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+tags: 645f666f9bcd5a90fca523b33c5a78b7
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run `folktexts` benchmark with a custom ACS task\n",
+    "\n",
+    "This notebook describes how to define a custom prediction task based on \n",
+    "American Community Survey (ACS) data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.0.22'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import folktexts\n",
+    "folktexts.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** Change the `DATA_DIR` variable to the ACS data path on your file system (if any, otherwise will attempt to download data)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define custom task objects appropriately:\n",
+    "\n",
+    "In this example, we'll try to predict whether someone is or ever was part of the Military, based on a series of demographic features.\n",
+    "\n",
+    "We must define objects of the following classes:\n",
+    "- `Threshold`: Used to binarize the target column (not needed if target is already binary);\n",
+    "- `MultipleChoiceQA`: Defines a question and answer scheme for the target column;\n",
+    "- `TaskMetadata`: Defines which columns to use as features, and which to use as the prediction target;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select the target column (among those defined in `folktexts.acs.acs_columns`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TARGET_COLUMN = \"MIL\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define threshold to be applied to target column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.threshold import Threshold\n",
+    "target_threshold = Threshold(4, \"!=\")\n",
+    "\n",
+    "# data[\"MIL\"] != 4 means \"Is on active duty in the military or was in the past\"\n",
+    "# data[\"MIL\"] == 4 means \"Never served in the military\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define question and answer interface used for prompting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.qa_interface import MultipleChoiceQA\n",
+    "from folktexts.qa_interface import Choice\n",
+    "\n",
+    "target_column_qa = MultipleChoiceQA(\n",
+    "    column=target_threshold.apply_to_column_name(TARGET_COLUMN),\n",
+    "    text=\"Has this person ever served in the military?\",\n",
+    "    choices=(\n",
+    "        Choice(\"Yes, this person is now on active duty or was in the past\", 1),\n",
+    "        Choice(\"No, this person has never served in the military\", 0),\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define task metadata:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.acs import ACSTaskMetadata\n",
+    "\n",
+    "task = ACSTaskMetadata.make_task(\n",
+    "    name=\"ACSMilitary\",\n",
+    "    description=\"predict if a person has ever served in the military\",\n",
+    "    features=[\n",
+    "        \"AGEP\", \"SCHL\", \"MAR\", \"POBP\", \"WKHP\", \"SEX\", \"RAC1P\", \"ST\", \"CIT\", \"DIS\", \"PINCP\",\n",
+    "    ],\n",
+    "    target=TARGET_COLUMN,\n",
+    "    target_threshold=target_threshold,\n",
+    "    sensitive_attribute=\"RAC1P\",\n",
+    "    multiple_choice_qa=target_column_qa,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load ACS dataset for the custom task:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "CPU times: user 38 s, sys: 19.3 s, total: 57.3 s\n",
+      "Wall time: 57.4 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.acs.acs_dataset import ACSDataset\n",
+    "acs_dataset = ACSDataset.make_from_task(\n",
+    "    task=task,\n",
+    "    cache_dir=DATA_DIR,)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** This dataset will contain all samples in the ACS PUMS file, you can\n",
+    "now filter the `acs_dataset.data` if you want to use only a portion of the data \n",
+    "(e.g, only people in California, or only people above 18 years old).\n",
+    "\n",
+    "As an example, we'll filter the ACS data to contain only people above 18 years old."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original number of samples: 3236107\n",
+      "Parsed number of samples: 2580544\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Original number of samples: {len(acs_dataset.data)}\")\n",
+    "\n",
+    "acs_dataset.data = acs_dataset.data[acs_dataset.data[\"AGEP\"] >= 18]\n",
+    "print(f\"Parsed number of samples: {len(acs_dataset.data)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Optionally,** you can subsample the dataset to have faster but noisier results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<folktexts.acs.acs_dataset.ACSDataset at 0x14584395d510>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "acs_dataset.subsample(0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load LLM from huggingface or from local disk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAME_OR_PATH = \"meta-llama/Meta-Llama-3-8B\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9adceb50efa487fa02fd702a7ef8bb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4.39 s, sys: 6.29 s, total: 10.7 s\n",
+      "Wall time: 13.1 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.llm_utils import load_model_tokenizer\n",
+    "model, tokenizer = load_model_tokenizer(MODEL_NAME_OR_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create `LLMClassifier` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.classifier import TransformersLLMClassifier\n",
+    "\n",
+    "llm_clf = TransformersLLMClassifier(\n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    task=task,\n",
+    "    batch_size=10,\n",
+    "    context_size=600,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Validation:** Check that model prompts with the custom task make sense, or if anything needs to be adjusted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following data corresponds to a survey respondent. The survey was conducted among US residents in 2018. Please answer the question based on the information provided. The data provided is enough to reach an approximate answer.\n",
+      "\n",
+      "Information:\n",
+      "- The age is: 50 years old.\n",
+      "- The highest educational attainment is: Regular high school diploma.\n",
+      "- The marital status is: Married.\n",
+      "- The place of birth is: Nevada.\n",
+      "- The usual number of hours worked per week is: 50 hours.\n",
+      "- The sex is: Male.\n",
+      "- The race is: White.\n",
+      "- The resident state is: Nevada.\n",
+      "- The citizenship status is: Born in the United States.\n",
+      "- The disability status is: No disability.\n",
+      "- The yearly income is: $70,000.\n",
+      "\n",
+      "Question: Has this person ever served in the military?\n",
+      "A. Yes, this person is now on active duty or was in the past.\n",
+      "B. No, this person has never served in the military.\n",
+      "Answer:\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_sample, _y_sample = acs_dataset.sample_n_train_examples(n=1)\n",
+    "print(llm_clf.encode_row(X_sample.iloc[0], question=llm_clf.task.question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run benchmark on the custom task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.benchmark import Benchmark\n",
+    "bench = Benchmark(llm_clf=llm_clf, dataset=acs_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1f553e570e043d0aa2567237fafc552",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4min 25s, sys: 5.32 s, total: 4min 31s\n",
+      "Wall time: 4min 30s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 2581,\n",
+       " 'n_positives': 259,\n",
+       " 'n_negatives': 2322,\n",
+       " 'model_name': 'Meta-Llama-3-8B',\n",
+       " 'accuracy': 0.8082138705927935,\n",
+       " 'tpr': 0.305019305019305,\n",
+       " 'fnr': 0.694980694980695,\n",
+       " 'fpr': 0.13565891472868216,\n",
+       " 'tnr': 0.8643410852713178,\n",
+       " 'balanced_accuracy': 0.5846801951453114,\n",
+       " 'precision': 0.20050761421319796,\n",
+       " 'ppr': 0.15265401007361487,\n",
+       " 'log_loss': 0.6253456831680403,\n",
+       " 'brier_score_loss': 0.21635791438469001,\n",
+       " 'accuracy_ratio': 0.79638671875,\n",
+       " 'accuracy_diff': 0.20361328125,\n",
+       " 'fpr_ratio': 0.0,\n",
+       " 'fpr_diff': 1.0,\n",
+       " 'fnr_ratio': 0.0,\n",
+       " 'fnr_diff': 1.0,\n",
+       " 'precision_ratio': 0.0,\n",
+       " 'precision_diff': 1.0,\n",
+       " 'balanced_accuracy_ratio': 0.4435516271595018,\n",
+       " 'balanced_accuracy_diff': 0.5282227307398932,\n",
+       " 'tpr_ratio': 0.0,\n",
+       " 'tpr_diff': 1.0,\n",
+       " 'ppr_ratio': 0.0,\n",
+       " 'ppr_diff': 1.0,\n",
+       " 'tnr_ratio': 0.0,\n",
+       " 'tnr_diff': 1.0,\n",
+       " 'equalized_odds_ratio': 0.0,\n",
+       " 'equalized_odds_diff': 1.0,\n",
+       " 'roc_auc': 0.6900338877083063,\n",
+       " 'ece': 0.3593739652181054,\n",
+       " 'ece_quantile': 0.3593739652181053,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/ACSMilitary_subsampled-0.01_seed-42_hash-1434010626.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': False,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'Meta-Llama-3-8B',\n",
+       "  'model_hash': 1160994164,\n",
+       "  'task_name': 'ACSMilitary',\n",
+       "  'task_hash': 4147552870,\n",
+       "  'dataset_name': 'ACSMilitary_subsampled-0.01_seed-42_hash-1434010626',\n",
+       "  'dataset_subsampling': 0.01,\n",
+       "  'dataset_hash': 1434010626},\n",
+       " 'benchmark_hash': 190963433,\n",
+       " 'results_dir': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433',\n",
+       " 'results_root_dir': '/lustre/home/acruz/folktexts/notebooks',\n",
+       " 'current_time': '2024.08.30-14.57.43',\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "bench.run(results_root_dir=\".\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bench.plot_results();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.doctrees/nbsphinx/notebooks/custom-dataset-example.ipynb b/.doctrees/nbsphinx/notebooks/custom-dataset-example.ipynb
new file mode 100644
index 0000000..edd1013
--- /dev/null
+++ b/.doctrees/nbsphinx/notebooks/custom-dataset-example.ipynb
@@ -0,0 +1,763 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run `folktexts` benchmark with a different data source\n",
+    "\n",
+    "This notebook describes how to use the `folktexts` pipeline to run a benchmark on a different\n",
+    "tabular dataset (distinct from the ACS tasks provided out-of-the-box).\n",
+    "\n",
+    "In this example, we have mapped 11 columns of the [Medical Expenditure Panel Survey (MEPS)](https://meps.ahrq.gov/mepsweb/index.jsp) dataset (survey panels 19, 20, and 21).\n",
+    "\n",
+    "Data codebook available [at this link](https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_codebook.jsp?PUFId=H192)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.0.22'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import folktexts\n",
+    "\n",
+    "folktexts.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define mappings for each dataset column using `ColumnToText`: each value of each\n",
+    "column should be mapped to a meaningful text representation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.col_to_text import ColumnToText\n",
+    "\n",
+    "# AGE\n",
+    "meps_age_col = ColumnToText(\n",
+    "    \"AGE\",\n",
+    "    short_description=\"age\",\n",
+    "    value_map=lambda x: f\"{int(x)} years old\",\n",
+    ")\n",
+    "\n",
+    "# REGION: Census region\n",
+    "meps_region_col = ColumnToText(\n",
+    "    \"REGION\",\n",
+    "    short_description=\"US region\",\n",
+    "    value_map={\n",
+    "        1: \"Northeast\",\n",
+    "        2: \"Midwest\",\n",
+    "        3: \"South\",\n",
+    "        4: \"West\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# SEX\n",
+    "meps_sex_col = ColumnToText(\n",
+    "    \"SEX\",\n",
+    "    short_description=\"sex\",\n",
+    "    value_map={\n",
+    "        1: \"Male\",\n",
+    "        2: \"Female\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# MARRY / MARRY31X\n",
+    "meps_marital_status = ColumnToText(\n",
+    "    \"MARRY\",\n",
+    "    short_description=\"marital status\",\n",
+    "    value_map={\n",
+    "        1: \"Married\",\n",
+    "        2: \"Widowed\",\n",
+    "        3: \"Divorced\",\n",
+    "        4: \"Separated\",\n",
+    "        5: \"Never married\",\n",
+    "        6: \"Inapplicable - Under 16 years old\",\n",
+    "        7: \"Married during current survey round\",\n",
+    "        8: \"Widowed during current survey round\",\n",
+    "        9: \"Divorced during current survey round\",\n",
+    "        10: \"Separated during current survey round\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# HONRDC: Honorably discharged from military\n",
+    "meps_education_col = ColumnToText(\n",
+    "    \"HONRDC\",\n",
+    "    short_description=\"honorably discharged status\",\n",
+    "    value_map={\n",
+    "        1: \"Yes, honorably discharged from military\",\n",
+    "        2: \"Not part of military or not honorably discharged\",\n",
+    "        3: \"Inapplicable - Under 16 years old\",\n",
+    "        4: \"Inapplicable - Now on active duty\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# RTHLTH / RTHLTH31: Perceived health status\n",
+    "meps_health_status_col = ColumnToText(\n",
+    "    \"RTHLTH\",\n",
+    "    short_description=\"self-rated health status\",\n",
+    "    value_map={\n",
+    "        1: \"Excellent\",\n",
+    "        2: \"Very good\",\n",
+    "        3: \"Good\",\n",
+    "        4: \"Fair\",\n",
+    "        5: \"Poor\",\n",
+    "        -1: \"Inapplicable - Missing data\",\n",
+    "        -7: \"Inapplicable - Refused to answer\",\n",
+    "        -8: \"Inapplicable - Don't know\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# MNHLTH / MNHLTH31: Perceived mental health status\n",
+    "meps_mental_health_status_col = ColumnToText(\n",
+    "    \"MNHLTH\",\n",
+    "    short_description=\"self-rated mental health status\",\n",
+    "    value_map={\n",
+    "        1: \"Excellent\",\n",
+    "        2: \"Very good\",\n",
+    "        3: \"Good\",\n",
+    "        4: \"Fair\",\n",
+    "        5: \"Poor\",\n",
+    "        -1: \"Inapplicable - Missing data\",\n",
+    "        -7: \"Inapplicable - Refused to answer\",\n",
+    "        -8: \"Inapplicable - Don't know\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# POVCAT / POVCAT15: Poverty category\n",
+    "meps_poverty_category_col = ColumnToText(\n",
+    "    \"POVCAT\",\n",
+    "    short_description=\"poverty category\",\n",
+    "    value_map={\n",
+    "        1: \"Poor\",\n",
+    "        2: \"Near poor\",\n",
+    "        3: \"Low income\",\n",
+    "        4: \"Middle income\",\n",
+    "        5: \"High income\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# INSCOV / INSCOV15: Insurance coverage\n",
+    "meps_insurance_coverage_col = ColumnToText(\n",
+    "    \"INSCOV\",\n",
+    "    short_description=\"insurance coverage\",\n",
+    "    value_map={\n",
+    "        1: \"Private insurance\",\n",
+    "        2: \"Public insurance\",\n",
+    "        3: \"Uninsured\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# DIABDX: Diabetes diagnosis\n",
+    "meps_diabetes_col = ColumnToText(\n",
+    "    \"DIABDX\",\n",
+    "    short_description=\"diabetes diagnosis\",\n",
+    "    value_map={\n",
+    "        1: \"Yes, diagnosed with diabetes\",\n",
+    "        2: \"No, not diagnosed with diabetes\",\n",
+    "        -1: \"Inapplicable - Under 17 years old\",\n",
+    "    },\n",
+    "    missing_value_fill=\"Inapplicable - Under 17 years old\",\n",
+    ")\n",
+    "\n",
+    "# HIBPDX: High blood pressure diagnosis\n",
+    "meps_high_blood_pressure_col = ColumnToText(\n",
+    "    \"HIBPDX\",\n",
+    "    short_description=\"high blood pressure diagnosis\",\n",
+    "    value_map={\n",
+    "        1: \"Yes, diagnosed with high blood pressure\",\n",
+    "        2: \"No, not diagnosed with high blood pressure\",\n",
+    "        -1: \"Inapplicable - Under 17 years old\",\n",
+    "    },\n",
+    "    missing_value_fill=\"Inapplicable - Under 17 years old\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the target column mapping and the question to prompt the LLM with by instantiating a `MultipleChoiceQA` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.qa_interface import MultipleChoiceQA, Choice\n",
+    "\n",
+    "TARGET_COL = \"UTILIZATION\"\n",
+    "\n",
+    "utilization_qa = MultipleChoiceQA(\n",
+    "    column=TARGET_COL,\n",
+    "    text=\"Was is this person's estimated number of doctor visits in the past year?\",\n",
+    "    choices=(\n",
+    "        Choice(\"More than 10 doctor visits (high health-care utilization)\", 1),\n",
+    "        Choice(\"10 or fewer doctor visits (low health-care utilization)\", 0),\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# UTILIZATION: Number of doctor visits\n",
+    "meps_utilization_col = ColumnToText(\n",
+    "    TARGET_COL,\n",
+    "    short_description=\"doctor visits\",\n",
+    "    question=utilization_qa,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optionally, you can define a **numeric question prompt that asks for a verbalized probability** by instantiating a `DirectNumericQA` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.qa_interface import DirectNumericQA\n",
+    "\n",
+    "utilization_numeric_qa = DirectNumericQA(\n",
+    "    column=TARGET_COL,\n",
+    "    text=(\n",
+    "        \"What is the probability that this person has high health-care utilization? \"\n",
+    "        \"(i.e., more than 10 doctor visits per year)\"\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the prediction task be instantiating a `TaskMetadata` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper dict to access ColumnToText objects by column name\n",
+    "meps_columns_map: dict[str, object] = {\n",
+    "    col_mapper.name: col_mapper\n",
+    "    for col_mapper in globals().values()\n",
+    "    if isinstance(col_mapper, ColumnToText)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.task import TaskMetadata\n",
+    "\n",
+    "meps_task = TaskMetadata(\n",
+    "    name=\"health-care utilization\",\n",
+    "    description=(\n",
+    "        \"predict whether an individual had low or high healthcare utilization \"\n",
+    "        \"in the past year by their number of doctor visits\"\n",
+    "    ),\n",
+    "    features=[col.name for col in meps_columns_map.values() if col.name != TARGET_COL],\n",
+    "    target=TARGET_COL,\n",
+    "    cols_to_text=meps_columns_map,\n",
+    "    sensitive_attribute=\"SEX\",\n",
+    "    multiple_choice_qa=utilization_qa,\n",
+    "    direct_numeric_qa=utilization_numeric_qa,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Set** whether to use numeric risk prompting or the default multiple-choice Q&A prompting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meps_task.use_numeric_qa = False\n",
+    "# meps_task.use_numeric_qa = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load MEPS data and instantiate the `Dataset` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.dataset import Dataset\n",
+    "\n",
+    "DATA_PATH = Path(folktexts.__file__).parent.parent / \"notebooks\" / \"data\" / \"meps.csv\"\n",
+    "meps_df = pd.read_csv(DATA_PATH)\n",
+    "\n",
+    "dataset = Dataset(\n",
+    "    data=meps_df,\n",
+    "    task=meps_task,\n",
+    "    test_size=0.4,\n",
+    "    val_size=0,\n",
+    "    subsampling=0.1,   # NOTE: Optional, for faster but noisier results!\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Optionally,** you can subsample the data to get faster but noisier results, with:\n",
+    "```py\n",
+    "dataset.subsample(0.1)  # Keeps only 10% of the data\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load huggingface LLM:\n",
+    "\n",
+    "**Note:** Update `MODEL_NAME_OR_PATH` as needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAME_OR_PATH = \"meta-llama/Meta-Llama-3-8B\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e35aa130a0844ed9910d21349c8c5c44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from folktexts.classifier import TransformersLLMClassifier\n",
+    "from folktexts.llm_utils import load_model_tokenizer\n",
+    "\n",
+    "model, tokenizer = load_model_tokenizer(MODEL_NAME_OR_PATH)\n",
+    "\n",
+    "llm_clf = TransformersLLMClassifier(\n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    task=meps_task,\n",
+    "    batch_size=20,\n",
+    "    context_length=800,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example LLM prompt for this task:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following data corresponds to a survey respondent. The survey was conducted among US residents in 2018. Please answer the question based on the information provided. The data provided is enough to reach an approximate answer.\n",
+      "\n",
+      "Information:\n",
+      "- The age is: 37 years old.\n",
+      "- The US region is: West.\n",
+      "- The sex is: Female.\n",
+      "- The marital status is: Never married.\n",
+      "- The honorably discharged status is: Not part of military or not honorably discharged.\n",
+      "- The self-rated health status is: Good.\n",
+      "- The self-rated mental health status is: Good.\n",
+      "- The poverty category is: Poor.\n",
+      "- The insurance coverage is: Private insurance.\n",
+      "- The diabetes diagnosis is: No, not diagnosed with diabetes.\n",
+      "- The high blood pressure diagnosis is: No, not diagnosed with high blood pressure.\n",
+      "\n",
+      "Question: Was is this person's estimated number of doctor visits in the past year?\n",
+      "A. More than 10 doctor visits (high health-care utilization).\n",
+      "B. 10 or fewer doctor visits (low health-care utilization).\n",
+      "Answer:\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_sample, _y_sample = dataset.sample_n_train_examples(n=1)\n",
+    "print(llm_clf.encode_row(X_sample.iloc[0], question=llm_clf.task.question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run benchmark on MEPS data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.benchmark import BenchmarkConfig, Benchmark\n",
+    "bench = Benchmark(llm_clf=llm_clf, dataset=dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a6560c0c807478387e3a96afddf1214",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/99 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 45s, sys: 5.43 s, total: 2min 51s\n",
+      "Wall time: 2min 50s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 1963,\n",
+       " 'n_positives': 354,\n",
+       " 'n_negatives': 1609,\n",
+       " 'model_name': 'Meta-Llama-3-8B',\n",
+       " 'accuracy': 0.630667345899134,\n",
+       " 'tpr': 0.7033898305084746,\n",
+       " 'fnr': 0.2966101694915254,\n",
+       " 'fpr': 0.3853325046612803,\n",
+       " 'tnr': 0.6146674953387197,\n",
+       " 'balanced_accuracy': 0.6590286629235971,\n",
+       " 'precision': 0.286536248561565,\n",
+       " 'ppr': 0.44268976057055526,\n",
+       " 'log_loss': 0.6640880361799258,\n",
+       " 'brier_score_loss': 0.23555848017340178,\n",
+       " 'fnr_ratio': 0.7005952380952382,\n",
+       " 'fnr_diff': 0.1007343124165554,\n",
+       " 'fpr_ratio': 0.9499246987951807,\n",
+       " 'fpr_diff': 0.019768133174791924,\n",
+       " 'ppr_ratio': 0.9785014080325124,\n",
+       " 'ppr_diff': 0.009620674692892017,\n",
+       " 'precision_ratio': 0.7380730854374539,\n",
+       " 'precision_diff': 0.086496795041585,\n",
+       " 'tpr_ratio': 0.8681980959035723,\n",
+       " 'tpr_diff': 0.1007343124165554,\n",
+       " 'balanced_accuracy_ratio': 0.9408797810568554,\n",
+       " 'balanced_accuracy_diff': 0.04048308962088176,\n",
+       " 'tnr_ratio': 0.968370986920333,\n",
+       " 'tnr_diff': 0.01976813317479187,\n",
+       " 'accuracy_ratio': 0.9913632326092872,\n",
+       " 'accuracy_diff': 0.0054705390193721115,\n",
+       " 'equalized_odds_ratio': 0.7005952380952382,\n",
+       " 'equalized_odds_diff': 0.1007343124165554,\n",
+       " 'roc_auc': 0.7183612307886781,\n",
+       " 'ece': 0.30890763123834214,\n",
+       " 'ece_quantile': 0.3089076312383417,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/health-care utilization_subsampled-0.1_seed-42_hash-1494059445.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': False,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'Meta-Llama-3-8B',\n",
+       "  'model_hash': 2852898721,\n",
+       "  'task_name': 'health-care utilization',\n",
+       "  'task_hash': 3442708664,\n",
+       "  'dataset_name': 'health-care utilization_subsampled-0.1_seed-42_hash-1494059445',\n",
+       "  'dataset_subsampling': 0.1,\n",
+       "  'dataset_hash': 1494059445},\n",
+       " 'benchmark_hash': 1075925211,\n",
+       " 'results_dir': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211',\n",
+       " 'results_root_dir': '/lustre/home/acruz/folktexts/notebooks',\n",
+       " 'current_time': '2024.08.30-14.54.40',\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "bench.run(results_root_dir=\".\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bench.plot_results();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 1963,\n",
+       " 'n_positives': 354,\n",
+       " 'n_negatives': 1609,\n",
+       " 'model_name': 'Meta-Llama-3-8B',\n",
+       " 'accuracy': 0.630667345899134,\n",
+       " 'tpr': 0.7033898305084746,\n",
+       " 'fnr': 0.2966101694915254,\n",
+       " 'fpr': 0.3853325046612803,\n",
+       " 'tnr': 0.6146674953387197,\n",
+       " 'balanced_accuracy': 0.6590286629235971,\n",
+       " 'precision': 0.286536248561565,\n",
+       " 'ppr': 0.44268976057055526,\n",
+       " 'log_loss': 0.6640880361799258,\n",
+       " 'brier_score_loss': 0.23555848017340178,\n",
+       " 'fnr_ratio': 0.7005952380952382,\n",
+       " 'fnr_diff': 0.1007343124165554,\n",
+       " 'fpr_ratio': 0.9499246987951807,\n",
+       " 'fpr_diff': 0.019768133174791924,\n",
+       " 'ppr_ratio': 0.9785014080325124,\n",
+       " 'ppr_diff': 0.009620674692892017,\n",
+       " 'precision_ratio': 0.7380730854374539,\n",
+       " 'precision_diff': 0.086496795041585,\n",
+       " 'tpr_ratio': 0.8681980959035723,\n",
+       " 'tpr_diff': 0.1007343124165554,\n",
+       " 'balanced_accuracy_ratio': 0.9408797810568554,\n",
+       " 'balanced_accuracy_diff': 0.04048308962088176,\n",
+       " 'tnr_ratio': 0.968370986920333,\n",
+       " 'tnr_diff': 0.01976813317479187,\n",
+       " 'accuracy_ratio': 0.9913632326092872,\n",
+       " 'accuracy_diff': 0.0054705390193721115,\n",
+       " 'equalized_odds_ratio': 0.7005952380952382,\n",
+       " 'equalized_odds_diff': 0.1007343124165554,\n",
+       " 'roc_auc': 0.7183612307886781,\n",
+       " 'ece': 0.30890763123834214,\n",
+       " 'ece_quantile': 0.3089076312383417,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/health-care utilization_subsampled-0.1_seed-42_hash-1494059445.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': False,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'Meta-Llama-3-8B',\n",
+       "  'model_hash': 2852898721,\n",
+       "  'task_name': 'health-care utilization',\n",
+       "  'task_hash': 3442708664,\n",
+       "  'dataset_name': 'health-care utilization_subsampled-0.1_seed-42_hash-1494059445',\n",
+       "  'dataset_subsampling': 0.1,\n",
+       "  'dataset_hash': 1494059445},\n",
+       " 'benchmark_hash': 1075925211,\n",
+       " 'results_dir': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211',\n",
+       " 'results_root_dir': '/lustre/home/acruz/folktexts/notebooks',\n",
+       " 'current_time': '2024.08.30-14.54.41',\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bench.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.doctrees/nbsphinx/notebooks/detailed-example.ipynb b/.doctrees/nbsphinx/notebooks/detailed-example.ipynb
new file mode 100644
index 0000000..6210949
--- /dev/null
+++ b/.doctrees/nbsphinx/notebooks/detailed-example.ipynb
@@ -0,0 +1,1223 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e85e7f8a-dcfa-489a-9a8e-77225d2e3499",
+   "metadata": {},
+   "source": [
+    "# Example: Run ACS benchmark task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "98180e30-da85-41fb-a360-35b6acacb170",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "folktexts.__version__='0.0.21'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import folktexts\n",
+    "print(f\"{folktexts.__version__=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e5c41432-8ee0-4fbb-b37b-4eea92504382",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7a4b1e7f-a893-488e-96f1-4094857115f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "logging.getLogger().setLevel(logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04d5e737-9b3c-468a-bd68-b1c161f57c84",
+   "metadata": {},
+   "source": [
+    "## Set important local paths\n",
+    "**NOTE:** Can be ignored if you haven't previously downloaded the model, just use `load_model_tokenizer` with the model's name on Huggingface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cf6dfe9-4ddc-448f-afb9-0e4802ae480d",
+   "metadata": {},
+   "source": [
+    "Set your root directory (_**change as appropriate**_):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "048f506d-83a6-4911-b5f3-2fc290ed9fd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/fast/groups/sf')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ROOT_DIR = Path(\"/fast/groups/sf\")\n",
+    "ROOT_DIR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cb6c30e-5af1-4733-8732-788cba086eaf",
+   "metadata": {},
+   "source": [
+    "Directory where LLMs are saved (_**change as appropriate**_):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a7e60a72-e0c4-4fa8-a5d5-402f82098872",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODELS_DIR = ROOT_DIR / \"huggingface-models\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efbc677d-5208-4a8a-aab8-9cb7282ca39f",
+   "metadata": {},
+   "source": [
+    "Directory where data is saved or will be saved to (_**change as appropriate**_):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8040b3cd-da53-4b62-8aae-58b2bb7cba69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = ROOT_DIR / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "985344ec-6c9c-4e8b-8d8e-26f009937a7c",
+   "metadata": {},
+   "source": [
+    "Other configs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4316d94-110f-4014-a823-95300ac87eb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAME = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
+    "# MODEL_NAME = \"google/gemma-2b\"    # Smaller model that is faster to run\n",
+    "\n",
+    "TASK_NAME = \"ACSIncome\"\n",
+    "\n",
+    "RESULTS_ROOT_DIR = ROOT_DIR / \"folktexts-results\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7e0f598d-dd37-471f-a0d4-4b3c1ae07167",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Loading model '/lustre/fast/fast/groups/sf/huggingface-models/meta-llama--Meta-Llama-3-8B-Instruct'\n",
+      "INFO:accelerate.utils.modeling:We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set `max_memory` in to a higher value to use more memory (at your own risk).\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0a08cdd63fa4bf4815578e005a80497",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Moving model to device: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "from folktexts.llm_utils import load_model_tokenizer, get_model_folder_path\n",
+    "model_folder_path = get_model_folder_path(model_name=MODEL_NAME, root_dir=MODELS_DIR)\n",
+    "model, tokenizer = load_model_tokenizer(model_folder_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ab088b1f-37ba-4d2e-b5d2-b829787b7fed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_dir = RESULTS_ROOT_DIR / Path(model_folder_path).name\n",
+    "results_dir.mkdir(exist_ok=True, parents=True)\n",
+    "results_dir"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d87da67-3857-421f-b761-81814ea5498d",
+   "metadata": {},
+   "source": [
+    "### Construct LLM Classifier\n",
+    "\n",
+    "**NOTE:** Also compatible with models hosted through a web API by using the `WebAPILLMClassifier` class instead of `TransformersLLMClassifier`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e664c322-4da5-49b3-a755-3ba0a075ee64",
+   "metadata": {},
+   "source": [
+    "Load prediction task (which maps tabular data to text):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7f0b725d-d97a-40a9-a71d-88fb5fefbddf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Changing Q&A mode for task 'ACSIncome' to multiple-choice.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from folktexts.acs import ACSTaskMetadata\n",
+    "task = ACSTaskMetadata.get_task(TASK_NAME, use_numeric_qa=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0cfa6291-5c06-4618-a14d-86e49f2f4969",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.classifier import TransformersLLMClassifier\n",
+    "llm_clf = TransformersLLMClassifier(\n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    task=task,\n",
+    "    batch_size=32,\n",
+    "    context_size=1000,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53f969d6-9d0e-48c2-9b34-a57aef0edb5c",
+   "metadata": {},
+   "source": [
+    "### Load Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "820004cb-25ce-4802-85c3-6ab251881141",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "CPU times: user 58 s, sys: 27.5 s, total: 1min 25s\n",
+      "Wall time: 1min 26s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.acs import ACSDataset\n",
+    "dataset = ACSDataset.make_from_task(task=task, cache_dir=DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8145eddd-09da-4db9-8779-c61b04c3d777",
+   "metadata": {},
+   "source": [
+    "Optionally, subsample to quickly get approximate results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c977cac3-162c-4cde-8594-e6795f62e29a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Train size: 13316, Test size:  1665, Val size:   1665;\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dataset.subsampling=0.01\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.subsample(0.01)\n",
+    "print(f\"{dataset.subsampling=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3b4ce61-ecbc-4491-bd7b-0cfae38c6c10",
+   "metadata": {},
+   "source": [
+    "### Load and run ACS Benchmark\n",
+    "\n",
+    "**_Note:_** Helper constructors exist at `Benchmark.make_acs_benchmark` and `Benchmark.make_benchmark` that avoid the above boilerplate code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "10d362b6-aac8-49dd-84c3-0ce799ffabba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:\n",
+      "** Benchmark initialization **\n",
+      "Model: meta-llama--Meta-Llama-3-8B-Instruct;\n",
+      "Task: ACSIncome;\n",
+      "Hash: 516109582;\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from folktexts.benchmark import BenchmarkConfig, Benchmark\n",
+    "\n",
+    "bench = Benchmark(llm_clf=llm_clf, dataset=dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a648f32-05f4-4057-8624-2a39bfe7ab55",
+   "metadata": {},
+   "source": [
+    "Here's an example prompt for the current prediction task:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a83832fc-47c4-4e14-adc9-6393f197e2c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following data corresponds to a survey respondent. The survey was conducted among US residents in 2018. Please answer the question based on the information provided. The data provided is enough to reach an approximate answer.\n",
+      "\n",
+      "Information:\n",
+      "- The age is: 37 years old.\n",
+      "- The class of worker is: Owner of non-incorporated business, professional practice, or farm.\n",
+      "- The highest educational attainment is: Regular high school diploma.\n",
+      "- The marital status is: Married.\n",
+      "- The occupation is: Painters and paperhangers.\n",
+      "- The place of birth is: New Jersey.\n",
+      "- The relationship to the reference person in the survey is: The reference person itself.\n",
+      "- The usual number of hours worked per week is: 40 hours.\n",
+      "- The sex is: Male.\n",
+      "- The race is: White.\n",
+      "\n",
+      "Question: What is this person's estimated yearly income?\n",
+      "A. Below $50,000.\n",
+      "B. Above $50,000.\n",
+      "Answer:\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_sample, _y_sample = dataset.sample_n_train_examples(n=1)\n",
+    "print(llm_clf.encode_row(X_sample.iloc[0], question=llm_clf.task.question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e107c703-17d4-4543-8f56-73c7b9583589",
+   "metadata": {},
+   "source": [
+    "Optionally, you can fit the model's threshold on a few data samples.\n",
+    "\n",
+    "This is generally quite fast as it is _not fine-tuning_; it only changes one parameter: the `llm_clf.threshold`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "16f88399-1b9e-4fd3-a1ca-0aa9f2b91080",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0e4d1784cc8416eb3f30a9b542754b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n",
+      "WARNING:root:Setting meta-llama--Meta-Llama-3-8B-Instruct threshold to 0.7918923636748516.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.57 s, sys: 382 ms, total: 7.95 s\n",
+      "Wall time: 9 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TransformersLLMClassifier(encode_row=functools.partial(&lt;function encode_row_prompt at 0x14802eedf600&gt;, task=ACSTaskMetadata(name=&#x27;ACSIncome&#x27;, description=&quot;predict whether an individual&#x27;s income is above $50,000&quot;, features=[&#x27;AGEP&#x27;, &#x27;COW&#x27;, &#x27;SCHL&#x27;, &#x27;MAR&#x27;, &#x27;OCCP&#x27;, &#x27;POBP&#x27;, &#x27;RELP&#x27;, &#x27;WKHP&#x27;, &#x27;SEX&#x27;, &#x27;RAC1P&#x27;], target=&#x27;PINCP&#x27;, cols_to_text={&#x27;AGEP&#x27;: &lt;folktexts.col_to_text.ColumnToText...\n",
+       "\t128253: AddedToken(&quot;&lt;|reserved_special_token_248|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128254: AddedToken(&quot;&lt;|reserved_special_token_249|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128255: AddedToken(&quot;&lt;|reserved_special_token_250|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TransformersLLMClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>TransformersLLMClassifier(encode_row=functools.partial(&lt;function encode_row_prompt at 0x14802eedf600&gt;, task=ACSTaskMetadata(name=&#x27;ACSIncome&#x27;, description=&quot;predict whether an individual&#x27;s income is above $50,000&quot;, features=[&#x27;AGEP&#x27;, &#x27;COW&#x27;, &#x27;SCHL&#x27;, &#x27;MAR&#x27;, &#x27;OCCP&#x27;, &#x27;POBP&#x27;, &#x27;RELP&#x27;, &#x27;WKHP&#x27;, &#x27;SEX&#x27;, &#x27;RAC1P&#x27;], target=&#x27;PINCP&#x27;, cols_to_text={&#x27;AGEP&#x27;: &lt;folktexts.col_to_text.ColumnToText...\n",
+       "\t128253: AddedToken(&quot;&lt;|reserved_special_token_248|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128254: AddedToken(&quot;&lt;|reserved_special_token_249|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128255: AddedToken(&quot;&lt;|reserved_special_token_250|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "})</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "TransformersLLMClassifier(encode_row=functools.partial(<function encode_row_prompt at 0x14802eedf600>, task=ACSTaskMetadata(name='ACSIncome', description=\"predict whether an individual's income is above $50,000\", features=['AGEP', 'COW', 'SCHL', 'MAR', 'OCCP', 'POBP', 'RELP', 'WKHP', 'SEX', 'RAC1P'], target='PINCP', cols_to_text={'AGEP': <folktexts.col_to_text.ColumnToText...\n",
+       "\t128253: AddedToken(\"<|reserved_special_token_248|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128254: AddedToken(\"<|reserved_special_token_249|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128255: AddedToken(\"<|reserved_special_token_250|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "})"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "X_sample, y_sample = dataset.sample_n_train_examples(n=50)\n",
+    "llm_clf.fit(X_sample, y_sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dcc20d7-732f-450d-b02d-b94c34b41fc8",
+   "metadata": {},
+   "source": [
+    "Run benchmark..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4dae3a36-2b7b-473b-916b-35711e985140",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Test data features shape: (1665, 10)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48b6fc00f210464b9d0174c7909c0b1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/53 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:\n",
+      "** Test results **\n",
+      "Model: meta-llama--Meta-Llama-3-8B-Instruct;\n",
+      "\t ECE:       24.8%;\n",
+      "\t ROC AUC :  82.8%;\n",
+      "\t Accuracy:  73.8%;\n",
+      "\t Bal. acc.: 74.1%;\n",
+      "\n",
+      "INFO:root:Skipping group 'American Indian' as it's too small.\n",
+      "INFO:root:Skipping group 'Alaska Native' as it's too small.\n",
+      "INFO:root:Skipping group 'American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races' as it's too small.\n",
+      "INFO:root:Skipping group 'Native Hawaiian and Other Pacific Islander' as it's too small.\n",
+      "INFO:root:Skipping group 'Some other race alone (non-White)' as it's too small.\n",
+      "INFO:root:Skipping group 'Two or more races' as it's too small.\n",
+      "INFO:root:Skipping group American Indian plot as it's too small.\n",
+      "INFO:root:Skipping group Alaska Native plot as it's too small.\n",
+      "INFO:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it's too small.\n",
+      "INFO:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it's too small.\n",
+      "INFO:root:Skipping group Some other race alone (non-White) plot as it's too small.\n",
+      "INFO:root:Skipping group Two or more races plot as it's too small.\n",
+      "INFO:root:Saving JSON file to '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/results.bench-3191680725.json'\n",
+      "INFO:root:Saved experiment results to '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/results.bench-3191680725.json'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3min 51s, sys: 7.12 s, total: 3min 58s\n",
+      "Wall time: 4min 23s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': np.float64(0.7918923636748516),\n",
+       " 'n_samples': 1665,\n",
+       " 'n_positives': 605,\n",
+       " 'n_negatives': 1060,\n",
+       " 'model_name': 'meta-llama--Meta-Llama-3-8B-Instruct',\n",
+       " 'accuracy': 0.7375375375375376,\n",
+       " 'tpr': 0.7553719008264462,\n",
+       " 'fnr': 0.24462809917355371,\n",
+       " 'fpr': 0.27264150943396226,\n",
+       " 'tnr': 0.7273584905660377,\n",
+       " 'balanced_accuracy': 0.7413651956962419,\n",
+       " 'precision': 0.6126005361930295,\n",
+       " 'ppr': 0.44804804804804804,\n",
+       " 'log_loss': 0.8504629791262521,\n",
+       " 'brier_score_loss': np.float64(0.24624856734149014),\n",
+       " 'ppr_ratio': 0.0,\n",
+       " 'ppr_diff': 0.5961538461538461,\n",
+       " 'precision_ratio': 0.0,\n",
+       " 'precision_diff': 1.0,\n",
+       " 'balanced_accuracy_ratio': 0.0,\n",
+       " 'balanced_accuracy_diff': 1.0,\n",
+       " 'accuracy_ratio': 0.0,\n",
+       " 'accuracy_diff': 1.0,\n",
+       " 'tnr_ratio': 0.0,\n",
+       " 'tnr_diff': 1.0,\n",
+       " 'fnr_ratio': 0.0,\n",
+       " 'fnr_diff': 1.0,\n",
+       " 'fpr_ratio': 0.0,\n",
+       " 'fpr_diff': 1.0,\n",
+       " 'tpr_ratio': 0.0,\n",
+       " 'tpr_diff': 1.0,\n",
+       " 'equalized_odds_ratio': 0.0,\n",
+       " 'equalized_odds_diff': 1.0,\n",
+       " 'roc_auc': np.float64(0.8282707001403399),\n",
+       " 'ece': 0.2475394044687882,\n",
+       " 'ece_quantile': 0.24753940446878786,\n",
+       " 'predictions_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/ACSIncome_subsampled-0.01_seed-42_hash-373553086.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': False,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'meta-llama--Meta-Llama-3-8B-Instruct',\n",
+       "  'model_hash': 1896439497,\n",
+       "  'task_name': 'ACSIncome',\n",
+       "  'task_hash': 662640833,\n",
+       "  'dataset_name': 'ACSIncome_subsampled-0.01_seed-42_hash-373553086',\n",
+       "  'dataset_subsampling': 0.01,\n",
+       "  'dataset_hash': 373553086},\n",
+       " 'plots': {'roc_curve_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "bench.run(results_root_dir=results_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4dfbfa5f-a83d-4391-a2d9-afe0431ccd77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Skipping group 'American Indian' as it's too small.\n",
+      "INFO:root:Skipping group 'Alaska Native' as it's too small.\n",
+      "INFO:root:Skipping group 'American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races' as it's too small.\n",
+      "INFO:root:Skipping group 'Native Hawaiian and Other Pacific Islander' as it's too small.\n",
+      "INFO:root:Skipping group 'Some other race alone (non-White)' as it's too small.\n",
+      "INFO:root:Skipping group 'Two or more races' as it's too small.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Skipping group American Indian plot as it's too small.\n",
+      "INFO:root:Skipping group Alaska Native plot as it's too small.\n",
+      "INFO:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it's too small.\n",
+      "INFO:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it's too small.\n",
+      "INFO:root:Skipping group Some other race alone (non-White) plot as it's too small.\n",
+      "INFO:root:Skipping group Two or more races plot as it's too small.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bench.plot_results();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2f2e7d60-9fe4-4290-a2a9-7ec22b210cdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 0.7375375375375376,\n",
+      " 'accuracy_diff': 1.0,\n",
+      " 'accuracy_ratio': 0.0,\n",
+      " 'balanced_accuracy': 0.7413651956962419,\n",
+      " 'balanced_accuracy_diff': 1.0,\n",
+      " 'balanced_accuracy_ratio': 0.0,\n",
+      " 'brier_score_loss': np.float64(0.24624856734149014),\n",
+      " 'config': {...},\n",
+      " 'ece': 0.2475394044687882,\n",
+      " 'ece_quantile': 0.24753940446878786,\n",
+      " 'equalized_odds_diff': 1.0,\n",
+      " 'equalized_odds_ratio': 0.0,\n",
+      " 'fnr': 0.24462809917355371,\n",
+      " 'fnr_diff': 1.0,\n",
+      " 'fnr_ratio': 0.0,\n",
+      " 'fpr': 0.27264150943396226,\n",
+      " 'fpr_diff': 1.0,\n",
+      " 'fpr_ratio': 0.0,\n",
+      " 'log_loss': 0.8504629791262521,\n",
+      " 'model_name': 'meta-llama--Meta-Llama-3-8B-Instruct',\n",
+      " 'n_negatives': 1060,\n",
+      " 'n_positives': 605,\n",
+      " 'n_samples': 1665,\n",
+      " 'plots': {...},\n",
+      " 'ppr': 0.44804804804804804,\n",
+      " 'ppr_diff': 0.5961538461538461,\n",
+      " 'ppr_ratio': 0.0,\n",
+      " 'precision': 0.6126005361930295,\n",
+      " 'precision_diff': 1.0,\n",
+      " 'precision_ratio': 0.0,\n",
+      " 'predictions_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/ACSIncome_subsampled-0.01_seed-42_hash-373553086.test_predictions.csv',\n",
+      " 'roc_auc': np.float64(0.8282707001403399),\n",
+      " 'threshold': np.float64(0.7918923636748516),\n",
+      " 'tnr': 0.7273584905660377,\n",
+      " 'tnr_diff': 1.0,\n",
+      " 'tnr_ratio': 0.0,\n",
+      " 'tpr': 0.7553719008264462,\n",
+      " 'tpr_diff': 1.0,\n",
+      " 'tpr_ratio': 0.0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "pprint(bench.results, depth=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0b5be1e-4c00-4b48-adf4-3eee02b8d6e1",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/notebooks/minimal-example.ipynb b/.doctrees/nbsphinx/notebooks/minimal-example.ipynb
new file mode 100644
index 0000000..25111b8
--- /dev/null
+++ b/.doctrees/nbsphinx/notebooks/minimal-example.ipynb
@@ -0,0 +1,400 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "181f6c46",
+   "metadata": {},
+   "source": [
+    "# Evaluate model calibration using folktexts\n",
+    "\n",
+    "**Prerequisite**: Install `folktexts` package with `pip install folktexts` or follow the setup guide in the [README](https://github.com/socialfoundations/folktexts).\n",
+    "\n",
+    "**Summary**: The script loads a language model from Huggingface and demonstrates how to use folktexts to get insights into model calibration, and plot the benchmark results. \n",
+    "\n",
+    "### 1. Check folktexts is installed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f3867376",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "folktexts.__version__='0.0.21'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import folktexts\n",
+    "print(f\"{folktexts.__version__=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2129c830",
+   "metadata": {},
+   "source": [
+    "### 2. Load Model from Huggingface\n",
+    "\n",
+    "We use the _Mistral 7B (instruct)_ model for this demo. The workflow can be similarly applied to any model/tokenizer pair.\n",
+    "\n",
+    "**Note:** _Set_ `model_name_or_path` to the **model's name on huggingface** or to the **path to a saved pretrained model**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3dc758de-b86f-4adb-9ed0-0148831da070",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef6ed53409ed404795c07a024033a991",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from folktexts.llm_utils import load_model_tokenizer\n",
+    "\n",
+    "# Note: make sure you have the necessary persmissions on Huggingface to download the model\n",
+    "# Note: use gpt2 for the demo if you need a smaller model\n",
+    "\n",
+    "model_name_or_path = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
+    "# model_name_or_path = \"gpt2\"\n",
+    "\n",
+    "model, tokenizer = load_model_tokenizer(model_name_or_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25a2a5f7",
+   "metadata": {},
+   "source": [
+    "### 3. Create default benchmarking tasks\n",
+    "\n",
+    "We generate ACSIncome benchmark using folktexts. \n",
+    "\n",
+    "**NOTE:** We will subsample the reference data for faster runtime. This should be removed for obtaining reproducible reslts.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed481a4-5258-468f-8fb5-91ed794d1503",
+   "metadata": {},
+   "source": [
+    "### Benchmark configuration\n",
+    "- The `subsampling` and `numeric_risk_prompting` key-word arguments are examples of optional benchmark configurations. See this [page for a list of available configs](https://github.com/socialfoundations/folktexts?tab=readme-ov-file#benchmark-options)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bbb17808",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Received non-standard ACS argument 'subsampling' (using subsampling=0.01 instead of default subsampling=None). This may affect reproducibility.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "Using zero-shot prompting.\n",
+      "CPU times: user 52.4 s, sys: 1min 30s, total: 2min 22s\n",
+      "Wall time: 2min 26s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.benchmark import Benchmark, BenchmarkConfig\n",
+    "\n",
+    "# Note: This argument is optional. Omit, or set to 1 for reproducible benchmarking on the full data\n",
+    "subsampling_ratio = 0.01\n",
+    "\n",
+    "bench = Benchmark.make_acs_benchmark(\n",
+    "    model= model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    task_name=\"ACSIncome\",\n",
+    "    subsampling=subsampling_ratio,\n",
+    "    numeric_risk_prompting=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f785fc1",
+   "metadata": {},
+   "source": [
+    "### 4. Run benchmark\n",
+    "\n",
+    "Results will be saved in a folder RESULTS_DIR. There is \n",
+    "* `.json` file contains evaluated metrics\n",
+    "* `.cvs` file contains risk scores of each datapoint\n",
+    "* folder called `imgs/` contains figures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "62b90528",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffde879b1f5e453abb55e7b423379f7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/105 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n",
+      "WARNING:root:Failed to compute ECE quantile: The smallest edge difference is numerically 0.\n",
+      "WARNING:root:Skipping group American Indian plot as it's too small.\n",
+      "WARNING:root:Skipping group Alaska Native plot as it's too small.\n",
+      "WARNING:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it's too small.\n",
+      "WARNING:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it's too small.\n",
+      "WARNING:root:Skipping group Some other race alone (non-White) plot as it's too small.\n",
+      "WARNING:root:Skipping group Two or more races plot as it's too small.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 1665,\n",
+       " 'n_positives': 605,\n",
+       " 'n_negatives': 1060,\n",
+       " 'model_name': 'mistralai--Mistral-7B-Instruct-v0.2',\n",
+       " 'accuracy': 0.6816816816816816,\n",
+       " 'tpr': 0.856198347107438,\n",
+       " 'fnr': 0.14380165289256197,\n",
+       " 'fpr': 0.4179245283018868,\n",
+       " 'tnr': 0.5820754716981132,\n",
+       " 'balanced_accuracy': 0.7191369094027756,\n",
+       " 'precision': 0.5390218522372529,\n",
+       " 'ppr': 0.5771771771771772,\n",
+       " 'log_loss': 0.5812465405486003,\n",
+       " 'brier_score_loss': np.float64(0.19836672672672676),\n",
+       " 'tpr_ratio': 0.0,\n",
+       " 'tpr_diff': 1.0,\n",
+       " 'balanced_accuracy_ratio': 0.0,\n",
+       " 'balanced_accuracy_diff': 1.0,\n",
+       " 'accuracy_ratio': 0.0,\n",
+       " 'accuracy_diff': 1.0,\n",
+       " 'fnr_ratio': 0.0,\n",
+       " 'fnr_diff': 1.0,\n",
+       " 'ppr_ratio': 0.0,\n",
+       " 'ppr_diff': 0.6442307692307693,\n",
+       " 'precision_ratio': 0.0,\n",
+       " 'precision_diff': 1.0,\n",
+       " 'tnr_ratio': 0.0,\n",
+       " 'tnr_diff': 1.0,\n",
+       " 'fpr_ratio': 0.0,\n",
+       " 'fpr_diff': 1.0,\n",
+       " 'equalized_odds_ratio': 0.0,\n",
+       " 'equalized_odds_diff': 1.0,\n",
+       " 'roc_auc': np.float64(0.814200842039607),\n",
+       " 'ece': 0.16251051051051124,\n",
+       " 'ece_quantile': None,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/ACSIncome_subsampled-0.01_seed-42_hash-1233880265.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': True,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'mistralai--Mistral-7B-Instruct-v0.2',\n",
+       "  'model_hash': 2545663199,\n",
+       "  'task_name': 'ACSIncome',\n",
+       "  'task_hash': 127998692,\n",
+       "  'dataset_name': 'ACSIncome_subsampled-0.01_seed-42_hash-1233880265',\n",
+       "  'dataset_hash': 1233880265},\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RESULTS_DIR = \"res\"\n",
+    "bench.run(results_root_dir=RESULTS_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20636ce4",
+   "metadata": {},
+   "source": [
+    "### 4. Visualize results\n",
+    "\n",
+    "We can also visualize the results inline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "edd5bf8f-cd70-4276-9c14-f1e0012aee0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Skipping group American Indian plot as it's too small.\n",
+      "WARNING:root:Skipping group Alaska Native plot as it's too small.\n",
+      "WARNING:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it's too small.\n",
+      "WARNING:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it's too small.\n",
+      "WARNING:root:Skipping group Some other race alone (non-White) plot as it's too small.\n",
+      "WARNING:root:Skipping group Two or more races plot as it's too small.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve.pdf',\n",
+       " 'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve.pdf',\n",
+       " 'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution.pdf',\n",
+       " 'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution_per_label.pdf',\n",
+       " 'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve_per_subgroup.pdf',\n",
+       " 'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve_per_subgroup.pdf'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bench.plot_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b83085bc-9df7-43b0-9282-cfd4c83713b7",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/notebooks/minimal-example_web-API-model.ipynb b/.doctrees/nbsphinx/notebooks/minimal-example_web-API-model.ipynb
new file mode 100644
index 0000000..0a05386
--- /dev/null
+++ b/.doctrees/nbsphinx/notebooks/minimal-example_web-API-model.ipynb
@@ -0,0 +1,376 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "181f6c46",
+   "metadata": {},
+   "source": [
+    "# Evaluate model calibration using folktexts\n",
+    "\n",
+    "**Prerequisite**: Install `folktexts` package with optional model API dependencies: `pip install 'folktexts[apis]'`\n",
+    "\n",
+    "**Summary**: The script demonstrates how to use folktexts to get insights into model calibration on a **model hosted through a web API**.\n",
+    "\n",
+    "### 1. Check folktexts is installed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f3867376",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "folktexts.__version__='0.0.21'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import folktexts\n",
+    "print(f\"{folktexts.__version__=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2129c830",
+   "metadata": {},
+   "source": [
+    "### 2. Load model API using `litellm`\n",
+    "\n",
+    "We use OpenAI's _**GPT-4o-mini**_ model for this demo. The workflow can be similarly applied to any compatible model.\n",
+    "\n",
+    "**Note:** _Set_ `model_name` to the model's name. See the `litellm` [list of compatible web-API providers and models](https://docs.litellm.ai/docs/providers/openai#openai-chat-completion-models)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3dc758de-b86f-4adb-9ed0-0148831da070",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_name = \"openai/gpt-4o-mini\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c58aff1e-082d-4f3f-b061-1f1028d3520d",
+   "metadata": {},
+   "source": [
+    "### 3. Set `OPENAI_API_KEY` (or key to respective API provider)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28d73863-f448-4370-b26a-24576e1498f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-key-here\"  # NOTE: Substitute with your key here!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25a2a5f7",
+   "metadata": {},
+   "source": [
+    "### 3. Create default benchmarking tasks\n",
+    "\n",
+    "We generate ACSIncome benchmark using folktexts. \n",
+    "\n",
+    "**NOTE:** We will subsample the reference data for faster runtime. This should be removed for obtaining reproducible reslts.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed481a4-5258-468f-8fb5-91ed794d1503",
+   "metadata": {},
+   "source": [
+    "### Benchmark configuration\n",
+    "- The `subsampling` and `numeric_risk_prompting` key-word arguments are examples of optional benchmark configurations. See this [page for a list of available configs](https://github.com/socialfoundations/folktexts?tab=readme-ov-file#benchmark-options)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bbb17808",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Received non-standard ACS argument 'subsampling' (using subsampling=0.005 instead of default subsampling=None). This may affect reproducibility.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "Using zero-shot prompting.\n",
+      "CPU times: user 52.6 s, sys: 1min 23s, total: 2min 16s\n",
+      "Wall time: 2min 21s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.benchmark import Benchmark, BenchmarkConfig\n",
+    "\n",
+    "# Note: This argument is optional. Omit, or set to 1 for reproducible benchmarking on the full data\n",
+    "subsampling_ratio = 0.005\n",
+    "\n",
+    "bench = Benchmark.make_acs_benchmark(\n",
+    "    model=model_name,\n",
+    "    task_name=\"ACSIncome\",\n",
+    "    subsampling=subsampling_ratio,\n",
+    "    numeric_risk_prompting=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f785fc1",
+   "metadata": {},
+   "source": [
+    "### 4. Run benchmark\n",
+    "\n",
+    "Results will be saved in a folder RESULTS_DIR. There is \n",
+    "* `.json` file contains evaluated metrics\n",
+    "* `.cvs` file contains risk scores of each datapoint\n",
+    "* folder called `imgs/` contains figures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "62b90528",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2706f59a1b1b4a7492f9d9376ba60cd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/52 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to compute ECE quantile: The smallest edge difference is numerically 0.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 832,\n",
+       " 'n_positives': 305,\n",
+       " 'n_negatives': 527,\n",
+       " 'model_name': 'openai/gpt-4o-mini',\n",
+       " 'accuracy': 0.7884615384615384,\n",
+       " 'tpr': 0.6885245901639344,\n",
+       " 'fnr': 0.3114754098360656,\n",
+       " 'fpr': 0.15370018975332067,\n",
+       " 'tnr': 0.8462998102466793,\n",
+       " 'balanced_accuracy': 0.7674122002053069,\n",
+       " 'precision': 0.7216494845360825,\n",
+       " 'ppr': 0.34975961538461536,\n",
+       " 'log_loss': 0.8249689807687466,\n",
+       " 'brier_score_loss': np.float64(0.15596153846153846),\n",
+       " 'tpr_ratio': 0.0,\n",
+       " 'tpr_diff': 0.782608695652174,\n",
+       " 'precision_ratio': 0.0,\n",
+       " 'precision_diff': 0.9,\n",
+       " 'tnr_ratio': 0.8177339901477833,\n",
+       " 'tnr_diff': 0.18226600985221675,\n",
+       " 'fnr_ratio': 0.21739130434782608,\n",
+       " 'fnr_diff': 0.782608695652174,\n",
+       " 'ppr_ratio': 0.0,\n",
+       " 'ppr_diff': 0.47619047619047616,\n",
+       " 'accuracy_ratio': 0.7,\n",
+       " 'accuracy_diff': 0.30000000000000004,\n",
+       " 'balanced_accuracy_ratio': 0.5961800818553888,\n",
+       " 'balanced_accuracy_diff': 0.33867276887871856,\n",
+       " 'fpr_ratio': 0.0,\n",
+       " 'fpr_diff': 0.18226600985221675,\n",
+       " 'equalized_odds_ratio': 0.0,\n",
+       " 'equalized_odds_diff': 0.782608695652174,\n",
+       " 'roc_auc': np.float64(0.8337263197187919),\n",
+       " 'ece': 0.032091346153846276,\n",
+       " 'ece_quantile': None,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/ACSIncome_subsampled-0.005_seed-42_hash-305608976.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': True,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'openai/gpt-4o-mini',\n",
+       "  'model_hash': 920159687,\n",
+       "  'task_name': 'ACSIncome',\n",
+       "  'task_hash': 127998692,\n",
+       "  'dataset_name': 'ACSIncome_subsampled-0.005_seed-42_hash-305608976',\n",
+       "  'dataset_hash': 305608976},\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RESULTS_DIR = \"res\"\n",
+    "bench.run(results_root_dir=RESULTS_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20636ce4",
+   "metadata": {},
+   "source": [
+    "### 4. Visualize results\n",
+    "\n",
+    "We can also visualize the results inline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "edd5bf8f-cd70-4276-9c14-f1e0012aee0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve.pdf',\n",
+       " 'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve.pdf',\n",
+       " 'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution.pdf',\n",
+       " 'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution_per_label.pdf',\n",
+       " 'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve_per_subgroup.pdf',\n",
+       " 'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve_per_subgroup.pdf'}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bench.plot_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b83085bc-9df7-43b0-9282-cfd4c83713b7",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/notebooks/paper-plots-and-tables.ipynb b/.doctrees/nbsphinx/notebooks/paper-plots-and-tables.ipynb
new file mode 100644
index 0000000..ee45e93
--- /dev/null
+++ b/.doctrees/nbsphinx/notebooks/paper-plots-and-tables.ipynb
@@ -0,0 +1,4094 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a436d249-9dfc-4d25-9695-549cb440ea18",
+   "metadata": {},
+   "source": [
+    "# Render paper plots and tables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a7df2c18-8ad5-4733-b77e-68afde7064a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import logging\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a247e64e-8d4c-4dc7-beb2-cf70e1492fc2",
+   "metadata": {},
+   "source": [
+    "**Note:** _Change_ the following path to the aggregated results file in your local system \n",
+    "(can be obtained using the `parse-acs-results.ipynb` notebook)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6d4d0fde-5cea-4ed8-b496-00627aac840f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ACS_AGG_RESULTS_PATH = Path(\"../results\") / \"aggregated-results.2024-08.csv\"\n",
+    "ACS_AGG_RESULTS_PATH = Path(ACS_AGG_RESULTS_PATH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ddc248c4-a5c5-4c95-9f2a-3d386c830520",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "results_df.shape=(210, 64)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>penai/gpt-4o-mini__ACSTravelTime__-1__Num</th>\n",
+       "      <td>0.570906</td>\n",
+       "      <td>0.315286</td>\n",
+       "      <td>0.624340</td>\n",
+       "      <td>0.553706</td>\n",
+       "      <td>0.070915</td>\n",
+       "      <td>0.876372</td>\n",
+       "      <td>0.267584</td>\n",
+       "      <td>0.149517</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.382569</td>\n",
+       "      <td>...</td>\n",
+       "      <td>GPT 4o mini (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.510013</td>\n",
+       "      <td>0.450000</td>\n",
+       "      <td>0.570968</td>\n",
+       "      <td>0.223652</td>\n",
+       "      <td>0.384149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>penai/gpt-4o-mini__ACSTravelTime__-1__QA</th>\n",
+       "      <td>0.551154</td>\n",
+       "      <td>0.119294</td>\n",
+       "      <td>0.812222</td>\n",
+       "      <td>0.588927</td>\n",
+       "      <td>0.113947</td>\n",
+       "      <td>0.823835</td>\n",
+       "      <td>0.404025</td>\n",
+       "      <td>0.393327</td>\n",
+       "      <td>0.393301</td>\n",
+       "      <td>0.274655</td>\n",
+       "      <td>...</td>\n",
+       "      <td>GPT 4o mini (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.998499</td>\n",
+       "      <td>0.602202</td>\n",
+       "      <td>0.970688</td>\n",
+       "      <td>0.593339</td>\n",
+       "      <td>0.365205</td>\n",
+       "      <td>0.772858</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 64 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           accuracy  accuracy_diff  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num  0.570906       0.315286   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA   0.551154       0.119294   \n",
+       "\n",
+       "                                           accuracy_ratio  balanced_accuracy  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num        0.624340           0.553706   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA         0.812222           0.588927   \n",
+       "\n",
+       "                                           balanced_accuracy_diff  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num                0.070915   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA                 0.113947   \n",
+       "\n",
+       "                                           balanced_accuracy_ratio  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num                 0.876372   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA                  0.823835   \n",
+       "\n",
+       "                                           brier_score_loss       ece  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num          0.267584  0.149517   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA           0.404025  0.393327   \n",
+       "\n",
+       "                                           ece_quantile  equalized_odds_diff  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num           NaN             0.382569   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA       0.393301             0.274655   \n",
+       "\n",
+       "                                           ...              name  is_inst  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num  ...  GPT 4o mini (it)     True   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA   ...  GPT 4o mini (it)     True   \n",
+       "\n",
+       "                                           num_features  uses_all_features  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num            -1               True   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA             -1               True   \n",
+       "\n",
+       "                                           fit_thresh_on_100  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num           0.250000   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA            0.998499   \n",
+       "\n",
+       "                                           fit_thresh_accuracy  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num             0.510013   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA              0.602202   \n",
+       "\n",
+       "                                           optimal_thresh  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num        0.450000   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA         0.970688   \n",
+       "\n",
+       "                                           optimal_thresh_accuracy  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num                 0.570968   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA                  0.593339   \n",
+       "\n",
+       "                                          score_stdev  score_mean  \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num    0.223652    0.384149  \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA     0.365205    0.772858  \n",
+       "\n",
+       "[2 rows x 64 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_df = pd.read_csv(ACS_AGG_RESULTS_PATH, index_col=0)\n",
+    "print(f\"{results_df.shape=}\")\n",
+    "results_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a70081bb-71f2-4dc6-95d4-0919b0eb93b9",
+   "metadata": {},
+   "source": [
+    "Remove `gemma-2-*` results (need to investigate why results are so poor):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f461c5d8-aca5-47ba-b6a6-bc18a441c1ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "results_df.shape=(170, 64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_df = results_df.drop(index=[id_ for id_ in results_df.index if \"gemma-2-\" in id_.lower()])\n",
+    "print(f\"{results_df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b658cc4b-e0b0-4dd0-a4c6-00c5ed79d4bf",
+   "metadata": {},
+   "source": [
+    "## Run baseline ML classifiers on the benchmark ACS tasks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9f02d293-7725-4754-bce8-caa40c93a362",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5299d2a9-3723-44cc-a88b-fbba1d631eef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ALL_TASKS = [\n",
+    "    \"ACSIncome\",\n",
+    "    \"ACSMobility\",\n",
+    "    \"ACSEmployment\",\n",
+    "    \"ACSTravelTime\",\n",
+    "    \"ACSPublicCoverage\",\n",
+    "]\n",
+    "\n",
+    "model_col = \"config_model_name\"\n",
+    "task_col = \"config_task_name\"\n",
+    "numeric_prompt_col = \"config_numeric_risk_prompting\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64b72e20-a5d6-442b-8ba9-f7402f87b77b",
+   "metadata": {},
+   "source": [
+    "List all baseline classifiers here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1749fee2-ea21-41e9-b7f0-9ae5d0868409",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
+    "from xgboost import XGBClassifier    # NOTE: requires `pip install xgboost`\n",
+    "\n",
+    "baselines = {\n",
+    "    \"LR\": LogisticRegression(),\n",
+    "    \"GBM\": HistGradientBoostingClassifier(),\n",
+    "    \"XGBoost\": XGBClassifier(),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "72fb9307-a5c1-4805-8656-cc352cc736d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.acs.acs_dataset import ACSDataset\n",
+    "from folktexts.evaluation import evaluate_predictions\n",
+    "from collections import defaultdict\n",
+    "\n",
+    "def fit_and_eval(\n",
+    "    clf,\n",
+    "    X_train, y_train,\n",
+    "    X_test, y_test, s_test,\n",
+    "    fillna=False,\n",
+    "    save_scores_df_path: str = None,\n",
+    ") -> dict:\n",
+    "    \"\"\"Fit and evaluate a given classifier on the given data.\"\"\"\n",
+    "    assert len(X_train) == len(y_train) and len(X_test) == len(y_test) == len(s_test)\n",
+    "\n",
+    "    train_nan_count = X_train.isna().any(axis=1).sum()\n",
+    "    if fillna and train_nan_count > 0:\n",
+    "        # Fill NaNs with value=-1\n",
+    "        X_train = X_train.fillna(axis=\"columns\", value=-1)\n",
+    "        X_test = X_test.fillna(axis=\"columns\", value=-1)\n",
+    "\n",
+    "    # Fit on train data\n",
+    "    clf.fit(X_train, y_train)\n",
+    "\n",
+    "    # Evaluate on test data\n",
+    "    y_test_scores = clf.predict_proba(X_test)[:, -1]\n",
+    "    test_results = evaluate_predictions(\n",
+    "        y_true=y_test.to_numpy(),\n",
+    "        y_pred_scores=y_test_scores,\n",
+    "        sensitive_attribute=s_test,\n",
+    "        threshold=0.5,\n",
+    "    )\n",
+    "\n",
+    "    # Optionally, save test scores DF\n",
+    "    if save_scores_df_path:\n",
+    "        scores_df = pd.DataFrame({\n",
+    "            \"risk_score\": y_test_scores,\n",
+    "            \"label\": y_test,\n",
+    "        })\n",
+    "        scores_df.to_csv(save_scores_df_path)\n",
+    "        test_results[\"predictions_path\"] = save_scores_df_path\n",
+    "\n",
+    "    return test_results\n",
+    "\n",
+    "def run_baselines(baselines, tasks) -> dict:\n",
+    "    \"\"\"Run baseline classifiers on all acs tasks.\"\"\"\n",
+    "    baseline_results = defaultdict(dict)\n",
+    "\n",
+    "    # Prepare progress bar\n",
+    "    progress_bar = tqdm(\n",
+    "        total=len(tasks) * len(baselines),\n",
+    "        leave=True,\n",
+    "    )\n",
+    "\n",
+    "    for task in tasks:\n",
+    "        progress_bar.set_postfix({\"task\": task})\n",
+    "\n",
+    "        # Load ACS task data\n",
+    "        acs_dataset = ACSDataset.make_from_task(task=task, cache_dir=DATA_DIR)\n",
+    "    \n",
+    "        # Get train/test data\n",
+    "        X_train, y_train = acs_dataset.get_train()\n",
+    "        X_test, y_test = acs_dataset.get_test()\n",
+    "    \n",
+    "        # Get sensitive attribute test data\n",
+    "        s_test = None\n",
+    "        if acs_dataset.task.sensitive_attribute is not None:\n",
+    "            s_test = acs_dataset.get_sensitive_attribute_data().loc[y_test.index]\n",
+    "    \n",
+    "        for clf_name, clf in baselines.items():\n",
+    "            progress_bar.set_postfix({\"task\": task, \"clf\": clf_name})\n",
+    "\n",
+    "            try:\n",
+    "                baseline_results[task][clf_name] = fit_and_eval(\n",
+    "                    clf=clf,\n",
+    "                    X_train=X_train, y_train=y_train,\n",
+    "                    X_test=X_test, y_test=y_test, s_test=s_test,\n",
+    "                    fillna=(clf_name == \"LR\"),\n",
+    "                    save_scores_df_path=ACS_AGG_RESULTS_PATH.parent / f\"baseline_scores.{clf_name}.{task}.csv\"\n",
+    "                )\n",
+    "            except Exception as err:\n",
+    "                logging.error(err)\n",
+    "            finally:\n",
+    "                progress_bar.update()\n",
+    "\n",
+    "    return baseline_results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f359a7a3-af13-4516-a07f-7d09f71f99ac",
+   "metadata": {},
+   "source": [
+    "Flatten results and add extra columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4580a3a8-279d-4286-a8d0-01d228b0d92e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parse_baseline_results(baseline_results) -> list:\n",
+    "    \"\"\"Flatten and parse baseline results.\"\"\"\n",
+    "    parsed_results_list = list()\n",
+    "    \n",
+    "    for task, task_results in baseline_results.items():\n",
+    "    \n",
+    "        for clf, clf_results in task_results.items():\n",
+    "            parsed_results = clf_results.copy()\n",
+    "    \n",
+    "            parsed_results[\"config_task_name\"] = task\n",
+    "            parsed_results[\"config_model_name\"] = clf\n",
+    "            parsed_results[\"name\"] = clf\n",
+    "            parsed_results[\"num_features\"] = -1\n",
+    "            parsed_results[\"uses_all_features\"] = True\n",
+    "    \n",
+    "            parsed_results_list.append(parsed_results)\n",
+    "\n",
+    "    return parsed_results_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70b0dd85-79f0-4a6b-a2e1-72fbeb84d85a",
+   "metadata": {},
+   "source": [
+    "Check if baseline results were already computed. If so, load csv; otherwise, compute and save."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e04b361c-780c-4e04-a817-6b2409db84e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading pre-computed baseline results from ../results/baseline-results.GBM.LR.XGBoost.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>threshold</th>\n",
+       "      <th>n_samples</th>\n",
+       "      <th>n_positives</th>\n",
+       "      <th>n_negatives</th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>tpr</th>\n",
+       "      <th>fnr</th>\n",
+       "      <th>fpr</th>\n",
+       "      <th>tnr</th>\n",
+       "      <th>...</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>predictions_path</th>\n",
+       "      <th>config_task_name</th>\n",
+       "      <th>config_model_name</th>\n",
+       "      <th>name</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>new_index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>XGBoost_ACSPublicCoverage</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>113829</td>\n",
+       "      <td>33971</td>\n",
+       "      <td>79858</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.801650</td>\n",
+       "      <td>0.515175</td>\n",
+       "      <td>0.484825</td>\n",
+       "      <td>0.076486</td>\n",
+       "      <td>0.923514</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.368044</td>\n",
+       "      <td>0.839742</td>\n",
+       "      <td>0.004371</td>\n",
+       "      <td>0.004271</td>\n",
+       "      <td>/fast/groups/sf/folktexts-results/2024-07-03/b...</td>\n",
+       "      <td>ACSPublicCoverage</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GBM_ACSIncome</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>166450</td>\n",
+       "      <td>61233</td>\n",
+       "      <td>105217</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.813584</td>\n",
+       "      <td>0.727973</td>\n",
+       "      <td>0.272027</td>\n",
+       "      <td>0.136594</td>\n",
+       "      <td>0.863406</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.630389</td>\n",
+       "      <td>0.890792</td>\n",
+       "      <td>0.007721</td>\n",
+       "      <td>0.007146</td>\n",
+       "      <td>/fast/groups/sf/folktexts-results/2024-07-03/b...</td>\n",
+       "      <td>ACSIncome</td>\n",
+       "      <td>GBM</td>\n",
+       "      <td>GBM</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           threshold  n_samples  n_positives  n_negatives  \\\n",
+       "new_index                                                                   \n",
+       "XGBoost_ACSPublicCoverage        0.5     113829        33971        79858   \n",
+       "GBM_ACSIncome                    0.5     166450        61233       105217   \n",
+       "\n",
+       "                           model_name  accuracy       tpr       fnr       fpr  \\\n",
+       "new_index                                                                       \n",
+       "XGBoost_ACSPublicCoverage         NaN  0.801650  0.515175  0.484825  0.076486   \n",
+       "GBM_ACSIncome                     NaN  0.813584  0.727973  0.272027  0.136594   \n",
+       "\n",
+       "                                tnr  ...  equalized_odds_diff   roc_auc  \\\n",
+       "new_index                            ...                                  \n",
+       "XGBoost_ACSPublicCoverage  0.923514  ...             0.368044  0.839742   \n",
+       "GBM_ACSIncome              0.863406  ...             0.630389  0.890792   \n",
+       "\n",
+       "                                ece  ece_quantile  \\\n",
+       "new_index                                           \n",
+       "XGBoost_ACSPublicCoverage  0.004371      0.004271   \n",
+       "GBM_ACSIncome              0.007721      0.007146   \n",
+       "\n",
+       "                                                            predictions_path  \\\n",
+       "new_index                                                                      \n",
+       "XGBoost_ACSPublicCoverage  /fast/groups/sf/folktexts-results/2024-07-03/b...   \n",
+       "GBM_ACSIncome              /fast/groups/sf/folktexts-results/2024-07-03/b...   \n",
+       "\n",
+       "                            config_task_name  config_model_name     name  \\\n",
+       "new_index                                                                  \n",
+       "XGBoost_ACSPublicCoverage  ACSPublicCoverage            XGBoost  XGBoost   \n",
+       "GBM_ACSIncome                      ACSIncome                GBM      GBM   \n",
+       "\n",
+       "                           num_features  uses_all_features  \n",
+       "new_index                                                   \n",
+       "XGBoost_ACSPublicCoverage            -1               True  \n",
+       "GBM_ACSIncome                        -1               True  \n",
+       "\n",
+       "[2 rows x 42 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "BASELINE_RESULTS_PATH = ACS_AGG_RESULTS_PATH.parent / (\"baseline-results.\" + \".\".join(sorted(baselines.keys())) + \".csv\")\n",
+    "\n",
+    "# If saved results exists: load\n",
+    "if BASELINE_RESULTS_PATH.exists():\n",
+    "    print(f\"Loading pre-computed baseline results from {BASELINE_RESULTS_PATH.as_posix()}\")\n",
+    "    baselines_df = pd.read_csv(BASELINE_RESULTS_PATH, index_col=0)\n",
+    "\n",
+    "# Compute baseline results\n",
+    "else:\n",
+    "    print(f\"Computing baseline results and saving to {BASELINE_RESULTS_PATH.as_posix()}\")\n",
+    "    # Compute baseline results\n",
+    "    baseline_results = run_baselines(baselines, tasks=ALL_TASKS)\n",
+    "\n",
+    "    # Parse results\n",
+    "    parsed_results_list = parse_baseline_results(baseline_results)\n",
+    "\n",
+    "    # Construct DF\n",
+    "    baselines_df = pd.DataFrame(parsed_results_list, index=[r[\"name\"] for r in parsed_results_list])\n",
+    "    \n",
+    "    # Save DF to disk\n",
+    "    baselines_df.to_csv(BASELINE_RESULTS_PATH)\n",
+    "\n",
+    "# Untie indices\n",
+    "baselines_df[\"new_index\"] = [(r[\"name\"] + \"_\" + r[task_col]) for _, r in baselines_df.iterrows()]\n",
+    "baselines_df = baselines_df.set_index(\"new_index\", drop=True)\n",
+    "\n",
+    "# Show 2 random rows\n",
+    "baselines_df.sample(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "07b4ddea-bd88-4bee-87cc-b6aaa6af5630",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "all_results_df.shape=(185, 64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_results_df = pd.concat((results_df, baselines_df))\n",
+    "print(f\"{all_results_df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b657133-120f-4ddb-95ef-b7106b9511fb",
+   "metadata": {},
+   "source": [
+    "## Prepare results table for each task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "317e5744-31e3-48ef-b051-484f79b01a8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_metrics = [\"ece\", \"brier_score_loss\", \"roc_auc\", \"accuracy\", \"fit_thresh_accuracy\", \"score_stdev\"] #, \"score_mean\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9df28300-70ee-47d7-b7ad-23d06ed415cc",
+   "metadata": {},
+   "source": [
+    "Add model size and model family columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "41009307-905b-4498-b98e-972b589f68ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "config_task_name   model_family\n",
+       "ACSEmployment      -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "ACSIncome          -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "ACSMobility        -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "ACSPublicCoverage  -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "ACSTravelTime      -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "Name: accuracy, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from folktexts.llm_utils import get_model_size_B\n",
+    "\n",
+    "all_results_df[\"model_size\"] = [\n",
+    "    (\n",
+    "        get_model_size_B(row[\"name\"], default=300)  # Set GPT 4o size as the largest (300B)\n",
+    "        if row[\"name\"] not in baselines else \"-\"\n",
+    "    )\n",
+    "    for _, row in all_results_df.iterrows()\n",
+    "]\n",
+    "\n",
+    "def get_model_family(model_name) -> str:\n",
+    "    if \"gpt\" in model_name.lower():\n",
+    "        return \"OpenAI\"\n",
+    "    elif \"llama\" in model_name.lower():\n",
+    "        return \"Llama\"\n",
+    "    elif \"mistral\" in model_name.lower() or \"mixtral\" in model_name.lower():\n",
+    "        return \"Mistral\"\n",
+    "    elif \"gemma\" in model_name.lower():\n",
+    "        return \"Gemma\"\n",
+    "    elif \"yi\" in model_name.lower():\n",
+    "        return \"Yi\"\n",
+    "    elif \"qwen\" in model_name.lower():\n",
+    "        return \"Qwen\"\n",
+    "    else:\n",
+    "        return \"-\"\n",
+    "\n",
+    "all_results_df[\"model_family\"] = [get_model_family(row[model_col]) for _, row in all_results_df.iterrows()]\n",
+    "all_results_df.groupby([task_col, \"model_family\"])[\"accuracy\"].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "027d43fb-8290-499c-847f-70e1315a2ae3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_sort_key(name, task_df):\n",
+    "    \"\"\"Sort key for paper table rows.\"\"\"\n",
+    "\n",
+    "    if \"gpt\" in name.lower():\n",
+    "        key = 11\n",
+    "    elif \"llama\" in name.lower():\n",
+    "        key = 10\n",
+    "    elif \"mixtral\" in name.lower():\n",
+    "        key = 9\n",
+    "    elif \"mistral\" in name.lower():\n",
+    "        key = 8\n",
+    "    elif \"yi\" in name.lower():\n",
+    "        key = 7\n",
+    "    elif \"gemma\" in name.lower():\n",
+    "        key = 6\n",
+    "    else:\n",
+    "        return 0\n",
+    "\n",
+    "    row = task_df.loc[name]\n",
+    "    # return (1e5 * key) + (row[\"model_size\"] * 100) + int(row[\"is_inst\"] * 10) + (1 if row[numeric_prompt_col] else 0)\n",
+    "    return (row[\"model_size\"] * 1e9) + int(row[\"is_inst\"] * 1e6) + 1e3 * (1 if row[numeric_prompt_col] else 0) + key\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "871c31ae-6a64-4b8a-9773-f1d430be9b36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def latex_colored_float_format(val, all_values, higher_is_better=True):\n",
+    "    \"\"\"Map a cell's value to its colored latex code.\n",
+    "\n",
+    "    Current definition:\n",
+    "    - use cyan color gradient for good values;\n",
+    "    - use orange color gradient for bad values;\n",
+    "    - use no color for anything in between;\n",
+    "    \"\"\"\n",
+    "    min_val, max_val = np.min(all_values), np.max(all_values)\n",
+    "\n",
+    "    low_pct_val, high_pct_val = [\n",
+    "        min_val + (max_val - min_val) * interp_point\n",
+    "        for interp_point in [0.1, 0.9]\n",
+    "    ]\n",
+    "\n",
+    "    # Use rounded value or original value for coloring?\n",
+    "    # > Using rounded value for consistency in table\n",
+    "    # val = np.round(val, decimals=2)\n",
+    "\n",
+    "    # Use no color for middle 33% of values\n",
+    "    if low_pct_val <= val <= high_pct_val:\n",
+    "        return f\"{val:.2f}\"\n",
+    "\n",
+    "    if val < low_pct_val:\n",
+    "        color = \"orange\" if higher_is_better else \"cyan\"\n",
+    "        color_value = 100 * (\n",
+    "            (low_pct_val - val) / (low_pct_val - min_val))\n",
+    "        \n",
+    "    elif val > high_pct_val:\n",
+    "        color = \"cyan\" if higher_is_better else \"orange\"\n",
+    "        color_value = 100 * (\n",
+    "            (val - high_pct_val) / (max_val - high_pct_val))\n",
+    "\n",
+    "    else:\n",
+    "        raise RuntimeError(f\"{val}\")\n",
+    "\n",
+    "    # Note: halving `color_value` to have softer colors\n",
+    "    color_value /= 4\n",
+    "\n",
+    "    return (\n",
+    "        r\"\\cellcolor{\"\n",
+    "        + f\"{color}!{color_value:.1f}\"\n",
+    "        + r\"} \"\n",
+    "        + f\"{val:.2f}\"\n",
+    "    )\n",
+    "\n",
+    "higher_is_better_cols = {\"roc_auc\", \"accuracy\", \"fit_thresh_accuracy\"}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c1a03d1-eac4-49c3-9243-3291bfa1640e",
+   "metadata": {},
+   "source": [
+    "## Output latex results tables - colored!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61778ae8-af37-404f-946c-dda26fef6836",
+   "metadata": {},
+   "source": [
+    "Set whether to use **_Numeric_ prompting** or **_multiple-choice Q&A_ prompting**!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "eb0096df-e27a-4f10-a42a-63dbeeae0dce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NUMERIC_PROMPTING = False\n",
+    "NUMERIC_PROMPTING = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8461cf3-b042-4ad3-9cf5-ca383ad66514",
+   "metadata": {},
+   "source": [
+    "Whether to **combine results from both prompting schemes** in the latex tables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "40f8b070-8c7b-4935-af5b-4fa4789c1bc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "COMBINE_PROMPTING_RESULTS = True\n",
+    "# COMBINE_PROMPTING_RESULTS = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "477e4f80-2c42-458b-a98c-b32a95c7d67a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*** ACSINCOME ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & \\cellcolor{cyan!25.0} 0.05 & \\cellcolor{cyan!25.0} 0.16 & \\cellcolor{cyan!20.6} 0.83 & \\cellcolor{cyan!24.4} 0.78 & 0.24 & 0.24 & \\cellcolor{cyan!17.6} 0.85 & 0.74 \\\\\n",
+      "Mixtral 8x22B (it) & 0.11 & \\cellcolor{cyan!15.5} 0.17 & \\cellcolor{cyan!25.0} 0.84 & \\cellcolor{cyan!18.9} 0.77 & 0.21 & \\cellcolor{cyan!3.6} 0.22 & \\cellcolor{cyan!11.2} 0.85 & \\cellcolor{cyan!11.1} 0.76 \\\\\n",
+      "Mixtral 8x22B & 0.13 & \\cellcolor{cyan!3.6} 0.18 & \\cellcolor{cyan!9.6} 0.82 & \\cellcolor{cyan!2.4} 0.74 & \\cellcolor{cyan!13.2} 0.17 & \\cellcolor{cyan!21.6} 0.19 & \\cellcolor{cyan!16.5} 0.85 & 0.68 \\\\\n",
+      "Llama 3 70B (it) & 0.25 & 0.23 & \\cellcolor{cyan!22.1} 0.84 & 0.67 & 0.27 & 0.27 & \\cellcolor{cyan!25.0} 0.86 & 0.69 \\\\\n",
+      "Llama 3 70B & 0.27 & 0.24 & \\cellcolor{cyan!6.7} 0.82 & 0.54 & 0.20 & \\cellcolor{cyan!14.9} 0.20 & \\cellcolor{cyan!20.8} 0.86 & 0.70 \\\\\n",
+      "Mixtral 8x7B (it) & 0.10 & \\cellcolor{cyan!15.5} 0.17 & \\cellcolor{cyan!22.8} 0.84 & \\cellcolor{cyan!12.8} 0.76 & \\cellcolor{cyan!16.8} 0.16 & \\cellcolor{cyan!25.0} 0.18 & \\cellcolor{cyan!19.7} 0.86 & \\cellcolor{cyan!25.0} 0.78 \\\\\n",
+      "Mixtral 8x7B & \\cellcolor{cyan!4.0} 0.07 & \\cellcolor{cyan!13.1} 0.17 & \\cellcolor{cyan!3.7} 0.81 & \\cellcolor{cyan!25.0} 0.78 & \\cellcolor{cyan!10.6} 0.17 & \\cellcolor{cyan!11.5} 0.21 & 0.83 & 0.65 \\\\\n",
+      "Yi 34B (it) & 0.22 & 0.21 & 0.80 & 0.48 & \\cellcolor{cyan!1.3} 0.19 & \\cellcolor{cyan!18.8} 0.19 & \\cellcolor{cyan!19.7} 0.86 & 0.72 \\\\\n",
+      "Yi 34B & 0.15 & 0.19 & \\cellcolor{cyan!17.7} 0.83 & 0.61 & 0.25 & \\cellcolor{cyan!2.5} 0.22 & \\cellcolor{cyan!13.3} 0.85 & 0.62 \\\\\n",
+      "Llama 3 8B (it) & 0.23 & 0.23 & \\cellcolor{cyan!0.1} 0.81 & 0.67 & 0.32 & 0.30 & \\cellcolor{cyan!13.3} 0.85 & 0.62 \\\\\n",
+      "Llama 3 8B & 0.14 & 0.24 & 0.63 & \\cellcolor{orange!3.7} 0.40 & 0.25 & 0.26 & 0.81 & \\cellcolor{orange!20.8} 0.38 \\\\\n",
+      "Mistral 7B (it) & 0.16 & 0.19 & \\cellcolor{cyan!14.7} 0.83 & 0.70 & 0.21 & \\cellcolor{cyan!4.7} 0.22 & 0.83 & \\cellcolor{cyan!16.5} 0.77 \\\\\n",
+      "Gemma 7B (it) & 0.33 & 0.30 & 0.78 & 0.42 & \\cellcolor{orange!14.7} 0.61 & \\cellcolor{orange!4.7} 0.59 & \\cellcolor{cyan!3.8} 0.84 & \\cellcolor{orange!25.0} 0.37 \\\\\n",
+      "Mistral 7B & \\cellcolor{orange!15.7} 0.36 & 0.32 & 0.75 & 0.49 & 0.20 & 0.23 & 0.80 & 0.73 \\\\\n",
+      "Gemma 7B & 0.15 & 0.20 & 0.80 & 0.73 & 0.24 & 0.27 & 0.76 & \\cellcolor{orange!25.0} 0.37 \\\\\n",
+      "Gemma 2B (it) & 0.28 & 0.31 & \\cellcolor{orange!25.0} 0.50 & \\cellcolor{orange!25.0} 0.37 & \\cellcolor{orange!25.0} 0.63 & \\cellcolor{orange!25.0} 0.63 & 0.73 & \\cellcolor{orange!25.0} 0.37 \\\\\n",
+      "Gemma 2B & \\cellcolor{orange!25.0} 0.37 & \\cellcolor{orange!25.0} 0.37 & \\cellcolor{orange!25.0} 0.50 & 0.63 & \\cellcolor{cyan!25.0} 0.14 & 0.25 & \\cellcolor{orange!25.0} 0.62 & 0.45 \\\\\n",
+      "LR & 0.03 & 0.18 & 0.79 & 0.74 & 0.03 & 0.18 & 0.79 & 0.74 \\\\\n",
+      "GBM & 0.01 & 0.13 & 0.89 & 0.81 & 0.01 & 0.13 & 0.89 & 0.81 \\\\\n",
+      "XGBoost & 0.00 & 0.13 & 0.90 & 0.82 & 0.00 & 0.13 & 0.90 & 0.82 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n",
+      "*** ACSMOBILITY ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & 0.22 & 0.25 & 0.49 & \\cellcolor{cyan!25.0} 0.73 & 0.26 & 0.26 & \\cellcolor{cyan!0.6} 0.57 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Mixtral 8x22B (it) & 0.05 & \\cellcolor{cyan!17.9} 0.20 & \\cellcolor{cyan!25.0} 0.54 & \\cellcolor{cyan!25.0} 0.73 & 0.40 & 0.40 & \\cellcolor{orange!12.8} 0.51 & 0.39 \\\\\n",
+      "Mixtral 8x22B & 0.13 & 0.22 & 0.49 & \\cellcolor{cyan!25.0} 0.73 & \\cellcolor{cyan!10.2} 0.11 & \\cellcolor{cyan!21.2} 0.21 & 0.55 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Llama 3 70B (it) & 0.05 & \\cellcolor{cyan!10.7} 0.20 & 0.52 & \\cellcolor{cyan!25.0} 0.73 & 0.20 & \\cellcolor{cyan!1.5} 0.25 & 0.57 & 0.58 \\\\\n",
+      "Llama 3 70B & 0.06 & \\cellcolor{cyan!7.1} 0.20 & 0.53 & \\cellcolor{cyan!20.7} 0.73 & 0.22 & \\cellcolor{cyan!4.8} 0.24 & 0.55 & 0.53 \\\\\n",
+      "Mixtral 8x7B (it) & 0.11 & 0.21 & 0.51 & \\cellcolor{cyan!25.0} 0.73 & 0.26 & 0.26 & \\cellcolor{cyan!12.8} 0.58 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Mixtral 8x7B & 0.24 & 0.25 & \\cellcolor{orange!25.0} 0.48 & \\cellcolor{cyan!25.0} 0.73 & \\cellcolor{cyan!0.8} 0.14 & \\cellcolor{cyan!18.9} 0.21 & 0.57 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Yi 34B (it) & 0.23 & 0.25 & 0.50 & \\cellcolor{orange!25.0} 0.27 & \\cellcolor{cyan!18.2} 0.09 & \\cellcolor{cyan!23.1} 0.20 & 0.56 & \\cellcolor{cyan!15.4} 0.72 \\\\\n",
+      "Yi 34B & 0.15 & 0.23 & 0.52 & 0.44 & \\cellcolor{cyan!25.0} 0.07 & \\cellcolor{cyan!25.0} 0.20 & 0.57 & \\cellcolor{cyan!20.7} 0.73 \\\\\n",
+      "Llama 3 8B (it) & 0.11 & 0.21 & 0.49 & \\cellcolor{cyan!23.4} 0.73 & 0.15 & \\cellcolor{cyan!13.7} 0.22 & 0.56 & \\cellcolor{cyan!5.3} 0.70 \\\\\n",
+      "Llama 3 8B & 0.14 & 0.21 & 0.51 & \\cellcolor{cyan!19.1} 0.72 & \\cellcolor{cyan!12.5} 0.10 & \\cellcolor{cyan!21.7} 0.20 & 0.55 & \\cellcolor{cyan!24.5} 0.73 \\\\\n",
+      "Mistral 7B (it) & 0.17 & 0.23 & 0.49 & \\cellcolor{cyan!24.5} 0.73 & 0.26 & 0.26 & 0.57 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Gemma 7B (it) & \\cellcolor{orange!8.9} 0.25 & 0.26 & \\cellcolor{orange!3.1} 0.49 & \\cellcolor{cyan!22.3} 0.73 & 0.25 & 0.26 & \\cellcolor{cyan!25.0} 0.58 & \\cellcolor{cyan!21.8} 0.73 \\\\\n",
+      "Mistral 7B & \\cellcolor{orange!25.0} 0.27 & \\cellcolor{orange!25.0} 0.27 & 0.50 & \\cellcolor{cyan!25.0} 0.73 & 0.20 & \\cellcolor{cyan!8.6} 0.23 & 0.53 & \\cellcolor{cyan!23.4} 0.73 \\\\\n",
+      "Gemma 7B & 0.19 & 0.24 & 0.49 & \\cellcolor{cyan!25.0} 0.73 & 0.41 & 0.37 & \\cellcolor{orange!25.0} 0.50 & \\cellcolor{orange!25.0} 0.27 \\\\\n",
+      "Gemma 2B (it) & \\cellcolor{cyan!25.0} 0.02 & \\cellcolor{cyan!25.0} 0.20 & 0.50 & \\cellcolor{cyan!25.0} 0.73 & \\cellcolor{orange!25.0} 0.73 & \\cellcolor{orange!25.0} 0.73 & 0.52 & \\cellcolor{orange!25.0} 0.27 \\\\\n",
+      "Gemma 2B & \\cellcolor{orange!25.0} 0.27 & \\cellcolor{orange!25.0} 0.27 & 0.50 & \\cellcolor{cyan!25.0} 0.73 & 0.25 & 0.26 & 0.51 & 0.34 \\\\\n",
+      "LR & 0.02 & 0.19 & 0.61 & 0.74 & 0.02 & 0.19 & 0.61 & 0.74 \\\\\n",
+      "GBM & 0.01 & 0.17 & 0.74 & 0.76 & 0.01 & 0.17 & 0.74 & 0.76 \\\\\n",
+      "XGBoost & 0.00 & 0.16 & 0.74 & 0.76 & 0.00 & 0.16 & 0.74 & 0.76 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n",
+      "*** ACSEMPLOYMENT ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & 0.23 & 0.23 & 0.80 & 0.73 & 0.28 & 0.29 & 0.79 & 0.65 \\\\\n",
+      "Mixtral 8x22B (it) & 0.06 & \\cellcolor{cyan!21.9} 0.14 & \\cellcolor{cyan!24.4} 0.87 & \\cellcolor{cyan!16.5} 0.79 & \\cellcolor{orange!23.0} 0.38 & \\cellcolor{orange!8.0} 0.39 & 0.60 & 0.51 \\\\\n",
+      "Mixtral 8x22B & 0.15 & 0.18 & 0.82 & \\cellcolor{cyan!20.7} 0.80 & 0.21 & 0.24 & \\cellcolor{cyan!25.0} 0.86 & 0.52 \\\\\n",
+      "Llama 3 70B (it) & \\cellcolor{cyan!0.7} 0.05 & \\cellcolor{cyan!25.0} 0.14 & \\cellcolor{cyan!25.0} 0.88 & \\cellcolor{cyan!25.0} 0.81 & 0.17 & \\cellcolor{cyan!17.1} 0.19 & \\cellcolor{cyan!23.3} 0.85 & \\cellcolor{cyan!15.4} 0.73 \\\\\n",
+      "Llama 3 70B & 0.05 & \\cellcolor{cyan!16.7} 0.15 & \\cellcolor{cyan!13.6} 0.86 & \\cellcolor{cyan!5.8} 0.78 & 0.25 & 0.26 & \\cellcolor{cyan!2.3} 0.82 & 0.52 \\\\\n",
+      "Mixtral 8x7B (it) & 0.07 & \\cellcolor{cyan!16.7} 0.15 & \\cellcolor{cyan!23.2} 0.87 & \\cellcolor{cyan!7.2} 0.78 & 0.22 & 0.24 & \\cellcolor{cyan!6.8} 0.82 & \\cellcolor{cyan!13.7} 0.73 \\\\\n",
+      "Mixtral 8x7B & 0.08 & 0.17 & 0.81 & 0.73 & 0.30 & 0.31 & \\cellcolor{cyan!1.1} 0.81 & \\cellcolor{orange!24.1} 0.45 \\\\\n",
+      "Yi 34B (it) & 0.15 & 0.21 & 0.81 & 0.51 & 0.14 & 0.21 & 0.79 & 0.69 \\\\\n",
+      "Yi 34B & 0.13 & 0.23 & 0.66 & 0.50 & 0.08 & 0.23 & 0.70 & 0.62 \\\\\n",
+      "Llama 3 8B (it) & 0.08 & 0.17 & 0.82 & 0.77 & 0.07 & \\cellcolor{cyan!25.0} 0.19 & 0.79 & \\cellcolor{cyan!25.0} 0.74 \\\\\n",
+      "Llama 3 8B & 0.15 & 0.23 & 0.75 & \\cellcolor{orange!25.0} 0.46 & 0.34 & 0.34 & 0.76 & \\cellcolor{orange!25.0} 0.45 \\\\\n",
+      "Mistral 7B (it) & \\cellcolor{cyan!7.4} 0.04 & 0.19 & 0.79 & 0.69 & \\cellcolor{orange!6.6} 0.35 & 0.36 & 0.72 & 0.63 \\\\\n",
+      "Gemma 7B (it) & \\cellcolor{cyan!8.8} 0.04 & 0.22 & 0.71 & 0.60 & \\cellcolor{orange!10.0} 0.36 & 0.38 & 0.59 & 0.58 \\\\\n",
+      "Mistral 7B & 0.14 & 0.19 & 0.80 & \\cellcolor{cyan!12.9} 0.79 & 0.26 & 0.30 & 0.76 & \\cellcolor{orange!25.0} 0.45 \\\\\n",
+      "Gemma 7B & \\cellcolor{orange!25.0} 0.35 & \\cellcolor{orange!25.0} 0.38 & \\cellcolor{orange!1.6} 0.50 & 0.51 & 0.15 & 0.25 & 0.65 & \\cellcolor{orange!4.1} 0.48 \\\\\n",
+      "Gemma 2B (it) & 0.12 & 0.27 & \\cellcolor{orange!25.0} 0.46 & \\cellcolor{orange!25.0} 0.46 & \\cellcolor{orange!25.0} 0.38 & \\cellcolor{orange!25.0} 0.41 & \\cellcolor{orange!25.0} 0.42 & \\cellcolor{orange!16.3} 0.46 \\\\\n",
+      "Gemma 2B & \\cellcolor{cyan!25.0} 0.01 & 0.23 & 0.57 & 0.53 & \\cellcolor{cyan!25.0} 0.01 & 0.24 & 0.63 & 0.54 \\\\\n",
+      "LR & 0.02 & 0.15 & 0.86 & 0.78 & 0.02 & 0.15 & 0.86 & 0.78 \\\\\n",
+      "GBM & 0.00 & 0.12 & 0.91 & 0.83 & 0.00 & 0.12 & 0.91 & 0.83 \\\\\n",
+      "XGBoost & 0.00 & 0.12 & 0.91 & 0.83 & 0.00 & 0.12 & 0.91 & 0.83 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n",
+      "*** ACSTRAVELTIME ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & 0.15 & 0.27 & 0.58 & 0.57 & 0.39 & 0.40 & 0.65 & 0.55 \\\\\n",
+      "Mixtral 8x22B (it) & 0.12 & \\cellcolor{cyan!23.7} 0.24 & \\cellcolor{cyan!23.5} 0.64 & \\cellcolor{cyan!25.0} 0.59 & 0.31 & 0.33 & 0.66 & \\cellcolor{cyan!17.1} 0.59 \\\\\n",
+      "Mixtral 8x22B & 0.30 & 0.34 & 0.57 & \\cellcolor{cyan!2.4} 0.58 & 0.20 & 0.28 & 0.63 & \\cellcolor{orange!25.0} 0.44 \\\\\n",
+      "Llama 3 70B (it) & 0.12 & \\cellcolor{cyan!22.5} 0.24 & \\cellcolor{cyan!25.0} 0.64 & 0.53 & 0.15 & \\cellcolor{cyan!22.6} 0.24 & \\cellcolor{cyan!25.0} 0.70 & \\cellcolor{cyan!25.0} 0.60 \\\\\n",
+      "Llama 3 70B & 0.08 & \\cellcolor{cyan!12.4} 0.25 & 0.52 & 0.46 & 0.09 & \\cellcolor{cyan!21.4} 0.24 & 0.67 & 0.55 \\\\\n",
+      "Mixtral 8x7B (it) & 0.09 & \\cellcolor{cyan!25.0} 0.24 & 0.61 & 0.57 & \\cellcolor{orange!25.0} 0.45 & \\cellcolor{orange!25.0} 0.45 & 0.66 & 0.52 \\\\\n",
+      "Mixtral 8x7B & 0.07 & \\cellcolor{cyan!16.2} 0.25 & 0.57 & \\cellcolor{cyan!2.4} 0.58 & 0.28 & 0.32 & 0.60 & \\cellcolor{orange!25.0} 0.44 \\\\\n",
+      "Yi 34B (it) & \\cellcolor{cyan!3.7} 0.06 & \\cellcolor{cyan!11.2} 0.25 & 0.50 & \\cellcolor{orange!25.0} 0.44 & 0.35 & 0.36 & 0.65 & 0.56 \\\\\n",
+      "Yi 34B & 0.14 & 0.27 & 0.53 & \\cellcolor{orange!25.0} 0.44 & \\cellcolor{cyan!4.5} 0.08 & \\cellcolor{cyan!25.0} 0.24 & 0.62 & 0.56 \\\\\n",
+      "Llama 3 8B (it) & 0.11 & \\cellcolor{cyan!12.4} 0.25 & 0.56 & 0.56 & 0.19 & 0.28 & 0.60 & 0.57 \\\\\n",
+      "Llama 3 8B & 0.12 & 0.26 & \\cellcolor{orange!25.0} 0.48 & \\cellcolor{orange!25.0} 0.44 & 0.08 & \\cellcolor{cyan!7.1} 0.25 & 0.53 & 0.56 \\\\\n",
+      "Mistral 7B (it) & 0.11 & \\cellcolor{cyan!11.2} 0.25 & 0.55 & 0.56 & \\cellcolor{orange!2.7} 0.41 & 0.42 & 0.59 & 0.57 \\\\\n",
+      "Gemma 7B (it) & 0.10 & \\cellcolor{cyan!4.9} 0.26 & \\cellcolor{orange!4.9} 0.49 & \\cellcolor{orange!25.0} 0.44 & \\cellcolor{orange!12.0} 0.42 & 0.43 & 0.53 & 0.56 \\\\\n",
+      "Mistral 7B & \\cellcolor{orange!25.0} 0.44 & \\cellcolor{orange!25.0} 0.44 & 0.50 & 0.56 & \\cellcolor{cyan!22.5} 0.05 & \\cellcolor{cyan!14.2} 0.25 & 0.57 & 0.56 \\\\\n",
+      "Gemma 7B & \\cellcolor{cyan!25.0} 0.03 & \\cellcolor{cyan!13.7} 0.25 & 0.52 & 0.55 & \\cellcolor{cyan!25.0} 0.04 & \\cellcolor{cyan!19.0} 0.24 & 0.61 & 0.58 \\\\\n",
+      "Gemma 2B (it) & 0.19 & 0.28 & 0.50 & 0.56 & 0.34 & 0.36 & \\cellcolor{orange!9.3} 0.49 & 0.56 \\\\\n",
+      "Gemma 2B & \\cellcolor{orange!25.0} 0.44 & \\cellcolor{orange!25.0} 0.44 & 0.50 & 0.56 & 0.09 & \\cellcolor{cyan!4.7} 0.26 & \\cellcolor{orange!25.0} 0.48 & \\cellcolor{orange!25.0} 0.44 \\\\\n",
+      "LR & 0.04 & 0.24 & 0.58 & 0.56 & 0.04 & 0.24 & 0.58 & 0.56 \\\\\n",
+      "GBM & 0.02 & 0.20 & 0.75 & 0.69 & 0.02 & 0.20 & 0.75 & 0.69 \\\\\n",
+      "XGBoost & 0.02 & 0.19 & 0.77 & 0.70 & 0.02 & 0.19 & 0.77 & 0.70 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n",
+      "*** ACSPUBLICCOVERAGE ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & 0.10 & 0.20 & 0.68 & \\cellcolor{cyan!17.6} 0.73 & 0.33 & 0.34 & \\cellcolor{cyan!25.0} 0.71 & 0.60 \\\\\n",
+      "Mixtral 8x22B (it) & \\cellcolor{cyan!25.0} 0.04 & \\cellcolor{cyan!25.0} 0.18 & \\cellcolor{cyan!11.0} 0.71 & \\cellcolor{cyan!25.0} 0.75 & 0.24 & 0.25 & \\cellcolor{cyan!11.7} 0.70 & \\cellcolor{cyan!12.7} 0.72 \\\\\n",
+      "Mixtral 8x22B & \\cellcolor{orange!15.5} 0.29 & \\cellcolor{orange!3.4} 0.29 & 0.54 & \\cellcolor{cyan!0.5} 0.70 & 0.32 & 0.30 & 0.59 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Llama 3 70B (it) & 0.13 & 0.20 & \\cellcolor{cyan!25.0} 0.73 & \\cellcolor{cyan!24.4} 0.75 & 0.16 & \\cellcolor{cyan!16.2} 0.21 & \\cellcolor{cyan!7.0} 0.69 & \\cellcolor{cyan!25.0} 0.75 \\\\\n",
+      "Llama 3 70B & 0.12 & 0.21 & 0.64 & 0.53 & 0.18 & \\cellcolor{cyan!8.3} 0.22 & 0.67 & 0.63 \\\\\n",
+      "Mixtral 8x7B (it) & \\cellcolor{cyan!0.3} 0.06 & \\cellcolor{cyan!18.5} 0.19 & 0.69 & \\cellcolor{cyan!22.2} 0.74 & 0.20 & \\cellcolor{cyan!7.4} 0.23 & \\cellcolor{cyan!12.7} 0.70 & \\cellcolor{cyan!23.3} 0.74 \\\\\n",
+      "Mixtral 8x7B & 0.20 & 0.25 & 0.56 & 0.70 & 0.41 & 0.37 & 0.57 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Yi 34B (it) & 0.22 & 0.24 & 0.57 & \\cellcolor{orange!25.0} 0.31 & \\cellcolor{cyan!16.7} 0.06 & \\cellcolor{cyan!25.0} 0.19 & 0.67 & \\cellcolor{cyan!19.4} 0.74 \\\\\n",
+      "Yi 34B & 0.09 & 0.20 & 0.67 & 0.64 & \\cellcolor{cyan!25.0} 0.04 & \\cellcolor{cyan!17.2} 0.21 & 0.59 & \\cellcolor{cyan!2.1} 0.70 \\\\\n",
+      "Llama 3 8B (it) & 0.17 & 0.22 & 0.64 & 0.68 & 0.11 & \\cellcolor{cyan!12.7} 0.21 & 0.59 & \\cellcolor{cyan!7.7} 0.71 \\\\\n",
+      "Llama 3 8B & 0.20 & 0.25 & 0.51 & \\cellcolor{orange!6.2} 0.34 & 0.41 & 0.38 & 0.55 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Mistral 7B (it) & 0.07 & 0.20 & 0.67 & 0.65 & 0.30 & 0.30 & 0.61 & \\cellcolor{cyan!0.4} 0.70 \\\\\n",
+      "Gemma 7B (it) & 0.18 & 0.24 & 0.57 & 0.61 & 0.30 & 0.34 & \\cellcolor{orange!14.6} 0.46 & 0.50 \\\\\n",
+      "Mistral 7B & \\cellcolor{orange!22.1} 0.30 & \\cellcolor{orange!18.5} 0.30 & 0.50 & 0.70 & 0.29 & 0.30 & \\cellcolor{orange!25.0} 0.45 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Gemma 7B & 0.18 & 0.26 & 0.48 & 0.70 & 0.15 & \\cellcolor{cyan!3.4} 0.23 & 0.49 & 0.49 \\\\\n",
+      "Gemma 2B (it) & 0.24 & \\cellcolor{orange!14.2} 0.29 & \\cellcolor{orange!25.0} 0.42 & 0.42 & \\cellcolor{orange!25.0} 0.70 & \\cellcolor{orange!25.0} 0.70 & 0.54 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Gemma 2B & \\cellcolor{orange!25.0} 0.30 & \\cellcolor{orange!25.0} 0.30 & 0.50 & 0.70 & 0.26 & 0.28 & 0.54 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "LR & 0.03 & 0.19 & 0.70 & 0.72 & 0.03 & 0.19 & 0.70 & 0.72 \\\\\n",
+      "GBM & 0.01 & 0.14 & 0.83 & 0.80 & 0.01 & 0.14 & 0.83 & 0.80 \\\\\n",
+      "XGBoost & 0.00 & 0.14 & 0.84 & 0.80 & 0.00 & 0.14 & 0.84 & 0.80 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils import prettify_model_name\n",
+    "\n",
+    "for task in ALL_TASKS:\n",
+    "    task_df = all_results_df[all_results_df[task_col] == task]\n",
+    "\n",
+    "    # Sort table rows\n",
+    "    sorted_df_index = sorted(\n",
+    "        task_df.index.tolist(),\n",
+    "        key=lambda id_: model_sort_key(id_, task_df),\n",
+    "        reverse=True,\n",
+    "    )\n",
+    "\n",
+    "    latex_tab_cols = [numeric_prompt_col] + table_metrics\n",
+    "\n",
+    "    # latex_table = task_df.sort_values([\"model_family\", \"model_size\", \"is_inst\"], ascending=False).set_index(model_col)[table_metrics].round(3)\n",
+    "    latex_table = task_df.loc[sorted_df_index].set_index(model_col)[latex_tab_cols].round(3)\n",
+    "\n",
+    "    # Keep rows for a single selected prompting scheme\n",
+    "    latex_table = latex_table[\n",
+    "        (latex_table[numeric_prompt_col] == NUMERIC_PROMPTING)\n",
+    "        | latex_table[numeric_prompt_col].isna()\n",
+    "    ]\n",
+    "\n",
+    "    # But optionally join with the columns of the other prompting scheme\n",
+    "    if COMBINE_PROMPTING_RESULTS:\n",
+    "        cols_to_keep = [\"ece\", \"brier_score_loss\", \"roc_auc\", \"accuracy\"]\n",
+    "        latex_table = latex_table[cols_to_keep]\n",
+    "\n",
+    "        latex_table_other_prompt = task_df.loc[sorted_df_index].set_index(model_col)\n",
+    "        latex_table_other_prompt = latex_table_other_prompt[\n",
+    "            (latex_table_other_prompt[numeric_prompt_col] != NUMERIC_PROMPTING)\n",
+    "            | latex_table_other_prompt[numeric_prompt_col].isna()\n",
+    "        ][cols_to_keep].round(3)\n",
+    "\n",
+    "        latex_table = latex_table.join(\n",
+    "            latex_table_other_prompt,\n",
+    "            how=\"left\",\n",
+    "            lsuffix=\" (num)\" if NUMERIC_PROMPTING else \" (mult. choice)\",\n",
+    "            validate=\"1:1\",\n",
+    "        )\n",
+    "    \n",
+    "    # Fill NaNs\n",
+    "    latex_table = latex_table.fillna(\"-\")\n",
+    "\n",
+    "    # Prettify model names\n",
+    "    latex_table[\"Model\"] = [\n",
+    "        prettify_model_name(id_) if id_ not in baselines.keys() else id_\n",
+    "        for id_, row in latex_table.iterrows()\n",
+    "    ]\n",
+    "    latex_table.set_index(\"Model\", drop=True, inplace=True)\n",
+    "\n",
+    "    for col in set(latex_table.columns.tolist()) - {\"score_stdev\", numeric_prompt_col, \"model_family\"}:\n",
+    "        index_without_baselines = [name for name in latex_table.index if name not in baselines]\n",
+    "        index_baselines = list(baselines.keys())\n",
+    "\n",
+    "        col_data = latex_table.loc[index_without_baselines, col].copy()\n",
+    "\n",
+    "        new_col_data = [\n",
+    "            latex_colored_float_format(\n",
+    "                val=col_data.loc[id_], all_values=col_data,\n",
+    "                higher_is_better=any(col_name in col for col_name in higher_is_better_cols),\n",
+    "            )\n",
+    "            for id_ in index_without_baselines\n",
+    "        ] + [\n",
+    "            latex_table.loc[id_, col]\n",
+    "            for id_ in index_baselines\n",
+    "        ]\n",
+    "\n",
+    "        # Set compatible dtype\n",
+    "        latex_table = latex_table.astype({col: str})\n",
+    "\n",
+    "        # Set new data\n",
+    "        latex_table.loc[index_without_baselines + index_baselines, col] = new_col_data\n",
+    "\n",
+    "    # Drop prompting column of not combining both\n",
+    "    latex_table = latex_table.drop(columns=numeric_prompt_col, errors=\"ignore\")\n",
+    "\n",
+    "    # Rename columns to be latex compatible\n",
+    "    latex_table = latex_table.rename(columns={col: col.replace(\"_\", \" \") for col in latex_table.columns})\n",
+    "\n",
+    "    print(f\"*** {task.upper()} ***\\n\")\n",
+    "\n",
+    "    latex_table_str = latex_table.to_latex(float_format=\"%.2f\")\n",
+    "    print(latex_table_str)\n",
+    "\n",
+    "    tables_dir = ACS_AGG_RESULTS_PATH.parent / \"tables\"\n",
+    "    tables_dir.mkdir(exist_ok=True)\n",
+    "    with open(tables_dir / f\"{task}-table.{COMBINE_PROMPTING_RESULTS=}.{NUMERIC_PROMPTING=}.tex\", \"w\") as f_out:\n",
+    "        print(latex_table_str, file=f_out)\n",
+    "    \n",
+    "    print(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d4564ed-1e41-4322-8c87-74fc34265bf5",
+   "metadata": {},
+   "source": [
+    "## Render paper plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82cc9102-66a6-4001-9c36-cf88fa8209e6",
+   "metadata": {},
+   "source": [
+    "Filter by the currently selected prompting scheme:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "37b2b004-44e8-46d3-aa45-2cf168b5e638",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_results_df_original = all_results_df.copy()\n",
+    "\n",
+    "all_results_df = all_results_df[\n",
+    "    (all_results_df[numeric_prompt_col] == NUMERIC_PROMPTING)\n",
+    "    | (all_results_df[numeric_prompt_col].isna())\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "efd4cfdf-ae7e-4770-8bd6-0347342517c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
+       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
+       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
+       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
+       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
+       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
+       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
+       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"whitegrid\", rc={\"grid.linestyle\": \"--\"})\n",
+    "plt.rc(\"figure\", figsize=(4.5, 3.5), dpi=200)\n",
+    "\n",
+    "palette_name = \"tab10\"   # \"colorblind\"\n",
+    "palette = sns.color_palette(palette_name)\n",
+    "sns.set_palette(palette_name)\n",
+    "palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e93c2236-8546-439c-8927-fde4f8ea7350",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMGS_DIR = ACS_AGG_RESULTS_PATH.parent / \"imgs\"\n",
+    "IMGS_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "def save_fig(fig, name, add_prompt_suffix=True):\n",
+    "    num_or_multiple_choice = \"numeric-prompt\" if NUMERIC_PROMPTING else \"multiple-choice-prompt\"\n",
+    "    save_path = (IMGS_DIR / name).with_suffix(\".pdf\")\n",
+    "    if add_prompt_suffix:\n",
+    "        save_path = save_path.with_suffix(f\".{num_or_multiple_choice}.pdf\")\n",
+    "\n",
+    "    fig.savefig(save_path, bbox_inches=\"tight\")\n",
+    "    print(f\"Saved figure to '{save_path.as_posix()}'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbdedf0b-ccb1-4887-8474-b1e661f0fe01",
+   "metadata": {},
+   "source": [
+    "Load scores distributions for each model (and with varying degrees of information)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "32dcc2e9-b122-4931-a4be-811efd7fa6cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_path_col = \"predictions_path\"\n",
+    "\n",
+    "def load_model_scores_df(df_row: pd.Series) -> pd.DataFrame:\n",
+    "    \"\"\"Loads csv containing model scores corresponding to the given DF row.\"\"\"\n",
+    "    if predictions_path_col in df_row and not pd.isna(df_row[predictions_path_col]):\n",
+    "        return pd.read_csv(df_row[predictions_path_col], index_col=0)\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1f7cfb2d-3195-4659-97b2-8b0ada073f23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "465640a48d654dd3999164c4cc8efc3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scores_df_map = {\n",
+    "    id_: load_model_scores_df(row)\n",
+    "    for id_, row in tqdm(all_results_df.iterrows(), total=len(all_results_df))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a55adb53-12c1-4784-885c-6e0597f255e2",
+   "metadata": {},
+   "source": [
+    "#### 1st page illustrative plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "0c8819b1-0033-4d44-ab1e-a4b55a4ebd65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_task = \"ACSIncome\"\n",
+    "\n",
+    "# example_model = \"Mistral-7B-v0.1\"\n",
+    "example_model = \"Meta-Llama-3-70B\"\n",
+    "\n",
+    "baseline_model = \"XGBoost\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "deb6b51b-d49c-4551-85a9-f73cef23287a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "      <th>model_size</th>\n",
+       "      <th>model_family</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Meta-Llama-3-70B__ACSIncome__-1__Num</th>\n",
+       "      <td>0.543953</td>\n",
+       "      <td>0.134809</td>\n",
+       "      <td>0.771466</td>\n",
+       "      <td>0.636284</td>\n",
+       "      <td>0.078752</td>\n",
+       "      <td>0.887756</td>\n",
+       "      <td>0.237630</td>\n",
+       "      <td>0.270157</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.191080</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.89560</td>\n",
+       "      <td>0.784722</td>\n",
+       "      <td>0.8956</td>\n",
+       "      <td>0.784722</td>\n",
+       "      <td>0.253299</td>\n",
+       "      <td>0.638033</td>\n",
+       "      <td>70</td>\n",
+       "      <td>Llama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num</th>\n",
+       "      <td>0.665731</td>\n",
+       "      <td>0.086680</td>\n",
+       "      <td>0.880953</td>\n",
+       "      <td>0.722658</td>\n",
+       "      <td>0.061832</td>\n",
+       "      <td>0.919317</td>\n",
+       "      <td>0.230512</td>\n",
+       "      <td>0.246092</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.232483</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.75718</td>\n",
+       "      <td>0.735764</td>\n",
+       "      <td>0.8239</td>\n",
+       "      <td>0.784848</td>\n",
+       "      <td>0.290286</td>\n",
+       "      <td>0.613968</td>\n",
+       "      <td>70</td>\n",
+       "      <td>Llama</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 66 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               accuracy  accuracy_diff  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num           0.543953       0.134809   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num  0.665731       0.086680   \n",
+       "\n",
+       "                                               accuracy_ratio  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                 0.771466   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num        0.880953   \n",
+       "\n",
+       "                                               balanced_accuracy  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                    0.636284   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num           0.722658   \n",
+       "\n",
+       "                                               balanced_accuracy_diff  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                         0.078752   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num                0.061832   \n",
+       "\n",
+       "                                               balanced_accuracy_ratio  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                          0.887756   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num                 0.919317   \n",
+       "\n",
+       "                                               brier_score_loss       ece  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                   0.237630  0.270157   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num          0.230512  0.246092   \n",
+       "\n",
+       "                                               ece_quantile  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                    NaN   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num           NaN   \n",
+       "\n",
+       "                                               equalized_odds_diff  ...  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                      0.191080  ...   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num             0.232483  ...   \n",
+       "\n",
+       "                                               num_features  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                     -1   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num            -1   \n",
+       "\n",
+       "                                               uses_all_features  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                        True   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num               True   \n",
+       "\n",
+       "                                               fit_thresh_on_100  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                     0.89560   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num            0.75718   \n",
+       "\n",
+       "                                               fit_thresh_accuracy  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                      0.784722   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num             0.735764   \n",
+       "\n",
+       "                                               optimal_thresh  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                   0.8956   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num          0.8239   \n",
+       "\n",
+       "                                               optimal_thresh_accuracy  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                          0.784722   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num                 0.784848   \n",
+       "\n",
+       "                                               score_stdev  score_mean  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num              0.253299    0.638033   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num     0.290286    0.613968   \n",
+       "\n",
+       "                                              model_size  model_family  \n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                  70         Llama  \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num         70         Llama  \n",
+       "\n",
+       "[2 rows x 66 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Data for baseline model\n",
+    "baseline_row = all_results_df[(all_results_df[\"name\"] == baseline_model) & (all_results_df[task_col] == example_task)].iloc[0]\n",
+    "\n",
+    "# Data for base and instruct models\n",
+    "example_df = all_results_df[\n",
+    "    (all_results_df[task_col] == example_task)\n",
+    "    & (all_results_df[\"base_name\"] == example_model)\n",
+    "    & (all_results_df[numeric_prompt_col] == NUMERIC_PROMPTING)\n",
+    "]\n",
+    "\n",
+    "# Sort examples_df to have (base, instruct) ordering\n",
+    "example_df = example_df.sort_values(\"is_inst\", ascending=True)\n",
+    "example_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "fa917ee4-0c17-4857-9bf9-76de9d9a1e6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3388197/4070898515.py:38: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax1.set_xticklabels([\"Base\", \"Instr.\"])\n",
+      "/tmp/ipykernel_3388197/4070898515.py:90: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax3.set_xticklabels([\"Base\", \"Instr.\"])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/teaser-Meta-Llama-3-70B.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2200x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ALPHA = 0.7\n",
+    "N_BINS = 20\n",
+    "\n",
+    "fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(11, 2.5), gridspec_kw=dict(wspace=0.4))\n",
+    "\n",
+    "###\n",
+    "# ROC barplot\n",
+    "###\n",
+    "sns.barplot(\n",
+    "    data=example_df,\n",
+    "    x=\"is_inst\",\n",
+    "    y=\"roc_auc\",\n",
+    "    hue=\"name\",\n",
+    "    alpha=ALPHA,\n",
+    "    width=0.5,\n",
+    "    ax=ax1,\n",
+    ")\n",
+    "\n",
+    "# Add horizontal line and label\n",
+    "ax1.axhline(y=baseline_row[\"roc_auc\"], label=baseline_model, ls=\"--\", color=\"black\", xmin=0.05, xmax=0.95)\n",
+    "ax1.text(\n",
+    "    x=1.15,\n",
+    "    y=baseline_row[\"roc_auc\"] + 1e-3,\n",
+    "    s=baseline_model,\n",
+    "    color=\"black\",\n",
+    "    fontsize=9,\n",
+    "    ha='center',\n",
+    "    va='bottom',\n",
+    "    backgroundcolor='white',\n",
+    "    zorder=-1,\n",
+    ")\n",
+    "\n",
+    "ax1.set_ylim(0, baseline_row[\"roc_auc\"] + 9e-2)\n",
+    "# ax1.set_ylim(0.5, baseline_row[\"roc_auc\"] + 7e-2)\n",
+    "ax1.set_ylabel(\"ROC AUC\")\n",
+    "ax1.set_xlabel(None)\n",
+    "ax1.legend().remove()   # Remove the legend\n",
+    "ax1.set_xticklabels([\"Base\", \"Instr.\"])\n",
+    "ax1.set_title(\"Predictive signal\" + r\"$\\uparrow$\")\n",
+    "\n",
+    "###\n",
+    "# Score distribution\n",
+    "###\n",
+    "bins = np.histogram_bin_edges([], bins=N_BINS, range=(0, 1))\n",
+    "for id_ in example_df.index.tolist():\n",
+    "    sns.histplot(\n",
+    "        scores_df_map[id_][\"risk_score\"],\n",
+    "        alpha=ALPHA * 0.9,\n",
+    "        stat=\"proportion\",\n",
+    "        bins=bins,\n",
+    "        zorder=100,\n",
+    "        ax=ax2,\n",
+    "    )\n",
+    "\n",
+    "# # Draw baseline score distribution\n",
+    "# sns.histplot(\n",
+    "#     scores_df_map[baseline_row.name][\"risk_score\"],\n",
+    "#     alpha=1,\n",
+    "#     bins=bins,\n",
+    "#     stat=\"proportion\",\n",
+    "#     fill=False,\n",
+    "#     color=palette[2],\n",
+    "#     edgecolor=palette[2],\n",
+    "#     hatch=\"/\",\n",
+    "#     zorder=-1,\n",
+    "#     ax=ax2,\n",
+    "# )\n",
+    "\n",
+    "ax2.set_xlabel(None)\n",
+    "ax2.set_title(\"Score distribution\")\n",
+    "\n",
+    "###\n",
+    "# Calibration error\n",
+    "###\n",
+    "sns.barplot(\n",
+    "    data=example_df,\n",
+    "    x=\"is_inst\",\n",
+    "    y=\"ece\",\n",
+    "    hue=\"name\",\n",
+    "    alpha=ALPHA,\n",
+    "    width=0.5,\n",
+    "    ax=ax3,\n",
+    ")\n",
+    "\n",
+    "# Add horizontal line and label\n",
+    "ax3.axhline(y=baseline_row[\"ece\"], label=baseline_model, ls=\"--\", color=\"black\", xmin=0.05, xmax=0.95)\n",
+    "\n",
+    "ax3.set_ylabel(\"ECE\")\n",
+    "ax3.set_xlabel(None)\n",
+    "ax3.set_xticklabels([\"Base\", \"Instr.\"])\n",
+    "ax3.set_title(\"Calibration error\" + \"$\\downarrow$\")\n",
+    "# ax3.legend(\n",
+    "#     loc=\"center left\",\n",
+    "#     bbox_to_anchor=(1.05, 0.5),\n",
+    "# )\n",
+    "\n",
+    "hs, ls = ax3.get_legend_handles_labels()\n",
+    "ax3.legend().remove()\n",
+    "ax2.legend(handles=hs[:2], labels=ls[:2], loc=\"upper center\")\n",
+    "\n",
+    "save_fig(fig, f\"teaser-{example_model}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36a530b0-4e32-4993-9d4d-6cd8fa4330da",
+   "metadata": {},
+   "source": [
+    "#### Score distributions for base/instruct pairs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "dc652329-ac67-48d1-8f2c-632c2acfe483",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting score distributions for the following task: ACSIncome.\n",
+      "len(task_df)=20\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Plotting score distributions for the following task: {example_task}.\")\n",
+    "\n",
+    "task_df = all_results_df[\n",
+    "    (all_results_df[task_col] == example_task)\n",
+    "    & (\n",
+    "        (all_results_df[numeric_prompt_col] == NUMERIC_PROMPTING)\n",
+    "        | (all_results_df[numeric_prompt_col].isna())                  # Baseline models will have NaNs in this col.\n",
+    "    )]\n",
+    "print(f\"{len(task_df)=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42f4f271-b74c-492d-88cf-ce44724c8a76",
+   "metadata": {},
+   "source": [
+    "Sort model pairs as wished for the paper plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f422a80a-b5f4-413f-a4e7-2bde1611d36d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('penai/gpt-4o-mini', 'penai/gpt-4o-mini'),\n",
+       " ('Meta-Llama-3-8B', 'Meta-Llama-3-8B-Instruct'),\n",
+       " ('Meta-Llama-3-70B', 'Meta-Llama-3-70B-Instruct'),\n",
+       " ('Mistral-7B-v0.1', 'Mistral-7B-Instruct-v0.2'),\n",
+       " ('Mixtral-8x7B-v0.1', 'Mixtral-8x7B-Instruct-v0.1'),\n",
+       " ('Mixtral-8x22B-v0.1', 'Mixtral-8x22B-Instruct-v0.1'),\n",
+       " ('Yi-34B', 'Yi-34B-Chat'),\n",
+       " ('gemma-2b', 'gemma-1.1-2b-it'),\n",
+       " ('gemma-7b', 'gemma-1.1-7b-it')]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_model_families = [\"OpenAI\", \"Llama\", \"Mistral\", \"Yi\", \"Gemma\", \"-\"]\n",
+    "\n",
+    "model_pairs = sorted([\n",
+    "        (row[\"base_name\"], row[model_col])\n",
+    "        for id_, row in task_df[task_df[\"is_inst\"] == True].iterrows()\n",
+    "    ],\n",
+    "    key=lambda val: (\n",
+    "        1e6 * sorted_model_families.index((row := task_df[task_df[model_col] == val[0]].iloc[0])[\"model_family\"])\n",
+    "        + row[\"model_size\"]\n",
+    "    )\n",
+    ")\n",
+    "model_pairs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "de08f766-c37d-4c27-9261-684b97c6b921",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/score-distribution.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2400x1200 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "PLOT_THRESHOLD_LINE = False\n",
+    "\n",
+    "N_PLOTS = len(model_pairs) + 1  # plus 1 for the XGBoost baseline\n",
+    "\n",
+    "N_COLS = 5\n",
+    "N_ROWS = math.ceil(N_PLOTS / N_COLS)\n",
+    "\n",
+    "fig, axes = plt.subplots(\n",
+    "    nrows=N_ROWS, ncols=N_COLS,\n",
+    "    figsize=(12, 3 * N_ROWS),\n",
+    "    sharey=True, sharex=False,\n",
+    "    gridspec_kw=dict(\n",
+    "        hspace=0.5,\n",
+    "        wspace=0.06,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Plot settings\n",
+    "plot_config = dict(\n",
+    "    bins=N_BINS,\n",
+    "    binrange=(0, 1),\n",
+    "    stat=\"percent\",\n",
+    ")\n",
+    "\n",
+    "# Plot score distribution for model pairs\n",
+    "for idx, (base_name, instr_name) in enumerate(model_pairs):\n",
+    "    ax_row = idx // N_COLS\n",
+    "    ax_col = idx % N_COLS\n",
+    "\n",
+    "    ax = axes[ax_row, ax_col]\n",
+    "    base_row = task_df[task_df[model_col] == base_name].iloc[0]\n",
+    "    it_row = task_df[task_df[model_col] == instr_name].iloc[0]\n",
+    "\n",
+    "    # Get scores\n",
+    "    base_scores = scores_df_map[base_row.name][\"risk_score\"]\n",
+    "    it_scores = scores_df_map[it_row.name][\"risk_score\"]\n",
+    "\n",
+    "    ax.set_title(base_row[\"name\"])\n",
+    "\n",
+    "    ax.set_xlabel(\"risk scores\")\n",
+    "    if ax_col == 0:\n",
+    "        ax.set_ylabel(\"density (%)\")\n",
+    "\n",
+    "    if ax_row == 0:\n",
+    "        ax.set_ylim(0, 70)\n",
+    "\n",
+    "    ax.set_xlim(-0.03, 1.03)\n",
+    "\n",
+    "    # Render plot\n",
+    "    if \"gpt\" not in instr_name.lower():\n",
+    "        sns.histplot(base_scores, label=\"Base\", color=palette[0], ax=ax, **plot_config)\n",
+    "    sns.histplot(it_scores, label=\"Instruction-tuned\", color=palette[1], ax=ax, **plot_config)\n",
+    "\n",
+    "    # Draw faint vertical line with t^* of each model\n",
+    "    threshold_col = \"optimal_thresh\"\n",
+    "    # threshold_col = \"fit_thresh_on_100\"\n",
+    "\n",
+    "    if PLOT_THRESHOLD_LINE:\n",
+    "        ax.axvline(x=base_row[threshold_col], ls=\"--\", color=palette[0], alpha=0.5)\n",
+    "        ax.axvline(x=it_row[threshold_col], ls=\"--\", color=palette[1], alpha=0.5)\n",
+    "\n",
+    "    # Render legend on a few sub-plots\n",
+    "    if (ax_col == 0 and ax_row == 1) or (ax_col == N_COLS-1 and ax_row == 0):\n",
+    "        ax.legend(loc=\"upper center\")\n",
+    "\n",
+    "        if PLOT_THRESHOLD_LINE:\n",
+    "            ax.text(\n",
+    "                x=base_row[threshold_col] - 5e-2,\n",
+    "                y=43,\n",
+    "                s=\"$t^*$\",\n",
+    "                color=palette[0],\n",
+    "                fontsize=10,\n",
+    "                ha='center',\n",
+    "                va='bottom',\n",
+    "                # zorder=-1,\n",
+    "            ) #.set_bbox(dict(facecolor='white', alpha=0.5))\n",
+    "            \n",
+    "            ax.text(\n",
+    "                x=it_row[threshold_col] + 5e-2,\n",
+    "                y=43,\n",
+    "                s=\"$t^*$\",\n",
+    "                color=palette[1],\n",
+    "                fontsize=10,\n",
+    "                ha='center',\n",
+    "                va='bottom',\n",
+    "                # zorder=-1,\n",
+    "            )\n",
+    "        \n",
+    "\n",
+    "# Plot score distribution for XGBoost baseline\n",
+    "baseline_name = \"XGBoost\"\n",
+    "ax = axes[1, -1]\n",
+    "ax.set_title(baseline_name)\n",
+    "\n",
+    "baseline_row = task_df[task_df[\"name\"] == baseline_name].iloc[0]\n",
+    "xgboost_scores = scores_df_map[baseline_row.name][\"risk_score\"]\n",
+    "\n",
+    "sns.histplot(\n",
+    "    xgboost_scores,\n",
+    "    label=baseline_name,\n",
+    "    color=palette[2],\n",
+    "    ax=ax,\n",
+    "    **plot_config,\n",
+    ")\n",
+    "\n",
+    "ax.set_xlabel(\"risk scores\")\n",
+    "\n",
+    "# # Remove unused plot?\n",
+    "# axes[1, 3].remove()\n",
+    "\n",
+    "axes[1, 4].set_ylabel(\"density (%)\")\n",
+    "axes[1, 4].set_yticks([0, 20, 40, 60])\n",
+    "axes[1, 4].set_yticklabels([0, 20, 40, 60])\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "# Save figure\n",
+    "save_fig(fig, f\"score-distribution\" + (\"_with-threshold\" if PLOT_THRESHOLD_LINE else \"\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1e20b4f-5e3f-4c22-8309-b31f2befa29d",
+   "metadata": {},
+   "source": [
+    "## Calibration curves"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "831d0a85-c162-4f42-8f5e-d57da37da536",
+   "metadata": {},
+   "source": [
+    "Base models vs Instruction-tuned models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "79f2f793-da86-4797-bdf6-b2aee4a20c36",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "len(df)=17\n"
+     ]
+    }
+   ],
+   "source": [
+    "TASK = \"ACSIncome\"\n",
+    "\n",
+    "# df = results_df[(results_df[task_col] == TASK) & (results_df[numeric_prompt_col] == NUMERIC_PROMPTING)]\n",
+    "\n",
+    "df = all_results_df[(all_results_df[task_col] == TASK) & (all_results_df[numeric_prompt_col] == NUMERIC_PROMPTING)]\n",
+    "print(f\"{len(df)=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "e980b891-543f-4c9a-ae24-970af760c64b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-base-and-instr.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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884/SxJJMJsOiRYuEk+k2bdpg5cqVaNmypUK53Nxc/Pbbb9i4cSPkcjm++OILtG/fXmPTh9y+fRtisRjt27fH999/r1BvTk4OPv30U2E0oo+PD27fvo2wsDD0798fK1euRJ06dYTyaWlp+OCDDxAUFAS5XI4ff/wRO3bsUHrcmzdvYu3atcLjnj17YtWqVQojI8ViMX766Sf88ccfAPLvPN+2bRumTZumtM6lS5cKSTpLS0t8//33RaYKunDhAj7++GNs3bq1RNNzXbt2DWvWrIFcLoehoSHmzZuHyZMnF0nChYWF4aOPPsLDhw8RFhaGNWvWqEz2qiOVSvHLL78IjxctWoRp06YpjTU1NRX+/v4KF9GIiIg07fbt2wgMDIS7uzvWrFmjcHFXLpdjx44d+PbbbwEAFy9eRFBQEDp16qRQhyZ+3+bNmwcgf+rHgkTdsGHDMGrUqFK9Hx8fH+Tl5WH+/PmYOXOmQgxisbhUdZXFhQsXFJJ0Xbp0wfLly5VeNI+Ojoafn5/CNIXDhw/H8OHD4efnJyTq2rZti+XLl2skPrFYjA8//FD4/OvUqYMffvgBb7zxhkK5y5cv46OPPkJKSgqSkpKwaNEi7Nmzp9j+1Zo1a5CXl4fFixdj8uTJCrNOxMfHY9asWYiIiIBMJsO6detU9iVLaurUqQDy270gUefl5aV0WsrK9tdff0EsFmPkyJFYvHgxrK2thX3Z2dlYtmwZjh49CgD45ZdfMGnSJJiZmRWppyLOK7Zs2SIklEQiERYsWICZM2cqjEB78uQJFixYgNu3b+PevXvl/jxU+eabb2BnZ4e1a9cWmX70zz//xJo1awAA+/fvR9OmTYUk1Lp16+Dm5iaUzcvLw5o1a7B9+3YA6j/TtLQ0LFq0SEg0NWnSBGvXrkXr1q0Vyh0+fBiff/45cnJyEBUVhSVLluDXX39V+j4q4jNNTEzE/PnzhSTdiBEjsGjRItStW1ehXFJSElasWIHTp0/j1atXWLBgAQ4ePFjsiEJlfv31VyFJN2DAAHz11VdKp1LNzc3F9evX4e/vX+pjUPXx6tUrxMbGCr95RETVgVQqRW5uLl69egWRSISGDRuWe2atwpioI6Iq48KFC0hJSVF4TiaT4dWrV4iIiBDu4AXyk3Tff/+92voWLVqkdr9IJEKfPn3w66+/ClPs/PXXX0oTdQXrlgDAt99+qzRJB+SP3vP09ISnp6fS/YcPH0ZAQAAAoF27dti+fTtMTEyKlDM2NsacOXMgl8uxceNGZGVl4ffff8fKlSvVvqeSEovFcHJywp9//lnkJNXExATffPMNrl+/jtTUVERFRSEqKgpdunTBhg0bitwxYmVlhTVr1qB///7Iy8tDYGAgEhMTYWdnV+S469atE+6ka9++PTZt2gQjIyOFMkZGRvjkk08gFouF9VU2btyIMWPGFEm4XrlyRbhYJRKJsHHjRqVr0Xl5eeHnn3/GpEmTIJFI1H42MpkMK1asgEwmAwD8+OOP6N+/v9KyLVq0wLZt2zBixAgkJSVh3759ePfdd1GvXj21x3jd48ePhe9+hw4d1N7la21tjdGjR5eqfiIiotISi8Vo0qQJtm/fDnNzc4V9IpEIU6dOxY0bN4Tp9o4ePVokUVeVft+kUikWLFiA9957r8i+1/siFXHslStXChcse/fujY0bN6qcIt3BwQHz58+v0Jhed+TIETx48ABA/sic33//vUjCBwC6d++OLVu2YMKECZBKpbh37x6OHTuGESNGqK1fLBbjyy+/xLhx44rsq1+/PtauXYthw4ZBLpcjMDAQCQkJRZIO1YVYLMbQoUOVrhVoamqKb7/9Fjdu3EB8fDyysrJw/vx5DB48uEhZTZ9XvHr1SmFk7Jw5c/Duu+8Wqc/JyQlbt27FsGHDlE4PrykikQhbt25F8+bNi+ybPn06Ll26hKtXr0IqlWLVqlWoU6cOfH19FW4oBPLvfv/0009x8eJFPHnyRO1nun37dmFqVisrK2zbtg3169cvUm7YsGEwNTXFnDlzAOSv+a3sZoWK+kx//PFHYeTl5MmTVU7vb2tri/Xr12P69Om4fv06IiIicPLkSaXvvTgF58NGRkZYtWpVkd+FAsbGxvDy8iqy1jnVHMqSdCKRqEwJYiKiqiQvL0/4t00ulyM2NlajyTom6oioyggJCRHueFXFzs4OK1asQL9+/TR23LZt28LZ2RmPHj0Skj6vKxhNB6DYNQjU2bZtm7C9cuVKpSfThc2aNQs7duxAeno6jh07hi+++EJjQ6s/+ugjpXeSAvnTPHp5eeHQoUPCc5999pnKYzds2BDt27dHcHAw5HI5QkNDi4xqe/TokcKUL8uXL1d7YezDDz/E0aNHkZKSgoyMDGGducL27t0rbA8YMEBpkq5Ax44dMWTIEOEOZVXOnj2LqKgoAPnTJqlK0hWws7PD1KlTsXbtWkgkEpw4cULl6D9VCk+XWp7vFxERkSYtWrRI5cVYABg9erSQqFPWh6tKv29169bFzJkztXLsU6dOITY2FgBgZmaGb7/9VmWSTlv27NkjbI8fP15pkq5AmzZtMGbMGGEtsN27dxebqHNxcVGapCu8393dHXfv3hX6kn369Cndm9ARhoaGWLx4scr9xsbGGDJkCH7//XcA+bOKKEusaPq84siRI8JIsnr16qlNrNvY2GDevHn4/PPP1R6zPMaNG6c0SVdgyJAhuHr1qvB49uzZRZJ0BfT19TFo0CD8/PPPAPL/vXr9M5XL5Qrrcb7//vtKk3QF+vfvj549ewrrsO3evbtIoq4iPtPk5GQcPnwYQP55yMcff6y2vL6+PhYuXCj8/R0+fLhMibqC82FTU1O1vwtUs8lkMoUknYWFBWxsbGBmZqZyKQsiIl0hl8uRlZWF5ORkZGRkCMk6FxcXjVyr1exEmkREFSwxMRFz587Fxx9/rHQRa1WePHmCI0eOYNOmTVizZg2++uorfPnll8J/r169ApA/7VLB2naFFR4h9foC5SWVkJAgrIHSrFkzhSlZVDE2NhbW6CgYWagJJiYmxd7l6OLiImw7OjoWG2/hE+mCdSAKu379urDdokULtReAgPwLWUOHDhUeF9wxXFjh54q7QAQAI0eOLLZM4UXPCx9fncIJwhs3bpToNYUVvggQEBAgrE1DRESkLcbGxkVuunld4d/ygkRUYVXp923AgAFaS45dunRJ2B4yZIjWk5avy8jIQGhoqPD4rbfeKvY1Y8aMEbZDQkKQlZWltvzAgQOLrbNFixbCtrLvU3Xh4eGhdOaJwor726qI84rC/erBgwcXO9J08ODBJZpSvqwGDBigdn/hcxWg+O9Ycecqjx49Ekaz6evrY/jw4cXGWPjvIDAwsMj+ivhMr169KswQ0r9/fxgbGxcbZ9u2bYUbNG/evFlseWUKzofT0tJw/PjxMtVB1V/BhWsgP0nXqFEjmJubM0lHRNWCSCSCubk5GjVqJMz4JZfLFW5OLI+qdRsfEdVoc+bMUbpuRFZWFmJjY3Hx4kX8/vvvwl2EYWFh2Llzp9K58QucP38e69evx/3790scR0pKSpG7JwcNGiQkmtauXYurV6/C29sb3bp1K/E0h7dv3xa2c3Jy8OWXX5bodQWLlQPA8+fPS3QiXpwmTZoUexJY+HNt1qxZsXUWLq/sR6rgYgKQP+1lSXTo0EGY/vL1Nnzx4oXCOjZt27Yttr527dpBJBKpnSv/1q1bwvapU6cURgGqUpDoBaA00Vuc+vXro127drh9+zZevXqFUaNGYfjw4ejfvz86dOhQZG08IiKiiubk5FRsX6Hw2lrKfvur0u/b62tMVabCfcDX19qqCsLDw4Wpyc3MzODq6lrsa1q0aAEzMzNkZWUhLy8PDx48QIcOHVSWL0mdtWvXFrY1dcGjKno9waRMcX9bFXFeUbivXpDQU8fCwgIuLi4Vtk5dcZ9T4XMPS0tLlUsTFCjuMy18ruHk5KTwfVSl8Hc+MTERL168UIijIj7Twm0fHh5e4rYvkJaWhqysLJUzq6gyaNAgbNmyBUD+zCfHjx/H4MGD0blzZ5UjGanmSU9PF7ZtbGyYoCOiakkkEsHGxkboT6Snp6NWrVrlrpeJOiKq8szMzNC8eXM0b94cQ4cOxZgxY/DixQtERkZi9erVWLVqldLX+fj4YOPGjaU+XuFpLguMGTMGly5dwpkzZwAA165dE6bJbNCgATw8PNC5c2f07dtX5V3SCQkJwnZMTAx27txZ6thKM4pQnZLMn1x4DvmSlC98l3rBQuOFFU6qNWjQoNj6gPwpNQu8vn5h4fpMTU1LdHe6hYUFLC0tFU4gXle4ncpyt6i6utX55ptvMHXqVCQlJSErKwu7d+/G7t27YWBgADc3N3Tq1Andu3dH165dOb8/ERFVuJL89hdO5Cn77Qeqzu9bSS66V5SCtaSA/PXnqprCfaz69euX6MKqnp4e6tWrh8ePHxepQ5nX1xlWpri+ZHEJCUdHR0ydOrXY42ibJvrVFXFeUbhvrW7Kx8Lq169fYYm64j6n0p6rFC6vqXMVW1tbGBsbIzc3F0D+30HhRF1FfKaF2/7GjRtlms0jPT291Im69957D4GBgbh9+zbkcjlOnz6N06dPA8i/CdTDwwNdu3ZF7969S/T3TtWTWCwGkH8Ru7TfMSIiXVIwpa9cLhf+7SsvJuqISKfY29tjzpw5wtz9hw8fxocfflhk+pgrV64oJOnat2+PUaNGwd3dHfXq1YO5ubnC1COTJ08WpiuRyWRFjquvr4+NGzdi37592LZtGx4+fCjsi4uLQ1xcHI4cOYKVK1di+PDh+PjjjxXu2gQUR12VVcHdzuVV2jvbNHEnXOEpkUraaS98p/3rCdTCj4tbk+P1OtUl08p7B3dZ26hZs2Y4dOgQfv31Vxw8eFD4vkilUoSGhiI0NBRbt26Fvb095s6dqzDVDhERkaZp6i74qvL7Vpq+gqYV7rNUxQuXheMrzShHdf2012ni+1RcMsrT01MnEnWa+Cwq4ryicF+9pN+DihwVW5rPSdPnKqX9OyhI1L3+d1ARn6km2l7VjRXqmJmZwdfXF76+vti5c6fClKxRUVGIiorC/v37YWpqirfffhvz5s3T6r+7pB0F/67o6+tzNB0RVWsikQj6+vqQSqUau1bLRB0R6Zzu3bsL21KpFEFBQUUWxP7jjz+E7dGjR+Obb75R21Es7uICkP+P8JgxYzBmzBg8efIEQUFBuHnzJoKDgxEdHQ0AkEgk2LdvHwIDA7Fnzx6FUV6FT7r69OmDX375pfg3W40UvjBV3DomBQoWXwdQZNHywo9zcnJKHEfhOpUxNTUVToAPHDhQ7Fp6mmRra4tly5bhk08+we3btxEcHIxbt27h5s2bQgLxxYsXWLZsGcLDw7Fs2bJKi42IiKisqvvvm7KbvAozNzcXRi+VtA9UmQr3qYrrJxWmrp9WkxTX/hWhIs4rzMzMhD5wSb8Hpfm+VHWFz1U09XdQEZ9p4bb/7LPP8L///a+EkZafkZERZsyYgenTpyM8PBxBQUG4desWgoOD8eLFCwD58f/xxx8IDg7Gjh07mKwjIiIqISbqiEjnvD567vUF1vPy8oTRcXp6eli0aFGxd3PFxcWVKgYnJyc4OTlh7NixAIAnT55gz5492LFjB/Ly8vDs2TNs3LgRy5cvF15ja2srbCclJZXqeNVB4aRlSddxK9y2r09ZVbi+7OxspKSkFDutVUZGRrF3odapU0coU7CgfGUzMjKCp6cnPD09AeQngK9du4Zff/1VmN7G19cXw4YNQ5s2bbQSIxERUWnpwu9bSabzfF1J+hYFibqYmJgSrVVVmQr3n54/fw65XF5s31kmk+H58+dK66go4eHhFX4MQHHayZLcIa2JEU6lVRHnFTY2NsJ7iY+PL9HfYFnWZq6qynKu8vLlS2E0HaD8fEXTn2nhttfWuYpIJIKbmxvc3NwwefJkAPlr/Pn6+sLPzw8AcOfOHezcuRMzZszQSoxERES6Rk/bARARldbro6f09BT/KUtJSYFEIgGQf2GkuMWtHz58WOy6GsVxcnLC4sWLMXfuXOG5s2fPKpRp27atsB0WFlYl76iuSC1atBC2b926VaLX3Lx5U9h+fWSbvb29wgn1nTt3iq3vzp07kMvlassUbqfCx9cmQ0ND9OzZE9u2bYOLi4vw/Llz57QYFRERUfmU5vetsqbQKjwiJj09vdh+Q1xcXLHTZhdOzF2/fr1c8VXE5+Dq6iqs35WZmVmihNiDBw+Evqy+vj7c3Nw0HpcmlOXzKry+VmpqarHlIyIiSn2M8qqI84rCffXbt28XWz4zMxORkZHlPm5VUfhc4/HjxyVq+8LnCnZ2dgrr0wEV85kWTvZVlXMVIP/zW7VqlcL0xa+fDxMREZFqTNQRkc55fXHt10+ICifuSjIl4u7duzUTGPKnninw+t2tDg4OcHZ2BvDfFJk1SZcuXYTt+/fv48GDB2rLZ2dn4/jx40pfX6Bz587C9qFDh4qN4eDBg8WW6dWrl7C9f/9+hbtktc3IyAjdunUTHr98+VKL0RAREWlGSX7fCq8tXJb1lUrKwsJCWGc4OzsbT548UVv+xIkTxdbZo0cPYfvYsWNITk4uc3zGxsbCdsGNaeVlYWGB1q1bC48PHDhQ7GsK92PbtGlTJdfeA8r2vWnYsKGwHRYWprbsixcvtJIsqYjzisL96hMnThT7/Tp+/DjEYnG5j1tVODs7CzO35OXl4fDhw8W+pvDnXvjzU/acpj7THj16CKM+b926Vew5VWVTdz5MREREqjFRR0Q6Z9u2bcK2SCQqksCxtraGpaUlgPypaAqmwVTmxo0bJUrUlfSCSuEpgJSN5Js5c6aw/dNPP5VqCh9tTW2iKc7OzujUqZPw+KuvvlJ7svrTTz8JF+osLCwwdOjQImUK37F54sQJBAUFqazvxo0bOHr0aLFxDhgwAI6OjgDyP/MVK1YUezd9gczMzDLd0ZyWllbi9U0KT4dTeEQhERFRVaPJ37eC5BkAYS2kilJ4xIq6pNXz58+xZcuWYut78803heRPVlYWlixZUuZkY+HPISEhoUx1KDNu3Dhhe+fOnWov/oeGhmLPnj3C4/Hjx2ssDk0rPBVhSb83hdv/2LFjahMnq1at0ljCtLQ0fV7h7e0trH8WHx+P3377TeXrU1JSsGHDhlJEW/WJRCJhWQMA2LRpk9rvjL+/P86fPy88VvZ3UBGfqb29PYYNGwYAkMvl+OSTT4od1VtAJpOV6UYBsVhcojXdAcV/y4ub2YaIaqacnBxs3LgRw4YNQ7t27eDq6gpXV1d888032g5NI/z8/IT3FBMTU2T/4sWL4erqqnBjQ2EFr/Xx8anoUGuEPn36wNXVFYsXL9Z2KMVioo6IdEZ6ejqWL1+uMB3S0KFDFebpB/JH1Hl5eQmPFy9ejLt37xap7/jx45g1axby8vKKvQu4d+/eWL58OQIDA1VecAoJCcFXX30lPO7Zs2eRMsOGDRMSi5mZmXj77bfx999/q7wAkJGRgcOHD2Py5MkKdeuqDz/8UJhaKTg4GHPnzi1y17xYLMbatWsVErJz5swpsjg7AHTr1k24U1Uul+ODDz7AhQsXipS7fPky3n//fchkMoW1Z5TR19fHihUrhDj9/Pwwa9YsPHr0SOVrwsLC8P3336NXr15KO2LF8ff3x4ABA/DHH3+ofL1YLMZff/2FkydPCs8p+44RERFVFZr8fSs8Naa/v3+FjuQpfHPQ1q1bFWIrcPv2bUyaNAlpaWnF9i0MDAzw+eefC9Mwnjt3DjNmzFDZt4iJicH69euVzgTQvHlzYfvOnTulXmdZFW9vb2H6SolEgnfeeUfpNJ1Xr17FzJkzhURjq1atMGTIEI3EUBEKf16XL18u0XpyvXv3FvqdsbGxWLp0aZFZOlJTU/HJJ5/gxIkTCqP2KpOmzyssLS3xzjvvCI83bNiALVu2FFmnLyoqCtOnT0dCQkKx331dM3XqVGG2ltTUVEydOlXpqMpjx45h0aJFwuPevXsr3JBYoKI+0wULFgij/8LDw/HWW2/h8uXLKss/f/4c27Ztw8CBAxVmLCmphIQE9OrVC2vWrEFISIjKcleuXFG4sMxzFaKKFRAQICR1Xv+vbdu26N27N95//30cOXKkQmcjKA2JRIL//e9/8PHxQXh4OLKzs7UdElGVYVB8ESKiynHhwgWla8VlZ2cjNjYWd+7cUThJbtKkCT777DOldb333ns4c+YMcnJyEBsbi7Fjx6Jdu3ZwcnKCWCzG7du3hQtGY8eORVRUlNqRdzk5OdizZw/27NkDc3NztGjRAg0aNICpqSlSU1Px+PFjhfUEbGxsMGfOnCL16Ovr46effsL06dNx//59ZGRk4IsvvsD333+Pdu3awd7eHvr6+khLS8OTJ0/w+PFjoUM1YMCAkn2QVViHDh2waNEifPfddwDyL1T16tULnTt3Rv369ZGWloaAgACFNSH69++P//3vfyrr/OabbzB+/HgkJSUhLS0Ns2bNgouLC1q2bAmRSIT79+8LdxhPmzYNp06dQmxsrNo433jjDaxYsQIrVqxAXl4eLl68iEuXLqFZs2ZwdXWFubk5cnJykJiYiAcPHpRrCqsCz549w3fffYfvvvsODRo0gKurqzCiICkpCXfu3FH4XLy9vdGhQ4dyH5eIiKgiaer3rWfPnjAxMUFOTg7CwsIwePBgeHp6olatWkICrFu3bujevXu5Yx4yZAj+/PNPPHjwABKJBPPmzUOrVq3g5uYGmUyG8PBw3L9/HwAwd+5c+Pn5Fdu36N27Nz788EOsXbsWQP5adUOGDIGbmxuaNWsGMzMzpKWlITw8XJhuU1k/187ODu3bt8etW7eQm5uL4cOHo0ePHrCzsxOmf3dwcMDbb79dqvdsZGSEdevWYdKkSUhOTkZiYiKmTp0KNzc3YZ2tsLAwhZF2derUwdq1a6t0sqZNmzaoX78+4uPjkZiYiEGDBqFbt26oXbu28L1xd3fH4MGDhdeYmpri/fffx/fffw8AOHz4MK5cuYLOnTvDwsIC8fHxCA4ORnZ2NlxcXNC9e3f8+eeflf7eKuK8YtasWbhy5Qpu3rwJuVyOtWvXYseOHfD09ISZmRmePn2KGzduIC8vD23btoWDg0OJZqzQFVZWVli7di1mzpwpTH07cuRItG3bFs7OzpBIJLhz5w6ePn0qvKZJkyb49ttvVdZZEZ+pvb09fv75Z8yaNQspKSl48uQJZsyYAXt7e7Rp0wY2NjaQSCRISUlBZGRkmW4kfF16ejr+/PNP/Pnnn7C2tkaLFi1gb28PY2NjvHz5EuHh4YiOjlb4XKZMmVLu4xJR2eTk5CAuLg5xcXHw9/fH9u3b8csvvwhJfm35999/cevWLQDAqFGjMGLECGH0e+FR8KR7Jk+ejMDAQHh6esLX11fb4egkJuqIqMoICQlRe4deYX369MGXX36pcjqNZs2aYe3atfjoo4+QnZ0NuVyOW7duCR2CAuPGjcOyZcswY8YMtcczMzMTpjTMzMxEcHCwyrJubm5Yt25dkbXzCtSuXRu7d+/GqlWrsG/fPkilUmRkZKi9C9LExAStWrVSG6OumDFjBmrVqoXVq1cjIyMDYrEYly5dKlJOX18fEydOxOLFi4ULKco4ODhg27ZtmDNnDqKiogAAERERiIiIUCg3duxYLFq0CKdOnSpRnGPHjkXjxo3xxRdfICoqCnK5HJGRkWoXeG/evDmsrKxKVH9hZmZmEIlEwhSbBR1qZfT09DB+/HgsWbKk1MchIiKqTJr8fbO0tMTixYuxcuVKyOVyREdHK1wULjieJhJ1BgYG2LhxI6ZNmyYc4969ewrrJItEIsyePRsffPAB/Pz8SlTvrFmz0KhRI3zzzTdISkqCXC5HWFiYynXQVM34sHTpUkydOhWZmZlIT0/HsWPHFPZ7enqWOlEH5E9TvmvXLnz44YdCIvLBgwdKp8Fs1aoVfvrpJzRu3LjUx6lMenp6+OKLLzB37lxIJBIkJiYWGak4cuRIhUQdAEyfPh1RUVHYu3cvgPx1E18fidSuXTts2LAB//zzT4W+B3U0fV5hZGSE3377DQsWLBD654mJiUW+Y+3bt8eGDRuwbt06zb2ZKqJTp07Ytm0bPvroI0RHR0Mul+P27du4fft2kbJvvPEG1q5dq3Y6+or6TNu0aYP9+/dj6dKluHbtGoD86V1Pnz6t8jW2trbCFP+lYWhoCCMjI2G0ZmpqqnBMZTw9PbFu3boqu3YlUXU0YcIEhd/+rKwshIaG4s8//0RsbCxCQkLw/vvv459//lF7faWiXb16FUD+jUdff/21MJNRTbJ69WqsXr1a22FQFcREHRFVeUZGRrC0tETjxo3Rrl07DB06VGHBe1X69euHI0eOYNu2bbh8+TLi4+Ohr6+PunXrokOHDhg1apTSKUqUCQgIQHBwMAIDAxESEoKnT5/i5cuXyM3NhYmJCerVq4dWrVphwIAB6NOnj3BHsyomJiZYuXIlZs6cicOHD+P69euIiopCamoqZDIZLC0t4eDgADc3N3Tp0gU9e/aEhYVFiWLVBWPGjEHfvn2xd+9eXLx4EVFRUUhLS4O5uTnq1auHN954A6NHj0azZs1KVF/z5s1x+PBh7NmzB8ePH8eTJ0+QnZ0NOzs7uLu7Y8yYMejWrVup4+zSpQuOHz+OM2fO4Pz587hz5w6SkpKQkZEBExMT2NraomnTpmjfvj169uwp3HFeWgMHDsTly5dx+fJl3Lx5U7gjNT09HUD+xckmTZrAw8MDI0aMKPHnQkREpE2a/n2bMGECXFxcsGfPHty5cwcJCQnCDVma5uDggMOHD+Ovv/7CqVOnEBUVBbFYjLp166Jjx46YMGEC2rZtW+p6Bw8ejF69euHgwYO4ePEiwsPDkZycjLy8PFhZWcHJyQkeHh4YMGAAWrZsqbQOd3d3IbaAgABER0cjKyuryHR6ZeHk5IT9+/fj33//xalTp3D37l1h5gAbGxu0bdsWAwYMwIABA7R6oa80evfujf3792Pnzp24efMm4uLikJWVpfZ7o6enh6+//hr9+/fHnj17cPfuXaSmpsLa2hpNmzbFsGHDMHz48CoxmlDT5xUWFhb4/fffcerUKfj5+SE0NBSpqamoXbs2nJ2d4e3tjWHDhlWJ915R2rVrh+PHj+Pw4cM4c+YMHjx4gJcvX8LAwAB2dnbw8PDAkCFDSnxjQEV9pg0bNsS2bdtw69Yt/PvvvwgKCsLz58+Rnp4OfX19WFtbw9HREa1bt0b37t3h6ekJA4PSXwa0t7dHQEAArl+/juDgYNy7dw/Pnj1DcnIyJBIJzM3N0aBBA2F06htvvFHqYxBR+dSpU0dhmnAg/98yb29vjBkzBk+fPsXdu3dx7tw5lWujVYaC9XUbNWpUI5N0ROqI5BVxVkNERERERERERERENUJkZCSkUikMDAwU1gilihEQECBMMTtnzhzMnTtXabl9+/Zh6dKlAIC3334bX3zxRaXF+LqaMD2in5+fMH25v78/GjVqVKrXu7q6AlDfplVRVW3bPn36IDY2FiNHjtT4SEZN/5vHEXVERERERERERERERNVMmzZthG1lU5Dn5eXh8OHD+Pfff3Hv3j2kpqbC3NwcTZs2xZtvvokJEybAxMREad2vJ2eioqKwY8cOXL58GS9evEBOTg78/f3Rt29fhdcFBgYKCSkgf4Tw2bNnNfSO88lkMhw/fhwnT55ESEiIMENA/fr10bp1a/Tv3x99+/YtMpo5IiICZ86cwY0bNxAZGYnk5GQYGhoK6wRPmDAB7dq1K3NcixcvxoEDB0r8nq9evYodO3YgNDQUaWlpqFu3Lry8vDB79myVS+68niysW7cudu3ahePHj+Pp06dITU1VSASKxWJhFow7d+7g2bNnyMrKgoWFBRo3boyePXti4sSJSqd6Lng/BV5vW0B1+7569Qq7du3CuXPnEBUVhYyMDFhbW6N169YYMWJEiWZwuHDhAv766y+EhIQgOzsb9erVQ69evTB9+nSVn09VxUQdEREREREREREREVE1U3jK29enyY6Li8N7771XZD3a1NRU3Lx5Ezdv3sTu3buxefNmODk5qT3OmTNn8PHHHyMrK0tzwZdRTEwM5syZo3Qd4KioKERFReHo0aPYsWMHOnfuLOwrPEqxMIlEgqdPn+Lp06c4ePAgZs2ahUWLFlXoewCAjRs3wsfHR+G5mJgY7Ny5E4cPH8avv/6Kjh07qq0jJSVF5WdRYPny5QrJtgKpqalITU3F3bt38ddff+Hnn3+Gh4dH2d7Ma65du4YFCxYgNTVV4fnExEScO3cO586dg5eXF3788UeYm5srrWPVqlXYtm2bwnNRUVHYtm0bDh8+jC1btmgk1srCRB0RERERERERERERUTUTEREhbNetW1fYTklJwdtvv434+HgYGRlh7Nix6NSpExo2bIisrCxcuXIFO3bswNOnTzFz5kwcOHAAlpaWSo8RFxeHjz/+GCYmJnjvvffQsWNH6OvrIyQkBGZmZjhy5AgA4LPPPkNoaChat26NVatWCa/X5LqnSUlJmDBhgrAeXpcuXTBy5Eg0bdoUQH6i6/r16zh58mSR1+bl5cHMzAxeXl7o0qULmjZtCgsLC7x8+RIPHz6Er68vYmNjsWXLFjRp0gSjR4/WWNyvO3/+PEJDQ+Hk5IR33nkHrq6uyMjIwIkTJ7B37168evUKs2fPxtGjR1G/fn2V9SxduhQREREYMWIEBg8eDFtbW8THxyt85lKpFA4ODujfvz/c3d3RoEED6OvrIy4uDlevXsX+/fuFUXhHjx5FnTp1hNcuXLgQ06dPV9m2QNH2vXHjBmbOnAmJRAJbW1tMmjQJbm5uqFu3LhISEoR1Yi9cuIDFixcXSVYCwLZt24QkXd26dTF79my0adMGubm5OH/+PLZv34758+cjOzu7LB+/VjBRR0RERERERERERERUjUilUmzdulV47OnpKWx//fXXiI+PR8OGDbF9+3Y4ODgovLZz584YOHAgJk6ciOjoaPz+++9YuHCh0uPExMSgbt262LNnDxo0aCA837ZtWwAQpkw0MzMT/t/FxUUzb/I1K1asEJJ0H330EWbOnKmwv02bNhg8eDA+/fRTSKVShX1ubm64cOECatWqVaTeHj16YOLEiXj33Xdx5coVbNq0CSNGjIC+vn6FvI/Q0FC0atUKvr6+CiPKunbtig4dOuDTTz9FRkYGVq9ejfXr16usJzw8HF9//TXGjBkjPNeqVSuFMvPmzYODg0ORaSbd3d0xYMAAvP322xg/fjySk5Ph6+uLBQsWCGXs7e1hb29f4raVSCT4+OOPIZFI0KNHD/j4+MDU1FQhtt69e6NTp074/PPPcerUKVy5cgXdunUTyrx8+RI//fQTgPxpNffs2QM7Ozthf6dOndC9e3e88847Rdq4KtPTdgBEREREREREREREVHMkJiYq/KfugvrrZSUSicqySUlJCmXFYrHKsi9fvlQom5ubq7JscnJyicumpKSo3FcZsrKyEBgYiGnTpuH27dsA8hMagwcPBpCfWDtx4gQA4PPPPy+SpCvQsmVLvP322wDy1z1TZ9GiRQpJOm14/Pgxzpw5AwDo169fkSRdYebm5rCyslJ4zsbGRmmSroCRkRE++eQTAEBsbKza6SQ14csvv1Q67eOIESPQs2dPAPlTjiYmJqqso0uXLgpJOmUaN26sdi04V1dXoQ5/f/+ShK7SsWPHEBsbC2NjY3z33XcKSbrCxo4dK6yv+Pp378CBA8JIuU8//VQhSVega9euxb7vqoaJOiIiIiIiIiIiIiKqNHXr1lX4Lzw8XGXZJk2aKJQtSD4p06JFC4WyV69eVVnWw8NDoWxBkkeZHj16KJQ9fPiwyrJvvvmmyn0VYePGjXB1dRX+a9++PSZPnozAwEAAQJ06dbBp0yYYGRkBAC5cuIC8vDyYmpoKCR9VOnXqBABISEhAXFyc0jKGhoYYNGiQBt9R2Vy4cAFyuRwA8L///a/c9YnFYsTFxeHhw4eIiIhARESEUD+AImv7aZKLiwtat26tcn/BtJtSqVRoZ2W8vb1Lfey0tDQ8e/YMkZGRwvsuSGA+fPhQbaK8OGfPngWQ/70qGGmpSsH6e6//vV+7dg0AYGVlhb59+6p8fUVOTVoROPXl/3v58iXu3r2Lu3fvIiQkBCEhIcJihiNHjsTq1as1fsyjR4/Cz88P4eHhSE9Ph62tLTw8PDBx4kS0b99e48cjIiIiIlKF/WEiIiIiouqjUaNGGDBgAGbMmKGwrlhoaCgAIDs7Gy1btixxfUlJSUpHzTVp0gTGxsblD7ic7t+/DyA/cVgw7WZpZWVlwdfXF8eOHcPDhw+Rl5ensmxFjpx0d3dXu79gtBmQvw7hkCFDlJZzdXUt0fHCw8Oxbds2XLp0Se0IPZlMhvT0dIXvU2kUfPcuX75c4tiSkpKKxArkJ+UNDFSnt1q0aAFDQ8NyJRYrExN1/++NN96otGPl5ORg3rx5uHDhgsLzcXFxiIuLw7Fjx/DBBx9gzpw5lRYTEREREdVs7A8TEREREemeCRMmCFNUikQiGBsbo3bt2rC0tFRa/uXLl2U6TsF0g69TN11kZSpInFlZWQmjB0sjJiYGU6dORUxMTInKq5v+tLyKS4TZ2toK2wU3Vyrz+vSeyuzduxcrVqwo8XpuOTk5JSqnTHJycqlf8/rx0tLSABT/GRkYGMDa2lpt4rEqYaJOiQYNGqBp06a4fPlyhdS/ZMkS4aJE586dMWXKFNStWxcRERHYvHkznj17Bh8fH9jZ2WHcuHEVEgMRERERkSrsDxMRERER6YY6derAxcWlxOULRonVrl0bO3bsKPHrGjVqpPR5fX39EtdRlX3yySeIiYmBSCTCqFGjMGTIEDg7O8PGxgaGhoYQiUSQyWRo0aIFAChMg6lp6taMKw09PfUrnz169EhI0tWpUwczZsxAly5d0LBhQ5ibm8PQ0BAAsG/fPixduhRA+d53wXevZ8+e+Pjjj8tcD6C5z6iqYKLu/33wwQdwd3eHu7s7bG1tERMTo3aO07K6du0ajh07BgDo3bs3Nm3aJPxj1qZNG/Tp0wejR49GXFwcfvjhBwwcOLBEmW8iIiIiovJgf5iIiIiIKktCQoLC49q1a6ssGxUVpfDY2tpaZdmwsDCFRIK6fuSNGzcgk8mEx+pGhl26dElhGkR1ZU+dOqVyX1VQ8PllZmbC2dm52iTaCr5DaWlpEIvFpRpV9+jRI9y4cQMAMHv2bCxcuFBpOXWj1zTp9eke1e1X9/dQnAMHDkAqlUJfXx++vr5wdnZWWq5gFFt5WVtbIyEhARKJpFTJ5cKsrKyQmJhY7GcklUorrb00QX1KtQaZN28eevfurTBstCL8+eefAPKHXq5YsaLIP4Q2Njb46KOPAADp6enYu3dvhcZDRERERASwP0xERERElcfOzk7hP3VrTb1etmCUjzK2trYKZdUla+rUqaNQVt06azY2NiUuqy7pWBUUrEsnFouFNcOqg1atWgEAJBIJbt++XarXPnz4UNgePHiwynKV9XmFhISUeH/z5s3LfJyC9+3m5qYySQdo7n0XfPdCQ0MhFovLVEdBgu/Bgwdqp+t88OCBzqxPBzBRV6kyMjJw7do1AEDXrl1Rr149peX69+8PCwsLAMCZM2cqLT4iIiIioorE/jARERERkXb17t1bmDZw+/btWo5Gc7y8vMr8vgqPllS1Fh8A/P3332ULrpQiIiJw//59lfv3798PIH/aUU9PzzIfpyDRlZWVpbJMQkICzp49q7aegsR1ccm3Pn36AABevXoFPz+/0oQq6Nq1K4D80Y3nzp1TWa7gM9IVTNRVopCQECGLq+4PyMjICO3atSvyGiIiIiIiXcb+MBERERGRdjVt2hQDBw4EABw7dgxbt25VWz46OhpHjx6t8LhcXV3h6uoqJHNKy8nJCf379weQf7Pf77//rrJsVlaWwnSOjo6OwraqBNKuXbvg7+9fptjK4vPPP1eaQDty5Iiw3ne/fv1Qt27dMh+jSZMmAICnT5/i5s2bRfZnZ2dj0aJFyMnJUVuPnZ0dgPzviro17EaOHIn69esDANasWYOgoCC19QYHByMwMLBIHSYmJgCAVatWKZ0CMzAwEP/884/auqsarlFXiR49eiRsN23aVG1ZJycnXL58GVKpFE+fPkWzZs00FodMJhPuDDAwMKh2Cy8SERFRxZPL5cLdd6ampsUuUk0EsD9MRERE1Qf7w6TLVqxYgdDQUERHR2P16tXw9/fH8OHD0bx5cxgZGSE1NRUPHjzApUuXcP36dfTv3x9Dhw7VdtjF+uKLL3D79m0kJCTg+++/x6VLlzBy5Eg0bdoUIpEIsbGxCAgIwPHjx7FhwwZ07twZQP6UjC4uLoiIiMCePXuQnp6O4cOHw87ODs+fP8fhw4dx8uRJdOjQQWlCS9Nat26N0NBQjB49GjNnzoSLiwtevXqFkydPYs+ePQAAc3NzfPLJJ+U6zrBhw+Dr6wuZTIbZs2djxowZ8PDwgLGxMUJDQ7F9+3ZERUUV+747dOgAPz8/vHz5EqtWrcKwYcNgaWkJIP98q2HDhgDyb8j86aefMHnyZGRlZWHq1KkYPHgw+vXrh0aNGkEmkyExMRH37t3D6dOnERERgc8//1zhJk9bW1vMnz8fa9asQWxsLEaNGoXZs2fD3d0dYrEYFy5cwLZt22Bvb4/s7GwkJyeX6zOqLEzUVaLnz58L2/b29mrLFmSWASA+Pl6jFyays7Px4MEDjdVHRERENZubmxvMzc21HQbpAPaHiYiIqDpif5h0jbW1NXbv3o0FCxYgODgYQUFBakc36cr329bWFrt27cL777+PiIgIXL9+HdevXy/2dSKRCN999x2mTp2KtLQ0nDhxAidOnFAo4+LigvXr16NHjx4VFb6gV69e6NWrFzZu3IjPPvusyH4LCwv88ssvaNSoUbmO06ZNG8ydOxc+Pj5IT0/Hjz/+WKTM9OnT0bx5c7WJusGDB2Pz5s2Ijo7G9u3bFaYebdiwocLUme3atYOvry8WLFiA+Ph4HDlyBEeOHFFZd8GSCK/HFBcXB19fX7x48QJffvmlwv7atWvjp59+woIFC9S9/SqFibpKlJmZKWybmZmpLWtqaipsq5sjloiIiIhIV7A/TERERERUNdjZ2WHnzp04f/48jh49itu3byMpKQlSqRSWlpZwdHRE+/bt0adPH3Tq1Enb4ZaYg4MDDh48iMOHD+Pff//FvXv3kJqaCn19fdSrVw/t2rXDm2++iY4dOyq8rkWLFjh48CA2b96MS5cuISEhAebm5mjcuDEGDRqEiRMnCmuxVYa5c+eiXbt2+OuvvxAaGoq0tDTUrVsXXl5emD17tsr1vktrzpw5cHd3x44dOxASEoKsrCzUqVMHbdq0wfjx49GtW7di15MzNzfH33//jc2bN+PKlSuIi4tTu9Zfu3btcOrUKfj5+eHcuXO4f/8+UlJSoKenBxsbGzg7O6NTp0548803Vc7EsmzZMvTo0QM7duxAaGgosrOzUa9ePfTs2RPvvPOOxj6fyiKSq5s0tAaLiYlB3759AeTPe7p69epy17lkyRJhEcMzZ87AwcFBZdl9+/Zh6dKlAIDvvvsOw4cPL/fxC+Tm5iI0NBRA/vy7hS+CFKavry8M25fJZAqLaipjaGgobOfl5UEmk5W4vFQqVTt/rUgkgoHBf3nl4tYpKRx74akIVCk85VFp36umY9fT04O+vj4kEolwp7ezs7PCMQtjO2kn9oJ2KihbXFuxnbQTO9tJtaraTkD+BfmC6fGUtRXbSTuxv95O6enpePr0KYD8aTkq84SFKgf7w/z3Rluxsz+sWlVsp4Ky7Gf9h+1UsvJsp/+wnVSrqu0EsD/8usjISEilUhgYGKB58+baDoeIqEJp+t88jqirRIV/sMVisdqyhfcXLI6oKYXX4DAzMyv2bubKYGRkVKHlK7KzVFGx6+npwdraGkD+EN/CnUdtYTspV9Xaiu2kHNtJcyo69qrUVmynfHK5HOvXr4e9vT0mTJgAQHE0FNf3opJif1g1/ntTVFX6PSjAdlKuqrUV20k5tpPmsJ00V15X2kkikWDRokWYOnUqPDw8ALA/TEREmqP9M50apPBcvsVN31N4aGhFXjjQdoePVDMwMICzs7O2w6ASYFvpBraT7mBbVS15eXlYuHAhfHx8YGRkhEaNGqFHjx7sQ1CZsD9MpcHfA93BttINbCfdwHaqetLT0zF27FicPHkSe/fuxfXr1+Ho6Mg+BBERaYyetgOoSQrPi/rixQu1ZePj44Xt+vXrV1hMRERERKpIJBKMHDkSPj4+APJHOI0YMQLh4eFajox0FfvDREREpEsSEhLQo0cPnDx5EgDw/PlzDBkyBKmpqdoNjIiIqhUm6ipR4TuiHj9+rLbskydPAOTfSeXo6FihcREREREpo6wfkpycjHPnzmkpItJ17A8TERGRLrG2tkadOnUUnnv06BFu376tnYCIiKhaYqKuErm7uwuL2gYGBqosJxaLhR/8wq+pCMUtlEvaU7CA9IMHD9hOVRzbSjewnXQH26rqEIlE+Omnn+Dt7Q0gf92OXbt24d1332XbUJmwP0ylwd8D3cG20g1sJ93AdqpajIyM4Ofnh5YtWwIA7OzscO7cOfTq1YvtQ0REGsNEXSWysLBA165dAQDXrl3D8+fPlZY7ffo0MjIyAAD9+vWrtPioapHL5cjMzERmZibkcrm2wyE12Fa6ge2kO9hWVYu+vj52796NN998E6dPn8aECRO0HRLpMPaHqTT4e6A72Fa6ge2kG9hOVY+1tTWOHTsGLy8vXLt2DV26dNF2SEREVM0wUadBfn5+cHV1haurq7CWy+umT58OAJBKpVi5ciXy8vIU9icnJ+OHH34AANSqVQtjxoyp2KCJiIiIimFubo6TJ0+iZ8+e2g6Fqjj2h4mIiKg6atKkCc6fP68wjTcREZGmGGg7gKoiODgYz549Ex6npKQI20+fPoWfn59C+VGjRpXpOF27dsWQIUNw7NgxnD17FtOmTcPUqVNRt25dRERE4Ndff0VcXBwA4KOPPoKVlVWZjkNERERUUnK5HH/++ScmTpwIExMTbYdDWsL+MBEREdVUOTk52LlzJ6ZPnw6RSKTtcIiIqIZhou7/7du3DwcOHFC67+bNm7h586bCc2W9MAEA3377LTIyMnDhwgUEBAQgICBAYb+enh7ef/99jBs3rszHICIiIioJsViMWbNmYfv27Th16hR2794NPT1OulATsT9MRERENdHLly8xYsQIXL58GQkJCfjss8+0HRIREdUwTNRpgYmJCbZs2YIjR47gwIEDePDgAdLT02FrawsPDw9MmjQJ7du313aYREREVM2lpqZi9OjROHv2LADgn3/+gZOTE1avXq3lyKi6Y3+YiIiIqoJHjx5h0KBBiIyMBAAsWbIETZo04brMRERUqZio+3+rV68u90WpUaNGlerOYm9vb3h7e5frmERERERlFRsbi6CgIIXnvv/+e0yePBmtWrXSUlSkLewPExERUU0TGhqKhw8fKjw3b948eHt7w8LCQktRERFRTcN5jYiIiIhqqFatWmH//v0wMMi/d8vMzAwHDx5kko6IiIiIaoThw4dj3bp1wuOGDRvC39+fSToiIqpUHFFXw3ENmqpLX18fjRo1Erap6mJb6Qa2k+5gW1Wu/v3749dff8XSpUtx9OhRdOzYsUSvYx+Cqgt+l6su/h7oDraVbmA76Qa2U+WbP38+Hj9+jIsXL+LYsWNo2LBhiV7HPgQREWkKE3U1HDt9VZe+vj7s7e21HQaVANtKN7CddAfbqvLNmDEDb731FqysrEr8GvYhqLrgd7nq4u+B7mBb6Qa2k25gO1U+kUiEH3/8EdnZ2aUaScc+BBERaQpv/SAiIiKq5mQyGcLCwtSWKU2SjoiIiIhIl2RkZODp06cq9+vr63O6SyIi0hom6oiIiIiqsezsbIwdOxadO3dGSEiItsMhIiIiIqpUcXFx6NmzJ/r164ekpCRth0NERFQEp76s4SQSCYyMjLQdBikhFouFC6ru7u5spyqMbaUb2E66g22lOQkJCRg+fDiuX78OABg8eDCuX79e4nU31JFIJOWug6gqYH+46uLvge5gW+kGtpNuYDtpVmhoKAYPHozo6GgAwPDhw+Hv7w8TE5Ny183+MBERaQpH1BERERFVU6tWrRKSdAAQExODUaNGQSaTaTEqIiIiIqLKMXfuXCFJBwBXr17FvHnztBgRERFRUUzUEREREVVTq1atwhtvvCE8trKywqpVq6Cnxy4gEREREVV/O3fuRKNGjYTHzs7O+Oijj7QYERERUVG8SkNERERUTZmYmODQoUNo3rw5GjdujCtXrqBPnz7aDouIiIiIqFI0aNAAx48fh6WlJbp27Ypr167BxcVF22ERaV2fPn3g6uqKxYsXazsUIgLXqCMiIiKq1mxtbXHixAmYmZmhfv362g6HiIiIiKhSubu74/z582jRogVMTU21HQ6RRgQEBGDKlCkAgDlz5mDu3LlajqhmOH/+PEJCQhASEoLo6GgkJycjIyMDZmZmcHBwgKenJ8aOHYumTZtq5HgZGRnYt28f/P39ERkZiYyMDBgZGaFhw4bo2LEjJkyYoPbmg8mTJyMwMFDpPgMDA1haWsLZ2RleXl4YO3YsrK2tNRI3lR4TdUREREQ6TiKRQE9PD/r6+kr3Ozs7V3JERERERESVJzc3F8bGxir3d+jQoRKjIaLqSCqVYvbs2Ur3paen4969e7h37x7++usvzJs3D7NmzSrX8e7fv4/3338f8fHxCs9LJBJEREQgIiICe/bswYIFC8p0LKlUipSUFAQHByM4OBjbtm2Dj48PPDw8yhU3lY3WEnXJycmIjo5GYmIisrOzYWBggFq1aqF+/fpwdHRUeaGJiIiIiP6Tnp6OMWPGwMXFBRs2bIBIJNJ2SFRC7A8TERERlV9UVBQGDx6MRYsWYcaMGdoOh7TA2+cyEl/llrseO0tjHJnbXQMRUXVlaWkJT09PtG3bFg4ODrCzs4OJiQkSEhIQGBiI/fv349WrV1i7di0sLS0xYcKEMh0nPT0dM2fORFJSEgCgU6dOmDBhAhwcHJCSkoIrV65g165dkEgkWLt2LRo2bIghQ4aorfPIkSMKjyUSCaKjo3Ho0CGcPXsWL1++xHvvvYd///0XNjY2ZYqbyq7SEnVZWVnw9/fHxYsXERQUhBcvXqgsa2RkhBYtWqB79+7o168f3NzcKitMIiIiIp0RExODwYMHIyQkBKdOnULTpk2xcOFCbYdFKrA/TERERKRZQUFBGDp0KBISEjB79mw4ODjgzTff1HZYVMkSX+XieXqOtsOgas7AwAABAQEqb6js27cvJk2ahNGjRyMtLQ0bNmzA2LFjy3QD5t69e4Uk3cCBA7F+/XqF/V5eXujcuTPef/99AMAvv/xSbKJO2RSZrVq1wsCBA/Hpp5/i4MGDSEtLw969e1WOHKSKU+GJuvv378PX1xf//vsvcnLy/8GUy+VqX5Obm4vbt2/jzp072LRpE5o1a4aJEydi+PDhnEtaw3indtVlYGAAV1dXYZuqLraVbmA76Q62VcmIxWJ4eXnh8ePHwnOLFi2Co6MjRo0aVeHHZx+i5Ngfrtr4Xa66+HugO9hWuoHtpBvYTiX37NkzeHl5ITs7GwCQl5eHt956C5cvX0abNm0q/PjsQxDVPMX93Ts4OGDgwIHYs2cPkpOT8fjxYzRv3rzUx7l165awXZCMe13fvn3RsmVL3L9/X1i/zsLCotTHAoAZM2bg4MGDAICQkJAy1UHlU2G/+Pfv38f69etx8eJFAP9djLC1tUWbNm3QqlUr1KlTB1ZWVrCyskJOTg7S0tKQlpaGqKgohISEIDw8HFKpFJGRkVi5ciXWr1+Pd955B5MnT4aRkVFFhV6j6OnpaTsEUkFPT6/M/7hS5WJb6Qa2k+5gW5WMkZERvv76a7z99tvCc7a2tmjYsGGlHJ99iOKxP6wb+F2uuvh7oDvYVrqB7aQb2E4l17hxY8ybNw9r1qwRnnN0dIS1tXWlHJ99CKrOEhIScPr0aQQEBODBgwdISEiAVCpF7dq10bp1a3h7e2PgwIEq/w4CAgIwZcoUAMCOHTvg6emJffv2wc/PD48fP4ZEIkHTpk0xadIkjBgxQnidWCzG/v37ceDAAURFRUEikcDV1RVTpkzB4MGDVcablpaGM2fO4Nq1a7h//z7i4+MhkUhgZWUFNzc3vPnmmxg5cmSlnEOZm5sL27m5ZZuSVSKRCNsODg4qyzk4OOD+/ftFXlNajRo1ErbFYnGZ66Gyq5BE3WeffYZDhw5BJpMBAFq2bIlhw4bhzTffRIMGDUpcj1gsRnBwMI4cOYLTp08jJSUFP/zwA3bt2oU1a9agY8eOFRE+ERERkU6YMGECoqKisGTJEri6uuL48eNo2rSptsMisD9MREREVBm+/fZbREVFYc+ePejXrx/27dsHKysrbYdFpNPy8vLg5eUlnMsUlpCQgLNnz+Ls2bPYt28ffHx8FBJTykilUrz33ns4d+6cwvMhISH49NNPERoaimXLliEtLQ0ffPABgoKCFMrdunULt27dwrNnz/Duu+8qPcbIkSMRGxtb5PmkpCRcvnwZly9fxt9//40tW7bAzs6uuI+gzHJycnD27FkA+cn8Jk2alKkeJycnnD9/HgAQHR0tjLR+XXR0NADA2toatWvXLtOxACAuLk7Yrl+/fpnrobKrkETdgQMHYGhoiLfeegvTpk2Dk5NTmeoxMjLCG2+8gTfeeAMrV67EiRMnsHnzZjx+/BjXr1/nhQkNUPYPLlUNMpkMWVlZAAAzMzPeqVWFsa10A9tJd7CtSmfx4sUwNjbG//73v0pd8Jl9CPXYH9Yd/C5XXfw90B1sK93AdtINbKfS0dPTw7Zt29C+fXssXLiwUmccYB+CqquCmUC6dOmCnj17wsXFBTY2NsjMzER0dDT27t2LW7du4cqVK/jyyy8VRrUqs379ety5cwfe3t7w9vaGra0toqKi4OPjgydPnsDX1xd9+vSBr68vbt26hQkTJqB///6wtrZGWFgY1q9fj4SEBGzYsAF9+/ZVOpVkXl4e2rZti169eqFly5aoU6cOJBIJYmJicPjwYVy6dAn379/Hhx9+CF9fX41+XhKJBImJibh16xZ+++03REVFAQBGjx5d5hHSY8aMwY4dO5CXl4dff/0VP/74Y5Ey58+fF0bTjR8/vszxA8Aff/whbPft27dcdVHZVEii7u2338bMmTM1mn01MjLC8OHDMWzYMJw4cYI/hhqSl5en7RBIBalUivDwcACAu7s7p7eqwthWuoHtpDvYVqUjEonw4YcfVvpx2YdQj/1h3cHvctXF3wPdwbbSDWwn3cB2Kj0TExN8+umnlX5c9iHK52VG2aYEVEZWzPrPJSWH6npSs8TIk5X8OHUsjDURklbo6+vj33//haOjY5F9np6eGD16NDZs2IBNmzbh0KFDeO+999SOHLtz5w6WLFmCqVOnCs+1atUKnp6eGDBgADIzM7Fo0SKkpKRg48aN6Nevn0K51q1bY+TIkcjLy8OePXuwbNmyIsfYvn270hg6dOiAYcOGYf/+/ViyZAkCAwNx7do1dO3atXQfymtiYmLUJrS6d++OxYsXl7l+Z2dnLF++HF9++SWOHz+Oly9fYvz48WjUqBFSUlJw7do1/PXXX8KxZs+eXWydERERCo8lEgliY2Nx+PBhnD59GgAwePBg9OzZs8xxU9lVSKJu+fLlFVEtgPyLUermoyUiIiKqTl6+fInZs2fjhx9+KPO0GVT52B8mIiIi0owHDx5g2bJl2LZtG9fvq0Y8vj6j7RCKyMxVnXwd8+s1RCZklLiuqNVDNBGSVohEIqVJusI++OAD7Nq1CykpKTh79iymT5+usmzbtm0VknQF7Ozs0L9/fxw8eBDJyckYPHiwQpKugJubGzw8PBAUFIQbN24oPUZx58qjR4+Gr68vwsLCcObMmXIn6lSpXbs2li9fjgEDBkBfX79cdY0fPx4tW7bEb7/9hlOnTiEgIEBhf+PGjTF79myMGDECBgbFp3m8vb1V7nNycsKsWbMwcuTIcsVMZVchiToiIiIiKr+HDx9i8ODBiIyMxP3793HlypVyzTtPRERERKRLLly4gBEjRiA1NRW5ubk4ePBgkYvfT0a/BWlSUrmPZWBrC6f9+8pdD1F1I5PJkJiYiMzMTEilUuH5evXqISUlBQ8ePFD7enU3Gbq5uQnbQ4aoTm66ubkhKChIWJNNHblcjqSkJGRkZEAikQjP29vbIywsrNh4S8Le3h5HjhwBkD+69sWLF7h06RL27duHFStWIDo6ukSj3NTJyMjA/v37cfXqVaX7o6OjcejQITg5OcHDw6Ncx3ry5An27NmDRo0awdPTs1x1UdkwUUdERERUBd25cwf9+vVD0v9fdAgLC8OoUaNw8uTJip8CabMXkJGgtoiBcW2gq0/FxkFERERENdaBAwcwbtw44UL70aNHMX/+fPj4+EAkEgnlpElJkL54oa0wiaoluVyOw4cPY9++fbh79y5ycnJUlk1JSVFbl7r1ui0tLYVtdaPiCsplZmaqLHP+/Hns3r0bQUFBassVF29JGBoawsXFRXjcokUL9OrVC2PGjMHUqVOxbt06REVFYdWqVWWqPzExEdOmTUNkZCRMTU2xaNEiDBo0CPXq1UNmZiaCg4Oxfv16BAYGYurUqfjuu++KnXWlYFrlAjKZDCkpKbhx4wY2bdqE27dvY/r06fjhhx8wcODAMsVNZccVaYmIiIiqIEdHR9jb2ys89/DhQ8TExFT8wTMSgFdxav8TZfBiCBERERFVnFatWilcxAeAwMBAZGVlaSkiopohNzcXs2bNwieffILAwEC1SToAxe43MTFRuU9PT69U5ZSt0y2Xy7F06VLMnj0b58+fV5ukK0m85eHm5ob58+cDAPz8/HD58uUy1fPVV18hMjISIpEImzdvxqxZs+Dg4ABDQ0NYW1ujX79++Pvvv+Hs7AyJRIIlS5YIN/mWlJ6eHurUqYM333wTu3btQpMmTYS6UlNTyxQ3lV2FjKjbuHFjRVRbxJw5cyrlOERERESVzdraGseOHUOXLl3w/PlztGvXDkePHkXDhg21HRqVAPvDREREROXj4uKCgwcPol+/fhCLxRg+fDh27doFMzMzbYdGGnBjWdG1yMpq0PpLSHiVW+56zI1Vrym2992uyJPJy30MXfDLL7/g4sWLAABPT0+8/fbbaNWqFWxtbWFiYiIkzSZOnIjg4GBthgoA2LdvH/bty5+2tkWLFpg6dSratGkDe3t7mJqaCtPlfvLJJzh06FCFx9O3b1+sXLkSAHDy5El07969VK9PS0vD6dOnAQBvvPEGOnfurLScubk53n33XXz88cfIzs7GsWPHlK4FWBLm5uaYMGECVq1ahczMTJw8eRLjxo0rU11UNhWWqCs8BL2i8MIEERERVWeOjo44evQoVq9ejT///LPIHcVUdbE/TERERFR+PXr0wPbt23H9+nWsXbu2yPp0pLvqWBhrrC49DfW7RVBdj7VZBS8/UEXI5XIh6dWxY0ds375dYdRbYWlpaZUZmkp79+4FkH/+/Pfff6scmVdZ8drY2AjbcXFxpX79kydPhJGDLVu2VFu2VatWwvbjx49LfazCmjZtKmxHRESUqy4qvQpdo04ur7i7DCrjwgcRERGRtnl4eAgnHqR72B8mIiIiKp/x48dj/Pjx2g6DqEZITU1FYmIiAGDgwIEqk3SZmZl48uRJZYamUmRkJACgT58+KpN0crkc9+7dq5R4XhRaM7MsI4AL35CQl5entqxUKhW2DQzKl+opfKzC9VLlqNBE3dGjR9GsWTON1hkREYFhw4ZptM6azNDQUNshkApGRkbw8PDQdhhUAmwr3cB20h01ra3i4+Nx4sQJTJ8+XduhUAVgf7jqY3+46qppvwe6jG2lG9hOuqEmttOtW7cQHx+PwYMHl/g1/3wbhKx0MQBA2mwB0FT9xWxVjMTp6HTjuzK9ln0Iqo4KJ2uys7NVltu7d2+VSeYUxKxu/Up/f38hAVnR/v33X2HbxcWl1K9v2LAhRCIR5HJ5sVOLBgUFCduNGjUq9bEKCw0NFbbr169frrqo9JSnxKsw3jlMRERE1UVoaCg6d+6MGTNmYOfOndoOh3QE+8NERERUXRw/fhw9evTAW2+9pXDBuThZ6WJkpuYiMzUXuYa1kGtcu0z/iY1qVeC7I9I9NjY2qFUr/+/i6NGjEIvFRcrcvXsX69evr+zQVHJ0dAQAnDt3DqmpqUX2P3v2TFgzrjzOnDmDhIQEtWWCgoKwadMmAPkj3IYMGVLq49jY2KBdu3YA8j/rAwcOKC0XGxuLX3/9FUD+OaKXl1epj1W4rsLXJMpTF5VNhY6oIyIiIiLlzpw5g9GjRyM9PR0AMG3aNDRq1IgdYiIiIiKqEX755RfMmTNHWItp6NChuH79OpycnLQcGZFuCQsLg5+fX7HlunTpggYNGqgto6enB29vb+zcuRPh4eGYMGECpk2bBkdHR2RkZODChQvYtWsXzMzMULduXURFRWnoXZTdiBEj8N133yEhIQHjxo3DzJkz4eLigtzcXFy/fh3bt2+HWCxGq1atyjX95ZkzZ7BgwQL06tULXbt2RbNmzVCrVi2IxWI8e/YM586dw4kTJ4R/095//32Fdd9KY+HChZg2bRry8vLw2Wef4dq1axg0aBDq1auHzMxMBAYGYvv27UJicvTo0cUe6/V152QyGVJTUxEcHAxfX1+hLm9vb7Ro0aJMcVPZVUiibseOHQDKP9xSmUaNGgn1U/kVN88taU9eXh6SkpIAALa2tlwwuQpjW+kGtpPuqCltFRYWJiTpAEAikeDjjz9GQEAAR0xVA+wP6w72h6uumvJ7UB2wrXQD20k31JR2ksvlCAoKEi5oA0BCQgK+/fZb/Pbbb1qMrOTYh6Cqwt/fH/7+/sWW27RpU7GJOiA/SXTz5k2EhYUhNDQUixYtUthvbW0NHx8fbNiwoUok6qZMmYKrV6/i8uXLiIqKwtKlSxX2m5iYYM2aNbhw4UK516mTSCQ4ffo0Tp8+rbKMiYkJFixYgGnTppX5OJ07d8aaNWuwfPlyZGVl4dChQzh06JDSskOGDMEXX3xRbJ3e3t7Flhk8eDC+/fbbUsdL5VchiTpPT8+KqBYAYGpqWqH11zSFO0RUteTl5SEmJgYAULt27WrbOa8O2Fa6ge2kO2pKW82dOxePHj0Spgzp2rUrDh06xCRdNcH+sO5gf7jqqim/B9UB20o3sJ10Q01pJ5FIhF9//RXPnj0TEgyTJk3Cxo0btRxZybEPUXXYWRpXqXp0naWlJXbv3o2tW7fixIkTePr0KfT19VG/fn14eXlh6tSpqFevnrbDFBgaGmLz5s3YvXs3Dh48iEePHkEul8Pe3h5du3bFlClT4OzsjAsXLpTrOB9//DE6deqE4OBgRERE4OXLl3j58iX09PRgZWWF5s2bo3PnzhgxYgTq1q1b7vfl7e2NTp06Yc+ePbh27RqePHmCjIwMGBkZwd7eHu3atcOIESPQpUuXMtUvEolgZmaG+vXrC3V16tSp3HFT2Yjkcrlc20FQ5RKLxQgJCQEAuLm5wdzcXMsRkTKF28nd3R1GRkZajohUYVvpBraT7qhJbZWXl4fRo0fD0NAQO3bsgKmpqbZDyre2BfAqTm0RsYktQvr/A6D6txNVP+wP64aa9Hug69hWuoHtpBtqWjulpaWhe/fuGDFiBL788ssS37S2bfEVZKbmlvv4xrkp6HZtGQDAwN4ezS+cL/FrMzMz8eDBAwA1o62KExkZCalUCgMDAzRv3lzb4RARVShN/5vHNeqIiIiItERfXx9///03jIyMoKenp+1wiIiIiIgqlZWVFQICAmBmZqbtUIiIiLSGiToiIiKiChQVFQVzc3PY2dkp3W9iYlLJERERERER5Rt3dBySspPKXY+tqS32DN2jdN/t27fRqlUrGBoaKt3PJB0REdV0TNQRERERVZCgoCAMHToUTZs2xdmzZ6vO1JZERERERACSspOQkJVQYfXv2bMHU6dOxdtvv40//viD6zETEREpUeXnWDpw4ABatGiBli1bajsUIiIiohI7dOgQvLy8kJCQgOvXr2Py5MlccJ7KhP1hIiIi0jVyuRxr1qzB+PHjkZubi61bt+Kbb77RSN2yzEzkpaZqpC4iIqKqoMon6oD8H3e5XK7tMIiIiIhKJDc3FwsWLEB2drbw3P79+7Fu3TotRkW6jP1hIiIi0iVRUVFYuXKlwnOff/45zpw5U656xc+eIWr8BMhzc8pVDxERUVWiE4k6oppIJBLB3Nwc5ubmnBqiimNb6Qa2k+6oDm1lbGyMI0eOoFatWsJz/fr1w8yZM7UYFRGRbqkOvwc1BdtKN7CddEN1aScnJyfs3r1b4T2899576NWrV5nrzLh0CU/eGoPcyEgNREhERFR1cI26Gk7VQr6kfYaGhnBzc9N2GFQCbCvdwHbSHdWlrVq3bo39+/dj0KBBmDx5MjZv3szfXaIqiH+XVVd1+T2oCdhWuoHtpBuqUzsNHz4cP/30E+bPn4/vv/8eixYtKlPyUS6X4+VvvyPxxx8BTc8woKcPA3t7AICBrW2pXso+BBERaUqFJeo+++wzjdTz7NkzjdRDREREVNn69euHoKAgtG3bVrfuiLaoW3wZY5uKj0PHsT9MRERENd28efPQs2dPtGvXrkyvl2VmIm7pMrz691/NBvb/DOzs0PyP8xVSNxERUUlVWKLuwIEDunVBioiIiKgMMjIyYGFhoXJ/WS9KaNXsC0CeFNBX01UUi4GQkMqLSQexP0xERETa5u1zGYmvclXuz6qXC+hXbAxl7Q+Lnz1DzAdzlE51KTI2KWdUREREVUeFT33JRe+rNqlUCiMjI22HQUpIpVI8ffoUAODo6AgDA85UW1WxrXQD20l36FJbnT9/HmPHjsWOHTswcOBAbYejOTIZsLkn0KAd0HUOYN+ySBGpVFr5ceko9oerNvaHqy5d+j2o6dhWuoHtpBs03U6Jr3LxPD1H5f4Jjy1gKlF901lJSUxEwJhyVyPIuHQZsYsWQZaerrhDJILdwoXQf2wNpKpOQFYG9oeJiEhTKqxXZm1tjbS0NHTv3h0rV64scz0nT57Ed999p8HIqDBeOKq6ZDIZUlNTAQAODg7aDYbUYlvpBraT7tCVtvrrr78wffp0SCQSjBkzBpcvX0bbtm21HZZmRPwLJNzL/+/2TsC5L/DWn4CptVCEfYjisT+sG/hdrrp05feA2Fa6gu2kGyq7nUzFejDP1St3PZkyKRITE2FnZ6eBqABZVlaRJJ1erVpouPYHWPToASy+opHjlAf7EEREpCkVlqhzd3fHpUuX8PjxYzRs2LDM9dSuXVuDURERERGV37lz5zB58mThcUZGBoYMGYKAgIBy9XuqjKs+io8zXgAmVtqJRYexP0xEREQ1hRzAsGHDcO7cOZiYlH9ayloD3kTOrFl4uWULAMC4eXM02ugDI0fHctdNRERU1ZT/lhkV3N3dAQDx8fFITk6uqMMQERERVTovLy9MmDBB4bmOHTtWj4RKajQQe0PxuTfmAlxrrdTYHyYiIqKapFevXhqdTtpu/jyY9+wBywED0OTv3QpJOrNaRjC3Ni73f2a1OP01ERFpX4WNqGvTpo2wHRISAi8vr4o6FBEREVGl0tPTw9atWxETE4NLly5h/vz5WLt2LfT19bUdWvlZOwAL7wFBvwFBvwP6xkCrUdqOSiexP0xEREQ1hZ5IhFWrVmm0TpG+PhqtXw+RiQlEhW4ay84QY+ySTho9FhERkTZVeKJOLpeX68JE48aNMXLkSE2GRkRERFRuxsbGOHDgAA4dOoTp06drOxzNsrADei8Bui8EXj4EDHincVmwP0xEREQ1hUiv9JN2yeVyvPztdxg2aACroUOUltEzNVV4/PxJGg6tu4VOQ53Qrn9j6Olx1gciItJ9FZaos7GxwYMHD8pdj4eHBzw8PDQQEREREZFm1alTp/ol6QozNAXquWs7Cp3F/jARERGRcrLMTMQtWYpXJ09CZGIC42bOMHFzU/saSW4ezmy9D6lEhmsHHiEqJAn9/tcStWxN1b6OiIioqquwNeqIiIiIdN2xY8cwfvx4SKVSbYdCRERERKRh8v//r3LlZWQgavwEvDp5Mj+KnBzEfDAH0pQUta+7si8SaQnZwuP4h2mICHxRobESERFVhgobUUe6ofAc31S16OnpoW7dusI2VV1sK93AdtIdVaWtfvnlF8yZMwcymQw2NjbYtGkTfzcL4WdB1QW/y1VXVfk9oOKxrXQD20k3VGY7GViGAqLKT9TpW1jAtEMH5EZGCs9J4uKQde0aag0erPQ1crkcJuaGEIkA+f+HbO9UCx0GNK6MkJViH4KIiDSFiboazsCAX4GqysDAAA4ODtoOg0qAbaUb2E66oyq01VdffYXly5cLj3/55Rc0bdoUH330kRajqkByOVDKCw3sQ1B1we9y1VUVfg+oZNhWuoHtpBsqs52kr1oB8guVcqzX2S9dgtzwcGTfvg29WrXQcO0PsOjRQ2V5kUiELiOc0bh1Hfhvu4+sdDH6/a8l9PS1l3RmH4KIiDSFt1ARERERvaZPnz4wNjZWeC4kJARyeeXfcVwp7vwN/DkQeHAMkMm0HQ0RERERVQo9aOrSoL6odPXoGRmh4Yb1MO/WDU779qpN0hXWoJk1xi31xNAP2sLa3qwsoRIR8s95XV1dsXjxYm2HQkTgiDoiIiKiIrp164bt27dj/PjxAIDPP/8cK1eurJ7T28jlwFUfIOEe8OwaUKcZ8ObXgOsgbUdGREREROUkL2YNOrnUAkBmuY9jY2KjvH6JBCJDQ6X7DOvWReM/fi/5QTZ7ARkJMALQEACOljrMfBZ1gdnaGUlIpCkBAQGYMmUKAGDOnDmYO3euliOqGc6fP4+QkBCEhIQgOjoaycnJyMjIgJmZGRwcHODp6YmxY8eiadOm5TpOnz59EBsbW6rX+Pv7o1GjRir3Hz16FH5+fggPD0d6ejpsbW3h4eGBiRMnon379mrrdnV1VbnP2NgYtWvXRosWLTBw4EAMHTqUI47LgJ9YDSeRSGBkZKTtMEgJiUSCsLAwAECLFi1gqKJjS9rHttINbCfdUVXaaty4cYiOjkadOnUwbdo0rcRQKR6fy0/SFXj58L+FP9SQSCQVGBRR5WF/uOqqKr8HVDy2lW5gO+kGTbbTk6RMvMwQayq0Usu4eBHPv/wKjf/4HUaOjhqoMAF4FVf+ejSE/WGimkUqlWL27NlK96Wnp+PevXu4d+8e/vrrL8ybNw+zZs2qtNgsLS1hZ2endF9OTg7mzZuHCxcUb1CIi4tDXFwcjh07hg8++ABz5swp07Fzc3Px/PlzPH/+HOfOncOOHTuwZcsW2Nralqm+moqJOqIqSi6XC52+ajvVWjXBttINbCfdUZXaqtquSVfY7V2Kj+s0A1wGaicWIqJCqtLvAanHttINbCfdoKl2OvvgBeb/fRtSWX4dekYJkMtMIJfW0kic6sjlcrzcvAWJ69cDcjli5sxFk793Q8/cvMR1yPJkyMuTw9BIvwIjpWrj/0dblhtHW1IxLC0t4enpibZt28LBwQF2dnYwMTFBQkICAgMDsX//frx69Qpr166FpaUlJkyYUKbj/PHHH8XeDHD16lWsWrUKADBo0KAiy3cUWLJkiZCk69y5M6ZMmYK6desiIiICmzdvxrNnz+Dj4wM7OzuMGzdO7TFbt24tHLNAVlYWIiMj8ddff+HBgwe4d+8e5s2bh127dqmohZRhoo6IiIhqrH/++QeOjo7o3LmztkPRnuE/A836509/+SIE6PoBoMdljImIiIh0kUwmx6ZzD7HuTIQwSYLIIBWmjf8A5PrIejYDckmdijt+ZibiPluCV6dOCc/lRkYibslSNPzpxxJPJX/z5DOEBzxHv2ktYd+k4pOLpOOq2GhLqp4MDAwQEBAAfX3lNxD07dsXkyZNwujRo5GWloYNGzZg7NixKsur4+TkVGyZn3/+WdgePny40jLXrl3DsWPHAAC9e/fGpk2bhHjatGmDPn36YPTo0YiLi8MPP/yAgQMHwsrKSuUxzczM4OLiUuT5du3awdvbGyNHjsTjx49x48YN3Lp1q9gpNek/vApDRERENY5cLseaNWswbtw4eHt74/Hjx9oOSXsMjIC244B3LwFTDgFty3bHHxERERFpV0auFO/tvIG1pwsl6fQzYNr4D+gZpkHPKBlmTX6BnnHhhIbmRleKnz5F1PjxCkk6gUgEeQmnikx4mo6go0+Q+iIL+7+7gaBjTyDLk2ksTiKisiou6ebg4ICBA/NnqElOTq6waw2vXr3C2bNnhWN27NhRabk///wTQH6SccWKFUXit7GxEWYSSk9Px969e8sck4mJCSZOnCg8DgkJKXNdNZHWRtQdPHgQANCvXz9YWFgAyP+C+fv7AwBGjBihpciIiIioOpNKpZgzZw42b94MAEhMTMTgwYNx9epV2NjYaDk6LRKJgKa9tB1FjcL+MBEREWnKo8QMzPa9gYcJGf89qZcLU4dt0DdO/O8pgwwY1/0X2dHT//+Zko1wK448NxdPxoyFLD1dcYdIBLsPF6LOO++UaDSdRJyHM1vvQ/b/U3bKZXIEHX2CJu62UL76EhFVhISEBJw+fRoBAQF48OABEhISIJVKUbt2bbRu3Rre3t4YOHAg9FTMxhIQEIApU6YAAHbs2AFPT0/s27cPfn5+ePz4MSQSCZo2bYpJkyYpnPeIxWLs378fBw4cQFRUFCQSCVxdXTFlyhQMHjxYZbxpaWk4c+YMrl27hvv37yM+Ph4SiQRWVlZwc3PDm2++iZEjR1bK2tTmhab5zc3NrZBjnDhxQqhb1XljRkYGrl27BgDo2rUr6tWrp7Rc//79YWFhgYyMDJw5cwbvvPNOmeNq1KiRsC0Wa2+NVF2ktUTd4sWLIRKJ0Lp1azRr1gwA8Pz5cyxevBh6enq8MEFEREQVQi6XF7mrLTw8XFjwmaiysD9MREREmuAf9gIL/r6NV7nS/54USWHWyBf6pjEKZUWSerBIm4zsrFRI86SQyzUzUi0vJbVIkk6vVi00XLsWFj26l7ie3EwpDE0UL1d6DGoCu8aWGomTiIqXl5cHLy8vyGRF/31ISEjA2bNncfbsWezbtw8+Pj4KiSllpFIp3nvvPZw7d07h+ZCQEHz66acIDQ3FsmXLkJaWhg8++ABBQUEK5W7duoVbt27h2bNnePfdd5UeY+TIkYiNjS3yfFJSEi5fvozLly/j77//xpYtW2BnV3Fp/5ycHGGkm56eHpo0aVIhxym46VMkEqmc9jIkJERY587T01NlXUZGRmjXrh0uX74svMbQ0LBMcRVugwYNGpSpjpqqSq5Rx0WNiYiIqKIYGhpi79696NGjhzAVw/fff4+5c+dqOTKi/7A/TERERMWRyeTwOfsQP56JeH0PLB3+AcwfKjxb37w+dgzagXrm9RAZGYmuXbsitb0bZKYmqGNTByamJqULQC5HXloa5Dm5MJJKFXYZN2+ORps2wqhx41JVaVHbGKM+7oAbJ54i+HgU7Bws0HFIk9LFRUTlUnAu0qVLF/Ts2RMuLi6wsbFBZmYmoqOjsXfvXty6dQtXrlzBl19+iTVr1qitb/369bhz5w68vb3h7e0NW1tbREVFwcfHB0+ePIGvry/69OkDX19f3Lp1CxMmTED//v1hbW2NsLAwrF+/HgkJCdiwYQP69u2L5s2bFzlGXl4e2rZti169eqFly5aoU6cOJBIJYmJicPjwYVy6dAn379/Hhx9+CF9fX41+XhKJBImJibh16xZ+++03REVFAQBGjx4tzJyiSdHR0bh58yYAwMPDAw4ODkrLPXr0SNhu2rSp2jqdnJxw+fJlSKVSPH36VLiRtDRycnKwa9cuAPlr2XXt2rXUddRkVTJRR0RERFSRrKyscOzYMfTq1QurV6/GmDFjtB0SEREREVGJvcqR4MN/7uD0/Rev7ZGjXtMTyDS+q/BsbePa2NJ/C+qZ50991rx5cxw6dAiTJk3Cvn374OHhUarji58+RcycOciNfFhkn+WggWjw9dfQK2aUjSr6+nrwHOqExq1sYGxqAH195VPrkY7LTFJ8bGIN6Ku4VF2krBWgX7YRP+qpuVkuKxkoPALV2BIwMFZeNjsFMK2t2dAqkb6+Pv799184OjoW2efp6YnRo0djw4YN2LRpEw4dOoT33ntP7cixO3fuYMmSJZg6darwXKtWreDp6YkBAwYgMzMTixYtQkpKCjZu3Ih+/foplGvdujVGjhyJvLw87NmzB8uWLStyjO3btyuNoUOHDhg2bBj279+PJUuWIDAwENeuXSt3EikmJgZ9+/ZVub979+5YvHhxuY6hyqFDh4RkqrpZWJ4/fy5s29vbq62zfv36wnZ8fLzKRF1WVhYiIhRvDsnJyUF4eDh27dqFhw8fQiQS4aOPPkLt2rr7N6ANTNTVcMUtgEnaY2BgINztYGDAP9WqjG2lG9hOuqOy2srBwQFhYWGVMkd9lZInBXYMA1wGAh5T809yy4B9CKou+F2uuvjbrTvYVrqB7aQbStJODxMyMNs3GI8SM4vsa93qOp7KLik8Z2Zghl/6/wKD+Ewc3PIVBs/9CEYmpujWrRvCw8NL3R/OuHgRsR99XHQ9Oj091P1wIWxmzCjRenTFqedUtn5qZWEfopy+d1Z8/P51oG4L5WV/cgckWf89nnkWaFi65HKJSNWsJ7Z1EJD44L/HY7YBrUYqL+s7Eph1XpORVSqRSKQ0SVfYBx98gF27diElJQVnz57F9OnTVZZt27atQpKugJ2dHfr374+DBw8iOTkZgwcPVkjSFXBzc4OHhweCgoJw48YNpccoborJ0aNHw9fXF2FhYThz5kyFjfaqXbs2li9fjgEDBlTYvxGHDh0CAJiYmGDQoEEqy2Vm/vcbYWZmprZOU1NTYTsrK0tludDQUHh7e6vc3717d8ycORNdunRRezwqij2zGk7Vgp+kfXp6erzzQEewrXQD20l3aLKtzp8/Dy8vL5UXCmpckg4AHhwBnl7J/+/Cd/nJur7LVd8NqgL7EFRd8LtcdfG3W3ewrXQD20k3FNdOp++/wMI9t5GRqzjVpJ4IGNztIS68PKTwvAEMsKHPBti9MsU/az5DblYm9n21DCM/WwFTC8tS9Yflcjlebt6CxPXrgdem6dazsspfj657txLXp+vYh6CaQiaTITExEZmZmZAWmua2Xr16SElJwYMHD9S8Ghg8eLDKfW5ubsL2kCFD1JYLCgpCdHR0sfHK5XIkJSUhIyNDWKMNyB9VFhYWVmy8JWFvb48jR44AyJ9288WLF7h06RL27duHFStWIDo6GrNnzy73cV538+ZNPHv2DADQt29ftVNr5ub+l3gu7t/6wvtzcnLKHF9AQABMTU3h6OioMEqPisdEHREREVU7MpkMn3/+Ob799lusXLkSy5cv13ZIVYNcDlz1+e+x+FV+wk6/BiYsiYiIiHSITCbHev9IrPePLLKvtpkhpvZPxh8RvyvukANPfn6Cp6Iw3Lzsg9ys/NEV8Q/DseeLTzH2i1Uwq1WyUWuyzEzEfbYEr06dKrLP2MUFjTb6lHo9OgCQSvJgYMiRaURVjVwux+HDh7Fv3z7cvXtXbfImJSVFbV1OTk4q91laWgrb6kbFFZQrPErsdefPn8fu3bsRFBSktlxx8ZaEoaEhXFxchMctWrRAr169MGbMGEydOhXr1q1DVFQUVq1aVe5jFXbw4EFhW920lwBgbPzfzbhisVht2cL7TUxUr1fq6elZZI0/iUSCFy9e4MKFC/Dx8cHp06dx584dbNu2Dc7Ozipqotfx1o8aTi5XM/cyaZVcLodYLIZYLGY7VXFsK93AdtId5W2r3NxcTJo0Cd9++y0A4IsvvsCOHTs0HaZuen4XiH1tqpA35gJlmJqIf0dUXfC7XHXxt1t3sK10A9tJN6hqp4iEV9h0ruh6cC3r18LnY/SwPXJNkX2x22IhD83B3b2+yEpLVdhnXa8+TMxVj8QoTPz0KaLGj1eapLMcNBBN/t5dpiSdTCbHkQ13cNY3DOIcafEvqGL4d0TVVW5uLmbNmoVPPvkEgYGBxY6wKm6/usRP4ZGpJSknk8mK7JPL5Vi6dClmz56N8+fPq03SlSTe8nBzc8P8+fMBAH5+frh8+bLG6haLxfj3338B5E8b2q2b+hHM5oXWCVU3nSUAZGdnC9vFTZP5OkNDQzRq1AgTJ06Er68vDA0NkZCQoHQtQVKNI+pqOKlUqpBdp6pDIpEgJCQEAODu7l4zp2fTEWwr3cB20h3lbavr169jz549Cs+988476N69u7DeR41Vvy0w4wxwzQcIOwLUagS0GF6mqgpPeUKky9gfrrr426072Fa6ge2kG1S1k1u9WvjCuyU+P3RPKDuiXQNM9tLHu/4zIJUr9s1e7H+B3GsZWPVmT0jMFC9+1xHnwe2oPx4d9S82HnluLvLS0opMdamJ9ehun36GuMhUxEWmIjY8Bf2mtUJ956q9Ll1h7A+X08ePFB+bWKsuuyDktbIV9D1RtxzAtBOAvFCSyNhSddnJBzQXkxb88ssvuHjxIoD8EVRvv/02WrVqBVtbW5iYmAhJs4kTJyI4OFiboQIA9u3bh3379gHIH9k2depUtGnTBvb29jA1NRXWivvkk0+E9d0qUt++fbFy5UoAwMmTJ9G9e3eN1Hv27FmkpaUBALy9vYtdA69evXrC9osXL+Du7q6ybHx8vLBdnikrmzdvjp49e8Lf3x83b97EkydP1I6opP8wUUdERETVipeXF9avX4+5c+cCyF/k3cfHh0m6Ag6dAIcdQPITIC0G0Gd3kIiIiEgXTOriiJDYNOy/GYslg1tgercmeCV5hZZ1WuJmwk2h3MQWE/HILhxmPSKKJOlqZeWiw6NYyGVylDXNpIn16BKfvULA4cfC4/SkHJzZdh8TV3SGnj4nAKsRzG0rpmy5qEk6m9mUvBpT3V0PVC6XC0mvjh07Yvv27SrXYyxIGmnb3r17AQCOjo74+++/VY7Mq6x4bWz++67ExcVprN7STHsJQGHaycePH6spCTx58gQAYGBgAEdHxzLFV6Bp06bw98+/ESQiIoKJuhLiLx8RERFVO3PmzMHChQthYWGBI0eOVMgizjrPxglw6qHtKIiIiIiohEQiEb4c3hr/zO6KGd2dIBKJUMuoFjb334xeDr0AAEObDsWH7Raiv2N9NK6jmCwwzxGj0+M4GMrKPmWjsYsLnPb+U64kHQC8Ss6BvkGhy5IioO+UFkzSEWlZamoqEhMTAQADBw5UmaTLzMwUkjvaFhmZv3Znnz59VCbp5HI57t27p3Sfpr148ULYLu00kqokJycL02i2aNECrq6uxb7G3d0dhoaGAIDAwECV5cRiMW7fvl3kNWWVl5endJvU468fERERVUs//PADbt26hUGDBmk7FCIiIiKiEnmVI0FArOr1k0wM9eHhqJiAMzEwwY+9fsRiz8VY0eUL/OuzFtH37iqWEUvh+TgexnlF13cqqfKsR/e6pu3sMG6ZpzDVZYc3HdGguXW56yWi8imcWCm8btnr9u7dW2Wmfy2IWd06bP7+/kICsqIVrCMHAC4uLhqp8+jRo5BIJABKNpoOACwsLNC1a1cAwLVr1/D8+XOl5U6fPo2MjAwAQL9+/coda2hoqLBdePpNUo9zHREREVUzT0a/BWlSUrnrMbC1hdP+fRqIqGLI5XKkpKQoTCtRmJ6eHpo1a1bJURERERERlU3ki1eYtSMYT19mYVnP2lCznBCA/PXscnJyYGlpCQM9A7zt9jZObfZBZOBVhXJG0jx4Po6DqaTsF9X1LCzQcN26Mq9Hp4yVnSlGLOqAsCtxcOta9jWRiEhzbGxsUKtWLaSnp+Po0aP43//+V2RN07t372L9+vVairAoR0dHRERE4Ny5c0hNTYW1tbXC/mfPnglrxpXHmTNn0KZNG9StW1dlmaCgIGzatAlA/jSSQ4YMKfdxgf+mvTQwMIC3t3eJXzd9+nRcvHgRUqkUK1euxMaNGxXWtktOTsYPP/wAAKhVqxbGjBlTrjjPnz8vjN6rXbs22rRpU676ahIm6oiIiKoZaVISpIWmWqiO8vLysGDBAhw7dgzXr19X21EmIiIiIqrqTt57jg/33EamOH9kyLrrqfDyyIaz/X8XyMV5YgCAkb4R0tPTMWbMGEilUpw4cQJGRka4uHMrQs+dUqjXQCZHp8dxsMiVlCs+PXNzjSbphHr1RGjVo2HJCltoqM+vqXqIqoiwsDD4+fkVW65Lly5o0KCB2jJ6enrw9vbGzp07ER4ejgkTJmDatGlwdHRERkYGLly4gF27dsHMzAx169ZFVFSUht5F2Y0YMQLfffcdEhISMG7cOMycORMuLi7Izc3F9evXsX37dojFYrRq1apc01+eOXMGCxYsQK9evdC1a1c0a9YMtWrVglgsxrNnz3Du3DmcOHECMln+yOX3338fTZs2Lff7e/jwoRB3jx49UKdOnRK/tmvXrhgyZAiOHTuGs2fPYtq0aZg6dSrq1q2LiIgI/Prrr8I6eh999BGsrKzU1peVlYWIiAiF56RSKV68eIHz588L6xsCwKJFi2BgwPRTSfGTUiI2Nha+vr44f/48nj9/DiMjIzg4OGDQoEGYOHEiTE1Ny32MmJgY7N69G9euXcOzZ8+QnZ0Nc3NzNG3aFN27d8eECRNK9UdHRERUU2RmZmLChAk4cuQIAGDYsGE4e/asxuZ+r3Zu7wairwNd5wC2zbUdDekI9oeJiIgqV8TzV0KSDgAyxHIsP3wfO2fmT1uWJ8vDpxc/RYYkA5+4fILRw0YjJCQEADBr1iy8N2IIgo8oXqjXNzSE54t0WGWLK++NVKTZF7QdAVGV5O/vD39//2LLbdq0qdhEHQAsXLgQN2/eRFhYGEJDQ7Fo0SKF/dbW1vDx8cGGDRuqRKJuypQpuHr1Ki5fvoyoqCgsXbpUYb+JiQnWrFmDCxculHudOolEgtOnT+P06dMqy5iYmGDBggWYNm1auY5VoGA0HQAMHz681K//9ttvhSRrQEAAAgICFPbr6enh/fffx7hx44qtKzQ0tNgRfYaGhpg/f365R+fVNEzUvebs2bP4+OOPhXlZgfz5eNPS0hAaGoq9e/diy5YtcHR0LPMxDh48iC+++AI5OYpzjqelpeHWrVu4desWfH19sW7dOnTrVr7FeYmIiKqb+fPnC0k6AAgICMCUKVOwd+/eCrnLV6fJZMDldUBSBHBjG+AyCOj9GVC/rbYjoyqM/WEiIqLK90HvZgiJTcOp+/kzYzjXNsA3w1sCyJ/y/avrX+HMszMAgNG3R+P+k/vCa//euROtpOnQK1SfSE8PQ+d/CvlHi1EVVpGS5cmgp69XfMHSCDsKmFoDDT0Aw/LfREQ6jKMtNcrS0hK7d+/G1q1bceLECTx9+hT6+vqoX78+vLy8MHXq1Cq19pihoSE2b96M3bt34+DBg3j06BHkcjns7e3RtWtXTJkyBc7OzrhwoXzJ/o8//hidOnVCcHAwIiIi8PLlS7x8+RJ6enqwsrJC8+bN0blzZ4wYMUJjs/7IZDLh+ketWrXQt2/fUtdhYmKCLVu24MiRIzhw4AAePHiA9PR02NrawsPDA5MmTUL79u3LHKO+vj4sLS3RuHFjdO7cGWPGjCnXuWJNJZLL5XJtHNjNzQ0ikQhHjhwR1o+JjIyEt7c3RCIRwsLCKj2m+/fvY8KECcjJyYGZmRlmz56Nzp07IycnB8ePH8c///wDAGjSpAn2798PCwuLUh/jxo0bmDRpEmQyGfT09DBixAj07dsXdevWRXx8PA4cOIBz584ByP8jOnr0KBwcHDT6PsVisXDXVevWrWFsbKzR+kkz5HK5sEiooaEhLz5XYWwr3VCT2inSq5dGpr40sLdH8wvnyx9QKRXXVs+fP0eXLl3w9OlTAICRkRG2bduGCRMmVHqsVV7ESWDXWMXnJu0HmpV/gejc3FxhkWh3d/ci6xZQ8dgfZn+Y1KtJv926jm2lG9hOVV9GrhQjNl1B6waW+NK7BSzNTCASibDh5gb8FvKbQtmcJzl4uPIhAMDOzg57//LFo+N+SImPBQAMfH8hWnn1rRLnBq+Sc3Bw3U10GeGM5h3tyx2L4MfWQFo0oGcINGgPDPgWcOikufqLwf6wosjISEilUhgYGKB5c87kQUTVm6b/zeOIukK++eYb5OTkwMDAAH/++adCJrlr165wdHTE999/j6ioKGzduhVz584t9TG2bNkizFO7bNkyTJw4UdjXpk0bDBgwAKtXr8bWrVuRk5ODrVu3Yvny5eV/cyqwY151iUSiGt/J0xVsK93AdtIdxbVVvXr1cPz4cbzxxhvQ09PDwYMH0bNnz0qMUIdc/1nxcd2WgHPp78BThn2I6on9YapK+NutO9hWuoHtVPVZGBtg/7tvoJapgfD79Nf9v4ok6Qz1DDGjzQws0luEZs2a4fjx43B2dkanzp2xf9UXaNmjD1p5aabPV15ymRz+2+8jPSkHp36/h6iQJPQc7wpj03JekkyNzk/SAYBMAsQEAsalv4GoPNiHICIiTdHwmPOSe/DgAcLCwoS7hwGgefPmwvOV7e7duwgODgYAjB49Wulwz+nTp8PZ2RkAsGPHDuFOtNK4desWgPy5fAtflCjsgw8+ELZv375d6mMQERFVdy1btsSRI0dw9epVJunUGfEr0H0hYPL/C0J3nQPwgkKVwf4w+8NERFSz5MnkWHcqHLeepagsY2X232jHI4+OYE3QGoX9eiI9fN/zeywYtQB79+7FtWvXhN9mMytrTPjqB3gMKf0aRhXlztloxIanCo8jAl4g6MiT8lf87LriYxNrwNa1/PUSERFpgdYSdVXNmTNnhO3Ro0crLVMwNQ8ApKenF1l4sSQKLmY0atRIZRlLS0vUrl1boXxFKbibmaoemUyGlJQUpKSksJ2qOLaVbmA76Y6CtkpOTlbbVj169ICbm1slRqaDatUH+q0AFt4HhqwF3N/SWNX8O6p+2B+mqoa/3bqDbaUb2E7alZYtwYztQdhw9iHe++smEl7lKC1X0E7HHxzH8itFR5Qv77IcfR3zR8uNGjUKNjY2CvsNDA01H3wZyeVyvIzJUHjOwsYYnbydyl+5vgFQrw0g+v9Lm427AHqVe5mTf0dERKQpTNT9vxs3bgAAzMzM0KpVK5XlOnX6b67rmzdvlvo4Tk75nZGYmBiVZTIyMpCSkqJQvqLk5eVVaP1UdlKpFI8fP8bjx48hlVaFpZ9JFbaVbmA76Q6pVIrw8HBMmTIFP//8c/EvoOIZWwCd3gEMNLcOF/sQ1Q/7w1TV8Ldbd7CtdAPbSXvCn7/C8I2XcT48EQDwPD0HH+y8CbG0aKJHKpXiZMhJLAtcBqlcsZ3md5iPftbdcG3/bsjl8kqJvTxEIhH6TG2BftNawsjUABAB/aa2LP+0lwDQaiTw7iXg06fAJD+g2/zy11lK7EMQEZGmMFH3/x49egQAaNy4MQwMVHcYmjZtWuQ1pTF+/HgAQGpqKnbv3q20zKZNm4qUJyIiqilSU1Mxd+5cHDt2DPPnz8fx48e1HRJRjcD+MBERkeadCInHyJ+vIOpllsLzt56l4qaSKTAjUyOx9slaSOSKI8ont5yM8Y1GYd/Xn+PqPztxavMGyCozUZTxAljbQvG/zV7FvkwkEsG1cz2M/9wTvSe6oaFrbc3GZVILaNYXcHxDs/USERFVIg3cwqL7cnNzhTt269Wrp7aslZUVzMzMkJWVhefPn5f6WKNHj8aNGzdw8OBBfPnll7h37x769OkDOzs7xMfH49ChQ8K0Q++++y7eeKNiOxoSiQRisVjpPgMDA+j9/7QBMpms2DvuCi9KnZeXp/bOIpFIBMNC0zFIpVK1Uwbo6ekpXDCSSCRq7x4rHLtcLi92yiRDw//mgC/uvVZ07Pr6+tDX11d4Tl38bCftxK6snQpepwzbSTux19R20hS5XA6xWFyp7ZSVlQUvLy88ePAAQH47jB07FmfPnkW7du0AVJ920tTf04HvbyM7XQyIgP//n/8nB9TdaP1aeVNLQ4z8uJ3K4q//PVX0dIRUudgfZn+4qv9+sz+cr6q3U8HrlGE7aSd2tpNqFd1OIj19rD0Vjp/PF72p5e34/ahvJMHt7/7G7f+vHwDy5HlIzk6GN+oolJeLAfPLd/Bb5jQhORd67jQiA65i1i/b1U75qLGRd7I84NULxbohR55UWqJ2MrbQQzNPW8hksirVTuX5e2J/mIiINIWJOgCZmZnCtpmZWbHlTU1NkZWVhaysrGLLvk5fXx9r1qxB7969sXnzZuzduxd79+5VKNO5c+dKuSgBqL8L2tXVFRYWFgDyL5yGh4errcvDw0PYTkpKUjudkbm5ucK6Qk+fPkVqaqrK8nXr1oWDg4PwOCwsTG2HqGnTpgrrmoSEhKiN3d3dXTjBSEtLw+PHj1WWNTQ0RJs2bYTH8fHxSEhIUFne2tpaWNgZyP/MC3/nXteoUSPY29srPFdwwVoZtpNy2mgnQHVbsZ2UYzulqixfnnbS1Ml4QXtXZjuZmZmhe/fuCm2Ul5eHa9euCSfF1aWdNPX3lJ6cCXFm+dtcKlUfz+t/T2UZSUVVF/vDyvHfG+XYH05VWb6mtxNQ/ftZbCdFbCfl9E0t8eP1VFyISCyyr1ltQ9gnZEOc/grKbhMxhR6UTYCVk55W5DlJbg5e/R979x3fVPU+cPyTpHvRFtoyStlllw1liTJlL1EBFQERREFFUBEF9afgV3AiKkNkKAjILFNBAZG9y96UltG9Z8bvj9jQ0N0mTdo+79eLF8m9597zpKdpTu5zzznxcdy6k/vP3SEjw2zTaWVkZPDg3r1S207FfT9Jf1gIIYSpyNSX6O8gzpT1zpvcZHYQUlNzXvg3P9evX2fTpk1cuXIlx/2nT5/m999/58GDBznuF0IIIcqy8ePH07t3bwAqVqzI4sWL6dSpk4WjEqJsk/6wEEIIYRq34zJ4ZeOtHJN0XWs68n9PeKJUKHI4svAcXd2wd3I2yblMpTSsnSeEEEJYG4XOTJ+gO3fu5MknnzTHqU0uOjqa9u3bA9CnTx+++uqrPMt36NCBqKgo/P39CQoKKlRdx48fZ8KECSQkJFCtWjVef/11OnbsSIUKFYiKimLPnj18++23xMbG4u3tzdKlS6lXr16RX1tO0tPTDXcs1alTJ9e7pmXKC8vEnjmVQtZ2atCgQa4XzaSdLBN71ikvCtJW0k6Wib28ttPVLo+jNsHFbZW3NzV3/1niU8gkJSVx9uxZ5s6dy6effmp0lyuUnXYy1ftp1QdHSYrLedq+wnB2t2PEx21z3f9oO8XGxhruIs56p7N4SPrDOZP+sF5p/HtTUrFLfzh31thOUL76WdJOxqSdjO0494Dpm86TnG78c7BRKpjRpz4j2viiUChYOvklkmKi84yvIFw8KzJuwc95xn6rew80eYwqKygbRw31Bj4y9aVrFTSvnzO009XjDwjeF8rjI+vh4umQ83msoJ0yFfd7i/SHjV29ehX1f1OhmrrvJoQQ1sbUf/PMNvXllClTuHHjBhMnTjRXFSbj7Pzw7qOCTN+TkpICFGxaoKzS09OZMmUKCQkJeHl5sWbNGry8vAz7K1euzMiRI2nbti1Dhw4lPDycd955hw0bNhSqnsKwtbUtUEdCqVQWqsOR2zz4ucnaMSqIgtzpnUmhUBQq9sK+VnPGnvUYaSdj1thOmcflF5e0U+6knXJX0Neq0+nQJCQU6ty5ya29zd1Ome0zY8YM6tSpk2c7lNZ2AhO+n0x0RzYULp6ivv/KE+kPZyf94dyVir83uZD+cO7KWztlHldW+1kg7ZSX0t5OQxceJSIhLf/CuUhIzSApPXuispKLPT8815I2NT2N4jOVvNop49490Omw8fY27jMmPtCvOVcINg7ZyytQGNoxMSaNfasuk5asZv3/TvPYs/74t/XJ87UW6f0UeQGO/wR+7cEvEDxq5dofLonvLUIIIYQpmC1Rp9VqmT9/PtevX2fOnDlWfVeJvb097u7uxMbGcv/+/TzLxsXFGS5eVK5cuVD17N+/3zB9z3PPPWd0USKrevXqMWDAANatW8f58+e5dOmS0TzuonzIeieYKTvxwvSkrUqH8tBOOrWaezNnoSvCmlElLTk5mY8++oj3338fV1dXo33loa1E+SD94eykPywKQz4PSg9pq9JB2ilvEQlp3I8v2pTOuWle3Z0fn2tF5Qo5jy4zJ51Gw92330ETEYFD48ZUnTcX+1q19Du/aAgJppteWqfVsWf5BdKS9aPd0lPU7P75Aq6e9lSt52GyegC4/hecXKH/B1CrC4zaYto6hBBCiBJmtjXqOnTogE6nY/v27Tz//PNERkaaqyqTqFu3LgAhISF5DqPPumjto1Nx5SfrsY0aNcqzbOPGjXM8ztTk7h/rlbkIckBAgLSTlZO2Kh3Kejtp09IIfeMN4sw46sRUwsPD6dq1K59//jlPP/10ts/dst5WZYW0Tf6kP5z3sdIfFvmRz4PSQ9qqdJB2KlnPtK7OmvGBOSbp8pvu0RSilvxE8rFjAKSeP8/NIUNJOnzYLHUlxqYRG258s2Dd1t5Uqetu+spCHnkNlfxNX0cByftICCGEqZgtUbd48WJGjBiBTqfj7NmzDBs2jEuXLpmrumJr1aoVoL/D//z587mWO/ZfJwegZcuWhaoj6xQQec3bDhhdHCnM1BFCCCHKH01iEnfGTyBx9x5Lh5Kvy5cvExgYyJEjRwD9Gl6vvvqqLDovyiTpD2cn/WEhhBDlwaeDm/DZ0KbY22T//NqzZw8RERFmrT8lOJiI+fONtqk83HHI5yaZonL1dODZD9pRv51+pL2zuz1dhtc3/chNrRbuPJKo8ws0bR1CCCGEBZgtUadSqZg5cyYzZ85EpVJx7949hg8fzu7du81VZbF0797d8Hj9+vU5ltFqtWzatAkANzc32rVrV6g6fH19DY+PHz+eZ9msF0CyHieEEEJkpY6JIWT0aJJzuDtW5e6OjY9P0f9VqmTyeFUqFQmPrKG3Zs0abt++bfK6hLA06Q9nJ/1hIYQQZZ2nsx0j29XINUl1NOkoCgfzTT2qTUoibOpUyDo6Xqmk2uefo3JzM1u99o42dB/diF7jmtD9xYY4OJthtJkmHbq8Cw0HgPN/02f7tTd9PUIIIUQJM9sadZlGjBhBrVq1eOONN4iLi2Py5Mm8/vrrjB8/3txVF0pAQACtW7fm+PHjrF+/nsGDB9OiRQujMkuXLuX69esAvPDCC9mGuB85coQXXngBgMGDB/PZZ58Z7W/fvj2Ojo6kpKSwevVqBgwYQP369bPFsm/fPv78808AfHx8aNiwocle56PUarVVr5dSnqnVau7duwdAlSpVCr0Isig50lalQ1lsp4z79wkZ+xLp/302ZVI6O+P7w/c4t21rochyV7duXbZs2cITTzxBWloafn5+bN++nZo1axrKlMW2KovymhpRGJP+8EPSHxaFIZ8HpYe0Vekg7VQy7FS53xO/985e1sSuYYhDZTDtcngG9z+dTcbtEKNtlSaMx6l1a/NU+Ii6rbzNd3JbB2g/Uf9Pp4OYm1Chmvnqy4f0h4UQQpiK2UbUZdW+fXt+++03atSogVar5euvv2batGmkp6eXRPUFNmPGDBwcHFCr1YwZM4aFCxdy+vRpDh8+zMyZM5k7dy4ANWvWZPTo0YU+v5ubG+PGjQMgKSmJZ599li+//JLDhw9z8eJF/vnnHz788EMmTpyIVqsF4K233kKpNF8zyVRj1kur1RIeHk54eLjh90FYJ2mr0qGstVP6rVvcHjEyW5JO5emJ34rlVpmky9S+fXt++eUX2rZty5EjR4zWoYKy11bmdP9GHGlJ5l/jJCfShygc6Q/rSX9YFIZ8HpQe0lalg7STZZ14cIKp+6ai0eU99XNxxO/YkW3Nasdmzag0caLZ6rQYhQI8a1s0BOlDCCGEMJUSu32qVq1a/P7770yePJlDhw6xdetW7ty5w4IFC6hYsWJJhZGnRo0a8dVXXzFt2jQSExP58ssvs5WpWbMmixYtwsXFpUh1TJw4kbi4OFasWEFycjILFy5k4cKF2crZ2try5ptvMnDgwCLVI4QQouxKvXiRkJfGoYmKMtpuU6UKfj/9hH3tWhaKrOCeeuopBg8eLOtOFdORLTdQZ8iFttJC+sN60h8WQghR3lyOvsykPZNI06SZrY6Mu3e5N3OW0TalszNV581FYeLRkzqdPk8mhBBCCNMokRF1mVxdXVmyZAnDhw9Hp9Nx5swZnnrqKRYuXMi+ffsIDw8vyXBy1LVrV7Zs2cKLL75IzZo1cXR0xM3NjSZNmjB16lQ2bdpEjRo1inx+hULBe++9x++//86zzz6Lv78/zs7OqFQqXF1dady4MaNHjyYoKIixY8ea8JUJIYQoC5KPH+f28y9kS9LZ1a5NzVW/Wk2SLj4+nn379uVZRpJ0xde0lqztV9pIf1j6w0IIIcqHmzdvEhwczJ2EO0zYPYGEjIT8DyoinUZD2Ntvo31kLejKMz/Arnp1k9e3N34CxxOHotVJtk4IIYQwhRKfkFylUvHOO+9w8+ZNDh8+zP379/n6668N+z09PWnQoAENGjSgYcOGNGzYkNq1a+e6CK85VKtWjenTpzN9+vRCHdeuXTsuX75coLJNmjShSZMmRQlPCCFEOZWwdy9hr7+BLs34TlyHxo2pvngRNp6eForMWGhoKH369OHq1av8/fffBAYGWjqkUkun0xH7IBmPys7Zd2o11Lz5IQrmokOSnqWJ9If1pD8shBCirDp69Cj9+/fHzsOOhh83JDIl0mi/vcoeMN0UmFGLF5Ny/ITRNre+fXEbMMBkdWS6nhrIhZReANwObU/3iBQqeDmavB4hhBCiPCnRRN2DBw/49ddfWbt2LXFxcSgUimzzOUdFRXHw4EEOHjxo2GZvb4+/vz9r164tyXCFEEIIqxEXtJW706fDIwuWO7Vpg+8P36Mq4hR0pnb69Gn69u3L3bt3ARgwYACHDh2iTp06Fo6sdFGna7hy7AFn/w4l5m4SL8zpgHMFe+NCl7aijL2JnSKFNJ11tL/In/SHhRBCiLItJTWFxx/vT5oijVqv1eJeyj2j/a18WlHBTktSUrRp6jtzhoj53xlts61WjcofzjL5TT5JGg/2xr1ieH4/tQ4bvzjJ8//XHpWtmSftkvk2hShRGzZsMNy0t2fPHnx9fS0cUcl4/vnnOXr0KG3btmXlypWWDkeUIyWSqDt79izLly9n165daDQaw8WIGjVqMGbMGPz8/Lh48SKXLl3i4sWL3Lx5E3WWC5GpqakEBweXRKhCCCGEVUq7di1bks6la1eqffkFSgcHC0WV3bJlywxJOoCIiAhefPFF9u/fX6KjgUozjUbLr7MOkxjzcOTk+f1htO1f27jgQf0FGRtFqiTqSgHpDwshhBDlQ2JCIqnqVGq+VRNHP+ORZvU96jO/63xWbX7NNJXpdIRNnQaaLKPzlEqqzv0clauraerIIjS9KWk645keAgfWNn+SDuCnnmDrCH7twS8QqrcDOyfz1yuElTpy5AgvvPCC4bmTkxMHDx7E0THvEa6pqal07NiRxMREw7YVK1bQrl07s8VansyfP5/vvvsu/4JZvPbaa0yaNMloW9ZE6aMUCgVOTk5UrVqVli1b8vTTT8tMJWWA2RJ1Wq2WnTt3smLFCs6cOQNguCAREBDA2LFj6dmzp+GiXfv27Q3Hpqenc+3aNcPFikuXLhV4Ch0hhBCiLPJ643U0sbHErlkDQIWBA6ny6ScmXxi+uObNm8fly5fZuXMnAPXr12f58uWSpCsElUqJXyNPLvz78O7rc//cpVXvmqhs/rsIotNB9w/h0Hdg+SXNRC6kPyyEEEKUP54V3bGf1gCVv/HU5L4uvvzY40dc7UyXQNPEx5Nx547RtkqvvIJTy5YmqyOr+o77cVM9YHfcG8RrKlPH5RT1A58wS11GUmIg9Bigg5v/rYM95g/wk8SCEJmSk5PZvXs3/fv3z7Pcnj17jJJ0JS00NJRu3boBMGfOHIYMGWKxWKxFrVq1ClVep9ORlJTE1atXuXr1KmvXruXll19mypQpZopQlASzXd3r1q0b9+/fBx5ekHjsscd46aWXaNu2bZ7H2tnZ0ahRIxo1amSu8MR/5MKp9VIqlbi7uxseC+slbVU6lPZ2UigUVJ75AdqEeFSVKuHz7rsorPB12NjYsHbtWjp37kyFChXYuHEjnoVcO6+0t5UpNH2iulGiztZeRXxkysO16hQKqNlR/+/UPog33RonBSV9iPxJf7h0kN9l6yWfB6WHtFXpIO2Uu3txKUQlpeVfMF861J7rUDkbJ+kqOlRkUY9FVHKsZII6stSWkmr03LFFCyq9MiH/A128czsjJEXq+5oKJaAABzdQPHw9VUjgGY/PORrVl9bVT6BQvFX0F1BQd/5L0mVS2UPV5uavNx/Sh7Aez2x9JttakEVRybESa/qtMUFEJcve3p60tDQ2b96cb6Ju8+bNRsfkZsiQIZJEK4IRI0bQq1evPMtotVpGjhxJYmIiLi4udO/ePc/yb7zxhiG5mXl8TEwMR44cYfny5SQnJ7Nw4UKqV6/OsGHDTPI6RMkzW6Lu3j39xSUbGxv69OnDSy+9hL+/v7mqE0VkY2UjMcRDNjY2sqZTKSFtVTqUhXZSqFRU/fxzUKms+kuhq6sru3btwt3dHXt7+/wPeERZaKu8xEWkELwvlJh7yfSf1CzHMpV8Xajm747KRknTJ3yp0bgiCmXObe7k7gTK9GLH5eRmV6jy0ofIn/SHSwf5XbZeZf3zoCyRtiodpJ1ydvhGFK+tOkmGRpd/4XzYe+9A7XzUaJurrSsLeyykult1wzZndw/gv1ERMVnWqlMocHBxxcbWNs96dBoNmqgo7LJMk610caHq3M8LNuPG+H0FeDW5swM6FesMhRRyyPh5tVZgU/jvGaYmfQjrEZkSSXhy+Z1qpGvXruzYsYODBw8SERGBl5dXjuWioqL4999/Af1Nhdu3by/JMMuFihUrUrFixTzL7Nu3zzCq8cknn8Qhn+VMfHx8cvwe2b59e9q1a8fo0aPR6XQsWbJEEnWlmNk+UZycnHj66acZNWoUVapUMVc1QgghRJmiTUtD/eABdn5+Oe63lqkuo6OjcXZ2zjUR5+PjU8IRWb/k+HT+XnmRW+eiDDcEh9+Ox7uGW47l+09qXqD1Pp5+r40pwxQmJP1hIYQQwrpduBvPyCVH0GiLn6RT2EZh63HQaJu9yp753eZT37O+0fbn5nwNwMkdQfy9bOHDc6Dg+Tlf4+aV24g30KnV3B71IinnbxltrzxrFna+vkULXpMBqryTgxbV/jV9ci7kEIQchlqPWToiIaxKx44dOX78OBEREWzbto0XX3wxx3Jbt25FrVbj5eVFhw4dJFFnIZmjGgEGDRpUrHO1b9+eRo0acf78eW7dumUYpSdKH7Nd7du7dy9ubjlfeBJCCCFEdprEREInvkr6zZvUWL2q6F+0zezatWv06dOHdu3asWLFCqse3WdNHJxtiAxNNJq15+zfoXR/MeepDQuSpBPWTfrDQgghhHVrWMWV/gFV2HT6brHPpcuoSErIGJz8VoAyFZVCxbwu82jl0yrn8lotp3dtNdpWp3XbPJN0AJGLFpFy4oTRNrcB/anQv1/Rg/99jH4NOO9G4NMIGg/WJ8ashXNFaNhP/08IkY1KpaJv374sW7aMzZs355qoy0wQ9evXD5VKlWOZTBs2bGD69OmAfl073xyuT5w7d45Vq1Zx4sQJHjx4gEajwdPTk4oVK9KsWTM6depE165dDdcM6tc3vmlh+vTphjoyvfbaa0yaNCnHGLy9vVm1ahXbt2/n9u3bxMbGGpVPT0/nwIEDHDhwgDNnzhASEkJycjIuLi74+fnx2GOPMXLkyEIvz2FKiYmJ7NmzBwBfX19at25d7HP6+vpy/vx5QP8zEKWT2RJ1clGidMjIyMDOrnBTXYmSkZGRwfXr1wGoU6cOtvlMfSEsR9qqdLD2dlJHR3Nn3Muk/te5Chkzlpq//oJNLlNWWMrBgwcZMGAAUVFRXL16ldq1a/PRRx+ZtA5rb6uiUqqUNH6sGkc23zBsu3YinMee8cfO0TpGShZGRkaGpUOwetIfLh2kP2y9yurnQVkkbVU6SDtlp1AomDMkgMsPErl4Lx6nmvNR2CQU76Q6G2wUNnzY4UMer/54rsVuB58m5l6Y0bYWT/bPs52ST50icsH3RsfY+vpSeebM4sUcfgES7un/Xd8DlfxJr9iMXYvP0aZfLSrXqlC885dR0h8W1mTgwIEsW7aMCxcucPXqVerVq2e0/9q1a4ZkzsCBA7l48WKx6lu2bBn/+9//0Gq1Rtvv37/P/fv3OX/+PKtWreLkyZM4OzsXqy6AmJgYXnvttTzjnjlzJhs3bsy2PTY2ltjYWM6ePcsvv/zC999/T6tWlrkZYceOHaSm6tcXHThwoElufL57V3+ziYODg0WTkKJ4St9VISHKCZ1OR1JSkuGxsF7SVqWDtbdT+OdzDUk6gIyQEELffJMaK1dazYi1xMREBg4cSFRUlGHbxx9/TP369RkxYoTJ6rH2tsqNTqvj9vkoMtI01Gud89SfjTtV5fi2W6hslTTsUIWmj1creJIuIxX2zoZWo8GzlgkjF0II61RaPw/KI2mr0kHaKWeOdioWPteKYT8eJMUuEZ0qvtjn9HDwYmDdgXmWObUzyOh5RV8/qjcOICMjI8d20iQmcnfa26DRPDxIpaLq3M9RFWeas4wUiL5hvM27Mf/+fo2Q89HcuRhD6941aNWnJiqVzPgghLVq1KgR9erV4+rVq2zevJmpU6ca7c8cTefv70/Dhg2Llai7dOmSIUnn6+vLc889R4MGDXB3dycpKYmbN29y+PBh/vrrL6PjgoKCCA8PZ+zYsQC88cYbdOvWzahMbuu7zZgxgytXrjBo0CD69OlDpUqVuHfvntHNDGq1murVq9OjRw+aNm1K1apVUalU3L17l4MHD7J+/XrDKLytW7fmu5acOZhy2kuAI0eOGBKwXbt2Lfb5hOVIok4IIYSwAj7T3yX14kXSLl8GQOXpSeX33rOaJB2Ai4sLy5YtY8CAAYa75po3b06XLl0sHJllpaWouXTwHmf3hhIfkYKzuz21W3jleCHD0dWOfq8F4F3TDTuHQnbDgtfCv9/AwfnQsD90mAy+xZ8mQwghhBCirNPpdITGpFDd0ynH/X4VnTgyozvd1s0hPLn49eXXh499cJ8bp44bbWvxZL88j7v/8cdkhIYabav06kScWrQoeqAAEZdAl3VEjIKbD3y4cOAKoL8Z7di2W6QkZNBlRP2czyFEEUSnRhf5WCcbJxxsHEwYjV5eNzHEpcWh0Wly3f8oT4eSH9k0cOBA5s2bx9atW3nrrbcMf1N0Oh1BQUGGMsW1a9cutFotTk5OrFmzhkqVKhntb926NcOGDSMhIQFHR0fDdn9/f5ycHv4d9vHxwd/fv0B1Xr58mU8++YRhw4YZtjVu3NiozOTJk6levXq2v6VNmzalV69ejBgxgmeffZbo6GhWrlzJG2+8UdCXbBKhoaEcP67/29+yZUv8/PwKdNyDBw+4cuWK4blWqyUmJoZjx46xfPlytFotPj4+Jf56hGmZJVGXOdzSx8cn3/luC0uj0fDgwQMAqlatatJzCyGEEJaiqlABvyWLuTXyOXTqDPx++gn7WtY3aqpv377Mnz+fV199ld69e7NmzRpcXV0tHZZFxYUnc2DdVcPzpNg0bp6OpG6rnNcX8W1QhC9sWi0c/E7/WKeFC5shJQZGBeV9nLAY6Q8LIYQQ1iEhNYOp685w/FYMQZM6UdXdMf+DzOz0H9sgS0LA3smZhp2fyLV8XNBW4rcY9/scW7Wi0vjxxQ/GpylMPALh5+HBBbiwmQt/nAEe/pxs7VU071G9+HUJkUWXNUW/4fO9du8xvMFwE0ajl6zOPVM/ascorsddL/C5gkcFmyKkQhkwYABffvkl9+7d48iRIwQGBgL6UVf37t1DqVTSv3//YtcTGRkJQM2aNbMl6bIy5bWCwMBAoyRdTvJLfNWvX59hw4axfPly9uzZU+KJrc2bNxuSwYMHDy7wcV9//TVff/11jvvs7OwYN24czz//PD4+Oc/sI0oHs4xZ79q1K927d+fmzZsmP/eNGzcM5xdCCCHKEhsvL/yW/kTNVausMkmXaeLEiaxfv54tW7aU+yQdgHcNN3xqGa9FdvbvO6at5NpuiLxsvK3DZNPWIUxK+sNCCCGE5V0LT2TQgn/Zdf4BUUnpvPLLCVIzCj4ixhwyUlM59/cfRtsaP94dO4ecE4gZd+9y/5E1oZWurlT7/H8oTHEzkMoGvBtAk6HQ7QPwbkDv1Odo67IKBfqfVaen61HBK+fRiGaVEgMJ90u+XiFKKR8fH9q1awcYT7GY+TgwMNAkyRwvLy9Av+7d2bNni32+gihKgjEuLo6QkBCuXr3KlStXuHLlimEd8WvXrpX4OpNbtmwBwN7ent69e5vknOnp6WzatIn169dnWy9QlC5mm/rS3POdy3zqQgghSiudTpfrtDZ2vr4lHE3OtFotSmXu9/MMGTKkBKOxvNSkDDRqLc4V7HPcH9DVlz9/ugBAxWouNAiskmc7F5pPY2j/GpxYDukJ4NUA6kqSxtpJf1gIIYSwnJ3n7jN13RkS09SGbWdC4/go6DxzhgRYLK6LB/aS9t8adAAoFDTv1TfX8jZeXniMGEHU4sWGUXiVP5yFbbVq5gnQ1hmlQksbl3X42Z/iqsckGnbIfbSfWZ1bD9veAo+a4Nce/HtB44KPQhGiPBo0aBCHDh3ijz/+YNasWYB+qkowzbSXoJ9pZ9GiRaSnpzN8+HA6d+5Mly5daNWqFfXq1TPLEh716xds6t3Lly+zbNky/vnnHyIiInItp9VqiY+PL7F16k6fPs2tW7cA6NatW6Fuep4zZ062azBJSUlcv36dtWvXsm7dOr755hsuXbrE119/nee1HGG9zLpGnTWtqyOEEEJYg7gtW4jfth3f+d+isLOzdDg5Cg4O5rnnnuO3336jYcOGlg7HoiJDEwn++w5Xjj7Av60PTzyf88+jTgtvQjvG0CCwMlXqupu+D1ShGvT6FLq8rU/WufuB9LNKBekPCyGEECVLo9Xx5Z+XWfB39inq3J1s6d2kigWi0tPpdJzatdVoW63mrfConPtU1gpbW7ynvIlzx47cfecdnAMDqdA398ReVmtnHyM5Pr1wQaYOgPRuD58nOXN3znGefq9N4c5jCiGH9f/H3NL/02okUSdEPnr06MGHH35IYmIie/bsQafTkZSUhJOTEz179jRJHXXq1OGLL77ggw8+IC4ujr///pu///4bAA8PDzp37swzzzxD69amW1O9QoUK+ZZZt24dH374IWq1Ot+yAKmpqcUNq8A2bdpkeDxo0KBin8/Z2ZmAgAACAgKoWLEiP/74I7t27WL9+vX5ThEqrJNZE3VjxozBxsa0VRT0jSaEEEKYy82hT6H+b072wtAmJ6NNSADgcpu22NetQ631600dXrHs3r2boUOHEh8fT58+fTh8+HC5nef8wr93+XvlJcPzK0cf0H5IXRycbbOVVdko6ZpLEs+kHCpAR5nysjSR/rAQQghRcmKT05n822n2X8k+iqJRFTcWPt+K6p4WmMLxP6EXzxEZcstoW4te/Qp0rHO7ttTetBFssvdFc5Mcn05SbFphQkS/Nl2WaThTgcIm+0wlM1GXyS/QMnEIs9j3zL4iH+tkY573cV7nXd57ORqdZafOLQhnZ2e6d+9OUFCQ0Zpo3bt3x8nJdD+3Xr160aFDB7Zv386BAwc4fvw40dHRxMTEsGXLFrZs2cLgwYOZPXu2SUZ45XeO69evG5J0FStWZOzYsQQGBlKtWjWcnZ2xtdX/7fz999+ZMWMGUHIzlKSnp7Njxw4AKlWqRKdOnUx6/tGjR7No0SK0Wq0k6koxs059mbnIvbBeMhTWeqlUKnz/mwJPZYp554XZSFuVDqZsJ3VkJOpifsbp0tJIu3HTtNMjFtPu3bvp3bu3IQlw69Yt+vfvz969e03aoc+PtbynajSpiFKpQKvVd97VGVou/HuXlj1rWCwmayJ9iPxJf7h0kN9l62Utnwcif9JWpUNZb6cLd+MZ/8tx7kSnZNs3pEU1Ph3cFEe7vF93YmIimPFj4dTOIKPnHlWqUrNZS6NtebWTyt3dfMFZm+RoSI0z3lajg2ViyYX0IYrH08HT0iFkk9d38wr2+Y/oshaDBg0iKCiIf//912ibqbm6uvLMM8/wzDPPAPpk2Z49e1i5ciXh4eFs3LiRhg0bMmrUKJPX/aiNGzeiVqtRqVSsXLmSOnXq5FguLi4ux+3mtHfvXmJjYwH9Wnum/gx2d3fH09OTyMhILl++nP8BwiqZJVE3eLAMQy8tymLnvKxQqVTldhRLaSNtVTpYZTtZSYIuU4cOHWjZsiVHjx41bLOxsSElJaXEE3Ul2Va5JUudK9hTp5U3V4/pEy1KpYKUhJJdbNqaSR8ib9IfLj3kd9l6WeVnt8iRtFXpUJbbadOpMN7dcJbUDK3Rdhulgvf7NmRUh5oFujnO0dGR5LRks8QYHxnBtWPGI8Sa9+yL4pFkT1lup0Jx8oR3bkH4Bf3IurCTUKlga1SVFOlDCGvVvn17vLy8DGu0eXt70759e7PXW6dOHerUqUP//v3p06cPycnJ7NixwyhRZ64bla9duwZAgwYNck3SAZw7d84s9efF1NNe5iTzhmuNxvpHfYqcmSVRN2fOHHOcVgghhChTVK6uVjOaDsDJyYktW7bQvn17bt68ybBhw1i+fDmOjo75H1zKaDRabpyM4OzfobTo6Uft5l45lgt4wpfQyzE06VyVxo9Vw7mCfQlHKkor6Q8LIYQQ5peh0TJ7+0V+/vdWtn2VXOz5fmRL2tYq+KgdcyZeHF1c6TZmAqd2biUqNARbewcaP949W7mEv/7GtlpVHOpbV1LKIpQqqNxU/08IUWAqlYqBAweyYsUKAAYOHFiiI0CrVKlCzZo1uXDhAjExMUb77O0ffqdOTzfdtLqZiark5NxvtggPD+evv/4yWZ0FERMTw/79+wF9ErFBgwYmryM0NNQwYq9KFcutwyqKx6xr1AkhhBCidPHx8WH79u2sXr2aWbNmlcnpXC4evMfhzddJjtN/KQjeG5pros6nlhujZndAZWOBn8PFrWDnDLUft7rRl0IIIYQQlhaRkMarq05y9GZ0tn0t/Nz5YWQrKldwsEBkObN1cKBZjz4EdO/NnfPBxD64h72Ts1GZ9Dt3uDttGrqMDLynTsXj+ees6sY+IUTpMW3aNKZNm2aWc+/evZu2bdvi5uaW4/579+5x48YNAMNUvpnc3d2xtbUlIyODkJAQk8VUs2ZN/vnnH27fvs3Jkydp2dJ4WuGUlBTeeustUlNTTVZnQWzbto2MDP3MPOYYTafVavniiy8Mz7t06WLyOkTJKJFE3d69e/nnn38ICwtDq9Xi7e1N27Zt6d27t2EhR2EZGRkZ2NnZWToMkYP09HSCg4MBaNq0qbSTFZO2Kh2knQquQYMGfPTRRxar39xtpVBgSNIBhF6KIfpuEp5VnXMoq0BlY4GLI5oM2PEOxIeCT1PoMAmaDAGV9fSbMr9siIKR/rD1kv6w9ZLP7tJD2qp0KEvtdCokhld+Ocn9+OwXXEe282Nm/0bY21jntIQKhQK/JgH4NQkw2q5Tq7k7dRrapCQAHsyeTcL+/VT/fgHKUtxWZZn0h0V5tXz5cqZOnUqXLl0IDAykTp06uLq6EhcXx7lz5/jll18MCbHhw4cbHWtjY0PTpk05efIk69evp1GjRjRs2BAbG32aokKFCrgXYT3OAQMGsHLlSrRaLePHj2fs2LG0atUKe3t7zp07x/Lly7l16xYtW7bk5MmTxf4ZFFTmtJc2Njb079+/SOd48OABV65cMdqWkpLCtWvXWLduHadOnQL0SdBx48YVK15hOWZN1EVGRvLqq69y9uzZbPvWr1/Pt99+y4IFC6gvw/mFEEKIEnP06FF27drFBx98YOlQLKJua2/+XX+N1MSHX6wvHrpHx6F1LRjVI85v0ifpAB4Ew8aXwbMWVG9r0bBE4Ul/WAghhDCtVUdC+HDLedI1xuvR2dko+WRgE55uUz3fc6xZswa1Ws3IkSPNFWahxfy2hpQzZ4y2qSpVlCSdEMIqpaSksHPnTnbu3JnjfqVSyaRJk+jePfsUv+PHj2fChAnExsby1ltvGe177bXXmDRpUqHjCQgIYNKkScyfP5/4+Hi++uqrbGXGjBlDvXr1SixRd/36dcMNMh07dqRSpUpFOs/XX3/N119/nWcZX19fvv32W7y8cp4tSFg/syXqNBoNr7zyiuGXMSehoaGMHTuWLVu24OlZ8DnDhRBCCFE0mzdvZvjw4aSkpODp6cmrr75q6ZBMLik2jfP/hNGsux/2jtm7Oja2Khp3qsqJnbepUrcCTR/3pXYLK+rM6nRwaL7xNt+2kqQrhaQ/LIQQQphOmlrDrM3n+e3YnWz7qlZw4IfnWtGsunue59DpdHz++ee8++672NraUq1aNR5//HHzBFxI7k8PIyM0lOhlywDQ+vhQ6d13LRuUEKVEJceiJUDMdZ6y7osvvmDv3r0cOXKE69evExkZSUxMDHZ2dlSrVo3WrVvz7LPP5roe2+OPP86yZctYsWIFwcHBxMTEmGSE6muvvUbTpk0N501OTqZixYoEBATw7LPP0rFjRzZs2FDsegpqy5YthsemnvbS3t4eDw8PGjRoQNeuXRkwYACOjo4mrUOULLMl6nbs2EFwcLB+SL+fHy+//DIBAQHY2Nhw5coVfv75Z06fPk1UVBQ///xztuy5EEIIIUzru+++Y/Lkyeh0OgAmT55MjRo16Nevn4UjM4171+M4+/cdbpyMQKvVYe9kS7NuOd9R3fQJX+q09MbLz7WEoywAnRZavQgHv4Po6/ptHQp/R6GwPOkPCyGEEKZxNzaFV349yZk7sdn2ta9dkfkjWlDJxT7Pc2i1WiZOnMjChQsB/bSFgwcP5uDBgzRs2NAcYReK0s4On3ffwT6wHWEz3if91YkonZwsHZZlnF0LHjWhSnOwkRGFIn9r+q2xdAglrl27dly+fLnIxw8ZMoQhQ4YUab+3tzdPP/00Tz/9dJHrDwwMJDAwsFgx5qRLly55rtOW3zlXrlxZqPry8uabb/Lmm28W6diivHZRuinNdeIdO3YAUK1aNdatW8fQoUOpV68etWrVolevXvz666+0adMGnU6X6xBZIYQQQpiOq6urIUkH+osVS5YssWBEpnVmTwjXjoej1epfY/DeUHRaXY5lnSvYW2eSDkCpgtZj4LXj8OwqaPIUNOhr6ahEEUh/WAghhCi+wzei6D//QI5JunGda7FybNt8k3Sgn4bt0dEGsbGxrF271lShZpMUG8PO77/i/rUr+Rf+j1OHDqR++QXaOnXMFlfh5dynNouMVNj8KvzUAz6rDj/3gajrJVe/EEIIYQFmS9RdvHgRhULB6NGjcXNzy7ZfpVIxefJkQD/lT2JiorlCEUIIIQQwatQoZs2aZXg+evRo1q1bZ8GITCvgCV+j53ERKdw+H2WhaExAqdQn6J76SZ+8E6WO9IeFEEKIotPpdPx04CYjlxwhKindaJ+jrYpvh7dgRt9G2KgKfmlr3rx5DB482Oj5zJkzTRbzo878uYPz+/bw64wprJrxFpcPHSjYgTZmmwCraHQlmKi7dxo0/7W3OhVuHwSniiVXvxBCCGEBZvvkj46OBqBp06a5lmnSpInhcUxMDC4uLuYKRwghhBDArFmzuHnzJv7+/rz33nsoFApLh1RgOp2OqLAkKvnm3F+oUteditVciApLxMZWiX9gZdy9y+l0QcIqSH9YCCGEKJqUdA3vbjjL5tN3s+2rUdGJH59rRcMq2W+CyY9KpeKXX36hT58+vPbaazz11FNG+3Nan0qj1aDVabFV2Ra4nkqOldCoMzi7e4dh271rl7l15gT123cqdNzlSsgh4+c+TcDR3SKhCCGEECXFbIm61NRUFAoFTnnMp511yoH09PRcywkhhBDCNBQKBcuWLTN5gm7t7GMkxxf/s9zJzY5BU5sZbctI13DlyH2C94YSFZbEyI8CcffJ3r9QKBS06VuT+KhUGnaogoNzwS+mCGEO0h8WQgghiubwzagck3RP1Pfi62daUMGp6P08Jycn/v777xz7w6Zc5+rSv/tIio0x2tbiyf4A6DIyuPfRR1QcPRp7M05xqVFrTXMihdkm5MrO3g28G0P4BUAHfnmvYSWEEEKUBVYzll5XksPohYFKJVNpWSsbGxvq169veCysl7RV6WDKdtKlpZkiJLPZv38/tWvXxtfXN8f95hhFlxyfTlKsaX4uWdtKpVSx+qOjxEWkGPYH7w2l8zP+OR5bp6W3SWIQ+ZM+hOlJf9gy5HfZekkfq/SQtiodSlM7PVHfmwld6vDjvodrk03uVo83utVDqcy/L7tx40Z69eqV680yJTGrxKmdW42eV2vQCO+atQGIWLCAuN/XE791Gz7vvov7M08bYjLp95Zc1mu2am3G6v+lxMCdo+BaxdIR5Ur6EEIIIUylBG+JEdZIqZRfAWulVCpxcXHBxcVF2snKSVuVDqZqp7igIDSxsaYLzMR++eUXunfvTt++fYmPj7d0OEWSta1UNipqN/cy2n/x0D3SU9UWis5M0pPhxDL9/6WE/L0TZYX8Llsv6WOVHtJWpUNpa6dpverTqW4lXO1tWPxCa6b08M83SafT6Xj//fcZMmQIzz//PFqtiUaUFdKDG9e4e+Wi0bbM0XTJx44RtXARALrUVO5/+CHhn31mKGfKdrKxK8WJJEcP8O8FVQIsHUmuSsP7SAghROlg9luoVq1ahaenp0nKvfbaa6YKSwghhCi0mN/WcP+jjywdRo50Oh2ffPIJM2fOBODs2bM8/fTTBAUFYWtbuqeAbNKlGqd3hxjWsHdytSM+MjXXtepKpTOrYNtbsPsjaDsO2owDF6/8jxOlgvSHhRBCiMJTKRXMH96CmOR0anvl3+9LS0tjzJgxrFq1CoANGzbw9ttvM2/evALVdyfhDj5OPtip7IoVN8CpXcaj6Vw8PKnbpj2auDjC3n4Hso6it7HBrV//YtcphBBCiNLL7Im61atX57k/c2h/fuVALkyYg6XuLhP502q1JCfrR1Y4OTnJnVpWTNqqdChuO0X9tJTwuXPNEZpJJCYm8uuvvxpt27VrF0uWLOGVV16xUFRF82hbuVVypFYzL9QZWgKe8MWvkSeKAkx5VGpoNXDoe/3jlGjY9z+4HwzD8+8bWZL0IQpO+sPWTX6XrZf0sUoPaavSwVzt1H/+ASISijYFulqrRaPVYW+jwsvVnqBJnYz2ezjb4eFcsMRZSEgI27ZtM9r2xRdfMHToUNq3b5/nsTqdjil7pxCbFsu4puMYVHdQkRN2yfFxXPp3n9G2gB69UapUhM36EPW9e0b7vF6fjGPTJobn8n4qPaQPIYQQwlTMmqgz5TobJTF/eHmk0WgsHYLIhVqt5vLlywA0bdoUO7vi39UnzEPaqnQoTjtpk5KI+e03c4VmEq6urmzfvp3AwEAiIiIAeP3113n55ZctHFnh5dRWvcY1RqkqoxcpLu+A6OvG29pNsEwshSB9iIKR/rD1k99l6yV9rNJD2qp0MFc7RSSkcT8+tZhnySh2HPXq1WPDhg306tULtVqNSqXi+++/zzdJB/BXyF9cir4EwP8d/j8WBy9mac+lVHerXug4gv/6A03Gw9ejsrEhoNuTxG3YSMLOnUZlnQIDqTh2rNG2oraTRqNF9Uh/2ckth2N1WkgK198slsnFG5S5z8KR43mE9CGEEEKYjNkSdStWrDDXqYUQQogSpXR2xu/nn7k9ciTqBw8M25QuxZt60aZSJVOEZ1C7dm2CgoLo1q0bn376Ka+//rpJz29JZTZJB+BaBep2h2u79c8rN4Vaj1k2JmES0h8WQgghCk5rgptbunbtypIlS5g0aRJr167lySefLEC9Wr4/873RNicbJ6q6VC10/VqNhjN/bjfa5t++M7axcdz59FOj7aoKFaj6v89QmGDE3OUj9zm56zYD32hhlFR7+r02uR+UEgvhFyH8PLQcAqoslwjDTkBcqH795IwkqNaq2DEKIYQQIndmS9S1bdvWXKcWQgghSpydbzX8fl7K7edfoOLYsVQcM9rSIeWoXbt23LhxA29vb0uHUiSmHH1Uavi2gufWw4PzcGgB1O0GMnKqTJD+sBBCCFFwiWlqk5xn1KhR9O7du8D94b9C/uJKzBWjbROaTUClVBW67usnjpAQGWG0rXm3Jwmb9ja6/6azzFT5k//D1sen0HU86uLBu/y18hLoYPPXpxg0pQWOLgUYAefoDjXa6/89at/ncCXL6L+uH0DVFsWONU9Xd8PlbeDXAfwCwb3woxmFEEKI0qoM354uhBBCmJZ97drU2b7N4km6W7du5bm/tCbpoJxP7efTGAZ9D02GWjoSIYQQQogS5+pQsHvJdTodN2/ezLNMQfvDOY2mq1OhDj1r9CzQ8Y86tXOr0fPKdephs+MPUoODjba7DxuGW48eRaojqytH7/PXCn2SDiD6bhJbvjlNemoxk562TsbPM5JzLmdKV3bA8aWw4SX4uglsLF3rbAshhBDFIYk6IYQQIgttejq6PBYFV1WoUILRZPfDDz9Qr1491q1bZ9E4hBBCCCGEMCUF+d+wpdFomDx5Mk2bNuXUqVPFrnNPyB6uxlw12lbU0XSRIbe4c/6s0bZG9RoTtXix0Ta7WrXwmf5u4YPNQdV6HrhVcjDaVq2+B7b2hY/fiN0jibr0EkjUhRw2fu5V3/x1CiGEEFZCEnVCCCHEf7RJSdwZP577H39sdVMwarVapk2bxsSJE1Gr1Tz//PMcPHjQ0mEJIYQQQghRIpKSkhg8eDDfffcdSUlJ9OvXjzt37hT5fFqdlu9P5zCarmbRRtOd/mOb0XNHVzccl/0CWb9X2NpS7Yt5KJ0eSYQVkYuHPQPfbIGLpz0ALZ+sQcehdYs/S4VbNfCsA5UDwK89uPuZINo8pMTqp4HPyi+HKTmFEEKIMsosibo//vjDHKc1ePDgAadPnzZrHUIIIcoXTXw8IWNfIvnQYWJ/W0P43HlWlazbsmUL8+bNMzxPS0tjwIAB3L1714JRCSFyI/1hIYQQwrTmzp1LUFCQ4fndu3fp168fGRkZRTrfn7f/5FrsNaNtE5pPQKko2qUyOydnbB0cDc9raZXoHjwwKuP9xhs4NGpUpPPnxq2iI4PebEHHp+oSOLC2aaaSf+I9mHwSJvwDY3ZC+4nFP2eedNDjI/DvDY4eoLKHqs3NXKcQQghhPcySqJs8eTIDBw5k586d+RcuhHv37vHhhx/So0cPDhw4YNJzCyGEKL90Wi0h48aRkuWid/TSpUQtWWK5oB4xcOBAJkyYYLRt4sSJVKlSxUIRiWK5sQ/iQi0dhTAj6Q8LIYQQpjV9+nQee+wxw3M7OzveffddbG1tC30urU7Lj2d+NNpW171ukdemA3hsxIuM/2E5T7w4Hnc3d3yOnTba79yhPZ6jXyzy+fNSwcuJ5t398k/SRVyBi0EQdR3ymO6/xDl6QMfXYcRvMO0GvHYUbOwtHZUQQghRYgq2Um8h+fn5cfnyZd58800+//xz+vfvT79+/ahXr16hz5WcnMyff/7J1q1bOXToEGq1GhsbG/z8zDzsvpwoSodWlAw7OztatWpl6TBEAUhblQ55tZNCqaTimLGEvfmm4Quryt0d5w4dSjLEPCkUCubPn8/t27f5888/Wbx4MS+++KKlwzKLMv+eUqfBhpchKQKaDIH2r5XKO4alD5E36Q+XHvK7bL3K/OdBGSJtVTqU9nayt7dn48aNdOjQgfDwcDZv3kznzp2LdK4/bv+RbTTdK81eKfJoOkOMTk40btAEx+OzIENj2K5yd6fKnM9QKPM/f07tpNPpiAxNxKu6a7Hi48Jm+PsT/WNbJ2j2LPT7qnjnNDWlEjxqWjqKApE+hBBCCFMxS6Ju27ZtLF++nCVLlnD37l0WLVrEokWLqFGjBs2bN6dp06Y0bNiQihUr4ubmRoUKFUhNTSUuLo64uDhu3bpFcHAwZ8+eJTg4mLS0NMP0Yz179uTNN9+kVq1a5ghdCCFEOeXWqyfa2Z9y793p2Hh54ffzUuzr1rV0WEZsbGxYs2YNZ86coVOnTpYORxRV8O+QeP+/x+v0/149Bl7+lo1LmJT0h4UQQgjT8/T0ZPv27WRkZFC/fv0inUOj1fDjaePRdPU86tG9Rvdix6dLT+fu1GmQkmK0vcqnn2Dr4120c+p0HFh3leC9YfR6qTF1WhbtPACEZ1kHLiMZlGa5LCiEEEKIQjLLJ7KtrS0vvfQSw4cPZ9WqVaxatYp79+5x69Ytbt++zebNmwt0nsyLEXZ2dvTo0YNRo0YREBBgjpCFEEII3AcNAsCpZUvsLDhSJXO0TE5cXV0lSVea6XRw6DvjbTU7S5KuDJL+sBBCCFE0Op0OrVaLSqXKcX/t2rWLdf4/b//J9bjrRttMMZoOIOLbb0k9f95om/uzz+DarVuRzqfT6tj/2xXO7Q8D4I8l53lygpJaAZWKFuCDC8bPvU27Xp4QQgghisast844Ozszbtw4xo4dy7///suOHTs4cuQIYWFh+R5rb29PQEAA3bp1Y9CgQbi7u5sz1HJLo9HkX0hYhEajITIyEoBKlSrl+iVFWJ60VelQ0HbKTNZZgk6n4/PPP2fHjh3s2rULe/vyuS5DmX5PZaToE3Mxt/R3MQN0mGTRkIpK+hAFI/1h6ye/y9arTH8elDHSVqVDaWin9PR0xo0bh4ODAz/++GP+a64Vkkar4YczPxht8/fwp5tf0RJpWSUdPkzUT0uNttnVqYPPO+8ULsYs7RR7S2tI0gFotTr+WHyO5z5pj3OFQn5X0OnArSokR0JylH6bT+PCnUMYkT6EKMs2bNjA9OnTAdizZw++vr4Wjqhkde3albCwMAYPHsxnn31m6XBEOVAiY9yVSiWdO3c2zB3+4MEDTp48yYMHD4iOjiY2NhZ7e3s8PT3x9PTE39+fJk2ayFzPJUBrTYsHCyMajYbQ0FAAPDw8rPJLlNCTtiodMttJeeUKdh4eeDz5pKVDMqJWq3n11VdZtGgRAKNHj+aXX35BWYB1LCwpI12Dysa0MZbp95SdE/T5HB5/F44vhZv7oG4PS0dVJNKHKBzpD1sv+V22XmX686CMkbYqHay9nbQ6Lb179+avv/4CoE6dOrz99tsmrSMiJSLbyLnijKYLOXcGr5q1sc1Qc/ftd/TJsP8obG2pNm8uSkfHQp0zazs1ad6Exp2rcv6fu/pzKhU88UKDwifpABQKeGGTPsbEcP00mD5Nci6bGgdhJyA9CdKTQaeB5iMKX2dB6HT62Eoh6UMISzpy5AgvvPCC4bmTkxMHDx7EMZ+/OampqXTs2JHExETDthUrVtCuXTuzxVqeabVadu7cydatWzl//jzR0dEolUoqVqxIQEAAgwcPpkuXLmar/91332Xjxo2FOmbOnDkMGTLEaNv8+fP57rvvciyvVCpxdnamevXqtG3blmeeeabYo9/LI4tMRu3j40Pv3r0tUbUQQohyTHnuHPZffsV9jQZbZ2dcirj4vDm8/PLL/Pzzz4bnq1evpnbt2nzyyScWjCpvWo2WnQvPYWunxNHVNMkEJzc7k5zH6jl5wmNT9f9EuST9YSGEEMJYZEQEx/5L0gG888471KxZk6efftpkdVR2rsz6Aev58/af/HjmR1QKFV39uhbpXOmpKWye9ykatRo/lR3V4mNxy7Lf660pODRsWKx4FQoFXYbXR6PRceXwfXoWd406/UnB1Uf/LzeRV2Hl4IfPbRzNk6jTauHbZlDJH/wCwa8D+LYBm3LynUAIE0pOTmb37t30798/z3J79uwxStJZStZEY1lNFMbFxTFx4kSOHz+ebV9YWBhhYWHs2LGDXr16MW/ePOzsrONvX2HXQtdqtSQkJHDhwgUuXLjAr7/+yvTp0xk5cqSZIiybZNVYIYQQ5ULS33uxnzsPhVoNQOikyfgtXoRTmzYWjkxv8uTJrFu3zqjDbM3T3Ol0OvatukzIef20OZVrV+DZ91vg4FL8hF16enqxzyGEEEIIIUoXV1dXo+dOTk75jgwpCqVCSa+avehRowcRydlH2BXUhX1/kZ6in8r8ZkY6N/196XrhNg5qDc4dO+KZZaRLcSiUCp54rgFNu1TDu4Zb/geYgp2z8XN1Cmg1oDTxKMyISxAbov93bbd+25vnoUL5mmJPFN/NoU+h/m/K2OKwqVSJWut/N0FEJcve3p60tDQ2b96cb6Iuc63szGNyM2TIkGyjqkThTJkyxZCk8/X1ZezYsfj7+6NWqzl//jyLFy8mJiaGXbt24e7uzscff2zyGN58803GjBmTZ5n4+Hief/55tFotNWvWpEWLFnmWnz17Nk2bNjU812q1hIeHs3//fn777TcyMjL4+OOPqVWrFh06dDDJ6ygPJFEnhBCizIvbuo3777yDIssaArrUVGJ+W2M1ibrmzZuzdu1a+vfvj62tLStXruSpp56ydFi5OrHjNhf+vWd4fv9GHNu+P8uQaS1NvpaI1VvYRT99UHG5eMP4fcU/jxBCCCFEKeTo6MTnn3/O22+/TeXKldm6dSutWrUyW31KhRIf5zxGleVBp9NxatdWo20+8ck4qDWoPDyoMmc2ChNOYa9UKkouSQdg65R9W0Yy2Ltm314cIYeMn1fwkySdKBJ1ZCTqBw8sHYbFdO3alR07dnDw4EEiIiLw8vLKsVxUVBT//vsvAN26dWP79u0lGWa5EhwczIEDBwCoXr06mzZtwsXFxbA/MDCQ3r17M3DgQOLj41m3bh2vv/46FStWNGkcPj4++Pjk/Vm3atUqw1S+AwcOzPecvr6++Pv7G21r0KABjz32GA0bNuS9994D4KeffpJEXSFIok4IIUSZFrNuHfdnzjJaLwLAtfeTVJ0z20JR5ax379789NNP+Pv70759e0uHkyef2m7YOahIT9UnP1W2Sjo+Vbf8JelAn6RLuGvpKIQQQgghSr2pU6eiVqsZMWIENWrUKPH6CzoqJ8JORbSn8Wi/GpFx+gc6HaGvTCzUqJyMdA1ndofQomfJv+Yc2bmAi48+YWfnol9nWas2fT0hh42f+wWavg4hyoGOHTty/PhxIiIi2LZtGy+++GKO5bZu3YparcbLy4sOHTpIos6MTp06ZXg8atQooyRdpqpVqzJkyBCWLVuGVqvlzJkzdO1atOmYiyNzlKVCoShQoi4vQ4cOZe7cucTExBAcHGyK8MoNSdQJIYQos6KWLSP8s/9l2+46eBDVPvkEhcrEU7eYwKhRoywdQoFUb+DJkGmt2PrdGRJj0+g5pjGVa1ewdFhCCCGEEKKEebna57hdq9MRnmA8rZq3qz3KXG7s8nK1R6FQMH36dJPHWFB7dEmkeTrkWy7dxni0nEKn40x1bzJfmb0uiZcKWGdGmoZt358h7HIskXcSefwF//wPMjfnijD1ivnr6fl/0KCPPmEXcghqdjR/nUKUQSqVir59+7Js2TI2b96ca6IuMyHTr18/VPlcD9mwYYPh7/GePXvw9X042vWDDz5g7dq1AHz99de5rr29Z88eJk6cCMCgQYP43//+R2hoKN26dTMq90IOUwXPmTPHMPXm/Pnz+e677wC4fPkyCQkJrFixgj///JPQ0FASEhKMyicnJ7N3717+/fdfzp07R2hoKKmpqbi6ulK3bl2eeOIJnn32WZydnbPVayoZGRmGx9WrV8+1nJ+fX47HQPF+zgV169YtTp8+DUCbNm2oVq1agY/NTbVq1YiJiZFlTQpJEnVCCCHKHJ1OR+T33xM5/7ts+zJ69cRr1iyLJOm0Wi2zZs3i8ccfz9YxLY0qVnNh6NutuXs1htotcp5aQwghhBBClG1BkzrluP1BfCrtZu8x2rZ5QhtmvjuVd999lzp16pREeBy5dwQHGweaeTXLt2yaUkFqEb4n6BQK0uyyXGLTaAt0XHqKmq0LznDvmn403vVTEaCESs11KJQmnKki4jJc2QnejcGnEbhWAWuYCcO1MjQerP8nhCiWgQMHsmzZMi5cuMDVq1epV6+e0f5r165x/vx5Q9mLFy8Wua7p06dz9OhRbt26xaxZs2jRogWVK1c2KhMREcH7778P6BM3H3zwQZHry+rWrVuMGTOGsLCwXMuMHz+eo0ePZtseExPDsWPHOHbsGKtWrWLRokVm+yyqVauW4fGdO3dyLRcSEpLjMVAyP+dNmzYZHg8aNKhQx+bm7l39jENVqlQxyfnKC0nUCWGlFAqF4c6OcjmVXCkibWVddDod4XPnEb10afadw57CdsQIlBZI0qWlpTF69GhWr17N/Pnz+ffff2ncuHGJx2FqLh72+LetnH/BQpD3lBBCCJDPg9JE2qp0KOl2Umt12bYNHNCfYwf/Yf/+/Rw6dAhPT08zx6Dmk8OfcCv+Fh2rdWRis4kEeAWYtc7CiA1PJiIkwWjbnQsxeDfxxMnDxnTtdGMf/Dnz4XO/DjBmh2nOLYSwCo0aNaJevXpcvXqVzZs3M3XqVKP9maPp/P39adiwYbESdU5OTsydO5fhw4cTFxfHO++8w7Jly4z+Zk2fPp3o6GhUKhVz5841TP3o4+NDUFAQwcHBhrXMZs+eTdOmTY3qeDQhlWny5MmEh4fz/PPP07VrV9zc3Lh9+zZVq1Y1lFGr1fj7+9O1a1eaNm2Kt7c3Op2OsLAwdu/ezY4dOwgNDeXVV19l8+bN2NvnPDK8ODp16oSvry+hoaGsWLGCoUOH4uRkvP7n/fv32bhxIwCtWrXKtu5bcX7OBaHT6diyZQsAjo6O9OrVq6gv12Djxo1ER0cDlIkb1EuSJOrKOVtbW0uHIHJha2tLgwYNLB2GKABpK+uh02q5/9HHxK5Zk22f11tTqDRunAWigoSEBPr27cs///wDQFxcHH369OHw4cNyh1EO5D1VOkgfQpQV8rtsveTzoPSQtiodSrqdNJrsibrjR48AcOXKFQYNGsSff/5plgukmXbc3MGt+FsA/Bv2L/+G/cuK3ito4d3CbHUWhncNN/pODGDrgrNoMrQ4ONsy4I3meFV3NW1F4eeNn1fwzbmcKBTpQxSP+r+L+SahLdgo1nzpsv/dyqSJjUVXiHpszHwjQk4GDhzIvHnz2Lp1K2+99ZYhoaPT6QgKCjKUMYWAgABee+01vv76aw4fPszSpUsZO3YsACtXrjRc/xg3bhytWrUyHGdra4u/vz8xMTGGbb6+vtmSVLm5evUqixcvplOnh6O5mzRpYlRmzpw51KxZM9uxzZo1o0+fPjz11FOMHTuWmzdvsmXLFoYNG1bg111QdnZ2fPHFF0yYMIGQkBAGDhzI2LFjqVevHmq1mvPnz7NkyRLi4uKoXr06s2fPzvE8Rf05F8SxY8cMIxO7d+9e4CRfaGgoHh4ehudarZbIyEj279/PqlWrAKhXrx5jxowpVDzlnSTqhBBClAk6tZq7098j/r/OZ1Y+Mz/Ac8QIC0Sl5+zsjLe3t9G2sLAwDh48yNChQy0UVcFoMrTsWXGRFj39TH/BQAghhBBClFnqHC5o67Qaw+OzZ89y6dIlmjXLf0rKotWvZuHZhUbbGno2pLlXc7PUV1S+DTzp80pT/v7lEv1ebUbFagUfDVFgDy4YP/dpZPo6hCikqx2sb01CbVJSrvtuPfcc6deuF/hcDS8VfcRaUQ0YMIAvv/ySe/fuceTIEQIDAwE4cuQI9+7dQ6lU0r9/f5PV9/LLL/PPP/9w4sQJvvrqKzp27IiNjQ1z584FoGnTpkyaNMlk9QEMHjzYKEmXk5ySdFl16NCBrl27snv3bvbs2WOWRB1A8+bN2bRpE8uXL2flypXMmjXLaL+TkxOvv/46I0aMwN3dPdfzmOvnnHXay8GDCz4FceZIyJy4urry2muvMWLECNzc3AodU3mmzL+IEEIIYd206emEvvFG9iSdUkmVz+ZYNEmnD0PJypUrDZ1kFxcXtm7davVJOp1Ox18rL3L12AM2zjtJyIUoS4ckhBBCCCFKiZymvqz631RmNWrU4ODBg2ZL0gFsv7md2/G3jbZNbD7RKqdn9WtUkec+am+eJB1A9bbg2xbs/ju/T5O8ywshSiUfHx/atWsHPJzqMuvjwMBAfHx8TFafSqXi888/x8XFhYyMDN566y3eeust0tLScHR0ZO7cudjYmHacUFESjdHR0dy6dYsrV64Y/mVOvXzp0iWTxpeVTqdj27Zt7Ny5k4yMjGz7k5OTCQoKYvfu3Xmexxw/57S0NHbt2gXof2/at29fqONzk5CQwLp169i+fbtJzleeyIi6ck6tVmNnZ2fpMEQO1Go1t2/rv1TUqFHD5B9swnSkrSxLm5xM6GuTSDp40HiHrS3V5s3DrVdPwPLt5OjoyObNmxk2bBjffvutWS9KmMqRzTe4cvQBABlpGrZ9d5Zuoxvi38a0a9I9ytJtJQpGrVZbOgQhTEL6w9ZLPg9KD2mr0qGk20n9yNSXSgVs27aVKVOmsGrVqlzXHzJJ3Vo1C88Yj6ZrVLERXXy7mK3O/Oh0ujyThCpb/b30ZmmnXp/q/9dqIe4OOFfK/5jwi5D4ANKTIT0JqgSAV/3ixwKQGA4KZcHisGLSHxbWaNCgQRw6dIg//vjDMIIrMyFjqmkvs/L19WXmzJm8/fbbXLt2zbB9+vTp1KpVy+T11a9fsL9DJ06cYOXKlRw6dIjY2Nhcy2WdgtOUtFotb775Jjt37gTgqaeeYuTIkdSpUweNRsPFixdZsmQJf/31FzNmzODy5cvMmDEj1/OZ+ue8e/duEhMTAf1ITKWy4OO5VqxYYUgIg/7zLTExkUuXLrF8+XL+/PNPZs2axc2bN5k+fXqhYyuvrHJEXXp6OpGRkWhNNb+wyJUuj7mXhWVptVpiY2OJjY2V94KVk7ayHE1CAiHjXs6WpFPY21P9+wWGJB1YRzt5e3uzd+/eUpGk06i13LseZ7RNaavE3dsplyNMxxraSuRP+hDmJf3hkiO/y9ZLPg9KD2mr0qGk20nzyIg6G6WS5s2bs2fPHrMm6QC23dhGSEKI0baJzSw3mi4+KoX1n58gKiwx37JmbSelEjxqgJ1z/mV3TocVA+G34bDhJbj6h+niOPwDzK0D81vD5tfgWt6jSayV9CGENerRoweOjo4kJiayZ88edu/eTVJSEk5OTvTs2TP/ExTBwIEDadu2reF5x44deeaZZ8xSV4UKFfItM3/+fEaMGMGOHTvyTNIBpKammigyY6tXrzYk6SZNmsSnn35Ko0aNsLe3x8nJiVatWvHDDz8YkqcrVqzgr7/+yvOcpvw5Z532ctCgQUU6RyaFQoGrqytt2rThu+++o1+/fgAsW7aMg4/eVC9yVaK3uSUlJXH8+HEAWrdujbOzcccgOjqaWbNm8ffff6PRaHBycmLYsGFMmTJF7nIVQghhRB0Tw52XxpF63nhhdKWzM9V//AGnNm1KPCaNRsPPP//M6NGjUalUOZaxxql2cqKyUdJ/cjP2LLvItRPhKJQKer3UGO8aMse4EMUh/WEhhBBlWXx8PFu2bOG5557LtkadSqnvB5u7P5zT2nRNKjbhMd/HzFpvbuIiUtj01UkSo9PY/PUpBr/VEo/KBUiUWdqjybz0ZNOdO+Sw/v+oq/p/Lt5Qt7vpzi9KhXoH/zXZuW4OHIQ6IqLY51E65/7erPnLL+hKwc0ozs7OdO/enaCgIDZv3mxIKHfv3h0nJ/PceHvu3DlOnTpleH7p0iWioqKoWLGiyevK7VpLpkOHDvHdd98BUL16dcaMGUOrVq2oWrUqjo6OhlHK33zzDd9//73J48u0bt06QN8eL7/8cq7lpkyZYpiadP369XTt2jXXsqb6OUdGRvLvv/r3X+PGjalbt26hjs/PSy+9xNatWwH9a+rQoYNJz19WlWii7o8//mD69OlUrlw5W4ZYq9Uybtw4Lly4YPgDkpSUxPLlywkLC2P+/PklGaoQQggrlhEeTsiYMdkWclZWqIDfksU4Nm1a4jElJSUxfPhwgoKCOH/+PF999VWJx2BqNrYqeo5tjIunA+7ejtRsWrqnphHCGkh/WAghRFkVGhpKnz59CA4ORqPR0LiL8TpCNqqSuWFt642t3Em4Y7TtleavWOSGubiIFDZ+cZKk2DQAUhIy2PSVPllXEjNVFMujibqMJNOcV50GYSeMt/mZZm0kUbrY/LdGmEkUYtq+POU1Pa27u2nqKAGDBg0iKCjIkIzJ3GYOqampTJs2jYyMDJycnEhJSSEqKooZM2bw448/mqXOvKxduxbQj7xbu3atYS26R8XFxeW43VSuX9dfr6pbt26eN1xWrlyZSpUqERkZyY0bN3ItZ8qfc1BQEBqNBoDBgwcX6tiCqF27tuHxlStXTH7+sqpEp748cOAAoM/gPzrv6fbt2zn/36iIRo0a8eKLL9KoUSN0Oh27d+9m//79JRmqEEIIK5UeGsbt557PlqRTVapEjRUrLJKku3fvHl26dCEoKAiAr7/+mm+//bbE4zAHhVJBx6F1ady5mqVDEaJMkP6wEEKIsuj06dO0a9eO4OBgQH83/dFjxskYG6X5E2UZ2oxsa9M1rdSUztU6m73unDi62uJW0cFom4OzLXYOpWAdR6eK4OwF7jXAuzE4meimvegboMz6+hXgW/KzoQhRlrVv3x4vLy/UajVqtRpvb2/atzdPQvyzzz4zJJg++ugjRo8eDcDff//N6tWrcz3OXDdPZK7f1q5du1yTdKAfnWZOmSP3CrKWZWaZvNYkLerPOSeZ017a2trSt2/fQh1bEFlfs6zlWXAlmqi7cuUKCoWCli1bZtuX+QvSuHFj1q5dy7vvvsuaNWsICAgw2i+EEKL8Srtxk9vPPUdGiPF6EzZVqlDzl5U41Pe3SFxXr17l7NmzRtveffdd7t+/b5F4hBDWS/rDQgghyqLDhw9z9+5dw3O1Ws2XX39tVEZlqhEvedh6fSuhiaFG215pZpnRdAB2Djb0e60Z3jX108dXqu7CoCktcHIrBdNZPzkHpl2DN87CxIPQcbJpzuvdEN69DeP+hl5zIPAVcHQ3zbmFEIB+esiBAwdiZ2eHnZ0dAwcOzHaToCns27fPkCTq168fAwYM4M0336RBgwYA/O9//+PmzZs5Hmtvb294nJ6ebrKYMhNDycm5T9d74cIFzpw5Y7I6c+Lr6wvorxfFx8fnWu7KlSuGdfSqVcv5Buni/JwfdfnyZS5dugTAY489lmcys6iyJkGrVKli8vOXVSWaqIuOjgYe/qJmysjI4Pjx4ygUCkaOHGnIHtva2vLss8+i0+myXQAVQghRvqReusTt559H/Ujyy65GDWr++gt2NWtaJjD0nZuffvrJ8NzDw4Ndu3ZRuXJli8VUGOp0DZcP35PF0ItMfm6i4KQ/LIQQoiyaMGECb731luF5/fr1+XT2HKMy5h5Rl6HNyLY2XUClADpV61Sg4yNCbpFmhhjtHG3oP6kZDTtWYeAbLXB0KcEkXdR1CHodjiyCWwcgObrk6s6LyhaqtYT2E/UJQSGEyU2bNo3g4GCCg4OZOnWqyc8fHR3Ne++9B+iTMbNmzQLAzs6OefPmYW9vT0pKCtOmTctxVJWXl5fhccgjN2MXR40aNQA4efIkt2/fzjHut99+22T15eaJJ54A9EnIOXPm5Hi9JS0tjU8++STbMVkV9+f8qKw3f5pjOtT09HS++eYbw/MuXbqYvI6yqkTH2mfO/Wpra2u0PTg4mNTUVBQKBZ07G09HUPO/C6+RkZElEqMQQgjro46JIWTUi2gemUPc3t8fv5+WYJOlg2cpzz//PLdu3WLZsmVs376d+vXrWzqkAtFpdexedoHrJyO4fyOezs/6oyyBaYnKFvl5iYKT/rAQQoiy6vPPP+fWrVtERESwceNGTj3IAB6uFacycx9z6/WthCWGGW0ryNp0GnUGRzau5cjGdejMNPLOwdmWrs83NMu58xR2Ak4se/i8gh+8GVzycQghypwZM2YQGRmJUqnkf//7H25uboZ99erV46233mL27NkEBwezYMECXn/9daPjq1atSuXKlbl//z5Lly6lcuXK1KpVC5VKBUDFihVxcXEpdFyDBg3i77//Jjk5meeee46XX36Zxo0bA3Dq1Cl+/vlnIiMjadGiBadOnSrGTyBvL774Ir///jtRUVFs2LCB27dv8+yzz1K7dm20Wi0XLlxg5cqVhqk669Spk+N6ccX9OWel0WgMS7a4u7vz+OOPF+m1hYaG4uHhYbQtMTGRixcvsnr1aq5evQrok6bPPPNMkeooj0o0Uefg4EBSUhJRUVFG248fPw6An58flSpVynaMMB9LTf8g8qdUKvH29jY8FtZL2sr8bDw8qPTqRB5kuSvXISAAv0ULC7ygc0m00/vvv8/kyZOpUKGCWc5vDv9uuMb1kxEAnNsfRmJsGj1faoytncpiMcl7qnSQPkTRSH/Y+sjvsvWSz4PSQ9qqdDB3OymVSlauXIlSqcTe3h71PeOZMGxU5vt7m+NoOq8AOlbtmOdx969dYdfCb4kMuWW22ArLpO304Lzxc59GxTufMCJ9CFFerVmzhr/++guA0aNH065du2xlXnjhBfbv38+BAwdYuHAhjz32GC1atDAqM378eD766CNCQ0OZOHGi0b45c+YwZMiQQsf25JNPMmTIEDZs2EB4eLjRiDXQTws6ffp04uPjzZqo8/T05KeffuK1114jNDSUEydOcOLEiRzLNmzYkAULFmBnZzzi2lQ/50z//vsvERH66z99+vTJVl9BZY7wy0uDBg1YsGCBfJcthBJN1Pn5+XHx4kWOHj1Kp04Ppx74888/USgUtGmTffHYzOmBzDFfqsh7kUphWTY2NlSvXt3SYYgCkLYqGZ4vvIA2KYmIb77FqU0bfH/4AZWLc4GPN0U7paenExISQt26dXPcr1AoSlWSLiIkgTO77xhtu3slhoSoVDyrFPxna2ryniodpA9RNNIftj7yu2y95POg9JC2Kh1M0U6RkZFoNBp8fHxy3O/o6Gh4rNEaT/NlzhF1GZoM+tTqw68XfyVZrV+XaGKzibkmUjLS0zi49ldObN2ETqc1W1xFYdL306OJOm9J1JmS9CGsh80jN7pZ+jxl2a1bt/jss88AfYLpjTfeyLGcQqFg9uzZDBgwgNjYWKZNm8bmzZtxdn54rWHEiBFUqlSJNWvWcPHiReLi4go0fWN+5syZQ2BgIGvXruXixYtkZGTg5eVF69atee655wgICGD+/PnFric/DRs2JCgoiI0bN/LXX39x+fJlYmNjUSgUVKxYkYYNG/Lkk0/Sp0+fbDOumPLnnGnz5s2Gx6ae9tLR0RFPT08aN25Mr169ePLJJ+VvZCEpdCW4IM28efNYsmQJLi4ufPHFF7Ru3ZoNGzbw6aefolAo+O677+jWrZvRMT/99BNz586lefPm/PbbbyUVapmWnp5OcLB+qoOmTZsWOXsuhBAlTafTEbdxE269n0SZ5SJASYiNjWXIkCFcunSJw4cP4+fnV6L1m8vV4w/YvewCWrUOpVJBv8nNqN5AkgGFsrALJIYX/zwu3jB+X/HPU0KkP1E00h+2DvL7K4QQhXft2jX69OmDu7s7e/fuxcnJKc/yW87cZfLqh6MV/H1c+ONN865VE5Maw4oLK7gQdYEfu/+YY6Iu9NJ5/vjxG2Lu3c1+Ap0OTDBKykGj5dXftxf7PMV2YhncPqRP2EVehkE/QNOnLB1VmSH9CWNXr15FrVZjY2NDvXr1LB2OEEKYlan/5pVoWvOFF17gt99+IykpiQkTJhjtq1OnDl27ds12zN69e1EoFDRqJHf9CCFEeadQKHAfkn3ObnO7desWffv25cKFCwD07duXAwcOlKrRc7mp19oH5wp2bP8hmE5P15MkXUEkR4NTlp9TKUquCcuT/rAQQojS6ODBgwwYMMAwdfPIkSP5/fffDWsJ5SSgWgU+HdwEtUaHWqujgqNtrmVNxcPBg9dbvo5Op8uWpEtPTeGfVcs5/cc2fULuEZ6elUh8cJ902+JfKtOqrCRh0+pF/T8ATQYUZvRgWiJEXIaMJEhP0h/boG/x4jm+FKo0h8oBoJKRFkIIIUSmEv1U9Pb25scff+TNN980zIcKUL16db755ptsnaiQkBDD3K0dOnQoyVDLjYyMjHJ/x4+1ysjI4OLFi4B+iPOjQ6CF9ZC2Mq3oX3/FoWEjnFrmPKd2URWnnd59911Dkg7g3LlzjBo1ik2bNpk0RkupWs+D5/6vPQ7O1vG7a9XvqSu74PexMPgHaNjf0tFYVEZGhqVDKJWkP2x9pD9svaz680AYkbYqHYraThqNhpdeeslofdVNmzbxf//3f3z44Ye5HlezkjM1K1lmOvVHP09vnz3NH4vmEx/xIFtZlY0NbXsPwHPBEv6p5IQyM4enVKCqWAmFKu914jLSNKQlP5yqzcZOiXtV7yLHbrb3k6qQ57kfDD8/+fC5fQWYHlL0+uNCYeub+se2zuDbGoYtM74BrpSR/rAQQghTKfHbV1q3bs2ePXs4efIkkZGReHl50apVqxznLA0PDzcsJNmxY94LAAtR1uh0OkOnrwRnqBVFIG1lGjqdjqiFC4n4+huUrq7UWL4MBxOOHilOO/3www+cOXOGS5cuAVC5cmU++OADk8VmDawlSQdW/J66fw5+HwPpibDmeej+IXR83STTI4nyRfrDQhSM1X4eiGykrUqHoraTSqVi/fr1dOjQgdjYWACaN2/OuHHjzBGmSaUmJbJv5VLO/f1Hjvur1K1PzwmTSfnfXBJjYugUE2PYV3Xu51ToX7Abs07uus2hjdep18aH7i82RJlPci8vVvN+sntkatOMpOJNDRpy2Phc986Ag3uRwxNCCCHKEouMM7ezsyMwMDDfcq1bt6Z169YlEJEQQghL0ul0RHz5JVGLlwCgTUggZOxL1PhlJfZ16lg4OvDw8GD79u0EBgbi5eXFtm3bqFGjhqXDKpSMNA2aDC0OLtaTkCtVEiNg1TP6JB0AOtg9Cyr5Q4M+Fg1NlE7SHxZCCFGaNGzYkE2bNtGjRw+6d+/OmjVrcHV1tXRY+dr+7Vxunj6RbbuNnT0dn3mOln0GkBC0lYjde4z2u/bogVu/fgWup2WvGnhUdqJG00oolWXkJi7bR0ZDatWgSQcb+6KdL+SQ8XO/QFAWPaEphBBClCUyIbQQQgjL02hIu3LVeFNMDEmHDltFog6gVq1a7N69m+rVq+Pu7m7pcApFq9Hyx5JzxIan0H9SM9wqOVo6pNLHoQLU7gKnf324rekwqN/bcjEJIYQQQpSgLl26sH//flq3bp3jKPCStvnaZm7E3eDFxi/i4eCRY5mOzzzPrbOn0Gkfrs3m26gJPcdPxqNyVTLu3+f+J58aHaPy9KTyRx9mmz4zP7WaeRX+RVgzO2ewcdSPrLN11j9XpxU9UedSWX+TW+QV/XO//G9YEkIIIcoLy/esrFBYWBgrV65k79693L9/Hzs7O6pXr07v3r0ZOXIkjo6mu8B58OBBtmzZwokTJ4iIiEClUlGpUiXq169PYGAgAwcOxNnZMnO6CyFESVHY2FDtm6+5M34CyUeOAOAzYwaez40s0TjS0tKws7PL9Ut506ZNSzQeU9DpdOxfc5Vbwfo1RX7/33H6vdYM7xpuFo6slLGxg4ELoGJd2PMRVG8HA76TaS9FmSX9YSGEKJ/S0tKwt889EVOQ0eAlIV2Tznenv+N+0n1+u/QbIxqOYFSjUbg/MpWiT+26tBkwlKOb1mHr4EiX50YT0O1JFEolOp2OezPeR5uQYHRMlY8/wsYz+7ppV48/oHpDT6uaMt6s3KrA+/dNd74u0/T/kiLhzhHwbmi6cwshhBClnEUTdSEhIZw6dYrIyEhSUlIYMWIEnjl0hkrSX3/9xbRp00hMTDRsS0lJIS4ujnPnzrFu3ToWLVpU7CnP4uLimD59Onv27Mm2LzExkVu3brFr1y5atGhBw4bSeRFClH1KBwd8Fyzgzssv4z50KO5Dh5Ro/eHh4QwYMIBBgwbx7rvvlmjd5hS8N5Tz+8MMz1MSMtjxYzDPfdwela1MNVMoCgV0nqK/qFCtNdg6WDoiUQZIf1j6w0IIYS3OnTvHgAEDmD9/Pn379rV0OHnadG0T95P0SaRkdTJLzi4hsEog7aq0y1a2/dDhpCUl0nbQMNwqeRu2x65ZQ9K//xqVrTBwAK7du2c7x5m/7nBg7VW8a7gy4I0W2Dta+X3vcWGwaQJ4NwafRvr/q7awjqkmnStBA+v+/RJCCCFKmkV6FufPn2f27NmcPHnSaPuTTz5pdGHi119/5bvvvsPV1ZVt27Zha2veu5YuXLjAm2++SWpqKk5OTowfP5527dqRmprK9u3bWbt2Lbdu3eLll19m/fr1uLi4FKmehIQERo8ezfnz5wHo0aMHvXr1ws/PD6VSyf379zl69Ch//JHzYsdCCFFWqVycqbFyBQqVqkTrvXz5Mr179+bmzZscOXKEmjVr8uyzz5ZoDOZSu7kX5/+5S/TdJABUNkp6jGksSbrikOkuhQlIf1j6w0IIYU12797N0KFDiY+P55lnnmHfvn20atWqWOdc8s8N5v91DRulAhuVgi7+Xnz+VLNix5quSWfR2UX6JzqoG+ZM84gqtBrZMsfyNnZ2dH/pVeNzhITw4PO5xuV8fPB5771sx5/84zaHNlwHIPx2Alvnn6H/5GbYOVhxsu7BObi5X/8PwL4CvHvbsjEJIYQQIlcl3qv4+++/ef3118nIyECn0xm25zTN2MCBA5k3bx6xsbHs3buXHj16mDW2Tz/9lNTUVGxsbFi6dCktWrQw7Gvfvj01atRg7ty53Lp1i59//plJkyYVqZ7/+7//4/z589jZ2fH111/TrVs3o/1NmzalR48evPfee2g0mmK9JiGEsDaaxERQq1H9t85b//kHiEhIK/Z5vVztCZrUqdDHxcTE0LFjR6KiogzbRo0aRbVq1ejcuXOx47I0Fw8HhkxrxY4fgwm7HEO3FxtStZ67pcMSolyT/rD0h4UQwpqcPHmS3r17o1arAUhKSqJfv34cOXIEPz+/Ip83OV1DXEqG4XnWx8Wx8epGHiQ/wDlFRYfgilSLdAQyOLF1I+0GP53v8TqNhrvvvYcuOdloe5VPPkFVoYLRtrTkDM7+FWq07f6NOG6ciqBB+yrFfi1m8+C88XOfRjJluxBCCGHFSvR2+vDwcKZMmUJ6ejp169Zl8eLF2e4izsrFxYWuXbsCsH//frPGdvbsWY4fPw7A0KFDjS5KZBozZgx16tQBYMWKFWRkFL6Tefz4cTZv3gzAG2+8ke2iRFYKhcLsCzSrSnjUiig4GxsbateuTe3ata1ioW6RO2mrgtPExhIyegwhL49Hk6gf4RWRkMb9+NRi/8sv2ZdbO3l4eDB9+nSjslWqVKFixYqm/wFYiL2jDf0nNaPfa82o19rH0uHky+LvqegbsPM9UKeXfN2liPQhikb6w9IfFgVn8c8DUWDSVqVDbu3UokULhg8fblS2Vq1axV6PVK3VGT23McG0i+madBafXUz92y4M2l/1vySd3qHfVxEVeiffc0QvX0HK8RNG29yffQaXztlv+rN3smXgG81xdLMzbGs/uI5Zk3QmeT+FXzB+7t2o+IGJbKQPIYQQwlRKNFG3bNkyUlJSqFq1Kr/++iudO3fGyckpz2PatWuHTqczTItjLrt37zY8Hjp0aI5llEolgwYNAiA+Pp4jR44Uup5ff/0VAFdXV5577rnCB2piSmuYn1zkSKlU4uHhgYeHh7STlZO2Khh1RAS3n3+B1OBgUs+eJfSVV9CmppZY/Xm105QpU5g4cSIArVu35vDhwzRqVLa+zKpslNRoUjqSjxZ9T6XEwK9Pw+EF8MsQ/XORI/l7VzTSH5b+sCg46WOVHtJWpUNu7aRQKFiyZAmPP/44AMOGDWPPnj14eXkVqz61Rmv0XKUs/oiutYeX0+JvaH++IrYa4981jVrN1aMH8zw+7do1Ir7+2mibra8vPtOm5XqMR2VnfbLO1ZaOT9WlZa/irdGaH5O8n1o8D49Ph4YDoGJdqNzUtEEKQPoQQgghTKdEb3X7559/UCgUjBkzBjc3twIdU7t2bQBCQ0PzKVk8J07o76ZycnKicePGuZZr06aN4fHJkyfp1Kng06ylp6ezZ88eADp06IC9vT0AGo2G8PBwNBoNXl5ehu1CCFFWZNy9S8joMaTffrguQvKxY9yf9SFUMO80bgWhUCj45ptvqF69OpMmTcLZ2dnSIRWJTqfLceo8UUCaDFg7CqKu6p/f+geWdIfnN4J70ad9EiIr6Q9Lf1gIIayRnZ0dGzZs4KeffmLKlCkmSUCMaOfH4/W9UWu1aLQ6KjoX/W+7Vqvh6NYN3P1tE5U1Dtn2u3h40n3cq9Rp1S7Xc+gyMrj77nR06VlmTVAoqDpnNsp8+v8Vq7ow4sNAHJzNu1ZsgS3sAonhBS+/73P9v0e5eMP4fbkfF3MbUqIhPQnSk8HLHzxqFi7WM79B2Emo0R782oNr5cIdL4QQQpQDJZqou3v3LgABAQEFPiZzgfrkR+YON7Xr1/ULA/v5+eU5tUDmhZKsxxTUpUuXSEvTT83m7+9PYmIi33zzDZs2bSI+Ph4AW1tb2rRpw4QJE2jXLvcOpqlkXRdFWBedTmeYTsrW1lYuvlsxaau8pd+6xe0xY1DfvWe03aZyZSqOHw+/3SiROPJrJxsbG959990SicUcNBotO34Mpm5Lb+teL6MALPaeenAO7hw13mbjAI4eJVN/KSN9iKKR/rD0h0XBSR+r9JC2Kh3yaycPDw+mTp1qsvp8PZzw9ch71HhBRN65zR8/fsu9a5dRkf13q8kTPeny/BgcnF3yPs/ixaSeO2e0zXPUKJyy3ICSl5JK0hXo/ZQYDgl3zR/Mpolw+8DD570/h3bjC3eO8xvhyk44ulD/vPNU6PaB6WK0IOlDCCGEMJUSTdRlLgSv1WrzKflQQkICQL5TAhVHWloaMTH6qa0qV877zp4KFSrg5OREcnIy9+/fL1Q9WS9k6HQ6hg4dyq1bt4zKZGRkcPDgQQ4dOsSUKVN4+eWXC1VHYaWkpOT6JcrGxsZwF51WqzUsLJ0bO7uHc7ZrNBpDe+dEoVBga/uwk6tWq/P8vVAqlUYXjDIyMvLsEGWNPWsnNzdZO7/5vVZzx65SqVCpVGRkZBAcHAxAgwYNjOrMStrJMrFntlNm2fzaqry2U8qlS9wZ+xKaqCjjc1avTtVFC1H6+gKmSdTp0JGebrymWNZ2unnzJuPGjeO9994jMDCwTLWTTqfjn9XXuR0cxe3gKBKiUwnoXjXPi2TW+n4CfTLi0qVLQM7vKbO1U6XGKJ/fgs265yHxPjh7o376V7QKe0jPeb268vp3D/R9CFF40h+W/nAma/78tpa/N9Ifzs4a2ymzrPSHH7LWdjp06BCffvopM2fOpEmTJlbfTjqtllPbNnFo/W9oNdnjSHdWMuzV9/Fr2twQb27tlHbxIpHf/2Ace53aeL35xn+xaNi/+jI1mlakeqPsN2lZ2/vJFnJIWRaeDh0Zj/RzjV6rnfFIQ3VKPNpHyuf5WnVabEMOG8Wq9apvWIenNL+fQPrDQgghTKdEE3WVKlUiLCyMO3fu0Lx58wIdc/bsWQCqVDHf6ICkpCTD44JcAHF0dCQ5ObnQdzXHxcUZHi9evJi0tDQ6d+7M5MmTadCgAYmJiezatYsvvviChIQEvvjiC2rXrk337t0LVU9h5HUXdP369Y3u4L58+XKe52rVqpXhcWRkZJ7TMzk7O9OgQQPD89u3bxMbG5treW9vb6pXr254fvHixTw7c7Vr18bDQ9+5ztrJzU3Tpk0NXzDi4uK4cSP3xIGtra3RXfD37t0jPDz3KSfc3d2pU6eO4fn169eNfuce5evri4+Pj9G2zAvWOZF2ypkl2glyb6vy2E4pwcHcHvsSuv9GSGTS+voS/87bxEdHY/vfxWdTyCm2zHY6ffo0ffv25e7du0RHR7NgwYIcp1Urre1050QqYSfTDM+PBt3kzu0warRzzPX81vx+yvo+yuk9Zd52cqPBuL/g99HQaw63YzXE3sr9d748/90r7EgqoSf9YekPZ7LWz2+wvr83IP3hTNbeTiD9YbDOdtq8eTPDhw8nJSUFOzs7ZsyYkeNNCtbSTgn373J99zZi7+Z8vgs14nmi51jiUBnaI9d2ysjA4f0PUGZJ8OiUSrz+7/9Q2tuj1WjZteQst07HcOnwfRr0cqZCNePLZdb2fmqh05okUZfT77NRO9kZ9wkiwm5y95Hyeb2fHOJv0jg11qh8vHtj3POo/1HW+H7KWl4IIYQwhRJd9bR169bodDp27txZoPLp6emsWbMGhUJB27ZtzRZX5vQ7QK53aWaV2UFITU0tVD1ZL2SkpaXRsWNHFi5cSEBAAHZ2dnh6ejJ8+HB+/PFHw11gX375pQylF0KUOsnHjhHy4uhsSTpNrVqkvj8DPEpuKsF9+/bRuXNnw3Rzp0+f5qOPPirUaBZrptPp0KqNPydUtkoq1rKS9TNKowrVYMwu8G2Vf1khCkn6w9IfFkKIkrZo0SIGDx5sGP2zadMmli9fbuGocpfw4B4nVi7OMUkX55zB9sD7JLStTEOPJgU6n+369SgfSSCqBw7AoUkTNBotfy69wK3T+lHlOg1c/iOJ+Ht5jyQsN2yd0SltUdu6ke7gjVaV+42AOdHYuhBWfwxxXm3R2DiT5uiNzrWqmYIVQgghSi+FrgS/9R45coRRo0ahUChYsmQJHTt2BPRD+RUKBUFBQdStWxfQX5R455132LFjB0qlks2bN1OvXj2zxBUdHU379u0B6NOnD1999VWe5Tt06EBUVBT+/v4EBQUVuJ6ffvqJzz9/uHjvxo0badSoUY5lJ0+ezK5duwDYvHmz0V1nxZWenm64Y6lOnTq53jVtTVNelMepSbK2k0z1o2eN7QQUqK3KUzulHT5M2OTX0T1y8dahRQsqfzcflaurUeyd5+7nfnzhLvTmxMfNnn+mPma0TaVScf/+fdq1a0dYWJhhe7169fj777/x8vIyKl+a2+niP/c58Ps1FMCT45vi26hCnrFb6/sJ9CN7LDL1Jdb3frLmdoqNjTXcRZz1TmeRN+kPS384k/y9kf7wo7GX1nYC6Q8/ytra6dixYzzxxBNGN1d069aNoKAgo892sI520ul0bJn3CSFnTxm2adFxvnY8p+vFoVHpWNJtCS28W+Qae2Y7pZ4+TdiLoyFLPXYNGuD76y/YOTmh08GeZRe4cvSBYb9SpaD72IbUaOJZ6NgN5zDz+8l2fjMUJlijTudahYxJZ422Gb1WrRa1Vmua16rVQOJ9bDxrlOr3k/SHc3f16lXUajU2NjZm67MKIYS1MPXfvBKd+rJdu3b06dOH7du3M2HCBF544QV69epl2B8WFkZ8fDwnT55k7dq13LlzB4VCwbPPPmvWP/DOzg/n3C7I9D2Zd6EVdp2QrPV4enrmelECoHPnzoYLE8HBwSa9MJGVra1tgToSSqWyUB2ORzsv+cnaMSqIgtzpnUmhUBQq9sK+VnPGnvUYaSdj1thOmcflF1dZbqf4XX8QNnUqPPJly7ljR3znf4vSjOsrKcg5tmrVqrF9+3Y6depEQkICLVu2ZNu2bfmuwVTa2qlZNz9cPR1JTkindnOvPMs+ytreT1nL5/eeKnI7pSdD4gPwrJVnefm7Z7ryQk/6w9Ifzo38vcn/GGknY9bYTpnHlef+8KOsoZ0CAwP55ZdfGDZsGDqdjv79+7N69WocHfMeHVXcdopNTiddo8VGqUSlVOBgazypU16vtdf4SSx761UyUlOId9Oyr8kDotz1a6O1q9KOdr7t8oxFoVBgo1YT/sEHRkk6ha0t1f73P+z/+zxSKKDbqIZo1DqunwxHZaukz4Sm+DWumOf5ren9VBy5fYcyUCqxURZuMq48X6uDcd+7NL6filNeCCGEyE2JJuoAPvvsM5KSkti3bx9Lly5l6dKlhjtjJkyYYCiXeQdLz549mTFjhlljsre3x93dndjYWO7fv59n2bi4OMPFi/wu8j4q67oi+R2bdX9M4YMcbQABAABJREFUTEyh6hFCCEuI3bSJe+/NMPoiDODaoztVv/gCpQXvLgwICGD16tX88MMPzJgxA09Pz/wPKoVqtyhcgq7c0mph48tw6wA88wvU7GTpiEQ5I/1hCnSs9IeFEMJ0hg4dyv/+9z9u3LjB2LFjSyTBMHPzebaceTjq69Un6jCtV8FuunCr5M3jL7xEYnQUzp0aEn5+EQfCDgAwsdnEAp0j/IsvybgdYrSt0uRJONT3N9qmVCnpMbYRKhsFDTtUwbdB2fyuIIQQQgjrVeKJOjs7OxYuXMjatWtZsmQJISEhOZarXLky48ePZ/jw4SUSV926dTl+/DghISGGIYs5ybpobdYFZwtaR6a8poMAjIbmF+bOQSGEsIToVat48PH/ZdvuNqA/VWfPRlHIOxnNoUePHoW+oCzKqD0fwcX/pupbMQj6fwMtRlo0JFG+SH9YT/rDQghRsl5//XXDlIolQaN9ZB3lLCOz0pKT2f/rUuq2DqRWi9Y5Hh/Q7eGI8x+q/MCZiDPsu7OPlj4t861bp9Ggjogw2ubYrBkVx4zJsbxKpaTHmMb5nlcIIcqLDRs2MH36dAD27NmDr6+vhSMqWV27diUsLIzBgwfz2WefWTocUQ5Y7Mrp008/zdNPP821a9c4d+4cUVFRaDQaPDw8aNiwIY0bNzbcWVwSWrVqxfHjx0lOTub8+fM0a9Ysx3LHjh0zPG7ZMv/OYVbVqlWjatWq3L17l7CwMHQ6Xa6vMesFGx8fn0LVI4QQJSlqyRLC532Rbbv7s89QeeZMFIWcKqWo1Go169atY9iwYSVSnyVoMrSc2HmLFr1qYGsnF62L5MJm+Pfrh8+1GbBrOvg/Cc55T3EkhKlJf1j6w0IIYUp79+7F3t7esOaopakfmWnDRqn/e3/z1HH+WPwdiVGR3Dh1nBfnfY99AaZSbubVjGZeOX82PUqhUlHtm6+J37qV+//3Cbr0dLz+bzY6hZKS+2QVQgjzOXLkCC+88ILhuZOTEwcPHsx3WuPU1FQ6duxIYmKiYduKFSto1y7vKYVF0Wi1Wnbu3MnWrVs5f/480dHRKJVKKlasSEBAAIMHD6ZLly5mjeHatWvs3buXY8eOcfXqVSIiIgwxNGvWjEGDBuUbQ0ZGBocOHeLAgQOcPXuWmzdvkpiYiKOjI9WrVycwMJARI0ZQvXr1PM+TmQDNia2tLe7u7tSrV4/u3bszZMiQfH+fyxKLD3GoW7eu0Z21ltK9e3cWLlwIwPr163O8MKHVatm0aRMAbm5uRfoD1rNnT5YtW0ZiYiKHDh2iQ4cOOZb7448/DI9btWpV6HoKqrDzdYuSY2trS9OmTQ2PhfUqr22l0+mI+OYbon5cmG2f59gxeE+dWqIXmMMjwhnxf2Nwd3enR48e2faX9nbSaXXsWXGRq8ceEHIhmr4TA3B0LZuLlZu1rep0A//ecGWH/rnSBp5eIUm6IpA+hOlIf1j6wyJnpf2zuzyRtrK8X375hTFjxlChQgUOHTqU4+dKSbfToyPqlOnJ7FjwJRf2/2XYlhgVyf5fl9Jj3Gsmr1+hUFChf3+c2rYl7tR5tm+IxrNKKl2fb4hCab3pujLzftLp9IsAlmHShxDWJDk5md27d9O/f/88y+3Zs8coSWcpWRONZTVRGBcXx8SJEzl+/Hi2fWFhYYSFhbFjxw569erFvHnzzLIm6TvvvGP4/pZbDNu3b6dTp0589dVXuLm5ZSsXHR1N7969iY2NzbYvISGBCxcucOHCBVauXMm0adMYNWpUkWLNyMggIiKCiIgIDh48yM8//8yiRYuoXbt2kc5X2pToJ8rq1avp3bs37u7uJVltgQQEBNC6dWuOHz/O+vXrGTx4MC1atDAqs3TpUq5fvw7ACy+8kK3DlPUPTG7DYkeNGsXq1atJS0tjzpw5rF69GhcXF6Mymzdv5ujRowA8/vjjRmt5mFpJXkQXhVPYRZWF5ZTHttJptTyY8xkxK1dm2+f1+mQqTphQ4n9ftFotarWap556igMHDhi+3GYq7e10ePN1rh57AMCDm/H8/vkJ+r/WDHef/O8+Lm3M2lb2LvDsr/DnTDj0HfT9Amo/bp66yjjpQxSN9IelPywKrrR/dpcn0laWo9Pp+OSTT5g5cyYAkZGR9OnTh0OHDlGxovGNSCXdThmah4m6OknXSV/zCxdSErKVu3hgHx2GjcTZ3cMscWicPdlzwoXIO/GE34pHZaOky4j6Vvv336reTxkpEBcK6UmQkQxaDdTqnP9xKbEwvyX4tgW/QPBrD76tQVm2ZgWx1t+h8mjt7GMkx6cX+zxObnY8/V4bE0RUsuzt7UlLS2Pz5s35Juo2b95sdExuhgwZwpAhQ0waZ3kzZcoUQ5LO19eXsWPH4u/vj1qt5vz58yxevJiYmBh27dqFu7s7H3/8scljePBAfx3J3d2dXr160bZtW6pVq4ZKpeLixYv8/PPP3Lx5kwMHDjBhwgR++eUXlI/MjJWenm5I0jVs2JBu3boREBBApUqVSEhIYP/+/fzyyy+kpaUxe/ZsHBwceOaZZ/KMy9vbm59++sloW2pqKjdv3mTdunUcO3aMO3fuMH78eLZt22Y9n4tmVKKJuo8++ohPP/2UTp06MWDAALp164a9vX1JhpCnGTNmMHz4cFJTUxkzZgwTJkygXbt2pKamsn37dtasWQNAzZo1GT16dJHqqFq1KpMnT2bu3LlcuXKFp556inHjxlG/fn0SExP5888/Wb16NQAuLi6GuYCFEMJa6DQa7s2cSdz6Ddn2+Ux/F88i3jljKvHx8cyYMYMtW7ZYNA5TSopL4/w/d422pcSnk5GW9/pOIhdKFfT6FBoNguql70uYKN2kPyz9YSGEMCWtVsuhQ4eMtl29epVvv/2Wjz76qMjn/WX6GyTFxhQrtgZJ6VTR2RFrU4F6yTfQ5VDGr2lzer48yShJl6HJQKlQojJBUken1RE0/zSRdx6OHjn/z10cXe1oN6B83KFfLHeOwIqBD587VYK3r+d/XOgxSI7Sz2JxZQeo7GH6nTKXqBPWIzk+naTY3JNOZV3Xrl3ZsWMHBw8eJCIiAi8vrxzLRUVF8e+//wLQrVs3tm/fXpJhlivBwcEcOHAAgOrVq7Np0yajmxMDAwPp3bs3AwcOJD4+nnXr1vH6669nu8mmuKpUqcLHH3/M4MGDsyW7AgICGDBgAGPHjuXEiROcOHGCLVu2MGjQIKNyCoWCjh07MnnyZJo3b56tjsDAQHr16sULL7xAamoqc+fOpW/fvtluxszK1tYWf3//bNsDAgLo378/o0aN4ujRo4SEhPDHH3/Qr1+/Ir3+0qTEx2ir1Wr27dvHvn37cHJyonv37vTv358OHTpky9aWtEaNGvHVV18xbdo0EhMT+fLLL7OVqVmzJosWLcrzFy0/L730EnFxcSxevJibN2/y3nvvZStTsWJFFixYQM2aNYtcT0FoH5kzXlgPrVZLXFwcABUqVLD4+0Pkrjy1lS49nbB33iFhx07jHQoFVf7vY9yfesoygWXRu3dvfv3112zbS3M7OVewZ8jUVgR9d5rE6DQUSgVPvtwELz9XS4dmFiXWVpKkKxbpQxSd9IelPywKpjR/dpc30laWo1KpWLNmDY899hinT58G4I033jCMsMuqMO2UFBtDYnRUsWKzAyoBXunZz2Pv5EyX58fS5Ike2UYlrbq0io1XNzKh+QR61uiJUlGw3ydNYiKqRz6bFEoFbfrWYsfCYLT/jfBzrmCHf1vrXfvUqt5Pts7GzzOSC3bc7YPGz6u1BBvruTHJVKQPIaxFx44dOX78OBEREWzbto0XX3wxx3Jbt25FrVbj5eVFhw4dJFFnRqdOnTI8HjVqVI7fnapWrcqQIUNYtmwZWq2WM2fO0LVrV5PGMWfOnDz3Ozo68uGHHxpGYu7cuTNbos7Hx4elS5fmeZ5mzZoxYsQIli5dSkJCAgcPHqRnz55FilmpVDJmzBjDDCvBwcHlIlFXop/2v/32GyNHjsTT0xOdTkdSUhJbtmxh3LhxPPbYY8yePZuzZ8+WZEjZdO3alS1btvDiiy9Ss2ZNHB0dcXNzo0mTJkydOpVNmzZRo0aNYtfz1ltvsXr1agYOHEi1atWws7PD1dWVpk2b8vrrr7Nr165sUw2Zg0YjozGslVqt5saNG9y4cQO1Wm3pcEQeyktbaVNTCZ00OXuSzsaGal/Ms3iSzs7OjgkTJrBlyxZcXbMnsEp7O3lWdeapd1pTqboLj4+sj1/jsrumWmlvq/JC+hBFI/3hh6Q/LPIjnwelh7SVZbm6urJ161Zq1KjBN998w1dffYVKlX3kkiXaKaeJAWu3asuoLxbQtGvPbEm65Ixklp5byvW460zbN42hW4Zy/H72tX0eFb99O9d7PUnCX39l21czoBK9XmqCQqnAxcOeQW+1xKOycw5nsQ5W9X6yyyFRV5DkVMhh4+d+7U0XkxWRPoSwFiqVir59+wIPp7bMSea+fv365fg5kdWGDRuoX78+9evXJzQ01GjfBx98YNi3Y8eOXM+xZ88eQ7l33nkHgNDQUOrXr2+YLh/0U+pnlsv8t2HDw1mc5s+fb9gO+nXRFixYwKBBg2jdunW28snJyWzfvp0ZM2YwcOBAWrVqRePGjQkMDOS5557jp59+IikpKc/XX1wZGRmGx9WrV8+1nJ+fX47HQPF+zoXh7++Ph4d+ZPudO3cKfXymrOsMhoSEFPk8oJ8qNFN6evGntS0NSnREXfPmzWnevDnvvfceBw8eJCgoiN27d5OcnExkZCQrV65k5cqV+Pn50b9/f/r372+SiwCFVa1aNaZPn17oaXbatWvH5cuXC1y+RYsWJXLxQQghikublMSdia+SfOSI0XaFnR3Vvv4a165PFPncXq4P76wMT0gl65rzdiol6RrjL4KV3RxyPE9FZ1e+n/x9mV4nwLmCPU+92xqVSu5SL5B/v4HkaOg2C+TOfmElpD9sTPrDQghhGtWqVePChQs4OVnv+sVKB2eefHkiDTo8lmuffd2VdUSnRhueX4u9hp0q73VpMsLDuf/Rx2ji4gid+CoVBg/G573pqLLcvFe7hRdPvtyESr4uuFVyNM0LKg/s/vt9Utrok3a2zqBOfbg9N0MXw+1DEHJIn7Sr0cH8sQpRzg0cOJBly5Zx4cIFrl69Sr169Yz2X7t2jfPnzxvKXrx4sch1TZ8+naNHj3Lr1i1mzZpFixYtqFy5slGZiIgI3n//fUD/GfXBBx8Uub6sbt26xZgxYwgLC8u1zPjx4w2jsbKKiYnh2LFjHDt2jFWrVrFo0SLq1KljkrgeVatWLcPjvJJfWRNaWY+Bkv05ZyYJizOKO2tCLb9EcH7u3n24/Is51yu3JiU+9SXoG6pz58507tyZtLQ09uzZQ1BQEAcOHCAjI4Pbt2+zYMECFixYQJMmTRgwYAB9+vQx+RytQgghCkan06F95G4jhZMT1Rd8h3P74t0dGTSpEwBHbkTxzCLjOy9/erE1z/+k71w52Sqo7unE2gkdqeBoW6w6SzNJ0hXQxSD4cxagg+jrMHhR/hcUhChB0h8WQghRWCdPnqRFixa5JrmsOUl31bkOj496mYYdG+daJnM0XVadqnUiwCsg12N0Oh33Z85C899UkQBxGzfi0qULbk/2Mipbu3nOazaJPFTwg/cjwCbvZGn243whYJj+nxCiRDRq1Ih69epx9epVNm/ezNSpU432Z46m8/f3p2HDhsVK1Dk5OTF37lyGDx9OXFwc77zzDsuWLTP6fJo+fTrR0dGoVCrmzp1rmPrRx8eHoKAggoODDdPfz549m6ZNmxrV8WhCKtPkyZMJDw/n+eefp2vXrri5uXH79m2qVq1qKKNWq/H396dr1640bdoUb29vdDodYWFh7N69mx07dhAaGsqrr77K5s2bzbJmeKdOnfD19SU0NJQVK1YwdOjQbJ/T9+/fZ+PGjQC0atUq25ptxfk5F8aFCxdITNSv5VqcxGXW5Gjt2kVfB1an0/Hzzz8D+vXxTD0dqLWy+NU+e3t7+vTpww8//MCBAwf4+OOPadNGv2aMTqcjODiY2bNn06VLFwtHKoQQ5ZfKxYXqixdhX68uAEpXV/x+WlLsJF1WW8/eM3reoLIrHetUIui1TnzdxY6wb4fjsPcrXOws/tFlVup0DVFhifkXFLm7ewrWjwP+G555MQiW9wNNRp6HCWEp0h8WQgiRnx9++IE2bdrkuPacNdMC27x7sdO7J/ZuFfIsu/byWqPRdAATm03M8xhdaqrhcZKjN8mOXrj2fjJbkk4UkVJZ+CSdEAWUkpBu9E+ryX1a1UfLavIoWzy6XPekJmUYx5CRewypSZb57jlw4EBAvxadTvfwteh0OoKCgozKFFdAQACvvfYaAIcPHzZaw2zlypX8888/AIwbN45WrVoZ9tna2uLv7280taGvry/+/v5G/9zc3HKs9+rVq/z444+8//77dOjQgSZNmtC3b1+jWTrmzJlDUFAQb775Jt27dycgIIBmzZrRp08fvvzyS3766SeUSiU3b95ky5YtJvl5PMrOzo4vvvgCDw8PQkJCGDhwIL/99hsnTpzgyJEjLF26lCFDhhAXF0f16tWZPXt2jucp6s+5MH788UfD4969exfpHOHh4YbpRz09PQkMDMyzfEZGBleuXDH6FxwcTFBQEKNGjeLQoUOAfm3zunXrFimm0sYiI+pyU6FCBZ5++mmefvppHjx4QFBQEIsWLSI+Pl7mfRZCCAuz8fCg+k8/ETb5dSrPmolDw4YmO7dao2V7sHGirn+zqiiVCo79sZ7x48ejVqvZsmULU6ZM4ZtvvjFZ3dZEq9Xx59ILhFyMptdLjanZtJKlQyqdIq6A9pEvRg0HgKr8jsQUpYf0h4UQQmSl1Wp55513mDdvHgCffPIJtWrVYsyYMRaOrGCSVU7ccNbfVa/KYzqt5Ixkfj7/s9G2ztU609SraS5H6CkdHfH94Xtur9zCgX2g1GkYMqkcTLPo4q3/X6eGxHDjfY4eYFPA6T0zzyOEBSyddsDo+bMz21Kxas6jgVbMOIg6/WFi7Kl3W+NTM+dETnFoMnJP1G2Yd5KYew9nGuo1rgl1W+X8Hgr69jTDprcxeXz5GTBgAF9++SX37t3jyJEjhmTJkSNHuHfvHkqlkv79+5usvpdffpl//vmHEydO8NVXX9GxY0dsbGyYO3cuAE2bNmXSpEkmqw9g8ODBdOrUKc8yNWvWzHN/hw4d6Nq1K7t372bPnj0MG2ae0b/Nmzdn06ZNLF++nJUrVzJr1iyj/U5OTrz++uuMGDECd3f3XM9jzp/zrl272LVrFwCNGzemZ8+ehT6HTqdj5syZhnX/Jk6cmO8oxfDw8Dx/FwMCAnj55Zfp0aNHoeMprawqUZfpypUrBAUFsW3bNhISEiwdjhBCiP/YentTY/Uqk68Dd/5uPNHJxovD9guoQlRUFFOnTjVaRP3bb78lMDCQ4cOHmzQGS9PpdPy77io3TkcAsP2HYLoM96dx52oWjqwUavYMuPrAmhcgLQ5aPAcdX7d0VCXuma3PEJkSWezzVHKsxJp+a0wQkSgM6Q8LIYQA/XSXX331ldG28ePH07p1awICcp8S0no8/N5go8z9O8Say2uyjaZ7pdkrBaohKiyRPWc9SbfV36i1dekNBk1xx9Uz57Wty4Tx+/T/7/0f7M0yCsPBHaZcAtsy/NqFELny8fGhXbt2HDp0iM2bNxsSdZnTXgYGBuLj42Oy+lQqFZ9//jkDBw4kMTGRt956CxsbG9LS0nB0dGTu3LnY2Jg2/VCURGN0dDTx8fFGa6h5enoCcOnSJZPF9iidTse2bdvYuXOnYQ24rJKTkwkKCsLb25unnnoq1/OY6+d8/fp1w5rkDg4OzJ07t0jX+3788Uf+/vtvQL9m+ciRIwt9jkedO3eOVatW4efnR/369Yt9vtLAahJ1d+/eZevWrWzdupWrV68CGIboOjg48MQTT1gyPCGEKDfSb9/GpnJllLnc/WLqJB1As+ruHJnejW3B99h69h5qjZYaFZ0BZzZt2kSPHj0MHaphw4YxePBgk8dgaddOhHP271DDc51Wx8EN16nd3AtHV5luptBqPw4v7YZ/v4G+X4EZfm+tXWRKJOHJ4fkXFFZD+sNCCCEe1bp1axYsWMCECRMM2957771sa/mUBqpcEnXJGcn8fM54NN1jvo/lO5ou08H110hNfHgBND4ylUMbrtHzpSZFD7a0CHgadBo4sxpiQ6DpU5KkE6KcGzRoEIcOHeKPP/4wjODKHDFlqmkvs/L19WXmzJm8/fbbXLt2zbB9+vTp1KpVy+T1FTRpc+LECVauXMmhQ4eIjY3NtVxMTIyJIjOm1Wp588032blzJwBPPfUUI0eOpE6dOmg0Gi5evMiSJUv466+/mDFjBpcvX2bGjBm5ns/UP+cHDx4wbtw4kpKSUCgUzJ49u0jr023ZssUw65Wvry9ffPEFyjxG0GeqVq0af/31l9E2tVpNVFQUhw4d4ttvv+XgwYOMGDGChQsX0rp160LHVtpYNFEXFxfHjh07CAoK4tSpU+h0OsPFCNX/s3ff4VEUbwDHv3eX3glJ6CX0jvQqXToIqCBVARVQUQELWH5iBUEFxIaoKCBNehORLlKk904oCSWE9H5lf3/EHDly6deSvJ/n4SE3O7v7JnNlbt+dGY2Gli1b0qdPHx577DE8PT3tGaoQNqdSqXB2djb+LBxXUWqrpNNnuPncc7g3bkz5ObNROdtuqsAgHzdGtglmZJtgkrUPpndr164dCxYsYOjQobz55ptMmzYtVx/6D3P0dqrySCA1mpfi4r93AVBrVPQYW79YJuks1laBNaDfNxaKSgjrkP6wEFlz9M9u8YC0lXWNGTOGq1ev8uWXXzJ//nyeffbZfB3H3u2U1Yi6ZReWEZVieqE0t6PpALqOrsfaWUe5H5Y25VbpKr60H1or/4HaWZ7ayT8YOr4N7SfD9X/Au4wNIsxGTCi4eKZNvymEsIvHHnuMqVOnEh8fz/bt21EUhYSEBDw8PPI1rWFuPP7446xcuZJ///0XgDZt2jBo0CCrnMvXN/v1TgHmzp3L119/navjJWdY69SSli5dakzSjR8/3rjOXLomTZrQpEkT3nzzTdatW8fChQtp1aoVnTp1yvKYlvo7R0dHM3r0aMLCwgB477336NWrV56Ps2vXLt5++20URSEwMJAFCxYQGBiY5+Okc3JyolSpUvTr14/WrVvTp08foqOjefPNN9m6davFR2c6Gpv/dsnJyWzfvp0NGzbwzz//GKczS78gUa9ePfr06UOvXr0ICJC1eazN2YYX4UXeODs7F5KpTERRaavEI0e4OWYshvh44nfs4NbkKZSd8Rkqjcbmsbg5m55zyJAh1K5d22Rx4Lxy9HbSOKnpMrIOXv5uHN1ync7P1KZ8zeL5BdfR20qkkT5E/kl/2LHIc9lxyedB4SFtZX3Tpk1jyJAhNGzYMN/HsHc7OWky32yXqE3kl9O/mJS1L9+eegHmR8Mpej2G+Hg0GS7Uunk50/fVRqyddQx3L2d6vdQAF7fCezEvX+2kVkPwo9YJKCECkmMgNQG0iVCiMniXNl932wdwagUE1YGKraDBIKjYwjpxOQDpQxTMqJmm64y5emT9uh3xienaky7Z1C0IjXPWyfEBrzdGMTxYwy6795k+rzxiybDyxNPTky5durBhwwbWrVtn/I7RpUsXPDw8rHLO06dPc+zYMePj8+fPc//+fUqWLGnxc2lyuEa1f/9+Y5KuQoUKjBo1iiZNmlC2bFnc3d2NyZ45c+bw7bffWjy+dL///juQ1h4vvPBClvUmTpxonJp01apV2SbqLPF3jo+P57nnnjPO4PLqq6/ma6rKgwcP8sorr6DVavH19eXHH3+kYsWKeT5OVoKCgujbty8LFy4kLCyMAwcO5Lg2YWFn057LG2+8wfbt20lKSgIeXIyoWLEiffr0oU+fPjku9iiEEMLyDMnJhL02AUN8vLEsdtMm3OrUpuTo0TaJISEhwexoEa3ewNlbsYSqS7F/12VuRibxQd+6uDjlfVSdo1OpVLTqV5XqTYMIKO9t73AKhxsHwa8i+Nj57mEhckn6w0IIIbKSVX8YQK1WFyhJl2dKzlXyytyIuiXnl2QeTfdI1qPpIhcsIHLhIsp8/BFe7doZyz18XOg3oRHObhqcXWx/o2GRtnQwhP774HHv2dB0pPm6Nw6k/R9+Nu1fqbpFOlEnCiYvs8fYbqaZrBN1bp65T8zmpa419OvXz3hTYMYya0hOTuaNN95Aq9Xi4eFBUlIS9+/f55133uH777+3yjmzs2LFCiBt5N2KFSuMa9E9LCYmxqpxXLlyBYBq1arh4pL187d06dIEBAQQERHB1atXs6xnib9zcnIy48aN49SpUwCMHj2aF198MQ+/VZqTJ08yduxYUlJS8PDwYP78+dSqZfmR7FWqVDH+fPHixSKfqLPpVc4NGzaQmJiIoij4+/szbNgwVqxYwdatWxk/frxclBBCCDtRu7lRbtaXqNwerGfg0aolJQYPtsn5165dS3BwMEeOHMm0LTFVz+Pf/MP4pceYseUCS/+9wa3oJJvEZS+SpMulexfht6fgx85w+6S9oxEiV6Q/LIQQ4mGKovDZZ59Rr1497ty5Y/dYLh3aT0KM5dfseXiNugRtAr+e+dWkrEP5DtQtWdfs/skXLnJvzlfowsO5+cIYbr/3P/TxCcbtHj4ukqSzBpeHksepCebrxYRCzA3TsoqtrBOTECJbrVq1IjAwEJ1Oh06nIygoiFatrPN6nD59ujHB9MEHHzByZFoif+fOnSxdujTL/aw1BXP6+m0tWrTIMkkHaaPTrCl95F767CnZSa+T3dSO+f07p9NqtYwfP944bebTTz/Nm2++meN+Dzt//jzPPfcciYmJuLq68v3331vtRiK9/sGyOLn5OxZ2Nk3Uubu707dvX+bPn8+ePXt49913ZXoMOysOT/LCSqfTcfPmTW7evCnt5OCKSlt5NG1K+a+/RuXsjFenTlT4/nvUVpoWIaM5c+YwYMAA7t27R+/evbl+/brJdl93Z3wemlLiZlRins/jSO2k1xqMo2hEZrlqq4T7sGQgpMRAbBj83B3Ob7ZtoMWcvV9HhZX0hx2PPJcdlyN9dovsSVvln06nY9y4cUyePJlr167Rp08fEhKySIRY4FzZtVP4tav8/tE7rP/8ExSDweLnf3hE3dLzS4lOiTYpy2o0nZKayvH3vyc08MHorOiVK0m5dNHicdqbw72eHk7UabP4LhZxCTSuDx67+UFg4V0rMDccon2EMEOj0fD444/j4uKCi4sLjz/+OGq15dMAu3fvNiaJevfuTd++fZkwYYJxdNVnn31GSEiI2X1dXR+8X6SmplospvTXZWJi1teNzp49y4kTJyx2TnPKly8PwKVLl4iNjc2y3sWLF4mOjgagXLlyZusU5O8MaQmvSZMmsWfPHiBtrbupU6fm9VciJCSE0aNHExMTg7OzM3PnzqVFC+uNms6YTC1TpujPomTTRN3+/fuZMWMGjz76aI7zyQrbkAvFjstgMBAeHk54eDgGK3xJEpZTlNrKq20bKi1eRPk5s1Fn6DRZy9Mz1/DB+tM4l6oGwJ07d+jZs6exk5Kugr9pwvBmZN5H1DlKOxn0Bv6Yd4pdv13AoC/czxdryVVbbX0HojJ0RLUJcHK5bQIUgPQh8kv6w45HnsuOy1E+u0XOpK3y75133mHevHnGx4cPH2bw4MFW+Ttm1U4J0VFs/WEuiya/ys0z1pulIOOIOkVR2Hptq8n2DhU6UKdkHbP7Hp+xhKM+3blQYzC3yqStVeU/aiQeBVjD2lHl+HqKuwOXtoFBn3mbNaQn6lRqcPEmy6kBq3aEKTdh9F/Q5QNo9VLa2nlFmPQhhCN74403OHXqFKdOneL111+3+PEjIyN5++23gbQkyvvvvw+Ai4sLn3/+Oa6uriQlJfHGG2+YTWoHBgYaf75x40am7flVqVIlAI4ePZrpRvD0uPMzkiyvOnbsCKQlIadNm2b2/SIlJYWPP/440z4ZFfTvrCgK7777Ln/++ScA3bp1Y9q0aXke0Xjr1i1GjhxJREQEGo2Gzz//nPbt2+fpGHlx5swZNm3aBKStB9q6desc9ij8bLpGnVuGKdWEEEI4JncbrX0Rl6zlWIwbPs364dOsH9qo20Ss/4yWLVtmWpsjOMCT2GQtFUp4UNHfg8oB1h/pZw2KorB76UWun74PQEJ0Cl2fq1uoF7u3m66fQNR1uLEv7XHZRtDvO/vGJEQuSH9YCCFERpMmTWLlypXG6axUKhUdOnSw2pRgDzu1cyu7fp1PapL1p5Z3ypC0UalU/NbzN9ZeWcv8k/O5nXCbFxuaXyfnzOrD7L9ZDkWddoPL+RqDcQksSc1XXrF6zA7p+G+w/UPwLgMNBsIjQyGwpvXO13sW9PkKnFwhp+elkytUaJ72TwhRpL3zzjtERESgVqv57LPP8PHxMW6rXr06kyZN4tNPP+XUqVN88803vPrqqyb7ly1bltKlS3Pnzh1+/vlnSpcuTXBwsPFmxpIlS+Ll5ZXnuPr168fOnTtJTExk2LBhvPDCC9Stmzal8rFjx1iwYAERERE0atSIY8eOFeAvkL1nn32WlStXcv/+fVavXs3169d5+umnqVKlCgaDgbNnz7Jo0SLjVJ1Vq1alf//+mY5T0L/zZ599xurVqwGoUaMGY8aMMa6fl5UaNWqYPI6KimLkyJHcvn0bgJEjR1KlShUuXsx6VLuvry+lSpXKcrtWq820v8FgICIign379rFkyRLjSMvRo0dTsmTJbGMuCuTKoBBCFCOKohAx92s0Jf3xHzrUrrFsO3eXFN2DO0SdfAJ486XRfPjOW5kuTMwd3MhmFyus6ciW65zde8v4+Prp+2z54TR9X3nEfkEVVp4lYcRa2PAqhOyBwcvApXAmcIUQQghRfAUFBbF582ZatWpFUlISixcv5oknnrDZ+V09PM0m6VRqtcWnv3x4jTpnjTNP1XiKx6s+zoHbB6hdsnamfQzJyYSt2Izi1zZjcCS37WeTGUAcjqLA8f/WIoq7Df/MgaQo6DvXeud8eOpLIUSxt3z5cnbs2AGkJW3MTX84YsQI9uzZw969e5k3bx7t2rWj0UOjoMeMGcMHH3xAaGgoL75oerPGtGnTGDBgQJ5j6969OwMGDGD16tWEh4ebjFiDtGlBp0yZQmxsrFUTdf7+/vz000+8/PLLhIaGcuTIEY4cOWK2bu3atfnmm29wcXExKbfE33nr1gej1y9evJirv+mFCxdMHl+8eJFr164ZH//444/8+OOP2R6jf//+TJ8+Pcvt4eHh9OnTJ9tjqFQqhg8fzmuvvZZjzEWBVRJ1t249uAhZtmxZs+X5kfFYQggh8kZRFMKnTyfy14UAqD088evfz27xbDhx2+RxnRIqPnp7stm6RSFJB1CitAcaZzV6bdpFD42zmma9gu0cVSHm5Jo2ii7+LniXtnc0QpiQ/rAQQojcqlmzJuvWrcPJyYlWrVrZ9NzVm7emfO16hJ5LWwfGydWVFo8/xfG/NpMQFWnRczlpzPfpXTQutCvfzuy2e3O+osLxpWgrx3Gtcg8AqpSIosvLeb94WySEHob7l0zLGg6xTyxCOBAPH5ecK9nwOEXZtWvXjAmY2rVrZ5lEUalUfPrpp/Tt25fo6GjeeOMN1q1bZzKD0pAhQwgICGD58uWcO3eOmJgYi6z9OG3aNFq2bMmKFSs4d+4cWq2WwMBAmjZtyrBhw2jQoAFz51rxBof/1K5dmw0bNrBmzRp27NjBhQsXiI6ORqVSUbJkSWrXrk337t3p2bMnzs7OJvta8u9cWKjVajw9PalQoQKNGzfmySefpHbtzDfxFFUqxQoTKqf/AVUqFWfPns1Unh8PH0vkX2pqKqdOnQKgVq1ahfKFWxxkbKf69etnuqtCOI7C0FaKXs+dqVOJ/n3lg0K1mnKzZuHTratdYrp4N46NJ26x/sQtrt1PZPagR+jXyPzCuZZgqXZa8ekhEmPzv9CxXmcgOUELCnQfU4+qjYLyfayiqjC8pgqDzr93JjwxvMDHCfIIYvtT2zOVJyQkcP78eUDayRzpDzs26Q8XDvJ5UHhIWxUOWbXT3auX+e2didR5tCNtnx6Bl39JFk95jYToqHyfSwHuxiSToPFgRbknAdgxqT1VAnM/jVni4cNcHz4CFAUFuFKlH6qAUvT88UXURfg5lu3rKeRv2Pou3D6e9rhEMLxyLOcpKYVVSH/Y1KVLl9DpdDg5OVG9enV7hyOEEFZl6fc8q4yoyyr3J4usCiGE7SlaLbfemkzs5s0PbVDQR0fbJIbFixezb98+vvnmG+PouBqlvJnYtSYTHqvBmVuxBAcUjoukibGpJESnFPg4rh5OkqTLLV0qOBXvL72i8JH+sBBCiHSKovDee+9RqlQpxo8fb9Nz61JTObJ5HeHXrtLtxQlm65SqUo3nvvoRn8AHfdNh02YX6LzJWj213ttiUpZxjbqcGBISuDXl7bSpHgEVUC10M8Gzfy/SSbocBT8KY3bD3bNwYgn4VZIknRBCCFEEWCVRN23atDyVCyGEsA5DSgphr00gfudO0w0aDWWnT8M3h/mgC0pRFD7++GP+97//AVC+fHnefvttkzoqlYp65XytGocjcnLR2DuEwiElHn7rCw0GQauX5UJEHugMBZ8yROSf9IeFEEIApKSkMGrUKJYsWYJaraZSpUr07dvX6udVFIVLB/9h9+IFxN67C0DNNu1B7Wy2fsYknSXoDJlvTEk2JHA1+g5V/KpkuZ9eb0CjUXN35ky0N2+abAt69VXcatSwaJyFVqk60PXjnOvZgi4Fji6Eii0hqA6o5XuOEEIIkVdWSdT1798/T+XCforKuk9FkVqtxs/Pz/izcFyO2laGhARuvvwyifsPmJSrnJ0pN+tLvLt0sXoML7/8Mt9++63x8TvvvEPlypUZMsT26yg4ajuJzIxtZdDjtG4M3DmV9u/+Zej5OWjMX2ASppzUVunmGUkfInvSHy485LnsuOSzu/CQtjJPq9XStWtX9uzZA4DBYGDw4MHs3r2bpk2bWu28d69eZuev8wk7f8akfO+SX3h03ETUGo3V28lJrWJkm8roDQo6g4Jer/DH9d/56cz3dA/uztiGY6nia5qwO7QphNDzUXRsnED0suUm29wbN8b/2WesGrOjcLjXk14LSVGQmpD2TzFAmQYPtt86DptfT/vZ1SctYff0UtBYty/qCKQPIYQQwlKK/qemyJaTkzwFHJWTkxNVq1a1dxgiFxyxrfSxsdx8YQxJx4+blKvc3Sn/9Vy82rSxSRxdunThu+++M5nq7fjx4/lK1G09c4fD16O4cT+Rm1GJDGlRkaEtKuV6f0dsJ2Gesa3+mAyXtz7YcOQXQAV9ZtspMpGR9CFEUSHPZccln92Fh7SVec7OznTu3NmYqIO0EXbnzp2zSqIuPiqSvcsWcmb3duOUkRlF3Q7DOTmByg0aWfzcD3Nz1vB+n7rGx7GpsXRfNRYFhT9C/mBLyBbeav4WQ2sPRVEUDq6/ypE/rgPwx7Gr1Fc7oflvdgCVuztlp32KSlM8Rmo53Ovp6i747ckHj33KwcQMa+be2P/g55RYiAkrFkk6kD6EEEIIy7HpJ8qhQ4eAtAVW3dzccrVPSkoKJ0+eBKBZs2ZWi00IIYoKXWQkN0Y/R8q5cyblai8vKsz7Ho8mTWwWS//+/fnyyy+ZMGECKpWK2bNn88orr+TrWFtO32H1sTDj40t34y0VpnBEigKeAaZl7v7QJn/PHyEchfSHhRCieHnvvfe4evUqv/76K15eXqxcuZJu3bpZ9Bza1BSOblrHwTUr0KYkm61TrlZdOj7zPKWqVLPouXPrt3O/EZcaZ3ysoNC0VFqy8vDma8YkHUCkVxVO132eBqe+R4VCqTffwKVS7m/QExbm8tBa4qkPfQ/LmKiDtBF1QgghhMgTmybqhg8fjlqtZv369VSrlrvO4d27d437nT17NucdhBCiGNPevcuNkaNIvXrVpFzj50eFH3/EvV7dLPa0ntdee407d+7QqlUrmnfoyjc7L9OnQVkqlvTI03Eq+JvWvxmZaMkwhaNRqaDd61CyKqwZCwY9PP0b+Ge9pklxdT32OqciTtG7Sm97hyJyQfrDQghRvKhUKn744Qf0ej2vv/46DRs2tNixFUXh4oG97PltAbH3ws3W8QksRfthI6neoo3dpumLTY1l0ZlFJmWPVXqMmv41AQhuGMCJ7TdJSXywvm7J+6dRoeDZujV+Tz9t03gdjqKANglc8vb9yWKcHzpv6kPfw0oEp/2LCkl7XKm1beISQgghihCbj9FWzEy/YM39RPa0Wi0uLi72DkOYodVquXLlCgBVq1bF2VnWZHJUjtJWqTdvcmPkKLShoSblToGBVPz5J1yrV7dLXADTp08HYP6eq8z88wIz/7xAwwp+PN2sAoObV8zVMR5O1N3IY6LOUdpJ5MykrWr0xvnZzRB9Tb70m/HX9b9475/3SNGlUM6rHI2CHkxlFeAekM2euZfVcbRarUWOXxxJf9ixSH/Ycclnd+EhbZU9FxcXFi1alHPFPLhz5RI7f53PrQvmb+BwdnOnRf+BNOn5OE7/vcfZq50Wn11MnDbOpGxsw7HGnwPKe9NrZBXWf3UCndqVWheWUPbOftReXpT55ONitw5Ypna6dwZ+6QV1+sEjQ6BiK7Dl2nUPj6jTuIAuFZz+++zsMT3tX9wduHEAKre1XWx2Jv1hIYQQluLwkykbDAYANMVkLnIh0imKQkJCgvFn4bgcoa1SrlzhxshR6MJN76R1LluWir8swKVi7pJh+TV//nw6dOhA9RySgRtO3jL+fOJmNDVLeeU+UVfC3eRxaFQSiqLk+ou7I7STyJ1MbVW+Sdo/YaQ1aJl1ZBaLzj646Pf67tdZ0XsFJd1LArC893J7hScsTPrDoriSz+7Co7i3lV6vZ+bMmYwbNw5fX1+rn2/7z99z/M+N5jeqVNTr8Bhtnx6Op18Jk02WbKdBGwcRkRSRYz1FUTLVc9W4Mm7bOCDthqBlvZah/2EGDU9cIsktkNLhadNEl3rnHZzLlClQnIVRpnY6viRtusnji9P+VX4Uns2i/a2hRDC8fjltRJ+Te9ZJQu/SULef7eISQgghihCHT9TdupV2UdfLy8vOkQghhGNKPnuWG6OfQx8VZVLuEhxMxQU/41y6tNXObTAYeOutt/j888+pVq0a+/fvJyDA/OibaxEJnAyNMSnr07Bsrs/18FSZSVo9EfGpBHq75j1wIQqxuwl3eX336xy/d9ykPDwxnO9OfMe7Ld+1T2DCaqQ/LIQQjishIYHBgwezYcMGduzYwaZNm6w+Us2rhL/Z8vK169HhmecpFVzVqucHiEiKIDzR/HSbOUnRp5jsG7txE3F//YUv4Bt7DQCvzp3x7fe4BSIt5PSpcOp307KKrWwbg8YJvAJte04hhBCimLFqoi79osLD7t27h4dH9nNrp6amcuPGDebMmYNKpcpxlIYQQhRHiUePcXPMGAxxplPJuNaqRcWffsSpZEmrnTspKYkRI0awcuVKAC5fvkzfvn3Zvn077u7umepvPGn6mVDS04VWVXIfXylvN1w0alL1BmPZjchESdQVBYoC+76CxiPAvUTO9Yux/bf2M/nvyUQmR2ba1qdKHyY2mWiHqER2pD8shBBF1+3bt+nTpw9HjhwB4K+//mLs2LH8+OOPVp2usUmvfpzcvsW4Lp1vUCnaDxtNteatHHqaSLVBjV9SKSI9bxvLfGP13Jn5kUk9jZ8fZT6Y6tC/i62obh2F5GjTwkcG2yUWIYQQQliPVRN1nTt3zlSmKAqjRo3K87Eef1zupBJCiIwS9u3j5ksvoyQlmZS7NWxAxR9+QGPlaXf0er1x7YR0+/fvZ+PGjTz11FOZ6m84cdvkcc/6ZXDS5H5tBbVaRbkS7oREJBjLQqMSaVJJEjuF3u7PYNc0OLYYhiwHr/L2jsjhGBQD80/O55vj36BgOlWVs9qZKS2m8GT1J+WClgOS/rAQQhRdycnJ3Lx506Rs8eLFTJw4kbp16xb4+FlN8+7k4kK7oSPZOu8rWvQfROMefY3r0DkqtUHDYxefpVxMDTbW+ZZw7+ugKAxZG40hNsWkbumpU3HKYpaO4kap0BImnIGTy+H4UvDwB/8q9g5LCCGEEBZm1URdVnOe52UudFdXV4YPH86TTz5pqbCEEKLQi9uxg7BXX0N5aPFqjxYtKP/NN2i8PLPY03K8vLzYuHEjLVu25ObNmzg5OTF//nyzSboLd+K4cNd01F920172mbuXe3EpmcojE1NNHr+z5jTTNp/P8jiB3q5sGF98FjMvlE6tTEvSAURchPmdUT3xCyBT/KWLTo5myt4p7A3bm2lbOa9yfNHhC+qWLPjFQGEd0h8WQoiiKzg4mA0bNtChQweSkpLw9/dn7dq1BU7SxUVGsHfJr7j7+NBhxPNm69Ro2ZYKdRvg4WP9NfHSrfj0EImxaf3xHknj0SuGHPb4jwJuOk+clLQpQfudfpXV9b+k4eUb1L1k2uf36dULn+7dLBp3oedTFtpOgDavZR5dJ4QQQogiwaqJumnTppk8njJlCiqVildffZVSpUpluZ9KpcLFxYWgoCBq166Np6f1LzgLIURhEbNxE7feegv0epNyr/btKTdnNmo3N5vFUrZsWTZv3kyvXr34+eefzY4cgczTXpb2caNpNiPh7sWlcCc2Ocfzx6foiE/R5S1o4Th0KbD9A9Oy5GhIjUcSdWlO3TvFpN2TuJ1wO9O2DuU78HHbj/F1td0FOpF30h8WQoiirXnz5ixZsoQ333yTDRs2ULNmzXwfS5uSzOENa/h3/Up0KSmoNRrqd+5OyXIVMtVVqVQ2TdIBJMamkhCdllhzxyffx1Gjoee50XT8+wPgQbLPKTCQ0u/JWrtZUqkca5r4fXMh7k7amnkVW4KnjIIUQggh8suqibr+/fubPJ4yZQoAXbp0oVq1atY8tRBCFElRv//Onf+9n7amVwbePbpT7rPPUNlhypt69epx6dIlXLI4t6IobDhhmqjr3aAMarVM0VfsObnCs5th6dNw93RaWbdpKNUeg1On7BubnSmKwrILy5hxaAY6g2kyWq1S82rjV3m27rOoVbmfPlbYh/SHhRCi6OvXrx89e/bMsj+cE0VROP/PbvYs+YX4+xHGcoNez57FP9P/rfctFapDUFAIDPsVz4duuCvz8Udo/PzsE5TInjYJUhPS/mkTwasUnFiW1off/3VanV5fQrPR9o1TCCGEKKSsmqh72MKFCwEoX17WnnEUarVc4HNUGo3G+FrRaDR2jkZkx1Ztdf+XXwif/lmmct8nBlDmww9RWenciqLw999/065duyzrZHdR4sytWK7dTzQpy27aS2uR15SD8qsAo7bAytFpP7cYg8ZgKPZt9eGBD1l5cWWm8pJuJZnZfibNSjezQ1SmpA+RP9IfdjzyXHZc8tldeBT1tkpNTeXIkSO0atUqyzr5TdLdunieXQvnc/vSBbPbQ8+dITbiHj4Bgfk6fkaO0k7JTvGsaR+CdzzU+W+JP7+nnsSrfXu7xeRIHKWdTPzcHW4ff/C4+3S4e8a0TqniNxW79CGEEEJYik0Tdc2bN7fl6UQuOEynT2Si0WiynRJLOA5btFXq9euEf/5FpvISw4dTaspkVFb6gqDT6XjppZf44YcfmD9/Ps8991yej/HwaLqK/h40KG/7qfrkNeXAXL1h8NK0kaIqlbQV0KpMq0yJuialmjCz3UwCPQp+oc4SpA+RP9IfdjzyXHZc8nlQeBTltoqOjmbAgAHs27eP7du306ZNG4scNzbiHnuX/sq5vbvMblep1NTv3JU2A4fh4etnkXM6Sjvp1XrCS6j4YIiGQSc8efpiAEFvvWXvsByGo7STCZeHpuC+shPIMMuLxhXKNrJpSI5A+hBCCCEsxSqJulu3HlyULVu2rNny/Mh4LCGEKE5cKlWi3MwZhE16HQxp6ziUHDeWwFdeQaWyzhSScXFxDBw4kC1btgAwduxYKlSoQLduuV/c3WBQ2HjSdG2t3g3KWC1mUYip5UtuRl0rd2VY+DAWn1sMwKh6oxjfaDxOapveYyUKQPrDQghR+F27do1evXpx9uxZAPr27cuBAweoXr16vo+pTU7m0IZVHFq/Gl1qitk6Fes1pMOI5wisFJzv8xQGilrFjjZevD1zLSpnZ3uH4zj0Olj3ItTqDTW6pU0Xb28PJ+q8gqD9ZLixH0IPQZmGjhGnEEIIUUhZ5WpP586dgbTFjdM7tACdOnXK98XZh48lhBDFjU+PHhgSk7j97rsETZpIyXyMbsuLP/74w5ikA9Dr9QwaNIirV6/i7++fq2McuxlFWHSSSZk9pr20JA+fB9MaKYpCapIOF3enPH++ZTxOsREfnvalXuTKxKYTuRF3gyerP0nHih3tHY7II+kPCyFE4ffDDz+YvO9GRkby1FNPcfTo0TxPeacYDJz7Zzd/L/mF+Mj7Zuv4lS5D++HPUbVJ82J1Y5sk6R5yZTucXJ72z70E1HsybapJjR1v2HL2MH3sWx46TE77Wa+FhIjM+wghhBAi16zyKa8oSr62CdvTarX5nktfWFdqaiqnTp0CoH79+tJODsyWbeX3xADc6tfDrUYNq50j3cCBAzl27BjTp08HwM3NjZ9++inXSTqADSdMR9NVC/KiVmlvi8aZW5Zqp4Fv239tsELp4lZYMQJ6z4JHBmdbtTi9/8WkxODran4qWGe1M990/sbGEeWeVqu1dwgOTfrDhYf0hx1Xcfo8KOyKalt9+OGHnDx5kk2bNgFQunRpfvrppzwn6XRaLSs+mJzlOnSuHp60fOJpGnXvjcbJekkrR2mnEvEK332tA0CtukPIsicJXpV5bd7iynB0EcZnWFIU3Dpm3yQdQM/PoceMtJF1zh6Q8TWgcQafMvaLzY6kPyyEEMJSrPJJP23atDyVCyGEeEBRlGzvoLVFki7dJ598wrVr19i2bRvr16+nVatWud5Xb2bayz4Nyharu4PFf+6egZWjQJcEa8fC/cvQ8R3TL/jF0J7QPUz5ewrvtHiHnlV62jscYWHSHxZCiMLPycmJZcuW0b59e1JSUti0aROVKlXK+3GcnfEvWyFTok6lUtOgS3daDxyKh4/t13C2F7UCJePSHxnQRchoLKPESFSX/jQty+EmN5vwcow1koUQQuSsZs2aALz88suMHz/eKueYPHkya9asoVy5cuzYsaPAx5szZw7ffvstHTp0YN68eSbbDh48yIgRIwBYuHAhLVq0yNc5FEWhb9++XLx4kU8//ZQnnniiwHFbklUSdf37989TuRBCiDSGpCTCXpuA36CBeHfqZO9wUKvVLFiwgDt37lC5cuU87Xvw6n0i4k3X3ejdsHjeaVmsJdyHJYMgNe5B2d+fQ6m6UG+A/eKyI71BzzfHv2H+qfkATN0/lZr+NanqV9XOkQlLkv6wEEIUDV5eXmzatAk3Nzf8/PzyfZy2Tw/n4oG9aFOSAahY/xE6jniOgIqVLROoKBqc3NB3n0HS/p/xjjyJonFBVc+xLiQKIRyTTqdj+/bt7N27l+PHjxMREUFsbCxubm74+/tTo0YNGjVqRLdu3ahQoUKWx+nUqRNhYWGZyp2cnPDy8qJKlSq0bt2agQMHUqpUKeP29ORQQWzfvp3y5csX+DivvfYaf/zxh8WPK6zj1q1b/PTTTwC89NJLVjuPSqVi7NixTJw4kVmzZtGjRw88PDxy3tFGivet7EII4UD0cXHceO554nfvJuzV10jYt88m5zUYDERHR2e53c3NLc9JOoAND42mq1vWh6qBXnk+jr2lJuvY8sNpou4k2DuUwsndD2r3MS2r9yTULZ7JioikCMb8NcaYpANI0iUxcddEErWJdoxMCCGEKL4SExNJTk7Ocnvp0qVzlaRTDAYMBr3ZbV7+JWne7ylKlClHvzf/x5PvfCRJOpGZiweGhkO52GY2pzotRt/nm7R16oQQIhvbt2+nZ8+evPLKK6xYsYKLFy8SGRmJTqcjPj6eGzdusG3bNmbOnEmXLl0YM2YMFy9ezNM5dDod0dHRHD16lK+//pqePXvy119/Wek3yr+dO3eaJOmE4/vuu+9ISUnh0UcfpUGDBnnef+7cudSsWTNXyeIePXoQHBzMvXv3+O233/ITrtXYeZJrIYQQAPr4BG48O5LkM2cAULRabr70MhV/+hGPxo2tdt6UlBRGjhzJhQsX2L17N15elkmkafUG/jj90LSXDcta5Ni29s/Ky1w5Gs61UxG0HlCN+u3LoVLL9J25ptZA92lQshpsfgPKNYHHv4FiOAXq0btHeX3369xLupdpW1W/qijIumVCCCGErYWHh9O3b1+Cg4P57bff8rz+XLqw82fZ+et86nboTKNuvc3WadpnAM36DrDqOnSi6Ej1LIuhTn17h2EqfZ3dYtiXF45t8ZTXSIiOKvBxPP1KMGza7IIHZEPffvstX331lXEd7ObNm9OxY0dq1qyJn58fSUlJ3Lt3j8OHD7Nz507CwsLYtWsXpUqV4sMPP8zyuEFBQcZRTpCWqAsLC2PVqlXs3LmT+Ph4JkyYwIoVK6hTpw4bNmzI8lijR48mPDw80zEflnGEXn4kJCQYf6eSJUty//79Ah2vsLpwwfyauJY0ffp0pk+fXuDj3L17lzVr1gAwatQos3VatGhhsd9JrVbz7LPP8v777/Pzzz8zYsQIXF1dLXLsgnLIRF1qaiqxsbH4+/vnu5MshBC25vrue1xLSMj3Gmz62FiTxyoXF1RO1nubvn//Pv379+fvv/8GYPDgwaxZswYnC5xz7+UIohNNF9buVb/wTXsZcjKCs3tvAaDXGvh7+UXiI5Np/UQ1O0dWCDUbDSWrQlBdcHazdzQ2pSgKC88uZNaRWegV07vsnVROTGw6kWG1h8n6jcKE9IeFEML6zp8/T8+ePQkJCeHgwYMEBwfz6aef5ukYsffC2fPbAi7sT+tTx9y7S+02HXAzcwOck7Mk6ISDm9ce4sOz3m7QQuJ90Lik/XNyAbVL5npeQTBmt/XiFOIhCdFRxEcWv6TMypUrmTNnDgABAQF8+eWXWa7f1aNHD95++202bdrErFmzcjy2s7MzNWrUMCmrU6cOjz32GNOnT2fBggVotVq+++475s6dm6nuw8fK6piWNHv2bG7dukWrVq0oXbq0MQEkHNeSJUvQarUEBQXRsmVLm5yze/fufPzxx0RGRrJp0yYGDHCMZVlsmqhLSEjg8OHDADRt2hRPT0+T7ZGRkbz//vvs3LkTvV6Ph4cHTz31FBMnTsTFxcwHvxBCOBBVdDT6qILfwQWgCQig4k8/4VbTeh2YESNGGJN0ABs3bmTixIl89dVXBT72hhO3TB43ruhHBX/Hmfc5NxRF4cS2GyZlTq4a6rYrnCMDHUKVDvaOwObiUuN475/32H5je6ZtQR5BfNH+Cx4JesT2gQm7kf6wEEI4htTUVLp3787169eNZdOmTaNatWpZ3tFtsn9yEv+uXcnhjavRax/coJYcF8uB1UvpMOJ5q8RtXzL6v8iLD4e4WznX0yWn/UvJuaoQwjpu375tHD3m5eXFkiVLqFSpUrb7aDQa+vbtS4cOHYzfSfJj/PjxLF26lOTkZPbu3YvBYLD7zYWnTp1i8eLFuLi48P777zNv3jy7xiNyZjAYWL16NQC9evWy2XPIz8+Ptm3bsnPnTlauXOkwiTqbvoK2bt3KmDFjeP/993F3dzfZZjAYeP7559m2bRs6nQ5FUUhISODXX39l0qRJtgxTCCHsSuXuTqVFC62apAOYNWsW/v7+xsdeXl706tWrwMdN1urZeuauSVlhnPZSpVLR++WGNOj4YMHhR5+qjm9g4Uo4Cvs5H3meQRsHmU3StSrTit/7/C5JumJI+sNCCOEYXFxc+P7779FoNMaySpUq5Xg3t2IwcHrXNn5+bQwH1yw3SdKlu37qBHqdzuIx21Nygpak+My/qxBCCPtYsGABKSlp2fIJEybkmKTLyMfHh06dOuX73J6enlStWhVIW+c1Ojo638eyBJ1Ox3vvvYfBYOCFF14gODg4z8e4cOEC7733Hl27dqVhw4Y0atSIXr168emnnxIaGlqg+A4ePGhcQ+3gwYMoisLvv//O4MGDadGiBY0bN+bJJ59k7dq1JvulpqaydOlSBg4cSPPmzWnUqBFPP/00mzdvzvZ86eeaO3dupm2rV682bg8NDcVgMLB8+XKefvppmjVrxiOPPEKfPn347rvvSEpKyvIckydPpmbNmgV6Hh05coTw8LRR3F27ds2y3sN/v4d/l6+//tpYll4v4z9z7detWzcAjh49yu3btzNttwebjqjbu3cvAF26dMmUId28eTNnzpxBpVJRp04dmjdvzr///svZs2fZtm0be/bsoV27drYMt1jI+KVEOBYnJyfjIpiWmIpQWE96W91ydsb80vF5o/HxwTUfnYq8qlGjBuvWraNz584EBgayadMmGjZsWODj7rpwj/iUBxcmVCrHmPYyP68pJxcNjw6qQeUGAVw5Gk7tNvb/PRxaZAjsnQU9PgNn95zrZ6EovP+tubSGTw5+Qore9DZjFSrGNhzLmAZj0KgL92ew9CHyR/rDjkeey46rKHweFBeFta26d+/Od999xwsvvECTJk3YuHEjpUuXzrJ+6LnT7Fr4I3evXja73c3Ti1ZPDaHhYz3ROODfIb/tFBeZzIa5JzDoZESd1VzdBWUeAXe/Qvt6Ko6kDyHsRVEU1q1bB6QlzewxIsg5w3TOBoPB5ufPaMGCBZw7d47KlSszZsyYPO8/b948Zs+enen3uHz5MpcvX2bp0qV89NFH9OvXr8Cx6nQ6xo0bx86dO03KT506xVtvvcXp06d59913iYmJ4aWXXuLQoUMm9Y4dO8axY8e4ceMGY8eOLVAsycnJjBo1iv3795uUX7x4kYsXL7Jjxw5+/fVXPDysc8N6etLN2dmZevXqWeUcWUm//qkoCn///TcDBw606fnNsekn/sWLF1GpVDRu3DjTtvSMcd26dVm2bBlOTk5otVqGDh3KqVOnWLt2rVyYsAJ7D0sWWVOr1XiZWddAOJ70tiqM60u1bduW1atX07BhQ8qXL5/zDrmw4aTpVCktgv0J8rH/mmQFeU1VqO1Phdr+OVcszpKiYclAiLgId0/D00vBO38LQRfm979kXTKfHPyEtZfXZtrm5+rHtEen0bZcW9sHZgXSh8gf6Q87HnkuO67C/HlQ3BTmtnr++edxd3enf//+maYjThcTfoc9v/3CxQN7zW5XqdU80rUXrZ4cjLu3jzXDLZD8ttP+1ZeJup1ghYgEAMkxsGQQKArU6oX6kSF4VekIGknSOTrpQwh7uXjxonEUW9OmTa2WSMmKTqfj6tWrQFqSxc/Pz6bnz+jmzZt88803ALz//vt5Xi7gt99+48svvwTA39+f559/nsaNG6PX69m/fz8//fQTiYmJTJ48mRIlStC+ffsCxTtnzhxOnDhBnz596NOnDwEBAVy7do25c+cSEhLCokWL6NSpE4sWLeLYsWMMHjyYxx57DD8/P86dO8ecOXMIDw/nq6++onPnzlSvXj3fsbz77rucOHGC/v3706NHDwICArh9+zY//vgjx44d4+TJk3z33XdWm90lffrVGjVq5GuZhy5dulCvXj2WLFnC0qVLAdiwYUOmeqVKZb42FRwcjI+PD7Gxsfz777/FL1EXGRkJkOlisFar5fDhw6hUKoYOHWq8Y8jZ2Zmnn36akydPcvLkSVuGKoQQRYaiKNkmES0x3WW6xFQdO86ZLj5eGKe9FHmk18Lvz6Ql6QDCjsD8TjBiHQRUs29sNhYSE8LGqxszlTcIaMDn7T+njJeMyizupD8shBC2l1N/eNiwYWbLU5MSObj2d45sWmt2ikuA4Eea0H74c5QsX8EisTqidoNrcv9WApG3JFlnFWfWpq33BnBmNZxbD5MugGeAXcMSwtoSY2Pyva+zmxvOLq4WjCaNomQ9cjgpPg4lDyPHPHx8LRGSWRcuXDD+XKdOHaudJyvLly8nNjYWgCZNmth19O/UqVNJSkqid+/etG7dOk/7RkZGMnPmTACCgoJYsWIFZco8+M7epEkTOnXqxNChQ0lMTOS9995j+/btJqMJ8+rEiRO8/fbbPPPMM8ayunXr0rx5c7p160ZCQgKTJk0iKiqKr7/+mi5dupjUq1evHv3790ev17N8+XLefffdfMdy7NgxZsyYweOPP25yjnbt2vHEE09w8eJFVqxYwauvvmrxNlYUhRMnTgBQu3btfB3Dx8cHHx8fSpYsaSyrUSN3ywipVCpq167NwYMHOXPmTL7Ob2k2fRXFxKS9AT/8ZD516hTJycmoVCoeffRRk22VK1cGICIiwiYxFjf2HpossmYwGEhMTATAw8ND7tRyYOltlV2Hzl4SEhIYOnQoI0eONPngtZZt58JJ0j6YAFSjVtGjXt4TE4HelulwZzyOvKasKOIShB01LXPzzfeIusLcVrVL1uatZm/xycFPjGVDaw9lUpNJOGvy35l3RNKHyB/pDzseeS47rsL8eVDcOHJb3bx5k6FDh/Ltt9/maVqlmPC7LHl3Eokx0Wa3+5erQIfhowlu1NRCkVpfftvJzdOZ3i83ZNF7+9EbdCQ5JaCoDJSIV1Dn4yuQS2ps3ncqyo4vMXmoVO9GguIG8fEO93oSpqQPUTDfPT803/t2GjWWRt16WzCaNNrkrNfkWv7+W9wPvZHrY01anvkGTkuJiooy/uzvn/XsPwaDgcuXzU/XDGmjinKbdNLpdISFhbF69Wrmz59vLB89enSu9reGdevWsXfvXry9vZkyZUqe91+1apVxHbYpU6aYJOnS1alThxdeeIHZs2dz9+5dtm3bRo8ePfIdc8OGDU2SdOkCAwN57LHHWLt2LZGRkfTs2dMkSZeuVq1aNGnShEOHDnHkyJF8xwFp68KZu1bo4uLC0KFDef/994mOjuby5cvUqlWrQOd6WExMjLFPkjHRZkvp5w0NDc3xpi5bsGmizs3NjYSEBO7fv29Snj7MsWLFigQEBGTaR1iPXm+JFbWENeh0OuMdMvXr18/XEGBhG+lt5abV4khfoW7fvk2fPn04cuQIW7duZdeuXTRv3tyq57wfn4KXq5Nxjbq21QLw98z7c3fDeMtPDZjTa+rejTguHb5Liz5V0Dg7UksWAqXqwOi/0qa+jL4OnkEwZDm4eufrcIX9/W9QzUEcDT/K7pu7+aDNB3Sv3N3eIVmF9CHyR/rDjkeey46rsH8eFCeO2lbHjx+nV69e3Lp1i549e3LgwAHKls3dbA8+gUH4liqdKVHn5uVN66eG0KBLD4dchy47BWknb383wnsdYOOttaQ4p11Y++5rHSXjrBJq8aFLBa8gUDuDIW3Upq7eQId8PYnMpA8h7CUh4cEIZ3f3rNeHj4+Pp0+fPllu3759e5bLoISFhRnXyzRHpVLx6quv2m1q/qioKKZPnw7AhAkTMn2Hyo30tdl8fHx47LHHsqz31FNPMXv2bOM+BUnU9ezZM8ttGZNh2c1+VatWLQ4dOsTNmzfzHQeQ7XOjbt26xp9DQ0MtnqhLn2kG0v7+9uDrmzbqNTU1ldjYWONje7HplciKFSsC8O+//5qU//XXX6hUKpo1a5Zpn/RGy+7uACGEEKbu3r1Ly5YtjXfXJCUl0adPH0JCQqx63pFtgjn8bhe+H9aYXg3KMKBxOauez1J0Wj1/LTjLsa03+H36Ye6Hxds7pMInqBY8vwOqdIDBy8Cv6E7/lBOVSsXUVlNZ1ntZkU3SifyT/rAQQtjG4cOHefTRR7l1K2395Js3b9KnTx/i43PXz1OpVHR65gXjY7VGQ+MefRk15wcade9T6JJ0uWEwKFnOEnLw9kFW3VtiTNIBeBiK1mwBduHkAoMWwesXocdMqNIRpVrmERRCCJFRxjVV00eE2YqXlxddunRh8eLFjBs3zqbnzuizzz4jMjKSBg0aMHjw4Hwd4+LFtOU76tSpk+3IwoCAAMqVK2eyT34FBwdnuc3b+8HNzumzqmRXL2PCNj+qVKmS5baM6w7mtu+UF+kzzQB2S5BlPK+tX0fm2LRn2bp1a86ePcuSJUto0qQJTZs2ZfXq1Zw6dQqVSkXHjh0z7ZN+F1FQUJAtQxVCiEItKCiIzp07s2DBAmOZTqfjzp072XYKLMHNWUP3emXono8pL+3lwNqrRN1O6+DcD4tnxbRDPP5aI8pW87NvYIWNZwAMXwt2ni7AFg7ePsjl6MsMrW1+uhYPZw+Cfa37WhOFk/SHhRDCNurWrUv9+vWNd8tD2gWtyMhIvLy8jGXalGScXc2PXC5drQZ12nUiOT6OdsNGUbJc0b0RSZuiZ+tPZyhdxYcm3SubbEvRp/DRgY9MyjpcdMY9wf4XtYoMD39o8ULav9RUe0cjhHBwGZMoGUcmPczHx8dkPTuAyZMns2bNmhzPERQUxE8//WR8rNFo8Pb2JjAw0O5TBO7fv581a9ag0Wj44IMP8j1FcHqyKDdTLwYGBhIWFmaSYMqP7GZLyfh75KZeQaffze4cGdvYGtP8uro+WKomOTnZ4sfPjYzntec6i8YYbHmyESNGsGzZMhISEhg7dqzJtqpVq9KpU6dM++zatQuVSmWXhTGFEKKwUqlUzJs3j5s3b7Jt2zaqVKnC5s2bs522oLiKj0rh9J4wk7KSZb0oVdk+Q+8LvSKepDMoBn469RNfH/8aRVGo4luFVmVb2TssUYhIf1gIIWzD3d2ddevW0apVK65cuUK7du1Ys2aNcXSywaDn9M5t/LN8ET1enkTlBo3MHqfrmPFonIr2yLHE2FQ2fXuS8GuxXDsZgZefKzVbPrjpbuGZhVyPvW583PGEgbFb7HNRTQhRdIyb/1u+93W20tTwzm5ZTyM56IPPUBxkXcKM0xCeO3fOKudwdnamRo0aVjl2Qf34448A1KtXj5CQELOzR4WGhhp/3rlzp/Hz39yUkvZOPBZHGWeLKWjyM78ynjfjaEZ7sWmiLigoiO+//54JEyZw7949Y3mFChWYM2dOphfFjRs3jNO2tW7d2pahCiFEoefs7MzKlSt57bXXmDFjBoGBgfYOySF5lXDlybeasm3BGe6HJaBxVtPl2TponGSdOrNSEyH6RtpUl8VMTEoMU/6ewt9hfxvLJv89mRW9V1DKs5QdIxOFifSHhRDCdgIDA9m8eTOzZ89m1qxZxru3b545yc5f53PvetqFvd0Lf6TiZ1+h1mgyHaOoJ+l0qXpWf36EmPAHo+N2LDyPh68rFWqnXUR7utbT3E28y4oLK+h+SM/IbY5xoVoIUbh5+Nh3PShzskvYuHvZ/0J+uho1auDn50d0dDSHDx8mKSkp27XqiprU/0YenzhxgokTJ+ZY/+OPPzb+nDFR5+vry71794iIiMjxGOnf3ey9jllR4UiJupIlS5qM8LMXm4/pa9q0Kdu3b+fo0aNEREQQGBhIkyZNzA4vDA8P58UXXwSgTZs2tg5VCCEKPV9fX5PpL4V5AeW9eGpyMw6uv4qXvyv+ZT1z3qk4MhhgzQtwZSc8+TPU6GbviGzmdMRpJu2axK2EWyblkcmRLL+wnFcav2KnyERhJP1hIYSwnRo1avDtt98CEH3nNrsX/8zlQ/tN6kTcvM6pHX/S8LGe9gjRrpxcNNRrV45/Vl42ljm7aUxuWvN28eadFu/w+N+pOG373R5hCiGEyEClUtG3b18WLlxIfHw8a9asYciQIfYOq9CpUaMG9+7d4+zZs+h0uiynP7x//75xzVtHHWVY2Li4uFC5cmWuXbvGtWvXCnSs/I6ITD9vtWrVCnR+S7HL5JsuLi60bNkyx3pNmzaladOmNohICCEKp+joaGbMmMHUqVNxcXGxWxzJWj1uzpnvQC5MNM5qWj/hGB/ODmvHR3BuQ9rPS5+Gbp9Ci7FFerpLRVFYfmE5Mw7NQGvQmmxTq9SMbzSeUfVG2Sk6UZhJf1gIISzj8uXLrFy5ksmTJ2dZJyUxgQOrl3Psj/XodTqzdW5fulgsE3UAj3SpSHxkCid23MTb343e4xviX+bBjWuKohD++ec4/ZQ5Saf29ESdYc2/dDFJWpJS9cbHHq4afNzMj050CgiwwG9RyOi1oCnaozWFENY1cuRIli9fTkpKCl9++SWPPvooFSoU3bVUM1q0aFGOdTKuxbd9+3bKly+fqU6rVq34559/iI2NZevWrfTsab4fsHLlShRFMe4jLKNJkyZcu3aNU6dOFeg4Ga+Hpqam5ur6aHx8vHHK1IYNGxbo/JZi/1XyhBBC5Mu1a9fo2bMn586d4/bt2/z88892mVf7Tkwynb7YRcdaQfRpUJYONQMLfdJOmHHhD9j75YPHigF2ToO6A8C7aE77mKhN5IP9H7A5ZHOmbf5u/sxsN5PmZZrbITIhhBBCAOzbt4++ffty//593N3defXVV022Gwx6Tu/4i73LF5EUa35apZLlK9LhmeezXKOuuGjzZDWcXNTU71geT98H0z8pBgN3PvyQ6GXLM+0TOHEiAS88b/Z4E5cfZ/WxB+tAj2lfhSk9als+8MJq8QBABY8Mgdp9wEVm9BBC5E3ZsmV59913ee+994iLi2PIkCHMmjUr25v8FEUhNjbWhlE6tieeeIJvvvmGpKQkPvvsM5o0aUKpUqbXN86fP8/3338PQKlSpejSpYs9Qi2SmjZtyqpVq4iKiuLmzZv5TjQHBQUZf75x40auRsidOnXKmHx1lJlr7J6oi4iI4NKlS0RHRwPg5+dH9erVCSiOd1TZgbOz3MHlqFxcXGjSpIm9wxC5kN5Wl1xcMH9/ruUdOXKEXr16cffuXQB++eUXqlSpwnvvvWejCB7YdOo2ial6Np28zaaTtynr68betzqhVjvWKCt5TRVQ1U5QfyCcWpH2WKWBgb9aJUnnCG11NfoqE3ZN4GrM1UzbGgc1Zmb7mQR5BJnZs/iQPoTlSH/YvuS57Lgc4fNA5I492mrlypUMGzaMlJQUACZMmEClSpXo168fADdOn2DXr/O5d+Oa2f3dvX1oM2gY9Tt1M7s2XVGUXTup1Cpa9qtqUqbodNya8jaxGzZkql/qf+/hn800azqDYvLYycG+G9hV1HUI2ZP2c8hu2DQJnlkP5dLaRt77Cg/pQwh7GzhwIHfv3uXrr78mPDycoUOH0rJlSzp27EjNmjXx9fVFr9cTERHBmTNn2LJlC5cuXQJAo9EU++ewv78/b7zxBh9++CF37txhwIABPP/88zRu3BidTse+ffv46aefSExMRKVS8dFHHxX7v5kltW/fHmdnZ7RaLQcOHMh3oq5Rowc3W02bNo2xY8cSGBhoHMxQrly5TNOaHjhwAIASJUo4zGeuXRJ1iqKwfPlyfvvtNy5fvmy2TrVq1Rg2bBgDBw60ywgRIYTIKyWLaXSsci5FIS4uzqTsu+++Y/z48fj5+dksDoANJ0zX7GpVNcDhknQZnd9/G09fVyrU8c+5snjAyRUG/AAB1WHnJ9DrC6ja0d5RWcUfIX/w/r73SdIlZdo2su5Ixjcej7NaOueiYKQ/LIQQBZOSkmJM0kHa++rMmTNp17wZe35bwJXDB8zup9Y40ahHH1oOGISbZ+YpG4sqvc7AvxtCaPRYRdy8su/HHA8/Tilnf3TvzSB+23bTjWo1ZT79BL//EqJZnu+hRJ1Grc6iZjF0YpnpY7UTBNW1TyxCiEJv/Pjx1KpVi88++4ybN29y4MABYxLCHJVKRdu2bXnzzTczjR4rjoYOHUpcXBxz5swhIiKCadOmZarj4uLCRx99RPv27e0QYdFVsmRJOnXqxJ9//smGDRt46qmn8nWcSpUq0aNHD/744w/27t3L3r17Tbabm/p048aNAPTs2dNhkq82T9TFxMQwbtw4jh07BmAcYviwy5cvM3XqVNatW8f333+Pj4+PLcMUQog8MSQloY8xP52ONTRt2pSlS5fSr18/FEWhbt26bN682eZJuoj4FE6Hmf7efRqWsWkMeRF5K4FdSy6g1xpo0LE8rfpXxcmleNxBbREqFbR/E2p0hzIN7B2NxaXqU5l5aCbLLizLtM3L2YuP235M54qd7RCZKGqkPyyEEAU3dOhQQkJCjDNK9OjRgy/+9w6/vv4SBr35G+iqNm1J+2EjKVGmnC1DtbuUJB1b5p0i9HwUty5F8fhrjbLsA8enxjPlrwmM+u0e9a7qTTc6O1Pu88/x6dY1x3PqDAaTxzKiLoMLD02rXu8JcHazTyxCFBGefiUc6ji29thjj9GxY0f++usv9u7dy/Hjx7l//z5xcXG4ubnh5+dHjRo1aNSoET169Cg2a9nl1tixY+nQoQO//fYbBw4cIDw8HLVaTZkyZWjTpg3PPPOM2TXuRMENHDiQP//8k0OHDnH37t18J49nzpxJvXr1+PPPPwkJCSEhIQHDQ32RdMeOHSM0NBSAwYMH5zt2S1MpWV0ZsAJFURg2bBhHjhwB0qb16dGjBw0bNjRO7RMREcHJkyf5448/iIqKQqVS0aRJExYvXmyrMIu81NRU4yKNderUwd3d3c4RCXPSh6YDBAQEoCkm07EURnq9nmtvvEnqZtMvXCo3NzS+vnk+nlNAAMGrVuaq7ty5c1m7di2rVq2yeZIuXWRCKltO32HDiVtcvBvHgbc746xxvDtmU1O0/D79ENG3k41l/mU9eXJyU5wlWedQ7PX+N27bOPaG7c1UXsu/Fl+2/5IKPvJlJqOkpCTOnj0LQP369XO1YLOQ/rCjkP5w4SD94cLDXm2lKArPPfcczs7OfP311yh6HQsmjCPu/j2TegEVK9NhxHNUqv+ITeJyJPFRKWz8+gT3w+KNZWVre9P7xUY4O2e+d/vzHR9Q9dNl1Ao1LVe5uVF+7ly8Hm2bq/M+9+shtp0LNz5+o1tNXuqY85oxxYI2KS1Zd3wpXNkOo7dB+QfTbtn1vW9ee4gPz7leTryCYMzugh/HwUl/2NSlS5fQ6XQ4OTlRvXp1e4cjhCgEFEWhT58+XLp0iddee41x48ZZ/ZzvvPMOK1eupG3btvz000/5Po6l3/NsOqJuw4YNHDlyBJVKRe/evXn//ffx8so81US/fv2YNGkSH374IevWrePIkSNs3LiR3r172zLcYiGrzLKwP71eb8zulyhRQi5MOLCo1aszJelcq1en8orlqK184W/8+PG8+OKLdn1++Hu6MKRFRYa0qEhiqs4hk3QA5w/cNknSAZSvVUKSdOak38Njp6n27PX+N7T2UP4J+weFB/cwPVH9CSY3n4ybk9zl/DDpQ+SP9IcdjzyXHZf0hwsPe7WVSqXihx9+QK1Wp00R7OTEo0OfZfNXMwFw9/Gl7aDh1Ov0GGp18X3+pCRqTR7fuRTH/bA4Slc2HTly8uJe6nywjOC7pvurPT2pMO97PJo2zfU5ZY26bDi7p42iq/cExN1NS2plYNf3vmKQXLMk6UMIIUTBqFQqJk6cyLhx4/j111955pln8PDwsNr5bt26xbp16wB45ZVXrHae/LDp1dT0uT+bNWvGzJkzzV6USOfp6clnn31Gs2bNUBSF9evX2ypMIYTIteQLF4n45FOTMrWHB+XmzLFIki48PJxDhw5lW8eRLlp5uNhl6dNcqdmiFJVbuaH6789VorQHrfpVtW9QjmrfV2mL2uu1OdctQtqWa8sLDV4AwFXjykdtPmJq66mSpBMWJf1hIYTIm9OnT3P9+nWTsuT4B6PDNBqNyTqetVq3o3ydejTtM4DRc36gQZfuxTpJ51XCld4vN8TFPa2f7uyuok5vT/zLeprUS7odStRzL2VK0uHrTcVffslTkg7MrVEniTqzvEvZ7eY4IYQQwhF06tSJpk2bEhUVxW+//WbVc82bNw+tVkv37t1p2LChVc+VVza9onr27FlUKhXDhg3L9T7Dhw/n0KFDxqHkQgjhKPTxCYS99hpKsukorTIff4RrleACH//8+fP07NmT6Oho9u/fT82aNQt8zOJMpVZRup4rvuWcuH1YRYchtWR9OnPObYS/3gcUiLwKT/0C7n52Dsp2xjUcR0RSBINrDaamv7zmhOVJf1gIIXJv27ZtPPHEE1SoUIG9e/fi5uTEgdVLObntT4Z/NsfsWnMqlYqB732KSu2YszzYQ8lyXvQcW589yy9Q8VENbj6mf5vUmzc5P/QpgsJTTcv9PKm5cDFuNWrk+Zw6vYyoE0IIIUTu/O9//2Pr1q1WHU2nKArlypXj5Zdf5oknnrDaefLLpom66OhogDwtvpheNyYmxhohCSFEviiKwp3//Y/UkBCTcp9Bg/Dp2bPAx9+zZw/9+vUjKioKgJ49e7J//36CgoJy2FPkxL2Ehr4T6uHq6mrvUBzPreOw+nlIn/rx6k74uTu8sKtILXB/LeYalXwqmdx9n06j1jC19VTbByWKDekPCyFE7vz888+MGTMGnU7HubNnmTB0IE1LlyQ5Pg6A3YsX0O+Nd83uK0m6zMrVLEH/Nxtx5sxpk/LU0DCuDhmM2z3Tz5ioEs40XLIct+D8zUChe2hKQI2DTo8vhBBCCPurWbOm1QcoqFQqXnjhBaueoyBs2lPy9vYG0qZyy61799IWgc5uWiAhhLC1qKVLiX1oXTp9cDABb7xukePPnTvXmKQDuHr1KuPHj7fIsQVmEzQCiLkJBr1pWcNBRSZJpygKC88spP+6/qy6tMre4YhiSvrDQgiRs5SUFL744gt0Oh01SgUwseuj1PNyMSbpAK4cPsD1U8ftF6SDMRgU7lzN/oYOtZlRbU6BAVwOMk2q3fIH7/lz8Mxnkg6gfjk/2lQrSYtgf5pWKkEpb7lJTgghhBAiKzZN1FWvXh2A1atX53qfVatWmewrhBD2lnTqNOHTppuUKR4epL4yHpWLi0XO8csvv9CkSRPj46ZNmzJnzhyLHNsS5u2+wntrT/NvSCSGh9afEIVY7T7w7CbwDEx73GgYtHnNriFZSnxqPJN2T2Lm4ZnoFB3TDk7j7H2ZRlDYnvSHhRAiZ66uriz75WfGdW7DC+1bUNrXO1MdjZMTkbdC7RCd49Gl6vnzh9Os+fwoN87cz9O+G0P/5H89YjldMS2Jdy0ITnw4kPr1OhYopv/1qcNvz7Vk+ZhWrBzXmq51SxfoeIXe8SWw/xuIz/2NOkIIIYQoPmyaqOvWrRuKovDXX38xd+5cFCX7i7vffPMNW7duRaVS0b17dxtFKYQQ2YtcsABFqzUpSx0zBsWC01J6enqyYcMGKlasSN++fdm1axelSzvGl1tFUVh88DqLDlxn4Lz9tJ6+g+3nHl513r4UReHo1uskxaXmXFmYqtAMnt8BTUZCr1lFYnH7C5EXeHrT0/x1/S9jWaohlYm7JhKXGpfNnkJYnvSHhRAie0nxcez85Qe2zfqUqiX9zNap3qI1z375PY269bZtcA4oOV7LutnHuHr8HgaDwpYfTnPvRu76N1HJUcw8NBOts4oZT6r5s5GKb58rw5j2b1o56mJGUeDvL+HPt+GLWrBkEIQdtXdUQgghhHAgNl2jbuDAgSxevJiQkBC+/fZbtm7dyoABA2jQoAElS5ZEpVIRERHByZMnWbNmDZcuXQKgSpUqDBw40JahCmF3KpUKT09P48/CcZSdPg2nwEAif/0VAL9nnyGmfTvAsm1VpkwZ/vnnH8qUKYNGo7HYcbPTZ+5e7sWlZFtHqzdwP+FBAuxObDJvrjyJc4Z1JwK9Xdkwvq3V4szJuX232b/6Cse33aTT8FpUrh8gr6m88KsIfWbb7fSWbKu1l9fy8YGPSdFnfl73rtIbDyfrLVQshDnSHxYi9+Szu/CwRFvpdTpObvuDfb8vMZniMqOgylXp8MxzVKhTP9+xFjVn993iztVY42Ntip4/vj/F0A9aonE2vTf74Xb6/PDnRKdEA5DsquKn7hrmdvwfns6eNou/WAg9DPfTPs9R9HBxS7azVsh7nxBCCFH82DRR5+zszPz583n22WcJDQ3l8uXLzJgxI8v6iqJQoUIF5s+fj5OTTUMtNpydne0dgsiCs7MztWrVsncYwgyViwulpkzGvWkToleupPSkSZTJ52spPDycgIAA1FkseF++fPmChJpn9+JSuBObnOf9Mibu7C3mXiJ/r0j7IpwUm8qmb07SuHslWvWrKq+pQsIS73/JumSm/zvd7Fp0vq6+TGs7jUfLP1qgcxR30ofIH+kPOx55Ljsu6Q8XHgVpq7t375J4O5RdC38kMuym2Toevn60HTyCuu07o1bb5ga2wqJRl4pE3Ijj0uG0KRVd3J3o9EztTEk6gOS//6Z648Zo/PzYf2s/66+sN9n+WKXH6FChgy3CLl5OLDF9XCIYKrbMsrq89xUe0ocQQghhKTad+hLSLjqvX7+ekSNH4u3tjaIoZv95e3szatQo1q5dS9myZW0dphBC5MjnsceoOG8eqnx2zo8fP06jRo14++23LRxZ8fbPysvoUvQmZaWDfewUjQO7vh8iQ+wdhVXcjL3J8D+Gm03S1StZjxW9V0iSTtiV9IeFECLN2rVrGd6lPaunvW82SadxdqZ5v6cYPecH6nfsKkk6M1RqFZ2fqUO5Gn54lXBlwOuNKV+zRKZ6UctXEPrSy9x4YQwJMRF8uP9Dk+1ezl5Mbj7ZVmEXL/WfggZPg/N/Mzk8MqRITC8vhBBCCMuxy225Hh4evPXWW0yYMIEzZ85w6dIloqOjAfDz86N69erUrVsXFxcXe4QnhBBW98cffzBw4EDi4+P57LPPCA4OZsyYMfYOq0joMLQWBsM5rp+6D0CdNmUIbhho56gczL2LsHQQqJ3g6SXZ3tFb2Oy4sYN3975LnDbzlFlP13yaN5q9gYtG+hfC/qQ/LIQo7ubMmcOECRPwcXOlY41gXJxMk3A1Wral3dBn8Q1yjHWaHZnGWU2PsfXRphjwKuGaafv9n34mfOZMAJJPnuTqmOeJ7HoHMvzJJzSZQJCH5dbcFhlUap32L+VzOLsOqnSwd0RCCCGEcDB2nT/HxcWFRo0a0ahRI3uGUazpdDq5AOSgdDod169fB6BSpUoy3ZWdKAYDSkoKanf3LOvkta1u377NgAEDSE5+MMXkSy+9RLVq1ejcubNlAi/GPHxc6PViA87uvcWpXWG0eao6IK8po8RIWDIQkmPSHv/aBx7/Bho4ztpX+WkrnUHHV0e/YsGZBZm2uTu5M7XVVHpW6WnxWIsznU5n7xCKBOkP25/0hx2XfHYXHnltq507d/Laa68BEJOUzK4LV+hatwYAQcFV6fjM85SvXc+qMRc2iqJg0Clmp7QEcPVwxtXM0rv62FgiFy0yKXM6fp5FPcYzrdQhDt89zCOBj/BkjSctGu8nm84Sn6JDo1bhpFYzrGUlqgV5WfQchY6rNzQalmM1ee8rPKQ/bEqj0aDT6dDr9SiKImssCiGKLEVR0OvTZvPSaCwz44PNp740R6fTERkZSWRkpHzI2ZiiKPYOQWTBYDAQHR1NdHQ0BoPB3uEUWxHff0/IU0+RcvlylnXy2lZlypTh66+/Nilr27atXKS1IJVKRd1HyzHwnWa4uKV9sZXX1H+2fwBRGaa81KfCufXgQJ8HeW2re4n3GP3naLNJuiq+VVjaa6kk6axA+hCWJf1h+5HnsuOSz+7CIzdtFR953/hzhw4dePnll42Pd52/SpLaiS4vjGfYp7MkSfcQvd7AjkXn2fLDKQz6vL0WND4+VPz5JzT+/sYy7YD+VHp6ND93+5kPW3/I+63eR62y7OWhNcdusfTfmyw+cINf9l3jbj7WwS6u5L2v8JA+hKn0G58URSExMdHO0QghhPUkJiYaPwMsddOn3W7LuXz5MsuWLWPfvn1cu3bN+IupVCoqVapE69atGTRoEDVq1LBXiEKIYi5h/34i5n4NikLIUwMp8+EH+PbpY5Fjjx49mpCQED755BOGDBnCzz//jKtr5mlqRMGo1XIHXyaPfQQxYXD5r7THZRpC/3mFep2MqJQozt4/m6m8R3APpraaioezmdvLhXAA0h8WQhQH90NvsGvhj9wNucLoOT/g6uGJSqVi9uzZXL9+nQ0bNvC/Dz5gypQpqNUOcS+xQ0lN1vHnD6e5cTYSgD3LLtJ+SM08jVRxrVKFij/9yPURz5Dcpw+6Xj1RqVSoVCr6V+9vlbj1DyWYNNIvF6LI8/HxIS4ubQmCyMhIPDw8ZFSdEKLIURSFyMhI42MfHx+LHNfmiTqDwcCMGTNYtGgRBoMh090niqIQEhLCtWvXWLZsGUOHDmXy5Mk27bCHhYWxaNEidu3axZ07d3BxcaFChQr06NGDoUOH4p7NFHj5lZSURO/evQkNDQWgXLly7Nixw+LnEULkjjY8nLDX3zCOMlKSkrg1eQru9evjUrmyRc7x0Ucf0aBBA5566inpvArbcfOBwcvgz7fh3AYYvBxcPO0dVYHUKFGD91q9xzt73wHAWe3MW83eYmDNgfLaEg5J+sPmSX9YiKIlMTaG/SuXcOKvP1D+S9ocWL2c9sNGAWnTBC1dupQdO3bQx0I3wxU1iqLwx/enCD0fZSw78/ctvEu60aR75Twdy612bSpuWM/ZsDCLxddn7l7uxaWY3RadqDV5/OJvR3HRmP8cC/R2ZcP4thaLSwhhH15eXqhUKhRFIT4+ntDQUPz9/SVhJ4QoEtJHC0dGRhIfHw+k3WTr5WWZqb1tnqibNGkSW7ZsMV6QqF69OvXr1ycgIACAiIgITp06xaVLl9Dr9SxatIjw8HBmz55tk/h27NjBG2+8YfxjQ9pFg5iYGE6fPs3vv//ODz/8QKVKlSx63q+++sp4UUIIYX9KaipOgYHo7z+YoifwtVfznKTLbl52lUrFwIGOsy5YYZScoCXkxD1qtSojHf+80DhBzxnQ/i3wLGnvaCyib9W+HL17lH239vFlhy+pFyBTZgnHJf1h86Q/LETRoNdpOfLXH+xftYSUhASTbUc3r6dBl+6UKF0WAE9PT0nSZUOlUtGsVzC3L8eg16UlO52c1fiXMX+TlSE1FZWzc5b9Yo2/P1gwUXcvLoU7uZzSMjIh1WLnLRSib4IuGQKq2zsSIWxGrVZTrlw5wsLCjMm6+Ph4VCqVxdZwEkIIe0lffzOdSqWiXLlyFruh1qaJuk2bNvHHH3+gUqmoVasWH374IQ0aNDBb9+TJk0ydOpWzZ8/y559/smnTJnr16mXV+M6ePcuECRNITk7Gw8ODMWPG0KJFC5KTk9m8eTMrVqzg2rVrvPDCC6xatcpi2dKzZ8/y66+/4urqipOTEwkPfZkRQtieS/nyVF62lLuffEr077/j1b49JUePztMx/vnnH8aPH8/GjRspW7aslSIt3vYsu8ilQ3e5cvQeHYfXwtNXpg/NkyKSpEs3pcUUkrRJ+Ln52TsUIbIk/eGszyv9YSEKN0VRuH/lIksWziP6zi2zddx9fIiLiDAm6kTOylb3o8vIOvz542ncPJ3p9WIDSlfxzVRPH59A6Esv4Va3LkFvvG6SrNt+YztnIs7wbK1nbRh5Mbf/azj4PZRvBo8MgboDwN3P3lEJYXXe3t4myTpI+3yQNZiFEEVJepLO29vbYse0aaJu+fLlAFSuXJklS5bg4ZH1mjENGjRg8eLFPPHEE4SEhLBs2TKrX5j45JNPSE5OxsnJiZ9//plGjRoZt7Vq1YpKlSoxc+ZMrl27xoIFCxg/fnyBz6nX63n33XfR6/W89NJLrFq1Si5MCOEg1G5ulPnoQzxbtcSjVStUebhD4vfff2f48OGkpKTQq1cv9uzZY9E3bwGXDt3l0qG7AFw/fZ9lH/5Lj7H1KFu9hJ0jcyDaZHB2s3cUFqEoCr9f/J3wxHBebvSy2TquGldcNZKsFY5N+sOZSX9YiMLvfugNTv6+iKhrV8xuNwCtBgyi+eNP4uJm+alzi7pqTYLQptSiTFU//Epl/tzQR0dzY8wYkk+cJPHgQTTeXgSMGwdAXGocnx74lPCkcLaEbGFwwGDqeNWx9a9QvOhS4eSKtJ9DD6X9i70Nnd6xb1xC2Ii3tzc1atQgPj6e2NhYUlNT0ev19g5LCCEKRKPR4OLigo+PD15eXhZfmsKmiboLFy6gUql4/vnns70okc7Dw4Pnn3+et99+mwsXLlg1tpMnT3L48GEAnnjiCZOLEulGjRrF6tWruXLlCgsXLmTs2LE4OzsX6LwLFy7kzJkzBAcH8/zzz7Nq1aoCHU8IYXk+PXvmqf7SpUsZMmSI8fHx48cZNGgQ69evx8nJ5jMOF0kpSTp2LzX9XFAUBZ8AufBjlBIHP/eAWj2hwxQoxFODJmoT+ejAR2y8uhGAuiXr0rFiRztHJUT+SH84M+kPC1F4JcbGsO/3JZz86w8UxWC2ztHrYWw+dZ4xwfVoO2i4jSMsOmq3Nj8KURcRwY3Rz5GS4TPi3pyvUPv44D90KHOOziE8KRyAm/E3mRE/g3ervEt96tsk7mLp0lZIijQta/i0fWIRwk7UajU+Pj74+PjYOxQhhCgUbLciPZCamjYnec2aNXO9T3pdrVabQ82C2bZtm/HnJ554wmwdtVpNv379AIiNjeXgwYMFOmdYWBhfffUVAB988AEuLi4FOl5+yJpOjkutVhMUFERQUJDFM/TCsh5uq8cee4xq1aplqpeSYn6hdZF3ru5OdBlZB3efB++b7QfXxKtE1qPHitVryqCHlaPh7inY/RmsGp02uq6QyNhW1+OuM3TzUGOSDuCdve9wM+6mHSMUIH2I/JL+sCnpD4vsFKvP7kJGr9NyZNNafn71BU5s3WQ2SRcaHcvc7ftYcvA40YnJpKammqzrIR5QDAr711wh/HpsnvbT3rrF9WHDTZJ0AJoSJfBo1Ijj4cdZcWGFybYm/k1oVamVvKasKTkGPAMfPK7YCkpWzfXu8t5XeEgfQgghhKXY9BO/XLlyAMTFxeV6n/RF7NP3tZYjR44AaXct161bN8t6zZo1M/589OjRAp3zgw8+IDExkccff5wWLVoU6Fj5JaN7HJeTkxMVKlSgQoUK0k42ELvlTwyp+Vvg/OG2CggIYPPmzZQsmbb+19ixY1m/fj2enuYXfRf5U7l+AIP/15wqjwRSvVkpqjcrlW39YvWa2vouXPrzwePTq2DDq/aLJ4/S2+qM7gzDtgzjcvRlk+1x2jj2hO6xU3QiXZF/HVmJ9IdNSX9YZKdYfXYXMn98M4tdC38kJTHzNLVe/iXp8fIkekx6l7CYOFQqFV999RVffvmlXNQ2Q6fVs/WnMxz98zobvz5BzL2kXO2Xeu0a14YNI/XaNZNyp1KlqPTbYjQ1q/PB/g9QeJAcddO48XGHj6lYsaK8pqyp0VCYeA4GL4PafaHxiDztLu99hYe0jxBCCEuxaaKua9euKIrC1q1bc73Pn3/+iUqlokuXLlaMDK5cSZtLP6cOa5UqVTLtkx+bNm1i9+7d+Pr6Mnny5HwfRwhRcLFb/iTstde4PngIqTctM0qnevXqrF+/npkzZ/Ltt99KB95K3L1c6D6mHp1H1LZ3KI7FvwqoMnzEu5eA9m/aL5480uq1TDs4jTf2vEGiLtFkm5ezF7M6zGJo7aF2ik6IgpH+8APSHxai8Grco0+mMicXV1o9OZhRs+ZR59GOdO3alfnz57NmzRqLrGdZFKUk6djw1QkuH0mbmjIpTsuGucdJis/+BsLkCxe5Nmw4ulu3TcqdK1Sg0m+Lca1ShQVnFmS62emlR16ivHd5y/4SwjyNM9TsAYMWwSNDcq4vhBBCiGLNpleOR44cyYYNG1i+fDlNmzalZw7rPm3ZsoXly5dTvnx5Ro8ebbW4UlJSiIqKAqB06dLZ1vX19cXDw4PExETu3LmTr/PFxMTw6aefAjBp0iT8/f3zdRxLyG4KJScnJ+M0CwaDAZ1Ol+2xMk5VpNfrs10oVqVSmaxnotPpMBjMr2kAaVM/ZLxgpNVqs502JWPsiqLkOFWUs7Oz8e7OnH5Xa8eu0WjQaDTGx6k5jPKSdipY7NobN7j1Ttqi3slnzhAy4AnKfjYdt0cfLXA7NW3alKZNm6LVagtNO2W849YSbPl60pt5qRTb11Oz59B5l8dpzXOgS0H3xK8o3hUgw+/vqO97t+NvM3HXRE7fP52pXnW/6sxoO4MqJR4kCQp1OxXyzydrT8NYVEl/OI30h+X9RvrDmWMvTO0UULkqNVu348K+tBHutdt2oMWTQ/AuGYDCgzZLX7c5NTVV2skMg2LItIxwXEQyYZciqVjX/Pti6pmz3Bo7Fn1MjOk5q1ah7Lx5EBTEpYhLzDsxz2R7Lf9aDKszzKKvJ0t9d1BQzMblKO0E8r5XGF5Ptohd+sNCCCGsxaaJOm9vbxYsWMCECROYNGkSGzduZMCAAdSvXx9/f39UKhX379/n1KlTrFmzhh07dlCvXj1mz56Nt7e31eJKSHgwXYeHh0eO9d3d3UlMTCQxMTHHuubMmDGDiIgIGjVqxMCBA/N1DEs5f/58lttq1qyJl5cXAImJiVx4aN77hzVp0sT4c0REBKGhoVnW9fT0pFatWsbH169fJzo6Osv6QUFBVKhQwfj43Llz2XaIqlSpQokSJYC0jtOpU6eyjb1+/frGjmtMTAxXr17Nsq6zszMNGjQwPr59+zbh4eFZ1vfz86Nq1Qfz0V+5csXkOfew8uXLU6pUKbRabY6/J0g7ZSVX7ZSaitv7U1FnaA9DXBy6+/dz1U4nT57k4sWLvPDCCznGXljaydJfNBzl9QS5e44VlnYyJ9PrKTEIt1ZzcIu/QXSMFzz0uzvi+94/Yf8w+e/JRKdEZ6rzqN+jDC87nJhrMSS6JhaddiqEn0/psutDiKxJfziN9Ifl/Ub6w2kcuZ0uXrhAVPgd3H1LmK1fp2tvLp09Q+W2nXjs6SGcPHkSHhrhlZG0k3kVH9WQnODF/bB4nN00NOlfihhDGKdOhWWqqz53DrcvvoQk0+kxDZUrE/PGG8TcvYty5w4zrs0g1fAg0aNWqZnaaiqKXrFof9hS3x2y+ps6UjvJ95boLOsX13YC6Q8LIYSwHJsm6mrXfjA1maIo7Ny5k507d2ZZX1EUTp8+ne00PyqVirNnzxYorpSUFOPPGe+8yUp6ByE5OTnP5zp06BCrVq3CycmJqVOnyhz9Iku5ubNMFIzLwkWob9wwKfN9vC9+Tz7J3Ry+6CxZsoQ333wTvV5PYGCgSedeWF78PT0aJ/Dzy/8xsrszsqhK9gkm2SfY3mHkSK/o+eb4N8w7MS/TndnOKmeGlx1OuxLt7BSdEJYl/WHpD4vck/6wfSiKwuXDB9jz8/eonJxpOmIMKrXpqhkGg4HP537Ndz8sxHvpGnbWqWenaAs/J1c1vV9uyJYfTtFhaE2SlGjCwzMnCtTHT+A6ezY8/JqoXYvkiRPhv5ss9kbv5VzCOZMqQ2sPpW5A3RxHaQnHUBy/twghhBDFnUqxYQ8g4x05lqJSqTh37lzOFbMRGRlJq1atAOjZsyezZs3Ktn7r1q25f/8+NWrUYMOGDbk+T2pqKn379iUkJIRRo0bx1ltvZarTqVMnwsLCKFeuHDt27MjbL5KHONLvWKpatWqWd03LVAr2iT19KoWM7VSrVq0sL5pJO+Uv9rgNGwh/512TfVyqVSV4xQrUHh7Zxj516lSmTZtmfOzm5sb3339PvXr1smyrwtJOA74/wL0Ma2IkpeqJTX4Qq1oFgd6u2cauQkWgtysbxre1yOtJm6Jn1fRjJMam0uLxYB7pVBGVWpUpdnMyTk2Sm9dUYWkncwrr+15UchTvHniXA7cPZNoW5BLE5+0+p25gXZNyaSf7xP7wVD/R0dHG9cky3ukssif9YekPp5P3G+kPPxy7I7RTZOgNdi38kZtnThrLO40eR90Ojxkfp6Sk8Mwzz7BmzRpjWfny5fnhhx8ICgqSfhb5aydFUVCpVGZjj9/6F3cnT4aHjuPZpg2lvvwClbs7AJHJkTy56UliUh9Mi1nGswxrH1+Lh7OHxfvDj36+h7uxKVluz61SPq78/Xrmm7IcsZ3SZWonxQCoSJ/LtCD9LPneYsoRP5/SSX9YCCGEpdh0RN3LL79sy9Plmqenp/Hn3Ezfk/TfNBO5mRYoo++++46QkBDKlCnjMItpOzs756ojoVar89TheLjzkpOMHaPcyM2d3ulUKlWeYs/r72rN2DPuI+1kqiDtlHLpEvc+/sT0/O7ulJ8zB/V/r+vsYk+fYiRdcnIyGzdupF69erlqK0dup42vmH5BfnPlCVYcfjAtSre6pfluWBNyyxKvp39WnCfuftqIjX0rr3DjdCTdnq+Hm6dzvl5PkLvXlCO3k5GiwN+fQ+NnwCXIWFxY3vcUReG1ra+ZXY+usXdjRpcfTd3AutnGVijaKQuFpZ0sVV+kkf6w9IezIu83Oe8j7WTKku2UEB3FPysWc2rH1rT+RQYHVi6lTtuOuP73endycsr0e4WGhrJ3714GDBhQdPpZWchPO4UOHsKd5BIExF8ir2OIDUlJGGJjM5V7P9aFsl98gTpDLLMPzjZJ0gG82/JdPJwzv1dbop1Uef5tsj5Obv6mDv16uvgnbJkMDYdAw0HgV9Fkc7H/3pKFwv75JP1hIYQQliKJOsDV1RU/Pz+io6O5c+dOtnVjYmKMFy9Kly6dp/PMnz8fgFatWmU5xVH6sRMTE9m0aRMA/v7+xjuchRAFY0hIIPS1CSgPretQ5sMPcM3l9JVTpkwhJCSEH3/8EYBJkyYxaNAgi8fqCP4NiTR53DzY/KL21nLj7H3O7r1lUqZLNeDibtOPL8e1Zybs/ASOLIQhy6FUHXtHlCcqlYqJTSfy/Nbn0Stpd/VqVBpebvgyj2gfkenwRJEk/WHpDwvhSHRaLUc3r+PgmuWkPtQ/TucbVIqkuFhjok6tVvPrr78SGhrKvn37cHJy4rvvvqNRo0a2DL3QMOgNnHRqQViFJgSHbCT4+h8FPqZP3z6U/fRTVBmSEP+E/cOmq5tM6nWr3I125WX68AKZ1x7is17jzCgpEnTJsPPjtH/OnuDm+2C7VxCM2W29OIUQQghRqMmVzv9Uq1aNw4cPc+PGDXQ6XZZ33WRctDava1KlD+dfvXo1q1evzrZuVFQUEydOBKB58+ZyYUIIM0KeeBJdRESe9tHHxKA8tJ6OpkQJfPv0yfUxVCoV3377Lbdu3aJPnz6MGjUqxwWwC6O7sclcu286qsLWibqy1fxo2KkCJ3bcBMDZVUOXkXVQqyWBw+lVaUk6gJgb8FNXeOoXqJ71OlaOqFnpZrzS+BVmHZlFoHsgM9vPpH6J+kXyNSWEo5P+sBDFh6IoXP53P7t/+5mYu+aT894lA2k39Flqtm6X6eYZNzc31q1bR48ePZg+fTqPPvqofHaboU3R8+ePpwkrkTYjRUhwb1xToih7J/OU37nl9/QgSv/vfybrBibpkvjowEcm9bydvZncfHK+zyP+Ex8OcbdyrvcwbULaPyGEEEKIXJBE3X+aNGnC4cOHSUxM5MyZMzRs2NBsvUOHDhl/bty4sa3CE0KYoYuIQHf3boGPo8rn9EsbN25EpVIV2UXZHx5N5+3mRK3SPjaNwclFQ9uB1anUoCQ7fj1Hs97B+Aa62zQGh6TXwa7ppmXaBFCyXmvCkY2sO5IUXQpP1XyKAPeAIvuaEsLRSX9YiOLhbsgVdi2cT+jZzFNPAzi5utLi8ado0rsfzq5uWR4nICCAf//9t0j3hwvqztUYrp++b1J2ocbT+Eedxy0lOs/HU3t4UPr99zMlTlP1qdQLqEdYfJixbGLTiQS4B+Qr7txQFIXY5OzXFhNCCCGEELkjibr/dOnShXnz5gGwatUqsxcmDAYDa9euBcDHx4cWLVrk6RwXLlzIsU6nTp0ICwujXLly7NixI0/HF0LkUxbT623ZsoUWLVpQokSJLHYr2qO6Dl8zTdQ1q+yPJp8j2VZ8eojE2AJewFEUTu8Oo06bsgU7TlGgcYJnN8OyIRD6b1pZt0+hRjf7xpWNf2//S7PSzcy+blQqFeMeGWeHqIQQGUl/WIiiLSE6ir3LFnF611+Z1qFLV7d9Z9o8PRxv/wAUReH3339nwIABWa5nVdT7wwVVobY/7QbVYM+yiwCoDVrqnP0lX0k6ALW3t9m/ua+rL5+3/5w+Vfrw8cGPKetZlgHVBxQk9Bz9tDeExNTCeZOYEEIIIYSjkUTdfxo0aEDTpk05fPgwq1aton///pnm2P/555+5cuUKACNGjMi0aOzBgwcZMWIEAP3792f69IdGOzigvCwgLGzLycmJKlWqGH8WtjV79mwmTpxIhw4d2LJlS7YLVhfVtnqnVx36PlKOf0Mi+TfkPh1qBub7WImxqSREpxQ8qAJcDCpy7eQVCM9sgHUvpa1/0WKsvSMyK0WfwvR/p7Py4krebPYmw+sMz3GfItdWRZT0IYoe6Q8LRyOfB5ajGAys+GAKkbdCzW4vW7MOHUc8R+lqNQDQ6XS89NJL/PDDD7z00kvMnTs326SctFXW6ncoz42vFxDq25AGp+bhF3s1553yqX2F9jQr3YzY1FjUKnWm7ZZqp00nb/PxpnP53l9kT15PhYf0IYQQQliKfOJn8M477zB48GCSk5MZNWoUY8eOpUWLFiQnJ7N582aWL18OQOXKlRk5cqSdo7UMtTpz5104BrVaneVILmE9er2eiRMn8tVXXwGwc+dOnnvuOX799dcsL04U1bZycVLTpFIJmlQqwbgOeVuDyBEVyXZydoMnfgSDvkBJTGu5GXeTSbsmcS4y7ULOl4e/pF5APRoFNcp2vyLZVkWQ9CGKJukPC0cinweWo1Krad7vKbZ8O8uk3CcwiHZDR1KjZVtjXzcuLo6BAweyZcsWAL755huqVq3KhAkTsjy+tFX2qt/bTtlLm/M9ki4vPJw98HD2MLvNEu106FokE1Ycz1Tu6+6Mu3P+khaB3q4FiqmokddT4SF9CCGEEJYiiboM6tSpw6xZs3jjjTeIj4/nyy+/zFSncuXK/PDDD3h5edkhQiGEtYWHh7NixQqTskWLFjFgwAD69etnn6CEyI5KlTYVpoPZeWMn7/zzDnGpccYynaLj9d2vs6HfhiwvIAkh7Ev6w0IUXXUe7cjxPzdy58olnF3daN7vv3XoXEyTJOfOncs07ewbb7xB3759qVq18N+8ZS2pSTpc3M33yVRgkySdtV25F8/zCw+TqjOYlL/VvVaRuLFPCCGEEMJe5NaPh3Tq1In169fz7LPPUrlyZdzd3fHx8aFevXq8/vrrrF27lkqVKtk7TItRslibQNifoiikpqaSmpoq7WRDZcqUYdOmTXh6ehrLPv74Yx5//PEs95G2KhwKdTvFhNk7glzTGXTMOjKLV3a+YpKkA3B3cmdCkwk5JukKdVsVI9I2RZf0h4WjkM+DvIuPiiQuMsLsNpVaTYdnXqBu+86Mmj2PlgMGZUrSATRv3pxff/3V+NjNzY3ly5dnm6Qrzm2lGBT2r7nMckusyZzbcyoK8anx+dovv+10Ly6FZxf8S3Si1qR8WMuKjG1fJc+xiKwV59dTYSPtI4QQwlJUinyqFDupqamcOnUKgFq1apkkJITjyNhO9evXz3aNtOLqUvsO6O7eLfBxnEqVovruXSZlGzduZODAgcyfP5+hQ4dmu7+0Vc5+mfyPRdao8/Rz5dnpbfK1b6Ftp0vbYNlg6PoJtHjB3tFkKyIpgjd2v8Hhu4czbQv2DWZWh1lU9cv5butC21bFTEJCAufPnweknUThI/3hwkE+D3JPl5rKkU1rObj2dyo3bETfiW8X+JjTp0/niy++YP369bRq1SrbusW1rfRaA9sXnuPSobTvJEGVvOk3sTHOrqZTQFr6e8uf1/7k04Of8mazN+kZ3DPb9QMzym87JabqePqHA5wMjTEp71wriHnDm+CkKcL3gH9RG+JuFfw43mVhUu7W9Suur6fCSPrDQgghLKUI96aEECL/evfuTUhISI5JOiGs6u5Z+P1Z0KfCH2/A5jdAr7N3VGYdunOIpzY8ZTZJ171yd5b2WpqrJJ0QQgghck9RFC7s38uCiWPZu2wh2uQkLh3cx82zpwp87LfeeovTp0/nmKQrzv5ZfdmYpAMIvx7H1p/OWHWUTUxKDNMOTiMyOZLJf09m3LZxhMaFWu18Or2B8UuOZUrSNSjvy9whjYp2kk4IIYQQwkas0qPavn0727dvJzEx0RqHF0IImyhVqpS9Q7CLM7diWHzgOpfD42QqD3tKioIlgyDj9JH//gCnV9ovJjMMioGfTv3Ec1ufIyLJdKotJ7UTU5pPYUa7GXg6y2gVUbxIf1gIYW13rlxi+dS32Dh7OrH3wk227fr1RwwGfbb7GwwGbty4keV2lUpVbPvDudWkeyW8/d2Mj1VqFcENA3I9wi0/Zh+dzf3k+8bH/9z6h9MRp61yLkVRmLrhDNvPmz6/Kvi789MzzfBwcbx1koUQQgghCiOr9Kpeeukl1Go169evp1q1asbyKVOmoFKpeO211wgKCrLGqYUQQhTQppO3+XbXFQD8PV0Y2qIik7rWtHNUxZCbHzwyBHZPf1BW7wloMMhuIT0sJiWGd/95l103d2XaVtqzNF+0/4IGgQ1sHpcQjkD6w0IIa4mPvM/eZQs5s3u7+QoqFUHBVdClpuLi5m62SnJyMiNHjmTnzp0cOHCAypUrWy/gIszT15U+rzRk1Ywj6PUK3V+oR6W6Ja12Pq0hlZUXTW/aale+Hd0qd7PK+b7ffZXFB0yTuX4ezvwysjmB3pnXNxRCCCGEEPljtdufzI3CWLNmDSqVilGjRsmFCSGEw9DptDlXKkb+DYk0/hyZkIpBRtXZh0oFHadAyWqw7kUo8wg8/m1auQO4EHmBV3e+Slh8WKZtbcq1YXrb6fi5+dk+MCEciPSHhRCWpE1N4ciGNfy7biXalGSzdcrXrkeHEc9Rqko1s9sB7t+/T79+/di7dy8APXv25J9//qFEiRJWibuoK1Hak14vNsDJRUNgRW+zdQzJ5tsrr2JT4kweuzu5806Ld6wygm/d8TA+23LepMzFSc38EU2pGuhl8fMJIYQQQhRnVknUubi4oNVqZaofIUShEBFxn7i4OLy9zX+xLk6StXpOhEablDUPtt5dwSIXGjwFJSqn/XN2y6m2zbhqXIlOiTYpU6HixUde5IUGL6BWyXoloniT/rAQwlIUReHCvj3sWfILcRH3zNbxDSpFu2GjqN68dY5JmylTphiTdADnzp1j4MCBbN261apTNhZlZar5Zbkt4vt5GGJistyeFzpFR8bLOOMbjaesV1mLHDujA1fv88bvJzOVzxr4CM0q+1v8fEIIIYQQxZ1VrqKlz2N/+PBhaxxeCCEsysvLS5J0/4lN1tKrfhnK+KYlhNQqaFKpYHdXG/QGtCnZr5EiclChGXgF2jsKE5V9K/Nh6w+Nj0u4luD7x75nbMOxkqQTAukPCyEs4/blCyz93xts+mqm2SSdi7s7jw55lme/+I4aLdrkKtE2c+ZM6tata3zs5eXFpEmTJEmXBUVROPrndc78nXkWgdxwf+QRywb0nzol6zCk1hCrHDtZq8dJY/p8eKdnbXo1KGOV8wkhhBBCFHdWGVHXqlUrVqxYwZdffsnJkyepXLkyTk4PTrVkyRL8/fN+F9bLL79syTCFEAJIuzgh0gR5uzH76UYoikJoVBLn78Th5Vqwj4p7N+JJTdJZKELhSLpW7srwe8M5ee8kn7f/nNKepe0dkhAOQ/rDQoiCSoqPY8UHb6NLTcm8UaWifsfHaDNoOJ5+ebupytfXl82bN9OiRQs0Gg2bNm2iYcOGFoq6aDEYFP5efpHTu8NQqdLWpKvcICBPx/Bs2QK1lxeG+HiLxaVRaZjaaioatcZix8yoQ80glr/QipG/HCIiPoVnW1fmuUeDrXIuIYQQQggBKsXc4hkFdPv2bfr37090dLTJXXnpp8rvnXrnzp2zSHzFXWpqKqdOnQKgXr16uLrKItCOSFEUtNq0tdOcnZ3lDlczLrXvgO7u3QIfx6lUKarv3pXv/aWtcjZv/C50WkOBj+Pp58qz09vka1+HbqfIENj5KfT6Atx87B1NJoqiZPn30hq0oICzxtmi53PYthJGKSkpnD59GoD69evj4uJi54gci/SHHZv0hwsH+TyAvcsWcXDNcpOyCnXq0+GZ5wmqXKVAxz5x4gQlS5akfPnyBToOFM22MhgUtsw7RciJCGOZk4uafhMbU6py3vprlvrect8bxr3sxLN1n2VS00l53j+v7XQzMpFf9l3j7Z610agLf5vm2f0r8EsviLtd8GN5l4VJufsML4qvp6JK+sNCCCEsxSoj6sqUKcOaNWv49ttv2b9/P3fv3kWr1aJSqVAUBSvkBkU+SYfPcalUKunkPUQXGUnC3r349u1r71BMSFvlzMXdCZ021a4xOGw7JUXDkkEQcQHunIIhy6FEJXtHBYBWr+XLI1+iM+h4p+U7Zus4qy2XoEvnsG0lTEgfInvSHy485LnsuArD58HiKa+REB1V4ON4+pVg2LTZmcqb93uS0zu3khAdhV+pMrQbPopqTVvm6nmr1+tRq9VZ1rXkKLrC0FZ5pVar8C/jaZKo06UauHc91myiTtHpQKMx+/d2CsjbKLx0BsVARFIECmmfGdGeUM6rHOMajsvX8fLaThX8PXivd518navQu38FfuoKKXE2P3VRfD0VVdKHEEIIYSlWSdRB2sWJjz76yKSsVq1aqFQqNmzYQLVq1ax1aiFEEZR05gyh48eju3UbtZcX3p062TskkYFiUAi/HkepYPN3F6uK4x24uaHXwu/PpiXpAO6dg/mdYMQ6KF3PrqHdSbjD67tf58S9EwA0CmpEzyo97RqTEIWN9IeFKPoSoqOIj7xf4ONklbx3cXOnw4jniLsfQaMefXFyzt0NMvHx8QwePJgWLVrw7rvvFji+4qrF41WIj0rhwsE7qJ1UdHmmDtWblcpUT3fvHqETJuDTtRv+I4Zn2h68amW+zv/m7jf549ofJmXftXwXD2ePfB1P5FLcHVjUHxL/S9KqncGjJBRkHWavIMvEJoQQQogiyWqJOlE4GAwFn4pOWIfBYCAmJgZIW0dCrS7Al4JCLmbjJm6/+y5KcjIAt954k8orlpu9M1VRFCIjI0lKSgLA29sbX1/fbI+f3ztc0xX3trpzNYa/l1/k3s14nn63Of5lPe0dklkO2U5R19NG0WXkFQR+Fe0Tz3/23drH5D2TiUp5MEJg6v6p1PKvRRW/gk2zlRsO2VYiE+lDiKJCnsuOqzh9HqT+13c1p1ab9nk61u3bt+nduzdHjx5l48aNVK5cmWHDhhU0xGwV1bZSqVR0HF4LndZA/fblKFcz83qAScePE/rKq+jCw0k6dhy32rXwaNaswOfeE7onU5KuZ3BP2pZrm+9jmmunb3ZeJsjblaeaVihQvEWGosCKERB9/UGZQQs1ukHfr2wSQlF9PRVF0ocQQghhKTZN1E2bNg2A0qVL2/K0Iht6vd7eIYgs6HQ6rl69Cshc57rwcGOSDsCQkMCdjz82e2dqz549+ePvPSZl3377LePG5W96mFzFV0TaymBQUOdx5NvOxec5u/eW8fHelZfoM76hQ04B4pDtFFANnt+eNvXlvfPgGZg29aWd1qkzKAbmnZzHd8e/M06xlE5v0HM5+rJNEnUO2VYiE+lD5I/0hx2PPJcdV3H6PNAmJxETfhffoMyjtfIiMTGRNm3aEBISYiwbNWoUFSpUoH37vCX88qIot5XGSU33F8zPdBC1fAV3Pv4Y/ltPDL2e0NcmELxqJc4FeJ/XGXR8evBTkzIfFx/ebPZmvo8Jmdtpw6lwZv6ZNrNDWHQSr3au7pD9eJtSqaDrJ7DkKUj676a1Mg2h68c2C6Eov56KGulDCCGEsBSb3pbTv39/+vfvj5eXly1PK4Qo5PxHPotP3z7Gx+5NmlBu5kyzdSdNmoST04N7EJycnPDwkKlhcqI3KLSYtp3BPxxg1l8X2XclghRdzl863L1Np1+6eTaS66cyT//k4eOCp59rgf95+BTBL6klKsPorVCzFzy91G6j6aKSo3hx24t8e/zbTEm68l7lWdxzMV0rd7VLbEIUJdIfFkJkZc+SXwp8DA8PD1577TWTMm9vbxmRk4PLR8JJTdblur4hJYXb773Hnffff5Ck+4/K1QX9f6Oh8stJ7cSMdjOoUaKGsez1pq9T0r1kgY6b0b4r93lr1Unj49nbLjF51SlZQxWgQjMY9Sf4lIcSwTB0pd1upBNCCCFE8WD3qS8jIiK4dOkS0dHRAPj5+VG9enUCCjgVnRCi6FCpVJT58ENSr1zFrX49Sr/9Nqos7irs3Lkz8+fPZ+TIkfj4+LB69Wo6d+5s44gLn3O3Y7kXl8K9uBT2X70P2+Hg250p5aPJdr/G3Spxft9tEmJSAfAr5YGTa+Z9Br5d8Ol/ijQ3Xxi8xG6nP3nvJJN2T+JOwp1M2zpW6MjHbT/Gx0UuTghhLdIfFkKgUlGmWg0URSnwiKZXXnmFq1evMmfOHKpUqcLmzZupWbOmhQItWhRF4d8NIRzefI2Kdf3p+WIDNJrsk5raO3cIfeVVkk+ezLTNo2VLyn35BU7+/gWOrUFgA5b1Xsais4s4fOcw/ar1K/AxMzpyIxqdwTQpV7O0t4yoSxdYM+1mOn2qrC8nhBBCCKuzS6JOURSWL1/Ob7/9xuXLl83WqVatGsOGDWPgwIHSURRCoHZzo9KihahzMTru2WefJTo6mi5dulCvnvlpaoSpf0MiTR5XLulBKR+3HPdzcXOi1YBq7Fl6gWa9g6nfoTwaJ7lju7BQFIUl55fw+eHP0RlM7yLXqDS80vgVRtYdKZ/DQliB9IeFEBl5+vrRtHd/ix3viy++wNvbm/HjxxMUJEkGc/R6A7sWnef8gbQblW6ciWT3bxfoOLxWlu+5Cf/+S9iEiejvZ55Bwn/UKIImTkDlZLnLLM5qZ0bVG2WV/tj4jlUJ9HFn6vozGBQY3TaYUW2DLXqOQs+3nL0jEEIIIUQxYfNEXUxMDOPGjePYsWMAWU6rcPnyZaZOncq6dev4/vvv8fGRO/mFKMoUg4GI77/Hs3lzPJo2NVsnN0m6dA9P+SOy93Cirnlw2l3ASXGpHFx/leBHAqlU1/xUOzWalaJiXX/cvYrgtJSWlJoIEReh7CP2jgSABG0CU/dNZcu1LZm2BbgHMKPdDJqVlpGQQliD9IeFEA9TWXhqSo1Gw0cffWTRYxY1CVEpXHtoyvZz+25To3kpytcyHRGnKApRixZx97MZ8NCaVCp3d8p+8jE+PXtaLVZr3awxolVlyvi688fp27zTs7ZVzuHwUuLA1dveUQghhBCimLNpok5RFF588UWOHj0KpE3r06NHDxo2bGic2iciIoKTJ0/yxx9/EBUVxbFjx3jxxRdZvHixLUMVQtiQPj6BW5PfIn7bdqL8/Qle+TvOZctmWV+n0/H6668zcuRIGjZsaMNIiyZFUfj3mmmirlklf05sv8m/G0NITdIRdjGa8v8rYXYqIJVaJUm6nBgMsHYsXNgC/b6F+k/aNZzLUZeZsGsC12KvZdrWrHQzZrSbQYC7TLknhDVIf1gIYQlRUVFMnDiRGTNmEBgYaO9wCiWfAHd6vdSAtbOOodcaUKmg3eCamZJ0hqQkbv/vfWI3bMh0DOeKFSk/dy5uNWtk2lZYPFanFI/VKWXvMOzjzFrYNAmGLIfy5m8WFUIIIYSwBZsm6jZs2MCRI0dQqVT07t2b999/Hy8vr0z1+vXrx6RJk/jwww9Zt24dR44cYePGjfTu3duW4QohbCD1+nVCX36ZlEtp037pIyMJfXk8lX5bjNrdPVP92NhYBg4cyJ9//snKlSs5cOAA5cuXt3XYRcqVe/FEJqSalFVIUNj7+4Op2KLvJnJ6VxgNO1ewdXhFw86P4ey6tJ9XjYb7V6D9m2CHqewMioHXd79uNkk3ut5oXm70Mk5quy9hK0SRJf1hIURBXbt2jZ49e3Lu3DnOnz/Pjh07cDfTbxY5K13Fl66j67JtwVkeG12X4AamNyqlhoYSOv4VUs6dy7SvZ7tHKTdzJhpf3wLHoSgKf13/i04VO0k/zFZC9sDq59PWoPu1DwxcCNUfs3dUQgghhCimbNoD3LhxIwDNmjVj5syZ2db19PTks88+49atWxw6dIj169fLhQlRrKhUKpydnY0/F0Xxe/8hbOJEDLGxJuXJ58+TeOgQXu3amZTfvXuXrl27cvK/hdvDwsLo1asXf//9t12nAyvsbXXwoWkvy/i60fzR8tzcf5fw63HG8mNbr1OvfblCuwad3drp0jb4+wvTsv3fQKOh4Gv7JLNapebjth8z4o8RaA1aALxdvPm07ad0qNDB5vGYU9hfU0JkR/rDQuSefB5kduLECbp168bdu3cBOHDgAMOHD2fFihWoLTx9Zl4U5raq8kggwz9plWmGiPi9/3Br0iT0MTGZ9ik5biyBL7+MSqPJ8riDNg4iIikiVzGk6FKISY3BSe2Ej4uPSbIuwD2A5b2X5/K3Me/nvSG0rR5AsL9boW0ni7p7FpYOSUvSAWgTYenT8MIuKF3frqFB4X49CSGEECJ/bJqoO3v2LCqVimHDhuV6n+HDh3Po0CHOnj1rxciKr/TOn3A8zs7ONGjQwN5hWIWiKET+vIDwL75ImxIwA42vL+VmfYln69aZ9vPz88P3oTtWL168yLFjx2jfvr1VY85OYW8rc+vTqTVq2g6sweqZRwCo3qwUrfpXLbRJOrBjO1XpAM2eg0M/pj1WaWDgr3ZJ0qWrF1CPt5q9xccHP6a2f22+7PAl5b0dZ2RqYX9NFRfSh8gf6Q87HnkuOy75PMgsKCgIV1dXk7J//vmHmzdvUqlSJTtF5fhtlRibiodP1lO1Z0zSKYrC/fk/cm/27EzfVdSenpT9bDreXbrkeM6IpAjCE8PzFKfOoCMyOTLninmw4tBNPtx4Fm83J+YNb0JrB24nmylRCSq2gMvbHpQ1fgZK1bNfTBk4+utJPCB9CCGEEJZi0yuu0dHRAHmapi69boyZu9iEEIWPITmZW2++RfjMmZm++LrWqEHllb+bTdIBuLq6smbNGmrUSFsDIiAggJ07d9o1SVeYGQwK92/Fc/Bq5kQdQJmqvjTvE8yA1xvTdXRdvP3d7BFm4adxgl5fQI8ZoFJDr8+hakd7R8XAmgP5sPWHLOq5yKGSdEIUddIfFkIURJkyZdi8ebNxNom6dety4MABuybpHN3xbTdY9N5+7lzN+T1UH59A2GsTuPfll5m+q7hUqULl31fkKknnKHZfvMeUNacAiEvW8czP/7L2WJido3IALp4weBnUH5j2uHZf6DnTLtPSCyGEEEKAjRN13t7eAISH5/6usnv37gGYXbtDCFG4aG/d4vqQoWYXYvfu2pXKS5fgUiH7NdBKlizJ5s2badu2LQcOHKBly5bWCrdIu3Upit+nHWLVzCNERyebbGsRXNL4c7NewZSp5mfj6IqoFmPgxQPQdJRNTqcz6FhzaQ0GxWB2u0qlon/1/rhqXM1uF0JYh/SHhRAFVbduXVavXk337t3Zu3evJOmyoBgU9q64xD8rL6NL0bPp25NE303Msn5KSAjXnh5E3J9/Ztrm/VgXKq9YjmuVKtYM2aLO3IrhxcVH0BsUY5lWrxCXrLVjVA5E4wz956XdUDdgPqiznsZUCCGEEMLabJqoq169OgCrV6/O9T6rVq0y2VdYlk6ns3cIIgs6nY6bN29y8+bNItFOiYcPE/LkUyQ/PG2XSkXga69Sbs5s1J6euTpW1apV2bNnD1WrVrVCpHlXmNpKrzfw54+nWfPFMSJuxqNN0tMm+cF0HSU9XagamLt2KGwcop0Ca9rkNBFJEYz5awz/2/c/5p+cb5NzWpJDtJXIkbRN/kh/2PHIc9lxyedB1jp37szmzZvx8/OzdyiAY7bVmb23OLHjpvFxcryWDXOPk5KUOb64HTu59tRAUi9fMd2gUhH42muUmzMHTSG6WSIsOomRCw6RkKo3KX/h0cp0KK9xqHayK7U6bYp6Z8eaOcQRX0/CPGkfIYQQlmLTRF23bt1QFIW//vqLuXPnoihKtvW/+eYbtm7dikqlonv37jaKsnjJqQ2E/RgMBsLDwwkPD8dgMD8iprCIWraM68+ORB9pOsWi2tOT8t98Q8DYsSaLZCcnJ/PLL79k+/x0pEW1C1NbaTRqHh5g9UiqhpL6tL9n82B/h/rbWpJN2klR0v7Z0ZG7Rxi4YSD/3vkXgG+Of8P+W/vtGlNeFabXVHEmfYj8kf6w45HnsuMqzp8H4eHhrFmzJts6jtRnc8S2qt2mDBXr+puU1WlbFhe3ByOnFIOBe1/NJfTFFzHEx5vUVfv6UuGHeQSMHYNKXXjWaY5J0jJywb+Ex6WYlPdpWJaJnas5XDtZXSF8j3fE15MwT/oQQgghLMXJlicbOHAgixcvJiQkhG+//ZatW7cyYMAAGjRoQMmSJVGpVERERHDy5EnWrFnDpUuXAKhSpQoDB/6fvfsOj6L6Gjj+3ZreIIUSIAmhN+kdRLHQFFCxUMSOgg3F8lNsrx1BFBs2FAUEUZFqQQELUkLvLYEQSkJ6z7Z5/wgElt1AytbkfJ6HB/bOzJ0T7m72zpw7945yZahCCAdQDAZOv/oa2YsW2WzTx8QQ/dGHNtPHZGRkMHz4cP755x8yMjJ44oknXBVurdFrZFOO7krHbCy96MtUK+jOXl+cW59OVNH6WXB6F9wwy+UjcxVF4es9XzNz60zMyvnR0woKz//7PCtHrpRpLoXwANIfFqLmUBSFkoL8y+9YSfv372fw4MEkJyezfPlySdJXkUaj5rr72rJkxjYyTuRz1bhWtOher2y7OTeXk089Tf7atTbH+rRoQfQHsy47Lb+nMZgsTPhmCwdTrd+X3WLr8M4t7VFZzOUcWUMpCqycAj5BcPULsgadEEIIITyWSxN1Op2Ozz77jPHjx5OSksLhw4d5++23y91fURQaNWrEZ599hlbr0lCFENVkOnOGlEcepWjbNpttAf370XDaNDTBwVblR44cYdCgQWU3JZ988kmaNGnCzTff7JKYa4vgcD86XtOYHWtSWEUh2/VmlLPXrJKoq4Z9y+H3FwAFso/BrfMgMMIlp8415DL1n6n8efxPm21R/lFMv3K6JOmE8BDSHxai5tj2y3KMJSWX37ES1q1bx4gRI8jKygLglltu4Z9//qFDhw4OPU9toffVMmRie7JPF9KwRZjVthNPPknBX3/bHBM8ZAj1/+8V1P7+rgrTIRRF4ekfdvJfYoZVeXxkIJ+N7YKPVoPBUMsSdX9Ng81np4EvOANDZ4JGvkuFEEII4Xlc3kOJjo5m6dKlzJo1ix9++IHc3Fy7+wUHB3PzzTczceJEAiq4bpUQwjMU7dpFyqSHMaWm2myre//9RDz6CCqN7WLd27dvL0vSnfPQQw8xaNAg+T1QCQU5JWxcmkjHaxoTVs/+/1un65pwMkrHtp92lpUF+WppWS/Y7v7iMk5uhx/vA84+mnh8I3wxEB5cD3rnvnf3Zexj8trJpOSn2Gzr1aAXb/Z9kzDfMDtHCiHcRfrDQni/5N07WDvXeh1YjU6HX1DV+lIBoaXf1atXry5L0gHk5+czceJE/v77b4+a6tIZFr2+mcJcQ7Xr8Q/WM+p/XcteB4T4EBBiO2Ap8sknObo5AaWoqLRAoyFyypPUufNOr/y/nv7bQX7adsKqLCLIh6/u6kqIv66co2qwhC9hzWvnX2/7Bkry4Jav5Mk6IYQQQngctwwl8vf35+mnn+bxxx9nz549HDp0iOzsbABCQ0Np1qwZbdq0Qa/XuyM8IUQ1ZC9ZwukXXkQxWF9kq/z8aPD6awQPGlTusTfddBNvvfUWTz/9NAANGzZkxYoVcnOygsxGCzv+PE7CqqMYi80UZBsY9rD90dc6Hw1bTmVblXWNqYNGLRetVVKYAaqL1i7pNM6pSTpFUfjp8E+8tuE1DJaLPm+oeLDDg9zf/n40atukuBDC/aQ/LIT3ykk7zbKZb6FctHbU4IefpHn33tWq++WXX+bIkSMsWLAAgC5durB48WKvTBxVVmGugYJsxz6heCm+zZvT4PXXOPH4ZDR16tBwxgwCenR32fkdaf7GZD5Yc9iqzF+vYc74rkSHedeTgQ6j1pb2z8sW6FZB6xslSSeEEEIIj+TWZ/71ej0dO3akY8eO7gxDCOEAislE2rRpZH4912abrkEDoj/6EN+WLS9bz5QpU0hMTOS///5jxYoVREdHOyPcGmn7H8lsWJJY9jp5TwZHd6UT0y7c7v6bkjKtXsu0l9UQfzXc8xvMvxVyjkOHO6DPZKedrshUxGsbXuPnIz/bbAv1CeWtvm/Rq2Evp51fCOE40h8WwrsYiov4edqrFOdZPwnb46bbqp2kA1Cr1cyZM4eUlBTCwsKYP3++DFpzouBBgzBlZhI0YAC6Bg3cHU6VrNmfxtSfd1uVadQqPhzdibYNQ9wUlQfoNA78w2HxXWAqhsHToO1Id0clhBBCCGGXTM4thKg2U1YWJyZPpvC/DTbb/Lt3p+HMd9GGVWzqPZVKxQcffEBRURFBQUGODrVGa3dlNDv/TLGaMmjuZztYGQVcNHDUoiik5VmPWP70r0S++veoVVlEkA/LHu7jpIhrmKg2cO8f8Pd0uPZVp43WPZZ7jMlrJ3Mw66DNtvYR7Znefzr1Auo55dxCCCFEbaYoCr9+NJMzyUetypt26U6vm+9w2Hl8fHxYvnw5AQEBaOxMFy8qpyQxEX3jxqjKWeezzujRLo7IcXal5DBx/lbMFsWq/NXhbRnQItJNUXmQloNh7BJI/g+63efuaIQQQgghyiWJulquNkyh4q3UajWhoaFl//ZUxQcOkDJxEsYU2/WxwsaOJeqpKah01msi5Ofnk5WVRaNGjezWqdVqvSpJ5yltpffV0nNEU/74eh8Au/Qm/vY1UpBXseMzC6q/Jognc0k7BUXB4LedUzfw38n/eHzt4xQYC2y2jWk1hsmdJ6PTeP8aJJ7ymRKXJn0IUVPIe9lzedr3wcafFnFw479WZXUaNmLQxCdQVTK+lJQUQkNDCQwMtLs9ONi71g32tLY6J2fpUk5NfYE6Y8cQ+eST7g7HoY5nFnLXV5spNJityicNiOf2bo3tHuOp7eRUTXqW/vEitbKdvJT0IYQQQjiKJOpqOW05owqF+2m1Wpo2beruMC4p97ffOPn0M+cXYD9LpdNR76WXCL3JdmqRU6dOMXToUAoKCli/fj116nj/dIuubCuL2UJBjoGgOr52t7foXo/TiTlMO3iCPSVGl8TkLbzhM3U5McExaNXWv7f9tf683Ptlro+53k1ROV5NaKvaQPoQoqaQ97Ln8qTvg8MJG/l34TdWZT4BAQyf8jw+/pVbA2zbtm0MGTKETp06sWTJkhrxHvSktgJQjEZS355G1jelbZbx+Rf4tmlzyfWyvUlOoZHxczaRnm89Q8aIjg154trm5R7nae0k7JN28h414fe3EEIIzyBDc4QQVWc22yTptBERNPn2G7tJuj179tCjRw+2bt3KgQMHGDlyJCUlrlsw3tsd35/Jwtc2s2zWDixmi919VGoVV45uSYaPi4OrTY6th7R9bjl1/cD6vNn3TVRn5zKND43nu6Hf1agknRBCCOFpMlKSWfXBO1ZlKpWaoY88RVj9hpWqa9WqVfTr149Tp06xYsUKHnnkERRFufyBosJMZ85w6MoBZH37rVX5yeeepyQxyU1ROU6Jycx93yRw5Iz1DAu9mtblrZva174nfEry4KcJkHfa3ZEIIYQQQlSZJOqEEFUWPGgQde+/v+y1X4cOxPywGL8OHWz2VRSFBx98kOTk5LKydevW8fjjj7skVm9WUmhk5cc7WTpzO5knC8g6VcDuv066O6zaKf0wLLgdvrgWDv/hlhD6NOzDAx0eYGjcUOYNnkdsSKxb4hBCCCFqg+L8fJZM+z8MFw1O6zt6PDFXdK5UXQUFBdx1113k5+eXlX388cd8/vnnDolVnGUxY87IQH3RtKKhw4ejj65cYtXTWCwKT36/k01JmVblLaKC+GRsZ/TaWnaLx1QC342GHQtK++cZR9wdkRBCCCFEldSyXpy4mNEoU+N5KqPRyP79+9m/f79Ht1PEo48Q0K8vITffRONv5qKLtL9ouUqlYt68edSvX7+sLC4ujkcffdRVoTqNs9tK56slL7PYqmzTskSK8z33feGJqt1OhZkw/xYozoaSXJh3C2x2zo01i2LBYC5/zcAHOzzI631ex19Xuam2vIW3/P6r7aRtRE0h72XP5e7vA4vFzIr33yb79Cmr8lZ9rqTL0BGVri8gIIAffvgBH5/zUx/079+fm266qdqxupu728oetb8/ITffhEqvp/7rr1Pvhamo9Hqnn1dRFNKL0p1S91u/7mfZDusBe1HBPsy5qyvBvpdfp9gT26nKLJbSJ+mS1pW+zj5Wmqw7ud2tYTlCjWqnGk7aRwghhKNIok4ID6UoCgUFBRQUFHj0dDgqjYboDz6g/v/9H+rLXPg2atSIFStWEBAQQI8ePdiwYQMtWrRwUaTO4+y2UqtV9B1lvdZEYB1fCvPKT+QIW9Vup3VvQWbiBRWa4cgacHCbZxdnM/GPibz838vlxqlWqWv0tEbe8vtPCCGEc7n7++Dv+V9zdMdWq7KouHiueeDhKn8P9+7dm7lz5wIwevRofv311xqxZrO726o89aZOJeb7RYSOrHxitao+3P4hNy+9GZPF5NB6v/nvKLPXJVqVBfpomTO+Gw1C/SpUh6e2U5UUZsCp7bblPkEuD8XRalQ7CSGEEKJCZNVTIcQlKYpCxqefoW/SmODr7a+DdbkE3YU6duzImjVraNu2LX5+FbugFNCgWSjxnSNJOZBFjxvjaNW7AWp1zU3UeKSBL0F+Kuz5qfR1vfYw8lNwYMJs15ldPLHuCU4VlI7c7xjZkZub3+yw+oUQQghRcVFx8Wj1PpgMpWsq+4eEcsMTz6HTV28x4FGjRlG/fn369OlTowfeeAK1jw++LhwY+P3B75m9czYAKlSE+oSi11TvKb5wv3B+35vKi0v3WJVr1So+Gt2J1g2Cq1W/1wqMgLt/hXk3w6kdoAuA0d9D3abujkwIIYQQotIkUSeEKJelsJCTzz1H3qpfUPn5oY+Jwbdly8seZzAY0F8iede1a1dHhlkj5GeVkLAyiR7Dm+IbYH/amr63NkejVeHjf/lpbYQT6Pzgpi+hbjxsmwd3LAR9gEOqVhSF7w58x9ub37Yaff3GxjdoXbc1reu2dsh5hBBCCFFxLXv1o06DaH5+51XyMzMZNvlZgsMjKnTs5frDffv2dVSYwkOsO76OVze8WvZaQaHQWMjiYYuJCoiqcr07jmdz66f/Ybnowao3RrajX/OKvR9rrMBIuHM5LL4bej4EDTu5OyIhhBBCiCqRqS+FEHaZ8/M5esdo8lb9AoBSVETKxEmYsrIuedzRo0fp2LFj2ZQ+4tJMBjMJK5OY9+J/7Pn7JAkrjpa7r3+wXpJ07qZWw1XPw0P/QXADh1RZaCzk6b+e5vWNr9tMkRSkD6LEXOKQ8wghhBCi8iJj4hj9+rvc8MSzRLdsU6Fj1q9fT3x8PBs3bnRydMJT7E7fzZS/pmBRLFblT3d7ulpJOoDf96ZSbLSu97GBzbilS6Nq1Vtj+AaXPknX9Cp3RyKEEEIIUWVufaIuPz+flJQUCgoKsFgsl91fnsIRwnXUAQH4tWtLyf79ZWXGkycp/O8/ggcPtntMQkICQ4cOJTU1lXvvvZdGjRoxYMAAV4Xslf5ZfJg9f50oe71rbQqt+zagTn3HPKklnMQv1CHVHMk+wuS1k0nMSbTZ1jmqM9P6TSPCv5aPlBaihpP+sBCezz84hKadu1do30WLFjFu3DhKSkoYNmwYGzZsIC4uzskRCnc6nnuciX9MpMhUZFV+X7v7GNViVLXrf+La5gT5anljVel12S2do3n06mbVrrdGkSlkhRBCCOHl3JKoW7RoEfPnz+fgwYMVXhhXpVKxd+9eJ0cmhDhHpVIRNXUqJYcOU7R9O+qgIBrOmE5gOdP0HD16lP79+1NYWAiA0WhkxIgRrF+/ntatZdq+8nS8pjH715/CbCq9OWuxKGxalsj197dzc2S1nKHAYdNalmdF4gpe/u9lm5s6AHe3vZuHOz6MVi0zVAtRU0l/WIia55dffuHWW28te33mzBkGDx7M+vXrqVOnjhsj80zF+Ub+/fEwna9vQmikv7vDqZLM4kwmrJ5AZnGmVfkNTW/g4Y4PO+QcKpWKB/o3pX6oH0u3n+D1ke1q39qGqXsg4wi0vsHdkQghhBBCOIVL7wCazWYefvhh1qxZA1DhmxJCCPdQ6/U0fP89Tj3zDFFTp+ITG1vuvjExMUyYMIEZM2aUlUVHRxMYGOiKUL1WSIQfVwxsxJZfjoEKWvdpQPdhMurarUry4cvrIaYPXPcaqDUOrd5gNvD25rdZeGChzbYgXRCv9XmNAY3lSVQhairpDwvhmXb+8Ss+/v606Fn1teOuvvpqrrnmGn7//feysqZNm6LTydTlF0vcfoa18w9QlGsg90wRwx/v6O6QKq3IVMTDfzxMcl6yVXmP+j14qedLDk+m3dChAcPa1699SbqsY/DNSMhPhSHToes97o5ICCGEEMLhXJqo++677/jzzz8BCA8PZ+TIkbRp04aQkBDUalkuzx3k/91zaTQaoqOjy/7tTIrFgqqc94IuMpLGX35ZoXqmTZvG0aNH+fHHH7nqqqv44YcfCA0NdWCknulybWU2WTAUm/AL1Ns9vtP1TchOK6LzoCZENApyaqy1WYU+UxYz/HAvpO4q/ZOZCDd/AT6OaZeT+Sd5Yu0T7M7YbbOtVZ1WTL9yOo2CZL0RV/7+E1UnfYiqkf6w55H/d8/lqu+DlL27+eOLj7CYzZw5lkTvUWPK7Rtfik6n4/vvv6dv377s2rWLCRMmMGvWLLTamv+EfGXaat/6U/w5d1/Z65OHstm1LsWp8Tma2WLmqb+eYmf6TqvyFmEtePfKd9FpnJOcrW6Szuv6WAXp8O1IyD9d+nrFZMhPgyufqdHTXXpdO9Vi0ocQQgjhKC69YliyZAkA8fHxzJs3j5CQEFeeXtghnT7PpdFoiIqq3sLjFVG4bRunX3iB6I8+Qt+oegkCtVrNN998Q+fOnXnyySfR6+0npmqaS7XVsd0Z/PP9IUKj/BnyUHu7++h9tVx/f1tnhiio4Gfq9xfg4Krzrw/9Ckseglu/qfb5/0r5i2f/fpZcQ67Ntpua3cSz3Z/FR+NT7fPUBK76/SeqR/oQVSP9Yc8j72XP5Yrvg9z0NJa++wYWsxmAjT8t4kzyUW588jnUVXiqPiQkhBUrVvDzzz8zceLEWvP0U2XaqmmnCDavSCIvo7isbOuvyYB3PGGsKApvbHqDtcfXWpXXD6jPRwM/IlBf9RlFFm9JoXOTMGLDnTMFu9f1sbbPh4zD1mWJa6HvZNDW3H6z17VTLSZ9CCGEEI7i0kTdkSNHUKlUPPTQQ3JTQggPkL14MadffgXFaCTloYnEfLcAdUD1Lgr9/f353//+56AIvVdBdglrvt3Psd0ZAGSnFpK8J4PGbeq6OTJxSfXag0YPZkPpa78wGPhStas1Woy8k/COTZLOV+PL8z2e58b4G6t9DiGEd5D+sBCew1hSzM/TXqMoN8eqPLxRkyol6c5p1KgRkyZNqm54NZbeV8tV41rx87vbAIhpV5crR7dkxUc7ARWmM2dKZzmoav12BkU50he7v7CZwjxIH8THAz8m0j+yyvX+svs0UxbvIMxfz2fjutC5SVh1Q/V+vR4uXTt63ZulryNbwx3f1egknRBCCCFqJ7fMwRF7iXWuhBDOpxiNpL7xJlnz55eVlRw6xMln/0fD92ZecuTv/v37eemll/jyyy/x9/fORd9dQeejIe2Y9U2Cf74/xK0tw9BoZHoMj9XhVghtDN/dASV5cOs8qNu02tXq1Dre6f8Oo1eMpthcOnq8SXATZlw5g+ZhzatdvxDC+0h/WAj3UhSFXz95n7SjR6zKYzt2ofetYy557O+//87PP//MrFmzas0Tc44W3SKMTtc3IayePy2610OlUjHqf10pTEjg2LjnwGJxd4h2LTuyjPe2vmdVplPreH/A+zQNrXqfccuxLB79bhuKApkFBu74bAPv3daR69vWq27I3k2lggHPQmAE/Ps+jPmhdCCdEEIIIUQN49K7xTExMQBkZ2e78rTiEoxGo7tDEOUwGAxs2bKFLVu2YDAYHFavKTOT5LvvsUrSnWPJz0cpKir32HXr1tGzZ08WLlzImDFjMJurPtK1JrHXVno/LT2GW1+s63w0FOU6ri1F5VT4M9WkJ9z3B9z8JcT0dtj5m4c1Z2rPqQBc0+QavhvynSTpyuGs33/CsaQPUTXSH/Y88l72XM78Pti89AcOrP/LqiysQTRDHplyyafpvvzySwYPHsyHH37I//3f/zk0Jm92cVsV5xvZsOQIZnP5Cbeew5vSskd9VCoVitFI2rszOTbuTo9N0imKwprja2zKX+/7Ol3qdalW3TNXH6TEdP7nLjFZOJSaV6067fHaPlbXe2HiRghu4O5IXMJr26kWkj6EEEIIR3HpE3WDBw9m7969rF27lp49e7ry1EIIoHjvXo5PmoTp5CmbbXXuuovIJyajKmex+x9//JHbbrutrCP6008/8dRTTzF9+nSnxuzNWvWsz+51JyjILqHniKalo4XVrhl1HRHkmOlgHFWP16kTV/rHwW5oegMRfhH0qN9DRuALUUtJf1gI90vctpm/F3xtVab382f4lOfx8S9/GvhXX32VqVOnlr1+8cUXiYmJYdy4cU6L1Rsl7Ujn30VHKMozotGp6Trk0k8QlyQlcXLKUxTv3u2iCKtGpVLxdr+3eX3j63x/8HsApnSZwvUx11e77o/HdGbivK2sO3gGgNu6NmLSVfHVrrdG0fm5OwIhhBBCCKdxaaJu3LhxLF++nAULFnDNNdfQpUv1Rp0JISouZ/kKTj3/PEpxsVW5yseH+q/+HyHDhl3y+JYtW+Lv709Ozvk1PP766y8KCwu9agrMRa9vptABT7X5B+u5/oG27FqTQuchje3uo1KruPbeNvgH69H7unam4WUP93Hp+USpPEMe3+z9hvvb349Wbb/NezaQG/NC1GbSHxbCvTJPprDy/XdAUc4XqlQMeXQKdRpEX/LYDh06oFarsVzwxNdvv/0miboLHN9czInt+8teJ6w4SmyHcMKjg2z2VRSF7EXfk/rmm5ec1cOTaNVapvaYSv2A+mSVZDGujWPaPtBHy+d3dmHqkt2cyinm/4a3rX2DuhLXQeMesv6cEEIIIWoll059qdfr+eKLL2jTpg133XUXb7/9Nvv27aOkpMSVYQhRqyhmM2nvvMPJJ5+0SdJp69Wjybx5l03SAbRu3Zoff/wRnU4HwI033sjatWu9KkkHUJhroCC7pNp/stMKmf/SRravPs7+9afLPV9opL/Lk3SiAhQF1rwO2ckOq3J/5n5uXX4rH+/4mFnbZjmsXiFEzSL9YSHcp6SwgCXTXqWksMCqvM9t44jr2PWyxw8bNoz333+/7PVTTz3F3LlzHR6nNwuL0aK64C6DxaLwz/eHbfYzZWaSMulhTr/4otck6c5RqVTc1/4+pnSZ4tB6dRo1b4xsx+yxndHVtjWtD6yCb0bAvFtK14kWQgghhKhlXHr3uFWrVmX/VhSFOXPmMGfOnAodq1Kp2Lt3r7NCE6JGMufkcOLJKRT8/bfNNr/OnYl+byba8PAK13fVVVfx+eefs2XLFmbMmIFGU/76HTWdsfj8+nwJK4/RbqQfWt9adkHtxdT/zoC/3oKEOXD7dxDduVr1/XToJ17b+Bol5tIb7V/u/pIrIq5gQOMBjghXCFGDSH9YCPewWMysnPUOWSdTrMpb9OxLtxtvrnA9EydO5NixY8TFxTFhwgRHh+n1AiO0dBgYzfbfSv+fY9qHc+XoFlb75P/9Nyf/9z/MZ9JtjtfHxoJahSW/wGZbZVXmOqcqnPHEm0qlwldXy66xkjfA9+NBMUPSOvhqCIz+AQIj3B2ZEEIIIYTLuDRRp1w4vYid10IIxyk5fJjjEydiPGb7xFDo7bdR79lnUen1la533LhxMr3PRUoKTJzYUUKT7rJugjcIO7kW7ZY3S18UpMFXg2HEbGgzvNJ1FZuKeW3jayw5vMRm27SEafSN7lvuFJhCiNpJ+sNCuMf6RfNI3LrZqiwiJo7rHny00gmXt99+25Gh1TidrmtMamIebfo2pHm3qLL/X8VgIPXtaWR9+63d40Jvv42op55C7ef+PrVFsTB7x2xubXkrdXzrOLTuP/al0js+vPYl5OwxGeCHe8F0wcwvp3bAjgXQ+xH3xSWEEEII4WIuvXs4adIkV55OiFor748/ODnlKSyFhdYbdDrqPf88YbeOKvfYbdu2kZaWxnXXXefkKL2bRqfGbLSgUqto1ace/k3y3R2SqAjFTL1D863LTCVVWgsjOTeZyWsncyDrgM22duHtmN5/uiTphBA2pD8shOvtX/8XG39aZFXmFxTM8CefR+fja7P/zz//TPPmza2egBXnFeUbSN6TSYvu9exu1+jUjHiik20CVKul5IjtNJiaOnWo/9qrBA3wnJkIpidMZ+7euSxPXM4nAz+hUXAjh9S7YucpJs7fSteYMD4b14VQ/8oPnKxRtHoYNbd0ysvCs09YXjEGej3s3riEEEIIIVxMEnVC1CCKxUL6xx+TPusDm22a8HCi338P/06dyj1+5cqVjBpVmsT7+++/6dixo9Ni9XY+floiWgbRc3hTgiL07Nq1y90hiYpQaTjYawbt97+D+uhfpWXXvQYtBlWqmj+O/cHz/z5PvtE2QXtHyzt4ssuT6DQ6R0QshKhhpD8shOsZigpRazRYzKVTl6s1GoZNfpbgiEibfWfOnMnkyZNp0qQJGzZsICoqytXherQj29JYN/8ARflGgur60iA+1O5+9p5SVKnVNHjjDRJvHI4lJweAgP79aPDaa06fprIyvtn7DXP3lq49mJyXzJhVY/jw6g9pG962WvVuPprJ44u2n/13FiM/Xs/Xd3WjUR3vWvPb4Rp2gnt+g2+GQ2RrGPYeOGFaUSGEEEIITyZD/Wu52rzGmKfTarW0aNGi7N+XY84v4NSzz5D3+2qbbb5t2xL9wSx09eyPegWYPXs2EydOxHz2BsaQIUPYuHEjjRo5ZvRoTaNSqxg6sQMAFoulUm0l3OPCzxSdF8MvTwEq6PFQheswWoy8t+U9vt77tc02P60fL/d6mUGxlUv6CVuV/f0n3EP6EKKmkPey53LU90H7q68nrH5Dls14g6K8XAbceT+NWrez2sdsNjN58mTef/99AI4ePcoNN9zAmjVr8Pev5YmUs/74ei/7/ztd9vrPufu49flu6PSaCreVrl496r/8MieffprIp58i7PbbnbLWW1X9dvQ3pm2eZlWWW5JLriG3WvUeOZPPvV8nYDBZysoSzxTw+95U7u4TW626K8Nj+1h1m8I9q8EnCDQeFJebeGw7CRvShxBCCOEo8o1fy6nVaneHIMqhVqsJDAys0L6GY8dImTSJkkO2U8mE3HgD9V5+GbWv7bQ+5yiKwt9//12WpAM4deoUr776KrNnz6588LVMZdpKuI9NOw2dCYqlwiN20wrTmLJuClvTttpsaxrSlBlXziAuNM5B0dZu8pnyDtKHEDWFvJc9lyO/Dxq1bseYN2ay7991dLh2sM32kpIS1q9fb1W2adMmvv76ax588EGHxODtwuoHWL3OSSsiYcVReo5oatNW5pwcNCEhdusJvv46/Dp2RBdl+0SjO21J3cKzfz+LgvXaoS/3fpleDXpVud4zeSWMn7OJnCKjVfmYHo25q3dMleutCo/uYwXJ06vneHQ7CSvShxBCCOEobk/Upaenc+jQIbKzswEIDQ2lWbNmhHvQ1BdCeLL8f/7lxOTJWHIvGuWpVhP19FOEjRt32VGqKpWKL774gpSUFNatWwfA6NGjy0YUC1EjqVSgqtgIyI2nNvLUX0+RWZxps21I3BBe6PEC/joZbS+EqBrpDwvhGsERkXQffovdbf7+/ixbtowePXpw7NgxAF599VUmTJjgyhA92hUDG5O47QypSaXXHTHtw2l/VbTVPub8AlJfe43CTZuIXfITmqAgu3V5WpIuMTuRR/58BIPFYFX+SMdHuKHpDVWut9Bg4p6vN3M8s8iq/OqWkbw0rI1HPU3odBYL5KZAaGN3RyKEEEII4XHckqhTFIWFCxcyb948Dh+2fQIIID4+njFjxjBq1Kja1Xl1MYvFcvmdhFtYLBYKCwuB0hsHF4/UUhSFzC/nkDZ9eulFzwU0ISE0fHcGAb0qPvLTx8eHn376iV69enHzzTfzyiuvyGevgi7XVsKNso5CWAxQ9XZambiSZ/95Foti/TnTqXU80+0Zbml+i3xWHEw+U95B+hDVI/1hzyHvZc/l6u+DevXqsXLlSq666ipmzJjBHXfc4dTzeRu1WsXVd7bi53e30XNkPM27RZX9brJYLGRt2EDG1BcwnzgBQOqrr9LgrbfcGXKFnCk8w4OrH7SZ3vKW5rdwb7t7q1yvyWzh4fnb2JmSY1XePjqEWXd0RKtxff/GrX2s36fCtm/g9oXQpKfrzuuFpC/sPaQPIYQQwlFUiqIol9/NcXJycnjwwQfZtm0bUHqTwm5gZzv8HTt25JNPPiE4ONhlMdZ0BoOBXbt2AdCyZUsCAgIuc4RwhwvbqV27duj1+rJtluJiTk19gdxly2yO82nWjOiPPkRfxbXlCgoKatx7QlEUUpNyObQ5lV1rU3DEb72AUB/Gv9kbuHRbCQeZ3R/y0yp3jKkYijJL17rQB6GgUKgOYn+/TyrVTulF6dyy7BbSi9LLyhoENGD6ldNpG962cjGJCpHPlHcoKChg//79gLRTZUl/2P2kP+wdqvJ9cOLAPowlxcS071jl89bE/nBFFeUbyMsoJrJJ+b9vzEYLGp114qCkpIRDt9+BZu9eq/KGM6YTPNh2qlFPUWAsYPwv49mfud+q/MroK3l3wLto1VUb26woCs8v2c28jclW5Y3q+PHjg72JCPKpcszV4bY+1r/vwe8vlP5b6ws3z4GWnvu+cDfpC3sP6Q8LIYRwFJc+UacoCg899BBbt5au7xMaGsqgQYPo0KFD2dQ+6enp7Ny5k1WrVpGVlcW2bdt46KGH+Pbbb10ZqhAeSzEYODZmLMW7d9tsC7r2Whq88TrqS9xY2LlzJ61bty53UeqaeFNi++/HWf+j/acVhJfIT4O8k1U7tiQPSvJQATpf42V3v1i4XzjT+k3j3t/uxayY6Rfdj9f7vE6Ij/11V4QQ4lKkPyyE8+RlpLN0+msU5ebSf+zddBp8o92nUbdu3UqnTp3Kracm9ocr4sjWNNYtOIBao+b2F7rh46+zu9/FSTooHVhguP9+fJ99FlVR6TSPl7om8QRGi5HJayfbJOnahbfjrX5vVTlJB/DJukSbJF2ov46v7urmtiSd2xxYdT5JB6WD6b4fD4/ugOD6bgtLCCGEEMKTuPT5+WXLlrFlyxZUKhXDhg1j9erVvPjiiwwfPpw+ffrQp08fhg8fzgsvvMDq1au58cYbURSFLVu2sHz5cleGKoTHUun1BA648qJCFRGPPUrD92Ze8oJ40aJFdOvWjYkTJ5Y7er8mimlf190hCC/XpV4XHu/8OI90fIRZV82SJJ0QosqkPyyEcxgNJfz8zmsU5mSjKBbWzv2cXz56F5Px/CAdRVF45ZVX6Ny5Mx9//LEbo/UsRoOZXz/fzS+f7qYoz0hBdgn/LK78IDclIhzDXeMB8OvUidifl3js03SKovDS+pdYf3K9VXmjoEbMumpWtdYe/nn7Cd76xTr5p9eq+WxcF5pGBFa5Xq8V2x+aX29ddsP7kqQTQgghhLiASxN1524udO3alWnTphEYWH4nNSAggLfeeouuXbuiKApLly51VZhCeLzwBx8k6JqBQOlI1egPPyR8woRy169RFIW3336bW2+9lZKSEj799FOmTZvmypCdKvNUAQmrjmIx258fPqxeAOGNauFFsaiUQmMh+Yb8crff2eZO7mt/H2qVrBEhhKg66Q8L4XiKovD7px+QmnjIqrwgOwv12XXADAYDd999Ny+++CIAkyZNYuXKlS6P1RNpdWpKCqxnHdi//hTHdmdUui5z795EzZhOk7lfo4+OdlSIDvfB9g9YesT6d2qYTxifDPyEun5VH+S3ITGDKd/vtCpTqeDdUVfQNaZOlev1anp/uHUeXDGm9PW1r0KH29wbkxBCCCGEh3Hp1Jd79+5FpVIxZsyYCh8zduxYNm/ezN6L5roXwtsk3XQzpvT0y+94lqIo+J4dAXxUpytLwmnDw4n9YTH133gTeIaIxx7Dp2nTS9aVmJjICy+8YFX29NNP07VrVwYMGFC5H8RDKBaFLb8e43BCKhknCgCIahJMo9b2L4DjO0diNlrIzy7BWGx2ZajCCyRmJzJ57WTiQuOY3n96uUlvIYSoLukPC+F4W1YsYd/fa6zKQuvVZ+ijT6NWawBYvXo1X331Vdl2i8XCqFGj2L9/P9EenFByBZVKxYCxrVjwysayfnJsh3C7A90K1q8n/eNPaPTJx+XO5BE4cCCqcqbZ9wQrElfw6c5Prcp8Nb58cPUHNA5uXOV6D6Xmcf/cBAwXDR58bnArhrSv5U+PabRw4wfQZjg0u8bd0QghhBBCeByXPhaQnZ0NUKkLoXP75uTkOCMkIVzGlJ6OKTW1wn/MaWmos7JQZ2VhTks7v+1ssk8TGED0rFmXTdIBNG3a1GZdmwkTJtC3b1+n/KyuoFKrSNp+pixJB3AoIbXc/Tte05jbX+yO3tdzbxoI91iVtIrbVtzGkZwj/H7sd77Z+427QxJC1GDSHxbCsY7u2Mpf386xKtP5+jF8ylR8L3hidfDgwUydOrXstVqt5o033qj1Sbpzgur40vumeHwCtFxzT2sGTWhHQMj5tdQsJSWkvvEmyXffQ+HmzaS++aYbo62eXg160SGiQ9lrtUrNtP7TaB/Rvsp1puYWM37OZnKLTVbl43vFcE+f2CrXW6OoVJKkE0IIIYQoh0sTdUFBQQCkpaVV+JgzZ84AXHJaICHE5d18881l012+/fbbfPTRR2g9eKTrORZL+WvpxXeOsnqduP0MZpP96S/VGrU8JSWsGMwGXt/4Ok/99RRFpqKy8ne3vMu2tG1ujEwIUZNJf1gIx8k6fZIV772Nolj3/wY//CR1o22fjHr55ZcZM2YMfn5+/PTTTzz88MOuCtUjFOUZKMozlLu9dZ8GjH65B8271rPqNxcfPMjRW0aR+fXXZWXZ3y8m9/ffnRqvs4T5hvH5tZ9zVaOrAHiu+3Nc2ejKKteXX2Li7q82cyK7yKr82tZRTB3aunZdgxiL3R2BEEIIIYRXcmmirlmzZgD8+OOPFT7mhx9+sDpWCFF1TzzxBAkJCUyZMsWjLxgLcw3sWpvCT9O3smbuvnL3i+8SWfZv/xA9LbrXw1gi01qKyztVcIrxv4xnwf4FNtuahjalrm/V1yYRQohLkf6wEI5hKCrk52mvUlxgvb5sr1Gjie/S3e4xKpWKzz//nI0bN3LDDTe4IkyPcXhLGgte2cjaeQdQFPsD4VQqFX6B+rLXisVC5ty5HL35FkoOHrTZP+/X35wWr7P5an2ZceUMPrjqA0a1GFXleoxmCxPnbWXPyVyr8o6NQ3nvto5o1J57zeVwhkL4ehj8+hxY7A+eFEIIIYQQ9rn0cZrrrruOTZs28fvvvzNr1iwmTZp0yWTBhx9+yG+//YZKpeL66693YaS1h06nc3cIwsEKCgoIKGe9CJVKRefOnV0cUeUc2ZrGr5/t5tz9g/TjGvobzWh1Gpt9g+r40nNEU6Jig6kfH4q6AhfC/sH6y+5TERfWo9frPf7/VZTS6/QU1Sti9K+jySmxnUJuZLORPNvtWXy1vm6ITpwjnynvIH2IqpH+sOeR97LnKu/7QLFYWPnBDDJSkq3Km3XvRY8Rt16yP+zj40O7du2cEq8nKs43sm7BAQ5vKX2KN3H7GQ4lpNK8a71LHmdMS+PU/56j4J9/bLap9Hoin3ySsDGjy8q88btbo9bQv1H/Kh+vKApTl+xm3cEzVuUxdf35fFwX/PS21y/u5rR2Mhvh+zshZVPpn4L00jXpNPL7tSq88fNUW0kfQgghhKO4NFE3atQovv32W5KSkvjoo4/47bffGDlyJO3bt6du3bqoVCrS09PZuXMnP/30E4cOHQIgLi6OUaOqPspNiNriyy+/5Pnnn+fvv/+maQXWrvNE9eJCuHCMr6HYTPKeTOKuiLC7f6frmlSq/lH/61qN6IQ3MwOf+KuZvfohFKxHkvtofHiu+3OMaDbCPcEJIWoN6Q8LUX3//bCAIwkbrMrCG8dw7YRHmfLUU6xevZq///67bKrZ2kxRFE4czLIq++u7g0S3qFPuALa81as59fxUzGfX1LyQT4sWNJj2Nr7NmzsjXIcrNhWjVWvRqh1/6+ODPw/z3ebjVmV1AvR8dVc36gb6lHNUDbXsUTh0wROWO78DjRZu/NB9MQkhhBBCeBGXJup0Oh2fffYZ48ePJyUlhcOHD/P222+Xu7+iKDRq1IjPPvvMK9bSEsJdFEVh6tSpvPbaawAMHjyY9evXU7eu503fZyg2cXRXOrEdItDZGWUaEOpDg/hQTh7KLitL2n6m3ESdEBWRqVbzdGRdNvip4aIkXeOgxsy4cgYt6rRwT3BCiFpF+sNCVM+hjev5b7H11NW+gUFc//CTjBl3Z9lUsaNGjWLZsmW1/nPjF6Sn320t+PWz3WVlDeJD7e5rKSwk9Y03yf7+e7vb64wfT8Tjj6H28Y4klMliYsq6KViwMK3fNPx1/g6r+4ctKUz/3Xo6UB+tms/v7EJMuP2nOWu02H6wcyFYTKWvfUOh5yS3hiSEEEII4U1cukYdQHR0NEuXLuWuu+4iKCgIRVHs/gkKCuLuu+9myZIlNGjQwNVh1hpms6znVRPMmDGjLEkHcPDgQYYPH05JSYkbo7KWuP0Mv3y6mzlT/uH3L/ZybFdGufvGd45E66OhWZdIBk1oR//Rnp1AMZvNpKamkpqaKp8pD7TdR88tDeuxwc/PZtvAxgP5buh3kqTzMPKZ8g7SNlUn/WHPIu9lz3Xx98GZ5KOs+nCG1T4qtZqhjz3Nk89NLUvSAfzyyy9MnDjR1SF7pPjOkTTtFIlPgJZr7mnNoAntbJ6mK9q1i6QRI+0m6bSRkTT+8guinnm63CSdp313K4rC6xtfZ23KWv5K+Yu7f72bjKLyrz8q49/D6Tz9w06rMpUK3rutI50ahznkHM7itHbqcBvc/h3o/EHrB3csgshWjqu/lvG0z5Mon7SPEEIIR3HL8EJ/f3+efvppHn/8cfbs2cOhQ4fIPjutRmhoKM2aNaNNmzbo9Y5ZS6qyTpw4wTfffMPatWs5ffo0er2eRo0aMWjQIEaPHo2fnZu9FVVUVMTff//Nv//+y+7du0lOTqawsJDAwEBiYmLo06cPt912GxERrnl6yCKLPLuE8cQJLEVFTqv/vvvuY+7cuezcef6CsVevXh41X/rONSmcOHB+2p3DW1KJ7xxpd9+WPevTsld9u0/ceSKz2UxKSgoAYWFhaDTeEbfHMptg/zJY/wEMmwn1qraOjAJ8GxzEjDqhmC5a/0mj0vB458cZ13rcJdeGEu4hnynvIH2I6pH+sPSHxeVd/H1wYt8ejCXFVvtcOe5emrS7ghdffJFffvmF9PR0ALRaLT179nR5zK6WdNPNmNLTMap90VqKKa9X00jjRzRqVAkFHL5om6WgAEt+vt3jgq65hnqvvIw27NIJKE/77v5i9xd8f/B80nFPxh4m/jGRBUMWVKvvt/90LhO+2YLJYj1DwwtDW3N920uv++cJnNpOza6BcUuhKAsad3dcvbWQp32eRPmkDyGEEMJR3DoPiF6vp2PHjnTs2NGdYVj5888/mTJlCvkXXKgUFRWRk5PD7t27+f777/n0009p0qRy62IB7N+/n9tvv53CwkKbbdnZ2Wzfvp3t27fz1Vdf8X//938MHjy4Wj+LcB9jWhqFGzdRsHEDhRs3YTx+/PIHVUNwcDArVqygR48enDp1ig8//JAJEyY49ZyV1axLpFWi7tiuDAzFJvS+tr+GdD5yIVJrbZ8Pa9+E7GOlr//7EEZ8UqWqDCpYEhhgk6QL1YbyzpXv0L2h3EAQQrif9IfPk/6wuJwrrhtCYJ26rPxgOsbiItr0H0jH64cBEB8fz9KlSxkwYAC+vr788MMPXH311W6O2PlM6emctDTgQNNbiT26guiTf9vd79xUOqYK1qvy96fec/8jZORIrxvUtOzIMt7b+p5VmU6tY0rXKdX+WV5bsY+8Euv/xXv7xHJX79hq1VtjNJL1wIUQQgghqqJ2T9h/kb179/L4449TXFyMv78/DzzwAN27d6e4uJiVK1eyaNEijh49yv33388PP/xAYGBgperPz88vuynRqVMnBgwYQNu2bQkNDSUzM5PffvuN77//nvz8fJ588kkCAgLo37+/M35U4WCmrCyrxJwhMdHlMURHR7NixQpOnDjh0ptaFkvpAvWHE9JQFIWrxtqf4iSuYwR/LTiIxaKgVqto0DyU4nyj3USdqMWyj59P0gHs+h6umlqlqnwUmJGWzq0N61GgLr091a3EzJiWr9AxwnNuiAshhCeR/rBwtm+ffYyC7KzL70jp9IVGY2lSZLNOW5Zk0fn4oNFoGHjvQ1aJl549e/L9998TFxdHmzZtHB+8hynMNbCj4c2kBrcG4EjTEdTN3INfcWa16vXt0J6Gb7+NvgrJeHdbf3I9L/z7glWZChVv9H2DzlGdq13/B7d34v5vEtiYVPp/PLhdPf43uJZN8agopXN9CiGEEEIIh5E75Bd47bXXKC4uRqvV8uWXX1qNbO7ZsydNmjRh2rRpHD16lDlz5vDwww9Xqn61Ws2gQYOYNGkS8fHxNtv79OlDv379mDRpEmazmVdffZV+/fp53QjG2sCcm0thQgIFG0oTcyUHDrg7JAA6dOhAhw4dXHa+04k5rPx4J0V5RgA0WjV9bm6G3s/2V4tfoJ6O1zYmqK4vTTtG4hvoOdNyCg/S9V74ZwaYzk5rZTHB9nlVrq6JycSrZzKYHBnOfdm53FusY6822EHBCiFEzSP9YeFsBdlZ5GdWfq0ww0WvA+vURWtnathhw4ZVMTLvk5GSX5akAzBrfNjfYgxX7JiFCuUSR5ZDrSZ8wgTCH5yAyoOm0K+o/Zn7mbx2MibF+om3KV2ncF3MdQ45R4i/jrn3dGPK9zs5mV3EjFFXoFbXot9PZw7C8sdg5GcQ0tDd0QghhBBC1BhOSdSdPHmy7N8XLnx/YXlVXFiXo+3cuZOEhAQAbrrpJrvTD9199938+OOPHDlyhLlz5zJhwoRKrQHWqVMnOnXqdMl9Bg4cyLXXXsuvv/5KcnIye/furRWjQSvi3PoL1aUNDyf2h8WVOsZSUEDh1q0UbtxIwYaNFO/dC5Wdi1ylKh19WE25ubnVrsNRQqP8KSk8fyFsNllI2nGGFj3q292/x/CmrgpNeCpFgWP/QuOeoLYzxWlAXbjiDkj4Ehp0hF6PQKsbIGFOlU85sLCIJSdOEWc0YfANr0bwQghRcdIftk/6w0I4TqPWdWiYtYUTYeefFNOaijBr9GjNJZWrTKOhyTff4N/J/bMO3Lr8VtKLKnfdZ1EsZBZlYsH6Gm1s67GMbT3WkeHho9Uw89YrKDSa8dXVoin7c07ANyMgNwW+uBbG/ggRLdwdlRBCCCFEjeCURN25tQBUKhV79+61Ka+Ki+tytNWrV5f9+6abbrK7j1qtZvjw4UyfPp3c3Fw2btxInz59HB5L9+7d+fXXXwFITk6WGxNnmdLTMaWmuuRcFoOBoq1bKdi4kcINGynatQtMFV3RoZQ6MBD/bt0I6N4N/x49OH7/Aw6JPzc3l6+++orx48dXu67LURSFM8l5hET642PnKTnfAB2NW9fh6K7zo6IPJaSVm6gTtZjZCHt+gvXvw+ldMOobaH2D/X17PwZtb4YmvSo0rc5xrZZPQ4OZmp6J7bj6UnHGyn1+hRCiuqQ/XD3SHxaXYzBc/Ixd7dQi7XfS/WKxaPQ0P7SIyLQtVOX5Lm3duh6RpANIL0onrTCt2vX4aHx4ssuTDojIllqtItCnFk1QVJQF395UmqSD0r+/vA7uXA712ro3NiGEEEKIGsApPUulnKeGyiv3BFu2bAHA39//kjcCunY9vzjy1q1bnXJj4sKLTo2mFo3Q8yCm06dJHn9XpY5R+fvj36kTAT2649+9B76tW6FyUvvdd999REdHM3DgQKfUn3Ein0MJqRxOSCPnTBEDxrakdW/7I/jju0RxdFcG4Y0CadYlivjOkU6JSXi5hWPh4Krzr9fPKj9RF9ak9M+FAu2/r/7Qq5gaqCZPrcJH68/zBeU/6aqgYFQHVTZyIYSoEukPV4/0h8XlpKdncOTIEZo2rfkzNphNFjRatd1tWouBdrs/xbckG70xr+onqYHTy4b4hKBW2f9/q4iNiRl0bByGvpz/+1rFUFA6Jf2F6sRBWIxbwhFCCCGEqGmckqh74403KlXuCY4cOQJA48aN0WrL/2+Ji4uzOcbRNm3aVPbv2nDh6Q6KyUTxvn1o69RB19B2bn1do0Zo69fHdOpUuXWo9Hr8OnY8m5jrjl/btqjsrJPhDCaTiR07djgtUbdpWRKJ28+UvT68Ja3cRF3cFRGMfrkHoVH+TonFG6hUKgICAsr+Lexoe5N1oi5lEyRvhMbdK3b8A+usXposJt7f+j5z9pyfEnOhn5qO177NkLghdqswGY0cP3KEAKSdPJ18pkRNIP3h6pH+sLgcRbFw4MCBGv/+OJSQyr+LDzPs4Q7UbRhod5/g/OMujspWTfvu/uvgGe7+ajM94ury8ZhOBPl635p99lS5nUKi4e5fYf4tcGIL1G0Gd3wPPvbfk6J6atrnSQghhBCX55RE3YgRIypV7m4lJSVkZWUBUK9evUvuGxISgr+/P4WFhZw+fdrhsezfv59160pvSDdv3twlF57lTRuj1WpRq0tHD1osFkyXmfpRf0GSymw2Yzaby91XpVJZrWdiMpmwXGLNt3NxOII5K4uDPXpiyc8n7MEHqfNg6doq5zrA535Wv65dyVu69PyBWi2+7dri1607QT174tfxCtQ+PmWxGwHs/F+q1epL3uyqLJVKxddff81tt92GwWBwSjvFXFHHKlGXsj+LojwDOj+1bTupwD9Mi8FgsPlZjUbjJZ8cuDB2RVEwGo2XjN1eO5WnKu+xysSu0WjKRvjrdLqym5aKotj9THna58nl7dRsMLrghqhyT5TWExaLqTALjMZKx55lyOLJdU+yNW2rzfa5e+ZydcOry0ZPSzuVz1M/T+fiv1RbSTu5J/aL20lcmvSHq076w/bVpN83jhAeHs7AgQOd1h8+x13tVJRn4N/vj5C0vXSK+dVf7+XGxzvg46u3aidHPaWrKAomk8kz+sOOevBYKf+zfal22nsqjwe/3YLJovDP4XRu/ng9X47vQsOw0sSJp32eXNZOuiC4/Qc0vz2Due9Tpa8NBq/4PJWnRrZTOaSdHBO79IeFEEI4Sy2aVL18BQUFZf/297/8U0F+fn4UFhZSWFjo0DgMBgPPPfdcWYfp8ccfd2j99uzfv7/cbS1atCAwsHSEXGFhIQcOHLhkXZ07n1/EPD09nZSUlHL3DQgIoGXLlmWvjx07RnZ2drn7R0Y6bjpFxWBAOdvRTV+7hhN9etOuXbuyjmtOTg6JiYloohuijYvD0qY15tatsTRvTqGvLzqdjnrt25fVd+rUKdLSyl9DITQ01KE3mEJDQwlq04Zdu3YBlW+n5rFtOLwljbyMYloMCLHbTmYU1Nrzs5sE1fEhN72YfMuZy7ZTo0aNyl7v27fvkp3uuLg4wsLCgNIO8bmfqTz22qk8Op2O9tVopyNHjlj9brhYdHQ0UVFRZa8vF7unfZ4c2k6KQnDaJiKO/szRTv+jdaeedtspMnoYYaf+4XTTUeTU6wkFGnT79lWqnY5znJmHZ5JRnGGzrXtId+6Kuos9u/eUlUk7lU8+T/ZJO1W8nS7VhxDeR/rD9snvG/uq+/vGEVQqdZX7w97QTse3FHNie8n5OJPz+W1+AteP7WrVTkajEUcMaTQajRw7dswjvr8vd9O+oi71vi+vncwWhUd/TafAcD6JcSA1n/nrE5kypN1l6z3Hk7+/q91OTe6H5GwgG/COz1N5anQ7XUTayT7pDwshhPAULk3Ubd68GSj9kvX19a3QMSUlJezcuROwXg/DkUpKzl8AXTjypjznOgjFxcUOjeOVV15h9+7dQOlo66uuusqh9Qtb6kOH7T4FB2Du3Rtz794OO5c2PBy4/IgujVaDRn1+hJbNkyShoZQ/9q18xbkWjqwtZEPqeqB0GYomXezfiNPoVES21KPX6+h2bSsimwShUqnIP3LG7v6i9qpz/DfqHfkOv7yjAIQfWw6detrdNy3uJtKajqrSeSyKhZXpK/kx9UcsWH9+NCoNd9S7g6vqXCVTwwghPJ70hy9N+sNClGrYwYeMRCPF2ef7PYWZ55+gUxQFw44dqC5xQ1lUnkat4rHuobz+TxY5JaX/970b+XJ3T9vlErxFncUjoTiz7HU7o/3r33O0a7RwdnYKf8VCu7NPLxl96rC/3yfOC1QIIYQQohZTKS5c0b5ly5ao1WqWLl1KfHx8hY5JTk7m2muvRa1Ws3fvXqfElZmZSc+epTeWBw8ezLvvvnvJ/Xv16kVGRgbNmzdn2bJlDolh9uzZzJgxAyi9cTN37twKjWauCoPBUDZiKT4+Hj8/P7v7edpUCklXD8SUmnrJGCpLFxtLvfdmEtCsmdunUlAUhVmzZnH77bfTsGFDTCYTx44dw2Kx0LBhw3KnC6poO5kMZr59bhPGkvPt0ueWeFr1LX96K5nyomJTXphMJpKSkgDKbStP+zw5qp00P92LZt/PZa+VwHrw2E5UWh/AMe2Ua8jlhf9e4O+Tf9scX8+/Hm/1eYu2ddvarV/aqXye+nmC0lHBJ06UTpNqr62kndwT+8XtlJOTw+HDhwHrkc7i0qQ/XD7pD9eO3zezH7yT/EzbJ+MrKyCsDne//zlQc9spNSmXZTN3ovfX0vuWpsR1DEevL5368uRzz5Hzw4+X/DkrQxMZSdyff3hEf3jQkkGkFZX/BExFRfpFsmr4KrvbLtdOyZmF3PfNNsID9cy5szP+vnqP/DzZi90mllntUeWVv/Z6RSlB9TE+vNOm3Fs+T/Z4UjvJdUv5PLWdQPrDQgghHMflU19WNS/ozHziuUV6gQpN31NUVARUbFqgivjuu+/KbkrExcXx6aefOu2mxMW0Wm2FOhJqtbpSHY7Kztvt6DUrLkUdGEi9F1/Ev3s3dHam1Kzsz1rZ2O2NUjcYDEyYMIGvvvqKhQsXsnbtWnQ6Xdn0Ek2aNKlQTIYic2n8frYx6fUQ0z6cQ5vPJzuPbDtDh6sbVzh2R/ys5VGpVJX6f3dHO5XHYrGQm5sLVKytPO3zVK126vMoXJCoU+Wfhj1LoMOtQPXbaU/GHp5Y+wQn8k/Y7NunYR/e6PMGob6hFaq7VrfTZXjS5+lc/RVtK2mn8rminUTVSH/YlvSHa9fvG0co72fytna68HN98c/UqEU4V49vTaNWdfAPtv6ZAnr1cmiiTqVS2fxsbusPO2qCBBUVfi9c/LPG19Pz40O9UatUBPpbb/O0z9Pl28kx/6EqLv9zu/vzdCHva6fz5LqlfJ7UTlWpXwghhCiPI6azd6pzI1+cuVirj48PoaGhAJw+ffqS++bk5JTdvKhXr/wnkSpq+fLlvPzyy0DpSKk5c+ZQp06datcryqcOCCBk2FC7STp3yM7OZvDgwXz11VcAJCQkMHr06EuOcLuQocjEgQ2nWP7hDuY89Q/71pc/WrJZ19K51H38tbTqVZ+ug2OrHb+oBdIPlb+tYWdocnaK2PAWcMMsaH1jtU+pKAqLDixi7MqxNkk6FSomXTGJD6/+sMJJOiGE8GbSHxai5lEUhUMJqfw0fSsmo/1+v2Iy0bxrpE2SDiD4mmvQnJ1aXzhHWICeEP/K3bQXQgghhBCiKjx+6MfJkycByhbJdZb4+HgSEhJITk7GZDKVOyrmwkVrq7sg+h9//MHTTz+NxWIhIiKCr776yiE3O4R3KSkp4ciRI1Zly5YtY/369WU3zC7l7+8Psf+C5NyhhFQ6XN3I7r6NW9VhyEPtadS6Dhqtx+fphTtZLHBwFayfBcn/wcRNENHC/r5XvwDFuRA/ENSOeV/N2zePtza/ZVNex7cOb/Z9k54N7K+DJ4QQNZH0h0VNolxiCrLaojDXwLoFB0jcVrr+86ZlSfQaeX4qXGNaGtmLF5O9cBEN3nidgF69bOpQ6fWE3nIzGZ9+BhUc4CdsHcsooEndgMvvKIQQQgghhBM5NVF37qbCxc6cOXPZqWwMBgPJycm89957qFQqmjVr5owQy3Tu3JmEhAQKCwvZs2cPHTp0sLvf5s2by/7dqVOnKp/vv//+47HHHsNkMhEaGsqcOXNo3LjiUxCKmiMqKoqVK1fSq1cvsrOz8ff3Z8GCBfTt27ds7ZRLadoxwipRl5qUS256EcHhtmutaHRqYtrLyFtxGYoCX14HKZvOl/33QenTcvY07uHwEIY1HcY3e7/hZMH575GOkR2Z1m8aUQFRDj+fEEI4i/SHyyf94dqpuCDf3SG43R9f7SV5b2bZ6+2/JxPbIZyQzENkLVhA7m+/w9k1mDLnz7ebqAMIf+ABcn78yeHreNcWa/ancd/cBB4b2IyJA+LL1sASQgghhBDC1ZyaqLv66qttyhRF4e677650XTfeWP2p1C5l4MCBzJ49G4AffvjB7o0Ji8XCkiVLAAgODqZ79+5VOtfWrVt56KGHMBgMBAUF8cUXXzj9xkuN4MR1WdytVatWLFmyhHHjxvHDDz/QpUsXDAYDFrNCToqJhORj9Bph/z3SqFUdfAK0lBSUXsxrtGrOHM+zm6gTokJUKojtZ52o2/EdDHgeglyTJAvxCWH6ldMZt2ocRouRca3H8Vjnx9CpZfohIYR3kf6wfdIfrr18/AMozMl2dxhu1eumeFIObsZiKr2+URTY9sKnxG6ZY7Nv/p9rMJ46ha5+fZttal9fp8daU+1KyWHi/K2YLArv/HaQE9lF/N+NbdFqZNYRIYQQQgjhek7thSqKYvWnvPJL/dHr9dxzzz3cfPPNzgyV9u3b06VLF6D0xsS2bdts9vnyyy/LpigcN26czSKzGzdupEWLFrRo0YJnnnnG7nn27dvHAw88QGFhIf7+/syePZu2bds6+KepeYypaZiystwdhlP179+fgwcP0qVLF4oLjKybd5At3+Zy4LdCtv16nJwzhXaP02jVNOsSRZN2dRk4vhV3T+tD046esf6e8GLd7gfNBeuh+IdD5pHy93eCtuFtmdpjKu9e+S5Tuk6RJJ0QwitJf9iW9IdrN7UT11r0FnUbBtKpRzAAOmM+bfZ8QYydJB0AFgt5f/7pwujcK9eQS3ZJtlPPcTyzkLu+2kyh4fyUoQs2HWfJdvtPQAshhBBCCOFsTn2i7o033rB6/eyzz6JSqXj00UeJiir/qQyVSoVerycyMpJWrVoREOCaOeOfe+45br/9doqLi7n77ruZMGEC3bt3p7i4mJUrV7Jw4UIAYmJiuOuuuypdf3JyMvfccw+5ubkAPProowQFBXHw4MFyj6lbty5169at2g9UQxTt2EHKpIfBaHR3KNVisVj477//6N27d7n7+Pj4AKD31ZC8Jwuz4fy2QwlpdBkUY/e4frc1l6laRMUpCiRvgN0/wKC37a8rFxQF7W+Fk9ug18PQZiRo9bb7VdPpgtP4anwJ9Q21u31EsxEOP6cQQriS9IetSX+4dsvPz8do8O4+fXUoRiN5f/xJ1vz5BG5OoEnMUBql/IneaDsdqMrXl+ChQwi77Xb82rYpt05tuGOmtXdUPdVxIPMAj615DMOFF0EOllNoZPycTaTnl1iVj+jYkJs6NXTaeYUQQgghhLgUpybqRoywvsH67LPPAqXT6sTHx9s7xK1at27Nu+++y5QpU8jPz2fGjBk2+8TExPDpp58SGBhY6foTEhLIyMgoe33xjRt7Jk2axMMPP1zpc1WUpyd3spcs4fQLL6IYnHex5gpFRUWMHTuWn376iZ9//pmhQ4eiWBTMJgtave2oYrVGTdwV4ez95/zac4cvkajz9Has6dRqNZGRkWX/9lgWC+z7GdZ/ACcSSsuaXQPNr7O//6C3QOdfOhWmE6w/sZ5n/n6G1uGt+ejqj1CrnPt/5zXtJKStvIR891SM9IetSX+4dlAsFnavXU3rfgPQaEufujx16hRDhw5laHQdgn193ByhcymKwuGENM4cz6PXyHiMqWlkf/892YsWYUpLA0qntmmatNTmWF2TxoTdfjuhI0agCQm57Llif1js6PCrpLrf3UuPLOWV/16hxFxy+Z2rqMRk5r5vEjhypsCqvGdcXd66qb38LhAeQ/rC3kN+bwghhHAUpybqLjZ37lwAoqOjXXnaSrnqqqtYunQpc+fOZe3ataSmpqLT6WjcuDHXX389Y8aMwc+v5qz9pdW69C1QYYrJRNo708n86it3h1JtZ86c4YYbbmDDhg0APPHQ8xiOhZKXoqJ1nwZ0HRJr97jmXeuVJerqNw0hvkskikVBpZaOoKfRarU0atTI3WFcnkoF696GtL3ny9bPKj9Rp3fO0xtmi5lPd37Kxzs+RkHh3xP/8unOT5nQYYJTzneO17STkLbyEp7ah/B00h/2PPJedqzC3BxWfTiDo9u3kJ58lAHj72fPnj0MHjyY5ORk+tbpjVqtJiIiEk011gMLCA1zYNSOU5hrYN2CAyRuOwOA75qF+P25AMzm8g9SqwkcMICw228noFdPVF54Y76q390Gs4G3N7/NwgMLnRDVeRaLwhOLdrApKdOqvHlUIJ+M7Yxe633/5zYKz/5s/nXcG4eoNukLew/pQwghhHAUlXLhYhmiVjAYDOzatQuAdu3aodc7fjq76jDn5HDi8ckUrF/vlPq1UVE0W7fWKXXbM3PmTB5//HEAhnQZz6DOY8u2hdUP4I4Xu9s9zmJR2L0uhdgOEQTVkYXihYNsmwc/P2Rddt8aaNjJJafPKs7imb+fYf1J68+3ChWfX/s53ep3c0kcQojq8/T+hBCXIu9f5zixfy/L33uL/MzzT03eMPl/vPrR7LIk9TnXXXcdv/zyi6tDdCqz2cK3z68nP+v8bCA+xZl03/waWnOxzf6aOnUIveUWwkbdgq5h7Zt28XTBaSavncyu9F1OqT/SP5I/bvkDgDdW7WP2ukSr7VHBPvz0UG8ahHrxwINTO+HASjj0O5zYAlc9D/2etN5neivIc8D6e0EN4Il91a9HiBpE+hNCCCEcxaVDP/Ly8vj6668BGDVqVNmj/OVJS0tj0aJFANx99934+/s7PUbhXiWHD3N84kSMx5Jttmnq1EGl0wLVe6LM1esvPProo2zbto25c+ey7/hmq0Rd1qkCMk7kU7eh7dRRarWK9gNkFJ2ogsLM8kfStrsZ/ngF8k9DSGPo+RCEN3NJWDvO7OCJtU+QWphqs21AowG0rNvSJXEIIYQ7SX9Y1ESKxcLmZT/yz3dzUSwWq22/fTqL92Z8wP79+9m0aRMAcXFxvP/++y6PM+mmmzGlp1e7Hm14uM2Uk8UHD5K1YAENt53hQMzIsvIS3zqkRnam4al/y8r8OnUi7PbbCbruWtS19KbuhlMbeGrdU2SVZNlsC/MJQ6fRVfsc4X6l131z/ztqk6QL0Gv4cnxX707SAWz7BjZ9ev714T9sE3VCCCGEEMLjuTRRt3TpUj744AOaNGnCpEmTLrt/REQEy5YtIzk5maioKG655RYXRFm7GI1Gjxnxk/fnGk5OmYKlwHrNAHQ66r/0IqE33eSewCohO7WQY3syaD8gumyucpVKxWeffcbx48c5ciQRn0A1Jfnnb2Ac35dpN1FnNBrZt690xGKrVq3Q6ap/sSqcwyPaSlEgaV3p+nMpm+DxPeATZLuf1geueQU0Omh1A2ic/zWgKArz98/nnc3vYFJMVts0Kg2PdXqMO9vc6fT5/T2inUSFSFt5B6PR6O4QvJL0hz2PJ/WHvVFRXi6/fPQuiVs322wLCA1j8MNTCK1bl2XLltGjRw+ioqJYunQpERERl63b0d8HpvR0TKm2A4aqSjEYyFu9msz58ylK2AJAA1SkhrQhO6wFOkMeLQ4tJPLMNlR+foQMG0bY7bfh26qVw2LwFJVtq8UHF9sk6Xw1vrzQ8wWGNR3msLh+35vKS0v3WJVp1So+HtOZNg0uvwag25lNkHsCwprY3x4/0DpRd3wjFOeArxf8bKJc0hf2HtIfFkII4SguTdT9/fffqFQqBg0aVKH9VSoVQ4YM4aOPPmLNmjVyY6KGUhSFjNmzOfPe+6XJhgtowsOJfv99/Dt1dFN0l2c2Wdjxx3EOJaSSfjwfgAbxoUQ0Pp8k0ev1/PjjjxQVFZH4bz5JO9KJ7xJJsy5R1Glgfx0wRVHKOn0yQ61nc3tbmUrgi2vh1PbzZVu/KX1azp4Ot7okLIB8Qz4vrn+R3479ZrMtwi+Caf2n0Tmqs0ticXs7iQqTthI1mfSHRU1y8uB+ls98i7yMMzbbGrdtz+CHp5StJRcZGcmff/5JVFRUhdc49PTvg6zvviP19TesylQotDowj6SYIcQf+ZHA6AjC7vsfISOGowmyM4iqhqhsW73U8yUOZB7gaO5RABoFNeLdK9+lRZ0WDotp+/FsHl6wFctF4bw+sh39ml8+Uew2hZmwf3npdJaJ6yAwAh7eYn/fmD6g8QFzSelrjR5S90KTnq6LVzicp//uE0IIIYTjuTRRd25EUMeOFU+6XHHFFQDs37/fGSEJN7MUFnLyf8+RZ2d9Ct+2bYn+YBa6evXcEFnFqTUqdv91gryM8+tOHN6SapWoAwgNDSU0NJSIG830uine6U8PiVpE6wMh0daJug0fQbf7XfLEXHkOZh3kibVPlN2AuVC3et14q99bZVMSCSFEbSH9YVETKIrCluU/8feCr7GYzdYbVSp63nQbPW66DbVaY7UpJibGdUE6iQKcCm6DdscZGg0bRto701EMBqt9/IzZdG90irCn38e/Rw/p99sRqA9k5oCZ3L7idrrX685rfV8jWB/ssPqTMwq556vNFButp2J99OpmjOri4csLZB2FpQ+ff12SA5lJUCfWdl99AFxxB+j8odlAaNwLdLK+uRBCCCGEt3HpHdyMjNJFxS+3FseFzk2Jku6AtQSEZ1GMRo6NGUvx3r0224JvGEb9V15B7es5FxmKoti9yFapVDRuF8qetafLyg4lpNFjeFO7+2t1GpsyIaqt18OlI2/PMeRD+kGIau2WcJYdWcYr/71CsbnYZtt97e7joSseQqt2XxJRCCHcRfrDwtsV5+fzy8fvciRho822IqOJgvox9LpltBsiu7wNMfdSElf1NckU1Ji0flg0eg5/u5/bX+hO8ODB5CxZApTOBhI26hZCR43y+MGGnqBpaFPmD55PXGgcapXaYfVmFRgYP2cTGQXWCdSbO0fz2EDXrM18WVnHILih/UF19a8A/3AovOB3/uHV0O0++3UNm+mMCIUQQgghhAu59C6pj48PhYWFFBUVVfiYc/tqNJLcqGlUOh1Bg663TtSp1UQ+8QR17r7LI0aeFuUbSNx2hkMJaUQ2CaLXyHibfVJSUnjlvSe5qUPpot1GpZgmbRpiMljQ+cj7VjjIqR1wbD30eND+9kbdIborFGZAz4nQ4Q7Q+7s2RqDEXMKbm95k8cHFNtuC9EG80ecN+jfq7/K4hBDCU0h/WHizU4cPsHzmW+SeSbPZlpydx5y/NpJXXELDVm0rtAajq5VoAynROeapraI8I399d5B+d9yOMSWFsDtuJ2jgQFSy3mEZo9nIxzs+5o5Wd5Q7i0J8mO31VXUUG83cOzeBxHTrdc/7NgvnjZHt3HeNqShw5M/ShNvh1aUD6u75HRp1s91XrYb4q2HnwvNl6QddF6sQQgghhHA5lybqIiIiOHbsGLt376ZTp04VOmb37t0AhIfL9Gg1Ud1776Vk/wFyV6xAHRREwxnTCezb191hAbBv/UnWfHsA5eyiBjlphfQc3hSV+vzFnaIo3HDDDWzbto1ASwMOnNjK4ZM7eD3udfr7PO2u0EVNoSilF/Lr34ekvwAVNLsW6ja13Velglu/hYAIULvvRq4aNQczbW8ktK7bmun9pxMdFO2GqIQQwnNIf1h4q8MJG1k24w0sZpP1BpWKf44ks3TLLixn11J69NFHiY2NZciQIW6I1HUOb0mj/VWdafLtN+4OxeOkFqTyxLon2HFmB1vTtvLZtZ+hU+ucek6LReHxhdvZcizLqrxlvSA+Gt0JncZxT+1VmkoFvz4HZ/adLzu82n6iDqDVsNJ1qOMHlv4Jrl/1cweef4Jb4fzaZzqdDhWVSFwGVvxJcCGEEEIIUTkuTdR16dKFo0ePMn/+fG6//XZ0ukt31I1GI/Pnz0elUtG5c2cXRSlcSaVSUf/V/wMgfNJEfGLtzLvvJpFNgsuSdAD5WSWcTsqlftOQsjKVSsVHH33EgAEDWLbpC6D0Blz//vLEkHAAQz4svhtKcs8WKKVrzw2Zbn//IPdPsaTT6Hin/zvcsvwWckpyABjVfBRPdXsKH42Pm6MTQgj3k/6w8FYNmrfEPzSU/Izz0/H5BQUzeNIThO/YzZLx48vKW7VqRdu2bd0Qpev4Benod1sLq2sDUSohNYFn1z9LZnEmAFtStzBzy0ymdJ3i1PO+tnIfq3aftiqrH+LLV3d1I8jXuUlCAAwFpX/KS2jFX22dqDv0Owz4n/19Ww0r/eMID6wr+6fRYGDXrl0AtGvXDr08ASqEEEII4RFcOqRs5MiRABw7downnnjiklP+FBUV8cQTT3D06FGrY4V3Ukymcrep/fxoOP0dlyfpjAYzR7alWSXjLlSnQQBh9aynDjyyxXaanx49ejBv3jxUKhXNmzfnv//+o0ePHk6JWdQyPkHQebx12bZ5UJjplnAqqn5gfd7s+yb+Wn9e7zl/OZwAAHgmSURBVPM6U3tOlSSdEEKcJf1h4a38g0MY+shTqNSll5ANW7Zh7NvvE3NFZ+68805eeuklAK6++mr+/fdfmjRp4sZonUttMXL7C92J7yxPGF1IURRWnlnJQ2seKkvSnbPwwEJO5Z9y2rm//CeJL/5JsioL8tEy566u1Atx4rrnmYmwfhbMvRHeioE1r5W/b7NrrF/nHAdDofNiE0IIIYQQXsOlT9R16tSJIUOGsGLFCn7//Xd27drFLbfcQpcuXYiIiADgzJkzbN68mcWLF3P69GlUKhXXXXcd3bqVMyWEqBZXrHVSvHcvJx6fTP033sC/U0enn+9SFEUhaUc6h7ekkbQzHVOJmRFPdKRBszCbfVUqFfFdotj26zGatAunWZdImrSta7fekSNH8t133zFw4EDq1KnjkFi1Wi1xcXFl/xaeq9ptZTKAtpzRrN0nlD5FZzFBQCR0u99hU1veuvxW0ovSL7/jZYT7hbNw6EKrsj4N+/DrTb8S6hta7fodRT5T3kPayjvIemlVI/1hzyPv5Ypr2LI1/e4YT1FeLr1vHYv6gv+7F154gUaNGjFmzBiHPaXjqd8HOnMRfkHyJNKFii3FzMmcw1+pf9lsaxjYkJkDZlI/sBrTN17CL7tP8X8r9lqV6TQqZo/tTMt6jlmTsFz7lsPvU8+/PvxH6fT19tbCa9wTYvpCbL/Sp+vqX+HyKes99TMlrEk7eQ/pQwghhHAUlaIo9h8ncpKSkhIefPBB1q9ff8mFnM+F1bt3bz766CN8fORpDEcxuHC6i9yVKzn5v+dQiovRhIcTu/h7dPXcOz3fd/+3iYwT+WWv2/ZrSP87Wtjdt6TQiEqtQu+rRVEU9y0+LmqmlC2l689lJsIDf9m/oAf48zUIbQTtRoHOcSOCr/7+atIKbZ8SraxI/0j+uOUPB0QkhPAmruxP1DTSH3Y/ef+Wr6SwEMViwTcw0Gabt/eHP79nCSW66idufIy53PvF8OoHVEMczjrM42sf52juUZtt/aL78Xqf1wnxcc4UoVuOZXHHZxsoMVmsymeM6sDITg5YG1lRIDsZwsp5QjR1L3zc07ps4iaIsH99KYSoWaQ/IYQQwlFcvpqyj48PX3zxBf/73/+IiopCURS7f+rXr8/zzz/P559/LjclvFT+unWcmPwESnExAOb0dFImPYzl7Gt3ie9iPUXNkW1pWMwWu/v6+OvQ+2rJyMjg2muvZf369a4IUdR0+Wfgy0Hw+VWwdwmc3gmJa8rf/6rnoNM4hybphBBCuI/0h4WnSk06wrfPPMqqD6ejWKz7x/v376dfv34cP37cTdFVj/HUKbDY7/OLqvsl6RfuWHmHTZJOhYqJV0xk1lWznJakS0ov4N6vN9sk6Z68tnn1knSGQtj9Iyx5CKa3gI97lc6AYU9kKwhqcP61Wgund1X93EIIIYQQolZyyzP0KpWKcePGMXbsWPbt28fevXvJysoCICwsjDZt2tCyZUuvHq3pLZz5QGVA794E9OpFwQXJLZWPD5aiItS+jk84WCwKpw5nczghDb2flp4jmtrdr1mXSDb+nFgajwrqNgykKN9IQIj9G2BHjhxh0KBBHDp0iG3btrFhwwbi4+MdHv/FFEXBaDQCoNPp5PPgwSrdVv51oeCiJ9nWz4KmVzkpQucqMpW/vpInkc+U95C28g4unpShxpH+sOeQ93Lp/8HO1atY8/VnmI1GslNPsXnZj3S78WYA1q1bx/Dhw8nOzmbIkCH8888/BAc7eUpBHPN9YM7LI+PTz8icOxc6Tb38AaJCjBYjMxJm8O2+b222hehDeKvfW/Ru2Ntp5zdbFB74JoGsQqNV+W1dGzFxQDWv1Qz5sPgu67LjG0qnrLyYSgVX3AEFZ0rXoIvtB77OSUw6gvSxvIO0k/eQPoQQQghHcetk1yqVitatW9O6dWt3hlGrmUwmp43QVmm1NHx3BkmjRmE8lkzoqFHUe/45VE6YCuD4/kxWz9lLYU7pSEefAC3dbohFo7F9aDQkwp82fRtQt2EgTTtF4h9cfjyHDh2iV69epKeXruOVkZHBoEGD+O+//wgPD3f4z3Eho9EoUyh4iUq3lVoNPSfB8sfOlx3fXPqkXWCE8wJ1EoPZ4BVTYclnyntIW3kHk8nk7hBqBOkPu58z+8PewFBUyG+ffsCB9dZri/3z3VwatGjFnmMp3HDDDRgMpf3sc+sqLl++HJ1O59TYqvN9oBgMZH23kPSPPsKcne2kCGunM4VneHLdk2xN22qzLcY3hlnXzCKmToxTY9CoVbw6vB33zU0gp6g0odG/eQSvDm9bsT5p/hnQ+4M+wHZbYGTp+nGntp8vO7zafqIO4GrvSQBLH8s7SDt5D+kPCyGEcBRZlVY4lSYkhEYffUTh5gTCbrvVaecJCfcrS9IBlBSYSNmXRZO2de3uf+XolhWqNzY2lm7durFy5cqysqKiItLS0pyeqBNeLv0wZByCFoPsb+9wG/z5Kmj00GMCdLoT/EJdGqKjhPiEeHySTgghhBC20o4msnzmm2SdOmmzLSounuC6EXSNrE9sbCwHDhwo25aZmUlubi5169rva7uToijk/fobae/OwHgs2d3h1DjHco8x/pfxpBel22zrF9aPMfXH0CCwgZ0jHa9bbB1+eLAn4+dsJsRPx4ejO6G1M1CzTEoCHPylNOl2chvc9AW0u9n+vvEDrRN1afsdGrsQQgghhBAXkkSdqDZFUTBnZ6MNC7O73adpU3ya2p+GsjLnyDxZQGikPxqd7cVXcLgfUbHBpCbllpUd3pJabqKuorRaLQsXLqRfv35s27aNDh06sGLFCho2bFitekUNpSiojm+ATZ/AgZWlibfH95aO1r2Yzg/uXAp1m4FWRkgKIYQQwnUURWHXn7+yZs6nmIy2a291HnIjfe8Yj0Zb+sTcypUr6dGjB2fOnOHGG29k3rx5BATYeRLJzQq3biXtrbcp2rHD7nbF9Uu01zgNAxsSGxJrlajTq/U83eVpmhZW75qvKuIjg/jxoV6gQKDPZW5vrHsLDv12/vXhP8pP1DW/Hk7tKJ3OMn4g1IlzXNBCCCGEEEJcxG2JOkVR2LdvH/v37ycrK4vi4uLLzu08adIkF0UnKspSXMypF16gaPsOYhctRBMa6tD6M08VcDghlcNb0sg6Xcjgh9oT297+k2zxnSNJTcqlbsMA4jtHEd8l0iExBAYGsnz5cqZOncrMmTMJCgpySL2i5vEpPIlu+djzBUVZsH0edLvP/gFRbVwTmBBCCI8k/WHhDobiIlZ//hH7/l5js83HP4DrHnqMZl17WpXHxcWxbNkyFi9ezJtvvolGo3FVuBVSkpjEmXdnkPf76nL3yb56PAaT89fVq+m0ai1v93ubW5fdSlpRGg0CGjBjwAyaBTUrm6rP1SKDzq5/bjZCYQYE1bO/Y/zAixJ1q8FiKZ2W/mKNusKYxY4PVgghhBBCCDvckqj76aef+OCDDzh50naKlUuRGxOexZiaSsqkhyk+e0F2YvJkGn36KSqt495Wa7/dz6kjOWWvDyeklpuoa9mjPo1b16VOA8eP7m3QoAFffPGFw+sVNUtJQEMsTQeiPnLBTaL/PoQud4Pas25oCSGEcC/pDwt3SE8+yrJ33yTzZIrNtqi4Zgx7/GlCIu0nObp370737t2dHWKlmDIySP/wQ7IWLgKz2e4+2o7dONzpXhKPGEFm6naIcL9wpl85nS92fcH/9f4/Qn1Dy9YwdJa0vOLzCbkL5aWWzmRxeDUkroNG3WDsj/YriR9o/dpQANnHoE6s4wMWQgghhBCiElyeqHv33Xf59NNPLztaGEClUlVoP+F6hdu2kfLII5jPnJ/ypGD9f6RNe4eoZ59x2Hniu0RZJeqSdqRjMpjR6m2THr6BOnwDq7ag/alTp5g9ezYvvPACansjKoW4kKJAOWuymbtPPJ+o8wmB1jeCqdj+QvVOZFEsnCo4RVJOEonZiSTllv6tUqn46vqvXBqLEEIIa9IfFpX17bOPUZCdVa06jCUllBQWlPZjLtJx0DBC23ZmwY9LmDBhQrXO4wqWoiIyv/qKjM8+x1JYaHcfXZPGRE5+gpzGnUl8z/5UmFWlqiXXC4XGQvx1dqZwB66IvIJZV89ySRybj2Yy9ouNTLmuJff0uSiplrwelj92/vWxf8FYVDrN/MXqNi2d0jK8eemUlo16yBT0QgghhBDCI7g0Ubdjxw5mz56NSqWid+/eTJkyBUVRGDFiBCqVir1795Kdnc3u3btZsGABf/75J507d+a9994jPNz+U1TC9bJ/+JHTL72EYjRalav9/fHv1rXC9RRkl3B4SxolRSa6DbU/irFppwj+WXSw7H6CT4CWnDNF1G0YWOX4L7Znzx4GDx5McnIyJpOJV1991WF1ixombR/89wFYzDDiE7u7KE16Q8uhENMHOo4BH+dOlWowGziWe4zEnEQScxJJykkiKSeJozlHKTYX2+yvVWsxWUxo1bJEqRBCuIP0h0VVFGRnkZ+Z4fB69X7+XPfgoxzJzKX/gAHk5+dTt25dbrnlFoefyxEUs5mcn37izPuzMKWl2d1HExZG+EMPEXbrKFR6PcFA+6ui2fmn7VOEVaWpW711sD2dyWJi5paZrEtZx/wh8wnSu2/q/yNn8nnuq18oNobwf8v3kpJVyPNDWqNRnx00F3clqNSgWM4GXwxH/4VmA+1XeMdCl8QthBBCCCFEZbj0Tu2CBQuA0mkEZ8+ejVar5dChQ2XbVSoVYWFh9O3bl759+zJ//nxeeeUV7r33XhYtWoReL6Pd3EkxmUh9+22y5n5js03XuDGNPvwAn2bNLltPxsl8/lpwkJOHs0EBrV5Nx2sao/OxfUouIMSH+C5R+AXqaNY1iqiYYFRqx81Z8+effzJixAhyc3MBeO2114iNjeWee+5x2DlEDZB1FFZOOb+mhUoDVz4LYU1s91Wp4LZ5Tg1n8+nNfL3na5JykkjJT8Fy7sZEBZgsJlLyUogJiXFegEIIIcol/WHhKSJjmzLssWdY+PNSJk6ciPns1JFjx46lYcOG9OrVy80RXkBRKPj7b7JmvkfJBZ+XC6l8fKhz553Uve9eNBetKd1zRFNS9meReaoA5AHVS0ovSmfKuikkpCYA8PxXPZiZZ7nsrKE6FNqdHcipW6ujbJ7RwEh4YF3lgjCb4MiflBz4Df3W5fzGKbryIWcIY86/R2keFcTt3RqX7usXBtFd4fjG88en7io/USeEEEIIIYQHcmmibtu2bahUKsaOHYu2AuuY3XHHHWzYsIHff/+d+fPnM378eOcHWctUpB0ATFlZnHh8MoUbNthsC+jVk4YzZqAJDa1QXX6B+tLpLM9eJJsMFo7uSqdZlyi7+197T5sK1VsVJSUlFBQUWJW98847jB071u03wnQ6He3atSv7t3Aj35DSkbnnKGbY+Alc/wbguLZSFIXTBadLn4jLPcrtLW9HZWeKzXxDPutSKnnD4wJJOUm1MlEnnynvIW3lHSrahxDWpD/seWrje/mK64bQf+y9aHU6cnNzy5J0UNo/njFjhkck6nQ6Hc01WjJmzOD0pk32d1KpCBk+nIhHH0FXz/76elqdhuvvb8uSGdsozHXuWmrebFvaNp5Y+wRnis6Ulf3po+bLghzuycm75LEqoOzqyXZSh0pS4Id78CnJpdHZkv6anSw29+fqlpHc0jnaevc2IyGkUel0lk2vKk0OCrukj+UdpJ28R23sQwghhHAOl36jpJ2dnqTZBU9dXbgemNFotOmE3Hjjjfz222+sWrVKbkw4gb0kwMWKDx4kZeIkjMeP22yrc+edRE55EtVFnZOSIhMajcruWnL+wXqiW4RyfN/5dTYOJ6SVm6hzpkGDBvHRRx/xwAMPANCzZ09+/vlntyfpoLRtPCEOQelI3U5jS5Nz5+xcCANfAq1PpdvKaDaSnJdcNlVlYk4iidmJHM09SpGpqGy/qxtfTVSA7eciLjSuwucK0gURGxpLbHAscaFxxAbHckXkFRU+viaRz5T3kLbyDhXpQwhb0h/2PLXrvaxi6GNP0aJn37KSJ598kqSkJD7++GMARo8ezRdffOGuAK0oBgOnHnoIc2am3e0BffoQ+eQTKA3j+GPBAboOCSx3ivywegEEhPo4ZHYO/+Ca9R2lKArz9s1jesJ0TIrJaptOUQixVHz2hgorySudTt4v1HabRgdx/WHfsrKiKzU7OVj/Bmbd0RGt5qI1AntMADx/bUVPIH0s7yDt5D1qVx9CCCGEM7k0UWcylXb6614wp7+///nFqTMzM4mKsr4pXe/sqMhjx465IEJxsdzff+fk08+gXLRIu0qno94rrxA6YnhZmaHYxNFd6RxOSOPYngyuGtuKFt3tj2qN7xLF8X1Z6Hw1xHWIoHk31yfpzrn//vtJTEzkyJEjzJ07Fz8/OwuPi5ovJwUK0qHBFfa393gQNn1a+nRd1/ug232g9blklSXmEg5kHrBKyB3NOcrxvOOYFfMljwVIzEm0m6hrGNiwbK25cyL9I4kLiSMuJI7YkNiyv8P9wuXiQQghPIj0h4U7BYSFWSXpoPQm4/vvv09ycjIdO3bklVde8Zi+g9rHh/AJE0h9/XWrcp+WLYmc8iSBvXtzbHcGf/zfJopyDWSnFnLzM13Q6mwHCwKM+l/F19OuLQqNhby0/iVWHV1ls62eycS7qem0NTjoKcQzB+DAKji8GpL/g6umQp/H7O8bP9AqUXdV2Bm6j+uCv16eXhFCCCGEEDWPS3u5derUIS0tjfz8/LKyunXrotFosFgsJCYm2tyYOHOmdNqNi6cnFI6x6I1NmIrsXYgrWPIL0GadpOtFSTptRATRH8zCr0MHq/LVc/aStCO97PXhLWnlJurirojAN0BH4zZ1yr2QdqXXz178Xzii3d0sFgs5OTkAhISEeFRsNcqpHbD+A9jzI0S1gfvXla4zd7GwGLjje2jSC/Tnb6gqioLZYiYvt3Q6oAvbKiknidErR1c5tMScRHo26GlTrlVreazTY9TxrUNsSCyxIbEE6AKqfJ7aQj5T3kPayjtYnPGERS0g/WHPU5vey+Ul4LRaLUuWLPG4KbwsFgtcfx2ar7/GfOIE2nr1iHjsUUKGDUOl0ZC47QyrZu8q2z/jRAEbliTS55bLr5tdU9y6/FbSi9Ivv6MdZouZ7JJsuwPIehosvHXqNGGO/HxsnA0JFzyteXj1pRN1jXuVrjUXPxD/qHb4S3+g2qSP5R2knbxHbepDCCGEcC6XXonFx8eTlpZGYmIiXbp0AUCv1xMfH8/BgwdZuXIlPXta35T++eefAYiMlHnmnaEo14ihoLwV1XX46IOtSnzbtyd61ix0UbbtEdcxwipRl7wng5JCIz7+tnOq+wboiLsiolqxV8bRo0cpKiqiVatWdrd7YsfXZDKRmJgIQLt27WTqC2dI+gu+Hnb+9akdcPQfiO1rs6vRYuR4ZBxJp/4jKTeJxOzSp+SScpP44MoPUJ0qvfF1YVs1CW6CChUK5X3G7AvQBRAXEkeQPqjcfe5sc2el6hTymfIm0lbe4cI1rUTFSX/Y89Sm9/Klbii6K0mnWCzkrlqFf+fONmvMmUwmko4fR3PrKOqbzUTcdRdqX9+y7Y3b1qFuw0AyTpxPfB/anErXITF2r0FqovSidNIK0xxa533t7mPimo/ROPoGdPxA60Rd8n9QnAu+pdecJSYzPtqzgzhDouFu26f8RPVIH8s7SDt5j9rUhxBCCOFcLr0a69KlC//++y8bN25k1KhRZeWDBw/mwIED/PDDD0RERDBo0CCKior48ccfWbVqFSqVin79+rkyVHERi0qNYdB4iq+9iVg7STqA2A4RqLX7sZjOJyXSjubRqHUdV4VpV0JCAkOHDsXHx4eNGzeWTR8lBE16Q2gTyD4/lVj++vc4GhR2frrK7ESScpM4nnvcZs2Oc5JykojDdt04P60fDQIbcCL/hN3jIv0iy56Iiw05v4ZcpH+kx0w5JYQQwrGkPyzcKSMjg5KSEnx8Lj19t6sUbNpE2rR3KN61i5CRI2nw+mt29zN37kxYu3aoL7pZrdVpuOae1nz/egJmk4WYdnUZMLZVrUnSOVqQLojX+77OlY2uhDUfO/4Esf1ArQOL8WyBClJ3Q5NebEjMYPLC7Xw8pjMdGoU6/txCCCGEEEJ4MJcm6gYOHMh7773HmjVryM/PJzCwdKHvcePGsWjRIk6cOMHHH39ctpD5OcHBwTzwwAOuDFWcpaBmf4vbSW/UE0OhBvXSY7Tq0wjfQNuLXx8/LbHtwzEWm4nvEklsh9LpLd3p559/5o477qDw7PSdQ4cOZd26dQQEyDSBAlBroOckDKum8FC9SJJ0OtJMB2DF7ZWq5mjuUeK0tok6gGahzdCpdeeTcWfXjosNib3kE3OuEO4X7lH1CCFEbSD9YeFOJSUl3HXXXXz77bdun1Eic/58Ul/5v7LXOT/9RJ0778S3RfNK1VO3QSB9RpVOddmmbwMZ7FRFWpWW74Z+R+Pgxs47iU8gtLsFdH7Q7BqI6Qs+gRxKzeP+uQnkFpu47dMNzLq9IwNbu28NcyGEEEIIIVzNpYm6Zs2aMXfuXMxmMybT+SdT/Pz8mDt3LlOmTGHr1q02x0ybNk2egnIbhdToPpjPNpfFonBkWxpt+ja0u/d197ZFpfaMi2OLxcK0adPKknQAW7Zs4fnnn+fdd991Y2TCVUwZR0hZP4NEH1+S6rema72utI9ob71Tx9HoN3/OoQCFTEtxlc6TlJsE5Tw4+v5V73vsDaOFQxe6OwQhhKh1pD8sqkSp3DTal7JgwQKuu+467rzTvdNoB19zDWnvTEc511dXFNLeeYfGn31qs2/WMSOmFmbKm/2tbT/71yai4ur41nFuku6cEdaDENJyixk/ZzO5xaW/D4uMZu7/JoHv7u9Jt1j3zswihBBCCCGEq7h8IYJu3brZLW/YsCHz588nMTGRw4cPYzKZiImJoXXr1i6OUFxIpVYR0z6CI9vOlJUd3lJ+os5TknRQuu7cjz/+SM+ePcvmd7/66qt56aWX3BuYcLhCY6H1unFndpGUup1jliJM55Jkp37joQ4P2Sbq9AEwcSOxv95FZuqWy54r3C/c5sm4Jv5NOH3ktN39PTVJJ4QQwn2kPywqqigvlx2/raQgO8thdU6YMIHRo0c7rL6q0kZEUPeeu0mf9QEA+pgYwm4dhaIoZf0nQ5GJw2sLST9kRFtylCtvt7/etHCAi7usZdNTOk9+iYm7vtrMiewiq/KBraLo3CTM6ecXQgghhBDCU7hnxfBLiIuLIy7O/hRywh1UxHeJKkvURcYEE9POe6a5i4yMZNWqVfTs2ZMbbriB2bNny0LMXm5b2jYOZh48n5jLTeJ0gf0kGRclyRJzEsvdLzYkli1nE3VqlZrowGirZNy5PyE+ITaHGwwGTlNODEIIIUQlSX9YAPy76FsSlv2EyVDisDpDQkJ45aOPXDaQSDGZKDlyBN8WLexurzt+PHm//U7oraMIu+UWVLrz0+anHcvll093kZdRmjDas+4Uce0jadymrktir/WKMp1avclsYeK8rew5mWtV3rFxKO/d1hGNBw0AFUIIIYQQwtlcmqhbsmQJALGxsXTo0MGVpxbV0KRdXXqOaEp850iCw/3cHU6lNW/enK1bt9K4cWN5uskLmCwmik3FBOoD7W5/c9Ob7M3YW6W6k3KSyt02In4EPer3IC4kjibBTdBrJKErhBDC8aQ/LCpKpVI7NEkHEBQU5JL+sKIo5K9ZQ9o70zGlpxP/269oQkNt9lMHBBC75Ce7MWl1GgpzrZ/q+uPrfYx+uQd6P48bb1rzaH3BUOCUqhVF4fklu1l38IxVeZO6/nw+rgt+eo1TziuEEEIIIYSncukVzjPPPINKpWL69OlyY8KL6PQaOl3XxN1hXFJaWhqRkZHlbm/SxLPjt0elUqE7O6q4JiYYC42FHMs9RmJO6XSV5/4+lnuMoXFDeaX3K3aPiwuJq3Siro5aT2xYc1pFXlHuPu0j2ttOi1lBNb2tagppJ+8hbSVqMukPi4q64rohbP55MSajwd2hVErRrl2kvfU2hQkJZWXpn8wm6pmn7e5f3u/5Og0C6H5jLOsXHwFA56uh103x6HwlieMSTkzUfbjmMN9tPm5VVidAz1d3daNuoI9TzilKSR/LO0g7CSGEELWPSxN1QUFB5OfnExMT48rTihpu3bp1DB8+nFdeeYWHH37Y3eE4jE6no337qiWOPIWiKGQWZ1ol4s79OVlwstzjLvXkW6w22G65ChUNAxsSFxpHbHAscSGxxJ3eT2z7sYSENq72z3IpNaGtagNpJ+8hbeUddBdMUScqTvrDnsdd72WjoYR9f60h5orOBIdH2Gz3Dw6hzZVXs+vP31BrtA5/us7RDMePc+bdd8lducpmW9a8eYSNGY0+OrpSdV5xdWNO7M/GUGxi4PjWXjm7hzMlZieSb8h3TuVOml3ix60pvPPbQasyH62az8Z1ITY8wCnnFOdJH8s7SDt5D+kPCyGEcBSXJuqio6PZv38/OTk5rjytqMHmzZvHXXfdhdFo5LHHHqNJkybccMMN7g5LAO9tfY/vD35PTknlP++JOYkoimI7enDXYpr/9R4twkKINRqJM5mJHfgGsfU7ExMSg4/mohG4zavxAwghhBBOIP1hUZibw47fVrLt1+UU5ebQZdhI+o+52+6+3UfeSrfho1gw9UnyMz0zUWfKyiLjk0/InL8AjEa7+/g0a4Yl335CyWQwoy1nqkOVSsU197RB56NBLWuWAZBelM6qpFUsT1xe5engK8bx/9//Hk7nqcU7rc+igvdu60jnJmEOP58QQgghhBDewqWJuoEDB7Jv3z7WrFlDz549XXlqUQPt2bOHsWPHoigKABaLhdtuu43169dzxRVXuDe4GqrYVMyx3GNlT8hpVBoe6PBAuftXJUkHkGvIJbM4k7p+da03xA3gSqPClSdPny87uR/a3FGl8wghhBCuJv3h2ivr1Am2rPiZPev+sHo6bufqVfQYeSs+/rZPEwXVCXdliJViKSkh69tvSf9kNpa8PLv76Bo0IOLxxwkeMhiVWm2z/fi+TP74eh9Xjm5BTDv7P6uPrEdHkamINclrWJa4jP9O/odZMbs7pErbfzqXCd9swWRRrMpfGNqa69vWc1NUQgghhBBCeAaXXvXceeed/PDDDyxYsIABAwbIzQlRLW3atOHll1/mhRdeKCu79tpradasmRujchyTycSpU6cAqF+/Plqt6z6u2cXZNmvHJeYkcjL/JArnL64j/SPLTdTFhcRd9jwqVDQIbEBsSCxxIXFWf4f52hlVG1AXrhgNCV+UvlbrwFhcpZ/RkdzZVqLipJ28h7SVdzCZTO4OwStJf9jzmEwm9HrnTPMHcPLgPjYv/ZHDCRtAUWy2G4qK2PXHr3QZNrLcOgJCy3/aKCMjg6KiorLXQUFBhISEWO1jysgAiwVNahqH+l9Z+R/iLG14ODHfLyJ3+XLSZs7EdPKU3f3UwcGEP/AAYWNGo/axXXPMZDSzYUkiO/4oXafsz7n7uG1qd/yDrduhtn8fnC44zQfbPmB18moKjM5ZL84VTuUUcdeczeSVWH9v3Nsnlrt6x7opqtqptn+mvIW0k/eQ/rAQQghHcem3fWBgIHPmzOGRRx7h3nvvZeTIkQwdOpQWLVoQEhIii+SKSnv++edJTEzkq6++4tFHH2X69OloNDVjgXmLxUJaWhoAUVFRTjvPkewj/HviX6s15LJKsip0bFphGgXGAgJ0tiPAY0POX3Tr1XqahDQpXTsuNK4sGdckuAl+2gvWGsk7DRtnQ+Me0Pw6+yftORF2/wCdx0P3ByC4QWV+XKdwVVuJ6pF28h7SVt5BsZNwEJcn/WHP44z3ssVi5kjCRhKW/cTJg/suuW+T9h2p1/TS83WPeWNmuduKioq46qqr2Lx5Mx9++CEPPGA7iOpQ/ysxpaYCUJ1biorBQNLNN1Oy1/7PpNLpCBs9mvAJD6AJDS23nsTtZ8qSdABFeUbWfLOPwQ+1t/oM1PbvAz+tHyuTVmK02J9SFECNGgsWF0ZVORYU7pqzmVM51oPrBrerx/8Gt3JTVLVXbf9MeQtpJ+8h/WEhhBCO4tJEXatW5zviiqKwePFiFi9eXKFjVSoVe/c6cw5+4Y1UKhWzZ89myJAh3Hzzze4OxyOVmEs4mX/SKnF2oS2pW5iWMK3K9R/NOUqb8DY25U1Dm/LBVR8QFxJHg8AGaNSXSKCmH4J/ZsKuRWA2QHS38hN1dZvCkwdBazs6WwghhPB00h+u2YwlxexZ9ydbVvxE9mn7T5sBqDUaWvbuT5ehI4hoUr0nivz8/Fi6dCnbt2/nmmuuqVZdl2POysKcZX9AV/CQIUQ8/hj66OjL1tOsSxSHNqdxdGf6+brNCsYSM3rf2vfkiN21mS0WQjIS6e9bn9WFyVab/LR+XN34aobFDWPq+qmkFaY5PqjASKuX6QUlWCyXviF9buuFP0lqrh/7S6ynRu3SJIwZo66QdQeFEEIIIYQ4y6VXQRePNJGRJ6Ki7F68nqXX6yVJR+l6cOeeiEvMSSx7Qu5E/glUqNg8ejM6jc7muPISeJfSIKB0usrYkFgC9YF29/HT+tG/Uf+KVXhqB2z/9vzrlE2QvBEad7e/vyTphBBCeCnpD9dMiqKw4cfv2LpqGcV5ueXup/fzp8M1g+h4/TCC6lZu/blL9YcjIiKcnqQrj3/XrkQ+NQW/du0qfIxKpeKqsS1Z8H+bMBSa6DmyKe2vjEZVixI3hcZC/kj+gxWJK4gOiub5Hs9b77D4Lti7hKH+fqyOikANdK/fk2FNh3F146vx1/k7N8AH1lm9HPr6H5zOrf6U8xq1is/GdcFXVzNmQRFCCCGEEMIRXJqomzRpkitPJ2qIlStX8uqrr7Jq1Sqb9TZqG4tiIbUg1e76cZnFmZc8NjkvmaahTW3Ky0vUadVaYoJjyhJy56arjAmOcfyNgdY3wu8vQm7K+bL170PjeY49jxBCCOFm0h+umVQqFenHk8tN0gXVjaDT4Btod9V1+PhXvh/13nvv8ddff7Fo0SKPmeZd37QpkU8+QeCVV1Zpyla/ID3X3dsG3wAddRvaH/hV05gtZjae3sjyI8tZnbyaIlPp2oJB+iCmdJ2Cj+aCwWiNusPeJfQrLGJKRhbX40/kuNng5dPj1vHXExbgvDUhhRBCCCGE8EaSqBMe7ZNPPmHixIlYLBZuvvlmVq5ciU5n+1RYbbA1dSsTVk8ou6CvrMScRLuJurq+delRvwdR/lHEhcaVrSPXMLAhWrUDf0UU54JKDT52bsRodNDjQfjtOdD4QIfboKf8vhBCCFHzSH+45uo6dAQH//vbqiwiJo6uw0bSvEcfNNrK96vMZjOTJ0/m/fffB+DJJ5/k3XffdUi8l7O581MY9MG2G9Rq1IGBqP384FcV/LrefgWKgqHYTHC4L7dNtT9LQsPmYQ6M2HMdyDzA8sTlrExcSVqR7TSVeYY8/k75m4FNBp4vbHYt/PosOmBcbh6QB6d3Qv0OLovbGTS16KlJIYQQQgghKqr2LQAgrOhLclCVmMvd7mOpWlLIEd59910mT55c9nr16tU88MADfPHFF1UateuJcg25VtNVJuUk8Vz356gXUM9m30j/yCon6QBO5dtfJ0WlUvHZtZ9Vud7LykmBjZ/Alq+h72To87j9/TqNA0M+dLnbZk0MIYQQQghn+e6FKZgKC8rdHhAaxpg3ZmKxmDmSsJHYjl3R2hk4Vi++OQ1btuHE/j3EXNGZrsNG0qhN+2r1W++55x6+/vrrstczZ84kNjaWRx55pMp1VpRBH0yJTzmJtBKgxFCherJTCy85bWdNlVqQysqklSxPXM7BrIOX3f+Xo79YJ+rqNoWw2NKBbs2vg2bXQERLm+PC/So3hWp5HFWPEEIIIYQQovKclqhbsmQJAAMHDiQwsHZMZeKNuh/7Ek1u+etoaMPDgTGuC+gCw4cP58033yQt7fyoU4PBgMlk8qqn6hRFIbXw/HSVFybm0ovSbfa/tcWtdhN19QPq46PxocRcUu65tCotjYMbl01TeW7KypiQGAJ0AQ79uSpk02fwyzNgMZW+3jgbekwErZ3pbnyD4cpnXBufEEII4UTSH/YORdnZlOSX3x9WFIVtvy5ny4ol5KSe5roHH6PtlQPt7jvgzvvQaLWEN45xSGzjx49n/vz5GI3GsrKCgvKTip7IbFLYtfYE7QdEuzsUpys0FrI6eTXLjixj46mNKFx6DUq1otAzrBXD2o1nQKMB1htVKrh/Dfhd+qnDhUMXVjdsIYQQQgghhJs5LVH3zDPPoFKpaNu2LfHx8TbbMzMzmT9/PiBTALlT44Xf4V+FdTJcITY2luXLl9O/f3+KioqYOnUqL7/8ssePxi00FjJv3zyrxFyhqbDCxyflJNGnYR/UajWhoaEAqNVqNGoNMcExHMg6QIAuoGyKygsTctFB0ejUHpTEbNjpfJIOIO8U7P4BrrjdfTE5wcVtJTyTtJP3kLbyDp7+fewJpD9cMxRkZ/Hnl5+UvU5Y9iNt+l9t9zMQFWfbztVx5ZVX8uWXXzJ27Fj0ej1z5szhjjvucOg5XGHziiRa9aqPzqdy6+t50/eBKfMog1fdSkYF+v2tSgwMzS9gcEEB4VFDIG6I/R0vk6QTorK86TNVm0k7eQ/pDwshhHAUt019mZGRwQcffIBKpZIbE26krcJaGa7UtWtXFixYQEZGBnfffbe7wwEg35BPUk4S9QPr250iRqvW8sH2D7AolirVn5iTWFqPVkvTptZryr3R9w2C9cFE+kd6R4ewYWdo0huO/Xu2QAVn9rs1JGew11bC80g7eQ9pK+/g6X0IbyD9YS+hWD8VlZGSzNHtW4jt2MUlpx8zZgypqal07dqVfv36ueScjqTWqLjpqc6VTtKBl3wfKAp8cQ3alM30Dq/D0iD7T8/WC6jHkNghDM08Q/z6j6FxT+h2DbQY7OKARW3mFZ8pIe3kRaQ/LIQQwlHkG0V4vBtvvNHl51QUhTNFZ8qeikvMTiQpN4mk7KSyBeBf7PkiNze/2eZYvUZPdGA0yXnJFTqXRqWhUVCjsukqu9brWu6+zcKaVe0HcpaiLEj4EloOg4jm9vfpOQlOboMrRkPPh6BOnGtjFEIIIYRwoMjYpmhcPA37E0884dLzOZJfkI7QSM+cwaOyThecZl/GPgY0vmCaSpUKAiIAGJpfYJWoC9AFcE2TaxgWN4wu9bqgVqmhMBP6TAG/UBdHL4QQQgghhPBUkqgTbrdo0SIARo0a5fJzGy1GUvJSbNaPS8pJIt+Yf8ljzz35Zk9cSJxNos5f6281TeW5vxsFNUKn8aDpKisi6yhs+Bi2fgPGgtLXN8yyv2/z6+HxPeBfx5URCiGEEEI4VFynrnQZOoLo1u0cOrOBoii8+eabXHfddXTq1Mlh9XoOL5gFojwWM/nJ6/l919esMKaxKe8oPhof1t661nr952bXwIGVdCsuoYHRRLxZYdg1M7iy8VX4an2t65Q+sRBCCCGEEOIikqir5YxGI3q93i3nVhSFadOm8fTTT6PX62nQoAF9+vRxaQxDfxzKyYKTVTr2Uom6fo36ERUQZZWYi/KPqtRNHaPRyJEjRwBo2rQpOheP3L6krd/AxvNrtbDjOxjwPARF2e6rVtf4GxIe3VaijLST95C28g5Go9HdIQjhElofH8a8/i51oxs7vG6DwcADDzzAV199xaxZs9iwYQONGzv+PF5pdn+U/FRMptL1jrVaLaqqJP0CI+GBdZU6xGQxsf6vV1l+YCFr9GqKL1gjqthczOpjq7kx/oJZP5pdC4DGL4xloT3RN7sOGg8ErXuus4Qoj/SxvIO0k/eQ/rAQQghHkUSdcAuLxcLEiRP55JPSZI/BYODGG29k/fr1tGjRokp1KopCRnGG1VSVidmJ9G7Ymzvb3Gn3mEZBjaqUqFOr1JjMpnK339L8lkrXeTFFUSgoKCj7t0fpdj+sfx/MhtLXZgNs/gyuet69cbmJR7eVKCPt5D2krYQQnsQ3INApSbrc3FxGjBjBn3/+CcCpU6cYMmQI//zzDyEhIQ4/n9fJT0OVdwpX3Z5WFIW9GXtZlriMVUmryCzOBF/7l8vLEpdZJ+pCouH+dVCvHXp15dfiE8JVpI/lHaSdhBBCiNpHEnXCLVQqFRqN9UVsZmYmCxcu5IUXXrjksSaLiRP5J0oTcufWkDv7d54hz2b/QL39Bd0BYkJi2Hh6Y7nb/bR+xATHEBcaR2xwbNnfjYMbo9fU8BGyxiLQ+JQ+EXexoChofyts+wZUGmgzAloNc32MQgghhBBeytfXF4vFYlW2d+9e1qxZw/Dhw90TVG1RkgeJa+H0Lk52Hc+KxBUsS1xGUk7SZQ8N0gXROKgxFsVSuubcOQ2ucFq4QgghhBBCiJpNEnW13NhfxpJjySl3e7hfOAuHLnT4eVUqFTNnzuTYsWMsX74cgGnTpvHEE0/Y3X/xwcWsP7mepJwkjuUew2ip+PQCidmXXksOoK5vXZu142JDYokKiLK+AK8NCtJh8+f/3959x0dR7f8ff6dCGj2hQwDd0C+gCIhIVQTpnUtTEPGnqKAioBcLXMWK/V6KhaIgIKHDVboiGATkSwKXhN4hECAQQkjb3x+5WTbJbupml0lez8eDB5OZM+ecmbOTfHbOzDnSrjlSz6+kkMdsp2s1VipRSmr5jFSGIZoAAADywtvbW6GhoWrdurX++9//ytfXV4sWLVKPHj1cWq8b/tVU4vZVl9ah0Ny4KIWOlk7u0HofLy0J8NfuU4ty3M3TLD1UspK61fu72jX8u0p4lHBCZQEAAAAUF4XeUbdw4UKVK5d1fqorV65Ylr/88stc5TV27FiH1QtpLidc1tVk13wR9/T01MKFC9W5d2f1eaqPHm77sN053P6K/ksbTm7IVzknb5xUcmqyPN2zfty71+mux2s/rtIlGF5IkrT9E2nre1JyQtrPO76w31EXVFd67F3n1Q0AAIMiHoY9ZcuW1bp169SrVy99/fXXuv/++11Sj0Qvf12o2FwXKrVUnH811Tm63KnlD1wzUJdvXc64sqyHVKZKnvKpkJKixecu2k/gW146v09KTdKekv7a7VMy2/wal75H3Wp00mP1/66yJcvmqS4AAAAAkFuF3lG3aJH9JxTTO2W++uqrXOXFjQnjSklN0bm4c5Z546z/vzHshubdnqc9e/ZoUTfbn5f0N99yo6RHSQWXDlat0rUsb8fZG9c9wDsgX8dTZPlXvNNJJ0knt0tn90hV73NdnQAAMDjiYWQnODhYf/31l90H1pzhaO2eOl/5QcvPFyq1dGr5l29dVnR8dMaVHm5y+NdVD0+pTkfpQKi6x93U4lJZvwtU9a+qbrW7qVvtbgouHezY8gEAAADAhkLtqHPkpLeu/OKK3EtITtCJ6ycyzBu37/Q+XTVfVWJqYrb7Hr9+XGaz2WZb2+qoK1eyXNb540rXUmW/ysVvuEpHadhP2jRVunH+zrqjW+ioAwAgn4iHkZqaqtDQUPXt29duGxZ223pWqJDt9qq3o3RedzrqbvpVlldyXKHWqTCd8fTQGn8//eznq29vSBnehTN1lg6EqnGSWTXkqVNKVoB3gB4Lfkzd63RXk8AmXGsAAAAAnKrQOurmz59fWFnjLvZNxDea+X8z87XvzaSbio6PVkW/ilm21StfT8PrD1ft0rUtHXNlSpYpYG2Lodtx0v8tkv42SCph421CT2+pxRhp8ztSo/5Sq+ekSg2dX08AAIoA4mHcunVLw4YN07JlyzR9+nRNmjTJJfWo+N1CnToYo7otK9vcbk41K+qNnbp+OW1kBf+yJZSc6KWkm7mfF/pucMvNTSMqB2lvyTtDWv6nRIoGWye691Fp4A9yq91WL5zfIQ83Dz1c7WF5e3g7vb4AAAAAIBViR90DDzxQWFnDiVJSU/Tbmd8sb8cdjz2u64nXtbyn7XkrapWqla9yvN29FVw6WLGJsTY76ir5VdKE5hPylbdReXh4qFq1apblArlxQdo1W/rzGynhmpSSJLV61nba+0dJjQdKpfI2J0hx5tC2QqGhnYyDtjIGd3feXs8J8XDxdunSJfXo0UN//PGHJGny5MmqWbOmBg8enMOejpGakqrj/3dZh3ae18kDV2RONSuwRoDKV/HPktbN3U0N2lRVzLk41W1VWdVMZTXvtR1Oqacj3fDw0N5MfzfWlHDP2FHnW06q102S1Dm4s/MqZ3CBASVySGFWSmqqJMnD3V2S7bcSc84HhYkYyxhoJ+MgHgYAOEqhz1EHY4tJiNGzm7J26MTejlXpEqWzrK9dJvu55Pw8/GQqb7LMHZc+j1wVvyrycCcAtebh4aGKFbN2WubL+lelgyvv/PzHv6QHnk6bpyOzkqXS/iHXHNpWKDS0k3HQVsbAjSMge2FhYQoLC8uw7umnn9ajjz6q8uXLO6UOvy6OUnzsneHnI3de0IN977GZtlnnmk6pk7Pt93LTqeunVKNUDVdXxdBWP/+Qq6sAByDGMgbayTiIhwEAjkJHHfLleOxxNQlqkmV9zVI15e7mrkq+lRSQFKAda3Yo4WyCkqKTNO3FaXrx6ReZ88EVWj6bsaMu9rR0cIXUqJ/LqgQAAFCUdevWTR999JFefvllSVLp0qW1bNkyp3XSuXu4K+SBSvprwynLusiwC2rZq7bcPVz4BkBKknTpkHTuL+nqCanjG4VSTOmUFD12M17dUn1UPaB6oZQBAAAAAI5ARx3yxV5HnY+nj8L+HqaSnmnzQnwT/43GjRunJUuWqEuXLk6u5V1sVlspLrrg+fgHSWO2pS2npkr2hl2o3kKq1lw682faz6bHpHL5G6YUAAAAuTN+/HgdO3ZMq1ev1rp169SgQQOH5Z2clKIT+2OUmpoqU/NKNtOEtLrTUefp7a7q9cspMSFFJf1y7qjzLeWYOdsy5HP5iDSztZSccGfdg887pBxJktmsR+JvqVvcTbWJvyUvSQrwkXhQEAAAAMBdjI465CjAO8AyTGX6/w0rNLSbPr2TTpJGjRqlxx9/XJUq2b55UGzFRUs3zjkmr4Tr0t55aXPQDVshla+TNY2bm/TQeCnqZ6nVc1JgiGPKhiQpMTFR4eHhkqRGjRrJ29sxN7bgWLSTcdBWxpCUlOTqKgAO4VOmjLy8vexu9ytTNt95u7m56bPPPtObb76pwMDAfOdjLfZSvPZtPK3Df17U7fhkBZQvqXvvqyg396ydUeWr+KvBw1VVMThAdZoFybtk7r/+DXited4qlpoqXT2e9qZcve6Sp425yMrWlMzmjOvO7ctbOdkITEnRjOjLDssPucffbmOgnYyBdjIO4mEAgKPQUYdsVfCpoM39N9sdrtJsNuv06dOqUcP+nA900hWixBvSJw2k29fTfv7jX9LjH9tOW/fxtH8AAACwGDT1Q/n5+eV7/8Xv7FLctYRczFMTle1W31LetjvHMo3EcDuhhiJOT7T8fCMmQefe6aGqvkds5ttOks5LCrcaicGRUpKlH/pKZ/+SbsemrXt6q1Slada0Hl5SpUbS2d131p37y2FV4b05AAAAAEZERx2y5e7mbreTLiUlRePHj9e8efP0+++/q2FD+2/ZoZCYU6XEm3d+/usHqd1rkp9z5j4BAAAozq5fv67Tx8/LxzNAUnLhFJJpJIZA8zmV8zylK8l3HpQ7dKWxqqb8WjjlS9L1c1KJgLR/mXl4SrFn7nTSSWmdb7Y66qS09Wd3S2WD05YDQ6QLawul2gAAAABgBC6cRRxGdvPmTfXp00dffPGFrl+/rq5du+rcOQcN5Yjc8/aX3K3625NvSYdWu64+AAAAxcSZM2fUpk0bJSQk5Jw4l8xmsy4ci9XWhZG6dOqGzTRublKIzxbLz1W9w1XD23FvpVn88W9p4SDpoxBpRj3p8C/202bulMvuLbmHX5FePS69+H9S/7mM+AAAAACg2OONOuTLe++9p1WrVll+Pn36tLp3766wsDB5evKxcho3D6lhP2n/YqleN6nV81KNFq6uFQAAQJH3zDPPaP/+/erfyDH5JSemaOFbYbp2MV6S5O4mBdawPa9wSMltSjF7KaTkVpXyvJT/Qs3mtJ4/W45sko5suPPzub+khn1tp63SVApfmrbsFyR5ZTOUaADD4gMAAACANXpUkC+vvfaaNm/erB07dkiSvL299corr9BJ5wrtJ0ttX5XK13F1TQAAAIqNWbNmqWXLlg7LLzXVbOmkk6SoPy+qdb97ZWvmOz+Pq2ruvzTvhZzbJx3/Na3T7dxfUtOhaW+42VKlacaOurPZvCUX0lUqUzNtn1JV7Hf+AQAAAACyoFcF+eLj46OVK1eqVatWiomJ0cqVK9WmTRtXV6t4Khvs6hoAAAAUO1WrVtXatWu15qNDDsnPy9tDSQkplp9vxyfr+P7Luschuf/PvoXSrll3fs5uiMqqzTL+fPW4/TfwytVK++dqbh5SQJWM6/yDXFMXAAAAAMglOuqQbxUqVNC6deuUmpqqkBDbw/IAAAAARVXjxo31R4XLSorPOW1O3NzdVNVURmejrqlUoI/qtaqkSrVLSVsLnrdFXuaSq3qf1PK5tH2qNJXK1b7735QLqCiN3OTqWgAAAABAntBRhxylpKTIw8PWoDvSvffe6+TaAHcfT09PS2c1w7/evWgn46CtjMFebAAYTU6f5exiYUny9i6hpPjbDqlLix61JUmV6pSWW2F0imXuqLt+VrpxMa2DKzP/IOmxdx1fBxRJ/O02BtrJGGgn4yAeBgA4Cn/xi7kKPhXkleJle6NZirsYp5deekmfffaZcysGGIi7u7v8/f1dXQ3kgHYyDtrKGNzd3V1dBcAhsvssx8TEqHfv3nriiSc0cuTIQq9L5XvKFG4BFe6VqreUgupKVZqlddz5li/cMlEs8LfbGGgnY6CdjIN4GADgKHTUFXM/dvtR3t7eWdZfu3ZNffv21a7Nu7RLu1SrVi2NGzfO+RUEAAAAXODo0aPq0qWLDh8+rJ07d6p69ep65JFHXF2tgnH3kEb97OpaZFHBp8JdlQ8AAAAAOBMddTacPXtWCxYs0NatW3XhwgV5e3urevXq6tKli4YMGSIfHx+HlLNt2zYtWbJE4eHhunLlisqVK6dGjRppwIABatu2rUPKyElqamqWdSkpKerYsaP27t1rWffSSy+pZs2a6t27t1PqBRhJamqq4uPTJqfx9fXlqbq7FO1kHLSVMdiKIVB0FPd4ODo6Wi1bttTly5clScnJyerXr5+2b9+uRo0aOaVexcnibottrufvgXHQVsZAOxkD7WQcxMMAAEehoy6TzZs3a8KECYqLi7Osu3XrlmJjYxUREaGlS5dq9uzZqlmzZr7LSE1N1ZQpU/TTTz9lWH/x4kVdvHhRGzduVP/+/TV16tRCD8hSUlKyrPPw8NDLL7+sIUOGWNb5+PgwNjpgR3JysiIjIyVJjRo1svmWKlyPdjIO2soYbMUQKBqIh6WgoCCNHDlSH3zwgWVdQECAzGZzodYFGfH3wDhoK2OgnYyBdjIO4mEAgKPwWI6VgwcPavz48YqLi5Ovr6/Gjx+vH3/8UXPnztWAAQMkSSdOnNDTTz+d4cZFXn3yySeWmxL169fXjBkztHTpUs2YMUP169eXJC1dulSffvppgY8pv/7+97/rn//8pySpUqVK+vXXX9W9e3eX1QcAAACFj3j4junTp1uO+W9/+5vCwsLUuHFjl9UHAAAAAFA08YqUlXfeeUcJCQny9PTUt99+q6ZNm1q2tWrVSjVr1tSHH36oEydO6LvvvtPzzz+f5zKOHz+ub7/9VpLUsGFD/fDDDypZsqQkqXHjxurQoYOGDh2qiIgIffPNN+rbt2+BnlYuiNdee02pqakaPny4y+oAAAAA5yEevsPd3V3z5s1TrVq19PrrrysgIMDpdQAAAAAAFH28Ufc/+/fv1+7duyVJffv2zXBTIt3IkSNVp04dSdL8+fOVlJSU53LmzZun5ORkSdKUKVMsNyXS+fj4aMqUKZLShjuYO3dunstwFDc3N02ZMoVOOgAAgGKAeDirkiVL6r333qOTDgAAAABQaOio+5+NGzdalvv27Wszjbu7u3r16iVJun79usLCwvJUhtls1qZNmyRJtWvXVpMmTWyma9KkiWrVqiVJ2rRpU6HOhfH666/r5MmThZY/AAAAjKG4xsMTJ07UzZs3Cy1/AAAAAACyQ0fd/+zZs0eS5OvrqwYNGthN17x5c8vy3r1781TGmTNnFB0dnSUfWx544AFJ0sWLF3XmzJk8lZMXixYtUteuXXXt2rVCKwM2+AdJAVWy/WcOqKzEkhWUWLKCzAGVbafzD3L1kQAAgCKiuMbDS5cu1eDBg5WSklJoZQAAAAAAYA9z1P3P0aNHJUk1atSQp6f901K7du0s++TWkSNHbOaTUznHjh1T9erV81RWdqyfSC5fvrwuX76sJ554Qt999528vb0t2zw8POTuntaXm5qamuPNCy8vL8tySkqKUlNTc50+OTk52yel3dzcMrRLTsMsWdfdbDZbhleyx9PTU25ubpLyfqz5qvvQdXbTu7u7y8PDQ0lJSTp06JAkqU6dOhnKtOaRkEA7uaDu6e2UOW18fLzNfbmeXFN32sm+u7WdpLT2sV7OvD/t5Jq6Z26nW7duWZYL820nOE9xjofDwsI0ceJEvfXWW5ZrTsrd7xvPkmZ5+7llWZ9Xnj5mm2/2eZYoK7ekxALnby5RVsnZvDl4N/6+yc3fbom/C66qO3GWfbRT7tLTTnfQTvbdre0kEQ8DAByHjjpJt2/f1tWrVyVJlSpVyjZt6dKl5evrq/j4eF24cCFP5Vinz6kc6+3nz5/PUzk5sQ6CFixYYFk+duyYQ8uB4+T1Jhhch7YyBtrJOGgrY0hOTlaJEiVcXQ0UAPFwmsjIyDznVa+7jyQfR1TL8pBWBq2+cEje/yvAcXk5GX8PjIO2MgbayRhoJ+MgHgYAFARDX0oZnlz19fXNMb2PT9oXcesn/h1dTnoZ+SkHAAAAyAviYQAAAAAAXIM36pT2BHE6e8MLWksfHjIhIaHQyrEegjKv5eTEx8dHdevWlZRxCAEAAIDcsh6qyLpDBcZEPEw8DAAA8oZ4GADgKHTUSRleTc9pPGpJSkxMmyOiZMmShVZOehn5KScn7u7u8vPzc2ieAACg+GF4n6KDeBgAACDviIcBAI7A0JdShi/puRlWJ32y2NwMC5TfcqwnpM1rOQAAAEBeEA8DAAAAAOAadNQp7emXMmXKSMo4wb0tsbGxlpsK1hPc54Z1+pzKsd5euXLlPJUDAAAA5AXxMAAAAAAArkFH3f/cc889kqRTp05Zxpe25dixY5blOnXq5KuMzPnkVE7t2rXzVA4AAACQV8TDAAAAAAA4Hx11/3PfffdJShuC58CBA3bT/fnnn5blZs2a5amMatWqKSgoKEs+2ZVTsWJFVatWLU/lAAAAAHlFPAwAAAAAgPPRUfc/nTp1siwvW7bMZprU1FStWLFCklSqVCm1aNEiT2W4ubmpY8eOktKeEN63b5/NdPv27bM8QdyxY0e5ubnlqRwAAAAgr4iHAQAAAABwPjrq/qdx48a6//77JaXdmPjrr7+ypPn222919OhRSdLw4cPl5eWVYXtYWJhCQkIUEhKiSZMm2SxnxIgR8vDwkCRNmzZNCQkJGbYnJCRo2rRpkiRPT0+NGDGiYAcGAAAA5ALxMAAAAAAAzkdHnZXXX39dJUuWVHJyskaOHKlZs2Zp3759+uOPP/TGG2/oww8/lCQFBwfrySefzFcZtWrV0qhRoyRJERERGjx4sNatW6fw8HCtW7dOgwcPVkREhCRp1KhRCg4OdsixAQAAADkhHgYAAAAAwLk8XV2Bu0n9+vX1ySefaMKECYqLi9OMGTOypAkODtbs2bPl7++f73LGjx+vmJgYLVu2TAcPHtT48eOzpOnXr5/GjRuX7zIAAACAvCIeBgAAAADAueioy6RDhw5atWqV5s+fr61bt+rixYvy8vJSjRo19Nhjj2no0KHy8fEpUBnu7u5699131blzZy1evFjh4eG6evWqypYtq0aNGmngwIFq27atg44IAAAAyD3iYQAAAAAAnMfNbDabXV0JAAAAAAAAAAAAoLhhjjoAAAAAAAAAAADABeioAwAAAAAAAAAAAFyAjjoAAAAAAAAAAADABeioAwAAAAAAAAAAAFyAjjoAAAAAAAAAAADABeioAwAAAAAAAAAAAFyAjjoAAAAAAAAAAADABeioAwAAAAAAAAAAAFzA09UVQP6dPXtWCxYs0NatW3XhwgV5e3urevXq6tKli4YMGSIfHx+HlLNt2zYtWbJE4eHhunLlisqVK6dGjRppwIABatu2rUPKKMoKs51u3bql3377Tb///rsiIiJ06tQpxcfHy9/fX8HBwXrooYc0aNAgBQYGOvCIii5nXVPWbt26pW7duunMmTOSpKpVq2rz5s0OL6cocWY77dixQ6tWrdKePXt06dIleXh4qEKFCgoJCVHLli3Vs2dP+fn5Oay8osQZ7XTmzBktWrRIO3fu1KlTp3Tr1i35+fmpdu3aeuihhzR48GCVL1/eAUdT9MTExGj//v3av3+/wsPDFR4ermvXrkmSevfurffee8/hZa5Zs0ahoaGKjIzU9evXVaFCBd13330aMmSImjZt6vDyUPQRCxsH8bAxEAsbB/GwMRAP392IhwEAdxM3s9lsdnUlkHebN2/WhAkTFBcXZ3N7cHCwZs+erZo1a+a7jNTUVE2ZMkU//fST3TT9+/fX1KlT5e7Oy5m2FGY7HTp0SIMHD1Z8fHy26fz9/TVt2jR17do1z2UUJ864pmx5//339e2331p+5uZE9pzVTrGxsZo8ebI2bdqUbboVK1aoXr16BSqrKHJGO61YsUJvvvmmEhIS7KYpU6aMZsyYodatW+e7nKIqJCTE7jZH35hISEjQCy+8oG3bttnc7u7urueee05jx451WJko+oiFjYN42BiIhY2DeNgYiIfvfsTDAIC7CR11BnTw4EENHjxYCQkJ8vX11ZgxY9SiRQslJCRo3bp1WrJkiaS0wG/ZsmXy9/fPVzkff/yxZs+eLUmqX7++nnrqKVWvXl2nT5/W119/rYMHD0qSxowZo5deeskxB1eEFHY77d69W0OGDJEkNWvWTO3bt1fDhg1VpkwZXblyRb/88ouWLl2q1NRUeXh46N///jdPfdvhrGvKVrn9+vWTp6enPD09dfPmTW5OZMNZ7XTjxg2NGDFCBw4ckCQ98sgj6ty5s2rUqCF3d3dduHBBu3bt0i+//KKZM2dyYyITZ7TTnj17NHToUKWmpsrd3V29evVSx44dFRQUpPPnz2v58uXasmWLJKlkyZJas2aNqlev7tDjNDrrGxNVqlRR7dq1tX37dkmOvzHx0ksvae3atZKkFi1aaPjw4QoKClJUVJRmzZqlU6dOSZKmTp2qgQMHOqxcFF3EwsZBPGwMxMLGQTxsDMTDxkA8DAC4q5hhOH//+9/NJpPJXL9+ffPevXuzbJ8zZ47ZZDKZTSaT+fPPP89XGceOHTPXr1/fbDKZzH369DHfunUrw/b4+Hhznz59LPU4ceJEvsopygq7nfbs2WN+8cUXzYcPH7abZsOGDeaQkBCzyWQyd+rUyZyamprncooDZ1xTmSUnJ5t79+5tNplM5i+//NLcvn17s8lkMrdv394h+RdFzmqnCRMmmE0mk7lhw4bmjRs32k2XmppqTkpKync5RZUz2unpp5+25PH999/bTDN9+nRLmrfffjtf5RRln332mXnz5s3mS5cumc1ms/n06dOW8zVx4kSHlbNjxw5LvmPGjDEnJydn2B4TE2Nu166d2WQyme+//37ztWvXHFY2ii5iYeMgHjYGYmHjIB42BuJhYyAeBgDcTRijxWD279+v3bt3S5L69u1rcwzrkSNHqk6dOpKk+fPnKykpKc/lzJs3T8nJyZKkKVOmqGTJkhm2+/j4aMqUKZKk5ORkzZ07N89lFGXOaKdmzZrp008/1T333GM3TadOnfToo49Kkk6dOmV58ht3OOuaymz+/Pk6cOCAatWqpdGjRxc4v6LOWe20e/durVy5UpI0btw4dezY0W5aNzc3eXoy1as1Z7XTX3/9JSltKJ/0Nykye+655yzL+/bty3MZRd0LL7yg9u3bq0KFCoVaTvpwZp6ennrrrbfk4eGRYXu5cuX0yiuvSJKuX7+upUuXFmp9YHzEwsZBPGwMxMLGQTxsDMTDxkE8DAC4m9BRZzAbN260LPft29dmmvRhD6S0P/JhYWF5KsNsNlvGoa9du7aaNGliM12TJk1Uq1YtSdKmTZtkZhRVC2e0U261aNHCspw+nALucEVbnT17Vp9//rkk6e2335a3t3eB8isOnNVOP/zwgyQpICBAQ4cOzXtFizlntVP6zYxq1arZTRMQEKCyZctmSA/niouL086dOyVJrVq1UqVKlWyme+SRRyxDPll/hgBbiIWNg3jYGIiFjYN42BiIh2GNeBgAkFt01BnMnj17JEm+vr5q0KCB3XTNmze3LO/duzdPZZw5c0bR0dFZ8rHlgQcekCRdvHhRZ86cyVM5RZkz2im3EhMTLcuZn9yCa9rq7bffVnx8vHr27JnhxhHsc0Y7JSYmWm7MPvjggypRooQkKSUlRefPn9eZM2d0+/btvFa9WHHW9ZR+Yzy7vztxcXG6evVqhvRwrvDwcMtNofR4wRZvb29LR4j1PoAtxMLGQTxsDMTCxkE8bAzEw7BGPAwAyC066gzm6NGjkqQaNWpkO8RE7dq1s+yTW0eOHLGZT07lHDt2LE/lFGXOaKfc2rVrl2U5fXgN3OHstlq7dq22bdum0qVLa9KkSfnOp7hxRjsdOnTIcuPBZDIpLi5O77zzjlq2bKl27dqpY8eOuu+++/Tkk08W2hP/Rues62nQoEGSpGvXrmnRokU203z11VdZ0sO5rNs2p3gi/eZRcnKyTp48Waj1grERCxsH8bAxEAsbB/GwMRAPwxrxMAAgtxhM3EBu375teRrK3uvy6UqXLi1fX1/Fx8frwoULeSrHOn1O5VhvP3/+fJ7KKaqc1U65cejQIW3btk1S2hctbkxk5Oy2io2N1bvvvitJevnll1WuXLl85VPcOKudrL9Emc1m9e3bVydOnMiQJikpSTt27NDOnTv10ksv6emnn85TGUWZM6+nvn37as+ePVqxYoWmTp2qAwcOqEOHDgoMDNT58+e1cuVKy5AxzzzzjB588MG8HxAKzLptK1asmG3aypUrW5bPnz+f7XxTKL6IhY2DeNgYiIWNg3jYGIiHkRnxMAAgt+ioM5CbN29aln19fXNM7+Pjo/j4eMXHxxdaOT4+PpblvJZTVDmrnXKSmJio119/XSkpKZKk8ePHOzT/osDZbfXBBx/o8uXLatq0qQYMGJCvPIojZ7VTbGysZXnOnDm6ffu22rRpoxdeeEF169ZVXFycfv75Z3388ce6ceOGPv74Y9WuXVudOnXKUzlFlTOvJw8PD73//vtq3769Zs2apaVLl2aZdL1FixbclHAx4gk4GrGwcRAPGwOxsHEQDxsD8TAyI6YAAOQWQ18aiPVY8F5eXjmmT5+UOyEhodDKsZ74O6/lFFXOaqecTJ06VREREZKk3r17q0OHDg7NvyhwZlv9+eefWrZsmTw9PfXWW2/Jzc0tz3kUV85qJ+svQ7dv31br1q01a9YsNW7cWN7e3ipXrpwGDx6smTNnyt097c/njBkzZDab81ROUeXs331Hjx7VihUrFBUVZXP7vn379NNPP+nixYv5yh8FZ/2ZsI4XbCGeQG4QCxsH8bAxEAsbB/GwMRAPIzPiYQBAbtFRZyDpEzlLytXEsumTppcsWbLQyrGemD2v5RRVzmqn7KQ/USdJjRo10htvvOGwvIsSZ7VVYmKipkyZIrPZrOHDh6tu3bp5q2gx54rffZL0yiuvyMPDI0u6+++/X4888oiktC/HkZGReSqnqHLm777du3dr4MCB2rJliypWrKgPPvhAv//+uyIiIrRt2za98cYb8vHx0dq1a9WvXz8dPnw4z2Wg4Kw/E9bxgi3EE8gNYmHjIB42BmJh4yAeNgbiYWRGPAwAyC066gzEz8/Pspyb1+Bv3bolKXdDLuS3nPQy8lNOUeWsdrLnxx9/1IwZMySlTVY8e/Zs2sYOZ7XVv//9bx0/flyVK1fW888/n7dKwiW/+8qVK6f69evbTdumTRvLcnh4eJ7KKaqc1U6JiYl66aWXdOPGDQUGBmrx4sXq2bOnKlSoIC8vL1WqVElDhgzR999/rxIlSig6OloTJ07M28HAIYgn4GjEwsZBPGwMxMLGQTxsDMTDyIyYAgCQW8xRZyAlSpRQmTJldO3atRwnG46NjbUEATlNYpyZdfqcyrHebj3xbXHmrHayZc2aNXr77bclSVWrVtV3333HJO3ZcFZbzZkzR5LUqlUrbdmyxWaa9Lzj4+O1du1aSWlfjlu1apWnsooiZ7WT9e+wnPa13p4+YXxx56x2+vXXXy3D9wwdOlSBgYE20917773q0aOHli5dqgMHDujQoUM8we9k1m178eJFNWrUyG7a8+fPW5aJJ2APsbBxEA8bA7GwcRAPGwPxMDIjHgYA5BYddQZzzz33aPfu3Tp16pSSk5Pl6Wm7CY8dO2ZZrlOnTp7LsJVPTuXUrl07T+UUZc5op8w2bdqkiRMnKjU1VYGBgZo7d65DbnYUdc5oq/RhT0JDQxUaGppt2qtXr+qll16SJD3wwAPcnPgfZ//uS0lJyTZtamqqZdnWcEDFlTPayXrf7J7ylqQGDRpYhj07duwYNyaczLptc4onjh8/Lkny9PRUzZo1C7VeMDZiYeMgHjYGYmHjIB42BuJhWCMeBgDkFkNfGsx9990nKe1JwwMHDthN9+eff1qWmzVrlqcyqlWrpqCgoCz5ZFdOxYoVVa1atTyVU5Q5o52s7dy5U+PGjVNycrLKlCmj7777TjVq1Mh3fsWJs9sK+eOMdqpataqqVKkiSTp79qzMZrPdtKdOnbIsV6xYMU/lFGXOaCfrG0E53UBKTk62uR+co1GjRvLy8pIk7dq1y266xMRE7du3L8s+gC3EwsZBPGwMxMLGQTxsDMTDsEY8DADILTrqDKZTp06W5WXLltlMk5qaqhUrVkiSSpUqpRYtWuSpDDc3N3Xs2FFS2hM/6cFCZvv27bM8EdSxY0e5ubnlqZyizBntlG7v3r169tlnlZiYqICAAH3zzTe6995785VXceSMtoqMjMzxX9WqVSWlfTlOX7dgwYL8HVQR5Kxr6tFHH5UkxcXFaefOnXbT/fLLL5bl9C/jcE47Wd8I3717d7ZprW+AcAPd+fz9/S1vQuzcudPuEFAbNmxQXFycpIyfIcAWYmHjIB42BmJh4yAeNgbiYVgjHgYA5BYddQbTuHFj3X///ZLSgr6//vorS5pvv/1WR48elSQNHz48y5M4YWFhCgkJUUhIiCZNmmSznBEjRlietpo2bZoSEhIybE9ISNC0adMkpb2WP2LEiIIdWBHjrHb673//qzFjxig+Pl6+vr6aNWuWGjZs6OCjKdqc1VYoGGf+7itRooQkafr06ZYvS9ZWrlxpeRqyXbt2zB9gxRnt1KpVK/n4+EiSFi1apMjISJt12bZtmzZs2CAp7SnvevXq5f/AYFNoaKilrb744gubaUaOHCkp7Wnut99+O8tT31euXNFHH30kKe1GVf/+/Qu30jA8YmHjIB42BmJh4yAeNgbi4eKFeBgA4CjMUWdAr7/+ugYPHqyEhASNHDlSzzzzjFq0aKGEhAStW7dOixcvliQFBwfrySefzFcZtWrV0qhRozR79mxFRERo8ODBGj16tKpXr67Tp09rzpw5OnjwoCRp1KhRCg4OdtThFRmF3U6nTp3SqFGjdP36dUnSiy++qICAAEVFRdndp3z58ipfvnz+DqgIc8Y1hYJ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",
+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "from utils import prettify_model_name\n",
+    "\n",
+    "base_models_to_plot = [m for m in df[\"base_name\"].unique() if \"gemma\" not in m.lower()]\n",
+    "\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-8B\",\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mistral-7B-v0.1\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    # \"Yi-34B\",\n",
+    "]\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(8, 3), gridspec_kw=dict(wspace=0.33), sharey=False)\n",
+    "\n",
+    "# Plot base models\n",
+    "for ax, model_list in zip(axes, (base_models_to_plot, instr_models_to_plot)):\n",
+    "    \n",
+    "    for idx, m in enumerate(model_list):\n",
+    "\n",
+    "        curr_row = df[df[model_col] == m].iloc[0]\n",
+    "        curr_scores = scores_df_map[curr_row.name]\n",
+    "\n",
+    "        disp = CalibrationDisplay.from_predictions(\n",
+    "            y_true=curr_scores[\"label\"],\n",
+    "            y_prob=curr_scores[\"risk_score\"],\n",
+    "            n_bins=10,\n",
+    "            strategy=\"quantile\",\n",
+    "            name=prettify_model_name(curr_row[\"base_name\"]),\n",
+    "            linestyle=[\"-.\", \":\", \"--\"][idx % 3],\n",
+    "            ax=ax,\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlim(0, 1)\n",
+    "    ax.set_ylim(0, 1)\n",
+    "\n",
+    "ylabel = \"Fraction of positives \" + r\"$\\left(\\mathrm{P}[Y=1]\\right)$\"\n",
+    "axes[0].set_ylabel(ylabel)\n",
+    "axes[1].set_ylabel(ylabel)\n",
+    "\n",
+    "xlabel = \"Mean predicted probability\" # + r\" $(\\mathbb{E}[R])$\"\n",
+    "axes[0].set_xlabel(xlabel)\n",
+    "axes[1].set_xlabel(xlabel)\n",
+    "\n",
+    "axes[0].set_title(\"Base models\")\n",
+    "axes[1].set_title(\"Instruction-tuned models\")\n",
+    "\n",
+    "axes[0].legend().remove()\n",
+    "\n",
+    "# Legent to the right of the right-most plot\n",
+    "h, l = axes[1].get_legend_handles_labels()\n",
+    "l[0] = \"Perf. calibrated\"\n",
+    "axes[1].legend(handles=h, labels=l, loc=\"upper left\", bbox_to_anchor=(1.1, 1.1))\n",
+    "\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"calibration-curves-base-and-instr.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1875bb47-5e9d-499a-bb6b-c497513d4046",
+   "metadata": {},
+   "source": [
+    "### Same plot but only with large models (> 30B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "0177214e-8ae2-4a23-a561-0c4626f6a278",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-base-and-instr.large-models.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "from utils import prettify_model_name\n",
+    "\n",
+    "base_models_to_plot = [m for m in df[\"base_name\"].unique() if \"gemma\" not in m.lower()]\n",
+    "\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    \"Yi-34B\",\n",
+    "]\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(8, 3), gridspec_kw=dict(wspace=0.33), sharey=False)\n",
+    "\n",
+    "# Plot base models\n",
+    "for ax, model_list in zip(axes, (base_models_to_plot, instr_models_to_plot)):\n",
+    "    \n",
+    "    for idx, m in enumerate(model_list):\n",
+    "\n",
+    "        curr_row = df[df[model_col] == m].iloc[0]\n",
+    "        curr_scores = scores_df_map[curr_row.name]\n",
+    "\n",
+    "        disp = CalibrationDisplay.from_predictions(\n",
+    "            y_true=curr_scores[\"label\"],\n",
+    "            y_prob=curr_scores[\"risk_score\"],\n",
+    "            n_bins=10,\n",
+    "            strategy=\"quantile\",\n",
+    "            name=prettify_model_name(curr_row[\"base_name\"]),\n",
+    "            linestyle=[\"-.\", \":\", \"--\"][idx % 3],\n",
+    "            ax=ax,\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlim(0, 1)\n",
+    "    ax.set_ylim(0, 1)\n",
+    "\n",
+    "ylabel = \"Fraction of positives \" + r\"$\\left(\\mathrm{P}[Y=1]\\right)$\"\n",
+    "axes[0].set_ylabel(ylabel)\n",
+    "axes[1].set_ylabel(ylabel)\n",
+    "\n",
+    "xlabel = \"Mean predicted probability\" # + r\" $(\\mathbb{E}[R])$\"\n",
+    "axes[0].set_xlabel(xlabel)\n",
+    "axes[1].set_xlabel(xlabel)\n",
+    "\n",
+    "axes[0].set_title(\"Base models\")\n",
+    "axes[1].set_title(\"Instruction-tuned models\")\n",
+    "\n",
+    "axes[0].legend().remove()\n",
+    "\n",
+    "# Legent to the right of the right-most plot\n",
+    "h, l = axes[1].get_legend_handles_labels()\n",
+    "l[0] = \"Perf. calibrated\"\n",
+    "axes[1].legend(handles=h, labels=l, loc=\"upper left\", bbox_to_anchor=(1.1, 1.1))\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"calibration-curves-base-and-instr.large-models.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c69130d-0fe1-42a8-98a5-f071469a18f0",
+   "metadata": {},
+   "source": [
+    "### Smaller to use for both prompting schemes side by side"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "87b843d0-0082-4eca-9bb7-70bf1a53d35c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-base-and-instr.large-models.smaller.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "from utils import prettify_model_name\n",
+    "\n",
+    "base_models_to_plot = [m for m in df[\"base_name\"].unique() if \"gemma\" not in m.lower()]\n",
+    "\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    \"Yi-34B\",\n",
+    "]\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(5, 2.2), gridspec_kw=dict(wspace=0.15), sharey=True)\n",
+    "\n",
+    "# Plot base models\n",
+    "for ax, model_list in zip(axes, (base_models_to_plot, instr_models_to_plot)):\n",
+    "    \n",
+    "    for idx, m in enumerate(model_list):\n",
+    "\n",
+    "        curr_row = df[df[model_col] == m].iloc[0]\n",
+    "        curr_scores = scores_df_map[curr_row.name]\n",
+    "\n",
+    "        disp = CalibrationDisplay.from_predictions(\n",
+    "            y_true=curr_scores[\"label\"],\n",
+    "            y_prob=curr_scores[\"risk_score\"],\n",
+    "            n_bins=10,\n",
+    "            strategy=\"quantile\",\n",
+    "            name=prettify_model_name(curr_row[\"base_name\"]),\n",
+    "            linestyle=[\"-.\", \":\", \"--\"][idx % 3],\n",
+    "            ax=ax,\n",
+    "            ms=3.5,\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlim(0, 1)\n",
+    "    ax.set_ylim(0, 1)\n",
+    "\n",
+    "ylabel = \"Fraction of positives \" + r\"$\\left(\\mathrm{P}[Y=1]\\right)$\"\n",
+    "axes[0].set_ylabel(ylabel)\n",
+    "# axes[1].set_ylabel(ylabel)\n",
+    "axes[1].set_ylabel(\"\")\n",
+    "\n",
+    "# xlabel = \"Mean predicted probability\" # + r\" $(\\mathbb{E}[R])$\"\n",
+    "xlabel = \"Mean risk score\"\n",
+    "axes[0].set_xlabel(xlabel)\n",
+    "axes[1].set_xlabel(xlabel)\n",
+    "\n",
+    "axes[0].set_title(\"Base models\")\n",
+    "axes[1].set_title(\"Instruction-tuned\")\n",
+    "\n",
+    "# Set prettier x ticks\n",
+    "xticks = (0, 0.25, 0.5, 0.75, 1)\n",
+    "xticks_labels = (\"0.0\", \"0.25\", \"0.5\", \"0.75\", \"1.0\")\n",
+    "axes[0].set_xticks(xticks, labels=xticks_labels)\n",
+    "axes[1].set_xticks(xticks, labels=xticks_labels)\n",
+    "\n",
+    "axes[0].legend().remove()\n",
+    "\n",
+    "# Legent to the right of the right-most plot\n",
+    "if NUMERIC_PROMPTING:\n",
+    "    h, l = axes[1].get_legend_handles_labels()\n",
+    "    l[0] = \"Perf. calibrated\"\n",
+    "    axes[1].legend(handles=h, labels=l, loc=\"upper left\", bbox_to_anchor=(1, 1))\n",
+    "else:\n",
+    "    axes[1].legend().remove()\n",
+    "\n",
+    "# Figure title\n",
+    "if NUMERIC_PROMPTING:\n",
+    "    title = \"Numeric Prompting\"\n",
+    "else:\n",
+    "    title = \"Multiple-choice Prompting\"\n",
+    "plt.suptitle(title, y=1.1, fontsize=14)\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"calibration-curves-base-and-instr.large-models.smaller.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90cf83cb-cfc9-472f-92d7-605da9bb2704",
+   "metadata": {},
+   "source": [
+    "## Calibration per sub-group\n",
+    "Plot calibration curves per race sub-group, for each model pair."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b7f29ce1-b64a-4879-9a67-c305d1c4492b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "CPU times: user 41.3 s, sys: 28.3 s, total: 1min 9s\n",
+      "Wall time: 1min 9s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "acs_dataset = ACSDataset.make_from_task(task=TASK, cache_dir=DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19829429-ba87-471b-84cb-7b01829c5616",
+   "metadata": {},
+   "source": [
+    "Plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "98cb0f15-4434-4022-ba38-675b7c38db9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Omit small groups from the plots (results can be too noisy)\n",
+    "OMIT_GROUPS_BELOW_PCT = 4\n",
+    "N_BINS = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "f5a2f2e6-f557-401e-8f85-4d8ea0c0db72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sens_attr_col = \"RAC1P\"\n",
+    "sens_attr_map = {\n",
+    "    1: \"White\",\n",
+    "    2: \"Black\",\n",
+    "    6: \"Asian\",\n",
+    "    # 8: \"Other\",   # some other race alone\n",
+    "}\n",
+    "\n",
+    "name_to_sens_attr_map = {val: key for key, val in sens_attr_map.items()}\n",
+    "\n",
+    "def plot_cali_curve(model_id, ax, df):\n",
+    "    \"\"\"Plot sub-group calibration curves using sklearn\"\"\"\n",
+    "\n",
+    "    curr_row = df.loc[model_id]\n",
+    "    curr_scores_df = scores_df_map[curr_row.name]\n",
+    "\n",
+    "    y_true = curr_scores_df[\"label\"].to_numpy()\n",
+    "    y_scores = curr_scores_df[\"risk_score\"].to_numpy()\n",
+    "\n",
+    "    sens_attr = acs_dataset.data.loc[curr_scores_df.index][sens_attr_col].to_numpy()\n",
+    "\n",
+    "    for idx, group_val in enumerate(np.unique(sens_attr)):\n",
+    "        group_filter = sens_attr == group_val\n",
+    "\n",
+    "        if np.sum(group_filter) < len(group_filter) * 0.01 * OMIT_GROUPS_BELOW_PCT:\n",
+    "            # print(f\"Skipping group={group_val}\")\n",
+    "            continue\n",
+    "\n",
+    "        disp = CalibrationDisplay.from_predictions(\n",
+    "            y_true=y_true[group_filter],\n",
+    "            y_prob=y_scores[group_filter],\n",
+    "            n_bins=N_BINS,\n",
+    "            # strategy=\"uniform\",\n",
+    "            strategy=\"quantile\",\n",
+    "            name=str(sens_attr_map.get(group_val, group_val)),\n",
+    "            linestyle=[\"-.\", \":\", \"--\"][idx % 3],\n",
+    "            ax=ax,\n",
+    "        )\n",
+    "\n",
+    "    # Set other plot settings\n",
+    "    ax.set_title(curr_row[\"name\"])\n",
+    "    return disp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "798752d9-8abb-49d9-b874-24c54c523e93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.evaluation import bootstrap_estimate\n",
+    "\n",
+    "def plot_cali_curve_ci(model_id, ax, df, ci=95):\n",
+    "    \"\"\"Plot sub-group calibration curves with bootstrap confidence intervals\"\"\"\n",
+    "\n",
+    "    curr_row = df.loc[model_id]\n",
+    "    curr_scores_df = scores_df_map[curr_row.name]\n",
+    "\n",
+    "    y_true = curr_scores_df[\"label\"].to_numpy()\n",
+    "    y_scores = curr_scores_df[\"risk_score\"].to_numpy()\n",
+    "\n",
+    "    sens_attr = acs_dataset.data.loc[curr_scores_df.index][sens_attr_col].to_numpy()\n",
+    "\n",
+    "    # Compute bins\n",
+    "    if len(np.unique(y_scores)) == 1:\n",
+    "        ax.set_title(curr_row[\"name\"])\n",
+    "        return\n",
+    "\n",
+    "    y_scores_binned, bin_edges = pd.qcut(y_scores, q=N_BINS, retbins=True, duplicates=\"drop\")\n",
+    "    bin_names = y_scores_binned.categories   # Name used for each bin: \"(bin_low_range, bin_high_range)\"\n",
+    "\n",
+    "    # To hold plot render artifacts\n",
+    "    plot_artifacts = {}\n",
+    "\n",
+    "    for idx, group_val in enumerate(np.unique(sens_attr)):\n",
+    "        group_filter = sens_attr == group_val\n",
+    "\n",
+    "        if (\n",
+    "            np.sum(group_filter) < len(group_filter) * 0.01 * OMIT_GROUPS_BELOW_PCT\n",
+    "            or group_val not in sens_attr_map.keys()\n",
+    "        ):\n",
+    "            # print(f\"Skipping group={group_val}\")\n",
+    "            continue\n",
+    "\n",
+    "        group_labels = y_true[group_filter]\n",
+    "        group_scores = y_scores[group_filter]\n",
+    "\n",
+    "        group_cali_points = []\n",
+    "        for b in bin_names:\n",
+    "            bin_filter = y_scores_binned == b\n",
+    "\n",
+    "            group_labels_current_bin = y_true[group_filter & bin_filter]\n",
+    "            group_scores_current_bin = y_scores[group_filter & bin_filter]\n",
+    "\n",
+    "            results = bootstrap_estimate(\n",
+    "                eval_func=lambda labels, scores, sensattr: {\n",
+    "                    \"mean_score\": np.mean(scores),\n",
+    "                    \"mean_label\": np.mean(labels),\n",
+    "                },\n",
+    "                y_true=group_labels_current_bin,\n",
+    "                y_pred_scores=group_scores_current_bin,\n",
+    "                sensitive_attribute=None,\n",
+    "            )\n",
+    "\n",
+    "            # Store plot point for this bin\n",
+    "            group_cali_points.append((\n",
+    "                results[\"mean_score_mean\"],    # x coordinate\n",
+    "                results[\"mean_label_mean\"],    # y coordinate\n",
+    "                (                              # y coordinate c.i. range\n",
+    "                    results[\"mean_label_low-percentile\"],\n",
+    "                    results[\"mean_label_high-percentile\"],\n",
+    "                ),\n",
+    "            ))\n",
+    "\n",
+    "        # Get group label from sensitive attribute map\n",
+    "        group_label = str(sens_attr_map.get(group_val, group_val))\n",
+    "        \n",
+    "        # Plot this group's calibration curve w/ conf. intervals!\n",
+    "        line, caps, bars = ax.errorbar(\n",
+    "            # x=[mean_score for mean_score, *_ in group_cali_points],\n",
+    "            x=[(bin_edges[idx-1] + bin_edges[idx]) / 2. for idx in range(1, len(bin_edges))],\n",
+    "            y=[mean_label for _, mean_label, _ in group_cali_points],\n",
+    "            yerr=np.hstack(\n",
+    "                [\n",
+    "                    np.abs(np.array(yerr_range).reshape(2, 1) - y)\n",
+    "                    for _x, y, yerr_range in group_cali_points]\n",
+    "            ),\n",
+    "            fmt=\"o\", markersize=1,\n",
+    "            linestyle=[\"-.\", \"-\", \"--\"][idx % 3],\n",
+    "            linewidth=2,\n",
+    "            label=group_label,\n",
+    "        )\n",
+    "        plot_artifacts[group_label] = (line, caps, bars)\n",
+    "    \n",
+    "    # Global plot settings\n",
+    "    ax.plot(\n",
+    "        [0,1], [0,1],\n",
+    "        ls=\"dotted\", color=\"black\",\n",
+    "    )\n",
+    "\n",
+    "    # Draw custom legend\n",
+    "    handles = []\n",
+    "    for group, (l, c, b) in plot_artifacts.items():\n",
+    "        l.set_label(group)\n",
+    "        l.set_markersize(0)\n",
+    "        handles.append(l)\n",
+    "\n",
+    "    # Perfectly calibrated line\n",
+    "    perf_cal_line = plt.Line2D([], [], ls=\":\", color=\"black\", label=\"Perf. calibrated\")\n",
+    "\n",
+    "    # Confidence interv. marker line\n",
+    "    conf_int_line = plt.Line2D([], [], lw=0, ms=10, marker=\"|\", markeredgewidth=2, color=\"grey\", label=\"$95\\%$ conf. int.\")\n",
+    "    \n",
+    "    ax.legend(handles=handles + [conf_int_line, perf_cal_line])\n",
+    "    ax.set_title(curr_row[\"name\"])\n",
+    "\n",
+    "    return disp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e1895eb-cdf3-4f14-8424-aaad256821bb",
+   "metadata": {},
+   "source": [
+    "### Plot sub-group calibration (with confidence intervals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "b3802fc0-f903-4666-b4c9-de43d38bf46c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "818d39dfd7f14966b96c35233df9c3a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-8B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-70B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mistral-7B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mixtral-8x7B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mixtral-8x22B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Yi-34B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.None.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "\n",
+    "# Get results for the current task\n",
+    "task_df = all_results_df[all_results_df[task_col] == TASK]\n",
+    "\n",
+    "# Get all base/instruct model pairs\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-8B\",\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mistral-7B-v0.1\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    \"Yi-34B\",\n",
+    "]\n",
+    "# NOTE: Gemma models have degenerate distributions that always predict the same score, can't plot calibration properly...\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "base_models_to_plot += [None for _ in range(len(instr_models_to_plot) - len(base_models_to_plot))]\n",
+    "\n",
+    "base_it_pairs = list(zip(base_models_to_plot, instr_models_to_plot))\n",
+    "\n",
+    "for base_model, it_model in tqdm(base_it_pairs):\n",
+    "    fig, axes = plt.subplots(ncols=2, figsize=(7, 2.8), gridspec_kw=dict(wspace=0.35), sharey=False)\n",
+    "\n",
+    "    for idx, ax, m in zip(range(len(axes)), axes, (base_model, it_model)):\n",
+    "        if m is None:\n",
+    "            ax.remove()\n",
+    "            continue\n",
+    "\n",
+    "        curr_row = task_df[(task_df[model_col] == m) & (task_df[\"uses_all_features\"])].iloc[0]\n",
+    "        curr_id = curr_row.name\n",
+    "\n",
+    "        # # Original sklearn calibration plot:\n",
+    "        # plot_cali_curve(curr_id, ax=ax, df=task_df)\n",
+    "\n",
+    "        # # Plot with confidence intervals:\n",
+    "        plot_cali_curve_ci(curr_id, ax=ax, df=task_df)\n",
+    "        \n",
+    "        # Remove left plot legend\n",
+    "        if idx == 0:\n",
+    "            ax.get_legend().remove()\n",
+    "        else:\n",
+    "            ax.get_legend().set(loc=\"upper left\", bbox_to_anchor=(1.1, 1))\n",
+    "\n",
+    "        ax.set_xlim(0, 1)\n",
+    "        ax.set_ylim(0, 1)\n",
+    "\n",
+    "        ax.set_xlabel(\"Mean risk score\")\n",
+    "        ax.set_ylabel(\"Fraction of positives\")\n",
+    "\n",
+    "    plt.plot()\n",
+    "    save_fig(fig, f\"calibration-curves-per-subgroup.{base_model}.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef3001ff-8557-4097-b3a9-f506bff952ca",
+   "metadata": {},
+   "source": [
+    "### Smaller figures to plot both prompting schemes side by side"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "5a35436c-7606-4ebf-b021-b3072aa7625c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd4b354cb3c140238849fe9f583968d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-8B.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-70B.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mistral-7B-v0.1.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mixtral-8x7B-v0.1.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mixtral-8x22B-v0.1.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Yi-34B.smaller.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.None.smaller.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "\n",
+    "# Get results for the current task\n",
+    "task_df = all_results_df[all_results_df[task_col] == TASK]\n",
+    "\n",
+    "# Get all base/instruct model pairs\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-8B\",\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mistral-7B-v0.1\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    \"Yi-34B\",\n",
+    "]\n",
+    "# NOTE: Gemma models have degenerate distributions that always predict the same score, can't plot calibration properly...\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "base_models_to_plot += [None for _ in range(len(instr_models_to_plot) - len(base_models_to_plot))]\n",
+    "\n",
+    "base_it_pairs = list(zip(base_models_to_plot, instr_models_to_plot))\n",
+    "\n",
+    "for base_model, it_model in tqdm(base_it_pairs):\n",
+    "    fig, axes = plt.subplots(ncols=2, figsize=(4.8, 2.2), gridspec_kw=dict(wspace=0.15), sharey=True)\n",
+    "\n",
+    "    for idx, ax, m in zip(range(len(axes)), axes, (base_model, it_model)):\n",
+    "        if m is None:\n",
+    "            ax.remove()\n",
+    "            continue\n",
+    "\n",
+    "        curr_row = task_df[(task_df[model_col] == m) & (task_df[\"uses_all_features\"])].iloc[0]\n",
+    "        curr_id = curr_row.name\n",
+    "\n",
+    "        # # Original sklearn calibration plot:\n",
+    "        # plot_cali_curve(curr_id, ax=ax, df=task_df)\n",
+    "\n",
+    "        # # Plot with confidence intervals:\n",
+    "        plot_cali_curve_ci(curr_id, ax=ax, df=task_df)\n",
+    "        \n",
+    "        ax.set_xlim(0, 1)\n",
+    "        ax.set_ylim(0, 1)\n",
+    "\n",
+    "        ax.set_xlabel(\"Mean risk score\")\n",
+    "        if idx == 0:\n",
+    "            ax.set_ylabel(\"Fraction of positives\")\n",
+    "\n",
+    "        # Set prettier x-ticks\n",
+    "        xticks = (0, 0.25, 0.5, 0.75, 1)\n",
+    "        xticks_labels = (\"0.0\", \"0.25\", \"0.5\", \"0.75\", \"1.0\")\n",
+    "        ax.set_xticks(xticks, labels=xticks_labels)\n",
+    "\n",
+    "    # Prepare legend\n",
+    "    axes[0].legend().remove()\n",
+    "    if NUMERIC_PROMPTING:\n",
+    "        axes[1].get_legend().set(loc=\"upper left\", bbox_to_anchor=(1.03, 1))\n",
+    "    else:\n",
+    "        axes[1].legend().remove()\n",
+    "\n",
+    "    # Figure title\n",
+    "    if NUMERIC_PROMPTING:\n",
+    "        title = \"Numeric Prompting\"\n",
+    "    else:\n",
+    "        title = \"Multiple-choice Prompting\"\n",
+    "\n",
+    "    # Don't use suptitle for GPT4 (no base model)\n",
+    "    if base_model is not None:\n",
+    "        plt.suptitle(title, y=1.1, fontsize=14)\n",
+    "    else:\n",
+    "        model_title = axes[1].get_title()\n",
+    "        plt.title(f\"{title}\\n{model_title}\")\n",
+    "    \n",
+    "    plt.plot()\n",
+    "    save_fig(fig, f\"calibration-curves-per-subgroup.{base_model}.smaller.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79d97774-23e4-42e9-a52c-8d7d5504eac7",
+   "metadata": {},
+   "source": [
+    "## Score distribution per sub-group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "47bca57c-3e4b-496a-8f1b-880b6ded9639",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    2\n",
+       "2    1\n",
+       "8    1\n",
+       "Name: RAC1P, dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sens_attr_data = acs_dataset.data[sens_attr_col]\n",
+    "sens_attr_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "d768a5b5-7816-41e8-a4da-763986ed994a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad627657fb0044bb9f3620c89dc7d648",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/score-per-subgroup.Meta-Llama-3-8B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Meta-Llama-3-70B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Mistral-7B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Mixtral-8x7B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Mixtral-8x22B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Yi-34B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.None.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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UNtu2bcPmzZvdEo8le/bsUZaffPJJm51mALh48aKrQ3KY6Wltt912G8LDw12+T9N9lJeXe8V8bUREROT92C+1j/3S+nF2v9R03t68vLwGbcvZTONp166dByMh8l4s6BJRo3HrrbfW+QV38ODBaNasmUfiUalU6N27Nz799FPcdtttyv3bt2+v09ZdIyPy8/OV5S5duthsq9frcejQIVeH5JC8vDyzTv+4cePcst/aF2Hw1AgWIiIiEgv7pfaxX1o/zu6XxsXFKctnzpxp0LZMNTSuiooKs+J9fHx8Q0MiapRY0CWiRkOtVmP06NFm97n6tDZHSJKEgQMHKrcLCwvrtPH19VWWjafkuYJKdf1j395cWVu3bsXly5ddFkt9bNiwAQaDAQDQvHlzDB482C37PXbsmLLs5+eH0NBQt+yXiIiIxMZ+qX3sl9aPs/uliYmJyvLx48cbtC1TDX39nDx5Uvn7duzYEc2bN3dabESNCefQJaJG5cknn8TIkSOV2926dXPZvkpLS+Hr62vWabEmNzdXWbZ05V/TDpkrT3mKjo7GqVOnANSMyLD29ykqKsI777zjsjjqy/S0thEjRsDPz8/l+8zNzTU7DTE5OZkjdImIiMhh7Jfaxn6p41zRL+3Xrx/UajX0ej3+/PNP6HQ6+Pg0vETU0NfPgQMHlOVbb721wfEQNVYcoUtEjUrz5s2RkJCg/HPlBayOHj2KQYMGYd68eUpntDa9Xo9Nmzbhyy+/VO6744476rS76aablOXdu3crFyhwNtMRGZ999hk2bNhQp83Ro0fxwAMPIDc3F4GBgS6Joz6OHDmCrKws5bY7TmtLT0/H9OnTzU5te+ihh1y+XyIiImo82C+1jf1Sx7iqX9qiRQvccsstAGouOJeWltbgbQINf/388ccfyvKdd97plJiIGiOO0CUiaoDLly9j/vz5mD9/Plq3bo34+Hi0bt0aarUaBQUFOHr0qNn8YL1798Zf/vKXOttJTExEZGQkcnNzcfnyZYwYMQK33XYbQkNDlV/fExISzEZ53Ihx48Zh8eLFOHv2LKqrq/Hyyy/js88+Q3x8PPz8/HDixAmkp6cDqJmvasCAAVi4cGGD9tlQpp37jh07omfPng3e5rx58xAUFGR2n8FgwLVr15CZmYmTJ0+arZs2bRpHCBAREZFXY7/U9Rpbv3T06NHYv38/gJppLZzxfBry+iktLVXmJ27ZsqXZfM9EZI4FXSKiG+Tv7w8fHx9lbqjLly/bnNtr2LBhmDNnjtl8YUYqlQpvvPEGnn32WWi1Wly+fNnsdC6gptPb0I6zr68v/u///g+PPvoosrOzAQBZWVlmIw0A4JZbbsGHH36INWvWNGh/DVVdXY3vv/9eue2sURC1/7bW+Pv74/nnn8fDDz/slP0SERERuQL7pa7XGPulI0eOxNy5c1FeXo6ffvoJL730UoOncmjI62f79u2orq4GANx9991OmQKCqLHiu4OI6AbdfPPN+P333/H777/j4MGDyMjIwPnz51FcXAyDwYDg4GBER0cjKSkJY8aMMbvwgCUDBw7EN998gxUrVuDQoUO4ePEiysvLIcuyU+Pu1KkT1q9fjxUrVuDnn3/GmTNnoNVq0bp1a3Tp0gWjRo3CiBEjXHpaoKN27dqF4uJiADWdw7vvvtul+wsMDERoaCi6dOmCfv36YcyYMRbnliMiIiLyJuyXul5j7Jc2a9YM48ePx5dffomcnBz8/vvvThkVe6Ovn7Vr1wKouajglClTGhwHUWMmyc7+RCYiIiIiIiIiIq+Xk5OD4cOHQ6vVYvDgwfj00089EsepU6cwatQoyLKMUaNG4f333/dIHESi4EXRiIiIiIiIiIiaoKioKEycOBFAzZQHtae8cJeFCxdClmX4+Pjgueee80gMRCJhQZeIiIiIiIiIqIl65plnEBwcDFmW8cknn7h9/+fPn1fmJ77vvvvQoUMHt8dAJBoWdImIiIiIiIiImqhWrVrh2WefBQBs2rQJ6enpbt3/hx9+CK1Wi7CwMDz//PNu3TeRqDiHLhEREREREREREZEgOEKXiIiIiIiIiIiISBAs6BIREREREREREREJggVdIiIiIiIiIiIiIkGwoEtEREREREREREQkCBZ0iYiIiIiIiIiIiATBgi4RERERERERERGRIFjQJSIiIiIiIiIiIhIEC7pEREREREREREREgmBBl4iIiIiIiIiIiEgQPp4OwBMefPBB7Nu3r16PWbZsGZKTky2u27VrF9asWYO0tDQUFRUhLCwMCQkJuPfee5GSkuKMkK0yGAyoqKgAAPj4+ECSJJfuj4iIiBofWZah0+kAAAEBAVCp+Js/iYP9YSIiImoo0frDTbKgW18qlQodO3asc7/BYMCsWbPw9ddfm92fl5eHvLw8bN26Fffccw/eeustl70QKioqcPz4cZdsm4iIiJqe+Ph4BAUFeToMIoexP0xERETOJEJ/uEkWdOfMmaP8im/NqVOnMHPmTABA//79ERERUafNf/7zH6WY261bNzzyyCOIjo5GdnY2Fi5ciGPHjmHt2rUICwvDCy+84PwnQkRERERERERERE1KkyzoRkdH222zYcMGZfnuu++us/7MmTNYvHgxAKBHjx5YsWIF/P39AQCJiYkYNGgQHnjgAaSnp2PRokWYMGECOnTo4KRncJ2Pz/UUxsbGIjAw0On7oIbTarXKyJH4+HhoNBoPR0TWMFdiYJ7EwVyJoby8HFlZWQDM+xZEImB/WAw8HoiDuRID8yQO5koMovWHvT9CDzAYDNi4cSMAIDAwEHfddVedNkuXLlXm1pg1a5ZSzDUKCAjArFmzMGnSJOh0OixZsgRvvPGG02M1nSPM19cXvr6+Tt8HNZxKpUJISAgAwM/PT4gPh6aKuRID8yQO5koMxj4NAM4/SsJhf1gMPB6Ig7kSA/MkDuZKDKL1h/kqsmDPnj3Iy8sDAAwfPhwBAQFm62VZxrZt2wAAMTExSEpKsridpKQkdOrUCWfOnMG2bdvwj3/8w6UvCn4oeC8fHx/ExsZ6OgxyAHMlBuZJHMyVGNiHoMaCr2XvxeOBOJgrMTBP4mCuxCBaH8K7L9nmIevXr1eWLU23kJOTg/z8fABAnz59bG6rb9++AGoulJaTk+O8IImIiIiIiIiIiKjJYUG3lrKyMmzduhUA0K5dOyQnJ9dpc+rUKWU5JibG5vZM158+fdpJURIREREREREREVFTJNZ4Yjf4+eefUV5eDgAYPXq0xSkSLl26pCy3adPG5vZM1+fm5jopSsuMcVvi4+MDlaqmfm8wGMzmBrHEdO4xvV4PvV5vta0kSWaTeut0OhgMBqvtVSqV2VB2rVYLWZYdil2WZWi1WpuxazQaJW/2nqurY1er1VCr1dBqtcjKyoLBYECHDh2sToLOPHkmdmOejG1PnjwJAFZzxTx5JnbmyTpvzRNQ80PpuXPnAFjOFfPkmdhr58lWH4Iap9LSUuzatQtpaWlIT09HXl4eioqKUFVVhWbNmqFz58644447MHHiRISGhtrd3qFDh7By5UocPHgQBQUFaN68OeLj4zFu3DiMGjXKDc+ohlar5Ry6XsrYHwZqLl7HiwJ5L+ZKDMyTOJgrMdjry3sbFnRrMZ1uYezYsRbblJWVKcv2rqJrOv+uq78sGT8gLImLi0NwcLASR2Zmps1t9erVS1kuKCiwOV1EUFAQ4uPjldvnzp1DcXGx1fbh4eGIjo5WbmdkZNh848TExChfJLRaLdLS0mzGnpCQoHTkS0pKbI6M1mg0SExMVG7n5uYq02lYEhISYjb3TVZWltnrobaoqChERERAlmWlnfHqlpYwT5a5K09ATVGmoqICgPVcMU+WMU/FVts31TwB5vmxlCvmyTJ358lWH4IapyNHjuCFF16wuK6oqAj79u3Dvn37sGjRIrz77ru4/fbbrW5r3rx5+PTTT81+dCgoKMDu3buxe/dubNy4ER9//DH8/Pyc/jxIHKb9YVs/OJHnMVdiYJ7EwVyRK7Cga+LSpUvYt28fgOsXNLOkqqpKWbb3y4rpCIHKykonRElERERE1HCRkZFITk5G9+7dERkZidatW8NgMODSpUvYvHkztmzZgitXruDJJ5/E119/bfYDidFXX32F+fPnAwDat2+Pxx9/HF26dEF+fj6WLVuGvXv3YufOnXjttdfw/vvvu/spEhERETVKksyfBxQLFixQOppvvvkmJk+ebLHdwoUL8e677wIAPv/8c9xxxx1Wt7lr1y489thjAID/9//+Hx5++GGnxlxdXa2MCIqNjbU6YpintHomduMpraZ5io+P55QL8M48AXAoV8yTZ2Jnnqzz1jwBNWe1GEfmWsoV8+SZ2Gvnqbi4WBmlazqSmBovvV5v9hqwZOvWrXj66acBAEOHDlUKt0bFxcUYMmQIrl27hrZt2+Kbb75BWFiY2T6efvpp7NixAwCwbNkyi9enaKjax4SgoCCn74MazjRP/JzxbsyVGJgncTBXYjD93iJCnjhC18SGDRsA1HxZHDlypNV2pp1Ee9MoGE8JBuxPz9BQGo3GoRecSqWq1wuz9pc+e0y/UDqiPvPHSJJUr9jr+1xdGbvpY5gnc96YJ+Pj7MXFPFnHPFnXlPJk2t5erpgn69yZJ2oaHHnvDBkyBJ06dcKZM2dw4MCBOuvXrl2La9euAQBeeukls2KucR9vvvkmfvnlF+j1eixatMglBV0iIiKipkbl6QC8RVpaGk6dOgUAGDhwIFq0aGG1remFzkwvkGaJ6frIyMgGRklERERE5D7GgQymU44Zbdu2DQAQHByMoUOHWnx8mzZt0L9/fwDAnj17UFpa6qJIiYiIiJoOFnT/yzg6F7B+MTSjzp07K8u2LmhSe31MTMyNBUdERERE5GanT59WTj2s3Y+trq7GkSNHANRce8LWiPO+ffsqj0lPT3dRtERERERNB6dcQM08cz/88AMAICwszOacuEDNlaHDw8ORn5+P/fv322xrXB8REYGoqCjnBOwClZWVKC4uRnl5uc35COnGmM57eObMGWXORWoYtVoNX19fNG/eHMHBwfy7EhERNVBFRQXy8vKwY8cOLFy4UJnredq0aWbtzp49q/QZ7Q1aMF2flZWFfv36OTlqIiIioqaFBV0Av/zyC4qKigAAo0aNsjtPnSRJGDx4MFatWoXTp08jNTUVSUlJddqlpqYqI3QHDx6sXAjFVW6kmCXLMnJzc1FSUuKCiMhIlmX4+fkBqLlAiK0L25DjdDodqqqqcO3aNUiShHbt2qFZs2YN2qZarVZ+fKnP3JzkXsyTOJgrMfAHsaZt3bp1ePXVV62uf+yxxzB69Giz+0ynFTOdjsyS+kxX1lB6vR7V1dUW1/EijJ6J3TjfufF4oNPpmKf/8sY8GZeN71truWKePBM782Sdt+YJqPlb2soV8+SZ2C3lSSQs6KJ+0y0YTZs2DWvWrIFer8fs2bOxYsUK+Pv7K+srKysxe/ZsADUv8NqjGlzhRr4oFxYW1inm1vfCK0SeotfrlQ9oWZZx4cKFBhd11Wo1IiIinBUiuQjzJA7mSgwstpMlXbt2xVtvvYXExMQ668rKypRlexf+NV1v74LCDXXy5Emr6+Li4hAcHKzEkZmZaXNbvXr1UpYLCgqQk5NjtW1QUBDi4+OV2+fOnUNxcbHV9uHh4YiOjlZuZ2Rk2PwSHBMTg9DQUAA1X1CNV0u3xvTq3CUlJTanidNoNGY5zs3NRX5+vtX2ISEhiI2NVW5nZWWZvR5qi4qKQkREhHI8OHjwoM3CPvNkmbvyBNQcE4w5spYr5sky5qnYavummicAOHbsmLJsKVfMk2XuzpOtPoQ3avKVu5KSEuzYsQMA0KVLF3Tv3t2hx3Xq1AnTp0/HggULkJ6ejsmTJ+PRRx9FdHQ0srOz8fnnnytv2unTp6Njx46uego3rLq6GpcvX1Zuh4eHIyQkhF/qSBiyLKO8vBxFRUUoLS1VirpdunThaDMiIiIHDRkyBD169ABQMyghOzsbP/74I7Zs2YIXX3wRr732GgYOHGj2GNOLpJmOnrHEdCRRZWWlEyMnIiIiapok2db44yZg1apVePPNNwEAf/vb3/DII484/FiDwYDXX38d33zzjdU2EydOxOzZs11WXKqurlZ+ATH9pcMRRUVFyMvLAwC0bNkS4eHhLomRyNVkWUZOTo5y5ex27dqhefPmHo6KiEgcDelPUOO1fv16vPLKK5AkCf/6178wfvx4Zd2PP/6IGTNmAADefPNNTJ482ep2srKyMHLkSADAAw88gFmzZjk1TtPXb3x8vNUCM09p9UzstU9ptTbVghHz5JnYmSfrmCfLmCfmqXbsoueprKxMuRisCP3hJj9C1zjdglqtrjM3mD0qlQpz5szBsGHDsHr1aqSlpeHKlSsIDQ1FQkICJk2ahJSUFFeEbZFWq63XC8506DmLX65lMBhQUVEBAAgICODoUSeTJAlhYWFKQffq1as3/JpmUUMMzJM4mCsx2Ot8U9M0duxY7Ny5Ez/++CNmz56NQYMGISQkBEDNKZxG9qZRMF1vb3oGZ3Dkc0alUtXr86j2lz576juFmb1RzqYkSapX7PV9rq6K/UaOB8yTda6Mvb65Yp6sY56sayp5MnI0V8yTde7Ik0iafEH3q6++avA2UlJS3Fq4dRbjr0SSJCkX7CISVWBgICRJgizLdn8BJSIiIscMHjwYP/74I8rLy/Hrr78qAyDqc6Gz+lxAjYiIiIjs4zDBJsw4VF2tVitD3olEJUmS8sukaFenJCIi8lZhYWHK8sWLF5Xljh07KsddWxc0qb3e9GIlRERERHRjWNAlIiIiauIa+ylpdOOM11sAzKdL8PX1Va48nZqaavPsmH379imPMV58jYiIiIhuHAu6RERERERk0U8//aQsd+nSxWzd4MGDAQClpaXYsmWLxcdfunQJe/bsAQD0798fwcHBLoqUiIiIqOlo8nPoEhEREYlg+/ntKKoscsm2Q9WhCEGIS7ZN3mndunX4y1/+YvM6CkuWLMGuXbsAAFFRUejdu7fZ+nvuuQefffYZrl27hvfffx+33norQkNDlfV6vR5vvvmmMhXS9OnTXfBMiLxIxvdAWYFrth3UCug6yjXbJiIi4bCgS0RERCSAosoi5Jfnu2TbkkZiQbeJmT9/Pv7973/jrrvuQq9evRAdHY2goCCUlpbixIkT2LhxIw4dOgSgZkqO2bNn17mKdkhICF566SW88cYbuHDhAu6991488cQT6NKlC/Lz87F06VLs3bsXADBq1CgkJye7/XkSuVVZAXAt19NREBFRE8CCLhERERFRE1RcXIw1a9ZgzZo1Vtu0adMGc+bMwa233mpx/X333Yf8/Hx8+umnOH/+PF577bU6bVJSUjBnzhynxU1ERETU1LGg24jUHjVB3kOSJPj7+yvL7hAXFwcAeOaZZ/Dss8/e0Db27t2LqVOnAgCWLVvWJEbW+Pj4KH87Hx9+RHor5kkczJUYJJV7jk3kPRYuXIhdu3bh0KFDOHfuHAoLC1FcXAw/Pz+0bNkSXbt2xZ133okRI0YgICDA5raee+45DBgwACtWrMDBgwdRUFCA5s2bIz4+HuPHj8eoUe47TZz9Ye/F44E4mCsxME/iYK7EIFofgq+kRkSl4jXuvJXBYECfPn1QVlaGbt264dtvv7XaVpZl9OvXD8XFxQCAf/3rX5g4caLV9t9++y1eeeUVAMAbb7yB+++/36mxNzUqlYoXbBEA8yQO5koM7vqxkbxHTEwMYmJi8NBDDzlle7fccgtuueUWp2yrIdgf9l48HoiDuRID8yQO5koMovUhxIqWSFBqtRo9e/YEABw/fhylpaVW2548eVIp5gLAwYMHbW77wIEDynKfPn0aFmg9zZs3D3FxccqvjURERERERERE5FocoduIGAwGl+9j89FLKCytdvl+3KllsC+GdW/j0n3IsoxevXph9+7dMBgMOHToEO644w6LbY0FWrVaDb1eb1awtdU+NDQUnTt3dmrcycnJyMzMdOo2vZ3BYEB5eTkAIDAwULhf6ZoK5kkczJUYZFn2dAhETuGO/jDdGB4PxMFciYF5EgdzJQbR+hAs6DYier3e5fsoLK1G3tVKl++nsZFlGYmJicrtAwcO2C3oDh8+HD/88APOnz+PvLw8RERE1GlbWFiIs2fPAgB69erFU2adQKfTKUXshIQE+Pr6ejgisoR5EgdzJQbZwIIuNQ7u6A/TjeHxQBzMlRiYJ3EwV2IQrQ/BnwWI3KR79+7w8/MDAJujbo3rhg0bhvbt2wOwPu2CJ6dbICIiIiIiIiIi9+MIXSI38fX1Rffu3XHo0CGkpaWhurq6zi9z2dnZyMvLA1Az4rZXr144f/48Dhw4gJEjR9bZpmlBt3fv3jb3f+TIESxZsgQHDhxAUVERQkND0a9fPzzxxBOIjY21+Ji9e/di6tSpAIBly5YhOTkZALBu3Tq8+uqrZm0tzaO7bds2REVFmd2n1+vx3Xff4aeffsLRo0dRXFyMoKAgxMTE4K677sLkyZPh7+9v87kQERERERERETVVHKFL5EbGqz9XV1fjzz//rLPeWKDt2LEjWrVqhV69egEA9u/fb3F7xpG7wcHB6Nq1q9X9rlixApMnT8YPP/yAvLw8aLVa5Ofn47vvvsOECROsbt/ZLl68iPHjx+OVV17Bzp07cfnyZWi1WhQXF+PQoUOYO3cuxowZgzNnzrglHiIiIiIiIiIi0XCELpEbGQu6QE3xtvY0CcaCrrGd8f8nT55ESUkJWrRoobQtLS3F8ePHAQA9e/aEWq22uM/du3fjyJEj6NKlC6ZOnYouXbqgqqoKW7ZswbJly1BRUYGXX34ZmzdvdngunyFDhqBHjx5YuXIlVq1aBQDYuHFjnXam8/5euXIF999/P3Jzc+Hr64t7770Xffr0Qbt27VBeXo7ffvsNy5Ytw7lz5/Doo4/i22+/RbNmzRyKh4iIiIiIiIioqWBBl8iNEhMT4ePjA51OZ3EeXeN9xukTYmNjERoaiitXruDQoUMYOHCg0vbw4cPKpN225s9NTU1FSkoK5s+fb1aw7d27N0JCQvDhhx/i4sWL2LVrF4YOHerQ82jevDmaN2+Oli1bKvd16dLF5mPefvtt5Obmol27dli6dCmio6PN1icnJ2P48OGYMmUKsrOzsXDhQsycOdOheIiIiIiIiIiImgpOuUDkRoGBgcrUCKYFWQAoLCzE2bNnAUCZagG4Pkq3dgHY0flz/fz88M4771gcffvggw9Co9FY3L4z5eTk4McffwQAzJo1q04x16hbt264//77AdTM00tEREREREREROZY0CVyM2PxtaysDBkZGcr9xnlsW7VqhY4dOyr3G4u7tQuuxvlz/fz8kJCQYHV/t956q9lIWlPBwcHKvrKzs+v3ROph165d0Ov1CAgIwB133GGzrXG0cX5+Pi5evOiymIiIiIiIiIiIRMQpFxoR40hL8j4qlQpBQUEAagq6X3zxBYCaIm2PHj2UZcB8dK6xPQAcPXoUlZWV8Pf3R3V1NY4cOQIAuPnmm23OfRsTE2MzNuO8vGVlZfV9Wg5LT08HAFRUVKBbt24OP66goABt27Z1VVgW+fr61skBeR/mSRzMlRhUav7GT40D+8Pei8cDcTBXYmCexMFciUG0PgR770Ru1rt3b0iSBOD6KFvTZdMLpwE10xAEBARAq9UiNTUVAJCWloaqqiple7YEBATYXK9S1XwMGAwGx59EPRUWFt7Q4yoqKpwcCRERERERERGR2DhCl8jNQkJCcNNNN+HEiRPKqNzS0lIcP34cQN0RuhqNBgkJCdi3bx8OHDiAfv36mRWCbV0QzVsY5woODQ3FsmXLHH5cVFSUq0IiIiIiIiIiIhISC7qNiOkFtsi7yLIMnU4HAPDx8UHv3r1x4sQJFBUVISsrCzk5OTAYDAgMDLQ4JUGvXr2Ugi5wfb5djUaDpKQktz2PGxUSEgKgZlqH2NhYqNVqzwZkg16vR0FBAYCa+Yy9OdamjHkSB3MlBleepUHkTuwPey8eD8TBXImBeRIHcyUG0foQnHKhEeGXMe8lyzKqq6tRXV0NWZbNpkk4ePCgMuI2KSnJ4oe7cdTun3/+ierqahw+fBhAzXQMgYGBbngGlhmnjrDHWKSurq5W5tP1Vnq9Hjk5OcjJyRHuA70pYZ7EwVwJQvZ0AETOwf6w9+LxQBzMlRiYJ3EwV2IQrQ/Bgi6RB5gWdPfv36+MuLU2UXrPnj2hVqtRXl6OdevW4dq1a3W24wmmF2Orrq622m7gwIFK8Xfp0qUuj4uIiIiIiIiIqLFiQZfIAyIiItC+fXsAwN69e5VRq9YKusHBwejSpQsAYOHChcr9np4/Nzw8XFk+f/681XYxMTEYPnw4AOCHH37AF198YXO72dnZ+P77750TJBERERERERFRI8I5dKleWgb72m8kGE89p969e+P8+fPIy8sDUDO3rq35cHv16oWMjAxkZ2cDAFQqldUCsLv07NlTWX7nnXfwxBNPoHXr1spo3Hbt2sHHp+Zj5s0330R6ejqys7Mxd+5cbNu2DXfffTduuukm+Pr6ori4GMePH8evv/6KP/74A0OHDsWoUaM88ryIiIiIiIiIiLwVC7pUL8O6t/F0CI1G7969sW7dOuV2165dERAQYLV9r1698OWXXyq3u3TpgubNm7s0Rns6dOiAESNG4Mcff8Tu3buxe/dus/Xbtm1DVFQUgJoLo61atQozZszAgQMHzKaasCQoKMilsRMRERERERERiYgFXSIPqT1dgr3RtrXXe3q6BaN3330XPXr0wObNm3HmzBmUlZVZnUy8devWWLFiBXbu3Invv/8eqampKCgogE6nQ7NmzdChQwf07NkTgwYN8prnR0RERERERETkTVjQJfKQ9u3bIzMz0+H2ERER9WrvaNvly5dbXZecnGx3OxqNBo888ggeeeQRh2O78847ceeddzrcnoiIiIiIiIiIarCgS+QmKhWvQSgCSZKU6R6McwGT92GexMFcERERwOOBSJgrMTBP4mCuyBVY0G1ENBqNp0MgK1Qqlc35ccl7aDQaxMfHezoMsoN5EgdzJQaVmj86UuPA/rD34vFAHMyVGJgncTBXYhCtD8HeOxEREREREREREZEgWNAlIiIiIiIiIiIiEgSnXGhEdDodfH19PR0GWSDLMqqqqgAAfn5+nDfHi+l0Opw7dw4A0KFDB/j48GPSGzFP4mCuxGAwGDwdApFTsD/svXg8EAdzJQbmSRzMlRh0Op2nQ6gXjtBtRGRZ9nQIZIUsy9Dr9dDr9cyTlzMYDCguLkZxcTELHF6MeRIHcyUIHpqokWA/y3vxeCAO5koMzJM4mCsxiNaHYEGXiIiIiIiIiIiISBAs6BIREREREREREREJggVdIiIiIiIiIiIiIkGwoEtEREREREREREQkCBZ0iYiIiIiIiIiIiATBgi4RERERERERERGRIHw8HQA5jyRJng6BrJAkCRqNRlkm76VSqRAeHq4sk3dinsTBXAmChyZqJNjP8l48HoiDuRID8yQO5koMovUhWNBtRHx8mE5vJUkSfH19PR0GOcDHxwfR0dGeDoPsYJ7EwVyJgV8uqLFgf9h78XggDuZKDMyTOJgrMYjWhxArWhe6ePEivv76a+zcuRMXL15EWVkZwsLC0K5dOyQnJ2PEiBHo0qWL1cfv2rULa9asQVpaGoqKihAWFoaEhATce++9SElJceMzISIiIiIiIiIiosaKBV0Ay5cvxwcffIDy8nKz+y9duoRLly7h4MGDKC0txd///vc6jzUYDJg1axa+/vprs/vz8vKQl5eHrVu34p577sFbb73F0S9ERERERERERETUIE2+oPvpp5/io48+AgB07NgR9957LxISEtCsWTMUFxfj2LFj2LJli9Vi7H/+8x+lmNutWzc88sgjiI6ORnZ2NhYuXIhjx45h7dq1CAsLwwsvvODS56LVanlav5cyGAyorKwEAPj7+7O478W0Wi0yMjIAAF27dlXmPibvwjyJg7kSg0Fv8HQIRE7B/rD34vFAHMyVGJgncTBXYtBqtZ4OoV6adEF3z549SjF37NixePvtt+u8sfr374/p06ejurq6zuPPnDmDxYsXAwB69OiBFStWwN/fHwCQmJiIQYMG4YEHHkB6ejoWLVqECRMmoEOHDi5+VuStZFn2dAjkAFmWlQ9y5sx7MU/iYK6IiAjg8UAkzJUYmCdxMFfkCk12mKDBYMCbb74JAIiPj8e//vUvm7+SWPqlf+nSpdDpdACAWbNmKcVco4CAAMyaNQsAoNPpsGTJEucET0RERERERERERE1Sky3o7t69G2fPngUAPProo/W+mp0sy9i2bRsAICYmBklJSRbbJSUloVOnTgCAbdu28dcYcoq9e/ciLi4OcXFx2Lt3r6fDqWPdunVKfDk5OZ4Oh4iIiIiIiIio0WiyUy789NNPAABJknDnnXcq9xcXF6O4uBghISEICQmx+vicnBzk5+cDAPr06WNzX3379sWZM2eQl5eHnJwcREdHNzh+EtOBAwfw2GOPWV0fGBiI8PBwJCYmYvz48ejfv78boyMiIiIiIiIiIm/XZAu6f/75JwCgXbt2CA4OxsaNG7FgwQKcOHFCaWO8SNqDDz5YZ8qFU6dOKcsxMTE292W6/vTp0yzoklXl5eU4e/Yszp49i++++w5jx47FnDlzoFarPR0aERERERERERF5gSZZ0DUYDDh9+jQAIDQ0FG+//TaWL19ep93Zs2fxP//zP9iyZQsWLFiA5s2bK+suXbqkLLdp08bm/kzX5+bmNjR8q7RarcWLtwGAj48PVKqaGTYMBgN0Oh0MBoMyBYTBYH51a2NboGZ6CUmSapYzvgdKL9fZvnE9AMiQAVszS0iABJP29qahMGlvd9s30F4Kbg10HaXEYiseSZLMnmvtv5u99qbuu+8+TJ482ey+kpIS/Pnnn1iyZAkKCwuxfv16REREYMaMGWbtLMVoL3agbl5d9VxNtyvLssXH1m7vrNgtrdNqtTa3r1arLRbNrV3l0tL7yRbTH4T0ej30er3VtpIkmc3nbXyvWqNSqcymjLH3XE1jN52c3xqNRqPkyd5zdXXszJN13pwn09gtPQ/mybHYAcCgN1h9vpIkmX1O2vq7WGpvkG1/xhMRERERkfdokgXda9euKV/oTpw4gbS0NLRu3Rovv/wyUlJS4Ofnh7S0NLz33ntITU3F4cOH8dprr2H+/PnKNsrKypTlwMBAm/sLCAhQlsvLy538bK7Lysqyui4uLg7BwcFKDJmZmQCufxmtqKgwax8UFKQs6/V65QuuriQX8tWLZm0lSYLG5/qXTr1OZ/OLoVpl/mVfp9XWFF6t8FH7QDJ+6ZRlaHV2vjD7aABjodBggE5v4wszJGhMipY6nc5qURyoKVSYXvyuqqrKZnHA19fX6sX2mjdvXme09k033YTk5GQMGjQI48ePR1VVFb788ks8/PDDZtupqqqqsz17satUKrPXYlVVlc0v/BqNxqxwUllZabOw4efnZ3Eu6srKyjqvL6DmfWEsbOj1eovPyUiSJLP3mVartVg40ev1FtdlZWWZvWdri4qKQkRERJ37jx8/brG9tfeTNb169VKWCwoKbM4rHBQUhPj4eOX2uXPnUFxcbLV9eHi42esoIyPDZlEpJiYGoaGhAGr+jmlpaTZjT0hIUF4HJSUlyo9hlmg0GiQmJiq3c3NzlalpLAkJCUFsbKxym3m6rjHlyTQ/lnLFPFlWO08AUHK1BEVXiyy29/PzM/vh+dq1azZjDwoKMvtcLSsra6K9QiIiIiIi8TTJrrtpcamqqgoBAQFYtmyZ2dQIffr0wdKlSzFp0iQcP34cW7ZswZ9//ombb75ZeZyRtYKdUe2iGDU9kiTBz8/P4fadO3dGSkoKfv75Z5SVleHMmTPo0qWLCyMko/peIJE8g3mipig0NBRaP9s/ahrZug6AJS1atACs1+mJhMFpqryXj4+P8n2Lx3HvxlyJgXkSB3MlBtH6EE3ylVR7PtyJEydanAfX398fM2fOxOOPPw4A2LRpk1LQNS3O2TvF0nTUpOnoTmeLj4+3Wlw2/dAIDAxEQkICzpw5A71eD7VabTZyszbTF7WP2gfwsV3AVvuooZZtvBFqzUBg9wNNMl/W2Nm/aXtJJUEj2WlfKxZbb+La0yfYK9Ia20uSBB8fH7P2Go2mzt/ddPtRUVHKcu3RtZb2Wzv21NRU7Ny5E4cOHcKZM2dQUlICX19ftGnTBn369MEDDzxgNurO1nPdtWsXNm7ciMOHD6OwsBB6vR6tW7dGt27dMHDgQAwfPtzq383f39/i6+vixYuYPn06zp49i8DAQMyfP9/hi8BpNBqLrxu1Wm1xXWxsrN1TxI1UKhUSEhJs7t/S+8lRrVq1Ukb0WVL7NdahQweb826bnjINAF27drV7iriRRqOxG7vpZ0qLFi1stq8de2RkpMURtUa1Y2eermsseQLAPJloSJ6IyDG138fkPVQqlc3PbPIezJUYmCdxMFdiEK0P0SQLusZTO40GDBhgtW3//v3h4+MDnU5ndiql6ZQE9qZRMB0RbG96hoaofYq8NSqVCr6+vlCpVMp0AbZeuKZfKiVJUqYzsNq+ZhJbh9XnS2u9t30DsdQnnvq+4Wv/LW09/uLF61NbtGvXzqytpRhNY1+3bh1effXVOm20Wi2ysrKQlZWFtWvX4u9//zumTJliNYYrV65g5syZ2LNnT511Fy5cwIULF7BlyxZIkoTx48c7/DyzsrLw8MMP49KlSwgJCcHnn39e59RiW2zlydI6e6Poa3PkfWRkfD85yto8sNbU9xfc+jxXSZJc+lxdGTvAPFnDPFkncp4AIKeoHJkFJfV6jMNahaB9/VJLREREREQe0iQLur6+vggLC0NRUc08dLYuaubn54fQ0FBcvnxZaV/7MaYXSLPEdH1kZOSNhm2X3YuLkccYL9TlaI6ysrKwc+dOAEBSUhJatWpVr/3p9Xq0aNECgwcPRu/evdGhQwcEBgYiPz8fR48exfLly3HlyhXMnj0bMTExFkfGVlRUYOrUqThx4gQAoHv37pg0aRJuuukm+Pr64tKlS9i/fz82bdpUr9iOHDmCRx99FMXFxQgPD8cXX3yBzp0712sbrmR6YSPTCxiRd2GexMFcOU+lzoDSKtsXjbtRVToDwIIuNQLsD3svHg/EwVyJgXkSB3MlBtH6EE2yoAvUzFG6b98+ALB5USvg+pWiTUf2mBagbF3UpPZ6S1M7OItOp6vXPK3kPrIso6Kiwmzu5cLCQqVYamxz7do1HD58GEuXLkVlZSWaNWtmcaStPXfccQdGjRpVZ6qDbt264c4778TUqVMxZcoUZGZmYt68eRYLuh9++KES35QpUzBr1iyzA0+PHj0wZMgQvPjii7h69apDce3ZswdPPfUUysvL0aFDByxevNhsaglvYHphI9MLGJF3YZ7EwVwRkTuxP+y9eDwQB3MlBuZJHMyVGHQ61wyccJUmW9Dt06ePUtDNzs5Gt27dLLYrLS3FlStXAMBsjsGoqCiEh4cjPz8f+/fvt7kv4/qIiAivK16R56xatQqrVq2yuE6lUuG+++7DX//6V3Tq1Kne27Y1HyYANGvWDM899xyefvppHDx4EFeuXDGb0+fq1atYvXo1gJqRuX//+9+t/oro6+vr0AjirVu3YubMmaiurkZcXBwWLVqE1q1b1+NZERERERERERGRWDP+OtFdd92lLG/ZssVquy1btijDrnv16qXcL0kSBg8eDKBmBG5qaqrFx6empiojdAcPHsyh9eQQg8GATZs2YdWqVWYX1btR5eXlyMnJwcmTJ3HixAmcOHHCbG7I48ePm7X/448/lLmfp06d2uCrPa5btw7PPfccqqur0bNnT3z55Zcs5hIRERERERER3YAmO0I3Pj4ed9xxB3755Rf88MMPmDBhQp3Tzi9fvowPP/wQQM08JxMmTDBbP23aNKxZswZ6vR6zZ8/GihUr4O/vr6yvrKzE7NmzAdRM1zBt2jTXPikSyjPPPINnn33W7L7KykqcO3cOGzZswNKlS7F06VKkp6dj0aJFdaZPsKeoqAhLlizB5s2bce7cOZvzwRhHoRsdO3ZMWTb9IeNGLF26FMuXL4csyxgwYADmz59f7+dCREREzpeWloZdu3bh0KFDOHXqFIqKiqDRaBAeHo5bbrkFEyZMQO/evW1uw9qFWC155513zC6iSkREREQ3pskWdAHgtddeQ2pqKq5evYrHH38c06ZNQ0pKCvz8/HDkyBEsWLBAuaDZ888/X+c09k6dOmH69OlYsGAB0tPTMXnyZDz66KOIjo5GdnY2Pv/8c6UwNn36dHTs2NHdT5EE4+/vj7i4OLz88svo2LEjZs2ahYMHD+L//u//MHPmTIe3k56ejunTp6O4uNih9qZz+wLmBd7w8HCH92vJsmXLAABhYWGYN28ei7lEREReYMqUKThw4ECd+7VaLc6ePYuzZ89i3bp1GDt2LGbPns35/oiIiIi8SJMu6Hbq1An/+7//i+effx4FBQVYsGABFixYYNZGkiQ88cQTePTRRy1uY+bMmSgsLMQ333yDY8eOWSy6TZw4ETNmzHDFU6BGbOLEiXj//fdRXFyMb775xuGCbnV1NWbMmIHi4mJoNBo88MADGDx4MDp27IgWLVooX8iys7MxZMgQAK69muOwYcOwefNmFBUV4eWXX8aHH35odoFBIiIicr/8/HwANT/cDh8+HL1790ZkZCQMBgNSU1OxePFi5OXlYf369dDpdHj//fftbnPRokU2fwhu06aN0+InIiIiasqafFWld+/e+P777/Hll19i69atyMnJgVarRevWrdG3b188+OCDVi+YBtRcvGrOnDkYNmwYVq9ejbS0NOUCUwkJCZg0aRJSUlLc+IyosVCpVOjQoQOKi4tx+fLlOhcus+aPP/5AdnY2AOCNN97APffcY7GdrdG7pvvJz89HdHR0/YI38fLLL6NVq1ZYsWIFtmzZghdeeAEffPABi7pEREQeFBMTg5kzZ2LYsGF15spPSkrCmDFjMHnyZJw9exbff/897rvvPvTp08fmNjt27MgLABMRERG5ASsqqClePfvss3XmM62PlJQUFm7J6XQ6nbKs1+sdesypU6eU5REjRlhtl56ebnVd9+7dleUDBw40qKALALNmzYJOp8Pq1auxefNm/O1vf8N7773X4IutERER0Y357LPPbK4PCwvDK6+8gieeeAIAsHnzZrsFXSIiIiJyDxZ0GxGOePRekiQhICAAfn5+Dj+moqICWVlZAGrm1nVkdC5gXgSuqKhAcHBwnTYGgwFr1661uo3k5GQEBgaivLwcy5cvx5gxYxpUfJUkCf/85z+V/W7atAkqlQrvvvsuVCrVDW/XFTQaDRISEpRl8k7MkziYKyJxJScnK8vnz5/3YCSOY3/Ye/F4IA7mSgzMkziYKzGI1ofwrkoKNYgkSZ4OgayQJAkqlapeOZo3bx4qKysBAAMGDHC4oGp68b1vv/3WYpv3338fR48etbqN5s2bY9KkSQCAo0ePYs6cOVbn2dVqtSgsLLQblyRJmD17tnJ16++//x6vvPIKDAaD3ce6kyRJ8PX1ha+vL99TXox5EgdzRSSu6upqZdnbfoC1hp8z3ovHA3EwV2JgnsTBXIlBtNyIVX4mzwtq5ekInM9Dz6mwsBAnTpwwu6+qqgrnzp3D+vXr8euvvwIA/Pz88Nxzzzm83QEDBqBly5YoLCzEhx9+iJycHAwdOhShoaE4f/481qxZgz179uCWW27BoUOHrG7n+eefx2+//YYTJ07gyy+/xOHDh3HfffehS5cu0Gg0uHTpEg4cOIAffvgBM2bMUAq1tkiShH/9618wGAxYv349NmzYALVajTlz5gj34UlERNTY7d+/X1mOjY212/7VV1/FmTNnUFxcjKCgIHTo0AH9+/fH/fffj4iICFeGSkRERNSksKDbiLhlpGPXUa7fRyMkyzL0er3ZPLirVq3CqlWrbD4uLCwM7777LuLi4hzeV2BgIP7973/j6aefRlVVFVavXo3Vq1ebtenbty/+8Y9/YNQo6/kMCAjA0qVL8dxzz2H//v04evQoZs2a5XAc1qhUKrzzzjvQ6/XYuHEj1q1bB7VajdmzZ3tFUddgMKCkpAQA0KJFC2FGJDU1zJM4mCsiMRkMBixYsEC5bWtefqN9+/Ypy8XFxSguLsaff/6JL774Aq+99hruu+8+l8Rqqqqqyuo6Hx8f5TPIYDCYTVNlia+vr7Jcux9XmyRJZqfR6nQ6m31zlUpldmqnVqu1ejZU7dhlWYZWq7UZu0ajUfpV9p6rq2NXq9VQq9XK8UCn06FZs2ZWjwei5cl0m7Isw9iblSHDoLf9/UylVkH67yNkWbb9fU42wPR8PVflCaj5uxvPwLOWK9HyZC12Ud9PxliYp+u8NU8AUFlZiWvXrgGwnCvmyTOx186TrT6EN2JBtxFx9KJZ5H6yLKOqqsqhD7eQkBB07twZKSkpGD9+PFq0aFHv/d1+++345ptvsGDBAvzxxx+4cuUKmjVrhs6dO2P06NGYOHEiLl68aHc7YWFh+PLLL7FlyxZs3LgRf/75J4qKiiBJEsLDw9G9e3cMGTIEw4YNq1d8KpUK//73v2EwGPDDDz9g7dq1UKlU+Oc//+nxoq5Op8Pp06cBAAkJCWYHS/IezJM4mCsiMS1ZsgRHjhwBANx1113o0aOH1bbR0dEYOnQoevbsiTZt2gAAcnJysHnzZmzevBlVVVV44403IEmSMqWTq9Q++8lUXFyccm2B8vJyZGZm2txWr169lOWCggLk5ORYbRsUFIT4+Hjl9rlz51BcXGy1fXh4uNlFZzMyMmz2E2NiYpTrKWi1WqSlpdmM3fTztqSkRPkctkSj0SAxMVG5nZubi/z8fKvtQ0JCzEZsZ2VloayszGr7qKgoREREmB0PbBEtT4GBgejatSsAoEpbDf//3m/QG5CXn2cz9ojwCKWYUFlViStXrlht62cIREuT267KE1Bz7LY3b7ZoeTLVGN5PAPNUm7fmCYDN6Q4B5skad+fJVh/CG7GgS+RGvXv3RkZGRoNHqCUnJ9v9kL/pppvw7rvvWl0fFRVldxtGQ4cOxdChQx2Ob/z48XanYFCr1fjggw/wwQcfOLxdIiIicr19+/bh/fffBwC0bNkSb775ptW2Q4cOxbhx4+r8IJuYmIiRI0dix44dePbZZ6HVavHOO+9g0KBBaN26tSvDJyIiImr0JNnW+GPyetXV1covIPHx8QgKCnL4sSdPnoROp4OPjw9uuukmV4VIqDmVoKKiAkDNVAY85dg1nPGaNn1PcTSh92KexMFcOc/c3UtwNC/bJdu+JaIT7gioGWHGPDVtJ0+exJQpU1BSUgI/Pz8sWrQIffr0adA2P/30U3z00UcAgBkzZuDJJ590RqgK08+Z2NhYBAYGWmzHU1o9E7vxlNba31usXeldtDyZTbmw/wtIpZdqlp095UKLtlD3edipsZsyPfXYkVyJlidrsYv6fgKYp9q8NU8AUFZWhuPHjwOwnCvmyTOx185TcXExsrKyAIjRH+YIXSIiIiIiQnZ2Nh5++GGUlJQoZ9I0tJgLAJMmTcLHH38MWZaxf/9+pxd0TWk0Goe+gKlUqnp9Uav9pc8e0y+UjrBW3LTEeLV0R9X3uboydtPHNMY8mY5UlyDVKxZJstNeqjvnZn3cSJ6Mj7OXA9HyZKoxvJ+Mj2OervO2PJm2t5cr5sk6d+ZJBBwmSERERETUxOXl5eGhhx5Cfn4+JEnCnDlzMGTIEKdsu2XLlggJCVH2Q0REREQNw4IuEREREVETVlRUhIcffhjZ2TVTesyaNQtjx4516j48fdFTIiIiosaEBV0iIiIioibq2rVreOSRR3Dq1CkAwIsvvogpU6Y4dR9FRUW4cuUKgJqrZxMRERFRw3AOXSI34cgUMZhOvM6ceS/mSRzMFZH3qqiowGOPPYajR48CAJ544gk89thjTt/P6tWrlYuSOGNOXhITjwfiYK7EwDyJg7kiV2BBtxERbQLnpkSlUlm94jJ5F41Gg8TERE+HQXYwT+Jgroi8U3V1NZ555hkcOnQIADB16lTMnDmzXtvIycnB1atX0a1bN6ttduzYgU8++QQA4O/vjwkTJtx40A5gf9h78XggDuZKDMyTOJgrMYjWh2BBl4iIiIioiXnxxRexe/duAEC/fv0wceJEnDhxwmp7jUaDTp06md134cIFTJ06FT179sTAgQMRHx+PsLAwAEB2djY2b96MzZs3K6NzX375ZURERLjoGRERERE1HSzoEhERERE1MT///LOy/Mcff2DMmDE227dr1w7bt2+3uO7w4cM4fPiw1ccGBATg1VdfxaRJk24sWCIiIiIyw4JuI6LT6eDr6+vpMMgCWZah1WoB1Ixw4bw53kun0yE3NxcAEBkZCR8ffkx6I+ZJHMwVUePVvXt3vPvuu0hNTUV6ejouX76MK1euQKfToUWLFujcuTP69++Pe+65By1btnRLTOwPey8eD8TBXImBeRIHcyUGnU7n6RDqha+iRsR4Oht5H9OCro+PDwu6XsxgMCA/Px8AeFqoF2OexMFcEXmnzMzMBm8jODgYY8aMsTu6153YH/ZePB6Ig7kSA/MkDuZKDKL1IVSeDoCIiIiIiIiIiIiIHMOCLhEREREREREREZEgWNAlIiIiIiIiIiIiEgQLukRERERERERERESCYEGXiIiIiIiIiIiISBAs6BIREREREREREREJwsfTAZDzSJLk6RDICkmSoFarlWXyXiqVCiEhIcoyeSfmSRzMFRG5E/tZ3ovHA3EwV2JgnsTBXIlBtD4EC7qNiI8P0+mtJEmCv7+/p8PAoEGDcOHCBYwbNw5z5871dDheycfHB7GxsZ4Og+xgnsTBXBGRO7E/7L14PBAHcyUG5kkczJUYROtD8KcBIg/at28f4uLilH+HDh3ydEhEREREREREROTFWNAl8qBvv/3W7Pb69es9EwgREREREREREQlBrPHEZJNWq4Wvr6+nwyALDAYDqqqqAAB+fn5QqVSorKzE5s2bAQCBgYEoLy/HTz/9hNdff91ledy+fbtLttuYaLVaZGVlAQBiY2Oh0Wg8HBFZwjyJg7kiIndif9h78XggDuZKDMyTOJgrMWi1Wk+HUC8s6FK9bD+/HUWVRZ4Ow6nC/MMwqP0gl+/HYDCY3d6yZQvKysoAAK+//jpee+01lJSUYPv27Rg+fLjL4yHLZFlW8iLLsoejIWuYJ3EwV0REBPB4IBLmSgzMkziYK3IFFnSpXooqi5Bfnu/pMBoF4/QKcXFxmDBhAj7//HOcOXMG69evZ0GXiIiIiIiIiIgsYkGXyAPy8/OxZ88eAMCYMWOU/3/00UfYvXs3ioqKEBYWZvXxeXl5WL58OX777TecP38elZWVaNGiBVq2bImbbroJAwYMwF133YXg4GCzxw0aNAgXLlzAuHHjMHfuXItxbdmyBXv37sXx48eRn58PnU6H0NBQ9OjRA6NHj8bw4cOhUlmefnvv3r2YOnUqAGDZsmVITk7Gpk2bsHr1amRmZqK8vByRkZEYPHgwHnvsMYSEhNzIn4+IiIiIiIiIqMliQZfIAzZu3Ai9Xg+VSoXRo0cDAEaPHo2PP/4YWq0W33//vVIYre3AgQN4/PHHUVpaanZ/YWEhCgsLceLECfzwww8IDQ3FwIEDHY5Jr9cjJSWlztQQQE2hd/v27di+fTu+/vprzJs3D0FBQTa3ZzAY8Le//Q3fffed2f1nz57FokWLsHXrVqxYsQKtW7d2OEYiIiIiIiIioqaOBV0iD9iwYQMAoG/fvoiIiAAAREdHo2fPnjh06BDWr19vsaBbXV2NmTNnorS0FEFBQZg8eTKSk5PRsmVLaLVa5OTk4PDhw9iyZUu9YzLO5dOvXz/ccccd6NKlC8LCwlBWVobs7GysXbsWhw8fxm+//Ya33noL//73v21u76OPPsLhw4cxZMgQjB07Fm3btkVBQQFWrlyJnTt34ty5c3jnnXfwwQcf1DtWIiIiIiIiIqKmigVdIjfLyMhAZmYmgOvTLRiNGTMGhw4dwtGjR3Hq1Cl07tzZbP3BgweRn18zh/H7779fZwRuUlISRo0ahVdffRWVlZX1ikutVuOnn35Chw4d6qzr27cvJkyYgI8//hiffPIJNmzYgCeffBIdO3a0ur3Dhw9jxowZePLJJ83uv+OOO/DII49g9+7d2Lx5s93pJYiIiIiIiIiI6DrLE2ESkcsYR+f6+/tj2LBhZutGjBgBjUYD4PpF00wVFBQoy3369LG6Dx8fnzrz59ojSZLFYq6pp59+GqGhoZBlGdu3b7fZtnv37njiiScs7uevf/0rAECn0+Hw4cP1ipOIiIiIiIiIqCnjCN1GxNqFqsjzJEmCr68vdDodfvjhBwDAwIED6xRdQ0JCkJKSgq1bt2Ljxo144YUXzPJqOt/sN998g2nTprksZoPBgMuXL6OsrAw6nU65v02bNrhy5QqOHz9u8/GjR4+GJEkW13Xv3l1Zzs7Odk7ATqJWqxEVFaUsk3dinsTBXBGRO7E/7L14PBAHcyUG5kkczJUYROtDsKDbiPCDwXtJkgSNRoPffvtNGWVbe7oFozFjxmDr1q24dOkS9u7di/79+yvrevXqhejoaGRnZ2POnDnYuHEjhg4dit69eyMhIQG+vr4NilOWZXz33Xf4+uuvceTIEZvTNly5csXmtmJiYqyuCwkJUZbLysrqHacrqdVqZV5j8l7MkziYKyJyJ/aHvRePB+JgrsTAPImDuRKDaH0IFnSJ3Mg4jUJISAhuv/12i20GDhyI5s2b4+rVq1i/fr1ZQVej0eD//u//8NxzzyErKwtpaWlIS0sDUDOFQ+/evTF27FiMHDmy3h9GVVVVeOaZZ/DLL7841N7eHL3+/v5W15n+8mUwGBwLkIiIiIiIiIiIWNAlcpdr164p884WFxejR48edh/z888/44033kBgYKByX+fOnbFx40bs2LED27dvx4EDB3Du3DlUVlZi9+7d2L17N7744gt8/vnnaNmypcPx/e///q9SzO3bty/uv/9+dO/eHa1atYK/v79ShJ0yZQoOHDhQn6dOREREREREREROwoJuI6LVaht8yj25hsFgwPr161FVVVWvx5WXl+Pnn3/G2LFjze5Xq9UYMmQIhgwZAgDIz8/Hr7/+ihUrVuDo0aM4evQo/vGPf+CTTz5xaD+yLOPrr78GAPTu3RtLly61On9MSUlJvZ6DaKqrq5VRz86YxoJcg3kSB3NFRO7E/rD34vFAHMyVGJgncTBXYtBqtZ4OoV5Y0CVyk++//x5AzYXNXn31Vbvt/+d//geXLl3Chg0b6hR0awsPD8eECRMwZswYTJo0CUePHsXOnTtRWVlpc+oDo+LiYly+fBkAMHz4cKvF3LKyMpw5c8bu9oiIiIiIiIiIyDVY0CVyg5ycHPz5558AgLvuugt/+ctf7D4mNTUVy5Ytwx9//IG8vDyHJlHXaDTo06cPjh49Cp1Oh6tXrzpU0NXr9cpyRUWF1XZr166FTqezuz0iIiIiIiIiInINy8PwiMipNmzYAFmWAdQUdB0xbNgwADXTNWzYsAEAlPlyramursb+/fsBAIGBgQgLC3NoX2FhYWjevDmAmpHE1dXVddocOXIEH330kUPbIyIiIiIiIiIi12iyI3Tj4uIcate3b18sX77cZptdu3ZhzZo1SEtLQ1FREcLCwpCQkIB7770XKSkpzgiXBPfdd98BqCmc9u7d26HH3HLLLWjdujUuX76M7777Do899hj27NmDTz/9FL1790ZKSgri4uIQFhaGyspKnD17Fl999RWOHj0KAJg4cSJ8fBx7i6tUKowePRorVqxAZmYmJk+ejIceeggdOnRAaWkpdu3ahZUrVyIwMBDh4eE4e/bsDf0diIiIiIiIiIioYZpsQdcZDAYDZs2apVxMyigvLw95eXnYunUr7rnnHrz11ltW5yQVTZi/YyM+ReLq53Tw4EGcP38eADBw4ECHXwsqlQpDhw7FypUrcfLkSaSnpwOoed3t27cP+/bts/rYwYMH48UXX6xXnDNnzsShQ4eQkZGB9PT0Oo8PCQnBvHnz8PHHH7OgS0RERERERETkIU2+oDt58mTcf//9VtcHBARYXfef//xHKeZ269YNjzzyCKKjo5GdnY2FCxfi2LFjWLt2LcLCwvDCCy84PXZPGNR+kKdDEI5xugSgptBaH3fddRdWrlwJAFi/fj1mzpyJuLg4/P7778jIyEB+fj4KCwsBAK1atUJiYiLGjh2LO++8s95xNmvWDKtWrcIXX3yBH3/8EefOnYNarUZkZCRSUlIwbdo0tGnTpt7bJSIiIiIiIiIi52nyBd2WLVuiS5cu9X7cmTNnsHjxYgBAjx49sGLFCuXiU4mJiRg0aBAeeOABpKenY9GiRZgwYQI6dOjg1NhJDG+99RbefPNNmxcbs6Z///7IzMw0u++uu+5yeB7e2rZv325zfUBAAJ566ik89dRTVtvYmoIkOTm5TrzWONqOiIiIiIiIiIiua/IF3Ru1dOlS6HQ6AMCsWbOUYq5RQEAAZs2ahUmTJkGn02HJkiV44403XBqTWq126fbpxkmSpLxGJEnycDRki4+PjzLHtqNzEJP7MU/iYK6IyJ3YH/ZePB6Ig7kSA/MkDuZKDKL1IRrHxK5uJssytm3bBgCIiYlBUlKSxXZJSUno1KkTAGDbtm2QZdmlcTWWeXobI0mSoFaroVarWdD1ciqVCsHBwQgODuZ7yosxT+JgrojInfg54714PBAHcyUG5kkczJUYRMuNWNF6iZycHOTn5wMA+vTpY7Nt3759AdRcKC0nJ8flsREREREREREREVHj1eTHev/000/48ccfceHCBahUKrRu3Ro9e/bEuHHj0K9fP4uPOXXqlLIcExNjc/um60+fPo3o6GjnBG6BwWBw2bapYWRZVvKjUqk4SteLGQwGlJeXAwACAwOF+5WuqWCexMFcEZE7sT/svXg8EAdzJQbmSRzMlRhE60M0+YKuaXEWAM6dO4dz585h/fr1GDJkCObOnYtmzZqZtbl06ZKy3KZNG5vbN12fm5vrhIitq6ystPrB4OPjo6wzGAzQ6XQwGAzKNBC1X7im25Fl2e50EfVpL0mSWUHT3pvGtL0jsdS3vTtil2UZlZWVAAB/f3+rBV1Xxl7f9qLmydI6rVZrc/vG6TAAQKfTKRdsi4+Ph0ajqdPe0vvJFl9fX2VZr9dDr9dbbStJktk+je9Va1Qqldk8TPaeq2nssixDq9XajF2j0Sh5svdcXR0782Sdt+YJACoqKmzminlyLPb/7gSybC1+889s6+2ubx8wbe/aaaGI3MXWZwJ5lumxOyEhwezznLwLcyUG5kkczJUYROtDNNmCbkBAAAYNGoT+/fujU6dOCAoKQlFREfbt24evvvoKxcXF2Lp1K5566iksXrzY7EtVWVmZshwYGGh3P0bGX2RcJSsry+q6uLg4BAcHK3EYP0yMX0YrKirM2gcFBSnLOp0O1dXVVretUqnMnmdVVZXNN4JGozH7AKusrLT5RdLPz0/5gi3Lcp1YawsICFC+1Or1elRVVVltK0mSWQ61Wq3NL+RqtdrsAnhVVVU2iwO+vr51vpAbC7uW+Pv7K4UQg8Fgsy3APNWm1+strsvKyjJ739YWFRWFiIiIOvcfP37cYntr7ydrevXqpSwXFBTYnH4lKCgI8fHxyu1z586huLjYavvw8HCzkf8ZGRk2X8MxMTEIDQ0FUPN3TEtLsxm7aYejpKQEp0+fttpWo9EgMTFRuZ2bm6tMT2NJSEgIYmNjldvM03WNKU+m+bGUK+bJstp5AoCqqkqrf3sfHx/4+1//jK+oqITBYP0z3s/PDxrN9c94bXUVYLtLQ0REREREXqLJFnR/+eUXNG/evM79t912Gx588EE8+uijOHbsGPbt24dVq1Zh6tSpShvTwpOlUWGmahfEiIiIiIiIiIiIiG5Uky3oWirmGrVq1Qoff/wxRowYAa1Wiy+//NKsoOvn56cs2zu90nTEpOnITleIjY21OmLY9BTSwMBAJCQk4MyZM9Dr9VCr1WYjNy091vT0WXv8/Pzsnspvyt7fxbS9JEk2Y63d3t5zq02j0Zj9rWxtGzB/LTjSHrA/5YJR7RG19jBPNduxtC42NtbuKeKW2DqV38j4fnJUq1atlBF9ltT+u3fo0MHm3Nu1p1np2rWr3VPEjTQajd3YTZ9/ixYtbLavHXtkZKTFEbVGtWNnnq5rTHmKj49XRuZam3LBiHm6ztJxws/P3+zMjFqPMLsVEOD4ZzYAaHxtH8+IiIiIiMh7NNmCrj3R0dG49dZbsWvXLpw7dw55eXnKF17TL1P2plEwPe3c3vQMDVX7FHlrVCoVfH19oVKpzC7UZU3tuVTtqW/7+kwI7upYXBm76T4ceZzIz9WTsVtaZ28kvTWOvKeM7ydH1Z5f1B5bPzBYUp/nKklSvWKv73N1Zey1H8c8XedteTJtby9XzJPdnUCSHPssdrTd9fa8WCcRERERkShY0LUhNjYWu3btAgCzgq7phc5ML5Bmien6yMhIF0RJREREREREROR6289vR1FlkUu2HeYfhkHtB7lk20SNDQu6NlgbrdK5c2dl2dYFTWqvj4mJcU5gRERERERERERuVlRZhPxy6xfUJSL3qP/54k1IVlaWsmw6v2BUVBTCw8MBAPv377e5DeP6iIgIREVFuSBKIjKyNdclEREREREREVFjwBG6VmRnZ+O3334DALRv396soCtJEgYPHoxVq1bh9OnTSE1NRVJSUp1tpKamKiN0Bw8e7PL56eo7r6FarYZOp4Ner4fBYLih+WDJMSqVysaFbMgZ9Ho99Ho9AOsXz3KEr68vevXq5aywyEWYJ3EwV0TkTjc6Hzu5Ho8H4mCuxMA8iYO5EoNofYgmWcHbvn07dDqd1fUFBQV47rnnoNVqAQD3339/nTbTpk1TikazZ89GZWWl2frKykrMnj0bQM1FWKZNm+as8J3G37/mCtiyLKO0tNTD0RA1THFxsbLs6gsQEhERERERERF5SpMcofv2229Dq9Vi2LBhSEpKQrt27eDv748rV65g7969WL16Na5cuQIA6NWrF6ZMmVJnG506dcL06dOxYMECpKenY/LkyXj00UcRHR2N7OxsfP755zh27BgAYPr06ejYsaM7n6JDmjdvjpKSEgDXL94WHBzMkbokDFmWUVVVhatXr6KwsFC5PzQ01INRERERERERERG5TpMs6AJAfn4+li9fjuXLl1ttM2zYMLz99tvw9fW1uH7mzJkoLCzEN998g2PHjmHmzJl12kycOBEzZsxwVtg2GU83d1RQUBACAgJQUVEBvV6PCxcuQJKkBp2uTtYZ53d19dQbTYler68zb26LFi3g5+fXoG0WFBQAAFq1asX3g5dinsTBXBGRO9W3P0zuw+OBOJgrMTBP4mCuxCBaH6JJFnTnzp2Lffv2ITU1FdnZ2SguLkZpaSkCAwPRpk0b9OzZE+PGjUPPnj1tbkelUmHOnDkYNmwYVq9ejbS0NFy5cgWhoaFISEjApEmTkJKS4qZnBRgMhnq1lyQJ7du3x/nz51FRUQGgpuhoazoKujGyLCtTeGg0GhZ1XaR169Zo2bJlg7ah1+uRk5MDoGakLw+23ol5EgdzRUTuVN/+MLkPjwfiYK7EwDyJg7kSg2h9iCZZ0O3bty/69u3rtO2lpKS4tXDrTCqVCh06dEBZWRmuXbumjNYl5zIYDErR3N/fn9NaOIlKpYKvry+CgoIQHBxsdTQ9EREREREREVFj0SQLumROkiQEBwcjODjY06E0WtXV1UhLSwNQM/8yC49ERERERERERHQjOEyQiIiIiIiIiIiISBAs6BIREREREREREREJggVdIiIiIiIiIiIiIkFwDl0iIiIiIiIiIrIrp6gcmQUlLtl2XCte14fIUSzoErmBJEkICgpSlsl7MVdiYJ7EwVwRea+0tDTs2rULhw4dwqlTp1BUVASNRoPw8HDccsstmDBhAnr37u3w9nbt2oU1a9YgLS0NRUVFCAsLQ0JCAu69916kpKS48JmQCHg8EAdzJQZP5alSZ0Bplc5l226M+J4iV2BBtxHRaDSeDoGs0Gg0iI+P93QY5ADmSgzMkziYKyLvNGXKFBw4cKDO/VqtFmfPnsXZs2exbt06jB07FrNnz4avr6/VbRkMBsyaNQtff/212f15eXnIy8vD1q1bcc899+Ctt96CSuXaGd/YH/ZePB6Ig7kSA/MkDuZKDKL1IVjQJSIiIiJqYvLz8wEA4eHhGD58OHr37o3IyEgYDAakpqZi8eLFyMvLw/r166HT6fD+++9b3dZ//vMfpZjbrVs3PPLII4iOjkZ2djYWLlyIY8eOYe3atQgLC8MLL7zgludHRERE1JixoEtERERE1MTExMRg5syZGDZsGNRqtdm6pKQkjBkzBpMnT8bZs2fx/fff47777kOfPn3qbOfMmTNYvHgxAKBHjx5YsWIF/P39AQCJiYkYNGgQHnjgAaSnp2PRokWYMGECOnTo4PonSERERNSIufacJ3Irnc4189hQw+l0OmRlZSErK4t58nLMlRiYJ3EwV0Te6bPPPsPIkSPrFHONwsLC8Morryi3N2/ebLHd0qVLlff2rFmzlGKuUUBAAGbNmgWg5vNgyZIlTojeOn7OeC8eD8TBXImBeRIHcyUG0XLDgm4jIsuyp0MgKwwGA4qLi1FcXAyDoXFO9N5YMFdiYJ7EwVwRiSs5OVlZPn/+fJ31sixj27ZtAGpG/CYlJVncTlJSEjp16gQA2LZtm0v7rOwPey8eD8TBXImBeRIHcyUG0foQLOgSEREREVEd1dXVyrKli5nl5OQoc/Famo7BVN++fQHUXCgtJyfHiVESERERNT0s6BIRERERUR379+9XlmNjY+usP3XqlLIcExNjc1um60+fPu2E6IiIiIiaLl4UjYiIiIiIzBgMBixYsEC5PWLEiDptLl26pCy3adPG5vZM1+fm5johQsu0Wq3ZyGJTPj4+ykhjg8Fgd648X19fZVmv10Ov11ttK0kSNBqNclun09k8rValUsHH5/pXMa1Wa/NUT9PYZVmGVqu1GbtGo4EkSQDsP1dXx65Wq+vM1WwrftHyZLpNWZYh/fd+GTIMetunVqvUKkj/fYQsy7ZPxZYNMP0ruiNPxsdZIlqerMXeGN5PxsdZ4qw8mcUpy5DlmuckSdfHCNbEbeuUdUn5O9a0t/B3MXnuBoOh0eTJNHZLz4PvJ8/EbitPImBBl4iIiIiIzCxZsgRHjhwBANx1113o0aNHnTZlZWXKcmBgoM3tBQQEKMvl5eVOirKurKwsq+vi4uIQHBysxJCZmWlzW7169VKWCwoKbE4VERQUhPj4eOX2uXPnUFxcbLV9eHg4oqOjldsZGRk2v0jGxMQgNDQUQM0XzrS0NJuxJyQkKF/4S0pKbI6K1mg0SExMVG7n5uYqU2lYEhISYjZiOysry+y1UFtUVBQiIiLM7jt+/LjV9qLlKTAwEF27dgUAVGmrYbwsoEFvQF5+ns3YI8IjlGJCZVUlrly5YrWtnyEQLU1uuyNPgPVciZYnU43t/QS4Pk9hYWHKXOhVVZUoKyuDSqU2++yvqqq0WZzTaDTw87t+4czy8vI6xbaqqkqz9cbYRc+TaX4s5YrvJ8vcnSdbfQhvxCkXiIiIiIhIsW/fPrz//vsAgJYtW+LNN9+02K6qqkpZNh1BY4npaKLKykobLYmIiIjIHo7QJSIiIiIiAMDJkyfxzDPPQKfTwc/PDx999BFatmxpsa2fn5+ybO80RdNpEPz9/W20bJjY2Firo4VNT8MMDAxEQkKCw9tt1aqVMgLJEtPTiAGgQ4cOZiOcaqt9kbmuXbvaPaXVSKPR2I3dtMDeokULm+1rxx4ZGWlxBKBR7dhjY2PtntJaW3x8vNUfAUTLk+k2/TS+wH9/51CpVYgIt/53NLYx8vfzt92+RYjZTXfkCbCeK9HyZKqxvZ8A1+fJNE4/P38EBQXVae/n5w8/P9tTLpiy9FltOoLXdL3oeYqPj1dG5lrKFd9Plrk7T7GxsUKN0mVBtxGp/WIn76FSqRAeHq4sk/dirsTAPImDuSISR3Z2Nh5++GGUlJRArVbjgw8+QJ8+fay2N/1Cb28ahYqKCmXZ3vQMDeHr62s2GtgalUrlUDsja/NWWmP6BdcR9kY4m5IkqV6x1/e5uip20+OBn5+fQ/sRLU+m38ckSPWKRZLstJfMj6GufI3VN1ei5cmUqO8nYyweyZMkmc2de/1uCbWLtrZY2gZM3kOm/UaR8wTU5MfRXPH9ZJ2r81SfWLwBC7qNSH1f3OQ+Pj4+Nn8FI+/BXImBeRIHc0Ukhry8PDz00EPIz8+HJEmYM2cOhgwZYvMxphc6M71AmiWm6yMjIxsWrA3sD3svHg/EwVyJgXkSB3MlBtH6EBwqQ0RERETUhBUVFeHhhx9GdnY2AGDWrFkYO3as3cd17txZWbZ1UZPa62NiYm4sUCIiIiICwIIuEREREVGTde3aNTzyyCM4deoUAODFF1/ElClTHHpsVFSUcgrp/v37bbY1ro+IiEBUVFQDIiYiIiIiscYTk01arVa4OT+aCq1Wi4yMDAA1k4rXdy4Xch/mSgzMkziYKyLvVVFRgcceewxHjx4FADzxxBN47LHHHH68JEkYPHgwVq1ahdOnTyM1NRVJSUl12qWmpiojdAcPHuzS6z6wP+y9eDwQB3MlBubJebaf346iyiKXbT/ENwRDOtZMY6TT6YQ7tb+psHeBV2/DVxGRG8iyrHw42LrKInkecyUG5kkczBWRd6qursYzzzyDQ4cOAQCmTp2KmTNn1ns706ZNw5o1a6DX6zF79mysWLEC/v7Xr1JeWVmJ2bNnA6iZm27atGnOeQIkHB4PxMFciYF5cp6iyiLkl+e7bPsGvUFZZq7IWVjQJSIiIiJqYl588UXs3r0bANCvXz9MnDgRJ06csNpeo9GgU6dOde7v1KkTpk+fjgULFiA9PR2TJ0/Go48+iujoaGRnZ+Pzzz/HsWPHAADTp09Hx44dXfJ8iIiIiJoSFnSJiIiIiJqYn3/+WVn+448/MGbMGJvt27Vrh+3bt1tcN3PmTBQWFuKbb77BsWPHLI70nThxImbMmNGgmImIiIioBgu6RERERER0w1QqFebMmYNhw4Zh9erVSEtLw5UrVxAaGoqEhARMmjQJKSkpng6TiIiIqNFgQZeIiIiIqInJzMx0+jZTUlJYuCUiIiJyA5WnAyAiIiIiIiIiIiIix7CgS0RERERERERERCQITrnQiKjVak+HQFb4+PggJiZGWSbvxVyJgXkSB3NFRO7E/rD34vFAHMyVGJgncUgqSVnmccp7iZYbvusbEZWKA669lUqlQmhoqKfDIAcwV2JgnsTBXBGRO7E/7L14PBAHcyUG5kkcknS9oMvjlPcSLTdiRUtERERERERERETUhHGEbiMiy7KnQyArZFmGVqsFAGg0GrNf6Mi7MFdiYJ7EwVwRkTuxP+y9eDwQB3MlBuZJHKbHJlmWmSsvJVofgiN0GxGdTufpEMgKrVaLtLQ0pKWlKQdd8k7MlRiYJ3EwV0TkTuwPey8eD8TBXImBeRKHbLheKORxynuJlhu3F3SnTp2KadOm4cKFCw4/Ji8vT3kcEREREZHI2B8mIiIiooZw+5QL+/btgyRJqKiocPgxVVVVyuOIiIiIiETG/jARERERNQSnXCAiIiIiIiIiIiIShBAF3fLycgCAn5+fhyMhIiIiInI/9oeJiIiIyEiIgu4vv/wCAIiIiPBwJERERERE7sf+MBEREREZuXwO3VdffdXi/R9++CGaNWtm87HV1dXIzs5GWloaJElC3759XREiEREREZHLsD9MRERERM7k8oLut99+W+fiDbIsY9u2bQ49XpZlAECLFi3w2GOPOT0+IiIiIiJXYn+YiIiIiJzJ5QXdtm3bmt2+ePEiJElC69at4eNjffeSJMHPzw+tW7dGz549MXnyZJ5iZoetvyd5lkajQUJCgrJM3ou5EgPzJA7mioj9YXdif9h78XggDuZKDMyTOCTV9R91eZzyXqLlxuXRbt++3ex2fHw8AGDx4sXo3Lmzq3ffpNQe+UHeQ5Ik+Pr6ejoMcgBzJQbmSRzMFRH7w+7E/rD34vFAHMyVGJgncZgem3ic8l6i5cbt5ec+ffoAAAICAty9a4e9++67WLhwoXJ72bJlSE5OtvmYXbt2Yc2aNUhLS0NRURHCwsKQkJCAe++9FykpKa4OmYiIiIgEIUJ/mIiIiIi8l9sLusuXL3f3LuslIyMDS5Yscbi9wWDArFmz8PXXX5vdn5eXh7y8PGzduhX33HMP3nrrLahUKidHWzcW8k4GgwElJSUAaua/c/VrgW4ccyUG5kkczBVRXd7eHxYZ+8Pei8cDcTBXYmCexGGcCx+oyRtz5Z1E60PwVWTCWJzV6XRo2bKlQ4/5z3/+oxRzu3Xrhg8++ABr167FBx98gG7dugEA1q5diw8//NBVYSv0er3L90E3RqfT4fTp0zh9+jR0Op2nwyEbmCsxME/iYK6IyJ3YH/ZePB6Ig7kSA/MkDtlwvaDL45T3Ei03XjHjb2lpKcrKyhz649W+qIQzLVu2DGlpaYiJicHQoUPx2Wef2Wx/5swZLF68GADQo0cPrFixAv7+/gCAxMREDBo0CA888ADS09OxaNEiTJgwAR06dHBZ/EREREQkJm/pDxMRERGR9/NYQfe3337DypUrcfDgQeU0AXskScKxY8dcEs/Fixfx0UcfAQD++c9/Yu/evXYfs3TpUuWXsFmzZinFXKOAgADMmjULkyZNgk6nw5IlS/DGG284P3giIiIiEo639YeJiIiISAwemXLh7bffxiOPPILt27ejuLgYsiw7/M9V3nrrLZSXl2PcuHHo27ev3fayLGPbtm0AgJiYGCQlJVlsl5SUhE6dOgEAtm3b5tLnQERERERi8Mb+MBERERGJwe0jdDdu3Igvv/wSAODn54chQ4age/fuHp3Ee9OmTdixYwdCQkLw8ssvO/SYnJwc5OfnA7h+pWJr+vbtizNnziAvLw85OTmIjo5ucMxEREREJCZv7A8TERERkTjcXtBdvXo1ACAyMhJLly5F+/bt3R2CmatXr2LOnDkAgJdeeglhYWEOPe7UqVPKckxMjM22putPnz7Ngi4RERFRE+Zt/WEiIiIiEovbC7qZmZmQJAlPP/20V3Re3333XVy+fBm33HILJk6c6PDjLl26pCy3adPGZlvT9bm5ufUP0kFarRbV1dUW1/n4+CgjPgwGg92rYPr6+irLer3e5gU6JEmCRqNRbut0OhgMBqvtVSoVfHyuv/S0Wq3N0wdNY5dlGVqt1mbsGo0GkiQBsP9cXR27Wq2GWq02u89W/MyTZ2K3lCfj4yxhnjwTO/NknTfnyTR2S8+DeXIs9v/uBLJsLX5J2XZNU+vP07h9wLQ9T+N3J2/rDxMRERGRWNxe0DV+eenWrZu7d13HgQMHsHbtWvj4+OCf//yn2Rche8rKypTlwMBAm20DAgKU5fLy8voH6qCsrCyr6+Li4hAcHKzEkJmZaXNbvXr1UpYLCgqQk5NjtW1QUBDi4+OV2+fOnUNxcbHV9uHh4WajlDMyMmx+CY6JiUFoaCiAmi/XaWlpNmNPSEhQvvCXlJTg9OnTVttqNBokJiYqt3Nzc5WpNCwJCQlBbGyscjsrK8vstVBbVFQUIiIilC/mWq0Wx48ft9qeebLMXXkCYPY5YC1XzJNlzFOx1fZNNU+AeX4s5Yp5sqx2ngCgqqrS6t/ex8cH/v7X+xsVFZUwGKwXu/38/KDRXC+Oa6urANvdGXIib+oPE7mL6Q9V9fneRe7HXImBeRITc0XO4vaCbrt27ZCVleXSwqYjqqurMWvWLMiyjGnTpqFLly71enxVVZWyXGcETS2mo4kqKyvrFyg1CsYv5gcPHvR0KGSHvfczeQfmiYhE5i394caIxwfvZemHKvJOzJUYmCdxqNTX58c3PRuMvItofQi3v5KGDh2KrKws7NmzB71793b37hWfffYZTp8+jbZt2+KZZ56p9+P9/PyUZXunWJpOg+Dv71/vfTkqPj7e6gvQ9EMjMDAQCQkJDm+3VatWyggkS2r/wtShQweb8wTXvthH165d7Z7SaqTRaOzGbvo3aNGihc32tWOPjIw0G1lWW+3YY2Nj7Z56bMpe7MyTZcwT8wQwT7YwT5Y1pjwBgJ+fP4KCgqw9wuxWQIDt/kbt7fv6ua5/QnV5S3+YiIiIiMTk9oLuww8/jA0bNmDp0qUYOXKk2Wmc7pKVlYXPPvsMAPD666/bnTLBEtMvVPZGV1RUVCjLN7IvR2k0GrPRwNaoVCqH2hlZm7fSmvr+4lSfX0EkSapX7PV9rq6MHYBLY2eerGOeLGOerGOerGOeLHN1nv67E0iSyn47wOF25Bne0B8mIiIiInG5vaDbrFkzLFq0CE888QQmT56M559/HqNGjUKLFi3cFsPSpUuh1WoRHR2NyspK/PDDD3XanDx5Uln+448/UFBQAAAYOHAgAgMDzS50ZnqBNEtM10dGRjY0fKt0Ol39vxySW+h0OuWCeJGRkTzNwosxV2JgnsTBXBHV5Q394caK/WHvxeOBOJgrMTBP4jC9KK9Op2OuvJS9izN7G7e/igYPHgygZi7Zq1ev4u2338a//vUvhIaG2p2OQJIkbN26tcExGKdAyM7OxgsvvGC3/aeffqosb9u2DYGBgejcubNyn62LmtReHxMTU99wHcYrVHsvg8GgXHTI1qnN5HnMlRiYJ3EwV0R1eUN/uLFif9h78XggDuZKDMyTQEwOTTxOeS/RcuP2gu6FCxfMbsuyDFmWUVhYaPex3nQ1wKioKISHhyM/Px/79++32da4PiIiAlFRUe4Ij4iIiIi8VGPpDxMRERGRZ7i9oDtu3Dh377KOuXPnYu7cuTbbzJs3D/PnzwcALFu2DMnJyWbrJUnC4MGDsWrVKpw+fRqpqalISkqqs53U1FRlhO7gwYPZCSciIiJq4ryhP0xERERE4nJ7Qfedd95x9y5dZtq0aVizZg30ej1mz56NFStWmJ0mV1lZidmzZwOouRDLtGnTPBUqEREREXmJxtQfJiIiIiL34yWQG6BTp06YPn06ACA9PR2TJ0/Gpk2bkJaWhk2bNmHy5MlIT08HAEyfPh0dO3b0YLREREREREREREQkOl5ar4FmzpyJwsJCfPPNNzh27BhmzpxZp83EiRMxY8YM9wdHREREREREREREjYrHC7qVlZVIT09HQUEBKisrMWTIEAQHB3s6LIepVCrMmTMHw4YNw+rVq5GWloYrV64gNDQUCQkJmDRpElJSUjwdJhERERF5KdH7w0RERETkXh4r6Obm5uKDDz7ATz/9BJ1Op9zfo0cPdO7cWbm9du1arF69Gs2aNcPixYvddlGxZ599Fs8++6zD7VNSUjxeuOUF17yXSqVCSEiIskzeq9HnKuN7oKzAddsPagV0HeW67f9Xo89TI8JcEVnn7f1hEfFv472axPEgqJWY266lSeSqEWCeBGJyaOJxynuJlhuPFHT//PNPPPbYY7h69SpkWVbut/THGzRoEN566y3odDrs3r0bt99+uztDFYqPj8cHXJMVPj4+iI2N9XQY5IBGn6uyAuBarqejaLBGn6dGhLkisoz9Yddgf9h7NYnjgRt+1HaHJpGrRoB5EodpwZ3HKe8lWm7c/jPO1atX8dRTT6GkpAStWrXCG2+8gY0bN1pt37JlS9xxxx0AgF27drkrTCIiIiIil2B/mIiIiIgawu3l52XLlqGwsBChoaFYvXo12rZta/cxt956K7Zt24YjR464IUIiIiIiItdhf5iocdp+ZAmKik65ZNthYZ0xKPGvLtk2ERGJx+0F3R07dkCSJDz00EMOdV4BKHOInT9/3pWhCU+r1cLX19fTYZAFWq0WWVlZAIDY2FhoNBoPR0TWMFdiYJ7EwVwR1cX+sOuwP+y9msLxoOjaReRfyXLNxjWBrtmuBU0hV40B8yQOg96gLOt0OuFO7W8qtFqtp0OoF7e/ioyd0N69ezv8mBYtWgAASktLXRITkavJsoyysjJlmbwXcyUG5kkczBVRXewPU1PE44E4mCsxME9iYq7IWdw+h25VVRWA+k02XF5eDgDw9/d3SUxERERERO7C/jARERERNYTbR+i2bNkSly5dQk5ODhITEx16TEZGBgAgPDzclaEREREReS1/HxWC/VzTdfP1kVyyXbKM/WEiIiIiagi3F3QTExNx6dIl/Prrrxg5cqTd9rIsY+3atZAkqV6npRERERE1JlFhgfD1b+GSbUf6BwJlLtk0WcD+MBERERE1hNunXBg9ejRkWcbGjRuVkQa2zJ07F8ePHwcAjB071sXRERERERG5FvvDRERERNQQbh+hO2TIECQnJ2Pv3r3461//iueffx7Dhg1T1ut0OuTl5eHQoUNYvnw5Dh8+DEmSMHToUNxyyy3uDpeIiIjIK5TlnUbehXSXbLt5VC8goL1Ltk11sT9MRERERA3h9oIuAMybNw9//etfcezYMcyePRuzZ8+GJNXM3TZu3DiztrIsIykpCXPnzvVEqEREREReQV9VgeqKqy7ZtqGqEghwyabJCvaHiYiIiOhGeaSg27x5c3z11Vf45JNPsHLlSly7ds1iu4CAAEyZMgXPPfccfH193RyleFQqt8+gQQ5Sq9WIiopSlsl7MVdiYJ7EwVyJgZdEcz/2h12D/WHvxeOBOJgrMTBPAjHpaPE45b1Ey41HCroA4Ovri5kzZ+Lxxx/H/v37kZaWhqKiIuj1eoSEhKBbt2649dZb0axZM0+FKBx+iHsvtVqNiIgIT4dBDmCuxMA8iYO5IrKO/WHnY3/Ye/F4IA7mSgzMkzhMC4U8Tnkv0XLjsYKuUWBgIFJSUpCSkuLpUIiIiIiI3I79YSIiIiKqD48XdImIiIiIyP0KCwtx5MgRHDlyBGlpaUhLS0NxcTGAmnl8HZmzd926dXj11Vcd2t8777yD8ePHNyRkIq9WWq1D3rUql2w7METnku0SEZGYPFLQvXjxIgCgVatWducCq6qqQmFhIQCgbdu2Lo9NZFqtlnOreanq6mqkpaUBABISEpgnL8ZciYF5EgdzJQbZ0wE0Qd7QH7711ludti1vwf6w92oKxwODXka13uCybbtLU8hVY8A8icNg8rmg1Wqh0Wg8GA1Zo9VqPR1Cvbi9oLt79248+uijCAwMxPbt2+1+6FRUVOAvf/kLqqqq8MUXXyA5OdlNkRIREREROZ839ofbtm2LmJgY7N69+4a3sWjRIoSHh1td36ZNmxveNhERERFd5/aC7k8//QRZljFkyBC0aNHCbvuQkBAMGzYM69evx6ZNm1jQJSIiIiKheUt/+Omnn0ZCQgISEhLQqlUr5OTkYPDgwTe8vY4dOypXXCciIiIi11HZb+JcqampkCQJt912m8OPGTBggPJYIiIiIiKReUt/+LnnnsPAgQPRqlUrp22TiIiIiFzP7QXdCxcuAKj5Bd9R7du3BwDk5OS4IiQiIiIiIrdhf5iIiIiIGsLtBV2drubqnGq12uHHGNtWVbnmiqFERERERO7C/jAREVHToVJfL71JkuTBSKgxcXtBNzQ0FACQnZ3t8GOMbUNCQlwREhERERGR2zTW/vCrr76KAQMGoEePHkhOTsa9996L//znP8jLy/N0aERERESNitsvita1a1dcvnwZmzZtwvDhwx16zKZNmwAAN910kytDIyIiIiJyucbaH963b5+yXFxcjOLiYvz555/44osv8Nprr+G+++5zeQxarRbV1dUW1/n4+EClqhnPYjAYlJHS1vj6+irLer0eer3ealtJkqDRaJTbOp0OBoPBanuVSgUfn+tfxbRaLWRZttreNHZZlqHVam3GrtFolFFg9p6rq2NXq9V1RqPbil+0PNVuc50MWP+z/Dcg5T+Ajb/hf7dmxh15Mj7OEtHyZC32xvB+Mj7OEmflySxOWYYs1zwnSbo+RrAmbluvY8lsdKpxG2ZMnrvBYHBbnnKKynH88hUboUuQYBI7DDafak0c19t3aRWkLFt6TfD95JnYa7+f7MXubdxe0B08eDB27dqFLVu24Mcff8SIESNstt+0aRN+/vlnSJKEIUOGuClKMdXntD1yLx8fH8TFxSnL5L2YKzEwT+JgrsTAk//cq7H1h6OjozF06FD07NkTbdq0AVAz1+/mzZuxefNmVFVV4Y033oAkSZg0aZJLY8nKyrK6Li4uDsHBwQCA8vJyZGZm2txWr169lOWCggKb8xcHBQUhPj5euX3u3DkUFxdbbR8eHo7o6GjldkZGhs0vkjExMcrIbq1Wi7S0NJuxJyQkKF/4S0pKcPr0aattNRoNEhMTldu5ubnIz8+32j4kJASxsbHK7aysLJSVlVltHxUVhYiICOV4kJmZiePHj1ttL1qe2rRpg3bt2gEAZNMKjwyrPy4Y+fr6Kh/ABtkAndZ6YcNgMC/YuCpPgPnx2lquRMuTqcbwfgLcm6ewsDB06tQJAFBVVYmysjKoVGoEBgYq7auqKm0W5zQaDfz8/JXb5eXldYptVVWVZuuNsbs6T6WVVcgrKrHa3sfHB/7+AWax1X5PmvLz84NGc73oerW0XFnOzc1FUVGRWXu+nyxz5/vJ2F4kbv9mNXbsWHz22We4cOECXnrpJRw5cgRTp05FZGSkWbvc3FwsWbIEX375JSRJQmRkJO655x53hysU468i5H1UKpXyAU3ejbkSA/MkDuaKqK7G1B8eOnQoxo0bV2dOwMTERIwcORI7duzAs88+C61Wi3feeQeDBg1C69atPRQteRKPB+Lg90oxME9ETZsk2xp/7CIZGRmYMmUKysvLlc5fZGQkwsPDAQD5+fnIzc0FUDNkOzAwEF9++SW6devm7lC9XnV1tfILiOkvHUREFh1YAlzLdd32m0UCvf/quu0TNWGLt/4TJ7MPu2Tb3dr3R7eQmpGf7E+4hzf2h3NycjB48GAAwLhx4zB37lynbPfTTz/FRx99BACYMWMGnnzySads18i0PxwfH292eqYpntLqmdhrn9Jqb9SqaHkybWP+Oe3cKRc6t78F04e84dTYTTX2PFmLne8nc45MuTD31y+QnncegPOnXOgR0R6v3P6QEq+78jR39xdIv3S+QbHX3r7p+U/dw6OU58UpF7z3/VRWVqaMdhehP+yRcx+7du2KtWvX4m9/+xuOHTsGALh48aJZp9Woe/fu+J//+R+zYdNkma0XNnmWwWBAeXnNaRaBgYH8NdWLMVdiYJ7EwVwRWdaU+sOTJk3Cxx9/DFmWsX//fqcXdE2p1WqHvoCpVKp6fVGzNm+lNfWdYsZaEdoSSZLqFXt9n6urYr+R44HIeQKk+s1nI9luXHutK2M3GAxKodCRXImcJ1HfT4AH8yRJZoXc63ebFzHtsbQN0/eB6fNxdZ5qCraO91Hr0/a/m1dYn3f7+nq+nyxz7We2eNOYemwyu9jYWKxbtw6//fYbduzYgYyMDFy5UjMJdWhoKLp164ZBgwahf//+ngpROLZ+lSHP0ul0yjw4IvzS05QxV2JgnsTBXInB7adrEYCm0x9u2bIlQkJCcOXKFeTl5bl0X+wPey8eD8TBXImBeRKISUfLdOQxeRfR+hBuL+hevHgRQM0vSCEhIbjttttw2223uTsMIiIiIiKPaIr94dpz7BIRkZj8fVQI9nNNKcnfh4VOIke5vaA7aNAgSJKE119/HVOmTHH37omIiIiIPKqp9YeLioqUkcfGOYKJiEhMUWGB8PVv4ZJthwcGumS7RI2R23/+8Pf3B1BzSgARERERUVPT1PrDq1evVuYE7tOnj4ejISIiIhKf20foRkRE4Pz587yAFxERERE1SY2lP5yTk4OrV6+iW7duVtvs2LEDn3zyCYCaQvaECRPcFR4REblAWd5p5F1Id8m2g9r1ALq4ZNNEjY7bC7q33XYbzp8/j4MHDyIpKcnduyciIiIi8ihv6Q8fOHAA58+fV24bp0UAgHPnzmHdunVm7cePH292+8KFC5g6dSp69uyJgQMHIj4+HmFhYQCA7OxsbN68GZs3b1ZG57788suIiIhw1dMhIiI30FdVoLriqsu2TUSOcXtBd+rUqfj222+xePFijBo1ip06IiIiImpSvKU//PXXX+Pbb7+1uO7QoUM4dOiQ2X21C7pGhw8fxuHDh63uJyAgAK+++iomTZp048ESERERkcLtc+h27NgR7733HioqKnDvvfdi48aNqK6udncYREREREQe0Vj6w927d8e7776LKVOm4Oabb0bbtm0REBAAjUaDVq1aoV+/fpg5cya2bdvGYi4RERGRE3lkhC4AhIWFIScnBy+//DL+/ve/o0OHDmjRogVUKus1ZkmSsHTpUneFKhyNRuPpEMgKX19f9OrVy9NhkAOYKzEwT+JgrsQgeTqAJsZb+sNz587F3Llzb/jxwcHBGDNmDMaMGeOUeJyB/WHvxeOBOJgrMTBPApGu97R8fNxehiMHidaHcPsrad++fZBMXsyyLKO6uhonT560+hhJkiDLstnjiIiIiIhExP4wERERETWE2wu6ffr0cfcuiYiIiIi8BvvDRERERNQQbi/oLl++3N27bDL0er2nQyAr9Ho9CgoKAACtWrWCWq32cERkDXMlBuZJHMwVUV3sD7sO+8Pei8cDcTBXYmCeRCIrS3q9nrnyUqL1ITh5RyNiMBg8HQJZodfrkZOTAwAIDQ3lB7gXY67EwDyJg7kSg2y/CZEQ2B/2XjweiIO5EgPzJBCTjpYss9flrUTrQ1i/4gIREREREREREREReRWvGKF76dIlXL58GZWVlUhISIC/v7+nQyIiIiIichv2h4mIiIjIUR4r6JaWlmLhwoX49ttvkZ+fr9y/ceNGdO7cWbn9ww8/4Oeff0azZs3w9ttveyJUIiIiIiKnY3+YiIiIiG6ERwq6Z8+exWOPPYbs7Gyz+UMkSarT9uabb8bf/vY3yLKMsWPHonfv3g3ef2lpKXbt2oW0tDSkp6cjLy8PRUVFqKqqQrNmzdC5c2fccccdmDhxIkJDQ+1u79ChQ1i5ciUOHjyIgoICNG/eHPHx8Rg3bhxGjRrV4HiJiIiIqHHxdH+YiMirZHwPlBW4bvtBrYCu/G5ORI2H2wu6VVVVePzxx3H+/HkEBARgypQp6NOnDx5//HGL7aOiopCcnIw//vgD27dvd0oH9siRI3jhhRcsrisqKsK+ffuwb98+LFq0CO+++y5uv/12q9uaN28ePv30U7PJkwsKCrB7927s3r0bGzduxMcffww/P78Gx01ERERE4vOG/jARkVcpKwCu5Xo6CiIiYbi9oLtq1SqcO3cOAQEBWLlyJbp27Wr3MXfccQf27NmD1NRUp8URGRmJ5ORkdO/eHZGRkWjdujUMBgMuXbqEzZs3Y8uWLbhy5QqefPJJfP3114iPj6+zja+++grz588HALRv3x6PP/44unTpgvz8fCxbtgx79+7Fzp078dprr+H99993WuxEREREJC5v6Q8TERERkZjcXtD9+eefIUkSpk6d6lDnFYBSTD137pxTYkhOTsbOnTutrh85ciS2bt2Kp59+GlqtFvPnz1cKt0bFxcV47733AABt27bF6tWrERYWpqwfOHAgnn76aezYsQPff/897r33XiQnJzslfhKPJEkICgpSlsl7MVdiYJ7EwVwR1eUN/WEid+PxQBzMlRiYJ4EwPeQCbi/oZmVlAQAGDBjg8GNCQkIAAFevXnVKDGq12m6bIUOGoFOnTjhz5gwOHDhQZ/3atWtx7do1AMBLL71kVsw17uPNN9/EL7/8Ar1ej0WLFrm8oKvRaFy6fbpxGo3G4ihv8j7MlRiYJ3EwV2Lg9wz38ob+cGPF/rD34vFAHMyVGJgnkVzvafn4eORSVuQA0foQKnfvsLy8HAAQGBjo8GOqq6sBuP+Fb/y1q6qqqs66bdu2AQCCg4MxdOhQi49v06YN+vfvDwDYs2cPSktLXRQpEREREYlCpP4wEREREXkftxd0jaMLLly44PBjTp48CQBo3bq1K0Ky6PTp0zh+/DgAICYmxmxddXU1jhw5AgBISkqCr6+v1e307dtXeUx6erqLoiUiIiIiUYjSHyYiIiIi7+T2gm737t0BAPv373f4MRs2bIAkSUhKSnJRVDUqKipw9uxZfPHFF3jwwQeh0+kAANOmTTNrd/bsWej1egB1i721ma43nl7nKsZ4yfvodDpkZWUhKyuLefJyzJUYmCdxMFdikD0dQBPjzf1h0fFzxnvxeCAO5koMzJNIrve0mCvvJVpu3H7O1rBhw7Bz506sWbMGDz30ENq2bWuz/ZIlS7B//35IkoS//OUvTo9n3bp1ePXVV62uf+yxxzB69Giz+y5duqQst2nTxub2TdebPs4VqqurrZ6G5+PjA5Wqpn5vMBjsvlBNRx3r9XqlgG2JJElmc43odDoYDAar7VUqlVmcWq0Wsmz9q6Rp7LIsQ6vV2oxdo9Eok8Lbe66ujl2tVkOtVsNgMKC4uBhAzWvC2j6YJ8/EbsyTMRZ7uRItT6bblGVZmcFJhgyD3vq2AUClVkH67yNkWbYZCyTAdIZy5um6pvp+AmqmLbKVK+bJsdiB/34VsBEPTC+IYqudpfbkVt7WH25MbL1nybNMj93R0dGeDYZsYq7EwDwJhIcmIYjWh3B7Qffuu+/GkiVLkJmZiQcffBD/+Mc/cMcddyjrJUmCLMtIS0vD0qVLsWnTJkiShN69eyMlJcVtcXbt2hVvvfUWEhMT66wrKytTlu3NfWa63jhfmqvYGgEcFxeH4OBgJY7MzEyb2+rVq5eyXFBQgJycHKttg4KCzCZjP3funHJgsSQ8PNzsgJORkWHzS3BMTAxCQ0MB1Hy5TktLsxl7QkKC8oW/pKQEp0+fttpWo9GY5Tg3Nxf5+flW24eEhCA2Nla5nZWVZfZ6qC0qKgoRERFm9xmn8rCEebLME3kCrOdKtDwFBgYqV1Gv0lbD/7/3G/QG5OXn2Yw9IjxCKc5VVlXiypUrVtuqVWpEtIhSbhcWFtp8rsxT03g/mebHUq6YJ8tq5wkADHq9Mo9qbSqVCj4mBWCtTgfZRvHax8cHKpPCu8FgvTBOzidKf5iIiIiIvJPbC7oqlQr/+7//i/vvvx8XLlzAE088AX9/f2XEyoMPPoiysjLlC4ssy2jfvj0+/PBDl8QzZMgQ9OjRAwBQWVmJ7Oxs/Pjjj9iyZQtefPFFvPbaaxg4cKDZY0wvkmbvKnimI4kqKyudGDkRERERicjb+sNEREREJBaPXCa3bdu2WL9+PWbPno2ffvoJFRUVyrqioiJlWZIkjBgxAm+++SZatGjhkliaN2+O5s2bK7cTExPxl7/8BevXr8crr7yCp556Cv/6178wfvx4pY2fn5+ybO/0StORNP7+/jZaNlxsbKzVEcOmp5AGBgYiISHB4e22atVKGYFkiVTrlM0OHTrYPOXDeHqqUdeuXe2e0mqk0Wjsxm5aZG/RooXN9rVjj4yMtDgC0Kh27LGxsXZPPa4tPj7e6g8BzJNlnsgTYD1XouXJdJt+Gl/gv79JqdQqRIRb/zsa2xj5+/nbbl/r7O2WLVvafK7MU9N4P8XHxysjcy3linmyrHbsAKBSq21eiNVsX1amYLJGpbL8/iLX8ab+MBERiUeSJLtnDBNR4+WRgi5Qcwrn+++/jxdeeAE7d+5Eeno6ioqKoNfrERISgv/f3p3HR1Xf+x9/zySTHQkhBBDCkigBarggIKK2KFi3tiqiIgWlimhrlYrLo73tA1vFKu2t9nbxV8UVlFpWsbV6WwXl1mpZ5RJkUUBWIRCWQAghM5n5/ZHmMElmJplkzsx8Z17Px0MfJzPf+Z7v8Jlzzmc+c873DBw4UJdddpn69u0bk/Fdf/31+uCDD/TOO+9o5syZGj16tHVH4uzsbKtdS9Mo+D9v987W5XK16oue0+ls9RdCqfl8iC0JNo9vMC2d5ezP4XCENfZw36udY/d/DXFqLB7j1PC6lsZlWpz8i0QOOcIai8MRXnviFFwybU/+7VuKFXFqYR31K2rtgMLqG7ER7/kwACB+uVwua1q1lk40A5B4olbQ/eCDD/SPf/xD+/btk9frVUFBgUaMGKGrrrpKEydOjNYwwjJmzBi98847qq6u1j/+8Q/r5mjh3OgsnBuoAQAAIHGZmA8DAAAg/the0K2oqND3v/99bdiwodlzixcv1m9+8xs988wzKikpsXsoYcvLy7OWv/zyS2u5T58+SklJUV1dXcgbmkhq9Lz/TWUAAACQHEzOhwEAbfe3Tw/ocFXgG5q2V15Wqq4qPduWvgHEP1sLunV1dfre974X8q7Pe/fu1ZQpU/TnP/+5UQE1HpSXn7n7u/90CWlpaRo0aJA++eQTrV+/XrW1tUEvm1y1apX1moabr9kl0Hx7iA9Op1MFBQXWMuIXsTIDcTIHsUKyMz0fNg35cPzieGAOYhU5h6tqVX7cnpuj+3xnahDs++Ic4TGCaduRrQXdd955R2VlZXI4HOrVq5fuuusuDRo0SKmpqfrss8/08ssva/369Tp8+LBefvllPfjgg3YOJ2z/8z//Yy3369ev0XNjxozRJ598oqqqKr377rv6xje+0ez1Bw4c0McffyxJGjlypHJycmwdb7hzAyJ6UlNTQ96IB/GDWJmBOJmDWJnBrPTVLKbnw6YhH45fHA/MQazM4HCcKbaz74t3ZzItYhW/TIuNrT+3vfPOO5KkHj16aOHChRo3bpzOPfdc9e3bV1deeaXmzZun4cOHy+fzNSqe2m3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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot settings\n",
+    "plot_config = dict(\n",
+    "    bins=20,\n",
+    "    binrange=(0, 1),\n",
+    "    stat=\"percent\",\n",
+    ")\n",
+    "\n",
+    "for base_model, it_model in tqdm(base_it_pairs):\n",
+    "    fig, axes = plt.subplots(ncols=2, figsize=(8, 3), gridspec_kw=dict(wspace=0.33), sharey=False)\n",
+    "\n",
+    "    for idx, ax, m in zip(range(len(axes)), axes, (base_model, it_model)):\n",
+    "        if m is None:\n",
+    "            ax.remove()\n",
+    "            continue\n",
+    "\n",
+    "        model_row = task_df[(task_df[model_col] == m) & (task_df[\"uses_all_features\"])].iloc[0]\n",
+    "        model_id = model_row.name\n",
+    "\n",
+    "        scores_df = scores_df_map[model_id]\n",
+    "        scores_df[\"group\"] = sens_attr_data.loc[scores_df.index]\n",
+    "\n",
+    "        # sns.histplot(\n",
+    "        #     data=scores_df,\n",
+    "        #     x=\"risk_score\",\n",
+    "        #     hue=\"group\",\n",
+    "        #     ax=ax,\n",
+    "        #     **plot_config,\n",
+    "        # )\n",
+    "\n",
+    "        for group_val, group_name in sens_attr_map.items():\n",
+    "            group_scores_df = scores_df[sens_attr_data.loc[scores_df.index] == group_val]\n",
+    "            sns.histplot(\n",
+    "                group_scores_df[\"risk_score\"],\n",
+    "                label=group_name,\n",
+    "                ax=ax,\n",
+    "                alpha=0.5,\n",
+    "                **plot_config,\n",
+    "            )\n",
+    "\n",
+    "        # Remove left plot legend\n",
+    "        if idx == 0:\n",
+    "            ax.legend(loc=\"upper left\")\n",
+    "\n",
+    "        ax.set_xlim(0, 1)\n",
+    "        # ax.set_ylim(0, 1)\n",
+    "\n",
+    "        ax.set_title(model_row[\"name\"])\n",
+    "\n",
+    "    plt.plot()\n",
+    "    save_fig(fig, f\"score-per-subgroup.{base_model}.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77754ec9-7368-4edd-bf26-465710a99e73",
+   "metadata": {},
+   "source": [
+    "## Compute mean under-/over-estimation of risk score\n",
+    "It's basocally akin to computing ECE without the absolute..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "323b2ad2-1d01-4a9c-ab88-5f5079a7d014",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_confidence_bias_metric_helper(data):\n",
+    "    \"\"\"Helper to compute the under- / over-confidence metric.\n",
+    "    \"\"\"\n",
+    "    mean_label = data[\"label\"].mean()\n",
+    "    mean_score = data[\"risk_score\"].mean()\n",
+    "\n",
+    "    if mean_score < 0.5:\n",
+    "        return ((1 - mean_score) - (1 - mean_label))\n",
+    "    else:\n",
+    "        return (mean_score - mean_label)\n",
+    "\n",
+    "def compute_confidence_bias_metric(model_id, quantiled_bins=False, scores_df=None):\n",
+    "    if scores_df is None:\n",
+    "        scores_df = scores_df_map[model_id]\n",
+    "\n",
+    "    discretization_func = pd.qcut if quantiled_bins else pd.cut\n",
+    "    scores_df[\"score_bin\"] = discretization_func(scores_df[\"risk_score\"], 10, labels=range(10))\n",
+    "    \n",
+    "    bias_metric_val = (1 / len(scores_df)) * sum(\n",
+    "        len(bin_data) * compute_confidence_bias_metric_helper(bin_data)\n",
+    "        for bin_ in range(10)\n",
+    "        if len(bin_data := scores_df[scores_df[\"score_bin\"] == bin_]) > 0\n",
+    "    )\n",
+    "    return float(bias_metric_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "57d97476-15cf-4eba-a53b-468b1f23f1ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81416b6ae6a041bdacc00c142bcdb7b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "confidence_scores = {\n",
+    "    m: compute_confidence_bias_metric(m)\n",
+    "    for m in tqdm(scores_df_map.keys())\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "66363a5b-9104-4da5-a96f-ad59eb464634",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Meta-Llama-3-8B-Instruct__ACSPublicCoverage__-1__Num     -0.161384\n",
+       "Mixtral-8x7B-Instruct-v0.1__ACSPublicCoverage__-1__Num   -0.061690\n",
+       "Mixtral-8x7B-Instruct-v0.1__ACSEmployment__-1__Num       -0.056597\n",
+       "Meta-Llama-3-70B-Instruct__ACSPublicCoverage__-1__Num    -0.052221\n",
+       "gemma-7b__ACSIncome__-1__Num                             -0.033482\n",
+       "                                                            ...   \n",
+       "gemma-7b__ACSEmployment__-1__Num                          0.341868\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num                       0.355603\n",
+       "gemma-2b__ACSIncome__-1__Num                              0.367876\n",
+       "Mistral-7B-v0.1__ACSTravelTime__-1__Num                   0.438141\n",
+       "gemma-2b__ACSTravelTime__-1__Num                          0.438312\n",
+       "Name: under_over_score, Length: 100, dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_results_df[\"under_over_score\"] = all_results_df.apply(lambda row: confidence_scores[row.name], axis=1)\n",
+    "all_results_df.sort_values(\"under_over_score\", ascending=True)[\"under_over_score\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "a558de58-e4e9-4105-8fbd-4abafe41fca2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TASK = \"ACSIncome\"\n",
+    "# TASK = \"ACSTravelTime\"\n",
+    "# TASK = \"ACSMobility\"\n",
+    "# TASK = \"ACSEmployment\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "987310f0-4078-47d0-b841-b43e83446e4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/under_over_score.ACSIncome.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_df = all_results_df[\n",
+    "    (all_results_df[task_col] == TASK)\n",
+    "    & (~all_results_df[numeric_prompt_col].isna())\n",
+    "]\n",
+    "\n",
+    "# Omit Gemma models bc they're garbage :)\n",
+    "plot_df = plot_df.drop(index=[id_ for id_ in plot_df.index if \"gemma\" in id_.lower() or \"yi\" in id_.lower()])\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(4, 3))\n",
+    "sns.barplot(\n",
+    "    data=plot_df.sort_values(\"under_over_score\", ascending=True),\n",
+    "    x=\"under_over_score\",\n",
+    "    y=\"name\",\n",
+    "    # y=task_col,\n",
+    "    hue=\"is_inst\",\n",
+    "    ax=ax,\n",
+    ")\n",
+    "\n",
+    "handles, _ = plt.gca().get_legend_handles_labels()\n",
+    "plt.legend(handles=handles, labels=[\"Base\", \"Instruct\"])\n",
+    "\n",
+    "plt.ylabel(\"\")\n",
+    "plt.xlabel(r\"$\\mathrm{R}_\\mathrm{bias}$\")\n",
+    "\n",
+    "plt.title(f\"Risk score confidence bias\")\n",
+    "\n",
+    "save_fig(fig, f\"under_over_score.{TASK}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e37f6688-c821-43ee-899a-a6308b723bf3",
+   "metadata": {},
+   "source": [
+    "## Compute risk score fairness per sub-group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "46e74470-cf67-43da-855a-727a37912916",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    2\n",
+       "2    1\n",
+       "8    1\n",
+       "Name: RAC1P, dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sens_attr_data = acs_dataset.data[sens_attr_col]\n",
+    "sens_attr_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "6c50ef72-2027-4cb8-8279-89880376652c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_signed_score_bias(scores_df, quantiled_bins=False, n_bins=10):\n",
+    "    discretization_func = pd.qcut if quantiled_bins else pd.cut\n",
+    "    scores_df[\"score_bin\"] = discretization_func(scores_df[\"risk_score\"], n_bins, duplicates=\"drop\", retbins=False)\n",
+    "    bin_categories = scores_df[\"score_bin\"].dtype.categories\n",
+    "\n",
+    "    return (1 / len(scores_df)) * sum(\n",
+    "        len(bin_data) * (bin_data[\"risk_score\"].mean() - bin_data[\"label\"].mean())\n",
+    "        for bin_ in bin_categories\n",
+    "        if len(bin_data := scores_df[scores_df[\"score_bin\"] == bin_]) > 0\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "a6dfaa3b-1a75-44fc-9f72-3dcb61051223",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_signed_subgroup_score_bias(model, group: int | str) -> float:\n",
+    "    scores_df = scores_df_map[model]\n",
+    "    if isinstance(group, str):\n",
+    "        group = name_to_sens_attr_map[group]\n",
+    "\n",
+    "    scores_df = scores_df[sens_attr_data.loc[scores_df.index] == group]\n",
+    "    # return compute_confidence_metric(model, scores_df=scores_df)\n",
+    "    return compute_signed_score_bias(scores_df)\n",
+    "\n",
+    "\n",
+    "def compute_calibration_fairness(model: str, priv_group: int | str, unpriv_group: int | str) -> float:\n",
+    "    return (\n",
+    "        compute_signed_subgroup_score_bias(model, group=priv_group)\n",
+    "        - compute_signed_subgroup_score_bias(model, group=unpriv_group)\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "2770de00-0ade-4c9a-8a06-684957f2b169",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3388197/3464681156.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  scores_df[\"score_bin\"] = discretization_func(scores_df[\"risk_score\"], n_bins, duplicates=\"drop\", retbins=False)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>White_score_bias</th>\n",
+       "      <th>Black_score_bias</th>\n",
+       "      <th>Asian_score_bias</th>\n",
+       "      <th>White_v_Black_score_bias</th>\n",
+       "      <th>White_v_Asian_score_bias</th>\n",
+       "      <th>Asian_v_Black_score_bias</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mistral-7B-v0.1__ACSIncome__-1__Num</th>\n",
+       "      <td>0.353045</td>\n",
+       "      <td>0.329428</td>\n",
+       "      <td>0.319214</td>\n",
+       "      <td>0.023617</td>\n",
+       "      <td>0.033831</td>\n",
+       "      <td>-0.010215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gemma-1.1-2b-it__ACSIncome__-1__Num</th>\n",
+       "      <td>0.260100</td>\n",
+       "      <td>0.403764</td>\n",
+       "      <td>0.202488</td>\n",
+       "      <td>-0.143664</td>\n",
+       "      <td>0.057612</td>\n",
+       "      <td>-0.201276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Yi-34B-Chat__ACSIncome__-1__Num</th>\n",
+       "      <td>0.210531</td>\n",
+       "      <td>0.229600</td>\n",
+       "      <td>0.212839</td>\n",
+       "      <td>-0.019069</td>\n",
+       "      <td>-0.002308</td>\n",
+       "      <td>-0.016761</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     White_score_bias  Black_score_bias  \\\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num          0.353045          0.329428   \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num          0.260100          0.403764   \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num              0.210531          0.229600   \n",
+       "\n",
+       "                                     Asian_score_bias  \\\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num          0.319214   \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num          0.202488   \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num              0.212839   \n",
+       "\n",
+       "                                     White_v_Black_score_bias  \\\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num                  0.023617   \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num                 -0.143664   \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num                     -0.019069   \n",
+       "\n",
+       "                                     White_v_Asian_score_bias  \\\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num                  0.033831   \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num                  0.057612   \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num                     -0.002308   \n",
+       "\n",
+       "                                     Asian_v_Black_score_bias  \n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num                 -0.010215  \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num                 -0.201276  \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num                     -0.016761  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calibration_per_subgroup_df = pd.DataFrame([\n",
+    "    pd.Series(\n",
+    "        {\n",
+    "            f\"{group}_score_bias\": compute_signed_subgroup_score_bias(model_id, group)\n",
+    "            for group in sens_attr_map.values()\n",
+    "        },\n",
+    "        name=model_id,\n",
+    "    )\n",
+    "    for model_id in task_df.index\n",
+    "])\n",
+    "\n",
+    "calibration_per_subgroup_df[\"White_v_Black_score_bias\"] = \\\n",
+    "    calibration_per_subgroup_df[\"White_score_bias\"] - calibration_per_subgroup_df[\"Black_score_bias\"]\n",
+    "\n",
+    "calibration_per_subgroup_df[\"White_v_Asian_score_bias\"] = \\\n",
+    "    calibration_per_subgroup_df[\"White_score_bias\"] - calibration_per_subgroup_df[\"Asian_score_bias\"]\n",
+    "\n",
+    "calibration_per_subgroup_df[\"Asian_v_Black_score_bias\"] = \\\n",
+    "    calibration_per_subgroup_df[\"Asian_score_bias\"] - calibration_per_subgroup_df[\"Black_score_bias\"]\n",
+    "\n",
+    "calibration_per_subgroup_df.sample(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1efc1204-874f-4e5e-b1b4-716e5af1d044",
+   "metadata": {},
+   "source": [
+    "**Note:** This is an example of comparing risk score bias between different sub-groups. A more detailed analysis must be conducted to draw any sort of conclusions regarding prediction fairness of the LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "ca1a5631-3070-4d93-9258-b527dac8fe85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/score_bias.Asian_v_Black_score_bias.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "GROUP_A = \"Asian\"\n",
+    "# GROUP_A = \"White\"\n",
+    "GROUP_B = \"Black\"\n",
+    "# GROUP_B = \"Asian\"\n",
+    "\n",
+    "COLUMN = f\"{GROUP_A}_v_{GROUP_B}_score_bias\"\n",
+    "\n",
+    "plot_df = calibration_per_subgroup_df.join(results_df, how=\"left\", validate=\"1:1\")\n",
+    "plot_df = plot_df.drop(index=[id_ for id_ in plot_df.index if \"gemma\" in id_.lower()])   # omit Gemma models bc they're garbage :)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(3, 3))\n",
+    "sns.barplot(\n",
+    "    data=plot_df.sort_values(COLUMN, ascending=True),\n",
+    "    x=COLUMN,\n",
+    "    y=\"name\",\n",
+    "    hue=\"is_inst\",\n",
+    "    ax=ax,\n",
+    ")\n",
+    "\n",
+    "handles, _ = plt.gca().get_legend_handles_labels()\n",
+    "plt.legend(handles=handles, labels=[\"Base\", \"Instruct\"])\n",
+    "\n",
+    "plt.ylabel(\"\")\n",
+    "plt.xlabel(\"$\\Delta_{SCE}$\")\n",
+    "\n",
+    "plt.title(\n",
+    "    \"Signed risk score bias\\n\"\n",
+    "    + r\"($SCE_{\" + GROUP_A\n",
+    "    + r\"} - SCE_{\" + GROUP_B\n",
+    "    + r\"}$)\"\n",
+    ")\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"score_bias.{COLUMN}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "bc511e0a-2928-464e-a09b-ebd2a9928b9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>White_score_bias</th>\n",
+       "      <th>Black_score_bias</th>\n",
+       "      <th>Asian_score_bias</th>\n",
+       "      <th>White_v_Black_score_bias</th>\n",
+       "      <th>White_v_Asian_score_bias</th>\n",
+       "      <th>Asian_v_Black_score_bias</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>...</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mixtral-8x22B-v0.1__ACSIncome__-1__Num</th>\n",
+       "      <td>0.054322</td>\n",
+       "      <td>-0.069436</td>\n",
+       "      <td>0.130153</td>\n",
+       "      <td>0.123758</td>\n",
+       "      <td>-0.075831</td>\n",
+       "      <td>0.199589</td>\n",
+       "      <td>0.740583</td>\n",
+       "      <td>0.148527</td>\n",
+       "      <td>0.828562</td>\n",
+       "      <td>0.761428</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Mixtral 8x22B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.769300</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>0.740595</td>\n",
+       "      <td>0.379947</td>\n",
+       "      <td>0.411113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num</th>\n",
+       "      <td>0.110399</td>\n",
+       "      <td>0.055872</td>\n",
+       "      <td>0.156710</td>\n",
+       "      <td>0.054527</td>\n",
+       "      <td>-0.046311</td>\n",
+       "      <td>0.100837</td>\n",
+       "      <td>0.767882</td>\n",
+       "      <td>0.096716</td>\n",
+       "      <td>0.885938</td>\n",
+       "      <td>0.770218</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Mixtral 8x22B (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.769967</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>0.767882</td>\n",
+       "      <td>0.298323</td>\n",
+       "      <td>0.474955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>penai/gpt-4o-mini__ACSIncome__-1__Num</th>\n",
+       "      <td>-0.001300</td>\n",
+       "      <td>-0.019960</td>\n",
+       "      <td>-0.000407</td>\n",
+       "      <td>0.018661</td>\n",
+       "      <td>-0.000893</td>\n",
+       "      <td>0.019553</td>\n",
+       "      <td>0.777393</td>\n",
+       "      <td>0.080767</td>\n",
+       "      <td>0.904748</td>\n",
+       "      <td>0.758922</td>\n",
+       "      <td>...</td>\n",
+       "      <td>GPT 4o mini (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.777302</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>0.775512</td>\n",
+       "      <td>0.317359</td>\n",
+       "      <td>0.363743</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 70 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 White_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                   0.054322   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num          0.110399   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                   -0.001300   \n",
+       "\n",
+       "                                                 Black_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                  -0.069436   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num          0.055872   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                   -0.019960   \n",
+       "\n",
+       "                                                 Asian_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                   0.130153   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num          0.156710   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                   -0.000407   \n",
+       "\n",
+       "                                                 White_v_Black_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                           0.123758   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num                  0.054527   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                            0.018661   \n",
+       "\n",
+       "                                                 White_v_Asian_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                          -0.075831   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num                 -0.046311   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                           -0.000893   \n",
+       "\n",
+       "                                                 Asian_v_Black_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                           0.199589   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num                  0.100837   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                            0.019553   \n",
+       "\n",
+       "                                                 accuracy  accuracy_diff  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num           0.740583       0.148527   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num  0.767882       0.096716   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num            0.777393       0.080767   \n",
+       "\n",
+       "                                                 accuracy_ratio  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                 0.828562   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num        0.885938   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                  0.904748   \n",
+       "\n",
+       "                                                 balanced_accuracy  ...  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                    0.761428  ...   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num           0.770218  ...   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                     0.758922  ...   \n",
+       "\n",
+       "                                                               name  is_inst  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                Mixtral 8x22B    False   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num  Mixtral 8x22B (it)     True   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num              GPT 4o mini (it)     True   \n",
+       "\n",
+       "                                                 num_features  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                   -1.0   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num          -1.0   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                    -1.0   \n",
+       "\n",
+       "                                                 uses_all_features  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                        True   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num               True   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                         True   \n",
+       "\n",
+       "                                                 fit_thresh_on_100  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                        0.65   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num               0.65   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                         0.65   \n",
+       "\n",
+       "                                                 fit_thresh_accuracy  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                      0.769300   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num             0.769967   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                       0.777302   \n",
+       "\n",
+       "                                                 optimal_thresh  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                     0.45   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num            0.55   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                      0.35   \n",
+       "\n",
+       "                                                 optimal_thresh_accuracy  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                          0.740595   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num                 0.767882   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                           0.775512   \n",
+       "\n",
+       "                                                 score_stdev  score_mean  \n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num              0.379947    0.411113  \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num     0.298323    0.474955  \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num               0.317359    0.363743  \n",
+       "\n",
+       "[3 rows x 70 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot_df.sort_values(COLUMN, ascending=False).head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "750f97e8-44cc-465b-a2fc-c08b626063d7",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "# Plots comparing different prompting schemes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "972c2e80-6c0e-4f37-be60-039677d2464a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Plot all models / i.t. / base?\n",
+    "IT_OR_BASE = \"any\"\n",
+    "# IT_OR_BASE = \"it\"\n",
+    "# IT_OR_BASE = \"base\"\n",
+    "\n",
+    "### Plot which metric\n",
+    "METRIC = \"ece\"\n",
+    "# METRIC = \"roc_auc\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "f42aa360-45b3-4c6d-a8f7-11f10ebacee0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting ALL models\n",
+      "model_names=['Gemma 2B', 'Gemma 7B', 'Mistral 7B', 'Llama 3 8B', 'Yi 34B', 'Mixtral 8x7B', 'Llama 3 70B', 'Mixtral 8x22B', 'Gemma 2B (it)', 'Gemma 7B (it)', 'Mistral 7B (it)', 'Llama 3 8B (it)', 'Yi 34B (it)', 'Mixtral 8x7B (it)', 'Llama 3 70B (it)', 'Mixtral 8x22B (it)', 'GPT 4o mini (it)']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prepare data\n",
+    "df = all_results_df_original.copy()\n",
+    "\n",
+    "min_max_per_task = {\n",
+    "    task: (np.min(values), np.max(values))\n",
+    "    for task in ALL_TASKS\n",
+    "    if len(values := df[(df[task_col] == task) & (~df[numeric_prompt_col].isna())][METRIC]) > 0\n",
+    "}\n",
+    "\n",
+    "# Sort models\n",
+    "sorted_df_index = sorted(\n",
+    "    df.index.tolist(),\n",
+    "    key=lambda id_: model_sort_key(id_, df),\n",
+    "    reverse=True,\n",
+    ")\n",
+    "df = df.loc[sorted_df_index]\n",
+    "\n",
+    "if IT_OR_BASE == \"it\":\n",
+    "    print(\"Plotting IT models\")\n",
+    "    df = df[df[\"is_inst\"] == True]\n",
+    "elif IT_OR_BASE == \"base\":\n",
+    "    print(\"Plotting BASE models\")\n",
+    "    df = df[df[\"is_inst\"] == False]\n",
+    "else:\n",
+    "    print(\"Plotting ALL models\")\n",
+    "    df = df.sort_values(by=[\"is_inst\", \"model_size\"], ascending=False)\n",
+    "\n",
+    "# Data for multiple-choice prompting\n",
+    "df_mult_choice = df[df[numeric_prompt_col] == False].set_index([\"name\", task_col], drop=False)\n",
+    "\n",
+    "# Data for numeric risk prompting\n",
+    "df_numeric = df[df[numeric_prompt_col] == True].set_index([\"name\", task_col], drop=False)\n",
+    "\n",
+    "# Sorted list of model names\n",
+    "model_names = df_numeric[\"name\"].unique().tolist()[::-1]\n",
+    "n_models = len(model_names)\n",
+    "print(f\"{model_names=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "91a7f4e9-6af2-44b4-a8ca-0e17cbcea1ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/ece_comparison_diff_prompts.any-models.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1133.33 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import FancyArrowPatch\n",
+    "\n",
+    "# Create a figure and axis\n",
+    "fig, axes = plt.subplots(ncols=len(ALL_TASKS), figsize=(8, n_models / 3), sharey=True)\n",
+    "\n",
+    "for task_i, task in enumerate(ALL_TASKS):\n",
+    "\n",
+    "    # Get current plot axis\n",
+    "    ax = axes[task_i]\n",
+    "        \n",
+    "    # Plot arrows for each model\n",
+    "    for i, model_name in enumerate(model_names):\n",
+    "\n",
+    "        curr_mult_choice_metric = df_mult_choice.loc[model_name, task][METRIC]\n",
+    "        curr_numeric_metric = df_numeric.loc[model_name, task][METRIC]\n",
+    "        \n",
+    "        ax.scatter(\n",
+    "            curr_mult_choice_metric, i,\n",
+    "            color=palette[0], marker=\"s\",\n",
+    "            label='Multiple-choice' if i == 0 else \"\",\n",
+    "        )\n",
+    "        ax.scatter(\n",
+    "            curr_numeric_metric, i,\n",
+    "            color=palette[1], marker=\"o\",\n",
+    "            label='Numeric' if i == 0 else \"\",\n",
+    "        )\n",
+    "\n",
+    "        # Draw a FancyArrowPatch arrow from before to after fine-tuning\n",
+    "        if METRIC == \"ece\":\n",
+    "            curr_metric_improved = curr_numeric_metric <= curr_mult_choice_metric\n",
+    "        else:\n",
+    "            curr_metric_improved = curr_numeric_metric >= curr_mult_choice_metric\n",
+    "        \n",
+    "        arrow = FancyArrowPatch(\n",
+    "            (curr_mult_choice_metric, i), (curr_numeric_metric, i),\n",
+    "            arrowstyle='->',\n",
+    "            color=palette[2] if curr_metric_improved else palette[3],\n",
+    "            mutation_scale=15,\n",
+    "            lw=1.5,\n",
+    "        )\n",
+    "        ax.add_patch(arrow)\n",
+    "\n",
+    "        # Set axes ranges according to overall min/max values\n",
+    "        lo, hi = min_max_per_task[task]\n",
+    "        if METRIC == \"ece\":\n",
+    "            lo = max(lo - 5e-2, 0)\n",
+    "            hi = min(hi + 5e-2, 0.475)\n",
+    "        elif METRIC == \"roc_auc\":\n",
+    "            lo = max(lo - 5e-2, 0.475)\n",
+    "            hi = min(hi + 4e-2, 1)\n",
+    "\n",
+    "        if task == \"ACSIncome\" and METRIC == \"roc_auc\":\n",
+    "            lo = 0.58\n",
+    "        ax.set_xlim(lo, hi)\n",
+    "    \n",
+    "    # Set y-ticks and y-labels\n",
+    "    ax.set_yticks(range(n_models))\n",
+    "    ax.set_yticklabels(model_names)\n",
+    "\n",
+    "    # # Prettify x-axis ticks\n",
+    "    # xticks = ax.get_xticks()\n",
+    "    # ax.set_xticks(xticks, [f\"{x}\" for x in xticks])\n",
+    "    \n",
+    "    # Add labels and title\n",
+    "    # ax.set_xlabel('ECE')\n",
+    "    ax.set_title(task[3:])\n",
+    "\n",
+    "    # Add horizontal line separating instruct and base models\n",
+    "    if IT_OR_BASE == \"any\":\n",
+    "        n_instruct_models = len(df_mult_choice[(df_mult_choice[\"is_inst\"] == True) & (df_mult_choice[task_col] == task)])\n",
+    "        line_y = n_instruct_models - 1.5\n",
+    "        ax.axhline(line_y, color='black', linestyle='--', linewidth=1) #, label=\"instruct / base\")\n",
+    "\n",
+    "        # Add text label near the horizontal line\n",
+    "        if task_i > len(ALL_TASKS) - 3:\n",
+    "            text_kwargs = dict(\n",
+    "                horizontalalignment='right',\n",
+    "                transform=ax.get_yaxis_transform(),  # Use axis coordinates for y-axis\n",
+    "                fontsize=9, color='black',\n",
+    "            )\n",
+    "\n",
+    "            ax.text(1, line_y + 0.1, 'i.t.', verticalalignment='bottom', **text_kwargs)\n",
+    "            ax.text(1, line_y - 0.1, 'base', verticalalignment='top', **text_kwargs)\n",
+    "        \n",
+    "\n",
+    "\n",
+    "# Add legend for marker\n",
+    "axes[0].legend(loc=\"upper right\", bbox_to_anchor=(-0.15, 0.01), title=\"Prompting scheme\")\n",
+    "\n",
+    "# X-axis label\n",
+    "if METRIC == \"ece\":\n",
+    "    x_axis_label = \"Expected Calibration Error (ECE)\"\n",
+    "elif METRIC == \"roc_auc\":\n",
+    "    x_axis_label = \"Predictive Power (ROC AUC)\"\n",
+    "\n",
+    "fig.text(0.5, 0.02 if IT_OR_BASE == \"any\" else -0.04, x_axis_label, horizontalalignment=\"center\", verticalalignment=\"center\")\n",
+    "\n",
+    "# Save figure\n",
+    "save_fig(fig=fig, name=f\"{METRIC}_comparison_diff_prompts.{IT_OR_BASE}-models.pdf\", add_prompt_suffix=False)\n",
+    "\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3db3e60-220d-4a15-bc46-c0815b39e6df",
+   "metadata": {},
+   "source": [
+    "## Scatter plot of ECE vs AUC for a few models (before and after numeric prompting)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "e77dd307-0e63-4f1d-b755-9025566e1947",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/comparison_diff_prompts.any-models.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2600x440 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_METRIC = \"ece\"\n",
+    "Y_METRIC = \"roc_auc\"\n",
+    "\n",
+    "# Filter out small models\n",
+    "df_mult_choice = df_mult_choice[df_mult_choice[\"model_size\"] > 50]\n",
+    "df_numeric = df_numeric[df_numeric[\"model_size\"] > 50]\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=len(ALL_TASKS), figsize=(len(ALL_TASKS) * 2.6, 2.2), gridspec_kw=dict(wspace=0.4))\n",
+    "\n",
+    "unique_model_names = df_mult_choice.name.unique()\n",
+    "markers = dict(zip(unique_model_names, [\"s\", \"o\", \"D\", \"P\", \"X\", \"<\", \">\"]))\n",
+    "\n",
+    "for task_i, task in enumerate(ALL_TASKS):\n",
+    "\n",
+    "    # Create a figure and axis\n",
+    "    # fig, ax = plt.subplots(figsize=(2.5, 2.5))\n",
+    "\n",
+    "    ax = axes[task_i]\n",
+    "        \n",
+    "    # Plot arrows for each model\n",
+    "    for i, model_name in enumerate(df_mult_choice.name.unique()):\n",
+    "\n",
+    "        # Get data for each plotting scheme\n",
+    "        curr_mult_choice_row = df_mult_choice.loc[model_name, task]\n",
+    "        curr_numeric_row = df_numeric.loc[model_name, task]\n",
+    "\n",
+    "        # Get points to plot\n",
+    "        curr_mult_choice_point = (curr_mult_choice_row[X_METRIC], curr_mult_choice_row[Y_METRIC])\n",
+    "        curr_numeric_point = (curr_numeric_row[X_METRIC], curr_numeric_row[Y_METRIC])\n",
+    "\n",
+    "        # Plot point for multiple-choice prompting\n",
+    "        ax.scatter(\n",
+    "            *curr_mult_choice_point,\n",
+    "            color=palette[0], marker=markers[model_name],\n",
+    "            label='Multiple-choice' if i == 0 else \"\",\n",
+    "        )\n",
+    "\n",
+    "        # Plot point for numeric-style prompting\n",
+    "        ax.scatter(\n",
+    "            *curr_numeric_point,\n",
+    "            color=palette[1], marker=markers[model_name],\n",
+    "            label='Numeric' if i == 0 else \"\",\n",
+    "        )\n",
+    "\n",
+    "        if curr_numeric_point[0] <=  curr_mult_choice_point[0] and curr_numeric_point[1] >=  curr_mult_choice_point[1]:\n",
+    "            arrow_color = palette[2]\n",
+    "        elif curr_numeric_point[0] >  curr_mult_choice_point[0] and curr_numeric_point[1] <  curr_mult_choice_point[1]:\n",
+    "            arrow_color = palette[3]\n",
+    "        else:\n",
+    "            arrow_color = \"grey\"\n",
+    "\n",
+    "        # Draw a FancyArrowPatch arrow from before to after fine-tuning\n",
+    "        arrow = FancyArrowPatch(\n",
+    "            curr_mult_choice_point, curr_numeric_point,\n",
+    "            arrowstyle='->',\n",
+    "            color=arrow_color,\n",
+    "            mutation_scale=15,\n",
+    "            lw=1,\n",
+    "            ls=\"--\",\n",
+    "        )\n",
+    "        ax.add_patch(arrow)\n",
+    "\n",
+    "    ### Plot configs\n",
+    "    # Add labels and title\n",
+    "    ax.set_title(task[3:])\n",
+    "    ax.set_xlabel(\"ECE\")\n",
+    "    if task_i == 0:\n",
+    "        ax.set_ylabel(\"AUC\")\n",
+    "\n",
+    "# Create custom global legend\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "# Create a global legend for markers (model names)\n",
+    "marker_handles = [Line2D([0], [0], marker=markers[model], color='w', markerfacecolor='black', markersize=8, label=model) for model in unique_model_names]\n",
+    "\n",
+    "# Create a global legend for colors (prompting schemes)\n",
+    "color_handles = [\n",
+    "    Line2D([0], [0], marker='o', color='w', markerfacecolor=palette[0], markersize=8, label=\"Multiple-choice\"),\n",
+    "    Line2D([0], [0], marker='o', color='w', markerfacecolor=palette[1], markersize=8, label=\"Numeric\")\n",
+    "]\n",
+    "\n",
+    "# Position the legends outside the subplots\n",
+    "fig.legend(handles=marker_handles, title=\"Models\", loc='upper left', bbox_to_anchor=(0.13, -0.12), ncol=min(4, len(unique_model_names)))\n",
+    "fig.legend(handles=color_handles, title=\"Prompting Scheme\", loc='upper right', bbox_to_anchor=(0.9, -0.12), ncol=2)\n",
+    "\n",
+    "# Save figure\n",
+    "save_fig(fig=fig, name=f\"comparison_diff_prompts.{IT_OR_BASE}-models.pdf\", add_prompt_suffix=False)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/notebooks/parse-acs-results.ipynb b/.doctrees/nbsphinx/notebooks/parse-acs-results.ipynb
new file mode 100644
index 0000000..6b99e2d
--- /dev/null
+++ b/.doctrees/nbsphinx/notebooks/parse-acs-results.ipynb
@@ -0,0 +1,589 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "04c2f12d-f989-4ac9-b90f-3464a8bcca96",
+   "metadata": {},
+   "source": [
+    "# Fetch and parse ACS benchmark results under a given directory\n",
+    "Each ACS benchmark run outputs a json file. This script collects all such files under a given root directory, parses them, and aggregates them into a more easily digestable pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b241208-d10f-43cf-a486-84c54bbf43c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faf6afa4-8648-4d35-9312-65636ea5d0b2",
+   "metadata": {},
+   "source": [
+    "**[Action required]** Set `RESULTS_ROOT_DIR` to the root results directory path:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26089a60-81c0-4736-8ba5-99572ec01398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RESULTS_ROOT_DIR = Path(\"/fast/groups/sf\") / \"folktexts-results\" / \"2024-08-28_2\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d1af7f-d013-40a0-80de-cb9ad65cff6d",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root data directory (needed only to train baseline ML methods):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b18dad87-a1ed-495b-93b6-4c8af4043ddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db7c98c5-2942-4f88-984a-8c9014afe761",
+   "metadata": {},
+   "source": [
+    "Important results columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e96f0c4f-5683-4150-8cca-93a883f20154",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_col = \"config_model_name\"\n",
+    "task_col = \"config_task_name\"\n",
+    "numeric_prompt_col = \"config_numeric_risk_prompting\"\n",
+    "\n",
+    "feature_subset_col = \"config_feature_subset\"\n",
+    "predictions_path_col = \"predictions_path\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4296ffdf-a9af-43f7-bd0c-20f4ae5f9619",
+   "metadata": {},
+   "source": [
+    "Helper function to parse each dictionary containing benchmark results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0cf4a6c8-ec28-4e66-9f6d-8a4e977d7b60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import (\n",
+    "    num_features_helper,\n",
+    "    parse_model_name,\n",
+    "    get_non_instruction_tuned_name,\n",
+    "    prettify_model_name,\n",
+    ")\n",
+    "\n",
+    "def parse_results_dict(dct) -> dict:\n",
+    "    \"\"\"Parses results dict and brings all information to the top-level.\"\"\"\n",
+    "\n",
+    "    # Make a copy so we don't modify the input object\n",
+    "    dct = dct.copy()\n",
+    "\n",
+    "    # Discard plots' paths\n",
+    "    dct.pop(\"plots\", None)\n",
+    "\n",
+    "    # Bring configs to top-level\n",
+    "    config = dct.pop(\"config\", {})\n",
+    "    for key, val in config.items():\n",
+    "        dct[f\"config_{key}\"] = val\n",
+    "\n",
+    "    # Parse model name\n",
+    "    dct[model_col] = parse_model_name(dct[model_col])\n",
+    "    dct[\"base_name\"] = get_non_instruction_tuned_name(dct[model_col])\n",
+    "    dct[\"name\"] = prettify_model_name(dct[model_col])\n",
+    "\n",
+    "    # Is instruction-tuned model?\n",
+    "    dct[\"is_inst\"] = dct[\"base_name\"] != dct[model_col] or \"(it)\" in dct[\"name\"].lower()\n",
+    "\n",
+    "    # Log number of features\n",
+    "    dct[\"num_features\"] = num_features_helper(dct[feature_subset_col], max_features_return=-1)\n",
+    "    dct[\"uses_all_features\"] = (dct[feature_subset_col] is None) or (dct[\"num_features\"] == -1)\n",
+    "\n",
+    "    if dct[feature_subset_col] is None:\n",
+    "        dct[feature_subset_col] = \"full\"\n",
+    "\n",
+    "    # Assert all results are at the top-level\n",
+    "    assert not any(isinstance(val, dict) for val in dct.values())\n",
+    "    return dct\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02009a86-e099-4ec0-8676-8d66190ceddb",
+   "metadata": {},
+   "source": [
+    "Iteratively search the root directory for results files matching the given regex:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aaefa99d-dbd7-40a3-a3c1-7571d3811409",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b984f67ca7644ba998983af5385ba147",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 241 benchmark results.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils import find_files, load_json\n",
+    "\n",
+    "# Results file name pattern\n",
+    "pattern = r'^results.bench-(?P<hash>\\d+)[.]json$'\n",
+    "\n",
+    "# Find results files and aggregate\n",
+    "results = {}\n",
+    "for file_path in tqdm(find_files(RESULTS_ROOT_DIR, pattern)):\n",
+    "    results[Path(file_path).parent.name] = parse_results_dict(load_json(file_path))\n",
+    "\n",
+    "if len(results) == 0:\n",
+    "    raise RuntimeError(f\"Couldn't find any results at {RESULTS_ROOT_DIR}\")\n",
+    "else:\n",
+    "    print(f\"Found {len(results)} benchmark results.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ecc0d33-902d-492a-89c6-d9729fe69fa1",
+   "metadata": {},
+   "source": [
+    "Aggregate results into a single DataFrame, generate a unique identifier for each row, and drop potential duplicates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "94f1900f-fea3-4872-b722-ee1cd3f5f7e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.shape=(241, 58)\n",
+      "Dropping 31 duplicates!\n",
+      "df.shape=(210, 58)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(list(results.values()))\n",
+    "\n",
+    "def row_id(row) -> str:\n",
+    "    \"\"\"Unique row identifier.\"\"\"\n",
+    "    numeric_or_multiple_choice = \"Num\" if row[numeric_prompt_col] else \"QA\"\n",
+    "    return f\"{row[model_col]}__{row[task_col]}__{row['num_features']}__{numeric_or_multiple_choice}\"\n",
+    "\n",
+    "print(f\"{df.shape=}\")\n",
+    "df[\"id\"] = df.apply(row_id, axis=1)\n",
+    "\n",
+    "# Drop duplicates\n",
+    "len_with_dups = len(df)\n",
+    "df = df.drop_duplicates(subset=[\"name\", \"is_inst\", \"num_features\", task_col, numeric_prompt_col])\n",
+    "df = df.set_index(\"id\", drop=True, verify_integrity=True)\n",
+    "\n",
+    "if len_with_dups != len(df):\n",
+    "    print(f\"Dropping {len_with_dups - len(df)} duplicates!\")\n",
+    "    print(f\"{df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97cd24df-3c1f-49c0-bb50-a6fdba22e0fb",
+   "metadata": {},
+   "source": [
+    "Load scores DFs and analyze score distribution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "43ba0fbf-b886-4f2a-8a01-564aa6f0f2c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_model_scores_df(df_row: pd.Series) -> pd.DataFrame:\n",
+    "    \"\"\"Loads csv containing model scores corresponding to the given DF row.\"\"\"\n",
+    "    if predictions_path_col in df_row and not pd.isna(df_row[predictions_path_col]):\n",
+    "        return pd.read_csv(df_row[predictions_path_col], index_col=0)\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "54e22896-1d3d-4d95-bb90-2b453f58f09a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64f4e6718312499b84fb8c9637ff10fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/210 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from folktexts.evaluation import compute_best_threshold\n",
+    "from sklearn import metrics\n",
+    "from scipy import stats\n",
+    "\n",
+    "# Number of samples used to fit the one-parameter binarization threshold!\n",
+    "N = 100\n",
+    "\n",
+    "fit_thresh_col = f\"fit_thresh_on_{N}\"\n",
+    "fit_acc_col = f\"fit_thresh_accuracy\"\n",
+    "\n",
+    "optimal_thres_col = \"optimal_thresh\"\n",
+    "optimal_acc_col = \"optimal_thresh_accuracy\"\n",
+    "\n",
+    "score_stdev_col = \"score_stdev\"\n",
+    "score_mean_col = \"score_mean\"\n",
+    "\n",
+    "scores_stats = dict()\n",
+    "for row_id, row in tqdm(df.iterrows(), total=len(df)):\n",
+    "\n",
+    "    # Load model scores\n",
+    "    scores_df = load_model_scores_df(row)\n",
+    "    if scores_df is None:\n",
+    "        logging.error(f\"Couldn't find scores for {row_id}\")\n",
+    "\n",
+    "    # Extract scores and labels\n",
+    "    risk_scores = scores_df[\"risk_score\"].to_numpy()\n",
+    "    labels = scores_df[\"label\"].to_numpy()\n",
+    "\n",
+    "    # Sample N rows to fit threshold\n",
+    "    scores_df_sample = scores_df.sample(n=N, random_state=42)\n",
+    "\n",
+    "    # Compute optimal threshold on each data sample\n",
+    "    fit_thr = compute_best_threshold(y_true=scores_df_sample[\"label\"], y_pred_scores=scores_df_sample[\"risk_score\"])\n",
+    "    opt_thr = compute_best_threshold(y_true=labels, y_pred_scores=risk_scores)\n",
+    "\n",
+    "    # Evaluate accuracy\n",
+    "    fit_acc = metrics.accuracy_score(labels, (risk_scores >= fit_thr).astype(int))\n",
+    "    opt_acc = metrics.accuracy_score(labels, (risk_scores >= opt_thr).astype(int))\n",
+    "\n",
+    "    # Save results\n",
+    "    scores_stats[row_id] = {\n",
+    "        fit_thresh_col: fit_thr,\n",
+    "        fit_acc_col: fit_acc,\n",
+    "        optimal_thres_col: opt_thr,\n",
+    "        optimal_acc_col: opt_acc,\n",
+    "        score_stdev_col: np.std(risk_scores),\n",
+    "        score_mean_col: np.mean(risk_scores),\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1a58b87-2de8-473a-badb-934951d1bcdc",
+   "metadata": {},
+   "source": [
+    "Update results DF with scores statistics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0ba74c81-c008-4010-a9a3-1b511ae445df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scores_stats_df.shape=(210, 6)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>gemma-2-27b__ACSTravelTime__-1__Num</th>\n",
+       "      <td>0.539392</td>\n",
+       "      <td>0.258000</td>\n",
+       "      <td>0.656000</td>\n",
+       "      <td>0.504500</td>\n",
+       "      <td>0.100184</td>\n",
+       "      <td>0.823204</td>\n",
+       "      <td>0.388077</td>\n",
+       "      <td>0.347071</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.221264</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Gemma 2 27B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.13000</td>\n",
+       "      <td>0.514676</td>\n",
+       "      <td>0.210000</td>\n",
+       "      <td>0.524692</td>\n",
+       "      <td>0.311014</td>\n",
+       "      <td>0.219864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gemma-1.1-2b-it__ACSEmployment__-1__QA</th>\n",
+       "      <td>0.462874</td>\n",
+       "      <td>0.235384</td>\n",
+       "      <td>0.631881</td>\n",
+       "      <td>0.427372</td>\n",
+       "      <td>0.091640</td>\n",
+       "      <td>0.800718</td>\n",
+       "      <td>0.408398</td>\n",
+       "      <td>0.382143</td>\n",
+       "      <td>0.376308</td>\n",
+       "      <td>0.212855</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Gemma 2B (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.02033</td>\n",
+       "      <td>0.482054</td>\n",
+       "      <td>0.007577</td>\n",
+       "      <td>0.485395</td>\n",
+       "      <td>0.232183</td>\n",
+       "      <td>0.216562</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 64 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        accuracy  accuracy_diff  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num     0.539392       0.258000   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA  0.462874       0.235384   \n",
+       "\n",
+       "                                        accuracy_ratio  balanced_accuracy  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num           0.656000           0.504500   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA        0.631881           0.427372   \n",
+       "\n",
+       "                                        balanced_accuracy_diff  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num                   0.100184   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA                0.091640   \n",
+       "\n",
+       "                                        balanced_accuracy_ratio  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num                    0.823204   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA                 0.800718   \n",
+       "\n",
+       "                                        brier_score_loss       ece  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num             0.388077  0.347071   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA          0.408398  0.382143   \n",
+       "\n",
+       "                                        ece_quantile  equalized_odds_diff  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num              NaN             0.221264   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA      0.376308             0.212855   \n",
+       "\n",
+       "                                        ...           name  is_inst  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num     ...    Gemma 2 27B    False   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA  ...  Gemma 2B (it)     True   \n",
+       "\n",
+       "                                        num_features  uses_all_features  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num               -1               True   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA            -1               True   \n",
+       "\n",
+       "                                        fit_thresh_on_100  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num               0.13000   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA            0.02033   \n",
+       "\n",
+       "                                        fit_thresh_accuracy  optimal_thresh  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num                0.514676        0.210000   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA             0.482054        0.007577   \n",
+       "\n",
+       "                                        optimal_thresh_accuracy score_stdev  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num                    0.524692    0.311014   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA                 0.485395    0.232183   \n",
+       "\n",
+       "                                        score_mean  \n",
+       "gemma-2-27b__ACSTravelTime__-1__Num       0.219864  \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA    0.216562  \n",
+       "\n",
+       "[2 rows x 64 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_stats_df = pd.DataFrame(scores_stats.values(), index=list(scores_stats.keys()))\n",
+    "print(f\"{scores_stats_df.shape=}\")\n",
+    "\n",
+    "results_df = pd.concat((df, scores_stats_df), axis=\"columns\")\n",
+    "results_df.sample(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d04ff64-39c4-43cc-9df2-fa4e099c4e56",
+   "metadata": {},
+   "source": [
+    "Check if any results are missing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f1faf926-de0f-442e-baf7-c3029f5386e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiments_per_model_task_pair = results_df.groupby([model_col, task_col]).nunique().max(axis=None)\n",
+    "\n",
+    "for m in results_df[model_col].unique():\n",
+    "    for t in results_df[task_col].unique():\n",
+    "        match_ = results_df[(results_df[model_col] == m) & (results_df[task_col] == t)]\n",
+    "        if len(match_) < experiments_per_model_task_pair:\n",
+    "            print(f\"Couldn't find all results for m={m}, t={t} ({len(match_)} < {experiments_per_model_task_pair})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6e859fc-9d82-436d-a238-11fd008da44c",
+   "metadata": {},
+   "source": [
+    "Finally, save results DF to the results root directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9725afa2-b6f8-46b1-86e7-57ae82ef05d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import get_current_timestamp\n",
+    "results_df.to_csv(Path(RESULTS_ROOT_DIR) / f\"aggregated_results.{get_current_timestamp()}.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3a6beb5-b44b-4f81-ae1a-d027afb2c5f4",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/notebooks/parse-acs-results_using-feature-subsets.ipynb b/.doctrees/nbsphinx/notebooks/parse-acs-results_using-feature-subsets.ipynb
new file mode 100644
index 0000000..2ca3445
--- /dev/null
+++ b/.doctrees/nbsphinx/notebooks/parse-acs-results_using-feature-subsets.ipynb
@@ -0,0 +1,1275 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "04c2f12d-f989-4ac9-b90f-3464a8bcca96",
+   "metadata": {},
+   "source": [
+    "# Fetch and parse ACS benchmark results under a given directory\n",
+    "Each ACS benchmark run outputs a json file. This script collects all such files under a given root directory, parses them, and aggregates them into a more easily digestable pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b241208-d10f-43cf-a486-84c54bbf43c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faf6afa4-8648-4d35-9312-65636ea5d0b2",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root results directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26089a60-81c0-4736-8ba5-99572ec01398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RESULTS_ROOT_DIR = Path(\"/fast/groups/sf\") / \"folktexts-results\" / \"2024-07-03_feature-subsets\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d1af7f-d013-40a0-80de-cb9ad65cff6d",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root data directory (needed only to train baseline ML methods):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b18dad87-a1ed-495b-93b6-4c8af4043ddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db7c98c5-2942-4f88-984a-8c9014afe761",
+   "metadata": {},
+   "source": [
+    "Important results columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e96f0c4f-5683-4150-8cca-93a883f20154",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_col = \"config_model_name\"\n",
+    "task_col = \"config_task_name\"\n",
+    "\n",
+    "feature_subset_col = \"config_feature_subset\"\n",
+    "predictions_path_col = \"predictions_path\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4296ffdf-a9af-43f7-bd0c-20f4ae5f9619",
+   "metadata": {},
+   "source": [
+    "Helper function to parse each dictionary containing benchmark results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0cf4a6c8-ec28-4e66-9f6d-8a4e977d7b60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import (\n",
+    "    num_features_helper,\n",
+    "    parse_model_name,\n",
+    "    get_non_instruction_tuned_name,\n",
+    "    prettify_model_name,\n",
+    ")\n",
+    "\n",
+    "def parse_results_dict(dct) -> dict:\n",
+    "    \"\"\"Parses results dict and brings all information to the top-level.\"\"\"\n",
+    "\n",
+    "    # Make a copy so we don't modify the input object\n",
+    "    dct = dct.copy()\n",
+    "\n",
+    "    # Discard plots' paths\n",
+    "    dct.pop(\"plots\", None)\n",
+    "\n",
+    "    # Bring configs to top-level\n",
+    "    config = dct.pop(\"config\", {})\n",
+    "    for key, val in config.items():\n",
+    "        dct[f\"config_{key}\"] = val\n",
+    "\n",
+    "    # Parse model name\n",
+    "    dct[model_col] = parse_model_name(dct[model_col])\n",
+    "    dct[\"base_name\"] = get_non_instruction_tuned_name(dct[model_col])\n",
+    "    dct[\"name\"] = prettify_model_name(dct[model_col])\n",
+    "\n",
+    "    # Is instruction-tuned model?\n",
+    "    dct[\"is_inst\"] = dct[\"base_name\"] != dct[model_col]\n",
+    "\n",
+    "    # Log number of features\n",
+    "    dct[\"num_features\"] = num_features_helper(dct[feature_subset_col], max_features_return=-1)\n",
+    "    dct[\"uses_all_features\"] = (dct[feature_subset_col] is None) or (dct[\"num_features\"] == -1)\n",
+    "\n",
+    "    if dct[feature_subset_col] is None:\n",
+    "        dct[feature_subset_col] = \"full\"\n",
+    "\n",
+    "    # Assert all results are at the top-level\n",
+    "    assert not any(isinstance(val, dict) for val in dct.values())\n",
+    "    return dct\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02009a86-e099-4ec0-8676-8d66190ceddb",
+   "metadata": {},
+   "source": [
+    "Iteratively search the root directory for results files matching the given regex:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aaefa99d-dbd7-40a3-a3c1-7571d3811409",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3293cf3c9da64e4eaab95a251b59aad5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 195 benchmark results.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils import find_files, load_json\n",
+    "\n",
+    "# Results file name pattern\n",
+    "pattern = r'^results.bench-(?P<hash>\\d+)[.]json$'\n",
+    "\n",
+    "# Find results files and aggregate\n",
+    "results = {}\n",
+    "for file_path in tqdm(find_files(RESULTS_ROOT_DIR, pattern)):\n",
+    "    results[Path(file_path).parent.name] = parse_results_dict(load_json(file_path))\n",
+    "\n",
+    "if len(results) == 0:\n",
+    "    raise RuntimeError(f\"Couldn't find any results at {RESULTS_ROOT_DIR}\")\n",
+    "else:\n",
+    "    print(f\"Found {len(results)} benchmark results.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ecc0d33-902d-492a-89c6-d9729fe69fa1",
+   "metadata": {},
+   "source": [
+    "Aggregate results into a single DataFrame, and generate a unique identifier for each row:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "94f1900f-fea3-4872-b722-ee1cd3f5f7e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.shape=(195, 58)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>config_population_filter</th>\n",
+       "      <th>config_reuse_few_shot_examples</th>\n",
+       "      <th>config_seed</th>\n",
+       "      <th>config_task_hash</th>\n",
+       "      <th>config_task_name</th>\n",
+       "      <th>base_name</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX__7</th>\n",
+       "      <td>0.630994</td>\n",
+       "      <td>0.281751</td>\n",
+       "      <td>0.663274</td>\n",
+       "      <td>0.500705</td>\n",
+       "      <td>0.007359</td>\n",
+       "      <td>0.985495</td>\n",
+       "      <td>0.215502</td>\n",
+       "      <td>0.111277</td>\n",
+       "      <td>0.116464</td>\n",
+       "      <td>0.031550</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>42</td>\n",
+       "      <td>591377254</td>\n",
+       "      <td>ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX</td>\n",
+       "      <td>Mixtral-8x22B-v0.1</td>\n",
+       "      <td>Mixtral 8x22B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>7</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Meta-Llama-3-8B__ACSIncome_RAC1P__1</th>\n",
+       "      <td>0.369300</td>\n",
+       "      <td>0.286688</td>\n",
+       "      <td>0.362849</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.279789</td>\n",
+       "      <td>0.220760</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>42</td>\n",
+       "      <td>664576746</td>\n",
+       "      <td>ACSIncome_RAC1P</td>\n",
+       "      <td>Meta-Llama-3-8B</td>\n",
+       "      <td>Llama 3 8B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX__7</th>\n",
+       "      <td>0.477681</td>\n",
+       "      <td>0.190822</td>\n",
+       "      <td>0.677577</td>\n",
+       "      <td>0.508865</td>\n",
+       "      <td>0.210830</td>\n",
+       "      <td>0.678362</td>\n",
+       "      <td>0.392382</td>\n",
+       "      <td>0.354748</td>\n",
+       "      <td>0.353959</td>\n",
+       "      <td>0.294444</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>42</td>\n",
+       "      <td>591377254</td>\n",
+       "      <td>ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX</td>\n",
+       "      <td>gemma-2-27b</td>\n",
+       "      <td>Gemma 2 27B (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 58 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    accuracy  accuracy_diff  \\\n",
+       "id                                                                            \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...  0.630994       0.281751   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                 0.369300       0.286688   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...  0.477681       0.190822   \n",
+       "\n",
+       "                                                    accuracy_ratio  \\\n",
+       "id                                                                   \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...        0.663274   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                       0.362849   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...        0.677577   \n",
+       "\n",
+       "                                                    balanced_accuracy  \\\n",
+       "id                                                                      \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...           0.500705   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                          0.500000   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...           0.508865   \n",
+       "\n",
+       "                                                    balanced_accuracy_diff  \\\n",
+       "id                                                                           \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...                0.007359   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                               0.000000   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...                0.210830   \n",
+       "\n",
+       "                                                    balanced_accuracy_ratio  \\\n",
+       "id                                                                            \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...                 0.985495   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                                1.000000   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...                 0.678362   \n",
+       "\n",
+       "                                                    brier_score_loss  \\\n",
+       "id                                                                     \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...          0.215502   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                         0.279789   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...          0.392382   \n",
+       "\n",
+       "                                                         ece  ece_quantile  \\\n",
+       "id                                                                           \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...  0.111277      0.116464   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                 0.220760           NaN   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...  0.354748      0.353959   \n",
+       "\n",
+       "                                                    equalized_odds_diff  ...  \\\n",
+       "id                                                                       ...   \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...             0.031550  ...   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                            0.000000  ...   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...             0.294444  ...   \n",
+       "\n",
+       "                                                    config_population_filter  \\\n",
+       "id                                                                             \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...                      None   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                                     None   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...                      None   \n",
+       "\n",
+       "                                                    config_reuse_few_shot_examples  \\\n",
+       "id                                                                                   \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...                           False   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                                          False   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...                           False   \n",
+       "\n",
+       "                                                    config_seed  \\\n",
+       "id                                                                \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...           42   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                          42   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...           42   \n",
+       "\n",
+       "                                                    config_task_hash  \\\n",
+       "id                                                                     \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...         591377254   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                        664576746   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...         591377254   \n",
+       "\n",
+       "                                                                              config_task_name  \\\n",
+       "id                                                                                               \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...  ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                                            ACSIncome_RAC1P   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...  ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX   \n",
+       "\n",
+       "                                                             base_name  \\\n",
+       "id                                                                       \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...  Mixtral-8x22B-v0.1   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                    Meta-Llama-3-8B   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...         gemma-2-27b   \n",
+       "\n",
+       "                                                                name  is_inst  \\\n",
+       "id                                                                              \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...     Mixtral 8x22B    False   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                       Llama 3 8B    False   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...  Gemma 2 27B (it)     True   \n",
+       "\n",
+       "                                                   num_features  \\\n",
+       "id                                                                \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...            7   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                           1   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...            7   \n",
+       "\n",
+       "                                                    uses_all_features  \n",
+       "id                                                                     \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...              False  \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                             False  \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...              False  \n",
+       "\n",
+       "[3 rows x 58 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(list(results.values()))\n",
+    "\n",
+    "def row_id(row) -> str:\n",
+    "    \"\"\"Unique row identifier.\"\"\"\n",
+    "    return f\"{row[model_col]}__{row[task_col]}__{row['num_features']}\"\n",
+    "\n",
+    "df[\"id\"] = df.apply(row_id, axis=1)\n",
+    "df = df.set_index(\"id\", drop=True, verify_integrity=True)\n",
+    "\n",
+    "print(f\"{df.shape=}\")\n",
+    "df.sample(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fd59aee-3802-4430-a977-243dd44f8f62",
+   "metadata": {},
+   "source": [
+    "Drop potential duplicates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "69ef57de-0059-4c40-9eae-fb4e6b3bf582",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parsed_df = df.drop_duplicates(subset=[\"name\", \"is_inst\", \"num_features\", task_col])\n",
+    "if len(parsed_df) != len(df):\n",
+    "    print(f\"Found {len(df) - len(parsed_df)} duplicates! dropping rows...\")\n",
+    "    df = parsed_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97cd24df-3c1f-49c0-bb50-a6fdba22e0fb",
+   "metadata": {},
+   "source": [
+    "Load scores DFs and analyze score distribution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "43ba0fbf-b886-4f2a-8a01-564aa6f0f2c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_model_scores_df(df_row: pd.Series) -> pd.DataFrame:\n",
+    "    \"\"\"Loads csv containing model scores corresponding to the given DF row.\"\"\"\n",
+    "    if predictions_path_col in df_row and not pd.isna(df_row[predictions_path_col]):\n",
+    "        return pd.read_csv(df_row[predictions_path_col], index_col=0)\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9bc88155-08a3-4cc2-bb8d-aaec96350f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "133e0fbc9ecc4262990da41e34a12425",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/195 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scores_df_map = {\n",
+    "    id_: load_model_scores_df(row)\n",
+    "    for id_, row in tqdm(parsed_df.iterrows(), total=len(parsed_df))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "54e22896-1d3d-4d95-bb90-2b453f58f09a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2325fd86cbdc436e84da4bc8be53fd98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/195 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from folktexts.evaluation import compute_best_threshold\n",
+    "from sklearn import metrics\n",
+    "from scipy import stats\n",
+    "\n",
+    "# Number of samples used to fit the one-parameter binarization threshold!\n",
+    "N = 100\n",
+    "\n",
+    "fit_thresh_col = f\"fit_thresh_on_{N}\"\n",
+    "fit_acc_col = f\"fit_thresh_accuracy\"\n",
+    "\n",
+    "optimal_thres_col = \"optimal_thresh\"\n",
+    "optimal_acc_col = \"optimal_thresh_accuracy\"\n",
+    "\n",
+    "score_stdev_col = \"score_stdev\"\n",
+    "score_mean_col = \"score_mean\"\n",
+    "\n",
+    "scores_stats = dict()\n",
+    "for row_id, row in tqdm(df.iterrows(), total=len(df)):\n",
+    "\n",
+    "    # Load model scores\n",
+    "    scores_df = scores_df_map[row_id]\n",
+    "    if scores_df is None:\n",
+    "        logging.error(f\"Couldn't find scores for {row_id}\")\n",
+    "\n",
+    "    # Extract scores and labels\n",
+    "    risk_scores = scores_df[\"risk_score\"].to_numpy()\n",
+    "    labels = scores_df[\"label\"].to_numpy()\n",
+    "\n",
+    "    # Sample N rows to fit threshold\n",
+    "    scores_df_sample = scores_df.sample(n=N, random_state=42)\n",
+    "\n",
+    "    # Compute optimal threshold on each data sample\n",
+    "    fit_thr = compute_best_threshold(y_true=scores_df_sample[\"label\"], y_pred_scores=scores_df_sample[\"risk_score\"])\n",
+    "    opt_thr = compute_best_threshold(y_true=labels, y_pred_scores=risk_scores)\n",
+    "\n",
+    "    # Evaluate accuracy\n",
+    "    fit_acc = metrics.accuracy_score(labels, (risk_scores >= fit_thr).astype(int))\n",
+    "    opt_acc = metrics.accuracy_score(labels, (risk_scores >= opt_thr).astype(int))\n",
+    "\n",
+    "    # Save results\n",
+    "    scores_stats[row_id] = {\n",
+    "        fit_thresh_col: fit_thr,\n",
+    "        fit_acc_col: fit_acc,\n",
+    "        optimal_thres_col: opt_thr,\n",
+    "        optimal_acc_col: opt_acc,\n",
+    "        score_stdev_col: np.std(risk_scores),\n",
+    "        score_mean_col: np.mean(risk_scores),\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1a58b87-2de8-473a-badb-934951d1bcdc",
+   "metadata": {},
+   "source": [
+    "Update results DF with scores statistics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0ba74c81-c008-4010-a9a3-1b511ae445df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4</th>\n",
+       "      <td>0.567786</td>\n",
+       "      <td>0.183621</td>\n",
+       "      <td>0.743500</td>\n",
+       "      <td>0.636874</td>\n",
+       "      <td>0.105719</td>\n",
+       "      <td>0.843667</td>\n",
+       "      <td>0.273436</td>\n",
+       "      <td>0.237165</td>\n",
+       "      <td>0.237165</td>\n",
+       "      <td>0.630823</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yi 34B (chat)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.531214</td>\n",
+       "      <td>0.582998</td>\n",
+       "      <td>0.562204</td>\n",
+       "      <td>0.597789</td>\n",
+       "      <td>0.192288</td>\n",
+       "      <td>0.606465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6</th>\n",
+       "      <td>0.422379</td>\n",
+       "      <td>0.183270</td>\n",
+       "      <td>0.609219</td>\n",
+       "      <td>0.538078</td>\n",
+       "      <td>0.085607</td>\n",
+       "      <td>0.857950</td>\n",
+       "      <td>0.303729</td>\n",
+       "      <td>0.306175</td>\n",
+       "      <td>0.306175</td>\n",
+       "      <td>0.232553</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yi 34B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>6</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.731161</td>\n",
+       "      <td>0.659718</td>\n",
+       "      <td>0.679091</td>\n",
+       "      <td>0.610856</td>\n",
+       "      <td>0.117468</td>\n",
+       "      <td>0.675475</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 64 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  accuracy  accuracy_diff  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4     0.567786       0.183621   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6  0.422379       0.183270   \n",
+       "\n",
+       "                                                  accuracy_ratio  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4           0.743500   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6        0.609219   \n",
+       "\n",
+       "                                                  balanced_accuracy  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4              0.636874   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6           0.538078   \n",
+       "\n",
+       "                                                  balanced_accuracy_diff  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                   0.105719   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6                0.085607   \n",
+       "\n",
+       "                                                  balanced_accuracy_ratio  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                    0.843667   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6                 0.857950   \n",
+       "\n",
+       "                                                  brier_score_loss       ece  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4             0.273436  0.237165   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6          0.303729  0.306175   \n",
+       "\n",
+       "                                                  ece_quantile  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4         0.237165   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6      0.306175   \n",
+       "\n",
+       "                                                  equalized_odds_diff  ...  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                0.630823  ...   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6             0.232553  ...   \n",
+       "\n",
+       "                                                           name  is_inst  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4     Yi 34B (chat)     True   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6         Yi 34B    False   \n",
+       "\n",
+       "                                                  num_features  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                4   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6             6   \n",
+       "\n",
+       "                                                  uses_all_features  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                 False   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6              False   \n",
+       "\n",
+       "                                                  fit_thresh_on_100  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4              0.531214   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6           0.731161   \n",
+       "\n",
+       "                                                  fit_thresh_accuracy  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                0.582998   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6             0.659718   \n",
+       "\n",
+       "                                                  optimal_thresh  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4           0.562204   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6        0.679091   \n",
+       "\n",
+       "                                                  optimal_thresh_accuracy  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                    0.597789   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6                 0.610856   \n",
+       "\n",
+       "                                                 score_stdev  score_mean  \n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4       0.192288    0.606465  \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6    0.117468    0.675475  \n",
+       "\n",
+       "[2 rows x 64 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_stats_df = pd.DataFrame(scores_stats.values(), index=list(scores_stats.keys()))\n",
+    "\n",
+    "results_df = pd.concat((df, scores_stats_df), axis=\"columns\")\n",
+    "results_df.sample(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d04ff64-39c4-43cc-9df2-fa4e099c4e56",
+   "metadata": {},
+   "source": [
+    "Check if any results are missing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "dbb3b379-7697-4b54-a33d-09d503210f87",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "config_model_name\n",
+      "Meta-Llama-3-70B                9\n",
+      "Meta-Llama-3-70B-Instruct       9\n",
+      "Meta-Llama-3-8B                10\n",
+      "Meta-Llama-3-8B-Instruct       10\n",
+      "Mistral-7B-Instruct-v0.2       10\n",
+      "Mistral-7B-v0.1                10\n",
+      "Mixtral-8x22B-Instruct-v0.1     9\n",
+      "Mixtral-8x22B-v0.1              9\n",
+      "Mixtral-8x7B-Instruct-v0.1      9\n",
+      "Mixtral-8x7B-v0.1              10\n",
+      "Yi-34B                         10\n",
+      "Yi-34B-Chat                    10\n",
+      "gemma-1.1-2b-it                10\n",
+      "gemma-1.1-7b-it                10\n",
+      "gemma-2-27b                    10\n",
+      "gemma-2-27b-it                 10\n",
+      "gemma-2-9b                     10\n",
+      "gemma-2-9b-it                  10\n",
+      "gemma-2b                       10\n",
+      "gemma-7b                       10\n",
+      "Name: config_task_name, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(results_df.groupby([model_col])[task_col].count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6e859fc-9d82-436d-a238-11fd008da44c",
+   "metadata": {},
+   "source": [
+    "Finally, save results DF to the results root directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9725afa2-b6f8-46b1-86e7-57ae82ef05d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import get_current_timestamp\n",
+    "results_df.to_csv(Path(RESULTS_ROOT_DIR) / f\"aggregated_results.{get_current_timestamp()}.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3a6beb5-b44b-4f81-ae1a-d027afb2c5f4",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5d721ce1-f3f3-4a56-a2fa-fb63f652128d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1f77b4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff7f0e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2ca02c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d62728;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8c564b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e377c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7f7f7f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bcbd22;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#17becf;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
+      ],
+      "text/plain": [
+       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
+       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
+       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
+       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
+       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
+       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
+       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
+       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"whitegrid\", rc={\"grid.linestyle\": \"--\"})\n",
+    "plt.rc(\"figure\", figsize=(4.5, 3.5), dpi=200)\n",
+    "\n",
+    "palette_name = \"tab10\"   # \"colorblind\"\n",
+    "palette = sns.color_palette(palette_name)\n",
+    "sns.set_palette(palette_name)\n",
+    "palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "919690a2-8f87-451b-b778-0740127cd135",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMGS_DIR = RESULTS_ROOT_DIR / \"imgs\"\n",
+    "IMGS_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "def save_fig(fig, name):\n",
+    "    fig.savefig((IMGS_DIR / name).with_suffix(\".pdf\"), bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5d64604-8c0e-40f9-8130-fca5fff63194",
+   "metadata": {},
+   "source": [
+    "## Score distributions of an example model with increasing evidence\n",
+    "Score distribution should get more discriminative (less Gaussian-shaped) with increasing evidence.,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2742c1eb-6440-4ffe-b2b2-25886902976e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EXAMPLE_MODEL = \"Meta-Llama-3-70B-Instruct\"\n",
+    "# EXAMPLE_MODEL = \"Meta-Llama-3-70B\"\n",
+    "EXAMPLE_MODEL = \"Mixtral-8x7B-Instruct-v0.1\"\n",
+    "# EXAMPLE_MODEL = \"Mixtral-8x7B-v0.1\"\n",
+    "# EXAMPLE_MODEL = \"Mistral-7B-v0.1\"\n",
+    "# EXAMPLE_MODEL = \"Mistral-7B-Instruct-v0.2\"\n",
+    "# EXAMPLE_MODEL = \"Yi-34B\"\n",
+    "# EXAMPLE_MODEL = \"Yi-34B-Chat\"\n",
+    "# EXAMPLE_MODEL = \"Meta-Llama-3-8B\"\n",
+    "# EXAMPLE_MODEL = \"Meta-Llama-3-8B-Instruct\"\n",
+    "# EXAMPLE_MODEL = \"Mixtral-8x22B-Instruct-v0.1\"\n",
+    "# EXAMPLE_MODEL = \"Mixtral-8x22B-v0.1\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54ae284c-79af-4940-89ff-35327283cff6",
+   "metadata": {},
+   "source": [
+    "**TODO?** plot xgboost score distribution as features are added! to show below the LLM score distribution as what true uncertainty quantification looks like..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9be9c590-589f-4ed1-a3cc-9e83e7bd6034",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ku0S+A4WKjCfjJTIeKETkO/kuke/ILQrrQJ6kmt6kqdq1a6enn35aK1eu1J///GctXbpUH3/8sWKxmFatWqVVq1bpueee05NPPqnBgwcn3ceXvvQlRaNRffrpp6qoqNA555yj0aNHZ9WuOtdee60uvfRS/eEPf9A777yj5cuX68CBA9qxY4cqKir00ksv6dZbb9WUKVNycrxElmXlZD/p7m9Td6WcJD344IMp/8YNNTxR6Natm1588UUtWbJEf/nLX/T+++9r3bp1ikQiWrZsmZYtW6bnnntOM2bM0GmnnZbZEwHQqsh38l0i34FCRL6T7xL5DhQqMp6Ml8h4oBCR7+S7RL4jtyisAx43aNCg+LQrdfccmT9/vv7yl79oz549uuOOO7Ro0SKVlJQ02rZTp0569NFHNXHiRG3cuFFTp06Vbdu69NJLc9K2E088Uddff72uv/56OY6jNWvW6LXXXtPs2bO1f/9+Pf300yovL9eFF14Yb0+dXbt2NetYiW+WiVO9JBONRrV3795G2zVHx44d48slJSXq169fRvupM3z4cA0fPlxS7VWDS5YsUUVFhd59911t2rRJU6ZM0YIFC7I6BgBvId9rke8ACg35Xot8B1CIyPhaZDyAQkO+1yLfASn1pSAAPKddu3YaOXKknnjiCV133XWSat8cP/jgg5TbdOvWTTNnzlTPnj0VjUb1z//8z1q0aFHO22bbts4880zdfffdev755+O///Of/xxfPuOMM+JXui1btqxZ+y8qKlLv3r0lSStXrky77urVqxWJRCQp4zfjxLYuX748o32k0qlTJ40ePVozZ87UyJEjJUlr1qyJ378GgP+Q770lke8ACg/53lsS+Q6gMJHxvSWR8QAKD/neWxL5Dv+isA4UqLorr6T6901J5uSTT9bMmTPVo0cPRaNR3X333Xr99ddbrG1nnnlm/Cq1uqvWpNor0Oqmc/nzn/+sHTt2NGu/dc/573//e9o39jlz5jTaprk6d+6ss846S5L0xz/+UV988UVG+zme5ryOAPyBfCffARQm8p18B1C4yHgyHkBhIt/Jd/gPhXXAgzZv3qz33nsv7Tr/93//F1/u0aPHcfd5yimnaNasWTr11FMViUR011136c0338yofQsXLlQ4HE75eGVlpfbt2ydJOvXUU+s9dsstt0iSjhw5orvuuksHDhxIuZ/t27fX+/maa66J35Nl2rRpOnjwYKNt3n777fibeuIUPpn4p3/6J0m10//ceeed2r9/f8p1a2pqNHv2bFVXV8d/t2bNGq1ZsyblNq7r6p133pFUe8+ahn8rAIWHfK9FvgMoNOR7LfIdQCEi42uR8QAKDflei3wH6uMe64AHff7555o4caJOP/10XXjhhSovL1fXrl0l1b7RLVy4MD69yxlnnKGvfOUrTdrvqaeeqpkzZ+q6667Ttm3bdMcdd+jJJ5/U+eef36z2Pfroo3rggQc0atQoDR06VL1791abNm20d+9effDBB/rd734nSQoEArrqqqvqbTty5EhdeeWVmjNnjj788EONHj1aEyZM0Nlnn6127dqpqqpKq1at0sKFCzVgwABNnz49vm3//v11ww036Nlnn9XHH3+ssWPH6pZbbtEZZ5yhI0eO6I033tALL7ygWCymUCikBx98sFnPq6ERI0Zo4sSJmjVrlt5//32NHj1a3/nOdzRkyBB17NhRhw8f1qZNm7Rs2TK99tpr2rdvn8aMGaPi4mJJtW/q9913n8rLy3XBBRfozDPPVJcuXRSNRrVlyxbNmzcvfnI2cuTI+GsMoHCR7+Q7gMJEvpPvAAoXGU/GAyhM5Dv5DiRDYR3wsHXr1mndunUpH+/Tp4+eeOKJ+H1ImqKsrEyzZs3ShAkTtGPHDt1+++369a9/rXPPPbdZbdu/f7/mz5+v+fPnJ328qKhIP/7xj1VeXt7osQcffFAlJSWaPXu2du7cqV/84hdJ9zFgwIBGv7v33nt15MgR/f73v9emTZs0bdq0Ruu0b99e//Vf/6UzzjijWc8pmX/9139Vhw4d9Otf/1q7du3SE088kXLdNm3aKBAINPp9ZWWlKisrU243ePBg/fSnP826rQC8g3wn3wEUJvKdfAdQuMh4Mh5AYSLfyXcgEYV1wIOGDh2qF154QW+//bZWrFih7du3a/fu3aqpqVGHDh00YMAAXXTRRRo3bpyKioqavf+ePXtq1qxZuu6667Rz505NnjxZTz/9dJPvhTJz5ky98cYbWrZsmT799FPt3r1b+/fvV0lJiXr27Kmvfe1r+u53v6uysrKk2wcCAU2bNk3jxo1TRUWFli5dqp07dyoSiahjx47q37+//uEf/kHf+ta3Gm1r27YeeOABffOb39SLL76oDz74QLt371ZRUZHKyso0YsQITZo0SZ07d2723yUZy7J0++2369vf/rZefPFFvfvuu9qyZYsOHDigkpISde/eXWeccYbOO+88XXjhhSopKYlve/nll+vEE0/UO++8o8rKSu3YsUN79uxRNBrViSeeqIEDB2r06NH65je/GZ9eB0BhI9/JdwCFiXwn3wEULjKejAdQmMh38h1IxnJd1813IwAAAAAAAAAAAAAAMBWXXwAAAAAAAAAAAAAAkAaFdQAAAAAAAAAAAAAA0qCwDgAAAAAAAAAAAABAGhTWAQAAAAAAAAAAAABIg8I6AAAAAAAAAAAAAABpUFgHAAAAAAAAAAAAACANCusAAAAAAAAAAAAAAKRBYR0AAAAAAAAAAAAAgDQorAMAAAAAAAAAAAAAkAaFdQAAAAAAAAAAAAAA0qCwDgAAAAAAAAAAAABAGhTWAQAAAAAAAAAAAABIg8I6AAAAAAAAAAAAAABpUFgHAAAAAAAAAAAAACANCusAAAAAAAAAAAAAAKRBYR0AAAAAAAAAAAAAgDQorAMAAAAAAAAAAAAAkAaFdQAAAAAAAAAAAAAA0qCwDgAAAAAAAAAAAABAGhTWAQAAAAAAAAAAAABIg8I6AAAAAAAAAAAAAABpUFgHAAAAAAAAAAAAACANCusAAAAAAAAAAAAAAKRBYR0AAAAAAAAAAAAAgDT+P8Q+xBOZg5n2AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 2400x600 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get data to plot\n",
+    "plot_df = results_df[results_df[model_col] == EXAMPLE_MODEL].sort_values(\"num_features\")\n",
+    "# Skip every other row in plot\n",
+    "plot_df = plot_df[[idx % 2 == 0 for idx in range(len(plot_df))]]\n",
+    "# plot_df = plot_df.iloc[[0, 2, 3, 5, 7]]\n",
+    "\n",
+    "# # For current Llama-3-8B-Instruct results\n",
+    "# plot_df = plot_df[[False, True, False, True, True, False, True]]\n",
+    "\n",
+    "N_PLOTS = min(10, len(plot_df))\n",
+    "\n",
+    "N_COLS = 5\n",
+    "N_ROWS = math.ceil(N_PLOTS / N_COLS)\n",
+    "\n",
+    "fig, axes = plt.subplots(\n",
+    "    nrows=N_ROWS, ncols=N_COLS,\n",
+    "    figsize=(12, 3 * N_ROWS),\n",
+    "    sharey=True, sharex=False,\n",
+    "    gridspec_kw=dict(\n",
+    "        hspace=0.5,\n",
+    "        wspace=0.06,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Plot settings\n",
+    "plot_config = dict(\n",
+    "    bins=(N_BINS := 20),\n",
+    "    binrange=(0, 1),\n",
+    "    stat=\"percent\",\n",
+    ")\n",
+    "\n",
+    "# Plot score distribution for model pairs\n",
+    "current_featureset = set()\n",
+    "\n",
+    "for idx, (id_, row) in enumerate(plot_df.iterrows()):\n",
+    "    ax_row = idx // N_COLS\n",
+    "    ax_col = idx % N_COLS\n",
+    "\n",
+    "    ax = axes[ax_row, ax_col] if N_ROWS > 1 else axes[ax_col]\n",
+    "\n",
+    "    # Get scores\n",
+    "    scores = scores_df_map[row.name][\"risk_score\"]\n",
+    "\n",
+    "    # Added features\n",
+    "    added_features = set(row[\"config_feature_subset\"]) - current_featureset\n",
+    "\n",
+    "    # Set title with added features compared to last plot\n",
+    "    title_str = \"+ \" if len(current_featureset) > 0 else \"\"\n",
+    "    title_str += \", \".join(added_features)\n",
+    "    current_featureset |= added_features\n",
+    "\n",
+    "    ax.set_title(title_str)\n",
+    "\n",
+    "    ax.set_xlabel(\"risk scores\")\n",
+    "    if ax_col == 0:\n",
+    "        ax.set_ylabel(\"density (%)\")\n",
+    "\n",
+    "    ax.set_xlim(-0.03, 1.03)\n",
+    "\n",
+    "    # Render plot\n",
+    "    sns.histplot(scores, ax=ax, color=palette[1 if row[\"is_inst\"] else 0], **plot_config)\n",
+    "\n",
+    "    # Add text box with distribution stats\n",
+    "    textstr = '\\n'.join([\n",
+    "        r\"$\\text{ROC}=%.2f$\" % (row[\"roc_auc\"],),\n",
+    "        r\"$\\text{ECE}=%.2f$\" % (row[\"ece\"],),\n",
+    "        r\"$\\sigma=%.2f$\" % (row[\"score_stdev\"],),\n",
+    "        r\"$\\mu=%.2f$\" % (row[\"score_mean\"],),\n",
+    "    ])\n",
+    "\n",
+    "    props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
+    "\n",
+    "    if row[\"is_inst\"]:\n",
+    "        # Center-aligned text box\n",
+    "        ax.text(0.5, 0.95, textstr, transform=ax.transAxes, fontsize=9,\n",
+    "            verticalalignment='top', horizontalalignment='center', bbox=props)\n",
+    "    else:\n",
+    "        # Right-aligned text box\n",
+    "        ax.text(0.95, 0.95, textstr, transform=ax.transAxes, fontsize=9,\n",
+    "            verticalalignment='top', horizontalalignment='right', bbox=props)\n",
+    "\n",
+    "        # # Left-aligned text box\n",
+    "        # ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=9,\n",
+    "        #     verticalalignment='top', bbox=props)\n",
+    "\n",
+    "# Remove unused axes\n",
+    "[\n",
+    "    (axes[N_ROWS - 1, idx] if N_ROWS > 1 else axes[idx]).remove()\n",
+    "    for idx in range(N_COLS-1, len(plot_df) - N_COLS * (N_ROWS - 1) - 1, -1)\n",
+    "];\n",
+    "\n",
+    "# Set suptitle\n",
+    "plt.suptitle(plot_df[\"name\"].iloc[0], y=1.05, fontsize=15)\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"increasing-evidence.{EXAMPLE_MODEL}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "06d0f410-3fe5-4dd3-913d-4369aa8cfacf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Label prevalence: 36.9%\n"
+     ]
+    }
+   ],
+   "source": [
+    "prevalence = scores_df_map[row.name]['label'].mean()\n",
+    "print(f\"Label prevalence: {prevalence:.1%}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41dfe9c8-52c3-4e86-ac51-2f73540963bb",
+   "metadata": {},
+   "source": [
+    "- [ ] **TODO:** Make the same plot for XGBoost to have a plot of a calibrated baseline..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6047a8d-2577-4123-833a-a7ead8ca8251",
+   "metadata": {},
+   "source": [
+    "## Plots for XGBoost baseline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0e899105-2370-4ee3-b304-aea95d7d6380",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bea01302-f19e-4322-a092-836f5cf65969",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5606a544-28a0-4805-9878-829fdfbd1fd8",
+   "metadata": {},
+   "source": [
+    "## Change in ECE and ROC with increasing evidence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a2494f73-152a-47f7-a9d6-d15092227b22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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18FZkPVlP1jsPWQ9vRdaT9WS985D18FZkPVlP1jsPWe9ZAZ7uAOAMly5dciyHhYU5fft2u11nzpzR4cOHtX//fsd/0dHRkkpeYecKgwYNKnfdHXfc4bgibePGjeW269y5c5WvNsvLy9OJEyd08OBBx2u22+2O9a583T/99JMkyWKx6A9/+EO57e666y7VqlWrxHPKcuONN5Z7EouIiHD8IXb8+PFq9ff8+fOO5Tp16lRrG5Xl6vHuDKGhoapRo4Yk6cyZMx7uDXwFWU/WF39OWch69yLr4QpkPVlf/DllIevdi6yHK5D1ZH3x55SFrHcvsh6uQNaT9cWfUxay3r3I+urjG+nwCTVr1nQsZ2dnO227a9as0Zw5c7R58+YSYXi5ovu9uEJwcLBat25d4fo2bdooKSnJMX1IWeLj4yu1v6ysLM2ePVvff/+9Dh48qIKCgnLbuvJ1HzhwQFLhPVoquvIuJCREbdq00aZNmyp8/S1btqxwf0Un04p+zxXJyMhwLJc11U55EhIS1K1btyrty1XjvUh1+lSW2rVrKycnxyV9hH8i68l6st55yHp4K7KerCfrnYesh7ci68l6st55yHp4K7KerCfrnYes9ywK6fAJdevWdSyfO3fO8Pbsdrv++te/6ttvv61U+5ycHMP7LE/dunUd9zUpT9E9N4qfHC5XmZNFcnKyRowYUempPXJzcyvVrjqKpv+pzFQo9evXl1Tx67/SlWABAYUTdNhstkr2sKTiU9Dk5OQoIiKiWtupjIiICAUHB8tqters2bMu249RReMjKIhTDZyDrCfrJbLe25D1cDaynqyXyHpvQ9bD2ch6sl4i670NWQ9nI+vJeoms9zZkffXwbsEnFL8CbPfu3Ya39+233zpOym3atNGIESPUvn17RUdHKywszHGinDhxov79738b3p87XOnkLhW+nuTkZFksFg0aNEj33HOPYmNjFRkZqeDgYFksFtlsNsf9T4pPG+MqFovF5ftwhuJX4WVkZLj0xCxJcXFx2r17t1JTU3X27FnHH2fewmaz6eLFi5KqdsUfUBGy/srIetci60si6+EKZP2VkfWuRdaXRNbDFcj6KyPrXYusL4mshyuQ9VdG1rsWWV8SWV99FNLhE1q1aqV69eopLS1NW7duVWZmpqFg/OabbyRJzZs319y5cx33jrhcRVdUOUt6eroKCgoqPLEWXeVk5F4fhw4d0tatWyVJY8aM0YQJE8rtjzsUXbVYmSu4iu7p4ep7nVSk+In5woULiomJcen+unbt6vgjdM2aNRXel8YTLl686LhasFGjRh7uDXwFWU/WS2S9NyHr4QpkPVkvkfXehKyHK5D1ZL1E1nsTsh6uQNaT9RJZ703I+uoL8HQHAGewWCwaOHCgpMJ7hhSdWKur6H4fvXv3LvekbLfbnXI13ZVYrVb99ttv5a7Pz893rI+Li6v2fg4ePOhY7tevX7ntdu3aVeF2nHVFWqtWrSQVTl9z/vz5cttZrVbt3btXkrHXb1TxfR8+fNjl+ysa75L0r3/9q9pT3LjKkSNHHMtFv0vAKLKerJfIem9C1sMVyHqyXiLrvQlZD1cg68l6iaz3JmQ9XIGsJ+slst6bkPXVRyEdPuOxxx5z3Fdj2rRpOnToUKWeZ7PZtGjRohKPFRQUSCo8yZdn5cqVjiurXG3BggXlrluxYoXjSrsePXpUex9Fr1mSsrOzy203d+7cCrcTGhrqWM7Ly6t2f4pei91u1/z588ttt2zZMseUJEZev1HXXXed47Xv3LnT5fuLj49X7969JUl79+7VJ598UunnbtmyRcePH3dV1ySVfA/at2/v0n3Bv5D1ZD1ZXzlkPcyMrCfryfrKIethZmQ9WU/WVw5ZDzMj68l6sr5yyHrvRiEdPiM6Olovv/yypMIT6rBhw7Rp06YKn3Pw4EH98Y9/1MyZM0s83rx5c0nS6tWry5wa5dixY3rttdec0/FKmDt3rrZs2VLq8TNnzmjKlCmSpLCwsBJXPVVV0WuWpMTExDLbfPXVV1q5cmWF26lfv75j2Uj49+nTRw0aNJAkffLJJ9q3b1+pNidPntQ777wjqfD1Dxo0qNr7MyokJEQdOnSQ5J4TsyS9/vrrjnutTJ06VVOnTq3wj6GsrCx9+OGHeuyxx5SZmenSvu3YsUOS1LhxY7Vs2dKl+4J/IevJerKerIfvI+vJerKerIfvI+vJerKerIfvI+vJerKerPcF3CMdPmXw4ME6deqUpk2bpnPnzmnYsGG6+eab1bt3b8XGxqp27drKyMjQ4cOHtXbtWq1bt04FBQVq3bp1ie0MGDBAU6ZMUWpqqh566CE98cQTiouLU25urn7++Wd98cUXysvL07XXXuvy6WIiIyMVFhamkSNH6rHHHtOtt96qkJAQ7dy5U5988olSU1MlSePHj1dUVFS199O2bVvFxcVp//79mjdvni5cuKD+/furfv36OnXqlBYtWqRly5apc+fO2rZtW7nbadOmjUJDQ5Wbm6upU6cqKChIjRs3VkBA4XU70dHR5U6/U1xISIjeeOMNjR07VpmZmRoyZIhGjRqlHj16KDAwUNu3b9eMGTN07tw5SdLEiRNL3PfEE26//XZt2rRJO3bsqPR9f5KTk1WvXr0rtqtXr16JP3qkwj+CPvnkE40dO1Znz57VP/7xD/373//Wfffdp86dOysyMlJWq1WnT5/Wzz//rOXLl1c47Y4z+iQVXpWYlJQkqfAPLMDZyHqy3pPI+kJkPVyNrCfrPYmsL0TWw9XIerLek8j6QmQ9XI2sJ+s9iawvRNYbQyEdPudPf/qTWrVqpbffflspKSlav3691q9fX277Vq1a6fnnny/x2PDhw7Vx40atX79eR44c0UsvvVRifY0aNfTOO+9o7dq1Lj8xh4WFaerUqXriiSc0ffp0TZ8+vVSbYcOG6fHHHze0H4vFoilTpmjEiBHKyMjQ0qVLtXTp0hJt4uLiNHXqVN1yyy3lbiciIkLDhg3TP//5T+3evVsjR44ssT4hIUHdunWrVJ969eqlyZMn65VXXtGlS5c0bdo0TZs2rUSbwMBAjR8/Xo888kglX6nr9O/fX3//+9+Vm5urH374QQMGDLjic1588cVKbXv48OGlxqEktWvXTl9//bVef/11rVmzRikpKRVOGxMeHq5Ro0YpNjbWZX3avHmzTp06JUm6//77K7UtoKrI+uoh640j6wuR9XAHsr56yHrjyPpCZD3cgayvHrLeOLK+EFkPdyDrq4esN46sL0TWG0MhHT6pb9++6tWrl5YtW6Yff/xRO3fu1Pnz53Xp0iVFREQoJiZGHTp00J133qlu3brJYrGUeH5wcLCmT5+uOXPmaOHChTp06JDsdruio6PVo0cPDR8+XLGxsVq7dq1bXk+7du20YMECzZw5U2vXrtXp06cVFhamdu3aadiwYerZs6dT9tOmTRstXLhQ06dP17p165SamqqaNWuqWbNmuvvuuzV06NAS91Qpz3/913+pRYsWWrhwoQ4ePKiLFy+WuKdLVQwcOFBdunTRF198oQ0bNujkyZOy2Wxq0KCBunfvrkcffVTx8fHV2raz1atXT3379tV3332nxYsXV+rE7AwxMTGaPn26duzYoeXLlyspKUknT55Uenq6goODFRUVpbZt2+rmm29Wv379KnXlnRHfffedpMJx265dO5fuC/6NrK8est4Ysr4QWQ93Ieurh6w3hqwvRNbDXcj66iHrjSHrC5H1cBeyvnrIemPI+kJkvTEWu91u93QnAMBX/Prrr3rwwQcVGBioFStWKCYmxtNdcqvMzEzddtttunDhgt59913dc889nu4SADgdWU/WA/B9ZD1ZD8D3kfVkPQDfR9aT9UYFeLoDAOBLOnTooL59+6qgoKDMaX183ZdffqkLFy7ommuu0d133+3p7gCAS5D1ZD0A30fWk/UAfB9ZT9YD8H1kPVlvFIV0AHCyCRMmKCgoSImJiY57j/iDrKwszZo1S5I0ceJEBQRwigHgu8h6sh6A7yPryXoAvo+sJ+sB+D6ynqw3gnukA4CTtWzZUm+99ZaOHz+uEydOqGHDhp7uklucOHFCQ4cOVZ06dZx2HyAA8FZkPVkPwPeR9WQ9AN9H1pP1AHwfWU/WG8E90gEAAAAAAAAAAAAAKIbv8QMAAAAAAAAAAAAAUAyFdAAAAAAAAAAAAAAAiqGQDgAAAAAAAAAAAABAMRTSAQAAAAAAAAAAAAAohkI6AAAAAAAAAAAAAADFUEgHAAAAAAAAAAAAAKAYCukAAAAAAAAAAAAAABRDIR0AAAAAAAAAAAAAgGIopAMAAAAAAAAAAAAAUAyFdAAAAAAAAAAAAAAAiqGQDgAAAAAAAAAAAABAMRTSAQAAAAAAAAAAAAAohkI6AAAAAAAAAAAAAADFUEgHAAAAAAAAAAAAAKAYCukAAAAAAAAAAAAAABRDIR0AAAAAAAAAAAAAgGIopAMAAAAAAAAAAAAAUAyFdAAAAAAAAAAAAAAAiqGQDgAAAAAAAAAAAABAMRTSAQAAAAAAAAAAAAAo5v8Bq6C1DqK3iRIAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 2400x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = results_df\n",
+    "\n",
+    "# PLOT_BASE = True\n",
+    "PLOT_BASE = False\n",
+    "\n",
+    "if PLOT_BASE:\n",
+    "    models_to_plot = [\n",
+    "        \"Meta-Llama-3-8B\",\n",
+    "        \"Meta-Llama-3-70B\",\n",
+    "        \"Mistral-7B-v0.1\",\n",
+    "        \"Mixtral-8x7B-v0.1\",\n",
+    "        \"Mixtral-8x22B-v0.1\",\n",
+    "    ]\n",
+    "else:\n",
+    "    models_to_plot = [\n",
+    "        \"Meta-Llama-3-8B-Instruct\",\n",
+    "        \"Meta-Llama-3-70B-Instruct\",\n",
+    "        \"Mistral-7B-Instruct-v0.2\",\n",
+    "        \"Mixtral-8x7B-Instruct-v0.1\",\n",
+    "        \"Mixtral-8x22B-Instruct-v0.1\",\n",
+    "    ]\n",
+    "\n",
+    "fig, axes = plt.subplots(1,len(models_to_plot), sharey=True, figsize=(12, 2.5), gridspec_kw=dict(wspace=0.1))\n",
+    "\n",
+    "num_features = [2,4,6,8,10]\n",
+    "\n",
+    "for idx, model_name in enumerate(models_to_plot):\n",
+    "    this_data = data[data[model_col] == model_name]\n",
+    "    vals_ece = []\n",
+    "    vals_acc = []\n",
+    "\n",
+    "    for nf in num_features:\n",
+    "        this_row = (this_data[this_data[\"num_features\"] == nf]).iloc[0]\n",
+    "        vals_ece.append(this_row[\"ece\"])\n",
+    "        vals_acc.append(this_row[\"roc_auc\"])\n",
+    "\n",
+    "    ax = axes[idx]\n",
+    "    ax.plot(vals_ece, vals_acc, \"-o\")\n",
+    "    ax.set_title(this_row[\"name\"])\n",
+    "    ax.set_ylim([0.50, 0.9])\n",
+    "\n",
+    "    if PLOT_BASE:\n",
+    "        ax.set_xlim([0.0, 0.29])\n",
+    "    else:\n",
+    "        ax.set_xlim([0.1, 0.58])\n",
+    "\n",
+    "    ax.plot(vals_ece[-1], vals_acc[-1], '*', ms=13, label='all features',color = 'tab:orange')\n",
+    "\n",
+    "    ax.set_xlabel(\"Calibration (ECE)\")\n",
+    "    if idx == 0:\n",
+    "        ax.set_ylabel((\"Predictive Signal (ROC)\"))\n",
+    "\n",
+    "    if idx == 0:\n",
+    "        ax.legend(loc=\"upper left\" if PLOT_BASE else \"lower left\")\n",
+    "    elif idx == len(models_to_plot) - 1:\n",
+    "        ax.legend(loc=\"lower right\")\n",
+    "\n",
+    "\n",
+    "save_fig(fig, f\"feature-ablation.{'.'.join(models_to_plot)}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bde315a6-81fc-4830-8281-e32925829be5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/notebooks/parse-feature-importance-results.ipynb b/.doctrees/nbsphinx/notebooks/parse-feature-importance-results.ipynb
new file mode 100644
index 0000000..9d40549
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+++ b/.doctrees/nbsphinx/notebooks/parse-feature-importance-results.ipynb
@@ -0,0 +1,1253 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "04c2f12d-f989-4ac9-b90f-3464a8bcca96",
+   "metadata": {},
+   "source": [
+    "# Fetch and parse feature importance results under a given directory\n",
+    "\n",
+    "This script collects all feature importance results files under a given root directory, parses them, and aggregates them into a more easily digestable pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b241208-d10f-43cf-a486-84c54bbf43c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faf6afa4-8648-4d35-9312-65636ea5d0b2",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root results directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26089a60-81c0-4736-8ba5-99572ec01398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RESULTS_ROOT_DIR = Path(\"/fast/groups/sf\") / \"folktexts-results\" / \"2024-07-03\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d1af7f-d013-40a0-80de-cb9ad65cff6d",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root data directory (needed only to train baseline ML methods):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b18dad87-a1ed-495b-93b6-4c8af4043ddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db7c98c5-2942-4f88-984a-8c9014afe761",
+   "metadata": {},
+   "source": [
+    "Function to parse and flatten results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eeded803-6ba7-49b2-81b6-836549f3e10b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import operator\n",
+    "from functools import reduce\n",
+    "\n",
+    "def parse_results_dict(dct: dict) -> dict:\n",
+    "    return reduce(\n",
+    "        operator.or_,\n",
+    "        [{\n",
+    "            f\"{feat}_imp_mean\": dct[feat][\"imp_mean\"],\n",
+    "            f\"{feat}_imp_std\": dct[feat][\"imp_std\"]\n",
+    "        } for feat in dct],\n",
+    "    )\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02009a86-e099-4ec0-8676-8d66190ceddb",
+   "metadata": {},
+   "source": [
+    "Iteratively search the root directory for results files matching the given regex:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "aaefa99d-dbd7-40a3-a3c1-7571d3811409",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a998789eb64b4cb6b29af43c4d373d03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from utils import find_files, load_json\n",
+    "import re\n",
+    "\n",
+    "# Results file name pattern\n",
+    "pattern = r'^feature-importance[.](?P<task>\\w+)[.](?P<model>.*?)[.]json$'\n",
+    "\n",
+    "# Find results files and aggregate\n",
+    "from collections import defaultdict\n",
+    "results = defaultdict(dict)\n",
+    "\n",
+    "for file_path in tqdm(find_files(RESULTS_ROOT_DIR, pattern)):\n",
+    "    m = re.match(pattern, Path(file_path).name)\n",
+    "    results[m[\"task\"]][m[\"model\"]] = parse_results_dict(load_json(file_path))\n",
+    "\n",
+    "if len(results) == 0:\n",
+    "    raise RuntimeError(f\"Couldn't find any results at {RESULTS_ROOT_DIR}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ecc0d33-902d-492a-89c6-d9729fe69fa1",
+   "metadata": {},
+   "source": [
+    "Aggregate results into a single DataFrame, and generate a unique identifier for each row:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5e89e632-a448-4b0e-8d8e-621f6c74bed7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "llms_df.shape=(20, 20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "llms_df = pd.DataFrame(results[\"ACSIncome\"].values(), results[\"ACSIncome\"].keys())\n",
+    "print(f\"{llms_df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c374c14e-0e81-4813-9983-309ce6215648",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Currently discarding Gemma 2 results as some extra analysis is needed:\n",
+    "llms_df = llms_df[[\"gemma-2-\" not in id_.lower() for id_ in llms_df.index]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cbe9d8e6-8af1-4663-a976-6c9d5020cc20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AGEP_imp_mean</th>\n",
+       "      <th>AGEP_imp_std</th>\n",
+       "      <th>COW_imp_mean</th>\n",
+       "      <th>COW_imp_std</th>\n",
+       "      <th>MAR_imp_mean</th>\n",
+       "      <th>MAR_imp_std</th>\n",
+       "      <th>OCCP_imp_mean</th>\n",
+       "      <th>OCCP_imp_std</th>\n",
+       "      <th>POBP_imp_mean</th>\n",
+       "      <th>POBP_imp_std</th>\n",
+       "      <th>...</th>\n",
+       "      <th>RAC1P_imp_std</th>\n",
+       "      <th>RELP_imp_mean</th>\n",
+       "      <th>RELP_imp_std</th>\n",
+       "      <th>SCHL_imp_mean</th>\n",
+       "      <th>SCHL_imp_std</th>\n",
+       "      <th>SEX_imp_mean</th>\n",
+       "      <th>SEX_imp_std</th>\n",
+       "      <th>WKHP_imp_mean</th>\n",
+       "      <th>WKHP_imp_std</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>google--gemma-2b</th>\n",
+       "      <td>0.030893</td>\n",
+       "      <td>0.003416</td>\n",
+       "      <td>0.005972</td>\n",
+       "      <td>0.001597</td>\n",
+       "      <td>0.004709</td>\n",
+       "      <td>0.002058</td>\n",
+       "      <td>0.033321</td>\n",
+       "      <td>0.004408</td>\n",
+       "      <td>0.011186</td>\n",
+       "      <td>0.003455</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.002855</td>\n",
+       "      <td>0.004928</td>\n",
+       "      <td>0.002963</td>\n",
+       "      <td>0.047764</td>\n",
+       "      <td>0.002630</td>\n",
+       "      <td>-0.000595</td>\n",
+       "      <td>0.002619</td>\n",
+       "      <td>0.009022</td>\n",
+       "      <td>0.003113</td>\n",
+       "      <td>Gemma 2B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>google--gemma-1.1-2b-it</th>\n",
+       "      <td>0.057288</td>\n",
+       "      <td>0.001795</td>\n",
+       "      <td>0.002239</td>\n",
+       "      <td>0.000988</td>\n",
+       "      <td>0.003562</td>\n",
+       "      <td>0.000592</td>\n",
+       "      <td>0.054559</td>\n",
+       "      <td>0.001415</td>\n",
+       "      <td>0.002923</td>\n",
+       "      <td>0.000761</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000334</td>\n",
+       "      <td>0.010951</td>\n",
+       "      <td>0.000447</td>\n",
+       "      <td>0.073909</td>\n",
+       "      <td>0.001346</td>\n",
+       "      <td>0.003064</td>\n",
+       "      <td>0.000409</td>\n",
+       "      <td>0.028131</td>\n",
+       "      <td>0.001265</td>\n",
+       "      <td>Gemma 2B (it)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         AGEP_imp_mean  AGEP_imp_std  COW_imp_mean  \\\n",
+       "google--gemma-2b              0.030893      0.003416      0.005972   \n",
+       "google--gemma-1.1-2b-it       0.057288      0.001795      0.002239   \n",
+       "\n",
+       "                         COW_imp_std  MAR_imp_mean  MAR_imp_std  \\\n",
+       "google--gemma-2b            0.001597      0.004709     0.002058   \n",
+       "google--gemma-1.1-2b-it     0.000988      0.003562     0.000592   \n",
+       "\n",
+       "                         OCCP_imp_mean  OCCP_imp_std  POBP_imp_mean  \\\n",
+       "google--gemma-2b              0.033321      0.004408       0.011186   \n",
+       "google--gemma-1.1-2b-it       0.054559      0.001415       0.002923   \n",
+       "\n",
+       "                         POBP_imp_std  ...  RAC1P_imp_std  RELP_imp_mean  \\\n",
+       "google--gemma-2b             0.003455  ...       0.002855       0.004928   \n",
+       "google--gemma-1.1-2b-it      0.000761  ...       0.000334       0.010951   \n",
+       "\n",
+       "                         RELP_imp_std  SCHL_imp_mean  SCHL_imp_std  \\\n",
+       "google--gemma-2b             0.002963       0.047764      0.002630   \n",
+       "google--gemma-1.1-2b-it      0.000447       0.073909      0.001346   \n",
+       "\n",
+       "                         SEX_imp_mean  SEX_imp_std  WKHP_imp_mean  \\\n",
+       "google--gemma-2b            -0.000595     0.002619       0.009022   \n",
+       "google--gemma-1.1-2b-it      0.003064     0.000409       0.028131   \n",
+       "\n",
+       "                         WKHP_imp_std     model_name  \n",
+       "google--gemma-2b             0.003113       Gemma 2B  \n",
+       "google--gemma-1.1-2b-it      0.001265  Gemma 2B (it)  \n",
+       "\n",
+       "[2 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from utils import prettify_model_name\n",
+    "\n",
+    "# Add prettified model name\n",
+    "llms_df[\"model_name\"] = [prettify_model_name(m[m.find(\"--\")+2:]) for m in llms_df.index]\n",
+    "llms_df = llms_df.sort_values(\"model_name\")\n",
+    "\n",
+    "llms_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b83b99c-de78-4792-b712-305929207a94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['AGEP', 'COW', 'MAR', 'OCCP', 'POBP', 'RAC1P', 'RELP', 'SCHL', 'SEX', 'WKHP']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "all_features = [m[\"name\"] for col in llms_df.columns if (m := re.fullmatch(r\"^(?P<name>[a-zA-Z0-9]+)_imp_mean$\", col))]\n",
+    "\n",
+    "feat_imp_cols = [f\"{col}_imp_mean\" for col in all_features]\n",
+    "feat_imp_std_cols = [f\"{col}_imp_std\" for col in all_features]\n",
+    "\n",
+    "all_features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba2fe6d9-5a91-4424-bf09-5584caf3574b",
+   "metadata": {},
+   "source": [
+    "## Get feature importance for GBM and LR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f7f90d13-0b1c-467f-99ce-62b902958111",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "CPU times: user 43.1 s, sys: 26.9 s, total: 1min 10s\n",
+      "Wall time: 1min 19s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.acs.acs_dataset import ACSDataset\n",
+    "acs_income_dt = ACSDataset.make_from_task(task=\"ACSIncome\", cache_dir=DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "401a71fd-fbc9-496f-92c2-1fa721873d41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, y_train = acs_income_dt.get_train()\n",
+    "X_test, y_test = acs_income_dt.get_test()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c316b332-0c67-421e-9321-d718d91fd838",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
+    "from xgboost import XGBClassifier    # NOTE: requires `pip install xgboost`\n",
+    "\n",
+    "baselines = {\n",
+    "    \"GBM\": HistGradientBoostingClassifier(),\n",
+    "    \"LR\": LogisticRegression(),\n",
+    "    \"XGBoost\": XGBClassifier(),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5876f670-bc7a-4733-8281-05d4fc150465",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parse_feat_imp_results(results) -> dict:\n",
+    "    parsed_dict = dict()\n",
+    "    for idx, feat in enumerate(X_train.columns.tolist()):\n",
+    "        parsed_dict[f\"{feat}_imp_mean\"] = results[\"importances_mean\"][idx]\n",
+    "        parsed_dict[f\"{feat}_imp_std\"] = results[\"importances_std\"][idx]\n",
+    "\n",
+    "    return parsed_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "10e14028-aa11-47d5-bd15-dbaa4cc92875",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b9ef16acef94f42a75dda6f3e91873a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/acruz/miniconda3/envs/folktexts/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.inspection import permutation_importance\n",
+    "baseline_feat_imp_results = dict()\n",
+    "\n",
+    "for name, clf in tqdm(baselines.items()):\n",
+    "    # Fit classifier\n",
+    "    clf.fit(X_train, y_train)\n",
+    "\n",
+    "    # Compute permutation importance\n",
+    "    r = permutation_importance(\n",
+    "        estimator=clf,\n",
+    "        X=X_test,\n",
+    "        y=y_test,\n",
+    "        scoring=\"roc_auc\",\n",
+    "    )\n",
+    "\n",
+    "    baseline_feat_imp_results[name] = r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b3543466-3b2e-4221-be63-49e1e7bd5e7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AGEP_imp_mean</th>\n",
+       "      <th>AGEP_imp_std</th>\n",
+       "      <th>COW_imp_mean</th>\n",
+       "      <th>COW_imp_std</th>\n",
+       "      <th>SCHL_imp_mean</th>\n",
+       "      <th>SCHL_imp_std</th>\n",
+       "      <th>MAR_imp_mean</th>\n",
+       "      <th>MAR_imp_std</th>\n",
+       "      <th>OCCP_imp_mean</th>\n",
+       "      <th>OCCP_imp_std</th>\n",
+       "      <th>...</th>\n",
+       "      <th>POBP_imp_std</th>\n",
+       "      <th>RELP_imp_mean</th>\n",
+       "      <th>RELP_imp_std</th>\n",
+       "      <th>WKHP_imp_mean</th>\n",
+       "      <th>WKHP_imp_std</th>\n",
+       "      <th>SEX_imp_mean</th>\n",
+       "      <th>SEX_imp_std</th>\n",
+       "      <th>RAC1P_imp_mean</th>\n",
+       "      <th>RAC1P_imp_std</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>GBM</th>\n",
+       "      <td>0.057407</td>\n",
+       "      <td>0.000544</td>\n",
+       "      <td>0.006616</td>\n",
+       "      <td>0.000135</td>\n",
+       "      <td>0.038793</td>\n",
+       "      <td>0.000252</td>\n",
+       "      <td>0.002003</td>\n",
+       "      <td>0.000039</td>\n",
+       "      <td>0.061977</td>\n",
+       "      <td>0.000385</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000078</td>\n",
+       "      <td>0.013170</td>\n",
+       "      <td>0.000146</td>\n",
+       "      <td>0.075559</td>\n",
+       "      <td>0.000622</td>\n",
+       "      <td>0.013678</td>\n",
+       "      <td>0.000182</td>\n",
+       "      <td>0.000804</td>\n",
+       "      <td>0.000042</td>\n",
+       "      <td>GBM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LR</th>\n",
+       "      <td>0.002650</td>\n",
+       "      <td>0.000024</td>\n",
+       "      <td>-0.000173</td>\n",
+       "      <td>0.000084</td>\n",
+       "      <td>-0.000614</td>\n",
+       "      <td>0.000018</td>\n",
+       "      <td>0.022415</td>\n",
+       "      <td>0.000363</td>\n",
+       "      <td>0.092150</td>\n",
+       "      <td>0.000710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000185</td>\n",
+       "      <td>0.049616</td>\n",
+       "      <td>0.000824</td>\n",
+       "      <td>0.071947</td>\n",
+       "      <td>0.000480</td>\n",
+       "      <td>0.003057</td>\n",
+       "      <td>0.000042</td>\n",
+       "      <td>0.000509</td>\n",
+       "      <td>0.000119</td>\n",
+       "      <td>LR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>XGBoost</th>\n",
+       "      <td>0.054982</td>\n",
+       "      <td>0.000185</td>\n",
+       "      <td>0.008279</td>\n",
+       "      <td>0.000207</td>\n",
+       "      <td>0.035391</td>\n",
+       "      <td>0.000434</td>\n",
+       "      <td>0.002507</td>\n",
+       "      <td>0.000076</td>\n",
+       "      <td>0.075997</td>\n",
+       "      <td>0.000635</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000098</td>\n",
+       "      <td>0.013292</td>\n",
+       "      <td>0.000332</td>\n",
+       "      <td>0.072216</td>\n",
+       "      <td>0.000690</td>\n",
+       "      <td>0.012949</td>\n",
+       "      <td>0.000112</td>\n",
+       "      <td>0.001207</td>\n",
+       "      <td>0.000072</td>\n",
+       "      <td>XGBoost</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         AGEP_imp_mean  AGEP_imp_std  COW_imp_mean  COW_imp_std  \\\n",
+       "GBM           0.057407      0.000544      0.006616     0.000135   \n",
+       "LR            0.002650      0.000024     -0.000173     0.000084   \n",
+       "XGBoost       0.054982      0.000185      0.008279     0.000207   \n",
+       "\n",
+       "         SCHL_imp_mean  SCHL_imp_std  MAR_imp_mean  MAR_imp_std  \\\n",
+       "GBM           0.038793      0.000252      0.002003     0.000039   \n",
+       "LR           -0.000614      0.000018      0.022415     0.000363   \n",
+       "XGBoost       0.035391      0.000434      0.002507     0.000076   \n",
+       "\n",
+       "         OCCP_imp_mean  OCCP_imp_std  ...  POBP_imp_std  RELP_imp_mean  \\\n",
+       "GBM           0.061977      0.000385  ...      0.000078       0.013170   \n",
+       "LR            0.092150      0.000710  ...      0.000185       0.049616   \n",
+       "XGBoost       0.075997      0.000635  ...      0.000098       0.013292   \n",
+       "\n",
+       "         RELP_imp_std  WKHP_imp_mean  WKHP_imp_std  SEX_imp_mean  SEX_imp_std  \\\n",
+       "GBM          0.000146       0.075559      0.000622      0.013678     0.000182   \n",
+       "LR           0.000824       0.071947      0.000480      0.003057     0.000042   \n",
+       "XGBoost      0.000332       0.072216      0.000690      0.012949     0.000112   \n",
+       "\n",
+       "         RAC1P_imp_mean  RAC1P_imp_std  model_name  \n",
+       "GBM            0.000804       0.000042         GBM  \n",
+       "LR             0.000509       0.000119          LR  \n",
+       "XGBoost        0.001207       0.000072     XGBoost  \n",
+       "\n",
+       "[3 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "baselines_df = pd.DataFrame([\n",
+    "    pd.Series(parse_feat_imp_results(r) | {\"model_name\": name}, name=name) for name, r in baseline_feat_imp_results.items()\n",
+    "])\n",
+    "baselines_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "02560b20-98b7-427a-994e-71f3e1a84c03",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.shape=(19, 21)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.concat((llms_df, baselines_df), axis=0)\n",
+    "print(f\"{df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "36c8f364-eaa6-4bee-ac15-4d03c5314e4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import get_current_timestamp\n",
+    "df.to_csv(Path(RESULTS_ROOT_DIR) / f\"feature_importance.{get_current_timestamp()}.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f4c4ba2-00f9-45cd-b9e0-b9ce3e0edc9d",
+   "metadata": {},
+   "source": [
+    "# Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "53df0763-7d81-4edd-8e6b-f444546b9d82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMGS_DIR = Path(RESULTS_ROOT_DIR) / \"imgs\"\n",
+    "IMGS_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "def save_fig(fig, name):\n",
+    "    fig.savefig((IMGS_DIR / name).with_suffix(\".pdf\"), bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e3ca551a-2d21-4eb6-a573-a504687e0d1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1f77b4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff7f0e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2ca02c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d62728;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8c564b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e377c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7f7f7f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bcbd22;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#17becf;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
+      ],
+      "text/plain": [
+       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
+       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
+       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
+       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
+       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
+       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
+       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
+       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"whitegrid\", rc={\"grid.linestyle\": \"--\"})\n",
+    "plt.rc(\"figure\", figsize=(4.5, 3.5), dpi=200)\n",
+    "\n",
+    "palette_name = \"tab10\"\n",
+    "# palette_name = \"colorblind\"\n",
+    "palette = sns.color_palette(palette_name)\n",
+    "sns.set_palette(palette_name)\n",
+    "palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8a97c7b0-7021-4727-8a8c-cf10806d573a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def barplot_feat_imp(row: pd.Series, stdev_values=None, ax=None, **kwargs):\n",
+    "    if ax is None: ax = plt.gca()\n",
+    "\n",
+    "    labels = [name[:name.find(\"_\")] for name, val in row.items()]\n",
+    "    sns.barplot(x=row, y=labels, ax=ax, **kwargs)\n",
+    "\n",
+    "    ax.errorbar(\n",
+    "        x=row.values,\n",
+    "        y=labels,\n",
+    "        xerr=stdev_values * 2,  # 2 stdev ~> 95% c.i.\n",
+    "        fmt='none', c='grey', capsize=0,\n",
+    "    )\n",
+    "\n",
+    "    ax.set_title(\"Mean feature importance (ACSIncome)\")\n",
+    "    ax.set_xlabel(\"Importance for ROC metric\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d71958d8-eaf8-4448-8f32-d88c017182f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('OCCP_imp_mean', 'OCCP_imp_std'),\n",
+       " ('SCHL_imp_mean', 'SCHL_imp_std'),\n",
+       " ('WKHP_imp_mean', 'WKHP_imp_std'),\n",
+       " ('AGEP_imp_mean', 'AGEP_imp_std'),\n",
+       " ('RELP_imp_mean', 'RELP_imp_std'),\n",
+       " ('MAR_imp_mean', 'MAR_imp_std'),\n",
+       " ('SEX_imp_mean', 'SEX_imp_std'),\n",
+       " ('COW_imp_mean', 'COW_imp_std'),\n",
+       " ('POBP_imp_mean', 'POBP_imp_std'),\n",
+       " ('RAC1P_imp_mean', 'RAC1P_imp_std')]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feat_imp_cols_sorted = df[feat_imp_cols].mean().sort_values(ascending=False).index.tolist()\n",
+    "feat_imp_std_cols_sorted = [col.replace(\"_imp_mean\", \"_imp_std\") for col in feat_imp_cols_sorted]\n",
+    "list(zip(feat_imp_cols_sorted, feat_imp_std_cols_sorted))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b2372ab-fc18-443a-bf26-b979b9e3c03e",
+   "metadata": {},
+   "source": [
+    "Mean feature importance among all models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "62b9793d-a99b-40c9-b091-da1067e5f96d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "barplot_feat_imp(df[feat_imp_cols_sorted].mean(), df[feat_imp_std_cols_sorted].mean())\n",
+    "ax = plt.gca()\n",
+    "ax.set_xlim(left=0, right=ax.get_xlim()[-1])\n",
+    "save_fig(plt.gcf(), \"mean-feat-imp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "68cfa49f-499d-4320-8511-408a8671e791",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_feat_imp_subplots(df):\n",
+    "    N_COLS = 4\n",
+    "    N_ROWS = math.ceil(len(df) / N_COLS)\n",
+    "\n",
+    "    cols_order = df[feat_imp_cols_sorted].mean().sort_values(ascending=False).index.tolist()\n",
+    "    std_cols_order = [col.replace(\"_imp_mean\", \"_imp_std\") for col in cols_order]\n",
+    "    \n",
+    "    fig, axes = plt.subplots(nrows=N_ROWS, ncols=N_COLS, figsize=(12, 2.8 * N_ROWS), sharey=True, sharex=True)\n",
+    "    \n",
+    "    for idx, m in enumerate(df.index):\n",
+    "        ax_row = idx // N_COLS\n",
+    "        ax_col = idx % N_COLS\n",
+    "    \n",
+    "        ax = axes[ax_row, ax_col]\n",
+    "        row = df.loc[m]\n",
+    "        \n",
+    "        barplot_feat_imp(\n",
+    "            row[cols_order], stdev_values=row[std_cols_order],\n",
+    "            ax=ax,\n",
+    "            color=palette[0] if m not in baselines else palette[2],\n",
+    "        )\n",
+    "        \n",
+    "        ax.set_title(row[\"model_name\"])\n",
+    "\n",
+    "    # Set xlim\n",
+    "    max_xlim = max(ax_i.get_xlim()[-1] for ax_row in axes for ax_i in ax_row)\n",
+    "    axes[0,0].set_xlim(left=0, right=max_xlim)\n",
+    "\n",
+    "    # Remove extra axes\n",
+    "    [axes[N_ROWS-1, N_COLS - 1 - i].remove() for i in range((N_ROWS * N_COLS) - len(df))];\n",
+    "\n",
+    "    plt.plot()\n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a70eb7d-1e7e-4dc8-9a0e-ee79bcc6d6cf",
+   "metadata": {},
+   "source": [
+    "Feature importance for each model, plus baselines:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9f64cbcb-3a1d-4831-9d9e-46e4081d0e9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2400x2800 with 19 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_feat_imp_subplots(df)\n",
+    "save_fig(fig, \"all-feat-imps\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "2827713d-0828-4e69-9618-0c3304c6a29c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR:root:Could not infer model size from name 'GBM'\n",
+      "ERROR:root:Could not infer model size from name 'LR'\n",
+      "ERROR:root:Could not infer model size from name 'XGBoost'\n"
+     ]
+    }
+   ],
+   "source": [
+    "from folktexts.llm_utils import get_model_size_B\n",
+    "large_models_filter = np.array([(get_model_size_B(m, default=-1) >= 40) for m in df.index])\n",
+    "# baselines_filter = np.array([(m in baselines) for m in df.index])\n",
+    "baselines_filter = np.array([(m == \"XGBoost\") for m in df.index])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68160fcf-3c16-46ec-8222-7a4ddf105503",
+   "metadata": {},
+   "source": [
+    "Plot feature importance among large models (>= 40B):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "085d4f05-85ce-4047-9671-859a1c307aea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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g4GCNHj1ao0ePNnV84uLi9NZbb+nPP/80nvP19VW9evVUrFgxRUZG6t9//9WFCxcUGxurr7/+WiEhIfr4449N1Z+cd955x+64VKxYUbVq1VLhwoUVGxurkJAQnTx5MslMQwAAAMhbSLIDAAAgzwsODtbQoUONH9m7d++uYcOGqVSpUnblrl+/rg8//FDr1q3TrVu39Oabb2r58uXJjuhs3ry5+vfvr9atWydJHkoJU9Nu2LBB77//vkJCQrRkyRJ17txZTZo0sSuXOApy5MiRRpK9a9euqU59mxXmzp2r2NhY9e7dW8OGDbMbZWmxWIzjkhnHN7McPHhQMTExatiwoT777DOVL1/eeO327dsaMWKEMeJ3xowZOnjwoI4fP65HHnlE48aNU/HixY3y4eHhev3117Vnzx5ZrVZNmzZN33//fapt+Prrr2WxWNSlSxd9+OGHdv0nPDxco0eP1l9//WWUbd26tRo1apSknvDwcA0bNsxIsFeqVElTp07VfffdZ1du5cqV+uCDD3T79m2dP39e7733nr7++utU2zlt2jR5eHhozJgx6tWrl92owZiYGHl6emrMmDGS7JPsic+lxlnfoeRMmjRJcXFxGjlypPr06WOXzLty5YoGDhyokydPKj4+Xp9//nmKf7crV65o6NChRoK9XLlyGjt2rB588MEkZcPDw7V27Vr9999/ydY1btw4I8FeqVIlffTRR2revLldmbi4OP3888+aMGGCYmJiNH36dDVv3tzUzRuOZOaxnjp1qqxWq8aMGaNnn31Wrq6uxmu7d+/WK6+8oqioKJ05c0Zz587V119/LR8fH3366afq0KGDXV1r1qzR8OHDFRcXp61bt2rPnj1q2rRpsvsdNWqUkWD38vLS+PHj9fjjj9uVOXz4sN566y1dvHjRGLm6cuXKJMtc3G3WrFmKiYnRyy+/rNdff10FChQwXgsLC9PQoUO1c+dOSdLnn3+uTp06JTuq9syZM5o8ebLxuFWrVpo4caLdudFisWjGjBmaM2eOPv/8c4ftygwuLi7q0aOHunfvrkaNGiU7rX5MTIy+//57TZs2TbGxsRo7dqzatGljauT9tGnTVLBgQU2cOFEPP/xwknoTffXVV0aC3dXVVe+884769etn158uXryot99+WwcOHNDRo0dT3XdOiU2LFy9WTEyMnnrqKY0YMcLuO3j16lX1799fZ8+e1e3bt/XVV1/p5MmTioyM1JAhQzRw4EC7acpPnTql/v37Kzg4WNevX9fChQs1ePDgZPc7b948u0TywIEDNWTIELv6goODNXLkSG3dulWSNHnyZDVo0ED169dPUl9oaKjee+89I8Hu7++vL774wu6GhPj4eM2bN09Tp07VpEmTTB2fmTNnGgn2kiVLasyYMXrkkUeSfKfWrl2rDz74QLdu3dIvv/yili1bmr7Zw1ZAQIDWr18vKeHc8cUXXyRZIibRxYsX9fvvv9vFfgAAAOQdrqkXAQAAAHKHTZs26aOPPkr1v7unV502bZpu3LghKWEt4UmTJiX5kV2SSpQooS+//FItWrSQJJ08edJuZJWtYcOGqVOnTskmrKSExEW7du3skpk//PBDuj53domNjVWvXr00ZsyYJMkUDw8PI/mRGcc3s8TExKhy5cqaP3++XYJdkvLnz69PPvnEWDv6/PnzWrRokVq0aKHp06cn+ZG9cOHCmjRpkpGI2b17t4KDg1Ntg8ViUZs2bTR58uQk/adw4cKaNm2amjVrJikhYTF16tRk61m4cKHR1wsXLqwFCxYkSbBLCTdtTJkyxXi8YcMG7dmzJ9V2xsbG6tNPP9VTTz2VJNnhjLVpM/M7FBMTo7Fjx6p///5J1lguU6aMpk6danym3bt3J5m2P9HUqVMVGhoqSfLz89PPP/+cbIJdSvgbPPPMMxoxYkSS1/bu3avly5dLShjNu2TJkiQJdklyc3PTc889p3HjxklKSLrPmjXL1Gd2JCuOde/eve0SolLCuuIDBw40Hn/++eeKjo7WzJkzkyTYJemxxx6zu8Ho7iUdEu3cuVObN282Hk+bNi1Jgl2S6tatqwULFhgjfa9cuWLqRpiYmBi98sorGj58uF2CXUoYCTx16lR5eXlJSkjCJY7Av9vMmTONKeJr1qyp2bNnJzk3enh46O2331afPn2STJ+fFTw9PTVhwgQ1b948xXXrPT099dJLLxmzaty8eVMrVqwwVX98fLxmz56dJMGeWK+UcIPK/PnzjeffeustDRgwIEl/Kl++vObNmyc/Pz9TxyqnxKaYmBh17dpVH3/8cZLvYOnSpTV+/Hjj8bJly3T06FG9/vrrGjx4cJJzbfXq1fXuu+8aj9esWZPsPiMiIuzWux8wYICGDRuWpL6SJUtq9uzZxlIKsbGxKcacBQsWGMezRIkSWrBgQZIR/66urnr55Zc1dOhQu+URUnLp0iXNmTNHUsJ3a/HixXr00UeTvWmlU6dOxqwIUsL3y2q1prqPu9nOxNO3b98UE+xSQp8bNGiQ2rVrl+b9AAAAIPcjyQ4AAIB7xuHDh/Xjjz+m+l9ISIjxnpCQEK1cuVJSwo/J77zzjsN9uLm56a233jIeJ743verXr2/8CJ3cusE5Wb58+VI9Xtl9fNNj+PDhRoLsbj4+Pkl+cB81alSSZE8iPz8/Y5Sx1WpNds3uu7m4uGj06NEp1unu7m43ze7evXuNWQ4SWa1W/fLLL8bj1157TWXKlElxn4888ohdcnjJkiWptrNevXrq2rVrquUyW3q+Q/7+/nr66acdvp6YVErp7xYUFKS1a9cajz/88EOVKFEiLU03fPfdd8b2iBEjUp2uv0ePHsaSEVu3bjUS/ZktPce6Zs2aDtcq7ty5s93j9u3bO1yr2TZZnlLy+ueffza227Vrp4ceeijF+sqVK6dXXnnFePzTTz+lmpgrVqyYXn/99RRfL1GihN15Irl2hoeHa926dcbjd999V/ny5UuxzjfffDPFmyByip49exrbZvtHhw4dUpyNINHvv/9uLLPg5+enAQMGpFi2UKFCxgwwjuSk2OTh4ZHszTeJGjdurLJlyxqPS5QoYddn7/boo48aN0ScPXvWGKlva9WqVYqKijLqc7TsiO2sJFLCEhnJxZzffvvNePzaa685HN390ksvyc/PL8XXE33//ffGWvGvvfaaKlSo4LB8ixYt9MADD0hKmCni2LFjqe7jbrbHy8zSKQAAAMi7mC4eAAAAedr27duN0VSPPPKIwyRHovr168vLy0tRUVF26yen5Ny5czpy5IguXLigiIgIxcTE2CVxEtfvDQsL05UrVxwmQ3OSVq1aqXDhwg7LZMXxdab8+fM7HLUmJSRgE1WsWFE1a9Z0WL569erGyLhLly6l2oaGDRummkioUaOGateubSQQdu3aZSRdpYTkQuKoeTc3N3Xr1i3V/fbq1csY/bt79+5Uyyc3MjizOPs71LFjx1T3WatWLSM5mty6u9u3bzemRa5UqVKKI9hTExsbq+3bt0tKuImjbdu2pt7XvHlznT17VlarVfv371f79u3Ttf+7OftYJzci3Vb58uWN77uZ8rbfv5TWQ961a5exbZv4TUnPnj31+eefKz4+XsHBwTp79qzDNbfbtm2b6rmsdu3axk0YybXzwIEDxrmxZMmSDm8skBL6Rvv27U2PEM8M8fHxOnLkiAICAnT16lVFREQY34G7HT9+3FSdZs4jtn/PTp06JZl94m4dOnTQmDFjjMR8cnJSbGrSpEmqN+hUr15dly9flpTQ/xzNFpI/f35VqFBBZ86ckdVqVWBgoGrUqGFXJnE5Aynhb5A/f36H+69Xr578/f118uRJSY5jjru7u7p06eKwPg8PD3Xu3NkYpZ6STZs2Gdup1ZmoRYsWxvT2+/btU506dUy9L1Hp0qWN7RUrVuipp55KMmMFAAAAIJFkBwAAwD1k8ODBGjJkSJrek7jGqySdOHFCH330UZreHx4erqioqGRHPm/cuFFffvllmkZShYaG5poku5kfrjPz+GaGSpUqpTglciLbGwuqVauWap225ZMbUXi3Bg0apFomsVxi37o7oWXb5ypXrqyiRYumWp/tuu7BwcEKCgqSr69viuXTmrhIj8z6Dt2dcEqO7TFL7u9m27cTp+9PjxMnThgJZnd3d33yySem3nf48GFj++rVq+nef6LMOta2SfGUFCxY0DgG1atXd1g2te9TUFCQMWW1ZN+vU1KsWDFVqlTJGJ177Ngxh0l2M58pcVmJlNpp+5297777Upy5wlb9+vWzJckeGxurRYsWacGCBab7mtnZFcycRwICAozt5NYCv1uBAgVUvXp1hzOH5KTYlFqflxJG6CdyRtyx7X+Js62kplGjRkaS/e7zhO3jKlWq2LU3JanFutDQUJ0/f15SQlLedip4R06fPm1sX7lyxdR7bLVp08a4meLo0aPq1KmTnnzySbVp00a1a9c2loABAAAASLIDAAAgT7Nda3nfvn3at29fmuu4efNmkh/aZ8yYYfoHYVuRkZFpfk92MTONamYd38ySuDazI7Y/sJspbzvqMqVRn7ZspwV2xDa5absEwt2PzdZXokQJ5cuXzxj9GRoa6jDJntnT6Gbmd8jMtNup/d1sE7nly5c32bqkbL8jYWFh+vHHH9NcR3h4eLr3L+WsY51a+dT+LrZ9P3/+/Kb7qZ+fn5FkTy1B7IzvvW07zd5Y5ej7mFliYmI0aNAgY2SwWWZjmZm/j+2xsh1l7Ejp0qUdJtlzUmxKa39ydv8zM2373eXu/o6kpz+nFpsSR8ZLksViSde58ebNm2l+T9GiRTV+/HiNGDFCFotFV65c0YwZMzRjxgx5eXmpfv36atq0qdq1a6datWqluX4AAADcO0iyAwAAIE9LnPo4I+7+AXvbtm12CauGDRuqR48eqlu3rkqXLi1vb2+7qV779OljTM8dHx+f4fZkldSml5Uy5/hmJhcXl0wtb4aZ4yrJLrlzd0IrcVSwpDRNc1ugQAEjyZ5akszM9MrpldnfIWf83WyPT0YSbc74jiSuWZweOe1YZ/RvY/t3SUvfd/R9upsz+o/td9Tsd97b2zvD+02rmTNnGgl2FxcXderUSY8++qj8/f3l6+urfPny2c3+kThLRGrr2icy89nTcz5L7TuZk2JTdsSdjB5TRzHHbH9Obb/ZeW58/PHHVaVKFc2aNUsbN240lhaIiorSjh07tGPHDk2fPl116tTRe++9pyZNmmS4rQAAAMh9SLIDAAAgT7P9kXfUqFHq169fhuucN2+esd2zZ0998sknDn8Uzymj1zMjwZ8Zx/ded/v2bVPlbJMadyffbJMh0dHRpvdtWzY7EnqJcsN3yPb42P4t0sr2b1WjRg2tXLkyQ+1Kq9xwrNPC9u+Slr7v6PuUGWz/7un5zmeFmJgYLVq0yHg8ceJEde/ePcXyZpbDSA8vLy8j4Wr2b5paubwem9JzTM3GHLP9ObX92tbp4+OTrtkGMqJWrVqaOXOmbt68qT179mjfvn3av3+/jhw5YiTdjx49qr59+2rq1Knq1KlTlrYPAAAA2S/1Rb8AAACAe1iJEiWMbdupSdMrLi7OGOXp6uqqYcOGpTrq7PLlyxneb3Jsp4s1M5rLGaPG7ubs45sXmO0Ptmsj373muu0UzGbXpL1x44Yxij25OrNKTvoOOVK8eHFj+9KlS06p5/r16xlqU1rllmOdFrZ9//bt20mWUkhJYGCgsZ0Vfd92H2bXOTdbzln+/fdfI7FavXp1hwl2KfP6hu2xCgoKMvWe1I5VXo9Ntt8Ts383R9+R9MSc1MrZnhsjIiLSdNOMMxUqVEjt27fXu+++q59++kk7d+7UhAkTjOnu4+LiNG7cONM3FwAAAODeQZIdAAAAeVq9evWM7f3792e4vtDQUGOEU/Hixe1+JE7O6dOnU13/V0rf9LC2axuHhYWlWv7kyZNp3kdqnH1884JDhw6ZKnfw4EFju3bt2nav2T4+e/asqb+/7d+nZMmS2bL+s5R53yFna9CggbG9a9eudNdTq1YtYzr2Gzdu6L///sto00zLLcc6LXx9fe0+x4EDB1J9T0hIiM6fP288vvv7lBls13I+fPiwqenVDx8+nJlNSsJ23XJ/f/9Uy+/ZsydT2mF7rMycH2/fvq1Tp045LJPXY5PtMTXzHbm7XGoxx8xNe7YxLDmlSpWyW9/dbDszm4+Pj3r06KGFCxca5+7Q0NAc0z4AAABkHZLsAAAAyNNat25tjPg+cOCAAgICMlSfq+v/XWKbGdW0ZMkSU/Xarolsdh1YPz8/Y/v48eMOyx4+fDhDo3FT4uzjmxfs379fFy9edFjm1KlTOnr0qPG4WbNmdq9XrVpVJUuWlJQwys7MFOS//vqrsd28efO0NDlVtuu3JyZ1U5JZ3yFnu//++42+ff78eW3ZsiVd9eTPn18tWrQwHi9evNgp7TMjtxzrtLLtv8uWLUu1/LJly4zlMkqVKqUqVapkWtsSNWzY0FjLPDg4WDt37nRYPjIyUuvXr8/0dtmyvbkrtVHE8fHx+uWXXzKlHbbnt7Vr16YaA//8889U+3Nej02255zVq1fbzWKSnMOHD+vEiRPG47tjRJUqVYyYExsbq99//91hfWbKSNJDDz1kbGfludGMChUqqHr16sbjGzduZGNrAAAAkB1IsgMAACBP8/X1VdeuXSVJVqtV7777rul1ZePj45NMRVykSBEVLFhQUsL064lTMSdn3759ppNWRYoUMbbNTpdrO1Jv+fLlKZaLjY3VJ598YqrOtHL28c0LrFarPvnkkxRHtsbFxWn8+PHG48aNG6tq1ap2ZVxcXPTUU08Zj2fNmuWw3/z999/auHGj8fiZZ55JZ+uTl5b+m1nfIWfz9fW1W4N37Nix6Z7u/eWXXza2f/jhB23fvt30ezMy1XVuOdZp9fTTTxvb69atc3gDRGBgoL7++mu796Zn5pC0KlKkiNq3b288/uyzzxQTE5Ni+enTp2fKkh6OlC9f3tjes2ePw/3PnTs30xLVnTt3Nm7UuXTpkhYsWJBi2Vu3bunLL79Mtc68Hpu6dOlirHkeHBysmTNnplg2JibGLuY0b948yY0orq6u6tmzp/F41qxZDo/R/PnzTd3YN2DAALm5uUlK+C4vXbo01fckSu+50ezfNi4uzm62h9RmAgEAAMC9hyQ7AAAA8rw333zTGIF14sQJPfnkk9q6dWuK5a9evaoFCxaoY8eOWrNmjd1rrq6uatOmjfF45MiR+vfff5PUsWbNGg0cOFBxcXHGD92O2E7V+/fffztMxiR67LHHjJGqBw4c0JQpU5KszX716lW98sorOnDggN1oeWdy5vHNCzw8PLRhwwaNHDkySdInPDxcb7/9tjHq1cXFRW+//Xay9bzwwgvGlO9hYWF64YUXkp3RYPXq1Ro2bJjxuG3btmratKmzPo4k2Y32++OPPxyWzazvUGYYNmyYcQNBYGCgnn766RQTujdv3tTPP/+syZMnJ3mtWbNmeuKJJyQl3PQycOBAzZkzR5GRkcnWdefOHa1fv16DBg3SoEGD0t3+3HSs06JFixZ68MEHjcdvvPGG1q5dm6TckSNH1L9/f928eVOSVKZMGfXt2zfL2jl48GBjNPvRo0c1aNCgJDdqWCwWffHFF1qwYEGmnaNTUrt2beMccuvWLQ0dOjTJTTIxMTH68ssvNXXq1EzrG0WKFFH//v2Nx1OnTtWCBQuM2QcSXbp0SS+99JICAwNNHau8HJt8fHz02muvGY+/+eYbffHFF0muLa5fv67XXnvNmNrd3d3dLl7YeuGFF4y12oODg9W/f3+dOXPGrkx8fLzmz5+vadOmGX3fkQoVKtid49577z1NmjQpxUR4bGystm7dqnfeecc4p6bVZ599pt69e2v58uXGueFuoaGhGj16tJHI9/HxUcOGDdO1PwAAAORe7tndAAAAACC7+fr66quvvtLAgQMVGhqqc+fO6cUXX5Svr6/q1aunYsWKyWKxKDQ0VKdOnUp19NWgQYO0fv163b59W4GBgXrqqafUoEEDVa5cWTExMTp48KBRx1NPPaXz5887HEEqSQ8++KDy58+v27dv6/jx43rsscfUrFkzFSpUyBh12apVKz3wwAPGe/z8/PTMM88YU6x+++23Wr16tZo0aaJ8+fLpwoUL2r9/vywWi+6//36VKFHC1LTiaeXs43uve+WVV/T9999r+fLlWrdunVq0aKHixYsrODhYu3btUlRUlF3ZJk2aJFtP4cKFNXXqVL388suKjo7WuXPn9MQTT6h+/fqqWrWqLBaLDh06ZLcGeKVKlfTpp586/TN16NDBSF5NmTJFmzdvVvXq1e0SYa+++qoKFy4sKXO+Q5mhTJky+uKLL/Taa68pKirKSPL5+fmpbt26Kly4sKKionT+/HkFBATIYrHYjV629dFHHyk4OFhbt26VxWLR559/rtmzZ6tevXoqW7asPD09dfPmTV24cEGnTp0ykmF16tTJ0GfILcc6rSZMmKBnn31WFy5cUFRUlN5880198cUXqlevnjw8PHTmzBkdOnTImDHCy8tLU6dOVaFChbKsjdWrV9fw4cM1YcIESdLWrVvVtm1bNWvWTGXLllV4eLj27NmjkJAQeXh46O2339bEiRMlKUtG27u6umro0KF67733JEnbtm1Tx44d1bBhQ5UtW1ZhYWHavXu3wsPDJSX04eHDh2dKW15//XVt375d//77r+Lj4zVhwgTNnz9fTZo0kZeXly5evKi9e/cqNjZWDRs2VLly5bRq1SrjcyQnr8emF198Ufv27dOGDRskSbNnz9aSJUvUvHlzFS5cWFeuXNGuXbvsEu/vvPOO6tevn2x9xYoV0yeffKIhQ4YoLi5OAQEB6ty5sxo3bqxKlSopKipKe/bsMUZ/jxo1ytQsOoMHD1ZgYKCWLVsmq9Wq+fPna9GiRbrvvvtUoUIF5c+fX5GRkQoMDNSJEyeMGGk7g0paWK1W7d27V3v37pWbm5uqVKmiKlWqqHDhwrp9+7aCgoKMa6dEI0aMUP78+dO1PwAAAOReJNkBAAAAJUyt/ttvv+n999/Xjh07JCVMa71u3boU31OiRAlVrFgxyfPVqlXT1KlTNXz4cEVHR8tqterAgQM6cOCAXbmnn35ao0eP1osvvphq+woWLKiRI0dq3LhxslqtunjxYpJ1u728vOyS7FLCyNTAwEBt2rRJknT58uUkifS2bdtq8uTJmZJcTeTM43uv8/Pz05w5c/TGG28oODhYf//9d5Iybm5uevnll/XWW285rKtp06ZasGCBhg8frosXL8pqtergwYPGqERb999/v6ZOnapixYo566MYnnjiCa1cuVJ79uyR1WrVrl27tGvXLrsyvXv3NpLsmfEdyiwtW7bUkiVLNGLECGO67MDAQAUGBiZbPqXRvp6envrmm280c+ZMfffdd4qOjlZ0dHSS42TLw8NDDRo0yFD7c9OxTosSJUpoyZIlGjZsmDHzw/nz53X+/PkkZStWrKgpU6bYLbGRVfr166e4uDhNmzZNFotFMTExSUZTFyxYUJMnTzamTJcSRs5mhZ49e+rChQvGlPpRUVHatm2bXZl8+fLpvffeU5cuXTItye7p6al58+ZpyJAhxt8zKChIq1evtivXsGFDzZgxw7gZQXJ8rPJybHJ1ddXMmTM1YcIELVmyRHFxcQoLC9Off/6ZpGzBggX13nvvqUePHg7rbN++vT7//HONHj1at27dUnx8vPbs2aM9e/YYZTw9PTV69Gi1atXKVJLdxcVFEydOVJ06dTRjxgyFh4fLYrEke56yfU+jRo1SrTs53t7exnZcXJxOnTqlU6dOpVh25MiRdsuzAAAAIO8gyQ4AAAD8f35+flqwYIEOHDigP/74Q3v27NHVq1d18+ZNubm5qUiRIqpYsaLuu+8+PfDAA2rWrJnc3ZO/pH744Ye1atUqLViwQFu3btWVK1fk5uamUqVKqVGjRurRo0e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",
+      "text/plain": [
+       "<Figure size 2400x1120 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_feat_imp_subplots(df[large_models_filter | baselines_filter])\n",
+    "plt.suptitle(\"Feature importance among large models\")\n",
+    "\n",
+    "save_fig(fig, \"feat-imps-large-models\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "078abc0b-cb4e-4434-bf65-185da6459f7c",
+   "metadata": {},
+   "source": [
+    "Feature importance averaged among all large models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "b232fa0a-30ec-4dd6-8bb8-d501ff69c89b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cols_order = df[large_models_filter][feat_imp_cols_sorted].mean().sort_values(ascending=False).index.tolist()\n",
+    "std_cols_order = [col.replace(\"_imp_mean\", \"_imp_std\") for col in cols_order]\n",
+    "\n",
+    "barplot_feat_imp(df[large_models_filter][cols_order].mean(), df[large_models_filter][std_cols_order].mean())\n",
+    "ax = plt.gca()\n",
+    "ax.set_xlim(left=0, right=ax.get_xlim()[-1])\n",
+    "\n",
+    "plt.title(\"Mean feature importance\\n(model size > 50B; ACSIncome)\")\n",
+    "save_fig(fig, \"mean-feat-imp-large-models\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70515b7b-80f7-4f2d-a015-273fc1a5e31d",
+   "metadata": {},
+   "source": [
+    "Plot feature importance for each model separately:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "e5cd171b-ce3f-44ae-9afe-6dd6831d6e59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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/Wh988AHi4+OxY8cOaDQarFq1CuvWrUOzZs1Qt25dODk5ITs7G/Hx8YiJiREmthfXnq2TSqX46KOP8OWXXwIATp48ib59+6Jly5aoWbMm0tPTcfbsWWRkZAAAvvnmG3z22Wcmi+fo0aPCtaRSKQYNGlTuNnfv3o1r164VeT83Nxf37t1DVFRUgTXeWrZsiUmTJpX7ukTlxcKMiMqtVq1a2LhxI9atW4dVq1YJq8OnpqbixIkTxZ4nkUjQunVrvPPOOyXegSeRSLBgwQI0bdoUv/zyCzIyMqBQKHDp0qVibwiQSCRo1apV+T6YiA0dOhQPHz4UlorIycnByZMnCxzj6OiIL7/8EsHBwSYtzHSHMTt27Ijq1auXu01DnpWpNXToUHz55ZcGPVqMyNRYmBFRhXB2dsaECRMwatQonDhxAqdPn8bVq1eRkpKC9PR0qNVqVK5cGR4eHggICEBgYCC6d+9eYL5TaUJDQzFkyBDs2rULp06dQnR0NFJTUyGXy+Hq6gofHx80atQI7dq1Q9euXQvc0UlFTZ06FV26dMH69etx4cIFpKamwtXVFdWrV0eXLl3w+uuvo379+iaNoXDxbuq1y5ycnFCpUiU0bNgQLVu2xKBBg9CwYUOTXpPIGBKNqZdxJiIiIiKDcPI/ERERkYVgYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGFYGFGREREZCFYmBERERFZCBZmRERERBaChRkRERGRhWBhRkRERGQhWJgRERERWQgWZkREREQWgoUZERERkYWwN3cAZF5qtRq5ubkAAHt7e0gkEjNHREREZB00Gg2USiUAwNnZGVJp+fu7WJjZuNzcXERHR5s7DCIiIqsWEBAAV1fXcrfDoUwiIiIiC8EeMxtnb/+/fwK+vr5wcXExYzRkCgqFQugVDQgIgEwmM3NEZArMs/gxx5ZHNye6v0/Lg4WZjdOdU+bg4AAHBwczRkOmIJVK4e7uDgBwdHSssB8eZFmYZ/Fjji1bRc3RZlZJwG9ycbK3t4evr6+5wyATY57Fjzm2DZxjRkRERGQhWJgRERERWQiOXZFAoVBwjpkIKRQK3LlzB0D+DR6cMCxOzLP4Mce2gYUZkchpNBpkZ2cL2yROzLP4Mce2gUOZRERERBaChRkRERGRhWBhRkRERGQhWJgRERERWQgWZkREREQWgoUZERERkYXgchkkkEpZp4uRnZ0dateuLWyTODHP4scc2wYWZiTgN7o42dnZwcfHx9xhkIkxz+LHHNsGdpEQERERWQgWZkREREQWgkOZJOCzMsVJLpcjKioKABAYGMgcixTzLH7MsW1gjxkRERGRhWCPGQns7fnPQYxkMhkCAwOFbRIn5ln8mGPbwN/EJJBIJOYOgUxAIpFwyMMGMM/ixxzbBhZmJFCp1UhMzzZ3GERERALPys6ws6F1NlmYkSA1Kw8DF+42dxhERESCfWFD4ePuau4wXhjbKUGJiIiILBwLMyIiIiILwcKMiIiIyEKwMCMiIiKyECzMiIiIiCyExd+VmZmZid27d+PYsWO4c+cOUlJSYG9vD09PTzRr1gw9evRAnz59YGdnZ3Cbqamp+Pvvv3Hy5EncunULaWlpkMvlqFKlCho2bIhWrVphwIAB8PPzK7UttVqNo0eP4vjx47h06RKSk5Px7NkzuLq6wsfHR4ixa9euehcEDA0NxdmzZ4u8L5VKUalSJdSpUwft2rXDm2++iXr16hn8GYmIiMj6SDQajcbcQRRn8+bNWLx4MdLT00s87qWXXsLXX3+NNm3alHicWq3Gb7/9hj/++APZ2aWv19WpUyd8+eWXeOmll/TuP3v2LObOnYvY2NhS2/L29sbUqVMREhJS4P3iCrPCZDIZZsyYgZEjR5Z6rDF0n71WvZ4vl8sgIiKLYsnLZZji+aUW22O2cOFCrFq1CkD+o4L69++PoKAg1KpVC3K5HPfu3cPevXtx5swZ3L59G++88w4WLVqEvn376m0vLy8PH3/8MY4ePQogv9Dp378/XnnlFdSuXRtOTk5ISUlBVFQUDh8+jJs3b+LkyZPYtGkTwsLCirS3bds2zJkzBwqFAgDQokUL9OrVC40bN4a7uzsyMzPx6NEjHD9+HMeOHUNSUhLmzZtXpDDTtWfPHmFbrVYjISEBBw4cwI4dO6BQKDB37lzUqlUL3bp1K+uXlYiIiCyYRRZm69evF4qy6tWr47fffkPjxo0LHNOmTRsMGzYMe/bswYwZMyCXyzFt2jTUq1evyLEA8NVXXwlF2csvv4wff/wRtWvXLnJc165d8cEHH+Do0aOYN2+e3vhOnz6NmTNnQq1Ww8XFBfPnz9dbEHbo0AHDhg1DXFwcFi9ejBMnTpT4uQsPnQYEBKBbt25o2rQpvv32W2g0GixdupSFGRERkUhZ3OT/+Ph4LFy4EADg4uKC1atX6y20tIKDg4UCSi6X4/PPP0fh0dlDhw5hx44dAAB/f3+sXr1ab1Gmq3v37ti2bRs6dOhQ4P3c3FxMmzYNarUaUqkU//nPf4rtpdOqXbs2lixZgpkzZ5Z4XHFGjhyJWrVqAQCuX7+OlJSUMrVDREREls3iCrM1a9bg+fPnAID33nsPDRo0KPWcgQMHokuXLgCA2NhYoWdM67fffhO258+fDxcXF4NiqVy5Mnr06FHgvW3btuHp06cA8gumdu3aGdQWAAwePNjgY3VJpVI0a9ZMeP348eMytUNERESWzaIKM41Gg127dgEAnJyc8Oabbxp87ttvvy1sb9++XdiOiYnBtWvXAOQPfzZt2rRcMWrblkgkGD16dLnaMoa9/f9GndVq9Qu7LhEREb04FjXH7NatW8IdmG3atEGlSpUMPveVV16Bk5MT8vLycOHCBeH9c+fOCdtdu3YtV3xZWVm4efMmAKBBgwaoW7duudozhu6dn97e3i/sukREROXhAFW5zs/NyUF20dWmjObqapl3dhZmUYVZdHS0sN2kSROjzrWzs0NAQAAuX76M1NRUJCYmwsfHp0CbusOBZRETEyP0VpW3580Yx44dw61btwAAderUQY0aNV7YtYmIiMqjr9vDcp2/5vdfKySOOXPmVEg7pmZRhVlaWpqw7enpafT51apVE7bT09Ph4+NTYA00Dw+PcsWn25butUxBd7mMpUuXCu+/++67Jr0uERERmY9FFWa6i74aOkFfl+45WVlZFdKmLt22nJ2dy9WWPv7+/iXuHz58uFHz7oiIiMi6WFRhpjv+m5OTY/T5uue4ublVSJu6dNvKzc0tV1uGcnZ2RqtWrTBq1CgEBQW9kGsSERGReVhUYVa1alVhOzk52ejzddf3cnd3L/D/wvvLQretssRXGt2V/+3s7ODq6govLy+jngNKRERkSQ5kle9GuQ1TX4NXlfKNeFkTiyrMdIfybty4YdS5KpUKMTExAPLnkvn4+ADIXz1f6/r16+jUqVO54pNKpVCr1UbHZwhDHppORERkTeQoX+eCs4uL1dxRWREsah0zPz8/oVfq/PnzyMzMNPjcU6dOCcOLug8zb9u2rbB9/PjxcsXn5uYmPIXg3r17ePDgQbnaIyIiItJlUYWZRCLBoEGDAOQ/dHzz5s0Gn/vf//5X2B4yZIiw7e/vLyyTcf78eVy/fr1cMWofQq7RaLBu3bpytUVERESky6IKMwAYPXo0HBwcAADLli0zqFfq77//xrFjxwDk97p17969wP5JkyYJ2zNmzDD4JoBnz54hPDy8wHtDhw6Fl5cXgPyHrZ89e9agtgBg586dBh9LREREtsfiCrPatWvj888/B5B/F+WYMWMKLBJb2L59+/DFF18AAGQyGb7//ntIJJICx/Tq1Ut4TmVMTAzGjBmD+Pj4EuM4fvw4Xn/9dZw+fbrA+87Ozli0aJEw12zixIn4559/Smzr8ePH+OSTT/Dtt9+WeBwRERHZNoua/K8VGhqKhw8fYu3atXj8+DGGDh2KAQMGICgoCDVr1oRSqcTdu3exd+9eoXBycHDAokWLhDlghX311VfIyMjA0aNHceXKFfTt2xf9+vVD586dUatWLTg5OSE5ORk3btzAoUOHShzy7NixI7799lvMmTMHOTk5+PDDD9GiRQv06dMHjRs3RpUqVZCZmYlHjx7hxIkTCA8Ph1wuN+oRU0RERGR7LLIwA4CwsDA0bNgQP/30E9LT07Fr1y7hAeeF+fr64ptvvikw6b8wZ2dn/Prrr1i+fDlWrlyJ7OzsEtsEgG7duuGtt97Su2/o0KGoU6cO5s6di9jYWFy+fBmXL18utq2aNWvi008/LXY/ERERkcUWZgAwYsQIDBgwALt378axY8dw+/ZtpKamws7ODp6enggMDESPHj3Qt29fg9b6kkqleP/99zFixAj8/fffOHnyJGJjY5GWlgaFQoEqVaqgYcOGaNWqFQYOHAhfX98S22vXrh127dqFo0eP4tixY7h06RKSk5ORmZkJFxcXVK9eXYixa9eusLe36C83ERERmZlEo9FozB0EmY9cLkdUVBQAoHo9XwxcuNvMEREREf3PvrCh8HG3zHXMdH+HBgYGCjcvlofFTf4nIiIislUszIiIiIgsBAszIiIiIgvBwoyIiIjIQvA2QRJ4uDlhX9hQc4dBREQk8KzsbO4QXigWZiSwk0rh4+5k7jCIiIhsFgszEnDlFHHSaDRQKBQA8h9bVviRZSQOzLP4Mce2gXPMSKBUKs0dApmAQqFAVFQUoqKihB/qJD7Ms/gxx7aBhRkRERGRhWBhRkRERGQhOMeMBJyvIE5SqRTe3t7CNokT8yx+zLFtYGFGAj5kXZzs7e1Rp04dc4dBJsY8ix9zbBtYchMRERFZCBZmRERERBaCY1ckUKnVSEzPNncYRGQkz8rOsJNKIZfLER0djcaNG0Mmk5k7LKpgCoUCN2/eBADmWMRYmJEgNSsPAxfuNncYRGSkfWFD4ePuCiD/lzcXixYn3QVmmWPx4lAmERERkYVgYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGFeOGFmUqlQqtWreDv748hQ4aUeKxGo0H79u3h7+8Pf39/bN26tcTjd+zYIRz7119/Ce+HhoYK75dmzZo1wrEdO3ZEdHS0sO+XX34R9kVGRpbaFgAEBQXB398fQUFBevdPnz5daFP3v4CAALRp0wYDBw7E119/LdwiTUREROL1wgszOzs7tGzZEgAQHR2NrKysYo+9desW0tPThdcXLlwose3z588L223btjU6tj/++APz5s0DAHh5eWHdunUICAgwup2KoNFokJmZiZiYGPz1118ICQnBkiVLzBILEVkHe3t7NGzYkI9XEyltfpljcTNLZtu2bYuIiAio1WpcvHgRr776qt7jtIWWnZ0dVCpVgcKrpOOrVq2Kl156yaiYli9fjp9++gkA4OPjgzVr1qBBgwZGtVFeK1euFB5Qq1arkZKSgmPHjuGvv/6CUqnEb7/9Bm9vb4wcOfKFxkVE1kEqlaJq1armDoNMhPm1DWaZY9amTRthu6RiS7uvb9++AICHDx8iMTFR77EpKSm4f/8+AKB169aQSCQGx/PLL78IRVnNmjXx3//+94UXZQBQv359+Pn5wc/PDwEBAejUqRPCwsLwyy+/CMcsW7YMKpXqhcdGREREpmeWwqx58+ZwdHQEYFhh1qdPH9StWxdA8cOZZR3G/PHHH7Fs2TIAQO3atfHf//5XuJalCAoKEorZ1NRUXL9+3cwREZEl0mg0kMvlXBVepLT5ZY7FzSyFmYODA5o3bw4AiIqKglwuL3LMo0ePhN6x1q1bo3Xr1gCKL+R039ftkSvJwoUL8Z///AdAfm/V+vXrUatWLcM/yAuk/XoBwOPHj80YCRFZKoVCgaioKOGxPSQu2vwyx+JmtuUytMWTXC7HlStXiuzXFlr169eHp6enUJidO3dOb3vanjQ3Nzc0bty41Ot/++23WLVqFQCgYcOGWLduHapXr278B3lBdCd6qtVqM0ZCRC+aA1Ql/pebk4Ps7Gzk5OTg+fPnyPn/rwv/R0SWz2y3dbRt2xbLly8HkF+EFR5+1BZmrVq1KvD/W7duISMjA1WqVBGOzcrKEpa1aNmyJezs7Iq9rkajwddff40NGzYAAPz8/PDnn3/C09Ozgj6ZacTGxgrb2hsEiMg29HV7WOL+Nb//WuD1oUOH9B43Z86cCouJiEzDbIVZixYtYG9vD6VSqXd4UvuetmfN19cXVatWRVpaGi5evIju3bsLx166dEmYEF/a/LJZs2Zhy5YtAIDGjRtj1apV8PDwMDr+uLg4g+6OqYju5ujoaJw4cQIA4OzsjMDAwHK3SURERJbHbIWZq6srGjdujKioKKGw0vZ0Fb7DUqtVq1Y4cuQIzp8/X6AwM2Z+mbYo8/X1xZo1awr0vBnjyy+/LNN5htJoNEhOTsaxY8ewePFiofAMDQ0VbpwgIiIicTHrCnVt27ZFVFQUsrOzcfPmTTRr1gzA/+aReXp6on79+sLxrVu3FgozXdr5ZY6OjqX2JkkkEmg0GsTFxeHGjRvo2LFjBX6i8unRo0eJ+7t164YPP/zwBUVDREREL5pZC7PWrVsLE/DPnz8vFGbawku3twz4X2/Y9evXkZeXBycnJ8jlcly9ehUA8PLLL8PBwaHEa86aNQvffPMNnj9/jsmTJ+OPP/4w+C5OXWvXrkX79u1LPS4oKAjx8fFGt68lk8kQGBiI4cOHY9CgQUatz0ZE4nAgq+QlfDZMfQ1eVVygUChw48YNNGnSBDKZ7AVFR0QVyayFWZs2bYQerAsXLmDMmDEA/tcDpp3wr9WkSRM4OzsjNzcXly9fRocOHRAVFYXnz58L7ZVm5MiRyMvLw/fff4/c3FxMnDgRf/75Z4HlKMxFd+V/qVQKFxcXeHp6llpsEpG4yVH8DU0A4OziAldXV8jlcjg6OsLFxYU/N4islNmWywAAd3d3NGrUCMD/esl077As3GOm7T3SPV53wVlDF5YdN24cpkyZIlzv3XffLfCwcnPRXfn/pZdeQs2aNfnDlYgMpv0Zyd4ycdLmlzkWN7MWZgAKrGh/584dXLhwAWq1Gi4uLmjSpEmR4wsvNKudjyaTydCiRQuDr/vBBx9g/PjxAICMjAyMHTsWd+7cKc9HISIyK4lEAgcHB055ECltfpljcbOYwgzI7/3S9oC1aNFC73pk2sLsypUrkMvluHTpEoD8YU4XFxejrv3ZZ58hNDQUQP6doGPGjMGjR4/K9DmIiIiIysuiCrNz584JPWCFhzG1tAvI5uTkYPv27cjMzCzSjjHCwsLwxhtvAACSkpLw9ttv48mTJ2Vqi4jInNRqNdLS0vh0EJHS5pc5FjezF2Y+Pj7CQ8MjIyNx7do1AMUXZm5ubvDz8wMA/PHHH8L7xjy4XJdEIsHXX3+N4OBgAEB8fDzGjBmDp0+flqk9IiJzUSqVuHv3LpRKpblDIRPQ5pc5FjezF2bA/3q7EhMTIZfLYW9vX+J8MW3Rph12lEqlxRZyhpBKpVi4cCH69OkDALh//z7eeecdpKamlrlNIiIiImNZVGGm1bhxYzg7Oxd7fOEizM/PD5UrVy5XDHZ2dli8eDG6desGIP+ZnOPGjcOzZ8/K1S4RERGRoSyiMCs8DFla71fh/WUdxixMJpNh6dKleOWVVwAAN27cwPjx45GdnV0h7RMRERGVRKLRaDTmDoLMRy6XIyoqCgBQvZ4vBi7cbeaIiMhY+8KGwsfdVfh+DgwM5BqIIqT785o5tgymyIlF9JgREREREQszIiIiIoth1mdlEhFRxZLJZFwVXqQkEonwKCbmWLxYmBERiYSDgwOaN29u7jDIRGQyGfNrA1iYkcDDzQn7woaaOwwiMpJn5eKXFyIi68LCjAR2Uil83J3MHQYREZHN4uR/EnDlFHHSaDRQKpVQKpXMsYhpNBrI5XI8evSIj+sRKaVSiUePHjHHIsfCjAT8RhcnhUKBK1eu4MqVK1AoFOYOh0xEoVAgKioKSUlJfMC1SKnVaiQlJTHHIsfCjIiIiMhCsDAjIiIishAszIiIiIgsBAszIiIiIgvB5TJIYG/Pfw5iJJPJEBgYKGyTODHP4scc2wb+JiYBH/EhThKJBA4ODuYOg0yMeRY/5tg2sDAjgUqtRmJ6trnDICIiC+FZ2Rl2Us56epFYmJEgNSsPAxfuNncYRERkIfaFDYWPu6u5w7ApLIOJiIiILAQLMyIiIiILwcKMiIiIyEKwMCMiIiKyECzMiIiIiCyE1dyVmZOTg127diE8PBzR0dFIT0+HRqOBm5sbatWqBT8/P7Rs2RJdunRBjRo1Smzr8ePH+Pvvv3Hq1Ck8ePAAqampUKvVqFKlCvz8/NC6dWsEBwejTp06Rc6NjIzE6NGjAQAffPABpkyZUmrs06dPx44dOwAAR44cQe3atQvs3759O2bMmAEAmD9/PkJCQgz6mhAREZG4WEVhdunSJXzyySd4/PhxkX1paWlIS0vDtWvXsH37dnh6euLkyZN623n+/DkWL16MDRs2QC6XF9mflJSEpKQkREREYOnSpejbty+++OKLUgs9IiIioopg8YXZvXv3MG7cOGRn5y98GhQUhD59+qBBgwaQyWRIS0tDdHQ0Tp06hcjIyGLbSU1NxeTJk3H58mUAgKurK1577TV06NAB1atXh729PZKTk3Hx4kUcOnQI9+/fx/79+9GiRQuMGTPmBXxSIiIisnUWX5gtWbJEKMqKG+br1KkTxo0bh9TUVOzfv7/IfrVajY8//lgoyrp3747vvvsO1apVK3JsUFAQPvnkE+zevRvff/99xX4YIiIiohJYdGGmUqlw/PhxAECzZs1KnXvl4eGBkSNHFnl/7dq1Qm9a586dsWzZshIf2C2VSjF48GB06NAB9+7dK8cnICIiIjKcRRdmqampyMvLAwDUq1evTG3I5XKsXLkSAODo6Ih58+aVWJTpql69OqpXr16m6xIREREZy6ILM5lMJmzfuXOnTG1EREQgKSkJANC3b1/4+PhUSGxERETWxgEqo47PzclBtqz043S5uvLZmuVh0YWZu7s7atWqhfj4eERHR2PFihV49913ITXiSffnzp0Ttrt162aCKImIiKxDX7eHRh2/5vdfjb7GnDlzjD6H/seiCzMAGDVqFBYuXAgAWLx4MTZu3IigoCC0atUKgYGBetca0xUdHS1sN23atEJjS0lJQWxsbKnHPXv2rEKvS0REROJk8YXZmDFjcPv2bWzbtg0AEB8fj3Xr1mHdunUAAE9PT7Rr1w7BwcHo3r07JBJJgfPT09OFbX13YZbHhg0bsGHDhgptk4iIiGyXxT+SSSqVYt68eVi1ahW6dOlSZOJ+cnIy9u3bh8mTJ+P111/Hw4cFu2m1S20AgLOz8wuJmYiIiKgsLL7HTKtTp07o1KkTsrKycOHCBURFReHatWs4f/48MjMzAQDXrl3DW2+9he3bt8Pb2xtAwUmIubm5cHNzq7CYyvJIJiIiInM5kFXXqOM3TH0NXlVcTBQN6WM1hZmWm5sbunbtiq5duwLIXw5jz549WLhwITIyMvD06VP8/PPP+O677wDk30CglZycXKGFGRERkTWRw86o451dXHiX5Qtm8UOZpXFwcMDQoUOxePFi4b1Dhw5BrVYDAAICAoT3b9y48cLjIyIiIjKU1RdmWl26dBEeNp6RkSFM+m/btq1wzLFjx8wQGREREZFhRFOYARDmlenq3Lmz8P6BAweQmJj4osMiIiIiMohoCrPc3Fzcvn0bQP48tKpVqwLIH+ocN24cAOD58+f48ssvoVIZtvJxYmIiTp8+bZqAiYiIiAqx6MIsOzsbw4YNw9GjR4U5Y/qo1WrMnTtXWBojKCiowHpmo0ePRvv27QHkP6Lp/fffR2pqarHtaTQa7NmzByEhIYiJiamgT0NERERUMou/K/Pq1auYNGkSfHx80LNnT7Ro0QI1a9aEm5sbnj17hhs3bmDbtm3CCvyVKlXCRx99VKANqVSKn376CZMmTcKVK1dw9OhR9OzZE8HBwejQoQOqV68Oe3t7JCcn4/Llyzh48CDu3r1rjo+LCxcuGHRcUFBQgTtOiYiIyPpZdGFmb28PLy8vPH36FImJiVi/fj3Wr19f7PH169fH4sWLUbt27SL7PDw8sG7dOvzwww/YsGEDsrOzsXHjRmzcuFFvWxKJBMHBwejXr1+FfR5DbN26FVu3bi31uJ07d7IwIyIiEhmLLswcHR3x77//4vLlyzh16hSuXLmCe/fuISUlBc+fP4ezszO8vb0REBCAHj16oHfv3nBwcCixvbCwMLzzzjvYu3cvTp8+jfv37yM1NRUajQZVqlSBn58f2rZti+DgYNSqVesFfloiIiKydRKNRqMxdxBkPnK5HFFRUQCA6vV8MXDhbjNHRERElmJf2FD4uHOB2eLo/g4NDAwssXPIUBY9+Z+IiIjIlrAwIyIiIrIQLMyIiIiILIRFT/6nF8vDzQn7woaaOwwiIrIQnpWdzR2CzWFhRgI7qRQ+7k7mDoOIiMhmsTAjAW/QFSeNRgOFQgEAkMlkBZ6KQeLBPIsfc2wbOMeMBEql0twhkAkoFApERUUhKipK+KFO4sM8ix9zbBvYY0YCmUxm7hDIBBwcHNC6dWtzh0EmxjyLH3NsG9hjRkRERGQhWJgRERERWQgOZZJApVKZOwQyAZVKheTkZACAp6cn7OzszBwRmQLzLH7MsW1gYUYCtVpt7hDIBFQqFeLi4gAAVatW5Q9zkWKexY85tg0cyiSBvT3rdCIiInNiYUYCrolDRERkXuwiIYFKrUZiera5wxAlz8rOsJPy7yAiIioZCzMSpGblYeDC3eYOQ5T2hQ2Fj7urucMgIiILxz/hiYiIiCwECzMiIiIiC8GhTCKRk0gkcHV1FbZJnJhn8WOObQMLMyKRk8lkCAgIMHcYZGLMs/gxx7bBqgqzs2fPIjQ0VHi9YcMGtGrVyqg2Hj58iP379+P06dN4+PAh0tLSoFQqUalSJdSuXRtNmjRBt27d0KlTp2If6r19+3bMmDHDqOvOmDEDY8aMKfBeaGgozp49W+RYqVSKSpUqoU6dOmjXrh3efPNN1KtXz6jrERERkfWxqsJsx44dBV7v3LnT4MLs2bNnWLBgAXbt2gWlUllkf0pKClJSUnDlyhVs2LAB1apVw/jx4zF69OgXvrqyWq1GRkYGMjIycO3aNaxbtw4zZszAyJEjX2gcRERE9GJZTWGWl5eHf/75BwDg4uKCnJwcHDhwADNnzoSDg0OJ5z569Ajjx4/HvXv3AADu7u4YMGAA2rRpA29vbzg7OyM1NRV3795FREQETp06hZSUFCxYsACvvfYavLy8im37448/Ro8ePUqNv6Q2AGDPnj3CtlqtRkJCAg4cOIAdO3ZAoVBg7ty5qFWrFrp161bqtYh0KZVKPHjwAABQr149PuFBpJhn8WOObYPVZPXQoUPIzs5f/HTmzJn48ssvkZGRgfDwcPTt27fY83JycjBx4kShKHvjjTfwxRdfwM3NrcixXbp0wdtvv41Hjx7h119/xfbt20uNy8fHB35+fmX8VP9TuI2AgAB069YNTZs2xbfffguNRoOlS5eyMCOjqdVqpKenAwDq1Klj3mDIZJhn8WOObYPVLJexc+dOAIC/vz+GDh2KBg0aFHi/OD/88APu3LkDAHjrrbcwd+5cvUWZrjp16mD+/PlYvnw5HB0dyx17eYwcORK1atUCAFy/fh0pKSlmjYeIiIhMxyoKs6SkJJw+fRoAMHDgwAL/j4iIQGpqqt7zkpOTsXXrVgBAjRo18MUXXxh13aCgIFSuXLmsYVcIqVSKZs2aCa8fP35sxmiIiIjIlKyiMNuzZw9UKhWkUimCg4MBAMHBwZBIJFAoFNi7d6/e8/bt24fnz58DAIYNGwYnJ6cXFnNF0p1HoFarzRgJERERmZJVzDHbtWsXAKBdu3bw8fEBkD/c2LJlS1y8eBE7d+7E6NGji5x37tw5YbtLly4vJlgTiI2NFba9vb3NGIltcICqwtvMzclBtv7VV8pFu9gkERGJg8UXZjdv3kRMTAyA/w1fag0cOBAXL17E9evXcfv2bbz00ksF9mvPk0qlJluULzExsUDhVJyy3iBw7Ngx3Lp1C0B+MVqjRo0ytUOG6+v2sMLbXPP7rxXeJgDMmTPHJO0SEZF5WHxhpp3c7+TkhD59+hTY169fP3z33XdQKBTYuXMnPvvsswL709LSAACVKlUqcUmNp0+fCscWVqVKFaGXTp+ffvoJP/30U6mfQ1skGkJ3uYylS5cK77/77rsGt0FERETWx6ILM6VSKcwf6969e5G7Kd3d3dG1a1ccPnwYe/bswSeffAKp9H/T5rTLazg7O5d4nRUrVmDt2rV69w0ZMgQLFiwoz8cwiL+/f4n7hw8fjjfffNPkcZD4SKVSYQhc9/uDxIV5Fj/m2DZYdGEWERGB5ORkAEWHMbUGDhyIw4cPIyEhAZGRkejYsaOwz9XVFc+ePUNubq7JYpw/fz5CQkJM0razszNatWqFUaNGISgoyCTXIPGzt7fnmkc2gHkWP+bYNlh0YaYdxnR3dy928n737t1RuXJlPHv2DDt37ixQmLm7u+PZs2fIzMyEXC4vdjgzLCwMYWFhwuu4uDiDVvOvSLor/9vZ2cHV1RVeXl4v/HFQBBzIqlvhbW6Y+hq8qrhUeLtERCQuFluYZWZmIjw8HACQnp5eYC2v4hw8eBBz5syBi0v+L8CAgAA8fPgQarUaMTExCAwMNGnM5VERTw+giiFHxRfDzi4uvIOSiIhKZbGD1Pv37xfWIDNUTk4ODh48KLxu27atsH3ixIkKi43ImigUCly9ehVXr16FQqEwdzhkIsyz+DHHtsFie8y0w5heXl6YMWNGqcd///33SEhIwK5duzB48GAAQP/+/bFo0SLI5XJs2bIF48aNM/sjloheNI1GI/wQ12g0Zo6GTIV5Fj/m2DZYZGH26NEjXLx4EQDQp08fDBgwoNRzLl++jLVr1+LMmTNITEyEj48PPD09MXToUGzYsAGPHz/GokWLMHPmTFOHT0RERFQmFjmUuWvXLuGvgcJrlxVHe5xarRaeFAAAn332mfDA83Xr1uGrr74SltEozrNnz8oSNhEREVG5WGSPmbawqlatGtq0aWPQOa1atYKXlxeePn2K3bt3Y8KECQAANzc3/Oc//8GECRNw//59bNiwAQcOHMBrr72GNm3awNvbGy4uLsjKysKDBw9w6tQpHDp0SGi3tOdrGrryv5ubG2rWrGnQZyEiIiLbZHGF2YULF/DwYf4jcXr27GnwInpSqRS9evXCX3/9hVu3buHatWvCnZz16tXD5s2bMX/+fOzevRtpaWlYt24d1q1bV2x71apVw7vvvovQ0NASr2voyv89evTAr7+a5rE8REREJA4WV5jpDkMaOoyp1bt3b/z1118A8m8e0F1io0qVKliwYAEmT56Mffv24cyZM3jw4AHS09OhUqlQuXJl1KhRA82aNUPnzp3RtWtXyGQmeOo0ERERUTEkGt7aYdPkcjmioqIAANXr+WLgwt1mjkic9oUNhY+7edYx081xYGBgic+NJevFPIsfc2x5TJETi+sxI6KKZW9vj4YNGwrbJE7Ms/gxx7aBmSUSOalUiqpVq5o7DDIx5ln8mGPbYJHLZRARERHZIvaYEYmc7mrhMpkMEonEzBGRKTDP4scc2wYWZiTwcHPCvrCh5g5DlDwrO5vt2gqFghOGbQDzLH7MsW1gYUYCO6kUPu4lL6hLREREpsM5ZiTgyilERETmxcKMBEql0twhEBER2TQWZkREREQWgoUZERERkYVgYUZERERkIViYEREREVkILpdBAj57TZxkMhkCAwOFbRIn5ln8mGPbwN/EJOAq0uIkkUi4EKUNYJ7Fjzm2DRzKJCIiIrIQ7DEjgUqtRmJ6tlmu7VnZGXZS/p1gCmq1GhkZGQCAKlWqQMqvsygxz+LHHNsGFmYkSM3Kw8CFu81y7X1hQ+Hj7mqWa4udUqnE3bt3AfD5emLGPIsfc2wbWG4TERERWQgWZkREREQWgoUZERERkYVgYUZERERkIWxy8n9OTg527dqF8PBwREdHIz09HRqNBm5ubqhVqxb8/PzQsmVLdOnSBTVq1Chw7vTp07Fjxw6jrrdz5040btxYeP3BBx/g0KFDAIDVq1ejY8eOJZ5/5MgRvPfeewCAwYMHY+HChUZdn4iIiKyDzRVmly5dwieffILHjx8X2ZeWloa0tDRcu3YN27dvh6enJ06ePFnhMcyePRuRkZF49uwZZs+ejT179sDJyUnvsZmZmfjqq68AANWqVcOMGTMqPB4iIiKyDDZVmN27dw/jxo1Ddnb+Wl1BQUHo06cPGjRoAJlMhrS0NERHR+PUqVOIjIwstb2VK1fC29u71OPq1atX4LW3tzemTZuGWbNm4eHDh/j555/xxRdf6D33+++/R1JSEgBg5syZcHd3L/V6RLokEonw+BY+3UG8mGfxY45tg00VZkuWLBGKsvnz5yMkJKTIMZ06dcK4ceOQmpqK/fv3l9he/fr1Ubt27TLFMmzYMOzZswdnz57FmjVrMGDAADRr1qzAMZGRkdiyZQuA/CKyf//+ZboW2TaZTIbmzZubOwwyMeZZ/Jhj22Azk/9VKhWOHz8OAGjWrJneokyXh4cHRo4cabJ4JBIJvv32Wzg6OkKlUiEsLAxKpVLYn5eXh1mzZglz37TDmURERCReNlOYpaamIi8vD0DRoUVzqVevHj744AMAQHR0NP744w9h39KlS/HgwQMAwLRp0+Dj42OWGImIiOjFsZnCTDsuDwB37twxYyQFjR07Fk2aNAEA/N///R/u3r2La9euYfXq1QCAtm3bYvjw4WaMkKydUqnEo0eP8OjRowK9siQuzLP4Mce2wWbmmLm7u6NWrVqIj49HdHQ0VqxYgXfffdfsD4G1t7fHt99+i2HDhkEul2PmzJnIycmBSqWCo6Mj5s6da/WTPB2gKvWY3JwcZMtKPQwA4OrKZ2oaQ61WCzeQsOdVvJhn8WOObYPNFGYAMGrUKGENsMWLF2Pjxo0ICgpCq1atEBgYiDp16hjV3v3795GTk1PiMc7OzqW227RpU4wZMwYrV67EhQsXhPc/+OADNGjQwKiYLFFft4elHrPm918Nbm/OnDnlCYeIiMhi2VRhNmbMGNy+fRvbtm0DAMTHx2PdunVYt24dAMDT0xPt2rVDcHAwunfvXmpP1bhx40q9Zrt27YT2S/Lhhx9i3759ePLkCQCgSZMmGDt2bKnnERERkXjYzBwzAJBKpZg3bx5WrVqFLl26wN6+YF2anJyMffv2YfLkyXj99dfx8GHpPT0V5cGDB0hOThZee3h4FImPiIiIxM0mf/N36tQJnTp1QlZWFi5cuICoqChcu3YN58+fR2ZmJgDg2rVreOutt7B9+/ZiF5E9cuRImdcx06VdLkOhUAjvRUREYM+ePQgODi53+0RERGQdbLIw03Jzc0PXrl3RtWtXAIBcLseePXuwcOFCZGRk4OnTp/j555/x3XffmTSOtWvXIioqCkD+8OiePXuQlJSEefPmoXPnzqhatapJr29qB7LqlnrMhqmvwauKywuIhoiIyHLZ1FBmaRwcHDB06FAsXrxYeO/QoUNQq9Umu+ajR4/w888/AwD8/PwwdepUzJo1C0D+2mtieGC5HHal/ufs4gJXV1eD/iMiIhIrFmZ6dOnSBTVq1AAAZGRkID093WTXmj17NnJzcyGVSvHdd99BJpOhd+/e6NmzJwBgx44dOH36tMmuT+InlUrh7u4Od3d3sy8PQ6bDPIsfc2wbmNliGPJw8vLatm0bTp06BQAIDQ0t8Ay02bNnw83NDUD+8hDPnz83eTwkTvb29vD19YWvry9vKBEx5ln8mGPbwMJMj9zcXNy+fRtA/jw0U8zxSk5OFoYpa9WqhY8//rjAfh8fH3z66acA8u/YXLZsWYXHQERERJbFZgqz7OxsDBs2DEePHi1xzpharcbcuXORnZ0NAAgKCjLJyvvffPMNMjIyAABff/01XFyKTnwfMWIEWrVqBQBYtWoVoqOjKzwOIiIishw21Rd69epVTJo0CT4+PujZsydatGiBmjVrws3NDc+ePcONGzewbds2xMbGAgAqVaqEjz76qNj2DFn5HwC8vLwK9LodPnwY//zzDwBg0KBB6NKli97zJBIJ5s6di8GDB0OhUGDmzJnYvHkz5xaQURQKhfB8WF9f3wLPjSXxYJ7Fjzm2DTZTmNnb28PLywtPnz5FYmIi1q9fj/Xr1xd7fP369bF48eIS1ykzZOV/AJgxYwbGjBkDAMjMzMTXX38NIH8R2RkzZpR47ksvvYSJEydi2bJliIqKwrp16/D2228bdF0iANBoNEIPsEajMXM0ZCrMs/gxx7bBZgozR0dH/Pvvv7h8+TJOnTqFK1eu4N69e0hJScHz58/h7OwMb29vBAQEoEePHujduzccHBwqPI7vv/9eeAjtl19+adD8tYkTJ2L//v24c+cOfvrpJ/Tq1Qs1a9as8NiIiIjIvGymMAPybzVu1aqVMG+rLBYsWIAFCxaU+fy5c+di7ty5Rp3j4OCAffv2lfmaREREZB04WYmIiIjIQrAwIyIiIrIQLMyIiIiILAQLMyIiIiILYVOT/6lkHm5O2Bc21CzX9qzsbJbr2gI7Ozth2Rc7OzszR0OmwjyLH3NsG1iYkcBOKoWPu5O5w6AKZmdnBx8fH3OHQSbGPIsfc2wbOJRJREREZCFYmBERERFZCA5lkkChUJjkaQdkXnK5HFFRUQCAwMBA5likmGfxY45tA3vMiIiIiCwECzMiIiIiC8HCjIiIiMhCsDAjIiIishAszEhgb897QYiIiMyJhRkJJBKJuUMgIiKyaewiIYFKrUZiera5wxAdz8rOsJPybyAiIiodCzMSpGblYeDC3eYOQ3T2hQ2Fj7ur2a5vb28Pf39/YZvEiXkWP+bYNjCzRCInlUrh5uZm7jDIxJhn8WOObQPHV4iIiIgsBHvMiEROrVYjJycHAODi4gIp57uJEvMsfsyxbWBWiUROqVQiJiYGMTExUCqV5g6HTIR5Fj/m2DaIqscsMjISo0ePFl67uLjg1KlTcHZ2LvG8vLw8dOrUCVlZWcJ7a9euRfv27Us87+zZswgNDRVeb9iwAa1atTI6Tl1OTk5wd3dHQEAAevXqhYEDB/JBtURERDZC1D1mOTk5OHz4cKnHHTlypEBRZqgdO3YUeL1z506j2ygsLy8PCQkJOHbsGMLCwhASEoK4uLhyt0tERESWT7SFmaOjIwBg165dpR6rPUZ7jiHy8vLwzz//AMjvmQOAAwcOQC6XGxXniBEjsGfPHuG/1atXIywsDNWrVwcA3Lp1C5MnT4ZKpTKqXSIiIrI+oi3MgoKCAACnTp3C06dPiz0uJSUFJ0+eBAD06NHD4PYPHTqE7Oz8xVhnzpwJAMjIyEB4eLhRcVarVg1+fn7Cfx07dsTo0aPx999/o1atWgCA2NhYHDp0yKh2iYiIyPqItjDr1KkTvLy8oFKp8Pfffxd73N69e6FUKuHl5YVXXnnF4Pa1w5b+/v4YOnQoGjRoUOD98nJzc8PkyZOF16dOnaqQdomIiMhyibYws7Ozw4ABAwCUPJyp3ffaa6/Bzs7OoLaTkpJw+vRpAMDAgQML/D8iIgKpqalljluXdoVnAEhISKiQNomIiMhyibYwA4BBgwYBAG7cuIFbt24V2X/79m1cv369wLGG2LNnD1QqFaRSKYKDgwEAwcHBkEgkUCgU2Lt3bwVED8hkMmGbj98gIiISP1H/tm/SpAkaNWqEW7duYdeuXfjss88K7Nf2lvn5+aFx48a4efOmQe1qz2vXrh18fHwAAHXq1EHLli1x8eJF7Ny5s9jlMIxx584dYVs734zMywHG34SRm5ODbFnpx+nj6lr+Z2w6ODigdevW5W6HLBvzLH7MsW0QdWEG5PeE/fDDD9i7dy8+/fRTSCQSAIBGo8GePXuEYwx18+ZNxMTEAPjf8KXWwIEDcfHiRVy/fh23b9/GSy+9VOa4VSoVVq5cKbzu06dPmduiitPX7aHR56z5/dcyX2/OnDllPpeIiKyPqIcygfxiSSqV4smTJ4iMjBTej4yMxJMnTwoMRxpCO7nfycmpSLHUr18/YfixrDcBpKam4vTp0xg1ahRu3LgBIL8oa9OmTZnaIyIiIush+sLMx8dHWMFf9yYA7XaHDh2E4cjSKJVKYf5Y9+7d4ebmVmC/u7s7unbtCiB/HpparS61zWXLlsHf31/4r2PHjhgzZgwuXrwIZ2dnvPPOO1i8eLFB8RHpo1KpkJiYiMTERK6HJ2LMs/gxx7ZB9IUZAAwePBgAcPDgQeTl5RVYHNaYYcyIiAgkJycDKDqMqaV9PyEhoUAPXVkEBAQgNDS0wE0ARMZSqVSIi4tDXFwcf5iLGPMsfsyxbRD9HDMA6NWrF7766itkZWXhyJEj0Gg0yM7OhouLC3r37m1wO9rhSXd3d3Tp0kXvMd27d0flypXx7Nkz7Ny5Ex07diyxzREjRuCtt94CkP9Nl5CQgH/++Qe7du3CpUuXEBoaiq1bt8LDw8PgOMl0DmTVNfqcDVNfg1cVFxNEQ0REYmMThZmrqyt69uyJPXv2YNeuXdBoNACAnj17Co9TKk1mZqawqn96ejqaNWtW6jkHDx7EnDlzSryGduV/rcaNG6N79+5o3749pk+fjvj4eISFhWH58uUGxUmmJYdha93pcnZxqZC7K4mISPxsYigT+N9w5smTJ4VV9LXvGWL//v14/vy5UdfMycnBwYMHjTpHa8iQIcLNBeHh4cKCtkRERCReNtFjBgAdO3aEl5eX8NxMb2/vUocZdWmHMb28vDBjxoxSj//++++RkJCAXbt2GVUA6po6dSoOHz4MlUqFJUuWGBUvERERWR+bKczs7OwwaNAgrF27FkD+pH+p1LAOw0ePHuHixYsA8peu0D7qqSSXL1/G2rVrcebMGSQmJhp856euBg0aoF+/fti7dy+uXLmCkydPolOnTka3Q0RERNbBZoYyAWDatGmIiopCVFRUkacAlER3XpqhC71qj1Or1SU+q7M0EydOFBbF5TwzIiIicbOpwqystIVVtWrVDF7otVWrVvDy8gIA7N69u8zX9vPzQ1BQEADg3LlzOH/+fJnbItskkUjg6uoKV1dXocgn8WGexY85tg0szEpx4cIFPHyY/xienj17Gjz8KZVK0atXLwDArVu3cO3atTLHMGnSJGGbvWZkLJlMhoCAAAQEBHBNPBFjnsWPObYNLMxKoTsMaezzKnXXSCvrI5oAoHnz5sLcsoiICFy9erXMbREREZHlkmi0k6fIJsnlckRFRQEAqtfzxcCFZR92Jf32hQ2FjzvXMSMiEhvd36GBgYFwcHAod5s2c1cmka1SKpV48OABAKBevXqwt+e3vRgxz+LHHNsGDmUSiZxarUZ6ejrS09OhVqvNHQ6ZCPMsfsyxbWBhRkRERGQhWJgRERERWQgOUJPAw80J+8KGmjsM0fGs7GzuEIiIyEqwMCOBnVQKH3cnc4dBRERksziUSQKunEJERGReLMxIoFQqzR0CERGRTeNQJgn47DVxkkql8Pb2FrZJnJhn8WOObQMLMxJwsUJxsre3R506dcwdBpkY8yx+zLFtYMlNREREZCFYmBERERFZCI5dkUChUFTIA1jJsigUCty8eRMA0LhxY8hkMjNHRKbAPIsfc2wbWJiRwM7OztwhkAloNBooFAphm8SJeRY/5tg2cCiTBLwrk4iIyLxYmBERERFZCBZmRERERBaChRkRERGRhWBhRkRERGQhWJgRERERWQgul0Ekcvb29mjYsKGwTeLEPIsfc2wbrC6zkZGRGD16tN59Tk5OcHd3R0BAAHr16oWBAweWuGCqv7+/UdcOCAjArl27Cry3fft2zJgxAwAwf/58hISEGNVmUFAQ4uPji7xvb28PNzc3NGzYEK+88greeOMN+Pj4GNW2sbhchjhJpVJUrVrV3GGQiTHP4scc2wZRDWXm5eUhISEBx44dQ1hYGEJCQhAXF2fusMpEqVQiPT0dFy9exLJly9C/f38cOnTI3GERERGRCVldj5muESNG4K233hJep6Sk4NatW1i5ciUSEhJw69YtTJ48GTt37ixxVftmzZph/vz5pV7PycmpQuLWx9vbGytXrhReK5VKxMfHY9u2bTh69CiysrIwdepUbN68GU2aNDFJDFxJWpx0VwuXyWTsGRUp5ln8mGPbYNWFWbVq1eDn51fgvY4dOyIkJAQDBw5EfHw8YmNjcejQIfTt27fYdlxcXIq086LJZLIiMTRp0gS9evXCggUL8Oeff0KhUGD58uX45ZdfzBQlWSOFQoGoqCgAQGBgIJ+HKlLMs/gxx7ZBVEOZWm5ubpg8ebLw+tSpU2aMpvymTJki9NZFRERArVabOSIiIiIyBVEWZkDBif0JCQlmjKT8XF1d4evrCwDIyclBenq6eQMiIiIikxBtYSaTyYRtMdxWrPt52GNGREQkTqItzO7cuSNs16pVy4yRlJ9SqcTdu3cB5Bdo7u7u5g2IiIiITML6u5L0UKlUBe5w7NOnT4nH5+TkIDY2ttR2q1evjsqVK5c7PmNt2rQJz549AwC0bt1aFD2AREREVJSofsOnpqYiJiYGS5cuxY0bNwDkF2Vt2rQp8bxr164hODi41PbLsoBsWWmXy9i+fTt+//134f1x48a9kOsTERHRi2fVhdmyZcuwbNkyvfucnZ3x5ptv4tNPP33BUZVNfHx8iU8ikEgk+Oijj/Dqq6++wKiIiIjoRbLqwqwkAQEBCA0NLTBpvjjt2rXDunXrXkBUxnNzc0OHDh3wzjvvlNrzR6SPTCZDYGCgsE3ixDyLH3NsG6y6MNNd+V+lUiEhIQH//PMPdu3ahUuXLiE0NBRbt26Fh4eHmSMtXeGV/+3s7FCpUiV4eXm9sNWduYq0OEkkEi5EaQOYZ/Fjjm2DVRdmhVf+b9y4Mbp374727dtj+vTpiI+PR1hYGJYvX27GKA2jb+V/IiIisi2iXC5jyJAhwp2Y4eHhOH36tJkjsg58VqY4qdVqpKWlIS0tjWvgiRjzLH7MsW0QZWEGAFOnThUeXL5kyRIzR0NkPtp18O7evQulUmnucMhEmGfxY45tg2gLswYNGqBfv34AgCtXruDkyZNmjoiIiIioZKItzABg4sSJwoR2a5hnRkRERLbNqif/l8bPzw9BQUE4cuQIzp07h/Pnz+tdcsLQlf8B4KWXXoJUqr+evXDhgkFtBAUF8bFKREREVISoCzMAmDRpEo4cOQIgv9dMd0kKLUNX/geAc+fOFftYpq1bt2Lr1q2ltrFz504WZkRERFSEqIcyAaB58+bo1KkTACAiIgJXr141c0RERERE+lldj1n79u0RExNj1DmrVq3S+76x7egTEhJSrudnhoeHlzsGIiIiEgerK8yIyDgSiUR4fAuf7iBezLP4Mce2gYUZCfiNLk4ymQzNmzc3dxhkYsyz+DHHtkH0c8yIiIiIrAULMyIiIiILwaFMEvBZmeKkVCrx5MkTAECNGjVgb89vezFinsWPObYNzCqRyKnVaiQlJQEAfHx8zBwNmQrzLH7MsW3gUCYJ2GNGRERkXizMSKBSqcwdAhERkU1jYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGF4HIZJOCaOOIklUrh7u4ubJM4Mc/ixxzbBv4mJgGflSlO9vb28PX1NXcYZGLMs/gxx7aBhRkJVGo1EtOzy3SuZ2Vn2PEvOCIionJhYUaC1Kw8DFy4u0zn7gsbCh931wqOiIiIyLawMCMSOYVCgTt37gAAfH19IZPJzBwRmQLzLH7MsW1gYUYkchqNBtnZ2cI2iRPzLH7MsW3gpCAiIiIiC8HCjIiIiMhCsDAjIiIishBWP8dMLpfj4MGD+Pfff3H16lWkpaUhKysLbm5uqFWrFgIDA9GnTx906NCh1AX5jh8/jsOHD+PChQtITk5Gbm4uPDw8ULduXbz66qsYOHAgfHx8ij1/wIABuH37Ntzd3XHmzJkS1wUbPHgwbt68CQB477338NFHHxV77NmzZxEaGgoAePfddzFt2rQSPwcRERFZJ6suzA4ePIgFCxYgPj6+yL709HSkp6fj+vXr2LhxI+rXr48ZM2agW7duRY69desWZs2ahUuXLhXZl5CQgISEBJw9exa//vorJkyYgEmTJuktulq3bo3bt28jPT0dt2/fRqNGjfTGnZmZiZiYGOH1+fPnS/ycuvvbtm1b4rFERERkvay2MPu///s/LF26VHjdqVMnBAUFwdfXF5UrV0ZGRgbu3buH8PBwnDp1Cvfv38eSJUuKFGbnz5/H5MmT8ezZMwBA06ZNMWTIEPj7+8PFxQWJiYk4ceIEdu7ciZycHPz000+4desWFi5cWORW5bZt22LTpk1Cu8UVZhcvXoRarYadnR1UKhWuXr0KhUJR7K3PFy5cAJD/CI7WrVuX6etFREREls8qC7Nt27YJRVm1atXw008/oV27dkWOe+WVVzBy5EjExsZi/vz5SE1NLbA/ISEB77//Pp49ewaJRIJp06Zh7NixBXrDmjVrhh49emDcuHF4//33ERMTg7///hs1atQoMqSo25t1/vx5jBgxQm/82h6wV199FZGRkcjJyUFUVBRatWpV5FiVSiX05Pn7+6NSpUqGfImIBHZ2dqhdu7awTeLEPIsfc2wbrG7yf2JiIubOnQsAcHFxwbp16/QWZbr8/PywcuVKjB07tsD7s2bNQnp6OgDgo48+wrhx44qdF1anTh38+eef8Pb2BgCsXLkSV65cKXBM9erVUatWLQAlD09q97Vr1w4vv/xyicffvHlTWLemTZs2JX1MIr3s7Ozg4+MDHx8f/jAXMeZZ/Jhj22B1hdnq1auRm5sLAPjwww8NfqCrVCrFoEGDhNfR0dH4999/AeQXbhMmTCi1jWrVqmHGjBkA8hf3++OPP4oco+01S0hIQFxcXJH9z58/R1RUFID8Qks7NKkdriyM88uIiIhsh1UVZhqNBjt27ACQ31s2bNiwMrelbQcARo0aZfBfH3369BHuzDx8+DAyMjIK7Nft1dLXC3blyhUoFAo4OzujSZMmQmGmnXdWmG7BxsKMiIhI3KyqMLt16xbS0tIA5N8B6ebmVua2zp07J2x3797d4PPs7OyEGwjUanWROzkLzzMrTPte8+bNYW9vj5dffhl2dnZ49uwZYmNjixyvLcwaNmwIDw8Pg+OsKA5QGfRfbk4OsrOzDf6PXhy5XI4LFy7gwoULkMvl5g6HTIR5Fj/m2DZY1eT/6OhoYbtp06blaktbBHl5eQnzxgzVpEmTAjHp3ulZv359eHl54enTp3oLM22hpe1Zc3V1RUBAAK5fv47z588jICBAOPbu3btISUkBYL7esr5uDw06bs3vvxrV7pw5c8oSDhERkahZVY+ZdqI+kD/fq6wyMzOhUCgAAJ6enkafr3uObkxa2uHJe/fuCYUVUPAOS91lL7TbhQs53WFMTvwnIiISP6sqzHSHwJydnSukHRcXF6PP1z0nKyuryP7ihjNv3LiB7Oxs2NnZoUWLFsL7xRVmnPhPRERkW6yqMHN1dRW2tXdmlrednJwco8/XPUffPLfibgDQ9oAFBAQUiEF7/NOnT/HgwYMi59aqVQs1atQwOk4iIiKyLlY1x8zd3V3Y1h0iNJabmxvs7e2hVCqRnJxs9Pm65+jGpOXn54fKlSvj2bNnBYYjtYVW4dX7PT09Ua9ePTx48ADnz59HvXr1kJiYKCy3Yc5hzANZdQ06bsPU1+BVxfjeRyIiIvofq+ox050Yf/369TK3I5FI4O/vDyC/l+rp06dGnX/jxg29MWnpPjopOjpaGO7UFmn6HqtUeDjTUoYx5bAz6D9nFxe4uroa/B8REREVZVWFWaNGjVC1alUA+UWOvvldhtLthQoPDzf4PLVajePHjwPIL8D0PUZJt32VSoWLFy/izp07wiOh9PWAadvRFmS6y3lw4j8REZFtsKrCTCKRYMiQIQDy53lt2bKlzG2FhIQI2+vXr4dKpTLovH/++QcJCQkAgJ49e6Jy5cp6j9Mtpi5cuCAUXPXq1dN7J6i2x+zhw4dISkoSete8vLzQoEEDg2Ij0sfe3h7+/v7w9/eHvb1VzV4gIzDP4scc2warKswAYMyYMcIdmUuXLsWdO3cMOk+tVmP37t3C64CAAHTu3BkAEBMTg5UrV5baRmpqKhYsWAAgv0h89913iz22adOmQpznzp0TesD0DWMCBReQPXz4MG7dulXi8USGkkqlcHNzg5ubG6RSq/uWJwMxz+LHHNsGq8usj48PZs2aBSC/1yw0NBRnz54t8Zzbt2/j3XffLVJ8zZ07V+jx+vHHH7F69WpoNBq9bcTHx+Odd94ResvGjRsnPIBcH5lMJiyJERUVhcjISAAlF1ra4cxVq1YJcXCZDCIiItthlX2hQ4cORUJCApYuXYqUlBSEhoaic+fOCAoKgq+vLypXroyMjAzcu3cPx48fx4kTJ6BSqYpM1K9ZsyaWLVuG9957D1lZWZg/fz727NmDIUOGwN/fH87OzkhKSsKJEyewc+dOYZmM/v374+OPPy41zjZt2uD06dOQy+VISkoCUHJh1rp1axw+fBiPHj0q0AZReajVauHfrouLC//SFinmWfyYY9tglYUZALz//vto1KgRFixYgPj4eERERCAiIqLY4xs1aoRp06YVeb99+/b466+/MGvWLFy5cgXXrl3DtWvX9Lbh7OyM8ePH47333oNEIik1xsJFVbVq1UqcL1a4aKtSpYpw9yhRWSmVSsTExAAAAgMD4eDgYOaIyBSYZ/Fjjm2D1RZmANC7d29069YN//zzD/79919ERUUhNTUV2dnZcHNzQ61atfDyyy+jT58+aN++fbHFlL+/PzZv3oxjx47h0KFDuHjxIp4+fYq8vDxUrVoV9evXR5cuXTBo0CD4+PgYHF+LFi0gk8mExz8VdwenVpMmTeDs7CwsntuqVSuDCkAiIiISB6suzADAwcEBwcHBCA4OLndb3bp1K/BA8vJycnIqtvdNH5lMhsuXL1fY9YmIiMi6cICaiIiIyEKwMCMiIiKyECzMiIiIiCwECzMiIiIiC2H1k/+p4ni4OWFf2NAynetZ2bmCoyEiIrI9LMxIYCeVwsfdydxhUAVzcHDgo71sAPMsfsyxbeBQJhEREZGFYGFGREREZCE4lEkClUpl7hDIBFQqFZKTkwEAnp6esLOzM3NEZArMs/gxx7aBhRkJ1Gq1uUMgE1CpVIiLiwMAVK1alT/MRYp5Fj/m2DZwKJOIiIjIQrAwIyIiIrIQLMxIYG/PkW0iIiJzYmFGAolEYu4QiIiIbBq7SEigUquRmJ5d7nY8KzvDTsqan4iIyFgszEiQmpWHgQt3l7udfWFD4ePuWgERERER2RYWZkQiJ5FI4OrqKmyTODHP4scc2wYWZkQiJ5PJEBAQYO4wyMSYZ/Fjjm0DJwIRERERWQgWZkREREQWgkOZRCKnVCrx4MEDAEC9evW4Xp1IMc/ixxzbBovOamRkJEaPHq13n5OTEzw8PNC4cWP069cP/fr1K/Uf6cOHD7F7925EREQgPj4eaWlpcHV1hbe3N9q3b4++ffuiTZs2pcYVGhqKs2fP6t1nb2+PSpUqwdfXF127dsUbb7wBd3f3YtuaPn06duzYUeR9iUQCNzc31KxZE61bt8Ybb7yBxo0blxobUWFqtRrp6ekAgDp16pg3GDIZ5ln8mGPbYNGFWUny8vLw+PFjPH78GEeOHMGaNWuwfPlyeHl5FTlWLpfjhx9+wF9//QWFQlFgX3p6OtLT0xEbG4t169aha9eu+Oabb1C9evUyxaVUKpGWlobz58/j/PnzWL16NX755Re0bt3aqHY0Gg0yMzMRExODmJgYbNy4ERMmTMDUqVPLFBcRERFZPqspzEaMGIG33npLeJ2Tk4Nr165h1apViI+PR1RUFN577z1s3ry5wG3EWVlZeO+99xAZGQkAcHd3x5AhQ9CxY0dUq1YNmZmZuHbtGrZt24Z79+7h+PHjGD58OFatWgVfX99S49qzZ0+B1wqFAo8ePcKuXbsQHh6OlJQUTJ48GQcOHICHh0eJba1cuRLe3t4A8v8ySklJwbFjx/DXX39BqVTit99+g7e3N0aOHGnw142IiIish9UUZtWqVYOfn1+B91q0aIHg4GAMGzYMDx48wNWrV3H06FEEBQUJx4SFhQlFWfv27bFkyRJUq1atQDsdO3bEmDFjsHjxYvz5559ISEjApEmTsGPHDri5uZUYV+GYAKBp06bo27cvvvjiC+zcuRMZGRnYsmULJk6cWGJb9evXR+3atQu816lTJ3Ts2BGTJ08GACxbtgxvvvkm7OzsSmyLiIiIrI/V35VZpUoVTJgwQXh94sQJYXvfvn04cOAAACAgIAD/+c9/ihRlWjKZDNOnT8ewYcMA5M9HW7x4cbliGzdunLAdFRVV5naCgoKEuW+pqam4fv16ueIiIiIiy2T1hRkANG/eXNh+/PixsP37778L23PnzoWzs3OpbU2fPh2enp4AgG3btiE1NbXMcen2fsnl8jK3AxT/GYmIiEg8rGYosyS6d2OqVCoAQHR0NG7cuAEAaNmyZYHCpiRubm4ICQnBihUr8Pz5c+zdu7fYO0NLo1tA1ahRo0xtaOl+RrVaXa62ipOXmwsHqMrdTm5ODrJlFRDQ/6d9BAkREZHYiaIwi42NFba1k+fPnz8vvNe9e3ej2gsKCsKKFSuEdspamK1cuVLY7tGjR5na0NL3GSva1r/Woq9bXrnbWfP7rxUQzf/MmTOnQtuzNVKpVPg3I5WKopOc9GCexY85tg1WX5gplUr8+eefwut27doByO8x02rSpIlRbTZu3BhSqRRqtRoxMTElHqtbMAH5d2XGx8dj9+7dOHToEACgf//+ePXVV42KQVd0dLQwd87Z2RmBgYFlbotsj729Pdc8sgHMs/gxx7bBagsz7XIZv/zyCy5fvgwAqFWrFvr37w8ASEtLE47Vt7ZZSZycnODq6orMzExhMb/iBAcHF7uvQYMGmDBhAoYMGWLU9YH8dcySk5Nx7NgxLF68WBiiDQ0NhaOjo9HtERERkeWzmsJs2bJlWLZsWbH7q1Wrhv/7v/+Dg4MDACA7O1vY5+LiYvT1XFxckJmZiaysLOOD/f/u3buHTZs2oXbt2kJPXklKG+7s1q0bPvzwwzLHQ0RERJbNagqz4tSuXRt9+vTBuHHjCiyFoTthPCcnx+h2teeUto5Z4aFOtVqNtLQ0XLhwAf/3f/+Hy5cvY+zYsfjhhx/Qt29fo+OQyWQIDAzE8OHDMWjQoAKL51a0198ajVFLD5S7nQ1TX4NXFeOLYTINhUKBmzdvAsgfppfJKvDODLIYzLP4Mce2wWoKM92V/yUSCRwdHVG1alVUqlRJ7/FVq1YVtp8+fYqAgACDr5WXlyf0uJX0nEt9pFIpqlWrht69e6NTp04ICQnB/fv38eWXX6JDhw4ltqe78r9UKoWLiws8PT2FXkBTc3J2hhzlX7jW2cWFd1JaEI1GIzyKTKPRmDkaMhXmWfyYY9tgNbd1aFf+9/PzQ6NGjVC3bt1iizIA8Pf3F7a1f2EY6ubNm8KSFLrtGMvV1RUjRowAkD+0+s8//5R4fP369YXP+NJLL6FmzZovrCgjIiIi87OawsxYbdu2FbbDw8ONOlf3eN12yqJhw4bCduE7OImIiIh0ibYwCwgIQOPGjQEAly5dMviRSFlZWdixYwcAwNHREQMGDChXHNq7KYH8pT2IiIiIiiPawgwAxo8fL2zPnj0beXmlL576/fff4+nTpwCAoUOHwsPDo1wxXLt2Tdgu7+r/REREJG6iLswGDBiA3r17AwBu3LiByZMnF/vsS4VCge+//x6bNm0CANStWxeffvppua4fHx+P9evXC6+7du1arvaIiIhI3KzmrsyymjdvHtLS0nDu3DmcOnUK/fr1Q0hICDp06ABPT09kZWXh2rVr2Lp1K+7evQsAqF69OpYvX17qUhlA0XljarUa6enpOH/+PNatWycsUBscHCwMrRIRERHpI/rCrFKlSli5ciUWLVqEDRs2ID09HatWrcKqVav0Ht+lSxfMnTvX4GHHklb+1+rfvz/mzZtnVNxEFcXe3l64CcXeXvTf8jaLeRY/5tg22ERmHR0dMXPmTISGhmL37t2IiIhAfHw80tPT4erqCm9vb7Rt2xb9+vUr912YEokELi4uqFGjBlq0aIHBgweXu02i8pBKpQXW9SNxYp7Fjzm2DRINV6mzaXK5XLhjtXo9XwxcuLvcbe4LGwofdy4wS0RE4qb7OzQwMLBC1h61iR4zIlumu1q4TCYz6WO9yHyYZ/Fjjm2DqO/KJKL8O46joqIQFRUl/FAn8WGexY85tg0szIiIiIgsBAszIiIiIgvBOWYk8HBzwr6woeVux7OycwVEQ0REZHtYmJHATiqFj7uTucMgIiKyWRzKJAFXTiEiIjIvFmYkUCqV5g6BiIjIprEwIyIiIrIQnGNGAj57TZxkMhkCAwOFbRIn5ln8mGPbwN/EJOAq0uIkkUgq5DEhZNmYZ/Fjjm0DhzKJiIiILAR7zEjAuzLFSa1WIyMjAwBQpUoVSKX8e0yMmGfxY45tA7NKAg5lipNSqcTdu3dx9+5d3nkrYsyz+DHHtoGFGREREZGFYGFGREREZCFYmBERERFZCBZmRERERBaChRkRERGRhWBhRkRERGQhuI4ZkchJJBLh8S1cEkW8mGfxY45tg0kLs8jISIwePVrvPicnJ7i7uyMgIAC9evXCwIEDDX7URFxcHHr27CksiPrDDz8gODjY6PgeP36Mv//+G6dOncKDBw+QmpoKtVqNKlWqwM/PD61bt0ZwcDDq1Kmj9/yUlBRcvXoVV69eRVRUFKKiopCeng4AGDJkCBYsWFBqDNOnT8eOHTuKvC+RSODm5oaaNWuidevWeOONN9C4cWOjPyORTCZD8+bNzR0GmRjzLH7MsW0wW49ZXl4eEhISkJCQgGPHjmH16tX47bffULt27VLP3blzZ4FV6nfu3GlUYfb8+XMsXrwYGzZsgFwuL7I/KSkJSUlJiIiIwNKlS9G3b1988cUXqFGjRoHjXnnlFYOvaSyNRoPMzEzExMQgJiYGGzduxIQJEzB16lSTXZOIiIjM64UVZiNGjMBbb70lvE5JScGtW7ewcuVKJCQk4NatW5g8eTJ27twJOzu7EtvavXs3AMDFxQU5OTk4ffo0kpKS4O3tXWocqampmDx5Mi5fvgwAcHV1xWuvvYYOHTqgevXqsLe3R3JyMi5evIhDhw7h/v372L9/P1q0aIExY8YU227NmjXRsGFDRERElP7FKMbKlSuFz6BWq5GSkoJjx47hr7/+glKpxG+//QZvb2+MHDmyzNcgIiIiy/XCCrNq1arBz8+vwHsdO3ZESEgIBg4ciPj4eMTGxuLQoUPo27dvse1cvHgRDx48AAB8+eWXmDNnDlQqFfbs2YNx48aVGINarcbHH38sFGXdu3fHd999h2rVqhU5NigoCJ988gl2796N77//Xm9777//PgIDAxEYGAhPT0/ExcWhR48eJcZQkvr16xfpMezUqRM6duyIyZMnAwCWLVuGN998s9TilUhLqVTiyZMnAIAaNWrA3p5TS8WIeRY/5tg2mP2uTDc3N6HoAIBTp06VePzOnTsBAFWrVsXgwYPRsWNHAMCuXbtKvdbatWsRGRkJAOjcuTOWLVumtyjTkkqlGDx4MLZv3w5/f/8i+z/88EN0794dnp6epV67PIKCgtCmTRsA+T1+169fN+n1SFzUarUwPK9Wq80dDpkI8yx+zLFtMHthBqBA0ZOQkFDscXK5HAcOHAAA9OvXDzKZDAMHDgQAxMTE4ObNmyWeu3LlSgCAo6Mj5s2bZ/BfG9WrVxcKQHPRnfD5+PFjM0ZCREREpmIR/aDa238BlFgshYeHIyMjAwCEgqxXr17CXLOdO3cWe+diREQEkpKSAAB9+/aFj49PRYX/Quh+XUz1l1JOTg4UCoXBx7u6upokDiIiIltlEYXZnTt3hO1atWoVe5x2GLNu3bpo2bIlgPwbAHr06IE9e/Zg7969+Pzzz/XOvzp37pyw3a1bt4oJ/AWKjY0Vtg25yaEs/vOf/yAvL8/g4+fMmWOSOIiIiGyV2YcyVSqVMMQIAH369NF7XGpqqnDHY+GlMbS9Z8nJycXeFRkdHS1sN23atFwxv2jR0dE4ceIEAMDZ2RmBgYFmjoiIiIhMwWyFWWpqKk6fPo1Ro0bhxo0bAPKLMu0k98L27t0rDLNpCzGtTp06wcvLC8D/etUK0y78CqDECf+WQqPR4OnTp9iyZQvGjBkDlUoFAAgNDYWjo6OZoyMiIiJTeGFDmcuWLcOyZcv07nN2dsabb76JTz/9tNjztQVX8+bNUb9+/QL77Ozs0L9/f6xZswbh4eHIysqCm5tbgWOys7MLXM8SlbbURrdu3fDhhx++oGiIiIjoRbOIOWYBAQEIDQ0tcBOArlu3bglLRBTuLdMaOHAg1qxZg7y8POzfvx/Dhg0rsF93onpubm6Rws1SyWQyBAYGYvjw4Rg0aJBJn482ceLEYnNA1ksqlcLd3V3YJnFinsWPObYNZln5X6VSISEhAf/88w927dqFS5cuITQ0FFu3boWHh0eRc7W9Zfb29hgwYIDe9ps1awZfX1/cuXMHu3btKlKYaf8xA/lz0SyxMNNd+V8qlcLFxQWenp4GP0O0vFxcXF7YtejFsbe3h6+vr7nDIBNjnsWPObYNZlv5v3HjxujevTvat2+P6dOnIz4+HmFhYVi+fHmB89RqNfbs2QMgf9VjQ9YTO3/+POLi4gqsoh8QECAsXnvjxo0iw6GWQN/K/0RERGQ7zN4XOmTIEOFOzPDwcJw+fbrA/tOnTyMxMdGoNjUaTZEnAbRt21bYPnbsWNmCJSIiIjIhi5hjNnXqVBw+fBgqlQpLliwp0CumHcZ0cHDAvHnzSh1X/+OPP3Djxg3s3r0b77//vvB+586d4e3tjaSkJBw4cACffvqp1S0yS1QWCoVCWCvQ19eX8whFinkWP+bYNlhEYdagQQP069cPe/fuxZUrV3Dy5El06tQJ2dnZOHz4MID8JTEKr1+mz9OnT3Hjxg3cv38fly5dEhaidXBwwLhx4zB//nw8f/4cX375JVasWGHQw8ATExNx9+5dsz+WiagsNBqNcFeyRqMxczRkKsyz+DHHtsHsQ5laEydOFO441M4zO3jwIHJycgAUv/BsYX369BHaKbym2ejRo9G+fXsA+Y9oev/995GamlpsWxqNBnv27EFISAhiYmKM+jxERERExrKIHjMA8PPzQ1BQEI4cOYJz587h/PnzQmElk8kQFBRkUDs1atRAYGAgrl69iv379yMsLEy401AqleKnn37CpEmTcOXKFRw9ehQ9e/ZEcHAwOnTogOrVq8Pe3h7Jycm4fPkyDh48iLt37xZ7rfPnz+Phw4fC67S0NGH7wYMH2L59e4HjQ0JCDP1yEBERkQ2ymMIMACZNmoQjR44AAH755RecPXsWANC+fXtUqVLF4Hb69OmDq1evIiMjA0ePHi3Q2+bh4YF169bhhx9+wIYNG5CdnY2NGzdi48aNetuSSCQIDg5Gv379iuzbunUrduzYofe8ixcv4uLFiwXeY2FGREREJbGYoUwgf1X/Tp06AQDOnDkDtVoNwPBhTC3d4/U9osnR0RFhYWE4ePAgPv30U7zyyiuoWbMmnJyc4OjoCG9vb3Tu3BlTp07FkSNHsGjRIt4oQERERCYn0XAGoU2Ty+WIiooCAAQGBnKBWRFijm0D8yx+zLHlMUVOLKrHjIiIiMiWWdQcMyKqeHZ2dsITJQxZHoasE/MsfsyxbWBhRiRydnZ2nCNpA5hn8WOObQOHMomIiIgsBAszIiIiIgvBoUwS8AZdceKdXLaBeRY/5tg2sMeMBEql0twhEBER2TQWZkREREQWgoUZERERkYVgYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGF4DpmJOCz18TJ3t4e/v7+wjaJE/MsfsyxbWBmSSCVsgNVjKRSKdzc3MwdBpkY8yx+zLFt4G9iIiIiIgvBHjMSqNVqc4dAJqBWq5GTkwMAcHFxYc+oSDHP4scc2wZmlQQqlcrcIZAJKJVKxMTEICYmho/dEjHmWfyYY9vAwoyIiIjIQrAwIyIiIrIQLMyIiIiILAQLMyIiIiILwcKMiIiIyEJwuQwbp9FohG2FQgG5XG7GaMgUFAqF3m0SF+ZZ/Jhjy6ObB93fp+Uh0VRUS2SVsrOzER0dbe4wiIiIrFpAQABcXV3L3Q6HMomIiIgsBHvMbJxarUZubi6A/IfiSiQSM0dERERkHTQajbDYr7Ozc4U8jYGFGREREZGF4FAmERERkYVgYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGFYGFGREREZCFYmBERERFZCBZmRERERBaChRkRERGRhWBhRkRERGQhWJgRERERWQgWZkREREQWgoUZERERkYVgYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGFYGFGREREZCHszR0AlS4+Ph7r1q3DsWPHkJCQAAcHB9SpUwf9+vXDyJEj4ezsXCHXOX78ODZv3oyoqCikpqbCw8MDgYGBeOONN9C1a1eD2lAqldiyZQv27NmDu3fvIicnB97e3njllVcQGhqKRo0aVUisYmMNOU5OTsbRo0dx5swZ3Lx5E0+ePIFCoYC7uzv8/f3Ru3dvDBo0CE5OThUSqxhZQ56LEx0djaFDh0KpVAIAhgwZggULFlRIvGJibTlWKBTYs2cPDhw4gNjYWCQnJ8PV1RXe3t5o3rw5OnfujH79+lVIzGQYiUaj0Zg7CCpeeHg4pk2bhqysLL3769evjxUrVqBevXplvoZarcasWbOwdevWYo8ZNmwYvvnmG0ilxXeypqamYsKECYiKitK738HBAbNnz8awYcPKHKsYWUOON2/ejK+++goqlarE69SvXx8///wzAgICyhyrWFlDnktqd/jw4bh69arwHguzoqwtx9HR0Zg2bRpiY2OLPaZSpUo4f/58meMl47Ews2A3btzAiBEjkJeXBxcXF0ycOBHt27dHXl4e9u3bh82bNwPI/2bftm0b3NzcynSdxYsXY8WKFQCAJk2a4N1330WdOnXw6NEj/PHHH7hx4wYAYOLEifjkk0/0tqFSqRAaGooLFy4AAHr37o1hw4bB3d0dV65cwfLly5GSkgKpVIrffvutzH+1i4215PjXX3/Fzz//DJlMhu7du6NTp07w9fWFq6srHj58iC1btiAiIgIA4OHhgR07dqB69eplilWMrCXPxVm7di2+++47VKtWDSkpKQBYmBVmbTmOjo7G22+/jfT0dDg6OmLYsGHo1KkTfHx8oFAocP/+fZw4cQIXLlzAsWPHyhQrlZGGLNZbb72l8fPz0zRp0kRz8eLFIvt///13jZ+fn8bPz0+zdOnSMl3j7t27miZNmmj8/Pw0ISEhmtzc3AL7c3JyNCEhIUIc9+/f19vOli1bhFi++uqrIvvv37+vadWqlcbPz0/Tq1cvjUKhKFO8YmMtOf7zzz81ixYt0qSkpBR7nfnz5wuxTp8+vUyxipW15FmfJ0+eaFq2bKnx9/fX7NixQ4jziy++KFOcYmVNOc7Ly9P07t1b4+fnp+nWrZvm3r17xV7z+fPnZYqVyo6T/y3U1atXhe7joUOHomXLlkWOGTt2LHx9fQHk/0WrUCiMvs6aNWuEOSOzZs0qMj/I2dkZs2bNApA/f2z16tV621m1ahUAwN3dHZ9//nmR/fXq1cPEiRMBAA8ePMChQ4eMjlVsrCnHY8aMwWeffQYPD49ir/PJJ5/Ay8sLAHDo0CGo1WqjYxUja8qzPl9//TWys7MxZMgQtGnTxui4bIG15XjlypW4f/8+pFIpfvrpJ9SvX7/Yazo4OBgdJ5UPCzMLdfjwYWF76NCheo+RSqUYPHgwAODZs2eIjIw06hoajQZHjhwBADRs2BAtWrTQe1yLFi3QoEEDAMCRI0egKTT6fe/ePdy5cwcA0Ldv32Intw4ZMkTY1v18tsqacmwIBwcHtGrVCgCQmZmJ9PR0o9sQI2vO84EDBxAeHl7sH1yUz5pyrFKpsHHjRgDAK6+8gpdfftmoOMj0WJhZKO1cLRcXFzRt2rTY49q2bStsX7x40ahrxMXFISkpqUg7+rRr1w4AkJiYiLi4OL2x6h6nj5eXl/CXmbGxipE15dhQcrlc2DZmcrmYWWueMzMz8e233wIApk2bhqpVqxoVky2xphxfunQJiYmJAICgoCDh/efPn+Phw4dITEws9SYfMi3+5LRQ2h6ounXrwt6++FVNGjZsWOQcQ92+fVtvO6Vd5+7du3pjNaadJ0+eICcnx+BYxciacmwIhUKBy5cvAwA8PT3h7u5udBtiZK15XrRoEZ4+fYo2bdoU2wtE+awpx9rvUQDw8/PD/fv3MWXKFLRu3Rq9evXCq6++ivbt2+Pzzz/Hw4cPjYqRKgYLMwv0/PlzpKWlAUCpd7ZVqVIFLi4uAICEhASjrqN7fGnX0d3/5MmTYtvx8fEpsZ0aNWoAyO+WNzZeMbG2HBti8+bNwmfq27ev0eeLkbXm+cKFC9i8eTNkMhm++uorSCQSo+KxJdaWY92C8O7duxgyZAgOHjxYYM5bZmYmdu3ahcGDB+PUqVNGxUnlx8LMAmVnZwvb2m/ikmjndBnbA2XMdXTnjRW+jm47rq6uZW7Hllhbjkvz6NEjLFmyRLjOhAkTjDpfrKwxz3K5HLNnz4ZGo8GYMWO4KHQprC3HunM/v/vuO+Tk5GDMmDE4ePAgoqKicPjwYYwbNw4SiQTZ2dn46KOP8PjxY6NipfJhYWaBnj9/LmzLZLJSj9feNZOXl2ey6+jemVP4OhXVji2xthyXJDc3Fx988AEyMzMB5N8tVlrPqa2wxjyvWLECt2/fRq1atfD+++8bFYctsrYc5+bmFmjzww8/xIwZM1CvXj3hKQWff/45pk6dCiD/RoX//Oc/RsVK5cPCzAI5OjoK24bcUq2dcG3so3CMuY7upO7C16modmyJteW4OEqlEh999BGio6MBACNGjEBISIhRMYqZteX57t27wi/hWbNmVdjjg8TM2nKs207VqlUxfvx4vW2MGzdOWP7mwIEDZbpTm8qGhZkF0h0ONKS7W/sXkCHd6GW9ju5fWYWvo9uObne7se3YEmvLsT4ajQbTp0/H8ePHAQD9+vXD7NmzjYpP7KwpzxqNBnPmzIFcLkevXr3QvXt3o2KwVdaU48LttGvXrth1yuzt7dGhQwcA+cOfjx49MipeKjs+xNwCOTo6wt3dHenp6aVOEM3IyBC+SY19BI7u8aVdR3e/dgK/vnYSExNLXIRUOxFVIpHY9CN7rC3H+nz99dfYs2cPAODVV1/FokWLuERGIdaU58uXL+Ps2bMAgJYtW+Lvv/8ucm5qaqqwHRcXJxzTqFEj+Pn5GRWzWFhTjgu3U9r3ue7+1NRU1K1b16BYqXxYmFmol156CefPn8fDhw+hVCqLvQVb91Zo7arSxlxDXzulXafwrdq617179y4aN25cajs1atSw6R4zwLpyXNiiRYuwYcMGAPlrKv3yyy8Gza+xRdaSZ93hr++//77Ua547dw7nzp0DAHzwwQc2W5gB1pNjAAVu5ihtvTLd/XZ2dgbFSeXHP28tVOvWrQHkd1lfv3692OO0PxgBCKuuG6p27drw9vYu0k5J1/Hx8UHt2rX1xgpA+Itbn6dPn+L+/ftlilWMrCnHun799Vf88ccfAIDAwED89ttvNj1fsDTWmmcynDXlWPexWqUtJK07fMkbel4cFmYWqmfPnsL2tm3b9B6jVquxc+dOAEDlypXRvn17o64hkUjQo0cPAPl/YekuPKjr8uXLwl9gPXr0KLKmUYMGDYS//g4cOFBgfoOuHTt2CNu6n89WWVOOtdasWYOff/4ZQP7ilH/88Qfc3NyMisnWWEue27dvj5iYmBL/0z4SCMh/xJr2/SlTphgVr9hYS44BoE6dOmjSpAkAIDIyUriburCsrCxhDbO6desKRSGZHgszC9W8eXPhL5tt27bh0qVLRY5ZtWqVsFjg6NGjiwwlRUZGwt/fH/7+/pg+fbre67z99ttCF/XcuXOL3Fqdl5eHuXPnAsifDPr222/rbWfs2LEA8ieJLlq0qMj+hw8fCnd71atXD7169dL/wW2IteV427ZtmD9/PgCgfv36+PPPP7m6vwGsLc9kPGvLsfZOzJycHMybN0/vMQsWLEBWVhYA4M0339R7DJkG55hZsLCwMIwYMQJ5eXkYO3YsJk2ahPbt2yMvLw/79u3Dpk2bAOT/knznnXfKdI0GDRpg3LhxWLFiBa5du4YRI0Zg/PjxqFOnDh49eoTff/8dN27cAJB/+7T2WZeFDRkyBNu2bcPFixexfv16JCcnY9iwYahSpQquXr2KX3/9FVlZWZBKpQgLCyvxsSW2xFpyfPjwYcyaNQsajQZubm4ICwtDampqgcnghdWuXdvm5xFqWUueqeysKcf9+/fHzp07cfz4cWzfvh1JSUkYMWIEatasiSdPnmDTpk3C3dZNmjTBqFGjyhQvlY1Ew8VJLFp4eDimTZsm/OVSWP369bFixQrUq1evyL7IyEiMHj0aQH7htGDBAr1tqNVqzJw5s9gueAB4/fXXMXfu3BLvuktNTcWECRMQFRWld7+DgwNmz56NYcOGFduGLbKGHE+fPr3AULQh1q5da/RwjZhZQ55LExcXJwynlRSHrbKmHGdnZ+PDDz9EREREsccEBgZi+fLlwnpm9GKw28LCBQUFYffu3Vi7di2OHTuGxMREyGQy1K1bF3379sWoUaPKvQikVCrFvHnz0KdPH2zatAlRUVFIS0tD1apVERgYiOHDh6Nr166ltuPh4YGNGzdi8+bN2Lt3L+7cuYPc3Fx4e3ujY8eOGD16NB/vooc15ZjKjnkWP2vKsaurK/744w/s27cPO3bsQHR0NNLT01GpUiU0btwYAwYMwODBg3k3phmwx4yIiIjIQnDyPxEREZGFYGFGREREZCFYmBERERFZCBZmRERERBaChRkRERGRhWBhRkRERGQhWJgRERERWQgWZkREREQWgoUZERERkYVgYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGFYGFGREREZCFYmBERERFZCBZmRERERBaChRkR0Qvw+PFjzJ49Gz179kRgYCD8/f3h7++Pw4cPmzs0ojKZPn06/P39ERQUZO5QRMXe3AEQlVdkZCRGjx4NAPjggw8wZcoUM0dEVNDjx48REhKCtLQ0c4diFN3vrcKcnJzg7u6OgIAA9OrVCwMHDoSDg4PBbatUKhw8eBBHjhxBVFQUkpOToVAo4OnpiYYNG6Jbt24YOHAgKleubFTMcrkcBw8exL///ourV68iLS0NWVlZcHNzQ61atRAYGIg+ffqgQ4cOkErZN0GWh4UZEQEA/P39AbC4NYXly5cjLS0N9vb2+Pjjj9G2bVu4uLgAAGrWrGnm6MomLy8PCQkJSEhIwLFjx7B69Wr89ttvqF27dqnnnj9/HnPmzMHt27eL7IuPj0d8fDxOnDiBX375BZ9++ineeOMNg2I6ePAgFixYgPj4+CL70tPTkZ6ejuvXr2Pjxo2oX78+ZsyYgW7duhnUtjXj97Z1YWFGRGRip06dAgD06NED48ePN3M0ZTNixAi89dZbwuuUlBTcunULK1euREJCAm7duoXJkydj586dsLOzK7adAwcOYNq0aZDL5QCAdu3aYeDAgWjQoAEcHBzw+PFjhIeH4++//0Z6ejpmzZqFe/fu4Ysvvigxvv/7v//D0qVLhdedOnVCUFAQfH19UblyZWRkZODevXsIDw/HqVOncP/+fSxZssQmCjNTWbBgARYsWGDuMESHhRkRkYklJiYCABo0aGDmSMquWrVq8PPzK/Bex44dERISgoEDByI+Ph6xsbE4dOgQ+vbtq7eNGzdu4LPPPoNCoYBMJsP8+fMRHBxc4JjmzZujb9++eOeddzBp0iQkJCRg1apVqFOnToHCUNe2bduEoqxatWr46aef0K5duyLHvfLKKxg5ciRiY2Mxf/58pKamluVLQWRSHGAnIjIxhUIBALC3F9/fwm5ubpg8ebLwWts7WJharcbnn38ufC3mzZtXpCjT1bhxY6xevVoY8l24cCEeP35c5LjExETMnTsXAODi4oJ169bpLcp0+fn5YeXKlRg7dmzJH47IDMT3U4KoEN0JzGvXrkW7du2wdetWbN++HXfv3oVCoUDDhg0xatQoDB48WDhPLpdj27Zt2LFjB+7fvw+FQgF/f3+MHj0a/fv313utuLg49OjRAwAwf/58hISEYP/+/di0aRNiYmKQnZ2NmjVrolevXhg/fnypE5vlcjm2bNmCAwcO4NatW8jKykKVKlXQpEkTvPbaawgODi52AvP06dOxY8cO1KpVC+Hh4UhKSsKaNWtw7NgxPH78GDk5OVi7di1mzJhRYE7OsmXLsGzZsgJtDRkypMCQRVJSEg4dOoTIyEhER0cjKSkJSqUSVatWRbNmzRAcHIy+ffsWG1vhnLRv3x779u0Tvk45OTmoUaMGevTogQkTJsDd3b3ErxMAHD9+HHv27MGlS5eQkpIClUoFLy8vNG3aFN27d0e/fv3g7Oys99zr169j06ZNiIyMRFJSEjQaDXx8fNChQweMGTOmTD1d27dvx4wZMwq8V/hrW/jrCgCpqalYs2YNjh8/jri4ODx//hxeXl5o3bo1hg8fjjZt2hR7zaCgIMTHxwvtXrt2Df/9739x9uxZJCUlQaFQICYmxujPUhrtHCYASEhI0HtMeHg4bt26BQB49dVXMXDgwFLbbdCgASZPnozFixcjLy8Pa9euxfTp0wscs3r1auTm5gIAPvzwQ/j6+hoUs1QqxaBBgww6VteL/Hmi6+nTp/jvf/+LEydOIC4uDjk5OahWrRpatGiB4cOH45VXXilyjvbfg1Zp39uFP1vbtm2xfft27Nq1C3fu3EFqaioGDx4sHF/4Z0xxsrKysHnzZhw7dgx37txBRkYGHBwcULduXbRu3Rr9+/dH69atS/0a2AoWZmRTlEolJk+ejKNHjxZ4PyoqCl988QWuXbuGmTNnIiMjA++//z7OnTtX4LhLly7h0qVLePjwISZNmlTq9b788kts27atwHv37t3DihUrsHPnTqxevbrYXyRxcXEYP3487t69W+D95ORk/Pvvv/j333+xadMm/Prrr6UWLpcvX8akSZMq5K5AlUqFrl27Qq1WF9mXlJSE8PBwhIeHY+vWrfjll1/g6upaYntqtRrTpk3D7t27C7x///59rFy5EocPH8b69evh5eWl9/y0tDRMnToVp0+fLrJPO5H84MGDAICQkJAi1164cCHWrFkDjUZT5Pr379/H1q1bMXv2bAwfPrzEz1ERIiIi8NFHHyErK6vA+9rPsXv3bowcORIzZ84s9Y7CDRs24Ntvv4VSqTRlyAAAmUwmbBfXK7hz505h++233za47TfffBPLli3D8+fPsWPHDnzxxReQSCQAAI1Ggx07dgDI7y0bNmxYGaIvuxf182T37t2YM2cOcnJyCryfkJCAAwcO4MCBA3j99dfx9ddfV1iv7PPnzzFu3Lhie0ANderUKXzyySdFfvYoFArcvHkTN2/exH//+1+T/MFgrViYkU35+eefceXKFQQHByM4OBienp64f/8+fvnlF9y7dw/r1q1DUFAQ1q1bh0uXLmHEiBHo1asX3N3dcfPmTfz8889ISkrC0qVL0aNHDzRq1KjYa/3111+IiopC8+bNMWbMGNSrVw8pKSnYsWMH9u/fj6SkJLz77rvYs2cP3NzcCpybnZ2NMWPG4NGjRwCAnj17YujQofD29kZcXBzWr1+Ps2fP4sKFC5g0aRLWr19f7ITr7OxsTJkyBc+fP8ekSZPQqVMnODk5ITY2Fl5eXli5ciUUCoUwrFR4kjcAVKlSRdjWFjAdOnTAq6++Cj8/P3h4eCA7OxuPHj3Cli1bcOnSJZw8eRLffPMNFi5cWGpOLl26hJ49e2Lw4MGoWbMmkpOT8ddff+HYsWN48OAB5s+fjx9//LHIubm5uRg9ejRiY2MBAE2bNsXw4cPRqFEjODg4ICEhAefOncO+ffv0Xnvu3Ln466+/AABt27bFkCFDUKdOHTg5OSEmJgZr1qzBrVu3MHv2bHh6egq9oYbo2bMnmjVrBgDFfm11v643b97EpEmThPlXI0eORFBQEJydnXHz5k2sWLFCyL2zszOmTZtW7LWjoqKwe/duVK9eHePGjUOzZs2gVCpx4cIFg+M3xp07d4TtWrVq6T3m/PnzAABnZ2d07NjR4LYrV66Mtm3bIiIiAunp6bh9+7bwfXfr1i3hF37r1q2LfB+Z2ov4ebJv3z58/vnn0Gg0qFOnDkaNGgVfX194eHggPj4eW7duxfHjx7F161a4ubkV6KU19ntb1w8//ICYmBgEBQUhJCRE+L7Mzs42+Otz5swZjB8/HkqlEnZ2dhg0aBB69OiBGjVq4Pnz57hz5w7+/fffIoWtzdMQWbkzZ85o/Pz8NH5+fpqlS5eWuN/Pz0+zevXqIsckJSVpWrZsqfHz89N06NBB4+/vrzl06FCR427evKkJCAjQ+Pn5aebOnVtk/6NHjwpca/z48RqFQlHkuGXLlgnHLFy4sMj+BQsWCPuXLFlSZL9ardZ8+umnwjHr168vcswXX3wh7G/RooXm5s2bRY7RVdLXsPC179+/X+IxP//8s8bPz0/j7++vuXfvXpH9hXPy66+/6r3O2LFjNX5+fpomTZpoUlJSihwzb948oY2vv/5ao1ar9cbz/PlzzdOnTwu8FxERIZy7efNmvefl5eVpRo8erfHz89N0795dby4NYcjXdujQoRo/Pz9N48aNNSdOnCiyPz09XdO/f3+Nn5+fJiAgQBMbG1vkmO7duwvXeu211zQZGRllilertO8tjUajUSqVmsGDBwvHnTt3rsgxCQkJwv433njD6Dh++OEH4fzdu3cL7+/atUt4/8cffzS63bJ4kT9PUlJSNK1bt9b4+flpZsyYUey/vx9//FH4d3Hnzp0i+w393i782fT97NGl/RnTvXv3Ivvy8vI0nTt31vj5+WlefvllzZkzZ4pt5/HjxyVex9Zw8j/ZlJdfflnvMIqXlxd69eoFIH+OT79+/dCzZ88ixwUEBAhzIUrrfXBwcMDcuXP1Di1MnjxZuMNt27ZtwtIBQP5clK1btwIAGjVqpHfdIYlEgq+++koYwly/fn2Jsbz77rsICAgo8RhDSSQS1KtXr8Rj3n//fVStWhUajabEuSdAfi+XvmEciUSCMWPGAMgfMrp06VKB/c+ePcOmTZuENsLCwoQhrsIcHBzg6elZ4L0VK1YAAPr06VPsEJijoyNmz54NIH84MTIyssTPUlZXr15FVFQUAGDYsGHo3LlzkWOqVKmCb775BkD+EKy2p684c+bMMXpxVmOkpqbi9OnTGDVqFG7cuAEg/2upbw6c7jBW4TwYolq1asJ2enq63m3dY14UU/882bBhAzIzM+Hj44Ovvvqq2GHKKVOmwMfHB2q1Grt27SrPRxLUr1+/XGue7dy5E0lJSQCAqVOnon379sUeW6NGjTJfR4xYmJFNKWmSrW7hMmDAgFKP0w4zFqdTp07w8fHRu08qlQoTg9PT04VfbABw7do1PHv2DED+xNzihijd3NzQr18/AMDt27eFH4L6GDLRuqzUajUSExNx9+5dxMbGIjY2Fnfu3EH16tUBANHR0SWeHxwcXGxB1bRpU2G78Nf7zJkzwqTv0aNHl7h2VmFZWVk4e/YsgPxioiS+vr6oWrUqABQpDiuK7jye119/vdjjWrduLcxJLGnuT40aNUq8SaAsli1bJjxGyt/fHx07dsSYMWNw8eJFODs745133sHixYv1nqs7Z057l6UxdOcp6ralO6xW3I0dpmTqnyfaP2q6detW4lMV7O3t0aJFCwAV92+0f//+Rn1PFaYdnnRxcTF4gWDKxzlmZFNKuruuUqVKwnb9+vVLPa60uRaBgYEl7m/evLmwHRsbK/xg1d65BuT/RV6Sl19+GRs2bBDO8/b2LnKMi4sL6tSpU2I7xtJoNNi9eze2bt2Kq1evIi8vr9hjS7vhoGHDhsXu072pofDXW7eYNfaOrhs3bgg3L3zyySf45JNPDDovOTnZqOsYSptzmUyGxo0bl3jsyy+/jDt37uDBgweQy+V6f2Hr3iX5IgQEBCA0NLTATQC6dOd+FZ7Abgjd3Ou2pVuwaYv0F8mUP09UKpXwR82mTZuE3uHSVNS/0fL+G7p58yaA/D+uzFE0WzMWZmRTnJycit2ne5ebIcfpuytRV2lDK8UNz2RkZAjbHh4eJbahOyyke56uih7Oev78OT744AP8+++/Bh1fUtEGGJ6Twl9v3YJPX0FakpSUFKOO1yrts5SVNv/u7u6l3lWnzblGo8GzZ8/0Dg2aYghTd+K4SqVCQkIC/vnnH+zatQuXLl1CaGgotm7dqvffrLbHEShb4aCbL91iXXe7rDktD1P+PMnIyCjTHbUV9W+0uJsCDKX9/izubmoqHgszIhMpbnjuRbdRnuEIfZYvXy4UZe3atcNbb72Fpk2bwtPTE05OTsIvmpEjRwp34lka3V+C3/y/9u40JKq2jQP4f4zKxNJMzRaQelSYksIlFLLNpCDaJhEU0bSdCsuEVip4iha/VEopgZZlOSZIoVGD66RSoSlaEg6WuUSSaYobTZrPB5nDTLM01oye3vf/+zQw5xzvmTNnvOY6931d//4LHx8fs/b7039W48XS5xzQr/wvlUqxZs0aBAQE4Pjx4/j48SNOnTqFlJQUvX1dXV0xc+ZMfP36FQ0NDRgeHh7TGLWzo9q3CLUf19fXj/Ulidrw8LDwOCwszGgz+Z8Zy1qOFRu8TxwGZkRW8qvMgLEsgPY//87OTpO3S7T/xngEDSMjI8LCBH9/f2RkZBj9AjeWwbMU7SzM58+fx3S7Vvv9trW11Ws1NN404+nu7sbQ0JDJrJnmnEskEqtO7jeXTCZDSUkJFAoFiouL8fz5c71yGBKJBH5+figsLMTg4CCeP39ucIGDIb29vUKA7+joCA8PD+E5T09PIeB79eoV+vr6xr1khrX8XKJmoj+jYzVz5ky0t7ejo6Njoofy12FITGQlmlV25jyvXb9I+3Ftba3JY9TV1Rncz1q6u7uFL1pTlf37+/vR1NRk1bFoLwwYa2ZOKpUK2cjq6mqLjut3aM6dpuimKZpz7u7ubnJC+HiKj48XMmBXrlwxuI1MJhMe37171+xjZ2dnC7fnZDKZThZZIpEIxx0YGEBOTs6Yxy5WU6ZMET4XYviMjtWiRYsAjC5mmoj5f38zBmZEVlJRUWF0peSPHz+EiuUODg46QYa3t7eQCXn48KHRuWx9fX148uQJAMDDw2PM86x+NnXqVADQKd3xM+3bK6a+bHNycqxecT4gIEBY4Xf37l2dsf2Kk5OTsNgiPz9/wptZa7fT+blThLaamho0Njbq7TPRFixYIKwQrq2tRUVFhd42wcHBworS0tJSPH78+JfHbW5uxvXr1wGMZjYN3c6LiYkRJpcnJSXpFLs15cePH3rdJsQmODgYAPD+/XuUlZX99nHMubYtTTP2wcFBsxcu0CgGZkRWolarcebMGYMBw82bN4Vq9aGhoTqZjylTpgglE1QqFW7cuKG3/8jICM6dOydMsI2MjPzj8Wom6ba0tBjdxsnJSQga8/PzDX7R19XV4dq1a388nl+ZMWOG0Capvr4eFy5c0GurpPH9+3e9yeGaxtt9fX2Ii4sTSpQYolarce/ePXz79s1Co9e1ZMkSoUtATk6OwfZSvb29Qk01GxsbREREWGUsv2vv3r1CNsvQPDMbGxskJiYKc6COHz9utCMDMFpmJSYmRljFeezYMcydO1dvu9mzZ+P06dMARrNmUVFRQikUYxobG7Fr1y6kpaWZ9+ImSHR0tPDj48SJEzortg0pLS01WJ7GnGvb0jZv3iyUC7p69arJc2Ksv+r/K84xI7ISb29vlJSUICIiQqcl08OHD4VsgZubG/bv36+374EDB1BQUIDW1lYkJydDpVJh27ZtcHFxQVtbm9CYGgB8fHws0sfRx8cHbW1tKC4uhlwuh6+vr/BL297eHrNmzYKNjQ02bdqEe/fuoaGhAREREYiNjYW7uzv6+vqgVCpx//592NnZwdXVFR8+fPjjcZly6NAhVFRUQKVSITMzEzU1NQgPD4eXlxcmT56M9vZ2VFVV4fHjxzh8+LBOr8xVq1YhOjoad+7cQWVlJTZs2IDw8HD4+fnB0dERAwMDaGlpQVVVFQoKCtDT04OtW7cK74mlnT9/HmFhYfj+/Tt2796NqKgorFmzRqclk6bW1Y4dO0Q358jLywvBwcEoKipCZWUlqqqq9GqpeXt7IzExEUePHoVarUZ8fDzkcjk2bdqEhQsXYvLkyfj06ROKi4uRn58vZF137Nih10pIW2hoKNrb25GUlITOzk5ERUUhKChIyNLNmDEDPT09aGpqglKpRFlZGYaHhy1WdNlanJ2dcfnyZcTFxaGjowOhoaGQyWRYuXIl3NzcMDQ0hPb2dtTV1UGhUKC1tRWpqal6r8uca9vSpk6disTEROzcuRODg4OIjY3F5s2bERISAjc3N6jVarx//x5KpRLFxcV48+aNxcfwt2JgRmQlkZGRqKysRG5uLuLj4/We1/Sp1K53pGFvb4/bt28LTcwVCgUUCoXedr6+vkhJSbHIKrydO3dCoVBArVbj7NmzOs/JZDJcunQJwOh8ourqarx9+xZv3rxBQkKCzraOjo5ITk5GUlKS1QOzadOmISMjA3FxcaisrER9fb2QPTHHyZMn4eDggJSUFHR0dCA5OdnotnZ2dlZZ7aghlUqRmpoqNDFPT09Henq63naRkZF677lY7Nu3D0VFRQBGs2aGMlIbNmyAi4sLzp49i3fv3uHly5dGOyo4OjriyJEjZv3wOHDgADw9PXHp0iV8/PgR5eXlKC8vN7q9p6enyX6jYrFu3TrcuHEDJ06cQHd3N+RyOeRyucFtbWxsDNYMM/fatrTAwECkpqYiISEBPT09yM3NRW5urlX+1v8SBmZEVnTx4kUsX74c2dnZUKlUGBgYwNy5cxESEoI9e/aYXEk5f/58PHr0CDk5OXj69ClUKhX6+/vh4OAAqVQqNE621LJ2qVSK7OxspKWlobq6Gl++fDF4q3L69OnIysrCrVu38OTJEzQ3N2PSpEmYM2cOVq1ahe3btwtV/8eDk5MTMjMzUVBQgLy8PNTW1qKrqwsSiQSurq5YvHgxQkJCDFb4l0gkOHjwILZs2QK5XI4XL16gra0Nvb29sLW1xZw5cyCVShEUFISQkBCT9agsISgoCAUFBcjIyIBSqURrayvUajWcnZ3h5+eH8PBwi1f0t6QlS5Zg+fLlqKioQHl5Oerq6nQKKWssW7YMeXl5UCgUKCoqwuvXr/HlyxcMDQ3ByckJ//zzD1avXo0tW7aMaeXpunXrsHr1aigUCjx79gyvX79GV1cX+vv7YW9vj3nz5mHp0qVYv349AgICLFKOZjxoMpEPHjyAUqlEY2Mjenp6MGnSJDg7O8PT0xOBgYFYv369wfZG5l7b1rBixQoUFhYiKysLJSUlaGpqQm9vL6ZNmwZ3d3f4+/tj48aN4zKWv4VkxNikDCIas7a2NqxduxbAaFCmfeuMiIjoVzj5n4iIiEgkGJgRERERiQQDMyIiIiKRYGBGREREJBIMzIiIiIhEgqsyiYiIiESCGTMiIiIikWBgRkRERCQSDMyIiIiIRIKBGREREZFIMDAjIiIiEgkGZkREREQiwcCMiIiISCQYmBERERGJBAMzIiIiIpFgYEZEREQkEgzMiIiIiESCgRkRERGRSDAwIyIiIhIJBmZEREREIsHAjIiIiEgkGJgRERERiQQDMyIiIiKRYGBGREREJBIMzIiIiIhE4j/7VDZUnw1+YgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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HjKgS6NChAx4/fgxA/0nuhtJc5sHOzk7Y5oZKJzc3V2vXhsWLFxc5ZFjUcGhZevr0qdb6eCUdxjRE9+7d8euvvwIA0tLScPv27VIt/6K5SG15D/saKiUlRTguz4SXTA97zIgqAc31tmJjY8slOdN88jAjI4PzYspIWFiYMFzYrFmzYhMgzScky9O+ffugVCoB5M2rq4jlHPInKKV9WrF+/frCsb7z9iqK5i4Prq6uRoyExIaJGVEl0K9fP61J5upFTcuSs7Oz1pCp5krwVHLqvSGB4h88APIWzq0ImsOYAwYMKPVEfH3kH27UXDC4JDT3sLx//36p6tJUFkPYMTExwrHmwsRETMyIKgEbGxut+TkHDx4UViYvS7169RKON2/eXOb1V0Wav+SL64VUKpXYvn17eYeEGzduaCUyJVm7rCRu3ryp9bq0q+F7eXkJX9979+4V+vCFoTSX8tDngZL8lEoloqOjhddlsf0UVR5MzIgqiQkTJggbewPAl19+WebJ2bhx44SnDI8dO4bdu3frfS3XatKtQYMGwvGlS5eKfMjijz/+QGRkZLnHtGfPHuG4SZMmaNOmTbnfM/+TvnZ2dmjVqlWp6nRwcEDz5s0B5PXG5U/8Skqzd1qzx1Nft27dEoavX3nlFW7HRFqYmBFVEtWqVcPKlSuFuWDZ2dmYOnUqPvvsM9y8ebPQJ/iUSiVCQ0P12u+vYcOGmDJlivB61qxZWLJkidZEZk1yuRwhISGYPn16hfW6mJoWLVoIv5hfvHiBTz/9VGv+EZD3gMDPP/+MZcuW6dxrsyzl5uYKe5ACFdNblpiYiE8//VQrcRo9erTBWz/p4u3tLRxfuHCh1PUBeXPX1E9SxsXFGTynUzMOzV5oIoBPZRJVKg0aNMCOHTvw4YcfIjIyEiqVCocPH8bhw4fh4OCAli1bolatWrCxsUF2djbi4+MRFRWFtLQ0rXo6d+5c6ErpU6dORVxcHPbs2QOVSoV169YhKCgIrVq1QsOGDWFlZYXMzEzExcUhKipK6BkQ28rrYiGVSvHpp59i1qxZAICzZ8+if//+aNu2LerVq4e0tDRcvHgR6enpAIDvvvsOX375ZbnFc+rUKeFeUqkUgwcPLnWduvbKBPL+eLh//z7Cw8O1hhnbtm2LyZMnl/q+QN4OFf/9738BAMePH8ekSZNKXaeZmRl69+6NgwcPAgDGjBmDHj16oG7dukKPcs2aNQv9DJo92fruoEFVBxMzokrG1dUVW7duRVBQENatWycsE5CSkoIzZ84Uep1EIkH79u3x/vvvF/kEnkQiweLFi9GyZUusWrUK6enpkMlkuHbtWqEPBEgkErRr1650H6wSGzZsGB49eiQsFZGVlYWzZ89qlalWrRpmzZoFX1/fck3MNIcxu3btWiZ7TOqzV6basGHDMGvWrDJb26tJkyZo3749rly5gvDwcDx+/Fhr+LikPv/8c4SGhuLZs2fIzs7G0aNHtc67urrqTMyePn2K69evAwBatmzJif9UABMzokrI2toaEydOxOjRo3HmzBmcP38eYWFhSE5ORlpaGpRKJWrUqAEHBwd4enrCy8sL3t7eBv3CCggIwNChQ7Fv3z6cO3cOkZGRSElJQW5uLmxtbeHi4oJmzZqhU6dO6Nmzp9YTnVTQtGnT0KNHD2zatAlXrlxBSkoKbG1tUadOHfTo0QNvvfUWGjduXK4x5E/ey3vtMisrK1SvXh1NmzZF27ZtMXjwYDRt2rTM7/Pee+/hypUrUKlU2LlzJ6ZNm1bqOl1dXbFv3z789ddfOHv2LB48eIDMzMxiHzDYtWuXMK3gvffeK3UcVPlIVNwXgoiIKjGlUolBgwYhJiYGjo6OOHnyZJlskm4omUyGPn36ID4+Hq6urvjnn3/KZB4dVS6c/E9ERJWaVCoV9oZNSkoy6GnisnTgwAFhodupU6cyKSOdmJgREVGlN2DAAGHZj99//73U+3AaSi6X47fffgOQt6BsWTxUQZUTEzMiIqr0JBIJ5s6dC6lUiri4OK010yrCzp078eDBAwDA3Llzhac3ifLjHDMiIiIikWCPGREREZFIMDEjIiIiEgkmZkREREQiwcSMiIiISCSYmBERERGJBBMzIiIiIpFgYkZEREQkEkzMiIiIiESCiRkRERGRSJgbOwAyLqVSiezsbACAubk5JBKJkSMiIiIyDSqVCnK5HABgbW0NqbT0/V1MzKq47OxsREZGGjsMIiIik+bp6QlbW9tS18OhTCIiIiKRYI9ZFWdu/r8m4ObmBhsbGyNGQ2QYmUwm9Ph6enrCwsLCyBER6Y/t1/Rp/htq/j4tDSZmVZzmnDJLS0tYWloaMRoiw0ilUtjb2wMAqlWrVmY/GIkqAttv5VJWc7TZCkjAHwpkaszNzeHm5mbsMIhKhO2XdOEcMyIiIiKRYGJGREREJBIcuyKBTCbjHDMyKTKZDPfu3QOQ9/AKJ0+TKWH7JV2YmBGRyVKpVMjMzBSOiUwJ2y/pwqFMIiIiIpFgYkZEREQkEkzMiIiIiESCiRkRERGRSDAxIyIiIhIJJmZEREREIsHlMkgglTJPJ9NiZmaG+vXrC8dEpoTtl3RhYkYC/mAgU2NmZgYXFxdjh0FUImy/pAu7SIiIiIhEgokZERERkUhwKJME3CuTTE1ubi7Cw8MBAF5eXmy/ZFLYfkkX9pgRERERiQR7zEhgbs7mQKbFwsICXl5ewjGRKWH7JV34m5gEEonE2CEQGUQikXD4h0wW2y/pwsSMBAqlEglpmcYOg4iISG+ONaxhVonW4WRiRoKUjBz4Ldlv7DCIiIj0dmj2MLjY2xo7jDJTeVJMIiIiIhPHxIyIiIhIJJiYEREREYkEEzMiIiIikWBiRkRERCQSon8q88WLF9i/fz+Cg4Nx7949JCcnw9zcHI6OjmjVqhV69+6Nfv36wczMTO86U1JS8Pfff+Ps2bO4c+cOUlNTkZubi5o1a6Jp06Zo164dBg0aBHd392LrUiqVOHXqFE6fPo1r164hKSkJz58/h62tLVxcXIQYe/bsqXMBwYCAAFy8eLHA+1KpFNWrV0eDBg3QqVMnvPPOO2jUqJHen5GIiIhMj0SlUqmMHURhtm/fjmXLliEtLa3Icq+88gq+/fZbdOjQochySqUSv/76K/744w9kZha/Xle3bt0wa9YsvPLKKzrPX7x4EQsWLEB0dHSxdTk7O2PatGnw9/fXer+wxCw/CwsLzJw5E6NGjSq2rCE092qr08iNy2UQEZFJMeZyGeWx36loe8yWLFmCdevWAcjbKmjgwIHw8fGBq6srcnNzcf/+fRw8eBAXLlzA3bt38f777+Onn35C//79ddaXk5ODzz77DKdOnQKQl+gMHDgQr732GurXrw8rKyskJycjPDwcx48fx+3bt3H27Fls27YNs2fPLlDfrl27MH/+fMhkMgBAmzZt0LdvXzRv3hz29vZ48eIFHj9+jNOnTyM4OBiJiYlYuHBhgcRM04EDB4RjpVKJ+Ph4HDlyBHv27IFMJsOCBQvg6uqKXr16lfTLSkRERCImysRs06ZNQlJWp04d/Prrr2jevLlWmQ4dOmD48OE4cOAAZs6cidzcXEyfPh2NGjUqUBYAvvnmGyEpe/XVV/Gf//wH9evXL1CuZ8+emDp1Kk6dOoWFCxfqjO/8+fOYM2cOlEolbGxssGjRIp0JYZcuXTB8+HDExsZi2bJlOHPmTJGfO//QqaenJ3r16oWWLVvi+++/h0qlwsqVK5mYERERVVKim/wfFxeHJUuWAABsbGywfv16nYmWmq+vr5BA5ebm4quvvkL+0dljx45hz549AAAPDw+sX79eZ1KmydvbG7t27UKXLl203s/Ozsb06dOhVCohlUrx22+/FdpLp1a/fn0sX74cc+bMKbJcYUaNGgVXV1cAwM2bN5GcnFyieoiIiEjcRJeYbdiwAS9fvgQAfPjhh2jSpEmx1/j5+aFHjx4AgOjoaKFnTO3XX38VjhctWgQbGxu9YqlRowZ69+6t9d6uXbvw7NkzAHkJU6dOnfSqCwCGDBmid1lNUqkUrVq1El4/efKkRPUQERGRuIkqMVOpVNi3bx8AwMrKCu+8847e17733nvC8e7du4XjqKgoREREAMgb/mzZsmWpYlTXLZFIMGbMmFLVZQhz8/+NOiuVygq7LxEREVUcUc0xu3PnjvAEZocOHVC9enW9r33ttddgZWWFnJwcXLlyRXj/0qVLwnHPnj1LFV9GRgZu374NAGjSpAkaNmxYqvoMofnkp7Ozc4Xdl4iIyFgsoSi2THZWFjILrkalk62t+Dc7F1ViFhkZKRy3aNHCoGvNzMzg6emJ69evIyUlBQkJCXBxcdGqU3M4sCSioqKE3qrS9rwZIjg4GHfu3AEANGjQAHXr1q2wexMRERlLf7tHxZbZ8Psvetc3f/780oRTIUSVmKWmpgrHjo6OBl9fu3Zt4TgtLQ0uLi5aa6A5ODiUKj7NujTvVR40l8tYuXKl8P4HH3xQrvclIiIi4xFVYqa56Ku+E/Q1aV6TkZFRJnVq0qzL2tq6VHXp4uHhUeT5ESNGGDTvjoiIiEyLqBIzzbHfrKwsg6/XvMbOzq5M6tSkWVd2dnap6tKXtbU12rVrh9GjR8PHx6dC7klERETGIarErFatWsJxUlKSwddrru9lb2+v9f/850tCs66SxFcczZX/zczMYGtrCycnJ4P2ASUiIqosjmQU/5Ddlmlvwqlm6UbExERUiZnmUN6tW7cMulahUCAqKgpA3lwyFxcXAHmr56vdvHkT3bp1K1V8UqkUSqXS4Pj0oc+m6URERFVFLorvmLC2sTGJpy31Jap1zNzd3YVeqcuXL+PFixd6X3vu3DlheFFzM/OOHTsKx6dPny5VfHZ2dsIuBPfv38fDhw9LVR8RERGRJlElZhKJBIMHDwaQt+n49u3b9b72r7/+Eo6HDh0qHHt4eAjLZFy+fBk3b94sVYzqTchVKhWCgoJKVRcRERGRJlElZgAwZswYWFpaAgACAwP16pX6+++/ERwcDCCv183b21vr/OTJk4XjmTNn6v0QwPPnz3Hy5Emt94YNGwYnJycAeZutX7x4Ua+6AGDv3r16lyUiIqKqR3SJWf369fHVV18ByHuKcuzYsVqLxOZ36NAhfP311wAACwsL/Pjjj5BIJFpl+vbtK+xTGRUVhbFjxyIuLq7IOE6fPo233noL58+f13rf2toaP/30kzDXbNKkSfjnn3+KrOvJkyf4/PPP8f333xdZjoiIiKo2UU3+VwsICMCjR4+wceNGPHnyBMOGDcOgQYPg4+ODevXqQS6XIyYmBgcPHhQSJ0tLS/z000/CHLD8vvnmG6Snp+PUqVO4ceMG+vfvjwEDBqB79+5wdXWFlZUVkpKScOvWLRw7dqzIIc+uXbvi+++/x/z585GVlYVPPvkEbdq0Qb9+/dC8eXPUrFkTL168wOPHj3HmzBmcPHkSubm5Bm0xRURERFWPKBMzAJg9ezaaNm2KFStWIC0tDfv27RM2OM/Pzc0N3333ndak//ysra3xyy+/YPXq1Vi7di0yMzOLrBMAevXqhXfffVfnuWHDhqFBgwZYsGABoqOjcf36dVy/fr3QuurVq4cvvvii0PNEREREok3MAGDkyJEYNGgQ9u/fj+DgYNy9excpKSkwMzODo6MjvLy80Lt3b/Tv31+vtb6kUik++ugjjBw5En///TfOnj2L6OhopKamQiaToWbNmmjatCnatWsHPz8/uLm5FVlfp06dsG/fPpw6dQrBwcG4du0akpKS8OLFC9jY2KBOnTpCjD179oS5uai/3ERERGRkEpVKpTJ2EGQ8ubm5CA8PBwDUaeQGvyX7jRwRERGR/g7NHgYXe+OsY6b5O9TLy0t4eLE0RDf5n4iIiKiqYmJGREREJBJMzIiIiIhEgokZERERkUjwMUESONhZ4dDsYcYOg4iISG+ONayNHUKZYmJGAjOpFC72VsYOg4iIqMpiYkYCrpxCpkalUkEmkwHI25It/3ZsRGLG9ku6cI4ZCeRyubFDIDKITCZDeHg4wsPDhV9wRKaC7Zd0YWJGREREJBJMzIiIiIhEgnPMSMD5DWRqpFIpnJ2dhWMiU8L2S7owMSMBN1knU2Nubo4GDRoYOwyiEmH7JV2YohMRERGJBBMzIiIiIpHg2BUJFEolEtIyjR0GiYBjDWuYmcCcF5lMhtu3bwMAmjdvDgsLCyNHRKQ/tl/ShYkZCVIycuC3ZL+xwyARODR7GFzsbY0dRrE0F+jkAslkath+SRfx/0lMREREVEUwMSMiIiISCSZmRERERCLBxIyIiIhIJJiYEREREYlEhSdmCoUC7dq1g4eHB4YOHVpkWZVKhc6dO8PDwwMeHh7YuXNnkeX37NkjlN28ebPwfkBAgPB+cTZs2CCU7dq1KyIjI4Vzq1atEs6FhoYWWxcA+Pj4wMPDAz4+PjrPz5gxQ6hT8z9PT0906NABfn5++Pbbb4VHqomIiKjyqvDEzMzMDG3btgUAREZGIiMjo9Cyd+7cQVpamvD6ypUrRdZ9+fJl4bhjx44Gx/bHH39g4cKFAAAnJycEBQXB09PT4HrKgkqlwosXLxAVFYXNmzfD398fy5cvN0osRGJlbm6Opk2bomnTptxSjEwO2y/pYpSW0LFjR4SEhECpVOLq1at4/fXXdZZTJ1pmZmZQKBRaiVdR5WvVqoVXXnnFoJhWr16NFStWAABcXFywYcMGNGnSxKA6Smvt2rXChrZKpRLJyckIDg7G5s2bIZfL8euvv8LZ2RmjRo2q0LiIxEoqlaJWrVrGDoOoRNh+SRejzDHr0KGDcFxUsqU+179/fwDAo0ePkJCQoLNscnIyHjx4AABo3749JBKJ3vGsWrVKSMrq1auHv/76q8KTMgBo3Lgx3N3d4e7uDk9PT3Tr1g2zZ8/GqlWrhDKBgYFQKBQVHhsRERGVP6MkZq1bt0a1atUA6JeY9evXDw0bNgRQ+HBmSYcx//Of/yAwMBAAUL9+ffz111/CvcTCx8dHSGZTUlJw8+ZNI0dEJA4qlQq5ubnIzc3lyulkcth+SRejJGaWlpZo3bo1ACA8PBy5ubkFyjx+/FjoHWvfvj3at28PoPBETvN9zR65oixZsgS//fYbgLzeqk2bNsHV1VX/D1KB1F8vAHjy5IkRIyESD5lMhvDwcISHhwtb2xCZCrZf0sVoy2Wok6fc3FzcuHGjwHl1otW4cWM4OjoKidmlS5d01qfuSbOzs0Pz5s2Lvf/333+PdevWAQCaNm2KoKAg1KlTx/APUkE0J4YqlUojRkKVgSUURf6XnZWFzMzMIv8jIqKyZ7THQDp27IjVq1cDyEvC8g8/qhOzdu3aaf3/zp07SE9PR82aNYWyGRkZwrIWbdu2hZmZWaH3ValU+Pbbb7FlyxYAgLu7O/788084OjqW0ScrH9HR0cKx+gEBopLqb/eoyPMbfv+l2Drmz59fVuEQEdH/M1pi1qZNG5ibm0Mul+scnlS/p+5Zc3NzQ61atZCamoqrV6/C29tbKHvt2jVhQnxx88vmzp2LHTt2AACaN2+OdevWwcHBweD4Y2Nj9Xqapiy6pyMjI3HmzBkAgLW1Nby8vEpdJxEREYmP0RIzW1tbNG/eHOHh4UJipe7pyv+EpVq7du1w4sQJXL58WSsxM2R+mTopc3Nzw4YNG7R63gwxa9asEl2nL5VKhaSkJAQHB2PZsmVC4hkQECA8OEFERESVi1FXtOvYsSPCw8ORmZmJ27dvo1WrVgD+N4/M0dERjRs3Fsq3b99eSMw0qeeXVatWrdjeJIlEApVKhdjYWNy6dQtdu3Ytw09UOr179y7yfK9evfDJJ59UUDRERERU0YyamLVv316YgH/58mUhMVMnXpq9ZcD/esNu3ryJnJwcWFlZITc3F2FhYQCAV199FZaWlkXec+7cufjuu+/w8uVLTJkyBX/88YfeT3Fq2rhxIzp37lxsOR8fH8TFxRlcv5qFhQW8vLwwYsQIDB482KD12YgKcySj6CVhtkx7E041bSooGiIiUjNqYtahQwehB+vKlSsYO3YsgP/1gKkn/Ku1aNEC1tbWyM7OxvXr19GlSxeEh4fj5cuXQn3FGTVqFHJycvDjjz8iOzsbkyZNwp9//qm1HIWxaK78L5VKYWNjA0dHx2KTTSJD5aLwB2QAwNrGBra2thUUDRERqRltuQwAsLe3R7NmzQD8r5dM8wnL/D1m6t4jzfKaC87qu7Ds+PHj8fHHHwv3++CDD7Q2KzcWzZX/X3nlFdSrV49JGVER1D8TvLy8YGFhYexwiAzC9ku6GDUxA6C1ov29e/dw5coVKJVK2NjYoEWLFgXK519oVj0fzcLCAm3atNH7vlOnTsWECRMAAOnp6Rg3bhzu3btXmo9CRBVMIpHA0tISlpaWHOYnk8P2S7qIJjED8nq/1D1gbdq00bkemToxu3HjBnJzc3Ht2jUAecOcNjaGzYn58ssvERAQACDvSdCxY8fi8ePHJfocRERERKUlqsTs0qVLQg9Y/mFMNfUCsllZWdi9ezdevHhRoB5DzJ49G2+//TYAIDExEe+99x6ePn1aorqIqGIplUqkpqYiNTWVO2KQyWH7JV2Mnpi5uLgIm4aHhoYiIiICQOGJmZ2dHdzd3QEAf/zxh/C+IRuXa5JIJPj222/h6+sLAIiLi8PYsWPx7NmzEtVHRBVHLpcjJiYGMTExkMvlxg6HyCBsv6SL0RMz4H+9XQkJCcjNzYW5uXmR88XUSZt62FEqlRaayOlDKpViyZIl6NevHwDgwYMHeP/995GSklLiOomIiIgMJarETK158+awtrYutHz+JMzd3R01atQoVQxmZmZYtmwZevXqBSBvT87x48fj+fPnpaqXiIiISF+iSMzyD0MW1/uV/3xJhzHzs7CwwMqVK/Haa68BAG7duoUJEyYgMzOzTOonIiIiKopEpVKpjB0EGU9ubi7Cw8MBAHUaucFvyX4jR0RicGj2MLjYi3+BWc326+XlxXX/yKSw/Zq+8vg3FEWPGRERERExMSMiIiISDaPulUlEVBoSiUTYyoYrp5OpYfslXZiYEZHJsrCwQOvWrY0dBlGJsP2SLkzMSOBgZ4VDs4cZOwwSAccahS9XQ0RE5YeJGQnMpFK42FsZOwwiIqIqi4kZCbhyCpkauVwu7G1bt25dmJvzRxqZDrZf0oVPZZKAe7WRqVEqlUhMTERiYiI3gSaTw/ZLujAxIyIiIhIJJmZEREREIsHEjIiIiEgkmJgRERERiQQTMxLwiSAiIiLjYmJGAm4JQkREZFzsIiGBQqlEQlqmscOgSsCxhjXMpOX/d59UKoW9vb1wTGRK2H5JFyZmJEjJyIHfkv3GDoMqgUOzh8HF3rbc72Nubg43N7dyvw9ReWD7JV2YohMRERGJBBMzIiIiIpHgUCYRmSyZTIZ79+4BANzc3GBhYWHkiIj0x/ZLujAxIyKTpVKpkJmZKRwTmRK2X9KFQ5lEREREImEyPWZZWVnYt28fTp48icjISKSlpUGlUsHOzg6urq5wd3dH27Zt0aNHD9StW7fIup48eYK///4b586dw8OHD5GSkgKlUomaNWvC3d0d7du3h6+vLxo0aFDg2tDQUIwZMwYAMHXqVHz88cfFxj5jxgzs2bMHAHDixAnUr19f6/zu3bsxc+ZMAMCiRYvg7++v19eEiIiIKheTSMyuXbuGzz//HE+ePClwLjU1FampqYiIiMDu3bvh6OiIs2fP6qzn5cuXWLZsGbZs2YLc3NwC5xMTE5GYmIiQkBCsXLkS/fv3x9dff11sokdERERUFkSfmN2/fx/jx48XxuF9fHzQr18/NGnSBBYWFkhNTUVkZCTOnTuH0NDQQutJSUnBlClTcP36dQCAra0t3nzzTXTp0gV16tSBubk5kpKScPXqVRw7dgwPHjzA4cOH0aZNG4wdO7YCPikRERFVdaJPzJYvXy4kZYUN83Xr1g3jx49HSkoKDh8+XOC8UqnEZ599JiRl3t7e+OGHH1C7du0CZX18fPD5559j//79+PHHH8v2wxAREREVQdSJmUKhwOnTpwEArVq1KnbulYODA0aNGlXg/Y0bNwq9ad27d0dgYGCRG3ZLpVIMGTIEXbp0wf3790vxCYiIiIj0J+rELCUlBTk5OQCARo0alaiO3NxcrF27FgBQrVo1LFy4sMikTFOdOnVQp06dEt2XiMqfmZmZ8DCNmZmZkaMhMgzbL+ki6sRMc7E99SJ8hgoJCUFiYiIAoH///nBxcSmT2IiqKksoii2TnZWFTAPWyrS1Ldm+mmZmZvyeJpPF9ku6iDoxs7e3h6urK+Li4hAZGYk1a9bggw8+gFSq//Jrly5dEo579epVDlESVS397R4VW2bD778YVOf8+fNLGg4RUaUi6sQMAEaPHo0lS5YAAJYtW4atW7fCx8cH7dq1g5eXl861xjRFRkYKxy1btizT2JKTkxEdHV1suefPn5fpfYmIiKhyEn1iNnbsWNy9exe7du0CAMTFxSEoKAhBQUEAAEdHR3Tq1Am+vr7w9vaGRCLRuj4tLU041vUUZmls2bIFW7ZsKdM6iUh/ubm5CA8PBwB4eXnB0tLSyBER6Y/tl3QR/ZZMUqkUCxcuxLp169CjR48CE/eTkpJw6NAhTJkyBW+99RYePdIeZlEvtQEA1tbWFRIzERERUUmIvsdMrVu3bujWrRsyMjJw5coVhIeHIyIiApcvX8aLFy8AABEREXj33Xexe/duODs7A9CeVJydnQ07O7syi6kkWzIRmbojGQ2LLbNl2ptwqmlTAdEQEVUuJpOYqdnZ2aFnz57o2bMngLyu4AMHDmDJkiVIT0/Hs2fP8PPPP+OHH34AkPcAgVpSUlKZJmZEVVEuin+s39rGpsRPWhIRVWWiH8osjqWlJYYNG4Zly5YJ7x07dgxKpRIA4OnpKbx/69atCo+PiIiISF8mn5ip9ejRQ9hsPD09XZj037FjR6FMcHCwESIjIiIi0k+lScwACPPKNHXv3l14/8iRI0hISKjosIiIiIj0UmkSs+zsbNy9exdA3jy0WrVqAcgb6hw/fjwA4OXLl5g1axYUiuJXLgeAhIQEnD9/vnwCJiIiIspH1IlZZmYmhg8fjlOnTglzxnRRKpVYsGCBsDSGj4+P1npmY8aMQefOnQHkbdH00UcfISUlpdD6VCoVDhw4AH9/f0RFRZXRpyGismZubg4PDw94eHjovQcukViw/ZIuom8JYWFhmDx5MlxcXNCnTx+0adMG9erVg52dHZ4/f45bt25h165dwgr81atXx6effqpVh1QqxYoVKzB58mTcuHEDp06dQp8+feDr64suXbqgTp06MDc3R1JSEq5fv46jR48iJibGGB8XV65c0aucj4+P1hOnRFWRVCrlk9Zksth+SRdRJ2bm5uZwcnLCs2fPkJCQgE2bNmHTpk2Flm/cuDGWLVuG+vXrFzjn4OCAoKAgLF26FFu2bEFmZia2bt2KrVu36qxLIpHA19cXAwYMKLPPo4+dO3di586dxZbbu3cvEzMiIqJKRtSJWbVq1fDvv//i+vXrOHfuHG7cuIH79+8jOTkZL1++hLW1NZydneHp6YnevXvjjTfeKHJLi2rVqmH27Nl4//33cfDgQZw/fx4PHjxASkoKVCoVatasCXd3d3Ts2BG+vr5wdXWtwE9LRIZSKpXIysoCANjY2EAqFfXsDCItbL+ki0SlUqmMHQQZj+ZebXUaucFvyX4jR0SVwaHZw+BiX/4LzHKvQTJlbL+mrzz+DZmeExEREYkEEzMiIiIikWBiRkRERCQSop78TxXLwc4Kh2YPM3YYVAk41rA2dghERCaJiRkJzKRSuNhbGTsMIiKiKotDmSTgA7pERETGxcSMBHK53NghEBERVWkcyiSBhYWFsUMgMoilpSXat29v7DCISoTtl3RhjxkRERGRSDAxIyIiIhIJDmWSQKFQGDsEIoMoFAokJSUBABwdHWFmZmbkiIj0x/ZLujAxI4FSqTR2CEQGUSgUiI2NBQDUqlWLv9jIpLD9ki4cyiSBuTnzdCIiImNiYkYCiURi7BCIiIiqNHaRkEChVCIhLdPYYejFsYY1zKT8u4KIiCoXJmYkSMnIgd+S/cYOQy+HZg+Di72tscMgIiIqU+xyICIiIhIJJmZEREREIsGhTCIyWRKJBLa2tsIxkSlh+yVdmJgRkcmysLCAp6enscMgKhG2X9LFpBKzixcvIiAgQHi9ZcsWtGvXzqA6Hj16hMOHD+P8+fN49OgRUlNTIZfLUb16ddSvXx8tWrRAr1690K1bt0I39d69ezdmzpxp0H1nzpyJsWPHar0XEBCAixcvFigrlUpRvXp1NGjQAJ06dcI777yDRo0aGXQ/IiIiMj0mlZjt2bNH6/XevXv1TsyeP3+OxYsXY9++fZDL5QXOJycnIzk5GTdu3MCWLVtQu3ZtTJgwAWPGjKnw1ZiVSiXS09ORnp6OiIgIBAUFYebMmRg1alSFxkFEREQVy2QSs5ycHPzzzz8AABsbG2RlZeHIkSOYM2cOLC0ti7z28ePHmDBhAu7fvw8AsLe3x6BBg9ChQwc4OzvD2toaKSkpiImJQUhICM6dO4fk5GQsXrwYb775JpycnAqt+7PPPkPv3r2Ljb+oOgDgwIEDwrFSqUR8fDyOHDmCPXv2QCaTYcGCBXB1dUWvXr2KvRdRVSGXy/Hw4UMAQKNGjbh7BZkUtl/SxWRawbFjx5CZmbf46Zw5czBr1iykp6fj5MmT6N+/f6HXZWVlYdKkSUJS9vbbb+Prr7+GnZ1dgbI9evTAe++9h8ePH+OXX37B7t27i43LxcUF7u7uJfxU/5O/Dk9PT/Tq1QstW7bE999/D5VKhZUrVzIxI9KgVCqRlpYGAGjQoIFxgyEyENsv6WIyy2Xs3bsXAODh4YFhw4ahSZMmWu8XZunSpbh37x4A4N1338WCBQt0JmWaGjRogEWLFmH16tWoVq1aqWMvjVGjRsHV1RUAcPPmTSQnJxs1HiIiIio/JpGYJSYm4vz58wAAPz8/rf+HhIQgJSVF53VJSUnYuXMnAKBu3br4+uuvDbqvj48PatSoUdKwy4RUKkWrVq2E10+ePDFiNERERFSeTCIxO3DgABQKBaRSKXx9fQEAvr6+kEgkkMlkOHjwoM7rDh06hJcvXwIAhg8fDisrqwqLuSxpzjtQKpVGjISIiIjKk0kkZvv27QMAdOrUCS4uLgDyhhvbtm0LoPDhzEuXLgnHPXr0KN8gy1F0dLRw7OzsbMRIjM8SClhCgeysLGRmZmr9R0REZOpEP/n/9u3biIqKAvC/4Us1Pz8/XL16FTdv3sTdu3fxyiuvaJ1XXyeVSsttEb+EhAStxKkwJX1AIDg4GHfu3AGQl4zWrVu3RPVUFv3tHgEANvz+S4Fz8+fPr+hwiIiIypToEzN1b5iVlRX69eundW7AgAH44YcfIJPJsHfvXnz55Zda51NTUwEA1atXL3JJjWfPngll86tZs6bQS6fLihUrsGLFimI/hzpJ1IfmchkrV64U3v/ggw/0roOIiIhMj6gTM7lcLswf8/b2LvA0pb29PXr27Injx4/jwIED+PzzzyGV/m90Vj28ZW1tXeR91qxZg40bN+o8N3ToUCxevLg0H0MvHh4eRZ4fMWIE3nnnnXKPg8iUSKVSYXhf83ufyBSw/ZIuok7MQkJCkJSUBKDgMKaan58fjh8/jvj4eISGhqJr167COVtbWzx//hzZ2dnlFuOiRYvg7+9fLnVbW1ujXbt2GD16NHx8fMrlHkSmzNzcnOs/kcli+yVdRJ2YqYcx7e3tC5287+3tjRo1auD58+fYu3evVmJmb2+P58+f48WLF8jNzS10OHP27NmYPXu28Do2Nlav1fzLkubK/2ZmZrC1tYWTk1OFbwcldkcyGgIAtkx7E041bYwcDRERUdkSbWL24sULnDx5EgCQlpamtZZXYY4ePYr58+fDxibvF7anpycePXoEpVKJqKgoeHl5lWvMpVEWuwdUBbnIS1StbWxga2tr5GiIiIjKlmgHtQ8fPiysQaavrKwsHD16VHjdsWNH4fjMmTNlFhsRiYNMJkNYWBjCwsIgk8mMHQ6RQdh+SRfR9piphzGdnJwwc+bMYsv/+OOPiI+Px759+zBkyBAAwMCBA/HTTz8hNzcXO3bswPjx442+xRIRlR2VSiX8QlOpVEaOhsgwbL+kiygTs8ePH+Pq1asAgH79+mHQoEHFXnP9+nVs3LgRFy5cQEJCAlxcXODo6Ihhw4Zhy5YtePLkCX766SfMmTOnvMMnIiIiKhFRDmXu27dP+Osh/9plhVGXUyqVwk4BAPDll18KG54HBQXhm2++KXaV+OfPn5ckbCIiIqJSEWWPmTqxql27Njp06KDXNe3atYOTkxOePXuG/fv3Y+LEiQAAOzs7/Pbbb5g4cSIePHiALVu24MiRI3jzzTfRoUMHODs7w8bGBhkZGXj48CHOnTuHY8eOCfUWt7+mviv/29nZoV69enp9FiIiIqqaRJeYXblyBY8e5W2706dPH70X3ZNKpejbty82b96MO3fuICIiQniSs1GjRti+fTsWLVqE/fv3IzU1FUFBQQgKCiq0vtq1a+ODDz5AQEBAkffVd+X/3r1745dfCm4jRERERKQmusRMcxhS32FMtTfeeAObN28GkPfwgOYSGzVr1sTixYsxZcoUHDp0CBcuXMDDhw+RlpYGhUKBGjVqoG7dumjVqhW6d++Onj17wsLComw+FBEREZEeJCo+ClKl5ebmIjw8HABQp5Eb/JbsN3JE+jk0exhc7LmOWVWn2X69vLyK3BOXSGzYfk1fefwbiq7HjIhIX+bm5mjatKlwTGRK2H5JF7YEIjJZUqkUtWrVMnYYRCXC9ku6iHK5DCIiIqKqiD1mRGSyNFdOt7CwgEQiMXJERPpj+yVdmJiRwMHOCodmDzN2GHpxrGFt7BBIBGQyGSdPk8li+yVdmJiRwEwqhYt90QvqEhERUfnhHDMScOUUIiIi42JiRgK5XG7sEIiIiKo0JmZEREREIsHEjIiIiEgkmJgRERERiQQTMyIiIiKR4HIZJOBebWRqLCws4OXlJRwTmRK2X9KFv4lJwFWnydRIJBIuykkmi+2XdOFQJhEREZFIsMeMBAqlEglpmYWed6xhDTMpc3kSD6VSifT0dABAzZo1IWX7JBPC9ku6MDEjQUpGDvyW7C/0/KHZw+Bib1uBEREVTS6XIyYmBgD3GiTTw/ZLujA9JyIiIhIJJmZEREREIsHEjIiIiEgkmJgRERERiUSVnPyflZWFffv24eTJk4iMjERaWhpUKhXs7Ozg6uoKd3d3tG3bFj169EDdunW1rp0xYwb27Nlj0P327t2L5s2bC6+nTp2KY8eOAQDWr1+Prl27Fnn9iRMn8OGHHwIAhgwZgiVLlhh0fyIiIjINVS4xu3btGj7//HM8efKkwLnU1FSkpqYiIiICu3fvhqOjI86ePVvmMcybNw+hoaF4/vw55s2bhwMHDsDKykpn2RcvXuCbb74BANSuXRszZ84s83iIiIhIHKpUYnb//n2MHz8emZl5a3X5+PigX79+aNKkCSwsLJCamorIyEicO3cOoaGhxda3du1aODs7F1uuUaNGWq+dnZ0xffp0zJ07F48ePcLPP/+Mr7/+Wue1P/74IxITEwEAc+bMgb29fbH3I6oqJBKJsJUNd64gU8P2S7pUqcRs+fLlQlK2aNEi+Pv7FyjTrVs3jB8/HikpKTh8+HCR9TVu3Bj169cvUSzDhw/HgQMHcPHiRWzYsAGDBg1Cq1attMqEhoZix44dAPKSyIEDB5boXkSVlYWFBVq3bm3sMIhKhO2XdKkyk/8VCgVOnz4NAGjVqpXOpEyTg4MDRo0aVW7xSCQSfP/996hWrRoUCgVmz54NuVwunM/JycHcuXOFuW/q4UwiIiKqvKpMYpaSkoKcnBwABYcWjaVRo0aYOnUqACAyMhJ//PGHcG7lypV4+PAhAGD69OlwcXExSoxERERUcapMYqYexweAe/fuGTESbePGjUOLFi0AAP/9738RExODiIgIrF+/HgDQsWNHjBgxwogREomXXC7H48eP8fjxY60eZyJTwPZLulSZxMze3h6urq4A8nqn1qxZA6VSaeSoAHNzc3z//fcwMzNDbm4u5syZgzlz5kChUKBatWpYsGCBaCaFZmdlCXP0iMRAqVQiMTERiYmJovh+JjIE2y/pUqUm/48ePVpYA2zZsmXYunUrfHx80K5dO3h5eaFBgwYG1ffgwQNkZWUVWcba2rrYelu2bImxY8di7dq1uHLlivD+1KlT0aRJE4NiKk8bfv8FADB//nwjR0JERFQ5VanEbOzYsbh79y527doFAIiLi0NQUBCCgoIAAI6OjujUqRN8fX3h7e1dbE/V+PHji71np06dhPqL8sknn+DQoUN4+vQpAKBFixYYN25csdcRERFR5VFlhjIBQCqVYuHChVi3bh169OgBc3PtvDQpKQmHDh3ClClT8NZbb+HRo0cVFtvDhw+RlJQkvHZwcCgQHxEREVVuVfI3f7du3dCtWzdkZGTgypUrCA8PR0REBC5fvowXL14AACIiIvDuu+9i9+7dhS4ie+LEiRKvY6ZJvVyGTCYT3gsJCcGBAwfg6+tb6vqJiIjINFSpHrP87Ozs0LNnT0ydOhW//vorzp07h4ULF6JmzZoAgGfPnuHnn38u9zg2btyI8PBwAHnDo+pEcOHChUhNTS33++vrvQkf4ssvvzR2GERERJVWlU7M8rO0tMSwYcOwbNky4b1jx46V69Myjx8/FpI/d3d3TJs2DXPnzgWQt/aamDYst7axga2trbHDICIiqrSYmOnQo0cP1K1bFwCQnp6OtLS0crvXvHnzkJ2dDalUih9++AEWFhZ444030KdPHwDAnj17cP78+XK7P5Epk0qlsLe3h729PaRS/jgj08L2S7qwJRRCn83JS2vXrl04d+4cACAgIEBrz7R58+bBzs4OQN7yFC9fviz3eIhMjbm5Odzc3ODm5saHZcjksP2SLkzMdMjOzsbdu3cB5M1Dq1WrVpnfIykpSRimdHV1xWeffaZ13sXFBV988QWAvCc2AwMDyzwGIiIiEpcqk5hlZmZi+PDhOHXqVJFzxpRKJRYsWCCscO/j41MuK+9/9913SE9PBwB8++23sLGxKVBm5MiRaNeuHQBg3bp1iIyMLPM4iIiISDyqVN9pWFgYJk+eDBcXF/Tp0wdt2rRBvXr1YGdnh+fPn+PWrVvYtWsXoqOjAQDVq1fHp59+Wmh9+qz8DwBOTk5avW7Hjx/HP//8AwAYPHgwevToofM6iUSCBQsWYMiQIZDJZJgzZw62b9/OuQhE/08mkwl737q5uWntiUskdmy/pEuVSczMzc3h5OSEZ8+eISEhAZs2bcKmTZsKLd+4cWMsW7asyHXK9Fn5HwBmzpyJsWPHAgBevHiBb7/9FkDeIrIzZ84s8tpXXnkFkyZNQmBgIMLDwxEUFIT33ntPr/sSVXYqlUro3VapVEaOhsgwbL+kS5VJzKpVq4Z///0X169fx7lz53Djxg3cv38fycnJePnyJaytreHs7AxPT0/07t0bb7zxBiwtLcs8jh9//BGJiYkAgFmzZuk1f23SpEk4fPgw7t27hxUrVqBv376oV69emcdGRERExiVRMU2v0nJzc4XFbes0coPfkv2Flj00exhc7LmOGYmHZvv18vIqlz+miMoL26/pK49/Q05WIiIiIhIJJmZEREREIsHEjIiIiEgkmJgRERERiUSVeSqTiudgZ4VDs4cVet6xhnUFRkNUPDMzM2FJGzMzMyNHQ2QYtl/ShYkZCcykUrjYWxk7DCK9mZmZwcXFxdhhEJUI2y/pwqFMIiIiIpFgYkZEREQkEhzKJIFMJuMCh2RSuEAnmTK2X9KFPWZEREREIsHEjIiIiEgkmJgRERERiQQTMyIiIiKRYGJGAnNzPgtCRERkTEzMSCCRSIwdAhERUZXGLhISKJRKJKRllmmdjjWsYSZl/k9ERKQPJmYkSMnIgd+S/WVa56HZw+Bib1umdRKpmZubw8PDQzgmMiVsv6QLWwIRmSypVAo7Oztjh0FUImy/pAvHmIiIiIhEgj1mRGSylEolsrKyAAA2NjaQcj4jmRC2X9KFrYCITJZcLkdUVBSioqIgl8uNHQ6RQdh+SZdK1WMWGhqKMWPGCK9tbGxw7tw5WFtbF3ldTk4OunXrhoyMDOG9jRs3onPnzkVed/HiRQQEBAivt2zZgnbt2hkcpyYrKyvY29vD09MTffv2hZ+fHze2JSIiqiIqdY9ZVlYWjh8/Xmy5EydOaCVl+tqzZ4/W67179xpcR345OTmIj49HcHAwZs+eDX9/f8TGxpa6XiIiIhK/SpuYVatWDQCwb9++Ysuqy6iv0UdOTg7++ecfAHk9cwBw5MgR5ObmGhTnyJEjceDAAeG/9evXY/bs2ahTpw4A4M6dO5gyZQoUCoVB9RIREZHpqbSJmY+PDwDg3LlzePbsWaHlkpOTcfbsWQBA79699a7/2LFjyMzMW4x1zpw5AID09HScPHnSoDhr164Nd3d34b+uXbtizJgx+Pvvv+Hq6goAiI6OxrFjxwyql4iIiExPpU3MunXrBicnJygUCvz999+Fljt48CDkcjmcnJzw2muv6V2/etjSw8MDw4YNQ5MmTbTeLy07OztMmTJFeH3u3LkyqZeIiIjEq9ImZmZmZhg0aBCAoocz1efefPNNmJmZ6VV3YmIizp8/DwDw8/PT+n9ISAhSUlJKHLcm9YrQABAfH18mdRIREZF4VdrEDAAGDx4MALh16xbu3LlT4Pzdu3dx8+ZNrbL6OHDgABQKBaRSKXx9fQEAvr6+kEgkkMlkOHjwYBlED1hYWAjH3K6DiIio8qvUiVmLFi3QrFkzALp7zdTvubu7o3nz5nrXq76uU6dOcHFxAQA0aNAAbdu2BVB2w5n37t0TjtXzzUyFJRSwhALZWVnIzMwU/iMqS5aWlmjfvj3at2/PZWXI5LD9ki6VOjED/tcTdvDgQahUKuF9lUqFAwcOaJXRx+3btxEVFQXgf8OXaurXN2/exN27d0sVt0KhwNq1a4XX/fr1K1V9Fa2/3SP0t3uEDb//gqVLlwr/ERERUeEqfWLm5+cHqVSKp0+fIjQ0VHg/NDQUT58+1RqO1Ie6N8zKyqpAsjRgwABh+LGkvWYpKSk4f/48Ro8ejVu3bgHIS8o6dOhQovqIiIjIdFT6xMzFxUVYwV9zOFN93KVLF2E4sjhyuVyYP+bt7Q07Ozut8/b29ujZsyeAvHloSqWy2DoDAwPh4eEh/Ne1a1eMHTsWV69ehbW1Nd5//30sW7ZMr/iIqhqFQoGEhAQkJCRwrT8yOWy/pEulT8wAYMiQIQCAo0ePIicnR2txWEOGMUNCQpCUlASg4DCmmvr9+Ph4rR66kvD09ERAQIDWQwBE9D8KhQKxsbGIjY3lLzYyOWy/pEuVeNSvb9+++Oabb5CRkYETJ05ApVIhMzMTNjY2eOONN/SuRz08aW9vjx49eugs4+3tjRo1auD58+fYu3cvunbtWmSdI0eOxLvvvgsg75s0Pj4e//zzD/bt24dr164hICAAO3fuhIODg95xisGRjIYAgC3T3oRTTRsjR0NERGQaqkRiZmtriz59+uDAgQPYt2+f8BBAnz59hO2UivPixQthVf+0tDS0atWq2GuOHj2K+fPnF3kP9cr/as2bN4e3tzc6d+6MGTNmIC4uDrNnz8bq1av1ilMscpG3Jpy1jQ1sbW2NHA0REZFpqBJDmcD/hjPPnj0rrKKvfk8fhw8fxsuXLw26Z1ZWFo4ePWrQNWpDhw4VHi44efKksKAtERERVV5VoscMALp27QonJydh30xnZ+dihxk1qYcxnZycMHPmzGLL//jjj4iPj8e+ffsMSgA1TZs2DcePH4dCocDy5csNipeIiIhMT5VJzMzMzDB48GBs3LgRQN6kf6lUvw7Dx48f4+rVqwDylq5Qb/VUlOvXr2Pjxo24cOECEhIS9H7yU1OTJk0wYMAAHDx4EDdu3MDZs2fRrVs3g+shIiIi01BlhjIBYPr06QgPD0d4eDi+/PJLva/TnJem70Kv6nJKpbLIvTqLM2nSJEgkEgAwuXlmREREZJgqlZiVlDqxql27tt4LvbZr1w5OTk4AgP3795f43u7u7vDx8QEAXLp0CZcvXy5xXUSVjUQiga2tLWxtbYU/YIhMBdsv6cLErBhXrlzBo0ePAOQ9xanv8KdUKkXfvn0BAHfu3EFERESJY5g8ebJwzF4zov+xsLCAp6cnPD09ud4fmRy2X9KFiVkxNIchDd2vUnONtNJsbN66dWthbllISAjCwsJKXBcRERGJl0SlubM3VTm5ubkIDw8HANRp5Aa/JSUfdtXl0OxhcLHnOmZERFT5aP4O9fLygqWlZanrrDJPZRJR5SOXy/Hw4UMAQKNGjWBuzh9pZDrYfkkXDmUSkclSKpVIS0tDWloalEqlscMhMgjbL+nCxIyIiIhIJJiYEREREYkEB7RJ4GBnhUOzh5VpnY41rMu0PiIiosqMiRkJzKRSuNhbGTsMIiKiKotDmSTgyilERETGxcSMBHK53NghEBERVWkcyiQB92ojUyOVSuHs7CwcE5kStl/ShYkZCbi4IZkac3NzNGjQwNhhEJUI2y/pwhSdiIiISCSYmBERERGJBMeuSCCTycpkA1aiiiKTyXD79m0AQPPmzWFhYWHkiIj0x/ZLujAxIyKTpVKpIJPJhGMiU8L2S7pwKJMEfCqTiIjIuJiYkcDMzMzYIRAREVVpTMyIiIiIRIKJGREREZFIMDEjIiIiEgkmZkREREQiweUyiMhkmZubo2nTpsIxkSlh+yVdTK4lhIaGYsyYMTrPWVlZwd7eHp6enujbty/8/PyKXDDVw8PDoHt7enpi3759Wu/t3r0bM2fOBAAsWrQI/v7+BtXp4+ODuLi4Au+bm5vDzs4OTZs2xWuvvYa3334bLi4uBtVtKC6XQaZGKpWiVq1axg6DqETYfkmXSjWUmZOTg/j4eAQHB2P27Nnw9/dHbGysscMqEblcjrS0NFy9ehWBgYEYOHAgjh07ZuywiIiIqByZXI+ZppEjR+Ldd98VXicnJ+POnTtYu3Yt4uPjcefOHUyZMgV79+4tco2uVq1aYdGiRcXez8rKqkzi1sXZ2Rlr164VXsvlcsTFxWHXrl04deoUMjIyMG3aNGzfvh0tWrQolxi48jSZGs2V0y0sLNjrSyaF7Zd0MenErHbt2nB3d9d6r2vXrvD394efnx/i4uIQHR2NY8eOoX///oXWY2NjU6CeimZhYVEghhYtWqBv375YvHgx/vzzT8hkMqxevRqrVq0yUpRE4iKTyRAeHg4A8PLy4l6vZFLYfkmXSjWUqWZnZ4cpU6YIr8+dO2fEaErv448/FnrrQkJCoFQqjRwRERERlYdKmZgB2hP74+PjjRhJ6dna2sLNzQ0AkJWVhbS0NOMGREREROWi0iZmFhYWwnFleAxZ8/Owx4yIiKhyqrSJ2b1794RjV1dXI0ZSenK5HDExMQDyEjR7e3vjBkRERETlwvS7knRQKBRaTzj269evyPJZWVmIjo4utt46deqgRo0apY7PUNu2bcPz588BAO3bt68UPYBERERUUKX6DZ+SkoKoqCisXLkSt27dApCXlHXo0KHI6yIiIuDr61ts/SVZQLak1Mtl7N69G7///rvw/vjx4yvk/kRERFTxTDoxCwwMRGBgoM5z1tbWeOedd/DFF19UcFQlExcXV+ROBBKJBJ9++ilef/31CoyKiIiIKpJJJ2ZF8fT0REBAgNak+cJ06tQJQUFBFRCV4ezs7NClSxe8//77xfb8EVU1FhYW8PLyEo6JTAnbL+li0omZ5sr/CoUC8fHx+Oeff7Bv3z5cu3YNAQEB2LlzJxwcHIwcafHyr/xvZmaG6tWrw8nJqcJWg+aq02RqJBIJF+Ukk8X2S7qYdGKWf+X/5s2bw9vbG507d8aMGTMQFxeH2bNnY/Xq1UaMUj+6Vv4nIiKiqqVSLpcxdOhQ4UnMkydP4vz580aOyDRwr0wyNUqlEqmpqUhNTeX6fmRy2H5Jl0qZmAHAtGnThI3Lly9fbuRoiKg8qNf4i4mJgVwuN3Y4RAZh+yVdKm1i1qRJEwwYMAAAcOPGDZw9e9bIEREREREVrdImZgAwadIkYUK7KcwzIyIioqrNpCf/F8fd3R0+Pj44ceIELl26hMuXL+tcckLflf8B4JVXXoFUqjufvXLlil51+Pj4cFslIiIiKqBSJ2YAMHnyZJw4cQJAXq+Z5pIUavqu/A8Aly5dKnRbpp07d2Lnzp3F1rF3714mZkRERFRApR7KBIDWrVujW7duAICQkBCEhYUZOSIiIiIi3SQqrpFQpeXm5iI8PBwA0KpVK1SrVs3IERHpT7P9enl5cbFOMilsv6avPP4NK/1QJhFVXhKJRNjKhjtXkKlh+yVdmJiRgD8YyNRYWFigdevWxg6DqETYfkmXSj/HjIiIiMhUMDEjIiIiEgkOZZKAz4GQqZHL5Xj69CkAoG7dujA35480Mh1sv6QLWwEJFAqFsUMgMohSqURiYiIAwMXFxcjREBmG7Zd04VAmCdhjRkREZFxMzIiIiIhEgokZERERkUgwMSMiIiISCSZmRERERCLBxIyIiIhIJLhcBgm4hg6ZGqlUCnt7e+GYyJSw/ZIu/E1MAu6VSabG3Nwcbm5uxg6DqETYfkkXpugkUCiVUCiVxg6DiIioymJiRoKUjBwkPc82dhhERERVFocyichkyWQy3Lt3DwDg5uYGCwsLI0dEpD+2X9KFiRkRmSyVSoXMzEzhmMiUsP2SLhzKJCIiIhIJJmZEREREIsHEjIiIiEgkTH6OWW5uLo4ePYp///0XYWFhSE1NRUZGBuzs7ODq6govLy/069cPXbp0KXYBv9OnT+P48eO4cuUKkpKSkJ2dDQcHBzRs2BCvv/46/Pz84OLiUuj1gwYNwt27d2Fvb48LFy4UuS7YkCFDcPv2bQDAhx9+iE8//bTQshcvXkRAQAAA4IMPPsD06dOL/BxERERkmkw6MTt69CgWL16MuLi4AufS0tKQlpaGmzdvYuvWrWjcuDFmzpyJXr16FSh7584dzJ07F9euXStwLj4+HvHx8bh48SJ++eUXTJw4EZMnT9aZdLVv3x53795FWloa7t69i2bNmumM+8WLF4iKihJeX758ucjPqXm+Y8eORZYlIiIi02Wyidl///tfrFy5UnjdrVs3+Pj4wM3NDTVq1EB6ejru37+PkydP4ty5c3jw4AGWL19eIDG7fPkypkyZgufPnwMAWrZsiaFDh8LDwwM2NjZISEjAmTNnsHfvXmRlZWHFihW4c+cOlixZUuDR5o4dO2Lbtm1CvYUlZlevXoVSqYSZmRkUCgXCwsIgk8kKfVT6ypUrAPK27Gjfvn2Jvl5EREQkfiaZmO3atUtIymrXro0VK1agU6dOBcq99tprGDVqFKKjo7Fo0SKkpKRonY+Pj8dHH32E58+fQyKRYPr06Rg3bpxWb1irVq3Qu3dvjB8/Hh999BGioqLw999/o27dugWGFDV7sy5fvoyRI0fqjF/dA/b6668jNDQUWVlZCA8PR7t27QqUVSgUQk+eh4cHqlevrs+XiKhKMDMzQ/369YVjIlPC9ku6mNzk/4SEBCxYsAAAYGNjg6CgIJ1JmSZ3d3esXbsW48aN03p/7ty5SEtLAwB8+umnGD9+fKHzwho0aIA///wTzs7OAIC1a9fixo0bWmXq1KkDV1dXAEUPT6rPderUCa+++mqR5W/fvi2sc9OhQ4eiPiZRlWNmZgYXFxe4uLjwFxuZHLZf0sXkErP169cjOztv26BPPvlE7w1gpVIpBg8eLLyOjIzEv//+CyAvcZs4cWKxddSuXRszZ84EkLcY4B9//FGgjLrXLD4+HrGxsQXOv3z5EuHh4QDyEi310KR6uDI/zi8jIiKqOkwqMVOpVNizZw+AvN6y4cOHl7gudT0AMHr0aL3/WunXr5/wZObx48eRnp6udV6zV0tXL9iNGzcgk8lgbW2NFi1aCImZet5ZfpoJGxMzIiKiys2kErM7d+4gNTUVQN4TkHZ2diWu69KlS8Kxt7e33teZmZkJDxAolcoCT3Lmn2eWn/q91q1bw9zcHK+++irMzMzw/PlzREdHFyivTsyaNm0KBwcHveMsiZzsbGRnZSEzM7PAf0RilJubiytXruDKlSvIzc01djhEBmH7JV1MavJ/ZGSkcNyyZctS1aVOgpycnIR5Y/pq0aKFVkyaT3o2btwYTk5OePbsmc7ETJ1oqXvWbG1t4enpiZs3b+Ly5cvw9PQUysbExCA5ORlAxfSW7dy8ETk5OTrPzZ8/v9zvT0REVNWZVI+ZeqI+kDffq6RevHgBmUwGAHB0dDT4es1rNGNSUw9P3r9/X0isAO0nLDWXvVAf50/kNIcxOfGfiIio8jOpxExzSM3a2rpM6rGxsTH4es1rMjIyCpwvbDjz1q1byMzMhJmZGdq0aSO8X1hixon/REREVYtJJWa2trbCsfrJzNLWk5WVZfD1mtfomudW2AMA6h4wT09PrRjU5Z89e4aHDx8WuNbV1RV169Y1OE4iIiIyLSY1x8ze3l441hwiNJSdnR3Mzc0hl8uRlJRk8PWa12jGpObu7o4aNWrg+fPnWsOR6kQr/+r9jo6OaNSoER4+fIjLly+jUaNGSEhIEJbbqKhhzLfeHQMzM3M41TS8F5GIiIhKz6R6zDQnxt+8ebPE9UgkEnh4eADI66V69uyZQdffunVLZ0xqmlsnRUZGCsOd6iRN17ZK+YczjTGMaWVtDWsbG9ja2hb4j4iIiMqfSSVmzZo1Q61atQDkJTm65nfpS7MX6uTJk3pfp1Qqcfr0aQB5CZiubZQ061coFLh69Sru3bsnbAmlqwdMXY86IdNczoMT/4mIiKoGk0rMJBIJhg4dCiBvnteOHTtKXJe/v79wvGnTJigUCr2u++effxAfHw8A6NOnD2rUqKGznGYydeXKFSHhatSokc4nQdU9Zo8ePUJiYqLQu+bk5IQmTZroFRtRVWNubg4PDw94eHjA3NykZmYQsf2STiaVmAHA2LFjhScyV65ciXv37ul1nVKpxP79+4XXnp6e6N69OwAgKioKa9euLbaOlJQULF68GEBekvjBBx8UWrZly5ZCnJcuXRJ6wHQNYwLaC8geP34cd+7cKbI8EeX1WtvZ2cHOzg5Sqcn9OKMqju2XdDG5luDi4oK5c+cCyOs1CwgIwMWLF4u85u7du/jggw8KJF8LFiwQerz+85//YP369VCpVDrriIuLw/vvvy/0lo0fP17YgFwXCwsLYUmM8PBwhIaGAig60VIPZ65bt06Ig8tkEBERVR0m2Xc6bNgwxMfHY+XKlUhOTkZAQAC6d+8OHx8fuLm5oUaNGkhPT8f9+/dx+vRpnDlzBgqFosBE/Xr16iEwMBAffvghMjIysGjRIhw4cABDhw6Fh4cHrK2tkZiYiDNnzmDv3r3CMhkDBw7EZ599VmycHTp0wPnz55Gbm4vExEQARSdm7du3x/Hjx/H48WOtOohIN6VSKXxf2tjYsNeBTArbL+likokZAHz00Udo1qwZFi9ejLi4OISEhCAkJKTQ8s2aNcP06dMLvN+5c2ds3rwZc+fOxY0bNxAREYGIiAiddVhbW2PChAn48MMPIZFIio0xf1JVu3btIueL5U/aatasKTw9SkQFyeVyREVFAQC8vLxgaWlp5IiI9Mf2S7qYbGIGAG+88QZ69eqFf/75B//++y/Cw8ORkpKCzMxM2NnZwdXVFa+++ir69euHzp07F5pMeXh4YPv27QgODsaxY8dw9epVPHv2DDk5OahVqxYaN26MHj16YPDgwXBxcdE7vjZt2sDCwkLY/qmwJzjVWrRoAWtra2Hx3Hbt2umVABIREVHlYNKJGQBYWlrC19cXvr6+pa6rV69eWhuSl5aVlVWhvW+6WFhY4Pr162V2fyIiIjItHNAmIiIiEgkmZkREREQiwcSMiIiISCSYmBERERGJhMlP/qey42Bnxce1iYiIjIiJGQnMpFKYcYFDMiGWlpbctoxMFtsv6cLfwkREREQiwcSMiIiISCQ4lEkChUJh7BCIDKJQKJCUlAQAcHR0hJmZmZEjItIf2y/pwsSMBEql0tghEBlEoVAgNjYWAFCrVi3+YiOTwvZLunAok4iIiEgkmJgRERERiQQTMxKYm3Nkm4iIyJiYmJFAIpEYOwQiIqIqjV0kJFAolUhIy9R5zrGGNRefJSIiKmdMzEiQkpEDvyX7dZ47NHsYXOxtKzgiIiKiqoWJGRGZLIlEAltbW+GYyJSw/ZIuTMyIyGRZWFjA09PT2GEQlQjbL+nCSUNEREREIsHEjIiIiEgkOJRJRCZLLpfj4cOHAIBGjRpxLT4yKWy/pIuoW0FoaCjGjBmj85yVlRUcHBzQvHlzDBgwAAMGDCi2UT969Aj79+9HSEgI4uLikJqaCltbWzg7O6Nz587o378/OnToUGxcAQEBuHjxos5z5ubmqF69Otzc3NCzZ0+8/fbbsLe3L7SuGTNmYM+ePQXel0gksLOzQ7169dC+fXu8/fbbaN68ebGxEVUlSqUSaWlpAIAGDRoYNxgiA7H9ki6iTsyKkpOTgydPnuDJkyc4ceIENmzYgNWrV8PJyalA2dzcXCxduhSbN2+GTCbTOpeWloa0tDRER0cjKCgIPXv2xHfffYc6deqUKC65XI7U1FRcvnwZly9fxvr167Fq1Sq0b9/eoHpUKhVevHiBqKgoREVFYevWrZg4cSKmTZtWoriIiIhI/EwmMRs5ciTeffdd4XVWVhYiIiKwbt06xMXFITw8HB9++CG2b9+u9dhxRkYGPvzwQ4SGhgIA7O3tMXToUHTt2hW1a9fGixcvEBERgV27duH+/fs4ffo0RowYgXXr1sHNza3YuA4cOKD1WiaT4fHjx9i3bx9OnjyJ5ORkTJkyBUeOHIGDg0ORda1duxbOzs4A8v6SSk5ORnBwMDZv3gy5XI5ff/0Vzs7OGDVqlN5fNyIiIjIdJpOY1a5dG+7u7lrvtWnTBr6+vhg+fDgePnyIsLAwnDp1Cj4+PkKZ2bNnC0lZ586dsXz5ctSuXVurnq5du2Ls2LFYtmwZ/vzzT8THx2Py5MnYs2cP7Ozsiowrf0wA0LJlS/Tv3x9ff/019u7di/T0dOzYsQOTJk0qsq7GjRujfv36Wu9169YNXbt2xZQpUwAAgYGBeOedd2BmZlZkXURERGR6TP6pzJo1a2LixInC6zNnzgjHhw4dwpEjRwAAnp6e+O233wokZWoWFhaYMWMGhg8fDiBvPtqyZctKFdv48eOF4/Dw8BLX4+PjI8x9S0lJwc2bN0sVFxEREYmTySdmANC6dWvh+MmTJ8Lx77//LhwvWLAA1tbWxdY1Y8YMODo6AgB27dqFlJSUEsel2fuVm5tb4nqAwj8jERERVR6VIjHTfBpToVAAACIjI3Hr1i0AQNu2bbUSm6LY2dnB398fAPDy5UscPHiwxHFpJlB169YtcT2A9mdUKpWlqksfllBo/ZedlYXMzMwC/xEREVHZMZk5ZkWJjo4WjtWT5y9fviy85+3tbVB9Pj4+WLNmjVBPYUt2FGft2rXCce/evUtUh5quz1ie+ts90nq94fdfdJabP39+ucdCVBipVCp8P0illeLvTKpC2H5JF5NPzORyOf7880/hdadOnQDk9ZiptWjRwqA6mzdvDqlUCqVSiaioqCLLaiZMQN5TmXFxcdi/fz+OHTsGABg4cCBef/11g2LQFBkZKcyds7a2hpeXV4nrIqpMzM3Nuf4TmSy2X9LFZBMz9XIZq1atwvXr1wEArq6uGDhwIAAgNTVVKKtrbbOiWFlZwdbWFi9evBAW/yuMr69voeeaNGmCiRMnYujQoQbdH8hbxywpKQnBwcFYtmyZMEQbEBCAatWqGVwfERERiZ/JJGaBgYEIDAws9Hzt2rXx3//+F5aWlgCgNf/JxsbG4PvZ2NjgxYsXyMjIMDzY/3f//n1s27YN9evXF3ryilLccGevXr3wySeflDgeIiIiEjeTScwKU79+ffTr1w/jx4/XWgrD1tZWOM7KyjK4XvU1xa1jln+oU6lUIjU1FVeuXMF///tfXL9+HePGjcPSpUvRv39/g+OwsLCAl5cXRowYgcGDB2stnluejmQ01Hq9ZdqbcKppeIJLVJ5kMhlu374NIG8KgoWFhZEjItIf2y/pYjKJmebK/xKJBNWqVUOtWrVQvXp1neVr1aolHD979gyenp563ysnJ0focStqn0tdpFIpateujTfeeAPdunWDv78/Hjx4gFmzZqFLly5F1qe58r9UKoWNjQ0cHR2FXsCKlAvtBWytbWy0kl0iMVCpVMI2ayqVysjREBmG7Zd0MZnHQNQr/7u7u6NZs2Zo2LBhoUkZAHh4eAjH6r9I9HX79m1hSQrNegxla2uLkSNHAsgbWv3nn3+KLN+4cWPhM77yyiuoV6+eUZIyIiIiMg6TScwM1bFjR+H45MmTBl2rWV6znpJo2rSpcJz/CU4iIiIiTZU2MfP09ETz5s0BANeuXdN7S6SMjAzs2bMHAFCtWjUMGjSoVHGon6YE8pb2ICIiIipMpU3MAGDChAnC8bx585CTk1PsNT/++COePXsGABg2bBgcHBxKFUNERIRwXNrV/4mIiKhyq9SJ2aBBg/DGG28AAG7duoUpU6YUuvelTCbDjz/+iG3btgEAGjZsiC+++KJU94+Li8OmTZuE1z179ixVfURERFS5mcxTmSW1cOFCpKam4tKlSzh37hwGDBgAf39/dOnSBY6OjsjIyEBERAR27tyJmJgYAECdOnWwevXqYpfKAArOG1MqlUhLS8Ply5cRFBQkLFDr6+srDK0SERER6VLpE7Pq1atj7dq1+Omnn7BlyxakpaVh3bp1WLdunc7yPXr0wIIFC/Qedixq5X+1gQMHYuHChQbFTUTFMzc3Fx6wMTev9D/OqJJh+yVdqkRLqFatGubMmYOAgADs378fISEhiIuLQ1paGmxtbeHs7IyOHTtiwIABpX4KUyKRwMbGBnXr1kWbNm0wZMiQUtdJRLpJpVKtNQuJTAnbL+kiUXFVuyotNzdXeGK1TiM3+C3Zr7PcodnD4GLPBWaJiIjUNH+Henl5lcnao1Wix4yIKifNldMtLCwqbMsyorLA9ku6VOqnMomocpPJZAgPD0d4eLjwC47IVLD9ki5MzIiIiIhEgokZERERkUhwjhkJHOyscGj2MJ3nHGtYV3A0REREVQ8TMxKYSaVwsbcydhhERERVFocyScCVU4iIiIyLiRkJ5HK5sUMgIiKq0piYEREREYkE55iRgHu1kamxsLCAl5eXcExkSth+SRf+JiYBV50mUyORSMpkCxQiY2D7JV04lElEREQkEuwxIwGfyiRTo1QqkZ6eDgCoWbMmpFL+rUmmg+2XdGErIAGHMsnUyOVyxMTEICYmhk8Vk8lh+yVdmJgRERERiQQTMyIiIiKRYGJGREREJBJMzIiIiIhEgokZERERkUgwMSMiIiISCa5jRkQmSyKRCFvZcLkXMjVsv6RLuSZmoaGhGDNmjM5zVlZWsLe3h6enJ/r27Qs/Pz+9t6aIjY1Fnz59hAVRly5dCl9fX4Pje/LkCf7++2+cO3cODx8+REpKCpRKJWrWrAl3d3e0b98evr6+aNCggc7rk5OTERYWhrCwMISHhyM8PBxpaWkAgKFDh2Lx4sXFxjBjxgzs2bOnwPsSiQR2dnaoV68e2rdvj7fffhvNmzc3+DMSVWYWFhZo3bq1scMgKhG2X9LFaD1mOTk5iI+PR3x8PIKDg7F+/Xr8+uuvqF+/frHX7t27V2uV+r179xqUmL18+RLLli3Dli1bkJubW+B8YmIiEhMTERISgpUrV6J///74+uuvUbduXa1yr732mt73NJRKpcKLFy8QFRWFqKgobN26FRMnTsS0adPK7Z5ERERkXBWWmI0cORLvvvuu8Do5ORl37tzB2rVrER8fjzt37mDKlCnYu3cvzMzMiqxr//79AAAbGxtkZWXh/PnzSExMhLOzc7FxpKSkYMqUKbh+/ToAwNbWFm+++Sa6dOmCOnXqwNzcHElJSbh69SqOHTuGBw8e4PDhw2jTpg3Gjh1baL316tVD06ZNERISUvwXoxBr164VPoNSqURycjKCg4OxefNmyOVy/Prrr3B2dsaoUaNKfA8iIiISrwpLzGrXrg13d3et97p27Qp/f3/4+fkhLi4O0dHROHbsGPr3719oPVevXsXDhw8BALNmzcL8+fOhUChw4MABjB8/vsgYlEolPvvsMyEp8/b2xg8//IDatWsXKOvj44PPP/8c+/fvx48//qizvo8++gheXl7w8vKCo6MjYmNj0bt37yJjKErjxo0L9Bh269YNXbt2xZQpUwAAgYGBeOedd4pNXomqArlcjqdPnwIA6tatC3NzTpsl08H2S7oY/alMOzs7IekAgHPnzhVZfu/evQCAWrVqYciQIejatSsAYN++fcXea+PGjQgNDQUAdO/eHYGBgTqTMjWpVIohQ4Zg9+7d8PDwKHD+k08+gbe3NxwdHYu9d2n4+PigQ4cOAPJ6/G7evFmu9yMyFUqlUph6oFQqjR0OkUHYfkkXoydmALSSnvj4+ELL5ebm4siRIwCAAQMGwMLCAn5+fgCAqKgo3L59u8hr165dCwCoVq0aFi5cqPdfJ3Xq1BESQGPRnCD65MkTI0ZCRERE5UUUiZn6cWEARSZLJ0+eRHp6OgAICVnfvn1hY2MD4H+9abqEhIQgMTERANC/f3+4uLiUNuwKpfl1Ka+/rLKyspCZmYnMzMxyqZ+IiIiKJorE7N69e8Kxq6troeXUiVfDhg3Rtm1bAHkPAKjndR08eBAKhULntZcuXRKOe/XqVcqIK150dLRwrM9DDiXx22+/YenSpVi6dGm51E9ERERFM3piplAohCFGAOjXr5/OcikpKcITj/mXxlD3niUlJRX6VGRkZKRw3LJly1LFXNEiIyNx5swZAIC1tTW8vLyMHBERERGVB6MlZikpKTh//jxGjx6NW7duAchLytST3PM7ePAgZDIZgP8lYmrdunWDk5MTgMKHM9ULvwIocsK/WKhUKjx79gw7duzA2LFjhZ7AgIAAVKtWzcjRERERUXmosGdzAwMDERgYqPOctbU13nnnHXzxxReFXq9OuFq3bo3GjRtrnTMzM8PAgQOxYcMGnDx5EhkZGbCzs9MqozlvytraumQfopwVt9RGr1698Mknn1RQNERERFTRRLFoiqenJwICArQeAtB0584dYYmI/L1lan5+ftiwYQNycnJw+PBhDB8+XOu8ra2tcJydnV0gcRMrCwsLeHl5YcSIERg8eHC57qc2adKkQv8NiMRIKpXC3t5eOCYyJWy/pItRVv5XKBSIj4/HP//8g3379uHatWsICAjAzp074eDgUOBadW+Zubk5Bg0apLP+Vq1awc3NDffu3cO+ffsKJGbqxg/kzUUTY2KmufK/VCqFjY0NHB0d9d5DtLRsbGwq7F5EZcHc3Bxubm7GDoOoRNh+SRejrfzfvHlzeHt7o3PnzpgxYwbi4uIwe/ZsrF69Wus6pVKJAwcOAMhbJVmf9cQuX76M2NhYrVX0PT09hcVrb926VWA4VAx0rfxPREREVYfR+06HDh0qPIl58uRJnD9/Xuv8+fPnkZCQYFCdKpWqwE4AHTt2FI6Dg4NLFiwRERFRORLFHLNp06bh+PHjUCgUWL58uVavmHoY09LSEgsXLix2HP6PP/7ArVu3sH//fnz00UfC+927d4ezszMSExNx5MgRfPHFFya3yCwRaZPJZMI6iG5ubpwjSSaF7Zd0EUVi1qRJEwwYMAAHDx7EjRs3cPbsWXTr1g2ZmZk4fvw4gLwlMfKvX6bLs2fPcOvWLTx48ADXrl0TFqK1tLTE+PHjsWjRIrx8+RKzZs3CmjVr9NoMPCEhATExMUbflomItKlUKuGJa5VKZeRoiAzD9ku6GH0oU23SpEnCE4fqeWZHjx5FVlYWgMIXns2vX79+Qj351zQbM2YMOnfuDCBvi6aPPvoIKSkphdalUqlw4MAB+Pv7IyoqyqDPQ0RERGQoUfSYAYC7uzt8fHxw4sQJXLp0CZcvXxYSKwsLC/j4+OhVT926deHl5YWwsDAcPnwYs2fPFp40lEqlWLFiBSZPnowbN27g1KlT6NOnD3x9fdGlSxfUqVMH5ubmSEpKwvXr13H06FHExMQUeq/Lly/j0aNHwuvU1FTh+OHDh9i9e7dWeX9/f32/HERERFQFiSYxA4DJkyfjxIkTAIBVq1bh4sWLAIDOnTujZs2aetfTr18/hIWFIT09HadOndLqbXNwcEBQUBCWLl2KLVu2IDMzE1u3bsXWrVt11iWRSODr64sBAwYUOLdz507s2bNH53VXr17F1atXtd5jYkZERERFEc1QJpC3qn+3bt0AABcuXIBSqQSg/zCmmmZ5XVs0VatWDbNnz8bRo0fxxRdf4LXXXkO9evVgZWWFatWqwdnZGd27d8e0adNw4sQJ/PTTT3xQgIiIiMqdRMUZh1Vabm4uwsPDAQBeXl5cYJZMCtsvmTK2X9NXHv+GouoxIyIiIqrKRDXHjIjIEGZmZsJuGfosfUMkJmy/pAsTMyIyWWZmZpz/SSaL7Zd04VAmERERkUgwMSMiIiISCQ5lkoAP6JKp4VNtZMrYfkkX9piRQC6XGzsEIiKiKo2JGREREZFIMDEjIiIiEgkmZkREREQiwcSMiIiISCSYmBERERGJBBMzIiIiIpHgOmYk4F5tZGrMzc3h4eEhHBOZErZf0oUtgQRSKTtQybRIpVLY2dkZOwyiEmH7JV34m5iIiIhIJNhjRgKlUmnsEIgMolQqkZWVBQCwsbFhry+ZFLZf0oWtgAQKhcLYIRAZRC6XIyoqClFRUdxSjEwO2y/pwsSMiIiISCSYmBERERGJBBMzIiIiIpFgYkZEREQkEkzMiIiIiESCy2VUcSqVSjiWyWTIzc01YjREhpHJZDqPiUwB26/p0/x30/x9WhoSVVnVRCYpMzMTkZGRxg6DiIjIpHl6esLW1rbU9XAok4iIiEgk2GNWxSmVSmRnZwPI20RXIpEYOSIiIiLToFKphMWBra2ty2T3BiZmRERERCLBoUwiIiIikWBiRkRERCQSTMyIiIiIRIKJGREREZFIMDEjIiIiEgkmZkREREQiwcSMiIiISCSYmBERERGJBBMzIiIiIpFgYkZEREQkEkzMiIiIiESCiRkRERGRSDAxIyIiIhIJJmZEREREIsHEjIiIiEgkmJgRERERiQQTMyIiIiKRYGJGREREJBLmxg6AihcXF4egoCAEBwcjPj4elpaWaNCgAQYMGIBRo0bB2tq6TO5z+vRpbN++HeHh4UhJSYGDgwO8vLzw9ttvo2fPnnrVIZfLsWPHDhw4cAAxMTHIysqCs7MzXnvtNQQEBKBZs2ZlEiuZDlNovzNmzMCePXv0us+JEydQv379sgiZTEB5tl+lUomYmBiEhYUhLCwM4eHhiIqKgkwmAwBs3LgRnTt31ru+7Oxs/PXXXzhy5AgeP36M3Nxc1KlTB7169UJAQABcXV1LHCtVHIlKpVIZOwgq3MmTJzF9+nRkZGToPN+4cWOsWbMGjRo1KvE9lEol5s6di507dxZaZvjw4fjuu+8glRbeyZqSkoKJEyciPDxc53lLS0vMmzcPw4cPL3GsZFpMpf0yMSNdyrv97tmzBzNmzCj0vCGJ2cOHDzFx4kQ8ePBA53k7OzssXboU3t7eJQmVKhB7zETs1q1bmDZtGnJycmBjY4NJkyahc+fOyMnJwaFDh7B9+3Y8ePAAEydOxK5du2BnZ1ei+yxfvlz4pdaiRQt88MEHaNCgAR4/fow//vgDt27dwo4dO+Dg4IDPP/9cZx0KhQJTp04VkrI33ngDw4cPh729PW7cuIHVq1cjOTkZ8+bNg7Ozs949cGS6TKn9qjk7O2Pt2rVFlnFxcSlRnGRaKqL9avaLWFhYwN3dHTKZDNHR0QbVk5GRoZWUvf322xg4cCCsrKwQGhqK3377DRkZGZg2bRq2bNmC5s2bGxwrVSAVida7776rcnd3V7Vo0UJ19erVAud///13lbu7u8rd3V21cuXKEt0jJiZG1aJFC5W7u7vK399flZ2drXU+KytL5e/vL8Tx4MEDnfXs2LFDiOWbb74pcP7Bgweqdu3aqdzd3VV9+/ZVyWSyEsVLpsOU2u/XX3+tcnd3V3l7e5coDqp8KqL93rhxQ7Vx40bVtWvXVDk5OSqVSqVauXKlUO+FCxf0qmfFihXCNb///nuB81euXBG+T0aPHl2iWKnicPK/SIWFheHy5csAgGHDhqFt27YFyowbNw5ubm4A8rq81fMSDLFhwwbI5XIAwNy5c2FlZaV13traGnPnzgWQN39s/fr1OutZt24dAMDe3h5fffVVgfONGjXCpEmTAOR1uR87dszgWMl0mFr7JdJUUe23devWCAgIQJs2bVCtWrUSxSqTyRAUFAQAcHNzw7hx4wqUadeuHYYNGwYAuHjxIsLCwkp0L6oYTMxE6vjx48Kx+hsqP6lUiiFDhgAAnj9/jtDQUIPuoVKpcOLECQBA06ZN0aZNG53l2rRpgyZNmgDIm1+jyjct8f79+7h37x4AoH///oVOhh06dKhwrPn5qPIxpfZLlF9FtN+yEhoaihcvXgAAhgwZUug8Sn9/f+GYP3/FjYmZSF25cgUAYGNjg5YtWxZarmPHjsLx1atXDbpHbGwsEhMTC9SjS6dOnQAACQkJiI2N1RmrZjldnJyc0Lhx4xLFSqbFlNovUX4V0X7Lir4/f1u1aiX80cyfv+LGxEyk1D1QDRs2hLl54c9oNG3atMA1+rp7967Oeoq7T0xMjM5YDann6dOnyMrK0jtWMi2m1H41paamYvTo0ejcuTNatWqF7t27Y/z48fjrr7+QnZ1tUHxkuiqi/ZYVfX/+mpubo2HDhgWuIfFhYiZCL1++RGpqKgCgTp06RZatWbMmbGxsAADx8fEG3UezfHH30Tz/9OnTQusp7om1unXrAsgbhjI0XjINptZ+NWVlZeHSpUtIS0uDTCbDs2fPEBISggULFqBfv37saagCKqr9lhX1fW1sbFCjRo0iy6p//qakpCA3N7fcY6OS4XIZIpSZmSkcq7/pi2JtbY2srCyDe6AMuY/mvLH899Gsx9bWtsT1UOVgau0XACQSCdq0aQNvb2+0aNECjo6OePnyJaKjo7Fz506EhYUhISEB48ePx6ZNm9CiRQuDYiXTUVHtt6yo49U3Vs3rLC0tyy0uKjkmZiL08uVL4djCwqLY8upvrpycnHK7j+Y3cP77lFU9VDmYWvsFgJkzZ+rsbWjbti3efvttrFixAr/++iuysrIwZ84c7Nq1CxKJxKB4yTRUVPstK+p4DYlV8zoSHw5lipDmY9P6PIKt7pLOv1RAWd5Hs9s7/33Kqh6qHEyt/QIocghIIpFg2rRp6Nq1KwDg5s2bHNKsxCqq/ZYVdbyGxKp5HYkPEzMR0hwO1Kd7XD0pWZ+u7JLeR3Pic/77aNajOQxgaD1UOZha+9XXiBEjhONLly6VqA4Sv4pqv2VFHa8hsWpeR+LDxEyEqlWrBnt7ewDFTyhNT08XviGLm6ian2b54u6jeV49gVRXPQkJCUXWo554LZFIDI6XTIOptV99vfLKK8Jxce2cTFdFtd+yor5vVlYWnj9/XmRZ9c9fBwcHzi8TMSZmIqX+JfDo0SNhZXNdNB/9V69Cbeg98tdT3H3yP5KteV9966lbty57zCoxU2q/+uKcsqqjItpvWdH3569cLsfjx48LXEPiw8RMpNq3bw8g76+gmzdvFlpOc0ilXbt2Bt2jfv36cHZ2LlBPUfdxcXFB/fr1dcYK5G33UZhnz54Jm+waGiuZFlNqv/rSXDdNfV+qnCqi/ZYVfX/+RkRECL17/PkrbkzMRKpPnz7C8a5du3SWUSqV2Lt3L4C8ycudO3c26B4SiQS9e/cGkPeX1vXr13WWu379uvCXWO/evQv0HDRp0kT4C+zIkSOFLsS5Z88e4Vjz81HlY0rtV1/btm0TjovbaYBMW0W037LSqVMnVK9eHQCwd+/eQrcc2717t3DMn7/ixsRMpFq3bo0OHToAyPvBcO3atQJl1q1bJ6zgPGbMmAKPS4eGhsLDwwMeHh6YMWOGzvu89957MDMzAwAsWLCgwCPfOTk5WLBgAYC8laPfe+89nfWoN85NS0vDTz/9VOD8o0eP8NtvvwHI29C8b9++uj84VQqm1H6vX78ubO2ki0qlwvLly3Hu3DkAgKenp1YvBVU+FdV+y4KlpSUCAgIA5K3ov3bt2gJlrl27JiSYnTp1QuvWrcstHio9rmMmYrNnz8bIkSORk5ODcePGYfLkyejcuTNycnJw6NAh4S/4xo0b4/333y/RPZo0aYLx48djzZo1iIiIwMiRIzFhwgQ0aNAAjx8/xu+//45bt24BAMaPHy/sdZnf0KFDsWvXLly9ehWbNm1CUlIShg8fjpo1ayIsLAy//PILMjIyIJVKMXv27CK3OaHKwVTa75kzZ7BmzRr06NED3bp1g5ubG2rUqIHc3FxERUVh165duHHjBoC8BToXLFjA+WZVQEW0X0C7JwsAbt++LRyfOXMGcXFxwuuGDRsKCaOm8ePH49ChQ3jw4AF++uknPHr0CAMHDoSVlRVCQ0Px66+/Qi6Xw8rKCrNmzSpxrFQxJKrC+j1JFE6ePInp06cjIyND5/nGjRtjzZo1aNSoUYFzoaGhGDNmDIC8xGnx4sU661AqlcKimYV56623sGDBAkilhXeypqSkYOLEiQgPD9d53tLSEvPmzcPw4cMLrYMqF1Nov6tWrUJgYGCxn6VevXpYunQpe8uqkIpovx4eHnrHU1Q9Dx8+xMSJE4V5vPnZ2dlh6dKl8Pb21vt+ZBzsthA5Hx8f7N+/Hxs3bkRwcDASEhJgYWGBhg0bon///hg9erTWNhslIZVKsXDhQvTr1w/btm1DeHg4UlNTUatWLXh5eWHEiBHo2bNnsfU4ODhg69at2L59Ow4ePIh79+4hOzsbzs7O6Nq1K8aMGYNmzZqVKlYyLabQfv39/VG7dm1cv34dUVFRSE5ORlpaGszNzVGrVi20aNEC3t7e8PX15aKcVUxFtN+y0qhRI+zZswebNm3CkSNH8OjRI8hkMtSpUwc9e/bEmDFj4OrqauwwSQ/sMSMiIiISCU7+JyIiIhIJJmZEREREIsHEjIiIiEgkmJgRERERiQQTMyIiIiKRYGJGREREJBJMzIiIiIhEgokZERERkUgwMSMiIiISCSZmRERERCLBxIyIiIhIJJiYEREREYkEEzMiIiIikWBiRkRERCQSTMyIiIiIRIKJGREREZFIMDEjIqoAT548wbx589CnTx94eXnBw8MDHh4eOH78uLFDIyqRGTNmwMPDAz4+PsYOpVIxN3YARKUVGhqKMWPGAACmTp2Kjz/+2MgREWl78uQJ/P39kZqaauxQDKL5vZWflZUV7O3t4enpib59+8LPzw+WlpZ6161QKHD06FGcOHEC4eHhSEpKgkwmg6OjI5o2bYpevXrBz88PNWrUMCjm3NxcHD16FP/++y/CwsKQmpqKjIwM2NnZwdXVFV5eXujXrx+6dOkCqZR9EyQ+TMyICADg4eEBgMlteVi9ejVSU1Nhbm6Ozz77DB07doSNjQ0AoF69ekaOrmRycnIQHx+P+Ph4BAcHY/369fj1119Rv379Yq+9fPky5s+fj7t37xY4FxcXh7i4OJw5cwarVq3CF198gbfffluvmI4ePYrFixcjLi6uwLm0tDSkpaXh5s2b2Lp1Kxo3boyZM2eiV69eetVtyvi9bVqYmBERlbNz584BAHr37o0JEyYYOZqSGTlyJN59913hdXJyMu7cuYO1a9ciPj4ed+7cwZQpU7B3716YmZkVWs+RI0cwffp05ObmAgA6deoEPz8/NGnSBJaWlnjy5AlOnjyJv//+G2lpaZg7dy7u37+Pr7/+usj4/vvf/2LlypXC627dusHHxwdubm6oUaMG0tPTcf/+fZw8eRLnzp3DgwcPsHz58iqRmJWXxYsXY/HixcYOo9JhYkZEVM4SEhIAAE2aNDFyJCVXu3ZtuLu7a73XtWtX+Pv7w8/PD3FxcYiOjsaxY8fQv39/nXXcunULX375JWQyGSwsLLBo0SL4+vpqlWndujX69++P999/H5MnT0Z8fDzWrVuHBg0aaCWGmnbt2iUkZbVr18aKFSvQqVOnAuVee+01jBo1CtHR0Vi0aBFSUlJK8qUgKlccYCciKmcymQwAYG5e+f4WtrOzw5QpU4TX6t7B/JRKJb766ivha7Fw4cICSZmm5s2bY/369cKQ75IlS/DkyZMC5RISErBgwQIAgI2NDYKCgnQmZZrc3d2xdu1ajBs3rugPR2QEle+nBFE+mhOYN27ciE6dOmHnzp3YvXs3YmJiIJPJ0LRpU4wePRpDhgwRrsvNzcWuXbuwZ88ePHjwADKZDB4eHhgzZgwGDhyo816xsbHo3bs3AGDRokXw9/fH4cOHsW3bNkRFRSEzMxP16tVD3759MWHChGInNufm5mLHjh04cuQI7ty5g4yMDNSsWRMtWrTAm2++CV9f30InMM+YMQN79uyBq6srTp48icTERGzYsAHBwcF48uQJsrKysHHjRsycOVNrTk5gYCACAwO16ho6dKjWkEViYiKOHTuG0NBQREZGIjExEXK5HLVq1UKrVq3g6+uL/v37Fxpb/n+Tzp0749ChQ8LXKSsrC3Xr1kXv3r0xceJE2NvbF/l1AoDTp0/jwIEDuHbtGpKTk6FQKODk5ISWLVvC29sbAwYMgLW1tc5rb968iW3btiE0NBSJiYlQqVRwcXFBly5dMHbs2BL1dO3evRszZ87Uei//1zb/1xUAUlJSsGHDBpw+fRqxsbF4+fIlnJyc0L59e4wYMQIdOnQo9J4+Pj6Ii4sT6o2IiMBff/2FixcvIjExETKZDFFRUQZ/luKo5zABQHx8vM4yJ0+exJ07dwAAr7/+Ovz8/Iqtt0mTJpgyZQqWLVuGnJwcbNy4ETNmzNAqs379emRnZwMAPvnkE7i5uekVs1QqxeDBg/Uqq6kif55oevbsGf766y+cOXMGsbGxyMrKQu3atdGmTRuMGDECr732WoFr1O1Brbjv7fyfrWPHjti9ezf27duHe/fuISUlBUOGDBHK5/8ZU5iMjAxs374dwcHBuHfvHtLT02FpaYmGDRuiffv2GDhwINq3b1/s16CqYGJGVYpcLseUKVNw6tQprffDw8Px9ddfIyIiAnPmzEF6ejo++ugjXLp0SavctWvXcO3aNTx69AiTJ08u9n6zZs3Crl27tN67f/8+1qxZg71792L9+vWF/iKJjY3FhAkTEBMTo/V+UlIS/v33X/z777/Ytm0bfvnll2ITl+vXr2Py5Mll8lSgQqFAz549oVQqC5xLTEzEyZMncfLkSezcuROrVq2Cra1tkfUplUpMnz4d+/fv13r/wYMHWLt2LY4fP45NmzbByclJ5/WpqamYNm0azp8/X+CceiL50aNHAQD+/v4F7r1kyRJs2LABKpWqwP0fPHiAnTt3Yt68eRgxYkSRn6MshISE4NNPP0VGRobW++rPsX//fowaNQpz5swp9onCLVu24Pvvv4dcLi/PkAEAFhYWwnFhvYJ79+4Vjt977z29637nnXcQGBiIly9fYs+ePfj6668hkUgAACqVCnv27AGQ11s2fPjwEkRfchX182T//v2YP38+srKytN6Pj4/HkSNHcOTIEbz11lv49ttvy6xX9uXLlxg/fnyhPaD6OnfuHD7//PMCP3tkMhlu376N27dv46+//iqXPxhMFRMzqlJ+/vln3LhxA76+vvD19YWjoyMePHiAVatW4f79+wgKCoKPjw+CgoJw7do1jBw5En379oW9vT1u376Nn3/+GYmJiVi5ciV69+6NZs2aFXqvzZs3Izw8HK1bt8bYsWPRqFEjJCcnY8+ePTh8+DASExPxwQcf4MCBA7Czs9O6NjMzE2PHjsXjx48BAH369MGwYcPg7OyM2NhYbNq0CRcvXsSVK1cwefJkbNq0qdAJ15mZmfj444/x8uVLTJ48Gd26dYOVlRWio6Ph5OSEtWvXQiaTCcNK+Sd5A0DNmjWFY3UC06VLF7z++utwd3eHg4MDMjMz8fjxY+zYsQPXrl3D2bNn8d1332HJkiXF/ptcu3YNffr0wZAhQ1CvXj0kJSVh8+bNCA4OxsOHD7Fo0SL85z//KXBtdnY2xowZg+joaABAy5YtMWLECDRr1gyWlpaIj4/HpUuXcOjQIZ33XrBgATZv3gwA6NixI4YOHYoGDRrAysoKUVFR2LBhA+7cuYN58+bB0dFR6A3VR58+fdCqVSsAKPRrq/l1vX37NiZPnizMvxo1ahR8fHxgbW2N27dvY82aNcK/vbW1NaZPn17ovcPDw7F//37UqVMH48ePR6tWrSCXy3HlyhW94zfEvXv3hGNXV1edZS5fvgwAsLa2RteuXfWuu0aNGujYsSNCQkKQlpaGu3fvCt93d+7cEX7ht2/fvsD3UXmriJ8nhw4dwldffQWVSoUGDRpg9OjRcHNzg4ODA+Li4rBz506cPn0aO3fuhJ2dnVYvraHf25qWLl2KqKgo+Pj4wN/fX/i+zMzM1Pvrc+HCBUyYMAFyuRxmZmYYPHgwevfujbp16+Lly5e4d+8e/v333wKJbZWnIjJxFy5cULm7u6vc3d1VK1euLPK8u7u7av369QXKJCYmqtq2batyd3dXdenSReXh4aE6duxYgXK3b99WeXp6qtzd3VULFiwocP7x48da95owYYJKJpMVKBcYGCiUWbJkSYHzixcvFs4vX768wHmlUqn64osvhDKbNm0qUObrr78Wzrdp00Z1+/btAmU0FfU1zH/vBw8eFFnm559/Vrm7u6s8PDxU9+/fL3A+/7/JL7/8ovM+48aNU7m7u6tatGihSk5OLlBm4cKFQh3ffvutSqlU6ozn5cuXqmfPnmm9FxISIly7fft2ndfl5OSoxowZo3J3d1d5e3vr/LfUhz5f22HDhqnc3d1VzZs3V505c6bA+bS0NNXAgQNV7u7uKk9PT1V0dHSBMt7e3sK93nzzTVV6enqJ4lUr7ntLpVKp5HK5asiQIUK5S5cuFSgTHx8vnH/77bcNjmPp0qXC9fv37xfe37dvn/D+f/7zH4PrLYmK/HmSnJysat++vcrd3V01c+bMQtvff/7zH6Fd3Lt3r8B5fb+38382XT97NKl/xnh7exc4l5OTo+revbvK3d1d9eqrr6ouXLhQaD1Pnjwp8j5VDSf/U5Xy6quv6hxGcXJyQt++fQHkzfEZMGAA+vTpU6Ccp6enMBeiuN4HS0tLLFiwQOfQwpQpU4Qn3Hbt2iUsHQDkzUXZuXMnAKBZs2Y61x2SSCT45ptvhCHMTZs2FRnLBx98AE9PzyLL6EsikaBRo0ZFlvnoo49Qq1YtqFSqIueeAHm9XLqGcSQSCcaOHQsgb8jo2rVrWuefP3+Obdu2CXXMnj1bGOLKz9LSEo6OjlrvrVmzBgDQr1+/QofAqlWrhnnz5gHIG04MDQ0t8rOUVFhYGMLDwwEAw4cPR/fu3QuUqVmzJr777jsAeUOw6p6+wsyfP9/gxVkNkZKSgvPnz2P06NG4desWgLyvpa45cJrDWPn/HfRRu3Zt4TgtLU3nsWaZilLeP0+2bNmCFy9ewMXFBd98802hw5Qff/wxXFxcoFQqsW/fvtJ8JEHjxo1LtebZ3r17kZiYCACYNm0aOnfuXGjZunXrlvg+lRETM6pSippkq5m4DBo0qNhy6mHGwnTr1g0uLi46z0mlUmFicFpamvCLDQAiIiLw/PlzAHkTcwsborSzs8OAAQMAAHfv3hV+COqiz0TrklIqlUhISEBMTAyio6MRHR2Ne/fuoU6dOgCAyMjIIq/39fUtNKFq2bKlcJz/633hwgVh0veYMWOKXDsrv4yMDFy8eBFAXjJRFDc3N9SqVQsACiSHZUVzHs9bb71VaLn27f+vvfuPibr+4wD+PAwFhnJeSGA1ysS6JAixsEmJxHRjqV3MDWJnoP1aOgrcSmvlVq2MrVWQwtq0SI0jHNPE2Q0BT2HWIBin5LyhhOC6BRgXv/LkvO8f7D77HPeDO7iDj9+ej78+270/Hz7c53Ofe937/X693onCnER3c3+ioqLcJglMxddffy0sI/Xwww/jqaeeQk5ODlpaWhAcHIzc3Fx8/vnnTvcVz5mzZVl6QzxPUXws8bCaq8QOf/L388T2oyYlJcXtqgp33XUXHn/8cQC+u0fT09O9+kxNZBueDAkJ8bhAMI3jHDP6T3GXXTd//nxh+4EHHpi03WRzLR577DG3r8fFxQnbBoNBeLDaMteA8V/k7sTHx6O8vFzYLyIiwqFNSEgI7r//frfH8ZbVasVPP/2Eo0ePQq/X499//3XZdrKEgyVLlrh8TZzUMPH9Fgez3mZ0/f7770LyQkFBAQoKCjzar6+vz6u/4ynbNQ8MDIRSqXTbNj4+HleuXEFXVxfMZrPTL2xxluRMeOSRR6BWq+2SAMTEc78mTmD3hPjai48lDthsQfpM8ufzxGKxCD9qKioqhN7hyfjqHp3uPXTp0iUA4z+uZiNovpMxMKP/lKCgIJevibPcPGnnLCtRbLKhFVfDMyaTSdhWKBRujyEeFhLvJ+br4aybN29ix44dOHv2rEft3QVtgOfXZOL7LQ74nAWk7vT393vV3may/2WqbNdfLpdPmlVnu+ZWqxX//POP06FBfwxhiieOWywWGI1GaLVaHD9+HK2trVCr1Th69KjTe9bW4whMLXAQXy9xsC7enuo1nQ5/Pk9MJtOUMmp9dY+6SgrwlO3z6SqbmlxjYEbkJ66G52b6GNMZjnCmpKRECMqefPJJvPjii1i+fDnCw8MRFBQkfNFkZ2cLmXhSI/4S/PDDD5GQkODRftP9spopvr7mgGPlf6VSibVr1yIpKQm7du3C9evX8d5776GkpMRh34iICCxcuBB///03Ll++DIvF4tU5intHxUOE4u329nZv/yVJs1gswvbmzZtdLiY/kateS29xgffZw8CMyE8m6xlw1Qsg/vLv7+93O1wi/hszETRYrVYhMWHlypUoKytz+QB31YPnK+JemL/++sur4Vrx+x0UFOSw1NBMs53PwMAAxsbG3Paa2a65TCbz6+R+T6lUKtTX10Or1aKurg7nz593KIchk8mQmJiI06dPY3R0FOfPn3ea4ODM4OCgEODL5XIsXbpUeC0mJkYI+H777TcMDQ3NeMkMf5lYoma271FvLVy4EEajEb29vbN9KncchsREfmLLsvPkdXH9IvF2W1ub22Po9Xqn+/nLwMCA8KB1V9l/eHgYnZ2dfj0XcWKAtz1zSqVS6I1saWnx6XlNhe3a2YpuumO75tHR0W4nhM+k/Px8oQfsiy++cNpGpVIJ24cOHfL42BUVFcLwnEqlsutFlslkwnFHRkZQWVnp9blL1dy5c4X7Qgr3qLceffRRAOPJTLMx/+9OxsCMyE8aGxtdZkrevn1bqFgeFhZmF2TExsYKPSHHjh1zOZdtaGgIp06dAgAsXbrU63lWE82bNw8A7Ep3TCQeXnH3sK2srPR7xfmkpCQhw+/QoUN25zYZhUIhJFtUV1fP+mLW4uV0Jq4UIdba2oqOjg6HfWbbgw8+KGQIt7W1obGx0aFNamqqkFF65swZnDx5ctLjdnV1Yd++fQDGezadDefl5OQIk8uLiorsit26c/v2bYfVJqQmNTUVAHD16lWcO3duysfx5LPta7ZzHx0d9ThxgcYxMCPyE7PZjA8++MBpwPDNN98I1eozMjLsej7mzp0rlEwwGAzYv3+/w/5WqxUfffSRMME2Ozt72udrm6R77do1l20UCoUQNFZXVzt90Ov1enz11VfTPp/JLFiwQFgmqb29HZ988onDsko2t27dcpgcblt4e2hoCHl5eUKJEmfMZjOOHDmCmzdv+ujs7cXFxQmrBFRWVjpdXmpwcFCoqRYQEICsrCy/nMtUvfbaa0JvlrN5ZgEBASgsLBTmQO3atcvligzAeJmVnJwcIYvznXfeweLFix3a3XPPPXj//fcBjPeaqdVqoRSKKx0dHXj55Zdx4MABz/65WbJlyxbhx8fu3bvtMradOXPmjNPyNJ58tn1t48aNQrmgL7/80u01cbW+6n8V55gR+UlsbCzq6+uRlZVltyTTsWPHhN6CyMhIvPHGGw77bt++HTU1Neju7kZxcTEMBgNeeOEFLFq0CD09PcLC1ACQkJDgk3UcExIS0NPTg7q6Omg0GqxYsUL4pR0aGoq7774bAQEB2LBhA44cOYLLly8jKysLubm5iI6OxtDQEHQ6HX744QeEhIQgIiICf/zxx7TPy50333wTjY2NMBgMOHz4MFpbW5GZmYlly5YhMDAQRqMRzc3NOHnyJN566y27tTLXrFmDLVu24Pvvv0dTUxPS09ORmZmJxMREyOVyjIyM4Nq1a2hubkZNTQ1MJhOef/554T3xtY8//hibN2/GrVu38Morr0CtVmPt2rV2SzLZal1t3bpVcnOOli1bhtTUVNTW1qKpqQnNzc0OtdRiY2NRWFiIt99+G2azGfn5+dBoNNiwYQOWLFmCwMBA/Pnnn6irq0N1dbXQ67p161aHpYTEMjIyYDQaUVRUhP7+fqjVaiQnJwu9dAsWLIDJZEJnZyd0Oh3OnTsHi8Xis6LL/hIeHo7PPvsMeXl56O3tRUZGBlQqFZ555hlERkZibGwMRqMRer0eWq0W3d3dKC0tdfi/PPls+9q8efNQWFiIbdu2YXR0FLm5udi4cSPS0tIQGRkJs9mMq1evQqfToa6uDhcvXvT5OdypGJgR+Ul2djaamppQVVWF/Px8h9dt61SK6x3ZhIaG4rvvvhMWMddqtdBqtQ7tVqxYgZKSEp9k4W3btg1arRZmsxl79uyxe02lUmHv3r0AxucTtbS04NKlS7h48SJ27txp11Yul6O4uBhFRUV+D8yCg4NRVlaGvLw8NDU1ob29Xeg98cS7776LsLAwlJSUoLe3F8XFxS7bhoSE+CXb0UapVKK0tFRYxPzgwYM4ePCgQ7vs7GyH91wqXn/9ddTW1gIY7zVz1iOVnp6ORYsWYc+ePbhy5Qp+/fVXlysqyOVyFBQUePTDY/v27YiJicHevXtx/fp1NDQ0oKGhwWX7mJgYt+uNSsW6deuwf/9+7N69GwMDA9BoNNBoNE7bBgQEOK0Z5uln29dWrVqF0tJS7Ny5EyaTCVVVVaiqqvLL3/p/wsCMyI8+/fRTrF69GhUVFTAYDBgZGcHixYuRlpaGV1991W0m5X333Yfjx4+jsrISP//8MwwGA4aHhxEWFgalUiksnOyrtHalUomKigocOHAALS0t6OvrczpUOX/+fJSXl+Pbb7/FqVOn0NXVhTlz5iAqKgpr1qzBSy+9JFT9nwkKhQKHDx9GTU0NTpw4gba2Nty4cQMymQwRERFYvnw50tLSnFb4l8lk2LFjBzZt2gSNRoNffvkFPT09GBwcRFBQEKKioqBUKpGcnIy0tDS39ah8ITk5GTU1NSgrK4NOp0N3dzfMZjPCw8ORmJiIzMxMn1f096W4uDisXr0ajY2NaGhogF6vtyukbPPEE0/gxIkT0Gq1qK2txYULF9DX14exsTEoFAo89NBDSElJwaZNm7zKPF23bh1SUlKg1Wpx9uxZXLhwATdu3MDw8DBCQ0Nx7733Ij4+HuvXr0dSUpJPytHMBFtP5I8//gidToeOjg6YTCbMmTMH4eHhiImJwapVq7B+/Xqnyxt5+tn2h6effhqnT59GeXk56uvr0dnZicHBQQQHByM6OhorV67Ec889NyPncqeQWV1NyiAir/X09ODZZ58FMB6UiYfOiIiIJsPJ/0REREQSwcCMiIiISCIYmBERERFJBAMzIiIiIolgYEZEREQkEczKJCIiIpII9pgRERERSQQDMyIiIiKJYGBGREREJBEMzIiIiIgkgoEZERERkUQwMCMiIiKSCAZmRERERBLBwIyIiIhIIhiYEREREUkEAzMiIiIiiWBgRkRERCQRDMyIiIiIJIKBGREREZFEMDAjIiIikggGZkREREQSwcCMiIiISCIYmBERERFJBAMzIiIiIolgYEZEREQkEf8Df6RQChOJkcoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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qqaeE19pJnj7Fl+Sozt0U7t27h8ePHwuvy9u3szz29vbCZ1Or1YiLi6tSfdoq8zSrNu1FZTt06FAtMZG4MTEjsnBNmzYVEpmEhATcu3ev1LK7d+/W2exZrEz9mSoyTykzMxOLFy8WXrdr165GhqzCw8OF45YtW6Jjx44VruPVV18VjlevXl3qQwAPHz7UmeNX2h6RlbVu3Tqd19WxLZP2Asjnzp2rcn0a2kOu2sOShlCpVMK+oBKJpMxFmqn2YGJGZOGcnJyEX9IqlQrvvfdeidXXVSoVNm7ciI8//hgymczgld9NxdSfac6cOXjvvfdw7NixUpfzUKlUOHr0KEaOHKkTW3VvBwUUrSe2e/du4XVFe8s0Xn75ZTRv3hxAUUI5YcKEEqvRJyYmYuLEicLQrIeHB1566aXKBV5MXl4eVqxYgQ0bNgjvdenSpVp6kgICAoRj7Qckqkp7Tb69e/dW6NorV64gKysLQNEixQ0bNqy2uEi8+FQmkRnYsmULDhw4YHD59957D3379jW4/Pvvv49x48ZBpVIhJiYGwcHB6NSpE5o1a4bc3FycPXtWWPpg6tSp2Lp1q+i3hzHlZ1KpVNi/fz/++usv2NnZwcvLC+7u7nBwcIBCoUBqaiquXr2KtLQ0netmzZpVLb0/xR0+fFh4uEEqlWLIkCGVqkcul2PFihUYNWoUcnNzERMTgwEDBqBHjx5wc3NDUlISoqKihCE7BwcHrFixosQ6daXRt1cmULQeWlJSEi5evKgz/6thw4b4z3/+U6nPUlz37t3h5uaG5ORkREVFISsrq9z19AwxYMAAnDhxAgCwZMkSHDt2DK1bt9aZ3zd58mS98wq1v+e5ebnlYGJGZAZSU1MrtB1LRZ8w7NmzJ+bOnYsvvvgCSqUSCoUCp0+fxunTp4UyUqkUU6ZMwaRJk7B169YK1W8KpvxM2r90c3NzcfHiRVy8eLHU8s2aNcOcOXPQu3fvaotBm/YwZs+ePau0jZGPjw/WrFmDjz76CPfv34dCocDx48dLlGvWrBmWLFlS7q4P2gzZK1Ojc+fOWLhwITw8PAyuvyxSqRQhISFYtWoVFAoF9u/fj5CQkCrXO2zYMERGRuLMmTNQq9WIiooq0SM3evRovYmZZoeGOnXq4MUXX6xyLGQemJgREQBg1KhR6Ny5M9auXYuoqCikpKTAxsYGbm5u6NGjB4YPH27Ujbarg6k+03//+1+MHTtWWPz01q1bSEpKQm5uLqysrFC3bl24u7ujXbt2CAgIgL+/v85uBtUpPT1dJ3GqyNplpenUqRMiIyOxc+dO7N27F7du3UJmZiYcHR3RsmVLDBw4EEOHDoW9vX2V7wUU9dQ5ODjA3d0dvr6+GDRokM5TotXl1Vdfxc8//wyFQoGtW7dWS2Iml8vxyy+/YNu2bdi3bx8SEhKQmZlZ7oMAp0+fFp5qDQoKgpOTU5VjIfMgUYv9uXciIiIjmTNnjtB7Gh4ebrI/Rj744APs2bMHMpkMu3btqpEndUmcOPmfiIjo/7311lvCUPRPP/1kkhju3r2Lffv2AQCCg4OZlFkYJmZERET/r3Hjxnj99dcBFM3xun79utFjWLVqFQoLC2Fra4v33nvP6Pcn02JiRkREpGXKlClwc3NDYWEhlixZYtR7X79+HREREQCASZMmoUmTJka9P5ke55gRERERiQR7zIiIiIhEgokZERERkUgwMSMiIiISCSZmRERERCLBxIyIiIhIJJiYEREREYkEEzMiIiIikWBiRkRERCQSTMyIiIiIRMLK1AGQaalUKuTl5QEArKysIJFITBwRERGReVCr1VAqlQAAW1tbSKVV7+9iYmbh8vLyEBsba+owiIiIzJqPjw/s7e2rXA+HMomIiIhEgj1mFs7K6n9NwNPTE3Z2diaMhiyNQqEQemx9fHwgl8tNHBFZCrY9qg7a7Uj792lVMDGzcNpzyqytrWFtbW3CaMjSSKVSODo6AgDq1KlTbT/YiMrDtkfVrbrmaLMlkoA/mMjYrKys4OnpaeowyAKx7ZFYcY4ZERERkUgwMSMiIiISCY5dkUChUHCOGRmVQqHAjRs3ABQ9fMIJ2GQsbHskVkzMiMhk1Go1cnJyhGMiY2HbI7HiUCYRERGRSDAxIyIiIhIJJmZEREREIsHEjIiIiEgkmJgRERERiQQTMyIiIiKR4HIZJJBKmaeTcclkMjRt2lQ4JjIWtj0SKyZmJOAPJzI2mUwGNzc3U4dBFohtj8SKXSREREREIsHEjIiIiEgkOJRJAu6VScZWUFCA6OhoAICvry/bHxkN2x6JFXvMiIiIiESCPWYksLJicyDjksvl8PX1FY6JjIVtj8SKv4lJIJFITB0CWRiJRMIhJDIJtj0SKyZmJChUqZCcmWPqMIiIiAzmXM8Wslq0DicTMxKkZ+cjeHGkqcMgIiIy2O6w4XBztDd1GNWm9qSYRERERGaOiRkRERGRSDAxIyIiIhIJJmZEREREIsHEjIiIiEgkRP9UZlZWFiIjI3HkyBHcuHEDaWlpsLKygrOzM9q3b4++fftiwIABkMlkBteZnp6OP//8E3///TcSEhKQkZGBgoIC1K9fH61atULnzp3xwgsvwMvLq9y6VCoVDh8+jKNHj+LChQtITU3F48ePYW9vDzc3NyHG3r17613EMDQ0FKdPny7xvlQqRd26ddGsWTN0794dr7zyClq0aGHwZyQiIiLzI1Gr1WpTB1GarVu3YunSpcjMzCyz3FNPPYXPPvsMXbt2LbOcSqXC999/j59//hk5OeWv1+Xv749PPvkETz31lN7zp0+fxvz58xEfH19uXa6urpg6dSpCQkJ03i8tMStOLpdj1qxZGD16dLllK0J7v7hGLTy5XAYREZkVUy6XURN7roq2x2zx4sVYs2YNgKKtggYPHozAwEC4u7ujoKAAt27dwh9//IF//vkH169fxxtvvIGvvvoKAwcO1Ftffn4+PvjgAxw+fBhAUaIzePBgPPPMM2jatClsbGyQlpaG6OhoHDhwANeuXcPff/+N3377DWFhYSXq2759O+bNmweFQgEA6NixI/r37482bdrA0dERWVlZuHfvHo4ePYojR44gJSUFCxYsKJGYadu1a5dwrFKpkJSUhL179yI8PBwKhQLz58+Hu7s7+vTpU9kvKxEREYmYKBOzjRs3CklZo0aN8P3336NNmzY6Zbp27YoRI0Zg165dmDVrFgoKCjB9+nS0aNGiRFkA+PTTT4Wk7Omnn8Z///tfNG3atES53r1745133sHhw4exYMECvfGdOnUKs2fPhkqlgp2dHRYuXKg3IezRowdGjBiB+/fvY+nSpTh+/HiZn7v40KmPjw/69OmDdu3a4YsvvoBarcby5cuZmBEREdVSopv8n5iYiMWLFwMA7OzssHbtWr2JlkZQUJCQQBUUFODjjz9G8dHZ/fv3Izw8HADg7e2NtWvX6k3KtAUEBGD79u3o0aOHzvt5eXmYPn06VCoVpFIpfvjhh1J76TSaNm2KZcuWYfbs2WWWK83o0aPh7u4OALh69SrS0tIqVQ8RERGJm+gSs3Xr1uHJkycAgLfeegstW7Ys95rg4GD06tULABAfHy/0jGl8//33wvHChQthZ2dnUCz16tVD3759dd7bvn07Hj58CKAoYerevbtBdQHA0KFDDS6rTSqVon379sLrBw8eVKoeIiIiEjdRJWZqtRoREREAABsbG7zyyisGX/v6668Lxzt27BCO4+LicOXKFQBFw5/t2rWrUoyauiUSCcaMGVOluirCyup/o84qlcpo9yUiIiLjEdUcs4SEBOEJzK5du6Ju3boGX/vMM8/AxsYG+fn5OHfunPD+mTNnhOPevXtXKb7s7Gxcu3YNANCyZUs0b968SvVVhPaTn66urka7LxERkalYo7DcMnm5ucgpuRqVXvb24t/sXFSJWWxsrHDctm3bCl0rk8ng4+ODixcvIj09HcnJyXBzc9OpU3s4sDLi4uKE3qqq9rxVxJEjR5CQkAAAaNasGRo3bmy0exMREZnKQIe75ZZZ99N3Btc3b968qoRjFKJKzDIyMoRjZ2fnCl/fsGFD4TgzMxNubm46a6A5OTlVKT7turTvVRO0l8tYvny58P6bb75Zo/clIiIi0xFVYqa96KuhE/S1aV+TnZ1dLXVq067L1ta2SnXp4+3tXeb5kSNHVmjeHREREZkXUSVm2mO/ubm5Fb5e+xoHB4dqqVObdl15eXlVqstQtra26Ny5M1577TUEBgYa5Z5ERERkGqJKzBo0aCAcp6amVvh67fW9HB0ddf5f/HxlaNdVmfjKo73yv0wmg729PVxcXCq0DygREVFtsTe7/IfsNk99ES71qzYiJiaiSsy0h/JiYmIqdG1hYSHi4uIAFM0lc3NzA1C0er7G1atX4e/vX6X4pFIpVCpVheMzhCGbphMREVmKApTfMWFrZ2cWT1saSlTrmHl5eQm9UmfPnkVWVpbB1548eVIYXtTezLxbt27C8dGjR6sUn4ODg7ALwa1bt3Dnzp0q1UdERESkTVSJmUQiwZAhQwAUbTq+detWg6/99ddfheNhw4YJx97e3sIyGWfPnsXVq1erFKNmE3K1Wo0NGzZUqS4iIiIibaJKzABgzJgxsLa2BgCsXLnSoF6pP//8E0eOHAFQ1OsWEBCgc37y5MnC8axZswx+CODx48c4dOiQznvDhw+Hi4sLgKLN1k+fPm1QXQCwc+dOg8sSERGR5RFdYta0aVN8/PHHAIqeohw7dqzOIrHF7d69GzNmzAAAyOVyfPnll5BIJDpl+vfvL+xTGRcXh7FjxyIxMbHMOI4ePYqXXnoJp06d0nnf1tYWX331lTDXbNKkSfjrr7/KrOvBgweYNm0avvjiizLLERERkWUT1eR/jdDQUNy9exfr16/HgwcPMHz4cLzwwgsIDAxEkyZNoFQqcfPmTfzxxx9C4mRtbY2vvvpKmANW3KeffopHjx7h8OHDuHTpEgYOHIhBgwbh2Wefhbu7O2xsbJCamoqYmBjs37+/zCHPnj174osvvsC8efOQm5uL9957Dx07dsSAAQPQpk0b1K9fH1lZWbh37x6OHz+OQ4cOoaCgoEJbTBEREZHlEWViBgBhYWFo1aoVvv76a2RmZiIiIkLY4Lw4T09PfP755zqT/ouztbXFd999h1WrVmH16tXIyckps04A6NOnD1599VW954YPH45mzZph/vz5iI+Px8WLF3Hx4sVS62rSpAk+/PDDUs8TERERiTYxA4BRo0bhhRdeQGRkJI4cOYLr168jPT0dMpkMzs7O8PX1Rd++fTFw4ECD1vqSSqV4++23MWrUKPz555/4+++/ER8fj4yMDCgUCtSvXx+tWrVC586dERwcDE9PzzLr6969OyIiInD48GEcOXIEFy5cQGpqKrKysmBnZ4dGjRoJMfbu3RtWVqL+chMREZGJSdRqtdrUQZDpFBQUIDo6GgDQqIUnghdHmjgiIiIiw+0OGw43R9OsY6b9O9TX11d4eLEqRDf5n4iIiMhSMTEjIiIiEgkmZkREREQiwcSMiIiISCT4mCAJnBxssDtsuKnDICIiMphzPVtTh1CtmJiRQCaVws3RxtRhEBERWSwmZiTgyilkbGq1GgqFAkDRlmrFt1MjqilseyRWnGNGAqVSaeoQyMIoFApER0cjOjpa+CVJZAxseyRWTMyIiIiIRIKJGREREZFIcI4ZCTjHgoxNKpXC1dVVOCYyFrY9EismZiTgJutkbFZWVmjWrJmpwyALxLZHYsU/E4iIiIhEgokZERERkUhw7IoEhSoVkjNzTB0GmRnneraQVXKOjkKhwLVr1wAAbdq0gVwur87QiErFtkdixcSMBOnZ+QheHGnqMMjM7A4bDjdH+0pdq73IJxc4JmNi2yOx4lAmERERkUgwMSMiIiISCSZmRERERCLBxIyIiIhIJJiYEREREYmE0ROzwsJCdO7cGd7e3hg2bFiZZdVqNfz8/ODt7Q1vb29s27atzPLh4eFC2U2bNgnvh4aGCu+XZ926dULZnj17IjY2Vji3YsUK4VxUVFS5dQFAYGAgvL29ERgYqPf8zJkzhTq1//Px8UHXrl0RHByMzz77THism4iIiGovoydmMpkMnTp1AgDExsYiOzu71LIJCQnIzMwUXp87d67Mus+ePSscd+vWrcKx/fzzz1iwYAEAwMXFBRs2bICPj0+F66kOarUaWVlZiIuLw6ZNmxASEoJly5aZJBaimmJlZYVWrVqhVatW3BKMjIptj8TKJK2xW7duOHHiBFQqFc6fP4/nnntObzlNoiWTyVBYWKiTeJVVvkGDBnjqqacqFNOqVavw9ddfAwDc3Nywbt06tGzZskJ1VNXq1auFTXVVKhXS0tJw5MgRbNq0CUqlEt9//z1cXV0xevRoo8ZFVFOkUikaNGhg6jDIArHtkViZZI5Z165dheOyki3NuYEDBwIA7t69i+TkZL1l09LScPv2bQBAly5dIJFIDI5nxYoVQlLWpEkT/Prrr0ZPygDAw8MDXl5e8PLygo+PD/z9/REWFoYVK1YIZVauXInCwkKjx0ZEREQ1zySJWYcOHVCnTh0AhiVmAwYMQPPmzQGUPpxZ2WHM//73v1i5ciUAoGnTpvj111+Fe4lFYGCgkMymp6fj6tWrJo6IqHqo1WoUFBSgoKCAq6+TUbHtkViZJDGztrZGhw4dAADR0dEoKCgoUebevXtC71iXLl3QpUsXAKUnctrva/fIlWXx4sX44YcfABT1Vm3cuBHu7u6GfxAj0ny9AODBgwcmjISo+igUCkRHRyM6OlrYHofIGNj2SKxMtlyGJnkqKCjApUuXSpzXJFoeHh5wdnYWErMzZ87orU/Tk+bg4IA2bdqUe/8vvvgCa9asAQC0atUKGzZsQKNGjSr+QYxEe3KqSqUyYSRkqaxRqPe/vNxc5OTklPkfEREZxmSPonTr1g2rVq0CUJSEFR9+1CRmnTt31vl/QkICHj16hPr16wtls7OzhWUtOnXqBJlMVup91Wo1PvvsM2zevBkA4OXlhV9++QXOzs7V9MlqRnx8vHCseUCAyJgGOtzV+/66n74r99p58+ZVdzhERLWSyRKzjh07wsrKCkqlUu/wpOY9Tc+ap6cnGjRogIyMDJw/fx4BAQFC2QsXLggT4subXzZnzhz8/vvvAIA2bdpgzZo1cHJyqnD89+/fN+iJnuroIo+NjcXx48cBALa2tvD19a1ynURERCQ+JkvM7O3t0aZNG0RHRwuJlaanq/gTlhqdO3fGwYMHcfbsWZ3ErCLzyzRJmaenJ9atW6fT81YRn3zySaWuM5RarUZqaiqOHDmCpUuXColnaGio8OAEERER1S4mXVWvW7duiI6ORk5ODq5du4b27dsD+N88MmdnZ3h4eAjlu3TpIiRm2jTzy+rUqVNub5JEIoFarcb9+/cRExODnj17VuMnqpq+ffuWeb5Pnz547733jBQNERERGZtJE7MuXboIE/DPnj0rJGaaxEu7twz4X2/Y1atXkZ+fDxsbGxQUFODy5csAgKeffhrW1tZl3nPOnDn4/PPP8eTJE0yZMgU///yzwU9xalu/fj38/PzKLRcYGIjExMQK168hl8vh6+uLkSNHYsiQIRVan42oOu3N1r+MzOapL8Klvp2RoyEiqp1Mmph17dpV6ME6d+4cxo4dC+B/PWCaCf8abdu2ha2tLfLy8nDx4kX06NED0dHRePLkiVBfeUaPHo38/Hx8+eWXyMvLw6RJk/DLL7/oLEdhKtor/0ulUtjZ2cHZ2bncZJPIGAqg/6EaWzs72NvbGzkaIqLayWTLZQCAo6MjWrduDeB/vWTaT1gW7zHT9B5pl9decNbQhWXHjx+Pd999V7jfm2++qbNZualor/z/1FNPoUmTJkzKqFbTfE/7+vpCLpebOhyyIGx7JFYmTcwA6Kxof+PGDZw7dw4qlQp2dnZo27ZtifLFF5rVzEeTy+Xo2LGjwfd95513MGHCBADAo0ePMG7cONy4caMqH4WIKkgikcDa2hrW1tYcpiejYtsjsRJNYgYU9X5pesA6duyodz0yTWJ26dIlFBQU4MKFCwCKhjnt7Co2z+Wjjz5CaGgogKInQceOHYt79+5V6nMQERERVZWoErMzZ84IPWDFhzE1NAvI5ubmYseOHcjKyipRT0WEhYXh5ZdfBgCkpKTg9ddfx7///lupuoioYlQqFTIyMpCRkcEdLcio2PZIrEyemLm5uQmbhkdFReHKlSsASk/MHBwc4OXlBQD4+eefhfcrsnG5NolEgs8++wxBQUEAgMTERIwdOxYPHz6sVH1EZDilUombN2/i5s2bUCqVpg6HLAjbHomVyRMz4H+9XcnJySgoKICVlVWZ88U0SZtm2FEqlZaayBlCKpVi8eLFGDBgAADg9u3beOONN5Cenl7pOomIiIgqSlSJmUabNm1ga2tbavniSZiXlxfq1atXpRhkMhmWLl2KPn36ACjak3P8+PF4/PhxleolIiIiMpQoErPiw5Dl9X4VP1/ZYczi5HI5li9fjmeeeQYAEBMTgwkTJiAnJ6da6iciIiIqi0StVqtNHQSZTkFBAaKjowEAjVp4InhxpIkjInOzO2w43Bwrt8Csdvvz9fXlun1kNGx7VB1qoh2JoseMiIiIiJiYEREREYmGSffKJCLLJpFIhO1wuPo6GRPbHokVEzMiMhm5XI4OHTqYOgyyQGx7JFZMzEjg5GCD3WHDTR0GmRnneqUvbUNERBXDxIwEMqkUbo42pg6DiIjIYjExIwFXTiFjUyqVwt60jRs3hpUVfySRcbDtkVjxqUwScL84MjaVSoWUlBSkpKRwI2kyKrY9EismZkREREQiwcSMiIiISCSYmBERERGJBBMzIiIiIpFgYkYCPpVERERkWkzMSMBtSYiIiEyLXSQkKFSpkJyZY+owRMW5ni1kUv79UlOkUikcHR2FYyJjYdsjsWJiRoL07HwEL440dRiisjtsONwc7U0dRq1lZWUFT09PU4dBFohtj8SKfyYQERERiQQTMyIiIiKR4FAmEZmMQqHAjRs3AACenp6Qy+UmjogsBdseiRUTMyIyGbVajZycHOGYyFjY9kisOJRJREREJBJm02OWm5uLiIgIHDp0CLGxscjMzIRarYaDgwPc3d3h5eWFTp06oVevXmjcuHGZdT148AB//vknTp48iTt37iA9PR0qlQr169eHl5cXunTpgqCgIDRr1qzEtVFRURgzZgwA4J133sG7775bbuwzZ85EeHg4AODgwYNo2rSpzvkdO3Zg1qxZAICFCxciJCTEoK8JERER1S5mkZhduHAB06ZNw4MHD0qcy8jIQEZGBq5cuYIdO3bA2dkZf//9t956njx5gqVLl2Lz5s0oKCgocT4lJQUpKSk4ceIEli9fjoEDB2LGjBnlJnpERERE1UH0idmtW7cwfvx4YS5AYGAgBgwYgJYtW0IulyMjIwOxsbE4efIkoqKiSq0nPT0dU6ZMwcWLFwEA9vb2ePHFF9GjRw80atQIVlZWSE1Nxfnz57F//37cvn0be/bsQceOHTF27FgjfFIiIiKydKJPzJYtWyYkZaUN8/n7+2P8+PFIT0/Hnj17SpxXqVT44IMPhKQsICAA//nPf9CwYcMSZQMDAzFt2jRERkbiyy+/rN4PQ0RERFQGUSdmhYWFOHr0KACgffv25c69cnJywujRo0u8v379eqE37dlnn8XKlSvL3LBbKpVi6NCh6NGjB27dulWFT0BERERkOFEnZunp6cjPzwcAtGjRolJ1FBQUYPXq1QCAOnXqYMGCBWUmZdoaNWqERo0aVeq+RFQ+mUwmPAwjk8lMHA1ZErY9EitRJ2baC/5pFgKsqBMnTiAlJQUAMHDgQLi5uVVLbGT+rFFYbpm83FzkGLDupL0999OsDJlMxu9JMgm2PRIrUSdmjo6OcHd3R2JiImJjY/Hjjz/izTffhFRq+PJrZ86cEY779OlTA1GSuRrocLfcMut++s6guubNm1fVcIiIiMSdmAHAa6+9hsWLFwMAli5dii1btiAwMBCdO3eGr6+v3rXGtMXGxgrH7dq1q9bY0tLSEB8fX265x48fV+t9iYiIqHYSfWI2duxYXL9+Hdu3bwcAJCYmYsOGDdiwYQMAwNnZGd27d0dQUBACAgIgkUh0rs/MzBSO9T2FWRWbN2/G5s2bq7VOIktSUFCA6OhoAICvry+sra1NHBFZCrY9EivRb8kklUqxYMECrFmzBr169SoxcT81NRW7d+/GlClT8NJLL+HuXd3hKc1SGwBga2trlJiJiIiIKkP0PWYa/v7+8Pf3R3Z2Ns6dO4fo6GhcuXIFZ8+eRVZWFgDgypUrePXVV7Fjxw64uroC0J2UnZeXBwcHh2qLqTJbMpF47M1uXm6ZzVNfhEt9OyNEQ0REZEaJmYaDgwN69+6N3r17Ayjqjt61axcWL16MR48e4eHDh/jmm2/wn//8B0DRAwQaqamp1ZqYkXkrQPmPyNva2fGJSyIiMhrRD2WWx9raGsOHD8fSpUuF9/bv3w+VSgUA8PHxEd6PiYkxenxEREREhjL7xEyjV69ewmbjjx49Eib9d+vWTShz5MgRE0RGREREZJhak5gBEOaVaXv22WeF9/fu3Yvk5GRjh0VERERkkFqTmOXl5eH69esAiuahNWjQAEDRUOf48eMBAE+ePMEnn3yCwsLyV3wHgOTkZJw6dapmAiYiIiIqRtSJWU5ODkaMGIHDhw8Lc8b0UalUmD9/vrA0RmBgoM56ZmPGjIGfnx+Aoi2a3n77baSnp5dan1qtxq5duxASEoK4uLhq+jREVJyVlRW8vb3h7e1t8B62RNWBbY/ESvSt8fLly5g8eTLc3NzQr18/dOzYEU2aNIGDgwMeP36MmJgYbN++XViBv27dunj//fd16pBKpfj6668xefJkXLp0CYcPH0a/fv0QFBSEHj16oFGjRrCyskJqaiouXryIffv24ebNm6b4uDh37pxB5QIDA3WeOCUyR1KplE9Kk0mw7ZFYiToxs7KygouLCx4+fIjk5GRs3LgRGzduLLW8h4cHli5diqZNm5Y45+TkhA0bNmDJkiXYvHkzcnJysGXLFmzZskVvXRKJBEFBQRg0aFC1fR5DbNu2Ddu2bSu33M6dO5mYERER1TKiTszq1KmDY8eO4eLFizh58iQuXbqEW7duIS0tDU+ePIGtrS1cXV3h4+ODvn374vnnny9zW406deogLCwMb7zxBv744w+cOnUKt2/fRnp6OtRqNerXrw8vLy9069YNQUFBcHd3N+KnJbI8KpUKubm5AAA7OztIpaKeXUG1CNseiZVErVarTR0EmY72fnGNWngieHGkiSMSl91hw+HmyAVmawr3KyRTYduj6lAT7Yh/IhARERGJBBMzIiIiIpFgYkZEREQkEqKe/E/G5eRgg91hw00dhqg417M1dQhERGRBmJiRQCaVws3RxtRhEBERWSwOZZKAD+gSERGZFhMzEiiVSlOHQEREZNE4lEkCuVxu6hDIwlhbW6NLly6mDoMsENseiRV7zIiIiIhEgokZERERkUhwKJMEhYWFpg6BLExhYSFSU1MBAM7OzpDJZCaOiCwF2x6JFRMzEqhUKlOHQBamsLAQ9+/fBwA0aNCAvxzJaNj2SKw4lEkCKyvm6URERKbExIwEEonE1CEQERFZNHaRkKBQpUJyZo5J7u1czxYyKf9OICIiy8bEjATp2fkIXhxpknvvDhsON0d7k9ybiIhILNhFQURERCQSTMyIiIiIRIJDmURkMhKJBPb29sIxkbGw7ZFYMTEjIpORy+Xw8fExdRhkgdj2SKzMKjE7ffo0QkNDhdebN29G586dK1TH3bt3sWfPHpw6dQp3795FRkYGlEol6tati6ZNm6Jt27bo06cP/P39S93Ue8eOHZg1a1aF7jtr1iyMHTtW573Q0FCcPn26RFmpVIq6deuiWbNm6N69O1555RW0aNGiQvcjIiIi82NWiVl4eLjO6507dxqcmD1+/BiLFi1CREQElEplifNpaWlIS0vDpUuXsHnzZjRs2BATJkzAmDFjjL4itEqlwqNHj/Do0SNcuXIFGzZswKxZszB69GijxkFERETGZTaJWX5+Pv766y8AgJ2dHXJzc7F3717Mnj0b1tbWZV577949TJgwAbdu3QIAODo64oUXXkDXrl3h6uoKW1tbpKen4+bNmzhx4gROnjyJtLQ0LFq0CC+++CJcXFxKrfuDDz5A3759y42/rDoAYNeuXcKxSqVCUlIS9u7di/DwcCgUCsyfPx/u7u7o06dPufciMhdKpRJ37twBALRo0YK7T5DRsO2RWJlNS9y/fz9ycooWP509ezY++eQTPHr0CIcOHcLAgQNLvS43NxeTJk0SkrKXX34ZM2bMgIODQ4myvXr1wuuvv4579+7hu+++w44dO8qNy83NDV5eXpX8VP9TvA4fHx/06dMH7dq1wxdffAG1Wo3ly5czMaNaRaVSITMzEwDQrFkz0wZDFoVtj8TKbJbL2LlzJwDA29sbw4cPR8uWLXXeL82SJUtw48YNAMCrr76K+fPn603KtDVr1gwLFy7EqlWrUKdOnSrHXhWjR4+Gu7s7AODq1atIS0szaTxERERUc8wiMUtJScGpU6cAAMHBwTr/P3HiBNLT0/Vel5qaim3btgEAGjdujBkzZlTovoGBgahXr15lw64WUqkU7du3F14/ePDAhNEQERFRTTKLxGzXrl0oLCyEVCpFUFAQACAoKAgSiQQKhQJ//PGH3ut2796NJ0+eAABGjBgBGxsbo8VcnbTnPqhUKhNGQkRERDXJLBKziIgIAED37t3h5uYGoGi4sVOnTgBKH848c+aMcNyrV6+aDbIGxcfHC8eurq4mjKTm5OXmCnMIiYiILJXoJ/9fu3YNcXFxAP43fKkRHByM8+fP4+rVq7h+/TqeeuopnfOa66RSaY0tJJicnKyTOJWmsg8IHDlyBAkJCQCKktHGjRtXqh6xW/fTdwCAefPmmTgSIiIi0xF9YqbpDbOxscGAAQN0zg0aNAj/+c9/oFAosHPnTnz00Uc65zMyMgAAdevWLXNJjYcPHwpli6tfv77QS6fP119/ja+//rrcz6FJEg2hvVzG8uXLhffffPNNg+sgIiIi8yPqxEypVArzxwICAko8Teno6IjevXvjwIED2LVrF6ZNmwap9H+js5qhMVtb2zLv8+OPP2L9+vV6zw0bNgyLFi2qyscwiLe3d5nnR44ciVdeeaXG4yAyJqlUKgzPa3/vEtU0tj0SK1EnZidOnEBqaiqAksOYGsHBwThw4ACSkpIQFRWFnj17Cufs7e3x+PFj5OXl1ViMCxcuREhISI3UbWtri86dO+O1115DYGBgjdyDyJSsrKy4hhSZBNseiZWoEzPNMKajo2Opk/cDAgJQr149PH78GDt37tRJzBwdHfH48WNkZWWhoKCg1OHMsLAwhIWFCa/v379v0Gr+1Ul75X+ZTAZ7e3u4uLgYfTsoU3l9wltwqW9n6jCIiIhMSrSJWVZWFg4dOgQAyMzM1FnLqzT79u3DvHnzYGdX9Avex8cHd+/ehUqlQlxcHHx9fWs05qqojt0DzJmtnR3s7e1NHQYREZFJiXZgfc+ePcIaZIbKzc3Fvn37hNfdunUTjo8fP15tsRFR9VAoFLh8+TIuX74MhUJh6nDIgrDtkViJtsdMM4zp4uKCWbNmlVv+yy+/RFJSEiIiIjB06FAAwODBg/HVV1+hoKAAv//+O8aPH2/yLZaI6H/UarXwS1GtVps4GrIkbHskVqJMzO7du4fz588DAAYMGIAXXnih3GsuXryI9evX459//kFycjLc3Nzg7OyM4cOHY/PmzXjw4AG++uorzJ49u6bDJyIiIqoUUQ5lRkRECH/BFF+7rDSaciqVStgpAAA++ugjYcPzDRs24NNPPy13hfnHjx9XJmwiIiKiKhFlj5kmsWrYsCG6du1q0DWdO3eGi4sLHj58iMjISEycOBEA4ODggB9++AETJ07E7du3sXnzZuzduxcvvvgiunbtCldXV9jZ2SE7Oxt37tzByZMnsX//fqHe8vbXNHTlfwcHBzRp0sSgz0JERESWSXSJ2blz53D37l0AQL9+/Qxe+E8qlaJ///7YtGkTEhIScOXKFeFJzhYtWmDr1q1YuHAhIiMjkZGRgQ0bNmDDhg2l1tewYUO8+eabCA0NLfO+hq7837dvX3z33XcGfRYiIiKyTKJLzLSHIQ0dxtR4/vnnsWnTJgBFDw9oL7FRv359LFq0CFOmTMHu3bvxzz//4M6dO8jMzERhYSHq1auHxo0bo3379nj22WfRu3dvyOXy6vlQRERERAaQqPk4ikUrKChAdHQ0AKBRC08EL440SRy7w4bDzZHrmFka7fbn6+tb5p62RNWJbY+qQ020I9H1mBGR5bCyskKrVq2EYyJjYdsjsWJrJCKTkUqlaNCgganDIAvEtkdiJcrlMoiIiIgsEXvMiMhktFdfl8vlkEgkJo6ILAXbHokVEzMSODnYYHfYcJPc27merUnuS6alUCg4AZtMgm2PxIqJGQlkUincHMteUJeIiIhqDueYkYArpxAREZkWEzMSKJVKU4dARERk0ZiYEREREYkEEzMiIiIikWBiRkRERCQSTMyIiIiIRILLZZCA+8WRscnlcvj6+grHRMbCtkdixd/EJODK12RsEomEC3uSSbDtkVhxKJOIiIhIJNhjRoJClQrJmTnVUpdzPVvIpMz7qWwqlQqPHj0CANSvXx9SthkyErY9EismZiRIz85H8OLIaqlrd9hwuDnaV0tdVHsplUrcvHkTAPcrJONi2yOx4p8IRERERCLBxIyIiIhIJJiYEREREYkEEzMiIiIikbDIyf+5ubmIiIjAoUOHEBsbi8zMTKjVajg4OMDd3R1eXl7o1KkTevXqhcaNG+tcO3PmTISHh1fofjt37kSbNm2E1++88w72798PAFi7di169uxZ5vUHDx7EW2+9BQAYOnQoFi9eXKH7ExERkXmwuMTswoULmDZtGh48eFDiXEZGBjIyMnDlyhXs2LEDzs7O+Pvvv6s9hrlz5yIqKgqPHz/G3LlzsWvXLtjY2Ogtm5WVhU8//RQA0LBhQ8yaNava4yEiIiJxsKjE7NatWxg/fjxycorW6goMDMSAAQPQsmVLyOVyZGRkIDY2FidPnkRUVFS59a1evRqurq7llmvRooXOa1dXV0yfPh1z5szB3bt38c0332DGjBl6r/3yyy+RkpICAJg9ezYcHR3LvR+RuZBIJMJ2ONx5goyJbY/EyqISs2XLlglJ2cKFCxESElKijL+/P8aPH4/09HTs2bOnzPo8PDzQtGnTSsUyYsQI7Nq1C6dPn8a6devwwgsvoH379jploqKi8PvvvwMoSiIHDx5cqXsRiZVcLkeHDh1MHQZZILY9EiuLmfxfWFiIo0ePAgDat2+vNynT5uTkhNGjR9dYPBKJBF988QXq1KmDwsJChIWFQalUCufz8/MxZ84cYe6bZjiTiIiIai+LSczS09ORn58PoOTQoqm0aNEC77zzDgAgNjYWP//8s3Bu+fLluHPnDgBg+vTpcHNzM0mMREREZDwWk5hp5hIAwI0bN0wYia5x48ahbdu2AIBvv/0WN2/exJUrV7B27VoAQLdu3TBy5EgTRkhUc5RKJe7du4d79+7p9BgT1TS2PRIri0nMHB0d4e7uDqCod+rHH3+ESqUycVSAlZUVvvjiC8hkMhQUFGD27NmYPXs2CgsLUadOHcyfP9/sJqZaoxB5ubnCfD6i0qhUKqSkpCAlJUUU349kOdj2SKwsavL/a6+9JqwBtnTpUmzZsgWBgYHo3LkzfH190axZswrVd/v2beTm5pZZxtbWttx627Vrh7Fjx2L16tU4d+6c8P4777yDli1bVigmMRjocBfrfvoOADBv3jwTR0NERGQ+LCoxGzt2LK5fv47t27cDABITE7FhwwZs2LABAODs7Izu3bsjKCgIAQEB5fZUjR8/vtx7du/eXai/LO+99x52796Nf//9FwDQtm1bjBs3rtzriIiIqPawmKFMAJBKpViwYAHWrFmDXr16wcpKNy9NTU3F7t27MWXKFLz00ku4e/eu0WK7c+cOUlNThddOTk4l4iMiIqLazSJ/8/v7+8Pf3x/Z2dk4d+4coqOjceXKFZw9exZZWVkAgCtXruDVV1/Fjh07Sl1E9uDBg5Vex0ybZrkMhUIhvHfixAns2rULQUFBVa6fiIiIzINF9ZgV5+DggN69e+Odd97B999/j5MnT2LBggWoX78+AODhw4f45ptvajyO9evXIzo6GkDR8KgmEVywYAEyMjJq/P7VbW92c7w+4S189NFHpg6FiIjIrFh0YlactbU1hg8fjqVLlwrv7d+/v0af2Ll3756Q/Hl5eWHq1KmYM2cOgKK118xxw/ICyGBrZwd7e3tTh0JERGRWmJjp0atXLzRu3BgA8OjRI2RmZtbYvebOnYu8vDxIpVL85z//gVwux/PPP49+/foBAMLDw3Hq1Kkauz+RKUmlUjg6OsLR0RFSKX8ckfGw7ZFYsTWWwpDNyatq+/btOHnyJAAgNDRUZ9+2uXPnwsHBAUDRkhNPnjyp8XiIjM3Kygqenp7w9PTkwy5kVGx7JFZMzPTIy8vD9evXARTNQ2vQoEG13yM1NVUYpnR3d8cHH3ygc97NzQ0ffvghgKInNleuXFntMRAREZG4WExilpOTgxEjRuDw4cNlzhlTqVSYP3++sGp9YGBgjay8//nnn+PRo0cAgM8++wx2dnYlyowaNQqdO3cGAKxZswaxsbHVHgcRERGJh0X1316+fBmTJ0+Gm5sb+vXrh44dO6JJkyZwcHDA48ePERMTg+3btyM+Ph4AULduXbz//vul1mfIyv8A4OLiotPrduDAAfz1118AgCFDhqBXr156r5NIJJg/fz6GDh0KhUKB2bNnY+vWrZwPQbWGQqEQ9q719PTU2dOWqCax7ZFYWUxiZmVlBRcXFzx8+BDJycnYuHEjNm7cWGp5Dw8PLF26tMx1ygxZ+R8AZs2ahbFjxwIAsrKy8NlnnwEoWkR21qxZZV771FNPYdKkSVi5ciWio6OxYcMGvP766wbdl0js1Gq10DutVqtNHA1ZErY9EiuLSczq1KmDY8eO4eLFizh58iQuXbqEW7duIS0tDU+ePIGtrS1cXV3h4+ODvn374vnnn4e1tXW1x/Hll18iJSUFAPDJJ58YNH9t0qRJ2LNnD27cuIGvv/4a/fv3R5MmTao9NiIiIjItiZp/Kli0goICYXHbRi08Ebw4slrq3R02HG6OXMeMyqbd/nx9fWvkjyEifdj2qDrURDviZCUiIiIikWBiRkRERCQSTMyIiIiIRIKJGREREZFIWMxTmVQ+Jwcb7A4bXi11OdezrZZ6qHaTyWTCkjQymczE0ZAlYdsjsWJiRgKZVAo3RxtTh0EWRCaTwc3NzdRhkAVi2yOx4lAmERERkUgwMSMiIiISCQ5lkkChUHCRRTIqLvJJpsK2R2LFHjMiIiIikWBiRkRERCQSTMyIiIiIRIKJGREREZFIMDEjgZUVnwUhIiIyJSZmJJBIJKYOgYiIyKKxi4QEhSoVkjNzKnydcz1byKTM8YmIiKqKiRkJ0rPzEbw4ssLX7Q4bDjdH+xqIiGo7KysreHt7C8dExsK2R2LF1khEJiOVSuHg4GDqMMgCse2RWHH8iYiIiEgk2GNGRCajUqmQm5sLALCzs4OUcxXJSNj2SKzYEonIZJRKJeLi4hAXFwelUmnqcMiCsO2RWNWqHrOoqCiMGTNGeG1nZ4eTJ0/C1ta2zOvy8/Ph7++P7Oxs4b3169fDz8+vzOtOnz6N0NBQ4fXmzZvRuXPnCsepzcbGBo6OjvDx8UH//v0RHBzMzXWJiIgsRK3uMcvNzcWBAwfKLXfw4EGdpMxQ4eHhOq937txZ4TqKy8/PR1JSEo4cOYKwsDCEhITg/v37Va6XiIiIxK/WJmZ16tQBAERERJRbVlNGc40h8vPz8ddffwEo6pkDgL1796KgoKBCcY4aNQq7du0S/lu7di3CwsLQqFEjAEBCQgKmTJmCwsLCCtVLRERE5qfWJmaBgYEAgJMnT+Lhw4ellktLS8Pff/8NAOjbt6/B9e/fvx85OUWLsc6ePRsA8OjRIxw6dKhCcTZs2BBeXl7Cfz179sSYMWPw559/wt3dHQAQHx+P/fv3V6heIiIiMj+1NjHz9/eHi4sLCgsL8eeff5Za7o8//oBSqYSLiwueeeYZg+vXDFt6e3tj+PDhaNmypc77VeXg4IApU6YIr0+ePFkt9RIREZF41drETCaT4YUXXgBQ9nCm5tyLL74ImUxmUN0pKSk4deoUACA4OFjn/ydOnEB6enql49amWZUaAJKSkqqlTiIiIhKvWpuYAcCQIUMAADExMUhISChx/vr167h69apOWUPs2rULhYWFkEqlCAoKAgAEBQVBIpFAoVDgjz/+qIboAblcLhxzyxAiIqLar1YnZm3btkXr1q0B6O8107zn5eWFNm3aGFyv5rru3bvDzc0NANCsWTN06tQJQPUNZ964cUM41sw3E6O83Fxhvh1RRVhbW6NLly7o0qULl4Uho2LbI7Gq1YkZ8L+esD/++ANqtVp4X61WY9euXTplDHHt2jXExcUB+N/wpYbm9dWrV3H9+vUqxV1YWIjVq1cLrwcMGFCl+mrSup++w5IlS0wdBhERkdmr9YlZcHAwpFIp/v33X0RFRQnvR0VF4d9//9UZjjSEpjfMxsamRLI0aNAgYfixsr1m6enpOHXqFF577TXExMQAKErKunbtWqn6iIiIyHzU+sTMzc1NWMFfezhTc9yjRw9hOLI8SqVSmD8WEBAABwcHnfOOjo7o3bs3gKJ5aCqVqtw6V65cCW9vb+G/nj17YuzYsTh//jxsbW3xxhtvYOnSpQbFR2RuCgsLkZycjOTkZK7VR0bFtkdiVesTMwAYOnQoAGDfvn3Iz8/XWRy2IsOYJ06cQGpqKoCSw5gamveTkpJ0eugqw8fHB6GhoToPARDVJoWFhbh//z7u37/PX45kVGx7JFYW8ahf//798emnnyI7OxsHDx6EWq1GTk4O7Ozs8Pzzzxtcj2Z40tHREb169dJbJiAgAPXq1cPjx4+xc+dO9OzZs8w6R40ahVdffRVA0Q+KpKQk/PXXX4iIiMCFCxcQGhqKbdu2wcnJyeA4je31CW/Bpb6dqcMgIiIyexaRmNnb26Nfv37YtWsXIiIihIcA+vXrJ2ynVJ6srCxhVf/MzEy0b9++3Gv27duHefPmlXkPzcr/Gm3atEFAQAD8/Pwwc+ZMJCYmIiwsDKtWrTIoTlOwtbODvb29qcMgIiIyexYxlAn8bzjz77//FlbR17xniD179uDJkycVumdubi727dtXoWs0hg0bJjxccOjQIWFBWyIiIqq9LKLHDAB69uwJFxcXYd9MV1fXcocZtWmGMV1cXDBr1qxyy3/55ZdISkpCREREhRJAbVOnTsWBAwdQWFiIZcuWVSheIiIiMj8Wk5jJZDIMGTIE69evB1A06V8qNazD8N69ezh//jyAoqUrNFs9leXixYtYv349/vnnHyQnJxv85Ke2li1bYtCgQfjjjz9w6dIl/P333/D3969wPURERGQeLGYoEwCmT5+O6OhoREdH46OPPjL4Ou15aYYu9Kopp1KpytyrszyTJk2CRCIBAFHPMyMiIqKqs6jErLI0iVXDhg0NXui1c+fOcHFxAQBERkZW+t5eXl4IDAwEAJw5cwZnz56tdF1EYiORSGBvbw97e3vhDxAiY2DbI7FiYlaOc+fO4e7duwCKnuI0dPhTKpWif//+AICEhARcuXKl0jFMnjxZOGavGdUmcrkcPj4+8PHx4Xp9ZFRseyRWTMzKoT0MWdH9KrXXSKvKxuYdOnQQ5padOHECly9frnRdREREJF4StfbO3mRxCgoKEB0dDQBo1MITwYsrPuy6O2w43By5jhkREVkW7d+hvr6+sLa2rnKdFvNUJhGJj1KpxJ07dwAALVq0gJUVfySRcbDtkVhxKJOITEalUiEzMxOZmZlQqVSmDocsCNseiRUTMyIiIiKRYGJGREREJBIcVCeBk4MNdocNr/B1zvVsayAaIiIiy8PEjAQyqRRujjamDoOIiMhicSiTBFw5hYiIyLSYmJFAqVSaOgQiIiKLxqFMEnC/ODI2qVQKV1dX4ZjIWNj2SKyYmJGACyySsVlZWaFZs2amDoMsENseiRX/TCAiIiISCSZmRERERCLBsSsSKBSKatmAlchQCoUC165dAwC0adMGcrncxBGRpWDbI7FiYkYCzjEjY1Or1VAoFMIxkbGw7ZFYcSiTBHwqk4iIyLTYRUKCQpUKyZk5Jd53rmcLGR8nJyIiqnFMzEiQnp2P4MWRJd7fHTYcbo72JoiIiIjIsrAbhIiIiEgkmJgRERERiQQTMyIiIiKR4BwzIjIZKysrtGrVSjgmMha2PRIrs2uNUVFRGDNmjN5zNjY2cHR0hI+PD/r374/g4OAyF0z19vau0L19fHwQERGh896OHTswa9YsAMDChQsREhJSoToDAwORmJhY4n0rKys4ODigVatWeOaZZ/Dyyy/Dzc2tQnUTiZ1UKkWDBg1MHQZZILY9EqtaNZSZn5+PpKQkHDlyBGFhYQgJCcH9+/dNHValKJVKZGZm4vz581i5ciUGDx6M/fv3mzosIiIiqkFm12OmbdSoUXj11VeF12lpaUhISMDq1auRlJSEhIQETJkyBTt37oRMJiu1nvbt22PhwoXl3s/GxqZa4tbH1dUVq1evFl4rlUokJiZi+/btOHz4MLKzszF16lRs3boVbdu2rbE4iIxJe/V1uVzORY7JaNj2SKzMOjFr2LAhvLy8dN7r2bMnQkJCEBwcjMTERMTHx2P//v0YOHBgqfXY2dmVqMfY5HJ5iRjatm2L/v37Y9GiRfjll1+gUCiwatUqrFixwkRRElUvhUKB6OhoAICvry/3aiWjYdsjsapVQ5kaDg4OmDJlivD65MmTJoym6t59912ht+7EiRNQqVQmjoiIiIhqQq1MzADdif1JSUkmjKTq7O3t4enpCQDIzc1FZmamaQMiIiKiGlFrEzO5XC4c14ZHobU/D3vMiIiIaqdam5jduHFDOHZ3dzdhJFWnVCpx8+ZNAEUJmqOjo9HubY1C5OXmIien5ObmREREVL3MvytJj8LCQp0nHAcMGFBm+dzcXMTHx5dbb6NGjVCvXr0qx1dRv/32Gx4/fgwA6NKli1F7AAc63MW6n74DAMybN89o9yUiIrJEtSoxS09PR1xcHJYvX46YmBgARUlZ165dy7zuypUrCAoKKrf+yiwgW1ma5TJ27NiBn376SXh//PjxRrk/ERERGZ9ZJ2YrV67EypUr9Z6ztbXFK6+8gg8//NDIUVVOYmJimTsRSCQSvP/++3juueeMGBUREREZk1knZmXx8fFBaGiozqT50nTv3h0bNmwwQlQV5+DggB49euCNN94ot+ePyNzI5XL4+voKx0TGwrZHYmXWiZn2yv+FhYVISkrCX3/9hYiICFy4cAGhoaHYtm0bnJycTBxp+Yqv/C+TyVC3bl24uLiYdEXqvdnNsXnqi3Cpb2eyGKj2kkgkXNiTTIJtj8TKrBOz4iv/t2nTBgEBAfDz88PMmTORmJiIsLAwrFq1yoRRGkbfyv9iUAAZbO3sYG9vb+pQiIiIar1auVzGsGHDhCcxDx06hFOnTpk4IiLSR6VSISMjAxkZGVyfj4yKbY/EqlYmZgAwdepUYePyZcuWmTgaItJHs0bfzZs3oVQqTR0OWRC2PRKrWpuYtWzZEoMGDQIAXLp0CX///beJIyIiIiIqW61NzABg0qRJwsR5c5hnRkRERJbNrCf/l8fLywuBgYE4ePAgzpw5g7Nnz+pdcsLQlf8B4KmnnoJUqj+fPXfunEF1BAYGGnVbJSIiIjIPtToxA4DJkyfj4MGDAIp6zbSXpNAwdOV/ADhz5kyp2zJt27YN27ZtK7eOnTt3MjEjIiKiEmr1UCYAdOjQAf7+/gCAEydO4PLlyyaOiIiIiEg/s+sx8/PzQ1xcXIWuWbNmjd73K1qPPiEhIVXaP/PQoUNVjoGIiIhqB7NLzIio9pBIJMJ2OKbc4YIsD9seiRUTMyIyGblcjg4dOpg6DLJAbHskVrV+jhkRERGRuWBiRkRERCQSHMokgZODDXaHDS/xvnM9WxNEQ5ZAqVTi33//BQA0btwYVlb8kUTGwbZHYsWWSAKZVAo3RxtTh0EWRKVSISUlBQDg5uZm4mjIkrDtkVhxKJMEarXa1CEQERFZNCZmJFAqlaYOgYiIyKIxMSMiIiISCSZmRERERCLBxIyIiIhIJJiYEREREYkEl8sgAdfxIWOTSqVwdHQUjomMhW2PxIq/iUnAjXzJ2KysrODp6WnqMMgCse2RWDExI0GhSoXkzByd95zr2ULGvyaJiIiMgokZCdKz8xG8OFLnvd1hw+HmaG+iiIiIiCwLEzMiMhmFQoEbN24AADw9PSGXy00cEVkKtj0SKyZmRGQyarUaOTk5wjGRsbDtkVhx8hARERGRSDAxIyIiIhIJJmZEREREImH2c8wKCgqwb98+HDt2DJcvX0ZGRgays7Ph4OAAd3d3+Pr6YsCAAejRo0e5iwgePXoUBw4cwLlz55Camoq8vDw4OTmhefPmeO655xAcHAw3N7dSr3/hhRdw/fp1ODo64p9//ilzXbChQ4fi2rVrAIC33noL77//fqllT58+jdDQUADAm2++ienTp5f5OYiIiMg8mXVitm/fPixatAiJiYklzmVmZiIzMxNXr17Fli1b4OHhgVmzZqFPnz4lyiYkJGDOnDm4cOFCiXNJSUlISkrC6dOn8d1332HixImYPHmy3qSrS5cuuH79OjIzM3H9+nW0bt1ab9xZWVmIi4sTXp89e7bMz6l9vlu3bmWWJSIiIvNltonZt99+i+XLlwuv/f39ERgYCE9PT9SrVw+PHj3CrVu3cOjQIZw8eRK3b9/GsmXLSiRmZ8+exZQpU/D48WMAQLt27TBs2DB4e3vDzs4OycnJOH78OHbu3Inc3Fx8/fXXSEhIwOLFi0s8Xt2tWzf89ttvQr2lJWbnz5+HSqWCTCZDYWEhLl++DIVCUerj2ufOnQNQtG1Ily5dKvX1IiIiIvEzy8Rs+/btQlLWsGFDfP311+jevXuJcs888wxGjx6N+Ph4LFy4EOnp6Trnk5KS8Pbbb+Px48eQSCSYPn06xo0bp9Mb1r59e/Tt2xfjx4/H22+/jbi4OPz5559o3LhxiSFF7d6ss2fPYtSoUXrj1/SAPffcc4iKikJubi6io6PRuXPnEmULCwuFnjxvb2/UrVvXkC8RkVmQyWRo2rSpcExkLGx7JFZmN/k/OTkZ8+fPBwDY2dlhw4YNepMybV5eXli9ejXGjRun8/6cOXOQmZkJAHj//fcxfvz4UueFNWvWDL/88gtcXV0BAKtXr8alS5d0yjRq1Aju7u4Ayh6e1Jzr3r07nn766TLLX7t2TVhrp2vXrmV9TCKzI5PJ4ObmBjc3N/5yJKNi2yOxMrvEbO3atcjLywMAvPfeewZvQiuVSjFkyBDhdWxsLI4dOwagKHGbOHFiuXU0bNgQs2bNAlC0IOHPP/9cooym1ywpKQn3798vcf7JkyeIjo4GUJRoaYYmNcOVxXF+GRERkeUwq8RMrVYjPDwcQFFv2YgRIypdl6YeAHjttdcM/otpwIABwpOZBw4cwKNHj3TOa/dq6esFu3TpEhQKBWxtbdG2bVshMdPMOytOO2FjYkZERFS7mVVilpCQgIyMDABFT0A6ODhUuq4zZ84IxwEBAQZfJ5PJhAcIVCpViSc5i88zK07zXocOHWBlZYWnn34aMpkMjx8/Rnx8fInymsSsVatWcHJyMjjO6mCNQuTl5iInJ0cYTiWqTgUFBTh37hzOnTuHgoICU4dDFoRtj8TKrBKz2NhY4bhdu3ZVqkuTBLm4uAjzxgzVtm1bvTEBgIeHB1xcXADoT8w0iZamZ83e3h4+Pj56y9+8eRNpaWkATNNbNtDhLtb99B2WLFmCJUuWGP3+RERElsasEjPNRH2gaL5XZWVlZUGhUAAAnJ2dK3y99jXaMWlohidv3bolJFaA7hOW2steaI6LJ2baw5ic+E9ERFT7mVVipj2cZmtrWy312NnZVfh67Wuys7NLnC9tODMmJgY5OTmQyWTo2LGj8H5piRkn/hMREVkWs0rM7O3thWPNk5lVrSc3N7fC12tfo2+eW2kPAGh6wHx8fHRi0JR/+PAh7ty5U+Jad3d3NG7cuMJxEhERkXkxqwVmHR0dhWPtIcKKcnBwgJWVFZRKJVJTUyt8vfY12jFpeHl5oV69enj8+LHOcKQm0Sq+er+zszNatGiBO3fu4OzZs2jRogWSk5OF5TZMNYy5N7s5Nk99ES71K96rSERERBVnVj1mmknyAHD16tVK1yORSODt7Q2gqJfq4cOHFbo+JiZGb0wa2lsnxcbGCsOdmiRN37ZKxYczxTCMWQAZbO3sYG9vr9PDR0RERDXDrBKz1q1bo0GDBgCKkhx987sMpd0LdejQIYOvU6lUOHr0KICiBEzfNkra9RcWFuL8+fO4ceOGsCWUvh4wTT2ahEx7OQ9O/CciIrIMZpWYSSQSDBs2DEDRPK/ff/+90nWFhIQIxxs3bkRhYaFB1/31119ISkoCAPTr1w/16tXTW047mTp37pyQcLVo0ULvk6CaHrO7d+8iJSVF6F1zcXFBy5YtDYqNyNxYWVnB29sb3t7esLIyq5kVZObY9kiszCoxA4CxY8cKT2QuX74cN27cMOg6lUqFyMhI4bWPjw+effZZAEBcXBxWr15dbh3p6elYtGgRgKIk8c033yy1bLt27YQ4z5w5I/SA6RvGBHQXkD1w4AASEhLKLE9UG0ilUjg4OMDBwQFSqdn9OCIzxrZHYmV2rdHNzQ1z5swBUNRrFhoaitOnT5d5zfXr1/Hmm2+WSL7mz58v9Hj997//xdq1a6FWq/XWkZiYiDfeeEPoLRs/frywAbk+crlcWBIjOjoaUVFRAMpOtDTDmWvWrBHi4DIZRERElsMs+2+HDx+OpKQkLF++HGlpaQgNDcWzzz6LwMBAeHp6ol69enj06BFu3bqFo0eP4vjx4ygsLCwxUb9JkyZYuXIl3nrrLWRnZ2PhwoXYtWsXhg0bBm9vb9ja2iIlJQXHjx/Hzp07hWUyBg8ejA8++KDcOLt27YpTp06hoKAAKSkpAMpOzLp06YIDBw7g3r17OnUQ1VYqlUr4vrKzs2PPBRkN2x6JlVkmZgDw9ttvo3Xr1li0aBESExNx4sQJnDhxotTyrVu3xvTp00u87+fnh02bNmHOnDm4dOkSrly5gitXruitw9bWFhMmTMBbb70FiURSbozFk6qGDRuWOV+seNJWv3594elRotpIqVQiLi4OAODr6wtra2sTR0SWgm2PxMpsEzMAeP7559GnTx/89ddfOHbsGKKjo5Geno6cnBw4ODjA3d0dTz/9NAYMGAA/P79Skylvb29s3boVR44cwf79+3H+/Hk8fPgQ+fn5aNCgATw8PNCrVy8MGTIEbm5uBsfXsWNHyOVyYfun0p7g1Gjbti1sbW2FxXM7d+5sUAJIREREtYNZJ2YAYG1tjaCgIAQFBVW5rj59+qBPnz5VD+r/2djYlNr7po9cLsfFixer7f5ERERkXjioTkRERCQSTMyIiIiIRIKJGREREZFIMDEjIiIiEgmzn/xP1cfJwQa7w4brvOdcz9ZE0RAREVkeJmYkkEmlcHO0MXUYZEGsra257RiZBNseiRWHMomIiIhEgokZERERkUhwKJMEhYWFpg6BLExhYSFSU1MBAM7OzpDJZCaOiCwF2x6JFRMzEqhUKlOHQBamsLAQ9+/fBwA0aNCAvxzJaNj2SKw4lElEREQkEkzMiIiIiESCiRkJrKw4sk1ERGRKTMxIIJFITB0CERGRRWMXCQkKVSokZ+YAKFrxXyZl3k5ERGRM/M1LgvTsfAz+z3YM/s92pD7OM3U4REREFoc9ZkRkMhKJBPb29sIxkbGw7ZFYMTEjIpORy+Xw8fExdRhkgdj2SKw4lElEREQkEkzMiIiIiESCQ5lEZDJKpRJ37twBALRo0YJr6ZHRsO2RWIm6JUZFRWHMmDF6z9nY2MDJyQlt2rTBoEGDMGjQoHK/se7evYvIyEicOHECiYmJyMjIgL29PVxdXeHn54eBAweia9eu5cYVGhqK06dP6z1nZWWFunXrwtPTE71798bLL78MR0fHUuuaOXMmwsPDS7wvkUjg4OCAJk2aoEuXLnj55ZfRpk2bcmMjMicqlQqZmZkAgGbNmpk2GLIobHskVqJOzMqSn5+PBw8e4MGDBzh48CDWrVuHVatWwcXFpUTZgoICLFmyBJs2bYJCodA5l5mZiczMTMTHx2PDhg3o3bs3Pv/8czRq1KhScSmVSmRkZODs2bM4e/Ys1q5dixUrVqBLly4VqketViMrKwtxcXGIi4vDli1bMHHiREydOrVScREREZH4mU1iNmrUKLz66qvC69zcXFy5cgVr1qxBYmIioqOj8dZbb2Hr1q06jz5nZ2fjrbfeQlRUFADA0dERw4YNQ8+ePdGwYUNkZWXhypUr2L59O27duoWjR49i5MiRWLNmDTw9PcuNa9euXTqvFQoF7t27h4iICBw6dAhpaWmYMmUK9u7dCycnpzLrWr16NVxdXQEU/TWXlpaGI0eOYNOmTVAqlfj+++/h6uqK0aNHG/x1IyIiIvNhNolZw4YN4eXlpfNex44dERQUhBEjRuDOnTu4fPkyDh8+jMDAQKFMWFiYkJT5+flh2bJlaNiwoU49PXv2xNixY7F06VL88ssvSEpKwuTJkxEeHg4HB4cy4yoeEwC0a9cOAwcOxIwZM7Bz5048evQIv//+OyZNmlRmXR4eHmjatKnOe/7+/ujZsyemTJkCAFi5ciVeeeUVyGSyMusiIiIi82P2T2XWr18fEydOFF4fP35cON69ezf27t0LAPDx8cEPP/xQIinTkMvlmDlzJkaMGAGgaD7a0qVLqxTb+PHjhePo6OhK1xMYGCjMfUtPT8fVq1erFBcRERGJk9knZgDQoUMH4fjBgwfC8U8//SQcz58/H7a2tuXWNXPmTDg7OwMAtm/fjvT09ErHpd37VVBQUOl6gNI/IxEREdUetSIx034as7CwEAAQGxuLmJgYAECnTp10EpuyODg4ICQkBADw5MkT/PHHH5WOSzuBaty4caXrAXQ/o0qlqlJdhsjLza3xexAREZGuWpGYxcfHC8eayfNnz54V3gsICKhQfdpz1LTrqajVq1cLx3379q10PYD+z1iT1v30XY3fg0gqlcLV1RWurq6QSmvFjyMyE2x7JFZmM/m/NEqlEr/88ovwunv37gCKesw02rZtW6E627RpA6lUCpVKhbi4uDLLaidMQNFTmYmJiYiMjMT+/fsBAIMHD8Zzzz1XoRi0xcbGCnPnbG1t4evrW+m6iMTEysqKa0iRSbDtkViZbWKmWS5jxYoVuHjxIgDA3d0dgwcPBgBkZGQIZfWtbVYWGxsb2NvbIysrS1iAsDRBQUGlnmvZsiUmTpyIYcOGVej+QNE6ZqmpqThy5AiWLl0qDNGGhoaiTp06Fa6PiIiIxM9sErOVK1di5cqVpZ5v2LAhvv32W1hbWwMAcnJyhHN2dnYVvp+dnR2ysrKQnZ1d8WD/361bt/Dbb7+hadOmQk9eWcob7uzTpw/ee++9SsdDRERE4mY2iVlpmjZtigEDBmD8+PE6S2HY29sLx7mVmMiuuaa8dcyKD3WqVCpkZGTg3Llz+Pbbb3Hx4kWMGzcOS5YswcCBAysch1wuh6+vL0aOHIkhQ4boLJ5bk16f8JZR7kOWTaFQ4Nq1awCKphDI5XITR0SWgm2PxMpsEjPtlf8lEgnq1KmDBg0aoG7dunrLN2jQQDh++PAhfHx8DL5Xfn6+0ONW1j6X+kilUjRs2BDPP/88/P39ERISgtu3b+OTTz5Bjx49yqxPe+V/qVQKOzs7ODs7C72AxmRbiV5GoopSq9XCNmlqtdrE0ZAlYdsjsTKbR1E0K/97eXmhdevWaN68ealJGQB4e3sLx5q/igx17do1YUkK7Xoqyt7eHqNGjQJQNLT6119/lVnew8ND+IxPPfUUmjRpYpKkjIiIiEzDbBKziurWrZtwfOjQoQpdq11eu57KaNWqlXBc/AlOIiIiIm21NjHz8fFBmzZtAAAXLlwweEuk7OxshIeHAwDq1KmDF154oUpxaJ6mBIqW9iAiIiIqTa1NzABgwoQJwvHcuXORn59f7jVffvklHj58CAAYPnw4nJycqhTDlStXhOOqrv5PREREtVutTsxeeOEFPP/88wCAmJgYTJkypdS9LxUKBb788kv89ttvAIDmzZvjww8/rNL9ExMTsXHjRuF17969q1QfERER1W5m81RmZS1YsAAZGRk4c+YMTp48iUGDBiEkJAQ9evSAs7MzsrOzceXKFWzbtg03b94EADRq1AirVq0qd6kMoOS8MZVKhczMTJw9exYbNmwQFqgNCgoShlaJiIiI9Kn1iVndunWxevVqfPXVV9i8eTMyMzOxZs0arFmzRm/5Xr16Yf78+QYPO5a18r/G4MGDsWDBggrFTWQJrKyshAdkrKxq/Y8jEhG2PRIri2iNderUwezZsxEaGorIyEicOHECiYmJyMzMhL29PVxdXdGtWzcMGjSoyk9hSiQS2NnZoXHjxujYsSOGDh1a5TqJaiupVKqz5iCRsbDtkVhJ1FxZz6IVFBQIT6w2auGJ4MWRAIDdYcPh5mhf1qVEREQWTft3qK+vb7WsPWoRPWZEJE7aq6/L5XKjbTlGxLZHYlWrn8okInFTKBSIjo5GdHS08EuSyBjY9kismJgRERERiQQTMyIiIiKR4BwzEjg52GB32HAAgHM9WxNHQ0REZHmYmJFAJpXCzdHG1GEQERFZLA5lkoArpxAREZkWEzMSKJVKU4dARERk0ZiYEREREYkE55iRgPvFkbHJ5XL4+voKx0TGwrZHYsXfxCTgytdkbBKJpFq2MCGqKLY9EisOZRIRERGJBHvMSMCnMsnYVCoVHj16BACoX78+pFL+rUjGwbZHYsWWSAIOZZKxKZVK3Lx5Ezdv3uRTwWRUbHskVkzMSFCoUqFQpTJ1GERERBaLiRkJ0rPzkfo4z9RhEBERWSwmZkREREQiwcSMiIiISCSYmBERERGJBBMzIiIiIpHgOmZEZDISiUTYDofLtZAxse2RWNVoYhYVFYUxY8boPWdjYwNHR0f4+Pigf//+CA4ONnh7jPv376Nfv37CgqhLlixBUFBQheN78OAB/vzzT5w8eRJ37txBeno6VCoV6tevDy8vL3Tp0gVBQUFo1qyZ3uvT0tJw+fJlXL58GdHR0YiOjkZmZiYAYNiwYVi0aFG5McycORPh4eEl3pdIJHBwcECTJk3QpUsXvPzyy2jTpk2FPyORmMnlcnTo0MHUYZAFYtsjsTJZj1l+fj6SkpKQlJSEI0eOYO3atfj+++/RtGnTcq/duXOnzir1O3furFBi9uTJEyxduhSbN29GQUFBifMpKSlISUnBiRMnsHz5cgwcOBAzZsxA48aNdco988wzBt+zotRqNbKyshAXF4e4uDhs2bIFEydOxNSpU2vsnkRERGRaRkvMRo0ahVdffVV4nZaWhoSEBKxevRpJSUlISEjAlClTsHPnTshksjLrioyMBADY2dkhNzcXp06dQkpKClxdXcuNIz09HVOmTMHFixcBAPb29njxxRfRo0cPNGrUCFZWVkhNTcX58+exf/9+3L59G3v27EHHjh0xduzYUutt0qQJWrVqhRMnTpT/xSjF6tWrhc+gUqmQlpaGI0eOYNOmTVAqlfj+++/h6uqK0aNHV/oeREREJF5GS8waNmwILy8vnfd69uyJkJAQBAcHIzExEfHx8di/fz8GDhxYaj3nz5/HnTt3AACffPIJ5s2bh8LCQuzatQvjx48vMwaVSoUPPvhASMoCAgLwn//8Bw0bNixRNjAwENOmTUNkZCS+/PJLvfW9/fbb8PX1ha+vL5ydnXH//n307du3zBjK4uHhUaLH0N/fHz179sSUKVMAACtXrsQrr7xSbvJKZA6USiX+/fdfAEDjxo1hZcVpr2QcbHskViZ/KtPBwUFIOgDg5MmTZZbfuXMnAKBBgwYYOnQoevbsCQCIiIgo917r169HVFQUAODZZ5/FypUr9SZlGlKpFEOHDsWOHTvg7e1d4vx7772HgIAAODs7l3vvqggMDETXrl0BFPX4Xb16tUbvR2QsKpVKmDqg4nZgZERseyRWJk/MAOgkPUlJSaWWKygowN69ewEAgwYNglwuR3BwMAAgLi4O165dK/Pa1atXAwDq1KmDBQsWGPwXUqNGjYQE0FS0J6k+ePDAhJEQERFRTRFFYqZ5ZBlAmcnSoUOH8OjRIwAQErL+/fvDzs4OwP960/Q5ceIEUlJSAAADBw6Em5tbVcM2Ku2vS039dZefl4e83Fzk5OQI/xEREZHxiGJQ/caNG8Kxu7t7qeU0iVfz5s3RqVMnAEUPAPTt2xe7du3CH3/8gY8//ljv/KszZ84Ix3369KmewI0oPj5eODbkIYfK2LZpPfLz83XemzdvXo3ci4iIiEoyeY9ZYWGhMMQIAAMGDNBbLj09XXjisfjSGJres9TU1FKfioyNjRWO27VrV6WYjS02NhbHjx8HANja2sLX19fEEREREVFNMFlilp6ejlOnTuG1115DTEwMgKKkTDPJvbg//vgDCoUCwP8SMQ1/f3+4uLgAKH04U7PwK4AyJ/yLhVqtxsOHD/H7779j7NixKCwsBACEhoaiTp06Jo6OiIiIaoLRhjJXrlyJlStX6j1na2uLV155BR9++GGp12sSrg4dOsDDw0PnnEwmw+DBg7Fu3TocOnQI2dnZcHBw0CmjPV/K1ta2ch+ihpW31EafPn3w3nvvGSkaIiIiMjZRzDHz8fFBaGiozkMA2hISEoQlIor3lmkEBwdj3bp1yM/Px549ezBixAid8/b29sJxXl5eicRNrORyOXx9fTFy5EgMGTKkRvd0e+nVMZDJrOBS367G7kGkTSqVwtHRUTgmMha2PRIrk6z8X1hYiKSkJPz111+IiIjAhQsXEBoaim3btsHJyanEtZreMisrK7zwwgt662/fvj08PT1x48YNRERElEjMNN+AQNFcNDEmZtor/0ulUtjZ2cHZ2dngPUSrysbWFlZWcp0klqgmWVlZwdPT09RhkAVi2yOxMtnK/23atEFAQAD8/Pwwc+ZMJCYmIiwsDKtWrdK5TqVSYdeuXQCKVmo2ZD2xs2fP4v79+zqr6Pv4+AiL18bExJQYDhUDfSv/ExERkeUwef/tsGHDhCcxDx06hFOnTumcP3XqFJKTkytUp1qtLrETQLdu3YTjI0eOVC5YIiIiohokijlmU6dOxYEDB1BYWIhly5bp9IpphjGtra2xYMGCcucC/Pzzz4iJiUFkZCTefvtt4f1nn30Wrq6uSElJwd69e/Hhhx+a3SKzRLWNQqEQ1jH09PQsdZ4pUXVj2yOxEkVi1rJlSwwaNAh//PEHLl26hL///hv+/v7IycnBgQMHABQtiVF8/TJ9Hj58iJiYGNy+fRsXLlwQFqK1trbG+PHjsXDhQjx58gSffPIJfvzxR4M2A09OTsbNmzdNvi0TUW2jVquFJ6bVarWJoyFLwrZHYmXyoUyNSZMmCU8cauaZ7du3D7m5uQBKX3i2uAEDBgj1FF/TbMyYMfDz8wNQtEXT22+/jfT09FLrUqvV2LVrF0JCQhAXF1ehz0NERERUUaLoMQMALy8vBAYG4uDBgzhz5gzOnj0rJFZyuRyBgYEG1dO4cWP4+vri8uXL2LNnD8LCwoSnGqVSKb7++mtMnjwZly5dwuHDh9GvXz8EBQWhR48eaNSoEaysrJCamoqLFy9i3759uHnzZqn3Onv2LO7evSu8zsjIEI7v3LmDHTt26JQPCQkx9MtBREREFkg0iRkATJ48GQcPHgQArFixAqdPnwYA+Pn5oX79+gbXM2DAAFy+fBmPHj3C4cOHdXrbnJycsGHDBixZsgSbN29GTk4OtmzZgi1btuitSyKRICgoCIMGDSpxbtu2bQgPD9d73fnz53H+/Hmd95iYERERUVlEM5QJFK3q7+/vDwD4559/oFKpABg+jKmhXV7fFk116tRBWFgY9u3bhw8//BDPPPMMmjRpAhsbG9SpUweurq549tlnMXXqVBw8eBBfffUVHxQgIiKiGidRc9ajRSsoKEB0dDQAoFELT1hZyeHmyAVmyTi025+vr6/RFlMmYtuj6lAT7UhUPWZERERElkxUc8yIyLLIZDJhtwtDlq4hqi5seyRWTMyIyGRkMhnnb5JJsO2RWDExI4GTgw3nWRAREZkQ55iRQCaVQlbOlldERERUc9hjRgI+oEvGxifjyFTY9kis2D1CAqVSaeoQiIiILBoTMyIiIiKRYGJGREREJBJMzIiIiIhEgokZERERkUgwMSMiIiISCSZmRERERCLBdcxIwP3iyNisrKzg7e0tHBMZC9seiRVbIwmkXPWfjEwqlcLBwcHUYZAFYtsjseJvYiIiIiKRYI8ZCVQqlalDIAujUqmQm5sLALCzs2OvLRkN2x6JFVsiCQoLC00dAlkYpVKJuLg4xMXFcUswMiq2PRIrJmZEREREIsHEjIiIiEgkmJgRERERiQQTMyIiIiKRYGJGREREJBJcLsPCqdVq4VihUKCgoMCE0ZClUSgUeo+JahrbHlUH7baj/fu0KiTq6qqJzFJOTg5iY2NNHQYREZFZ8/Hxgb29fZXr4VAmERERkUiwx8zCqVQq5OXlASjayFcikZg4IiIiIvOgVquFBYptbW2rZQcJJmZEREREIsGhTCIiIiKRYGJGREREJBJMzIiIiIhEgokZERERkUgwMSMiIiISCSZmRERERCLBxIyIiIhIJJiYEREREYkEEzMiIiIikWBiRkRERCQSTMyIiIiIRIKJGREREZFIMDEjIiIiEgkmZkREREQiwcSMiIiISCSYmBERERGJBBMzIiIiIpFgYkZEREQkElamDoDKl5iYiA0bNuDIkSNISkqCtbU1mjVrhkGDBmH06NGwtbWtlvscPXoUW7duRXR0NNLT0+Hk5ARfX1+8/PLL6N27t0F1KJVK/P7779i1axdu3ryJ3NxcuLq64plnnkFoaChat25dLbGScZhD25s5cybCw8MNus/BgwfRtGnT6giZjKAm259KpcLNmzdx+fJlXL58GdHR0YiLi4NCoQAArF+/Hn5+fgbXl5eXh19//RV79+7FvXv3UFBQgEaNGqFPnz4IDQ2Fu7t7pWMlyyJRq9VqUwdBpTt06BCmT5+O7Oxsvec9PDzw448/okWLFpW+h0qlwpw5c7Bt27ZSy4wYMQKff/45pNLSO1nT09MxceJEREdH6z1vbW2NuXPnYsSIEZWOlYzHXNoeE7PaqabbX3h4OGbOnFnq+YokZnfu3MHEiRNx+/ZtvecdHBywZMkSBAQEVCZUsjDsMROxmJgYTJ06Ffn5+bCzs8OkSZPg5+eH/Px87N69G1u3bsXt27cxceJEbN++HQ4ODpW6z7Jly4RfjG3btsWbb76JZs2a4d69e/j5558RExOD33//HU5OTpg2bZreOgoLC/HOO+8ISdnzzz+PESNGwNHREZcuXcKqVauQlpaGuXPnwtXV1eAeODINc2p7Gq6urli9enWZZdzc3CoVJxmXMdqfdp+EXC6Hl5cXFAoF4uPjK1RPdna2TlL28ssvY/DgwbCxsUFUVBR++OEHZGdnY+rUqdi8eTPatGlT4VjJwqhJtF599VW1l5eXum3bturz58+XOP/TTz+pvby81F5eXurly5dX6h43b95Ut23bVu3l5aUOCQlR5+Xl6ZzPzc1Vh4SECHHcvn1bbz2///67EMunn35a4vzt27fVnTt3Vnt5ean79++vVigUlYqXjMOc2t6MGTPUXl5e6oCAgErFQeJjjPZ36dIl9fr169UXLlxQ5+fnq9VqtXr58uVCvf/8849B9Xz99dfCNT/99FOJ8+fOnRPa+WuvvVapWMmycPK/SF2+fBlnz54FAAwfPhydOnUqUWbcuHHw9PQEUNTtrpkbURHr1q2DUqkEAMyZMwc2NjY6521tbTFnzhwARfPH1q5dq7eeNWvWAAAcHR3x8ccflzjfokULTJo0CUBRt//+/fsrHCsZh7m1PapdjNX+OnTogNDQUHTs2BF16tSpVKwKhQIbNmwAAHh6emLcuHElynTu3BnDhw8HAJw+fRqXL1+u1L3IcjAxE6kDBw4Ix5pv6uKkUimGDh0KAHj8+DGioqIqdA+1Wo2DBw8CAFq1aoWOHTvqLdexY0e0bNkSQNEcHXWxaYm3bt3CjRs3AAADBw4sdULusGHDhGPtz0fiYk5tj2ofY7S/6hIVFYWsrCwAwNChQ0udBxkSEiIc82cflYeJmUidO3cOAGBnZ4d27dqVWq5bt27C8fnz5yt0j/v37yMlJaVEPfp0794dAJCcnIz79+/rjVW7nD4uLi7w8PCoVKxkPObU9qj2MUb7qy6G/uxr37698Acrf/ZReZiYiZSmB6p58+awsir9GY1WrVqVuMZQ169f11tPefe5efOm3lgrUs+///6L3Nxcg2Ml4zGntqctIyMDr732Gvz8/NC+fXs8++yzGD9+PH799Vfk5eVVKD4yHWO0v+pi6M8+KysrNG/evMQ1RPowMROhJ0+eICMjAwDQqFGjMsvWr18fdnZ2AICkpKQK3Ue7fHn30T7/77//llpPeU+9NW7cGEDRUFZF46WaZ25tT1tubi7OnDmDzMxMKBQKPHz4ECdOnMD8+fMxYMAA9lSYAWO1v+qiua+dnR3q1atXZlnNz7709HQUFBTUeGxkvrhchgjl5OQIx5ofPGWxtbVFbm5uhXugKnIf7Xljxe+jXY+9vX2l6yHTM7e2BwASiQQdO3ZEQEAA2rZtC2dnZzx58gTx8fHYtm0bLl++jOTkZIwfPx4bN25E27ZtKxQrGY+x2l910cRraKza11lbW9dYXGTemJiJ0JMnT4RjuVxebnnNN3h+fn6N3Uf7h0jx+1RXPWR65tb2AGDWrFl6eys6deqEl19+GV9//TW+//575ObmYvbs2di+fTskEkmF4iXjMFb7qy6aeCsSq/Z1RPpwKFOEtB/dNuQxcE23ePHlBqrzPtpd78XvU131kOmZW9sDUOYQkkQiwdSpU9GzZ08AwNWrVzmkKWLGan/VRRNvRWLVvo5IHyZmIqQ9HGhIF71mYrMh3emVvY/25Oni99GuR3sooqL1kOmZW9sz1MiRI4XjM2fOVKoOqnnGan/VRRNvRWLVvo5IHyZmIlSnTh04OjoCKH9S66NHj4QfCuVNli1Ou3x599E+r5nEqq+e5OTkMuvRTN6WSCQVjpdqnrm1PUM99dRTwnF5bZRMx1jtr7po7pubm4vHjx+XWVbzs8/JyYnzy6hMTMxESvOL5O7du8Lq6PpoLx+gWQm7ovcoXk959yn+WLj2fQ2tp3HjxuwxEylzanuG4pwy82GM9lddDP3Zp1Qqce/evRLXEOnDxEykunTpAqDoL7GrV6+WWk57WKZz584VukfTpk3h6upaop6y7uPm5oamTZvqjRUo2nKkNA8fPhQ2+q1orGQ85tT2DKW9bprmviROxmh/1cXQn31XrlwRevf4s4/Kw8RMpPr16yccb9++XW8ZlUqFnTt3AiiaAO3n51ehe0gkEvTt2xdA0V97Fy9e1Fvu4sWLwl+Dffv2LdH70LJlS+GvwL1795a6mGd4eLhwrP35SFzMqe0Z6rfffhOOy9tpgEzLGO2vunTv3h1169YFAOzcubPULcN27NghHPNnH5WHiZlIdejQAV27dgVQ9MPpwoULJcqsWbNGWEV6zJgxJR7ZjoqKgre3N7y9vTFz5ky993n99dchk8kAAPPnzy/x2Hl+fj7mz58PoGj16tdff11vPZrNezMzM/HVV1+VOH/37l388MMPAIo2NO/fv7/+D04mZ05t7+LFi8LWTvqo1WosW7YMJ0+eBAD4+Pjo9HKQ+Bir/VUHa2trhIaGAiha0X/16tUlyly4cEFIMLt3744OHTrUWDxUO3AdMxELCwvDqFGjkJ+fj3HjxmHy5Mnw8/NDfn4+du/eLfQCeHh44I033qjUPVq2bInx48fjxx9/xJUrVzBq1ChMmDABzZo1w7179/DTTz8hJiYGADB+/Hhhr8vihg0bhu3bt+P8+fPYuHEjUlNTMWLECNSvXx+XL1/Gd999h+zsbEilUoSFhZW51QqZnrm0vePHj+PHH39Er1694O/vD09PT9SrVw8FBQWIi4vD9u3bcenSJQBFC3zOnz+f883MgDHaH6DbkwUA165dE46PHz+OxMRE4XXz5s2FhFHb+PHjsXv3bty+fRtfffUV7t69i8GDB8PGxgZRUVH4/vvvoVQqYWNjg08++aTSsZLlkKhL63slUTh06BCmT5+O7Oxsvec9PDzw448/okWLFiXORUVFYcyYMQCKEqdFixbprUOlUgkLb5bmpZdewvz58yGVlt7Jmp6ejokTJyI6OlrveWtra8ydOxcjRowotQ4SD3NoeytWrMDKlSvL/SxNmjTBkiVL2FtmRozR/ry9vQ2Op6x67ty5g4kTJwpzaItzcHDAkiVLEBAQYPD9yHKx20LkAgMDERkZifXr1+PIkSNITk6GXC5H8+bNMXDgQLz22ms6W31UhlQqxYIFCzBgwAD89ttviI6ORkZGBho0aABfX1+MHDkSvXv3LrceJycnbNmyBVu3bsUff/yBGzduIC8vD66urujZsyfGjBmD1q1bVylWMh5zaHshISFo2LAhLl68iLi4OKSlpSEzMxNWVlZo0KAB2rZti4CAAAQFBXFRTzNjjPZXXVq0aIHw8HBs3LgRe/fuxd27d6FQKNCoUSP07t0bY8aMgbu7u6nDJDPBHjMiIiIikeDkfyIiIiKRYGJGREREJBJMzIiIiIhEgokZERERkUgwMSMiIiISCSZmRERERCLBxIyIiIhIJJiYEREREYkEEzMiIiIikWBiRkRERCQSTMyIiIiIRIKJGREREZFIMDEjIiIiEgkmZkREREQiwcSMiIiISCSYmBERERGJBBMzIiIjePDgAebOnYt+/frB19cX3t7e8Pb2xoEDB0wdGlGlzJw5E97e3ggMDDR1KLWKlakDIKqqqKgojBkzBgDwzjvv4N133zVxRES6Hjx4gJCQEGRkZJg6lArR/t4qzsbGBo6OjvDx8UH//v0RHBwMa2trg+suLCzEvn37cPDgQURHRyM1NRUKhQLOzs5o1aoV+vTpg+DgYNSrV69CMRcUFGDfvn04duwYLl++jIyMDGRnZ8PBwQHu7u7w9fXFgAED0KNHD0il7Jsg8WFiRkQAAG9vbwBMbmvCqlWrkJGRASsrK3zwwQfo1q0b7OzsAABNmjQxcXSVk5+fj6SkJCQlJeHIkSNYu3Ytvv/+ezRt2rTca8+ePYt58+bh+vXrJc4lJiYiMTERx48fx4oVK/Dhhx/i5ZdfNiimffv2YdGiRUhMTCxxLjMzE5mZmbh69Sq2bNkCDw8PzJo1C3369DGobnPG723zwsSMiKiGnTx5EgDQt29fTJgwwcTRVM6oUaPw6quvCq/T0tKQkJCA1atXIykpCQkJCZgyZQp27twJmUxWaj179+7F9OnTUVBQAADo3r07goOD0bJlS1hbW+PBgwc4dOgQ/vzzT2RmZmLOnDm4desWZsyYUWZ83377LZYvXy689vf3R2BgIDw9PVGvXj08evQIt27dwqFDh3Dy5Encvn0by5Yts4jErKYsWrQIixYtMnUYtQ4TMyKiGpacnAwAaNmypYkjqbyGDRvCy8tL572ePXsiJCQEwcHBSExMRHx8PPbv34+BAwfqrSMmJgYfffQRFAoF5HI5Fi5ciKCgIJ0yHTp0wMCBA/HGG29g8uTJSEpKwpo1a9CsWTOdxFDb9u3bhaSsYcOG+Prrr9G9e/cS5Z555hmMHj0a8fHxWLhwIdLT0yvzpSCqURxgJyKqYQqFAgBgZVX7/hZ2cHDAlClThNea3sHiVCoVPv74Y+FrsWDBghJJmbY2bdpg7dq1wpDv4sWL8eDBgxLlkpOTMX/+fACAnZ0dNmzYoDcp0+bl5YXVq1dj3LhxZX84IhOofT8liIrRnsC8fv16dO/eHdu2bcOOHTtw8+ZNKBQKtGrVCq+99hqGDh0qXFdQUIDt27cjPDwct2/fhkKhgLe3N8aMGYPBgwfrvdf9+/fRt29fAMDChQsREhKCPXv24LfffkNcXBxycnLQpEkT9O/fHxMmTCh3YnNBQQF+//137N27FwkJCcjOzkb9+vXRtm1bvPjiiwgKCip1AvPMmTMRHh4Od3d3HDp0CCkpKVi3bh2OHDmCBw8eIDc3F+vXr8esWbN05uSsXLkSK1eu1Klr2LBhOkMWKSkp2L9/P6KiohAbG4uUlBQolUo0aNAA7du3R1BQEAYOHFhqbMX/Tfz8/LB7927h65Sbm4vGjRujb9++mDhxIhwdHcv8OgHA0aNHsWvXLly4cAFpaWkoLCyEi4sL2rVrh4CAAAwaNAi2trZ6r7169Sp+++03REVFISUlBWq1Gm5ubujRowfGjh1bqZ6uHTt2YNasWTrvFf/aFv+6AkB6ejrWrVuHo0eP4v79+3jy5AlcXFzQpUsXjBw5El27di31noGBgUhMTBTqvXLlCn799VecPn0aKSkpUCgUiIuLq/BnKY9mDhMAJCUl6S1z6NAhJCQkAACee+45BAcHl1tvy5YtMWXKFCxduhT5+flYv349Zs6cqVNm7dq1yMvLAwC899578PT0NChmqVSKIUOGGFRWmzF/nmh7+PAhfv31Vxw/fhz3799Hbm4uGjZsiI4dO2LkyJF45plnSlyjaQ8a5X1vF/9s3bp1w44dOxAREYEbN24gPT0dQ4cOFcoX/xlTmuzsbGzduhVHjhzBjRs38OjRI1hbW6N58+bo0qULBg8ejC5dupT7NbAUTMzIoiiVSkyZMgWHDx/WeT86OhozZszAlStXMHv2bDx69Ahvv/02zpw5o1PuwoULuHDhAu7evYvJkyeXe79PPvkE27dv13nv1q1b+PHHH7Fz506sXbu21F8k9+/fx4QJE3Dz5k2d91NTU3Hs2DEcO3YMv/32G7777rtyE5eLFy9i8uTJ1fJUYGFhIXr37g2VSlXiXEpKCg4dOoRDhw5h27ZtWLFiBezt7cusT6VSYfr06YiMjNR5//bt21i9ejUOHDiAjRs3wsXFRe/1GRkZmDp1Kk6dOlXinGYi+b59+wAAISEhJe69ePFirFu3Dmq1usT9b9++jW3btmHu3LkYOXJkmZ+jOpw4cQLvv/8+srOzdd7XfI7IyEiMHj0as2fPLveJws2bN+OLL76AUqmsyZABAHK5XDgurVdw586dwvHrr79ucN2vvPIKVq5ciSdPniA8PBwzZsyARCIBAKjVaoSHhwMo6i0bMWJEJaKvPGP9PImMjMS8efOQm5ur835SUhL27t2LvXv34qWXXsJnn31Wbb2yT548wfjx40vtATXUyZMnMW3atBI/exQKBa5du4Zr167h119/rZE/GMwVEzOyKN988w0uXbqEoKAgBAUFwdnZGbdv38aKFStw69YtbNiwAYGBgdiwYQMuXLiAUaNGoX///nB0dMS1a9fwzTffICUlBcuXL0ffvn3RunXrUu+1adMmREdHo0OHDhg7dixatGiBtLQ0hIeHY8+ePUhJScGbb76JXbt2wcHBQefanJwcjB07Fvfu3QMA9OvXD8OHD4erqyvu37+PjRs34vTp0zh37hwmT56MjRs3ljrhOicnB++++y6ePHmCyZMnw9/fHzY2NoiPj4eLiwtWr14NhUIhDCsVn+QNAPXr1xeONQlMjx498Nxzz8HLywtOTk7IycnBvXv38Pvvv+PChQv4+++/8fnnn2Px4sXl/ptcuHAB/fr1w9ChQ9GkSROkpqZi06ZNOHLkCO7cuYOFCxfiv//9b4lr8/LyMGbMGMTHxwMA2rVrh5EjR6J169awtrZGUlISzpw5g927d+u99/z587Fp0yYAQLdu3TBs2DA0a9YMNjY2iIuLw7p165CQkIC5c+fC2dlZ6A01RL9+/dC+fXsAKPVrq/11vXbtGiZPnizMvxo9ejQCAwNha2uLa9eu4ccffxT+7W1tbTF9+vRS7x0dHY3IyEg0atQI48ePR/v27aFUKnHu3DmD46+IGzduCMfu7u56y5w9exYAYGtri549expcd7169dCtWzecOHECmZmZuH79uvB9l5CQIPzC79KlS4nvo5pmjJ8nu3fvxscffwy1Wo1mzZrhtddeg6enJ5ycnJCYmIht27bh6NGj2LZtGxwcHHR6aSv6va1tyZIliIuLQ2BgIEJCQoTvy5ycHIO/Pv/88w8mTJgApVIJmUyGIUOGoG/fvmjcuDGePHmCGzdu4NixYyUSW4unJjJz//zzj9rLy0vt5eWlXr58eZnnvby81GvXri1RJiUlRd2pUye1l5eXukePHmpvb2/1/v37S5S7du2a2sfHR+3l5aWeP39+ifP37t3TudeECRPUCoWiRLmVK1cKZRYvXlzi/KJFi4Tzy5YtK3FepVKpP/zwQ6HMxo0bS5SZMWOGcL5jx47qa9eulSijrayvYfF73759u8wy33zzjdrLy0vt7e2tvnXrVonzxf9NvvvuO733GTdunNrLy0vdtm1bdVpaWokyCxYsEOr47LPP1CqVSm88T548UT98+FDnvRMnTgjXbt26Ve91+fn56jFjxqi9vLzUAQEBev8tDWHI13b48OFqLy8vdZs2bdTHjx8vcT4zM1M9ePBgtZeXl9rHx0cdHx9fokxAQIBwrxdffFH96NGjSsWrUd73llqtViuVSvXQoUOFcmfOnClRJikpSTj/8ssvVziOJUuWCNdHRkYK70dERAjv//e//61wvZVhzJ8naWlp6i5duqi9vLzUs2bNKrX9/fe//xXaxY0bN0qcN/R7u/hn0/ezR5vmZ0xAQECJc/n5+epnn31W7eXlpX766afV//zzT6n1PHjwoMz7WBpO/ieL8vTTT+sdRnFxcUH//v0BFM3xGTRoEPr161einI+PjzAXorzeB2tra8yfP1/v0MKUKVOEJ9y2b98uLB0AFM1F2bZtGwCgdevWetcdkkgk+PTTT4UhzI0bN5YZy5tvvgkfH58yyxhKIpGgRYsWZZZ5++230aBBA6jV6jLnngBFvVz6hnEkEgnGjh0LoGjI6MKFCzrnHz9+jN9++02oIywsTBjiKs7a2hrOzs467/34448AgAEDBpQ6BFanTh3MnTsXQNFwYlRUVJmfpbIuX76M6OhoAMCIESPw7LPPlihTv359fP755wCKhmA1PX2lmTdvXoUXZ62I9PR0nDp1Cq+99hpiYmIAFH0t9c2B0x7GKv7vYIiGDRsKx5mZmXqPtcsYS03/PNm8eTOysrLg5uaGTz/9tNRhynfffRdubm5QqVSIiIioykcSeHh4VGnNs507dyIlJQUAMHXqVPj5+ZVatnHjxpW+T23ExIwsSlmTbLUTlxdeeKHccpphxtL4+/vDzc1N7zmpVCpMDM7MzBR+sQHAlStX8PjxYwBFE3NLG6J0cHDAoEGDAADXr18XfgjqY8hE68pSqVRITk7GzZs3ER8fj/j4eNy4cQONGjUCAMTGxpZ5fVBQUKkJVbt27YTj4l/vf/75R5j0PWbMmDLXziouOzsbp0+fBlCUTJTF09MTDRo0AIASyWF10Z7H89JLL5VarkuXLsKcxLLm/jRu3LjMhwQqY+XKlcI2Ut7e3ujZsyfGjh2L8+fPw9bWFm+88QaWLl2q91rtOXOapywrQnueonZd2sNqpT3YUZNq+ueJ5o+aPn36lLmrgpWVFTp27Aig+tro4MGDK/Q9VZxmeNLOzs7gBYKpCOeYkUUp6+m6unXrCsceHh7llitvroWvr2+Z5zt06CAcx8fHCz9YNU+uAUV/kZfl6aefxubNm4XrXF1dS5Sxs7NDs2bNyqynotRqNSIjI7Ft2zZcvnwZ+fn5pZYt74GDVq1alXpO+6GG4l9v7WS2ok90xcTECA8vTJs2DdOmTTPoutTU1Ardx1Caf3O5XI42bdqUWfbpp5/GjRs3cOfOHRQUFOj9ha39lKQx+Pj4IDQ0VOchAG3ac7+KT2A3hPa/vXZd2gmbJkk3ppr8eVJYWCj8UfPbb78JvcPlqa42WtU2dO3aNQBFf1yZImk2Z0zMyKLY2NiUek77KTdDyul7KlFbeUMrpQ3PPHr0SDh2+r/27j2kqf+NA/h7RmVhuZaZXUC6GKykMCuDrMykIMpaEmhi9xsVlgVdqaCizH8qpZSgi11nhhQZNUxtpVRoipZEw7LSaOQllzdamt8/ZIezdnHapqff7/36a7DPOX62s7M9fi7Po1DYPYd4Wkh8nJizp7N+/vyJ7du34+nTpw61txe0AY5fkz/fb3HAZy0gtae2trZL7U06ey3dZbr+crm80111pmve3t6OHz9+WJ0adMUUpnjheFtbG/R6PTQaDe7du4fi4mLExMTgzp07Vj+zphFHoHuBg/h6iYN18ePuXtO/4crvE4PB0K0dtc76jNraFOAo0/1pazc12cbAjMhFbE3P9fQ5/mY6wprk5GQhKJsxYwZWrlyJSZMmwcvLC+7u7sIPTXR0tLATT2rEP4JHjx5FQECAQ8f97Y9VT3H2NQcsM/8rlUrMmzcPQUFB2LdvH758+YKDBw8iOTnZ4lhvb28MGTIE379/x7t379DW1talPopHR8VThOLHZWVlXX1JktbW1iY8XrFihc1i8n+yNWrZVSzw3nsYmBG5SGcjA7ZGAcQ//rW1tXanS8R/oyeChvb2dmFjwrRp05CammrzC9zWCJ6ziEdhvn371qXpWvH77e7ublFqqKeZ+lNfX4/W1la7o2amay6TyVy6uN9RKpUKubm50Gg0yMnJwfPnzy3SYchkMgQGBuLx48doaWnB8+fPrW5wsKahoUEI8OVyOcaPHy885+fnJwR8r169QmNjY4+nzHCVP1PU9PZntKuGDBkCvV6P6urq3u7KP4chMZGLmHbZOfK8OH+R+HFJSYndc5SWllo9zlXq6+uFL1p7mf2bmppQUVHh0r6INwZ0dWROqVQKo5FFRUVO7Vd3mK6dKemmPaZr7uvra3dBeE+Ki4sTRsBOnz5ttY1KpRIeX7t2zeFzp6WlCdNzKpXKbBRZJpMJ521ubkZ6enqX+y5V/fr1Ez4XUviMdtXEiRMBdGxm6o31f/8yBmZELpKfn29zp+Tv37+FjOWenp5mQYa/v78wEnL37l2ba9kaGxvx8OFDAMD48eO7vM7qT/379wcAs9QdfxJPr9j7sk1PT3d5xvmgoCBhh9+1a9fM+tYZhUIhbLbIzMzs9WLW4nI6f1aKECsuLkZ5ebnFMb1tzJgxwg7hkpIS5OfnW7QJDQ0VdpQ+efIEDx486PS8nz59wrlz5wB0jGxam85bs2aNsLg8MTHRLNmtPb9//7aoNiE1oaGhAIAPHz7g2bNn3T6PI/e2s5n63tLS4vDGBerAwIzIRYxGIw4fPmw1YLhw4YKQrT4iIsJs5KNfv35CygSdTofz589bHN/e3o5jx44JC2yjo6P/ur+mRbqfP3+22UahUAhBY2ZmptUv+tLSUpw9e/av+9OZwYMHC2WSysrKcOLECYuySia/fv2yWBxuKrzd2NiI2NhYIUWJNUajETdu3MDPnz+d1HtzkydPFqoEpKenWy0v1dDQIORUc3NzQ1RUlEv60l2bN28WRrOsrTNzc3NDQkKCsAZq3759NisyAB1pVtasWSPs4ty7dy9Gjhxp0W748OE4dOgQgI5Rs5iYGCEVii3l5eXYsGEDLl686NiL6yWrVq0S/vnYv3+/2Y5ta548eWI1PY0j97azhYeHC+mCzpw5Y/ea2Kqv+v+Ka8yIXMTf3x+5ubmIiooyK8l09+5dYbTAx8cHW7dutTh227ZtyMrKQmVlJZKSkqDT6bB8+XIMGzYMVVVVQmFqAAgICHBKHceAgABUVVUhJycHarUaU6dOFf7T9vDwwNChQ+Hm5oYlS5bgxo0bePfuHaKiorB27Vr4+vqisbERWq0WN2/exMCBA+Ht7Y2PHz/+db/s2bFjB/Lz86HT6XD9+nUUFxcjMjISEyZMQN++faHX61FYWIgHDx5g586dZrUy586di1WrVuHq1asoKCjAokWLEBkZicDAQMjlcjQ3N+Pz588oLCxEVlYWDAYDli1bJrwnznb8+HGsWLECv379wsaNGxETE4N58+aZlWQy5bpat26d5NYcTZgwAaGhocjOzkZBQQEKCwstcqn5+/sjISEBe/bsgdFoRFxcHNRqNZYsWYKxY8eib9+++Pr1K3JycpCZmSmMuq5bt86ilJBYREQE9Ho9EhMTUVtbi5iYGAQHBwujdIMHD4bBYEBFRQW0Wi2ePXuGtrY2pyVddhUvLy+cOnUKsbGxqK6uRkREBFQqFebMmQMfHx+0trZCr9ejtLQUGo0GlZWVSElJsXhdjtzbzta/f38kJCRg/fr1aGlpwdq1axEeHo6wsDD4+PjAaDTiw4cP0Gq1yMnJwZs3b5zeh38VAzMiF4mOjkZBQQEyMjIQFxdn8bypTqU435GJh4cHrly5IhQx12g00Gg0Fu2mTp2K5ORkp+zCW79+PTQaDYxGI44cOWL2nEqlQnx8PICO9URFRUV4+/Yt3rx5g927d5u1lcvlSEpKQmJiossDswEDBiA1NRWxsbEoKChAWVmZMHriiAMHDsDT0xPJycmorq5GUlKSzbYDBw50yW5HE6VSiZSUFKGI+aVLl3Dp0iWLdtHR0RbvuVRs2bIF2dnZADpGzayNSC1atAjDhg3DkSNH8P79e7x8+dJmRQW5XI5du3Y59I/Htm3b4Ofnh/j4eHz58gV5eXnIy8uz2d7Pz89uvVGpWLBgAc6fP4/9+/ejvr4earUaarXaals3NzerOcMcvbedbebMmUhJScHu3bthMBiQkZGBjIwMl/yt/yUMzIhc6OTJk5g1axbS0tKg0+nQ3NyMkSNHIiwsDJs2bbK7k3L06NG4d+8e0tPT8ejRI+h0OjQ1NcHT0xNKpVIonOysbe1KpRJpaWm4ePEiioqKUFNTY3WqctCgQbh16xYuX76Mhw8f4tOnT+jTpw9GjBiBuXPnYvXq1ULW/56gUChw/fp1ZGVl4f79+ygpKUFdXR1kMhm8vb0xadIkhIWFWc3wL5PJsH37dixduhRqtRovXrxAVVUVGhoa4O7ujhEjRkCpVCI4OBhhYWF281E5Q3BwMLKyspCamgqtVovKykoYjUZ4eXkhMDAQkZGRTs/o70yTJ0/GrFmzkJ+fj7y8PJSWlpolUjaZPn067t+/D41Gg+zsbLx+/Ro1NTVobW2FQqHAuHHjEBISgqVLl3Zp5+mCBQsQEhICjUaDp0+f4vXr16irq0NTUxM8PDwwatQoTJkyBQsXLkRQUJBT0tH0BNNI5O3bt6HValFeXg6DwYA+ffrAy8sLfn5+mDlzJhYuXGi1vJGj97YrzJ49G48fP8atW7eQm5uLiooKNDQ0YMCAAfD19cW0adOwePHiHunLv0LWbmtRBhF1WVVVFebPnw+gIygTT50RERF1hov/iYiIiCSCgRkRERGRRDAwIyIiIpIIBmZEREREEsHAjIiIiEgiuCuTiIiISCI4YkZEREQkEQzMiIiIiCSCgRkRERGRRDAwIyIiIpIIBmZEREREEsHAjIiIiEgiGJgRERERSQQDMyIiIiKJYGBGREREJBEMzIiIiIgkgoEZERERkUQwMCMiIiKSCAZmRERERBLBwIyIiIhIIhiYEREREUkEAzMiIiIiiWBgRkRERCQRDMyIiIiIJIKBGREREZFE/AdKTml6AKuR0wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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NkydP1hnKfP311yvdbr169dCpUycAhTdWaJ5vWVmV/f45c+aMsF2RJykQAewxIyIJSE5ORmBgIAIDA+Hk5ARPT084OTnB1NQUKSkpuH79us68ri5dumD48OFF2mnfvj0aNWqER48eITk5GUOHDoW3tzfq168v9NJ4eXnp9AZWxOjRo7F+/XrExsaioKAAn3zyCX799Vd4enrC0tIS0dHRwmr/np6e6N27N9auXVupa1aWdvHYvHlznSUsKqq4Z2WqVCpkZWUhKioKMTExOvtee+01g/Uk+fr64ty5cwAKh4sN8X4q8/2TnZ0tzN9r0KCBznxIovJgYUZEorKysoKZmZkwpyc5ObnUOVmDBw/G4sWLdeZ5achkMixcuBAzZsyAXC5HcnJykec5jh49utKFmYWFBX755RdMmTJFeC7i7du3dXqkAKBTp05YsWIFduzYUanrVVZBQQH27dsnfG2o3jJ9npUJFOb4/fffxxtvvGGQ6wLAsGHDsHTpUuTm5uLgwYP4+OOPKz1EWpnvn9DQUGFdu5EjRxpkaJVqJ37nEJGonnvuOYSFhSEsLAwXLlzAzZs3cf/+fWRkZEClUsHOzg5NmzZFhw4d4OfnpzPxuzj9+/fHrl27sGXLFly8eBEPHz5Ebm6uwdc9a9GiBYKDg7FlyxYcOnQId+/ehVwuh5OTE9zd3TFixAgMHTq0SoeB9XXixAlhiQqZTFZlT3DQsLGxQf369eHu7o4ePXrAz8+vXE9V0EedOnXg7++PP/74A3FxcQgLCzNIL1VFv3/++usvAIU3z0yYMKHScVDtZaIWexlqIiKiCoiLi8OQIUMgl8sxYMAArF69WpQ4bt26hREjRkCtVmPEiBFYvny5KHFQzcDJ/0REZJSaNGmCF198EUDhUOLTQ8nVZe3atVCr1TAzM8N7770nSgxUc7AwIyIio/Xuu+/Czs4OarUaP/30U7Vf//79+8L8vZdffhnNmjWr9hioZmFhRkRERsvR0REzZswAAOzfv1+4G7a6rFixAnK5HA4ODnj//fer9dpUM3GOGREREZFEsMeMiIiISCJYmBERERFJBAszIiIiIolgYUZEREQkESzMiIiIiCSChRkRERGRRLAwIyIiIpIIFmZEREREEsHCjIiIiEgizMQOgMSlUqmQl5cHADAzM4OJiYnIERERERkHtVoNhUIBALC2toZMVvn+LhZmtVxeXh4iIyPFDoOIiMioeXp6wtbWttLtcCiTiIiISCLYY1bLmZn971vAzc0NNjY2IkZDcrlc6MH09PSEubm5yBHVbsyH9DAn0lLb86H9/rV/n1YGC7NaTntOmYWFBSwsLESMhmQyGezt7QEAlpaWBvtBp4phPqSHOZEW5uN/DDVHu/Z+glREbf6BkgozMzO4ubmJHQb9P+ZDepgTaWE+DI9zzIiIiIgkgoUZERERkURw7IoEcrmcc8xEJpfLcfv2bQCFN2PUtom0UsN8SA9zIi3Mh+GxMCOSELVajZycHGGbxMV8SA9zIi3Mh+FxKJOIiIhIIliYEREREUkECzMiIiIiiWBhRkRERCQRLMyIiIiIJIKFGREREZFEcLkMEshkrNPFZmpqiiZNmgjbJC7mQ3qYE2lhPgyPhRkJ+EMlPlNTU7i4uIgdBv0/5kN6mBNpYT4Mj10kRERERBLBwoyIiIhIIjiUSQI+K1N8BQUFiIiIAAB4eXkxHyJjPqSHOZEW5sPw2GNGREREJBHsMSOBmRm/HcRmbm4OLy8vYZvExXxID3MiLcyH4fE3MQlMTEzEDqHWMzEx4VCAhDAf0sOcSAvzYXgszEigVKmQmJEjdhhEREQ6HOtaw7SWrLXJwowEadn58Fu2V+wwiIiIdOyfOwYu9rZih1Etakf5SURERGQEWJgRERERSQQLMyIiIiKJYGFGREREJBEszIiIiIgkQvJ3ZWZlZWHv3r04fvw4bt++jdTUVJiZmcHR0RHt2rXDgAEDMHjwYJiamurdZlpaGv7++2/8999/iImJQXp6OgoKClCvXj20bNkSnTp1wvDhw+Hu7l5mWyqVCseOHcOJEydw6dIlpKSk4PHjx7C1tYWLi4sQY9++fYtdfC8gIABnz54t8rpMJkOdOnXQtGlTdOvWDS+//DKaNWum93skIiIi42OiVqvVYgdRkh07dmD58uXIyMgo9bhnn30WX3zxBbp06VLqcSqVCr/88gvWrl2LnJyy1+vy9vbGZ599hmeffbbY/WfPnsWiRYsQHR1dZlvOzs6YOXMm/P39dV4vqTB7mrm5OebMmYMJEyaUeWx5aD/nrGEzNy6XQUREkiPV5TKq4lmhku0xW7ZsGdavXw+g8FFBw4YNg4+PD1xdXVFQUIC7d+9i3759OHPmDG7duoXXX38d3377LYYMGVJse/n5+fjggw9w7NgxAIWFzrBhw9CrVy80adIEVlZWSE1NRUREBI4cOYKbN2/iv//+w59//om5c+cWaW/Xrl1YuHAh5HI5AKBDhw4YNGgQWrduDXt7e2RlZeHBgwc4ceIEjh8/jqSkJCxevLhIYaYtJCRE2FapVEhISMDBgwcRFBQEuVyORYsWwdXVFf369avox0pEREQSJsnCbMuWLUJR1rBhQ/zyyy9o3bq1zjFdunTB2LFjERISgjlz5qCgoACzZs1Cs2bNihwLAJ9//rlQlD333HP4/vvv0aRJkyLH9e3bF++++y6OHTuGxYsXFxvf6dOnMW/ePKhUKtjY2GDJkiXFFoQ9evTA2LFjERcXh+XLl+PkyZOlvu+nh049PT3Rr18/tG3bFl999RXUajVWrlzJwoyIiKiGktzk//j4eCxbtgwAYGNjgw0bNhRbaGn4+voKBVRBQQE++eQTPD06e/jwYQQFBQEAPDw8sGHDhmKLMm39+/fHrl270KNHD53X8/LyMGvWLKhUKshkMvz6668l9tJpNGnSBD/88APmzZtX6nElmTBhAlxdXQEA169fR2pqaoXaISIiImmTXGG2ceNGPHnyBADw9ttvo0WLFmWe4+fnhz59+gAAoqOjhZ4xjV9++UXYXrJkCWxsbPSKpW7duhgwYIDOa7t27UJycjKAwoKpW7duerUFAKNGjdL7WG0ymQzt2rUTvn748GGF2iEiIiJpk1RhplarsWfPHgCAlZUVXn75Zb3Pfe2114Tt3bt3C9tRUVG4du0agMLhz7Zt21YqRk3bJiYmmDhxYqXaKg8zs/+NOqtUqmq7LhEREVUfSc0xi4mJEe7A7NKlC+rUqaP3ub169YKVlRXy8/Nx4cIF4fVz584J23379q1UfNnZ2bh58yYAoEWLFnjmmWcq1V55aN/56ezsXG3XJSIiMgQLKCt8bl5uLnKKrjhVbra20ruz82mSKswiIyOF7TZt2pTrXFNTU3h6euLy5ctIS0tDYmIiXFxcdNrUHg6siKioKKG3qrI9b+Vx/PhxxMTEAACaNm2KRo0aVdu1iYiIDGGI3f0Kn7vxt9UGiWHhwoUGaacqSaowS09PF7YdHR3LfX6DBg2E7YyMDLi4uOisgebg4FCp+LTb0r5WVdBeLmPlypXC62+++WaVXpeIiIjEI6nCTHvRV30n6GvTPic7O9sgbWrTbsva2rpSbRXHw8Oj1P3jxo0r17w7IiIiMi6SKsy0x35zc3PLfb72OXZ2dgZpU5t2W3l5eZVqS1/W1tbo1KkTXn31Vfj4+FTLNYmIiEgckirM6tevL2ynpKSU+3zt9b3s7e11/v/0/orQbqsi8ZVFe+V/U1NT2NrawsnJqVzPASUiIpKig9kVv2Fu28wRcKpXuVEvYyGpwkx7KO/GjRvlOlepVCIqKgpA4VwyFxcXAIWr52tcv34d3t7elYpPJpNBpVKVOz596PPQdCIiImNUgIp3Mljb2BjFHZWGIKl1zNzd3YVeqfPnzyMrK0vvc8PCwoThRe2HmXft2lXYPnHiRKXis7OzE55CcPfuXdy7d69S7RERERFpk1RhZmJigpEjRwIofOj4jh079D73jz/+ELZHjx4tbHt4eAjLZJw/fx7Xr1+vVIyah5Cr1Wps3ry5Um0RERERaZNUYQYAEydOhIWFBQAgMDBQr16pv//+G8ePHwdQ2OvWv39/nf3Tpk0TtufMmaP3TQCPHz9GaGiozmtjxoyBk5MTgMKHrZ89e1avtgAgODhY72OJiIio9pFcYdakSRN88sknAArvopw0aZLOIrFP279/Pz799FMAgLm5Ob755huYmJjoHDNo0CDhOZVRUVGYNGkS4uPjS43jxIkTePHFF3H69Gmd162trfHtt98Kc82mTp2Kf/75p9S2Hj58iA8//BBfffVVqccRERFR7Sapyf8aAQEBuH//PjZt2oSHDx9izJgxGD58OHx8fNC4cWMoFArcuXMH+/btEwonCwsLfPvtt8IcsKd9/vnnyMzMxLFjx3DlyhUMGTIEQ4cORe/eveHq6gorKyukpKTgxo0bOHz4cKlDnj179sRXX32FhQsXIjc3F++99x46dOiAwYMHo3Xr1qhXrx6ysrLw4MEDnDx5EqGhoSgoKCjXI6aIiIio9pFkYQYAc+fORcuWLbFixQpkZGRgz549wgPOn+bm5oYvv/xSZ9L/06ytrbF69Wr8/PPPWLduHXJyckptEwD69euHV155pdh9Y8aMQdOmTbFo0SJER0fj8uXLuHz5coltNW7cGB999FGJ+4mIiIgkW5gBwPjx4zF8+HDs3bsXx48fx61bt5CWlgZTU1M4OjrCy8sLAwYMwJAhQ/Ra60smk+Gdd97B+PHj8ffff+O///5DdHQ00tPTIZfLUa9ePbRs2RKdOnWCn58f3NzcSm2vW7du2LNnD44dO4bjx4/j0qVLSElJQVZWFmxsbNCwYUMhxr59+8LMTNIfNxEREYnMRK1Wq8UOgsRTUFCAiIgIAEDDZm7wW7ZX5IiIiIh07Z87Bi720lvHTPt3qJeXl3DzYmVIbvI/ERERUW3FwoyIiIhIIliYEREREUkECzMiIiIiieBtgiRwsLPC/rljxA6DiIhIh2Nda7FDqDYszEhgKpPBxd5K7DCIiIhqLRZmJODKKeJTq9WQy+UACh8x9vTjxah6MR/Sw5xIC/NheJxjRgKFQiF2CLWeXC5HREQEIiIihH/sSDzMh/QwJ9LCfBgeCzMiIiIiiWBhRkRERCQRnGNGAs4NEJ9MJoOzs7OwTeJiPqSHOZEW5sPwWJiRgA9ZF5+ZmRmaNm0qdhj0/5gP6WFOpIX5MDyWt0REREQSwcKMiIiISCI4dkUCpUqFxIwcscOgWsSxrjVMJTwvRS6X4+bNmwCA1q1bw9zcXOSIiDmRFubD8FiYkSAtOx9+y/aKHQbVIvvnjoGLva3YYZRIe/FMLsAsDcyJtDAfhifdP1WJiIiIahkWZkREREQSwcKMiIiISCJYmBERERFJBAszIiIiIomo9sJMqVSiU6dO8PDwwOjRo0s9Vq1Wo3v37vDw8ICHhwd27txZ6vFBQUHCsVu3bhVeDwgIEF4vy8aNG4Vje/bsicjISGHfqlWrhH3h4eFltgUAPj4+8PDwgI+PT7H7Z8+eLbSp/Z+npye6dOkCPz8/fPHFF8LtyERERFRzVXthZmpqio4dOwIAIiMjkZ2dXeKxMTExyMjIEL6+cOFCqW2fP39e2O7atWu5Y1u7di0WL14MAHBycsLmzZvh6elZ7nYMQa1WIysrC1FRUdi6dSv8/f3xww8/iBILUW1lZmaGli1bomXLlnxkmUQwJ9LCfBieKJ9i165dcerUKahUKly8eBHPP/98scdpCi1TU1MolUqdwqu04+vXr49nn322XDH9/PPPWLFiBQDAxcUFGzduRIsWLcrVRmWtW7dOeBisSqVCamoqjh8/jq1bt0KhUOCXX36Bs7MzJkyYUK1xEdVWMpkM9evXFzsM0sKcSAvzYXiizDHr0qWLsF1asaXZN2TIEADA/fv3kZiYWOyxqampiI2NBQB07twZJiYmesezatUqoShr3Lgx/vjjj2ovygCgefPmcHd3h7u7Ozw9PeHt7Y25c+di1apVwjGBgYFQKpXVHhsRERFVPVEKs/bt28PS0hKAfoXZ4MGD8cwzzwAoeTizosOY33//PQIDAwEATZo0wR9//CFcSyp8fHyEYjYtLQ3Xr18XOSKi2kGtVqOgoAAFBQVc1VwimBNpYT4MT5TCzMLCAu3btwcAREREoKCgoMgxDx48EHrHOnfujM6dOwMouZDTfl27R640y5Ytw6+//gqgsLdqy5YtcHV11f+NVCPN5wUADx8+FDESotpDLpcjIiICERERwmNnSFzMibQwH4Yn2nIZmuKpoKAAV65cKbJfU2g1b94cjo6OQmF27ty5YtvT9KTZ2dmhdevWZV7/q6++wvr16wEALVu2xObNm9GwYcPyv5Fqoj2pUqVSiRgJkX4soCzzv7zcXOTk5Oj1HxFRbSDaLRRdu3bFzz//DKCwCHt6+FFTmHXq1Enn/zExMcjMzES9evWEY7Ozs4VlLTp27AhTU9MSr6tWq/HFF19g27ZtAAB3d3f8/vvvcHR0NNA7qxrR0dHCtuYGASIpG2J3v8xjNv62Wu/2Fi5cWJlwiIiMgmiFWYcOHWBmZgaFQlHs8KTmNU3PmpubG+rXr4/09HRcvHgR/fv3F469dOmSMCG+rPll8+fPx19//QUAaN26NdavXw8HB4dyxx8XF6fXnSiG6NqNjIzEyZMnAQDW1tbw8vKqdJtEREQkPaIVZra2tmjdujUiIiKEwkrT0/X0HZYanTp1wtGjR3H+/Hmdwqw888s0RZmbmxs2btyo0/NWHp999lmFztOXWq1GSkoKjh8/juXLlwuFZ0BAgHDjBBEREdUsoq4G17VrV0RERCAnJwc3b95Eu3btAPxvHpmjoyOaN28uHN+5c2ehMNOmmV9maWlZZm+SiYkJ1Go14uLicOPGDfTs2dOA76hyBgwYUOr+fv364b333qumaIiIiKi6iVqYde7cWZiAf/78eaEw0xRe2r1lwP96w65fv478/HxYWVmhoKAAV69eBQA899xzsLCwKPWa8+fPx5dffoknT55g+vTpWLt2rd53cWrbtGkTunfvXuZxPj4+iI+PL3f7Gubm5vDy8sK4ceMwcuTIcq3PRiSmg9llLzuzbeYIONWzqYZoiIiMg6iFWZcuXYQerAsXLmDSpEkA/tcDppnwr9GmTRtYW1sjLy8Ply9fRo8ePRAREYEnT54I7ZVlwoQJyM/PxzfffIO8vDxMnToVv//+u85yFGLRXvlfJpPBxsYGjo6OZRabRFJUgJJvwtGwtrGBra1tNURDRGQcRFsuAwDs7e3RqlUrAP/rJdO+w/LpHjNN75H28doLzuq7sOzkyZMxY8YM4XpvvvmmzsPKxaK98v+zzz6Lxo0bsygjEpHm3xwvLy+Ym5uLHQ6BOZEa5sPwRC3MAOisaH/79m1cuHABKpUKNjY2aNOmTZHjn15oVjMfzdzcHB06dND7uu+++y6mTJkCAMjMzMQbb7yB27dvV+atEFENY2JiAgsLC1hYWHAagUQwJ9LCfBieZAozoLD3S9MD1qFDh2LXI9MUZleuXEFBQQEuXboEoHCY08amfHNVPv74YwQEBAAovBN00qRJePDgQYXeBxEREVFlSaowO3funNAD9vQwpoZmAdnc3Fzs3r0bWVlZRdopj7lz5+Kll14CACQlJeG1117Do0ePKtQWEdUsKpUK6enpSE9P5xM3JII5kRbmw/BEL8xcXFyEh4aHh4fj2rVrAEouzOzs7ODu7g4AWLt2rfB6eR5crs3ExARffPEFfH19AQDx8fGYNGkSkpOTK9QeEdUcCoUCd+7cwZ07d6BQKMQOh8CcSA3zYXiiF2bA/3q7EhMTUVBQADMzs1Lni2mKNs2wo0wmK7GQ04dMJsOyZcswePBgAEBsbCxef/11pKWlVbhNIiIiovKSVGGm0bp1a1hbW5d4/NNFmLu7O+rWrVupGExNTbF8+XL069cPQOEzOSdPnozHjx9Xql0iIiIifUmiMHt6GLKs3q+n91d0GPNp5ubmWLlyJXr16gUAuHHjBqZMmYKcnByDtE9ERERUGhO1Wq0WOwgST0FBASIiIgAADZu5wW/ZXpEjotpk/9wxcLGX7gKz2j8fXl5eXFdQApgTaant+aiK9y+JHjMiIiIiYmFGREREJBmiPiuTiEjKTExMhMfMcFVzaWBOpIX5MDwWZkREJTA3N0f79u3FDoO0MCfSwnwYHgszEjjYWWH/3DFih0G1iGPdkpfFISKqjViYkcBUJoOLvZXYYRAREdVaLMxIwJVTxKdWq6FUKgEULnrMORviUigUwrNzGzVqBDMz/pMpNuZEWpgPw+NdmSTgc87EJ5fLceXKFVy5cgVyuVzscGo9lUqFpKQkJCUl8QHNEsGcSAvzYXgszIiIiIgkgoUZERERkUSwMCMiIiKSCBZmRERERBLB2ydIwLtpxGdubg4vLy9hm8TFfEgPc0I1HX8Tk4BLM4jPxMQEFhYWYodB/4/5kB7mhGo6FmYkUKpUSMzIETsMIiKDc6xrDVMZZ+8Ymkwmg729vbBNlcfCjARp2fnwW7ZX7DCIiAxu/9wxcLG3FTuMGsfMzAxubm5ih1GjsLwlIiIikggWZkREREQSwaFMIiIiqhC5XI7bt28DANzc3HinrAGwMCMiIqIKUavVyMnJEbap8jiUSURERCQRRtNjlpubiz179iA0NBSRkZHIyMiAWq2GnZ0dXF1d4e7ujo4dO6JPnz5o1KhRqW09fPgQf//9N8LCwnDv3j2kpaVBpVKhXr16cHd3R+fOneHr64umTZsWOTc8PBwTJ04EALz77ruYMWNGmbHPnj0bQUFBAICjR4+iSZMmOvt3796NOXPmAACWLFkCf39/vT4TIiIiqlmMojC7dOkSPvzwQzx8+LDIvvT0dKSnp+PatWvYvXs3HB0d8d9//xXbzpMnT7B8+XJs27YNBQUFRfYnJSUhKSkJp06dwsqVKzFkyBB8+umnZRZ6RERERIYg+cLs7t27mDx5sjCG7ePjg8GDB6NFixYwNzdHeno6IiMjERYWhvDw8BLbSUtLw/Tp03H58mUAgK2tLUaMGIEePXqgYcOGMDMzQ0pKCi5evIjDhw8jNjYWBw4cQIcOHTBp0qRqeKdERERU20m+MPvhhx+EoqykYT5vb29MnjwZaWlpOHDgQJH9KpUKH3zwgVCU9e/fH19//TUaNGhQ5FgfHx98+OGH2Lt3L7755hvDvhkiIiKiUki6MFMqlThx4gQAoF27dmXOvXJwcMCECROKvL5p0yahN613794IDAws9YHdMpkMo0aNQo8ePXD37t1KvAMiIiIi/Um6MEtLS0N+fj4AoFmzZhVqo6CgAOvWrQMAWFpaYvHixaUWZdoaNmyIhg0bVui6RERENZ2pqalwQ5upqanI0dQMki7MtBeq0yxgV16nTp1CUlISAGDIkCFwcXExSGxERCQeCyjLdXxebi5yKrD2qa0tn69ZGlNTU/5eNTBJF2b29vZwdXVFfHw8IiMjsWbNGrz55pvleoL9uXPnhO1+/fpVQZRERFTdhtjdL9fxG39bXaHrLFy4sELnEVWUpAszAHj11VexbNkyAMDy5cuxfft2+Pj4oFOnTvDy8ip2rTFtkZGRwnbbtm0NGltqaiqio6PLPO7x48cGvS4RERHVTJIvzCZNmoRbt25h165dAID4+Hhs3rwZmzdvBgA4OjqiW7du8PX1Rf/+/WFiYqJzfkZGhrBd3F2YlbFt2zZs27bNoG0SEREZi4KCAkRERAAAvLy8YGFhIXJExk/yj2SSyWRYvHgx1q9fjz59+hSZuJ+SkoL9+/dj+vTpePHFF3H/vm73tmapDQCwtraulpiJiIiIKkLyPWYa3t7e8Pb2RnZ2Ni5cuICIiAhcu3YN58+fR1ZWFgDg2rVreOWVV7B79244OzsD0J24mZeXBzs7O4PFVJFHMhERUeUdzH6mXMdvmzkCTvVsqigaIsMxmsJMw87ODn379kXfvn0BFHajhoSEYNmyZcjMzERycjJ+/PFHfP311wAKbyDQSElJMWhhRkRE4ihA+ZZmsLax4R2WZBQkP5RZFgsLC4wZMwbLly8XXjt8+DBUKhUAwNPTU3j9xo0b1R4fERERkb6MvjDT6NOnj/Cw8czMTGHSf9euXYVjjh8/LkJkRERERPqpMYUZAGFembbevXsLrx88eBCJiYnVHRYRERGRXmpMYZaXl4dbt24BKJyHVr9+fQCFQ52TJ08GADx58gSfffYZlEr9VoxOTEzE6dOnqyZgIiIioqdIujDLycnB2LFjcezYMWHOWHFUKhUWLVokLI3h4+Ojs57ZxIkT0b17dwCFj2h65513kJaWVmJ7arUaISEh8Pf3R1RUlIHeDRERUc1iZmYGDw8PeHh46P0caiqd5D/Fq1evYtq0aXBxccHAgQPRoUMHNG7cGHZ2dnj8+DFu3LiBXbt2CSvw16lTB++//75OGzKZDCtWrMC0adNw5coVHDt2DAMHDoSvry969OiBhg0bwszMDCkpKbh8+TIOHTqEO3fuiPF2ceHCBb2O8/Hx0bnjlIiIqLrJZDKudmBgki7MzMzM4OTkhOTkZCQmJmLLli3YsmVLicc3b94cy5cvF550r83BwQGbN2/Gd999h23btiEnJwfbt2/H9u3bi23LxMQEvr6+GDp0qMHejz527tyJnTt3lnlccHAwCzMiIqIaRtKFmaWlJf79919cvnwZYWFhuHLlCu7evYvU1FQ8efIE1tbWcHZ2hqenJwYMGIAXXnih1MdBWFpaYu7cuXj99dexb98+nD59GrGxsUhLS4NarUa9evXg7u6Orl27wtfXF66urtX4bomIiIyLSqVCbm4uAMDGxgYymaRnSBkFE7VarRY7CBKP9nPOGjZzg9+yvSJHRERkePvnjoGLPReYNbTa/qzMqnj/LG2JiIiIJIKFGREREZFEsDAjIiIikghJT/6n6uVgZ4X9c8eIHQYRkcE51rUWOwQivbAwI4GpTAYXeyuxwyAiIqq1WJiRgDfoik+tVkMulwMAzM3NdZ5gQdWP+ZAe5oRqOs4xI4FCoRA7hFpPLpcjIiICERERwi8fEg/zIT3MCdV0XMeslqvta9AQERFVFNcxIyIiIqrBWJgRERERSQQn/5NAqVSKHUKtp1QqkZKSAgBwdHSEqampyBHVbsyH9DAn0sJ8GB4LMxKoVCqxQ6j1lEol4uLiAAD169fnP3IiYz6khzmRFubD8DiUSQIzM9bpREREYmJhRgKuB0RERCQudpGQQKlSITEjR5RrO9a1hqmMfycQEVHtxsKMBGnZ+fBbtleUa++fOwYu9raiXJuIiEgq2EVBREREJBEszIiIiIgkgkOZRBJiYmICW1tbYZvExXxID3MiLcyH4bEwI5IQc3NzeHp6ih0G/T/mQ3qYE2lhPgzPqAqzs2fPIiAgQPh627Zt6NSpU7nauH//Pg4cOIDTp0/j/v37SE9Ph0KhQJ06ddCkSRO0adMG/fr1g7e3N8zNzYttY/fu3ZgzZ065rjtnzhxMmjRJ57WAgACcPXu2yLEymQx16tRB06ZN0a1bN7z88sto1qxZua5HRERExseoCrOgoCCdr4ODg/UuzB4/foylS5diz549UCgURfanpqYiNTUVV65cwbZt29CgQQNMmTIFEydOrPaVjFUqFTIzM5GZmYlr165h8+bNmDNnDiZMmFCtcRAREVH1MprCLD8/H//88w8AwMbGBrm5uTh48CDmzZsHCwuLUs998OABpkyZgrt37wIA7O3tMXz4cHTp0gXOzs6wtrZGWloa7ty5g1OnTiEsLAypqalYunQpRowYAScnpxLb/uCDDzBgwIAy4y+tDQAICQkRtlUqFRISEnDw4EEEBQVBLpdj0aJFcHV1Rb9+/cq8FhkvhUKBe/fuAQCaNWvGpzGIjPmQHuZEWpgPwzOaT/Dw4cPIySlc/HTevHn47LPPkJmZidDQUAwZMqTE83JzczF16lShKHvppZfw6aefws7Orsixffr0wWuvvYYHDx5g9erV2L17d5lxubi4wN3dvYLv6n+ebsPT0xP9+vVD27Zt8dVXX0GtVmPlypUszGo4lUqFjIwMAEDTpk3FDYaYDwliTqSF+TA8o1kuIzg4GADg4eGBMWPGoEWLFjqvl+S7777D7du3AQCvvPIKFi1aVGxRpq1p06ZYsmQJfv75Z1haWlY69sqYMGECXF1dAQDXr19HamqqqPEQERFR1TGKwiwpKQmnT58GAPj5+en8/9SpU0hLSyv2vJSUFOzcuRMA0KhRI3z66afluq6Pjw/q1q1b0bANQiaToV27dsLXDx8+FDEaIiIiqkpGUZiFhIRAqVRCJpPB19cXAODr6wsTExPI5XLs27ev2PP279+PJ0+eAADGjh0LKyuraovZkLTH7FUqlYiREBERUVUyisJsz549AIBu3brBxcUFQOFwY8eOHQGUPJx57tw5YbtPnz5VG2QVio6OFradnZ1FjKRqWECJvNxc5OTkCP8RERHVRpKf/H/z5k1ERUUB+N/wpYafnx8uXryI69ev49atW3j22Wd19mvOk8lkVbYAXmJiok7hVJKK3iBw/PhxxMTEACgsRhs1alShdqRsiN19bPxttc5rCxcuFCkaIiIi8Ui+MNP0hllZWWHw4ME6+4YOHYqvv/4acrkcwcHB+Pjjj3X2p6enAwDq1KlT6pIaycnJwrFPq1evntBLV5wVK1ZgxYoVZb4PTZGoD+3lMlauXCm8/uabb+rdBhERERkfSRdmCoVCmD/Wv3//IndT2tvbo2/fvjhy5AhCQkLw4YcfQib73+isZkjM2tq61OusWbMGmzZtKnbf6NGjsXTp0sq8Db14eHiUun/cuHF4+eWXqzwOEpdMJhOGq7W/l0kczIf0MCfSwnwYnqQLs1OnTiElJQVA0WFMDT8/Pxw5cgQJCQkIDw9Hz549hX22trZ4/Pgx8vLyqizGJUuWwN/fv0ratra2RqdOnfDqq6/Cx8enSq5B0mJmZsa1gCSE+ZAe5kRamA/Dk3RhphnGtLe3L3Hyfv/+/VG3bl08fvwYwcHBOoWZvb09Hj9+jKysLBQUFJQ4nDl37lzMnTtX+DouLk6v1fwNSXvlf1NTU9ja2sLJyanaHwclhoPZz2DbzBFwqmcjdihERESikmxhlpWVhdDQUABARkaGzlpeJTl06BAWLlwIG5vCX/Cenp64f/8+VCoVoqKi4OXlVaUxV4Yhnh5grApgCmsbG9ja2oodChERkagkOyB84MABYQ0yfeXm5uLQoUPC1127dhW2T548abDYiKqKXC7H1atXcfXqVcjlcrHDqfWYD+lhTqSF+TA8yfaYaYYxnZycMGfOnDKP/+abb5CQkIA9e/Zg1KhRAIBhw4bh22+/RUFBAf766y9MnjxZ9EcsEZVGrVYL/7ip1WqRoyHmQ3qYE2lhPgxPkoXZgwcPcPHiRQDA4MGDMXz48DLPuXz5MjZt2oQzZ84gMTERLi4ucHR0xJgxY7Bt2zY8fPgQ3377LebNm1fV4RMRERFViCSHMvfs2SNU3k+vXVYSzXEqlUp4UgAAfPzxx8IDzzdv3ozPP/+8zJXlHz9+XJGwiYiIiCpFkj1mmsKqQYMG6NKli17ndOrUCU5OTkhOTsbevXvx1ltvAQDs7Ozw66+/4q233kJsbCy2bduGgwcPYsSIEejSpQucnZ1hY2OD7Oxs3Lt3D2FhYTh8+LDQblnP19R35X87Ozs0btxYr/dCREREtZPkCrMLFy7g/v37AICBAwfqvWCdTCbDoEGDsHXrVsTExODatWvCnZzNmjXDjh07sGTJEuzduxfp6enYvHkzNm/eXGJ7DRo0wJtvvomAgIBSr6vvyv8DBgzA6tWryzyOiIiIai/JFWbaw5D6DmNqvPDCC9i6dSuAwpsHtJfYqFevHpYuXYrp06dj//79OHPmDO7du4eMjAwolUrUrVsXjRo1Qrt27dC7d2/07dsX5ubmhnlTRERERHowUfM2ilqtoKAAERERAICGzdzgt2yvKHHsnzsGLvZcx0w7H15eXqU+45WqHvMhPcyJtNT2fFTF+5dcjxlRbWZmZoaWLVsK2yQu5kN6mBNpYT4Mj58ikYTIZDLUr19f7DDo/zEf0sOcSAvzYXiSXC6DiIiIqDZijxmRhGivom1ubg4TExORI6rdmA/pYU6khfkwPBZmJHCws8L+uWNEubZjXWtRris1crm8Vk+klRrmQ3qYE2lhPgyPhRkJTGUyuNiXvqAuERERVR3OMSMBV04hIiISFwszEigUCrFDICIiqtVYmBERERFJBAszIiIiIolgYUZEREQkESzMiIiIiCSCy2WQgM85E5+5uTm8vLyEbRIX8yE9zIm0MB+Gx9/EJOCKzeIzMTHhAo0SwnxID3MiLcyH4XEok4iIiEgi2GNGJCEqlQqZmZkAgHr16kEm499OYmI+pIc5kRbmw/D4CRJJiEKhwJ07d3Dnzh0u+CsBzIf0MCfSwnwYHgszIiIiIolgYUZEREQkESzMiIiIiCSChRkRERGRRNTKuzJzc3OxZ88ehIaGIjIyEhkZGVCr1bCzs4Orqyvc3d3RsWNH9OnTB40aNdI5d/bs2QgKCirX9YKDg9G6dWvh63fffReHDx8GAGzYsAE9e/Ys9fyjR4/i7bffBgCMGjUKy5YtK9f1iYiIyDjUusLs0qVL+PDDD/Hw4cMi+9LT05Geno5r165h9+7dcHR0xH///WfwGBYsWIDw8HA8fvwYCxYsQEhICKysrIo9NisrC59//jkAoEGDBpgzZ47B4yEiIiJpqFWF2d27dzF58mTk5OQAAHx8fDB48GC0aNEC5ubmSE9PR2RkJMLCwhAeHl5me+vWrYOzs3OZxzVr1kzna2dnZ8yaNQvz58/H/fv38eOPP+LTTz8t9txvvvkGSUlJAIB58+bB3t6+zOuR8TIxMREea8InMYiP+ZAe5kRamA/Dq1WF2Q8//CAUZUuWLIG/v3+RY7y9vTF58mSkpaXhwIEDpbbXvHlzNGnSpEKxjB07FiEhITh79iw2btyI4cOHo127djrHhIeH46+//gJQWEQOGzasQtci42Fubo727duLHQb9P+ZDepgTaWE+DK/WTP5XKpU4ceIEAKBdu3bFFmXaHBwcMGHChCqLx8TEBF999RUsLS2hVCoxd+5cncX58vPzMX/+fGHum2Y4k4iIiGquWlOYpaWlIT8/H0DRoUWxNGvWDO+++y4AIDIyEmvXrhX2rVy5Evfu3QMAzJo1Cy4uLqLESERERNWn1hRmmjFwALh9+7aIkeh644030KZNGwDATz/9hDt37uDatWvYsGEDAKBr164YN26ciBFSdVIoFHjw4AEePHjAx5tIAPMhPcyJtDAfhldrCjN7e3u4uroCKOydWrNmDVQqlchRAWZmZvjqq69gamqKgoICzJs3D/PmzYNSqYSlpSUWLVpUbRMqc3NzkZOTI8zDo+qnUqmQlJSEpKQkSXx/1nbMh/QwJ9LCfBherZr8/+qrrwprgC1fvhzbt2+Hj48POnXqBC8vLzRt2rRc7cXGxiI3N7fUY6ytrctst23btpg0aRLWrVuHCxcuCK+/++67aNGiRbliqoxff/1VGO5duHBhtV2XiIiICtWqwmzSpEm4desWdu3aBQCIj4/H5s2bsXnzZgCAo6MjunXrBl9fX/Tv37/MnqrJkyeXec1u3boJ7Zfmvffew/79+/Ho0SMAQJs2bfDGG2+UeR4RERHVHLVmKBMAZDIZFi9ejPXr16NPnz4wM9OtS1NSUrB//35Mnz4dL774Iu7fv19tsd27dw8pKSnC1w4ODkXiIyIiopqtVv7m9/b2hre3N7Kzs3HhwgVERETg2rVrOH/+PLKysgAA165dwyuvvILdu3eXuIjs0aNHK7yOmTbNchlyuVx47dSpUwgJCYGvr2+l2yciIiLjUCsLMw07Ozv07dsXffv2BQAUFBQgJCQEy5YtQ2ZmJpKTk/Hjjz/i66+/rtI4Nm3ahIiICACFw6MhISFISkrC4sWL0bt3b9SvX79Kr68xdepUnbtXiYiIqHrVqqHMslhYWGDMmDFYvny58Nrhw4er9E6TBw8e4McffwQAuLu7Y+bMmZg/fz6AwrXXqvOB5TY2NrC1tYWtrW21XZOIiIj+h4VZMfr06YNGjRoBADIzM5GRkVFl11qwYAHy8vIgk8nw9ddfw9zcHC+88AIGDhwIAAgKCsLp06er7PokLTKZDPb29rC3t4dMxh9PsTEf0sOcSAvzYXj8FEugz8PJK2vXrl0ICwsDAAQEBOg8b2zBggWws7MDULh0xZMnT6o8HhKfmZkZ3Nzc4Obmxps/JID5kB7mRFqYD8NjYVaMvLw83Lp1C0DhPLSqmOOVkpIiDFO6urrigw8+0Nnv4uKCjz76CEDhHZuBgYEGj4GIiIikpdYUZjk5ORg7diyOHTtW6pwxlUqFRYsWCavf+/j4VMnK+19++SUyMzMBAF988QVsbGyKHDN+/Hh06tQJALB+/XpERkYaPA4iIiKSjlrV73j16lVMmzYNLi4uGDhwIDp06IDGjRvDzs4Ojx8/xo0bN7Br1y5ER0cDAOrUqYP333+/xPb0WfkfAJycnHR63Y4cOYJ//vkHADBy5Ej06dOn2PNMTEywaNEijBo1CnK5HPPmzcOOHTs4jl+DyeVy4Vmubm5uvEtWZMyH9DAn0sJ8GF6tKczMzMzg5OSE5ORkJCYmYsuWLdiyZUuJxzdv3hzLly8vdZ0yfVb+B4A5c+Zg0qRJAICsrCx88cUXAAoXkZ0zZ06p5z777LOYOnUqAgMDERERgc2bN+O1117T67pkfNRqtdBbq1arRY6GmA/pYU6khfkwvFpTmFlaWuLff//F5cuXERYWhitXruDu3btITU3FkydPYG1tDWdnZ3h6emLAgAF44YUXYGFhYfA4vvnmGyQlJQEAPvvsM73mr02dOhUHDhzA7du3sWLFCgwaNAiNGzc2eGxEREQkLhM1S9xaraCgQFjc1svLq0qKUdIf8yEtzIf0MCfSUtvzURXvn5OViIiIiCSChRkRERGRRLAwIyIiIpIIFmZEREREElFr7sokMgampqbCEi2mpqYiR0PMh/QwJ9LCfBgeCzMiCTE1NYWLi4vYYdD/Yz6khzmRFubD8DiUSURERCQRLMyIiIiIJIJDmSSQy+W1bnFAqantizVKDfMhPcyJtDAfhsceMyIiIiKJYGFGREREJBEszIiIiIgkgoUZERERkUSwMCOBmRnvBSEiIhITCzMSmJiYiB0CERFRrcYuEhIoVSokZuRUSduOda1hKuPfAURERKVhYUaCtOx8+C3bWyVt7587Bi72tlXSdk1iZmYGDw8PYZvExXxID3MiLcyH4fFTJJIQmUwGOzs7scOg/8d8SA9zIi3Mh+FxbImIiIhIIthjRiQhKpUKubm5AAAbGxvIOC9PVMyH9DAn0sJ8GB4/QSIJUSgUiIqKQlRUFBQKhdjh1HrMh/QwJ9LCfBhejeoxCw8Px8SJE4WvbWxsEBYWBmtr61LPy8/Ph7e3N7Kzs4XXNm3ahO7du5d63tmzZxEQECB8vW3bNnTq1KnccWqzsrKCvb09PD09MWjQIPj5+fGhsERERLVEje4xy83NxZEjR8o87ujRozpFmb6CgoJ0vg4ODi53G0/Lz89HQkICjh8/jrlz58Lf3x9xcXGVbpeIiIikr8YWZpaWlgCAPXv2lHms5hjNOfrIz8/HP//8A6CwZw4ADh48iIKCgnLFOX78eISEhAj/bdiwAXPnzkXDhg0BADExMZg+fTqUSmW52iUiIiLjU2MLMx8fHwBAWFgYkpOTSzwuNTUV//33HwBgwIABerd/+PBh5OQULsY6b948AEBmZiZCQ0PLFWeDBg3g7u4u/NezZ09MnDgRf//9N1xdXQEA0dHROHz4cLnaJSIiIuNTYwszb29vODk5QalU4u+//y7xuH379kGhUMDJyQm9evXSu33NsKWHhwfGjBmDFi1a6LxeWXZ2dpg+fbrwdVhYmEHaJSIiIumqsYWZqakphg8fDqD04UzNvhEjRsDU1FSvtpOSknD69GkAgJ+fn87/T506hbS0tArHrU2zmjIAJCQkGKRNIiIikq4aW5gBwMiRIwEAN27cQExMTJH9t27dwvXr13WO1UdISAiUSiVkMhl8fX0BAL6+vjAxMYFcLse+ffsMED1gbm4ubPNRF0RERDVfjS7M2rRpg1atWgEovtdM85q7uztat26td7ua87p16wYXFxcAQNOmTdGxY0cAhhvOvH37trCtmW9mrPJyc4U5eVQyCwsLdO7cGZ07d+YyKRLAfEgPcyItzIfh1ejCDPhfT9i+ffugVquF19VqNUJCQnSO0cfNmzcRFRUF4H/Dlxqar69fv45bt25VKm6lUol169YJXw8ePLhS7Ylt42+r8d1334kdBhERkaTV+MLMz88PMpkMjx49Qnh4uPB6eHg4Hj16pDMcqQ9Nb5iVlVWRYmno0KHC8GNFe83S0tJw+vRpvPrqq7hx4waAwqKsS5cuFWqPiIiIjEeNL8xcXFyEFfy1hzM12z169BCGI8uiUCiE+WP9+/eHnZ2dzn57e3v07dsXQOE8NJVKVWabgYGB8PDwEP7r2bMnJk2ahIsXL8La2hqvv/46li9frld8ZPyUSiUSExORmJjIteskgPmQHuZEWpgPw6vxhRkAjBo1CgBw6NAh5Ofn6ywOW55hzFOnTiElJQVA0WFMDc3rCQkJOj10FeHp6YmAgACdmwCoZlMqlYiLi0NcXBz/kZMA5kN6mBNpYT4Mr1bc6jdo0CB8/vnnyM7OxtGjR6FWq5GTkwMbGxu88MILerejGZ60t7dHnz59ij2mf//+qFu3Lh4/fozg4GD07Nmz1DbHjx+PV155BUDhN3hCQgL++ecf7NmzB5cuXUJAQAB27twJBwcHveOUotemvA2nejZih0FERCRptaIws7W1xcCBAxESEoI9e/YINwEMHDhQeJxSWbKysoRV/TMyMtCuXbsyzzl06BAWLlxY6jU0K/9rtG7dGv3790f37t0xe/ZsxMfHY+7cufj555/1ilOqrG1sYGtrK3YYREREklYrhjKB/w1n/vfff8Iq+prX9HHgwAE8efKkXNfMzc3FoUOHynWOxujRo4WbC0JDQ4UFbYmIiKjmqhU9ZgDQs2dPODk5Cc/NdHZ2LnOYUZtmGNPJyQlz5swp8/hvvvkGCQkJ2LNnT7kKQG0zZ87EkSNHoFQq8cMPP5QrXiIiIjI+taYwMzU1xciRI7Fp0yYAhZP+ZTL9OgwfPHiAixcvAihcukLzqKfSXL58GZs2bcKZM2eQmJio952f2lq0aIGhQ4di3759uHLlCv777z94e3uXux0iIiIyDrVmKBMAZs2ahYiICERERODjjz/W+zzteWn6LvSqOU6lUpX6rM6yTJ06FSYmJgBg9PPMiIiIqHS1qjCrKE1h1aBBA70Xeu3UqROcnJwAAHv37q3wtd3d3eHj4wMAOHfuHM6fP1/htkj6TExMYGtrC1tbW6EgJ/EwH9LDnEgL82F4LMzKcOHCBdy/fx9A4V2c+g5/ymQyDBo0CAAQExODa9euVTiGadOmCdvsNavZzM3N4enpCU9PT65fJwHMh/QwJ9LCfBgeC7MyaA9Dlvd5ldprpFXmwebt27cX5padOnUKV69erXBbREREJF0mau0ne1OtU1BQgIiICABAw2Zu8FtW8WHX0uyfOwYu9lzHjIiIag7t36FeXl6wsLCodJu15q5MImOgUChw7949AECzZs1gZsYfUTExH9LDnEgL82F4HMokkhCVSoWMjAxkZGRApVKJHU6tx3xID3MiLcyH4bEwIyIiIpIIFmZEREREEsHBYBI42Flh/9wxVdK2Y13rKmmXiIioJmFhRgJTmQwu9lZih0FERFRrcSiTBFw5hYiISFwszEigUCjEDoGIiKhW41AmCficM/HJZDI4OzsL2yQu5kN6mBNpYT4Mj4UZCbgwoPjMzMzQtGlTscOg/8d8SA9zIi3Mh+GxvCUiIiKSCBZmRERERBLBsSsSyOVygzyAlSpOLpfj5s2bAIDWrVvD3Nxc5IhqN+ZDepgTaWE+DI+FGQk4x0x8arUacrlc2CZxMR/Sw5xIC/NheBzKJAHvyiQiIhIXu0hIoFSpkJiRY/B2Hetaw5S3URMREZWJhRkJ0rLz4bdsr8Hb3T93DFzsbQ3eLhERUU3DbgwiIiIiiWBhRkRERCQRLMyIiIiIJIJzzIgkxMzMDC1bthS2SVzMh/QwJ9LCfBie0X2K4eHhmDhxYrH7rKysYG9vD09PTwwaNAh+fn6lLpjq4eFRrmt7enpiz549Oq/t3r0bc+bMAQAsWbIE/v7+5WrTx8cH8fHxRV43MzODnZ0dWrZsiV69euGll16Ci4tLudom4yOTyVC/fn2xw6D/x3xID3MiLcyH4dWoocz8/HwkJCTg+PHjmDt3Lvz9/REXFyd2WBWiUCiQkZGBixcvIjAwEMOGDcPhw4fFDouIiIiqkNH1mGkbP348XnnlFeHr1NRUxMTEYN26dUhISEBMTAymT5+O4OBgmJqalthOu3btsGTJkjKvZ2VlZZC4i+Ps7Ix169YJXysUCsTHx2PXrl04duwYsrOzMXPmTOzYsQNt2rSpsjhIXNqraJubm3PRX5ExH9LDnEgL82F4Rl2YNWjQAO7u7jqv9ezZE/7+/vDz80N8fDyio6Nx+PBhDBkypMR2bGxsirRT3czNzYvE0KZNGwwaNAhLly7F77//Drlcjp9//hmrVq0SKUqqanK5HBEREQAALy8vPrtUZMyH9DAn0sJ8GF6NGsrUsLOzw/Tp04Wvw8LCRIym8mbMmCH01p06dQoqlUrkiIiIiKgq1MjCDNCd2J+QkCBiJJVna2sLNzc3AEBubi4yMjLEDYiIiIiqRI0tzMzNzYXtmnALr/b7YY8ZERFRzWT8FUsJbt++LWy7urqKGEnlKRQK3LlzB0BhgWZvby9uQHqygBIAkJebi5z/ryttbfnMTCIiopLUyMJMqVTq3OE4ePDgUo/Pzc1FdHR0me02bNgQdevWrXR85fXnn3/i8ePHAIDOnTsbTQ/gELv7AICNv60WXlu4cKFY4RAREUmecfyG11NaWhqioqKwcuVK3LhxA0BhUdalS5dSz7t27Rp8fX3LbL8iC8hWlGa5jN27d+O3334TXp88eXK1XJ+IiIiqn1EXZoGBgQgMDCx2n7W1NV5++WV89NFH1RxVxcTHx5f6JAITExO8//77eP7556sxKiIiIqpORl2YlcbT0xMBAQE6k+ZL0q1bN2zevLkaoio/Ozs79OjRA6+//nqZPX9k/MzNzeHl5SVsk7iYD+lhTqSF+TA8oy7MtFf+VyqVSEhIwD///IM9e/bg0qVLCAgIwM6dO+Hg4CBypGV7euV/U1NT1KlTB05OTka7kvLB7GcAANtmjoBTPRuRozEOJiYmXKBRQpgP6WFOpIX5MDyjLsyeXvm/devW6N+/P7p3747Zs2cjPj4ec+fOxc8//yxilPopbuV/Y1eAwsdgWdvY8G5MIiIiPdTIdcxGjx4t3IkZGhqK06dPixwRkX5UKhXS09ORnp7O9eokgPmQHuZEWpgPw6uRhRkAzJw5U3hw+Q8//CByNET60axZd+fOHSgUCrHDqfWYD+lhTqSF+TC8GluYtWjRAkOHDgUAXLlyBf/995/IERERERGVrsYWZgAwdepUYeK8McwzIyIiotrNqCf/l8Xd3R0+Pj44evQozp07h/Pnzxe75IS+K/8DwLPPPguZrPh69sKFC3q14ePjYzSPVSIiIqLqU6MLMwCYNm0ajh49CqCw10x7SQoNfVf+B4Bz586V+FimnTt3YufOnWW2ERwczMKMiIiIiqjRQ5kA0L59e3h7ewMATp06hatXr4ocEREREVHxjK7HrHv37oiKiirXOevXry/29fK2Uxx/f/9KPT8zNDS00jEQERFRzWB0hRlRTWZiYiI81sRYn/hQkzAf0sOcSAvzYXgszIgkxNzcHO3btxc7DPp/zIf0MCfSwnwYXo2fY0ZERERkLFiYEREREUkEhzJJ4GBnhf1zxxi8Xce61gZvs6ZSKBR49OgRAKBRo0YwM+OPqJiYD+lhTqSF+TA8foIkMJXJ4GJvJXYYtZpKpUJSUhIAwMXFReRoiPmQHuZEWpgPw+NQJgnUarXYIRAREdVqLMxIoFAoxA6BiIioVmNhRkRERCQRLMyIiIiIJIKFGREREZFEsDAjIiIikggul0ECrj8jPplMBnt7e2GbxMV8SA9zIi3Mh+HxNzEJ+ABa8ZmZmcHNzU3sMOj/MR/Sw5xIC/NheCzMSKBUqZCYkVPh8x3rWsOUfzERERFVGAszEqRl58Nv2d4Kn79/7hi42NsaMCIiIqLahYUZkYTI5XLcvn0bAODm5gZzc3ORI6rdmA/pYU6khfkwPBZmRBKiVquRk5MjbJO4mA/pYU6khfkwPE4IIiIiIpIIFmZEREREEsHCjIiIiEgijH6OWUFBAQ4dOoR///0XV69eRXp6OrKzs2FnZwdXV1d4eXlh8ODB6NGjR5mL3504cQJHjhzBhQsXkJKSgry8PDg4OOCZZ57B888/Dz8/P7i4uJR4/vDhw3Hr1i3Y29vjzJkzpa4LNmrUKNy8eRMA8Pbbb+P9998v8dizZ88iICAAAPDmm29i1qxZpb4PIiIiMk5GXZgdOnQIS5cuRXx8fJF9GRkZyMjIwPXr17F9+3Y0b94cc+bMQb9+/YocGxMTg/nz5+PSpUtF9iUkJCAhIQFnz57F6tWr8dZbb2HatGnFFl2dO3fGrVu3kJGRgVu3bqFVq1bFxp2VlYWoqCjh6/Pnz5f6PrX3d+3atdRjiYiIyHgZbWH2008/YeXKlcLX3t7e8PHxgZubG+rWrYvMzEzcvXsXoaGhCAsLQ2xsLH744Ycihdn58+cxffp0PH78GADQtm1bjB49Gh4eHrCxsUFiYiJOnjyJ4OBg5ObmYsWKFYiJicGyZcuK3BbctWtX/Pnnn0K7JRVmFy9ehEqlgqmpKZRKJa5evQq5XF7ibcYXLlwAUPi4i86dO1fo8yIiIiLpM8rCbNeuXUJR1qBBA6xYsQLdunUrclyvXr0wYcIEREdHY8mSJUhLS9PZn5CQgHfeeQePHz+GiYkJZs2ahTfeeEOnN6xdu3YYMGAAJk+ejHfeeQdRUVH4+++/0ahRoyJDitq9WefPn8f48eOLjV/TA/b8888jPDwcubm5iIiIQKdOnYocq1QqhZ48Dw8P1KlTR5+PiIyUqakpmjRpImyTuJgP6WFOpIX5MDyjm/yfmJiIRYsWAQBsbGywefPmYosybe7u7li3bh3eeOMNndfnz5+PjIwMAMD777+PyZMnlzgvrGnTpvj999/h7OwMAFi3bh2uXLmic0zDhg3h6uoKoPThSc2+bt264bnnniv1+Js3bwprxHTp0qW0t0k1gKmpKVxcXODi4sJ/5CSA+ZAe5kRamA/DM7rCbMOGDcjLywMAvPfee3o/PFUmk2HkyJHC15GRkfj3338BFBZub731VpltNGjQAHPmzAFQuJDe2rVrixyj6TVLSEhAXFxckf1PnjxBREQEgMJCSzM0qRmufBrnlxEREdUeRlWYqdVqBAUFASjsLRs7dmyF29K0AwCvvvqq3pX+4MGDhTszjxw5gszMTJ392r1axfWCXblyBXK5HNbW1mjTpo1QmGnmnT1Nu2BjYUZERFSzGVVhFhMTg/T0dACFd0Da2dlVuK1z584J2/3799f7PFNTU+EGApVKVeROzqfnmT1N81r79u1hZmaG5557Dqampnj8+DGio6OLHK8pzFq2bAkHBwe946wOFlDq/JeXm4ucnJxi/yP9FBQU4MKFC7hw4QIKCgrEDqfWYz6khzmRFubD8Ixq8n9kZKSw3bZt20q1pSmCnJychHlj+mrTpo1OTNp3ejZv3hxOTk5ITk4utjDTFFqanjVbW1t4enri+vXrOH/+PDw9PYVj79y5g9TUVADS7C0bYndf5+uNv60u8diFCxdWdThERERGz6h6zDQT9YHC+V4VlZWVBblcDgBwdHQs9/na52jHpKEZnrx7965QWAG6d1hqL3uh2X66kNMexuTEfyIioprPqAoz7SExa2trg7RjY2NT7vO1z8nOzi6yv6ThzBs3biAnJwempqbo0KGD8HpJhRkn/hMREdUuRlWY2draCtuaOzMr205ubm65z9c+p7h5biXdAKDpAfP09NSJQXN8cnIy7t27V+RcV1dXNGrUqNxxEhERkXExqjlm9vb2wrb2EGF52dnZwczMDAqFAikpKeU+X/sc7Zg03N3dUbduXTx+/FhnOFJTaD29er+joyOaNWuGe/fu4fz582jWrBkSExOF5TakOox5MPsZna+3zRwBp3rl74EkIiKiQkbVY6Y9Mf769esVbsfExAQeHh4ACnupkpOTy3X+jRs3io1JQ/vRSZGRkcJwp6ZIK+6xSk8PZxrDMGYBTHX+s7axga2tbbH/ERERUdmMqjBr1aoV6tevD6CwyClufpe+tHuhQkND9T5PpVLhxIkTAAoLsOIeo6TdvlKpxMWLF3H79m3hkVDF9YBp2tEUZNrLeUi1x4yIiIgMy6gKMxMTE4wePRpA4Tyvv/76q8Jt+fv7C9tbtmyBUqnU67x//vkHCQkJAICBAweibt26xR6nXUxduHBBKLiaNWtW7J2gmh6z+/fvIykpSehdc3JyQosWLfSKjYyfmZkZPDw84OHhATMzo5ppUCMxH9LDnEgL82F4RlWYAcCkSZOEOzJXrlyJ27dv63WeSqXC3r17ha89PT3Ru3dvAEBUVBTWrVtXZhtpaWlYunQpgMIi8c033yzx2LZt2wpxnjt3TugBK24YE9BdQPbIkSOIiYkp9XiqmWQyGezs7GBnZweZzOh+PGsc5kN6mBNpYT4Mz+g+RRcXF8yfPx9AYa9ZQEAAzp49W+o5t27dwptvvlmk+Fq0aJHQ4/X9999jw4YNUKvVxbYRHx+P119/Xegtmzx5svAA8uKYm5sLS2JEREQgPDwcQOmFlmY4c/369UIcUp1fRkRERIZnlP2OY8aMQUJCAlauXInU1FQEBASgd+/e8PHxgZubG+rWrYvMzEzcvXsXJ06cwMmTJ6FUKotM1G/cuDECAwPx9ttvIzs7G0uWLEFISAhGjx4NDw8PWFtbIykpCSdPnkRwcLCwTMawYcPwwQcflBlnly5dcPr0aRQUFCApKQlA6YVZ586dceTIETx48ECnDao9VCqV8H1mY2PDv0BFxnxID3MiLcyH4RllYQYA77zzDlq1aoWlS5ciPj4ep06dwqlTp0o8vlWrVpg1a1aR17t3746tW7di/vz5uHLlCq5du4Zr164V24a1tTWmTJmCt99+GyYmJmXG+HRR1aBBg1Lniz1dtNWrV0+4e5RqB4VCgaioKACAl5cXLCwsRI6odmM+pIc5kRbmw/CMtjADgBdeeAH9+vXDP//8g3///RcRERFIS0tDTk4O7Ozs4Orqiueeew6DBw9G9+7dSyymPDw8sGPHDhw/fhyHDx/GxYsXkZycjPz8fNSvXx/NmzdHnz59MHLkSLi4uOgdX4cOHWBubi48/qmkOzg12rRpA2tra2Hx3E6dOulVABIREVHNYNSFGQBYWFjA19cXvr6+lW6rX79+Og8krywrK6sSe9+KY25ujsuXLxvs+kRERGRcOBhMREREJBEszIiIiIgkgoUZERERkUSwMCMiIiKSCKOf/E+G42Bnhf1zx1T4fMe61gaMhoiIqPYxUZe01D3VCgUFBYiIiADANWiIiIjKoyp+h3Iok4iIiEgiWJgRERERSQTnmJFAqVSKHUKtp1QqkZKSAgBwdHSEqampyBHVbsyH9DAn0sJ8GB4LMxKoVCqxQ6j1lEol4uLiAAD169fnP3IiYz6khzmRFubD8DiUSURERCQRLMyIiIiIJIKFGQnMzDiyTUREJCYWZiQwMTEROwQiIqJajV0kJFCqVEjMyAFQuIq/qYx1OxERUXXib14SpGXnY9jXuzDs611IeZwndjhERES1DnvMiCTExMQEtra2wjaJi/mQHuZEWpgPw2NhRiQh5ubm8PT0FDsM+n/Mh/QwJ9LCfBgehzKJiIiIJIKFGREREZFEcCiTSEIUCgXu3bsHAGjWrBnXlhMZ8yE9zIm0MB+GJ+lPMDw8HBMnTix2n5WVFRwcHNC6dWsMHToUQ4cOLfMb4v79+9i7dy9OnTqF+Ph4pKenw9bWFs7OzujevTuGDBmCLl26lBlXQEAAzp49W+w+MzMz1KlTB25ubujbty9eeukl2Nvbl9jW7NmzERQUVOR1ExMT2NnZoXHjxujcuTNeeukltG7duszYyLipVCpkZGQAAJo2bSpuMMR8SBBzIi3Mh+FJujArTX5+Ph4+fIiHDx/i6NGj2LhxI37++Wc4OTkVObagoADfffcdtm7dCrlcrrMvIyMDGRkZiI6OxubNm9G3b198+eWXaNiwYYXiUigUSE9Px/nz53H+/Hls2LABq1atQufOncvVjlqtRlZWFqKiohAVFYXt27fjrbfewsyZMysUFxEREUmf0RRm48ePxyuvvCJ8nZubi2vXrmH9+vWIj49HREQE3n77bezYsUPnlt3s7Gy8/fbbCA8PBwDY29tj9OjR6NmzJxo0aICsrCxcu3YNu3btwt27d3HixAmMGzcO69evh5ubW5lxhYSE6Hwtl8vx4MED7NmzB6GhoUhNTcX06dNx8OBBODg4lNrWunXr4OzsDKDwr5DU1FQcP34cW7duhUKhwC+//AJnZ2dMmDBB78+NiIiIjIfRFGYNGjSAu7u7zmsdOnSAr68vxo4di3v37uHq1as4duwYfHx8hGPmzp0rFGXdu3fHDz/8gAYNGui007NnT0yaNAnLly/H77//joSEBEybNg1BQUGws7MrNa6nYwKAtm3bYsiQIfj0008RHByMzMxM/PXXX5g6dWqpbTVv3hxNmjTRec3b2xs9e/bE9OnTAQCBgYF4+eWXYWpqWmpbREREZHyM/q7MevXq4a233hK+PnnypLC9f/9+HDx4EADg6emJX3/9tUhRpmFubo7Zs2dj7NixAArnoy1fvrxSsU2ePFnYjoiIqHA7Pj4+wty3tLQ0XL9+vVJxERERkTQZfWEGAO3btxe2Hz58KGz/9ttvwvaiRYtgbW1dZluzZ8+Go6MjAGDXrl1IS0urcFzavV8FBQUVbgco+T0SERFRzVEjCjPtuzGVSiUAIDIyEjdu3AAAdOzYUaewKY2dnR38/f0BAE+ePMG+ffsqHJd2AdWoUaMKtwPovkeVSlWptvSRl5tb5dcgIiIiXTWiMIuOjha2NZPnz58/L7zWv3//crWnPUdNu53yWrdunbA9YMCACrcDFP8eq9LG31ZX+TWoKJlMBmdnZzg7O0MmqxE/nkaN+ZAe5kRamA/DM5rJ/yVRKBT4/fffha+7desGoLDHTKNNmzblarN169aQyWRQqVSIiooq9VjtggkovCszPj4ee/fuxeHDhwEAw4YNw/PPP1+uGLRFRkYKc+esra3h5eVV4bZI2szMzLgWkIQwH9LDnEgL82F4RluYaZbLWLVqFS5fvgwAcHV1xbBhwwAA6enpwrHFrW1WGisrK9ja2iIrK0tYOK8kvr6+Je5r0aIF3nrrLYwePbpc1wcK1zFLSUnB8ePHsXz5cmGINiAgAJaWluVuj4iIiKTPaAqzwMBABAYGlri/QYMG+Omnn2BhYQEAyMnJEfbZ2NiU+3o2NjbIyspCdnZ2+YP9f3fv3sWff/6JJk2aCD15pSlruLNfv3547733KhwPERERSZvRFGYladKkCQYPHozJkyfrLIVha2srbOdWYCK75pyy1jF7eqhTpVIhPT0dFy5cwE8//YTLly/jjTfewHfffYchQ4aUOw5zc3N4eXlh3LhxGDlypM7iuVXptSlvV8t1SJdcLsfNmzcBFA6pm5ubixxR7cZ8SA9zIi3Mh+EZTWGmvfK/iYkJLC0tUb9+fdSpU6fY4+vXry9sJycnw9PTU+9r5efnCz1upT3nsjgymQwNGjTACy+8AG9vb/j7+yM2NhafffYZevToUWp72iv/y2Qy2NjYwNHRUegFrE7WFehlpMpTq9XCY8PUarXI0RDzIT3MibQwH4ZnNLdQaFb+d3d3R6tWrfDMM8+UWJQBgIeHh7Ctqeb1dfPmTWFJCu12ysvW1hbjx48HUDi0+s8//5R6fPPmzYX3+Oyzz6Jx48aiFGVEREQkDqMpzMqra9euwnZoaGi5ztU+XrudimjZsqWw/fQdnERERETaamxh5unpidatWwMALl26pPcjkbKzsxEUFAQAsLS0xPDhwysVh+ZuSqBwaQ8iIiKiktTYwgwApkyZImwvWLAA+fn5ZZ7zzTffIDk5GQAwZswYODg4VCqGa9euCduVXf2fiIiIarYaXZgNHz4cL7zwAgDgxo0bmD59eonPvpTL5fjmm2/w559/AgCeeeYZfPTRR5W6fnx8PLZs2SJ83bdv30q1R0RERDWb0dyVWVGLFy9Geno6zp07h7CwMAwdOhT+/v7o0aMHHB0dkZ2djWvXrmHnzp24c+cOAKBhw4b4+eefy1wqAyg6b0ylUiEjIwPnz5/H5s2bhQVqfX19haFVIiIiouLU+MKsTp06WLduHb799lts27YNGRkZWL9+PdavX1/s8X369MGiRYv0HnYsbeV/jWHDhmHx4sXliptqJzMzM+GGEe0H15M4mA/pYU6khfkwvFrxKVpaWmLevHkICAjA3r17cerUKcTHxyMjIwO2trZwdnZG165dMXTo0ErfhWliYgIbGxs0atQIHTp0wKhRoyrdJtUeMplMZw0+EhfzIT3MibQwH4ZnouaKcLVaQUGBcMdqw2Zu8Fu2FwCwf+4YuNjblnYqERFRrab9O9TLy8sga4/Wih4zImOhvYq2ubl5tT2Ci4rHfEgPcyItzIfh1ei7MomMjVwuR0REBCIiIoR/7Eg8zIf0MCfSwnwYHgszIiIiIolgYUZEREQkEZxjRgIHOyvsnzsGAOBY11rkaIiIiGofFmYkMJXJ4GJvJXYYREREtRaHMknAlVOIiIjExcKMBAqFQuwQiIiIajUWZkREREQSwTlmJOBzzsRnbm4OLy8vYZvExXxID3MiLcyH4fE3MQm4YrP4TExMDPJIDzIM5kN6mBNpYT4Mj0OZRERERBLBHjMS8K5M8alUKmRmZgIA6tWrB5mMfzuJifmQHuZEWpgPw+MnSAIOZYpPoVDgzp07uHPnDu+SlQDmQ3qYE2lhPgyPhRkJlCoVlCqV2GEQERHVWizMSJCWnY+Ux3lih0FERFRrsTAjIiIikggWZkREREQSwcKMiIiISCJYmBERERFJBNcxI5IQExMT4bEmXL5EfMyH9DAn0sJ8GF6VFmbh4eGYOHFisfusrKxgb28PT09PDBo0CH5+fno/1iEuLg4DBw4UFkT97rvv4OvrW+74Hj58iL///hthYWG4d+8e0tLSoFKpUK9ePbi7u6Nz587w9fVF06ZNiz0/NTUVV69exdWrVxEREYGIiAhkZGQAAEaPHo2lS5eWGcPs2bMRFBRU5HUTExPY2dmhcePG6Ny5M1566SW0bt263O+RjIu5uTnat28vdhj0/5gP6WFOpIX5MDzReszy8/ORkJCAhIQEHD9+HBs2bMAvv/yCJk2alHlucHCwzir1wcHB5SrMnjx5guXLl2Pbtm0oKCgosj8pKQlJSUk4deoUVq5ciSFDhuDTTz9Fo0aNdI7r1auX3tcsL7VajaysLERFRSEqKgrbt2/HW2+9hZkzZ1bZNYmIiEhc1VaYjR8/Hq+88orwdWpqKmJiYrBu3TokJCQgJiYG06dPR3BwMExNTUtta+/evQAAGxsb5Obm4vTp00hKSoKzs3OZcaSlpWH69Om4fPkyAMDW1hYjRoxAjx490LBhQ5iZmSElJQUXL17E4cOHERsbiwMHDqBDhw6YNGlSie02btwYLVu2xKlTp8r+MEqwbt064T2oVCqkpqbi+PHj2Lp1KxQKBX755Rc4OztjwoQJFb4GERERSVe1FWYNGjSAu7u7zms9e/aEv78//Pz8EB8fj+joaBw+fBhDhgwpsZ2LFy/i3r17AIDPPvsMCxcuhFKpREhICCZPnlxqDCqVCh988IFQlPXv3x9ff/01GjRoUORYHx8ffPjhh9i7dy+++eabYtt755134OXlBS8vLzg6OiIuLg4DBgwoNYbSNG/evEiPobe3N3r27Inp06cDAAIDA/Hyyy+XWbyScVIoFHj06BEAoFGjRjAz4zRQMTEf0sOcSAvzYXii35VpZ2cnFB0AEBYWVurxwcHBAID69etj1KhR6NmzJwBgz549ZV5r06ZNCA8PBwD07t0bgYGBxRZlGjKZDKNGjcLu3bvh4eFRZP97772H/v37w9HRscxrV4aPjw+6dOkCoLDH7/r161V6PRKPSqUShtJVfDyW6JgP6WFOpIX5MDzRCzMAOkVPQkJCiccVFBTg4MGDAIChQ4fC3Nwcfn5+AICoqCjcvHmz1HPXrVsHALC0tMTixYv1ruwbNmwoFIBi0Z5c+fDhQxEjISIioqoiicJMc6stgFKLpdDQUGRmZgKAUJANGjQINjY2AP7Xm1acU6dOISkpCQAwZMgQuLi4VDbsaqX9uVTVXyX5eXnIy81FTk5Okf+IiIio6kliMPj27dvCtqura4nHaQqvZ555Bh07dgRQeAPAgAEDEBISgn379uGTTz4pdv7VuXPnhO1+/foZJvBqFB0dLWzrc5NDRezcugn5+fnF7lu4cGGVXJOIiIj+R/QeM6VSKQwxAsDgwYOLPS4tLU244/HppTE0vWcpKSkl3hUZGRkpbLdt27ZSMVe3yMhInDx5EgBgbW0NLy8vkSMiIiKiqiBaYZaWlobTp0/j1VdfxY0bNwAUFmWaSe5P27dvH+RyOYD/FWIa3t7ecHJyAlDycKZm4VcApU74lwq1Wo3k5GT89ddfmDRpEpRKJQAgICAAlpaWIkdHREREVaHahjIDAwMRGBhY7D5ra2u8/PLL+Oijj0o8X1NwtW/fHs2bN9fZZ2pqimHDhmHjxo0IDQ1FdnY27OzsdI7RnidlbW1dsTdRxcpaaqNfv3547733qikaIiIiqm6SmGPm6emJgIAAnZsAtMXExAhLRDzdW6bh5+eHjRs3Ij8/HwcOHMDYsWN19tva2grbeXl5RQo3qTI3N4eXlxfGjRuHkSNHVumzyF58ZSJMTc3gVM+myq5BpZPJZLC3txe2SVzMh/QwJ9LCfBieKCv/K5VKJCQk4J9//sGePXtw6dIlBAQEYOfOnXBwcChyrqa3zMzMDMOHDy+2/Xbt2sHNzQ23b9/Gnj17ihRmmm8coHAumhQLM+2V/2UyGWxsbODo6Kj3M0Qry8raGmZm5jpFLFUvMzMzuLm5iR0G/T/mQ3qYE2lhPgxPtJX/W7dujf79+6N79+6YPXs24uPjMXfuXPz8888656lUKoSEhAAoXGFYn/XEzp8/j7i4OJ1V9D09PYXFa2/cuFFkOFQKilv5n4iIiGoP0fsdR48eLdyJGRoaitOnT+vsP336NBITE8vVplqtLvIkgK5duwrbx48fr1iwRERERFVIEnPMZs6ciSNHjkCpVOKHH37Q6RXTDGNaWFhg8eLFZY5hr127Fjdu3MDevXvxzjvvCK/37t0bzs7OSEpKwsGDB/HRRx8Z3SKzVPPJ5XJhXT83N7cS511S9WA+pIc5kRbmw/AkUZi1aNECQ4cOxb59+3DlyhX8999/8Pb2Rk5ODo4cOQKgcEmMp9cvK05ycjJu3LiB2NhYXLp0SViI1sLCApMnT8aSJUvw5MkTfPbZZ1izZo1eDwNPTEzEnTt3RH8sE9V8arVauINYrVaLHA0xH9LDnEgL82F4og9lakydOlW441Azz+zQoUPIzc0FUPLCs08bPHiw0M7Ta5pNnDgR3bt3B1D4iKZ33nkHaWlpJbalVqsREhICf39/REVFlev9EBEREZWXJHrMAMDd3R0+Pj44evQozp07h/PnzwuFlbm5OXx8fPRqp1GjRvDy8sLVq1dx4MABzJ07V7irUSaTYcWKFZg2bRquXLmCY8eOYeDAgfD19UWPHj3QsGFDmJmZISUlBZcvX8ahQ4dw586dEq91/vx53L9/X/g6PT1d2L537x52796tc7y/v7++HwcRERHVQpIpzABg2rRpOHr0KABg1apVOHv2LACge/fuqFevnt7tDB48GFevXkVmZiaOHTum09vm4OCAzZs347vvvsO2bduQk5OD7du3Y/v27cW2ZWJiAl9fXwwdOrTIvp07dyIoKKjY8y5evIiLFy/qvMbCjIiIiEojmaFMoHBVf29vbwDAmTNnoFKpAOg/jKmhfXxxj2iytLTE3LlzcejQIXz00Ufo1asXGjduDCsrK1haWsLZ2Rm9e/fGzJkzcfToUXz77be8UYCIiIiqnImas/VqtYKCAkRERAAAGjZzg5mZOVzsucCsWLTz4eXlVW2LC1PxmA/pYU6kpbbnoyrev6R6zIiIiIhqM0nNMSOq7UxNTYWnP+izlAtVLeZDepgTaWE+DI+FGZGEmJqacj6jhDAf0sOcSAvzYXgszEjgYGdV6+YHEBERSQnnmJHAVCaDaRmPvCIiIqKqwx4zEvAGXfHV9jucpIb5kB7mRFqYD8Nj9wgJFAqF2CEQERHVaizMiIiIiCSChRkRERGRRLAwIyIiIpIIFmZEREREEsHCjIiIiEgiWJgRERERSQTXMSMBn3MmPjMzM3h4eAjbJC7mQ3qYE2lhPgyPnyIJZFz1X3QymQx2dnZih0H/j/mQHuZEWpgPw+NvYiIiIiKJYI8ZCVQqldgh1HoqlQq5ubkAABsbG/Ziioz5kB7mRFqYD8PjJ0gCpVIpdgi1nkKhQFRUFKKioviILAlgPqSHOZEW5sPwWJgRERERSQQLMyIiIiKJYGFGREREJBEszIiIiIgkgoUZERERkURwuYxaTq1WC9tyuRwFBQUiRkNyubzYbRIH8yE9zIm01PZ8aL9n7d+nlWGiNlRLZJRycnIQGRkpdhhERERGzdPTE7a2tpVuh0OZRERERBLBHrNaTqVSIS8vD0DhA2hNTExEjoiIiMg4qNVqYWFda2trgzz5gIUZERERkURwKJOIiIhIIliYEREREUkECzMiIiIiiWBhRkRERCQRLMyIiIiIJIKFGREREZFEsDAjIiIikggWZkREREQSwcKMiIiISCJYmBERERFJBAszIiIiIolgYUZEREQkESzMiIiIiCSChRkRERGRRLAwIyIiIpIIFmZEREREEsHCjIiIiEgiWJgRERERSYSZ2AFQ2eLj47F582YcP34cCQkJsLCwQNOmTTF06FBMmDAB1tbWBrnOiRMnsGPHDkRERCAtLQ0ODg7w8vLCSy+9hL59++rVhkKhwF9//YWQkBDcuXMHubm5cHZ2Rq9evRAQEIBWrVoZJFYxGUM+UlJScOzYMZw5cwY3b97Eo0ePIJfLYW9vDw8PD7zwwgsYOXIkrKysDBKrmIwhHyWJjIzEmDFjoFAoAACjR4/G0qVLDRKvmIwtJ3K5HCEhITh48CCio6ORkpICW1tbODs7o3379ujduzeGDh1qkJjFYEz5uHTpEnbs2IGLFy8iKSlJ59+twYMHY9SoUbCwsDBIvFJlolar1WIHQSULDQ3FrFmzkJ2dXez+5s2bY82aNWjWrFmFr6FSqTB//nzs3LmzxGPGjh2LL7/8EjJZyZ2saWlpeOuttxAREVHsfgsLCyxYsABjx46tcKxiM4Z87NixA59//jmUSmWp12nevDl+/PFHeHp6VjhWsRlDPkprd9y4cbh69arwWk0ozIwtJ5GRkZg1axaio6NLPKZOnTo4f/58heMVk7HkQ61W4+uvv8bmzZtLvVarVq2wZs0aNG7cuMLxSh0LMwm7ceMGxo8fj/z8fNjY2GDq1Kno3r078vPzsX//fuzYsQNA4Q/Wrl27YGdnV6HrLF++HGvWrAEAtGnTBm+++SaaNm2KBw8eYO3atbhx4wYAYOrUqfjwww+LbUOpVCIgIAAXLlwAALzwwgsYO3Ys7O3tceXKFfz8889ITU2FTCbDL7/8UuEeBjEZSz5Wr16NH3/8Eebm5ujfvz+8vb3h5uYGW1tb3L9/H3/99RdOnToFAHBwcEBQUBAaNmxYoVjFZCz5KMmmTZvw9ddfo0GDBkhNTQVg/IWZseUkMjISr732GjIyMmBpaYmxY8fC29sbLi4ukMvliI2NxcmTJ3HhwgUcP368QrGKyZjy8euvv+L7778HANja2uL1119Hp06dYGNjg7t37+L3338Ximd3d3cEBQXBzKyGDvqpSbJeeeUVtbu7u7pNmzbqixcvFtn/22+/qd3d3dXu7u7qlStXVugad+7cUbdp00bt7u6u9vf3V+fl5ensz83NVfv7+wtxxMbGFtvOX3/9JcTy+eefF9kfGxur7tSpk9rd3V09aNAgtVwur1C8YjKWfPz+++/qb7/9Vp2amlridZYsWSLEOnv27ArFKjZjyUdxHj16pO7YsaPaw8NDHRQUJMT56aefVihOqTCmnOTn56tfeOEFtbu7u7pfv37qu3fvlnjNJ0+eVChWsRlLPgoKCtRdunRRu7u7q9u2bau+ceNGkWPkcrl67NixQrwHDhyoULzGgJP/Jerq1atC1/mYMWPQsWPHIse88cYbcHNzA1D417dcLi/3dTZu3CjMb5k/f36ROUfW1taYP38+gML5Yxs2bCi2nfXr1wMA7O3t8cknnxTZ36xZM0ydOhUAcO/ePRw+fLjcsYrJmPIxadIkfPzxx3BwcCjxOh9++CGcnJwAAIcPH4ZKpSp3rGIypnwU54svvkBOTg5Gjx6NLl26lDsuKTK2nKxbtw6xsbGQyWRYsWIFmjdvXuI1jXFOkzHl4/bt23j8+DEAoH///mjdunWRY8zMzITfIQBw+fLlcsdqLFiYSdSRI0eE7TFjxhR7jEwmw6hRowAAjx8/Rnh4eLmuoVarcfToUQBAy5Yt0aFDh2KP69ChA1q0aAEAOHr0KNRPjX7fvXsXt2/fBgAMGTKkxImko0ePFra1358xMKZ86MPCwgKdOnUCAGRlZSEjI6PcbYjJmPNx8OBBhIaGlvhHjLEyppwolUps374dANCrVy8899xz5YrDGBhTPrQLwiZNmpR4vWeeeabYc2oaFmYSpZmrZWNjg7Zt25Z4XNeuXYXtixcvlusacXFxSEpKKtJOcbp16wYASExMRFxcXLGxah9XHCcnJ+Gv0vLGKjZjyoe+CgoKhO3yTFqXAmPNR1ZWFr766isAwKxZs1C/fv1yxSRlxpSTS5cuITExEQDg4+MjvP7kyRPcv38fiYmJZd48I3XGlI/mzZvDxMREaLMk9+/fF7Y1hV5NZFz/Gtcimh6oZ555ptQJji1btixyjr5u3bpVbDtlXefOnTvFxlqedh49eoTc3Fy9YxWbMeVDH3K5XBgKcHR0hL29fbnbEJOx5uPbb79FcnIyunTpUmIvhrEyppxoD4O5u7sjNjYWM2bMQOfOnTFo0CA8//zz6N69Oz755BOdYsCYGFM+6tSpg+HDhwMAjh07hsjIyCLnKxQK4QYD7eNrIhZmEvTkyROkp6cDQJl3y9WrVw82NjYAgISEhHJdR/v4sq6jvf/Ro0cltuPi4lJqO40aNQJQ2AVe3njFYmz50MeOHTuE9zRkyJByny8mY83HhQsXsGPHDpibm+Pzzz8XeghqAmPLiXYBcufOHYwePRqHDh3SGR7LysrCnj17MGrUKISFhZUrTrEZWz4AYM6cOWjbti3kcjkmTJiAwMBAhIWF4fLlywgKCsKYMWNw+fJlWFtbY9myZTWqt/lpNfReU+OWk5MjbGt+YEpjbW2N3NzccvdAlec62vPGnr6Odju2trYVbkeqjC0fZXnw4AF++OEH4TpvvfVWuc4XmzHmo6CgAAsWLIBarcakSZNqxELL2owtJ9pzKr/++ms8efIEkyZNwiuvvIJGjRohMTER27Ztw/r165GTk4P3338fe/bsMZq1s4wtH0Bhz/2WLVuwY8cOrFmzBqtWrdLZb2JigrFjx+L1118XblioqdhjJkFPnjwRts3Nzcs8XnPHUH5+fpVdR/uupKevY6h2pMrY8lGavLw8vPvuu8jKygJQeBdVWb2cUmOM+VizZg1u3boFV1dXvPPOO+WKwxgYW07y8vJ02nzvvfcwZ84cNGvWTFgV/5NPPsHMmTMBFE6M//XXX8sVq5iMLR8aZ86cwd69e5GSklJkn+ZGg6CgIJ35sTURCzMJsrS0FLb1ufNE801a3sfrlOc62j8IT1/HUO1IlbHloyQKhQLvv/++MH9j/Pjx8Pf3L1eMUmBs+bhz547wS33+/PkGe/yNlBhbTrTbqV+/PqZMmVJsG5MnTxaWlTl48GCF7oAWg7HlAyhcdmP69Om4du0aunbtit9//x0XLlxAREQE9u/fjzfeeAOZmZn47bff8Nprr+n01tU0LMwkSHs4UJ+uZc1ff/p0WVf0Otp/YT59He12yvphKa0dqTK2fBRHrVZj9uzZOHHiBABg6NChWLBgQbnikwpjyodarcbChQtRUFCAQYMGoX///uWKwVgYU06ebqdbt24lrlNmZmaGHj16ACgc/nzw4EG54hWLseUjMjISS5cuhVqtRq9evbBx40b06tULdnZ2sLCwgJubGz799FN8+eWXAArvHn16qLMm4RwzCbK0tIS9vT0yMjLKnIyZmZkp/ECU97E62seXdR3t/ZoJ/MW1k5iYWOrCpppJnyYmJkbzGCBjy0dxvvjiC4SEhAAAnn/+eXz77bdGt0SGhjHl4/Llyzh79iwAoGPHjvj777+LnJuWliZsx8XFCce0atUK7u7u5YpZLMaUk6fbKevnR3t/WlqazlpaUmVs+di9e7ewyPWMGTNgampabBsvvvgifvvtN8TGxiIoKAiffvppjbqJRoOFmUQ9++yzOH/+PO7fvw+FQlHi7c7atx2Xd0Lks88+W2w7ZV3n6duita97586dYldtfrqdRo0aGU2PGWBc+Xjat99+i23btgEoXGto1apVes07kTJjyYf28M0333xT5jXPnTuHc+fOAQDeffddoynMAOPJCQCdmy/KWq9Me39JBYMUGVM+tO+SLW3NNaDwWZyxsbHIyMhAamoqHB0dyxOyUTDOP5lrgc6dOwMo7B6+fv16icdp/hEHIKzkrq8mTZrA2dm5SDulXcfFxaXIysyaWAEIvQPFSU5ORmxsbIViFZsx5UPb6tWrsXbtWgCAl5cXfvnlF6OZ21caY81HTWZMOdF+DFZZCzRrD18a040yxpQP7aJR83inkmjvN6ZCuTxYmEnUwIEDhe1du3YVe4xKpUJwcDAAoG7duujevXu5rmFiYoIBAwYAKPxrpqRnj12+fFn4a2fAgAFFuo5btGgh/KV18OBBnbkE2oKCgoRt7fdnDIwpHxobN27Ejz/+CKBwEc21a9fCzs6uXDFJlbHko3v37oiKiir1P80jbYDCx5ZpXp8xY0a54hWbseQEAJo2bYo2bdoAAMLDw4W7lJ+WnZ0trGH2zDPPCEWIMTCmfGgXaprnexZHLpfj0qVLAAoXmTW2hbH1xcJMotq3by/8Vbdr1y7hm1Hb+vXrhS7giRMnFhmeCg8Ph4eHBzw8PDB79uxir/Paa68Jf3UsWrSoyG3M+fn5WLRoEYDCv2pee+21Ytt54403ABROkP3222+L7L9//75wZ1qzZs0waNCg4t+4RBlbPnbt2oUlS5YAKHzcye+//16j/hEztnzUBsaWE82dmLm5uVi8eHGxxyxduhTZ2dkAgJdffrnYY6TKmPKhfVPM8uXLhc/8aatWrUJycjIAoG/fvjVyfhnAOWaSNnfuXIwfPx75+fl44403MG3aNHTv3h35+fnYv38//vzzTwCFv3hff/31Cl2jRYsWmDx5MtasWYNr165h/PjxmDJlCpo2bYoHDx7gt99+w40bNwAU3jquedbl00aPHo1du3bh4sWL2LJlC1JSUjB27FjUq1cPV69exerVq5GdnQ2ZTIa5c+eW+ogQqTKWfBw5cgTz58+HWq2GnZ0d5s6di7S0NJ1J5k9r0qSJUc35A4wnH7WJMeVk2LBhCA4OxokTJ7B7924kJSVh/PjxaNy4MR49eoQ///xTuIu5TZs2ePXVVysUr5iMJR+9e/dGjx49cObMGURFRWHkyJGYOHEi2rdvD0tLS9y7dw+7du3CyZMnARTe1VkT1wPUMFEby8IstVRoaChmzZpV4l8QzZs3x5o1a9CsWbMi+8LDwzFx4kQAhYXT0qVLi21DpVJh3rx5JXZ3A4V3wyxatKjUO/nS0tLw1ltvISIiotj9FhYWWLBgAcaOHVtiG1JnDPmYPXu2zrCxPjZt2lTuYQwpMIZ8lCUuLk4YDiotDmNhTDnJycnBe++9h1OnTpV4jJeXF37++WdhPTNjYyz5yMzMxIwZMxAeHl7q+3FwcMDy5cvRq1evUo8zZsbXbVHL+Pj4YO/evdi0aROOHz+OxMREmJub45lnnsGQIUPw6quvVnrBSplMhsWLF2Pw4MH4888/ERERgfT0dNSvXx9eXl4YN24c+vbtW2Y7Dg4O2L59O3bs2IF9+/bh9u3byMvLg7OzM3r27ImJEyca/aNojCkftQHzIT3GlBNbW1usXbsW+/fvR1BQECIjI5GRkYE6deqgdevWGD58OEaNGmXUk8yNJR/16tXDxo0bcfToUezbtw8RERFISUmBUqlEnTp18Oyzz+L555/H2LFja9S0jOKwx4yIiIhIIjj5n4iIiEgiWJgRERERSQQLMyIiIiKJYGFGREREJBEszIiIiIgkgoUZERERkUSwMCMiIiKSCBZmRERERBLBwoyIiIhIIliYEREREUkECzMiIiIiiWBhRkRERCQRLMyIiIiIJIKFGREREZFEsDAjIiIikggWZkREREQSwcKMiKgaPHz4EAsWLMDAgQPh5eUFDw8PeHh44MiRI2KHRlQhs2fPhoeHB3x8fMQOpUYxEzsAosoKDw/HxIkTAQDvvvsuZsyYIXJERLoePnwIf39/pKenix1KuWj/bD3NysoK9vb28PT0xKBBg+Dn5wcLCwu921YqlTh06BCOHj2KiIgIpKSkQC6Xw9HRES1btkS/fv3g5+eHunXrlivmgoICHDp0CP/++y+uXr2K9PR0ZGdnw87ODq6urvDy8sLgwYPRo0cPyGTsmyDpYWFGRAAADw8PACxuq8LPP/+M9PR0mJmZ4YMPPkDXrl1hY2MDAGjcuLHI0VVMfn4+EhISkJCQgOPHj2PDhg345Zdf0KRJkzLPPX/+PBYuXIhbt24V2RcfH4/4+HicPHkSq1atwkcffYSXXnpJr5gOHTqEpUuXIj4+vsi+jIwMZGRk4Pr169i+fTuaN2+OOXPmoF+/fnq1bcz4s21cWJgREVWxsLAwAMCAAQMwZcoUkaOpmPHjx+OVV14Rvk5NTUVMTAzWrVuHhIQExMTEYPr06QgODoapqWmJ7Rw8eBCzZs1CQUEBAKBbt27w8/NDixYtYGFhgYcPHyI0NBR///03MjIyMH/+fNy9exeffvppqfH99NNPWLlypfC1t7c3fHx84Obmhrp16yIzMxN3795FaGgowsLCEBsbix9++KFWFGZVZenSpVi6dKnYYdQ4LMyIiKpYYmIiAKBFixYiR1JxDRo0gLu7u85rPXv2hL+/P/z8/BAfH4/o6GgcPnwYQ4YMKbaNGzdu4OOPP4ZcLoe5uTmWLFkCX19fnWPat2+PIUOG4PXXX8e0adOQkJCA9evXo2nTpjqFobZdu3YJRVmDBg2wYsUKdOvWrchxvXr1woQJExAdHY0lS5YgLS2tIh8FUZXiADsRURWTy+UAADOzmve3sJ2dHaZPny58rekdfJpKpcInn3wifBaLFy8uUpRpa926NTZs2CAM+S5btgwPHz4sclxiYiIWLVoEALCxscHmzZuLLcq0ubu7Y926dXjjjTdKf3NEIqh5/0oQPUV7AvOmTZvQrVs37Ny5E7t378adO3cgl8vRsmVLvPrqqxg1apRwXkFBAXbt2oWgoCDExsZCLpfDw8MDEydOxLBhw4q9VlxcHAYMGAAAWLJkCfz9/XHgwAH8+eefiIqKQk5ODho3boxBgwZhypQpZU5sLigowF9//YWDBw8iJiYG2dnZqFevHtq0aYMRI0bA19e3xAnMs2fPRlBQEFxdXREaGoqkpCRs3LgRx48fx8OHD5Gbm4tNmzZhzpw5OnNyAgMDERgYqNPW6NGjdYYskpKScPjwYYSHhyMyMhJJSUlQKBSoX78+2rVrB19fXwwZMqTE2J7OSffu3bF//37hc8rNzUWjRo0wYMAAvPXWW7C3ty/1cwKAEydOICQkBJcuXUJqaiqUSiWcnJzQtm1b9O/fH0OHDoW1tXWx516/fh1//vknwsPDkZSUBLVaDRcXF/To0QOTJk2qUE/X7t27MWfOHJ3Xnv5sn/5cASAtLQ0bN27EiRMnEBcXhydPnsDJyQmdO3fGuHHj0KVLlxKv6ePjg/j4eKHda9eu4Y8//sDZs2eRlJQEuVyOqKiocr+XsmjmMAFAQkJCsceEhoYiJiYGAPD888/Dz8+vzHZbtGiB6dOnY/ny5cjPz8emTZswe/ZsnWM2bNiAvLw8AMB7770HNzc3vWKWyWQYOXKkXsdqq85/T7QlJyfjjz/+wMmTJxEXF4fc3Fw0aNAAHTp0wLhx49CrV68i52i+HzTK+tl++r117doVu3fvxp49e3D79m2kpaVh1KhRwvFP/xtTkuzsbOzYsQPHjx/H7du3kZmZCQsLCzzzzDPo3Lkzhg0bhs6dO5f5GdQWLMyoVlEoFJg+fTqOHTum83pERAQ+/fRTXLt2DfPmzUNmZibeeecdnDt3Tue4S5cu4dKlS7h//z6mTZtW5vU+++wz7Nq1S+e1u3fvYs2aNQgODsaGDRtK/EUSFxeHKVOm4M6dOzqvp6Sk4N9//8W///6LP//8E6tXry6zcLl8+TKmTZtmkLsClUol+vbtC5VKVWRfUlISQkNDERoaip07d2LVqlWwtbUttT2VSoVZs2Zh7969Oq/HxsZi3bp1OHLkCLZs2QInJ6diz09PT8fMmTNx+vTpIvs0E8kPHToEAPD39y9y7WXLlmHjxo1Qq9VFrh8bG4udO3diwYIFGDduXKnvwxBOnTqF999/H9nZ2Tqva97H3r17MWHCBMybN6/MOwq3bduGr776CgqFoipDBgCYm5sL2yX1CgYHBwvbr732mt5tv/zyywgMDMSTJ08QFBSETz/9FCYmJgAAtVqNoKAgAIW9ZWPHjq1A9BVXXf+e7N27FwsXLkRubq7O6wkJCTh48CAOHjyIF198EV988YXBemWfPHmCyZMnl9gDqq+wsDB8+OGHRf7tkcvluHnzJm7evIk//vijSv5gMFYszKhW+fHHH3HlyhX4+vrC19cXjo6OiI2NxapVq3D37l1s3rwZPj4+2Lx5My5duoTx48dj0KBBsLe3x82bN/Hjjz8iKSkJK1euxIABA9CqVasSr7V161ZERESgffv2mDRpEpo1a4bU1FQEBQXhwIEDSEpKwptvvomQkBDY2dnpnJuTk4NJkybhwYMHAICBAwdizJgxcHZ2RlxcHLZs2YKzZ8/iwoULmDZtGrZs2VLihOucnBzMmDEDT548wbRp0+Dt7Q0rKytER0fDyckJ69atg1wuF4aVnp7kDQD16tUTtjUFTI8ePfD888/D3d0dDg4OyMnJwYMHD/DXX3/h0qVL+O+///Dll19i2bJlZebk0qVLGDhwIEaNGoXGjRsjJSUFW7duxfHjx3Hv3j0sWbIE33//fZFz8/LyMHHiRERHRwMA2rZti3HjxqFVq1awsLBAQkICzp07h/379xd77UWLFmHr1q0AgK5du2L06NFo2rQprKysEBUVhY0bNyImJgYLFiyAo6Oj0Buqj4EDB6Jdu3YAUOJnq/253rx5E9OmTRPmX02YMAE+Pj6wtrbGzZs3sWbNGiH31tbWmDVrVonXjoiIwN69e9GwYUNMnjwZ7dq1g0KhwIULF/SOvzxu374tbLu6uhZ7zPnz5wEA1tbW6Nmzp95t161bF127dsWpU6eQkZGBW7duCT93MTExwi/8zp07F/k5qmrV8e/J/v378cknn0CtVqNp06Z49dVX4ebmBgcHB8THx2Pnzp04ceIEdu7cCTs7O51e2vL+bGv77rvvEBUVBR8fH/j7+ws/lzk5OXp/PmfOnMGUKVOgUChgamqKkSNHYsCAAWjUqBGePHmC27dv499//y1S2NZ6aiIjd+bMGbW7u7va3d1dvXLlylL3u7u7qzds2FDkmKSkJHXHjh3V7u7u6h49eqg9PDzUhw8fLnLczZs31Z6enmp3d3f1okWLiux/8OCBzrWmTJmilsvlRY4LDAwUjlm2bFmR/UuXLhX2//DDD0X2q1Qq9UcffSQcs2XLliLHfPrpp8L+Dh06qG/evFnkGG2lfYZPXzs2NrbUY3788Ue1u7u72sPDQ3337t0i+5/OyerVq4u9zhtvvKF2d3dXt2nTRp2amlrkmMWLFwttfPHFF2qVSlVsPE+ePFEnJyfrvHbq1Cnh3B07dhR7Xn5+vnrixIlqd3d3df/+/YvNpT70+WzHjBmjdnd3V7du3Vp98uTJIvszMjLUw4YNU7u7u6s9PT3V0dHRRY7p37+/cK0RI0aoMzMzKxSvRlk/W2q1Wq1QKNSjRo0Sjjt37lyRYxISEoT9L730Urnj+O6774Tz9+7dK7y+Z88e4fXvv/++3O1WRHX+e5Kamqru3Lmz2t3dXT1nzpwSv/++//574fvi9v+1d+9BUZVvHMC/i3EdlItcrQZTITYIQixsoAQiGSk0ImYggkDtilHgTGlO4mhTxkxTQQnjjBYpAUEMJI7tICAKAwbBsIDmDpcQLJJLLFddgf39sbPvHNgLu7CL68/n89cZzruHs3t2zz77vs/zvp2dCvs1/WzPf27K7j1c8ntMUFCQwr5bt25JAwICpG5ublJvb29pfX29yuP8/fffav/P/YaS/8l9xdvbW+kwir29PZ5//nkAshyfbdu2ISQkRKGdu7s7y4VYqPfBxMQER44cUTq08M4777AKt19++YVNHQDIclGKiooAAK6urkrnHeLxeDh06BAbwszNzVV7Lrt374a7u7vaNpri8XhwcXFR2yYpKQk2NjaQSqVqc08AWS+XsmEcHo+HhIQEALIho+bm5jn7R0dHUVBQwI5x4MABNsQ1n4mJCezs7Ob87fjx4wCA0NBQlUNgpqamOHjwIADZcOLly5fVPpfFEgqFaG1tBQBERUUhICBAoY2VlRUOHz4MQDYEK+/pUyUtLU3ryVm1MTw8jLq6Orz22mu4cuUKANlrqSwHjjuMNf86aGL16tVse2RkROk2t81y0ff9JC8vD2NjY3B0dMShQ4dUDlO+9957cHR0xOzsLEpLS5fylJi1a9cuac6zkpIS3Lx5EwCQkpICPz8/lW2dnZ0X/X/+H1FgRu4r6pJsuYHLCy+8sGA7+TCjKv7+/nB0dFS6z8jIiCUGj4yMsC82AGhra8Po6CgAWWKuqiFKS0tLbNu2DQDQ0dHBboLKaJJovVizs7P4999/0dXVBZFIBJFIhM7OTjg5OQEA/vzzT7WPDw8PVxlQeXh4sO35r3d9fT1L+o6Pj1c7d9Z84+Pj+P333wHIggl11q9fDxsbGwBQCA51hZvH88orr6hs5+vry3IS1eX+ODs7qy0SWIxvv/2WLSP16KOP4umnn0ZCQgKamppgbm6OxMREfPnll0ofy82Zk1dZaoObp8g9FndYTVVhhz7p+34i/1ETGBiodlWFBx54AE888QQA3b1Hw8LCtPpMzScfnrSwsNB4gmAiQzlm5L6irrpu5cqVbHvt2rULtlso1+Lxxx9Xu9/Ly4tti0QidmOVV64Bsl/k6nh7eyMvL489zsHBQaGNhYUFHn74YbXH0ZZUKsWvv/6KoqIiCIVC3Lp1S2XbhQoO1q1bp3Ift6hh/uvNDWa1rei6cuUKK15ITU1FamqqRo8bHBzU6v9oSn7NjY2Nwefz1bb19vZGZ2cnenp6IJFIlH5hc6skl4O7uzvi4uLmFAFwcXO/5iewa4J77bnH4gZs8iB9OenzfjIzM8N+1BQUFLDe4YXo6j261PfQ1atXAch+XN2NoPleRoEZua+YmZmp3MetctOknbKqRK6FhlZUDc+IxWK2bWtrq/YY3GEh7uO4dD2cdfv2bezZswcXL17UqL26oA3Q/JrMf725AZ+ygFSdoaEhrdrLLfRcFkt+/a2trResqpNfc6lUitHRUaVDg/oYwuQmjs/MzKC/vx8CgQClpaVobm5GXFwcioqKlL5n5T2OwOICB+714gbr3O3FXtOl0Of9RCwWL6qiVlfvUVVFAZqSfz5VVVMT1SgwI0RPVA3PLfcxljIcoUxWVhYLyp566im8+uqr8PDwgJ2dHczMzNgXTWxsLKvEMzTcL8HDhw/Dx8dHo8ct9ctquej6mgOKM//z+XwEBQXBz88P+/btw40bN3DgwAFkZWUpPNbBwQE2Njb477//cO3aNczMzGh1jtzeUe4QIXe7vb1d26dk0GZmZth2VFSUysXk51PVa6ktWuD97qHAjBA9WahnQFUvAPfLf2hoSO1wCfd/LEfQIJVKWWHCpk2bkJOTo/IGrqoHT1e4vTA3b97UariW+3qbmZkpLDW03OTnMzIygunpabW9ZvJrzuPx9Jrcr6mIiAhUVVVBIBCgsrISdXV1CtNh8Hg8+Pr64vz585iamkJdXZ3SAgdlxsbGWIBvbW2NDRs2sH2urq4s4Pvjjz8wPj6+7FNm6Mv8KWru9ntUWzY2Nujv78fAwMDdPpV7DoXEhOiJvMpOk/3c+Yu42y0tLWqPIRQKlT5OX0ZGRtiNVt3M/hMTE+ju7tbruXALA7TtmePz+aw3sqmpSafntRjyayefdFMd+TV3cXFRmxC+nFJSUlgP2FdffaW0TUREBNs+deqUxscuKChgw3MRERFzepF5PB477uTkJAoLC7U+d0NlYmLC3heG8B7V1mOPPQZAVsx0N/L/7mUUmBGiJ7W1tSorJWdnZ9mM5VZWVnOCDE9PT9YTUlJSojKXbXx8HOfOnQMAbNiwQes8q/lMTU0BYM7UHfNxh1fU3WwLCwv1PuO8n58fq/A7derUnHNbiK2tLSu2KCsru+uLWXOX05m/UgRXc3MzOjo6FB5ztz3yyCOsQrilpQW1tbUKbYKDg1lF6YULF3D27NkFj9vT04PvvvsOgKxnU9lwXkJCAksuz8jImDPZrTqzs7MKq00YmuDgYABAV1cXLl26tOjjaPLZ1jX5uU9NTWlcuEBkKDAjRE8kEgkOHjyoNGA4fvw4m60+MjJyTs+HiYkJmzJBJBLh2LFjCo+XSqU4cuQIS7CNjY1d8vnKk3SvX7+uso2trS0LGsvKypTe6IVCIb755psln89CVq1axZZJam9vx2effaawrJLcnTt3FJLD5Qtvj4+PIzk5mU1RooxEIkFubi5u376to7Ofy8vLi60SUFhYqHR5qbGxMTanmpGREWJiYvRyLov11ltvsd4sZXlmRkZGSE9PZzlQ+/btU7kiAyCbZiUhIYFVcX700UdYs2aNQjtHR0d88sknAGS9ZnFxcWwqFFU6Ojqwe/dunDhxQrMnd5fEx8ezHx/79++fU7GtzIULF5ROT6PJZ1vXtm/fzqYL+vrrr9VeE1Xrq96vKMeMED3x9PREVVUVYmJi5izJVFJSwnoLnJyc8O677yo8NikpCeXl5ejt7UVmZiZEIhFefvll2Nvbo6+vjy1MDQA+Pj46WcfRx8cHfX19qKysRH5+PjZu3Mh+aVtaWmL16tUwMjJCeHg4cnNzce3aNcTExCAxMREuLi4YHx9HdXU1fvrpJ1hYWMDBwQF//fXXks9Lnffffx+1tbUQiUQ4ffo0mpubER0dDTc3NxgbG6O/vx+NjY04e/YsPvjggzlrZW7ZsgXx8fH48ccf0dDQgLCwMERHR8PX1xfW1taYnJzE9evX0djYiPLycojFYrz00kvsNdG1Tz/9FFFRUbhz5w7eeOMNxMXFISgoaM6STPK5rnbu3GlwOUdubm4IDg5GRUUFGhoa0NjYqDCXmqenJ9LT0/Hhhx9CIpEgJSUF+fn5CA8Px7p162BsbIx//vkHlZWVKCsrY72uO3fuVFhKiCsyMhL9/f3IyMjA0NAQ4uLiEBAQwHrpVq1aBbFYjO7ublRXV+PSpUuYmZnR2aTL+mJnZ4cvvvgCycnJGBgYQGRkJCIiIvDss8/CyckJ09PT6O/vh1AohEAgQG9vL7KzsxWelyafbV0zNTVFeno6du3ahampKSQmJmL79u0ICQmBk5MTJBIJurq6UF1djcrKSrS1ten8HO5VFJgRoiexsbFoaGhAcXExUlJSFPbL16nkznckZ2lpiR9++IEtYi4QCCAQCBTabdy4EVlZWTqpwtu1axcEAgEkEgnS0tLm7IuIiMDRo0cByPKJmpqacPXqVbS1tWHv3r1z2lpbWyMzMxMZGRl6D8zMzc2Rk5OD5ORkNDQ0oL29nfWeaOLjjz+GlZUVsrKyMDAwgMzMTJVtLSws9FLtKMfn85Gdnc0WMT958iROnjyp0C42NlbhNTcUb7/9NioqKgDIes2U9UiFhYXB3t4eaWlp6OzsxOXLl1WuqGBtbY3U1FSNfngkJSXB1dUVR48exY0bN1BTU4OamhqV7V1dXdWuN2ootm7dimPHjmH//v0YGRlBfn4+8vPzlbY1MjJSOmeYpp9tXdu8eTOys7Oxd+9eiMViFBcXo7i4WC//6/8JBWaE6NHnn38Of39/FBQUQCQSYXJyEmvWrEFISAjefPNNtZWUDz30EEpLS1FYWIjffvsNIpEIExMTsLKyAp/PZwsn66qsnc/no6CgACdOnEBTUxMGBweVDlWuXLkSeXl5+P7773Hu3Dn09PRgxYoVcHZ2xpYtW/D666+zWf+Xg62tLU6fPo3y8nKcOXMGLS0tGB4eBo/Hg4ODAzw8PBASEqJ0hn8ej4c9e/Zgx44dyM/PR319Pfr6+jA2NgYzMzM4OzuDz+cjICAAISEhauej0oWAgACUl5cjJycH1dXV6O3thUQigZ2dHXx9fREdHa3zGf11ycvLC/7+/qitrUVNTQ2EQuGciZTlnnzySZw5cwYCgQAVFRVobW3F4OAgpqenYWtri/Xr1yMwMBA7duzQqvJ069atCAwMhEAgwMWLF9Ha2orh4WFMTEzA0tISDz74ILy9vREaGgo/Pz+dTEezHOQ9kT///DOqq6vR0dEBsViMFStWwM7ODq6urti8eTNCQ0OVLm+k6WdbH5555hmcP38eeXl5qKqqQnd3N8bGxmBubg4XFxds2rQJL7744rKcy72CJ1WVlEEI0VpfXx+ee+45ALKgjDt0RgghhCyEkv8JIYQQQgwEBWaEEEIIIQaCAjNCCCGEEANBgRkhhBBCiIGgwIwQQgghxEBQVSYhhBBCiIGgHjNCCCGEEANBgRkhhBBCiIGgwIwQQgghxEBQYEYIIYQQYiAoMCOEEEIIMRAUmBFCCCGEGAgKzAghhBBCDAQFZoQQQgghBoICM0IIIYQQA0GBGSGEEEKIgaDAjBBCCCHEQFBgRgghhBBiICgwI4QQQggxEBSYEUIIIYQYCArMCCGEEEIMBAVmhBBCCCEGggIzQgghhBADQYEZIYQQQoiBoMCMEEIIIcRA/A+Q1a/E+W/fLAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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G2ysoKMDbb7+NQ4cOASgqqIYOHYonn3wSTZs2hYODAzIyMhAbG4sDBw7g8uXL+Pvvv/Hbb78hPDy8RHtbt27F/PnzoVQqAQCdOnXCwIED0bZtW7i4uODhw4e4ffs2jhw5gsOHDyMtLQ0LFy4sUQDqi4yMFLY1Gg1SUlKwd+9ebN++HUqlEgsWLICXlxf69etX3i8rERERUc0sADdu3CgUf40aNcKqVavQtm1bg326dOmC0aNHIzIyErNnz0ZhYSFmzJiBFi1alNgXAD744AOh+Hv88cfxxRdfoGnTpiX269u3L9544w0cOnQICxcuNBrf8ePHMWfOHGg0Gjg5OWHRokVGC88ePXpg9OjRuHPnDpYuXYqjR4+Wet3Fh5z9/PzQr18/tG/fHh9//DG0Wi2WL1/OApCIiIgqpMYNdCcnJ2PJkiUAisbi165da7Sg0wkODhYKtcLCQrz33nsoPqq9f/9+bN++HQDg6+uLtWvXGi3+9AUGBmLr1q3o0aOHwfv5+fmYMWMGNBoN5HI5vvvuO5O9jjpNmzbFsmXLMGfOnFL3M2X8+PHw8vICAFy6dAkZGRnlaoeIiIgIqIEF4Lp16/Do0SMAwGuvvYZWrVqVeUxISAj69OkDAEhISBB6+nRWrVolbC9atAhOTk5mxVKvXj3079/f4L2tW7fi3r17AIoKs27dupnVFgCMGDHC7H31yeVydOjQQXh99+7dcrVDREREBNSwAlCr1WLnzp0AAAcHBzz//PNmH/vCCy8I29u2bRO24+PjcfHiRQBFw8bt27evUIy6tmUyGSZOnFihtiyhvzilRqOptvMSERFR7VOj7gFMTEwUZvx26dIFdevWNfvYJ598Eg4ODigoKMDp06eF90+ePCls9+3bt0Lx5eTk4PLlywCAVq1aoXnz5hVqzxL6M409PDyq7bxERERVxR5qi4/Jz8tDbslV1czi7Fx5s4fFrkYVgFeuXBG227VrZ9GxNjY28PPzw7lz55CZmYnU1FR4enoatKk/jFoe8fHxQu9bRXsSLXH48GEkJiYCAJo1a4bGjRtX27mJiIiqymDFLYuPWffDt+U+3/z588t9bG1TowrArKwsYdvNzc3i411dXYXt7OxseHp6Gqwh2LBhwwrFp9+W/rmqgv4yMMuXLxfef/nll6v0vERERFJib2+PgIAAa4dR7WpUAai/OLO5EzX06R+Tk5NTKW3q02/L0dGxQm0Zo3sYtSljxoyx6L5IIiIiImNqVAGoPzaveyyLJfSP0T3YuaJt6tNvKz8/v0JtmcvR0RGdO3fGhAkTEBQUVC3nJCIiotqtRhWADRo0ELbT09MtPl5/fTwXFxeD/xf/vDz02ypPfGXRfxKIjY0NnJ2d4e7ubtFzjomIiMRib47lkyk3TR8G9/oVG9HTp1arhd/pbm5ukvmdW6MKQP0h0Li4OIuOVavViI+PB1B0r5+npyeAoqdp6Fy6dAm9evWqUHxyuRwajcbi+MxR/EkgREREtVkhLC+2HJ2cKnU2r1qtxp07dwAUdURJpQCsUesA+vj4CL1sp06dwsOHD80+9tixY8KwbJcuXYT3u3btKmwfOXKkQvEpFArhqSTXr1/HzZs3K9QeERERkTXUqAJQJpNh+PDhAICCggJs3rzZ7GN//vlnYXvkyJHCtq+vr7D8y6lTp3Dp0qUKxRgaGgqgaNHqDRs2VKgtIiIiImuoUQUgAEycOBH29vYAgBUrVpjVy/bHH3/g8OHDAIp6EQMDAw0+nzp1qrA9e/ZssyeDPHjwAFFRUQbvjRo1Cu7u7gCAjRs34sSJE2a1BQA7duwwe18iIiKiqlLjCsCmTZvivffeA1A0a3fSpEkGizkXt3v3bsycORMAYGdnh08//RQymcxgn4EDBwrP4Y2Pj8ekSZOQnJxcahxHjhzBs88+i+PHjxu87+joiM8++0y4F/DVV1/Fn3/+WWpbd+/exTvvvIOPP/641P2IiIiIqkONmgSiExYWhlu3bmH9+vW4e/cuRo0ahWeeeQZBQUFo0qQJVCoVrl27hl27dgkFmr29PT777DPhHr3iPvjgA9y/fx+HDh3C+fPnMXjwYAwZMgS9e/eGl5cXHBwckJ6ejri4OOzfv7/UoeKePXvi448/xvz585GXl4e33noLnTp1wqBBg9C2bVvUr18fDx8+xO3bt3H06FFERUWhsLDQokfbEREREVWVGlkAAkB4eDhat26NL7/8EtnZ2di5cyd27txpdF9vb2989NFHBpM/inN0dMS3336LlStXYvXq1cjNzS21TQDo168fxo0bZ/SzUaNGoVmzZliwYAESEhJw7tw5nDt3zmRbTZo0wbvvvmvycyIiIqLqUmMLQAAYO3YsnnnmGURERODw4cO4evUqMjMzYWNjAzc3N/j7+6N///4YPHiwWdO25XI5Xn/9dYwdOxZ//PEH/v77byQkJCArKwtKpRL169dH69at0blzZ4SEhMDb27vU9rp164adO3fi0KFDOHz4MM6ePYv09HQ8fPgQTk5OaNSokRBj3759YWtbo7/cREREkiOTyYRlZYrfQlabybRardbaQZD1FBYWIjY2FgDQqIU3QpZEWDkiIiIi03aHj4KnS+WtA1gR+r9D/f39hUmsYlDjJoEQERERUdViAUhEREQkMbwpjYiIiCRLpVIJaw63aNFCMvfrsweQiIiIJEuj0SA7OxvZ2dnQaDTWDqfaSKPMJbM0VDhgd/goa4dBRERkkls9R2uHUCuwACSBjVwOTxcHa4dBREREVYwFIAm4IpD4aLVaKJVKAEWPQpTSGla1BXMofswhiRHvASSBSqWydghkIaVSidjYWMTGxgq/gEhcmEPxYw5JjFgAEhEREUkMC0AiIiIiieE9gCTgfSviI5fL4eHhIWyT+DCH4scciptU88cCkARSWfyyNrG1tUWzZs2sHQZVAHMofsyhuEk1f9IpdYmIiIgIAAtAIiIiIsnhmB8J1BoNUrNzrR0GUY3iVs8RNlV4X5BSqcTly5cBAG3btoWdnV2VnYuqBnMoblLNHwtAEmTmFCBkSYS1wyCqUXaHj4Kni3OVta+/iDAXYxcn5lDcpJo/DgETERERSQwLQCIiIiKJYQFIREREJDEsAImIiIgkhgUgERERkcRUewGoVqvRuXNn+Pr6YuTIkaXuq9Vq0b17d/j6+sLX1xdbtmwpdf/t27cL+/7yyy/C+2FhYcL7ZVm3bp2wb8+ePXHlyhXhs6+//lr4LCYmpsy2ACAoKAi+vr4ICgoy+vmsWbOENvX/8/PzQ5cuXRASEoIPP/xQmKJOREREVFHVXgDa2NjgiSeeAABcuXIFOTk5JvdNTExEdna28Pr06dOltn3q1Clhu2vXrhbH9uOPP2LhwoUAAHd3d2zYsAF+fn4Wt1MZtFotHj58iPj4ePzyyy8IDQ3FsmXLrBILEVUdW1tbtG7dGq1bt+bjGEWKORQ3qebPKlfatWtXREdHQ6PR4MyZM3jqqaeM7qcr6GxsbKBWqw0KvNL2b9CgAR577DGLYlq5ciW+/PJLAICnpyfWrVuHVq1aWdRGRa1evVp4ILVGo0FGRgYOHz6MX375BSqVCqtWrYKHhwfGjx9frXERUdWRy+Vo0KCBtcOgCmAOxU2q+bPKPYBdunQRtksr6nSfDR48GABw69YtpKamGt03IyMDN27cAAAEBARAJpOZHc/XX38tFH9NmjTBzz//XO3FHwC0bNkSPj4+8PHxgZ+fH3r16oXw8HB8/fXXwj4rVqyAWq2u9tiIiIio9rBKAdixY0fUqVMHgHkF4KBBg9C8eXMApoeByzv8+8UXX2DFihUAgKZNm+Lnn38WzlVTBAUFCUVzZmYmLl26ZOWIiKiyaLVaFBYWorCwUFJPIahNmENxk2r+rFIA2tvbo2PHjgCA2NhYFBYWltjn9u3bQm9fQEAAAgICAJguGPXf1+9hLM2SJUvw3XffASjqfdu4cSO8vLzMv5BqpPt6AcDdu3etGAkRVSalUonY2FjExsYKj6MicWEOxU2q+bPaMjC6Iq2wsBDnz58v8bmuoGvZsiXc3NyEAvDkyZNG29P1DCoUCrRt27bM83/88cdYs2YNAKB169bYsGEDGjVqZPmFVBP9G1M1Go0VIyESL3uoLf4vPy8Pubm5Fv9HRFSTWW26S9euXbFy5UoARcVe8WFbXQHYuXNng/8nJibi/v37qF+/vrBvTk6OsFzLE088ARsbG5Pn1Wq1+PDDD7Fp0yYAgI+PD3766Se4ublV0pVVjYSEBGFbN1GEiCwzWHHL4mPW/fBtuc41f/78ch1HRFQdrFYAdurUCba2tlCpVEaHdXXv6XoKvb290aBBA2RlZeHMmTMIDAwU9j179qwwMaKs+//mzp2L33//HQDQtm1brFmzBg0bNrQ4/jt37pg1a6gyupOvXLmCo0ePAgAcHR3h7+9f4TaJiIhIuqxWADo7O6Nt27aIjY0VCjhdz13xGb06nTt3xsGDB3Hq1CmDAtCS+/90xZ+3tzfWrVtn0JNoiffff79cx5lLq9UiPT0dhw8fxtKlS4UCNywsTJhAQ0RERFQeVl3xsGvXroiNjUVubi4uX76MDh06APjffX5ubm5o2bKlsH9AQIBQAOrT3f9Xp06dMnvHZDIZtFot7ty5g7i4OPTs2bMSr6hi+vfvX+rn/fr1w1tvvVVN0RAREVFtZdUCMCAgQJiIcerUKaEA1BV4+r1/wP969y5duoSCggI4ODigsLAQFy5cAAA8/vjjsLe3L/Wcc+fOxUcffYRHjx5h2rRp+PHHH82eNaxv/fr16N69e5n7BQUFITk52eL2dezs7ODv748xY8Zg+PDhFq1vSESG9uZYvsTTpunD4F7fqQqiISKyHqsWgF26dBF65E6fPo1JkyYB+F+Pnm7ih067du3g6OiI/Px8nDt3Dj169EBsbCwePXoktFeW8ePHo6CgAJ9++iny8/Px6quv4qeffjJYZsVa9J8EIpfL4eTkBDc3tzKLWiIyTyFMTxAzxdHJCc7OzlUQDRGR9VhtGRgAcHFxQZs2bQD8r9dPf0Zv8R5AXW+Y/v76C0ObuwD05MmT8eabbwrne/nll4VzWpP+k0Aee+wxNGnShMUfUS2n+7nm7+8POzs7a4dD5cAciptU82fVAhCAwRMukpKScPr0aWg0Gjg5OaFdu3Yl9i++ILTufkE7Ozt06tTJ7PO+8cYbeOWVVwAA9+/fx0svvYSkpKSKXAoRkcVkMhns7e1hb2/PWzxEijkUN6nmr8YUgEBRb56uR69Tp05G1/PTFYDnz59HYWEhzp49C6BoeNjJybL7dP773/8iLCwMQNHM40mTJuH27dvlug4iIiIisahRBeDJkyeFHr3iw786uoWe8/LysG3bNjx8+LBEO5YIDw/Hc889BwBIS0vDCy+8gH///bdcbRERWUqj0SArKwtZWVl8yo9IMYfiJtX8Wb0A9PT0RPPmRTPzYmJicPHiRQCmC0CFQgEfHx8AwI8//ii8b+79f8XJZDJ8+OGHCA4OBgAkJydj0qRJuHfvXrnaIyKyhEqlwrVr13Dt2jWoVCprh0PlwByKm1TzZ/UCEPhf711qaioKCwtha2tb6v18uuJQN1wrl8tNFozmkMvlWLJkCQYNGgQAuHHjBl588UVkZmaWu00iIiKimqpGFYA6bdu2haOjo8n9ixd7Pj4+qFevXoVisLGxwdKlS9GvXz8ARc8cnjx5Mh48eFChdomIiIhqmhpRABYfvi2rN6/45+Ud/i3Ozs4Oy5cvx5NPPgkAiIuLwyuvvILc3NxKaZ+IiIioJpBptVqttYMg6yksLERsbCwAoFELb4QsibByREQ1y+7wUfB0qbqFoPW/B/39/bn2pwgxh+JWkfyJOfc1ogeQiIiIiKoPC0AiIiIiibHqs4CJiKROJpMJj5+S0lMIahPmUNykmj8WgEREVmRnZ4eOHTtaOwyqAOZQ3KSaPxaAJGiocMDu8FHWDoOoRnGrZ3pJKiIisWIBSAIbuRyeLg7WDoOIiIiqGAtAEnBFIPHRarVQq9UAihYzl9L9K7WFSqUSnj/euHFj2Nryx7LYMIfiJtX8cRYwCaT0DMTaQqlU4vz58zh//jyUSqW1w6Fy0Gg0SEtLQ1pamqQeRF+bMIfiJtX8sQAkIiIikhgWgEREREQSwwKQiIiISGJYABIRERFJjDSmupBZpDLzqTaxs7ODv7+/sE1ERGQO/sYnAZcQER+ZTAZ7e3trh0FERCLDApAEao0Gqdm51g6DyKrc6jnCRl59d8fI5XK4uLgI2yQ+zKG4STV/LABJkJlTgJAlEdYOg8iqdoePgqeLc7Wdz9bWFt7e3tV2Pqp8zKG4STV/0il1iYiIiAgAC0AiIiIiyeEQMBGRFSmVSiQlJQEAvL29OZtbhJhDcZNq/lgAEhFZkVarRW5urrBN4sMciptU88chYCIiIiKJEU0PYF5eHnbu3ImoqChcuXIF2dnZ0Gq1UCgU8PLygo+PD5544gn06dMHjRs3LrWtu3fv4o8//sCxY8dw8+ZNZGZmQqPRoH79+vDx8UFAQACCg4PRrFmzEsfGxMRg4sSJAIA33ngDb775Zpmxz5o1C9u3bwcAHDx4EE2bNjX4fNu2bZg9ezYAYNGiRQgNDTXra0JERERUHqIoAM+ePYt33nkHd+/eLfFZVlYWsrKycPHiRWzbtg1ubm74+++/jbbz6NEjLF26FJs2bUJhYWGJz9PS0pCWlobo6GgsX74cgwcPxsyZM8ssKImIiIjEpMYXgNevX8fkyZOF8fmgoCAMGjQIrVq1gp2dHbKysnDlyhUcO3YMMTExJtvJzMzEtGnTcO7cOQCAs7Mzhg0bhh49eqBRo0awtbVFeno6zpw5g/379+PGjRvYs2cPOnXqhEmTJlXDlRIRERFVjxpfAC5btkwo/kwNj/bq1QuTJ09GZmYm9uzZU+JzjUaDt99+Wyj+AgMD8cknn8DV1bXEvkFBQXjnnXcQERGBTz/9tHIvhoiIiKgGqNEFoFqtxpEjRwAAHTp0KPPeuIYNG2L8+PEl3l+/fr3QO9i7d2+sWLECtramL10ul2PEiBHo0aMHrl+/XoErICIiIqp5anQBmJmZiYKCAgBAixYtytVGYWEhVq9eDQCoU6cOFi5cWGrxp69Ro0Zo1KhRuc5LRGQOGxsbYWKYjY2NlaOh8mAOxU2q+avRBaD+Yoy6RRotFR0djbS0NADA4MGD4enpWSmxEVHNYg91pbSTn5eH3EpaB9bZuexnCtvY2PDnksgxh+Im1fzV6ALQxcUFXl5eSE5OxpUrV/D999/j5Zdfhlxu/vKFJ0+eFLb79etXBVESUU0wWHGrUtpZ98O3ldIOAMyfP7/S2iIiqkw1ugAEgAkTJmDJkiUAgKVLl+LXX39FUFAQOnfuDH9/f6Nr9em7cuWKsN2+fftKjS0jIwMJCQll7vfgwYNKPS8RERFRRdT4AnDSpEm4evUqtm7dCgBITk7Ghg0bsGHDBgCAm5sbunXrhuDgYAQGBkImkxkcn52dLWwbm/VbEZs2bcKmTZsqtU0ikpbCwkLExsYCAPz9/WFvb2/liMhSzKG4STV/Nf5RcHK5HAsXLsSaNWvQp0+fEhM40tPTsXv3bkybNg3PPvssbt0yHAbSLSEDAI6OjtUSMxEREVFNVuN7AHV69eqFXr16IScnB6dPn0ZsbCwuXryIU6dO4eHDhwCAixcvYty4cdi2bRs8PDwAGN6EnZ+fD4VCUWkxledRcERUNfbmNK+UdjZNHwb3+k6V0hYRUU0lmgJQR6FQoG/fvujbty+Aoq7byMhILFmyBPfv38e9e/fw1Vdf4ZNPPgFQNJFEJz09vVILQCKqOQpROcs3ODo5mTV7l4hIzGr8EHBZ7O3tMWrUKCxdulR4b//+/dBoNAAAPz8/4f24uLhqj4+IiIiophF9AajTp08fNG7cGABw//59YfJH165dhX0OHz5shciIiIiIapZaUwACEO7709e7d2/h/b179yI1NbW6wyIiIiKqUWpNAZifn4+rV68CKLpPsEGDBgCKhognT54MAHj06BHef/99qNXmPTEgNTUVx48fr5qAiYiIiKykRheAubm5GD16NA4dOiTc02eMRqPBggULhCVfgoKCDNYDnDhxIrp37w6g6NFwr7/+OjIzM022p9VqERkZidDQUMTHx1fS1RARlWRrawtfX1/4+vqa/ZxyqlmYQ3GTav5q/JVeuHABU6dOhaenJwYMGIBOnTqhSZMmUCgUePDgAeLi4rB161bhiRx169bFf/7zH4M25HI5vvzyS0ydOhXnz5/HoUOHMGDAAAQHB6NHjx5o1KgRbG1tkZ6ejnPnzmHfvn24du2aNS4Xp0+fNmu/oKAggxnORCROcrmcqxOIHHMoblLNX40uAG1tbeHu7o579+4hNTUVGzduxMaNG03u37JlSyxduhRNmzYt8VnDhg2xYcMGfP7559i0aRNyc3Px66+/4tdffzXalkwmQ3BwMIYMGVJp12OOLVu2YMuWLWXut2PHDhaAREREVC41ugCsU6cO/vrrL5w7dw7Hjh3D+fPncf36dWRkZODRo0dwdHSEh4cH/Pz80L9/fzz99NOlPsKlTp06CA8Px4svvohdu3bh+PHjuHHjBjIzM6HValG/fn34+Piga9euCA4OhpeXVzVeLRFJkUajQV5eHgDAyckJcnmNvjOHjGAOxU2q+ZNptVqttYMg69F/BmKjFt4IWRJh5YiIrGt3+Ch4ulTfQtBSfQ5pbcIciltF8ifm3EujzCUiIiIiAQtAIiIiIolhAUhEREQkMTV6EghVr4YKB+wOH2XtMIisyq2eo7VDICKqciwASWAjl8PTxcHaYRAREVEVYwFIAk4IFx+tVgulUgkAsLOzM3gCDhERkSm8B5AEKpXK2iGQhZRKJWJjYxEbGysUgkRERGXhOoASJ+Y1jIiIiKxJzL9D2QNIREREJDEsAImIiIgkhpNASKBWq60dAllIrVYjPT0dAODm5gYbGxsrR0SWYg7FjzkUN6nmjwUgCTQajbVDIAup1WrcuXMHANCgQQPJ/OCqTZhD8WMOxU2q+eMQMAlsbfn3ABERkRSwACQB15AjIiKSBnb5kECt0SA1O9fgPbd6jrCR8+8EIiKi2oQFIAkycwoQsiTC4L3d4aPg6eJspYiIiIioKrBrh4iIiEhiWAASERERSQyHgIlETCaTwdnZWdgm8WEOxY85FDep5o8FIJGI2dnZwc/Pz9phUAUwh+LHHIqbVPMnqgLwxIkTCAsLE15v2rQJnTt3tqiNW7duYc+ePTh+/Dhu3bqFrKwsqFQq1K1bF02bNkW7du3Qr18/9OrVC3Z2dkbb2LZtG2bPnm3ReWfPno1JkyYZvBcWFoYTJ06U2Fcul6Nu3bpo1qwZunXrhueffx4tWrSw6HxEREREpoiqANy+fbvB6x07dphdAD548ACLFy/Gzp07oVKpSnyekZGBjIwMnD9/Hps2bYKrqyteeeUVTJw4sdpXBddoNLh//z7u37+PixcvYsOGDZg9ezbGjx9frXEQERFR7SSaArCgoAB//vknAMDJyQl5eXnYu3cv5syZA3t7+1KPvX37Nl555RVcv34dAODi4oJnnnkGXbp0gYeHBxwdHZGZmYlr164hOjoax44dQ0ZGBhYvXoxhw4bB3d3dZNtvv/02+vfvX2b8pbUBAJGRkcK2RqNBSkoK9u7di+3bt0OpVGLBggXw8vJCv379yjwXSYdKpcLNmzcBAC1atODTXESIORQ/5lDcpJo/0Vzl/v37kZtbtEjxnDlz8P777+P+/fuIiorC4MGDTR6Xl5eHV199VSj+nnvuOcycORMKhaLEvn369MELL7yA27dv49tvv8W2bdvKjMvT0xM+Pj7lvKr/Kd6Gn58f+vXrh/bt2+Pjjz+GVqvF8uXLWQCSAY1Gg+zsbABAs2bNrBsMlQtzKH7MobhJNX+iWQZmx44dAABfX1+MGjUKrVq1MnjflM8//xxJSUkAgHHjxmHBggVGiz99zZo1w6JFi7By5UrUqVOnwrFXxPjx4+Hl5QUAuHTpEjIyMqwaDxEREYmfKArAtLQ0HD9+HAAQEhJi8P/o6GhkZmYaPS49PR1btmwBADRu3BgzZ8606LxBQUGoV69eecOuFHK5HB06dBBe371714rREBERUW0gigIwMjISarUacrkcwcHBAIDg4GDIZDIolUrs2rXL6HG7d+/Go0ePAACjR4+Gg4NDtcVcmfTvR9BoNFaMhIiIiGoDURSAO3fuBAB069YNnp6eAIqGaZ944gkApoeBT548KWz36dOnaoOsQgkJCcK2h4dHtZ3XHmrk5+UhNzdXuP+SiIiIxK/GTwK5fPky4uPjAfxv2FcnJCQEZ86cwaVLl3D16lU89thjBp/rjpPL5VW2yGNqaqpBgWZKeSeKHD58GImJiQCKit7GjRuXq53yGKy4hXU/fCu8nj9/frWdm4iIiKpOjS8Adb17Dg4OGDRokMFnQ4YMwSeffAKlUokdO3bgv//9r8HnWVlZAIC6deuWulTMvXv3hH2Lq1+/vtDraMyXX36JL7/8sszr0BWj5tBfBmb58uXC+y+//LLZbRARERGZUqMLQJVKJdzfFxgYWGL2rouLC/r27YsDBw4gMjIS77zzDuTy/41q64YtHR0dSz3P999/j/Xr1xv9bOTIkVi8eHFFLsMsvr6+pX4+ZswYPP/881UeB4mLXC4XbgvQ/7dP4sEcih9zKG5SzV+NLgCjo6ORnp4OoOTwr05ISAgOHDiAlJQUxMTEoGfPnsJnzs7OePDgAfLz86ssxkWLFiE0NLRK2nZ0dETnzp0xYcIEBAUFVck5SNxsbW0ltW5VbcQcih9zKG5SzV+NLgB1w78uLi4mJ3EEBgaiXr16ePDgAXbs2GFQALq4uODBgwd4+PAhCgsLTQ4Dh4eHIzw8XHh9584ds57uUZn0nwRiY2MDZ2dnuLu7V/tj6PTtzWmOTdOHwb2+k9ViICIiospXYwvAhw8fIioqCgCQnZ1tsBaeKfv27cP8+fPh5FRUsPj5+eHWrVvQaDSIj4+Hv79/lcZcEZXxNJHKVggbODo5wdnZ2dqhEBERUSWqsYPde/bsEdbwM1deXh727dsnvO7atauwffTo0UqLjaimUCqVuHDhAi5cuAClUmntcKgcmEPxYw7FTar5q7E9gLrhX3d3d8yePbvM/T/99FOkpKRg586dGDFiBABg6NCh+Oyzz1BYWIjff/8dkydPtvqj3Ygqk1arFX5gabVaK0dD5cEcih9zKG5SzV+NLABv376NM2fOAAAGDRqEZ555psxjzp07h/Xr1+Off/5BamoqPD094ebmhlGjRmHTpk24e/cuPvvsM8yZM6eqwyciIiKq0WrkEPDOnTuFKrz42n+m6PbTaDTCk0MA4L///S9atWoFANiwYQM++OCDMp9q8eDBg/KETURERCQKNbIHUFfAubq6okuXLmYd07lzZ7i7u+PevXuIiIjAlClTAAAKhQLfffcdpkyZghs3bmDTpk3Yu3cvhg0bhi5dusDDwwNOTk7IycnBzZs3cezYMezfv19ot6znB5v7JBCFQoEmTZqYdS1EREREVanGFYCnT5/GrVu3AAADBgwwe1FGuVyOgQMH4pdffkFiYiIuXrwozBxu0aIFNm/ejEWLFiEiIgJZWVnYsGEDNmzYYLI9V1dXvPzyywgLCyv1vOY+CaR///749ttvy9yPiIiIqKrVuAJQf/jW3OFfnaeffhq//PILgKJJJPpLx9SvXx+LFy/GtGnTsHv3bvzzzz+4efMmsrOzoVarUa9ePTRu3BgdOnRA79690bdvX9jZ2VXORRERERHVIDKtlKa8UAmFhYWIjY0FADRq4Y2QJREGn+8OHwVPF64DWFPp58/f37/UZ15TzcQcih9zKG4VyZ+Yc1/jegCJyHy2trZo3bq1sE3iwxyKH3MoblLNn3SulKgWksvlaNCggbXDoApgDsWPORQ3qeavRi4DQ0RERERVhz2ARCKmv4K9nZ0dZDKZlSMiSzGH4scciptU88cCkAQNFQ7YHT7K4D23eo5WiobMoVQqRXsDMhVhDsWPORQ3qeaPBSAJbORyeLqUvvA1ERERiR/vASQBVwQiIiKSBhaAJFCpVNYOgYiIiKoBC0AiIiIiiWEBSERERCQxLACJiIiIJIYFIBEREZHEcBkYEkjpGYi1hZ2dHfz9/YVtEh/mUPyYQ3GTav74G58EUln9vDaRyWSSWbS0tmIOxY85FDep5o9DwEREREQSwx5AEqg1GqRm51bpOdzqOcJGzr87KotGo8H9+/cBAPXr14ecX1vRYQ7FjzkUN6nmjwUgCTJzChCyJKJKz7E7fBQ8XZyr9BxSolKpcO3aNQDSeoZlbcIcih9zKG5SzZ80ylwiIiIiErAAJCIiIpIYFoBEREREEsMCkIiIiEhiJDkJJC8vDzt37kRUVBSuXLmC7OxsaLVaKBQKeHl5wcfHB0888QT69OmDxo0bGxw7a9YsbN++3aLz7dixA23bthVev/HGG9i/fz8AYO3atejZs2epxx88eBCvvfYaAGDEiBFYsmSJRecnIiIi0ie5AvDs2bN45513cPfu3RKfZWVlISsrCxcvXsS2bdvg5uaGv//+u9JjmDdvHmJiYvDgwQPMmzcPkZGRcHBwMLrvw4cP8cEHHwAAXF1dMXv27EqPh4iIiKRFUgXg9evXMXnyZOTmFq11FxQUhEGDBqFVq1aws7NDVlYWrly5gmPHjiEmJqbM9lavXg0PD48y92vRooXBaw8PD8yYMQNz587FrVu38NVXX2HmzJlGj/3000+RlpYGAJgzZw5cXFzKPB9Jh0wmEx5dxCe5iBNzKH7MobhJNX+SKgCXLVsmFH+LFi1CaGhoiX169eqFyZMnIzMzE3v27Cm1vZYtW6Jp06blimX06NGIjIzEiRMnsG7dOjzzzDPo0KGDwT4xMTH4/fffARQVq0OHDi3Xuaj2srOzQ8eOHa0dBlUAcyh+zKG4STV/kpkEolarceTIEQBAhw4djBZ/+ho2bIjx48dXWTwymQwff/wx6tSpA7VajfDwcKhUKuHzgoICzJ07V7g3UTcMTERERFRRkikAMzMzUVBQAKDkkKy1tGjRAm+88QYA4MqVK/jxxx+Fz5YvX46bN28CAGbMmAFPT0+rxEhERES1j2QKQN34PgAkJSVZMRJDL730Etq1awcA+Oabb3Dt2jVcvHgRa9euBQB07doVY8aMsWKEVJOpVCrcvn0bt2/fNuhBJvFgDsWPORQ3qeZPMgWgi4sLvLy8ABT1tn3//ffQaDRWjgqwtbXFxx9/DBsbGxQWFmLOnDmYM2cO1Go16tSpgwULFtSKm1LtoYY91MjPy0Nubq7wH1WMRqNBWloa0tLSasS/Z7Iccyh+zKG4STV/kpoEMmHCBGENvaVLl+LXX39FUFAQOnfuDH9/fzRr1syi9m7cuIG8vLxS93F0dCyz3fbt22PSpElYvXo1Tp8+Lbz/xhtvoFWrVhbFVFMNVtwCAKz74VuD9+fPn2+NcIiIiCRNUgXgpEmTcPXqVWzduhUAkJycjA0bNmDDhg0AADc3N3Tr1g3BwcEIDAwss+dt8uTJZZ6zW7duQvuleeutt7B79278+++/AIB27drhpZdeKvM4IiIiIktJZggYAORyORYuXIg1a9agT58+sLU1rH/T09Oxe/duTJs2Dc8++yxu3bpVbbHdvHkT6enpwuuGDRuWiI+IiIioMkiywujVqxd69eqFnJwcnD59GrGxsbh48SJOnTqFhw8fAgAuXryIcePGYdu2bSYXez548GC51wHUp1sGRqlUCu9FR0cjMjISwcHBFW6fiIiISJ8kC0AdhUKBvn37om/fvgCAwsJCREZGYsmSJbh//z7u3buHr776Cp988kmVxrF+/XrExsYCKBpWjoyMRFpaGhYuXIjevXujQYMGVXr+6rA3pzkAYNP0YXCv72TlaIiIiKRNUkPAZbG3t8eoUaOwdOlS4b39+/dX6ayg27dv46uvvgIA+Pj4YPr06Zg7dy6AorULdZNWxK4QNiiEDRydnODs7Cz8R0RERNWPBaARffr0QePGjQEA9+/fR3Z2dpWda968ecjPz4dcLscnn3wCOzs7PP300xgwYAAAYPv27Th+/HiVnZ/ETS6Xw8XFBS4uLpDL+e0sRsyh+DGH4ibV/EnnSi1k6r6/yrR161YcO3YMABAWFmbwLMJ58+ZBoVAAKFoq5dGjR1UeD4mPra0tvL294e3tzUlDIsUcih9zKG5SzR8LQCPy8/Nx9epVAEX3CVbFPXjp6enC8K6Xlxfefvttg889PT3x7rvvAiiaIbxixYpKj4GIiIikSTIFYG5uLkaPHo1Dhw6Vek+fRqPBggULhKdUBAUFVcmTOD766CPcv38fAPDhhx/CyankxIixY8eic+fOAIA1a9bgypUrlR4HERERSY90+joBXLhwAVOnToWnpycGDBiATp06oUmTJlAoFHjw4AHi4uKwdetWJCQkAADq1q2L//znPybbM+dJIADg7u5u0It44MAB/PnnnwCA4cOHo0+fPkaPk8lkWLBgAUaMGAGlUok5c+Zg8+bNkrpHgUqnVCqFZ1t7e3sbPPOaxIE5FD/mUNykmj/JFIC2trZwd3fHvXv3kJqaio0bN2Ljxo0m92/ZsiWWLl1a6jp/5jwJBABmz56NSZMmAQAePnyIDz/8EEDRYs+zZ88u9djHHnsMr776KlasWIHY2Fhs2LABL7zwglnnpdpPq9UKvdVardbK0VB5MIfixxyKm1TzJ5kCsE6dOvjrr79w7tw5HDt2DOfPn8f169eRkZGBR48ewdHRER4eHvDz80P//v3x9NNPw97evtLj+PTTT5GWlgYAeP/99826v/DVV1/Fnj17kJSUhC+//BIDBw5EkyZNKj02IiIikgbJFIBA0VTvzp07C/fVlcfixYuxePHich+/YMECLFiwwKJj7O3tsXv37nKfk4iIiEgfbyYjIiIikhgWgEREREQSwwKQiIiISGJYABIRERFJjKQmgVDpGiocsDt8VJWew62eY5W2LzU2NjbCUkU2NjZWjobKgzkUP+ZQ3KSaPxaAJLCRy+Hp4mDtMMgCNjY28PT0tHYYVAHMofgxh+Im1fxxCJiIiIhIYlgAEhEREUkMh4BJoFQqq+TpJ1R1CgsLERsbCwDw9/dn/kSIORQ/5lDcpJo/9gASERERSQwLQCIiIiKJYQFIREREJDEsAImIiIgkhgUgCWxtOSeIiIhIClgAkkAmk1k7BCIiIqoG7PIhgVqjQWp2rlXO7VbPETZy/j1CRERUHVgAkiAzpwAhSyKscu7d4aPg6eJslXOLma2tLXx9fYVtEh/mUPyYQ3GTav6kc6VEtZBcLodCobB2GFQBzKH4MYfiJtX8ccyNiIiISGLYA0gkYhqNBnl5eQAAJycnyHkfpegwh+LHHIqbVPMnjaskqqVUKhXi4+MRHx8PlUpl7XCoHJhD8WMOxU2q+atVPYAxMTGYOHGi8NrJyQnHjh2Do6NjqccVFBSgV69eyMnJEd5bv349unfvXupxJ06cQFhYmPB606ZN6Ny5s8Vx6nNwcICLiwv8/PwwcOBAhISESObB1ERERFQ9anUPYF5eHg4cOFDmfgcPHjQo/sy1fft2g9c7duywuI3iCgoKkJKSgsOHDyM8PByhoaG4c+dOhdslIiIi0qm1BWCdOnUAADt37ixzX90+umPMUVBQgD///BNAUU8jAOzduxeFhYUWxTl27FhERkYK/61duxbh4eFo1KgRACAxMRHTpk2DWq22qF0iIiIiU2ptARgUFAQAOHbsGO7du2dyv4yMDPz9998AgP79+5vd/v79+5GbW7Ro8pw5cwAA9+/fR1RUlEVxurq6wsfHR/ivZ8+emDhxIv744w94eXkBABISErB//36L2iUiIiIypdYWgL169YK7uzvUajX++OMPk/vt2rULKpUK7u7uePLJJ81uXzfc6+vri1GjRqFVq1YG71eUQqHAtGnThNfHjh2rlHaJiIiIam0BaGNjg2eeeQZA6cPAus+GDRsGGxsbs9pOS0vD8ePHAQAhISEG/4+OjkZmZma549anW5kcAFJSUiqlTSIiIqJaWwACwPDhwwEAcXFxSExMLPH51atXcenSJYN9zREZGQm1Wg25XI7g4GAAQHBwMGQyGZRKJXbt2lUJ0QN2dnbCtpQeT0NERERVq1YXgO3atUObNm0AGO8F1L3n4+ODtm3bmt2u7rhu3brB09MTANCsWTM88cQTACpvGDgpKUnY1t0PWJvYQy38l5+Xh9zcXIP/qGz29vYICAhAQEAAlwsSKeZQ/JhDcZNq/mp9t9Lw4cPx+eefY9euXXj33Xchk8kAAFqtFpGRkcI+5rp8+TLi4+MB/G/YVyckJARnzpzBpUuXcPXqVTz22GPljlutVmP16tXC60GDBpW7rZpqsOKWsL3uh29LfD5//vzqDIeIiEgyanUPIFBUlMnlcvz777+IiYkR3o+JicG///5rMIxrDl3vnoODQ4mibMiQIcKwbXl7ATMzM3H8+HFMmDABcXFxAIqKvy5dupSrPSIiIqLian0B6OnpKTzRQ38YWLfdo0cPYRi3LCqVSri/LzAwEAqFwuBzFxcX9O3bF0DRfYIajabMNlesWAFfX1/hv549e2LSpEk4c+YMHB0d8eKLL2Lp0qVmxUfSo1arkZqaitTUVK4VKVLMofgxh+Im1fzV+gIQAEaMGAEA2LdvHwoKCgwWcbZk+Dc6Ohrp6ekASg7/6ujeT0lJMehxLA8/Pz+EhYUZTAYh0qdWq3Hnzh3cuXNHUj+4ahPmUPyYQ3GTav5q/T2AADBw4EB88MEHyMnJwcGDB6HVapGbmwsnJyc8/fTTZrejG9Z1cXFBnz59jO4TGBiIevXq4cGDB9ixYwd69uxZaptjx47FuHHjABT9I0xJScGff/6JnTt34uzZswgLC8OWLVvQsGFDs+MUi705zYXtTdOHwb2+kxWjISIikg5JFIDOzs4YMGAAIiMjsXPnTmi1WgDAgAEDhMe4leXhw4fCUz6ys7PRoUOHMo/Zt28f5s+fX+o5dE8C0Wnbti0CAwPRvXt3zJo1C8nJyQgPD8fKlSvNilNMCvG/dRcdnZzg7OxsxWiIiIikQxJDwMD/hoH//vtv4akauvfMsWfPHjx69Miic+bl5WHfvn0WHaMzcuRIYZJJVFSUsPA0ERERUUVJogcQAHr27Al3d3fhucAeHh5lDs/q0w3/uru7Y/bs2WXu/+mnnyIlJQU7d+60qNDUN336dBw4cABqtRrLli2zKF4iIiIiUyRTANrY2GD48OFYv349gKLJH3K5eR2gt2/fxpkzZwAULcmie8Rcac6dO4f169fjn3/+QWpqqtkzjfW1atUKQ4YMwa5du3D+/Hn8/fff6NWrl8XtEBEREemTzBAwAMyYMQOxsbGIjY3Ff//7X7OP079v0NwFmXX7aTSaUp9FXJZXX31VWLy6Nt4HSERERNVPUgVgeekKOFdXV7MXZO7cuTPc3d0BABEREeU+t4+PD4KCggAAJ0+exKlTp8rdFtU+MpkMzs7OcHZ2Fv5QIHFhDsWPORQ3qeaPBWAZTp8+jVu3ih5ZNmDAALOHjeVyOQYOHAgASExMxMWLF8sdw9SpU4Vt9gKSPjs7O/j5+cHPz4/rRYoUcyh+zKG4STV/LADLoD98a+nzePXXGCzvo+EAoGPHjsK9f9HR0bhw4UK52yIiIiKSaXU3t5EkFRYWIjY2FgDQqIU3QpaUf7i6InaHj4KnC9cBJCIi8dD/Herv7w97e3srR2Q+ycwCJqqNVCoVbt68CQBo0aIFbG35LS02zKH4MYfiJtX8cQiYSMQ0Gg2ys7ORnZ0NjUZj7XCoHJhD8WMOxU2q+WMBSERERCQxLACJiIiIJEYaA91kloYKB+wOH2WVc7vVc7TKeYmIiKSIBSAJbORyeLo4WDsMIiIiqmIcAiYBVwQiIiKSBhaAJFCpVNYOgYiIiKoBh4BJIKVnINYWcrkcHh4ewjaJD3MofsyhuEk1fywASSCVxS9rE1tbWzRr1szaYVAFMIfixxyKm1TzJ51Sl4iIiIgAsAAkIiIikhyO+ZFAqVSK6kHWVJSzy5cvAwDatm0LOzs7K0dElmIOxY85FDep5o8FIAl4D6D4aLVaKJVKYZvEhzkUP+ZQ3KSaPw4Bk4CzgImIiKSBXT4kUGs0SM3Otcq53eo5wkZC0++JiIisiQUgCTJzChCyJMIq594dPgqeLs5WOTcREZHUsMuFiIiISGJYABIRERFJDAtAIiIiIonhPYBEImZra4vWrVsL2yQ+zKH4MYfiJtX8ie5KY2JiMHHiRKOfOTg4wMXFBX5+fhg4cCBCQkJKXdjY19fXonP7+flh586dBu9t27YNs2fPBgAsWrQIoaGhFrUZFBSE5OTkEu/b2tpCoVCgdevWePLJJ/Hcc8/B09PTorap9pPL5WjQoIG1w6AKYA7FjzkUN6nmr1YNARcUFCAlJQWHDx9GeHg4QkNDcefOHWuHVS4qlQrZ2dk4c+YMVqxYgaFDh2L//v3WDouIiIhqAdH1AOobO3Ysxo0bJ7zOyMhAYmIiVq9ejZSUFCQmJmLatGnYsWMHbGxsTLbToUMHLFq0qMzzOTg4VErcxnh4eGD16tXCa5VKheTkZGzduhWHDh1CTk4Opk+fjs2bN6Ndu3ZVFgeJi/4K9nZ2dlzMW4SYQ/FjDsVNqvkTdQHo6uoKHx8fg/d69uyJ0NBQhISEIDk5GQkJCdi/fz8GDx5ssh0nJ6cS7VQ3Ozu7EjG0a9cOAwcOxOLFi/HTTz9BqVRi5cqV+Prrr60UJdU0SqUSsbGxAAB/f38+y1mEmEPxYw7FTar5q1VDwDoKhQLTpk0TXh87dsyK0VTcm2++KfQ+RkdHQ6PRWDkiIiIiErNaWQAChhM8UlJSrBhJxTk7O8Pb2xsAkJeXh+zsbOsGRERERKJWawtAOzs7Ybs2TOvWvx72ABIREVFFiL8yMiEpKUnY9vLysmIkFadSqXDt2jUARYWgi4uLdQOqZPZQIz8vD7n/X+M6O/OZwERERFWpVhaAarXaYEbtoEGDSt0/Ly8PCQkJZbbbqFEj1KtXr8LxWeq3337DgwcPAAABAQG1okdT32DFLaz74Vvh9fz5860YDRERUe1XqyqJzMxMxMfHY/ny5YiLiwNQVPx16dKl1OMuXryI4ODgMtsvz0LP5aVbBmbbtm344YcfhPcnT55cLecnIiKi2kvUBeCKFSuwYsUKo585Ojri+eefx7vvvlvNUZVPcnJyqU8mkclk+M9//oOnnnqqGqMiIiKi2kjUBWBp/Pz8EBYWZjB5wpRu3bphw4YN1RCV5RQKBXr06IEXX3yxzJ5Mkh47Ozv4+/sL2yQ+zKH4MYfiJtX8iboA1H8SiFqtRkpKCv7880/s3LkTZ8+eRVhYGLZs2YKGDRtaOdKyFX8SiI2NDerWrQt3d/davyr53pzm2DR9GNzrO1k7FNGRyWSSWbS0tmIOxY85FDep5k/UBWDxJ4G0bdsWgYGB6N69O2bNmoXk5GSEh4dj5cqVVozSPMaeBCIVhbCBo5MTZ/8SERFVk1q5DuDIkSOFmb9RUVE4fvy4lSMiqhoajQZZWVnIysri+pAixRyKH3MoblLNX60sAAFg+vTpsLGxAQAsW7bMytEQVQ3dGpHXrl2DSqWydjhUDsyh+DGH4ibV/NXaArBVq1YYMmQIAOD8+fP4+++/rRwRERERUc1QawtAAHj11VeFCRRiuA+QiIiIqDqIehJIWXx8fBAUFISDBw/i5MmTOHXqlNGlVMx9EggAPPbYY5DLjdfNp0+fNquNoKCgWvc4NyIiIhKPWl0AAsDUqVNx8OBBAEW9gPpLreiY+yQQADh58qTJx8Ft2bIFW7ZsKbONHTt2sAAkIiIiq6nVQ8AA0LFjR/Tq1QsAEB0djQsXLlg5IiIiIiLrEl0PYPfu3REfH2/RMWvWrDH6vqXtGBMaGlqh5wNHRUVVOAYiIiIiS4iuACSi/5HJZMKji2r7E2NqK+ZQ/JhDcZNq/lgAEomYnZ0dOnbsaO0wqAKYQ/FjDsVNqvmr9fcAEhEREZEhFoBEREREEsMhYBI0VDhgd/goq5zbrZ6jVc4rdiqVCv/++y8AoHHjxrC15be02DCH4scciptU8yeNqySz2Mjl8HRxsHYYZAGNRoO0tDQAgKenp5WjofJgDsWPORQ3qeaPQ8Ak0Gq11g6BiIiIqgELQBKoVCprh0BERETVgAUgERERkcSwACQiIiKSGBaARERERBLDApCIiIhIYrgMDAmksvZRbSKXy+Hi4iJsk/gwh+LHHIqbVPPH3/gkkNJDsGsLW1tbeHt7WzsMqgDmUPyYQ3GTav5YAJJArdEgNTtXeO1WzxE2EvpriIiISCpYAJIgM6cAIUsihNe7w0fB08XZihERERFRVWABSCRiSqUSSUlJAABvb2/Y2dlZOSKyFHMofsyhuEk1fywAiURMq9UiNzdX2CbxYQ7FjzkUN6nmjzd4EREREUkMC0AiIiIiiWEBSERERCQxor8HsLCwEPv27cNff/2FCxcuICsrCzk5OVAoFPDy8oK/vz8GDRqEHj16lLnA45EjR3DgwAGcPn0a6enpyM/PR8OGDdG8eXM89dRTCAkJgaenp8njn3nmGVy9ehUuLi74559/Sl1Xb8SIEbh8+TIA4LXXXsN//vMfk/ueOHECYWFhAICXX34ZM2bMKPU6iIiIiEoj6gJw3759WLx4MZKTk0t8lp2djezsbFy6dAm//vorWrZsidmzZ6Nfv34l9k1MTMTcuXNx9uzZEp+lpKQgJSUFJ06cwLfffospU6Zg6tSpRou7gIAAXL16FdnZ2bh69SratGljNO6HDx8iPj5eeH3q1KlSr1P/865du5a6LxEREVFZRFsAfvPNN1i+fLnwulevXggKCoK3tzfq1auH+/fv4/r164iKisKxY8dw48YNLFu2rEQBeOrUKUybNg0PHjwAALRv3x4jR46Er68vnJyckJqaiqNHj2LHjh3Iy8vDl19+icTERCxZsqTEVPGuXbvit99+E9o1VQCeOXMGGo0GNjY2UKvVuHDhApRKpcmp56dPnwZQ9IiagICAcn29iIiIiHREWQBu3bpVKP5cXV3x5Zdfolu3biX2e/LJJzF+/HgkJCRg0aJFyMzMNPg8JSUFr7/+Oh48eACZTIYZM2bgpZdeMujd69ChA/r374/Jkyfj9ddfR3x8PP744w80bty4xFCsfu/cqVOnMHbsWKPx63r0nnrqKcTExCAvLw+xsbHo3LlziX3VarXQM+nr64u6deua8yUiibCxsUHTpk2FbRIf5lD8mENxk2r+RDcJJDU1FQsWLAAAODk5YcOGDUaLP30+Pj5YvXo1XnrpJYP3586di+zsbADAf/7zH0yePNnkfXvNmjXDTz/9BA8PDwDA6tWrcf78eYN9GjVqBC8vLwClD+vqPuvWrRsef/zxUve/fPmysD5Rly5dSrtMkiAbGxt4enrC09NTUj+4ahPmUPyYQ3GTav5EVwCuXbsW+fn5AIC33nrL7Ac4y+VyDB8+XHh95coV/PXXXwCKCsQpU6aU2Yarqytmz54NoGixyB9//LHEPrpewJSUFNy5c6fE548ePUJsbCyAooJON6SrG+Ytjvf/ERERUWUTVQGo1Wqxfft2AEW9f6NHjy53W7p2AGDChAlmV/2DBg0SZgIfOHAA9+/fN/hcv5fOWK/e+fPnoVQq4ejoiHbt2gkFoO6+wOL0C0MWgERERFQZRFUAJiYmIisrC0DRjFuFQlHutk6ePClsBwYGmn2cjY2NMJFEo9GUmDlc/D7A4nTvdezYEba2tnj88cdhY2ODBw8eICEhocT+ugKwdevWaNiwodlxVpQ91MjPy0Nubq7wH9U8hYWFOH36NE6fPo3CwkJrh0PlwByKH3MoblLNn6gmgVy5ckXYbt++fYXa0hVb7u7uwn195mrXrp1BTPozi1u2bAl3d3fcu3fPaAGoK+h0PYXOzs7w8/PDpUuXcOrUKfj5+Qn7Xrt2DRkZGQCqv/dvsOIW1v3wrcF78+fPr9YYiIiIqGqIqgdQN2EDKLofr7wePnwIpVIJAHBzc7P4eP1j9GPS0Q3rXr9+XSjgAMMZvfrLuei2ixeM+sO/nABCRERElUVUBaD+MKSjo2OltOPk5GTx8frH5OTklPjc1DBwXFwccnNzYWNjg06dOgnvmyoAOQGEiIiIqoKoCkBnZ2dhWzcTuKLt5OXlWXy8/jHG7kM0NRFE16Pn5+dnEINu/3v37uHmzZsljvXy8kLjxo0tjpOIiIjIGFHdA+ji4iJs6w+tWkqhUMDW1hYqlQrp6ekWH69/jH5MOj4+PqhXrx4ePHhgMIyrK+iKP83Dzc0NLVq0wM2bN3Hq1Cm0aNECqampwjIy1hj+3ZvTHJumD4N7fct7SImIiKhmE1UPoP4EiUuXLpW7HZlMBl9fXwBFvW737t2z6Pi4uDijMenoP7LtypUrwjCxrhg09ji34sPA1h7+LYQNHJ2c4OzsLPxHREREtYOoCsA2bdqgQYMGAIqKKWP335lLv1ctKirK7OM0Gg2OHDkCoKjQM/b4Nv321Wo1zpw5g6SkJOFRdMZ69HTt6Ao//WVqOAGEiIiIKpOoCkCZTIaRI0cCKLoP7/fffy93W6GhocL2xo0boVarzTruzz//REpKCgBgwIABqFevntH99Iu206dPC4VdixYtjM481vUA3rp1C2lpaUJvobu7O1q1amVWbCQ9tra28PX1ha+vL2xtRXVHB/0/5lD8mENxk2r+RFUAAsCkSZOEGcDLly9HUlKSWcdpNBpEREQIr/38/NC7d28AQHx8PFavXl1mG5mZmVi8eDGAomL05ZdfNrlv+/bthThPnjwp9OgZG/4FDBd6PnDgABITE0vdnwgo6oVWKBRQKBSQy0X37UxgDmsD5lDcpJo/0V2pp6cn5s6dC6CoFzAsLAwnTpwo9ZirV6/i5ZdfLlHkLViwQOjB++KLL7B27VpotVqjbSQnJ+PFF18Uev8mT56Mxx9/3OQ57ezshKVeYmNjERMTA6D0gk43DLxmzRohDi7/QkRERJVNlH2do0aNQkpKCpYvX46MjAyEhYWhd+/eCAoKgre3N+rVq4f79+/j+vXrOHLkCI4ePQq1Wl1iwkaTJk2wYsUKvPbaa8jJycGiRYsQGRmJkSNHwtfXF46OjkhLS8PRo0exY8cOYfmXoUOH4u233y4zzi5duuD48eMoLCxEWloagNILwICAABw4cAC3b982aIPIFI1GI/y7dHJyktRfr7UFcyh+zKG4STV/oiwAAeD1119HmzZtsHjxYiQnJyM6OhrR0dEm92/Tpg1mzJhR4v3u3bvjl19+wdy5c3H+/HlcvHgRFy9eNNqGo6MjXnnlFbz22muQyWRlxli8eHN1dS31fr7ixWH9+vWF2cpExqhUKsTHxwMA/P39YW9vb+WIyFLMofgxh+Im1fyJtgAEgKeffhr9+vXDn3/+ib/++guxsbHIzMxEbm4uFAoFvLy88Pjjj2PQoEHo3r27yaLN19cXmzdvxuHDh7F//36cOXMG9+7dQ0FBARo0aICWLVuiT58+GD58ODw9Pc2Or1OnTrCzsxMeO2dqxrBOu3bt4OjoKCxy3blzZ7MKTSIiIiJLiLoABAB7e3sEBwcjODi4wm3169cP/fr1q3hQ/8/BwcFkb6IxdnZ2OHfuXKWdn4iIiMgYaQx0ExEREZGABSARERGRxLAAJCIiIpIYFoBEREREEiP6SSBUeRoqHLA7fJTw2q2eoxWjISIioqoi05p69AVJQmFhIWJjYwFIa/0jIiKiihLz71AOARMRERFJDAtAIiIiIonhPYAkUKvV1g6BLKRWq5Geng4AcHNzg42NjZUjIksxh+LHHIqbVPPHApAEGo3G2iGQhdRqNe7cuQMAaNCggWR+cNUmzKH4MYfiJtX8cQiYiIiISGJYABIRERFJDAtAEtja8o4AIiIiKWABSAKZTGbtEIiIiKgasAAkgVqjQWp2LtScDEJERFSrsQAkQWZOAYZ+shXpD/KtHQoRERFVId70RSRiMpkMzs7OwjaJD3MofsyhuEk1fywAiUTMzs4Ofn5+1g6DKoA5FD/mUNykmj8OARMRERFJDAtAIiIiIonhEDCRiKlUKty8eRMA0KJFC67lKELMofgxh+Im1fzV6KuMiYnBxIkTjX7m4OCAhg0bom3bthgyZAiGDBlSZtJu3bqFiIgIREdHIzk5GVlZWXB2doaHhwe6d++OwYMHo0uXLmXGFRYWhhMnThj9zNbWFnXr1oW3tzf69u2L5557Di4uLibbmjVrFrZv317ifZlMBoVCgSZNmiAgIADPPfcc2rZtW2ZsJC0ajQbZ2dkAgGbNmlk3GCoX5lD8mENxk2r+anQBWJqCggLcvXsXd+/excGDB7Fu3TqsXLkS7u7uJfYtLCzE559/jl9++QVKpdLgs+zsbGRnZyMhIQEbNmxA37598dFHH6FRo0blikulUiErKwunTp3CqVOnsHbtWnz99dcICAiwqB2tVouHDx8iPj4e8fHx+PXXXzFlyhRMnz69XHERERER6YimABw7dizGjRsnvM7Ly8PFixexZs0aJCcnIzY2Fq+99ho2b95sMI07JycHr732GmJiYgAALi4uGDlyJHr27AlXV1c8fPgQFy9exNatW3H9+nUcOXIEY8aMwZo1a+Dt7V1mXJGRkQavlUolbt++jZ07dyIqKgoZGRmYNm0a9u7di4YNG5ba1urVq+Hh4QGg6C+SjIwMHD58GL/88gtUKhVWrVoFDw8PjB8/3uyvGxEREVFxoikAXV1d4ePjY/Bep06dEBwcjNGjR+PmzZu4cOECDh06hKCgIGGf8PBwofjr3r07li1bBldXV4N2evbsiUmTJmHp0qX46aefkJKSgqlTp2L79u1QKBSlxlU8JgBo3749Bg8ejJkzZ2LHjh24f/8+fv/9d7z66qulttWyZUs0bdrU4L1evXqhZ8+emDZtGgBgxYoVeP7552FjY1NqW0RERESmiH4WcP369TFlyhTh9dGjR4Xt3bt3Y+/evQAAPz8/fPfddyWKPx07OzvMmjULo0ePBlB0v+DSpUsrFNvkyZOF7djY2HK3ExQUJNybmJmZiUuXLlUoLiIiIpI20ReAANCxY0dh++7du8L2Dz/8IGwvWLAAjo6OZbY1a9YsuLm5AQC2bt2KzMzMcsel35tXWFhY7nYA09dIREREZKlaUQDqz/5Vq9UAgCtXriAuLg4A8MQTTxgUUKVRKBQIDQ0FADx69Ai7du0qd1z6hVrjxo3L3Q5geI0ajaZCbZUlPy8Pubm5VXoOIiIisp5aUQAmJCQI27pJFKdOnRLeCwwMtKg9/XsI9dux1OrVq4Xt/v37l7sdwPg1VpV1P3yLzz//vErPQZVDLpfDw8MDHh4ekMtrxbez5DCH4scciptU8yeaSSCmqFQq/PTTT8Lrbt26ASjqAdRp166dRW22bdsWcrkcGo0G8fHxpe6rX5gBRbOAk5OTERERgf379wMAhg4diqeeesqiGPRduXJFuLfR0dER/v7+5W6LahdbW1tJrVtVGzGH4scciptU8yfaAlC3DMzXX3+Nc+fOAQC8vLwwdOhQAEBWVpawr7G1AUvj4OAAZ2dnPHz4UFgc0pTg4GCTn7Vq1QpTpkzByJEjLTo/ULQOYHp6Og4fPoylS5cKQ9thYWGoU6eOxe0RERER6YimAFyxYgVWrFhh8nNXV1d88803sLe3BwCDe9icnJwsPp+TkxMePnyInJwcy4P9f9evX8dvv/2Gpk2bCj2TpSlrmLhfv3546623yh0PERERESCiAtCUpk2bYtCgQZg8ebLBEi/Ozs7Cdl5ensXt6o4pax3A4kPEGo0GWVlZOH36NL755hucO3cOL730Ej7//HMMHjzY4jjs7Ozg7++PMWPGYPjw4QaLXFeVF155De71LS+aqfoplUpcvnwZQNGtC3Z2dlaOiCzFHIofcyhuUs2faApA/SeByGQy1KlTBw0aNEDdunWN7t+gQQNh+969e/Dz8zP7XAUFBUIPYmnP8TVGLpfD1dUVTz/9NHr16oXQ0FDcuHED77//Pnr06FFqe/pPApHL5XBycoKbm5vQq1ldHJ2cDApoqrm0Wq3weEOtVmvlaKg8mEPxYw7FTar5E810F92TQHx8fNCmTRs0b97cZPEHAL6+vsK2rrI31+XLl4WlVvTbsZSzszPGjh0LoGhI+s8//yx1/5YtWwrX+Nhjj6FJkybVXvwRERFR7SeaAtBSXbt2FbajoqIsOlZ/f/12yqN169bCdvEZw0RERETWUGsLQD8/P7Rt2xYAcPbsWbMfxZaTk4Pt27cDAOrUqYNnnnmmQnHoZu8CRUvWEBEREVlbrS0AAeCVV14RtufNm4eCgoIyj/n0009x7949AMCoUaPQsGHDCsVw8eJFYbuiTwMhIiIiqgy1ugB85pln8PTTTwMA4uLiMG3aNJPP9lUqlfj000/x22+/AQCaN2+Od999t0LnT05OxsaNG4XXffv2rVB7RERERJVBNLOAy2vhwoXIysrCyZMncezYMQwZMgShoaHo0aMH3NzckJOTg4sXL2LLli24du0aAKBRo0ZYuXJlmUvAACXv69NoNMjOzsapU6ewYcMGYSHp4OBgYUiaiIiIyJpqfQFYt25drF69Gp999hk2bdqE7OxsrFmzBmvWrDG6f58+fbBgwQKzh2tLexKIztChQ7Fw4UKL4iYyh62trTDRyNa21n8710rMofgxh+Im1fxJ4krr1KmDOXPmICwsDBEREYiOjkZycjKys7Ph7OwMDw8PdO3aFUOGDKnwrF+ZTAYnJyc0btwYnTp1wogRIyrcJpEpcrncYM1LEh/mUPyYQ3GTav5kWimtekglFBYWCjOkG7XwRsiSCOwOHwVPFy4ETUREVBr936H+/v6iWrtXEj2ARLWV/gr2dnZ21fKoQKpczKH4MYfiJtX81epZwES1nVKpRGxsLGJjY4UfYCQuzKH4MYfiJtX8sQAkIiIikhgWgEREREQSwwKQBA0VDtgdPgpu9RytHQoRERFVIU4CIYGNXA5PFwdrh0FERERVjD2AJOCKQERERNLAApAEKpXK2iEQERFRNWABSERERCQxvAeQBFJ6BmJtYWdnB39/f2GbxIc5FD/mUNykmj/+xieBVFY/r01kMpmoHj1EJTGH4scciptU88chYCIiIiKJYQ8gCTgLWHw0Gg3u378PAKhfvz7kcv5NJzbMofgxh+Im1fxJ4yrJLBwCFh+VSoVr167h2rVrnMUtUsyh+DGH4ibV/LEHkARqjQap2bkG77nVc4SNRP4aIiIikgoWgCTIzClAyJIIg/d2h4+Cp4uzlSIiIiKiqsCuHSIiIiKJYQFIREREJDEsAImIiIgkhgUgERERkcRwEgiRiMlkMuHRRVzGR5yYQ/FjDsVNqvmr0gIwJiYGEydONPqZg4MDXFxc4Ofnh4EDByIkJMTsR7HcuXMHAwYMEBYu/vzzzxEcHGxxfHfv3sUff/yBY8eO4ebNm8jMzIRGo0H9+vXh4+ODgIAABAcHo1mzZkaPz8jIwIULF3DhwgXExsYiNjYW2dnZAICRI0di8eLFZcYwa9YsbN++vcT7MpkMCoUCTZo0QUBAAJ577jm0bdvW4muk2s3Ozg4dO3a0dhhUAcyh+DGH4ibV/FmtB7CgoAApKSlISUnB4cOHsXbtWqxatQpNmzYt89gdO3YYPLVix44dFhWAjx49wtKlS7Fp0yYUFhaW+DwtLQ1paWmIjo7G8uXLMXjwYMycORONGzc22O/JJ580+5yW0mq1ePjwIeLj4xEfH49ff/0VU6ZMwfTp06vsnERERCQN1VYAjh07FuPGjRNeZ2RkIDExEatXr0ZKSgoSExMxbdo07NixAzY2NqW2FRFRtFadk5MT8vLycPz4caSlpcHDw6PMODIzMzFt2jScO3cOAODs7Ixhw4ahR48eaNSoEWxtbZGeno4zZ85g//79uHHjBvbs2YNOnTph0qRJJttt0qQJWrdujejo6LK/GCasXr1auAaNRoOMjAwcPnwYv/zyC1QqFVatWgUPDw+MHz++3OcgIiIiqrYC0NXVFT4+Pgbv9ezZE6GhoQgJCUFycjISEhKwf/9+DB482GQ7Z86cwc2bNwEA77//PubPnw+1Wo3IyEhMnjy51Bg0Gg3efvttofgLDAzEJ598AldX1xL7BgUF4Z133kFERAQ+/fRTo+29/vrr8Pf3h7+/P9zc3HDnzh3079+/1BhK07JlyxI9oL169ULPnj0xbdo0AMCKFSvw/PPPl1kkkzSoVCr8+++/AIDGjRvD1pa39YoNcyh+zKG4STV/Vp8FrFAohOIGAI4dO1bq/jt27AAANGjQACNGjEDPnj0BADt37izzXOvXr0dMTAwAoHfv3lixYoXR4k9HLpdjxIgR2LZtG3x9fUt8/tZbbyEwMBBubm5lnrsigoKC0KVLFwBFPZiXLl2q0vOReGg0GuGWBY1GY+1wqByYQ/FjDsVNqvmzegEIwKC4SklJMblfYWEh9u7dCwAYMmQI7OzsEBISAgCIj4/H5cuXSz129erVAIA6depg4cKFZlf5jRo1EgpNa9G/QfXu3btWjISIiIjErkYUgLrp1wBKLcqioqJw//59ABAKv4EDB8LJyQnA/3oHjYmOjkZaWhoAYPDgwfD09Kxo2NVK/+tSlX+h2ENt8F9+Xh5yc3NL/EdERETiVSMGupOSkoRtLy8vk/vpCrzmzZvjiSeeAFA0EaR///6IjIzErl278N577xm9P+7kyZPCdr9+/Son8GqUkJAgbJsz2aW8BituGbxe98O3RvebP39+lcVAREREVcvqPYBqtVoYmgWAQYMGGd0vMzNTmGFbfMkXXW9genq6yVm4V65cEbbbt29foZir25UrV3D06FEAgKOjI/z9/a0cEREREYmZ1QrAzMxMHD9+HBMmTEBcXByAouJPN9mhuF27dkGpVAL4X8Gn06tXL7i7uwMwPQysW6AZQKkTP2oKrVaLe/fu4ffff8ekSZOgVqsBAGFhYahTp46VoyMiIiIxq7Yh4BUrVmDFihVGP3N0dMTzzz+Pd9991+TxusKuY8eOaNmypcFnNjY2GDp0KNatW4eoqCjk5ORAoVAY7KN/35qjo2P5LqKKlbWETL9+/fDWW29VUzRERERUW9WIewD9/PwQFhZmMBlEX2JiorD0SfHeP52QkBCsW7cOBQUF2LNnD0aPHm3wubOzs7Cdn59fokCsqezs7ODv748xY8Zg+PDhVf6cwr05zQ1eb5o+DO71nar0nFR+crkcLi4uwjaJD3MofsyhuEk1f1Z5EoharUZKSgr+/PNP7Ny5E2fPnkVYWBi2bNmChg0bljhW1/tna2uLZ555xmj7HTp0gLe3N5KSkrBz584SBaAuuUDRvYI1sQDUfxKIXC6Hk5MT3NzczH5GcmUohOEEGkcnJ4PimWoWW1tbeHt7WzsMqgDmUPyYQ3GTav6s9iSQtm3bIjAwEN27d8esWbOQnJyM8PBwrFy50uA4jUaDyMhIAEWrdZuzHt+pU6dw584dg6dq+Pn5CYtMx8XFlRhGrgmMPQmEiIiIqLJZva9z5MiRwszfqKgoHD9+3ODz48ePIzU11aI2tVptiSeDdO3aVdg+fPhw+YIlIiIiqgVqxD2A06dPx4EDB6BWq7Fs2TKDXj7d8K+9vT0WLlxY5vj8jz/+iLi4OEREROD1118X3u/duzc8PDyQlpaGvXv34t133xXdYtBExSmVSmEdTW9vb5P30VLNxRyKH3MoblLNX40oAFu1aoUhQ4Zg165dOH/+PP7++2/06tULubm5OHDgAICipV6Kr/9nzL179xAXF4cbN27g7NmzwoLR9vb2mDx5MhYtWoRHjx7h/fffx/fff2900ejiUlNTce3aNas/Do6oOK1WK8xw12q1Vo6GyoM5FD/mUNykmj+rDwHrvPrqq8IMV919gPv27UNeXh4A0wtEFzdo0CChneJrAk6cOBHdu3cHUPRouNdffx2ZmZkm29JqtYiMjERoaCji4+Mtuh4iIiKimqpG9AACgI+PD4KCgnDw4EGcPHkSp06dEgo4Ozs7BAUFmdVO48aN4e/vjwsXLmDPnj0IDw8XZtHK5XJ8+eWXmDp1Ks6fP49Dhw5hwIABCA4ORo8ePdCoUSPY2toiPT0d586dw759+3Dt2jWT5zp16hRu3frfo9OysrKE7Zs3b2Lbtm0G+4eGhpr75SAiIiKqMjWmAASAqVOn4uDBgwCAr7/+GidOnAAAdO/eHfXr1ze7nUGDBuHChQu4f/8+Dh06ZNB72LBhQ2zYsAGff/45Nm3ahNzcXPz666/49ddfjbYlk8kQHByMIUOGlPhsy5Yt2L59u9Hjzpw5gzNnzhi8xwKQiIiIaoIaMwQMFD3lo1evXgCAf/75BxqNBoD5w786+vsbezRcnTp1EB4ejn379uHdd9/Fk08+iSZNmsDBwQF16tSBh4cHevfujenTp+PgwYP47LPPOGGEiIiIag2ZVkp3PFIJhYWFiI2NBQA0auGNkCURBp/vDh8FTxcuBF1T6efP39+/WhcNp8rBHIofcyhuFcmfmHNfo3oAiYiIiKjq1ah7AInIMjY2NsLTY8xZ0ohqHuZQ/JhDcZNq/lgAEomYjY0N708VOeZQ/JhDcZNq/lgAkqChwgG7w0cZvOdWz9FK0RAREVFVYQFIAhu5HJ4uDtYOg4iIiKoYC0AScEK4+Ih5BhoVYQ7FjzkUN6nmj7OASaBSqawdAhEREVUDFoBEREREEsMCkIiIiEhiWAASERERSQwLQCIiIiKJYQFIREREJDEsAImIiIgkhusAkkBKz0CsLWxtbeHr6ytsk/gwh+LHHIqbVPMnnSulMsnl7BAWG7lcDoVCYe0wqAKYQ/FjDsVNqvnjb3wiIiIiiWEPIAk0Go21QyALaTQa5OXlAQCcnJzYiytCzKH4MYfiJtX8SeMqySxqtdraIZCFVCoV4uPjER8fz0f5iRRzKH7MobhJNX8sAImIiIgkhgUgERERkcSwACQiIiKSGBaARERERBLDApCIiIhIYrgMjMRptVphW6lUorCw0IrRkKWUSqXRbRIP5lD8mENxq0j+9PfX/30qBjKt2CKmSpWbm4srV65YOwwiIiJR8/Pzg7Ozs7XDMBuHgImIiIgkhj2AEqfRaJCfnw+g6CHYMpnMyhERERGJg1arFRaPdnR0FNVTRFgAEhEREUmMeEpVIiIiIqoULACJiIiIJIYFIBEREZHEsAAkIiIikhgWgEREREQSwwKQiIiISGJYABIRERFJDAtAIiIiIolhAUhEREQkMSwAiYiIiCSGBSARERGRxLAAJCIiIpIYFoBEREREEsMCkIiIiEhiWAASERERSQwLQCIiIiKJYQFIREREJDEsAImIiIgkxtbaAUhdcnIyNmzYgMOHDyMlJQX29vZo1qwZhgwZgvHjx8PR0bFSznPkyBFs3rwZsbGxyMzMRMOGDeHv74/nnnsOffv2NasNlUqF33//HZGRkbh27Rry8vLg4eGBJ598EmFhYWjTpo1Z7WRmZmLDhg04cOAAkpOTAQBeXl4YMGAAJk6ciAYNGpT7Oq1BDDlMT0/HoUOH8M8//+Dy5cv4999/oVQq4eLiAl9fXzz99NMYPnw4HBwcSm3H19fXrFi7deuGDRs2mH1t1iSG/M2aNQvbt2836zwHDx5E06ZNS92nuq65utT0HG7btg2zZ8+26FwjR47E4sWLS7zP70HLaDQaXLt2DRcuXMCFCxcQGxuL+Ph4KJVKAMD69evRvXt3s9vLz8/Hzz//jL179+L27dsoLCxEo0aN0K9fP4SFhcHLy8usdmrC96BMq9Vqq/wsZFRUVBRmzJiBnJwco5+3bNkS33//PVq0aFHuc2g0GsydOxdbtmwxuc/o0aPx0UcfQS433SGcmZmJKVOmIDY21ujn9vb2mDdvHkaPHl1qPOfPn8frr7+Oe/fuGf3c3d0d3377LTp27FhqOzWFGHK4efNmfPDBB1Cr1aWep2XLlvjqq6/g5+dncp/a9stHDPkDKrcArI5rrk5iyGF5CsB33nkHr776aon3+T1ome3bt2PWrFkmP7ekALx58yamTJmCGzduGP1coVDg888/R2BgYKnt1JTvQRaAVhIXF4exY8eioKAATk5OePXVV9G9e3cUFBRg9+7d2Lx5M4Cifwhbt26FQqEo13mWLl2K77//HgDQrl07vPzyy2jWrBlu376NH3/8EXFxcQCAV199Fe+8847RNtRqNcLCwnD69GkAwNNPP43Ro0fDxcUF58+fx8qVK5GRkQG5XI5Vq1aZ/Ev433//RWhoKDIzM2Fra4tJkyYJ3yiHDh3C2rVroVKp4Orqim3btqFRo0bluubqIpYcfvvtt/jqq69gZ2eHwMBA9OrVC97e3nB2dsatW7fw+++/Izo6GgDQsGFDbN++3eTXXvfLZ+zYsRg3bpzJmB0dHdGsWbNyXW91EUv+gP8VgB4eHli9enWp52vVqhXs7Oyses3VRSw5fPDgAVJSUso8z5tvvokbN25ALpfj0KFDRr8P+T1oGf3i287ODj4+PlAqlUhISABgfgGYk5ODUaNGCcXfc889h6FDh8LBwQExMTH47rvvkJeXB0dHR2zatAlt27a12jWbTUtWMW7cOK2Pj4+2Xbt22jNnzpT4/IcfftD6+PhofXx8tMuXLy/XOa5du6Zt166d1sfHRxsaGqrNz883+DwvL08bGhoqxHHjxg2j7fz+++9CLB988EGJz2/cuKHt3Lmz1sfHRztw4ECtUqk02s6MGTOEdnbv3l3i8z/++EP4fObMmeW44uollhz+9NNP2s8++0ybkZFh8jyLFi0SYp01a5bJ/Sp6PTWJWPKn1Wq1M2fO1Pr4+GgDAwPLFYdOdVxzdRJTDsty9epVIdYXXnjB5H5iyk9ZqiN/58+f165fv1579uxZbUFBgVar1WqXL18utPvPP/+Y1c6XX34pHPPDDz+U+Pz06dPCv5MJEyaYbKcmfQ9yEogVXLhwAadOnQIAjBo1Ck888USJfV566SV4e3sDKPoLRXe/giXWrVsHlUoFAJg7d26J+7scHR0xd+5cAEX3961du9ZoO2vWrAEAuLi44L333ivxeYsWLYShips3b2L//v0l9rl37x4iIyMBAL1798aQIUNK7DN06FD07t0bALBz506Tw8Q1gZhyOGnSJPz3v/9Fw4YNTZ7nnXfegbu7OwBg//790Gg0FscqJmLKX2WprmuuLrUthzt27BC2R4wYUa42xKS68texY0eEhYWhU6dOqFOnTrliVSqVwnC6t7c3XnrppRL7dO7cGaNGjQIAnDhxAhcuXCixT037HmQBaAUHDhwQtnX/YIqTy+XCD4EHDx4gJibGonNotVocPHgQANC6dWt06tTJ6H6dOnVCq1atABTdO6QtdkfA9evXkZSUBAAYPHiwyRtTR44cKWzrX59OVFSUUFSYumYACA0NBVB0z01UVJTJ/axNTDk0h729PTp37gwAePjwIbKzsy1uQ0xqW/7MUR3XXJ1qUw41Go3wB7KTkxOefvppi44XIzH9e4yJicHDhw8BFBXnpu7V1f3+Aoz/Hqxp18wC0Ap099I5OTmhffv2Jvfr2rWrsH3mzBmLznHnzh2kpaWVaMeYbt26AQBSU1Nx584do7Hq72eMu7s7WrZsaTJW/XZKi6ci11ydxJRDcxUWFgrbpU0Iqg1qY/7KUh3XXJ1qUw5jYmLw77//Aii6x9rJycmi48VITP8ezf092KFDB6GTpLTfgzXlmrkMjBXoetSaN28OW1vTKWjdunWJY8x19epVo+2UdZ5r164Z3Disf15z2rlx4wb+/fdf5OXlGfwQ08VTt25dYajRGA8PDygUCuTk5Fh8zdVJTDk0h1KpxLlz5wAAbm5ucHFxKXX/vXv3Ys+ePUhOToZcLoe7uzueeOIJjBw5Ej169LDo3NYg1vxlZWVhwoQJSExMRG5urrCMT2BgIEaNGlXq0hHVcc3VSaw5NKY8w7/8Hqw+5v4etLW1RfPmzREfH2801pp2zbX7z/wa6NGjR8jKygKAMme51q9fXyiizJlBpk9//7LOo/+57q9QY+14enqW2k7jxo0BFA2bFI83NTXVrFj027H0mquL2HJojs2bNwvXNHjw4DL3v3r1KpKSklBQUIC8vDzcvHkTO3bswAsvvIDXX39dGC6picScv7y8PJw8eRLZ2dlQKpW4d+8eoqOjsWDBAgwaNMhkb0F1XXN1EXMOi8vLy8O+ffsAFP3sM7d44/dg9dGd18nJCfXq1St1X93vr8zMTINRlZp4zewBrGa5ubnCtjnd/I6OjsjLy0NeXl6VnUe/16D4efTbcXZ2rnA75l5z8XPXJGLLYVlu376NZcuWCeeZMmVKqecJCgpCz5490apVKzg7OyMzMxMnTpzAr7/+iuzsbBw4cACvvfYa1qxZY3I5EmsSY/5kMhk6deqEwMBAtGvXDm5ubnj06BESEhKwZcsWXLhwAampqZg8eTI2btyIdu3alTsWXTzluebqIsYcmrJv3z5h/+HDh0Mmk5V5Hn4PVq/y/P7SHWdvb2/QhiXtVPU1swCsZo8ePRK2zfnG1P3jKSgoqLLz6M5h7DyV3Y4l16x/7ppEbDksTX5+Pt544w2ht2Du3Lml9vT+9ddfRv8C7tWrF8LCwvDKK68gLi4OJ06cwKZNmzBx4kSzY6kuYszf7NmzjX7dn3jiCTz33HP48ssvsWrVKuTl5WHOnDnYunWrQSFRXddcXcSYQ1MiIiKE7eHDh5e5P78Hq195fn/pH1d8u6ZcM4eAq5n+NHRzpnfrupDLekRXRc6j301d/DyV3Y4l11zeKftVTWw5NEWlUuE///kPrly5AqBoYVn9WWzGlDb84ebmhuXLlws/3H7++Wez4qhuYsxfaV93mUyG6dOno2fPngCAS5culRgKrq5rri5izKExqampOH78OADg8ccfL/M+Q4Dfg9ZQnt9f+scV364p18wCsJrpD6Oa07Wbn58PwLwu4/KeR3cOY+fRb6esIVlz2rHkmssacrYWseXQGK1Wi1mzZuHIkSMAgCFDhmDevHkWxWdMs2bN8OSTTwIoWhNSd+9nTVIb8mfMmDFjhO2TJ0+WOxb9eGrqbNTaksOIiAhheSxzev/Mwe/Bylee31/6xxXfrinXzAKwmtWpU0eYYVnWzZ33798X/qFY+lg0/f3LOo/+57obWI21U9YPEt2NzzKZrES8umFFc25o1bVTUx8FJ7YcGvPhhx8K64499dRT+Oyzzypt6RfdIqZA2f9mrKE25M+Yxx57TNgu/nWvrmuuLrUlh7rhXzs7OzzzzDMWxVYafg9WLt158/Ly8ODBg1L31f3+atiwocFwcE28ZhaAVqD7QX3r1i1hhXljrl27Jmzrf0Nbco7i7ZR1nuJDEPrnNbedxo0bl/irRRfPw4cPS33CR1pamvCAbEuvuTqJKYfFffbZZ9i0aROAovWmvv7660q9Ubysm9hrAjHnz5Syvu7Vcc3VSew5vHTpkvA82sDAwDKXXrIEvwcrl7m/B1UqFW7fvl3iGJ2ads0sAK0gICAAQNFfE5cuXTK5n/4wju4pDeZq2rQpPDw8SrRT2nk8PT3RtGlTo7ECRY+3MeXevXvCQ7KNxarfTmnxVOSaq5OYcqjv22+/xY8//ggA8Pf3x6pVqyr9HhP9davKWjrIWsSav9Lor1mnO6++6rjm6iT2HOqv/VdZw786/B6sXOb+Hrx48aLQc1fa78Gacs0sAK1gwIABwvbWrVuN7qPRaIQfEPXq1UP37t0tOodMJkP//v0BFP01oVvkt7hz584Jf23079+/xF+OrVq1Ev4C2bt3r8H9Dfq2b98ubOtfn05QUJAwxGjqmgFg27ZtAIqeRBEUFGRyP2sTUw511q1bh6+++goA4OPjgx9//BEKhcKimMpy+/Zt/P333wCKFjutqb98xJi/svz222/CtrGnVlTHNVcnMedQpVLhjz/+AAA0aNAAffv2tSiu0vB7sPJ169YNdevWBVBUuJt61J/u9xdg/PdgTbtmFoBW0LFjR3Tp0gVA0T+Cs2fPlthnzZo1wl9xEydOLDFEFxMTA19fX/j6+mLWrFlGz/PCCy/AxsYGALBgwYIS08kLCgqwYMECAEUrmL/wwgtG29E9+Do7OxufffZZic9v3bqF7777DgDQokULDBw4sMQ+7u7uCA4OBgBER0dj7969JfbZs2cPoqOjART9RVzaE0OsTWw53Lp1KxYtWgQAaNmyJX766SeLh5yioqJKHbZIT0/HW2+9JcxwGzdunEXtVycx5e/cuXPC48iM0Wq1WLZsGY4dOwYA8PPzM+ixqMxrrknElMPijh49ioyMDADAsGHDzP4683vQ8vxVBnt7e4SFhQEo6l1dvXp1iX3Onj0rFHXdunVDx44dS+xT074HuQ6glYSHh2Ps2LEoKCjASy+9hKlTp6J79+4oKCjA7t27hb/mW7ZsiRdffLFc52jVqhUmT56M77//HhcvXsTYsWPxyiuvoFmzZrh9+zZ++OEHxMXFAQAmT54sPMu3uJEjR2Lr1q04c+YMNm7ciPT0dIwePRr169fHhQsX8O233yInJwdyuRzh4eEmH3Ezffp0HD16FJmZmXj33Xdx8eJF9OvXDwBw+PBh/PTTTwCKbp59++23y3XN1UksOTxw4ADmzp0LrVYLhUKB8PBwZGZmIjMz0+R5mzZtWuI+zo8//hhKpRKDBg1Cp06d4OXlBQcHB2RlZSEmJga//fabsNJ9QEAAxo8fX65rri5iyd/Ro0fx/fffo0+fPujVqxe8vb1Rr149FBYWIj4+Hlu3bsX58+cBFC0eu2DBApM9UNVxzdVJLDksrjyPfgP4PVhe+j1zAHD58mVh++jRo0hOThZeN2/eXCjS9E2ePBm7d+/GjRs38Nlnn+HWrVsYOnQoHBwcEBMTg1WrVkGlUsHBwQHvv/++1a/ZHDKtqb5MqnJRUVGYMWOGMOmhuJYtW+L7779HixYtSnwWExMjLPA5cuRILF682GgbGo1GWBjWlGeffRYLFiwodRZoZmYmpkyZgtjYWKOf29vbY968eRg9erTJNgDg/PnzeP31101OBHF3d8c333yDxx9/vNR2agox5HDWrFkGQ/TmWL9+fYmhh6CgIIMflKYMGjQIH3/8cZmPTKoJxJC/r7/+GitWrCjzWpo0aYLPP//caO+fvopcc00khhzqe/DgAXr37o1Hjx6hTZs22LVrV6n76+P3oCFz8+fr62t2PKW1c/PmTUyZMkW43704hUKBzz//HIGBgaWeo6Z8D7IH0IqCgoIQERGB9evX4/Dhw0hNTYWdnR2aN2+OwYMHY8KECaU+3N0ccrkcCxcuxKBBg/Dbb78hNjYWWVlZaNCgAfz9/TFmzBiz7j9p2LAhfv31V2zevBm7du1CUlIS8vPz4eHhgZ49e2LixIlo06ZNme08/vjjwjUfPHgQd+7cAVDU49S/f3+88MILaNCgQYWuuTqJKYcVtXjxYpw4cQLnzp3D7du3kZ2djZycHDg5OaFRo0bCg+ifeOKJKo+lsoghf6GhoXB1dcW5c+cQHx+PjIwMZGdnw9bWFg0aNEC7du0QGBiI4OBgsxZPr45rrk5iyKG+vXv3Ck+FCAkJsSgOfg9aV4sWLbB9+3Zs3LgRe/fuxa1bt6BUKtGoUSP07dsXEydOhJeXV5nt1JRrZg8gERERkcRwEggRERGRxLAAJCIiIpIYFoBEREREEsMCkIiIiEhiWAASERERSQwLQCIiIiKJYQFIREREJDEsAImIiIgkhgUgERERkcSwACQiIiKSGBaARERERBLDApCIiIhIYlgAEhEREUkMC0AiIiIiiWEBSERERCQxLACJiIiIJIYFIBFRNbh79y7mzZuHAQMGwN/fH76+vvD19cWBAwesHRpRucyaNQu+vr4ICgqydihUDrbWDoCoomJiYjBx4kQAwBtvvIE333zTyhERGbp79y5CQ0ORlZVl7VAsov+9VZyDgwNcXFzg5+eHgQMHIiQkBPb29ma3rVarsW/fPhw8eBCxsbFIT0+HUqmEm5sbWrdujX79+iEkJAT16tWzKObCwkLs27cPf/31Fy5cuICsrCzk5ORAoVDAy8sL/v7+GDRoEHr06AG5nH0gJF0sAIkIAODr6wuARXRVWLlyJbKysmBra4u3334bXbt2hZOTEwCgSZMmVo6ufAoKCpCSkoKUlBQcPnwYa9euxapVq9C0adMyjz116hTmz5+Pq1evlvgsOTkZycnJOHr0KL7++mu8++67eO6558yKad++fVi8eDGSk5NLfJadnY3s7GxcunQJv/76K1q2bInZs2ejX79+ZrUtZvzeJmNYABIRVbFjx44BAPr3749XXnnFytGUz9ixYzFu3DjhdUZGBhITE7F69WqkpKQgMTER06ZNw44dO2BjY2Oynb1792LGjBkoLCwEAHTr1g0hISFo1aoV7O3tcffuXURFReGPP/5AdnY25s6di+vXr2PmzJmlxvfNN99g+fLlwutevXohKCgI3t7eqFevHu7fv4/r168jKioKx44dw40bN7Bs2TJJFIBVZfHixVi8eLG1w6ByYgFIRFTFUlNTAQCtWrWyciTl5+rqCh8fH4P3evbsidDQUISEhCA5ORkJCQnYv38/Bg8ebLSNuLg4/Pe//4VSqYSdnR0WLVqE4OBgg306duyIwYMH48UXX8TUqVORkpKCNWvWoFmzZgYFqL6tW7cKxZ+rqyu+/PJLdOvWrcR+Tz75JMaPH4+EhAQsWrQImZmZ5flSENUKvAGCiKiKKZVKAICtbe37m1uhUGDatGnCa11vZ3EajQbvvfee8LVYuHBhieJPX9u2bbF27VphqHzJkiW4e/duif1SU1OxYMECAICTkxM2bNhgtPjT5+Pjg9WrV+Oll14q/eKIarHa99OIqBj9G9nXr1+Pbt26YcuWLdi2bRuuXbsGpVKJ1q1bY8KECRgxYoRwXGFhIbZu3Yrt27fjxo0bUCqV8PX1xcSJEzF06FCj57pz5w769+8PAFi0aBFCQ0OxZ88e/Pbbb4iPj0dubi6aNGmCgQMH4pVXXinzBvfCwkL8/vvv2Lt3LxITE5GTk4P69eujXbt2GDZsGIKDg03eyD5r1ixs374dXl5eiIqKQlpaGtatW4fDhw/j7t27yMvLw/r16zF79myDe6ZWrFiBFStWGLQ1cuRIg6GetLQ07N+/HzExMbhy5QrS0tKgUqnQoEEDdOjQAcHBwRg8eLDJ2IrnpHv37ti9e7fwdcrLy0Pjxo3Rv39/TJkyBS4uLqV+nQDgyJEjiIyMxNmzZ5GRkQG1Wg13d3e0b98egYGBGDJkCBwdHY0ee+nSJfz222+IiYlBWloatFotPD090aNHD0yaNKlcPXfbtm3D7NmzDd4r/rUt/nUFgMzMTKxbtw5HjhzBnTt38OjRI7i7uyMgIABjxoxBly5dTJ4zKCgIycnJQrsXL17Ezz//jBMnTiAtLQ1KpRLx8fEWX0tZdPeYAUBKSorRfaKiopCYmAgAeOqppxASElJmu61atcK0adOwdOlSFBQUYP369Zg1a5bBPmvXrkV+fj4A4K233oK3t7dZMcvlcgwfPtysffVV588Tfffu3cPPP/+Mo0eP4s6dO8jLy4Orqys6deqEMWPG4MknnyxxjO7fg05Z39vFr61r167Ytm0bdu7ciaSkJGRmZmLEiBHC/sV/xpiSk5ODzZs34/Dhw0hKSsL9+/dhb2+P5s2bIyAgAEOHDkVAQECZXwOqXCwASVJUKhWmTZuGQ4cOGbwfGxuLmTNn4uLFi5gzZw7u37+P119/HSdPnjTY7+zZszh79ixu3bqFqVOnlnm+999/H1u3bjV47/r16/j++++xY8cOrF271uQvrDt37uCVV17BtWvXDN5PT0/HX3/9hb/++gu//fYbvv322zILpHPnzmHq1KmVMgtVrVajb9++0Gg0JT5LS0tDVFQUoqKisGXLFnz99ddwdnYutT2NRoMZM2YgIiLC4P0bN25g9erVOHDgADZu3Ah3d3ejx2dlZWH69Ok4fvx4ic90Ewr27dsHAAgNDS1x7iVLlmDdunXQarUlzn/jxg1s2bIF8+bNw5gxY0q9jsoQHR2N//znP8jJyTF4X3cdERERGD9+PObMmVPmDNZNmzbh448/hkqlqsqQAQB2dnbCtqlezh07dgjbL7zwgtltP//881ixYgUePXqE7du3Y+bMmZDJZAAArVaL7du3Ayjq/Rs9enQ5oi+/6vp5EhERgfnz5yMvL8/g/ZSUFOzduxd79+7Fs88+iw8//LDSepkfPXqEyZMnm+zRNdexY8fwzjvvlPjZo1QqcfnyZVy+fBk///xzlfxhQqVjAUiS8tVXX+H8+fMIDg5GcHAw3NzccOPGDXz99de4fv06NmzYgKCgIGzYsAFnz57F2LFjMXDgQLi4uODy5cv46quvkJaWhuXLl6N///5o06aNyXP98ssviI2NRceOHTFp0iS0aNECGRkZ2L59O/bs2YO0tDS8/PLLiIyMhEKhMDg2NzcXkyZNwu3btwEAAwYMwKhRo+Dh4YE7d+5g48aNOHHiBE6fPo2pU6di48aNJm+8z83NxZtvvolHjx5h6tSp6NWrFxwcHJCQkAB3d3esXr0aSqVSGI4rfrM/ANSvX1/Y1hVKPXr0wFNPPQUfHx80bNgQubm5uH37Nn7//XecPXsWf//9Nz766CMsWbKkzJycPXsWAwYMwIgRI9CkSROkp6fjl19+weHDh3Hz5k0sWrQIX3zxRYlj8/PzMXHiRCQkJAAA2rdvjzFjxqBNmzawt7dHSkoKTp48id27dxs994IFC/DLL78AALp27YqRI0eiWbNmcHBwQHx8PNatW4fExETMmzcPbm5uQu+uOQYMGIAOHToAgMmvrf7X9fLly5g6dapwf9z48eMRFBQER0dHXL58Gd9//72Qe0dHR8yYMcPkuWNjYxEREYFGjRph8uTJ6NChA1QqFU6fPm12/JZISkoStr28vIzuc+rUKQCAo6MjevbsaXbb9erVQ9euXREdHY3s7GxcvXpV+L5LTEwUCouAgIAS30dVrTp+nuzevRvvvfcetFotmjVrhgkTJsDb2xsNGzZEcnIytmzZgiNHjmDLli1QKBQGvc6Wfm/r+/zzzxEfH4+goCCEhoYK35e5ublmf33++ecfvPLKK1CpVLCxscHw4cPRv39/NG7cGI8ePUJSUhL++uuvEgU0VRMtkcj9888/Wh8fH62Pj492+fLlpX7u4+OjXbt2bYl90tLStE888YTWx8dH26NHD62vr692//79Jfa7fPmy1s/PT+vj46NdsGBBic9v375tcK5XXnlFq1QqS+y3YsUKYZ8lS5aU+Hzx4sXC58uWLSvxuUaj0b777rvCPhs3biyxz8yZM4XPO3XqpL18+XKJffSV9jUsfu4bN26Uus9XX32l9fHx0fr6+mqvX79e4vPiOfn222+Nnuell17S+vj4aNu1a6fNyMgosc/ChQuFNj788EOtRqMxGs+jR4+09+7dM3gvOjpaOHbz5s1GjysoKNBOnDhR6+Pjow0MDDSaS3OY87UdNWqU1sfHR9u2bVvt0aNHS3yenZ2tHTp0qNbHx0fr5+enTUhIKLFPYGCgcK5hw4Zp79+/X654dcr63tJqtVqVSqUdMWKEsN/JkydL7JOSkiJ8/txzz1kcx+effy4cHxERIby/c+dO4f0vvvjC4nbLozp/nmRkZGgDAgK0Pj4+2tmzZ5v89/fFF18I/y6SkpJKfG7u93bxazP2s0ef7mdMYGBgic8KCgq0vXv31vr4+Ggff/xx7T///GOynbt375Z6HqoanARCkvL4448bHX5yd3fHwIEDARTdgzVkyBAMGDCgxH5+fn7CvSpl9abY29tjwYIFRodkpk2bJsyo3Lp1q7AkBlB0r9CWLVsAAG3atDG6bpdMJsMHH3wgDP1u3Lix1Fhefvll+Pn5lbqPuWQyGVq0aFHqPq+//joaNGgArVZb6r1BQFGvnbHhL5lMhkmTJgEoGmo7e/aswecPHjzAb7/9JrQRHh4uDA0WZ29vDzc3N4P3vv/+ewDAoEGDTA4d1qlTB/PmzQNQNAwbExNT6rWU14ULFxAbGwsAGD16NHr37l1in/r16+Ojjz4CUDR0reu5NGX+/PkWL6JsiczMTBw/fhwTJkxAXFwcgKKvpbF7FPWH/4rnwRyurq7CdnZ2ttFt/X2qS1X/PNm0aRMePnwIT09PfPDBByaHd9988014enpCo9Fg586dFbkkQcuWLSu0ZuCOHTuQlpYGAJg+fTq6d+9uct/GjRuX+zxUfiwASVJKu9lav0B65plnytxPNzxrSq9eveDp6Wn0M7lcLtwgnp2dLfwCBYCLFy/iwYMHAIpu0DY1tKtQKDBkyBAAwNWrV4UftsaYc8N9eWk0GqSmpuLatWtISEhAQkICkpKS0KhRIwDAlStXSj0+ODjYZOHWvn17Ybv41/uff/4Rbv6fOHFiqWvPFZeTk4MTJ04AKCpaSuPt7Y0GDRoAQIkitLLo32f17LPPmtwvICBAuGe0tHuzGjduXOpkkfJYsWKF8Pg6X19f9OzZE5MmTcKZM2fg6OiIF198EUuXLjV6rP49jbpZvZbQv49Uvy394UhTE3yqUlX/PNH98dSvX79Sn7Jia2uLTp06Aai8f6NDhw616HuqON2wrpOTk9kLeVP14j2AJCmlzeasW7eusN2yZcsy9yvrXhh/f/9SP+/YsaOwnZCQIPwA182UBIp6GErz+OOPY9OmTcJxHh4eJfZxcnJCs2bNSm3HUlqtFhEREdiyZQsuXLiAgoICk/uWNfGkdevWJj/Tn9xS/OutXzRbOoMwLu7/2rv7mKauNw7g32JQNCi1IkO3hLiJWSfRIG6YyKYyoomZL5WYwEh9ne8GByabbnEm27I5/tmETMgSnUycRQzRiHENAlYhamAQUGZsEEQwa8bL6HiLlcrvD9KTW/tCCy3wy/1+/mrSc29Pe2/b555zn/P8JZJY0tPTkZ6e7tF27e3tXr2Op2zHPDAwEGq12m3bxYsX4/Hjx2hubobFYnEaGEizcsfC22+/Da1Wa5cMIiW9N+/VRAZPSI+9dF/SwNB2MTCW/Pl7YrVaxcVTfn6+GO0ejq/O0dGeQw8fPgQwdBE3HsE5DY8BIMlKUFCQy+ekWZWetHOWBSs13JSUq2kts9ksHqtUKrf7kE6nSbeT8vU04PPnz3Hw4EHcunXLo/bugkPA82Py6uctDSydBb7udHR0eNXeZrj3MlK2469UKofN4rQd88HBQfz3339Op1T9MfUrTSCwWq0wmUzQ6/W4cuUKampqoNVqcenSJafnrG0EFRhZgCI9XtKLAunjkR7T0fDn74nZbB5RBrevzlFXySGesn0/XWXv0/hjAEjkJ66mNcd6H6OZxnEmOztbBH/vvfcePv74YyxcuBChoaEICgoSf2gpKSki83Oikf7Zfv3114iOjvZou9H+KY4VXx9zwLESiFqtxqpVqxAbG4sjR47g2bNn+PLLL5Gdne2wbVhYGGbOnIl///0Xjx49gtVq9aqP0tFe6dSq9HF9fb23b2lCs1qt4vHmzZvF+nzDcTUK663hlhmi/38MAIn8ZLiRDlejGtIgo6Ojw+00k/Q1xiI4GRwcFAkqS5cuRW5urss/Clcjkr4iHVX6559/vJrmln7eQUFBDiXOxpqtP11dXRgYGHA7Cmg75gqFwq9JHp7SaDQoKyuDXq9HaWkp7ty547DMi0KhQExMDG7cuIH+/n7cuXPHaaKLM93d3eJCQqlUYv78+eK5yMhIEVj++eef6OnpGfOlYPzl1aWXxvsc9dbMmTNhMpnQ1tY23l0hFxjiE/mJLavTk+el639JH9fW1rrdR11dndPt/KWrq0v8oLur9NHb24umpia/9kWaIOLtSKNarRajq9XV1T7t10jYjp1tcVx3bMc8IiLCbWLAWEpLSxMjej/++KPTNhqNRjw+d+6cx/vOz88X05oajcZuVFyhUIj99vX1oaCgwOu+T1STJ08W58VEOEe99c477wAYSmobj/szaXgMAIn8pKKiwmVm7suXL0UFg5CQELtgJioqSozsXL582eW9hj09Pbh+/ToAYP78+V7fB/eqKVOmAIDdkjSvkk5LuftRLygo8HsFitjYWJFReu7cObu+DUelUomkm6KiInR2dvqjix6TlvF6tXKMVE1NDRoaGhy2GW/z5s0TGem1tbWoqKhwaBMfHy8ymG/evIlr164Nu9/m5mb8/PPPAIZGap1Ng27btk0kGWRmZtotSu3Oy5cvHarPTDTx8fEAgMbGRty+fXvE+/Hku+1rtr739/d7nMBCY4sBIJGfWCwWfPXVV04Dk19++UVUr0hMTLQbyZk8ebJYCsRoNOLUqVMO2w8ODuKbb74RN1qnpKSMur+2m7WfPn3qso1KpRLBaVFRkdM/lLq6Opw8eXLU/RnOjBkzRHm2+vp6fPfddw7l3GxevHjhkCSwb98+AEOBdGpqqlh6xxmLxYLz58/j+fPnPuq9vUWLFomqIQUFBU7L2nV3d4s1CQMCApCcnOyXvozUnj17xOics/sAAwICkJGRIe5RO3LkiMsKLcDQ8kHbtm0TWcOff/455s6d69Dutddew7FjxwAMjQJqtVqxxI8rDQ0N+OSTT3D69GnP3tw42bJli7jIOXr0qN0KAc7cvHnT6bJLnny3fW39+vViGayffvrJ7TFxVT+a/Iv3ABL5SVRUFMrKypCcnGxXCu7y5cti9CM8PBz79+932PbAgQMoLi5GS0sLsrKyYDQasWnTJsyePRutra3Iy8sTP6jR0dE+qVMbHR2N1tZWlJaWQqfTYcmSJWLkIDg4GLNmzUJAQADWrVuH8+fP49GjR0hOTsb27dsRERGBnp4eGAwG/P7775g2bRrCwsLw5MmTUffLnUOHDqGiogJGoxF5eXmoqalBUlISFixYgMDAQJhMJlRVVeHatWv49NNP7WoBr1ixAlu2bMFvv/2GyspKrF27FklJSYiJiYFSqURfXx+ePn2KqqoqFBcXw2w2Y+PGjeIz8bVvv/0WmzdvxosXL7Br1y5otVqsWrXKrhScba24HTt2TLh7whYsWID4+HiUlJSgsrISVVVVDmsRRkVFISMjA5999hksFgvS0tKg0+mwbt06vPnmmwgMDMTff/+N0tJSFBUViVHkHTt2OJQwk0pMTITJZEJmZiY6Ojqg1WoRFxcnRh1nzJgBs9mMpqYmGAwG3L59G1ar1WeLo/tLaGgofvjhB6SmpqKtrQ2JiYnQaDT44IMPEB4ejoGBAZhMJtTV1UGv16OlpQU5OTkO78uT77avTZkyBRkZGdi5cyf6+/uxfft2rF+/HgkJCQgPD4fFYkFjYyMMBgNKS0vx4MEDn/eB3GMASOQnKSkpqKysRGFhIdLS0hyet9Xhla4XZhMcHIyzZ89i165daGxshF6vh16vd2i3ZMkSZGdn+yTrc+fOndDr9bBYLDh+/LjdcxqNBidOnAAwdL9XdXU1Hj58iAcPHuDw4cN2bZVKJbKyspCZmen3AHDq1KnIzc1FamoqKisrUV9fL0aDPPHFF18gJCQE2dnZaGtrQ1ZWlsu206ZN80t2rY1arUZOTg4OHTqEnp4enDlzBmfOnHFol5KS4vCZTxR79+5FSUkJgKFRQGcjbGvXrsXs2bNx/PhxPH78GPfu3XNZYUWpVCI9Pd2jC5wDBw4gMjISJ06cwLNnz1BeXo7y8nKX7SMjI93WU54oVq9ejVOnTuHo0aPo6uqCTqeDTqdz2jYgIMDpmnuefrd9bdmyZcjJycHhw4dhNptRWFiIwsJCv7wWeY8BIJEfff/991i+fDny8/NhNBrR19eHuXPnIiEhAbt373abufvGG2/gypUrKCgowB9//AGj0Yje3l6EhIRArVaLAvS+Wq5BrVYjPz8fp0+fRnV1Ndrb251O8U6fPh0XLlzAr7/+iuvXr6O5uRmTJk3CnDlzsGLFCmzdulVUARkLKpUKeXl5KC4uxtWrV1FbW4vOzk4oFAqEhYVh4cKFSEhIcFrxQ6FQ4ODBg9iwYQN0Oh3u3r2L1tZWdHd3IygoCHPmzIFarUZcXBwSEhLcrufmC3FxcSguLkZubi4MBgNaWlpgsVgQGhqKmJgYJCUl+bzChy8tWrQIy5cvR0VFBcrLy1FXV2e34LnNu+++i6tXr0Kv16OkpAT3799He3s7BgYGoFKp8NZbb2HlypXYsGGDV5nOq1evxsqVK6HX63Hr1i3cv38fnZ2d6O3tRXBwMF5//XUsXrwYa9asQWxsrE+WWRoLtpHVixcvwmAwoKGhAWazGZMmTUJoaCgiIyOxbNkyrFmzxmlZNU+/2/7w/vvv48aNG7hw4QLKysrQ1NSE7u5uTJ06FREREVi6dCk++uijMekL2VMMurpphoi81traig8//BDAUPAnnXIkIiKaKJgEQkRERCQzDACJiIiIZIYBIBEREZHMMAAkIiIikhkGgEREREQywyxgIiIiIpnhCCARERGRzDAAJCIiIpIZBoBEREREMsMAkIiIiEhmGAASERERyQwDQCIiIiKZYQBIREREJDMMAImIiIhkhgEgERERkcwwACQiIiKSGQaARERERDLDAJCIiIhIZhgAEhEREckMA0AiIiIimWEASERERCQzDACJiIiIZIYBIBEREZHMMAAkIiIikhkGgEREREQy8z8l9VBcd24D2gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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x/PhxXL9+HT169EDPnj3RoUMHVK9eHRUqVEBMTAxu376NI0eOFDjk2bZtW3z//feYNWsWUlNTMWHCBDRp0gTdu3eHt7c3KleujKSkJDx9+hSnTp1CcHAwMjIyjHrEFBEREZU/kizMAGDatGmoW7cufv75ZyQkJGD37t3CA85zc3d3x3fffZdj0n9u1tbW+PXXX7F8+XKsWrUKKSkpBbYJAJ07d8b777+vd9/AgQNRs2ZNzJ49GxEREbh27RquXbuWb1vVqlXDl19+me9+IiIiIskWZgAwePBg9O7dG3v27MGJEydw7949xMXFwczMDE5OTvDx8UGXLl3Qo0cPg9b6UiqVGDduHAYPHoy//voLZ86cQUREBOLj46FSqVC5cmXUrVsXzZo1Q58+feDu7l5ge61atcLu3btx/PhxnDhxAlevXkVMTAySkpJgY2ODqlWrCjF26tQJ5uaSTjcRERGJTKHRaDRiB0HiycjIQGhoKADAra4bAjYEiBwRERFR0QQND4KLnYvJrqf7O9THx0e4ebE4JDf5n4iIiKi8YmFGREREJBEszIiIiIgkgoUZERERkUTwNkESOFg7IGh4kNhhEBERFYmjjaPYIRQbCzMSmCnNTHo3CxEREeXEwowEXDnFdDQaDVQqFYDsR4nlfowYlR7mXjzMvTiYd3nhHDMSZGZmih1CuaFSqRAaGorQ0FDhByaZBnMvHuZeHMy7vLAwIyIiIpIIFmZEREREEsE5ZiTgvAPTUSqVcHFxEbbJdJh78TD34mDe5YWFGQn4kHXTMTc3R82aNcUOo1xi7sXD3IuDeZcXls5EREREEsHCjIiIiEgiOHZFgix1FqKTo8UOg4hIthxtHGGmNBM7jBxUKhXu3LkDAPD29oaFhYXIEVFBWJiRID4tHgEbAsQOg4hItoKGB0nuCSq6C8xyIXHp41AmERERkUSwMCMiIiKSCBZmRERERBLBwoyIiIhIIliYEREREUmEyQuzrKwsNGvWDJ6enujfv3+Bx2o0GrRu3Rqenp7w9PTE9u3bCzw+KChIOPbPP/8U3g8MDBTeL8zatWuFY9u2bYuwsDBh39KlS4V9ISEhhbYFAH5+fvD09ISfn5/e/VOmTBHa1P3Py8sLLVq0QJ8+ffDtt98KtzoTERFR2WXywszMzAxNmzYFAISFhSE5OTnfY+/evYuEhATh9eXLlwts+9KlS8J2y5YtjY7t999/x5w5cwAAzs7OWL9+Pby8vIxupyRoNBokJSUhPDwcf/75JwYMGIDFixeLEgsREcmXubk56tati7p16/LRezIgyleoZcuWOH36NNRqNa5cuYK33npL73HaQsvMzAxZWVk5Cq+CjndwcEC9evWMimn58uX4+eefAQCurq5Yu3Yt6tSpY1QbxbVq1SrhQbNqtRqxsbE4ceIE/vzzT2RmZmLFihVwcXHBkCFDTBoXERHJl1KphIODg9hhkIFEmWPWokULYbugYku7r0ePHgCAJ0+e4MWLF3qPjY2NxaNHjwAAzZs3h0KhMDiepUuXCkVZtWrVsGHDBpMXZQBQu3ZteHh4wMPDA15eXmjfvj2mTZuGpUuXCscsW7YMWVlZJo+NiIiISp8ohVnjxo1hZWUFwLDCrHv37njjjTcA5D+cWdRhzJ9++gnLli0DANSoUQMbNmwQriUVfn5+QjEbFxeHW7duiRwRERHJhUajQUZGBjIyMrjyvwyIUphZWlqicePGAIDQ0FBkZGTkOebp06dC71jz5s3RvHlzAPkXcrrv6/bIFWT+/Pn47bffAGT3Vm3cuBHVq1c3/IOYkDZfAPD8+XMRIyEiIjlRqVQIDQ1FaGio8Ggmki7RlsvQFk8ZGRm4fv16nv3aQqt27dpwcnISCrOLFy/qbU/bk2ZnZwdvb+9Cr//9999j9erVAIC6deti/fr1qFq1qvEfxER0J2yq1WoRIyEiIl0Wagvhv7TUNKSkpOT5j8hQot2e0bJlSyxfvhxAdhGWe/hRW5g1a9Ysx//v3r2LxMREVK5cWTg2OTlZWNaiadOmMDMzy/e6Go0G3377LTZt2gQA8PDwwB9//AEnJ6cS+mSlIyIiQtjW3iBARETi803wFbbXLF+j95hZs2aZKBqSO9EKsyZNmsDc3ByZmZl6hye172l71tzd3eHg4ID4+HhcuXIFvr7/+0a4evWqMCG+sPllM2bMwLZt2wAA3t7eWL16NapUqWJ0/M+ePTPoLpeS6DYOCwvDqVOnAADW1tbw8fEpdptEREQkPaIVZra2tvD29kZoaKhQWGl7unLfYanVrFkzHDt2DJcuXcpRmBkzv0xblLm7u2Pt2rU5et6M8fXXXxfpPENpNBrExMTgxIkTWLRokVB4BgYGCjdOEBERUdki6kpzLVu2RGhoKFJSUnDnzh00atQIwP/mkTk5OaF27drC8c2bNxcKM13a+WVWVlaF9iYpFApoNBo8e/YMt2/fRtu2bUvwExVPly5dCtzfuXNnTJgwwUTREBERkamJWpg1b95cmIB/6dIloTDTFl66vWXA/3rDbt26hfT0dFSoUAEZGRm4ceMGAODNN9+EpaVlgdecMWMGvvvuO7x+/Rpjx47F77//bvBdnLrWrVuH1q1bF3qcn58fIiMjjW5fy8LCAj4+Phg0aBD69u1r1PpsRERU+o7bHxe21w5eCydbac9ZJmkTtTBr0aKF0IN1+fJlDBs2DMD/esC0E/61GjRoAGtra6SlpeHatWto06YNQkND8fr1a6G9wgwZMgTp6elYsGAB0tLSMHr0aPzxxx85lqMQi+7K/0qlEjY2NnByciq02CQiIvGolP+bS2xtYw1bW1sRoyG5E225DACwt7dH/fr1Afyvl0z3DsvcPWba3iPd43UXnDV0YdmRI0fi008/Fa730Ucf5XhYuVh0V/6vV68eqlWrxqKMiIiKRfu708fHBxYWFmKHQ4UQtTADkGNF+/v37+Py5ctQq9WwsbFBgwYN8hyfe6FZ7Xw0CwsLNGnSxODrjh8/HqNGjQIAJCYmYsSIEbh//35xPgoREZHkKBQKWFpawtLSktNhZEAyhRmQ3ful7QFr0qSJ3vXItIXZ9evXkZGRgatXrwLIHua0sbEx6tr//ve/ERgYCCD7TtBhw4bh6dOnRfocRERERMUlqcLs4sWLQg9Y7mFMLe0Csqmpqdi5cyeSkpLytGOMadOm4d133wUAREdH48MPP8Q///xTpLaIiIikRq1WIz4+HvHx8XxyjAyIXpi5uroKDw0PCQnBzZs3AeRfmNnZ2cHDwwMA8PvvvwvvG/Pgcl0KhQLffvst/P39AQCRkZEYNmwYXr58WaT2iIiIpCQzMxMPHjzAgwcPkJmZKXY4VAjRCzPgf71dL168QEZGBszNzQucL6Yt2rTDjkqlMt9CzhBKpRLz589H9+7dAQCPHj3C8OHDERcXV+Q2iYiIiIwlqcJMy9vbG9bW1vken7sI8/DwQKVKlYoVg5mZGRYtWoTOnTsDyH4m58iRI/Hq1atitUtERERkKEkUZrmHIQvr/cq9v6jDmLlZWFhgyZIlaNeuHQDg9u3bGDVqFFJSUkqkfSIiIqKCKDQajUbsIEg8GRkZCA0NBQC41XVDwIYAkSMiIpKvoOFBcLFzETuMHHR/zvv4+HB9zBJUGrmVRI8ZEREREbEwIyIiIpIMUZ+VSURERKVLoVAIj2Liyv/Sx8KMiIioDLOwsEDjxo3FDoMMxMKMBA7WDggaHiR2GEREsuVo4yh2CCRzLMxIYKY0k9zdREREROUJCzMScOUU09FoNMjKygKQvbgx532YDnMvHuZeHJmZmcIzoN3c3GBuzl/9UsavDgkyMzNhZWUldhjlgkql4rpCImHuxcPci0OtViM6OhpA9vOpSdq4XAYRERGRRLAwIyIiIpIIFmZEREREEsHCjIiIiEgiWJiRwMzMTOwQiIiIyjUWZiTgretERETi4nIZRCJQKpWwt7cXtsl0mHvxMPfiYN7lhYUZCdhjZjrm5uZwd3cXO4xyibkXD3MvDuZdXlg6ExEREUkECzMiIiIiieBQJgn4rEzTUalUuH//PgDA3d0dFhYWIkdUfjD34mHuxcG8ywsLMyIRaDQapKSkCNtkOsy9eJh7cTDv8sKhTCIiIiKJkE2PWWpqKnbv3o3g4GCEhYUhISEBGo0GdnZ2qF69Ojw8PNC0aVN07NgRbm5uBbb1/Plz/PXXXzh79iweP36MuLg4qNVqVK5cGR4eHmjevDn8/f1Rs2bNPOeGhIRg6NChAIDx48fj008/LTT2KVOmICgoCABw7Ngx1KhRI8f+nTt3YurUqQCAuXPnYsCAAQblhIiIiMoWWRRmV69exRdffIHnz5/n2RcfH4/4+HjcvHkTO3fuhJOTE86cOaO3ndevX2PRokXYtGkTMjIy8uyPjo5GdHQ0Tp8+jSVLlqBHjx6YPHlyoYUeERERUUmQfGH28OFDjBw5Uhgf9/PzQ/fu3VGnTh1YWFggPj4eYWFhOHv2LEJCQvJtJy4uDmPHjsW1a9cAALa2tnjnnXfQpk0bVK1aFebm5oiJicGVK1dw5MgRPHr0CAcOHECTJk0wbNgwE3xSIiIiKu8kX5gtXrxYKMryG+Zr3749Ro4cibi4OBw4cCDPfrVajc8//1woynx9ffHDDz/A0dExz7F+fn744osvsGfPHixYsKBkPwwRERFRASRdmGVlZeHkyZMAgEaNGhU696pKlSoYMmRInvfXrVsn9KZ16NABy5Ytg7l5/h9dqVSiX79+aNOmDR4+fFiMT0BERERkOEkXZnFxcUhPTwcA1KpVq0htZGRkYNWqVQAAKysrzJkzp8CiTFfVqlVRtWrVIl2XqCBmZmbCTSBmZmYiR1O+MPfiYe7FwbzLi6QLM91F8LSL4xnr9OnTiI6OBgD06NEDrq6uJRJbWcRnZZqOmZkZ/y2KhLkXD3MvDuZdXiS9jpm9vT2qV68OAAgLC8PKlSuhVquNauPixYvCdufOnUsyPCIiIqISJekeMwD44IMPMH/+fADAokWLsHnzZvj5+aFZs2bw8fHRu9aYrrCwMGG7YcOGJRpbbGwsIiIiCj3u1atXJXpdIiIiKpskX5gNGzYM9+7dw44dOwAAkZGRWL9+PdavXw8AcHJyQqtWreDv7w9fX988w3EJCQnCtr67MItj06ZN2LRpU4m2KSY+qsN0MjIyEBoaCgDw8fGBpaWlyBGVH8y9eJh7cTDv8iLpoUwg+w7JOXPmYPXq1ejYsWOeifsxMTHYv38/xo4di3/961948uRJjv3apTYAwNra2iQxExERERWF5HvMtNq3b4/27dsjOTkZly9fRmhoKG7evIlLly4hKSkJAHDz5k28//772LlzJ1xcXABkLySrlZaWBjs7uxKLqSiPZCIiIiLKj+R7zHKzs7NDp06dMH78eKxYsQJnz57FnDlzULlyZQDAy5cv8csvvwjH29vbC9sxMTGmDpeIiIjIYLIrzHKztLTEwIEDsWjRIuG9I0eOCHdvenl5Ce/fvn3b5PERERERGUr2hZlWx44dhYeNJyYmCpP+W7ZsKRxz4sQJESIjIiIiMkyZKcwACPPKdHXo0EF4/+DBg3jx4oWpwyIiIiIySJkpzNLS0nDv3j0A2fPQHBwcAGQPdY4cORIA8Pr1a3z99dfIysoyqM0XL17g3LlzpRMwERERUS6SLsxSUlIQEBCA48ePF7jiv1qtxuzZs4WlMfz8/HKsZzZ06FC0bt0aQPYjmsaNG4e4uLh829NoNNi7dy8GDBiA8PDwEvo0RP9jbm4OT09PeHp6GvzsVioZzL14mHtxMO/yIvmv0I0bNzBmzBi4urqia9euaNKkCapVqwY7Ozu8evUKt2/fxo4dO4QV+CtWrIjPPvssRxtKpRI///wzxowZg+vXr+P48ePo2rUr/P390aZNG1StWhXm5uaIiYnBtWvXcPjwYTx48ECMj4vLly8bdJyfn1+OO05LAp+VaTpKpbJEl24hwzH34mHuxcG8y4ukCzNzc3M4Ozvj5cuXePHiBTZu3IiNGzfme3zt2rWxaNEi1KhRI8++KlWqYP369Vi4cCE2bdqElJQUbN68GZs3b9bblkKhgL+/P3r27Flin8cQ27dvx/bt2ws9bteuXSVemBEREZG4JF2YWVlZ4e+//8a1a9dw9uxZXL9+HQ8fPkRsbCxev34Na2truLi4wMvLC126dMHbb79d4KMmrKysMG3aNAwfPhz79u3DuXPn8OjRI8TFxUGj0aBy5crw8PBAy5Yt4e/vLzxAvbzgI5lMR61WIzU1FQBgY2MDpVLSswrKFOZePMy9OJh3eVFo+Nu4XNN9hlqjRo1gZWUlckTlA59dJx7mXjzMvTiY99JTGrll2UxEREQkESzMiIiIiCSChRkRERGRRLAwIyIiIpIIFmZEREREEsHCjAS8QZeIiEhcLMxIYOgzRImIiKh0cB2zco7r2xARERUN1zEjIiIiKsNYmBERERFJhKSflUmmxTlmppOVlYWYmBgAgJOTE8zMzESOqPxg7sXD3IuDeZcXFmYkUKvVYodQbmRlZeHZs2cAAAcHB/6gNCHmXjzMvTiYd3nhUCYJzM1ZpxMREYmJhRkJFAqF2CEQERGVa+wiIUGWOgvRydGFHudo4wgzJbvCiYiIShoLMxLEp8UjYENAoccFDQ+Ci52LCSIiIiIqXziUSURERCQRLMyIiIiIJIJDmUQiUCgUsLW1FbbJdJh78TD34mDe5YWFGZEILCws4OXlJXYY5RJzLx7mXhzMu7zIqjC7cOECAgMDhdebNm1Cs2bNjGrjyZMnOHDgAM6dO4cnT54gPj4emZmZqFixImrUqIEGDRqgc+fOaN++PSwsLPS2sXPnTkydOtWo606dOhXDhg3L8V5gYCAuXLiQ51ilUomKFSuiZs2aaNWqFd577z3UqlXLqOsRERGR/MiqMAsKCsrxeteuXQYXZq9evcK8efOwe/duZGZm5tkfGxuL2NhYXL9+HZs2bYKjoyNGjRqFoUOHmnyVZLVajcTERCQmJuLmzZtYv349pk6diiFDhpg0DiIiIjIt2RRm6enpOHToEADAxsYGqampOHjwIKZPnw5LS8sCz3369ClGjRqFhw8fAgDs7e3Ru3dvtGjRAi4uLrC2tkZcXBwePHiA06dP4+zZs4iNjcW8efPwzjvvwNnZOd+2P//8c3Tp0qXQ+AtqAwD27t0rbKvVakRFReHgwYMICgqCSqXC7NmzUb16dXTu3LnQa5H0ZWZm4vHjxwCAWrVq8akLJsTci4e5FwfzLi+y+eocOXIEKSkpAIDp06fj66+/RmJiIoKDg9GjR498z0tNTcXo0aOFouzdd9/F5MmTYWdnl+fYjh074sMPP8TTp0/x66+/YufOnYXG5erqCg8PjyJ+qv/J3YaXlxc6d+6Mhg0b4vvvv4dGo8GSJUtYmJURarUaCQkJAICaNWuKG0w5w9yLh7kXB/MuL7JZLmPXrl0AAE9PTwwcOBB16tTJ8X5+Fi5ciPv37wMA3n//fcyePVtvUaarZs2amDt3LpYvXw4rK6tix14cQ4YMQfXq1QEAt27dQmxsrKjxEBERUemRRWEWHR2Nc+fOAQD69OmT4/+nT59GXFyc3vNiYmKwfft2AICbmxsmT55s1HX9/PxQqVKlooZdIpRKJRo1aiS8fv78uYjREBERUWmSRWG2d+9eZGVlQalUwt/fHwDg7+8PhUIBlUqFffv26T1v//79eP36NQAgICAAFSpUMFnMJUl3PoBarRYxEiIiIipNsijMdu/eDQBo1aoVXF1dAWQPNzZt2hRA/sOZFy9eFLY7duxYukGWooiICGHbxUUaz6jUzvcjIiKikiP5yf937txBeHg4gP8NX2r16dMHV65cwa1bt3Dv3j3Uq1cvx37teUqlstQW13vx4kWOwik/Rb1B4MSJE7h79y6A7GLUzc2tSO2UtIULF2LWrFlih0FERFSmSL4w0/aGVahQAd27d8+xr2fPnvjhhx+gUqmwa9cu/Pvf/86xPz4+HgBQsWLFApfUePnypXBsbpUrVxZ66fT5+eef8fPPPxf6ObRFoiF0l8tYsmSJ8P5HH31kcBtEREQkP5IuzDIzM4X5Y76+vnnuprS3t0enTp1w9OhR7N27F1988QWUyv+NzmqH26ytrQu8zsqVK7Fu3Tq9+/r374958+YV52MYxNPTs8D9gwYNwnvvvVfqcZBpKJVKYVha998slT7mXjzMvTiYd3mRdGF2+vRpxMTEAMg7jKnVp08fHD16FFFRUQgJCUHbtm2Ffba2tnj16hXS0tJKLca5c+diwIABpdK2tbU1mjVrhg8++AB+fn6lcg0Sh7m5OdcTEglzLx7mXhzMu7xIujDTDmPa29vnO3nf19cXlSpVwqtXr7Br164chZm9vT1evXqFpKQkZGRk5DucOW3aNEybNk14/ezZM4NW8y9Juiv/m5mZwdbWFs7OziZ/HJShcg8bExERUfFJtjBLSkpCcHAwACAhISHHWl75OXz4MGbNmgUbGxsA2avnP3nyBGq1GuHh4fDx8SnVmIujJJ4eYEq2trZih0BERFTmSHaw+cCBA8IaZIZKTU3F4cOHhdctW7YUtk+dOlVisREVl0qlwo0bN3Djxg2oVCqxwylXmHvxMPfiYN7lRbI9ZtphTGdnZ0ydOrXQ4xcsWICoqCjs3r0b/fr1AwD06tULP/74IzIyMrBt2zaMHDlS9EcsEQGARqMRfkBqNBqRoylfmHvxMPfiYN7lRZKF2dOnT3HlyhUAQPfu3dG7d+9Cz7l27RrWrVuH8+fP48WLF3B1dYWTkxMGDhyITZs24fnz5/jxxx8xffr00g6fiIiIqEgkOZS5e/duoarPvXZZfrTHqdVq4UkBQPYkde0Dz9evX49vvvmm0FXrX716VZSwiYiIiIpFkj1m2sLK0dERLVq0MOicZs2awdnZGS9fvsSePXvw8ccfAwDs7Ozw22+/4eOPP8ajR4+wadMmHDx4EO+88w5atGgBFxcX2NjYIDk5GY8fP8bZs2dx5MgRod3Cnq9p6Mr/dnZ2qFatmkGfhYiIiMonyRVmly9fxpMnTwAAXbt2NXgxPKVSiW7duuHPP//E3bt3cfPmTeFOzlq1amHr1q2YO3cu9uzZg/j4eKxfvx7r16/Ptz1HR0d89NFHCAwMLPC6hq7836VLF/z6668GfRYiIiIqnyRXmOkOQxo6jKn19ttv488//wSQffOA7hIblStXxrx58zB27Fjs378f58+fx+PHj5GQkICsrCxUqlQJbm5uaNSoETp06IBOnTrBwsKiZD4UERERkQEUGt6iUa5lZGQgNDQUAOBW1w0BGwIKPSdoeBBc7FxKO7QyTTfvPj4+BT7LlUoWcy8e5l4czHvpKY3cSq7HjKg8MDc3R926dYVtMh3mXjzMvTiYd3nhV4hIBEqlEg4ODmKHUS4x9+Jh7sXBvMuLJJfLICIiIiqP2GNGJALdlbgtLCygUChEjqj8YO7Fw9yLg3mXFxZmJHCwdkDQ8KBCj3O0cTRBNGWbSqXiZFyRMPfiYe7FwbzLCwszEpgpzXi3JRERkYg4x4wEXDmFiIhIXCzMSJCZmSl2CEREROUaCzMiIiIiiWBhRkRERCQRLMyIiIiIJIKFGREREZFEcLkMEvAZaqZjYWEBHx8fYZtMh7kXD3MvDuZdXvibmARcDdp0FAoFF3kUCXMvHuZeHMy7vHAok4iIiEgi2GNGgix1FqKTow0+3tHGEWZKs1KMqOxSq9VITEwEAFSuXBlKJf9GMhXmXjzMvTiYd3lhYUaC+LR4BGwIMPj4oOFBfIRTEWVmZuLBgwcA+Ow6U2PuxcPci4N5lxeWzUREREQSwcKMiIiISCJYmBERERFJBAszIiIiIokol5P/U1NTsXv3bgQHByMsLAwJCQnQaDSws7ND9erV4eHhgaZNm6Jjx45wc3PLce6UKVMQFBRk1PV27doFb29v4fX48eNx5MgRAMCaNWvQtm3bAs8/duwYPvnkEwBAv379MH/+fKOuT0RERPJQ7gqzq1ev4osvvsDz58/z7IuPj0d8fDxu3ryJnTt3wsnJCWfOnCnxGGbOnImQkBC8evUKM2fOxN69e1GhQgW9xyYlJeGbb74BADg6OmLq1KklHg8RERFJQ7kqzB4+fIiRI0ciJSUFAODn54fu3bujTp06sLCwQHx8PMLCwnD27FmEhIQU2t6qVavg4lL4chG1atXK8drFxQWTJk3CjBkz8OTJE/zyyy+YPHmy3nMXLFiA6OjstcWmT58Oe3v7Qq9H0qdQKIRHo/CJC6bF3IuHuRcH8y4v5aowW7x4sVCUzZ07FwMGDMhzTPv27TFy5EjExcXhwIEDBbZXu3Zt1KhRo0ixBAQEYO/evbhw4QLWrl2L3r17o1GjRjmOCQkJwbZt2wBkF5G9evUq0rVIeiwsLNC4cWOxwyiXmHvxMPfiYN7lpdxM/s/KysLJkycBAI0aNdJblOmqUqUKhgwZUmrxKBQKfP/997CyskJWVhamTZuGzMxMYX96ejpmzJghzH3TDmcSERFR2VVuCrO4uDikp6cDyDu0KJZatWph/PjxAICwsDD8/vvvwr4lS5bg8ePHAIBJkybB1dVVlBiJiIjIdMpNYaYdXweA+/fvixhJTiNGjECDBg0AAP/5z3/w4MED3Lx5E2vWrAEAtGzZEoMGDRIxQioNmZmZePr0KZ4+fZqjp5RKH3MvHuZeHMy7vJSbwsze3h7Vq1cHkN07tXLlSqjVapGjAszNzfH999/DzMwMGRkZmD59OqZPn46srCxYWVlh9uzZkp6sqZ2zR8ZRq9WIjo5GdHS0JP4dlifMvXiYe3Ew7/JSbgozAPjggw+E7UWLFqFr1674/vvvsX//fjx9+tTo9h49eoSIiIgC/zOk3YYNG2LYsGEAgMuXL+POnTsAstc7q1OnjtFxmdLChQvFDoGIiKjMKFd3ZQ4bNgz37t3Djh07AACRkZFYv3491q9fDwBwcnJCq1at4O/vD19f30J7qkaOHFnoNVu1aiW0X5AJEyZg//79+OeffwAADRo0wIgRIwo9j4iIiMqOctVjplQqMWfOHKxevRodO3aEuXnOujQmJgb79+/H2LFj8a9//QtPnjwxWWyPHz9GTEyM8LpKlSp54iMiIqKyrVz+5m/fvj3at2+P5ORkXL58GaGhobh58yYuXbqEpKQkAMDNmzfx/vvvY+fOnfkuInvs2LEir2OmS7tchkqlEt47ffo09u7dC39//2K3T0RERPJQrnrMcrOzs0OnTp0wfvx4rFixAmfPnsWcOXNQuXJlAMDLly/xyy+/lHoc69atQ2hoKIDs4VFtIThnzhzEx8eX+vWL49///rfYIRAREZUZ5bowy83S0hIDBw7EokWLhPeOHDlSqnexPH36VCj+PDw8MHHiRMyYMQNA9tprUn9gua2trdghEBERlRkszPTo2LEj3NzcAACJiYlISEgotWvNnDkTaWlpUCqV+OGHH2BhYYG3334bXbt2BQAEBQXh3LlzpXZ9EodSqYS9vT3s7e2hVPLb0JSYe/Ew9+Jg3uWFX6F8GPJw8uLasWMHzp49CwAIDAzM8SyzmTNnws7ODgAwa9YsvH79utTjIdMxNzeHu7s73N3deZOHiTH34mHuxcG8ywsLMz3S0tJw7949ANnz0BwcHEr8GjExMcIwZfXq1fH555/n2O/q6oovv/wSQPYdm8uWLSvxGIiIiEhayk1hlpKSgoCAABw/frzAOWNqtRqzZ88WVrT38/MrlZX3v/vuOyQmJgIAvv32W9jY2OQ5ZvDgwWjWrBkAYPXq1QgLCyvxOIiIiEg6ylWf5o0bNzBmzBi4urqia9euaNKkCapVqwY7Ozu8evUKt2/fxo4dOxAREQEAqFixIj777LN823v06BFSU1MLva6zs3OOXrejR4/i0KFDAIC+ffuiY8eOes9TKBSYPXs2+vXrB5VKhenTp2Pr1q2cI1AGqFQq4Zmt7u7uOZ7lSqWLuRcPcy8O5l1eyk1hZm5uDmdnZ7x8+RIvXrzAxo0bsXHjxnyPr127NhYtWlTgOmWGrPwPAFOnThUeuZSUlIRvv/0WQPYislOnTi3w3Hr16mH06NFYtmwZQkNDsX79enz44YcGXZekS6PRCL2yGo1G5GjKF+ZePMy9OJh3eSk3hZmVlRX+/vtvXLt2DWfPnsX169fx8OFDxMbG4vXr17C2toaLiwu8vLzQpUsXvP3227C0tCzxOBYsWIDo6GgAwNdff23Q/LXRo0fjwIEDuH//Pn7++Wd069YN1apVK/HYiIiISFzlpjADsm8ZbtasmTBvqyjmzZuHefPmFfn82bNnY/bs2UadY2lpif379xf5mkRERCQPnKxEREREJBEszIiIiIgkgoUZERERkUSwMCMiIiKSiHI1+Z8K5mDtgKDhQQYf72jjWIrRlG1mZmbCUixmZmYiR1O+MPfiYe7FwbzLCwszEpgpzeBiV/rPCKXsH46urq5ih1EuMffiYe7FwbzLC4cyiYiIiCSChRkRERGRRHAokwQqlapUnnZAeWVkZCA0NBQA4OPjw7ybEHMvHuZeHMy7vLDHjIiIiEgiWJgRERERSQQLMyIiIiKJYGFGREREJBGc/E8Cc3P+czAVCwsL+Pj4CNtEREQACzPSoVAoxA6h3FAoFLwzioiI8mBhRoIsdRaik6PFDoPy4WjjCDMlH6dCRFSWsTAjQXxaPAI2BIgdBuUjaHgQH5lVAszNzeHp6Slsk+kw9+Jg3uWFXyEiKleUSiXs7OzEDqNcYu7FwbzLC+/KJCIiIpII9pgRUbmiVquRmpoKALCxsYFSyb9PTYW5FwfzLi/86hBRuZKZmYnw8HCEh4cjMzNT7HDKFeZeHMy7vJSpHrOQkBAMHTpUeG1jY4OzZ8/C2tq6wPPS09PRvn17JCcnC++tW7cOrVu3LvC8CxcuIDAwUHi9adMmNGvWzOg4dVWoUAH29vbw8vJCt27d0KdPHy6rQEREVE6U6R6z1NRUHD16tNDjjh07lqMoM1RQUFCO17t27TK6jdzS09MRFRWFEydOYNq0aRgwYACePXtW7HaJiIhI+spsYWZlZQUA2L17d6HHao/RnmOI9PR0HDp0CEB2zxwAHDx4EBkZGUbFOXjwYOzdu1f4b82aNZg2bRqqVq0KALh79y7Gjh2LrKwso9olIiIi+SmzhZmfnx8A4OzZs3j58mW+x8XGxuLMmTMAgC5duhjc/pEjR5CSkgIAmD59OgAgMTERwcHBRsXp6OgIDw8P4b+2bdti6NCh+Ouvv1C9enUAQEREBI4cOWJUu0RERCQ/ZbYwa9++PZydnZGVlYW//vor3+P27duHzMxMODs7o127dga3rx229PT0xMCBA1GnTp0c7xeXnZ0dxo4dK7w+e/ZsibRLRERE0lVmCzMzMzP07t0bQMHDmdp977zzDszMDHvcTXR0NM6dOwcA6NOnT47/nz59GnFxcUWOW5d2pWYAiIqKKpE2iYiISLrKbGEGAH379gUA3L59G3fv3s2z/969e7h161aOYw2xd+9eZGVlQalUwt/fHwDg7+8PhUIBlUqFffv2lUD0gIWFhbDNx2gQERGVfWW6MGvQoAHq168PQH+vmfY9Dw8PeHt7G9yu9rxWrVrB1dUVAFCzZk00bdoUQMkNZ96/f1/Y1s43o/LFQm0h/JeWmoaUlBThPyoaS0tLNG/eHM2bN+dSNCbG3IuDeZeXMt8N07dvXyxcuBD79u3Dl19+CYVCAQDQaDTYu3evcIyh7ty5g/DwcAD/G77U6tOnD65cuYJbt27h3r17qFevXpHjzsrKwqpVq4TX3bt3L3JbJF++Cb7C9prla3LsmzVrlomjISKi0lame8yA7GJJqVTin3/+QUhIiPB+SEgI/vnnnxzDkYbQ9oZVqFAhT7HUs2dPYfixqL1mcXFxOHfuHD744APcvn0bQHZR1qJFiyK1R0RERPJR5gszV1dXYQV/3eFM7XabNm2E4cjCZGZmCvPHfH19YWdnl2O/vb09OnXqBCB7HpparS60zWXLlsHT01P4r23bthg2bBiuXLkCa2trDB8+HIsWLTIoPiIqXFZWFl68eIEXL15wfUATY+7FwbzLS5kvzACgX79+AIDDhw8jPT09x+Kwxgxjnj59GjExMQDyDmNqad+PiorK0UNXFF5eXggMDMxxEwARFU9WVhaePXuGZ8+e8ZeUiTH34mDe5aXMzzEDgG7duuGbb75BcnIyjh07Bo1Gg5SUFNjY2ODtt982uB3t8KS9vT06duyo9xhfX19UqlQJr169wq5du9C2bdsC2xw8eDDef/99ANnfPFFRUTh06BB2796Nq1evIjAwENu3b0eVKlUMjpPKjuP2x4XttYPXwsnWScRoiIiotJWLwszW1hZdu3bF3r17sXv3bmg0GgBA165dhccpFSYpKUlY1T8hIQGNGjUq9JzDhw9j1qxZBV5Du/K/lre3N3x9fdG6dWtMmTIFkZGRmDZtGpYvX25QnFS2qJQqYdvaxhq2trYiRkNERKWtXAxlAv8bzjxz5oywir72PUMcOHAAr1+/NuqaqampOHz4sFHnaPXv31+4uSA4OFhY0JaIiIjKrnLRYwYAbdu2hbOzs/DcTBcXl0KHGXVphzGdnZ0xderUQo9fsGABoqKisHv3bqMKQF0TJ07E0aNHkZWVhcWLFxsVLxEREclPuSnMzMzM0LdvX6xbtw5A9qR/pdKwDsOnT5/iypUrALKXrtA+6qkg165dw7p163D+/Hm8ePHC4Ds/ddWpUwc9e/bEvn37cP36dZw5cwbt27c3uh0iIiKSh3IzlAkAkyZNQmhoKEJDQ/Hvf//b4PN056UZutCr9ji1Wl3gszoLM3r0aGFRXM4zIyIiKtvKVWFWVNrCytHR0eCFXps1awZnZ2cAwJ49e4p8bQ8PD/j5+QEALl68iEuXLhW5LSICFAoFbG1tYWtrK/zRQ6bB3IuDeZcXFmaFuHz5Mp48eQIg+y5OQ4c/lUolunXrBgC4e/cubt68WeQYxowZI2yz14yoeCwsLODl5QUvLy+uEWhizL04mHd5YWFWCN1hSGOfV6m7RlpxHmzeuHFjYW7Z6dOncePGjSK3RURERNKl0GgnT1G5lJGRgdDQUACAW103BGwIEDkiyk/Q8CC42LmIHQYREf0/3d+hPj4+sLS0LHab5eauTCIiIPuZt48fPwYA1KpVC+bm/DFoKsy9OJh3eeFQJhGVK2q1GgkJCUhISIBarRY7nHKFuRcH8y4vLMyIiIiIJIKFGREREZFEcKCZBA7WDggaHiR2GJQPRxtHsUMgIqJSxsKMBGZKM971R0REJCIWZiTgyimmo9FooFKpAGQv/sjVuImICOAcM9KRmZkpdgjlhkqlEp7bqi3QiIiI2GNGAvbamI5SqYSLi4uwTabD3IuHuRcH8y4vLMxIwEUHTcfc3Bw1a9YUO4xyibkXD3MvDuZdXlg6ExEREUkECzMiIiIiieDYFQlUKlWJPICVCqdSqXDnzh0AgLe3NywsLESOqPxg7sXD3IuDeZcXFmYk4Bwz0zE3N4eXl5ewTabD3ItHd5kYLs9jOsy7vPCnEgl4V6bpKBQK9k6KhLknIiljYUaCLHUWopOjxQ6DiPLhaOMIM6WZ2GEQUSliYUaC+LR4BGwIEDsMIspH0PAgPjaNqIzjXZlEREREEsHCjIiIiEgiWJgRERERSQTnmBERkUmYm5ujbt26wjaZBvMuL7L7CoWEhGDo0KF691WoUAH29vbw8vJCt27d0KdPnwJvi/f09DTq2l5eXti9e3eO93bu3ImpU6cCAObOnYsBAwYY1aafnx8iIyPzvG9ubg47OzvUrVsX7dq1w7vvvgtXV1ej2iYikhKlUgkHBwexwyh3mHd5KVNDmenp6YiKisKJEycwbdo0DBgwAM+ePRM7rCLJzMxEQkICrly5gmXLlqFXr144cuSI2GERERFRKZJdj5muwYMH4/333xdex8bG4u7du1i1ahWioqJw9+5djB07Frt27YKZWf5r/zRq1Ahz584t9HoVKlQokbj1cXFxwapVq4TXmZmZiIyMxI4dO3D8+HEkJydj4sSJ2Lp1Kxo0aFBqcRARlRbdFegtLCy4qLWJMO/yIuvCzNHRER4eHjnea9u2LQYMGIA+ffogMjISEREROHLkCHr06JFvOzY2NnnaMTULC4s8MTRo0ADdunXDvHnz8Mcff0ClUmH58uVYunSpSFESERWdSqVCaGgoAMDHx4dPYDAR5l1eytRQppadnR3Gjh0rvD579qyI0RTfp59+KvTWnT59Gmq1WuSIiIiIqDSUycIMyDmxPyoqSsRIis/W1hbu7u4AgNTUVCQkJIgbEBEREZWKMluYWVhYCNtl4fZg3c/DHjMiIqKySf4VSz7u378vbFevXl3ESIovMzMTDx48AJBdoNnb24sbEBGVGgu1Rb770lLTkKJIyXe/ra1taYRERCZUJguzrKysHHc4du/evcDjU1NTERERUWi7VatWRaVKlYodn7G2bNmCV69eAQCaN29eJnoAiUg/3wTffPetWb6mwHNnzZpVwtEQkamVqd/wcXFxCA8Px5IlS3D79m0A2UVZixYtCjzv5s2b8Pf3L7T9oiwgW1Ta5TJ27tyJ//73v8L7I0eONMn1iYiIyPRkXZgtW7YMy5Yt07vP2toa7733Hr788ksTR1U0kZGRBT6JQKFQ4LPPPsNbb71lwqiIiIjIlGRdmBXEy8sLgYGBOSbN56dVq1ZYv369CaIynp2dHdq0aYPhw4cX2vNHRCRlFhYW8PHxEbbJNJh3eZF1Yaa78n9WVhaioqJw6NAh7N69G1evXkVgYCC2b9+OKlWqiBxp4XKv/G9mZoaKFSvC2dmZqzQTlSPH7Y/nu2/t4LVwsnUyYTQlS6FQcHFTETDv8iLrwiz3yv/e3t7w9fVF69atMWXKFERGRmLatGlYvny5iFEaRt/K/0RU/qiUqnz3WdtY885LojKuTK5j1r9/f+FOzODgYJw7d07kiIiISK1WIz4+HvHx8VyP0YSYd3kpk4UZAEycOFF4cPnixYtFjoaIiLRrMj548ACZmZlih1NuMO/yUmYLszp16qBnz54AgOvXr+PMmTMiR0RERERUsDJbmAHA6NGjhYnzcphnRkREROWbrCf/F8bDwwN+fn44duwYLl68iEuXLuldcsLQlf8BoF69elAq9dezly9fNqgNPz8/PlaJiIiI8ijThRkAjBkzBseOHQOQ3WumuySFlqEr/wPAxYsX830s0/bt27F9+/ZC29i1axcLMyIiIsqjTA9lAkDjxo3Rvn17AMDp06dx48YNkSMiIiIi0k92PWatW7dGeHi4UeesXr1a7/vGtqPPgAEDivX8zODg4GLHQERERGWD7AozIiKSJ4VCITwSiE80MR3mXV5YmBERkUlYWFigcePGYodR7jDv8lLm55gRERERyQULMyIiIiKJ4FAmCRysHRA0PEjsMIgoH442jmKHUCyZmZn4559/AABubm4wN+evIFNg3uWFXx0SmCnN4GLnInYYRFRGqdVqREdHAwBcXV1Fjqb8YN7lhYUZCTQajdghlBsajQYqlQpA9sRc3illOsw9EUkZ55iRIDMzU+wQyg2VSoXQ0FCEhoYKRQKZBnNPRFLGwoyIiIhIIliYEREREUkECzMiIiIiiWBhRkRERCQRvCuTBFzbxnSUSiXs7e2FbTId5l48zL04mHd54W9iEnDZANMxNzeHu7u72GGUS8y9eJh7cTDv8sLSmYiIiEgiWJgRERERSQSHMknAlf9NR6VS4f79+wAAd3d3WFhYiBxR+cHci4e5FwfzLi8szIhEoNFokJKSImyT6TD34mHuxcG8ywuHMomIiIgkgoUZERERkUSwMCMiIiKSCNnPMcvIyMDhw4fx999/48aNG4iPj0dycjLs7OxQvXp1+Pj4oHv37mjTpk2hC+udPHkSR48exeXLlxETE4O0tDRUqVIFb7zxBt566y306dMHrq6u+Z7fu3dv3Lt3D/b29jh//nyB64L169cPd+7cAQB88skn+Oyzz/I99sKFCwgMDAQAfPTRR5g0aVKBn4OIiIjkSdaF2eHDhzFv3jxERkbm2ZeQkICEhATcunULmzdvRu3atTF16lR07tw5z7F3797FjBkzcPXq1Tz7oqKiEBUVhQsXLuDXX3/Fxx9/jDFjxugtupo3b4579+4hISEB9+7dQ/369fXGnZSUhPDwcOH1pUuXCvycuvtbtmxZ4LFEREQkX7ItzP7zn/9gyZIlwuv27dvDz88P7u7uqFSpEhITE/Hw4UMEBwfj7NmzePToERYvXpynMLt06RLGjh2LV69eAQAaNmyI/v37w9PTEzY2Nnjx4gVOnTqFXbt2ITU1FT///DPu3r2L+fPn57nluGXLltiyZYvQbn6F2ZUrV6BWq2FmZoasrCzcuHEDKpUq31uYL1++DCD7URrNmzcvUr6IiIhI+mRZmO3YsUMoyhwdHfHzzz+jVatWeY5r164dhgwZgoiICMydOxdxcXE59kdFRWHcuHF49eoVFAoFJk2ahBEjRuToDWvUqBG6dOmCkSNHYty4cQgPD8dff/0FNze3PEOKur1Zly5dwuDBg/XGr+0Be+uttxASEoLU1FSEhoaiWbNmeY7NysoSevI8PT1RsWJFQ1JEEmdmZoYaNWoI22Q6zL14mHtxMO/yIrvJ/y9evMDs2bMBADY2Nli/fr3eokyXh4cHVq1ahREjRuR4f8aMGUhISAAAfPbZZxg5cmS+88Jq1qyJP/74Ay4uLgCAVatW4fr16zmOqVq1KqpXrw6g4OFJ7b5WrVrhzTffLPD4O3fuCOvPtGjRoqCPWWx8VqbpmJmZwdXVFa6urvxBaWLMvXiYe3Ew7/Iiu8JszZo1SEtLAwBMmDDB4AezKpVK9O3bV3gdFhaGv//+G0B24fbxxx8X2oajoyOmTp0KIHuRvt9//z3PMdpes6ioKDx79izP/tevXyM0NBRAdqGlHZrUDlfmxvllRERE5YesCjONRoOgoCAA2b1lAQEBRW5L2w4AfPDBBwb/FdG9e3fhzsyjR48iMTExx37dXi19vWDXr1+HSqWCtbU1GjRoIBRm2nlnuekWbCzMiIiIyjZZFWZ3795FfHw8gOw7IO3s7Irc1sWLF4VtX19fg88zMzMTbiBQq9V57uTMPc8sN+17jRs3hrm5Od58802YmZnh1atXiIiIyHO8tjCrW7cuqlSpYnCcRcFHdZhORkYGLl++jMuXLyMjI0PscMoV5l48zL04mHd5kVVhFhYWJmw3bNiwWG1piyBnZ2dh3pihGjRooDcmAKhduzacnZ0B6C/MtIWWtmfN1tYWXl5eeo9/8OABYmNjAbC3jIiIqDyQVWGmnagPZM/3KqqkpCSoVCoAgJOTk9Hn656jG5OWdnjy4cOHQmEF5LzDUnfZC+127sJMdxiztCf+ExERkfhkVZhp704EAGtr6xJpx8bGxujzdc9JTk7Osz+/4czbt28jJSUFZmZmaNKkifB+foUZJ/4TERGVL7IqzGxtbYVt7Z2ZxW0nNTXV6PN1z9E3zy2/GwC0PWBeXl45YtAe//LlSzx+/DjPudWrV4ebm5vRcRIREZG8yKows7e3F7Z1hwiNZWdnB3Pz7LV1Y2JijD5f9xzdmLQ8PDxQqVIlADmHI7WFVu7V+52cnFCrVq0cx7x48UJYboPDmEREROWDrAoz7SR5ALh161aR21EoFPD09ASQ3Uv18uVLo86/ffu23pi0dB+dFBYWJgx3aos0fY9Vyj2cyWFMIiKi8kdWhVn9+vXh4OAAILvI0Te/y1C6vVDBwcEGn6dWq3Hy5EkA2QWYvsco6baflZWFK1eu4P79+8IjofT1gGnb0RZkust5sMeMiIiofJBVYaZQKNC/f38A2fO8tm3bVuS2BgwYIGxv3LgRWVlZBp136NAhREVFAQC6du0qDFnmpltMXb58WSi4atWqpfdOUG2P2ZMnTxAdHS30rjk7O6NOnToGxUbyYW5uDk9PT3h6egrD6mQazL14mHtxMO/yIqvCDACGDRsm3JG5ZMkS3L9/36Dz1Go19uzZI7z28vJChw4dAADh4eFYtWpVoW3ExcVh3rx5ALKLxI8++ijfYxs2bCjEefHiRaEHTN8wJpBzAdmjR4/i7t27BR5fGvisTNNRKpWws7ODnZ0dlErZfRvKGnMvHuZeHMy7vMjuK+Tq6ooZM2YAyO41CwwMxIULFwo85969e/joo4/yFF+zZ88Werx++uknrFmzJt/V7yMjIzF8+HCht2zkyJHCA8j1sbCwEJbECA0NRUhICICCCy3tcObq1auFODi/jIiIqPyQZZ/mwIEDERUVhSVLliA2NhaBgYHo0KED/Pz84O7ujkqVKiExMREPHz7EyZMncerUKWRlZeWZqF+tWjUsW7YMn3zyCZKTkzF37lzs3bsX/fv3h6enJ6ytrREdHY1Tp05h165dwjIZvXr1wueff15onC1atMC5c+eQkZGB6OhoAAUXZs2bN8fRo0fx9OnTHG2YCh/JZDpqtVr492RjY8O/Yk2IuRcPcy8O5l1eZFmYAcC4ceNQv359zJs3D5GRkTh9+jROnz6d7/H169fHpEmT8rzfunVr/Pnnn5gxYwauX7+Omzdv4ubNm3rbsLa2xqhRo/DJJ58YNOyXu6hydHQscL5Y7qKtcuXKwt2jVLZkZmYiPDwcAODj4wNLS0uRIyo/mHvxMPfiYN7lRbaFGQC8/fbb6Ny5Mw4dOoS///4boaGhiIuLQ0pKCuzs7FC9enW8+eab6N69O1q3bp1vMeXp6YmtW7fixIkTOHLkCK5cuYKXL18iPT0dDg4OqF27Njp27Ii+ffvC1dXV4PiaNGkCCwsL4fFP+d3BqdWgQQNYW1sLi+c2a9aM876IiIjKEVkXZgBgaWkJf39/+Pv7F7utzp07o3PnzsUP6v9VqFAh3943fSwsLHDt2rUSuz4RERHJCweaiYiIiCSChRkRERGRRLAwIyIiIpIIFmZEREREEsHCjIiIiEgiFBquKlquZWRkIDQ0FADXtyEiIjJGafwOZY8ZERERkUSwMCMiIiKSCNkvMEslJysrS+wQyo2srCzExMQAAJycnGBmZiZyROUHcy8e5l4czLu8sDAjgVqtFjuEciMrKwvPnj0DADg4OPAHpQkx9+Jh7sXBvMsLhzKJiIiIJIKFGREREZFEsDAjgbk5R7aJiIjExMKMBAqFQuwQiIiIyjV2kZAgS52F6ORog451tHGEmZITSImIiEoSCzMSxKfFI2BDgEHHBg0PgoudSylHREREVL6wMCMSgUKhgK2trbBNpsPci4e5FwfzLi8szIhEYGFhAS8vL7HDKJeYe/Ew9+Jg3uWFk/+JiIiIJIKFGREREZFEcCiTSASZmZl4/PgxAKBWrVpcQ86EmHvxMPfiYN7lRdJfnZCQEAwdOlTvvgoVKqBKlSrw9vZGz5490bNnz0L/sT158gR79uzB6dOnERkZifj4eNja2sLFxQWtW7dGjx490KJFi0LjCgwMxIULF/TuMzc3R8WKFeHu7o5OnTrh3Xffhb29fb5tTZkyBUFBQXneVygUsLOzQ7Vq1dC8eXO8++678Pb2LjQ2kge1Wo2EhAQAQM2aNcUNppxh7sXD3IuDeZcXSRdmBUlPT8fz58/x/PlzHDt2DGvXrsXy5cvh7Oyc59iMjAwsXLgQf/75J1QqVY59CQkJSEhIQEREBNavX49OnTrhu+++Q9WqVYsUV2ZmJuLj43Hp0iVcunQJa9aswdKlS9G8eXOj2tFoNEhKSkJ4eDjCw8OxefNmfPzxx5g4cWKR4iIiIiLpk01hNnjwYLz//vvC69TUVNy8eROrV69GZGQkQkND8cknn2Dr1q05bgdOTk7GJ598gpCQEACAvb09+vfvj7Zt28LR0RFJSUm4efMmduzYgYcPH+LkyZMYNGgQVq9eDXd390Lj2rt3b47XKpUKT58+xe7duxEcHIzY2FiMHTsWBw8eRJUqVQpsa9WqVXBxyV4bTK1WIzY2FidOnMCff/6JzMxMrFixAi4uLhgyZIjBeSMiIiL5kE1h5ujoCA8PjxzvNWnSBP7+/ggICMDjx49x48YNHD9+HH5+fsIx06ZNE4qy1q1bY/HixXB0dMzRTtu2bTFs2DAsWrQIf/zxB6KiojBmzBgEBQXBzs6uwLhyxwQADRs2RI8ePTB58mTs2rULiYmJ2LZtG0aPHl1gW7Vr10aNGjVyvNe+fXu0bdsWY8eOBQAsW7YM7733HszMuOo+ERFRWSP7uzIrV66Mjz/+WHh96tQpYXv//v04ePAgAMDLywu//fZbnqJMy8LCAlOmTEFAQPbK90+ePMGiRYuKFdvIkSOF7dDQ0CK34+fnJ8x9i4uLw61bt4oVFxEREUmT7AszAGjcuLGw/fz5c2H7v//9r7A9e/ZsWFtbF9rWlClT4OTkBADYsWMH4uLiihyXbu9XRkZGkdsB8v+MREREVHaUicJM927MrKwsAEBYWBhu374NAGjatGmOwqYgdnZ2GDBgAADg9evX2LdvX5Hj0i2g3NzcitwOkPMzqtXqYrVVXBZqC6SlpiElJQUpKSmixkJERFSWlInCLCIiQtjWTp6/dOmS8J6vr69R7enOUdNtx1irVq0Strt06VLkdgD9n1Esvgm+WLN8DRYuXIiFCxeKGotcKZVKuLi4wMXFBUplmfg2lA3mXjzMvTiYd3mRzeT//GRmZuKPP/4QXrdq1QpAdo+ZVoMGDYxq09vbG0qlEmq1GuHh4QUeq1swAdl3ZUZGRmLPnj04cuQIAKBXr1546623jIpBV1hYmDB3ztraGj4+PkVui6TB3Nyc6wmJhLkXD3MvDuZdXmRbmGmXy1i6dCmuXbsGAKhevTp69eoFAIiPjxeO1be2WUEqVKgAW1tbJCUlCYvy5cff3z/ffXXq1MHHH3+M/v37G3V9IHsds5iYGJw4cQKLFi0ShmgDAwNhZWVldHtEREQkfbIpzJYtW4Zly5blu9/R0RH/+c9/YGlpCQA55j7Z2NgYfT0bGxskJSUhOTnZ+GD/38OHD7FlyxbUqFFD6MkrSGHDnZ07d8aECROKHA8RERFJm2wKs/zUqFED3bt3x8iRI3MshWFraytsp6amGt2u9pzC1jHLPdSpVqsRHx+Py5cv4z//+Q+uXbuGESNGYOHChejRo4fRcVhYWMDHxweDBg1C3759cyyeK5bj9sexdvBaONk6iR2KbKlUKty5cwdA9tC5hYWFyBGVH8y9eJh7cTDv8iKbwkx35X+FQgErKys4ODigYsWKeo93cHAQtl++fAkvLy+Dr5Weni70uBX0nEt9lEolHB0d8fbbb6N9+/YYMGAAHj16hK+//hpt2rQpsD3dlf+VSiVsbGzg5OQk9AJKhUqpgrWNdY7il4yj0WiEx4NpNBqRoylfmHvxMPfiYN7lRTa3Z2hX/vfw8ED9+vXxxhtv5FuUAYCnp6ewrf1LwVB37twRlqTQbcdYtra2GDx4MIDsodVDhw4VeHzt2rWFz1ivXj1Uq1ZNckUZERERlR7ZFGbGatmypbAdHBxs1Lm6x+u2UxR169YVtnPfwUlERESkq8wWZl5eXvD29gYAXL161eBHIiUnJyMoKAgAYGVlhd69excrDu3dlED20h5ERERE+SmzhRkAjBo1StieOXMm0tPTCz1nwYIFePnyJQBg4MCBqFKlSrFiuHnzprBd3NX/iYiIqGwr04VZ79698fbbbwMAbt++jbFjx+b77EuVSoUFCxZgy5YtAIA33ngDX375ZbGuHxkZiY0bNwqvO3XqVKz2iIiIqGyTzV2ZRTVnzhzEx8fj4sWLOHv2LHr27IkBAwagTZs2cHJyQnJyMm7evInt27fjwYMHAICqVati+fLlhS6VAeSdN6ZWq5GQkIBLly5h/fr1wgK1/v7+wtAqERERkT5lvjCrWLEiVq1ahR9//BGbNm1CQkICVq9ejdWrV+s9vmPHjpg9e7bBw44Frfyv1atXL8yZM8eouKlsMzc3F24M0X1APZU+5l48zL04mHd5KRdfISsrK0yfPh2BgYHYs2cPTp8+jcjISCQkJMDW1hYuLi5o2bIlevbsWey7MBUKBWxsbODm5oYmTZqgX79+xW6Tyh6lUpljrT0yHeZePMy9OJh3eVFouNpcuZaRkSHcsepW1w0BGwIMOi9oeBBc7FxKMzQiIiJJ0/0d6uPjUyJrj5aLHjMiqdFdidvCwkISj9oqL5h78TD34mDe5aVM35VJJFUqlQqhoaEIDQ0VfmCSaTD34mHuxcG8ywsLMyIiIiKJYGFGREREJBGcY0YCB2sHBA0PMuhYRxvHUo6GiIio/GFhRgIzpRnvtCQiIhIRhzJJwJVTiIiIxMXCjASZmZlih0BERFSusTAjIiIikgjOMSMBn6FmOhYWFvDx8RG2yXSYe/Ew9+Jg3uWFv4lJwNWgTUehUJTIozvIeMy9eJh7cTDv8sKhTCIiIiKJYI8ZCXhXpumo1WokJiYCACpXrgylkn8jmQpzLx7mXhzMu7zwq0MCDmWaTmZmJh48eIAHDx7wblgTY+7Fw9yLg3mXFxZmRERERBLBwoyIiIhIIliYEREREUkECzMiIiIiiWBhRkRERCQRLMyIiIiIJILrmBGJQKFQCI9G4TIlpsXci4e5FwfzLi+lWpiFhIRg6NChevdVqFAB9vb28PLyQrdu3dCnTx+DHxnx7NkzdO3aVVgQdeHChfD39zc6vufPn+Ovv/7C2bNn8fjxY8TFxUGtVqNy5crw8PBA8+bN4e/vj5o1a+o9PzY2Fjdu3MCNGzcQGhqK0NBQJCQkAAD69++PefPmFRrDlClTEBQUlOd9hUIBOzs7VKtWDc2bN8e7774Lb29voz8jSZOFhQUaN24sdhjlEnMvHuZeHMy7vIjWY5aeno6oqChERUXhxIkTWLNmDVasWIEaNWoUeu6uXbtyrFK/a9cuowqz169fY9GiRdi0aRMyMjLy7I+OjkZ0dDROnz6NJUuWoEePHpg8eTLc3NxyHNeuXTuDr2ksjUaDpKQkhIeHIzw8HJs3b8bHH3+MiRMnlto1iYiISFwmK8wGDx6M999/X3gdGxuLu3fvYtWqVYiKisLdu3cxduxY7Nq1C2ZmZgW2tWfPHgCAjY0NUlNTce7cOURHR8PFxaXQOOLi4jB27Fhcu3YNAGBra4t33nkHbdq0QdWqVWFubo6YmBhcuXIFR44cwaNHj3DgwAE0adIEw4YNy7fdatWqoW7dujh9+nThycjHqlWrhM+gVqsRGxuLEydO4M8//0RmZiZWrFgBFxcXDBkypMjXICIiIukyWWHm6OgIDw+PHO+1bdsWAwYMQJ8+fRAZGYmIiAgcOXIEPXr0yLedK1eu4PHjxwCAr7/+GrNmzUJWVhb27t2LkSNHFhiDWq3G559/LhRlvr6++OGHH+Do6JjnWD8/P3zxxRfYs2cPFixYoLe9cePGwcfHBz4+PnBycsKzZ8/QpUuXAmMoSO3atfP0GLZv3x5t27bF2LFjAQDLli3De++9V2jxStKWmZmJf/75BwDg5uYGc3NO9zQV5l48zL04mHd5Ef2uTDs7O6HoAICzZ88WePyuXbsAAA4ODujXrx/atm0LANi9e3eh11q3bh1CQkIAAB06dMCyZcv0FmVaSqUS/fr1w86dO+Hp6Zln/4QJE+Dr6wsnJ6dCr10cfn5+aNGiBYDsHr9bt26V6vWo9KnVamHIXK1Wix1OucLci4e5FwfzLi+iF2YAchQ9UVFR+R6XkZGBgwcPAgB69uwJCwsL9OnTBwAQHh6OO3fuFHjuqlWrAABWVlaYM2eOwX81VK1aVSgAxaI7cfP58+ciRkJERESlRRKFmfY2XgAFFkvBwcFITEwEAKEg69atG2xsbAD8rzdNn9OnTyM6OhoA0KNHD7i6uhY3bJPSzUtp/cWTmpqKlJSUUmmbiIiICieJwuz+/fvCdvXq1fM9Tlt4vfHGG2jatCmA7BsAtPO69u3bh6ysLL3nXrx4Udju3LlzMSM2vYiICGHbkJsciuK3337DwoULS6VtIiIiKpzohVlWVpYwxAgA3bt313tcXFyccMdj7qUxtL1nMTEx+d4VGRYWJmw3bNiwWDGbWlhYGE6dOgUAsLa2ho+Pj8gRERERUWkQrTCLi4vDuXPn8MEHH+D27dsAsosy7ST33Pbt2weVSgXgf4WYVvv27eHs7Awg/+FM7cKvAAqc8C8VGo0GL1++xLZt2zBs2DChJzAwMBBWVlYiR0dERESlwWT3zC5btgzLli3Tu8/a2hrvvfcevvzyy3zP1xZcjRs3Ru3atXPsMzMzQ69evbB27VoEBwcjOTkZdnZ2OY7RnTtlbW1dtA9RygpbaqNz586YMGGCiaIhIiIiU5PEYiZeXl4IDAzMcROArrt37wpLROTuLdPq06cP1q5di/T0dBw4cAABAQE59tva2grbaWlpeQo3qbKwsICPjw8GDRqEvn37lupzzkaPHp3v14BKllKphL29vbBNpsPci4e5FwfzLi+irPyflZWFqKgoHDp0CLt378bVq1cRGBiI7du3o0qVKnnO1faWmZubo3fv3nrbb9SoEdzd3XH//n3s3r07T2Gm/UcJZM9Fk2Jhprvyv1KphI2NDZycnAx+hmhx2djYmOxa5Z25uTnc3d3FDqNcYu7Fw9yLg3mXF9FW/vf29oavry9at26NKVOmIDIyEtOmTcPy5ctznKdWq7F3714A2asXG7Ke2KVLl/Ds2bMcq+h7eXkJi9fevn07z3CoFOhb+Z+IiIjKD9H7NPv37y/ciRkcHIxz587l2H/u3Dm8ePHCqDY1Gk2eJwG0bNlS2D5x4kTRgiUiIiIqRZKYYzZx4kQcPXoUWVlZWLx4cY5eMe0wpqWlJebMmVPo+Pjvv/+O27dvY8+ePRg3bpzwfocOHeDi4oLo6GgcPHgQX375pewWmaWyQ6VSCev3ubu7c26fCTH34mHuxcG8y4skCrM6deqgZ8+e2LdvH65fv44zZ86gffv2SElJwdGjRwFkL4mRe/0yfV6+fInbt2/j0aNHuHr1qrAQraWlJUaOHIm5c+fi9evX+Prrr7Fy5UqDHgb+4sULPHjwQPTHMlHZodFohDuFNRqNyNGUL8y9eJh7cTDv8iL6UKbW6NGjhTsOtfPMDh8+jNTUVAD5LzybW/fu3YV2cq9pNnToULRu3RpA9iOaxo0bh7i4uHzb0mg02Lt3LwYMGIDw8HCjPg8RERGRsSTRYwYAHh4e8PPzw7Fjx3Dx4kVcunRJKKwsLCzg5+dnUDtubm7w8fHBjRs3cODAAUybNk2401CpVOLnn3/GmDFjcP36dRw/fhxdu3aFv78/2rRpg6pVq8Lc3BwxMTG4du0aDh8+jAcPHuR7rUuXLuHJkyfC6/j4eGH78ePH2LlzZ47jBwwYYGg6iIiIqBySTGEGAGPGjMGxY8cAAEuXLsWFCxcAAK1bt0blypUNbqd79+64ceMGEhMTcfz48Ry9bVWqVMH69euxcOFCbNq0CSkpKdi8eTM2b96sty2FQgF/f3/07Nkzz77t27cjKChI73lXrlzBlStXcrzHwoyIiIgKIpmhTCB7Vf/27dsDAM6fPw+1Wg3A8GFMLd3j9T2iycrKCtOmTcPhw4fx5Zdfol27dqhWrRoqVKgAKysruLi4oEOHDpg4cSKOHTuGH3/8kTcKEBERUalTaDgTsFzLyMhAaGgoAMDHx4cLzJoI8y4e5l48zL04mPfSUxq5lVSPGREREVF5Jqk5ZkTlhZmZmfCUB0OWbKGSw9yLh7kXB/MuLyzMiERgZmbGeYsiYe7Fw9yLg3mXFw5lEhEREUkECzMiIiIiieBQJgl4g67p8C4p8TD34mHuxcG8ywt7zEiQmZkpdghERETlGgszIiIiIolgYUZEREQkESzMiIiIiCSChRkRERGRRLAwIyIiIpIIFmZEREREEsF1zEjAZ6iZjrm5OTw9PYVtMh3mXjzMvTiYd3nhV4gESiU7UE1FqVTCzs5O7DDKJeZePMy9OJh3eeFvYiIiIiKJYI8ZCdRqtdghlBtqtRqpqakAABsbG/ZWmhBzLx7mXhzMu7zwq0OCrKwssUMoNzIzMxEeHo7w8HA+CsvEmHvxMPfiYN7lhYUZERERkUSwMCMiIiKSCBZmRERERBLBwoyIiIhIIliYEREREUkEl8so5zQajbCtUqmQkZEhYjTlh0ql0rtNpY+5Fw9zLw7mvfTo5lP392lxKDQl1RLJUkpKCsLCwsQOg4iISNa8vLxga2tb7HY4lElEREQkEewxK+fUajXS0tIAZD/cVqFQiBwRERGRPGg0GmHRXmtr6xJ5qgILMyIiIiKJ4FAmERERkUSwMCMiIiKSCBZmRERERBLBwoyIiIhIIliYEREREUkECzMiIiIiiWBhRkRERCQRLMyIiIiIJIKFGREREZFEsDAjIiIikggWZkREREQSwcKMiIiISCJYmBERERFJBAszIiIiIolgYUZEREQkESzMiIiIiCSChRkRERGRRLAwIyIiIpIIc7EDoMJFRkZi/fr1OHHiBKKiomBpaYmaNWuiZ8+eGDJkCKytrUvkOidPnsTWrVsRGhqKuLg4VKlSBT4+Pnj33XfRqVMng9rIzMzEtm3bsHfvXjx48ACpqalwcXFBu3btEBgYiPr165dIrKYgh7zHxMTg+PHjOH/+PO7cuYN//vkHKpUK9vb28PT0xNtvv42+ffuiQoUKJRKrqcgh9/kJCwvDwIEDkZmZCQDo378/5s2bVyLxmoLccq9SqbB3714cPHgQERERiImJga2tLVxcXNC4cWN06NABPXv2LJGYS5Oc8n716lVs3boVV65cQXR0dI6fOd27d0e/fv1gaWlZIvGWRwqNRqMROwjKX3BwMCZNmoTk5GS9+2vXro2VK1eiVq1aRb6GWq3GjBkzsH379nyPCQgIwHfffQelMv9O1ri4OHz88ccIDQ3Vu9/S0hIzZ85EQEBAkWM1FTnkfevWrfjmm2+QlZVV4HVq166NX375BV5eXkWO1ZTkkPuC2h00aBBu3LghvCenwkxuuQ8LC8OkSZMQERGR7zEVK1bEpUuXihyvKcgl7xqNBj/88APWr19f4LXq16+PlStXolq1akWOtzxjYSZht2/fxuDBg5Geng4bGxuMHj0arVu3Rnp6Ovbv34+tW7cCyP6m3bFjB+zs7Ip0nUWLFmHlypUAgAYNGuCjjz5CzZo18fTpU/z++++4ffs2AGD06NH44osv9LaRlZWFwMBAXL58GQDw9ttvIyAgAPb29rh+/TqWL1+O2NhYKJVKrFixosi9EaYgl7z/+uuv+OWXX2BhYQFfX1+0b98e7u7usLW1xZMnT7Bt2zacPn0aAFClShUEBQWhatWqRYrVVOSS+/ysW7cOP/zwAxwdHREbGwtAPoWZ3HIfFhaGDz/8EAkJCbCyskJAQADat28PV1dXqFQqPHr0CKdOncLly5dx4sSJIsVqCnLK+2+//YaffvoJAGBra4vhw4ejWbNmsLGxwcOHD/HHH38IRbKHhweCgoJgbs6BOaNpSLLef/99jYeHh6ZBgwaaK1eu5Nn/3//+V+Ph4aHx8PDQLFmypEjXePDggaZBgwYaDw8PzYABAzRpaWk59qempmoGDBggxPHo0SO97Wzbtk2I5Ztvvsmz/9GjR5pmzZppPDw8NN26ddOoVKoixWsKcsn7H3/8ofnxxx81sbGx+V5n7ty5QqxTpkwpUqymJJfc6/PPP/9omjZtqvH09NQEBQUJcU6ePLlIcZqanHKfnp6uefvttzUeHh6azp07ax4+fJjvNV+/fl2kWE1FLnnPyMjQtGjRQuPh4aFp2LCh5vbt23mOUalUmoCAACHeAwcOFCne8o6T/yXqxo0bQvf7wIED0bRp0zzHjBgxAu7u7gCy/1JXqVRGX2ft2rXCXJgZM2bkmYtkbW2NGTNmAMieP7ZmzRq97axevRoAYG9vj6+++irP/lq1amH06NEAgMePH+PIkSNGx2oKcsr7sGHD8O9//xtVqlTJ9zpffPEFnJ2dAQBHjhyBWq02OlZTkVPu9fn222+RkpKC/v37o0WLFkbHJSa55X7VqlV49OgRlEolfv75Z9SuXTvfa0p5rpOc8n7//n28evUKAODr6wtvb+88x5ibmws/5wHg2rVrRsdKvCtTso4ePSpsDxw4UO8xSqUS/fr1AwC8evUKISEhRl1Do9Hg2LFjAIC6deuiSZMmeo9r0qQJ6tSpAwA4duwYNLlGvx8+fIj79+8DAHr06JHvJNX+/fsL27qfT0rklHdDWFpaolmzZgCApKQkJCQkGN2Gqcg59wcPHkRwcHC+f5hInZxyn5WVhc2bNwMA2rVrhzfffNOoOKRETnnXLQhr1KiR7/XeeOMNveeQ4ViYSZR2rpaNjQ0aNmyY73EtW7YUtq9cuWLUNZ49e4bo6Og87ejTqlUrAMCLFy/w7NkzvbHqHqePs7Oz8JetsbGaipzybqiMjAxh25iJ7KYm19wnJSXh+++/BwBMmjQJDg4ORsUkBXLK/dWrV/HixQsAgJ+fn/D+69ev8eTJE7x48aLQG2KkQk55r127NhQKhdBmfp48eSJsaws9Mo50f0qXc9oeqDfeeKPAyZN169bNc46h7t27p7edwq7z4MEDvbEa084///yD1NRUg2M1FTnl3RAqlUoYTnBycoK9vb3RbZiKXHP/448/4uXLl2jRokW+vR5SJ6fc6w6PeXh44NGjR/j000/RvHlzdOvWDW+99RZat26Nr776KkeRIEVyynvFihXRu3dvAMDx48cRFhaW5/zMzEzhBgPd48k4LMwk6PXr14iPjweAQu+iq1y5MmxsbAAAUVFRRl1H9/jCrqO7/59//sm3HVdX1wLbcXNzA5DdvW5svKVNbnk3xNatW4XP1KNHD6PPNxW55v7y5cvYunUrLCws8M033wg9CnIit9zrFiYPHjxA//79cfjw4RzDZklJSdi9ezf69euHs2fPGhWnqcgt7wAwdepUNGzYECqVCkOGDMGyZctw9uxZXLt2DUFBQRg4cCCuXbsGa2trzJ8/X5a9x1LA+1glKCUlRdjWfjMWxNraGqmpqUb3QBlzHd15Y7mvo9uOra1tkdsRm9zyXpinT59i8eLFwnU+/vhjo843JTnmPiMjAzNnzoRGo8GwYcNktXiyLrnlXnee5A8//IDXr19j2LBheP/99+Hm5oYXL15g06ZNWL16NVJSUvDZZ59h9+7dkltTS255B7J73Tdu3IitW7di5cqVWLp0aY79CoUCAQEBGD58uHDDAhmPPWYS9Pr1a2HbwsKi0OO1dx2lp6eX2nV072zKfZ2Sakdscst7QdLS0jB+/HgkJSUByL4Tq7DeTDHJMfcrV67EvXv3UL16dYwbN86oOKREbrlPS0vL0eaECRMwdepU1KpVS1gt/6uvvsLEiRMBZE+Y/+2334yK1RTklnet8+fPY8+ePYiJicmzT3ujQVBQUI65rWQcFmYSZGVlJWwbcleL9hvA2MfuGHMd3W+y3NcpqXbEJre85yczMxOfffaZMAdk8ODBGDBggFExmprccv/gwQPhl/2MGTNK7HE5YpBb7nXbcXBwwKhRo/S2MXLkSGGpmIMHDxbprubSJLe8A9nLbowdOxY3b95Ey5Yt8ccff+Dy5csIDQ3F/v37MWLECCQmJuK///0vPvzwwxy9dWQ4FmYSpDscaEi3tfYvSEO6w4t6Hd2/UnNfR7edwr4RC2pHbHLLuz4ajQZTpkzByZMnAQA9e/bEzJkzjYpPDHLKvUajwaxZs5CRkYFu3brB19fXqBikRk65z91Oq1at8l2nzNzcHG3atAGQPfz59OlTo+ItbXLLe1hYGObNmweNRoN27dph7dq1aNeuHezs7GBpaQl3d3dMnjwZ3333HYDsu0dzD3WSYTjHTIKsrKxgb2+PhISEQid6JiYmCt9sxj5uR/f4wq6ju187gV9fOy9evChwwVPthFKFQiG5xwPJLe/6fPvtt9i7dy8A4K233sKPP/4o6SUytOSU+2vXruHChQsAgKZNm+Kvv/7Kc25cXJyw/ezZM+GY+vXrw8PDw6iYS5uccp+7ncK+J3T3x8XF5VhjS2xyy/vOnTuFBao//fRTmJmZ6W3jX//6F/773//i0aNHCAoKwuTJk2V5U4yYWJhJVL169XDp0iU8efIEmZmZ+d5KrXtLs7GTLevVq6e3ncKuk/uWa93rPnjwQO+K0LnbcXNzk1yPGSCvvOf2448/YtOmTQCy1ytaunSpQXNXpEIuudcd7lmwYEGh17x48SIuXrwIABg/frzkCjNAPrkHkOMmi8LWK9Pdn18hISY55V33btiC1lwDsp/F+ejRIyQkJCA2NhZOTk7GhFzuSf9P6XKqefPmALK7nm/dupXvcdof+ACEFd4NVaNGDbi4uORpp6DruLq65ln1WRsrAKEnQZ+XL1/i0aNHRYrVVOSUd12//vorfv/9dwCAj48PVqxYIbk5fIWRa+7LAjnlXvdxV4Utuqw7fCnFm1/klHfdolH7eKf86O6XYkEsdSzMJKpr167C9o4dO/Qeo1arsWvXLgBApUqV0Lp1a6OuoVAo0KVLFwDZfynl91yza9euCX9JdenSJU+3dJ06dYS/4g4ePJhjnoKuoKAgYVv380mJnPKutXbtWvzyyy8Ashfc/P3332FnZ2dUTFIgl9y3bt0a4eHhBf6nfQQOkP0oMu37n376qVHxmopccg8ANWvWRIMGDQAAISEhwp3HuSUnJwtrmL3xxhtCcSIlcsq7bqGmfb6nPiqVClevXgWQvcislBe1lioWZhLVuHFj4S/DHTt2CP/Qda1evVroXh46dGieYauQkBB4enrC09MTU6ZM0XudDz/8UPiLZvbs2XlukU5PT8fs2bMBZP/F9OGHH+ptZ8SIEQCyJ9n++OOPefY/efJEuIutVq1a6Natm/4PLjK55X3Hjh2YO3cugOxHpvzxxx+y/UEot9yXJXLLvfZOzNTUVMyZM0fvMfPmzUNycjIA4L333tN7jNjklHfdm1wWLVok5Da3pUuX4uXLlwCATp06cX5ZEXCOmYRNmzYNgwcPRnp6OkaMGIExY8agdevWSE9Px/79+7FlyxYA2b+Qhw8fXqRr1KlTByNHjsTKlStx8+ZNDB48GKNGjULNmjXx9OlT/Pe//8Xt27cBZN9+rn3WZW79+/fHjh07cOXKFWzcuBExMTEICAhA5cqVcePGDfz6669ITk6GUqnEtGnTCnz8iNjkkvejR49ixowZ0Gg0sLOzw7Rp0xAXF5dj4nluNWrUkOTcPi255L4sklPue/XqhV27duHkyZPYuXMnoqOjMXjwYFSrVg3//PMPtmzZItyZ3KBBA3zwwQdFitcU5JL3Dh06oE2bNjh//jzCw8PRt29fDB06FI0bN4aVlRUeP36MHTt24NSpUwCy7+qU8/p+YlJopLa4C+UQHByMSZMm5fvXSe3atbFy5UrUqlUrz76QkBAMHToUQHbhNG/ePL1tqNVqTJ8+Pd+udCD7TpvZs2cXeIdfXFwcPv74Y4SGhurdb2lpiZkzZyIgICDfNqRCDnmfMmVKjuFhQ6xbt87ooRBTk0PuC/Ps2TNh+KigOKRGTrlPSUnBhAkTcPr06XyP8fHxwfLly4X1zKRKLnlPTEzEp59+ipCQkAI/T5UqVbBo0SK0a9euwONIP+l2WxAAwM/PD3v27MG6detw4sQJvHjxAhYWFnjjjTfQo0cPfPDBB8Ve3FKpVGLOnDno3r07tmzZgtDQUMTHx8PBwQE+Pj4YNGgQOnXqVGg7VapUwebNm7F161bs27cP9+/fR1paGlxcXNC2bVsMHTpUNo+tkVPeyxrmXjxyyr2trS1+//137N+/H0FBQQgLC0NCQgIqVqwIb29v9O7dG/369ZPF5HO55L1y5cpYu3Ytjh07hn379iE0NBQxMTHIyspCxYoVUa9ePbz11lsICAiQ7ZQKKWCPGREREZFEcPI/ERERkUSwMCMiIiKSCBZmRERERBLBwoyIiIhIIliYEREREUkECzMiIiIiiWBhRkRERCQRLMyIiIiIJIKFGREREZFEsDAjIiIikggWZkREREQSwcKMiIiISCJYmBERERFJBAszIiIiIolgYUZEREQkESzMiIiIiCSChRkRkQk8f/4cM2fORNeuXeHj4wNPT094enri6NGjYodGVCRTpkyBp6cn/Pz8xA6lTDEXOwCi4goJCcHQoUMBAOPHj8enn34qckREOT1//hwDBgxAfHy82KEYRfd7K7cKFSrA3t4eXl5e6NatG/r06QNLS0uD287KysLhw4dx7NgxhIaGIiYmBiqVCk5OTqhbty46d+6MPn36oFKlSkbFnJGRgcOHD+Pvv//GjRs3EB8fj+TkZNjZ2aF69erw8fFB9+7d0aZNGyiV7Jsg6WFhRkQAAE9PTwAsbkvD8uXLER8fD3Nzc3z++edo2bIlbGxsAADVqlUTObqiSU9PR1RUFKKionDixAmsWbMGK1asQI0aNQo999KlS5g1axbu3buXZ19kZCQiIyNx6tQpLF26FF9++SXeffddg2I6fPgw5s2bh8jIyDz7EhISkJCQgFu3bmHz5s2oXbs2pk6dis6dOxvUtpzxe1teWJgREZWys2fPAgC6dOmCUaNGiRxN0QwePBjvv/++8Do2NhZ3797FqlWrEBUVhbt372Ls2LHYtWsXzMzM8m3n4MGDmDRpEjIyMgAArVq1Qp8+fVCnTh1YWlri+fPnCA4Oxl9//YWEhATMmDEDDx8+xOTJkwuM7z//+Q+WLFkivG7fvj38/Pzg7u6OSpUqITExEQ8fPkRwcDDOnj2LR48eYfHixeWiMCst8+bNw7x588QOo8xhYUZEVMpevHgBAKhTp47IkRSdo6MjPDw8crzXtm1bDBgwAH369EFkZCQiIiJw5MgR9OjRQ28bt2/fxr///W+oVCpYWFhg7ty58Pf3z3FM48aN0aNHDwwfPhxjxoxBVFQUVq9ejZo1a+YoDHXt2LFDKMocHR3x888/o1WrVnmOa9euHYYMGYKIiAjMnTsXcXFxRUkFUaniADsRUSlTqVQAAHPzsve3sJ2dHcaOHSu81vYO5qZWq/HVV18JuZgzZ06eokyXt7c31qxZIwz5zp8/H8+fP89z3IsXLzB79mwAgI2NDdavX6+3KNPl4eGBVatWYcSIEQV/OCIRlL2fEkS56E5gXrduHVq1aoXt27dj586dePDgAVQqFerWrYsPPvgA/fr1E87LyMjAjh07EBQUhEePHkGlUsHT0xNDhw5Fr1699F7r2bNn6NKlCwBg7ty5GDBgAA4cOIAtW7YgPDwcKSkpqFatGrp164ZRo0YVOrE5IyMD27Ztw8GDB3H37l0kJyejcuXKaNCgAd555x34+/vnO4F5ypQpCAoKQvXq1REcHIzo6GisXbsWJ06cwPPnz5Gamop169Zh6tSpOebkLFu2DMuWLcvRVv/+/XMMWURHR+PIkSMICQlBWFgYoqOjkZmZCQcHBzRq1Aj+/v7o0aNHvrHl/pq0bt0a+/fvF/KUmpoKNzc3dOnSBR9//DHs7e0LzBMAnDx5Env37sXVq1cRGxuLrKwsODs7o2HDhvD19UXPnj1hbW2t99xbt25hy5YtCAkJQXR0NDQaDVxdXdGmTRsMGzasSD1dO3fuxNSpU3O8lzu3ufMKAHFxcVi7di1OnjyJZ8+e4fXr13B2dkbz5s0xaNAgtGjRIt9r+vn5ITIyUmj35s2b2LBhAy5cuIDo6GioVCqEh4cb/VkKo53DBABRUVF6jwkODsbdu3cBAG+99Rb69OlTaLt16tTB2LFjsWjRIqSnp2PdunWYMmVKjmPWrFmDtLQ0AMCECRPg7u5uUMxKpRJ9+/Y16Fhdpvx5ouvly5fYsGEDTp06hWfPniE1NRWOjo5o0qQJBg0ahHbt2uU5R/vvQauw7+3cn61ly5bYuXMndu/ejfv37yMuLg79+vUTjs/9MyY/ycnJ2Lp1K06cOIH79+8jMTERlpaWeOONN9C8eXP06tULzZs3LzQH5QULMypXMjMzMXbsWBw/fjzH+6GhoZg8eTJu3ryJ6dOnIzExEePGjcPFixdzHHf16lVcvXoVT548wZgxYwq93tdff40dO3bkeO/hw4dYuXIldu3ahTVr1uT7i+TZs2cYNWoUHjx4kOP9mJgY/P333/j777+xZcsW/Prrr4UWLteuXcOYMWNK5K7ArKwsdOrUCWq1Os++6OhoBAcHIzg4GNu3b8fSpUtha2tbYHtqtRqTJk3Cnj17crz/6NEjrFq1CkePHsXGjRvh7Oys9/z4+HhMnDgR586dy7NPO5H88OHDAIABAwbkufb8+fOxdu1aaDSaPNd/9OgRtm/fjpkzZ2LQoEEFfo6ScPr0aXz22WdITk7O8b72c+zZswdDhgzB9OnTC72jcNOmTfj++++RmZlZmiEDACwsLITt/HoFd+3aJWx/+OGHBrf93nvvYdmyZXj9+jWCgoIwefJkKBQKAIBGo0FQUBCA7N6ygICAIkRfdKb6ebJnzx7MmjULqampOd6PiorCwYMHcfDgQfzrX//Ct99+W2K9sq9fv8bIkSPz7QE11NmzZ/HFF1/k+dmjUqlw584d3LlzBxs2bCiVPxjkioUZlSu//PILrl+/Dn9/f/j7+8PJyQmPHj3C0qVL8fDhQ6xfvx5+fn5Yv349rl69isGDB6Nbt26wt7fHnTt38MsvvyA6OhpLlixBly5dUL9+/Xyv9eeffyI0NBSNGzfGsGHDUKtWLcTGxiIoKAgHDhxAdHQ0PvroI+zduxd2dnY5zk1JScGwYcPw9OlTAEDXrl0xcOBAuLi44NmzZ9i4cSMuXLiAy5cvY8yYMdi4cWO+E65TUlLw6aef4vXr1xgzZgzat2+PChUqICIiAs7Ozli1ahVUKpUwrJR7kjcAVK5cWdjWFjBt2rTBW2+9BQ8PD1SpUgUpKSl4+vQptm3bhqtXr+LMmTP47rvvMH/+/EK/JlevXkXXrl3Rr18/VKtWDTExMfjzzz9x4sQJPH78GHPnzsVPP/2U59y0tDQMHToUERERAICGDRti0KBBqF+/PiwtLREVFYWLFy9i//79eq89e/Zs/PnnnwCAli1bon///qhZsyYqVKiA8PBwrF27Fnfv3sXMmTPh5OQk9IYaomvXrmjUqBEA5Jtb3bzeuXMHY8aMEeZfDRkyBH5+frC2tsadO3ewcuVK4WtvbW2NSZMm5Xvt0NBQ7NmzB1WrVsXIkSPRqFEjZGZm4vLlywbHb4z79+8L29WrV9d7zKVLlwAA1tbWaNu2rcFtV6pUCS1btsTp06eRkJCAe/fuCd93d+/eFX7hN2/ePM/3UWkzxc+T/fv346uvvoJGo0HNmjXxwQcfwN3dHVWqVEFkZCS2b9+OkydPYvv27bCzs8vRS2vs97auhQsXIjw8HH5+fhgwYIDwfZmSkmJwfs6fP49Ro0YhMzMTZmZm6Nu3L7p06QI3Nze8fv0a9+/fx99//52nsC33NEQyd/78eY2Hh4fGw8NDs2TJkgL3e3h4aNasWZPnmOjoaE3Tpk01Hh4emjZt2mg8PT01R44cyXPcnTt3NF5eXhoPDw/N7Nmz8+x/+vRpjmuNGjVKo1Kp8hy3bNky4Zj58+fn2T9v3jxh/+LFi/PsV6vVmi+//FI4ZuPGjXmOmTx5srC/SZMmmjt37uQ5RldBOcx97UePHhV4zC+//KLx8PDQeHp6ah4+fJhnf+6vya+//qr3OiNGjNB4eHhoGjRooImNjc1zzJw5c4Q2vv32W41ardYbz+vXrzUvX77M8d7p06eFc7du3ar3vPT0dM3QoUM1Hh4eGl9fX71fS0MYktuBAwdqPDw8NN7e3ppTp07l2Z+QkKDp1auXxsPDQ+Pl5aWJiIjIc4yvr69wrXfeeUeTmJhYpHi1Cvve0mg0mszMTE2/fv2E4y5evJjnmKioKGH/u+++a3QcCxcuFM7fs2eP8P7u3buF93/66Sej2y0KU/48iY2N1TRv3lzj4eGhmTp1ar7//n766Sfh38X9+/fz7Df0ezv3Z9P3s0eX9meMr69vnn3p6emaDh06aDw8PDRvvvmm5vz58/m28/z58wKvU95w8j+VK2+++abeYRRnZ2d069YNQPYcn549e6Jr1655jvPy8hLmQhTW+2BpaYnZs2frHVoYO3ascIfbjh07hKUDgOy5KNu3bwcA1K9fX++6QwqFAt98840whLlx48YCY/noo4/g5eVV4DGGUigUqFWrVoHHjBs3Dg4ODtBoNAXOPQGye7n0DeMoFAoMGzYMQPaQ0dWrV3Psf/XqFbZs2SK0MW3aNGGIKzdLS0s4OTnleG/lypUAgO7du+c7BGZlZYWZM2cCyB5ODAkJKfCzFNWNGzcQGhoKAAgICECHDh3yHFO5cmV89913ALKHYLU9ffmZNWuW0YuzGiMuLg7nzp3DBx98gNu3bwPIzqW+OXC6w1i5vw6GcHR0FLYTEhL0buseYyql/fNk06ZNSEpKgqurK7755pt8hyk//fRTuLq6Qq1WY/fu3cX5SILatWsXa82zXbt2ITo6GgAwceJEtG7dOt9j3dzcinydsoiFGZUrBU2y1S1cevfuXehx2mHG/LRv3x6urq569ymVSmFicEJCgvCLDQBu3ryJV69eAciemJvfEKWdnR169uwJALh3757wQ1AfQyZaF5VarcaLFy/w4MEDREREICIiAvfv30fVqlUBAGFhYQWe7+/vn29B1bBhQ2E7d77Pnz8vTPoeOnRogWtn5ZacnIwLFy4AyC4mCuLu7g4HBwcAyFMclhTdeTz/+te/8j2uefPmwpzEgub+uLm5FXiTQFEsW7ZMeIyUp6cn2rZti2HDhuHKlSuwtrbG8OHDsWjRIr3n6s6Z095laQzdeYq6bekOq+V3Y0dpKu2fJ9o/ajp37lzgUxXMzc3RpEkTACX3b7RXr15GfU/lph2etLGxMXiBYMrGOWZUrhR0d13FihWF7dq1axd6XGFzLXx8fArc37hxY2E7IiJC+MGqvXMNyP6LvCBvvvkmNm3aJJzn4uKS5xgbGxvUrFmzwHaMpdFosGfPHmzfvh03btxAenp6vscWdsNB3bp1892ne1ND7nzrFrPG3tF1+/Zt4eaFL774Al988YVB58XExBh1HUNpv+YWFhbw9vYu8Ng333wT9+/fx+PHj5GRkaH3F7buXZKm4OXlhcDAwBw3AejSnfuVewK7IXS/9rpt6RZs2iLdlErz50lWVpbwR82WLVuE3uHClNS/0eL+G7pz5w6A7D+uxCia5YyFGZUrFSpUyHef7l1uhhyn765EXYUNreQ3PJOYmChsV6lSpcA2dIeFdM/TVdLDWa9fv8b48ePx999/G3R8QUUbYPjXJHe+dQs+fQVpQWJjY406Xquwz1JU2q+/vb19oXfVab/mGo0Gr1690js0WBpDmLoTx7OyshAVFYVDhw5h9+7duHr1KgIDA7F9+3a9/2a1PY5A0QoH3a+XbrGuu13Ur2lxlObPk8TExCLdUVtS/0bzuynAUNrvz/zupqb8sTAjKiX5Dc+Zuo3iDEfos3z5cqEoa9WqFd5//300bNgQTk5OqFChgvCLZsiQIcKdeFKj+0vwu+++Q9OmTQ06r7i/rEylpL/mQN6V/729veHr64vWrVtjypQpiIyMxLRp07B8+fI857q4uMDBwQHx8fEIDw9HVlaWUTHq9o7qDhHqbt+6dcvYjyRpWVlZwnZAQEC+D5PPLb9eS2PxAe/iYWFGVEoK6xnIrxdA95d/bGxsgcMlutcwRdGg0WiEGxNatGiBtWvX5vsDPL8evJKi2wsTHR1t1HCtbr4rVKiQ51FDpqaNJyEhAZmZmQX2mmm/5gqFolQn9xuqf//+OH78OA4dOoTg4GCcO3cuz3IYCoUCzZs3x9GjR5GWloZz587pvcFBn6SkJKHAt7e3R7169YR99evXFwq+y5cvIzk52eRLZpSW3EvUiP1v1FgODg6IiorCy5cvxQ5FdlgSE5US7V12huzXXb9Id/v69esFtnHjxg2955WWhIQE4QdtQSv7p6Sk4OHDh6Uai+6NAcb2zHl7ewu9kVeuXCnRuIpC+7XTLrpZEO3XvFatWgVOCDeliRMnCj1gixcv1ntM//79he3169cb3PaWLVuE4bn+/fvn6EVWKBRCu6mpqdi2bZvRsUuVpaWl8O9CCv9GjdWgQQMA2TcziTH/T85YmBGVkjNnzuR7p6RarRZWLK9cuXKOIqNRo0ZCT8iuXbvyncuWnJyMAwcOAADq1atn9Dyr3KysrAAgx9IduekOrxT0w3bbtm2lvuJ869athTv81q9fnyO2wlSpUkW42WLfvn2iP8xa93E6uZ8Uoevq1au4d+9ennPEVqdOHeEO4evXr+PMmTN5jvHz8xPuKD1x4gT++uuvQtt9/Pgx/vOf/wDI7tnUN5w3bNgwYXL5kiVLcix2WxC1Wp3naRNS4+fnBwB48OABTp06VeR2DPneLmna2NPS0gy+cYGysTAjKiUZGRmYOXOm3oJh5cqVwmr1AwcOzNHzYWlpKSyZEBERgV9//TXP+RqNBrNnzxYm2A4ZMqTY8Won6T558iTfY6pUqSIUjfv27dP7g/7GjRv45Zdfih1PYSpVqiQ8JunWrVuYM2dOnscqaalUqjyTw7UP3k5OTsaECROEJUr0ycjIwMaNG/H69esSij6nxo0bC08J2LZtm97HSyUlJQlrqimVSgwePLhUYimq0aNHC71Z+uaZKZVKLFiwQJgDNWXKlHyfyABkL7MybNgw4S7OyZMno1q1anmOc3V1xYwZMwBk95oFBgYKS6Hk5969e/joo4+watUqwz6cSIYOHSr88TF16tQcd2zrc+LECb3L0xjyvV3S+vTpIywX9PPPPxf4Ncnv+arlFeeYEZWSRo0a4fjx4xg8eHCORzLt2rVL6C2oWrUqPvnkkzznjhs3DkeOHMHTp0+xdOlSREREYMCAAXB2dsazZ8+EB1MDQNOmTUvkOY5NmzbFs2fPEBwcjM2bN6NZs2bCX9p2dnZwdHSEUqmEv78/Nm7ciPDwcAwePBjDhw9HrVq1kJycjJMnT+LPP/+EjY0NXFxc8OjRo2LHVZDPPvsMZ86cQUREBDZs2ICrV6/ivffeg4eHBywsLBAVFYVLly7hr7/+wueff57jWZmdOnXC0KFDsW7dOly8eBG9evXCe++9h+bNm8Pe3h6pqal48uQJLl26hCNHjiAxMRH9+vUTclLSvv/+ewQEBEClUmHUqFEIDAyEr69vjkcyade6GjFihOTmHHl4eMDPzw/Hjh3DxYsXcenSpTxrqTVq1AgLFizAV199hYyMDEycOBGbN2+Gv78/6tatCwsLC/zzzz8IDg7Gvn37hF7XESNG5HmUkK6BAwciKioKS5YsQWxsLAIDA9GhQwehl65SpUpITEzEw4cPcfLkSZw6dQpZWVkltuhyaXFycsL8+fMxYcIEvHz5EgMHDkT//v3x1ltvoWrVqsjMzERUVBRu3LiBQ4cO4enTp1ixYkWez2XI93ZJs7KywoIFCzBy5EikpaVh+PDh6NOnD7p27YqqVasiIyMDDx48wMmTJxEcHIybN2+WeAxyxcKMqJQMGTIEFy9exM6dOzFx4sQ8+7XPqdRd70jLzs4Oa9asER5ifujQIRw6dCjPcc2aNcPy5ctL5C68kSNH4tChQ8jIyMCsWbNy7Ovfvz/mzZsHIHs+0ZUrV3Dnzh3cvHkTX375ZY5j7e3tsXTpUixZsqTUCzNra2usXbsWEyZMwMWLF3Hr1i2h98QQX3/9NSpXrozly5fj5cuXWLp0ab7H2tjYlMrdjlre3t5YsWKF8BDz1atXY/Xq1XmOGzJkSJ6cS8WYMWNw7NgxANm9Zvp6pHr16gVnZ2fMmjUL9+/fR0hISL5PVLC3t8cXX3xh0B8e48aNQ/369TFv3jxERkbi9OnTOH36dL7H169fv8DnjUrF22+/jV9//RVTp05FQkICNm/ejM2bN+s9VqlU6l0zzNDv7ZLWpk0brFixAl9++SUSExOxc+dO7Ny5s1SuVZawMCMqRXPnzkX79u2xZcsWREREIDU1FdWqVUPXrl3x8ccfF3gnZY0aNbB7925s27YNBw8eREREBFJSUlC5cmV4e3sLD04uqdvavb29sWXLFqxatQpXrlxBTEyM3qHKihUrYtOmTfjjjz9w4MABPH78GGZmZnBzc0OnTp3w4YcfCqv+m0KVKlWwYcMGHDlyBHv37sX169cRFxcHhUIBFxcXNGzYEF27dtW7wr9CocD48ePRt29fbN68GefPn8ezZ8+QlJSEChUqwM3NDd7e3ujQoQO6du1a4HpUJaFDhw44cuQI1q5di5MnT+Lp06fIyMiAk5MTmjdvjvfee6/EV/QvSY0bN0b79u1x5swZnD59Gjdu3MixkLJWy5YtsXfvXhw6dAjHjh1DaGgoYmJikJmZiSpVqsDd3R2dO3dG3759jbrz9O2330bnzp1x6NAh/P333wgNDUVcXBxSUlJgZ2eH6tWr480330T37t3RunXrElmOxhS0PZFbt27FyZMnce/ePSQmJsLMzAxOTk6oX78+2rRpg+7du+t9vJGh39uloWPHjjh69Cg2bdqE48eP4+HDh0hKSoK1tTVq1aqFFi1a4J133jFJLHKh0OQ3KYOIjPbs2TN06dIFQHZRpjt0RkREVBhO/iciIiKSCBZmRERERBLBwoyIiIhIIliYEREREUkECzMiIiIiieBdmUREREQSwR4zIiIiIolgYUZEREQkESzMiIiIiCSChRkRERGRRLAwIyIiIpIIFmZEREREEsHCjIiIiEgiWJgRERERSQQLMyIiIiKJYGFGREREJBEszOj/2q1jAQAAAIBB/tbT2FEUAQATYgYAMCFmAAATYgYAMCFmAAATYgYAMCFmAAATYgYAMCFmAAATARnFzeRpnbh5AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for idx, row in df.iterrows():\n",
+    "    fig, ax = plt.subplots(figsize=(3,3))\n",
+    "\n",
+    "    barplot_feat_imp(\n",
+    "        row[cols_order], row[std_cols_order], ax=ax,\n",
+    "        color=palette[0] if row[\"model_name\"] not in baselines else palette[2],\n",
+    "    )\n",
+    "    ax.set_title(row[\"model_name\"])\n",
+    "\n",
+    "    ax.set_xlim(left=0, right=ax.get_xlim()[-1])\n",
+    "\n",
+    "    save_fig(fig, f\"feat-imp_{idx}\")\n",
+    "    plt.plot()\n",
+    "    # plt.close(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3a6beb5-b44b-4f81-ae1a-d027afb2c5f4",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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+
+
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+  <meta charset="utf-8" />
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+  <title>folktexts.acs.acs_columns &mdash; folktexts 0.0.25 documentation</title>
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+             
+  <h1>Source code for folktexts.acs.acs_columns</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Module to hold ACS column mappings from values to natural text.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+
+<span class="kn">from</span> <span class="nn">..col_to_text</span> <span class="kn">import</span> <span class="n">ColumnToText</span>
+<span class="kn">from</span> <span class="nn">..qa_interface</span> <span class="kn">import</span> <span class="n">Choice</span><span class="p">,</span> <span class="n">DirectNumericQA</span><span class="p">,</span> <span class="n">MultipleChoiceQA</span>
+<span class="kn">from</span> <span class="nn">._utils</span> <span class="kn">import</span> <span class="n">parse_pums_code</span>
+<span class="kn">from</span> <span class="nn">.acs_thresholds</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">acs_employment_threshold</span><span class="p">,</span>
+    <span class="n">acs_health_insurance_threshold</span><span class="p">,</span>
+    <span class="n">acs_income_threshold</span><span class="p">,</span>
+    <span class="n">acs_mobility_threshold</span><span class="p">,</span>
+    <span class="n">acs_poverty_ratio_threshold</span><span class="p">,</span>
+    <span class="n">acs_public_coverage_threshold</span><span class="p">,</span>
+    <span class="n">acs_travel_time_threshold</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Path to ACS codebook files</span>
+<span class="n">ACS_CODEBOOK_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="vm">__file__</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span> <span class="o">/</span> <span class="s2">&quot;data&quot;</span>
+
+<span class="c1"># AGEP: Age</span>
+<span class="n">acs_age</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;AGEP&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="si">}</span><span class="s2"> years old&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># COW: Class of Worker</span>
+<span class="n">acs_class_of_worker</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;COW&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;class of worker&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Working for a for-profit private company or organization&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Working for a non-profit organization&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Working for the local government&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Working for the state government&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;Working for the federal government&quot;</span><span class="p">,</span>
+        <span class="mi">6</span><span class="p">:</span> <span class="s2">&quot;Owner of non-incorporated business, professional practice, or farm&quot;</span><span class="p">,</span>
+        <span class="mi">7</span><span class="p">:</span> <span class="s2">&quot;Owner of incorporated business, professional practice, or farm&quot;</span><span class="p">,</span>
+        <span class="mi">8</span><span class="p">:</span> <span class="s2">&quot;Working without pay in a for-profit family business or farm&quot;</span><span class="p">,</span>
+        <span class="mi">9</span><span class="p">:</span> <span class="s2">&quot;Unemployed and last worked 5 years ago or earlier or never worked&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># SCHL: Educational Attainment</span>
+<span class="n">acs_schooling</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;SCHL&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;highest educational attainment&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;N/A - no schooling completed&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Nursery school / preschool&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Kindergarten&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;1st grade only&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;2nd grade&quot;</span><span class="p">,</span>
+        <span class="mi">6</span><span class="p">:</span> <span class="s2">&quot;3rd grade&quot;</span><span class="p">,</span>
+        <span class="mi">7</span><span class="p">:</span> <span class="s2">&quot;4th grade&quot;</span><span class="p">,</span>
+        <span class="mi">8</span><span class="p">:</span> <span class="s2">&quot;5th grade&quot;</span><span class="p">,</span>
+        <span class="mi">9</span><span class="p">:</span> <span class="s2">&quot;6th grade&quot;</span><span class="p">,</span>
+        <span class="mi">10</span><span class="p">:</span> <span class="s2">&quot;7th grade&quot;</span><span class="p">,</span>
+        <span class="mi">11</span><span class="p">:</span> <span class="s2">&quot;8th grade&quot;</span><span class="p">,</span>
+        <span class="mi">12</span><span class="p">:</span> <span class="s2">&quot;9th grade&quot;</span><span class="p">,</span>
+        <span class="mi">13</span><span class="p">:</span> <span class="s2">&quot;10th grade&quot;</span><span class="p">,</span>
+        <span class="mi">14</span><span class="p">:</span> <span class="s2">&quot;11th grade&quot;</span><span class="p">,</span>
+        <span class="mi">15</span><span class="p">:</span> <span class="s2">&quot;12th grade, no diploma&quot;</span><span class="p">,</span>
+        <span class="mi">16</span><span class="p">:</span> <span class="s2">&quot;Regular high school diploma&quot;</span><span class="p">,</span>
+        <span class="mi">17</span><span class="p">:</span> <span class="s2">&quot;GED or alternative credential&quot;</span><span class="p">,</span>
+        <span class="mi">18</span><span class="p">:</span> <span class="s2">&quot;Some college, less than 1 year&quot;</span><span class="p">,</span>
+        <span class="mi">19</span><span class="p">:</span> <span class="s2">&quot;Some college, 1 or more years, no degree&quot;</span><span class="p">,</span>
+        <span class="mi">20</span><span class="p">:</span> <span class="s2">&quot;Associate&#39;s degree&quot;</span><span class="p">,</span>
+        <span class="mi">21</span><span class="p">:</span> <span class="s2">&quot;Bachelor&#39;s degree&quot;</span><span class="p">,</span>
+        <span class="mi">22</span><span class="p">:</span> <span class="s2">&quot;Master&#39;s degree&quot;</span><span class="p">,</span>
+        <span class="mi">23</span><span class="p">:</span> <span class="s2">&quot;Professional degree beyond a bachelor&#39;s degree&quot;</span><span class="p">,</span>
+        <span class="mi">24</span><span class="p">:</span> <span class="s2">&quot;Doctorate degree&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># MAR: Marital Status</span>
+<span class="n">acs_marital_status</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;MAR&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;marital status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Married&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Widowed&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Divorced&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Separated&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;Never married&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># OCCP: Occupation</span>
+<span class="n">acs_occupation</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;OCCP&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;occupation&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span>
+        <span class="n">parse_pums_code</span><span class="p">,</span>
+        <span class="n">file</span><span class="o">=</span><span class="n">ACS_CODEBOOK_DIR</span> <span class="o">/</span> <span class="s2">&quot;OCCP.txt&quot;</span><span class="p">,</span>
+        <span class="n">postprocess</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">4</span><span class="p">:]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">.</span><span class="n">capitalize</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">(),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="c1"># POBP: Place of Birth</span>
+<span class="n">acs_place_of_birth</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;POBP&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;place of birth&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span>
+        <span class="n">parse_pums_code</span><span class="p">,</span>
+        <span class="n">file</span><span class="o">=</span><span class="n">ACS_CODEBOOK_DIR</span> <span class="o">/</span> <span class="s2">&quot;POBP.txt&quot;</span><span class="p">,</span>
+        <span class="n">postprocess</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">[:</span> <span class="n">x</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)]</span> <span class="k">if</span> <span class="s2">&quot;/&quot;</span> <span class="ow">in</span> <span class="n">x</span> <span class="k">else</span> <span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">strip</span><span class="p">(),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="c1"># RELP: Relationship to Reference Person</span>
+<span class="n">acs_relationship</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;RELP&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;relationship to the reference person in the survey&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">0</span><span class="p">:</span> <span class="s2">&quot;The reference person itself&quot;</span><span class="p">,</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Husband/wife&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Biological son or daughter&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Adopted son or daughter&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Stepson or stepdaughter&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;Brother or sister&quot;</span><span class="p">,</span>
+        <span class="mi">6</span><span class="p">:</span> <span class="s2">&quot;Father or mother&quot;</span><span class="p">,</span>
+        <span class="mi">7</span><span class="p">:</span> <span class="s2">&quot;Grandchild&quot;</span><span class="p">,</span>
+        <span class="mi">8</span><span class="p">:</span> <span class="s2">&quot;Parent-in-law&quot;</span><span class="p">,</span>
+        <span class="mi">9</span><span class="p">:</span> <span class="s2">&quot;Son-in-law or daughter-in-law&quot;</span><span class="p">,</span>
+        <span class="mi">10</span><span class="p">:</span> <span class="s2">&quot;Other relative&quot;</span><span class="p">,</span>
+        <span class="mi">11</span><span class="p">:</span> <span class="s2">&quot;Roomer or boarder&quot;</span><span class="p">,</span>
+        <span class="mi">12</span><span class="p">:</span> <span class="s2">&quot;Housemate or roommate&quot;</span><span class="p">,</span>
+        <span class="mi">13</span><span class="p">:</span> <span class="s2">&quot;Unmarried partner&quot;</span><span class="p">,</span>
+        <span class="mi">14</span><span class="p">:</span> <span class="s2">&quot;Foster child&quot;</span><span class="p">,</span>
+        <span class="mi">15</span><span class="p">:</span> <span class="s2">&quot;Other non-relative&quot;</span><span class="p">,</span>
+        <span class="mi">16</span><span class="p">:</span> <span class="s2">&quot;Institutionalized group quarters population&quot;</span><span class="p">,</span>
+        <span class="mi">17</span><span class="p">:</span> <span class="s2">&quot;Non-institutionalized group quarters population&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># WKHP: Usual Hours Worked per Week</span>
+<span class="n">acs_work_hours</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;WKHP&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;usual number of hours worked per week&quot;</span><span class="p">,</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;N/A (less than 16 years old, or did not work during the past 12 months)&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="si">}</span><span class="s2"> hours&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># SEX: Sex</span>
+<span class="n">acs_sex</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;SEX&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;sex&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Male&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Female&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># RAC1P: Race</span>
+<span class="n">acs_race</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;RAC1P&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;race&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;White&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Black or African American&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;American Indian&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Alaska Native&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="p">(</span>
+            <span class="s2">&quot;American Indian and Alaska Native tribes specified; or American &quot;</span>
+            <span class="s2">&quot;Indian or Alaska Native, not specified and no other races&quot;</span><span class="p">),</span>
+        <span class="mi">6</span><span class="p">:</span> <span class="s2">&quot;Asian&quot;</span><span class="p">,</span>
+        <span class="mi">7</span><span class="p">:</span> <span class="s2">&quot;Native Hawaiian and Other Pacific Islander&quot;</span><span class="p">,</span>
+        <span class="mi">8</span><span class="p">:</span> <span class="s2">&quot;Some other race alone (non-White)&quot;</span><span class="p">,</span>
+        <span class="c1"># 8: &quot;Some other race alone&quot;,</span>
+        <span class="mi">9</span><span class="p">:</span> <span class="s2">&quot;Two or more races&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># PINCP: Yearly Income</span>
+<span class="n">acs_income</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;PINCP&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;yearly income&quot;</span><span class="p">,</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;N/A (less than 15 years old)&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;$</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="si">:</span><span class="s2">,</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># PINCP: Yearly Income (Thresholded)</span>
+<span class="n">acs_income_qa</span> <span class="o">=</span> <span class="n">MultipleChoiceQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_income_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;PINCP&quot;</span><span class="p">),</span>
+    <span class="n">text</span><span class="o">=</span><span class="s2">&quot;What is this person&#39;s estimated yearly income?&quot;</span><span class="p">,</span>
+    <span class="n">choices</span><span class="o">=</span><span class="p">(</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;Below $50,000&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;Above $50,000&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">acs_income_numeric_qa</span> <span class="o">=</span> <span class="n">DirectNumericQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_income_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;PINCP&quot;</span><span class="p">),</span>
+    <span class="n">text</span><span class="o">=</span><span class="p">(</span>
+        <span class="s2">&quot;What is the probability that this person&#39;s estimated yearly income is &quot;</span>
+        <span class="s2">&quot;above $50,000 ?&quot;</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">acs_income_target_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="n">acs_income_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;PINCP&quot;</span><span class="p">),</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;yearly income&quot;</span><span class="p">,</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;N/A (less than 15 years old)&quot;</span><span class="p">,</span>
+    <span class="n">question</span><span class="o">=</span><span class="n">acs_income_qa</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># PUBCOV: Public Health Coverage (Original)</span>
+<span class="n">acs_pubcov_og_qa</span> <span class="o">=</span> <span class="n">MultipleChoiceQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="s2">&quot;PUBCOV&quot;</span><span class="p">,</span>
+    <span class="n">text</span><span class="o">=</span><span class="s2">&quot;Does this person have public health insurance coverage?&quot;</span><span class="p">,</span>
+    <span class="n">choices</span><span class="o">=</span><span class="p">(</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;Yes, person is covered by public health insurance&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;No, person is not covered by public health insurance&quot;</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>  <span class="c1"># NOTE: value=2 for no public coverage!</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">acs_pubcov</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;PUBCOV&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;public health coverage status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Covered by public health insurance&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Not covered by public health insurance&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">question</span><span class="o">=</span><span class="n">acs_pubcov_og_qa</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># PUBCOV: Public Health Coverage (Thresholded)</span>
+<span class="n">acs_pubcov_qa</span> <span class="o">=</span> <span class="n">MultipleChoiceQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_public_coverage_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;PUBCOV&quot;</span><span class="p">),</span>
+    <span class="n">text</span><span class="o">=</span><span class="s2">&quot;Does this person have public health insurance coverage?&quot;</span><span class="p">,</span>
+    <span class="n">choices</span><span class="o">=</span><span class="p">(</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;Yes, person is covered by public health insurance&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;No, person is not covered by public health insurance&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>  <span class="c1"># NOTE: value=0 for no public coverage!</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">acs_pubcov_numeric_qa</span> <span class="o">=</span> <span class="n">DirectNumericQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_public_coverage_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;PUBCOV&quot;</span><span class="p">),</span>
+    <span class="n">text</span><span class="o">=</span><span class="p">(</span>
+        <span class="s2">&quot;What is the probability that this person is covered by public health insurance?&quot;</span>
+    <span class="p">),</span>  <span class="c1"># NOTE: value=1 for yes, 0 for no</span>
+<span class="p">)</span>
+
+<span class="n">acs_pubcov_target_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="n">acs_public_coverage_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;PUBCOV&quot;</span><span class="p">),</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;public health coverage status&quot;</span><span class="p">,</span>
+    <span class="n">question</span><span class="o">=</span><span class="n">acs_pubcov_qa</span><span class="p">,</span>
+    <span class="n">use_value_map_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># DIS: Disability Status</span>
+<span class="n">acs_disability</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;DIS&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;disability status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;With a disability&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;No disability&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># ESP: Employment Status of Parents</span>
+<span class="n">acs_emp_parents</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;ESP&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;employment status of parents&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Living with two parents, both employed&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Living with two parents, only Father is employed&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Living with two parents, only Mother is employed&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Living with two parents, neither employed&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;Living with Father, and Father is employed&quot;</span><span class="p">,</span>
+        <span class="mi">6</span><span class="p">:</span> <span class="s2">&quot;Living with Father, and Father is not employed&quot;</span><span class="p">,</span>
+        <span class="mi">7</span><span class="p">:</span> <span class="s2">&quot;Living with Mother, and Mother is employed&quot;</span><span class="p">,</span>
+        <span class="mi">8</span><span class="p">:</span> <span class="s2">&quot;Living with Mother, and Mother is not employed&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;N/A (not own child of householder, and not child in subfamily)&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># CIT: Citizenship Status</span>
+<span class="n">acs_citizenship</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;CIT&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;citizenship status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Born in the United States&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Born in Puerto Rico, Guam, the U.S. Virgin Islands, or the Northern Marianas&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Born abroad of American parents&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Naturalized US citizen&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;Not a US citizen&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># MIG: Mobility Status</span>
+<span class="n">acs_mobility</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;MIG&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;mobility status over the last year&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Lived in the same house one year ago&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Lived in a different house, outside the United States and Puerto Rico, one year ago&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Lived in a different house in the United States one year ago&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># MIG: Mobility Status (Thresholded)</span>
+<span class="n">acs_mobility_qa</span> <span class="o">=</span> <span class="n">MultipleChoiceQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_mobility_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;MIG&quot;</span><span class="p">),</span>      <span class="c1"># NOTE: Thresholded by MIG!=1</span>
+    <span class="n">text</span><span class="o">=</span><span class="s2">&quot;Has this person moved in the last year?&quot;</span><span class="p">,</span>
+    <span class="n">choices</span><span class="o">=</span><span class="p">(</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;No, person has lived in the same house for the last year&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;Yes, person has moved in the last year&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">acs_mobility_numeric_qa</span> <span class="o">=</span> <span class="n">DirectNumericQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_mobility_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;MIG&quot;</span><span class="p">),</span>      <span class="c1"># NOTE: Thresholded by MIG!=1</span>
+    <span class="n">text</span><span class="o">=</span><span class="p">(</span>
+        <span class="s2">&quot;What is the probability that this person has moved in the last year?&quot;</span>
+    <span class="p">),</span>  <span class="c1"># NOTE: Question should relate to probability of MIG!=1</span>
+<span class="p">)</span>
+
+<span class="n">acs_mobility_target_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="n">acs_mobility_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;MIG&quot;</span><span class="p">),</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;mobility status over the last year&quot;</span><span class="p">,</span>
+    <span class="n">question</span><span class="o">=</span><span class="n">acs_mobility_qa</span><span class="p">,</span>
+    <span class="n">use_value_map_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># MIL: Military Service Status</span>
+<span class="n">acs_military</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;MIL&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;military service status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Now on active duty&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;On active duty in the past, but not now&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Only on active duty for training in Reserved/National Guard&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Never served in the military&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;N/A (less than 17 years old)&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># ANC: Ancestry</span>
+<span class="n">acs_ancestry</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;ANC&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;ancestry&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Single ancestry&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Multiple ancestry&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Unclassified&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Not reported&quot;</span><span class="p">,</span>
+        <span class="mi">8</span><span class="p">:</span> <span class="s2">&quot;N/A (information suppressed for certain area codes)&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># ANC1P: Detailed Ancestry</span>
+<span class="n">acs_detailed_ancestry</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;ANC1P&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;detailed ancestry&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span>
+        <span class="n">parse_pums_code</span><span class="p">,</span>
+        <span class="n">file</span><span class="o">=</span><span class="n">ACS_CODEBOOK_DIR</span> <span class="o">/</span> <span class="s2">&quot;ANC1P.txt&quot;</span><span class="p">,</span>
+        <span class="n">postprocess</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">strip</span><span class="p">(),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="c1"># NATIVITY: Nativity</span>
+<span class="n">acs_nativity</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;NATIVITY&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;nativity&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Native&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Foreign born&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># LANX: Language other than English spoken at home</span>
+<span class="n">acs_lanx</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;LANX&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;language other than English spoken at home&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Speaks a language other than English at home.&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Speaks only English at home.&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">use_value_map_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;N/A (less than 5 years old)&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># LANP: Language spoken at home</span>
+<span class="n">acs_lanp</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;LANP&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;language spoken at home (other than English)&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span>
+        <span class="n">parse_pums_code</span><span class="p">,</span>
+        <span class="n">file</span><span class="o">=</span><span class="n">ACS_CODEBOOK_DIR</span> <span class="o">/</span> <span class="s2">&quot;LANP.txt&quot;</span><span class="p">,</span>
+        <span class="n">postprocess</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">strip</span><span class="p">(),</span>
+    <span class="p">),</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;N/A (speaks only English at home)&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># ENG: Ability to Speak English</span>
+<span class="n">acs_eng</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;ENG&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;ability to speak English&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Very well&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Well&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Not well&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Not at all&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;Native (speaks only English)&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># NOP: Nativity of Parents</span>
+<span class="n">acs_nop</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;NOP&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;nativity of parents&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Living with both parents, both of whom are native&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Living with both parents, Father only is foreign born&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Living with both parents, Mother only is foreign born&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Living with both parents, both of whom are foreign born&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;Living with Father only, who is native&quot;</span><span class="p">,</span>
+        <span class="mi">6</span><span class="p">:</span> <span class="s2">&quot;Living with Father only, who is foreign born&quot;</span><span class="p">,</span>
+        <span class="mi">7</span><span class="p">:</span> <span class="s2">&quot;Living with Mother only, who is native&quot;</span><span class="p">,</span>
+        <span class="mi">8</span><span class="p">:</span> <span class="s2">&quot;Living with Mother only, who is foreign born&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="p">(</span>
+        <span class="s2">&quot;N/A (not own child of householder and not child in subfamily)&quot;</span>
+        <span class="c1"># OR greater than 17 years old</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="c1"># DEAR: Hearing Status</span>
+<span class="n">acs_hearing</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;DEAR&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;hearing status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;With hearing difficulty&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;No hearing difficulty&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># DEYE: Vision Status</span>
+<span class="n">acs_vision</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;DEYE&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;vision status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;With vision difficulty&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;No vision difficulty&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># DREM: Cognitive Status</span>
+<span class="n">acs_cognitive</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;DREM&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;cognition status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;With cognitive difficulty&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;No cognitive difficulty&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;N/A (less than 5 years old)&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># ESR: Employment Status</span>
+<span class="n">acs_employment</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;ESR&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;employment status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Civilian employed, at work&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Civilian employed, with a job but not at work&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Unemployed&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Armed forces, at work&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;Armed forces, with a job but not at work&quot;</span><span class="p">,</span>
+        <span class="mi">6</span><span class="p">:</span> <span class="s2">&quot;Not in labor force&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;N/A (less than 16 years old)&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># ESR: Employment Status (Thresholded)</span>
+<span class="n">acs_employment_qa</span> <span class="o">=</span> <span class="n">MultipleChoiceQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_employment_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;ESR&quot;</span><span class="p">),</span>
+    <span class="n">text</span><span class="o">=</span><span class="s2">&quot;What is this person&#39;s employment status?&quot;</span><span class="p">,</span>
+    <span class="n">choices</span><span class="o">=</span><span class="p">(</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;Employed civilian&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;Unemployed or in the military&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">acs_employment_numeric_qa</span> <span class="o">=</span> <span class="n">DirectNumericQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_employment_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;ESR&quot;</span><span class="p">),</span>
+    <span class="n">text</span><span class="o">=</span><span class="p">(</span>
+        <span class="s2">&quot;What is the probability that this person is an employed civilian?&quot;</span>
+        <span class="c1"># &quot;What is the probability that this person is currently employed?&quot;</span>
+    <span class="p">),</span>  <span class="c1"># NOTE: Question should relate to probability of ESR==1</span>
+<span class="p">)</span>
+
+<span class="n">acs_employment_target_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="n">acs_employment_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;ESR&quot;</span><span class="p">),</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;employment status&quot;</span><span class="p">,</span>
+    <span class="n">question</span><span class="o">=</span><span class="n">acs_employment_qa</span><span class="p">,</span>
+    <span class="n">use_value_map_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># ST: State</span>
+<span class="n">acs_state</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;ST&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;resident state&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span>
+        <span class="n">parse_pums_code</span><span class="p">,</span>
+        <span class="n">file</span><span class="o">=</span><span class="n">ACS_CODEBOOK_DIR</span> <span class="o">/</span> <span class="s2">&quot;ST.txt&quot;</span><span class="p">,</span>
+        <span class="n">postprocess</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[:</span><span class="n">x</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)]</span><span class="o">.</span><span class="n">strip</span><span class="p">(),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="c1"># HISP: Hispanic Origin (Detailed)</span>
+<span class="n">acs_hispanic</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;HISP&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;Hispanic origin&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span>
+        <span class="n">parse_pums_code</span><span class="p">,</span>
+        <span class="n">file</span><span class="o">=</span><span class="n">ACS_CODEBOOK_DIR</span> <span class="o">/</span> <span class="s2">&quot;HISP.txt&quot;</span><span class="p">,</span>
+        <span class="n">postprocess</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">strip</span><span class="p">(),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="c1"># FER: Parenthood Status</span>
+<span class="n">acs_parenthood</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;FER&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;person has given birth within the last year&quot;</span><span class="p">,</span>
+    <span class="n">use_value_map_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Person has given birth within the last year.&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Person has not given birth within the last year.&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;N/A (less than 15 years old, or greater than 50 years old, or male)&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># JWMNP: Commute Time</span>
+<span class="n">acs_commute_time</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;JWMNP&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;commute time&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="si">}</span><span class="s2"> minutes&quot;</span><span class="p">,</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;N/A (not a worker, or worker who worked at home)&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># JWMNP: Commute Time (Thresholded)</span>
+<span class="n">acs_commute_time_qa</span> <span class="o">=</span> <span class="n">MultipleChoiceQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_travel_time_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;JWMNP&quot;</span><span class="p">),</span>
+    <span class="n">text</span><span class="o">=</span><span class="s2">&quot;What is this person&#39;s commute time?&quot;</span><span class="p">,</span>
+    <span class="n">choices</span><span class="o">=</span><span class="p">(</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;Longer than 20 minutes&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;Less than 20 minutes&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">acs_commute_time_numeric_qa</span> <span class="o">=</span> <span class="n">DirectNumericQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_travel_time_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;JWMNP&quot;</span><span class="p">),</span>
+    <span class="n">text</span><span class="o">=</span><span class="p">(</span>
+        <span class="s2">&quot;What is the probability that this person&#39;s commute time is longer than 20 minutes?&quot;</span>
+    <span class="p">),</span>  <span class="c1"># NOTE: Question should relate to probability of JWMNP&gt;20</span>
+<span class="p">)</span>
+
+<span class="n">acs_travel_time_target_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="n">acs_travel_time_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;JWMNP&quot;</span><span class="p">),</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;commute time&quot;</span><span class="p">,</span>
+    <span class="n">question</span><span class="o">=</span><span class="n">acs_commute_time_qa</span><span class="p">,</span>
+    <span class="n">use_value_map_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># JWTR: Commute Method</span>
+<span class="n">acs_commute_method</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;JWTR&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;means of transportation to work&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Car, truck, or van&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Bus or trolley bus&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Streetcar or trolley car&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Subway or elevated&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;Railroad&quot;</span><span class="p">,</span>
+        <span class="mi">6</span><span class="p">:</span> <span class="s2">&quot;Ferryboat&quot;</span><span class="p">,</span>
+        <span class="mi">7</span><span class="p">:</span> <span class="s2">&quot;Taxicab&quot;</span><span class="p">,</span>
+        <span class="mi">8</span><span class="p">:</span> <span class="s2">&quot;Motorcycle&quot;</span><span class="p">,</span>
+        <span class="mi">9</span><span class="p">:</span> <span class="s2">&quot;Bicycle&quot;</span><span class="p">,</span>
+        <span class="mi">10</span><span class="p">:</span> <span class="s2">&quot;Walked&quot;</span><span class="p">,</span>
+        <span class="mi">11</span><span class="p">:</span> <span class="s2">&quot;Worked at home&quot;</span><span class="p">,</span>
+        <span class="mi">12</span><span class="p">:</span> <span class="s2">&quot;Other method&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+
+<div class="viewcode-block" id="describe_income_to_poverty_ratio_pct">
+<a class="viewcode-back" href="../../../source/folktexts.acs.html#folktexts.acs.acs_columns.describe_income_to_poverty_ratio_pct">[docs]</a>
+<span class="k">def</span> <span class="nf">describe_income_to_poverty_ratio_pct</span><span class="p">(</span><span class="n">ratio_pct</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Describe the income-to-poverty ratio in natural language.&quot;&quot;&quot;</span>
+    <span class="n">context_msg</span><span class="p">:</span> <span class="nb">str</span>
+
+    <span class="k">if</span> <span class="n">ratio_pct</span> <span class="o">&lt;</span> <span class="mi">90</span><span class="p">:</span>
+        <span class="n">context_msg</span> <span class="o">=</span> <span class="s2">&quot;below the poverty line&quot;</span>
+    <span class="k">elif</span> <span class="mi">90</span> <span class="o">&lt;=</span> <span class="n">ratio_pct</span> <span class="o">&lt;</span> <span class="mi">110</span><span class="p">:</span>
+        <span class="n">context_msg</span> <span class="o">=</span> <span class="s2">&quot;at or near the poverty line&quot;</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">context_msg</span> <span class="o">=</span> <span class="s2">&quot;above the poverty line&quot;</span>
+
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">ratio_pct</span><span class="p">)</span><span class="si">}</span><span class="s2">% of the poverty line income, which is </span><span class="si">{</span><span class="n">context_msg</span><span class="si">}</span><span class="s2">&quot;</span></div>
+
+
+
+<span class="c1"># POVPIP: Income-to-Poverty Ratio</span>
+<span class="n">acs_poverty_ratio</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;POVPIP&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;income-to-poverty ratio&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="n">describe_income_to_poverty_ratio_pct</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># POVPIP: Income-to-Poverty Ratio (Thresholded)</span>
+<span class="n">acs_poverty_ratio_qa</span> <span class="o">=</span> <span class="n">MultipleChoiceQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_poverty_ratio_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;POVPIP&quot;</span><span class="p">),</span>
+    <span class="n">text</span><span class="o">=</span><span class="p">(</span>
+        <span class="s2">&quot;Is this person&#39;s income-to-poverty ratio below 2.5 ? &quot;</span>
+        <span class="s2">&quot;That is, is this person&#39;s annual income below 2.5 times the poverty line income?&quot;</span><span class="p">,</span>
+    <span class="p">),</span>
+    <span class="n">choices</span><span class="o">=</span><span class="p">(</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;Yes, this person earns below 2.5 times the poverty line income&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;No, this person earns above 2.5 times the poverty line income.&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">acs_poverty_ratio_target_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="n">acs_poverty_ratio_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;POVPIP&quot;</span><span class="p">),</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;income-to-poverty ratio is below 2.5&quot;</span><span class="p">,</span>
+    <span class="n">question</span><span class="o">=</span><span class="n">acs_poverty_ratio_qa</span><span class="p">,</span>
+    <span class="n">use_value_map_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># GCL: Grandparent Living with Grandchildren</span>
+<span class="n">acs_gcl_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;GCL&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;grandparent living with grandchildren&quot;</span><span class="p">,</span>
+    <span class="n">use_value_map_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Household includes grandparent living with grandchildren&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Household does not include grandparents living with grandchildren&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;N/A (less than 30 years old, or living in institutional group quarters)&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># PUMA: Public Use Microdata Area Code</span>
+<span class="c1"># TODO: assign meaningful natural-text values to PUMA codes</span>
+<span class="c1"># &gt; Consult: https://www2.census.gov/geo/pdfs/reference/puma/2010_PUMA_Names.pdf</span>
+<span class="n">acs_puma_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;PUMA&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;Public Use Microdata Area (PUMA) code&quot;</span><span class="p">,</span>
+    <span class="n">use_value_map_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;Public Use Microdata Area (PUMA) code: </span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">,</span>
+    <span class="c1"># missing_value_fill=&quot;N/A (less than 16 years old)&quot;,</span>
+<span class="p">)</span>
+
+<span class="c1"># POWPUMA: Place of Work PUMA</span>
+<span class="n">acs_powpuma_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;POWPUMA&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;place of work PUMA&quot;</span><span class="p">,</span>
+    <span class="n">use_value_map_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;Public Use Microdata Area (PUMA) code for the place of work: </span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">,</span>
+    <span class="c1"># missing_value_fill=&quot;N/A (not a worker, or worker who worked at home)&quot;,</span>
+<span class="p">)</span>
+
+<span class="c1"># HINS2: Health Insurance Coverage through Private Company</span>
+<span class="n">acs_health_ins_2_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;HINS2&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;acquired health insurance directly from an insurance company&quot;</span><span class="p">,</span>
+    <span class="n">use_value_map_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Person has purchased insurance directly from an insurance company&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="p">(</span>
+            <span class="s2">&quot;Person has not purchased insurance directly from an insurance &quot;</span>
+            <span class="s2">&quot;company (is either uninsured or insured through another source)&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># HINS2: Health Insurance Coverage through Private Company (Thresholded)</span>
+<span class="n">acs_health_ins_2_qa</span> <span class="o">=</span> <span class="n">MultipleChoiceQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_health_insurance_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;HINS2&quot;</span><span class="p">),</span>
+    <span class="n">text</span><span class="o">=</span><span class="s2">&quot;Has this person purchased health insurance directly from an insurance company?&quot;</span><span class="p">,</span>
+    <span class="n">choices</span><span class="o">=</span><span class="p">(</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;Yes, this person has health insurance through a private company&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;No, this person either has insurance through other means or is uninsured&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">acs_health_ins_2_numeric_qa</span> <span class="o">=</span> <span class="n">DirectNumericQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">acs_health_insurance_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;HINS2&quot;</span><span class="p">),</span>
+    <span class="n">text</span><span class="o">=</span><span class="p">(</span>
+        <span class="s2">&quot;What is the probability that this person has purchased health &quot;</span>
+        <span class="s2">&quot;insurance directly through a private company?&quot;</span>
+    <span class="p">),</span>  <span class="c1"># NOTE: Question should relate to probability of HINS2==1</span>
+<span class="p">)</span>
+
+<span class="n">acs_health_ins_2_target_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="n">acs_health_insurance_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="s2">&quot;HINS2&quot;</span><span class="p">),</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;acquired health insurance directly from an insurance company&quot;</span><span class="p">,</span>
+    <span class="n">question</span><span class="o">=</span><span class="n">acs_health_ins_2_qa</span><span class="p">,</span>
+    <span class="n">use_value_map_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+
+           </div>
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+             
+  <h1>Source code for folktexts.acs.acs_dataset</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Module to access ACS data using the folktables package.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">folktables</span> <span class="kn">import</span> <span class="n">ACSDataSource</span>
+<span class="kn">from</span> <span class="nn">folktables.load_acs</span> <span class="kn">import</span> <span class="n">state_list</span>
+
+<span class="kn">from</span> <span class="nn">..dataset</span> <span class="kn">import</span> <span class="n">Dataset</span>
+<span class="kn">from</span> <span class="nn">.acs_tasks</span> <span class="kn">import</span> <span class="n">ACSTaskMetadata</span>
+
+<span class="n">DEFAULT_DATA_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;~/data&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">expanduser</span><span class="p">()</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span>
+<span class="n">DEFAULT_TEST_SIZE</span> <span class="o">=</span> <span class="mf">0.1</span>
+<span class="n">DEFAULT_VAL_SIZE</span> <span class="o">=</span> <span class="mf">0.1</span>
+<span class="n">DEFAULT_SEED</span> <span class="o">=</span> <span class="mi">42</span>
+
+<span class="n">DEFAULT_SURVEY_YEAR</span> <span class="o">=</span> <span class="s2">&quot;2018&quot;</span>
+<span class="n">DEFAULT_SURVEY_HORIZON</span> <span class="o">=</span> <span class="s2">&quot;1-Year&quot;</span>
+<span class="n">DEFAULT_SURVEY_UNIT</span> <span class="o">=</span> <span class="s2">&quot;person&quot;</span>
+
+
+<div class="viewcode-block" id="ACSDataset">
+<a class="viewcode-back" href="../../../source/folktexts.acs.html#folktexts.acs.acs_dataset.ACSDataset">[docs]</a>
+<span class="k">class</span> <span class="nc">ACSDataset</span><span class="p">(</span><span class="n">Dataset</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Wrapper for ACS folktables datasets.&quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">data</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+        <span class="n">full_acs_data</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+        <span class="n">task</span><span class="p">:</span> <span class="n">ACSTaskMetadata</span><span class="p">,</span>
+        <span class="n">test_size</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">DEFAULT_TEST_SIZE</span><span class="p">,</span>
+        <span class="n">val_size</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">DEFAULT_VAL_SIZE</span><span class="p">,</span>
+        <span class="n">subsampling</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">42</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_full_acs_data</span> <span class="o">=</span> <span class="n">full_acs_data</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span>
+            <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+            <span class="n">test_size</span><span class="o">=</span><span class="n">test_size</span><span class="p">,</span>
+            <span class="n">val_size</span><span class="o">=</span><span class="n">val_size</span><span class="p">,</span>
+            <span class="n">subsampling</span><span class="o">=</span><span class="n">subsampling</span><span class="p">,</span>
+            <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
+        <span class="p">)</span>
+
+<div class="viewcode-block" id="ACSDataset.make_from_task">
+<a class="viewcode-back" href="../../../source/folktexts.acs.html#folktexts.acs.acs_dataset.ACSDataset.make_from_task">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">make_from_task</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">task</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">ACSTaskMetadata</span><span class="p">,</span>
+        <span class="n">cache_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">survey_year</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">DEFAULT_SURVEY_YEAR</span><span class="p">,</span>
+        <span class="n">horizon</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">DEFAULT_SURVEY_HORIZON</span><span class="p">,</span>
+        <span class="n">survey</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">DEFAULT_SURVEY_UNIT</span><span class="p">,</span>
+        <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DEFAULT_SEED</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Construct an ACSDataset object from a given ACS task.</span>
+
+<span class="sd">        Can customize survey sample parameters (survey year, horizon, survey type).</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        task : str | ACSTaskMetadata</span>
+<span class="sd">            The name of the ACS task or the task object itself.</span>
+<span class="sd">        cache_dir : str | Path, optional</span>
+<span class="sd">            The directory where ACS data is (or will be) saved to, by default</span>
+<span class="sd">            uses DEFAULT_DATA_DIR.</span>
+<span class="sd">        survey_year : str, optional</span>
+<span class="sd">            The year from which to load survey data, by default DEFAULT_SURVEY_YEAR.</span>
+<span class="sd">        horizon : str, optional</span>
+<span class="sd">            The time horizon of survey data to load, by default DEFAULT_SURVEY_HORIZON.</span>
+<span class="sd">        survey : str, optional</span>
+<span class="sd">            The name of the survey unit to load, by default DEFAULT_SURVEY_UNIT.</span>
+<span class="sd">        seed : int, optional</span>
+<span class="sd">            The random seed, by default DEFAULT_SEED.</span>
+<span class="sd">        **kwargs</span>
+<span class="sd">            Extra key-word arguments to be passed to the Dataset constructor.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Create &quot;folktables&quot; sub-folder under the given cache dir</span>
+        <span class="n">cache_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">cache_dir</span> <span class="ow">or</span> <span class="n">DEFAULT_DATA_DIR</span><span class="p">)</span><span class="o">.</span><span class="n">expanduser</span><span class="p">()</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span> <span class="o">/</span> <span class="s2">&quot;folktables&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">cache_dir</span><span class="o">.</span><span class="n">exists</span><span class="p">():</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Creating cache directory &#39;</span><span class="si">{</span><span class="n">cache_dir</span><span class="si">}</span><span class="s2">&#39; for ACS data.&quot;</span><span class="p">)</span>
+            <span class="n">cache_dir</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="c1"># Parse task if given a string</span>
+        <span class="n">task_obj</span> <span class="o">=</span> <span class="n">ACSTaskMetadata</span><span class="o">.</span><span class="n">get_task</span><span class="p">(</span><span class="n">task</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="n">task</span>
+
+        <span class="c1"># Load ACS data source</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Loading ACS data...&quot;</span><span class="p">)</span>
+        <span class="n">data_source</span> <span class="o">=</span> <span class="n">ACSDataSource</span><span class="p">(</span>
+            <span class="n">survey_year</span><span class="o">=</span><span class="n">survey_year</span><span class="p">,</span> <span class="n">horizon</span><span class="o">=</span><span class="n">horizon</span><span class="p">,</span> <span class="n">survey</span><span class="o">=</span><span class="n">survey</span><span class="p">,</span>
+            <span class="n">root_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="o">.</span><span class="n">as_posix</span><span class="p">(),</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Get full ACS dataset</span>
+        <span class="n">full_acs_data</span> <span class="o">=</span> <span class="n">data_source</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span>
+            <span class="n">states</span><span class="o">=</span><span class="n">state_list</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
+
+        <span class="c1"># Parse data for this task</span>
+        <span class="n">parsed_data</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_parse_task_data</span><span class="p">(</span><span class="n">full_acs_data</span><span class="p">,</span> <span class="n">task_obj</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span>
+            <span class="n">data</span><span class="o">=</span><span class="n">parsed_data</span><span class="p">,</span>
+            <span class="n">full_acs_data</span><span class="o">=</span><span class="n">full_acs_data</span><span class="p">,</span>
+            <span class="n">task</span><span class="o">=</span><span class="n">task_obj</span><span class="p">,</span>
+            <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">task</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ACSTaskMetadata</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_task</span>
+
+    <span class="nd">@task</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">task</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_task</span><span class="p">:</span> <span class="n">ACSTaskMetadata</span><span class="p">):</span>
+        <span class="c1"># Parse data rows for new ACS task</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parse_task_data</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_full_acs_data</span><span class="p">,</span> <span class="n">new_task</span><span class="p">)</span>
+
+        <span class="c1"># Re-make train/test/val split</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_test_indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_make_train_test_val_split</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">test_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rng</span><span class="p">)</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Check if sub-sampling is necessary (it&#39;s applied only to train/test/val indices)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">subsampling</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_subsample_train_test_val_indices</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">subsampling</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_task</span> <span class="o">=</span> <span class="n">new_task</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_parse_task_data</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">full_df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">task</span><span class="p">:</span> <span class="n">ACSTaskMetadata</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Parse a DataFrame for compatibility with the given task object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        full_df : pd.DataFrame</span>
+<span class="sd">            Full DataFrame. Some rows and/or columns may be discarded for each</span>
+<span class="sd">            task.</span>
+<span class="sd">        task : ACSTaskMetadata</span>
+<span class="sd">            The task object used to parse the given data.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        parsed_df : pd.DataFrame</span>
+<span class="sd">            Parsed DataFrame in accordance with the given task.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Pre-process the data if necessary</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="n">ACSTaskMetadata</span><span class="p">)</span> <span class="ow">and</span> <span class="n">task</span><span class="o">.</span><span class="n">folktables_obj</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">parsed_df</span> <span class="o">=</span> <span class="n">task</span><span class="o">.</span><span class="n">folktables_obj</span><span class="o">.</span><span class="n">_preprocess</span><span class="p">(</span><span class="n">full_df</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">parsed_df</span> <span class="o">=</span> <span class="n">full_df</span>
+
+        <span class="c1"># Threshold the target column if necessary</span>
+        <span class="k">if</span> <span class="n">task</span><span class="o">.</span><span class="n">target</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">task</span><span class="o">.</span><span class="n">target_threshold</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">task</span><span class="o">.</span><span class="n">get_target</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">parsed_df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+            <span class="n">parsed_df</span><span class="p">[</span><span class="n">task</span><span class="o">.</span><span class="n">get_target</span><span class="p">()]</span> <span class="o">=</span> <span class="n">task</span><span class="o">.</span><span class="n">target_threshold</span><span class="o">.</span><span class="n">apply_to_column_data</span><span class="p">(</span><span class="n">parsed_df</span><span class="p">[</span><span class="n">task</span><span class="o">.</span><span class="n">target</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">parsed_df</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
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+
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+  <h1>Source code for folktexts.acs.acs_tasks</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;A collection of ACS prediction tasks based on the folktables package.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">asdict</span><span class="p">,</span> <span class="n">dataclass</span>
+
+<span class="kn">import</span> <span class="nn">folktables</span>
+<span class="kn">from</span> <span class="nn">folktables</span> <span class="kn">import</span> <span class="n">BasicProblem</span>
+
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">hash_dict</span>
+<span class="kn">from</span> <span class="nn">..col_to_text</span> <span class="kn">import</span> <span class="n">ColumnToText</span> <span class="k">as</span> <span class="n">_ColumnToText</span>
+<span class="kn">from</span> <span class="nn">..qa_interface</span> <span class="kn">import</span> <span class="n">DirectNumericQA</span><span class="p">,</span> <span class="n">MultipleChoiceQA</span>
+<span class="kn">from</span> <span class="nn">..task</span> <span class="kn">import</span> <span class="n">TaskMetadata</span>
+<span class="kn">from</span> <span class="nn">..threshold</span> <span class="kn">import</span> <span class="n">Threshold</span>
+<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">acs_columns</span><span class="p">,</span> <span class="n">acs_questions</span>
+<span class="kn">from</span> <span class="nn">.acs_thresholds</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">acs_employment_threshold</span><span class="p">,</span>
+    <span class="n">acs_health_insurance_threshold</span><span class="p">,</span>
+    <span class="n">acs_income_threshold</span><span class="p">,</span>
+    <span class="n">acs_mobility_threshold</span><span class="p">,</span>
+    <span class="n">acs_poverty_ratio_threshold</span><span class="p">,</span>
+    <span class="n">acs_public_coverage_threshold</span><span class="p">,</span>
+    <span class="n">acs_travel_time_threshold</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Map of ACS column names to ColumnToText objects</span>
+<span class="n">acs_columns_map</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">object</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="n">col_mapper</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">col_mapper</span>
+    <span class="k">for</span> <span class="n">col_mapper</span> <span class="ow">in</span> <span class="n">acs_columns</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">col_mapper</span><span class="p">,</span> <span class="n">_ColumnToText</span><span class="p">)</span>
+<span class="p">}</span>
+
+
+<div class="viewcode-block" id="ACSTaskMetadata">
+<a class="viewcode-back" href="../../../source/folktexts.acs.html#folktexts.acs.acs_tasks.ACSTaskMetadata">[docs]</a>
+<span class="nd">@dataclass</span>
+<span class="k">class</span> <span class="nc">ACSTaskMetadata</span><span class="p">(</span><span class="n">TaskMetadata</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A class to hold information on an ACS prediction task.&quot;&quot;&quot;</span>
+
+    <span class="c1"># The ACS task object from the folktables package</span>
+    <span class="n">folktables_obj</span><span class="p">:</span> <span class="n">BasicProblem</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="ACSTaskMetadata.make_task">
+<a class="viewcode-back" href="../../../source/folktexts.acs.html#folktexts.acs.acs_tasks.ACSTaskMetadata.make_task">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">make_task</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">features</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
+        <span class="n">target</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">sensitive_attribute</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">target_threshold</span><span class="p">:</span> <span class="n">Threshold</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">multiple_choice_qa</span><span class="p">:</span> <span class="n">MultipleChoiceQA</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">direct_numeric_qa</span><span class="p">:</span> <span class="n">DirectNumericQA</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">description</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">folktables_obj</span><span class="p">:</span> <span class="n">BasicProblem</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ACSTaskMetadata</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Create an ACS task object from the given parameters.&quot;&quot;&quot;</span>
+        <span class="c1"># Resolve target column name</span>
+        <span class="n">target_col_name</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">target_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">target_threshold</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">target</span><span class="p">)</span>
+
+        <span class="c1"># Get default Q&amp;A interfaces for this task&#39;s target column</span>
+        <span class="k">if</span> <span class="n">multiple_choice_qa</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">multiple_choice_qa</span> <span class="o">=</span> <span class="n">acs_questions</span><span class="o">.</span><span class="n">acs_multiple_choice_qa_map</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">target_col_name</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">direct_numeric_qa</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">direct_numeric_qa</span> <span class="o">=</span> <span class="n">acs_questions</span><span class="o">.</span><span class="n">acs_numeric_qa_map</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">target_col_name</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span>
+            <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
+            <span class="n">features</span><span class="o">=</span><span class="n">features</span><span class="p">,</span>
+            <span class="n">target</span><span class="o">=</span><span class="n">target</span><span class="p">,</span>
+            <span class="n">cols_to_text</span><span class="o">=</span><span class="n">acs_columns_map</span><span class="p">,</span>
+            <span class="n">sensitive_attribute</span><span class="o">=</span><span class="n">sensitive_attribute</span><span class="p">,</span>
+            <span class="n">target_threshold</span><span class="o">=</span><span class="n">target_threshold</span><span class="p">,</span>
+            <span class="n">multiple_choice_qa</span><span class="o">=</span><span class="n">multiple_choice_qa</span><span class="p">,</span>
+            <span class="n">direct_numeric_qa</span><span class="o">=</span><span class="n">direct_numeric_qa</span><span class="p">,</span>
+            <span class="n">description</span><span class="o">=</span><span class="n">description</span><span class="p">,</span>
+            <span class="n">folktables_obj</span><span class="o">=</span><span class="n">folktables_obj</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ACSTaskMetadata.make_folktables_task">
+<a class="viewcode-back" href="../../../source/folktexts.acs.html#folktexts.acs.acs_tasks.ACSTaskMetadata.make_folktables_task">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">make_folktables_task</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">target_threshold</span><span class="p">:</span> <span class="n">Threshold</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">description</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ACSTaskMetadata</span><span class="p">:</span>
+
+        <span class="c1"># Get the task object from the folktables package</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">folktables_task</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">folktables</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Could not find task &#39;</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39; in folktables package.&quot;</span><span class="p">)</span>
+
+        <span class="n">acs_task</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">make_task</span><span class="p">(</span>
+            <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
+            <span class="n">features</span><span class="o">=</span><span class="n">folktables_task</span><span class="o">.</span><span class="n">features</span><span class="p">,</span>
+            <span class="n">target</span><span class="o">=</span><span class="n">folktables_task</span><span class="o">.</span><span class="n">target</span><span class="p">,</span>
+            <span class="n">sensitive_attribute</span><span class="o">=</span><span class="n">folktables_task</span><span class="o">.</span><span class="n">group</span><span class="p">,</span>
+            <span class="n">target_threshold</span><span class="o">=</span><span class="n">target_threshold</span><span class="p">,</span>
+            <span class="n">description</span><span class="o">=</span><span class="n">description</span><span class="p">,</span>
+            <span class="n">folktables_obj</span><span class="o">=</span><span class="n">folktables_task</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">acs_task</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="n">hashable_params</span> <span class="o">=</span> <span class="n">asdict</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="n">hashable_params</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;cols_to_text&quot;</span><span class="p">)</span>
+        <span class="n">hashable_params</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;folktables_obj&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">hash_dict</span><span class="p">(</span><span class="n">hashable_params</span><span class="p">),</span> <span class="mi">16</span><span class="p">)</span></div>
+
+
+
+<span class="c1"># Instantiate folktables tasks</span>
+<span class="n">acs_income_task</span> <span class="o">=</span> <span class="n">ACSTaskMetadata</span><span class="o">.</span><span class="n">make_folktables_task</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;ACSIncome&quot;</span><span class="p">,</span>
+    <span class="n">target_threshold</span><span class="o">=</span><span class="n">acs_income_threshold</span><span class="p">,</span>
+    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;predict whether an individual&#39;s income is above $50,000&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">acs_public_coverage_task</span> <span class="o">=</span> <span class="n">ACSTaskMetadata</span><span class="o">.</span><span class="n">make_folktables_task</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;ACSPublicCoverage&quot;</span><span class="p">,</span>
+    <span class="n">target_threshold</span><span class="o">=</span><span class="n">acs_public_coverage_threshold</span><span class="p">,</span>
+    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;predict whether an individual is covered by public health insurance&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">acs_mobility_task</span> <span class="o">=</span> <span class="n">ACSTaskMetadata</span><span class="o">.</span><span class="n">make_folktables_task</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;ACSMobility&quot;</span><span class="p">,</span>
+    <span class="n">target_threshold</span><span class="o">=</span><span class="n">acs_mobility_threshold</span><span class="p">,</span>
+    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;predict whether an individual had the same residential address one year ago&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">acs_employment_task</span> <span class="o">=</span> <span class="n">ACSTaskMetadata</span><span class="o">.</span><span class="n">make_folktables_task</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;ACSEmployment&quot;</span><span class="p">,</span>
+    <span class="n">target_threshold</span><span class="o">=</span><span class="n">acs_employment_threshold</span><span class="p">,</span>
+    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;predict whether an individual is employed&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">acs_travel_time_task</span> <span class="o">=</span> <span class="n">ACSTaskMetadata</span><span class="o">.</span><span class="n">make_folktables_task</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;ACSTravelTime&quot;</span><span class="p">,</span>
+    <span class="n">target_threshold</span><span class="o">=</span><span class="n">acs_travel_time_threshold</span><span class="p">,</span>
+    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;predict whether an individual has a commute to work that is longer than 20 minutes&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">acs_income_poverty_ratio_task</span> <span class="o">=</span> <span class="n">ACSTaskMetadata</span><span class="o">.</span><span class="n">make_folktables_task</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;ACSIncomePovertyRatio&quot;</span><span class="p">,</span>
+    <span class="n">target_threshold</span><span class="o">=</span><span class="n">acs_poverty_ratio_threshold</span><span class="p">,</span>
+    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;predict whether an individual&#39;s income-to-poverty ratio is below 2.5&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Dummy/test ACS task to predict health insurance coverage using all other available features</span>
+<span class="n">acs_full_task</span> <span class="o">=</span> <span class="n">ACSTaskMetadata</span><span class="o">.</span><span class="n">make_task</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;ACSHealthInsurance-test&quot;</span><span class="p">,</span>
+    <span class="n">features</span><span class="o">=</span><span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">({</span>
+        <span class="o">*</span><span class="n">acs_income_task</span><span class="o">.</span><span class="n">features</span><span class="p">,</span>
+        <span class="o">*</span><span class="n">acs_public_coverage_task</span><span class="o">.</span><span class="n">features</span><span class="p">,</span>
+        <span class="o">*</span><span class="n">acs_mobility_task</span><span class="o">.</span><span class="n">features</span><span class="p">,</span>
+        <span class="o">*</span><span class="n">acs_employment_task</span><span class="o">.</span><span class="n">features</span><span class="p">,</span>
+        <span class="o">*</span><span class="n">acs_travel_time_task</span><span class="o">.</span><span class="n">features</span><span class="p">,</span>
+    <span class="p">})),</span>
+    <span class="n">target</span><span class="o">=</span><span class="s2">&quot;HINS2&quot;</span><span class="p">,</span>
+    <span class="n">target_threshold</span><span class="o">=</span><span class="n">acs_health_insurance_threshold</span><span class="p">,</span>
+    <span class="n">description</span><span class="o">=</span><span class="p">(</span>
+        <span class="s2">&quot;predict whether an individual has purchased health insurance directly &quot;</span>
+        <span class="s2">&quot;from an insurance company (as opposed to being insured through an &quot;</span>
+        <span class="s2">&quot;employer, Medicare, Medicaid, or any other source)&quot;</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
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+          <li class="breadcrumb-item"><a href="../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">folktexts.benchmark</li>
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+             
+  <h1>Source code for folktexts.benchmark</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;A benchmark class for measuring and evaluating LLM calibration.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">dataclasses</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoModelForCausalLM</span><span class="p">,</span> <span class="n">AutoTokenizer</span>
+
+<span class="kn">from</span> <span class="nn">._io</span> <span class="kn">import</span> <span class="n">load_json</span><span class="p">,</span> <span class="n">save_json</span>
+<span class="kn">from</span> <span class="nn">._utils</span> <span class="kn">import</span> <span class="n">hash_dict</span><span class="p">,</span> <span class="n">is_valid_number</span><span class="p">,</span> <span class="n">get_current_timestamp</span>
+<span class="kn">from</span> <span class="nn">.acs.acs_dataset</span> <span class="kn">import</span> <span class="n">ACSDataset</span>
+<span class="kn">from</span> <span class="nn">.acs.acs_tasks</span> <span class="kn">import</span> <span class="n">ACSTaskMetadata</span>
+<span class="kn">from</span> <span class="nn">.classifier</span> <span class="kn">import</span> <span class="n">LLMClassifier</span><span class="p">,</span> <span class="n">TransformersLLMClassifier</span><span class="p">,</span> <span class="n">WebAPILLMClassifier</span>
+<span class="kn">from</span> <span class="nn">.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span>
+<span class="kn">from</span> <span class="nn">.evaluation</span> <span class="kn">import</span> <span class="n">evaluate_predictions</span>
+<span class="kn">from</span> <span class="nn">.plotting</span> <span class="kn">import</span> <span class="n">render_evaluation_plots</span><span class="p">,</span> <span class="n">render_fairness_plots</span>
+<span class="kn">from</span> <span class="nn">.prompting</span> <span class="kn">import</span> <span class="n">encode_row_prompt</span><span class="p">,</span> <span class="n">encode_row_prompt_few_shot</span>
+<span class="kn">from</span> <span class="nn">.task</span> <span class="kn">import</span> <span class="n">TaskMetadata</span>
+
+<span class="n">DEFAULT_SEED</span> <span class="o">=</span> <span class="mi">42</span>
+<span class="n">DEFAULT_FIT_THRESHOLD_N</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">DEFAULT_ROOT_RESULTS_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="BenchmarkConfig">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.benchmark.BenchmarkConfig">[docs]</a>
+<span class="nd">@dataclasses</span><span class="o">.</span><span class="n">dataclass</span><span class="p">(</span><span class="n">frozen</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">eq</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">class</span> <span class="nc">BenchmarkConfig</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A dataclass to hold the configuration for risk-score benchmark.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    numeric_risk_prompting : bool, optional</span>
+<span class="sd">        Whether to prompt for numeric risk-estimates instead of multiple-choice</span>
+<span class="sd">        Q&amp;A, by default False.</span>
+<span class="sd">    few_shot : int | None, optional</span>
+<span class="sd">        Whether to use few-shot prompting with a given number of examples, by</span>
+<span class="sd">        default None.</span>
+<span class="sd">    reuse_few_shot_examples : bool, optional</span>
+<span class="sd">        Whether to reuse the same samples for few-shot prompting (or sample new</span>
+<span class="sd">        ones every time), by default False.</span>
+<span class="sd">    batch_size : int | None, optional</span>
+<span class="sd">        The batch size to use for inference.</span>
+<span class="sd">    context_size : int | None, optional</span>
+<span class="sd">        The maximum context size when prompting the LLM.</span>
+<span class="sd">    correct_order_bias : bool, optional</span>
+<span class="sd">        Whether to correct the ordering bias in multiple-choice Q&amp;A when</span>
+<span class="sd">        prompting the LLM, by default True.</span>
+<span class="sd">    feature_subset : list[str] | None, optional</span>
+<span class="sd">        Whether to use a subset of the standard feature set for the task. The</span>
+<span class="sd">        list should contain the names of the columns of features to use.</span>
+<span class="sd">    population_filter : dict | None, optional</span>
+<span class="sd">        Optional population filter for this benchmark; must follow the format</span>
+<span class="sd">        `{&quot;column_name&quot;: &quot;value&quot;}`.</span>
+<span class="sd">    seed : int, optional</span>
+<span class="sd">        Random seed -- to set for reproducibility.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">numeric_risk_prompting</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="n">few_shot</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">reuse_few_shot_examples</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">context_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">correct_order_bias</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="n">feature_subset</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">population_filter</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DEFAULT_SEED</span>
+
+<div class="viewcode-block" id="BenchmarkConfig.default_config">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.benchmark.BenchmarkConfig.default_config">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">default_config</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">**</span><span class="n">changes</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the default configuration with optional changes.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="o">**</span><span class="n">changes</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BenchmarkConfig.update">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.benchmark.BenchmarkConfig.update">[docs]</a>
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">changes</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">BenchmarkConfig</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Update the configuration with new values.&quot;&quot;&quot;</span>
+        <span class="n">possible_keys</span> <span class="o">=</span> <span class="n">dataclasses</span><span class="o">.</span><span class="n">asdict</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
+        <span class="n">valid_changes</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">changes</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">possible_keys</span><span class="p">}</span>
+
+        <span class="c1"># Log config changes</span>
+        <span class="k">if</span> <span class="n">valid_changes</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Updating benchmark configuration with: </span><span class="si">{</span><span class="n">valid_changes</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Log unused kwargs</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">valid_changes</span><span class="p">)</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">changes</span><span class="p">):</span>
+            <span class="n">unused_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">changes</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">possible_keys</span><span class="p">}</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unused config arguments: </span><span class="si">{</span><span class="n">unused_kwargs</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">dataclasses</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">valid_changes</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BenchmarkConfig.load_from_disk">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.benchmark.BenchmarkConfig.load_from_disk">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">load_from_disk</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Load the configuration from disk.&quot;&quot;&quot;</span>
+        <span class="n">obj</span> <span class="o">=</span> <span class="n">load_json</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="o">**</span><span class="n">obj</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid configuration file &#39;</span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="BenchmarkConfig.save_to_disk">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.benchmark.BenchmarkConfig.save_to_disk">[docs]</a>
+    <span class="k">def</span> <span class="nf">save_to_disk</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Save the configuration to disk.&quot;&quot;&quot;</span>
+        <span class="n">save_json</span><span class="p">(</span><span class="n">dataclasses</span><span class="o">.</span><span class="n">asdict</span><span class="p">(</span><span class="bp">self</span><span class="p">),</span> <span class="n">path</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generates a unique hash for the configuration.&quot;&quot;&quot;</span>
+        <span class="n">cfg</span> <span class="o">=</span> <span class="n">dataclasses</span><span class="o">.</span><span class="n">asdict</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;feature_subset&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;feature_subset&quot;</span><span class="p">])</span> <span class="k">if</span> <span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;feature_subset&quot;</span><span class="p">]</span> <span class="k">else</span> <span class="kc">None</span>
+        <span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;population_filter_hash&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">hash_dict</span><span class="p">(</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;population_filter&quot;</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;population_filter&quot;</span><span class="p">]</span> <span class="k">else</span> <span class="kc">None</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">hash_dict</span><span class="p">(</span><span class="n">cfg</span><span class="p">),</span> <span class="mi">16</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="Benchmark">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.benchmark.Benchmark">[docs]</a>
+<span class="k">class</span> <span class="nc">Benchmark</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Measures and evaluates risk scores produced by an LLM.&quot;&quot;&quot;</span>
+
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Standardized configurations for the ACS data to use for benchmarking.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">ACS_DATASET_CONFIGS</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="c1"># ACS survey configs</span>
+        <span class="s2">&quot;survey_year&quot;</span><span class="p">:</span> <span class="s2">&quot;2018&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;horizon&quot;</span><span class="p">:</span> <span class="s2">&quot;1-Year&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;survey&quot;</span><span class="p">:</span> <span class="s2">&quot;person&quot;</span><span class="p">,</span>
+
+        <span class="c1"># Data split configs</span>
+        <span class="s2">&quot;test_size&quot;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span>
+        <span class="s2">&quot;val_size&quot;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span>
+        <span class="s2">&quot;subsampling&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+
+        <span class="c1"># Fixed random seed</span>
+        <span class="s2">&quot;seed&quot;</span><span class="p">:</span> <span class="mi">42</span><span class="p">,</span>
+    <span class="p">}</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">llm_clf</span><span class="p">:</span> <span class="n">LLMClassifier</span><span class="p">,</span>
+        <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span>
+        <span class="n">config</span><span class="p">:</span> <span class="n">BenchmarkConfig</span> <span class="o">=</span> <span class="n">BenchmarkConfig</span><span class="o">.</span><span class="n">default_config</span><span class="p">(),</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;A benchmark object to measure and evaluate risk scores produced by an LLM.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        llm_clf : LLMClassifier</span>
+<span class="sd">            A language model classifier object (can be local or web-hosted).</span>
+<span class="sd">        dataset : Dataset</span>
+<span class="sd">            The dataset object to use for the benchmark.÷</span>
+<span class="sd">        config : BenchmarkConfig, optional</span>
+<span class="sd">            The configuration object used to create the benchmark parameters.</span>
+<span class="sd">            **NOTE**: This is used to uniquely identify the benchmark object for</span>
+<span class="sd">            reproducibility; it **will not be used to change the benchmark</span>
+<span class="sd">            behavior**. To configure the benchmark, pass a configuration object</span>
+<span class="sd">            to the Benchmark.make_benchmark method.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span> <span class="o">=</span> <span class="n">llm_clf</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_y_test_scores</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_results_root_dir</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Path</span><span class="p">]</span> <span class="o">=</span> <span class="n">DEFAULT_ROOT_RESULTS_DIR</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_results</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_plots</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="c1"># Log initialization</span>
+        <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">** Benchmark initialization **</span><span class="se">\n</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;Model: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2">;</span><span class="se">\n</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;Task: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">;</span><span class="se">\n</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;Hash: </span><span class="si">{</span><span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="si">}</span><span class="s2">;</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">configs_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+        <span class="n">cnf</span> <span class="o">=</span> <span class="n">dataclasses</span><span class="o">.</span><span class="n">asdict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="p">)</span>
+
+        <span class="c1"># Add info on model, task, and dataset</span>
+        <span class="n">cnf</span><span class="p">[</span><span class="s2">&quot;model_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_name</span>
+        <span class="n">cnf</span><span class="p">[</span><span class="s2">&quot;model_hash&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span><span class="p">)</span>
+        <span class="n">cnf</span><span class="p">[</span><span class="s2">&quot;task_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">name</span>
+        <span class="n">cnf</span><span class="p">[</span><span class="s2">&quot;task_hash&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="p">)</span>
+        <span class="n">cnf</span><span class="p">[</span><span class="s2">&quot;dataset_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">name</span>
+        <span class="n">cnf</span><span class="p">[</span><span class="s2">&quot;dataset_subsampling&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">subsampling</span>
+        <span class="n">cnf</span><span class="p">[</span><span class="s2">&quot;dataset_hash&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">cnf</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">results</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># Add benchmark configs to the results</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_results</span><span class="p">[</span><span class="s2">&quot;config&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">configs_dict</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_results</span><span class="p">[</span><span class="s2">&quot;benchmark_hash&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_results</span><span class="p">[</span><span class="s2">&quot;results_dir&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">results_dir</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_results</span><span class="p">[</span><span class="s2">&quot;results_root_dir&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">results_root_dir</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_results</span><span class="p">[</span><span class="s2">&quot;current_time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">get_current_timestamp</span><span class="p">()</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_results</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">task</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">task</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">model_name</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">model_name</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">results_root_dir</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Path</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">Path</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_results_root_dir</span><span class="p">)</span>
+
+    <span class="nd">@results_root_dir</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">results_root_dir</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_results_root_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">path</span><span class="p">)</span><span class="o">.</span><span class="n">expanduser</span><span class="p">()</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">results_dir</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Path</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the results directory for this benchmark.&quot;&quot;&quot;</span>
+        <span class="c1"># Get subfolder name</span>
+        <span class="n">subfolder_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2">_bench-</span><span class="si">{</span><span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="n">subfolder_dir_path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">results_root_dir</span><span class="p">)</span> <span class="o">/</span> <span class="n">subfolder_name</span>
+
+        <span class="c1"># Create subfolder directory if it does not exist</span>
+        <span class="n">subfolder_dir_path</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">subfolder_dir_path</span>
+
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="n">hash_params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+            <span class="n">llm_clf_hash</span><span class="o">=</span><span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span><span class="p">),</span>
+            <span class="n">dataset_hash</span><span class="o">=</span><span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="p">),</span>
+            <span class="n">config_hash</span><span class="o">=</span><span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="p">),</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">hash_dict</span><span class="p">(</span><span class="n">hash_params</span><span class="p">),</span> <span class="mi">16</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_get_predictions_save_path</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_split</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Path</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Standardized path to file containing predictions for the given data split.&quot;&quot;&quot;</span>
+        <span class="k">assert</span> <span class="n">data_split</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;train&quot;</span><span class="p">,</span> <span class="s2">&quot;val&quot;</span><span class="p">,</span> <span class="s2">&quot;test&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">results_dir</span> <span class="o">/</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">data_split</span><span class="si">}</span><span class="s2">_predictions.csv&quot;</span>
+
+<div class="viewcode-block" id="Benchmark.run">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.benchmark.Benchmark.run">[docs]</a>
+    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">results_root_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span><span class="p">,</span> <span class="n">fit_threshold</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">bool</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Run the calibration benchmark experiment.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        results_root_dir : str | Path</span>
+<span class="sd">            Path to root directory under which results will be saved.</span>
+<span class="sd">        fit_threshold : int | bool, optional</span>
+<span class="sd">            Whether to fit the binarization threshold on a given number of</span>
+<span class="sd">            training samples, by default 0 (will not fit the threshold).</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        float</span>
+<span class="sd">            The benchmark metric value. By default this is the ECE score.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_results</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Benchmark was already run. Overriding previous results.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Update results directory</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">results_root_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">results_root_dir</span><span class="p">)</span>
+
+        <span class="c1"># Get test data</span>
+        <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">get_test</span><span class="p">()</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test data features shape: </span><span class="si">{</span><span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Get sensitive attribute data if available</span>
+        <span class="n">s_test</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">sensitive_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">s_test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">get_sensitive_attribute_data</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">y_test</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+
+        <span class="c1"># Get LLM risk-estimate predictions for each row in the test set</span>
+        <span class="n">test_predictions_save_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_predictions_save_path</span><span class="p">(</span><span class="s2">&quot;test&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_y_test_scores</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span>
+            <span class="n">data</span><span class="o">=</span><span class="n">X_test</span><span class="p">,</span>
+            <span class="n">predictions_save_path</span><span class="o">=</span><span class="n">test_predictions_save_path</span><span class="p">,</span>
+            <span class="n">labels</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span>  <span class="c1"># used only to save alongside predictions in disk</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_y_test_scores</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">_get_positive_class_scores</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_y_test_scores</span><span class="p">)</span>
+
+        <span class="c1"># If requested, fit the threshold on a small portion of the train set</span>
+        <span class="k">if</span> <span class="n">fit_threshold</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">fit_threshold</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
+                <span class="n">fit_threshold</span> <span class="o">=</span> <span class="n">DEFAULT_FIT_THRESHOLD_N</span>
+            <span class="k">elif</span> <span class="ow">not</span> <span class="n">is_valid_number</span><span class="p">(</span><span class="n">fit_threshold</span><span class="p">)</span> <span class="ow">or</span> <span class="n">fit_threshold</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid fit_threshold=</span><span class="si">{</span><span class="n">fit_threshold</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+            <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Fitting threshold on </span><span class="si">{</span><span class="n">fit_threshold</span><span class="si">}</span><span class="s2"> train samples&quot;</span><span class="p">)</span>
+            <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">sample_n_train_examples</span><span class="p">(</span><span class="n">fit_threshold</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+        <span class="c1"># Evaluate test risk scores</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_results</span> <span class="o">=</span> <span class="n">evaluate_predictions</span><span class="p">(</span>
+            <span class="n">y_true</span><span class="o">=</span><span class="n">y_test</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(),</span>
+            <span class="n">y_pred_scores</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_y_test_scores</span><span class="p">,</span>
+            <span class="n">sensitive_attribute</span><span class="o">=</span><span class="n">s_test</span><span class="p">,</span>
+            <span class="n">threshold</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">threshold</span><span class="p">,</span>
+            <span class="n">model_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">model_name</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Save predictions save path</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_results</span><span class="p">[</span><span class="s2">&quot;predictions_path&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_predictions_save_path</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span>
+
+        <span class="c1"># Log main results</span>
+        <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">** Test results **</span><span class="se">\n</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;Model: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2">;</span><span class="se">\n</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2"> ECE:       </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_results</span><span class="p">[</span><span class="s1">&#39;ece&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">.1%</span><span class="si">}</span><span class="s2">;</span><span class="se">\n</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2"> ROC AUC :  </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;roc_auc&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">.1%</span><span class="si">}</span><span class="s2">;</span><span class="se">\n</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2"> Accuracy:  </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">.1%</span><span class="si">}</span><span class="s2">;</span><span class="se">\n</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2"> Bal. acc.: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">.1%</span><span class="si">}</span><span class="s2">;</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
+
+        <span class="c1"># Render plots</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">plot_results</span><span class="p">(</span><span class="n">show_plots</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Failed to render plots: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Save results to disk</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">save_results</span><span class="p">()</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_results</span></div>
+
+
+<div class="viewcode-block" id="Benchmark.plot_results">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.benchmark.Benchmark.plot_results">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot_results</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">show_plots</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Render evaluation plots and save to disk.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        show_plots : bool, optional</span>
+<span class="sd">            Whether to show plots, by default True.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        plots_paths : dict[str, str]</span>
+<span class="sd">            The paths to the saved plots.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_results</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;No results to plot. Run the benchmark first.&quot;</span><span class="p">)</span>
+
+        <span class="n">imgs_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">results_dir</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;imgs&quot;</span>
+        <span class="n">imgs_dir</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">get_test</span><span class="p">()</span>
+
+        <span class="n">plots_paths</span> <span class="o">=</span> <span class="n">render_evaluation_plots</span><span class="p">(</span>
+            <span class="n">y_true</span><span class="o">=</span><span class="n">y_test</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(),</span>
+            <span class="n">y_pred_scores</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_y_test_scores</span><span class="p">,</span>
+            <span class="n">eval_results</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">results</span><span class="p">,</span>
+            <span class="n">model_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">model_name</span><span class="p">,</span>
+            <span class="n">imgs_dir</span><span class="o">=</span><span class="n">imgs_dir</span><span class="p">,</span>
+            <span class="n">show_plots</span><span class="o">=</span><span class="n">show_plots</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Plot fairness plots if sensitive attribute is provided</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">sensitive_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">s_test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">get_sensitive_attribute_data</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">y_test</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+
+            <span class="n">plots_paths</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">render_fairness_plots</span><span class="p">(</span>
+                <span class="n">y_true</span><span class="o">=</span><span class="n">y_test</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(),</span>
+                <span class="n">y_pred_scores</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_y_test_scores</span><span class="p">,</span>
+                <span class="n">sensitive_attribute</span><span class="o">=</span><span class="n">s_test</span><span class="p">,</span>
+                <span class="n">eval_results</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">results</span><span class="p">,</span>
+                <span class="n">model_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">model_name</span><span class="p">,</span>
+                <span class="n">group_value_map</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">sensitive_attribute_value_map</span><span class="p">(),</span>
+                <span class="n">imgs_dir</span><span class="o">=</span><span class="n">imgs_dir</span><span class="p">,</span>
+                <span class="n">show_plots</span><span class="o">=</span><span class="n">show_plots</span><span class="p">,</span>
+            <span class="p">))</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_results</span><span class="p">[</span><span class="s2">&quot;plots&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">plots_paths</span>
+
+        <span class="k">return</span> <span class="n">plots_paths</span></div>
+
+
+<div class="viewcode-block" id="Benchmark.save_results">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.benchmark.Benchmark.save_results">[docs]</a>
+    <span class="k">def</span> <span class="nf">save_results</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">results_root_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Save the benchmark results to disk.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        results_root_dir : str | Path, optional</span>
+<span class="sd">            Path to root directory under which results will be saved. By default</span>
+<span class="sd">            will use `self.results_root_dir`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">results</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;No results to save. Run the benchmark first.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Update results directory if provided</span>
+        <span class="k">if</span> <span class="n">results_root_dir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">results_root_dir</span> <span class="o">=</span> <span class="n">results_root_dir</span>
+
+        <span class="c1"># Save results to disk</span>
+        <span class="n">results_file_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;results.bench-</span><span class="si">{</span><span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="si">}</span><span class="s2">.json&quot;</span>
+        <span class="n">results_file_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">results_dir</span> <span class="o">/</span> <span class="n">results_file_name</span>
+
+        <span class="n">save_json</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">results</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="n">results_file_path</span><span class="p">)</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Saved experiment results to &#39;</span><span class="si">{</span><span class="n">results_file_path</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span><span class="si">}</span><span class="s2">&#39;&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Benchmark.make_acs_benchmark">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.benchmark.Benchmark.make_acs_benchmark">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">make_acs_benchmark</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">task_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="o">*</span><span class="p">,</span>
+        <span class="n">model</span><span class="p">:</span> <span class="n">AutoModelForCausalLM</span> <span class="o">|</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="p">:</span> <span class="n">AutoTokenizer</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">data_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">max_api_rpm</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">config</span><span class="p">:</span> <span class="n">BenchmarkConfig</span> <span class="o">=</span> <span class="n">BenchmarkConfig</span><span class="o">.</span><span class="n">default_config</span><span class="p">(),</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Benchmark</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Create a standardized calibration benchmark on ACS data.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        task_name : str</span>
+<span class="sd">            The name of the ACS task to use.</span>
+<span class="sd">        model : AutoModelForCausalLM | str</span>
+<span class="sd">            The transformers language model to use, or the model ID for a webAPI</span>
+<span class="sd">            hosted model (e.g., &quot;openai/gpt-4o-mini&quot;).</span>
+<span class="sd">        tokenizer : AutoTokenizer, optional</span>
+<span class="sd">            The tokenizer used to train the model (if using a transformers</span>
+<span class="sd">            model). Not required for webAPI models.</span>
+<span class="sd">        data_dir : str | Path, optional</span>
+<span class="sd">            Path to the directory to load data from and save data in.</span>
+<span class="sd">        max_api_rpm : int, optional</span>
+<span class="sd">            The maximum number of API requests per minute for webAPI models.</span>
+<span class="sd">        config : BenchmarkConfig, optional</span>
+<span class="sd">            Extra benchmark configurations, by default will use</span>
+<span class="sd">            `BenchmarkConfig.default_config()`.</span>
+<span class="sd">        **kwargs</span>
+<span class="sd">            Additional arguments passed to `ACSDataset` and `BenchmarkConfig`.</span>
+<span class="sd">            By default will use a set of standardized configurations for</span>
+<span class="sd">            reproducibility.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        bench : Benchmark</span>
+<span class="sd">            The ACS calibration benchmark object.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Handle non-standard ACS arguments</span>
+        <span class="n">acs_dataset_configs</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">ACS_DATASET_CONFIGS</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">acs_dataset_configs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="ow">and</span> <span class="n">kwargs</span><span class="p">[</span><span class="n">arg</span><span class="p">]</span> <span class="o">!=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">ACS_DATASET_CONFIGS</span><span class="p">[</span><span class="n">arg</span><span class="p">]:</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Received non-standard ACS argument &#39;</span><span class="si">{</span><span class="n">arg</span><span class="si">}</span><span class="s2">&#39; (using &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">arg</span><span class="si">}</span><span class="s2">=</span><span class="si">{</span><span class="n">kwargs</span><span class="p">[</span><span class="n">arg</span><span class="p">]</span><span class="si">}</span><span class="s2"> instead of default </span><span class="si">{</span><span class="n">arg</span><span class="si">}</span><span class="s2">=</span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="n">ACS_DATASET_CONFIGS</span><span class="p">[</span><span class="n">arg</span><span class="p">]</span><span class="si">}</span><span class="s2">). &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;This may affect reproducibility.&quot;</span><span class="p">)</span>
+                <span class="n">acs_dataset_configs</span><span class="p">[</span><span class="n">arg</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">arg</span><span class="p">)</span>
+
+        <span class="c1"># Update config with any additional kwargs</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="c1"># Fetch ACS task and dataset</span>
+        <span class="n">acs_task</span> <span class="o">=</span> <span class="n">ACSTaskMetadata</span><span class="o">.</span><span class="n">get_task</span><span class="p">(</span>
+            <span class="n">name</span><span class="o">=</span><span class="n">task_name</span><span class="p">,</span>
+            <span class="n">use_numeric_qa</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">numeric_risk_prompting</span><span class="p">)</span>
+
+        <span class="n">acs_dataset</span> <span class="o">=</span> <span class="n">ACSDataset</span><span class="o">.</span><span class="n">make_from_task</span><span class="p">(</span>
+            <span class="n">task</span><span class="o">=</span><span class="n">acs_task</span><span class="p">,</span>
+            <span class="n">cache_dir</span><span class="o">=</span><span class="n">data_dir</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">acs_dataset_configs</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">make_benchmark</span><span class="p">(</span>
+            <span class="n">task</span><span class="o">=</span><span class="n">acs_task</span><span class="p">,</span>
+            <span class="n">dataset</span><span class="o">=</span><span class="n">acs_dataset</span><span class="p">,</span>
+            <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+            <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+            <span class="n">max_api_rpm</span><span class="o">=</span><span class="n">max_api_rpm</span><span class="p">,</span>
+            <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Benchmark.make_benchmark">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.benchmark.Benchmark.make_benchmark">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">make_benchmark</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="o">*</span><span class="p">,</span>
+        <span class="n">task</span><span class="p">:</span> <span class="n">TaskMetadata</span> <span class="o">|</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span>
+        <span class="n">model</span><span class="p">:</span> <span class="n">AutoModelForCausalLM</span> <span class="o">|</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="p">:</span> <span class="n">AutoTokenizer</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>    <span class="c1"># WebAPI models have no local tokenizer</span>
+        <span class="n">max_api_rpm</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">config</span><span class="p">:</span> <span class="n">BenchmarkConfig</span> <span class="o">=</span> <span class="n">BenchmarkConfig</span><span class="o">.</span><span class="n">default_config</span><span class="p">(),</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Benchmark</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Create a calibration benchmark from a given configuration.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        task : TaskMetadata | str</span>
+<span class="sd">            The task metadata object or name of the task to use.</span>
+<span class="sd">        dataset : Dataset</span>
+<span class="sd">            The dataset to use for the benchmark.</span>
+<span class="sd">        model : AutoModelForCausalLM | str</span>
+<span class="sd">            The transformers language model to use, or the model ID for a webAPI</span>
+<span class="sd">            hosted model (e.g., &quot;openai/gpt-4o-mini&quot;).</span>
+<span class="sd">        tokenizer : AutoTokenizer, optional</span>
+<span class="sd">            The tokenizer used to train the model (if using a transformers</span>
+<span class="sd">            model). Not required for webAPI models.</span>
+<span class="sd">        max_api_rpm : int, optional</span>
+<span class="sd">            The maximum number of API requests per minute for webAPI models.</span>
+<span class="sd">        config : BenchmarkConfig, optional</span>
+<span class="sd">            Extra benchmark configurations, by default will use</span>
+<span class="sd">            `BenchmarkConfig.default_config()`.</span>
+<span class="sd">        **kwargs</span>
+<span class="sd">            Additional arguments for easier configuration of the benchmark.</span>
+<span class="sd">            Will simply use these values to update the `config` object.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        bench : Benchmark</span>
+<span class="sd">            The calibration benchmark object.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Update config with any additional kwargs</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="c1"># Handle TaskMetadata object</span>
+        <span class="n">task</span> <span class="o">=</span> <span class="n">TaskMetadata</span><span class="o">.</span><span class="n">get_task</span><span class="p">(</span><span class="n">task</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="n">task</span>
+        <span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">numeric_risk_prompting</span><span class="p">:</span>
+            <span class="n">task</span><span class="o">.</span><span class="n">use_numeric_qa</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">feature_subset</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">feature_subset</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">task</span> <span class="o">=</span> <span class="n">task</span><span class="o">.</span><span class="n">create_task_with_feature_subset</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">feature_subset</span><span class="p">)</span>
+            <span class="n">dataset</span><span class="o">.</span><span class="n">task</span> <span class="o">=</span> <span class="n">task</span>
+
+        <span class="c1"># Check dataset is compatible with task</span>
+        <span class="k">if</span> <span class="n">dataset</span><span class="o">.</span><span class="n">task</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">task</span> <span class="ow">and</span> <span class="n">dataset</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">name</span> <span class="o">!=</span> <span class="n">task</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Dataset task &#39;</span><span class="si">{</span><span class="n">dataset</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39; does not match the &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;provided task &#39;</span><span class="si">{</span><span class="n">task</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">population_filter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">population_filter</span><span class="p">)</span>
+
+        <span class="c1"># Get prompting function</span>
+        <span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">few_shot</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Using few-shot prompting (n=</span><span class="si">{</span><span class="n">config</span><span class="o">.</span><span class="n">few_shot</span><span class="si">}</span><span class="s2">)!&quot;</span><span class="p">)</span>
+            <span class="n">encode_row_function</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span>
+                <span class="n">encode_row_prompt_few_shot</span><span class="p">,</span>
+                <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+                <span class="n">n_shots</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">few_shot</span><span class="p">,</span>
+                <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span>
+                <span class="n">reuse_examples</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">reuse_few_shot_examples</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Using zero-shot prompting.&quot;</span><span class="p">)</span>
+            <span class="n">encode_row_function</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">encode_row_prompt</span><span class="p">,</span> <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">)</span>
+
+        <span class="c1"># Parse LLMClassifier parameters</span>
+        <span class="n">llm_inference_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;correct_order_bias&quot;</span><span class="p">:</span> <span class="n">config</span><span class="o">.</span><span class="n">correct_order_bias</span><span class="p">}</span>
+        <span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">batch_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">llm_inference_kwargs</span><span class="p">[</span><span class="s2">&quot;batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">batch_size</span>
+        <span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">context_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">llm_inference_kwargs</span><span class="p">[</span><span class="s2">&quot;context_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">context_size</span>
+        <span class="k">if</span> <span class="n">max_api_rpm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="n">llm_inference_kwargs</span><span class="p">[</span><span class="s2">&quot;max_api_rpm&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">max_api_rpm</span>
+
+        <span class="c1"># Create LLMClassifier object</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="n">llm_clf</span> <span class="o">=</span> <span class="n">WebAPILLMClassifier</span><span class="p">(</span>
+                <span class="n">model_name</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+                <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+                <span class="n">encode_row</span><span class="o">=</span><span class="n">encode_row_function</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">llm_inference_kwargs</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Using webAPI model: </span><span class="si">{</span><span class="n">model</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">llm_clf</span> <span class="o">=</span> <span class="n">TransformersLLMClassifier</span><span class="p">(</span>
+                <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+                <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+                <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+                <span class="n">encode_row</span><span class="o">=</span><span class="n">encode_row_function</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">llm_inference_kwargs</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Using local transformers model: </span><span class="si">{</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span>
+            <span class="n">llm_clf</span><span class="o">=</span><span class="n">llm_clf</span><span class="p">,</span>
+            <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span>
+            <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
+        <span class="p">)</span></div>
+</div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
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diff --git a/_modules/folktexts/classifier/base.html b/_modules/folktexts/classifier/base.html
new file mode 100644
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+
+
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for folktexts.classifier.base</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Module containing the base class for all LLM risk classifiers.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span>
+<span class="kn">from</span> <span class="nn">tqdm.auto</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+<span class="kn">from</span> <span class="nn">folktexts.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span>
+<span class="kn">from</span> <span class="nn">folktexts.evaluation</span> <span class="kn">import</span> <span class="n">compute_best_threshold</span>
+<span class="kn">from</span> <span class="nn">folktexts.prompting</span> <span class="kn">import</span> <span class="n">encode_row_prompt</span> <span class="k">as</span> <span class="n">default_encode_row_prompt</span>
+<span class="kn">from</span> <span class="nn">folktexts.qa_interface</span> <span class="kn">import</span> <span class="n">DirectNumericQA</span><span class="p">,</span> <span class="n">MultipleChoiceQA</span>
+<span class="kn">from</span> <span class="nn">folktexts.task</span> <span class="kn">import</span> <span class="n">TaskMetadata</span>
+
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">hash_dict</span><span class="p">,</span> <span class="n">hash_function</span>
+
+<span class="n">DEFAULT_CONTEXT_SIZE</span> <span class="o">=</span> <span class="mi">600</span>
+<span class="n">DEFAULT_BATCH_SIZE</span> <span class="o">=</span> <span class="mi">16</span>
+
+<span class="n">SCORE_COL_NAME</span> <span class="o">=</span> <span class="s2">&quot;risk_score&quot;</span>
+<span class="n">LABEL_COL_NAME</span> <span class="o">=</span> <span class="s2">&quot;label&quot;</span>
+
+
+<div class="viewcode-block" id="LLMClassifier">
+<a class="viewcode-back" href="../../../source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier">[docs]</a>
+<span class="k">class</span> <span class="nc">LLMClassifier</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span><span class="p">,</span> <span class="n">ABC</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;An interface to produce risk scores and class predictions with an LLM.&quot;&quot;&quot;</span>
+
+    <span class="n">DEFAULT_INFERENCE_KWARGS</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s2">&quot;context_size&quot;</span><span class="p">:</span> <span class="n">DEFAULT_CONTEXT_SIZE</span><span class="p">,</span>
+        <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="n">DEFAULT_BATCH_SIZE</span><span class="p">,</span>
+    <span class="p">}</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">model_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">task</span><span class="p">:</span> <span class="n">TaskMetadata</span> <span class="o">|</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">custom_prompt_prefix</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">encode_row</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">],</span> <span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">correct_order_bias</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">42</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">inference_kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Creates an LLMClassifier object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        model_name : str</span>
+<span class="sd">            The model name or ID.</span>
+<span class="sd">        task : TaskMetadata | str</span>
+<span class="sd">            The task metadata object or name of an already created task.</span>
+<span class="sd">        custom_prompt_prefix : str, optional</span>
+<span class="sd">            A custom prompt prefix to supply to the model before the encoded</span>
+<span class="sd">            row data, by default None.</span>
+<span class="sd">        encode_row : Callable[[pd.Series], str], optional</span>
+<span class="sd">            The function used to encode tabular rows into natural text. If not</span>
+<span class="sd">            provided, will use the default encoding function for the task.</span>
+<span class="sd">        threshold : float, optional</span>
+<span class="sd">            The classification threshold to use when outputting binary</span>
+<span class="sd">            predictions, by default 0.5. Must be between 0 and 1. Will be</span>
+<span class="sd">            re-calibrated if `fit` is called.</span>
+<span class="sd">        correct_order_bias : bool, optional</span>
+<span class="sd">            Whether to correct ordering bias in multiple-choice Q&amp;A questions,</span>
+<span class="sd">            by default True.</span>
+<span class="sd">        seed : int, optional</span>
+<span class="sd">            The random seed - used for reproducibility.</span>
+<span class="sd">        **inference_kwargs</span>
+<span class="sd">            Additional keyword arguments to be used at inference time. Options</span>
+<span class="sd">            include `context_size` and `batch_size`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1"># Set classifier metadata</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_model_name</span> <span class="o">=</span> <span class="n">model_name</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_task</span> <span class="o">=</span> <span class="n">TaskMetadata</span><span class="o">.</span><span class="n">get_task</span><span class="p">(</span><span class="n">task</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="n">task</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_custom_prompt_prefix</span> <span class="o">=</span> <span class="n">custom_prompt_prefix</span>
+
+        <span class="c1"># TODO: Add default custom_prompt_prefix to row encoder here?</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_encode_row</span> <span class="o">=</span> <span class="n">encode_row</span> <span class="ow">or</span> <span class="n">partial</span><span class="p">(</span>
+            <span class="n">default_encode_row_prompt</span><span class="p">,</span>
+            <span class="n">task</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_threshold</span> <span class="o">=</span> <span class="n">threshold</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_correct_order_bias</span> <span class="o">=</span> <span class="n">correct_order_bias</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_seed</span> <span class="o">=</span> <span class="n">seed</span>
+
+        <span class="c1"># Default inference kwargs</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_inference_kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">DEFAULT_INFERENCE_KWARGS</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_inference_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">inference_kwargs</span><span class="p">)</span>
+
+        <span class="c1"># Fixed sklearn parameters</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_is_fitted</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate a unique hash for this object.&quot;&quot;&quot;</span>
+
+        <span class="c1"># All parameters that affect the model&#39;s behavior</span>
+        <span class="n">hash_params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+            <span class="n">model_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="p">,</span>
+            <span class="n">task_hash</span><span class="o">=</span><span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="p">),</span>
+            <span class="n">custom_prompt_prefix</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">custom_prompt_prefix</span><span class="p">,</span>
+            <span class="n">correct_order_bias</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">correct_order_bias</span><span class="p">,</span>
+            <span class="n">threshold</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">,</span>
+            <span class="n">encode_row_hash</span><span class="o">=</span><span class="n">hash_function</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">encode_row</span><span class="p">),</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">hash_dict</span><span class="p">(</span><span class="n">hash_params</span><span class="p">),</span> <span class="mi">16</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">model_name</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_name</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">task</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TaskMetadata</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_task</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">custom_prompt_prefix</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_custom_prompt_prefix</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">encode_row</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">],</span> <span class="nb">str</span><span class="p">]:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_encode_row</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">threshold</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_threshold</span>
+
+    <span class="nd">@threshold</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">threshold</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">value</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Threshold must be between 0 and 1; got </span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Clip threshold to valid range</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_threshold</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> threshold to </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_threshold</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">correct_order_bias</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_correct_order_bias</span>
+
+    <span class="nd">@correct_order_bias</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">correct_order_bias</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_correct_order_bias</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> correct_order_bias to </span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">seed</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_seed</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">inference_kwargs</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_inference_kwargs</span>
+
+<div class="viewcode-block" id="LLMClassifier.set_inference_kwargs">
+<a class="viewcode-back" href="../../../source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.set_inference_kwargs">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_inference_kwargs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Set inference kwargs for the model.&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_inference_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">__sklearn_is_fitted__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check fitted status and return a Boolean value.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_is_fitted&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_fitted</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_get_positive_class_scores</span><span class="p">(</span><span class="n">risk_scores</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function to get positive class scores from risk scores.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">risk_scores</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">risk_scores</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">risk_scores</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_make_predictions_multiclass</span><span class="p">(</span><span class="n">pos_class_scores</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Converts positive class scores to multiclass scores.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">([</span><span class="mi">1</span> <span class="o">-</span> <span class="n">pos_class_scores</span><span class="p">,</span> <span class="n">pos_class_scores</span><span class="p">])</span>
+
+<div class="viewcode-block" id="LLMClassifier.fit">
+<a class="viewcode-back" href="../../../source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">false_pos_cost</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">false_neg_cost</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Uses the provided data sample to fit the prediction threshold.&quot;&quot;&quot;</span>
+
+        <span class="c1"># Compute risk estimates for the data</span>
+        <span class="n">y_pred_scores</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_positive_class_scores</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Compute the best threshold for the given data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">compute_best_threshold</span><span class="p">(</span>
+            <span class="n">y</span><span class="p">,</span> <span class="n">y_pred_scores</span><span class="p">,</span>
+            <span class="n">false_pos_cost</span><span class="o">=</span><span class="n">false_pos_cost</span><span class="p">,</span>
+            <span class="n">false_neg_cost</span><span class="o">=</span><span class="n">false_neg_cost</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Update sklearn is_fitted status</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_is_fitted</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_load_predictions_from_disk</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">predictions_save_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span><span class="p">,</span>
+        <span class="n">data</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Attempts to load pre-computed predictions from disk.&quot;&quot;&quot;</span>
+
+        <span class="c1"># Load predictions from disk</span>
+        <span class="n">predictions_save_path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">predictions_save_path</span><span class="p">)</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="s2">&quot;.csv&quot;</span><span class="p">)</span>
+        <span class="n">predictions_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">predictions_save_path</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="c1"># Check if index matches our current dataframe</span>
+        <span class="k">if</span> <span class="n">predictions_df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">predictions_df</span><span class="p">[</span><span class="n">SCORE_COL_NAME</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;Saved predictions do not match the current dataframe.&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="LLMClassifier.predict">
+<a class="viewcode-back" href="../../../source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.predict">[docs]</a>
+    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">data</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+        <span class="n">predictions_save_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">labels</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns binary predictions for the given data.&quot;&quot;&quot;</span>
+        <span class="n">risk_scores</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span>
+            <span class="n">data</span><span class="p">,</span>
+            <span class="n">predictions_save_path</span><span class="o">=</span><span class="n">predictions_save_path</span><span class="p">,</span>
+            <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_positive_class_scores</span><span class="p">(</span><span class="n">risk_scores</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="LLMClassifier.predict_proba">
+<a class="viewcode-back" href="../../../source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.predict_proba">[docs]</a>
+    <span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">data</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+        <span class="n">predictions_save_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">labels</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns probability estimates for the given data.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data : pd.DataFrame</span>
+<span class="sd">            The DataFrame to compute risk estimates for.</span>
+<span class="sd">        predictions_save_path : str | Path, optional</span>
+<span class="sd">            If provided, will save the computed risk scores to this path in</span>
+<span class="sd">            disk. If the path exists, will attempt to load pre-computed</span>
+<span class="sd">            predictions from this path.</span>
+<span class="sd">        labels : pd.Series | np.ndarray, optional</span>
+<span class="sd">            The labels corresponding to the provided data. Not required to</span>
+<span class="sd">            compute predictions. Will only be used to save alongside predictions</span>
+<span class="sd">            to disk.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        risk_scores : np.ndarray</span>
+<span class="sd">            The risk scores for the given data.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Validate arguments</span>
+        <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">predictions_save_path</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
+                <span class="s2">&quot;** Ignoring `labels` argument as `predictions_save_path` was not provided. **&quot;</span>
+                <span class="s2">&quot;The `labels` argument is only used in conjunction with &quot;</span>
+                <span class="s2">&quot;`predictions_save_path` to save alongside predictions to disk. &quot;</span><span class="p">)</span>
+
+        <span class="c1"># Check if `predictions_save_path` exists and load predictions if possible</span>
+        <span class="k">if</span> <span class="n">predictions_save_path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">Path</span><span class="p">(</span><span class="n">predictions_save_path</span><span class="p">)</span><span class="o">.</span><span class="n">exists</span><span class="p">():</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_load_predictions_from_disk</span><span class="p">(</span><span class="n">predictions_save_path</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Loaded predictions from </span><span class="si">{</span><span class="n">predictions_save_path</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_make_predictions_multiclass</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Failed to load predictions from </span><span class="si">{</span><span class="n">predictions_save_path</span><span class="si">}</span><span class="s2">. &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;Re-computing predictions and overwriting local file...&quot;</span>
+                <span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;`data` must be a pd.DataFrame, received </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="si">}</span><span class="s2"> instead.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Compute risk estimates</span>
+        <span class="n">risk_scores</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_risk_estimates_for_dataframe</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="c1"># Save to disk if `predictions_save_path` is provided</span>
+        <span class="k">if</span> <span class="n">predictions_save_path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">predictions_save_path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">predictions_save_path</span><span class="p">)</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="s2">&quot;.csv&quot;</span><span class="p">)</span>
+
+            <span class="n">predictions_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">risk_scores</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="n">SCORE_COL_NAME</span><span class="p">])</span>
+            <span class="n">predictions_df</span><span class="p">[</span><span class="n">LABEL_COL_NAME</span><span class="p">]</span> <span class="o">=</span> <span class="n">labels</span>
+            <span class="n">predictions_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">predictions_save_path</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_make_predictions_multiclass</span><span class="p">(</span><span class="n">risk_scores</span><span class="p">)</span></div>
+
+
+    <span class="nd">@abstractmethod</span>
+    <span class="k">def</span> <span class="nf">_query_prompt_risk_estimates_batch</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">prompts_batch</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
+        <span class="o">*</span><span class="p">,</span>
+        <span class="n">question</span><span class="p">:</span> <span class="n">MultipleChoiceQA</span> <span class="o">|</span> <span class="n">DirectNumericQA</span><span class="p">,</span>
+        <span class="n">context_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Query model with a batch of prompts and return risk estimates.&quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Calling an abstract method :: Use one of the subclasses of LLMClassifier.&quot;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="LLMClassifier.compute_risk_estimates_for_dataframe">
+<a class="viewcode-back" href="../../../source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.compute_risk_estimates_for_dataframe">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_risk_estimates_for_dataframe</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Compute risk estimates for a specific dataframe (internal helper function).</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        df : pd.DataFrame</span>
+<span class="sd">            The dataframe to compute risk estimates for.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        risk_scores : np.ndarray</span>
+<span class="sd">            The risk estimates for each row in the dataframe.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1"># Initialize risk scores and other constants</span>
+        <span class="n">fill_value</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="n">risk_scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">))</span>
+        <span class="n">risk_scores</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">fill_value</span><span class="p">)</span>    <span class="c1"># fill with -1&#39;s</span>
+
+        <span class="n">batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_inference_kwargs</span><span class="p">[</span><span class="s2">&quot;batch_size&quot;</span><span class="p">]</span>
+        <span class="n">context_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_inference_kwargs</span><span class="p">[</span><span class="s2">&quot;context_size&quot;</span><span class="p">]</span>
+        <span class="n">num_batches</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">)</span>
+
+        <span class="c1"># Get questions to ask</span>
+        <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">question</span>
+        <span class="n">questions</span> <span class="o">=</span> <span class="p">[</span><span class="n">q</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">correct_order_bias</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">DirectNumericQA</span><span class="p">):</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;No need to correct ordering bias for DirectNumericQA prompting.&quot;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">MultipleChoiceQA</span><span class="p">):</span>
+                <span class="n">questions</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">MultipleChoiceQA</span><span class="o">.</span><span class="n">create_answer_keys_permutations</span><span class="p">(</span><span class="n">q</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unknown question type &#39;</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">q</span><span class="p">)</span><span class="si">}</span><span class="s2">&#39;; cannot correct ordering bias.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Compute risk estimates per batch</span>
+        <span class="k">for</span> <span class="n">batch_idx</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">num_batches</span><span class="p">),</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Computing risk estimates&quot;</span><span class="p">):</span>
+            <span class="n">start_idx</span> <span class="o">=</span> <span class="n">batch_idx</span> <span class="o">*</span> <span class="n">batch_size</span>
+            <span class="n">end_idx</span> <span class="o">=</span> <span class="nb">min</span><span class="p">((</span><span class="n">batch_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">))</span>
+            <span class="n">batch_data</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">start_idx</span><span class="p">:</span><span class="n">end_idx</span><span class="p">]</span>
+
+            <span class="n">batch_risk_scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">batch_data</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">questions</span><span class="p">)))</span>
+            <span class="k">for</span> <span class="n">q_idx</span><span class="p">,</span> <span class="n">q</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">questions</span><span class="p">):</span>
+
+                <span class="c1"># Encode batch data into natural text prompts</span>
+                <span class="n">data_texts_batch</span> <span class="o">=</span> <span class="p">[</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">encode_row</span><span class="p">(</span>
+                        <span class="n">row</span><span class="p">,</span>
+                        <span class="n">question</span><span class="o">=</span><span class="n">q</span><span class="p">,</span>
+                        <span class="n">custom_prompt_prefix</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">custom_prompt_prefix</span><span class="p">,</span>
+                    <span class="p">)</span>
+                    <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">batch_data</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span>
+                <span class="p">]</span>
+
+                <span class="c1"># Query the model with the batch of data</span>
+                <span class="n">risk_estimates_batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_query_prompt_risk_estimates_batch</span><span class="p">(</span>
+                    <span class="n">prompts_batch</span><span class="o">=</span><span class="n">data_texts_batch</span><span class="p">,</span>
+                    <span class="n">question</span><span class="o">=</span><span class="n">q</span><span class="p">,</span>
+                    <span class="n">context_size</span><span class="o">=</span><span class="n">context_size</span><span class="p">,</span>
+                <span class="p">)</span>
+
+                <span class="c1"># Store risk estimates for current question</span>
+                <span class="n">batch_risk_scores</span><span class="p">[:,</span> <span class="n">q_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">risk_estimates_batch</span>
+
+            <span class="n">risk_scores</span><span class="p">[</span><span class="n">start_idx</span><span class="p">:</span> <span class="n">end_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch_risk_scores</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="c1"># Check that all risk scores were computed</span>
+        <span class="k">assert</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">risk_scores</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">risk_scores</span></div>
+
+
+<div class="viewcode-block" id="LLMClassifier.compute_risk_estimates_for_dataset">
+<a class="viewcode-back" href="../../../source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.compute_risk_estimates_for_dataset">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_risk_estimates_for_dataset</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Computes risk estimates for each row in the dataset.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        dataset : Dataset</span>
+<span class="sd">            The dataset to compute risk estimates for.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        results : dict[str, np.ndarray]</span>
+<span class="sd">            The risk estimates for each data type in the dataset (usually &quot;train&quot;,</span>
+<span class="sd">            &quot;val&quot;, &quot;test&quot;).</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">data_types</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;train&quot;</span><span class="p">:</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_train</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span>
+            <span class="s2">&quot;test&quot;</span><span class="p">:</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_test</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span>
+        <span class="p">}</span>
+        <span class="k">if</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_val</span><span class="p">()</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">data_types</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_val</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="n">results</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="n">data_type</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_risk_estimates_for_dataframe</span><span class="p">(</span>
+                <span class="n">df</span><span class="o">=</span><span class="n">df</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="k">for</span> <span class="n">data_type</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">data_types</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+        <span class="p">}</span>
+
+        <span class="k">return</span> <span class="n">results</span></div>
+</div>
+
+</pre></div>
+
+           </div>
+          </div>
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diff --git a/_modules/folktexts/classifier/transformers_classifier.html b/_modules/folktexts/classifier/transformers_classifier.html
new file mode 100644
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+          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">folktexts.classifier.transformers_classifier</li>
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+      </li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for folktexts.classifier.transformers_classifier</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Module for using huggingface transformers models as classifiers.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoModelForCausalLM</span><span class="p">,</span> <span class="n">AutoTokenizer</span>
+
+<span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">query_model_batch_multiple_passes</span>
+<span class="kn">from</span> <span class="nn">folktexts.qa_interface</span> <span class="kn">import</span> <span class="n">DirectNumericQA</span><span class="p">,</span> <span class="n">MultipleChoiceQA</span>
+<span class="kn">from</span> <span class="nn">folktexts.task</span> <span class="kn">import</span> <span class="n">TaskMetadata</span>
+
+<span class="kn">from</span> <span class="nn">.._utils</span> <span class="kn">import</span> <span class="n">hash_dict</span>
+<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">LLMClassifier</span>
+
+
+<div class="viewcode-block" id="TransformersLLMClassifier">
+<a class="viewcode-back" href="../../../source/folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier">[docs]</a>
+<span class="k">class</span> <span class="nc">TransformersLLMClassifier</span><span class="p">(</span><span class="n">LLMClassifier</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Use a huggingface transformers model to produce risk scores.&quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">model</span><span class="p">:</span> <span class="n">AutoModelForCausalLM</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="p">:</span> <span class="n">AutoTokenizer</span><span class="p">,</span>
+        <span class="n">task</span><span class="p">:</span> <span class="n">TaskMetadata</span> <span class="o">|</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">custom_prompt_prefix</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">encode_row</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">],</span> <span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">correct_order_bias</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">42</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">inference_kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Creates an LLMClassifier based on a huggingface transformers model.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        model : AutoModelForCausalLM</span>
+<span class="sd">            The torch language model to use for inference.</span>
+<span class="sd">        tokenizer : AutoTokenizer</span>
+<span class="sd">            The tokenizer used to train the model.</span>
+<span class="sd">        task : TaskMetadata | str</span>
+<span class="sd">            The task metadata object or name of an already created task.</span>
+<span class="sd">        custom_prompt_prefix : str, optional</span>
+<span class="sd">            A custom prompt prefix to supply to the model before the encoded</span>
+<span class="sd">            row data, by default None.</span>
+<span class="sd">        encode_row : Callable[[pd.Series], str], optional</span>
+<span class="sd">            The function used to encode tabular rows into natural text. If not</span>
+<span class="sd">            provided, will use the default encoding function for the task.</span>
+<span class="sd">        threshold : float, optional</span>
+<span class="sd">            The classification threshold to use when outputting binary</span>
+<span class="sd">            predictions, by default 0.5. Must be between 0 and 1. Will be</span>
+<span class="sd">            re-calibrated if `fit` is called.</span>
+<span class="sd">        correct_order_bias : bool, optional</span>
+<span class="sd">            Whether to correct ordering bias in multiple-choice Q&amp;A questions,</span>
+<span class="sd">            by default True.</span>
+<span class="sd">        seed : int, optional</span>
+<span class="sd">            The random seed - used for reproducibility.</span>
+<span class="sd">        **inference_kwargs</span>
+<span class="sd">            Additional keyword arguments to be used at inference time. Options</span>
+<span class="sd">            include `context_size` and `batch_size`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Transformers objects for the model and tokenizer</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_model</span> <span class="o">=</span> <span class="n">model</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_tokenizer</span> <span class="o">=</span> <span class="n">tokenizer</span>
+
+        <span class="c1"># Fetch name for transformers model</span>
+        <span class="n">model_name</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_model</span><span class="o">.</span><span class="n">name_or_path</span><span class="p">)</span><span class="o">.</span><span class="n">name</span>
+
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
+            <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+            <span class="n">custom_prompt_prefix</span><span class="o">=</span><span class="n">custom_prompt_prefix</span><span class="p">,</span>
+            <span class="n">encode_row</span><span class="o">=</span><span class="n">encode_row</span><span class="p">,</span>
+            <span class="n">correct_order_bias</span><span class="o">=</span><span class="n">correct_order_bias</span><span class="p">,</span>
+            <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span>
+            <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">inference_kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate a unique hash for the LLMClassifier object.&quot;&quot;&quot;</span>
+
+        <span class="c1"># All parameters that affect the model&#39;s behavior</span>
+        <span class="n">hash_params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+            <span class="n">super_hash</span><span class="o">=</span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__hash__</span><span class="p">(),</span>
+            <span class="n">model_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_model</span><span class="o">.</span><span class="n">num_parameters</span><span class="p">(),</span>
+            <span class="n">tokenizer_vocab_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_tokenizer</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">hash_dict</span><span class="p">(</span><span class="n">hash_params</span><span class="p">),</span> <span class="mi">16</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">model</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">AutoModelForCausalLM</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">tokenizer</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">AutoTokenizer</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_tokenizer</span>
+
+    <span class="k">def</span> <span class="nf">_query_prompt_risk_estimates_batch</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">prompts_batch</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
+        <span class="o">*</span><span class="p">,</span>
+        <span class="n">question</span><span class="p">:</span> <span class="n">MultipleChoiceQA</span> <span class="o">|</span> <span class="n">DirectNumericQA</span><span class="p">,</span>
+        <span class="n">context_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Query model with a batch of prompts and return risk estimates.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        prompts_batch : list[str]</span>
+<span class="sd">            A batch of string prompts to query the model with.</span>
+<span class="sd">        question : MultipleChoiceQA | DirectNumericQA</span>
+<span class="sd">            The question (`QAInterface`) object to use for querying the model.</span>
+<span class="sd">        context_size : int, optional</span>
+<span class="sd">            The maximum context size to consider for each input (in tokens).</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        risk_estimates : np.ndarray</span>
+<span class="sd">            The risk estimates for each prompt in the batch.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># TODO: Add support for any unicode character used as a prefix to &quot; A&quot;.</span>
+
+        <span class="c1"># Query model</span>
+        <span class="n">last_token_probs_batch</span> <span class="o">=</span> <span class="n">query_model_batch_multiple_passes</span><span class="p">(</span>
+            <span class="n">text_inputs</span><span class="o">=</span><span class="n">prompts_batch</span><span class="p">,</span>
+            <span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span>
+            <span class="n">tokenizer</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">,</span>
+            <span class="n">context_size</span><span class="o">=</span><span class="n">context_size</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference_kwargs</span><span class="p">[</span><span class="s2">&quot;context_size&quot;</span><span class="p">],</span>
+            <span class="n">n_passes</span><span class="o">=</span><span class="n">question</span><span class="o">.</span><span class="n">num_forward_passes</span><span class="p">,</span>
+            <span class="n">digits_only</span><span class="o">=</span><span class="kc">True</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">question</span><span class="p">,</span> <span class="n">DirectNumericQA</span><span class="p">)</span> <span class="k">else</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Decode model output</span>
+        <span class="n">risk_estimates_batch</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">question</span><span class="o">.</span><span class="n">get_answer_from_model_output</span><span class="p">(</span>
+                <span class="n">ltp</span><span class="p">,</span>
+                <span class="n">tokenizer_vocab</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_tokenizer</span><span class="o">.</span><span class="n">vocab</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="k">for</span> <span class="n">ltp</span> <span class="ow">in</span> <span class="n">last_token_probs_batch</span>
+        <span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">risk_estimates_batch</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
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diff --git a/_modules/folktexts/classifier/web_api_classifier.html b/_modules/folktexts/classifier/web_api_classifier.html
new file mode 100644
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+      <li class="breadcrumb-item active">folktexts.classifier.web_api_classifier</li>
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+      </li>
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for folktexts.classifier.web_api_classifier</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Module for using a language model through a web API for risk classification.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">re</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="kn">from</span> <span class="nn">folktexts.qa_interface</span> <span class="kn">import</span> <span class="n">DirectNumericQA</span><span class="p">,</span> <span class="n">MultipleChoiceQA</span>
+<span class="kn">from</span> <span class="nn">folktexts.task</span> <span class="kn">import</span> <span class="n">TaskMetadata</span>
+
+<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">LLMClassifier</span>
+
+
+<div class="viewcode-block" id="WebAPILLMClassifier">
+<a class="viewcode-back" href="../../../source/folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier">[docs]</a>
+<span class="k">class</span> <span class="nc">WebAPILLMClassifier</span><span class="p">(</span><span class="n">LLMClassifier</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Use an LLM through a web API to produce risk scores.&quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">model_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">task</span><span class="p">:</span> <span class="n">TaskMetadata</span> <span class="o">|</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">custom_prompt_prefix</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">encode_row</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">],</span> <span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">correct_order_bias</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">max_api_rpm</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5000</span><span class="p">,</span>    <span class="c1"># NOTE: OpenAI Tier 1 limit is only 500 RPM !</span>
+        <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">42</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">inference_kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Creates an LLMClassifier object that uses a web API for inference.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        model_name : str</span>
+<span class="sd">            The model ID to be resolved by `litellm`.</span>
+<span class="sd">        task : TaskMetadata | str</span>
+<span class="sd">            The task metadata object or name of an already created task.</span>
+<span class="sd">        custom_prompt_prefix : str, optional</span>
+<span class="sd">            A custom prompt prefix to supply to the model before the encoded</span>
+<span class="sd">            row data, by default None.</span>
+<span class="sd">        encode_row : Callable[[pd.Series], str], optional</span>
+<span class="sd">            The function used to encode tabular rows into natural text. If not</span>
+<span class="sd">            provided, will use the default encoding function for the task.</span>
+<span class="sd">        threshold : float, optional</span>
+<span class="sd">            The classification threshold to use when outputting binary</span>
+<span class="sd">            predictions, by default 0.5. Must be between 0 and 1. Will be</span>
+<span class="sd">            re-calibrated if `fit` is called.</span>
+<span class="sd">        correct_order_bias : bool, optional</span>
+<span class="sd">            Whether to correct ordering bias in multiple-choice Q&amp;A questions,</span>
+<span class="sd">            by default True.</span>
+<span class="sd">        max_api_rpm : int, optional</span>
+<span class="sd">            The maximum number of requests per minute allowed for the API.</span>
+<span class="sd">        seed : int, optional</span>
+<span class="sd">            The random seed - used for reproducibility.</span>
+<span class="sd">        **inference_kwargs</span>
+<span class="sd">            Additional keyword arguments to be used at inference time. Options</span>
+<span class="sd">            include `context_size` and `batch_size`.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
+            <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+            <span class="n">custom_prompt_prefix</span><span class="o">=</span><span class="n">custom_prompt_prefix</span><span class="p">,</span>
+            <span class="n">encode_row</span><span class="o">=</span><span class="n">encode_row</span><span class="p">,</span>
+            <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span>
+            <span class="n">correct_order_bias</span><span class="o">=</span><span class="n">correct_order_bias</span><span class="p">,</span>
+            <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">inference_kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Initialize total cost of API calls</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_total_cost</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="c1"># Set maximum requests per minute</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_api_rpm</span> <span class="o">=</span> <span class="n">max_api_rpm</span>
+        <span class="k">if</span> <span class="s2">&quot;MAX_API_RPM&quot;</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_api_rpm</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;MAX_API_RPM&quot;</span><span class="p">))</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;MAX_API_RPM environment variable is set. &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Overriding previous value of </span><span class="si">{</span><span class="n">max_api_rpm</span><span class="si">}</span><span class="s2"> with </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">max_api_rpm</span><span class="si">}</span><span class="s2">.&quot;</span>
+            <span class="p">)</span>
+
+        <span class="c1"># Check extra dependencies</span>
+        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_webAPI_deps</span><span class="p">(),</span> <span class="s2">&quot;Web API dependencies are not installed.&quot;</span>
+
+        <span class="c1"># Check OpenAI API key was passed</span>
+        <span class="k">if</span> <span class="s2">&quot;OPENAI_API_KEY&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;OpenAI API key not found in environment variables&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Set-up litellm API client</span>
+        <span class="kn">import</span> <span class="nn">litellm</span>
+        <span class="n">litellm</span><span class="o">.</span><span class="n">success_callback</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">track_cost_callback</span><span class="p">]</span>
+
+        <span class="kn">from</span> <span class="nn">litellm</span> <span class="kn">import</span> <span class="n">completion</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">text_completion_api</span> <span class="o">=</span> <span class="n">completion</span>
+
+        <span class="c1"># Get supported parameters</span>
+        <span class="kn">from</span> <span class="nn">litellm</span> <span class="kn">import</span> <span class="n">get_supported_openai_params</span>
+        <span class="n">supported_params</span> <span class="o">=</span> <span class="n">get_supported_openai_params</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">supported_params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Failed to get supported parameters for model &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">supported_params</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">supported_params</span><span class="p">)</span>
+
+        <span class="c1"># Set litellm logger level to WARNING</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;LiteLLM&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">WARNING</span><span class="p">)</span>
+
+<div class="viewcode-block" id="WebAPILLMClassifier.check_webAPI_deps">
+<a class="viewcode-back" href="../../../source/folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.check_webAPI_deps">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">check_webAPI_deps</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Check if litellm dependencies are available.&quot;&quot;&quot;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="kn">import</span> <span class="nn">litellm</span>  <span class="c1"># noqa: F401</span>
+        <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span>
+                <span class="s2">&quot;Please install extra API dependencies with &quot;</span>
+                <span class="s2">&quot;`pip install &#39;folktexts[apis]&#39;` &quot;</span>
+                <span class="s2">&quot;to use the WebAPILLMClassifier.&quot;</span>
+            <span class="p">)</span>
+            <span class="k">return</span> <span class="kc">False</span>
+        <span class="k">return</span> <span class="kc">True</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_query_webapi_batch</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">prompts_batch</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
+        <span class="o">*</span><span class="p">,</span>
+        <span class="n">question</span><span class="p">:</span> <span class="n">MultipleChoiceQA</span> <span class="o">|</span> <span class="n">DirectNumericQA</span><span class="p">,</span>
+        <span class="n">context_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">dict</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Query the web API with a batch of prompts and returns the json response.</span>
+
+<span class="sd">        TODO! Retry on non-successful API calls (e.g., RPM exceeded).</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        prompts_batch : list[str]</span>
+<span class="sd">            A batch of string prompts to query the model with.</span>
+<span class="sd">        question : MultipleChoiceQA | DirectNumericQA</span>
+<span class="sd">            The question (`QAInterface`) object to use for querying the model.</span>
+<span class="sd">        context_size : int, optional</span>
+<span class="sd">            The maximum context size to consider for each input (in tokens).</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        responses_batch : list[dict]</span>
+<span class="sd">            The returned JSON responses for each prompt in the batch.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Adapt number of forward passes</span>
+        <span class="c1"># &gt; Single token answers should require only one forward pass</span>
+        <span class="k">if</span> <span class="n">question</span><span class="o">.</span><span class="n">num_forward_passes</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">num_forward_passes</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="c1"># NOTE: Models often generate &quot;0.&quot; instead of directly outputting the fractional part</span>
+        <span class="c1"># &gt; Therefore: for multi-token answers, extra forward passes may be required</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">num_forward_passes</span> <span class="o">=</span> <span class="n">question</span><span class="o">.</span><span class="n">num_forward_passes</span> <span class="o">+</span> <span class="mi">2</span>    <span class="c1"># +2 tokens for &quot;0.&quot;</span>
+
+        <span class="n">api_call_params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+            <span class="n">temperature</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+            <span class="n">max_tokens</span><span class="o">=</span><span class="n">num_forward_passes</span><span class="p">,</span>
+            <span class="n">stream</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="n">seed</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">seed</span><span class="p">,</span>
+            <span class="n">logprobs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">top_logprobs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">api_call_params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">supported_params</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Unsupported API parameters for model &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2">&#39;: &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">set</span><span class="p">(</span><span class="n">api_call_params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="n">supported_params</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+        <span class="c1"># Get system prompt depending on Q&amp;A type</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">question</span><span class="p">,</span> <span class="n">DirectNumericQA</span><span class="p">):</span>
+            <span class="c1"># system_prompt = &quot;Please respond with number.&quot;</span>
+            <span class="n">system_prompt</span> <span class="o">=</span> <span class="s2">&quot;Please respond with number representing the estimated probability.&quot;</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">question</span><span class="p">,</span> <span class="n">MultipleChoiceQA</span><span class="p">):</span>
+            <span class="n">system_prompt</span> <span class="o">=</span> <span class="s2">&quot;Please respond with a single letter.&quot;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unknown question type &#39;</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">question</span><span class="p">)</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Query model for each prompt in the batch</span>
+        <span class="n">responses_batch</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">prompt</span> <span class="ow">in</span> <span class="n">prompts_batch</span><span class="p">:</span>
+
+            <span class="c1"># Construct prompt messages object</span>
+            <span class="n">messages</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="p">{</span><span class="s2">&quot;role&quot;</span><span class="p">:</span> <span class="s2">&quot;system&quot;</span><span class="p">,</span> <span class="s2">&quot;content&quot;</span><span class="p">:</span> <span class="n">system_prompt</span><span class="p">},</span>
+                <span class="p">{</span><span class="s2">&quot;role&quot;</span><span class="p">:</span> <span class="s2">&quot;user&quot;</span><span class="p">,</span> <span class="s2">&quot;content&quot;</span><span class="p">:</span> <span class="n">prompt</span><span class="p">},</span>
+            <span class="p">]</span>
+
+            <span class="c1"># Query the model API</span>
+            <span class="c1"># TODO: Retry on non-successful API calls (e.g., RPM exceeded).</span>
+            <span class="n">response</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">text_completion_api</span><span class="p">(</span>
+                <span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="p">,</span>
+                <span class="n">messages</span><span class="o">=</span><span class="n">messages</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">api_call_params</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">responses_batch</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">response</span><span class="p">)</span>
+
+            <span class="c1"># Sleep for short period to avoid rate-limiting (max 5K RPM for OpenAI API)</span>
+            <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">60</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_api_rpm</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">responses_batch</span>
+
+    <span class="k">def</span> <span class="nf">_decode_risk_estimate_from_api_response</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">response</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span>
+        <span class="n">question</span><span class="p">:</span> <span class="n">MultipleChoiceQA</span> <span class="o">|</span> <span class="n">DirectNumericQA</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Decode model output from API response to get risk estimate.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        response : dict</span>
+<span class="sd">            The response from the API call.</span>
+<span class="sd">        question : MultipleChoiceQA | DirectNumericQA</span>
+<span class="sd">            The question (`QAInterface`) object to use for querying the model.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        risk_estimate : float</span>
+<span class="sd">            The risk estimate for the API query.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Get response message</span>
+        <span class="n">response_message</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">choices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">message</span><span class="o">.</span><span class="n">content</span>
+
+        <span class="c1"># Get top token choices for each forward pass</span>
+        <span class="n">token_choices_all_passes</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">choices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">logprobs</span><span class="p">[</span><span class="s2">&quot;content&quot;</span><span class="p">]</span>
+
+        <span class="c1"># Construct dictionary of token to linear token probability for each forward pass</span>
+        <span class="n">token_probs_all_passes</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="p">{</span>
+                <span class="n">token_metadata</span><span class="p">[</span><span class="s2">&quot;token&quot;</span><span class="p">]:</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">token_metadata</span><span class="p">[</span><span class="s2">&quot;logprob&quot;</span><span class="p">])</span>
+                <span class="k">for</span> <span class="n">token_metadata</span> <span class="ow">in</span> <span class="n">top_token_logprobs</span><span class="p">[</span><span class="s2">&quot;top_logprobs&quot;</span><span class="p">]</span>
+            <span class="p">}</span>
+            <span class="k">for</span> <span class="n">top_token_logprobs</span> <span class="ow">in</span> <span class="n">token_choices_all_passes</span>
+        <span class="p">]</span>
+
+        <span class="c1"># Decode model output into risk estimates</span>
+        <span class="c1"># 1. Construct vocabulary dict for this response</span>
+        <span class="n">vocab_tokens</span> <span class="o">=</span> <span class="p">{</span><span class="n">tok</span> <span class="k">for</span> <span class="n">forward_pass</span> <span class="ow">in</span> <span class="n">token_probs_all_passes</span> <span class="k">for</span> <span class="n">tok</span> <span class="ow">in</span> <span class="n">forward_pass</span><span class="p">}</span>
+
+        <span class="n">token_to_id</span> <span class="o">=</span> <span class="p">{</span><span class="n">tok</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tok</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">vocab_tokens</span><span class="p">)}</span>
+        <span class="n">id_to_token</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">tok</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tok</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">vocab_tokens</span><span class="p">)}</span>
+
+        <span class="c1"># 2. Parse `token_probs_all_passes` into an array of shape (num_passes, vocab_size)</span>
+        <span class="n">token_probs_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
+            <span class="p">[</span>
+                <span class="n">forward_pass</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">id_to_token</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">vocab_tokens</span><span class="p">))</span>
+            <span class="p">]</span>
+            <span class="k">for</span> <span class="n">forward_pass</span> <span class="ow">in</span> <span class="n">token_probs_all_passes</span>
+        <span class="p">])</span>
+        <span class="c1"># NOTE: token_probs.shape = (num_passes, vocab_size)</span>
+
+        <span class="c1"># Get risk estimate</span>
+        <span class="n">risk_estimate</span> <span class="o">=</span> <span class="n">question</span><span class="o">.</span><span class="n">get_answer_from_model_output</span><span class="p">(</span>
+            <span class="n">token_probs_array</span><span class="p">,</span>
+            <span class="n">tokenizer_vocab</span><span class="o">=</span><span class="n">token_to_id</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Sanity check numeric answers based on global model response:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">question</span><span class="p">,</span> <span class="n">DirectNumericQA</span><span class="p">):</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">numeric_response</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;[-+]?\d*\.\d+|\d+&quot;</span><span class="p">,</span> <span class="n">response_message</span><span class="p">)</span><span class="o">.</span><span class="n">group</span><span class="p">()</span>
+                <span class="n">risk_estimate_full_text</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">numeric_response</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">risk_estimate</span><span class="p">,</span> <span class="n">risk_estimate_full_text</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">):</span>
+                    <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+                        <span class="sa">f</span><span class="s2">&quot;Numeric answer mismatch: </span><span class="si">{</span><span class="n">risk_estimate</span><span class="si">}</span><span class="s2"> != </span><span class="si">{</span><span class="n">risk_estimate_full_text</span><span class="si">}</span><span class="s2"> &quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot;from response &#39;</span><span class="si">{</span><span class="n">response_message</span><span class="si">}</span><span class="s2">&#39;.&quot;</span>
+                    <span class="p">)</span>
+
+                    <span class="c1"># Using full text answer as it more tightly relates to the ChatGPT web answer</span>
+                    <span class="n">risk_estimate</span> <span class="o">=</span> <span class="n">risk_estimate_full_text</span>
+
+            <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Failed to extract numeric response from message=&#39;</span><span class="si">{</span><span class="n">response_message</span><span class="si">}</span><span class="s2">&#39;;</span><span class="se">\n</span><span class="s2">&quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;Falling back on standard risk estimate of </span><span class="si">{</span><span class="n">risk_estimate</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">risk_estimate</span>
+
+    <span class="k">def</span> <span class="nf">_query_prompt_risk_estimates_batch</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">prompts_batch</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
+        <span class="o">*</span><span class="p">,</span>
+        <span class="n">question</span><span class="p">:</span> <span class="n">MultipleChoiceQA</span> <span class="o">|</span> <span class="n">DirectNumericQA</span><span class="p">,</span>
+        <span class="n">context_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Query model with a batch of prompts and return risk estimates.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        prompts_batch : list[str]</span>
+<span class="sd">            A batch of string prompts to query the model with.</span>
+<span class="sd">        question : MultipleChoiceQA | DirectNumericQA</span>
+<span class="sd">            The question (`QAInterface`) object to use for querying the model.</span>
+<span class="sd">        context_size : int, optional</span>
+<span class="sd">            The maximum context size to consider for each input (in tokens).</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        risk_estimates : np.ndarray</span>
+<span class="sd">            The risk estimates for each prompt in the batch.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        RuntimeError</span>
+<span class="sd">            Raised when web API call is unsuccessful.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1"># Query model through web API</span>
+        <span class="n">api_responses_batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_query_webapi_batch</span><span class="p">(</span>
+            <span class="n">prompts_batch</span><span class="o">=</span><span class="n">prompts_batch</span><span class="p">,</span>
+            <span class="n">question</span><span class="o">=</span><span class="n">question</span><span class="p">,</span>
+            <span class="n">context_size</span><span class="o">=</span><span class="n">context_size</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Parse API responses and decode model output</span>
+        <span class="n">risk_estimates_batch</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_decode_risk_estimate_from_api_response</span><span class="p">(</span><span class="n">response</span><span class="p">,</span> <span class="n">question</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">response</span> <span class="ow">in</span> <span class="n">api_responses_batch</span>
+        <span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">risk_estimates_batch</span>
+
+<div class="viewcode-block" id="WebAPILLMClassifier.track_cost_callback">
+<a class="viewcode-back" href="../../../source/folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.track_cost_callback">[docs]</a>
+    <span class="k">def</span> <span class="nf">track_cost_callback</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">kwargs</span><span class="p">,</span>
+        <span class="n">completion_response</span><span class="p">,</span>
+        <span class="n">start_time</span><span class="p">,</span>
+        <span class="n">end_time</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Callback function to cost of API calls.&quot;&quot;&quot;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">response_cost</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;response_cost&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_total_cost</span> <span class="o">+=</span> <span class="n">response_cost</span>
+
+        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Failed to track cost of API calls: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Destructor to report total cost of API calls.&quot;&quot;&quot;</span>
+        <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Total cost of API calls: $</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_total_cost</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
+
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+             
+  <h1>Source code for folktexts.cli.download_models</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/env python3</span>
+<span class="kn">import</span> <span class="nn">gc</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">argparse</span> <span class="kn">import</span> <span class="n">ArgumentParser</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+<span class="c1"># Temporary directory to use as download cache</span>
+<span class="n">TMP_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;/tmp/&quot;</span><span class="p">)</span>
+
+<span class="c1"># Default list of models to download</span>
+<span class="n">DEFAULT_MODEL_LIST</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="c1"># Google Gemma2 models</span>
+    <span class="s2">&quot;google/gemma-2b&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;google/gemma-1.1-2b-it&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;google/gemma-7b&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;google/gemma-1.1-7b-it&quot;</span><span class="p">,</span>
+
+    <span class="s2">&quot;google/gemma-2-9b&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;google/gemma-2-9b-it&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;google/gemma-2-27b&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;google/gemma-2-27b-it&quot;</span><span class="p">,</span>
+
+    <span class="c1"># Meta Llama3 models</span>
+    <span class="s2">&quot;meta-llama/Meta-Llama-3-8B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;meta-llama/Meta-Llama-3-8B-Instruct&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;meta-llama/Meta-Llama-3-70B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;meta-llama/Meta-Llama-3-70B-Instruct&quot;</span><span class="p">,</span>
+
+    <span class="c1"># Mistral AI models</span>
+    <span class="s2">&quot;mistralai/Mistral-7B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mistralai/Mistral-7B-Instruct-v0.2&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mistralai/Mixtral-8x7B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mistralai/Mixtral-8x7B-Instruct-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mistralai/Mixtral-8x22B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mistralai/Mixtral-8x22B-Instruct-v0.1&quot;</span><span class="p">,</span>
+
+    <span class="c1"># Yi models</span>
+    <span class="s2">&quot;01-ai/Yi-34B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;01-ai/Yi-34B-Chat&quot;</span><span class="p">,</span>
+
+    <span class="c1"># Qwen2 models</span>
+    <span class="c1"># &quot;Qwen/Qwen2-1.5B&quot;,</span>
+    <span class="c1"># &quot;Qwen/Qwen2-1.5B-Instruct&quot;,</span>
+    <span class="c1"># &quot;Qwen/Qwen2-7B&quot;,</span>
+    <span class="c1"># &quot;Qwen/Qwen2-7B-Instruct&quot;,</span>
+    <span class="c1"># &quot;Qwen/Qwen2-72B&quot;,</span>
+    <span class="c1"># &quot;Qwen/Qwen2-72B-Instruct&quot;,</span>
+<span class="p">]</span>
+
+
+<div class="viewcode-block" id="setup_arg_parser">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.download_models.setup_arg_parser">[docs]</a>
+<span class="k">def</span> <span class="nf">setup_arg_parser</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">ArgumentParser</span><span class="p">:</span>
+    <span class="c1"># Init parser</span>
+    <span class="n">parser</span> <span class="o">=</span> <span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;Download huggingface transformer models and tokenizers to disk&quot;</span><span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--model&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[string] Model name on huggingface hub - can provide multiple models!&quot;</span><span class="p">,</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">action</span><span class="o">=</span><span class="s2">&quot;append&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--save-dir&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[string] Directory to save the downloaded models to&quot;</span><span class="p">,</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--tmp-cache-dir&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[string] Cache dir to temporarily download models to&quot;</span><span class="p">,</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">default</span><span class="o">=</span><span class="n">TMP_DIR</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">parser</span></div>
+
+
+
+<div class="viewcode-block" id="is_bf16_compatible">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.download_models.is_bf16_compatible">[docs]</a>
+<span class="k">def</span> <span class="nf">is_bf16_compatible</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Checks if the current environment is bfloat16 compatible.&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_bf16_supported</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="main">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.download_models.main">[docs]</a>
+<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
+    <span class="c1"># Parse command-line arguments</span>
+    <span class="n">parser</span> <span class="o">=</span> <span class="n">setup_arg_parser</span><span class="p">()</span>
+    <span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
+
+    <span class="c1"># Process arguments</span>
+    <span class="n">model_list</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">model</span> <span class="ow">or</span> <span class="n">DEFAULT_MODEL_LIST</span>
+    <span class="n">save_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">save_dir</span><span class="p">)</span><span class="o">.</span><span class="n">expanduser</span><span class="p">()</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span>
+    <span class="n">save_dir</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="n">cache_dir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">tmp_cache_dir</span><span class="p">)</span><span class="o">.</span><span class="n">expanduser</span><span class="p">()</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span>
+    <span class="n">cache_dir</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">model_name</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">model_list</span><span class="p">):</span>
+        <span class="c1"># Create sub-folder to save this model to</span>
+        <span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">get_model_folder_path</span>
+        <span class="n">curr_save_dir</span> <span class="o">=</span> <span class="n">get_model_folder_path</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">root_dir</span><span class="o">=</span><span class="n">save_dir</span><span class="p">)</span>
+
+        <span class="c1"># If model already exists on disk, skip</span>
+        <span class="k">if</span> <span class="n">Path</span><span class="p">(</span><span class="n">curr_save_dir</span><span class="p">)</span><span class="o">.</span><span class="n">exists</span><span class="p">():</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Model &#39;</span><span class="si">{</span><span class="n">model_name</span><span class="si">}</span><span class="s2">&#39; already exists at &#39;</span><span class="si">{</span><span class="n">curr_save_dir</span><span class="si">}</span><span class="s2">&#39;&quot;</span><span class="p">)</span>
+            <span class="k">continue</span>
+
+        <span class="c1"># Download model to tmp dir</span>
+        <span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">load_model_tokenizer</span>
+        <span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">load_model_tokenizer</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">)</span>
+
+        <span class="c1"># Save model and tokenizer to disk</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Saving </span><span class="si">{</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">curr_save_dir</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="n">curr_save_dir</span><span class="p">)</span>
+        <span class="n">tokenizer</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="n">curr_save_dir</span><span class="p">)</span>
+
+        <span class="c1"># Delete references to the model and tokenizer and force garbage collection</span>
+        <span class="k">del</span> <span class="n">model</span>
+        <span class="k">del</span> <span class="n">tokenizer</span>
+        <span class="n">gc</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
+
+        <span class="c1"># Empty VRAM if GPU is available</span>
+        <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">empty_cache</span><span class="p">()</span></div>
+
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+    <span class="n">main</span><span class="p">()</span>
+</pre></div>
+
+           </div>
+          </div>
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diff --git a/_modules/folktexts/cli/eval_feature_importance.html b/_modules/folktexts/cli/eval_feature_importance.html
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--- /dev/null
+++ b/_modules/folktexts/cli/eval_feature_importance.html
@@ -0,0 +1,351 @@
+
+
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+             
+  <h1>Source code for folktexts.cli.eval_feature_importance</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/env python</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">argparse</span> <span class="kn">import</span> <span class="n">ArgumentParser</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.inspection</span> <span class="kn">import</span> <span class="n">permutation_importance</span>
+
+<span class="kn">from</span> <span class="nn">folktexts._io</span> <span class="kn">import</span> <span class="n">save_json</span><span class="p">,</span> <span class="n">save_pickle</span>
+<span class="kn">from</span> <span class="nn">folktexts.classifier</span> <span class="kn">import</span> <span class="n">LLMClassifier</span>
+<span class="kn">from</span> <span class="nn">folktexts.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span>
+<span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">get_model_folder_path</span><span class="p">,</span> <span class="n">load_model_tokenizer</span>
+
+<span class="c1"># Local paths</span>
+<span class="c1"># DEFAULT_ROOT_DIR = Path(&quot;/fast/groups/sf&quot;)            # CLUSTER dir</span>
+<span class="n">DEFAULT_ROOT_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;~&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">expanduser</span><span class="p">()</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span>     <span class="c1"># LOCAL dir</span>
+
+<span class="n">DEFAULT_MODELS_DIR</span> <span class="o">=</span> <span class="n">DEFAULT_ROOT_DIR</span> <span class="o">/</span> <span class="s2">&quot;huggingface-models&quot;</span>
+<span class="n">DEFAULT_DATA_DIR</span> <span class="o">=</span> <span class="n">DEFAULT_ROOT_DIR</span> <span class="o">/</span> <span class="s2">&quot;data&quot;</span>
+<span class="n">DEFAULT_RESULTS_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span>
+
+<span class="n">DEFAULT_TASK_NAME</span> <span class="o">=</span> <span class="s2">&quot;ACSIncome&quot;</span>
+
+<span class="n">DEFAULT_CONTEXT_SIZE</span> <span class="o">=</span> <span class="mi">600</span>
+<span class="n">DEFAULT_BATCH_SIZE</span> <span class="o">=</span> <span class="mi">16</span>
+<span class="n">DEFAULT_SEED</span> <span class="o">=</span> <span class="mi">42</span>
+
+<span class="n">DEFAULT_SUBSAMPLING</span> <span class="o">=</span> <span class="mf">0.1</span>           <span class="c1"># NOTE: by default, uses 10% of the dataset</span>
+<span class="n">DEFAULT_PERMUTATION_REPEATS</span> <span class="o">=</span> <span class="mi">5</span>
+
+
+<div class="viewcode-block" id="setup_arg_parser">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.eval_feature_importance.setup_arg_parser">[docs]</a>
+<span class="k">def</span> <span class="nf">setup_arg_parser</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">ArgumentParser</span><span class="p">:</span>
+
+    <span class="c1"># Init parser</span>
+    <span class="n">parser</span> <span class="o">=</span> <span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;Evaluate LLM feature importance on a given ACS task.&quot;</span><span class="p">)</span>
+
+    <span class="c1"># List of command-line arguments, with type and helper string</span>
+    <span class="n">cli_args</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="p">(</span><span class="s2">&quot;--model&quot;</span><span class="p">,</span>
+         <span class="nb">str</span><span class="p">,</span>
+         <span class="s2">&quot;[str] Model name or path to model saved on disk&quot;</span><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--task&quot;</span><span class="p">,</span>
+         <span class="nb">str</span><span class="p">,</span>
+         <span class="s2">&quot;[str] Name of the ACS task to run the experiment on&quot;</span><span class="p">,</span>
+         <span class="kc">False</span><span class="p">,</span>
+         <span class="n">DEFAULT_TASK_NAME</span><span class="p">,</span>
+         <span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--results-dir&quot;</span><span class="p">,</span>
+         <span class="nb">str</span><span class="p">,</span>
+         <span class="s2">&quot;[str] Directory under which this experiment&#39;s results will be saved&quot;</span><span class="p">,</span>
+         <span class="kc">False</span><span class="p">,</span>
+         <span class="n">DEFAULT_RESULTS_DIR</span><span class="p">,</span>
+         <span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--data-dir&quot;</span><span class="p">,</span>
+         <span class="nb">str</span><span class="p">,</span>
+         <span class="s2">&quot;[str] Root folder to find datasets on&quot;</span><span class="p">,</span>
+         <span class="kc">False</span><span class="p">,</span>
+         <span class="n">DEFAULT_DATA_DIR</span><span class="p">,</span>
+         <span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--models-dir&quot;</span><span class="p">,</span>
+         <span class="nb">str</span><span class="p">,</span>
+         <span class="s2">&quot;[str] Root folder to find huggingface models on&quot;</span><span class="p">,</span>
+         <span class="kc">False</span><span class="p">,</span>
+         <span class="n">DEFAULT_MODELS_DIR</span><span class="p">,</span>
+         <span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--scorer&quot;</span><span class="p">,</span>
+         <span class="nb">str</span><span class="p">,</span>
+         <span class="s2">&quot;[str] Name of the scorer to use for evaluation&quot;</span><span class="p">,</span>
+         <span class="kc">False</span><span class="p">,</span>
+         <span class="s2">&quot;roc_auc&quot;</span><span class="p">,</span>
+         <span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--batch-size&quot;</span><span class="p">,</span>
+         <span class="nb">int</span><span class="p">,</span>
+         <span class="s2">&quot;[int] The batch size to use for inference&quot;</span><span class="p">,</span>
+         <span class="kc">False</span><span class="p">,</span>
+         <span class="n">DEFAULT_BATCH_SIZE</span><span class="p">,</span>
+         <span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--context-size&quot;</span><span class="p">,</span>
+         <span class="nb">int</span><span class="p">,</span>
+         <span class="s2">&quot;[int] The maximum context size when prompting the LLM&quot;</span><span class="p">,</span>
+         <span class="kc">False</span><span class="p">,</span>
+         <span class="n">DEFAULT_CONTEXT_SIZE</span><span class="p">,</span>
+         <span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--subsampling&quot;</span><span class="p">,</span>
+         <span class="nb">float</span><span class="p">,</span>
+         <span class="s2">&quot;[float] Which fraction of the dataset to use (if omitted will use all data)&quot;</span><span class="p">,</span>
+         <span class="kc">False</span><span class="p">,</span>
+         <span class="n">DEFAULT_SUBSAMPLING</span><span class="p">,</span>
+         <span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--fit-threshold&quot;</span><span class="p">,</span>
+         <span class="nb">int</span><span class="p">,</span>
+         <span class="s2">&quot;[int] Whether to fit the prediction threshold, and on how many samples&quot;</span><span class="p">,</span>
+         <span class="kc">False</span><span class="p">,</span>
+         <span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--seed&quot;</span><span class="p">,</span>
+         <span class="nb">int</span><span class="p">,</span>
+         <span class="s2">&quot;[int] Random seed -- to set for reproducibility&quot;</span><span class="p">,</span>
+         <span class="kc">False</span><span class="p">,</span>
+         <span class="n">DEFAULT_SEED</span><span class="p">,</span>
+         <span class="p">),</span>
+    <span class="p">]</span>
+
+    <span class="k">for</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">cli_args</span><span class="p">:</span>
+        <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+            <span class="n">arg</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+            <span class="nb">type</span><span class="o">=</span><span class="n">arg</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">help</span><span class="o">=</span><span class="n">arg</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+            <span class="n">required</span><span class="o">=</span><span class="p">(</span><span class="n">arg</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">arg</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span> <span class="k">else</span> <span class="kc">True</span><span class="p">),</span>    <span class="c1"># NOTE: required by default</span>
+            <span class="n">default</span><span class="o">=</span><span class="p">(</span><span class="n">arg</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">arg</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">4</span> <span class="k">else</span> <span class="kc">None</span><span class="p">),</span>     <span class="c1"># default value if provided</span>
+        <span class="p">)</span>
+    <span class="k">return</span> <span class="n">parser</span></div>
+
+
+
+<div class="viewcode-block" id="parse_feature_importance">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.eval_feature_importance.parse_feature_importance">[docs]</a>
+<span class="k">def</span> <span class="nf">parse_feature_importance</span><span class="p">(</span><span class="n">results</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">columns</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Parse the results dictionary of sklearn&#39;s permutation_importance.&quot;&quot;&quot;</span>
+    <span class="n">parsed_r</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">columns</span><span class="p">):</span>
+        <span class="n">parsed_r</span><span class="p">[</span><span class="n">col</span><span class="p">][</span><span class="s2">&quot;imp_mean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">importances_mean</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+        <span class="n">parsed_r</span><span class="p">[</span><span class="n">col</span><span class="p">][</span><span class="s2">&quot;imp_std&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="o">.</span><span class="n">importances_std</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">parsed_r</span></div>
+
+
+
+<div class="viewcode-block" id="compute_feature_importance">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.eval_feature_importance.compute_feature_importance">[docs]</a>
+<span class="k">def</span> <span class="nf">compute_feature_importance</span><span class="p">(</span>
+    <span class="n">llm_clf</span><span class="p">:</span> <span class="n">LLMClassifier</span><span class="p">,</span>
+    <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span>
+    <span class="n">scorer</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">results_dir</span><span class="p">:</span> <span class="n">Path</span><span class="p">,</span>
+    <span class="n">fit_threshold</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">seed</span><span class="o">=</span><span class="n">DEFAULT_SEED</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+
+    <span class="c1"># Get train and test data</span>
+    <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_test</span><span class="p">()</span>
+    <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="n">permutation_kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+        <span class="n">X</span><span class="o">=</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span>
+        <span class="n">scoring</span><span class="o">=</span><span class="n">scorer</span><span class="p">,</span>
+        <span class="n">n_repeats</span><span class="o">=</span><span class="n">DEFAULT_PERMUTATION_REPEATS</span><span class="p">,</span>
+        <span class="n">random_state</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Optionally, fit the LLM classifier&#39;s threshold on a few data samples.</span>
+    <span class="k">if</span> <span class="n">fit_threshold</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fit_threshold</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+        <span class="n">X_train_sample</span><span class="p">,</span> <span class="n">y_train_sample</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">sample_n_train_examples</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="n">fit_threshold</span><span class="p">)</span>
+        <span class="n">llm_clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_sample</span><span class="p">,</span> <span class="n">y_train_sample</span><span class="p">)</span>
+
+    <span class="c1"># LLM feature importance</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">permutation_importance</span><span class="p">(</span><span class="n">llm_clf</span><span class="p">,</span> <span class="o">**</span><span class="n">permutation_kwargs</span><span class="p">)</span>
+    <span class="n">llm_imp_file_path</span> <span class="o">=</span> <span class="n">results_dir</span> <span class="o">/</span> <span class="sa">f</span><span class="s2">&quot;feature-importance.</span><span class="si">{</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2">.pkl&quot;</span>
+    <span class="n">save_pickle</span><span class="p">(</span><span class="n">obj</span><span class="o">=</span><span class="n">r</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="n">llm_imp_file_path</span><span class="p">)</span>
+    <span class="n">save_json</span><span class="p">(</span>
+        <span class="n">parse_feature_importance</span><span class="p">(</span><span class="n">results</span><span class="o">=</span><span class="n">r</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">X_test</span><span class="o">.</span><span class="n">columns</span><span class="p">),</span>
+        <span class="n">path</span><span class="o">=</span><span class="n">llm_imp_file_path</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="s2">&quot;.json&quot;</span><span class="p">))</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;LLM feature importance:&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">r</span><span class="o">.</span><span class="n">importances_mean</span><span class="o">.</span><span class="n">argsort</span><span class="p">()[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">X_test</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;8</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">r</span><span class="o">.</span><span class="n">importances_mean</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot; +/- </span><span class="si">{</span><span class="n">r</span><span class="o">.</span><span class="n">importances_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="n">X_test</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span></div>
+
+
+
+<div class="viewcode-block" id="main">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.eval_feature_importance.main">[docs]</a>
+<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
+    <span class="c1"># Parse arguments from command line</span>
+    <span class="n">args</span> <span class="o">=</span> <span class="n">setup_arg_parser</span><span class="p">()</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
+
+    <span class="c1"># Set logging level</span>
+    <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">()</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">)</span>
+
+    <span class="c1"># Resolve model folder path</span>
+    <span class="n">model_folder_path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">get_model_folder_path</span><span class="p">(</span><span class="n">model_name</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">root_dir</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">models_dir</span><span class="p">))</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">model_folder_path</span><span class="o">.</span><span class="n">exists</span><span class="p">()</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">model_folder_path</span><span class="o">.</span><span class="n">is_dir</span><span class="p">():</span>
+        <span class="n">model_folder_path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model</span><span class="p">)</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span>
+
+    <span class="c1"># Load model and tokenizer</span>
+    <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Loading model from </span><span class="si">{</span><span class="n">model_folder_path</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">load_model_tokenizer</span><span class="p">(</span><span class="n">model_folder_path</span><span class="p">)</span>
+
+    <span class="c1"># Create results directory if needed</span>
+    <span class="c1"># Set-up results directory</span>
+    <span class="kn">from</span> <span class="nn">folktexts.cli._utils</span> <span class="kn">import</span> <span class="n">get_or_create_results_dir</span>
+    <span class="n">results_dir</span> <span class="o">=</span> <span class="n">get_or_create_results_dir</span><span class="p">(</span>
+        <span class="n">model_name</span><span class="o">=</span><span class="n">Path</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model</span><span class="p">)</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+        <span class="n">task_name</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">task</span><span class="p">,</span>
+        <span class="n">results_root_dir</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">results_dir</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Saving results to </span><span class="si">{</span><span class="n">results_dir</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Load Task and Dataset</span>
+    <span class="kn">from</span> <span class="nn">folktexts.acs</span> <span class="kn">import</span> <span class="n">ACSTaskMetadata</span>
+    <span class="n">task</span> <span class="o">=</span> <span class="n">ACSTaskMetadata</span><span class="o">.</span><span class="n">get_task</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">task</span><span class="p">)</span>
+
+    <span class="kn">from</span> <span class="nn">folktexts.acs</span> <span class="kn">import</span> <span class="n">ACSDataset</span>
+    <span class="n">dataset</span> <span class="o">=</span> <span class="n">ACSDataset</span><span class="o">.</span><span class="n">make_from_task</span><span class="p">(</span>
+        <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+        <span class="n">cache_dir</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">data_dir</span><span class="p">,</span>
+        <span class="n">seed</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Optionally, subsample dataset</span>
+    <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">subsampling</span><span class="p">:</span>
+        <span class="n">dataset</span><span class="o">.</span><span class="n">subsample</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">subsampling</span><span class="p">)</span>      <span class="c1"># subsample in-place</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dataset</span><span class="o">.</span><span class="n">subsampling</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Construct LLM Classifier</span>
+    <span class="kn">from</span> <span class="nn">folktexts.classifier</span> <span class="kn">import</span> <span class="n">TransformersLLMClassifier</span>
+    <span class="n">llm_clf</span> <span class="o">=</span> <span class="n">TransformersLLMClassifier</span><span class="p">(</span>
+        <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+        <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+        <span class="n">batch_size</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
+        <span class="n">context_size</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">context_size</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Compute feature importance</span>
+    <span class="n">compute_feature_importance</span><span class="p">(</span>
+        <span class="n">llm_clf</span><span class="p">,</span>
+        <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span>
+        <span class="n">scorer</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">scorer</span><span class="p">,</span>
+        <span class="n">results_dir</span><span class="o">=</span><span class="n">results_dir</span><span class="p">,</span>
+        <span class="n">fit_threshold</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">fit_threshold</span><span class="p">,</span>
+        <span class="n">seed</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span><span class="p">,</span>
+    <span class="p">)</span></div>
+
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+    <span class="n">main</span><span class="p">()</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
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diff --git a/_modules/folktexts/cli/experiments.html b/_modules/folktexts/cli/experiments.html
new file mode 100644
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+++ b/_modules/folktexts/cli/experiments.html
@@ -0,0 +1,248 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>folktexts.cli.experiments &mdash; folktexts 0.0.25 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=80d5e7a1" />
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+<li class="toctree-l1"><a class="reference internal" href="../../../notebooks.html">Example notebooks</a></li>
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+      <li><a href="../../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">folktexts.cli.experiments</li>
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+      </li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for folktexts.cli.experiments</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;General constants and helper classes to run the main experiments on htcondor.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">asdict</span><span class="p">,</span> <span class="n">dataclass</span><span class="p">,</span> <span class="n">field</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">ClassVar</span>
+
+<span class="kn">import</span> <span class="nn">classad</span>
+<span class="kn">import</span> <span class="nn">htcondor</span>
+
+<span class="kn">from</span> <span class="nn">folktexts._utils</span> <span class="kn">import</span> <span class="n">hash_dict</span>
+
+<span class="c1"># Cluster settings</span>
+<span class="n">DEFAULT_JOB_BID</span> <span class="o">=</span> <span class="mi">25</span>            <span class="c1"># htcondor bid (min. is 15 apparently...)</span>
+<span class="n">DEFAULT_JOB_CPUS</span> <span class="o">=</span> <span class="mi">4</span>            <span class="c1"># number of CPUs per experiment (per cluster job)</span>
+<span class="n">DEFAULT_JOB_MEMORY_GB</span> <span class="o">=</span> <span class="mi">62</span>      <span class="c1"># GBs of memory</span>
+<span class="n">DEFAULT_GPU_MEMORY_GB</span> <span class="o">=</span> <span class="mi">30</span>      <span class="c1"># GBs of GPU memory</span>
+
+<span class="n">MAX_RUNNING_PRICE</span> <span class="o">=</span> <span class="mi">1500</span>        <span class="c1"># Max price for running a job</span>
+
+
+<div class="viewcode-block" id="Experiment">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.experiments.Experiment">[docs]</a>
+<span class="nd">@dataclass</span>
+<span class="k">class</span> <span class="nc">Experiment</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A generic experiment to run on the cluster.&quot;&quot;&quot;</span>
+    <span class="n">executable_path</span><span class="p">:</span> <span class="nb">str</span>
+    <span class="n">env_vars</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+    <span class="n">kwargs</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span><span class="n">default_factory</span><span class="o">=</span><span class="nb">dict</span><span class="p">)</span>
+
+    <span class="n">job_cpus</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DEFAULT_JOB_CPUS</span>
+    <span class="n">job_gpus</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">job_memory_gb</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DEFAULT_JOB_MEMORY_GB</span>
+    <span class="n">job_gpu_memory_gb</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DEFAULT_GPU_MEMORY_GB</span>
+    <span class="n">job_bid</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DEFAULT_JOB_BID</span>
+
+    <span class="n">_all_experiments</span><span class="p">:</span> <span class="n">ClassVar</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="s2">&quot;Experiment&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">def</span> <span class="nf">__post_init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># Add experiment to the class-level list</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_all_experiments</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+<div class="viewcode-block" id="Experiment.get_all_experiments">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.experiments.Experiment.get_all_experiments">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get_all_experiments</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_all_experiments</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Attribute &#39;</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39; not found in Experiment.&quot;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="Experiment.hash">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.experiments.Experiment.hash">[docs]</a>
+    <span class="k">def</span> <span class="nf">hash</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate a hexadecimal hash that uniquely identifies the experiment&#39;s arguments.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Get hash of the experiment&#39;s arguments</span>
+        <span class="n">kwargs_for_hash</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+            <span class="n">executable_path</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">executable_path</span><span class="p">,</span>
+            <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># These kwargs shouldn&#39;t be used to generate a unique hash</span>
+        <span class="n">kwargs_for_hash</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;results_dir&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">kwargs_for_hash</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;hash&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">hash_dict</span><span class="p">(</span><span class="n">kwargs_for_hash</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Experiment.to_dict">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.experiments.Experiment.to_dict">[docs]</a>
+    <span class="k">def</span> <span class="nf">to_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">asdict</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="launch_experiment_job">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.experiments.launch_experiment_job">[docs]</a>
+<span class="k">def</span> <span class="nf">launch_experiment_job</span><span class="p">(</span><span class="n">exp</span><span class="p">:</span> <span class="n">Experiment</span><span class="p">):</span>
+
+    <span class="c1"># Name/prefix for cluster logs related to this job</span>
+    <span class="n">cluster_job_log_name</span> <span class="o">=</span> <span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">exp</span><span class="o">.</span><span class="n">results_dir</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;log.$(Cluster).$(Process)&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span>
+
+    <span class="c1"># Construct executable cmd-line arguments</span>
+    <span class="n">cmd_line_args</span> <span class="o">=</span> <span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
+        <span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;--</span><span class="si">{</span><span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span><span class="w"> </span><span class="s1">&#39;-&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">bool</span><span class="p">)</span> <span class="ow">and</span> <span class="n">value</span> <span class="ow">is</span> <span class="kc">True</span>
+            <span class="k">else</span> <span class="sa">f</span><span class="s2">&quot;--</span><span class="si">{</span><span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span><span class="w"> </span><span class="s1">&#39;-&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">=</span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">exp</span><span class="o">.</span><span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Construct job description</span>
+    <span class="n">job_description</span> <span class="o">=</span> <span class="n">htcondor</span><span class="o">.</span><span class="n">Submit</span><span class="p">({</span>
+        <span class="s2">&quot;executable&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">sys</span><span class="o">.</span><span class="n">executable</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>  <span class="c1"># correct env for the python executable</span>
+        <span class="s2">&quot;arguments&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">exp</span><span class="o">.</span><span class="n">executable_path</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">cmd_line_args</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;output&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">cluster_job_log_name</span><span class="si">}</span><span class="s2">.out&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;error&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">cluster_job_log_name</span><span class="si">}</span><span class="s2">.err&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;log&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">cluster_job_log_name</span><span class="si">}</span><span class="s2">.log&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;request_cpus&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">exp</span><span class="o">.</span><span class="n">job_cpus</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;request_gpus&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">exp</span><span class="o">.</span><span class="n">job_gpus</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;request_memory&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">exp</span><span class="o">.</span><span class="n">job_memory_gb</span><span class="si">}</span><span class="s2">GB&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;request_disk&quot;</span><span class="p">:</span> <span class="s2">&quot;10GB&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;jobprio&quot;</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">exp</span><span class="o">.</span><span class="n">job_bid</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">1000</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;notify_user&quot;</span><span class="p">:</span> <span class="s2">&quot;andre.cruz@tuebingen.mpg.de&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;notification&quot;</span><span class="p">:</span> <span class="s2">&quot;error&quot;</span><span class="p">,</span>
+
+        <span class="c1"># Environment variables</span>
+        <span class="s2">&quot;environment&quot;</span><span class="p">:</span> <span class="n">exp</span><span class="o">.</span><span class="n">env_vars</span> <span class="ow">or</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
+
+        <span class="c1"># GPU requirements</span>
+        <span class="s2">&quot;requirements&quot;</span><span class="p">:</span> <span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;(TARGET.CUDAGlobalMemoryMb &gt; </span><span class="si">{</span><span class="n">exp</span><span class="o">.</span><span class="n">job_gpu_memory_gb</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">1_000</span><span class="si">}</span><span class="s2">)&quot;</span>
+        <span class="p">)</span> <span class="k">if</span> <span class="n">exp</span><span class="o">.</span><span class="n">job_gpus</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
+
+        <span class="c1"># Concurrency limits:</span>
+        <span class="c1"># &gt; each job uses this amount of resources out of a pool of 10k</span>
+        <span class="s2">&quot;concurrency_limits&quot;</span><span class="p">:</span> <span class="s2">&quot;user.folktexts:100&quot;</span><span class="p">,</span>     <span class="c1"># 100 jobs in parallel</span>
+
+        <span class="s2">&quot;+MaxRunningPrice&quot;</span><span class="p">:</span> <span class="n">MAX_RUNNING_PRICE</span><span class="p">,</span>
+        <span class="s2">&quot;+RunningPriceExceededAction&quot;</span><span class="p">:</span> <span class="n">classad</span><span class="o">.</span><span class="n">quote</span><span class="p">(</span><span class="s2">&quot;restart&quot;</span><span class="p">),</span>
+    <span class="p">})</span>
+
+    <span class="c1"># Submit job to the htcondor scheduler</span>
+    <span class="n">schedd</span> <span class="o">=</span> <span class="n">htcondor</span><span class="o">.</span><span class="n">Schedd</span><span class="p">()</span>
+    <span class="n">submit_result</span> <span class="o">=</span> <span class="n">schedd</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">job_description</span><span class="p">)</span>
+
+    <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+        <span class="sa">f</span><span class="s2">&quot;Launched </span><span class="si">{</span><span class="n">submit_result</span><span class="o">.</span><span class="n">num_procs</span><span class="p">()</span><span class="si">}</span><span class="s2"> processe(s) with &quot;</span>
+        <span class="sa">f</span><span class="s2">&quot;cluster-ID=</span><span class="si">{</span><span class="n">submit_result</span><span class="o">.</span><span class="n">cluster</span><span class="p">()</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">submit_result</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
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+
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diff --git a/_modules/folktexts/cli/launch_experiments_htcondor.html b/_modules/folktexts/cli/launch_experiments_htcondor.html
new file mode 100644
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+++ b/_modules/folktexts/cli/launch_experiments_htcondor.html
@@ -0,0 +1,374 @@
+
+
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+      <li class="breadcrumb-item active">folktexts.cli.launch_experiments_htcondor</li>
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for folktexts.cli.launch_experiments_htcondor</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/env python3</span>
+<span class="sd">&quot;&quot;&quot;Launch htcondor jobs for all ACS benchmark experiments.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">argparse</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
+
+<span class="kn">from</span> <span class="nn">folktexts._io</span> <span class="kn">import</span> <span class="n">load_json</span><span class="p">,</span> <span class="n">save_json</span>
+<span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">get_model_folder_path</span><span class="p">,</span> <span class="n">get_model_size_B</span>
+
+<span class="kn">from</span> <span class="nn">.experiments</span> <span class="kn">import</span> <span class="n">Experiment</span><span class="p">,</span> <span class="n">launch_experiment_job</span>
+
+<span class="c1"># All ACS prediction tasks</span>
+<span class="n">ACS_TASKS</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="s2">&quot;ACSIncome&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;ACSEmployment&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;ACSMobility&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;ACSTravelTime&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;ACSPublicCoverage&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1">################</span>
+<span class="c1"># Useful paths #</span>
+<span class="c1">################</span>
+<span class="n">ROOT_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;/fast/groups/sf&quot;</span><span class="p">)</span>
+<span class="c1"># ROOT_DIR = Path(&quot;~&quot;).expanduser().resolve()               # on local machine</span>
+
+<span class="c1"># ACS data directory</span>
+<span class="n">ACS_DATA_DIR</span> <span class="o">=</span> <span class="n">ROOT_DIR</span> <span class="o">/</span> <span class="s2">&quot;data&quot;</span>
+
+<span class="c1"># Models save directory</span>
+<span class="n">MODELS_DIR</span> <span class="o">=</span> <span class="n">ROOT_DIR</span> <span class="o">/</span> <span class="s2">&quot;huggingface-models&quot;</span>
+
+
+<span class="c1">##################</span>
+<span class="c1"># Global configs #</span>
+<span class="c1">##################</span>
+<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="n">CONTEXT_SIZE</span> <span class="o">=</span> <span class="mi">750</span>
+
+<span class="n">JOB_CPUS</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">JOB_MEMORY_GB</span> <span class="o">=</span> <span class="mi">60</span>
+<span class="n">JOB_BID</span> <span class="o">=</span> <span class="mi">250</span>
+
+<span class="c1"># LLMs to evaluate</span>
+<span class="n">LLM_MODELS</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="c1"># Google Gemma2 models</span>
+    <span class="s2">&quot;google/gemma-2b&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;google/gemma-1.1-2b-it&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;google/gemma-7b&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;google/gemma-1.1-7b-it&quot;</span><span class="p">,</span>
+
+    <span class="s2">&quot;google/gemma-2-9b&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;google/gemma-2-9b-it&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;google/gemma-2-27b&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;google/gemma-2-27b-it&quot;</span><span class="p">,</span>
+
+    <span class="c1"># Meta Llama3 models</span>
+    <span class="s2">&quot;meta-llama/Meta-Llama-3-8B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;meta-llama/Meta-Llama-3-8B-Instruct&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;meta-llama/Meta-Llama-3-70B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;meta-llama/Meta-Llama-3-70B-Instruct&quot;</span><span class="p">,</span>
+
+    <span class="c1"># Mistral AI models</span>
+    <span class="s2">&quot;mistralai/Mistral-7B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mistralai/Mistral-7B-Instruct-v0.2&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mistralai/Mixtral-8x7B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mistralai/Mixtral-8x7B-Instruct-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mistralai/Mixtral-8x22B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;mistralai/Mixtral-8x22B-Instruct-v0.1&quot;</span><span class="p">,</span>
+
+    <span class="c1"># Yi models</span>
+    <span class="s2">&quot;01-ai/Yi-34B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;01-ai/Yi-34B-Chat&quot;</span><span class="p">,</span>
+
+    <span class="c1"># Qwen2 models</span>
+    <span class="c1"># &quot;Qwen/Qwen2-1.5B&quot;,</span>
+    <span class="c1"># &quot;Qwen/Qwen2-1.5B-Instruct&quot;,</span>
+    <span class="c1"># &quot;Qwen/Qwen2-7B&quot;,</span>
+    <span class="c1"># &quot;Qwen/Qwen2-7B-Instruct&quot;,</span>
+    <span class="c1"># &quot;Qwen/Qwen2-72B&quot;,</span>
+    <span class="c1"># &quot;Qwen/Qwen2-72B-Instruct&quot;,</span>
+<span class="p">]</span>
+
+
+<span class="c1"># Function that defines common settings among all LLM-as-clf experiments</span>
+<div class="viewcode-block" id="make_llm_clf_experiment">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.launch_experiments_htcondor.make_llm_clf_experiment">[docs]</a>
+<span class="k">def</span> <span class="nf">make_llm_clf_experiment</span><span class="p">(</span>
+    <span class="n">executable_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">model_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">task</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">results_dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">env_vars_str</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Experiment</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create an experiment object to run.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Get model size</span>
+    <span class="n">model_size_B</span> <span class="o">=</span> <span class="n">get_model_size_B</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+
+    <span class="c1"># Get model path</span>
+    <span class="n">model_path</span> <span class="o">=</span> <span class="n">get_model_folder_path</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">root_dir</span><span class="o">=</span><span class="n">MODELS_DIR</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">Path</span><span class="p">(</span><span class="n">model_path</span><span class="p">)</span><span class="o">.</span><span class="n">exists</span><span class="p">()</span> <span class="ow">and</span> <span class="s2">&quot;use_web_api_model&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">FileNotFoundError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Model folder not found at &#39;</span><span class="si">{</span><span class="n">model_path</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Split experiment and job kwargs</span>
+    <span class="n">job_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">val</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;job_&quot;</span><span class="p">)}</span>
+    <span class="n">experiment_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">val</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">job_kwargs</span><span class="p">}</span>
+
+    <span class="c1"># Set default job kwargs</span>
+    <span class="n">job_kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">&quot;job_cpus&quot;</span><span class="p">,</span> <span class="n">JOB_CPUS</span><span class="p">)</span>
+    <span class="n">job_kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">&quot;job_gpus&quot;</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">model_size_B</span> <span class="o">/</span> <span class="mi">40</span><span class="p">))</span>     <span class="c1"># One GPU per 40B parameters</span>
+    <span class="n">job_kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">&quot;job_memory_gb&quot;</span><span class="p">,</span> <span class="n">JOB_MEMORY_GB</span><span class="p">)</span>
+    <span class="n">job_kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">&quot;job_gpu_memory_gb&quot;</span><span class="p">,</span> <span class="mi">35</span> <span class="k">if</span> <span class="n">model_size_B</span> <span class="o">&lt;</span> <span class="mi">5</span> <span class="k">else</span> <span class="mi">60</span><span class="p">)</span>
+    <span class="n">job_kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">&quot;job_bid&quot;</span><span class="p">,</span> <span class="n">JOB_BID</span><span class="p">)</span>
+
+    <span class="c1"># Set default experiment kwargs</span>
+    <span class="n">n_shots</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">experiment_kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;few_shot&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="n">experiment_kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">&quot;batch_size&quot;</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">BATCH_SIZE</span> <span class="o">/</span> <span class="n">n_shots</span><span class="p">))</span>
+    <span class="n">experiment_kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">&quot;context_size&quot;</span><span class="p">,</span> <span class="n">CONTEXT_SIZE</span> <span class="o">*</span> <span class="n">n_shots</span><span class="p">)</span>
+    <span class="n">experiment_kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">&quot;data_dir&quot;</span><span class="p">,</span> <span class="n">ACS_DATA_DIR</span><span class="o">.</span><span class="n">as_posix</span><span class="p">())</span>
+    <span class="c1"># experiment_kwargs.setdefault(&quot;fit_threshold&quot;, FIT_THRESHOLD)</span>
+
+    <span class="c1"># Define experiment</span>
+    <span class="n">exp</span> <span class="o">=</span> <span class="n">Experiment</span><span class="p">(</span>
+        <span class="n">executable_path</span><span class="o">=</span><span class="n">executable_path</span><span class="p">,</span>
+        <span class="n">env_vars</span><span class="o">=</span><span class="n">env_vars_str</span><span class="p">,</span>
+        <span class="n">kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+            <span class="n">model</span><span class="o">=</span><span class="n">model_path</span> <span class="k">if</span> <span class="s2">&quot;use_web_api_model&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">else</span> <span class="n">model_name</span><span class="p">,</span>
+            <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+            <span class="n">results_dir</span><span class="o">=</span><span class="n">results_dir</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">experiment_kwargs</span><span class="p">,</span>
+        <span class="p">),</span>
+        <span class="o">**</span><span class="n">job_kwargs</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Create LLM results directory</span>
+    <span class="n">save_json</span><span class="p">(</span>
+        <span class="n">obj</span><span class="o">=</span><span class="n">exp</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(),</span>
+        <span class="n">path</span><span class="o">=</span><span class="n">Path</span><span class="p">(</span><span class="n">results_dir</span><span class="p">)</span> <span class="o">/</span> <span class="sa">f</span><span class="s2">&quot;experiment.</span><span class="si">{</span><span class="n">exp</span><span class="o">.</span><span class="n">hash</span><span class="p">()</span><span class="si">}</span><span class="s2">.json&quot;</span><span class="p">,</span>
+        <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">exp</span></div>
+
+
+
+<div class="viewcode-block" id="setup_arg_parser">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.launch_experiments_htcondor.setup_arg_parser">[docs]</a>
+<span class="k">def</span> <span class="nf">setup_arg_parser</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">:</span>
+    <span class="c1"># Init parser</span>
+    <span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;Launch experiments to evaluate LLMs as classifiers.&quot;</span><span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--executable-path&quot;</span><span class="p">,</span>
+        <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[string] Path to the executable script to run.&quot;</span><span class="p">,</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--results-dir&quot;</span><span class="p">,</span>
+        <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[string] Directory under which results will be saved.&quot;</span><span class="p">,</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--model&quot;</span><span class="p">,</span>
+        <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[string] Model name on huggingface hub - can provide multiple!&quot;</span><span class="p">,</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">action</span><span class="o">=</span><span class="s2">&quot;append&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--task&quot;</span><span class="p">,</span>
+        <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[string] ACS task name to run experiments on - can provide multiple!&quot;</span><span class="p">,</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">action</span><span class="o">=</span><span class="s2">&quot;append&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--dry-run&quot;</span><span class="p">,</span>
+        <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Construct folder structure and print experiments without launching them.&quot;</span><span class="p">,</span>
+        <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--experiment-json&quot;</span><span class="p">,</span>
+        <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[string] Path to an experiment JSON file to load. Will override all other args.&quot;</span><span class="p">,</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--environment&quot;</span><span class="p">,</span>
+        <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="p">(</span>
+            <span class="s2">&quot;[string] String defining environment variables to be passed to &quot;</span>
+            <span class="s2">&quot;launched jobs, in the form &#39;VAR1=val1;VAR2=val2;...&#39;.&quot;</span>
+        <span class="p">),</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">parser</span></div>
+
+
+
+<div class="viewcode-block" id="main">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.launch_experiments_htcondor.main">[docs]</a>
+<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
+    <span class="c1"># Parse command-line arguments</span>
+    <span class="n">parser</span> <span class="o">=</span> <span class="n">setup_arg_parser</span><span class="p">()</span>
+    <span class="n">args</span><span class="p">,</span> <span class="n">extra_kwargs</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_known_args</span><span class="p">()</span>
+
+    <span class="c1"># Parse extra kwargs</span>
+    <span class="kn">from</span> <span class="nn">._utils</span> <span class="kn">import</span> <span class="n">cmd_line_args_to_kwargs</span>
+    <span class="n">extra_kwargs</span> <span class="o">=</span> <span class="n">cmd_line_args_to_kwargs</span><span class="p">(</span><span class="n">extra_kwargs</span><span class="p">)</span>
+
+    <span class="c1"># Prepare command-line arguments</span>
+    <span class="n">models</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">model</span> <span class="ow">or</span> <span class="n">LLM_MODELS</span>
+    <span class="n">tasks</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">task</span> <span class="ow">or</span> <span class="n">ACS_TASKS</span>
+    <span class="n">executable_path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">executable_path</span><span class="p">)</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">executable_path</span><span class="o">.</span><span class="n">exists</span><span class="p">()</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">executable_path</span><span class="o">.</span><span class="n">is_file</span><span class="p">():</span>
+        <span class="k">raise</span> <span class="ne">FileNotFoundError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Executable script not found at &#39;</span><span class="si">{</span><span class="n">executable_path</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Set-up results directory</span>
+    <span class="n">Path</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">results_dir</span><span class="p">)</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="c1"># Load experiment from JSON file if provided</span>
+    <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">experiment_json</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Launching job for experiment at &#39;</span><span class="si">{</span><span class="n">args</span><span class="o">.</span><span class="n">experiment_json</span><span class="si">}</span><span class="s2">&#39;...&quot;</span><span class="p">)</span>
+        <span class="n">exp</span> <span class="o">=</span> <span class="n">Experiment</span><span class="p">(</span><span class="o">**</span><span class="n">load_json</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">experiment_json</span><span class="p">))</span>
+        <span class="n">all_experiments</span> <span class="o">=</span> <span class="p">[</span><span class="n">exp</span><span class="p">]</span>
+
+    <span class="c1"># Otherwise, run all experiments planned</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">all_experiments</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">make_llm_clf_experiment</span><span class="p">(</span>
+                <span class="n">executable_path</span><span class="o">=</span><span class="n">executable_path</span><span class="o">.</span><span class="n">as_posix</span><span class="p">(),</span>
+                <span class="n">model_name</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+                <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+                <span class="n">results_dir</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">results_dir</span><span class="p">,</span>
+                <span class="n">env_vars_str</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">environment</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">extra_kwargs</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span>
+            <span class="k">for</span> <span class="n">task</span> <span class="ow">in</span> <span class="n">tasks</span>
+        <span class="p">]</span>
+
+    <span class="c1"># Log total number of experiments</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Launching </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">all_experiments</span><span class="p">)</span><span class="si">}</span><span class="s2"> experiment(s)...</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">exp</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">all_experiments</span><span class="p">):</span>
+        <span class="n">cluster_id</span> <span class="o">=</span> <span class="n">launch_experiment_job</span><span class="p">(</span><span class="n">exp</span><span class="p">)</span><span class="o">.</span><span class="n">cluster</span><span class="p">()</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">args</span><span class="o">.</span><span class="n">dry_run</span> <span class="k">else</span> <span class="kc">None</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">2</span><span class="si">}</span><span class="s2">. cluster-id=</span><span class="si">{</span><span class="n">cluster_id</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">pprint</span><span class="p">(</span><span class="n">exp</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(),</span> <span class="n">indent</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span></div>
+
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+    <span class="n">main</span><span class="p">()</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
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+</html>
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diff --git a/_modules/folktexts/cli/rerun_experiment.html b/_modules/folktexts/cli/rerun_experiment.html
new file mode 100644
index 0000000..92fad0b
--- /dev/null
+++ b/_modules/folktexts/cli/rerun_experiment.html
@@ -0,0 +1,154 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
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+  <meta charset="utf-8" />
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+      <li class="breadcrumb-item active">folktexts.cli.rerun_experiment</li>
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+             
+  <h1>Source code for folktexts.cli.rerun_experiment</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/env python3</span>
+<span class="sd">&quot;&quot;&quot;Helper script to re-run a single experiment locally.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">argparse</span> <span class="kn">import</span> <span class="n">ArgumentParser</span>
+<span class="kn">from</span> <span class="nn">subprocess</span> <span class="kn">import</span> <span class="n">call</span>
+
+<span class="kn">from</span> <span class="nn">folktexts._io</span> <span class="kn">import</span> <span class="n">load_json</span>
+
+<span class="kn">from</span> <span class="nn">.experiments</span> <span class="kn">import</span> <span class="n">Experiment</span>
+
+
+<div class="viewcode-block" id="setup_arg_parser">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.rerun_experiment.setup_arg_parser">[docs]</a>
+<span class="k">def</span> <span class="nf">setup_arg_parser</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">ArgumentParser</span><span class="p">:</span>
+    <span class="c1"># Init parser</span>
+    <span class="n">parser</span> <span class="o">=</span> <span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;Re-run a single experiment from its JSON config file.&quot;</span><span class="p">)</span>
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--experiment-json&quot;</span><span class="p">,</span>
+        <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[string] Path to an experiment JSON file to load.&quot;</span><span class="p">,</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="c1"># TODO: add over-writable key-word arguments</span>
+
+    <span class="k">return</span> <span class="n">parser</span></div>
+
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
+
+    <span class="c1"># Parse command-line arguments</span>
+    <span class="n">parser</span> <span class="o">=</span> <span class="n">setup_arg_parser</span><span class="p">()</span>
+    <span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
+
+    <span class="c1"># Load experiment from JSON file</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Running experiment from config file at &#39;</span><span class="si">{</span><span class="n">args</span><span class="o">.</span><span class="n">experiment_json</span><span class="si">}</span><span class="s2">&#39;...&quot;</span><span class="p">)</span>
+    <span class="n">exp</span> <span class="o">=</span> <span class="n">Experiment</span><span class="p">(</span><span class="o">**</span><span class="n">load_json</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">experiment_json</span><span class="p">))</span>
+
+    <span class="c1"># Run the experiment</span>
+    <span class="n">cmdline_args</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">cmd</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">exp</span><span class="o">.</span><span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">cmd</span> <span class="ow">in</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;--</span><span class="si">{</span><span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span><span class="w"> </span><span class="s1">&#39;-&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
+    <span class="p">]</span>
+
+    <span class="n">call</span><span class="p">([</span><span class="n">exp</span><span class="o">.</span><span class="n">executable_path</span><span class="p">]</span> <span class="o">+</span> <span class="n">cmdline_args</span><span class="p">)</span>
+</pre></div>
+
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diff --git a/_modules/folktexts/cli/run_acs_benchmark.html b/_modules/folktexts/cli/run_acs_benchmark.html
new file mode 100644
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--- /dev/null
+++ b/_modules/folktexts/cli/run_acs_benchmark.html
@@ -0,0 +1,307 @@
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+             
+  <h1>Source code for folktexts.cli.run_acs_benchmark</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/env python3</span>
+<span class="sd">&quot;&quot;&quot;Runs the LLM calibration benchmark from the command line.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">import</span> <span class="nn">json</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">from</span> <span class="nn">argparse</span> <span class="kn">import</span> <span class="n">ArgumentParser</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+
+<span class="n">DEFAULT_ACS_TASK</span> <span class="o">=</span> <span class="s2">&quot;ACSIncome&quot;</span>
+
+<span class="n">DEFAULT_BATCH_SIZE</span> <span class="o">=</span> <span class="mi">16</span>
+<span class="n">DEFAULT_CONTEXT_SIZE</span> <span class="o">=</span> <span class="mi">600</span>
+<span class="n">DEFAULT_SEED</span> <span class="o">=</span> <span class="mi">42</span>
+
+
+<div class="viewcode-block" id="setup_arg_parser">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.run_acs_benchmark.setup_arg_parser">[docs]</a>
+<span class="k">def</span> <span class="nf">setup_arg_parser</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">ArgumentParser</span><span class="p">:</span>
+
+    <span class="c1"># Init parser</span>
+    <span class="n">parser</span> <span class="o">=</span> <span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;Benchmark risk scores produced by a language model on ACS data.&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Define a custom argument type for a list of strings</span>
+    <span class="k">def</span> <span class="nf">list_of_strings</span><span class="p">(</span><span class="n">arg</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">arg</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
+
+    <span class="c1"># List of command-line arguments, with type and helper string</span>
+    <span class="n">cli_args</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="p">(</span><span class="s2">&quot;--model&quot;</span><span class="p">,</span>         <span class="nb">str</span><span class="p">,</span> <span class="s2">&quot;[str] Model name or path to model saved on disk&quot;</span><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--results-dir&quot;</span><span class="p">,</span>   <span class="nb">str</span><span class="p">,</span> <span class="s2">&quot;[str] Directory under which this experiment&#39;s results will be saved&quot;</span><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--data-dir&quot;</span><span class="p">,</span>      <span class="nb">str</span><span class="p">,</span> <span class="s2">&quot;[str] Root folder to find datasets on&quot;</span><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--task&quot;</span><span class="p">,</span>          <span class="nb">str</span><span class="p">,</span> <span class="s2">&quot;[str] Name of the ACS task to run the experiment on&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">DEFAULT_ACS_TASK</span><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--few-shot&quot;</span><span class="p">,</span>      <span class="nb">int</span><span class="p">,</span> <span class="s2">&quot;[int] Use few-shot prompting with the given number of shots&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--batch-size&quot;</span><span class="p">,</span>    <span class="nb">int</span><span class="p">,</span> <span class="s2">&quot;[int] The batch size to use for inference&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">DEFAULT_BATCH_SIZE</span><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--context-size&quot;</span><span class="p">,</span>  <span class="nb">int</span><span class="p">,</span> <span class="s2">&quot;[int] The maximum context size when prompting the LLM&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">DEFAULT_CONTEXT_SIZE</span><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--fit-threshold&quot;</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="s2">&quot;[int] Whether to fit the prediction threshold, and on how many samples&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--subsampling&quot;</span><span class="p">,</span>   <span class="nb">float</span><span class="p">,</span> <span class="s2">&quot;[float] Which fraction of the dataset to use (if omitted will use all data)&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">),</span>
+        <span class="p">(</span><span class="s2">&quot;--seed&quot;</span><span class="p">,</span>          <span class="nb">int</span><span class="p">,</span> <span class="s2">&quot;[int] Random seed -- to set for reproducibility&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">DEFAULT_SEED</span><span class="p">),</span>
+    <span class="p">]</span>
+
+    <span class="k">for</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">cli_args</span><span class="p">:</span>
+        <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+            <span class="n">arg</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+            <span class="nb">type</span><span class="o">=</span><span class="n">arg</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">help</span><span class="o">=</span><span class="n">arg</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+            <span class="n">required</span><span class="o">=</span><span class="p">(</span><span class="n">arg</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">arg</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span> <span class="k">else</span> <span class="kc">True</span><span class="p">),</span>    <span class="c1"># NOTE: required by default</span>
+            <span class="n">default</span><span class="o">=</span><span class="p">(</span><span class="n">arg</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">arg</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">4</span> <span class="k">else</span> <span class="kc">None</span><span class="p">),</span>     <span class="c1"># default value if provided</span>
+        <span class="p">)</span>
+
+    <span class="c1"># Add special arguments (e.g., boolean flags or multiple-choice args)</span>
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--use-web-api-model&quot;</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[bool] Whether use a model hosted on a web API (instead of a local model)&quot;</span><span class="p">,</span>
+        <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span>
+        <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--dont-correct-order-bias&quot;</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[bool] Whether to avoid correcting ordering bias, by default will correct it&quot;</span><span class="p">,</span>
+        <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_false&quot;</span><span class="p">,</span>
+        <span class="n">default</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--numeric-risk-prompting&quot;</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[bool] Whether to prompt for numeric risk-estimates instead of multiple-choice Q&amp;A&quot;</span><span class="p">,</span>
+        <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span>
+        <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--reuse-few-shot-examples&quot;</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[bool] Whether to reuse the same samples for few-shot prompting (or sample new ones every time)&quot;</span><span class="p">,</span>
+        <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span>
+        <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Optionally, receive a list of features to use (subset of original list)</span>
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--use-feature-subset&quot;</span><span class="p">,</span>
+        <span class="nb">type</span><span class="o">=</span><span class="n">list_of_strings</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[str] Optional subset of features to use for prediction, comma separated&quot;</span><span class="p">,</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--use-population-filter&quot;</span><span class="p">,</span>
+        <span class="nb">type</span><span class="o">=</span><span class="n">list_of_strings</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="p">(</span>
+            <span class="s2">&quot;[str] Optional population filter for this benchmark; must follow &quot;</span>
+            <span class="s2">&quot;the format &#39;column_name=value&#39; to filter the dataset by a specific value.&quot;</span>
+        <span class="p">),</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--max-api-rpm&quot;</span><span class="p">,</span>
+        <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[int] Maximum number of API requests per minute (if using a web-hosted model)&quot;</span><span class="p">,</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
+        <span class="s2">&quot;--logger-level&quot;</span><span class="p">,</span>
+        <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
+        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;[str] The logging level to use for the experiment&quot;</span><span class="p">,</span>
+        <span class="n">choices</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;DEBUG&quot;</span><span class="p">,</span> <span class="s2">&quot;INFO&quot;</span><span class="p">,</span> <span class="s2">&quot;WARNING&quot;</span><span class="p">,</span> <span class="s2">&quot;ERROR&quot;</span><span class="p">,</span> <span class="s2">&quot;CRITICAL&quot;</span><span class="p">],</span>
+        <span class="n">required</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">default</span><span class="o">=</span><span class="s2">&quot;WARNING&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">parser</span></div>
+
+
+
+<div class="viewcode-block" id="main">
+<a class="viewcode-back" href="../../../source/folktexts.cli.html#folktexts.cli.run_acs_benchmark.main">[docs]</a>
+<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Prepare and launch the LLM-as-classifier experiment using ACS data.&quot;&quot;&quot;</span>
+
+    <span class="c1"># Setup parser and process cmd-line args</span>
+    <span class="n">parser</span> <span class="o">=</span> <span class="n">setup_arg_parser</span><span class="p">()</span>
+    <span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
+
+    <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">()</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">logger_level</span><span class="p">)</span>
+    <span class="n">pretty_args_str</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="nb">vars</span><span class="p">(</span><span class="n">args</span><span class="p">),</span> <span class="n">indent</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">sort_keys</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Current python executable: &#39;</span><span class="si">{</span><span class="n">sys</span><span class="o">.</span><span class="n">executable</span><span class="si">}</span><span class="s2">&#39;&quot;</span><span class="p">)</span>
+    <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Received the following cmd-line args: </span><span class="si">{</span><span class="n">pretty_args_str</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Parse population filter if provided</span>
+    <span class="n">population_filter_dict</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">use_population_filter</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">folktexts.cli._utils</span> <span class="kn">import</span> <span class="n">cmd_line_args_to_kwargs</span>
+        <span class="n">population_filter_dict</span> <span class="o">=</span> <span class="n">cmd_line_args_to_kwargs</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">use_population_filter</span><span class="p">)</span>
+
+    <span class="c1"># Load model and tokenizer</span>
+    <span class="c1"># &gt; Web-hosted LLM</span>
+    <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">use_web_api_model</span><span class="p">:</span>
+        <span class="n">model</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">model</span>
+        <span class="n">tokenizer</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="c1"># &gt; Local LLM</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">load_model_tokenizer</span>
+        <span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">load_model_tokenizer</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
+
+    <span class="c1"># Fill ACS Benchmark config</span>
+    <span class="kn">from</span> <span class="nn">folktexts.benchmark</span> <span class="kn">import</span> <span class="n">BenchmarkConfig</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">BenchmarkConfig</span><span class="p">(</span>
+        <span class="n">few_shot</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">few_shot</span><span class="p">,</span>
+        <span class="n">numeric_risk_prompting</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">numeric_risk_prompting</span><span class="p">,</span>
+        <span class="n">reuse_few_shot_examples</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">reuse_few_shot_examples</span><span class="p">,</span>
+        <span class="n">batch_size</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
+        <span class="n">context_size</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">context_size</span><span class="p">,</span>
+        <span class="n">correct_order_bias</span><span class="o">=</span><span class="ow">not</span> <span class="n">args</span><span class="o">.</span><span class="n">dont_correct_order_bias</span><span class="p">,</span>
+        <span class="n">feature_subset</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">use_feature_subset</span> <span class="ow">or</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">population_filter</span><span class="o">=</span><span class="n">population_filter_dict</span><span class="p">,</span>
+        <span class="n">seed</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Create ACS Benchmark object</span>
+    <span class="kn">from</span> <span class="nn">folktexts.benchmark</span> <span class="kn">import</span> <span class="n">Benchmark</span>
+    <span class="n">bench</span> <span class="o">=</span> <span class="n">Benchmark</span><span class="o">.</span><span class="n">make_acs_benchmark</span><span class="p">(</span>
+        <span class="n">task_name</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">task</span><span class="p">,</span>
+        <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+        <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+        <span class="n">data_dir</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">data_dir</span><span class="p">,</span>
+        <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
+        <span class="n">subsampling</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">subsampling</span><span class="p">,</span>
+        <span class="n">max_api_rpm</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">max_api_rpm</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Set-up results directory</span>
+    <span class="kn">from</span> <span class="nn">folktexts.cli._utils</span> <span class="kn">import</span> <span class="n">get_or_create_results_dir</span>
+    <span class="n">results_dir</span> <span class="o">=</span> <span class="n">get_or_create_results_dir</span><span class="p">(</span>
+        <span class="n">model_name</span><span class="o">=</span><span class="n">Path</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model</span><span class="p">)</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+        <span class="n">task_name</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">task</span><span class="p">,</span>
+        <span class="n">results_root_dir</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">results_dir</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Saving results to </span><span class="si">{</span><span class="n">results_dir</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Run benchmark</span>
+    <span class="n">bench</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">results_root_dir</span><span class="o">=</span><span class="n">results_dir</span><span class="p">,</span> <span class="n">fit_threshold</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">fit_threshold</span><span class="p">)</span>
+    <span class="n">bench</span><span class="o">.</span><span class="n">save_results</span><span class="p">()</span>
+
+    <span class="c1"># Save results</span>
+    <span class="kn">import</span> <span class="nn">pprint</span>
+    <span class="n">pprint</span><span class="o">.</span><span class="n">pprint</span><span class="p">(</span><span class="n">bench</span><span class="o">.</span><span class="n">results</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">sort_dicts</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="c1"># Finish</span>
+    <span class="kn">from</span> <span class="nn">folktexts._utils</span> <span class="kn">import</span> <span class="n">get_current_timestamp</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Finished experiment successfully at </span><span class="si">{</span><span class="n">get_current_timestamp</span><span class="p">()</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+    <span class="n">main</span><span class="p">()</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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+   
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\ No newline at end of file
diff --git a/_modules/folktexts/col_to_text.html b/_modules/folktexts/col_to_text.html
new file mode 100644
index 0000000..317544a
--- /dev/null
+++ b/_modules/folktexts/col_to_text.html
@@ -0,0 +1,255 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>folktexts.col_to_text &mdash; folktexts 0.0.25 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=80d5e7a1" />
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+      <script src="../../_static/custom.js?v=882eb7ae"></script>
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+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../readme.html">Readme file</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../source/modules.html">API reference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../notebooks.html">Example notebooks</a></li>
+</ul>
+
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+
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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+      <li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">folktexts.col_to_text</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for folktexts.col_to_text</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span>
+
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">.qa_interface</span> <span class="kn">import</span> <span class="n">MultipleChoiceQA</span><span class="p">,</span> <span class="n">QAInterface</span>
+
+
+<div class="viewcode-block" id="ColumnToText">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.col_to_text.ColumnToText">[docs]</a>
+<span class="k">class</span> <span class="nc">ColumnToText</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Maps a single column&#39;s values to natural text.&quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">short_description</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">value_map</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">object</span><span class="p">,</span> <span class="nb">str</span><span class="p">]</span> <span class="o">|</span> <span class="n">Callable</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">question</span><span class="p">:</span> <span class="n">QAInterface</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">connector_verb</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;is:&quot;</span><span class="p">,</span>
+        <span class="n">missing_value_fill</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;N/A&quot;</span><span class="p">,</span>
+        <span class="n">use_value_map_only</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructs a `ColumnToText` object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        name : str</span>
+<span class="sd">            The column&#39;s name.</span>
+<span class="sd">        short_description : str</span>
+<span class="sd">            A short description of the column to be used before different</span>
+<span class="sd">            values. For example, short_description=&quot;yearly income&quot; will result</span>
+<span class="sd">            in &quot;The yearly income is [...]&quot;.</span>
+<span class="sd">        value_map : dict[int | str, str] | Callable, optional</span>
+<span class="sd">            A map between column values and their textual meaning. If not</span>
+<span class="sd">            provided, will try to infer a mapping from the `question`.</span>
+<span class="sd">        question : QAInterface, optional</span>
+<span class="sd">            A question associated with the column. If not provided, will try to</span>
+<span class="sd">            infer a multiple-choice question from the `value_map`.</span>
+<span class="sd">        connector_verb : str, optional</span>
+<span class="sd">            Which verb to use when connecting the column&#39;s description to its</span>
+<span class="sd">            value; by default &quot;is&quot;.</span>
+<span class="sd">        missing_value_fill : str, optional</span>
+<span class="sd">            The value to use when the column&#39;s value is not found in the</span>
+<span class="sd">            `value_map`, by default &quot;N/A&quot;.</span>
+<span class="sd">        use_value_map_only : bool, optional</span>
+<span class="sd">            Whether to only use the `value_map` for mapping values to text,</span>
+<span class="sd">            or whether natural language representation should be generated using</span>
+<span class="sd">            the `connector_verb` and `short_description` as well.</span>
+<span class="sd">            By default (False) will construct a natural language representation</span>
+<span class="sd">            of the form:</span>
+<span class="sd">            `&quot;The [short_description] [connector_verb] [value_map.get(val)]&quot;.`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="n">name</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_short_description</span> <span class="o">=</span> <span class="n">short_description</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span> <span class="o">=</span> <span class="n">value_map</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_question</span> <span class="o">=</span> <span class="n">question</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_connector_verb</span> <span class="o">=</span> <span class="n">connector_verb</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_missing_value_fill</span> <span class="o">=</span> <span class="n">missing_value_fill</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_use_value_map_only</span> <span class="o">=</span> <span class="n">use_value_map_only</span>
+
+        <span class="c1"># If a `question` was provided and `value_map` was not</span>
+        <span class="c1"># &gt; infer `value_map` from question (`value_map` is required for `__getitem__`)</span>
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_question</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_question</span><span class="p">,</span> <span class="n">MultipleChoiceQA</span><span class="p">)</span>
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span> <span class="ow">is</span> <span class="kc">None</span>
+        <span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_question</span><span class="o">.</span><span class="n">get_value_to_text_map</span><span class="p">()</span>
+
+        <span class="c1"># If `value_map` was provided and `question` was not</span>
+        <span class="c1"># &gt; infer `question` from value map (if possible)</span>
+        <span class="k">elif</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_question</span> <span class="ow">is</span> <span class="kc">None</span>
+        <span class="p">):</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Inferring multiple-choice question for column &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_question</span> <span class="o">=</span> <span class="n">MultipleChoiceQA</span><span class="o">.</span><span class="n">create_question_from_value_map</span><span class="p">(</span>
+                    <span class="n">column</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+                    <span class="n">value_map</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span><span class="p">,</span>
+                    <span class="n">attribute</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">short_description</span><span class="p">,</span>
+                <span class="p">)</span>
+
+        <span class="c1"># Else, warn if both were provided (as they may use inconsistent value maps)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_question</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Got both `value_map` and `question` for column &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39;. &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Please make sure value mappings are consistent:&quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">- `value_map`: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">- `question`: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_question</span><span class="o">.</span><span class="n">choices</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+        <span class="c1"># Else, log critical error -- ColumnToText object is incomplete</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Must provide either a `value_map` or a `question` for column &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39; but neither was provided.&quot;</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">name</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_name</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">short_description</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_short_description</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">question</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">QAInterface</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_question</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;No question provided for column &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_question</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">value_map</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the value map function for this column.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">callable</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="k">def</span> <span class="nf">_helper_func</span><span class="p">(</span><span class="n">value</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">value</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span><span class="p">:</span>
+                    <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Could not find value &#39;</span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">&#39; in value map for column &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_missing_value_fill</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">_helper_func</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Invalid value map type &#39;</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_value_map</span><span class="p">)</span><span class="si">}</span><span class="s2">&#39; &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;for column &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the textual representation of the given data value.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)):</span>
+            <span class="n">value</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+
+        <span class="c1"># NOTE: `value != value` is a check for NaN values</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_missing_value_fill</span> <span class="k">if</span> <span class="n">value</span> <span class="o">!=</span> <span class="n">value</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">value_map</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ColumnToText.get_text">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.col_to_text.ColumnToText.get_text">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_text</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the natural text representation of the given data value.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_use_value_map_only</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="p">[</span><span class="n">value</span><span class="p">]</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;The </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">short_description</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_connector_verb</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="bp">self</span><span class="p">[</span><span class="n">value</span><span class="p">]</span><span class="si">}</span><span class="s2">.&quot;</span></div>
+</div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
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diff --git a/_modules/folktexts/dataset.html b/_modules/folktexts/dataset.html
new file mode 100644
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--- /dev/null
+++ b/_modules/folktexts/dataset.html
@@ -0,0 +1,494 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>folktexts.dataset &mdash; folktexts 0.0.25 documentation</title>
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+<li class="toctree-l1"><a class="reference internal" href="../../readme.html">Readme file</a></li>
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+             
+  <h1>Source code for folktexts.dataset</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;General Dataset functionality for text-based datasets.</span>
+
+<span class="sd">TODO</span>
+<span class="sd">----</span>
+<span class="sd">- Re-assess if the Dataset needs permanent access to the task;</span>
+<span class="sd">    - The task is already in the LLMClassifier;</span>
+<span class="sd">    - Maybe the Dataset should simply receive the `task` object whenever a</span>
+<span class="sd">    method needs it.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="kn">from</span> <span class="nn">._utils</span> <span class="kn">import</span> <span class="n">hash_dict</span><span class="p">,</span> <span class="n">is_valid_number</span>
+<span class="kn">from</span> <span class="nn">.task</span> <span class="kn">import</span> <span class="n">TaskMetadata</span>
+
+<span class="n">DEFAULT_TEST_SIZE</span> <span class="o">=</span> <span class="mf">0.1</span>
+<span class="n">DEFAULT_VAL_SIZE</span> <span class="o">=</span> <span class="mf">0.1</span>
+<span class="n">DEFAULT_SEED</span> <span class="o">=</span> <span class="mi">42</span>
+
+
+<div class="viewcode-block" id="Dataset">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.dataset.Dataset">[docs]</a>
+<span class="k">class</span> <span class="nc">Dataset</span><span class="p">:</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">data</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+        <span class="n">task</span><span class="p">:</span> <span class="n">TaskMetadata</span><span class="p">,</span>
+        <span class="n">test_size</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">DEFAULT_TEST_SIZE</span><span class="p">,</span>
+        <span class="n">val_size</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">DEFAULT_VAL_SIZE</span><span class="p">,</span>
+        <span class="n">subsampling</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DEFAULT_SEED</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Construct a Dataset object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data : pd.DataFrame</span>
+<span class="sd">            The dataset&#39;s data in pandas DataFrame format.</span>
+<span class="sd">        task : TaskMetadata</span>
+<span class="sd">            The metadata for the prediction task.</span>
+<span class="sd">        test_size : float, optional</span>
+<span class="sd">            The size of the test set, as a fraction of the total dataset size,</span>
+<span class="sd">            by default 0.1.</span>
+<span class="sd">        val_size : float, optional</span>
+<span class="sd">            The size of the validation set, as a fraction of the total dataset</span>
+<span class="sd">            size, by default 0.1.</span>
+<span class="sd">        subsampling : float, optional</span>
+<span class="sd">            Whether to use sub-sampling, and which fraction of the data to keep.</span>
+<span class="sd">            By default will not use sub-sampling (`subsampling=None`).</span>
+<span class="sd">        seed : int, optional</span>
+<span class="sd">            The random state seed, by default 42.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_task</span> <span class="o">=</span> <span class="n">task</span>
+
+        <span class="c1"># Validate task</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_task</span><span class="p">,</span> <span class="n">TaskMetadata</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Invalid `task` type: </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_task</span><span class="p">)</span><span class="si">}</span><span class="s2">. &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Expected `TaskMetadata`.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Validate data for this task</span>
+        <span class="n">task</span><span class="o">.</span><span class="n">check_task_columns_are_available</span><span class="p">(</span>
+            <span class="n">available_cols</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
+        <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_test_size</span> <span class="o">=</span> <span class="n">test_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_val_size</span> <span class="o">=</span> <span class="n">val_size</span> <span class="ow">or</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_train_size</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_test_size</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_size</span>
+        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_size</span> <span class="o">&gt;</span> <span class="mi">0</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_seed</span> <span class="o">=</span> <span class="n">seed</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_seed</span><span class="p">)</span>
+
+        <span class="c1"># Make train/test/val split</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_test_indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_make_train_test_val_split</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">test_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rng</span><span class="p">)</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Subsample the train/test/val data (if requested)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_subsampling</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">subsampling</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">subsample</span><span class="p">(</span><span class="n">subsampling</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">data</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span>
+
+    <span class="nd">@data</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_data</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+        <span class="c1"># Check if task columns are in the data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">check_task_columns_are_available</span><span class="p">(</span>
+            <span class="n">new_data</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Update data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="n">new_data</span>
+
+        <span class="c1"># Reset train/test/val indices</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_test_indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_make_train_test_val_split</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">test_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rng</span><span class="p">)</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Set subsampling to None</span>
+        <span class="c1"># (data was manually set, any subsampling will have to be re-done)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_subsampling</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">task</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TaskMetadata</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_task</span>
+
+    <span class="nd">@task</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">task</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_task</span><span class="p">:</span> <span class="n">TaskMetadata</span><span class="p">):</span>
+        <span class="c1"># Check if task columns are in the data</span>
+        <span class="n">new_task</span><span class="o">.</span><span class="n">check_task_columns_are_available</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_task</span> <span class="o">=</span> <span class="n">new_task</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">train_size</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_size</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">test_size</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_test_size</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">val_size</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_size</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">subsampling</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+        <span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_subsampling&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">seed</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_seed</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">name</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;A unique name for this dataset.&quot;&quot;&quot;</span>
+        <span class="n">subsampling_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;subsampled-</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">subsampling</span><span class="si">:</span><span class="s2">.3</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">subsampling</span> <span class="k">else</span> <span class="s2">&quot;full&quot;</span>
+        <span class="n">seed_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;seed-</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_seed</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="n">hash_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;hash-</span><span class="si">{</span><span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">subsampling_str</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">seed_str</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">hash_str</span><span class="si">}</span><span class="s2">&quot;</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_make_train_test_val_split</span><span class="p">(</span>
+        <span class="n">data</span><span class="p">,</span>
+        <span class="n">test_size</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
+        <span class="n">val_size</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
+        <span class="n">rng</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">Generator</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span>
+        <span class="c1"># Permute indices</span>
+        <span class="n">indices</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+
+        <span class="c1"># Split train/test</span>
+        <span class="n">train_size</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">test_size</span> <span class="o">-</span> <span class="n">val_size</span>
+        <span class="n">train_indices</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[:</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span> <span class="o">*</span> <span class="n">train_size</span><span class="p">)]</span>
+        <span class="n">test_indices</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span>
+            <span class="nb">len</span><span class="p">(</span><span class="n">train_indices</span><span class="p">):</span>
+            <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">train_size</span> <span class="o">+</span> <span class="n">test_size</span><span class="p">))]</span>
+
+        <span class="c1"># Split val if requested</span>
+        <span class="k">if</span> <span class="n">val_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">val_size</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">val_indices</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span>
+                <span class="nb">len</span><span class="p">(</span><span class="n">train_indices</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_indices</span><span class="p">):]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">val_indices</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="n">train_indices</span><span class="p">,</span>
+            <span class="n">test_indices</span><span class="p">,</span>
+            <span class="n">val_indices</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_subsample_train_test_val_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">subsampling</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Subsample the dataset in-place.&quot;&quot;&quot;</span>
+
+        <span class="c1"># Check argument is valid</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">is_valid_number</span><span class="p">(</span><span class="n">subsampling</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="p">(</span><span class="mi">0</span> <span class="o">&lt;</span> <span class="n">subsampling</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;`subsampling=</span><span class="si">{</span><span class="n">subsampling</span><span class="si">}</span><span class="s2">` must be in the range (0, 1]&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Update train/test/val indices</span>
+        <span class="n">new_train_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">)</span> <span class="o">*</span> <span class="n">subsampling</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">)</span>
+        <span class="n">new_test_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_test_indices</span><span class="p">)</span> <span class="o">*</span> <span class="n">subsampling</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">[:</span> <span class="n">new_train_size</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_test_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_test_indices</span><span class="p">[:</span> <span class="n">new_test_size</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">new_val_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span><span class="p">)</span> <span class="o">*</span> <span class="n">subsampling</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span><span class="p">[:</span> <span class="n">new_val_size</span><span class="p">]</span>
+
+        <span class="c1"># Log new dataset size</span>
+        <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Train size: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">)</span><span class="si">}</span><span class="s2">, &quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;Test size:  </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_test_indices</span><span class="p">)</span><span class="si">}</span><span class="s2">, &quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;Val size:   </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span><span class="p">)</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span><span class="w"> </span><span class="ow">is</span><span class="w"> </span><span class="ow">not</span><span class="w"> </span><span class="kc">None</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="mi">0</span><span class="si">}</span><span class="s2">;&quot;</span>
+        <span class="p">)</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span>
+
+<div class="viewcode-block" id="Dataset.subsample">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.dataset.Dataset.subsample">[docs]</a>
+    <span class="k">def</span> <span class="nf">subsample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">subsampling</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Subsamples this dataset in-place.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">subsampling</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Got `subsampling=</span><span class="si">{</span><span class="n">subsampling</span><span class="si">}</span><span class="s2">`, skipping...&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="bp">self</span>
+
+        <span class="c1"># Update train/test/val indices</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_subsample_train_test_val_indices</span><span class="p">(</span><span class="n">subsampling</span><span class="p">)</span>
+
+        <span class="c1"># Update subsampling factor</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_subsampling</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_subsampling</span> <span class="ow">or</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">subsampling</span>
+        <span class="k">return</span> <span class="bp">self</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_filter_inplace</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">population_feature_values</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;Dataset&quot;</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Subset the dataset in-place: keep only samples with the given feature values.&quot;&quot;&quot;</span>
+        <span class="c1"># Check argument is of valid type</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">population_feature_values</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Invalid `population_feature_values` type: &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">population_feature_values</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Check argument keys are valid columns</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">population_feature_values</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Invalid `population_feature_values` keys; columns don&#39;t exist &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;in the dataset: </span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="n">population_feature_values</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Create boolean filter based on the given feature values</span>
+        <span class="n">population_filter</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">population_feature_values</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="n">population_filter</span> <span class="o">&amp;=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">==</span> <span class="n">value</span><span class="p">)</span>
+
+        <span class="c1"># Update train/test/val indices</span>
+        <span class="n">train_pop_filter</span> <span class="o">=</span> <span class="n">population_filter</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">]</span>
+        <span class="n">test_pop_filter</span> <span class="o">=</span> <span class="n">population_filter</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_test_indices</span><span class="p">]</span>
+        <span class="n">val_pop_filter</span> <span class="o">=</span> <span class="n">population_filter</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">[</span><span class="n">train_pop_filter</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_test_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_test_indices</span><span class="p">[</span><span class="n">test_pop_filter</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span><span class="p">[</span><span class="n">val_pop_filter</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span>
+
+        <span class="k">return</span> <span class="bp">self</span>
+
+<div class="viewcode-block" id="Dataset.filter">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.dataset.Dataset.filter">[docs]</a>
+    <span class="k">def</span> <span class="nf">filter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">population_feature_values</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Filter dataset rows in-place.&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_filter_inplace</span><span class="p">(</span><span class="n">population_feature_values</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Dataset.get_features_data">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.dataset.Dataset.get_features_data">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_features_data</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">features</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="Dataset.get_target_data">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.dataset.Dataset.get_target_data">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_target_data</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">get_target</span><span class="p">()]</span></div>
+
+
+<div class="viewcode-block" id="Dataset.get_sensitive_attribute_data">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.dataset.Dataset.get_sensitive_attribute_data">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_sensitive_attribute_data</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">sensitive_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">sensitive_attribute</span><span class="p">]</span>
+        <span class="k">return</span> <span class="kc">None</span></div>
+
+
+<div class="viewcode-block" id="Dataset.get_data_split">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.dataset.Dataset.get_data_split">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_data_split</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">split</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="n">split</span> <span class="o">==</span> <span class="s2">&quot;train&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_train</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="n">split</span> <span class="o">==</span> <span class="s2">&quot;test&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_test</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="n">split</span> <span class="o">==</span> <span class="s2">&quot;val&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_val</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid split &#39;</span><span class="si">{</span><span class="n">split</span><span class="si">}</span><span class="s2">&#39;&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Dataset.get_train">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.dataset.Dataset.get_train">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_train</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">train_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">train_data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">features</span><span class="p">],</span> <span class="n">train_data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">get_target</span><span class="p">()]</span></div>
+
+
+<div class="viewcode-block" id="Dataset.sample_n_train_examples">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.dataset.Dataset.sample_n_train_examples">[docs]</a>
+    <span class="k">def</span> <span class="nf">sample_n_train_examples</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">reuse_examples</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return a set of samples from the training set.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        n : int</span>
+<span class="sd">            The number of example rows to return.</span>
+<span class="sd">        reuse_examples : bool, optional</span>
+<span class="sd">            Whether to reuse the same examples for consistency. By default will</span>
+<span class="sd">            sample new examples each time (`reuse_examples=False`).</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        X, y : tuple[pd.DataFrame, pd.Series]</span>
+<span class="sd">            The features and target data for the sampled examples.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># TODO: make sure examples are class-balanced?</span>
+        <span class="k">if</span> <span class="n">reuse_examples</span><span class="p">:</span>
+            <span class="n">example_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">[:</span><span class="n">n</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">example_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">example_indices</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">features</span><span class="p">],</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">example_indices</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">get_target</span><span class="p">()],</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Dataset.get_test">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.dataset.Dataset.get_test">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_test</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">test_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_test_indices</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">test_data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">features</span><span class="p">],</span> <span class="n">test_data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">get_target</span><span class="p">()]</span></div>
+
+
+<div class="viewcode-block" id="Dataset.get_val">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.dataset.Dataset.get_val">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_val</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">None</span>
+        <span class="n">val_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">val_data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">features</span><span class="p">],</span> <span class="n">val_data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">get_target</span><span class="p">()]</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the i-th training sample.&quot;&quot;&quot;</span>
+        <span class="n">curr_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">curr_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">curr_indices</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">curr_data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">features</span><span class="p">],</span> <span class="n">curr_data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">get_target</span><span class="p">()]</span>
+
+    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Iterates over the training data.&quot;&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">)):</span>
+            <span class="k">yield</span> <span class="bp">self</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="n">hashable_params</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;data_shape&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
+            <span class="s2">&quot;task&quot;</span><span class="p">:</span> <span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">task</span><span class="p">),</span>
+            <span class="s2">&quot;train_size&quot;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_train_indices</span><span class="p">),</span>
+            <span class="s2">&quot;test_size&quot;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_test_indices</span><span class="p">),</span>
+            <span class="s2">&quot;val_size&quot;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_indices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="mi">0</span><span class="p">,</span>
+            <span class="s2">&quot;subsampling&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">subsampling</span> <span class="ow">or</span> <span class="mi">1</span><span class="p">,</span>
+            <span class="s2">&quot;seed&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">seed</span><span class="p">,</span>
+        <span class="p">}</span>
+
+        <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">hash_dict</span><span class="p">(</span><span class="n">hashable_params</span><span class="p">),</span> <span class="mi">16</span><span class="p">)</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
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diff --git a/_modules/folktexts/evaluation.html b/_modules/folktexts/evaluation.html
new file mode 100644
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+++ b/_modules/folktexts/evaluation.html
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+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
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+  <meta charset="utf-8" />
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for folktexts.evaluation</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Module to map risk-estimates to a variety of evaluation metrics.</span>
+
+<span class="sd">Notes</span>
+<span class="sd">-----</span>
+<span class="sd">Code based on the `error_parity.evaluation` module,</span>
+<span class="sd">at: https://github.com/socialfoundations/error-parity/blob/main/error_parity/evaluation.py</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">statistics</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Optional</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">netcal.metrics</span> <span class="kn">import</span> <span class="n">ECE</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">brier_score_loss</span><span class="p">,</span> <span class="n">confusion_matrix</span><span class="p">,</span> <span class="n">log_loss</span><span class="p">,</span> <span class="n">roc_auc_score</span><span class="p">,</span> <span class="n">roc_curve</span>
+
+<span class="kn">from</span> <span class="nn">._utils</span> <span class="kn">import</span> <span class="n">is_valid_number</span><span class="p">,</span> <span class="n">join_dictionaries</span><span class="p">,</span> <span class="n">safe_division</span>
+
+
+<div class="viewcode-block" id="evaluate_binary_predictions">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.evaluation.evaluate_binary_predictions">[docs]</a>
+<span class="k">def</span> <span class="nf">evaluate_binary_predictions</span><span class="p">(</span><span class="n">y_true</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Evaluates the provided binary predictions on common performance metrics.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    y_true : np.ndarray</span>
+<span class="sd">        The true class labels.</span>
+<span class="sd">    y_pred : np.ndarray</span>
+<span class="sd">        The binary predictions.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    dict</span>
+<span class="sd">        A dictionary with key-value pairs of (metric name, metric value).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Compute confusion matrix</span>
+    <span class="n">tn</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">tp</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+    <span class="n">tn</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">tp</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="p">(</span><span class="n">tn</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">tp</span><span class="p">))</span>
+
+    <span class="n">total</span> <span class="o">=</span> <span class="n">tn</span> <span class="o">+</span> <span class="n">fp</span> <span class="o">+</span> <span class="n">fn</span> <span class="o">+</span> <span class="n">tp</span>
+    <span class="n">pred_pos</span> <span class="o">=</span> <span class="n">tp</span> <span class="o">+</span> <span class="n">fp</span>
+    <span class="n">pred_neg</span> <span class="o">=</span> <span class="n">tn</span> <span class="o">+</span> <span class="n">fn</span>
+    <span class="k">assert</span> <span class="n">pred_pos</span> <span class="o">+</span> <span class="n">pred_neg</span> <span class="o">==</span> <span class="n">total</span>
+
+    <span class="n">label_pos</span> <span class="o">=</span> <span class="n">tp</span> <span class="o">+</span> <span class="n">fn</span>
+    <span class="n">label_neg</span> <span class="o">=</span> <span class="n">tn</span> <span class="o">+</span> <span class="n">fp</span>
+    <span class="k">assert</span> <span class="n">label_pos</span> <span class="o">+</span> <span class="n">label_neg</span> <span class="o">==</span> <span class="n">total</span>
+
+    <span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="c1"># Accuracy</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">tp</span> <span class="o">+</span> <span class="n">tn</span><span class="p">)</span> <span class="o">/</span> <span class="n">total</span>
+
+    <span class="c1"># True Positive Rate (Recall)</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;tpr&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">safe_division</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">label_pos</span><span class="p">,</span> <span class="n">worst_result</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="c1"># False Negative Rate (1 - TPR)</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;fnr&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">safe_division</span><span class="p">(</span><span class="n">fn</span><span class="p">,</span> <span class="n">label_pos</span><span class="p">,</span> <span class="n">worst_result</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">assert</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;tpr&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;fnr&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span>
+
+    <span class="c1"># False Positive Rate</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;fpr&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">safe_division</span><span class="p">(</span><span class="n">fp</span><span class="p">,</span> <span class="n">label_neg</span><span class="p">,</span> <span class="n">worst_result</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># True Negative Rate</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;tnr&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">safe_division</span><span class="p">(</span><span class="n">tn</span><span class="p">,</span> <span class="n">label_neg</span><span class="p">,</span> <span class="n">worst_result</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="k">assert</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;tnr&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;fpr&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span>
+
+    <span class="c1"># Balanced accuracy</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;balanced_accuracy&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;tpr&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;tnr&quot;</span><span class="p">])</span>
+
+    <span class="c1"># Precision</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;precision&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">safe_division</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">pred_pos</span><span class="p">,</span> <span class="n">worst_result</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="c1"># Positive Prediction Rate</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;ppr&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">safe_division</span><span class="p">(</span><span class="n">pred_pos</span><span class="p">,</span> <span class="n">total</span><span class="p">,</span> <span class="n">worst_result</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">results</span></div>
+
+
+
+<div class="viewcode-block" id="evaluate_binary_predictions_fairness">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.evaluation.evaluate_binary_predictions_fairness">[docs]</a>
+<span class="k">def</span> <span class="nf">evaluate_binary_predictions_fairness</span><span class="p">(</span>
+    <span class="n">y_true</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">y_pred</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">sensitive_attribute</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">return_groupwise_metrics</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">min_group_size</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.04</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Evaluates fairness of the given predictions.</span>
+
+<span class="sd">    Fairness metrics are computed as the ratios between group-wise performance</span>
+<span class="sd">    metrics.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    y_true : np.ndarray</span>
+<span class="sd">        The true class labels.</span>
+<span class="sd">    y_pred : np.ndarray</span>
+<span class="sd">        The discretized predictions.</span>
+<span class="sd">    sensitive_attribute : np.ndarray</span>
+<span class="sd">        The sensitive attribute (protected group membership).</span>
+<span class="sd">    return_groupwise_metrics : bool, optional</span>
+<span class="sd">        Whether to return group-wise performance metrics (bool: True) or only</span>
+<span class="sd">        the ratios between these metrics (bool: False), by default False.</span>
+<span class="sd">    min_group_size : float, optional</span>
+<span class="sd">        The minimum fraction of samples (as a fraction of the total number of</span>
+<span class="sd">        samples) that a group must have to be considered for fairness</span>
+<span class="sd">        evaluation, by default 0.04. This is meant to avoid evaluating metrics</span>
+<span class="sd">        on very small groups which leads to noisy and inconsistent results.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    dict</span>
+<span class="sd">        A dictionary with key-value pairs of (metric name, metric value).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># All unique values for the sensitive attribute</span>
+    <span class="n">unique_groups</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">sensitive_attribute</span><span class="p">)</span>
+
+    <span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">groupwise_metrics</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">unique_metrics</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+
+    <span class="c1"># Helper to compute key/name of a group-wise metric</span>
+    <span class="k">def</span> <span class="nf">group_metric_name</span><span class="p">(</span><span class="n">metric_name</span><span class="p">,</span> <span class="n">group_name</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_name</span><span class="si">}</span><span class="s2">_group=</span><span class="si">{</span><span class="n">group_name</span><span class="si">}</span><span class="s2">&quot;</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">unique_groups</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Found a single unique sensitive attribute: </span><span class="si">{</span><span class="n">unique_groups</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">s_value</span> <span class="ow">in</span> <span class="n">unique_groups</span><span class="p">:</span>
+        <span class="c1"># Indices of samples that belong to the current group</span>
+        <span class="n">group_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">sensitive_attribute</span> <span class="o">==</span> <span class="n">s_value</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">group_indices</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">min_group_size</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_true</span><span class="p">):</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Skipping group </span><span class="si">{</span><span class="n">s_value</span><span class="si">}</span><span class="s2"> with </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">group_indices</span><span class="p">)</span><span class="si">}</span><span class="s2"> samples&quot;</span><span class="p">)</span>
+            <span class="k">continue</span>
+
+        <span class="c1"># Filter labels and predictions for samples of the current group</span>
+        <span class="n">group_labels</span> <span class="o">=</span> <span class="n">y_true</span><span class="p">[</span><span class="n">group_indices</span><span class="p">]</span>
+        <span class="n">group_preds</span> <span class="o">=</span> <span class="n">y_pred</span><span class="p">[</span><span class="n">group_indices</span><span class="p">]</span>
+
+        <span class="c1"># Evaluate group-wise performance</span>
+        <span class="n">curr_group_metrics</span> <span class="o">=</span> <span class="n">evaluate_binary_predictions</span><span class="p">(</span><span class="n">group_labels</span><span class="p">,</span> <span class="n">group_preds</span><span class="p">)</span>
+
+        <span class="c1"># Add group-wise metrics to the dictionary</span>
+        <span class="n">groupwise_metrics</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
+            <span class="p">{</span>
+                <span class="n">group_metric_name</span><span class="p">(</span><span class="n">metric_name</span><span class="p">,</span> <span class="n">s_value</span><span class="p">):</span> <span class="n">metric_value</span>
+                <span class="k">for</span> <span class="n">metric_name</span><span class="p">,</span> <span class="n">metric_value</span> <span class="ow">in</span> <span class="n">curr_group_metrics</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+            <span class="p">}</span>
+        <span class="p">)</span>
+
+        <span class="n">unique_metrics</span> <span class="o">=</span> <span class="n">unique_metrics</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="n">curr_group_metrics</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+
+    <span class="c1"># Compute ratios and absolute diffs</span>
+    <span class="k">for</span> <span class="n">metric_name</span> <span class="ow">in</span> <span class="n">unique_metrics</span><span class="p">:</span>
+        <span class="n">curr_metric_results</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">groupwise_metrics</span><span class="p">[</span><span class="n">curr_group_metric_name</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">group_name</span> <span class="ow">in</span> <span class="n">unique_groups</span>
+            <span class="k">if</span> <span class="p">(</span><span class="n">curr_group_metric_name</span> <span class="o">:=</span> <span class="n">group_metric_name</span><span class="p">(</span><span class="n">metric_name</span><span class="p">,</span> <span class="n">group_name</span><span class="p">))</span> <span class="ow">in</span> <span class="n">groupwise_metrics</span>
+        <span class="p">]</span>
+
+        <span class="c1"># Metrics&#39; ratio</span>
+        <span class="n">ratio_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_name</span><span class="si">}</span><span class="s2">_ratio&quot;</span>
+
+        <span class="c1"># NOTE: should this ratio be computed w.r.t. global performance?</span>
+        <span class="c1"># - i.e., min(curr_metric_results) / global_curr_metric_result;</span>
+        <span class="c1"># - same question for the absolute diff calculations;</span>
+        <span class="n">results</span><span class="p">[</span><span class="n">ratio_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">safe_division</span><span class="p">(</span>
+            <span class="nb">min</span><span class="p">(</span><span class="n">curr_metric_results</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">curr_metric_results</span><span class="p">),</span>
+            <span class="n">worst_result</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Metrics&#39; absolute difference</span>
+        <span class="n">diff_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_name</span><span class="si">}</span><span class="s2">_diff&quot;</span>
+        <span class="n">results</span><span class="p">[</span><span class="n">diff_name</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">curr_metric_results</span><span class="p">)</span> <span class="o">-</span> <span class="nb">min</span><span class="p">(</span><span class="n">curr_metric_results</span><span class="p">)</span>
+
+    <span class="c1"># ** Equalized odds **</span>
+    <span class="c1"># default value: use maximum constraint violation for TPR and FPR equality</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;equalized_odds_ratio&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span>
+        <span class="n">results</span><span class="p">[</span><span class="s2">&quot;fnr_ratio&quot;</span><span class="p">],</span>
+        <span class="n">results</span><span class="p">[</span><span class="s2">&quot;fpr_ratio&quot;</span><span class="p">],</span>
+    <span class="p">)</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;equalized_odds_diff&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span>
+        <span class="n">results</span><span class="p">[</span><span class="s2">&quot;tpr_diff&quot;</span><span class="p">],</span>  <span class="c1"># same as FNR diff</span>
+        <span class="n">results</span><span class="p">[</span><span class="s2">&quot;fpr_diff&quot;</span><span class="p">],</span>  <span class="c1"># same as TNR diff</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Optionally, return group-wise metrics as well</span>
+    <span class="k">if</span> <span class="n">return_groupwise_metrics</span><span class="p">:</span>
+        <span class="n">results</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">groupwise_metrics</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">results</span></div>
+
+
+
+<div class="viewcode-block" id="compute_best_threshold">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.evaluation.compute_best_threshold">[docs]</a>
+<span class="k">def</span> <span class="nf">compute_best_threshold</span><span class="p">(</span>
+    <span class="n">y_true</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">y_pred_scores</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="o">*</span><span class="p">,</span>
+    <span class="n">false_pos_cost</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+    <span class="n">false_neg_cost</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computes the binarization threshold that maximizes accuracy.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    y_true : np.ndarray</span>
+<span class="sd">        The true class labels.</span>
+<span class="sd">    y_pred_scores : np.ndarray</span>
+<span class="sd">        The predicted risk scores.</span>
+<span class="sd">    false_pos_cost : float, optional</span>
+<span class="sd">        The cost of a false positive error, by default 1.0</span>
+<span class="sd">    false_neg_cost : float, optional</span>
+<span class="sd">        The cost of a false negative error, by default 1.0</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    best_threshold : float</span>
+<span class="sd">        The threshold value that maximizes accuracy for the given predictions.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Compute TPR and FPR for all possible thresholds</span>
+    <span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred_scores</span><span class="p">)</span>
+
+    <span class="c1"># Compute the cost of each threshold</span>
+    <span class="n">costs</span> <span class="o">=</span> <span class="n">false_pos_cost</span> <span class="o">*</span> <span class="n">fpr</span> <span class="o">+</span> <span class="n">false_neg_cost</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">tpr</span><span class="p">)</span>
+
+    <span class="c1"># Get the threshold that minimizes the cost</span>
+    <span class="n">best_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">costs</span><span class="p">)]</span>
+    <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">best_threshold</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="evaluate_predictions">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.evaluation.evaluate_predictions">[docs]</a>
+<span class="k">def</span> <span class="nf">evaluate_predictions</span><span class="p">(</span>
+    <span class="n">y_true</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">y_pred_scores</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="o">*</span><span class="p">,</span>
+    <span class="n">sensitive_attribute</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;best&quot;</span><span class="p">,</span>
+    <span class="n">model_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Evaluates predictions on common performance and fairness metrics.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    y_true : np.ndarray</span>
+<span class="sd">        The true class labels.</span>
+<span class="sd">    y_pred_scores : np.ndarray</span>
+<span class="sd">        The predicted scores.</span>
+<span class="sd">    sensitive_attribute: np.ndarray, optional</span>
+<span class="sd">        The sensitive attribute data. Will compute fairness metrics if provided.</span>
+<span class="sd">    threshold : float | str, optional</span>
+<span class="sd">        The threshold to use for binarizing the predictions, or &quot;best&quot; to infer</span>
+<span class="sd">        which threshold maximizes accuracy.</span>
+<span class="sd">    model_name : str, optional</span>
+<span class="sd">        The name of the model to be used on the plots, by default None.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    results : dict</span>
+<span class="sd">        A dictionary with key-value pairs of (metric name, metric value).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Compute threshold if necessary</span>
+    <span class="k">if</span> <span class="n">threshold</span> <span class="o">==</span> <span class="s2">&quot;best&quot;</span><span class="p">:</span>
+        <span class="n">threshold</span> <span class="o">=</span> <span class="n">compute_best_threshold</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred_scores</span><span class="p">)</span>
+    <span class="k">assert</span> <span class="n">is_valid_number</span><span class="p">(</span><span class="n">threshold</span><span class="p">)</span> <span class="ow">and</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">threshold</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">,</span> \
+        <span class="sa">f</span><span class="s2">&quot;Invalid threshold: </span><span class="si">{</span><span class="n">threshold</span><span class="si">}</span><span class="s2">&quot;</span>
+
+    <span class="c1"># Save initial results&#39; statistics</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s2">&quot;threshold&quot;</span><span class="p">:</span> <span class="n">threshold</span><span class="p">,</span>
+        <span class="s2">&quot;n_samples&quot;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_true</span><span class="p">),</span>
+        <span class="s2">&quot;n_positives&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y_true</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
+        <span class="s2">&quot;n_negatives&quot;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_true</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y_true</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
+        <span class="s2">&quot;model_name&quot;</span><span class="p">:</span> <span class="n">model_name</span><span class="p">,</span>
+    <span class="p">}</span>
+
+    <span class="c1"># Compute binary predictions using the default threshold</span>
+    <span class="n">y_pred_binary</span> <span class="o">=</span> <span class="p">(</span><span class="n">y_pred_scores</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+
+    <span class="c1"># Evaluate binary predictions</span>
+    <span class="n">results</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">evaluate_binary_predictions</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred_binary</span><span class="p">))</span>
+
+    <span class="c1"># Add loss functions as proxies for calibration</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;log_loss&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">log_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred_scores</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;brier_score_loss&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">brier_score_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred_scores</span><span class="p">))</span>
+
+    <span class="c1"># Evaluate fairness metrics</span>
+    <span class="k">if</span> <span class="n">sensitive_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">results</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">evaluate_binary_predictions_fairness</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred_binary</span><span class="p">,</span> <span class="n">sensitive_attribute</span><span class="p">))</span>
+
+    <span class="c1"># Compute additional metrics</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;roc_auc&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred_scores</span><span class="p">))</span>
+
+    <span class="c1"># Compute Expected Calibration Error</span>
+    <span class="c1"># TODO: re-implement ECE scorer to avoid including 10 other dependencies for this one metric...</span>
+    <span class="n">class_preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="mi">1</span> <span class="o">-</span> <span class="n">y_pred_scores</span><span class="p">,</span> <span class="n">y_pred_scores</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">n_bins</span> <span class="o">=</span> <span class="mi">10</span>
+    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;ece&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">ECE</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="n">n_bins</span><span class="p">,</span> <span class="n">equal_intervals</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">measure</span><span class="p">(</span><span class="n">class_preds</span><span class="p">,</span> <span class="n">y_true</span><span class="p">)</span>
+
+    <span class="c1"># Try to compute ECE with quantile bins</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">results</span><span class="p">[</span><span class="s2">&quot;ece_quantile&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">ECE</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="n">n_bins</span><span class="p">,</span> <span class="n">equal_intervals</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">measure</span><span class="p">(</span><span class="n">class_preds</span><span class="p">,</span> <span class="n">y_true</span><span class="p">)</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Failed to compute ECE quantile: </span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">results</span><span class="p">[</span><span class="s2">&quot;ece_quantile&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">return</span> <span class="n">results</span></div>
+
+
+
+<div class="viewcode-block" id="bootstrap_estimate">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.evaluation.bootstrap_estimate">[docs]</a>
+<span class="k">def</span> <span class="nf">bootstrap_estimate</span><span class="p">(</span>
+    <span class="n">eval_func</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">],</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]],</span>
+    <span class="o">*</span><span class="p">,</span>
+    <span class="n">y_true</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">y_pred_scores</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">sensitive_attribute</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">k</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">200</span><span class="p">,</span>
+    <span class="n">confidence_pct</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">95</span><span class="p">,</span>
+    <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">42</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computes bootstrap estimates of the given evaluation function.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    eval_func : Callable[[np.ndarray, np.ndarray, np.ndarray], dict[str, float]]</span>
+<span class="sd">        The evaluation function to run for each bootstrap sample. Must follow</span>
+<span class="sd">        the signature `eval_func(y_true, y_pred_scores, sensitive_attribute)`.</span>
+<span class="sd">    y_true : np.ndarray</span>
+<span class="sd">        The true labels.</span>
+<span class="sd">    y_pred_scores : np.ndarray</span>
+<span class="sd">        The predicted scores.</span>
+<span class="sd">    sensitive_attribute : np.ndarray, optional</span>
+<span class="sd">        Optionally, provide the sensitive attribute data to compute fairness</span>
+<span class="sd">        metrics, by default None.</span>
+<span class="sd">    k : int, optional</span>
+<span class="sd">        How many bootstrap samples to draw, by default 200.</span>
+<span class="sd">    confidence_pct : float, optional</span>
+<span class="sd">        The confidence interval to use, in percentage, by default 95.</span>
+<span class="sd">    seed : int, optional</span>
+<span class="sd">        The random seed, by default 42.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    results : dict[str, float]</span>
+<span class="sd">        A dictionary containing bootstrap estimates for a variety of metrics.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_true</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_pred_scores</span><span class="p">)</span>
+    <span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
+
+    <span class="c1"># Draw k bootstrap samples with replacement</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">k</span><span class="p">):</span>
+        <span class="c1"># Indices of current bootstrap sample</span>
+        <span class="n">indices</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_true</span><span class="p">),</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">y_true</span><span class="p">))</span>
+
+        <span class="c1"># Evaluate predictions on this bootstrap sample</span>
+        <span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+            <span class="n">eval_func</span><span class="p">(</span>
+                <span class="n">y_true</span><span class="p">[</span><span class="n">indices</span><span class="p">],</span>            <span class="c1"># ground-truth labels</span>
+                <span class="n">y_pred_scores</span><span class="p">[</span><span class="n">indices</span><span class="p">],</span>     <span class="c1"># predicted risk scores</span>
+                <span class="n">sensitive_attribute</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">sensitive_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>  <span class="c1"># sensitive attributes</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+
+    <span class="c1"># Compute statistics from bootstrapped results</span>
+    <span class="n">all_metrics</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+
+    <span class="n">bt_mean</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">bt_stdev</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">bt_percentiles</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="n">low_percentile</span> <span class="o">=</span> <span class="p">(</span><span class="mi">100</span> <span class="o">-</span> <span class="n">confidence_pct</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="n">confidence_percentiles</span> <span class="o">=</span> <span class="p">[</span><span class="n">low_percentile</span><span class="p">,</span> <span class="mi">100</span> <span class="o">-</span> <span class="n">low_percentile</span><span class="p">]</span>
+
+    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">all_metrics</span><span class="p">:</span>
+        <span class="n">metric_values</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="p">[</span><span class="n">m</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">results</span><span class="p">]</span>
+
+        <span class="n">bt_mean</span><span class="p">[</span><span class="n">m</span><span class="p">]</span> <span class="o">=</span> <span class="n">statistics</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">metric_values</span><span class="p">)</span>
+        <span class="n">bt_stdev</span><span class="p">[</span><span class="n">m</span><span class="p">]</span> <span class="o">=</span> <span class="n">statistics</span><span class="o">.</span><span class="n">stdev</span><span class="p">(</span><span class="n">metric_values</span><span class="p">)</span>
+        <span class="n">bt_percentiles</span><span class="p">[</span><span class="n">m</span><span class="p">]</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">metric_values</span><span class="p">,</span> <span class="n">confidence_percentiles</span><span class="p">))</span>
+
+    <span class="c1"># Join bootstrap results for all metrics</span>
+    <span class="k">return</span> <span class="n">join_dictionaries</span><span class="p">(</span>
+        <span class="o">*</span><span class="p">(</span>
+            <span class="p">{</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric</span><span class="si">}</span><span class="s2">_mean&quot;</span><span class="p">:</span> <span class="n">bt_mean</span><span class="p">[</span><span class="n">metric</span><span class="p">],</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric</span><span class="si">}</span><span class="s2">_stdev&quot;</span><span class="p">:</span> <span class="n">bt_stdev</span><span class="p">[</span><span class="n">metric</span><span class="p">],</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric</span><span class="si">}</span><span class="s2">_low-percentile&quot;</span><span class="p">:</span> <span class="n">bt_percentiles</span><span class="p">[</span><span class="n">metric</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric</span><span class="si">}</span><span class="s2">_high-percentile&quot;</span><span class="p">:</span> <span class="n">bt_percentiles</span><span class="p">[</span><span class="n">metric</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+            <span class="p">}</span>
+            <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">bt_mean</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+        <span class="p">)</span>
+    <span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="evaluate_predictions_bootstrap">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.evaluation.evaluate_predictions_bootstrap">[docs]</a>
+<span class="k">def</span> <span class="nf">evaluate_predictions_bootstrap</span><span class="p">(</span>
+    <span class="n">y_true</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">y_pred_scores</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="o">*</span><span class="p">,</span>
+    <span class="n">sensitive_attribute</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;best&quot;</span><span class="p">,</span>
+    <span class="n">k</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">200</span><span class="p">,</span>
+    <span class="n">confidence_pct</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">95</span><span class="p">,</span>
+    <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">42</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computes bootstrap estimates of classification metrics for the given predictions.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    y_true : np.ndarray</span>
+<span class="sd">        The true labels.</span>
+<span class="sd">    y_pred_scores : np.ndarray</span>
+<span class="sd">        The score predictions.</span>
+<span class="sd">    sensitive_attribute : np.ndarray, optional</span>
+<span class="sd">        The sensitive attribute data. Will compute fairness metrics if provided.</span>
+<span class="sd">    threshold : float | str, optional</span>
+<span class="sd">        The threshold to use for binarizing the predictions, or &quot;best&quot; to infer</span>
+<span class="sd">        which threshold maximizes accuracy, by default &quot;best&quot;.</span>
+<span class="sd">    k : int, optional</span>
+<span class="sd">        How many bootstrap samples to draw, by default 200.</span>
+<span class="sd">    confidence_pct : float, optional</span>
+<span class="sd">        How large of a confidence interval to use when reporting lower and upper</span>
+<span class="sd">        bounds, by default 95 (i.e., 2.5 to 97.5 percentile of results).</span>
+<span class="sd">    seed : int, optional</span>
+<span class="sd">        The random seed, by default 42.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    results : dict[str, float]</span>
+<span class="sd">        A dictionary containing bootstrap estimates for a variety of metrics.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">bootstrap_estimate</span><span class="p">(</span>
+        <span class="n">eval_func</span><span class="o">=</span><span class="k">lambda</span> <span class="n">labels</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">sens_attr</span><span class="o">=</span><span class="kc">None</span><span class="p">:</span> <span class="n">evaluate_predictions</span><span class="p">(</span>
+            <span class="n">y_true</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span>
+            <span class="n">y_pred_scores</span><span class="o">=</span><span class="n">scores</span><span class="p">,</span>
+            <span class="n">sensitive_attribute</span><span class="o">=</span><span class="n">sens_attr</span><span class="p">,</span>
+            <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span>
+        <span class="p">),</span>
+        <span class="n">y_true</span><span class="o">=</span><span class="n">y_true</span><span class="p">,</span>
+        <span class="n">y_pred_scores</span><span class="o">=</span><span class="n">y_pred_scores</span><span class="p">,</span>
+        <span class="n">sensitive_attribute</span><span class="o">=</span><span class="n">sensitive_attribute</span><span class="p">,</span>
+        <span class="n">k</span><span class="o">=</span><span class="n">k</span><span class="p">,</span>
+        <span class="n">confidence_pct</span><span class="o">=</span><span class="n">confidence_pct</span><span class="p">,</span>
+        <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
+    <span class="p">)</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
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+          SphinxRtdTheme.Navigation.enable(true);
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+</html>
\ No newline at end of file
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new file mode 100644
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+  <h1>Source code for folktexts.llm_utils</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Common functions to use with transformer LLMs.&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">re</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoModelForCausalLM</span><span class="p">,</span> <span class="n">AutoTokenizer</span>
+
+<span class="c1"># Will warn if the sum of digit probabilities is below this threshold</span>
+<span class="n">PROB_WARN_THR</span> <span class="o">=</span> <span class="mf">0.5</span>
+
+
+<div class="viewcode-block" id="query_model_batch">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.llm_utils.query_model_batch">[docs]</a>
+<span class="k">def</span> <span class="nf">query_model_batch</span><span class="p">(</span>
+    <span class="n">text_inputs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
+    <span class="n">model</span><span class="p">:</span> <span class="n">AutoModelForCausalLM</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">:</span> <span class="n">AutoTokenizer</span><span class="p">,</span>
+    <span class="n">context_size</span><span class="p">:</span> <span class="nb">int</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Queries the model with a batch of text inputs.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    text_inputs : list[str]</span>
+<span class="sd">        The inputs to the model as a list of strings.</span>
+<span class="sd">    model : AutoModelForCausalLM</span>
+<span class="sd">        The model to query.</span>
+<span class="sd">    tokenizer : AutoTokenizer</span>
+<span class="sd">        The tokenizer used to encode the text inputs.</span>
+<span class="sd">    context_size : int</span>
+<span class="sd">        The maximum context size to consider for each input (in tokens).</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    last_token_probs : np.array</span>
+<span class="sd">        Model&#39;s last token *linear* probabilities for each input as an</span>
+<span class="sd">        np.array of shape (batch_size, vocab_size).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">model_device</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">device</span>
+
+    <span class="c1"># Tokenize</span>
+    <span class="n">token_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="o">-</span><span class="n">context_size</span><span class="p">:]</span> <span class="k">for</span> <span class="n">text</span> <span class="ow">in</span> <span class="n">text_inputs</span><span class="p">]</span>
+    <span class="n">idx_last_token</span> <span class="o">=</span> <span class="p">[</span><span class="n">tok_seq</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">tok_seq</span> <span class="ow">in</span> <span class="n">token_inputs</span><span class="p">]</span>
+
+    <span class="c1"># Pad</span>
+    <span class="n">tensor_inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">pad_sequence</span><span class="p">(</span>
+        <span class="n">token_inputs</span><span class="p">,</span>
+        <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">padding_value</span><span class="o">=</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token_id</span><span class="p">,</span>
+    <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">model_device</span><span class="p">)</span>
+
+    <span class="c1"># Mask padded context</span>
+    <span class="n">attention_mask</span> <span class="o">=</span> <span class="n">tensor_inputs</span><span class="o">.</span><span class="n">ne</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token_id</span><span class="p">)</span>
+
+    <span class="c1"># Query: run one forward pass, i.e., generate the next token</span>
+    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+        <span class="n">logits</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">input_ids</span><span class="o">=</span><span class="n">tensor_inputs</span><span class="p">,</span> <span class="n">attention_mask</span><span class="o">=</span><span class="n">attention_mask</span><span class="p">)</span><span class="o">.</span><span class="n">logits</span>
+
+    <span class="c1"># Probabilities corresponding to the last token after the prompt</span>
+    <span class="n">last_token_logits</span> <span class="o">=</span> <span class="n">logits</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">idx_last_token</span><span class="p">)),</span> <span class="n">idx_last_token</span><span class="p">]</span>
+    <span class="n">last_token_probs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">last_token_logits</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">last_token_probs</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="query_model_batch_multiple_passes">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.llm_utils.query_model_batch_multiple_passes">[docs]</a>
+<span class="k">def</span> <span class="nf">query_model_batch_multiple_passes</span><span class="p">(</span>
+    <span class="n">text_inputs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
+    <span class="n">model</span><span class="p">:</span> <span class="n">AutoModelForCausalLM</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">:</span> <span class="n">AutoTokenizer</span><span class="p">,</span>
+    <span class="n">context_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">n_passes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">digits_only</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Queries an LM for multiple forward passes.</span>
+
+<span class="sd">    Greedy token search over multiple forward passes: Each forward pass takes</span>
+<span class="sd">    the highest likelihood token from the previous pass.</span>
+
+<span class="sd">    NOTE: could use model.generate in the future!</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    text_inputs : list[str]</span>
+<span class="sd">        The batch inputs to the model as a list of strings.</span>
+<span class="sd">    model : AutoModelForCausalLM</span>
+<span class="sd">        The model to query.</span>
+<span class="sd">    tokenizer : AutoTokenizer</span>
+<span class="sd">        The tokenizer used to encode the text inputs.</span>
+<span class="sd">    context_size : int</span>
+<span class="sd">        The maximum context size to consider for each input (in tokens).</span>
+<span class="sd">    n_passes : int, optional</span>
+<span class="sd">        The number of forward passes to run.</span>
+<span class="sd">    digits_only : bool, optional</span>
+<span class="sd">        Whether to only sample for digit tokens.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    last_token_probs : np.array</span>
+<span class="sd">        Last token *linear* probabilities for each forward pass, for each text</span>
+<span class="sd">        in the input batch. The output has shape (batch_size, n_passes, vocab_size).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># If `digits_only`, get token IDs for digit tokens</span>
+    <span class="n">allowed_tokens_filter</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">vocab</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">digits_only</span><span class="p">:</span>
+        <span class="n">allowed_token_ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
+            <span class="n">tok_id</span>
+            <span class="k">for</span> <span class="n">token</span><span class="p">,</span> <span class="n">tok_id</span> <span class="ow">in</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">vocab</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">token</span><span class="o">.</span><span class="n">isdecimal</span><span class="p">()</span>
+        <span class="p">])</span>
+
+        <span class="n">allowed_tokens_filter</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">vocab</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
+        <span class="n">allowed_tokens_filter</span><span class="p">[</span><span class="n">allowed_token_ids</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="c1"># Current text batch</span>
+    <span class="n">current_batch</span> <span class="o">=</span> <span class="n">text_inputs</span>
+
+    <span class="c1"># For each forward pass, add one token to each text in the batch</span>
+    <span class="n">last_token_probs</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="nb">iter</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_passes</span><span class="p">):</span>
+        <span class="c1"># Query the model with the current batch</span>
+        <span class="n">current_probs</span> <span class="o">=</span> <span class="n">query_model_batch</span><span class="p">(</span><span class="n">current_batch</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">context_size</span><span class="p">)</span>
+
+        <span class="c1"># Filter out probabilities for tokens that are not allowed</span>
+        <span class="n">current_probs</span><span class="p">[:,</span> <span class="o">~</span><span class="n">allowed_tokens_filter</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="c1"># Sanity check digit probabilities</span>
+        <span class="k">if</span> <span class="nb">iter</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">digits_only</span><span class="p">:</span>
+            <span class="n">total_digit_probs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">current_probs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">probs</span> <span class="o">&lt;</span> <span class="n">PROB_WARN_THR</span> <span class="k">for</span> <span class="n">probs</span> <span class="ow">in</span> <span class="n">total_digit_probs</span><span class="p">):</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Digit probabilities are too low: </span><span class="si">{</span><span class="n">total_digit_probs</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Add the highest likelihood token to each text in the batch</span>
+        <span class="n">next_tokens</span> <span class="o">=</span> <span class="p">[</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">probs</span><span class="p">)])</span> <span class="k">for</span> <span class="n">probs</span> <span class="ow">in</span> <span class="n">current_probs</span><span class="p">]</span>
+        <span class="n">current_batch</span> <span class="o">=</span> <span class="p">[</span><span class="n">text</span> <span class="o">+</span> <span class="n">next_token</span> <span class="k">for</span> <span class="n">text</span><span class="p">,</span> <span class="n">next_token</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">current_batch</span><span class="p">,</span> <span class="n">next_tokens</span><span class="p">)]</span>
+
+        <span class="c1"># Store the probabilities of the last token for each text in the batch</span>
+        <span class="n">last_token_probs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">current_probs</span><span class="p">)</span>
+
+    <span class="c1"># Cast output to np.array with correct shape</span>
+    <span class="n">last_token_probs_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">last_token_probs</span><span class="p">)</span>
+    <span class="n">last_token_probs_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">last_token_probs_array</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="k">assert</span> <span class="n">last_token_probs_array</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">text_inputs</span><span class="p">),</span> <span class="n">n_passes</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">vocab</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">last_token_probs_array</span></div>
+
+
+
+<div class="viewcode-block" id="add_pad_token">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.llm_utils.add_pad_token">[docs]</a>
+<span class="k">def</span> <span class="nf">add_pad_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Add a pad token to the model and tokenizer if it doesn&#39;t already exist.</span>
+
+<span class="sd">    Here we&#39;re using the end-of-sentence token as the pad token. Both the model</span>
+<span class="sd">    weights and tokenizer vocabulary are untouched.</span>
+
+<span class="sd">    Another possible way would be to add a new token `[PAD]` to the tokenizer</span>
+<span class="sd">    and update the tokenizer vocabulary and model weight embeddings accordingly.</span>
+<span class="sd">    The embedding for the new pad token would be the average of all other</span>
+<span class="sd">    embeddings.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">tokenizer</span><span class="o">.</span><span class="n">add_special_tokens</span><span class="p">({</span><span class="s1">&#39;pad_token&#39;</span><span class="p">:</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token</span><span class="p">})</span></div>
+
+
+
+<div class="viewcode-block" id="is_bf16_compatible">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.llm_utils.is_bf16_compatible">[docs]</a>
+<span class="k">def</span> <span class="nf">is_bf16_compatible</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Checks if the current environment is bfloat16 compatible.&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_bf16_supported</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="load_model_tokenizer">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.llm_utils.load_model_tokenizer">[docs]</a>
+<span class="k">def</span> <span class="nf">load_model_tokenizer</span><span class="p">(</span><span class="n">model_name_or_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">AutoModelForCausalLM</span><span class="p">,</span> <span class="n">AutoTokenizer</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Load a model and tokenizer from the given local path (or using the model name).</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    model_name_or_path : str | Path</span>
+<span class="sd">        Model name or local path to the model folder.</span>
+<span class="sd">    kwargs : dict</span>
+<span class="sd">        Additional keyword arguments to pass to the model `from_pretrained` call.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    tuple[AutoModelForCausalLM, AutoTokenizer]</span>
+<span class="sd">        The loaded model and tokenizer, respectively.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Loading model &#39;</span><span class="si">{</span><span class="n">model_name_or_path</span><span class="si">}</span><span class="s2">&#39;&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Load tokenizer from disk</span>
+    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_name_or_path</span><span class="p">)</span>
+
+    <span class="c1"># Set default keyword arguments for loading the pretrained model</span>
+    <span class="n">model_kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+        <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span> <span class="k">if</span> <span class="n">is_bf16_compatible</span><span class="p">()</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span>
+        <span class="n">trust_remote_code</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">device_map</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">model_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="c1"># Load model from disk</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+        <span class="n">model_name_or_path</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">model_kwargs</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Add pad token to the tokenizer if it doesn&#39;t already exist</span>
+    <span class="n">add_pad_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">)</span>
+
+    <span class="c1"># Move model to the correct device</span>
+    <span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span>
+    <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
+        <span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cuda&quot;</span>
+    <span class="k">elif</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">mps</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
+        <span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;mps&quot;</span>
+    <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Moving model to device: </span><span class="si">{</span><span class="n">device</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="n">device</span><span class="p">:</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span></div>
+
+
+
+<div class="viewcode-block" id="get_model_folder_path">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.llm_utils.get_model_folder_path">[docs]</a>
+<span class="k">def</span> <span class="nf">get_model_folder_path</span><span class="p">(</span><span class="n">model_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">root_dir</span><span class="o">=</span><span class="s2">&quot;/tmp&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the folder where the model is saved.&quot;&quot;&quot;</span>
+    <span class="n">folder_name</span> <span class="o">=</span> <span class="n">model_name</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">,</span> <span class="s2">&quot;--&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">root_dir</span><span class="p">)</span> <span class="o">/</span> <span class="n">folder_name</span><span class="p">)</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="get_model_size_B">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.llm_utils.get_model_size_B">[docs]</a>
+<span class="k">def</span> <span class="nf">get_model_size_B</span><span class="p">(</span><span class="n">model_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">default</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the model size from the model name, in Billions of parameters.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">regex</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;((?P&lt;times&gt;\d+)[xX])?(?P&lt;size&gt;\d+)[bB]&quot;</span><span class="p">,</span> <span class="n">model_name</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">regex</span><span class="p">:</span>
+        <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">regex</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="s2">&quot;size&quot;</span><span class="p">))</span> <span class="o">*</span> <span class="nb">int</span><span class="p">(</span><span class="n">regex</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="s2">&quot;times&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">default</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">default</span>
+
+    <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Could not infer model size from name &#39;</span><span class="si">{</span><span class="n">model_name</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">default</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
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diff --git a/_modules/folktexts/plotting.html b/_modules/folktexts/plotting.html
new file mode 100644
index 0000000..4e3b936
--- /dev/null
+++ b/_modules/folktexts/plotting.html
@@ -0,0 +1,377 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>folktexts.plotting &mdash; folktexts 0.0.25 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=80d5e7a1" />
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+      <script src="../../_static/custom.js?v=882eb7ae"></script>
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+<li class="toctree-l1"><a class="reference internal" href="../../readme.html">Readme file</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../source/modules.html">API reference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../notebooks.html">Example notebooks</a></li>
+</ul>
+
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+      <li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">folktexts.plotting</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for folktexts.plotting</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Module to plot evaluation results.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">sklearn.calibration</span> <span class="kn">import</span> <span class="n">CalibrationDisplay</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">RocCurveDisplay</span>
+
+<span class="kn">from</span> <span class="nn">._utils</span> <span class="kn">import</span> <span class="n">safe_division</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+    <span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+    <span class="n">HAS_PLOTTING_DEPS</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">&quot;whitegrid&quot;</span><span class="p">,</span> <span class="n">rc</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;grid.linestyle&quot;</span><span class="p">:</span> <span class="s2">&quot;--&quot;</span><span class="p">})</span>
+<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+    <span class="n">HAS_PLOTTING_DEPS</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="n">_error_msg</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="s2">&quot;Plotting functions will not work as optional plotting dependencies are not installed. &quot;</span>
+    <span class="s2">&quot;Please install `matplotlib` and `seaborn` to enable plotting.&quot;</span>
+<span class="p">)</span>
+
+
+<span class="c1"># Minimum fraction of the dataset size to consider a group for plotting</span>
+<span class="n">GROUP_SIZE_THRESHOLD</span> <span class="o">=</span> <span class="mf">0.04</span>
+
+
+<span class="k">def</span> <span class="nf">_check_plotting_deps</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">HAS_PLOTTING_DEPS</span><span class="p">:</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="n">_error_msg</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">HAS_PLOTTING_DEPS</span>
+
+
+<div class="viewcode-block" id="save_fig">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.plotting.save_fig">[docs]</a>
+<span class="k">def</span> <span class="nf">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">fig_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">imgs_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span><span class="p">,</span> <span class="nb">format</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;pdf&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper to save a matplotlib figure to disk.&quot;&quot;&quot;</span>
+    <span class="n">path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">imgs_dir</span><span class="p">)</span> <span class="o">/</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fig_name</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="n">path</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;.</span><span class="si">{</span><span class="nb">format</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">resolve</span><span class="p">()</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">path</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="render_evaluation_plots">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.plotting.render_evaluation_plots">[docs]</a>
+<span class="k">def</span> <span class="nf">render_evaluation_plots</span><span class="p">(</span>
+    <span class="n">y_true</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">y_pred_scores</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="o">*</span><span class="p">,</span>
+    <span class="n">eval_results</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{},</span>
+    <span class="n">model_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">imgs_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">show_plots</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Renders evaluation plots for the given predictions.&quot;&quot;&quot;</span>
+    <span class="c1"># Check if plotting dependencies are available</span>
+    <span class="k">if</span> <span class="n">_check_plotting_deps</span><span class="p">()</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">{}</span>
+
+    <span class="c1"># Initialize vars</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">model_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot; - </span><span class="si">{</span><span class="n">model_name</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">if</span> <span class="n">model_name</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+
+    <span class="c1"># Helper function to show or save plot</span>
+    <span class="k">def</span> <span class="nf">show_or_save</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">fig_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">show_plots</span><span class="p">:</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">imgs_dir</span><span class="p">:</span>
+            <span class="n">results</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fig_name</span><span class="si">}</span><span class="s2">_path&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">fig_name</span><span class="p">,</span> <span class="n">imgs_dir</span><span class="p">)</span>
+
+    <span class="c1"># ### ### ### ###</span>
+    <span class="c1"># Plot ROC curve</span>
+    <span class="c1"># ### ### ### ###</span>
+    <span class="n">disp</span> <span class="o">=</span> <span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="o">=</span><span class="n">y_pred_scores</span><span class="p">,</span> <span class="n">plot_chance_level</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">disp</span><span class="o">.</span><span class="n">figure_</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;ROC Curve&quot;</span> <span class="o">+</span> <span class="n">model_str</span><span class="p">)</span>
+
+    <span class="c1"># Plot ROC point</span>
+    <span class="k">if</span> <span class="s2">&quot;fpr&quot;</span> <span class="ow">in</span> <span class="n">eval_results</span> <span class="ow">and</span> <span class="s2">&quot;tpr&quot;</span> <span class="ow">in</span> <span class="n">eval_results</span> <span class="ow">and</span> <span class="s2">&quot;threshold&quot;</span> <span class="ow">in</span> <span class="n">eval_results</span><span class="p">:</span>
+        <span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span> <span class="o">=</span> <span class="n">eval_results</span><span class="p">[</span><span class="s2">&quot;fpr&quot;</span><span class="p">],</span> <span class="n">eval_results</span><span class="p">[</span><span class="s2">&quot;tpr&quot;</span><span class="p">]</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+            <span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="s2">&quot;ro&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+            <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;threshold=</span><span class="si">{</span><span class="n">eval_results</span><span class="p">[</span><span class="s1">&#39;threshold&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+    <span class="n">show_or_save</span><span class="p">(</span><span class="n">disp</span><span class="o">.</span><span class="n">figure_</span><span class="p">,</span> <span class="s2">&quot;roc_curve&quot;</span><span class="p">)</span>
+
+    <span class="c1"># ### ### ### ### ### ###</span>
+    <span class="c1"># Plot calibration curve</span>
+    <span class="c1"># ### ### ### ### ### ###</span>
+    <span class="n">disp</span> <span class="o">=</span> <span class="n">CalibrationDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_prob</span><span class="o">=</span><span class="n">y_pred_scores</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;quantile&quot;</span><span class="p">)</span>
+    <span class="n">disp</span><span class="o">.</span><span class="n">figure_</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Calibration Curve&quot;</span> <span class="o">+</span> <span class="n">model_str</span><span class="p">)</span>
+    <span class="n">show_or_save</span><span class="p">(</span><span class="n">disp</span><span class="o">.</span><span class="n">figure_</span><span class="p">,</span> <span class="s2">&quot;calibration_curve&quot;</span><span class="p">)</span>
+
+    <span class="c1"># ### ### ### ### ### ###</span>
+    <span class="c1"># Plot score distribution</span>
+    <span class="c1"># ### ### ### ### ### ###</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">y_pred_scores</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;frequency&quot;</span><span class="p">,</span> <span class="n">kde</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Predicted Risk Score&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Frequency&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Score Distribution&quot;</span> <span class="o">+</span> <span class="n">model_str</span><span class="p">)</span>
+    <span class="n">show_or_save</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">(),</span> <span class="s2">&quot;score_distribution&quot;</span><span class="p">)</span>
+
+    <span class="c1"># ### ### ### ### ### ### ### ### ### ###</span>
+    <span class="c1"># Plot distribution of scores per label #</span>
+    <span class="c1"># ### ### ### ### ### ### ### ### ### ###</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">kdeplot</span><span class="p">(</span>
+        <span class="n">data</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;score&quot;</span><span class="p">:</span> <span class="n">y_pred_scores</span><span class="p">,</span> <span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="n">y_true</span><span class="p">}</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+        <span class="n">x</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span>
+        <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;label&quot;</span><span class="p">,</span>
+        <span class="n">multiple</span><span class="o">=</span><span class="s2">&quot;fill&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">y_pred_scores</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">y_pred_scores</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Predicted Risk Score&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Score Distribution per Label&quot;</span> <span class="o">+</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot; - </span><span class="si">{</span><span class="n">model_name</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">if</span> <span class="n">model_name</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">))</span>
+    <span class="n">show_or_save</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">(),</span> <span class="s2">&quot;score_distribution_per_label&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">results</span></div>
+
+
+
+<div class="viewcode-block" id="render_fairness_plots">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.plotting.render_fairness_plots">[docs]</a>
+<span class="k">def</span> <span class="nf">render_fairness_plots</span><span class="p">(</span>  <span class="c1"># noqa: C901</span>
+    <span class="n">y_true</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">y_pred_scores</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="o">*</span><span class="p">,</span>
+    <span class="n">sensitive_attribute</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">eval_results</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{},</span>
+    <span class="n">model_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">group_value_map</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="nb">int</span><span class="p">],</span> <span class="nb">str</span><span class="p">],</span>
+    <span class="n">group_size_threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">GROUP_SIZE_THRESHOLD</span><span class="p">,</span>
+    <span class="n">imgs_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">Path</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">show_plots</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Renders fairness plots for the given predictions.&quot;&quot;&quot;</span>
+    <span class="c1"># Check if plotting dependencies are available</span>
+    <span class="k">if</span> <span class="n">_check_plotting_deps</span><span class="p">()</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">{}</span>
+
+    <span class="c1"># Plot fairness plots if sensitive attribute is provided</span>
+    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">sensitive_attribute</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_true</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_pred_scores</span><span class="p">),</span> \
+        <span class="s2">&quot;All arrays should have the same length.&quot;</span>
+
+    <span class="c1"># Initialize vars</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">model_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot; - </span><span class="si">{</span><span class="n">model_name</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">if</span> <span class="n">model_name</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+    <span class="n">n_groups</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">sensitive_attribute</span><span class="p">))</span>
+    <span class="k">assert</span> <span class="n">n_groups</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;At least 2 groups are required for fairness plots.&quot;</span>
+
+    <span class="c1"># Helper function to show or save plot</span>
+    <span class="k">def</span> <span class="nf">show_or_save</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">fig_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">show_plots</span><span class="p">:</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">imgs_dir</span><span class="p">:</span>
+            <span class="n">results</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fig_name</span><span class="si">}</span><span class="s2">_path&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">fig_name</span><span class="p">,</span> <span class="n">imgs_dir</span><span class="p">)</span>
+
+    <span class="c1"># Set group-wise colors and global color</span>
+    <span class="n">palette</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">(</span><span class="n">n_colors</span><span class="o">=</span><span class="n">n_groups</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">global_color</span> <span class="o">=</span> <span class="n">palette</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">group_colors</span> <span class="o">=</span> <span class="n">palette</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+    <span class="n">group_line_styles</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="s2">&quot;:&quot;</span><span class="p">,</span> <span class="s2">&quot;-.&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">n_groups</span> <span class="o">//</span> <span class="mi">3</span><span class="p">)</span>
+
+    <span class="c1"># ###</span>
+    <span class="c1"># Plot group-specific ROC curves</span>
+    <span class="c1"># ###</span>
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">s_value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">sensitive_attribute</span><span class="p">)):</span>
+        <span class="n">is_first_group</span> <span class="o">=</span> <span class="p">(</span><span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">is_last_group</span> <span class="o">=</span> <span class="p">(</span><span class="n">idx</span> <span class="o">==</span> <span class="n">n_groups</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="c1"># If it&#39;s the first group</span>
+        <span class="k">if</span> <span class="n">is_first_group</span><span class="p">:</span>
+            <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+
+            <span class="c1"># Plot global ROC point</span>
+            <span class="k">if</span> <span class="s2">&quot;fpr&quot;</span> <span class="ow">in</span> <span class="n">eval_results</span> <span class="ow">and</span> <span class="s2">&quot;tpr&quot;</span> <span class="ow">in</span> <span class="n">eval_results</span><span class="p">:</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+                    <span class="n">eval_results</span><span class="p">[</span><span class="s2">&quot;fpr&quot;</span><span class="p">],</span> <span class="n">eval_results</span><span class="p">[</span><span class="s2">&quot;tpr&quot;</span><span class="p">],</span>
+                    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;P&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                    <span class="n">color</span><span class="o">=</span><span class="n">global_color</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Global&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Get group-specific data</span>
+        <span class="n">group_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">sensitive_attribute</span> <span class="o">==</span> <span class="n">s_value</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+        <span class="n">group_y_true</span> <span class="o">=</span> <span class="n">y_true</span><span class="p">[</span><span class="n">group_indices</span><span class="p">]</span>
+        <span class="n">group_y_pred_scores</span> <span class="o">=</span> <span class="n">y_pred_scores</span><span class="p">[</span><span class="n">group_indices</span><span class="p">]</span>
+
+        <span class="c1"># If the group is too small of a fraction, skip (curve will be too erratic)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">group_indices</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">sensitive_attribute</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">group_size_threshold</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Skipping group &#39;</span><span class="si">{</span><span class="n">group_value_map</span><span class="p">(</span><span class="n">s_value</span><span class="p">)</span><span class="si">}</span><span class="s2">&#39; as it&#39;s too small.&quot;</span><span class="p">)</span>
+            <span class="k">continue</span>
+
+        <span class="c1"># Plot group-specific ROC curve</span>
+        <span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span>
+            <span class="n">y_true</span><span class="o">=</span><span class="n">group_y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="o">=</span><span class="n">group_y_pred_scores</span><span class="p">,</span>
+            <span class="n">plot_chance_level</span><span class="o">=</span><span class="n">is_last_group</span><span class="p">,</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+
+            <span class="c1"># Group-specific visuals</span>
+            <span class="n">linestyle</span><span class="o">=</span><span class="n">group_line_styles</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">group_colors</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
+            <span class="n">name</span><span class="o">=</span><span class="n">group_value_map</span><span class="p">(</span><span class="n">s_value</span><span class="p">),</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Plot group-specific ROC point</span>
+        <span class="k">if</span> <span class="s2">&quot;threshold&quot;</span> <span class="ow">in</span> <span class="n">eval_results</span><span class="p">:</span>
+            <span class="n">threshold</span> <span class="o">=</span> <span class="n">eval_results</span><span class="p">[</span><span class="s2">&quot;threshold&quot;</span><span class="p">]</span>
+            <span class="n">tn</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">tp</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">confusion_matrix</span><span class="p">(</span>
+                <span class="n">group_y_true</span><span class="p">,</span> <span class="p">(</span><span class="n">group_y_pred_scores</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">),</span>
+                <span class="n">labels</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+            <span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+            <span class="n">group_fpr</span> <span class="o">=</span> <span class="n">safe_division</span><span class="p">(</span><span class="n">fp</span><span class="p">,</span> <span class="n">fp</span> <span class="o">+</span> <span class="n">tn</span><span class="p">,</span> <span class="n">worst_result</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">group_tpr</span> <span class="o">=</span> <span class="n">safe_division</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">tp</span> <span class="o">+</span> <span class="n">fn</span><span class="p">,</span> <span class="n">worst_result</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+                <span class="n">group_fpr</span><span class="p">,</span> <span class="n">group_tpr</span><span class="p">,</span>
+                <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                <span class="n">color</span><span class="o">=</span><span class="n">group_colors</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
+                <span class="c1"># label=group_value_map(s_value),</span>
+            <span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;ROC curve per sub-group&quot;</span> <span class="o">+</span> <span class="n">model_str</span><span class="p">)</span>
+    <span class="n">show_or_save</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="s2">&quot;roc_curve_per_subgroup&quot;</span><span class="p">)</span>
+
+    <span class="c1"># ###</span>
+    <span class="c1"># Plot group-specific calibration curves</span>
+    <span class="c1"># ###</span>
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">s_value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">sensitive_attribute</span><span class="p">)):</span>
+        <span class="n">group_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">sensitive_attribute</span> <span class="o">==</span> <span class="n">s_value</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+        <span class="n">group_y_true</span> <span class="o">=</span> <span class="n">y_true</span><span class="p">[</span><span class="n">group_indices</span><span class="p">]</span>
+        <span class="n">group_y_pred_scores</span> <span class="o">=</span> <span class="n">y_pred_scores</span><span class="p">[</span><span class="n">group_indices</span><span class="p">]</span>
+        <span class="n">is_first_group</span> <span class="o">=</span> <span class="p">(</span><span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">is_first_group</span><span class="p">:</span>
+            <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+
+        <span class="c1"># If the group is too small of a fraction, skip (curve will be too erratic)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">group_indices</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">sensitive_attribute</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">group_size_threshold</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Skipping group </span><span class="si">{</span><span class="n">group_value_map</span><span class="p">(</span><span class="n">s_value</span><span class="p">)</span><span class="si">}</span><span class="s2"> plot as it&#39;s too small.&quot;</span><span class="p">)</span>
+            <span class="k">continue</span>
+
+        <span class="c1"># Plot global calibration curve</span>
+        <span class="n">CalibrationDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span>
+            <span class="n">y_true</span><span class="o">=</span><span class="n">group_y_true</span><span class="p">,</span> <span class="n">y_prob</span><span class="o">=</span><span class="n">group_y_pred_scores</span><span class="p">,</span>
+            <span class="n">n_bins</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;quantile&quot;</span><span class="p">,</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+
+            <span class="c1"># Group-specific visuals</span>
+            <span class="n">linestyle</span><span class="o">=</span><span class="n">group_line_styles</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">group_colors</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
+            <span class="n">name</span><span class="o">=</span><span class="n">group_value_map</span><span class="p">(</span><span class="n">s_value</span><span class="p">),</span>
+        <span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Calibration curve per sub-group&quot;</span> <span class="o">+</span> <span class="n">model_str</span><span class="p">)</span>
+    <span class="n">show_or_save</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="s2">&quot;calibration_curve_per_subgroup&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">results</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
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+
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diff --git a/_modules/folktexts/prompting.html b/_modules/folktexts/prompting.html
new file mode 100644
index 0000000..c41dcc0
--- /dev/null
+++ b/_modules/folktexts/prompting.html
@@ -0,0 +1,295 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>folktexts.prompting &mdash; folktexts 0.0.25 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=80d5e7a1" />
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+          <li class="breadcrumb-item"><a href="../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">folktexts.prompting</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
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+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for folktexts.prompting</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Module to map risk-estimation questions to different prompting techniques.</span>
+
+<span class="sd">e.g.,</span>
+<span class="sd">- multiple-choice Q&amp;A vs direct numeric Q&amp;A;</span>
+<span class="sd">- zero-shot vs few-shot vs CoT;</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span>
+
+<span class="kn">from</span> <span class="nn">.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span>
+<span class="kn">from</span> <span class="nn">.qa_interface</span> <span class="kn">import</span> <span class="n">QAInterface</span>
+<span class="kn">from</span> <span class="nn">.task</span> <span class="kn">import</span> <span class="n">TaskMetadata</span>
+
+<span class="n">SYSTEM_PROMPT</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
+<span class="s2">You are a helpful assistant. You answer multiple-choice questions based on the information provided.</span>
+<span class="s2">&quot;&quot;&quot;</span>
+
+<span class="n">ACS_TASK_DESCRIPTION</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
+<span class="s2">The following data corresponds to a survey respondent. </span><span class="se">\</span>
+<span class="s2">The survey was conducted among US residents in 2018. </span><span class="se">\</span>
+<span class="s2">Please answer the question based on the information provided. </span><span class="se">\</span>
+<span class="s2">The data provided is enough to reach an approximate answer.</span>
+<span class="s2">&quot;&quot;&quot;</span>
+
+<span class="n">ACS_FEW_SHOT_TASK_DESCRIPTION</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
+<span class="s2">The following data corresponds to different survey respondents. </span><span class="se">\</span>
+<span class="s2">The survey was conducted among US residents in 2018. </span><span class="se">\</span>
+<span class="s2">Please answer each question based on the information provided. </span><span class="se">\</span>
+<span class="s2">The data provided is enough to reach an approximate answer for each person.</span>
+<span class="s2">&quot;&quot;&quot;</span>
+
+<span class="n">ANTHROPIC_CHAT_PROMPT</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;If had to select one of the options, my answer would be&quot;&quot;&quot;</span>
+<span class="n">GEMMA_CHAT_PROMPT</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;The provided information suggests that the answer is&quot;&quot;&quot;</span>
+
+
+<div class="viewcode-block" id="encode_row_prompt">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.prompting.encode_row_prompt">[docs]</a>
+<span class="k">def</span> <span class="nf">encode_row_prompt</span><span class="p">(</span>
+    <span class="n">row</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">,</span>
+    <span class="n">task</span><span class="p">:</span> <span class="n">TaskMetadata</span><span class="p">,</span>
+    <span class="n">question</span><span class="p">:</span> <span class="n">QAInterface</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">custom_prompt_prefix</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">add_task_description</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Encode a question regarding a given row.&quot;&quot;&quot;</span>
+    <span class="c1"># Get the question to ask</span>
+    <span class="n">question</span> <span class="o">=</span> <span class="n">question</span> <span class="ow">or</span> <span class="n">task</span><span class="o">.</span><span class="n">question</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="p">(</span><span class="n">ACS_TASK_DESCRIPTION</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="k">if</span> <span class="n">add_task_description</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
+        <span class="o">+</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="si">{</span><span class="n">custom_prompt_prefix</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="k">if</span> <span class="n">custom_prompt_prefix</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
+        <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
+<span class="s2">Information:</span>
+<span class="si">{</span><span class="n">task</span><span class="o">.</span><span class="n">get_row_description</span><span class="p">(</span><span class="n">row</span><span class="p">)</span><span class="si">}</span>
+
+<span class="si">{</span><span class="n">question</span><span class="o">.</span><span class="n">get_question_prompt</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;&quot;&quot;</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="encode_row_prompt_few_shot">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.prompting.encode_row_prompt_few_shot">[docs]</a>
+<span class="k">def</span> <span class="nf">encode_row_prompt_few_shot</span><span class="p">(</span>
+    <span class="n">row</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">,</span>
+    <span class="n">task</span><span class="p">:</span> <span class="n">TaskMetadata</span><span class="p">,</span>
+    <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span>
+    <span class="n">n_shots</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">question</span><span class="p">:</span> <span class="n">QAInterface</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">reuse_examples</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">custom_prompt_prefix</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Encode a question regarding a given row using few-shot prompting.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    row : pd.Series</span>
+<span class="sd">        The row that the question will be about.</span>
+<span class="sd">    task : TaskMetadata</span>
+<span class="sd">        The task that the row belongs to.</span>
+<span class="sd">    n_shots : int, optional</span>
+<span class="sd">        The number of example questions and answers to use before prompting</span>
+<span class="sd">        about the given row, by default 3.</span>
+<span class="sd">    reuse_examples : bool, optional</span>
+<span class="sd">        Whether to reuse the same examples for consistency. By default will</span>
+<span class="sd">        resample new examples each time (`reuse_examples=False`).</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    prompt : str</span>
+<span class="sd">        The encoded few-shot prompt.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Take `n_shots` random samples from the train set</span>
+    <span class="n">X_examples</span><span class="p">,</span> <span class="n">y_examples</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">sample_n_train_examples</span><span class="p">(</span><span class="n">n_shots</span><span class="p">,</span> <span class="n">reuse_examples</span><span class="o">=</span><span class="n">reuse_examples</span><span class="p">)</span>
+
+    <span class="c1"># Start with task description</span>
+    <span class="n">prompt</span> <span class="o">=</span> <span class="n">ACS_FEW_SHOT_TASK_DESCRIPTION</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span>
+
+    <span class="c1"># Get the question to ask</span>
+    <span class="n">question</span> <span class="o">=</span> <span class="n">question</span> <span class="ow">or</span> <span class="n">task</span><span class="o">.</span><span class="n">question</span>
+
+    <span class="c1"># Add `n` example rows with respective labels</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_shots</span><span class="p">):</span>
+        <span class="n">prompt</span> <span class="o">+=</span> <span class="p">(</span>
+            <span class="n">encode_row_prompt</span><span class="p">(</span>
+                <span class="n">X_examples</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
+                <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+                <span class="n">add_task_description</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="n">custom_prompt_prefix</span><span class="o">=</span><span class="n">custom_prompt_prefix</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">question</span><span class="o">.</span><span class="n">get_answer_key_from_value</span><span class="p">(</span><span class="n">y_examples</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+
+    <span class="c1"># Add the target row without its label</span>
+    <span class="n">prompt</span> <span class="o">+=</span> <span class="n">encode_row_prompt</span><span class="p">(</span>
+        <span class="n">row</span><span class="p">,</span>
+        <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+        <span class="n">add_task_description</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">custom_prompt_prefix</span><span class="o">=</span><span class="n">custom_prompt_prefix</span><span class="p">,</span>
+        <span class="n">question</span><span class="o">=</span><span class="n">question</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">prompt</span></div>
+
+
+
+<div class="viewcode-block" id="encode_row_prompt_chat">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.prompting.encode_row_prompt_chat">[docs]</a>
+<span class="k">def</span> <span class="nf">encode_row_prompt_chat</span><span class="p">(</span>
+    <span class="n">row</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">,</span>
+    <span class="n">task</span><span class="p">:</span> <span class="n">TaskMetadata</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="p">:</span> <span class="n">AutoTokenizer</span><span class="p">,</span>
+    <span class="n">question</span><span class="p">:</span> <span class="n">QAInterface</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">chat_template_kwargs</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+    <span class="c1"># TODO: implement two functions</span>
+    <span class="c1"># - one for gemma-like models that are not compatible with system prompts</span>
+    <span class="c1"># - and another for regular models compatible with system prompts</span>
+    <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;NOTE :: Untested feature!!&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">apply_chat_template</span><span class="p">(</span>
+        <span class="n">tokenizer</span><span class="p">,</span>
+        <span class="p">(</span>
+            <span class="n">SYSTEM_PROMPT</span>
+            <span class="o">+</span> <span class="n">encode_row_prompt</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">task</span><span class="p">,</span> <span class="n">question</span><span class="o">=</span><span class="n">question</span><span class="p">)</span>
+        <span class="p">),</span>
+        <span class="o">**</span><span class="n">chat_template_kwargs</span><span class="p">,</span>
+    <span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="apply_chat_template">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.prompting.apply_chat_template">[docs]</a>
+<span class="k">def</span> <span class="nf">apply_chat_template</span><span class="p">(</span>
+    <span class="n">tokenizer</span><span class="p">:</span> <span class="n">AutoTokenizer</span><span class="p">,</span>
+    <span class="n">user_prompt</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">system_prompt</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">chat_prompt</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">ANTHROPIC_CHAT_PROMPT</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+    <span class="c1"># Add system prompt</span>
+    <span class="n">conversation</span> <span class="o">=</span> <span class="p">([</span>
+        <span class="p">{</span><span class="s2">&quot;role&quot;</span><span class="p">:</span> <span class="s2">&quot;system&quot;</span><span class="p">,</span> <span class="s2">&quot;content&quot;</span><span class="p">:</span> <span class="n">system_prompt</span><span class="p">}</span>
+    <span class="p">]</span> <span class="k">if</span> <span class="n">system_prompt</span> <span class="k">else</span> <span class="p">[])</span>
+
+    <span class="c1"># Add user prompt</span>
+    <span class="n">conversation</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s2">&quot;role&quot;</span><span class="p">:</span> <span class="s2">&quot;user&quot;</span><span class="p">,</span> <span class="s2">&quot;content&quot;</span><span class="p">:</span> <span class="n">user_prompt</span><span class="p">})</span>
+
+    <span class="c1"># Using the Anthropic-style chat prompt</span>
+    <span class="n">conversation</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s2">&quot;role&quot;</span><span class="p">:</span> <span class="s2">&quot;assistant&quot;</span><span class="p">,</span> <span class="s2">&quot;content&quot;</span><span class="p">:</span> <span class="n">chat_prompt</span><span class="p">})</span>
+
+    <span class="c1"># Default kwargs</span>
+    <span class="n">kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">&quot;add_generation_prompt&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+    <span class="c1"># Apply prompt template</span>
+    <span class="n">filled_prompt</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">apply_chat_template</span><span class="p">(</span>
+        <span class="n">conversation</span><span class="o">=</span><span class="n">conversation</span><span class="p">,</span>
+        <span class="n">tokenize</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Make sure no special tokens follow the `CHAT_PROMPT`;</span>
+    <span class="c1"># &gt; some models add a newline character and/or a &lt;end_of_turn&gt; token</span>
+    <span class="n">filled_prompt</span> <span class="o">=</span> <span class="n">filled_prompt</span><span class="p">[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">chat_prompt</span><span class="p">)</span> <span class="o">+</span> <span class="n">filled_prompt</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="n">chat_prompt</span><span class="p">)]</span>
+    <span class="k">return</span> <span class="n">filled_prompt</span></div>
+
+</pre></div>
+
+           </div>
+          </div>
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+
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new file mode 100644
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+<!DOCTYPE html>
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+      <li class="breadcrumb-item active">folktexts.qa_interface</li>
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+      </li>
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for folktexts.qa_interface</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Interface for question-answering with LLMs.</span>
+
+<span class="sd">- Create different types of questions (direct numeric, multiple-choice).</span>
+<span class="sd">- Encode questions and decode model outputs.</span>
+<span class="sd">- Compute risk-estimate from model outputs.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">dataclasses</span>
+<span class="kn">import</span> <span class="nn">itertools</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">re</span>
+<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span>
+<span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterator</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">._utils</span> <span class="kn">import</span> <span class="n">hash_dict</span>
+
+<span class="c1"># Minimum probability density assigned to all valid answers</span>
+<span class="c1"># &gt; small models will be worse at using valid answers...</span>
+<span class="n">ANSWER_PROB_THRESHOLD</span> <span class="o">=</span> <span class="mf">0.1</span>
+
+<span class="c1"># Default answer keys for multiple-choice questions</span>
+<span class="n">_ALPHABET</span> <span class="o">=</span> <span class="s2">&quot;ABCDEFGHIJKLMNOPQRSTUVWXYZ&quot;</span>
+
+
+<div class="viewcode-block" id="QAInterface">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.QAInterface">[docs]</a>
+<span class="nd">@dataclass</span><span class="p">(</span><span class="n">frozen</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">class</span> <span class="nc">QAInterface</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;An interface for a question-answering system.&quot;&quot;&quot;</span>
+
+    <span class="n">column</span><span class="p">:</span> <span class="nb">str</span>
+    <span class="n">text</span><span class="p">:</span> <span class="nb">str</span>
+    <span class="n">num_forward_passes</span><span class="p">:</span> <span class="nb">int</span>
+
+<div class="viewcode-block" id="QAInterface.get_question_prompt">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.QAInterface.get_question_prompt">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_question_prompt</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns a question and answer key.&quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+
+<div class="viewcode-block" id="QAInterface.get_answer_from_model_output">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.QAInterface.get_answer_from_model_output">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_answer_from_model_output</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">last_token_probs</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+        <span class="n">tokenizer_vocab</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Decodes the model&#39;s output into an answer for the given question.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        last_token_probs : np.ndarray</span>
+<span class="sd">            The model&#39;s last token probabilities for the question. The first</span>
+<span class="sd">            dimension corresponds to the number of forward passes as specified</span>
+<span class="sd">            by `self.num_forward_passes`.</span>
+<span class="sd">        tokenizer : dict[str, int]</span>
+<span class="sd">            The tokenizer&#39;s vocabulary.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        answer : float</span>
+<span class="sd">            The answer to the question.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">hash_dict</span><span class="p">(</span><span class="n">dataclasses</span><span class="o">.</span><span class="n">asdict</span><span class="p">(</span><span class="bp">self</span><span class="p">)),</span> <span class="mi">16</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="DirectNumericQA">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.DirectNumericQA">[docs]</a>
+<span class="nd">@dataclass</span><span class="p">(</span><span class="n">frozen</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">class</span> <span class="nc">DirectNumericQA</span><span class="p">(</span><span class="n">QAInterface</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Represents a direct numeric question.</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    For example, the prompt could be &quot;</span>
+<span class="sd">    Q: What is 2 + 2?</span>
+<span class="sd">    A: &quot;</span>
+<span class="sd">    With the expected answer being &quot;4&quot;.</span>
+
+<span class="sd">    If looking for a direct numeric probability, the answer prompt will be</span>
+<span class="sd">    framed as so: &quot;</span>
+<span class="sd">    Q: What is the probability, between 0 and 1, of getting heads on a coin flip?</span>
+<span class="sd">    A: 0.&quot;</span>
+<span class="sd">    So that we can extract a numeric answer with at most 2 forward passes.</span>
+<span class="sd">    This is done automatically by passing the kwarg `answer_probability=True`.</span>
+
+<span class="sd">    Note that some models have multi-digit tokens in their vocabulary, so we</span>
+<span class="sd">    need to correctly assess which tokens in the vocabulary correspond to valid</span>
+<span class="sd">    numeric answers.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">num_forward_passes</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span>     <span class="c1"># NOTE: overrides superclass default</span>
+    <span class="n">answer_probability</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
+
+<div class="viewcode-block" id="DirectNumericQA.get_question_prompt">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.DirectNumericQA.get_question_prompt">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_question_prompt</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="n">question_prompt</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;&quot;&quot;Question: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">text</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">answer_probability</span><span class="p">:</span>
+            <span class="n">question_prompt</span> <span class="o">+=</span> <span class="s2">&quot;Answer (between 0 and 1): 0.&quot;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">question_prompt</span> <span class="o">+=</span> <span class="s2">&quot;Answer: &quot;</span>
+
+        <span class="k">return</span> <span class="n">question_prompt</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_get_numeric_tokens</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tokenizer_vocab</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the indices of tokens that correspond to numbers.</span>
+
+<span class="sd">        This can include digits (&quot;0&quot;-&quot;9&quot;), multi-digit tokens (e.g., &quot;100&quot;), and</span>
+<span class="sd">        the decimal point (&quot;.&quot;).</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">numeric_tokens</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="n">key</span><span class="p">:</span> <span class="n">token_id</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">token_id</span> <span class="ow">in</span> <span class="n">tokenizer_vocab</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">isdigit</span><span class="p">()</span>
+        <span class="p">}</span>
+
+        <span class="k">if</span> <span class="s2">&quot;.&quot;</span> <span class="ow">in</span> <span class="n">tokenizer_vocab</span><span class="p">:</span>
+            <span class="n">numeric_tokens</span><span class="p">[</span><span class="s2">&quot;.&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tokenizer_vocab</span><span class="p">[</span><span class="s2">&quot;.&quot;</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">numeric_tokens</span>
+
+<div class="viewcode-block" id="DirectNumericQA.get_answer_from_model_output">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.DirectNumericQA.get_answer_from_model_output">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_answer_from_model_output</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">last_token_probs</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+        <span class="n">tokenizer_vocab</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Outputs a numeric answer inferred from the model&#39;s output.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        last_token_probs : np.ndarray</span>
+<span class="sd">            The last token probabilities of the model for the question.</span>
+<span class="sd">            The first dimension must correspond to the number for forward passes</span>
+<span class="sd">            as specified by `num_forward_passes`.</span>
+<span class="sd">        tokenizer_vocab: dict[str, int],</span>
+<span class="sd">            The tokenizer&#39;s vocabulary.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        answer : float | int</span>
+<span class="sd">            The numeric answer to the question.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        Eventually we could run a search algorithm to find the most likely</span>
+<span class="sd">        answer over multiple forward passes, but for now we&#39;ll just take the</span>
+<span class="sd">        argmax on each forward pass.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">numeric_tokens_vocab</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_numeric_tokens</span><span class="p">(</span><span class="n">tokenizer_vocab</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">last_token_probs</span><span class="p">)</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_forward_passes</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Expected </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">num_forward_passes</span><span class="si">}</span><span class="s2"> forward passes, got &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">last_token_probs</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+
+        <span class="n">answer_text</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">ltp</span> <span class="ow">in</span> <span class="n">last_token_probs</span><span class="p">:</span>
+            <span class="c1"># Get the probability of each numeric token</span>
+            <span class="n">num_tokens_probs</span> <span class="o">=</span> <span class="p">{</span>
+                <span class="n">num_token</span><span class="p">:</span> <span class="n">ltp</span><span class="p">[</span><span class="n">token_id</span><span class="p">]</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ltp</span><span class="p">[</span><span class="n">token_id</span><span class="p">],</span> <span class="nb">float</span><span class="p">)</span> <span class="k">else</span> <span class="n">ltp</span><span class="p">[</span><span class="n">token_id</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+                <span class="k">for</span> <span class="n">num_token</span><span class="p">,</span> <span class="n">token_id</span> <span class="ow">in</span> <span class="n">numeric_tokens_vocab</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+            <span class="p">}</span>
+
+            <span class="c1"># Get the most likely numeric token</span>
+            <span class="n">most_likely_numeric_token</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">num_tokens_probs</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">num_tokens_probs</span><span class="o">.</span><span class="n">get</span><span class="p">)</span>
+            <span class="n">answer_text</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">most_likely_numeric_token</span><span class="p">)</span>
+
+            <span class="n">logging</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Total prob. assigned to numeric tokens: </span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">num_tokens_probs</span><span class="o">.</span><span class="n">values</span><span class="p">())</span><span class="si">:</span><span class="s2">.2%</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Filter out any non-numeric characters</span>
+        <span class="n">match_</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;[-+]?\d*\.\d+|\d+&quot;</span><span class="p">,</span> <span class="n">answer_text</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="n">match_</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;Could not find numeric answer in &#39;</span><span class="si">{</span><span class="n">answer_text</span><span class="si">}</span><span class="s2">&#39;.&quot;</span>
+        <span class="n">numeric_answer_text</span> <span class="o">=</span> <span class="n">match_</span><span class="o">.</span><span class="n">group</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">answer_probability</span> <span class="ow">and</span> <span class="s2">&quot;.&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">numeric_answer_text</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;0.</span><span class="si">{</span><span class="n">numeric_answer_text</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">numeric_answer_text</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="Choice">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.Choice">[docs]</a>
+<span class="nd">@dataclass</span><span class="p">(</span><span class="n">frozen</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">eq</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">class</span> <span class="nc">Choice</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Represents a choice in multiple-choice Q&amp;A.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    text : str</span>
+<span class="sd">        The text of the choice. E.g., &quot;25-34 years old&quot;.</span>
+<span class="sd">    data_value : object</span>
+<span class="sd">        The categorical value corresponding to this choice in the data.</span>
+<span class="sd">    numeric_value : float, optional</span>
+<span class="sd">        A meaningful numeric value for the choice. E.g., if the choice is &quot;25-34</span>
+<span class="sd">        years old&quot;, the numeric value could be 30. The choice with the highest</span>
+<span class="sd">        numeric value can be used as a proxy for the positive class. If not</span>
+<span class="sd">        provided, will try to use the `choice.value`.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">text</span><span class="p">:</span> <span class="nb">str</span>
+    <span class="n">data_value</span><span class="p">:</span> <span class="nb">object</span>
+    <span class="n">numeric_value</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="Choice.get_numeric_value">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.Choice.get_numeric_value">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_numeric_value</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the numeric value of the choice.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">numeric_value</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">numeric_value</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_value</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="MultipleChoiceQA">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA">[docs]</a>
+<span class="nd">@dataclass</span><span class="p">(</span><span class="n">frozen</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">eq</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>    <span class="c1"># NOTE: kw_only=True requires Python 3.10</span>
+<span class="k">class</span> <span class="nc">MultipleChoiceQA</span><span class="p">(</span><span class="n">QAInterface</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Represents a multiple-choice question and its answer keys.&quot;&quot;&quot;</span>
+
+    <span class="n">num_forward_passes</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span>     <span class="c1"># NOTE: overrides superclass default</span>
+    <span class="n">choices</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Choice</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataclasses</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="n">default_factory</span><span class="o">=</span><span class="nb">tuple</span><span class="p">)</span>
+    <span class="n">_answer_keys_source</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataclasses</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="n">default_factory</span><span class="o">=</span><span class="k">lambda</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">_ALPHABET</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="nf">__post_init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">choices</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Choices must be provided.&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">choices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_answer_keys_source</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Number of choices must be less than or equal to the number of answer keys.&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">hash_dict</span><span class="p">(</span><span class="n">dataclasses</span><span class="o">.</span><span class="n">asdict</span><span class="p">(</span><span class="bp">self</span><span class="p">)),</span> <span class="mi">16</span><span class="p">)</span>
+
+<div class="viewcode-block" id="MultipleChoiceQA.create_question_from_value_map">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.create_question_from_value_map">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">create_question_from_value_map</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">column</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="n">value_map</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">],</span>
+        <span class="n">attribute</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;MultipleChoiceQA&quot;</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructs a question from a value map.&quot;&quot;&quot;</span>
+        <span class="n">choices</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">Choice</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">value</span><span class="p">))</span> <span class="k">for</span> <span class="n">value</span><span class="p">,</span> <span class="n">text</span> <span class="ow">in</span> <span class="n">value_map</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
+
+        <span class="c1"># Set default question text</span>
+        <span class="n">kwargs</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">&quot;text&quot;</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;What is this person&#39;s </span><span class="si">{</span><span class="n">attribute</span><span class="si">}</span><span class="s2">?&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span>
+            <span class="n">column</span><span class="o">=</span><span class="n">column</span><span class="p">,</span>
+            <span class="n">choices</span><span class="o">=</span><span class="n">choices</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="MultipleChoiceQA.create_answer_keys_permutations">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.create_answer_keys_permutations">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">create_answer_keys_permutations</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">question</span><span class="p">:</span> <span class="s2">&quot;MultipleChoiceQA&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterator</span><span class="p">[</span><span class="s2">&quot;MultipleChoiceQA&quot;</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Yield questions with all permutations of answer keys.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        question : Question</span>
+<span class="sd">            The template question whose answer keys will be permuted.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        permutations : Iterator[Question]</span>
+<span class="sd">            A generator of questions with all permutations of answer keys.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">perm</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">permutations</span><span class="p">(</span><span class="n">question</span><span class="o">.</span><span class="n">choices</span><span class="p">):</span>
+            <span class="k">yield</span> <span class="n">dataclasses</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">question</span><span class="p">,</span> <span class="n">choices</span><span class="o">=</span><span class="n">perm</span><span class="p">)</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">answer_keys</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_answer_keys_source</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">choices</span><span class="p">)]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">key_to_choice</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Choice</span><span class="p">]:</span>
+        <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">answer_keys</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">choices</span><span class="p">))</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">choice_to_key</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Choice</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
+        <span class="k">return</span> <span class="p">{</span><span class="n">choice</span><span class="p">:</span> <span class="n">key</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">choice</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">key_to_choice</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+
+<div class="viewcode-block" id="MultipleChoiceQA.get_value_to_text_map">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_value_to_text_map">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_value_to_text_map</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">object</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the map from choice data value to choice textual representation.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="p">{</span><span class="n">choice</span><span class="o">.</span><span class="n">data_value</span><span class="p">:</span> <span class="n">choice</span><span class="o">.</span><span class="n">text</span> <span class="k">for</span> <span class="n">choice</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">choices</span><span class="p">}</span></div>
+
+
+<div class="viewcode-block" id="MultipleChoiceQA.get_answer_key_from_value">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_answer_key_from_value">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_answer_key_from_value</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the answer key corresponding to the given data value.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">choice</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">choices</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">choice</span><span class="o">.</span><span class="n">data_value</span> <span class="o">==</span> <span class="n">value</span><span class="p">:</span>
+                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">choice_to_key</span><span class="p">[</span><span class="n">choice</span><span class="p">]</span>
+
+        <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Could not find choice for value: </span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="kc">None</span></div>
+
+
+<div class="viewcode-block" id="MultipleChoiceQA.get_answer_from_text">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_answer_from_text">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_answer_from_text</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Choice</span><span class="p">:</span>
+        <span class="n">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">text</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">key_to_choice</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">key_to_choice</span><span class="p">[</span><span class="n">text</span><span class="p">]</span>
+
+        <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Could not find answer for text: </span><span class="si">{</span><span class="n">text</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="kc">None</span></div>
+
+
+<div class="viewcode-block" id="MultipleChoiceQA.get_question_prompt">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_question_prompt">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_question_prompt</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="n">choice_str</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">. </span><span class="si">{</span><span class="n">choice</span><span class="o">.</span><span class="n">text</span><span class="si">}</span><span class="s2">.&quot;</span>
+            <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">choice</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">key_to_choice</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
+<span class="s2">Question: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">text</span><span class="si">}</span>
+<span class="si">{</span><span class="n">choice_str</span><span class="si">}</span>
+<span class="s2">Answer:&quot;&quot;&quot;</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_decode_model_output_to_choice_distribution</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">last_token_probs</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+        <span class="n">tokenizer_vocab</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Decodes the model&#39;s output into an answer distribution.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        last_token_probs : np.ndarray</span>
+<span class="sd">            The model&#39;s last token probabilities for the question.</span>
+<span class="sd">        tokenizer_vocab: dict[str, int],</span>
+<span class="sd">            The tokenizer&#39;s vocabulary.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        answers : dict[Choice, float]</span>
+<span class="sd">            How much probability the model places on each answer choice.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        Answer-key tokens may be prefixed with a space, so we need to check</span>
+<span class="sd">        both &quot;A&quot; and &quot; A&quot; templates.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">def</span> <span class="nf">_get_choice_token_id</span><span class="p">(</span><span class="n">choice</span><span class="p">:</span> <span class="n">Choice</span><span class="p">,</span> <span class="n">prefix</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot; &quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+            <span class="n">choice_answer_text</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">prefix</span><span class="si">}{</span><span class="bp">self</span><span class="o">.</span><span class="n">choice_to_key</span><span class="p">[</span><span class="n">choice</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="k">if</span> <span class="n">choice_answer_text</span> <span class="ow">in</span> <span class="n">tokenizer_vocab</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">tokenizer_vocab</span><span class="p">[</span><span class="n">choice_answer_text</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="kc">None</span>
+
+        <span class="c1"># Different models may use different prefixes to represent white space</span>
+        <span class="c1"># or word boundaries; here we try a few common ones</span>
+        <span class="n">prefixes</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="s2">&quot;_&quot;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\u2581</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\u0120</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\u010A</span><span class="s2">&quot;</span><span class="p">]</span>
+
+        <span class="c1"># Map probabilities to choice values</span>
+        <span class="n">answers_per_prefix</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="n">prf</span><span class="p">:</span> <span class="p">{</span>
+                <span class="n">choice</span><span class="p">:</span> <span class="n">last_token_probs</span><span class="p">[</span><span class="n">choice_token_id</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+                <span class="k">for</span> <span class="n">choice</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">choices</span>
+                <span class="k">if</span> <span class="p">(</span><span class="n">choice_token_id</span> <span class="o">:=</span> <span class="n">_get_choice_token_id</span><span class="p">(</span><span class="n">choice</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="n">prf</span><span class="p">))</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="p">}</span>
+            <span class="k">for</span> <span class="n">prf</span> <span class="ow">in</span> <span class="n">prefixes</span>
+        <span class="p">}</span>
+
+        <span class="c1"># Choose the prefix with the highest probability density</span>
+        <span class="n">best_prefix</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">answers_per_prefix</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">prf</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span><span class="n">answers_per_prefix</span><span class="p">[</span><span class="n">prf</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
+        <span class="n">answers</span> <span class="o">=</span> <span class="n">answers_per_prefix</span><span class="p">[</span><span class="n">best_prefix</span><span class="p">]</span>
+
+        <span class="c1"># Log prefix information in debug mode</span>
+        <span class="k">for</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">choice_probs</span> <span class="ow">in</span> <span class="n">answers_per_prefix</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;prefix=&#39;</span><span class="si">{</span><span class="n">prefix</span><span class="si">}</span><span class="s2">&#39; has density </span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">choice_probs</span><span class="o">.</span><span class="n">values</span><span class="p">())</span><span class="si">:</span><span class="s2">.2%</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Normalize probabilities to sum to 1</span>
+        <span class="n">answers_sum_prob</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">answers</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
+
+        <span class="c1"># Log total probability density assigned to answers</span>
+        <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Answers have </span><span class="si">{</span><span class="n">answers_sum_prob</span><span class="si">:</span><span class="s2">.2%</span><span class="si">}</span><span class="s2"> probability assigned.&quot;</span>
+        <span class="k">if</span> <span class="n">answers_sum_prob</span> <span class="o">&lt;</span> <span class="n">ANSWER_PROB_THRESHOLD</span><span class="p">:</span>
+            <span class="n">id_to_tok</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">tokenizer_vocab</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+            <span class="n">argmax_token</span> <span class="o">=</span> <span class="n">id_to_tok</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">last_token_probs</span><span class="p">)]</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="n">msg</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot; Argmax token: &#39;</span><span class="si">{</span><span class="n">argmax_token</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">{</span>
+            <span class="n">choice</span><span class="p">:</span> <span class="n">prob</span> <span class="o">/</span> <span class="n">answers_sum_prob</span>
+            <span class="k">for</span> <span class="n">choice</span><span class="p">,</span> <span class="n">prob</span> <span class="ow">in</span> <span class="n">answers</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+        <span class="p">}</span>
+
+<div class="viewcode-block" id="MultipleChoiceQA.get_answer_from_model_output">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_answer_from_model_output">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_answer_from_model_output</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">last_token_probs</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+        <span class="n">tokenizer_vocab</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Decodes the model&#39;s output into an answer for the given question.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        last_token_probs : np.ndarray</span>
+<span class="sd">            The model&#39;s last token probabilities for the question. The first</span>
+<span class="sd">            dimension corresponds to the number of forward passes as specified</span>
+<span class="sd">            by `self.num_forward_passes`.</span>
+<span class="sd">        tokenizer_vocab: dict[str, int],</span>
+<span class="sd">            The tokenizer&#39;s vocabulary.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        answer : float</span>
+<span class="sd">            The answer to the question.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">last_token_probs</span><span class="o">.</span><span class="n">ndim</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">last_token_probs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Multiple (</span><span class="si">{</span><span class="n">last_token_probs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">) forward passes &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;detected: using only the first pass.&quot;</span><span class="p">)</span>
+
+            <span class="c1"># Using only 1st forward pass results</span>
+            <span class="n">last_token_probs</span> <span class="o">=</span> <span class="n">last_token_probs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="n">answers</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_decode_model_output_to_choice_distribution</span><span class="p">(</span>
+            <span class="n">last_token_probs</span><span class="o">=</span><span class="n">last_token_probs</span><span class="p">,</span>
+            <span class="n">tokenizer_vocab</span><span class="o">=</span><span class="n">tokenizer_vocab</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">sorted_choices_by_value</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span>
+            <span class="n">answers</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span>
+            <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">choice</span><span class="p">:</span> <span class="n">choice</span><span class="o">.</span><span class="n">get_numeric_value</span><span class="p">(),</span>
+        <span class="p">)</span>
+
+        <span class="c1"># If binary question, return probability of positive answer</span>
+        <span class="c1"># &gt; positive answer always has the highest numeric value</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">answers</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">positive_choice</span> <span class="o">=</span> <span class="n">sorted_choices_by_value</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="k">return</span> <span class="n">answers</span><span class="p">[</span><span class="n">positive_choice</span><span class="p">]</span>
+
+        <span class="c1"># Compute risk estimate by summing weighted choices</span>
+        <span class="n">risk_estimate</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span>
+            <span class="n">choice</span><span class="o">.</span><span class="n">get_numeric_value</span><span class="p">()</span> <span class="o">*</span> <span class="n">prob</span>
+            <span class="k">for</span> <span class="n">choice</span><span class="p">,</span> <span class="n">prob</span> <span class="ow">in</span> <span class="n">answers</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+        <span class="p">)</span>
+
+        <span class="n">logging</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Risk estimate: </span><span class="si">{</span><span class="n">risk_estimate</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">risk_estimate</span></div>
+</div>
+
+</pre></div>
+
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for folktexts.task</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Definition of a generic TaskMetadata class.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">dataclasses</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span><span class="p">,</span> <span class="n">field</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">ClassVar</span><span class="p">,</span> <span class="n">Iterable</span>
+
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="kn">from</span> <span class="nn">._utils</span> <span class="kn">import</span> <span class="n">hash_dict</span>
+<span class="kn">from</span> <span class="nn">.col_to_text</span> <span class="kn">import</span> <span class="n">ColumnToText</span>
+<span class="kn">from</span> <span class="nn">.qa_interface</span> <span class="kn">import</span> <span class="n">DirectNumericQA</span><span class="p">,</span> <span class="n">MultipleChoiceQA</span><span class="p">,</span> <span class="n">QAInterface</span>
+<span class="kn">from</span> <span class="nn">.threshold</span> <span class="kn">import</span> <span class="n">Threshold</span>
+
+
+<div class="viewcode-block" id="TaskMetadata">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.task.TaskMetadata">[docs]</a>
+<span class="nd">@dataclass</span>
+<span class="k">class</span> <span class="nc">TaskMetadata</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A base class to hold information on a prediction task.&quot;&quot;&quot;</span>
+
+    <span class="n">name</span><span class="p">:</span> <span class="nb">str</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;The name of the task.&quot;&quot;&quot;</span>
+
+    <span class="n">features</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;The names of the features used in the task.&quot;&quot;&quot;</span>
+
+    <span class="n">target</span><span class="p">:</span> <span class="nb">str</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;The name of the target column.&quot;&quot;&quot;</span>
+
+    <span class="n">cols_to_text</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">ColumnToText</span><span class="p">]</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A mapping between column names and their textual descriptions.&quot;&quot;&quot;</span>
+
+    <span class="n">sensitive_attribute</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;The name of the column used as the sensitive attribute data (if provided).&quot;&quot;&quot;</span>
+
+    <span class="n">target_threshold</span><span class="p">:</span> <span class="n">Threshold</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;The threshold used to binarize the target column (if provided).&quot;&quot;&quot;</span>
+
+    <span class="n">multiple_choice_qa</span><span class="p">:</span> <span class="n">MultipleChoiceQA</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;The multiple-choice question and answer interface for this task.&quot;&quot;&quot;</span>
+
+    <span class="n">direct_numeric_qa</span><span class="p">:</span> <span class="n">DirectNumericQA</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;The direct numeric question and answer interface for this task.&quot;&quot;&quot;</span>
+
+    <span class="n">description</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A description of the task, including the population to which the task pertains to.&quot;&quot;&quot;</span>
+
+    <span class="n">_use_numeric_qa</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Whether to use numeric Q&amp;A instead of multiple-choice Q&amp;A prompts. Default is False.&quot;&quot;&quot;</span>
+
+    <span class="c1"># Class-level task storage</span>
+    <span class="n">_tasks</span><span class="p">:</span> <span class="n">ClassVar</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="s2">&quot;TaskMetadata&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span><span class="n">default</span><span class="o">=</span><span class="p">{},</span> <span class="n">init</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="nb">repr</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">__post_init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># Check if this task had already been created</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">TaskMetadata</span><span class="o">.</span><span class="n">_tasks</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;A task with `name=&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39;` already exists. Overwriting...&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Add this task to the class-level dictionary</span>
+        <span class="n">TaskMetadata</span><span class="o">.</span><span class="n">_tasks</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span>
+
+        <span class="c1"># Check all required columns are provided by the `cols_to_text` map</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">check_task_columns_are_available</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cols_to_text</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+
+        <span class="c1"># Check target is provided</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;No target column provided for task &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39;. &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Will not be able to generate predictions or use task Q&amp;A prompts. &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Will still be able to generate row descriptions.&quot;</span>
+            <span class="p">)</span>
+            <span class="k">return</span>
+
+        <span class="c1"># If no question is explicitly provided, use the question from the target column</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">multiple_choice_qa</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">direct_numeric_qa</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">target</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;No question was explicitly provided for task &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39;. &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Inferring from target column&#39;s default question (</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">get_target</span><span class="p">()</span><span class="si">}</span><span class="s2">).&quot;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cols_to_text</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">get_target</span><span class="p">()]</span><span class="o">.</span><span class="n">_question</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">question</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cols_to_text</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">get_target</span><span class="p">()]</span><span class="o">.</span><span class="n">_question</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">set_question</span><span class="p">(</span><span class="n">question</span><span class="p">)</span>
+
+        <span class="c1"># Make sure Q&amp;A related attributes are consistent</span>
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_use_numeric_qa</span> <span class="ow">is</span> <span class="kc">True</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">direct_numeric_qa</span> <span class="ow">is</span> <span class="kc">None</span>
+            <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_use_numeric_qa</span> <span class="ow">is</span> <span class="kc">False</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">multiple_choice_qa</span> <span class="ow">is</span> <span class="kc">None</span>
+        <span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Inconsistent Q&amp;A attributes provided.&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="n">hashable_params</span> <span class="o">=</span> <span class="n">dataclasses</span><span class="o">.</span><span class="n">asdict</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="n">hashable_params</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;cols_to_text&quot;</span><span class="p">)</span>
+        <span class="n">hashable_params</span><span class="p">[</span><span class="s2">&quot;question_hash&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">question</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">hash_dict</span><span class="p">(</span><span class="n">hashable_params</span><span class="p">),</span> <span class="mi">16</span><span class="p">)</span>
+
+<div class="viewcode-block" id="TaskMetadata.check_task_columns_are_available">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.task.TaskMetadata.check_task_columns_are_available">[docs]</a>
+    <span class="k">def</span> <span class="nf">check_task_columns_are_available</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">available_cols</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
+        <span class="n">raise_</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Checks if all columns required by this task are available.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        available_cols : list[str]</span>
+<span class="sd">            The list of column names available in the dataset.</span>
+<span class="sd">        raise_ : bool, optional</span>
+<span class="sd">            Whether to raise an error if some columns are missing, by default True.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        all_available : bool</span>
+<span class="sd">            True if all required columns are present in the given list of</span>
+<span class="sd">            available columns, False otherwise.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">required_cols</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">features</span> <span class="o">+</span> <span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">get_target</span><span class="p">()]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target</span> <span class="k">else</span> <span class="p">[])</span>
+        <span class="n">missing_cols</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">required_cols</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">available_cols</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">raise_</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">missing_cols</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;The following required task columns were not found in the dataset: &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="n">missing_cols</span><span class="p">)</span><span class="si">}</span><span class="s2">;&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">missing_cols</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span>   <span class="c1"># Return True if all columns are present</span></div>
+
+
+<div class="viewcode-block" id="TaskMetadata.get_target">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.task.TaskMetadata.get_target">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_target</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Resolves the name of the target column depending on `self.target_threshold`.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;No target column provided for task </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="kc">None</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_threshold</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">target</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="TaskMetadata.set_question">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.task.TaskMetadata.set_question">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_question</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">question</span><span class="p">:</span> <span class="n">QAInterface</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Sets the Q&amp;A interface for this task.&quot;&quot;&quot;</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting question for task &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39; to &#39;</span><span class="si">{</span><span class="n">question</span><span class="o">.</span><span class="n">text</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">question</span><span class="p">,</span> <span class="n">MultipleChoiceQA</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">multiple_choice_qa</span> <span class="o">=</span> <span class="n">question</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_use_numeric_qa</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">question</span><span class="p">,</span> <span class="n">DirectNumericQA</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">direct_numeric_qa</span> <span class="o">=</span> <span class="n">question</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_use_numeric_qa</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid question type: </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">question</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">use_numeric_qa</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Getter for whether to use numeric Q&amp;A instead of multiple-choice Q&amp;A prompts.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_use_numeric_qa</span>
+
+    <span class="nd">@use_numeric_qa</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">use_numeric_qa</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">use_numeric_qa</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Setter for whether to use numeric Q&amp;A instead of multiple-choice Q&amp;A prompts.&quot;&quot;&quot;</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Changing Q&amp;A mode for task &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39; to &quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="s1">&#39;numeric&#39;</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="n">use_numeric_qa</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="s1">&#39;multiple-choice&#39;</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_use_numeric_qa</span> <span class="o">=</span> <span class="n">use_numeric_qa</span>
+
+<div class="viewcode-block" id="TaskMetadata.get_task">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.task.TaskMetadata.get_task">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">get_task</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">use_numeric_qa</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TaskMetadata</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Fetches a previously created task by its name.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        name : str</span>
+<span class="sd">            The name of the task to fetch.</span>
+<span class="sd">        use_numeric_qa : bool, optional</span>
+<span class="sd">            Whether to set the retrieved task to use verbalized numeric Q&amp;A</span>
+<span class="sd">            instead of the default multiple-choice Q&amp;A prompts. Default is False.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        task : TaskMetadata</span>
+<span class="sd">            The task object with the given name.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            Raised if the task with the given name has not been created yet.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_tasks</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Task &#39;</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39; has not been created yet.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Retrieve the task object</span>
+        <span class="n">task</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_tasks</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
+
+        <span class="c1"># Set Q&amp;A interface type</span>
+        <span class="n">task</span><span class="o">.</span><span class="n">use_numeric_qa</span> <span class="o">=</span> <span class="n">use_numeric_qa</span>
+
+        <span class="k">return</span> <span class="n">task</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">question</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">QAInterface</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Getter for the Q&amp;A interface for this task.&quot;&quot;&quot;</span>
+
+        <span class="c1"># Resolve direct numeric Q&amp;A vs multiple-choice Q&amp;A</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_use_numeric_qa</span><span class="p">:</span>
+            <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">direct_numeric_qa</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">multiple_choice_qa</span>
+
+        <span class="k">if</span> <span class="n">q</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;No Q&amp;A interface provided for task </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">q</span>
+
+<div class="viewcode-block" id="TaskMetadata.get_row_description">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.task.TaskMetadata.get_row_description">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_row_description</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Encode a description of a given data row in textual form.&quot;&quot;&quot;</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="p">]</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
+                <span class="s2">&quot;- &quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">cols_to_text</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">get_text</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">row</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+            <span class="p">)</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="TaskMetadata.sensitive_attribute_value_map">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.task.TaskMetadata.sensitive_attribute_value_map">[docs]</a>
+    <span class="k">def</span> <span class="nf">sensitive_attribute_value_map</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns a mapping between sensitive attribute values and their descriptions.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sensitive_attribute</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;No sensitive attribute provided for this task.&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="p">{}</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">cols_to_text</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">sensitive_attribute</span><span class="p">]</span><span class="o">.</span><span class="n">value_map</span></div>
+
+
+<div class="viewcode-block" id="TaskMetadata.create_task_with_feature_subset">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.task.TaskMetadata.create_task_with_feature_subset">[docs]</a>
+    <span class="k">def</span> <span class="nf">create_task_with_feature_subset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feature_subset</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="nb">str</span><span class="p">]):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Creates a new task with a subset of the original features.&quot;&quot;&quot;</span>
+        <span class="c1"># Convert iterable to list</span>
+        <span class="n">feature_subset</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">feature_subset</span><span class="p">)</span>
+
+        <span class="c1"># Check if features are a subset of the original features</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">set</span><span class="p">(</span><span class="n">feature_subset</span><span class="p">)</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;`feature_subset` must be a subset of the original features; &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;following features are not in the original set: &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">set</span><span class="p">(</span><span class="n">feature_subset</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+        <span class="c1"># Return new TaskMetadata object</span>
+        <span class="k">return</span> <span class="n">dataclasses</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="n">name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">_&quot;</span> <span class="o">+</span> <span class="s2">&quot;_&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">feature_subset</span><span class="p">)),</span>
+            <span class="n">features</span><span class="o">=</span><span class="n">feature_subset</span><span class="p">,</span>
+        <span class="p">)</span></div>
+</div>
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
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diff --git a/_modules/folktexts/threshold.html b/_modules/folktexts/threshold.html
new file mode 100644
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+++ b/_modules/folktexts/threshold.html
@@ -0,0 +1,170 @@
+
+
+<!DOCTYPE html>
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+  <meta charset="utf-8" />
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+      <li class="breadcrumb-item active">folktexts.threshold</li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for folktexts.threshold</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Helper function for defining binarization thresholds.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">dataclasses</span>
+<span class="kn">import</span> <span class="nn">operator</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">ClassVar</span>
+
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+
+<div class="viewcode-block" id="Threshold">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.threshold.Threshold">[docs]</a>
+<span class="nd">@dataclasses</span><span class="o">.</span><span class="n">dataclass</span><span class="p">(</span><span class="n">frozen</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">eq</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">class</span> <span class="nc">Threshold</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A class to represent a threshold value and its comparison operator.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    value : float | int</span>
+<span class="sd">        The threshold value to compare against.</span>
+<span class="sd">    op : str</span>
+<span class="sd">        The comparison operator to use. One of &#39;&gt;&#39;, &#39;&lt;&#39;, &#39;&gt;=&#39;, &#39;&lt;=&#39;, &#39;==&#39;, &#39;!=&#39;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">value</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">int</span>
+    <span class="n">op</span><span class="p">:</span> <span class="nb">str</span>
+
+    <span class="n">valid_ops</span><span class="p">:</span> <span class="n">ClassVar</span><span class="p">[</span><span class="nb">dict</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s2">&quot;&gt;&quot;</span><span class="p">:</span> <span class="n">operator</span><span class="o">.</span><span class="n">gt</span><span class="p">,</span>
+        <span class="s2">&quot;&lt;&quot;</span><span class="p">:</span> <span class="n">operator</span><span class="o">.</span><span class="n">lt</span><span class="p">,</span>
+        <span class="s2">&quot;&gt;=&quot;</span><span class="p">:</span> <span class="n">operator</span><span class="o">.</span><span class="n">ge</span><span class="p">,</span>
+        <span class="s2">&quot;&lt;=&quot;</span><span class="p">:</span> <span class="n">operator</span><span class="o">.</span><span class="n">le</span><span class="p">,</span>
+        <span class="s2">&quot;==&quot;</span><span class="p">:</span> <span class="n">operator</span><span class="o">.</span><span class="n">eq</span><span class="p">,</span>
+        <span class="s2">&quot;!=&quot;</span><span class="p">:</span> <span class="n">operator</span><span class="o">.</span><span class="n">ne</span><span class="p">,</span>
+    <span class="p">}</span>
+
+    <span class="k">def</span> <span class="nf">__post_init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">op</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">valid_ops</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid comparison operator &#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="si">}{</span><span class="bp">self</span><span class="o">.</span><span class="n">value</span><span class="si">}</span><span class="s2">&quot;</span>
+
+<div class="viewcode-block" id="Threshold.apply_to_column_data">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.threshold.Threshold.apply_to_column_data">[docs]</a>
+    <span class="k">def</span> <span class="nf">apply_to_column_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Applies the threshold operation to a pandas Series or scalar value.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">):</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">valid_ops</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">](</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="nb">float</span><span class="p">,</span> <span class="nb">int</span><span class="p">)):</span>
+            <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">valid_ops</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">op</span><span class="p">](</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">value</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid data type &#39;</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="si">}</span><span class="s2">&#39;.&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Threshold.apply_to_column_name">
+<a class="viewcode-back" href="../../source/folktexts.html#folktexts.threshold.Threshold.apply_to_column_name">[docs]</a>
+    <span class="k">def</span> <span class="nf">apply_to_column_name</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">column_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Standardizes naming of thresholded columns.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">column_name</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
+
+           </div>
+          </div>
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diff --git a/_modules/index.html b/_modules/index.html
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+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Overview: module code &mdash; folktexts 0.0.25 documentation</title>
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+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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+<li class="toctree-l1"><a class="reference internal" href="../readme.html">Readme file</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../source/modules.html">API reference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../notebooks.html">Example notebooks</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">folktexts</a>
+      </nav>
+
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+      <li class="breadcrumb-item active">Overview: module code</li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>All modules for which code is available</h1>
+<ul><li><a href="folktexts/acs/acs_columns.html">folktexts.acs.acs_columns</a></li>
+<li><a href="folktexts/acs/acs_dataset.html">folktexts.acs.acs_dataset</a></li>
+<li><a href="folktexts/acs/acs_tasks.html">folktexts.acs.acs_tasks</a></li>
+<li><a href="folktexts/benchmark.html">folktexts.benchmark</a></li>
+<li><a href="folktexts/classifier/base.html">folktexts.classifier.base</a></li>
+<li><a href="folktexts/classifier/transformers_classifier.html">folktexts.classifier.transformers_classifier</a></li>
+<li><a href="folktexts/classifier/web_api_classifier.html">folktexts.classifier.web_api_classifier</a></li>
+<li><a href="folktexts/cli/download_models.html">folktexts.cli.download_models</a></li>
+<li><a href="folktexts/cli/eval_feature_importance.html">folktexts.cli.eval_feature_importance</a></li>
+<li><a href="folktexts/cli/experiments.html">folktexts.cli.experiments</a></li>
+<li><a href="folktexts/cli/launch_experiments_htcondor.html">folktexts.cli.launch_experiments_htcondor</a></li>
+<li><a href="folktexts/cli/rerun_experiment.html">folktexts.cli.rerun_experiment</a></li>
+<li><a href="folktexts/cli/run_acs_benchmark.html">folktexts.cli.run_acs_benchmark</a></li>
+<li><a href="folktexts/col_to_text.html">folktexts.col_to_text</a></li>
+<li><a href="folktexts/dataset.html">folktexts.dataset</a></li>
+<li><a href="folktexts/evaluation.html">folktexts.evaluation</a></li>
+<li><a href="folktexts/llm_utils.html">folktexts.llm_utils</a></li>
+<li><a href="folktexts/plotting.html">folktexts.plotting</a></li>
+<li><a href="folktexts/prompting.html">folktexts.prompting</a></li>
+<li><a href="folktexts/qa_interface.html">folktexts.qa_interface</a></li>
+<li><a href="folktexts/task.html">folktexts.task</a></li>
+<li><a href="folktexts/threshold.html">folktexts.threshold</a></li>
+</ul>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
new file mode 100644
index 0000000..b7bf7cf
--- /dev/null
+++ b/_sources/index.rst.txt
@@ -0,0 +1,75 @@
+.. folktexts documentation master file, created by
+   sphinx-quickstart on Wed Dec 13 15:35:43 2023.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+.. error-parity documentation master file, created by
+   sphinx-quickstart on Thu Nov 23 16:53:42 2023.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+
+Welcome to :code:`folktexts`' documentation!
+============================================
+
+The :code:`folktexts` package enables you to benchmark and evaluate 
+LLM-generated risk scores.
+
+We encode unrealizable tabular prediction tasks as natural language text tasks,
+and prompt LLMs for the probability of a target variable being true.
+The correct solutions for each task often require expressing uncertainty, as the
+target variable is not uniquely determined by the input features.
+
+Folktexts is compatible with any huggingface transformer model and models 
+available through web APIs (e.g., OpenAI API).
+
+Five tabular data tasks are provided out-of-the-box, using the American
+Community Survey as a data source: `ACSIncome`, `ACSMobility`, `ACSTravelTime`, 
+`ACSEmployment`, and `ACSPublicCoverage`. These tasks follow the same name, 
+feature columns, and target columns as those put forth by `Ding et al. (2021)`_ 
+in the `folktables`_ python package.
+
+
+Full code available on the `GitHub repository`_, 
+including various `jupyter notebook examples`_ .
+
+Check out the following sub-pages:
+
+.. toctree::
+   :maxdepth: 1
+
+   Readme file <readme>
+   API reference <source/modules>
+   Example notebooks <notebooks>
+
+
+Citing
+======
+
+The :code:`folktexts` package is the basis for the following `publication`_:
+
+.. code-block:: bib
+
+   @inproceedings{cruz2024evaluating,
+      title={Evaluating language models as risk scores},
+      author={Andr\'{e} F. Cruz and Moritz Hardt and Celestine Mendler-D\"{u}nner},
+      booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
+      year={2024},
+      url={https://openreview.net/forum?id=qrZxL3Bto9}
+   }
+
+All additional supplementary materials are available in the `GitHub repository`_.
+
+
+Indices
+=======
+
+* :ref:`genindex`
+* :ref:`modindex`
+
+
+.. _publication: https://arxiv.org/abs/2407.14614
+.. _folktables: https://github.com/socialfoundations/folktables
+.. _Ding et al. (2021): https://arxiv.org/abs/2108.04884
+.. _GitHub repository: https://github.com/socialfoundations/folktexts
+.. _jupyter notebook examples: https://github.com/socialfoundations/folktexts/tree/main/notebooks
diff --git a/_sources/notebooks.rst.txt b/_sources/notebooks.rst.txt
new file mode 100644
index 0000000..66f78f5
--- /dev/null
+++ b/_sources/notebooks.rst.txt
@@ -0,0 +1,11 @@
+Notebooks Gallery
+=================
+
+.. nbgallery::
+   notebooks/minimal-example.ipynb
+   notebooks/detailed-example.ipynb
+   notebooks/custom-dataset-example.ipynb
+   notebooks/custom-acs-task-example.ipynb
+   notebooks/paper-plots-and-tables.ipynb
+   notebooks/minimal-example_web-API-model.ipynb
+   notebooks/parse-acs-results.ipynb
diff --git a/_sources/notebooks/README.md.txt b/_sources/notebooks/README.md.txt
new file mode 100644
index 0000000..98caa36
--- /dev/null
+++ b/_sources/notebooks/README.md.txt
@@ -0,0 +1,13 @@
+# Example notebooks using `folktexts`
+
+| File | Category | Description |
+| ---- | - | ----------- |
+| [minimal-example.ipynb](minimal-example.ipynb) | Usage example | **[Start here]** A minimal example of how to use `folktexts` to evaluate model calibration and other statistics. |
+| [detailed-example.ipynb](detailed-example.ipynb) | Usage example | Detailed example of running an ACS benchmark on a given LLM. |
+| [custom-acs-task-example.ipynb](custom-acs-task-example.ipynb) | Usage example | Example of how to define and benchmark LLMs on a custom ACS task. |
+| [custom-dataset-example.ipynb](custom-dataset-example.ipynb) | Usage example | Example of using `folktexts` with a different dataset (the [MEPS](https://meps.ahrq.gov/mepsweb/index.jsp) dataset). |
+| [minimal-example_web-API-model.ipynb](minimal-example_web-API-model.ipynb) | Usage example | A minimal example of how to use `folktexts` with models hosted through a web API. |
+| [paper-plots-and-tables.ipynb](paper-plots-and-tables.ipynb) | Plotting and analysis | Generate paper plots and tables using benchmark results. |
+| [parse-acs-results.ipynb](parse-acs-results.ipynb) | Parsing results | Aggregates and parses all ACS benchmark results saved under a given directory. |
+| [parse-feature-importance-results.ipynb](parse-feature-importance-results.ipynb) | Parsing results | Aggregates, parses, and plots results relating to feature importance. |
+| [parse-acs-results_using-feature-subsets.ipynb](parse-acs-results_using-feature-subsets.ipynb) | Parsing results | Aggregates, parses and plots results relating ablations on subsets of features. |
diff --git a/_sources/notebooks/custom-acs-task-example.ipynb.txt b/_sources/notebooks/custom-acs-task-example.ipynb.txt
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run `folktexts` benchmark with a custom ACS task\n",
+    "\n",
+    "This notebook describes how to define a custom prediction task based on \n",
+    "American Community Survey (ACS) data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.0.22'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import folktexts\n",
+    "folktexts.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** Change the `DATA_DIR` variable to the ACS data path on your file system (if any, otherwise will attempt to download data)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define custom task objects appropriately:\n",
+    "\n",
+    "In this example, we'll try to predict whether someone is or ever was part of the Military, based on a series of demographic features.\n",
+    "\n",
+    "We must define objects of the following classes:\n",
+    "- `Threshold`: Used to binarize the target column (not needed if target is already binary);\n",
+    "- `MultipleChoiceQA`: Defines a question and answer scheme for the target column;\n",
+    "- `TaskMetadata`: Defines which columns to use as features, and which to use as the prediction target;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select the target column (among those defined in `folktexts.acs.acs_columns`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TARGET_COLUMN = \"MIL\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define threshold to be applied to target column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.threshold import Threshold\n",
+    "target_threshold = Threshold(4, \"!=\")\n",
+    "\n",
+    "# data[\"MIL\"] != 4 means \"Is on active duty in the military or was in the past\"\n",
+    "# data[\"MIL\"] == 4 means \"Never served in the military\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define question and answer interface used for prompting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.qa_interface import MultipleChoiceQA\n",
+    "from folktexts.qa_interface import Choice\n",
+    "\n",
+    "target_column_qa = MultipleChoiceQA(\n",
+    "    column=target_threshold.apply_to_column_name(TARGET_COLUMN),\n",
+    "    text=\"Has this person ever served in the military?\",\n",
+    "    choices=(\n",
+    "        Choice(\"Yes, this person is now on active duty or was in the past\", 1),\n",
+    "        Choice(\"No, this person has never served in the military\", 0),\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define task metadata:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.acs import ACSTaskMetadata\n",
+    "\n",
+    "task = ACSTaskMetadata.make_task(\n",
+    "    name=\"ACSMilitary\",\n",
+    "    description=\"predict if a person has ever served in the military\",\n",
+    "    features=[\n",
+    "        \"AGEP\", \"SCHL\", \"MAR\", \"POBP\", \"WKHP\", \"SEX\", \"RAC1P\", \"ST\", \"CIT\", \"DIS\", \"PINCP\",\n",
+    "    ],\n",
+    "    target=TARGET_COLUMN,\n",
+    "    target_threshold=target_threshold,\n",
+    "    sensitive_attribute=\"RAC1P\",\n",
+    "    multiple_choice_qa=target_column_qa,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load ACS dataset for the custom task:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "CPU times: user 38 s, sys: 19.3 s, total: 57.3 s\n",
+      "Wall time: 57.4 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.acs.acs_dataset import ACSDataset\n",
+    "acs_dataset = ACSDataset.make_from_task(\n",
+    "    task=task,\n",
+    "    cache_dir=DATA_DIR,)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** This dataset will contain all samples in the ACS PUMS file, you can\n",
+    "now filter the `acs_dataset.data` if you want to use only a portion of the data \n",
+    "(e.g, only people in California, or only people above 18 years old).\n",
+    "\n",
+    "As an example, we'll filter the ACS data to contain only people above 18 years old."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original number of samples: 3236107\n",
+      "Parsed number of samples: 2580544\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Original number of samples: {len(acs_dataset.data)}\")\n",
+    "\n",
+    "acs_dataset.data = acs_dataset.data[acs_dataset.data[\"AGEP\"] >= 18]\n",
+    "print(f\"Parsed number of samples: {len(acs_dataset.data)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Optionally,** you can subsample the dataset to have faster but noisier results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<folktexts.acs.acs_dataset.ACSDataset at 0x14584395d510>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "acs_dataset.subsample(0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load LLM from huggingface or from local disk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAME_OR_PATH = \"meta-llama/Meta-Llama-3-8B\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9adceb50efa487fa02fd702a7ef8bb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4.39 s, sys: 6.29 s, total: 10.7 s\n",
+      "Wall time: 13.1 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.llm_utils import load_model_tokenizer\n",
+    "model, tokenizer = load_model_tokenizer(MODEL_NAME_OR_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create `LLMClassifier` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.classifier import TransformersLLMClassifier\n",
+    "\n",
+    "llm_clf = TransformersLLMClassifier(\n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    task=task,\n",
+    "    batch_size=10,\n",
+    "    context_size=600,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Validation:** Check that model prompts with the custom task make sense, or if anything needs to be adjusted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following data corresponds to a survey respondent. The survey was conducted among US residents in 2018. Please answer the question based on the information provided. The data provided is enough to reach an approximate answer.\n",
+      "\n",
+      "Information:\n",
+      "- The age is: 50 years old.\n",
+      "- The highest educational attainment is: Regular high school diploma.\n",
+      "- The marital status is: Married.\n",
+      "- The place of birth is: Nevada.\n",
+      "- The usual number of hours worked per week is: 50 hours.\n",
+      "- The sex is: Male.\n",
+      "- The race is: White.\n",
+      "- The resident state is: Nevada.\n",
+      "- The citizenship status is: Born in the United States.\n",
+      "- The disability status is: No disability.\n",
+      "- The yearly income is: $70,000.\n",
+      "\n",
+      "Question: Has this person ever served in the military?\n",
+      "A. Yes, this person is now on active duty or was in the past.\n",
+      "B. No, this person has never served in the military.\n",
+      "Answer:\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_sample, _y_sample = acs_dataset.sample_n_train_examples(n=1)\n",
+    "print(llm_clf.encode_row(X_sample.iloc[0], question=llm_clf.task.question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run benchmark on the custom task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.benchmark import Benchmark\n",
+    "bench = Benchmark(llm_clf=llm_clf, dataset=acs_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1f553e570e043d0aa2567237fafc552",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4min 25s, sys: 5.32 s, total: 4min 31s\n",
+      "Wall time: 4min 30s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 2581,\n",
+       " 'n_positives': 259,\n",
+       " 'n_negatives': 2322,\n",
+       " 'model_name': 'Meta-Llama-3-8B',\n",
+       " 'accuracy': 0.8082138705927935,\n",
+       " 'tpr': 0.305019305019305,\n",
+       " 'fnr': 0.694980694980695,\n",
+       " 'fpr': 0.13565891472868216,\n",
+       " 'tnr': 0.8643410852713178,\n",
+       " 'balanced_accuracy': 0.5846801951453114,\n",
+       " 'precision': 0.20050761421319796,\n",
+       " 'ppr': 0.15265401007361487,\n",
+       " 'log_loss': 0.6253456831680403,\n",
+       " 'brier_score_loss': 0.21635791438469001,\n",
+       " 'accuracy_ratio': 0.79638671875,\n",
+       " 'accuracy_diff': 0.20361328125,\n",
+       " 'fpr_ratio': 0.0,\n",
+       " 'fpr_diff': 1.0,\n",
+       " 'fnr_ratio': 0.0,\n",
+       " 'fnr_diff': 1.0,\n",
+       " 'precision_ratio': 0.0,\n",
+       " 'precision_diff': 1.0,\n",
+       " 'balanced_accuracy_ratio': 0.4435516271595018,\n",
+       " 'balanced_accuracy_diff': 0.5282227307398932,\n",
+       " 'tpr_ratio': 0.0,\n",
+       " 'tpr_diff': 1.0,\n",
+       " 'ppr_ratio': 0.0,\n",
+       " 'ppr_diff': 1.0,\n",
+       " 'tnr_ratio': 0.0,\n",
+       " 'tnr_diff': 1.0,\n",
+       " 'equalized_odds_ratio': 0.0,\n",
+       " 'equalized_odds_diff': 1.0,\n",
+       " 'roc_auc': 0.6900338877083063,\n",
+       " 'ece': 0.3593739652181054,\n",
+       " 'ece_quantile': 0.3593739652181053,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/ACSMilitary_subsampled-0.01_seed-42_hash-1434010626.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': False,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'Meta-Llama-3-8B',\n",
+       "  'model_hash': 1160994164,\n",
+       "  'task_name': 'ACSMilitary',\n",
+       "  'task_hash': 4147552870,\n",
+       "  'dataset_name': 'ACSMilitary_subsampled-0.01_seed-42_hash-1434010626',\n",
+       "  'dataset_subsampling': 0.01,\n",
+       "  'dataset_hash': 1434010626},\n",
+       " 'benchmark_hash': 190963433,\n",
+       " 'results_dir': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433',\n",
+       " 'results_root_dir': '/lustre/home/acruz/folktexts/notebooks',\n",
+       " 'current_time': '2024.08.30-14.57.43',\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "bench.run(results_root_dir=\".\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bench.plot_results();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/notebooks/custom-dataset-example.ipynb.txt b/_sources/notebooks/custom-dataset-example.ipynb.txt
new file mode 100644
index 0000000..edd1013
--- /dev/null
+++ b/_sources/notebooks/custom-dataset-example.ipynb.txt
@@ -0,0 +1,763 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run `folktexts` benchmark with a different data source\n",
+    "\n",
+    "This notebook describes how to use the `folktexts` pipeline to run a benchmark on a different\n",
+    "tabular dataset (distinct from the ACS tasks provided out-of-the-box).\n",
+    "\n",
+    "In this example, we have mapped 11 columns of the [Medical Expenditure Panel Survey (MEPS)](https://meps.ahrq.gov/mepsweb/index.jsp) dataset (survey panels 19, 20, and 21).\n",
+    "\n",
+    "Data codebook available [at this link](https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_codebook.jsp?PUFId=H192)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.0.22'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import folktexts\n",
+    "\n",
+    "folktexts.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define mappings for each dataset column using `ColumnToText`: each value of each\n",
+    "column should be mapped to a meaningful text representation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.col_to_text import ColumnToText\n",
+    "\n",
+    "# AGE\n",
+    "meps_age_col = ColumnToText(\n",
+    "    \"AGE\",\n",
+    "    short_description=\"age\",\n",
+    "    value_map=lambda x: f\"{int(x)} years old\",\n",
+    ")\n",
+    "\n",
+    "# REGION: Census region\n",
+    "meps_region_col = ColumnToText(\n",
+    "    \"REGION\",\n",
+    "    short_description=\"US region\",\n",
+    "    value_map={\n",
+    "        1: \"Northeast\",\n",
+    "        2: \"Midwest\",\n",
+    "        3: \"South\",\n",
+    "        4: \"West\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# SEX\n",
+    "meps_sex_col = ColumnToText(\n",
+    "    \"SEX\",\n",
+    "    short_description=\"sex\",\n",
+    "    value_map={\n",
+    "        1: \"Male\",\n",
+    "        2: \"Female\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# MARRY / MARRY31X\n",
+    "meps_marital_status = ColumnToText(\n",
+    "    \"MARRY\",\n",
+    "    short_description=\"marital status\",\n",
+    "    value_map={\n",
+    "        1: \"Married\",\n",
+    "        2: \"Widowed\",\n",
+    "        3: \"Divorced\",\n",
+    "        4: \"Separated\",\n",
+    "        5: \"Never married\",\n",
+    "        6: \"Inapplicable - Under 16 years old\",\n",
+    "        7: \"Married during current survey round\",\n",
+    "        8: \"Widowed during current survey round\",\n",
+    "        9: \"Divorced during current survey round\",\n",
+    "        10: \"Separated during current survey round\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# HONRDC: Honorably discharged from military\n",
+    "meps_education_col = ColumnToText(\n",
+    "    \"HONRDC\",\n",
+    "    short_description=\"honorably discharged status\",\n",
+    "    value_map={\n",
+    "        1: \"Yes, honorably discharged from military\",\n",
+    "        2: \"Not part of military or not honorably discharged\",\n",
+    "        3: \"Inapplicable - Under 16 years old\",\n",
+    "        4: \"Inapplicable - Now on active duty\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# RTHLTH / RTHLTH31: Perceived health status\n",
+    "meps_health_status_col = ColumnToText(\n",
+    "    \"RTHLTH\",\n",
+    "    short_description=\"self-rated health status\",\n",
+    "    value_map={\n",
+    "        1: \"Excellent\",\n",
+    "        2: \"Very good\",\n",
+    "        3: \"Good\",\n",
+    "        4: \"Fair\",\n",
+    "        5: \"Poor\",\n",
+    "        -1: \"Inapplicable - Missing data\",\n",
+    "        -7: \"Inapplicable - Refused to answer\",\n",
+    "        -8: \"Inapplicable - Don't know\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# MNHLTH / MNHLTH31: Perceived mental health status\n",
+    "meps_mental_health_status_col = ColumnToText(\n",
+    "    \"MNHLTH\",\n",
+    "    short_description=\"self-rated mental health status\",\n",
+    "    value_map={\n",
+    "        1: \"Excellent\",\n",
+    "        2: \"Very good\",\n",
+    "        3: \"Good\",\n",
+    "        4: \"Fair\",\n",
+    "        5: \"Poor\",\n",
+    "        -1: \"Inapplicable - Missing data\",\n",
+    "        -7: \"Inapplicable - Refused to answer\",\n",
+    "        -8: \"Inapplicable - Don't know\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# POVCAT / POVCAT15: Poverty category\n",
+    "meps_poverty_category_col = ColumnToText(\n",
+    "    \"POVCAT\",\n",
+    "    short_description=\"poverty category\",\n",
+    "    value_map={\n",
+    "        1: \"Poor\",\n",
+    "        2: \"Near poor\",\n",
+    "        3: \"Low income\",\n",
+    "        4: \"Middle income\",\n",
+    "        5: \"High income\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# INSCOV / INSCOV15: Insurance coverage\n",
+    "meps_insurance_coverage_col = ColumnToText(\n",
+    "    \"INSCOV\",\n",
+    "    short_description=\"insurance coverage\",\n",
+    "    value_map={\n",
+    "        1: \"Private insurance\",\n",
+    "        2: \"Public insurance\",\n",
+    "        3: \"Uninsured\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# DIABDX: Diabetes diagnosis\n",
+    "meps_diabetes_col = ColumnToText(\n",
+    "    \"DIABDX\",\n",
+    "    short_description=\"diabetes diagnosis\",\n",
+    "    value_map={\n",
+    "        1: \"Yes, diagnosed with diabetes\",\n",
+    "        2: \"No, not diagnosed with diabetes\",\n",
+    "        -1: \"Inapplicable - Under 17 years old\",\n",
+    "    },\n",
+    "    missing_value_fill=\"Inapplicable - Under 17 years old\",\n",
+    ")\n",
+    "\n",
+    "# HIBPDX: High blood pressure diagnosis\n",
+    "meps_high_blood_pressure_col = ColumnToText(\n",
+    "    \"HIBPDX\",\n",
+    "    short_description=\"high blood pressure diagnosis\",\n",
+    "    value_map={\n",
+    "        1: \"Yes, diagnosed with high blood pressure\",\n",
+    "        2: \"No, not diagnosed with high blood pressure\",\n",
+    "        -1: \"Inapplicable - Under 17 years old\",\n",
+    "    },\n",
+    "    missing_value_fill=\"Inapplicable - Under 17 years old\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the target column mapping and the question to prompt the LLM with by instantiating a `MultipleChoiceQA` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.qa_interface import MultipleChoiceQA, Choice\n",
+    "\n",
+    "TARGET_COL = \"UTILIZATION\"\n",
+    "\n",
+    "utilization_qa = MultipleChoiceQA(\n",
+    "    column=TARGET_COL,\n",
+    "    text=\"Was is this person's estimated number of doctor visits in the past year?\",\n",
+    "    choices=(\n",
+    "        Choice(\"More than 10 doctor visits (high health-care utilization)\", 1),\n",
+    "        Choice(\"10 or fewer doctor visits (low health-care utilization)\", 0),\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# UTILIZATION: Number of doctor visits\n",
+    "meps_utilization_col = ColumnToText(\n",
+    "    TARGET_COL,\n",
+    "    short_description=\"doctor visits\",\n",
+    "    question=utilization_qa,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optionally, you can define a **numeric question prompt that asks for a verbalized probability** by instantiating a `DirectNumericQA` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.qa_interface import DirectNumericQA\n",
+    "\n",
+    "utilization_numeric_qa = DirectNumericQA(\n",
+    "    column=TARGET_COL,\n",
+    "    text=(\n",
+    "        \"What is the probability that this person has high health-care utilization? \"\n",
+    "        \"(i.e., more than 10 doctor visits per year)\"\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the prediction task be instantiating a `TaskMetadata` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper dict to access ColumnToText objects by column name\n",
+    "meps_columns_map: dict[str, object] = {\n",
+    "    col_mapper.name: col_mapper\n",
+    "    for col_mapper in globals().values()\n",
+    "    if isinstance(col_mapper, ColumnToText)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.task import TaskMetadata\n",
+    "\n",
+    "meps_task = TaskMetadata(\n",
+    "    name=\"health-care utilization\",\n",
+    "    description=(\n",
+    "        \"predict whether an individual had low or high healthcare utilization \"\n",
+    "        \"in the past year by their number of doctor visits\"\n",
+    "    ),\n",
+    "    features=[col.name for col in meps_columns_map.values() if col.name != TARGET_COL],\n",
+    "    target=TARGET_COL,\n",
+    "    cols_to_text=meps_columns_map,\n",
+    "    sensitive_attribute=\"SEX\",\n",
+    "    multiple_choice_qa=utilization_qa,\n",
+    "    direct_numeric_qa=utilization_numeric_qa,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Set** whether to use numeric risk prompting or the default multiple-choice Q&A prompting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meps_task.use_numeric_qa = False\n",
+    "# meps_task.use_numeric_qa = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load MEPS data and instantiate the `Dataset` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.dataset import Dataset\n",
+    "\n",
+    "DATA_PATH = Path(folktexts.__file__).parent.parent / \"notebooks\" / \"data\" / \"meps.csv\"\n",
+    "meps_df = pd.read_csv(DATA_PATH)\n",
+    "\n",
+    "dataset = Dataset(\n",
+    "    data=meps_df,\n",
+    "    task=meps_task,\n",
+    "    test_size=0.4,\n",
+    "    val_size=0,\n",
+    "    subsampling=0.1,   # NOTE: Optional, for faster but noisier results!\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Optionally,** you can subsample the data to get faster but noisier results, with:\n",
+    "```py\n",
+    "dataset.subsample(0.1)  # Keeps only 10% of the data\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load huggingface LLM:\n",
+    "\n",
+    "**Note:** Update `MODEL_NAME_OR_PATH` as needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAME_OR_PATH = \"meta-llama/Meta-Llama-3-8B\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e35aa130a0844ed9910d21349c8c5c44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from folktexts.classifier import TransformersLLMClassifier\n",
+    "from folktexts.llm_utils import load_model_tokenizer\n",
+    "\n",
+    "model, tokenizer = load_model_tokenizer(MODEL_NAME_OR_PATH)\n",
+    "\n",
+    "llm_clf = TransformersLLMClassifier(\n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    task=meps_task,\n",
+    "    batch_size=20,\n",
+    "    context_length=800,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example LLM prompt for this task:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following data corresponds to a survey respondent. The survey was conducted among US residents in 2018. Please answer the question based on the information provided. The data provided is enough to reach an approximate answer.\n",
+      "\n",
+      "Information:\n",
+      "- The age is: 37 years old.\n",
+      "- The US region is: West.\n",
+      "- The sex is: Female.\n",
+      "- The marital status is: Never married.\n",
+      "- The honorably discharged status is: Not part of military or not honorably discharged.\n",
+      "- The self-rated health status is: Good.\n",
+      "- The self-rated mental health status is: Good.\n",
+      "- The poverty category is: Poor.\n",
+      "- The insurance coverage is: Private insurance.\n",
+      "- The diabetes diagnosis is: No, not diagnosed with diabetes.\n",
+      "- The high blood pressure diagnosis is: No, not diagnosed with high blood pressure.\n",
+      "\n",
+      "Question: Was is this person's estimated number of doctor visits in the past year?\n",
+      "A. More than 10 doctor visits (high health-care utilization).\n",
+      "B. 10 or fewer doctor visits (low health-care utilization).\n",
+      "Answer:\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_sample, _y_sample = dataset.sample_n_train_examples(n=1)\n",
+    "print(llm_clf.encode_row(X_sample.iloc[0], question=llm_clf.task.question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run benchmark on MEPS data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.benchmark import BenchmarkConfig, Benchmark\n",
+    "bench = Benchmark(llm_clf=llm_clf, dataset=dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a6560c0c807478387e3a96afddf1214",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/99 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 45s, sys: 5.43 s, total: 2min 51s\n",
+      "Wall time: 2min 50s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 1963,\n",
+       " 'n_positives': 354,\n",
+       " 'n_negatives': 1609,\n",
+       " 'model_name': 'Meta-Llama-3-8B',\n",
+       " 'accuracy': 0.630667345899134,\n",
+       " 'tpr': 0.7033898305084746,\n",
+       " 'fnr': 0.2966101694915254,\n",
+       " 'fpr': 0.3853325046612803,\n",
+       " 'tnr': 0.6146674953387197,\n",
+       " 'balanced_accuracy': 0.6590286629235971,\n",
+       " 'precision': 0.286536248561565,\n",
+       " 'ppr': 0.44268976057055526,\n",
+       " 'log_loss': 0.6640880361799258,\n",
+       " 'brier_score_loss': 0.23555848017340178,\n",
+       " 'fnr_ratio': 0.7005952380952382,\n",
+       " 'fnr_diff': 0.1007343124165554,\n",
+       " 'fpr_ratio': 0.9499246987951807,\n",
+       " 'fpr_diff': 0.019768133174791924,\n",
+       " 'ppr_ratio': 0.9785014080325124,\n",
+       " 'ppr_diff': 0.009620674692892017,\n",
+       " 'precision_ratio': 0.7380730854374539,\n",
+       " 'precision_diff': 0.086496795041585,\n",
+       " 'tpr_ratio': 0.8681980959035723,\n",
+       " 'tpr_diff': 0.1007343124165554,\n",
+       " 'balanced_accuracy_ratio': 0.9408797810568554,\n",
+       " 'balanced_accuracy_diff': 0.04048308962088176,\n",
+       " 'tnr_ratio': 0.968370986920333,\n",
+       " 'tnr_diff': 0.01976813317479187,\n",
+       " 'accuracy_ratio': 0.9913632326092872,\n",
+       " 'accuracy_diff': 0.0054705390193721115,\n",
+       " 'equalized_odds_ratio': 0.7005952380952382,\n",
+       " 'equalized_odds_diff': 0.1007343124165554,\n",
+       " 'roc_auc': 0.7183612307886781,\n",
+       " 'ece': 0.30890763123834214,\n",
+       " 'ece_quantile': 0.3089076312383417,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/health-care utilization_subsampled-0.1_seed-42_hash-1494059445.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': False,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'Meta-Llama-3-8B',\n",
+       "  'model_hash': 2852898721,\n",
+       "  'task_name': 'health-care utilization',\n",
+       "  'task_hash': 3442708664,\n",
+       "  'dataset_name': 'health-care utilization_subsampled-0.1_seed-42_hash-1494059445',\n",
+       "  'dataset_subsampling': 0.1,\n",
+       "  'dataset_hash': 1494059445},\n",
+       " 'benchmark_hash': 1075925211,\n",
+       " 'results_dir': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211',\n",
+       " 'results_root_dir': '/lustre/home/acruz/folktexts/notebooks',\n",
+       " 'current_time': '2024.08.30-14.54.40',\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "bench.run(results_root_dir=\".\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bench.plot_results();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 1963,\n",
+       " 'n_positives': 354,\n",
+       " 'n_negatives': 1609,\n",
+       " 'model_name': 'Meta-Llama-3-8B',\n",
+       " 'accuracy': 0.630667345899134,\n",
+       " 'tpr': 0.7033898305084746,\n",
+       " 'fnr': 0.2966101694915254,\n",
+       " 'fpr': 0.3853325046612803,\n",
+       " 'tnr': 0.6146674953387197,\n",
+       " 'balanced_accuracy': 0.6590286629235971,\n",
+       " 'precision': 0.286536248561565,\n",
+       " 'ppr': 0.44268976057055526,\n",
+       " 'log_loss': 0.6640880361799258,\n",
+       " 'brier_score_loss': 0.23555848017340178,\n",
+       " 'fnr_ratio': 0.7005952380952382,\n",
+       " 'fnr_diff': 0.1007343124165554,\n",
+       " 'fpr_ratio': 0.9499246987951807,\n",
+       " 'fpr_diff': 0.019768133174791924,\n",
+       " 'ppr_ratio': 0.9785014080325124,\n",
+       " 'ppr_diff': 0.009620674692892017,\n",
+       " 'precision_ratio': 0.7380730854374539,\n",
+       " 'precision_diff': 0.086496795041585,\n",
+       " 'tpr_ratio': 0.8681980959035723,\n",
+       " 'tpr_diff': 0.1007343124165554,\n",
+       " 'balanced_accuracy_ratio': 0.9408797810568554,\n",
+       " 'balanced_accuracy_diff': 0.04048308962088176,\n",
+       " 'tnr_ratio': 0.968370986920333,\n",
+       " 'tnr_diff': 0.01976813317479187,\n",
+       " 'accuracy_ratio': 0.9913632326092872,\n",
+       " 'accuracy_diff': 0.0054705390193721115,\n",
+       " 'equalized_odds_ratio': 0.7005952380952382,\n",
+       " 'equalized_odds_diff': 0.1007343124165554,\n",
+       " 'roc_auc': 0.7183612307886781,\n",
+       " 'ece': 0.30890763123834214,\n",
+       " 'ece_quantile': 0.3089076312383417,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/health-care utilization_subsampled-0.1_seed-42_hash-1494059445.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': False,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'Meta-Llama-3-8B',\n",
+       "  'model_hash': 2852898721,\n",
+       "  'task_name': 'health-care utilization',\n",
+       "  'task_hash': 3442708664,\n",
+       "  'dataset_name': 'health-care utilization_subsampled-0.1_seed-42_hash-1494059445',\n",
+       "  'dataset_subsampling': 0.1,\n",
+       "  'dataset_hash': 1494059445},\n",
+       " 'benchmark_hash': 1075925211,\n",
+       " 'results_dir': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211',\n",
+       " 'results_root_dir': '/lustre/home/acruz/folktexts/notebooks',\n",
+       " 'current_time': '2024.08.30-14.54.41',\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bench.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/notebooks/detailed-example.ipynb.txt b/_sources/notebooks/detailed-example.ipynb.txt
new file mode 100644
index 0000000..6210949
--- /dev/null
+++ b/_sources/notebooks/detailed-example.ipynb.txt
@@ -0,0 +1,1223 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e85e7f8a-dcfa-489a-9a8e-77225d2e3499",
+   "metadata": {},
+   "source": [
+    "# Example: Run ACS benchmark task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "98180e30-da85-41fb-a360-35b6acacb170",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "folktexts.__version__='0.0.21'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import folktexts\n",
+    "print(f\"{folktexts.__version__=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e5c41432-8ee0-4fbb-b37b-4eea92504382",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7a4b1e7f-a893-488e-96f1-4094857115f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "logging.getLogger().setLevel(logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04d5e737-9b3c-468a-bd68-b1c161f57c84",
+   "metadata": {},
+   "source": [
+    "## Set important local paths\n",
+    "**NOTE:** Can be ignored if you haven't previously downloaded the model, just use `load_model_tokenizer` with the model's name on Huggingface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cf6dfe9-4ddc-448f-afb9-0e4802ae480d",
+   "metadata": {},
+   "source": [
+    "Set your root directory (_**change as appropriate**_):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "048f506d-83a6-4911-b5f3-2fc290ed9fd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/fast/groups/sf')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ROOT_DIR = Path(\"/fast/groups/sf\")\n",
+    "ROOT_DIR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cb6c30e-5af1-4733-8732-788cba086eaf",
+   "metadata": {},
+   "source": [
+    "Directory where LLMs are saved (_**change as appropriate**_):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a7e60a72-e0c4-4fa8-a5d5-402f82098872",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODELS_DIR = ROOT_DIR / \"huggingface-models\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efbc677d-5208-4a8a-aab8-9cb7282ca39f",
+   "metadata": {},
+   "source": [
+    "Directory where data is saved or will be saved to (_**change as appropriate**_):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8040b3cd-da53-4b62-8aae-58b2bb7cba69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = ROOT_DIR / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "985344ec-6c9c-4e8b-8d8e-26f009937a7c",
+   "metadata": {},
+   "source": [
+    "Other configs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4316d94-110f-4014-a823-95300ac87eb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAME = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
+    "# MODEL_NAME = \"google/gemma-2b\"    # Smaller model that is faster to run\n",
+    "\n",
+    "TASK_NAME = \"ACSIncome\"\n",
+    "\n",
+    "RESULTS_ROOT_DIR = ROOT_DIR / \"folktexts-results\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7e0f598d-dd37-471f-a0d4-4b3c1ae07167",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Loading model '/lustre/fast/fast/groups/sf/huggingface-models/meta-llama--Meta-Llama-3-8B-Instruct'\n",
+      "INFO:accelerate.utils.modeling:We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set `max_memory` in to a higher value to use more memory (at your own risk).\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0a08cdd63fa4bf4815578e005a80497",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Moving model to device: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "from folktexts.llm_utils import load_model_tokenizer, get_model_folder_path\n",
+    "model_folder_path = get_model_folder_path(model_name=MODEL_NAME, root_dir=MODELS_DIR)\n",
+    "model, tokenizer = load_model_tokenizer(model_folder_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ab088b1f-37ba-4d2e-b5d2-b829787b7fed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_dir = RESULTS_ROOT_DIR / Path(model_folder_path).name\n",
+    "results_dir.mkdir(exist_ok=True, parents=True)\n",
+    "results_dir"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d87da67-3857-421f-b761-81814ea5498d",
+   "metadata": {},
+   "source": [
+    "### Construct LLM Classifier\n",
+    "\n",
+    "**NOTE:** Also compatible with models hosted through a web API by using the `WebAPILLMClassifier` class instead of `TransformersLLMClassifier`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e664c322-4da5-49b3-a755-3ba0a075ee64",
+   "metadata": {},
+   "source": [
+    "Load prediction task (which maps tabular data to text):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7f0b725d-d97a-40a9-a71d-88fb5fefbddf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Changing Q&A mode for task 'ACSIncome' to multiple-choice.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from folktexts.acs import ACSTaskMetadata\n",
+    "task = ACSTaskMetadata.get_task(TASK_NAME, use_numeric_qa=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0cfa6291-5c06-4618-a14d-86e49f2f4969",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.classifier import TransformersLLMClassifier\n",
+    "llm_clf = TransformersLLMClassifier(\n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    task=task,\n",
+    "    batch_size=32,\n",
+    "    context_size=1000,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53f969d6-9d0e-48c2-9b34-a57aef0edb5c",
+   "metadata": {},
+   "source": [
+    "### Load Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "820004cb-25ce-4802-85c3-6ab251881141",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "CPU times: user 58 s, sys: 27.5 s, total: 1min 25s\n",
+      "Wall time: 1min 26s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.acs import ACSDataset\n",
+    "dataset = ACSDataset.make_from_task(task=task, cache_dir=DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8145eddd-09da-4db9-8779-c61b04c3d777",
+   "metadata": {},
+   "source": [
+    "Optionally, subsample to quickly get approximate results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c977cac3-162c-4cde-8594-e6795f62e29a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Train size: 13316, Test size:  1665, Val size:   1665;\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dataset.subsampling=0.01\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.subsample(0.01)\n",
+    "print(f\"{dataset.subsampling=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3b4ce61-ecbc-4491-bd7b-0cfae38c6c10",
+   "metadata": {},
+   "source": [
+    "### Load and run ACS Benchmark\n",
+    "\n",
+    "**_Note:_** Helper constructors exist at `Benchmark.make_acs_benchmark` and `Benchmark.make_benchmark` that avoid the above boilerplate code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "10d362b6-aac8-49dd-84c3-0ce799ffabba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:\n",
+      "** Benchmark initialization **\n",
+      "Model: meta-llama--Meta-Llama-3-8B-Instruct;\n",
+      "Task: ACSIncome;\n",
+      "Hash: 516109582;\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from folktexts.benchmark import BenchmarkConfig, Benchmark\n",
+    "\n",
+    "bench = Benchmark(llm_clf=llm_clf, dataset=dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a648f32-05f4-4057-8624-2a39bfe7ab55",
+   "metadata": {},
+   "source": [
+    "Here's an example prompt for the current prediction task:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a83832fc-47c4-4e14-adc9-6393f197e2c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following data corresponds to a survey respondent. The survey was conducted among US residents in 2018. Please answer the question based on the information provided. The data provided is enough to reach an approximate answer.\n",
+      "\n",
+      "Information:\n",
+      "- The age is: 37 years old.\n",
+      "- The class of worker is: Owner of non-incorporated business, professional practice, or farm.\n",
+      "- The highest educational attainment is: Regular high school diploma.\n",
+      "- The marital status is: Married.\n",
+      "- The occupation is: Painters and paperhangers.\n",
+      "- The place of birth is: New Jersey.\n",
+      "- The relationship to the reference person in the survey is: The reference person itself.\n",
+      "- The usual number of hours worked per week is: 40 hours.\n",
+      "- The sex is: Male.\n",
+      "- The race is: White.\n",
+      "\n",
+      "Question: What is this person's estimated yearly income?\n",
+      "A. Below $50,000.\n",
+      "B. Above $50,000.\n",
+      "Answer:\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_sample, _y_sample = dataset.sample_n_train_examples(n=1)\n",
+    "print(llm_clf.encode_row(X_sample.iloc[0], question=llm_clf.task.question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e107c703-17d4-4543-8f56-73c7b9583589",
+   "metadata": {},
+   "source": [
+    "Optionally, you can fit the model's threshold on a few data samples.\n",
+    "\n",
+    "This is generally quite fast as it is _not fine-tuning_; it only changes one parameter: the `llm_clf.threshold`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "16f88399-1b9e-4fd3-a1ca-0aa9f2b91080",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0e4d1784cc8416eb3f30a9b542754b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n",
+      "WARNING:root:Setting meta-llama--Meta-Llama-3-8B-Instruct threshold to 0.7918923636748516.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.57 s, sys: 382 ms, total: 7.95 s\n",
+      "Wall time: 9 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TransformersLLMClassifier(encode_row=functools.partial(&lt;function encode_row_prompt at 0x14802eedf600&gt;, task=ACSTaskMetadata(name=&#x27;ACSIncome&#x27;, description=&quot;predict whether an individual&#x27;s income is above $50,000&quot;, features=[&#x27;AGEP&#x27;, &#x27;COW&#x27;, &#x27;SCHL&#x27;, &#x27;MAR&#x27;, &#x27;OCCP&#x27;, &#x27;POBP&#x27;, &#x27;RELP&#x27;, &#x27;WKHP&#x27;, &#x27;SEX&#x27;, &#x27;RAC1P&#x27;], target=&#x27;PINCP&#x27;, cols_to_text={&#x27;AGEP&#x27;: &lt;folktexts.col_to_text.ColumnToText...\n",
+       "\t128253: AddedToken(&quot;&lt;|reserved_special_token_248|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128254: AddedToken(&quot;&lt;|reserved_special_token_249|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128255: AddedToken(&quot;&lt;|reserved_special_token_250|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TransformersLLMClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>TransformersLLMClassifier(encode_row=functools.partial(&lt;function encode_row_prompt at 0x14802eedf600&gt;, task=ACSTaskMetadata(name=&#x27;ACSIncome&#x27;, description=&quot;predict whether an individual&#x27;s income is above $50,000&quot;, features=[&#x27;AGEP&#x27;, &#x27;COW&#x27;, &#x27;SCHL&#x27;, &#x27;MAR&#x27;, &#x27;OCCP&#x27;, &#x27;POBP&#x27;, &#x27;RELP&#x27;, &#x27;WKHP&#x27;, &#x27;SEX&#x27;, &#x27;RAC1P&#x27;], target=&#x27;PINCP&#x27;, cols_to_text={&#x27;AGEP&#x27;: &lt;folktexts.col_to_text.ColumnToText...\n",
+       "\t128253: AddedToken(&quot;&lt;|reserved_special_token_248|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128254: AddedToken(&quot;&lt;|reserved_special_token_249|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128255: AddedToken(&quot;&lt;|reserved_special_token_250|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "})</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "TransformersLLMClassifier(encode_row=functools.partial(<function encode_row_prompt at 0x14802eedf600>, task=ACSTaskMetadata(name='ACSIncome', description=\"predict whether an individual's income is above $50,000\", features=['AGEP', 'COW', 'SCHL', 'MAR', 'OCCP', 'POBP', 'RELP', 'WKHP', 'SEX', 'RAC1P'], target='PINCP', cols_to_text={'AGEP': <folktexts.col_to_text.ColumnToText...\n",
+       "\t128253: AddedToken(\"<|reserved_special_token_248|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128254: AddedToken(\"<|reserved_special_token_249|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128255: AddedToken(\"<|reserved_special_token_250|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "})"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "X_sample, y_sample = dataset.sample_n_train_examples(n=50)\n",
+    "llm_clf.fit(X_sample, y_sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dcc20d7-732f-450d-b02d-b94c34b41fc8",
+   "metadata": {},
+   "source": [
+    "Run benchmark..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4dae3a36-2b7b-473b-916b-35711e985140",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Test data features shape: (1665, 10)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48b6fc00f210464b9d0174c7909c0b1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/53 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:\n",
+      "** Test results **\n",
+      "Model: meta-llama--Meta-Llama-3-8B-Instruct;\n",
+      "\t ECE:       24.8%;\n",
+      "\t ROC AUC :  82.8%;\n",
+      "\t Accuracy:  73.8%;\n",
+      "\t Bal. acc.: 74.1%;\n",
+      "\n",
+      "INFO:root:Skipping group 'American Indian' as it's too small.\n",
+      "INFO:root:Skipping group 'Alaska Native' as it's too small.\n",
+      "INFO:root:Skipping group 'American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races' as it's too small.\n",
+      "INFO:root:Skipping group 'Native Hawaiian and Other Pacific Islander' as it's too small.\n",
+      "INFO:root:Skipping group 'Some other race alone (non-White)' as it's too small.\n",
+      "INFO:root:Skipping group 'Two or more races' as it's too small.\n",
+      "INFO:root:Skipping group American Indian plot as it's too small.\n",
+      "INFO:root:Skipping group Alaska Native plot as it's too small.\n",
+      "INFO:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it's too small.\n",
+      "INFO:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it's too small.\n",
+      "INFO:root:Skipping group Some other race alone (non-White) plot as it's too small.\n",
+      "INFO:root:Skipping group Two or more races plot as it's too small.\n",
+      "INFO:root:Saving JSON file to '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/results.bench-3191680725.json'\n",
+      "INFO:root:Saved experiment results to '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/results.bench-3191680725.json'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3min 51s, sys: 7.12 s, total: 3min 58s\n",
+      "Wall time: 4min 23s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': np.float64(0.7918923636748516),\n",
+       " 'n_samples': 1665,\n",
+       " 'n_positives': 605,\n",
+       " 'n_negatives': 1060,\n",
+       " 'model_name': 'meta-llama--Meta-Llama-3-8B-Instruct',\n",
+       " 'accuracy': 0.7375375375375376,\n",
+       " 'tpr': 0.7553719008264462,\n",
+       " 'fnr': 0.24462809917355371,\n",
+       " 'fpr': 0.27264150943396226,\n",
+       " 'tnr': 0.7273584905660377,\n",
+       " 'balanced_accuracy': 0.7413651956962419,\n",
+       " 'precision': 0.6126005361930295,\n",
+       " 'ppr': 0.44804804804804804,\n",
+       " 'log_loss': 0.8504629791262521,\n",
+       " 'brier_score_loss': np.float64(0.24624856734149014),\n",
+       " 'ppr_ratio': 0.0,\n",
+       " 'ppr_diff': 0.5961538461538461,\n",
+       " 'precision_ratio': 0.0,\n",
+       " 'precision_diff': 1.0,\n",
+       " 'balanced_accuracy_ratio': 0.0,\n",
+       " 'balanced_accuracy_diff': 1.0,\n",
+       " 'accuracy_ratio': 0.0,\n",
+       " 'accuracy_diff': 1.0,\n",
+       " 'tnr_ratio': 0.0,\n",
+       " 'tnr_diff': 1.0,\n",
+       " 'fnr_ratio': 0.0,\n",
+       " 'fnr_diff': 1.0,\n",
+       " 'fpr_ratio': 0.0,\n",
+       " 'fpr_diff': 1.0,\n",
+       " 'tpr_ratio': 0.0,\n",
+       " 'tpr_diff': 1.0,\n",
+       " 'equalized_odds_ratio': 0.0,\n",
+       " 'equalized_odds_diff': 1.0,\n",
+       " 'roc_auc': np.float64(0.8282707001403399),\n",
+       " 'ece': 0.2475394044687882,\n",
+       " 'ece_quantile': 0.24753940446878786,\n",
+       " 'predictions_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/ACSIncome_subsampled-0.01_seed-42_hash-373553086.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': False,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'meta-llama--Meta-Llama-3-8B-Instruct',\n",
+       "  'model_hash': 1896439497,\n",
+       "  'task_name': 'ACSIncome',\n",
+       "  'task_hash': 662640833,\n",
+       "  'dataset_name': 'ACSIncome_subsampled-0.01_seed-42_hash-373553086',\n",
+       "  'dataset_subsampling': 0.01,\n",
+       "  'dataset_hash': 373553086},\n",
+       " 'plots': {'roc_curve_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "bench.run(results_root_dir=results_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4dfbfa5f-a83d-4391-a2d9-afe0431ccd77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Skipping group 'American Indian' as it's too small.\n",
+      "INFO:root:Skipping group 'Alaska Native' as it's too small.\n",
+      "INFO:root:Skipping group 'American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races' as it's too small.\n",
+      "INFO:root:Skipping group 'Native Hawaiian and Other Pacific Islander' as it's too small.\n",
+      "INFO:root:Skipping group 'Some other race alone (non-White)' as it's too small.\n",
+      "INFO:root:Skipping group 'Two or more races' as it's too small.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Skipping group American Indian plot as it's too small.\n",
+      "INFO:root:Skipping group Alaska Native plot as it's too small.\n",
+      "INFO:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it's too small.\n",
+      "INFO:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it's too small.\n",
+      "INFO:root:Skipping group Some other race alone (non-White) plot as it's too small.\n",
+      "INFO:root:Skipping group Two or more races plot as it's too small.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bench.plot_results();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2f2e7d60-9fe4-4290-a2a9-7ec22b210cdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 0.7375375375375376,\n",
+      " 'accuracy_diff': 1.0,\n",
+      " 'accuracy_ratio': 0.0,\n",
+      " 'balanced_accuracy': 0.7413651956962419,\n",
+      " 'balanced_accuracy_diff': 1.0,\n",
+      " 'balanced_accuracy_ratio': 0.0,\n",
+      " 'brier_score_loss': np.float64(0.24624856734149014),\n",
+      " 'config': {...},\n",
+      " 'ece': 0.2475394044687882,\n",
+      " 'ece_quantile': 0.24753940446878786,\n",
+      " 'equalized_odds_diff': 1.0,\n",
+      " 'equalized_odds_ratio': 0.0,\n",
+      " 'fnr': 0.24462809917355371,\n",
+      " 'fnr_diff': 1.0,\n",
+      " 'fnr_ratio': 0.0,\n",
+      " 'fpr': 0.27264150943396226,\n",
+      " 'fpr_diff': 1.0,\n",
+      " 'fpr_ratio': 0.0,\n",
+      " 'log_loss': 0.8504629791262521,\n",
+      " 'model_name': 'meta-llama--Meta-Llama-3-8B-Instruct',\n",
+      " 'n_negatives': 1060,\n",
+      " 'n_positives': 605,\n",
+      " 'n_samples': 1665,\n",
+      " 'plots': {...},\n",
+      " 'ppr': 0.44804804804804804,\n",
+      " 'ppr_diff': 0.5961538461538461,\n",
+      " 'ppr_ratio': 0.0,\n",
+      " 'precision': 0.6126005361930295,\n",
+      " 'precision_diff': 1.0,\n",
+      " 'precision_ratio': 0.0,\n",
+      " 'predictions_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/ACSIncome_subsampled-0.01_seed-42_hash-373553086.test_predictions.csv',\n",
+      " 'roc_auc': np.float64(0.8282707001403399),\n",
+      " 'threshold': np.float64(0.7918923636748516),\n",
+      " 'tnr': 0.7273584905660377,\n",
+      " 'tnr_diff': 1.0,\n",
+      " 'tnr_ratio': 0.0,\n",
+      " 'tpr': 0.7553719008264462,\n",
+      " 'tpr_diff': 1.0,\n",
+      " 'tpr_ratio': 0.0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "pprint(bench.results, depth=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0b5be1e-4c00-4b48-adf4-3eee02b8d6e1",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/notebooks/minimal-example.ipynb.txt b/_sources/notebooks/minimal-example.ipynb.txt
new file mode 100644
index 0000000..25111b8
--- /dev/null
+++ b/_sources/notebooks/minimal-example.ipynb.txt
@@ -0,0 +1,400 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "181f6c46",
+   "metadata": {},
+   "source": [
+    "# Evaluate model calibration using folktexts\n",
+    "\n",
+    "**Prerequisite**: Install `folktexts` package with `pip install folktexts` or follow the setup guide in the [README](https://github.com/socialfoundations/folktexts).\n",
+    "\n",
+    "**Summary**: The script loads a language model from Huggingface and demonstrates how to use folktexts to get insights into model calibration, and plot the benchmark results. \n",
+    "\n",
+    "### 1. Check folktexts is installed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f3867376",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "folktexts.__version__='0.0.21'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import folktexts\n",
+    "print(f\"{folktexts.__version__=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2129c830",
+   "metadata": {},
+   "source": [
+    "### 2. Load Model from Huggingface\n",
+    "\n",
+    "We use the _Mistral 7B (instruct)_ model for this demo. The workflow can be similarly applied to any model/tokenizer pair.\n",
+    "\n",
+    "**Note:** _Set_ `model_name_or_path` to the **model's name on huggingface** or to the **path to a saved pretrained model**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3dc758de-b86f-4adb-9ed0-0148831da070",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef6ed53409ed404795c07a024033a991",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from folktexts.llm_utils import load_model_tokenizer\n",
+    "\n",
+    "# Note: make sure you have the necessary persmissions on Huggingface to download the model\n",
+    "# Note: use gpt2 for the demo if you need a smaller model\n",
+    "\n",
+    "model_name_or_path = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
+    "# model_name_or_path = \"gpt2\"\n",
+    "\n",
+    "model, tokenizer = load_model_tokenizer(model_name_or_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25a2a5f7",
+   "metadata": {},
+   "source": [
+    "### 3. Create default benchmarking tasks\n",
+    "\n",
+    "We generate ACSIncome benchmark using folktexts. \n",
+    "\n",
+    "**NOTE:** We will subsample the reference data for faster runtime. This should be removed for obtaining reproducible reslts.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed481a4-5258-468f-8fb5-91ed794d1503",
+   "metadata": {},
+   "source": [
+    "### Benchmark configuration\n",
+    "- The `subsampling` and `numeric_risk_prompting` key-word arguments are examples of optional benchmark configurations. See this [page for a list of available configs](https://github.com/socialfoundations/folktexts?tab=readme-ov-file#benchmark-options)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bbb17808",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Received non-standard ACS argument 'subsampling' (using subsampling=0.01 instead of default subsampling=None). This may affect reproducibility.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "Using zero-shot prompting.\n",
+      "CPU times: user 52.4 s, sys: 1min 30s, total: 2min 22s\n",
+      "Wall time: 2min 26s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.benchmark import Benchmark, BenchmarkConfig\n",
+    "\n",
+    "# Note: This argument is optional. Omit, or set to 1 for reproducible benchmarking on the full data\n",
+    "subsampling_ratio = 0.01\n",
+    "\n",
+    "bench = Benchmark.make_acs_benchmark(\n",
+    "    model= model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    task_name=\"ACSIncome\",\n",
+    "    subsampling=subsampling_ratio,\n",
+    "    numeric_risk_prompting=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f785fc1",
+   "metadata": {},
+   "source": [
+    "### 4. Run benchmark\n",
+    "\n",
+    "Results will be saved in a folder RESULTS_DIR. There is \n",
+    "* `.json` file contains evaluated metrics\n",
+    "* `.cvs` file contains risk scores of each datapoint\n",
+    "* folder called `imgs/` contains figures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "62b90528",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffde879b1f5e453abb55e7b423379f7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/105 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n",
+      "WARNING:root:Failed to compute ECE quantile: The smallest edge difference is numerically 0.\n",
+      "WARNING:root:Skipping group American Indian plot as it's too small.\n",
+      "WARNING:root:Skipping group Alaska Native plot as it's too small.\n",
+      "WARNING:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it's too small.\n",
+      "WARNING:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it's too small.\n",
+      "WARNING:root:Skipping group Some other race alone (non-White) plot as it's too small.\n",
+      "WARNING:root:Skipping group Two or more races plot as it's too small.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 1665,\n",
+       " 'n_positives': 605,\n",
+       " 'n_negatives': 1060,\n",
+       " 'model_name': 'mistralai--Mistral-7B-Instruct-v0.2',\n",
+       " 'accuracy': 0.6816816816816816,\n",
+       " 'tpr': 0.856198347107438,\n",
+       " 'fnr': 0.14380165289256197,\n",
+       " 'fpr': 0.4179245283018868,\n",
+       " 'tnr': 0.5820754716981132,\n",
+       " 'balanced_accuracy': 0.7191369094027756,\n",
+       " 'precision': 0.5390218522372529,\n",
+       " 'ppr': 0.5771771771771772,\n",
+       " 'log_loss': 0.5812465405486003,\n",
+       " 'brier_score_loss': np.float64(0.19836672672672676),\n",
+       " 'tpr_ratio': 0.0,\n",
+       " 'tpr_diff': 1.0,\n",
+       " 'balanced_accuracy_ratio': 0.0,\n",
+       " 'balanced_accuracy_diff': 1.0,\n",
+       " 'accuracy_ratio': 0.0,\n",
+       " 'accuracy_diff': 1.0,\n",
+       " 'fnr_ratio': 0.0,\n",
+       " 'fnr_diff': 1.0,\n",
+       " 'ppr_ratio': 0.0,\n",
+       " 'ppr_diff': 0.6442307692307693,\n",
+       " 'precision_ratio': 0.0,\n",
+       " 'precision_diff': 1.0,\n",
+       " 'tnr_ratio': 0.0,\n",
+       " 'tnr_diff': 1.0,\n",
+       " 'fpr_ratio': 0.0,\n",
+       " 'fpr_diff': 1.0,\n",
+       " 'equalized_odds_ratio': 0.0,\n",
+       " 'equalized_odds_diff': 1.0,\n",
+       " 'roc_auc': np.float64(0.814200842039607),\n",
+       " 'ece': 0.16251051051051124,\n",
+       " 'ece_quantile': None,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/ACSIncome_subsampled-0.01_seed-42_hash-1233880265.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': True,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'mistralai--Mistral-7B-Instruct-v0.2',\n",
+       "  'model_hash': 2545663199,\n",
+       "  'task_name': 'ACSIncome',\n",
+       "  'task_hash': 127998692,\n",
+       "  'dataset_name': 'ACSIncome_subsampled-0.01_seed-42_hash-1233880265',\n",
+       "  'dataset_hash': 1233880265},\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RESULTS_DIR = \"res\"\n",
+    "bench.run(results_root_dir=RESULTS_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20636ce4",
+   "metadata": {},
+   "source": [
+    "### 4. Visualize results\n",
+    "\n",
+    "We can also visualize the results inline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "edd5bf8f-cd70-4276-9c14-f1e0012aee0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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r1q34/fffERwcjN69e2Pw4MFKdzAxMjKSOjcgervfvHkzrl69KvXwEr/Q1EZMTAy2bNmC2NhYlJWVSXxXVFREX1tNxG/MYqytrWFsbCx1D2xtbaWuz8HBAZ06dcLZs2dRXl4OgUAgs725NhYsWIDFixejR48e8PPzQ/fu3TF06FA0b95c6WPIyodCoRD79u3Dn3/+ibS0NAgEAvo7eXmlvLxc6mVBVkni/v37yM7Oltsz1c3NTaLDRN++fcFisbB3716MGDEC3t7edcrLNWtGxGYozrMmJiZYsGABVq9ejS5duqBdu3bo0aMHhg4dKvNaakOWrsrC5XLBZrPldjapDYFAUGveCwsLw4YNG5CdnY3GjRvj/v37yM3NxYABA+htxUbI5/Oljl1RUSH1siSP0tJSTJs2DSUlJfjzzz/VXpjQeQMMCAigq8F69+6NMWPGYP78+Th//jwtljgTJCUlyexJBgBPnjyR2NbT05NOr20fRYiP8fTpU6mu6qpQ/eFRndoy0sCBA+nepz4+Pjh37hz+97//SZQWhUIhunTpUusblbhaVBYrV67EiRMn6M/BwcEK25tqe5OtLV1cTSMLFouFzZs3IzY2FteuXcPNmzfx1VdfISIiAn/99ZdSP5LqJRQxAoEAkyZNQkFBAaZMmQJPT09YWFggOzsbixcvlts5iMvlYuLEifD09MTixYvh4uICY2Nj3LhxA7///rvcfUNCQiQ+r1q1CsHBwVJj2fbt2ydzSMegQYPw7bff4vXr1+jWrZtKL1thYWHo0KEDLl26hNu3b2P37t3YuXMntmzZQrcrKkJWPty+fTt+/vlnjBgxAp999hlsbW3BZrPx448/yr23UVFREuN7gXe/zeqcOXMGbDZbolZBGTp16oT9+/cjOjoa3t7eas3L1a9r4sSJCA0NxeXLl3Hr1i38/PPP+O2337B37174+voqFausHru1ld5qe0bUhczMzFrz3oABA7B+/XqcO3cOEydOxLlz52BtbU0PNwHevbDk5ORIvSzk5OTI7ThYHT6fjzlz5uDJkyfYvXs3I01JOm+A1eFwOPjiiy8wYcIEHDhwgG7sFlf1nT17FjNmzJCZgU+ePAkAdNvhe++9B1tbW0RGRmL69Ol1qoro2bMnduzYgdOnTytlgLa2tjKr/jIyMlQ6b+/evfHdd9/RVRUvXryQaH8AgBYtWqC0tLROXYqnTJki0e7CROlWGQIDAxEYGIh58+bhzJkzWLBgAaKiojBy5Mg6VfM8ffoUL168wOrVqzF06FA6vXovy9q4evUq+Hw+fv31V4l2wnv37incNyIiQuKzl5cXbG1tpdJra8Pu06cPvv/+e8TGxtZpWI2zszPGjh2LsWPHIjc3F8OGDcP27dtpA6yLlhcuXMD777+PH3/8USK9sLAQ9vb2te4XEhIidd014fP5uHjxIoKDg1XuTFNVVQXgXc0Ok3m5RYsWmDx5MiZPnowXL15g6NCh2LNnD9atWwegbrra2NjIrB2p+Yxo0aIFhEIhnj17Bh8fn1qPJyuGRo0a1Zr3mjdvjoCAAJw7dw7jxo3DxYsX0bt3b4maCfH54uPjJcwuOzsbWVlZGDVqlMLrFAqFWLRoEe7cuYNNmzYhODhY4T51QefbAGsi7ha/d+9eVFRUAADMzc0xefJkPH/+XOYD4vr16zhx4gRCQkIQGBhI7zNlyhQ8e/YM69atk/nWeurUKanuz9UJCgpC165dceTIEVy+fFnqez6fj9WrV9OfmzdvjtTUVInqh6SkJPz7779KXz8g+pGEhITg3LlziIyMhLGxsVQpdsCAAXj48CFu3rwptX9hYSH9oJCFl5cXOnfuTP/5+/urFF99KSgokLof4h+duNrF3NwcgPyq1JqIS4TVj01RFPbt26dwX/ELUvV9xdXyiqiuZefOneHs7AxTU1Op9OrtTtWxtLTEDz/8gDlz5sjsIl8bAoFAqrrR0dERzs7OEtVX5ubmKrdhcjgcqXt07tw5hW2Nzs7OUtddkxs3bqCwsFCltk4x165dA/Dugc5EXi4rK6OfPWJatGgBS0tLKV1VyZ/i4xQVFUkMJ3n16hXdA1pM7969wWazsW3bNqnah+r3RVYMivJeWFgYYmNjcezYMeTn50tUfwJAq1at4OnpicOHD0uUTA8ePAgWi0X3RAdEv5Fnz55J5a/ly5cjKioK33//Pfr27ausPCqjVyVAMeHh4fjss89w/PhxuqPD1KlTkZiYiJ07dyI2NhZ9+/aFmZkZYmJicPr0abRs2VLCjADR22FKSgr27NmDe/fuoV+/fnBycsLr169x+fJlxMXF4dChQ3JjWbNmDSZPnozZs2ejZ8+e6NSpE8zNzfHy5UtERUXh1atX9FjADz/8EL///jvCw8Px4YcfIjc3F4cOHYKXl5fKbZFhYWH48ssv8eeffyIkJETqzTY8PBxXr17F9OnTMWzYMPj5+aGsrAxPnz7FhQsXcOXKFakONtrCiRMncPDgQfTu3RstWrRASUkJDh8+DCsrK7oqxszMDF5eXjh37hzc3d1hZ2eHVq1aya1G8fT0RIsWLbB69WpkZ2fDysoKFy5cUOoh1aVLFxgbG2P69On4+OOPUVJSgiNHjsDR0VGqjY8Jhg0bpvI+JSUl6N69O/r164c2bdrAwsIC//zzD+Lj4yV6hfr5+SEqKgqrVq1C27ZtYWFhodBoe/TogW3btmHJkiUICgrC06dPcebMGZXaFmvjzJkzMDExUdjWmZCQgFOnTtHXevfuXVy4cAFBQUFS1c7q5MWLF5g4cSL69+8PLy8vcDgcXL58Ga9fv5aosvXz88PBgwfxyy+/wM3NjW7PlUdYWBjWrVuH2bNnY/z48SgvL8fBgwfh4eFBj90ERO2f06dPxy+//IIxY8agb9++MDExQXx8PJydnenez3WJYcCAAVi9ejVWr14NOzs7mS8pCxcuxIwZMzB58mQMHDgQT58+xYEDBzBy5EiJdslLly5hyZIlWLVqFd3r+/fff8eff/6JoKAgmJmZ0fdQTJ8+fdQ2KYBeGmDfvn3RokUL7Nmzh+5JxeFwsGnTJpw8eRJHjhzBzz//TM8FOmvWLJlzgbLZbKxZswa9evXC4cOHsWfPHhQXF8Pe3h4dO3bEl19+iaCgILmxODg44NChQ/jzzz8RFRWFjRs3orKyEk2bNkVoaCgmTJhAbys24c2bN2PVqlXw8vLCmjVrcPbsWdy/f18lDcS9skpKSiR6f4oxNzfHH3/8gR07duD8+fM4efIkrKys4O7ujjlz5tTaYUMbCA4ORnx8PKKiovD69WtYW1sjICAA69atk3jArlixAsuXL8eqVatQWVmJ2bNnyzVAY2NjbN++HStWrMCOHTtgamqKPn36YOzYsQq7iXt6emLz5s3YtGkTVq9eDScnJ4wePRoODg746quv1Hbt6sTMzAyjR4/G7du3cfHiRVAUhRYtWuD777/HmDFj6O3GjBmDxMREHD9+HL///judd+Uxffp0lJWV4cyZM4iKioKvry927NiB9evX1ytmcU/XHj16KMyjZ8+exdmzZwGIOj25uLggPDxcZs9IddKkSRMMHDgQd+7cwenTp8HhcODp6YlNmzZJmPasWbOQkZGBXbt2oaSkBMHBwQrNx97eHlu3bsVPP/2EtWvXolmzZvjiiy/w8uVLCQMEgM8++wzNmjXD/v37sXHjRpibm0sNC6pLDE2aNEFQUBD+/fdfjBw5kh77WJ2ePXti69at2Lp1K5YvXw4HBwdMmzZNqeEv4tLtw4cP8fDhQ6nvr1y5ojYDZFHyWqQJBAKBQNBT9K4NkEAgEAgEZSAGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSI00H0NAIhUJUVVWBzWaDxWJpOhwCgUAgqAhFURAKhTAyMgKbXfdynEYN8MGDB9i9ezf+++8/5OTkYNu2bejdu7fcfe7du4effvoJycnJcHFxwYwZMzB8+HClz1lVVYX4+Pj6hk4gEAgEDdO2bVuYmJjUeX+NGmBpaSm8vb0xYsQIzJ49W+H2PB4P06ZNw8cff4x169bhzp07+Oabb9CoUSN07dpVqXOK3xbatm0LDocDgUCAFy9ewN3dHRwOp17Xo48QfRRDNJIP0UcxRCP51NRHIBAgPj6+XqU/QMMG2L17d3Tv3l3p7Q8dOoRmzZph8eLFAICWLVsiJiYGv//+u9IGKK725HA4dEYrKiqS+EyQhOijGKKRfIg+itFXjSiKQkWVEHyBEPwq0V9F1bv/8wUCVFQKUSEQQiikZB5DKBSiMCsPLVu2lNCnvs1YOtUGGBsbi06dOkmkhYSE4Mcff1T5WAKBQOJfQCQyRb27ASwWC2w2W2IbeenidkVZ6eLjK5PO4XDoOu6a6TVjrC1dHGN9r0m8rz5dk7rvU828pA/XpM77JI5JKBTSb++6fk2KYlf1mlT9nTF9TRVVFHIKy5BXykd+KR/5JZXILxX95RZXIL+UjxK+APwqISoF7wyNNrpKAfgC6q3BScaoKlWFOXgduRGOA+bCu3Vr+LjaSulUV3TKAF+/fg0nJyeJNCcnJxQXF6O8vBxmZmZKH0tWOyCXy0Vubi792cXFBa6urkhNTUVhYSGd7ubmBicnJyQlJaG8vJxO9/Lygq2tLeLi4iQypq+vL0xMTBAbGytxvsDAQPD5fCQkJNBpbDYbQUFBKCwsREpKCp1uZmYGPz8/5OXl4eXLl3S6jY0NWrVqhaysLGRmZtLpjo6OcHd3r/c1eXp6AgAeP36sN9fE1H2Kj4/Xu2sC1HefsrOz0axZM726JnXdJ29vbwDSzyV1XJOFlTUcXVvgyYt0pKZlo7CCQiFfCAHHFEJjS6S9ykdOURkKK4QoqhCiqJJCRZXskpg6MOawYMJhgwMhjDgsGLNZMOGwYGNlAVBCVJRX0NvyC3MQc+RrVLzOQNXVrXgzKQCxr9iwtrZWSywsquargobw9vZW2AmmX79+GD58OKZNm0an3bhxA1OnTsWjR4+UMkCBQIDY2Fi6DVAoFCI/P582Vn19C6/rNQFAfn4+bG1tJerbdfma1H2fqqqqkJ+fD3t7e7DZbL24JnXeJ/FvzMHBAUZGRnpxTYpiV/WaACAvLw92dnZyf2dCIYWiiiq8KatCXglfVBor4SOvlI+8Ev7bdHFaJfJK+Cgqr5I6lzKYcFhwsDSBvYUJ7C1N4GBhAgcrE9iZG8HewgSWpkYwMWLD3JgDE2MOjFiAiREbpkZsmBixYWYs+t6YDVEahw1jDhtGRhyJaxJT8z7xeDz06tULqamp8PT0xNGjR+Hv70/rGh8fj8DAwHpVGetUCdDJyQmvX7+WSHv9+jWsrKxUKv0B79oAORwOnJ2dFW7b0OksFktmem2NvqqmqxJLzVJ3XY+jTdekrnQWiwVjY2OpPKTr16TO+1TzN6YP18REjI0aNQIA8KuESMoqxEPuGzzivUHamzLkl7ytiiythKCWdjJ5sFgQGZmFMRwsTeg/e4tq/1q9NTlLkeFZmnAYHSqm6D7dunWLNr/r16+jefPmao9BpwwwMDAQf//9t0TaP//8g8DAwDofUyAQICkpCW3atNG7xmd1QPRRDNFIPkSf2qEoCmn5Zfj3ZR6uPnoObikbjzMKwa+S325mbWoEe0txycwY9pYmcLR8V1Kzr2ZyDhYmsDE3BoetW+Oex40bB0DUWdLV1RWPHz9Wex7SqAGWlJSAy+XSn9PS0pCYmAhbW1u4urpi/fr1yM7Oxpo1awAAH3/8MQ4cOIA1a9ZgxIgRuHv3Ls6dO4cdO3bUK47qdfQEaYg+iiEayYfoI6KovBJxaQWI5b3BQ24+Ynlv8LqYL7WdrbkxApvbIaiFHVo2snpnbpYmsLMwhqmRfr5I8Hg8mJub07VOYhMUCASM5CGNGuB///2HCRMm0J9XrVoFABg2bBh++ukn5OTkSDREN2/eHDt27MCqVauwb98+NGnSBCtWrFB6CASBQCA0FFUCIZ5mFyOW9waxvHw85L5BSk4xava6MGKz4ONijaZmVejT3gvt3Rzg7mhhcDNV8Xg89OjRA5aWlrh69arcphd1oVEDfP/99/HkyZNav//pp59k7nPy5EkGoyIQCATVySooFxkd7w1iuW8Qn16AUr50Z5dm9uYIbG5Hl/D8XG1hzBYN8woMdDXIamKx+Ynb/MrKyhrkvDrVBsgEbDYbXl5e9Z5RQF8h+iiGaCQffdSnlF+F+LdVmeK/zALpKjorUyO0a2771vDsEdjcDo2sTaW2oyhK7zRSlprmJ6vDC1N5yOANkMViwdbWVtNhaC1EH8UQjeSj6/oIhRRSXxfjX+5bs+O+wZPsIqnemGwW0LqxNYJa2COouR0C37bfKdP5RNc1qivKmB/AnD4Gb4ACgQBxcXEICAgwyKoHRRB9FEM0ko+u6ZNbXPG2k4rI8B6lvZE5lq6xjenbakxRya5tU1tYmtbtkaprGqkDZc0PYE4fgzdAQHpAJkESoo9iiEby0VZ9KqoEeJxRiFjuG1HbHS8fvDzp9iczYzYCmopKdeLSnYutuVpj0VaNmKKyshKVlZVKj/NjQh9igAQCwSCgKAovc0vpNruH3HwkZBaiUiA9sNzL2Uqio4p3Y2sYcQyvfY5JxMZnbGzMyCB3ZSAGSCAQ9JKC0krEpona7GJ5ojF3+aWVUts5WJqISnVvS3YBzexga26sgYj1Hy6Xi4SEBPTv3x/Au7mGNYXBGyCbzYavr69B9r5SBqKPYohG8mkIfSoFQiRlFr0bhsB7g9ScEqntTDhs+LraIKjF29Jdc3s0dzDX+Jg7Q8hDXC4XPXv2BI/Hw+nTp2kTVAam9DF4AwRQrxWFDQGij2KIRvJRpz4URSGjoFzUbvd2NpX49AJUyJg+zM3R4q3R2SGwhT18XKy1dhYVfc5DYvMTd3jx8/NT+RhM6GPwBigUCt8OQK3frOL6CtFHMUQj+dRXn+KKKsSlveuVGct7g5yiCqntbMyM0O6t2QW1sEe75nZwsNQNU9HnPFTT/OoysTVT+hi8ARIIBO1BIKSQ/KrobelOZHZPXxXJnD6sjYs1PcA8qIUdPBwtwdaxCZ/1HXWYH5MQAyQQCBrjVWE53Wb3kJuP+LQClMiYPqyp3bvpwwJb2MHf1RbmJvpVUtI3Xr16pdXmBxADJBAIDUQZX4DE7IK3vTJFhpchY/owCxMO2jUTGZ24/c7ZRrX1Pgmax8nJCd27dwcArTQ/QItWhG8oxCvCi+uSxSsQi1dqJkhC9FEM0Uga0fRhJW+nDhP1zEzKkp4+jMUCWjtb0+PtAlvYoZWztc6tXVdf9DUPCYVCvH79WuGi44qoqU/N53hdISVAAHw+X+UV5Q0Joo9iDF2jvBI+Hr0t1T3kiVYyL5QxfVgja1OJAeYBzexgVcfpw/QNfchDPB4PmzdvxqpVq2BkZAQ2m11v8xPDhD4Gn/OEQiESEhL0sveVOiD6KMbQNKqoEiAxs4gu2cXy3uBlbqnUdqZGbLRtaot2zWxhK8jHsK6BaOZgqVclHHWhD3mo+tyeALB27Vq1HZspfQzeAAkEgnxieW+Q8qoYjzMK8JD7BgkZheALpMfceTayfDfmrrk92rhYw5jDpqurXO00P+CcwAw1J7aeO3eupkNSCmKABAJBJimvivFjVCKuJr2S+s7OwpieSSWwhR0Cm9nB1oJMH2aIqLKqg7ZBDBDQ6+mH1AHRRzH6pNGbUj42XU7G/rsvUSWkYMRmIaiFHXxdbOilf9wcLVQqzemTPkyhixo1pPkxoY/B9wIlEAgiKgVC7L/7EpsuJ6OgTDRpdG8fZywJ80HLRlYajo6gbQgEArRr1w6PHz9u8JKfup7juvfKoWYoikJBQQEM7D1AaYg+itF1jSiKwtWkbPTb9DeWnklAQVklvBtbY3/4+9j1Scd6m5+u69MQ6KJGHA4HGzZsgJ+fH+Pmx5Q+Bm+AQqEQKSkpBrcYpbIQfRSjyxo9ySrChD33Mfn3aKTmlMDR0gQrh/kjcm4IQlo5qeUcuqxPQ6FLGlU3ob59++LRo0eMl/yY0oe0ARIIBkhucQU2XHqKg/e5EFKiZYImhbhjVk8v2JiRziwE2fB4PIwdOxa7du1C69atAUCnm5KIARIIBkRFlQB7/3mBLVdSUFQhGqg+wL8JlgzwQQtHCw1HR9Bmqnd4mTJlCm7cuKHzw1qIAQI6P/sC0xB9FKPtGlEUhQuPs7HqXCI9aN3P1QbfDfLF+56OjJ9f2/XRBrRZo5q9PQ8cONDg5seEPqQXKIGg5/yXXoDlZxNw73keANF0ZAv7eWNE+2Zk+SCCQrRxnB+ZC1RNCIVC5OXlwcHBQSfH4TAN0Ucx2qrRq8JyrLv4BEdi0kBRoqnJpnbzxPTuLWHZgPNvaqs+2oS2aqQt5seUPgZvgBRF4eXLl7C3t9d0KFoJ0Ucx2qZReaUAu289x7ZrKSh9u7bekHauWDSgDZramTd4PNqmjzairRrNnz9f4+YHMKePwRsggaAvUBSFM3GZWH0uCelvygAAgc3t8O0gX7znpl0PVoJusGPHDgDA+vXrNV7tyQTEAAkEPSCW9wbLzyYg5mU+AMDV1gyLBrTB4ABX0s5HUImSkhJYWloCAOzt7XH48GENR8QcxAAB2NjYaDoErYbooxhNaZRZUIY155/gxMN0AIC5MQczerTEp109YW6iPZ28SB5SjDZoxOVy0bNnT8ydOxefffaZpsORgAl9SC9QAkEHKeVXYfuNVPz29zOUV4pmx/jwvWb4sp83Gttob3d6gvYiNr/U1FS0bNkSjx49okuC2gbpBaomhEIhsrKy0KRJE63qfaUtEH0U05AaCYUUTjxMx5oLScgurAAABLs74NtBvmjbzJbRc9cVkocUo2mNqpufp6cnrl27plXmx5Q+Bm+AFEUhMzMTjRs31nQoWgnRRzENpVH0izwsO5uAuLQCAEBzB3N8NcAH/f2baPWMHCQPKUaTGtU0P20Y51cTpvQxeAMkELQdXl4pfjqfhMi4TACAlakRZod6YWJnd5gZk2p8Qt3RBfNjEmKABIKWUlReiV+uP8PuW8/BrxKCzQI+6tgCX/RpjUbWppoOj6AHnD171mDNDyAGCBaLBUdHR62uQtIkRB/FqFsjgZDCkWge1l18itfFona+Ll6O+GagL3xcNN9TUFVIHlKMpjSaOXMmAGDw4MFabX5M6UN6gRIIWsQ/z15j+dlEJGYWAgA8nSzxVZgPevk4EwMhqIW0tDTY2trC2tpa06HUGbIivJoQCoV48eKFTixEqQmIPopRh0bPX5fg033RGLPzHhIzC2FjZoRvB/ni/Ofd0Nu3sU6bH8lDimkojbhcLrp3747+/fujqKiI0XOpE6b0MXgDpCgKubm5MLCCsNIQfRRTH40Kyiqx4mwC+m68gUsJ2eCwWfikkxtufNkT4SEeMDHS/Z8oyUOKaQiNqnd4ycrKQkFBAWPnUjdM6WPwbYAEgiaoEghx8D4XGy49RX5pJQCgp3cjfD3QB17Ouls1RdBOZPX2bNasmabD0jjEAAmEBubG0xysOJuA5FfFAIBWzlb4ZpAvurdupOHICPqIoQ91kIfBGyCLxYKLi4tOt7EwCdFHMTI1Sk4G9uwBXrwA3N2ByZORYtsEKyITcf1JDgDA3sIYX/T1xuiOzWHE0f2qztogeUgxTGmkL+bHlD4Gb4BsNhuurq6aDkNrIfooRkqjiAhgyhSAxQIoChSLBWr1GvwWNhfX/XvDmMPCxM7umB3aCrbmxpoLvIEgeUgxTGlUWFiIwsJCnTY/gDl99Pe1U0kEAgGSk5MhEAg0HYpWQvRRjIRGycki8xMKAYEAEArBEgjAooRYFfUzxjrycXFed3w90NcgzA8geUgZmNLI398f165d02nzA5jTx+ANEBC9JRFqh+ijGLFGwt27QcmopmFB9Ba7Mu8+PJy0Z5LhhoLkIcWoSyMej4dbt27Rn/39/XXa/MQwkYeIARII9YSiKKTkVWJFZCIuRd2rdawSi6JEbYIEAkPweDz06NED/fr1kzBBgmwMvg2QQKgrKa+KcDo2A6ceZeBlbimAXDSydEIvsADIGK/EYok6xBAIDCA2P3GHFzc3N02HpPUYvAGyWCy4ubmRHmq1QPSRJONNGc48ysCp2AwkZL6rkjEzYqOPb2O06/4FOIOOy/Q/UBQQHt5wwWoJJA8ppr4a1TQ/XW/zqwlTecjgDZDNZsPJyUnTYWgtRB8RWQXl+DEqEacfZdBpRmwWurduhCGBrujt0xiWpm9/Trt3i4zubS9Q+t/duwEvLw1dgeYgeUgx9dFI380PYC4PGbwBCgQCJCUloU2bNmRybBkYuj78KiH23H6OzVeSUcoX9UB738MBQwJdEebvAntLE2mNJk4EQkJEhiceBxgebpDmB5A8pAx11SgrK0vvzQ9gLg8ZvAECQHl5uaZD0GoMVZ+byTn4/vRjpOaUAADat7DDsg/84d/UVmpbKY28vIBVqxoiTJ3AUPOQKtRFIwcHB7Rr1w4A9Nb8xDCRh4gBEgg1SMsvxYqziTj/OAsA4GRlgsUDfDA8qCnYbNKORdAeTExMcOjQIeTl5aFJkyaaDkfnqJMBZmRkICMjA2VlZXBwcECrVq1gYmKi7tgIhAalvFKAnX+nYtv1FJRXCsFhszChkxvm9WkNGzPDGLRO0H54PB4iIiLw7bffgsViwcTEhJhfHVHaANPS0nDw4EFERUUhKytLYlkKY2NjdOjQAaNGjUK/fv3AZuvO8EI2mw0vLy+dirkhMRR9riZlY+mZhLfDGYBgDwcs+8APbZooXoHdUDSqK0QfxSirUfUOLwDw3XffNUR4GoepPKTUivArVqzAiRMnEBISgp49eyIgIADOzs4wMzNDQUEBnj59ipiYGERGRoLD4eDHH39EQECAWgNVF2RFeEJ1uLmlWHb2MS4nvgIAOFub4uuBPhjSzpV02ydoFYbQ21NZ1PUcV6oEaG5ujsuXL8Pe3l7qO0dHR3Tq1AmdOnXC7Nmz8ffffyMrK0trDbAmAoEAcXFxCAgIIIYoA33Vp7xSgF+uP8P2G8/ArxLCiM1CeIgH5vRqBStT1VoG9FUjdUH0UYwijQzd/JjKQ0r90ufPn6/0Abt161bnYDRFbVNXEUTokz4UReFiQjaWn01AWn4ZAKCLlyOWDvGr10K0+qQRExB9FFObRoZufmKYyEOkFyjBYEjNKcbSMwm48VS0Hp+rrRm+GeSLAf5NSHUnQSvh8/no3bu3wZsfU6itRfHZs2fo1auXug5HIKgNiqLwy/UU9Nv0N248zYEJh41ZPVvi8vzuCGtLFmolaC8mJiZYvnw5WrduTcyPAdRWAqysrERGRobiDbUMNpsNX19f0kOtFnRdn/JKARYfi8PJWFHe7OHdCN8P9lPrkkS6rhHTEH0UI0+jUaNGYejQoQY91IypPKS0Aa5SMKtFXl5evYPRFIacsZRBV/XJKarA1D+i8ZD7BkZsFpZ+4IcxwS0YKfHpqkYNBdFHMWKNuFwuPv30U+zatYsu8RH9mNFAaQPct28ffHx8YGkp+825tLRUbUE1JEKhkAyLkIOu6pOYWYgpe6OR/qYMtubG+HVse3T2YmZCZl3VqKEg+ihGrJGDgwPd5jdlyhRcuHBB06FpBUzlIaUNsEWLFvjkk0/wwQcfyPw+MTERw4cPVzmAAwcOYPfu3cjJyUGbNm3w7bffyh1C8fvvv+PgwYPIzMyEvb09+vXrh/nz58PU1FTlcxP0k0sJ2fjs0EOU8gXwdLLErk86wLORlabDIhDkkpWVhVGjRtEdXnbt2qXpkPQepStU/f398fjx41q/Z7FYUGJMvQRRUVFYtWoVZs2ahRMnTqBNmzYIDw9Hbm6uzO3PnDmD9evXY/bs2YiKisLKlSsRFRWFDRs2qHRegn5CURR23HiGqX9Eo5QvQBcvR5yY2YWYH0Hr4XK5mDZtGunt2cAoXQJcvHgx+Hx+rd+3adMGSUlJKp08IiICo0aNwogRIwAAS5cuxfXr13Hs2DFMnTpVavuHDx+iffv2GDx4MACgWbNmGDRoEB49eqTSeQHRwMrq/wKiYnZ1E2exWGCz2RLbyEtns9lgsVgy08XHVyadw+GAoiiZ6TVjrC1dHGN9r0m8r7ZfU6WQwlfH43Hs33QAwNjg5vhusC9MjY0Yv08185Im7pM25z1xTEKhEBwORy+uSVHsqlwTl8tF7969kZ6eDg8PD1y5cgWurq4QCAQ6e03idFmx1/WaANnP7fqgtAE2atRILScUw+fz8fjxY0ybNo1OY7PZ6Ny5Mx4+fChzn6CgIJw+fZqeEYDH4+HGjRu1VsvKIz4+nv6/g4MD2Gw2Xr58KVH6dHFxgaurK1JTU1FY+G71bzc3Nzg5OSEpKUliiQ4vLy/Y2toiLi5O4ib6+vrCxMQEsbGxEjEEBgaCz+cjISFBQoOgoCAUFhYiJSWFTjczM4Ofnx/y8vLw8uVLOt3GxgatWrVCVlYWMjMz6XRHR0e4u7uDy+XW+5oCAwO1+prM7Rrhh0vpuP8iD2wAk4KsMaAFH0UFb2DagPcpPj5eo/dJ2/Peq1ev0LRpU726JnXcp88++wypqanw8PDAli1bkJubS59DV69J3ffpzZs3AN49t62t6z5pRXWUmguUCbKzs9GtWzccOnQIQUFBdPqaNWvw4MEDHDlyROZ++/btw5o1a0BRFKqqqvDxxx9j6dKlSp9XPIdc27Zt6TeOiooKWFhYgKIonXgTasi3OxaLhYqKChgbG0v0ntSWa0p+VYxP98WAl18GK1MjbBndDt1aNZJ7TUyUAMvLy2FmZgYWi6XXb+F1uSaKolBeXg5zc3NSApRxTZmZmZg2bRo2btwIT09PpX5n2n5N8mKvyzUJBAKUlZXRvzGBQID4+PiGmQtUW7h37x527NiB77//HgEBAeByuVi5ciW2bduGWbNmqXQsDodDC5uUlCRXSE2kix+kNaltHIyq6crGIhAIkJCQUKs+mryma09eYe6fD1FUUYUWDhbYM7GDzOnMmL5PLBYLT548kdCooe+TOtPVfZ8EAgGtj7piVDVdW35PYqqqquiOe82aNcPp06cRGxtba5y6cE1M3icAUr8xdaCxkan29vbgcDhSHV5yc3Ph5CS7u/rPP/+MIUOGYOTIkfD29kafPn0wb948/Pbbb2SuQQOCoijsufUc4b8/QFFFFd73cMCpWV3qNZcngdBQ8Hg8tG3bFvv27dN0KAaPxgzQxMQEfn5+uHPnDp0mFApx584diSrR6pSXl0u9rYjfBjRUk0toYCoFQnx14j8sO5sAIQV81KE5/gh/H/aWZKAwQfsRT2ydnJyMFStWoKKiQtMhGTQarQKdNGkSFi1aBH9/fwQEBGDv3r0oKyujxxMuXLgQjRs3plej6NmzJyIiIuDr60tXgf7888/o2bNnvYrFZIom+WiLPm9K+Zix/1/cSc0FiwV8HeaD8BAPrZjLU1s00laIPtKrOly5ckVi/DLRSD5M6FMnA3zw4AHMzMzQtm1bOi0+Ph7l5eXo2LGj0scJCwtDXl4eNm/ejJycHPj4+GDXrl10FWhmZqbERc+YMQMsFgubNm1CdnY2HBwc0LNnT8ybN68ulwFAVIKsrcRJ0B59nuUUY8reaDx/XQJLEw42jw5CL5/Gmg4LgPZopK0QfRQvaUQ0kg9T+tSpF2ibNm3g6emJqKgoOm3AgAF48eIFEhMT1Rqguqm5kjBFUSgsLISNjY1WlCS0DW3Q51bya8w8EIPC8io0tTPH7okd0KaJjUZikYU2aKTNGLo+yqznZ+gaKaKmPupaEb5OZcorV64gIiJCIu3333/H5cuX6xyIphAKhUhJSSGdaGpB0/r8cfclPom4j8LyKrznZo9Ts7tolfkBmtdI2zF0fQ4cOKBwhhdD10gRTOlTpyrQpk2bSqU1bqwd1VEE/aBKIMTyswnYe0c0oHd4UFP8OLwtzIzJZMoE3WLRokUAgLFjx5LpzbQMnRoHSDAMCsoqMfvPf3Ez+TUAYGF/b8zo3pJUDRF0hoyMDDg4ONADtxcvXqzpkAgyUMoAO3bsqPTD5/79+/UKSBOYmZlpOgStpiH1efG6BOF7H+BZTgnMjTnY+FEg+vs3abDz1xWSh+RjSPqI2/y8vb1x/Phxpa/dkDSqC0zoo5QBfvXVV2o/sbbA4XDg5+en6TC0lobU525qLqbvj8Gb0kq42Jph54QO8G9q2yDnrg8kD8nHkPSp3uEFEC0U7urqqnA/Q9KoLjClj1IGOGzYMLWfWFsQCoXIy8ujJ8QmSNJQ+vz1gIuvT/yHKiGFds3tsHP8e3C20Y03YpKH5GMo+sjq7amM+QGGo1FdYUqfOh2Jy+Vi48aN+OKLL+ipzG7cuIHk5GS1BdZQUBSFly9fkplkaoFpfQRCCivOJmDRsXhUCSkMCnDBX1P/pzPmB5A8pAhD0EeZoQ7yMASN6gNT+qhsgPfv38fgwYMRFxeHixcvorS0FIBootItW7aoNTiCflNUXolP90Vj163nAIB5vVtjy+gg0tOToFPU1/wImkNlA1y/fj0+//xzREREwNjYmE7/3//+J7XuE4FQG7y8Unz46x1cTXoFUyM2to4Jwme9W5GengSdIzMzEzk5OcT8dBCVh0E8ffoU69atk0p3cHBAfn6+WoJqaGxstGtgtbahbn2iX+Rh2h8xyC3hw9naFDsndEC75nZqPUdDQ/KQfPRZn+DgYFy+fBkuLi71Mj991kgdMKGPygZobW2NnJwcqRudmJiok4PhORwOWrVqpekwtBZ163P83zQsPhYPvkAIP1cb7PqkA1xszdV2fE1A8pB89FEfHo+H3Nxceo3D4ODgeh1PHzVSJ0zpo3IV6MCBA7Fu3Trk5OSAxWJBKBQiJiYGq1evxtChQ9UeINMIhUJkZGSQKYhqQV36CIUU1pxPwheHH4EvEKK/XxMcmd5J580PIHlIEfqmj7jNLzQ0VG3NPvqmkbphSh+VDXDevHnw9PREjx49UFpaioEDB2LcuHEICgrCjBkz1BpcQ0BRFDIzM0nvq1pQhz4lFVWYvj8Gv1x/BgCY3dMLv4xtDwsT/ZiIiOQh+eiTPtU7vNjb28PR0VEtx9UnjZiAKX1UfgKZmJhgxYoVmDlzJpKTk1FSUgJfX1+4u7urNTCCfpDxpgxT9kYjIbMQJhw2Vn/YFsOCmmk6LAJBZUhvT/2jzq/grq6ucHFxAQDSc48gk4fcfHy6LwaviyvgZGWCHeM74D03e02HRSCoDDE//aROBnjkyBHs3bsXL168AAC4u7vjk08+wciRI9UZW4PAYrHg6OhITLwW6qrP6UcZWHDkEfhVQrRpYo1dn3RAM3sLhqLULCQPyUfX9UlPT2fc/HRdI6ZhSh+VDfDnn3/G77//jnHjxtE9oGJjY/Hjjz8iIyMDn332mVoDZBo2m02qb+Wgqj5CIYVNV5Kx+YpoVqDePs7Y9HEQrEz1o71PFiQPyUfX9bG3t4ebmxsAMFby03WNmIYpfVR+Kh08eBDLly/HoEGD6LRevXrB29sby5cv1zkDFAqF4HK5aNGiBZmDTwaq6MPLK8VXJ+LpZYymdfPEwv5twGHr91styUPy0XV9LCwscPbsWeTn58tcC1Ud6LpGTMOUPiofqaqqCv7+/lLpfn5+EAgEagmqIaEoCrm5uaT3VS2oos9nhx7iZvJrGLFZWDMiAEvCfPTe/ACShxShi/pwuVxs2rSJjtnCwoIx8wN0U6OGhCl9VDbADz74AAcPHpRKP3z4MAYPHqyWoAi6x5OsIvzLfQMA+G3CexjVkXQQIOgmXC4XPXv2xLx58/Dzzz9rOhwCgyhVBbpq1Sr6/ywWC0eOHMHt27fRrl07AEBcXBwyMjJ0ciA8of68KirH9P0xAIC+vo0R2kb3ZgQiEIB35ifu8DJixAhNh0RgEKUMMCEhQeKzeGFCLpcLALCzs4OdnZ1OLofEYrHg4uJCel/VgiJ9KqoEmLD7Pp6/LkFTO3P8MMTwFvUkeUg+uqJPTfNryKEOuqKRpmBKH6UM8I8//lDrSbUJNput9KKVhogifQ5HpyEpqwiOliY4MOV9uNrp/tRmqkLykHx0QR9Nmh+gGxppEqb0MfjuRgKBAMnJyTrZgachkKdPpUCI7W+nN5sT6gV3J8uGDk8rIHlIPtquT1lZGUJDQzU6yF3bNdI0TOlTp8FZ8fHxOHfuHDIzM1FZWSnx3datW9USWENSWFio6RC0mtr0OfFvOtLflMHJyhQfB7do4Ki0C5KH5KPN+pibm+PLL7/EunXrcPXqVY3N8KLNGmkDTOijcgkwMjISo0ePRmpqKi5duoSqqiokJyfj7t27sLa2VnuABO2kSiDEtuspAICp3TzIKu4EnWbatGmIi4sj05sZGCob4Pbt27FkyRJs374dxsbG+Prrr3H+/HkMGDCAnhuUoP+cjcvEy9xS2FsYY+z7bpoOh0BQCR6Ph2HDhuH169d0mrm54bVfGzoqGyCPx0P37t0BiFaGKC0tBYvFwsSJE3H48GG1B8g0LBYLbm5upPdVLcjSRyiksPWaqPQXHuIBSz2e5kwZSB6Sj7bpI57Y+uTJk/j00081HQ4A7dNI22BKH5UN0MbGBiUlJQAAZ2dneuhDYWEhysrK1BpcQ8Bms+Hk5ESmH6oFWfqc+y8LKa+KYWNmhAmd3TUXnJZA8pB8tEmfmqs6bN68WdMhAdAujbQRpvRR+WgdO3bEP//8AwDo378/Vq5ciW+++Qbz589Hp06d1BpcQyAQCPD48WPS+6oWaupDURS2XBW99Ezs4gEbM2NNhqcVkDwkH23RR5uXNNIWjbQVpvRRue7q22+/RUVFBQBgxowZMDY2xr///ou+ffvq5IrwAFBeXq7pELSa6vpcTnyFpKwiWJpwMLmLu+aC0jJIHpKPpvXRZvMTo2mNtB0m9FHZAO3s7Oj/s9lsTJ06VZ3xELSY6qW/8Z3cYWdhouGICATl+OSTT7Ta/AiaQSkDLC4uVvqAVlZWdQ6GoN38nfwacWkFMDNmY0pXD02HQyAoza5duzBlyhTs3buXmB+BRikD7NChg8LeNxRFgcViITExUS2BNRRsNhteXl6k8bkWxPqwWCxsebvI7dj33eBkZarhyLQHkofkoyl9qqqqYGQkesR5enri6tWrDXp+VSB5SD5M6aOUAe7bt0+tJ9UmWCwWbG1tNR2G1iLW559nrxH9Mh8mRmxM7eap6bC0CpKH5KMJfXg8Hvr27YvVq1djyJAhDXruukDykHyY0kcpAwwODlb7ibUFgUCAuLg4BAQEgMMhs5nURKzPlgeiBuiPOjRHYxszDUelXZA8JJ+G1qd6h5dFixYhLCyMLglqKyQPyYcpfUh5G4BQKNR0CFpNwqty3EnNgxGbhek9Wmo6HK2E5CH5NJQ+NXt7Xrx4UevNTwzJQ/JhQh9igASFHE0UdYIa0b4ZmhrgckcE3UAXhjoQtAtigAS5xKUV4GEWHxw2CzN7ktIfQTsh5keoCwZvgGw2G76+vqT3VS38ciMVADCknSvcHA1zvT9FkDwkn4bQZ/v27TptfiQPyYcpfepUOV5VVYX79++Dy+Vi0KBBsLKyQnZ2NqysrGBpqXsPSRMTMqBbFomZhbiUkA0WC5hF2v7kQvKQfJjWZ/ny5QCA6dOn65z5iSF5SD5M6KOynaanp2Pw4MGYOXMmli1bhvz8fADAzp07sXr1arUHyDRCoRCxsbGkAVoG4hUfOjU1g4eThYaj0V5IHpIPU/pkZ2ejqqoKgKiEsHLlSp01P5KH5MOUPiob4MqVK+Hv74/79+/D1PTdYOg+ffrg7t27ag2OoDlSXhUhKj4TADDCR/dK9QT9hsfjoXPnzpgwYQJtggSCqqhcBRoTE4ODBw9KFUebNm2K7OxstQVG0Czbrj0DRQG9fZzhbkfaJQjaQ/UOLwCQm5uLxo0bazgqgi6i8pNNKBTKLIZmZWXpZPsfQZqXuSU4FZsOAJhNen4StAhZvT2J+RHqisoG2KVLF+zdu1ciraSkBFu2bKFXitcl2Gw2AgMDSe+ravxy7RmEFNDDuxHaNbcn+iiA5CH5qEsffR7qQPKQfJjSR+WjLV68GP/++y/CwsLA5/OxYMEChIaGIjs7GwsWLFBrcA0Fn8/XdAhaQ1p+KY79mwYAmBPqBYDoowxEI/nUVx99Nj8xJA/Jhwl9VDbAJk2a4NSpU5g2bRo++eQT+Pj4YMGCBTh58iQcHR3VHiDTCIVCJCQkkN5Xb9lxIxVVQgqdWzriPTcHoo8SEI3kow59kpOTkZ6errfmR/KQfJjSR+VOMBUVFTA1NcUHH3yg1kAImie7sBx/RfMAAHNCW2k4GgLhHaGhoYiKikKrVq30zvwImkPlEmCnTp2waNEi3L59m7yt6Bk7bqSCXyVEBzd7/M/TQdPhEAwcLpeLp0+f0p9DQ0OJ+RHUisoGuHr1apSVlWHmzJno1q0bVq5cifj4eCZiazBIwzPwurgCf95/CQCY06uVxALIRB/FEI3ko6o+XC4XPXv2RI8ePSRMUJ8heUg+TOjDoiiKqsuOxcXFuHDhAiIjI3H37l00b94cgwcPxuzZs9Udo1oRCASIjY1FYGAgWXerGj+dS8L2G8/QrpktTs7qImGABEJDIjY/fe7wQqgf6nqO19lSraysMGLECOzZswenT5+Gubk5tm3bVudANAVFUSgoKEAd3wP0gvwSPv648wIAMDtUsvRH9FEM0Ug+quhjqOZH8pB8mNKnzgZYUVGBqKgozJw5E8OGDUNBQQHCw8PVGVuDIBQKkZKSYtDtmRH/vEAJXwAfFxv09nGW+I7ooxiikXyU1cdQzQ8geUgRTOmjci/Qmzdv4uzZs7h8+TKMjIzQr18/7NmzBx07dlRrYISGobC8EhG3nwMQjfsjVZ8ETZCWlmaw5kfQHCob4OzZs9GjRw+sXr0a3bt3h7GxMRNxERqIff+8QFF5FbycrdDfr4mmwyEYKJaWlnBwEPU8JuZHaChUNsDbt2/DysqKiVg0hpmZmaZD0AglFVXYfUtU+pvd0wtstuzSn6HqowpEI/ko0sfe3h4XL15ESUkJmjVr1kBRaRckD8mHCX2UMsDi4mIJ0ysuLq51W10zRw6HAz8/P02HoREO3HuJ/NJKuDtaYFCAi8xtDFkfZSEayac2fXg8Hi5duoTJkycDEJmgvb19Q4enFZA8JB+m9FHKADt27Ihbt27B0dERHTp0kNlORFEUWCwWEhMT1R4kkwiFQuTl5cHBwcGgxuGUVwrw29+i0t/Mnl4w4si+dkPVRxWIRvKRpU/NJY3EJmiokDwkH6b0UcoA9+7dC1tbWwDAvn371HZybYCiKLx8+dLg3jwP3efidXEFmtqZY1hQ01q3M1R9VIFoJJ+a+tSc2LpPnz4ajlDzkDwkH6b0UcoAg4OD6f83a9YMLi4uUqVAiqKQmZmp1uAIzFBRJcD2G6I37xk9WsK4ltIfgaBuDGFVB4LuoPKTr1evXsjLy5NKf/PmDXr16qVyAAcOHEBoaCjatm2LkSNHIi4uTu72hYWFWLp0KUJCQuDv749+/frhxo0bKp/XkDkak4aswnI0tjHFyA6G2eGA0PAQ8yNoGyr3AhW39dWktLQUpqamKh0rKioKq1atwtKlS9GuXTvs3bsX4eHhOH/+vMyllfh8PiZNmgRHR0f8/PPPaNy4MTIyMmBjY6PqZUhQ3/11iUqBEL9efwYAmNatJUyNFE8jZEj61BWikXzYbDZ69epFzE8OJA/Jhwl9lDbAVatWAQBYLBY2bdoEc3Nz+juBQIC4uDi0adNGpZNHRERg1KhRGDFiBABg6dKluH79Oo4dO4apU6dKbX/s2DEUFBTg0KFD9PjD+naZ5nA4aNXKcJb+OfkwHWn5ZXCyMsHo4BYKtzc0feoC0Ug+HA4HQUFBCA8Px+7du4n5yYDkIfkwpY/SBpiQkABAVAJ8+vSpxAB4ExMTtGnTRqWeXHw+H48fP8a0adPoNDabjc6dO+Phw4cy97l69SoCAwOxbNkyXLlyBQ4ODhg0aBA+/fRTlSdEFQgEAES9i169egUXFxf6+sSwWCyw2Wx6W0XpbDYbLBZLZrr4XMqkczgcUBQlM10oFErNhycrXRxj9XSBkMK2aykAgPAQD5hw3ulQ2zUBQHZ2Nho1aiTR+0pbrkle7A11n6qqqpCdnY3GjRuDzWbrxTWp8z4JhUJkZ2dj4cKFmDNnDiwsLCTi1MVrUhS7qtcEAFlZWXB2dlbqd6YL16TO+1RVVYWsrCz6NyZLv7qgtAH+8ccfAIAlS5bg66+/rvd4v/z8fAgEAqmqTkdHR7prdE14PB7u3r2LwYMH47fffgOXy8XSpUtRVVWl8ioUNZdwatKkCXg8HnJzc+k0FxcXuLq6IjU1FYWFhXS6m5sbnJyckJSUhPLycjrdy8sLtra2iIuLk7iJvr6+MDExQWxsrMQ5AwMDwefz6ZcLQJQ5goKCUFhYiJSUFDrdzMwMfn5+yMvLw8uXL+l0GxsbtGrVCllZWRKdkBwdHeHu7g4ul0tf001uGV7klsLOwhidGwkk4qntmjw9PZGZmYns7GytvCZAe+5Tdna23l0TUPf7dPfuXaxYsQKLFy+GpaUlAFGNTXJyss5eE1P3ydvbG1lZWcjKytKba1L3fcrOzkZ2djYAwNraGuqgzssh1Zfs7Gx069YNhw4dQlBQEJ2+Zs0aPHjwAEeOHJHap1+/fqioqMCVK1foEl9ERAR2796NW7duKXVe8TIabdu2BYfDgUAgQHx8PAIDA8FisbT+Tajub+EUwrbcRvKrYszv0xoze3gqdU0UReHRo0e0Xtp0TYpib6j7VFlZifj4eFojfbim+t6n9PR0usPL6NGjMX/+fAQEBMDY2Fhnr4nJ+6Tq70wXrkmd96myshJxcXEyn9v1WQ5JqRLg7Nmz8dNPP8HKykphSWvr1q1Kndje3h4cDkfizQMAcnNz4eTkJHOfRo0awcjISOKCPT09kZOTAz6fDxMTE6XODYB+UFWntgGWtQnMZDqLxZKZXluMitIvJmQi+VUxrM2M8EkXd6VjEWdqWXrVFntt6eq+pvrEoq706tdUXSN9uSZlYqyZXrO3548//ojc3Fx6O128JkXp9Y2xLr8zbb+muqQruqba9KkrSg2DqF7ctLa2lvunLCYmJvDz88OdO3foNKFQiDt37kiUCKvTvn17cLlciTeEFy9eoFGjRiqZX3VYLBYcHR31ehUEiqKw5aqoWmFiZ3fYmCk/gbkh6FNfiEbvkDXUoUWLFkQfBZA8JB/G9KE0SGRkJOXv708dP36cSklJob799luqQ4cOVE5ODkVRFPXll19S69ato7fPyMiggoKCqGXLllGpqanUtWvXqE6dOlG//PKL0uesqqqioqOjqaqqKrVfj7ZyOSGLclt0lvL99hyVV1yh6XAIegqXy6U8PT0pAJSnpyfF5XI1HRJBT1HXc1zlcYDl5eWgKIoeBpGeno5Lly7By8sLISEhKh0rLCwMeXl52Lx5M3JycuDj44Ndu3bRVaCZmZkSxXMXFxfs3r0bq1atwpAhQ9C4cWNMmDABn376qaqXQSMUCsHlctGiRQu9nIOPoihsflv6G9fJDfaWqpWU9V0fdUA0EuWzUaNGyRznR/RRDNFIPkzpo7IBzpw5E3369MHo0aNRWFiIkSNHwtjYGPn5+Vi8eDHGjBmj0vHGjRuHcePGyfxO3PO0OkFBQTh8+LCqYdcKRVHIzc3V23FJN5Nf4xHvDcyM2ZgS4ql4hxrouz7qgGgkqqLauXMnPv30Uxw+fFhCC6KPYohG8mFKH5Wt9PHjx+jQoQMA4MKFC3BycsK1a9ewevVqmYZF0Cxb35b+Rge3QCNr1WbqIRAUUb093t/fH//88w95iBN0BpUNsLy8nB7Tc+vWLfTt2xdsNhuBgYHIyMhQe4CEunM3NRf3X+TBhMPGtG4tNR0OQc/g8XgICgqSmIuXdOIg6BIqG2CLFi1w+fJlZGZm4tatW+jSpQsA0fAFXVsMFxD9YGWtbqEPbLmaDAAY2aEZmtjWbTVlfdZHXRiiRuLennFxcZg7d67U2K3qGKI+qkI0kg9T+qhsgLNmzcKaNWsQGhqKgIAAesjC7du34ePjo9bgGgI2mw1XV1e9a3iOeZmP2ym5MGKzMKNH3Ut/+qqPOjE0jWoOdTh79qzcazc0feoC0Ug+TOmjcieY/v3747333kNOTo7E5NedOnVC79691RpcQyAQCOgfsjoHWGqarW9Lf8PbN0Uze4s6H0df9VEnhqRRXZY0MiR96grRSD5M6aOyAQKiGVkaNWpEz1vXpEkTBAQEqC2ohqb6fHf6QHxaAa49yQGbBczs4VXv4+mbPkxgCBrVZz0/Q9CnvhCN5MOEPioboFAoxC+//IKIiAiUlpYCACwtLTFp0iTMmDGDFOG1gK3XRKW/Ie1c4e5kqeFoCPrC2rVryXp+BL1CZQPcuHEjjh49ivnz56N9+/YAgJiYGGzduhV8Ph/z5s1Te5AE5UnKKsSFx9lgsYDZofUv/REIYtatWwcA+PLLL4n5EfQClQ3wxIkTWLFiBXr16kWntWnTBo0bN8bSpUt1zgBZLBbc3Nz0pveVeNxfmL8LvJzrv2SIvunDBPqs0evXr+k5GE1MTLB582aVj6HP+qgLopF8mNJH5frKgoICeHpKzyji6emJgoICtQTVkLDZbDg5OelF1e2znGJExovW+5rVUz2lP33Shyn0VSMul4v3338fs2bNklpeRxX0VR91QjSSD1P6qHy0Nm3a4MCBA1LpBw4ckOgVqisIBAI8fvxYbSsMa5Jt11JAUUBvn8bwdbVRyzH1SR+m0EeNuFwuevbsidTUVFy4cEFq2TJV0Ed91A3RSD5M6aNyFeiXX36JadOm4Z9//kFgYCAAIDY2FpmZmdi5c6dag2soqq9urKtwc0txKlY0E88cNbf96YM+TKNPGlU3P3GHl9rW6FQWfdKHKYhG8mFCH5VLgMHBwTh//jz69OmDoqIiFBUVoU+fPjh//jw9Ryih4fn1RgoEQgrdWjdCu+Z2mg6HoKPIMj/S4YWgr6hUAkxLS8M///yDyspKDBw4EK1bt2YqLoIKpL8pw9GYNADAXNLzk1BHiPkRDA2lDfDu3buYPn06XQw1MjLCypUr8cEHHzAWXEPAZrPh5eWl043PO248Q6WAQidPR3Rwd1DrsfVBH6bRF41iY2Px8uVLtZufvujDJEQj+TClj9JH+/nnn9G5c2f8/fffuHfvHkaOHIm1a9eqNRhNwGKxYGtrq7Pdj18VluPQAx4A9bf9AbqvT0OgLxoNGTIEx48fV3vJT1/0YRKikXyY0kdpA0xOTsYXX3wBZ2dn2NraYuHChcjLy0N+fr5aA2poBAIBHj58qLO9r377OxX8KiHec7NHp5aOaj++ruvTEOiyRjweDzwej/48ZMgQtVd76rI+DQXRSD5M6aO0ARYXF8Pe3p7+bG5uDlNTUxQXF6s1IE0gbykXbSa3uAIH7nEBiEp/TL096qo+DYkuaiSe27NHjx4SJsgEuqhPQ0M0kg8T+qjUCebmzZuwtn43uwhFUbhz5w6ePn1Kp1WfIYbALLtvPUdZpQABzWzRvXUjTYdD0CFqTmxNIBgiKhng4sWLpdK+++47+v8sFguJiYn1j4qgkDelfOy78xIAMLsnc6U/gv5Rn1UdCAR9QmkDTEpKYjIOjcFms+Hr66tzva8ibr9AcUUV2jSxRm+fxoydR1f1aUh0SSNNmJ8u6aMpiEbyYUofojYAExMTTYegEkXllYi4/RyAaMUHNpvZ0p+u6aMJdEEjTZb8dEEfTUM0kg8T+ihlgLGxsUofsKysDMnJyXWNp8ERCoWIjY3VqQbofXdeorC8Ci0bWWKAvwuj59JFfRoaXdHIyMgIJiYmDW5+uqKPJiEayYcpfZQywIULFyI8PBznzp2jF8GtSUpKCjZs2IA+ffrg8ePHag2S8I5SfhV233pX+uMwXPoj6A8uLi64evUqafMjEN6iVBtgZGQkDh48iE2bNmHBggVwd3eHs7MzTE1NUVBQgNTUVJSWlqJPnz7YvXs3vL29mY7bYPnzHhd5JXy4OVpgcICrpsMhaDk8Hg93797FyJEjAYhMkEAgiFDKAI2NjTFhwgRMmDAB8fHxiImJQUZGBsrLy+Ht7Y2JEyfi/fffh52dHcPhGjbllQLs+DsVADCzR0sYcUgTLqF2xG1+z5+LagzEJkggEESwqPqsdKmDCAQCxMbGIjAwEBwOBxRFQSgUgs1ma/1Qgr3/vMD3px+jqZ05ri3oARMj5g1Ql/TRFNqokTYNddBGfbQNopF8aupT8zleV0gRAgCfz9d0CArhVwmx/cYzAMD07p4NYn70uXVAH02jTRppk/mJ0SZ9tBWikXyY0MfgDVAoFCIhIUHre18d+zcNmQXlcLY2xcgODfcw0xV9NIk2aaSN5qdN+mgrRCP5MKWPwRugLlApEOKX6ykAgGndW8LMuO5FfoL+kp+fr3XmRyBoM8QAdYDTsRng5ZXB0dIEY4JbaDocgpZiZ2eH4cOHE/MjEJSkXgZYUVGhrjg0ijZPPyQQUth2TVT6m9LVE+YmDV/602Z9tAVt0IjFYmHNmjWIjo7WOvPTBn20HaKRfJjQR+UjCoVCbNu2DV27dkVQUBC9jMqmTZtw5MgRtQfINBwOB0FBQfXqScQkkfGZSH1dAjsLY4zv5Nbg59d2fbQBTWrE4/Ewffp0lJeXAxCZYPVly7QBkocUQzSSD1P6qGyAv/zyC06cOIEvv/wSxsbGdHrr1q1x9OhRtQbXEFAUhYKCAmjjaBChkMK2q6LS3+QuHrAyVWnxDrWgzfpoC5rSSNzhZceOHfj8888b9NyqQPKQYohG8mFKH5UN8NSpU1i+fDmGDBkiUST19vZGamqqWoNrCIRCIVJSUrSy99XFhGw8yS6CtakRPunsrpEYtFkfbUETGtXs7fn111832LlVheQhxRCN5MOUPiobYHZ2Nlq0kO6IQVEUqqqq1BIUQaTnlquiScU/6ewOW3NjBXsQDAVtHOpAIOgiKhugl5cXoqOjpdLPnz8PHx8ftQRFAK4/ycHjjEJYmHAwOcRD0+EQtARifgSC+lC5UWnmzJlYvHgxsrOzQVEULl68iOfPn+PkyZPYsWMHEzEyjpmZmaZDkICiKGx+W/ob9z83OFhqdp0wbdNHG2kIjYRCIT744AOdND+ShxRDNJIPE/rUaS7Q6OhobNu2DUlJSSgtLYWvry9mzZqFkJAQtQeobtQ1hxyT3Ep+jXG778HUiI2bi3rC2Zr8MAgi7t69i5kzZ+LUqVM6Y34EgrpR13O8Tt0KO3TogIiIiDqfVJsQCoXIy8uDg4OD1ozDEbf9jQ5uoXHz00Z9tA2mNaIoip4g+X//+x+io6N16l6QPKQYopF8mNJH5SP16tUL+fn5UumFhYXo1auXWoJqSCiKwsuXL7Wm+/H953m49zwPJhw2pnX31HQ4WqePNsKkRlwuF8HBwfj333/pNF17QJI8pBiikXyY0kflX1J6errMrqh8Ph/Z2dlqCcqQEZf+PuzQDC625hqOhqBJuFwuevbsiejoaEybNo08HAkENaN0FeiVK1fo/9+8eRPW1tb0Z6FQiDt37qBp06bqjc7AeMjNx83k1+CwWZjRvaWmwyFoELH5iTu8HD9+nKwTRyCoGaUNcNasWQBEUy0tXrxY8iBGRmjatKlUuq5gY2Oj6RAAAFvfzvoyLKgpmjtYaDiad2iLPtqMOjWqaX661NuzNkgeUgzRSD5M6KNyL9DQ0FAcPXoUDg4Oag+mIdDWXqD/pRdg0JZbYLOAK/N7wMPJUtMhETSAPpofgaBuNLYi/NWrV3XW/GQhFAqRkZGh8SmIxCs+DG7nqlXmpy36aDPq1OiHH37QO/MjeUgxRCP5MKVPnYZBlJaW4sGDB8jIyEBlZaXEdxMmTFBLYA0FRVHIzMxE48aNNRbD0+winPsvCwAwq6eXxuKQhTboo+2oU6OtW7cCAJYuXaoX5geQPKQMRCP5MKWPygaYkJCAqVOnoqysDGVlZbC1tUV+fj7Mzc3h4OCgcwaoDYjb/gb4N0HrxtYKtiboGwUFBbCxsQGLxYKFhQX27Nmj6ZAIBINA5SrQVatWoWfPnnjw4AFMTU1x+PBhXLt2DX5+fli0aBETMeo1qTnFOBuXAQCYHapdpT8C8/B4PLRv3x7fffcdGeZAIDQwKhtgYmIiJk2aBDabDQ6HAz6fDxcXF3z55ZfYsGEDEzEyCovFgqOjo8a6mP9y/RmEFNCrjTP8XG01EoM8NK2PLlBXjapPbP3nn3+isLCQoQg1C8lDiiEayYcpfVQ2QCMjI3omCkdHR2RkiEovVlZWyMrKUmtwDQGbzYa7u7tGZtfg5ZXixMN0AMCcXq0a/PzKoEl9dIW6aCRrVQdbW+17AVIHJA8phmgkH6b0Uflovr6+iI+PBwB07NgRmzdvxunTp/Hjjz+iVSvtfIjLQygU4sWLFxrpffXrjWcQCCl0beWEwOZ2DX5+ZdCkPrqCqhoZ2pJGJA8phmgkH6b0UdkA582bh0aNGtH/t7GxwQ8//ID8/HwsW7ZMrcE1BBRFITc3t8HbXzILynA0Og0AMCdUe18cNKWPLqGKRoZmfgDJQ8pANJIPU/qo3Au0bdu29P8dHR2xe/dutQZkKOy4kQq+QIj3PRwQ7KE/4yoJ8rlx44ZBmR+BoM2orUL18ePHmDZtmroOp9e8KirHwftcAMBcLW37IzDDuHHj8McffxDzIxC0AJVKgDdv3sQ///wDY2NjjBw5Es2bN8ezZ8+wfv16XLt2TScWxK0Ji8WCi4tLg/a+2nXzOSqqhAhqYYfOLR0b7Lx1QRP66BqKNOLxeDA3N4eTkxMAkQkaEiQPKYZoJB+m9FHaAI8cOYJvv/0Wtra2KCwsxJEjR7B48WKsWLECAwYMwNmzZ9Gype6tYMBms+Hq6tpg58sr4WP/3ZcAgLmhrbQ+wze0PrqIPI3EbX6Wlpa4evUqbYKGBMlDiiEayYcpfZSuAt23bx8WLFiAe/fuYdOmTcjPz8fBgwdx5swZLFu2TCfNDxBNqpqcnAyBQNAg59tz6zlK+QK0bWqLHt6NGuSc9aGh9dFFatOoeoeXkpISlJWVaShCzULykGKIRvJhSh+lDZDH46F///4AgL59+8LIyAhffvklmjRpotaANEFDDUAuKKvE3n9eABDN+qLtpT8x+jpAW53U1MgQe3vKg+QhxRCN5MOEPkobYHl5OczNRSuUs1gsGBsbw9nZWe0B6TN7/3mBoooqeDe2Rh8fMumtvkLMj0DQDVTqBHPkyBFYWIgWahUIBDh+/Djs7e0ltiGTYdfO8X9F4/5m9mwJNls3Sn8E1SDmRyDoDkoboKurKw4fPkx/dnJywqlTpyS2YbFYOmeALBYLbm5ujFdHvioqx4vcUrBYQA9v3Sk5N5Q+ukx1jaqqqlBVVUXMrxokDymGaCQfpvRR2gCvXr2q1hNX58CBA9i9ezdycnLQpk0bfPvttwgICFC4X2RkJL744gv06tULv/zyS53OzWazG6RnXvSLfABAmyY2sDU3Zvx86qKh9NFlqmvk4eGB69evw8jIiJjfW0geUgzRSD5M6aPxmVejoqKwatUqzJo1CydOnECbNm0QHh6O3NxcufulpaVh9erV6NChQ73OLxAI8PjxY8Z7Xz14kQcA6Ohur2BL7aKh9NFlXrx4gR07dtAaeXh4EPOrBslDiiEayYcpfeq0Irw6iYiIwKhRozBixAgAopWwr1+/jmPHjmHq1Kky9xEIBFiwYAHmzJmDmJiYOvUOEgspEAhQXl4OQDThavW55lgsFthstpTotaWz2WywWCyZ6dFvDbB9Czv6e/HM5jUneOVwOKAoSmZ6zRhrSxfHWN9roigK5eXlMq9JVuy6cE3y7pOq18TlchEaGgoejwdXV1eEhYXp/DWp+z6Jf2NCoRAcDkcvrklR7Kpek6q/M124JnXfp+r6qMsINWqAfD5fago1NpuNzp074+HDh7Xut23bNjg6OmLkyJGIiYmp07nFK1pUh8vlSpQ8XVxc4OrqitTUVAmTdXNzg5OTE5KSkmjzBAAvLy/Y2toiLi5O4ia6tWyNxxmi/S2KMxAbmw0ACAwMBJ/PR0JCAr0tm81GUFAQCgsLkZKSQqebmZnBz88PeXl5ePnyJZ1uY2ODVq1aISsrC5mZmXS6o6Mj3N3d631Nnp6eAERT3VW/Jl9fX5iYmCA2NlZCQ124ptruk6rX5OTkhO7du+PFixdo2rQp2Gw2kpKSdPqamLxP2dnZaNasmV5dk7ruk7e3NwDp55IuX5M671N+fr6EPtbW1lAHLEqD049nZ2ejW7duOHToEIKCguj0NWvW4MGDBzhy5IjUPtHR0fjiiy9w8uRJODg4YPHixSgsLFS6DVAgECA2NhZt27al30bj4+MRGBgIFovFyJvQ7We5mLDnAZrZm+PGgu4S2wPa/XZHURQePXpE66Uodl24JnW8sfJ4PPTq1Qupqanw8PDAli1b0KdPH3A4HJ29JnGMTJQA4+PjERAQAGNjY724JkWx16UEqMrvTBeuSZ33qbKyEnFxcTKf29X1UhWNV4GqQnFxMRYuXIjly5fDwaF+KyiIH1RsNhteXl70Taxt2/qkR798AwDo6O4gcx9ZaSwWS2Z6bQtCqpqubOwURcHLywvGxsYy9VFFG225pvqmVzc/T09PXLt2Dba2thIa6do1VUfd90n8GzMyMlJbjKqma3veq8vvTNuvqS7ptV2TkZGRXH3qSp0MkMvl4tixY+DxePj666/h6OiIGzduwNXVVaVFce3t7cHhcKQ6vOTm5srs8cPj8ZCeno4ZM2bQaeK3BV9fX5w/fx4tWrRQ6VpYLBbjK3E/eC7uAKN7yx41hD66RE5ODhnnpyIkDymGaCQfpvRRuRfo/fv3MXjwYMTFxeHixYsoLS0FADx58gRbtmxR6VgmJibw8/PDnTt36DShUIg7d+5IVImK8fT0xJkzZ3Dy5En6LzQ0FO+//z5OnjxZp2nZBAIBHj58yFjvq0qBEA95ovprXesBCjCvj67h6OiI7t27S5gf0Ug+RB/FEI3kw5Q+KpcA169fj88//xyTJk2SMKn//e9/2L9/v8oBTJo0CYsWLYK/vz8CAgKwd+9elJWVYfjw4QCAhQsXonHjxpg/fz5MTU3RunVrif1tbGwAQCpdFWrWOauTxxmFKK8Uws7CGC0bWTF2HiZhUh9dg81mY9euXcjNzUWjRu8mMycayYfooxiikXyY0EdlA3z69CnWrVsnle7g4ED31FGFsLAw5OXlYfPmzcjJyYGPjw927dpFV4FmZmbWWketC4irPzu4OZDpz3QUHo+HzZs3Y9WqVTAyMgKbzZYwPwKBoJuobIDW1tbIycmRavdITExE48Z1m+B53LhxtS4S+scff8jd96effqrTORsKXR0ATxBRfW5PAFi7dq2GIyIQCOpC5aLVwIEDsW7dOuTk5IDFYkEoFCImJgarV6/G0KFDGQiRWdhsNnx9fRkpZVIUheiXolJxBx3sAAMwq4+2U3Ni67lz58rczpA1Ugaij2KIRvJhSh+VjzZv3jx4enqiR48eKC0txcCBAzFu3DgEBQVJ9M7UJUxMTBg57rOcEuSV8GFqxEbbprrbw4spfbQZVVd1MESNVIHooxiikXyY0EdlAzQxMcGKFStw6dIl7NixA2vWrMG5c+ewdu3aeg1I1BRCoRCxsbGMNLCKpz8LbG4HEyPdfLNjUh9tRVXzM0SNVIHooxiikXyY0kflNsDo6Gh06NABrq6ucHV1VWsw+saDF+LhD7pZ/WmICAQCDBgwgIzzIxAMAJWLJRMnTkRoaCg2bNggMWcbQRq6A4wHMUBdgcPhYMOGDfDz8yPmRyDoOSob4N9//43Jkyfj/v37GDRoED744APs2rULWVlZTMSns2QXloObVwo2S7QCBEG7qT4XYt++ffHo0SNifgSCnqOyATo4OGDcuHE4dOgQLl26hP79+9MzsujaavCAqHdRYGCg2nsXVV8A19pMdxbArQlT+mgTPB4P3bp1w5MnT+g0VdqzDUGj+kD0UQzRSD5M6VOvozVv3hxTp07F/Pnz0bp1azx48EBdcTUofD5f7ccUV38G60H1JxP6aAviDi+3bt3ClClTpGbRVxZ91kgdEH0UQzSSDxP61NkAY2Ji8MMPPyAkJATz589Hq1atsGPHDnXG1iAIhUIkJCSovXeR2AA76PgAeKb00QZq9vb8888/6zTTvD5rpA6IPoohGsmHKX3qNBdoZGQkXr16hS5duuDrr79Gr169YG5urtbAdJmi8kokZooWkiQ9QLUTVYc6EAgE/UNlA3zw4AHCw8MxYMCAeq/Jp6/8y30DIQW0cLBAYxszTYdDqAExPwKBANTBAA8dOsREHBpF/R1g9KP6U4y+NczPnz9f7eanbxqpG6KPYohG8mFCH6UM8MqVK+jWrRuMjY1x5coVudv26tVLLYE1FBwOR+bag/Xh3QTYul9CZkIfTSNuq16/fr1azE8fNVInRB/FEI3kw5Q+ShngrFmzcPv2bTg6OmLWrFm1bsdisZCYmKi24BoCiqJQWFgIGxubOnWAqAm/SoiH3DcA9MMA1a2PpigpKYGlpSUAwN7eHocPH1bbsfVFI6Yg+iiGaCQfpvRRqkyZlJQER0dH+v+1/ema+QGi3kUpKSlq6130X0YBKqqEsLcwRstGlmo5piZRtz6agMfjoV27dti0aRMjx9cHjZiE6KMYopF8mNJH5UrVkydPyhyPwefzcfLkSXXEpNO8a/9zIG9yWoC4w8uzZ8+wdetWlJSUaDokAoGgJahsgEuWLEFRUZFUeklJCZYsWaKWoHSZ+8/FE2DrRwcYXaZmb89r167R1aAEAoGgsgFSFCWzZJOdnQ1ra2u1BNXQmJmpZ6iCUEgh5qX+dIARoy59GpKGHuqgixo1JEQfxRCN5MOEPkoPgxg6dChYLBZYLBY++eQTGBm921UgECAtLQ1du3ZVe4BMw+Fw4Ofnp5Zjpb4uRn5pJcyM2fBz1d0FcKujTn0aioY2P13UqCEh+iiGaCQfpvRR2gB79+4NAEhMTERISIhEVZKxsTGaNm2Kvn37qj1AphEKhcjLy4ODg0O9x5mIqz91eQHcmqhTn4YiMjKyQQe566JGDQnRRzFEI/kwpY/SBjh79mwAQNOmTREWFgZTU1O1BaFJKIrCy5cvYW9f/zY7cQeYYD2q/lSnPg3F9OnTAQADBw5skBledFGjhoTooxiikXyY0kflmWCGDRum1gD0iQcv3/UAJTQsaWlpsLW1pduhxSZIIBAItaGUAQYHB+P8+fNwcHBAx44d5Xbvv3//vtqC0yWyCsrByysDmwUEkQVwGxRxm1+TJk1w/vx5ne2MRSAQGhalDHDJkiWwsrKi/69v49tsbGzqfQzx9Ge+rrq9AK4s1KEPU1Tv8AIABQUFGjFAbdZIGyD6KIZoJB8m9FHKAKtXew4fPlztQWgSDoeDVq1a1fs49AB4N/2q/lSXPkwgq7dns2bNGjwObdZIGyD6KIZoJB+m9FG5O83jx4/x5MkT+vPly5cxc+ZMbNiwQSdXNBYKhcjIyKj3FDv3X4gHwOuXAapLH3WjTUsaaatG2gLRRzFEI/kwpY/KBvjdd9/hxYsXAEQPoXnz5sHc3Bznz5/H2rVr1RpcQ0BRFDIzM0FRVJ2PUVheiaQs8QK4+tWLSx36qBttMj9AOzXSJog+iiEayYcpfVQ2wBcvXsDHxwcAcO7cOQQHB2P9+vVYtWoVLl68qNbgdIV/X+aDogA3Rws4kwVwGaewsBBFRUVaYX4EAkF3UXkYBEVRdDH0zp076NGjBwDAxcUF+fn5ag1OV3igp+1/2oqfnx+uXbsGGxsbYn4EAqHOqFwC9Pf3x6+//oqTJ0/iwYMHtAGmpaXByclJ3fExDovFgqOjY716tj542/4X7KFf1Z+AevRRBzweD7du3aI/+/n5aY35aYtG2grRRzFEI/kwpY/KBvjVV18hISEBy5cvx/Tp0+Hm5gYAuHDhgk6uaMxms+Hu7l7n6XUqqgR4xHsDQD8HwNdXH3UgbvPr16+fhAlqC9qgkTZD9FEM0Ug+TOmjchVomzZtcObMGan0hQsX6uTNEwqF4HK5aNGiRZ3i/y9dtACuo6UJPJ30b6md+upTX2p2eBG/cGkTmtZI2yH6KIZoJB+m9Knzkf777z+cOnUKp06dwuPHj2FqagpjY90bAE5RFHJzc+vcu0hc/dnB3V4vqy/qq0990LbenrWhSY10AaKPYohG8mFKH5VLgLm5ufj888/x4MEDemR+YWEh3n//fWzcuBEODvpXDSgP8QB4fRv/p2l0xfwIBILuonIJcPny5SgtLUVkZCTu37+P+/fv4+zZsyguLsaKFSuYiFFrEQqpaiVAYoDqIisri5gfgUBgHJVLgDdv3kRERARatmxJp3l5eeH777/H5MmT1RpcQ8BiseDi4lKn6suUnGIUlFXC3JgDP1f9nMevPvrUFQcHB7Rr1w4AdML8NKGRLkH0UQzRSD5M6aOyAQqFQpltfUZGRjo5jQ+bzYarq2ud9hWP/wtqYQdjjn42XNdHn7piYmKCQ4cOIS8vD02aNGnQc9cFTWikSxB9FEM0kg9T+qj81P7f//6HlStXIjs7m07Lzs7GqlWr0KlTJ7UG1xAIBAIkJydDIBCovO+D5/q//l999FEFHo+HpUuX0i9RJiYmOmF+QMNppKsQfRRDNJIPU/qoXAL87rvvMGPGDPTq1Yt+QGVlZaFVq1Y6ORcoIOrEUxfoAfB6bIBA3fVRlppLGn3//feMno8JmNZI1yH6KIZoJB8m9FHZAF1cXHDixAncuXMHz549AwC0bNkSnTt3Vntw2kzGmzKkvykDh81CIFkAt87U7O2pi+3IBAJBN1HJAKOionDlyhVUVlaiU6dOGD9+PFNxaT30ArguNrAyVfk9ggAy1IFAIGgWpZ/cf/75J5YtWwY3NzeYmZnh0qVL4HK5WLRoEZPxMQ6LxYKbm5vKvYui9XT9v5rUVR9F6JP5MaWRvkD0UQzRSD5M6aN0J5gDBw5g9uzZuHDhAk6dOoWffvoJBw8eVGswmoDNZsPJyUnl6XUe0APg9W8C7OrUVR958Pl89O7dWy/MD2BGI32C6KMYopF8mNJH6aPxeDwMHTqU/jx48GBUVVXh1atXag2ooREIBHj8+LFKvYsKyirxJLsIgH73AAXqpo8iTExMsHz5crRu3VrnzQ9gRiN9guijGKKRfJjSR2kD5PP5sLCweLcjmw1jY2NUVFSoNSBNUF5ertL24gVwPZws0cjalKGotAdV9VGGUaNGIT4+XufNTwwTGukTRB/FEI3kw4Q+KvXe2LRpE8zNzenPlZWV+PXXX2FtbU2nLVmyRH3RaSnvFsDV7+pPdcLlcjFlyhTs3r2bNj0TExMNR0UgEAwZpQ2wY8eOeP78uURaUFAQeDwe/dlQGnAfkAmwVYLL5aJnz55ITU3FlClTcOHCBU2HRCAQCMob4B9//MFkHBqDzWbDy8tL6cbV8koBHvEKAIiWQNJ3VNWnJtXNz9PTE7t27VJzhJqnvhrpO0QfxRCN5MOUPgY/gI3FYsHW1lbp7f9LLwBfIISTlQk89HAB3Jqoqk91apqfPnR4kUV9NDIEiD6KIRrJhyl9DP51QyAQ4OHDh0r3LrpPt/85GESVr6r6iDEU8wPqrpGhQPRRDNFIPkzpY/AGCEClVSyiq60AbyjUZZWPmTNnGoT5idHFlVAaEqKPYohG8mFCH2KAKiAUUvQK8MEepAOMPHbt2oWBAwcahPkRCATdxODbAFXh6asiFJZXwcKEA18X/VwAtz5UVFTA1FQ0LrJJkyY4e/ashiMiEAiE2qlTCTA6OhoLFizARx99RK8LePLkSURHR6s1uIaAzWbD19dXqd5F4uWPglrYwUhPF8CtibL68Hg8tG3bFvv27WugyLQHVfKQIUL0UQzRSD5M6aPy0S5cuIDw8HCYmZkhISEBfD4fAFBcXIwdO3aoNbiGQtkB2dEGOv5PkT7iia2Tk5OxYsUKvZgdSFXIoH75EH0UQzSSDxP6qGyAv/76K5YuXYoVK1bAyOhdDWr79u2RkJCg1uAaAqFQiNjYWKUaWMUrwBuSASrSp+aqDleuXKGrQQ0FVfKQIUL0UQzRSD5M6aOyAT5//hwdOnSQSre2ttbrFY3T35Qho6BctABucztNh6MV6NOSRgQCwfBQ2QCdnJzA5XKl0mNiYvT64Seu/vR3tYElWQCXmB+BQNB5VDbAUaNGYeXKlXj06BFYLBays7Nx+vRprF69GqNHj2YiRq3g/tvqT31f/khZ/vzzT2J+BAJBp1G5KDN16lQIhUJMnDgRZWVlGDduHExMTDB58mSMHz+eiRgZhc1mIzAwUGHvoncrwBvOAHigdn0WLlwIABgzZozBm5+yechQIfoohmgkH6b0UdkAWSwWZsyYgfDwcHC5XJSWlqJly5awtNTdeTH5fD7MzMxq/b6g1HAWwJWFWJ+MjAw4ODjAzMwMLBYLixYt0nRoWoOiPGToEH0UQzSSDxP61NlOTUxM4OXlhYCAgHqb34EDBxAaGoq2bdti5MiRiIuLq3Xbw4cPY8yYMejYsSM6duyIiRMnyt1eEUKhEAkJCXJ7F0W/FFV/ejpZwsnK8Ho4JiQk4OXLl+jatSuGDx9OFu6sgTJ5yJAh+iiGaCQfpvRRuQQ4fvx4uZNAqzoQOioqCqtWrcLSpUvRrl077N27F+Hh4Th//jwcHR2ltr937x4GDhyI9u3bw8TEBLt27cLkyZMRGRmJxo0bq3o5SvHAAOf/rE5WVhZGjRqF1NRUAEBeXh5cXV01HBWBQCDUD5VLgD4+PmjTpg395+XlhcrKSiQkJKB169YqBxAREYFRo0ZhxIgR8PLywtKlS2FmZoZjx47J3H79+vUYO3YsfHx80LJlS6xYsQJCoRB37txR+dzKYsgL4PJ4PEybNk2iwwsxPwKBoA+oXAL86quvZKZv2bIFpaWlKh2Lz+fj8ePHmDZtGp3GZrPRuXNnPHz4UKljlJWVoaqqSuW1osTLaggEArpEKxQKQVEUvQ2LxQJfQCEu7Q0AoH0LW3p7NpsttTQHm80Gi8WSmS4+vjLpHA4HFEXJTK8ZY23p4hhrS68Zo6x0Ho+HXr16IT09HR4eHrhy5QpcXV0hEAh09prEMarzPonzhPh4+nBN6rxPYn2EQiE4HI5eXJOi2FW9JoqiVLpWXbgmdd+n6udQ17JIahvQNmTIEIwcOVKljhH5+fkQCARSVZ2Ojo50dZsi1q1bB2dnZ3Tu3FmleOPj4yXOx+Fw8OLFC+Tm5tLpLi4uSKswQ6WAgp0pG/ncp3jDY8HNzQ1OTk5ISkqSaA/z8vKCra0t4uLiJG6ir68vTExMEBsbKxFDYGAg+Hy+xAw6bDYbQUFBKCwsREpKCp1uZmYGPz8/5OXl4eXLl3S6jY0NWrVqhaysLGRmZkpck7u7O7hcrtQ1ubq6IjU1VWLigprXlJWVhWnTpiE9PR2enp7YunUrcnNz6WPp4jUxfZ/i4+P17poA9d2nnJwcvbsmdd6ndu3a6d01qes+FRQUgKIo+rltbW0NdcCiar4q1JGTJ09i3bp1uHXrltL7ZGdno1u3bjh06BCCgoLo9DVr1uDBgwc4cuSI3P1/++037Nq1C/v27UObNm2UOqdAIEBsbCzatm1Lv3EUFRXBzs4OFEVJvQn9eiMVay88wQD/xtg6OohO1/c31vv376Nfv35o1KgRTp8+DW9vb4m2X128puoxKnOfhEIhKisrJWoIZF2TQCBASUkJLC0twWKxtPqaFKUzcZ8oikJJSQmsrKxICbCWa2KxWCguLoaFhYVSvzNduKb63CdjY2NwOBw6XSAQoLCwENbW1vS54uPjERgYSG9XF1QuAc6ePVviM0VRyMnJwX///YeZM2eqdCx7e3twOByJtw8AyM3NhZOTk9x9d+/ejd9++w0RERFKm191OBwOLWxqamqtQr5r/3OU+r424dWRLn6Q1qS2cTCqpiuKpVOnTrh8+TKcnZ2Rm5tbazy6dE2qpPP5fDx//lypXmcURaGyshLGxsZyO4gZKkQfxRCNpLGzs0OTJk3oz/Ke03VFZQOsWfRksVjw8PDA3LlzERISotKxTExM4Ofnhzt37qB3794AQHdoGTduXK377dy5E9u3b8fu3bvRtm1bVS9BaQRCCjEvxQPg9b8DDI/HQ05ODtq3bw8ACA4OhkAgkHpB0XcoikJmZiY4HA6aN2+ucPAtRVEoKyuDubk5eXjJgOijGKLROyiKQmlpKV69egVAVCXLFCoZoEAgwPDhw9G6dWuVO53UxqRJk7Bo0SL4+/sjICAAe/fuRVlZGYYPHw5ANONI48aNMX/+fACias/Nmzdj/fr1aNq0KXJycgAAFhYWah+M/ySrCEXlVbA04cDHRT11ztqKeG7PvLw8XLlyhTZBQ6SqqgqlpaVwdXWFhYWFwu3F1TbiCQIIkhB9FEM0ksTc3BwA8OrVKzg7OzN2HpUMkMPhYPLkyYiKilKbAYaFhSEvLw+bN29GTk4OfHx8sGvXLroKNDMzU+IN/NChQ6isrMTcuXMljjN79mzMmTOnTjHUNruAeAB8ezd7vV4At+bE1o0aNZL43tBmpxC3Zaiy/hiZwko+RB/FEI0kEb98iquGmXgOqVwF2qpVK6Slpal1/sdx48bVWuX5xx9/SHy+evWq2s4LiEzdz89P5nf0AHg3/a3+VLSqgzx99B1l38RZLBb9xkqQhuijGKKRNNV/f0w9h1R+5fj888+xevVqXLt2Da9evUJxcbHEn64hFArx+vVrqc4OFEW9WwDXQz9ngFFmSaPa9CG8Q9yBQU0dqvUOoo9iiEbyYeo5pHQJcOvWrZg8eTKmTp0KAJgxY4aEQ4sHciYmJqo1QKahKAovX76Evb2kyaXllyGrsBxGeroAbnp6ulLr+dWmD0ESPp8PIyOyTmRtEH0UQzSqHaaeQ0qrvW3bNowePVrluT51FXH7n19TW1iY6F+mtLe3h5ubGwCQ9fwMDG9vb2zbto3uec0U9+7dw4QJE3D//n36wX758mWsXr0aaWlpGDduHHx8fPDjjz8iOjqakRhSU1Mxfvx4XLhwAVZWVoycw9CYN28e2rZti8mTJ2s6lHqj9JNdXDQPDg5mLBht4v5zUftfsJ5OgG1hYYGzZ88iPz8fTZs21XQ4BDWRk5OD7du34/r168jOzoajoyN8fHzwySefoFOnTg0aS1BQEG7dugVra2uUlZUBAL777jsMHz4c48ePh6WlJYyMjNC9e3fGYtiwYQPGjRsn0/z69++PtLQ0XLt2TarjV2hoKCZMmICJEydKpG/ZsgWXL1/GqVOn6DRNaX7u3Dn8/PPPSE9Ph7u7OxYsWKBQy9OnT2PXrl14+fIlrK2t0bVrVyxcuJAuWSUnJ2Pz5s14/Pgx0tPTsWTJEikNZsyYgXHjxmHkyJFqm5FFU6jUBqiv3XNtbGyk0qJf6N8K8FwuFxs3bqRfZiwsLJQyP1n6GBoURaGUX1XrX4UAcr+vy5+q7UFpaWkYPnw47t69i4ULF+LMmTPYtWsX3n//fSxdupQhZWrHxMQEjRo1oidAKCkpQW5uLkJCQtC4cWNYWVnBzMxM5qovqlBZWSkzPSMjA9evX6eHVFUnOjoaFRUV6NevH06cOFHnc6tTc1UGeP/777+YP38+PvzwQ5w8eRK9evXCrFmz8PTp01r3iYmJwaJFi/Dhhx/i7Nmz2LRpE+Lj4/Htt9/S25SVlaFZs2aYP3++1EuBmNatW6N58+Y4ffq08henBph4DqlUt9evXz+FJnj//v16BdTQcDgctGrVSiItv4SP5FeiDj0d3PSjBMjlctGzZ096jtV58+YptZ8sfQwNiqLw4fY79KQIDUUHN3scmd5J6RfPpUuXgsVi4ciRIxLjF1u1aoURI0bUut/atWtx+fJlZGVlwcnJCYMHD8asWbNgbGwMAEhKSsLKlSvx33//gcViwd3dHUuXLkXbtm2Rnp6O5cuXIyYmBpWVlWjatCkWLlyI7t2701WgDx48QGJiIiZMmAAA+OSTTwCIlk5LT0+XqgK9fPkytm3bhpSUFDg7O2PYsGGYPn06XY3q7e2N77//Hn///Tfu3r2L8PBwmUOgzp07B29vb5nLpB07dgyDBg1Cx44dsXLlSrpvg6rUVfOasFgslbr579u3D127dsWUKVMAiDon/vPPP9i/fz+WLVsmc5/Y2Fg0bdqUvg/NmzfHRx99hJ07d9LbBAQEICAgAIBo5Z3a6NmzJyIjIzF27FilY64PTD2HVDLAOXPm6HyRtyZCoRBZWVlo0qQJPQ4n+u2DzrORJRz1YAHc6ubn6emJDz/8UOl9ZeljiGh73cebN29w8+ZNzJs3T+bgfXlvz5aWlli1ahWcnZ3x9OlTfPvtt7C0tMSnn34KAFiwYAF8fHzwww8/gMPhIDExkTbHZcuWobKyEvv374eFhQVSUlKkzk9RFPz9/XHu3DkMGDAAW7ZsQVBQEGxtbZGeni6xbXR0NBYtWoRvvvkGHTp0AJfLpUso1adh3Lp1K+bPn4+vv/661pJTdHQ0/P39pdKLi4tx/vx5HD58GJ6eniguLkZ0dDQ6dOhQq0ayqI/mp0+fxvfffy+RJu5IKGbnzp21xhQbGytVNRkSEoLLly/Xes7AwEBs3LgRN27cQLdu3ZCbm4sLFy7UqQo6ICAA27dvB5/PV2m8bF1h6jmkkgEOHDiw3tUV2oZ42qvqb4ni6s9gPaj+rGl+qnZ4kaWPocFisXBkeieUVcpegkU0dVMZLCzUO42VuTFH6eNxuVxQFAVPT0+Vz1N9Dt9mzZrh+fPniIyMpA0wIyMD4eHhaNmyJQDA3d2d3j4jIwP9+vWDt7c3AMjNW+Jnh62tba3Va1u3bsXUqVMxbNgw+nifffYZ1q5dK2GAgwYNUljCysjIkGmAUVFRcHNzo0sUYWFhOHr0qMoGWB/NQ0ND0a5dO/qzrKnQ5P3mXr9+LTVfsqOjI16/fl3rPu+99x7Wrl2Lzz//HHw+H1VVVejZsye+++47leN3dnZGZWUlcnJyGqQPAVPPIaUNUF/b/2TxNLsIABDQzE6zgdST+pof4R0sFqvW3sAURQFVHFiYGGnsd1Kf8WNRUVHYt28feDweSktLUVVVJdFpZNKkSfjmm29w6tQpdO7cGf3790eLFi0AABMmTMAPP/yAW7duoXPnzujbt2+dJqcXk5SUhH///Rfbt2+n0wQCASoqKmiDACDT2GpSXl4OU1PpGpxjx45hyJAh9OchQ4Zg/Pjx+Oabb1TqKVofza2srCTOJZ7/suZqEOokJSUFK1euxKxZsxASEoKcnBysWbMG33//PX788UeVjiWurq2+LJIuonIvUEMgu7ACAOBiq7tTgJWVlSE0NJSYn4Hg5uYGFoul9DqaYh4+fIgFCxZgzpw5CAkJgbW1NSIjIxEREUFvM2fOHAwaNAg3btzA33//jc2bN2Pjxo3o06cPRo4ciZCQEFy/fh23b9/Gb7/9hkWLFmH8+PF1uo7S0lLMmTMHffv2lfquupkpM0ervb29xHp2gMgEYmNjERcXh3Xr1tHpAoEAUVFRGDVqFABRtbCsiT3ES/IAddccqH8VqJOTk1RpT9EqOjt27ED79u3pdsM2bdrA3NwcY8eOxeeff67SnJsFBQUAoPPjg5U2wKSkJCbj0BgsFguOjo4SGe9VkcgAnW10t/3P3NwcCxcuxNq1a3H16tU6m58sfQjSaHoAs52dHUJCQnDgwAGMHz9eyiAKCwtltkk9fPgQrq6umDFjBp2WkZEhtZ2Hhwc8PDwwceJEfPHFFzh27Bj69OkDQDRb/+jRozF69GisX78ehw8fljJAIyMjVFVVKbwOX19fPH/+nB6jWh98fX0lFlcFgKNHj6Jjx45S1X7Hjx/H0aNHaQP08PDAf//9J3XMhIQEeHh4AKi75oDsKtCayyHJq+4LDAzE3bt3JdoB//nnHwQGBta6T3l5ea1LuqlawHn69CmaNGkCB4eGaSZi6jlkuL0a3sJms+Hu7k43rFYJhMgteWuA1rpbAgSAqVOnIi4url4lv5r6EKRhsVgwNTXV+EvC999/D6FQiJEjR+LChQt48eIFnj17hn379uGjjz6SuY+bmxsyMzMRGRkJLpeLffv2SXSkKC8vx7Jly3Dv3j2kp6cjJiYG8fHxdHvgypUrcfPmTfB4PDx+/Bj37t2jvxOjij6zZs3CqVOnsHXrViQnJ+PZs2eIjIzExo0bVdYjJCQEsbGx9OTmlZWVOHXqFAYOHIjWrVtL/I0cORKPHj1CcnIyAGDixIm4ceMGfv31Vzx79gxPnz7Fxo0bERsbS/eiBOqmOSCqAnVzc6P/3N3d0apVK7i7u9Np8nqFTpgwATdv3sSePXvw7NkzbNmyBf/995/EnMrr16/HwoUL6c89e/bEpUuX8Oeff4LH4yEmJgYrVqxAQEAAbbZ8Ph+JiYlITEwEn89HdnY2EhMTJVaYB0RDKrp06aLC3agfTD2H9G+KExURCoXgcrlo0aIF2Gw2XhfzQVEAh82CoyXzvZvUCY/Hw9y5c7Fz5066KqS+E+zW1IcgDUVRdG84TZpg8+bNcfz4cWzfvh2rV6/Gq1ev4ODgAD8/P/zwww8y9+nVqxc++eQTLFu2DHw+Hz169MCMGTOwdetWAKIHz5s3b7Bo0SK8fv0a9vb26Nu3L70ai1AoxLJly5CVlQUrKyt07doVS5YskTgHRVGoqKhQqpTRtWtXbN++Hdu2bcPOnTthZGQET09PjBw5UmU9unXrBg6Hg3/++Qddu3bF1atX8ebNG7rkWp2WLVuiZcuWOHr0KJYsWYL27dtj586d2LZtGyIiIsBisdC6dWv8/vvvaN26Nb1fXTSXhap5qH379li3bh02bdqEDRs2wN3dHdu2bZOILScnB5mZmfTn4cOHo6SkBAcOHMDq1athbW2N//3vf/jyyy/pbV69eoWhQ4fSn/fs2YM9e/YgODiYXpigoqICly9fxq5du5S+vvrC2HOIMjCqqqqo6OhoqqqqSubnWG4+5bboLPW/Hy9rMkyV4XK5lKenJwWAGjp0qNqOW1MfQ6CsrIxKSEigysrKlNpeKBRSxcXFlFAoZDgy3UST+uzfv5+aPHlyg59XVXQpDx04cICaNGkS4+ep/jtU9NyuKwZfAqwJ3f5nrTvtfzVXddi8ebOmQyIQtIKPPvoIhYWFKC4uJnOBqgljY2N88803mg5DLRADrEF2oahbr7ONbrT/KbOkEYFgqBgZGUl08CHUn7pUR2srBt+ow2Kx4OLiQte7vxIboA6UABvC/GrqQ5CNeGYUgmyIPoohGtUOU88hgy8BstlsuLq60p/FVaCNdaAEOHHiRMZLfjX1IUjDYrEaZDooXYXooxiikXyYeg4ZfAlQIBAgOTmZ7iqdrUMlwF27diE0NJTRas+a+hCkoSgK5eXlBjVZhCoQfRRDNJIPU88hgy8BApCYLULbS4BVVVX0oGsPDw9cuXKF8XPWnE2DIA15QZAP0UcxRCP5MPEcMvgSYE3E06Bp4ywwPB4Pbdu2bfB1uAgEAkEfIQZYDW2eBUbc4SUpKQmLFi1SalopAoFAINSOwRsgi8WiJ7XV1llgavb2vHjxYoPNPVldH0LtkA4M8iH6KIZoVDtMPYcM3gDZbDacnJzAZrPpDjCNrEzBZmvHA1/T4/yq60OQDYvFkpjEWNvx9vaWu3CqulGXPsePH1d5zb76cu/ePXh7eytsf7pz5w4GDBhQ53Y8XctD9WXUqFG4cOGC0tsz9Rwy+KeaQCDA48ePIRAIqnWA0Y72P02bHyCpD0E21NvFTLWhB19OTg6WL1+OXr16wd/fH927d8f06dNx584djcWkTfowxdq1azFjxgyp1RbKy8sRHByM999/H3w+X2o/8ctITY0WL14ssVAxALx8+RJLlixBt27d4O/vj9DQUHzxxReIj49n7sIAHDhwAKGhoWjbti1GjhyJuLg4udsfP34c3t7eEn9t27aV2GbGjBlYv349hEKhUjEw9RwivUDxblFHbZsFZvv27Voxw4uuL3qpLkpKSmSmU28ne64+e39t2wKit9nqk5TXtq2lpaVK8aWlpWH06NGwsbHBwoUL0bp1a1RVVeHWrVtYunQpzp8/r9Lx1ImyDzpdJDo6GlwuF/369ZP67sKFC/Dy8gJFUbh8+TLCwsJqPY48jeLj4zFx4kS0atUKy5Ytg6enJ0pKSnDlyhWsXr0a+/fvV8u11CQqKgqrVq3C0qVL0a5dO+zduxfh4eE4f/48HB0da93PyspKIr/VLNl269YN33zzDf7++2/06NFDqViYeA4RA6yGts0Dunz5cgDA9OnTyfRmWoC8uST79euHc+fO0Z+dnZ1RWloqc9vu3bvj+vXr9Gd3d3epxU0B1ddoW7p0KVgsFo4cOSKxNl2rVq0wYsQIiW3z8/Mxa9Ys3Lp1C40bN8aiRYvQq1cvAKK37W+//RZ3797F69ev4eLigjFjxuCTTz6h91+8eDEKCwvx3nvvISIiApWVlQgLC8NXX31Fz2jC5/Px888/4+zZs8jNzYWLiwumTp1KT6X19OlTrFmzBjExMTA3N0eXLl2wZMkSldaYu3z5MrZt24aUlBQ4Oztj2LBhmD59OoyMjDB//nwIBAJs2rSJ3r6yshIhISFYsmQJhg4dCqFQiJ07d+Kvv/7C69ev4e7ujpkzZ6J///5KxxAVFYXOnTvLXH3+6NGjGDJkCCiKwtGjR+UaYG1QFIUlS5bAzc0Nf/75p0Q1oI+Pj8TyTOomIiICo0aNovPP0qVLcf36dRw7dgxTp06tdT8Wi4VGjRrV+j2Hw0G3bt0QGRmptAEygcFXgVZHPA2aJscAZmdn0z082Ww2Vq5cScyPoJA3b97g5s2bGDt2rMzV0msuzLp161YMGDAAp0+fRrdu3bBgwQK8efMGgKgk0qRJE/z888+IjIzErFmzsHHjRkRFRUkc4969e+Byudi7dy9++uknnDhxAidOnKC/X7hwISIjI/H111/j2LFjWLp0KV2qLSwsxCeffAJfX18cPXoUu3btQm5uLj7//HOlrzk6OhqLFi3ChAkTEBUVhWXLltFLEwHA4MGDce3aNYkS9q1bt1BeXo7evXsDEK2SfvLkSSxduhSRkZGYOHEivvzyS9y/f1+lOPz9/aXSuVwuYmNjMWDAAAwYMADR0dFIT09X+rhiEhMTkZycjMmTJ8tsA6tt0V1AVIsUFBQk90/WAsiA6AXm8ePH6Ny5M53GZrPRuXNnPHz4UG7MpaWl6NmzJ7p3744ZM2bQ6yxWJyAgADExMXKPwzQGXwJks9nw8vKS6ASjqRKguM2vY8eO2L9/v8ZXGQck9TF0iouLZabLKqm9evWq1uPU1PLFixf1igsQPWwpioKnp6dS2w8bNgyDBg0CAHzxxRf4448/EBcXh27dusHY2Jhe7w8QrXkXGxuL8+fPS5RgbG1t8d1334HD4aBly5bo3r077ty5g1GjRuH58+c4d+4cIiIi0KlTJwgEArRu3ZquCtu/fz98fX3xxRdf0Mf78ccf0b17dzx//pxedV0eW7duxdSpUzFs2DA6zs8++wxr167F7NmzERISAnNzc1y6dIle4+7s2bMIDQ2FlZUV+Hw+duzYgYiICAQFBdHHiImJwV9//YXg4GCltMzIyICzs7NU+rFjx9CtWzfY2toCEC3Qe/z4ccyZM0fmcWSVIIF3+UPZe1udjz/+GAMGDJC7jazYAVEtgUAgkKrqdHR0RGpqaq3H8/DwwI8//ghvb28UFRVhz549+PjjjxEZGYkmTZpInDczMxNCoVDh84Wp55Dmn7AahsVi0RlUk7PAVO/wAgC5ubn0Ks2apLo+ho4qbXJMbVsbqlaXent70/+3sLCAlZUV8vLy6LQDBw7g2LFjyMjIQEVFBSorK9GmTRuJY3h5eUl0+mjUqBGePn0KQFRq4XA46NixI1gsltTLXFJSEu7du0cbT3W4XK5SBpiUlIR///2XLvEBourbiooKlJWVwdzcHAMGDMCZM2cwdOhQlJaW4sqVK9iwYQMAUaeSsrIyTJ48WeK4lZWV8PHxUXh+MeXl5VLmJRAIcOLECXz99dd02pAhQ7BmzRrMmjVL6kEuSyN1YGdnBzs7O7UfVx7ikmX1z2FhYTh06JBECd/MzAxCoRB8Pl+i/VwWTD2HDN4ABQIB4uLiEBAQoLFZYGT19tQG8wMk9anZw40gQtyDz9zcXGPd2MVjpOS9mVen5soDLBaL7oQRGRmJ1atXY9GiRQgKCoKlpSV2796NR48eSexT84HNYrFoI67+QJOlj7iKbMGCBVKxyWs7qk5paSnmzJmDvn37Sn0nNqTBgwdj/PjxyM3Nxe3bt2FqaoquXbvS+wOiatCavzdVxuTZ29tLDZO4desWsrOzMW/ePIl0gUCAO3fuoEuXLgBELz9FRUVSGhUWFsLa2hqAqI0YAFJTU+Hr66t0XICoCnTHjh1yt4mMjJQ50bS9vT04HA5yc3Ml0nNzc+Hk5KR0DMbGxvDx8QGXy5VILygogIWFhULzA5h7Dhm8AQKiNg9NzQKjDUMdFKHPPfjUhaa7+NvZ2SEkJAQHDhzA+PHjpdoBCwsL5bYVVefff/9FUFAQxo4dS6fVfHgponXr1hAKhXjw4AE6deokpY+fnx8uXLiApk2b1rnk4+vri+fPn8PNza3Wbdq3b48mTZogKioKf//9N/r370+bf8uWLWFiYoKMjAylqztriyMlJUUi7ejRoxg4cCCmT58ukb59+3YcPXqUNkAPDw88fvwYQ4cOpTUSCARISkqiOwv5+PjAy8sLe/bsQVhYmFTpUd69rU8VqImJCfz8/HDnzh26zVQoFOLOnTsYN26c3GNWRyAQ4OnTp+jevbtE+tOnT1UqaTPxHCIG+BZNzAKjC+ZH0B2+//57jB49GiNHjsTcuXPh7e0NgUCA27dv4+DBgxK9VOXh5uaGkydP4ubNm2jWrBlOnTqF+Ph4NGvWTOlYmjVrhmHDhuGrr77C119/DTc3N+Tl5SEvLw9hYWEYM2YMDh8+jC+++AJTpkyBnZ0dXr58iaioKKxYsUKpt/xZs2Zh+vTpcHV1Rb9+/cBms5GUlISnT59KlLwGDRqEQ4cO4cWLF9i7dy+dbmVlhcmTJ2PVqlWgKArvvfceioqK8O+//8LKyopuW1RESEiIROefvLw8XLt2Db/88gtat24tse0HH3yA2bNn482bN7Czs8OkSZPw9ddfw8PDA+3btwdFUdi/fz8KCwtpA2SxWFi1ahUmTpyIMWPGYMaMGfQwiGvXruH27du1DoOobxXopEmTsGjRIvj7+yMgIAB79+5FWVkZhg8fTm+zcOFCNG7cGPPnzwcgapsNDAyEm5sbCgsLsXv3bmRkZEgtpBsTE0O/CGgKYoBvEbf/NeQsMMnJyUhPTyfmR1ALzZs3p3tBrl69Gq9evYKDgwP8/Pzwww8/KH2cjz/+GImJiZg3bx5YLBYGDhyIMWPG4O+//1Ypnh9++AEbNmzA0qVL8ebNG7i6umLatGkAgMaNG+PgwYNYt24dwsPDwefz4erqiq5duyrd0aFr167Yvn07tm3bhp07d8LIyAienp5SD9ohQ4Zg+/btaNq0Kd577z2J7z7//HM4ODhgx44dSEtLg7W1NXx9faVKbvIYPHgw1q5dS7/Injx5Eubm5ujUqZPUtp06dYKZmRlOnz6NCRMmYNCgQaAoChEREVi/fj3Mzc3h5+eH/fv3S1QzBgQE4NixY9i+fTu++eYb5Ofnw9nZGUFBQfjqq6+UjlVVwsLCkJeXh82bNyMnJwc+Pj7YtWuXRGyZmZkS96ywsBDffvstcnJyYGtrCz8/Pxw6dAheXl70NtnZ2Xj48CHWrl3LWOzKwKI0XXfTwAgEAsTGxiIwMBAcDodeh+tmagGm/hGDds1scWp2SIPFc/XqVbRq1UprzU+sj5mZmcFM01ReXk73RFSmfYKiKFAUBRaLZTAaqYIh6LN69WqUlJRg2bJlddrfEDSqztq1a1FYWEiPdZZF9d+hqampxHOo5nO8rpC+7RDVdb8qejsPKMPtf1wuF0+ePKE/h4aGaq35iSGT9CrGEB5a9UHf9ZkxYwZcXV3r1U6l7xpVx9HREZ999plK+zDxHDJ4AxQKhYiNjUV2gXgQPHM9QLlcLnr27IkePXpImKA2I9aHdISRT22zvhBE6Ls+NjY2mD59er3Gqem7RtWZPHmySj1JmXoOGbwBislmeAyg2Pz+396Zh1VVrQ38x+iEqSGKmgqigTLIQbwWoV5nU7HyXkrtwiURh5ynwNSuoIYaKSpqiGRONzNF00T7MhPlYpRajlDiBAgKKIiEwAH29wedHcfDdJDBOOv3POdR1l57rXe9e+317vXutdd748YNmjZtWuZuHQKBQCCoO4QB/IP0WtwHtLTxEwteBAKB4NlAGMA/SMuunRmgMH4CgUDwbKLzBlBfXx9HR0fuyYtgam4GmJSU9Jc3fir9iL1AK0a4tCtG6KdyhI7Kp7bGIfEdIJCbl8f930uCVdbkDLB58+ZyaJe/ovFTUZW9+nQd1RJ2QdkI/VSO0FHF1MY4pPOP9cXFxZz5+Uqt7ALTsmVLvv32W6Kiov6yxq+4uJirV6+KVaCV8Pjx4/oW4ZlG6KdyhI7Kp7bGIZ03gACZj0uUWhO7wCQlJfHpp5/Kf7ds2VKrLaQEgpokNjYWa2trjc2aa5uIiAicnZ2fqozk5GSsra2Ji4srN099tU/QMBAGEMjMKwKe/htA1d6e3t7eakZQoKNcuwYLF8K4cSX/lhEUtKbx8PBgxYoVtV5PQ2L37t0MHDgQe3t73N3duXjxYoX5IyIisLa2VvvZ29ur5ZEkiXXr1uHq6oqDgwNeXl41EvdRULMIAwhk5pXsBvc0u8A8ubH1kCFDakq8ekcsgKkcjXc327aBjQ189BHs3Vvyr40NfPZZvcinDQUFBTVe5rP6bisyMpLAwECmTZvGgQMHsLGxwdvbWyME0JOYmJgQHR0t/77//nu142FhYezcuZOlS5eyd+9emjRpgre3N/n5+eWW+azq6FmhNsYhnR/ZDAwMaNSyJBxIdWeADTmqg4GBAQqFQsQCrAA9PT2aNm365wB27RpMnAjFxVBUpP6vtzc8ETqnpvDz8+PHH39kx44d8szkzp07AFy5coUxY8bQs2dPxo4dqxY3cMOGDbz22mt8+eWXDBw4EAcHB6BkU+NFixbx0ksv4eTkhKenJ/Hx8fJ58fHxeHh4oFAocHJyYsyYMVy6dElNptOnTzNixAheeeUVJk6cSFpamnysuLiYkJAQ+vXrh52dHa+99lqlG25HRUUxbNgwHBwc8PDwkNtXXbZt28abb77JP/7xD7p27Yq/vz+NGzdm//79FZ6np6eHmZmZ/Cu9q4kkSezYsYOpU6cyePBgbGxsWL16NWlpaRw/frzc8tT6kECN2hqHdN4ASpJEckbJ+4PqrABtyMYPSvTz8OHDeo939ywjSRKFhYV/6ujTT6G8gUxPD8LDa0WORYsWoVAoePPNN+WZSbt27QBYu3Ytfn5+7N+/HwMDA40IAomJiXzzzTeEhIRw8OBBAGbNmsX9+/cJCwsjIiICW1tb/v3vf5OVlQXA/PnzMTc3Z9++fURERODj46MWaDcvL49PP/2UVatWsX37dlJTU1m1apV8fMeOHWzbtg1fX18OHTqEq6sr7777brmuwtTUVKZPn86AAQM4ePAg7u7ufPzxx2p5UlJS5Ijk5f1UEeQLCgq4cuUKLi4u8vn6+vq4uLjw888/V6hrVUDf/v37M3XqVK6Vcm8nJyeTnp6uVm7z5s3p2bNnueVq9CGBGrU1Dun8ZxDFxcXcTssCtN8F5tGjRw3a+EGJfhISEp561/WGTn5+/p/fcd26BeXdqJJUcrwWaN68OUZGRjRu3FiOqq6a6c2ZM0cO+jpp0iQmTZpEfn6+HDldqVSyevVq+bOds2fPcvHiRc6cOSNvQuzr68vx48f55ptveOutt0hJScHb2xsrKyvgz8jlKpRKJf7+/nTs2JHc3FzefvttNm3aJB8PDw/Hx8eHkSNHArBgwQJiY2PZvn07//nPfzTa9/nnn9OpUyf8/PwA6NKlC7/99hthYWFynjZt2sgGvDxatGgBQGZmJkVFRZiamqodNzU1VZshP4mlpSUffvgh1tbWPHr0iE8//ZSxY8dy5MgRzM3NSU9Pl8t5styMjIxyy1XrQwI1amsc0nkDCPDgsWoRjHYzwObNm+Pj40NYWFiDNH6CamJhUfEM8AlDURdYW1vL/1cZx/v379O+fXsA2rdvLxs/gF9//ZXc3Fz69OmjVk5eXp4cHf6dd95h8eLFfPXVV7i4uDB8+HA6deok523SpAmdOnWSn9rNzMzkd2s5OTmkpaXh5OSkVr6Tk5Oam7U0169fl92zKhwdHdX+NjQ0rDBCfE2gmkmW/nvEiBHs2bOH2bNn12rdgppFGEAgM++PzyCqsQuMn58f06ZNo3nz5jUtluCvyoQJsHp12cckqeQ9YB1jaPjnra56z1T6m6omTZqo5f/9998xMzNj586dGmWp+vqMGTMYNWoUUVFRnDp1ivXr17N27Vp5AVjpOlX11raLLyUlRZ5RlsfkyZOZMmUKrVq1wsDAQGPBy/3797WKVGBkZET37t3lB4PSDxht2rRRK9fGxqbK5QpqH503gIVFxTz8wwBWZQaYlJTEwoUL2bx5szwQNHTjJ3aBqRy1FWrdupW85/P2LpnxSdKf/4aHQ6nI2DWNkZFRjXwsbGtrS0ZGBgYGBhV+x2ppaYmlpSVeXl7MnTuX/fv3l7kC+skVfCYmJrRp04bz58/LrlmA8+fPa8zyVFhZWXHixAm1tAsXLqj9rY0L1NjYGFtbW86cOcPgwYOBPzbGOHOGf/3rXxWWUZqioiJ+++03+vfvD8ALL7yAmZkZZ86coXv37kDJjPfChQuMGzeu3HLEauuKqY1xSOcNYObjIiSqtgtM6QUvALt27aoDCesXAwMDbG1t61uMZxo9PT2NGRReXuDqWmLwbt0qcXt6e9eq8QPo0KEDFy5cIDk5maZNm1bbGLq4uODo6Mi0adNYsGABFhYWpKWlERUVxeDBg+nWrRurV69m2LBhvPDCC9y9e5dLly4xdOhQjbJU+nlyhaO3tzcbNmygU6dO2NjYEBERQXx8PEFBQWXKNHbsWHlRjbu7O1euXOHAgQNqebR1gb7zzjv4+vpiZ2eHg4MD27dv5/Hjx4wZM0bO895779G2bVvmzZsHQEhICI6OjnTu3Jns7GzCw8NJSUnB3d1dbq+npyebN2+mc+fOvPDCC6xbt442bdrIhrY8HQnKprbGIZ03gHcflmw/ZGZiXOEuME+u9gwMDKwrEeuV4uJiHjx4wPPPPy+eUMtBtYLP0NBQfZDv2hXquJ9MmDABPz8/Ro4cSV5eXrX7qZ6eHlu2bCE4OJiFCxeSmZlJ69atcXZ2pnXr1ujr65OVlYWvry8ZGRm0atWKoUOHMnPmTI2yylvh6OnpSU5ODitXruTBgwdYWVmxadMmjcU0Ktq3b8+GDRsIDAxk165dODg4MGfOHI0VrdowYsQIHjx4wPr160lPT6d79+5s3bpVzQWampqq1vezs7NZsmQJ6enptGjRAltbW/bs2UPXUg83Pj4+PH78mA8++IDs7Gx69erF1q1b5UVH5elIow8JgNobh/QkHVt3W1RUxC+//CKvJvrmciqTd53HoUMLDs1wLfOchv6pQ0U8qS9dIC8vj5s3b2JpaVklt4skSeTm5orvuMpB6KdyhI40KX0fGhkZqY1DNTUu6fwjfdofYZDK+wRCl42fQCAQNGR03gDezynZ9snURPP9nyRJvPnmm8L4CQQCQQNE5w1gZq4SgOfLWACjp6dHWFgYL730kk4bv+eee66+RXjm0RX3cHUR+qkcoaOKqY1xSOcXwWQ9LjGApiZ/ukCLiorkzmhnZ0dMTIzO+uUNDAzo1q1bfYvxTKOnpyc+FakAoZ/KETqqmNoah8QM8I9I8C2alDwLJCUloVAoOHnypJxHV40flKy+SklJ0cmAuFVdHyZJEgUFBWIfx3IQ+qkcoSNNSuuitsYhnTeAD/4wgC2bGMkLXi5dusTMmTMpKiqqZ+nqH0mSSE1N1akbUzX71yYskFKprC1xGgRCP5UjdKRObm4uULK5Q22NQ8+EC3T37t2Eh4eTnp6OjY0NS5YsKXc3CICjR4+ybt067ty5g4WFBfPnz5d3YdAWlQs0Pyudv49+Q17wcuTIEeGT11EMDQ1p2rQp6enpGBkZVfrdkSRJ5Ofno6+vr9PegvIQ+qkcoaM/UX0SkpaWRsuWLeXPHmqDejeAqoCU/v7+9OzZk+3bt+Pt7c2xY8c0dlOHkq2S5s2bx9y5cxkwYACHDx9m2rRpRERE8OKLL2pdf+bvBRRmp/Pu29NJun1LrPYUoKenR7t27bh58ya3b9+uNL8kSSiVSoyMjHR+8CoLoZ/KETrSpGXLlpibm9dqHfVuAEsHpATw9/fn5MmT7N+/n0mTJmnk37FjB3379mXixIkAzJ49m5iYGHbt2kVAQIBWdecXFvEw4y73Pl9IYdZdYfzKQE9PD1NTU527KY2NjenWrVuV3KDFxcWkpqbSrl07sVtOGQj9VI7QkTpGRkZqHrjaGofq1QCqAlJOnjxZTqssIOUvv/yCl5eXWpqrq2u5kZbLo6ioiPu/K8n+MUI2fidOnKB9+/bydFtPTw99fX2N6Xd56Sr3RVnpgMYL3PLSDQwMkCSpzPTi4mINP3hZ6SoZy0vXpk0WFhYUFRWpHfurt6mq16m0C7SiNpXevutZb1NF6bV1nSwsLBpcmyqSvTpt6tKlS4NrU1myV7VNqvJUhrBjx45IkqQxFj0N9WoAqxOQMiMjQyNUSWWBJsvi0qVLFBRJdB4+kUdNDDixKxhJkvjll1/kPO3ataN9+/bcuHGD7OxsOb1z5860bt2a+Ph48vLy5PSuXbvSokULLl68qHZxe/TogbGxsVrZUBLLrKCggKtXr8pp+vr6KBQKsrOzSUhIkNMbN26Mra0tDx48UHPLPffcc3Tr1o27d++SmpqqphMLCwsSExPVwr1o2yYrKyuysrLIzMxsMG1qiNfpr9Amc3NzOnTo0KDaVFPXycbGhnv37pGZmdlg2lST1ykjI4OkpCQ5vaYi8NTrXqD37t2jX79+7NmzRy3A5OrVq/npp5/48ssvNc6xs7Nj5cqVjBo1Sk7bvXs3GzduJCYmptI6VXvI2dvbY2BgQM7jAn6Nv0IvhUIjXtmz8iRUOr2un+4kSeLChQuyvhpCm2r6OimVSi5duiTrqCG0qSavU1FREZcuXcLBwQEjI6MG0abKZNe2TdreZ3+FNtXkdVIqlVy8eFHWj6pPPe1eoPU6A6xOQMrWrVtrzPa0DWAJyAOVSRNjDP7wK5fney9PwbWZrqenV2Z6eTJqm15VWUq7Ico656/YpppML92m0jpqKG2qioxVTVf93ZDaVFMyVuc+e9bbVJ30ytpUnn6qS70awOoEpHR0dOSHH35Qew8YExODo6NjlepUPemoOtyT/wrUEfqpHKGjihH6qRyho4opb7x+WgdmvYdDioyMxNfXl4CAADkg5dGjRzl69CitW7fWCEZ5/vx5PDw8mDdvHv379ycyMpLQ0NAqfwZRUFDApUuXartZAoFAIKhl7O3tMTauOJB5RdT7ZxCVBaR8Mhilk5MTQUFBBAcHs2bNGiwsLNi4cWOVvwE0NDTE3t5efHAqEAgEf1FU7woNDZ/OhNX7DFAgEAgEgvpAfHEpEAgEAp1EGECBQCAQ6CTCAAoEAoFAJxEGUCAQCAQ6iTCAAoFAINBJhAEUCAQCgU4iDKBAIBAIdBJhAAUCgUCgk+iEAdy9ezcDBw7E3t4ed3d3Ll68WGH+o0ePMnz4cOzt7XFzcyMqKqqOJK0ftNHP3r17GT9+PL1796Z37954eXlVqs+GgLZ9SMWRI0ewtrbm3XffrWUJ6xdt9ZOdnY2/vz+urq7Y2dkxbNgwcZ89wWeffcawYcNwcHCgf//+fPjhh+Tn59eRtHXLTz/9xJQpU3B1dcXa2rpK8V1jY2N54403sLOzY8iQIURERGhfsdTAOXLkiGRrayvt27dPunbtmrR48WLJ2dlZysjIKDP/uXPnpO7du0thYWFSQkKCtHbtWsnW1lb69ddf61jyukFb/cydO1fatWuXdPXqVSkhIUHy8/OTevXqJd29e7eOJa87tNWRiqSkJKlv377S+PHjpalTp9aRtHWPtvrJz8+XxowZI/n4+Ehnz56VkpKSpNjYWCkuLq6OJa87tNXRoUOHJDs7O+nQoUNSUlKSdPr0aemVV16RPvzwwzqWvG44efKktGbNGun//u//pBdffFH69ttvK8yfmJgo9ezZUwoMDJQSEhKknTt3St27d5dOnTqlVb0N3gD+85//lPz9/eW/i4qKJFdXVyk0NLTM/LNmzZImTZqklubu7i4tWbKkVuWsL7TVz5MUFhZKCoVCOnDgQC1JWP9UR0eFhYXSW2+9Je3du1fy9fVt0AZQW/3897//lQYNGiQVFBTUlYj1jrY68vf3lzw9PdXSAgMDpbFjx9aqnM8CVTGAq1evlkaOHKmWNnv2bGnChAla1dWgXaAFBQVcuXIFFxcXOU1fXx8XFxd+/vnnMs/55ZdfePnll9XSXF1dNaIaNwSqo58nefz4MYWFhbRo0aK2xKxXqqujjRs3Ympqiru7e12IWW9URz8nTpzA0dGRgIAAXFxcGDVqFJ988kmDDQVUHR0pFAquXLkiu0mTkpKIioqif//+dSLzs05NjdP1Hg2iNsnMzKSoqAhTU1O1dFNTU27cuFHmORkZGRrBdU1NTTWC8DYEqqOfJwkKCqJNmzZqN3dDojo6Onv2LPv27ePgwYN1IGH9Uh39JCUl8cMPP+Dm5saWLVtITEzE39+fwsJCpk+fXhdi1ynV0ZGbmxuZmZmMHz8eSZIoLCxk7NixTJkypS5EfuYpa5xu3bo1OTk55OXl0bhx4yqV06BngILaZcuWLURGRhISEkKjRo3qW5xngpycHN577z2WLVvG888/X9/iPJNIkoSpqSnLli3Dzs6OESNGMGXKFPbs2VPfoj0zxMbGEhoayn/+8x8iIiIICQkhKiqKjRs31rdoDYoGPQNs1aoVBgYG3L9/Xy39/v37Gk8PKlq3bq0x26so/1+Z6uhHRXh4OFu2bGHbtm3Y2NjUppj1irY6SkpK4s6dO0ydOlVOKy4uBqBHjx4cO3aMTp061a7QdUh1+pCZmRmGhoYYGBjIaV26dCE9PZ2CgoKnCnD6LFIdHa1bt47Ro0fLLnRra2tyc3P54IMPmDp1qlqMVF2krHE6IyMDExOTKs/+oIHPAI2NjbG1teXMmTNyWnFxMWfOnEGhUJR5jqOjIz/88INaWkxMDI6OjrUpar1QHf0AhIWFsWnTJrZu3Yq9vX1diFpvaKujLl26cPjwYQ4ePCj/Bg4cSJ8+fTh48CDm5uZ1KX6tU50+5OTkRGJiovxgAHDr1i3MzMwanPGD6ukoLy9Pw8ipHhgkEcK15sZp7dbn/PU4cuSIZGdnJ0VEREgJCQnSkiVLJGdnZyk9PV2SJElasGCBFBQUJOc/d+6c1KNHDyk8PFxKSEiQ1q9f3+A/g9BGP6GhoZKtra107NgxKS0tTf7l5OTUVxNqHW119CQNfRWotvpJSUmRFAqFFBAQIN24cUP6/vvvpZdfflnatGlTfTWh1tFWR+vXr5cUCoX09ddfS4mJiVJ0dLQ0ePBgadasWfXUgtolJydHunr1qnT16lXpxRdflLZt2yZdvXpVunPnjiRJkhQUFCQtWLBAzq/6DGLVqlVSQkKCtGvXrmp9BtGgXaAAI0aM4MGDB6xfv5709HS6d+/O1q1bZddDamqq2pOWk5MTQUFBBAcHs2bNGiwsLNi4cSMvvvhifTWhVtFWP3v27EGpVDJz5ky1cqZPn86MGTPqVPa6Qlsd6Rra6qddu3aEh4cTGBjI6NGjadu2LZ6envj4+NRXE2odbXU0depU9PT0CA4O5t69ezz//PMMGDCAOXPm1FcTapXLly/j6ekp/x0YGAjAG2+8wcqVK0lPTyc1NVU+3rFjR0JDQwkMDGTHjh2Ym5uzfPly+vbtq1W9epIk5tMCgUAg0D1097FVIBAIBDqNMIACgUAg0EmEARQIBAKBTiIMoEAgEAh0EmEABQKBQKCTCAMoEAgEAp1EGECBQCAQ6CTCAAoEAoFAJxEGUFAuERERODs717cY1cba2prjx49XmMfPz4933323jiR6tggODmbJkiV1UldsbCzW1tZkZ2dXmG/gwIF89tlntSqLtnXU1H1Qlf6oLQkJCfTr14/c3NwaLVdXEAawgePn54e1tbXG7/bt2/UtGhEREbI8NjY29OvXj4ULF2rsml9doqOj6devHwDJyclYW1sTFxenlmfRokWsXLmyRuorjw0bNsjt7N69O/3792fJkiVkZWVpVU5NGuv09HR27NihFl+udF+xs7NjyJAhhISEUFhY+NT1KRQKoqOjad68OVC+Udm3bx9vvfXWU9fXUNi8eTNjx46lZ8+eZeqra9euODo6sm3btnqQ7q9Pg98LVAB9+/aV99ZT8azEqjMxMeHYsWMUFxcTHx/P+++/T1paGuHh4U9dtpmZWaV5VANybdOtWze2bdtGcXEx169f5/333+fRo0cEBwfXSf1P8uWXX6JQKOjQoYNauqqvFBQUEBUVRUBAAEZGRkyePPmp6jM2Nq7S9XhW+uWzglKpZPjw4Tg6OrJv374y84wZM4bFixczefJkDA3FkK4NYgaoA6gGn9I/AwMDtm3bhpubG46OjvTv35+lS5fy+++/l1tOfHw8Hh4eKBQKnJycGDNmDJcuXZKPnz17lvHjx+Pg4ED//v1Zvnx5pa4ZPT09zMzMaNu2Lf3798fDw4OYmBjy8vIoLi4mJCSEfv36YWdnx2uvvcapU6fkcwsKCggICMDV1RV7e3sGDBhAaGiofLy0y2nQoEEAvP7661hbW+Ph4QGoz6q++OILXF1d1cL0QMnGxAsXLpT/Pn78OG+88Qb29vYMGjSoSrMkAwMDuZ0uLi4MHz6cmJgY+XhRURHvv/8+AwcOxMHBgWHDhrF9+3b5+IYNGzhw4ADfffedPEuLjY0FSjZSnjVrFs7Ozvztb39j6tSpJCcnVyhPZGQkAwcO1EhX9ZUOHTowfvx4XFxcOHHiBAAPHz7kvffeo3fv3vTs2ZOJEydy69Yt+dw7d+4wZcoUevfujaOjIyNHjiQqKgpQd4HGxsaycOFCHj16JLdlw4YNgLp7ct68ecyePVtNPqVSKYeWgpKwQqGhobLeRo8ezbFjxyps+5NU9T44fvw4Q4cOxd7eHm9vb7XNmVXHte0XlTFz5ky8vLwq3IzfxcWFhw8f8tNPPz1VXbqIMIA6jJ6eHosWLeLrr79m5cqV/PDDD3z00Ufl5p8/fz7m5ubs27ePiIgIfHx8MDIyAiAxMREfHx+GDh3KoUOHWLt2LefOnWPZsmVaydS4cWOKi4spLCxkx44dbNu2DV9fXw4dOoSrqyvvvvuuPOju3LmTEydOEBwczLFjx/joo480ZjQqvvzySwA+++wzoqOj5QG3NMOHDycrK0s2LABZWVmcPn2a0aNHAyVG3tfXF09PTyIjIwkICCAiIoJPPvmkym1MTk4mOjpa1h2UDOTm5uasW7eOI0eOMG3aNNauXUtkZCQAEyZM4NVXX6Vv375ER0cTHR2NQqFAqVTi7e1Ns2bN2L17N59//jlNmzZl4sSJFBQUlFl/VlYWCQkJ2NnZVSpro0aNUCqVQMnDwuXLl9m8eTNffPEFkiQxadIk+XhAQAAFBQXs2rWLw4cPM3/+fJo2bapRpkKh4P3338fExERuy4QJEzTyubm58f3336sZo+joaPLy8hg8eDAAoaGhHDx4EH9/f44cOYKXlxcLFizgxx9/rLRtKqpyH+Tl5bF582ZWrVrF559/TnZ2tlpkhur0Cw8PD/z8/KosZ3kYGxvTvXt3zp49+9Rl6RrCAOoAJ0+eRKFQyD9VKCMvLy9eeuklXnjhBV5++WVmz57N0aNHyy0nJSUFFxcXrKyssLCw4NVXX5WjwYeGhuLm5oaXlxcWFhY4OTmxaNEiDh48SH5+fpXkvHXrFp9//jl2dnaYmJgQHh6Oj48PI0eOpEuXLixYsAAbGxt5ZpSamkrnzp3p1asXHTp0wNnZmVGjRpVZtsq11rJlS8zMzGjZsqVGnhYtWtCvXz8OHz4sp33zzTe0atWKPn36ABASEsKkSZN444036NixI6+88gqzZs1iz549Fbbtt99+Q6FQ4ODgwKBBg7h27Zpa+B8jIyNmzpyJvb09HTt2ZPTo0YwZM0aezTRr1ozGjRurzeaNjY2JjIykuLiYFStWYG1tjZWVFYGBgaSmppZrBFJTU5EkiTZt2pQrryRJxMTEEB0dTZ8+fbh16xYnTpxg+fLlODs7Y2NjQ1BQEPfu3ZNn2SkpKTg5OWFtbU3Hjh0ZMGAAvXv31ijb2NiY5s2by7N/MzMzmjVrppHP1dWVJk2a8O2338ppX3/9NQMHDsTExISCggJCQ0P58MMP6du3Lx07dmTMmDGMHj2aL774osLrUZqq3AdKpZIPPvgAhUKBnZ0dK1eu5Oeff+bixYtA9fpFu3btquQWrgpt2rQhJSWlRsrSJYTDWAfo06cPS5culf9u0qQJUBJBOTQ0lBs3bpCTk0NRURH5+fk8fvxYzlOad955h8WLF/PVV1/JbrxOnToBJe7RX3/9Vc14SJJEcXExycnJWFlZlSnbo0ePUCgUFBcXk5+fT69evVi+fDk5OTmkpaXh5OSklt/JyYn4+HigJFbYhAkTGD58OH379uXvf/87rq6uT6UrNzc3lixZwtKlSzE2Nubw4cOMHDlSjtUWHx/P+fPn1Z7sK9MbgKWlJZs3byY/P59Dhw4RFxfHv/71L7U8u3fvZv/+/aSkpJCfn49SqZQfMMojPj6exMREDT3l5+eTmJhY5jl5eXlAyezuSVQPS0qlEkmSGDVqFDNmzODMmTMYGhrSs2dPOW+rVq2wtLTk+vXrAHh6erJ06VKio6NxcXFh6NChlcpfEYaGhrz66qscPnyY119/ndzcXL777jvWrFkDwO3bt3n8+LHG7FGpVNK9e/cq11OV+8DQ0BB7e3v5HCsrK5577jmuX7+Og4NDtfrF6tWrtdJHRTRq1IjHjx/XWHm6gjCAOkCTJk3o3LmzWlpycjKTJ09m3LhxzJkzhxYtWnDu3DkWLVqEUqks84adMWMGo0aNIioqilOnTrF+/XrWrl3LkCFDyM3NZezYsfK7tdK0a9euXNmaNWvGgQMH0NfXx8zMjMaNGwOQk5NTabtsbW357rvvOHXqFDExMcyePRsXFxfWr19f6bnlMXDgQBYvXszJkyext7fn7Nmzau//cnNzmTFjBkOHDtU4tyyDosLIyEi+BvPnz2fSpEmEhITI77iOHDnCqlWr8PX1RaFQ0KxZM8LDw7lw4UKF8ubm5mJra0tQUJDGsfIWlLRq1Qooeaf3ZB7Vw5KRkRFt2rTRalGFu7s7rq6unDx5kv/9739s2bIFX1/fMvtEVXFzc8PDw4P79+/zv//9j0aNGslBT1Xvl0NDQ2nbtq3aecbGxlUqvzr3QVlUt1/UFA8fPpQfRgVVRxhAHeXKlStIkoSfn588u6nI/anC0tISS0tLvLy8mDt3Lvv372fIkCH06NGDhIQEDUNbGfr6+mWeY2JiQps2bTh//jx/+9vf5PTz58/j4OCglm/EiBGMGDGCYcOGMXHiRLKysjRcnKr3bUVFRRXK06hRI4YOHcrhw4e5ffs2lpaW2Nraysd79OjBzZs3tW7nk0ydOpV///vfjBs3jrZt23L+/HkUCgVvv/22nOfJGZyRkZHGAh1bW1uOHj2KqakpJiYmVaq7U6dOmJiYcP36dSwtLdWOlfWwBCUznsLCQi5cuCDPNjMzM7l58yZdu3aV87Vr145x48Yxbtw4Pv74Y/bu3VumATQyMqr0WkDJjN/c3JzIyEhOnTrF8OHD5WtpZWWFsbExKSkpan1EG6p6HxQWFnL58mW57924cYPs7GzZs1FT/aK6XLt2jWHDhtVL3X9lxDtAHaVz584olUp27txJUlISBw8erPB9RV5eHgEBAcTGxnLnzh3OnTvHpUuX5AHAx8eHn3/+mYCAAOLi4rh16xbHjx8nICCg2jJ6e3sTFhZGZGQkN27cICgoiPj4eDw9PYGS1Xtff/01169f5+bNmxw7dgwzMzOee+45jbJMTU1p3Lgxp0+fJiMjg0ePHpVbr5ubGydPnmT//v24ubmpHZs2bRpfffUVISEhXLt2jevXr3PkyBHWrl2rVdsUCgXW1tbyqtXOnTtz+fJlTp8+zc2bNwkODlZbYQvQoUMHfv31V27cuMGDBw9QKpW4ubnRqlUrpk6dytmzZ0lKSiI2Npbly5dz9+7dMuvW19fHxcWFc+fOVVleCwsLBg0axJIlSzh79izx8fEsWLCAtm3byitsV6xYwenTp0lKSuLKlSvExsaW6/ru0KEDubm5nDlzhgcPHlTovhs1ahR79uwhJiZG7XqYmJgwYcIEAgMDOXDgAImJiVy5coWdO3dy4MCBKrWrqveBkZERy5Yt48KFC1y+fJmFCxfi6OgoG8Tq9Iv33nuPjz/+uEL5UlJSiIuLIyUlhaKiIuLi4oiLi1NbGJScnMy9e/dwcXGpUpsFfyJmgDqKjY0NCxcuJCwsjDVr1uDs7MzcuXPx9fUtM7++vj5ZWVn4+vqSkZFBq1atGDp0qLygxsbGhp07dxIcHMz48eMB6NixIyNGjKi2jJ6enuTk5LBy5UoePHiAlZUVmzZtwsLCAihxn27dupXbt2+jr6+Pvb09W7ZskZ/kS2NoaMjixYvZuHEj69evx9nZmZ07d5ZZ70svvUSLFi24efOmhgHs27cvn3zyCRs3biQsLAxDQ0O6dOmCu7u71u3z8vLCz88PHx8fxo4dS1xcHHPmzEFPT4+RI0cyfvx4tc8+3nzzTX788Uf+8Y9/kJuby44dO+jTpw+7du0iKCiI6dOn8/vvv9O2bVtefvnlCmeE//znP1myZAkLFiwoU19lERgYyIoVK5gyZQpKpRJnZ2e2bNkiz8iKi4sJCAjg7t27mJiY0LdvXzX3cWmcnJwYO3Yss2fPJisri+nTpzNjxowy844ePZpPPvmEDh060KtXL7Vjs2fP5vnnnyc0NJTk5GSaN29Ojx491D7wr4iq3geNGzfGx8eHefPmce/ePZydnVmxYoV8vDr9IjU1tVLdr1+/Xs2Yv/766wDytYcS9/krr7xS7gpoQfnoSZIk1bcQAoGgbpEkCXd3d7y8vMpdOSt49ikoKGDYsGEEBQVpPBwIKke4QAUCHURPT49ly5bVyDZngvojNTWVyZMnC+NXTcQMUCAQCAQ6iZgBCgQCgUAnEQZQIBAIBDqJMIACgUAg0EmEARQIBAKBTiIMoEAgEAh0EmEABQKBQKCTCAMoEAgEAp1EGECBQCAQ6CTCAAoEAoFAJ/l/etz04vS3XGoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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4cYKzZ89adLwlDBw40CxxKjY2lvbt25v0VMahQ4fIzs5m7NixZk9+w4cPN0V2bmTUqFFmN9tDhw6RkZHBvffeazYW3rdvX5o3b86ff/5ZQ1U111UTRo4caZb8FxsbiyRJ3HnnnWbHxcbGkpKSgl6vr7SuuujjQYMGlfvxUKvVph8zURTJzs5Gr9cTHR3NkSNHqqyvbJ+VlJSQlZVFREQEXl5e1Z5bU9RqNbfddhtr164F4JdffiEkJKTCG3ZFlEaoNm3aZNH3uiK0Wi2jR48uV1728yh9cOvUqROFhYWmB6/asGHDBkpKSiodmnB2dmbu3LnMnTuXOXPm8NZbb+Hm5sbEiRM5c+ZMrdq+9957zf7u2LGj2Q+6l5cXhYWFZkOi1tK4cWMzh8EaRFHk999/p1+/fhVGq2v62zJkyBAyMjLMhig2bNiAKIpm+YFFRUUVOoWlZdbOgvjqq6/Yvn07L7zwgtVR1RpFGl577TWaNWvG1atXWb58OXv27CknqPSHv9R5qIgbHYvSH8WqzqmO5ORkGjVqhI+PT43rqIiKMmFvu+023nvvPRISEnjiiSeQJIn169fTu3dvk5bSm3BV4cOrV69W+oNXSpMmTcz+FgSBJk2amIaGatKOpdm9zzzzDE899RSDBw+mVatW3HLLLYwcObLKKFJ13KgHoGnTpqxbtw4wfkmuXr1q9n5gYCCXLl0CMIWOS3FyciIsLKzCtm7UWVpHReHJ5s2bW5RrUhnV6aoIg8FgCvuW4u3tXe2TY2hoqNnfnp6eAISEhJQrF0WRq1evVhqKrIs+rux6WrlyJd9//z1nzpyhpKSk2uNLKSoq4uuvv2bFihWkpaWZRQpvvDbKkp+fbzadW61W4+fnx9WrVykqKjKVazSaCu8Tw4cPZ8GCBRw7doxff/2VoUOHWvyjMHToUJYuXcqrr77KzJkz6d69O7feeiu33Xabxcm3QUFBFfb9iRMnmDVrFjt37jQ9YJVS1eeRmZlplh/g5uZW4YPZmjVr8PHxqfTJU61Wl0uY69OnD4MGDeLjjz/m888/r9G17OzsXM7Z9Pb2NssJu++++1i3bh2PPfYYQUFB9OzZkyFDhlj1lFyb2QyZmZnk5eXRsmXLGp1f2bXXu3dvPD09SUhIMEVxEhISaNOmjdn9ycXFpcK8hdIyaxKBExISmDVrFnfddVeVuWyVUSOnITY21uRtDRw4kPvuu48XXniB9evXmy7G0nGcY8eOmZ66b+T48eNmx5aOAR0/frzSc+qCym4AVSUUVdQpQUFBdOrUiXXr1vHEE0+QmJjIpUuXTGNugOlG99JLL1U6flsXsxZq0o6lF1rnzp3ZuHEjmzZtYtu2bSxbtox58+bx5ptvMmbMmCrPrWmSVkJCQrlwfun1Yi31kSFdl8lnKSkpDBgwwKxs/vz51YY3K/sRqqy8quG52vRxKRV9zqtXr2bq1KkMHDiQCRMm4O/vj1qt5uuvv6423P/222+bcnLi4uLw9PREEIRqc3C+//57Zs+ebfo7LCyMzZs3884777By5UpTeZcuXViwYEG589u3b09ERATvvPMOFy5csDgpEIyfwcKFC9m1axd//vknW7duJSEhgZ9//pnvv//eolytij7H3Nxcxo0bh4eHB8888wwRERE4Oztz+PBhPvrooyqjGnfddZdZwvbkyZPLTZu9dOkSe/fuZezYsVZNXQ0ODqZZs2bs2bMHqNm1bMln4u/vz6pVq/j777/566+/+Ouvv1ixYgWjRo3i/ffft8jWij7XmvwW1ITKrr3SqPTGjRt5/fXXycjI4J9//mHKlClm5wcGBpKenl6u3suXLwPXhymqY9u2bbz00kv07duXN998s0Zaap0IqVarmTJlCg888AALFy40zVnu2LEjXl5e/Prrrzz55JMVXhirVq0CMOVCdOzYEW9vb9auXcsTTzxRo2TIiIgI/v77b7KzsyuNNpQ+befm5pqVlz6BWsOQIUN48803OX36NAkJCbi6uprldpROafLw8LBqWsuNnDt3zuxvSZI4d+6caRyrrtqpDB8fH+68807uvPNO8vPzGTduHJ9//rnpB8Xb27vCH4HKPtMb9YAxWlIaLbjllluYO3duuWNKn66Tk5Pp1q2bqVyv13Px4kWLxvVK6zhz5ky5MdozZ86YPcHXta6KCAwMLKe1NlGcmlJdH9ckBLthwwbCw8OZPXu22fmfffaZReeOGjXKbJZHcXFxlU/VYByOKk0whOvO8aOPPsqIESNM5VWFZW+//Xa+/PJLWrRoYfVaCiqViu7du9O9e3emTZvGV199xSeffMKuXbvo0aNHjT7H3bt3k52dzezZs+ncubOp3JIx+Q8//NAsfH3jNEswJntKkmT2+ViKwWAwRXbq81rWarX079+f/v37I4oib7zxBj///DNPPfUUTZo0qdHnaulvgZ+fHx4eHpw4caLK+iqzoaprb8iQIaxcuZIdO3Zw6tQpJEliyJAhZue3bt2affv2IYqi2UPBwYMHcXV1tSip88CBA0yePJno6GhmzZpV46nCdTLlsmvXrsTGxjJv3jzTxenq6sojjzzCmTNn+OSTT8qd8+eff7Jy5UpuueUW4uLiTOc8+uijnDp1io8++qjCp4nVq1dz8ODBSm0ZNGgQkiSZPWmUUlqfh4cHvr6+7N271+z9n376yWLNpQwePBi1Ws3atWtZv369KSO2lOjoaCIiIvj+++8rHHa5MZRXGatWrTILSa5fv5709HRTeK6u2qmIrKwss7/d3d2JiIgwC5eFh4dz+vRps3aOHTtmyhq/kd9//91seuPBgwc5cOCASU+jRo3o0aOH2b9SnT4+PixZssRsjH7NmjVm4cyqiI6Oxt/fn8WLF5tp2LJlC6dOnaJv3771pqsinJ2dy2mtbriqrrGkj0tnK9x4g62KUse/7Hf5wIEDpmnWlpxblgULFlT7FBgeHm72WZY6EJGRkWbl0dHRldYxZswYJk+ebPWshOzs7HJlpU5H6WdZk8+x9Iei7Oeo0+ksumd17NjRTHdlTkNoaKiZs2UJZ86c4cyZMybHoL6u5RuvT5VKZXpAuPFzrc6pLIulvwUqlYqBAwfyxx9/VLiiZWm/VGZDVddejx498PHxISEhgXXr1hEbG1uuj2677TauXLnCb7/9ZirLzMxk/fr19OvXz2z4Jzk52SzPDoyTDCZOnEhYWBhff/11raKvdbYqyYQJE3j22WdZsWKFKall4sSJHD16lG+//ZbExEQGDRqEi4sL+/bt45dffqFFixblQkuPPvooJ0+e5Pvvv2fXrl0MHjyYgIAArly5wu+//87BgwdZvHhxpXZ069aNkSNHsmDBAs6dO0evXr0QRZF9+/bRtWtXxo0bBxhvCt988w2vvPIK0dHR7N27t0bJPP7+/nTt2pW5c+eSn59vlrwCxott+vTpPPbYYwwbNozRo0cTFBREWloau3btwsPDg6+++qradry9vbnvvvsYPXq0acplkyZNTAuq1FU7FXH77bfTpUsX2rVrh4+PD0lJSWzYsMH0WYIxBPrDDz8wYcIE7rrrLjIyMli8eDGRkZEVOjERERHce++93Hvvveh0OubPn4+Pj0+56as3otVqefrpp3n77bd58MEHGTJkCBcvXmTFihXl8hwqQ6PR8OKLLzJt2jTGjRvH7bffbppyGRYWZraU8M3SZWss6eN27doBMH36dG655RbUajW33357lfX27duX3377jUmTJtG3b18uXLhg+vyqWka+9NzVq1fj4eFBZGQkiYmJbN++vc7zlSoiLCysRitffvHFF+zdu5c+ffoQFhZGRkYGP/30E8HBwaYf5NJkzsWLF+Pu7o6bm1uFPxRliY+Px9vbm6lTpzJ+/HgEQWD16tW1mhFWyr///svx48eZOHFilU/rer2e1atXA8YfydK+FEXRNPOhvnj11VfJycmhW7duBAUFcenSJX788UfatGljGt5u06YNarWab7/9lqtXr6LVaunWrZtpymplWPpbMGXKFLZt28b48eMZO3YsLVq0ID09nfXr1/PTTz/h5eVVIxs0Gg233nora9eupbCwsEJHdfDgwcTFxTFt2jROnjyJr68vixYtwmAwlLtOS+9fmzdvBoxJsxMmTCA3N5cJEyaUS/SOiIggPj6+ys+oLHXmNAwaNMj0pDt27FjUajVqtZpZs2axatUqli5dyqeffkpJSQkRERFMmjSJRx55pNw4u0ql4oMPPmDAgAEsWbKE77//nry8PHx9fencuTP/+c9/qhU4Y8YMoqKiWLZsGR988AGenp5ER0ebnTdp0iQyMzPZsGED69ato3fv3nz33XflwtWWMHToULZv3467uzt9+vQp937Xrl35+eef+d///sePP/5IQUEBgYGBxMbGcvfdd1vUxhNPPMHx48f55ptvyM/Pp3v37rz++utmc9Xrop2KGD9+PJs3b2bbtm3odDpCQ0N57rnnmDBhgumYUgfws88+Y8aMGURGRvLBBx/w66+/Vrh4yahRo1CpVMybN4+MjAxiY2P5v//7P4vG5saNG4ckScydO5f333+f1q1b8+WXXzJ9+nSL8zRGjx6Ni4sL3377LR999BFubm4MHDiQ//znP2ahw5upy5ZY0seDBg1i/PjxrF27ll9++QVJkqp1GkaPHs2VK1f4+eef+fvvv4mMjOTDDz9k/fr1FX5+ZXnllVdQqVSsWbOG4uJiOnTowNy5cxu0A9a/f38uXrzI8uXLycrKwtfXly5duvD000+bElU1Gg3vvfceH3/8MW+88QZ6vZ4ZM2ZU6TT4+vry1Vdf8f777zNr1iy8vLwYMWIE3bt3N+ujmrBmzRrAuEZFVeh0Ol566SXT3x4eHsTExPDhhx/W6L5pDSNGjGDJkiX89NNP5ObmEhgYyJAhQ3j66adNUZjAwEDefPNNvv76a1555RUMBgPz58+v1mmw9LcgKCiIJUuW8Omnn7JmzRry8vIICgqid+/epif3mtpQmkArCEK5oQkwRt2++eYbPvjgAxYsWEBxcTExMTHMmDHDlAtYGdnZ2aaFACuaYn/HHXdY5TQIUl24qgr1xq5du3jggQfMlg9VKI8oiqZM9dL18W8mFy5cYMCAAbz00ku1vokrKCgoNFTkvQC/gl1SXFxcLiy7atUqsrOzy62spqCgoKBQd9h25ycFhRqQmJjIjBkzuO222/Dx8eHIkSMsW7aMVq1aKdEYBQUFhXpEcRoU7I6wsDCCg4NZsGABOTk5eHt7M3LkSF588cWbsquigoKCglxRchoUFBQUFBQULELJaVBQUFBQUFCwCMVpUFBQUFBQULAIJaehhuzZs4c5c+Zw6NAh0tPT+eKLL6rdL2PXrl289957nDhxgpCQEJ588skKd7OrClEU0ev1qFSqWu3aqaCgoGBrJElCFEWcnJws3tBLwbYoTkMNKSgoICoqijvvvLPCbbhv5Pz58zz++OPcc889fPTRR+zYsYNXX32VwMBAq7Zr1ev1FS5jqqCgoGCvxMTEKEnMdoLiNNSQPn36VLj6Y2UsXryYxo0bmzbfadGiBfv27eOHH36wymko9cZLdxlNSkoiJiamRpt72RsGg0HRa2dIooTuwlXEAn2597ThnqjdjTsq6rOKKEkrQHB34ljGaWJiYlCpVBQdzyp3XnVoQz1Qexl/gAy5xegu5aNyc8I54vpKn0UnspAM1uWAa4LccPI1rvxnyC9Bd/4qgrMal2bX91YoOpODVGz5DomiKHIqI5l2PdqjVqsRi/QUn81FcFLhEuljOq74XC5iYfnPsCqcfJ3RBBl3HZb0IkUnswFwbX19G2rdhasY8kpuOFMCUQTVtWtO1EPqIdO7ajcJrb9xV01Jgvwrvpx3UlEcriIuKAZObUaXpeJEejpFoo5wF3881caVa/OcNGQ18qWgSG92D1OiDPaD4jTcJBITE8stS3rLLbfw7rvvWlVP2SEJlUpF8+bNTUt2i6JotuiRIAioVKpyG/xUVl465FFROVBu+93KytVqtSnseGP5jTZWVl5qY9lylUpFixYt0Gg05eq2V01V2a5Wq4mMjCx3Q23omiRRoiT5KkWHMihMuoJY7kfJiN+j7VBf+8HVHc8mZ81pXKL9aX57c6PNEmQvOFbhuVXhd39r1G2NP4y6s1fJ/vlfnJt74/JotMnG7J9PIFn5I+w9ojnOPcIwGAzoUwvIXnAMpyA3XJ+NN/Vf7urTGNILrao39JZGqFQqYx25JWQvOIbKXYPLf427WarVavJ+S0Z31vINrgDcugXjPdy4xLBUZDB+lgK4Tu8Bunw4tYn8TRqKUizZUOr6Bkeuqr9w1X5grFcSuFq8Bh/gre4/8OVtn6NefDdFJU/hZzDuw2MAssvUdCH8CF3GDcXJycm06Zwy1Go/KE7DTeLKlSsEBASYlQUEBJCXl0dRUZHVu46VHaLw9/enadOmJCcnk5GRYSoPCQkhNDSU06dPm+2o16RJEwICAjh27BhFRUWm8sjISLy9vTl48KDZD0nbtm3RarXldiaMi4tDp9Nx5MgRU5lKpSI+Pp7c3FxOnjxpKndxcaFdu3ZkZmaabR/t5eVFy5YtSU1NNa2PXp0mHx8fh9NUVT8dPny44Ws6d47cf9NxvmDA5bwBddF1x0LUgMFDBYJxwzEnjYbCggJOnD2FPsfo0DRRB6AJ9yTDkEve6Ws/upJEWJg7giCU29zKzc0NSZIoLDT/gXZzd6dQ1HH02megTTPg6a/GvZGbmSYfTxGNhwZnFxdKdDpKSq47Nk5OTmidndEVF5vtpJqrz8cTOH36NAXnsvDyU1GoLcYpM9N07Tk761D7GTU5OzujdnKi4IaNzVxdXc005RRlc+HAAWM/iSWU+KkQtQYSExNN/ST5OFHid915FAQBVzc39CUl13cilSSc0KN1EjDoiig4txH9/B1oi67gXKxD4/0COmd/EhMTcSrOpP1vD6EpeRCDEMuNSC4+CAGRFBYUIIl6XPKM28Pnq2CPezEBbq3ooFchSQIluemcFQoJ92rCgYMHifJpjZgnkVpyFp0AIZIKl2uXab7amXynYry9vcnJyTG79hTsA2WdhjogKiqq2kTIwYMHM3r0aB5//HFT2ZYtW5g4cSIHDhyw2GkwGIw3k9LQ3qFDh4iJiTE9fTtypMFgMHDo0CHat29f7nOxV01V2S6KIocOHaJdu3ZmwxMNTZMgCFz+fD8ll67/OArOalzb+ePczg/nSB8EJ1WVWks16XQ6Dh06RHR0NGq12i76qTbXXuk1HR0djUajqVyTrgDx8lHIPo9w9RLkpqDKS0Vs3Amp07UNvPLTUX8cRaW0HYl41w9GTZKIat4w8AhC8ApF9AgCz1AkrxDwDEHwCkXl7E5OYQ4eWg9TFW/tfIsVJ1cwptUYXunyiqlcQkKtUlvUTwaDgcOHDxMTE4MoiiQlJREXF2e3Q3ByQ4k03CRKt/cuy5UrV/Dw8KjR3ualXzBJkkxfzMrGBSv7MtZnuSAIFZZXZqOl5aU3cUfSVFWboiiahp8sOd7acms1CYKA/lI+Rccy8RwQYQoraxt7os8owrWtP64xAbi08jU5CtbaKElSOc0NvZ9qU16qt/SzVEsGcLqWFJh/BX64HeHKv6glsVwdKn0RdL32IOLRCNwDwS0AvELAM/Ta/yHgFQp+LcpoUsOEDdfruaFevajnyY2PsztlN+vuXEewezAAw1sMx9vFm9ua3lara1IUxUr7SaFhozgNN4m4uDj++usvs7Lt27cTFxdnG4MUFGqCXiT9myQknQHnVr6m5EKvQU3wGd4CQaMktFmMKELGSXwv/YmQuQbSDhn/RXSHMXONx7j6Qc4FkERw8we/5tedAM8QCI65Xp9KBf+pWbj/fO55zuSeoXfj3gA4qZwo0hehl/TsTt3NiBYjAOgU3IlOwZ1qJVvBvlGchhqSn59PcnKy6e8LFy5w9OhRvL29CQ0NZebMmaSlpfHBB8aEoXvuuYeFCxfywQcfcOedd7Jz507WrVvH119/bSsJCgqVIkkSJSn5FCZdoeRSHv4PtTMORWjUuMUFIhbpzSIJag9lulyViIbrsxEkCX4YBpf2oy7Jp/mNx6Zdn6mASgXjVoBPBHgGQz0kDB6+cph71t6Dp9aTLXdvQaMyDpO81PklPLWeRHhF1HmbCvaL4jTUkEOHDvHAAw+Y/p4xYwYAd9xxB++99x7p6elmCWPh4eF8/fXXzJgxg/nz5xMcHMz06dOtmm55IyqVirZt28pmupKit36RJAl9WgEFB9MpPHgF/ZXrSYb6tAI0wcbpe76jW9abDQ7Rx1fTIDUJUg9e+z8JXLzhsU3G9wUBinKgJB/JyQUpsA1CSHuEkBgIjoVGbc3ri+haZ6adyj7FL6d+IcIzgjtb3QlAa7/WNHJrRDPvZmQWZhLkHgRAu4B2ddZuWcr28Y05EAoNHyUR0s4oTYSMi4szfenksjpkaYKborduKUnLp+DgFQqT0tFfLjMbwUnAJcoPt9gAXNr4o9LW//izXfWxJJk/+f/yNBxfD/mXyx+rdob/XgL1tee083vAxQvJrzkiqnrTqxeNsz+cVMZ2l/27jDd3vElb/7b8POxn03E6gw6t+uZEi8r2sSiKpvuZkt9gHyiRBjtGbl84RW/dUXK5gMKD6RQkXUGfVmY6o1rApZUvbu0DcWnth8rl5t4iGmwfF+fB5SPm0YPcSzDl6HXHoTD7msMgMCe0GYfdPBnTqAvdm90GwTGkFKbz4d4P8dB48FbPtwAQDQbe//190rXp3NHyDlNOwZXCK7y76120ai3v9XrPZMbPx35mV+ouhjYbysAmxtlaubpc3tj+BgAf9/3YdOyS40uYvX82r3d/nQFNBgDQL7wfu1J2MaDJACRJup7IepMcBjDvYwX7Q3EaFBRkxpUfDlN0LPN6gVrApaUvrrEBuLb1v+mOQoPixujB1pmwfyFkngbKB2VzLh9mUerfnMo+xYe9XoCez0KjNiT+PZU/z/9Jz6bdoaXxxz0v6wQbz23Ez8XPrI7ThafZlbqLjkEdTWVF+iI2ntuIq5Or2bFHMo+w8dxG2vpfH8LQGXRsPLexnG2nc06TVZzF1otbTU6Dv6s/H/b50NpPRUHBhIzvDgoKjo8+o5DCwxl49AxFUBvzBJwCXeFfAZeWPrjGBhodBVcZ3goMesg4USb/4JDx9aRd4H5tIbaiXMg8ZXxdOlshKNr4f3AsJa7efH3ga/SSnifaP0ELnxYAjG01lltCbyGuUZypuUDXQF7t+irOTs5mZvTy7cWAqAF0COpgKvNx9uHVrq+iVplHW4Y1H0Zbv7bEBF6fNeGucefVrq+WkzeoySD6hfczc0YUFGqLDO8UCgryQBIlLn95ADGvBE2wOy6tfAHw7N0Yr37hqNw0NrbQRiQtgx2z4fJR0BeVfz81CVr0M76Ouw+a94GgGM6LRcw7Mg+VoOK/0cbdaQOAR2MfJcIzglCPUFMVvRqXT3D2cfHh7tZ3lyuP9ogmrpX5cIyH1qPCYzsHd6ZzcGezMlcn1wqPLeuEKCjUFUoipJ2hJEIqeitCn11E4cErFJ/Owf+Btggq4/FZq0+iv1KIV78InJtbsseAbal1H0sS5F68nndQ+m/0txB+7cd23zxY84zxtdajTOTg2r9GbUFTfsG1g+kHuT/hflzULmweuxlPrWctlJaaK69rGpRESHtHiTTYOTqdrkYrStorit7r6LOLKUwyznrQJV+9fk5yLs5NjQ6Cz4gWdvdjVKM+PrsNtrxndBAKK9gZM/XgdachcgCMmWd0EHybGddCuIHEy4n8cPgHogOieTTGuExzTEAMD7R9gJ5hPXFzcrNWVqXI7ZoGeWp2FBSnwY4RRZEjR47IxktX9Bq3ei5IukLhwSvozpXZ9VAAbVNv3GIDcAq8/oNmbw5DpX1clHM95yA1CdKSjEmH0ca1BpBEOHNtxVVBDYGtIbhMBCEk7npd3o2N/26g7GyC81fPsyl5E8cyjzEheoJpj43/dP7PzdHrwJTVrGB/KE6DgoKN0aXkowl2QxAE40qMqQVoQ9xN7xuu6ig8kkXBwXSjo1BmQFHb1Au32EBcowNQeznYqowZp2Dja8YoQXZy+fcv/nPdaQhpDyNmGx2EwNYVDi9Uxm9nf2PBkQXc1+Y+hjQbAsDAJgM5kXWCYS2G2Z3jpaBQnyhOg4KCDcndlEzuxnNoGnvg3jmY/D2plFzIw+vWJrj3DcNzbzGXl+01dxSaeOEaE4BbTABqb+fKK7cX9MWw62s4thYhagi4XksidHKBY79eP847wjx6EFom0c/FCzqMt6i50jSuUmfg36x/SUxPxE3jZnIaXJ1cmdJpSu21KSg4GIrTYOfY9XK7NcCR9OpS8sndeA6Akot5ZF84CdceanM3nkPb2gfJXQ2SAW24p3F6ZEwATj4O4CiAMWnx2K/w26uQdRYAwdUPVZs+xve9QmHIh9CojdFZcPWtdZM/HPqBn4//zMy+M01rHYyKHIW7xp3hLYbXuv6a4EjXtKXIUbOjoDgNdoxarSY+Pt7WZtw0HE2vJtgNTWMPSi7mXY8kSIAAmjAPnEM9iRwZjzRUxMnPwZLGUpNg/TQ4u9X4t0cw3PIcQvN+xDdqff24rhNr1YxBNJitdXAk4wgX8i6w5tQak9PQ2LMxD0c/XKt2aoqjXdOWUFazwWCwsTUK1qI4DXaMJEnk5ubi5eUli3FXR9ErSRK65KsUHkzHvWMQ2RfybjgA3DsHA5AnFuLl62UDK+uR7bON0QUk4xBEj6eh53Pg7GHs45ycWvdxiaGE9/e8z+/nfmflyJX4uhijFA+0e4BejXuZlmC2NY5yTVtDWc0K9ocSI7JjRFHk5MmTstkpzt716rOLyf0jmbSZ+0j/8gB52y5x9e+LpiEJEwLk70nFYDDYtd5Kiehm/L/daJi8B/q/Cs4eQO36uMRQYnqtUWtIupJERlGG2RLL0QHRDG8xvNzyzLbC3q/pmiBHzY6EEmlQUKhHRJ2BosMZ5P+TRvHJbNMwhKBV4dzc5/oeEAKmoQkkKLmQZ76RlL1SmreQcwG6PWksa9wJJu+FgMg6aeJK4RWm75zOscxj/HrHr6YdHZ/t8CwCAl1D6m5raQUFuaM4DQoKdYwkSejO5VKw7zIFB9ORiq+P2zo398atYxCu0QGonNXXZ0+ElZ89oQl2h1QbCqktZfMW1FqIGgK+TY3v1dJhKNQXmqIF3lpv9qXtI7s4m/2X95uWWe4R2qNWbSgoKJRHcRrsHLmtqtaQ9eqziynYl0bBP2noM67vaaD2c8G9QyPcOgSVS2j0GhCBS1t/0zoN7l2CTes0GAyGBq23UvLS4Y/p8M9846JLamdj3oJbgEWnV6X5WOYxpu+cjlpQM2/IPMA4FPFGjzcI9wynlW+rOpFwM7HLPq4lctTsKCh7T9gZZfeekMsKcvbC1a0XyFl7BjAOP7jGBOLeMQhtUy/TXhAOTel6C399CMXXVqtsdwcMfBN8m9SoSlESKdQX4q4xLnZ1ueAyty67FRUqfrvrNwLdAuvKegUboNzP7A8l0mDHiKJIZmYmfn5+spj33BD1SqJkcgjc4hpRdDwLt/hGpuGH2tAQ9VZJ/hX4413QFxqXbb7tPWjS3aoqymr+6+JfvLf7PboEd+Gtnm8B0MitEe/3fp+OjTo6hMNgd31cB5TVrGB/KE6DHSNJEufOncPXt/aL3tgDDU1v3s4UCg+mE/BINIKTCrWnlsBHY+qs/oamt0Kyk8EnwvjaOwwGvgHOntD+3go3gqqKYkMxuhKdSbOX1ouLeRfZdnEbelFvSnC8reltdSzCdthFH9cxctTsSMjDtVVQqGMMV3XkrDtD8ekc8v9Js7U5N5+8dFjzLHza3rjDZCndnoD4+612GBYeXUi/Jf34+d+fTWXxjeL5qM9HrLljjclhUFBQsC2K06CgUAPUnlr8x7cx7hFxbSEmWaAvhm2fwecdYN8PxkTH039YXU1mUabZugpuTm5c1V1lZ8pOU5kgCAxuOhg3Td1tQ62goFA7FPfdzpHbqmq21CsWlKDPLELb2BMAl0hfXCLrN8TaYPpXkuB4Amx4BbKMyZ6EtL+Wt2Dd1MbpO6ez/MRyPuj9Abc2uRWAQU0HEeweTKdGnTh75mwdG9+waTB9fBORo2ZHQZZOw6VLl7h06RKFhYX4+fnRsmVLtFr721ZYrVbTsmVLW5tx07ClXn1GIVd+OIwhr4RGT7ZH06j+n34bVP+uehIOLDK+9giCAa9B+/ssGoa4cPUCjT0bm/720HigF/X8k/aPyWlw17jTPdSYNNlgNN8EGlQf3yTKalb2nrA/ZOM0XLhwgUWLFpGQkEBqaiplZ5pqNBo6derE2LFjGTx4sN1kMYuiSGpqKsHBwXZjc22wlV7d+atcmXcYMa/EuBW1eHNmKTeo/m3RHw6tgB6T4ZbnjcmO1SBKIo/+9ih7UvewbPgyovyiALi39b0MbT60wjUVGpTmm4Dc9IK5ZgX7QxZX6fTp0xk5ciQXLlzg2WefZe3atezdu5dDhw6xbds2vvnmGzp27Mhnn33GiBEjOHjwoK1NtghJkkhJSUEuS23YQm/hkQzSvzmImFeCJsSdRpPaG1dqvAnYrH/1Otj+OSQtu14WfRc8848xwlCJwyBKIqezT5v+VgkqvLXeqAU1SVeSTOVB7kGVLsKkXNOOjxw1OxKyiDS4urry+++/VzjFx9/fn+7du9O9e3cmT57MX3/9RWpqKrGxsTawVKEhkbfjEtm/nAIJnFv54n9/a1TODvyVkSQ4vg5+ewUyTxuHIVoNNjoJKhV4N6701MyiTO5fez+XCy6zeexmvJ29AXi+4/O80u0VAlwtWw1SQUGhYePAd8DrvPDCCxYf27t373q0RMEekESJnHVnyNt6EQD3LsH4jGyBoHbgwFzaYeM+EWe2GP8uzVuoZOZCsaGY87nnifQ17iHh6+yLq8YVjVrDv1n/mvZ/iPCKuCnmKygo3Bxk4TQ4KoIg4O/vjyDIYIlibo5eqcRA5pJ/KUy6AoDX4KZ49m1sk8/4pvRv/hX4453r0yfVztB9EvSaUukwxOErh3ls42N4ajxZd+c6VIIKQRD4qM9HhLiH1GrbaeWadnzKalaGKOwPB350so5Tp04xYMAAW5thFSqViqZNm8omgaq+9RryS0j/7pDRYVAL+N0ThVe/cJvd0OtUb+oh4/ADGP9PPWR8nXka9n5vdBjajoTJu2Hg62YOQ2ZRplmuQgufFsZqkEjJTzGVN/duXiuHAZRrWg7IUbMjofTaNUpKSrh06ZKtzbAKURQ5e/Ysoija2pSbQn3q1WcUkv7lAXTnchFcnAh4JBq3uEZ13o411JneLR/CVz3h236w53v4sofx7y0fQngX6DsNHkqAsfOvb119jfVn1jNgyQDe3fWuqczFyYWFQxey/s71hHmE1c62G1CuacdHjpodCdkMT8yYMaPK9zMzM2+SJXWHJElkZGQQHh5ua1NuCvWpN2/bJfRXClH7OBPwSPRNWYehOupEb+oh4zbVACkH4NLz19/7YzpEDYG+U01Fp3NOo1VpTesqxAbGYpAMFOgLKDYU46x2BqCZd7Oa21QFyjXt+MhRsyMhG6dh/vz5tGnTBnf3iqfLFRQU3GSLFBoS3kONP4Ke/cJRe9rfQl+VotKAZyhcvWQcgihFECAkHoLamYq+TPyS/x34H2NajeG17q8BEOoRSsLoBLPFmRQUFOSLbJyGiIgIHnzwQUaOHFnh+0ePHmX06NE32SoFW1J4JAOX1n4IKgHBSYXPiBa2NskiLmYXkpWvw9ddS5hPJTkEGadg0T1w5d8K3xYliX1RfYkszsbXxTgVuUNQB9SCmkJ9odmxisOgoKBQimxyGqKjozl8+HCl79tjJq8gCISEhMgm87ou9WYnnCFj/hFy1p6u/mAbUZHei9mF9P/oT4Z9/jf9P/qTi9mFxsWYTm2Go2uun+zdGHJTQHC6ltRo/pm92CiQR84u45dTv5jKOgd3ZuNdG5nRq+qhvPpEuaYdHzlqdiRkE2mYOnUqOp2u0vdbt27NsWPHbqJFtUelUhEaGmprM24adalXG+YBAqi9tEiS1CBvYDfqvZhdyJ4zmRTrRUCih7gPcdkCxMtbUelywa85tB5mHHpwcoZxywEonjuIP91c6V+oQyMZQFDTrbCQ7a7OFGcnX29PUBHoFnizZZqhXNOOT1nNyt4T9odsnIbAQNveDOsDg8HA6dOnad68OWq12tbm1Dt1qdetfSCaUHc0gbZPeKyMsnpTr+ro/9GfFOtFuqiO8bLTIjqqTsCFa8e6BaBu2gv0RaC5NmQR0RVJkrgrMpqz+lw+L/Gkb9yj8M98RqQkMqzDJNx6/td2AitAuaYdn7KaFewP2TgNjkpubq6tTbip1FRvSXoB2StP4nd3lHHTKWjQDkMppXqz8nUU60WeVq/gBY1xT4hCScsiQ3/WGrry5oSHiA73I7Mok+3nNzGs+TDAGAru3WoUxacTKOr1EjQfAh0fxiXtMARH20xXVSjXtOMjR82OguI0KDg8xWdzyJh/BLFAT9bqUwQ80NbWJlmNr7sWZycVmw3xPCOtZKmhD58Z7iRV8sXZSYWvpyt5ujwGLxtMkaGItn5tae5jfJKbFDeJFzq9gEq4lsIkCA3WYVBQUGjYKE6DgkNTcDCdzCXHQS+hCffE945IW5tkOUU5hB79FiGjEWGD3mLzi33ZcyaWHj/7kI4vKu1lJvTL4ZH4O0yzKLqFdCO9MJ1c3fUnObdK9o9QUFBQsBbFabBjBEGgSZMmDTKJrz6wRq8kSeRtvUhOwhkAXNr643dPFCqtHYwb64th97eotn5ESGEW0mkNdJ1ImE9jaOZHrpM/KvVF3Jt/yuqLLjzfc4Tp1A/7fIiLk4sNja8dyjXt+JTVbG8z1hQUp8GuUalUBATIZ8thS/VKokT2mlPk7zDui+DRIxTvYc0RVA38xiwaIGkpbH4HcpKNkyQDWyMNeI29BZfIzz1J3/C+bH6xL5l5xfx396809Q4nR5eDh9YDwK4dBlCuaTlQVrMye8L+kKXTsGfPHlxcXIiJiTGVJSUlUVRUROfOnW1omXUYDAaOHTtG69atZZF5bYleUWcgc9Exio5mggDeQ5vjcUtow3+SSzsMKyZC2rWNpDxDEftO5ag2jnOai7z82wTCPcPp07gPYT6uhPm4siJkKRq1xrZ21zHKNe34lNWsYH/I0mkYP348zZs3JyEhwVT20ksvcfbsWY4ePWpDy6ynqKjI1ibcVKrSa7iq48q8w5RcyAMnFX53R+EWYydPce6NIOssxc7ebI4bSUDM3XQI6U5RYiK9W/WmkVsjugR3oVBfaMpRcDSHoRTlmnZ85KjZUZCl07Bp0yacnMyl//DDD+j1ehtZpFBbSi4XcGXuIQxZxajcnPB/sB3OTbxsbVblZJ6Gw6ug1xTj3x6BMHY+c7IS+fLID/Q8WkKHkO4AuDq58tudv6FWyeNJVEFBoeEiS6chLKz8dr5BQUE2sEShLtBdzCP9uySkQj1O/i74PxyNJqCSPRlsjSTB7m9h4/+h1xfxp7OKqNZ3Eu4VDpEDGJbbklXnNtA+sL1ZkpjiMCgoKDQEZOk0OAoqlYrIyEhUKnlsIVKZXqcAF5x8nBECXfF/oC1qjwa6S2XeZVg9CU78BsDbzdqy4ugc7hHzeaXbKwBEeEWw4c4NpsxyOfUvKNe0HCirWRTF6k9QaFDIwmno3LmzxYlwu3fvrmdr6g5BEPD29ra1GTeNsnpLn8IFQUDl7ETAhGhUzmoETQN9Ij++nuNrJxN29Qoeame49S2GhLdn89aXyu33UHqtyq1/QX6a5aYX5KnZkZCF0/Df/zas9fXrCoPBwMGDB4mNjZVF5nWp3ph2MVxdewa1rwtefcMBGm50AWDzdF4/MocV/h5M9Yzk/hHzIagtXSWJ38f8jrPaucLT5Na/ID/NctML5poV7A9ZOA133HGHrU2oN+QW3hNFkeLjWeTvSgUVuEYHNMj8hcyiTHycfYxLN4d1os3+r1B7CqS1HwtBxmWsBUGo1GEoRW79C/LTLDe9IE/NjoJ8BtLKkJyczCeffMKUKVPIyMgAYMuWLZw4ccLquhYuXEj//v2JiYlhzJgxHDx4sMrjf/jhBwYPHkxsbCx9+vTh3Xffpbi4uEY65IpLWz88+4Xjf3+bBukwvLdrBrcuHcj2S9uNBVG3MeK+BDbctZEpXV6yrXEKCgoKtUB2TsPu3bsZPnw4Bw8e5LfffqOgoACA48eP8/nnn1tVV0JCAjNmzGDSpEmsXLmS1q1bM2HCBJMjciNr1qxh5syZTJ48mYSEBN555x0SEhL4+OOPa63L0Sm5XIBYeH1KrPfgpri2axhrMBjEMqva5VxEOLYGnVjCtjO/mYrdQmIJcldm6CgoKNg3snMaZs6cyXPPPcfcuXPRaK4vjtOtWzcSExOtqmvu3LmMHTuWO++8k8jISN58801cXFxYvnx5hcfv37+fDh06MHz4cBo3bswtt9zCsGHDqo1OVIZKpaJt27YOn3lddCqby/9LJOun47Rp1brB6JUkia8PfM2g5YNIzk02rrvwZQ8eSD7GwsvZvNzollrVL5f+LYvcNMtNL8hTsyMhi5yGsvz777989NFH5cr9/PzIysqyuB6dTsfhw4d5/PHHTWUqlYoePXqwf//+Cs+Jj4/nl19+MSUBnT9/ni1btjBy5EirdRgMBiRJQq1WI4qi6f+yc/sFQUClUpVb372ycpVKhSAIFZZD+XHIysrVajWSJFVYfqONlZWX2pi3L5XsFSfBICHpRdSSyqS/IWhKTE/kcsFlVqx9nOeOG4cjgkPiCbnzO0S/5khl2i210dJ+AtBqtRgMBrPZPw2xn+ry2lOr1SbNjqKpMttLv8Oluh1BU3XlkiSh1WqRJEnZe8IOkZ3T4OnpSXp6OuHh4WblR48etWqBp6ysLAwGA/7+/mbl/v7+nD59usJzhg8fTlZWFvfddx+SJKHX67nnnnt44oknrNaRlJRkeu3n50ezZs1ITk42GxoJCQkhNDSU06dPk5t7favkJk2aEBAQwLFjx8yWc42MjMTb25uDBw+afcnbtm2LVqstF4mJi4tDp9Nx5MgRU5lKpSI+Pp7c3FxOnjxpKndxcaFdu3ZkZmZy7tw5U7mXlxctW7YkNTWVlJQUU7m/nx9+Z9Tk/mY8tqixmrROei6cOEJcXNxN1xTbPpYVR1aw/Nhyno54Ghe1Cy4uLjwe3Juhx/7mtivbkRBIbXkfeZ2epmVAJKmXLplr8venadOmFvdTeHg458+fx8XFpeH2k5WaquunpKQkh9PkiP1UW02iKNK8efNK75UKDRdBktnepO+//z4HDhzg008/ZfDgwaxcuZIrV67w8ssvM2rUKCZPnmxRPWlpafTu3ZvFixcTHx9vKv/ggw/Ys2cPS5cuLXfOrl27mDJlCs899xyxsbEkJyfzzjvvMGbMGCZNmmRRuwaDgcTERNNmW0lJScTGxqLRaBrEUwTU/slIMojk/nKGgr1pALj3CsVzUBNESSQpKYm4uLhyn0t9axJUAiNWjuDc1XP8t8t/GdtqrNH2DVNh9zdI3uGIo76CiO6VarX2aU8UReMU05gYs+l4DaWfaqKpun7S6XQkJSWZNDuCpqr6yWAwmPSWDpfau6bqyks1t2/fHlG8/p2Wy5RTe0d2kYbnn3+et956i759+2IwGLj99tsxGAwMGzaMJ5980uJ6fH19UavV5ZIeMzIyKt3q9tNPP2XEiBGMGTMGgKioKAoKCnjttdd48sknrRrju/FHpOz/VR17s8oFQaiwvDIbTTeXIj2ZPx2n+N8sEMBnRAs8uocC5kMS9Wm7JEkkZSSx8dxGXuz0omlo4LHYx0jNT2VQk1uvn3frW6BxRej1AmoX8wVrqtNqjY0VvWfLfrK0vKY23qjZETRVVa5Wq03XmaNoqq68Mk0KDRvZOQ1arZbp06fz1FNPceLECfLz82nbti1Nmza1up527dqxY8cOBg4cCBi96R07djBu3LgKzykqKir3pS390sgs4FMhhpxirvxwmJKUfASNCr97W+Pa1r/6E+uYAn0BEzdOpFBfSK/GvegW0g2AkS1GwIFFsGoy3LMQVGrQuBodBwUFBQUZIDunoZTQ0FBCQkKA68v2WsvDDz/Myy+/THR0NLGxscybN4/CwkJGjx4NGLfbDgoK4oUXXgCgX79+zJ07l7Zt25qGJz799FP69etXI49bpVIRFxfnEFnIJan5xl0qc3SoPDQEPNQObWNPs2PqS29qfip7UvcwvMVwANw17oxtNZYcXQ5BbtfyXAqzYM1zcGSV8e+DSyDu3jq140YcqX8tRW6a5aYXzDUrizzZH7J0GpYuXcq8efM4e/YsAE2bNuXBBx80DRtYytChQ8nMzOSzzz4jPT2dNm3a8N1335mGJ1JSUsxuBk8++SSCIDBr1izS0tLw8/OjX79+PP/88zXWotPpcHFxqfH5DYGik1lkLDiKVGzAqZErAQ9F4+RXsaaa6D2eeZxWvq1Mm0D9m/UvUX5RAFwuuMyQ5UMQEekY1JFQD+NQyIudX7xewZm/YOUTkHsRVE7QdxrEjq2ZWCtxhP61FrlplptekKdmR0F2iZCffvopP/zwA+PGjTMl1CUmJvLjjz/y0EMP8eyzz9rWwGooTYQsa7s9JxFJBom0WfvQpxeibeZNwPg2qNw0FR5bVruler8+8DWzE2fTzr8do1uOZvm/yzmSeYTJcZN5vL1xuuxjvz1GiVjCtC7TTM4EAHod/DEdtn0GSODXAu78FsI61la2RdREr70jN81y0wuOdw+TG7KLNCxatIi3336bYcOGmcoGDBhAVFQUb7/9doN3GhwNQS3g/0Bb8rZdwmdYcwSnugvTHsk4wuzE2QAczTzK2zvfNr03O3E2fcP7EuUXxef9P8fFqYKnnl8mw8Gfja87PAiD3wVnjzqzT0FBQcHekJ3ToNfriY6OLlferl07ZaERG6EJdMN3VGSd1/vX+b/w0HhQoC9AlMzHTiN9Imnl2wqgYocBoMczxqGJoR9Cm+F1bp+CgoKCvSGf7JtrjBw5kkWLFpUrX7JkCcOH298Pg70mUEmiRPHZHKtnjVijN7UgFb2kL+cwANzb+t7yCbB56XBk9fW/g6Ph2QM2dRjstX9rg9w0y00vyFOzoyCLSMOMGTNMrwVBYOnSpWzbto327dsDcPDgQS5dusSoUaNsZGHNUKvVZgtL2RNFxzPJmHcEbTMvAifGWjSDxVq9nYM688f5P9DpdYhcdxxUgoqVJ1cyptWY6+2e2AirnoLCTJjw2/W8Baeqt66uT+y5f2uK3DTLTS+Ya1aiu/aHLJyGskuYgnEoAoxbZAP4+Pjg4+NTo62xbYkkSeTm5uLl5VXjaaO2wpBdjKBRoQ3ztNh2a/VG+kaSWZQJGB0FURJN/x+6csg4i8IzAja+Bru/MZ4U2AYqG664ydhz/9YUuWmWm14w16xgf8jCaViwYIGtTagXRFHk5MmTdpl57NE9FLf4RkgGy4cnrNUb5RfF5LjJzE6cTRu/NoxuOZoVJ1ZwOOMwk+MmE6XTwTd9If2Y8YSuT8DAN4wLNjUA7Ll/a4rcNMtNL5hrVrA/ZOE0KDRMVC71f/k93v5x+ob3Na3TMKbVGGOE4cSf8G1/MOjAvRGM+hJaDqx3exQUFBTsGVk6DUlJSaxbt46UlBRKSkrM3ps9e7aNrJIHkl6kJCUfbbhn9QfXkjd3vMmKEyuMUYVr6y8IgmB8LW02OgxRQ2HE5+Be8X4hCgoKCgrXkV0K69q1a7n33ns5ffo0GzduRK/Xc+LECXbu3ImnZ/3/kNU19raqWuGhK1z+IpEr849Uf3AFWKNXkiRESUSigiGQLo/DPYvgnp8atMNgb/1bF8hNs9z0gjw1OwqyWxFy+PDh3HPPPdx///3Ex8fzyy+/0LhxY1577TUCAwN55plnbG1ildj7CnKXvzqA7mwuXgMj8BrYpF7buqq7SrGhGDcnN9w0btcK08AzqF7bVVBQsAx7v5/JEdlFGs6fP0+fPn0A406VBQUFCILAQw89xJIlS2xsnXWIosiVK1fsZtOXktR8dGdzQQXuXYKtPt9avZ5aTwJcA647DBf/gVnRsOEVsIPPzN76ty6Qm2a56QV5anYkZOc0eHl5kZ+fD0CjRo1M0yxzc3MpLCy0pWlWI0kS586ds5tttfN2pgDg2tYftZf16x9YozejMOPGk2H9VGMeQ14a2MHiMvbWv3WB3DTLTS/IU7MjIbtEyM6dO7N9+3aioqK47bbbeOedd9i5cyfbt2+ne/futjbPYRGL9RT8cxkA926h9dpWri6X0b+MJr5RPG90fwMfFx84tQnO7zKuwTBoer22r6CgoOCoyM5p+L//+z+Ki4sB41bVGo2Gf/75h0GDBvHkk0/a2DrHpWB/OpLOgFOgK84tvOu1rT0pe8gpzuF0zmncte7GKMMf11YF7TQBPK0fGlFQUFBQkKHT4OPjY3qtUqmYOHGi7YypA+xhVTVJksi/NjTh3jWkVivfWaJ3QJMBLBu+jHx9PhqVBk78Dhf3gpMr3PJcjdu2BfbQv3WN3DTLTS/IU7OjIAunIS8vz+JjPTzsZ+tjtVpNy5YtbW1GtejO5VKSmo+gUeHeoVGN67FGb6TvtV0zJQn+fNf4uvME8Kh5+zcbe+nfukRumuWmF8w1K3tP2B+ycBo6depU7dOtJEkIgsDRo0dvklW1RxRFUlNTCQ4ObtC7xpVGGVzbB6Jy09S4nur0Hs88jr+rPwGuZdZdyDwNl48Zoww9n61x27bAXvq3LpGbZrnpBXPNCvaHLJyG+fPn29qEekGSJFJSUggKarjrDhjydBQkXQHAo1tIreqqSq9e1DN161RS8lOY1W8W3UK6Gd/wbwHPJcHFfXYVZQD76N+6Rm6a5aYX5KnZkZCF09ClSxdbmyBbCvalgUFC09gDbeP6W3EzsygTF7ULTion2vi1MX/T3R9aDaq3thUUFBTkgiycBgXbIEkSebtSAfDoWrsoQ3U0cmvEwtsXkpybjLeztzGX4eI+aNypXttVUFBQkBPyGERzUARBwN/fv1azEeoTQRAIeKgdHr3CcG0fWCf1VaVXJaho6t3U+MeJ3+C7AbBwrNGBsEMaev/WB3LTLDe9IE/NjoTiNNgxKpWKpk2bNugEKk0jN3xub45KW/t15SvSeybnDMv+XYZBLJOFLUnw57V1GRq1Bju9OdlD/9Y1ctMsN70gT82OhNJrdowoipw9e1Y2a7hXpPejvR/x5o43+WjvR9cP/Hc9XNoPGnfo0bA3IKsKufUvyE+z3PSCPDU7ErJ0GvR6Pdu3b2fx4sWmNRzS0tJMe1LYC5IkkZGR0SDXcM/9/RwZi46hu2T5GhnVcaNeSZLoFtINPxc/xkaNLT3oepShy2MNetvr6mjI/VtfyE2z3PSCPDU7ErJLhLx48SKPPvooKSkp6HQ6evbsiYeHB99++y06nY633nrL1ibaPZJBJG9nCmJeCa7RAWhD62fBLEEQGN92PGOjxuKsvrYB1vF1kHIAtB52HWVQUFBQaIjILtLwzjvvEB0dze7du3F2vr7T4q233srOnTttaJnjIKhVBDwcjUfPUFzb+tV7eyaHwSzKMNE41VJBQUFBoc6QndOwb98+nnzySbRarVl5WFgYaWlpNrKqZgiCQEhI7fZyqC+0YR74DG+BoK67S6xUb64ul2c3P8uxzGPmB1xNgeKr16IMT9dZu7aiIfdvfSE3zXLTC/LU7EjIbnhCFMUKE3BSU1Nxd3e3gUU1R6VSERpav9tMNyRK9b6/+302n9/MxbyLLB2+9PrNxysUJu+FtEPgVv8RjvpGbv0L8tMsN71grlnZe8L+kF2koWfPnsybN8+sLD8/n88//5w+ffrYyKqaYTAYOHHiRIP64mWvO0Pm0n8puVxQ53WX6r0v6j6GNB3CC51eKP+0onaC0Lg6b9sWNMT+rW/kplluekGemh0J2TkNU6dO5Z9//mHo0KHodDpefPFF+vfvT1paGi+++KKtzbOa3NxcW5tgQiw2kL8zhYJ9aRhyiuuljdzcXEI9Qvmgzwd0D+1+rWERkpaBXlcvbdqShtS/Nwu5aZabXpCnZkdBdsMTwcHBrF69mrVr13L8+HEKCgq46667GD58OC4uLrY2z64pSLyMVGzAyd8F5xY+dV6/XtRX/MaxX2H5BGjUDp74G5RFYxQUFBTqBdk5DcXFxTg7OzNy5Ehbm+JQSJJk2gLbvVsIgqpuk5xKxBLuW3cfkepIWpW0wlN9bfMrUYQt7xtftx6qOAwKCgoK9Yjs7rDdu3fn5ZdfZtu2bXa/IpkgCDRp0qRBZCHrzl+lJCUfnFS4d6z7LW//SP6Df7P+ZXvudkTK9NuxNcbER2cv6D6pztu1JQ2pf28WctMsN70gT82OhOwiDe+//z5r1qzhqaeewtPTkyFDhjBixAhiYmJsbZrVqFQqAgIaxoqHpVEGt9gAVG6aOq//1ia3Mrv/bIoNxXi7eBsLRRH+vBZl6PYkuPrWebu2pCH1781CbprlphfMNSvJkPaH7CINt956K5999hnbtm3j+eef59SpU9x9990MHjyY2bNn29o8qzAYDBw+fNjmXzxDfgkFB9MB49BEfSAIAreE3kJYfth1vUd/gcuHwdnb6DQ4GA2lf28mctMsN70gT82OhOychlI8PDy48847+f777/nll19wdXXliy++sLVZVlNUVGRrEyjYlwZ6CU2YB9pwzzqt+0rhFXSG67MiTHrL5jI4YJShlIbQvzcbuWmWm16Qp2ZHQXbDE6UUFxezadMmfv31V7Zu3UpAQAATJkywtVl2hyRK5O0yDk14dK3bVd4kSeK/W//L+avnmdFrBjH+ZYaQirKNiznlXHTIKIOCgoJCQ0R2TsPWrVv59ddf+f3333FycmLw4MF8//33dO7c2dam2SXFJ7MxZBQhuKhxjQus07qvFF7hZPZJsouz8Xe5YR8JNz8YtxyupoGrT522q6CgoKBQMbJzGiZPnkzfvn15//336dOnDxpN3Sft3SxUKhWRkZGobDjNMK90mmWHIFRadZ3WHegWyK93/Mr+y/sJ9wpHkqTyej3rfqZGQ6Eh9O/NRm6a5aYXzDXb+ww2OSI7p2Hbtm14eNTPVs03G0EQ8Pb2tln7+uxiio5mAPWXAOmmcaNnWE/gml5PD9g6E+LHO7TDALbvX1sgN81y0wvy1OxIyMK9zcvLK/d3Zf/sCYPBwP79+22WhSwV6XFu5o1zc280jdzqrN7Mokz2pO4pV24wGDiz9hPY/DZ80wcMJXXWZkPE1v1rC+SmWW56QZ6aHQlZRBo6d+7M33//jb+/P506daowWU+SJARB4OjRozawsObYMrynCXYncGIsUkndfvn/l/g/fj7+M4/FPMYzHZ65/oZoIPj4fOPrTo+A2n6HlixFjuFbuWmWm16Qp2ZHQRZOw7x580zhsPnz59vYGsdD0NRdLoMkSagFNWpBTY/QHubtHFmFa945JBcfhK5P1FmbCgoKCgqWIQunoUuXLqbXjRs3JiSk/NRASZJISUm52abZLfn70nBp5YvaU1un9QqCwLSu03io3UOEeJTJkxANCH99AIDUbRKCi1edtqugoKCgUD2yyGkoy4ABA8jMzCxXnp2dzYABA2xgUc1RqVS0bdv2pmdel1wuIGvpv6S8vwexoH7yCswcBoBDKxAyTiC5+CJ0e7xe2mxo2Kp/bYncNMtNL8hTsyMhu14rzV24kYKCApydnW1gUe3Qauv2Sd8SpGID2ghPXFr61Nk+EzqDjln7ZnGl8Er5N0WDafVHqfsk4+ZUMsEW/Wtr5KZZbnpBnpodBVkMTwDMmDEDMIa/Z82ahaurq+k9g8HAwYMHad26ta3MqxGiKJKYmEhcXBxqdd2ukVAV2nBPGj0VV6cJkD8d/Yk5h+awKXkTq0etRiWU8Wf1RRA5EEmXxwGXbsSK4k3Vayts1b+2RG6a5aYXzDUr2B+ycRqOHDkCGCMN//77r9miTlqtltatW/PII4/Yyjy7pC4TIOMaxdHWvy33tr7X3GEA0LrDkPcQ+7+GeMi+ZrcoKCgoOBKycRoWLFgAwLRp03jllVccZoGnm03+vjRc2/jV+fbXcY3iWHT7oqoPcrK/4SMFBQUFR0J2OQ0zZsxQHIYaojt/1ZgA+eHeOl+bAUAlqMyjDAY9/PI0XNhb520pKCgoKFiPLCINkydP5r333sPDw4PJkydXeezs2bOtqnvhwoXMmTOH9PR0Wrduzf/93/8RGxtb6fG5ubl88sknbNy4kezsbMLCwvjvf/9Lnz59rGoXjFnIcXFxNy0LuXSfCdfWfnUyNCFJEi//9TLdQ7szMnJk+WGJpKXwz3w4thaeP4LKyfmm6rU1N7t/GwJy0yw3vWCuWVnkyf6QhdPg6elZ4evakpCQwIwZM3jzzTdp37498+bNY8KECaxfvx5/f/9yx+t0Oh5++GH8/f359NNPCQoK4tKlS3h51Xw2gE6nw8XFpTYyLEIsKKHgQDpQd/tM/HXhL9adXcfm85vpHtqdYPfg628a9HBtXQZ6PAMaF5Ckm6a3oSA3vSA/zXLTC/LU7CjIwmkonTlx4+vaMnfuXMaOHcudd94JwJtvvsmff/7J8uXLmThxYrnjly9fTk5ODosXLzYlYjZu3LjG7YuiyJEjR25K5nX+vsugF9GEuKONqBvHq0doD17s9CJ6UW/uMAAkLYHM0+DmD50fBW6u3oaA3PSC/DTLTS+Ya1awP2ThNJSlqKgISZJMUy4vXrzIxo0biYyM5JZbbrG4Hp1Ox+HDh3n88esLDalUKnr06MH+/fsrPGfz5s3ExcXx1ltvsWnTJvz8/Bg2bBiPPfaY1TeMspu9iNemIIqiiCRJpnJBEFCpVOU2hqmsXKVSIQhC+eMRyNt5CQC3LkGmkGJpSPXGEKNarUaSpArLy9qoQsW41uPK2y7qUW35AAEQezyD5OQKBoOZXbXVVJnttdVUVXmpjZb2U2lbjqTJ0n4q/d+RNFVke1m9jqKpuvLScyvSpNDwkZ3T8NRTT3Hrrbdy7733kpuby5gxY9BoNGRlZTF16lTuu+8+i+rJysrCYDCUG4bw9/fn9OnTFZ5z/vx5du7cyfDhw/nmm29ITk7mzTffRK/XV5trcSNJSUmm1xcuXKBZs2YkJyeTkZFhKg8JCSE0NJTTp0+Tm5trKm/SpAkBAQEcO3aMoqIiU3lkZCTe3t4cPHjQ7Msc6dIYQ0YRohOcUKUgJaYCEBcXh06nM01nBeNNIj4+ntzcXE6ePGkqd3FxoV27dmRmZnLizAk0ggZBEPDy8qJly5akpqaalvH2P7+epllnwC2A80GDuJKYWE5/bTW1bdsWrVZL4g1111TTuXPnTOUVaQLjtdG0aVOL+yk8PByAEydOOIym6vrp8OHDwPXr2xE0WdJPSUlJDqcJKu8ngKtXr1Z6r1RouAjSjW6qg9O1a1d+/PFHWrZsydKlS1mwYAGrVq1iw4YNfPbZZ6xbt86ietLS0ujduzeLFy8mPj7eVP7BBx+wZ88eli5dWu6cwYMHU1xczKZNm0yRhblz5zJnzhz+/vtvi9o1GAwkJiYSExMDwKFDh4iJiUGj0dTbU0TWT8cpOpyBW9dgvEc0NzserH8yemP7G1zMv8jLnV6mhU8L8ycjQwmqL7siZJ2FW99G7D7ZpMlgMHDo0CHat29f7nNx1EjDoUOHaNeunVkkyp41VddPOp2OQ4cOER0djVqtdghN1UUaSvWWDlnau6bqyg0GA4cPHyYmJgZRFE0Ok1yGZ+wd2UUaioqKcHd3B+Dvv/9m0KBBpmzeS5cuWVyPr68varXazGsHyMjIICAgoMJzAgMDcXJyMvtyNG/enPT0dHQ6nVVLq6rVatRqNR06dDCVVZaBXdmX0ZJyQ04xRUeNGj17hFZ4TkVlgiBUWJ5VnMW6s+soMhRRYCgwHXPddtGY+Lh/AXSeYKbpRr011VTT8so0Vfa5W1t+Y91qtdrMIbXERmvLb7am6sq1Wm2FfWzPmqoqr+iatndN1ZWXva5vdD4UGj7ymedzjYiICH7//XdSUlL4+++/6dmzJ2D8sbdm/QatVku7du3YsWOHqUwURXbs2FHpjb5Dhw4kJyebed1nz54lMDCwRmuxS5JETk5OuSeNuiRvdyqIoG3mhSbIvdb1+bv6s2rUKqZ2mUpco7jyB6g10HkCPPaHcSXIMtwMvQ0JuekF+WmWm16Qp2ZHQnZOw6RJk/jggw/o378/sbGxph/4bdu20aZNG6vqevjhh1myZAkrV67k1KlTvPHGGxQWFjJ69GgAXnrpJWbOnGk6/t577yU7O5t33nmHM2fO8Oeff/L1119z//3310iLKIqcPHmy3pKJJINI/h5j/oJHHU2zBAjzCOP+NtVormBTsfrW29CQm16Qn2a56QV5anYkZDc8cdttt9GxY0fTYkyldO/enYEDB1pV19ChQ8nMzOSzzz4jPT2dNm3a8N1335mGJ1JSUszCgSEhIcyZM4cZM2YwYsQIgoKCeOCBB3jsscfqRlwdU3Q0EzFXh8pDg2u7iodcLKXYUEx6QTqNPSuZYqrXwU9jIfZuiBkDatldmgoKCgoNHlnemQMDAwkMDCQ11fgUHRwcXOUqjlUxbtw4xo0bV+F7pftdlCU+Pp4lS5bUqK2bjWkxp07BCE61C0rNPzyfrw58xTMdnuHBdg+WP+DAIjj9B6QdhrYjFadBQUFBoQEiu+EJURSZPXs2HTt2pF+/fvTr149OnTrxxRdf2GW4rD5XVfO7Jwq/+1pbvQLk94e+Z+DSgXyR+AVgHMNMupKETtTh71p+pUz0OvjrI+PrW54HrVuldcttFTm56QX5aZabXpCnZkdBdo9zn3zyCcuWLeOFF14wZS3v27eP2bNno9PpeP75521soeWo1WratWtXb/ULahVusYHVHrfm1BoaezYmLjAOQRDI0+WRVpDGVd1VYz2CwKf9PmVX6i66BnctX0HiQshJBo8g6PRwpe3Ut96Ghtz0gvw0y00vmGtWZk/YH7JzGlauXMn06dMZMGCAqax169YEBQXx5ptv2pXTIIoimZmZ+Pn51emGN5JBBEFAUJVPRryRgpIC3t31LnklecwdPJdOwZ24p/U9DGwyED8XP9NxgiDQLaRb+Qr0Oth6LVn0ludB41ppW/Wlt6EiN70gP81y0wvmmhXsD3lcpWXIycmhefPm5cqbN29OTk6ODSyqOZIkce7cuTqfulSw/zKpH+whb3dK9cfqC7i1ya208m1FhyBj5KaRWyPa+rctv59ERST+CDnnwSMYOj5U5aH1pbehIje9ID/NctML8tTsSMjOaWjdujULFy4sV75w4UKz2RRypuBAOobsYsQCfbXHBrgG8FbPt1gybEn5ra2rw1ACf1kWZVBQUFBQsD2yG574z3/+w+OPP8727dtNu6wlJiaSkpLCt99+a1vjGggBD7Sl4OAVXKJ8LT5HrarBErBqDdzxFez+ptoog4KCgoKC7ZFdpKFLly6sX7+eW2+9latXr3L16lVuvfVW1q9fT6dOnWxtntV4eXnVeZ2CRo17xyDUHlWvUrnl/BYuF1yuXWPNesHdC0BjWTZ1fehtyMhNL8hPs9z0gjw1Owqy2rDqwoULbN++nZKSEjp37kyrVq1sbZLVlG5YVR8bvEh6EVSWJUBe1V1lwNIB6Aw6lo9YTgufFtY1JhqgJtEJBQUFh6E+72cK9YNshid27tzJE088Ydrm1cnJiXfeeYeRI0fa2LKaI4oiqampBAcH10nmdd7OFPJ3puA1MAK3uEZVHptRmEEbvzbkFOfQ3Lt8YmmV6Ivhq17Q+nbo9QI4W7bnR13rbejITS/IT7Pc9IK5ZgX7Qx5XKfDpp5/So0cP/vrrL3bt2sWYMWP48MMPbW1WrZAkiZSUlDrJQpYkifxdKeivFCIWVZ8A2dS7KfOGzOPHoT8iVLBPRJX8Mx+uHIcDi415DVbYWFd67QG56QX5aZabXpCnZkdCNk7DiRMnmDJlCo0aNcLb25uXXnqJzMxMsrKybG1ag6D4VA769EIErRq3+KqjDGXx0Fq+MygAJUWw9WPj615TwMnZuvMVFBQUFGyGbJyGvLw8fH2vzwZwdXXF2dmZvLw8G1rVcMjfZVyTwa1DI1TOVY9aHUg/QImhpGYN/TMfrl4CrzDo8EDN6lBQUFBQsAmyyWkA2Lp1K56enqa/JUlix44d/Pvvv6aysitFNnQEQcDf39/64YEbMOTqKDycAVS/BXZWURaPrH8ED60Hy4YvI9Ct+mWmTZQUwd81jzLUlV57QW56QX6a5aYXzDUrQxT2h6ychqlTp5Yre+2110yvBUHg6NGjN9OkWqFSqWjatGmt68nfkwqihLaJF5pg9yqPPZd7Dh9nH/xd/QlwtXK77H/mwdUU8GoM8eOttrOu9NoLctML8tMsN71grlnZe8L+kI3TcOzYMVubUOeIokhycjIRERE1zryWDBL515aLri7KABDXKI4Nd20gvSDduqcjUYQds42va5jLUBd67Qm56QX5aZabXjDXrGB/yOMqdVAkSSIjI6NWIb6iYxkYcnSo3J1wjbEscuCkciLEw7rtslGp4KG10OPpGkUZoG702hNy0wvy0yw3vSBPzY6ELJyGxMREi48tLCzkxIkT9WdMAyNvpzHK4N4pGMGp6svhYt7F2jXmEwGDpoNT1StNKigoKCg0TGThNLz00ktMmDCBdevWUVBQUOExJ0+e5OOPP+bWW2/l8OHDN9lC26C/UkjxiWwQwL1r1ZGD9IJ0hq0Yxr2/3ktBScWfYaUU2dfuoQoKCgoKFSOLnIa1a9eyaNEiZs2axYsvvkjTpk1p1KgRzs7O5OTkcPr0aQoKCrj11luZM2cOUVFRtjbZIgRBICQkpMaZ16VbX7u08sXJr+q9Hw6mHwTBODThpnGzvJGSQpjdBcI7w7BZ4G5l8mQZaqvX3pCbXpCfZrnpBXPNyhCF/SGrvScAkpKS2LdvH5cuXaKoqAhfX1/atm1L165d8fHxsbV51VJXa7VLkkTKO7sQ80rwf6Atrm39qz0nsyiTjMIMWvq2tLyhHf+DDdOMQxOT9ylDEwoKCiaUvSfsD1lEGsoSExNDTEyMrc2oEwwGA6dPn6Z58+bWf+H0Eq4xAZSk5OPS0rItsP1c/PBz8bO8DV0B/P2J8XWvF2vtMNRKrx0iN70gP81y0wvmmhXsD9k5DY5Gbm5ujc4TNCp8R0ZadGxBSYF1QxKl7P0e8i+DTxOIu8/68yugpnrtFbnpBflplptekKdmR0EWiZAKNedS3iX6/NyHaVunoRer38jKhK4Ats0yvu79H6s2plJQUFBQaJgoToNMKUkvQNRVvxrbn+f/pMhQRHpBOk4qKwJTe+dAfjr4NoX299TYTgUFBQWFhoMyPGHHCIJAkyZNapR5fWXOIQw5xQQ+2R7nCK9Kj7uvzX20D2yPKImWVy5Jxm2voU6jDLXRa4/ITS/IT7Pc9IK5Zpnl4TsEsnYaiouLcXa2362ZVSoVAQHWT2EUdQYkgwQSaAKrz1VoF9DOugYEASZshAOLILbuogw11WuvyE0vyE+z3PSCuWZl7wn7Q3bDE6Io8sUXX9CrVy/i4+M5f/48ALNmzWLp0qU2ts46DAYDhw8ftvqLp9KqCX2lKyGvdkXlWrnfaFV04Ua0btB5Aqjrzi+tqV57RW56QX6a5aYX5KnZkZCd0/C///2PlStX8p///AeN5nrYvFWrVixbtsyGltWMoqKiGp+r9qh8CuS53HMMXj6Yrw58ZV0I8cpJ4+ZU9URt9NojctML8tMsN70gT82OguychtWrV/P2228zYsQIs13loqKiOH36tA0ta1isPrma1PxUDqYftHy8tTgPvh8EX/aA7OT6NVBBQUFB4aYju5yGtLS0CrdklSQJvd6KKYV2TPp3SQhqAe9hzSvNaXii/RO09G1JsHuw5RXv+RYKMsDFGzxD68haBQUFBYWGguychsjISPbu3UtYWJhZ+fr162nTpo2NrKoZKpWKyMhIs4hJdUglBopPZYMEvqMrX4FOq9YypNkQy40pzoNtnxlf936pTnMZSqmJXntGbnpBfprlphfMNYv1OJSpUD/Izml46qmnmDp1KmlpaUiSxG+//caZM2dYtWoVX3/9ta3NswpBEPD29rbqnJLUApBA5a5B5VWH+0Ds/gYKM8GvBcSMqbt6y1ATvfaM3PSC/DTLTS/IU7MjIR/39hoDBw7kq6++YseOHbi6uvLZZ59x6tQpvvrqK3r27Glr86zCYDCwf/9+q7KQdZfyANCEuleYq3Ay6yQPrnuQ9WfWW25I8VXY/rnxdZ+X6yXKADXTa8/ITS/IT7Pc9II8NTsSsos0AHTq1Im5c+fa2ow6wdrwXsk1p0Eb6lHh+0v/Xco/l//Bz8WP25rdZlmlpVEG/0iIvtMqe6xFbuFMuekF+WmWm16Qp2ZHQXaRhgEDBpCVlVWuPDc3lwEDBtjAopuL7lI+AJpKnIZHoh/hqbinGNd2nOWVntth/L8eowwKCgoKCrZHdnf4ixcvVujl6nQ60tLSbGDRzUMySJSklDoN7hUeE+QexJPtn7Su4vuXwuk/oVnvWlqooKCgoNCQkY3TsGnTJtPrrVu34unpafpbFEV27NhRbkZFQ0elUtG2bVuLM6/1VwpALyJoVTj5u9adIYIALfrVXX2VYK1ee0duekF+muWmF8w1K8MU9odsnIZJkyYBxszdqVOnmr3n5OREWFhYuXJ7QKu1fAZESenQRIgHgso8CfJIxhF+Pv4zY6PG0s7fwr0mzm2HoHbGdRluEtbodQTkphfkp1luekGemh0F2bi3x44d49ixY4SEhLB9+3bT38eOHePQoUNs2LCBfv3q/2m5LhFFkcTERIu99bIzJ25kyfElrDixgvmH51vWeFEOLLoXZsXC5WMW21wbrNVr78hNL8hPs9z0gjw1OxKyiTSUsnnzZlubYDOqmjkxKnIURYYi7o6627LKdn0NRdkQEAUBLevQSgUFBQWFhorsnAaAgoIC9uzZw6VLlygpKTF774EHHrCRVfWLJElVzpyIaxRHXKM4yyorzIYds42v+74MqspXllRQUFBQcBxk5zQcOXKEiRMnUlhYSGFhId7e3mRlZeHq6oqfn5/DOg2G7GKkQj2oBDRBFe83YTG7vjYOTwS2hraj6sQ+BQUFBYWGj2xyGkqZMWMG/fr1Y8+ePTg7O7NkyRL++OMP2rVrx8svv2xr86xCpVIRFxdnUea1yl2D/4Nt8RnRAsHp+vGHrxxmTtIcMgozLGu0MBt2fGF83efmRhms0esIyE0vyE+z3PSCPDU7ErLrtaNHj/Lwww+jUqlQq9XodDpCQkL4z3/+w8cff2xr86xGp9NZdJxKq8a1jT8e3ULMyn88+iOz/pnFZ/s/s6zBXV9BcQ4EtrFJlMFSvY6C3PSC/DTLTS/IU7OjIDunwcnJyeTh+vv7c+nSJQA8PDxITU21pWlWI4oiR44cqVUWco/QHsQExHBXy7ssO6EgAwTVtVyGm3v51IVee0JuekF+muWmF+Sp2ZGQXU5D27ZtSUpKomnTpnTu3JnPPvuMrKwsVq9eTcuWjjsL4OqWCzgFuODSyhdBc31IYXiL4QxvMdzyioZ+CF2fAN9m9WClgoKCgkJDRnaRhueff57AwEDTay8vL9544w2ysrJ46623bGxd/WDILyFn3RkyFhxFMki1r9C/xU2PMigoKCgo2B7ZRRpiYmJMr/39/ZkzZ44Nrak9liQTSXoRt45BiAUlqFyMXX444zCpean0Ce+Dk8qCyyDrHCCBb9PaGVxL5JY8JTe9ID/NctML8tTsKCg9d43Dhw/z+OOP29oMq1Cr1cTHx6NWVz2DwcnbGb8xrQh48Pry0HOS5vDcn8/x2T8WJkDumA2ftodNb9fG5FphqV5HQW56QX6a5aYX5KnZkZCV07B161bef/99Pv74Y86fPw/AqVOneOqpp7jrrrtqlJizcOFC+vfvT0xMDGPGjOHgwYMWnbd27VqioqJ46qmnrG6zFEmSyMnJQZKsH3Jo7t0cPxc/bm9+u2UnnLy24VdonNVt1RW10WuPyE0vyE+z3PSCPDU7ErJxGpYuXcpjjz3GihUr+Pbbbxk7diyrV6/mnnvuISAggF9//ZVvv/3WqjoTEhKYMWMGkyZNYuXKlbRu3ZoJEyaQkVH1mgcXLlzg/fffp1OnTrWRhCiKnDx5slpnp+RyAZLB/JjJ8ZP5fczvRPlFVd9Q1lnIPAWC2qbbX1uq11GQm16Qn2a56QV5anYkZOM0zJ8/nxdffJFdu3Yxa9YssrKyWLRoEWvWrOGtt96iRYsWVtc5d+5cxo4dy5133klkZCRvvvkmLi4uLF++vNJzDAYDL774Ik8//TTh4eG1kWQRos5A2if7uPj6dsQC8yWzNSqNZZWcurZfR3iXm7qjpYKCgoJCw0I2iZDnz5/ntttuA2DQoEE4OTnxn//8h+Dg4BrVp9PpyuVBqFQqevTowf79+ys974svvsDf358xY8awb9++GrUNRuejFFEUUavViKJoFvITBIGS1HyQQOXihOSs4uiVo6gEFVH+UWZ1lNovCEL58pObEACxeT+kMu+VJjPd+MSgVquRJKnC8httrKxcEARUKpVZeVm7brSx9HiLNVVi+83WVJXtpW05kiZL+6n0f0fSVJHtZfU6iqbqykvPrUiTQsNHNk5DUVERrq6ugPELodFoaNSoUY3ry8rKwmAw4O/vb1bu7+/P6dOnKzxn7969LFu2jFWrVtW43VKSkpJMry9cuECzZs1ITk42GxoJCQnBy7h2FYUeBhITE/nk3CccuHqAlzu/TLwUT1FRken4yMhIvL29OXjw4PUvs6inw+ktABw3NKYgMdF0fFxcHDqdjiNHjpjKVCoV8fHx5ObmcvLkSVO5i4sL7dq1IzMzk3PnzpnKvby8aNmyJampqaSkpJjK/f39adq0aTlNpclTp0+fJjc311TepEkTAgICOHbsWPWaMK7XodVqSSyjx1aaQkJCCA0NLacpPDwcFxcXTpw44TCaquunw4cPA9evb0fQZEk/JSUlOZwmqLyfXFxcuHr1aqX3SoWGiyDJJBuldevWPPfcc7i5GTdr+uijj5gwYQK+vr5mx1m6YVVaWhq9e/dm8eLFxMfHm8o/+OAD9uzZw9KlS82Oz8vLY8SIEbz++uv06dMHgKlTp5Kbm8v//vc/i3UYDMYf/5iYGNMPaFVPETmrTpG/OxX3PmG4D2zMtG3T2Jy8mRUjVxDhEWFWd4VPEck7Uc8biuTqhzjluNleE3J5MlI0KZoUTfWjqaSkxOQwKbMp7APZRBpCQ0NZsmSJ6e+AgABWr15tdowgCBY7Db6+vqjV6nJJjxkZGQQEBJQ7/vz581y8eJEnn3zSVFb6RWrbti3r168nIiKi3HmVoVarEQSBzMxM/Pz8gIrnPusu5QHg3NgTrUbLzL4zyS7KxsfFp8q6TYTGwtgFCIVZqDXa6o+/hiAIFZZXNj/bknJRFE16K7vB1Gd5fWiqqk1RFLly5Qp+fn4VnmOPmqorL3tNl63TnjVVVV72mhYEodLj7UlTdeVlr2vFUbA/ZOM0bN68uU7r02q1tGvXjh07djBw4EDA+GXYsWMH48aNK3d88+bNWbNmjVnZrFmzyM/P55VXXqlRboUkSZw7d65ctMT0vkE05jQA2lAPU3lVDkM5nD2h7QirbasPqtPraMhNL8hPs9z0gjw1OxKycRrqg4cffpiXX36Z6OhoYmNjmTdvHoWFhYwePRqAl156iaCgIF544QWcnZ1p1aqV2fleXl4A5crrCn16IeglBGc1F53S8NX54aX1qpe2FBQUFBQcH8VpqAVDhw4lMzOTzz77jPT0dNq0acN3331nGp5ISUmx6XKpuovGoQlNqDtv7HqHxMuJvNvrXW5tcqtlFZz+E87tgDbDIDim2sMVFBQUFBwbxWmoJePGjatwOAJgwYIFVZ773nvv1br90mhFRZSkGIcmVMEuZBZlUmwopq1/W8srP7gEEhdCSUGDcRqq0uuIyE0vyE+z3PSCPDU7CorTYMeo1eoqt/MuuZYE6Rrmw7KOyzidc5owjzDLKpek60tHRw6oral1QnV6HQ256QX5aZabXjDXfOMsDIWGj2xWhHRERFHk0qVLFS6QIkkSukvGSIMm1B1BEGjhY8Wql2mHIS8VnFwhontdmVwrqtLriMhNL8hPs9z0gjw1OxKydBqSk5P55JNPmDJlimnK5JYtWzhx4oSNLbMOSZJISUmpcOMXQ1YxUpEe1AKqQGfrKz91LcrQ9BZwqsH59UBVeh0RuekF+WmWm16Qp2ZHQnZOw+7duxk+fDgHDx7kt99+o6CgAIDjx4/z+eef29i6ukN/pRBUcNE1nWG/DGf/5cqXtq6QBjY0oaCgoKBge2TnNMycOZPnnnuOuXPnotFc37CpW7du5ZZAtWdcWvniOq0N74Z/R2p+quW5DAC6fEjeYXzdQnEaFBQUFBSMyC4R8t9//+Wjjz4qV+7n50dWVpYNLKo5giDg7+9vWknuRvw9A1h8/zIOph+kkZsV+2xknASNK3gEQUDDSdKqTq+jITe9ID/NctML5pqVIQr7Q3ZOg6enJ+np6eW2pT569ChBQUE2sqpmqFQqmjZtWuUxrk6udA3pal3FIe3hP6ch9wI0oJuZJXodCbnpBflplpteMNeszJ6wP2Q3PHH77bfz0UcfkZ6ejiAIiKLIvn37eP/99xk1apStzbMKURQ5e/ZsuSxkQ56O1Nn/kLXqZM09ebUT+DatvZF1SGV6HRW56QX5aZabXpCnZkdCdk7D888/T/Pmzenbty8FBQXcfvvtjBs3jvj4eLPNpOwBSZLIyMgo5xiUXMpHfyGfcwePcTb3rHWVigbjGg0NkMr0Oipy0wvy0yw3vSBPzY6E7IYntFot06dP56mnnuLEiRPk5+fTtm1bhwoRpnlm8WHYHJzQ8JpzD+tO/mc+bP0Yuj4OPSbXj4EKCgoKCnaJ7JyGvXv30qlTJ0JDQwkNDbW1OfVCk5DmTHv4HZLSk/Bz8bPu5FObICfZuHS0goKCgoJCGWTnNDz00EM0atSIYcOGMWLECCIjI21tUo0RBIGQkJAKM6/DPMKsm2YJYNDD6b+MrxvgVMuq9DoictML8tMsN71grlkZorA/ZJfT8Ndff/HII4+we/duhg0bxsiRI/nuu+9ITU21tWlWo1KpCA0NNdtJ01Ck5+qW8xSdyLL+C3lxLxTngKsvhMbVrbF1QEV6HRm56QX5aZabXpCnZkdCdr3m5+fHuHHjWLx4MRs3buS2225j1apV9O/fnwceeMDW5lmFwWDgxIkTpmlLkiTx+sqp5Kw7y5Ulx6x/eildBbJ5P1Cp69ja2nOjXkdHbnpBfprlphfkqdmRkN3wRFnCw8OZOHEirVu35tNPP2XPnj22NslqcnNzTa8PXjmIlFoMgFOIm/WVnWr4S0eX1SsH5KYX5KdZbnpBnpodBdk6Dfv27WPNmjVs2LCB4uJiBgwYwJQpU2xtVq2ICYjBxf9uSJNwbext3ckFmXDxH+PrFv3r3jgFBQUFBbtHdk7DzJkzWbt2LZcvX6Znz5688sorDBgwAFdXV1ubVmtUggqfbDdKyEcb6mHdyfpi6PIY5FwAL8ecVaKgoKCgUDtk5zTs2bOHCRMmMGTIEPz8rJyO2MAQBIEmTZqYchckvUhJmnGqpMZap8ErBIZ+WNcm1ik36nV05KYX5KdZbnrBXLMye8L+kJ3TsHjxYlubUGeoVCoCAgIQJZFJmyYx0Lk3nQ2hCC5OqH2dbW1enVOqVy7ITS/IT7Pc9IK5ZiUZ0v6QhdOwadMmevfujUajYdOmTVUeO2BAw00CvBGDwcCxY8fI8c7hrwt/4XVVoDN3ow11t+7JJfcSZJyC8K7gpK0/g2tJqd7WrVujVje82R11jdz0gvw0y00vmGtWsD9k4TRMmjSJbdu24e/vz6RJkyo9ThAEjh49ehMtqz1FRUW0a96O/+v2fzTebszLsHpoImkZbPw/iLod7v2pHqysO4qKimxtwk1FbnpBfprlphfkqdlRkIXTcOzYsQpfOwoeWg/GRo3l8h8H0JGLU4ibdV/K84ngEQ5NB0ID/jKXhjKLiopk8VQmN70gP81y0wvmmkupL/1arVZZRKqOkYXTUJZVq1YxdOhQtFrzMLxOpyMhIcHutscuRRIlSlLyMbgKXHDORDqTZemJ0OQeaHI3eIbAmTP1a2gtkCQJJycnzp07J4vEMbnpBflplpteMNcM1Kt+lUpFs2bNyt3vFWqO7JyGadOm0atXL/z9/c3K8/PzmTZtml05DRISS3KXkH8pn64uHRCLDeR3cUbrorV8mdbiPPAoBsEJAlpCA75xSZKEwWBArVbL4gYrN70gP81y0wvmmgEKCwtxdXWtc/2iKHLp0iVSUlKIiIiQzedb38jOaZAkqcKLJy0tDU9PTxtYVHO2p2wnITmBbWnbWNt6CZIziKFaAgMDcXOzcEXI4ivgJICbDzjAWhUKCgr2gyRJiKKIi4tLvfyoBwYGcunSJfR6PRqNps7rlyOycRpGjRqFIAgIgsCDDz6Ik9N16QaDgQsXLtCrVy8bWmg9TT2bMjhgMM1Dm6N1dcEp0htBK1gXiiu+tpyrs1f9GFmHSJJUb08lDRG56QX5aZabXjDXXN+U3gsNBoPiNNQRsnEaBg4cCMDRo0e55ZZbcHd3N72n0WgICwtj0KBBtjKvRoR5hHFv8L3ExcYZw5tN3Mg5c8bym49eZ1wJEsDZyhkXNkJui8HITS/IT7Pc9MLN0ywXR+xmIhunYfLkyQCEhYUxdOhQnJ0db/Ejq1FrICAK9IWgks2loKCgoKBQQ2Q3F+WOO+5wCIchuyibY5nHuKy7jFQiIhaUWF+JIIDWDdz8qz+2gdChQwd+//13i4+fOnUqTz31VK3avHDhAlFRUXa3hoeCgoJCXSOLx8suXbqwfv16/Pz86Ny5c5Uhq927d99Ey2rOHxf+4M2db9I7tDd9z3QhY94RVO28oZP9jtulp6fz9ddfs2XLFlJTU/H09CQiIoIRI0Zwxx134OLiYmsTbzqOsJGatchNs9z0gjw1OwqycBqmTZuGh4eH6bUjjHO5OrnSyK0Rfq5+6DONi6QIrlZ0p64A8tPBxQdcy2+jfTG7kKx8Hb7uWsJ86v8Lfv78ee699148PT15/vnniYqKQqvVcvz4cZYsWUJQUBD9+8tvy25HuFatRW6a5aYX5KnZUZCF03DHHXeYXo8ePdqGltQdtzW9jdua3kZiYiJuccG4dwiiIK+A7CsXLaugKAcKM42LO93gNFzMLqT/R39SrBdxdlKx+cW+9e44vPHGG6jVapYvX242XTQ8PJyBAwdWmjh1/Phx3nnnHRITE3F1dWXQoEFMnTrVLNEVYPbs2fz444/odDqGDRvGq6++asqs/uuvv/jyyy85ceIEarWauLg4XnnlFSIiIupPsIUUFBRYPn3WQZCbZrnpBXlqdhRkl9Nw+PBhjh8/bvr7999/56mnnuLjjz9Gp9PZ0LLaoXJ1wsmz9lMtL2YXsudMJsV60XiYXmTPmUwuZhfWlanlyMrKYtu2bdx///2V3kgqejIpKChgwoQJeHt7s2zZMmbNmsX27dt5++23zY7bsWMHp06dYsGCBXz88cds3LiRL774wvR+YWEhDz/8MMuXL+eHH35AEAQmTZqEKIp1K1RBQUHBzpGd0/Daa69x9uxZwBgSf/7553F1dWX9+vV8+OGHtjXuZmHQQ0mB8bXz9QWtSiMMz/2ciOrab7RKgOd+TqT/R3/Wm+OQnJyMJEk0a9bMrLxr167Ex8cTHx9fYd/8+uuv6HQ63n//fVq1akX37t157bXXWL16NVeuXDEdp9Vqeffdd2nZsiV9+/blmWeeYf78+SanYPDgwQwaNIgmTZrQpk0b3n33Xf79919OnjxZL3oVFBQU7BXZOQ1nz56lTZs2AKxbt44uXbowc+ZMZsyYwW+//WZj6yxnV8ouXvzrRXaf3ErGnEPk/nHe8pN1V43/O7mYbYWdla8zRRjEa6MBpf8X60Wy8m9uJGbZsmWsWrWKyMjICqNAp06dIioqyiw60aFDB0RR5EyZPTSioqLMEq/i4+MpKCggJSUFMF4TU6ZMYcCAAXTo0MG0PXrp+woKCgoKRmSR01CW0mVLwRi27tu3LwAhISFkZVm4yVMD4FL+JX5P/p324mPoTueicnZC2z3QspMrGZrwddfi7KSiWC+iEowOQ+n/zk4qfN3rZ9OX0nXhz9ywWVZ4eDhAvc+aeOKJJwgLC2P69Ok0atQIURQZNmwYJSU1mMZax8hx3FdumuWmF+Sp2VGQXaQhOjqaL7/8klWrVrFnzx6T03DhwgUCAgJsa5wVxAXG8UrXV+gsxQKgCbVwRUdJgqJrkQZn8702wnxc2fxiX2bdHWcWaZh1d1y9JkP6+vrSs2dPfvzxRwoKCiw+r0WLFhw/ftzsnH/++ce0s10px48fN9uGNzExETc3N5OjeObMGZ588km6d+9OixYtyMnJqRthdYCyWqDjIze9IE/NjoLsnIb//ve/HDlyhLfffpsnnniCJk2aALBhwwbi4+NtbJ3lNPNuxpiWY3BLMwaLtKHu1ZxxDVEPKjWgAm15RyPMx5XOzfxwdjJeGs5OKjo386v32ROvv/46BoOBO++8k4SEBE6dOsXp06dZvXo1p0+fNu2IV5bhw4ej1WqZOnUq//77Lzt37uTtt99m5MiRZg6gTqfjlVde4eTJk2zZsoXPP/+ccePGoVKp8Pb2xsfHh59//plz586xY8cO3nvvvXrVag2FhfWXgNpQkZtmuekFeWp2FGQ3PNG6dWvWrFlTrvyll16ybCvpBoSkF3HKNXrsmlAP9JacpNZAozbGZMhK9JZGHG7mOg0RERGsXLmSr7/+mpkzZ5KWloZGoyEyMpJHHnmE++67r9w5rq6uzJkzh3feeYe77rrLbMplWbp3706TJk24//77TVMun376aQBUKhWffPIJ06dPZ9iwYTRr1oxXX32V8ePH17tmBQUFBXtDkGQaJzp06BCnTp0CIDIyknbt2tnYIsswGAwkJibSvG1zss6kop1/BcHVidDXulFcXMyZM2do1qyZQ66eKEmSaX63HBaHkZtekJ9muekFc81AveovKipy6HuiLZBdpCEjI4PnnnuOPXv24OVlTATMzc2la9eufPLJJ/j5+dnYQsvYlLyJHRs38jzj0IS4W/aFk0SQqDTCYA/I5cZaitz0gvw0y00vyFOzo2C/vx415O2336agoIC1a9eye/dudu/eza+//kpeXh7Tp0+3tXkWo1FpaKNrDoA2zMIkyOI8SD0IWefq0bL6QxAEWT2RyU0vyE+z3PSCPDU7ErKLNGzdupW5c+fSokULU1lkZCSvv/46jzzyiA0ts45hzYeR4dEEXcZVNCEWJkEW5wKScXdLO0SSJAwGA2q1WhY3HLnpBflplpteMNesYH/ILtIgiiIaTfmdIJ2cnOxq2WBJlChJyQdAHWzhnOfiiqda2hPFxcW2NuGmIje9ID/NctML8tTsKMjOaejWrRvvvPMOaWlpprK0tDRmzJhB9+7dbWiZdegzC5F0IpIKnAIsmN2g14H+2loFduw0KCgoKCjYDtkNT7z22ms8+eSTDBgwgODgYABSU1Np2bKlXe09cfzYIfyBDPerhKotCGuWrgKpcQeV7LpdQUFBQaEOkN2vR0hICCtXrjTtfAjGlQV79OhhY8usIzs3E3+8SHfKtOyE0qEJF/uOMtjbWhq1RW56QX6a5aYX5KnZUZCV05CQkMCmTZsoKSmhe/fudr2AT0hkBP8WnsSrUWj1CUWSVCafwavqYxswgiCYbTzl6MhNL8hPs9z0grlmmS4TZNfIxmn46aefeOutt2jSpAkuLi5s3LiR5ORkXn75ZVubViNatGpLZFQ7MjMzEUWxas9dEsEzCIrzQWO/G8VIkoRer8fJyUkWmeZy0wvy0yw3vWCuWcH+kE2MaOHChUyePJkNGzawevVq3nvvPRYtWlQn9fbv35+YmBjGjBnDwYMHKz12yZIl3HfffXTu3JnOnTvz0EMPVXl8dUiSxLlz56r31lVq8AgC/+Z2O92ylLJbZEdFRfH7779XeuyuXbuIiooiNze3XmzZsWMHQ4YMwWAw1Ev9QIVbgjsqOp2O/v37k5iYaGtTbipy6uNS5KjZUZCN03D+/HlGjRpl+nv48OHo9XouX75c4zoTEhKYMWMGkyZNYuXKlbRu3ZoJEyaQkZFR4fG7du3i9ttvZ/78+SxevJiQkBAeeeQRs5kclpKXnUvqv+coyrpaY/sbAosWLSI+Ph69/vrOGfn5+bRr167c8NGuXbvo0KEDycnJFtUdHx/P33//jaenMY9jxYoVdOrUqc5s//DDD3nyySfLDQ8VFRXRpUsXunbtWuHNsTJnZ+rUqTz11FNmZcnJyUybNo3evXsTHR1N//79mTJlCklJSXWmoyKscYZL+eGHHxg8eDCxsbH06dOHd999t9zUuqrq1Wq1PPLII3z66ad1rkdBQaFukI3ToNPpzPZwV6lUaDSaWs0Xnjt3LmPHjuXOO+8kMjKSN998ExcXF5YvX17h8TNnzuT++++nTZs2tGjRgunTpyOKIjt27LC67cTt25HmXSR/7/mqDxT1UJABhhKr27gZdO3alYKCAg4dOmQq27t3LwEBARw4cMCsf3bt2kVwcDAREREW1a3VagkMDKyXsO/evXtJTk5m8ODB5d7bsGEDkZGRNG/evMpISHUkJSUxbtw4zp49y1tvvUVCQgJffPEFzZs35/3336+N+VVirTMMsGbNGmbOnMnkyZNJSEjgnXfeISEhgY8//tiqeocPH05iYiInTpyoN30KCgo1R1aDSrNmzTJLOiopKeHLL780PYkCTJs2zaK6dDodhw8f5vHHHzeVqVQqevTowf79+y2qo7CwEL1ej7e3t4UKrmNwkkjTZFDorEMURdRqNaIoIkmS6R+AUHwV0o8jOblAQCvT+YIgGI9RqcHp+kYuQklB5cMdggo0ZZK2dPkVH+bsUWEdpjbL0KxZMwIDA9m1axft27cHYPfu3fTv359du3axf/9+unbtairv0qULcD2BKjMzk0mTJvH3338TFBTEyy+/TP/+/QGjk/Hggw+yZ88ejhw5YurbqKgoACZNmsTTTz+NTqfjk08+Ye3atVy9epWWLVvywgsvmNqtyPa1a9fSo0cPnJ2dy2latmwZI0aMQJIkli5dypAhQ8zqKbW/7Hll6y99b9q0aURERPDjjz+aRTNat27NAw88UOsksor6A4zO8JgxYxg9ejQAb7zxBn/++SfLly/nscceq7Cef/75hw4dOjBs2DAAwsLCGDZsGAcOHDC1UVrvnXfeiSRJpnqXLVvGxIkTEQQBLy8v4uLiWLt2LS1btqwzTQ2tvBRJklCr1VUe09Bsr05TdZRqLn2tYF/Ixmno3LkzZ86cMSuLj4/n/PnrT+rWPJFmZWVhMBjw9/c3K/f39+f06dMW1fHRRx/RqFGjGk339G4UAqOgMeFcvHiRpk2bkpKSQklJCYWFhaaVL7XFV2HuECpSJgD65gMovnMBAM7Ozjh9GIlQUlBhm4bw7hTdcz2K4vZFLEJBBU+fb+RgMBjMogQqlQpXV1f0er1ZyF6tVtO1a1d27tzJuHHjAGOuwMMPP4woimzfvp2YmBiKioo4ePAgo0aNQhAEioqMC1XNnj2bZ599lilTprBo0SJefPFF1q5di7e3t1n7bdq04cUXX+Srr75ixYoVuLi44O7uTkFBAW+//TanT5/m3XffJSIigo0bN/LYY4+xZMkSIiIiTGvll9W0Z88ehg4dCmCm6fz58yQmJjJ79mx0Oh0zZszg5MmThIaG4uTkhLOzM2BcEa+gwPg5azQatFotoihiMBgoKCjg2LFjnDx5kpkzZ1JSUmKmxdnZGS8vLwoKzB08V1dXBEHg888/5/vvv7/ez2UclVKWL19OixYtyvWTwWDg8OHDTJgwwWQfGCNC+/fvp6SkhJKS61GrUk0xMTH88ssv7N69m+joaFJTU9myZQu33347BQUFlJSUmOoF4xCOKIp06dKFffv2YTAYcHJyoqioiHbt2rFnzx4KCgpMmsraAuDm5oYkSRQWFprpvLGfqrv2XFxcKtWk0+nMhs1K+6m4uNgsj0Wr1aLRaEyayvaTk5MThYWFFfZTWU2FhYUOpwmq7ie9Xq+sDGmHyMZpWLBgga1NMOObb74hISGB+fPnm35IrCEmJgZBEEhLSzMtUhUSEsLZs2dxdXW9vg1sUdVJgGq12mzYpipUKpXFx1ZWr5OTU7ms6W7duvHuu++i1WopKiri+PHj9OjRA0EQWLRoEW5ubhw8eBCdTkeXLl2QJMn0mY0ePdr0RDxlyhQWLFjAyZMn6dWrl9nn6uXlhZ+fH4IgmA1vXLp0iV9++YXNmzcTFBQEwIQJE/j7779JSEhgypQpFWpKTU0lNDS0nKaEhAR69+6Nt7c3kiRxyy23sG7dOp5++mkzzc7OzuU+H5VKZWqjNM8lPDwcZ2fnCh3ayqbqjR8/nhEjRlT4XilhYWHlNIFxdVSDwUCjRo3Myhs1asSePXvQaDQVLsN+xx13cPXqVSZMmGDKjr/nnnuYNGlSuXoB0/UZFBREcnKy6cnTxcWF4OBgNm7caNZ+RddS6Y/PjVhz7QGVatJqtWi12nLllX1fK9t6ubJ+Kv1BLTuTwBE03ciNmiRJQqfTodFocHV1NXMoFBo+snEa6hpfX1/UanW5cd6MjAwCAgKqPHfOnDl88803zJ07l9atW9eo/dKbbFpaGiEhIYDxR0cQBNM/SgpBLIGH10NQdIVbYguC2nxGxX9OVtqmIKjMj32u8mS8yqI2FZV36dLFlNeQm5tL06ZN8ff3p3PnzkybNg2dTsfu3bsJDw83fbal9URFRZleu7m54eHhQWZm5vXPoEy7pX+XLT9x4gQGg8FsCAGMw08+Pj7l6iilqKjIdKMtLTcYDKxatYpXXnnFVD5ixAg++OADJk+ebDYt9kb7ytZTtlyv11d4bGWfJRivTV9f3wrfq66OsjZY0+bu3bv55ptveP3114mNjSU5OZl33nmH//3vf0yaNKmctqraLY041DQXxZprz5blpZSUlKDRaKo8rqHZXts8IUs0KzRMFKehhmi1Wtq1a8eOHTsYOHAggCmpsTTMXhHffvstX331FXPmzCEmJqbG7R/ZsRfD1itkeedBXFzFB5Uu6OQRaPlKkFoLd8y09tgqaNKkCcHBwezatYucnBw6d+4MGJ9CQ0JC+Oeff9i1axfdunUrd+6NT1OCIFi18VhBQQFqtZrly5eXmwVRVVTF19e33FTOv//+m7S0NJ5//nmzcoPBwI4dO+jZsyfw/+2dd1hUR9vG76U3lSI2RMFCkbqAEhEkWFBRiJqY2GNXjBpsYIkNC2hUEDWCJRYksdBEMeazK7aIihULglJDkV53Yef7g5cTl92FpUjb+V2Xl+ycOXOe+9RnnmmAsrIyCgoER73k5+cz/Wt0dHQAAB8+fICFhYXYegDA398fAQEBNeaJjIxkIiWfU19neM+ePXBxccGECRMAVDpzxcXFzLTtdSk3Pz8f6urqNdpPoVCaB+o0NICZM2fCw8MDxsbGMDU1xfHjx1FSUsKEy93d3dG5c2csX74cQGWThJ+fH3bt2gUtLS1kZmYCqPw4KSvX7QOcnZOF3nntkcGqYRrpqvUmWsEskNbW1vjnn3+Ql5fHtHsDgJWVFW7duoVnz55h4sSJDTqGrKyswJwKhoaGqKioQHZ2dp2GY/br1w9xcfxRmeDgYIwePRoLFizgS/f390dwcDDjNOjq6uLly5cYN24ck6eiogKvX79mPrqGhobo06cPAgMDMXbsWAGHJj8/H+3bC7+uEydOFIicVKeqmaA69XWGS0tLBSYY+7yzW13KjYuLg6GhYY32UyiU5oE6DQ3AyckJ2dnZ8PPzQ2ZmJgwNDXH48GGm5pSWlsb3Ij116hS4XC6WLFnCV86iRYsE2rxro4tyFwDF0FQUMaSQ8CpngARaxaqW1tbW8PT0RHl5OTNCAqhsuvD09ASXy4W1tXWDZpHT0tJCcXEx7t27B319fSgqKkJXVxfOzs5wd3fHqlWrYGhoiJycHCbP119/LbQsW1tbhIWFMb+zs7Nx/fp1/Pbbb9DT0+PL+80332DRokXIzc2FqqoqZs6cibVr10JXVxeDBg1CSUkJAgMDkZ+fzzgNLBYL27Ztw8yZMzFlyhS4urqiV69eKCoqwvXr13Hnzh2cPHlSqG2qqqpQVVWt93mqzRkGBB1iBwcHHD16FP369WOaJ/bs2QMHBwfGeRCnXACIiYnBzz//XG/7WxuSODOiJGpuK9Ar10CmTp0qsgZWvfPltWvXGu24XZS7oADx0FbrIXwKaZYU0LkfwCnkG1LZUrG2tkZpaSl69erFF67u378/ioqKoKury3RUrC8WFhaYOHEi3NzckJubyzhrXl5eOHDgALy9vZGRkQFVVVWYm5uLdBiAyvkEfv31V8THx6NXr14IDw+HoqKi0OXVBw4cCAUFBURERGD69OkYM2YMCCE4evQodu3aBUVFRRgZGeHkyZN82s3MzBASEgJ/f3/88ssvyMnJQadOncBms7FmzZoGnYuaqM0ZBgQdYldXV7BYLPj6+iI9PR3q6upwcHDga6oRp9yYmBgUFhZi5MiRX0xfS4LFYtWrI3Rr5nPNdMhl64NFJPCqRUdH49SpU0hKSoKfnx86d+6M8PBwdO/evVFnDPwSVFRUICYmBn3Ku6AgLB48HUV0n2cBKSkplJaWIiEhAbq6uiJ7Pbdmqnpdy8nJtYgOVNu3b0dRURE8PT2/SPktTW9T4Obmhj59+vB1nmzLSOI1/lwzUNmvSElJ6Yvob+vvxOZAYmaErOLvv//G7NmzoaCggFevXjFjnAsLC2vtPNaS4PEq2+ZLy0okylv/fIx5c+Pq6opu3brVqeNlXWlJer80HA4Henp6mDRpUnOb0qRI0jWuQhI1txUkzmk4cOAANm3ahC1btvC1q1lYWODVq1fNaFndeJZVOdzxQ8kHwY3lHCDrHVCYXrksNuWL0L59eyxYsKDmFUYpYiMnJwdXV1daI6RQWjAS97ZLSEgQ2gTRrl27L7YaYpPDKarsy1CS2+pXtaRQKBRKy0HinIaOHTsKXSXx0aNH0NbWbgaL6odZp8p1GgzVDAXbAqvWhGgFQy3rirBZ7toykqYXkDzNkqYXkEzNbQWJcxq+//57bN26FU+fPmWmYY6IiMD27dtbVVsq63+rSSgqKvGHxwn5z2lQaFtOA4vFkqgOY5KmF5A8zZKmF5BMzW0JiRtyOW/ePPB4PMyYMQMlJSWYOnUq5OTkMGvWLEybNq25zaszRYWFUKuo+G/ynwoOAB7AkgFkxVsnorVACEFZWZnItRjaGpKmF5A8zZKmF+DXTGl9SJzTwGKx4OrqitmzZyMxMRHFxcXo3bt3nWdkbG5SC1PRDsC/Jeno/vmGCg4AmcopntvgS6j6jI5tHUnTC0ieZknTC0im5raCxDkNVcjJyaFPnz7NbUa9ySjOQDu0Rx43l38DrwKATKuY0IlCoVAorQuJcxqmTZtWYxjwxIkTTWhN/dHo0QWxmXEoEnoFpQEZweVvKRQKhUJpCBLXEdLQ0BAGBgbMvz59+oDL5eLVq1cCawa0ZAyNzTF0xgTYOY3id4IUVYFO+oBSzctztzaSk5NhYGCA+Pj4RitTX18fV65cabTyGpuSkhK4u7vD0tIS+vr6IocEJycnQ19fH7GxsU1s4ZehaqbAxmDVqlVYuHBho5XXEFauXAl/f3+B9MbU21poDM23bt3CN99880UnV6MIgVAIIYT4+fkRb2/v5jajVsrLy0l0dDQpLy8X2FZSUkJevXpFSkpKmsGy+uPh4UH09PSYfwMGDCCzZs0isbGxTJ6kpCSip6dHXr161WjH1dPTI5cvX2608urD+fPniYGBAdm4caPAtqCgIPLVV1+RN2/ekIyMDMLj8YSWUV5eTjIyMgiXy/3S5opFTZqamvz8fJKXl9fcZpDY2FgyYMAAUlhYKLCtpvMVEhJCLC0thZYp7P69dOkSmTp1KrGwsCDm5uZkzJgxZO/evSQnJ6dRdAgjJyeHLFu2jLDZbGJpaUlWr14tVOfnZGRkkBUrVhAbGxtiampKxo4dSy5dusRsT0pKIqtXryYODg7ExMSEDB06lOzZs4eUlZXxlTN+/HgSFhYm8jit9Z3YkpG4SIMoXFxcEBIS0txmiA23uAyF6bl49s+TNtGpyM7ODlFRUYiKisKxY8cgIyMjsMQ0UDmXPGlFs1xyudwatwcHB2POnDmIjIxEWVkZ37bExETo6uqib9++0NQUvpoph8OBtLQ0NDU1W8zKgTVpqg1CCEpKGj41ekVFBXg8Htq1aydyCfGmJDAwECNGjBDocE0IwZkzZzB79ux6na/P8fHxwdKlS2FsbIxDhw7h/PnzWLVqFd68eYNz5841VIJIVqxYgbi4OBw9ehT+/v6Ijo7G+vXrReYnhGDFihVISEjAb7/9hjNnzmD48OFwc3NjZuWNj48HIQSenp6IjIzE6tWrcerUKfj4+PCVNX78eIGFASlfmOb0WFoSYWFhZNCgQc1tRq1URRpuRFwgSR63yN9eJ5ioQ0lCNHl1/yopSY/j26eIU0SKOEV8NVVOOYcUcYpIWXmZ0LwVvIr/8lZU5i0tLxUrb13x8PAgrq6ufGkPHz4kenp65NOnT4SQ/yINjx49Ijwej5SXl/PVRBwdHcmxY8cEyj579ixxcnIiRkZGZNCgQWTTpk3Mtuo1tT179pBBgwbxRTiqExQURIYOHUqMjIyIo6OjQC1HT0+PBAUFkfnz5xMzMzPi5+cnsqzExERiampK8vPzyYQJE0hERASzberUqXzRl6lTpxJCCHFwcCD79u0jK1euJGw2m3h4eAiNwrx9+5bMmzePsNlsYm5uTiZNmkQ+fvxICCHk6dOnZMaMGWTAgAHEwsKCTJkyhbx48UJAx5kzZ8jChQuJqakpGT58OLly5YpILeJoIuS/mvO1a9eIo6MjMTU1JYsXLybFxcUkNDSUfP3118TS0pJ4enryRdPKysqIt7c3sbW1JWZmZuS7774j9+/fFyj3ypUrZNSoUcTQ0JAkJSUJ3FsVFRXk4MGDZNiwYcTIyIjY29uT3377jdm+Y8cOxq4hQ4YQHx8fwuH8d0/7+fkRFxcXEhYWRhwcHIiFhQVxc3MjBQUFIs9JeXk5sbS0JNevXxd5vvLy8mo8X8L4/P59+vQp0dPTE/oMEEK+WLQlLi6O6OnpkWfPnjFpN2/eJPr6+uTff/8Vug+PxyNmZmYkLCyM8Hg8UlhYSHg8HhkwYAA5c+aMyGMdOnSIDBkyhC8tJSWF6OnpMfd2dWikofFpGVWTJmTRokV8vwkhyMzMxIsXL1pM26c4ECmghFWKcqnPogy5HwGeMlDBX7u1/sMaAHDzh5tQV1AHABx9eRR7n+zFt32/xUabjUzer898jZLyElz69hK0VLQAAKden8KOhzvgpOuE7YO3M3lHhoxETlkOwlzC0EetciTKubhz+E7vuwZpKyoqQkREBHr27AlVVVWheXg8Hrp06YI9e/ZAVVUVT548wfr166GpqQknJycAwB9//AFvb28sX74cgwcPRkFBAR4/fixQFiEEW7ZswfXr1xEUFISePXsKPebly5exbds2rF69GjY2Nrhx4wbWrFmDLl264KuvvmLy7du3D8uXL8fatWv/mz9DCKGhobC3t0e7du3g4uKC4OBgODs7AwD27t2LnTt34s2bN9i/fz9fG/Dvv/+On376SeBeriI9PR1Tp07FgAEDcPz4caioqODx48fMIkFFRUUYO3YsfvnlF6a8efPm4e+//4aKigqfjpUrV8Ld3R2BgYFYsWIFrl+/LvKa1KapitLSUgQGBsLHxwdFRUVYtGgRFi1ahHbt2uHgwYOIi4vDypUrYWlpyVxLT09PxMXFwcfHB506dcLly5cxZ84cnD9/Hjo6Oky5hw4dwpYtW6CqqgoNDQ0B+3bt2oWzZ89i9erVsLS0REZGBhISEpjtysrK8PLyQqdOnfD27VusW7cOysrKmDt3LpMnMTERV69ehb+/P/Lz8+Hm5oZDhw7xLQP+OW/evEFBQQGMjY2Fni9bW9saz5c4REREQElJCZMnTxa6vaZoy+jRo5Gamipyu6WlJQ4fPix025MnT9C+fXuYmJgwaTY2NpCSksKzZ88wfPhwofuZmZnh4sWLsLe3h4yMDBNlGTBggEg7CgoK0KFDB760bt26oWPHjoiOjkaPHj1E7ktpPCTOaWjXrh3fbxaLBV1dXSxZsgS2trbNZFXd+Wr4UJQOKUXmi8/CnfnJgKw+IN36OlbduHEDbDYbQOVSuZqamggICBC5GJSsrCyWLFnC/NbW1kZMTAwuXbrEfGgOHDiAmTNn4scff2TymZqa8pVTXl6OFStWIDY2Fn/++Sc6d+4s0sYjR45g3LhxmDJlCgBAV1cXMTEx+P333/mchjFjxuDbb7+tUS+Px0NYWBjz4XZycoK3tzeSkpKgra0NVVVVKCoqQlZWVqBp4quvvsKsWbOY38nJyXxlBwUFQUVFBbt372am69XV1WW2Dxw4kC//5s2bYWVlhYcPH8LBwYFJHzduHMaMGQMAWLZsGQIDA/Hs2TMMHjy4Xpqq4HK52LhxI/OSHzFiBCIiInDnzh0oKSmhW7dusLa2xv379+Hk5ITU1FSEhobi+vXrzPWZPXs2bt++jdDQUCxbtoyvXAMDA6H2FRYW4sSJE1i/fj3GjRsHAOjRowffWjSfVxy6d++OhIQEREZG8jkNhBB4eXkxDpaLiwvu3bsn0mlITU2FtLS0gBNTdb5WrlxZ4/kSh48fP0JbW7te0zMfPHiwxlUna1pALCsrC+rq6nxpMjIy6NChAzIzM0Xut337dqxZswZfffUVZGRkoKCggH379ol02D9+/IiTJ0/Cw8NDYFunTp1qdHoojYtEOQ0VFRUYP3489PT0BDzW1oastCzkZeVhpGf034c1LwXoqA9I8784Hkx+AABQlFFk0mYazcRUw6mQkeK/BW58fwMAoPDZPA8TDSbi277fQlqKv9Z86dtLAnm/6fNNvfRYW1tj48aNlTLy8vDnn39i7ty5OHv2LLS0tJh8n78Ug4KCEBISgtTUVJSVlYHL5TIfjE+fPiEjI0PgA1kdLy8vyMnJ4fTp0wIvv+rEx8fjhx9+4EuzsLAQGKYrrEZZnTt37qCkpAT29vYAAHV1dQwaNAghISFwc3Nj8glzmmorPzY2FlZWViI/IFlZWfD19cU///yDT58+gcfjoaSkRODFq6+vz/ytpKQEFRUVZGdnN1iToqIiX62wY8eO0NLSgrKyMgghkJeXh4aGBnOst2/foqKiAiNHjuQ7HofD4Yt6yMrK8tlcnfj4eHA4HD4HrzoXL17EiRMnkJSUhOLiYpSXl/NFXwBAS0uLL61Tp0749OmTyDJLS0uFTpt8584dFBcXY8iQIQBEny9xIA3oA/L589VUBAQEoKCgAEePHoWioiKioqLg5uaGoKAggWuYnp6OOXPmYOTIkfj+++8FypKXl0dJSUlTmS7xSJTTIC0tjVmzZuHixYut3mkAKqMkfDryUoCOEIg0KAmZTlpWWhay0oIfFaF5pWQhKyV+3vqgqKjIV8swMjKClZUVzpw5w1eDk5GRAYvFQmRkJLZv3w4PDw+w2WwoKyvjyJEjePr0KQCIPUWtjY0NIiMjERUVBRcXl3rZXh0lpdqn7w4ODkZubi7MzMyYNB6Phzdv3mDJkiWMs8BisQQ+NoqKiqiJ2paW9vDwQG5uLtauXYtu3bpBTk4OP/zwg0CnzepOB4vFqnF4m7iaqnfYZLFYTFrV31JSUsyxiouLIS0tjZCQEIHmns/PtYKCQo1zsNR2Tzx58gQrVqzA4sWLmSaDyMhIHD16lC+fsA6nNX201dTUUFJSAg6Hw9fMFBwcjLy8PFhaWjJp1c+XiooKSkpKwOPx+BzIquG3Vc6Ljo4OHj16BC6XW+doQ0OaJzp27CjgSJaXlyMvLw+amppC90lKSsIff/yBCxcuoE+fPiguLoa5uTkePXqEoKAgeHp6MnnT09Mxffp0sNlsbN68WWh5eXl5tTr8lMZDopwGAOjbty+Sk5Nb1YqWwnj36CkK76YhV7kEDrPGVb5M81MqN9bzw92SqPpYVu9NXjV64vHjx2Cz2UxTAQC+1UtVVFSgpaWFe/fu1VizHDp0KIYMGYLly5dDWloao0ePFpm3V69eePz4MRPaBoDHjx/XeWbRnJwcXL16FT4+Pnz78ng8TJo0CVFRUUwTQEVFBQghdVqXQF9fH2FhYSI/II8fP8aGDRuYiEBaWhpycnLqpKEhmmqCCBk9YWhoiIqKCmRnZwtd1l5cdHR0oKCggPv37wt9/p88eYJu3brB1dWVSWuMsLehoSEA4P3798zfVedr9+7d0NbWZtaeqH6+dHV1UV5ejtjYWBgZGTFlvnz5EsB/zU7Ozs4IDAzEH3/8wdccV0V+fr7Ifg0NaZ5gs9nIz8/HixcvmAjY/fv3wePxBJoCqyguLgYAgXtaWlqa77pXOQxGRkbw8vISGnUrKytDUlIS+vXrJ9JGSuMicU6Dm5sbtm/fjp9//hlGRkYCtcLqociWSnp6Gnr/2x7Zqv/z8jnFQPH/2hBbYZ8GDofDtIHm5+fj5MmTKC4u5mtjB/6r0fXs2RPh4eG4ffs2unfvjnPnzuH58+fo3v2/lTgWL16MDRs2QENDA4MHD0ZRUREeP34ssDDZ8OHDsWPHDri7u0NaWlogDF7FnDlz4ObmBkNDQ9jY2OD69eu4fPmyQE20Ns6dOwdVVVWMGjVK4MVpb2+P4OBgsT6wopgyZQoCAwOxbNkyzJs3D+3atUNMTAxMTU3Rq1cv6OjoICIiAiYmJigsLMSOHTtqjU40pabqtXZdXV04OzvD3d0dq1atgqGhIXJycnDv3j3o6+vj66+/FqtceXl5zJ07F7/++itkZWVhYWGB7OxsvHv3DhMmTEDPnj2RlpaGyMhImJiY4MaNG40y+Ze6ujqMjIzw6NEjxmn4/HyVlJRASUmJOW+fn6++ffvC1tYWa9aswapVq5h+Ftu2bYOTkxPTx8PMzAxz5szB9u3bkZ6ejuHDh6NTp05ITEzEn3/+CUtLS6HOBNCw5onevXvDzs4O69atw6ZNm8DlcrF582aMHj2asS09PR0//vgjduzYwdyD2tra2LBhA9zd3SEvL487d+7gzp07CAgIYPaZNm0aunXrBg8PD75oxucRjJiYGMjKysLc3LzeGih1Q2Kchn379mHWrFmYN28eAMDV1ZXv5VZVm2sts+ppKmoCKEN7mf81T5TkAJoGgJQMIN36Luvt27eZjqjKysro1asX9uzZA2tra6H5J06ciNjYWCxduhQsFgujR4/G5MmTcevWLSbPuHHjUFZWhmPHjmHHjh1QVVUV6RCMHDkSPB4P7u7ukJKSgqOjo0CeYcOGYc2aNfj999+xbds2aGlpYdu2bSJtFEVISAiGDx8uNHrg6OgId3f3GvsO1IaamhqOHz+OX3/9FdOmTYOUlBQMDQ2ZMPjWrVuxbt06jBs3Dl27dsXSpUuxY8eOeh8P+PKavLy8cODAAXh7eyMjIwOqqqowNzcX22GoYuHChZCWloafnx8yMjKgqamJiRMnAqiMOv3444/w9PQEh8PB119/DVdXV+zbt6/edlfx3Xff4dy5c5g6dSoA8c+Xuro6fHx84Ofnh/Xr1yMjIwNdunTBsGHDBEZ7rVy5EkZGRvjjjz9w6tQpEEKgra2NESNG8EXHGpudO3di8+bN+PHHH5lnp6ozLFDZQTUhIYHpdyArK4u9e/di//79cHV1RVFREXr27Alvb28m+nXnzh18/PgRHz9+FHA237x5w/wdGRkJZ2fnWpvsKI0HizSkB00rwtDQEFFRUXj//n2N+Woa8tMSqKioQExMDPqUd0FBWDxKu0lD9ydrSEtLo7S0FAkJCdDV1W1wzbElQghBcXExX62sLSNpeoG2q7m0tBQjR46Ej48PM0oIaLt6a+JzzQDqrT87OxujRo1CcHCwyObmtv5ObA5aX5W0nlT5Ri3dKagr7dq1EzkssS0iaTUKSdMLtE3NCgoK2L59u9C+I21Rb200huaUlBRs2LCh1fdPa21IjNMACHa8aQtIksMAtM1rWBOSphdou5pFNWO1Vb010RiaTUxM+CaVojQNEuU0jBgxotab9Z9//mkiaxrG04yn6IV2ePLvEwzlGUA6+EegIA8Y4Fn7zq2Yz8OakoCk6QUkT7Ok6QUkU3NbQaKchsWLFwvMCNlaIfhfV5SqLikZr4GyUkDyKi0UCoVCaSIkymkYPXq00PnoWyPGHY1QgiT0VOwJEF7luhOKnStHT1AoFAqF8gWQmAbxttZuKPO/CZykWTJAfipQwQFYMgBL9AJJFAqFQqE0BIlxGtrqyNIOHTpAKu9/MyG27wq0MeeoOpLWDippegHJ0yxpegHJ1NxWkJhY9uvXr5vbhEYloygdiv/7v2PO/1Y5bN/0C880NXWdUrm1I2l6AcnTLGl6AcnU3FaQmEhDWyO1KA0AkJKfApL9oTKxfXfRO7QRJG01O0nTC0ieZknTC0im5rYCdRpaKR26aOB192QUafIAJXWgiwmg0au5zWoWkpOToa+v32RTgE+ZMgXnz59vkmNJArdu3cI333xT4wqaFAqlZUCdhlaKiUV/OLh+jx7mxiDWrsCCKMBUcK351sSTJ09gaGjIrA8iLl27dkVUVBT69u37hSz7j6tXryIrK0voapgBAQEwNDQUuozw3r178c033wikC3N4CCE4ffo0vv/+ewwaNAj9+/fH+PHjcezYsS9aQ0tNTcW8efNgZmaGgQMHYvv27TWufggACQkJcHV1hbW1NSwsLDBp0iTcv3+fL8+9e/cwceJEsNlsDBo0CL/++itfuYMHD4aMjAwiIiK+iC4KhdJ4UKehldOWZoQMDg7G1KlT8fDhQ6SnpwvNI6wdVFpaGpqampCR+fJddAIDA/Htt98KPe8hISGYM2cOQkJCGnSMlStXYtu2bRgyZAgOHTqEsLAwLFy4EFevXsWdO3caVLYoKioqMH/+fHC5XJw6dQre3t4ICwuDn59fjfstWLAAFRUVOH78OEJDQ2FgYIAFCxYwK5a+fv0ac+fOha2tLcLDw+Hj44Nr165h165dfOWMHz8egYGBANreSKfakDS9gGRqbjMQSquivLycREdHE24Zh1SUlhNeKYeQigpCCCElJSXk1atXpKSkhG+firLyOv/jlfOY/XnlvMo0TrlY5daHwsJCYm5uTt6/f0/c3NzIgQMH+Lbn5uaSZcuWEWtra2JiYkKGDx9OgoODCSGEJCUlET09PfLq1SvmHK1evZo4ODgQExMT4ujoSI4dO8ZXnoeHB3F1dSWHDx8mgwYNIgMGDCAbN24kHA5HpI2fPn0i+vr65O3btwLbHjx4QOzs7AiHwyG2trbk0aNHfNv9/PyIi4uLwH7VbY+MjCR6enrk8uXLAnl5PB7Jz88XaV9DuHHjBjEwMCCZmZlM2h9//EEsLCxIWVmZ0H0+ffpE9PT0yMOHD5m0goICoqenR+7cuUMIIWTXrl1k/PjxfPtdvXqVmJiYkIKCAiYtJSWF6OnpkY8fPzamLEoLh8fjkcLCQsLj8WrPXA9EvRMp9UdiRk+0Ne7/3xXoRKkgTuMj7MuWg6VlCUwKFZo3df3dOpevPtkASqaV69aXvMxC9h+vIafbAZ3mmzJ5/t3+D3hFguHr7t52dT7eX3/9hV69eqFXr15wcXHBtm3bMH/+fKZGsmfPHrx//x7+/v7Q0NBAUlISSktLhZbF4/HQpUsX7NmzB6qqqnjy5AnWr18PTU1NODk5MfkePHgATU1NHD9+HImJiVi6dCkMDQ3x/ffCm3kePXoERUVF9O7dW2BbcHAwRo8eDVlZWYwZMwbBwcGwsLCo83k4f/48dHV1MWzYMBBCUFFRAWlpabBYLLBYrBpnNP189URhODs7w9NT+DTjMTEx0NPTQ8eOHZk0W1tbbNy4EXFxcejXr5/APmpqatDV1UV4eDj69esHOTk5nD59GhoaGjAyMgIAcDgcyMvL8+2noKCAsrIyvHz5klmPoVu3bujYsSMePnyIbt26MZrbOtWvsSTwuWZK64M6Da2UClLZaYzLLQOrvLRycqdWTHBwMFxcXAAAdnZ2KCgowD///MN8VFJTU2FoaIi+fftCSUmpxpXtZGVlsWTJEua3trY2YmJicOnSJT6noUOHDli/fj2kpaXRu3dv2Nvb4969eyKdhpSUFGhoaAg0TRQWFuLvv//G6dOnAQAuLi6YPHky1q5dC2Vl5Tqdh48fP0JXV5f5XVZWJvaY9vDw8Bq3q6ioiNyWlZXF5zAAYH5XNTVUh8Vi4dixY1i4cCEsLCwgJSUFdXV1HD58GB06dABQ6XgcP34cFy5cwKhRo5CVlYX9+/cLLbdTp05ITU2tk+a2gKTpBSRTc1uBOg2tFFMHG+R+lYPOl4OBtwDUdEXm7eZpU+fyWdL/fRgVjTqim6eNwLxRXTwaZ5nx+Ph4PH/+nPmYyMjIwMnJCcHBwYzTMGnSJCxZsgQvXryAnZ0dhg0bVmNNPigoCCEhIcxHiMvlwsDAgC9Pnz59+Go7mpqaePv2rcgyy8rKBGrNAHDhwgX06NGDKd/Q0BBaWlq4ePEiJkyYIP6JQMMmIevZs2e9960PhBBs2rQJGhoaCAoKgoKCAs6ePYsFCxYgODgYnTp1gq2tLdzd3bFhwwa4u7tDTk4OCxcuRHR0tIDzJS8vLzJ6RKFQWgbUaWiltFdsj/aK7ZFd9qkyQU1HZF4puYaFAVnSLLCEhBIbWm4VwcHBKC8vh53df80ahBDIyclh/fr1aNeuHezt7XHt2jVcvnwZ0dHRmDFjBqZMmQIPDw+B8iIjI7F9+3Z4eHiAzWZDWVkZR44cwdOnT/nyVe84yWKxavxoq6mpIS8vT6j979694wvh83g8hISEME6DiooKCgoKBPatSquKAujo6CA+Pl6kDTXRkOaJjh074tmzZ3xpWVlZACqdKWHcv38fN27cwMOHDxn7jYyMcPfuXYSHhzOjYGbOnIkZM2YgIyMDHTp0QEpKCnbt2oXu3fnnFcnLy4OamlrtQikUSrNBnYZWjmLJv5V/qIuONLRkysvLce7cOaxatQqDBg3i2/bTTz/hwoULmDRpEgBAXV0d33zzDX744QdYWVlhx44dQp2Gx48fg81mY8qUKUxaYmJig201NDREVlYW8vLymPD7mzdv8OLFCwQGBjJpQOUHcNq0aXj//j169+4NXV1dpKenCzQDvHz5EvLy8ujWrRuAyg/70qVLceXKFQwdOpSvNk4IQWFhoch+DQ1pnjA3N4e/vz8+ffrELOp29+5dqKiooE+fPkL3qRr+Wb0tnsViCcy5wGKx0LlzZwCVkZmuXbsy/R6AyihOUlIS+vXr16ZGBImDpOkFJFNzW4E6Da2UhGevkBedAun8fjDHwxojDS2ZGzduIC8vD999953Ax9DR0RHBwcGYNGkS9uzZAyMjI/Tt2xccDgc3btwQ2iERqAzTh4eH4/bt2+jevTvOnTuH58+fC9Rs60q/fv2gpqaGx48fw8HBAUBllMHU1BT9+/cXyG9iYoLg4GB4eHjA1tYWurq6WLZsGdzc3KCpqYmXL1/C19cX06dPZ5pJRo0ahcuXL2P58uVwdXXFoEGDoK6ujrdv3+LYsWOYNm0ahg0bJlJ3fbG1tUWfPn3g7u6OlStXIjMzE76+vpgyZQrk5OQAAM+ePYO7uzuOHz+Ozp07w9zcHO3bt8eqVavw008/QV5eHmfOnEFKSgq+/vprpuzDhw/Dzs4OUlJS+L//+z8cOnQIvr6+fE1DMTExkJWVBZvNhqKiYr11tDZYLJZE6QX4NTekOY7SPFCnoZWSkpyI3u/b453i/z6cNfRpaMkEBwfDxsZGaO15xIgROHz4MF6/fg1ZWVns3r0bKSkpkJeXh5WVFXbv3i20zIkTJyI2NhZLly4Fi8XC6NGjMXnyZNy6datBtkpLS2P8+PE4f/48HBwcwOFwEBERgblz5wrN7+joiKNHj2LZsmWQlZXFkSNH4OPjg+XLlyM7Oxvdu3fH9OnTMXPmTGYfFouFXbt24fTp0wgJCYG/vz+kpaXRs2dPjB07Fra2tg3SUJM2f39/bNy4ET/88AMUFRUxbtw4vg6lJSUlSEhIAJfLBQCm06Ovry9+/PFHcLlc9O3bF/v37+frP3Lr1i34+/uDw+HAwMAA+/fvh729Pd/xIyMj4ezsDAUFBXC5XMjIyEjEaAJCCMrLyyVGL8CvmdL6YBHq6rUqKioqEBMTA5nsUmhc5SFN+RWsOp8Ca85VlHI4SEhIgK6uLhQUFJrb1EaHEILi4mIoKSk12ws2MzMTY8aMQWhoKLS0vuwCYS1Bb1OQnZ2NUaNGITg4GN27d5cIzVVIyjX+nM81A/ii+ktLS9v0O7E5oA1LrRSd9joAALUOJuDNvgLQNsImQVNTE1u3bkVaWlpzm9JmSElJwYYNG2ocRkuhUFoGND5EodQRUX0KKPXDxMQEJiYmzW0GhUIRA1o9beXISli7oKTNIidpegHJ0yxpegHJ1NxWoE5DK+Vl1ksAQGLmPUg/P9PM1jQNLBYLCgoKEtP2K2l6AcnTLGl6AcnU3JagTkMrhcur7MFeDoAnw9/Bp632bSWEgMPhtFl91ZE0vYDkaZY0vUDTapak89pUUKehlWKgVjmkrS+HA9KuC4D/Qn4cTuteh6Imqob7SQqSpheQPM2SphdoOs1V70LaHNJ4SFaDeBtCQVYBXAAKhAAqXQFUTouspKSEzMxMyMrKtrlZ1wghKCsrg5SUlESENiVNLyB5miVNL8CvGcAX08/j8ZCZmQklJSU6J0QjQs9kAwkKCsKRI0eQmZkJAwMDrFu3DqampiLz//XXX9izZw9SUlKgo6ODFStWCEx0Ixac4v/+blc5PS+LxULXrl2RkJCAjx8/1r3MFg4hBFwuF7KyshLxgpU0vYDkaZY0vQC/ZgBfVL+UlBR69OghMee2KaBOQwO4ePEivLy8sGnTJpiZmeH48eOYPXs2Ll26xMzf/zmPHz/G8uXLsWzZMjg4OOD8+fP46aefEBoaCj09vTod+1N+IuQgh2wZeajL/LfyopycHDPVcluDx+MhLS0NXbt2bXNRFGFIml5A8jRLml6AXzMhBK9fvxZYcbaxkJOTk5jz2lTQGSEbwIQJE2BiYoL169cDqHwY7O3tMW3aNGaFv89xc3NDSUkJAgICmLTvv/8eBgYGIlcfrE7VjJCFb2PQ+6ke3ik/g8O6nxpHEIVCoTQhVe8zc3Nz2u+glUAjDfWEw+Hg5cuXmD9/PpMmJSUFGxsbPHnyROg+MTExmDFjBl+ara0trly5UufjK3VQwjuVWHCUc5l53Hk8Hl9vYRaLBSkpKVRUVPDtKyq9ql1RWDoAgZULRaVLS0uDECI0vbqNotKrbPw8ncfjITk5GT179hQoo7Vqqsl2QgiSk5OhpaXFV1tqzZpqu05cLhdJSUnQ1taGlJRUm9BU03Xi8XiM3qp299auqbZ0Ho+HlJQUaGtrC5RDaflQp6Ge5OTkoKKiQqAZQkNDA/Hx8UL3qb4sclX+rKysOh9fqrMe2o+q/DspKQm6urpITEzEp0+fmDxdu3ZFt27dEB8fj/z8fCa9Z8+e6NixI16/fo3S0lImvU+fPujQoQOePXvG95D369cPcnJyiImJ4bPB3NwcHA4Hr169+s8uKSmw2Wzk5+cjLi6OSVdQUICRkRGys7P5+lu0b98effv2xb///ss3NbOGhgZ0dHQENAFAjx492pQmUddJW1sbnz59QlFRUZvRVNt1evHiBXg8HnJyctqMJnGuU05OTpvTBIi+TjweDx06dBD5rqS0XGjzRD1JT0/H4MGDcerUKbDZbCZ9x44dePjwIc6ePSuwj7GxMby9vTFmzBgmLSgoCPv378fdu3fFOm5VOK9q2t3nz5/D1NQUsrKyLaIWAXy5mlFFRQWeP38Oc3NzgfPSWjXVZDuPx8OzZ89gYmLCF7ptzZpqu04cDgfPnz9nNLcFTTVdp6p72sTEhOkY2No11ZZepdnMzAw8Ho95pmnzROuARhrqiZqaGqSlpQVqwZ8+fRKIJlTRsWNHgahCTfmFIczHI4QIDfPVNb36A1/fdFEhx7qki7KxKn/1F0xr1lRTel1sbC2a2uJ1oprqll59G627th6o01BP5OTkYGRkhHv37jELGPF4PNy7dw9Tp04Vuo+5uTnu37/P16/h7t27QmvOoqh60J4/f86kff63JED1tn0kTbOk6QX4NdfkXFBaFtRpaAAzZ86Eh4cHjI2NYWpqiuPHj6OkpATjx48HALi7u6Nz585Yvnw5AGD69OmYNm0afv/9d9jb2+PixYt48eKF2CMngMoJnExMTCRqMhgKhdI2qWp2oZMvtR7olWoATk5OyM7Ohp+fHzIzM2FoaIjDhw8zzQ1paWl8vd4tLCywc+dO+Pr6Yvfu3dDR0cH+/fvrNEeDlJQU5OTkGl0LhUKhUCi1QTtCUigUCoVCEQs6VRaFQqFQKBSxoE4DhUKhUCgUsaBOA4VCoVAoFLGgTgOFQqFQKBSxoE4DhUKhUCgUsaBOA4VCoVAoFLGgTgOFQqFQKBSxoE5DCycoKAhDhgyBiYkJJkyYgGfPntWY/6+//sLIkSNhYmICZ2dn3Lx5s4ksbRzqovfMmTOYPHky+vfvj/79+2PGjBm1np+WRl2vbxWRkZHQ19fHwoULv7CFjU9dNefn52PTpk2wtbWFsbExRowY0aru67rqPXbsGEaMGAFTU1PY29tj27ZtKCsrayJrG8bDhw+xYMEC2NraQl9fH1euXKl1nwcPHmDcuHEwNjbG8OHDERoa2gSWUuoNobRYIiMjiZGREQkODibv3r0jv/zyC7GysiJZWVlC8z969IgYGhqSQ4cOkbi4OOLj40OMjIzImzdvmtjy+lFXvcuWLSMnT54kr169InFxcWTVqlXE0tKS/Pvvv01sef2oq94qkpKSiJ2dHZk8eTJxdXVtImsbh7pqLisrI+PHjydz584l0dHRJCkpiTx48IDExsY2seX1o656IyIiiLGxMYmIiCBJSUnk9u3bZNCgQWTbtm1NbHn9uHHjBtm9ezf5v//7P6Knp0cuX75cY/7ExERiZmZGvLy8SFxcHAkMDCSGhobk1q1bTWQxpa5Qp6EF891335FNmzYxvysqKoitrS0JCAgQmv/nn38m8+bN40ubMGECWbdu3Re1s7Goq97qlJeXEzabTcLCwr6QhY1LffSWl5eTH374gZw5c4Z4eHi0Oqehrpr/+OMPMnToUMLhcJrKxEalrno3bdpEpk+fzpfm5eVFJk6c+EXt/BKI4zTs2LGDjB49mi/Nzc2NzJo160uaRmkAtHmihcLhcPDy5UvY2NgwaVJSUrCxscGTJ0+E7hMTE4OBAwfypdna2iImJuZLmtoo1EdvdUpKSlBeXo4OHTp8KTMbjfrq3b9/PzQ0NDBhwoSmMLNRqY/ma9euwdzcHJ6enrCxscGYMWPg7+8vclnolkR99LLZbLx8+ZJpwkhKSsLNmzdhb2/fJDY3Na35nSWp0AWrWig5OTmoqKiAhoYGX7qGhgbi4+OF7pOVlcUslvV5/qysrC9mZ2NRH73V2blzJzp16sT3km6p1EdvdHQ0goODER4e3gQWNj710ZyUlIT79+/D2dkZBw8eRGJiIjZt2oTy8nIsWrSoKcyuN/XR6+zsjJycHEyePBmEEJSXl2PixIlYsGBBU5jc5Ah7Z3Xs2BGFhYUoLS2FgoJCM1lGEQWNNFDaBAcPHsTFixexb98+yMvLN7c5jU5hYSHc3d2xefNmqKurN7c5TQYhBBoaGti8eTOMjY3h5OSEBQsW4NSpU81t2hfhwYMHCAgIwIYNGxAaGop9+/bh5s2b2L9/f3ObRqEAoJGGFouamhqkpaXx6dMnvvRPnz4JeOZVdOzYUSCqUFP+lkR99FZx5MgRHDx4EEePHoWBgcGXNLPRqKvepKQkpKSkwNXVlUnj8XgAgH79+uHSpUvo0aPHlzW6gdTnGmtqakJGRgbS0tJMWq9evZCZmQkOh9Oil4mvj949e/bAxcWFaX7S19dHcXEx1q9fD1dXV0hJta16nrB3VlZWFlRUVGiUoYXStu7ANoScnByMjIxw7949Jo3H4+HevXtgs9lC9zE3N8f9+/f50u7evQtzc/MvaWqjUB+9AHDo0CH89ttvOHz4MExMTJrC1Eahrnp79eqF8+fPIzw8nPk3ZMgQWFtbIzw8HF26dGlK8+tFfa6xhYUFEhMTGQcJAD58+ABNTc0W7TAA9dNbWloq4BhUOUyEkC9nbDPRmt9ZEkszd8Sk1EBkZCQxNjYmoaGhJC4ujqxbt45YWVmRzMxMQgghK1euJDt37mTyP3r0iPTr148cOXKExMXFET8/v1Y35LIuegMCAoiRkRG5dOkSycjIYP4VFhY2l4Q6UVe91WmNoyfqqjk1NZWw2Wzi6elJ4uPjyfXr18nAgQPJb7/91lwS6kRd9fr5+RE2m00uXLhAEhMTSVRUFBk2bBj5+eefm0lB3SgsLCSvXr0ir169Inp6euTo0aPk1atXJCUlhRBCyM6dO8nKlSuZ/FVDLrdv307i4uLIyZMn6ZDLFg5tnmjBODk5ITs7G35+fsjMzIShoSEOHz7MhDbT0tL4aiUWFhbYuXMnfH19sXv3bujo6GD//v3Q09NrLgl1oq56T506BS6XiyVLlvCVs2jRIixevLhJba8PddXbFqir5q5du+LIkSPw8vKCi4sLOnfujOnTp2Pu3LnNJaFO1FWvq6srWCwWfH19kZ6eDnV1dTg4OGDp0qXNJaFOvHjxAtOnT2d+e3l5AQDGjRsHb29vZGZmIi0tjdmura2NgIAAeHl54cSJE+jSpQu2bNkCOzu7JredIh4sQtpgzItCoVAoFEqj07aqMRQKhUKhUL4Y1GmgUCgUCoUiFtRpoFAoFAqFIhbUaaBQKBQKhSIW1GmgUCgUCoUiFtRpoFAoFAqFIhbUaaBQKBQKhSIW1GmgUCgUCoUiFtRpoHxRQkNDYWVl1dxm1Bt9fX1cuXKlxjyrVq3CwoULm8iiloWvry/WrVvXJMd68OAB9PX1kZ+fX2O+IUOG4NixY1/Ulroeo7GeA3Hux7oSFxeHwYMHo7i4uFHLpbRNqNNAqZVVq1ZBX19f4N/Hjx+b2zSEhoYy9hgYGGDw4MFYvXq1wMqC9SUqKgqDBw8GACQnJ0NfXx+xsbF8edauXQtvb+9GOZ4o9u7dy+g0NDSEvb091q1bh9zc3DqV05gOTmZmJk6cOIEFCxbwlV9lp7GxMYYPH459+/ahvLy8wcdjs9mIiopCu3btAIj+EAcHB+OHH35o8PHaCgcOHMDEiRNhZmYm9Hz16dMH5ubmOHr0aDNYR2lt0LUnKGJhZ2fHzCNfhbq6ejNZw4+KigouXboEHo+H169fY82aNcjIyMCRI0caXLampmateao+Yl+avn374ujRo+DxeHj//j3WrFmDgoIC+Pr6Nsnxq3P27Fmw2WxoaWnxpVfdKxwOBzdv3oSnpydkZWUxf/78Bh1PTk5OrOvRUu7LlgKXy8XIkSNhbm6O4OBgoXnGjx+PX375BfPnz4eMDP0sUERDIw0Usah6YX/+T1paGkePHoWzszPMzc1hb2+PjRs3oqioSGQ5r1+/xrRp08Bms2FhYYHx48fj+fPnzPbo6GhMnjwZpqamsLe3x5YtW2oNm7JYLGhqaqJz586wt7fHtGnTcPfuXZSWloLH42Hfvn0YPHgwjI2N8c033+DWrVvMvhwOB56enrC1tYWJiQkcHBwQEBDAbP88HDx06FAAwNixY6Gvr49p06YB4K+9nz59Gra2tnxLOQOVCxGtXr2a+X3lyhWMGzcOJiYmGDp0qFi1cWlpaUanjY0NRo4cibt37zLbKyoqsGbNGgwZMgSmpqYYMWIEjh8/zmzfu3cvwsLCcPXqVSYa8ODBAwCVCyf9/PPPsLKywoABA+Dq6ork5OQa7bl48SKGDBkikF51r2hpaWHy5MmwsbHBtWvXAAB5eXlwd3dH//79YWZmhjlz5uDDhw/MvikpKViwYAH69+8Pc3NzjB49Gjdv3gTA3zzx4MEDrF69GgUFBYyWvXv3AuBvOli+fDnc3Nz47ONyucyS4kDlctUBAQHMeXNxccGlS5dq1F4dcZ+DK1euwNHRESYmJpg9ezbf4k1V2+t6X9TGkiVLMGPGjBoXrrOxsUFeXh4ePnzYoGNR2j7UaaA0CBaLhbVr1+LChQvw9vbG/fv38euvv4rMv2LFCnTp0gXBwcEIDQ3F3LlzISsrCwBITEzE3Llz4ejoiIiICPj4+ODRo0fYvHlznWxSUFAAj8dDeXk5Tpw4gaNHj8LDwwMRERGwtbXFwoULmQ9VYGAgrl27Bl9fX1y6dAm//vqrQM25irNnzwIAjh07hqioKOYj9TkjR45Ebm4u8zEGgNzcXNy+fRsuLi4AKh0jDw8PTJ8+HRcvXoSnpydCQ0Ph7+8vtsbk5GRERUUx5w6o/Ph16dIFe/bsQWRkJH766Sf4+Pjg4sWLAIBZs2Zh1KhRsLOzQ1RUFKKiosBms8HlcjF79mwoKysjKCgIf/75J5SUlDBnzhxwOByhx8/NzUVcXByMjY1rtVVeXh5cLhdApYP14sULHDhwAKdPnwYhBPPmzWO2e3p6gsPh4OTJkzh//jxWrFgBJSUlgTLZbDbWrFkDFRUVRsusWbME8jk7O+P69et8H/CoqCiUlpZi2LBhAICAgACEh4dj06ZNiIyMxIwZM7By5Ur8888/tWqrQpznoLS0FAcOHMD27dvx559/Ij8/n2/1yvrcF9OmTcOqVavEtlMUcnJyMDQ0RHR0dIPLorRtqNNAEYsbN26AzWYz/6qWo54xYwa++uordO/eHQMHDoSbmxv++usvkeWkpqbCxsYGvXv3ho6ODkaNGgUDAwMAlS9vZ2dnzJgxAzo6OrCwsMDatWsRHh6OsrIysez88OED/vzzTxgbG0NFRQVHjhzB3LlzMXr0aPTq1QsrV66EgYEBUwNPS0tDz549YWlpCS0tLVhZWWHMmDFCy64Ke6uqqkJTUxOqqqoCeTp06IDBgwfj/PnzTNrff/8NNTU1WFtbAwD27duHefPmYdy4cdDW1sagQYPw888/49SpUzVqe/v2LdhsNkxNTTF06FC8e/eOb4loWVlZLFmyBCYmJtDW1oaLiwvGjx/P1JqVlZWhoKDAFzWSk5PDxYsXwePxsHXrVujr66N3797w8vJCWlqayA9nWloaCCHo1KmTSHsJIbh79y6ioqJgbW2NDx8+4Nq1a9iyZQusrKxgYGCAnTt3Ij09nYnmpKamwsLCAvr6+tDW1oaDgwP69+8vULacnBzatWvHRJk0NTWhrKwskM/W1haKioq4fPkyk3bhwgUMGTIEKioq4HA4CAgIwLZt22BnZwdtbW2MHz8eLi4uOH36dI3X43PEeQ64XC7Wr18PNpsNY2NjeHt748mTJ3j27BmA+t0XXbt2FavJRhw6deqE1NTURimL0nahjVcUsbC2tsbGjRuZ34qKigCAu3fvIiAgAPHx8SgsLERFRQXKyspQUlLC5PmcmTNn4pdffsG5c+eYEHuPHj0AVDZdvHnzhu+DSwgBj8dDcnIyevfuLdS2goICsNls8Hg8lJWVwdLSElu2bEFhYSEyMjJgYWHBl9/CwgKvX78GAIwbNw6zZs3CyJEjYWdnh6+//hq2trYNOlfOzs5Yt24dNm7cCDk5OZw/fx6jR4+GlJQUo/Px48d8NcjazhsA6Orq4sCBAygrK0NERARiY2MxdepUvjxBQUEICQlBamoqysrKwOVyGadMFK9fv0ZiYqLAeSorK0NiYqLQfUpLSwFURhGqU+VgcrlcEEIwZswYLF68GPfu3YOMjAzMzMyYvGpqatDV1cX79+8BANOnT8fGjRsRFRUFGxsbODo61mp/TcjIyGDUqFE4f/48xo4di+LiYly9ehW7d+8GAHz8+BElJSUCUQoulwtDQ0OxjyPOcyAjIwMTExNmn969e6N9+/Z4//49TE1N63Vf7Nixo07noybk5eVRUlLSaOVR2ibUaaCIhaKiInr27MmXlpycjPnz52PSpElYunQpOnTogEePHmHt2rXgcrlCX3KLFy/GmDFjcPPmTdy6dQt+fn7w8fHB8OHDUVxcjIkTJzJ9BT6na9euIm1TVlZGWFgYpKSkoKmpCQUFBQBAYWFhrbqMjIxw9epV3Lp1C3fv3oWbmxtsbGzg5+dX676iGDJkCH755RfcuHEDJiYmiI6O5uvPUFxcjMWLF8PR0VFgX2Ef4SpkZWWZa7BixQrMmzcP+/btY9rsIyMjsX37dnh4eIDNZkNZWRlHjhzB06dPa7S3uLgYRkZG2Llzp8A2UZ0K1dTUAFT2Uaiep8rBlJWVRadOnerUsW7ChAmwtbXFjRs3cOfOHRw8eBAeHh5C7wlxcXZ2xrRp0/Dp0yfcuXMH8vLysLOzAwCmv0xAQAA6d+7Mt5+cnJxY5dfnORBGfe+LxiIvL49x4CkUUVCngVJvXr58CUIIVq1axdSia2qaqEJXVxe6urqYMWMGli1bhpCQEAwfPhz9+vVDXFycgHNSG1JSUkL3UVFRQadOnfD48WMMGDCASX/8+DFMTU358jk5OcHJyQkjRozAnDlzkJubK9D8UNV/oKKiokZ75OXl4ejoiPPnz+Pjx4/Q1dWFkZERs71fv35ISEios87quLq64scff8SkSZPQuXNnPH78GGw2G1OmTGHyVI8UyMrKCnTSNDIywl9//QUNDQ2oqKiIdewePXpARUUF79+/h66uLt82YQ4mUFmzLi8vx9OnT5moRk5ODhISEtCnTx8mX9euXTFp0iRMmjQJu3btwpkzZ4Q6DbKysrVeC6AystSlSxdcvHgRt27dwsiRI5lr2bt3b8jJySE1NZXvHqkL4j4H5eXlePHiBXPvxcfHIz8/n4mgNdZ9UV/evXuHESNGNMuxKa0H2qeBUm969uwJLpeLwMBAJCUlITw8vMb219LSUnh6euLBgwdISUnBo0eP8Pz5c+alOXfuXDx58gSenp6IjY3Fhw8fcOXKFXh6etbbxtmzZ+PQoUO4ePEi4uPjsXPnTrx+/RrTp08HUNnr/cKFC3j//j0SEhJw6dIlaGpqon379gJlaWhoQEFBAbdv30ZWVhYKCgpEHtfZ2Rk3btxASEgInJ2d+bb99NNPOHfuHPbt24d3797h/fv3iIyMhI+PT520sdls6OvrM6M9evbsiRcvXuD27dtISEiAr68v38gUANDS0sKbN28QHx+P7OxscLlcODs7Q01NDa6uroiOjkZSUhIePHiALVu24N9//xV6bCkpKdjY2ODRo0di26ujo4OhQ4di3bp1iI6OxuvXr7Fy5Up07tyZGZmydetW3L59G0lJSXj58iUePHggsllKS0sLxcXFuHfvHrKzs2sMrY8ZMwanTp3C3bt3+a6HiooKZs2aBS8vL4SFhSExMREvX75EYGAgwsLCxNIl7nMgKyuLzZs34+nTp3jx4gVWr14Nc3Nzxomoz33h7u6OXbt21WhfamoqYmNjkZqaioqKCsTGxiI2Npavc2hycjLS09NhY2MjlmaK5EIjDZR6Y2BggNWrV+PQoUPYvXs3rKyssGzZMnh4eAjNLyUlhdzcXHh4eCArKwtqampwdHRkOlUaGBggMDAQvr6+mDx5MgBAW1sbTk5O9bZx+vTpKCwshLe3N7Kzs9G7d2/89ttv0NHRAVDZtHH48GF8/PgRUlJSMDExwcGDB5ka4+fIyMjgl19+wf79++Hn5wcrKysEBgYKPe5XX32FDh06ICEhQcBpsLOzg7+/P/bv349Dhw5BRkYGvXr1woQJE+qsb8aMGVi1ahXmzp2LiRMnIjY2FkuXLgWLxcLo0aMxefJkviGm33//Pf755x98++23KC4uxokTJ2BtbY2TJ09i586dWLRoEYqKitC5c2cMHDiwxsjDd999h3Xr1mHlypVCz5cwvLy8sHXrVixYsABcLhdWVlY4ePAgU/Pn8Xjw9PTEv//+CxUVFdjZ2fE17XyOhYUFJk6cCDc3N+Tm5mLRokVYvHix0LwuLi7w9/eHlpYWLC0t+ba5ublBXV0dAQEBSE5ORrt27dCvXz++SatqQtznQEFBAXPnzsXy5cuRnp4OKysrbN26ldlen/siLS2t1nPv5+fH5wCNHTsWAJhrD1Q2bQ0aNEjkyCEKpQoWIYQ0txEUCqX1QQjBhAkTMGPGDJEjTigtHw6HgxEjRmDnzp0CDhWFUh3aPEGhUOoFi8XC5s2bG2WKaErzkZaWhvnz51OHgSIWNNJAoVAoFApFLGikgUKhUCgUilhQp4FCoVAoFIpYUKeBQqFQKBSKWFCngUKhUCgUilhQp4FCoVAoFIpYUKeBQqFQKBSKWFCngUKhUCgUilhQp4FCoVAoFIpYUKeBQqFQKBSKWPw/YtqrE4h9/XQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Skipping group American Indian plot as it's too small.\n",
+      "WARNING:root:Skipping group Alaska Native plot as it's too small.\n",
+      "WARNING:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it's too small.\n",
+      "WARNING:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it's too small.\n",
+      "WARNING:root:Skipping group Some other race alone (non-White) plot as it's too small.\n",
+      "WARNING:root:Skipping group Two or more races plot as it's too small.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve.pdf',\n",
+       " 'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve.pdf',\n",
+       " 'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution.pdf',\n",
+       " 'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution_per_label.pdf',\n",
+       " 'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve_per_subgroup.pdf',\n",
+       " 'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve_per_subgroup.pdf'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bench.plot_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b83085bc-9df7-43b0-9282-cfd4c83713b7",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/notebooks/minimal-example_web-API-model.ipynb.txt b/_sources/notebooks/minimal-example_web-API-model.ipynb.txt
new file mode 100644
index 0000000..0a05386
--- /dev/null
+++ b/_sources/notebooks/minimal-example_web-API-model.ipynb.txt
@@ -0,0 +1,376 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "181f6c46",
+   "metadata": {},
+   "source": [
+    "# Evaluate model calibration using folktexts\n",
+    "\n",
+    "**Prerequisite**: Install `folktexts` package with optional model API dependencies: `pip install 'folktexts[apis]'`\n",
+    "\n",
+    "**Summary**: The script demonstrates how to use folktexts to get insights into model calibration on a **model hosted through a web API**.\n",
+    "\n",
+    "### 1. Check folktexts is installed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f3867376",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "folktexts.__version__='0.0.21'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import folktexts\n",
+    "print(f\"{folktexts.__version__=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2129c830",
+   "metadata": {},
+   "source": [
+    "### 2. Load model API using `litellm`\n",
+    "\n",
+    "We use OpenAI's _**GPT-4o-mini**_ model for this demo. The workflow can be similarly applied to any compatible model.\n",
+    "\n",
+    "**Note:** _Set_ `model_name` to the model's name. See the `litellm` [list of compatible web-API providers and models](https://docs.litellm.ai/docs/providers/openai#openai-chat-completion-models)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3dc758de-b86f-4adb-9ed0-0148831da070",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_name = \"openai/gpt-4o-mini\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c58aff1e-082d-4f3f-b061-1f1028d3520d",
+   "metadata": {},
+   "source": [
+    "### 3. Set `OPENAI_API_KEY` (or key to respective API provider)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28d73863-f448-4370-b26a-24576e1498f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-key-here\"  # NOTE: Substitute with your key here!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25a2a5f7",
+   "metadata": {},
+   "source": [
+    "### 3. Create default benchmarking tasks\n",
+    "\n",
+    "We generate ACSIncome benchmark using folktexts. \n",
+    "\n",
+    "**NOTE:** We will subsample the reference data for faster runtime. This should be removed for obtaining reproducible reslts.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed481a4-5258-468f-8fb5-91ed794d1503",
+   "metadata": {},
+   "source": [
+    "### Benchmark configuration\n",
+    "- The `subsampling` and `numeric_risk_prompting` key-word arguments are examples of optional benchmark configurations. See this [page for a list of available configs](https://github.com/socialfoundations/folktexts?tab=readme-ov-file#benchmark-options)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bbb17808",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Received non-standard ACS argument 'subsampling' (using subsampling=0.005 instead of default subsampling=None). This may affect reproducibility.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "Using zero-shot prompting.\n",
+      "CPU times: user 52.6 s, sys: 1min 23s, total: 2min 16s\n",
+      "Wall time: 2min 21s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.benchmark import Benchmark, BenchmarkConfig\n",
+    "\n",
+    "# Note: This argument is optional. Omit, or set to 1 for reproducible benchmarking on the full data\n",
+    "subsampling_ratio = 0.005\n",
+    "\n",
+    "bench = Benchmark.make_acs_benchmark(\n",
+    "    model=model_name,\n",
+    "    task_name=\"ACSIncome\",\n",
+    "    subsampling=subsampling_ratio,\n",
+    "    numeric_risk_prompting=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f785fc1",
+   "metadata": {},
+   "source": [
+    "### 4. Run benchmark\n",
+    "\n",
+    "Results will be saved in a folder RESULTS_DIR. There is \n",
+    "* `.json` file contains evaluated metrics\n",
+    "* `.cvs` file contains risk scores of each datapoint\n",
+    "* folder called `imgs/` contains figures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "62b90528",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2706f59a1b1b4a7492f9d9376ba60cd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/52 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to compute ECE quantile: The smallest edge difference is numerically 0.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 832,\n",
+       " 'n_positives': 305,\n",
+       " 'n_negatives': 527,\n",
+       " 'model_name': 'openai/gpt-4o-mini',\n",
+       " 'accuracy': 0.7884615384615384,\n",
+       " 'tpr': 0.6885245901639344,\n",
+       " 'fnr': 0.3114754098360656,\n",
+       " 'fpr': 0.15370018975332067,\n",
+       " 'tnr': 0.8462998102466793,\n",
+       " 'balanced_accuracy': 0.7674122002053069,\n",
+       " 'precision': 0.7216494845360825,\n",
+       " 'ppr': 0.34975961538461536,\n",
+       " 'log_loss': 0.8249689807687466,\n",
+       " 'brier_score_loss': np.float64(0.15596153846153846),\n",
+       " 'tpr_ratio': 0.0,\n",
+       " 'tpr_diff': 0.782608695652174,\n",
+       " 'precision_ratio': 0.0,\n",
+       " 'precision_diff': 0.9,\n",
+       " 'tnr_ratio': 0.8177339901477833,\n",
+       " 'tnr_diff': 0.18226600985221675,\n",
+       " 'fnr_ratio': 0.21739130434782608,\n",
+       " 'fnr_diff': 0.782608695652174,\n",
+       " 'ppr_ratio': 0.0,\n",
+       " 'ppr_diff': 0.47619047619047616,\n",
+       " 'accuracy_ratio': 0.7,\n",
+       " 'accuracy_diff': 0.30000000000000004,\n",
+       " 'balanced_accuracy_ratio': 0.5961800818553888,\n",
+       " 'balanced_accuracy_diff': 0.33867276887871856,\n",
+       " 'fpr_ratio': 0.0,\n",
+       " 'fpr_diff': 0.18226600985221675,\n",
+       " 'equalized_odds_ratio': 0.0,\n",
+       " 'equalized_odds_diff': 0.782608695652174,\n",
+       " 'roc_auc': np.float64(0.8337263197187919),\n",
+       " 'ece': 0.032091346153846276,\n",
+       " 'ece_quantile': None,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/ACSIncome_subsampled-0.005_seed-42_hash-305608976.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': True,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'openai/gpt-4o-mini',\n",
+       "  'model_hash': 920159687,\n",
+       "  'task_name': 'ACSIncome',\n",
+       "  'task_hash': 127998692,\n",
+       "  'dataset_name': 'ACSIncome_subsampled-0.005_seed-42_hash-305608976',\n",
+       "  'dataset_hash': 305608976},\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RESULTS_DIR = \"res\"\n",
+    "bench.run(results_root_dir=RESULTS_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20636ce4",
+   "metadata": {},
+   "source": [
+    "### 4. Visualize results\n",
+    "\n",
+    "We can also visualize the results inline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "edd5bf8f-cd70-4276-9c14-f1e0012aee0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve.pdf',\n",
+       " 'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve.pdf',\n",
+       " 'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution.pdf',\n",
+       " 'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution_per_label.pdf',\n",
+       " 'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve_per_subgroup.pdf',\n",
+       " 'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve_per_subgroup.pdf'}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bench.plot_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b83085bc-9df7-43b0-9282-cfd4c83713b7",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/notebooks/paper-plots-and-tables.ipynb.txt b/_sources/notebooks/paper-plots-and-tables.ipynb.txt
new file mode 100644
index 0000000..ee45e93
--- /dev/null
+++ b/_sources/notebooks/paper-plots-and-tables.ipynb.txt
@@ -0,0 +1,4094 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a436d249-9dfc-4d25-9695-549cb440ea18",
+   "metadata": {},
+   "source": [
+    "# Render paper plots and tables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a7df2c18-8ad5-4733-b77e-68afde7064a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import logging\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a247e64e-8d4c-4dc7-beb2-cf70e1492fc2",
+   "metadata": {},
+   "source": [
+    "**Note:** _Change_ the following path to the aggregated results file in your local system \n",
+    "(can be obtained using the `parse-acs-results.ipynb` notebook)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6d4d0fde-5cea-4ed8-b496-00627aac840f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ACS_AGG_RESULTS_PATH = Path(\"../results\") / \"aggregated-results.2024-08.csv\"\n",
+    "ACS_AGG_RESULTS_PATH = Path(ACS_AGG_RESULTS_PATH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ddc248c4-a5c5-4c95-9f2a-3d386c830520",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "results_df.shape=(210, 64)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>penai/gpt-4o-mini__ACSTravelTime__-1__Num</th>\n",
+       "      <td>0.570906</td>\n",
+       "      <td>0.315286</td>\n",
+       "      <td>0.624340</td>\n",
+       "      <td>0.553706</td>\n",
+       "      <td>0.070915</td>\n",
+       "      <td>0.876372</td>\n",
+       "      <td>0.267584</td>\n",
+       "      <td>0.149517</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.382569</td>\n",
+       "      <td>...</td>\n",
+       "      <td>GPT 4o mini (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.510013</td>\n",
+       "      <td>0.450000</td>\n",
+       "      <td>0.570968</td>\n",
+       "      <td>0.223652</td>\n",
+       "      <td>0.384149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>penai/gpt-4o-mini__ACSTravelTime__-1__QA</th>\n",
+       "      <td>0.551154</td>\n",
+       "      <td>0.119294</td>\n",
+       "      <td>0.812222</td>\n",
+       "      <td>0.588927</td>\n",
+       "      <td>0.113947</td>\n",
+       "      <td>0.823835</td>\n",
+       "      <td>0.404025</td>\n",
+       "      <td>0.393327</td>\n",
+       "      <td>0.393301</td>\n",
+       "      <td>0.274655</td>\n",
+       "      <td>...</td>\n",
+       "      <td>GPT 4o mini (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.998499</td>\n",
+       "      <td>0.602202</td>\n",
+       "      <td>0.970688</td>\n",
+       "      <td>0.593339</td>\n",
+       "      <td>0.365205</td>\n",
+       "      <td>0.772858</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 64 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           accuracy  accuracy_diff  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num  0.570906       0.315286   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA   0.551154       0.119294   \n",
+       "\n",
+       "                                           accuracy_ratio  balanced_accuracy  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num        0.624340           0.553706   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA         0.812222           0.588927   \n",
+       "\n",
+       "                                           balanced_accuracy_diff  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num                0.070915   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA                 0.113947   \n",
+       "\n",
+       "                                           balanced_accuracy_ratio  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num                 0.876372   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA                  0.823835   \n",
+       "\n",
+       "                                           brier_score_loss       ece  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num          0.267584  0.149517   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA           0.404025  0.393327   \n",
+       "\n",
+       "                                           ece_quantile  equalized_odds_diff  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num           NaN             0.382569   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA       0.393301             0.274655   \n",
+       "\n",
+       "                                           ...              name  is_inst  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num  ...  GPT 4o mini (it)     True   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA   ...  GPT 4o mini (it)     True   \n",
+       "\n",
+       "                                           num_features  uses_all_features  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num            -1               True   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA             -1               True   \n",
+       "\n",
+       "                                           fit_thresh_on_100  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num           0.250000   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA            0.998499   \n",
+       "\n",
+       "                                           fit_thresh_accuracy  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num             0.510013   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA              0.602202   \n",
+       "\n",
+       "                                           optimal_thresh  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num        0.450000   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA         0.970688   \n",
+       "\n",
+       "                                           optimal_thresh_accuracy  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num                 0.570968   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA                  0.593339   \n",
+       "\n",
+       "                                          score_stdev  score_mean  \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num    0.223652    0.384149  \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA     0.365205    0.772858  \n",
+       "\n",
+       "[2 rows x 64 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_df = pd.read_csv(ACS_AGG_RESULTS_PATH, index_col=0)\n",
+    "print(f\"{results_df.shape=}\")\n",
+    "results_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a70081bb-71f2-4dc6-95d4-0919b0eb93b9",
+   "metadata": {},
+   "source": [
+    "Remove `gemma-2-*` results (need to investigate why results are so poor):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f461c5d8-aca5-47ba-b6a6-bc18a441c1ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "results_df.shape=(170, 64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_df = results_df.drop(index=[id_ for id_ in results_df.index if \"gemma-2-\" in id_.lower()])\n",
+    "print(f\"{results_df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b658cc4b-e0b0-4dd0-a4c6-00c5ed79d4bf",
+   "metadata": {},
+   "source": [
+    "## Run baseline ML classifiers on the benchmark ACS tasks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9f02d293-7725-4754-bce8-caa40c93a362",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5299d2a9-3723-44cc-a88b-fbba1d631eef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ALL_TASKS = [\n",
+    "    \"ACSIncome\",\n",
+    "    \"ACSMobility\",\n",
+    "    \"ACSEmployment\",\n",
+    "    \"ACSTravelTime\",\n",
+    "    \"ACSPublicCoverage\",\n",
+    "]\n",
+    "\n",
+    "model_col = \"config_model_name\"\n",
+    "task_col = \"config_task_name\"\n",
+    "numeric_prompt_col = \"config_numeric_risk_prompting\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64b72e20-a5d6-442b-8ba9-f7402f87b77b",
+   "metadata": {},
+   "source": [
+    "List all baseline classifiers here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1749fee2-ea21-41e9-b7f0-9ae5d0868409",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
+    "from xgboost import XGBClassifier    # NOTE: requires `pip install xgboost`\n",
+    "\n",
+    "baselines = {\n",
+    "    \"LR\": LogisticRegression(),\n",
+    "    \"GBM\": HistGradientBoostingClassifier(),\n",
+    "    \"XGBoost\": XGBClassifier(),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "72fb9307-a5c1-4805-8656-cc352cc736d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.acs.acs_dataset import ACSDataset\n",
+    "from folktexts.evaluation import evaluate_predictions\n",
+    "from collections import defaultdict\n",
+    "\n",
+    "def fit_and_eval(\n",
+    "    clf,\n",
+    "    X_train, y_train,\n",
+    "    X_test, y_test, s_test,\n",
+    "    fillna=False,\n",
+    "    save_scores_df_path: str = None,\n",
+    ") -> dict:\n",
+    "    \"\"\"Fit and evaluate a given classifier on the given data.\"\"\"\n",
+    "    assert len(X_train) == len(y_train) and len(X_test) == len(y_test) == len(s_test)\n",
+    "\n",
+    "    train_nan_count = X_train.isna().any(axis=1).sum()\n",
+    "    if fillna and train_nan_count > 0:\n",
+    "        # Fill NaNs with value=-1\n",
+    "        X_train = X_train.fillna(axis=\"columns\", value=-1)\n",
+    "        X_test = X_test.fillna(axis=\"columns\", value=-1)\n",
+    "\n",
+    "    # Fit on train data\n",
+    "    clf.fit(X_train, y_train)\n",
+    "\n",
+    "    # Evaluate on test data\n",
+    "    y_test_scores = clf.predict_proba(X_test)[:, -1]\n",
+    "    test_results = evaluate_predictions(\n",
+    "        y_true=y_test.to_numpy(),\n",
+    "        y_pred_scores=y_test_scores,\n",
+    "        sensitive_attribute=s_test,\n",
+    "        threshold=0.5,\n",
+    "    )\n",
+    "\n",
+    "    # Optionally, save test scores DF\n",
+    "    if save_scores_df_path:\n",
+    "        scores_df = pd.DataFrame({\n",
+    "            \"risk_score\": y_test_scores,\n",
+    "            \"label\": y_test,\n",
+    "        })\n",
+    "        scores_df.to_csv(save_scores_df_path)\n",
+    "        test_results[\"predictions_path\"] = save_scores_df_path\n",
+    "\n",
+    "    return test_results\n",
+    "\n",
+    "def run_baselines(baselines, tasks) -> dict:\n",
+    "    \"\"\"Run baseline classifiers on all acs tasks.\"\"\"\n",
+    "    baseline_results = defaultdict(dict)\n",
+    "\n",
+    "    # Prepare progress bar\n",
+    "    progress_bar = tqdm(\n",
+    "        total=len(tasks) * len(baselines),\n",
+    "        leave=True,\n",
+    "    )\n",
+    "\n",
+    "    for task in tasks:\n",
+    "        progress_bar.set_postfix({\"task\": task})\n",
+    "\n",
+    "        # Load ACS task data\n",
+    "        acs_dataset = ACSDataset.make_from_task(task=task, cache_dir=DATA_DIR)\n",
+    "    \n",
+    "        # Get train/test data\n",
+    "        X_train, y_train = acs_dataset.get_train()\n",
+    "        X_test, y_test = acs_dataset.get_test()\n",
+    "    \n",
+    "        # Get sensitive attribute test data\n",
+    "        s_test = None\n",
+    "        if acs_dataset.task.sensitive_attribute is not None:\n",
+    "            s_test = acs_dataset.get_sensitive_attribute_data().loc[y_test.index]\n",
+    "    \n",
+    "        for clf_name, clf in baselines.items():\n",
+    "            progress_bar.set_postfix({\"task\": task, \"clf\": clf_name})\n",
+    "\n",
+    "            try:\n",
+    "                baseline_results[task][clf_name] = fit_and_eval(\n",
+    "                    clf=clf,\n",
+    "                    X_train=X_train, y_train=y_train,\n",
+    "                    X_test=X_test, y_test=y_test, s_test=s_test,\n",
+    "                    fillna=(clf_name == \"LR\"),\n",
+    "                    save_scores_df_path=ACS_AGG_RESULTS_PATH.parent / f\"baseline_scores.{clf_name}.{task}.csv\"\n",
+    "                )\n",
+    "            except Exception as err:\n",
+    "                logging.error(err)\n",
+    "            finally:\n",
+    "                progress_bar.update()\n",
+    "\n",
+    "    return baseline_results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f359a7a3-af13-4516-a07f-7d09f71f99ac",
+   "metadata": {},
+   "source": [
+    "Flatten results and add extra columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4580a3a8-279d-4286-a8d0-01d228b0d92e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parse_baseline_results(baseline_results) -> list:\n",
+    "    \"\"\"Flatten and parse baseline results.\"\"\"\n",
+    "    parsed_results_list = list()\n",
+    "    \n",
+    "    for task, task_results in baseline_results.items():\n",
+    "    \n",
+    "        for clf, clf_results in task_results.items():\n",
+    "            parsed_results = clf_results.copy()\n",
+    "    \n",
+    "            parsed_results[\"config_task_name\"] = task\n",
+    "            parsed_results[\"config_model_name\"] = clf\n",
+    "            parsed_results[\"name\"] = clf\n",
+    "            parsed_results[\"num_features\"] = -1\n",
+    "            parsed_results[\"uses_all_features\"] = True\n",
+    "    \n",
+    "            parsed_results_list.append(parsed_results)\n",
+    "\n",
+    "    return parsed_results_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70b0dd85-79f0-4a6b-a2e1-72fbeb84d85a",
+   "metadata": {},
+   "source": [
+    "Check if baseline results were already computed. If so, load csv; otherwise, compute and save."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e04b361c-780c-4e04-a817-6b2409db84e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading pre-computed baseline results from ../results/baseline-results.GBM.LR.XGBoost.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>threshold</th>\n",
+       "      <th>n_samples</th>\n",
+       "      <th>n_positives</th>\n",
+       "      <th>n_negatives</th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>tpr</th>\n",
+       "      <th>fnr</th>\n",
+       "      <th>fpr</th>\n",
+       "      <th>tnr</th>\n",
+       "      <th>...</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>predictions_path</th>\n",
+       "      <th>config_task_name</th>\n",
+       "      <th>config_model_name</th>\n",
+       "      <th>name</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>new_index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>XGBoost_ACSPublicCoverage</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>113829</td>\n",
+       "      <td>33971</td>\n",
+       "      <td>79858</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.801650</td>\n",
+       "      <td>0.515175</td>\n",
+       "      <td>0.484825</td>\n",
+       "      <td>0.076486</td>\n",
+       "      <td>0.923514</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.368044</td>\n",
+       "      <td>0.839742</td>\n",
+       "      <td>0.004371</td>\n",
+       "      <td>0.004271</td>\n",
+       "      <td>/fast/groups/sf/folktexts-results/2024-07-03/b...</td>\n",
+       "      <td>ACSPublicCoverage</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GBM_ACSIncome</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>166450</td>\n",
+       "      <td>61233</td>\n",
+       "      <td>105217</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.813584</td>\n",
+       "      <td>0.727973</td>\n",
+       "      <td>0.272027</td>\n",
+       "      <td>0.136594</td>\n",
+       "      <td>0.863406</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.630389</td>\n",
+       "      <td>0.890792</td>\n",
+       "      <td>0.007721</td>\n",
+       "      <td>0.007146</td>\n",
+       "      <td>/fast/groups/sf/folktexts-results/2024-07-03/b...</td>\n",
+       "      <td>ACSIncome</td>\n",
+       "      <td>GBM</td>\n",
+       "      <td>GBM</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           threshold  n_samples  n_positives  n_negatives  \\\n",
+       "new_index                                                                   \n",
+       "XGBoost_ACSPublicCoverage        0.5     113829        33971        79858   \n",
+       "GBM_ACSIncome                    0.5     166450        61233       105217   \n",
+       "\n",
+       "                           model_name  accuracy       tpr       fnr       fpr  \\\n",
+       "new_index                                                                       \n",
+       "XGBoost_ACSPublicCoverage         NaN  0.801650  0.515175  0.484825  0.076486   \n",
+       "GBM_ACSIncome                     NaN  0.813584  0.727973  0.272027  0.136594   \n",
+       "\n",
+       "                                tnr  ...  equalized_odds_diff   roc_auc  \\\n",
+       "new_index                            ...                                  \n",
+       "XGBoost_ACSPublicCoverage  0.923514  ...             0.368044  0.839742   \n",
+       "GBM_ACSIncome              0.863406  ...             0.630389  0.890792   \n",
+       "\n",
+       "                                ece  ece_quantile  \\\n",
+       "new_index                                           \n",
+       "XGBoost_ACSPublicCoverage  0.004371      0.004271   \n",
+       "GBM_ACSIncome              0.007721      0.007146   \n",
+       "\n",
+       "                                                            predictions_path  \\\n",
+       "new_index                                                                      \n",
+       "XGBoost_ACSPublicCoverage  /fast/groups/sf/folktexts-results/2024-07-03/b...   \n",
+       "GBM_ACSIncome              /fast/groups/sf/folktexts-results/2024-07-03/b...   \n",
+       "\n",
+       "                            config_task_name  config_model_name     name  \\\n",
+       "new_index                                                                  \n",
+       "XGBoost_ACSPublicCoverage  ACSPublicCoverage            XGBoost  XGBoost   \n",
+       "GBM_ACSIncome                      ACSIncome                GBM      GBM   \n",
+       "\n",
+       "                           num_features  uses_all_features  \n",
+       "new_index                                                   \n",
+       "XGBoost_ACSPublicCoverage            -1               True  \n",
+       "GBM_ACSIncome                        -1               True  \n",
+       "\n",
+       "[2 rows x 42 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "BASELINE_RESULTS_PATH = ACS_AGG_RESULTS_PATH.parent / (\"baseline-results.\" + \".\".join(sorted(baselines.keys())) + \".csv\")\n",
+    "\n",
+    "# If saved results exists: load\n",
+    "if BASELINE_RESULTS_PATH.exists():\n",
+    "    print(f\"Loading pre-computed baseline results from {BASELINE_RESULTS_PATH.as_posix()}\")\n",
+    "    baselines_df = pd.read_csv(BASELINE_RESULTS_PATH, index_col=0)\n",
+    "\n",
+    "# Compute baseline results\n",
+    "else:\n",
+    "    print(f\"Computing baseline results and saving to {BASELINE_RESULTS_PATH.as_posix()}\")\n",
+    "    # Compute baseline results\n",
+    "    baseline_results = run_baselines(baselines, tasks=ALL_TASKS)\n",
+    "\n",
+    "    # Parse results\n",
+    "    parsed_results_list = parse_baseline_results(baseline_results)\n",
+    "\n",
+    "    # Construct DF\n",
+    "    baselines_df = pd.DataFrame(parsed_results_list, index=[r[\"name\"] for r in parsed_results_list])\n",
+    "    \n",
+    "    # Save DF to disk\n",
+    "    baselines_df.to_csv(BASELINE_RESULTS_PATH)\n",
+    "\n",
+    "# Untie indices\n",
+    "baselines_df[\"new_index\"] = [(r[\"name\"] + \"_\" + r[task_col]) for _, r in baselines_df.iterrows()]\n",
+    "baselines_df = baselines_df.set_index(\"new_index\", drop=True)\n",
+    "\n",
+    "# Show 2 random rows\n",
+    "baselines_df.sample(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "07b4ddea-bd88-4bee-87cc-b6aaa6af5630",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "all_results_df.shape=(185, 64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_results_df = pd.concat((results_df, baselines_df))\n",
+    "print(f\"{all_results_df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b657133-120f-4ddb-95ef-b7106b9511fb",
+   "metadata": {},
+   "source": [
+    "## Prepare results table for each task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "317e5744-31e3-48ef-b051-484f79b01a8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_metrics = [\"ece\", \"brier_score_loss\", \"roc_auc\", \"accuracy\", \"fit_thresh_accuracy\", \"score_stdev\"] #, \"score_mean\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9df28300-70ee-47d7-b7ad-23d06ed415cc",
+   "metadata": {},
+   "source": [
+    "Add model size and model family columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "41009307-905b-4498-b98e-972b589f68ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "config_task_name   model_family\n",
+       "ACSEmployment      -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "ACSIncome          -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "ACSMobility        -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "ACSPublicCoverage  -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "ACSTravelTime      -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "Name: accuracy, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from folktexts.llm_utils import get_model_size_B\n",
+    "\n",
+    "all_results_df[\"model_size\"] = [\n",
+    "    (\n",
+    "        get_model_size_B(row[\"name\"], default=300)  # Set GPT 4o size as the largest (300B)\n",
+    "        if row[\"name\"] not in baselines else \"-\"\n",
+    "    )\n",
+    "    for _, row in all_results_df.iterrows()\n",
+    "]\n",
+    "\n",
+    "def get_model_family(model_name) -> str:\n",
+    "    if \"gpt\" in model_name.lower():\n",
+    "        return \"OpenAI\"\n",
+    "    elif \"llama\" in model_name.lower():\n",
+    "        return \"Llama\"\n",
+    "    elif \"mistral\" in model_name.lower() or \"mixtral\" in model_name.lower():\n",
+    "        return \"Mistral\"\n",
+    "    elif \"gemma\" in model_name.lower():\n",
+    "        return \"Gemma\"\n",
+    "    elif \"yi\" in model_name.lower():\n",
+    "        return \"Yi\"\n",
+    "    elif \"qwen\" in model_name.lower():\n",
+    "        return \"Qwen\"\n",
+    "    else:\n",
+    "        return \"-\"\n",
+    "\n",
+    "all_results_df[\"model_family\"] = [get_model_family(row[model_col]) for _, row in all_results_df.iterrows()]\n",
+    "all_results_df.groupby([task_col, \"model_family\"])[\"accuracy\"].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "027d43fb-8290-499c-847f-70e1315a2ae3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_sort_key(name, task_df):\n",
+    "    \"\"\"Sort key for paper table rows.\"\"\"\n",
+    "\n",
+    "    if \"gpt\" in name.lower():\n",
+    "        key = 11\n",
+    "    elif \"llama\" in name.lower():\n",
+    "        key = 10\n",
+    "    elif \"mixtral\" in name.lower():\n",
+    "        key = 9\n",
+    "    elif \"mistral\" in name.lower():\n",
+    "        key = 8\n",
+    "    elif \"yi\" in name.lower():\n",
+    "        key = 7\n",
+    "    elif \"gemma\" in name.lower():\n",
+    "        key = 6\n",
+    "    else:\n",
+    "        return 0\n",
+    "\n",
+    "    row = task_df.loc[name]\n",
+    "    # return (1e5 * key) + (row[\"model_size\"] * 100) + int(row[\"is_inst\"] * 10) + (1 if row[numeric_prompt_col] else 0)\n",
+    "    return (row[\"model_size\"] * 1e9) + int(row[\"is_inst\"] * 1e6) + 1e3 * (1 if row[numeric_prompt_col] else 0) + key\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "871c31ae-6a64-4b8a-9773-f1d430be9b36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def latex_colored_float_format(val, all_values, higher_is_better=True):\n",
+    "    \"\"\"Map a cell's value to its colored latex code.\n",
+    "\n",
+    "    Current definition:\n",
+    "    - use cyan color gradient for good values;\n",
+    "    - use orange color gradient for bad values;\n",
+    "    - use no color for anything in between;\n",
+    "    \"\"\"\n",
+    "    min_val, max_val = np.min(all_values), np.max(all_values)\n",
+    "\n",
+    "    low_pct_val, high_pct_val = [\n",
+    "        min_val + (max_val - min_val) * interp_point\n",
+    "        for interp_point in [0.1, 0.9]\n",
+    "    ]\n",
+    "\n",
+    "    # Use rounded value or original value for coloring?\n",
+    "    # > Using rounded value for consistency in table\n",
+    "    # val = np.round(val, decimals=2)\n",
+    "\n",
+    "    # Use no color for middle 33% of values\n",
+    "    if low_pct_val <= val <= high_pct_val:\n",
+    "        return f\"{val:.2f}\"\n",
+    "\n",
+    "    if val < low_pct_val:\n",
+    "        color = \"orange\" if higher_is_better else \"cyan\"\n",
+    "        color_value = 100 * (\n",
+    "            (low_pct_val - val) / (low_pct_val - min_val))\n",
+    "        \n",
+    "    elif val > high_pct_val:\n",
+    "        color = \"cyan\" if higher_is_better else \"orange\"\n",
+    "        color_value = 100 * (\n",
+    "            (val - high_pct_val) / (max_val - high_pct_val))\n",
+    "\n",
+    "    else:\n",
+    "        raise RuntimeError(f\"{val}\")\n",
+    "\n",
+    "    # Note: halving `color_value` to have softer colors\n",
+    "    color_value /= 4\n",
+    "\n",
+    "    return (\n",
+    "        r\"\\cellcolor{\"\n",
+    "        + f\"{color}!{color_value:.1f}\"\n",
+    "        + r\"} \"\n",
+    "        + f\"{val:.2f}\"\n",
+    "    )\n",
+    "\n",
+    "higher_is_better_cols = {\"roc_auc\", \"accuracy\", \"fit_thresh_accuracy\"}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c1a03d1-eac4-49c3-9243-3291bfa1640e",
+   "metadata": {},
+   "source": [
+    "## Output latex results tables - colored!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61778ae8-af37-404f-946c-dda26fef6836",
+   "metadata": {},
+   "source": [
+    "Set whether to use **_Numeric_ prompting** or **_multiple-choice Q&A_ prompting**!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "eb0096df-e27a-4f10-a42a-63dbeeae0dce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NUMERIC_PROMPTING = False\n",
+    "NUMERIC_PROMPTING = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8461cf3-b042-4ad3-9cf5-ca383ad66514",
+   "metadata": {},
+   "source": [
+    "Whether to **combine results from both prompting schemes** in the latex tables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "40f8b070-8c7b-4935-af5b-4fa4789c1bc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "COMBINE_PROMPTING_RESULTS = True\n",
+    "# COMBINE_PROMPTING_RESULTS = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "477e4f80-2c42-458b-a98c-b32a95c7d67a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*** ACSINCOME ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & \\cellcolor{cyan!25.0} 0.05 & \\cellcolor{cyan!25.0} 0.16 & \\cellcolor{cyan!20.6} 0.83 & \\cellcolor{cyan!24.4} 0.78 & 0.24 & 0.24 & \\cellcolor{cyan!17.6} 0.85 & 0.74 \\\\\n",
+      "Mixtral 8x22B (it) & 0.11 & \\cellcolor{cyan!15.5} 0.17 & \\cellcolor{cyan!25.0} 0.84 & \\cellcolor{cyan!18.9} 0.77 & 0.21 & \\cellcolor{cyan!3.6} 0.22 & \\cellcolor{cyan!11.2} 0.85 & \\cellcolor{cyan!11.1} 0.76 \\\\\n",
+      "Mixtral 8x22B & 0.13 & \\cellcolor{cyan!3.6} 0.18 & \\cellcolor{cyan!9.6} 0.82 & \\cellcolor{cyan!2.4} 0.74 & \\cellcolor{cyan!13.2} 0.17 & \\cellcolor{cyan!21.6} 0.19 & \\cellcolor{cyan!16.5} 0.85 & 0.68 \\\\\n",
+      "Llama 3 70B (it) & 0.25 & 0.23 & \\cellcolor{cyan!22.1} 0.84 & 0.67 & 0.27 & 0.27 & \\cellcolor{cyan!25.0} 0.86 & 0.69 \\\\\n",
+      "Llama 3 70B & 0.27 & 0.24 & \\cellcolor{cyan!6.7} 0.82 & 0.54 & 0.20 & \\cellcolor{cyan!14.9} 0.20 & \\cellcolor{cyan!20.8} 0.86 & 0.70 \\\\\n",
+      "Mixtral 8x7B (it) & 0.10 & \\cellcolor{cyan!15.5} 0.17 & \\cellcolor{cyan!22.8} 0.84 & \\cellcolor{cyan!12.8} 0.76 & \\cellcolor{cyan!16.8} 0.16 & \\cellcolor{cyan!25.0} 0.18 & \\cellcolor{cyan!19.7} 0.86 & \\cellcolor{cyan!25.0} 0.78 \\\\\n",
+      "Mixtral 8x7B & \\cellcolor{cyan!4.0} 0.07 & \\cellcolor{cyan!13.1} 0.17 & \\cellcolor{cyan!3.7} 0.81 & \\cellcolor{cyan!25.0} 0.78 & \\cellcolor{cyan!10.6} 0.17 & \\cellcolor{cyan!11.5} 0.21 & 0.83 & 0.65 \\\\\n",
+      "Yi 34B (it) & 0.22 & 0.21 & 0.80 & 0.48 & \\cellcolor{cyan!1.3} 0.19 & \\cellcolor{cyan!18.8} 0.19 & \\cellcolor{cyan!19.7} 0.86 & 0.72 \\\\\n",
+      "Yi 34B & 0.15 & 0.19 & \\cellcolor{cyan!17.7} 0.83 & 0.61 & 0.25 & \\cellcolor{cyan!2.5} 0.22 & \\cellcolor{cyan!13.3} 0.85 & 0.62 \\\\\n",
+      "Llama 3 8B (it) & 0.23 & 0.23 & \\cellcolor{cyan!0.1} 0.81 & 0.67 & 0.32 & 0.30 & \\cellcolor{cyan!13.3} 0.85 & 0.62 \\\\\n",
+      "Llama 3 8B & 0.14 & 0.24 & 0.63 & \\cellcolor{orange!3.7} 0.40 & 0.25 & 0.26 & 0.81 & \\cellcolor{orange!20.8} 0.38 \\\\\n",
+      "Mistral 7B (it) & 0.16 & 0.19 & \\cellcolor{cyan!14.7} 0.83 & 0.70 & 0.21 & \\cellcolor{cyan!4.7} 0.22 & 0.83 & \\cellcolor{cyan!16.5} 0.77 \\\\\n",
+      "Gemma 7B (it) & 0.33 & 0.30 & 0.78 & 0.42 & \\cellcolor{orange!14.7} 0.61 & \\cellcolor{orange!4.7} 0.59 & \\cellcolor{cyan!3.8} 0.84 & \\cellcolor{orange!25.0} 0.37 \\\\\n",
+      "Mistral 7B & \\cellcolor{orange!15.7} 0.36 & 0.32 & 0.75 & 0.49 & 0.20 & 0.23 & 0.80 & 0.73 \\\\\n",
+      "Gemma 7B & 0.15 & 0.20 & 0.80 & 0.73 & 0.24 & 0.27 & 0.76 & \\cellcolor{orange!25.0} 0.37 \\\\\n",
+      "Gemma 2B (it) & 0.28 & 0.31 & \\cellcolor{orange!25.0} 0.50 & \\cellcolor{orange!25.0} 0.37 & \\cellcolor{orange!25.0} 0.63 & \\cellcolor{orange!25.0} 0.63 & 0.73 & \\cellcolor{orange!25.0} 0.37 \\\\\n",
+      "Gemma 2B & \\cellcolor{orange!25.0} 0.37 & \\cellcolor{orange!25.0} 0.37 & \\cellcolor{orange!25.0} 0.50 & 0.63 & \\cellcolor{cyan!25.0} 0.14 & 0.25 & \\cellcolor{orange!25.0} 0.62 & 0.45 \\\\\n",
+      "LR & 0.03 & 0.18 & 0.79 & 0.74 & 0.03 & 0.18 & 0.79 & 0.74 \\\\\n",
+      "GBM & 0.01 & 0.13 & 0.89 & 0.81 & 0.01 & 0.13 & 0.89 & 0.81 \\\\\n",
+      "XGBoost & 0.00 & 0.13 & 0.90 & 0.82 & 0.00 & 0.13 & 0.90 & 0.82 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n",
+      "*** ACSMOBILITY ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & 0.22 & 0.25 & 0.49 & \\cellcolor{cyan!25.0} 0.73 & 0.26 & 0.26 & \\cellcolor{cyan!0.6} 0.57 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Mixtral 8x22B (it) & 0.05 & \\cellcolor{cyan!17.9} 0.20 & \\cellcolor{cyan!25.0} 0.54 & \\cellcolor{cyan!25.0} 0.73 & 0.40 & 0.40 & \\cellcolor{orange!12.8} 0.51 & 0.39 \\\\\n",
+      "Mixtral 8x22B & 0.13 & 0.22 & 0.49 & \\cellcolor{cyan!25.0} 0.73 & \\cellcolor{cyan!10.2} 0.11 & \\cellcolor{cyan!21.2} 0.21 & 0.55 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Llama 3 70B (it) & 0.05 & \\cellcolor{cyan!10.7} 0.20 & 0.52 & \\cellcolor{cyan!25.0} 0.73 & 0.20 & \\cellcolor{cyan!1.5} 0.25 & 0.57 & 0.58 \\\\\n",
+      "Llama 3 70B & 0.06 & \\cellcolor{cyan!7.1} 0.20 & 0.53 & \\cellcolor{cyan!20.7} 0.73 & 0.22 & \\cellcolor{cyan!4.8} 0.24 & 0.55 & 0.53 \\\\\n",
+      "Mixtral 8x7B (it) & 0.11 & 0.21 & 0.51 & \\cellcolor{cyan!25.0} 0.73 & 0.26 & 0.26 & \\cellcolor{cyan!12.8} 0.58 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Mixtral 8x7B & 0.24 & 0.25 & \\cellcolor{orange!25.0} 0.48 & \\cellcolor{cyan!25.0} 0.73 & \\cellcolor{cyan!0.8} 0.14 & \\cellcolor{cyan!18.9} 0.21 & 0.57 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Yi 34B (it) & 0.23 & 0.25 & 0.50 & \\cellcolor{orange!25.0} 0.27 & \\cellcolor{cyan!18.2} 0.09 & \\cellcolor{cyan!23.1} 0.20 & 0.56 & \\cellcolor{cyan!15.4} 0.72 \\\\\n",
+      "Yi 34B & 0.15 & 0.23 & 0.52 & 0.44 & \\cellcolor{cyan!25.0} 0.07 & \\cellcolor{cyan!25.0} 0.20 & 0.57 & \\cellcolor{cyan!20.7} 0.73 \\\\\n",
+      "Llama 3 8B (it) & 0.11 & 0.21 & 0.49 & \\cellcolor{cyan!23.4} 0.73 & 0.15 & \\cellcolor{cyan!13.7} 0.22 & 0.56 & \\cellcolor{cyan!5.3} 0.70 \\\\\n",
+      "Llama 3 8B & 0.14 & 0.21 & 0.51 & \\cellcolor{cyan!19.1} 0.72 & \\cellcolor{cyan!12.5} 0.10 & \\cellcolor{cyan!21.7} 0.20 & 0.55 & \\cellcolor{cyan!24.5} 0.73 \\\\\n",
+      "Mistral 7B (it) & 0.17 & 0.23 & 0.49 & \\cellcolor{cyan!24.5} 0.73 & 0.26 & 0.26 & 0.57 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Gemma 7B (it) & \\cellcolor{orange!8.9} 0.25 & 0.26 & \\cellcolor{orange!3.1} 0.49 & \\cellcolor{cyan!22.3} 0.73 & 0.25 & 0.26 & \\cellcolor{cyan!25.0} 0.58 & \\cellcolor{cyan!21.8} 0.73 \\\\\n",
+      "Mistral 7B & \\cellcolor{orange!25.0} 0.27 & \\cellcolor{orange!25.0} 0.27 & 0.50 & \\cellcolor{cyan!25.0} 0.73 & 0.20 & \\cellcolor{cyan!8.6} 0.23 & 0.53 & \\cellcolor{cyan!23.4} 0.73 \\\\\n",
+      "Gemma 7B & 0.19 & 0.24 & 0.49 & \\cellcolor{cyan!25.0} 0.73 & 0.41 & 0.37 & \\cellcolor{orange!25.0} 0.50 & \\cellcolor{orange!25.0} 0.27 \\\\\n",
+      "Gemma 2B (it) & \\cellcolor{cyan!25.0} 0.02 & \\cellcolor{cyan!25.0} 0.20 & 0.50 & \\cellcolor{cyan!25.0} 0.73 & \\cellcolor{orange!25.0} 0.73 & \\cellcolor{orange!25.0} 0.73 & 0.52 & \\cellcolor{orange!25.0} 0.27 \\\\\n",
+      "Gemma 2B & \\cellcolor{orange!25.0} 0.27 & \\cellcolor{orange!25.0} 0.27 & 0.50 & \\cellcolor{cyan!25.0} 0.73 & 0.25 & 0.26 & 0.51 & 0.34 \\\\\n",
+      "LR & 0.02 & 0.19 & 0.61 & 0.74 & 0.02 & 0.19 & 0.61 & 0.74 \\\\\n",
+      "GBM & 0.01 & 0.17 & 0.74 & 0.76 & 0.01 & 0.17 & 0.74 & 0.76 \\\\\n",
+      "XGBoost & 0.00 & 0.16 & 0.74 & 0.76 & 0.00 & 0.16 & 0.74 & 0.76 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n",
+      "*** ACSEMPLOYMENT ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & 0.23 & 0.23 & 0.80 & 0.73 & 0.28 & 0.29 & 0.79 & 0.65 \\\\\n",
+      "Mixtral 8x22B (it) & 0.06 & \\cellcolor{cyan!21.9} 0.14 & \\cellcolor{cyan!24.4} 0.87 & \\cellcolor{cyan!16.5} 0.79 & \\cellcolor{orange!23.0} 0.38 & \\cellcolor{orange!8.0} 0.39 & 0.60 & 0.51 \\\\\n",
+      "Mixtral 8x22B & 0.15 & 0.18 & 0.82 & \\cellcolor{cyan!20.7} 0.80 & 0.21 & 0.24 & \\cellcolor{cyan!25.0} 0.86 & 0.52 \\\\\n",
+      "Llama 3 70B (it) & \\cellcolor{cyan!0.7} 0.05 & \\cellcolor{cyan!25.0} 0.14 & \\cellcolor{cyan!25.0} 0.88 & \\cellcolor{cyan!25.0} 0.81 & 0.17 & \\cellcolor{cyan!17.1} 0.19 & \\cellcolor{cyan!23.3} 0.85 & \\cellcolor{cyan!15.4} 0.73 \\\\\n",
+      "Llama 3 70B & 0.05 & \\cellcolor{cyan!16.7} 0.15 & \\cellcolor{cyan!13.6} 0.86 & \\cellcolor{cyan!5.8} 0.78 & 0.25 & 0.26 & \\cellcolor{cyan!2.3} 0.82 & 0.52 \\\\\n",
+      "Mixtral 8x7B (it) & 0.07 & \\cellcolor{cyan!16.7} 0.15 & \\cellcolor{cyan!23.2} 0.87 & \\cellcolor{cyan!7.2} 0.78 & 0.22 & 0.24 & \\cellcolor{cyan!6.8} 0.82 & \\cellcolor{cyan!13.7} 0.73 \\\\\n",
+      "Mixtral 8x7B & 0.08 & 0.17 & 0.81 & 0.73 & 0.30 & 0.31 & \\cellcolor{cyan!1.1} 0.81 & \\cellcolor{orange!24.1} 0.45 \\\\\n",
+      "Yi 34B (it) & 0.15 & 0.21 & 0.81 & 0.51 & 0.14 & 0.21 & 0.79 & 0.69 \\\\\n",
+      "Yi 34B & 0.13 & 0.23 & 0.66 & 0.50 & 0.08 & 0.23 & 0.70 & 0.62 \\\\\n",
+      "Llama 3 8B (it) & 0.08 & 0.17 & 0.82 & 0.77 & 0.07 & \\cellcolor{cyan!25.0} 0.19 & 0.79 & \\cellcolor{cyan!25.0} 0.74 \\\\\n",
+      "Llama 3 8B & 0.15 & 0.23 & 0.75 & \\cellcolor{orange!25.0} 0.46 & 0.34 & 0.34 & 0.76 & \\cellcolor{orange!25.0} 0.45 \\\\\n",
+      "Mistral 7B (it) & \\cellcolor{cyan!7.4} 0.04 & 0.19 & 0.79 & 0.69 & \\cellcolor{orange!6.6} 0.35 & 0.36 & 0.72 & 0.63 \\\\\n",
+      "Gemma 7B (it) & \\cellcolor{cyan!8.8} 0.04 & 0.22 & 0.71 & 0.60 & \\cellcolor{orange!10.0} 0.36 & 0.38 & 0.59 & 0.58 \\\\\n",
+      "Mistral 7B & 0.14 & 0.19 & 0.80 & \\cellcolor{cyan!12.9} 0.79 & 0.26 & 0.30 & 0.76 & \\cellcolor{orange!25.0} 0.45 \\\\\n",
+      "Gemma 7B & \\cellcolor{orange!25.0} 0.35 & \\cellcolor{orange!25.0} 0.38 & \\cellcolor{orange!1.6} 0.50 & 0.51 & 0.15 & 0.25 & 0.65 & \\cellcolor{orange!4.1} 0.48 \\\\\n",
+      "Gemma 2B (it) & 0.12 & 0.27 & \\cellcolor{orange!25.0} 0.46 & \\cellcolor{orange!25.0} 0.46 & \\cellcolor{orange!25.0} 0.38 & \\cellcolor{orange!25.0} 0.41 & \\cellcolor{orange!25.0} 0.42 & \\cellcolor{orange!16.3} 0.46 \\\\\n",
+      "Gemma 2B & \\cellcolor{cyan!25.0} 0.01 & 0.23 & 0.57 & 0.53 & \\cellcolor{cyan!25.0} 0.01 & 0.24 & 0.63 & 0.54 \\\\\n",
+      "LR & 0.02 & 0.15 & 0.86 & 0.78 & 0.02 & 0.15 & 0.86 & 0.78 \\\\\n",
+      "GBM & 0.00 & 0.12 & 0.91 & 0.83 & 0.00 & 0.12 & 0.91 & 0.83 \\\\\n",
+      "XGBoost & 0.00 & 0.12 & 0.91 & 0.83 & 0.00 & 0.12 & 0.91 & 0.83 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n",
+      "*** ACSTRAVELTIME ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & 0.15 & 0.27 & 0.58 & 0.57 & 0.39 & 0.40 & 0.65 & 0.55 \\\\\n",
+      "Mixtral 8x22B (it) & 0.12 & \\cellcolor{cyan!23.7} 0.24 & \\cellcolor{cyan!23.5} 0.64 & \\cellcolor{cyan!25.0} 0.59 & 0.31 & 0.33 & 0.66 & \\cellcolor{cyan!17.1} 0.59 \\\\\n",
+      "Mixtral 8x22B & 0.30 & 0.34 & 0.57 & \\cellcolor{cyan!2.4} 0.58 & 0.20 & 0.28 & 0.63 & \\cellcolor{orange!25.0} 0.44 \\\\\n",
+      "Llama 3 70B (it) & 0.12 & \\cellcolor{cyan!22.5} 0.24 & \\cellcolor{cyan!25.0} 0.64 & 0.53 & 0.15 & \\cellcolor{cyan!22.6} 0.24 & \\cellcolor{cyan!25.0} 0.70 & \\cellcolor{cyan!25.0} 0.60 \\\\\n",
+      "Llama 3 70B & 0.08 & \\cellcolor{cyan!12.4} 0.25 & 0.52 & 0.46 & 0.09 & \\cellcolor{cyan!21.4} 0.24 & 0.67 & 0.55 \\\\\n",
+      "Mixtral 8x7B (it) & 0.09 & \\cellcolor{cyan!25.0} 0.24 & 0.61 & 0.57 & \\cellcolor{orange!25.0} 0.45 & \\cellcolor{orange!25.0} 0.45 & 0.66 & 0.52 \\\\\n",
+      "Mixtral 8x7B & 0.07 & \\cellcolor{cyan!16.2} 0.25 & 0.57 & \\cellcolor{cyan!2.4} 0.58 & 0.28 & 0.32 & 0.60 & \\cellcolor{orange!25.0} 0.44 \\\\\n",
+      "Yi 34B (it) & \\cellcolor{cyan!3.7} 0.06 & \\cellcolor{cyan!11.2} 0.25 & 0.50 & \\cellcolor{orange!25.0} 0.44 & 0.35 & 0.36 & 0.65 & 0.56 \\\\\n",
+      "Yi 34B & 0.14 & 0.27 & 0.53 & \\cellcolor{orange!25.0} 0.44 & \\cellcolor{cyan!4.5} 0.08 & \\cellcolor{cyan!25.0} 0.24 & 0.62 & 0.56 \\\\\n",
+      "Llama 3 8B (it) & 0.11 & \\cellcolor{cyan!12.4} 0.25 & 0.56 & 0.56 & 0.19 & 0.28 & 0.60 & 0.57 \\\\\n",
+      "Llama 3 8B & 0.12 & 0.26 & \\cellcolor{orange!25.0} 0.48 & \\cellcolor{orange!25.0} 0.44 & 0.08 & \\cellcolor{cyan!7.1} 0.25 & 0.53 & 0.56 \\\\\n",
+      "Mistral 7B (it) & 0.11 & \\cellcolor{cyan!11.2} 0.25 & 0.55 & 0.56 & \\cellcolor{orange!2.7} 0.41 & 0.42 & 0.59 & 0.57 \\\\\n",
+      "Gemma 7B (it) & 0.10 & \\cellcolor{cyan!4.9} 0.26 & \\cellcolor{orange!4.9} 0.49 & \\cellcolor{orange!25.0} 0.44 & \\cellcolor{orange!12.0} 0.42 & 0.43 & 0.53 & 0.56 \\\\\n",
+      "Mistral 7B & \\cellcolor{orange!25.0} 0.44 & \\cellcolor{orange!25.0} 0.44 & 0.50 & 0.56 & \\cellcolor{cyan!22.5} 0.05 & \\cellcolor{cyan!14.2} 0.25 & 0.57 & 0.56 \\\\\n",
+      "Gemma 7B & \\cellcolor{cyan!25.0} 0.03 & \\cellcolor{cyan!13.7} 0.25 & 0.52 & 0.55 & \\cellcolor{cyan!25.0} 0.04 & \\cellcolor{cyan!19.0} 0.24 & 0.61 & 0.58 \\\\\n",
+      "Gemma 2B (it) & 0.19 & 0.28 & 0.50 & 0.56 & 0.34 & 0.36 & \\cellcolor{orange!9.3} 0.49 & 0.56 \\\\\n",
+      "Gemma 2B & \\cellcolor{orange!25.0} 0.44 & \\cellcolor{orange!25.0} 0.44 & 0.50 & 0.56 & 0.09 & \\cellcolor{cyan!4.7} 0.26 & \\cellcolor{orange!25.0} 0.48 & \\cellcolor{orange!25.0} 0.44 \\\\\n",
+      "LR & 0.04 & 0.24 & 0.58 & 0.56 & 0.04 & 0.24 & 0.58 & 0.56 \\\\\n",
+      "GBM & 0.02 & 0.20 & 0.75 & 0.69 & 0.02 & 0.20 & 0.75 & 0.69 \\\\\n",
+      "XGBoost & 0.02 & 0.19 & 0.77 & 0.70 & 0.02 & 0.19 & 0.77 & 0.70 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n",
+      "*** ACSPUBLICCOVERAGE ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & 0.10 & 0.20 & 0.68 & \\cellcolor{cyan!17.6} 0.73 & 0.33 & 0.34 & \\cellcolor{cyan!25.0} 0.71 & 0.60 \\\\\n",
+      "Mixtral 8x22B (it) & \\cellcolor{cyan!25.0} 0.04 & \\cellcolor{cyan!25.0} 0.18 & \\cellcolor{cyan!11.0} 0.71 & \\cellcolor{cyan!25.0} 0.75 & 0.24 & 0.25 & \\cellcolor{cyan!11.7} 0.70 & \\cellcolor{cyan!12.7} 0.72 \\\\\n",
+      "Mixtral 8x22B & \\cellcolor{orange!15.5} 0.29 & \\cellcolor{orange!3.4} 0.29 & 0.54 & \\cellcolor{cyan!0.5} 0.70 & 0.32 & 0.30 & 0.59 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Llama 3 70B (it) & 0.13 & 0.20 & \\cellcolor{cyan!25.0} 0.73 & \\cellcolor{cyan!24.4} 0.75 & 0.16 & \\cellcolor{cyan!16.2} 0.21 & \\cellcolor{cyan!7.0} 0.69 & \\cellcolor{cyan!25.0} 0.75 \\\\\n",
+      "Llama 3 70B & 0.12 & 0.21 & 0.64 & 0.53 & 0.18 & \\cellcolor{cyan!8.3} 0.22 & 0.67 & 0.63 \\\\\n",
+      "Mixtral 8x7B (it) & \\cellcolor{cyan!0.3} 0.06 & \\cellcolor{cyan!18.5} 0.19 & 0.69 & \\cellcolor{cyan!22.2} 0.74 & 0.20 & \\cellcolor{cyan!7.4} 0.23 & \\cellcolor{cyan!12.7} 0.70 & \\cellcolor{cyan!23.3} 0.74 \\\\\n",
+      "Mixtral 8x7B & 0.20 & 0.25 & 0.56 & 0.70 & 0.41 & 0.37 & 0.57 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Yi 34B (it) & 0.22 & 0.24 & 0.57 & \\cellcolor{orange!25.0} 0.31 & \\cellcolor{cyan!16.7} 0.06 & \\cellcolor{cyan!25.0} 0.19 & 0.67 & \\cellcolor{cyan!19.4} 0.74 \\\\\n",
+      "Yi 34B & 0.09 & 0.20 & 0.67 & 0.64 & \\cellcolor{cyan!25.0} 0.04 & \\cellcolor{cyan!17.2} 0.21 & 0.59 & \\cellcolor{cyan!2.1} 0.70 \\\\\n",
+      "Llama 3 8B (it) & 0.17 & 0.22 & 0.64 & 0.68 & 0.11 & \\cellcolor{cyan!12.7} 0.21 & 0.59 & \\cellcolor{cyan!7.7} 0.71 \\\\\n",
+      "Llama 3 8B & 0.20 & 0.25 & 0.51 & \\cellcolor{orange!6.2} 0.34 & 0.41 & 0.38 & 0.55 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Mistral 7B (it) & 0.07 & 0.20 & 0.67 & 0.65 & 0.30 & 0.30 & 0.61 & \\cellcolor{cyan!0.4} 0.70 \\\\\n",
+      "Gemma 7B (it) & 0.18 & 0.24 & 0.57 & 0.61 & 0.30 & 0.34 & \\cellcolor{orange!14.6} 0.46 & 0.50 \\\\\n",
+      "Mistral 7B & \\cellcolor{orange!22.1} 0.30 & \\cellcolor{orange!18.5} 0.30 & 0.50 & 0.70 & 0.29 & 0.30 & \\cellcolor{orange!25.0} 0.45 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Gemma 7B & 0.18 & 0.26 & 0.48 & 0.70 & 0.15 & \\cellcolor{cyan!3.4} 0.23 & 0.49 & 0.49 \\\\\n",
+      "Gemma 2B (it) & 0.24 & \\cellcolor{orange!14.2} 0.29 & \\cellcolor{orange!25.0} 0.42 & 0.42 & \\cellcolor{orange!25.0} 0.70 & \\cellcolor{orange!25.0} 0.70 & 0.54 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Gemma 2B & \\cellcolor{orange!25.0} 0.30 & \\cellcolor{orange!25.0} 0.30 & 0.50 & 0.70 & 0.26 & 0.28 & 0.54 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "LR & 0.03 & 0.19 & 0.70 & 0.72 & 0.03 & 0.19 & 0.70 & 0.72 \\\\\n",
+      "GBM & 0.01 & 0.14 & 0.83 & 0.80 & 0.01 & 0.14 & 0.83 & 0.80 \\\\\n",
+      "XGBoost & 0.00 & 0.14 & 0.84 & 0.80 & 0.00 & 0.14 & 0.84 & 0.80 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils import prettify_model_name\n",
+    "\n",
+    "for task in ALL_TASKS:\n",
+    "    task_df = all_results_df[all_results_df[task_col] == task]\n",
+    "\n",
+    "    # Sort table rows\n",
+    "    sorted_df_index = sorted(\n",
+    "        task_df.index.tolist(),\n",
+    "        key=lambda id_: model_sort_key(id_, task_df),\n",
+    "        reverse=True,\n",
+    "    )\n",
+    "\n",
+    "    latex_tab_cols = [numeric_prompt_col] + table_metrics\n",
+    "\n",
+    "    # latex_table = task_df.sort_values([\"model_family\", \"model_size\", \"is_inst\"], ascending=False).set_index(model_col)[table_metrics].round(3)\n",
+    "    latex_table = task_df.loc[sorted_df_index].set_index(model_col)[latex_tab_cols].round(3)\n",
+    "\n",
+    "    # Keep rows for a single selected prompting scheme\n",
+    "    latex_table = latex_table[\n",
+    "        (latex_table[numeric_prompt_col] == NUMERIC_PROMPTING)\n",
+    "        | latex_table[numeric_prompt_col].isna()\n",
+    "    ]\n",
+    "\n",
+    "    # But optionally join with the columns of the other prompting scheme\n",
+    "    if COMBINE_PROMPTING_RESULTS:\n",
+    "        cols_to_keep = [\"ece\", \"brier_score_loss\", \"roc_auc\", \"accuracy\"]\n",
+    "        latex_table = latex_table[cols_to_keep]\n",
+    "\n",
+    "        latex_table_other_prompt = task_df.loc[sorted_df_index].set_index(model_col)\n",
+    "        latex_table_other_prompt = latex_table_other_prompt[\n",
+    "            (latex_table_other_prompt[numeric_prompt_col] != NUMERIC_PROMPTING)\n",
+    "            | latex_table_other_prompt[numeric_prompt_col].isna()\n",
+    "        ][cols_to_keep].round(3)\n",
+    "\n",
+    "        latex_table = latex_table.join(\n",
+    "            latex_table_other_prompt,\n",
+    "            how=\"left\",\n",
+    "            lsuffix=\" (num)\" if NUMERIC_PROMPTING else \" (mult. choice)\",\n",
+    "            validate=\"1:1\",\n",
+    "        )\n",
+    "    \n",
+    "    # Fill NaNs\n",
+    "    latex_table = latex_table.fillna(\"-\")\n",
+    "\n",
+    "    # Prettify model names\n",
+    "    latex_table[\"Model\"] = [\n",
+    "        prettify_model_name(id_) if id_ not in baselines.keys() else id_\n",
+    "        for id_, row in latex_table.iterrows()\n",
+    "    ]\n",
+    "    latex_table.set_index(\"Model\", drop=True, inplace=True)\n",
+    "\n",
+    "    for col in set(latex_table.columns.tolist()) - {\"score_stdev\", numeric_prompt_col, \"model_family\"}:\n",
+    "        index_without_baselines = [name for name in latex_table.index if name not in baselines]\n",
+    "        index_baselines = list(baselines.keys())\n",
+    "\n",
+    "        col_data = latex_table.loc[index_without_baselines, col].copy()\n",
+    "\n",
+    "        new_col_data = [\n",
+    "            latex_colored_float_format(\n",
+    "                val=col_data.loc[id_], all_values=col_data,\n",
+    "                higher_is_better=any(col_name in col for col_name in higher_is_better_cols),\n",
+    "            )\n",
+    "            for id_ in index_without_baselines\n",
+    "        ] + [\n",
+    "            latex_table.loc[id_, col]\n",
+    "            for id_ in index_baselines\n",
+    "        ]\n",
+    "\n",
+    "        # Set compatible dtype\n",
+    "        latex_table = latex_table.astype({col: str})\n",
+    "\n",
+    "        # Set new data\n",
+    "        latex_table.loc[index_without_baselines + index_baselines, col] = new_col_data\n",
+    "\n",
+    "    # Drop prompting column of not combining both\n",
+    "    latex_table = latex_table.drop(columns=numeric_prompt_col, errors=\"ignore\")\n",
+    "\n",
+    "    # Rename columns to be latex compatible\n",
+    "    latex_table = latex_table.rename(columns={col: col.replace(\"_\", \" \") for col in latex_table.columns})\n",
+    "\n",
+    "    print(f\"*** {task.upper()} ***\\n\")\n",
+    "\n",
+    "    latex_table_str = latex_table.to_latex(float_format=\"%.2f\")\n",
+    "    print(latex_table_str)\n",
+    "\n",
+    "    tables_dir = ACS_AGG_RESULTS_PATH.parent / \"tables\"\n",
+    "    tables_dir.mkdir(exist_ok=True)\n",
+    "    with open(tables_dir / f\"{task}-table.{COMBINE_PROMPTING_RESULTS=}.{NUMERIC_PROMPTING=}.tex\", \"w\") as f_out:\n",
+    "        print(latex_table_str, file=f_out)\n",
+    "    \n",
+    "    print(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d4564ed-1e41-4322-8c87-74fc34265bf5",
+   "metadata": {},
+   "source": [
+    "## Render paper plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82cc9102-66a6-4001-9c36-cf88fa8209e6",
+   "metadata": {},
+   "source": [
+    "Filter by the currently selected prompting scheme:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "37b2b004-44e8-46d3-aa45-2cf168b5e638",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_results_df_original = all_results_df.copy()\n",
+    "\n",
+    "all_results_df = all_results_df[\n",
+    "    (all_results_df[numeric_prompt_col] == NUMERIC_PROMPTING)\n",
+    "    | (all_results_df[numeric_prompt_col].isna())\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "efd4cfdf-ae7e-4770-8bd6-0347342517c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
+       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
+       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
+       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
+       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
+       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"whitegrid\", rc={\"grid.linestyle\": \"--\"})\n",
+    "plt.rc(\"figure\", figsize=(4.5, 3.5), dpi=200)\n",
+    "\n",
+    "palette_name = \"tab10\"   # \"colorblind\"\n",
+    "palette = sns.color_palette(palette_name)\n",
+    "sns.set_palette(palette_name)\n",
+    "palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e93c2236-8546-439c-8927-fde4f8ea7350",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMGS_DIR = ACS_AGG_RESULTS_PATH.parent / \"imgs\"\n",
+    "IMGS_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "def save_fig(fig, name, add_prompt_suffix=True):\n",
+    "    num_or_multiple_choice = \"numeric-prompt\" if NUMERIC_PROMPTING else \"multiple-choice-prompt\"\n",
+    "    save_path = (IMGS_DIR / name).with_suffix(\".pdf\")\n",
+    "    if add_prompt_suffix:\n",
+    "        save_path = save_path.with_suffix(f\".{num_or_multiple_choice}.pdf\")\n",
+    "\n",
+    "    fig.savefig(save_path, bbox_inches=\"tight\")\n",
+    "    print(f\"Saved figure to '{save_path.as_posix()}'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbdedf0b-ccb1-4887-8474-b1e661f0fe01",
+   "metadata": {},
+   "source": [
+    "Load scores distributions for each model (and with varying degrees of information)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "32dcc2e9-b122-4931-a4be-811efd7fa6cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_path_col = \"predictions_path\"\n",
+    "\n",
+    "def load_model_scores_df(df_row: pd.Series) -> pd.DataFrame:\n",
+    "    \"\"\"Loads csv containing model scores corresponding to the given DF row.\"\"\"\n",
+    "    if predictions_path_col in df_row and not pd.isna(df_row[predictions_path_col]):\n",
+    "        return pd.read_csv(df_row[predictions_path_col], index_col=0)\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1f7cfb2d-3195-4659-97b2-8b0ada073f23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "465640a48d654dd3999164c4cc8efc3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scores_df_map = {\n",
+    "    id_: load_model_scores_df(row)\n",
+    "    for id_, row in tqdm(all_results_df.iterrows(), total=len(all_results_df))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a55adb53-12c1-4784-885c-6e0597f255e2",
+   "metadata": {},
+   "source": [
+    "#### 1st page illustrative plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "0c8819b1-0033-4d44-ab1e-a4b55a4ebd65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_task = \"ACSIncome\"\n",
+    "\n",
+    "# example_model = \"Mistral-7B-v0.1\"\n",
+    "example_model = \"Meta-Llama-3-70B\"\n",
+    "\n",
+    "baseline_model = \"XGBoost\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "deb6b51b-d49c-4551-85a9-f73cef23287a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "      <th>model_size</th>\n",
+       "      <th>model_family</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Meta-Llama-3-70B__ACSIncome__-1__Num</th>\n",
+       "      <td>0.543953</td>\n",
+       "      <td>0.134809</td>\n",
+       "      <td>0.771466</td>\n",
+       "      <td>0.636284</td>\n",
+       "      <td>0.078752</td>\n",
+       "      <td>0.887756</td>\n",
+       "      <td>0.237630</td>\n",
+       "      <td>0.270157</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.191080</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.89560</td>\n",
+       "      <td>0.784722</td>\n",
+       "      <td>0.8956</td>\n",
+       "      <td>0.784722</td>\n",
+       "      <td>0.253299</td>\n",
+       "      <td>0.638033</td>\n",
+       "      <td>70</td>\n",
+       "      <td>Llama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num</th>\n",
+       "      <td>0.665731</td>\n",
+       "      <td>0.086680</td>\n",
+       "      <td>0.880953</td>\n",
+       "      <td>0.722658</td>\n",
+       "      <td>0.061832</td>\n",
+       "      <td>0.919317</td>\n",
+       "      <td>0.230512</td>\n",
+       "      <td>0.246092</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.232483</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.75718</td>\n",
+       "      <td>0.735764</td>\n",
+       "      <td>0.8239</td>\n",
+       "      <td>0.784848</td>\n",
+       "      <td>0.290286</td>\n",
+       "      <td>0.613968</td>\n",
+       "      <td>70</td>\n",
+       "      <td>Llama</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 66 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               accuracy  accuracy_diff  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num           0.543953       0.134809   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num  0.665731       0.086680   \n",
+       "\n",
+       "                                               accuracy_ratio  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                 0.771466   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num        0.880953   \n",
+       "\n",
+       "                                               balanced_accuracy  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                    0.636284   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num           0.722658   \n",
+       "\n",
+       "                                               balanced_accuracy_diff  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                         0.078752   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num                0.061832   \n",
+       "\n",
+       "                                               balanced_accuracy_ratio  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                          0.887756   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num                 0.919317   \n",
+       "\n",
+       "                                               brier_score_loss       ece  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                   0.237630  0.270157   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num          0.230512  0.246092   \n",
+       "\n",
+       "                                               ece_quantile  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                    NaN   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num           NaN   \n",
+       "\n",
+       "                                               equalized_odds_diff  ...  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                      0.191080  ...   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num             0.232483  ...   \n",
+       "\n",
+       "                                               num_features  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                     -1   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num            -1   \n",
+       "\n",
+       "                                               uses_all_features  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                        True   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num               True   \n",
+       "\n",
+       "                                               fit_thresh_on_100  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                     0.89560   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num            0.75718   \n",
+       "\n",
+       "                                               fit_thresh_accuracy  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                      0.784722   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num             0.735764   \n",
+       "\n",
+       "                                               optimal_thresh  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                   0.8956   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num          0.8239   \n",
+       "\n",
+       "                                               optimal_thresh_accuracy  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                          0.784722   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num                 0.784848   \n",
+       "\n",
+       "                                               score_stdev  score_mean  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num              0.253299    0.638033   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num     0.290286    0.613968   \n",
+       "\n",
+       "                                              model_size  model_family  \n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                  70         Llama  \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num         70         Llama  \n",
+       "\n",
+       "[2 rows x 66 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Data for baseline model\n",
+    "baseline_row = all_results_df[(all_results_df[\"name\"] == baseline_model) & (all_results_df[task_col] == example_task)].iloc[0]\n",
+    "\n",
+    "# Data for base and instruct models\n",
+    "example_df = all_results_df[\n",
+    "    (all_results_df[task_col] == example_task)\n",
+    "    & (all_results_df[\"base_name\"] == example_model)\n",
+    "    & (all_results_df[numeric_prompt_col] == NUMERIC_PROMPTING)\n",
+    "]\n",
+    "\n",
+    "# Sort examples_df to have (base, instruct) ordering\n",
+    "example_df = example_df.sort_values(\"is_inst\", ascending=True)\n",
+    "example_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "fa917ee4-0c17-4857-9bf9-76de9d9a1e6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3388197/4070898515.py:38: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax1.set_xticklabels([\"Base\", \"Instr.\"])\n",
+      "/tmp/ipykernel_3388197/4070898515.py:90: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax3.set_xticklabels([\"Base\", \"Instr.\"])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/teaser-Meta-Llama-3-70B.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2200x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ALPHA = 0.7\n",
+    "N_BINS = 20\n",
+    "\n",
+    "fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(11, 2.5), gridspec_kw=dict(wspace=0.4))\n",
+    "\n",
+    "###\n",
+    "# ROC barplot\n",
+    "###\n",
+    "sns.barplot(\n",
+    "    data=example_df,\n",
+    "    x=\"is_inst\",\n",
+    "    y=\"roc_auc\",\n",
+    "    hue=\"name\",\n",
+    "    alpha=ALPHA,\n",
+    "    width=0.5,\n",
+    "    ax=ax1,\n",
+    ")\n",
+    "\n",
+    "# Add horizontal line and label\n",
+    "ax1.axhline(y=baseline_row[\"roc_auc\"], label=baseline_model, ls=\"--\", color=\"black\", xmin=0.05, xmax=0.95)\n",
+    "ax1.text(\n",
+    "    x=1.15,\n",
+    "    y=baseline_row[\"roc_auc\"] + 1e-3,\n",
+    "    s=baseline_model,\n",
+    "    color=\"black\",\n",
+    "    fontsize=9,\n",
+    "    ha='center',\n",
+    "    va='bottom',\n",
+    "    backgroundcolor='white',\n",
+    "    zorder=-1,\n",
+    ")\n",
+    "\n",
+    "ax1.set_ylim(0, baseline_row[\"roc_auc\"] + 9e-2)\n",
+    "# ax1.set_ylim(0.5, baseline_row[\"roc_auc\"] + 7e-2)\n",
+    "ax1.set_ylabel(\"ROC AUC\")\n",
+    "ax1.set_xlabel(None)\n",
+    "ax1.legend().remove()   # Remove the legend\n",
+    "ax1.set_xticklabels([\"Base\", \"Instr.\"])\n",
+    "ax1.set_title(\"Predictive signal\" + r\"$\\uparrow$\")\n",
+    "\n",
+    "###\n",
+    "# Score distribution\n",
+    "###\n",
+    "bins = np.histogram_bin_edges([], bins=N_BINS, range=(0, 1))\n",
+    "for id_ in example_df.index.tolist():\n",
+    "    sns.histplot(\n",
+    "        scores_df_map[id_][\"risk_score\"],\n",
+    "        alpha=ALPHA * 0.9,\n",
+    "        stat=\"proportion\",\n",
+    "        bins=bins,\n",
+    "        zorder=100,\n",
+    "        ax=ax2,\n",
+    "    )\n",
+    "\n",
+    "# # Draw baseline score distribution\n",
+    "# sns.histplot(\n",
+    "#     scores_df_map[baseline_row.name][\"risk_score\"],\n",
+    "#     alpha=1,\n",
+    "#     bins=bins,\n",
+    "#     stat=\"proportion\",\n",
+    "#     fill=False,\n",
+    "#     color=palette[2],\n",
+    "#     edgecolor=palette[2],\n",
+    "#     hatch=\"/\",\n",
+    "#     zorder=-1,\n",
+    "#     ax=ax2,\n",
+    "# )\n",
+    "\n",
+    "ax2.set_xlabel(None)\n",
+    "ax2.set_title(\"Score distribution\")\n",
+    "\n",
+    "###\n",
+    "# Calibration error\n",
+    "###\n",
+    "sns.barplot(\n",
+    "    data=example_df,\n",
+    "    x=\"is_inst\",\n",
+    "    y=\"ece\",\n",
+    "    hue=\"name\",\n",
+    "    alpha=ALPHA,\n",
+    "    width=0.5,\n",
+    "    ax=ax3,\n",
+    ")\n",
+    "\n",
+    "# Add horizontal line and label\n",
+    "ax3.axhline(y=baseline_row[\"ece\"], label=baseline_model, ls=\"--\", color=\"black\", xmin=0.05, xmax=0.95)\n",
+    "\n",
+    "ax3.set_ylabel(\"ECE\")\n",
+    "ax3.set_xlabel(None)\n",
+    "ax3.set_xticklabels([\"Base\", \"Instr.\"])\n",
+    "ax3.set_title(\"Calibration error\" + \"$\\downarrow$\")\n",
+    "# ax3.legend(\n",
+    "#     loc=\"center left\",\n",
+    "#     bbox_to_anchor=(1.05, 0.5),\n",
+    "# )\n",
+    "\n",
+    "hs, ls = ax3.get_legend_handles_labels()\n",
+    "ax3.legend().remove()\n",
+    "ax2.legend(handles=hs[:2], labels=ls[:2], loc=\"upper center\")\n",
+    "\n",
+    "save_fig(fig, f\"teaser-{example_model}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36a530b0-4e32-4993-9d4d-6cd8fa4330da",
+   "metadata": {},
+   "source": [
+    "#### Score distributions for base/instruct pairs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "dc652329-ac67-48d1-8f2c-632c2acfe483",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting score distributions for the following task: ACSIncome.\n",
+      "len(task_df)=20\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Plotting score distributions for the following task: {example_task}.\")\n",
+    "\n",
+    "task_df = all_results_df[\n",
+    "    (all_results_df[task_col] == example_task)\n",
+    "    & (\n",
+    "        (all_results_df[numeric_prompt_col] == NUMERIC_PROMPTING)\n",
+    "        | (all_results_df[numeric_prompt_col].isna())                  # Baseline models will have NaNs in this col.\n",
+    "    )]\n",
+    "print(f\"{len(task_df)=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42f4f271-b74c-492d-88cf-ce44724c8a76",
+   "metadata": {},
+   "source": [
+    "Sort model pairs as wished for the paper plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f422a80a-b5f4-413f-a4e7-2bde1611d36d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('penai/gpt-4o-mini', 'penai/gpt-4o-mini'),\n",
+       " ('Meta-Llama-3-8B', 'Meta-Llama-3-8B-Instruct'),\n",
+       " ('Meta-Llama-3-70B', 'Meta-Llama-3-70B-Instruct'),\n",
+       " ('Mistral-7B-v0.1', 'Mistral-7B-Instruct-v0.2'),\n",
+       " ('Mixtral-8x7B-v0.1', 'Mixtral-8x7B-Instruct-v0.1'),\n",
+       " ('Mixtral-8x22B-v0.1', 'Mixtral-8x22B-Instruct-v0.1'),\n",
+       " ('Yi-34B', 'Yi-34B-Chat'),\n",
+       " ('gemma-2b', 'gemma-1.1-2b-it'),\n",
+       " ('gemma-7b', 'gemma-1.1-7b-it')]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_model_families = [\"OpenAI\", \"Llama\", \"Mistral\", \"Yi\", \"Gemma\", \"-\"]\n",
+    "\n",
+    "model_pairs = sorted([\n",
+    "        (row[\"base_name\"], row[model_col])\n",
+    "        for id_, row in task_df[task_df[\"is_inst\"] == True].iterrows()\n",
+    "    ],\n",
+    "    key=lambda val: (\n",
+    "        1e6 * sorted_model_families.index((row := task_df[task_df[model_col] == val[0]].iloc[0])[\"model_family\"])\n",
+    "        + row[\"model_size\"]\n",
+    "    )\n",
+    ")\n",
+    "model_pairs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "de08f766-c37d-4c27-9261-684b97c6b921",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/score-distribution.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2400x1200 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "PLOT_THRESHOLD_LINE = False\n",
+    "\n",
+    "N_PLOTS = len(model_pairs) + 1  # plus 1 for the XGBoost baseline\n",
+    "\n",
+    "N_COLS = 5\n",
+    "N_ROWS = math.ceil(N_PLOTS / N_COLS)\n",
+    "\n",
+    "fig, axes = plt.subplots(\n",
+    "    nrows=N_ROWS, ncols=N_COLS,\n",
+    "    figsize=(12, 3 * N_ROWS),\n",
+    "    sharey=True, sharex=False,\n",
+    "    gridspec_kw=dict(\n",
+    "        hspace=0.5,\n",
+    "        wspace=0.06,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Plot settings\n",
+    "plot_config = dict(\n",
+    "    bins=N_BINS,\n",
+    "    binrange=(0, 1),\n",
+    "    stat=\"percent\",\n",
+    ")\n",
+    "\n",
+    "# Plot score distribution for model pairs\n",
+    "for idx, (base_name, instr_name) in enumerate(model_pairs):\n",
+    "    ax_row = idx // N_COLS\n",
+    "    ax_col = idx % N_COLS\n",
+    "\n",
+    "    ax = axes[ax_row, ax_col]\n",
+    "    base_row = task_df[task_df[model_col] == base_name].iloc[0]\n",
+    "    it_row = task_df[task_df[model_col] == instr_name].iloc[0]\n",
+    "\n",
+    "    # Get scores\n",
+    "    base_scores = scores_df_map[base_row.name][\"risk_score\"]\n",
+    "    it_scores = scores_df_map[it_row.name][\"risk_score\"]\n",
+    "\n",
+    "    ax.set_title(base_row[\"name\"])\n",
+    "\n",
+    "    ax.set_xlabel(\"risk scores\")\n",
+    "    if ax_col == 0:\n",
+    "        ax.set_ylabel(\"density (%)\")\n",
+    "\n",
+    "    if ax_row == 0:\n",
+    "        ax.set_ylim(0, 70)\n",
+    "\n",
+    "    ax.set_xlim(-0.03, 1.03)\n",
+    "\n",
+    "    # Render plot\n",
+    "    if \"gpt\" not in instr_name.lower():\n",
+    "        sns.histplot(base_scores, label=\"Base\", color=palette[0], ax=ax, **plot_config)\n",
+    "    sns.histplot(it_scores, label=\"Instruction-tuned\", color=palette[1], ax=ax, **plot_config)\n",
+    "\n",
+    "    # Draw faint vertical line with t^* of each model\n",
+    "    threshold_col = \"optimal_thresh\"\n",
+    "    # threshold_col = \"fit_thresh_on_100\"\n",
+    "\n",
+    "    if PLOT_THRESHOLD_LINE:\n",
+    "        ax.axvline(x=base_row[threshold_col], ls=\"--\", color=palette[0], alpha=0.5)\n",
+    "        ax.axvline(x=it_row[threshold_col], ls=\"--\", color=palette[1], alpha=0.5)\n",
+    "\n",
+    "    # Render legend on a few sub-plots\n",
+    "    if (ax_col == 0 and ax_row == 1) or (ax_col == N_COLS-1 and ax_row == 0):\n",
+    "        ax.legend(loc=\"upper center\")\n",
+    "\n",
+    "        if PLOT_THRESHOLD_LINE:\n",
+    "            ax.text(\n",
+    "                x=base_row[threshold_col] - 5e-2,\n",
+    "                y=43,\n",
+    "                s=\"$t^*$\",\n",
+    "                color=palette[0],\n",
+    "                fontsize=10,\n",
+    "                ha='center',\n",
+    "                va='bottom',\n",
+    "                # zorder=-1,\n",
+    "            ) #.set_bbox(dict(facecolor='white', alpha=0.5))\n",
+    "            \n",
+    "            ax.text(\n",
+    "                x=it_row[threshold_col] + 5e-2,\n",
+    "                y=43,\n",
+    "                s=\"$t^*$\",\n",
+    "                color=palette[1],\n",
+    "                fontsize=10,\n",
+    "                ha='center',\n",
+    "                va='bottom',\n",
+    "                # zorder=-1,\n",
+    "            )\n",
+    "        \n",
+    "\n",
+    "# Plot score distribution for XGBoost baseline\n",
+    "baseline_name = \"XGBoost\"\n",
+    "ax = axes[1, -1]\n",
+    "ax.set_title(baseline_name)\n",
+    "\n",
+    "baseline_row = task_df[task_df[\"name\"] == baseline_name].iloc[0]\n",
+    "xgboost_scores = scores_df_map[baseline_row.name][\"risk_score\"]\n",
+    "\n",
+    "sns.histplot(\n",
+    "    xgboost_scores,\n",
+    "    label=baseline_name,\n",
+    "    color=palette[2],\n",
+    "    ax=ax,\n",
+    "    **plot_config,\n",
+    ")\n",
+    "\n",
+    "ax.set_xlabel(\"risk scores\")\n",
+    "\n",
+    "# # Remove unused plot?\n",
+    "# axes[1, 3].remove()\n",
+    "\n",
+    "axes[1, 4].set_ylabel(\"density (%)\")\n",
+    "axes[1, 4].set_yticks([0, 20, 40, 60])\n",
+    "axes[1, 4].set_yticklabels([0, 20, 40, 60])\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "# Save figure\n",
+    "save_fig(fig, f\"score-distribution\" + (\"_with-threshold\" if PLOT_THRESHOLD_LINE else \"\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1e20b4f-5e3f-4c22-8309-b31f2befa29d",
+   "metadata": {},
+   "source": [
+    "## Calibration curves"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "831d0a85-c162-4f42-8f5e-d57da37da536",
+   "metadata": {},
+   "source": [
+    "Base models vs Instruction-tuned models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "79f2f793-da86-4797-bdf6-b2aee4a20c36",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "len(df)=17\n"
+     ]
+    }
+   ],
+   "source": [
+    "TASK = \"ACSIncome\"\n",
+    "\n",
+    "# df = results_df[(results_df[task_col] == TASK) & (results_df[numeric_prompt_col] == NUMERIC_PROMPTING)]\n",
+    "\n",
+    "df = all_results_df[(all_results_df[task_col] == TASK) & (all_results_df[numeric_prompt_col] == NUMERIC_PROMPTING)]\n",
+    "print(f\"{len(df)=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "e980b891-543f-4c9a-ae24-970af760c64b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-base-and-instr.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "from utils import prettify_model_name\n",
+    "\n",
+    "base_models_to_plot = [m for m in df[\"base_name\"].unique() if \"gemma\" not in m.lower()]\n",
+    "\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-8B\",\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mistral-7B-v0.1\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    # \"Yi-34B\",\n",
+    "]\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(8, 3), gridspec_kw=dict(wspace=0.33), sharey=False)\n",
+    "\n",
+    "# Plot base models\n",
+    "for ax, model_list in zip(axes, (base_models_to_plot, instr_models_to_plot)):\n",
+    "    \n",
+    "    for idx, m in enumerate(model_list):\n",
+    "\n",
+    "        curr_row = df[df[model_col] == m].iloc[0]\n",
+    "        curr_scores = scores_df_map[curr_row.name]\n",
+    "\n",
+    "        disp = CalibrationDisplay.from_predictions(\n",
+    "            y_true=curr_scores[\"label\"],\n",
+    "            y_prob=curr_scores[\"risk_score\"],\n",
+    "            n_bins=10,\n",
+    "            strategy=\"quantile\",\n",
+    "            name=prettify_model_name(curr_row[\"base_name\"]),\n",
+    "            linestyle=[\"-.\", \":\", \"--\"][idx % 3],\n",
+    "            ax=ax,\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlim(0, 1)\n",
+    "    ax.set_ylim(0, 1)\n",
+    "\n",
+    "ylabel = \"Fraction of positives \" + r\"$\\left(\\mathrm{P}[Y=1]\\right)$\"\n",
+    "axes[0].set_ylabel(ylabel)\n",
+    "axes[1].set_ylabel(ylabel)\n",
+    "\n",
+    "xlabel = \"Mean predicted probability\" # + r\" $(\\mathbb{E}[R])$\"\n",
+    "axes[0].set_xlabel(xlabel)\n",
+    "axes[1].set_xlabel(xlabel)\n",
+    "\n",
+    "axes[0].set_title(\"Base models\")\n",
+    "axes[1].set_title(\"Instruction-tuned models\")\n",
+    "\n",
+    "axes[0].legend().remove()\n",
+    "\n",
+    "# Legent to the right of the right-most plot\n",
+    "h, l = axes[1].get_legend_handles_labels()\n",
+    "l[0] = \"Perf. calibrated\"\n",
+    "axes[1].legend(handles=h, labels=l, loc=\"upper left\", bbox_to_anchor=(1.1, 1.1))\n",
+    "\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"calibration-curves-base-and-instr.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1875bb47-5e9d-499a-bb6b-c497513d4046",
+   "metadata": {},
+   "source": [
+    "### Same plot but only with large models (> 30B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "0177214e-8ae2-4a23-a561-0c4626f6a278",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-base-and-instr.large-models.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "from utils import prettify_model_name\n",
+    "\n",
+    "base_models_to_plot = [m for m in df[\"base_name\"].unique() if \"gemma\" not in m.lower()]\n",
+    "\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    \"Yi-34B\",\n",
+    "]\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(8, 3), gridspec_kw=dict(wspace=0.33), sharey=False)\n",
+    "\n",
+    "# Plot base models\n",
+    "for ax, model_list in zip(axes, (base_models_to_plot, instr_models_to_plot)):\n",
+    "    \n",
+    "    for idx, m in enumerate(model_list):\n",
+    "\n",
+    "        curr_row = df[df[model_col] == m].iloc[0]\n",
+    "        curr_scores = scores_df_map[curr_row.name]\n",
+    "\n",
+    "        disp = CalibrationDisplay.from_predictions(\n",
+    "            y_true=curr_scores[\"label\"],\n",
+    "            y_prob=curr_scores[\"risk_score\"],\n",
+    "            n_bins=10,\n",
+    "            strategy=\"quantile\",\n",
+    "            name=prettify_model_name(curr_row[\"base_name\"]),\n",
+    "            linestyle=[\"-.\", \":\", \"--\"][idx % 3],\n",
+    "            ax=ax,\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlim(0, 1)\n",
+    "    ax.set_ylim(0, 1)\n",
+    "\n",
+    "ylabel = \"Fraction of positives \" + r\"$\\left(\\mathrm{P}[Y=1]\\right)$\"\n",
+    "axes[0].set_ylabel(ylabel)\n",
+    "axes[1].set_ylabel(ylabel)\n",
+    "\n",
+    "xlabel = \"Mean predicted probability\" # + r\" $(\\mathbb{E}[R])$\"\n",
+    "axes[0].set_xlabel(xlabel)\n",
+    "axes[1].set_xlabel(xlabel)\n",
+    "\n",
+    "axes[0].set_title(\"Base models\")\n",
+    "axes[1].set_title(\"Instruction-tuned models\")\n",
+    "\n",
+    "axes[0].legend().remove()\n",
+    "\n",
+    "# Legent to the right of the right-most plot\n",
+    "h, l = axes[1].get_legend_handles_labels()\n",
+    "l[0] = \"Perf. calibrated\"\n",
+    "axes[1].legend(handles=h, labels=l, loc=\"upper left\", bbox_to_anchor=(1.1, 1.1))\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"calibration-curves-base-and-instr.large-models.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c69130d-0fe1-42a8-98a5-f071469a18f0",
+   "metadata": {},
+   "source": [
+    "### Smaller to use for both prompting schemes side by side"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "87b843d0-0082-4eca-9bb7-70bf1a53d35c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-base-and-instr.large-models.smaller.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "from utils import prettify_model_name\n",
+    "\n",
+    "base_models_to_plot = [m for m in df[\"base_name\"].unique() if \"gemma\" not in m.lower()]\n",
+    "\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    \"Yi-34B\",\n",
+    "]\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(5, 2.2), gridspec_kw=dict(wspace=0.15), sharey=True)\n",
+    "\n",
+    "# Plot base models\n",
+    "for ax, model_list in zip(axes, (base_models_to_plot, instr_models_to_plot)):\n",
+    "    \n",
+    "    for idx, m in enumerate(model_list):\n",
+    "\n",
+    "        curr_row = df[df[model_col] == m].iloc[0]\n",
+    "        curr_scores = scores_df_map[curr_row.name]\n",
+    "\n",
+    "        disp = CalibrationDisplay.from_predictions(\n",
+    "            y_true=curr_scores[\"label\"],\n",
+    "            y_prob=curr_scores[\"risk_score\"],\n",
+    "            n_bins=10,\n",
+    "            strategy=\"quantile\",\n",
+    "            name=prettify_model_name(curr_row[\"base_name\"]),\n",
+    "            linestyle=[\"-.\", \":\", \"--\"][idx % 3],\n",
+    "            ax=ax,\n",
+    "            ms=3.5,\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlim(0, 1)\n",
+    "    ax.set_ylim(0, 1)\n",
+    "\n",
+    "ylabel = \"Fraction of positives \" + r\"$\\left(\\mathrm{P}[Y=1]\\right)$\"\n",
+    "axes[0].set_ylabel(ylabel)\n",
+    "# axes[1].set_ylabel(ylabel)\n",
+    "axes[1].set_ylabel(\"\")\n",
+    "\n",
+    "# xlabel = \"Mean predicted probability\" # + r\" $(\\mathbb{E}[R])$\"\n",
+    "xlabel = \"Mean risk score\"\n",
+    "axes[0].set_xlabel(xlabel)\n",
+    "axes[1].set_xlabel(xlabel)\n",
+    "\n",
+    "axes[0].set_title(\"Base models\")\n",
+    "axes[1].set_title(\"Instruction-tuned\")\n",
+    "\n",
+    "# Set prettier x ticks\n",
+    "xticks = (0, 0.25, 0.5, 0.75, 1)\n",
+    "xticks_labels = (\"0.0\", \"0.25\", \"0.5\", \"0.75\", \"1.0\")\n",
+    "axes[0].set_xticks(xticks, labels=xticks_labels)\n",
+    "axes[1].set_xticks(xticks, labels=xticks_labels)\n",
+    "\n",
+    "axes[0].legend().remove()\n",
+    "\n",
+    "# Legent to the right of the right-most plot\n",
+    "if NUMERIC_PROMPTING:\n",
+    "    h, l = axes[1].get_legend_handles_labels()\n",
+    "    l[0] = \"Perf. calibrated\"\n",
+    "    axes[1].legend(handles=h, labels=l, loc=\"upper left\", bbox_to_anchor=(1, 1))\n",
+    "else:\n",
+    "    axes[1].legend().remove()\n",
+    "\n",
+    "# Figure title\n",
+    "if NUMERIC_PROMPTING:\n",
+    "    title = \"Numeric Prompting\"\n",
+    "else:\n",
+    "    title = \"Multiple-choice Prompting\"\n",
+    "plt.suptitle(title, y=1.1, fontsize=14)\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"calibration-curves-base-and-instr.large-models.smaller.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90cf83cb-cfc9-472f-92d7-605da9bb2704",
+   "metadata": {},
+   "source": [
+    "## Calibration per sub-group\n",
+    "Plot calibration curves per race sub-group, for each model pair."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b7f29ce1-b64a-4879-9a67-c305d1c4492b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "CPU times: user 41.3 s, sys: 28.3 s, total: 1min 9s\n",
+      "Wall time: 1min 9s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "acs_dataset = ACSDataset.make_from_task(task=TASK, cache_dir=DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19829429-ba87-471b-84cb-7b01829c5616",
+   "metadata": {},
+   "source": [
+    "Plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "98cb0f15-4434-4022-ba38-675b7c38db9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Omit small groups from the plots (results can be too noisy)\n",
+    "OMIT_GROUPS_BELOW_PCT = 4\n",
+    "N_BINS = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "f5a2f2e6-f557-401e-8f85-4d8ea0c0db72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sens_attr_col = \"RAC1P\"\n",
+    "sens_attr_map = {\n",
+    "    1: \"White\",\n",
+    "    2: \"Black\",\n",
+    "    6: \"Asian\",\n",
+    "    # 8: \"Other\",   # some other race alone\n",
+    "}\n",
+    "\n",
+    "name_to_sens_attr_map = {val: key for key, val in sens_attr_map.items()}\n",
+    "\n",
+    "def plot_cali_curve(model_id, ax, df):\n",
+    "    \"\"\"Plot sub-group calibration curves using sklearn\"\"\"\n",
+    "\n",
+    "    curr_row = df.loc[model_id]\n",
+    "    curr_scores_df = scores_df_map[curr_row.name]\n",
+    "\n",
+    "    y_true = curr_scores_df[\"label\"].to_numpy()\n",
+    "    y_scores = curr_scores_df[\"risk_score\"].to_numpy()\n",
+    "\n",
+    "    sens_attr = acs_dataset.data.loc[curr_scores_df.index][sens_attr_col].to_numpy()\n",
+    "\n",
+    "    for idx, group_val in enumerate(np.unique(sens_attr)):\n",
+    "        group_filter = sens_attr == group_val\n",
+    "\n",
+    "        if np.sum(group_filter) < len(group_filter) * 0.01 * OMIT_GROUPS_BELOW_PCT:\n",
+    "            # print(f\"Skipping group={group_val}\")\n",
+    "            continue\n",
+    "\n",
+    "        disp = CalibrationDisplay.from_predictions(\n",
+    "            y_true=y_true[group_filter],\n",
+    "            y_prob=y_scores[group_filter],\n",
+    "            n_bins=N_BINS,\n",
+    "            # strategy=\"uniform\",\n",
+    "            strategy=\"quantile\",\n",
+    "            name=str(sens_attr_map.get(group_val, group_val)),\n",
+    "            linestyle=[\"-.\", \":\", \"--\"][idx % 3],\n",
+    "            ax=ax,\n",
+    "        )\n",
+    "\n",
+    "    # Set other plot settings\n",
+    "    ax.set_title(curr_row[\"name\"])\n",
+    "    return disp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "798752d9-8abb-49d9-b874-24c54c523e93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.evaluation import bootstrap_estimate\n",
+    "\n",
+    "def plot_cali_curve_ci(model_id, ax, df, ci=95):\n",
+    "    \"\"\"Plot sub-group calibration curves with bootstrap confidence intervals\"\"\"\n",
+    "\n",
+    "    curr_row = df.loc[model_id]\n",
+    "    curr_scores_df = scores_df_map[curr_row.name]\n",
+    "\n",
+    "    y_true = curr_scores_df[\"label\"].to_numpy()\n",
+    "    y_scores = curr_scores_df[\"risk_score\"].to_numpy()\n",
+    "\n",
+    "    sens_attr = acs_dataset.data.loc[curr_scores_df.index][sens_attr_col].to_numpy()\n",
+    "\n",
+    "    # Compute bins\n",
+    "    if len(np.unique(y_scores)) == 1:\n",
+    "        ax.set_title(curr_row[\"name\"])\n",
+    "        return\n",
+    "\n",
+    "    y_scores_binned, bin_edges = pd.qcut(y_scores, q=N_BINS, retbins=True, duplicates=\"drop\")\n",
+    "    bin_names = y_scores_binned.categories   # Name used for each bin: \"(bin_low_range, bin_high_range)\"\n",
+    "\n",
+    "    # To hold plot render artifacts\n",
+    "    plot_artifacts = {}\n",
+    "\n",
+    "    for idx, group_val in enumerate(np.unique(sens_attr)):\n",
+    "        group_filter = sens_attr == group_val\n",
+    "\n",
+    "        if (\n",
+    "            np.sum(group_filter) < len(group_filter) * 0.01 * OMIT_GROUPS_BELOW_PCT\n",
+    "            or group_val not in sens_attr_map.keys()\n",
+    "        ):\n",
+    "            # print(f\"Skipping group={group_val}\")\n",
+    "            continue\n",
+    "\n",
+    "        group_labels = y_true[group_filter]\n",
+    "        group_scores = y_scores[group_filter]\n",
+    "\n",
+    "        group_cali_points = []\n",
+    "        for b in bin_names:\n",
+    "            bin_filter = y_scores_binned == b\n",
+    "\n",
+    "            group_labels_current_bin = y_true[group_filter & bin_filter]\n",
+    "            group_scores_current_bin = y_scores[group_filter & bin_filter]\n",
+    "\n",
+    "            results = bootstrap_estimate(\n",
+    "                eval_func=lambda labels, scores, sensattr: {\n",
+    "                    \"mean_score\": np.mean(scores),\n",
+    "                    \"mean_label\": np.mean(labels),\n",
+    "                },\n",
+    "                y_true=group_labels_current_bin,\n",
+    "                y_pred_scores=group_scores_current_bin,\n",
+    "                sensitive_attribute=None,\n",
+    "            )\n",
+    "\n",
+    "            # Store plot point for this bin\n",
+    "            group_cali_points.append((\n",
+    "                results[\"mean_score_mean\"],    # x coordinate\n",
+    "                results[\"mean_label_mean\"],    # y coordinate\n",
+    "                (                              # y coordinate c.i. range\n",
+    "                    results[\"mean_label_low-percentile\"],\n",
+    "                    results[\"mean_label_high-percentile\"],\n",
+    "                ),\n",
+    "            ))\n",
+    "\n",
+    "        # Get group label from sensitive attribute map\n",
+    "        group_label = str(sens_attr_map.get(group_val, group_val))\n",
+    "        \n",
+    "        # Plot this group's calibration curve w/ conf. intervals!\n",
+    "        line, caps, bars = ax.errorbar(\n",
+    "            # x=[mean_score for mean_score, *_ in group_cali_points],\n",
+    "            x=[(bin_edges[idx-1] + bin_edges[idx]) / 2. for idx in range(1, len(bin_edges))],\n",
+    "            y=[mean_label for _, mean_label, _ in group_cali_points],\n",
+    "            yerr=np.hstack(\n",
+    "                [\n",
+    "                    np.abs(np.array(yerr_range).reshape(2, 1) - y)\n",
+    "                    for _x, y, yerr_range in group_cali_points]\n",
+    "            ),\n",
+    "            fmt=\"o\", markersize=1,\n",
+    "            linestyle=[\"-.\", \"-\", \"--\"][idx % 3],\n",
+    "            linewidth=2,\n",
+    "            label=group_label,\n",
+    "        )\n",
+    "        plot_artifacts[group_label] = (line, caps, bars)\n",
+    "    \n",
+    "    # Global plot settings\n",
+    "    ax.plot(\n",
+    "        [0,1], [0,1],\n",
+    "        ls=\"dotted\", color=\"black\",\n",
+    "    )\n",
+    "\n",
+    "    # Draw custom legend\n",
+    "    handles = []\n",
+    "    for group, (l, c, b) in plot_artifacts.items():\n",
+    "        l.set_label(group)\n",
+    "        l.set_markersize(0)\n",
+    "        handles.append(l)\n",
+    "\n",
+    "    # Perfectly calibrated line\n",
+    "    perf_cal_line = plt.Line2D([], [], ls=\":\", color=\"black\", label=\"Perf. calibrated\")\n",
+    "\n",
+    "    # Confidence interv. marker line\n",
+    "    conf_int_line = plt.Line2D([], [], lw=0, ms=10, marker=\"|\", markeredgewidth=2, color=\"grey\", label=\"$95\\%$ conf. int.\")\n",
+    "    \n",
+    "    ax.legend(handles=handles + [conf_int_line, perf_cal_line])\n",
+    "    ax.set_title(curr_row[\"name\"])\n",
+    "\n",
+    "    return disp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e1895eb-cdf3-4f14-8424-aaad256821bb",
+   "metadata": {},
+   "source": [
+    "### Plot sub-group calibration (with confidence intervals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "b3802fc0-f903-4666-b4c9-de43d38bf46c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "818d39dfd7f14966b96c35233df9c3a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-8B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-70B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mistral-7B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mixtral-8x7B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mixtral-8x22B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Yi-34B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.None.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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dhJEQERGR2NjTg4iMLv8X88DAQACAtbU1fH19RTm+TCZDmzZtMHHiRKxYsQLnz5/Hli1b0KdPH6HM8ePHcfjwYVHiKciFCxeE1++//36hX9YBIDY21tghFVv+YRm6d+8ODw8Pox8z/zEyMzPLxZjcREREhsY6VNFYhyoZQ9eh8s8j8vz58zLty9Dyx1OrVi0TRkJERERiY6MHERldt27ddJ6u6tu3L6pUqWKSeKRSKTp27IiVK1eie/fuwvITJ07olBXrqcW4uDjhddOmTQstq1KpEBYWZuyQiuX58+daPzYMHz5clOO+PBmlqZ4uJSIiMibWoYrGOlTJGLoO5enpKbx+8OBBmfaVX1njysrK0mrgatasWVlDIiIiogqEjR5EZHQymQz+/v5ay4w9LENxSCQS9O7dW3ifmJioU8bKykp4nTekhDFIpf9ejosaD/nYsWOIj483WiwlsWfPHqjVagCAo6Mj+vbtK8pxb9y4Iby2traGi4uLKMclIiISE+tQRWMdqmQMXYdq3bq18PrWrVtl2ld+ZT1/7t69K+S3fv36cHR0NFhsREREVP5xTg8iEsX777+PgQMHCu9btGhhtGOlp6fDyspK68uSPk+fPhVeu7q66qzP/0XQmF3269Spg3v37gHIfVpSX35evHiB77//3mhxlFT+YRn8/PxgbW1t9GM+ffpUaxgNb29v9vQgIqJKi3WowrEOVXzGqEN16dIFMpkMKpUK//zzD5RKJSwsyv4zQ1nPn9DQUOF1t27dyhwPERERVSzs6UFEonB0dISXl5fwnzEnnY6MjESfPn2wbNky4Uvwy1QqFYKDg/HHH38Iy3r27KlTrkmTJsLrkJAQYaJGQ8v/tOSqVauwZ88enTKRkZEYO3Ysnj59Cjs7O6PEURIRERGIiooS3osxLMP169cxceJEraEZ3nnnHaMfl4iIyFRYhyoc61DFY6w6lJOTE9q3bw8gd5L4a9eulXmfQNnPn7///lt43atXL4PERERERBUHe3oQUaUUHx+P5cuXY/ny5XB3d0ezZs3g7u4OmUyGhIQEREZGao0B3bFjRwwaNEhnP61bt0aNGjXw9OlTxMfHw8/PD927d4eLi4vwZJyXl5fWE5ilMXz4cKxbtw7R0dGQy+X4+OOPsWrVKjRr1gzW1ta4c+cOrl+/DiB3TOIePXpgzZo1ZTpmWeX/UaF+/fpo165dmfe5bNky2Nvbay1Tq9VIS0vD7du3cffuXa11b731Fp/eIyIiMiDWoYyvstWh/P39cfnyZQC5Q4gZ4u8py/mTnp4uzJdStWpVrflniIiIyDyw0YOIKh0bGxtYWFgI4//Gx8cXOn7zgAED8N1332mNCZ1HKpXiiy++wPTp06FQKBAfH681HAGQ+2W7rF/Yrays8Ntvv2HSpEl4/PgxACAqKkrrKUAAaN++PRYvXowdO3aU6XhlJZfLsX//fuG9oZ5QfDm3+tjY2GDmzJmYMGGCQY5LRERErEOJoTLWoQYOHIiFCxciMzMThw4dwty5c8s8bFZZzp8TJ05ALpcDAIYOHWqQ4baIiIioYuHdn4gqnTZt2uD8+fM4f/48rly5gps3b+LRo0dITk6GWq2Gg4MD6tSpg7Zt22LIkCFaEzAWpHfv3ti1axe2bNmCsLAwxMbGIjMzExqNxqBxN2jQAEFBQdiyZQuOHDmCBw8eQKFQwN3dHU2bNsXgwYPh5+dn1GEtiuv06dNITk4GkPuldOjQoUY9np2dHVxcXNC0aVN06dIFQ4YMKXD8cCIiIio91qGMrzLWoapUqYIRI0bgjz/+QExMDM6fP2+Q3hWlPX927twJAJDJZAgICChzHERERFTxSDSGrnESERERERERkdmIiYmBr68vFAoF+vbti5UrV5okjnv37mHw4MHQaDQYPHgwFi1aZJI4iIiIyLQ4kTkRERERERERlVrt2rUxatQoALnDS708vJhY1qxZA41GAwsLC8yYMcMkMRAREZHpsdGDiIiIiIiIiMrkgw8+gIODAzQaDVasWCH68R89eiTMlzJ69GjUq1dP9BiIiIiofGCjBxERERERERGViZubG6ZPnw4ACA4OxvXr10U9/uLFi6FQKODq6oqZM2eKemwiIiIqXzinBxERERERERERERERVQrs6UFERERERERERERERJUCGz2IiIiIiIiIiIiIiKhSsDB1AKaSmJiIiIgIRERE4Nq1a7h27RqSk5MBAMOHD8fChQsNfsz9+/cjMDAQt2/fRmpqKtzc3NChQwcEBASgXbt2Bj8eERERUWXC+hsREREREREVxWwbPbp16ybasbKzszFjxgycPn1aa3lsbCxiY2Nx4MABTJs2DR988IFoMRERERFVNKy/ERERERERUVHMttEjv5o1a6Jhw4YICQkxyv4/+eQT4Quzt7c3xo8fDw8PD9y5cwerVq3Co0ePsGzZMri7u+ONN94wSgxERERElQnrb0RERERERFQQs230mDZtGry8vODl5QU3NzfExMSgb9++Bj/OhQsXcODAAQBA7969sWLFCshkMgBA69at0adPH4wcORKxsbH46aef4OvrCycnJ4PHQURERFTRsf5GRERERERERTHbicxnzJiB3r17w83NzajHWbduHQDAwsICX375pfCFOY+rqyvmzp0LAEhNTcXOnTuNGg8RERFRRcX6GxERERERERXFbBs9xJCeno4LFy4AALp27Yrq1asXWK5fv35wcHAAABw7dky0+IiIiIhIG+tvREREREREFRsbPYzo2rVrUCgUAIDOnTvrLWdlZYW2bdvqbENERERE4mL9jYiIiIiIqGIz2zk9xBAVFSW8btiwYaFlGzRogJCQECiVSjx8+BCNGzc2WBxqtRpZWVkAcodpkEgkBts3ERFRZaPRaKBUKgEAtra2kEr5jIg5KQ/1N9bdiIiIio91NyIiehkbPYzo2bNnwutq1aoVWrZGjRrC66dPnxq00SMrKwu3bt0y2P6IiIjMRbNmzWBvb2/qMEhE5aH+xrobERFR6bDuRkREAIe3MqqMjAzhtZ2dXaFlbW1thdeZmZlGi4mIiIiI9GP9jYiIiIiIqGJjTw8jysnJEV5bWVkVWjb/+uzsbIPGYWHx78dcr149rS/o+clkMqEbqFqthkqlKnS/lpaWwmuVSgW1Wl3s8kqlEhqNRm9ZiUSiFXdR42Tnjz1/11Z98g8VUdK/tajYlUol7t27ByD3KZOiSKVSyGQy4X1J/lZ+Tv8eN284kmbNmsHS0tLgsfNz+lf+fDdq1Ai2trZG+/fEz0k333n7LE/Xvcr0Oa1atQq//vorNm/eDED7HkrmoTzU31h302Xsa5hGoxF61zRu3LjQf/vl+RpWUT4n1t1Yd8tTWT+ngupv5e26V1k+p/DwcEybNg2rVq0CwLobERHl4t3AiKytrYXXcrm80LL519vY2Bg0jvzjQNva2paLrp5F/YhQ1vL5c29oRcWS/7O0tLQ0+t9qTBXlcyoo58aO3Zw/p/z5trOzK1Mu+DkVXb64+TbldU/s8sY0ffp0pKSkCO85l4L5KQ/1N9bdDKs4seT/LG1tbUsUf3m6hlWUz4l1N9bdDFXemMoSe3Fyburrnpjljcnb2xs//PCD8J51NyIiAji8lVHl/4Ja1JAHeZNVAkUPpUBERETGM2vWLFOHQCbE+hsREVHF0qVLF1OHQERE5QwbPYyoevXqwuvnz58XWvbp06fC6/yTYhIREZG4+ISgeWP9jYiIiIiIqGJjo4cRNWrUSHh9//79Qss+ePAAQO74k/Xq1TNqXERERObsxo0b8PPzw4sXL0wdCpVDrL8RERGVL5mZmRg5ciTOnTtn6lCIiKiCYKOHEXl5eQkTbF26dElvOblcjqtXr+psQ0RERIYVGhqKnj174tChQxg4cCDS09NNHRKVM6y/ERERlR/Jycno378/AgMDMWjQIOHeS0REVBhOZG5EDg4O6Nq1K86cOYMLFy7g2bNnWkMm5Dl69Kjwo4uPj49RY+IXcuOzsrJChw4dTB2GWWHOxcV8i4v5Npxz587Bz88PaWlpAICLFy9i2LBhOHDggNZkorxXmrfyVn/j+SgOXmvFxXyLi/kWH3NuGC9evEDfvn2Fho6UlBQMGDAAZ8+eRdOmTYVyvFcSEdHL2NOjDAIDA+Hp6QlPT08sW7aswDITJkwAACiVSnz11VdQqVRa61+8eIGffvoJAODo6IjXXnvNuEETERGZqdq1a8PR0VFrWUZGhtZk1FT5sf5GRERUMVSpUgV16tTRWqZQKJCSkmKiiIiIqKIw254eoaGhePTokfA+KSlJeP3w4UMEBgZqlR8xYkSpjtO1a1cMGjQIBw4cwIkTJ/DOO+/grbfegoeHB+7cuYPffvsNsbGxAIC5c+fCycmpVMchIiKiwtWrVw9Hjx5Fz549kZCQAB8fH+zevRsODg6mDo2KifU3IiIi82FpaYnt27fDz88Pp0+fRo0aNXDkyBG0atXK1KEREVE5Z7aNHn/99Rd2795d4LqwsDCEhYVpLSvtl2YA+O6775Ceno7Tp0/j4sWLuHjxotZ6qVSKqVOn4o033ij1MYrr5ScVyfBUKhUSEhIAAG5ubpDJZCaOqPJjzsXFfIuL+Tas5s2b49ChQ1i6dClWr16tNaxVHt4ryy9zrL/xfBQHr7XiYr7FxXyLjzk3HFtbW+zduxcTJ07EwoUL0ahRI50yvFcSEdHLzLbRQ0w2NjZYvXo19u3bh927d+PWrVtITU2Fm5sbOnTogLFjx6Jdu3aixKJWq0U5jjlTqVSIiYkBALi4uLCCKwLmXFzMt7iYb8Pr0KEDNm7cqHc975UElJ/6G89HcfBaKy7mW1zMt/iYc8NydHTEzp079a7nvZKIiF5mto0eCxcuxMKFC8u0jxEjRpToCUJ/f3/4+/uX6ZhERERUOI1Gg4iICLRp08bUoZCBsf5GRERUOf3zzz9o3bo1JBKJqUMhIqJKwGwbPYiIiKjyUalUmDJlCjZu3Ih9+/ZhwIABpg6JiIiIiAqxfft2jB07FtOnT8eiRYvY8FFCarUa6enpSE1NhVwu53BfRFQhyGQy2NnZwdnZGTY2NgbfPxs9iIiIqFKQy+UYO3asMPzB8OHDcfToUXTv3t3EkRERERFRQVavXo0pU6ZAo9Hgl19+gYuLCxYsWGDqsCqMtLQ0PHnyBBqNxtShEBGViFKpRE5ODpKSkuDk5IQaNWoYtNGbjR5ERERUKaxdu1ZrvOesrCwMHToU9+/fh6OjowkjIyIiIqKX3b9/H9OmTdP6wf7zzz+Ht7c3+vfvb8LIKoaCGjwkEgnnkCGiCkGpVAqvU1JSYGVlBTc3N4Ptn40eREREVClMnjwZ586dw5YtWwAAVlZWWLt2LRs8iIiIiMqhhg0bYv369Rg3bpywbPr06fDx8TFhVBWDWq3WavBwcHCAq6sr7OzsODwYEVUIKpUKycnJiIuLAwDEx8fD0dERVlZWBtm/1CB7ISIiIjIxqVSK9evXw9/fH/b29jh48CCGDh1q6rCIiIiISI+xY8di+fLlAHJ7eSxZsgRSKX+qKkp6erpWg0ft2rVhb2/PBg8iqjBkMhmqVq2KqlWrCsvS09MNtn/29CAyMIlEAnt7e+E1GR9zLi7mW1zMd8lYWlpi+/btuHv3Llq3bm3qcIioguC1VlzMt7iYb/Ex5yUzbdo0dOrUCZ07dzZ1KBVGamqq8NrV1ZXnGRFVWI6OjkhMTAQAZGRkwNXV1SD7ZaOHmbG0tDR1CJWepaUlmjVrZuowzApzLi7mW1zMd8nZ2tqWqcGD90oqT3g+ioPXWnEx3+JivsXHnJdcWRo8zPFeKZfLAeQ2qtnZ2Zk4GiKi0rO2toZEIoFGoxGubYbAPoNERERUoYSFhWHy5MlaE58RERERUfmUkpKCsWPHIiYmxtShVBoqlQpA7vAw7OVBRBWZRCKBTCYDkDtfkaGwpwcRERFVGGfOnIG/vz9SU1OhVCqxZs0aftEjIiIiKqfi4+MxYMAAhIeHIywsDGfOnIGbm5upwyIiokqOPT3MDJ+KNT6lUomoqChERUUx3yJhzsXFfIuL+f5XcHAwBgwYIIxhvG7dOsydO1eYxNFQzD3PVL7wfBQHr7XiYr7FxXyLjznPFRMTg1deeQXh4eEAgJs3b8LX11drPgpDMOccExFRwdjoYWYM/cMQ6VKr1UhOTkZycrJBu2WRfsy5uJhvcTHf/7Kzs9O5j129etWg434CvFdS+cLzURy81oqL+RYX8y0+5jxX3jjt+cXExCA2Ntagx+G9koiIXsZGDyIiIqoQevXqhZ07dwrjfQ4dOhQHDhyAtbW1iSMjIiIiope5u7vjyJEjqFu3LgCgXr16CAkJ4STvRERkdGz0ICIiogrD398fGzZswNtvv42//voLNjY2pg6JiIiIiPSoU6cOjh07ht69e+PcuXNo3LixqUMiIiIzwEYPIiIiqlDGjh2L9evXw8LCwtShEBEREVERmjRpghMnTqBWrVqmDoWoQvD09ISnpyeWLVtW6n1cvHhR2M/FixcNGB1RxcBGDyIiIipXVCoVHj58aOowiIiIiKiY7t+/b+oQiIxCpVKhffv28PT0xPDhwwstq9Fo4O3tLTQ2/PXXX4WW3717t1B269athgybyOyx0YOIiIjKDblcjoCAAHh7e+Pu3bumDoeIiIiIivD777/zR1uqtGQyGdq1awcAuHXrFtLT0/WWvXv3LpKTk4X3V65cKXTfoaGhwutOnTqVLdASWrZsmdDgQlQZsdGDiIiIyoXMzEwMGzYM27dvx/Pnz9GvXz/ExMSYOiwiIiIi0uPHH3/Ee++9B6VSifHjx2P//v2mDonI4PIaJNRqNcLCwvSWy2vEkMlkWu+LKu/i4mLw+W68vb1x+/Zt3L59G97e3gbdN1FFwEYPMyORSEwdQqUnlUrh4eEBDw8PSKX8JyYG5lxczLe4zCnfn332GQ4ePCi8f/jwIfz9/aFSqUSNg/dKKk94PorDnK615QHzLS7mW3zmkvMTJ07g448/Ft6rVCq89tpruHPnjqhx8F5JxtaxY0fhdWENGXnrfH19AQCPHj3C8+fPCyybmJiI6OhoAECHDh14HhMZWOW9+1KBOOmr8VlYWKBOnTqoU6cO8y0S5lxczLe4zCnfX375Jdq3by+8t7e3x48//ig8KSWWyp5nqlh4PorDnK615QHzLS7mW3zmkvPevXtj1qxZWss+/PBDNGnSRNQ4KnOOqXxo3bo1rK2tARSv0WPAgAGoW7cuAP1DXJlyaCsic8BGDyIiIioXHB0dcejQITRr1gwuLi44duwYfHx8TB0WERERERVAIpFg0aJFePvttwEAP/30E7799ls+sU6VjpWVFVq3bg0AuHbtGuRyuU6Zx48fC706OnTogA4dOgDQ30iSf3n+niQFiYiIwJw5c9CzZ0+0atUKr7zyCj766CNERUXp3ebixYvCnB0XL14UlgcGBsLT0xPLly8XluWVy/9fQcMMq1Qq7N69G5MnT0aPHj3QqlUreHt7Y8yYMVi/fj2ys7ML/TuIxMRGDyIiIio33N3dceTIEZw+fRpdunQxdThEREREVAipVIrff/8dR44cwYcffmjqcIiMJq9hQi6X459//tFZn9eIUb9+fbi5uQmNHpcvXy5wf3k9QBwcHNC8eXO9x92yZQvGjBmDAwcO4Pnz51AoFIiLi8PevXsxcuRIvfs3tNjYWIwYMQLz5s3DqVOnEB8fD4VCgeTkZISFhWHhwoUYMmQIHjx4IEo8REVhH0Azo1AoYGVlZeowKjWFQoGbN28CAJo3bw5LS0sTR1T5MefiYr7FZY75zhsOwlQUCoXJjk30MtbdxGGO11pTYr7FxXyLz1g5DwgOQER8hNay1u6tsWXgFoPsv7QsLCzQr18/kx2fdTcSQ6dOnfDrr78CyG3geHlIqrxGj7zhevP+f/fuXaSkpMDJyUkom56ejlu3bgEA2rVrp3c435CQEERERKBp06YYP348mjZtipycHBw9ehSbNm1CVlYWPv74Yxw+fLjY9UUfHx+0atUKW7duxbZt2wAA+/bt0ylXrVo14XVSUhLefPNNPH36FFZWVnj99dfRqVMn1KpVC5mZmTh37hw2bdqEhw8fYtKkSdi9ezeqVKlSrHiIjIWNHkQGptFohEqXRqMxcTTmgTkXF/MtrsqY75CQEFy6dAlz5swxdShERAAq57W2PGO+xcV8i6+y5TwhIQFff/01/ve//8HGxsbU4VApDF95ztQhCPxb18SEHg0KLbMu5AH2RcTqLN89tbuxwipS27ZtYWFhAaVSWeCQVXnL8nqENGrUCC4uLkhKSkJYWBh69+4tlA0PD4dKpQJQ+HweV69exauvvorly5drNWp07NgRzs7OWLx4MWJjY3H69OliNzw6OjrC0dERVatWFZY1bdq00G2+/fZbPH36FLVq1cLGjRt1HlDz9vaGr68vAgIC8PjxY6xZswazZ88uVjxExsJGDyIiIhLNwYMHMXLkSGRlZcHW1hbvv/++qUMiIiIiIj1iYmLQv39/3Lx5Ew8fPsRff/3F3kIVUPijZFOHIGhf16XIMk+Ss8pVzABgb2+P5s2b49q1a0KjRV4PjcTERERHRwOAMKwVkNvb4/jx4wgNDdVq9CjufB7W1tb4/vvvC+zFMW7cOKxYsQIKhQKhoaFG620VExODgwcPAgAWLFigt0d+ixYt8Oabb2LNmjUIDAxkoweZHOf0ICIiIlFs374dQ4YMQVZWFgBg2rRp2Lp1q4mjIiIiIqKC3Lt3Dz169BCG6tq7dy8mTJgAtVpt4siITCOvV0ZGRobw7wL4d94ONzc31K9fX1iubzLzvPk8rK2t4eXlpfd43bp10+qRkZ+Dg4NwrMePH5fsDymB06dPQ6VSwdbWFj179iy0bF5+4uLiEBur21OHSEzs6UFERESieP78OZRKpfBeo9EgMDAQY8aMgUQiMWFkREREROVfujxd3OOlpyMpKUlr2ZkzZxAXF4fq1auLGgtRedChQwesW7cOQG5DRqtWrYTXeevzy+vFERkZiezsbNjY2EAulyMiIndunjZt2hQ6F0fDhg0LjSdvnpCMjIxS/DXFc/36dQBAVlYWWrRoUeztEhISULNmTWOFRVQk9vQgIiIiUcyYMQNffvml8H7cuHH4888/2eBBREREVIjbL27j/WPv437KfVGP27ZtWxw4cAC2trYAgGbNmuHcuXNs8CCz1bFjR+G7S15vjfyv8yYvz9OiRQvY2tpCoVDg6tWrAIBr164hJydH2F9h8v7t6SOV5v6sa8zeV4mJiaXaLq93P5GpsKcHERERiebzzz9HcnIylEollixZIlTUiYiIiKhgwQ+CEfIkxCTH7tGjBwIDA/HVV19h7969cHd3N0kcVDbt6jqbOgRBLefCf8jPK1OeYs7j7OyMJk2a4M6dO0LvjvT0dNy6dQuAbk8PS0tLeHl54dKlSwgNDUWXLl20GksKm8S8vMibcN3FxQWbNm0q9na1a9c2VkhExcJGDyIiIhKNRCLBzz//LLwmIiIiosJNaDUBO+/sRJo8zSTH9/X1Rf/+/fmwSgW2e2p3U4dQIhN6NMCEHg1MHUaBOnbsiDt37uDFixeIiopCTEwM1Go17OzsChz+qUOHDkKjB/Dv/B+WlpZo27atmKGXirOzM4DcIbQaNWokTN5OVN7xjmVmeHEyPgsLCzRs2BANGzaEhQXbFcXAnIuL+RZXRcy3QqHQGf85P4lEUq4bPHivpPKE56M4KuK1tiJjvsXFfIvP0Dl3snbCxFYTDRCZfvHx8YWuL88NHrxXkpjyD0l15coVoedG27ZtCzwX83p//PPPP5DL5QgPDweQO/SVnZ2dCBEXrLjfx/IacuRyuTC/B1FFUH7vWmQU5bmiUllIpVK4uLjAxcWF+RYJcy4u5ltcFS3fmZmZGDZsGAYMGIC0NNM8jVhWFSHPZD54Poqjol1rKzrmW1zMt/iMkfM3m78JC6lxGq0WLVqEJk2aICwszCj7Nzae1ySm/I0ely9fFnpuvDy0VZ527dpBJpMhMzMTgYGBwnekoubzMLb8E6jL5XK95Xr37i00kGzcuNHocREZCu8MREREZBApKSnw9fVFcHAwLl++jKFDhyI7O9vUYRERERGVSyq1CkH3grAxsugfEm0tbFHd3rATiGs0GixYsABz585FSkoKBgwYIMxNQEQFq1atGurWrQsAuHjxotD7QV+jh4ODA5o2bQoAWLNmjbDc1PN5eHh4CK8fPXqkt1zDhg3h6+sLADhw4ADWr19f6H4fP36M/fv3GyZIojJgo4eZ0Wg0pg6h0tNoNJDL5ZDL5cy3SJhzcTHf4qpI+X7rrbdw9uxZ4f3JkycxYcIEE0ZUOuU9z2ReeD6KoyJdaysD5ltczLf4isq5RqPBiUcnMHLvSCw4twDLwpfhecbzIvfrauNq0DjXrVuHb7/9VnifkJCAfv36ITk52aDHMTae1yS2vF4az58/h1wuh4WFRaHzc+Q1iDx+/BhAbu8kfY0kYmnXrp3w+vvvv8fly5cRHR2Nhw8f4uHDh1AqlcL6L7/8EnXq1AEALFy4EGPHjsXOnTtx9epV3LhxA+fPn8e6devwzjvvoH///jhy5Ijofw/Ryzigp5lRKpWwtrY2dRiVmkKhwLVr1wAAXl5eWl0GyTiYc3Ex3+KqSPn+4YcfcP78eWFMaGdnZ3zwwQcmjqrk8lfwiUyNdTdxVKRrbWXAfIuL+RZfYTkPfRaKxWGL8U/8P8KyHFUOfov4DV90/ULUOAMCArB161acOHFCWDZjxgxh4uKKgnU3ElvHjh0RGBgovG/evDlsbW31lu/QoQP++OMP4X3Tpk3h6Oho1BiLUq9ePfj5+eHgwYMICQlBSEiI1vrjx4+jdu3aAHK/123btg2zZs1CaGio1rBeBbG3tzdq7ETFwUYPIiIiMghPT08cPnwYvXr1gq2tLY4cOYLWrVubOiwiIiIik7v94jYWhy1GyJOQAtfvvrsbb7V4C/Wd6osWk42NDYKCguDj44PLly9j1apVmDRpkmjHJ6qoXh6aqqheGy+vN/XQVnl+/PFHtGrVCocPH8aDBw+QkZEBtVpdYFl3d3ds2bIFp06dwv79+3H16lUkJCRAqVSiSpUqqFevHtq1a4c+ffqUm7+PzBsbPYiIiMhg2rVrh4MHD8Ld3R1NmjQxdThEREREJhWTHoPVkasRfD8YGugfhkmlUWFZ+DIs6rVIb5ktA7cYPL4qVarg4MGDOH/+PAYPHmzw/RNVRnXr1sXt27eLXb5atWolKl/csps3b9a7ztvbu8j9WFpa4t1338W7775b7Nh69eqFXr16Fbs8kamw0YOIiIgMqlu3bqYOgYiIiMikUpQp2Bu3F6dvnIZSXfTwSw2cGsCvgV+hZYavPIfwR8lay9rVdcbuqd3LEipcXV3Z4EFERJUKGz2IiIioRA4fPgyVSoWBAweaOhQiIiKiciVdno61EWux+c5m5KhziizvYeeBaW2nYUijIbCQFv4TzcIXc+Bpc0tr2e0XzQBcLHS7J0+eYMuWLfjoo48gkUiKjImIiKiiY6MHERERFdvOnTsREBAAmUyGw4cPo2fPnqYOiYiIiMjkclQ52H5rO36/9juSc5KLLO9o5YhJXpMwutlo2FjYGC2ue/fuoV+/foiOjkZmZia+/PJLox2LiIiovJCaOgAiIiKqGNasWYPRo0dDoVAgOzsb/v7+CAsLM3VYRERERCajUqsQdC8I/rv98WPoj0U2eNjIbDDJaxIOjjyIt1u9XWiDx8PEDLz5+994lpJdqtgiIiLQo0cPREdHAwC++uorLFmypFT7IiIiqkjY6EFERERF0mg0OHfuHNRqtbAsNTUV69evN2FURERERKYT8iQEI/eOxIJzC/A042mhZS0kFnjD8w0EjwjGjPYz4GjlWGj5W89SMeq3CzgflYhxay8iKUNe4vhu3bqFuLg4rWW//vorsrKySrwvIiKiioTDW5kZCwt+5MZmaWkJLy8v4TUZH3MuLuZbXOUl3xKJBL///juSk5MRFBQEAJg6dWqlfFqQ90oqT3g+iqO8XGvNBfMtLubbeKJTohGVElVkOb/6fvig3Qeo61i3WPsNe5SEd9ZfRkqWAgBwNy4db6+/hC0erkCsdlnP6lX07uf1119HUlISpkyZAgBo164dDh06BFtb22LFUVHwXklERC/jncHMcNIy45NIJLCysjJ1GGaFORcX8y2u8pRvCwsLbNu2DYMGDUKXLl3w7bffVsr7SmX8m6ji4vkojvJ0rTUHzLe4mG/jed3zdWy+sRmxGbEFru9esztmtJ+BFlVbFHufIXcT8N7mUGTKVVrL/4lJwSTbIVivuQwbiaLY+5s8eTKSkpJw4MAB7N+/H05OTsXetqLgvZKIiF7GRg8iIiIqNhsbGxw8eJA/nhAREZHZs5JZYVq7afg05FOt5V5uXpjVfhY61+hcov0duv4MM7aFQ65SF7g+S22JHFjBBsVv9ACAefPmYc6cOay/ERGR2eCcHmYm/1jsZBxqtRpJSUlISkpivkXCnIuL+RaXKfKdnZ0NpVKpd31l/8LM85rKE56P4uC9TVzMt7iYb+Ma1GAQGjs3BgDUd6yPX3r9gs2+m9HEpkmJcr4z9DGmbrmit8Gje+Oq2FJrF5wkGQWuT09PL3T/lbn+xvOaiIhexkYPM6NSqYouRGWiVCpx//593L9/v9AfDclwmHNxMd/iEjvfqamp8PX1xbvvvmu2XyB5r6TyhOejOHhvExfzLS7mu+TS5elYHr4c3/79bZFlZVIZPur0Eb7s+iV2D90Nn3o+UKlUJcr5upAH+OivCKg1Ba/v36Ia1r7VCfZS3R4eGo0GX375JTp06KAzabm54L2SiIhexuGtiIiICACQkJAAPz8/hIaG4vTp03B2dsYvv/zCcZKJiIjILOSocrD91nb8fu13JOckQwIJ3vB8A01cmhS6Xbea3Up1PI1Gg8XH7mLJ8bt6y4xoXwv/G9kaFjLdZ1bVGg1m/XkHy06cAAD4+vri5MmTlXLeDiIiopJgTw8iIiKCWq2Gr68vQkNDhWVLlizBDz/8YMKoiIiIiIxPpVYh6F4Q/Hf748fQH5GckwwA0ECDpeFLjXJMtVqDr/ffKLTB4+1u9fHTqDYFNngAwJencrDsRIzwPjw8HP7+/uzRQ0REZo+NHkRERASpVIqvvvoKFhb/dgL18PCAn5+fCaMiIiIiMh6NRoMTj05g5N6RWHBuAZ5mPNUpc+rxKYTHhRv0uEqVGh/visD6c9F6y8zs2wRf+LeAVKq/x+3kDlaoX9VGeC+RSBAQEKBVnyMiIjJHbPQgIiIiAMCgQYOwefNmSCQS1K1bF2fPnkWbNm1MHRYRERGRwYU+C8W4g+Mw8+RMRKVEFVp2SdgSgx03R6nCtK1h+OtKjN4ynw9ugdn9mhY5xGgtRymOzWmH6tWrw8LCAlu3bsXkyZMNFisREVFFxeZ/IiIiEowePRoajQY9evRAnTp1TB0OERERkUHdfnEbi8MWI+RJSLHKd6vZDTPbzzTIsTNylJi8+QpC7iUUuF4qAX4Y2RqvdSx+HayRhx2OHDmCmJgY9tAlIiL6f2z0ICIiIi1jxowxdQhEREREBvU47TFWXF2B4PvB0EBTZHkvNy/MbD8T3jW8DXL85Ew53tlwGeGPkgtcbyWTYumYtvBtVaPE+/by8oKXl1cZIyQiIqo82OhBZGASiQSWlpbCazI+5lxczLe4jJHvoKAgNG/eHJ6engbZHxFRRcd7m7iYb3GZe74TshKwOmI1dt7ZCaW66Am+6zvWx8z2M9G3bt9S5+vlnMelZmPc2ku4/TytwPK2ljKsHt8BrzRxL3D9/fv3ERYWhlHyjFLFQ0REZG7Y6GFm8ipeZDyWlpZo3bq1qcMwK8y5uJhvcRk63+vXr8e7776LWrVqISQkBHXr1jXYvisL3iupPOH5KA7e28TFfIvLXPOdLk/HhsgN2HRjE7KUWUWW97DzwNQ2UzG08VBYSMv2U0n+nD9+kYmxay/iYWJmgWUdbSyw/p3O6FDPpcD1169fh++AfnirUTJG9LaCzrzmKnmZYq0MeK8kIqKXsdGDiIjITPzyyy+YM2cOAODx48fo168fzp49Cw8PDxNHRkRERGQ4+6L24X+X/4fknOQiyzpaOeJdr3cxptkY2FjYGDSOu8/TMHbtRTxPzSlwvZuDNTZP7IzmNRwLXH/x4kVMecMXfw6Qo0ddq4IPkp1iqHCJiIgqDampAyAiIiLjk8vl2LZtm9ayO3fuYN26dSaKiIiIiMg4LKWWRTZ42Mhs8K7Xuzg48iDeafWOwRs8/nmcjNdXXdDb4FHL2RZ/Temqt8EDGg2e7P8BZ8ao0KNuIc+ryqwNEC0RieXixYvw9PSEp6cnLl68aOpwdAQGBgrxxcTEmDocolJjTw8zo1QqYWWl5wkRMgilUomnT58CAGrUqAELC/4zMzbmXFzMt7gMlW8rKysEBwfj1VdfxY0bNwAA8+bNw3/+8x+DxVpZKJVFj/dNJBbW3cTBe5u4mG9xmWO++9fvj3XX1+Hmi5s662QSGUY2GYnJbSbDw844vV1D7sRh0uYryFKoC1zf2MMBf0z0RnUnPQ0tGQnAvpkYITsOyIqYV8S6ShmjrfhYdyMxXLx4EePHj9e73s7ODh4eHmjdujVGjBiBrl27ihgdEb2s8td2SItGozF1CJWeWq1GXFwcAKBatWomjsY8MOfiYr7FZch8u7m54ciRI+jRowemTJnCBg89eK+k8oTnozh4bxMX8y2uipjvgOAARMRHaC1r7d4aWwZuKdb2UokUs9rPwuRjk7WW+9b3xQftPkA9x3oGi/Vlx248x9StYZArC27w8KrlhI0TOsPVXk+D9u1DwN4PgIz44h3QDCenfxnvlVQeZGZmIjo6GtHR0di7dy+GDRuG7777DjKZzNShEZklNnoQERGZkVq1auHatWtwcHAwdShEREREpaJUK4ucbLxrza7oXL0zLj27hG41u2FG+xloWbWlUePaHR6DuTsjoFIX/CO8dwNXrHmrI6rYFDDxdk46cORT4MoGo8ZIRGU3ZswYvPnmm8J7jUaDlJQUXL16FRs2bEBiYiKCgoJQvXp1zJ4924SREpkvNnoQERFVMnK5vNDhcNjgQURERBXR47THWHF1BZJzkvGbz2+FlpVIJPi408dIzkmGdw1vo8e2+UI0FuyJ1Lu+bzMPrAhoDxtL3ae+NY8vAYHvQZL0oPCD2LsXvwcIERlN1apV0bRpU53lnTt3Rp8+fTBixAjk5ORg8+bNmDZtGocqJTIBTmRORERUiSQmJqJHjx5YunSpqUMhIiIiMpgnaU8wJGgIDtw/gHNPzuHi06InAPZ09TR6g4dGo8GKk/cKbfAY2rYmfhvXQbfBQ6WA5vi30KzpX3iDh50bMHob4FLfMEETkdE0btwYvXr1AgBkZGTg/v37pg2IyEyxpwcREVEl8eTJE/Tv3x83btzA5cuX4ezsXOhke0REREQVRWJ2otb7JWFLsGXgFkhMOKeFRqPB9wdvYfUZ/T9qvtm5Nr4d1hpS6UtxJtyFZte7kDy9Wvi0HE39gCHLAAd3IORnwwROREZVq1Yt4bVcLi/x9levXsXJkycRFhaG+/fvIyUlBVZWVqhevTo6deqEcePGoXHjxsXa1+nTp7Fv3z6Eh4cjMTERKpUK7u7uaNmyJXr37g0/Pz/Y2tqWKL4nT55gwoQJiI6Ohp2dHVauXMmJ26ncYaMHERFRJZCWloZXXnkFDx78+5TghAkT4OLiAn9/fxNGRkRERGR41xKu4dijY+hXr59Jjq9Sa/Dp7mv48/JjvWVGNLPHF4OaaTd4aDTA5TXAkQWQKLP0H8DSHvD9Hmg/npOVE1UwsbGxwuuaNWuWaNvAwEDMnz9fZ7lCoUBUVBSioqKwc+dOfPrppwgICNC7n6SkJMyePRsXLlzQWffkyRM8efIER44cAQCMGDGi2PFFRUVhwoQJePbsGZydnfH777+jdevWxd6eSCxs9CAiIqoEqlSpgokTJ+Kzzz4TltWsWbPAsWaJiIiIyjOFSlGscr9H/A6fuj4m6e0RGZuCv67E6F0/rnUVDPO0144t9SmwZxoQdbzwndfuDIxYBbg2NFC0ZLaiTgJXtwBJ0aaOxLhc6gNtA4BGvU0dCaKionDq1CkAQNu2beHm5lai7VUqFZycnNC3b1907NgR9erVg52dHeLi4hAZGYnNmzcjKSkJ33zzDRo2bFhgD4usrCyMHz8ed+7cAQC0bNkSb7zxBpo0aQIrKys8e/YMly9fRnBwcIlii4iIwKRJk5CcnAwPDw+sX7++2D1OiMTGRg/ktnBu3rwZp06dwrNnz2BlZYU6derAz88PAQEBJe7mVZCYmBhs27YNFy5cwKNHj5CVlQV7e3s0bNgQPXr0wJgxY1C1alUD/DWFM2XXX3MhlUrh7OwsvCbjY87FxXyLqyT5/uSTT5CUlIRFixahadOmOHr0KOrWrStClJUL75XlH+tuZGi8t4mL+RZXRcz3i+wXha6XSWQY0WQEprSZYrLrZOvazvj5jbaY+Wc4NJp/l0skwDdDWqCrhxpAvpzf2APsmwlkJendp1oig7T3fKD7bEDGn2uKi/dKPaJOAltGAWqlqSMxvpjLQORuYOwuoGEvox8uMTFRaFAAcoe6S0tLQ3h4ODZu3Ijs7GxUqVKlwB4bRenZsycGDx6sU59t0aIFevXqhfHjxyMgIAC3b9/GsmXLCmz0WLx4sRBfQEAAFixYoPXvpFWrVvDx8cGHH36I1NTUYsV14cIFTJ06FZmZmahXrx7WrVuH2rVrl/jvIxKL2d9FT5w4gY8++gjp6enCsqysLKSkpOD69evYuXMnVq9ejXr16pX6GEFBQfjiiy+QnZ2ttTwlJQXh4eEIDw/H5s2b8fPPP6N79+6lPk5xWFiY/UdudBYWFmjUqJGpwzArzLm4mG9xlSTfEokEP/74I1xdXfHuu+/Cw8PDyNFVTrxXlm+su5Ex8N4mLuZbXBUt3xqNBkk5+hsGBtQfgOntpqOeY+mv84YypE1NpGUr8Onu6wAAS5kEv7zRFoNb5xvOJjsF2Pcf4J9the4rw6427Mf+AdRsZ8yQKyXeK/W4usU8GjzyqJVA+B+iNHps27YN27YV/G9aKpVi9OjRePvtt9GgQYMS77tatWqFrq9SpQpmzJiBadOm4cqVK0hKSoKLi4uwPjU1Fdu3bweQ28Pj008/1dswaGVlVayeKMeOHcPs2bMhl8vh6emJtWvXwt3dvQR/FZH4ytWdQS6XIzU1Fa6urqI8gXLjxg3Mnj0b2dnZsLOzw+TJk+Ht7Y3s7GwEBwdjx44diI6OxnvvvYddu3bBwcGhxMe4cuUK5s+fD7VaDalUimHDhqFv377w8PDA06dPsXv3bpw8eRLJycmYOnUq9u/fjzp16hjhryUiInMgkUjwySefmDoMMhOsu7HuRkRkaP/E/wO5SnfiXxuZDTb4bUDLqi1NEJV+Ad71kJKlwNLjd/Hb2A7o5ZnvoZPoEGD3+0DKo8J30nky7H2+BKzsjBorERmfWq1GcHAwrK2tMXfuXFhZWZVpf5mZmXjx4gWysrKg+f9uZZaWlsL6W7duafX2+Pvvv5GVlTtf0Pjx4yGTycp0/MDAQHz22WdQqVRo164dVq9eDUdHxzLtk0gMojR6ZGRkIDQ0FADQsWNH2Nvba61/8eIFvvjiC5w8eRIqlQp2dnZ47bXXMGfOnDJfHArz3//+F9nZ2bCwsMC6devQrt2/T1R07doV9erVw48//ojo6GisX78e06dPL/ExVq9eDbU6t1vrZ599pjXJUOvWrTFgwAAsXLgQ69evR3Z2NtavX4/PP/+87H8cERFVWnv27EHv3r1Z2SSjYd2NdTciIlMJuhdU4HIPO49y1+CRZ2qvxvBvXRN1XP+/0UKZA5z4Fji/DIBG/4ZVagBDVwCN+4oSJ5mZtgG5Qz6ZS28PqQXQbqwoh/rggw906pnZ2dl4+PAh9uzZg40bN2Ljxo24fv061q5dW+KhV1+8eIENGzbg8OHDePjwodDYUZCkJO2ecTdu3BBed+jQoUTHfdnGjRuxefNmaDQa9OjRA8uXLzfIMLJEYhCl0ePIkSOYP38+qlevjhMnTmitU6vVmDRpEm7cuCH8I87IyMDGjRvx5MkTLFu2zCgxRURECF/mR44cqfWlOc+ECRMQGBiIqKgobNq0CVOmTNFqTS2O8PBwAICzs7PWl+b8pk2bhvXr1wMArl69WqL9l5RCoTDqjxGUm+OoqCgAQKNGjUp8zlDJMefiYr7F9XK+V65ciVmzZqFnz544dOgQK51GoFAUb/LUyox1N9bdzA3vbeJivsVVkfKdrczG4ejDBa5ztC7fD3sIDR7PI6HZNQmSuMjCN2gxDBj8C2DnavTYKjvW3fRo1Dt3jovwP8xjIvN2Y0UZ2kofGxsbeHp64uOPP0b9+vWxYMECXLlyBb/99htmz55d7P1cv34dEydORHJycrHK5+TkaL3P3whS1uGON23aBABwdXXFsmXL+N2TKhRRGj1CQkIAAD4+PjpDHwQHByMyMhISiQQtWrRA586dcenSJdy4cQPHjh3DmTNn0LNnT4PHdOzYMeH1yJEjCyyTN6TBokWLkJqaiosXL6JHjx4lOk7ezbewyX2qVKkCFxcXJCUl8WZdCWg0GmRkZAivyfiYc3Ex3+LKy7dGo8HXX3+Nb7/9FgBw5swZvPbaa9i9e3e5/vGCKibW3Vh3Mze8t4mL+RZXRcr3iUcnkK5IL3CdVCL+JOzXn6RArlKjfV2Xogur1cDfK4DjX0NSwPBceVSWDpD5/wJ4vZY76zmRMTXsZdKGAHM1atQoLFq0CMnJydi1a1exGz3kcjlmzZqF5ORkWFpaYuzYsejbty/q168PJycn4UGYx48fw8fHB4Bxr+sDBgzA4cOH8eLFC3z88cdYvHgx59ChCkOUWsOdO3cgkUjQvn17nXVBQUEAcifX2bFjB+bNm4ft27ejdevWWusN7cqVKwAAOzs7tGypv4tsp06dhNdhYWElPk7epEUxMTF6y6SnpwstsaWZ5IiIiCq/pKQk/P7771rLDhw4gF27dpkoIqrMWHdj3Y2IyBT2Ru01dQiCi/cTMWb133h73SXcfJpaeOHkx8CmIcCRz4BCGjxOPlBizl1voPXrbPAgqsSkUinq1asHAIiPj9cZgkqfv//+G48fPwYAfPHFF5g3bx46deoEd3d3rZ6/hfUCyT+peVxcXCmi/9fHH38s9Hw+evQo5syZA6XSTIZLowpPlOa5Fy9eANB9Yk6hUCA0NBQSiQQBAQFCa6GlpSVGjx6NiIgIREREGCWmvO69devWLbSVsmHDhjrblMTo0aOxYMECJCcnY9u2bRgzZoxOmRUrVmiVNyaFQgG5vOBKmIWFhfA0p1qtLvJClv+Cq1KpoFKp9JaVSCRaTyIrlUphvOyCSKVSrc9FoVAU2nqdP3aNRlPkU5eWlpaQ/H8ls6i/taSxv7yvomKXyWRaE0vp+3zy8HPSVdB+DB07P6d/5X+vUCiM+u+Jn9O/+Xd1dUVQUBD8/PyESu7333+P0aNHm/y6V9k+Jz65z7ob626mv9e8zNjXsPyKymN5v4ZVhM+JdTfW3fLk/5yeZzzHhacX9JbVqDVQq9WifE4nb8Vhyh9XkKPM/VvHrb2IbRM7oV7Vfycal0qlsJDJgIgdQPBcIEd/w0iOUoP5x3Nww/FV7ArcXOp/TxYajc6Tq2qNBqr//1zzmNu/J9bdqDzKf94Xdk7nd+/ePeG1n5+f3nLXr1/Xuy7/w0GhoaGoU6dOsY6tz4IFC6BUKrF9+3YcPnwYH330EX766acyT5BOZGyiNHqkpKQAgM7wG9euXUN2djYkEgleeeUVrXX169cHACQkJBg8npycHKGVtXr16oWWdXJygp2dHTIzM/Hs2bMSH2vkyJG4cuUKgoKC8PXXXyMyMhJ9+vSBu7s7nj59ij179gjDNUyZMgXdunUr+R9UAoV9+ff09ISDgwMAIDMzE7dv3y50X/knREpISCj0iUh7e3s0a9ZMeP/w4cNCW6Y9PDy0Lsw3b94stCLTsGFDoTVboVDg2rVrhcbu5eUlVKxSUlJw//59vWUtLS2Fp1cB4OnTp4W2lr88sXBUVJTQnbwgtWvXRrVq1YT3RcXOz6l4ivqcnJ2d0ahRI+E9P6d/leRzunXrllH/PfFz0v6cLC0tsWjRIkyfPh3ffPMN5syZA8D01z1z+pzMBeturLuVp3sNYPxrWP7Yo6OjkZWVpbd8Rb6GlafP6WW81/zLXOtu++/vh1qj/4f6zMxMZGZmGv1z2vtPLOZsvwql+t9GgIR0Od78/QL+27sqqtrl/tBX3ckatcJ+BG4EFRpDun19jN6RCmkNL+zZuRPW1taQy+Wl+vfkmZkJh5fKZWZm4v7Nm6y7EZUjWVlZQh3OxsZGq/dFYfI3lGRlZQn/jvJTq9XYuXOn3n14e3sLdeHNmzdjyJAhZWqgkEgk+Oqrr4TjBgcHQyqV4scff9R5cISoPBGl0cPGxgYZGRlITEzUWp43GWXdunXh5uams42x5L+Z29nZFVIyl62trVDBKimZTIYffvgBvXv3xqpVq7Bz506di5O3t7coX5qJiKjia9OmDfbs2aP15ZDI0Fh3Y92NiEhMGo0Ge6L26CyXQIKfPX+Gi2XxfjA0RBwHrz3VavDIE5+pxu7bGXi3nSOqxF1GteOLgEz9DQsaSPC80RuI9XwbczpkwcvLC9bW1mWK73aP5QUu5+xuROXLsmXLkJ2dDQDo0aNHsRsd8h4iAoDdu3fjvffe0ymzaNEiREZG6t2Ho6Mj3njjDaxfvx6RkZH47rvv8Nlnnwk9x/JTKBRITU1F1apVC41LIpHgm2++gUqlQmBgIPbv3w+ZTIaFCxey4YPKLVEaPerWrYubN2/i0qVLWpNJHj16FBKJRGvs5Tx5wyq4uroaPJ6cnBzhdXEmf817uiLvglVSUVFRCAoKwp07dwpcf/XqVfz1119o1KiR1tMTxtCoUSO9Pxbk795qZ2cHLy+vYu/Xzc2t0Jbrly+u9erVK7SL3csXzebNmxfZpTuPpaVlkbHn/9ydnJwKLf9y7DVq1Cj0c1IqlUhN/bdrc6NGjYrsKpxfUbHzc9KlUChw69YtrWVFfU4vx87P6V9FfU75892sWTOj/nsy189JrVYLn5O+fJen615l+5yqVq1aqmGRKhPW3Vh3M/W95mXGvoblHwamfv36hQ6hVt6vYRXhc2LdjXW3PHmf07WEa3iQ8kBnvXd1b/Rs31N4b+zPSSKRYPHotkjbEIqQe9o9Fwe2qob/+TeE7ZlvIbuyttBjqR1r4W7LuUh3ayPkvLxd9/Kr6P+eWHcjsSUmJurUE3NycvDw4UMEBQXh7NmzAABra2vMmDGj2Pvt0aMHqlatisTERCxevBgxMTHo168fXFxc8OjRI+zYsQMXLlxA+/btC527bubMmTh37hzu3LmDP/74A+Hh4Rg9ejSaNm0KS0tLPHv2DKGhoThw4ABmzZqFESNGFBmbRCLBf//7X6jVagQFBWHPnj2QyWT47rvvCmxQITI1URo9unXrhhs3bmDr1q3o0KEDOnbsiMDAQFy7dg0SiQS9e/fW2Sav+6OHh4fB48n/dEVxxn7MG6+yNE8whoaGYsqUKUhLS0OtWrUwc+ZMdO/eHU5OTkhMTMTx48exdOlSHDhwAJcvX8a6devQpEmTEh+nuCwtLbXGy9RHKpUWq1yel8f4LEphXyQLUpwfOPJIJJISxV7Sv9WYsQMwauz8nPTj51Swoj6n/GMNA/ycClPcvzU2NhZDhw7Fjz/+iF69emmt03cN578n/UrzOZU0/sqIdTfW3V5W2esE+cent7CwKNH+y9s1rCQq2ueUX0W/15SEOdTd9E1gPrzJcL3xGetzsraQYdW4Dhi79iLCHyUDAMZ0rotvOysh2zwASCi4QVzQZgyUfb9F+t2HAAq+jvPfk36su1FFsG3bNmzbtq3QMq6urvjxxx/h6elZ7P3a2dnhhx9+wLRp05CTk4Pt27dj+/btWmU6d+6Mzz//HIMHD9a7H1tbW2zcuBEzZszA5cuXERkZiQULFhQ7Dn2kUim+//57qFQq7Nu3D4GBgZDJZPjmm2/Y8EHljiiNHuPHj8eff/6JjIwMTJkyRWtdo0aN0KdPH51tTp06BYlEghYtWhg8Hnt7e+F1cYY9yBvTtzjDKeQnl8sxZ84cpKWlwd3dHdu3b4e7u7uwvnr16ggICEDnzp0xcuRIxMXF4T//+Q8CAwNLdJySYLcz45PJZMLEr5zYSRzMubiYb+O5f/8++vXrh/v378Pf3x8nT55Eu3btmG+R8V7JuhvrbuaH9zZxMd/iqgj5bubaDE1cmuBu0l1hmYOlA/rU1b3fiMHe2gLr3+6EN1b9jT6ervjYPhiSdT8Aav0TcidmqnG+6hvwH/4bZCoVatfOnbi4vOa8MuG9ksoDS0tLODs7o3Hjxnj11VcxYsQIODk5lXg/r7zyCnbt2oXVq1fj77//RlJSEqpUqYLGjRvD398fo0aNQmxsbJH7cXV1xR9//IGjR49i3759+Oeff/DixQtIJBJ4eHigZcuW8PHxwYABA0oUn1QqxQ8//AC1Wo0DBw5g586dkEql+Oqrr9jwQeWKKI0eHh4e+O233zB79mzEx8cLy+vUqYMlS5bo/KN49OgRrly5AgBGGSvZ2toazs7OSE5OLnKCy5SUFOHLdVETZ77szJkzeP78OQBg7NixWl+a82vSpAmGDBmCnTt3IjIyErdu3dKamMuQWOEyPplMZvShLkgbcy4u5ts4Hj58iB49euDp06cAgPT0dPj6+uLMmTNG+RGZ9OO9knU3gHU3c8N7m7iYb3FVhHyPajoKI5uMxK0Xt7A3ai8O3D+APnX7wMbCePNFFcXZzgq7x9SA3f6pQMylQssevKvEhL1ZeJa+FmvcumLixInlPueVCe+VJAZvb2+hZ7Ox99OkSRP8+OOPetfXrl272LH069cP/fr1K3Z8I0aMKHK4K5lMhp9//hk///xzsfdLJDZRGj0AoGPHjjh+/DjCwsKQkJAAd3d3dOjQocBul3FxcZg6dSoAoHv37kaJp3HjxggNDcWjR4+gVCr1dv+8f/++8LpRo0YlOkb+bYv6waply5bCJJn379832hdnIiIqn2rVqoWuXbtqPTHu4uKi9YQ7kZhYd2PdjYhITBKJBM2rNkfzqs0xp8McZCgyTBeMRgOEbYTdoU+AQuJQSiwxY38qfg3NHXrR2tpab4M5ERERiUe0Rg8gd9zHLl26FFmuY8eO6Nixo1Fj6dChA0JDQ5GZmYnIyEi0adOmwHKXL18WXrdv375Ex8j/tIFKpSq0rFL5bzdZPqVARGR+LCwssHXrVvj7++Po0aNo06YNDh8+zKcEyaRYd9OPdTciIuOxlFnCWeZslH2nZSuw8lQUZvk0gbVFAdfv9Hhg3wzgdnDhO6rZDhYjfkdD+z1A6EdwcHDA3r17C5z3ioiIiMRltgMf+vj4CK937dpVYBm1Wo2goCAAgKOjI7y9vUt0jLxxU4HcSTELk/8Lev7tDK04k39S2cjlcly5cgVXrlzRmhSTjIc5FxfzbTzW1tbYvXs3PvjgA5w8eRLVqlVjvk2A98ryiXU3MiZea8XFfIuL+f5XYnoOxvz+N349FYVZf16FUqXWLnArGFjZpfAGD4kMePU/wMSjgFsTzJ07F9999x1OnDghNHgw5+LivZKIiF5mkkaPR48eYc+ePVi7di2WL1+OFy9eiB5D69athScSd+3ahfDwcJ0y69atQ1RUFIDcCT0tLS211l+8eBGenp7w9PTEvHnzdLbv2rUrbG1tAQDbtm3TO97e6dOncfToUQBAtWrV0Lx589L/YUREVKHZ29tj2bJlcHFxMXUoRALW3bSx7kZEVPE8TcnC66su4PqTVADAwevPMD/wGtRqDZCTDuydDvw5BshM0L8T14bAhMNA708A2b/3mPnz56NTp07G/hOIiIiomEQd3ioyMhLfffcdwsLCtJb7+vrC1dVVeL9lyxYsX74cVapUwYEDB3S+sBrKp59+ijFjxiA7OxsTJkzAlClT4O3tjezsbAQHB2P79u0AgPr16+Odd94p8f4dHR0xadIkLF26FBkZGRg9ejTGjRuHbt26wcnJCQkJCTh+/Dh27twJtTr3CZMPP/wQUqnZdsAhIjILhw8fRr9+/Xi9p3KPdTfW3YiIKoMHCRkYu+YiniRnaS3feSUGLVQ38fbz7yFJii50H5nN34DdsJ8BawcjRkpERESGIFqjx8mTJzFz5kwoFApoNBphuUQi0Sk7dOhQ/PTTT0hOTsapU6fQr18/o8TUokUL/PLLL/joo4+Qnp6On3/+WadM/fr1sXr1ajg4lK5iM3XqVKSkpGDTpk3IzMzEqlWrsGrVKp1ylpaWmD17NoYOHVqq4xARUfmn0Wjw7bff4vPPP8e0adOwbNmyAu+DROUB626suxERVQY3YlMxft0lJKTnaC23gBIzLQIx/tZeSKDWszUgt3TC2B3JuL37Ik71V8DF2tgRExERUVmJ8lhaXFwc5syZA7lcjsaNG+P333/XeWIwPwcHB/Tp0wcAcObMGaPG1qdPH+zduxdvv/026tevD1tbWzg6OqJVq1aYO3cugoKCUK9evVLvXyKR4JNPPsFff/2F0aNHo2nTprC3t4dMJkOVKlXQsmVLvPPOO9i3bx8mTpxowL+MiIjKE41Ggw8//BCff/45AGDFihXCa6LyhnU31t2IiIxJoVLg1otbRj/OlYcvMHr1BZ0Gj0aSJwi0+gLTLYIgK6TB45lzBzRaFI+dEWmIiIjAoEGDkJGRYeywiYiIqIxE6emxYcMGZGVloWbNmtiyZQscHR2L3Mbb2xsHDhxAZGSk0eOrVasW5s+fj/nz55doO29vb71jPb+sVatWaNWqVWnCIyKiSuDatWtYvny51rJvv/0WgwcPLvFky0TGxrob625ERMZ0OuY0Zp+ajaYuTTG00VAMajgIVW2rGvQYZ+7EY/LmK8hSqIRlEqgxXnYU8y22wkZSyOTXVg5Q9vsveo37HjFJ2cLiCxcuYOHChfjmm28MGisREREZlig9Pc6ePQuJRIIJEyYU60szADRs2BAAEBMTY8zQiIiIRNG6dWv88ccfWkMDrVixgg0eVC6x7kZERMa0594eAMCdpDv4MfRH9N3ZF9OPT0dKTopB9h987Skmbrys1eBRDS+w0fIHfGW5sfAGjzpdgCkhsOj0Ng4ePIQaNWoIq4YPH47PPvvMIDESERGR8YjS6BEbGwsg9wef4sobhzkzM9MoMREREYnt9ddfx+rVq2FhYYE//vgDU6dONXVIRAVi3Y2IiIwlISsBZ5+c1Vqm0qhwL/keqlhVKfP+d1x+jA+2hkGh+nc+qoHSv3HY+j/oKbumf0OpBdD3c+CdYMC1AQCgQYMGOHr0KFxdXfH2229jx44dsLbmpB5ERETlnSjDW6lUuU9XqNX6x8p8WVpaGgDAzs7OKDGZK5lMZuoQKj0LCwt4enoKr8n4mHNxMd9l8+6776JPnz7CU/FFYb7Fx3sl627lCc9HcfBaKy7mW1zlLd/B94Oh0qh0lg9pNARSSdmey1xz9j6+PXBTeO+IDHxpuREjZCGFb+jmCYz8HajRRmdVy5YtceXKFdStWxdSafHiK285r+x4ryQiopeJcvd1c3PDkydP8PjxY7Rt27ZY20RERACAVldSKrviVtKo9KRSqfC0K4mDORcX8112xW3wAJhvU+C9knW38oTnozh4rRUX8y2u8pbvvVF7C1zu38i/1PvUaDT4+egdLDtxT1jWRXoDiyx/RS1JYuEbe78P+HwBWNrqLVK/fv0SxVPecl7Z8V5JREQvE+XO0LFjR2g0Ghw6dKhY5eVyObZv3w6JRILOnTsbOToiIiLDiY6ORu/evfHw4UNTh0JUaqy7ERGRMdx6cQu3k27rLO9UvRNqV6ldqn2q1Rp8uTdSaPCwhhyfWGzBVsv/Ft7gUaUmMC4I8r5f4823JmLXrl2lOj4RERGVP6I0egwfPhwAcOLECZw7d67QsnK5HP/5z3/w6NEjSCQSvP7662KEaDZKMkwFlY5arUZ6ejrS09OZb5Ew5+JivvW7ceMGunfvjlOnTsHHxwfPnz8v8z6Zb/Exz6y7lSc8H8XBa624mG9xlad8501g/rIhjYaUan8KlRof7vwHGy/kPmzSTPIIQVYL8J7FAUglGv0bthwBvH8OGdU7Y8iQIdi2bRvefPNNHD16tFRxvKw85dwcMMdERPQyUYa38vb2xsCBAxEcHIwpU6Zg/PjxGDBggLD+yZMnSE1NRVhYGHbs2IHHjx9DIpFg9OjRaNKkiRghmo28MbrJeJRKJW7fzn16ycvLC1ZWViaOqPJjzsXFfBcsIiICffr0QWJi7hOF9+7dw4ABA3Dq1Ck4OzuXer/Mt/h4r2TdrTzh+SgOXmvFxXyLq7zkW6FS4MD9AzrLbS1s0b9ef+2Fa3yAmMvay2p3At49JrzNVqjwwdZwHLv5HFKo8a7sAD602AlriVJ/ENZOwOCfAa9RyMzMxIAB/YXGfblcjmHDhuHYsWPo2rVrqf9OoPzk3FzwXklERC8TbUathQsXIiMjA6dPn8a6deuwbt06SCQSAMCUKVOEchpN7tMY/fv3x6effipWeERERGVSu3Zt1KhRQ2j0AHLvaTk5OSaMiqj0WHcjIiJDOvvkLJJyknSW96vXD3aWdiXaV3qOEpM2huLC/UTUlsRjkeWv8JbeKnyjBj2BYb8CTrnDaNna2qJVq1ZaPRolEgmysrJKFAsRERGVP6LN9mRlZYVVq1bh66+/Rp06daDRaAr8r3r16vjiiy+wdOlSyGQyscIjIiIqE1dXVxw5ckSYpLxr1644deoUqlWrZuLIiEqHdTciIjIkfROYD200tET7ScqQI+D3v3HhfgJGSM/goNW8whs8ZNbAgO+BcXuEBg8gt4FjxYoVeOONNwDk1uVOnDiBPn36lCgeIiIiKn9E6+mR5/XXX8frr7+Oe/fu4fr160hMTIRKpYKLiwuaN2+Oli1bCk8REhERVSQ1atTAsWPH8MUXX+DXX3+Fvb29qUMiKjPW3YiIqKxeZL/A6cendZbXtK+JjtU7Fns/z1OzMW7tRcQ9f4qVlmsxUHap8A2qeQEjVgPVWhS4WiaTYdOmTbC3t8fs2bPRqlWrYsdCRERE5ZfojR55GjdujMaNG5vq8EREREbRoEEDbNq0ydRhEBkc625ERFRaBx8chFKjO9eGfyN/SCXFG4DikcIJAb+dR8Pkv7HZehWqSZILKS0Bus8Een8CWFgXul8rKyusXbu2WDEQEZlKnz598OTJEwwfPhwLFy40dThE5Z4ow1ulpaWJcRgiIiKj02g0uHSpiKcKiSo41t2IiMiQ9tzbU+Dy4g5tdVtdGwExQzEpdSU2Wv1QeIOHc13gnWCg31dCg0d4eDjkcnlJwyYi0uvSpUvw9PQU/gsLCzN1SESUjyiNHt27d8eMGTNw5MgRVjSIiKjC0mg0+Oijj+Dt7Y1169aZOhwio2HdjYiIDOVO0h3cfHFTZ3l7j/ao41inyO3D1Y3wlWI8Nsi+w3iLo4UXbhsATDkH1OsmLAoODka3bt0wduxYqFSqEsdPRFSQ3bt3a70PCgoyTSBEVCBRhreSy+U4evQojh49CgcHBwwYMACDBw9Gly5dxDg8ERFRmalUKkyePFkY/mDSpElwcnLCyJEjTRwZkeGx7kZERIay956eCcwbF93L45yqBcLUTbHJaiEsJGr9BW1dAf8lQIshWov//PNPjBs3DkqlEjt37oSTkxNWr17NuaiIqEyys7Nx+PBhAICdnR0yMzNx6NAhfPbZZ7CysjLKMU+cOGGU/RJVVhKNRqMx9kE+//xzHD58GCkpKbkH/f8KhoeHBwYNGgR/f380b97c2GGYLblcjmvXrgEAvLy8jHYBJiKqzIKCgjB8+HCtZdbW1oiKikKtWrVMFBUZA++brLuZGs9BIqosFGoF+u3sh8TsRK3lNjIbnHz9JBysHArc7va33kjIkcJOkoN20qjCD9K4HzB0BVClmtbixMRENGjQQGfIxg0bNuCtt94q+R9D5ZY53jfv3r0LpVIJCwsLNGnSxNThmJ19+/Zh7ty5AIDvvvsOn3zyCQBgyZIl8PX1NWVoRBWSMa5pogxv9fXXX+PcuXNYuXIlBg4cCGtra2g0Gjx//hzr16/HiBEjMGjQIKxatQoxMTFihERERFQiw4YNw3/+8x/hvUwmw+rVq9ngQZUS625ERGQI55+c12nwAIC+9frqbfAAgBcqO7hJUgtt8NBY2gGDfgYCduo0eABA1apVsXv3bq0fwMePH4+AgIAS/hVERNryhrLy9PTEyJEj0aBBA63lRGR6ogxvBQAWFhbo06cP+vTpg8zMTBw9ehT79+/HhQsXoFQqERUVhcWLF2Px4sVo27YthgwZAl9fX7i4uIgVIhERUaG+//57JCcnY8OGDdi+fTuGDi3e5JtEFRHrbkREVFZ7oko3gflTlQO6SvVPCqyu2R7SEb8Dbo0L3U/fvn3x559/YtSoUZg2bRoWL14MqVSUZz+JqJKKi4vDhQsXAABDhgwR/r9kyRKEhITgxYsXcHV11bv98+fPsXnzZpw7dw6PHj1CdnY2nJycULVqVTRp0gQ9evRA//794eCg3TDcp08fPHnyBMOHD8fChQsLjOvo0aO4ePEibt26hbi4OCiVSri4uKBVq1bw9/eHr6+v3mvgxYsXMX78eADApk2b4O3tjeDgYGzfvh23b99GZmYmatSogb59++K9996Ds7NzadJHJBpRhrcqzIsXL3Dw4EHs27cPV69eFZZLJBLIZDL06NEDv/32m+kCrATyd/Vs0aIFbG1tTRxR5aZSqZCQkAAAcHNzg0wmM3FElR9zLi5zz7dKpUJkZCRat24t2vHMOd+mkJWVhRs3bgAwnyESSoJ1N+Nj3U18vNaKi/kWl6nynS5PR8/tPaFQK7SWV7evjkMjDkEmLTiOxJR05PzshZqSFzrrVJABPT+C7NW5gMyy2LFcvXoVbdq0EW0uD57j4jLHuhuHtzKdtWvX4n//+x+kUilOnTqFatWq4fHjx+jXrx80Gg0+/fRTofHgZaGhoZg8eTLS09MLPcZvv/2G3r17ay0rrNFDpVKhVatWUKsLmfsIQPfu3bFs2TLY29vrrMvf6LFhwwYEBgZi796C52SqV68etmzZAnd390KPR1RcxrimidbTQx9XV1cEBAQgICAAMTEx2L9/Pw4cOCD8sadPnzZ1iJVKURdAKjuVSiUM9eHi4sIKrgiYc3GZe75lMploDR4A820KvFcWjnU3cfF8FAevteJivsVlqnw7WDkgcEgg9kbtxb77+/As4xkAwL+hv94GDwC4uG81BhbQ4BGncYbbxL8grdupxLG0bdu2xNuUBc9xcfFeSWLasye3B1vnzp1RrVru0Hp16tRBu3btEBYWhqCgoAIbPeRyOWbPno309HTY29tjzJgx8Pb2RtWqVaFQKBATE4Pw8HAcPXq0xDHlPc/epUsX9OzZE02bNoWrqysyMjLw+PFj7Ny5E+Hh4Th37hy+/vpr/PDDD4Xub8mSJQgPD4ePjw+GDRuGmjVrIiEhAVu3bsWpU6fw8OFDfP/99/j5559LHCuRWEze6JFf7dq10b9/f2RnZ+P58+dITU01dUhERGRmbt26hS+//BLr1q2DnZ2dqcMhKtdYdyMiosLUd6qPGe1n4IN2H+DSs0vYe28vhjQaon8DtRrdn28pcJXcqWGBDR6ZmZl47733MG/ePLRq1cpQoRNVKgHBhp3Lxq++H8a2GFtomT9u/IGD0QcNetwtAwu+Pojl5s2buH37NoB/h7bKM2TIEISFhSEyMhL37t1D48baw+9duXIFcXFxAIBFixbp9ORo27YtBg8ejPnz5yM7O7tEcclkMhw6dAj16tXTWde5c2eMHDkSS5cuxYoVK7Bnzx68//77qF+/vt79hYeHY9asWXj//fe1lvfs2RPvvvsuQkJCcPjw4SKH8iIypXLR6BEXF4cDBw5g3759uHnzptY6c+iWSERE5cOVK1fg6+uLhIQEpKSkYM+ePbwPERWAdTciIioJqUSKLjW6oEuNLoUXvHsETmn3dBY/UVdF7Sq649CnpKTA398fZ8+exYkTJxASEoKGDRsaKmyiSiMiPsKg+2vj3qbIMrEZsQY/rqnlTVRuY2ODAQMGaK3z8/PDf//7XygUCgQFBWHu3Lla6/OGvAOATp3091izsLDQmc+jKBKJpMAGj/ymTZuGrVu3IikpCSdOnMCECRP0lm3ZsiWmTJlS4HHefvtthISEQKlUIjw8HH379i1RrERiMVmjR3p6Og4dOoR9+/YhNDQUarVa6I4lkUjQqVMnYZIdIiIiYzt79iwGDRqEtLQ0AMChQ4cwbtw4bN26lUMSEIF1NyIiEsG5xXpWaICX5uNISEhA//79ER4eDgB4+vQpfHx8EBISgpo1axo3TiIyO0qlEvv37wcA9O7dW6dhwtnZGa+++iqOHTuGffv2Yc6cOVqThuef/2LXrl146623jBarWq1GfHw8MjIyoFQqheXVq1dHUlISbt26Vej2/v7+eudAatmypfD68ePHhgmYyAhEbfSQy+U4deoU9u3bhzNnzkAulwP4d+y5Zs2awd/fH4MHDxbGxSMiIhKDs7MzLCy0b4sPHz5ERkYGHB0dTRQVkWmx7kZERKJ59Dfw6EKBq2pJXwBopLXM1tZWZyjSpKQkxMbGstGDiAwuJCRE6K3x8tBWeYYMGYJjx47h2bNnuHjxIrp27Sqs69ChA+rUqYPHjx/ju+++w759+9CvXz907NgRXl5eZe4trdFosHfvXvz111+IiIgodIispKSkQvdVWI85Z2dn4XVGRkaJ4yQSiyiNHhcuXMC+fftw9OhRpKenA/j3y3LNmjXh7+8Pf39/nfHuiIiIxOLl5YXg4GD4+PggIyMDffv2RVBQUIm7FhNVBqy7ERGR6EIWl6i4vb099u/fj969e+Pq1auoXr06jhw5Ai8vL+PER0RmLW9oK2dnZ7zyyisFlunduzccHR2RmpqKoKAgrUYPS0tL/Pbbb5gxYwaioqJw7do1XLt2DUDucFkdO3bEsGHDMHDgwBKPNJCTk4MPPvgAZ86cKVb5ouYMsbGx0bsuf+8VtVpdvACJTECURo933nkHEolE+LLs7OwMX19f+Pv7o0OHDmKEQEREVKQuXbpgz549WLNmDdavX19oZY+oMmPdjYiIRBV3E7hT8gmPnZ2dcejQIbz11ltYsWIFGjVqVPRGRGaotXtrg+6vpn3Rvalq2tc0+HFNJS0tDSdOnAAAJCcno1WrVkVuc+TIEXzxxRdaPdIaN26Mffv24eTJkzhx4gRCQ0Px8OFDZGdnIyQkBCEhIVi/fj1+//13VK1atdjx/frrr0KDR+fOnfHmm2+iZcuWcHNzg42NjdBQERAQgNDQ0JL86UQVlmjDW1lbW6NPnz7w9/fHK6+8ojOECFFlIZFIYG9vL7wm42POxVXZ8923b99yNRlbZc83lV+su5E54bVWXMy3uCpEvs8tLfWm1apVw6FDhwwYTNlViJyTWdkycIvoxxzbYizGthgr+nGN4eDBg8jJySnRNpmZmThy5AiGDRumtVwmk8HHxwc+Pj4AgLi4OJw9exZbtmxBZGQkIiMj8fnnn2PFihXFOo5Go8Fff/0FAOjYsSM2btyo1Rsjv5SUlBL9DUQVmSjfXhcuXIh+/foJN30yHUtLS1OHUOlZWlqiWbNmpg7DrDDn4qro+dZoNLh37x6aNGli6lCKpaLnuyLivZJ1t/KE56M4eK0VF/MtLjHzfTj6MO6n3MeQRkNQy6FW8TZKiQGu7Si0yJ1EFZrWNkCAIuE5Li7eK8nY8oa2cnd3x/z584ss/7///Q/Pnj3Dnj17dBo9Xubh4YGRI0diyJAheOONNxAZGYlTp04hOzu7WCMPJCcnIz4+HgDg6+urt8EjIyMDDx48KHJ/RJWFKI0eRf0DJyIiEoNKpcL777+PrVu34vjx4/D29jZ1SETlEutuRERUGptubEJEfARWXl2JTtU7YUijIehfrz/sLO0KLK9Wa3B569fwViv17nNHpAJjA7Pw7fCH+PhdY0VORFSwx48fIywsDAAwYMAADBo0qMhtrl69ik2bNuHvv//G8+fPUa1atSK3sbS0RKdOnRAZGQmlUonU1NRiNXqoVCrhdVZWlt5yO3fuhFKp/1pLVNkU3PxHRERUycjlcrz55pv4/fffkZGRAT8/P1y/ft3UYRERERFVCg9SHiAiPkJ4f/nZZSw4twDTjk/Tu82pf26j1bMg3RUSGTAzAr/X+gmjd2VDoQb+s+seVq9ebYTIiYj027NnjzDP3YABA4q1TV45tVqNPXv2AIAwf4c+crkcly9fBgDY2dnB1dW1WMdydXWFo6MjAGD//v2Qy+U6ZSIiIrBkyZJi7Y+osuDgzGZGqVTCysrK1GFUakqlUriR1atXj2Ogi4A5F1dFzffixYuxY8e/QyckJSXBz88Pd+7cga2trQkjK1xFzXdFxiegqDxh3U0cvNaKi/kWl1j53hu1t8DlvvV9C1yu0Wjw9Ogy2Et0x8nP9hyGqNh0TJ48WfixEQCmTJmCVq1aoVu3boYJ2kh4jouLdTcyprxGi6pVq6Jjx47F2qZ9+/Zwd3dHfHw89u7di/feew8XLlzAypUr0bFjR7z66qvw9PSEq6srsrOzER0djT///BORkZEAgFGjRhX7uiGVSuHv748tW7bg9u3bGDNmDN555x3Uq1cP6enpOH36NLZu3Qo7Ozt4eHggOjq6VHkgqmgMeudt3rw5gNyJum7cuKGzvDRe3heVTf4KIxmHWq1GcnIyAKBOnTqmDcZMMOfiqqj5njVrFk6cOIHDhw8DAKysrLBs2bJy3eABVNx8V2TmdK9k3a38M6fz0ZR4rRUX8y0uMfKtUquwL2qfznJLqSV8GxTc6HH+1mP4ZuwBCpjnW9ZzFlrWbIlFixZhzpw5wvKpU6eiS5cuBovbWHiOi4v3SjKWK1eu4NGjRwAAHx8fvfNlvEwqlaJfv37YunUr7t69K4wwoFarcenSJVy6dEnvtn379sWHH35Yojhnz56NsLAw3Lx5E9evX9fZ3tnZGcuWLcPSpUvZ6EFmw6CNHvpuNLwBERGRqVlZWWHXrl0YMGAArl69iqCgIPj4+Jg6LCKTYt2NiIgM4eKzi3ie+Vxnee86veFk7VTgNrcO/orukjSd5U/ceqBWzdYAcn/IS0pKwjfffIPPPvsMX3/9NSSSAlpJiIiMIK+XB1D8oa3y9O/fH1u3bgWQOxH67Nmz4enpifPnz+PmzZuIi4tDYmIiAMDNzQ2tW7fGsGHD0KtXrxLHWaVKFWzbtg3r16/HwYMH8fDhQ8hkMtSoUQOvvvoq3nrrLVSvXr3E+yWqyAza6PHBBx+UaDkREZGY7O3tsX//fkRFRaFDhw6mDofI5Fh3IyIiQ9hzb0+By4c2HlrgcqVCjpHZuwtc5+Y3T+v9V199hV69eqFPnz5lC5KIqIS+/vprfP3116XatmvXrrh9+7bWsv79+6N///6l2t+JEycKXW9ra4upU6di6tSpests3rxZ7zpvb2+dePUpbjkiU2KjBxERmRVnZ2c2eBD9P9bdiIiorNLkaTjxSPfHODdbN3SrWfDcGxY398BZ/lRn+XNHL1Rr2ENrmUQiYYMHERERlUjxBqMjIiKqIMLCwjBv3jwOz0NEREQkgiPRR5CtytZZPrjhYFhIC3jOUqMBzi0pcF+O/T4COHwVERERlZFBe3roc/nyZQCAl5cXbGxsirVNTk4OIiIiAACdOnUyWmxERFR5nD17FoMHD0ZqaiokEgm+//57U4dEVCGx7kZERMW1J6rgoa2GNBpS8Ab3jgPPr+ksjk63glv93oYMjYiIiMyUKI0e48aNg1Qqxd69e9G4ceNibfP8+XNhuxs3bhg5QiIiqugOHjyIESNGIDs790nDhQsXwsXFBR9//LGJIyOqeFh3IyKi4niU+gjhceE6y1tUbYEmLk0K3ijklwIXf308BY+ujsCBAwdgbW1tyDCJiIjIzIjS6AGg1MOMcHgSw5Kwq7DRSaVSeHh4CK/J+JhzcZXXfGdlZUEul2stO3z4MObMmQMLC9FudwZXXvNdmfFemYt1t/KB56M4eK0VF/MtLmPmW18vj6GNCp7AHDGhwMMQ3cWpamy5poCL23U8evQITZroaTCpIHiOi4v3SiIielm5/RVIrVYDAGQymYkjqVwq8g9/FYWFhQXq1Klj6jDMCnMurvKa7xEjRmDNmjWYMGECAGDo0KH4888/K/x1r7zmuzKr6OeMqbDuZhw8H8XBa624mG9xGSvfao0a+6L26R5PaoGBDQYWvJGeXh6//C1Hjdr1cPTo0Qrf4AHwHBcb75VERPSycntniI2NBQA4ODiYOBIiIqoo3nnnHSQnJyM8PBzr1q3jFyAiEbHuRkRkXi4/u4ynGU91lveq3QvONs66GyTcBW4d0FmstHRAuKQpQkJ2o3bt2kaIlIiIiMyNUX4NyvvS+7L4+HjY2dkVuq1cLsejR4+wZMkSSCSSSvGUBxERiWf27NnQaDTs5k5UAqy7ERFRSe25V/wJzONSs/F8x9fwgu4QiBZdJuP4JwtYdyMiIiKDMUqjR9++fXWWaTQaYciRkhg6VM9YoFQqCoUCVlZWpg6jUlMoFLh58yYAoHnz5rC0tDRxRJUfcy4uU+dbpVIhISEB1apV01umMn1pNnW+zZFCoTB1CKJj3a38Yt1NHLzWiov5Fpcx8p2hyMCxR8d0lrvauKJH7R46y9fvP4nZzw8AL1fRLGwA7ymVqu4G8BwXmznW3YiIqHBGafTQN4FlSSa2tLa2xrhx4zBq1ChDhUUkCo1GI1S6OJmrOJhzcZky3wqFAuPHj8fly5dx9uxZ1KhRQ9TjmwLPbxID625k7nitFRfzLS5j5PtI9BFkKbN0lg9qOAiWUu0f+Ff8vh5O9w/CylqlG1vbAEgcPAwSU3nCc5yIiMi0jNLo8f3332u9nz9/PiQSCWbOnFnkk7lWVlbw8PBA8+bNYW9vb4zwiIioAsrMzMRrr72G4OBgAED//v1x+vRpuLq6mjgyooqPdTciIiqJvVF7C1w+tJF2b7+ff/4ZS/b+jeuvntEpq4YU0m7TjRIfERERmTejNHoMHz5c6/38+fMBAD4+PmjcuLExDklERJXcnDlzhAYPALh+/TqGDx+OU6dOVbohEYjExrobEREV1+O0xwh9HqqzvJlrM3i6egrv9+/fj7nzP8N3s15DFYlur5C0hoPg5NrAqLESERGReTJKo8fLNm3aBACoXbu2GIcjIqJK6KuvvsLx48dx7949AICdnR0+++wzNngQGQHrbkREpE9CVgIaOjXE/ZT7WstfnsB84MCB8HnnI0y0WVfgfpz6fWS0GImIiMi8idLo0blzZzEOQ0RElVi1atVw7NgxdO/eHRkZGQgODkbXrl1NHRZRpcS6GxER6dPOox2ChgYhMjESQfeCcPDBQWQqMjGwwUCtcnKVBr1rZsBdnaqzj5Sar8CpRhuxQiYiIiIzI0qjBxERkSHUq1cPR48ehUKhQOvWrU0dDhEREZFZkkgkaOXWCq3cWuHjTh/jesJ1VLWtqlVm5+VojFHuBaS62zv5sJcHERERGY9BGz2CgoKE18OGDStweWnk3xcREZm35s2bmzoEokqDdTciIiorK5kV2ldrr7VMoVLjzsmtGCd9rlM+zdULVRr0FCs8IiIiMkMGbfSYN28eJBIJJBKJ1pfdvOWl8fK+qGxkMpmpQ6j0LCws0LBhQ+E1GR9zLi5j5/vcuXO4d+8e3nrrLYPvuyLi+S0+c7pXsu5W/pnT+WhKvNaKi/kWl7HznZCQgMWLF+PLL78U9h8UFoM3cv4qsJeHQ9+5QCWfk43nuLh4ryQiopcZ/O6r0WhKtJzEJZUWUOskg5JKpXBxcTF1GGaFOReXMfN96NAhjBgxAjk5ObC3t8eoUaOMcpyKhOe3+MztXsm6W/lmbuejqfBaKy7mW1zGzPeTJ0/Qr18/3Lx5E0+fPsWaNWug1gCXjgfiNWm0TvkMh/qwb+5vlFjKE57j4uK9snxYu3YtYmJitJbVrl0bEydONFFERGTODNrocfz48RItJyIiyrNz504EBARAoVAAAN58801UqVIFAwYMMHFkRJUX625ERFRa9+7dQ79+/RAdHQ0AWLduHZydndFr/IcYmrEDKODhe9teswEpn8onIiIi4zJoo0etWrVKtJzEx6c2jU+j0Qg/2lpaWpZ6eBAqPuZcXMbKd0REhLBfAFAoFFi/fr3ZN3rw/BafOd0rWXcr/8zpfDQlXmvFxXyLy1j5fvLkCZ4+faq1bOfOnciu1gwrZJE65bOt3WDTZrRBjl3e8RwXF++VRGQosbGx+O2333D+/Hk8f/4ccrkcALBixQr4+PgY5BiBgYGYP38+gNyHzWrXrm2Q/ZI2Di5pZpRKJaytrU0dRqWmUChw7do1AICXlxesrKxMHFHlx5yLy1j5/vrrr5GUlIQVK1YAAAICArB+/XqD7Lsi4/ktPqVSaeoQiASsu4mD11pxMd/iMkS+U3JS4GTtpLXs1Vdfxc6dOzF8+HCoVCp4enriq993wf7I7AJ7eVj1+ACwtCnV31DR8BwXF+tuJKacnBz89ddfOHLkCG7fvo309HQ4OzujefPmGDZsGAYNGlTo9p6ensU6TufOnbF582a962/evIklS5bgypUryMnJQaNGjTBmzBi8/vrrhe43OzsbAwcOxJMnT7BhwwZ07dq1WPGYg9jYWIwYMQJJSUmmDoUMgI0eRERULkgkEixduhTJyclwcnLCsmXLOD4vERERkYlpNBq8eeBNOFg5YGijoRjYYCCcbZwBAP7+/tiwYQMWL16MAwcO4Ovtp7FMeklnH3ILB1h1miBy5EREhnX//n1MnToVDx480FoeHx+P+Ph4nDlzBoGBgVi6dCns7e2NFkdoaCgmTpyI7OxsYdmNGzewYMEC3Lt3D5988onebX/99Vc8efIEfn5+bPB4ya+//oqkpCRYWFhg1qxZ6NSpE+zs7AAANWvWNHF0JWfuPUpEa/TIysoCANja2ha4fvPmzTh48CCSkpJQu3ZtjBkzBn369BErPCIiKgekUik2bNgAmUzGYQCITIx1NyIiAoCr8VfxKO0RAOBG4g38GPojetXuhbdbvY027m0wduxYjB49GpceJqN73DZILXSHGpJ1ngjYOOksJyKqKBITEzFhwgRhWD9fX18MHz4cHh4eiIuLw+7du3Ho0CGEhIRgzpw5WLVqVaH7GzNmDN5880296/XVwdVqNT755BNkZ2ejQYMGmDNnDlxcXBAYGIjAwEBs3LgRAwYMQIcOHXS2ffjwIdatWwc7Ozvhx3D61/nz5wEAffv2xaRJk4x2nBEjRmDEiBFG2z/lEqXR48SJE5g2bRrs7Oxw+vRpODg4aK2fP38+goKCAOQ+RRIdHY2QkBDMmjULkydPFiNEIiISiUKhgFwu1/vki4UFOyESmRrrbkRElGfPvT1a75VqJY49Oga/Bn6Ae+4yCwsLbDl2GT/Lzupsr5JYQtZ1qhihEhEZzYoVK4QGjw8++ADTp08X1rVo0QK9evXC0qVLsWLFCpw6dQqHDh2Cr6+v3v1VrVoVTZs2LXEcYWFhePjwISwtLbF27VphLr5OnTohLi4OISEhCAwMLLDR45tvvoFcLseMGTNQrVq1Eh+7snv+/DkAoEGDBiaOhAxBlHFDQkJCoNFo0KdPH50vzaGhodi9ezcAwMbGBi1atIC1tTU0Gg2WLl2KO3fuiBEiERGJICsrCyNGjMDw4cORk5Nj6nCISA/W3YiICACylFnYe2evznJHK0f0qtNLeH/1cTJaPt4Ka4lCp6ymzRigSnVjhklEZFQqlQp79+ZeC2vVqoWpUwtuyJ02bZowDNLq1auNEsvNmzcBAO3atRMaPPLkzSeSVya/I0eO4OzZs2jYsCHefvtto8RW0SkUufcwPohZOYjS6HH16lVIJBJ4e3vrrNuxYwcAwMPDAwcPHkRgYCAOHjyIGjVqQK1WY/v27WKESERERpaamgo/Pz/s378fR48eRUBAACcdJCqnWHcjIiKNRoOpP0+FooCGDL8GfrCS/Ts599pjVzFWdlR3H5DAosdMo8ZJRGRs0dHRSEtLAwB069YNMpmswHIymQzdunUDAERGRuLx48cGjyUvDnd3d511bm5uAID09HSt5VlZWfj+++8BAJ9//jksLS0NHhcAXLlyBZ9++ikGDBiA9u3bo1WrVujZsycmT56MLVu2IDU1tcDt5HI5tmzZgnHjxqFLly5o1aoVunfvjkmTJmHPnj1Qq9V6j7ls2TJ4enoKE8Tn5ORgzZo1GD58ONq1a4d27dph1KhR+OOPPwr8/SEwMFBrewBYvny5sMzT0xPz5s0rY2b0HzMmJsbgf9PFixfh6empNYRZ3759tf4mT09PXLx40aB/V3kjStPVixcvABTcPejs2bOQSCQYO3YsqlfPffqjRo0aGDt2LH788UdcuqQ7CRoREVU8r7/+Ok6fPi2837VrF2bOnIkVK1aYMCoiKgjrbkREtGLFCpyKPwWHag4664Y1Hia8vvUsFTXv/QlHyyydckpPf1i6NTZmmERERpecnCy8rlq1aqFl8xoegNxGgDp16hg0lipVqgDInTz9ZQkJCQCg01P7119/RWxsrNEmL8/Ozsann36K/fv366x7/vw5nj9/jlOnTuHFixdaw4IBQExMDCZNmoT79+9rLU9ISMCZM2dw5swZbN++HStXroSzs3OhcSQkJODdd9/V6ely7do1XLt2DSEhIVi5ciWkUlH6ABhEZfybxCJqo8fL47ffvXsXSUlJkEgk6Nu3r9a6Vq1aAQBiY2PFCJGIiIzsm2++wblz54SnTpycnAqduI2ITId1NyIi8n3NF6sO6k7E29CpIVpWbSm8X338Jv5jcbDAfVj2nG20+IiIxJK/TvxyL4qX5fXEAIB79+7pLXfo0CEcPHgQT548gVQqhbu7O9q1a4fhw4ejS5cuerdr1qwZACA8PBzPnz/XmpvjwIEDAIDmzZsLyx48eCBMXm7oHgtA7sTqU6dOxblz5wAA9evXx5gxY9CqVSvY2toiPj4e4eHhOHhQ9z6RkZGBt99+W+gR4+Pjg5EjR8LDwwMxMTHYsmULLl26hCtXrmDKlCnYsmWL3l42QO5cK/fu3cO4cePQp08fODk54cGDB1i5ciWioqJw8uRJ7NixA6NHjxa28fHxEb7H+Pv7A9CdZN7JyansiSql0vxNXl5e2LdvH44fP47FixcDANauXQsPDw+tfdeuXVvMP0V0ojR6yGQyKBQKpKSkaC2/cuUKAMDV1RUNGzbUWpd3QnHMd8PiuHTGZ2lpCS8vL+E1GR9zLq7S5rtTp07Yt28ffH194eTkhCNHjqBNmzbGCrPS4PktPt4rWXcrT3g+ioPXWnEx3+Iqbb5PxZ0CJLrLhzYeCokkd0V0QgasbuxENctknXKKuq/Aslb70oRc4fEcFxfvlWRsdevWhaWlJRQKBS5fvlxo2fzr8yY+L8jLDSIPHz7Ew4cPERQUBB8fHyxcuFDo1ZFfhw4dULt2bcTExGDixImYNWsWnJ2dERQUhJCQEADA8OHDhfLffvstFAoFZs6cKfTSNqQ//vhDaPDo168ffv75Z1hZWWmV6dWrF2bOnKnTO2X58uVCg8f777+PWbNmCetatWqFAQMG4KOPPsK+ffsQHh6O7du3F/rg5PXr17F27VqtIXpbtmyJHj16YNCgQUhISMDWrVu1GggcHR3h6OiotZ/STjJvDKX5m+zs7NC0aVNcv35dWFa/fv1K38jxMlH6vuS1JL3cFefUqVOQSCTo0KGDzjZ5LaMuLi7GD9CM5FVOyXgkEgmsrKxgZWXFfIuEORdXWfLdq1cvoTLGBo/i4fktPuaZdbfyhOejOHitFRfzLa7S5Fuj0WDPvT06y6USKQY3HCy8/+3kHUyS6Q5nAph3Lw+e4+JijsnY7OzshB+db9++XeAwTgCwf/9+3LlzR3ifkZGhU8bW1haDBg3Ct99+iy1btiAoKAjr1q3DlClThOGbjh07hqlTpwoTa+cnlUrx3//+F1ZWVrh79y6mTZuGgIAA7Ny5EwAwbtw4dOzYEUBub5KQkBCjTV6uVquxdu1aAED16tXxww8/6DR45I87f68UuVyOv/76CwDQpEkTnWGvgNx/219++aWQly1bthQaz9ixYwuck9DZ2RkjRowAANy5c0erN055Vxn/JrGI0ujRsWNHaDQabNmyRRguISIiQmiBfOWVV3S2iYqKAlDwxDxERFRx+fr6okmTJqYOg4gKwbobEZF5i0iIQHRqtM7yrjW7wsMut2E8NjkL6f8EoZFU90lmhYcX0KiPscMkIhLN9OnThV5F8+bNw8qVKxEbGwuFQoHY2FisXLkS8+bN0+rdlZ2drbOfM2fO4Oeff8Zrr72Gjh07onnz5ujevTtmz56NAwcOoEWLFgCAS5cuYdu2bQXG0qVLF2zduhW9evVClSpVYGVlhWbNmuGrr77CZ599BgDIzMzEwoULAWhPXn79+nVMnjwZHTt2RJs2bTBq1CgEBweXKic3b97Es2fPAACvvfaaztC4hbl+/bowsfnw4cP1Dlvl4OAAPz8/ALm9Y+Li4vTuM294qoK0bJk7LKNGoylw8vDyqjL+TWIRpQ/gm2++id27dyMmJgY+Pj6oX78+oqKioFQq4eTkJJy8+f3999+QSCRo1KiR0eN78uQJNm/ejFOnTuHZs2ewsrJCnTp14Ofnh4CAANja2hrsWOfPn8fevXtx5coVxMfHQyaTwc3NDZ6enujSpQuGDh1aootESanVaqPtm3Kp1WphOBAnJydOJiQC5lxcReX76NGjsLe3R7du3UwRXqXD81t8vFey7pYf627mgddacTHf4ioq37GxsThw4AAmTZokLNt7b2+B+xrWaJjwevXpKLwrLbicZc/ZgBk/fc9zXFy8V5IY2rZti6+++gpffPEFFAoFlixZgiVLlmiVsbGxwccff4yvv/4agO78eAB0hlLKz83NDUuXLoWfnx8UCgX++OMPjB8/vsCyXl5eWLVKd96lPCtWrMDTp0/h6+srTF5+6dIlTJw4EXK5HM7OznBycsK1a9cwe/ZsPH36FBMnTiwyD/nduHFDeJ3Xu6S47t69K7wuahSINm3aCA1Ad+/e1ZmbIs/Lw+/ml39ejoJ64JRXlfFvEosojR4tW7bExx9/jP/973/IzMwU/lFYWFjg22+/hYODg1b5tLQ0nD59GgDQuXNno8Z24sQJfPTRR1oTEWVlZSElJQXXr1/Hzp07sXr1atSrV69Mx0lJScH8+fNx/PhxnXXp6emIjo7G4cOH0a5dO60JhwxNpVIZbd+US6lU4v79+wByb0L6uvaR4TDn4ios37t27cKYMWNgb2+P06dPo3Xr1qYKs9Lg+S0+3itZdwNYdzM3vNaKi/kWV2H5joqKQr9+/fDgwQMolUq8//77yFHl4GC07oSzVSyroHfd3gCAhPQcPAg9hLay+zrlFI71YNl8qJH+moqB57i4eK8ksYwaNQrNmzfHr7/+inPnziEzMxNAbh25Z8+emDt3rlYdtbAGDn3q1KmDbt264fTp03j48KHOZOXFERUVhY0bN8LOzg7z588HkNs4+Omnn0Iul2PcuHH49NNPIZFIcOTIEcyYMQO//PIL+vfvjzp16hT7OElJScLrkvb2zj93oKura6Fl3dzcCtzuZYU9+JS/8bkiXTMq498kFtFme3r77bfRtWtXHD58GAkJCXB3d8egQYMKbLG6ePEiWrVqBQDo3bu30WK6ceMGZs+ejezsbNjZ2WHy5Mnw9vZGdnY2goODsWPHDkRHR+O9997Drl27dL7gF1daWhreeecdREZGAsid2GfAgAGoW7cupFIpnj17hkuXLuHIkSOG/POIiES1bt06TJo0CWq1GsnJyejfvz/Onj3LoayIKijW3Vh3I6LK7fr16+jXr58wNMm0adPg5OQE126uSJPrjg3u28AX1jJrAMDakAeYAN05PwDA8pUZgIwTSxNR5dSyZUssX74cSqUS8fHxUCgUqFatGqytc6+Pe/b8e20s7XfhRo0aCQ8UlabR45tvvoFCocCMGTOEycvDwsLw6NEjuLq64uOPPxbmwunfvz969eqFkydPYt++fZg6dWqpYi4LzstDxiBqTcTT0xOenp5FlvPx8YGPj4/R4/nvf/+L7OxsWFhYYN26dWjXrp2wrmvXrqhXr97/sXffYVFcXwPHv7ssRUAERCzYe8NYYy9RsSW2mGaPvcZUo6Ya4y+mm1hijbEmMcZeoth7wyQq9t4rAtLZMu8fvGzE3UXAZXaB83keH2Hmzszh7uzM3T1z7+Wbb77h8uXL/PLLL1Yn1cmIzz//nBMnTuDm5sYPP/xAq1at0qwPDg4mJCSEDz74QDJzQogcSVEUVqxYkaZr+Z07d5g5cybfffedAyMTQjwNabtJ200IkXvt3r3bnPCAlPbc//73P5pOtpy3CaBz+ZTeG9EJeg7v38EYl2MWZQz5AtDV7Jk9AQshhBPR6XQULVrUYnnqQzNAlkc+eJokwIYNG9i/fz9ly5alX79+5uWnTp0CUpI2j/c+q127Ntu3b+f06dOZOpafn5/553v37mVqmNtHh2aKiIigTJkyNsvev3/f6nZCpCfPDix57NgxwsLCAOjWrVuaD82p+vfvb37DLly4EL1en+njhIWFmbO8b731lsWH5kdpNBrzpEhCCJGTaDQa/vjjjzSTGw8dOpSvv/7agVEJIXITabsJIYR9DRs2jM8++8z8e82aNVn21zL2395vUba0T2lqBKR8ebdw32X6mFZZ3aeu4TBwtd+8SkIIkZMYjUY2b94MQNGiRa22VzPiwoUL5p8z08sjLi7OPHn5xx9/nGZS9ZiYlB58+fPnt9gudVlqmYxKnUgbMLfTM+rRXjBHjx5Nt+yxY/8l2WUkiYzL6z1oHJb0UBSFq1evcuzYMY4dO8bVq1dRFEW142/ZssX8c7du3ayW0Wq1dOnSBYCHDx9y8ODBTB9nyZIlQMoFpFevXpkPVAghcghPT0/Wrl1L7dq1GTduHD/99BMuLi6ODksIYSfSdhNCiNzn448/5u2336Zx48Zs376dgw8PYlIsJ4XuXL4zGo2GuCQDm/Yc4HntAYsyRlcvqJe5SXCFECI3+fPPP7l58yYAr776apY+D1+7do29e/cCULJkyUwlPaZNm8adO3do164djRo1SrMuNbHxaA+/VKnLMjs0bOXKlc29XZYtW5apybSrV69unvNk1apVaUaNeFRsbCx//ZUyz1T58uVtTmIuLKUOuQaQnJzswEgcQ/VH03bv3s2SJUs4ePAgiYmJadZ5eHhQv359evXqRZMmTbI1jiNHjgApX9I9mpl8XL169cw///3335mKKzk52Tz5ZaNGjcwnm9Fo5O7duxiNRgoVKpTmJBRCiJysQIEC7NmzJ93JtoQQOYu03aTtJoTIvTQaDd999x1JSUm4u7uzeoflPB0aNLxQ9gUAfjt0lVf0q3HRWSa9Xer2g3x+FsuFECK3SG9+jf379/PFF18AULp0afr3729RZtu2bTRr1sxmT+H79+8zatQoc2/lHj16ZDi28+fPs2jRojSTlz+qcuXKQEqvimvXrpknLDcajWzYsCFNmYzSarUMGDCAiRMncvv2bcaMGcP3339vMXwWpEykfu/ePXP9ubm58dJLLzFv3jzOnj3LTz/9xMiRI9NsoygKn3/+uXnC9J49nWP4xNThf4OCgti2bZuDo7Ht0cnlr127ZnVuxlQ55W/KDNWSHsnJyYwbN878RrL2ZGBCQgI7d+5k586ddOjQgUmTJll9o9hDalexkiVLpjsswaMnxKPdyzLi9OnTJCUlAVCxYkViY2P58ccfWbVqFQ8fPgTA1dWVevXqMXToUOrXr5/ZP0MIIVSXlJSEoig2u0pKwkOI3EHabtJ2E0LkHsnJyTavzxqNBg8PD07cP8GFaMvrZoOiDSjiVYQkg5FlO/9htcsOizImrSvahiPsHLUQQjiXF154gXr16tGiRQvKly+Pm5sbt27dYvPmzaxduxaTyYSvry8//vij1YdkJk6ciF6vp23bttSsWZOgoCA8PDyIjIzk4MGDLF261PwFf506dTL1Jf+ECRMsJi9/VJ06dShRogTXrl1j4MCBvPfee3h7e7NgwQKuXr2KTqejY8eOma6Tnj17sn37dvbu3cvmzZvp2LEjPXr0oHr16uTLl4979+5x9OhR1q9fzwsvvJBmzr0RI0awefNmrl27xtSpUzl79iwvvvgihQoV4vr16yxevJhDhw4BUKtWLV599dVMx5eXValSBXd3d5KSkvjxxx/R6XQUK1YMrTZl4KfChQvj4eHh4Cizj2pJj/fee4/NmzejKAo6nY5GjRpRo0YNc9bp3r17HD9+nL1792IwGNiwYQMGg4Eff/zR7rEkJSWZLyLWLgSPKlCgAJ6ensTHx1vtApaeRz9oK4pCt27duHz5cpoyer2effv2sX//ft555x0GDx6cqWNkll6vt9mlSafTmU98k8mEwWBId1+PNpqNRmO6E3lqNJo0YwkaDAabXdcgJVv86Bcaer0+3SE0Ho1dUZQnjuHt6upq/sL2SX9rZmM3Go3m8hqN5omxu7i4pOny+KQuZ/I6WdLr9eh0OjQajbm8vWOX1ylFTEwML774IqVLl+btt99Gr9dn6/vJmV+nnut7cux+2gk8gwsGM7/NfMB+f2vq+Z26zFrsjr7uOfPrlJX3U1bmgciNpO0mbbec2CbIauyPbmM0GtO9Ljn7NSwnvE7SdlPvdVIUhQkTJrB69Wpmz56dbtttxdkVVvefOoF5ZJyeYfk242GwPA+U4JdJ9ghAazDI6wRW22/Odt3LTa+TtN2EWgwGA1u3bjX3DH5chQoV+Pbbb9PtMXH37l0WLVrEokWLbJZp27YtEydOzPDDROvWrePgwYOUKVOG119/3WoZrVbLF198wYABA7h8+bJFr4o333yTkiVLZuh4j+93+vTpjBkzhk2bNnH58mVzj5cn8fb2Zv78+QwaNIiLFy+yadMmNm3aZFGudu3azJgxQ4bPziRvb2969+7N3LlzOXHihEXvo4ULF+bqh7hUSXrs2LGD0NBQNBoN9evX54svviAoKMhq2Zs3b/LBBx9w4MABQkND2blzJ82bN7drPI+OMefp6fnE8vny5SM+Pp74+PhMHSc6Otr885w5c0hKSqJp06aMGjWKypUrExsby6ZNm/juu++IiYnhu+++o2zZsrRu3TpTx8mM9J54rFSpknn8vvj4eM6cOZPuvurUqWP++f79+1y/ft1mWS8vrzQX/StXrhAVFWWzfGBgoLmrHcCpU6fSbciULVsWP7+UrtR6vZ7jx4+nG3twcLD55hEdHc3FixdtlnV1daVGjRrm32/dusXdu3dtlvf19U1T/vTp0+mOa1i8ePE03SOfFLu8TtZl5XVKnewWUt4b8jqlsPU6RUVFMWrUKE6ePMmuXbvw9vamf//+efZ1svZhLz4+3nzM7HidTp8+bf5Z3k+2Zef7Ka+Qtpu03fJimyB1G2m7/ccZX6dUufleY6/XyWQyMXnyZH777Tcg5QutH3/8kbp161p9nWpSk9iAWPZF7SPKkBKfh9aDliVbAlDEQ0+npPUWx1fQcMqvDUnHj8vrZOV1Sm2/yfvJNmm7iZxi4sSJ7Nmzh+PHj3Pv3j3i4uLw9/enUqVKtGvXjk6dOqVJyj3uyy+/5NChQ/z7779cu3aNqKgoYmNj8fT0pEiRItSqVYuuXbtmagL02NhYvvrqKwA++eSTdBMlzz77LL/++itTp07lyJEj6PV6KlSoQL9+/XjhhRcyXhGPyZcvH1OmTOHAgQOsWLGCI0eOcP/+fYxGIwEBAVSuXJnnnnuO559/3mLb4sWLs3r1apYtW8bGjRs5e/YscXFxFChQgCpVqtCxY0c6duxoTqSKzHnvvfcoXbo0q1at4vz588TExKSbWM5NVEl6rFiR8sRI5cqVmTt3broXgGLFijFnzhxeeeUVTp8+zfLly+3+wTl12AIg3VhSpV4wHh/H+kke/aCdlJRE48aNmTVrljkz6e/vT/fu3alQoQK9e/fGZDLx/fff06pVK5vDxgghhCMYDAaGDRvGuXPnzMt++uknfH19CQ4OdmBkjqEoCncTbX+QFCKnk7abtN2EEDnf9OnTzQkPgLCwMMaNG0doaKjV8sXci/FKkVd4qfBLnIg9wZ6oPRRwLUA+3f8PXXpkAdrkhxbbRRVuRFL+zD8dLIQQOc3zzz9v9Yv7jHr22Wd59tln7RhRytP8u3fvznD54OBgZs+ebdcYUjVo0IAGDRpkejs3Nzd69uyZpTk73njjjTRDZtlSv379JyZgn7Q+s+VsefHFF3nxxRdtrrfn36TRaHj55Zd5+eWX0y33tH+TM9Io6fVhtJPmzZtz9+5dvvrqKzp16pShbdauXcvo0aMpXLgwO3futGs8Dx48oGHDhgB06NCByZMnp1u+UaNGREREULFiRdauXZvh4/z88898/fXX5t9XrlxJ1apVrZYdNWqUuQvX6tWrMz15UHqSk5PNT2JUrlzZ5pcFOaWr8JNil67CacnrZJ/Y5XWCxYsXM2DAAHO5QoUKsW7dOurVq5enXiejychn+z9j5fmVFuuzY3irjMQu76e0nvb9lJSUZH4q89GnLPMSabulkLabXMOscfZrmLxOKeR1grNnz9KyZUvu3btnjuPHH39k5MiRmX+dDMnw4zMQc9OijL7vXyhBdbMUu7xOtsn7yTppu6U4d+4chv8fTq5ChQqODgdIaUc93kunePHiaT5DCiGENdlxTVOlp8eDBw8AKF++fIa3SZ2EMnX8Znvy8vIy/5yRYQ8SEhKAjA2nYOs4/v7+Nj80AzRt2tT8wfn48eN2/eD8KI1Gk6EGgFarzVRD4fFG0JOkNwGpNRl5qjNVRv/GVJn9W58Uu8Fg4Nq1awAULVo0U7ED2Rp7bn2dDAYDt27dAlLqXKfTZWvskDdfp/79+xMbG8ubb75JsWLFWLJkCbVr107zdLO930+Pc/TrpDfqGbt7LKFXrD8hqdHaPq+z+rdaO78tjuvg697jHP06PSor76cnfZDPC6TtJm23x+WUNoE1GYn98WttZuJ3tmtYZkjbzbrc8jpVr16dTZs20aJFC+Li4pg8eTLDhg3LWtvt+B9WEx6UaoJrmUZ2iR1yz+v0pPabM1z3HpXTXydpuwkhhHicKkkPT09PHj58mKlxFh8+TOk2my9fPrvH4+7ujq+vL1FRUU+c4DI6Otr84fpJE2c+rmjRouafn7Tto+uz48uCVCp07MnzTCaTefzUR8c9FdlH6lw9o0aNAqBcuXLkz58/3SfIcpsEQwLv7HiHPTf2qHpcOb/VJ/dKabtlZFtpu+Uucq1Vl9S3emrVqsWqVas4evQojRo1ylrbzWSCvT9aX9fkraeKL7eSc1xdcq8UQgjxOFWSHmXKlOHo0aNs2LCBRo1sPwXyqA0bNpi3zQ7ly5cnLCyMq1evmrvPWPPoZGGPTvSV0WOketIkMY82PjPzVIMQQqht6NChT5y8MLeJTY5lxNYR/H33b0eHIoQqpO0mbTchRO7RuHFjfHx8sr6Ds3/B/bOWywtXh/Kts75fIUSuIsNYCSGciVaNg7Rs2RJFUVixYoV5Ysz0rFq1iuXLl6PRaGjVqlW2xFSnTh0gZYiEEydO2Cx3+PBh88+1a9fO1DGCgoIoVqwYADdu3Ej36YOrV6+af5YnQYQQjrRu3Tpu3Ljh6DCcRmRiJANCB0jCQ+Qp0naTtpsQIue4ePEimzdvzp6dKwrs+cH6usZvwiNDZQkhhBBCOAtVkh69evWiUKFCKIrChx9+yJAhQwgNDeXOnTvo9XoMBgN37twhNDSUIUOGMG7cOEwmE4GBgfTq1StbYmrd+r8nUpYvX261jMlkYtWqVQD4+PhQv379TB+nTZs2AMTGxrJ//36b5UJD/xsfPvVDvRBCqG3+/Pl07tyZNm3aEBER4ehwHO5O3B36bezHyYiTjg5FCFVJ203abkKInOHEiRM0adKETp06sWvXLrvu+9j1KPZtXwvXD1mu9C0J1V606/GEEEIIIexFlaSHp6cns2bNwsfHB0VR2LVrF2+++SYtWrSgRo0aBAcH06JFC95880127dqFoigUKFCAWbNmZcu40AA1atSgbt26QMoH53/++ceizLx587hw4QIAffr0sZjc6+DBg1SqVIlKlSoxduxYq8fp27cv7u7uAEyaNInY2FiLMqtXr+bQoZSGZIsWLdKMJy2EEGr54Ycf6NevHyaTiZMnT9K+fXtiYmIcHZbDXIu5Rt+NfbkQfcHqej93P5a+sJTjfY+b/y3psETlKIXIHtJ2k7abEML5HTp0iGbNmnHr1i0SExPp2LEjf/+d8Z6peqOeX8J/4Xac9bmSvgs9S+L276xv3PANcFFltGwhhBBCiExTJekBULVqVdasWUObNm3QarUoimL1n1arpW3btqxZs4bKlStna0wffvghHh4eGAwG+vfvz6xZs/j33385cOAAn3zyCd988w0ApUuXpl+/flk6RrFixcwT/549e5aXXnqJ5cuXEx4ezoEDB/j8888ZN24cAN7e3uafhRBCTfHx8cycOTPNssOHDzNv3jwHReRY5yPP0/evvtyItT7MV6BnIPPbzadqwaoqRyaEeqTtJm03IYRzmz9/Pg8ePDD//vDhQ8aMGZPh7Xfd2MX3R76nzZ9tGBw6mHUX15FgSADg+PVobp87QkuXfy039CwItbKnV58QQgghhD2o+mhG4cKFmTJlCnfv3uXQoUOcO3eOqKgoAHx9falQoQLPPvssgYGBqsRTtWpVJk+ezOjRo4mNjeX777+3KFO6dGlmz56Nt7d3lo8zcOBAoqOjmTNnDpcuXeKDDz6wKFOwYEGmT59O6dKls3wcIYTIKk9PT0JDQ2nSpAnXrl0DYMyYMeYv/vKSE/dPMHTLUKKSoqyuL5G/BHPazCHIO0jdwIRwAGm7SdtNCOG8pkyZwq1bt8zD+jVq1Ihly5ZlePs159cAoKCw/9Z+9t/aj5erF0tfWMr07fcZoltnfcNnh4Cb59OGL4QQQgiRbRzSHzUwMJAXXnjBEYe20LJlS9asWcPChQvZsWMHd+7cwdXVlZIlS9KuXTt69epll2Ea3n33XVq2bMlvv/1GWFgY9+7dw93dndKlS9OyZUt69+5N/vz57fAXpU8jE81lO61Wi6+vr/lnkf2kzu2jZMmSbNmyhaZNm/L222/bHPolN9d32O0wRm4bSZw+zur68r7lmR0ym0KehVSLKTfXt7OSe6UlabtJ2y23k2utuqS+7UOn0/Hbb7/x/PPPo9PpWLFiBV5eXhblrNX3g8QH7LpuOQeIr7svifG+HD+xnWnu+yzWK66eaJ4dZN8/JBeSc1xdcq8UQgjxOI2iKIqjgxDZKzk5mePHjwMQHByMm5ubgyMSQjizBw8e4O/v7+gwVLfr+i7e2fEOScYkq+urF6zOjNYz8PXwVTcwoTq5bwpHk3NQCJEZsbGxuLq6mucjyoglp5bw5aEvLZYPe2YYtfK/ys3f3qCbYb3lhg2GQ7tJTxOuEHaXF++b586dw2AwoNPpqFChgqPDEUKIp5Id1zSHzTwWERHB2bNnLYZICAgIcFRIQgiRJxiNRlxcXGyuz4sJj42XNzJu1zgMisHq+rqF6zKt1TS8XC2fnhQir5C2mxBCOIaiKJhMJpvtt6wM57f6/GqryzuW60gJLShss4xDq0PTcESmjyWEEEIIoTZVkx6KovD777/z66+/cv78eatlypcvT/fu3XnttdekG6gQQtjZgwcPeOGFFxg1ahSvvfaao8NxCnqTnhn/zrCZ8GhWvBnfNf8OD52HypEJ4XjSdhNCCMdSFIX33nuP+/fv88svv9jlOnvmwRlOPThlsbxO4TqUyF8Ctk9C8/8Tmj9KE/wyFCj+1McXQgghhMhuqiU9IiIiGDp0KOHh4UBK482a8+fP8/nnn7N8+XJmzpxJoULqjZueF+j1+jzR1dOR9Ho9Fy5cAKBcuXK4uro6OKLcT+o8Y27evEmbNm04ceIEhw8fxsfHhw4dOmR6P7mtvl21rswKmUWfv/pwK+5WmnXtSrfjiyZf4OriuL8xt9V3TqDX6x0dglOQtptzkLabOuRaqy6p74wxGAwMGTKEefPmASk97H744YdMz1/weH2vubDGarnO5TpDchwcmmV9R43fzNRx8zI5x9UlbTchhBCPUyXpkZycTN++fblw4QKKouDv70/79u0JDg42D4lw//59wsPD+euvv4iIiODEiRP069ePFStWyAc9kaMoikJcXJz5Z5H9pM6f7MGDBzRt2pSLFy8CKR+iu3XrRmhoKE2bNs3UvnJjfRfxKsKcNnPo+1dfIhIjAOhWoRsfN/gYF63tocDUkBvrWzg/abuJvEauteqS+s6Yfv36sXjxYvPvU6ZMwc/Pj/Hjx2dqP4/Wd7IxmXUX11mUyafLR5vSbeDIIkiItNxJxfYQWCVTx83L5BwXQgghHEuVMQjmz59vHhLhpZdeYsuWLXz88cd06dKFJk2a0KRJE7p06cJHH33Eli1beOWVVwC4cOEC8+fPVyNEIYTI1fz8/HjhhRfSLAsICJAnsh9RyqcUs0Jmkd8tP32r9uXThp86POEhhKNI200IIRzvtddeQ6f77zlFFxeXp57cc/+t/TxIfGCxvHXJ1nhp3WD/NOsbNnnrqY4rhBBCCKEmVZIe69evR6PR0KhRIyZOnIinp6fNsvny5WPChAk0btwYRVFYv369GiEKIUSuptFomDx5Mn379gWgQoUK7Nmzh8qVKzs4MudSyb8SKzqt4N2676Y7dISiKMzZdZEft5xTMToh1CNtNyGEcLznn3+eRYsWodFocHd3Z+XKlfTs2fOp9rnukmUvD4DO5TtD+HKIvma5skQDKNngqY4rhBBCCKEmVYa3unr1KgA9evTI8DY9evRg79695m2FEEI8Ha1Wy9y5cylcuDDvvPMOhQsXdnRITqmIV5F01ycbTHy8KpylYSlfCkzecta8rlZJX1YOb5yt8QmhBmm7CSGEc3jttdeIi4ujXLlytGjR4qn2FWuIZeeNnRbLi3oVpV7hurDyLesbSi8PIYQQQuQwqiQ93NzcSExMpGjRohneJrWsjAkthBD2o9Pp+OqrrxwdhuoSDAlsubKFjuU6PtV+ouKTGbb4b/ZfjLBTZEI4J2m7CSGE8xgwYECWt+25oSfH7h1Lt0w1n5ZcPbia0ndPWq4sVAUqtM3y8YUQQgghHEGV4a3KlCkDwK1btzK8TWrZ1G2FEEJkzMaNG0lMTHR0GE4jJjmGoZuH8sGeD5gfPv+p9mU0KVyPirdYHuDtxp4xz0kvD5FrSNtNCCHUc/LkSc6cOeOw4+/6uyR3//rS+srGb4JWla8NhBBC2FliYiLTpk2jU6dO1KxZk0qVKlGpUiX+97//OTo0u1ixYoX5b7p+/brF+rFjx1KpUiVatmxpdfvUbadOnZrdoeYJLVu2pFKlSowdO9bRoQAqJT1efPFFFEXh999/z/A2v//+OxqNhi5dumRfYEIIkctMnTqV9u3b0717dwwGg6PDcbjIxEgGbBrA33f/BuC7I9+x7OyyLO+voLc7P/etR373tB0lI+KSOXjRclJQIXIqabsJIYQ6wsLCaNasGSEhIQ4ZHrCYRxVKRd/hWa2VpItPcQh+SfWYhBDC2Rw8eND8Bfnj/5555hmee+45hg8fztq1a53mc7her+f1119n6tSpnDlzhoSEBEeHJISqVEl6vPzyyzRp0oQ9e/Ywfvx4kpKSbJZNTk5mwoQJ7N69m8aNG/Pqq6+qEWKeoZWndLKdi4sLxYsXp3jx4ri4uDg6nDxB6jxlYu0JEyYwatQoAFatWsXAgQMxmUx2P1ZOqe87cXfot7Efpx6cSrP88/2fs+Hihizvt2Lh/EzrWRvt/89z7unmwpzedelWp/jThGtTTqnv3ETuldJ2cyZyPqpDrrXqkvpOsWPHDlq2bElERATXrl0jJCSEu3fvqhpDxO0aDNOttb6y4QhwcVU1ntxCznF1yb1SOFJiYiI3b95k69atvPfee7z22mvcu3fP0WGxceNG/vnnHyDlgaaFCxeydu1a1q5dy+DBgx0cnXgavXv3plKlSvTu3dvRoTg1Veb0CAsLo1+/fkRHR7N06VK2bNlC+/btCQ4OpmDBgmg0Gu7fv8/x48fZuHEj9+/fJzg4mP79+xMWFmZzv/Xq1VMj/FxFGlzZz8XFRSaIVpnUOVy/fp1vv/02zbIFCxbQtWtXOnfubNdj5YT6vhZzjUGhg7gRe8NinYLCt2Hf0qJECzxdPbO0/+YVCzG+UzVm7rjA3L71qFrM52lDtikn1HduI/dKabs5Ezkf1SHXWnVJfac8sPLJJ58QExNjXnb27Fk+/PBD5syZo0oMOo0bPnd9CHE9Yhmfhy+a2n1UiSM3knNcXXKvFGrq3r07PXr0MP8eHx9PeHg48+bN48aNGxw/fpzhw4fzxx9/oNFoHBbnvn37AChUqBATJ07Mk++TL7/8ki+/tDF8o8j1VEl69O7dO80bPSIigsWLF6e7TXh4eLoTtmk0Gk6etDLRmhBC5EElSpRg3bp1tG3b1jyfx//+9z86derk4MjUdz7yPIM3D+ZegvWnawI9A5kTMifLCY9UfRqWpmutIPJ7yBOQIveRtpsQQmQvjUbDihUraN68ufna2Lp1ayZPnqxaDC4JNRiu3Ww9vvpDwN1btViEECKnKFiwIBUrVkyzrGbNmnTs2JGXX36ZK1eucOzYMbZv325zLgk1pPYclB5nIq9SrQ+goih2/yeEEOI/zZo1488//8TNzY3p06fzwQcfOPTJEkc4cf8E/Tb1s5nwKJG/BAvbL6Ssb1mb+4iITcrwPUYSHiI3k7abEEJkr4CAAEJDQyldujQvvvgi69atw9tbvUSD5m4ZOmv3WixXdPng2SGqxSGEELlBgQIF0gwbtXv3bgdGkzIELYCrq3xmFXmTKj09Fi5cqMZhRAbo9Xrc3NwcHUaulpyczPHjxwEIDg6W+laB1Pl/nn/+ec6dO0fJkiWz7RjOWt9ht8MYuW0kcfo4q+vL+5ZndshsCnkWsrmPf69FMXBBGK83KsXIlhWyK9RMcdb6zs30er2jQ3A4abs5D2m7qUOuteqS+v5PUFAQe/fuJTAwEJ3O/h/PbSWctSZfBiWdwFVntFinqd0bvAraPZa8RM5xdUnbTTiLGjVqmH++efOmxXqj0ciaNWvYuHEjJ06cICoqCi8vL8qWLUubNm3o3r07Hh4eVvfdu3dvDh06xLPPPsuiRYu4fPkyCxcuZM+ePdy5c4fExES2bt1Kq1at0mx36NAhKlWqZP49KCiIbdu22ekvTmEymdiwYQObNm3i+PHjPHjwAICiRYtSvXp1QkJCaNWqlUUC5uzZs2zZsoUjR45w7tw5Hjx4gKurK4UKFaJWrVp0796dmjVrZjmusWPHsnLlygz/zfv27WPhwoWEh4cTHR1NYGAgzZs3Z8iQITaHLFyxYgXjxo0DYOvWrQQGBvLrr7+yYcMGrly5QlRUFCNHjuSNN94AUu4Pe/bsYc+ePRw9epSrV68SHx+Pt7c3JUuWpFmzZvTs2RN/f3+bf0+qx19bsP36xsTE8Ouvv7J9+3YuX75MbGwsvr6+VK9enS5dutC2bdsnPjS7c+dOFi9ezPHjx0lISKBIkSK0aNGC/v37O+WQjqokPZ599lk1DiOEEHmCoijp3oyyM+HhrHZd38U7O94hyWh9suXqBaszo/UMfD18be5j3bGbvPvHUZIMJr4NPUvpAC9eqFEsmyIWwrlJ200IIeznSW23YsWyr71h67imB1Xp6bLSYrmicUHTcGS2xSOEELnZo8lrozFtUvnmzZsMGzaM06dPp1keFRXF33//zd9//81vv/3GrFmzKFOmTLrH2bJlC6NHjyY+Pt5+wWfR9evXGTlyJKdOnbJYd/nyZS5fvsy6detYuHAh9evXN687ePAgffpYzh2l1+u5cuUKV65cYdWqVQwePJh33303W/8GgGnTpjF16tQ0y65fv86SJUtYs2YNM2fOpG7duunuIzIy0mZdpPrkk0/SJC5SRUVFERUVxbFjx1i8eDE//fQTderUydof85j9+/fz1ltvERUVlWb5vXv32L59O9u3b6d58+ZMnjwZLy8vq/uYNGkS8+fPT7Ps8uXLzJ8/nzVr1jB79my7xGpPqiQ9hBBC2MetW7d45ZVXmDJlCrVq1XJ0OE5h4+WNjNs1DoNisLq+buG6TGs1DS9X6zdvRVGYuu08328+m2b5u38cpbifJzVL+No7ZCGEEELkEUajkSFDhlCjRg1GjRrl6HBQFODB83SLuY6XxvJhEU31buBXSv3AhBAiFzh79r/PlIGBgeafIyMj6dGjB7du3cLNzY1XXnmFevXqERQURHx8PHv37mXhwoVcuXKFQYMGsXLlSvLnz2/1GDdv3mT06NF4eHgwbNgw6tati4uLC8ePH8fT05O1a9cCMG7cOMLDw6levTqTJk0yb2/P4a7u379P9+7dzfOHNGjQgK5du1K2bMpw0tevX+fAgQNs2rTJYluj0YinpyfNmzenQYMGlC1bFm9vbyIiIjh//jyLFi3ixo0bzJ49m9KlS9OtWze7xf24HTt2EB4eTpkyZRg4cCCVKlUiNjaWv/76i2XLlhETE8OQIUNYt24dRYsWtbmfDz/8kLNnz9KlSxc6dOhAQEAAt27dSlPnBoOBEiVKEBISQnBwMMWKFcPFxYWbN2+yb98+li9fbu4dsm7dOgoW/K/n5dtvv03//v1tvrZg+foeOXKEQYMGodfrCQgIoFevXlSuXJnAwEDu3r3Lhg0bWLNmDTt37mTs2LEWiR+A+fPnmxMegYGB5nZNUlISO3bsYMGCBbz55pskJCRkpfqzjSQ9hBAih7h06RIhISFcuHCBtm3bsnv3bouujHnNinMr+Gz/Z5gUk9X1zYo347vm3+Ghs95FGODUrRh+3HrOYnmSwcTABWGsGdmYYr757BazEEIIIfKGpKQkevXqxZ9//gmAr6+v1ada1WSML4dy91lGuS8Ga51AGr+pekxCiOx3717aOQ/9/PxsDqn3eFlfX1+bX5Tfv38/zTB6BQoUsDmcW0REBCbTf5/bfHx8cHd3t1r2wYMHaXpKpFc2MjISPz8/q+vUZDAY+OWXX8y/P9pzeuLEidy6dYugoCAWLFhAiRIl0mxbv3592rVrR8+ePbl27Rpz587l7bfftnqc69evExgYyNKlS9P0FHzmmWcAzMMieXp6mv9/fOJ1exk/frw54fHee+8xaNCgNOtr1KhBhw4dGDNmDAZD2ocUK1euzM6dO/Hx8bHYb9OmTenZsydDhw5l7969TJ8+nS5dumTbhOzh4eFUq1aNRYsWpenp0LBhQ2rXrs2YMWOIjY3lyy+/5Mcff7S5nzNnzjBx4kRefvll87Jq1aqlKTNq1ChKlChh0RMzODiYtm3b0qNHD1577TUePHjAokWLeOutt8xlChcuTOHChTP82ur1ekaPHo1er6dp06ZMnTqVfPn++26jWrVqPPfcc9SrV4+PP/6Y0NBQ9u7dS+PGjc1lIiIi+OGHH4CUobOWLl1KoUL/DRler149mjRpwsCBAy1eY0dTbSJzIYQQWXfx4kWaNGnChQsXgJSGaEhICFevXnVwZI6z8MRCPt33qc2ER/vS7fnhuR/STXgAVC3mw/hO1ayuqx7kQ34PeT5ACCGEEJljNBrp3LmzOeEB0L9/f1avXu3AqCD5/nO87LKTgpoYy5XlQ6BIdfWDEkJku8DAwDT/zpw5Y7Ns6dKl05T9999/bZatUqVKmrL79u2zWbZOnTppym7ZssVm2aZNm6Ypu2bNGptl27RpY3OdGuLj4zl06BD9+vUz11VQUBAdOnQAUpIUf/31FwAff/yxRcIjVdWqVenRoweQMk9Eet59991sHRoxIy5evGh+DVu3bm2R8HiUl5cXBQoUSLPM39/fasIjlZubG++//z4AN27cSHfIKHuYMGGC1aGdunTpQrNmzYCUYcUeTwo+qkGDBmkSHtaULFky3SEvK1WqZN7H1q1bMxK6TevXr+fGjRu4u7vz9ddfp0l4POqVV14xz0fz+Lm3cuVKcw+OMWPGpEl4pGrYsOET/25HkKSHEELkAMWKFbPo1eHm5pbmSZm8QlEUfvr3J74J+8ZmmW4VujGp6SRctRnrutu7QSleb1Q6zbLXG5Vmbp+65PewX/dfIYQQQuQNLi4u5i9JHl3mSMaEEhBfhsEu660XaPKWqvEIIURONG3aNCpVqmT+V6tWLfMk4wAFCxZk+vTp5h4vO3fuxGg0ki9fPov7wuPq1asHwN27d61OhA4pwxe1b9/ejn9R1uzcudPcy+f1119/6v0lJydz8+ZNzp8/z9mzZzl79myaXkSPz4ViTxUrVqR6ddtJ/9ShtQwGg/l1tqZjx46ZPnZ0dDRXr17l3Llz5r87NRl0/vx59Hp9pveZKnVC83r16lmdGP1RqfOVPJ7k3L9/P5DSi6tVq1Y2t8/O4ceySh5fFUKIHMDDw4PVq1fTsmVLwsLCqFGjBps2baJIkSKODk1ViqLw9eGvWXxqsc0yr1d7nXfqvJPu0xPWfPR8FS5HxLH73H3Gd6xK74alnzJaIYQQQuRl48aN48GDB3z33Xd4e3ub23KqunPK/Khj0v3neF57kBJaK0+pBtWFUo0tlwshhMiQ4sWL07ZtWwYMGJBmHobw8HAAEhISqFq1aob3d//+fau9OUqXLm1zqC81nTx5EkhJwqQOrZVZ8fHxLFq0iPXr13P+/HmLyd8fFRkZmaVjZERwcHC661N7QUDKvC3PP/+81XIZHX78zJkzzJ8/n927d6fbc8RkMvHw4cM051NmpJ57e/bsyXBs9+/ft4gVUnp12RoSL3W9q6vrUyVp7E2SHkIIkUPkz5+fv/76i9GjR/P99987xbilagu9EppuwmNkzZEMrjE40wkPAJ2Llqnda3Hi5kMalM1ao0IIIYQQIpVGo+Gbb75BURReffXVNOO7q82YWARjbBWGun1gvUCTtyEL7SchhMhrunfvbh6GSqPR4O7ujp+fn82JxyMiIrJ0HFuTQqc3JJSaUpMQ6c3jkp7r16/Tt29frl+/nqHySUlJmT5GRj0pqRAQEGD+OSoqyma5x4fwsmbZsmWMHz8+w/NfJCYmZqicNQ8ePMj0No8fLzo6GnhyHel0Onx9fdNN4qhNkh55jKO7VOcFOp3OnEFNLwsq7Ccv1XlAQECaydEcwZH13aZUG7pV6Mbyc8st1o2pN4ZeVXs91f7ze7g6XcIjL53fzkLulcKZyPmoDrnWqisv1bdGo+G7775T9Zjbrm6jcVBj3F3+exI4OeI5mmuPUVV7xXKDgIpQqYOKEeZ+eekcdwZyr3yy1MmmU6X3AN3ly5fT/O7r62uz7KlTpywmMrflyJEjFhOZ27J7926LicxtCQ0NtbkuOxQsWDBTE4On/h1+fn4sXLgww9sVL17c6vLccr6///77XL9+HY1Gw4svvsjzzz9PuXLl8Pf3x9XVFY1Gg8lkokqVKgBpzjN7y8pDk9ZotenPInHhwgVzwqNgwYIMGDCABg0aEBQUhJeXF66uKUNr//nnn3z44YfA0/3dqedes2bNGD16dJb3A/arIzXJ3TePedIbUDw9rVaLt7e3o8PIU3Jbne/Zs4fGjRs77U3FkfWt0Wj4uMHHxOnj2Hh5Y0o8Gi3jG46na4WuVrd5mKjn3T+OMrxFOWqVzHm9Y3Lb+Z0TyL1SOBM5H9Uh11p15bb6PnbsGKVLl3aKp2/D74fz5vY3CcgXQO+qvTGiYEoKwPAwmKGuX1jfqNEokGuNXeW2c9zZyb3yyaxNPmyPso8+Af8kmRmi50nzDzzK2UdASE0axcXFUa5cuVyTtEit9+joaJKTkzPV2+PChQscOXIEgCFDhvD2229bLZderwp7enxIp/TWp5cEfJKVK1diMBhwcXFh0aJFlCtXzmq51N4VT8vX15e7d++i1+szlah7VIECBbh3794T68hgMKj2emWU3BmEEMJJKIrCxIkTadq0KRMnTnR0OE7LRevCF02/oFnxZui0Or5p9o3NhMfViHi6/bSPzSfvMGjhEW5EWe8iLIQQQgiRFbt27aJJkyZ07NjR5lAkavr5+M8A3E+4z+QjkzmlMWBKLkh1l1M0dDlpuUH+olDjFZWjFEKIvCN1Ho/k5GTzHAu5QbVq1QDQ6/UWk18/yfnz580/d+hgu6ehWvV1/PjxDK+vUKFClo+T+ndXrlzZZsID7Pd3p5574eHhJCcnZ2kfqcmS06dPpzsk1+nTp51qPg+wc9KjXr161K9fn4sXL6ZZfvjwYQ4fPvxU45AJ+3i0K6HIHiaTidjYWGJjY6W+VZIb6lxRFN577z0+/vhjAD755BOmTJni4Kisc4b6dtW68l3z7/il7S+0Kd3GapnDlx/Q5ae9nLsbC8D92CQGzD9MbFLGxs50Fs5Q33lNXqpnabs5v7x0PjqSXGvVlVvqe/369bRt25aYmBh27drFyy+/7NAP/BejL7L16tY0y0wacPG8TH/dRusbNRwBOsdPiJvb5JZzPKeQOhbO7LnnnjOP4rBgwQIHR2M/zZs3z/Lf9ejQZek9MPD7779nLbhMOnv2rHlidmuWL08ZXtvFxeWp5udKTRrEx8fbLHP37l22bduW7n5SJ7J/UiKjZcuWAMTExLBixYrMhGrWsGFDIKXXzfbt222WS60jZ2LXpEdMTAwPHz60uOH07t07U5PTiOzz6IVFZA+DwcCZM2c4c+ZMhicmEk8nN9T5gQMHmDx5cpplb7755hOfOHAEZ6lvD50HNQNrWl234u/r9JxzkAdxaRsBp2/H8OZv/2A0Zd94oPbmLPWdl+Sle6W03ZxfXjofHUmuterKDfWdmJjI0KFD0ySH169fz/fff++wmOYdn4eCZRvHM6o6XfjbcgOPAlDn9ewPLA/KDed4TiL3SuHMypYtS7t27YCU+8ST5ui8du0a69aty/a4KlWqRKVKlcxfjGdWmTJlCAkJAWDLli3MnTvXZtn4+Pg0QzaVKlXK/LOtL+N//fVXtm7danVddvj444+tJiPWrl3Lzp07AWjdujWBgYFZPkbp0qUBuHLlCn//bXlfTkhI4N13333ig2epQ9Bdu3Yt3Tk/unbtStGiRQH46quvOHz4cLr7DQsL49ChQxb78PDwAGDSpElWh7k6dOgQf/zxR7r7dgS7zumh1WpRFMXqTT07J5wRQoicrmHDhkybNo0RI0aYl02dOpXg4GAHRuUY12Ou4+3qja+Hb6a3NZkUvt98lmnbz9ssc/xGNDejEijh7/kUUQqRO0jbTQghssbDw4P169fTvHlz8xjWXbp04c0333RIPLfjbrP+4nqL5YpJx+Doe2g1Vq7p9QaCe34VohNCiLxt/PjxhIeHc+3aNb788ku2bt1K586dqVChAm5ubkRFRXH69Gl2797NgQMHCAkJ4YUXXnB02E/06aef8u+//3L37l2++eYbdu/eTdeuXSlbtiwajYYbN25w8OBBNmzYwJQpU6hfvz6QMuxSxYoVOXv2LEuXLuXhw4d07tyZQoUKcfv2bdasWcOmTZuoXbu21eSAvVWvXp3w8HC6devGoEGDqFixIjExMWzatImlS5cC4OXlxfvvv/9Ux+nUqROLFi3CZDIxZMgQBgwYQJ06dXB3dyc8PJwFCxZw+fLlJ/7dtWvXZsWKFURERDBp0iQ6depE/vwp93OdTkdQUBAAbm5u/PDDD/Tu3Zv4+Hj69u1Lhw4daN26NcWLF8dkMnHv3j1OnDjB5s2bOXv2LB9//HGa3iwBAQG8+eabfPXVV9y4cYMXX3yRIUOGEBwcTHJyMjt37mT+/PkULlyYhIQEHjx48FR1ZE92TXr4+PgQHR3N9evXqVy5sj13LYQQud7w4cOJjIzk008/Zf78+fTq1cvRIanufOR5Bm8eTGHPwsxtOxcvV68Mb5uQbOTdZf+y4fhtm2WqFfNhbt+6FC2Qzx7hCpHjSdtNCCGyrkaNGmzYsIHWrVvz0ksv8fPPP6PT2fUjdoYtOLEAg2KZwHaNCqY3lskQXNyh/lAVIhNCCOHr68tvv/3GW2+9RVhYmHkoWVu8vDL+OdiRAgIC+PXXXxk+fDhnz57lwIEDHDhw4InbaTQavv76a/r27Ut0dDR//fUXf/31V5oyFStW5Mcff6Rp06bZFb5ZixYtaNGiBdOmTWPcuHEW6729vZkxYwbFixd/quPUqFGDN954g6lTp/Lw4UOL0T4A+vfvT4UKFdJNenTo0IFZs2Zx7do1FixYkGZ4saCgoDTDY9WsWZNFixbx1ltvcevWLdauXcvatWtt7tvb29tqTDdv3mTRokXcuXOHCRMmpFnv5+fHDz/8wFtvvZXen686u7bIqlWrxr59+5g8eTKurq6UKVMmTaPv3r17eHpm/snaYsWK2TNMIYRwWh988AFdunQxTwqWl5y4f4KhW4YSlRTFvYR7jNw6khmtZ+Ch83jitncfJjJwYRjHrkfbLNOmamEmv1oTL3fHfBkhhDOStpsQQjydhg0bcvjwYSpXroxWa9fRozMsMjGS5ecsx9JWFC1Do+/hrrEyvFKtXuCd9SE6hBBCZE6hQoVYsmQJO3bsYN26dfz777/cv38fg8FA/vz5KVWqFLVq1aJly5bUq1fP0eFmWIkSJVi1ahVr1qxh48aNnDhxgqioKFxcXChSpAg1a9akTZs21K1bN812VapUYdWqVcyaNYvdu3dz9+5dvLy8KFmyJO3bt6dnz57muSvU8MYbb1CzZk0WL15MeHg40dHRBAYG0rx5c4YMGUKRIkXscpyRI0cSHBzMwoULOX78OPHx8RQsWJAaNWrw2muv0bhx4yfOv+Hl5cXvv//OrFmz2Lt3Lzdv3kx3bpSaNWsSGhrKihUr2L59OydPniQyMhKtVou/vz/lypWjXr16tGnThrJly1rdx0cffUTTpk1ZuHAh4eHhJCQkUKRIEZo1a8bAgQPtVj/2pFHsOHbBjh07GDp0qHkim1Sph3h8eUZoNJp0J5MRT5acnGyeF6By5co5JmOcUz1a38HBwbi5uTk4otxP6lxd2VHfYbfDGLltJHH6uDTLmxVvxg8tfsDVxdXmtuE3ohm0MIxb0bbHvRzSvCxj2lZGq838fcjR5PxWX1xcHKdPnwZyf51L2805SdtNfXKtVZfUt31N/3c6M4/OtFjuFV2eAw9sTIY66l/wL5O9geVhco6rKy+13VKdO3cOg8GATqejQoUKjg5HCCGeSnZc0+z6KEqLFi34+OOP8fb2RlEU879Ujy7LzD8hhMgtLl++TMeOHbl3756jQ3Eau67vYuiWoRYJj9R1s4/Ptrlt6InbvDxzv82Eh6uLhq9fqsG49lVyZMJDiOwmbTchhEhfcnIyffv2ZceOHY4Oxao4fRy/nvrV6rpxD69Y38izoCQ8hBBCCJGr2X2Mj549e9KtWzfCw8O5c+cOycnJjBs3Do1Gw5tvvknhwoXtfUghhMgRTp06RUhICDdu3KB9+/Zs27YNHx8fR4flUBsvb2TcrnFWx6AGqFu4Lq9Xe91iuaIozN51kS83nsbW96u+nq7M7FWHBmUL2jFiIXIfabsJIYR18fHxvPTSS/z111/mISEeH57D0f48+ycPkx9aLC8YW5jOBhvjxfs83ZjkQgghhBDOLlsGNvfw8EjTGEydBKZ169aUL18+Ow4phBBO7Z9//iEkJISIiAgAjhw5QseOHdm4cSP58uXNSbVXnFvBZ/s/w6SYrK5vVrwZ3zX/zmJOj2SDiY9WHeePsOs29122kBfz+tajdIAMCSNERkjbTQgh0oqJiaFDhw7s2bMHgNjYWNq1a8euXbuoWrWqg6NLkWxMZuGJhVbXfRR9yfaGutw/9I8QQggh8jZVZnNNnYAnr36x50xcXW2Piy/sw83NjTp16jg6jDwlJ9R5oUKF8Pb2Nic9AB48eMDDhw9z3LXRHvW98MRCvgn7xub69qXb87+m/8NVm/aaFRmXzNDFRzh46YHNbRuXL8hPPepQwDN3XO9ywvmd28i9UtpuzkTOR3XItVZdOaG+PT09LXq6JSYmcufOHadJeqy9sJa7CXctlheJ96Z18lUHRCRS5YRzPDeRe6UQQojHqZL0WLRokRqHEUIIp1W8eHE2b95M06ZNuXPnDvXr12fDhg34+/s7OjRVKYrCjKMzmHF0hs0y3Sp04+MGH+OidUmz/MK9WAbMP8zliHib23Z/tiQTOlfD1cWuU1YJkedI200Ikde5uLiwZMkSYmJiCA0Nxc/Pjw0bNtCgQQNHhwaA0WTklxO/WF03LloSHkIIIYTI2xz6rZDBYODBgwc8ePAAg8H6eO5CCJFbVKhQgU2bNvHSSy+xZcuWPJnw+Prw1+kmPF6v9jqfNvzUIuGx7/x9uk7fazPhodHAR89X4Yuu1SXhIUQ2krabECIvcXd3Z8WKFXTt2pWdO3c6TcIDYPPVzVyxMlF50SQdzyXGOiAiIYQQQgjnoUpPj0dduHCBX3/9lX379nHlyhWU/5+BVqPRUKpUKRo3bsxrr70m40dnE6PR6OgQcj2j0cj9+/cBCAgIwMXF5QlbiKeVk+r8mWeeYdmyZY4O46lkpb6NJiOf7f+MledX2iwzsuZIBtcYjEajSbP8t0NX+XhVOAaT9RnLvdxcmNK9Fq2q5M7JlnPS+Z1byL0yLWm7OZacj+qQa626clJ9e3l5sWLFCkeHkYaiKMw7Ps/quneib6OxukaoKSed47mB3CuFEEI8TtWkx3fffce8efMwmUzmD8ypFEXh0qVLXL58md9++40BAwbwzjvvqBlenmAyWZ8wWNiP0Wjk+vWUCZb9/PykgasCZ6pzRVE4ceIE1atXd1gM2S2z9a036hm7eyyhV0Jtlhn77Fh6VumZ9jgmhUkbTjF3j+2JOIN88zG3b12qFPXJxF+QszjT+Z1XyL3yP9J2czw5H9Uh11p1OVt9nzx5ksqVK6PV5ozeovtu7uPUg1MWy4skQ0ic7WFAhXqc7RzP7eReKYQQ4nGqJT0+//xzfv31V/MH5nLlylGjRg0KFSoEwL179zh+/Djnz5/HaDQyZ84c4uPj+eijj9QKUQghnoqiKIwZM4bvv/+elStX0rFjR0eH5HAJhgTe2fEOe27ssbpeq9HyWaPP6FK+S5rlsUkG3vztH7aetpycM1XNEr7M7lOHwPwe9gxZCPH/pO0mhMgL/vrrL1588UUGDBjA1KlTLXqcOqO5x+daXT7kYQQWX61rXECRp+CFEEIIkbeokvQ4cuQIS5YsQaPRUL58eSZMmEDt2rWtlv3nn3/49NNPOXv2LEuWLKFDhw42ywohhLMwGo0MGzaMOXPmAPDyyy+zceNGWrRo4djAHCg2OZYRW0fw992/ra7XaXV83exrQkqFpFl+IyqBAfMPc/p2jM19v1CjKN++/AwervLUnBDZQdpuQoi8YOnSpfTq1QuDwcD06dPx8/Pj888/d3RY6fr37r+E3QmzWF7IaKJTTJzlBm0+J7nOII4fPw5AcHAwbm5u2R2mEEIIIYRDqdJ/d+nSpQAUL16c3377Ld0PwrVq1WLJkiWUKFECgN9//12NEIUQ4qn8+uuv5oQHQFJSEp07dzaP5ZvXKIrCm9vftJnw8HDxYFrLaRYJj3+uRtJ52t50Ex5vtqrA1O61HJ/wmNsaxhdI+29ua8fGJISdSNtNCJHb3bx5k759+2IwGMzLJk6cyMqVtucfcwZB3kH0qdqHfLp8aZb3iYrGIpVRsALUG6RabEIIIYQQzkKVpEdYWBgajYbBgweTP3/+J5bPnz8/gwYNQlEUwsIsn2IRQghn06tXL/r162f+3cXFhalTpxIQEODAqBxHo9Ew9JmhuLu4W6zzdvVmVsgsGgc1TrN83bGbvDb7APdjk6zu002n5cfXavJ2SMUcMfSEEDmZtN2EELldsWLFWLhwYZo2Re/evZ1+eNJCnoUYXW80od1CGf7McAq4euNjNPFyTKxl4XaTQCe9OoTIjVLniTEajRbzrgkhRE6iKApGY8pQnPacX02VpEfqk85VqlTJ8DZVq1YFICIiIltiEkIIe9JoNMyePZtu3brh5ubGn3/+SZ8+fRwdlkPVK1KP71t8j07z30iKfu5+/Nz2Z2oX/u+pcUVRmLL1HCN//Yckg/VJCAO83fhtUAM61wzK9riFENJ2E0LkDa+88gqzZ88GYOTIkcyfPx+dTrVpL5+Kr4cvw2oOI9RUjOl37uL1+JeeFdpAhRDrGwshcrzUYeoURSE+Pt7B0QghRNYlJSWZk7f2HIJTlRadu7s7er2ehISEDG+TWlbGGxVC5BQ6nY4lS5Zw7Ngx6tWr5+hwnEKz4s2Y1HQS7+96n0KehZgTMoeyvmXN6xP1RsYsP8bqf2/a3EelwvmZ27cuJfw91QhZCIG03YQQecfAgQOpUqUKjRo1ynk9Sc+G4nl+CzUfX67VQdsvHBCQEEItPj4+xMSkDAn84MEDPD09c941TAghgIcPH5p/9vLystt+VUl6FC9enNOnT7Nt27YMfxG4bds2APP40ELkFBqNxvwmlUaHOpypzt3d3XN9wiOz9d2uTDtMiolnAp8hyPu/nhr3Y5MYvDCMv69G2dy2RaVCTO1ei/werk8dt10lxUDkZcvlhkS7H8qZzm+Rd0jbTeQ1cq1Vl7PVd+PGjZ9cyNkYkmHTB9bX1R8KARXMvzpbfecFUuciu3l7e6PRaFAUhdjYWK5fv46/v78kP4QQOYbRaCQqKirNSAHe3t52278qSY9mzZpx6tQpFi9eTLNmzWjYsGG65Q8cOMDixYvRaDQ0a9ZMjRDzDFdXJ/viMBdydXWlcuXKjg4jT1Gzzk+fPs0PP/zA1KlT8+z7KSv13aFsB4tlWo2G+7HJNrfp17g0H3aogs5FlZEYM+7yHlg1HOLuWa6Ls//E9XJNUV9efW8/StpuzkPOR3XItVZdatZ3QkICb7zxBh999BGlS5dW5ZjZSVEULtyLpfyFhRBxzrKAZwA0G51mkZzf6pM6V1devFdqtVqCgoK4ceOGOfERGxuLRqMxz/chhBDO6tF5PFIVKlQo5w1v1bdvX5YsWUJcXByDBg3ilVdeoVu3blSpUsU8QYnJZOLUqVMsX76cP/74A4PBQP78+enbt68aIQohxBP9/ffftG3blvv37xMXF8eCBQvsOslSThSVGIWvh2+WtvX3cmPe63Xp+tM+YhIN5uUuWg3jO1Wjd4NSdorSTvQJsHUCHJgB2JgsULE+J4kQOY203YQQuUF0dDSdOnVi165d7Ny5k927d1OkSBFHh/VUNobf5qNfd7An3xfks1ag1ceQz1flqIQQjpA/f/40iQ9I+SLRYDA8YUshhHAuBQoUoGDBgnbdp0ZRHp/xLHvs2bOHYcOGodfrzV3tXF1dKVCgABqNhqioKPR6PZBykXZ1dWXWrFk0atRIjfByteTkZI4fPw5AcHCwjLUtRBbs3r2bF154Ic1YgyNGjGDq1Kl5tvvwxssb+XTvp/zY8kcaFG2Q5f3sPneP1385jNGkkN9Dx089a9O0QiE7RmoH18Ng5VDrT1Q+qlBlGHFQnZhEtpH7ZgppuzmOnINCPL179+7Rrl07/v77b/OyGjVqsGPHDvz8/BwYWdYlG0yETN7J4Oip9NRttSxQJBgG7wStPOUt8pa8ft80mUzExsby8OFDkpOTLZ6eFkIIZ+Ti4oKnpye+vr54eHjYff+q9PQAaNKkCX/88Qcff/wx4eHhQMqN6d49y+FBqlevzsSJE6U7aDYwGAx5rgGgNoPBwJUrVwAoVaoUOp1qb7M8S40612q15i/3Uh0+fJj4+Hi7TrSUExgMBuYenMuM8zMwYWLUtlHMDplNzcCaWdpf0wqF+KxTNWbvusi81+tSPjC/fQN+GoYk2PEl7P0hY7043O0fu1xT1CdPx6WQtptzkLabOuRaqy416tvFxYXk5LTDaF69epVLly7lqKTH76d/517CPXpW6cnKsGi8Hpyku9s264XbfWU14SHnt/qkztWV19tuWq0WHx8ffHx8HB2KEEI4DVXvvFWqVOHPP//k2LFj7N+/n7NnzxIdHQ2kdGOpWLEiDRs2pEaNGmqGlaeo1LEnTzOZTERFRQEymata1Kjzxo0bs2LFCjp16oRer6dly5asWrUqzyU8ABadXMT089PNvycYEhi+dTi/tP2FSv6VsrTPXg1K8WLtIDzdnOgD4a1jKb077p5waBhyTVGf3Cv/I203x5PzUR1yrVWXGvXt7+9PaGgoTZo04eLFixQuXJjQ0NAcdb1KMiYx8+hMIhIjWHBiIUkP6vJlviNoTVauC1W7QGnrE7LL+a0+qXN1yb1SCCHE4xzy7VKNGjVyVGNTCCEA2rVrx5IlS/j9999ZsmRJtnS/c2aKojDj6AxmHJ1hsS4mOYbBmwfzZ8c/KeT539BUdx8mUii/e4aGAHOahIdRD3smw86vwJTeU2MabM7tIUQuI203IUROVLRoUbZs2ULv3r2ZP38+5cuXd3RImbL6/GoiEiMASDImQoE9fOSjcP9Bfvo+jDGXU3QeaEImOCpMIYQQQgin4yTfMAkhRM7w8ssv89JLL+W5eTwUReHrw1+z+NRim2U6letEQL4A8++HLz9g8MIw+jYqzVutK6oR5tO7expWDYWb/6RfLqhOytBXd8LViUsIIYQQWVKmTBl2796d49puBpOBeeHzLJdrNFR8bNguTaNR4FdKrdCEEEIIIZyeJD2EEOIRiqJw/fr1dLuh57QPzU/LaDLy2f7PWHl+pc0yI2uOZHCNwea6WX7kOuNWHCfZaOKHLecoE+BF55pBaoWceSYjHPgJtn4OxiTb5bSu0GIsNH4LXOQWKoQQQjiDq1evUrJkSZvrc2LbbdPlTdyIvWGxvGpSEg0SH2mr5C8GTd5SLzAhhBBCiBxA6+gAhBDCWRiNRoYOHUrt2rU5ffq0o8NxCnqjnvd3vZ9uwmPss2MZ8swQNBoNJpPCN5tO8+6yoyQb/5v4e/Sfx/j7aqQaIWdexAX4pQOEfpR+wqNwMAzeDs3ek4SHEEII4SSWLVtGhQoVmDfPsldETqUoCj+H/2x13cCoh6RJ4YRMALe8N8ecEEIIIUR6JOkhhBBAcnIyPXv2ZPbs2dy/f5+QkBCuXLni6LAcKsGQwKjtowi9Emp1vQYNn9b/lJ5VepqXnbr9kJk7L1qUTTaYGLwwjBtRCdkWb6aZTHBoDsxsAtcO2C6ncYFmo2HQNigSrF58QgghhEjX3Llzee2110hOTmbQoEEsX77c0SHZxe4buzkXec5ieelkPa3iH2lLlagPwS+pGJkQQgghRM4gSQ8hhAC++OILli5dav79+vXrtGvXjuTHxkzOK2KTYxm6eSh7buyxut5F48LwEsPpVLZTmuXVihXgs07VrG5Tp5Qffp6udo81S6KuwaIusOE90MfbLhdQEQZshpYfgc5NtfCEEEIIkb7Dhw8zaNAgTKaUnqUmk4kePXrw77//OjYwO5h7fK7V5f2jH6b9AN/uS8iBQ3cJIYQQQmQ3GZ8jj8mJ49nmNFqtlsDAQPPPIvvZo87fe+89Nm7cyMGDBwFwc3Pjf//7H25uee+L7sjESIZuGcrJiJNW13u4ePBh9Q+pU7CO1fru1aAUF+/FMW/vJfOyoc3L8X7bSmi1Dr4GKQr8sxg2fQBJD9MpqIGGI1KSHa75VAvPGrmmqE/ulcKZyPmoDrnWqsse9V2vXj3GjBnDV199ZV42cOBAatSoYZcYHeXInSP8c/cfi+WFDQZeiI37b0HNXhBUO0P7lPNbfVLn6pJ7pRBCiMdJ0iOP0enkJc9uOp0u3Umwhf3Zo869vb3ZsGEDzZs35+LFi6xatYqQkBA7RZhz3I2/y+DQwVyIvmB1vberN9NbTad24fQ/ZH/4fBUuR8Sx+9w9/tc1mFfqOsF7IuY2rBkF5zalX86vNHSZAaUaqRLWk8g1RX1yrxTORM5Hdci1Vl32qu9JkyYRFRXFrFmz+OCDD5g4cWKO//Lz5+PW5/LoGx2Dub+smze0+iTD+5TzW31S5+qSe6UQQojHyZ1BCCH+n7+/P6GhoVy9epX69es7OhzVXYu5xqDQQdyIvWF1vZ+7HzNDZlK1YNUn7stFq2FK91qcuf2QOqX87R1q5igKhC+H9e9CYlT6ZesNhNafgbu3KqEJIYQQIus0Gg3Tp0/n+eefp2PHjo4O56mdeXCG3Td2WywvYDTSLSb2vwXNRkP+wipGJoQQQgiRs0jSQwghHlG0aFGKFi3q6DBUdyHqAoNDB3M34a7V9YGegcwJmUNZ37IZ3qe3u87xCY+4CFj/DpxclX45nyDoPA3KtVQlLCGEEELYh4uLS65IeIDtXh49H8bgqSgpv/iVgQbDVIxKCCGEECLnkcEl8xi9Xu/oEHI9vV7PsWPHOHbsmNS3SjJT5//++y/ffvutSpHlDOcjz/P6xtdtJjxK5C/BwvYLKetblsi4ZHrPPcDSrYed/xw/tQ5+qv/khEfNXjB8v9MmPOSaoj6pZ+FM5HxUh1xr1ZWZ+n748CHvvvsu8fHxKkXnGNceXmPTFcshOPOZTPR4+Egvj7b/A517pvYt57f6pM7VJXUshBDicdLTQwg7UxTF3OhSUp/IEtkqo3W+d+9enn/+eaKjo9HpdLz11lsqRejcinkXo6RPSaLuRVmsK+9bntkhsynkWYgL92IZMP8wlyPiOXpNy1et/KnsjOd4QhT8NQaO/Z5+Oa9A6DQFKrVXJayskmuKEEJkP7nWqiuj9X3//n3at29PWFgYJ0+eZPXq1bi5uakVpqp+OfELJsVksfzlmFgKmP5/edkWUKlDpvct57f6pM6FEEIIx1I96REZGcm///7LtWvXiIuLw2g0PnGbkSNHqhCZECI327hxIy+++CIJCQkAvP322xQoUIB+/fo5ODLH83T15KdWP9F/U3/ORp41L69esDozWs/A18OXvefvM2zxER4mGgB4mGTiiz1R1K9pwN+Zvnw4vwVWvwExN9MvV+1FeP478HTw8FtC5ADSdhNCOML169dp06YNp06dAlLacr179+bXX3/FxcXFwdHZ1734e6w6v8piuU5R6B0dA4CicUHTdhLk8InahRBCCCHUoFrSIyIigi+++IJNmzZl6MPyo7L7g/ONGzdYtGgRO3bs4Pbt27i5uVGiRAnat29Pz549yZcvn92PmZCQwAsvvMD169cBCAoKYtu2bXY/jhAixdWrV80Jj1RLliyhb9++aLUy0l8B9wLMCpnF6xtf58rDK9QtXJdprabh5erFb4eu8vGqcAymtE+pXXto4K1lx5j3+rPoXBxch0kxEPoRHJmffrl8/inJjuovqhKWEDmZtN3SkrabEOqKiori9u3baZbt2LGDa9euUbp0accElU0WnVyE3mQ5PE+n2DiK/P/1V1NvABSuqnZoQgghhBA5kipJj+joaHr06MHVq1edrmvntm3bGD16NLGx/42TmpCQQHR0NOHh4SxbtozZs2dTqlQpux53ypQp5g/NQojsN3jwYCIjIxk7diwAHTt2ZOnSpZLweERAvgDmhMxh1rFZjH12LK5adyauO8ncPZdsbnP+bhx3YpII8rX/F4wZdnkPrBoGUVfTL1epA7zwA+QvrEpYQuRk0nazJG03IdRVvXp1/vrrL1q1akVcXBwlS5Zk8+bNuS7hEZ0UzdIzSy2WaxSFflEPATB5+KFtMU7t0IQQQgghcixVkh6zZ8/mypUrADRu3Jh+/fpRrVo1fH190Tiwe+7Jkyd5++23SUxMxNPTkyFDhlC/fn0SExPZsGEDf/zxB5cvX2bw4MEsX74cb29vux13wYIFuLu7o9PpiIuLs8t+hRDpGzNmDJGRkdy4cYN58+bh6urq6JCcTlHvooxvNJ7YJAMjFoex9bT1yc0BKvi7Mn/As45LeOgTYOsEOPBT+uXcC0D7L+GZ7jIkhBAZJG03y+NK200I9dWvX5/Vq1fz7rvvsm7dOooXL+7okOxu6ZmlxBssJ2lvHZ9AaUPKsKLalh/KkJxCCCGEEJmgStJj69ataDQamjdvzsyZM9U4ZIb873//IzExEZ1Ox7x586hVq5Z5XcOGDSlVqhTffPMNly9f5pdffuGNN9546mMajUY++ugjjEYjI0aMYPny5fLBWQgVTZo0CUVR8lwPD0VRUFDQap78d9+ISmDA/MOcvh1js0zjEh6MqFeAQvnd7Rlmxl0Pg5VDIeJc+uXKPgedp0GB3PcliRDZSdpu/5G2mxCO1apVK44cOZLr5vGAlPbZ2gtrra4bGBUNgKlQZbR1ZA46IYQQQojMUOVbv1u3bgHQs2dPNQ6XIceOHSMsLAyAbt26pfnQnKp///6UK1cOgIULF6LXW46zmlkLFy7kxIkTlClThkGDBj31/oQQaZlMJqKiomyu12g0eTLh8fXhrxm/bzwmxZRu2X+uRtJ52t50Ex4jW5Tl7foFcHdxwNPehiTY8hn8HJJ+wsPVC16YDL1XSsJDiCyQttt/pO0mRPZ78OBBuutzY8IDUtqlP7VYQMGIJhQy/Dd3UqP4BKomp1y/tO2/AhfVpuIUQgghhMgVVPnmz9PTE4CAgAA1DpchW7ZsMf/crVs3q2W0Wi1dunQB4OHDhxw8ePCpjnnjxg2mTJkCwGeffYabm9tT7S8rcusHBmei0+koW7YsZcuWRaeTDyhqSK3zEiVK0L9/f5577rl0Ex95idFk5NN9n7L41GJWnl/JN4e/sTk+/9qjN3lt9gHuxyZZXe+m0/LjazV5p00lypUrp/45fusozH4O9nwP6SVvSjaCYXuhbv9cMZyVXFPUJ/dKabulkrZb3iHXWnU9Wt+LFi2ibNmyT/1+zanm7rzBxMijbLx2g/H3Iiil1zMgOmUuD6Xy81C2xVMfQ85v9Umdq0vulUIIIR6nStKjYsWKQMoHR2dx5MgRIOVDfbVq1WyWq1evnvnnv//++6mO+dlnnxEfH0/nzp2pX7/+U+0rq/LaE+6OoNVq8fPzw8/PT+pbJVqtFg8PD/r3789vv/3Gv//+ywsvvEB8vOX4yHmJ3qjn/V3vs/L8SvOyxacWM+PojDTlFEXhxy3neOO3f0gyWE8mBHi78dugBnSuGaT+OW7Uw86vYU5LuHvCdjmdB7T9Al5fD/5lsj8ulcg1RX1Sz9J2SyVtt7xDrrXqSq3v+fPnM3DgQKKjo2nfvj3h4eGODk1VF+7FcufwSpq4nMAN6BYbx+rrt6iXmIRJ64amzUS7HEfOb/VJnatL6lgIIcTjVLkzvPrqqyiKwpo1a9Q4XIZcuHABgJIlS6b75EXZsmUttsmK9evXs3PnTgoUKMDYsWOzvB8hhHVDhgxh/fr15t/37t1L9+7dHRiRYyUYEhi1fRShV0It1s04OoOFJxYCkKg38tbSf5m85azNfVUqnJ+VwxtTp5RftsVr093TKUNZbf8fmAy2ywXVgSG7oeEIkA89Qjw1abtJ202I7LZs2TLeeecd8++RkZGEhIRw584dB0alrm83HGOcy6I0y1wADaBtNAL8y1rdTgghhBBCpE+VfpYdOnRg+/btrFu3jtmzZzN48GA1DmtTUlISkZGRABQpUiTdsgUKFMDT05P4+Hhu376dpeNFR0fzxRdfAPDuu+/i7++fpf3YQ3Jyss11Op3O/ISEyWTCYEjnC0ZIM8SD0WjEaDTaLKvRaHB1dTX/bjAYMJlsD0+j1WrTfKGh1+ttDsnzeOyKojxxDG9XV1c0/z/szZP+1szGrtFozLG6urpiMBjSjd3FxSVNd9z0XiOQ18kaRVEYPXo0mzdvNr9PPT09GTx4sM36zGzsOel1itXH8vbOt/n7nvUnnHVaHQHuAdx8EMOI347yz7Vom8dqUbEQ379UHW8PnflvVhTF/PfodDrc3Nzs/34yGdEemonLzklojNaH2wJQtK4Ym47G1PAN0OpwMRpzzOv0OFvn5OP1nVrXznTdy23vpyfFkxdI203abjm1TZDV2F1cXDI8B4yzX8NywuukKAqtW7emTZs2hIb+94BGnz598PX1tVqnue1ec+DCfUqdXUAp17sW5Q2ehdA1fdf8+9O+Tqq03f5fbnudsvq3Wmu/Odt1Lze9TtJ2E0II8ThVkh6HDx/mpZde4vr160yePJnQ0FBeeOEFypYtS758+Z64/aPDFNhDXFyc+efUMavTky9fPuLj47M8VM7XX3/N/fv3qVWrFq+88kqW9mEvZ86csbmuUqVKeHt7AxAfH59uWYA6deqYf75//z7Xr1+3WdbLy4vKlSubf79y5Uq6cy4EBgZSokQJ8++nTp1Kt1FYtmxZ/PxSnkLX6/UcP3483diDg4PNDavo6GguXrxos6yrqys1atQw/37r1i3u3rX8cJLKx8eHhw8fmo9z8eLFNOfc44oXL07hwoXNvz8pdnmdbPvhhx8YNmwYiqIwb948ihQpYvMYvr6+5sluIeVp4NzwOsUYYvj+yvdcSrhkdVsPFw9GlR5F0lV/Ov+6h3vxtj8M9WtcmtGty3HqZDpDSmH/95N73A1K//MV3pHpD3ER71OOyzXHkuBTDk6cAnLO62SNM72fMnvdy63vp7xM2m7Sdstr17ASJUqYY8iXLx8JCQk2y+fka5gzvU4An3zyCXFxcezdu5dx48bRrVs3m0Nc5aZ7zaXLl/lhRRjzdauslo+u8yYF3fObf89p76fc8jrlpPeTvE5R6e5fCCFE3qNK0qN3797mJxQATpw4wYkT6X+Jlkqj0XDy5Em7xpOU9N9Tw48+IWBLakMjMTEx08c6fPgwy5cvR6fTMX78+DT1IISwr/Lly7NmzRp8fHzw8/NLt6GfG0XqI/nm8jfcTLppdb23qzfTWk3j6N8GPtgbQYLB+tNcLhr4rHN1ejUope5TU4qJQpfXEHRqNi5G29dbRaPldvke3KrYG0X75Gu4ECLzpO0mbTch1ODh4cHKlSvZvn07DRs2zDNtt63nH/Jq3BK8dZbXqIc+FYmv0ImCDohLCCGEECK30Cjp9WG0k0ez8Jml0Wg4deqUHaOBBw8e0LBhQyBl+IbJkyenW75Ro0ZERERQsWJF1q5dm+HjJCcn06lTJy5dukT//v0ZM2aMRZmWLVty48YNgoKC2LZtW+b+kEzEkfokRrly5Ww+IZlTugo/KXZHdxU2GAzmczY4ODjNcFfWOHtX4ZzwOun1ek6fPg3890RUXuvSffXhVYZvG86NOOuTDvu5+zGj9QwOnfHk83UnMdn4U/N76JjWvRbNKwUC1l+nR+u7cuXKeHp6Pv37Kfo6uvVvor28y+a2AErBChg6TkcpVsvqemd/nbJyTj5e36n7dKbrXm57P8XGxprngnj0Kcu8RNpu0nbLqW2CrMZuMpnMdV6lSpV0541x9mtYTnid8nLb7VbMPT6cspx5esvrC4C+zwY0Jevb9XXKlrabDbnldYKn+1uttd+c7bqXm14nabsJIYR4nCo9PRYuXKjGYTLMy8vL/HNGhj1I7d6ekeEUHjVjxgwuXbpE0aJFeeONNzIXZDZxdXXNUANAq9VmqqHweCPoSdL7IGlNRp7qTKXRaDIVe2b/1uyMHcjW2HPD67Rv3z7u379Pp06d0i2fl16nK7FXGLxlMHcTrD8dGZgvkJ9az2LRrgQWH7D99HVJf0/mvV6P8oHe5mVPep0e/TAGWXg/ubjAsd9g4zhIjkmnpAYajkDT8iNcXZ88tE4qZ3qd7HFO2rqGy3XPtqy8TpmNPzeStpu03R7njG2CjMpI7I9+gZc650FGOds1LDPUeJ0iIiKYN28e7733Xro9p/LCveZExAl6rOtFYx8Tlx7qKKNP+0WwsfrLuJZtbLGdPV+np2675YHXKZW9/lZr13FnuO49Kqe/TtJ2E0II8ThVkh7PPvusGofJMHd3d3x9fYmKinriBJfR0dHmD9dPmjjzcXPmzAGgYcOGbN++3WqZ1H3Hx8ezfv16APz9/c1PMwoh/rNp0ya6du2K0Whkw4YNtGrVytEhOdyJiBMM3TyUqKQoq+uLexfn+2YzmLjyDrvP3be5n2dL+zOzdx38vVR8KirmNqwZBec2pV/OrzR0mQGlGqkSlhBC2m7SdhPCPm7cuEGbNm04efIkDx48YNKkSY4OyaFm/DMHEwZ2+8Ce/EVpHZ/AgKhoqiXrMbjkQxfymaNDFEIIIYTIFVRJejij8uXLExYWxtWrVzEYDDafhHh0srBHJ/rKiNTuqitWrGDFihXplo2MjOSdd94BUr5okA/OQqT1559/0qNHD/P7qnPnzmzdupX69es7ODLHCbsdxshtI4nTW590sLxveT6uO5mRCy9z4Z7tiQm71S7OFy9Wx12X8SeqnoqiQPhyWP8uJEalX7beQGj9Gbh7p19OCJHrSdtNiJzlwoULhISEcOnSJQC+/PJL/Pz8eP/99x0cmWNcjr7Mzhv/DYmnaDRs9vJkXz4Ptl29Qb5m70CBIAdGKIQQQgiRe2gdHYCj1KlTB0h5Si+9iTkPHz5s/rl27drZHpcQwrotW7akGfc2Li6OadOmOTAix9p9fTdDtwy1mfCoVrAab1T5ngE/n0034fF+u0p8+3IN9RIecfdhWV9YPiD9hIdPEPReCc9/JwkPIQQgbTchcpoTJ05w5cqVNMumT5/Ow4cPHRSRY/0QNhuwnDPhlYexuHgVQ9PIOYbUE0IIIYTIDRzS0yM8PJx9+/Zx7tw5oqOjAShQoAAVKlSgUaNGVK9ePdtjaN26NbNmzQJg+fLlPPPMMxZlTCYTq1atAsDHxyfTT5SfOXPmiWXUmAxTiNxg+vTpPHjwgGXLlgHQo0cP5s2b5+CoHGPT5U2M3T0Wg8n6hIB1C9eltf9Yhiw4hd5ofUJCD1ctP7xak3bVi2ZnqGmdWgfr3oK4e+mXq9kL2n0BHgVUCUsI8WTSdvuPtN2EyJhOnToxd+5c+vfvD0DFihXZvHkzPj4+T5wEObe5HXebbdc2wGNTmriZFPo8fIj7iz9CJuYsE0IIIYQQ6VM16XHmzBk++eQTjh07ZrPM5MmTeeaZZ/jss8+oVKlStsVSo0YN6tatS1hYGMuXL6dr167UqlUrTZl58+Zx4cIFAPr06WMxOdbBgwfp06cPAF27duXLL7/MtnjtJbMTmonMc3V1JTg42PyzsA8XFxcWL17Mw4cPKVu2LNOmTUOrTemslpfqfOW5lYzfPx6TYrK6vmlQM4L0g/lwxXmb+yjs487cPvUILp61pEKm6zshCv4aA8d+T7+cd2HoOAUqtctSXLlVXjq/nYXcK/8jbTfHk/NRHXKtzR79+vUjKiqKRYsWsXHjRgIDA4G8V99f7ZuNojFaLO8SG4t3kfpQtXO2Hj+v1bczkDpXl9wrhRBCPE61O8O+ffsYOnQoer0eRUl58lin0+Hr6wtAVFQUBkPKU8v//vsvL7/8MrNmzcrW8ZE//PBDunfvTmJiIv3792fo0KHUr1+fxMRENmzYwNKlSwEoXbo0/fr1y7Y41KTRaJ5cSDwVjUaDm5uKk0HnIW5ubqxevRo3N7c053JeqvM4fZzNhEdIybbEXnuZOSev29y+epAPc/vUo0gBjyzHkKn6Pr8FVr8BMTfTL1e9G3T4Fjz9sxxXbpWXzm9nIffKFNJ2cw5yPqpDrrXZ5+2332b48OG4u7ubl+Wl+o5IiGT7jZUWvTy0ikKf6Fg8Xvoasvl9npfq21lInatL7pVCCCEep0rS48GDB7z55pskJyej1Wp56aWXePnll6latao5I280Gjl58iTLli1j+fLlJCcn8+abb7Jp0yb8/PyyJa6qVasyefJkRo8eTWxsLN9//71FmdKlSzN79my8vWVMeSHUkDpvh60noh79wJwX9arai5jkGH46+lOa5c+X7kL4sRDCb9y3uW3baoWZ/GpNPN1UuPQnxUDoR3Bkfvrl8vmnzNtR/cXsj0kIkWHSdhNCZEZCQgL58tkenikvt98+3T4To8ZySNK2cfEUqdYDitZwQFRCCCGEELmbKhOZL1y4kJiYGFxdXZk1axaff/45NWrUSNMF0cXFheDgYCZMmMCsWbPQ6XTExMSwcOHCbI2tZcuWrFmzhtdff53SpUuTL18+fHx8qF69Ou+99x6rVq2iVKlS2RqDmkwm60+IC/sxmUxERkYSGRkp9Z1JiYmJdOvWjT59+mA0Wg4BYEteq/Ohzwyld9Xe5t9fr/Y679f5iNhE23U2rEU5ZvSsY5eExxPr+/IemNHoyQmPSh1g+AFJeDxBXju/nYHUs7TdnImcj+qQa23W/fjjj9SoUYNbt25leJu8Ut8P4mPZf3uZ1XV9Yg24t/lElTjySn07E6lzdUkdCyGEeJxGSR2vIBt17dqV06dP8/rrrzNmzJgMbfPVV1/xyy+/ULVqVVasWJHNEeZuycnJHD9+HIDKlSvj5eXl4Ihyt0frOzg4WLo1Z1BMTAydOnVix44dAAwZMoQZM2ZkqKtyXqxzRVEYv388xbyKMbjGYDQaDRfuxdJ1+l4eJv73NKGri4Yvugbzct0Sdju2zfpOjoetE+DgjPR34F4A2n8Fz7yW7cM55AZ58fx2tLi4OE6fPg3k3TqXtptjSdtNfXKtzTxFUfjss8/47LPPAKhevTo7d+7E3//JQ1XmlfoetmISe2J+tVjeND6BKTXeQtd4pCpx5JX6diZS5+qStpsQQojHqdLT4/r1lPHlW7ZsmeFtUsteu3YtW2ISQjgPRVHSJDwAZs2axaeffuq4oJycRqNhfMPxDHlmiDkxVK6QNzN61UGnTfnd19OVxQPq2zXhYdO1wzCr6ZMTHmWfg+H7oGZ3SXgI4cSk7SaEeJLvv//enPAACA8Pp0OHDiQlJTkwKudxIzqGfyOt9/LomeyFrv5glSMSQgghhMg7VEl6pDZ80xvn9XGpZZOTk7MlJiGE89BoNHzwwQdpnsgpUKAAbdq0cWBUzs9aL5jG5QP4vEt1yhbyYtXwxtQvWzB7gzAkwZbPYF4biDhvu5yrF7wwGXqvhALFszcmIcRTk7abEOJJevXqRfny5dMs69KlS56ev+NRE9d+R6xOb7G8dmIijdp/Azp5El0IIYQQIruokvQICAgA4NSpUxneJrVswYLZ/IWdEMIphISE8Ntvv6HVailUqBA7duygSZMmjg5LdbHJsby/631uxN7I8j66P1uSDaOaUjoge4dDyRd9Dt0vbWDP96CkM45uqcYwbC/U7S+9O4TIIaTtJoR4ksKFC7NlyxaCgoLQaDTMmDGDsWPHOjosp3DyVhRnE1ZaXdddVxpNRXmwRwghhBAiO6mS9Khbty6KojB79mxiY2OfWD42NpY5c+ag0WioW7euChEKIZzBiy++yJIlS9izZw81a9Z0dDiqi0yMZGDoQP669BeDQgdxL/5emvU3oxIwmTI2DZOHq0t2hJjCqKfI2UVU2T0c7b2TtsvpPKDtJOi7DvzLZF88Qgi7k7abECIjSpUqxebNm/n9998ZOnSoo8NxGjNWf8ZdN8sHQiom6WnbaboDIhJCCCGEyFtUSXq8+uqrQMr40D179jRP6GXN8ePH6d27N1evXk2zrRAib3jttdeoWLGio8NQ3d34u/Tb2I8TEScAuBZzjcGbBxOdFA3A3vP3affDLn7Yes6RYcLd0+gWtCfozC9oFKPtckF1YMhuaDgctKrcaoQQdiRtNyFERlWpUoVXXnnF0WE4jYSEeG66bLa6rodfXTSF8l47VwghhBBCbTo1DlKnTh169OjBr7/+ytmzZ3nllVcoX748zzzzjHkIhIiICI4ePcr58/+NCd+jRw/q1KmjRohCCJVs2bKFokWLUq1aNUeH4jSuxVxjUOggiyGtzkedZ9iWYYT4fcLEdRcxmhSmbD1H2QAvutQKUjdIkxH2T4dtE9Ea05mgVOsKLcZC47fARZVbjBAiG0jbTQiR6tatW+zZs4eXX37Z0aHkCP/u/ZyzbpbDeQYZTHTu8IP6AQkhhBBC5EGqfSP18ccf4+Hhwfz58zGZTJw7dy7Nh2QARUkZtkWr1dK/f3/ee+89tcITwm40Gg2urq7mn8V/VqxYQffu3QkICGDPnj2UKWOfIY9ycp1fiLrA4NDB3E24a3X9+Yjr7D9wEMUUYF72/p/HKOGfjzql/NUJMuICrBoO1w6kX65wMHSdCUWqqxNXHpGTz2+Rs0nbTeQlcq217uLFi4SEhHDp0iUURbFbj45cW9+x9/j54ipwt/yY3SuoNTqvAMttVJBr69uJSZ0LIYQQjqVRUj+tquTs2bP89ttv7Nu3jytXrqRZV6pUKRo1akT37t3z5PA22SU5Odk8LEVwcDBubm4OjkjkRfPnz2fAgAGYTCnjG5ctW5Y9e/ZQtGhRB0fmOCciTjB081CikqKsrg/MV4yrJ3qjT/KzWFfQy401bzQhyDdf9gVoMkHYz7D5E9DH2y6ncYGm70Cz90En1xeR88l9My1pu6lPzkHhDE6cOEFISAi3bt0CwNXVlTVr1tCuXTsHR+a8jq/sR4+HYRbLAxQNG3sewN3V0wFRCZH7yX1TCCHE41Qfe6RixYp8+umnQMqN6eHDhwD4+PjIjUmIXMpoNDJ37lxzwgNSnhycNm0a//vf/xwYmeOE3Q5j5LaRxOnjrK4v71ueWSGz2FY6kbErLMfSb1Q+gIJe2XjNjLoGq0fApZ3plwuomNK7I0iGsxEit5K2mxB50+rVq80JDwC9Xs/YsWMJCQnBxcXFgZE5qVvHiDmzgRIBflz7/yf8U/Uu20USHkIIIYQQKnLo7LJubm4EBAQQEBAgH5qFyMVcXFxYu3YtNWrUMC8bMmQIEyZMcGBUjrP7+m6GbhlqM+FRrWA1fmn7C4Gegbz2bEkGNU07DNhbrSsw5bWaeLhmwxcOigJ/L4KfGj4h4aGBhiNhyC5JeAiRh0jbTYi8Y9y4cYwYMcL8+zPPPMOmTZsk4WGNosDGsTRKTGTt9Vt8c/c+lZKSAciPC680eN/BAQohhBBC5C0yy2weYzAY5EuKbGYwGMxPxRUtWhSdTt5mAH5+foSGhtKkSRO6devGpEmT7Da+bU6q802XNzF291gMJoPV9XUL12Vqy6l4u3mbl41tX4VL9+PYde4+3778DJ2eKZY9wcXchjWj4NymdIspvqW523gCyUVqU1TjKjeSbJaTzu/cwmCw/v4UwhGk7aYOudZa0mg0TJkyhaioKC5dusT69evx9fW1y75zXX2fXAVX9gLgArSLi6dtXDx7vX24FzI+TbvOEXJdfecAUufqkrabEEKIx8mdN49ReQqXPMlkMnH3bsqk1IULF3ZwNM6lcOHChIWFUaBAAbvuN6fU+cpzKxm/fzwmxWR1fbPizfiu+Xd46DzSLHfRavjxtVpcuBdLjeK+9g9MUSB8Oax/FxKj0i9bbyD65h9y/cwluHvXqes7t8gp53duIvdK4UzkfFSHXGut02q1/PLLLyQnJ+Pl5WW3/eaq+tYnQOjHFos1QJPaQ+GZAerH9JhcVd85hNS5uuReKYQQ4nF2TXr06dMHSHkqaMGCBRbLs+LxfQkhnJvBYEj3SSZ7JzxyikUnF/H14a9trm9fuj3/a/o/XLWuVtd7ueuyJ+ERdx/WvwMnV6dfzqc4dJ4G5Z6D5GT7xyGEcAhpuwkhIP32m6urK66u1tsneZ2iKGyc/SHto69ZrsxfDJq8pXpMQgghhBDCzkmPQ4cOAVgMWXPo0CE0Gk2msu+p5e01/I0QIvvFxMTQpUsX2rVrx+jRox0djlNQFIWZR2fy09GfbJZpVvQFJjWdiItW5TGyT62DdW9B3L30y9XsBe2+AI+8mbASIjeTtpsQeZuiKHz++efs2bOHtWvX4u7u7uiQcpQD/xyjxd1FKd06HhcyAdzs1ztGCCGEEEJknF2THvXq1cvUciFE7hEREUGHDh04dOgQ27Ztw8/Pj4EDBzo6LIdSFIVvwr5h0clFNsskRzRj39XnuFEniZIFPdUJLCES/hoLx35Pv5x3Yeg4BSq1UycuIYTqpO0mRN5lMpl49913+eGHHwDo2bMnv//+u8w9kEEGo4mEvz4mn8ayB2xy0Xq4Bb/kgKiEEEIIIQTYOemxaJH1L/ZsLRdC5A6JiYm0aNGC8PBw87LBgwfj6+vLSy/lzQ98RpORCQcmsOLcCptlku6GkBzRkiT0DFhwmOXDG+Hjkc3DR5zfAqvfgJib6Zer3g06fAue/tkbjxDCoaTtJkTe9d5775kTHgDLly9nyJAhzJ07V3psZcD2zWsJ0e+0uk7f5gvcpA6FEEIIIRxG6+gAhBA5n4eHB6+//nqaZQULFqRs2bKOCcgJ3Eu4x85r1j8IAyTe7khyRCtSx0M4dzeWkb/+g8FofZLzp5YUA2vfhMXd0k945POHl36Bl+ZJwkMIIYTIxbp37463t3eaZXXq1JGEhy1zW8P4AjC+APGfFiJo30fszOfBKbe0D6ycD+qCV5lnHRSkEEIIIYQASXoIIezk3Xff5cMPPwSgePHi7N69m9q1azs4Kscp4lWE2W1m4+Pmk2a5omhIuPkS+sjGFttciYjjfmw2TBJ+aTfMaARH5qdfrlIHGHEQqr9o/xiEEEII4VTq1atnnsfDxcWFxYsXM3z4cEeH5bR6ujwguExJgsuU5IsAb8q5XGV8QEFeCSrK0MKFOOzhThwelH7lS0eHKoQQQgiR56kyYGvlypXRarWsWbOG8uXLZ2ibq1ev0qZNG7RaLSdPnszmCPMOeXIr+2m1Wnx9fc0/5yWff/45+fLlo1evXpQqVUq14zprnVf0q8iM1jMYGDqQBEMCiuJC4o3XMMQEW5R9tow/s3rVwc/LzX4BJMfD1glwcEb65dwLQPuv4JnXIAPXCGet79xK6lt9cq+UtpszkfNRHXn1WtuiRQuWLVsGQMeOHVU7bk6uby+Tibeioljj7cV9nQsAez3zsdczH5W0Bfkw6Ta1KOrgKNPKyfWdU0mdq0vulUIIIR6n2ix1iqKoup2wTiYmzH46nY5y5co5OgyH0Gg05t4eanLmOi+gLYd7xEDivX4m4eZrGOMqWpR5qU5xvugajJvOjh+Irh2GVUMh4nz65cq1hE7ToEBQhnftzPWdG0l9q0/ulSmk7eYc5HxUR16+1qqZ7EiVk+t7cFQ0vkYTvxTwsVh3xhRBgj7BAVGlLyfXd04lda4uuVcKIYR4nNPfGSRjL4RzCQ0NpV69evj5+Tk6FKd38GIEQxcfITI+CLTvg8kjzXqNBsa0q8yQZmXtd60zJMGOSbD3R1DSmR/E1QvaToQ6/TLUu0MIITJK2m5COJfLly9z584d6tev7+hQcrwSej29omPY4uXJNVdXi/VV/KvQsFhDB0QmhBBCCCEe5bRJj8jISADy5cvn4EiEEKkWLlxI//79efbZZ9m8eTNeXl6ODslpLQu7xgcrj6M3/v8Tz48lPPK5ujD51Zq0q17Efge9dRRWDoW7TxhWplRj6Dwd/MvY79hCiDxP2m5COJ+TJ08SEhJCfHw8O3fupEaNGo4OKcdK1Bt5LyoKV2CulV4eAAOCB0jiVwghhBDCCag6uGRGG4Dx8fEsWrQIgJIlS2ZnSHmOXq93dAi5nl6v5/Tp05w+fTpX1feUKVPo27cvRqOR/fv307VrV5KSkhwdFuC4Or8QdYE1F9akWWYyKXy18TSj/zz2X8LjMYV93Fk2tKH9Eh5GPez4Cua0TD/hofOAtpOg77qnSnjk1nPcWUl9q0/q+T/SdnM8OR/VkRuvtYcPH6ZZs2bcvHmTqKgo2rRpw7lz5xwdFpAz6/u5+BhaxiewJ58HZ9wt52Ar5VOK1iVbOyCyJ8uJ9Z3TSZ2rS+pYCCHE47Klp0erVq2sLu/fv/8Tx1pMTk7mwYMHmEwmNBoNzz33XHaEKES2URSFuLg488+5QXR0NF999VWaZZs3b2bBggUMHjzYQVH9xxF1fiLiBEM3DyU6KRqdRkeHsh2ITzbwztKjbDxx2+Z21YN8mNunHkUKeNgskyl3T8PKIXDr3/TLBdWBLjOhkOWcIpmVG89xZyb1LdQgbTeR1+XGa+13331HRESE+fc7d+7w7rvvsmbNmnS2UkdOq2/l70UMj0qpOCJpsgAAiF5JREFUy59t9PLoV60fLloXNcPKsJxW37mB1LkQQgjhWNmS9Lhx44bFMkVRuHPnTqb2U7NmTQYOHGivsIQQWVSgQAFCQ0Np1qwZDx48AGD06NEMGjTIwZE5RtjtMEZuG0mcPuWDzAd7PiApWcfPm/MRfuOhze3aVSvC968+g6ebHS69JiPsnwbb/gfGdHrcaF3huXHQ6E1wcdoRDYUQDiZtNyFyn3nz5nHz5k12794NQMOGDVmwYIGDo8p57u2cQ8Hto9EC/7q7cSSf5YMrgfkC6VhO/QnhhRBCCCGEddnyDVjXrl3T/L5y5Uo0Gg0tW7bEx8f6kzGp3N3dKVSoELVr16ZBgwYyJqoQTqJatWps3LiRVq1a8cEHHzB27FhHh+QQu6/v5u0db5P0SKLBqBj55MAY4iP7AeWsbje8RTnea1MJrdYO17SIC7BqGFw7mH65wsHQdSYUqf70xxRC5GrSdhMi9/H09GTt2rW0bNmSgIAAVqxYIfOxZVLs3jkU2v6e+fe5vgWslutTrQ9uLpZDXgkhhBBCCMfIlqTHpEmT0vy+cuVKAN5++23Kly+fHYcUQqigXr16nDlzhqJFizo6FIfYdHkTY3ePxWAyWK7UGHDz301CfNqkh6uLhkkv1uClOsWfPgCTCcJ+hs2fgD7edjmNCzR9B5q9Dzr5AC6EeDJpuwmROxUoUIDNmzfj5eWFu7u7o8PJUfQHf8Z7838Jj9Nuruz0zGdRzsfNh5cqvqRmaEIIIYQQ4glUGetk5MiRAPj7+6txOCHEU1AUJd2ndPNqwmPluZWM3z8ek2Kyut4QU5mEGz3TLPP1dGVWrzrUL1vw6QOIugqrR8ClXemXC6gEXWekzOEhhBBZJG03IXKO1PkCbLXf5H2ceaaDc3D967+ER7RWw3uBAVbL9qjSAy9X6UEjhBBCCOFMVE16CCGc24MHD+jatSsffvghbdq0cXQ4TmPRyUV8ffhrm+v10TVIvPkKj15SyxXyYt7r9ShV8Ck/BCsK/LMYNo6D5Jh0Cmqg0Uh47iNwtdMk6UKIPEvabkLkDIqiMHr0aDQaDV9//bUML2cPB2ej/Wu0+Vc98G5gIa64uloU1SjQo3IPFYMTQgghhBAZIbPaCiEAuHXrFm3atCE8PJywsDA2b95Mo0aNHB2WQymKwsyjM/np6E82yyRH1iPpdldAa17WpHwA03vWpkA+yw/HmfLwFqwdBedC0y/nVxq6zIRSDZ/ueEIIIYTIMYxGI0OGDOHnn38GwM/Pjw8++MDBUeVwB2bCxjFpFn1V0I+DViYvBwhAi5+HnxqRCSGEEEKITNA+ucjT+/vvv6lSpQo1atTgzp07Tyx/584dgoODqVq1KuHh4SpEmHdotaq85Hmai4sLxYsXp3jx4ri4uDg6nAy5c+cOTZo0Mb/f4uPjef755zl27JiDI8uY7KhzRVH4Juyb9BMeEU1Juv0ij15Ke9YvyS/96j1dwkNR4Pif8FODJyc86g2EoXtVTXjkxHM8J5P6Vp/cK6Xt5kzkfFRHTrvWKopC7969zQkPgA8//JAZM2Y4MKqMc8r63v+TRcLj9/zeLPXJb3OTwkrOeH86ZX3nclLn6pJ7pRBCiMepcmfYsGEDiqLQokULChcu/MTyhQsX5rnnnsNkMrFu3ToVIsw7pMGV/VxcXChcuDCFCxfOMfUdEBDAs88+m2aZl5cXrla68Tsje9e50WRk/P7xLDq5yGaZpHshJN3tAKQMI6HVwKcdqzKxS3VcXZ7i0hp3H5b1heUDIDHKdjmf4tB7FTz/Hbh7Z/14WZATz/GcTOpbfVLP0nZzJnI+qiOnXWs1Gg3t2rVLs8zFxQUvr5wxt4TT1fe+abBpXJpF+z3c+bJg+r04tOSM4cScrr7zAKlzdUkdCyGEeJwqSY8jR46g0Who1qxZhrdp0aIFAIcPH86mqIQQqVxcXFi4cCEdOnQAoHz58uzZs4cqVao4ODL16Y16xuwew4pzK2yWSbz9Asn3W5Ga8PByc2Fu37r0a1zm6cbSPrUOpteHk6vTL1ezFwzfB+Wey/qxhBAiHdJ2E8L59enThylTpgDg5ubG8uXL6dOnj4OjyoH2ToHQD9MsuqzT8W5gIYxPatcVznttZSGEEEKInECVOT2uXr0KpHyRmlFly5ZNs60QInu5urqybNky3nnnHcaPH0+RIkUcHZLqEgwJvLPjHfbc2GN1vaJoSLzVDUN0XfOyIN98/Px6XSoX8XmKA0fCX2Ph2O/pl/MuDB2nQKV26ZcTQoinJG03IXKGN954g/j4eOrVq0fLli0dHU7Os2cybBlvsfiqqw59zujEIYQQQgghrFAl6ZGUlASAu7t7hrdxc3MDICEhIVtiyqv0er25bkX2SE5O5vjx4wAEBwfnqPr29PRk5syZjg4j0+xR57HJsYzcNpIjd45YXa8oLiTeeA1DTLB5Wa2SvszuXZdC+TN+bbNwfgusfgNibqZfrno36PAtePpn/Vh2kpPP8ZxI6lt9er3e0SE4nLTdnIe03dSRk6+1Y8aMeXIhJ+MU9b37O9g6weqqZgmJNL5Wlz2lL5GkRKocmP05RX3nMVLn6pK2mxBCiMepMryVr68vADdvPuFLvUfcvn0bgPz5bU8cJ4TIvB07dmA0Gh0dhlOJTIxkYOhA2wkPkysJ1/qkSXh0eqYYvw1qkPWER1IMrH0TFndLP+GRzx9eng8vzXOKhIcQIm+QtpsQzuP06dPcuHHD0WHkLru+sZnwAPhE35f7xYazpusfVC9YHYAC7gXUik4IIYQQQjwlVZIe5cqVA2Dbtm0Z3mbLli0AlClTJltiEiIvmjp1Ks899xxDhgxBURRHh+M0Fp5cyImIE1bXKUZ3Eq72xxhXybzs7dYV+fG1mni4ZnHCvEu7YUYjODI//XKVOsCIg1Cta9aOI4QQWSRtNyGcQ1hYGE2aNKFNmzZEREQ4OpzcYcdXsG2izdUf6ftxMOAlfupVm2L5i/BLu18Y+sxQSuQvoWKQQgghhBDiaaiS9GjevDmKorB69WrCwsKeWP7w4cOsWbMGjUZjnhRTCJF1iqLw+eefM2rUKAB+/vln3n//fUl8/L/hNYfTvHhzyxVGL+KvDsaYkPIFnptOy5TutXizdYWsTVieHJ8yd8eCFyAqnTHv3QtAl5nw2q/gHZj54wghxFOStpsQjrdz505atmxJREQEJ0+epH379sTExDg6rJxt+yTY8YXN1R/q+xPq+QLz+tXDx8MVAA+dByNqjkCrUeWjsxBCCCGEsANVWm6vvvoqfn5+GI1GBg0axOLFi81jRT8qKSmJhQsXMnjwYAwGAz4+PvTo0UONEIXI1c6fP8/EiWmfaPv2228z9QRvbuaqdeXb5t/ybJFnzcsC8wXyWb2f0CYXByDA243fBzeg0zPFsnaQa4dhVlM4OCP9cuVawvD9ULM7ZCWxIoQQdiBtNyEcy2Qy8dZbb6VJchw+fJhPPvnEgVHlYIoC27+AnV/aLDJOP4CVLm2Z93o9gnzzqRicEEIIIYSwN1UmMvfy8uK7775j8ODBJCYm8r///Y/vv/+eatWqERiY8hTz3bt3CQ8PJzExEUVR0Ol0fP/993h7e6sRohC5WoUKFVi6dCkvvfSSeT6PiRMn0rJlSwdH5jw8dB5MaTmFQaGDiEyMZE6bORTPXxx94lV+2XuZuX3rUtzPM/M7NiTBjkmw90dQTLbLuXpB24lQp58kO4QQDidtNyEcS6vVsnr1apo0acK1a9cAaNWqFZ9//rmDI8uBFCVlOKvd31pdbVI0jDMMZJnpOeb2rkX1IJm7QwghhBAip1Ml6QHQqFEj5s6dy/vvv8/du3eJj4+3GC4hdaidwoUL8/XXX1O/fn21whMi1+vSpQvz5s2jX79+TJkyhREjRjg6JKfj5erFjNYzSDImEeiZ8qXeq/VK0qVWEO66LMzfcesorBwKd0+mX65UY+g8HfxlHHwhhPOQtpsQjlWyZEm2bNlCkyZNaNKkCb/99hvu7u6ODitnUZSUCcv3fG+xSg+MDgwgKrIpO5Ke4/PO1WhZubDNXS3psCQbAxVCCCGEEPakWtIDoEGDBmzZsoVVq1axfft2Tp06RWRkJAB+fn5UrVqV5557js6dO+Pm5qZmaELkCX369KFBgwZUrFjR0aE43LUH8QT55kOrTdurooC75dN9mU54GPWw+3vY9TWYDLbL6Tyg1adQfyhoZZxoIYTzkbabEI5VsWJFDhw4QMmSJdHpVP3olvMpCmwZD3t/sLr6K38/tnp5onj+TTPvavRq0EHV8IQQQgghRPbRKDKTca6XnJzM8ePHAahWrRoeHh4Ojih3M5lMxMfHA+Dp6YlWvszOdhmp8wO3DlCzUE08dB5sP32Xkb/+zeuNSzO6bWX7BnP3VErvjlv/pl8uqC50nQkBFex7fBXIOa4uqW/1JSYmcuLECQCCg4Ply3yhOmm7qU+uterK9vpWFNj8MeybanX1b/nz80WAX5pl3Sp048P6H+Lq4mrfWJyAnN/qkzpXl7TdhBBCPE7uvHmMNLayn1arxdvbG29vb9Xr+/bt27Rr144LFy6oelxHe1Kdrzy3kiGbh/DOjneYs/ssAxYcJi7ZyPTtF1h+5Lp9gjAZU+btmNUs/YSH1hVafQL9N+XIhAc49hzPi6S+1Sf1LJyJnI/qcNS11mg0MnToUH7//XfVjukMsrW+FQVCP7KZ8Njr4cGkgv4Wy5efW86Hez+0byxOQtoS6pM6V5fUsRBCiMdJH2khconLly8TEhLC+fPnCQkJYffu3QQFBTk6LIdbfHIxXx3+CoDdN3az7WE0JuU1UnO+Y1cco4S/J8+Wsfzwm2ERF2DVMLh2MP1yRYKhy0woUj3rxxJCCCFErpCcnEzv3r35448/+Pnnn/Hx8aFDBxli6akoCmz6AA78ZHX1RZ0rIwODUDR6i3UeLh68Xu31bA5QCCGEEEKoQdLheYzJZHJ0CLmeyWQiNjaW2NhY1er73LlzNGnShPPnzwNw6dIl2rRpQ0REhCrHdzRrda4oCjOOzjAnPFK5+hzDvchKIGVkP71RYciiMK5HxmflwHBwNsxskn7CQ+MCzd6HgdtyRcLDEed4Xib1rT6pZ+FM5HxUh9rX2uTkZDp37swff/wBgMFgoFu3buzatSvbj+0MsqW+FQU2jrWZ8HigceG1wuUxuFgmPAC+aPoFVQtWtU8sTkbaEuqTOleX1LEQQojHqdrTIzk5mbVr17JlyxbOnDlDZGQkiYmJ6W6j0Wg4efKkShHmfkaj0dEh5HoGg4EzZ84A6o0nGhgYSGBgIDdu3DAvS0pKIjY2loIFC2b78R3t8Tp3dXXl27BvWXhyodXybn6HMcRWwRib8sG2dZXCBObP5HjpUVdh9Qi49IQvJwIqQdcZEFQnc/t3Yo44x/MyqW/1yb3yP9J2czw5H9Wh9rXW1dWVihUrsnHjRvMyk8lEZGRkth7XWdi9vhUFNoyGw3Osrk5QtHQOrEmC2z2r60fUHEFIqZCni8GJSVtCfVLn6pJ7pRBCiMeplvS4dOkSI0aM4NKlS8jc6ULYV4ECBdi4cSNNmzbl7NmzBAcHs2nTJooWLero0FRnNBn5Yv8XLD+33GaZpHshGGOroNHAmHaVGdKsLBqNJmMHUBT4ZzFsHAfJMekU1ECjkfDcR+AqE9AKIXIeabsJkX00Gg2TJ08mOjqaBQsW4OXlxerVq2nVqpWjQ8t5TCbY8B6E/Wx1tV5xobNfU6K8Llpd3750e4bUGJKdEQohhBBCCJWpkvSIj49n0KBBXL9+Ha1WS6tWrfD39+ePP/5Ao9EwbNgwoqOjCQ8P5+jRo2g0GmrWrEnjxo3VCE+IXCEwMJDNmzfz3nvvMXPmTPz9n2KOihzKYDLw0f6PCL0aarPM+3XfZ/eRKuyOvs/kV2vSrnqRjB/g4S1YOwrO2d4/AH5loMsMKNUw4/sWQggnIm03IbKfVqtl7ty5aLVahgwZQv369R0dUs5jMsH6d+DIL1ZX6xUXunl14JbfUavrqxeszoTGEzL+8IsQQgghhMgRVEl6/P7771y/fh0XFxfmzp1Lw4YNOXfunHkM21GjRpnLnjx5kvfff5+jR4/y/PPP06tXLzVCFCJXKFmypPl9ldckmZKYfnU6x2KPWV2v1WgZ33A8XSt0pVsFA1ci4qlS1CdjO1cUOP5nylOEiVHpl603CEI+AzevzP0BQgjhRKTtJoQ6dDod8+bNc3QYOZPJBOvegr8XWF2tV1zo6foqFwP3YS2lEegZyJSWU/DQSY9cIYQQQojcRpWJzLdv345Go6Fdu3Y0bJj+k89Vq1Zl4cKF+Pv78+WXXxIeHq5GiELkGP/884+jQ3A6sfpYvr/8vc2Eh06r45tm39C1QlcAPN10GU94xN2HP/rAioHpJzx8ikPvVfD8t5LwEELkeNJ2E8J+Tp069cS5cEQmmUwpvW9tJDySFRdeZwAni4Wh0VhOcOzh4sHUllMp5FkouyMVQgghhBAOoErS4/z58wCEhFifHM5kStsQ9ff3p1+/fhgMBpYsWZLt8QmREyiKwhdffEHt2rWZNWuWo8NxGpGJkQzbNowz8Wesrk/9UNumdJvM7/zUOpheH06tSb9czV4wfB+Uey7zxxBCCCckbTch7GP37t00aNCA7t27YzAYHB1O7mAywpo34J9FVlcnKToGGkfwb1AYGhfryaYvmn5B1YJVszNKIYQQQgjhQKokPWJiUib7LVasmHmZm5ub+ef4+HiLbWrXrg3AoUOHsjk6IZyfoii8//77fPjhhwAMGzaM33//3cFROd7d+Lv029iPkw9OWl3v7erNzJCZNAlqkrkdJ0TCisGwtCfE37ddzrswdF8KXaaDR4HMHUMIIZyYtN2EeHobNmygTZs2PHz4kFWrVjFgwACLhKHIJJMRVo+EfxdbXa24uDHBayyHi4ajdbfehhtRcwQhpawndIUQQgghRO6gStLDwyNlnNRHJ4jLnz+/+edbt27Z3Pb+/XS+cBQij9i6dSvffvut+XdFUejdu7f5Sdy86FrMNfr81YcL0Resrvd19+Xntj9Tp3CdzO343Bb4qSEcW5p+uerdYPgBqNQuc/sXQogcQNpuQjydmJgY+vTpk2ZYq4ULFzJjxgwHRpXDmYywajgc/dX6ehd3NK/9iq5xIjpv623k9qXbM6TGkGwMUgghhBBCOANVJjIvXrw4Z86c4e7du+Zl/v7+FChQgIcPH/L3339ToUKFNNucOHECAFdXVzVCzDOkPrOfm5sbdepk8ov2J2jdujWffPIJEyZMMC/77rvvKF++vF2Pk1NciLrA4NDB3E24a3W9Se9DEcM7VPCtnPGdJsVA6EdwZH765fL5wwvfQ7WuGd93LpMd57iwTepbfXKvlLabM5H6VIe9r7X58+dnxYoVtG3b1pz46Ny5MwMGDLDbMXKyTNe30QCrhsHxP6yvd3GH7r+y1BDBivPWy1QvWJ0JjSekSebmFdKWUJ/UubrkXimEEOJxqvT0qF69OoDFxJYNGzZEURR+/vlnoqKizMuvXbvG7Nmz0Wg0VKlSRY0QhXB648eP54033sDFxYUFCxYwatQoR4fkECciTtB34+u2Ex7J/sRfGUpktD+RcckZ2+ml3TCj0ZMTHpWehxEH83TCQwiRN0jbTYin16xZM/788090Oh29e/fmzz//NPeiEplgNMDKIbYTHjoP6P4b//oEMOnQJKtFAj0DmdJyCh46qX8hhBBCiLxAlaRHo0aNUBSFbdu2pVneu3dvIOWDctu2bRk1ahSDBg2ic+fO5icLX3nlFTVCFMLpaTQafvjhB/bv30+fPn0cHY5DxOnjGBI6hOikKKvrjYmFib8ylCalK7J8WCMCfZ7wwTY5Hv4aCwtegKirtsu5F4Cus+C1JeAdmPU/QAghcghpuwlhH88//zz79u1j/vz56HSqdLLPXYwGWDEIwv+0vl6XD7r/DuVbUdm/Mm1KtbEo4uHiwdSWUynkWSibgxVCCCGEEM5ClaTHc8/9X3v3HR5F1fZx/Lub3oDQkRZAQkc6ggjSQVCaCAiCYsECKCqPgr0XFHsBFBFEBKRJUTo8IogC8hB6l95JIL3svH/kzZqQ3TSS2WT397kurmszc2bOPWeXnXv2zJzTnubNmxMUFMSxY//+sNi0aVMef/xxDMMgKiqKlStXsmHDBvvkmH379uWOO+4wI0SPkZKS4uoQ3F5KSgpnz57l7Nmz+d7eVquV5s2b5+s+i5LTl20kn+uDYWQeliAlrhKxxx7mnib1+Pa+5hQPyOYR5+N/wldtYHM2Y2vX6ACPbYKbBoIHDofgSEF+xiUztbf51M7K3QoTfR7NUZDftc2bN8dqNeWyq8jIUXunJMG8B2DXfMfrvQPgntlQoz0A/t7+vNv2XR5v9HiGYm/d+hZ1S9XNz/CLHOUS5lObm0ttLCIi1zLldqOAgABmzJjhcN2oUaNo1qwZc+fO5cCBA6SkpFC1alV69+5N165dzQjPo9hsNleH4PZSUlI4ceIEAKGhoXh5eeV423/++Yfx48fz1VdfZZgwVuC3A+d5bOY2rsbXxieuD/4V/r0ATo6pRsKJYTzQsAz/6VELb68sflhIToB1b8PvH4ORxf8HnyDo+gY0vV+dHde4ns+45J7a23w6Vyp3K0z0eTRHXr9rExMTGTlyJI8++iiNGzcuyBDdSrbtnZIEPw2HPT873D7J6o/P4LlQ7dYMyy0WC4/c9AjVi1fn+Q3P80CDB+hctXOBHENRolzCfGpzc+lcKSIi1yoUz1i3atWKVq1auToMEZfau3cvnTt35sSJE5w5c4alS5dq3Of/9/0f//Dyz7tIsRkAJEW2AGsC/uWWkny1NtYLQxnXuhRNKvhlPTnlqe2pk2Ce2511hVVvgV6fQ8lq+XcQIiJuRLmbCMTGxnLXXXfxyy+/sHDhQn777Tdq1arl6rCKvuRE+Ol+2LvE4eoYw4/748ZSb2dJXqhq4GXNnPt1CetCnZJ1qBRSqaCjFREREZFCyJROj4ULFwJQrVo1brrpJjOqFClStm3bRteuXblw4QIAa9asYdCgQcydO9ejx39OsRm8sXQ33/5+NNO6pEu3YiSVoKxXEyY/0IyEc0ey2FES/DYR/vse2JKdl/P2h44vQ8tHQMNQiIgHU+4mkrUrV67Qs2dPfvvtNwDOnz9P586d2bBhA1WqVHFxdEVYciLMvQ/2LXW4Osbw477EZ/nLqM2fvx/lxOU4Ph7YiEDfzPly5WKVCzhYERERESmsTPlV77nnnmPcuHGcPHnSjOpEipzAwMBMyw4cOEBkZKT5wRQSV+OTePC7vxx2eKRpGHorP49sR3i5YOc7OrcHvu4E697KusOjYjN4ZAO0ekwdHiLi8ZS7iWTN19c3040pFy9e5PDhwy6KyA0kJ8CcoU47PKINf4YmPsdfRm37svX7zrPvzFWzIhQRERGRIsKUW8hDQkKIjo4mLCzMjOpEipzatWuzfPlybrvtNq5evUqLFi1YtmwZpUqVcnVopjMMg79OHOTFn06z76zzi9hejW7g3X4N8ffxIjExMXMBWwps+gzWvAEpDtansfpA+/HQejR4ee5TNSIi6Sl3E8mav78/ixYtomPHjvz111+UKFGCZcuWadi3nPi6E74n/qJp2t+LgYpNIagM7P/V4SaJXkEMjR3LNiMci3cURnIQ4M2E/g1pXCXUpMBFREREpKgw5XbmSpVSx1KNiooyozqRIqlJkyYsWbKEnj17smrVKo/s8EixpTBy5XgeWDWYA5H7nJZ7qnM4Hw1ohL+PkwkBLx6Cb7vDypey7vAo3wAeXge3PqUODxGRdJS7iWQvJCSEZcuW0aFDB9avX68Oj+tx8aDTDg/8iuF73yKGDxqAr288gVWmEFDlGx7vWJ5ejSqaG6eIiIiIFAmmdHp06tQJwzBYu3atGdWJFFlt27Zl8eLFhISEuDoU0yWlJHHv4if47+klYI0joMpULL7nM5Tx87by6aDGjO5Y0/GE5YYN65av4ctb4Phm55VZvKDtf+DBNVC+fj4fiYhI0afcTSRnSpcuzerVq2nYsKGrQyna4p10sPoVg3sXQOXmdK1fhibNFmP1u4B30BFWX3meg5cPmhuniIiIiBQJptzaPGzYMObNm8esWbNo3759obsL6uTJk8yYMYN169Zx5swZfH19qVy5Mt27d2fw4MEEBATked9xcXH89ttv/P777+zcuZNjx44RGxtLcHAwYWFhtGnThoEDB1KmTJl8PCJxJYvFQlBQkP31tQ4fPkz16tXNDqtQi0uKo++8RziRsM2+zOodTWCVr4k9+ihGcglKB/sxZWhTh0MYWCwWinOFCn++gfe5LVlXVroW9PkydRgFyZPsPuOSv9Te4grK3ZS7eZqsvmsPHz5MtWrV9B1sNr/iMHSBPWd778/32HV5q331yeiTDPllCO+1fY+2ldq6KsoiQbmE+dTmIiIirmUxDMMwo6J//vmH0aNHc/DgQfr27UvPnj2pVasWxYsXd2kSsGbNGsaOHUt0dLTD9WFhYUyePJmqVavmet979+5l0KBBxMbGZlkuODiY119/ndtvvz3XdeREYmIiERERADRo0ABfX98CqUeyZhgG48aN45NPPmHFihW0adPG1SEVChdio+j90wNEGY6Hs0qJL0fluOeZet/NVCzh4Ecsw4C/Z8Cv4yExq4ksLdB6JLR/AXz88yd4EXFLOm+mUu7mnHI3z/Hrr7/St29fxo0bx4svvujqcNzD153gxF9Zl/EvDvcuhIpNAJi9dzZvbH7DYdGGZRoyo/sMrBZTBjEQkUJI500REbmWKU961KlTx/7aMAx++uknfvrppxxta7FY2L17d4HEtXv3bsaMGUN8fDyBgYGMGDGCli1bEh8fz7Jly5gzZw5Hjx7l4YcfZt68eQQHB+dq/9HR0faL5iZNmtC+fXvq169PiRIluHTpEitWrGDu3LlER0fzzDPPEBQURLt27QriUMXFUlJSeOyxx5g8eTIAPXr0YN26dTRu3NjFkbnWgQtnGPTzgyR4/eNwvWHzoY7fYL6771aC/Rx8XV05DYtHw4EVWVcUWg16fwlVC9edyiIihZVyN+VuAnPmzGHIkCEkJSXx0ksvUbx4cUaPHu3qsIo+W0rW6/1LwNBFcEMjADad2sTbf77tsGjZwLJ8eNuH6vAQERERkQxM6fS49mESkx4uydabb75JfHw83t7eTJ06NcMP0K1ataJq1apMmDCBo0eP8u233zJq1Khc7d9qtdK9e3dGjhzJjTfemGl9mzZtaNu2LSNHjiQlJYU33niDtm3b6vFXNzR58mR7hwfAlStX6NatG/v376d48eIujMx1fj9yiMdWP4LN54zD9UaKHx1DxzOxVx+8rNf8nzAMiPgJlj0D8ZFZV9T8Iej8KvgG5U/gIiIeQLmbcjdPd/jwYe655x5SUv79gf6JJ56gTp06dO7c2YWRFXFJ8amTljth8w/FOmwRVLgJgKNRR3l6/dOkGJk7Svy9/Pm0w6eUDSxbYOGKiIiISNFkSqfHyJEjzagmV3bs2MGWLalj//fr18/hHffDhw9n/vz5HDp0iOnTp/PII4/g4+OT4zqaNGlCkyZNsizTqVMnunTpwvLlyzl27Bi7d++mXr16uTuYXEhOTtajngUsOTmZf/5JfXKhatWqeHt78+CDD/LLL7+wePFiIPVHlXfffddjOzzmbP+b17Y8icXnksP1RnIQw298i6fadci8MuYCLBkDe37OupJilaD351D9tusPWDJw9BmXgqP2Nl9ycrKrQ3A55W7OKXdzT9d+11avXp1PPvmExx9/3F5myJAh3HbbbS6K0A0kJ8Lc+yDhisPVlwnhiZQXeN5SnVpAVEIUo9aM4qqT4UvfbPMmdUvVLbh43YhyCfOpzc2l3E1ERK7lsZ0eq1atsr/u16+fwzJWq5XevXvzwQcfcOXKFTZv3lwg8zC0bNmS5cuXA3Ds2LECvXAuLHdqujObzUZkZCQAlStXBsDHx4c5c+bQvXt3Nm7cyI8//kifPn1cGKVrGIbBhLX/5bvD47H6OL7gJbk4rzT/mLsaOphofM9iWPwkxF7IuqLGQ6DrW6njQUu+c/QZl4Kj9jafzpXK3bKj3M39OPqufeyxx7h8+TIvvPACjz/+OJ988glWq4ZRypOUZJj/IOz/xeHqSCOYQYnPsze+An9/uZHPBjfi+6MvcPTKUYflH2/0OF3CuhRgwO5FuYT51Obm0rlSRESule+dHgsXLgRS74LL7TjKZtq6dSsAgYGBWV6oNm/e3P5627ZtBXLhnJiYaH/t5eWV7/uXwsHf35+ff/6ZHTt2cMstt7g6HNMlpdh4YsFi1ke9hdXH8QSx1pRSfNlpMq2rhmdcEXcZfnkWdszOug6/knDnp/jU65lfYYuIuD3lbrmn3M1zjB8/nqZNm9K1a1cNY5ZXthRY9BjsXuRwdZQRyIDEF9hnVAHgakIyjyx7Ce/QPxyW7x7WnRENRxRYuCIiIiJS9OV7p8dzzz2HxWKhfv36DsdCvnTpEj/88APg2rsIDx06BECVKlWyfNS0evXqmbbJb3/++af9dY0aNQqkjjRJSUkZLtTT8/b2tt+9ZrPZsn1ENP1QCykpKRnGPL6WxWLJMLxEcnIyNpvNaXmr1ZrhfUlKSsry7o30sRuGQVJSUpax+/j42C9cszvW3MZ+7b7Sx+7n50fz5s0z/ViS/gcTZ+9PmqL4PkXFJfHAnJ847PUpVu8Eh/vys93Ad92/omapivY28PHxwXJwNfw8Eq6edn6QwKWKHThWfxR1a7bJ19jTeML75Ch2R/+f0v+dlJRUoP+f9D4ZmdrbUeyu/t5zt/cpu7Z0N8rdck+5W2ZFOXe79umNa/fdoUOHDPEV9u+wQvU+JSXi9cvTeDm5cSUSfwYlvmjv8ADwKfEH3qEbHZavX6o+r93yGhaLpcifa5S7pXLX98lR/lbYvvfc6X3ytNxNRESyZ/rAkhcvXuSzzz7DYrG47MI5ISGBy5cvA1C+fPksyxYvXpzAwEBiY2M5c8bxhMvXY+/evaxfvx6A8PDwAr9wzuriv1atWvY7PGNjY9m3b1+W+2ra9N/hhy5cuMCJEyeclg0KCqJ27dr2v//55x/7476OlC1bNsNjwHv27MkykalevTqhoaFAasIWERGRZewNGjSwJ1ZRUVEcPnzYaVkfHx8aNmxo//v06dOcO3fOYdmjR4+yfPlyHnroIXuSd+jQIWJiYpzuv1KlSpQrV87+d3axF7X36VxMMi//tYnYkt9hsTpOdgNtlXmz9n+IP3WJiFOp83xYk2NpeHo2XttnZHl8yT7F+Kfhk0TecFuG5Vm9TwAlSpTI8P/N09+n9HLz/2nv3r0F9v8J9D5d+z7t3bvX/rqwfO+BZ71Pnki5W0bK3RwrqrlbfHw8kydP5q233rIvO3r0KHFxcU73X5S/w0x9nyIjSfp5DGWPLnRY9io+DE14nj1GVfsyr8CD+JV3PG9bKd9SfNzhY/y9/QH3Ptcod3OuKL5PaflbYfneA896n0RExDN55Gxa6U/mgYGB2ZYPCAggNjaW2FjHQ/LkVWJiIs8//7z9LoYxY8bk6/7FXHv37mXUqFFcvnyZmJgYvZ//b1/8VuJKTcNicXy3TilLTV6vO4ZA73//LwZf2E7Y9vfwisv6x6rIcrfwz01jSPYrma8xi4hI4aLcTQpCdHQ0Tz31FNu2bWPnzp1MnDixUA/xVqQYBgG/v0Ookw6PGLy4L2E8O4x/f3S1+FwgoNJMLJbMd6f7WnwZX3c8ZQPLFlTEIiIiIuJGLEY+z/hUu3ZtLBYLixcvdjhEwoEDB7jjjjuwWCzs2bMnP6vOsdOnT3PbbbcB0KtXL957770sy992222cPn2aKlWqsHLlynyL44UXXmDu3LkA9OnTh3feeSff9p1eYmKi/U6MGjVqOP2xoKg8Kpxd7K54VHjDhg306dOHK1f+nZz7kUce4eOPP8ZisRTpR4Wv531adGgRb/z1BjbD8T5qF2vBN10/tN+xR1IsXmvfwGvLlCyPyfArRkqXt7HV7w//P7xB2h1UaXdEedoj3WYPkZDW3rVr1yYwMFCP3juQn8NbpW/vtH26+nsvJ7E7U9jfp+joaPvd9envsnRXyt1yTrmb++RuFy5c4I477mDbtm32ZU2aNOGTTz6hcePGWQ6hVti/wwrF+7T2LVj/rsMyCXhxX+J/2GRrkC6wWILCvsDqd8HhNu/e8i5dwrq41blGuVsqd32fHOVvrv7ey2nsjhT298nTcjcREcmeRz7p4efnZ3+dk7Ef007o/v7++RbDpEmT7BfNDRo04KWXXsq3fWfFx8cnRwmA1WrNVaJwbRKUnawuJB1Jn9Rkx2Kx5Cr23B6ro9hjY2MzPQ783//+l4SEBEJCQnK8b6BAYzfzfVp8aDGv/fma07KtynXk884T8PH6/22O/wkLHoFL2Yy/XqMjljs/xbt4xSyLFeRnDNznfcpOdv+f0l+MQf78f8qKp79Pzr7DXfG9l5Wi/j7lNn4peMrdlLullx/fYYZhcPHixQzL9u/fz/Hjx2nevHmu9l/YvsNyo0Dep98mOu3wSLb48HDCUxk7PEghoOIPTjs8Hm/0OLffeHum5UX9XJMbyt0cKyrvk6PvceVuzil3ExGR/GDNvoj7CQoKsr/OybAHaWP65mQ4hZz48ccfmThxIpA6rufkyZPzbd/ZSZ/gSv7p2bMn3333nb19W7duzU8//URAQICLI3OdVje0IqxYmMN13av24ssuH6R2eCQnwMqXYWrXrDs8fIKg50cwZB5c0+FhtVopW7YsZcuWzTQZqeQ/tbe51N7m07my8FHuJvmtYsWKrFq1yj5GfdmyZZk7dy633HKLvmuvx6YvYPWrDlcl48WjCaNYb7spw3K/ckvwDj7ocJvuYd0Z0XBEvofpaZRLmE9tbi6dK0VE5Foe+6RHiRIliIyMzHaCy6ioKPvFdXYTZ+bEkiVLePXV1AuBihUr8u2331KypHnzEeT2jg/JucGDBxMVFcXy5cuZPXt2vt5dWhSVDijN5M6TGfrrUM7E/Pv/7N46Qxnb/JnUxPTUdlj4KJzbnfXOqt4CvT6HktUcrvb29s4wiaAULLW3udTe5tO5svBR7iYF4cYbb2TFihUMHTqUOXPmEB4e7uqQirYtU2H5OIerUrDyROLjrLQ1y7Dc4nMJnxJbHG5Tv1R9XrvlNf2YmQ+US5hPbW4unStFRORaHnvLQdqY1ceOHctyfMnDhw/bX9eoUcNpuZxYvXo1zz77LDabjTJlyjBt2rR8uRiXwuOxxx5jwYIFHtfhcfh8NDZb5jFgKwRXYErnKZT0T/1x6PFGj6d2eNiSYd278HXHrDs8vP2h69swbInTDg8REfEMyt2kIDRs2JBt27apw+N6bf8BloxxuMpmWBib+DBLbTdnWmcklSQpqjEYGYeyKRtYlo87fPzvvG8iIiIiIrlQYN3hP/zwg8O74C5dumR//dlnn+VoXyNHjsy3uNI0bdqULVu2EBsby65du7jpppsclvvrr7/sr5s0aZLn+jZt2sSTTz5JcnIyJUqU4Ntvv6VKlSp53p+4hmEYnD9/nrJlyzot42mPL/+68zRPzt7Ofa2r8Vz32pnWhxUPY3Lnyfx97m8G1h4I5/akzt1xenvWO67YDPp8BaVrFkzgIiKSgXK3jJS7uY9z584pdytIO+fBosedrn4+eTjzbW0zr7DG43/DHHxCdlOnZB18rD7suLADfy9/Pu3wKWUDnb9nIiIiIiJZKbBOj1mzZjldl/aI8ueff56jfRXEhXOnTp2YNGkSAPPmzXN44Wyz2Vi4cCEAxYoVo2XLlnmqa9u2bTz22GMkJiYSEhLCN998Q82arvkhNykpKVeTiMm/UlJSGDlyJMuWLWPDhg1OH1dOSkpiz549ANSpU8dtJ1UzDIMv1x/ivV/3AfDV+kNULxPE3c0yt0utkrWoVeJG+P1jWPMGpCQ637HVB9qPh9ajwStnX1Ge0uaFhdrbXGpv8+Vkomx3pNztX8rd3MdPP/3EsGHDmDVrFnfeeafTcvquzaM9S2DeQ2DYHK5+JWkos1I6Zlpu9T2Hf6UZePmdB8DHy4dvunzDK5teoUPlDtQtVbdAw/Y0+nybT21uLk/N3URExLkCua3JMIx8+1dQGjZsSLNmqWPKzps3j7///jtTmalTp3LoUOrEykOHDs2UqGzevJlatWpRq1YtnnvuOYf17NmzhxEjRhAbG0tgYCCTJk2ifv36+Xw0UtCSkpIYMmQIX331FceOHaNLly6cP3/eYVnDMEhKSiIpKalAP8OulJCcwjNzd/De8p3Av8f4/III/jh8MfMGFw/Bt91h5UtZd3iUbwAPr4Nbn8pxhwd4RpsXJmpvc6m9xQzK3f6l3M19fPPNNwwYMIDY2Fjuvvtu1q1b57Ssvmvz4MBKmHsfGCkOV7+TNJBpKd0yLS9Z6jiBYZ/bOzzS+Hv7886t79AlrEtBROvR9Pk2n9pcRETEtfL9SY/p06fn9y4LzPPPP8+gQYOIj49n+PDhPPLII7Rs2ZL4+HiWLVvG7NmzAQgLC+P+++/P9f6PHTvGAw88wJUrVwB44oknCAkJYf/+/U63KVWqFKVKlcrbAUmBGT9+PD/++KP9771799KzZ082btyIl5dXFlu6pwNno1kccYDAqt+QHF2LxAudAUhKMXjk+60sGdWGSqGBYLPBX1+ndnYkxznfocUL2j4Dtz4D3rqbVUTETMrd/qXczX2sX7+eBx980P53QkICd9xxB1u3btX8Hfnh8HqYPQRsju+u/ii5L1+lZHyyxmKBpzqFsyn+ByIuJJgRpYiIiIh4qHzv9GjRokV+77LA1K1blw8//JCxY8cSHR3NxIkTM5UJCwtj8uTJBAcH53r/W7Zs4eLFf+96f/vtt7PdZuTIkYwaNSrXdUnBevbZZ1myZAl79+4FwMfHh//85z8e2eEBUDY0gUr1vuV8wgm8Ak5g2PxJunQrAHc0vIHyxfwh8ljq+M5H/pv1zkrXSp27o2Lex10XEZG8U+72L+Vu7qNt27YMHz6cqVOn2pfde++93HjjjS6Myk38swlmDYTkeIerv0q+g4+S+9n/tnhfJrjiIl655SX6NazJH8vMClREREREPFWBzelRVHTo0IGff/6Z6dOns27dOs6ePYuPjw9VqlShW7duDBkyhICAAFeHKS5WunRpVq5cSZs2bTh//jwLFiygSxfPfPT++NXjPLTiIc4nnLQv8y+3FIvNj/G33sd9rcPg7xnw63hIvJrFnizQeiS0fwF8/As8bhERcQ/K3SQnLBYLkydPJioqinnz5jFu3DjefPNN+/w0kkcntsLM/pAU63D1t8ldeSd5IJDazt7Buwis+BOGNY75J97hzvrTzItVRERERDyWx3d6AFSsWJFx48Yxbty4XG3XsmVL9u3b53R937596du37/WGJ4VEpUqVWLVqFefPn6dVq1auDsclDkUe4uEVD3Mu7lymdf4VFlCpZH344T9wYEXWOwqtBr2/hKqe2Y4iInJ9lLtJTnh5eTFz5kz69+/PgAEDXB1O0Xd6B3zfx+lNLT8kt+fV5KGABSzJ+JVdhm/JjfbZ33ac38Fnf39mWrgiIiIi4rnU6SGSCzfeeKPHDouw6+IuHln5CJEJkQ7XV/QtQe0lYyHmctY7av4QdH4VfIPyP0gRERGRdPz8/NThkR/O7YEZvSE+yuHqeSlteD75AcCCxecCAZVm4eV/MlO5qTunUq14tYKNVUREREQ8ntXVAYgUJv/73/8yjP3syWw2w/56y5ktPLD8AacdHjda/Pnu4E4qZdXhUawSDF0EPd5Xh4eIiIjki6tXr/LKK6+QlOR4Qm3JBxcPwfReEHvR4eolKS35T9IIDKx4F/sfwdU/ddjhkeZszNmCilREREREBNCTHh7HUyfezonff/+dHj16EBUVhZ+fH4MHD87Tfry9valevbr9dVF0/FIsj3y/lRd61CXZbzdj1o0hISXBYdl6SSl8deogJWw25ztsPAS6vgX+xQskXndo86JE7W0utbf5dK6UwkSfR+cuXLhA9+7d2bJlCwcPHmT69OlYrXm7p0vftU5cPgrf3QHRjjsqVqY05cmkx0mxpOBXbhG+oX9mubvu1bpz7Moxdl3cVQDBijP6fJtPbW4unStFRORaOvt6mLxeCLq75cuX06dPH+Li4gAYNmwYxYoV44477sj1vqxWK6Ghofkdomm2/nOZh6dv4WJMIiPmT8Wr3A8kG8kOyzaLi+fTs+cJNgyH6wkuB3d8ArW6FWDERb/Nixq1t7nU3ubTuVIKE30eHTt58iRdunRh9+7dAMycOZPixYvz2Wef5Wmycn3XOhB1Er67E644fmpjfUpDHk8ajc33IoEVf8DL3/kTHP5e/oxrOY4+N/ZhyC9DCipicUKfb/Opzc2lc6WIiFxLZwYRYOvWrfYOD4CUlBS++OILDGc/5rupRdtPMmjKH1yMScS7+F/Yysxw2uHRNjaOL7Pq8Kh/Fzz2R4F3eIiIiIjnOXbsGEeOHMmwbO7cuZw6dcpFEbmZq2dh+p0Q+Y/D1ZtS6jIi6Ulsxf9HYLXPsuzwqFG8BrN6zKJvzb556pASEREREcktdXp4GE/7ET+nxo0bx1NPPWX/u2fPnsyfPz9PF2aGYZCYmEhiYmKRaW/DMJi4cj9P/LidxGQbPqEbCLhhHhaL4/i7Rcfw0dnz+Ds6voCS0H8a3PUNBJYs2MD/X1Fs86JM7W0utbf51M5SmOjz6FirVq2YP38+Pj4+AFSuXJkNGzZQsWLFPO1P37XpxFxMncPj4kGHq7fYwnkgeRRUWETADT9hsTqfT6Vvzb7M6jmLG0NvLKhoJQf0+Taf2txcamMREbmWhrfyMMnJyfj5+bk6jELHYrHw/vvvExkZSXx8PNOmTbNfROdWUlISERERADRo0ABfX9/8DDXfxSel8Mzc/7Fkx2nAwLf0avzKrHJavt/VaF68cAmHo6bW7gk9P4TgsgUVrkNFrc2LOrW3udTe5ktOdvyEm4grKHdzrlu3bsycOZNXXnmFX3/9lcqVK+d5X/qu/X9xl2FGLzi/x+Hq/9mqc79lKJZqU/Dxu+B0N4HegbzU6iV6VO9RUJFKLujzbT61ubmUu4mIyLXU6SHy/ywWC5MnT8ZisXjMmKDnrsbz8PStbD8eCRj4lV2Kb6kNTssPi7rC05ciyfT8i19xuP09aDgANGyBiIiImKR///707t07zzerSDrxV+D7fnAmwuHqxNJ1GJF4G0boVKxW5z8w1i5Zm/fbvU/VYlULKlIRERERkSyp00M8is1mIy4ujqCgIIfrvbwcPr/glvacvsID0/7iVFQ8YMOvwnx8S2xxWv7xy5GMiLySucOjRke481MonrfhJERERESyEh0dTXBwsNP16vDIB4kx8MPdcHKrw9VXSofzSu2mxJxcmjkXTGdQ7UE83exp/LycP5008/aZJCYm6i54ERERESkwnnE7uwipjxgPHTqUnj17Zpi03BOt2XuWu77caO/w8K/4Y5YdHs9evMwj13Z4+ARBz49gyDx1eIiIiEiBmDZtGjfeeCN79jgebknyQVIcxqxBcGyT4/Wh1ZjUuAcrT/7X6S5CfEP48LYPGd9yfJYdHiIiIiIiZlCnh3iEuLg4+vXrx8yZM1m3bh0DBgwgKcn5pIvuyjAMvv7tMA9+t4WYxJT/X2rBSCrusLzVMHjt/EWGXLmacUXVNvDo79Dsfg1nJSIiIgXio48+4v777+fs2bN07tyZo0ePujok95OcyJXvBmE5st7x+uJVYNhiHmsx1ulwVQ1LN2TuHXPpVLVTAQYqIiIiIpJz6vQQj3DfffexePFi+9+LFy9mxIgRLozIfEkpNsYv2MkbS/dgM9KvsZByrgvhUaUzlPc2DCacu0Cf6Jh0C/2h2zswbDGUrGZK3CIiIuJ5ZsyYwZgxY+x/nzx5ks6dOxMZGem6oNxNSjInvh5EsRNrHa8PqQDDFkGJygT5BPF+u/fxsWYcSuz+evczrfs0KgbrqV8RERERKTzU6SEeYdy4cRQv/u/TDAEBAQwYMMCFEZkrKjaJ+779k1l/Hsu0rqblBAt8X2bOpW10+/8ODn+bjU/PnqdLbLphwCo2g0c2wM2PgodM9C4iIiKu0adPH1q2bJlhWb9+/TLkc5J3iYlJ/O/TAVQ6s8pxgaAyMPRnKFndvqh2ydqMbT4WgFC/UL7o+AVPNXsqU0eIiIiIiIiraSJz8QiNGjVi6dKldO7cGV9fX5YsWUKbNm1cHZYpjl6IYfi0vzh8ISbDcis2HvRaytPec/GzJAPw1vmLWIABV6JpmpDw/wV9oP14aD0avPSVISIiIgUvODiYZcuW0a5dO3bu3Mnbb7/Nc8895+qw3IPNxr4p93FTpOMOjwSf4vgNXQRlwjOtG1hrIFEJUfS5sQ/lgsoVdKQiIiIiInliMQzDyL6YFGWJiYlEREQAUL9+ffz8PHdywRUrVlC2bFkaNWpUYHUYhmGfL8THxweLC+e8+OPwRR75fiuRsRnnLwmznOZ9n0k0s+7PegflG0CfSVCuXgFGef0KU5t7ArW3udTe5ktISGDnzp0ANGjQAF9fXxdHJJ5Gudu/Tp8+zapVq7j33nsLtB6P+a41DFj6NGz5JsPiC15WMMA3xZ+JFSbw8ojBBdoGHtPehYTa23xqc3MpdxMRkWvptm0P4+nJVpcuXQq8DovFUiiSrDlbjvP8ggiSUtL6NQ0sGAz1Wslz3rMIsCQ639jiBW2fgVufAW/XH0t2Ckubewq1t7nU3ubz9HOlFC6e/nmsUKFCgXd4gId81xoGrHghU4fHJn8/nitbmuqJybQr8Rov9L+7wD93HtHehYja23xqc3N5+rlSREQyU6eHuJVNmzaRmJhIu3btXB2Ky9hsBu8u38uk9Yfty6z+JyhWdhETzl6is2VP1jsoXQv6fAUVmxRwpCIiIuLpLl68yPz583nooYdcHYr7W/MGbPrM/mcy8EVocb4uXgzDYuFSgBc314nC20tzt4mIiIhI0aZODw9js9lcHUKBWblyJb1798bLy4s1a9bQrFkzl8Rhs9mIiooCoHjx4lhNnPQ7NjGZJ3/czordZ+3LvAIOE1L5G1K8Uvj2hgRan7EQ5HBUOwu0HgntXwAff9Nizg+ubHNPpPY2l9rbfO58rpSix50/j6dOnaJLly7s2rWL6OhoxowZ47JY3P679r8T4Lf37X+e8fLi2bKl2OafMef74n9f0Kx8M5qWa1qg4bh9excyam/zqc3N5c7nShERyRudeT1MSkqKq0MoEPPnz6dnz57ExsZy9epVunXrxp492TzRUECSk5M5fPgwhw8fJjk52bR6T0fF0f+rTRk6PEoFbaV4lcmkeKW+7xH+fowqV4b4ax//Da0Gw3+FLm8UuQ4PcF2beyq1t7nU3uZz13OlFE3u+nk8fPgwbdq0YdeuXQA89dRTTJ061WXxuON3rX3qxo2fpT7l8f/WB/hzV8XymTo8AGyGjf/89z9cjr9coLG5Y3sXZmpv86nNzeWu50oREck7dXpIkWcYBrNmzSIx8d85Ki5evMgHH3zgwqjMteNEJL0++51dp678/xKDm4vNxVZ5DknX/C//K8CfsWVKYZ/avPlD8OjvUOVmEyMWERERT/bbb79x5MiRDMtee+014uLiXBSR+zAMg69/O8xD07di2zwFVjwPQBIwoWQJRpYvS5SXl9Pt65Ssg9Wiy0QRERERKbo0vJUUeRaLhRkzZnD58mVWr14NwMCBA/niiy9cHJk5fok4zZg524lPSn2ktyRX6FXyM+aXjcLmZEK3FIuFlOKV8en1GVS/zcRoRURERGDYsGGcOXOG5557DoCaNWuycuVKAgICXBxZ0RaXmMJz83ewaPsp+nutw3pkMgDHvb34T9nS7PTzc7qtt9Wbp5o+xZA6QzQpsIiIiIgUaer0ELfg7+/PwoUL6dSpE40bN+azzz7DK4s72NyBYRh8se4QE5bvsy/rav2TxqW+5/PSgYDji9Vu0TG8VbEbPt3eAf9iJkUrIiIiktGzzz7L5cuXWb58Ob/++ivlypVzdUhF2rGLsYz4fit7Tl/hTuvvvOs9BYAVgQG8XKYU0VnMKVApuBLvt3ufeqXrmRWuiIiIiEiBUaeHuI3g4GBWr15NYGCg29+dlpCcwrj5EczfdhKAYkTzis80zpbayeehJZxu1y8uhRc7fY5Xre4mRSoiIiLi3Ntvv82LL75IUFCQq0Mp0tbvP8/oWX8TFZdEN+ufTPT5kiSrwYSSocwuFpLltl2qduGV1q8Q4pt1ORERERGRokKdHlKkJCcnY7VasTq5U80TLpgvxSQyYsYW/jqaOsHkbdbtvO0zhe9LGUwvXsLpdsO8y/P0vT9iCSplUqQiIiIikJiYiK+vr8N1FovFI/K3gpL25O/7K/ZhGNDe+jef+HzKcV8rY8uUZZ+f43YH8LX68myLZ+kf3t/tbxgSEREREc+iGeqkyIiPj+euu+7iySefxDAMV4fjEv9cjKH357/z19HLBBHHW95T+Mb3PSaVsTC9uPOhqh6/oQNP37NCHR4iIiJiqk8//ZSbb76ZyMhIV4fiVhKTbazZe5bh0/5iwvLUDo9brBF85fMRy0P8GHBD+Sw7PMKKhfFDjx+4u9bd6vAQEREREbejJz2kSLh69Sq9evVi7dq1AISGhvLqq6+6OCrHLBYLPj4+9tf5KTTIFz9vK62su3jPezLlred5rkwpfg12fofksw0fY0jjR/M1jsKmINtcMlN7m0vtLSJFkWEYvP7667z88ssA9OzZkxUrVhAYGOjiyBwrCt+1ick2Nhw8z5Idp1m5+yxX45Pt61pY9vCp70ReLx3CopDgLPdzZ407eb7l8wT6uO69KArt7U7U3uZTm4uIiLiWxfDUW+Y9SGJiIhEREQA0aNDA6fAChZXNZqNt27b8/vvvGZZ/9NFHPPHEEy6Kynx9vvid3cfO8Zz3LO73Xk68xcJTZUvzW2CAw/JWLLzS+lX61OxjcqQiIkVbUT9vStHnDp/Bt99+m/Hjx2dY1rVrV5YsWYK3t+67yqmsOjrSNLYc4K3A93iufDEO+/o43VeAdwDPt3yeXjf2KsiQRURM5w7nTRERyV+64pBCz2q1MmrUKDZu3Ggf1qpYsWI0adLExZGZy9vrUSaHnqVdXDzRFgujypVhS4C/47IWb95t+y5dwrqYHKWIiIgI3H333XzyySecOXPGvqxdu3Z4eXm5MKqiIScdHWnqWY7wne+7GEY8sdbiTsvVDK3J++3ep3rx6gURsoiIiIhIoaJODykSBgwYQFRUFCNGjKBMmTIsX76cxo0buzosUzW0JdEuLp5Iq5VHypdhl5+fw3L+Xv582P5D2lRsY3KEIiIiIqlq1KjBypUradu2LZGRkXz++ec8+qh7D7d5PXLT0QFQx/IPvb02MNBrLcUssWCD985d4P4K5Ui5Ziidu8PvZmzzsfh7O75ZRkRERETE3ajTw8MkJycX2Uc9H374YVJSUujQoQO1atVydThOJScnc/r0aQAqVKiQb0M4lEhJ4ZyXFw+XL8MhJ+9hsE8wn3X8jKblmuZLnUVFQbW5OKb2Npfa23zJyVn/2ChipqKcu9WvX59ffvmFw4cPM2jQIFeHkyVXfNfmtqOjPBfp5bWR3l4bqGM9nml944RERl6O4uOSJYDUvPDl1i/TLaxbQYR/XXRuM5fa23xqc3MpdxMRkWvpzOthivoULkXhDkGbzca5c+cAKFeuXL7td4d/AK29vTjtJGEO9Qvlq85fUbdU3Xyrs6goqDYXx9Te5lJ7m6+onyvFvRT1z2PLli1p2bKlq8PIllnftbnt6AgmmuYhv+Ifsp22SecZdPVqluWHR13hz1KViAqtzPvtPqByscr5GX6+0bnNXGpv86nNzVXUz5UiIpL/1OkhhcratWupUaMGVapUcXUohU7sDfV5zxbHk5cieb9UKInphi4oG1CWKV2mUL2ExmkWERER85w+fZqIiAi6dNE8Ys7ktqPDm0QaBa+meLG/2B98lS3/Pw/KxYSgbDs9dvgHMqHfzwT4F8fXq2g+ISQiIiIicr3U6SGFxoIFCxg4cCBhYWH89ttvlC1b1tUhFTq7/fzYfc1cHpWCKzGlyxQqhVRyUVQiIiLiiY4cOULnzp05fvw4y5Yto2PHjq4OqVBJsRk8O28Hy3edybajA1KoFbiRMsV/51jwJfZ5W/9/+b8Tv+/x8+Ufb2+qOhnGZUVgAD+UKse0oDL5cwAiIiIiIkWUNfsiIgVv2rRp3HXXXSQmJrJ//366du1KZGSkq8Mq9Py9/Pmu+3fq8BARERFT7d69mzZt2nDo0CESExPp1asXmzdvdnVYhYqX1cI/F2Oy6PCwcUPgNlqUn0iVmuM5VXUp/ysRyWVv55doy4MCM/wda7GwJCiQh8qX5emypUmy6vJORERERERZsbhcYmIiH3zwATabzb5s+/btfPrppy6MqmioGVqTsoF6IkZERETM9e2333Lq1Cn73zExMYwePVrjql/j9gYVrllio3jAHm4q/zmVa47natU57Ak9x2Vvi8Ptr7U8KBAsVqjRgS+rN+K2KhUZV7Y0fwT4g8UC5erk/0GIiIiIiBQx6vQQl/P19WX58uWEhYXZlz300EOMHz/edUEVERZLzi6QRURERPLTO++8w913323/u2HDhixatMgjcpPEZBtr9p5l6Y7T2ZbtXr8CFouBv/8hapX7mso3voAt7DsOhx4nMpcDDZc0LDQu24ikJ3fAvQvYULoScXqyQ0REREQkE83pIYXCDTfcwKpVq2jTpg1Dhw7lnXfe8YiL5tyYeftMV4cgIiIiAoCXlxczZszgypUrREVFsXTpUkJDQ10dVoFxNBl5pdAAbm9Q3mnOGp8Yyw9bXqByzXVc9krilMNSWQu1GXQKrk7X+vfStFYfvK26fBMRERERyY6yZg9TmDsSatSowY4dOyhTpmhPvmi1WilRooT9tRQ8tbm51N7mUnubrzCfK8XzFObPo6+vL/PmzcNmsxEcHOzqcK5Ldt+14+ZHMG/biQzLTlyOI+JkFA0rlchY+Pw++N+P+EXMZUVICpd9cnfJVdxmo5P/DXSp1Z8WDe/D29vXYbmifEOMzm3mUnubT21ursJ8rhQREddQp4eH8fZ27Vtus9myTPqKeocHpLZxjRo1XB2GR1Gbm0vtbS61t/lcfa4USa8wfB6zyt8CAwMdLi9qsvuu7Vy3bKZOD4ClEadTOz2unoWdP8GO2XD6fwBYgK7W4kwtUTzb+kNSbHT0KUm3GnfQoumj+PiF5PVQigSd28yl9jaf2txcheFcKSIihYvODGKa6Oho+vbty5AhQxg6dKirwxERERGRLBiGwZtvvsnu3buZMWMGXl5erg6pQNhsBlZr1ncJ31arLIG+XsQmpgBg9TuDn/UqSds2Y1zYieXwWjBsmbbrFhPrtNMjJMVGe0sgXat2oVXzUfiElL/+gxEREREREXV6iDkuXbrE7bffzubNm1mzZg3FihWjd+/erg5LRERERBwwDINnnnmGiRMnAlCsWDG+/PJLtxlCJP0cHX8cusiaZ27D38d5p46/jxc3107m91MrKVZsK/F+UTSMT+Sl02fgkPN6aicmUSUpiWM+PgAE2Wy0T/aia8W2tG42Et+ytfP70EREREREPJ46PTxMUlISvr6OxwUuKNHR0bRr146dO3cCkJKSwoABA1i2bBkdO3Y0NRYzJCUlcehQ6tVvjRo18Pn/i1wpOGpzc6m9zaX2Nl9SUpKrQxCxc0XuBvDEE0/w6aef2v+eNGkSoaGhvP3226bHkl8cTUaeZs3uM1T3uwpk/K49GnWU5UeX8+vBhRxMOoFvGYj//212+PtyxsuL8ikpTuu0AL2vxnAgIIiuZZvTpsmj+FVtBW7SeZRXOreZS+1tPrW5uZS7iYjItdTpIQUuKCiI7t272zs9IPVuwbSJ3dyNYRjExMTYX0vBU5ubS+1tLrW3iLjCHXfcwaRJk0hMTLQvq1SpkgsjypusOjrS+2XXGe6rlfr62JVjrDm5huWHlrDvyuEs978iKJChV646XunlB7Vv56GGA6BGR3AyIbkn0rnNXGpv86nNRUREXEudHlLgLBYL7777LpGRkUyZMoVKlSqxcuVKatfW4/wiIiIihVHnzp2ZNWsW/fv3x2KxMG3aNIYMGeLqsHIkpx0d6a05uI/QEgf4+8pmju48nuO6ljvq9Ai7FRoOgLp3gn/2k5iLiIiIiEj+UqeHmMJisfDll19SvHhxRo4cSdWqVV0dkoiIiIhkoW/fvkydOpXQ0FDuvPNOV4eTpbx0dFi8L+NdLALfYv/DGnCSBedzX+8+Xx8irVZKlApP7eho0B9KVM7DEYiIiIiISH5Rp4eYxsvLiwkTJrg6DBERERHJoWHDhrk6BKfy1tERiXexCHxCduAVmPMnOtLzMQxuiY2ja4oPt914J8HdhkD5Bh4/T4eIiIiISGGhTg/JV+vWraNFixYEBga6OhQRERERycbRo0eJjY2lbt26rg4lR/LS0fEvg2Jhn2Hzic51vd6GQeu4eLrGJ3NblY4Uu3UwVGsHVq9c70tERERERAqWOj0k38yYMYP777+frl27smDBAnx9NVmjiIiISGG1Z88eOnfujGEYbNiwgWrVqrk6JIeur6MDQrlCT68/uNt3I0tjzzOzeEiOtvM2DFrGxdM1No4OZZtT/JZ7oHYP8A3Ky2GIiIiIiIhJ1Okh+eLTTz9l9OjRACxbtoxhw4bx/fff4+Wlu99ERERECpstW7bQrVs3Ll68CECnTp3YsGEDFSpUcHFkqfLW0WEDrAD4kUgn6zYG+G3kFuNvvEgBA5JjfLPs9PAyDFrEx9M1OpaOITUo0WgQ1O8HIeXy58BERERERKTAqdPDw1it1nzf5/nz53n55ZczLPvxxx/p2bMngwcPzvf6CjsvLy8qVapkfy0FT21uLrW3udTe5iuIc6VIXhXU5/HFF1+0d3gAHD58mDFjxvDjjz8WSH05kac5Orxi8A7ZhXexHWBYaXSyJYP8N9GFzfjbYsDIWP6mhETKJidzzvvfyyCrYdA8PoEuMbG0pxilGt6LtdFAKFMrvw9R/p/ObeZSe5tPbW4u5W4iInItdXp4mIJIuMqUKcPSpUvp1KkTsbGxADzzzDPcc889+V5XUeDl5UW5crob0Exqc3Opvc2l9jaffpyQwqSgPo8//PADt912Gzt27ACgZcuWfPHFFwVSV1by9ESHNRaf/+/o8Ao6hMViS11sGHzuv4aSNpvzTYEuMbHMLBZC0/gEusbE0inZm9J1ekHDAVClFejHswKnc5u51N7mU5ubS7mbiIhcS50eki9atWrFwoUL6dmzJy+//DLjxo3DYrG4OiwRERERcSA0NJQVK1bQpk0bqlatysKFCwkODjY9jgGTN/H3scjsC1pj8Q7ZjU+xCLyCDtg7OtKzWSysCgrk7qtZT1R+f9RVhl+No0yNztD2bqjZFXz883gEIiIiIiJS2KjTQ/JN586d2bNnD9WrV3d1KCIiIiKSjXLlyrF+/XpKliyJv7+5P/oPXjaYHed3kBDbCejkuJA1Hu/g3fgU24FX8AEslpRs97siu06Pyi0p23AA1OsDgSXzFryIiIiIiBRq6vTwMElJSfj6+hbY/tXhAYmJiURERADQoEGDAm1vSaU2N5fa21xqb/MlJSW5OgQRu4LO3W644YYC23dW9p25Cl7gXWwHiRfSdXpYE/AO3oN3sf/hHbQfizX7jo70/vL344LVSun0Q1yVrAE3DYQG/aFkNYfb6bvWXGpvc6m9zac2N5dyNxERuZY6PSTHLl++zMCBA3n99ddp0aKFq8MRERERkSwYhsGzzz5LmTJlGDt2rKvDccjL7xxWv+NY/S7hHbID7+B9WKw5mNfDgfoJCXSNjsUHILAU1L8rdZ6Oik1Aw66KiIiIiHgMdXpIjpw5c4YuXboQERHBli1b+O2336hbt66rwxIRERERB1JSUnjkkUf4+uuvAShRogQPPfSQi6P6V63yIew4n/o6sNqXDufoyIk6CYl0jYmhS0wslfGBWrenPtVRowN4+eRjxCIiIiIiUlSo00OyderUKdq2bcuhQ4cAuHTpEp07d2bDhg1Uq+Z4iAARERERcQ3DMLjnnnuYM2eOfdmIESMoUaIE/fv3d2FkmfnYDJKsuevwqJWQSNeYWLrExFI1OQWq3Qq3DYQ6d4B/sQKKVEREREREigp1eki2SpUqRY0aNeydHgAWi4X4+HgXRiUiIiIijlgsFlq0aJGh08NqtRIXF+fCqDLyOrObx69EUjc+kccrlM22/I2J/3Z0VE9KhrL1oMWA1CGsilc0IWIRERERESkq1Okh2fLz82P+/Pl06dKFjRs3UqNGDVatWkVYWJirQxMRERERB55++mkuX77Mm2++ia+vL7Nnz6Z3796uDsuu09UohkZeIRkITUnhspdXpjLVEpPoFhNL15gYaiQlQ0gFaD4MGg6E8vXND1pERERERIoEdXpIjgQFBbFkyRJGjhzJ+++/T4UKFVwdkoiIiIhk4fXXXychIYFu3brRsWNHV4eTQYvYGCD1YqRTTCxzi4UAUDUpia7RsXSLieXGpCQsvsFQ9264aQCE3QrWzJ0jIiIiIiIi6anTQ3IsNDSUmTNnujoMEREREckBi8XChAkTXB2GQ5Hpnuy4MzqG4jYb3WJiCU9MwmLxghs7QsMBqROT+wa6MFIRERERESlq1OnhYbwcDB2Q3p9//knz5s2xWCwmReR+vL29qVWrlv21FDy1ubnU3uZSe5svu3OliJmy+zzu37+fMmXKEBoaalJE+WNeiZI0jIsl0DBolJBIo4REDvn6Yen2LtTvB8FlTI1H37XmUnubS+1tPrW5uZS7iYjItayuDkDMZbU6f8u/+OILbr75Zl544QUTI3I/VquV4OBggoODs2xvyT9qc3Opvc2l9jaf2lkKk6w+j9u2beOWW26hR48exMTEmBjV9ZtgLU+gYWRYVqNsQ7j5EdM7PEDftWZTe5tL7W0+tbm51MYiInIt3XIgGIbB22+/zfPPPw/AW2+9RWhoKM8884yLIxMRERERR3777Td69uzJlStXuHDhAn369GHx4sX4+fm5OrSceXCVqyMQERERERE3pU4P4OTJk8yYMYN169Zx5swZfH19qVy5Mt27d2fw4MEEBATkSz3r169nzpw5REREcOnSJUqWLEmDBg24++67adeuXb7UkR2bzZZpWUREBC+++GKGZWPHjuWWW26hVatWpsTlTmw2G7GxsQAEBgbqrhMTqM3NpfY2l9rbfI7OlVK4eHrulpyczPDhw7ly5Yp92cqVK3n99dd54403TInL3ei71lxqb3Opvc2nNjeXcjcREbmWxTCuea7cw6xZs4axY8cSHR3tcH1YWBiTJ0+matWqea7DZrPx4osv8tNPPzkt079/f1577bUCSYYSExOJiIgAoHbt2gQFBWUq8/XXX/PQQw/Z/37ttdd44YUXNLdHHqRv7wYNGuDr6+viiNyf2txcam9zqb3NFxMTw969ewG1eWGk3C3Vrl27aNu2LZcuXQKgQ4cOLFy4kJCQkHyPxxPou9Zcam9zqb3NpzY3l3I3ERG5lkffbrB7927GjBlDdHQ0gYGBjBkzhh9//JFp06Zx9913A3D06FEefvhhpxfWOfHhhx/aL5rr1q3LxIkTmTt3LhMnTqRu3boAzJ07l48++ui6jymvHnzwQd5//30APvnkE1588UV1eIiIiEihotztX/Xq1ePXX38lODiY3r17s3TpUnV4iIiIiIiI4OHDW7355pvEx8fj7e3N1KlTady4sX1dq1atqFq1KhMmTODo0aN8++23jBo1Ktd1HDlyhKlTpwJQv359Zs6cib+/PwANGzakQ4cODBkyhJ07d/LNN9/Qr1+/67oz8Xo8/fTTdOzYkUaNGrmkfhEREZGsKHfLqHnz5mzatInatWvj7e3Rab2IiIiIiIidxz7psWPHDrZs2QJAv379Mlw0pxk+fDg1atQAYPr06SQlJeW6nu+++47k5GQAXnzxRftFc5qAgAD7fBrJyclMmzYt13XkJ3V4iIiISGGk3M2x+vXrq8NDREREREQkHY/t9Fi1apX9db9+/RyWsVqt9O7dG4ArV66wefPmXNVhGAarV68GoHr16k47FBo1akS1atUAWL16NQU5zcqjjz7KmTNnCmz/IiIiIgXBU3O3l19+mRUrVhTY/kVERERERNyNx3Z6bN26FYDAwEDq1avntFzz5s3tr7dt25arOk6cOMG5c+cy7ceRFi1aAHD27FlOnDiRq3pyY/ny5XTp0oXLly8XWB0iIiIi+c1Tc7cZM2bQp08fNm7cWGB1iIiIiIiIuBOPfRb+0KFDAFSpUiXLIQGqV6+eaZucOnjwoMP9ZFfP4cOHqVy5cq7qykr6uw9LlSrF2bNnueeee/juu+8ICgqyr/Py8sJqTe0Hs9lspKSkZLlfHx8f++uUlBRsNluOyycnJ2d5V6TFYsnwvmQ3PEX62A3DsA9L4Yy3t7d9ovbcHmt2saevOykpKdvYrVYrXl5eGbbJit6nzNLXm/Y6v2PX+/Sv9NvHxsZiGEaB/X/S+5S5vdP+Lkzfe+72PsXFxdn/Lsg7+CV3PDl3Axg2bBizZs2iTp069nVF4TvMmcL2HZZ+fVxcXJbxF/bvsKLwPil3U+6Wxl3fJ0f5W2H73nOn90m5m4iIXMsjOz0SEhLsTzqUL18+y7LFixcnMDCQ2NjYXA8Llb58dvWkX3/69Olc1ZOd9MnUjBkz7K+PHz+er/VIZnv37nV1CB5HbW6u3P6gKNdH7W2+5ORk/Pz8XB2Gx1Pu9i+d5wpe+s4vKXj6TJtLuYT51ObmUu4mIiLgocNbxcTE2F8HBgZmWz4gIABIvUOjoOpJqyMv9YiIiIi4M+VuIiIiIiIiklMe+6RHmvSPRTrj6+sLQHx8fIHVk1ZHXurJTkBAALVr1wYyPiIrIiIimaUfciL9D9viOsrdlLuJiIg4o9xNRESu5ZGdHukfdcxuLEqAxMREAPz9/QusnrQ68lJPdqxWa4a5O0RERCRrGhahcFHuJiIiIllR7iYiIul55PBW6S8iczIcQdqkWDkZTiGv9aSfeCu39YiIiIi4M+VuIiIiIiIiklMe2enh5+dHiRIlALKd4DIqKsp+0ZvdhJbXSl8+u3rSr69QoUKu6hERERFxZ8rdREREREREJKc8stMD4MYbbwTg2LFj9rEfHTl8+LD9dY0aNfJUx7X7ya6e6tWr56oeEREREXen3E1ERERERERywmM7PZo2bQqkDl2wa9cup+X++usv++smTZrkqo5KlSpRtmzZTPvJqp5y5cpRqVKlXNUjIiIi4u6Uu4mIiIiIiEhOeGynR6dOneyv582b57CMzWZj4cKFABQrVoyWLVvmqg6LxULHjh2B1LsBt2/f7rDc9u3b7XcLduzYEYvFkqt6RERERNydcjcRERERERHJCY/t9GjYsCHNmjUDUi+c//7770xlpk6dyqFDhwAYOnQoPj4+GdZv3ryZWrVqUatWLZ577jmH9QwbNgwvLy8AXn/9deLj4zOsj4+P5/XXXwfA29ubYcOGXd+BiYiIiLgh5W4iIiIiIiKSEx7b6QHw/PPP4+/vT3JyMsOHD2fSpEls376dP/74g5deeokJEyYAEBYWxv3335+nOqpVq8YDDzwAwM6dOxk0aBDLli0jIiKCZcuWMWjQIHbu3AnAAw88QFhYWL4cm4iIiIi7Ue4mIiIiIiIi2fF2dQCuVLduXT788EPGjh1LdHQ0EydOzFQmLCyMyZMnExwcnOd6xowZw8WLF5k3bx67d+9mzJgxmcrcddddPPnkk3muQ0RERMTdKXcTERERERGR7Hh0pwdAhw4d+Pnnn5k+fTrr1q3j7Nmz+Pj4UKVKFbp168aQIUMICAi4rjqsVitvvfUWXbt2Zfbs2URERHD58mVCQ0Np0KABAwYMoF27dvl0RCIiIiLuS7mbiIiIiIiIZMViGIbh6iBERERERERERERERESul0fP6SEiIiIiIiIiIiIiIu5DnR4iIiIiIiIiIiIiIuIW1OkhIiIiIiIiIiIiIiJuQZ0eIiIiIiIiIiIiIiLiFtTpISIiIiIiIiIiIiIibkGdHiIiIiIiIiIiIiIi4hbU6SEiIiIiIiIiIiIiIm5BnR4iIiIiIiIiIiIiIuIWvF0dgOTcyZMnmTFjBuvWrePMmTP4+vpSuXJlunfvzuDBgwkICMiXetavX8+cOXOIiIjg0qVLlCxZkgYNGnD33XfTrl27fKmjKCjI9o6Li+O3337j999/Z+fOnRw7dozY2FiCg4MJCwujTZs2DBw4kDJlyuTjERVuZn2+04uLi6Nnz56cOHECgIoVK7JmzZp8r6ewMrPNN27cyM8//8zWrVs5f/48Xl5elC5dmlq1anHzzTfTq1cvgoKC8q2+wsiM9j5x4gSzZs1i06ZNHDt2jLi4OIKCgqhevTpt2rRh0KBBlCpVKh+OpvC6ePEiO3bsYMeOHURERBAREUFkZCQAffr04Z133sn3OpcsWcL8+fPZt28fV65coXTp0jRt2pTBgwfTuHHjfK9Pig7lbuZS7mY+5W/mUu5mLuVu5lDuJiIiBcFiGIbh6iAke2vWrGHs2LFER0c7XB8WFsbkyZOpWrVqnuuw2Wy8+OKL/PTTT07L9O/fn9deew2r1b0fEirI9t67dy+DBg0iNjY2y3LBwcG8/vrr3H777bmuo6gx4/PtyLvvvsvUqVPtf3vSRbNZbR4VFcW4ceNYvXp1luUWLlxInTp1rquuwsyM9l64cCEvv/wy8fHxTsuUKFGCiRMncsstt+S5nsKuVq1aTtfl94VzfHw8o0ePZv369Q7XW61WHn/8cUaOHJlvdUrRodzNXMrdzKf8zVzK3cyl3M08yt1ERKQgqNOjCNi9ezeDBg0iPj6ewMBARowYQcuWLYmPj2fZsmXMmTMHSE285s2bR3BwcJ7q+eCDD5g8eTIAdevW5cEHH6Ry5cocP36cr7/+mt27dwMwYsQInnrqqfw5uEKooNt7y5YtDB48GIAmTZrQvn176tevT4kSJbh06RIrVqxg7ty52Gw2vLy8+PLLL936Lk2zPt+O6r3rrrvw9vbG29ubmJgYj7loNqvNr169yrBhw9i1axcAnTt3pmvXrlSpUgWr1cqZM2f4888/WbFiBV999ZXbXjib0d5bt25lyJAh2Gw2rFYrvXv3pmPHjpQtW5bTp0+zYMEC1q5dC4C/vz9LliyhcuXK+XqchUX6C+cbbriB6tWrs2HDBiD/L5yfeuopli5dCkDLli0ZOnQoZcuWZf/+/UyaNIljx44B8NprrzFgwIB8q1cKP+Vu5lLuZj7lb+ZS7mYu5W7mUu4mIiIFwpBC75577jHCw8ONunXrGtu2bcu0fsqUKUZ4eLgRHh5ufPLJJ3mq4/Dhw0bdunWN8PBwo2/fvkZcXFyG9bGxsUbfvn3tcRw9ejRP9RQFBd3eW7duNZ544gnjwIEDTsusXLnSqFWrlhEeHm506tTJsNlsua6nqDDj832t5ORko0+fPkZ4eLjx2WefGe3btzfCw8ON9u3b58v+Czuz2nzs2LFGeHi4Ub9+fWPVqlVOy9lsNiMpKSnP9RR2ZrT3ww8/bN/H999/77DM22+/bS/z6quv5qmeouDjjz821qxZY5w/f94wDMM4fvy4/bifffbZfKtn48aN9v2OGDHCSE5OzrD+4sWLxm233WaEh4cbzZo1MyIjI/Otbin8lLuZS7mb+ZS/mUu5m7mUu5lLuZuIiBQE937O3Q3s2LGDLVu2ANCvXz+H40sOHz6cGjVqADB9+nSSkpJyXc93331HcnIyAC+++CL+/v4Z1gcEBPDiiy8CkJyczLRp03JdR1FgRns3adKEjz76iBtvvNFpmU6dOtGlSxcAjh07Zr9T092Y9fm+1vTp09m1axfVqlXjoYceuu79FSVmtfmWLVtYtGgRAE8++SQdO3Z0WtZiseDt7Z5TTJnV3n///TeQOgRC2t3I13r88cftr7dv357rOoqK0aNH0759e0qXLl2g9aQNreLt7c0rr7yCl5dXhvUlS5bkmWeeAeDKlSvMnTu3QOORwkO5m7mUu5lP+Zu5lLuZS7mb+ZS7iYhIQVCnRyG3atUq++t+/fo5LJP2OCyknpw3b96cqzoMw7CP2Vq9enUaNWrksFyjRo2oVq0aAKtXr8Zww5HRzGjvnGrZsqX9ddpjtu7GFe198uRJPvnkEwBeffVVfH19r2t/RY1ZbT5z5kwAQkJCGDJkSO4DdRNmtXfaxXalSpWclgkJCSE0NDRDecmb6OhoNm3aBECrVq0oX768w3KdO3e2D3mR/rMg7k25m7mUu5lP+Zu5lLuZS7mbe1LuJiLiedTpUcht3boVgMDAQOrVq+e0XPPmze2vt23blqs6Tpw4wblz5zLtx5EWLVoAcPbsWU6cOJGreooCM9o7pxITE+2vr70LxV24or1fffVVYmNj6dWrV4YfJzyFGW2emJho/zGudevW+Pn5AZCSksLp06c5ceIECQkJuQ29SDLrM572o2ZW38vR0dFcvnw5Q3nJm4iICPuPD2nnRUd8fX3tP0an30bcm3I3cyl3M5/yN3MpdzOXcjf3pNxNRMTzqNOjkDt06BAAVapUyfIR4urVq2faJqcOHjzocD/Z1XP48OFc1VMUmNHeOfXnn3/aX6c9Pu1uzG7vpUuXsn79eooXL85zzz2X5/0UZWa0+d69e+0XxuHh4URHR/Pmm29y8803c9ttt9GxY0eaNm3K/fffX2B32xYWZn3GBw4cCEBkZCSzZs1yWObzzz/PVF7yJv17lN15M+1HiuTkZP75558CjUsKB+Vu5lLuZj7lb+ZS7mYu5W7uSbmbiIjncc+BON1EQkKC/c4OZ49fpilevDiBgYHExsZy5syZXNWTvnx29aRff/r06VzVU9iZ1d45sXfvXtavXw+kXni444Wz2e0dFRXFW2+9BcDTTz9NyZIl87SfosysNk9/UWEYBv369ePo0aMZyiQlJbFx40Y2bdrEU089xcMPP5yrOooCMz/j/fr1Y+vWrSxcuJDXXnuNXbt20aFDB8qUKcPp06dZtGiR/RH9Rx55hNatW+f+gMQu/XtUrly5LMtWqFDB/vr06dNZzgkgRZ9yN3MpdzOf8jdzKXczl3I396XcTUTE86jToxCLiYmxvw4MDMy2fEBAALGxscTGxhZYPQEBAfbXua2nsDOrvbOTmJjI888/T0pKCgBjxozJ1/0XFma393vvvceFCxdo3Lgxd999d572UdSZ1eZRUVH211OmTCEhIYFbb72V0aNHU7t2baKjo1m+fDkffPABV69e5YMPPqB69ep06tQpV/UUdmZ+xr28vHj33Xdp3749kyZNYu7cuZkmX2zZsqUumvOJzpvijHI3cyl3M5/yN3MpdzOXcjf3pfOmiIjn0fBWhVj6cVN9fHyyLZ82oV98fHyB1ZN+0sDc1lPYmdXe2XnttdfYuXMnAH369KFDhw75uv/Cwsz2/uuvv5g3bx7e3t688sorWCyWXO/DHZjV5ukvDhISErjllluYNGkSDRs2xNfXl5IlSzJo0CC++uorrNbU09DEiRPdboJds79TDh06xMKFC9m/f7/D9du3b+enn37i7Nmzedq//Cv9e5vdZLrufN6UzJS7mUu5m/mUv5lLuZu5lLu5L+VuIiKeR50ehVjaBHJAjibQSps80d/fv8DqST9BY27rKezMau+spN3lA9CgQQNeeumlfNt3YWNWeycmJvLiiy9iGAZDhw6ldu3auQvUjbjiOwXgmWeecTiha7NmzejcuTOQetG3b9++XNVT2Jn5nbJlyxYGDBjA2rVrKVeuHO+99x6///47O3fuZP369bz00ksEBASwdOlS7rrrLg4cOJDrOuRf6d/b9OdFR9z5vCmZKXczl3I38yl/M5dyN3Mpd3Nfyt1ERDyPOj0KsaCgIPvrnDxWGRcXB+TsUdy81pNWR17qKezMam9nfvzxRyZOnAikTq42efJkt2vj9Mxq7y+//JIjR45QoUIFRo0albsg3YwrvlNKlixJ3bp1nZa99dZb7a8jIiJyVU9hZ1Z7JyYm8tRTT3H16lXKlCnD7Nmz6dWrF6VLl8bHx4fy5cszePBgvv/+e/z8/Dh37hzPPvts7g5GMtB5U5xR7mYu5W7mU/5mLuVu5lLu5r503hQR8Tya06MQ8/Pzo0SJEkRGRmY7OVpUVJT95J3dpGvXSl8+u3rSr08/wZc7MKu9HVmyZAmvvvoqABUrVuTbb791+4kazWrvKVOmANCqVSvWrl3rsEzavmNjY1m6dCmQesHXqlWrXNVV2JnV5um/G3IzwW7axJHuwqz2/u9//2sf9mDIkCGUKVPGYbmaNWty5513MnfuXHbt2sXevXs99s7Z65X+PTp79iwNGjRwWjb9xNHudt6UzJS7mUu5m/mUv5lLuZu5lLu5L+VuIiKeR50ehdyNN97Ili1bOHbsGMnJyXh7O37LDh8+bH9do0aNXNfhaD/Z1VO9evVc1VMUmNHe11q9ejXPPvssNpuNMmXKMG3atHy5GC8KzGjvtEfT58+fz/z587Mse/nyZZ566ikAWrRo4VYXzWnM/k5Jm9TVGZvNZn/taBiFos6M9k6/bVZ3ZgLUq1fPPgzL4cOHdeGcR+nfo+zOm0eOHAHA29ubqlWrFmhcUjgodzOXcjfzKX8zl3I3cyl3c0/K3UREPI+GtyrkmjZtCqTewbRr1y6n5f766y/76yZNmuSqjkqVKlG2bNlM+8mqnnLlylGpUqVc1VMUmNHe6W3atIknn3yS5ORkSpQowbfffkuVKlXyvL+ixuz2FnPavGLFitxwww0AnDx5MstJLo8dO2Z/Xa5cuVzVUxSY0d7pf3DI7oeK5ORkh9tJ7jRo0MA+wemff/7ptFxiYiLbt2/PtI24N+Vu5lLuZj7lb+ZS7mYu5W7uSbmbiIjnUadHIdepUyf763nz5jksY7PZWLhwIQDFihWjZcuWuarDYrHQsWNHIPWuh7ST/LW2b99uvyuiY8eOWCyWXNVTFJjR3mm2bdvGY489RmJiIiEhIXzzzTfUrFkzT/sqqsxo73379mX7r2LFikDqBV/ashkzZuTtoAo5sz7jXbp0ASA6OppNmzY5LbdixQr767SLTHdiRnun/xFzy5YtWZZNf4Hujj9+miU4ONh+J/GmTZucDoGxcuVKoqOjgYyfBXFvyt3MpdzNfMrfzKXczVzK3dyTcjcREc+jTo9CrmHDhjRr1gxITbr+/vvvTGWmTp3KoUOHABg6dGimuxE2b95MrVq1qFWrFs8995zDeoYNG2a/c+T1118nPj4+w/r4+Hhef/11IPUxz2HDhl3fgRVSZrX3nj17GDFiBLGxsQQGBjJp0iTq16+fz0dT+JnV3vIvM79T/Pz8AHj77bftFw/pLVq0yH6n1W233eaWY+aa0d6tWrUiICAAgFmzZrFv3z6Hsaxfv56VK1cCqXdm1qlTJ+8H5ubmz59vb/NPP/3UYZnhw4cDqXdgvvrqq5nu1Lx06RLvv/8+kPqDSP/+/Qs2aCk0lLuZS7mb+ZS/mUu5m7mUuxVNyt1ERORamtOjCHj++ecZNGgQ8fHxDB8+nEceeYSWLVsSHx/PsmXLmD17NgBhYWHcf//9eaqjWrVqPPDAA0yePJmdO3cyaNAgHnroISpXrszx48eZMmUKu3fvBuCBBx4gLCwsvw6v0Cno9j527BgPPPAAV65cAeCJJ54gJCSE/fv3O92mVKlSlCpVKm8HVMiZ8fmWjMxo8xtuuIHRo0czYcIE9u/fz1133cVDDz1ErVq1iI6OZuXKlcyaNQtIvfNq3Lhx+XZ8hU1Bt3exYsV46KGH+OSTT4iJiWHgwIHce++9tG7dmuLFi3PhwgVWr17N3Llz7eNwP/3001it7nnfQ9o43GnST7L6zz//ZBobvm/fvnmqp1WrVvTo0YOlS5eyZs0a7r//foYNG0bZsmXZv38/X331FadOnQLgmWeeoXjx4nmqR4om5W7mUu5mPuVv5lLuZi7lbuZS7iYiIgVBnR5FQN26dfnwww8ZO3Ys0dHRTJw4MVOZsLAwJk+eTHBwcJ7rGTNmDBcvXmTevHns3r2bMWPGZCpz11138eSTT+a5jqKgoNt7y5YtXLx40f7322+/ne02I0eOZNSoUbmuqygw6/Mt/zKrzR988EGioqKYMmUKR44cYfz48ZnKlCpVis8//9ytf4wzo70fe+wxoqKimD59OrGxsUyaNIlJkyZlKufj48OYMWPo1atXnuopCn766ScWLFjgcN22bdvYtm1bhmV5vXAGeOutt4iOjmb9+vVs3ryZzZs3Z1hvtVp57LHHGDBgQJ7rkKJJuZu5lLuZT/mbuZS7mUu5m7mUu4mISEFQp0cR0aFDB37++WemT5/OunXrOHv2LD4+PlSpUoVu3boxZMgQ+yOyeWW1Wnnrrbfo2rUrs2fPJiIigsuXLxMaGkqDBg0YMGAA7dq1y6cjKtzMaG/5l9rbfGa1+dNPP02HDh2YNWsWW7Zs4fz58/j5+REWFkaHDh249957CQkJyYcjKtwKur0tFgvjx4/nzjvvZO7cuWzbto2TJ08SHx9PYGAgVapUoUWLFgwYMIBq1arl45F5Nn9/fyZPnszixYtZsGABe/fu5cqVK5QuXZqmTZsyZMgQGjdu7OowxUWUu5lLuYT51ObmUu5mLuVu7km5m4iI57AYhmG4OggREREREREREREREZHr5Z6DQoqIiIiIiIiIiIiIiMdRp4eIiIiIiIiIiIiIiLgFdXqIiIiIiIiIiIiIiIhbUKeHiIiIiIiIiIiIiIi4BXV6iIiIiIiIiIiIiIiIW1Cnh4iIiIiIiIiIiIiIuAV1eoiIiIiIiIiIiIiIiFtQp4eIiIiIiIiIiIiIiLgFdXqIiIiIiIiIiIiIiIhbUKeHiIiIiIiIiIiIiIi4BXV6iIiIiIiIiIiIiIiIW1Cnh4iIiIiIiIiIiIiIuAV1eoiIiIiIiIiIiIiIiFtQp4eIiIiIiIiIiIiIiLgFdXqIiIiIiIiIiIiIiIhbUKeHiIiIiIiIiIiIiIi4BXV6iIgUgPnz51OrVi1q1arFiRMn8m2/mzdvtu938+bN+bZfEREREU+m3E1ERETEfXi7OgARKRibN29m6NCh9r8DAwPZuHEjAQEBWW4XHx/PLbfcQnR0tH3Z9OnTadmyZYHFKiIiIuLplLuJiIiIiOQPPekh4iFiY2NZtWpVtuVWr16d4aJZRERERMyn3E1EREREJG/U6SHiAfz8/ABYtGhRtmXTyqRtI3nTt29f9u3bx759+6hUqZKrwxEREZEiRLmb+ZS7iYiIiLgPdXqIeIAOHToAsHHjRs6fP++03MWLF/n9998B6NixoymxiYiIiEhGyt1ERERERPJOnR4iHuCWW26hTJkypKSksHTpUqfllixZQnJyMmXKlKF169YmRigiIiIiaZS7iYiIiIjknSYyF/EAXl5e9OjRg2nTprFo0SLuu+8+h+XShkfo2bMnXl5eOd7/H3/8wYIFC9iyZQsXLlzAy8uLihUr0qZNG+677z7KlSvndNv9+/ezatUqtm7dyoEDB7h06RI+Pj6UKVOGxo0bM2jQIBo1auR0+08//ZTPPvsMgH379pGQkMCMGTNYunQpR48eBaBGjRr07t2bgQMH4u2d96+9WrVqATBy5EhGjRrFpk2bmDVrFv/73/+4cOEC5cqVY82aNQDMnz+fcePGAaljbTsaJmHnzp388MMPbN26lbNnz5KSkkLJkiUpVaoUN910E23atKFDhw5YLJZcxRkVFcXDDz/M9u3b8fb25u233+bOO+/M1T6uXLnCzJkzWbduHYcPHyY2NpaQkBBKlixJtWrVuOWWW+jSpQulS5d2uH1iYiILFixg9erV7Nmzh8uXL+Pt7U3FihVp1KgR3bp1o02bNg6PLSYmhpkzZ7J69WqOHDlCbGwsJUuWpFGjRvTp04f27ds7jfvee+/lzz//pEWLFsyYMYOjR48yffp0NmzYwNmzZ4mPj8/0fiQkJDB37lxWrlzJwYMHiYqKIiQkhFq1atGjRw/69OlzXZ8bERGR3FLuptxNuZtyNxEREck7ZQIiHqJXr15MmzaN3bt3c+DAAWrWrJlh/cGDB9m1a5e97J49e7LdZ0JCAuPGjXN4B+L+/fvZv38/P/74Ix988IF9mIb0Nm/ezNChQzMtT0pK4p9//uGff/5h4cKFPPzwwzz99NPZxnPhwgUefPDBTLFHREQQERHBhg0b+OKLL7Bar/8htw8//JCvvvoqz9tPmzaNd999F5vNlmH5mTNnOHPmDLt27eKHH35g27ZtBAUF5Xi/Z8+e5cEHH2T//v34+/vz8ccfc9ttt+UqtkOHDnHfffdx7ty5DMsvX77M5cuXOXToEKtWrcJmszFkyJBM2+/Zs4eRI0dy4sSJDMuTkpI4ePAgBw8e5KeffnL4g8Lu3bsZMWJEprrPnj3L8uXLWb58OV26dOH999/PduzyVatWMXbsWGJjY52W2bt3L4899hgnT57MsPzSpUts2rSJTZs2MXv2bL766iunPxKIiIgUBOVuyt1ySrmbcjcRERHJSJ0eIh6ibt261KxZkwMHDrBo0SKeeeaZDOvT7hQMDw+nTp062V44G4bB6NGjWbduHQDt27ene/fuVK5cGavVyo4dO/j22285deoUo0ePZtasWTRo0CDDPlJSUggMDKRdu3bcfPPNVK9eneDgYC5evMjBgweZMWMGJ0+eZPLkyYSFhdGvX78sYxo5ciQHDx7k3nvvpUOHDhQvXpwjR47wxRdfcOjQIdauXcucOXMYOHBgLlsvoxUrVrB//37Cw8O57777qFmzJgkJCTn6sQFSL9bSLporVarEkCFDqF27NiVKlCAmJoYjR47wxx9/2O88zKljx45x//33c+LECYKDg/nqq69o3rx5ro9v7NixnDt3Dh8fH/r370/btm0pXbo0hmFw5swZtm/fzqpVqxxue+jQIe655x77xWrnzp25/fbbqVy5MjabjSNHjvD777873P7s2bPcd999REVFYbFY6NOnDz169KBEiRIcPHiQb7/9lr1797JixQqee+45PvzwQ6fHcOrUKcaOHYu/vz+PPvoozZo1w8vLi4iICAIDAwH4559/GDJkCFevXiU4OJjBgwfTsGFDypcvT2RkJGvWrGH27NlERETw2GOPMXPmTHx8fHLdniIiInmh3E25W04pd1PuJiIiItcwRMQt/fHHH0Z4eLgRHh5uzJs3zzAMw5g8ebIRHh5utGvXzrDZbPayNpvNaNeunREeHm5MmTLFMAzDmDdvnn37P/74I9P+Z8+ebYSHhxv16tUz1q9f7zCGyMhIo0ePHkZ4eLgxcODATOsvXrxoREVFOT2GhIQE4/777zfCw8ON9u3bG8nJyZnKfPLJJ/Y469Wr5zDWy5cvG61btzbCw8ONO+64w2l92UmrJzw83Bg2bJiRkJDgtGz69jt+/HiGdR999JERHh5uNGrUyDh//rzTfVy5csVISUnJsCz9+5r+WPfu3WvccsstRnh4uHHzzTcbO3fuzNMxHjt2zL7/GTNmOC1ns9mMyMjITMv79OljhIeHG7Vr1zaWLFnidPtLly4ZcXFxGZaNGjXKXvecOXMybZOQkGDce++99jLr1q3LVGbIkCH29W3atDFOnjzpNIYBAwYY4eHhRu/evY2LFy86LLN+/Xqjdu3aRnh4uDF79myn+xIREbleyt3+pdwt55S7ZaTcTURERAzDMDSRuYgHufPOO7FarZw+fZrNmzfbl2/evJnTp09jtVq54447st2PYRhMmTIFSB2Ht23btg7LFS9enLFjxwKwbds2+zjNaUqWLEmxYsWc1uPr68t//vMfAE6ePJnt3XhDhgyhZcuWmZaXKFGCvn37AqlDN1y9ejXL/WTHarXyxhtv4Ovrm6ftL1y4AEBYWFiWj92HhITkaDiHbdu2ce+993L+/HkqVKjAzJkzqVevXp5iO3/+vP11s2bNnJazWCwUL148w7INGzbYh9m499576dGjh9PtQ0ND8ff3t/999uxZ+x2Et956K/3798+0ja+vL2+99ZZ9jOaZM2dmeSxPP/00N9xwg8N1W7Zs4e+//wbgnXfeoWTJkg7LtW3blq5duwKpY32LiIiYSbmbcrfsKHfLSLmbiIiIAKjTQ8SDlCtXzn5hmTYkQvrXN998c5YTV6Y5ePAgx44dA7BfVDiT/hH97du3Z1k2MTGRU6dOcfDgQfu40oZh2Nfv3bs3y+2zuuhPu5A0DCPTeMW51aRJE4eTW+ZUmTJlgNR23LFjx3XF8ttvvzF8+HCioqKoVq0as2bNonr16tcdG8CCBQtytW3acBkAw4YNy9W2f/75JykpKQDcddddTstVqlSJ1q1bZ9rmWj4+PnTv3t3pflavXg1AtWrV7JOcOpP2Gd65cyfJyclZlhUREclPyt2Uu+U0NlDulka5m4iIiGhODxEP07t3bzZt2sSKFSt4+eWXAVi+fDmQOglmTuzcudP+esCAATmuO/2daGliY2OZMWMGS5cu5eDBg04vhCB1MsasZHXBmP7OtpiYmBxE61x2F1rZ6dGjB5MnTyYxMZFBgwZx66230q5dO5o2bUrNmjWxWCw52s/y5cuZM2cOSUlJ1KtXj6+//trpXW85VblyZZo1a8aWLVuYNm0aGzZsoEuXLrRo0YJGjRoREBDgdNvdu3cDcMMNN1CxYsVc1XvgwAH765tuuinLsjfddBP//e9/iYuL4/jx44SFhWUqExYWluVkmWmf4SNHjuT4/UxKSiIqKopSpUrlqLyIiEh+UO6m3C0ryt2cU+4mIiLiudTpIeJhOnfuzCuvvEJ0dDSrV6/GMAxiYmIIDAykS5cuOdrHxYsX81R3fHx8hr9PnDjBsGHDcnz3XkJCQpbrs7qoSz/UQFYX5zmR1bAOOVGjRg0++OADXnzxRaKioli7di1r164FUocOuPXWWxkwYECWQxTAv0ME+Pr68vnnn1/3RXOaiRMn8sQTT/D3339z8OBBDh48yBdffIGPjw833XQTPXv2pG/fvpkuTNN+2Eh/x2FORUZG2l9nd2GafliJqKgoh2Wye48uXbqU8+DSiYuLy9N2IiIieaXcTblbdpS7OafcTURExDOp00PEwwQFBdGpUycWL17MokWL7EMQdOrUicDAwBztw2az2V9/9dVXOb4z7NoLov/85z+cOHECi8VC37596dGjBzVq1KBkyZL4+PhgsViw2WzUqVMHIMNwCa7k5eV13fvo2rUrrVu3ZtmyZWzYsIEtW7Zw6dIlLl++zM8//8zPP/9Mnz59eOutt5yODd2lSxdWrFhBYmIiY8aM4euvvyY4OPi6YytXrhw//vij/a7Sv/76i4MHD5KUlMSWLVvYsmULU6dOZfLkyVSrVu266ysI2b1HaT+e1K5dmwkTJuR4vzkZQkRERCQ/KXe7fsrdlLuJiIiIZ1Gnh4gH6t27N4sXL+b333/PsCynSpQoYX8dEhJCeHh4rmM4dOgQW7duBWDEiBGMGTPGYbn0d5G5m5CQEAYMGGAfZuLQoUOsXr2aGTNmcO7cORYsWECdOnWcjrE8ZMgQbrrpJiZMmMDff//Nww8/zJQpUwgKCsqX+Fq1akWrVq2A1DsBN23axOzZs/njjz84duwYY8aMYeHChfbyoaGhgOOhMLKT/jN18eJFKlSo4LRs2mSiQKYJOXNbX2xsbJ4+vyIiImZS7lY4KHf7l3I3ERERKcw0kbmIB2rVqhVlypQhOTmZ5ORkypYta79Ayom0u/cAtm3blqcYDh48aH99++23Oy2Xfgxqd1ejRg0efvhh5syZY79z85dffslymwcffJCnnnoKgK1btzJixIgCeYw/NDSU22+/ne+++44OHToAsGfPHo4ePWovU7duXQBOnTrFyZMnc7X/mjVr2l//73//y7Js2gSiAQEBVK5cOVf1pEmL9fjx43m60BcRETGTcrfCSblbKuVuIiIiUtio00PEA3l5edGrVy98fX3x9fWlV69eTh/Dd6RevXqUL18egDlz5mQ7XrMj6cdmzupC78cff8z1vou6ChUq2Cd4zG4CUEi92/KJJ54A4K+//mLEiBGZxuDOT+l/ZEkfX9oFNcC0adNytc8WLVrYhzWYN2+e03KnTp1i48aNmbbJrbRYDcNg+vTpedqHiIiIWZS7FW7K3ZS7iYiISOGiTg8RDzV27FgiIiKIiIjgmWeeydW2VquVESNGAKl3W/3nP/8hMTHRafno6Gi+//77DMuqVq1qfz1//nyH2/3www+sXr06V7EVBatWreLKlStO158+fZrDhw8DUKlSpRzt87HHHmPkyJEAbN68mUceeSRPP2js2bOHPXv2OF1vGIb9wtVisWQYE7x169bUq1cPgO+//56lS5c63c/ly5czXNyXK1eOTp06AfDf//6XBQsWZNomMTGR8ePHk5SUBMDgwYNzcWQZtWnThoYNGwLwzTffsGzZsizL79u3jzVr1uS5PhERkeul3M11lLspdxMREZGiRXN6iEieDBo0iI0bN7Jy5Up+/fVXdu/ezYABA2jYsCEhISFER0dz+PBh/vzzT9asWYOvry9Dhgyxb1+3bl3Cw8PZv38/s2fP5sqVK/Tq1YsyZcpw5swZfv75Z5YvX06TJk3yPAxDYfXdd9/xzDPP0K5dO26++WZq1KhBSEgIUVFR7Ny5k++//95+UTlo0KAc73fUqFGkpKTw5ZdfsmnTJh577DG+/PJLfH19c7yPPXv2MG7cOBo0aED79u2pV68epUuXJjk5mRMnTjB//nz7eOIdOnSgbNmyGbafMGECd911F7GxsTz11FP8+uuv3H777VSuXBmbzcY///zD77//zvLly1m8eHGGHwbGjx/PH3/8QVRUFOPHj2fr1q3cfvvtFCtWjMOHDzN16lT7RX337t1p165djo/LkQ8++ID+/fsTGRnJmDFj+Pnnn7n99tsJCwvDarVy8eJF9uzZw9q1a9m+fTvDhw/PcEekiIhIUaLcLe+Uuyl3ExERkaJFnR4ikicWi4UPP/yQN998kx9//JFjx44xYcIEp+VLliyZafv33nuPYcOGERUVxS+//JJpDOTw8HA+/vhjbr311gI5BleKi4vj119/5ddff3W43mq1MmrUKPsddDn15JNPkpKSwuTJk9mwYQMjR47ks88+y9XFM2C/k9SZxo0b8+abb2ZaXqNGDWbMmMHIkSM5ffo0K1asYMWKFTmqs3z58kybNo0RI0Zw7tw55s6dy9y5czOV69KlC++++27OD8aJKlWq8OOPPzJ69Gj279/P2rVrWbt2rdPy+TXJqIiIiCsod7s+yt0yU+4mIiIihZU6PUQkz3x8fHjllVcYNGgQc+fOZfPmzZw+fZrY2FgCAwOpVKkS9erVo23btrRv3z7T9nXq1GHhwoVMmjSJ3377jXPnzhEUFESVKlXo3r07gwcPxs/PzwVHVrA++OAD1q1bx+bNmzl06BAXLlzg8uXL+Pr6UrFiRZo1a8bAgQOpXbt2nvb/9NNPk5KSwjfffMP69esZPXo0n3zySY4unnv27EmpUqXYuHEjERERnD17losXL5KcnEypUqWoW7cut99+Oz169HA6lnj9+vX59ddfmTt3LqtWreLAgQNERUXh6+tLpUqVaNy4Md27d3c4/EPdunX59ddfmTlzJqtWreLIkSPExcURGhpKo0aN6NOnT77esVetWjUWLlzIL7/8wooVK4iIiODSpUukpKRQokQJqlWrRtOmTencubN9+AcREZGiSrlb3ih3U+4mIiIiRYvFMAzD1UGIiIiIiIiIiIiIiIhcL01kLiIiIiIiIiIiIiIibkGdHiIiIiIiIiIiIiIi4hbU6SEiIiIiIiIiIiIiIm5BnR4iIiIiIiIiIiIiIuIW1OkhIiIiIiIiIiIiIiJuQZ0eIiIiIiIiIiIiIiLiFtTpISIiIiIiIiIiIiIibkGdHiIiIiIiIiIiIiIi4hbU6SEiIiIiIiIiIiIiIm5BnR4iIiIiIiIiIiIiIuIW1OkhIiIiIiIiIiIiIiJuQZ0eIiIiIiIiIiIiIiLiFtTpISIiIiIiIiIiIiIibkGdHiIiIiIiIiIiIiIi4hbU6SEiIiIiIiIiIiIiIm5BnR4iIiIiIiIiIiIiIuIW1OkhIiIiIiIiIiIiIiJuQZ0eIiIiIiIiIiIiIiLiFtTpISIiIiIiIiIiIiIibkGdHiIiIiIiIiIiIiIi4hbU6SEiIiIiIiIiIiIiIm5BnR4iIiIiIiIiIiIiIuIW1OkhIiIiIiIiIiIiIiJuQZ0eIiIiIiIiIiIiIiLiFtTpISIiIiIiIiIiIiIibuH/ALUPPw4H7Wv1AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "\n",
+    "# Get results for the current task\n",
+    "task_df = all_results_df[all_results_df[task_col] == TASK]\n",
+    "\n",
+    "# Get all base/instruct model pairs\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-8B\",\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mistral-7B-v0.1\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    \"Yi-34B\",\n",
+    "]\n",
+    "# NOTE: Gemma models have degenerate distributions that always predict the same score, can't plot calibration properly...\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "base_models_to_plot += [None for _ in range(len(instr_models_to_plot) - len(base_models_to_plot))]\n",
+    "\n",
+    "base_it_pairs = list(zip(base_models_to_plot, instr_models_to_plot))\n",
+    "\n",
+    "for base_model, it_model in tqdm(base_it_pairs):\n",
+    "    fig, axes = plt.subplots(ncols=2, figsize=(7, 2.8), gridspec_kw=dict(wspace=0.35), sharey=False)\n",
+    "\n",
+    "    for idx, ax, m in zip(range(len(axes)), axes, (base_model, it_model)):\n",
+    "        if m is None:\n",
+    "            ax.remove()\n",
+    "            continue\n",
+    "\n",
+    "        curr_row = task_df[(task_df[model_col] == m) & (task_df[\"uses_all_features\"])].iloc[0]\n",
+    "        curr_id = curr_row.name\n",
+    "\n",
+    "        # # Original sklearn calibration plot:\n",
+    "        # plot_cali_curve(curr_id, ax=ax, df=task_df)\n",
+    "\n",
+    "        # # Plot with confidence intervals:\n",
+    "        plot_cali_curve_ci(curr_id, ax=ax, df=task_df)\n",
+    "        \n",
+    "        # Remove left plot legend\n",
+    "        if idx == 0:\n",
+    "            ax.get_legend().remove()\n",
+    "        else:\n",
+    "            ax.get_legend().set(loc=\"upper left\", bbox_to_anchor=(1.1, 1))\n",
+    "\n",
+    "        ax.set_xlim(0, 1)\n",
+    "        ax.set_ylim(0, 1)\n",
+    "\n",
+    "        ax.set_xlabel(\"Mean risk score\")\n",
+    "        ax.set_ylabel(\"Fraction of positives\")\n",
+    "\n",
+    "    plt.plot()\n",
+    "    save_fig(fig, f\"calibration-curves-per-subgroup.{base_model}.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef3001ff-8557-4097-b3a9-f506bff952ca",
+   "metadata": {},
+   "source": [
+    "### Smaller figures to plot both prompting schemes side by side"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "5a35436c-7606-4ebf-b021-b3072aa7625c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd4b354cb3c140238849fe9f583968d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-8B.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-70B.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mistral-7B-v0.1.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mixtral-8x7B-v0.1.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mixtral-8x22B-v0.1.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Yi-34B.smaller.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.None.smaller.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "\n",
+    "# Get results for the current task\n",
+    "task_df = all_results_df[all_results_df[task_col] == TASK]\n",
+    "\n",
+    "# Get all base/instruct model pairs\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-8B\",\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mistral-7B-v0.1\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    \"Yi-34B\",\n",
+    "]\n",
+    "# NOTE: Gemma models have degenerate distributions that always predict the same score, can't plot calibration properly...\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "base_models_to_plot += [None for _ in range(len(instr_models_to_plot) - len(base_models_to_plot))]\n",
+    "\n",
+    "base_it_pairs = list(zip(base_models_to_plot, instr_models_to_plot))\n",
+    "\n",
+    "for base_model, it_model in tqdm(base_it_pairs):\n",
+    "    fig, axes = plt.subplots(ncols=2, figsize=(4.8, 2.2), gridspec_kw=dict(wspace=0.15), sharey=True)\n",
+    "\n",
+    "    for idx, ax, m in zip(range(len(axes)), axes, (base_model, it_model)):\n",
+    "        if m is None:\n",
+    "            ax.remove()\n",
+    "            continue\n",
+    "\n",
+    "        curr_row = task_df[(task_df[model_col] == m) & (task_df[\"uses_all_features\"])].iloc[0]\n",
+    "        curr_id = curr_row.name\n",
+    "\n",
+    "        # # Original sklearn calibration plot:\n",
+    "        # plot_cali_curve(curr_id, ax=ax, df=task_df)\n",
+    "\n",
+    "        # # Plot with confidence intervals:\n",
+    "        plot_cali_curve_ci(curr_id, ax=ax, df=task_df)\n",
+    "        \n",
+    "        ax.set_xlim(0, 1)\n",
+    "        ax.set_ylim(0, 1)\n",
+    "\n",
+    "        ax.set_xlabel(\"Mean risk score\")\n",
+    "        if idx == 0:\n",
+    "            ax.set_ylabel(\"Fraction of positives\")\n",
+    "\n",
+    "        # Set prettier x-ticks\n",
+    "        xticks = (0, 0.25, 0.5, 0.75, 1)\n",
+    "        xticks_labels = (\"0.0\", \"0.25\", \"0.5\", \"0.75\", \"1.0\")\n",
+    "        ax.set_xticks(xticks, labels=xticks_labels)\n",
+    "\n",
+    "    # Prepare legend\n",
+    "    axes[0].legend().remove()\n",
+    "    if NUMERIC_PROMPTING:\n",
+    "        axes[1].get_legend().set(loc=\"upper left\", bbox_to_anchor=(1.03, 1))\n",
+    "    else:\n",
+    "        axes[1].legend().remove()\n",
+    "\n",
+    "    # Figure title\n",
+    "    if NUMERIC_PROMPTING:\n",
+    "        title = \"Numeric Prompting\"\n",
+    "    else:\n",
+    "        title = \"Multiple-choice Prompting\"\n",
+    "\n",
+    "    # Don't use suptitle for GPT4 (no base model)\n",
+    "    if base_model is not None:\n",
+    "        plt.suptitle(title, y=1.1, fontsize=14)\n",
+    "    else:\n",
+    "        model_title = axes[1].get_title()\n",
+    "        plt.title(f\"{title}\\n{model_title}\")\n",
+    "    \n",
+    "    plt.plot()\n",
+    "    save_fig(fig, f\"calibration-curves-per-subgroup.{base_model}.smaller.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79d97774-23e4-42e9-a52c-8d7d5504eac7",
+   "metadata": {},
+   "source": [
+    "## Score distribution per sub-group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "47bca57c-3e4b-496a-8f1b-880b6ded9639",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    2\n",
+       "2    1\n",
+       "8    1\n",
+       "Name: RAC1P, dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sens_attr_data = acs_dataset.data[sens_attr_col]\n",
+    "sens_attr_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "d768a5b5-7816-41e8-a4da-763986ed994a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad627657fb0044bb9f3620c89dc7d648",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/score-per-subgroup.Meta-Llama-3-8B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Meta-Llama-3-70B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Mistral-7B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Mixtral-8x7B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Mixtral-8x22B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Yi-34B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.None.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+Xe38+fPqpPZmlotSuPu2NkvdunXD/fffrz5+7rnnkJeXh4MHD+KNN95Qn3/22WfRunVrj8UBAA8++KDNRnNdjBgxQt3etm2bW+t25rrrrsPmzZsxZswYuw3i4OBgDBo0CIMGDXLYaLas03zboMFgwBNPPIHS0lJs2bIFn332GQDTHGCvvvpqnX+WRqMRTz/9tLqAR2xsLCZOnFinOgHTyArLW88mT57s8rxfpaWlWLJkSY1/y5cvx7Zt21BZWQmNRoNx48Zh/fr16NChQ53jJSIiagjYLrXGdinbpc74ql16+vRptaMYcO8UEXXVpEkTdeS5wWBAXl6ejyMiqolfMRBRnd1444149913oSgKVq5cieuvvx6A6Ral77//HgBwySWXoGvXrh6N46GHHsJvv/2GPXv2oLi4GE8++SQKCwvV28kGDx6MW265xaMxAMC1115b69fo9Xr8+eefOHToEE6fPo3S0lKrxREsVxM+cOCAW+J01YEDBzBr1iz1Fq6kpCS0b98eYWFhyM/Px+7du1FSUoL58+fjs88+w/PPP+/Sz3nq1KnYvXs3jh8/jqysLEyZMgW7du1S57W76667cNVVV9U5/tdff139sCFJEmbNmlXnRUcyMjIwbdo09fH111/v8irZrpJlWR3FM336dM6hS0RE5AK2S62xXcp2qTt4ol1aWFioboeEhHhkxPzFCggIQKNGjVBSUgIAKCgoQIsWLXwcFZE1dugSUZ21aNEC3bt3x/bt2/Hbb7/h9OnTaNKkCTZu3IizZ88C8OwoCDPzPFjDhw/H+fPn8ccff6j74uPjMWPGDI/HkJCQgMjISJfLV1RU4KOPPsKXX36JM2fOuPQaV8u5w4YNG/DYY49Br9cjPj4es2bNqjEfl/kYPvroI1RVVeG5555DWFiY0w8Q4eHheP3113H77bfDYDBYjSpITU1Vb32ri88//xzz589XHz/88MP4z3/+U6c6s7Oz8cADD6gjClJSUvDiiy/Wqo4WLVpg06ZNNZ7X6/UoKirCn3/+iYULF2Lnzp3IzMzEuHHj8MEHH+Dqq6+uU+xERET1HdulF7BdynapK3zVLi0rK1O3LadG8RfBwcFqh65lrET+glMuEJFbmBvGRqMRq1atAgB8++23AEzfPnuj4QyY5suy/Iba/P6vvvpqrRq0F6s2t2GVlJTgtttuw//+979aNYYtR0V40vHjx/Hkk09Cr9cjKCgIn376qc3FFYKDg/HYY4/hvvvuU5978cUXUV5e7vQ9LrvsMjz88MM16nvjjTdcvk3MnjVr1lh9WBo9enSN96qt/Px8jB8/HqdPnwZgOt/mzp2L8PDwOtVrptPpEB8fj8GDB+Ozzz7DzTffDMB0q9fTTz9ttRoyERER2cZ2qQnbpSZsl14cb7ZLzSOh/Yk/xkRkiR26ROQWQ4cORUhICADT/GSFhYX45ZdfAABdu3ZFYmKi12KJiYmxetykSRNcdtllXnnv2ny7PH36dPU2NZ1Oh1tuuQUffvgh1q1bh927d2P//v04dOgQDh06hI0bN6qv81bjYt68eeq30cOHD7daHMCW+++/X21AFhcXY/PmzS69T2xsrNXj5OTkOi+KsHnzZjz99NOQZRmA6daz2q4MXd2ZM2cwfvx4HD9+HIDpvPr0008RFxdXp3rtkSQJU6ZMUX+viouL8fXXX3vkvYiIiOoTtktN2C5lu9RdPNEuDQ0NVbct59L1F5YxWcZK5C/YoUtEbhEeHo4BAwYAAA4dOoTXX38dBoMBANRFBryhqKgITz/9tNVz+fn5mDlzptdicEVeXh7WrFkDANBoNJg7dy5eeuklDBgwAK1atUJYWBi0Wq1a3lujHyyZP/gAwBVXXOG0fEhICDp37qw+zsjIcPqao0eP1sjNvn378PHHH7seaDXbtm3D//3f/0Gv1wMA+vfvj1dffVVd2OBinD9/Hvfccw/++ecfAEBUVBQWLFjg8Q+EERER6irLgPcXHiEiIhIR26W1w3apCduljrm7XWrZeV5eXo6Kioo61edOer1enaIFqNnRT+QP2KFLRG5j2UBesWIFACAoKAhDhw71WgzPPfecettRy5Yt1cbnsmXLaqx07Evbtm1TRzT06dPHacP05MmT3gjLSn5+vrrt6m2BluXOnz/vsKxer8cTTzyhjrawXOX5/fffx19//eV6sP/atWsXHnzwQfUb9V69euGdd95BQMDFTxlfVlaGe++9F/v27QMANGrUCHPnzkWbNm0uus7aaNKkibrti/OAiIhIRGyXuo7tUrZLXeXOdmlsbKzVKPJTp07VqT53On36tPo7ERAQgPj4eB9HRFQTO3SJyG2uvPJKq4s8AAwYMACNGjXyyvt/+eWX6i1gwcHB+N///ocHHnhA3f/f//4XeXl5DuuQJMmjMZpZNkrbtm3rtPyOHTs8GY5NlivNujpHlmU5Z3l/9913sXfvXgCmBt2SJUtw0003ATDNzfXEE0/UagRIRkYGJk6cqDbEu3Tpgg8//LBOc55VVlbigQcewO7duwGYRnt8/PHH6Nix40XXWVuWoxXqMpqDiIioIWG71HVsl7Jd6ip3tkslSbI6344ePVqn+twpMzNT3W7Tpk2dOuGJPIWfDInIbbRaLW644Qar57x1W9uRI0cwa9Ys9fHTTz+N5ORkPPjgg+jSpQsAU6Pu6aefdjjXl2Vj0XxrlCdYNoCcLdJQXl6uLuThTc2bN1e3LVdmtqeiogJ//vmn+rhly5Z2y27fvh1z585VH8+cORMxMTF47rnn1NvFsrKy8NJLL7kU66FDhzBhwgR19EWHDh3wySef1Gm+K71ej0ceeQS///47ACAwMBAffvghunbtetF1XgzzfHYAODqAiIjIRWyXuo7tUrZLXeXudqnlFA4HDx6sc31mlr875ulWasMyFssYifwJO3SJyK0eeOABLF++XP3Xu3dvj79nVVUVnnjiCbUB2q9fP9x+++0ATI352bNnIywsDIDplrL58+fbrSsiIkJt1J45c8ZjjWfLOa5+/vlnGI1Gu2VnzZqFgoICj8ThSK9evdTtlStX4siRIw7Lz5kzB+fOnQNg+sbd1srDgGkV5aeeekpdGOKOO+5A3759AZjmvHv99dfVb8FXrFiBH374weH7Hj16FOPHj1dHYbRp0wZz586t0wgco9GIxx9/HFu2bAFgutXq7bfftntMnrJ+/XqcOHFCfezKnHFERERkwnapa9guZbvUFZ5ol1oew65du+pcn5nldBvORsLbYhmLt3/ORK5ihy4RuVVERATS0tLUf5YLKHjK22+/rc4j1aRJkxqLGSQmJlqtJPvWW29ZfbtsKTAwUP0GX6/Xe2x+syuuuEJdJTYrKwtPP/201cT7gGmur6lTp+LLL7/0ycqqd955J3Q6HQDTKIfx48erowIsVVRU4L333sOHH36oPjd06FC7CzNMmzYNubm5AIBLL70UTz31lNX+zp07W92SOG3aNLtzap08eRJ33323+sGiZcuW+PTTTxEdHV2LI7WmKAr++9//Yt26dQBMo1Zee+01dXEVb1AUBWvXrsWUKVPU5yIiIjBixAivxUBERCQ6tktdw3Yp26XOYvBUu7RXr17qPLq7d+9GVVVVnesErKcOWbduncOR8NUZDAbs3LkTAKDT6XDVVVe5JSYid+NEIEQktG3btuHTTz8FYPr2febMmTYbTcOHD8eWLVuwdu1a6PV6PP7441ixYoXVRPxmQ4YMwUcffQQAePLJJ/HNN9/gkksuURuRAGqsWFxbjRs3xvjx4/HBBx8AAFatWoVffvkFnTp1Qnx8PE6fPo3t27ejrKwMAQEBmDZtWp3fs7YSExPxzDPPYMaMGQCA3NxcjBs3DsnJyejQoQNCQkJw+vRp7Nq1CyUlJerrEhISMHXqVJt1rlixAt9//z0A04eU119/3eqWKLMHHngAv/76K3bv3q3ekrhgwYIac8k9+uijaiMcAJKSktTcOXPZZZfhxhtvrPH8559/jm+++UZ9fMkll2DXrl0ujxqw/JBmT3FxMaZPn17jeYPBgKKiIvz1119Wowm0Wi1eeumlOn0gICIiIs9iu9Rz2C41qW/t0tDQUAwcOBCrV69GaWkptm3bpo6QrotBgwbhzTffhKIo+OmnnzBs2DB06dJFHR0PANdeey3S0tJqvPb3339XR3dfffXViIiIqHM8RJ7ADl0iEpa5QWV5i1SfPn3sln/xxReRnp6OkydPIjMzE7NmzcILL7xQo9yECROwfv16HDlyBHq9Xr29yZI7GrEPPfQQTpw4gZUrV6rH8/PPP1uViYiIwCuvvILU1NQ6v9/FuOOOOxAeHo6XXnpJbdhkZmZaLRRg6YorrsCrr76KmJiYGvuOHz+uNsIB4PHHH7d7XOZbEm+88UacP38ev//+O+bOnYt7773XqlxRUZHV482bN7t8bGVlZTYbztXrPHbsGI4dO+Zyva40nEtLS7FkyRKX6mvWrBlmzJiB//znPy7HQERERN7FdqnnsV1aP9ulI0eOxOrVqwEA33//vVs6dFu3bo2JEyfi448/BgD8/fff+Pvvv63KXHrppTY7dC2n1eDdceTPOOUCEQnr+eefV78tbtu2LZ588kmH5SMiIvDaa6+pc5F98cUX2LRpU41yjRo1wvLly/HEE0+ge/fuiI6OthoF4S5arRavvvoqPvroI/Tr1w8xMTHQ6XSIiYlBx44d8X//939YvXo1Bg4c6Pb3ro3hw4dj8+bNeP755zFgwAC0aNECoaGhCAgIQGRkJNq1a4dRo0bhs88+w8KFC9G0adMadZhXBzav9Nu7d2+MGzfO4fsmJCRg2rRp6uN33nlHvYWxvtJoNIiIiEBycjJuuOEGvPHGG1i/fj07c4mIiPwc26XewXap93irXdqrVy91ioQffvhB7ayvq8mTJ2POnDkYMmQIEhIS1GlFHCktLcWaNWsAAK1atcLVV1/tlliIPEFSajOZCBERERERERERkZusWrUKTzzxBABgypQpuOuuu3wSx+eff44XX3wRADBjxgzceuutPomDyBUcoUtERERERERERD5x3XXX4dJLLwUALFiwAHq93usxGI1GzJ8/H4BpruKbb77Z6zEQ1QY7dImIiIiIiIiIyCc0Gg2eeuopAKYF777++muvx7Bq1SpkZ2cDAJ544gkEBHDJKfJv7NAlIiIiIiIiIiKf6dOnDwYMGAAAeP/999U5jr2hqqoK7777LgDgyiuvxJAhQ7z23kQXi3PoEhEREREREREREQmCI3SJiIiIiIiIiIiIBMEOXSIiIiIiIiIiIiJBsEOXiIiIiIiIiIiISBDs0CUiIiIiIiIiIiISBDt0iYiIiIiIiIiIiATBDl0iIiIiIiIiIiIiQbBDl4iIiIiIiIiIiEgQ7NAlIiIiIiIiIiIiEgQ7dImIiIiIiIiIiIgEEeDrAKhuZFlGeXk5ACAgIACSJPk4IiIiIhKNoigwGAwAgJCQEGg0/M6fxMH2MBEREdWVaO1hdugKrry8HAcPHvR1GERERFRPpKamIiwszNdhELmM7WEiIiJyJxHaw/7d3UxEREREREREREREKo7QFVxAwIUUJicnIzQ01IfRkD16vV4dOZKamgqdTufjiMge5koMzJM4mCsxlJWVITMzE4B124JIBGwPi4HXA3EwV2JgnsTBXIlBtPaw/0dIDlnOERYYGIjAwEAfRkP2aDQaREZGAgCCgoKE+OPQUDFXYmCexMFcicE8XxgAzj9KwmF7WAy8HoiDuRID8yQO5koMorWHeRbVI/yj4L8CAgKQnJzs6zDIBcyVGJgncTBXYmAbguoLnsv+i9cDcTBXYmCexMFciUG0NgTn0CUiIiIiIiIiIiISBDt0iYiIiIiIiIiIiAQh1nhickiv13POMD+l1+vVybWTk5M5CbofY67EwDyJg7kSg16v93UIRG7B9rD/4vVAHMyVGJgncTBXYhCtPcwOXSIvUBQFpaWl6jb5L+ZKDMyTOJgrIiICeD0QCXMlBuZJHMwVeQKnXCAiIiIiIiIiIiISBDt0iYiIiIiIiIiIiATBDl0iIiIiIiIiIiIiQbBDl4iIiIiIiIiIiEgQ7NAlIiIiIiIiIiIiEkSArwMg/1BRUYHi4mKUlZXBaDT6Opx6R5Zldfvo0aPQaPhdijtotVoEBgYiIiIC4eHh/LkSERERERERUb3HDt165GI6sxRFQW5uLkpKSjwQEZkpioKgoCAAgNFotOrgpYtnMBhQWVmJc+fOQZIktGjRAo0aNapTnVqtFgkJCeo2+SfmSRzMlRj4hRjVFzyX/RevB+JgrsTAPImDuRKDaG0IdujWIxfzh6GwsLBGZ25AAE8LEoPRaISiKABMneYnTpyoc6euVqtFfHy8u0IkD2GexMFciYEfLqi+4Lnsv3g9EAdzJQbmSRzMlRhEa0Ow564Bq6qqwunTp9XHcXFxiIyMFO4kpoZLURSUlZWhqKgI58+fVzt127ZtK9y3a0RERERERERErmCPRwN2/vx5dTsmJgYxMTHszCWhSJKEsLAwJCQkIDw8HICpk9fy3CYiIiIiIiIiqk84Qrce0ev1CAwMdLl8aWmpuh0REeGJkOhfsiyjvLwcABASEsLRo24mSRKio6PVjtyzZ89e9DldVVWFjIwMAEBaWlqtfqfIe5gncTBXYtDr9b4OgcgtatseJu/h9UAczJUYmCdxMFdiEK09zF6lBqyqqgqAqTPMvGAXkahCQ0MhSRKAC+c2EREREREREVF9wxG6DZgsywBMEz+bO8KIRCVJErRaLQwGA4xGo6/DISIiIiIiIgJgGoAEgH0v5Dbs0K1HdDqdr0MgIiIiIiIiIqJ/SZKEdu3a+ToMqmc45QIRERERERERERGRIDhCtx6R//4RqDhdi1e0AgLCACkUKC1wXlwTAIREXmR0REREREREREQN14ZjG1BcVeyRuqODo9H/kv4eqZv8T4Ps0L3zzjuxffv2Wr1m0aJF6Nmzp819W7ZswVdffYWMjAwUFRUhOjoaaWlpuPXWW9G3b193hOwSpawIKM11/QUhTQFtMKAogFGs1fyIiIiIiIiIiERSVF6EgkoXBtQROdEgO3RrS6PRoFWrVjWel2UZU6dOxfLly62ez8vLQ15eHjZs2IBbbrkF06dPh0bD2S2IiIiIiIiIiIiobhpkh+7MmTNRXl7usMzhw4cxadIkAECvXr0QHx9fo8xbb72ldua2b98eEyZMQGJiIrKzszF37lzs378fy5YtQ3R0NCZPnuz+A6lG47HVEiXTdAuaBnm6uIUkSQgODla3vSElJQUA8PDDD+ORRx65qDr++OMPjB07FoDjUer1SUBAgPqzCwjgOe+vmCdxMFdi0Gq1vg6ByC14LvsvXg/EwVyJgXkSh+W1SdJ4pz+Aak+0NkSD/K1PTEx0Wubbb79Vt2+88cYa+48ePYr58+cDADp27IglS5aoHXadOnVC//79cccdd2Dv3r2YN28eRowYgZYtW7rpCGzzWD+hJgAIj/VQ5Q2DLMvo3r07SktL0b59e3zzzTd2yyqKgiuuuALFxcUAgJdffhkjR460W/6bb77BM888AwCYNm0abr/9drfG3tBoNBqEh4f7OgxygnkSB3MlBt5JRPUFz2X/xeuBOJgrMTBP4rC8NnlrgBfVnmhtCLGi9RJZlrFq1SoAQGhoKAYPHlyjzMKFC2EwGAAAU6dOVTtzzUJCQjB16lQAgMFgwIIFCzwbNPk1rVaLLl26AAAOHjyI8+fP2y37zz//qJ25ALBr1y6Hde/cuVPd7t69e90CraX33nsPKSkp6jfDRERERERERETkWQ1yhK4z27ZtQ15eHgBg6NChCAkJsdqvKAo2btwIAEhKSkLnzp1t1tO5c2e0bt0aR48excaNG/H888979NsYRfFY1ap1+06h8HyV59/Ii2LCAzGkQ1OPvoeiKOjatSu2bt0KWZaxe/du9OnTx2ZZcwetVquF0Wi06rB1VD4qKgpt2rRxa9w9e/bEoUOH3Fqnv5NlGWVlZQBMX+iI9i1dQ8E8iYO5EoMsy74OgcgteC77L14PxMFciYF5Eocsy2p+FG903NBFEa0NwQ5dG1auXKlu25puIScnB/n5+QCcj4js0aMHjh49iry8POTk5Lg03cPFkr3wh6HwfBXyzlZ4/H3qG0VR0KlTJ/Xxzp07nXboDh06FGvWrMHx48eRl5dncx7nwsJCHDt2DADQtWtX3r7hBgaDQe3ETktLQ2BgoI8jIluYJ3EwV2IwGo2+DoHILXgu+y9eD8TBXImBeRKH0Wi80KErs0PXX4nWhmCHbjWlpaXYsGEDAKBFixY2F4E6fPiwup2UlOSwPsv9R44c8WyHrmy0ewJqNBq1s09RFMiyDAUKzH9KLmyZSLjQMagAqN5NWP1bJeuORMXhaGFTUYv6nXREW5d3XPfFlLekKIrDeCRJsjpWZ9/gWJbv0KEDgoKCUFlZiZ07d9p8rSRJaofu4MGDkZGRgePHj2PHjh249tpra5S3HL3brVs3m3Wa8w1YzwnjjmO1fL2iKFbnmPO8SrUq72rstvbp9XqH9Wu1WpsToOv1epvlAwIC1HhkWVanX7HHsnFlNNr/PQVMPxedTqc+NhgMDs8zjUZjtQiCs2O1jF1RFLvHaKbT6dQ8OTtWT8fOPNnnz3myjN3WcTBPvondUZ6IiIiIiMi/sUO3mvXr16u3Ldxwww02RzyeOnVK3W7a1PGt+pb7c3Nz3RSlbWfPnUPJ6Tyb+2JjYtUPwXq9HgWFBajSVQEBRsiKAr3e+kNloMWHSFmRUb0LxWg0qB8UJUlCQIDlh04jFMXRh07rD5GmD7SOPnQGQKMxd/wBBoPjD50BATp1gThZVmA0OuocsM6vwWBAVZX9KSW0Wq3VfMmVlZUOP2AHBgaqH8gDAwPRoUMH7N69GxkZGSgpKanxLWpBQYE63UeXLl1w2WWX4fjx4/jjjz/Qr1+/GvVbduh26dIF5eXlNcro9XqUl5dj//79+OKLL7Bz504UFRUhMjIS3bt3x/jx421+MaHT6bBnzx6MHTsWAPDJJ5+ga9euAIDvvvsOL7zwglX51NTUGnWsXr0azZs3t3rOaDRi7dq12LRpE/bv34/i4mKEhoaidevW6N+/P0aOHFljTmpJkhAaGmp1TLY6H4xGo819mZmZKC0trVHeLCEhweYI6IMHD9osn5KSoi5AUFZW5nRaCvPPDTDlOCcnx27ZsLAwq59lVlaW1ZzK1cXFxVl9UXTgwAGHHTNJSUmIiooCYPo5ZmRkOIzd8tv+kpISHDlyxG5ZnU5nNRI9NzdXvZvBlsjISCQnJ6uPmacL6lOeLPNjK1fMk23ezlNmZqbD2ImIiIiIyH9wkpVqLKdbGD58uM0ylh+QLDuZbLGcf9fcUUwN1+WXXw4AqKqqsvnB39xB26pVK8TGxqrzM+/evdtmfeYF08LDw212qJp99dVXuOuuu7BmzRrk5eVBr9fj9OnTWLt2Le644w6nC6+5S25uLsaMGYNp06Zhy5YtOH36NPR6PUpKSpCeno4333wTo0aNQlZWllfiISIiIiIiIiISDUfoWjh16hS2b98O4MKCZrZUVlaq25a3Q9piOQKzosKzc89GNGqEYKnm6DXA+nZ1nU6H+Lh4nA8KhEGrhUaSoNPZPxU0Us1+f63WfvmAAC0UpeZt0WbVBz1b3iLqrLwkwWo0sLPyGo0ESXJcvnostm7pvlC3dfBBQUFOYrEub+7QBYC9e/eid+/eVvvNHbeXX345NBoNrrjiCgCmkVNVVVVo3LixWra0tFQd7dalSxcEBQXZvL32jz/+QEZGBtq2bYuxY8eibdu2qKysxPr167F48WJUVFTg+eefxw8//GB1vlaPPTAwUP2C4pprrkGXLl3wxRdf4MsvvwRgGrVbfQqFSy65RP0dOXPmDCZMmIDc3FwEBgbilltuQY8ePdCiRQuUlpbi119/xeLFi5GdnY1HH30UX3/9NRo1amTz56rT6WyeN1qt1ua+5ORkp7ce25Kammrzd9yy/tDQUKSlpdmtu7rY2Fh1RJ8t1X/uLVu2dDhVS/XFD9q1a+f0FnEznU7nNHbL42/cuLHD8tVjb9asmc0RtWbVY2eeLqhPeUpNTVX/VtnKFfNkm7fzlJyczFG6RERERESCYIeuhe+++069fd7e6FzAuhPP2ZxzlrfvV7+N3N2qT2VgjyRJ0Gq1kCxmypVqzJJrUd5OHQ7eoUanrbN4alG6VnVfTCy1iae2K4l26tQJAQEBMBgM2LVrV43Xm0foduvWDZIkoU2bNoiKisKZM2eQnp5uNe1Cenq6Ondk9+7d7cb+559/om/fvnj//fetOmy7deuGqKgovP3228jNzcUvv/yCQYMG2Y1dkiQ13sjISERGRiI2Nlbdn5KS4vDYZ86cidzcXLRo0QILFy6s0alyxRVX4JprrsGYMWOQnZ2N+fPnY9KkSXZjsZcnW/ucffFij06nc7q4gEajqdUCBPbmgbXH2Rce1dXmWCVJqlXstT1WT8Ze/XXM0wX+lifL8s5yxTzZ5808ERERERGRf+OUCxa+/fZbAKaRiLYWoDILCwtTt51No2A5p6mz6Rmo/gsNDUW7du0AAHv27LFazKewsBDHjh0DYD1HpHlUr+V8udUfd+vWze57BgUF4ZVXXrHZcXDnnXeqH+Kr1+9OOTk5+P777wEAU6dOtTtCrn379rj99tsBACtWrPBYPEREREREREREomKH7r8yMjJw+PBhAEC/fv2sbm2vznKhM8sF0myx3N+sWbM6Rkn1gbnztbS0FAcOHFCf37FjBwDTLcStWrVSnzd37lbvcDXPexsUFOTwtt0rr7wSMTExNveFh4er75WdnV27A6mFLVu2wGg0IiQkBH369HFYtnv37gCA/Px8nDx50mMxERERERERERGJiFMu/Ms8OhdwPN0CALRp00bddrRCdfX9SUlJFxeci7SaWs1FQF6k0WjUkd3dunXDp59+CsDUSduxY0d1G7AenWsuDwD79u1DRUUFgoODUVVVhb/++gsAcNlllzm8bdfZeWf+8sLRauh1tXfvXgCmEevt27d3+XUFBQVo3ry5p8KyKTAwsEYOyP8wT+JgrsTAKReovuC57L94PRAHcyUG5kkcltcmjZbjKv2VaG0InkkwzYO7Zs0aAEB0dLTTEYQJCQmIi4sDcGFUpT3m/fHx8UhISHBDtCQ68/y4wIVRtpbblgunAaZpCEJCQqDX65Geng7ANKLcvDifo+kWAKgLmdljnhfXPH+0JxQWFl7U6yynLCEiIiIiIiIiIo7QBQD8/PPPKCoqAgBcf/31ThcekSQJAwYMwBdffIEjR44gPT0dnTt3rlEuPT1dHaE7YMCAWi7+RfVVZGQkLr30Uvz999/qqNzz58+rq8BX/5bVvHL69u3bsXPnTlxxxRVWHcHmKQr8mXmu4KioKCxatMjl1/FLECIiIiIiIiIia+zQRe2mWzAbN24cvvrqKxiNRsyYMQNLlixBcHCwur+iogIzZswAYFqZety4cW6N2RZZ8fhb0EVSFAUGgwGA6Xzo1q0b/v77bxQVFSEzMxM5OTmQZRmhoaE2pyTo2rWr2qELXBj5rdPpbH6Z4G8iIyMBmKZ1SE5OrtWq9N5mNBpRUFAAwDSfsT/H2pAxT+JgrsRguUgnkch4LvsvXg/EwVyJgXkSh9FoVPPjyTtjqW5Ea0M0+CkXSkpKsHnzZgBA27Zt0aFDB5de17p1a9xzzz0ATPODjh49GmvXrkVGRgbWrl2L0aNHq/OG3nPPPVaLXHmKorBH118pioKqqipUVVVBURSraRJ27dqljrjt3LmzzQuxedTun3/+iaqqKuzZsweAaTqG0NBQLxyBba6OOjd3UldVVam/F/7KaDQiJycHOTk5wv1Bb0iYJ3EwV2LghwuqL3gu+y9eD8TBXImBeRKH1bWJ3TZ+S7Q2RIMfobt27VpUVVUBAG688cZavXbSpEkoLCzE119/jf3792PSpEk1yowcORKPPfaYO0KlesSyQ3fHjh3IyckBUHO6BbMuXbpAq9WirKwMK1aswLlz52rU4wuWi7FVVVXZXZytX79+mD17NhRFwcKFC/Hmm296K0QiIiIiIiIionqlwY/QNU+3oNVqccMNN9TqtRqNBjNnzsScOXMwYMAAxMXFQafTIS4uDgMGDMCcOXPw8ssvq4tOEZnFx8fjkksuAQD88ccf6qhVex264eHhaNu2LQBg7ty56vO+nj/XvDggABw/ftxuuaSkJAwdOhQAsGbNGnz66acO683Ozsbq1avdEyQRERERERERUT3S4Efofvnll3Wuo2/fvujbt68bovF/MeG2R2CKzFfH1K1bNxw/fhx5eXkATHPrOpoPt2vXrjhw4ACys7MBmL5QsNcB7C1dunRRt1955RXcf//9aNKkiToVQ4sWLdRFBl944QXs3bsX2dnZmDVrFjZu3Igbb7wRl156KQIDA1FcXIyDBw/il19+we+//45Bgwbh+uuv98lxERERERERERH5qwbfoUu1M6RDU1+HUG9069YNK1asUB+3a9cOISEhdst37doVn332mfq4bdu2iIiI8GiMzrRs2RLXXHMNvv/+e2zduhVbt2612r9x40YkJCQAMC2M9sUXX+Cxxx7Dzp07sWPHDnVxN1vCwsI8GjsRERERERERkYjYoUvkI9WnS3A22rb6fl9Pt2A2e/ZsdOzYEevWrcPRo0dRWlpqdzLxJk2aYMmSJfjpp5+wevVqpKeno6CgAAaDAY0aNULLli3RpUsX9O/f32+Oj4iIiIiIiIjIn7BDl5yTDcD5AkAbAIRE+jqaeuOSSy7BoUOHXC4fHx9fq/Kull28eLHdfT179nRaj06nw4QJEzBhwgSXY7v66qtx9dVXu1yeiIiIiIiIiIhM2KFbn0ieqlgBZL0H628YuDieGCRJUqd7MM8FTP6HeRIHc0VERACvByJhrsTAPImD+SFPYIduPaLlHwm/pdFoHM6PS/5Dp9MhNTXV12GQE8yTOJgrMeh0Ol+HQOQWPJf9F68H4mCuxMA8icO8UDgAaLQc6OWvRGtD8EwiIiIiIiIiIiIiEgQ7dImIiIiIiIiIiIgEwSkX6hFZ8XUEZI+iKKisrAQABAUFcQ4dP2YwGJCVlQUAaNmypdXtMeQ/mCdxMFdiMBgMvg6ByC0MBgMCAwN9HQbZwOuBOJgrMTBP4jAYDGp+ZFn2cTRkj2jtYY7QrUcUhT26/kpRFBiNRhiNRubJz8myjOLiYhQXF/Ni68eYJ3EwV2LgtYnqC57L/ovXA3EwV2JgnsRhdW3iZcpvidaGYIcuERERERERERERkSDYoUtEREREREREREQkCHboEhEREREREREREQmCHbpEREREREREREREgmCHLhEREREREREREZEgAnwdABEREREReV9GRga2bNmC3bt34/DhwygqKoJOp0NcXBwuv/xyjBgxAt26dXNYx4oVKzBlyhSX3u+VV17BzTff7I7QiYiIiBo0dujWI5Ik+ToEskOSJOh0OnWb/JdGo0FcXJy6Tf6JeRIHcyUGXpsanjFjxmDnzp01ntfr9Th27BiOHTuGFStWYPjw4ZgxYwYCAwN9EGXt8Vz2X7weiIO5EgPzJA6raxMvU35LtDYEO3TrEY1Y516DIkmSMB+EGrqAgAAkJib6OgxygnkSB3MlhoAANgkbmvz8fABAXFwchg4dim7duqFZs2aQZRnp6emYP38+8vLysHLlShgMBrzxxhtO65w3b57auWBL06ZN3Ra/PTyX/RevB+JgrsTAPInD8trEznf/JVobQqxoiYiIiIiozpKSkjBp0iQMGTIEWq3Wal/nzp0xbNgwjB49GseOHcPq1atx2223oXv37g7rbNWqFRISEjwZNhERERGBi6IRERERETU4H3/8Ma699toanblm0dHReOaZZ9TH69at81ZoREREROQER+jWI0ZF8XUIZIcsy6ioqAAABAcH8zYLP6bX63HgwAEAQLt27dS5j8m/ME/iYK7EoNfrfR0C+aGePXuq28ePH/dhJK7T6/Wc5spP8XogDuZKDMyTOAwGg3o7v2yUfRwN2SNae5gduvUJ+3P9msIOdyEoiqL+IWfO/BfzJA7mikhcVVVV6ja/jKa64vVAHMyVGJgncTA/5Ans0CUiIiIiohp27NihbicnJzstP2XKFBw9ehTFxcUICwtDy5Yt0atXL9x+++2Ij4/3ZKhEREREDQo7dMkFEqAJMP0jv/DHH39g7NixAIBFixZZ3RLpD1asWIEpU6YAADZu3MgFUoiIiAQjyzLmzJmjPr7mmmucvmb79u3qdnFxMYqLi/Hnn3/i008/xbPPPovbbrvNI7Fa0uv1ViOLLQUEBKgjjWVZhsFgcFiX5dQNRqMRRqPRbllJkqxudzYYDJBl+7fVajQaq9W09Xq9wxFclrFbjsqzR6fTQZIkAM6P1dOxa7XaGnM1O4qfefJN7LbyZH6dLcyTb2Jnnuzz5zwZDAb1/YxyzZ+rRqNRY1cUxWEs5vrNZFlWY7GczsEcI/Pkep445QLVP5oAIDzW11HUCzt37sTEiRPt7g8NDUVcXBw6deqEm2++Gb169fJidEREREQmCxYswF9//QUAGDx4MDp27Gi3bGJiIgYNGoQuXbqgadOmAICcnBysW7cO69atQ2VlJaZNmwZJkjBq1CiPxp2ZmWl3X0pKCsLDwwEAZWVlOHTokMO6unbtqm4XFBQgJyfHbtmwsDCkpqaqj7OyslBcXGy3fFxcHBITE9XHBw4ccPhBMikpCVFRUQBMHzgzMjIcxp6WlqZ2zJSUlODIkSN2y+p0OnTq1El9nJubi/z8fLvlIyMjrUZsZ2ZmorS01G75hISEGiO0Dx48aLc882SbL/IE2M8V82Qb81Rst3xDztOxY8fQrl07AKYvPPNK8qzKx8bGqrHr9XoUFBTYrRsAmjdvrm6XlZXh7NmzAICAxhe6+A4fPoyysjLmqRZ5ctSG8Efs0CXyI2VlZTh27BiOHTuG7777DsOHD8fMmTPtrkBNRERE5G7bt2/HG2+8AQCIiYnBCy+8YLfsoEGDcNNNN6mjbcw6deqEa6+9Fps3b8YjjzwCvV6PV155Bf3790eTJk08GT4RERFRvccOXaqdA6uBUsffFgknLBZod73X33b06NG4/fbb1ceKoqCkpATp6elYsGABCgsLsXLlSjRt2hSTJk3yenxERETU8Pzzzz94+OGHYTAYEBQUhHfeeQcxMTF2yzdq1Mhhff369cODDz6Id955B+Xl5Vi+fDkeeOABd4etSk5ORmhoqM19lrdhhoaGIi0tzeV6Y2Nj1RFItlTv0G7ZsqXVCKfqqi8y165dO6e3tJrpdDqnsVveotq4cWOH5avH3qxZM4dzHlePPTk52ektrdWlpqZaxWiJebLNF3kC7OeKebKNeWKegJp5atWqlbodGRkJQ7D1NAOW9et0ulrNOx8aGoqQkBAAQEzohet1mzZt1CkXLDFPF1TPU3JyslCjdNmhS7VTWgCcy/V1FPVCTEwM2rZtW+P5Hj16oH///rj55ptRWVmJxYsX46GHHrKaz4iIiIjI3bKzszF+/HiUlJRAq9XizTffRPfu3etc76hRo/Duu+9CURTs2LHDox26Op3OpTaTRqOpVdvK3ryV9lh+wHWFvc5NWyRJqlXstT1WT8Zu+RrmyZo/5sn8OmdxMU/2MU/2NaQ8Wdav1Tj+uUqSVKufu2UnpkZr3THsLBZXNKQ8Xezvn6+wQ7ce0VT79oL8hyRJCAoKcvkPRJs2bXD11Vdj3bp1KC0txZEjR6zmvSHPCQgIQFJSkrpN/ol5EgdzJQZO7UN5eXm4++67kZ+fD0mSMHPmTAwcONAtdcfExCAyMhJnzpxBXl6e8xfUAc9l/8XrgTiYKzEwT+KwvDZJGvbb+CvR2hD8ra9H2J/rvyRJQkBAQK3+QLRo0ULdtrdasyPp6enYvHkzdu/ejSNHjqCkpASBgYFo2rQpunfvjjvvvBNt2rRxqa4tW7Zg1apV2LNnDwoLC2E0GtGkSRN06NAB/fr1wzXXXKPe5uGqEydOYPz48Th27BhCQ0Px4Ycf+sUicBqNxuGtReQfmCdxMFdiqH6LGjUsRUVFGD9+PLKzswEAU6dOxfDhw936HtVvm/QUnsv+i9cDcTBXYmCexGF5bfLW9ZBqT7Q2BDt0ifzUyZMn1W3LVSxdsWLFCkyZMqXG83q9HpmZmcjMzMSyZcvw3//+F2PGjLFbz5kzZzBp0iRs27atxr4TJ07gxIkTWL9+PQDg5ptvdjm+zMxMjB8/HqdOnUJkZCQ++eQTq9UriYiIyDvOnTuHCRMm4PDhwwCAxx9/3GHb4GIUFRXhzJkzAEyrZxMRERFR3bBDtx6xP9Uz+ZqiKOo/V2RmZuKnn34CAHTu3BmxsbG1ej+j0YjGjRtjwIAB6NatG1q2bInQ0FDk5+dj3759WLx4Mc6cOYMZM2YgKSnJ5sjY8vJyjB07Fn///TcAoEOHDhg1ahQuvfRSBAYG4tSpU9ixYwfWrl1bq9j++usv3HvvvSguLkZcXBw+/fRTl0cKe4OiKNDr9QBMc+jwG1T/xDyJg7kSg6vXJ6pfysvLMXHiROzbtw8AcP/992PixIluf5+lS5eq55g75uR1hOey/+L1QBzMlRiYJ3FYLk7G65T/Ei037NCtR2RZrJOvIVEUBeXl5aisrFSfKywsVDtLzWXOnTuHPXv2YOHChaioqECjRo1sjrR1pk+fPrj++utrTIPQvn17XH311Rg7dizGjBmDQ4cO4b333rPZofv222+r8Y0ZMwZTp061aiR07NgRAwcOxOOPP46zZ8+6FNe2bdvw4IMPoqysDC1btsT8+fORkJBQ6+PzJL1ej4yMDABAWloaF6PzU8yTOJgrMRgMBueFqF6pqqrCww8/jN27dwMAxo4di0mTJtWqjpycHJw9exbt27e3W2bz5s344IMPAADBwcEYMWLExQftAoPBgKCgII++B10cXg/EwVyJgXkSh8FgUNfTUdhv47dEaw+zQ5fIR7744gt88cUXNvdpNBrcdtttuOuuu9C6deta1x0fH+9wf6NGjfDoo4/ioYcewq5du3DmzBmr+ZfOnj2LpUuXAjCNzP3vf/9r9xvfwMBAl0YQb9iwAZMmTUJVVRVSUlIwb948NGnSpBZHRURERO7y+OOPY+vWrQCAK664AiNHjrT6ork6nU5Xo01y4sQJjB07Fl26dEG/fv2QmpqK6OhoAEB2djbWrVuHdevWqSNennrqKadtFCIiIiJyjh26RH5IlmWsXbsWQUFBeOKJJ+r8bWtZWRmKiopQXl6ufqgyf0MIAAcPHrQapfv777+jvLwcgGnETl1Xe1yxYgWee+45GI1GdOnSBXPmzEFERESd6iQiIqKLZ54DHzBd94cNG+awfIsWLbBp0yab+/bs2YM9e/bYfW1ISAimTJmCUaNGXVywRERERGSFHbpEPvLwww/jkUcesXquoqICWVlZ+Pbbb7Fw4UIsXLgQe/fuxbx582pMn+BMUVERFixYgHXr1iErK8vhfDDmhUrM9u/fr2537dq1Vu9b3cKFC7F48WIoioLevXvj/fffr/WxEBERkf/p0KEDZs+ejfT0dOzduxenT5/GmTNnYDAY0LhxY7Rp0wa9evXCLbfcgpiYGF+HS0RERFRvsEOXyI8EBwcjJSUFTz31FFq1aoWpU6di165d+Oijj2o1r93evXtxzz33oLi42KXylnP7AtYdvHVdjXrRokUAgOjoaLz33nvszCUiIvIDhw4dqnMd4eHhGDZsmNPRvUQ+dWA1UFrgufrDYoF213uufiIiIhvYoUvkp0aOHIk33ngDxcXF+Prrr13u0K2qqsJjjz2G4uJi6HQ63HHHHRgwYABatWqFxo0bq9M3ZGdnY+DAgQA8u5rjkCFDsG7dOhQVFeGpp57C22+/jYAA/ukhIiIiIi8oLQDO5fo6CiIiIrfS+DoAIrJNo9GgZcuWAKDewuiK33//HdnZ2QCAadOm4ZlnnkH37t3RpEkTq7l4HY3etVwgLT8//yKiv+Cpp57CmDFjAAA//vgjJk+eLNzqkURERERERERE/oIdukR+zLLj02g0uvSaw4cPq9vXXHON3XJ79+61u69Dhw7q9s6dO116X0emTp2qLoSybt06PPnkky4fDxERERERERERXcD7nv918uRJLF++HD/99BNOnjyJ0tJSREdHo0WLFujZsyeuueYatG3b1u7rt2zZgq+++goZGRkoKipCdHQ00tLScOutt6Jv375eOQaNRvLK+1DtSZKEkJAQBAUFufya8vJyZGZmAjDNrWs5atYRy07g8vJyhIeH1ygjyzKWLVtmt46ePXsiNDQUZWVlWLx4MYYNGwatVuty7NVJkoQXX3xRfd+1a9dCo9Fg9uzZ0Gj863slnU6HtLQ0dZv8E/MkDuZKDJwKh+oLnsv+i9cDcTBXYmCexGF5bZLYb+O3RGtD+FdPio8sXrwY1113HT744APs27cPZ86cQVVVFU6dOoVdu3bhww8/tNv5Jcsy/vvf/2LixInYsGED8vLyoNfrkZeXhw0bNmDixIl47rnnIMuyx4+Dfxb8lyRJ0Gg0kCTXs/Tee++hoqICANC7d2+XO1RbtWqlbn/zzTc2y7zxxhvYt2+f3ToiIiLUEbX79u3DzJkz7c6zq9frUVhY6DQuSZIwY8YM3HzzzQCA1atX45lnnvHK70ZtSJKEwMBABAYG1ipf5F3MkziYKzEwN1Rf8Fz2X7weiIO5EgPzJA7L/DBX/ku03IjV/ewBH374Id555x0Apo6wW2+9FWlpaWjUqBGKi4uxf/9+/Pjjj3ZHEb711ltYvnw5AKB9+/aYMGECEhMTkZ2djblz52L//v1YtmwZoqOjMXnyZK8dl8eExfo6Avfz0TEVFhbi77//tnqusrISWVlZWLlyJX755RcAQFBQEB599FGX6+3duzdiYmJQWFiIt99+Gzk5ORg0aBCioqJw/PhxfPXVV9i2bRsuv/xy7N692249//d//4dff/0Vf//9Nz777DPs2bMHt912G9q2bQudTodTp05h586dWLNmDR577DG1o9YRSZLw8ssvQ5ZlrFy5Et9++y20Wi1mzpwp3B9PIiIiIiIiIiJfaNAdutu2bVM7c4cPH46XXnqpxq0KvXr1wj333IOqqqoarz969Cjmz58PAOjYsSOWLFmC4OBgAECnTp3Qv39/3HHHHdi7dy/mzZuHESNGqItceYKdAZTu1e56L7xJ/aMoCoxGo9W8sV988QW++OILh6+Ljo7G7NmzkZKS4vJ7hYaG4tVXX8VDDz2EyspKLF26FEuXLrUq06NHDzz//PO4/nr7+QwJCcHChQvx6KOPYseOHdi3bx+mTp3qchz2aDQavPLKKzAajVi1ahVWrFgBrVaLGTNm+EWnrizLKCkpAQA0btzY76aEIBPmSRzMlRj87W4JoovFc9l/8XogDuZKDMyTOGRZVvNj785X8j3R2hAN9jdelmW88MILAIDU1FS8/PLLDuedCQwMrPHcwoUL1flKp06dqnbmmoWEhKgdYAaDAQsWLHBP8HbI/MPgtxRFQWVlJfR6vcNyOp0OTZo0Qa9evfDMM8/ghx9+QO/evWv9fv/5z3/w9ddfY9iwYYiLi4NOp0N0dDR69OiBGTNmYOHChQgJCXFaT3R0ND777DO8//77GDJkCJo2bYrAwEAEBQUhMTERQ4cOxeuvv+6wY9gWjUaDV199Fddddx0AYNmyZZg2bZpfXNwMBgOOHDmCI0eOWM1HTP6FeRIHcyUGLlRJ9QXPZf/F64E4mCsxME/isLw2KbLvP/OSbaK1IRrsCN2tW7fi2LFjAIB777231pMfK4qCjRs3AgCSkpLQuXNnm+U6d+6M1q1b4+jRo9i4cSOef/55vxiFWCuyAThfAGgDgJBIX0cjtG7duuHAgQN1/va0Z8+eOHTokMMyl156KWbPnm13f0JCgtM6zAYNGoRBgwa5HN/NN9/sdAoGrVaLN998E2+++abL9RIRERERERERNXQNdoTuDz/8AMA0p+fVV1+tPl9cXIxjx46huLjY4etzcnKQn58PAOjevbvDsj169AAA5OXlIScn5+KD9hkFkPWmjl0iIiIiIiIiIiLymQY7QvfPP/8EALRo0QLh4eFYtWoV5syZY7VIlXmRtDvvvLPGlAuHDx9Wt5OSkhy+l+X+I0eOIDEx0R2HQERERERERERERA1Mg+zQlWUZR44cAQBERUXhpZdewuLFi2uUO3bsGF577TX8+OOPmDNnDiIiItR9p06dUrebNm3q8P0s9+fm5tY1fLtk2Wh3zg+NRqNO9aAoCmRZhgIF5tlbLmyZSLgwLYRi8V/zs9Uni7acQkBRFIdzoUqSZDXthLOJpy3LO6v7Ysp7K3bL97D3Ok/GXtvyoubJ1j69Xu+wfq1WC61WW+N5e3MeBwQEqPHIsux0zirLL4SqL45XnSRJVvN5GwwGhz97jUZjNWWMs2O1jF1RFJfmdTbnydmxejp25sk+f86TZey2joN58k3sjvJERERERET+rUF26J47d079UPT3338jIyMDTZo0wVNPPYW+ffsiKCgIGRkZeP3115Geno49e/bg2Wefxfvvv6/WUVpaqm6HhoY6fD/LxafKysrcfDQXnD13DiWn82zui42JVT8E6/V6FBQWoEpXBQQYISsK9HrrD5WBFh8iZVmG0WiEpGhgfrayslL9GWo0GqtjrKysdPgBW6fTWX0gr6iocPihMygoSP2QqigKysvL7ZYFTD9v8wdmo9GIyspKu2UlSbLKn16vd/ihVqvVWi1+Z/lzsCUwMLDGYnsVFRV2ywcHB6sfsGVZdlgWAMLCwtRtg8GAqqoqu2UbQp6MRqPNfZmZmVa/s9UlJCQgPj6+xvMHDx60WT4lJQXh4eEATL/TzuYi7tq1q7pdUFDgcOqVsLAwpKamqo+zsrIcTgETFxdnNer/wIEDDs/hpKQkREVFATD9HDMyMhzGnpaWpp4HJSUl6pdhtuh0OnTq1El9nJubq05NY0tkZCSSk5PVx8zTBfUpT5b5sZUr5sk2b+cpMzPTYexEREREROQ/GuQcupYdTZWVlQgJCcGiRYswbNgwNG7cGMHBwejevTsWLlyofhD88ccf1WkazK8zq95hV131TjEiIiIiIiIiIiKii9EgR+hWnw935MiRNufBDQ4OxqRJk3DfffcBANauXYvLLrsMgGk0opmz2xQtR01aju50t4hGjRAs1Ry9Bljfrq7T6RAfF4/zQYEwaLXQSBJ0OvungkajgUYjAdoLZSyPv7qgoCCnt/JbcvYzsSwvSZLVKFNn5bVardPylnQ6ndUtq47qBhz/HKqXN28HBQXVqMdW+eojap0JCAiweTu6PfUxT1qt1ua+5ORkp7ceW8Zlfn2bNm1sfmFjWX9oaCjS0tJcjj02NlYd0WdL9Z97y5YtHc67bfm7DQDt2rVzeou4mU6ncxq75fE3btzYYfnqsTdr1szmiFqz6rEzTxfUlzwBQGpqqjrvvK1cMU+2eTtPycnJHKVLRB5lOZWMvbYw+QfmSgzMkziYH/KEBtmha76106x37952y/bq1QsBAQEwGAxWt1Ja3urubBoFyxHBzqZnqAudix16kiRBq9VCspgp13LO3BrlLf5rVv2DY/X6a/MHy1Fdda3b0+VdjV2j0dQ696IeqzdicVTe1j5no+irlzV/ceMKjUZT40siR+zNA2uPoy8YbKnNsUqSVKvYa3usnoydebLPn/IEmK6XruaKebLP03nyZPuEyJtqe+6T91SfSob8F3MlBuZJHJbtOI22Qd4oLwTR2hAN8kwKDAxEdHS0+tjRomZBQUHq6J+ioiKbr7FcIM0Wy/3NmjWrdbxEREREREREREREQAPt0AVMt32aOVrUCoC6cJTltyqWr3e0qEn1/bamdiAiIiIiIiIiIiJyRYOccgEAunfvju3btwMAsrOz0b59e5vlzp8/jzNnzgCA1dx1CQkJiIuLQ35+Pnbs2OHwvcz74+PjkZCQ4I7wbZLtT5VHPqYoijrXsk6n4xw6fsxgMCA3NxeAaUR9bW9zJu9gnsTBXInBYDD4OgQitzAYDLWazkQIB1YDpQWeqz8sFmh3vefq/xevB+JgrsTAPInDYDCo+XE2oJB8R7T2cIMdoTt48GB1+8cff7Rb7scff1QXFenatav6vCRJGDBgAADTCNz09HSbr09PT1dH6A4YMMCjHXmOFj8h3zJ36Or1eubJz8myjPz8fOTn5/Ni68eYJ3EwV2LgtYnqi3p5LpcWAOdyPffPk53FFng9EAdzJQbmSRxW16Z6eJmqL0RrQzTYDt3U1FT06dMHALBmzRps27atRpnTp0/j7bffBmAaVTlixAir/ePGjVMXY5kxYwYqKiqs9ldUVGDGjBkATNM1jBs3zt2HQURERERERERERA1Ig+3QBYBnn30WERERkGUZ9913H9544w3s3LkTGRkZWLJkCUaOHKkuaPZ///d/VlMuAEDr1q1xzz33AAD27t2L0aNHY+3atcjIyMDatWsxevRo7N27FwBwzz33oFWrVl49PiIiIiIiIiIiIqpfGvQkK61bt8b//vc//N///R8KCgowZ84czJkzx6qMJEm4//77ce+999qsY9KkSSgsLMTXX3+N/fv3Y9KkSTXKjBw5Eo899pgnDoGIiIiIiIiIiIgakAbdoQsA3bp1w+rVq/HZZ59hw4YNyMnJgV6vR5MmTdCjRw/ceeeddhdMAwCNRoOZM2diyJAhWLp0KTIyMnDmzBlERUUhLS0No0aNQt++fb14RERERERERERERFRfNfgOXQCIiorCI488gkceeeSi6+jbty87bomIiIiIiIiIiMijGvQcukREREREREREREQi4QjdekSSJF+HQHZIkgStVqtuk//SaDSIjIxUt8k/MU/iYK7EwGsT1Rc8l/0XrwfiYK7EwDyJw+raxMuU3xKtDcEO3XpEI9a516BIkoTg4GBfh4H+/fvjxIkTuOmmmzBr1ixfh+OXAgICkJyc7OswyAnmSRzMlRgCAtgkpPqB57L/4vVAHMyVGJgncVhem9j57r9Ea0PwTCIXSIBGB2jEOrlFsH37dqSkpKj/du/e7euQiIiIiIiIiIjIj7FDl5zTBADhsUBIpK8jqXe++eYbq8crV670TSBERERERERERCQEDrmsR4yK4usQyA5ZllFZWQkACAoKgkajQUVFBdatWwcACA0NRVlZGX744Qc899xzCAwM9EgcmzZt8ki99Yler0dmZiYAIDk5GTqdzscRkS3MkziYKzHo9Xpfh0DkFnq93mPtKKobXg/EwVyJgXkSh8FgUG/nl42yj6Mhe0RrD7NDtz7xQn/upuObUFRR5Pk38qLo4Gj0v6S/x99Hlq3/cP/4448oLS0FADz33HN49tlnUVJSgk2bNmHo0KEej4dsUxRFzYvCL0n8FvMkDuaKiIgAXg9EwlyJgXkSB/NDnsAOXaqVoooi5Jfl+zqMesE8vUJKSgpGjBiBTz75BEePHsXKlSvZoUtERERERERERDaxQ5fIB/Lz87Ft2zYAwLBhw9T/v/POO9i6dSuKiooQHR1t9/V5eXlYvHgxfv31Vxw/fhwVFRVo3LgxYmJicOmll6J3794YPHgwwsPDrV7Xv39/nDhxAjfddBNmzZplM64ff/wRf/zxBw4ePIj8/HwYDAZERUWhY8eOuOGGGzB06FC7K3P+8ccfGDt2LABg0aJF6NmzJ9auXYulS5fi0KFDKCsrQ7NmzTBgwABMnDgRkZGRF/PjIyIiIiIiIiJqsNihS+QDq1atgtFohEajwQ033AAAuOGGG/Duu+9Cr9dj9erVasdodTt37sR9992H8+fPWz1fWFiIwsJC/P3331izZg2ioqLQr18/l2MyGo3o27dvjakhAFNH76ZNm7Bp0yYsX74c7733HsLCwhzWJ8synnzySXz33XdWzx87dgzz5s3Dhg0bsGTJEjRp0sTlGImIiIiIiIiIGjp26BL5wLfffgsA6NGjB+Lj4wEAiYmJ6NKlC3bv3o2VK1fa7NCtqqrCpEmTcP78eYSFhWH06NHo2bMnYmJioNfrkZOTgz179uDHH3+sdUzmeX2uuOIK9OnTB23btkV0dDRKS0uRnZ2NZcuWYc+ePfj1118xffp0vPrqqw7re+edd7Bnzx4MHDgQw4cPR/PmzVFQUIDPP/8cP/30E7KysvDKK6/gzTffrHWsREREREREREQNFTt0ibzswIEDOHToEIAL0y2YDRs2DLt378a+fftw+PBhtGnTxmr/rl27kJ9vmsP4jTfeqDECt3Pnzrj++usxZcoUVFRU1CourVaLH374AS1btqyxr0ePHhgxYgTeffddfPDBB/j222/xwAMPoFWrVnbr27NnDx577DE88MADVs/36dMHEyZMwNatW7Fu3Tqn00sQEREREREREdEFtifCJCKPMY/ODQ4OxpAhQ6z2XXPNNdDpdAAuLJpmqaCgQN3u3r273fcICAioMX+uM5Ik2ezMtfTQQw8hKioKiqJg06ZNDst26NAB999/v833ueuuuwAABoMBe/bsqVWcREREREREREQNGUfo1iOSJPk6BLJDkiQEBgbCYDBgzZo1AIB+/frV6HSNjIxE3759sWHDBqxatQqTJ0+2WoDMcr7Zr7/+GuPGjfNYzLIs4/Tp0ygtLYXBYFCfb9q0Kc6cOYODBw86fP0NN9xg95zs0KGDup2dne2egN1Eq9UiISFB3Sb/xDyJg7kSg73FLolEw3PZf/F6IA7mSgzMkzisrk3stvFborUh2KFbj2j4h8FvSZIEnU6HX3/9VR1lW326BbNhw4Zhw4YNOHXqFP744w/06tVL3de1a1ckJiYiOzsbM2fOxKpVqzBo0CB069YNaWlpCAwMrFOciqLgu+++w/Lly/HXX385nLbhzJkzDutKSkqyuy8yMlLdLi0trXWcnqTVatV5jcl/MU/iYK7EwA+CVF/wXPZfvB6Ig7kSA/MkDstrk2idhg2JaG0IdugSeZF5GoXIyEj85z//sVmmX79+iIiIwNmzZ7Fy5UqrDl2dToePPvoIjz76KDIzM5GRkYGMjAwApikcunXrhuHDh+Paa6+t9R+jyspKPPzww/j5559dKu9sjt7g4GC7+ywvYrIsuxYgERERERERERGxQ5fIW86dO6fOO1tcXIyOHTs6fc369esxbdo0hIaGqs+1adMGq1atwubNm7Fp0ybs3LkTWVlZqKiowNatW7F161Z8+umn+OSTTxATE+NyfP/73//UztwePXrg9ttvR4cOHRAbG4vg4GC1E3bMmDHYuXNnbQ6diIiIiIiIiIjchB269YhRVnwdAtkhyzJWrlyJysrKWr2urKwM69evx/Dhw62e12q1GDhwIAYOHAgAyM/Pxy+//IIlS5Zg37592LdvH55//nl88MEHLr2PoihYvnw5AKBbt25YuHCh3VtBSkpKanUMoqmqqlJHPbtjGgvyDOZJHMyVGPR6va9DIHILvV7PvzN+itcDcTBXYmCexKHX69XFz2Uj71D1V6K1h9mhS+Qlq1evBmBa2GzKlClOy7/22ms4deoUvv322xodutXFxcVhxIgRGDZsGEaNGoV9+/bhp59+QkVFhcOpD8yKi4tx+vRpAMDQoUPtduaWlpbi6NGjTusjIiIiIiIiIiLPYIcukRfk5OTgzz//BAAMHjwY1113ndPXpKenY9GiRfj999+Rl5fn0oT3Op0O3bt3x759+2AwGHD27FmXOnSNRqO6XV5ebrfcsmXLYDAYnNZHRERERERERESeweX1iLzg22+/haKYpsQYPHiwS68ZMmQIANN0Dd9++y0AqPPl2lNVVYUdO3YAAEJDQxEdHe3Se0VHRyMiIgKAaSRxVVVVjTJ//fUX3nnnHZfqIyIiIiIiIiIiz+AIXSIv+O677wCYOk67devm0msuv/xyNGnSBKdPn8Z3332HiRMnYtu2bfjwww/RrVs39O3bFykpKYiOjkZFRQWOHTuGL7/8Evv27QMAjBw5EgEBrv2KazQa3HDDDViyZAkOHTqE0aNH4+6770bLli1x/vx5bNmyBZ9//jlCQ0MRFxeHY8eOXdTPgYiIiIiIiIiI6oYdulQr0cGujfgUiaePadeuXTh+/DgAoF+/fnbnp61Oo9Fg0KBB+Pzzz/HPP/9g7969AEwjdrdv347t27fbfe2AAQPw+OOP1yrOSZMmYffu3Thw4AD27t1b4/WRkZF477338O6777JDl4iIiIiIiIjIR9ihS7XS/5L+vg5BOObpEgBTR2ttDB48GJ9//jkAYOXKlZg0aRJSUlLw22+/4cCBA8jPz0dhYSEAIDY2Fp06dcLw4cNx9dVX1zrORo0a4YsvvsCnn36K77//HllZWdBqtWjWrBn69u2LcePGoWnTprWul4iIiIiIiIiI3IcdukQeNn36dLzwwgsOFxuzp1evXjh06JDVc4MHD3Z5Ht7qNm3a5HB/SEgIHnzwQTz44IN2yyxevNjuvp49e9aI1x5XyxERERERERER0QXs0K1HNJLkmYplA3C+ANAGACGRnnmPek6SJAQHB6vb5L8CAgKQkpKibpN/Yp7EwVyJQavV+joEIrfguey/eD0QB3MlBuZJHJbXJknD/gB/JVobgr/19Yjn+gkVQNYD/Ltz0SRJEu6PQ0Ol0WgQHh7u6zDICeZJHMyVGFyd353I3/Fc9l+8HoiDuRID8yQOy2sTB3j5L9HaEGJFS0RERERERERERNSAcYRuPaIovo6A7FEUBbIsAzB968Nv5fyXLMsoKysDAISGhgr3LV1DwTyJg7kSg/kaRSQ6nsv+i9cDcTBXYmCexCHLspofhR03fku0NgR/4+sRmX8Y/JaiKKioqEBFRQX/gPs5g8GAQ4cO4dChQzAYDL4Oh+xgnsTBXInBaDT6OgQit+C57L94PRAHcyUG5kkcltcmRWZ/gL8SrQ3BDl0iIiIiIiIiIiIPsLxDV6NlNxy5B88kIiIiIiIiIiIiIkFwDl0iIiIiogYoIyMDW7Zswe7du3H48GEUFRVBp9MhLi4Ol19+OUaMGIFu3bq5XN+WLVvw1VdfISMjA0VFRYiOjkZaWhpuvfVW9O3b14NHQkREJIacojIcKijxSN0pseEeqZf8Ezt0iYiIiIgamDFjxmDnzp01ntfr9Th27BiOHTuGFStWYPjw4ZgxYwYCAwPt1iXLMqZOnYrly5dbPZ+Xl4e8vDxs2LABt9xyC6ZPn85Fe4iIqEGr0BtxvtIzcx5XGMRa1Ivqhh26REREREQNTH5+PgAgLi4OQ4cORbdu3dCsWTPIsoz09HTMnz8feXl5WLlyJQwGA9544w27db311ltqZ2779u0xYcIEJCYmIjs7G3PnzsX+/fuxbNkyREdHY/LkyV45PiIiIqL6jB26REREREQNTFJSEiZNmoQhQ4ZAq9Va7evcuTOGDRuG0aNH49ixY1i9ejVuu+02dO/evUY9R48exfz58wEAHTt2xJIlSxAcHAwA6NSpE/r374877rgDe/fuxbx58zBixAi0bNnS8wdIREREVI/xniciqjcURfF1CEREREL4+OOPce2119bozDWLjo7GM888oz5et26dzXILFy6EwWC6dXTq1KlqZ65ZSEgIpk6dCgAwGAxYsGCBG6InIiIiatg4Qrce0Wqk2pWHEQZFhlGWIcsKNLV8PblOo9EgLCzM12HUa0ajEUajEQDsfjh1RWBgILp27equsMhDmCdxMFdi0Ol0vg6B/FDPnj3V7ePHj9fYrygKNm7cCMA04rdz58426+ncuTNat26No0ePYuPGjXj++echSZ5pd/Jc9l+8HoiDuRID8ySOgACLrjcPXf+o7kRrQ7BDtwELNp5HpTEcigKcL69ERFiw8xcR+ani4mJ1OzQ01HeBEBER1RNVVVXqtq3FzHJyctS5eG1Nx2CpR48eOHr0KPLy8pCTk4PExET3ButtB1YDpQWeqz+mDdC6t+fqJyIiIqE12A7dlJQUl8r16NEDixcvdlhmy5Yt+Oqrr5CRkYGioiJER0cjLS0Nt956K/r27euOcD0iwliIEqUJYNTj1JnzAIDwkCCO1CVhKIqCyspKnD17FoWFherzUVFRPoyKiIioftixY4e6nZycXGP/4cOH1e2kpCSHdVnuP3LkiPgduqUFwLlcz9UfFuu5uomIiEh4DbZD1x1kWcbUqVPVVX3N8vLykJeXhw0bNuCWW27B9OnTbY5qcHs8tZw+NMx4FiHGcyivlGBEBE4UnoMknYPWXqySBGjP1D3QBso8v6unbjFsiIxGY415cxs3boygoKA61VlQYBpxExsbW6fpG8hzmCdxMFdiME9ZQ2QmyzLmzJmjPr7mmmtqlDl16pS63bRpU4f1We7PzfVcR2hFRYXdfQEBAWqbXJZlde5fewIDA9Vto9EIWZbV2zGNsgxY/t5IgFZz4e+bLMsO5/aXJMnq84FRNgKWxWUZ5tqMigyNoqhtSAUKZKPsMHaNVgMJ/5ZXFMiyg/LKhfcCTHMdOyqv0Wisbh/W6/UOj1Wr1UKr1arXA4PBgOjoaLvXg7rmyWg0QpIk27lyd55Mlag/P1mW1fKKokCv1zuMXafTqXl1dqyWxwR4Lk+A6edo/v22lyt35MkeTx+rZezMkzXmyT2xW+YJACorK9XPqIqiQFGs38sUt7mfQHG6NowkWfbZWJS3eJ35mJgn1/PkqA3hjxp8h+7o0aNx++23290fEhJid99bb72ldua2b98eEyZMQGJiIrKzszF37lzs378fy5YtQ3R0NCZPnuz22Kur7YJQEhRcUnEQx4NTUV6pAJIWilYHg2SvQ1cLBHEdvYth+YfN8g8YuVeTJk0QExNTpzqMRiNycnIAmEb6svPJPzFP4mCuxOCws4capAULFuCvv/4CAAwePBgdO3asUaa0tFTddjbdkWWbuqyszE1R1vTPP//Y3ZeSkoLw8HA1hkOHDjmsy3JuyoKCAhQVFaFdu3YAgKIzRdAX5qn7A3WBiI29MKq2uKTY4QfDsLAwNI5ofKH+0wWmzsJ/hYSfhfl+o7MlZxGsr0BIsOlnKBtl5OXnwZH4uHj1721FZQXOnLE/KCNIDoVl6yk3N1edSsOWyMhIqxHbmZmZVudCdQkJCYiPj7e6Hlh+GVBdXfOUk5OD0NBQm7lyd54AQGcMRpN/t8+fP4+IiAgApo6EjIwMh7GnpaWpHWglJSU4cuSI3bI6nQ6dOnVSH3sqT4B1R6G9XLkjT/aEhYUhNTVVfZyVlWU1vVp1cXFxVqP+Dxw44LBTKSkpSb2jj3myxjzZVpc8AcDJkyfRunVrAEBVZUWN14aEhFp01MsoL3d8nQwPb6Ru6/V6VFZWAgAqKy/8PcvOzkZRURHzVIs8OWpD+KMG36EbExODtm3b1vp1R48exfz58wEAHTt2xJIlS9RVfTt16oT+/fvjjjvuwN69ezFv3jyMGDECLVu2dGvs7qCBjJYVB1CqjcA5bQzKteEwws6H7YBgICzSq/HVF7Iso7y8HAAQHBzslRHbDYFGo0FgYCDCwsIQHh5u9Y0yERERXZzt27fjjTfeAGBqK7/wwgs2y5k/QALOFxKxvEaLNgKGiIiIyN80+A7di7Vw4UJ1aPjUqVPVzlyzkJAQTJ06FaNGjYLBYMCCBQswbdo0X4TqlAQF4cYShBtLHBds1Ay49GqvxFTfVFVVqd9UtW7dmh2PRERE5Jf++ecfPPzwwzAYDAgKCsI777xj9+4XyymOnN1iabnAWvV2szslJyfbHS1seRtmaGgo0tLSXK43NjYW0dHR6uPoqGhAa9ExXe3mq8jGkVAiHN/Kb1V/k1jrW/kbRaibEY0joDl3YbSWRqtBfFw8HNFoLwweCA4Kdly+caTVw2bNmlmNWKpRd7WBCcnJyU5vaa0uNTXV7pcAdc1TVFSU1c/XKlfuzhMARFw4L8wjIQHTlxzOYrf8GTRu3Nhh+eqxeCNPgP1cuSNP9lQ/1pYtWzqcd7v6sbZr187pLeJmzJN9zNMFdc1Ts2bN1O3AwCCEhYXZjUer1dTY74hOp1N/VkFBF66viYmJaNGiBfNUizwlJycjMzPTbnl/ww7di6AoCjZu3AjANLy8c+fONst17twZrVu3xtGjR7Fx40Y8//zzvNWeiIiIiPxSdnY2xo8fj5KSEmi1Wrz55pvo3r273fKWHzidTaNgvlMJcD49Q13odDqXvjg33+Xjqurz7Gk1GsDBFDK1vRvLcl7Xfyu4sE/SmNay+JcEqVbT10iSk/LVpluz/HDuCmejs+29xht5Ahznqs55MlVisz5JkmoVe22P1Rt5Mr/OWVzuyJMjnjxW5sk+5sm+2ubJsn5JkqrNgVudVMt+I4vyFq+zd0zMk/vK+xrv+74IOTk56rwdjhq5ANCjRw8ApoXSHM0/Q0RERETkK3l5ebj77ruRn58PSZIwc+ZMDBw40OFrLBc6czQnavX9liOViIiIiKj2GvwI3R9++AHff/89Tpw4AY1GgyZNmqBLly646aabcMUVV9h8zeHDh9XtpKQkh/Vb7j9y5IjDoe1ERERERN5WVFSE8ePHIzs7G4BpOrHhw4c7fV2bNm3UbUeLmlTf76z9TERERESONfgOXcvOWcC04l9WVhZWrlyJgQMHYtasWWjUqJFVGcsRBpYjE2yx3J+bm+uGiO2TZSOMRqPNfRqNRh2GryiK09WsLW+tkBUZiqwAsqwul6bX69W5SCRJshqabjAYHNav0Wishspb1mVLQECAevuSoihO52jT6XTqscqyrM51bIunY7d1m4qj+C2P1VnsgPUCI0aj/fwDzFNt82R+nS3Mk29iZ57s8+c8WcZu6ziYJ9/E7ihP1LCcO3cOEyZMUNvEjz/+OMaMGePSaxMSEhAXF4f8/Hzs2LHDYVnz/vj4eCQkJNQtaCIiIqIGrsF26IaEhKB///7o1asXWrdujbCwMBQVFWH79u348ssvUVxcjA0bNuDBBx/E/PnzrT5UlZaWqtvO5gALCQlRt53NLVZXZ8+dQ8npPJv7YmNi1Q/Ber0eBYUFDutq3qy5ul1WVoazZ89CZwxGk3+fO3z4sHo8YWFhSE1NVctnZWWhuLjYbt1xcXFWI5UPHDjg8INkUlKSOhm7Xq9XFxezJy0tTT3WkpIShyNGdDodOnXqpD7Ozc1Vp9OwJTIyEsnJyerjzMxMq/OhuoSEBMTHx0OSJISFhaG0tBQHDx60Wz4lJUVdTKGsrAyHDh2yWxYAunbtqm4XFBQ4nNaDeXKeJ8B64nV7uWKebGOeiu2Wb6h5AqzzYytXzJNt3s6TSAtAkPuUl5dj4sSJ2LdvHwDg/vvvx8SJE11+vSRJGDBgAL744gscOXIE6enpNteWSE9PV8/3AQMGcE2JBsrcHjZvk/9irsTAPAmKqSI3abAduj///DMiIiJqPH/VVVfhzjvvxL333ov9+/dj+/bt+OKLLzB27Fi1TGVlpbrtbNJky5FEFRUVDkpSfabT6ZCamopdu3b5OhRyQrSJ0Bsq5omIqG6qqqrw8MMPY/fu3QCAsWPHYtKkSbWuZ9y4cfjqq69gNBoxY8YMLFmyBMHBF1bZrqiowIwZMwCYRrWPGzfOPQdgB68P/svcHib/x1yJgXkSh/ViXuzR9VeitSEkxdH9eA1YdnY2rrnmGuj1erRs2RLr169X982dOxezZ88GAHzyySfo06eP3Xq2bNmijnR4+umnMX78eLfGWVVVpY4Iam/cj8CzWTbLuWXKhYjm0PYwxc8pF1yLvfotrVVVVQ5j563HvomdebKPebKNeWKeqscuep4s7yCxHElM9dcjjzyitm+vuOIKPPvssw5HeOl0OrRu3drmvjfeeANz5swBALRv3x733nsvEhMTkZ2djU8++QT79+8HANx3332YPHmym4/Euj3stfN35wLgnAenU2uaBrS73jvv1agZ0O0uz9Xva/z5EZEfmbV1AfblZXuk7g7xiXim910eqbsh8El7og4a7AhdZxITE3HllVdiy5YtyMrKQl5ennprovm2BsD5NArl5eXqtrPpGepKq5Fszi9ZnSS5Vs5MI2kALYB/P7QCjr+5sP72ybnafAsiSVKtfqk0Gk2tynsydgAejd3e/KL2ME/2MU+2MU/2MU/2MU+2+VueRBuRQHVnOVjh999/x7BhwxyWb9GiBTZt2mRz36RJk1BYWIivv/4a+/fvtznSd+TIkXjsscfqFDMRERERmbBD14Hk5GRs2bIFAKw6dC0XOrNcIM0Wy/3NmjXzQJQXyBxr7bcMBgOyskyjp1u2bFnrD+bkPcyVGJgncTBXYnA2MprIEY1Gg5kzZ2LIkCFYunQpMjIycObMGURFRSEtLQ2jRo1C3759vRKLwWDw+xE1DRWvB+JgrsTAPInDYDBY5IcdN/5KtPYwf+MdsHfbWZs2bdRtRwuaVN+flJTknsDs4OwZ/kuWZXUhHssFd8j/MFdiYJ7EwVyJgW2IhsfZAoQXo2/fvl7ruLWH57L/4vVAHMyVGJgnQfEy5bdEa0NonBdpuCxXfLZcCTohIQFxcXEAgB07djisw7w/Pj4eCQkJHoiSiIiIiIiIiIiIGgp26NqRnZ2NX3/9FQBwySWXWHXoSpKEAQMGADCNwE1PT7dZR3p6ujpCd8CAAQ4XmiAiIiIiIiIiIiJypkF26G7atMnh3BgFBQV49NFH1dWqb7/99hplxo0bpy7EMmPGDFRUVFjtr6iowIwZMwCYFjIZN26cu8InIiIiIiIiIiKiBqpBzqH70ksvQa/XY8iQIejcuTNatGiB4OBgnDlzBn/88QeWLl2KM2fOAAC6du2KMWPG1KijdevWuOeeezBnzhzs3bsXo0ePxr333ovExERkZ2fjk08+wf79+wEA99xzD1q1auXNQyQiIiIiIiIiIqJ6qEF26AJAfn4+Fi9ejMWLF9stM2TIELz00kt2V8qdNGkSCgsL8fXXX2P//v2YNGlSjTIjR47EY4895q6wiYiIiIiIiIiIqAFrkB26s2bNwvbt25Geno7s7GwUFxfj/PnzCA0NRdOmTdGlSxfcdNNN6NKli8N6NBoNZs6ciSFDhmDp0qXIyMjAmTNnEBUVhbS0NIwaNcrnK/0SERERERERERFR/dEgO3R79OiBHj16uK2+vn37+kXHLRdd818ajQZxcXHqNvkv5koMzJM4mCsxsA1B9QXPZf/F64E4mCsxME+C4mXKb4nWhmiQHbr1lUasc69BCQgIQGJioq/DIBcwV2JgnsTBXIkhIIBNQqofeC77L14PxMFciYF5Eof1tYkdN/5KtDaEWNESEREREREREZFPrNt3CoXnqzxSd0x4IIZ0aOqRuonqG3boEhERERERERGRU4Xnq5B3tsLXYRA1eOzQrUeMiuLrEMgOvV6PAwcOAADatWsHnU7n44jIHuZKDMyTOJgrMej1el+HQOQWer0egYGBvg6DbOD1QBzMlRiYJ3EYDAaL2/nZb+OvRGsPs0O3PuHfBb+lKIr6x0Fhx7tfY67EwDyJg7kiIiKA1wORMFdiYJ4ExVSRm3ApRCIiIiIiIiIiIiJBsEOXiIiIiIiIiIiISBDs0CUiIiIiIiIiIiISBDt0iYiIiIiIiIiIiATBDl0iIiIiIiIiIiIiQbBDl4iIiIiIiIiIiEgQAb4OgNxHI0m+DoHsCAgIQFJSkrpN/ou5EgPzJA7mSgxardbXIRC5Bc9l/8XrgTiYKzEwT+LQaCzGUrLbxm+J1obgb309wv5c/6XRaBAVFeXrMMgFzJUYmCdxMFdisPqgQSQwnsv+i9cDcTBXYmCexGF9bWLHjb8SrQ0hVrREREREREREREREDRhH6NYjiq8DILsURYFerwcA6HQ6SBxO7beYKzEwT+JgrsSgKGxFUP3Ac9l/8XogDuZKDMyTOBRFscgPr1P+SrQ2BEfo1iOyLNbJ15Do9XpkZGQgIyNDveiSf2KuxMA8iYO5EoPBYPB1CERuwXPZf/F6IA7mSgzMkziMRuOFB+y28VuitSG83qE7duxYjBs3DidOnHD5NXl5eerriIiIiIhExvYwEREREdWF16dc2L59OyRJQnl5ucuvqaysVF9HRERERCQytoeJiIiIqC445QIRERERERERERGRIITo0C0rKwMABAUF+TgSIiIiIiLvY3uYiIiIiMyE6ND9+eefAQDx8fE+joSIiIiIyPvYHiYiIiIiM4/PoTtlyhSbz7/99tto1KiRw9dWVVUhOzsbGRkZkCQJPXr08ESIREREREQew/YwEREREbmTxzt0v/nmmxqLNyiKgo0bN7r0ekVRAACNGzfGxIkT3R4fEREREZEnsT1MRERERO7k8Q7d5s2bWz0+efIkJElCkyZNEBBg/+0lSUJQUBCaNGmCLl26YPTo0bzFzAmNhqse+yudToe0tDR1m/wXcyUG5kkczJUYHLXJqO7YHvYensv+i9cDcTBXYmCexKHVai88qIfdNpuOb0JRRZFH6o4Ojkb/S/p7pO7qRGtDeDzaTZs2WT1OTU0FAMyfPx9t2rTx9Ns3KPXw70K9IUkSAgMDfR0GuYC5EgPzJA7mSgzVR4+Se7E97D08l/0XrwfiYK7EwDyJw/raVP+uU0UVRcgvy/d1GHUmWhvC693P3bt3BwCEhIR4+62JiIiIiHyO7WEiIiIiqguvd+guXrzY22/ZYPw7vRr5IVmWUVJSAsA0/51Go/FxRGQPcyUG5kkczJUYZFn2dQgNCtvDnsNz2X/xeiAO5koMzJM4ZFm2yA87bvyVaG0I/sbXIzJ7dP2WwWDAkSNHcOTIERgMBl+HQw4wV2JgnsTBXInBaDT6OgQit+C57L94PRAHcyUG5kkcVh2F7LbxW6K1Ifxixt/z58+jtLTUpR9e9UUliIiIiIhEx/YwEREREbnKZx26v/76Kz7//HPs2rVLvU3AGUmSsH//fg9HRkRERETkeWwPExEREdHF8EmH7ksvvYQlS5YAABROE0BEREREDQzbw0RERER0sbzeobtq1Sp89tlnAICgoCAMHDgQHTp04CTeRERERNQgsD1MRERERHXh9Q7dpUuXAgCaNWuGhQsX4pJLLvF2CEREREREPsP2MBERERHVhdeHABw6dAiSJOGhhx5i45WIiIiIGhy2h4mIiIioLrw+QtdgMAAA2rdv7+23rv8kXwdA9kiSBJ1Op26T/2KuxMA8iYO5IqqJ7WFqiHg9EAdzJQbmSVBMFbmJ1zt0W7RogczMTJSVlXn7res9Lf+I+y2dTodOnTr5OgxyAXMlBuZJHMyVGMwfCMk72B72HJ7L/ovXA3EwV2JgnsQREGDZ9cZ+G38lWhvC61MuDBo0CACwbds2b781EREREZHPsT1MRERERHXh9Q7d8ePHqwtAZGZmevvtiYiIiIh8iu1hIiIiIqoLr3foNmrUCPPmzUNMTAxGjx6NJUuWoKSkxNth1Euy4usIyB6DwYDs7GxkZ2er8+aRf2KuxMA8iYO5EgNz411sD3sOz2X/xeuBOJgrMTBP4rDODztu/JVov0den0N3wIABAICKigqcPXsWL730El5++WVERUUhODjY4WslScKGDRu8EaaQFIV/GPyVLMvIz88HAMTHx/s4GnKEuRID8yQO5koMbEN4F9vDnsNz2X/xeiAO5koMzJOgeJnyW6K1IbzeoXvixAmrx4qiQFEUFBYWOn2tt1ZunD17NubOnas+XrRoEXr27OnwNVu2bMFXX32FjIwMFBUVITo6Gmlpabj11lvRt29fT4dMRERERIIQoT1MRERERP7L6x26N910k7ffslYOHDiABQsWuFxelmVMnToVy5cvt3o+Ly8PeXl52LBhA2655RZMnz4dGo3XZ7ggIiIiIj/j7+1hIiIiIvJvXu/QfeWVV7z9li4zd84aDAbExMS4NErirbfeUjtz27dvjwkTJiAxMRHZ2dmYO3cu9u/fj2XLliE6OhqTJ0/29CEQERERkZ/z5/YwEREREfk/r3fo+rNFixYhIyMDSUlJGDRoED7++GOH5Y8ePYr58+cDADp27IglS5ao85516tQJ/fv3xx133IG9e/di3rx5GDFiBFq2bOnx4yAiIiIiIiIgu7gcxac8t+hgZNNIJHqsdiIiIts4B8C/Tp48iXfeeQcA8OKLL0Kn0zl9zcKFC9VV8KZOnVpjEYuQkBBMnToVgGm1vNpM5UBERERERER1U6k34nylwWP/KvVGXx8iERE1QD7v0K2oqMDOnTvxww8/YOXKlTh//rxP4pg+fTrKyspw0003oUePHk7LK4qCjRs3AgCSkpLQuXNnm+U6d+6M1q1bAwA2btwo3Kp5RERERORZ/tIeJiIiIiIx+GzKhdzcXLz55pv44Ycf1FGugGnqgjZt2qiPly1bhqVLl6JRo0aYP3++R1b2Xbt2LTZv3ozIyEg89dRTLr0mJycH+fn5AIDu3bs7LNujRw8cPXoUeXl5yMnJQWKiZ27K4arH/kuj0SAyMlLdJv/FXImBeRIHcyUGtiF8w5/aw/UFfzb+i9cDcTBXYmCeBMXLlN8SrQ3hkw7dP//8ExMnTsTZs2etRqza+uH1798f06dPh8FgwNatW/Gf//zHrbGcPXsWM2fOBAA88cQTiI6Odul1hw8fVreTkpIclrXcf+TIEY916CqKAqPR9i0/Go1G/fkqigJZlh3WpdVq1W1ZkaHICiDLMD+r1+vV3EmSZDVFhcFgcFi/RqNBQMCFU8+yLlsCAgLUC5SiKNDr9Q5j1+l06rHKsmz1Aak6T8eu1Wqh1WoREBCA5ORkVFVVQZZlVFVV2SxveazOYgeAwMBAddtoNNrNP8A8uZIn83GYf0ft5Yp58k3szJN9/ponczyOcsU8+Sb26nniHUTe50/t4frE8veA/Iu5PUz+j7kSA/MkDutrk1idhg2JaG0Ir0d79uxZPPjggygpKUGTJk3w4IMPolu3brjhhhtslo+JiUGfPn2wadMmbNmyxe0N2NmzZ+P06dO4/PLLMXLkSJdfd+rUKXW7adOmDsta7s/Nza19kC4qLimGcjrP5r7YmFj1Q7Ber0dBYYHDupo3a65ul5WV4ezZs9AZg9Hk3+cOHz6MsrIyAEBYWBhSU1PV8llZWSguLrZbd1xcnFWn9oEDBxx+CE5KSkJUVJQae0ZGhsPY09LS1GMtKSnBkSNH7JbV6XTo1KmT+jg3N1cdeW1LZGSk1UUzMzMTpaWldssnJCQgPj5efews9pSUFISHhwMw/dwPHTrksHzXrl3V7YKCAuTk5NgtyzwxTwDz5AjzZBvz1DDydPDgQYexk3v5W3uYiIiIiMTi9Q7dRYsWobCwEFFRUVi6dCmaN2/u9DVXXnklNm7ciL/++sutsezcuRPLli1DQEAAXnzxxVoNr7b8kBQaGuqwbEhIiLpt7gQlIiIioobJX9rDhYWF+Ouvv/DXX38hIyMDGRkZ6pceN910E2bNmuW0jhUrVmDKlCkuvd8rr7yCm2++uS4hExERERF80KG7efNmSJKEu+++26XGKwB1DrHjx4+7LY6qqipMnToViqJg3LhxaNu2ba1eX1lZqW5b3hJpi+XtoRUVFbULtBYiGjVCsBRvc5/lnDo6nQ7xcbbL2RIaGoqQ4BAg4sJ0FG3atLGacsFSy5YtHU4rUX1+n3bt2jm9pdUy9rS0NIfxWuajcePGDstXj71Zs2ZWI5aqqx57cnKy01taAdPIrczMTISEhKBly5Z2zxnLYw0NDXV6rJZiY2PVkWK2ME/O8wSYcmX+EsZerpgn25gn5gmwzhMApKamIisrC4DtXDFPtnk7T8nJycjMzLRbntzLX9rDV155pdvq8hd6vd6q7U3+w9weBkx/c5x9hiLfYa7EwDyJw2AwWLQbOc2Vv3I2fZq/8XqHrrkR2q1bN5df07hxYwBw64q/H3/8MY4cOYLmzZvj4YcfrvXrg4KC1G1nSbecLzA4OLjW7+UqjUZb4wOaLZIkuVROrVfSAFoA1TqF7antvCO1ufBIklSrRrpGo6lVeU/FriiKOqpbp9O5FFNtY68+H6IzzJNtiqKgvLxcfZ2zuJgn+5gn+xpKnszlXc0V82SfN/JE3uMv7WFLzZs3R1JSErZu3XrRdcybNw9xcXF29zubpozqN8v2MOft9m/MlRiYJ0ExVeQmXu/QNY9src0HE/M0Be7qDM3MzMTHH38MAHjuueecTplgS1hYWI347DF/kAWcT89ARERERPWbP7SHAeChhx5CWloa0tLSEBsbi5ycHAwYMOCi62vVqhUSEhLcFh8RNRzr9p1C4XnbC0e7Q0x4IIZ04JdKRFR/eL1DNyYmBqdOnUJOTo7VYh+OHDhwAAAcfuNfGwsXLoRer0diYiIqKiqwZs2aGmX++ecfdfv3339HQYFpEbF+/fohNDTUaoSB5QJptljub9asWV3DJyIiIiKB+UN7GAAeffRRt9VFRFQXheerkHfWc9MTEhHVN17v0O3UqRNOnTqFX375Bddee63T8oqiYNmyZZAkqVa3pTlingIhOzsbkydPdlr+ww8/VLc3btyI0NBQdR4zAA5Xqa6+PykpqbbhEhEREVE94g/tYSIiIiISl8Z5Efe64YYboCgKVq1apY40cGTWrFk4ePAgAGD48OEejs51CQkJ6giJHTt2OCxr3h8fH8/b0IiIiIgauPrSHiYiIiIi3/B6h+7AgQPRs2dPGAwG3HXXXfj8889RWFio7jcYDMjLy8P333+P22+/HYsWLYIkSRg0aBAuv/xyt8Qwa9YsHDp0yOE/y4XSFi1apD5v7pCVJEmdY+zIkSNIT0+3+V7p6enqCN0BAwbUWLWaiIiIiBoWf2gPe8KUKVPQu3dvdOzYET179sStt96Kt956C3l5eb4OjYiIiKhe8fqUCwDw3nvv4a677sL+/fsxY8YMzJgxQ+3ovOmmm6zKKoqCzp07Y9asWb4I1aFx48bhq6++gtFoxIwZM7BkyRKrhSoqKiowY8YMAKZFL8aNG+erUImIiIjIj9SX9rCl7du3q9vFxcUoLi7Gn3/+iU8//RTPPvssbrvtNo/HoNfr1enVqgsICIBGYxrPIssyDAaDw7oCAwPVbaPRCFmWodPpTI9lGTAaLxSWAK1Gqz6UZdnhqvOSJKmxmOozWq98Lssw12ZUZGgURT0/FCiQjbLD2DVaDST8W15RIMsOyisX3gswfaHgqLxGo7Fa0E+v1zs8Vq1WC61Wa/WcXq+3W76ueTIajVYxKooCxXw8EiBJF37uiqIADmKHJFkNyFEUucYK9ZbHLsuyGruiKA6PEwB0Op1av7NjlSRJPf8A7+TJ/Dpb3JEny2OSZdNzACBJgKYOv0+ybKyRVlk2WmwzT/bYyhMAm7linlzPk8FgsP6bpFi/lylu898axWHdpvKWYzMtylu8znxMnjrW6vWaj83h9ebf+tXrmQvlLXk6T87OMX/jkw7diIgIfPnll/jggw/w+eef49y5czbLhYSEYMyYMXj00Uet/rD4i9atW+Oee+7BnDlzsHfvXowePRr33nsvEhMTkZ2djU8++QT79+8HANxzzz1o1aqVR+Ph6F//pdVq1dHdti7A5D+YKzEwT+JgrsRg+QGLvKO+tIcBIDExEYMGDUKXLl3UhYNzcnKwbt06rFu3DpWVlZg2bRokScKoUaM8GktmZqbdfSkpKQgPDwcAlJWV4dChQw7r6tq1q7pdUFCAoqIitGvXDgBQdKYI+sILI48DdYGIjY1VHxeXFKOiwv4CT2FhYWgc0fhC/acLTJ26/woJP4uof7fPlpxFsL4CIcEhAADZKCMv3/Go5/i4ePVvbkVlBc6cOWO3bJAcihiLx7m5ucjPz7dbPjIyEsnJyerjzMxMlJaW2i2fkJCA+Ph49XqQk5OjTiFiS13zlJOTg+joaLRu3RoAUFlZqcan0WoRGhqqlq+sqHDY6aML1CEo6MKAnbLSshqdAxGVlRf2l5Wpsev1emRkZDiMPS0tTf29Likpcbg2i06ns1pE0VN5Aqyv1/Zy5Y48hYaGqr9TZ4qKkF9o+juoq/b7VOLC71OExe/T6dMFVh2DABBkbATAdE6cP38eERERAJin6mzlCYDNXDFPruXJXL/5b1JVVWWN14aEhKr5NBpllJeX2a0bAMLDG6nber0elf/+HaqsvPDzz87ORlFREcLCwpCamqo+n5WVheLiYrt1x8XFITExUX184MABmx2dlueEmSzLTu/KMV8PANMgSEfXJ61Wi2aNmqmPCwsL1XPSlrrmyVEbwh/5pEMXMH3zM2nSJNx3333YsWMHMjIyUFRUBKPRiMjISLRv3x5XXnklGjVq5LwyH5o0aRIKCwvx9ddfY//+/Zg0aVKNMiNHjsRjjz3m8Vg07M/1W1qt1uoPBfkv5koMzJM4mCsxsLPdN+pDe3jQoEG46aabagws6NSpE6699lps3rwZjzzyCPR6PV555RX0798fTZo08VG05Evm64GjD+PkH3hNEAPzRNSw+axD1yw0NBR9+/ZF3759fR3KRdFoNJg5cyaGDBmCpUuXIiMjA2fOnEFUVBTS0tIwatQoYY+NiIiIiDxP5Paws87mfv364cEHH8Q777yD8vJyLF++HA888IDH4klNTa1xC6iZ5W2YoaGhSEtLc7ne2NhYREdHq4+jo6IBrcVItGoDKyIbR0KJcHzrsVX9TWKtb+VvFKFuRjSOgObchdFaGq0G8XGOvyjTaC+Mug8OCnZcvnGk1cNmzZo5/CKu+oj+5ORkp7e0WnL2c69rnqKioqxiDAoKQlhYmOlBtTwFBQcjyMmUC5ZCw0JrTLkQFBRkFa+ZTqdzGrvludq4cWOH5aufM/UhT5bHFBUdjUqt6edX/cbTxo0jEVGL36cmTWJr3Mof1fjCSGvziFWAeXLEnCcANnPFPLmeJ8sRr0FBwRf+JtmIR6vV1NjviE6nU/NseUdBYmIiWrRoUeNYW7ZsaRVPddWPtV27dnanXLD1WmcDOSzrDw4OrtXAj5iYGPWcdFY3UPs8paamOryDxN/4vEPXXz3yyCN45JFHXC4vaiOciIiIiMiTRo0ahXfffReKomDHjh0e7dDV6XQuTU2h0WhqNYVF9Xn2tBoN4GB0XG2nMbGcf/ffCi7skzRWPVwSpFqNzJMkJ+Ul61gtO4BcYa8D3Z7a/NzrmifAdPySnXxIklSz99ABSdLU6BS27NSwzLskSR491vqWJ43G9tyw5vprQ1P996nac8yTa+zN12svV8yTi/VLUrU5cKuTajmdpkV5i9fZOyZPHqvT600dy3s6T7Ut72s+6dA9efIkANM3Ps5+ySorK9VVf5s3b+7x2ERmlB1PnE2+U1VVpc75Yzm3D/kf5koMzJM4mCsxiLYIRH3QkNrDMTExiIyMxJkzZ5zOrVdXer2ef2f8FK8H4mCuxMA8icNyUTSHCzGST4nWHvb6Chhbt27FgAEDcMMNN6C8vNxp+fLyclx33XUYOHAg/vjjDy9ESERERETkOQ2xPczFe4mIiIjcx+sduj/88AMURcHAgQPRuHFjp+UjIyMxZMgQyLKMtWvXeiFCIiIiIiLPaWjt4aKiInUV67i4OB9HQ0RERCQ+r3fopqenQ5IkXHXVVS6/pnfv3upriYiIiIhE1tDaw0uXLlUXJenevbuPoyEiIiISn9c7dE+cOAEAaNWqlcuvueSSSwAAOTk5ngiJiIiIiMhr6kt7OCcnB/v373dYZvPmzfjggw8AmFazHjFihDdCIyIiIqrXvL4omsFgAIBarWRnLltZWemRmIiIiIiIvMVf2sM7d+7E8ePH1cfmaREAICsrCytWrLAqf/PNN1s9PnHiBMaOHYsuXbqgX79+SE1NRXR0NAAgOzsb69atw7p169TRuU899RTi4+PdFj8RERFRQ+X1Dt2oqCicPn0a2dnZ6NChg0uvyc7OBmCaP4yIiIiISGT+0h5evnw5vvnmG5v7du/ejd27d1s9V71D12zPnj3Ys2eP3fcJCQnBlClTMGrUqIsPloiIiIhUXp9yoV27dgBQqwUdzGUvvfRSj8REREREROQt9aU93KFDB8yePRtjxozBZZddhubNmyMkJAQ6nQ6xsbG44oorMGnSJGzcuJGduURERERu5PURugMGDMCWLVvw448/4vvvv8c111zjsPzatWuxfv16SJKEgQMHeilKMWkkydchkB0BAQFISUlRt8l/MVdiYJ7EwVyJoTa3/lPd+Ut7eNasWZg1a9ZFvz48PBzDhg3DsGHD3BZTXfFc9l+8HoiDuRID8yQOjcZiLCW7bfyWaG0Ir4/QHT58OFq0aAFFUfDEE0/g1VdfRW5ubo1yubm5eOWVV/Dkk09CkiQ0a9YMt9xyi7fDFQr7c/2XRqNBeHg4wsPDrf+Yk99hrsTAPImDuRIDc+NdbA97Ds9l/8XrgTiYKzEwT+Kwzg87bvyVaL9HXv8aJzAwEO+//z7GjBmDsrIyLFiwAAsWLECzZs0QFxcHAMjPz1cbtYqiIDQ0FO+//z4CAwO9HS4RERERkVuxPUxEREREdeGTcfnt2rXDsmXL8OSTT2L//v0AgJMnT1o1Ws06dOiA1157DcnJyb4IVSgWPzbyM7Iso6ysDAAQGhoq3Dc/DQlzJQbmSRzMlRhkWfZ1CA0O28OewXPZf/F6IA7mSgy+ylNMmOe+WPRk3b4ky7JFfthx469Ea0P4bKKV5ORkrFixAr/++is2b96MAwcO4MyZMwBMK/+2b98e/fv3R69evXwVonA45YL/MhgMOHToEAAgLS2No2v8GHMlBuZJHMyVGIxGo69DaJDYHnY/nsv+i9cDcTBXYvBVnoZ0bOqV96lPrDp02Z/rt0RrQ3i9Q/fkyZMATN8gRUZG4qqrrsJVV13l7TCIiIiIiHyC7WEiIiIiqguvd+j2798fkiThueeew5gxY7z99vXayeIKnDtV4rH6I5tGItFjtRMRERE1DGwPExGRqBalr0ZmUZ5H6k6OjsfYztd7pG6i+sbrHbrBwcGorKxEWlqat9+63qs0GHG+0uCx+kP0Yg0/JyIiIvJHbA8TEZGoTp4vwJEzJz1Sd0ig1iP1EtVHXp/dPD4+HoB4kw0TEREREbkD28NEREREVBde79A1zw+2a9cub781EREREZHPsT1MRERERHXh9Q7dsWPHIjg4GPPnz0denmfmXSEiIiIi8ldsDxMRERFRXXi9Q7dVq1Z4/fXXUV5ejltvvRWrVq1CVVWVt8MgIiIiIvIJtoeJiIiIqC68vija2LFjAQDR0dHIycnBU089hf/+979o2bIlGjduDI3Gfh+zJElYuHCht0IVjiRJvg6B7AgMDETXrl19HQa5gLkSA/MkDuZKDDqdztchNChsD3sOz2X/xeuBOJgrMTBP4ggIsOh6Y7+N3xKtDeH1Dt3t27dbdTwqioKqqir8888/dl8jSRIURWGHJREREREJj+1hIiIiIqoLr3fodu/e3dtvSURERETkN9geJiIiIqK68HqH7uLFi739lg2Hovg6ArLDaDSioKAAABAbGwutVuvjiMge5koMzJM4mCsxGI1GX4fQoLA97Dk8l/0XrwfiYK7EwDyJw2g0WuSH/Tb+SrQ2hNc7dMlz+GfBfxmNRuTk5AAAoqKieLH1Y8yVGJgncTBXYpBl2dchELkFz2X/xeuBOJgrMTBP4lAsB9+x48ZvidaGsL/iAhERERERERERERH5Fb8YoXvq1CmcPn0aFRUVSEtLQ3BwsK9DIiIiIiLyGraHiYiIiMhVPuvQPX/+PObOnYtvvvkG+fn56vOrVq1CmzZt1Mdr1qzB+vXr0ahRI7z00ku+CJWIiIiIyO3YHiYiIiKii+GTDt1jx45h4sSJyM7OtppLRJKkGmUvu+wyPPnkk1AUBcOHD0e3bt28GSoRERERkduxPUxEREREF8vrc+hWVlbivvvuw/HjxxEcHIwJEybg448/tls+ISEBPXv2BABs2rTJW2ESERERNRg6nc7XITQobA8TERERUV14fYTuF198gaysLISEhODzzz9Hu3btnL6mT58+2LZtG9LT0z0fIBERERGRB7E9TERERER14fUO3fXr10OSJIwdO9alxisApKamAgCysrI8GRqRx0iShLCwMHWb/BdzJQbmSRzMlftsOr4JRRVFHqk7ShuFSER6pG6qie1haoh4PRAHcyUG5klQTBW5idc7dDMzMwEAvXv/f3t3Hh5Vef5//DOTTHYgBEhYEpbEkoCELwiKqC0KKC6tijsKUsWtLlRcri7fC1vFWtuqbV2+VVzBUoosYrVaq6C0VssqP4IICsgShMQQEghZJzO/P2KGmWRmsp6ZeSbv13XVnnCeec4zc8/Muc895zznrFY/JjU1VZJ09OhRK4YUNfgSj1wOh8NzIIbIRqzMQJzMQaw6T2l1qYori1tu2A4xCTEUdEOIfNg6TB8SudgfmINYmYE4mSM21rv0Rt0mUpmWQ4R8Dt3KykpJUlJSUqsfU1tbK6nphwAAAAAwD/kwAAAAOiLkBd3GswsOHDjQ6sd8+eWXkqQ+ffpYMSQAAAAgZMiHAQAA0BEhL+iefPLJkqT169e3+jFvvPGGbDabRo0aZdGoooPb7Q73EBCA0+nUrl27tGvXLjmdznAPB0EQKzMQJ3MQKzO4XK5wD6FLIR+2Dt8zkYv9gTmIlRmIkzl840PdJlKZ9jkKeUF3ypQpcrvdeu211/T111+32P6VV17xJLsXXXSR1cMDLOFyuVRWVqaysjIOmiMcsTIDcTIHsTIExxYhRT5sHU5wiFzsD8xBrMxAnAzFbipimZZDhLyge8kllyg3N1c1NTWaMWOG1qxZ4/Oi2Ww2ud1ubdmyRffee69+85vfyGazaezYsZowYUKohwsAAAB0KvJhAAAAdETI76pgt9v1pz/9Sddee60OHDig2267TQkJCbLZGu70N2P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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot settings\n",
+    "plot_config = dict(\n",
+    "    bins=20,\n",
+    "    binrange=(0, 1),\n",
+    "    stat=\"percent\",\n",
+    ")\n",
+    "\n",
+    "for base_model, it_model in tqdm(base_it_pairs):\n",
+    "    fig, axes = plt.subplots(ncols=2, figsize=(8, 3), gridspec_kw=dict(wspace=0.33), sharey=False)\n",
+    "\n",
+    "    for idx, ax, m in zip(range(len(axes)), axes, (base_model, it_model)):\n",
+    "        if m is None:\n",
+    "            ax.remove()\n",
+    "            continue\n",
+    "\n",
+    "        model_row = task_df[(task_df[model_col] == m) & (task_df[\"uses_all_features\"])].iloc[0]\n",
+    "        model_id = model_row.name\n",
+    "\n",
+    "        scores_df = scores_df_map[model_id]\n",
+    "        scores_df[\"group\"] = sens_attr_data.loc[scores_df.index]\n",
+    "\n",
+    "        # sns.histplot(\n",
+    "        #     data=scores_df,\n",
+    "        #     x=\"risk_score\",\n",
+    "        #     hue=\"group\",\n",
+    "        #     ax=ax,\n",
+    "        #     **plot_config,\n",
+    "        # )\n",
+    "\n",
+    "        for group_val, group_name in sens_attr_map.items():\n",
+    "            group_scores_df = scores_df[sens_attr_data.loc[scores_df.index] == group_val]\n",
+    "            sns.histplot(\n",
+    "                group_scores_df[\"risk_score\"],\n",
+    "                label=group_name,\n",
+    "                ax=ax,\n",
+    "                alpha=0.5,\n",
+    "                **plot_config,\n",
+    "            )\n",
+    "\n",
+    "        # Remove left plot legend\n",
+    "        if idx == 0:\n",
+    "            ax.legend(loc=\"upper left\")\n",
+    "\n",
+    "        ax.set_xlim(0, 1)\n",
+    "        # ax.set_ylim(0, 1)\n",
+    "\n",
+    "        ax.set_title(model_row[\"name\"])\n",
+    "\n",
+    "    plt.plot()\n",
+    "    save_fig(fig, f\"score-per-subgroup.{base_model}.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77754ec9-7368-4edd-bf26-465710a99e73",
+   "metadata": {},
+   "source": [
+    "## Compute mean under-/over-estimation of risk score\n",
+    "It's basocally akin to computing ECE without the absolute..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "323b2ad2-1d01-4a9c-ab88-5f5079a7d014",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_confidence_bias_metric_helper(data):\n",
+    "    \"\"\"Helper to compute the under- / over-confidence metric.\n",
+    "    \"\"\"\n",
+    "    mean_label = data[\"label\"].mean()\n",
+    "    mean_score = data[\"risk_score\"].mean()\n",
+    "\n",
+    "    if mean_score < 0.5:\n",
+    "        return ((1 - mean_score) - (1 - mean_label))\n",
+    "    else:\n",
+    "        return (mean_score - mean_label)\n",
+    "\n",
+    "def compute_confidence_bias_metric(model_id, quantiled_bins=False, scores_df=None):\n",
+    "    if scores_df is None:\n",
+    "        scores_df = scores_df_map[model_id]\n",
+    "\n",
+    "    discretization_func = pd.qcut if quantiled_bins else pd.cut\n",
+    "    scores_df[\"score_bin\"] = discretization_func(scores_df[\"risk_score\"], 10, labels=range(10))\n",
+    "    \n",
+    "    bias_metric_val = (1 / len(scores_df)) * sum(\n",
+    "        len(bin_data) * compute_confidence_bias_metric_helper(bin_data)\n",
+    "        for bin_ in range(10)\n",
+    "        if len(bin_data := scores_df[scores_df[\"score_bin\"] == bin_]) > 0\n",
+    "    )\n",
+    "    return float(bias_metric_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "57d97476-15cf-4eba-a53b-468b1f23f1ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81416b6ae6a041bdacc00c142bcdb7b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "confidence_scores = {\n",
+    "    m: compute_confidence_bias_metric(m)\n",
+    "    for m in tqdm(scores_df_map.keys())\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "66363a5b-9104-4da5-a96f-ad59eb464634",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Meta-Llama-3-8B-Instruct__ACSPublicCoverage__-1__Num     -0.161384\n",
+       "Mixtral-8x7B-Instruct-v0.1__ACSPublicCoverage__-1__Num   -0.061690\n",
+       "Mixtral-8x7B-Instruct-v0.1__ACSEmployment__-1__Num       -0.056597\n",
+       "Meta-Llama-3-70B-Instruct__ACSPublicCoverage__-1__Num    -0.052221\n",
+       "gemma-7b__ACSIncome__-1__Num                             -0.033482\n",
+       "                                                            ...   \n",
+       "gemma-7b__ACSEmployment__-1__Num                          0.341868\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num                       0.355603\n",
+       "gemma-2b__ACSIncome__-1__Num                              0.367876\n",
+       "Mistral-7B-v0.1__ACSTravelTime__-1__Num                   0.438141\n",
+       "gemma-2b__ACSTravelTime__-1__Num                          0.438312\n",
+       "Name: under_over_score, Length: 100, dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_results_df[\"under_over_score\"] = all_results_df.apply(lambda row: confidence_scores[row.name], axis=1)\n",
+    "all_results_df.sort_values(\"under_over_score\", ascending=True)[\"under_over_score\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "a558de58-e4e9-4105-8fbd-4abafe41fca2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TASK = \"ACSIncome\"\n",
+    "# TASK = \"ACSTravelTime\"\n",
+    "# TASK = \"ACSMobility\"\n",
+    "# TASK = \"ACSEmployment\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "987310f0-4078-47d0-b841-b43e83446e4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/under_over_score.ACSIncome.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_df = all_results_df[\n",
+    "    (all_results_df[task_col] == TASK)\n",
+    "    & (~all_results_df[numeric_prompt_col].isna())\n",
+    "]\n",
+    "\n",
+    "# Omit Gemma models bc they're garbage :)\n",
+    "plot_df = plot_df.drop(index=[id_ for id_ in plot_df.index if \"gemma\" in id_.lower() or \"yi\" in id_.lower()])\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(4, 3))\n",
+    "sns.barplot(\n",
+    "    data=plot_df.sort_values(\"under_over_score\", ascending=True),\n",
+    "    x=\"under_over_score\",\n",
+    "    y=\"name\",\n",
+    "    # y=task_col,\n",
+    "    hue=\"is_inst\",\n",
+    "    ax=ax,\n",
+    ")\n",
+    "\n",
+    "handles, _ = plt.gca().get_legend_handles_labels()\n",
+    "plt.legend(handles=handles, labels=[\"Base\", \"Instruct\"])\n",
+    "\n",
+    "plt.ylabel(\"\")\n",
+    "plt.xlabel(r\"$\\mathrm{R}_\\mathrm{bias}$\")\n",
+    "\n",
+    "plt.title(f\"Risk score confidence bias\")\n",
+    "\n",
+    "save_fig(fig, f\"under_over_score.{TASK}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e37f6688-c821-43ee-899a-a6308b723bf3",
+   "metadata": {},
+   "source": [
+    "## Compute risk score fairness per sub-group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "46e74470-cf67-43da-855a-727a37912916",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    2\n",
+       "2    1\n",
+       "8    1\n",
+       "Name: RAC1P, dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sens_attr_data = acs_dataset.data[sens_attr_col]\n",
+    "sens_attr_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "6c50ef72-2027-4cb8-8279-89880376652c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_signed_score_bias(scores_df, quantiled_bins=False, n_bins=10):\n",
+    "    discretization_func = pd.qcut if quantiled_bins else pd.cut\n",
+    "    scores_df[\"score_bin\"] = discretization_func(scores_df[\"risk_score\"], n_bins, duplicates=\"drop\", retbins=False)\n",
+    "    bin_categories = scores_df[\"score_bin\"].dtype.categories\n",
+    "\n",
+    "    return (1 / len(scores_df)) * sum(\n",
+    "        len(bin_data) * (bin_data[\"risk_score\"].mean() - bin_data[\"label\"].mean())\n",
+    "        for bin_ in bin_categories\n",
+    "        if len(bin_data := scores_df[scores_df[\"score_bin\"] == bin_]) > 0\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "a6dfaa3b-1a75-44fc-9f72-3dcb61051223",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_signed_subgroup_score_bias(model, group: int | str) -> float:\n",
+    "    scores_df = scores_df_map[model]\n",
+    "    if isinstance(group, str):\n",
+    "        group = name_to_sens_attr_map[group]\n",
+    "\n",
+    "    scores_df = scores_df[sens_attr_data.loc[scores_df.index] == group]\n",
+    "    # return compute_confidence_metric(model, scores_df=scores_df)\n",
+    "    return compute_signed_score_bias(scores_df)\n",
+    "\n",
+    "\n",
+    "def compute_calibration_fairness(model: str, priv_group: int | str, unpriv_group: int | str) -> float:\n",
+    "    return (\n",
+    "        compute_signed_subgroup_score_bias(model, group=priv_group)\n",
+    "        - compute_signed_subgroup_score_bias(model, group=unpriv_group)\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "2770de00-0ade-4c9a-8a06-684957f2b169",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3388197/3464681156.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  scores_df[\"score_bin\"] = discretization_func(scores_df[\"risk_score\"], n_bins, duplicates=\"drop\", retbins=False)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>White_score_bias</th>\n",
+       "      <th>Black_score_bias</th>\n",
+       "      <th>Asian_score_bias</th>\n",
+       "      <th>White_v_Black_score_bias</th>\n",
+       "      <th>White_v_Asian_score_bias</th>\n",
+       "      <th>Asian_v_Black_score_bias</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mistral-7B-v0.1__ACSIncome__-1__Num</th>\n",
+       "      <td>0.353045</td>\n",
+       "      <td>0.329428</td>\n",
+       "      <td>0.319214</td>\n",
+       "      <td>0.023617</td>\n",
+       "      <td>0.033831</td>\n",
+       "      <td>-0.010215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gemma-1.1-2b-it__ACSIncome__-1__Num</th>\n",
+       "      <td>0.260100</td>\n",
+       "      <td>0.403764</td>\n",
+       "      <td>0.202488</td>\n",
+       "      <td>-0.143664</td>\n",
+       "      <td>0.057612</td>\n",
+       "      <td>-0.201276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Yi-34B-Chat__ACSIncome__-1__Num</th>\n",
+       "      <td>0.210531</td>\n",
+       "      <td>0.229600</td>\n",
+       "      <td>0.212839</td>\n",
+       "      <td>-0.019069</td>\n",
+       "      <td>-0.002308</td>\n",
+       "      <td>-0.016761</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     White_score_bias  Black_score_bias  \\\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num          0.353045          0.329428   \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num          0.260100          0.403764   \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num              0.210531          0.229600   \n",
+       "\n",
+       "                                     Asian_score_bias  \\\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num          0.319214   \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num          0.202488   \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num              0.212839   \n",
+       "\n",
+       "                                     White_v_Black_score_bias  \\\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num                  0.023617   \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num                 -0.143664   \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num                     -0.019069   \n",
+       "\n",
+       "                                     White_v_Asian_score_bias  \\\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num                  0.033831   \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num                  0.057612   \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num                     -0.002308   \n",
+       "\n",
+       "                                     Asian_v_Black_score_bias  \n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num                 -0.010215  \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num                 -0.201276  \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num                     -0.016761  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calibration_per_subgroup_df = pd.DataFrame([\n",
+    "    pd.Series(\n",
+    "        {\n",
+    "            f\"{group}_score_bias\": compute_signed_subgroup_score_bias(model_id, group)\n",
+    "            for group in sens_attr_map.values()\n",
+    "        },\n",
+    "        name=model_id,\n",
+    "    )\n",
+    "    for model_id in task_df.index\n",
+    "])\n",
+    "\n",
+    "calibration_per_subgroup_df[\"White_v_Black_score_bias\"] = \\\n",
+    "    calibration_per_subgroup_df[\"White_score_bias\"] - calibration_per_subgroup_df[\"Black_score_bias\"]\n",
+    "\n",
+    "calibration_per_subgroup_df[\"White_v_Asian_score_bias\"] = \\\n",
+    "    calibration_per_subgroup_df[\"White_score_bias\"] - calibration_per_subgroup_df[\"Asian_score_bias\"]\n",
+    "\n",
+    "calibration_per_subgroup_df[\"Asian_v_Black_score_bias\"] = \\\n",
+    "    calibration_per_subgroup_df[\"Asian_score_bias\"] - calibration_per_subgroup_df[\"Black_score_bias\"]\n",
+    "\n",
+    "calibration_per_subgroup_df.sample(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1efc1204-874f-4e5e-b1b4-716e5af1d044",
+   "metadata": {},
+   "source": [
+    "**Note:** This is an example of comparing risk score bias between different sub-groups. A more detailed analysis must be conducted to draw any sort of conclusions regarding prediction fairness of the LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "ca1a5631-3070-4d93-9258-b527dac8fe85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/score_bias.Asian_v_Black_score_bias.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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BLVu2qCTwByDRDUnZwcJAeR0FAgEAoHfv3kp/tsHBwcw4HG1tbbnnAYvFwtatW5m7NqmpqRKZvGRZsmRJlcfp9OnTzPK6deuq3H7ixIno2LEj8x7y8/OrrIeiXFxcpAb+Il988YXEHY9Lly6pbN/yiAfzjx8/rrRe/Nyqje9FY0fBPyGEVJOhoSEOHjyIY8eOYfDgwTJbC0tKSvD333/D3d0dixcvrtHg0MjISKbV38LCosrWuSZNmmD48OHV2oeuri6++uoruduID9ATZbGp6MGDB8yyi4uLQvseNGgQsyzeogxA4uKpe/fuVY6x6NChA3r37q3QfpUxevRold3RadGiBbN84cIFlZQpsmfPHmzfvh1CoRDa2trYvXs3ZsyYobLyxet+6dKlSoNaFfXw4UNmecaMGUpfuImfJ46OjrCwsJC7vaWlpURXmNDQ0Cr3Ia1Lmjgej8cEs02aNKny+yQi6hIkFAolxgnUlKura7W2UeQzUMSnT58QEhICT09P7Nu3D7/88gu2bdvG/N28eZPZVto4GfGxB3fv3kVubq5K6kXKUbcfQghRkqOjIxwdHZGXl4fQ0FBERkYiJiYGr169qpTxIjAwEDNmzMDFixeVapEX/wfZo0cPhfpq9+rVC/7+/grvo0OHDlWm8BRPoyltAGp+fj6Sk5MBlLe+Hjx4UKF9iwYfAuWDYMWJv3dF+0P37t1bagYaVejRo4fKyho7diwTtO7ZswePHz+Gi4sLBg8eLBFcVwefz8emTZvg6+sLoHzA64EDB/Dll1+qrN5AebeagwcPQiAQICgoCF9//TUmTZqEoUOHokuXLgoF8Tk5ORIXkbJSSCpCmfOkb9++TBe+inecKmrTpk2VaWTj4+OZ776WlhZ+/fVXheohfhftw4cPCr2mKiwWS2r3qIrEP6ucnBxkZWVJjCOqjoKCAhw4cABXr16VuKslj7Q7Hb169ULLli3x/v17vHv3DuPGjcPEiRPh5OSEnj17Kj1InZSj4J8QQmrI1NQUY8eOxdixYwGUt/49f/4cfn5+8Pf3Z/quvn79Gn/88Qc2b95c7X2Icp0DUDgjR3UHIBsZGVW5jfjFgeh9iRPP6sLlciUGXyqqqKhI4rEy770mWUuqUpMuLhW5ubnh4cOHCAgIAFCejz0kJARA+TwS/fr1w8CBAzF8+HCFuz/8/fffzLExMjLCyZMnq+yLrYxOnTphzZo12LVrF4RCId6+fYvff/8dv//+O5o1a4Y+ffpgwIABcifDy8nJYZZ1dHRqNGhe/DxRdA4O8bkCqupuo8jnn5WVxSwXFBQodf6rIn0sUD4AV5GGBlNTU+jq6jJpUfPy8pQK/kUZfN69e1et10m7SNDW1sauXbuwcOFClJSUID8/HydPnsTJkyehq6uLHj16YMCAARg6dCj69u1bq938PkfU7YcQQlRMS0sL/fv3x2+//QYvLy+JHOp//fUXSktLq12m+J0ERbNaVDfPvyr+gX78+LHGZVTsPiL+3vX19RUqozYn+1FlVhFNTU0cPHgQv/zyS6XuTO/evcP169exefNmDB06FJs2bUJBQUGVZYp3SRLlVq8tc+fOxdmzZ+Hg4CBx/hQWFiIoKAi///47xowZg2+//Rbx8fGVXi8e+NX0mImfJ4qWJX4+VdVSrchxr43zX1nVOU+r8znIsnr1aibwNzQ0xLfffosTJ04gICAAkZGRiI2NRXx8POLj43H27FnmdaKB/BXZ29vj2rVrcHV1lXgvojkKjhw5gunTp2PMmDHMxTNRDLX8E0JILerbty8WLVrEpN0sKytDdHS0zBSZsogHM4pePEibbKe2idezSZMmlfrv17RMaZM/SVMf711ZLBYLbm5ucHNzQ1JSEsLDw/Hs2TNEREQgLS0NQPldFF9fX4SFhcHHx0duK/SYMWOgp6eHixcvgs/nM1l2quqvrixRFqKcnByEhYXh2bNnePbsGWJjY5mBvCEhIZg8eTJOnTqFfv36Ma8Vv0Ct6TETP08ULUv8fFJmUjx5dbCxsWHSidaH6jQy1PRzePbsGdPNzsDAAH/99ZfcsTmKXmBYWVlh586d2Lp1KzMp2bNnz/DixQvm/SUnJ2Pp0qVYv349k7mIyEct/4QQUssq9rWuasIjacS7mijaJ1hVfYerw8zMjFkuLi5WOFiXRzzQrTgeQJb6eO+q0KFDB0yePBk7duxAQEAAbt++jTlz5jDZjVJTU6scR8FisfDjjz9i6tSpAMpbktesWYO///67Vutubm4OZ2dnbN68GX5+fnj06BE2btzI9JMvLS3F1q1bK71GhMPhSHSbqS5lzhPxWbpV0Z1L/PwX79JUHwoLCxUKsvPy8iRmQlbmcxB1VQPKM/lUNSi/ul2DDAwM8OWXX2LlypU4e/YsQkNDsX//fon5BPbs2SN1lmxSGQX/hBBSyyqm5FRmsFrXrl2Z5ejoaJm3ysXJSsVZm5o3by7R314Vg27F3/vz588Vek1tDfatax06dMD69euxfPly5rnAwMAqXye6AJgyZQqA8vEZdXEBIM7U1BTu7u74888/medev37N3M0AyoP/1q1bM49rkhZX/DxR9PiLbyeeyaomdRB9v3Nzc5GSklLjMpUlFAoRFRVV5Xbi3ylzc3Olxl2IX7TJmuBLXHh4eLX3IU5PTw9jxoyBl5cXcwHJ5XIlMkcR2Sj4J4SQWhYXFyfxWJnBqH369GEG22ZnZ1cZJLHZ7HrrBzts2DBm2dvbu8bliacBffnyJRITE+Vun5KSovBFgrpwcnJilhVtUWaxWPjpp58qXQDcunWrVuooS79+/SSy5FSs/9ChQ5llb29vhS5spRE/Tx48eFBlesjMzEyJYFH89crS09OTKEcV539NKJLtSzQhGaB8tiXx7GNVdTfKzMxkJlerKWNjY4l5DCglqGIo+CeEkGo4ffq01ElpZPn06ROOHDnCPDY3N5dooVSUsbGxRN7+33//HRwOR+b2Bw4cUMngQ2XMnTuX6aZy9+5d+Pn5KfxaaV2iOnXqJJGO8LfffmP6klckEAjwyy+/KB1A1jXxDDXyiHdjEu9aUhVpFwA//PCDSi4AFK17UVGRRPeTivV3d3dngsfIyEgcP35cqfoMGTKEyd7D4XDw22+/ydxWKBTil19+YebOaNu2Lb744gul9luR+CRl586dq9bvhTJdAuW5du0aXrx4IXP9kydP8M8//zCP3dzclNqPlZUVsywvsOfz+diyZQvzuctSnYnOxLt40YRgiqHgnxBCqiEqKgpz5szBpEmTcP78ebmtsC9evMDMmTORkJDAPDd//nyFcvRLs2zZMqb1PyYmBosXL660fy6Xiz/++ANnzpypt1zYbdu2xeLFi5nHGzduxM6dO2UGizweD8HBwVizZo3EzJ/iVq1axWSTCQ4OxurVqyulBC0uLsaaNWvw77//VjlfQUPx1VdfYcuWLQgLC5N5QRMdHY2ff/6ZeSzeUq6I2roAWLlyJRYuXIjbt2/LHNuRmZmJ1atXM8Fe+/btK8343KFDB4mBmnv27MHPP/8sM7NRVFQU1q9fj9evX0s8r6GhgdWrVzOPb9y4gc2bN1fq915cXIwNGzZIBL1r1qxR+ntZkb29PXMe83g8LFiwAEePHpXZ/76srAwBAQFYvHixxPemprS1tcHn87Fw4UKpFyBBQUFYtmwZc6E8ePDgKicPlMXR0ZH5foaFhWHnzp2V7gBkZ2dj+fLlCAoKqjIb07lz5/DNN9/A29tb5gURm83Gvn37mO6NmpqaEpO2Edko2w8hhCjh5cuXePnyJbZt24a2bduic+fOMDExgZaWFvLy8hAbGysxmBAARo4ciZkzZyq9zy5duuCHH37A9u3bAZQHwV999RXs7e3RqlUrFBYWIjw8HHl5edDW1sb333+PHTt2AFBNGs/qWLZsGTIyMnDlyhUIhUKcOnUKXl5e6NGjB9q2bQs9PT2w2WxkZGRITIwkaxKlgQMHYu7cuTh58iSA8lz2QUFBGDhwICwsLJCTk4MnT56gpKQEzZo1w+zZs+Hh4VFXb1dppaWl8PHxgY+PDwwNDdG1a1e0atUK+vr6KCgowNu3byWCXFNTUyxbtqza+xFdAAiFQvz111/MBQCLxcKYMWOUqrtQKERQUBCCgoKgra2NLl26oH379jAyMgKbzca7d+/w/Plz5qJGU1MTmzZtklrW999/j7dv3zITbp07dw4+Pj7o06cP2rRpA01NTeTk5ODly5dMMOju7l6pHGdnZ0RERDD59S9duoS///4bAwcOhLm5OXJzcxESEiKRDcjd3R2jRo1S6jOQZdu2bcjOzkZwcDC4XC727t2Lw4cPo2fPnmjVqhV0dHRQVFSE1NRUvH79mrmL1717d5XVoXnz5hgxYgQ8PT0xZ84c2NraomvXrhAKhYiJiZE4rywsLCQuMKurU6dO+Oabb5guRKdOncL169dhZ2cHMzMzZGRkIDw8HFwuF4aGhli7dm2lwd8VxcXF4aeffmJ+Y7t06QITExPweDxkZ2fj2bNnEsdx/vz5tTq/x+eEgn9CCKkGBwcHREVFSQT2qampSE1NlfkaPT09LFiwAAsXLpTIwa6Mb7/9Fnw+H/v27QOXywWHw0FwcLDENkZGRti1a5fEQGNlZhWuCRaLhR07dqB79+7w8PBAYWEhuFwuIiMjZQ7GZLFYEv13K1q7di00NTVx4sQJCAQClJSUMMGiSPPmzXHgwAEkJSWp9P3UFgMDAyaAYbPZiIiIkLmtra0t9u7dq/REWCwWC9u2bQMA5gJA1FKuzAWAeEpILpeLV69eyZwl18zMDNu2bZN510JLSwt//vkn9u/fj1OnToHD4YDL5SIsLAxhYWGVttfU1JR5Z2vLli0wNzfH4cOHweFwwGazpQ6S1tXVxdKlS7Fw4UJF3m616Ojo4NixYzh48CBOnz6NT58+4dOnTwgNDZX5Gm1tbfTu3Vul9VizZg3YbDZ8fX0RFxdXafwRUH7n5dChQxIDr5Xx448/Iicnh/k9ys7OrvS5t2jRAnv37pU6QaA48XNLKBQiJSVF5uBpbW1tLFq0SKmL4saKgn9CCKmGyZMnY/LkyUhISEB4eDieP3+Ot2/f4t27d0wfe0NDQ5ibm8PGxgYDBw7EmDFj0KxZM5XVYd68eRg2bBjOnz+PR48e4cOHD9DR0UHLli3x1VdfYerUqWjZsqVEZpemTZuqbP/VMWvWLEyYMAH+/v54/Pgx4uLikJeXBw6HA0NDQ1haWqJLly6wt7eHo6NjlS13q1evxujRo+Ht7Y0nT54gOzsbBgYGaN26NUaNGoXJkyfD1NRUbYL/0NBQREREICwsDNHR0UhJSUFubi7Kysqgp6eHFi1aoHv37hg9ejScnJxq3DVFlRcAhw8fxqtXrxASEoKoqCgkJibiw4cP+PTpE3R0dGBqagpra2s4OjrCxcWlygtQDQ0NrFq1ClOnToWfnx8eP36MlJQUFBQUQFNTE2ZmZujcuTMcHBzg7Ows9yJoyZIl+Oabb3Dp0iUEBwcjPT0dHz9+hJGREaysrDBkyBC4ubkpPBOwMjQ1NbFixQrMmjULV69exePHj5GYmIj8/HzweDwYGhqidevWsLa2xsCBA+Ho6KjyPuva2tr49ddfMWbMGPj6+iI6Opr5znTs2BHOzs6YMmWKSroI6uvr4/jx47h+/TquXr2KV69egc1mw9jYGFZWVhg9ejQmTJiAZs2ayb0IAsrHDY0aNQqPHz9GZGQk4uPjkZGRATabDRaLhaZNm6Jjx44YNGgQXF1da3zh0tiwhOoyKooQQki17Nu3jxlsvHr1aixYsKCea0QIIaS+0YBfQgj5DAmFQty+fZt5bGdnV4+1IYQQ0lBQ8E8IIZ+hM2fOIDk5GQBgaWkJe3v7+q0QIYSQBoH6/BNCiBq5ffs2Xrx4gcmTJ6NDhw6V1hcXF+P48eM4evQo85x43n1CCCGNGwX/hBCiRkpKSnDq1CmcOnUK7dq1g42NDUxMTMDlcvHu3Tu8ePFCIuf6oEGDMHv27HqsMSGEkIaEgn9CCFFT8tLfsVgsuLi44JdfflHZ5EWEEELUH2X7IYQQNcLhcPD48WM8fPgQMTExyM3NRX5+PkpLS9GkSRO0atUK/fv3h6urK7p161bf1SWEENLAUPBPCCGEEEJII0H3ggkhhBBCCGkkKPgnhBBCCCGkkaDgnxBCCCGEkEaCgn9CCCGEEEIaCQr+CSGEEEIIaSQo+CeEEEIIIaSRoOCfEEIamF9//RU2Njbo1asXPnz4UN/VIYSQGsnLy0Pfvn1hY2ODzZs313d1Gj2a4ZcQQhqQhIQEeHt7AwBmz56NFi1aKPza1NRU3LhxA8+ePUNiYiIKCwtRWloKfX19mJmZwcrKCt26dUO/fv3g4OAAXV1dhct+//49/vnnH0RERCAxMRGZmZkoLS2Frq4ujI2NYWNjg379+sHZ2RmtWrWSWc7Jkyexa9cuhfcrzszMDI8fP1bqtaRhoXO1cTE1NcXcuXPh4eGBy5cvY8qUKbCzs6vvajVaNMkXIYQ0IPPnz8e///4LAwMDBAYGwsTEpMrXZGZm4ueff0ZAQAAU/Uk3MDDA48ePoa+vL3e7pKQk7Nu3D3fv3oVAIKiyXA0NDTg6OmLNmjXo1KlTpfWrV6/GjRs3FKpjRcOGDcPRo0eVei1pGOhcbbw+fvyIYcOGobi4GPb29vDy8qrvKjVa1PJPCCENxNOnT/Hvv/8CANzc3BQK/ENCQvDdd9+hqKiIec7CwgLdu3eHmZkZWCwW8vLyEBcXh3fv3jHbmJubVxlMnT9/Hjt37kRZWRnznK6uLrp16wYrKyvo6+sjNzcXqampSEhIAAAIBALcv38fwcHBuH//PiwsLCTKjI2NZZaHDBmCdu3aVfkeRb744guFtyUND52rjZuRkRGmTp2KEydOICwsDI8fP6bPqZ5Q8E8IIQ3E8ePHAQAsFgvTp0+vcvsXL15g8eLF+PTpEwCgR48eWLNmDQYOHAgWi1Vp+7S0NNy+fRsXL16s8pb7zp07cerUKeZxixYtsGTJEowbNw5NmjSptH1GRgb++usvnDlzBqWlpdDT04O5ubnENqWlpUhOTmYer1ixAj179qzyfRL1R+cqAYCpU6fi5MmTEAqFOH78OAX/9YS6/RBCSAOQnJyMMWPGQCgUKnRLnMvlwtXVFW/evAEAfPXVVzhw4AB0dHSq3Befz8eHDx/QunVrqev//PNP7N+/n3k8atQobN++XWogVdG7d++wcuVK6OrqVnoPL168wOTJkwGUd7mIjIyEnp5elWUS9UbnKhH37bffIiQkBABw/fp1WFtb13ONGh/K9kMIIQ2An58f0wd67NixVW5///59JpjS1dXFb7/9plAwBQCampoyg6nw8HB4eHgwj7/++mvs379foWAKAFq1aoWzZ8/iv//9b6V1r169Ypbbtm1LwVQjQecqESf+++bn51ePNWm8KPgnhJAG4Pr168zyiBEjqtw+MDCQWe7Tpw9MTU1rXAc2m43169czgyW7d++O3377DRoa1ftXoaenB0dHx0rPi/ehtrGxqVllidqgc5WIGzFiBNPV68aNGwoP/CaqQ8E/IYTUM/EBjh07dkTz5s2rfE1aWhqzXNVgSEUdPXoU6enpAAAtLS389ttv1UqxWBXxgIpu9TcedK4ScWZmZujSpQsAIDs7G9HR0fVco8aHgn9CCKln4vnA+/fvr9BrRAMngfL+ycXFxTWqA5vNxoULF5jH48ePh62tbY3KFMfn85ksKwBUWjZp2OhcJRX169ePWab5EOoeBf+EEFLPXrx4wSwr2sXAysqKWc7Ly4O7uzuCgoLA5/OVqsOlS5eYFIwsFgvz589XqhxZkpKSUFpayjymrhSNB52rpKKuXbsyy1FRUfVYk8aJUn0SQkg9i4+PZ5Y7duyo0GsmTZqE27dvM49fvnyJhQsXwsTEBIMHD8bAgQPRv39/hcsTL6tXr14Kv05R4gMoAeDUqVNSUzxKM3jwYAwfPlyl9SF1h85VUpH4pGpxcXH1WJPGiYJ/QgipR0KhUGJCI0tLS4VeN3ToUKxZswZ79+6VaEHNz8/HjRs3mJlJ27ZtC2dnZ0yZMgWtWrWSWlZxcbFE69vIkSOVeStyifehBgBvb2+FX0v51dUbnaukIvHfuczMTPD5fGhqatZjjRoX6vZDCCH16OPHjxKzkioyq6/If//7X1y7dg0TJ06EgYGB1G1SU1Nx5MgRjB49Gvv372eyo4gLDw+XCMr69u1bjXegmIoBVXX06NFDhTWpGRcXF9jY2ODJkyf1XRW1QudqzY0bNw42NjYICwurtX3UVT3Ef+d4PB7y8/NVUTWiIGr5J4SQeiQ+GBJAtfOJd+7cGdu3b8fWrVsRGhqKx48fIzw8HLGxsRLBE4fDwZ9//omPHz9i8+bNEmVkZGRIPK6NAY7iAdWePXvw9ddfq3wfADBv3jwEBwcDALy8vGBvb6+ysjkcDt6+fQug4fcDP3fuHM6dO6fSMleuXIkxY8Yo/frGfK7Gx8dj/PjxUtfp6emhdevWcHR0xIIFC6Q2AJSWliIpKQksFkuiv3xdU1U9Kv7OVfwdJLWLgn9CCGlAlM15LcpXLspZnpeXh3/++Qeenp5MwAoA58+fh7u7u8QgzNzcXGbZ0NBQZsusst6/f4+CggLmcW0Fzvfu3WMCf6A8iFNl8P/u3TtYWVnB2Ni4Wndo6kN+fj6SkpJUWubHjx9VUk5jPFdF4wj09fUlgmYOh4OMjAwkJiYiMTERgYGBuHLlSqX3FR8fDz6fDysrKxgZGamkTspQVT0ot3/9om4/hBBSjyrmPRfvAlQTpqammDp1Kvz9/TFo0CDmeYFAIBEgA2AypwBQeHbU6hAfQKmjo4MOHTqofB8cDgc7d+4EADRt2hSA6gcStm/fHrdv38bFixdVWm5j1xjOVVG5gwYNwoULF5i/y5cv49GjR1i7di0AIDk5Gbdu3ZL5+m7duqmkPspSVT0q/s6pav4Hohhq+SeEkHpkZGQEXV1d5p9hfn6+SmZAFdHR0cHixYsl+qhnZ2dLbCMeRImnOFQV8W4UnTp1gpaW6v/1nD17FikpKWjfvj0mTZqEPXv2NOosIsuXL8fy5cvruxrV8jmfq6KgWVpXGU1NTcybNw9HjhxBUVERM3mZtHrVZ5cfVdYjLy+PWdbS0mrwd9I+NxT8E0JIPWKxWGjdujXT3eHDhw8SafBUoU2bNhKPK7aYWlhYMMtFRUUoKipiWs9VQTygqo0uP7m5uTh8+DAAYO3atcxMr2/evAGPx5MZwPH5fPj5+eHmzZuIj4/Hx48fYWhoCHNzc/Ts2RMTJ07EgAEDmO0nTpyImJgYHD16FMOGDZMoKysrC3fu3MGTJ0/w5s0b5OTkoKysDJaWlhg8eDCWLl0qNZPTN998g7i4OBw/fhxdunTBqVOnEBQUhMzMTDRv3hyjR4/GsmXLGk3L6Od4rgqFQqZcWS3mQqEQGhrlnTHE36OIvBZ3Zc89kSdPnuDy5ct49uwZsrOzmTEIX3zxBaZMmYK2bdsqVI+IiAisWrUKWVlZcHd3x9q1a2V+9zIzM5llS0tLyvRTxyj4J4SQemZjY8ME/0lJSRg8eLBKy8/KypJ4LP7PHJDMmCIUChEWFoYRI0aobP+1Hfzv3bsXxcXFGDRoEIYPH84EFhwOB4mJiVL3WVxcjP/+97+IjIxkLsBat26NvLw8pKSk4M2bN+jUqRMT/PN4PLx+/RqA9FbP8+fP48iRI9DV1YWFhQU6dOiA4uJipKSkwMfHB/fv34efn59EYCeqH1A+kHXFihVgsVjo0KED+Hw+0tLScOLECRQUFODXX39V+efWEH2O52pKSgrYbDYA2S3m9+7dQ0FBAfT09Cq9Hx6Px8w4rKpzDyifKXnDhg24c+cOgPLuV9bW1igpKUFcXBxevXqFPn36MMdAVj2EQiFOnDiBP/74Azo6Oti3bx+cnZ3lfiai8x6gGZTrAwX/hBBSz3r27Mn0862NripXrlxhlg0MDCpdXHTt2hUtWrTAhw8fAACenp5KBVRJSUmV+kgXFhZKZGhRdfD/6tUr+Pn5QUNDAxs2bABQ3pJobGyMgoICxMXFSd3n3r17ERkZiS+++ALbtm2TGFT66dMn3LlzRyIoefPmDTgcDszMzKS2otrZ2cHb2xu9evWSaO3MysrC999/j/DwcJw7dw6rVq1i1r1+/RpcLhcsFgs7d+7Ed999h5kzZ0JHRwdA+eRSO3fuxNWrV/Hjjz9CW1u75h9YA/c5nqui1vKmTZtK3NkoKytDeno6bty4gdOnT0NXVxfbt29H8+bNJV7/9u1blJWVwdzcvNI6QLlzj8vlYtmyZXj8+DE6d+6MzZs3Y9CgQcxkZnl5efD29oadnZ3cehQWFmLdunW4f/8+OnbsCA8PD3Tu3LnKz0T8d47mRqh7NOCXEELq2RdffMEsP336VOZ2cXFxOHjwIEpKShQu+9q1a7h06RLzeNasWZW6kLBYLCxYsIB5HBYWhmPHjim8j5KSEuzbtw/r1q2rtK5iznRra2uFy1XEr7/+CoFAgAkTJkgE66L9yLqYErV2/vTTTxKBP1A++NDV1VWivKr6Oo8YMQL9+vWr1M2hefPmmDJlCgAwdw4qlikUCvHDDz9g7ty5TOAPALNnz4a2tjZ4PB4T7KoLOlf/X0xMDIDybko2NjbMX8+ePeHs7IzDhw9j3LhxuHTpktQWc9HFg6wWcmXOvT/++AOPHz9Ghw4d4O3tDQcHB4lZjE1NTbFs2TKJC92K4xZevnyJCRMm4P79+xg9ejQuXbqkUOAPSP7Oif/+kbpBLf+EEFLPbG1t0apVK7x79w5v375FVlaW1Ba+yMhIeHh4wNvbG//5z3/g6uqKjh07Si0zJSUFx48fh6+vL5NWr0ePHli6dKnU7SdPnoy///4bERERAMrzmyclJWHZsmVo3bp1pe2FQiHi4+Ph7+8PX19fFBUVYfLkyZW2Ew+oTE1NpfZnVpaovgYGBli5cqXEui5duiAsLExm8C/KK/7w4UPMmDGjyn1VleWEz+cjODgYISEhSE5OBpvNBo/HA/D/XVkqttyLPhs7OzvMnDmzUpkCgYCZ0Ko2MtvUJjpXK5fbunVriWC6tLQUGRkZKCwsxJ07d2BnZyf1boOqz720tDR4enqCxWJh9+7daNasmULvQ7we3t7e2L59OwQCAdauXYt58+YpVAZQPkZHdDFiYWHRoCbxaywo+CeEkAbAxcUFR48eBQAEBARg+vTplbYR/fPNzc3F0aNHcfToUVhYWKBbt24wNTWFjo4OCgsLkZCQIJEvHQD69euHo0ePMoNhK9LW1oaHhwfmzp3LBCt+fn64cuUKrK2t0blzZzRp0gRsNhu5ubmIjY2VyIcOAN27d5dZZ6C81Xbbtm2KfygoH8ArbeKz0tJS7N69GwAwf/78ShdLVbX8jxs3Dn/99Re2bduGy5cvY8yYMXBycpLZcimv5f/FixdYt25dlXn127VrJ/FY9NnImkQqJSUFAoEApqamapcNhc7VyuWuXr0a48aNk1jH4/Fw/Phx/PHHH9i6dSu6du2KXr16SWyj6nPv+vXr4HK5GDp0aLUCb1E9bty4gYyMDJibm2Pfvn3VnksjICCAucgbN24cM9CZ1B0K/gkhpAGYOHEijh07BqFQiFu3bkkN/lu1agUTExPk5+czz2VnZ+PBgwcyyzU1NcXChQsxe/bsKv/JmpqawsfHB7t378bFixfB4XCYVtP4+HiZrzMwMMCoUaPw1VdfVVonHnzn5ubi/PnzcusgzsLCAlu2bJG67uTJk8jIyEDLli0xd+7cSutFwX9eXh4yMzMr9dPfunUrOnTogIsXLyImJgYxMTHYs2cPOnbsiPnz52PixInMtvKytcTHx+Pbb79FSUkJRo4cif/85z/o2rUrTExMmC48Tk5OyMjIkOi2IfpcAUjkthcn6i5S37ndlUHnarl3794xn4G0bjtaWlpYvHgx/Pz8kJqailu3bkkE/7Vx7j1+/BgApH4GsojXQzTXwqRJk5SaRO/27dvMsvj3jNQdCv4JIaQBaN++PYYNG4b79+8jLCwMycnJaN++vcQ2ixcvxoIFCxAZGYmIiAi8fPkSKSkpyMrKApvNBovFgpGREVq0aIEePXrAwcEBw4cPl+hHXhVdXV1s2rQJCxcuxPXr1xEWFoaEhAQUFBTg06dP0NfXh7GxMdq3bw8bGxsMGjQIAwYMkJqKsqysrFKrbnXIapXMzMzEiRMnAJTPyFqxpbSiuLi4SsG/lpYW5s6di7lz5+Lt27f4999/cfnyZSQkJGDDhg3Q0dFhWuTT0tJQXFwMQ0PDSq33+/btQ0lJCdzd3bFx40ap+xYNIhVvbRZlgNHV1ZV5t0FebviGjs7VcqJjqKenV+n7LK5NmzZITU1FTk6OxPPp6elMClpVnXvv378HUDmTkjyiejRp0gSnTp3CrFmzcOzYMdja2laZ2UdcWloaQkJCAAAODg61Nts3kY+Cf0IIaSAWLFiA+/fvAyhP37dp06ZK22hqaqJ///7o379/rdbF3Nwcc+bMwZw5c5QuQ1dXl2m9VqXff/8dJSUl0NfXh6GhocztCgsLweVyERcXB0dHR5nbdezYER07dsTs2bOxcOFC/Pvvv3j8+DET/IsPuBQfFAmACWSk9SEHgMuXLwMon8xNPNgStaJaW1vLzIWuzi3/AJ2rwP+fO126dJGby140oLviOIPaOPdEg7CrM0maeD169uyJXbt2YcWKFVi/fj1atmyJPn36KFTOxYsXmS4/8+fPV3j/RLWooxUhhDQQffv2xdChQwEAvr6+El0mSLnnz5/jxo0bAMq7/jx69Ejm35AhQwBUzuIii4aGBtO33tzcnHleVrcLDofDBFCiGZrFRURE4Ny5cwDKW+/FgzdRmdL6ngOKTQxFGj5F7t6Eh4czdx1E3/+Kr1fludeyZUsA8jOLVfU+Ro8ejdWrV6OsrAxLlixBWlpalWV8/PgRFy9eBADY29urfD4TojgK/gkhpAFZs2YNtLS0UFJSglOnTtV3dRoUoVCIX3/9FUKhEF9//TX69esnd3tpg379/f2xY8eOSgOBy8rKcObMGVy/fh3a2tr45ptvmHWyAjgdHR2mRfXo0aNMECYUCnHnzh0sWbKEybJSMcgXlSkr+BdlbZHW3YOoD3lpOvl8Pnx9fbF8+XIA5X3wHRwcJLaRNdi3JufemDFjAABeXl7MhbQIm83G5cuXcffu3SrrMX/+fLi5uSEvLw8LFixAYWGh3M/C09MTxcXF0NDQwJo1a+RuS2oXdfshhJAGxNraGtOnT8fZs2dx9uxZzJw5U+qkUo3R1atXERUVBQMDA6xdu7bK7bt06QKgvH+9qA94REQE/vrrL5w+fRrNmjVDmzZtmNl0RX3wt2/fjk6dOjHlyGuBX7p0KdatW4c7d+7gyZMnsLKywocPH5CTk4NZs2bh5cuXiIyMrPRa0cWHrFZ9UReUiq22RH3k5uYys02fP39eItAuLCxEeno6E7SPHj0aO3furFSGvDSfyp577u7uCAoKwtOnT7F69Wr8/PPPaN26NcrKypCSkgIul4s///xToXr8+OOPyMjIwOPHj7F8+XKcPHlS6mR0eXl5TGPGpEmTaGKvekbBPyGENDCbNm2S2t+/MWOz2dizZw8AYOHChQpdEImCf4FAgPj4ePTu3Rtubm4wMzPD06dPkZaWhjdv3oDFYqF169b44osvMHv2bIn+0dnZ2cjOzoa2trbEBYGIq6srNDQ0cPz4cSQlJeHdu3fo1asXZsyYgS+//JLJhiLe+ipepqyJpNS9vz+RTB2amJjILLNYLBgYGKBNmzbo3bs3JkyYgAEDBlR6fU5OjsrPPaB8fIOnpyfOnz+Pv//+G2/evEFCQgLMzMzQr18/fPnllxg4cKBC9dDS0sKBAwcwZcoUhIaG4n//+x927NhRqa6mpqZ49uyZgp8cqW0soWjkBSGEEEIIIeSzRn3+CSGEEEIIaSQo+CeEEEIIIaSRoOCfEEIIIYSQRoKCf0IIIYQQQhoJCv4JIYQQQghpJCj4J4QQQgghpJGg4J8QQgghhJBGgoJ/QgghhBBCGgkK/gkhhBBCCGkktOq7AoSQz5NAIMCnT58AlE8Bz2Kx6rlGhBBCiHoQCoXg8XgAAH19fWhoqK69noJ/Qkit+PTpE+Li4uq7GoQQQohas7W1haGhocrKo24/hBBCCCGENBLU8k8IqRVaWv//82JrawttbW1wuVzmboDoOdLw0XFTT3Tc1A8dM/VUG8dNvEzx/6eqQME/IaRWiPfx19bWho6ODjQ0NGBsbAwA0NXVVfkPGqkddNzUEx039UPHTD3V9nFT9Zg5OqsIIXVGS0sLnTp1qu9qkGqi46ae6LipHzpm6kndjhv1+SeEEEIIIaSRoOCfEEIIIYSQRoK6/RBC6gyXy0ViYiIAoFOnTjSYTU3QcVNPdNzUDx0z9aRux42Cf0JInREKhWCz2cwyUQ903NQTHTf1Q8dMPanbcaNuP4QQQgghhDQSFPwTQgghhBDSSFDwTwghhBBCSCNBwT8hhBBCCCGNBAX/hBBCCCGENBKU7YcQQggh1SLKblJUVITS0lLw+fz6rtJnQSAQMMtJSUnQ0KA2WnUg7bhpampCT08PTZs2haGhIVgsVj3WUBIF/4SQOqOpqYk2bdowy0Q90HFTT7V13AQCAVJTU/Hp0yeVlUnKCYVC6OrqAgD4fL5EUEkaLmnHjcfjoaysDIWFhdDX10fbtm0bzMUcBf+EkDqjqakJS0vL+q4GqSY6buqpNo6bUCisFPizWCy6KCSkAj6fz+T8//TpE1JTU9GuXbsGcQeAgn9CCCGEKITNZjOBv6amJlq0aIEmTZo0mBZNQhoKgUCA4uJifPjwAXw+H58+fQKbzUaTJk3qu2o04JcQQgghiikqKmKWW7RogaZNm1LgT4gUGhoaaNq0KVq0aME89/Hjx3qs0f+jln9CSJ3hcDiIjo4GANjZ2UFHR6eea0QUQcdNPdXGcSstLQVQ3tWnIbRgfm4EAgFzZ0VfX58urNSEvOPWpEkTsFgsCIXCBjNOhs4qQgghhChElNVHU1OTAlNCFCDK/AOgwWTFopZ/Qgghcmlra8POzo5ZJupB/LhpadG/e0JIOfo1IIQQIheLxaKuPmpI/LiJso4QQggF/4QQtcQXCJBT1DD6TxLSUJk31Ycmdc8hhIih4L8RcXJyQkZGBiZMmIAdO3bUd3UIqZGcok9w/vVyfVeDkAbt702TYGlsWN/VIIQ0IBT8q5HQ0FDMnj0bALBs2TIsX768nmvUOAQFBSE6OhrR0dFIS0tDXl4eiouLYWBgACsrK9jb22Py5Mno2LGjSvZXXFwMX19f3Lt3D69fv0ZxcTF0dHTQunVr9O/fH9OmTYO1tbXM18+aNQthYWFS12lpacHIyAidOnWCo6MjJk+eDGNjY5XUmxBCCCENHwX/hMjB4/GwcOFCqeuKiooQExODmJgYnDt3Dt999x0WLFhQo/29evUKS5Yswfv37yWe53K5SEhIQEJCAnx8fLBy5Uql9sXj8ZCfn4+IiAhERETgzJkz8PDwQL9+/WpUb0IIIYSoBwr+CamCkZER7O3t0atXL1hZWcHCwgJ6enrIyspCWFgYLl++jI8fP2LPnj0wMjLCtGnTlNpPUVER5s+fj5ycHADAgAEDMG3aNFhZWSE/Px+PHj2Ct7c3uFwu9uzZg9atW2PcuHFyy7x+/brEYy6Xi7S0NPj7+yMwMBC5ublYvHgxbt++DVNTU6XqXR1aWlqwsbFhlgkhhPw/FosFPT09Zln8jr80BgYGaN68OXr27ImJEyfCwcGhrqpKxFQ8bg0d/fclRA4tLS2EhoYyOXorGj58OGbOnIlJkyahsLAQBw4cwOTJk2VuL8+lS5eYwH/MmDHYv3+/xHpHR0cMHDgQS5YsAQAcPny4yuBfWveg7t27Y8yYMVi3bh2uXr2KwsJCXLp0SeYdDlXS0NCgiYEIqQfqEJCQ8uNUnf8fJSUlSE5ORnJyMq5duwZXV1f89ttvSv0PIsqr7nGrbxT8E1KFqr7QVlZWGDNmDHx8fJCXl4e3b9+iS5cu1d5PZGQksywK8CsaPnw4unXrhlevXjHjAZQNpufNm4erV68CADMLKCGEqNrnmJmrPrIoTZs2DdOnT2ceC4VCFBYW4vnz5zhz5gxyc3Nx9epVtGjRAqtWrarTuhH1QsE/YWRlZeHu3bsIDQ1FXFwcsrKywOPxYGJigh49esDFxQVjxoyROauj+O3Js2fPwt7eHr6+vvDz88Pbt2/B5XLRsWNHzJw5E66urszrOBwOLl++jCtXriA5ORlcLhc2NjaYPXs2nJ2dZda3sLAQAQEBCAkJwatXr/D+/XtwuVw0a9YMtra2GDVqFCZMmFAn+ckNDf8/m0ZZWZlSZXC5XGbZyspK5nZWVlZ49epVpddUV5s2bZhlDoejdDnVIRAIUFJSAqD8djXNEEpI3RAKhfXW+v85ZuaqrSxKQqEQAoEAACr9PpqZmUm9m2tvbw8nJydMnDgRZWVl8PLywtKlS2lujjpU8bg19DttFPwTAOVTTjs6OjInr7isrCwEBgYiMDAQvr6+8PDwkAh2peHxeFi8eDHu378v8Xx0dDTWrVuHly9fYvPmzSgsLMTSpUsRHh4usV1kZCQiIyORmpqKRYsWSd3HhAkTkJGRUen5nJwcBAcHIzg4GBcvXsSxY8dgYWFR1UegtNLSUgQGBgIo/9K3b99eqXI6dOiAoKAgAEBaWhrTN76itLQ0AICxsTFMTEyU2hcAvHv3jllu2bKl0uVUB4/HQ3x8PADAzs6O/jkRQogYoVCI0tJSAIC+vr7Cr+vcuTOGDRuGO3fugM1m4+3bt7C1ta2tapIKKh43Cv6JWhDN/jho0CAMHToU1tbWMDU1BZvNRlpaGi5duoTIyEg8evQI27Ztw86dO+WWt3//frx48QIuLi5wcXGBubk5kpOT4eHhgaSkJHh5ecHJyQleXl6IjIzEtGnTMHLkSBgbGyM2Nhb79+9HVlYWDhw4gOHDh0vtRsPn89GrVy8MGzYM3bp1g5mZGbhcLtLT03Ht2jU8fPgQr169wvfffw8vLy+Vfl5cLhfZ2dmIjIzE8ePHkZycDACYNGmS0t1w3NzccPbsWfD5fBw5cgT79u2rtE1QUBDT6j916lSl6w8AJ0+eZJaHDx9eo7IIIYTUr9atWzPL0u7mpqWl4e7duwgLC0NCQgIzxszMzAy9evXCxIkTMXToULn7KCoqwvnz5xEUFIS3b9+ipKQERkZGMDU1RYcOHTB48GCMGjUK5ubmUl9fVlaGS5cu4e7du3jz5g0KCwthZGQEGxsbjBs3DhMmTKBkEHWAPmECoLxf++3bt9GuXbtK6+zt7TFp0iQcOHAAhw4dgr+/PxYvXiy3hfvFixfYuHEj3N3dmee6d+8Oe3t7jB49Gmw2G6tXr0Z+fj4OHjyIESNGSGzXo0cPTJgwAXw+Hz4+Pti8eXOlfXh6ekqtQ9++fTF+/HhcvnwZGzduRFhYGEJCQmqcBSE9PV1ukDxkyBCsX79e6fI7deqELVu2YNu2bfj777+Rm5uLqVOnok2bNsjPz0dISAjOnTvH7EuRAboJCQkSj7lcLjIyMnDt2jXcvXsXAODs7FzlDz4hhJCGTfxubqtWrSTWpaWlSfyfrfi6d+/e4datWxg/fjy2b98uNQBPTEzEt99+i6ysLInn8/PzkZ+fj8TERAQEBEAgEGDmzJmVXh8XF4clS5ZUumOfl5eHkJAQhISEwMfHB0eOHJF58UBUg4J/AqB8pLq0wF/c0qVL4e3tjfz8fAQGBmLu3Lkyt+3Vq5dE4C9iYWGBkSNH4urVq8jLy4Ozs7PUHyRbW1v069cP4eHhePr0qdR9VNW9ZtKkSfDy8kJsbCwCAgJqLQWaiYkJtmzZgtGjR9d4tP/UqVPRrVs3HD9+HP/88w9CQ0Ml1rdt2xYLFy6Eq6urQq0jLi4uMtd16NABCxYswIQJE2pUZ0IIIfUrMTGR6Tbau3fvSsGzQCCAtrY2hgwZgsGDB6Nz585o1qwZCgsLkZSUBG9vb7x+/RrXrl2DlZUVvvvuu0r7WLNmDbKysqCtrQ03NzcMHToU5ubmEAqF+PDhA54/f46AgACp9UtJScHMmTPx8eNHNGnSBDNmzEDPnj3RokULFBQUIDAwED4+PoiOjsaSJUtw/vx5aGtrq/xzIuUo+CdSCQQCZGdng81mg8fjMc+3aNEC+fn5iIuLk/t6eQN1xfshyktVaWtri/DwcKaPuzxCoRA5OTkoLi6WGARraWmJ2NjYKuurCEtLSyZvPp/PR2ZmJh4+fAhfX1/8+OOPSEtLq3G6zOLiYly+fBmPHz+Wul6Uo79Dhw41npgrKSkJPj4+aNOmDezt7WtUFiGEkNqVm5srcTdXKBTi48ePiIyMhKenJ0pLS2FkZIQNGzZUeq2FhQUCAwPRvHnzSuscHBwwbdo0bNy4EX5+fjh9+jTmzJkDIyMjZpu0tDTExMQAANavX1+pZb9nz54YNWoU1qxZg6Kiokr7WLduHT5+/Ihu3brh5MmTleaVGTJkCIYNG4aFCxfixYsXuHLlCiZPnly9D4gojIJ/whAKhbh27Rp8fX0RFRXFDF6RJj8/X25ZHTp0kLlO/AdFXuu9aDs2my1zm6CgIFy4cAHh4eFyt6uqvorQ1taWyLTQtWtXDBs2DG5ubnB3d8fevXuRnJyM7du3K1V+dnY25syZg9evX0NfXx+rV6/G2LFj0aJFC7DZbERERGD//v0ICwuDu7s7du3aJfciCwAzuFZEIBAgPz8fT58+xaFDh/D8+XPMnTsXu3fvxpgxY5SqNyGEkNp34cIFXLhwQeo6DQ0NTJ06Fd9++63U/78GBgYwMDCQWTaLxcK6devg7++PkpISPH78GKNHj2bWZ2dnM8v9+/eXW06zZs0knouIiGBSWe/YsUPmhJJDhw7F6NGjcevWLfj5+VHwX4sozx4BUD4IZ8GCBVi7di3CwsLkBv4AqlwvmulOGvH0ZYpsJy0DkVAoxKZNm7Bw4UIEBQXJDfwVqW9N2NraYsWKFQAAPz8/BAcHK1XOzz//jNevX4PFYuHo0aNYsGABrKysoK2tDWNjY4wYMQIXL15Ep06dwOVysXHjRmbAlqI0NDRgZmaGUaNGwdvbG+3bt2fKKigoUKrehBBC6pdAIMDff/+NCxcuKJS6mcvl4sOHD0hMTERCQgISEhKQlZUFY2NjAKh0t1w8Y96VK1eqVbd79+4BKG8UlJXFTmTAgAEAgJcvX0r0OiCqRS3/BED5bLH//vsvgPIBvtOnT0f37t1hbm4OPT09JhCfMWMGIiIi6rOqAABfX1/4+voCKG+Bd3d3R8+ePWFpaQl9fX2m7/3atWvh7+9f6/UZPnw4fvrpJwDAnTt3MGTIkGq9vrCwkBmA+8UXX2DgwIFStzM0NMSiRYuwZs0afPr0CTdv3pQ6tkIRhoaGmDZtGrZv3w42m407d+5gypQpSpVFCCGkdi1btgzLly+XeK60tBQpKSnw9/eHp6cnPD098fLlS5w8ebJSqlAul4u//voL/v7+ePXqldx5YireLbeyskL//v0RERGBM2fOIDg4GKNGjYK9vT169+4tNy3py5cvAZR3Na0q+Beva2FhIczMzBTanlQPBf8EQqGQCaT79+8PT09PmZMvFRYW1mXVZLp06RIAoF27drh48aLMOwh1VV/x25jiGRcUlZSUxNzh6Natm9xtu3fvziy/ffu22vsS17FjR2a5Ymag2qCjo1PjsQqEkOpr6HnHSTkNDY0q59ERp6enBxsbG6xduxbt27fH//73Pzx9+hRHjhyRmOW3oKAAc+fOZfrtV0XaZJV79+7FihUrEBkZiTdv3uDNmzf4888/oa2tjV69euHrr7/GxIkToaurK/G6vLw8hd+PuE+f1GdW6Ooet/pGwT9BQUEB059P3gy+bDYbSUlJdVk1mV6/fg0AcHJykhn4C4VChX/oaiozM5NZltevUhbxLEF8Pl/utuK3QmuaD1l8X3SLlRBC1Nd//vMf7NmzBwUFBbh8+bJE8P/rr78y/w9HjBiBSZMmwcbGBmZmZtDV1WUuDocNG4b3798zc/+Is7S0xMWLFxESEoJ//vkH4eHhePPmDbhcLiIiIhAREYFTp07h2LFjEuMORP9nbG1t8fvvvyv8fiwtLZX6HEjVKPgnEgGgvCvtS5cuNZgAUVTnkpISmdvcu3dPYpBSbbp9+zazLG369aq0bt0aLBYLQqGwym5V4rMht2nTptr7Eie6HQvU3Sy/hBBCVE9DQwPt2rVjGvTy8/NhYmKC4uJi3Lp1C0B5+ufdu3fLLEORu+UODg5M6mzRHDQ+Pj548uQJUlNTsWrVKly9epXZXjSOoKSkRKn/j0T1aMAvgampKZo2bQoAuHHjhtTBQlFRUdi/f39dV00m0ZwE9+/flzpQNTU1lemDXxMBAQGVJjSpKDw8HIcOHQJQ3hIvL32pLKampujduzeA8s9a1oCqjIwMHDlyBED5bXxHR8dq70u8rPPnzzOPa1KWokQpUjMzM6u8w0EIUR1pLbmk4REKheByueByuUodM/EGOtFvbHJyMtO/X16GuMTERLkNatKYmJjA2dkZnp6ecHJyAgDExsYys94D/9+VNS0trc4a5OpaTY9bXaOWfzUVGxsLPz+/KrcbNGhQpZn+KtLQ0ICLiwvOnz+P+Ph4TJs2DXPmzEG7du1QXFyMBw8ewNvbGwYGBmjevLnEl7q+uLq6YteuXcjKysKUKVMwf/58WFtbo6ysDE+ePIGnpyc4HA66d+9eo64/AQEBWLlyJYYNGwYHBwd07twZTZs2BYfDQWpqKu7fv49bt24x/fWXLFki0Y++OlatWoU5c+aAz+djw4YNCAkJkUj1GRYWBk9PT+ZiZ9KkSVXuq2I/foFAgIKCAkRERMDLy4spy8XFBV27dlWq3tXB5/ORnp4OoPyfRk0nRSOEkM+JUChkGuCq+/v46dMnJCYmAigfC2BiYgJA8bv7Fy9erG51JTg4OCAwMBBA+R0BUSpvJycnnD9/HkKhEGfPnsXq1atrtJ+GqOJxa+hjbCj4V1P37t1j0mfJc+jQoSqDf6A88Hz27BliY2Px8uXLSl9OY2NjeHh44MCBAw0i+J89ezYeP36M4OBgJCcnY9OmTRLr9fT0sHPnTjx48KDG/f65XC7u3r3LZOORRk9PDytXrsScOXOU3s/AgQOxc+dObNmyBSUlJfD395eZqWjcuHHYunVrlWXKm+FXxNnZGb/99lu160sIIaTh8PDwYNJaDxkyhLl4aNu2LdOt9MqVK3B2dq4UnAYGBkrcCa4oNjYWAGQ2EgmFQmZyShaLhdatWzPrhgwZgp49eyIqKgonT55E165d5d6BiI+PR0ZGBnMngageBf8EQPmEWhcuXMDp06dx69YtpKSkQFNTEy1btoSjoyPc3d3RokWL+q4mQ1tbG0ePHsWFCxdw9epVJCYmQigUwtLSEg4ODpg9ezY6deqEBw8e1Gg/a9aswYABAxAREYGEhATk5uYiNzcXGhoaaNasGbp06YKBAwfC1dVV6syJ1eXi4oIBAwbAx8cHISEhSEpKQnFxMXR0dGBpaYnevXvD1dUVgwYNUqp8FosFAwMDtGzZkilLlFeZEEJIw1Vxhl+gPCtPSkoKrl69iocPHwIAdHV18d133zHbmJiYwNHREUFBQXj48CHmzp2LadOmoVWrVsjNzcU///yDK1euwMrKCkVFRVKz88TGxmLDhg2ws7PDV199xaQC5/F4SE9Ph5+fHx49egSgvKW/4v/DPXv2wM3NDQUFBVi1ahWuXbsGZ2dntG/fHhoaGsjNzUVsbCzu37/PTD5JwX/tYQnVoXMSIUTtcDgcREdHAwDs7Oygo6Mj9TllZRaw4fzrZZXUlZDP1d+bJsHS2BBCoVAlXRFev34NHo8HLS0tdOnSRaHXfI7fVdHnqmoCgYDpmqOvr4/w8HDMnj1b4debmpri999/rzTXzPv37zF9+nSZqahbtWqF48ePY8GCBcjIyMCECROwY8cOZr2fnx82bNhQ5f779OmDw4cPM12OxCUlJeG7775TKK308uXLsWzZsiq3aygqHreKWROV+d6o8v9lRdTyTwhRS+ZN9fH3pkn1XQ1CGjTzprInX6rLOnxu39WG8LkCYGaA79y5MxwdHTFx4kQ0a9as0nYtW7aEn58fjh8/jnv37uHdu3fQ1dVF69atMWLECMyePVvq60S+/vprmJmZ4fHjx4iOjkZmZiZyc3PB4/FgZmaGbt26wdnZGePGjZOZLrxDhw64evUqbt26hX/++QfR0dHIy8sDn8+HsbExOnTogH79+mHkyJES89kQ1aOWf0JIrajtln9CiOLqs+WfKK6qFmTSMFHLPyGEkM+KKI0dUN7S2NAzWZBy4sdNS0uLjhshBAAF/4SQOsRisZgp0CkQUR88Hg9v374FAHTq1Ana2tr1XCOiiIrHjVqR1QMdJ/WkTseNgn9CSJ3R1taGra1tfVeDVBMdN/VEx039aGhoQF+/YYwnIIpTt+OmPpcphBBCCCGEkBqh4J8QQgghhJBGgrr9EELqDI/HQ0pKCgCgXbt20NKinyB1QMdN/akq2w+pXUKhEGVlZQDKJ+uiY6Ye1O240S84IaTOCAQCFBQUAACsrKzqtzJEYbV53IQCPvjFOSotk/w/zSbmYGlo1nc1iIKEQiH4fD6z3NCDSFJO3Y4bBf+EEELqDb84B+/+GFHf1fhstVoZAK2mlvVdDUJIA0J9/gkhhBBCCGkkKPhXkJOTE2xsbLB+/fr6rgr5TPn6+sLGxgYuLi6oOPF2eno6bGxsYGNjAz8/vxrtZ8GCBbCxscGBAwdqVA4hhBBC1E+j6fYTGhqK2bNnAwCWLVuG5cuX13ONGoegoCBER0cjOjoaaWlpyMvLQ3FxMQwMDGBlZQV7e3tMnjwZHTt2VMn+iouL4evri3v37uH169coLi6Gjo4OWrdujf79+2PatGmwtraW+fpZs2YhLCxM6jotLS0YGRmhU6dOcHR0xOTJk2FsbKySerPZbOzbtw8AsGTJklrtL7hkyRI8ePAAp06dwuTJk9GiRYta2xchhBBCGhZq+Se1hsfjYeHChTh48CAePHiAt2/foqCgADweD0VFRYiJicHp06cxfvx4HDt2rMb7e/XqFb7++mts374dYWFhyM/PB5fLBZvNRkJCAry9veHq6qr0vng8HvLz8xEREYE9e/bA2dkZT58+rXG9AcDLyws5OTno3LkzxowZU+3X+/n5MXcG0tPT5W7bu3dvDB48GJ8+fcKRI0eUrTIhhBBC1FCjafkn9cPIyAj29vbo1asXrKysYGFhAT09PWRlZSEsLAyXL1/Gx48fsWfPHhgZGWHatGlK7aeoqAjz589HTk551pABAwZg2rRpsLKyQn5+Ph49egRvb29wuVzs2bMHrVu3xrhx4+SWef36dYnHXC4XaWlp8Pf3R2BgIHJzc7F48WLcvn0bpqamStUbAEpLS3H69GkAwLfffiu11b9NmzaIj49Xeh8VzZkzB48ePYKvry+WLFmC5s2bq6xsQgghhDRcFPyTWqOlpYXQ0FBoakpPMzd8+HDMnDkTkyZNQmFhIQ4cOIDJkyfL3F6eS5cuMYH/mDFjsH//fon1jo6OGDhwIJYsWQIAOHz4cJXBv7TuQd27d8eYMWOwbt06XL16FYWFhbh06RIWLlxY7TqLXLt2DQUFBdDR0cHo0aOVLqc6vvjiC5iZmSE3Nxc+Pj511g1OQ0ODudDQ0KAbj+qCjhshdYPFYkFbW5tZJupB3Y4b/YqTWlVVIG9lZcV0c8nLy8Pbt2+V2k9kZCSzLArwKxo+fDi6desGAMx4AGXNmzePWY6Ojla6HKB8oC8ADBs2DE2bNq1RWYrS1NSEs7MzgPIuQxUHGNcWLS0tWFlZwcrKiiaKUiN03NSfOgQkpPw46ejoQEdHh46ZGlG340a/4iqSlZWFu3fvIjQ0FHFxccjKygKPx4OJiQl69OgBFxcXjBkzRmarmfiA5LNnz8Le3h6+vr7w8/PD27dvweVy0bFjR8ycOROurq7M6zgcDi5fvowrV64gOTkZXC4XNjY2mD17NhPcSVNYWIiAgACEhITg1atXeP/+PbhcLpo1awZbW1uMGjUKEyZMgI6Ojko/J2kMDQ2ZZdEMedXF5XKZZXmTEFlZWeHVq1eVXlNdbdq0YZY5HI7S5WRkZODFixcAgFGjRsncLj09HcOHDwcAbN++HRMnTgQged6IiLYTd/bsWQwcOFDiuVGjRsHLywvv3r3D06dP0b9/f6XfByGEEELUAwX/KsDn8+Ho6AiBQFBpXVZWFgIDAxEYGAhfX194eHhIBLvS8Hg8LF68GPfv35d4Pjo6GuvWrcPLly+xefNmFBYWYunSpQgPD5fYLjIyEpGRkUhNTcWiRYuk7mPChAnIyMio9HxOTg6Cg4MRHByMixcv4tixY7CwsKjqI1BaaWkpAgMDAZR3J2jfvr1S5XTo0AFBQUEAgLS0NNjY2EjdLi0tDQBgbGwMExMTpfYFAO/evWOWW7ZsqXQ54pmFevfurXQ5yrCzs4Ompib4fD4ePnxIwT8hhBDSCFDwrwKiLhODBg3C0KFDYW1tDVNTU7DZbKSlpeHSpUuIjIzEo0ePsG3bNuzcuVNuefv378eLFy/g4uICFxcXmJubIzk5GR4eHkhKSoKXlxecnJzg5eWFyMhITJs2DSNHjoSxsTFiY2Oxf/9+ZGVl4cCBAxg+fDi6dOlSaR98Ph+9evXCsGHD0K1bN5iZmYHL5SI9PR3Xrl3Dw4cP8erVK3z//ffw8vJS6efF5XKRnZ2NyMhIHD9+HMnJyQCASZMmoUmTJkqV6ebmhrNnz4LP5+PIkSNM2kxxQUFBTKv/1KlTla4/AJw8eZJZltbSrqiIiAgA5Rcj8u5YyGJnZ4fr16/j3r17+OOPP5i6VRzAK36nQkRfXx+dO3dGfHy8zPSmqsblchEbGwsA6Nq1K9NHkjRsdNzUn1AoVIvuCI2dQCBAaWkpAEBPT4/G2KgJdTtuFPyrgKamJm7fvo127dpVWmdvb49JkybhwIEDOHToEPz9/bF48WK5LdwvXrzAxo0b4e7uzjzXvXt32NvbY/To0WCz2Vi9ejXy8/Nx8OBBjBgxQmK7Hj16YMKECeDz+fDx8cHmzZsr7cPT01NqHfr27Yvx48fj8uXL2LhxI8LCwhASEgIHB4fqfSgViHdbkWbIkCE1mkCtU6dO2LJlC7Zt24a///4bubm5mDp1Ktq0aYP8/HyEhITg3LlzzL4UGaCbkJAg8ZjL5SIjIwPXrl3D3bt3AQDOzs4YOnSo0vUWjVXo2rWrUq83MDCAtbU1Xr58yTzXvn17qcG+NN27d0d8fDxiY2PrJDgQCoVMd6u6GmdAao6OGyF1h75j6kmdjhsF/yrAYrGkBv7ili5dCm9vb+Tn5yMwMBBz586VuW2vXr0kAn8RCwsLjBw5ElevXkVeXh6cnZ0lAn8RW1tb9OvXD+Hh4TLz0FfVvWbSpEnw8vJCbGwsAgICahz8y2JiYoItW7Zg9OjRSmX5ETd16lR069YNx48fxz///IPQ0FCJ9W3btsXChQvh6uqq0KBFFxcXmes6dOiABQsWYMKECTWq84cPHwAAZmZmNSpHWaL9fvr0CdnZ2ZTykxBCCPnMUfBfCwQCAbKzs8Fms8Hj8ZjnW7Rogfz8fMTFxcl9vbyBura2tsyyvFSVtra2CA8PZ/q4yyMUCpGTk4Pi4mKJQbCWlpaIjY2tsr6KsLS0ZPLm8/l8ZGZm4uHDh/D19cWPP/6ItLS0GqXLBMpn9718+TIeP34sdb0oR3+HDh3Qr1+/Gu0rKSkJPj4+aNOmDezt7ZUqg8PhgM1mA0CdZfmpqFmzZsxyTk4OBf+EENKAiCd1WLZsWZ2lZSafNwr+VUQoFOLatWvw9fVFVFQU0/dLmvz8fLlldejQQeY6IyMjZlle671oO1FwKU1QUBAuXLiA8PBwudtVVV9FaGtrS+TN79q1K4YNGwY3Nze4u7tj7969SE5Oxvbt25UqPzs7G3PmzMHr16+hr6+P1atXY+zYsWjRogXYbDYiIiKwf/9+hIWFwd3dHbt27ZJ7kQWg0qRaAoEA+fn5ePr0KQ4dOoTnz59j7ty52L17t1Kz8hYUFDDLDSH4LykpqZc6EEIIUU+i5Bqf44XJrFmzEBYWBnt7e5WPfaxvFPyrQFlZGZYtW4Z///1Xoe3lXRgA5YNFZBEfRKLIdtIyEAmFQmzevJnJL1+VqupbE7a2tlixYgV++ukn+Pn5Ydy4cRgyZEi1y/n555/x+vVrsFgsHD16VCKtpbGxMUaMGAEHBwe4ubkhMTERGzduhL29PczNzRXeh4aGBszMzDBq1CgMHjwYEydORHJyMjZu3IhBgwbB2Ni4WnXW1dVllpVNcVpT4seWBnESQmqDUMAHvzinvquhUppNzMHSqFlXVULqCwX/KnD48GEm8Le3t8f06dPRvXt3mJubS4z6njFjBpPdpT75+voygX/Xrl3h7u6Onj17wtLSEvr6+kzf+7Vr18Lf37/W6zN8+HD89NNPAIA7d+5UO/gvLCxkBuB+8cUXlfLZixgaGmLRokVYs2YNPn36hJs3b0odW6EIQ0NDTJs2Ddu3bwebzcadO3cwZcqUapXRtGlTaGlpgcfjobCwUKl61JT4fsXvKhFCiKrwi3Pw7o/K49PUWauVAdBqalnf1SBEKRT815BQKGQC6f79+8PT01Nmiqf6CvAqunTpEgCgXbt2uHjxosw7CHVVX1NTU2ZZPH++opKSkpg7HKIZfGXp3r07s6zsbMIiHTt2ZJYrZgZSBIvFgomJCbKzsxtE8F+T+QoIIYQQoh4o+K+hgoICZGdnA4DcGXzZbDaSkpLqsmoyvX79GgDg5OQkM/AXCoWIiYmpk/pkZmYyywYGBtV+vXiWID6fL3db8QHYimT8kUd8X+LlVoe1tTWys7OZuQ6UpWyKTtF+27RpA319/RrVQRFaWlrMRVNNP39Sd+i4EVI3WCwW0yW0qt918cHAolnc//77b/j4+CA+Ph4lJSVo2bIlhg8fjgULFsjtmpqUlIRz584hNDQUGRkZ4HK5MDY2hpmZGbp164Yvv/wSI0aMgI6ODoDy+EF8otCDBw/i4MGDEmVOmDABO3bskFrXAQMGwM/PD/7+/khMTEReXh5cXV2Z7UXli5chzfr163HlyhW0bt2amTBUmry8PHh7eyM4OBgpKSkoKiqCgYEB2rdvjwEDBmD8+PFMQhVRmSJhYWGVJg6tuL/qHLeGgH7Fa0g8APz06ZPM7S5duqR0gKhqojrLG+B579495qKmtt2+fZtZFh8UrKjWrVuDxWJBKBRW2a1KfDZkRXPhyyKeW1/ZVvN+/frh0aNHSEpKQnFxsdKTnImPH+BwOAq/TvQeevXqpdR+q0tDQ6NGMyuT+kHHTf2pQ0BCyo+TMhfYAoEAa9aswbVr1ySeT05OxsmTJxEQEIDz58/DwsKi0mtv3bqFNWvWSGT7A8oTaWRnZyMuLg5+fn64fv26Uv+jKyorK8O8efNkZuZTtWvXrmHr1q2VYp6ioiJERUUhKioKt2/flnvxUBVlj1t9UZ+aNlCmpqZo2rQpioqKcOPGDXz77bfMlbFIVFQU9u/fX081rKxdu3ZISEjA/fv3UVBQUKk1IDU1lemDXxMBAQHo2bOn3PSR4eHhOHToEIDyFkV56UtlMTU1Re/evREZGYmoqChcuXJFav79jIwMHDlyBED5F9XR0bHa+xIv6/z588xjZcvq378/gPIf7pcvX2LQoEFKlSP+g56WlibRJUmWtLQ0JpPT4MGDldovIYSQ+rd//35ERkZixIgRcHV1RatWrZCTkwNvb28EBQUhJSUF27dvx969eyVel5OTg40bN4LL5cLMzAwzZsxA7969YWJigtLSUqSmpiIsLAz37t2TeN3JkyfB5XKZ+XCmTZuG6dOnS2wjnk1O3O7duxEfHw8nJydMnDiRqau8rIPKunr1KtatWwegvJFs8uTJ+PLLL2FhYYGSkhLEx8cjMDBQ4u77qlWrMHfuXGzYsAEvX75Ejx49KmUiVPcEGY0y+I+NjYWfn1+V2w0aNAitWrWSu42GhgZcXFxw/vx5xMfHY9q0aZgzZw7atWuH4uJiPHjwAN7e3jAwMEDz5s1r3L1DFVxdXbFr1y5kZWVhypQpmD9/PqytrVFWVoYnT57A09MTHA4H3bt3r1HXn4CAAKxcuRLDhg2Dg4MDOnfujKZNm4LD4SA1NRX379/HrVu3mP76S5YsUSholWbVqlWYM2cO+Hw+NmzYgJCQEIlUn2FhYfD09GTSa06aNKnKfVXsxy8QCFBQUICIiAh4eXkxZbm4uCg9Q2+fPn1gamqKvLw8hISEKB38d+3aFbq6uigrK8P+/fuhpaWFVq1aMd3QLC0tK3XxCgkJAVB+0fXVV18ptd/qEp8pVltbm1oj1QQdN/VXFzN4k5oTCoXMTLHVOV6RkZFYuXIlFi9eLPH80KFD8d///hfBwcG4c+cO8vLyJMbZBQUFMS3iZ86cqdSy37dvX7i6ulbK+lcxJbmZmZnCdwXi4+OxePFirFy5UtG3p5SsrCz8+OOPAMrrJ+399e/fHzNmzMD79++Z5ywtLWFpacl0QzYwMKjyvVU8bg39u9Yog/979+5VuoqV5tChQ1UG/0B54Pns2TPExsbi5cuXWL16tcR6Y2NjeHh44MCBAw0i+J89ezYeP36M4OBgJCcnY9OmTRLr9fT0sHPnTjx48KDG/f65XC7u3r3LZOORRk9PDytXrsScOXOU3s/AgQOxc+dObNmyBSUlJfD395eZqWjcuHHYunVrlWXKm+FXxNnZGb/99lu16yuio6MDV1dXnDp1Cjdv3sSqVauUKqdJkyaYNWsWTpw4gZiYmEozSIv6g4oTTbo2ZMgQiX8GtYnL5SI6OhoAYGdnV+kuGWmY6LgRUjeEQiHThbg647C6d++ORYsWVXqexWLh22+/RXBwMHg8HiIjIzF8+HBmfU5OeQrWZs2ayQ1w5aUWr6727dvXyZwA586dYz7Lbdu2yX1/NU14UfG4UfDfCBgZGeHChQs4ffo0bt26hZSUFGhqaqJly5ZwdHSEu7s7WrRoUd/VZGhra+Po0aO4cOECrl69isTERAiFQlhaWsLBwQGzZ89Gp06d8ODBgxrtZ82aNRgwYAAiIiKQkJCA3Nxc5ObmQkNDA82aNUOXLl0wcOBAuLq6qmRmWRcXFwwYMAA+Pj4ICQlh+tHr6OjA0tISvXv3hqurq9Kt6ywWCwYGBmjZsiVT1oABA2pcbzc3N5w6dQppaWl4/vw5evfurVQ5P/zwA9q3b4+rV6/izZs3+Pjxo8wB0JmZmcz4iIq3agkhhKgXFxcXmQGneJa7tLQ0iXWiLqOFhYUICAjAiBG1n5LV2dlZIlFHbbl//z4AwMrKSuKChzSi4H/gwIGVZmytjqoGgujr62PJkiVYsmSJzG3kzRCnaP0mTpyIiRMnVrnd8uXL5V5Za2lpYdasWZg1a5bMbXbs2CF3lH1VzMzMMGnSJEyaNEnpMqqrRYsWWLFiBVasWKHU6+tjFr+OHTti6NCh+Pfff+Hr6ys1+G/Tpk2V5weLxYKbmxvc3Nyq3Ofly5chEAjQqVMnDB06VNmqE1Jjmk3M0WplQH1X47Ol2UTxiQyJ+pLXjVV8XF/FfvVOTk7MuMVly5bB3t4eTk5O6N+/P7p27VorQXrFzDm1gcvlMpkN+/Xr1+Bb4utaown+CWnIVq9ejeDgYFy9ehVLly6t1Zz7bDYbnp6eAMqnZKcfRVKfWBqaNFkSITUkr1uOeApy0Rg7ERMTExw+fBjff/89MjMzERoaitDQUADl3UkdHBwwadIklY4LkzUQWJUKCwuZPvjSMhw1dtKT0hNC6pStrS2+/vprcLlcHD16tFb3df78eRQUFKBnz54YO3Zsre6LEFJ/hEIhOBwOOBwOEwgRUlH//v1x9+5d/P777/j666+ZbsrFxcW4e/cuFi1ahHnz5slNZ14dsuZDInWHWv4JaSC+//57tG3bFrq6urWamcPQ0BDLli3DyJEjqdWfkM8YDdQmitLV1cX48eMxfvx4AOVjAx48eAAvLy8kJycjODgY+/btw8aNG+ukPqL/TRXvVFQka76iZs2aQUNDAwKBoM7mLFInFPwT0kC0bNmyTjIgzJgxo9b3QQghRH1ZWVlh5syZcHV1xbhx4/DhwwfcunWrzoJ/Q0NDAOUTcckjK4OitrY2unTpgvj4eERERFCq2wro3gshhBBCCKmkSZMmsLOzAwBmUkhxotnlqzOzvCLatGkDAIiJiZHZZe3169dyE2GIximkp6crlN69otp6bw0BBf+EkDqjra0NOzs72NnZqf0MiY0JHTf1pK2tjb59+6Jv37503NQEi8WCvr5+neWKf/jwIbKysmSu//jxI6KiogD8f0AuTjSYNjU1VaX1sre3B1A+UdeNGzcqrS8uLq40R1FFM2fOZCbq2rJlS6WJO8V9+PCh0nOi95aWllblmJm6Pm41Rd1+CCF1hsViUb9jNUTHTT2pQxBCJNX17LA3b97E4sWL8cUXX2Dw4MGwtrZGs2bNwGazkZCQgPPnzyMzMxMAMHXq1Eqv79OnD9LT0xEYGIiLFy+ib9++TIt5kyZNYGZmplS9xo8fDw8PDybIT0lJwZdffgkWi4WXL1/izJkz+PDhA7p164ZXr15JLcPCwgJbt27FunXrkJuby6TCHjp0KCwsLMBms/H69WsEBgYiKSkJAQGSKYf79u0LPz8/5ObmYvv27Rg/fjyMjIwAlKdLb926NbOtOszqK46Cf0IIIfVOKOCDX5xT39X47Gg2MQdLo/YnVCLqi8vl4sGDB3In9pw6dSpmz55d6fl58+bhzp074HA42Lp1q8S6CRMmKD1XkKmpKX755ResXr0aZWVl8PDwgIeHB7NeT08Pu3btQlBQkMzgHwBcXV0hFArx448/orS0FF5eXlLn8xEP5EWcnZ1x9OhRpKWlwdPTk0mRLdq+qvmfGjIK/gkhdUYgEKCwsBDA/2djIA1fXRw3fnEO3v1R+7OLNjatVgZAq6llvQ54/BwncqutydOEQiEzM3tdzIK7YcMGfPHFF3jy5Ani4+ORnZ2NvLw8aGpqokWLFujTpw/+85//oH///lJf37VrV/j4+ODkyZN49uwZcnJyVNZHfuzYsWjdujWOHTuGp0+f4uPHjzA3N8egQYMwb948dOnSBUFBQVWWM2HCBAwePBjnz5/Hw4cPkZaWBjabjSZNmqBDhw4YNGgQk+VInKGhIS5evIijR4/i0aNHePfuncx0pxWPW0O/C8ASUvJfQkgt4HA4ldIMSnuONHx1cdx4RZkU/NcCVQf/r1+/Bo/Hg5aWFrp06aKCGhJxAoGACTD19fWpgURNVHXclPne1ObvLp1VDYyfnx9sbGxgY2OD9PT0+q5OnZk1axZsbGwwa9as+q5KvcnPz4e9vT1sbGyYAVbiVPUZ3bp1CzY2Nhg9ejS4XG6NyiKEEEKIeqFuP9UUGhoq0e/NwMAAjx8/hr6+vtzXlZaWYvDgwSguLmaeO3v2LAYOHFhrdW1MPDw8cPDgwWq9ZtmyZZXy6vv5+WHDhg1St2exWDAwMECrVq3Qt29fTJ48GT169FC6zhUdOHAAhYWFcHR0RM+ePVVWbkWjR49G586d8ebNG5w7dw5z5syptX0RQgghpGGhlv8aKikpqTRCXJp79+5JBP51LT09nbmj4OfnV2/1aEg6dOhQre2FQiGTHcDHxwf/+c9/sHfvXpXUJSMjA5cuXQIALF26VKkyRMdXfFCUNBoaGli0aBEA4NixYzJnSCSEEELI54da/mtAV1cXZWVl8Pf3h4uLi9xt/f39JV4jy8SJEzFx4kSV1rMxmD59OkaPHi13G4FAgBkzZqC4uBhNmjTBiBHy+xevXLkSw4cPl3h9fn4+QkND4enpiZKSEhw9ehRWVlZwc3OrUf2PHz8OLpeLvn37olevXlK3kZahQFnOzs74/fffkZmZCR8fH2r9J4QQQhoJavmvAScnJwDA48ePkZ2dLXO73NxcPHr0CAAkgkmiOmZmZrC2tpb7l5mZydx9GTNmDPT09OSWaWlpKfF6W1tbODg4YOXKlfjzzz+ZwXMnTpyoUd2LioqYi0NpGQdqg6amJpydnQEA586dg0AgqJP9EkIIIaR+UfBfA4MHD4aFhQX4fD5u3rwpc7sbN26Ax+PBwsICX3zxRR3WkIgTBdhAee7fmnBwcEC3bt0AAMnJyTXq0nXz5k2UlJRAW1sbY8aMqVG9qkN0tyo9PR2hoaF1tl9CCCGE1B8K/mtAU1MT48aNAyAZWFYkWvf1119XmbdXkWw/L1++xMaNGzF69Gj07t0bdnZ2cHR0xMSJE/HTTz/h3r17ElNR29jYSNxx2LBhA7MPaf3EK9aBw+HgzJkzmDx5MgYOHFhpew6Hg8DAQGzbtg2TJk3CgAED0L17dwwcOBBubm7w8PBAXl6e3Pdd24qLi3Hv3j0A5VOUy8pZXB3iU53XJK/xrVu3AJRPZ25iYiJzO1nZfpycnGBjY8M8PnjwYKXju379+krlde/enXkP0qZPrw0sFgva2trQ1tZu8HmQyf+j40ZI3VG32WJJOXU6btTnv4a++eYbnDlzBq9evcLr168r5W998+YNYmJimG1jY2NrtL8zZ85g586dlbppfPjwAR8+fEBMTAy8vb3x7NkzGBoa1mhfQHn6yWXLlsmt95YtW3DlypVKzxcUFKCgoABRUVE4d+4c/vzzT/Tr16/GdVLGrVu3UFpaCqD8OKjiC/ru3TsA5TMNmpqaKlUGh8NBZGQkAMjs61+bevXqhfT0dDx8+LBO9qetrV2rmYxI7aDjpv7UJShp7DQ0NGBgYFDf1SDVpG7HjYL/GurWrRu6dOmC169fw9/fHz/88IPEelGrv7W1Nbp27Vqj4D8uLo4J/Nu0aYOZM2fC1tYWxsbGYLPZSEpKwpMnTypNOX39+nVkZWVh3rx5ACoPZAXK+8xLs2nTJiQkJMDV1RXOzs4wNzfH+/fvoa2tzWzD4/FgZWWFkSNHws7ODq1atYKmpibevXuHx48f4/LlyygoKMCyZctw48YNmfuqTars8gOUp3wVXdSJxn4oIyoqirlrYGdnp1QZJ0+eBJfLZbrxTJs2DdOnT5fYplmzZlJf27NnT9y8eROZmZlISUlBu3btlKoDIYQQQtQDBf8q8M0332D37t24ceMGVq9ezbSwCIVCXL9+ndmmpu7cuQOBQAADAwP4+PjA3FxyevH+/fvDzc0NHz9+lJh3wNraWuKKVDSQVRHx8fH45ZdfJLLZdO/eXWKb7777DlZWVpValuzs7DB69GhMnz4dU6dORV5eHry8vLBy5UpF37JKpKenIyIiAgDQt29ftG3bVqHXZWZmIiEhgXksyvYTHh4OT09PCAQCWFpa1uj9iFr9ATBjCKqrYspS0eBnRYgfy5iYGAr+CSGEkM8cBf8qMH78eOzduxfv379HaGgoBg0aBKC8dfj9+/fQ0NCoMhWoInJycgAA7du3rxT4izMyMqrxvkQGDRpUZRrLqoJpGxsbuLm5wdPTE/fu3avz4N/f358ZAzFhwgSFX/fHH3/gjz/+kLpOR0cH8+fPx6xZs2Bpaal03TIzM5llZbsO1YT4PtPS0mp9fzweD+/fvwcAtGzZElpa9BOkDui4qT+hUEhdf9SAUChkZl6nMTbqQ92OGw34VQFLS0tmpl7x7iWi5UGDBtUoQBSxsLAAUD6OICoqqsblKUKZi5bCwkKkpqbi9evXSEhIQEJCApo2bQqgvO6iL0hduXbtGoDyORbGjh2rkjI5HA6uXr2Ky5cv1yhNpmggtL6+PnR0dFRSt+owNjZmluWlq1UVgUCArKwsZGVlUXpRNULHjYhoaJSHDXw+XyKxBFENURDJ5XLp81Uj8o6bUCgEn88H8P/fn/pGzTcq4urqipCQEPzzzz/YunUrgPJuOoBquvwAwLhx43Ds2DFwOBxMmzYNX375JRwdHdGvXz906dKlVq40xbPIyBMfH48zZ87g4cOHcoNIgUCAoqKiOuv3//z5cyQnJwMon2OhOndFtm/fXmnCNTabjcTERPz111+4dOkS9u/fj7i4OPzxxx9KfakLCwsBgLk4qmvi+/306VO91IEQoj50dHTA4XAgFApRVlZW5XwphDR2ZWVlzAVBfTTySdMwLkE+AyNHjoS+vj6TUjIgIABsNhsGBgYYNWqUSvbRqVMn7NmzB82aNQOPx8P9+/fx448/wsXFBQ4ODlizZg3Tt11VZA0UFXfp0iVMnDgRfn5+CrUei7Lu1IWrV68yy6oY6GtoaIiePXvil19+waJFiwCUX+RdvnxZqfJEPwTyZn2uTeL7pa4chJCqiGeRKyoqqseaEKIexL8nqsjCqAoU/KuIoaEhRowYAaC8u4+oy8+IESNUmv5p9OjRuHfvHrZt24ZRo0Yxfbbz8/Nx7do1zJgxA+vXr1fZrfmqWrMTExPx448/gsfjwczMDGvXroWfnx9CQ0Px8uVLxMfHIz4+Hr/++ivzmrq6lcnhcJgc+ubm5hgyZIhKy58zZw7z+Sgb/IuOX1FRUb3c4i0oKGCW6+vuAyFEfTRp0oRZzs3NRW5uLtOlgRDy//h8PvMdERH//tQnaupTIVdXV1y/fh2PHj2SeE7VjIyMMGXKFEyZMgVAeQB+7949eHl5ISsrC1euXEHXrl3h7u6u8n1XdOXKFfB4PGhqasLLywudOnWSup2oe0tdCgoKYoJbFxeXKidYqy5jY2OYmpoiJycH8fHxSpUhCv4FAgE+fvxY5wG4eItEy5Yt63TfhBD1o6OjAwsLC+Yur2gsiKamZoMf5KguRBdTqv6fRWqX+HET7+cvYmFh0WC6/VDwr0IODg4SP4rNmzeHg4NDre+3U6dO6NSpE1xcXODs7IySkhLcunVLIvivrR/lN2/eAABsbW1lBv5A+azEdU3VXX6k4fF4AKB0y5d4Ss7k5OQ6n0hJNB4CQKUJ6gghRBozMzNwOByJRh1q/VcNdcsaQ8pVddyaNWtWL3McyULBvwppamrim2++wdmzZwGUD/Sty5HdLVu2RPv27fHq1Svk5+dLrNPV1WWWRZNKqYIo+C0pKZG5TVZWVqWJx2pbfn4+/v33XwDlFya2trYq30d6ejpzZ0HZVvP+/fszy9HR0TUK/nV1dVFWVlat4xsdHQ2g/MdK2XkGCCGNC4vFQqtWrWBqaoqCggKUlJRQ8K8iAoGASb6gp6fXYLLDEPmkHTdNTU0YGBjA2Ni4wQ2Mp+BfxdasWYM1a9bUStkBAQGwt7eX2TXk/fv3ePv2LQCgTZs2EuuMjY2hra0NLpeL1NRUldWpffv2ePjwIVJSUvDs2TP07dtXYv2nT5+wevXqOh3kCwA3b95krsJro9VfIBBgz549zGNHR0elymnZsiVat26NjIwMREVFYcaMGUrXycLCAunp6dU6vqKUsb17966TgUgaGhpMelH6p6Y+6LgRafT09NCiRYv6rsZnhcfjISUlBQDQrl07SsSgJtTtuDXs2hEJnp6e+OGHH+Do6IhBgwahU6dOMDIyQmFhIV6+fIlz584xQfa0adMkXqulpQU7Ozs8e/YMly9fRrdu3dC1a1fmBG3WrJlEzndFjR8/Hl5eXhAIBFi4cCHmzZuHfv36QVdXFy9fvoSnpyeSk5PRt29fPHv2rMafgaJEXX60tLSUnmCt4gy/QPnFzJs3b3Dp0iVmdl5jY2PMnz9f6bo6OTnBy8sLoaGhNZqIp0+fPkhPT0dgYCAuXryIvn37Mnd8mjRpUumWY3FxMdPyLxqsXtu0tLTkdg8jDRMdN/VH3UfUA33X1JO6HTcK/tXMp0+fcPv2bdy+fVvqeg0NDSxfvlxqMLdw4UIsWrQIBQUFWL16tcS6ZcuWYfny5dWuT8+ePbF8+XJ4eHigqKgI+/btq7TN3Llz0aVLlzoL/hMTE5mgdvDgwXJnQ5ZH3gy/Im3atMGBAweYCdiUMXnyZHh5eeH9+/eIiIjAgAEDlCpn3rx5uHPnDjgcDjPXhMiECROwY8cOiefu3r2LsrIyaGlpYdy4cUrXnxBCCCHqg4J/NbJnzx4EBQUhNDQUiYmJyMnJQX5+PnR0dNC6dWv0798fU6dOldm/fdiwYThz5gzOnj2L6Oho5Ofnq2S23WXLlsHOzo4pt6SkBGZmZujZsyemTp2KwYMHw8/Pr8b7UZRoRl9A9V1+dHV1YWJiAltbWzg5OWH8+PHQ19evUZnW1tbo06cPIiMjcf36daWD/65du8LHxwcnT57Es2fPkJOTI7f//40bNwCUt/rX5OKFEEIIIeqDJaT5owmpd3///TdWrVqFZs2a4f79+7Xe/z4jIwMjR44En8+Hj48PevfurfJ9cDgc5g6MnZ0ddHR0wOVykZiYCKA8S5W2trbK90tUry6Om1DAB784R+XlNnaaTczB0tCsUZdCUnfoN1I91cZxk/Y/VFWo5Z+QBmDs2LE4ceIEYmJicP78eSxYsKBW93f06FHw+Xx8+eWXtRL4yyIUCsFms5lloh7q4rixNDSh1dSyVsomRF3Qb6R6UrfjRmkbCGkAWCwWkyXq9OnTclOn1tT79+/h5+cHTU3NWstMRQipf0KhEBwOBxwORy0CEkJI3aCWf0IaCAcHB2zevBkFBQV49+4dOnfuXCv7ef/+PRYuXAgrKyvY2NjUyj4IIfWPy+XWWrcBQoj6ouCfkAZk1qxZtb6Pvn37VpqPgRBCCCGNA3X7IYQQQgghpJGgln9CCCHkM6StrQ07OzsAaPAzjhJC6g79GhBCCCGfIRaLxfTzpwG/hBARCv4JIXVGU1MTbdq0YZaJeqDjVnv4AgFyij7VWvnmTfWhqUE9fNUFfdfUk7odNwr+CSF1RlNTE5aWlMtd3dBxqz05RZ/g/OvlWiv/702TYGlsSBN8qQn6rqkndTtu1BygBpycnGBjY4P169fXd1UIIYQQQogao5b/OhAaGorZs2cDAJYtW4bly5fXc40ah6CgIERHRyM6OhppaWnIy8tDcXExDAwMYGVlBXt7e0yePBkdO3as0X6cnJyQkZFRrdfcu3ePuUUozY0bN+Dn54f4+HgUFRXB3Nwc/fr1w4wZM9CnTx+5ZcvL3a+rqwsTExN07doVY8aMwddff00DAQkhhJBGhP7rk88Sj8fDwoULpa4rKipCTEwMYmJicO7cOXz33XdYsGBBndXNyMgIFhYWUteVlpbiu+++w4MHDySef/fuHd69e4ebN29i6dKlWLZsmVL7Lisrw4cPH/Dhwwfcv38fZ8+exbFjx2Bubq5UedXF4XBo0iE1RMdN/QmFQur6owbou6ae1O24UfBPPltGRkawt7dHr169YGVlBQsLC+jp6SErKwthYWG4fPkyPn78iD179sDIyAjTpk1Taj8nT54El8uVu83jx4+xfft2AMDYsWOhq6srdbuNGzcygf/AgQMxe/ZsNG/eHAkJCTh69ChSU1Ph4eEBCwsLTJkyRe4+e/TowexTpKSkBK9fv8a5c+cQFxeHmJgYfPfdd/D29lb07RJCCCFEjVHwTz5LWlpaCA0NlTnqfvjw4Zg5cyYmTZqEwsJCHDhwAJMnT1ZqlH6HDh2q3ObPP/9klr/55hup24SEhODmzZsAgK+++gqHDh1i6tOzZ084OTlh0qRJePfuHXbv3o0xY8agWbNmMvdpYGAAa2vrSs/37t0bLi4umDBhAt6+fYunT58iMjKyyu5EhBBCCFF/NOCXfLaqCuStrKwwZswYAEBeXh7evn1bK/X4+PEjAgMDmX32799f6nanTp0CUH7h8uOPP1aqv6mpKX744QcA5V2XLl26pHSd9PT0MGPGDOax6HYlIYQQQj5v1PL/GcjKysLdu3cRGhqKuLg4ZGVlgcfjwcTEBD169ICLiwvGjBkDDRm5nsUHJJ89exb29vbw9fWFn58f3r59Cy6Xi44dO2LmzJlwdXVlXsfhcHD58mVcuXIFycnJ4HK5sLGxwezZs+Hs7CyzvoWFhQgICEBISAhevXqF9+/fg8vlolmzZrC1tcWoUaMwYcKEOukzZ2hoyCyXlZXVyj5u3brFlC3++YkrLi5GSEgIAMDBwQEtWrSQut3IkSPRpEkTFBcXIyAgAP/973+Vrpf4gGMOh6N0OYQQQghRHxT8qzk+nw9HR0cIBIJK67KyshAYGIjAwED4+vrCw8NDItiVhsfjYfHixbh//77E89HR0Vi3bh1evnyJzZs3o7CwEEuXLkV4eLjEdpGRkYiMjERqaioWLVokdR8TJkyQmh0nJycHwcHBCA4OxsWLF3Hs2DGZA2NVobS0lGmR19DQQPv27WtlP1evXgVQPtumrC4/0dHRzLgBe3t7mWXp6Oigd+/eCA4OZl6jra2tVL3Ej0GrVq2UKoMQQggh6oWCfzUnmrJ90KBBGDp0KKytrWFqago2m420tDRcunQJkZGRePToEbZt24adO3fKLW///v148eIFXFxc4OLiAnNzcyQnJ8PDwwNJSUnw8vKCk5MTvLy8EBkZiWnTpmHkyJEwNjZGbGws9u/fj6ysLBw4cADDhw9Hly5dKu2Dz+ejV69eGDZsGLp16wYzMzNwuVykp6fj2rVrePjwIV69eoXvv/8eXl5eKv28uFwusrOzERkZiePHjyM5ORkAMGnSJDRp0kSl+wKAtLQ0PHv2DADQr18/WFlZSd0uMTGRWa4q9WiHDh0QHBwMHo+HlJQUdO7cudr1Ki0tZQb5GhgYwMHBodplEEIIIUT9UPCv5jQ1NXH79m20a9eu0jp7e3tMmjQJBw4cwKFDh+Dv74/FixfLbeF+8eIFNm7cCHd3d+a57t27w97eHqNHjwabzcbq1auRn5+PgwcPYsSIERLb9ejRAxMmTACfz4ePjw82b95caR+enp5S69C3b1+MHz8ely9fxsaNGxEWFoaQkJAaB6bp6ekYPny4zPVDhgyptQnU/P39mQs0WV1+AODDhw/MclWzBLZs2ZJZfv/+vczgv6SkBAkJCRLPlZaWIj4+Ht7e3njz5g1YLBZ++OEHmJiYVPVWCCGEEPIZoOBfzbFYLKmBv7ilS5fC29sb+fn5CAwMxNy5c2Vu26tXL4nAX8TCwgIjR47E1atXkZeXB2dnZ4nAX8TW1hb9+vVDeHg4nj59KnUfVXWvmTRpEry8vBAbG4uAgIBaa5U2MTHBli1bMHr0aKWy/CjC398fQPkA27Fjx8rcjs1mM8sGBgZyy9TX12eWS0pKZG738uVLuLi4yFw/ZMgQzJ8/H4MGDZK7P1XS0tJiJiGjycXUBx03QuoGfdfUk7odt4ZfQ1ItAoEA2dnZYLPZ4PF4zPMtWrRAfn4+4uLi5L5e3kBdW1tbZnncuHFytwsPD0daWlqV9RUKhcjJyUFxcbFErnxLS0vExsZWWV9FWFpa4vr16wDKuxxlZmbi4cOH8PX1xY8//oi0tDSZE4LVxLNnz5CamgqgPLWovG5F4oONqxroLL6+tLRU6fqFhoZCX18f7dq1k7ibUJs0NDRqpXsVqV103NQfTfClHui7pp7U7bhR8P8ZEAqFuHbtGnx9fREVFSU3IMzPz5dblryc9UZGRsyyvNZ70XbirdkVBQUF4cKFCwgPD5e7XVX1VYS2trZEvvuuXbti2LBhcHNzg7u7O/bu3Yvk5ORKE2LVlGigLyC/yw8AiUm/qsq8I75eT09P5nb29vaVxkxwuVxkZmbiwYMH8PDwwN27d/HixQucOXMGnTp1krtfQgghhKg/yvOv5srKyrBgwQKsXbsWYWFhVbYEV7VeXjApnipUke2kZSASCoXYtGkTFi5ciKCgILmBvyL1rQlbW1usWLECAODn54fg4GCVlc3hcHD79m0A5V2mBg8eLHd78SxM8rryAMCnT5+Y5aq6CFWkra2NNm3aYMaMGfDy8oK2tjaysrKkjs2oDQKBAMXFxSguLpZ6fpCGiY6b+hONPSING33X1JO6HTcK/tXc4cOH8e+//wIob+n9448/cPfuXURGRiI2Nhbx8fGIj4+XObFUXfP19YWvry+A8hb4HTt24O+//8bTp0/x6tUrpr6yUmKqmvhA4Dt37qis3MDAQBQWFgIAXFxcqhxTIJ7XPzMzU+6279+/Z5Zr0l2nS5cuGDp0KIDyLkpJSUlKl6UoHo/HHGPxbmmkYaPjRkjdoO+aelK340bdftSYUChkAun+/fvD09NT5kReokC0volmpW3Xrh0uXrwo8w5CXdXX1NSUWX737p3Kyq1Olx8AEl1uqpppWBSka2lpVTnYuyodO3bEvXv3AAAJCQlyu30RQgghRP1Ry78aKygoQHZ2NgDIncGXzWbXSauuIl6/fg0AcHJykhn4C4VCxMTE1El9xFvZq9uFRpa8vDymC1HXrl2ZDADy2NnZMZN1hYWFydyOw+Hg+fPnlV6jLD6fL3WZEEIIIZ8nCv7VmHiwJt4PvKJLly41mNtQojrL69d+79495qKmton65QOQGBRcEzdu3GAyFynS6g8ATZo0YVKahoSESOT9F3f37l0UFxcDgNRUq9X18uVLZlm86xEhhBBCPk8U/KsxU1NTNG3aFEB5wCktS0xUVBT2799f11WTSdRN5f79+ygoKKi0PjU1FT/99FON9xMQEICsrCy524SHh+PQoUMAyrvQyEtfWh2iLj9aWlpy8+xXJJp/gcfj4aeffqrUEp+Xl4fdu3cDAJo2bQo3N7ca1TMoKIi5y2BiYoKePXvWqDxCCCGENHzU57+OxcbGws/Pr8rtBg0ahFatWsndRkNDAy4uLjh//jzi4+Mxbdo0zJkzB+3atUNxcTEePHgAb29vGBgYoHnz5khOTlbRu1Ceq6srdu3ahaysLEyZMgXz58+HtbU1ysrK8OTJE3h6eoLD4aB79+416voTEBCAlStXYtiwYXBwcEDnzp3RtGlTcDgcpKam4v79+7h16xYzKn/JkiXo2LFjjd/fmzdvmHp/+eWXMDMzU/i1Dg4OGDduHG7evInAwEDMmTMH7u7uaN68ORISEnDkyBFmXMIPP/yAZs2ayS1P2gy/PB4PmZmZCAoKYsaLAMDq1avVYmISQgghhNQM/bevY/fu3WMGWMpz6NChKoN/AFi1ahWePXuG2NhYvHz5EqtXr5ZYb2xsDA8PDxw4cKBBBP+zZ8/G48ePERwcjOTkZGzatElivZ6eHnbu3IkHDx7UuN8/l8vF3bt3cffuXZnb6OnpYeXKlZgzZ06N9iUiPtBXmYxFv/32G3PhFhoaitDQUIn1GhoaWLJkCaZMmVJlWVXN8AuUp/5csWJFje8iEEIIIUQ9UPCv5oyMjHDhwgWcPn0at27dQkpKCjQ1NdGyZUs4OjrC3d29QfXl1tbWxtGjR3HhwgVcvXoViYmJEAqFsLS0hIODA2bPno1OnTrhwYMHNdrPmjVrMGDAAERERCAhIQG5ubnIzc2FhoYGmjVrhi5dumDgwIFwdXVF8+bNVfLeBAIBM5Nw06ZNJdKIKkpPTw/Hjh3D9evXceXKFcTFxaGoqAjm5ubo168fZs6ciT59+ihdR01NTRgZGaFt27YYOHAg3NzcapwxiBBCCCHqgyWkmT8IIbWAw+EgOjoaQHlmIh0dnXquESEND18gQE6R7IQNNWXeVB+aMjLBEUIartr8H0ot/4QQQkg90dTQgKWxYdUbEkKIilBzACGEEEIIIY0EtfwTQuoMn89HTk4OAMDc3Byampr1XCOiCDpu6omOm/qhY6ae1O24UfBPCKkzfD4f6enpAMrnFmjoP5CkHB039UTHTf3QMVNP6nbcqNsPIYQQQgghjQQF/4QQQgghhDQS1O2HEEII+Qxpa2vDzs4OAGgGb0IIg34NCCGEkM8Qi8VicoPTlD6EEBEK/gkhhJAGSCjgg1+cU6MyNJuYg6XRsAcfEkLqFgX/hBBCSAPEL87Buz9G1KiMVisDoNXUUkU1IoR8DmjAL1Gan58fbGxsYGNjw6S4IsrhcDgYNWoUbGxscPv27Urr169fDxsbGzg5OdVoP1FRUbCxsYG9vT0KCgpqVJYyWCwWDA0NYWhoCBaLVef7J8qh40ZI3aDvmnpSt+NGLf+N1IIFC/DgwQOwWCycO3cO/fv3r/I1ERERmDlzJoRCIYYNG4bRo0fXuB6JiYkICQlBdHQ0EhISkJubi/z8fGhqasLMzAx2dnb4+uuvMXz4cKW+UHFxcZg0aRJ4PB4AYMKECdixY4fUbUNDQzF79myZZRkYGKB58+bo2bMnJk6cCAcHh2rXR5azZ88iJSUF1tbWKvlcZenZsyeGDBmC4OBgHDx4EJs3b661fUmjra0NW1vbOt0nqTk6bupPHQISQt81daVux41a/hupbdu2oUmTJhAKhdi8eTPKysrkbs/hcLB582YIhUI0adIEP/30k0rqcfjwYfz888+4evUqXr16hczMTHA4HHz69Anp6em4desWli5ditmzZyM/P79aZQsEAvzvf/9jAv+aKikpQXJyMq5du4Zvv/0W69atA5/Pr3G5xcXFOH78OABg8eLFSv2TdnJygo2NDdavX1/ltkuXLgUAXLx4Ee/fv6/2vgghhBCivqjlv5Fq0aIFfvjhB/z4449ISkrCwYMHsXr1apnbHzp0CElJSQCANWvWoEWLFpg4cSImTpxYo3poaWmhV69e6Nu3L6ytrWFubg5TU1MUFRXh7du38PHxQUJCAsLCwrB48WJ4e3tDQ0Oxa9Zz584hKioKZmZmyM3NrVa9pk2bhunTpzOPhUIhCgsL8fz5c5w5cwa5ubm4evUqWrRogVWrVlWr7IouXLiAgoICtGrVCmPGjJG6zY4dO2Tesaiuvn37onfv3nj+/DlOnjxZ563/hBBCCKk/1PLfiE2dOhX29vYAgFOnTuHVq1dSt4uLi8OJEycAAPb29pgyZYrK6vDLL7/gr7/+wvr16zFx4kQMHToUPXr0wBdffIGZM2fi6tWrGDVqFAAgMjIS9+/fV6jcDx8+4I8//gCLxcLatWurXS8zMzNYW1szf6J+8gsWLMDZs2ehq6sLAPDy8gKHw6l2+SJ8Ph/nz58HAIwbN07hC5ua+vrrrwGUj9soLi6uk30CAI/HQ2JiIhITE1V2R4bUPjpu6o9SfaoH+q6pJ3U7bhT8N2IsFgu//PIL9PT0wOPxsGnTpkonLZ/Px8aNG8Hj8aCnp4dff/1VpX1Hq5p4RlNTE/PmzWMeR0REKFTuTz/9BDabjQkTJig0nqE6OnfujGHDhgEA2Gw23r59q3RZjx49YrreuLi4qKJ6CnF2doampibYbDZu3bpVZ/sVCAQoKChAQUEBBAJBne2X1AwdN0LqBn3X1JO6HTcK/hu5du3a4bvvvgMAvHr1CidPnpRYf/r0acTExAAAVqxYgbZt2zLr6irbj6GhIbOsSCv77du3ERgYCGNjY6Va/RXRunXratVJFlHg3b59e9jY2MjcTla2n1mzZsHGxgYZGRkAgCtXrjDHRPQ3a9asSuWZmZmhX79+AICbN28qXX9CCCGEqBcK/gm+/fZbZgr4gwcPMi3Zqamp8PDwAADY2dnB3d29XuonHpx27NhR7rYfP37EL7/8AqB8bILJ/7F353FRVv3j/18sA4IbioAbmZogLpjLrZjeWaiZK4qWW2pmqZVLZlpmZfUtNZcyl7sszZTScsddE61cCBVNcQHcFTdEEAQEZpj5/cFvrg8j+zADXPJ+Ph4+upjrXOecmcPQ+zrXWapVs0qdbt68qRzXrl3b7HzCwsIAaNGiRbHrVFRPP/00kPU0JTU1tcTLF0IIIUTJkwm/Ajs7O7788kv69++vrOrzyy+/8NFHH5GWloZGo2HmzJnY2ZXcLpHx8fFcvXqVdevWsXHjRgCqVatW4NCYuXPncvfuXdq0aUP//v2tUreLFy/y559/AlkBdI0aNczK5/bt20qPvfHmq6hmzpzJw4cPGTVqFLGxsXTu3Jl33nnHJI2Tk1Ou1/r6+gKg1Wr5999/eeaZZ8yqgxBCCCHUQ4J/AYC3tzejR49myZIlhIeH89prrym90mPGjMHLy8vqdRg2bBhHjhzJ9Vy1atVYvHgxVapUyfP68PBw1q5di0aj4dNPPy3W3IR79+4RHR2t/GwwGHjw4AEnTpxg5cqVpKWlUblyZaZNm2Z2GcePH1eOfXx8zMrD09MTyFpjGKBKlSqFbqumTZsqx6dPn5bgXwghhCgHJPgXirFjx7Jnzx7Onz9PaGgoAF5eXowZM6ZU6zVs2DDeeustqlevnmeajIwMPvnkEwwGA6+++iqNGjUqVplr1qxhzZo1uZ6ztbVl0KBBvPrqq9SvX9/sMu7cuaMcu7q6mp2PubKXKTs0CyGEEOWDBP9C4eDgwMyZMxk0aBCZmZnKcCAHB4cSKd84hMXYy3769GnWrFnDr7/+SkxMDF988UWeQ2x++OEHLly4QJ06dZRNrKxFr9ezY8cOHB0dee+998z+fOLj45XjqlWrWqp6hebo6EiFChVIS0vj7t27JV6+EEIIIUqeBP/ChK+vLzVr1uTGjRvUrFlTGRdeEoxDWIzatGnD4MGDmThxIvv372fAgAH89ttv1KxZ0yTdpUuXWLp0KQAff/xxnmPci2LcuHGMHz/e5LW0tDSuXr1KcHAwK1euZOXKlZw+fZrly5ebVWZiYqJynN9wJmuqUqUKaWlpPHz4sETKs7W1xd3dXTkW6iDtJkTJkO+aOqmt3cp+DUW55ujoyMyZM3FycuLWrVvMnTvX5LzBYGDGjBlkZGTQtWtXnn/+eavVpUKFCnh7ezN16lRmzJgBZM0z+P77783KL/sTg7S0NIvUsajS09OBgvdbsBR7e3s8PT3x9PQssTJF8Um7qZ8l92cR1iPfNXVSW7uV/RqKcq969eq0atWKQ4cOERISglarVSa4/vvvv8ok4ZYtW+a6Zn324TUxMTFKmkaNGpk9kXnAgAHMnz+f+/fvs2HDBiZNmlTkPLLPYUhMTKRSpUpm1cVcer2eBw8eAKX35EEIIYQQJUuCf6EKxvX6Hz58SEJCgvJ4LfsGW3PmzCkwn6NHj3L06FEga2iPucG/ra0t9erV4/79+9y9e5eEhIQi7ymQPfhPSkoy2TisJDx48EDZibBWrVolWrYQQgghSocM+xGqEBsbqxw7OzuXYk3+j06nU44zMzOLfH32G4/Lly9bpE5FceXKFeW4uKsjFZZWq+XUqVOcOnUKrVZbImWK4pN2Uz+DwVDaVRCFIN81dVJbu0nPvyjzbt++zYkTJwCoU6eOyfCYdu3aERUVle/1MTExdO7cGYB+/foxe/bsYtfp4cOHXLx4EciaC2DOTsLNmjXD0dGR9PR0IiIi6NGjh9n1cXR0BEyfhBQkIiJCOS6pid0Gg0H5wyjBiHpIuwlRMuS7pk5qazfp+Rel5vLly8p+Anl58OABkydPVr5UAQEBJVG1Ai1atEiZpNuxY0ezdj92cHCgRYsWgGkgbg43NzcArl27VuhrTp06BUDt2rVp0KBBscoXQgghhDpIz78oNbGxsbz66qs0btyYLl260LRpU2rUqIG9vT13797l+PHjbNiwQVmD3svLi9GjR5dI3R7d4ReyVsa5evUqmzdv5sCBA0BWj/uECRPMLqdz584cOXKEU6dOkZycbPak35YtWxIWFkZERAQ//PADzz77rLL8aIUKFfDw8DBJbzAYlB2cu3TpYnb9hRBCCKEuEvyLUhcZGUlkZGS+aZ577jlmzZplkTX8CyO/HX6Nqlevzty5c/H29ja7nICAAObPn096ejp79+6lb9++ZuUzZMgQfvvtN+7fv8/8+fOZP3++cq5t27YEBQWZpD969Ci3b98GoE+fPmbXXwghhBDqIsG/KDWtWrVi+fLlHD58mNOnT3Pnzh3i4uJIS0ujUqVK1KlTh6effpqePXvSunXr0q4uGo0GFxcXnnrqKTp16kRgYGCxd+atVq0aL7zwAtu2bWPr1q1mB/8eHh6sW7eOpUuXKoG9cQ3/3Gzbtg2A5s2b07x5c7PKFEIIIYT62BjUMDNBiMfYyZMnefnll7Gzs+OPP/6w+pKfycnJPP/88yQlJfH111/Ts2dPq5STkZGhzGVo3rw5Dg4Oub4myj5pt9Jh0GeSmRxXrDzsKtXAxtYOg8EgG32pgHzX1Mka7WbN3wXp+ReilLVo0YIXXniBPXv2sHTpUj7//HOrlvfrr7+SlJTEU089Rffu3a1alhDCfDa2dthX8Sg4oRBCFIGs9iNEGTBp0iTs7e3ZuHGjMhbfGlJTU1mxYgUAU6dOxda2ZP8E2Nvb06BBAxo0aKCKLdBFFmk3dTIYDOj1emUzP1H2yXdNndTWbmW/hkKUAw0aNODLL7/k+vXr3Lx5k5o1a1qlnJs3bzJ06FCqVq1Kp06drFJGfmxtbc3aE0GULmk3dbKxsZGhPioj3zV1Ulu7yZh/IYRVyNhVIYQQwjwy5l8I8VjIvguiRqORXkmVkHZTJ2k39ZE2Uye1tZuM+RdClBitVktERAQRERHKH0pR9km7qZO0m/pIm6mT2tpNev6FEEKIx5BGo1H28VDDJEQhRMmQvwZCCCHEY8jGxkYZJyzT+4QQRhL8CyGEEGWcuRt+GTf5EkIIIwn+hRBCiDIuMzmOmwu6FPm62u/slY3ChBAmHvsJv/7+/nh7e/PBBx+UdlWEyNfUqVPx9vbOdYffjRs34u3tjbe3NzExMWaX8fDhQ9q3b4+3tzdhYWHFqa4QQgghVKjM9/yHhYUxfPhwAMaNG8f48eNLuUblw59//qnMXL9+/Trx8fEkJyfj7OyMp6cnbdu25eWXX6ZBgwbFKsff358bN24U6ZqQkBDq1q2b5/lt27axceNGoqKiSEpKokaNGrRu3ZqhQ4fSsmXLfPP29vbO85yjoyPVqlXDx8eHF198kV69ellsEt2pU6fYsmULGo2G0aNHWyTP3Dg5OTFy5Ejmz5/PzJkz2bx5c5lfkkwIIYQQllPmg39R8nQ6HWPGjMn1XFJSEmfOnOHMmTP88ssvTJgwwarB6qMqV66Mm5tbrufS0tKYMGECf/31l8nrN2/e5ObNm2zfvp23336bcePGmVV2eno6t2/f5vbt2+zfv59Vq1bxww8/UKNGDbPyy27BggUYDAb69+9v1u6+H3zwAZs2baJOnTrs27cv37RDhgxh+fLlREZGsnPnTnr06GFutYUQQgihMhL8i1xVrlyZtm3b0qJFCzw9PXFzc6NChQrExsZy5MgRNmzYwIMHD5g/fz6VK1dm8ODBZpWzfPnyAtfEPXz4MLNmzQKge/fuODo65pruww8/VAL/du3aMXz4cNzd3YmOjmbp0qVcu3aNRYsW4ebmxsCBA/Mts1mzZkqZRqmpqZw/f55ffvmFyMhIzpw5w4QJE1i9enVh326uTp06xaFDhwB47bXXck0TGBhIYGBgscoxqlSpEi+//DI//PAD3333XYkG/9mXHtRoNCVWrigeaTchSoZ819RJbe0mwb/Iwd7enrCwMOzscl8honPnzrzyyiv079+fxMREFi5cyMsvv5xn+vzUr1+/wDT/+9//lOOAgIBc04SGhrJ9+3YAnn/+eZYsWaLUx9fXF39/f/r378/NmzeZN28eL774IlWrVs2zTGdnZ7y8vHK8/vTTT9O7d2/69evHpUuXCA8P58SJEwUOJ8rPypUrAWjRogX16tUzO5+i6NWrFz/88APR0dGEhYXRrl27Eik3+9KDQj2k3dRPhvepg3zX1Elt7fbYT/gV5ikokPf09OTFF18EID4+nkuXLlmlHg8ePFCGsXh6etKmTZtc0/30009A1o3Lp59+mqP+1atX57333gOyhi6tW7fO7DpVqFCBoUOHKj9HRESYndeDBw/Ys2cPAL179zY7n6Ly9vZWbm7Wr19fYuUKIYQQonSV+57/2NhY/vjjD8LCwoiMjCQ2NhadTke1atVo1qwZvXv35sUXX8TWNvf7pOwTkletWkXbtm1Zv349Gzdu5NKlS2i1Who0aMArr7xC3759lesyMjLYsGEDmzZt4sqVK2i1Wry9vRk+fHi+wzASExPZu3cvoaGhnD17llu3bqHVaqlatSqNGzfmhRdeoF+/fiVyB1qxYkXlOD093Spl7Ny5U8k7++eXXXJyMqGhoQC0b98+zzHzXbt2pVKlSiQnJ7N3715ef/11s+uVfcJxRkaG2fmEhIQo17/wwgt5ptu4cSPTpk1TrjGWv2jRIhYvXqyku3HjRq6TlqOionK81q1bN6Kjo9m7dy/p6el5DqeyJL1eT2JiIgBVq1bN83slyhZpN/UzGAzS+68C8l1TJ7W1W7kO/jMzM+nUqRN6vT7HudjYWPbt28e+fftYv349ixYtMgl2c6PT6XjzzTfZv3+/yesRERG8//77nD59mo8++ojExETefvttjh49apLuxIkTnDhxgmvXrjF27Nhcy+jXr1+uq+PExcVx8OBBDh48yG+//cYPP/yQ58RYS0hLS1N65G1tbXnyySetUs7mzZuBrEdqeQ35iYiIUOYNtG3bNs+8HBwcePrppzl48KByjblj87K3Qe3atc3KA1CW26xVqxYeHiW7FneLFi2ArLkMx44do0OHDlYvU6fTKU+JmjdvrqrHpOWZtJsQJUO+a+qktnYr18G/cbtzPz8/nn32Wby8vKhevTopKSlcv36ddevWceLECQ4dOsTnn3/OV199lW9+3377LSdPnqR379707t2bGjVqcOXKFRYtWsTly5cJCgrC39+foKAgTpw4weDBg+natSsuLi6cO3eOb7/9ltjYWBYuXEjnzp1p1KhRjjIyMzNp0aIFzz33HE2aNMHV1RWtVktMTAxbtmzhwIEDnD17lnfffZegoCCLfl5arZa7d+9y4sQJfvzxR65cuQJA//79qVSpkkXLArh+/TrHjx8HoHXr1nh6euaa7uLFi8pxQUuP1q9fn4MHD6LT6bh69SpPPfVUkeuVlpamTPJ1dnamffv2Rc7D6NixYwDKRKGiGjJkCN26dWPBggWEhITg7u7O8uXLC3Wtr6+vcnzkyJESCf6FEEIIUbrKdfBvZ2fHrl27cp1k2bZtW/r378/ChQtZsmQJwcHBvPnmm/n2cJ88eZIPP/yQESNGKK81bdqUtm3b0q1bN1JSUpg8eTIJCQksXryYLl26mKRr1qwZ/fr1IzMzk99//52PPvooRxkrV67MtQ6tWrWiT58+bNiwgQ8//JAjR44QGhparMAUICYmhs6dO+d5vmPHjlbbQC04OFi5QctryA/A7du3leOCes9r1aqlHN+6dSvP4D81NZXo6GiT19LS0oiKimL16tVcuHABGxsb3nvvPapVq1bQW8nVvXv3uHbtGgA+Pj5m5eHq6oqrqytVqlQBslYZyG2icm6qVq1K3bp1iYmJ4cyZM2aVL4QQQgh1KdfBv42NTYGrq7z99tusXr2ahIQE9u3bl+dSjJA1jCJ74G/k5uZG165d2bx5M/Hx8fTo0cMk8Ddq3LgxrVu35ujRo4SHh+daRkHDa/r3709QUBDnzp1j7969xQ7+81KtWjU++eQTunXrZtYqP4URHBwMZE2w7d69e57pUlJSlGNnZ+d883RyclKOU1NT80x3+vTpfCfgduzYkTfeeAM/P798y8tP9psWV1dXs/MpDldXV2JiYrh+/XqplC+EEEKIklWug/9H6fV67t69S0pKCjqdTnm9Zs2aJCQkEBkZme/1+U3Ubdy4sXLcs2fPfNMdPXq0UMGYwWAgLi6O5ORkk7XyPTw8OHfuXIH1LQwPDw+2bt0KZA05unPnDgcOHGD9+vV8+umnXL9+Pc8NwYrj+PHjSq94586d8x1WlH2ycUHj7LKfT0tLM7t+YWFhODk5Ua9ePZOnCUURHx+vHBt77kuacbnTuLi4UilfCCGEECWr3Af/BoOBLVu2sH79ek6dOpVvQJiQkJBvXvmtWV+5cmXlOL/ee2O67L3Zj/rzzz9Zs2YNR48ezTddQfUtjEeHkfj4+PDcc8/x0ksvMWLECL7++muuXLmSY0Os4jJO9IX8h/wAJqvUFLTyTvbzFSpUyDNd27Ztc8yZ0Gq13Llzh7/++otFixbxxx9/cPLkSX7++WcaNmyYb7m5Ma4MAOS754A1GW86Hj58WCrlCyGEEKJkle21iKwsPT2d0aNHM3XqVI4cOVJgT3BB5/MLJrMv+1SYdLmtQGQwGJg+fTpjxozhzz//zDfwL0x9i6Nx48ZMnDgRyFqG8uDBgxbLOyMjg127dgFZQ6YKmoiafRWm/IbygGmQW9AQoUdpNBrq1q3L0KFDCQoKQqPREBsbm+vcjMLIftNizbbKj/Gpib19ue8HEEIIIcqFch38f/fdd/z9999AVk/vggUL+OOPPzhx4gTnzp0jKiqKqKioPDeWKmnr169XNmTy8fFh9uzZ7Nixg/DwcM6ePavUN68lMS0t+0Tg3bt3Wyzfffv2Kb3ivXv3LnBOQfZ1/e/cuZNv2lu3binH5g7XAWjUqBHPPvsskDVE6fLly0XOI/tE4exPAUqSsdySGnZkY2ODRqNBo9HImuMqIu0mRMmQ75o6qa3dym13n8FgUALpNm3asHLlyjw3ZSitwOxRxl1p69Wrx2+//ZbnE4SSqm/16tWV45s3b1os36IM+QFMhtwUtNOwMUi3t7cvcLJ3QRo0aEBISAgA0dHR+Q77yk32z6+0g//i3AgVhUajMVliVKiDtJv6qSEgEfJdUyu1tVu57fm/f/8+d+/eBch3B9+UlBSzenWt4fz58wD4+/vnGfgbDIYSW7Yxey97UYfQ5CU+Pl4ZQuTj45PrbrWPat68ubJZ15EjR/JMl5GRwb///pvjGnNlZmbmelxYnp6eyupDxj0TzGXO/9j1er0ysdyc/Q6EEEIIoT7lNvjPHqzlN9lx3bp1Jiv/lCZjnfMb1x4SEqLc1FibcVw+UOi15Quybds2ZeWiwvT6A1SqVElZ0jQ0NNRkCc3s/vjjD5KTkwFyXWq1qE6fPq0cZx96VFgajUbZZTciIqJYdTGuYlTQhOfsLly4oPwuGeshhBBCiMdbuQ3+q1evroxz3rZtW65B06lTp/j2229Lump5Mg5T2b9/P/fv389x/tq1a3z22WfFLmfv3r3Exsbmm+bo0aMsWbIEyBpCk9/ypUVhHPJjb2+f7zr7jzLuv6DT6fjss89y9MTHx8czb948IGt8+0svvVSsev7555/KU4Zq1aqZ/bjPOJ8kKiqqSIH7o9zc3ICsjcOMNzgFOXXqlHJcUrv76nQ6rl+/zvXr18vMTbUomLSb+hk3TBRlm3zX1Elt7aaqMf/nzp1j48aNBabz8/Ojdu3a+aaxtbWld+/e/Prrr0RFRTF48GBGjhxJvXr1SE5O5q+//mL16tU4Ozvj7u5e7GEZltC3b1/mzJlDbGwsAwcO5I033sDLy4v09HT++ecfVq5cSUZGBk2bNi3W0J+9e/fyzjvv8Nxzz9G+fXueeuopqlSpQkZGBteuXWP//v3s3LlTWZHorbfeokGDBsV+fxcuXFDq/d///rdIG1+1b9+enj17sn37dvbt28fIkSMZMWIE7u7uREdH8/333yvzEt57770Cl9bMbYdfnU7HnTt3+PPPP5X5IgCTJ082e7Wczp07s3jxYrRaLceOHeOZZ54xK59WrVoBWUN5ZsyYwbBhw0wmFOc2vyE0NBTIWrnJ09PTrHKLSq/XKzeWBe3GLMoOaTchSoZ819RJbe2mquA/JCREmWCZnyVLlhQY/ANMmjSJ48ePc+7cOU6fPs3kyZNNzru4uLBo0SIWLlxYJoL/4cOHc/jwYQ4ePMiVK1eYPn26yfkKFSrw1Vdf8ddffxV73L9Wq+WPP/7gjz/+yDNNhQoVeOeddxg5cmSxyjLKPtHXnBWLZs6cqdy4hYWFERYWZnLe1taWt956i4EDBxaYV0E7/ELWsJ2JEycW6ylCkyZNlJu1rVu3mh38+/n58fTTT/Pvv/+ybds2tm3bZnI+KirK5OeHDx+yb98+APr06WNe5YUQQgihOqoK/i2tcuXKrFmzhhUrVrBz506uXr2KnZ0dtWrVolOnTowYMcKssdzWotFoWLp0KWvWrGHz5s1cvHgRg8GAh4cH7du3Z/jw4TRs2JC//vqrWOVMmTKF//znPxw7dozo6Gju3bvHvXv3sLW1pWrVqjRq1Ih27drRt29f3N3dLfLe9Hq9spNwlSpVTJYRLawKFSrwww8/sHXrVjZt2kRkZCRJSUnUqFGD1q1b88orr9CyZUuz62hnZ0flypV54oknaNeuHS+99FKxVwwCGDhwIJ988gl//PEHn332WYG7FOfG1taW5cuXs2zZMvbv38+1a9d4+PBhno/6Q0JCSE1NxdHRkcDAwOK+BSGEEEKohI1BBgIKUapSUlLo0qUL8fHxzJ07t0R64l999VVCQ0MZOHAgn3/+uVXKyMjIUCYyN2/eHAcHh1xfE2WftFvpM+gzyUyOK/J1dpVqYGNrh8FgkOU+VUC+a+pkjXaz5u9CuZ3wK0RZUbFiRd566y0Ali5dmuvuzpb077//EhoaikajYezYsVYtSwhhGTa2dthX8SjyPxvb/DdJFEKUPxL8C1EGDBo0CE9PTy5cuGCyhKo1LF68GMiaQ1KYuTFCCHUyGAxkZGSQkZEhq/0IIRTlesy/EGWFRqNh9uzZhIaGmrVhWGE9fPiQp59+mqeffppXX33VauUIIUqfVquVISRCiBwk+BeijGjTpo2y7r+1ODk5MW7cOKuWkR9bW1tcXFyUY6EO0m7qJO2mPtJm6qS2dpPgXwhRYuzt7WnYsGFpV0MUkbSbOkm7qY+0mTqprd3K/u2JEEIIIYQQwiIk+BdCCCGEEKKckGE/QogSo9VquXjxIgANGzZEo9GUco1EYUi7qZ+s868O8l1TJ7W1mwT/QogSYzAYSElJUY6FOki7lT2Zej1xSQ8LTFejihN2KpiAKLLId02d1NZuEvwLi9q4cSPTpk0DICQkhLp165ZyjUrGsGHDOHLkCG3btiUoKKi0qyOEeMzFJT2kx5cbCky3Y3p/PFwqlkCNhBBqIcF/ORMWFsbw4cOVn52dnTl8+DBOTk75XpeWlkaHDh1ITk5WXlu1ahXt2rWzWl3Lk0WLFimbbxXWuHHjGD9+vMlr2W++HmVjY4OzszO1a9emVatWvPzyyzRr1szsOgshhBBCfeRZYDmXmprK3r17C0wXEhJiEviXtJiYGLy9vfH29mbjxo2lVo+ypH79+kVKb3wsef78eX7//XcGDBjA119/baXaCSGEEKIskp7/cszR0ZH09HSCg4Pp3bt3vmmDg4NNrslLYGAggYGBFq1neTBkyBC6deuWbxq9Xs/QoUNJTk6mUqVKdOnSJd/077zzDp07dza5PiEhgbCwMFauXElqaipLly7F09OTl156ySLvQwghhBBlmwT/5Zi/vz87d+7k8OHD3L17Fzc3t1zT3bt3j0OHDgHQuXNnduzYUZLVLBdcXV1xdXXNN81ff/2lPH158cUXqVChQr7pPTw88PLyyvF6+/btadeuHSNHjsRgMLBs2TIJ/oUQQohyQob9lGMdOnTAzc2NzMxMtm/fnme6bdu2odPpcHNz45lnninBGorsjE9fAPr27VusvNq3b0+TJk0AuHLlSqkO6RJCCCFEyZGe/3LMzs6Onj178vPPPxMcHMyrr76aazpj0NmrVy/s7OzyzbMwq/2cPn2a1atXEx4ezp07d8jMzKR69eq4urrSokULOnbsiL+/v7Imtbe3t8n106ZNyzGpNfvk10fr4O7uzurVq9mxYwdXr17l/v37JukzMjI4ePAgBw8e5OTJk1y7do3U1FQqVarEE088wbPPPsvQoUOpXr16vu/dmpKTkwkJCQGgbt26tGnTpth51q1blzNnzgBZn0FJsLOzU34nCvpdEmWHtJsQJUO+a+qktnaT4L+cCwgI4Oeff+bs2bOcP3+eRo0amZy/cOGCEiAGBARw7ty5YpX3888/89VXX6HX601ev337Nrdv3+bMmTOsXr2a48ePU7Fi8ZenS0hIYNy4cfnW+5NPPmHTpk05Xr9//z7379/n1KlT/PLLL/zvf/+jdevWxa6TOXbu3ElaWhqQ1Q6W2Kzn5s2bAFSoUKHEbmzs7Ozw8PAokbKE5Ui7qZ9s8KUO8l1TJ7W1mwT/5VyTJk1o1KgR58+fJzg4mPfee8/kvLHX38vLCx8fn2IF/5GRkUrgX7duXV555RUaN26Mi4sLKSkpXL58mX/++Yd9+/aZXLd161ZiY2MZNWoUkHMiK5DnePnp06cTHR1N37596dGjBzVq1ODWrVsmu+/pdDo8PT3p2rUrzZs3p3bt2tjZ2XHz5k0OHz7Mhg0blKcF27ZtK3BsvjVYcsgPZC35aryp8/f3L3Z+QgghhFAHCf4FAQEBzJs3j23btjF58mSlh8hgMLB161YlTXHt3r0bvV6Ps7Mzv//+OzVq1DA536ZNG1566SUePHhgsu+Al5cXzs7Oys95TWTNTVRUFF988YXJhNamTZuapJkwYQKenp45esaaN29Ot27dGDJkCIMGDSI+Pp6goCDeeeedwr5li4iJieHYsWMAtGrViieeeKJQ1925c4fo6GjlZ+NqP0ePHmXlypXo9Xo8PDxK/P0IIYQQovRI8C/o06cPX3/9Nbdu3SIsLAw/Pz8gq3f41q1b2NraFrgUaGHExcUB8OSTT+YI/LOrXLlyscsy8vPzK3Alm4KCaW9vb1566SVWrlxJSEhIiQfLwcHBynbh/fr1K/R1CxYsYMGCBbmec3Bw4I033mDYsGEl+qgyIyODiIgIIOvmysHBocTKFuaTdlM/g8EgQ39UQL5r6qS2dpPVfgQeHh7KTr3Zh5cYj/38/CwSIBqXEr1w4QKnTp0qdn6FYc5NS2JiIteuXeP8+fNER0cTHR1NlSpVgKy6a7VaS1czX1u2bAGy9ljo3r27RfLMyMhg8+bNbNiwIcf8CyGEEEI8vqTnXwBZ48hDQ0PZs2cPM2bMALKG6YBlhvwA9OzZkx9++IGMjAwGDx7Mf//7Xzp16kTr1q1p1KiRVXqlHl0pKC9RUVH8/PPPHDhwgLt37+aZTq/Xk5SUVGLj/v/991+uXLkCZO2xUJSnIrNmzcqx4VpKSgoXL15k7dq1rFu3jm+//ZbIyEgWLFiAra30BQghhBCPOwn+BQBdu3bl008/VZaUNBgMpKSk4OzszAsvvGCRMho2bMj8+fP5+OOPSUxMZP/+/ezfvx+AatWq8d///peBAwdaZBlLo6pVqxaYZt26dXz66afodLpC5WlcdackbN68WTm2xETfihUr4uvri6+vL66urnz//ffs3r2bDRs2yEZfQgghRDkgXX0CyAoKu3TpAmQN9zEO+enSpYvJZNvi6tatGyEhIXz++ee88MILyhKTCQkJbNmyhaFDh/LBBx9YbChKQb3ZFy9eVAJ/V1dXpk6dysaNGwkLC+P06dNERUURFRXFl19+qVxjHH9vbRkZGezcuROAGjVq0LFjR4vmP3LkSOXz2bBhg0XzFkIIIUTZJD3/QtG3b1+2bt3KoUOHTF6ztMqVKzNw4EAGDhwIZAXgISEhBAUFERsby6ZNm/Dx8WHEiBEWL/tRmzZtQqfTYWdnR1BQEA0bNsw1XWJiotXr8qg///yT+/fvA1lzFyy9cYiLiwvVq1cnLi6OqKgoi+YthBBCiLJJev6Fon379ri5uaHT6dDpdLi7u9O+fXurl9uwYUNGjx7N2rVrlacMxh5vI2utUnHhwgUAGjdunGfgD1m7Epc0Sw/5yY1xqFNmZqZV8hdCCCFE2SLBv1DY2dkREBCAg4MDDg4OBAQElOgk0Fq1avHkk08CWcOAsnN0dFSOMzIyLFamMfhNTU3NM01sbGyOjcesLSEhgb///hvIujFp3LixxcuIiYlRnizUqlXL4vkLIYQQouyRYT/CxJQpU5gyZYpV8t67dy9t27ZVls181K1bt7h06RIAdevWNTnn4uKCRqNBq9Vy7do1i9XpySef5MCBA1y9epXjx4/TqlUrk/MPHz5k8uTJJTrJF2D79u3KkqLW6PXX6/XMnz9f+blTp04WLyM39vb2ygpM9vby50ctpN2EKBnyXVMntbVb2a+heGysXLmS9957j06dOuHn50fDhg2pXLkyiYmJnD59ml9++UUJsgcPHmxyrb29Pc2bN+f48eNs2LCBJk2a4OPjo3zJqlatiouLS5Hr1KdPH4KCgtDr9YwZM4ZRo0bRunVrHB0dOX36NCtXruTKlSu0atWK48ePF/szKCzjkB97e3uzN1h7dIdfyLqZuXDhAuvWrePEiRNA1o3VG2+8Uaz6FpatrS2VKlUqkbKE5Ui7qZ9s8KUO8l1TJ7W1mwT/okQ9fPiQXbt2sWvXrlzP29raMn78eGXloezGjBnD2LFjuX//PpMnTzY5N27cOMaPH1/k+vj6+jJ+/HgWLVpEUlIS33zzTY40r732Go0aNSqx4P/ixYvKToEdOnTIdzfk/OS3w69R3bp1WbhwobIBmxBCCCEebxL8ixIzf/58/vzzT8LCwrh48SJxcXEkJCTg4OBAnTp1aNOmDYMGDcpzfPtzzz3Hzz//zKpVq4iIiCAhIcEiu+2OGzeO5s2bK/mmpqbi6uqKr68vgwYNokOHDmzcuLHY5RSWcUdfsPyQH0dHR6pVq0bjxo3x9/enT58+ODk5WbSM/Oj1emV+hbOzs2wsphLSbupnMBik918F5LumTmprNxtDSS1aLoQoVzIyMpQnGM2bN8fBwSHX10TZJ+1W9ty5n0KPLwven2PH9P54uFSU4F8l5LumTtZoN2v+LkjPvxBCCKEyNao4sWN6/0KlE0KI7CT4F0IIIVTGztYWD5eKpV0NIYQKSfAvhBBCPIYMBoMyL8re3l6G/QghANnkSwghhHgsabVaIiIiiIiIUDY0FEIICf6FEEIIIYQoJyT4F0IIIYQQopyQMf9CCCHEY6px48ZoNBo0Gk1pV0UIUUbIOv9CCKuQ9aqFEEII88g6/0IIIYQwm0GfSWZyHAB2lWpgY2tXyjUSQpQWGfMvhBBCPOYyk+O4uaALNxd0UW4ChBDlk/T8CyFKTGZmJnFxWYFHjRo1sLOT3kc1kHZTp8zMTABpLxWR75o6qa3dyn3P/8aNG/H29sbb25uYmJjSrk6J8/f3x9vbmw8++KC0q1LuXbp0iWbNmtG8eXPu3LmT47yl2mrZsmV4e3szbNiwYuVjjszMTGJiYoiJiVECE1H2SbupU2ZmprSXysh3TZ3U1m5lvuc/LCyM4cOHKz87Oztz+PBhnJyc8r0uLS2NDh06kJycrLy2atUq2rVrZ7W6lmd6vZ5du3axbds2zpw5Q3x8PLa2tri6uuLr60u/fv3o1KmT1cr/4IMP2LRpU5GumTVrFoGBgSavLVq0iMWLF+ea3tbWlooVK+Lp6Unbtm0ZOHAgDRo0MLvOj5o9ezZarZYhQ4bg4eFhsXwfNXjwYH788UeOHDnCnj17eOGFF6xWlhBCCCHKFtX1/KemprJ3794C04WEhJgE/qUlLCxMebIQFhZW2tWxisTERIYNG8akSZMICQnh9u3bZGRkkJaWxo0bN9i5cyejR49mwoQJZGRklHZ1FfXr1y9Ser1ez4MHDzh79iw///wzffr04ddff7VIXY4fP85ff/2FRqNh9OjRRb4+JiZG+T3buHFjvmkrVqzIyJEjAVi4cCF6vd6sOgshhBBCfcp8z392jo6OpKenExwcTO/evfNNGxwcbHJNXgIDA3P0/oqieffddzl27BgAdevWZdSoUXh5eaHT6Thz5gw//vgjCQkJ7N69GxcXFz7//HOL12HSpEm89tpr+aZJSkpi2LBh6PV6nnzySVq2bJlv+pkzZ9K8eXPlZ71eT2xsLH///Te//fYbWq2Wzz//nPr16/PMM88Uq/7fffcdAC+++CK1atXKNc2+ffuKVUZ2Q4cO5X//+x/nz59n79690vsvhBBClBOqCv79/f3ZuXMnhw8f5u7du7i5ueWa7t69exw6dAiAzp07s2PHjpKsZrkSERHBwYMHAfD09GTz5s1UqlRJOe/n50f37t0JCAggKSmJdevWMXHiRFxdXS1aDw8PjwKHyqxevVrp5Q4ICCgwz7p16+Ll5WXyWuPGjXn22Wfx8fHhww8/BGD58uXFCv4vXbrEgQMHAOjTp4/Z+RRF5cqV6dSpE3v27CEoKEiCfyGEEKKcUNWwnw4dOuDm5kZmZibbt2/PM922bdvQ6XS4ubkVu0dW5O/EiRPK8YgRI0wCf6PatWsrT1f0ej0nT54ssfplZ3waZGNjU6jgPz/9+/enWrVqAMomHObauHEjBoMBV1fXEv19NT49O3LkCNeuXSuxcoUQQghRelQV/NvZ2dGzZ0/g/wK53BjP9erVq8DllvJb7efjjz9Wzu3cuTPPPEJCQpR077//PvB/Y7CzT1YePny4ki638dmLFi1SXgd48OABS5YsoW/fvrRp0yZH+tTUVHbs2MH06dMJCAigdevWNG3aFD8/P1555RWWL19OSkpKvu+/uLRarXLs6emZZ7onnngi12ugeJ9zYV25coV///0XgP/85z/UqVOnSNfnxphHcecxGN9z586dsbfP+2FcXqv9eHt707lzZ+XnadOm5fg9W7RoUY78nnvuORwdHYGsG2YhhBBCPP5UFfzD/w3XOHv2LOfPn89x/sKFC5w5c8YkrbmmTZvGk08+CcCMGTO4fft2jjR3797lo48+ArKCwY8//rhYZRpduXKFgIAAFi5cyLlz53jw4EGONGPGjGHSpEmsX7+eyMhIkpOT0el0JCQkcPToUebMmUOfPn24ePGiReqUm+yTZq9fv55nuuw9y49OtC2Jz3nz5s3Kcd++fYt0bV5u3rwJkOcY/cK4ceOGctPZokULi9SrsBwcHPDx8QHg77//LpEybWxsqFixIhUrVsTGxqZEyhTFJ+2mTjY2NtJeKiPfNXVSW7upasw/QJMmTWjUqBHnz58nODiY9957z+S8sdffy8sLHx8fzp07Z3ZZzs7OzJ07l8GDB5OYmMj777/Pzz//bNKw06ZNIz4+Hjs7O+bOnasMe/Hw8GDr1q1EREQoY8MfnUAKULNmzVzLnjBhArGxsQwbNgx/f3+qVKnC1atXqV27tpJGp9Ph5eWFv78/zZs3x93dHYPBwI0bN9i7dy87d+4kJiaGt99+m+DgYKWX15I6duxI3bp1iYmJYdWqVfTv3x9nZ2eTNLdv31aW4WzdunWOcfTF+ZwLw2AwsGXLFgCcnJzo1q2buW9XsWnTJuLj4wFMet2LyjhRGsjxu1FYW7duJTY2llGjRgHwzjvv5KhTXnMsmjdvzr///ktERATp6elW+R3JTqPR0LhxY6uWISxP2k2dNBpNaVdBFJF819RJbe2muuAfsnr0582bx7Zt25g8ebISJBoMBrZu3aqksQRfX1/GjRvHggUL+Oeff/jpp5+UICsoKEiZqPnGG2/QunVr5TqNRoOXlxcJCQnKa7lNIM3L+fPn+fHHH+nYsaPyWrNmzUzSzJo1S+kxz65Fixb06NGDAQMGMGrUKC5fvsyWLVt46aWXCv2+C8vBwYH58+czduxYrl27RkBAAKNGjaJRo0bKaj/Lli0jMTERT09PZs6cmWs+5n7OhXH06FFu3LgBQJcuXQp94xATE6OM64es+QpxcXH8/fffrF69GoBGjRoVuMpQfoxzJjQaDU899ZRZeXh5eZnccHl4eBT698z4O6XT6YiMjCzxpw9CCCGEKFmqDP779OnD119/za1btwgLC8PPzw/IWlP/1q1b2NraFrgUaFGMHj2aAwcOEB4ezjfffEOHDh2wt7dn7ty5QFbv6fjx4y1WHkC/fv1MAv/c5Bb4Z/fMM8/g7+/P3r17CQkJsUrwD/D000+zefNmVq5cSVBQEDNmzDA57+zszMSJExkyZAguLi555mOtzzn7kJ9+/foV+jrjE5vcVK5cmXHjxjFkyBCqVKlS5DoZGYc4ubi4lMp24NWrV1eOr1+/LsG/EEII8ZhTZfDv4eFBu3btCA0NJTg4WAn+jUN+/Pz8LLpDqp2dHXPmzCEgIIDk5GQmT56Mvb096enpODk5MXfu3HwnaprDnJuX+Ph4kpKSTCagGoO7yMhIi9XtUQaDge3bt7Nr164ck3kha2Ly1q1bcXd3Z8CAAXnmY43POT09nd27dwNZvzft27cv2pvLw4MHD1i3bh0uLi4MGjTI7HyMT4aqVq1qkXoVVfabsbi4OKuXp9PpuHr1KgD16tWz+PdGWIe0mzrpdDoAaS8Vke+aOqmt3cp27fLRt29fQkND2bNnj9LTbAzyLDXkJ7u6devyySefMHXqVC5cuKC8Pm3atCLvFFsYxhV/ChIeHk5QUBChoaHcv38/z3TZhx9Zkl6vZ9KkSezatQuAAQMGMHToUBo2bEhmZibnzp1j2bJl7Nu3j+nTpxMVFcX06dPzzM/Sn/PevXuVnZ779OmDrW3h57ivWrWKdu3aKT8bDAaSk5OJjIxk5cqV/PHHH8yYMYPLly8zbdq0ItcNUNqsOE8PiiN7uampqVYvT6/XK+85v9WhRNki7aZOsnu3+sh3TZ3U1m6qW+3HqGvXrjg5OZGcnExISAh79+4lJSUFZ2dnq21YFBAQQNu2bZWfO3TowMCBA61SVmF6ghctWsSQIUPYuXNnvoE/QFpamoVqZmrNmjVK4D9+/Hi+/PJLmjRpgqOjI87OzrRu3ZrvvvtOuSFbtWpVgTvVWvJztuQqPzY2NlSuXJn//Oc/LF68mF69egHw888/c/jwYbPyNE6wzW8XamvK/ntR1nsqhBBCCFF8qg3+K1asSJcuXYCs4T7GIT9dunTJsdqMpZw+fdpkU6vIyEju3btnlbIKGv8dGhrK4sWLgay7zBkzZrBlyxaOHTvGmTNniIqKIioqirfeessq9TNat24dkNUeo0ePzjPdu+++qxxv2LAh3zwt9TnHxcUpOz03bdrU7Am1eXn99deV44LeU16Mw7IKunmzlsTEROW4tJ4+CCGEEKLkqDb4h//ryT106JDS82qpNdwflZaWxpQpU9BqtTg7O2NjY8O9e/fyHcJiTWvXrgWynhCsXbuWIUOG4O3tTeXKlU16cLMHd9Zg3EPgqaeewsHBIc90NWvWpEaNGgBcunQpz3SW/Jy3bt1KZmYmULSJvoXVoEED5Tg6OtqsPIzBf1JSkkXqVFTZyy3OfgVCCCGEUAdVB//t27fHzc0NnU6HTqfD3d3dYhM6HzV79mwlaP3ss88YOXIkAPv372fNmjV5XmetzR6M4+HbtWtnsmLLo06fPm2V8o2MNxrGiWX5KczkM3M/59wYh/xoNBplZ2hLyv6eC/P+c2NckvPBgwfFeopk7u/Z5cuXleNGjRqZXb4QQggh1EHVwb+dnR0BAQE4ODjg4OBAQEBAkSZ0FtZff/2lBJ69evWiT58+TJo0SdnQ4auvvjIJorLLvmlS9lV4issYbOY3SfPs2bOcPHnSYmXmpm7dukDWvgT59V5HR0crQ1vq1KmTa5rifM6PioqKUlY4evbZZ/O9QTJX9hsrc3vN27RpoxxHRESYXRdzf8+M78HNzc1kAzkhhBBCPJ5UHfwDTJkyhYiICCIiInLs9msJ8fHxynrvtWrVUlYWcnBwYN68eTg6OvLw4UOmTJmSa++vm5ubcnzt2jWL1atevXoAHD9+XFle6tF6T5061WLl5eX5558HsgLOWbNmYTAYcqRJT0/niy++yHFNdsX9nB9lyYm+ucnIyODbb79Vfu7UqZNZ+fj6+irDpU6dOmV2fVxcXJTdPIvye2Yss0OHDmaXLYQQQgj1UH3wb23Tp08nLi4OW1tbvvrqK5NJkY0aNWLy5MlAVq/tkiVLclxfu3ZtatasCcBPP/1ESEgIly5d4urVq1y9elVZhrKojAFtamoqr7zyCkFBQRw/fpzjx4+zfPly+vTpw4ULF2jZsqVZ+RfWq6++iqurKwAbN25k6NChbNmyhdOnT3Pq1Cl+++03AgMDCQsLA6Bhw4a5jr8v7uecXWZmprLTs4uLC88995xZ7y0mJobo6GiTf8ePH+fXX38lMDCQ8PBwIOtGzNzViBwcHJTN3EJDQ83KA7KGUjVv3hzImny8bds2Ll68qPye5Tah+MqVK9y6dQvIWj2rJNja2uLu7o67u7tVntIJ65B2UydbW1tpL5WR75o6qa3dZG2/fPz+++/KspQjR440WfPdaPjw4fz9998cPHiQpUuX8uyzz+YIuMeMGcNnn31GTExMjtV3Zs2aRWBgYJHr9uKLLxIYGMjGjRuJjY016VmHrCFR06ZNIykpyWTlHEurXr06y5cvZ9y4ccTExBAeHq4ExY/y8fFhyZIlOSYGW+pzNjp06BB3794FoEePHvlORM5Pfjv8GjVu3JglS5ZQoUIFs8oAePnll9m3bx8nTpzgxo0beQ6LKsiYMWMYO3Ys9+/fV26WjMaNG5djd+Rt27YBWTdIzz77rHmVLyJ7e3tVrIEsTEm7qZMs36s+8l1TJ7W1W9m/PSklV65cYfbs2UBW0PrOO+/kms7GxoaZM2fi4uJCZmYmU6ZMISUlxSTNkCFDWLRoER07dsTV1dVif5BnzZrFnDlzaNOmDRUrVsTBwYE6deoQEBDAb7/9xogRIyxSTkF8fHzYunUrn3zyCR07dsTNzQ2NRoODgwO1atXC39+fOXPmsG7duhyBrSU/ZyPjsq9g+SE/Tk5O1KlThxdeeIH58+ezYcMGZd6DuZ599llq1qyp7JRsrueee46ff/6Zzp074+7urgwDyovx6Uj//v3NvkESQgghhLrYGHIbpC2EKFE//vgj8+bN48knn2Tnzp1Wf2x47Ngxhg4dikajYc+ePVaZ7JuRkaFMYm7evLncYAhRigz6TDKT4wCwq1QDG9v895IRQpQua/4/VHr+hSgDhg0bhoeHB1euXGHnzp1WL++7774Dsnr9S3KVH61Wy6lTpzh16hRarbbEyhXFI+2mTlqtVln9y8bWDvsqHthX8ZDAvwyT75o6qa3dJPgXogyoUKGCMib/u+++y3XVJEs5efIkBw8epGLFiowbN85q5eTGYDCg1WrRarVWfY/CsqTd1MnYbhkZGdJuKiHfNXVSW7vJbCAhyojAwEDu3buHVqslNjYWDw8Pq5Rz//59xo0bR5MmTUyWohVCPH6M+53I0DshhJEE/0KUEXZ2dowdO9bq5XTq1MnsfQmEEEIIoW4y7EcIIYQQQohyQnr+hRBCiMdU48aN0Wg0BS79K4QoPyT4F0IIIR5Txj1XhBDCSIJ/IYQQ4jGXqdcTl/TQ5LUaVZyws/KeIkKIskeCfyFEibG3t6dBgwbKsVAHaTd1yt5WcUkP6fHlBpPzO6b3x8OlYklXS+RDvmvqpLZ2k1v+x8DGjRvx9vbG29ubmJiY0q5OifP398fb25sPPvigtKsiCmBra0u1atWoVq2a1XcxFpYj7aZOtra20l4qI981dVJbu5X925PHQFhYGMOHD1d+dnZ25vDhwzg5OeV7XVpaGh06dCA5OVl5bdWqVbRr185qdS3P9Ho9u3btYtu2bZw5c4b4+HhsbW1xdXXF19eXfv36WXWJzA8++IBNmzYV6ZpZs2YRGBho8tqiRYtYvHhxrultbW2pWLEinp6etG3bloEDByq9FUIIIYR4/JX925PHUGpqKnv37i0wXUhIiEngX1rCwsKUJwthYWGlXR2rSExMZNiwYUyaNImQkBBu375NRkYGaWlp3Lhxg507dzJ69GgmTJhARkZGaVdXUb9+/SKl1+v1PHjwgLNnz/Lzzz/Tp08ffv31VyvVLieDwUBGRobsOKoy0m7qZDAYpL1URr5r6qS2dpOe/xLm6OhIeno6wcHB9O7dO9+0wcHBJtfkJTAwMEfvryiad999l2PHjgFQt25dRo0ahZeXFzqdjjNnzvDjjz+SkJDA7t27cXFx4fPPP7d4HSZNmsRrr72Wb5qkpCSGDRuGXq/nySefpGXLlvmmnzlzJs2bN1d+1uv1xMbG8vfff/Pbb7+h1Wr5/PPPqV+/Ps8884xF3kd+tFotERERgOw4qibSbuqk1WoBpL1URL5r6qS2dpPgv4T5+/uzc+dODh8+zN27d3Fzc8s13b179zh06BAAnTt3ZseOHSVZzXIlIiKCgwcPAuDp6cnmzZupVKmSct7Pz4/u3bsTEBBAUlIS69atY+LEibi6ulq0Hh4eHnh4eOSbZvXq1ej1egACAgIKzLNu3bp4eXmZvNa4cWOeffZZfHx8+PDDDwFYvnx5iQT/QgghhChdMuynhHXo0AE3NzcyMzPZvn17num2bduGTqfDzc1NgjIrO3HihHI8YsQIk8DfqHbt2srTFb1ez8mTJ0usftkZnwbZ2NgUKvjPT//+/alWrRqA0mMhhBBCiMebBP8lzM7Ojp49ewL/F8jlxniuV69e2NnZ5Ztnfqv9fPzxx8q5nTt35plHSEiIku79998HICYmBm9vb5PJysOHD1fSGf9t3LhROb9o0SLldYAHDx6wZMkS+vbtS5s2bXKkT01NZceOHUyfPp2AgABat25N06ZN8fPz45VXXmH58uWkpKTk+/6Ly/hoHLJ6/vPyxBNP5HoNFO9zLqwrV67w77//AvCf//yHOnXqFOn63BjzKEvzGIQQQghhPRL8lwJjj+3Zs2c5f/58jvMXLlzgzJkzJmnNNW3aNJ588kkAZsyYwe3bt3OkuXv3Lh999BGQFQx+/PHHxSrT6MqVKwQEBLBw4ULOnTvHgwcPcqQZM2YMkyZNYv369URGRpKcnIxOpyMhIYGjR48yZ84c+vTpw8WLFy1Sp9xknzR7/fr1PNNdu3Yt12ugZD7nzZs3K8d9+/Yt0rV5uXnzJgC1atWySH5CCCGEKNsk+C8FTZo0oVGjRkDuvf/G17y8vPDx8SlWWc7OzsydOxd7e3sSExN5//33c8xEnzZtGvHx8djZ2TF37lxl2IuHhwdbt25l5syZStqZM2eydetWk39dunTJtewJEyYQGxvLsGHDWLFiBRs2bODrr782CZx1Oh1eXl6MHTuWJUuWsG7dOtauXcs333xDz549sbW1JSYmhrfffjvfSc/F0bFjR+rWrQtkLaWampqaI83t27eVZThbt26dYxx9cT7nwjAYDGzZsgUAJycnunXrVqT3mJtNmzYRHx8PZM0rEUIIIcTjTyb8lpKAgADmzZvHtm3bmDx5MjY2NkBWkLd161YljSX4+voybtw4FixYwD///MNPP/3EqFGjAAgKCuLAgQMAvPHGG7Ru3Vq5TqPR4OXlRUJCgvJabhNI83L+/Hl+/PFHOnbsqLzWrFkzkzSzZs1Sesyza9GiBT169GDAgAGMGjWKy5cvs2XLFl566aVCv+/CcnBwYP78+YwdO5Zr164REBDAqFGjaNSokbLaz7Jly0hMTMTT09PkZig7cz/nwjh69Cg3btwAoEuXLoW+cYiJiVHG9UPWfIW4uDj+/vtvVq9eDUCjRo0KXGVICCGEEI8HCf5LSZ8+ffj666+5desWYWFh+Pn5AVlr6t+6dQtbW9sClwItitGjR3PgwAHCw8P55ptv6NChA/b29sydOxfIWppq/PjxFisPoF+/fiaBf25yC/yze+aZZ/D392fv3r2EhIRYJfgHePrpp9m8eTMrV64kKCiIGTNmmJx3dnZm4sSJDBkyBBcXlzzzsdbnnH3IT79+/Qp9nXE1n9xUrlyZcePGMWTIEKpUqVLkOgkhhBBCfST4LyUeHh60a9eO0NBQgoODleDfOOTHz8+vwGUfi8LOzo45c+YQEBBAcnIykydPxt7envT0dJycnJQhK5Zkzs1LfHw8SUlJJhNQq1evDkBkZKTF6vYog8HA9u3b2bVrV47JvJA1MXnr1q24u7szYMCAPPOxxuecnp7O7t27gazfm/bt2xftzeXhwYMHrFu3DhcXFwYNGmSRPAui0WiUfQc0Gk2JlCmKT9pNnaSt1Ee+a+qktnaT4L8U9e3bl9DQUPbs2aP0NBuDPEsN+cmubt26fPLJJ0ydOpULFy4or0+bNq3IO8UWhnHFn4KEh4cTFBREaGgo9+/fzzNd9uFHlqTX65k0aRK7du0CYMCAAQwdOpSGDRuSmZnJuXPnWLZsGfv27WP69OlERUUxffr0PPOz9Oe8d+9eZafnPn36YGtb+Kk6q1atol27dsrPBoOB5ORkIiMjWblyJX/88QczZszg8uXLTJs2rch1KyobG5syv/mJyEnaTZ2Mw0mFesh3TZ3U1m4y4bcUde3aFScnJ5KTkwkJCWHv3r2kpKTg7OzMCy+8YJUyAwICaNu2rfJzhw4dGDhwoFXKqlq1aoFpFi1axJAhQ9i5c2e+gT9AWlqahWpmas2aNUrgP378eL788kuaNGmCo6Mjzs7OtG7dmu+++065IVu1ahX79u3LN09Lfs6WXOXHxsaGypUr85///IfFixfTq1cvAH7++WcOHz5crLyFEEIIUfZJ8F+KKlasqKyUExwcrAz56dKlC87OzlYp8/Tp0yabWkVGRnLv3j2rlFXQ/gShoaEsXrwYyFpff8aMGWzZsoVjx45x5swZoqKiiIqK4q233rJK/YzWrVsHZLXH6NGj80z37rvvKscbNmzIN09Lfc5xcXHKTs9NmzblqaeeKnIe+Xn99deV44LekyXo9XoSEhJISEhQdioWZZ+0mzrp9XppL5WR75o6qa3dJPgvZcae3EOHDik9r5Zaw/1RaWlpTJkyBa1Wi7OzMzY2Nty7dy/fISzWtHbtWiDrCcHatWsZMmQI3t7eVK5c2WRcfGJiolXrYdxD4Kmnnsr3sV3NmjWpUaMGAJcuXcoznSU/561bt5KZmQkUbaJvYTVo0EA5jo6Otnj+j9LpdFy6dIlLly6h0+msXp6wDGk3ddLpdNJeKiPfNXVSW7tJ8F/K2rdvj5ubm/JH2t3d3WITOh81e/ZsJWj97LPPGDlyJAD79+9nzZo1eV5nrXGjxvHw7dq1Uyb15ub06dNWKd/IeKNRmC+sMU1+k3bN/ZxzYxzyo9FolJ2hLSn7e1bDHywhhBBCFI8E/6XMzs6OgIAAHBwccHBwICAgoEgTOgvrr7/+UgLPXr160adPHyZNmkTjxo0B+Oqrr7h8+XKu1zo6OirH2VfhKS5jsJnbplpGZ8+e5eTJkxYrMzfGDb7Onz9PUlJSnumio6OVeQl16tTJNU1xPudHRUVFKSscPfvss/neIJkr+42V7PIrhBBCPP4k+C8DpkyZQkREBBEREbz33nsWzz8+Pl5Z771WrVrKykIODg7MmzcPR0dHHj58yJQpU3Lt/XVzc1OOr127ZrF61atXD4Djx49z9erVXOs9depUi5WXl+effx7IurGZNWtWjp15IWu5zS+++CLHNdkV93N+lCUn+uYmIyODb7/9Vvm5U6dOFi9DCCGEEGWLBP/lwPTp04mLi8PW1pavvvrKZEOnRo0aMXnyZAAiIiJYsmRJjutr165NzZo1Afjpp58ICQnh0qVLXL16latXryrLUBaVMaBNTU3llVdeISgoiOPHj3P8+HGWL19Onz59uHDhAi1btjQr/8J69dVXcXV1BWDjxo0MHTqULVu2cPr0aU6dOsVvv/1GYGAgYWFhADRs2DDX8ffF/Zyzy8zMVHZ6dnFx4bnnnjPrvcXExBAdHW3y7/jx4/z6668EBgYSHh4OZN2IWWvVJyGEEEKUHbLO/2Pu999/V5alHDlypMma70bDhw/n77//5uDBgyxdupRnn302R8A9ZswYPvvsM2JiYnKsvjNr1iwCAwOLXLcXX3yRwMBANm7cSGxsrEnPOmQNiZo2bRpJSUkmK+dYWvXq1Vm+fDnjxo0jJiaG8PBwJSh+lI+PD0uWLMkxMdhSn7PRoUOHuHv3LgA9evQwe/3g/Hb4NWrcuDFLliyhQoUKZpUhhBBCCPWQnv/H2JUrV5g9ezaQFbS+8847uaazsbFh5syZuLi4kJmZyZQpU0hJSTFJM2TIEBYtWkTHjh1xdXW12G7As2bNYs6cObRp04aKFSvi4OBAnTp1CAgI4LfffmPEiBEWKacgPj4+bN26lU8++YSOHTvi5uaGRqPBwcGBWrVq4e/vz5w5c1i3bl2O8f6W/JyNjMu+guWH/Dg5OVGnTh1eeOEF5s+fz4YNG5R5D0IIIYR4vNkYchvgLIQQxZSRkUFERAQAzZs3x8HBAa1Wy7lz54CsGyU1bIMukHZTKa1Wi8FgwMHBgUy9nrikhybna1Rxws4KC0wI88l3TZ2s0W65/T/UUiT4F0JYhTX/cAkhhBCPM2v+P1Ru+YUQQgghhCgnJPgXQgghhBCinJDVfoQQJUan03Hr1i0gay8ES00cF9Yl7aZO0m7qI22mTmprt7JdOyHEY0Wv1xMbGwuAh4dHKddGFJa0mzpJu6mPtJk6qa3dZNiPEEIIIYQQ5YT0/AshhBCPqcaNG6PRaGTJSCGEQoJ/IYQQ4jFl3KxQCCGMJPgXQgghHnO5bfJlLtkcTAh1k+BfCCGEeMzFJT2kx5cbLJLXjun98XCpaJG8hBAlT27dRbm2ceNGvL298fb2JiYmprSrI4QQQghhVdLzL1Rr9OjR/PXXX9jY2PDLL7/Qpk2bAq85duwYr7zyCgaDgeeee45u3boVux4XL14kNDSUiIgIoqOjuXfvHgkJCdjZ2eHq6krz5s3p1asXnTt3xsbGpsj5R0ZG0r9/f3Q6HQD9+vVj9uzZuaYNCwtj+PDheebl7OyMu7s7vr6+BAYG0r59+yLXpzhsbW1xcXFRjoU6SLupk7SV+sh3TZ3U1m4S/AvV+vzzz+nZsyfJycl89NFHBAcH4+jomGf6jIwMPvroIwwGA5UqVeKzzz7j8OHDxa7Hd999x9atW3M9FxMTQ0xMDDt37qRt27YsXLiQatWqFTpvvV7Pxx9/rAT+xZWamsqVK1e4cuUKW7ZsoW/fvsycORM7OzuL5F8Qe3t7GjZsWCJlCcuRdlOnsr7RkMhJvmvqpLZ2k78MQrVq1qzJe++9x6effsrly5dZvHgxkydPzjP9kiVLuHz5MgBTpkyhZs2aBAYGEhgYWKx62Nvb06JFC1q1aoWXlxc1atSgevXqJCUlcenSJX7//Xeio6M5cuQIb775JqtXry50z8Avv/zCqVOncHV15d69e0Wq1+DBgxkyZIjys8FgIDExkX///Zeff/6Ze/fusXnzZmrWrMmkSZOKlLcQQggh1EmCf6FqgwYNYseOHRw5coSffvqJ7t2706RJkxzpIiMjWbZsGQBt27Zl4MCBFqvDF198kWcP2zPPPMPgwYN555132LNnDydOnGD//v107ty5wHxv377NggULsLGxYerUqbz//vtFqperqyteXl45Xm/bti3+/v4EBgaSnp5OUFAQb7/9tiwHKIQQQpQDZX9gkhD5sLGx4YsvvqBChQrodDqmT5+eY4hMZmYmH374ITqdjgoVKvDll1+aNfY+LwU9Wrezs2PUqFHKz8eOHStUvp999hkpKSn069evUPMZiuKpp57iueeeAyAlJYVLly5ZNP+8aLVaIiMjiYyMRKvVlkiZovik3dRJq9VKe6mMfNfUSW3tJsG/UL169eoxYcIEAM6ePcvy5ctNzq9YsYIzZ84AMHHiRJ544gnlXEmt9lOx4v8ti5eRkVFg+l27drFv3z5cXFyYOnWqVepUp06dItXJEgwGAykpKaSkpGAwGEqkTFF80m7qZDAYpL1URr5r6qS2dpPgXzwWXn31VZo3bw7A4sWLlZ7sa9eusWjRIgCaN2/OiBEjSqV+27dvV44bNGiQb9oHDx7wxRdfAFlzE4oyQbgobt68qRzXrl3bKmUIIYQQomyR4F88Fuzs7Pjyyy/RaDTKqj56vZ6PPvqItLQ0NBpNia5qAxAfH8+JEyf48MMP+f777wGoVq0avXv3zve6uXPncvfuXdq0aUP//v2tUreLFy/y559/AvD0009To0YNq5QjhBBCiLJFJvyKx4a3tzejR49myZIlhIeH89prrxEWFgbAmDFjcp38amnDhg3jyJEjuZ6rVq0aixcvpkqVKnleHx4eztq1a9FoNHz66afFmptw7949oqOjlZ8NBgMPHjzgxIkTrFy5krS0NCpXrsy0adPMLkMIIYQQ6iLBv3isjB07lj179nD+/HlCQ0MB8PLyYsyYMaVar2HDhvHWW29RvXr1PNNkZGTwySefYDAYePXVV2nUqFGxylyzZg1r1qzJ9ZytrS2DBg3i1VdfpX79+sUqRwghhBDqIcG/eKw4ODgwc+ZMBg0aRGZmpjIcqKSWsZw5cyYPHz5UetlPnz7NmjVr+PXXX4mJieGLL77Ic4jNDz/8wIULF6hTpw5vv/22Veup1+vZsWMHjo6OvPfee7LMpxBCCFFOSPAvHju+vr7UrFmTGzduULNmTXx9fUusbE9PT5Of27Rpw+DBg5k4cSL79+9nwIAB/Pbbb9SsWdMk3aVLl1i6dCkAH3/8MU5OTsWuy7hx4xg/frzJa2lpaVy9epXg4GBWrlzJypUrOX36NMuXL7dImUIIIYQo22TCrxBW5ujoyMyZM3FycuLWrVvMnTvX5LzBYGDGjBlkZGTQtWtXnn/+eavVpUKFCnh7ezN16lRmzJgBZM0zME5ItjY7Ozvq1q1L3bp1S3TytSgeaTd1srOzk/ZSGfmuqZPa2k16/oUoAdWrV6dVq1YcOnSIkJAQtFotGo0GgH///VeZJNyyZUuTZUGN4uPjleOYmBglTaNGjcyeyDxgwADmz5/P/fv32bBhA5MmTTIrn6Kws7PDw8PD6uUIy5J2Uyc1BCHClHzX1Elt7SbBvxAlxLhe/8OHD0lISMDd3R0w3WBrzpw5BeZz9OhRjh49CmQN7TE3+Le1taVevXrcv3+fu3fvkpCQYLU9BYQQQghRNsiwHyFKSGxsrHLs7OxcijX5PzqdTjnOzMwsxZoIIYQQoiRIz78QJeD27ducOHECgDp16lCpUiXlXLt27YiKisr3+piYGDp37gxAv379mD17drHr9PDhQy5evAhkzQUoiV7/jIwMIiIigKwdl2WVIXWQdlMn41NFaS/1kO+aOqmt3aTnX4hiuHz5srKfQF4ePHjA5MmT0Wq1AAQEBJRE1Qq0aNEi0tLSAOjYsaOMDxZCCCHKAen5F6IYYmNjefXVV2ncuDFdunShadOm1KhRA3t7e+7evcvx48fZsGEDd+/eBbI2HBs9enSJ1O3RHX4B0tPTuXr1Kps3b+bAgQNA1mpEEyZMKJE6CSGEEKJ0SfAvhAVERkYSGRmZb5rnnnuOWbNmldh6+vnt8GtUvXp15s6di7e3d4nUSQghhBClS4J/IYqhVatWLF++nMOHD3P69Gnu3LlDXFwcaWlpVKpUiTp16vD000/Ts2dPWrduXdrVRaPR4OLiwlNPPUWnTp0IDAykatWqpV0tIYQQQpQQCf7FY2nfvn2FShcYGEhgYKDZ5Wg0Gjp27EjHjh3NzqMw6tatW+CkYCjc5GEhRPlTo4oTO6b3t1heQgj1kuBfCCGEeMzZ2dri4VKxtKshhCgDJPgXQgghHlPGVcY0Gg02NjalXBshRFkgwb8QosTY29srk4vt7eXPj1pIu6mTvb09GRkZZGRkYG9vL8G/Csh3TZ3U1m5lv4ZCiMeGra2tyQZnQh2k3dRJ2k19pM3USW3tJpt8CSGEEEIIUU5Iz78QosTo9XpSU1MBcHZ2xtZW+h/UQNpNnaTd1EfaTJ3U1m5lu3ZCiMeKTqcjKiqKqKgodDpdaVdHFJK0mzrpdDpsbGxwcHCQ8f4qId81dVJbu0nPvxBCCPGY0mg0ODg4lHY1hBBliAT/QgghxGPOoM8kMznOInnZVaqBja2dRfISQpQ8Cf6FEEKIx1xmchw3F3SxSF6139mLfRUPi+QlhCh5MuZfPJa8vb3x9vZm0aJFVivjgw8+wNvbG39/f4vk9+233+Lt7c2YMWNynAsLC1PeU1hYmNllGAwGevfujbe3Nxs2bChOdYUQQgihQo9Vz79OpyMkJISDBw/y77//EhcXR1JSEhUqVKB69ep4eXnRsmVLunXrhqenZ575+Pv7c+PGjRyv29vbU6lSJRo0aMAzzzzDyy+/jIfH//V+GDd4KI6QkBDq1q1b7Hzeeecddu7cafF8hXXcvHmT5cuXA/D2229brRwbGxvGjh3Lu+++yzfffEP37t1xdna2WnlCCCGEKFsem57/kJAQevTowYQJE1i7di3R0dHEx8ej0+lITk7m2rVr7N27l7lz59KlSxfGjBlDdHR0kcrQ6XTcv3+f48ePs3jxYnr06MEff/xhpXdkvv3795sE/qLs++6770hPT+e///0vvr6+Rb5+0aJFypOBgnTv3p369etz9+5dfv31V3OqK4QQQgiVeix6/v/3v/+xcOFCDAYDAG3btuX555/H29sbFxcXHj58yN27dzl27Bj79+/nxo0b/Pnnn3h4ePD555/nma+7u7vSGwtZwf+NGzfYsGED+/fvJzk5mUmTJrF27VqaNGnC1q1b88xr1KhRxMbG5sjzUdmfJJgjJSVFeU+urq7cu3evWPmpVVRUlNXLmD17NrNnzy52Pnfu3GHTpk0AvPbaa7mmadeuncXek62tLa+++iozZszgp59+Yvjw4Tg6OlokbyGEEEKUbaoP/tevX8+3334LQI0aNfj6669p165drmm7d+/Ohx9+yPbt2/nmm28KzFuj0eDl5WXyWpMmTejatSuzZ89mxYoVaLVavvvuOxYtWpQj7aN55ZWnJS1YsICbN2/Svn17atasqQSVouxavXo1Wq0Wd3d3/Pz8SqTMF198kS+++IL4+Hi2b99OYGBgiZTr4OBA69atS6QsYTnSbuokS3yqj3zX1Elt7abqYT+3bt1SerkrVarE6tWr8wz8jezs7OjTpw/BwcE899xzZpc9fvx4KlSoAMDBgwfR6/Vm52UpERER/PLLLzg4ODBjxozSro4oBL1ez8aNGwHo2bNnie0K6OLiQseOHYGsG2ghhBBClA+qDv5XrFhBeno6AJMmTaJevXqFvrZKlSrFWqWlYsWKNGzYEIDU1FTu379vdl6WoNPp+Pjjj9Hr9YwePZr69esXOY+oqCg+/vhjXnjhBVq0aEHLli3p2bMnM2fOJCYmplj1e3S1GoPBwLp16xg8eDDt2rWjVatWDBgwgM2bN5tcl5GRwZo1a3j55Zdp27YtLVu2ZNCgQezYsSPf8vJb7Wfjxo3K+ZiYGPR6Pb///juDBg3iP//5D08//TS9e/fmu+++4+HDh3mWYYnVfsLDw4mNjQXghRdeyDNdXqv9GN/L4sWLldeM6bL/y639unXrBsDx48e5deuW2e9BCCGEEOqh2mE/BoOB4OBgICsQL6lhC9kZh/IApd7zv2LFCs6dO8eTTz6Z61KRBVm6dCkLFizI8T4uXLjAhQsXWLNmDf/v//0/+vbtW+y66nQ63nzzTfbv32/yekREBO+//z6nT5/mo48+IjExkbfffpujR4+apDtx4gQnTpzg2rVrjB07tlh1SUtL47XXXiM0NNTk9ejoaKKjo9m3bx8rV6602oo4xkBeo9HQrFkzq5SRlxYtWgBZ36UDBw7w8ssvW73MzMxM4uKyNhqqUaMGdnayUZAaSLupU2ZmJoC0l4rId02d1NZuqg3+o6Ojld72Nm3alPhyhTqdjkuXLgFZgZuLi0uJlp/d9evXWbJkCQAzZswo8jjPX3/9la+//hqA6tWr88Ybb9CqVSsyMzMJDQ1l+fLlpKam8sEHH1CtWjU6depUrPp+++23nDx5kt69e9O7d29q1KjBlStXWLRoEZcvXyYoKAh/f3+CgoI4ceIEgwcPpmvXrri4uHDu3Dm+/fZbYmNjWbhwIZ07d6ZRo0Zm1+Wjjz7i5MmT9OvXj+7du1OjRg1u3brFsmXLOHHiBKdOneK7775j8uTJxXrPeTl27BgAXl5eZo3P7dKlC82aNWP16tWsWbMGINeJ57lNJK9fvz5VqlQhKSmJI0eOlFjwb3wKUa1atTL/B1JkkXZTJwn+1Ue+a+qktnZTbfCffeWTJk2alHj5v//+O0lJSQC0bt0ae/vS+yg//fRTHj58SK9evXjmmWeKdG18fDxz584FslY3Wrt2LbVq1VLOt27dGn9/f4YOHUpqaioff/wxISEhJk89iurkyZN8+OGHjBgxQnmtadOmtG3blm7dupGSksLkyZNJSEhg8eLFdOnSxSRds2bN6NevH5mZmfz+++989NFHZtflxIkTzJkzh4CAAJMynn32Wfr37090dDRr165l4sSJFm9jg8HAyZMnAfDx8TErjypVqlClShVcXV2V1wo7odzGxgYfHx/CwsI4c+aMWeULIYQQQl1UO+Y/ISFBOa5evXqe6fR6vTKEI7d/Wq220GXqdDquXr3KN998w5dffqm8PmrUKPPehAUEBwdz8OBBKleuzLRp04p8/YYNG5Rx7dOmTTMJ/I2aNGnC6NGjgaxlKffu3VusOrdo0cIk8Ddyc3Oja9euQNZNSffu3U0Cf6PGjRsrs+rDw8OLVZcXXnjBJPA3cnBwYOjQoQDcv3+fCxcuFKuc3CQmJpKamgpgEryXJGO5MTExylK5QgghhHh8qbbnPyUlRTl2cnLKM11ycjK9e/fO83x+O9/euHEj302TbGxsmDhxIs8++2whamx5CQkJyjrzkyZNokaNGkXOwzjWvUqVKkrgnZuXXnqJBQsWKNd079696BX+//Xo0SPPc40bN1aOe/bsmW+6o0ePcv36dbPrAeT7u9G0aVPlOCYmxqRulhAfH68cV6lSxaJ5F1bVqlWBrInVSUlJys9CCCGEeDypNvivWLGicpzfiizWUKlSJfz8/Bg5ciRt2rQp0bKz++qrr4iPj8fX15fBgweblYdxl+MmTZrkO5SnRo0a1KlThxs3bhR5Z+RH5bcSUeXKlZXjJ598ssB02W8CzdGgQYM8z2Wfx5GcnFyscnKTmJioHJdW0J293IcPH0rwL4QQQjzmVBv8Zw/MsvegPqpKlSo5dkb94IMPCrX51aO78drZ2VG5cmXc3NywsbEpeqUtKDQ0lE2bNmFnZ8dnn31m9vrwxgC0MMNO3NzcuHHjhknQag7j/gi5yf4+CpOuuKss5VdG9ja2xmpO2XfVTUtLs3j+hZG93NKctyKEEEKIkqHa/9tnH4Jx7tw5q5Rh7d14i2PZsmUANGvWjMuXL3P58uUcabKv7b5//35lbkRuw2lK+2amPMo+V6W4N1Tmyl5u9qcuQgghhHg8qTb49/LywsXFhfv373Ps2DEePnyY79j/x01GRgaQtXLOu+++W2D6L774QjnOHvxXrVqVu3fvKuvT5ufu3bvKNaL4ylLw7+rqavIkwlpsbGyUIXtyw6ke0m7qJG2lPvJdUye1tZtqV/uxsbGhT58+QNZ47MIM4xE5GZ9snD17Fp1Ol2e6e/fucfPmTZNrRPE4ODgo8xquXLlSrLzM/WNjLPepp54qVvmFpdFoaNy4MY0bNy7WcrGiZEm7qZNGo5H2Uhn5rqmT2tpNtcE/wMiRI5Xeyq+//rrYK7+oSVBQEFFRUfn+69evn5I+JCREeT279u3bA5CUlMSePXvyLG/9+vXKUpDGa0TxGZcsjYiIKFY+2TcIMz4VKkhycrIyXMy4268QQgghHm+qDv5r166tbPD04MEDhgwZouyYmheDwaBsziWgf//+ynCpr776ijt37uRIExkZyffffw9k7RSb29r7wjzG1aISEhKKdfPq7u6uHF+7dq1Q10RERCg3dB06dDC7bCGEEEKoh2rH/Bu9/PLL3Llzh8WLFxMbG8vQoUPx8/Pj+eefx9vbm6pVq5KZmUlcXBxnzpxh165dnD9/HshavUcNj2esqXr16kyZMoXPP/+c27dvExgYyBtvvEGrVq3Q6XQcPnyY5cuXk5qaio2NDf/v//2/cv+ZWVKnTp3QaDRotVr++ecfPD09zcqnZcuWyvGsWbMYO3asyapUderUybGazz///ANkbUVufAJhbcaN8gDq1asnKwyphLSbOhmHckp7qYd819RJbe1WtmtXSOPHj6dx48Z89dVXXL9+nX/++UcJbHJjY2NDx44dmTp1Kh4eHiVY07Jp6NChPHjwgG+//Za4uDhmzZqVI42DgwP/7//9Pzp16lQKNXx8ubq64u/vz+7du9m6dSsvvfSSWfnUq1eP7t27s3PnTg4ePMjBgwdNzue2md22bduArE3XSuqGTq/Xc//+fQCzb3REyZN2UydrLFEsrEu+a+qktnZ7LIJ/gK5du/L888/zxx9/cPDgQf7991/u3bvHgwcPqFChAi4uLnh5edGyZUu6d++uisYpSWPHjuW5557j119/5Z9//iE2NhZbW1tq1apFhw4dGDFiRJ47IYviefnll9m9ezdHjx7lzp07Zt+Qzp07l2bNmrF7924uX75MSkpKnv/zP3HihLIUrLkbxAkhhBBCfWwMxkG/QohSYTAY6N27N+fPn+edd97hzTfftHqZ06dPZ/369XTs2NFkIztLysjIUCYyN2/eHAcHh1xfE2WftJs6GSf/Ozg4YNBnkplc8JLOhWFXqQY2tnYWyUuYku+aOlmj3az5u/DY9PwLoVY2Nja8++67vPnmm6xcuZIRI0bg7OxstfJu3rxJcHAwABMmTLBaOUKIssPG1g77KjLMVQih8tV+hHhc+Pv706ZNGxISEvj111+tWtbSpUvRarW8+OKLssSnEI85rVZLRkYG8pBfCGEkPf9ClBGffPIJe/bssWqvv8FgoE6dOowbN47+/ftbrRwhRNkQGRkJyBASIcT/keBfiDLC29sbb29vq5ZhY2PD6NGjrVqGEEIIIcouCf6FECXG1tZW2ZDM1lZGHaqFtJs6Sbupj7SZOqmt3ST4F0KUGHt7e1lmV4Wk3dRJ2k19pM3USW3tVvZvT4QQQgghhBAWIcG/EEIIIYQQ5YQM+xFClBitVsu5c+cA8PHxQaPRlHKNRGFIu6mTVqvFYDBYfJMvYX2ykZq6qO1vpAT/QogSYzAY0Gq1yrFQB2k3dcreVpnJcdxc0KUUayOKovY7e2VTNhVR299I1Q772bhxo7I0YkxMTGlXp8T5+/vj7e3NBx98UNpVERby7bff4u3tzZgxY3KcCwsLU37fw8LCzC7DYDDQu3dvvL292bBhQ3GqK4QQQggVslrPf1hYGMOHD1d+dnZ25vDhwzg5OeV7XVpaGh06dCA5OVl5bdWqVbRr185aVS3X9Ho9u3btYtu2bZw5c4b4+HhsbW1xdXXF19eXfv360alTJ6vW4cKFC/z5558cPXqU8+fPc/fuXaUOLVq0oG/fvgXWQavVEhoaysGDBzl16hSXL18mOTkZJycnPD098fPzY8iQIQXOxvf39+fGjRu5ntNoNLi4uNCoUSO6dOlCYGBggb/PhXXz5k2WL18OwNtvv22RPHNjY2PD2LFjeffdd/nmm2/o3r27VTcVE0IIIUTZUmLDflJTU9m7dy+9e/fON11ISIhJ4F9ast+8PK43H4mJibz11lscO3Ysx7kbN25w48YNdu7cSbdu3Zg3b55Vdod8//332bx5c67njHXYsWMHHTt25JtvvqFKlSo50sXHx9O9e3fu37+f49yDBw84e/YsZ8+eJSgoiClTpjBixAiz6qrVarl79y53797l8OHDrFixgh9++IEGDRqYlV923333Henp6fz3v//F19e3yNcvWrSIxYsXAxAVFZVv2u7du7No0SIuX77Mr7/+yhtvvGFWnYUQQgihPiUS/Ds6OpKenk5wcHCBwX9wcLDJNXkJDAwkMDDQovUsb959910l8K9bty6jRo3Cy8sLnU7HmTNn+PHHH0lISGD37t24uLjw+eefW7wOd+7cAcDFxYVu3brRtm1b6tSpg52dHefOnWPFihVcvnyZgwcPMnbsWH755ZccG2hkZGQogb+Pjw+dO3fG19eXGjVq8ODBA/7++29++eUX0tPTmTlzJhUqVGDgwIH51svd3V3piTdKS0vj8uXLrFu3jqNHj3L9+nXGjBnD9u3bi3VjdOfOHTZt2gTAa6+9lmuadu3aFRjUF5atrS2vvvoqM2bM4KeffmL48OE4OjpaJG8hhBBClG0lEvz7+/uzc+dODh8+zN27d3Fzc8s13b179zh06BAAnTt3ZseOHSVRvXIpIiKCgwcPAuDp6cnmzZupVKmSct7Pz4/u3bsTEBBAUlIS69atY+LEibi6ulq0HrVq1eLzzz+nX79+OQJoX19f+vTpw6hRowgPDyc8PJwtW7bQt29fk3Q2NjZ06NCBCRMm8PTTT+cow8/Pj27dujF8+HDS0tKYO3cuPXv2NHm/j9JoNHh5eeV43dfXl969ezNixAiOHDnCtWvX2LNnD7169TLr/QOsXr0arVaLu7s7fn5+ZudTFC+++CJffPEF8fHxbN++XW6khRBCiHKiRCb8dujQATc3NzIzM9m+fXue6bZt24ZOp8PNzY1nnnmmJKpWbp04cUI5HjFiRK6BcO3atZWgUK/Xc/LkSYvXY9asWQwcODDPnnMnJyc+/fRT5eddu3blSOPh4cFPP/2Ua+Bv1KJFC4YMGQJkDQU6fPiw2XW2tbU16aGPiIgwOy+9Xs/GjRsB6NmzZ4ltC+7i4kLHjh0BWL9+fYmUKYQQQojSVyKRhp2dHT179gT+b1hPboznevXqhZ1d/uvb5rfaz8cff6yc27lzZ555hISEKOnef/99AGJiYvD29jaZrDx8+HAlnfGfMWCDrPHWxtchK7hcsmQJffv2pU2bNjnSp6amsmPHDqZPn05AQACtW7emadOm+Pn58corr7B8+XJSUlLyff/FZVySCsh3EuwTTzyR6zVQvM+5KLy8vKhWrRoA169fL/L1RtnnbVy7ds3sfCBrmJRRRkaG2fmEh4cTGxsLwAsvvJBnurxW+zF+D4zj/YEcv6t5rYjVrVs3AI4fP86tW7fMfg9FYW9vT4MGDWjQoAH29rLSsFpIu6mTvb29tJcQJUBtfyNLbKnPgIAAAM6ePcv58+dznL9w4QJnzpwxSWuuadOm8eSTTwIwY8YMbt++nSPN3bt3+eijjwCoU6cOH3/8cbHKNLpy5QoBAQEsXLiQc+fO8eDBgxxpxowZw6RJk1i/fj2RkZEkJyej0+lISEjg6NGjzJkzhz59+nDx4kWL1Ck39evXV47zC6izB8nZr4GS/ZyNNx7F6RnPHqQXdHNZkJs3byrHtWrVMjsfYyCv0Who1qxZsepUVC1atACylv88cOBAiZRpa2tLtWrVqFatWok95RDFJ+2mTra2ttJeQpQAtf2NLLHbkyZNmtCoUSPOnz9PcHAw7733nsl5Y6+/l5cXPj4+yk5p5nB2dmbu3LkMHjyYxMRE3n//fX7++WdsbGyUNNOmTSM+Ph47Ozvmzp2rDHvx8PBg69atRERE8OGHHwIwc+ZMmjdvblJGzZo1cy17woQJxMbGMmzYMPz9/alSpQpXr16ldu3aShqdToeXlxf+/v40b94cd3d3DAYDN27cYO/evezcuZOYmBjefvttgoODrTIZs2PHjtStW5eYmBhWrVpF//79cyz5ePv2bWUiauvWrXOMgS/O51wUZ8+eVVaAatiwYZGvNzpy5IhyXJwVegwGAytWrACy5hv4+/ubnZdxwrWXl5dZk4a7dOlCs2bNWL16NWvWrAFg69atOdJ5eOTcLKZ+/fpUqVKFpKQkjhw5wssvv1zk8oUQQgihLiX6bCIgIIB58+axbds2Jk+erASJBoNBCViK2+tv5Ovry7hx41iwYAH//PMPP/30E6NGjQIgKChI6el84403aN26tXKdcaJnQkKC8lrdunVznfyZm/Pnz/Pjjz8q46mBHD26s2bNUnrMs2vRogU9evRgwIABjBo1isuXL7NlyxZeeumlQr/vwnJwcGD+/PmMHTuWa9euERAQwKhRo2jUqJGy2s+yZctITEzE09OTmTNn5pqPuZ9zUXz//ffKcffu3c3KIzY2Vhl6Vb169QIn1mq1WqKjo01eS09P58qVK6xbt07psX/99dd56qmnzKqTwWBQ5lH4+PiYlUeVKlWoUqWKyUTswv6u2tjY4OPjQ1hYmPLUzdqy74Ko0WhMbhRF2SXtpk7GnUalvYSwLrX9jSzR4L9Pnz58/fXX3Lp1i7CwMCUACwsL49atW9ja2ha4FGhRjB49mgMHDhAeHs4333xDhw4dsLe3Z+7cuQA0b96c8ePHW6w8gH79+pkE/rnJLfDP7plnnsHf35+9e/cSEhJileAf4Omnn2bz5s2sXLmSoKAgZsyYYXLe2dmZiRMnMmTIEFxcXPLMx5qf8+7du9m9ezcATZs2zXdcfF4MBgOffPKJMo/irbfeKvBpSmxsbL6/i76+vowePZquXbsWuT5GiYmJpKamAlh8FaXCMpYbExODwWCw+h8srVarTJBu3ry5VfaOEJYn7aZOxmBE2ksI61Lb38gSDf49PDxo164doaGhBAcHK8G/cciPn59frsMTzGVnZ8ecOXMICAggOTmZyZMnY29vT3p6Ok5OTsydO9fiEzPMuXmJj48nKSnJZEx69erVAYiMjLRY3R5lMBjYvn07u3btyjGZF7ImJm/duhV3d3cGDBiQZz7W+pwvXrzItGnTAKhQoQJz5841Kzj9/vvv2b9/P5A16Xfo0KFFzuNRp0+fZvXq1TzxxBPKRO+iio+PV45z27ysJFStWhXImg+RlJSk/CyEEEKIx1OJT0nu27cvoaGh7NmzR+lpNvbsWmrIT3Z169blk08+YerUqVy4cEF5fdq0aTkmsFpCYQPB8PBwgoKCCA0NzXVnWqPsw48sSa/XM2nSJGXpzAEDBjB06FAaNmxIZmYm586dY9myZezbt4/p06cTFRXF9OnT88zP0p/znTt3eOONN0hJScHGxoaZM2eaNd5/y5YtfPvtt0od58+fX6jJOHXq1GHfvn0mr+l0Ou7du0doaCgLFy7k8OHDDBkyhKVLl9KmTZsi1y0xMVE5Lq2gO3u5Dx8+lOBfCCGEeMyV+JTkrl274uTkRHJyMiEhIezdu5eUlBScnZ3NGtJRGAEBAbRt21b5uUOHDgXu8GquwgRPixYtYsiQIezcuTPfwB+ydpW1hjVr1iiB//jx4/nyyy9p0qQJjo6OODs707p1a7777jvlhmzVqlU5guFHWepzvn//PqNGjeLGjRtA1pKixqVii+LPP//kww8/xGAw4ObmxooVK/LcYK4w7O3t8fDwoG/fvvz222+4uLiQnJzM1KlT0el0Rc4v+9Aja7VzQbKXq4blyYQQQghRPCUe/FesWJEuXboAWcN9jEN+unTpkmO1GUs5ffq0yaZWkZGR3Lt3zyplFbSEZGhoqLImu6enJzNmzGDLli0cO3aMM2fOEBUVRVRUFG+99ZZV6me0bt06IKs9Ro8enWe6d999VznesGFDvnla4nNOTk7m9ddfV5aDnThxolnDdMLCwpgwYQJarZaqVauybNkykz0Lisvd3Z0+ffoAcOPGDf75558i52Ec2gWmTwFKUvZyK1euXCp1EEIIIUTJKZXFSPv27QvAoUOHlJ1Wja9ZWlpaGlOmTEGr1eLs7IyNjQ337t3LdwiLNa1duxbIekKwdu1ahgwZgre3N5UrVzbpebV2MGjcQ+Cpp57Kd2JKzZo1qVGjBgCXLl3KM50lPue0tDTefPNNZdLMqFGjzLoJOnXqFGPHjiU9PR1nZ2d+/PFHGjduXOR8CpJ9udBHVwYqjLIU/Lu6ulplSVkhhBBClC2lEvy3b98eNzc3dDodOp0Od3d32rdvb5WyZs+erQStn332GSNHjgRg//79yrroubHWqifG8fDt2rUzCf4edfr0aauUb2S80SjMcBVjmvyGhZj7ORtptVrGjx+vrMU/aNAgpk6dWuB1j4qMjOT1118nNTUVR0dHvv/+e2UzK0vLzMxUjs0Z9uPg4KCs/HTlypVi1cXc31djueYuVyqEEEIIdSmV4N/Ozo6AgAAcHBxwcHAgICDAKjui/fXXX0rg2atXL/r06cOkSZOUXuCvvvqKy5cv53pt9l7Q7KvwFJcxSDQu8Zibs2fPKuu/W0vdunWBrH0JkpKS8kwXHR2tzEuoU6dOrmmK8zlDVhA9efJk/v77byBr7sCnn35a1LfE5cuXGTVqFImJiWg0GhYtWkS7du2KnE9hZb9BM3eXX+PeB8anHebK/vSmsL+vycnJSrtY6wZJCCGEEGVLqe1BPGXKFCIiIoiIiMix268lxMfHKzv0zXZmdAAALldJREFU1qpVS1lZyMHBgXnz5uHo6MjDhw+ZMmVKrr222SeGXrt2zWL1qlevHgDHjx/n6tWrudbbnB7vonr++eeBrEBx1qxZymYw2aWnp/PFF1/kuCa74n7OBoOBjz76SFnxqVu3bsyaNavIPdk3b95k5MiRxMXFYWdnx7x58+jUqVOR8iiKM2fOsH37diBrQ49nnnnGrHyMqwQlJCRw/fp1s+vj7u6uHBf29zUiIkJp9w4dOphddlFoNBqaN29O8+bN0Wg0JVKmKD5pN3XSaDTSXkKUALX9jXxsl/eYPn06cXFx2Nra8tVXX5mso96oUSMmT57MzJkziYiIYMmSJUycONHk+tq1a1OzZk1u377NTz/9RM2aNalfv74yodfV1ZVKlSoVuV59+/Zl//79pKam8sorrzB69GiaNm0KwIkTJ1ixYgVxcXG0bNnSZPKspb366qusX7+ee/fusXHjRq5evcqgQYNo0KABer2es2fPEhQUpAxTatiwIf369cuRT3E/56+++krZedfLy4sxY8Yo8xHy8ugOtgkJCYwcOZJbt24BMHLkSBo0aJDvOPyqVavmu6dEbjv86vV64uLiOHz4MKtXr1Z62EeNGmX2Jl2dOnVCo9Gg1Wr5559/8PT0NCufli1bKsezZs1i7NixuLm5KTdRderUyTFsyzhJuVq1ambvvlxUNjY2ZX7zE5GTtJs6lfVdRoV4XKjtb+RjGfz//vvvyrKUI0eOzHXox/Dhw/n77785ePAgS5cu5dlnnzUJoADGjBnDZ599RkxMTI6Jp7NmzSIwMLDIdXvxxRcJDAxk48aNxMbGmvSsQ9aQqGnTppGUlGTV4L969eosX76ccePGERMTQ3h4OOHh4bmm9fHxYcmSJTl+sS3xOe/Zs0c5jo6OLtRnGhUVZfJzdHS0yZj5ZcuWsWzZsnzz6NevH7Nnz87zfEE7/ELWl33YsGG88847BdY5L66urvj7+7N79262bt1q9m7O9erVo3v37uzcuZODBw9y8OBBk/MhISHKUC+jbdu2AdCjRw9V9FQIIYQQovhKbdiPtVy5ckUJ6nx8fPIMzIwbR7m4uJCZmcmUKVNISUkxSTNkyBAWLVpEx44dcXV1tdg66LNmzWLOnDm0adOGihUr4uDgQJ06dQgICOC3335jxIgRFimnID4+PmzdupVPPvmEjh074ubmhkajwcHBgVq1auHv78+cOXNYt25djvH+lvyc1cLW1pbKlSvTpEkTXnnlFTZt2sT06dOL3bv28ssvA3D06FHu3Lljdj5z585lypQp+Pr6Urly5Xzn0Zw4cYKYmBgABg8ebHaZRaXX60lISCAhIQG9Xl9i5YrikXZTJ71eL+0lRAlQ299IG0Nug72FECXGYDDQu3dvzp8/zzvvvMObb75p9TKnT5/O+vXr6dixI8uXL7dKGRkZGcpE5ubNm+Pg4JDra6Lsk3ZTJ+PQRAcHBwz6TDKT40q5RqKw7CrVwMY2/32DRNlhjb+R1vy7+1gO+xFCTWxsbHj33Xd58803WblyJSNGjLDahneQNTnauLnehAkTrFaOEKLssLG1w75K3vOchBDlx2M37EcINfL396dNmzYkJCTw66+/WrWspUuXotVqefHFF2WJTyEec1qtloyMjFxXdBNlj8FgICMjQ9pMWJX0/AtRRnzyySfs2bPHqr3+BoOBOnXqMG7cOPr372+1coQQZUNkZCQgw7XUQqvVyhA7YXUS/AtRRnh7e+Pt7W3VMmxsbBg9erRVyxBCCCFE2SXDfoQQQgghhCgnpOdfCCGEeEw1btxYdvpVEeNOscZjoQ7Z281Sy8JbU9mvoRDisWFjY6P8D012H1UPaTd1MrabjBtXD7XtFCuyZG83NUzUluBfCFFiNBoNvr6+pV0NUUTSbuqUvec4U68nLulhKdZGiMdbjSpO2NnaqqKDRIJ/IYQQ4jEXl/SQHl9uKO1qCPHY2jG9Px4uFUu7GoUiE35L0caNG5UVXmJiYkq7OiXO398fb29vPvjgg9KuihBCCCFEuSA9//kICwtj+PDhys/Ozs4cPnwYJyenfK9LS0ujQ4cOJCcnK6+tWrWKdu3aWa2u5Zler2fXrl1s27aNM2fOEB8fj62tLa6urvj6+tKvXz86depk1TpcuHCBP//8k6NHj3L+/Hnu3r2r1KFFixb07du3wDpotVpCQ0M5ePAgp06d4vLlyyQnJ+Pk5ISnpyd+fn4MGTIET0/PfPPx9/fnxo0buZ7TaDS4uLjQqFEjunTpQmBgYIG/z5ak0+m4desWALVq1VLFxCgh7aZWOp0OUMcERCEeFwaDocwP/ZG/CEWQmprK3r176d27d77pQkJCTAL/0pL95uVxvflITEzkrbfe4tixYznO3bhxgxs3brBz5066devGvHnzrDKR6v3332fz5s25njPWYceOHXTs2JFvvvmGKlWq5EgXHx9P9+7duX//fo5zDx484OzZs5w9e5agoCCmTJnCiBEjzKqrVqvl7t273L17l8OHD7NixQp++OEHGjRoYFZ+RaXX64mNjQXAw8OjRMoUxSftpk56vb60qyCEKIMk+C8kR0dH0tPTCQ4OLjD4Dw4ONrkmL4GBgQQGBlq0nuXNu+++qwT+devWZdSoUXh5eaHT6Thz5gw//vgjCQkJ7N69GxcXFz7//HOL1+HOnTsAuLi40K1bN9q2bUudOnWws7Pj3LlzrFixgsuXL3Pw4EHGjh3LL7/8gq2t6Yi7jIwMJfD38fGhc+fO+Pr6UqNGDR48eMDff//NL7/8Qnp6OjNnzqRChQoMHDgw33q5u7uzfPlyk9fS0tK4fPky69at4+jRo1y/fp0xY8awfft2WWFCCCGEKAck+C8kf39/du7cyeHDh7l79y5ubm65prt37x6HDh0CoHPnzuzYsaMkq1muREREcPDgQQA8PT3ZvHkzlSpVUs77+fnRvXt3AgICSEpKYt26dUycOBFXV1eL1qNWrVp8/vnn9OvXL0cA7evrS58+fRg1ahTh4eGEh4ezZcsW+vbta5LOxsaGDh06MGHCBJ5++ukcZfj5+dGtWzeGDx9OWloac+fOpWfPnibv91EajQYvL68cr/v6+tK7d29GjBjBkSNHuHbtGnv27KFXr15mvX8hhBBCqIdM+C2kDh064ObmRmZmJtu3b88z3bZt29DpdLi5ufHMM8+UYA3LnxMnTijHI0aMyDUQrl27tvJ0Ra/Xc/LkSYvXY9asWQwcODDPnnMnJyc+/fRT5eddu3blSOPh4cFPP/2Ua+Bv1KJFC4YMGQJkDQU6fPiw2XW2tbXltddeU36OiIgwOy8hhBBCqIcE/4VkZ2dHz549gf8b1pMb47levXphZ2eXb575rfbz8ccfK+d27tyZZx4hISFKuvfffx+AmJgYvL29TSYrDx8+XEln/Ldx40bl/KJFi5TXISu4XLJkCX379qVNmzY50qemprJjxw6mT59OQEAArVu3pmnTpvj5+fHKK6+wfPlyUlJS8n3/xaXVapXj/CbBPvHEE7leA8X7nIvCy8uLatWqAXD9+vUiX2+Ufd7GtWvXzM4HsoZJGWVkZBQrLyGEEEKogwT/RRAQEADA2bNnOX/+fI7zFy5c4MyZMyZpzTVt2jSefPJJAGbMmMHt27dzpLl79y4fffQRAHXq1OHjjz8uVplGV65cISAggIULF3Lu3DkePHiQI82YMWOYNGkS69evJzIykuTkZHQ6HQkJCRw9epQ5c+bQp08fLl68aJE65aZ+/frKcX4BdfYgOfs1ULKfs/HG49Hx/kWRPUgv6OayIDdv3lSOa9WqVay8hBBCCKEOEvwXQZMmTWjUqBGQe++/8TUvLy98fHyKVZazszNz587F3t6exMRE3n///RxbRk+bNo34+Hjs7OyYO3euMuzFw8ODrVu3MnPmTCXtzJkz2bp1q8m/Ll265Fr2hAkTiI2NZdiwYaxYsYINGzbw9ddfmwTOOp0OLy8vxo4dy5IlS1i3bh1r167lm2++oWfPntja2hITE8Pbb7+d76Tn4ujYsaPSe71q1SpSU1NzpLl9+zabNm0CoHXr1jnGwBfncy6Ks2fPKitANWzYsMjXGx05ckQ5Ls4KPQaDgRUrVgBZ8w38/f3NzksIIYQQ6iETfosoICCAefPmsW3bNiZPnqys5WowGNi6dauSxhJ8fX0ZN24cCxYs4J9//uGnn35i1KhRAAQFBXHgwAEA3njjDVq3bq1cZ5zomZCQoLxWt27dXCd/5ub8+fP8+OOPdOzYUXmtWbNmJmlmzZql9Jhn16JFC3r06MGAAQMYNWoUly9fZsuWLbz00kuFft+F5eDgwPz58xk7dizXrl0jICCAUaNG0ahRI2W1n2XLlpGYmIinp6fJzVB25n7ORfH9998rx927dzcrj9jYWGXoVfXq1fHz88s3vVarJTo62uS19PR0rly5wrp16wgLCwPg9ddf56mnnjKrTkVla2uLi4uLcizUQdpNnaSthBC5keC/iPr06cPXX3/NrVu3CAsLUwKwsLAwbt26ha2tbYFLgRbF6NGjOXDgAOHh4XzzzTd06NABe3t75s6dC0Dz5s0ZP368xcoD6Nevn0ngn5vcAv/snnnmGfz9/dm7dy8hISFWCf4Bnn76aTZv3szKlSsJCgpixowZJuednZ2ZOHEiQ4YMUYKX3Fjzc969eze7d+8GoGnTprzwwgtFzsNgMPDJJ58o8yjeeustHB0d870mNjY2399FX19fRo8eTdeuXYtcH3PZ29sX68mHKB3Sbuokm3sJUfLK+gZfIMF/kXl4eNCuXTtCQ0MJDg5Wgn/jkB8/Pz+LboJjZ2fHnDlzCAgIIDk5mcmTJ2Nvb096ejpOTk7KkBVLMufmJT4+nqSkJJMx6dWrVwcgMjLSYnV7lMFgYPv27ezatSvHZF7Impi8detW3N3dGTBgQJ75WOtzvnjxItOmTQOgQoUKzJ0716w/DN9//z379+8Hsib9Dh06tMh5POr06dOsXr2aJ554QpnoLYQQQojHmwT/Zujbty+hoaHs2bNH6Wk29uxaashPdnXr1uWTTz5h6tSpXLhwQXl92rRpOSawWkJhA8Hw8HCCgoIIDQ3NdWdao+zDjyxJr9czadIkZenMAQMGMHToUBo2bEhmZibnzp1j2bJl7Nu3j+nTpxMVFcX06dPzzM/Sn/OdO3d44403SElJwcbGhpkzZ5rVe7plyxa+/fZbpY7z588v1OP8OnXqsG/fPpPXdDod9+7dIzQ0lIULF3L48GGGDBnC0qVLadOmTZHrJoQQQgh1kQGBZujatStOTk4kJycTEhLC3r17SUlJwdnZ2awhHYUREBBA27ZtlZ87dOhQ4A6v5qpatWqBaRYtWsSQIUPYuXNnvoE/ZO0qaw1r1qxRAv/x48fz5Zdf0qRJExwdHXF2dqZ169Z89913yg3ZqlWrcgTDj7LU53z//n1GjRrFjRs3gKwlRY1LxRbFn3/+yYcffojBYMDNzY0VK1bkucFcYdjb2+Ph4UHfvn357bffcHFxITk5malTp6LT6czOt7C0Wi2RkZFERkbm+qRGlE3Sbuqk1WqlvYQoYY8uGlIWSfBvhooVKyor5QQHBytDfrp06YKzs7NVyjx9+rTJplaRkZHcu3fPKmUVtIRkaGgoixcvBrLW158xYwZbtmzh2LFjnDlzhqioKKKionjrrbesUj+jdevWAVntMXr06DzTvfvuu8rxhg0b8s3TEp9zcnIyr7/+urIc7MSJE80aphMWFsaECRPQarVUrVqVZcuWmexZUFzu7u706dMHgBs3bvDPP/9YLO+8GAwGUlJSSElJUcUfSJFF2k2dDAaDtJcQIgcJ/s3Ut29fAA4dOqTstGp8zdLS0tKYMmUKWq0WZ2dnbGxsuHfvXr5DWKxp7dq1QNYTgrVr1zJkyBC8vb2pXLmyybj4xMREq9bDuIfAU089lefuugA1a9akRo0aAFy6dCnPdJb4nNPS0njzzTeVHXNHjRpl1k3QqVOnGDt2LOnp6Tg7O/Pjjz/SuHHjIudTkOzLhT66MpAQQgghHj8S/Jupffv2uLm5odPp0Ol0uLu70759e6uUNXv2bCVo/eyzzxg5ciQA+/fvZ82aNXleZ60Z58bx8O3atVMm9ebm9OnTVinfyHijUZjhKsY0+U3aNfdzNtJqtYwfP15Zi3/QoEFMnTq1wOseFRkZyeuvv05qaiqOjo58//33tGjRosj5FEZmZqZyXBLDfoQQQghRuiT4N5OdnR0BAQE4ODjg4OBAQECAVdZU/uuvv5TAs1evXvTp04dJkyYpvcBfffUVly9fzvXa7EtBZl+Fp7iMQWJum2oZnT17lpMnT1qszNwYN/g6f/48SUlJeaaLjo5W5iXUqVMn1zTF+ZwhK4iePHkyf//9N5A1d+DTTz8t6lvi8uXLjBo1isTERDQaDYsWLaJdu3ZFzqewst+gyS6/QgghxONPgv9imDJlChEREURERPDee+9ZPP/4+Hg+/PBDICswM64s5ODgwLx583B0dOThw4dMmTIl117b7BNDr127ZrF61atXD4Djx49z9erVXOttTo93UT3//PNA1o3NrFmzch3bmp6ezhdffJHjmuyK+zkbDAY++ugjZcWnbt26MWvWrCI/ebl58yYjR44kLi4OOzs75s2bR6dOnYqUR1GcOXOG7du3A1kbwz3zzDNWK0sIIYQQZYMs9VmGTZ8+nbi4OGxtbfnqq6+oUqWKcq5Ro0ZMnjyZmTNnEhERwZIlS5g4caLJ9bVr16ZmzZrcvn2bn376iZo1a1K/fn1lQq+rqyuVKlUqcr369u3L/v37SU1N5ZVXXmH06NE0bdoUgBMnTrBixQri4uJo2bKlyeRZS3v11VdZv3499+7dY+PGjVy9epVBgwbRoEED9Ho9Z8+eJSgoSBmm1LBhQ/r165cjn+J+zl999ZWy866XlxdjxoxR5iPk5dHdlhMSEhg5ciS3bt0CYOTIkTRo0CDfcfhVq1bNd0+J3Hb41ev1xMXFcfjwYVavXq08ERo1ahSurq751lkIIYQQ6ifBfxn1+++/K8tSjhw5MtehH8OHD+fvv//m4MGDLF26lGeffZaWLVuapBkzZgyfffYZMTExOSaezpo1i8DAwCLX7cUXXyQwMJCNGzcSGxtr0rMOWUOipk2bRlJSklWD/+rVq7N8+XLGjRtHTEwM4eHhhIeH55rWx8eHJUuW5JgYbInPec+ePcpxdHR0oT7TqKgok5+jo6O5cuWK8vOyZctYtmxZvnn069eP2bNn53m+oB1+IWteyLBhw3jnnXcKrLMQQggh1E+C/zLoypUrSlDn4+OTZ2Bm3DiqT58+3L9/nylTphAcHEzFihWVNEOGDKFGjRr8/vvvnDt3jsTERItM7Jw1axZ+fn6sXbuWc+fOodVqcXNzo02bNrzyyiv4+vqyaNGiYpdTEB8fH7Zu3cqmTZvYt28fUVFR3L9/HxsbG1xdXfHx8eHFF1+kR48eaDQak2st+Tmrha2tLRUrVsTT05NWrVoxYMAAfHx8Sqx8Ozs7Za5GQUvKirJD2k2dpK2EELmxMcgiwEIIK8jIyFCWPG3evHm+y7EKIazrzv0UenyZ/z4nQgjz7ZjeHw8Xy3UKWvP/odLzL4QQQjzmalRxYsf0/qVdDSEeWzWqOJV2FQpNgn8hhBDiMWdna2vRXkkhhHpJ8C+EKDEyFEidpN3UKSMjA61Wi0ajQaPRWG3jR2E5BoMBrVYLIG2mItnbzd7e3ir7PllS2a6dEEIIIcwWGRlJRESEEpiIsk2r1Sr7B0mbqUf2drPEoirWJsG/EEIIIYQQ5YQE/0IIIYQQQpQTEvwLIYQQQghRTkjwL4QQQgghRDkhwb8QQgghhBDlhAT/QgghhBBClBOyzr8QosTY29vj7e2tHAt1kHZTJ2k39ZE2Uye1tVvZr6EQ4rFha2tLpUqVSrsaooik3dRJ2k19pM3USW3tJsN+hBBCCCGEKCek518IUWL0ej2pqakAODs7l/kt0EUWaTd1knZTH2kzdVJbu5Xt2gkhHis6nY6oqCiioqJUsQW6yCLtpk7SbuojbaZOams3Cf6FEEIIIYQoJyT4F0IIIYQQopyQ4F8IIYQQQohyQoJ/IYQQQgghygkJ/oUQQgghhCgnZKlPIYRVGAwG5Vir1Zr899FjUbZJu6mTtJv6SJupkzXaLXs+2f9/agk2BkvnKIQQQEpKCpGRkaVdDSGEEELVGjduTMWKFS2Wnwz7EUIIIYQQopyQnn8hhFXo9XoePnwIgL29PTY2NqVcIyGEEEIdDAaDsmGYk5OTRXcNluBfCCGEEEKIckKG/QghhBBCCFFOSPAvhBBCCCFEOSHBvxBCCCGEEOWEBP9CCCGEEEKUExL8CyGEEEIIUU5I8C+EEEIIIUQ5IcG/EEIIIYQQ5YQE/0IIIYQQQpQTEvwLIYQQQghRTkjwL4QQQgghRDkhwb8QQgghhBDlhAT/QgghhBBClBMS/AshhBBCCFFOSPAvhBBCCCFEOSHBvxBCCCGEEOWEBP9CCCGEEEKUExL8CyGEEEIIUU5I8C+EEEIIIUQ5YV/aFRBCPL4ePnzIL7/8wq5du7h+/ToZGRnUrFmT5557jmHDhlGnTp1i5a/X6wkPD+fAgQOcOHGCS5cukZiYiIODA7Vr16ZNmzYMGjSIxo0bW+gdlQ/WbjeAW7ducerUKSIiIjh16hRnzpwhOTkZgHHjxjF+/Phil/E4uHHjBkFBQfz555/cvn0bBwcHPD096d69O0OHDsXJycki5fz111+sXbuWiIgI4uPjqV69Os2bN+fll1+mU6dOFimjPLFmu+n1ei5dusSpU6eU71BUVBRarRaAVatW0a5dO0u9lXLFmu328OFDDhw4wKFDhzh9+jTXrl0jNTWVSpUq8eSTT9KxY0cGDRqEm5ubBd9R7mwMBoPB6qUIIcqdq1evMnr0aK5cuZLr+UqVKjFv3jyef/55s8t47rnnuHXrVr5pbG1tee2113jvvfewsbExu6zyoiTa7caNG/j7++d5XoL/LPv27WPKlCnKTdGjnnzySX744Qfq1atndhl6vZ6PP/6Y9evX55nmpZde4vPPP8fWVgYLFIa1223Tpk188MEHeZ6X4N881my3yMhIBg8eTGpqar7pKlWqxP/7f/+PHj16FLmMopCefyGExSUnJ5sEkC+//DI9evSgQoUKhIWFsXTpUpKTk5k0aRJr1qzBx8fHrHJiY2MBqFevHi+88AKtWrXC3d2dtLQ0wsLCWLlyJYmJiSxbtgw7OzveffddS73Fx1JJtVv2PicbGxueeOIJ3N3dOXr0qCXexmPh7NmzTJo0ibS0NJydnRkzZgzt2rUjLS2NHTt2sHbtWq5cucLo0aPZsGEDlSpVMqucb775Rgn8mzRpwuuvv46npyfXr19n2bJlnD17lnXr1lG9enX5/hRCSbRb9u+PRqPBy8sLrVZLdHS0Jd9KuWLtdktOTlYC/1atWvH888/TrFkzXFxciI+PZ8+ePaxbt47k5GTee+89KlasaN0nbgYhhLCwBQsWGLy8vAxeXl6GH3/8Mcf58PBwQ5MmTQxeXl6GV155xexyBg4caPj7778Ner0+1/NXr141+Pn5Gby8vAxNmjQxXLt2zeyyyoOSarf4+HjD//73P8PBgwcN9+/fNxgMBsM///yjlL1w4UKz835cDBkyRPm9PX78eI7zP/74Y7E/r0uXLintGRgYaHj48KHJ+dTUVENgYKBSjytXrphVTnlSEu128uRJw6pVqwwnTpwwpKWlGQwGg2HhwoVKvv/880+x3kN5ZO12Cw8PN0ycONFw/vz5PNP88ccfBm9vb4OXl5ehS5f/r717j6m6/uM4/jrcFAxDlMAh5DVaKikVBeraT11iyabWKsM7ZVdXbbZK1KZs1crKZXNJMy9lS5vXzFITLd0UhuCFS6mZHa3QvCAm4OH2+4Odbwe5HjkcDnyfj83tcL6f7+fz+Z43wpvP9/P9fEY3+HvNFbiHB8ClysvL9cUXX0iS+vXrp5kzZ9YpExMTo0cffVSSlJmZqaNHj95UW19//bVGjBjR4HSeyMhIvfjii5KkiooK/fjjjzfVjhm4M27dunXT888/r2HDhunWW2+9+U53UEePHlVWVpYk6dFHH9XQoUPrlJk5c6b69esnqWaah32+tzNWr16tiooKSdL8+fPVuXPnWsf9/f01f/58STX/f1atWuV0G2birrhFR0drypQpGjJkiDp16tSyTsMtcYuJidGSJUvUv3//BsuMHj1aDz30kCTJarUqPz/fqTacQfIPwKUyMjJ09epVSdL48eMbnCc8ceJE43VrJuWOc1+tVmurtdPeeVrczMzxc7X/sXUjLy8vjR8/XpJUXFysjIwMp9qorq7W7t27JUl9+/bVkCFD6i03ZMgQ9enTR5K0e/fuWlNOUJs74gbX86S4uev3Fck/AJc6dOiQ8To2NrbBcoMGDTJWTsjOzm61/thsNuO1t7d3q7XT3nla3MzMHouAgAANHDiwwXL33Xef8drZWJw9e9Z4ZsaxnvrYvx/OnTuns2fPOtWOmbgjbnA9T4qbu35fkfwDcKnffvvNeN23b98Gy/n4+CgyMrLOOa7m+BBpY/0xO0+Lm5nZP9fIyEj5+DS8LodjnJyNxcmTJ+utp6l2Tp065VQ7ZuKOuMH1PClumZmZxmv7NKPWQPIPwKUKCwsl1YyidO3atdGyPXv2lCRdunSp1oiHq5SWlmr16tWSJD8/P40aNcrlbXQUnhQ3M7t+/bouX74sSQoLC2u07K233qqAgABJ/8WvuRzLN9WO4/GmltY1K3fFDa7lSXH75Zdf9NNPP0mS7rjjDpJ/AO3HtWvXJMn4IdkYxw1T7Oe50uLFi/XXX39JkpKSkhQaGuryNjoKT4qbmTl+ns7Eoqn1w1vSjmO8nW3HLNwVN7iWp8TNZrMpJSVFlZWVkqRXX33VpfXfiOQfgEtdv35dUs36003x8/Orc56rbN26VV9++aWkmtunr7zyikvr72g8JW5m5/h5OhOLsrKyVmvHMd7OtmMW7oobXMtT4rZo0SLl5uZKkiZMmNDoJoiuwCZfgElFRUW1uI533nmn1uovkoyl55qzFJrjlBFXLlmXkZGhlJQUSVJQUJCWLl1aZxnD9qojxw21P09nYuHs97cz7TjGu6P8P3I1d8UNruUJcVu+fLm++eYbSdLgwYO1YMECl9XdEEb+AbhUly5dJDXvtmhpaWmd81rq2LFjev7552Wz2RQQEKC0tLRWnTvZUbR13FDD8fN0JhbNmbJws+04xtvZdszCXXGDa7V13L7++mt9+OGHkmoeKE5LS3PL9wQj/4BJbd++vcV13HbbbXXeCwsL05EjR1RSUqLi4uJGHx61PzwYHBxca2rBzTpx4oSefvppXbt2TX5+flq2bJnuvvvuFtfrSTpi3PCfTp06KSgoSEVFRU0+VHjlyhUjYWnqYcUbOZZvqh3H4/aHvVGbu+IG12rLuG3btk0LFy6UJIWHh2vlypUKDg5ucb3NQfIPmFRrjYY71nvq1KkGNw+qqKjQmTNnXNYXq9WqGTNmqKioSD4+Pvroo48UFxfX4no9TUeLG+rq37+/srKyZLVaVVFR0eDyg47LbjobC8edRptavtPxOMvlNswdcYPrtUXcdu/erddff11VVVUKCQnRqlWr3PqHINN+ALjUPffcY7x2XLP4Rrm5ucYoSkxMTIvaLCws1PTp0/XPP//Iy8tL7777rkaPHt2iOs2mLeKG+tljUVJSory8vAbLOe5h4WwsevXqZdwBcqynsXZCQ0PVq1cvp9oxE3fEDa7n7rgdOHBAr7zyiioqKhQUFKSVK1cae6e4C8k/AJeKjY1VYGCgJGnz5s2qrq6ut9zGjRuN1y1J1C9evKjp06frzz//lCQtXLhQiYmJN12fWbk7bmiY4+e6YcOGestUVVVp8+bNkqSuXbvq/vvvd6oNi8Vi7Htx6tQpHT58uN5yhw8fNkY8R40aJYvF4lQ7ZuKOuMH13Bm37OxsvfDCC7LZbAoMDNSKFSs0YMCAm6qrJUj+AbiUn5+fpkyZIqlmF8QVK1bUKZOTk2P8kI2NjVV0dHS9dUVFRSkqKqrBZc+Ki4uVnJys33//XZL05ptv6vHHH3fFZZiOO+OGxkVHR+vee++VVJOM5OTk1Cnz+eefG7uMTp06tc4yhRkZGUYc3njjjXrbmTZtmry9vSVJqampdZYvLCsrU2pqqqSanZ2nTZvWsgvr4NwVN7iWu+JWUFCgZ599ViUlJQoICNDy5cs1aNAgF19N8zDnH4DLJScna/v27Tp9+rTef/99Wa1WPfzww+rcubMyMjL06aefqqKiQp07d9bcuXNvqg2bzaZZs2apoKBAkpSYmKj4+HgdP368wXP8/f0VERFxU+2ZgTviZvfzzz/rwoULxteO82kLCgpq3WEICAhQQkJCi9prb1JSUjRp0iSVlZVp5syZeu6553T//ferrKxM27dv17p16yRJvXv31owZM26qjT59+ig5OVlpaWnKzc3VpEmT9MwzzygiIkJnzpzRZ599pvz8fEk13xu9e/d21eV1WO6Im1T7Dpwk4+egJO3bt8+4EypJkZGRRnKL+rV23KxWq5KTk1VcXCxJevnllxUYGNjo76vu3bure/fuN3dBTbBUN3RvFwBa4I8//tCsWbN0+vTpeo/fcsstWrx4sf73v/81WId9Tfvw8HClp6fXOnb27Flj2kJzxcbG6osvvnDqHLNp7bjZTZkypdFnCxw1Vk9Hlp6ertdee03//vtvvcd79+6ttLQ03X777XWOZWRkaOrUqZJqNg169913662jqqpK8+bNa3C6gyQ99thjSk1NlZcXkwWawx1xc2a/j8bqwX9aM24bN27Um2++6VR/XnrpJc2ePdupc5qLkX8AreL222/Xpk2btHbtWv3www+yWq0qLy9XWFiYHnzwQU2dOlXh4eFt3U3cgLh5jpEjR2rr1q1as2aN9u7dq3PnzsnX11eRkZFKSEjQ5MmT5e/v36I2vLy89Pbbb2vMmDFat26djh07psuXL6tbt24aPHiwnnjiCT344IMuuiJzcEfc4Hpmihsj/wAAAIBJcA8PAAAAMAmSfwAAAMAkSP4BAAAAkyD5BwAAAEyC5B8AAAAwCZJ/AAAAwCRI/gEAAACTIPkHAAAATILkHwAAADAJkn8AAADAJEj+AQAAAJMg+QcAAABMguQfAAAAMAmSfwAAAMAkSP4BAAAAkyD5BwAAAEyC5B8AAAAwCZJ/AAAAwCRI/gEAaAcyMzMVFRVl/MvOzm7rLgFoh0j+AQBoBzZt2lTr682bN7dNRwC0ayT/AAB4uLKyMu3YsaPWez/88INsNlsb9QhAe0XyDwCAh9u1a5euXbsmSXrsscckSVeuXFF6enpbdgtAO0TyDwCAh7NP8QkODtbcuXMVGBhY630AaC6SfwAAPNj58+d14MABSVJCQoK6dOmiMWPGSJL279+vS5cutWX3ALQzJP8AAHiwb7/9VpWVlZKkcePGSZISExMlSeXl5dq2bVub9Q1A+0PyDwCAB9uyZYskKTw8XDExMZKk2NhYhYaGSmLqDwDnkPwDAOChCgoK9Ouvv0qqGfW3WCySJC8vLz3yyCOSpLy8PJ08ebLN+gigfSH5BwDAQzmO6tun+tT3NaP/AJqL5B8AAA9UUVFhzOePiorSgAEDah2/66671K9fP0k1zwVUVVW5vY8A2h+ftu4AAACoa//+/bpw4YKkuqP+domJiVqyZIkKCwuVkZGhuLi4BuvbvXu3NmzYoKNHj6qoqEj+/v7q3r27Bg8erLFjx2rkyJF1zsnNzdWmTZt08OBBnT9/XqWlpQoJCdHQoUM1YcIEjRgxolb5iRMnKi8vr9Hr2rp1q6Kiopq6fACthOQfAAAPZJ/KY7FYjFV+bjRu3DgtWbLEKF9f8m+z2TRnzhzt2LFDwcHBGjFihEJDQ1VSUqK8vDxt27ZN/v7+tZL/8vJypaamav369fL29lZcXJzi4+Pl4+OjkydPateuXfruu+80f/58TZ482Wjn+PHj6tatm5KSkurtr8ViUf/+/VvwqQBoKZJ/AAA8zNWrV43de++55x717Nmz3nIREREaOnSocnJytHPnTr311lsKCAioVeaDDz7Qjh079Pjjj2vevHnq1KlTrePnzp1TUVGR8XVlZaVmz56tPXv26IEHHtB7771nrCxkV1hYqNTUVIWHhxvvHT9+XOXl5YqJidHs2bNbcvkAWhFz/gEA8DDff/+9rl+/LqnhKT929uMlJSXauXNnrWM2m03r169X586d6038JSk0NLTWNJylS5caif+KFSvqJP6SFBYWpk8++UTDhg0z3svPz5ckDRo0qJlXCaAtkPwDAOBh7FN+fH19lZCQ0GjZsWPHysen5ka+fU8Au8uXL6ukpEReXl7GMqGNsVqtSktLU2BgoBYvXmzUWx+LxSI/Pz/j69zcXEkk/4CnY9oPAAAe5MyZM8rOzpYkDR8+XEFBQY2WDw4O1vDhw7V3714dPHhQ586dM0bre/TooaCgIBUVFWnGjBl66qmnFB8fr27dutVb18qVK1VZWalJkyYpJCTEqX7bR/737dunI0eO1DkeEhKiJ5980qk6AbgeyT8AAB5ky5Ytqq6ultT0lB+7xMRE7d27V1VVVdqyZYtmzZolSfL29taHH36ouXPnKisrS1lZWcZDtyNHjlRSUlKtaT325wya265dRUWFsRnZmjVr6i0zbtw4kn/AA5D8AwDgQexTdywWi0pLS/Xdd981eU5ZWZksFouqq6u1detWI/mXpGHDhik9PV0ZGRnat2+fDhw4oIKCAp04cUJr167V2rVrdeedd+rSpUsqLCxUly5d6uwp0JSTJ0/KZrNpzJgx+vjjj527YABuRfIPAICHOHTokKxWqySpurpaKSkpTtdx4sQJ5ebm1pp77+3trfj4eMXHx0uqmds/Z84cHTlyRMuWLdPHH3+sy5cvS6qZRtSc5wMc2ef7s34/4Pl44BcAAA9x4wO7N8v+wHBDIiMjNXPmTEnSn3/+KUny9/eXJF28eNGYdtRc9o29SP4Bz8fIPwAAHmLRokVatGiRW9o6f/68pJo/BCSpZ8+e6tGjhy5cuKBjx44pOjq6wXOrqqrk5fXf+KH9Yd8777yzFXsMwBUY+QcAoAPavHmzMjMz6z2Wk5OjZcuWycvLS1OmTJFU84yBfWfeBQsWqLCwsM55ZWVl+uqrr7R8+XLjvcrKSv3yyy+65ZZb1KtXr1a4EgCuxMg/AAAd0Pr163Xo0CFFRETo3nvv1W233abi4mL9+uuvys7Olp+fn1JTUxUTE2OcM2vWLOXn52vXrl1KSEjQiBEjFBERoaqqKp0+fVpZWVm6evWqPvjgA+Oc3377TWVlZQoJCdHSpUvr7Yuvr6+ee+65Vr9mAE2zVDs7sQ8AAHi8nJwc7dmzR5mZmfr777918eJF+fr6qlevXoqPj1dSUpIx5edG27dv18aNG5WXl6fi4mJ17dpVoaGhio6O1qhRoxQXF2ds8LVp0ya98cYbjfZl4MCB2rhxo8uvEYDzSP4BAAAAk2DOPwAAAGASJP8AAACASZD8AwAAACZB8g8AAACYBMk/AAAAYBIk/wAAAIBJkPwDAAAAJkHyDwAAAJgEyT8AAABgEiT/AAAAgEmQ/AMAAAAmQfIPAAAAmATJPwAAAGASJP8AAACASZD8AwAAACZB8g8AAACYBMk/AAAAYBIk/wAAAIBJkPwDAAAAJkHyDwAAAJgEyT8AAABgEv8HRbKTCIuHCKYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "GROUP_A = \"Asian\"\n",
+    "# GROUP_A = \"White\"\n",
+    "GROUP_B = \"Black\"\n",
+    "# GROUP_B = \"Asian\"\n",
+    "\n",
+    "COLUMN = f\"{GROUP_A}_v_{GROUP_B}_score_bias\"\n",
+    "\n",
+    "plot_df = calibration_per_subgroup_df.join(results_df, how=\"left\", validate=\"1:1\")\n",
+    "plot_df = plot_df.drop(index=[id_ for id_ in plot_df.index if \"gemma\" in id_.lower()])   # omit Gemma models bc they're garbage :)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(3, 3))\n",
+    "sns.barplot(\n",
+    "    data=plot_df.sort_values(COLUMN, ascending=True),\n",
+    "    x=COLUMN,\n",
+    "    y=\"name\",\n",
+    "    hue=\"is_inst\",\n",
+    "    ax=ax,\n",
+    ")\n",
+    "\n",
+    "handles, _ = plt.gca().get_legend_handles_labels()\n",
+    "plt.legend(handles=handles, labels=[\"Base\", \"Instruct\"])\n",
+    "\n",
+    "plt.ylabel(\"\")\n",
+    "plt.xlabel(\"$\\Delta_{SCE}$\")\n",
+    "\n",
+    "plt.title(\n",
+    "    \"Signed risk score bias\\n\"\n",
+    "    + r\"($SCE_{\" + GROUP_A\n",
+    "    + r\"} - SCE_{\" + GROUP_B\n",
+    "    + r\"}$)\"\n",
+    ")\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"score_bias.{COLUMN}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "bc511e0a-2928-464e-a09b-ebd2a9928b9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>White_score_bias</th>\n",
+       "      <th>Black_score_bias</th>\n",
+       "      <th>Asian_score_bias</th>\n",
+       "      <th>White_v_Black_score_bias</th>\n",
+       "      <th>White_v_Asian_score_bias</th>\n",
+       "      <th>Asian_v_Black_score_bias</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>...</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mixtral-8x22B-v0.1__ACSIncome__-1__Num</th>\n",
+       "      <td>0.054322</td>\n",
+       "      <td>-0.069436</td>\n",
+       "      <td>0.130153</td>\n",
+       "      <td>0.123758</td>\n",
+       "      <td>-0.075831</td>\n",
+       "      <td>0.199589</td>\n",
+       "      <td>0.740583</td>\n",
+       "      <td>0.148527</td>\n",
+       "      <td>0.828562</td>\n",
+       "      <td>0.761428</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Mixtral 8x22B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.769300</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>0.740595</td>\n",
+       "      <td>0.379947</td>\n",
+       "      <td>0.411113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num</th>\n",
+       "      <td>0.110399</td>\n",
+       "      <td>0.055872</td>\n",
+       "      <td>0.156710</td>\n",
+       "      <td>0.054527</td>\n",
+       "      <td>-0.046311</td>\n",
+       "      <td>0.100837</td>\n",
+       "      <td>0.767882</td>\n",
+       "      <td>0.096716</td>\n",
+       "      <td>0.885938</td>\n",
+       "      <td>0.770218</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Mixtral 8x22B (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.769967</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>0.767882</td>\n",
+       "      <td>0.298323</td>\n",
+       "      <td>0.474955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>penai/gpt-4o-mini__ACSIncome__-1__Num</th>\n",
+       "      <td>-0.001300</td>\n",
+       "      <td>-0.019960</td>\n",
+       "      <td>-0.000407</td>\n",
+       "      <td>0.018661</td>\n",
+       "      <td>-0.000893</td>\n",
+       "      <td>0.019553</td>\n",
+       "      <td>0.777393</td>\n",
+       "      <td>0.080767</td>\n",
+       "      <td>0.904748</td>\n",
+       "      <td>0.758922</td>\n",
+       "      <td>...</td>\n",
+       "      <td>GPT 4o mini (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.777302</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>0.775512</td>\n",
+       "      <td>0.317359</td>\n",
+       "      <td>0.363743</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 70 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 White_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                   0.054322   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num          0.110399   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                   -0.001300   \n",
+       "\n",
+       "                                                 Black_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                  -0.069436   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num          0.055872   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                   -0.019960   \n",
+       "\n",
+       "                                                 Asian_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                   0.130153   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num          0.156710   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                   -0.000407   \n",
+       "\n",
+       "                                                 White_v_Black_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                           0.123758   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num                  0.054527   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                            0.018661   \n",
+       "\n",
+       "                                                 White_v_Asian_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                          -0.075831   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num                 -0.046311   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                           -0.000893   \n",
+       "\n",
+       "                                                 Asian_v_Black_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                           0.199589   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num                  0.100837   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                            0.019553   \n",
+       "\n",
+       "                                                 accuracy  accuracy_diff  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num           0.740583       0.148527   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num  0.767882       0.096716   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num            0.777393       0.080767   \n",
+       "\n",
+       "                                                 accuracy_ratio  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                 0.828562   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num        0.885938   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                  0.904748   \n",
+       "\n",
+       "                                                 balanced_accuracy  ...  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                    0.761428  ...   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num           0.770218  ...   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                     0.758922  ...   \n",
+       "\n",
+       "                                                               name  is_inst  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                Mixtral 8x22B    False   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num  Mixtral 8x22B (it)     True   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num              GPT 4o mini (it)     True   \n",
+       "\n",
+       "                                                 num_features  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                   -1.0   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num          -1.0   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                    -1.0   \n",
+       "\n",
+       "                                                 uses_all_features  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                        True   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num               True   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                         True   \n",
+       "\n",
+       "                                                 fit_thresh_on_100  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                        0.65   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num               0.65   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                         0.65   \n",
+       "\n",
+       "                                                 fit_thresh_accuracy  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                      0.769300   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num             0.769967   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                       0.777302   \n",
+       "\n",
+       "                                                 optimal_thresh  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                     0.45   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num            0.55   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                      0.35   \n",
+       "\n",
+       "                                                 optimal_thresh_accuracy  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                          0.740595   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num                 0.767882   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                           0.775512   \n",
+       "\n",
+       "                                                 score_stdev  score_mean  \n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num              0.379947    0.411113  \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num     0.298323    0.474955  \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num               0.317359    0.363743  \n",
+       "\n",
+       "[3 rows x 70 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot_df.sort_values(COLUMN, ascending=False).head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "750f97e8-44cc-465b-a2fc-c08b626063d7",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "# Plots comparing different prompting schemes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "972c2e80-6c0e-4f37-be60-039677d2464a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Plot all models / i.t. / base?\n",
+    "IT_OR_BASE = \"any\"\n",
+    "# IT_OR_BASE = \"it\"\n",
+    "# IT_OR_BASE = \"base\"\n",
+    "\n",
+    "### Plot which metric\n",
+    "METRIC = \"ece\"\n",
+    "# METRIC = \"roc_auc\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "f42aa360-45b3-4c6d-a8f7-11f10ebacee0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting ALL models\n",
+      "model_names=['Gemma 2B', 'Gemma 7B', 'Mistral 7B', 'Llama 3 8B', 'Yi 34B', 'Mixtral 8x7B', 'Llama 3 70B', 'Mixtral 8x22B', 'Gemma 2B (it)', 'Gemma 7B (it)', 'Mistral 7B (it)', 'Llama 3 8B (it)', 'Yi 34B (it)', 'Mixtral 8x7B (it)', 'Llama 3 70B (it)', 'Mixtral 8x22B (it)', 'GPT 4o mini (it)']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prepare data\n",
+    "df = all_results_df_original.copy()\n",
+    "\n",
+    "min_max_per_task = {\n",
+    "    task: (np.min(values), np.max(values))\n",
+    "    for task in ALL_TASKS\n",
+    "    if len(values := df[(df[task_col] == task) & (~df[numeric_prompt_col].isna())][METRIC]) > 0\n",
+    "}\n",
+    "\n",
+    "# Sort models\n",
+    "sorted_df_index = sorted(\n",
+    "    df.index.tolist(),\n",
+    "    key=lambda id_: model_sort_key(id_, df),\n",
+    "    reverse=True,\n",
+    ")\n",
+    "df = df.loc[sorted_df_index]\n",
+    "\n",
+    "if IT_OR_BASE == \"it\":\n",
+    "    print(\"Plotting IT models\")\n",
+    "    df = df[df[\"is_inst\"] == True]\n",
+    "elif IT_OR_BASE == \"base\":\n",
+    "    print(\"Plotting BASE models\")\n",
+    "    df = df[df[\"is_inst\"] == False]\n",
+    "else:\n",
+    "    print(\"Plotting ALL models\")\n",
+    "    df = df.sort_values(by=[\"is_inst\", \"model_size\"], ascending=False)\n",
+    "\n",
+    "# Data for multiple-choice prompting\n",
+    "df_mult_choice = df[df[numeric_prompt_col] == False].set_index([\"name\", task_col], drop=False)\n",
+    "\n",
+    "# Data for numeric risk prompting\n",
+    "df_numeric = df[df[numeric_prompt_col] == True].set_index([\"name\", task_col], drop=False)\n",
+    "\n",
+    "# Sorted list of model names\n",
+    "model_names = df_numeric[\"name\"].unique().tolist()[::-1]\n",
+    "n_models = len(model_names)\n",
+    "print(f\"{model_names=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "91a7f4e9-6af2-44b4-a8ca-0e17cbcea1ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/ece_comparison_diff_prompts.any-models.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1133.33 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import FancyArrowPatch\n",
+    "\n",
+    "# Create a figure and axis\n",
+    "fig, axes = plt.subplots(ncols=len(ALL_TASKS), figsize=(8, n_models / 3), sharey=True)\n",
+    "\n",
+    "for task_i, task in enumerate(ALL_TASKS):\n",
+    "\n",
+    "    # Get current plot axis\n",
+    "    ax = axes[task_i]\n",
+    "        \n",
+    "    # Plot arrows for each model\n",
+    "    for i, model_name in enumerate(model_names):\n",
+    "\n",
+    "        curr_mult_choice_metric = df_mult_choice.loc[model_name, task][METRIC]\n",
+    "        curr_numeric_metric = df_numeric.loc[model_name, task][METRIC]\n",
+    "        \n",
+    "        ax.scatter(\n",
+    "            curr_mult_choice_metric, i,\n",
+    "            color=palette[0], marker=\"s\",\n",
+    "            label='Multiple-choice' if i == 0 else \"\",\n",
+    "        )\n",
+    "        ax.scatter(\n",
+    "            curr_numeric_metric, i,\n",
+    "            color=palette[1], marker=\"o\",\n",
+    "            label='Numeric' if i == 0 else \"\",\n",
+    "        )\n",
+    "\n",
+    "        # Draw a FancyArrowPatch arrow from before to after fine-tuning\n",
+    "        if METRIC == \"ece\":\n",
+    "            curr_metric_improved = curr_numeric_metric <= curr_mult_choice_metric\n",
+    "        else:\n",
+    "            curr_metric_improved = curr_numeric_metric >= curr_mult_choice_metric\n",
+    "        \n",
+    "        arrow = FancyArrowPatch(\n",
+    "            (curr_mult_choice_metric, i), (curr_numeric_metric, i),\n",
+    "            arrowstyle='->',\n",
+    "            color=palette[2] if curr_metric_improved else palette[3],\n",
+    "            mutation_scale=15,\n",
+    "            lw=1.5,\n",
+    "        )\n",
+    "        ax.add_patch(arrow)\n",
+    "\n",
+    "        # Set axes ranges according to overall min/max values\n",
+    "        lo, hi = min_max_per_task[task]\n",
+    "        if METRIC == \"ece\":\n",
+    "            lo = max(lo - 5e-2, 0)\n",
+    "            hi = min(hi + 5e-2, 0.475)\n",
+    "        elif METRIC == \"roc_auc\":\n",
+    "            lo = max(lo - 5e-2, 0.475)\n",
+    "            hi = min(hi + 4e-2, 1)\n",
+    "\n",
+    "        if task == \"ACSIncome\" and METRIC == \"roc_auc\":\n",
+    "            lo = 0.58\n",
+    "        ax.set_xlim(lo, hi)\n",
+    "    \n",
+    "    # Set y-ticks and y-labels\n",
+    "    ax.set_yticks(range(n_models))\n",
+    "    ax.set_yticklabels(model_names)\n",
+    "\n",
+    "    # # Prettify x-axis ticks\n",
+    "    # xticks = ax.get_xticks()\n",
+    "    # ax.set_xticks(xticks, [f\"{x}\" for x in xticks])\n",
+    "    \n",
+    "    # Add labels and title\n",
+    "    # ax.set_xlabel('ECE')\n",
+    "    ax.set_title(task[3:])\n",
+    "\n",
+    "    # Add horizontal line separating instruct and base models\n",
+    "    if IT_OR_BASE == \"any\":\n",
+    "        n_instruct_models = len(df_mult_choice[(df_mult_choice[\"is_inst\"] == True) & (df_mult_choice[task_col] == task)])\n",
+    "        line_y = n_instruct_models - 1.5\n",
+    "        ax.axhline(line_y, color='black', linestyle='--', linewidth=1) #, label=\"instruct / base\")\n",
+    "\n",
+    "        # Add text label near the horizontal line\n",
+    "        if task_i > len(ALL_TASKS) - 3:\n",
+    "            text_kwargs = dict(\n",
+    "                horizontalalignment='right',\n",
+    "                transform=ax.get_yaxis_transform(),  # Use axis coordinates for y-axis\n",
+    "                fontsize=9, color='black',\n",
+    "            )\n",
+    "\n",
+    "            ax.text(1, line_y + 0.1, 'i.t.', verticalalignment='bottom', **text_kwargs)\n",
+    "            ax.text(1, line_y - 0.1, 'base', verticalalignment='top', **text_kwargs)\n",
+    "        \n",
+    "\n",
+    "\n",
+    "# Add legend for marker\n",
+    "axes[0].legend(loc=\"upper right\", bbox_to_anchor=(-0.15, 0.01), title=\"Prompting scheme\")\n",
+    "\n",
+    "# X-axis label\n",
+    "if METRIC == \"ece\":\n",
+    "    x_axis_label = \"Expected Calibration Error (ECE)\"\n",
+    "elif METRIC == \"roc_auc\":\n",
+    "    x_axis_label = \"Predictive Power (ROC AUC)\"\n",
+    "\n",
+    "fig.text(0.5, 0.02 if IT_OR_BASE == \"any\" else -0.04, x_axis_label, horizontalalignment=\"center\", verticalalignment=\"center\")\n",
+    "\n",
+    "# Save figure\n",
+    "save_fig(fig=fig, name=f\"{METRIC}_comparison_diff_prompts.{IT_OR_BASE}-models.pdf\", add_prompt_suffix=False)\n",
+    "\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3db3e60-220d-4a15-bc46-c0815b39e6df",
+   "metadata": {},
+   "source": [
+    "## Scatter plot of ECE vs AUC for a few models (before and after numeric prompting)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "e77dd307-0e63-4f1d-b755-9025566e1947",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/comparison_diff_prompts.any-models.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2600x440 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_METRIC = \"ece\"\n",
+    "Y_METRIC = \"roc_auc\"\n",
+    "\n",
+    "# Filter out small models\n",
+    "df_mult_choice = df_mult_choice[df_mult_choice[\"model_size\"] > 50]\n",
+    "df_numeric = df_numeric[df_numeric[\"model_size\"] > 50]\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=len(ALL_TASKS), figsize=(len(ALL_TASKS) * 2.6, 2.2), gridspec_kw=dict(wspace=0.4))\n",
+    "\n",
+    "unique_model_names = df_mult_choice.name.unique()\n",
+    "markers = dict(zip(unique_model_names, [\"s\", \"o\", \"D\", \"P\", \"X\", \"<\", \">\"]))\n",
+    "\n",
+    "for task_i, task in enumerate(ALL_TASKS):\n",
+    "\n",
+    "    # Create a figure and axis\n",
+    "    # fig, ax = plt.subplots(figsize=(2.5, 2.5))\n",
+    "\n",
+    "    ax = axes[task_i]\n",
+    "        \n",
+    "    # Plot arrows for each model\n",
+    "    for i, model_name in enumerate(df_mult_choice.name.unique()):\n",
+    "\n",
+    "        # Get data for each plotting scheme\n",
+    "        curr_mult_choice_row = df_mult_choice.loc[model_name, task]\n",
+    "        curr_numeric_row = df_numeric.loc[model_name, task]\n",
+    "\n",
+    "        # Get points to plot\n",
+    "        curr_mult_choice_point = (curr_mult_choice_row[X_METRIC], curr_mult_choice_row[Y_METRIC])\n",
+    "        curr_numeric_point = (curr_numeric_row[X_METRIC], curr_numeric_row[Y_METRIC])\n",
+    "\n",
+    "        # Plot point for multiple-choice prompting\n",
+    "        ax.scatter(\n",
+    "            *curr_mult_choice_point,\n",
+    "            color=palette[0], marker=markers[model_name],\n",
+    "            label='Multiple-choice' if i == 0 else \"\",\n",
+    "        )\n",
+    "\n",
+    "        # Plot point for numeric-style prompting\n",
+    "        ax.scatter(\n",
+    "            *curr_numeric_point,\n",
+    "            color=palette[1], marker=markers[model_name],\n",
+    "            label='Numeric' if i == 0 else \"\",\n",
+    "        )\n",
+    "\n",
+    "        if curr_numeric_point[0] <=  curr_mult_choice_point[0] and curr_numeric_point[1] >=  curr_mult_choice_point[1]:\n",
+    "            arrow_color = palette[2]\n",
+    "        elif curr_numeric_point[0] >  curr_mult_choice_point[0] and curr_numeric_point[1] <  curr_mult_choice_point[1]:\n",
+    "            arrow_color = palette[3]\n",
+    "        else:\n",
+    "            arrow_color = \"grey\"\n",
+    "\n",
+    "        # Draw a FancyArrowPatch arrow from before to after fine-tuning\n",
+    "        arrow = FancyArrowPatch(\n",
+    "            curr_mult_choice_point, curr_numeric_point,\n",
+    "            arrowstyle='->',\n",
+    "            color=arrow_color,\n",
+    "            mutation_scale=15,\n",
+    "            lw=1,\n",
+    "            ls=\"--\",\n",
+    "        )\n",
+    "        ax.add_patch(arrow)\n",
+    "\n",
+    "    ### Plot configs\n",
+    "    # Add labels and title\n",
+    "    ax.set_title(task[3:])\n",
+    "    ax.set_xlabel(\"ECE\")\n",
+    "    if task_i == 0:\n",
+    "        ax.set_ylabel(\"AUC\")\n",
+    "\n",
+    "# Create custom global legend\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "# Create a global legend for markers (model names)\n",
+    "marker_handles = [Line2D([0], [0], marker=markers[model], color='w', markerfacecolor='black', markersize=8, label=model) for model in unique_model_names]\n",
+    "\n",
+    "# Create a global legend for colors (prompting schemes)\n",
+    "color_handles = [\n",
+    "    Line2D([0], [0], marker='o', color='w', markerfacecolor=palette[0], markersize=8, label=\"Multiple-choice\"),\n",
+    "    Line2D([0], [0], marker='o', color='w', markerfacecolor=palette[1], markersize=8, label=\"Numeric\")\n",
+    "]\n",
+    "\n",
+    "# Position the legends outside the subplots\n",
+    "fig.legend(handles=marker_handles, title=\"Models\", loc='upper left', bbox_to_anchor=(0.13, -0.12), ncol=min(4, len(unique_model_names)))\n",
+    "fig.legend(handles=color_handles, title=\"Prompting Scheme\", loc='upper right', bbox_to_anchor=(0.9, -0.12), ncol=2)\n",
+    "\n",
+    "# Save figure\n",
+    "save_fig(fig=fig, name=f\"comparison_diff_prompts.{IT_OR_BASE}-models.pdf\", add_prompt_suffix=False)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/notebooks/parse-acs-results.ipynb.txt b/_sources/notebooks/parse-acs-results.ipynb.txt
new file mode 100644
index 0000000..6b99e2d
--- /dev/null
+++ b/_sources/notebooks/parse-acs-results.ipynb.txt
@@ -0,0 +1,589 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "04c2f12d-f989-4ac9-b90f-3464a8bcca96",
+   "metadata": {},
+   "source": [
+    "# Fetch and parse ACS benchmark results under a given directory\n",
+    "Each ACS benchmark run outputs a json file. This script collects all such files under a given root directory, parses them, and aggregates them into a more easily digestable pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b241208-d10f-43cf-a486-84c54bbf43c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faf6afa4-8648-4d35-9312-65636ea5d0b2",
+   "metadata": {},
+   "source": [
+    "**[Action required]** Set `RESULTS_ROOT_DIR` to the root results directory path:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26089a60-81c0-4736-8ba5-99572ec01398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RESULTS_ROOT_DIR = Path(\"/fast/groups/sf\") / \"folktexts-results\" / \"2024-08-28_2\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d1af7f-d013-40a0-80de-cb9ad65cff6d",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root data directory (needed only to train baseline ML methods):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b18dad87-a1ed-495b-93b6-4c8af4043ddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db7c98c5-2942-4f88-984a-8c9014afe761",
+   "metadata": {},
+   "source": [
+    "Important results columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e96f0c4f-5683-4150-8cca-93a883f20154",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_col = \"config_model_name\"\n",
+    "task_col = \"config_task_name\"\n",
+    "numeric_prompt_col = \"config_numeric_risk_prompting\"\n",
+    "\n",
+    "feature_subset_col = \"config_feature_subset\"\n",
+    "predictions_path_col = \"predictions_path\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4296ffdf-a9af-43f7-bd0c-20f4ae5f9619",
+   "metadata": {},
+   "source": [
+    "Helper function to parse each dictionary containing benchmark results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0cf4a6c8-ec28-4e66-9f6d-8a4e977d7b60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import (\n",
+    "    num_features_helper,\n",
+    "    parse_model_name,\n",
+    "    get_non_instruction_tuned_name,\n",
+    "    prettify_model_name,\n",
+    ")\n",
+    "\n",
+    "def parse_results_dict(dct) -> dict:\n",
+    "    \"\"\"Parses results dict and brings all information to the top-level.\"\"\"\n",
+    "\n",
+    "    # Make a copy so we don't modify the input object\n",
+    "    dct = dct.copy()\n",
+    "\n",
+    "    # Discard plots' paths\n",
+    "    dct.pop(\"plots\", None)\n",
+    "\n",
+    "    # Bring configs to top-level\n",
+    "    config = dct.pop(\"config\", {})\n",
+    "    for key, val in config.items():\n",
+    "        dct[f\"config_{key}\"] = val\n",
+    "\n",
+    "    # Parse model name\n",
+    "    dct[model_col] = parse_model_name(dct[model_col])\n",
+    "    dct[\"base_name\"] = get_non_instruction_tuned_name(dct[model_col])\n",
+    "    dct[\"name\"] = prettify_model_name(dct[model_col])\n",
+    "\n",
+    "    # Is instruction-tuned model?\n",
+    "    dct[\"is_inst\"] = dct[\"base_name\"] != dct[model_col] or \"(it)\" in dct[\"name\"].lower()\n",
+    "\n",
+    "    # Log number of features\n",
+    "    dct[\"num_features\"] = num_features_helper(dct[feature_subset_col], max_features_return=-1)\n",
+    "    dct[\"uses_all_features\"] = (dct[feature_subset_col] is None) or (dct[\"num_features\"] == -1)\n",
+    "\n",
+    "    if dct[feature_subset_col] is None:\n",
+    "        dct[feature_subset_col] = \"full\"\n",
+    "\n",
+    "    # Assert all results are at the top-level\n",
+    "    assert not any(isinstance(val, dict) for val in dct.values())\n",
+    "    return dct\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02009a86-e099-4ec0-8676-8d66190ceddb",
+   "metadata": {},
+   "source": [
+    "Iteratively search the root directory for results files matching the given regex:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aaefa99d-dbd7-40a3-a3c1-7571d3811409",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b984f67ca7644ba998983af5385ba147",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 241 benchmark results.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils import find_files, load_json\n",
+    "\n",
+    "# Results file name pattern\n",
+    "pattern = r'^results.bench-(?P<hash>\\d+)[.]json$'\n",
+    "\n",
+    "# Find results files and aggregate\n",
+    "results = {}\n",
+    "for file_path in tqdm(find_files(RESULTS_ROOT_DIR, pattern)):\n",
+    "    results[Path(file_path).parent.name] = parse_results_dict(load_json(file_path))\n",
+    "\n",
+    "if len(results) == 0:\n",
+    "    raise RuntimeError(f\"Couldn't find any results at {RESULTS_ROOT_DIR}\")\n",
+    "else:\n",
+    "    print(f\"Found {len(results)} benchmark results.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ecc0d33-902d-492a-89c6-d9729fe69fa1",
+   "metadata": {},
+   "source": [
+    "Aggregate results into a single DataFrame, generate a unique identifier for each row, and drop potential duplicates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "94f1900f-fea3-4872-b722-ee1cd3f5f7e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.shape=(241, 58)\n",
+      "Dropping 31 duplicates!\n",
+      "df.shape=(210, 58)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(list(results.values()))\n",
+    "\n",
+    "def row_id(row) -> str:\n",
+    "    \"\"\"Unique row identifier.\"\"\"\n",
+    "    numeric_or_multiple_choice = \"Num\" if row[numeric_prompt_col] else \"QA\"\n",
+    "    return f\"{row[model_col]}__{row[task_col]}__{row['num_features']}__{numeric_or_multiple_choice}\"\n",
+    "\n",
+    "print(f\"{df.shape=}\")\n",
+    "df[\"id\"] = df.apply(row_id, axis=1)\n",
+    "\n",
+    "# Drop duplicates\n",
+    "len_with_dups = len(df)\n",
+    "df = df.drop_duplicates(subset=[\"name\", \"is_inst\", \"num_features\", task_col, numeric_prompt_col])\n",
+    "df = df.set_index(\"id\", drop=True, verify_integrity=True)\n",
+    "\n",
+    "if len_with_dups != len(df):\n",
+    "    print(f\"Dropping {len_with_dups - len(df)} duplicates!\")\n",
+    "    print(f\"{df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97cd24df-3c1f-49c0-bb50-a6fdba22e0fb",
+   "metadata": {},
+   "source": [
+    "Load scores DFs and analyze score distribution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "43ba0fbf-b886-4f2a-8a01-564aa6f0f2c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_model_scores_df(df_row: pd.Series) -> pd.DataFrame:\n",
+    "    \"\"\"Loads csv containing model scores corresponding to the given DF row.\"\"\"\n",
+    "    if predictions_path_col in df_row and not pd.isna(df_row[predictions_path_col]):\n",
+    "        return pd.read_csv(df_row[predictions_path_col], index_col=0)\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "54e22896-1d3d-4d95-bb90-2b453f58f09a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64f4e6718312499b84fb8c9637ff10fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/210 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from folktexts.evaluation import compute_best_threshold\n",
+    "from sklearn import metrics\n",
+    "from scipy import stats\n",
+    "\n",
+    "# Number of samples used to fit the one-parameter binarization threshold!\n",
+    "N = 100\n",
+    "\n",
+    "fit_thresh_col = f\"fit_thresh_on_{N}\"\n",
+    "fit_acc_col = f\"fit_thresh_accuracy\"\n",
+    "\n",
+    "optimal_thres_col = \"optimal_thresh\"\n",
+    "optimal_acc_col = \"optimal_thresh_accuracy\"\n",
+    "\n",
+    "score_stdev_col = \"score_stdev\"\n",
+    "score_mean_col = \"score_mean\"\n",
+    "\n",
+    "scores_stats = dict()\n",
+    "for row_id, row in tqdm(df.iterrows(), total=len(df)):\n",
+    "\n",
+    "    # Load model scores\n",
+    "    scores_df = load_model_scores_df(row)\n",
+    "    if scores_df is None:\n",
+    "        logging.error(f\"Couldn't find scores for {row_id}\")\n",
+    "\n",
+    "    # Extract scores and labels\n",
+    "    risk_scores = scores_df[\"risk_score\"].to_numpy()\n",
+    "    labels = scores_df[\"label\"].to_numpy()\n",
+    "\n",
+    "    # Sample N rows to fit threshold\n",
+    "    scores_df_sample = scores_df.sample(n=N, random_state=42)\n",
+    "\n",
+    "    # Compute optimal threshold on each data sample\n",
+    "    fit_thr = compute_best_threshold(y_true=scores_df_sample[\"label\"], y_pred_scores=scores_df_sample[\"risk_score\"])\n",
+    "    opt_thr = compute_best_threshold(y_true=labels, y_pred_scores=risk_scores)\n",
+    "\n",
+    "    # Evaluate accuracy\n",
+    "    fit_acc = metrics.accuracy_score(labels, (risk_scores >= fit_thr).astype(int))\n",
+    "    opt_acc = metrics.accuracy_score(labels, (risk_scores >= opt_thr).astype(int))\n",
+    "\n",
+    "    # Save results\n",
+    "    scores_stats[row_id] = {\n",
+    "        fit_thresh_col: fit_thr,\n",
+    "        fit_acc_col: fit_acc,\n",
+    "        optimal_thres_col: opt_thr,\n",
+    "        optimal_acc_col: opt_acc,\n",
+    "        score_stdev_col: np.std(risk_scores),\n",
+    "        score_mean_col: np.mean(risk_scores),\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1a58b87-2de8-473a-badb-934951d1bcdc",
+   "metadata": {},
+   "source": [
+    "Update results DF with scores statistics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0ba74c81-c008-4010-a9a3-1b511ae445df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scores_stats_df.shape=(210, 6)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>gemma-2-27b__ACSTravelTime__-1__Num</th>\n",
+       "      <td>0.539392</td>\n",
+       "      <td>0.258000</td>\n",
+       "      <td>0.656000</td>\n",
+       "      <td>0.504500</td>\n",
+       "      <td>0.100184</td>\n",
+       "      <td>0.823204</td>\n",
+       "      <td>0.388077</td>\n",
+       "      <td>0.347071</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.221264</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Gemma 2 27B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.13000</td>\n",
+       "      <td>0.514676</td>\n",
+       "      <td>0.210000</td>\n",
+       "      <td>0.524692</td>\n",
+       "      <td>0.311014</td>\n",
+       "      <td>0.219864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gemma-1.1-2b-it__ACSEmployment__-1__QA</th>\n",
+       "      <td>0.462874</td>\n",
+       "      <td>0.235384</td>\n",
+       "      <td>0.631881</td>\n",
+       "      <td>0.427372</td>\n",
+       "      <td>0.091640</td>\n",
+       "      <td>0.800718</td>\n",
+       "      <td>0.408398</td>\n",
+       "      <td>0.382143</td>\n",
+       "      <td>0.376308</td>\n",
+       "      <td>0.212855</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Gemma 2B (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.02033</td>\n",
+       "      <td>0.482054</td>\n",
+       "      <td>0.007577</td>\n",
+       "      <td>0.485395</td>\n",
+       "      <td>0.232183</td>\n",
+       "      <td>0.216562</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 64 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        accuracy  accuracy_diff  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num     0.539392       0.258000   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA  0.462874       0.235384   \n",
+       "\n",
+       "                                        accuracy_ratio  balanced_accuracy  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num           0.656000           0.504500   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA        0.631881           0.427372   \n",
+       "\n",
+       "                                        balanced_accuracy_diff  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num                   0.100184   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA                0.091640   \n",
+       "\n",
+       "                                        balanced_accuracy_ratio  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num                    0.823204   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA                 0.800718   \n",
+       "\n",
+       "                                        brier_score_loss       ece  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num             0.388077  0.347071   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA          0.408398  0.382143   \n",
+       "\n",
+       "                                        ece_quantile  equalized_odds_diff  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num              NaN             0.221264   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA      0.376308             0.212855   \n",
+       "\n",
+       "                                        ...           name  is_inst  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num     ...    Gemma 2 27B    False   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA  ...  Gemma 2B (it)     True   \n",
+       "\n",
+       "                                        num_features  uses_all_features  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num               -1               True   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA            -1               True   \n",
+       "\n",
+       "                                        fit_thresh_on_100  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num               0.13000   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA            0.02033   \n",
+       "\n",
+       "                                        fit_thresh_accuracy  optimal_thresh  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num                0.514676        0.210000   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA             0.482054        0.007577   \n",
+       "\n",
+       "                                        optimal_thresh_accuracy score_stdev  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num                    0.524692    0.311014   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA                 0.485395    0.232183   \n",
+       "\n",
+       "                                        score_mean  \n",
+       "gemma-2-27b__ACSTravelTime__-1__Num       0.219864  \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA    0.216562  \n",
+       "\n",
+       "[2 rows x 64 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_stats_df = pd.DataFrame(scores_stats.values(), index=list(scores_stats.keys()))\n",
+    "print(f\"{scores_stats_df.shape=}\")\n",
+    "\n",
+    "results_df = pd.concat((df, scores_stats_df), axis=\"columns\")\n",
+    "results_df.sample(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d04ff64-39c4-43cc-9df2-fa4e099c4e56",
+   "metadata": {},
+   "source": [
+    "Check if any results are missing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f1faf926-de0f-442e-baf7-c3029f5386e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiments_per_model_task_pair = results_df.groupby([model_col, task_col]).nunique().max(axis=None)\n",
+    "\n",
+    "for m in results_df[model_col].unique():\n",
+    "    for t in results_df[task_col].unique():\n",
+    "        match_ = results_df[(results_df[model_col] == m) & (results_df[task_col] == t)]\n",
+    "        if len(match_) < experiments_per_model_task_pair:\n",
+    "            print(f\"Couldn't find all results for m={m}, t={t} ({len(match_)} < {experiments_per_model_task_pair})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6e859fc-9d82-436d-a238-11fd008da44c",
+   "metadata": {},
+   "source": [
+    "Finally, save results DF to the results root directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9725afa2-b6f8-46b1-86e7-57ae82ef05d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import get_current_timestamp\n",
+    "results_df.to_csv(Path(RESULTS_ROOT_DIR) / f\"aggregated_results.{get_current_timestamp()}.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3a6beb5-b44b-4f81-ae1a-d027afb2c5f4",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/notebooks/parse-acs-results_using-feature-subsets.ipynb.txt b/_sources/notebooks/parse-acs-results_using-feature-subsets.ipynb.txt
new file mode 100644
index 0000000..2ca3445
--- /dev/null
+++ b/_sources/notebooks/parse-acs-results_using-feature-subsets.ipynb.txt
@@ -0,0 +1,1275 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "04c2f12d-f989-4ac9-b90f-3464a8bcca96",
+   "metadata": {},
+   "source": [
+    "# Fetch and parse ACS benchmark results under a given directory\n",
+    "Each ACS benchmark run outputs a json file. This script collects all such files under a given root directory, parses them, and aggregates them into a more easily digestable pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b241208-d10f-43cf-a486-84c54bbf43c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faf6afa4-8648-4d35-9312-65636ea5d0b2",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root results directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26089a60-81c0-4736-8ba5-99572ec01398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RESULTS_ROOT_DIR = Path(\"/fast/groups/sf\") / \"folktexts-results\" / \"2024-07-03_feature-subsets\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d1af7f-d013-40a0-80de-cb9ad65cff6d",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root data directory (needed only to train baseline ML methods):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b18dad87-a1ed-495b-93b6-4c8af4043ddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db7c98c5-2942-4f88-984a-8c9014afe761",
+   "metadata": {},
+   "source": [
+    "Important results columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e96f0c4f-5683-4150-8cca-93a883f20154",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_col = \"config_model_name\"\n",
+    "task_col = \"config_task_name\"\n",
+    "\n",
+    "feature_subset_col = \"config_feature_subset\"\n",
+    "predictions_path_col = \"predictions_path\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4296ffdf-a9af-43f7-bd0c-20f4ae5f9619",
+   "metadata": {},
+   "source": [
+    "Helper function to parse each dictionary containing benchmark results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0cf4a6c8-ec28-4e66-9f6d-8a4e977d7b60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import (\n",
+    "    num_features_helper,\n",
+    "    parse_model_name,\n",
+    "    get_non_instruction_tuned_name,\n",
+    "    prettify_model_name,\n",
+    ")\n",
+    "\n",
+    "def parse_results_dict(dct) -> dict:\n",
+    "    \"\"\"Parses results dict and brings all information to the top-level.\"\"\"\n",
+    "\n",
+    "    # Make a copy so we don't modify the input object\n",
+    "    dct = dct.copy()\n",
+    "\n",
+    "    # Discard plots' paths\n",
+    "    dct.pop(\"plots\", None)\n",
+    "\n",
+    "    # Bring configs to top-level\n",
+    "    config = dct.pop(\"config\", {})\n",
+    "    for key, val in config.items():\n",
+    "        dct[f\"config_{key}\"] = val\n",
+    "\n",
+    "    # Parse model name\n",
+    "    dct[model_col] = parse_model_name(dct[model_col])\n",
+    "    dct[\"base_name\"] = get_non_instruction_tuned_name(dct[model_col])\n",
+    "    dct[\"name\"] = prettify_model_name(dct[model_col])\n",
+    "\n",
+    "    # Is instruction-tuned model?\n",
+    "    dct[\"is_inst\"] = dct[\"base_name\"] != dct[model_col]\n",
+    "\n",
+    "    # Log number of features\n",
+    "    dct[\"num_features\"] = num_features_helper(dct[feature_subset_col], max_features_return=-1)\n",
+    "    dct[\"uses_all_features\"] = (dct[feature_subset_col] is None) or (dct[\"num_features\"] == -1)\n",
+    "\n",
+    "    if dct[feature_subset_col] is None:\n",
+    "        dct[feature_subset_col] = \"full\"\n",
+    "\n",
+    "    # Assert all results are at the top-level\n",
+    "    assert not any(isinstance(val, dict) for val in dct.values())\n",
+    "    return dct\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02009a86-e099-4ec0-8676-8d66190ceddb",
+   "metadata": {},
+   "source": [
+    "Iteratively search the root directory for results files matching the given regex:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aaefa99d-dbd7-40a3-a3c1-7571d3811409",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3293cf3c9da64e4eaab95a251b59aad5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 195 benchmark results.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils import find_files, load_json\n",
+    "\n",
+    "# Results file name pattern\n",
+    "pattern = r'^results.bench-(?P<hash>\\d+)[.]json$'\n",
+    "\n",
+    "# Find results files and aggregate\n",
+    "results = {}\n",
+    "for file_path in tqdm(find_files(RESULTS_ROOT_DIR, pattern)):\n",
+    "    results[Path(file_path).parent.name] = parse_results_dict(load_json(file_path))\n",
+    "\n",
+    "if len(results) == 0:\n",
+    "    raise RuntimeError(f\"Couldn't find any results at {RESULTS_ROOT_DIR}\")\n",
+    "else:\n",
+    "    print(f\"Found {len(results)} benchmark results.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ecc0d33-902d-492a-89c6-d9729fe69fa1",
+   "metadata": {},
+   "source": [
+    "Aggregate results into a single DataFrame, and generate a unique identifier for each row:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "94f1900f-fea3-4872-b722-ee1cd3f5f7e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.shape=(195, 58)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>config_population_filter</th>\n",
+       "      <th>config_reuse_few_shot_examples</th>\n",
+       "      <th>config_seed</th>\n",
+       "      <th>config_task_hash</th>\n",
+       "      <th>config_task_name</th>\n",
+       "      <th>base_name</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX__7</th>\n",
+       "      <td>0.630994</td>\n",
+       "      <td>0.281751</td>\n",
+       "      <td>0.663274</td>\n",
+       "      <td>0.500705</td>\n",
+       "      <td>0.007359</td>\n",
+       "      <td>0.985495</td>\n",
+       "      <td>0.215502</td>\n",
+       "      <td>0.111277</td>\n",
+       "      <td>0.116464</td>\n",
+       "      <td>0.031550</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>42</td>\n",
+       "      <td>591377254</td>\n",
+       "      <td>ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX</td>\n",
+       "      <td>Mixtral-8x22B-v0.1</td>\n",
+       "      <td>Mixtral 8x22B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>7</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Meta-Llama-3-8B__ACSIncome_RAC1P__1</th>\n",
+       "      <td>0.369300</td>\n",
+       "      <td>0.286688</td>\n",
+       "      <td>0.362849</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.279789</td>\n",
+       "      <td>0.220760</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>42</td>\n",
+       "      <td>664576746</td>\n",
+       "      <td>ACSIncome_RAC1P</td>\n",
+       "      <td>Meta-Llama-3-8B</td>\n",
+       "      <td>Llama 3 8B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX__7</th>\n",
+       "      <td>0.477681</td>\n",
+       "      <td>0.190822</td>\n",
+       "      <td>0.677577</td>\n",
+       "      <td>0.508865</td>\n",
+       "      <td>0.210830</td>\n",
+       "      <td>0.678362</td>\n",
+       "      <td>0.392382</td>\n",
+       "      <td>0.354748</td>\n",
+       "      <td>0.353959</td>\n",
+       "      <td>0.294444</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>42</td>\n",
+       "      <td>591377254</td>\n",
+       "      <td>ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX</td>\n",
+       "      <td>gemma-2-27b</td>\n",
+       "      <td>Gemma 2 27B (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 58 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    accuracy  accuracy_diff  \\\n",
+       "id                                                                            \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...  0.630994       0.281751   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                 0.369300       0.286688   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...  0.477681       0.190822   \n",
+       "\n",
+       "                                                    accuracy_ratio  \\\n",
+       "id                                                                   \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...        0.663274   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                       0.362849   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...        0.677577   \n",
+       "\n",
+       "                                                    balanced_accuracy  \\\n",
+       "id                                                                      \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...           0.500705   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                          0.500000   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...           0.508865   \n",
+       "\n",
+       "                                                    balanced_accuracy_diff  \\\n",
+       "id                                                                           \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...                0.007359   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                               0.000000   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...                0.210830   \n",
+       "\n",
+       "                                                    balanced_accuracy_ratio  \\\n",
+       "id                                                                            \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...                 0.985495   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                                1.000000   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...                 0.678362   \n",
+       "\n",
+       "                                                    brier_score_loss  \\\n",
+       "id                                                                     \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...          0.215502   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                         0.279789   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...          0.392382   \n",
+       "\n",
+       "                                                         ece  ece_quantile  \\\n",
+       "id                                                                           \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...  0.111277      0.116464   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                 0.220760           NaN   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...  0.354748      0.353959   \n",
+       "\n",
+       "                                                    equalized_odds_diff  ...  \\\n",
+       "id                                                                       ...   \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...             0.031550  ...   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                            0.000000  ...   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...             0.294444  ...   \n",
+       "\n",
+       "                                                    config_population_filter  \\\n",
+       "id                                                                             \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...                      None   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                                     None   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...                      None   \n",
+       "\n",
+       "                                                    config_reuse_few_shot_examples  \\\n",
+       "id                                                                                   \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...                           False   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                                          False   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...                           False   \n",
+       "\n",
+       "                                                    config_seed  \\\n",
+       "id                                                                \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...           42   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                          42   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...           42   \n",
+       "\n",
+       "                                                    config_task_hash  \\\n",
+       "id                                                                     \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...         591377254   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                        664576746   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...         591377254   \n",
+       "\n",
+       "                                                                              config_task_name  \\\n",
+       "id                                                                                               \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...  ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                                            ACSIncome_RAC1P   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...  ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX   \n",
+       "\n",
+       "                                                             base_name  \\\n",
+       "id                                                                       \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...  Mixtral-8x22B-v0.1   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                    Meta-Llama-3-8B   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...         gemma-2-27b   \n",
+       "\n",
+       "                                                                name  is_inst  \\\n",
+       "id                                                                              \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...     Mixtral 8x22B    False   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                       Llama 3 8B    False   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...  Gemma 2 27B (it)     True   \n",
+       "\n",
+       "                                                   num_features  \\\n",
+       "id                                                                \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...            7   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                           1   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...            7   \n",
+       "\n",
+       "                                                    uses_all_features  \n",
+       "id                                                                     \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...              False  \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                             False  \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...              False  \n",
+       "\n",
+       "[3 rows x 58 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(list(results.values()))\n",
+    "\n",
+    "def row_id(row) -> str:\n",
+    "    \"\"\"Unique row identifier.\"\"\"\n",
+    "    return f\"{row[model_col]}__{row[task_col]}__{row['num_features']}\"\n",
+    "\n",
+    "df[\"id\"] = df.apply(row_id, axis=1)\n",
+    "df = df.set_index(\"id\", drop=True, verify_integrity=True)\n",
+    "\n",
+    "print(f\"{df.shape=}\")\n",
+    "df.sample(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fd59aee-3802-4430-a977-243dd44f8f62",
+   "metadata": {},
+   "source": [
+    "Drop potential duplicates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "69ef57de-0059-4c40-9eae-fb4e6b3bf582",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parsed_df = df.drop_duplicates(subset=[\"name\", \"is_inst\", \"num_features\", task_col])\n",
+    "if len(parsed_df) != len(df):\n",
+    "    print(f\"Found {len(df) - len(parsed_df)} duplicates! dropping rows...\")\n",
+    "    df = parsed_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97cd24df-3c1f-49c0-bb50-a6fdba22e0fb",
+   "metadata": {},
+   "source": [
+    "Load scores DFs and analyze score distribution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "43ba0fbf-b886-4f2a-8a01-564aa6f0f2c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_model_scores_df(df_row: pd.Series) -> pd.DataFrame:\n",
+    "    \"\"\"Loads csv containing model scores corresponding to the given DF row.\"\"\"\n",
+    "    if predictions_path_col in df_row and not pd.isna(df_row[predictions_path_col]):\n",
+    "        return pd.read_csv(df_row[predictions_path_col], index_col=0)\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9bc88155-08a3-4cc2-bb8d-aaec96350f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "133e0fbc9ecc4262990da41e34a12425",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/195 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scores_df_map = {\n",
+    "    id_: load_model_scores_df(row)\n",
+    "    for id_, row in tqdm(parsed_df.iterrows(), total=len(parsed_df))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "54e22896-1d3d-4d95-bb90-2b453f58f09a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2325fd86cbdc436e84da4bc8be53fd98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/195 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from folktexts.evaluation import compute_best_threshold\n",
+    "from sklearn import metrics\n",
+    "from scipy import stats\n",
+    "\n",
+    "# Number of samples used to fit the one-parameter binarization threshold!\n",
+    "N = 100\n",
+    "\n",
+    "fit_thresh_col = f\"fit_thresh_on_{N}\"\n",
+    "fit_acc_col = f\"fit_thresh_accuracy\"\n",
+    "\n",
+    "optimal_thres_col = \"optimal_thresh\"\n",
+    "optimal_acc_col = \"optimal_thresh_accuracy\"\n",
+    "\n",
+    "score_stdev_col = \"score_stdev\"\n",
+    "score_mean_col = \"score_mean\"\n",
+    "\n",
+    "scores_stats = dict()\n",
+    "for row_id, row in tqdm(df.iterrows(), total=len(df)):\n",
+    "\n",
+    "    # Load model scores\n",
+    "    scores_df = scores_df_map[row_id]\n",
+    "    if scores_df is None:\n",
+    "        logging.error(f\"Couldn't find scores for {row_id}\")\n",
+    "\n",
+    "    # Extract scores and labels\n",
+    "    risk_scores = scores_df[\"risk_score\"].to_numpy()\n",
+    "    labels = scores_df[\"label\"].to_numpy()\n",
+    "\n",
+    "    # Sample N rows to fit threshold\n",
+    "    scores_df_sample = scores_df.sample(n=N, random_state=42)\n",
+    "\n",
+    "    # Compute optimal threshold on each data sample\n",
+    "    fit_thr = compute_best_threshold(y_true=scores_df_sample[\"label\"], y_pred_scores=scores_df_sample[\"risk_score\"])\n",
+    "    opt_thr = compute_best_threshold(y_true=labels, y_pred_scores=risk_scores)\n",
+    "\n",
+    "    # Evaluate accuracy\n",
+    "    fit_acc = metrics.accuracy_score(labels, (risk_scores >= fit_thr).astype(int))\n",
+    "    opt_acc = metrics.accuracy_score(labels, (risk_scores >= opt_thr).astype(int))\n",
+    "\n",
+    "    # Save results\n",
+    "    scores_stats[row_id] = {\n",
+    "        fit_thresh_col: fit_thr,\n",
+    "        fit_acc_col: fit_acc,\n",
+    "        optimal_thres_col: opt_thr,\n",
+    "        optimal_acc_col: opt_acc,\n",
+    "        score_stdev_col: np.std(risk_scores),\n",
+    "        score_mean_col: np.mean(risk_scores),\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1a58b87-2de8-473a-badb-934951d1bcdc",
+   "metadata": {},
+   "source": [
+    "Update results DF with scores statistics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0ba74c81-c008-4010-a9a3-1b511ae445df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4</th>\n",
+       "      <td>0.567786</td>\n",
+       "      <td>0.183621</td>\n",
+       "      <td>0.743500</td>\n",
+       "      <td>0.636874</td>\n",
+       "      <td>0.105719</td>\n",
+       "      <td>0.843667</td>\n",
+       "      <td>0.273436</td>\n",
+       "      <td>0.237165</td>\n",
+       "      <td>0.237165</td>\n",
+       "      <td>0.630823</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yi 34B (chat)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.531214</td>\n",
+       "      <td>0.582998</td>\n",
+       "      <td>0.562204</td>\n",
+       "      <td>0.597789</td>\n",
+       "      <td>0.192288</td>\n",
+       "      <td>0.606465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6</th>\n",
+       "      <td>0.422379</td>\n",
+       "      <td>0.183270</td>\n",
+       "      <td>0.609219</td>\n",
+       "      <td>0.538078</td>\n",
+       "      <td>0.085607</td>\n",
+       "      <td>0.857950</td>\n",
+       "      <td>0.303729</td>\n",
+       "      <td>0.306175</td>\n",
+       "      <td>0.306175</td>\n",
+       "      <td>0.232553</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yi 34B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>6</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.731161</td>\n",
+       "      <td>0.659718</td>\n",
+       "      <td>0.679091</td>\n",
+       "      <td>0.610856</td>\n",
+       "      <td>0.117468</td>\n",
+       "      <td>0.675475</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 64 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  accuracy  accuracy_diff  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4     0.567786       0.183621   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6  0.422379       0.183270   \n",
+       "\n",
+       "                                                  accuracy_ratio  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4           0.743500   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6        0.609219   \n",
+       "\n",
+       "                                                  balanced_accuracy  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4              0.636874   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6           0.538078   \n",
+       "\n",
+       "                                                  balanced_accuracy_diff  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                   0.105719   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6                0.085607   \n",
+       "\n",
+       "                                                  balanced_accuracy_ratio  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                    0.843667   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6                 0.857950   \n",
+       "\n",
+       "                                                  brier_score_loss       ece  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4             0.273436  0.237165   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6          0.303729  0.306175   \n",
+       "\n",
+       "                                                  ece_quantile  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4         0.237165   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6      0.306175   \n",
+       "\n",
+       "                                                  equalized_odds_diff  ...  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                0.630823  ...   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6             0.232553  ...   \n",
+       "\n",
+       "                                                           name  is_inst  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4     Yi 34B (chat)     True   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6         Yi 34B    False   \n",
+       "\n",
+       "                                                  num_features  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                4   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6             6   \n",
+       "\n",
+       "                                                  uses_all_features  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                 False   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6              False   \n",
+       "\n",
+       "                                                  fit_thresh_on_100  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4              0.531214   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6           0.731161   \n",
+       "\n",
+       "                                                  fit_thresh_accuracy  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                0.582998   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6             0.659718   \n",
+       "\n",
+       "                                                  optimal_thresh  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4           0.562204   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6        0.679091   \n",
+       "\n",
+       "                                                  optimal_thresh_accuracy  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                    0.597789   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6                 0.610856   \n",
+       "\n",
+       "                                                 score_stdev  score_mean  \n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4       0.192288    0.606465  \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6    0.117468    0.675475  \n",
+       "\n",
+       "[2 rows x 64 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_stats_df = pd.DataFrame(scores_stats.values(), index=list(scores_stats.keys()))\n",
+    "\n",
+    "results_df = pd.concat((df, scores_stats_df), axis=\"columns\")\n",
+    "results_df.sample(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d04ff64-39c4-43cc-9df2-fa4e099c4e56",
+   "metadata": {},
+   "source": [
+    "Check if any results are missing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "dbb3b379-7697-4b54-a33d-09d503210f87",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "config_model_name\n",
+      "Meta-Llama-3-70B                9\n",
+      "Meta-Llama-3-70B-Instruct       9\n",
+      "Meta-Llama-3-8B                10\n",
+      "Meta-Llama-3-8B-Instruct       10\n",
+      "Mistral-7B-Instruct-v0.2       10\n",
+      "Mistral-7B-v0.1                10\n",
+      "Mixtral-8x22B-Instruct-v0.1     9\n",
+      "Mixtral-8x22B-v0.1              9\n",
+      "Mixtral-8x7B-Instruct-v0.1      9\n",
+      "Mixtral-8x7B-v0.1              10\n",
+      "Yi-34B                         10\n",
+      "Yi-34B-Chat                    10\n",
+      "gemma-1.1-2b-it                10\n",
+      "gemma-1.1-7b-it                10\n",
+      "gemma-2-27b                    10\n",
+      "gemma-2-27b-it                 10\n",
+      "gemma-2-9b                     10\n",
+      "gemma-2-9b-it                  10\n",
+      "gemma-2b                       10\n",
+      "gemma-7b                       10\n",
+      "Name: config_task_name, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(results_df.groupby([model_col])[task_col].count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6e859fc-9d82-436d-a238-11fd008da44c",
+   "metadata": {},
+   "source": [
+    "Finally, save results DF to the results root directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9725afa2-b6f8-46b1-86e7-57ae82ef05d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import get_current_timestamp\n",
+    "results_df.to_csv(Path(RESULTS_ROOT_DIR) / f\"aggregated_results.{get_current_timestamp()}.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3a6beb5-b44b-4f81-ae1a-d027afb2c5f4",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5d721ce1-f3f3-4a56-a2fa-fb63f652128d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1f77b4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff7f0e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2ca02c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d62728;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8c564b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e377c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7f7f7f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bcbd22;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#17becf;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
+      ],
+      "text/plain": [
+       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
+       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
+       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
+       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
+       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
+       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
+       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
+       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"whitegrid\", rc={\"grid.linestyle\": \"--\"})\n",
+    "plt.rc(\"figure\", figsize=(4.5, 3.5), dpi=200)\n",
+    "\n",
+    "palette_name = \"tab10\"   # \"colorblind\"\n",
+    "palette = sns.color_palette(palette_name)\n",
+    "sns.set_palette(palette_name)\n",
+    "palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "919690a2-8f87-451b-b778-0740127cd135",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMGS_DIR = RESULTS_ROOT_DIR / \"imgs\"\n",
+    "IMGS_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "def save_fig(fig, name):\n",
+    "    fig.savefig((IMGS_DIR / name).with_suffix(\".pdf\"), bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5d64604-8c0e-40f9-8130-fca5fff63194",
+   "metadata": {},
+   "source": [
+    "## Score distributions of an example model with increasing evidence\n",
+    "Score distribution should get more discriminative (less Gaussian-shaped) with increasing evidence.,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2742c1eb-6440-4ffe-b2b2-25886902976e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EXAMPLE_MODEL = \"Meta-Llama-3-70B-Instruct\"\n",
+    "# EXAMPLE_MODEL = \"Meta-Llama-3-70B\"\n",
+    "EXAMPLE_MODEL = \"Mixtral-8x7B-Instruct-v0.1\"\n",
+    "# EXAMPLE_MODEL = \"Mixtral-8x7B-v0.1\"\n",
+    "# EXAMPLE_MODEL = \"Mistral-7B-v0.1\"\n",
+    "# EXAMPLE_MODEL = \"Mistral-7B-Instruct-v0.2\"\n",
+    "# EXAMPLE_MODEL = \"Yi-34B\"\n",
+    "# EXAMPLE_MODEL = \"Yi-34B-Chat\"\n",
+    "# EXAMPLE_MODEL = \"Meta-Llama-3-8B\"\n",
+    "# EXAMPLE_MODEL = \"Meta-Llama-3-8B-Instruct\"\n",
+    "# EXAMPLE_MODEL = \"Mixtral-8x22B-Instruct-v0.1\"\n",
+    "# EXAMPLE_MODEL = \"Mixtral-8x22B-v0.1\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54ae284c-79af-4940-89ff-35327283cff6",
+   "metadata": {},
+   "source": [
+    "**TODO?** plot xgboost score distribution as features are added! to show below the LLM score distribution as what true uncertainty quantification looks like..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9be9c590-589f-4ed1-a3cc-9e83e7bd6034",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2400x600 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get data to plot\n",
+    "plot_df = results_df[results_df[model_col] == EXAMPLE_MODEL].sort_values(\"num_features\")\n",
+    "# Skip every other row in plot\n",
+    "plot_df = plot_df[[idx % 2 == 0 for idx in range(len(plot_df))]]\n",
+    "# plot_df = plot_df.iloc[[0, 2, 3, 5, 7]]\n",
+    "\n",
+    "# # For current Llama-3-8B-Instruct results\n",
+    "# plot_df = plot_df[[False, True, False, True, True, False, True]]\n",
+    "\n",
+    "N_PLOTS = min(10, len(plot_df))\n",
+    "\n",
+    "N_COLS = 5\n",
+    "N_ROWS = math.ceil(N_PLOTS / N_COLS)\n",
+    "\n",
+    "fig, axes = plt.subplots(\n",
+    "    nrows=N_ROWS, ncols=N_COLS,\n",
+    "    figsize=(12, 3 * N_ROWS),\n",
+    "    sharey=True, sharex=False,\n",
+    "    gridspec_kw=dict(\n",
+    "        hspace=0.5,\n",
+    "        wspace=0.06,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Plot settings\n",
+    "plot_config = dict(\n",
+    "    bins=(N_BINS := 20),\n",
+    "    binrange=(0, 1),\n",
+    "    stat=\"percent\",\n",
+    ")\n",
+    "\n",
+    "# Plot score distribution for model pairs\n",
+    "current_featureset = set()\n",
+    "\n",
+    "for idx, (id_, row) in enumerate(plot_df.iterrows()):\n",
+    "    ax_row = idx // N_COLS\n",
+    "    ax_col = idx % N_COLS\n",
+    "\n",
+    "    ax = axes[ax_row, ax_col] if N_ROWS > 1 else axes[ax_col]\n",
+    "\n",
+    "    # Get scores\n",
+    "    scores = scores_df_map[row.name][\"risk_score\"]\n",
+    "\n",
+    "    # Added features\n",
+    "    added_features = set(row[\"config_feature_subset\"]) - current_featureset\n",
+    "\n",
+    "    # Set title with added features compared to last plot\n",
+    "    title_str = \"+ \" if len(current_featureset) > 0 else \"\"\n",
+    "    title_str += \", \".join(added_features)\n",
+    "    current_featureset |= added_features\n",
+    "\n",
+    "    ax.set_title(title_str)\n",
+    "\n",
+    "    ax.set_xlabel(\"risk scores\")\n",
+    "    if ax_col == 0:\n",
+    "        ax.set_ylabel(\"density (%)\")\n",
+    "\n",
+    "    ax.set_xlim(-0.03, 1.03)\n",
+    "\n",
+    "    # Render plot\n",
+    "    sns.histplot(scores, ax=ax, color=palette[1 if row[\"is_inst\"] else 0], **plot_config)\n",
+    "\n",
+    "    # Add text box with distribution stats\n",
+    "    textstr = '\\n'.join([\n",
+    "        r\"$\\text{ROC}=%.2f$\" % (row[\"roc_auc\"],),\n",
+    "        r\"$\\text{ECE}=%.2f$\" % (row[\"ece\"],),\n",
+    "        r\"$\\sigma=%.2f$\" % (row[\"score_stdev\"],),\n",
+    "        r\"$\\mu=%.2f$\" % (row[\"score_mean\"],),\n",
+    "    ])\n",
+    "\n",
+    "    props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
+    "\n",
+    "    if row[\"is_inst\"]:\n",
+    "        # Center-aligned text box\n",
+    "        ax.text(0.5, 0.95, textstr, transform=ax.transAxes, fontsize=9,\n",
+    "            verticalalignment='top', horizontalalignment='center', bbox=props)\n",
+    "    else:\n",
+    "        # Right-aligned text box\n",
+    "        ax.text(0.95, 0.95, textstr, transform=ax.transAxes, fontsize=9,\n",
+    "            verticalalignment='top', horizontalalignment='right', bbox=props)\n",
+    "\n",
+    "        # # Left-aligned text box\n",
+    "        # ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=9,\n",
+    "        #     verticalalignment='top', bbox=props)\n",
+    "\n",
+    "# Remove unused axes\n",
+    "[\n",
+    "    (axes[N_ROWS - 1, idx] if N_ROWS > 1 else axes[idx]).remove()\n",
+    "    for idx in range(N_COLS-1, len(plot_df) - N_COLS * (N_ROWS - 1) - 1, -1)\n",
+    "];\n",
+    "\n",
+    "# Set suptitle\n",
+    "plt.suptitle(plot_df[\"name\"].iloc[0], y=1.05, fontsize=15)\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"increasing-evidence.{EXAMPLE_MODEL}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "06d0f410-3fe5-4dd3-913d-4369aa8cfacf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Label prevalence: 36.9%\n"
+     ]
+    }
+   ],
+   "source": [
+    "prevalence = scores_df_map[row.name]['label'].mean()\n",
+    "print(f\"Label prevalence: {prevalence:.1%}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41dfe9c8-52c3-4e86-ac51-2f73540963bb",
+   "metadata": {},
+   "source": [
+    "- [ ] **TODO:** Make the same plot for XGBoost to have a plot of a calibrated baseline..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6047a8d-2577-4123-833a-a7ead8ca8251",
+   "metadata": {},
+   "source": [
+    "## Plots for XGBoost baseline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0e899105-2370-4ee3-b304-aea95d7d6380",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bea01302-f19e-4322-a092-836f5cf65969",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5606a544-28a0-4805-9878-829fdfbd1fd8",
+   "metadata": {},
+   "source": [
+    "## Change in ECE and ROC with increasing evidence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a2494f73-152a-47f7-a9d6-d15092227b22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2400x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = results_df\n",
+    "\n",
+    "# PLOT_BASE = True\n",
+    "PLOT_BASE = False\n",
+    "\n",
+    "if PLOT_BASE:\n",
+    "    models_to_plot = [\n",
+    "        \"Meta-Llama-3-8B\",\n",
+    "        \"Meta-Llama-3-70B\",\n",
+    "        \"Mistral-7B-v0.1\",\n",
+    "        \"Mixtral-8x7B-v0.1\",\n",
+    "        \"Mixtral-8x22B-v0.1\",\n",
+    "    ]\n",
+    "else:\n",
+    "    models_to_plot = [\n",
+    "        \"Meta-Llama-3-8B-Instruct\",\n",
+    "        \"Meta-Llama-3-70B-Instruct\",\n",
+    "        \"Mistral-7B-Instruct-v0.2\",\n",
+    "        \"Mixtral-8x7B-Instruct-v0.1\",\n",
+    "        \"Mixtral-8x22B-Instruct-v0.1\",\n",
+    "    ]\n",
+    "\n",
+    "fig, axes = plt.subplots(1,len(models_to_plot), sharey=True, figsize=(12, 2.5), gridspec_kw=dict(wspace=0.1))\n",
+    "\n",
+    "num_features = [2,4,6,8,10]\n",
+    "\n",
+    "for idx, model_name in enumerate(models_to_plot):\n",
+    "    this_data = data[data[model_col] == model_name]\n",
+    "    vals_ece = []\n",
+    "    vals_acc = []\n",
+    "\n",
+    "    for nf in num_features:\n",
+    "        this_row = (this_data[this_data[\"num_features\"] == nf]).iloc[0]\n",
+    "        vals_ece.append(this_row[\"ece\"])\n",
+    "        vals_acc.append(this_row[\"roc_auc\"])\n",
+    "\n",
+    "    ax = axes[idx]\n",
+    "    ax.plot(vals_ece, vals_acc, \"-o\")\n",
+    "    ax.set_title(this_row[\"name\"])\n",
+    "    ax.set_ylim([0.50, 0.9])\n",
+    "\n",
+    "    if PLOT_BASE:\n",
+    "        ax.set_xlim([0.0, 0.29])\n",
+    "    else:\n",
+    "        ax.set_xlim([0.1, 0.58])\n",
+    "\n",
+    "    ax.plot(vals_ece[-1], vals_acc[-1], '*', ms=13, label='all features',color = 'tab:orange')\n",
+    "\n",
+    "    ax.set_xlabel(\"Calibration (ECE)\")\n",
+    "    if idx == 0:\n",
+    "        ax.set_ylabel((\"Predictive Signal (ROC)\"))\n",
+    "\n",
+    "    if idx == 0:\n",
+    "        ax.legend(loc=\"upper left\" if PLOT_BASE else \"lower left\")\n",
+    "    elif idx == len(models_to_plot) - 1:\n",
+    "        ax.legend(loc=\"lower right\")\n",
+    "\n",
+    "\n",
+    "save_fig(fig, f\"feature-ablation.{'.'.join(models_to_plot)}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bde315a6-81fc-4830-8281-e32925829be5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/notebooks/parse-feature-importance-results.ipynb.txt b/_sources/notebooks/parse-feature-importance-results.ipynb.txt
new file mode 100644
index 0000000..9d40549
--- /dev/null
+++ b/_sources/notebooks/parse-feature-importance-results.ipynb.txt
@@ -0,0 +1,1253 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "04c2f12d-f989-4ac9-b90f-3464a8bcca96",
+   "metadata": {},
+   "source": [
+    "# Fetch and parse feature importance results under a given directory\n",
+    "\n",
+    "This script collects all feature importance results files under a given root directory, parses them, and aggregates them into a more easily digestable pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b241208-d10f-43cf-a486-84c54bbf43c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faf6afa4-8648-4d35-9312-65636ea5d0b2",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root results directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26089a60-81c0-4736-8ba5-99572ec01398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RESULTS_ROOT_DIR = Path(\"/fast/groups/sf\") / \"folktexts-results\" / \"2024-07-03\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d1af7f-d013-40a0-80de-cb9ad65cff6d",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root data directory (needed only to train baseline ML methods):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b18dad87-a1ed-495b-93b6-4c8af4043ddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db7c98c5-2942-4f88-984a-8c9014afe761",
+   "metadata": {},
+   "source": [
+    "Function to parse and flatten results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eeded803-6ba7-49b2-81b6-836549f3e10b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import operator\n",
+    "from functools import reduce\n",
+    "\n",
+    "def parse_results_dict(dct: dict) -> dict:\n",
+    "    return reduce(\n",
+    "        operator.or_,\n",
+    "        [{\n",
+    "            f\"{feat}_imp_mean\": dct[feat][\"imp_mean\"],\n",
+    "            f\"{feat}_imp_std\": dct[feat][\"imp_std\"]\n",
+    "        } for feat in dct],\n",
+    "    )\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02009a86-e099-4ec0-8676-8d66190ceddb",
+   "metadata": {},
+   "source": [
+    "Iteratively search the root directory for results files matching the given regex:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "aaefa99d-dbd7-40a3-a3c1-7571d3811409",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a998789eb64b4cb6b29af43c4d373d03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from utils import find_files, load_json\n",
+    "import re\n",
+    "\n",
+    "# Results file name pattern\n",
+    "pattern = r'^feature-importance[.](?P<task>\\w+)[.](?P<model>.*?)[.]json$'\n",
+    "\n",
+    "# Find results files and aggregate\n",
+    "from collections import defaultdict\n",
+    "results = defaultdict(dict)\n",
+    "\n",
+    "for file_path in tqdm(find_files(RESULTS_ROOT_DIR, pattern)):\n",
+    "    m = re.match(pattern, Path(file_path).name)\n",
+    "    results[m[\"task\"]][m[\"model\"]] = parse_results_dict(load_json(file_path))\n",
+    "\n",
+    "if len(results) == 0:\n",
+    "    raise RuntimeError(f\"Couldn't find any results at {RESULTS_ROOT_DIR}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ecc0d33-902d-492a-89c6-d9729fe69fa1",
+   "metadata": {},
+   "source": [
+    "Aggregate results into a single DataFrame, and generate a unique identifier for each row:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5e89e632-a448-4b0e-8d8e-621f6c74bed7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "llms_df.shape=(20, 20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "llms_df = pd.DataFrame(results[\"ACSIncome\"].values(), results[\"ACSIncome\"].keys())\n",
+    "print(f\"{llms_df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c374c14e-0e81-4813-9983-309ce6215648",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Currently discarding Gemma 2 results as some extra analysis is needed:\n",
+    "llms_df = llms_df[[\"gemma-2-\" not in id_.lower() for id_ in llms_df.index]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cbe9d8e6-8af1-4663-a976-6c9d5020cc20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AGEP_imp_mean</th>\n",
+       "      <th>AGEP_imp_std</th>\n",
+       "      <th>COW_imp_mean</th>\n",
+       "      <th>COW_imp_std</th>\n",
+       "      <th>MAR_imp_mean</th>\n",
+       "      <th>MAR_imp_std</th>\n",
+       "      <th>OCCP_imp_mean</th>\n",
+       "      <th>OCCP_imp_std</th>\n",
+       "      <th>POBP_imp_mean</th>\n",
+       "      <th>POBP_imp_std</th>\n",
+       "      <th>...</th>\n",
+       "      <th>RAC1P_imp_std</th>\n",
+       "      <th>RELP_imp_mean</th>\n",
+       "      <th>RELP_imp_std</th>\n",
+       "      <th>SCHL_imp_mean</th>\n",
+       "      <th>SCHL_imp_std</th>\n",
+       "      <th>SEX_imp_mean</th>\n",
+       "      <th>SEX_imp_std</th>\n",
+       "      <th>WKHP_imp_mean</th>\n",
+       "      <th>WKHP_imp_std</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>google--gemma-2b</th>\n",
+       "      <td>0.030893</td>\n",
+       "      <td>0.003416</td>\n",
+       "      <td>0.005972</td>\n",
+       "      <td>0.001597</td>\n",
+       "      <td>0.004709</td>\n",
+       "      <td>0.002058</td>\n",
+       "      <td>0.033321</td>\n",
+       "      <td>0.004408</td>\n",
+       "      <td>0.011186</td>\n",
+       "      <td>0.003455</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.002855</td>\n",
+       "      <td>0.004928</td>\n",
+       "      <td>0.002963</td>\n",
+       "      <td>0.047764</td>\n",
+       "      <td>0.002630</td>\n",
+       "      <td>-0.000595</td>\n",
+       "      <td>0.002619</td>\n",
+       "      <td>0.009022</td>\n",
+       "      <td>0.003113</td>\n",
+       "      <td>Gemma 2B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>google--gemma-1.1-2b-it</th>\n",
+       "      <td>0.057288</td>\n",
+       "      <td>0.001795</td>\n",
+       "      <td>0.002239</td>\n",
+       "      <td>0.000988</td>\n",
+       "      <td>0.003562</td>\n",
+       "      <td>0.000592</td>\n",
+       "      <td>0.054559</td>\n",
+       "      <td>0.001415</td>\n",
+       "      <td>0.002923</td>\n",
+       "      <td>0.000761</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000334</td>\n",
+       "      <td>0.010951</td>\n",
+       "      <td>0.000447</td>\n",
+       "      <td>0.073909</td>\n",
+       "      <td>0.001346</td>\n",
+       "      <td>0.003064</td>\n",
+       "      <td>0.000409</td>\n",
+       "      <td>0.028131</td>\n",
+       "      <td>0.001265</td>\n",
+       "      <td>Gemma 2B (it)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         AGEP_imp_mean  AGEP_imp_std  COW_imp_mean  \\\n",
+       "google--gemma-2b              0.030893      0.003416      0.005972   \n",
+       "google--gemma-1.1-2b-it       0.057288      0.001795      0.002239   \n",
+       "\n",
+       "                         COW_imp_std  MAR_imp_mean  MAR_imp_std  \\\n",
+       "google--gemma-2b            0.001597      0.004709     0.002058   \n",
+       "google--gemma-1.1-2b-it     0.000988      0.003562     0.000592   \n",
+       "\n",
+       "                         OCCP_imp_mean  OCCP_imp_std  POBP_imp_mean  \\\n",
+       "google--gemma-2b              0.033321      0.004408       0.011186   \n",
+       "google--gemma-1.1-2b-it       0.054559      0.001415       0.002923   \n",
+       "\n",
+       "                         POBP_imp_std  ...  RAC1P_imp_std  RELP_imp_mean  \\\n",
+       "google--gemma-2b             0.003455  ...       0.002855       0.004928   \n",
+       "google--gemma-1.1-2b-it      0.000761  ...       0.000334       0.010951   \n",
+       "\n",
+       "                         RELP_imp_std  SCHL_imp_mean  SCHL_imp_std  \\\n",
+       "google--gemma-2b             0.002963       0.047764      0.002630   \n",
+       "google--gemma-1.1-2b-it      0.000447       0.073909      0.001346   \n",
+       "\n",
+       "                         SEX_imp_mean  SEX_imp_std  WKHP_imp_mean  \\\n",
+       "google--gemma-2b            -0.000595     0.002619       0.009022   \n",
+       "google--gemma-1.1-2b-it      0.003064     0.000409       0.028131   \n",
+       "\n",
+       "                         WKHP_imp_std     model_name  \n",
+       "google--gemma-2b             0.003113       Gemma 2B  \n",
+       "google--gemma-1.1-2b-it      0.001265  Gemma 2B (it)  \n",
+       "\n",
+       "[2 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from utils import prettify_model_name\n",
+    "\n",
+    "# Add prettified model name\n",
+    "llms_df[\"model_name\"] = [prettify_model_name(m[m.find(\"--\")+2:]) for m in llms_df.index]\n",
+    "llms_df = llms_df.sort_values(\"model_name\")\n",
+    "\n",
+    "llms_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b83b99c-de78-4792-b712-305929207a94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['AGEP', 'COW', 'MAR', 'OCCP', 'POBP', 'RAC1P', 'RELP', 'SCHL', 'SEX', 'WKHP']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "all_features = [m[\"name\"] for col in llms_df.columns if (m := re.fullmatch(r\"^(?P<name>[a-zA-Z0-9]+)_imp_mean$\", col))]\n",
+    "\n",
+    "feat_imp_cols = [f\"{col}_imp_mean\" for col in all_features]\n",
+    "feat_imp_std_cols = [f\"{col}_imp_std\" for col in all_features]\n",
+    "\n",
+    "all_features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba2fe6d9-5a91-4424-bf09-5584caf3574b",
+   "metadata": {},
+   "source": [
+    "## Get feature importance for GBM and LR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f7f90d13-0b1c-467f-99ce-62b902958111",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "CPU times: user 43.1 s, sys: 26.9 s, total: 1min 10s\n",
+      "Wall time: 1min 19s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.acs.acs_dataset import ACSDataset\n",
+    "acs_income_dt = ACSDataset.make_from_task(task=\"ACSIncome\", cache_dir=DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "401a71fd-fbc9-496f-92c2-1fa721873d41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, y_train = acs_income_dt.get_train()\n",
+    "X_test, y_test = acs_income_dt.get_test()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c316b332-0c67-421e-9321-d718d91fd838",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
+    "from xgboost import XGBClassifier    # NOTE: requires `pip install xgboost`\n",
+    "\n",
+    "baselines = {\n",
+    "    \"GBM\": HistGradientBoostingClassifier(),\n",
+    "    \"LR\": LogisticRegression(),\n",
+    "    \"XGBoost\": XGBClassifier(),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5876f670-bc7a-4733-8281-05d4fc150465",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parse_feat_imp_results(results) -> dict:\n",
+    "    parsed_dict = dict()\n",
+    "    for idx, feat in enumerate(X_train.columns.tolist()):\n",
+    "        parsed_dict[f\"{feat}_imp_mean\"] = results[\"importances_mean\"][idx]\n",
+    "        parsed_dict[f\"{feat}_imp_std\"] = results[\"importances_std\"][idx]\n",
+    "\n",
+    "    return parsed_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "10e14028-aa11-47d5-bd15-dbaa4cc92875",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b9ef16acef94f42a75dda6f3e91873a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/acruz/miniconda3/envs/folktexts/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.inspection import permutation_importance\n",
+    "baseline_feat_imp_results = dict()\n",
+    "\n",
+    "for name, clf in tqdm(baselines.items()):\n",
+    "    # Fit classifier\n",
+    "    clf.fit(X_train, y_train)\n",
+    "\n",
+    "    # Compute permutation importance\n",
+    "    r = permutation_importance(\n",
+    "        estimator=clf,\n",
+    "        X=X_test,\n",
+    "        y=y_test,\n",
+    "        scoring=\"roc_auc\",\n",
+    "    )\n",
+    "\n",
+    "    baseline_feat_imp_results[name] = r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b3543466-3b2e-4221-be63-49e1e7bd5e7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AGEP_imp_mean</th>\n",
+       "      <th>AGEP_imp_std</th>\n",
+       "      <th>COW_imp_mean</th>\n",
+       "      <th>COW_imp_std</th>\n",
+       "      <th>SCHL_imp_mean</th>\n",
+       "      <th>SCHL_imp_std</th>\n",
+       "      <th>MAR_imp_mean</th>\n",
+       "      <th>MAR_imp_std</th>\n",
+       "      <th>OCCP_imp_mean</th>\n",
+       "      <th>OCCP_imp_std</th>\n",
+       "      <th>...</th>\n",
+       "      <th>POBP_imp_std</th>\n",
+       "      <th>RELP_imp_mean</th>\n",
+       "      <th>RELP_imp_std</th>\n",
+       "      <th>WKHP_imp_mean</th>\n",
+       "      <th>WKHP_imp_std</th>\n",
+       "      <th>SEX_imp_mean</th>\n",
+       "      <th>SEX_imp_std</th>\n",
+       "      <th>RAC1P_imp_mean</th>\n",
+       "      <th>RAC1P_imp_std</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>GBM</th>\n",
+       "      <td>0.057407</td>\n",
+       "      <td>0.000544</td>\n",
+       "      <td>0.006616</td>\n",
+       "      <td>0.000135</td>\n",
+       "      <td>0.038793</td>\n",
+       "      <td>0.000252</td>\n",
+       "      <td>0.002003</td>\n",
+       "      <td>0.000039</td>\n",
+       "      <td>0.061977</td>\n",
+       "      <td>0.000385</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000078</td>\n",
+       "      <td>0.013170</td>\n",
+       "      <td>0.000146</td>\n",
+       "      <td>0.075559</td>\n",
+       "      <td>0.000622</td>\n",
+       "      <td>0.013678</td>\n",
+       "      <td>0.000182</td>\n",
+       "      <td>0.000804</td>\n",
+       "      <td>0.000042</td>\n",
+       "      <td>GBM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LR</th>\n",
+       "      <td>0.002650</td>\n",
+       "      <td>0.000024</td>\n",
+       "      <td>-0.000173</td>\n",
+       "      <td>0.000084</td>\n",
+       "      <td>-0.000614</td>\n",
+       "      <td>0.000018</td>\n",
+       "      <td>0.022415</td>\n",
+       "      <td>0.000363</td>\n",
+       "      <td>0.092150</td>\n",
+       "      <td>0.000710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000185</td>\n",
+       "      <td>0.049616</td>\n",
+       "      <td>0.000824</td>\n",
+       "      <td>0.071947</td>\n",
+       "      <td>0.000480</td>\n",
+       "      <td>0.003057</td>\n",
+       "      <td>0.000042</td>\n",
+       "      <td>0.000509</td>\n",
+       "      <td>0.000119</td>\n",
+       "      <td>LR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>XGBoost</th>\n",
+       "      <td>0.054982</td>\n",
+       "      <td>0.000185</td>\n",
+       "      <td>0.008279</td>\n",
+       "      <td>0.000207</td>\n",
+       "      <td>0.035391</td>\n",
+       "      <td>0.000434</td>\n",
+       "      <td>0.002507</td>\n",
+       "      <td>0.000076</td>\n",
+       "      <td>0.075997</td>\n",
+       "      <td>0.000635</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000098</td>\n",
+       "      <td>0.013292</td>\n",
+       "      <td>0.000332</td>\n",
+       "      <td>0.072216</td>\n",
+       "      <td>0.000690</td>\n",
+       "      <td>0.012949</td>\n",
+       "      <td>0.000112</td>\n",
+       "      <td>0.001207</td>\n",
+       "      <td>0.000072</td>\n",
+       "      <td>XGBoost</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         AGEP_imp_mean  AGEP_imp_std  COW_imp_mean  COW_imp_std  \\\n",
+       "GBM           0.057407      0.000544      0.006616     0.000135   \n",
+       "LR            0.002650      0.000024     -0.000173     0.000084   \n",
+       "XGBoost       0.054982      0.000185      0.008279     0.000207   \n",
+       "\n",
+       "         SCHL_imp_mean  SCHL_imp_std  MAR_imp_mean  MAR_imp_std  \\\n",
+       "GBM           0.038793      0.000252      0.002003     0.000039   \n",
+       "LR           -0.000614      0.000018      0.022415     0.000363   \n",
+       "XGBoost       0.035391      0.000434      0.002507     0.000076   \n",
+       "\n",
+       "         OCCP_imp_mean  OCCP_imp_std  ...  POBP_imp_std  RELP_imp_mean  \\\n",
+       "GBM           0.061977      0.000385  ...      0.000078       0.013170   \n",
+       "LR            0.092150      0.000710  ...      0.000185       0.049616   \n",
+       "XGBoost       0.075997      0.000635  ...      0.000098       0.013292   \n",
+       "\n",
+       "         RELP_imp_std  WKHP_imp_mean  WKHP_imp_std  SEX_imp_mean  SEX_imp_std  \\\n",
+       "GBM          0.000146       0.075559      0.000622      0.013678     0.000182   \n",
+       "LR           0.000824       0.071947      0.000480      0.003057     0.000042   \n",
+       "XGBoost      0.000332       0.072216      0.000690      0.012949     0.000112   \n",
+       "\n",
+       "         RAC1P_imp_mean  RAC1P_imp_std  model_name  \n",
+       "GBM            0.000804       0.000042         GBM  \n",
+       "LR             0.000509       0.000119          LR  \n",
+       "XGBoost        0.001207       0.000072     XGBoost  \n",
+       "\n",
+       "[3 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "baselines_df = pd.DataFrame([\n",
+    "    pd.Series(parse_feat_imp_results(r) | {\"model_name\": name}, name=name) for name, r in baseline_feat_imp_results.items()\n",
+    "])\n",
+    "baselines_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "02560b20-98b7-427a-994e-71f3e1a84c03",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.shape=(19, 21)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.concat((llms_df, baselines_df), axis=0)\n",
+    "print(f\"{df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "36c8f364-eaa6-4bee-ac15-4d03c5314e4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import get_current_timestamp\n",
+    "df.to_csv(Path(RESULTS_ROOT_DIR) / f\"feature_importance.{get_current_timestamp()}.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f4c4ba2-00f9-45cd-b9e0-b9ce3e0edc9d",
+   "metadata": {},
+   "source": [
+    "# Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "53df0763-7d81-4edd-8e6b-f444546b9d82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMGS_DIR = Path(RESULTS_ROOT_DIR) / \"imgs\"\n",
+    "IMGS_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "def save_fig(fig, name):\n",
+    "    fig.savefig((IMGS_DIR / name).with_suffix(\".pdf\"), bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e3ca551a-2d21-4eb6-a573-a504687e0d1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1f77b4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff7f0e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2ca02c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d62728;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8c564b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e377c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7f7f7f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bcbd22;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#17becf;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
+      ],
+      "text/plain": [
+       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
+       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
+       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
+       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
+       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
+       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
+       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
+       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"whitegrid\", rc={\"grid.linestyle\": \"--\"})\n",
+    "plt.rc(\"figure\", figsize=(4.5, 3.5), dpi=200)\n",
+    "\n",
+    "palette_name = \"tab10\"\n",
+    "# palette_name = \"colorblind\"\n",
+    "palette = sns.color_palette(palette_name)\n",
+    "sns.set_palette(palette_name)\n",
+    "palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8a97c7b0-7021-4727-8a8c-cf10806d573a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def barplot_feat_imp(row: pd.Series, stdev_values=None, ax=None, **kwargs):\n",
+    "    if ax is None: ax = plt.gca()\n",
+    "\n",
+    "    labels = [name[:name.find(\"_\")] for name, val in row.items()]\n",
+    "    sns.barplot(x=row, y=labels, ax=ax, **kwargs)\n",
+    "\n",
+    "    ax.errorbar(\n",
+    "        x=row.values,\n",
+    "        y=labels,\n",
+    "        xerr=stdev_values * 2,  # 2 stdev ~> 95% c.i.\n",
+    "        fmt='none', c='grey', capsize=0,\n",
+    "    )\n",
+    "\n",
+    "    ax.set_title(\"Mean feature importance (ACSIncome)\")\n",
+    "    ax.set_xlabel(\"Importance for ROC metric\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d71958d8-eaf8-4448-8f32-d88c017182f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('OCCP_imp_mean', 'OCCP_imp_std'),\n",
+       " ('SCHL_imp_mean', 'SCHL_imp_std'),\n",
+       " ('WKHP_imp_mean', 'WKHP_imp_std'),\n",
+       " ('AGEP_imp_mean', 'AGEP_imp_std'),\n",
+       " ('RELP_imp_mean', 'RELP_imp_std'),\n",
+       " ('MAR_imp_mean', 'MAR_imp_std'),\n",
+       " ('SEX_imp_mean', 'SEX_imp_std'),\n",
+       " ('COW_imp_mean', 'COW_imp_std'),\n",
+       " ('POBP_imp_mean', 'POBP_imp_std'),\n",
+       " ('RAC1P_imp_mean', 'RAC1P_imp_std')]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feat_imp_cols_sorted = df[feat_imp_cols].mean().sort_values(ascending=False).index.tolist()\n",
+    "feat_imp_std_cols_sorted = [col.replace(\"_imp_mean\", \"_imp_std\") for col in feat_imp_cols_sorted]\n",
+    "list(zip(feat_imp_cols_sorted, feat_imp_std_cols_sorted))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b2372ab-fc18-443a-bf26-b979b9e3c03e",
+   "metadata": {},
+   "source": [
+    "Mean feature importance among all models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "62b9793d-a99b-40c9-b091-da1067e5f96d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "barplot_feat_imp(df[feat_imp_cols_sorted].mean(), df[feat_imp_std_cols_sorted].mean())\n",
+    "ax = plt.gca()\n",
+    "ax.set_xlim(left=0, right=ax.get_xlim()[-1])\n",
+    "save_fig(plt.gcf(), \"mean-feat-imp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "68cfa49f-499d-4320-8511-408a8671e791",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_feat_imp_subplots(df):\n",
+    "    N_COLS = 4\n",
+    "    N_ROWS = math.ceil(len(df) / N_COLS)\n",
+    "\n",
+    "    cols_order = df[feat_imp_cols_sorted].mean().sort_values(ascending=False).index.tolist()\n",
+    "    std_cols_order = [col.replace(\"_imp_mean\", \"_imp_std\") for col in cols_order]\n",
+    "    \n",
+    "    fig, axes = plt.subplots(nrows=N_ROWS, ncols=N_COLS, figsize=(12, 2.8 * N_ROWS), sharey=True, sharex=True)\n",
+    "    \n",
+    "    for idx, m in enumerate(df.index):\n",
+    "        ax_row = idx // N_COLS\n",
+    "        ax_col = idx % N_COLS\n",
+    "    \n",
+    "        ax = axes[ax_row, ax_col]\n",
+    "        row = df.loc[m]\n",
+    "        \n",
+    "        barplot_feat_imp(\n",
+    "            row[cols_order], stdev_values=row[std_cols_order],\n",
+    "            ax=ax,\n",
+    "            color=palette[0] if m not in baselines else palette[2],\n",
+    "        )\n",
+    "        \n",
+    "        ax.set_title(row[\"model_name\"])\n",
+    "\n",
+    "    # Set xlim\n",
+    "    max_xlim = max(ax_i.get_xlim()[-1] for ax_row in axes for ax_i in ax_row)\n",
+    "    axes[0,0].set_xlim(left=0, right=max_xlim)\n",
+    "\n",
+    "    # Remove extra axes\n",
+    "    [axes[N_ROWS-1, N_COLS - 1 - i].remove() for i in range((N_ROWS * N_COLS) - len(df))];\n",
+    "\n",
+    "    plt.plot()\n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a70eb7d-1e7e-4dc8-9a0e-ee79bcc6d6cf",
+   "metadata": {},
+   "source": [
+    "Feature importance for each model, plus baselines:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9f64cbcb-3a1d-4831-9d9e-46e4081d0e9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2400x2800 with 19 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_feat_imp_subplots(df)\n",
+    "save_fig(fig, \"all-feat-imps\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "2827713d-0828-4e69-9618-0c3304c6a29c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR:root:Could not infer model size from name 'GBM'\n",
+      "ERROR:root:Could not infer model size from name 'LR'\n",
+      "ERROR:root:Could not infer model size from name 'XGBoost'\n"
+     ]
+    }
+   ],
+   "source": [
+    "from folktexts.llm_utils import get_model_size_B\n",
+    "large_models_filter = np.array([(get_model_size_B(m, default=-1) >= 40) for m in df.index])\n",
+    "# baselines_filter = np.array([(m in baselines) for m in df.index])\n",
+    "baselines_filter = np.array([(m == \"XGBoost\") for m in df.index])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68160fcf-3c16-46ec-8222-7a4ddf105503",
+   "metadata": {},
+   "source": [
+    "Plot feature importance among large models (>= 40B):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "085d4f05-85ce-4047-9671-859a1c307aea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lZ2fr3Xff1WuvvVbovqmpqcYXN5I0ZMiQQvd79NFH9fvvv2vr1q06f/68xo4dq3nz5slisb226uDBg3rrrbeM1+PGjbP7rD97jhw5YrNEYmRkZKnGsWfNmjW6dOmSJKlSpUpq2bKl088BoPygPhZEfcxFfXS8PiYmJhp3YFauXFnNmzd3aLxWrVrpvvvu06lTp7R69WpJuRdDdO/evcC+eQ2rwjRr1kwBAQFKS0vTn3/+qVOnTik8PNyh2ACgtEJCQjR+/Hi9+OKLkqRPPvlEPXr00Kuvvmo0zG+66aYi61J+GzduNLbbtGnjUXPbihUrjO3w8HDVrVu3yH03b96shx9+2FhVxcvLS61atVKjRo2UmZmpnTt36vjx45Kk33//XUOGDNGCBQuKvADNarXqiSeesFnhJDg4WDfffLOqVaumkydPauPGjcrMzNT58+c1btw4nT9/Xg888ECh440dO9ZmrPr166tp06aqVq2asrKylJiYqAMHDiguLq7AsUFBQcbFD1FRUcZqRgMGDCh0xR9P+m8IAAAA56PJDpQTAwYM0FdffSUp95e5wpoIS5culZT7fLiilitzxCuvvKLt27crLi5OR48e1ZtvvqnXX3/d+DwnJ0fjxo0zrkRv0aKFnnzySZsxrr/+el1//fU6ceKE0UQIDg62uRPQjO3btysrK0sRERGaMWNGgaVG85ZklXK/GB42bJj69eunFi1aFPgiXZJSUlL0/vvva+7cucrKytL48eO1cuXKQvd1Nj8/P73wwgt64YUXJElff/21jhw5oocfflgRERGqXr26Tp48qS1btuiDDz4wftnv0aOHHn744ULH9Pb21ttvv63+/fvrwoUL2rx5s/79739r9OjRxj4ZGRl69tlnjS/lu3btqnvvvbfUP0fef09Jql69ujp16lTqsQrz22+/2Tz3cMiQIU5dAhFA+UR9tEV9pD46qz7GxMQYF6c0atRIPj6O/erYqVMnderUSRs3bjSa7A0aNChxjlssFjVu3Ni4cCEmJoYmBgC3GjBggJYvX67ffvtNWVlZGj58uFJSUiTl1trXX3/d5iK3wuQ1n/Ncf/31Lo3ZrJycHC1btkzvvvuu8d5DDz1U5M+TnJys5557zmiwN2jQQDNnzlSLFi1s9lu6dKleeuklpaen6+jRo5owYYI++uijQsecM2eOTVN81KhReuKJJ2xWl0lISNC4ceO0fv16SdL06dN1ww03FPhz3L9/v1GDAgIC9O677xZZl//66y8tX77cZgWk/P82W7dundFkf+KJJ+xeeAAAAIArE012oJy48cYb1aBBAx09elQ//PCDXn75ZVWuXNn4fNu2bTp27JgkqXfv3qpUqZLTY6hSpYqmT5+u4cOHKzs7W99++606depk3IH0+eefG7/UBgQEaMaMGfL19XV6HFLucnGhoaH6/PPPC73iPf8v3HXq1NGkSZPsjhcUFKQXX3xRaWlp+uqrr3T06FH99ttvTv8ivCj9+/eXj4+PJkyYoPT0dG3atEmbNm0qdN969erp/vvv1wMPPGD3y5o6dero1Vdf1bPPPitJmj17tjp06GA812/69Ok6cOCApNzl/vI3hEoqJyfHaGJJUp8+fUr8337p0qUFllnOzs5WcnKy9u7da+S3JA0dOtRougCo2KiPtqiP1Edn1cfY2Fhj+5prrnHKmM5yzTXXGE32/fv3m3pePAC40muvvaY+ffooNTXVaLBLuc3XBg0aFHv82bNnbS6EK+3qKaUxb948/fDDDzbvZWVl6dy5c9q1a5fi4+Ml5d6R/vTTT2v48OFFjvX555/r1KlTknJXyfnss89sHv+Sp1+/fvL399fjjz8uKffxJJs3b9ZNN91ks19KSorNsvsPPvignnvuuQLjhYaG6sMPP9TQoUMVExOjrKwszZw5U/PmzbPZL+/xQZI0fPhwu/+eqVevns1FeAAAAMDlaLID5Uj//v31r3/9SykpKVq9erX69OljfJb/LilnL4WbX9u2bfXoo48aS7JOmjRJrVq10rlz5zRz5kxjvwkTJqhhw4Yui0OSxowZ4/Rnmg4aNMi4IzI6OrrMmgiSdNddd6l9+/aaPXu2vvzyy0KfLVy5cmV1795d3bt3L/ZuiLwxf/31V0VFRSkrK0vPP/+8Fi9erC1btui///2vpNwvS9566y2H/iw3btyov//+23g9cODAEo8RHR2t6Ohou/s0aNBAb7zxhtq2bVvi8QFcuaiPtqiP1EdnOHHihLF91VVXOW1cZ8h/53phy/kCQFmrXbu2HnzwQb333nvGey1atNA///lPU8fnPb4lT5UqVYo9ZsmSJTZ3vxfGzGoheXd229OqVSu98cYbioiIKHKfnJwcffPNN8brMWPGFNpgz9O9e3fdfvvt+vXXXyVJCxYsKNBkX7ZsmdLS0iRJNWvW1FNPPVXkeH5+fnr55Zc1ePBgSbk1+PDhwzYXiuW/AMLZ/1YCAABAxUOTHShH+vfvr1mzZiknJ0dRUVFGEyEjI0MrV66UJF199dVq06aNS+N47LHHtGHDBm3fvl1JSUkaO3aszZX3PXr0MH6xdaU777yzxMfkLcMXGxurhIQEpaamKjs72/g8b7k3ScZzSMvKvn37NG3aNP3xxx+Scu/SatasmQIDA3X69Glt27ZNycnJmjt3rv773//afIFgz0svvaRt27bp+PHjOnbsmMaPH6+tW7caTYoRI0aoQ4cODsWev4kVERHh8HNbi3L06FGNGDFCQ4YM0fPPP++SO1IBlD/UR1vUR+qjM+rjmTNnjG17z0h3h+rVqxvbCQkJbowEAHJlZGTYPLdckqmLvvLkr7NS7so3xYmOjtbixYvt7lPSR3IUZdeuXbrnnnv08MMP69FHHy30sTGHDh0y5mRvb29TFzcOHjzYaLIXtlJNXu2Xci+Qy79aUWFatWqliIgIY0WajRs32jTZ8180tmTJEt19993y9/cvNk4AAACgMDTZgXKkTp06uummm7Rp0yZt2LBBCQkJCg0N1Zo1a3T+/HlJrr1LL0/e80wHDBiglJQUbdy40fgsPDxcU6ZMcXkMdevWLdEXvunp6froo4/01Vdf6dy5c6aOMbufM6xevVpPP/20MjMzFR4ermnTpunWW2+12SfvZ/joo4+UkZGhSZMmKTAwsNhmSlBQkGbMmKGhQ4cqKyvLZinAJk2aGMvlllZaWprNmAMGDCjVOFOnTlVkZKTNezk5OUpNTdWxY8e0evVqffbZZ0pLS9O8efO0f/9+zZkzx2bpYwAVE/Xxf6iP1Edn1ce85+lK8rgGRP548scJAO7ywQcf6NChQzbvxcTEaN68eabuZg8MDLR5nXf3dlmYN2+e2rVrZ/NeTk6OLly4oEOHDmnFihVasGCB0tLS9K9//UuHDh2yWaUnz969e43thg0b2lwQVZTWrVsb2wkJCTp16pTNaiX5L+y78cYbTf08rVu3Nprs+WOSpE6dOikgIEBpaWnas2ePevfurX/84x/q1KmTmjVrJm9vb1PnAAAAACSp4KWnADxaXpMgOztby5Ytk5R7BbaUe6V8WTQRpNznk02ePNnmvbxlVcvibqeSLO2WnJyse++9Vx9++GGJGgOX303gKsePH9fYsWOVmZmpSpUq6dNPPy3QQJByl8J9+umn9cgjjxjvvfrqq6a+XL7++uuN593lH2/mzJkOfwn/448/Gl8CeXt7q1+/fg6Nl5+Xl5eCgoLUvHlzPfXUU/rqq6+ML6A2bdqkjz/+2GnnAlC+UR9zUR9zUR+dWx8LW6LfnTwtHgAV2/79+/XJJ58Yr9u3b29sz5o1y+bxG0W5/N8IFy5cKPaYadOmKTY21uZ/lz+DvLS8vLxUtWpV3XjjjZo4caI+/vhjowG9fPnyQu+gT0xMNLZr165t6jw1a9a0WX3l8n+P5B+zTp06psbMv9/l41WvXl2vv/66fH19JUknT57Ue++9p3/84x9q27atRowYoffff7/MV+0BAABA+USTHShnevXqZdy9ExUVpbNnz+q3336TJLVp00b16tUrs1hCQkJsXoeGhur6668vk3MXt0xcfq+99prxS7Kvr68GDx6sDz74QD/88IO2bdumvXv3Gl9KrFmzxjiurL7AnTNnjvEl/IABA9SoUSO7+z/66KMKCgqSlPvsvp9//tnUeWrWrGnzulGjRjZL55VW/qVwb7vtNoWGhjo8ZlEaN26sYcOGGa/nz59vLMMMoGKjPuaiPlIfJefUx/x3i6enpzs0lrPlj8fT7rIHULFkZ2drwoQJyszMlCR17txZn3zyiZo0aSIp9470yy++K0xISIhNs/nPP/90TcCldOutt6p3797G6zlz5hTYJ//d9yWZm/Pve/mFfKUZM/9S+4VdGHjXXXfp22+/Vffu3Y1me965oqOjNWvWLA0YMECRkZHasmWL6Z8DAAAAFQ9NdqCcCQoKUteuXSVJsbGxmjFjhrKysiSVfhnS0khMTNSLL75o897p06f15ptvllkMZpw6dUrff/+9JMliseiTTz7R66+/rq5du6pBgwYKDAy0WRKurO7Oyy+vCSRJt9xyS7H7+/v764YbbjBex8TEFHvMkSNHCvy32bNnj/7973+bD7QQJ0+etFkOuSxy8LbbbjO2k5KSuMsAgCTqY0lRH3NRH4uW/+KDsnxEgBn572x05cULAFCcOXPmaM+ePZJy/y3y6quvysfHR2+88YZRR9evX1/ss9N9fX1tLsjbtWuX64IupY4dOxrbBw8eNJ6/nid/c7skj/LIv+/ly+aXZsz8jfnLx8vTtGlTzZ49Wxs2bNAHH3ygkSNH6sYbb7Rpuu/Zs0fDhw/XypUrTZ0XAAAAFQ9NdqAcyv9F7aJFiyRJlSpVUq9evcoshkmTJhm/VNevX9/4AuHbb7/V6tWryyyO4kRHRxt33N1+++3Ffkn/999/l0VYNk6fPm1sm11KOP9+KSkpdvfNzMzU888/b3zZ0LBhQ+Oz2bNnO/QFzpIlS2S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",
+      "text/plain": [
+       "<Figure size 2400x1120 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_feat_imp_subplots(df[large_models_filter | baselines_filter])\n",
+    "plt.suptitle(\"Feature importance among large models\")\n",
+    "\n",
+    "save_fig(fig, \"feat-imps-large-models\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "078abc0b-cb4e-4434-bf65-185da6459f7c",
+   "metadata": {},
+   "source": [
+    "Feature importance averaged among all large models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "b232fa0a-30ec-4dd6-8bb8-d501ff69c89b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cols_order = df[large_models_filter][feat_imp_cols_sorted].mean().sort_values(ascending=False).index.tolist()\n",
+    "std_cols_order = [col.replace(\"_imp_mean\", \"_imp_std\") for col in cols_order]\n",
+    "\n",
+    "barplot_feat_imp(df[large_models_filter][cols_order].mean(), df[large_models_filter][std_cols_order].mean())\n",
+    "ax = plt.gca()\n",
+    "ax.set_xlim(left=0, right=ax.get_xlim()[-1])\n",
+    "\n",
+    "plt.title(\"Mean feature importance\\n(model size > 50B; ACSIncome)\")\n",
+    "save_fig(fig, \"mean-feat-imp-large-models\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70515b7b-80f7-4f2d-a015-273fc1a5e31d",
+   "metadata": {},
+   "source": [
+    "Plot feature importance for each model separately:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "e5cd171b-ce3f-44ae-9afe-6dd6831d6e59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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/Wh988AHi4+OxY8cOaDQarFq1CuvWrUOzZs1Qt25dODk5ITs7G/Hx8YiJiREmthfXnq2TSqX46KOP8OWXXwIATp48ib59+6Jly5aoWbMm0tPTcfbsWWRkZAAAvvnmG3z22Wcmi+fo0aPCtaRSKQYNGlTuNnfv3o1r164VeT83Nxf37t1DVFRUgTXeWrZsiUmTJpX7ukTlxcKMiMqtVq1a2LhxI9atW4dVq1YJq8OnpqbixIkTxZ4nkUjQunVrvPPOOyXegSeRSLBgwQI0bdoUv/zyCzIyMqBQKHDp0qVibwiQSCRo1apV+T6YiA0dOhQPHz4UlorIycnByZMnCxzj6OiIL7/8EsHBwSYtzHSHMTt27Ijq1auXu01DnpWpNXToUHz55ZcGPVqMyNRYmBFRhXB2dsaECRMwatQonDhxAqdPn8bVq1eRkpKC9PR0qNVqVK5cGR4eHggICEBgYCC6d+9eYL5TaUJDQzFkyBDs2rULp06dQnR0NFJTUyGXy+Hq6gofHx80atQI7dq1Q9euXQvc0UlFTZ06FV26dMH69etx4cIFpKamwtXVFdWrV0eXLl3w+uuvo379+iaNoXDxbuq1y5ycnFCpUiU0bNgQLVu2xKBBg9CwYUOTXpPIGBKNqZdxJiIiIiKDcPI/ERERkYVgYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGFYGFGREREZCFYmBERERFZCBZmRERERBaChRkRERGRhWBhRkRERGQhWJgRERERWQgWZkREREQWgoUZERERkYWwN3cAZF5qtRq5ubkAAHt7e0gkEjNHREREZB00Gg2USiUAwNnZGVJp+fu7WJjZuNzcXERHR5s7DCIiIqsWEBAAV1fXcrfDoUwiIiIiC8EeMxtnb/+/fwK+vr5wcXExYzRkCgqFQugVDQgIgEwmM3NEZArMs/gxx5ZHNye6v0/Lg4WZjdOdU+bg4AAHBwczRkOmIJVK4e7uDgBwdHSssB8eZFmYZ/Fjji1bRc3RZlZJwG9ycbK3t4evr6+5wyATY57Fjzm2DZxjRkRERGQhWJgRERERWQiOXZFAoVBwjpkIKRQK3LlzB0D+DR6cMCxOzLP4Mce2gYUZkchpNBpkZ2cL2yROzLP4Mce2gUOZRERERBaChRkRERGRhWBhRkRERGQhWJgRERERWQgWZkREREQWgoUZERERkYXgchkkkEpZp4uRnZ0dateuLWyTODHP4scc2wYWZiTgN7o42dnZwcfHx9xhkIkxz+LHHNsGdpEQERERWQgWZkREREQWgkOZJOCzMsVJLpcjKioKABAYGMgcixTzLH7MsW1gjxkRERGRhWCPGQns7fnPQYxkMhkCAwOFbRIn5ln8mGPbwN/EJJBIJOYOgUxAIpFwyMMGMM/ixxzbBhZmJFCp1UhMzzZ3GERERALPys6ws6F1NlmYkSA1Kw8DF+42dxhERESCfWFD4ePuau4wXhjbKUGJiIiILBwLMyIiIiILwcKMiIiIyEKwMCMiIiKyECzMiIiIiCyExd+VmZmZid27d+PYsWO4c+cOUlJSYG9vD09PTzRr1gw9evRAnz59YGdnZ3Cbqamp+Pvvv3Hy5EncunULaWlpkMvlqFKlCho2bIhWrVphwIAB8PPzK7UttVqNo0eP4vjx47h06RKSk5Px7NkzuLq6wsfHR4ixa9euehcEDA0NxdmzZ4u8L5VKUalSJdSpUwft2rXDm2++iXr16hn8GYmIiMj6SDQajcbcQRRn8+bNWLx4MdLT00s87qWXXsLXX3+NNm3alHicWq3Gb7/9hj/++APZ2aWv19WpUyd8+eWXeOmll/TuP3v2LObOnYvY2NhS2/L29sbUqVMREhJS4P3iCrPCZDIZZsyYgZEjR5Z6rDF0n71WvZ4vl8sgIiKLYsnLZZji+aUW22O2cOFCrFq1CkD+o4L69++PoKAg1KpVC3K5HPfu3cPevXtx5swZ3L59G++88w4WLVqEvn376m0vLy8PH3/8MY4ePQogv9Dp378/XnnlFdSuXRtOTk5ISUlBVFQUDh8+jJs3b+LkyZPYtGkTwsLCirS3bds2zJkzBwqFAgDQokUL9OrVC40bN4a7uzsyMzPx6NEjHD9+HMeOHUNSUhLmzZtXpDDTtWfPHmFbrVYjISEBBw4cwI4dO6BQKDB37lzUqlUL3bp1K+uXlYiIiCyYRRZm69evF4qy6tWr47fffkPjxo0LHNOmTRsMGzYMe/bswYwZMyCXyzFt2jTUq1evyLEA8NVXXwlF2csvv4wff/wRtWvXLnJc165d8cEHH+Do0aOYN2+e3vhOnz6NmTNnQq1Ww8XFBfPnz9dbEHbo0AHDhg1DXFwcFi9ejBMnTpT4uQsPnQYEBKBbt25o2rQpvv32W2g0GixdupSFGRERkUhZ3OT/+Ph4LFy4EADg4uKC1atX6y20tIKDg4UCSi6X4/PPP0fh0dlDhw5hx44dAAB/f3+sXr1ab1Gmq3v37ti2bRs6dOhQ4P3c3FxMmzYNarUaUqkU//nPf4rtpdOqXbs2lixZgpkzZ5Z4XHFGjhyJWrVqAQCuX7+OlJSUMrVDREREls3iCrM1a9bg+fPnAID33nsPDRo0KPWcgQMHokuXLgCA2NhYoWdM67fffhO258+fDxcXF4NiqVy5Mnr06FHgvW3btuHp06cA8gumdu3aGdQWAAwePNjgY3VJpVI0a9ZMeP348eMytUNERESWzaIKM41Gg127dgEAnJyc8Oabbxp87ttvvy1sb9++XdiOiYnBtWvXAOQPfzZt2rRcMWrblkgkGD16dLnaMoa9/f9GndVq9Qu7LhEREb04FjXH7NatW8IdmG3atEGlSpUMPveVV16Bk5MT8vLycOHCBeH9c+fOCdtdu3YtV3xZWVm4efMmAKBBgwaoW7duudozhu6dn97e3i/sukREROXhAFW5zs/NyUF20dWmjObqapl3dhZmUYVZdHS0sN2kSROjzrWzs0NAQAAuX76M1NRUJCYmwsfHp0CbusOBZRETEyP0VpW3580Yx44dw61btwAAderUQY0aNV7YtYmIiMqjr9vDcp2/5vdfKySOOXPmVEg7pmZRhVlaWpqw7enpafT51apVE7bT09Ph4+NTYA00Dw+PcsWn25butUxBd7mMpUuXCu+/++67Jr0uERERmY9FFWa6i74aOkFfl+45WVlZFdKmLt22nJ2dy9WWPv7+/iXuHz58uFHz7oiIiMi6WFRhpjv+m5OTY/T5uue4ublVSJu6dNvKzc0tV1uGcnZ2RqtWrTBq1CgEBQW9kGsSERGReVhUYVa1alVhOzk52ejzddf3cnd3L/D/wvvLQretssRXGt2V/+3s7ODq6govLy+jngNKRERkSQ5kle9GuQ1TX4NXlfKNeFkTiyrMdIfybty4YdS5KpUKMTExAPLnkvn4+ADIXz1f6/r16+jUqVO54pNKpVCr1UbHZwhDHppORERkTeQoX+eCs4uL1dxRWREsah0zPz8/oVfq/PnzyMzMNPjcU6dOCcOLug8zb9u2rbB9/PjxcsXn5uYmPIXg3r17ePDgQbnaIyIiItJlUYWZRCLBoEGDAOQ/dHzz5s0Gn/vf//5X2B4yZIiw7e/vLyyTcf78eVy/fr1cMWofQq7RaLBu3bpytUVERESky6IKMwAYPXo0HBwcAADLli0zqFfq77//xrFjxwDk97p17969wP5JkyYJ2zNmzDD4JoBnz54hPDy8wHtDhw6Fl5cXgPyHrZ89e9agtgBg586dBh9LREREtsfiCrPatWvj888/B5B/F+WYMWMKLBJb2L59+/DFF18AAGQyGb7//ntIJJICx/Tq1Ut4TmVMTAzGjBmD+Pj4EuM4fvw4Xn/9dZw+fbrA+87Ozli0aJEw12zixIn4559/Smzr8ePH+OSTT/Dtt9+WeBwRERHZNoua/K8VGhqKhw8fYu3atXj8+DGGDh2KAQMGICgoCDVr1oRSqcTdu3exd+9eoXBycHDAokWLhDlghX311VfIyMjA0aNHceXKFfTt2xf9+vVD586dUatWLTg5OSE5ORk3btzAoUOHShzy7NixI7799lvMmTMHOTk5+PDDD9GiRQv06dMHjRs3RpUqVZCZmYlHjx7hxIkTCA8Ph1wuN+oRU0RERGR7LLIwA4CwsDA0bNgQP/30E9LT07Fr1y7hAeeF+fr64ptvvikw6b8wZ2dn/Prrr1i+fDlWrlyJ7OzsEtsEgG7duuGtt97Su2/o0KGoU6cO5s6di9jYWFy+fBmXL18utq2aNWvi008/LXY/ERERkcUWZgAwYsQIDBgwALt378axY8dw+/ZtpKamws7ODp6enggMDESPHj3Qt29fg9b6kkqleP/99zFixAj8/fffOHnyJGJjY5GWlgaFQoEqVaqgYcOGaNWqFQYOHAhfX98S22vXrh127dqFo0eP4tixY7h06RKSk5ORmZkJFxcXVK9eXYixa9eusLe36C83ERERmZlEo9FozB0EmY9cLkdUVBQAoHo9XwxcuNvMEREREf3PvrCh8HG3zHXMdH+HBgYGCjcvlofFTf4nIiIislUszIiIiIgsBAszIiIiIgvBwoyIiIjIQvA2QRJ4uDlhX9hQc4dBREQk8KzsbO4QXigWZiSwk0rh4+5k7jCIiIhsFgszEnDlFHHSaDRQKBQA8h9bVviRZSQOzLP4Mce2gXPMSKBUKs0dApmAQqFAVFQUoqKihB/qJD7Ms/gxx7aBhRkRERGRhWBhRkRERGQhOMeMBJyvIE5SqRTe3t7CNokT8yx+zLFtYGFGAj5kXZzs7e1Rp04dc4dBJsY8ix9zbBtYchMRERFZCBZmRERERBaCY1ckUKnVSEzPNncYRGQkz8rOsJNKIZfLER0djcaNG0Mmk5k7LKpgCoUCN2/eBADmWMRYmJEgNSsPAxfuNncYRGSkfWFD4ePuCiD/lzcXixYn3QVmmWPx4lAmERERkYVgYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGFeOGFmUqlQqtWreDv748hQ4aUeKxGo0H79u3h7+8Pf39/bN26tcTjd+zYIRz7119/Ce+HhoYK75dmzZo1wrEdO3ZEdHS0sO+XX34R9kVGRpbaFgAEBQXB398fQUFBevdPnz5daFP3v4CAALRp0wYDBw7E119/LdwiTUREROL1wgszOzs7tGzZEgAQHR2NrKysYo+9desW0tPThdcXLlwose3z588L223btjU6tj/++APz5s0DAHh5eWHdunUICAgwup2KoNFokJmZiZiYGPz1118ICQnBkiVLzBILEVkHe3t7NGzYkI9XEyltfpljcTNLZtu2bYuIiAio1WpcvHgRr776qt7jtIWWnZ0dVCpVgcKrpOOrVq2Kl156yaiYli9fjp9++gkA4OPjgzVr1qBBgwZGtVFeK1euFB5Qq1arkZKSgmPHjuGvv/6CUqnEb7/9Bm9vb4wcOfKFxkVE1kEqlaJq1armDoNMhPm1DWaZY9amTRthu6RiS7uvb9++AICHDx8iMTFR77EpKSm4f/8+AKB169aQSCQGx/PLL78IRVnNmjXx3//+94UXZQBQv359+Pn5wc/PDwEBAejUqRPCwsLwyy+/CMcsW7YMKpXqhcdGREREpmeWwqx58+ZwdHQEYFhh1qdPH9StWxdA8cOZZR3G/PHHH7Fs2TIAQO3atfHf//5XuJalCAoKEorZ1NRUXL9+3cwREZEl0mg0kMvlXBVepLT5ZY7FzSyFmYODA5o3bw4AiIqKglwuL3LMo0ePhN6x1q1bo3Xr1gCKL+R039ftkSvJwoUL8Z///AdAfm/V+vXrUatWLcM/yAuk/XoBwOPHj80YCRFZKoVCgaioKOGxPSQu2vwyx+JmtuUytMWTXC7HlStXiuzXFlr169eHp6enUJidO3dOb3vanjQ3Nzc0bty41Ot/++23WLVqFQCgYcOGWLduHapXr278B3lBdCd6qtVqM0ZCRC+aA1Ql/pebk4Ps7Gzk5OTg+fPnyPn/rwv/R0SWz2y3dbRt2xbLly8HkF+EFR5+1BZmrVq1KvD/W7duISMjA1WqVBGOzcrKEpa1aNmyJezs7Iq9rkajwddff40NGzYAAPz8/PDnn3/C09Ozgj6ZacTGxgrb2hsEiMg29HV7WOL+Nb//WuD1oUOH9B43Z86cCouJiEzDbIVZixYtYG9vD6VSqXd4UvuetmfN19cXVatWRVpaGi5evIju3bsLx166dEmYEF/a/LJZs2Zhy5YtAIDGjRtj1apV8PDwMDr+uLg4g+6OqYju5ujoaJw4cQIA4OzsjMDAwHK3SURERJbHbIWZq6srGjdujKioKKGw0vZ0Fb7DUqtVq1Y4cuQIzp8/X6AwM2Z+mbYo8/X1xZo1awr0vBnjyy+/LNN5htJoNEhOTsaxY8ewePFiofAMDQ0VbpwgIiIicTHrCnVt27ZFVFQUsrOzcfPmTTRr1gzA/+aReXp6on79+sLxrVu3FgozXdr5ZY6OjqX2JkkkEmg0GsTFxeHGjRvo2LFjBX6i8unRo0eJ+7t164YPP/zwBUVDREREL5pZC7PWrVsLE/DPnz8vFGbawku3twz4X2/Y9evXkZeXBycnJ8jlcly9ehUA8PLLL8PBwaHEa86aNQvffPMNnj9/jsmTJ+OPP/4w+C5OXWvXrkX79u1LPS4oKAjx8fFGt68lk8kQGBiI4cOHY9CgQUatz0ZE4nAgq+QlfDZMfQ1eVVygUChw48YNNGnSBDKZ7AVFR0QVyayFWZs2bYQerAsXLmDMmDEA/tcDpp3wr9WkSRM4OzsjNzcXly9fRocOHRAVFYXnz58L7ZVm5MiRyMvLw/fff4/c3FxMnDgRf/75Z4HlKMxFd+V/qVQKFxcXeHp6llpsEpG4yVH8DU0A4OziAldXV8jlcjg6OsLFxYU/N4islNmWywAAd3d3NGrUCMD/esl077As3GOm7T3SPV53wVlDF5YdN24cpkyZIlzv3XffLfCwcnPRXfn/pZdeQs2aNfnDlYgMpv0Zyd4ycdLmlzkWN7MWZgAKrGh/584dXLhwAWq1Gi4uLmjSpEmR4wsvNKudjyaTydCiRQuDr/vBBx9g/PjxAICMjAyMHTsWd+7cKc9HISIyK4lEAgcHB055ECltfpljcbOYwgzI7/3S9oC1aNFC73pk2sLsypUrkMvluHTpEoD8YU4XFxejrv3ZZ58hNDQUQP6doGPGjMGjR4/K9DmIiIiIysuiCrNz584JPWCFhzG1tAvI5uTkYPv27cjMzCzSjjHCwsLwxhtvAACSkpLw9ttv48mTJ2Vqi4jInNRqNdLS0vh0EJHS5pc5FjezF2Y+Pj7CQ8MjIyNx7do1AMUXZm5ubvDz8wMA/PHHH8L7xjy4XJdEIsHXX3+N4OBgAEB8fDzGjBmDp0+flqk9IiJzUSqVuHv3LpRKpblDIRPQ5pc5FjezF2bA/3q7EhMTIZfLYW9vX+J8MW3Rph12lEqlxRZyhpBKpVi4cCH69OkDALh//z7eeecdpKamlrlNIiIiImNZVGGm1bhxYzg7Oxd7fOEizM/PD5UrVy5XDHZ2dli8eDG6desGIP+ZnOPGjcOzZ8/K1S4RERGRoSyiMCs8DFla71fh/WUdxixMJpNh6dKleOWVVwAAN27cwPjx45GdnV0h7RMRERGVRKLRaDTmDoLMRy6XIyoqCgBQvZ4vBi7cbeaIiMhY+8KGwsfdVfh+DgwM5BqIIqT785o5tgymyIlF9JgREREREQszIiIiIoth1mdlEhFRxZLJZFwVXqQkEonwKCbmWLxYmBERiYSDgwOaN29u7jDIRGQyGfNrA1iYkcDDzQn7woaaOwwiMpJn5eKXFyIi68LCjAR2Uil83J3MHQYREZHN4uR/EnDlFHHSaDRQKpVQKpXMsYhpNBrI5XI8evSIj+sRKaVSiUePHjHHIsfCjAT8RhcnhUKBK1eu4MqVK1AoFOYOh0xEoVAgKioKSUlJfMC1SKnVaiQlJTHHIsfCjIiIiMhCsDAjIiIishAszIiIiIgsBAszIiIiIgvB5TJIYG/Pfw5iJJPJEBgYKGyTODHP4scc2wb+JiYBH/EhThKJBA4ODuYOg0yMeRY/5tg2sDAjgUqtRmJ6trnDICIiC+FZ2Rl2Us56epFYmJEgNSsPAxfuNncYRERkIfaFDYWPu6u5w7ApLIOJiIiILAQLMyIiIiILwcKMiIiIyEKwMCMiIiKyECzMiIiIiCyE1dyVmZOTg127diE8PBzR0dFIT0+HRqOBm5sbatWqBT8/P7Rs2RJdunRBjRo1Smzr8ePH+Pvvv3Hq1Ck8ePAAqampUKvVqFKlCvz8/NC6dWsEBwejTp06Rc6NjIzE6NGjAQAffPABpkyZUmrs06dPx44dOwAAR44cQe3atQvs3759O2bMmAEAmD9/PkJCQgz6mhAREZG4WEVhdunSJXzyySd4/PhxkX1paWlIS0vDtWvXsH37dnh6euLkyZN623n+/DkWL16MDRs2QC6XF9mflJSEpKQkREREYOnSpejbty+++OKLUgs9IiIioopg8YXZvXv3MG7cOGRn5y98GhQUhD59+qBBgwaQyWRIS0tDdHQ0Tp06hcjIyGLbSU1NxeTJk3H58mUAgKurK1577TV06NAB1atXh729PZKTk3Hx4kUcOnQI9+/fx/79+9GiRQuMGTPmBXxSIiIisnUWX5gtWbJEKMqKG+br1KkTxo0bh9TUVOzfv7/IfrVajY8//lgoyrp3747vvvsO1apVK3JsUFAQPvnkE+zevRvff/99xX4YIiIiohJYdGGmUqlw/PhxAECzZs1KnXvl4eGBkSNHFnl/7dq1Qm9a586dsWzZshIf2C2VSjF48GB06NAB9+7dK8cnICIiIjKcRRdmqampyMvLAwDUq1evTG3I5XKsXLkSAODo6Ih58+aVWJTpql69OqpXr16m6xIREREZy6ILM5lMJmzfuXOnTG1EREQgKSkJANC3b1/4+PhUSGxERETWxgEqo47PzclBtqz043S5uvLZmuVh0YWZu7s7atWqhfj4eERHR2PFihV49913ITXiSffnzp0Ttrt162aCKImIiKxDX7eHRh2/5vdfjb7GnDlzjD6H/seiCzMAGDVqFBYuXAgAWLx4MTZu3IigoCC0atUKgYGBetca0xUdHS1sN23atEJjS0lJQWxsbKnHPXv2rEKvS0REROJk8YXZmDFjcPv2bWzbtg0AEB8fj3Xr1mHdunUAAE9PT7Rr1w7BwcHo3r07JBJJgfPT09OFbX13YZbHhg0bsGHDhgptk4iIiGyXxT+SSSqVYt68eVi1ahW6dOlSZOJ+cnIy9u3bh8mTJ+P111/Hw4cFu2m1S20AgLOz8wuJmYiIiKgsLL7HTKtTp07o1KkTsrKycOHCBURFReHatWs4f/48MjMzAQDXrl3DW2+9he3bt8Pb2xtAwUmIubm5cHNzq7CYyvJIJiIiInM5kFXXqOM3TH0NXlVcTBQN6WM1hZmWm5sbunbtiq5duwLIXw5jz549WLhwITIyMvD06VP8/PPP+O677wDk30CglZycXKGFGRERkTWRw86o451dXHiX5Qtm8UOZpXFwcMDQoUOxePFi4b1Dhw5BrVYDAAICAoT3b9y48cLjIyIiIjKU1RdmWl26dBEeNp6RkSFM+m/btq1wzLFjx8wQGREREZFhRFOYARDmlenq3Lmz8P6BAweQmJj4osMiIiIiMohoCrPc3Fzcvn0bQP48tKpVqwLIH+ocN24cAOD58+f48ssvoVIZtvJxYmIiTp8+bZqAiYiIiAqx6MIsOzsbw4YNw9GjR4U5Y/qo1WrMnTtXWBojKCiowHpmo0ePRvv27QHkP6Lp/fffR2pqarHtaTQa7NmzByEhIYiJiamgT0NERERUMou/K/Pq1auYNGkSfHx80LNnT7Ro0QI1a9aEm5sbnj17hhs3bmDbtm3CCvyVKlXCRx99VKANqVSKn376CZMmTcKVK1dw9OhR9OzZE8HBwejQoQOqV68Oe3t7JCcn4/Llyzh48CDu3r1rjo+LCxcuGHRcUFBQgTtOiYiIyPpZdGFmb28PLy8vPH36FImJiVi/fj3Wr19f7PH169fH4sWLUbt27SL7PDw8sG7dOvzwww/YsGEDsrOzsXHjRmzcuFFvWxKJBMHBwejXr1+FfR5DbN26FVu3bi31uJ07d7IwIyIiEhmLLswcHR3x77//4vLlyzh16hSuXLmCe/fuISUlBc+fP4ezszO8vb0REBCAHj16oHfv3nBwcCixvbCwMLzzzjvYu3cvTp8+jfv37yM1NRUajQZVqlSBn58f2rZti+DgYNSqVesFfloiIiKydRKNRqMxdxBkPnK5HFFRUQCA6vV8MXDhbjNHRERElmJf2FD4uHOB2eLo/g4NDAwssXPIUBY9+Z+IiIjIlrAwIyIiIrIQLMyIiIiILIRFT/6nF8vDzQn7woaaOwwiIrIQnpWdzR2CzWFhRgI7qRQ+7k7mDoOIiMhmsTAjAW/QFSeNRgOFQgEAkMlkBZ6KQeLBPIsfc2wbOMeMBEql0twhkAkoFApERUUhKipK+KFO4sM8ix9zbBvYY0YCmUxm7hDIBBwcHNC6dWtzh0EmxjyLH3NsG9hjRkRERGQhWJgRERERWQgOZZJApVKZOwQyAZVKheTkZACAp6cn7OzszBwRmQLzLH7MsW1gYUYCtVpt7hDIBFQqFeLi4gAAVatW5Q9zkWKexY85tg0cyiSBvT3rdCIiInNiYUYCrolDRERkXuwiIYFKrUZiera5wxAlz8rOsJPy7yAiIioZCzMSpGblYeDC3eYOQ5T2hQ2Fj7urucMgIiILxz/hiYiIiCwECzMiIiIiC8GhTCKRk0gkcHV1FbZJnJhn8WOObQMLMyKRk8lkCAgIMHcYZGLMs/gxx7bBqgqzs2fPIjQ0VHi9YcMGtGrVyqg2Hj58iP379+P06dN4+PAh0tLSoFQqUalSJdSuXRtNmjRBt27d0KlTp2If6r19+3bMmDHDqOvOmDEDY8aMKfBeaGgozp49W+RYqVSKSpUqoU6dOmjXrh3efPNN1KtXz6jrERERkfWxqsJsx44dBV7v3LnT4MLs2bNnWLBgAXbt2gWlUllkf0pKClJSUnDlyhVs2LAB1apVw/jx4zF69OgXvrqyWq1GRkYGMjIycO3aNaxbtw4zZszAyJEjX2gcRERE9GJZTWGWl5eHf/75BwDg4uKCnJwcHDhwADNnzoSDg0OJ5z569Ajjx4/HvXv3AADu7u4YMGAA2rRpA29vbzg7OyM1NRV3795FREQETp06hZSUFCxYsACvvfYavLy8im37448/Ro8ePUqNv6Q2AGDPnj3CtlqtRkJCAg4cOIAdO3ZAoVBg7ty5qFWrFrp161bqtYh0KZVKPHjwAABQr149PuFBpJhn8WOObYPVZPXQoUPIzs5f/HTmzJn48ssvkZGRgfDwcPTt27fY83JycjBx4kShKHvjjTfwxRdfwM3NrcixXbp0wdtvv41Hjx7h119/xfbt20uNy8fHB35+fmX8VP9TuI2AgAB069YNTZs2xbfffguNRoOlS5eyMCOjqdVqpKenAwDq1Klj3mDIZJhn8WOObYPVLJexc+dOAIC/vz+GDh2KBg0aFHi/OD/88APu3LkDAHjrrbcwd+5cvUWZrjp16mD+/PlYvnw5HB0dyx17eYwcORK1atUCAFy/fh0pKSlmjYeIiIhMxyoKs6SkJJw+fRoAMHDgwAL/j4iIQGpqqt7zkpOTsXXrVgBAjRo18MUXXxh13aCgIFSuXLmsYVcIqVSKZs2aCa8fP35sxmiIiIjIlKyiMNuzZw9UKhWkUimCg4MBAMHBwZBIJFAoFNi7d6/e8/bt24fnz58DAIYNGwYnJ6cXFnNF0p1HoFarzRgJERERmZJVzDHbtWsXAKBdu3bw8fEBkD/c2LJlS1y8eBE7d+7E6NGji5x37tw5YbtLly4vJlgTiI2NFba9vb3NGIltcICqwtvMzclBtv7VV8pFu9gkERGJg8UXZjdv3kRMTAyA/w1fag0cOBAXL17E9evXcfv2bbz00ksF9mvPk0qlJluULzExsUDhVJyy3iBw7Ngx3Lp1C0B+MVqjRo0ytUOG6+v2sMLbXPP7rxXeJgDMmTPHJO0SEZF5WHxhpp3c7+TkhD59+hTY169fP3z33XdQKBTYuXMnPvvsswL709LSAACVKlUqcUmNp0+fCscWVqVKFaGXTp+ffvoJP/30U6mfQ1skGkJ3uYylS5cK77/77rsGt0FERETWx6ILM6VSKcwf6969e5G7Kd3d3dG1a1ccPnwYe/bswSeffAKp9H/T5rTLazg7O5d4nRUrVmDt2rV69w0ZMgQLFiwoz8cwiL+/f4n7hw8fjjfffNPkcZD4SKVSYQhc9/uDxIV5Fj/m2DZYdGEWERGB5ORkAEWHMbUGDhyIw4cPIyEhAZGRkejYsaOwz9XVFc+ePUNubq7JYpw/fz5CQkJM0razszNatWqFUaNGISgoyCTXIPGzt7fnmkc2gHkWP+bYNlh0YaYdxnR3dy928n737t1RuXJlPHv2DDt37ixQmLm7u+PZs2fIzMyEXC4vdjgzLCwMYWFhwuu4uDiDVvOvSLor/9vZ2cHV1RVeXl4v/HFQBBzIqlvhbW6Y+hq8qrhUeLtERCQuFluYZWZmIjw8HACQnp5eYC2v4hw8eBBz5syBi0v+L8CAgAA8fPgQarUaMTExCAwMNGnM5VERTw+giiFHxRfDzi4uvIOSiIhKZbGD1Pv37xfWIDNUTk4ODh48KLxu27atsH3ixIkKi43ImigUCly9ehVXr16FQqEwdzhkIsyz+DHHtsFie8y0w5heXl6YMWNGqcd///33SEhIwK5duzB48GAAQP/+/bFo0SLI5XJs2bIF48aNM/sjloheNI1GI/wQ12g0Zo6GTIV5Fj/m2DZYZGH26NEjXLx4EQDQp08fDBgwoNRzLl++jLVr1+LMmTNITEyEj48PPD09MXToUGzYsAGPHz/GokWLMHPmTFOHT0RERFQmFjmUuWvXLuGvgcJrlxVHe5xarRaeFAAAn332mfDA83Xr1uGrr74SltEozrNnz8oSNhEREVG5WGSPmbawqlatGtq0aWPQOa1atYKXlxeePn2K3bt3Y8KECQAANzc3/Oc//8GECRNw//59bNiwAQcOHMBrr72GNm3awNvbGy4uLsjKysKDBw9w6tQpHDp0SGi3tOdrGrryv5ubG2rWrGnQZyEiIiLbZHGF2YULF/DwYf4jcXr27GnwInpSqRS9evXCX3/9hVu3buHatWvCnZz16tXD5s2bMX/+fOzevRtpaWlYt24d1q1bV2x71apVw7vvvovQ0NASr2voyv89evTAr7+a5rE8REREJA4WV5jpDkMaOoyp1bt3b/z1118A8m8e0F1io0qVKliwYAEmT56Mffv24cyZM3jw4AHS09OhUqlQuXJl1KhRA82aNUPnzp3RtWtXyGQmeOo0ERERUTEkGt7aYdPkcjmioqIAANXr+WLgwt1mjkic9oUNhY+7edYx081xYGBgic+NJevFPIsfc2x5TJETi+sxI6KKZW9vj4YNGwrbJE7Ms/gxx7aBmSUSOalUiqpVq5o7DDIx5ln8mGPbYJHLZRARERHZIvaYEYmc7mrhMpkMEonEzBGRKTDP4scc2wYWZiTwcHPCvrCh5g5DlDwrO5vt2gqFghOGbQDzLH7MsW1gYUYCO6kUPu4lL6hLREREpsM5ZiTgyilERETmxcKMBEql0twhEBER2TQWZkREREQWgoUZERERkYVgYUZERERkIViYEREREVkILpdBAj57TZxkMhkCAwOFbRIn5ln8mGPbwN/EJOAq0uIkkUi4EKUNYJ7Fjzm2DRzKJCIiIrIQ7DEjgUqtRmJ6tlmu7VnZGXZS/p1gCmq1GhkZGQCAKlWqQMqvsygxz+LHHNsGFmYkSM3Kw8CFu81y7X1hQ+Hj7mqWa4udUqnE3bt3AfD5emLGPIsfc2wbWG4TERERWQgWZkREREQWgoUZERERkYVgYUZERERkIWxy8n9OTg527dqF8PBwREdHIz09HRqNBm5ubqhVqxb8/PzQsmVLdOnSBTVq1Chw7vTp07Fjxw6jrrdz5040btxYeP3BBx/g0KFDAIDVq1ejY8eOJZ5/5MgRvPfeewCAwYMHY+HChUZdn4iIiKyDzRVmly5dwieffILHjx8X2ZeWloa0tDRcu3YN27dvh6enJ06ePFnhMcyePRuRkZF49uwZZs+ejT179sDJyUnvsZmZmfjqq68AANWqVcOMGTMqPB4iIiKyDDZVmN27dw/jxo1Ddnb+Wl1BQUHo06cPGjRoAJlMhrS0NERHR+PUqVOIjIwstb2VK1fC29u71OPq1atX4LW3tzemTZuGWbNm4eHDh/j555/xxRdf6D33+++/R1JSEgBg5syZcHd3L/V6RLokEonw+BY+3UG8mGfxY45tg00VZkuWLBGKsvnz5yMkJKTIMZ06dcK4ceOQmpqK/fv3l9he/fr1Ubt27TLFMmzYMOzZswdnz57FmjVrMGDAADRr1qzAMZGRkdiyZQuA/CKyf//+ZboW2TaZTIbmzZubOwwyMeZZ/Jhj22Azk/9VKhWOHz8OAGjWrJneokyXh4cHRo4cabJ4JBIJvv32Wzg6OkKlUiEsLAxKpVLYn5eXh1mzZglz37TDmURERCReNlOYpaamIi8vD0DRoUVzqVevHj744AMAQHR0NP744w9h39KlS/HgwQMAwLRp0+Dj42OWGImIiOjFsZnCTDsuDwB37twxYyQFjR07Fk2aNAEA/N///R/u3r2La9euYfXq1QCAtm3bYvjw4WaMkKydUqnEo0eP8OjRowK9siQuzLP4Mce2wWbmmLm7u6NWrVqIj49HdHQ0VqxYgXfffdfsD4G1t7fHt99+i2HDhkEul2PmzJnIycmBSqWCo6Mj5s6da/WTPB2gKvWY3JwcZMtKPQwA4OrKZ2oaQ61WCzeQsOdVvJhn8WOObYPNFGYAMGrUKGENsMWLF2Pjxo0ICgpCq1atEBgYiDp16hjV3v3795GTk1PiMc7OzqW227RpU4wZMwYrV67EhQsXhPc/+OADNGjQwKiYLFFft4elHrPm918Nbm/OnDnlCYeIiMhi2VRhNmbMGNy+fRvbtm0DAMTHx2PdunVYt24dAMDT0xPt2rVDcHAwunfvXmpP1bhx40q9Zrt27YT2S/Lhhx9i3759ePLkCQCgSZMmGDt2bKnnERERkXjYzBwzAJBKpZg3bx5WrVqFLl26wN6+YF2anJyMffv2YfLkyXj99dfx8GHpPT0V5cGDB0hOThZee3h4FImPiIiIxM0mf/N36tQJnTp1QlZWFi5cuICoqChcu3YN58+fR2ZmJgDg2rVreOutt7B9+/ZiF5E9cuRImdcx06VdLkOhUAjvRUREYM+ePQgODi53+0RERGQdbLIw03Jzc0PXrl3RtWtXAIBcLseePXuwcOFCZGRk4OnTp/j555/x3XffmTSOtWvXIioqCkD+8OiePXuQlJSEefPmoXPnzqhatapJr29qB7LqlnrMhqmvwauKywuIhoiIyHLZ1FBmaRwcHDB06FAsXrxYeO/QoUNQq9Umu+ajR4/w888/AwD8/PwwdepUzJo1C0D+2mtieGC5HHal/ufs4gJXV1eD/iMiIhIrFmZ6dOnSBTVq1AAAZGRkID093WTXmj17NnJzcyGVSvHdd99BJpOhd+/e6NmzJwBgx44dOH36tMmuT+InlUrh7u4Od3d3sy8PQ6bDPIsfc2wbmNliGPJw8vLatm0bTp06BQAIDQ0t8Ay02bNnw83NDUD+8hDPnz83eTwkTvb29vD19YWvry9vKBEx5ln8mGPbwMJMj9zcXNy+fRtA/jw0U8zxSk5OFoYpa9WqhY8//rjAfh8fH3z66acA8u/YXLZsWYXHQERERJbFZgqz7OxsDBs2DEePHi1xzpharcbcuXORnZ0NAAgKCjLJyvvffPMNMjIyAABff/01XFyKTnwfMWIEWrVqBQBYtWoVoqOjKzwOIiIishw21Rd69epVTJo0CT4+PujZsydatGiBmjVrws3NDc+ePcONGzewbds2xMbGAgAqVaqEjz76qNj2DFn5HwC8vLwK9LodPnwY//zzDwBg0KBB6NKli97zJBIJ5s6di8GDB0OhUGDmzJnYvHkz5xaQURQKhfB8WF9f3wLPjSXxYJ7Fjzm2DTZTmNnb28PLywtPnz5FYmIi1q9fj/Xr1xd7fP369bF48eIS1ykzZOV/AJgxYwbGjBkDAMjMzMTXX38NIH8R2RkzZpR47ksvvYSJEydi2bJliIqKwrp16/D2228bdF0iANBoNEIPsEajMXM0ZCrMs/gxx7bBZgozR0dH/Pvvv7h8+TJOnTqFK1eu4N69e0hJScHz58/h7OwMb29vBAQEoEePHujduzccHBwqPI7vv/9eeAjtl19+adD8tYkTJ2L//v24c+cOfvrpJ/Tq1Qs1a9as8NiIiIjIvGymMAPybzVu1aqVMG+rLBYsWIAFCxaU+fy5c+di7ty5Rp3j4OCAffv2lfmaREREZB04WYmIiIjIQrAwIyIiIrIQLMyIiIiILAQLMyIiIiILYVOT/6lkHm5O2Bc21CzX9qzsbJbr2gI7Ozth2Rc7OzszR0OmwjyLH3NsG1iYkcBOKoWPu5O5w6AKZmdnBx8fH3OHQSbGPIsfc2wbOJRJREREZCFYmBERERFZCA5lkkChUJjkaQdkXnK5HFFRUQCAwMBA5likmGfxY45tA3vMiIiIiCwECzMiIiIiC8HCjIiIiMhCsDAjIiIishAszEhgb897QYiIiMyJhRkJJBKJuUMgIiKyaewiIYFKrUZiera5wxAdz8rOsJPybyAiIiodCzMSpGblYeDC3eYOQ3T2hQ2Fj7ur2a5vb28Pf39/YZvEiXkWP+bYNjCzRCInlUrh5uZm7jDIxJhn8WOObQPHV4iIiIgsBHvMiEROrVYjJycHAODi4gIp57uJEvMsfsyxbWBWiUROqVQiJiYGMTExUCqV5g6HTIR5Fj/m2DaIqscsMjISo0ePFl67uLjg1KlTcHZ2LvG8vLw8dOrUCVlZWcJ7a9euRfv27Us87+zZswgNDRVeb9iwAa1atTI6Tl1OTk5wd3dHQEAAevXqhYEDB/JBtURERDZC1D1mOTk5OHz4cKnHHTlypEBRZqgdO3YUeL1z506j2ygsLy8PCQkJOHbsGMLCwhASEoK4uLhyt0tERESWT7SFmaOjIwBg165dpR6rPUZ7jiHy8vLwzz//AMjvmQOAAwcOQC6XGxXniBEjsGfPHuG/1atXIywsDNWrVwcA3Lp1C5MnT4ZKpTKqXSIiIrI+oi3MgoKCAACnTp3C06dPiz0uJSUFJ0+eBAD06NHD4PYPHTqE7Oz8xVhnzpwJAMjIyEB4eLhRcVarVg1+fn7Cfx07dsTo0aPx999/o1atWgCA2NhYHDp0yKh2iYiIyPqItjDr1KkTvLy8oFKp8Pfffxd73N69e6FUKuHl5YVXXnnF4Pa1w5b+/v4YOnQoGjRoUOD98nJzc8PkyZOF16dOnaqQdomIiMhyibYws7Ozw4ABAwCUPJyp3ffaa6/Bzs7OoLaTkpJw+vRpAMDAgQML/D8iIgKpqalljluXdoVnAEhISKiQNomIiMhyibYwA4BBgwYBAG7cuIFbt24V2X/79m1cv369wLGG2LNnD1QqFaRSKYKDgwEAwcHBkEgkUCgU2Lt3bwVED8hkMmGbj98gIiISP1H/tm/SpAkaNWqEW7duYdeuXfjss88K7Nf2lvn5+aFx48a4efOmQe1qz2vXrh18fHwAAHXq1EHLli1x8eJF7Ny5s9jlMIxx584dYVs734zMywHG34SRm5ODbFnpx+nj6lr+Z2w6ODigdevW5W6HLBvzLH7MsW0QdWEG5PeE/fDDD9i7dy8+/fRTSCQSAIBGo8GePXuEYwx18+ZNxMTEAPjf8KXWwIEDcfHiRVy/fh23b9/GSy+9VOa4VSoVVq5cKbzu06dPmduiitPX7aHR56z5/dcyX2/OnDllPpeIiKyPqIcygfxiSSqV4smTJ4iMjBTej4yMxJMnTwoMRxpCO7nfycmpSLHUr18/YfixrDcBpKam4vTp0xg1ahRu3LgBIL8oa9OmTZnaIyIiIush+sLMx8dHWMFf9yYA7XaHDh2E4cjSKJVKYf5Y9+7d4ebmVmC/u7s7unbtCiB/HpparS61zWXLlsHf31/4r2PHjhgzZgwuXrwIZ2dnvPPOO1i8eLFB8RHpo1KpkJiYiMTERK6HJ2LMs/gxx7ZB9IUZAAwePBgAcPDgQeTl5RVYHNaYYcyIiAgkJycDKDqMqaV9PyEhoUAPXVkEBAQgNDS0wE0ARMZSqVSIi4tDXFwcf5iLGPMsfsyxbRD9HDMA6NWrF7766itkZWXhyJEj0Gg0yM7OhouLC3r37m1wO9rhSXd3d3Tp0kXvMd27d0flypXx7Nkz7Ny5Ex07diyxzREjRuCtt94CkP9Nl5CQgH/++Qe7du3CpUuXEBoaiq1bt8LDw8PgOMl0DmTVNfqcDVNfg1cVFxNEQ0REYmMThZmrqyt69uyJPXv2YNeuXdBoNACAnj17Co9TKk1mZqawqn96ejqaNWtW6jkHDx7EnDlzSryGduV/rcaNG6N79+5o3749pk+fjvj4eISFhWH58uUGxUmmJYdha93pcnZxqZC7K4mISPxsYigT+N9w5smTJ4VV9LXvGWL//v14/vy5UdfMycnBwYMHjTpHa8iQIcLNBeHh4cKCtkRERCReNtFjBgAdO3aEl5eX8NxMb2/vUocZdWmHMb28vDBjxoxSj//++++RkJCAXbt2GVUA6po6dSoOHz4MlUqFJUuWGBUvERERWR+bKczs7OwwaNAgrF27FkD+pH+p1LAOw0ePHuHixYsA8peu0D7qqSSXL1/G2rVrcebMGSQmJhp856euBg0aoF+/fti7dy+uXLmCkydPolOnTka3Q0RERNbBZoYyAWDatGmIiopCVFRUkacAlER3XpqhC71qj1Or1SU+q7M0EydOFBbF5TwzIiIicbOpwqystIVVtWrVDF7otVWrVvDy8gIA7N69u8zX9vPzQ1BQEADg3LlzOH/+fJnbItskkUjg6uoKV1dXocgn8WGexY85tg0szEpx4cIFPHyY/xienj17Gjz8KZVK0atXLwDArVu3cO3atTLHMGnSJGGbvWZkLJlMhoCAAAQEBHBNPBFjnsWPObYNLMxKoTsMaezzKnXXSCvrI5oAoHnz5sLcsoiICFy9erXMbREREZHlkmi0k6fIJsnlckRFRQEAqtfzxcCFZR92Jf32hQ2FjzvXMSMiEhvd36GBgYFwcHAod5s2c1cmka1SKpV48OABAKBevXqwt+e3vRgxz+LHHNsGDmUSiZxarUZ6ejrS09OhVqvNHQ6ZCPMsfsyxbWBhRkRERGQhWJgRERERWQgOUJPAw80J+8KGmjsM0fGs7GzuEIiIyEqwMCOBnVQKH3cnc4dBRERksziUSQKunEJERGReLMxIoFQqzR0CERGRTeNQJgn47DVxkkql8Pb2FrZJnJhn8WOObQMLMxJwsUJxsre3R506dcwdBpkY8yx+zLFtYMlNREREZCFYmBERERFZCI5dkUChUFTIA1jJsigUCty8eRMA0LhxY8hkMjNHRKbAPIsfc2wbWJiRwM7OztwhkAloNBooFAphm8SJeRY/5tg2cCiTBLwrk4iIyLxYmBERERFZCBZmRERERBaChRkRERGRhWBhRkRERGQhWJgRERERWQgul0Ekcvb29mjYsKGwTeLEPIsfc2wbrC6zkZGRGD16tN59Tk5OcHd3R0BAAHr16oWBAweWuGCqv7+/UdcOCAjArl27Cry3fft2zJgxAwAwf/58hISEGNVmUFAQ4uPji7xvb28PNzc3NGzYEK+88greeOMN+Pj4GNW2sbhchjhJpVJUrVrV3GGQiTHP4scc2wZRDWXm5eUhISEBx44dQ1hYGEJCQhAXF2fusMpEqVQiPT0dFy9exLJly9C/f38cOnTI3GERERGRCVldj5muESNG4K233hJep6Sk4NatW1i5ciUSEhJw69YtTJ48GTt37ixxVftmzZph/vz5pV7PycmpQuLWx9vbGytXrhReK5VKxMfHY9u2bTh69CiysrIwdepUbN68GU2aNDFJDFxJWpx0VwuXyWTsGRUp5ln8mGPbYNWFWbVq1eDn51fgvY4dOyIkJAQDBw5EfHw8YmNjcejQIfTt27fYdlxcXIq086LJZLIiMTRp0gS9evXCggUL8Oeff0KhUGD58uX45ZdfzBQlWSOFQoGoqCgAQGBgIJ+HKlLMs/gxx7ZBVEOZWm5ubpg8ebLw+tSpU2aMpvymTJki9NZFRERArVabOSIiIiIyBVEWZkDBif0JCQlmjKT8XF1d4evrCwDIyclBenq6eQMiIiIikxBtYSaTyYRtMdxWrPt52GNGREQkTqItzO7cuSNs16pVy4yRlJ9SqcTdu3cB5Bdo7u7u5g2IiIiITML6u5L0UKlUBe5w7NOnT4nH5+TkIDY2ttR2q1evjsqVK5c7PmNt2rQJz549AwC0bt1aFD2AREREVJSofsOnpqYiJiYGS5cuxY0bNwDkF2Vt2rQp8bxr164hODi41PbLsoBsWWmXy9i+fTt+//134f1x48a9kOsTERHRi2fVhdmyZcuwbNkyvfucnZ3x5ptv4tNPP33BUZVNfHx8iU8ikEgk+Oijj/Dqq6++wKiIiIjoRbLqwqwkAQEBCA0NLTBpvjjt2rXDunXrXkBUxnNzc0OHDh3wzjvvlNrzR6SPTCZDYGCgsE3ixDyLH3NsG6y6MNNd+V+lUiEhIQH//PMPdu3ahUuXLiE0NBRbt26Fh4eHmSMtXeGV/+3s7FCpUiV4eXm9sNWduYq0OEkkEi5EaQOYZ/Fjjm2DVRdmhVf+b9y4Mbp374727dtj+vTpiI+PR1hYGJYvX27GKA2jb+V/IiIisi2iXC5jyJAhwp2Y4eHhOH36tJkjsg58VqY4qdVqpKWlIS0tjWvgiRjzLH7MsW0QZWEGAFOnThUeXL5kyRIzR0NkPtp18O7evQulUmnucMhEmGfxY45tg2gLswYNGqBfv34AgCtXruDkyZNmjoiIiIioZKItzABg4sSJwoR2a5hnRkRERLbNqif/l8bPzw9BQUE4cuQIzp07h/Pnz+tdcsLQlf8B4KWXXoJUqr+evXDhgkFtBAUF8bFKREREVISoCzMAmDRpEo4cOQIgv9dMd0kKLUNX/geAc+fOFftYpq1bt2Lr1q2ltrFz504WZkRERFSEqIcyAaB58+bo1KkTACAiIgJXr141c0RERERE+lldj1n79u0RExNj1DmrVq3S+76x7egTEhJSrudnhoeHlzsGIiIiEgerK8yIyDgSiUR4fAuf7iBezLP4Mce2gYUZCfiNLk4ymQzNmzc3dxhkYsyz+DHHtkH0c8yIiIiIrAULMyIiIiILwaFMEvBZmeKkVCrx5MkTAECNGjVgb89vezFinsWPObYNzCqRyKnVaiQlJQEAfHx8zBwNmQrzLH7MsW3gUCYJ2GNGRERkXizMSKBSqcwdAhERkU1jYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGF4HIZJOCaOOIklUrh7u4ubJM4Mc/ixxzbBv4mJgGflSlO9vb28PX1NXcYZGLMs/gxx7aBhRkJVGo1EtOzy3SuZ2Vn2PEvOCIionJhYUaC1Kw8DFy4u0zn7gsbCh931wqOiIiIyLawMCMSOYVCgTt37gAAfH19IZPJzBwRmQLzLH7MsW1gYUYkchqNBtnZ2cI2iRPzLH7MsW3gpCAiIiIiC8HCjIiIiMhCsDAjIiIishBWP8dMLpfj4MGD+Pfff3H16lWkpaUhKysLbm5uqFWrFgIDA9GnTx906NCh1AX5jh8/jsOHD+PChQtITk5Gbm4uPDw8ULduXbz66qsYOHAgfHx8ij1/wIABuH37Ntzd3XHmzJkS1wUbPHgwbt68CQB477338NFHHxV77NmzZxEaGgoAePfddzFt2rQSPwcRERFZJ6suzA4ePIgFCxYgPj6+yL709HSkp6fj+vXr2LhxI+rXr48ZM2agW7duRY69desWZs2ahUuXLhXZl5CQgISEBJw9exa//vorJkyYgEmTJuktulq3bo3bt28jPT0dt2/fRqNGjfTGnZmZiZiYGOH1+fPnS/ycuvvbtm1b4rFERERkvay2MPu///s/LF26VHjdqVMnBAUFwdfXF5UrV0ZGRgbu3buH8PBwnDp1Cvfv38eSJUuKFGbnz5/H5MmT8ezZMwBA06ZNMWTIEPj7+8PFxQWJiYk4ceIEdu7ciZycHPz000+4desWFi5cWORW5bZt22LTpk1Cu8UVZhcvXoRarYadnR1UKhWuXr0KhUJR7K3PFy5cAJD/CI7WrVuX6etFREREls8qC7Nt27YJRVm1atXw008/oV27dkWOe+WVVzBy5EjExsZi/vz5SE1NLbA/ISEB77//Pp49ewaJRIJp06Zh7NixBXrDmjVrhh49emDcuHF4//33ERMTg7///hs1atQoMqSo25t1/vx5jBgxQm/82h6wV199FZGRkcjJyUFUVBRatWpV5FiVSiX05Pn7+6NSpUqGfImIBHZ2dqhdu7awTeLEPIsfc2wbrG7yf2JiIubOnQsAcHFxwbp16/QWZbr8/PywcuVKjB07tsD7s2bNQnp6OgDgo48+wrhx44qdF1anTh38+eef8Pb2BgCsXLkSV65cKXBM9erVUatWLQAlD09q97Vr1w4vv/xyicffvHlTWLemTZs2JX1MIr3s7Ozg4+MDHx8f/jAXMeZZ/Jhj22B1hdnq1auRm5sLAPjwww8NfqCrVCrFoEGDhNfR0dH4999/AeQXbhMmTCi1jWrVqmHGjBkA8hf3++OPP4oco+01S0hIQFxcXJH9z58/R1RUFID8Qks7NKkdriyM88uIiIhsh1UVZhqNBjt27ACQ31s2bNiwMrelbQcARo0aZfBfH3369BHuzDx8+DAyMjIK7Nft1dLXC3blyhUoFAo4OzujSZMmQmGmnXdWmG7BxsKMiIhI3KyqMLt16xbS0tIA5N8B6ebmVua2zp07J2x3797d4PPs7OyEGwjUanWROzkLzzMrTPte8+bNYW9vj5dffhl2dnZ49uwZYmNjixyvLcwaNmwIDw8Pg+OsKA5QGfRfbk4OsrOzDf6PXhy5XI4LFy7gwoULkMvl5g6HTIR5Fj/m2DZY1eT/6OhoYbtp06blaktbBHl5eQnzxgzVpEmTAjHp3ulZv359eHl54enTp3oLM22hpe1Zc3V1RUBAAK5fv47z588jICBAOPbu3btISUkBYL7esr5uDw06bs3vvxrV7pw5c8oSDhERkahZVY+ZdqI+kD/fq6wyMzOhUCgAAJ6enkafr3uObkxa2uHJe/fuCYUVUPAOS91lL7TbhQs53WFMTvwnIiISP6sqzHSHwJydnSukHRcXF6PP1z0nKyuryP7ihjNv3LiB7Oxs2NnZoUWLFsL7xRVmnPhPRERkW6yqMHN1dRW2tXdmlrednJwco8/XPUffPLfibgDQ9oAFBAQUiEF7/NOnT/HgwYMi59aqVQs1atQwOk4iIiKyLlY1x8zd3V3Y1h0iNJabmxvs7e2hVCqRnJxs9Pm65+jGpOXn54fKlSvj2bNnBYYjtYVW4dX7PT09Ua9ePTx48ADnz59HvXr1kJiYKCy3Yc5hzANZdQ06bsPU1+BVxfjeRyIiIvofq+ox050Yf/369TK3I5FI4O/vDyC/l+rp06dGnX/jxg29MWnpPjopOjpaGO7UFmn6HqtUeDjTUoYx5bAz6D9nFxe4uroa/B8REREVZVWFWaNGjVC1alUA+UWOvvldhtLthQoPDzf4PLVajePHjwPIL8D0PUZJt32VSoWLFy/izp07wiOh9PWAadvRFmS6y3lw4j8REZFtsKrCTCKRYMiQIQDy53lt2bKlzG2FhIQI2+vXr4dKpTLovH/++QcJCQkAgJ49e6Jy5cp6j9Mtpi5cuCAUXPXq1dN7J6i2x+zhw4dISkoSete8vLzQoEEDg2Ij0sfe3h7+/v7w9/eHvb1VzV4gIzDP4scc2warKswAYMyYMcIdmUuXLsWdO3cMOk+tVmP37t3C64CAAHTu3BkAEBMTg5UrV5baRmpqKhYsWAAgv0h89913iz22adOmQpznzp0TesD0DWMCBReQPXz4MG7dulXi8USGkkqlcHNzg5ubG6RSq/uWJwMxz+LHHNsGq8usj48PZs2aBSC/1yw0NBRnz54t8Zzbt2/j3XffLVJ8zZ07V+jx+vHHH7F69WpoNBq9bcTHx+Odd94ResvGjRsnPIBcH5lMJiyJERUVhcjISAAlF1ra4cxVq1YJcXCZDCIiItthlX2hQ4cORUJCApYuXYqUlBSEhoaic+fOCAoKgq+vLypXroyMjAzcu3cPx48fx4kTJ6BSqYpM1K9ZsyaWLVuG9957D1lZWZg/fz727NmDIUOGwN/fH87OzkhKSsKJEyewc+dOYZmM/v374+OPPy41zjZt2uD06dOQy+VISkoCUHJh1rp1axw+fBiPHj0q0AZReajVauHfrouLC//SFinmWfyYY9tglYUZALz//vto1KgRFixYgPj4eERERCAiIqLY4xs1aoRp06YVeb99+/b466+/MGvWLFy5cgXXrl3DtWvX9Lbh7OyM8ePH47333oNEIik1xsJFVbVq1UqcL1a4aKtSpYpw9yhRWSmVSsTExAAAAgMD4eDgYOaIyBSYZ/Fjjm2D1RZmANC7d29069YN//zzD/79919ERUUhNTUV2dnZcHNzQ61atfDyyy+jT58+aN++fbHFlL+/PzZv3oxjx47h0KFDuHjxIp4+fYq8vDxUrVoV9evXR5cuXTBo0CD4+PgYHF+LFi0gk8mExz8VdwenVpMmTeDs7CwsntuqVSuDCkAiIiISB6suzADAwcEBwcHBCA4OLndb3bp1K/BA8vJycnIqtvdNH5lMhsuXL1fY9YmIiMi6cICaiIiIyEKwMCMiIiKyECzMiIiIiCwECzMiIiIiC2H1k/+p4ni4OWFf2NAynetZ2bmCoyEiIrI9LMxIYCeVwsfdydxhUAVzcHDgo71sAPMsfsyxbeBQJhEREZGFYGFGREREZCE4lEkClUpl7hDIBFQqFZKTkwEAnp6esLOzM3NEZArMs/gxx7aBhRkJ1Gq1uUMgE1CpVIiLiwMAVK1alT/MRYp5Fj/m2DZwKJOIiIjIQrAwIyIiIrIQLMxIYG/PkW0iIiJzYmFGAolEYu4QiIiIbBq7SEigUquRmJ5d7nY8KzvDTsqan4iIyFgszEiQmpWHgQt3l7udfWFD4ePuWgERERER2RYWZkQiJ5FI4OrqKmyTODHP4scc2wYWZkQiJ5PJEBAQYO4wyMSYZ/Fjjm0DJwIRERERWQgWZkREREQWgkOZRCKnVCrx4MEDAEC9evW4Xp1IMc/ixxzbBovOamRkJEaPHq13n5OTEzw8PNC4cWP069cP/fr1K/Uf6cOHD7F7925EREQgPj4eaWlpcHV1hbe3N9q3b4++ffuiTZs2pcYVGhqKs2fP6t1nb2+PSpUqwdfXF127dsUbb7wBd3f3YtuaPn06duzYUeR9iUQCNzc31KxZE61bt8Ybb7yBxo0blxobUWFqtRrp6ekAgDp16pg3GDIZ5ln8mGPbYNGFWUny8vLw+PFjPH78GEeOHMGaNWuwfPlyeHl5FTlWLpfjhx9+wF9//QWFQlFgX3p6OtLT0xEbG4t169aha9eu+Oabb1C9evUyxaVUKpGWlobz58/j/PnzWL16NX755Re0bt3aqHY0Gg0yMzMRExODmJgYbNy4ERMmTMDUqVPLFBcRERFZPqspzEaMGIG33npLeJ2Tk4Nr165h1apViI+PR1RUFN577z1s3ry5wG3EWVlZeO+99xAZGQkAcHd3x5AhQ9CxY0dUq1YNmZmZuHbtGrZt24Z79+7h+PHjGD58OFatWgVfX99S49qzZ0+B1wqFAo8ePcKuXbsQHh6OlJQUTJ48GQcOHICHh0eJba1cuRLe3t4A8v8ySklJwbFjx/DXX39BqVTit99+g7e3N0aOHGnw142IiIish9UUZtWqVYOfn1+B91q0aIHg4GAMGzYMDx48wNWrV3H06FEEBQUJx4SFhQlFWfv27bFkyRJUq1atQDsdO3bEmDFjsHjxYvz5559ISEjApEmTsGPHDri5uZUYV+GYAKBp06bo27cvvvjiC+zcuRMZGRnYsmULJk6cWGJb9evXR+3atQu816lTJ3Ts2BGTJ08GACxbtgxvvvkm7OzsSmyLiIiIrI/V35VZpUoVTJgwQXh94sQJYXvfvn04cOAAACAgIAD/+c9/ihRlWjKZDNOnT8ewYcMA5M9HW7x4cbliGzdunLAdFRVV5naCgoKEuW+pqam4fv16ueIiIiIiy2T1hRkANG/eXNh+/PixsP37778L23PnzoWzs3OpbU2fPh2enp4AgG3btiE1NbXMcen2fsnl8jK3AxT/GYmIiEg8rGYosyS6d2OqVCoAQHR0NG7cuAEAaNmyZYHCpiRubm4ICQnBihUr8Pz5c+zdu7fYO0NLo1tA1ahRo0xtaOl+RrVaXa62ipOXmwsHqMrdTm5ODrJlFRDQ/6d9BAkREZHYiaIwi42NFba1k+fPnz8vvNe9e3ej2gsKCsKKFSuEdspamK1cuVLY7tGjR5na0NL3GSva1r/Woq9bXrnbWfP7rxUQzf/MmTOnQtuzNVKpVPg3I5WKopOc9GCexY85tg1WX5gplUr8+eefwut27doByO8x02rSpIlRbTZu3BhSqRRqtRoxMTElHqtbMAH5d2XGx8dj9+7dOHToEACgf//+ePXVV42KQVd0dLQwd87Z2RmBgYFlbotsj729Pdc8sgHMs/gxx7bBagsz7XIZv/zyCy5fvgwAqFWrFvr37w8ASEtLE47Vt7ZZSZycnODq6orMzExhMb/iBAcHF7uvQYMGmDBhAoYMGWLU9YH8dcySk5Nx7NgxLF68WBiiDQ0NhaOjo9HtERERkeWzmsJs2bJlWLZsWbH7q1Wrhv/7v/+Dg4MDACA7O1vY5+LiYvT1XFxckJmZiaysLOOD/f/u3buHTZs2oXbt2kJPXklKG+7s1q0bPvzwwzLHQ0RERJbNagqz4tSuXRt9+vTBuHHjCiyFoTthPCcnx+h2teeUto5Z4aFOtVqNtLQ0XLhwAf/3f/+Hy5cvY+zYsfjhhx/Qt29fo+OQyWQIDAzE8OHDMWjQoAKL51a0198ajVFLD5S7nQ1TX4NXFeOLYTINhUKBmzdvAsgfppfJKvDODLIYzLP4Mce2wWoKM92V/yUSCRwdHVG1alVUqlRJ7/FVq1YVtp8+fYqAgACDr5WXlyf0uJX0nEt9pFIpqlWrht69e6NTp04ICQnB/fv38eWXX6JDhw4ltqe78r9UKoWLiws8PT2FXkBTc3J2hhzlX7jW2cWFd1JaEI1GIzyKTKPRmDkaMhXmWfyYY9tgNbd1aFf+9/PzQ6NGjVC3bt1iizIA8Pf3F7a1f2EY6ubNm8KSFLrtGMvV1RUjRowAkD+0+s8//5R4fP369YXP+NJLL6FmzZovrCgjIiIi87OawsxYbdu2FbbDw8ONOlf3eN12yqJhw4bCduE7OImIiIh0ibYwCwgIQOPGjQEAly5dMviRSFlZWdixYwcAwNHREQMGDChXHNq7KYH8pT2IiIiIiiPawgwAxo8fL2zPnj0beXmlL576/fff4+nTpwCAoUOHwsPDo1wxXLt2Tdgu7+r/REREJG6iLswGDBiA3r17AwBu3LiByZMnF/vsS4VCge+//x6bNm0CANStWxeffvppua4fHx+P9evXC6+7du1arvaIiIhI3KzmrsyymjdvHtLS0nDu3DmcOnUK/fr1Q0hICDp06ABPT09kZWXh2rVr2Lp1K+7evQsAqF69OpYvX17qUhlA0XljarUa6enpOH/+PNatWycsUBscHCwMrRIRERHpI/rCrFKlSli5ciUWLVqEDRs2ID09HatWrcKqVav0Ht+lSxfMnTvX4GHHklb+1+rfvz/mzZtnVNxEFcXe3l64CcXeXvTf8jaLeRY/5tg22ERmHR0dMXPmTISGhmL37t2IiIhAfHw80tPT4erqCm9vb7Rt2xb9+vUr912YEokELi4uqFGjBlq0aIHBgweXu02i8pBKpQXW9SNxYp7Fjzm2DRINV6mzaXK5XLhjtXo9XwxcuLvcbe4LGwofdy4wS0RE4qb7OzQwMLBC1h61iR4zIlumu1q4TCYz6WO9yHyYZ/Fjjm2DqO/KJKL8O46joqIQFRUl/FAn8WGexY85tg0szIiIiIgsBAszIiIiIgvBOWYk8HBzwr6woeVux7OycwVEQ0REZHtYmJHATiqFj7uTucMgIiKyWRzKJAFXTiEiIjIvFmYkUCqV5g6BiIjIprEwIyIiIrIQnGNGAj57TZxkMhkCAwOFbRIn5ln8mGPbwN/EJOAq0uIkkUgq5DEhZNmYZ/Fjjm0DhzKJiIiILAR7zEjAuzLFSa1WIyMjAwBQpUoVSKX8e0yMmGfxY45tA7NKAg5lipNSqcTdu3dx9+5d3nkrYsyz+DHHtoGFGREREZGFYGFGREREZCFYmBERERFZCBZmRERERBaChRkRERGRhWBhRkRERGQhuI4ZkchJJBLh8S1cEkW8mGfxY45tg0kLs8jISIwePVrvPicnJ7i7uyMgIAC9evXCwIEDDX7URFxcHHr27CksiPrDDz8gODjY6PgeP36Mv//+G6dOncKDBw+QmpoKtVqNKlWqwM/PD61bt0ZwcDDq1Kmj9/yUlBRcvXoVV69eRVRUFKKiopCeng4AGDJkCBYsWFBqDNOnT8eOHTuKvC+RSODm5oaaNWuidevWeOONN9C4cWOjPyORTCZD8+bNzR0GmRjzLH7MsW0wW49ZXl4eEhISkJCQgGPHjmH16tX47bffULt27VLP3blzZ4FV6nfu3GlUYfb8+XMsXrwYGzZsgFwuL7I/KSkJSUlJiIiIwNKlS9G3b1988cUXqFGjRoHjXnnlFYOvaSyNRoPMzEzExMQgJiYGGzduxIQJEzB16lSTXZOIiIjM64UVZiNGjMBbb70lvE5JScGtW7ewcuVKJCQk4NatW5g8eTJ27twJOzu7EtvavXs3AMDFxQU5OTk4ffo0kpKS4O3tXWocqampmDx5Mi5fvgwAcHV1xWuvvYYOHTqgevXqsLe3R3JyMi5evIhDhw7h/v372L9/P1q0aIExY8YU227NmjXRsGFDRERElP7FKMbKlSuFz6BWq5GSkoJjx47hr7/+glKpxG+//QZvb2+MHDmyzNcgIiIiy/XCCrNq1arBz8+vwHsdO3ZESEgIBg4ciPj4eMTGxuLQoUPo27dvse1cvHgRDx48AAB8+eWXmDNnDlQqFfbs2YNx48aVGINarcbHH38sFGXdu3fHd999h2rVqhU5NigoCJ988gl2796N77//Xm9777//PgIDAxEYGAhPT0/ExcWhR48eJcZQkvr16xfpMezUqRM6duyIyZMnAwCWLVuGN998s9TilUhLqVTiyZMnAIAaNWrA3p5TS8WIeRY/5tg2mP2uTDc3N6HoAIBTp06VePzOnTsBAFWrVsXgwYPRsWNHAMCuXbtKvdbatWsRGRkJAOjcuTOWLVumtyjTkkqlGDx4MLZv3w5/f/8i+z/88EN0794dnp6epV67PIKCgtCmTRsA+T1+169fN+n1SFzUarUwPK9Wq80dDpkI8yx+zLFtMHthBqBA0ZOQkFDscXK5HAcOHAAA9OvXDzKZDAMHDgQAxMTE4ObNmyWeu3LlSgCAo6Mj5s2bZ/BfG9WrVxcKQHPRnfD5+PFjM0ZCREREpmIR/aDa238BlFgshYeHIyMjAwCEgqxXr17CXLOdO3cWe+diREQEkpKSAAB9+/aFj49PRYX/Quh+XUz1l1JOTg4UCoXBx7u6upokDiIiIltlEYXZnTt3hO1atWoVe5x2GLNu3bpo2bIlgPwbAHr06IE9e/Zg7969+Pzzz/XOvzp37pyw3a1bt4oJ/AWKjY0Vtg25yaEs/vOf/yAvL8/g4+fMmWOSOIiIiGyV2YcyVSqVMMQIAH369NF7XGpqqnDHY+GlMbS9Z8nJycXeFRkdHS1sN23atFwxv2jR0dE4ceIEAMDZ2RmBgYFmjoiIiIhMwWyFWWpqKk6fPo1Ro0bhxo0bAPKLMu0k98L27t0rDLNpCzGtTp06wcvLC8D/etUK0y78CqDECf+WQqPR4OnTp9iyZQvGjBkDlUoFAAgNDYWjo6OZoyMiIiJTeGFDmcuWLcOyZcv07nN2dsabb76JTz/9tNjztQVX8+bNUb9+/QL77Ozs0L9/f6xZswbh4eHIysqCm5tbgWOys7MLXM8SlbbURrdu3fDhhx++oGiIiIjoRbOIOWYBAQEIDQ0tcBOArlu3bglLRBTuLdMaOHAg1qxZg7y8POzfvx/Dhg0rsF93onpubm6Rws1SyWQyBAYGYvjw4Rg0aJBJn482ceLEYnNA1ksqlcLd3V3YJnFinsWPObYNZln5X6VSISEhAf/88w927dqFS5cuITQ0FFu3boWHh0eRc7W9Zfb29hgwYIDe9ps1awZfX1/cuXMHu3btKlKYaf8xA/lz0SyxMNNd+V8qlcLFxQWenp4GP0O0vFxcXF7YtejFsbe3h6+vr7nDIBNjnsWPObYNZlv5v3HjxujevTvat2+P6dOnIz4+HmFhYVi+fHmB89RqNfbs2QMgf9VjQ9YTO3/+POLi4gqsoh8QECAsXnvjxo0iw6GWQN/K/0RERGQ7zN4XOmTIEOFOzPDwcJw+fbrA/tOnTyMxMdGoNjUaTZEnAbRt21bYPnbsWNmCJSIiIjIhi5hjNnXqVBw+fBgqlQpLliwp0CumHcZ0cHDAvHnzSh1X/+OPP3Djxg3s3r0b77//vvB+586d4e3tjaSkJBw4cACffvqp1S0yS1QWCoVCWCvQ19eX8whFinkWP+bYNlhEYdagQQP069cPe/fuxZUrV3Dy5El06tQJ2dnZOHz4MID8JTEKr1+mz9OnT3Hjxg3cv38fly5dEhaidXBwwLhx4zB//nw8f/4cX375JVasWGHQw8ATExNx9+5dsz+WiagsNBqNcFeyRqMxczRkKsyz+DHHtsHsQ5laEydOFO441M4zO3jwIHJycgAUv/BsYX369BHaKbym2ejRo9G+fXsA+Y9oev/995GamlpsWxqNBnv27EFISAhiYmKM+jxERERExrKIHjMA8PPzQ1BQEI4cOYJz587h/PnzQmElk8kQFBRkUDs1atRAYGAgrl69iv379yMsLEy401AqleKnn37CpEmTcOXKFRw9ehQ9e/ZEcHAwOnTogOrVq8Pe3h7Jycm4fPkyDh48iLt37xZ7rfPnz+Phw4fC67S0NGH7wYMH2L59e4HjQ0JCDP1yEBERkQ2ymMIMACZNmoQjR44AAH755RecPXsWANC+fXtUqVLF4Hb69OmDq1evIiMjA0ePHi3Q2+bh4YF169bhhx9+wIYNG5CdnY2NGzdi48aNetuSSCQIDg5Gv379iuzbunUrduzYofe8ixcv4uLFiwXeY2FGREREJbGYoUwgf1X/Tp06AQDOnDkDtVoNwPBhTC3d4/U9osnR0RFhYWE4ePAgPv30U7zyyiuoWbMmnJyc4OjoCG9vb3Tu3BlTp07FkSNHsGjRIt4oQERERCYn0XAGoU2Ty+WIiooCAAQGBnKBWRFijm0D8yx+zLHlMUVOLKrHjIiIiMiWWdQcMyKqeHZ2dsITJQxZHoasE/MsfsyxbWBhRiRydnZ2nCNpA5hn8WOObQOHMomIiIgsBAszIiIiIgvBoUwS8AZdceKdXLaBeRY/5tg2sMeMBEql0twhEBER2TQWZkREREQWgoUZERERkYVgYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGF4DpmJOCz18TJ3t4e/v7+wjaJE/MsfsyxbWBmSSCVsgNVjKRSKdzc3MwdBpkY8yx+zLFt4G9iIiIiIgvBHjMSqNVqc4dAJqBWq5GTkwMAcHFxYc+oSDHP4scc2wZmlQQqlcrcIZAJKJVKxMTEICYmho/dEjHmWfyYY9vAwoyIiIjIQrAwIyIiIrIQLMyIiIiILAQLMyIiIiILwcKMiIiIyEJwuQwbp9FohG2FQgG5XG7GaMgUFAqF3m0SF+ZZ/Jhjy6ObB93fp+Uh0VRUS2SVsrOzER0dbe4wiIiIrFpAQABcXV3L3Q6HMomIiIgsBHvMbJxarUZubi6A/IfiSiQSM0dERERkHTQajbDYr7Ozc4U8jYGFGREREZGF4FAmERERkYVgYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGFYGFGREREZCFYmBERERFZCBZmRERERBaChRkRERGRhWBhRkRERGQhWJgRERERWQgWZkREREQWgoUZERERkYVgYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGFYGFGREREZCHszR0AlS4+Ph7r1q3DsWPHkJCQAAcHB9SpUwf9+vXDyJEj4ezsXCHXOX78ODZv3oyoqCikpqbCw8MDgYGBeOONN9C1a1eD2lAqldiyZQv27NmDu3fvIicnB97e3njllVcQGhqKRo0aVUisYmMNOU5OTsbRo0dx5swZ3Lx5E0+ePIFCoYC7uzv8/f3Ru3dvDBo0CE5OThUSqxhZQ56LEx0djaFDh0KpVAIAhgwZggULFlRIvGJibTlWKBTYs2cPDhw4gNjYWCQnJ8PV1RXe3t5o3rw5OnfujH79+lVIzGQYiUaj0Zg7CCpeeHg4pk2bhqysLL3769evjxUrVqBevXplvoZarcasWbOwdevWYo8ZNmwYvvnmG0ilxXeypqamYsKECYiKitK738HBAbNnz8awYcPKHKsYWUOON2/ejK+++goqlarE69SvXx8///wzAgICyhyrWFlDnktqd/jw4bh69arwHguzoqwtx9HR0Zg2bRpiY2OLPaZSpUo4f/58meMl47Ews2A3btzAiBEjkJeXBxcXF0ycOBHt27dHXl4e9u3bh82bNwPI/2bftm0b3NzcynSdxYsXY8WKFQCAJk2a4N1330WdOnXw6NEj/PHHH7hx4wYAYOLEifjkk0/0tqFSqRAaGooLFy4AAHr37o1hw4bB3d0dV65cwfLly5GSkgKpVIrffvutzH+1i4215PjXX3/Fzz//DJlMhu7du6NTp07w9fWFq6srHj58iC1btiAiIgIA4OHhgR07dqB69eplilWMrCXPxVm7di2+++47VKtWDSkpKQBYmBVmbTmOjo7G22+/jfT0dDg6OmLYsGHo1KkTfHx8oFAocP/+fZw4cQIXLlzAsWPHyhQrlZGGLNZbb72l8fPz0zRp0kRz8eLFIvt///13jZ+fn8bPz0+zdOnSMl3j7t27miZNmmj8/Pw0ISEhmtzc3AL7c3JyNCEhIUIc9+/f19vOli1bhFi++uqrIvvv37+vadWqlcbPz0/Tq1cvjUKhKFO8YmMtOf7zzz81ixYt0qSkpBR7nfnz5wuxTp8+vUyxipW15FmfJ0+eaFq2bKnx9/fX7NixQ4jziy++KFOcYmVNOc7Ly9P07t1b4+fnp+nWrZvm3r17xV7z+fPnZYqVyo6T/y3U1atXhe7joUOHomXLlkWOGTt2LHx9fQHk/0WrUCiMvs6aNWuEOSOzZs0qMj/I2dkZs2bNApA/f2z16tV621m1ahUAwN3dHZ9//nmR/fXq1cPEiRMBAA8ePMChQ4eMjlVsrCnHY8aMwWeffQYPD49ir/PJJ5/Ay8sLAHDo0CGo1WqjYxUja8qzPl9//TWys7MxZMgQtGnTxui4bIG15XjlypW4f/8+pFIpfvrpJ9SvX7/Yazo4OBgdJ5UPCzMLdfjwYWF76NCheo+RSqUYPHgwAODZs2eIjIw06hoajQZHjhwBADRs2BAtWrTQe1yLFi3QoEEDAMCRI0egKTT6fe/ePdy5cwcA0Ldv32Intw4ZMkTY1v18tsqacmwIBwcHtGrVCgCQmZmJ9PR0o9sQI2vO84EDBxAeHl7sH1yUz5pyrFKpsHHjRgDAK6+8gpdfftmoOMj0WJhZKO1cLRcXFzRt2rTY49q2bStsX7x40ahrxMXFISkpqUg7+rRr1w4AkJiYiLi4OL2x6h6nj5eXl/CXmbGxipE15dhQcrlc2DZmcrmYWWueMzMz8e233wIApk2bhqpVqxoVky2xphxfunQJiYmJAICgoCDh/efPn+Phw4dITEws9SYfMi3+5LRQ2h6ounXrwt6++FVNGjZsWOQcQ92+fVtvO6Vd5+7du3pjNaadJ0+eICcnx+BYxciacmwIhUKBy5cvAwA8PT3h7u5udBtiZK15XrRoEZ4+fYo2bdoU2wtE+awpx9rvUQDw8/PD/fv3MWXKFLRu3Rq9evXCq6++ivbt2+Pzzz/Hw4cPjYqRKgYLMwv0/PlzpKWlAUCpd7ZVqVIFLi4uAICEhASjrqN7fGnX0d3/5MmTYtvx8fEpsZ0aNWoAyO+WNzZeMbG2HBti8+bNwmfq27ev0eeLkbXm+cKFC9i8eTNkMhm++uorSCQSo+KxJdaWY92C8O7duxgyZAgOHjxYYM5bZmYmdu3ahcGDB+PUqVNGxUnlx8LMAmVnZwvb2m/ikmjndBnbA2XMdXTnjRW+jm47rq6uZW7Hllhbjkvz6NEjLFmyRLjOhAkTjDpfrKwxz3K5HLNnz4ZGo8GYMWO4KHQprC3HunM/v/vuO+Tk5GDMmDE4ePAgoqKicPjwYYwbNw4SiQTZ2dn46KOP8PjxY6NipfJhYWaBnj9/LmzLZLJSj9feNZOXl2ey6+jemVP4OhXVji2xthyXJDc3Fx988AEyMzMB5N8tVlrPqa2wxjyvWLECt2/fRq1atfD+++8bFYctsrYc5+bmFmjzww8/xIwZM1CvXj3hKQWff/45pk6dCiD/RoX//Oc/RsVK5cPCzAI5OjoK24bcUq2dcG3so3CMuY7upO7C16modmyJteW4OEqlEh999BGio6MBACNGjEBISIhRMYqZteX57t27wi/hWbNmVdjjg8TM2nKs207VqlUxfvx4vW2MGzdOWP7mwIEDZbpTm8qGhZkF0h0ONKS7W/sXkCHd6GW9ju5fWYWvo9uObne7se3YEmvLsT4ajQbTp0/H8ePHAQD9+vXD7NmzjYpP7KwpzxqNBnPmzIFcLkevXr3QvXt3o2KwVdaU48LttGvXrth1yuzt7dGhQwcA+cOfjx49MipeKjs+xNwCOTo6wt3dHenp6aVOEM3IyBC+SY19BI7u8aVdR3e/dgK/vnYSExNLXIRUOxFVIpHY9CN7rC3H+nz99dfYs2cPAODVV1/FokWLuERGIdaU58uXL+Ps2bMAgJYtW+Lvv/8ucm5qaqqwHRcXJxzTqFEj+Pn5GRWzWFhTjgu3U9r3ue7+1NRU1K1b16BYqXxYmFmol156CefPn8fDhw+hVCqLvQVb91Zo7arSxlxDXzulXafwrdq617179y4aN25cajs1atSw6R4zwLpyXNiiRYuwYcMGAPlrKv3yyy8Gza+xRdaSZ93hr++//77Ua547dw7nzp0DAHzwwQc2W5gB1pNjAAVu5ihtvTLd/XZ2dgbFSeXHP28tVOvWrQHkd1lfv3692OO0PxgBCKuuG6p27drw9vYu0k5J1/Hx8UHt2rX1xgpA+Itbn6dPn+L+/ftlilWMrCnHun799Vf88ccfAIDAwED89ttvNj1fsDTWmmcynDXlWPexWqUtJK07fMkbel4cFmYWqmfPnsL2tm3b9B6jVquxc+dOAEDlypXRvn17o64hkUjQo0cPAPl/YekuPKjr8uXLwl9gPXr0KLKmUYMGDYS//g4cOFBgfoOuHTt2CNu6n89WWVOOtdasWYOff/4ZQP7ilH/88Qfc3NyMisnWWEue27dvj5iYmBL/0z4SCMh/xJr2/SlTphgVr9hYS44BoE6dOmjSpAkAIDIyUriburCsrCxhDbO6desKRSGZHgszC9W8eXPhL5tt27bh0qVLRY5ZtWqVsFjg6NGjiwwlRUZGwt/fH/7+/pg+fbre67z99ttCF/XcuXOL3Fqdl5eHuXPnAsifDPr222/rbWfs2LEA8ieJLlq0qMj+hw8fCnd71atXD7169dL/wW2IteV427ZtmD9/PgCgfv36+PPPP7m6vwGsLc9kPGvLsfZOzJycHMybN0/vMQsWLEBWVhYA4M0339R7DJkG55hZsLCwMIwYMQJ5eXkYO3YsJk2ahPbt2yMvLw/79u3Dpk2bAOT/knznnXfKdI0GDRpg3LhxWLFiBa5du4YRI0Zg/PjxqFOnDh49eoTff/8dN27cAJB/+7T2WZeFDRkyBNu2bcPFixexfv16JCcnY9iwYahSpQquXr2KX3/9FVlZWZBKpQgLCyvxsSW2xFpyfPjwYcyaNQsajQZubm4ICwtDampqgcnghdWuXdvm5xFqWUueqeysKcf9+/fHzp07cfz4cWzfvh1JSUkYMWIEatasiSdPnmDTpk3C3dZNmjTBqFGjyhQvlY1Ew8VJLFp4eDimTZsm/OVSWP369bFixQrUq1evyL7IyEiMHj0aQH7htGDBAr1tqNVqzJw5s9gueAB4/fXXMXfu3BLvuktNTcWECRMQFRWld7+DgwNmz56NYcOGFduGLbKGHE+fPr3AULQh1q5da/RwjZhZQ55LExcXJwynlRSHrbKmHGdnZ+PDDz9EREREsccEBgZi+fLlwnpm9GKw28LCBQUFYffu3Vi7di2OHTuGxMREyGQy1K1bF3379sWoUaPKvQikVCrFvHnz0KdPH2zatAlRUVFIS0tD1apVERgYiOHDh6Nr166ltuPh4YGNGzdi8+bN2Lt3L+7cuYPc3Fx4e3ujY8eOGD16NB/vooc15ZjKjnkWP2vKsaurK/744w/s27cPO3bsQHR0NNLT01GpUiU0btwYAwYMwODBg3k3phmwx4yIiIjIQnDyPxEREZGFYGFGREREZCFYmBERERFZCBZmRERERBaChRkRERGRhWBhRkRERGQhWJgRERERWQgWZkREREQWgoUZERERkYVgYUZERERkIViYEREREVkIFmZEREREFoKFGREREZGFYGFGREREZCFYmBERERFZCBZmRERERBaChRkR0Qvw+PFjzJ49Gz179kRgYCD8/f3h7++Pw4cPmzs0ojKZPn06/P39ERQUZO5QRMXe3AEQlVdkZCRGjx4NAPjggw8wZcoUM0dEVNDjx48REhKCtLQ0c4diFN3vrcKcnJzg7u6OgIAA9OrVCwMHDoSDg4PBbatUKhw8eBBHjhxBVFQUkpOToVAo4OnpiYYNG6Jbt24YOHAgKleubFTMcrkcBw8exL///ourV68iLS0NWVlZcHNzQ61atRAYGIg+ffqgQ4cOkErZN0GWh4UZEQEA/P39AbC4NYXly5cjLS0N9vb2+Pjjj9G2bVu4uLgAAGrWrGnm6MomLy8PCQkJSEhIwLFjx7B69Wr89ttvqF27dqnnnj9/HnPmzMHt27eL7IuPj0d8fDxOnDiBX375BZ9++ineeOMNg2I6ePAgFixYgPj4+CL70tPTkZ6ejuvXr2Pjxo2oX78+ZsyYgW7duhnUtjXj97Z1YWFGRGRip06dAgD06NED48ePN3M0ZTNixAi89dZbwuuUlBTcunULK1euREJCAm7duoXJkydj586dsLOzK7adAwcOYNq0aZDL5QCAdu3aYeDAgWjQoAEcHBzw+PFjhIeH4++//0Z6ejpmzZqFe/fu4Ysvvigxvv/7v//D0qVLhdedOnVCUFAQfH19UblyZWRkZODevXsIDw/HqVOncP/+fSxZssQmCjNTWbBgARYsWGDuMESHhRkRkYklJiYCABo0aGDmSMquWrVq8PPzK/Bex44dERISgoEDByI+Ph6xsbE4dOgQ+vbtq7eNGzdu4LPPPoNCoYBMJsP8+fMRHBxc4JjmzZujb9++eOeddzBp0iQkJCRg1apVqFOnToHCUNe2bduEoqxatWr46aef0K5duyLHvfLKKxg5ciRiY2Mxf/58pKamluVLQWRSHGAnIjIxhUIBALC3F9/fwm5ubpg8ebLwWts7WJharcbnn38ufC3mzZtXpCjT1bhxY6xevVoY8l24cCEeP35c5LjExETMnTsXAODi4oJ169bpLcp0+fn5YeXKlRg7dmzJH47IDMT3U4KoEN0JzGvXrkW7du2wdetWbN++HXfv3oVCoUDDhg0xatQoDB48WDhPLpdj27Zt2LFjB+7fvw+FQgF/f3+MHj0a/fv313utuLg49OjRAwAwf/58hISEYP/+/di0aRNiYmKQnZ2NmjVrolevXhg/fnypE5vlcjm2bNmCAwcO4NatW8jKykKVKlXQpEkTvPbaawgODi52AvP06dOxY8cO1KpVC+Hh4UhKSsKaNWtw7NgxPH78GDk5OVi7di1mzJhRYE7OsmXLsGzZsgJtDRkypMCQRVJSEg4dOoTIyEhER0cjKSkJSqUSVatWRbNmzRAcHIy+ffsWG1vhnLRv3x779u0Tvk45OTmoUaMGevTogQkTJsDd3b3ErxMAHD9+HHv27MGlS5eQkpIClUoFLy8vNG3aFN27d0e/fv3g7Oys99zr169j06ZNiIyMRFJSEjQaDXx8fNChQweMGTOmTD1d27dvx4wZMwq8V/hrW/jrCgCpqalYs2YNjh8/jri4ODx//hxeXl5o3bo1hg8fjjZt2hR7zaCgIMTHxwvtXrt2Df/9739x9uxZJCUlQaFQICYmxujPUhrtHCYASEhI0HtMeHg4bt26BQB49dVXMXDgwFLbbdCgASZPnozFixcjLy8Pa9euxfTp0wscs3r1auTm5gIAPvzwQ/j6+hoUs1QqxaBBgww6VteL/Hmi6+nTp/jvf/+LEydOIC4uDjk5OahWrRpatGiB4cOH45VXXilyjvbfg1Zp39uFP1vbtm2xfft27Nq1C3fu3EFqaioGDx4sHF/4Z0xxsrKysHnzZhw7dgx37txBRkYGHBwcULduXbRu3Rr9+/dH69atS/0a2AoWZmRTlEolJk+ejKNHjxZ4PyoqCl988QWuXbuGmTNnIiMjA++//z7OnTtX4LhLly7h0qVLePjwISZNmlTq9b788kts27atwHv37t3DihUrsHPnTqxevbrYXyRxcXEYP3487t69W+D95ORk/Pvvv/j333+xadMm/Prrr6UWLpcvX8akSZMq5K5AlUqFrl27Qq1WF9mXlJSE8PBwhIeHY+vWrfjll1/g6upaYntqtRrTpk3D7t27C7x///59rFy5EocPH8b69evh5eWl9/y0tDRMnToVp0+fLrJPO5H84MGDAICQkJAi1164cCHWrFkDjUZT5Pr379/H1q1bMXv2bAwfPrzEz1ERIiIi8NFHHyErK6vA+9rPsXv3bowcORIzZ84s9Y7CDRs24Ntvv4VSqTRlyAAAmUwmbBfXK7hz505h++233za47TfffBPLli3D8+fPsWPHDnzxxReQSCQAAI1Ggx07dgDI7y0bNmxYGaIvuxf182T37t2YM2cOcnJyCryfkJCAAwcO4MCBA3j99dfx9ddfV1iv7PPnzzFu3Lhie0ANderUKXzyySdFfvYoFArcvHkTN2/exH//+1+T/MFgrViYkU35+eefceXKFQQHByM4OBienp64f/8+fvnlF9y7dw/r1q1DUFAQ1q1bh0uXLmHEiBHo1asX3N3dcfPmTfz8889ISkrC0qVL0aNHDzRq1KjYa/3111+IiopC8+bNMWbMGNSrVw8pKSnYsWMH9u/fj6SkJLz77rvYs2cP3NzcCpybnZ2NMWPG4NGjRwCAnj17YujQofD29kZcXBzWr1+Ps2fP4sKFC5g0aRLWr19f7ITr7OxsTJkyBc+fP8ekSZPQqVMnODk5ITY2Fl5eXli5ciUUCoUwrFR4kjcAVKlSRdjWFjAdOnTAq6++Cj8/P3h4eCA7OxuPHj3Cli1bcOnSJZw8eRLffPMNFi5cWGpOLl26hJ49e2Lw4MGoWbMmkpOT8ddff+HYsWN48OAB5s+fjx9//LHIubm5uRg9ejRiY2MBAE2bNsXw4cPRqFEjODg4ICEhAefOncO+ffv0Xnvu3Ln466+/AABt27bFkCFDUKdOHTg5OSEmJgZr1qzBrVu3MHv2bHh6egq9oYbo2bMnmjVrBgDFfm11v643b97EpEmThPlXI0eORFBQEJydnXHz5k2sWLFCyL2zszOmTZtW7LWjoqKwe/duVK9eHePGjUOzZs2gVCpx4cIFg+M3xp07d4TtWrVq6T3m/PnzAABnZ2d07NjR4LYrV66Mtm3bIiIiAunp6bh9+7bwfXfr1i3hF37r1q2LfB+Z2ov4ebJv3z58/vnn0Gg0qFOnDkaNGgVfX194eHggPj4eW7duxfHjx7F161a4ubkV6KU19ntb1w8//ICYmBgEBQUhJCRE+L7Mzs42+Otz5swZjB8/HkqlEnZ2dhg0aBB69OiBGjVq4Pnz57hz5w7+/fffIoWtzdMQWbkzZ85o/Pz8NH5+fpqlS5eWuN/Pz0+zevXqIsckJSVpWrZsqfHz89N06NBB4+/vrzl06FCR427evKkJCAjQ+Pn5aebOnVtk/6NHjwpca/z48RqFQlHkuGXLlgnHLFy4sMj+BQsWCPuXLFlSZL9ardZ8+umnwjHr168vcswXX3wh7G/RooXm5s2bRY7RVdLXsPC179+/X+IxP//8s8bPz0/j7++vuXfvXpH9hXPy66+/6r3O2LFjNX5+fpomTZpoUlJSihwzb948oY2vv/5ao1ar9cbz/PlzzdOnTwu8FxERIZy7efNmvefl5eVpRo8erfHz89N0795dby4NYcjXdujQoRo/Pz9N48aNNSdOnCiyPz09XdO/f3+Nn5+fJiAgQBMbG1vkmO7duwvXeu211zQZGRllilertO8tjUajUSqVmsGDBwvHnTt3rsgxCQkJwv433njD6Dh++OEH4fzdu3cL7+/atUt4/8cffzS63bJ4kT9PUlJSNK1bt9b4+flpZsyYUey/vx9//FH4d3Hnzp0i+w393i782fT97NGl/RnTvXv3Ivvy8vI0nTt31vj5+WlefvllzZkzZ4pt5/HjxyVex9Zw8j/ZlJdfflnvMIqXlxd69eoFIH+OT79+/dCzZ88ixwUEBAhzIUrrfXBwcMDcuXP1Di1MnjxZuMNt27ZtwtIBQP5clK1btwIAGjVqpHfdIYlEgq+++koYwly/fn2Jsbz77rsICAgo8RhDSSQS1KtXr8Rj3n//fVStWhUajabEuSdAfi+XvmEciUSCMWPGAMgfMrp06VKB/c+ePcOmTZuENsLCwoQhrsIcHBzg6elZ4L0VK1YAAPr06VPsEJijoyNmz54NIH84MTIyssTPUlZXr15FVFQUAGDYsGHo3LlzkWOqVKmCb775BkD+EKy2p684c+bMMXpxVmOkpqbi9OnTGDVqFG7cuAEg/2upbw6c7jBW4TwYolq1asJ2enq63m3dY14UU/882bBhAzIzM+Hj44Ovvvqq2GHKKVOmwMfHB2q1Grt27SrPRxLUr1+/XGue7dy5E0lJSQCAqVOnon379sUeW6NGjTJfR4xYmJFNKWmSrW7hMmDAgFKP0w4zFqdTp07w8fHRu08qlQoTg9PT04VfbABw7do1PHv2DED+xNzihijd3NzQr18/AMDt27eFH4L6GDLRuqzUajUSExNx9+5dxMbGIjY2Fnfu3EH16tUBANHR0SWeHxwcXGxB1bRpU2G78Nf7zJkzwqTv0aNHl7h2VmFZWVk4e/YsgPxioiS+vr6oWrUqABQpDiuK7jye119/vdjjWrduLcxJLGnuT40aNUq8SaAsli1bJjxGyt/fHx07dsSYMWNw8eJFODs745133sHixYv1nqs7Z057l6UxdOcp6ralO6xW3I0dpmTqnyfaP2q6detW4lMV7O3t0aJFCwAV92+0f//+Rn1PFaYdnnRxcTF4gWDKxzlmZFNKuruuUqVKwnb9+vVLPa60uRaBgYEl7m/evLmwHRsbK/xg1d65BuT/RV6Sl19+GRs2bBDO8/b2LnKMi4sL6tSpU2I7xtJoNNi9eze2bt2Kq1evIi8vr9hjS7vhoGHDhsXu072pofDXW7eYNfaOrhs3bgg3L3zyySf45JNPDDovOTnZqOsYSptzmUyGxo0bl3jsyy+/jDt37uDBgweQy+V6f2Hr3iX5IgQEBCA0NLTATQC6dOd+FZ7Abgjd3Ou2pVuwaYv0F8mUP09UKpXwR82mTZuE3uHSVNS/0fL+G7p58yaA/D+uzFE0WzMWZmRTnJycit2ne5ebIcfpuytRV2lDK8UNz2RkZAjbHh4eJbahOyyke56uih7Oev78OT744AP8+++/Bh1fUtEGGJ6Twl9v3YJPX0FakpSUFKOO1yrts5SVNv/u7u6l3lWnzblGo8GzZ8/0Dg2aYghTd+K4SqVCQkIC/vnnH+zatQuXLl1CaGgotm7dqvffrLbHEShb4aCbL91iXXe7rDktD1P+PMnIyCjTHbUV9W+0uJsCDKX9/izubmoqHgszIhMpbnjuRbdRnuEIfZYvXy4UZe3atcNbb72Fpk2bwtPTE05OTsIvmpEjRwp34lka3V+C3/y/9u40JKq2jQP4f4zKxNJMzRaQelSYksIlFLLNpCDaJhEU0bSdCsuEVip4iha/VEopgZZlOSZIoVGD66RSoSlaEg6WuUSSaYobTZrPB5nDTLM01oye3vf/+zQw5xzvmTNnvOY6931d//4LHx8fs/b7039W48XS5xzQr/wvlUqxZs0aBAQE4Pjx4/j48SNOnTqFlJQUvX1dXV0xc+ZMfP36FQ0NDRgeHh7TGLWzo9q3CLUf19fXj/Ulidrw8LDwOCwszGgz+Z8Zy1qOFRu8TxwGZkRW8qvMgLEsgPY//87OTpO3S7T/xngEDSMjI8LCBH9/f2RkZBj9AjeWwbMU7SzM58+fx3S7Vvv9trW11Ws1NN404+nu7sbQ0JDJrJnmnEskEqtO7jeXTCZDSUkJFAoFiouL8fz5c71yGBKJBH5+figsLMTg4CCeP39ucIGDIb29vUKA7+joCA8PD+E5T09PIeB79eoV+vr6xr1khrX8XKJmoj+jYzVz5ky0t7ejo6Njoofy12FITGQlmlV25jyvXb9I+3Ftba3JY9TV1Rncz1q6u7uFL1pTlf37+/vR1NRk1bFoLwwYa2ZOKpUK2cjq6mqLjut3aM6dpuimKZpz7u7ubnJC+HiKj48XMmBXrlwxuI1MJhMe37171+xjZ2dnC7fnZDKZThZZIpEIxx0YGEBOTs6Yxy5WU6ZMET4XYviMjtWiRYsAjC5mmoj5f38zBmZEVlJRUWF0peSPHz+EiuUODg46QYa3t7eQCXn48KHRuWx9fX148uQJAMDDw2PM86x+NnXqVADQKd3xM+3bK6a+bHNycqxecT4gIEBY4Xf37l2dsf2Kk5OTsNgiPz9/wptZa7fT+blThLaamho0Njbq7TPRFixYIKwQrq2tRUVFhd42wcHBworS0tJSPH78+JfHbW5uxvXr1wGMZjYN3c6LiYkRJpcnJSXpFLs15cePH3rdJsQmODgYAPD+/XuUlZX99nHMubYtTTP2wcFBsxcu0CgGZkRWolarcebMGYMBw82bN4Vq9aGhoTqZjylTpgglE1QqFW7cuKG3/8jICM6dOydMsI2MjPzj8Wom6ba0tBjdxsnJSQga8/PzDX7R19XV4dq1a388nl+ZMWOG0Capvr4eFy5c0GurpPH9+3e9yeGaxtt9fX2Ii4sTSpQYolarce/ePXz79s1Co9e1ZMkSoUtATk6OwfZSvb29Qk01GxsbREREWGUsv2vv3r1CNsvQPDMbGxskJiYKc6COHz9utCMDMFpmJSYmRljFeezYMcydO1dvu9mzZ+P06dMARrNmUVFRQikUYxobG7Fr1y6kpaWZ9+ImSHR0tPDj48SJEzortg0pLS01WJ7GnGvb0jZv3iyUC7p69arJc2Ksv+r/K84xI7ISb29vlJSUICIiQqcl08OHD4VsgZubG/bv36+374EDB1BQUIDW1lYkJydDpVJh27ZtcHFxQVtbm9CYGgB8fHws0sfRx8cHbW1tKC4uhlwuh6+vr/BL297eHrNmzYKNjQ02bdqEe/fuoaGhAREREYiNjYW7uzv6+vqgVCpx//592NnZwdXVFR8+fPjjcZly6NAhVFRUQKVSITMzEzU1NQgPD4eXlxcmT56M9vZ2VFVV4fHjxzh8+LBOr8xVq1YhOjoad+7cQWVlJTZs2IDw8HD4+fnB0dERAwMDaGlpQVVVFQoKCtDT04OtW7cK74mlnT9/HmFhYfj+/Tt2796NqKgorFmzRqclk6bW1Y4dO0Q358jLywvBwcEoKipCZWUlqqqq9GqpeXt7IzExEUePHoVarUZ8fDzkcjk2bdqEhQsXYvLkyfj06ROKi4uRn58vZF137Nih10pIW2hoKNrb25GUlITOzk5ERUUhKChIyNLNmDEDPT09aGpqglKpRFlZGYaHhy1WdNlanJ2dcfnyZcTFxaGjowOhoaGQyWRYuXIl3NzcMDQ0hPb2dtTV1UGhUKC1tRWpqal6r8uca9vSpk6disTEROzcuRODg4OIjY3F5s2bERISAjc3N6jVarx//x5KpRLFxcV48+aNxcfwt2JgRmQlkZGRqKysRG5uLuLj4/We1/Sp1K53pGFvb4/bt28LTcwVCgUUCoXedr6+vkhJSbHIKrydO3dCoVBArVbj7NmzOs/JZDJcunQJwOh8ourqarx9+xZv3rxBQkKCzraOjo5ITk5GUlKS1QOzadOmISMjA3FxcaisrER9fb2QPTHHyZMn4eDggJSUFHR0dCA5OdnotnZ2dlZZ7aghlUqRmpoqNDFPT09Henq63naRkZF677lY7Nu3D0VFRQBGs2aGMlIbNmyAi4sLzp49i3fv3uHly5dGOyo4OjriyJEjZv3wOHDgADw9PXHp0iV8/PgR5eXlKC8vN7q9p6enyX6jYrFu3TrcuHEDJ06cQHd3N+RyOeRyucFtbWxsDNYMM/fatrTAwECkpqYiISEBPT09yM3NRW5urlX+1v8SBmZEVnTx4kUsX74c2dnZUKlUGBgYwNy5cxESEoI9e/aYXEk5f/58PHr0CDk5OXj69ClUKhX6+/vh4OAAqVQqNE621LJ2qVSK7OxspKWlobq6Gl++fDF4q3L69OnIysrCrVu38OTJEzQ3N2PSpEmYM2cOVq1ahe3btwtV/8eDk5MTMjMzUVBQgLy8PNTW1qKrqwsSiQSurq5YvHgxQkJCDFb4l0gkOHjwILZs2QK5XI4XL16gra0Nvb29sLW1xZw5cyCVShEUFISQkBCT9agsISgoCAUFBcjIyIBSqURrayvUajWcnZ3h5+eH8PBwi1f0t6QlS5Zg+fLlqKioQHl5Oerq6nQKKWssW7YMeXl5UCgUKCoqwuvXr/HlyxcMDQ3ByckJ//zzD1avXo0tW7aMaeXpunXrsHr1aigUCjx79gyvX79GV1cX+vv7YW9vj3nz5mHp0qVYv349AgICLFKOZjxoMpEPHjyAUqlEY2Mjenp6MGnSJDg7O8PT0xOBgYFYv369wfZG5l7b1rBixQoUFhYiKysLJSUlaGpqQm9vL6ZNmwZ3d3f4+/tj48aN4zKWv4VkxNikDCIas7a2NqxduxbAaFCmfeuMiIjoVzj5n4iIiEgkGJgRERERiQQDMyIiIiKRYGBGREREJBIMzIiIiIhEgqsyiYiIiESCGTMiIiIikWBgRkRERCQSDMyIiIiIRIKBGREREZFIMDAjIiIiEgkGZkREREQiwcCMiIiISCQYmBERERGJBAMzIiIiIpFgYEZEREQkEgzMiIiIiESCgRkRERGRSDAwIyIiIhIJBmZEREREIsHAjIiIiEgkGJgRERERiQQDMyIiIiKRYGBGREREJBIMzIiIiIhE4j/7VDZUnw1+YgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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HjKgS6NChAx4/fgxA/0nuhtJc5sHOzk7Y5oZKJzc3V2vXhsWLFxc5ZFjUcGhZevr0qdb6eCUdxjRE9+7d8euvvwIA0tLScPv27VIt/6K5SG15D/saKiUlRTguz4SXTA97zIgqAc31tmJjY8slOdN88jAjI4PzYspIWFiYMFzYrFmzYhMgzScky9O+ffugVCoB5M2rq4jlHPInKKV9WrF+/frCsb7z9iqK5i4Prq6uRoyExIaJGVEl0K9fP61J5upFTcuSs7Oz1pCp5krwVHLqvSGB4h88APIWzq0ImsOYAwYMKPVEfH3kH27UXDC4JDT3sLx//36p6tJUFkPYMTExwrHmwsRETMyIKgEbGxut+TkHDx4UViYvS7169RKON2/eXOb1V0Wav+SL64VUKpXYvn17eYeEGzduaCUyJVm7rCRu3ryp9bq0q+F7eXkJX9979+4V+vCFoTSX8tDngZL8lEoloqOjhddlsf0UVR5MzIgqiQkTJggbewPAl19+WebJ2bhx44SnDI8dO4bdu3frfS3XatKtQYMGwvGlS5eKfMjijz/+QGRkZLnHtGfPHuG4SZMmaNOmTbnfM/+TvnZ2dmjVqlWp6nRwcEDz5s0B5PXG5U/8Skqzd1qzx1Nft27dEoavX3nlFW7HRFqYmBFVEtWqVcPKlSuFuWDZ2dmYOnUqPvvsM9y8ebPQJ/iUSiVCQ0P12u+vYcOGmDJlivB61qxZWLJkidZEZk1yuRwhISGYPn16hfW6mJoWLVoIv5hfvHiBTz/9VGv+EZD3gMDPP/+MZcuW6dxrsyzl5uYKe5ACFdNblpiYiE8//VQrcRo9erTBWz/p4u3tLRxfuHCh1PUBeXPX1E9SxsXFGTynUzMOzV5oIoBPZRJVKg0aNMCOHTvw4YcfIjIyEiqVCocPH8bhw4fh4OCAli1bolatWrCxsUF2djbi4+MRFRWFtLQ0rXo6d+5c6ErpU6dORVxcHPbs2QOVSoV169YhKCgIrVq1QsOGDWFlZYXMzEzExcUhKipK6BkQ28rrYiGVSvHpp59i1qxZAICzZ8+if//+aNu2LerVq4e0tDRcvHgR6enpAIDvvvsOX375ZbnFc+rUKeFeUqkUgwcPLnWduvbKBPL+eLh//z7Cw8O1hhnbtm2LyZMnl/q+QN4OFf/9738BAMePH8ekSZNKXaeZmRl69+6NgwcPAgDGjBmDHj16oG7dukKPcs2aNQv9DJo92fruoEFVBxMzokrG1dUVW7duRVBQENatWycsE5CSkoIzZ84Uep1EIkH79u3x/vvvF/kEnkQiweLFi9GyZUusWrUK6enpkMlkuHbtWqEPBEgkErRr1650H6wSGzZsGB49eiQsFZGVlYWzZ89qlalWrRpmzZoFX1/fck3MNIcxu3btWiZ7TOqzV6basGHDMGvWrDJb26tJkyZo3749rly5gvDwcDx+/Fhr+LikPv/8c4SGhuLZs2fIzs7G0aNHtc67urrqTMyePn2K69evAwBatmzJif9UABMzokrI2toaEydOxOjRo3HmzBmcP38eYWFhSE5ORlpaGpRKJWrUqAEHBwd4enrCy8sL3t7eBv3CCggIwNChQ7Fv3z6cO3cOkZGRSElJQW5uLmxtbeHi4oJmzZqhU6dO6Nmzp9YTnVTQtGnT0KNHD2zatAlXrlxBSkoKbG1tUadOHfTo0QNvvfUWGjduXK4x5E/ey3vtMisrK1SvXh1NmzZF27ZtMXjwYDRt2rTM7/Pee+/hypUrUKlU2LlzJ6ZNm1bqOl1dXbFv3z789ddfOHv2LB48eIDMzMxiHzDYtWuXMK3gvffeK3UcVPlIVNwXgoiIKjGlUolBgwYhJiYGjo6OOHnyZJlskm4omUyGPn36ID4+Hq6urvjnn3/KZB4dVS6c/E9ERJWaVCoV9oZNSkoy6GnisnTgwAFhodupU6cyKSOdmJgREVGlN2DAAGHZj99//73U+3AaSi6X47fffgOQt6BsWTxUQZUTEzMiIqr0JBIJ5s6dC6lUiri4OK010yrCzp078eDBAwDA3Llzhac3ifLjHDMiIiIikWCPGREREZFIMDEjIiIiEgkmZkREREQiwcSMiIiISCSYmBERERGJBBMzIiIiIpFgYkZEREQkEkzMiIiIiESCiRkRERGRSJgbOwAyLqVSiezsbACAubk5JBKJkSMiIiIyDSqVCnK5HABgbW0NqbT0/V1MzKq47OxsREZGGjsMIiIik+bp6QlbW9tS18OhTCIiIiKRYI9ZFWdu/r8m4ObmBhsbGyNGQ2QYmUwm9Ph6enrCwsLCyBER6Y/t1/Rp/htq/j4tDSZmVZzmnDJLS0tYWloaMRoiw0ilUtjb2wMAqlWrVmY/GIkqAttv5VJWc7TZCkjAHwpkaszNzeHm5mbsMIhKhO2XdOEcMyIiIiKRYGJGREREJBIcuyKBTCbjHDMyKTKZDPfu3QOQ9/AKJ0+TKWH7JV2YmBGRyVKpVMjMzBSOiUwJ2y/pwqFMIiIiIpFgYkZEREQkEkzMiIiIiESCiRkRERGRSDAxIyIiIhIJJmZEREREIsHlMkgglTJPJ9NiZmaG+vXrC8dEpoTtl3RhYkYC/mAgU2NmZgYXFxdjh0FUImy/pAu7SIiIiIhEgokZERERkUhwKJME3CuTTE1ubi7Cw8MBAF5eXmy/ZFLYfkkX9pgRERERiQR7zEhgbs7mQKbFwsICXl5ewjGRKWH7JV34m5gEEonE2CEQGUQikXD4h0wW2y/pwsSMBAqlEglpmcYOg4iISG+ONaxhVonW4WRiRoKUjBz4Ldlv7DCIiIj0dmj2MLjY2xo7jDJTeVJMIiIiIhPHxIyIiIhIJJiYEREREYkEEzMiIiIikWBiRkRERCQSon8q88WLF9i/fz+Cg4Nx7949JCcnw9zcHI6OjmjVqhV69+6Nfv36wczMTO86U1JS8Pfff+Ps2bO4c+cOUlNTkZubi5o1a6Jp06Zo164dBg0aBHd392LrUiqVOHXqFE6fPo1r164hKSkJz58/h62tLVxcXIQYe/bsqXMBwYCAAFy8eLHA+1KpFNWrV0eDBg3QqVMnvPPOO2jUqJHen5GIiIhMj0SlUqmMHURhtm/fjmXLliEtLa3Icq+88gq+/fZbdOjQochySqUSv/76K/744w9kZha/Xle3bt0wa9YsvPLKKzrPX7x4EQsWLEB0dHSxdTk7O2PatGnw9/fXer+wxCw/CwsLzJw5E6NGjSq2rCE092qr08iNy2UQEZFJMeZyGeWx36loe8yWLFmCdevWAcjbKmjgwIHw8fGBq6srcnNzcf/+fRw8eBAXLlzA3bt38f777+Onn35C//79ddaXk5ODzz77DKdOnQKQl+gMHDgQr732GurXrw8rKyskJycjPDwcx48fx+3bt3H27Fls27YNs2fPLlDfrl27MH/+fMhkMgBAmzZt0LdvXzRv3hz29vZ48eIFHj9+jNOnTyM4OBiJiYlYuHBhgcRM04EDB4RjpVKJ+Ph4HDlyBHv27IFMJsOCBQvg6uqKXr16lfTLSkRERCImysRs06ZNQlJWp04d/Prrr2jevLlWmQ4dOmD48OE4cOAAZs6cidzcXEyfPh2NGjUqUBYAvvnmGyEpe/XVV/Gf//wH9evXL1CuZ8+emDp1Kk6dOoWFCxfqjO/8+fOYM2cOlEolbGxssGjRIp0JYZcuXTB8+HDExsZi2bJlOHPmTJGfO//QqaenJ3r16oWWLVvi+++/h0qlwsqVK5mYERERVVKim/wfFxeHJUuWAABsbGywfv16nYmWmq+vr5BA5ebm4quvvkL+0dljx45hz549AAAPDw+sX79eZ1KmydvbG7t27UKXLl203s/Ozsb06dOhVCohlUrx22+/FdpLp1a/fn0sX74cc+bMKbJcYUaNGgVXV1cAwM2bN5GcnFyieoiIiEjcRJeYbdiwAS9fvgQAfPjhh2jSpEmx1/j5+aFHjx4AgOjoaKFnTO3XX38VjhctWgQbGxu9YqlRowZ69+6t9d6uXbvw7NkzAHkJU6dOnfSqCwCGDBmid1lNUqkUrVq1El4/efKkRPUQERGRuIkqMVOpVNi3bx8AwMrKCu+8847e17733nvC8e7du4XjqKgoREREAMgb/mzZsmWpYlTXLZFIMGbMmFLVZQhz8/+NOiuVygq7LxEREVUcUc0xu3PnjvAEZocOHVC9enW9r33ttddgZWWFnJwcXLlyRXj/0qVLwnHPnj1LFV9GRgZu374NAGjSpAkaNmxYqvoMofnkp7Ozc4Xdl4iIyFgsoSi2THZWFjILrkalk62t+Dc7F1ViFhkZKRy3aNHCoGvNzMzg6emJ69evIyUlBQkJCXBxcdGqU3M4sCSioqKE3qrS9rwZIjg4GHfu3AEANGjQAHXr1q2wexMRERlLf7tHxZbZ8Psvetc3f/780oRTIUSVmKWmpgrHjo6OBl9fu3Zt4TgtLQ0uLi5aa6A5ODiUKj7NujTvVR40l8tYuXKl8P4HH3xQrvclIiIi4xFVYqa56Ku+E/Q1aV6TkZFRJnVq0qzL2tq6VHXp4uHhUeT5ESNGGDTvjoiIiEyLqBIzzbHfrKwsg6/XvMbOzq5M6tSkWVd2dnap6tKXtbU12rVrh9GjR8PHx6dC7klERETGIarErFatWsJxUlKSwddrru9lb2+v9f/850tCs66SxFcczZX/zczMYGtrCycnJ4P2ASUiIqosjmQU/5Ddlmlvwqlm6UbExERUiZnmUN6tW7cMulahUCAqKgpA3lwyFxcXAHmr56vdvHkT3bp1K1V8UqkUSqXS4Pj0oc+m6URERFVFLorvmLC2sTGJpy31Jap1zNzd3YVeqcuXL+PFixd6X3vu3DlheFFzM/OOHTsKx6dPny5VfHZ2dsIuBPfv38fDhw9LVR8RERGRJlElZhKJBIMHDwaQt+n49u3b9b72r7/+Eo6HDh0qHHt4eAjLZFy+fBk3b94sVYzqTchVKhWCgoJKVRcRERGRJlElZgAwZswYWFpaAgACAwP16pX6+++/ERwcDCCv183b21vr/OTJk4XjmTNn6v0QwPPnz3Hy5Emt94YNGwYnJycAeZutX7x4Ua+6AGDv3r16lyUiIqKqR3SJWf369fHVV18ByHuKcuzYsVqLxOZ36NAhfP311wAACwsL/Pjjj5BIJFpl+vbtK+xTGRUVhbFjxyIuLq7IOE6fPo233noL58+f13rf2toaP/30kzDXbNKkSfjnn3+KrOvJkyf4/PPP8f333xdZjoiIiKo2UU3+VwsICMCjR4+wceNGPHnyBMOGDcOgQYPg4+ODevXqQS6XIyYmBgcPHhQSJ0tLS/z000/CHLD8vvnmG6Snp+PUqVO4ceMG+vfvjwEDBqB79+5wdXWFlZUVkpKScOvWLRw7dqzIIc+uXbvi+++/x/z585GVlYVPPvkEbdq0Qb9+/dC8eXPUrFkTL168wOPHj3HmzBmcPHkSubm5Bm0xRURERFWPKBMzAJg9ezaaNm2KFStWIC0tDfv27RM2OM/Pzc0N3333ndak//ysra3xyy+/YPXq1Vi7di0yMzOLrBMAevXqhXfffVfnuWHDhqFBgwZYsGABoqOjcf36dVy/fr3QuurVq4cvvvii0PNEREREok3MAGDkyJEYNGgQ9u/fj+DgYNy9excpKSkwMzODo6MjvLy80Lt3b/Tv31+vtb6kUik++ugjjBw5En///TfOnj2L6OhopKamQiaToWbNmmjatCnatWsHPz8/uLm5FVlfp06dsG/fPpw6dQrBwcG4du0akpKS8OLFC9jY2KBOnTpCjD179oS5uai/3ERERGRkEpVKpTJ2EGQ8ubm5CA8PBwDUaeQGvyX7jRwRERGR/g7NHgYXe+OsY6b5O9TLy0t4eLE0RDf5n4iIiKiqYmJGREREJBJMzIiIiIhEgokZERERkUjwMUESONhZ4dDsYcYOg4iISG+ONayNHUKZYmJGAjOpFC72VsYOg4iIqMpiYkYCrpxCpkalUkEmkwHI25It/3ZsRGLG9ku6cI4ZCeRyubFDIDKITCZDeHg4wsPDhV9wRKaC7Zd0YWJGREREJBJMzIiIiIhEgnPMSMD5DWRqpFIpnJ2dhWMiU8L2S7owMSMBN1knU2Nubo4GDRoYOwyiEmH7JV2YohMRERGJBBMzIiIiIpHg2BUJFEolEtIyjR0GiYBjDWuYmcCcF5lMhtu3bwMAmjdvDgsLCyNHRKQ/tl/ShYkZCVIycuC3ZL+xwyARODR7GFzsbY0dRrE0F+jkAslkath+SRfx/0lMREREVEUwMSMiIiISCSZmRERERCLBxIyIiIhIJJiYEREREYlEhSdmCoUC7dq1g4eHB4YOHVpkWZVKhc6dO8PDwwMeHh7YuXNnkeX37NkjlN28ebPwfkBAgPB+cTZs2CCU7dq1KyIjI4Vzq1atEs6FhoYWWxcA+Pj4wMPDAz4+PjrPz5gxQ6hT8z9PT0906NABfn5++Pbbb4VHqomIiKjyqvDEzMzMDG3btgUAREZGIiMjo9Cyd+7cQVpamvD6ypUrRdZ9+fJl4bhjx44Gx/bHH39g4cKFAAAnJycEBQXB09PT4HrKgkqlwosXLxAVFYXNmzfD398fy5cvN0osRGJlbm6Opk2bomnTptxSjEwO2y/pYpSW0LFjR4SEhECpVOLq1at4/fXXdZZTJ1pmZmZQKBRaiVdR5WvVqoVXXnnFoJhWr16NFStWAABcXFywYcMGNGnSxKA6Smvt2rXChrZKpRLJyckIDg7G5s2bIZfL8euvv8LZ2RmjRo2q0LiIxEoqlaJWrVrGDoOoRNh+SRejzDHr0KGDcFxUsqU+179/fwDAo0ePkJCQoLNscnIyHjx4AABo3749JBKJ3vGsWrVKSMrq1auHv/76q8KTMgBo3Lgx3N3d4e7uDk9PT3Tr1g2zZ8/GqlWrhDKBgYFQKBQVHhsRERGVP6MkZq1bt0a1atUA6JeY9evXDw0bNgRQ+HBmSYcx//Of/yAwMBAAUL9+ffz111/CvcTCx8dHSGZTUlJw8+ZNI0dEJA4qlQq5ubnIzc3lyulkcth+SRejJGaWlpZo3bo1ACA8PBy5ubkFyjx+/FjoHWvfvj3at28PoPBETvN9zR65oixZsgS//fYbgLzeqk2bNsHV1VX/D1KB1F8vAHjy5IkRIyESD5lMhvDwcISHhwtb2xCZCrZf0sVoy2Wok6fc3FzcuHGjwHl1otW4cWM4OjoKidmlS5d01qfuSbOzs0Pz5s2Lvf/333+PdevWAQCaNm2KoKAg1KlTx/APUkE0J4YqlUojRkKVgSUURf6XnZWFzMzMIv8jIqKyZ7THQDp27IjVq1cDyEvC8g8/qhOzdu3aaf3/zp07SE9PR82aNYWyGRkZwrIWbdu2hZmZWaH3ValU+Pbbb7FlyxYAgLu7O/788084OjqW0ScrH9HR0cKx+gEBopLqb/eoyPMbfv+l2Drmz59fVuEQEdH/M1pi1qZNG5ibm0Mul+scnlS/p+5Zc3NzQ61atZCamoqrV6/C29tbKHvt2jVhQnxx88vmzp2LHTt2AACaN2+OdevWwcHBweD4Y2Nj9Xqapiy6pyMjI3HmzBkAgLW1Nby8vEpdJxEREYmP0RIzW1tbNG/eHOHh4UJipe7pyv+EpVq7du1w4sQJXL58WSsxM2R+mTopc3Nzw4YNG7R63gwxa9asEl2nL5VKhaSkJAQHB2PZsmVC4hkQECA8OEFERESVi1FXtOvYsSPCw8ORmZmJ27dvo1WrVgD+N4/M0dERjRs3Fsq3b99eSMw0qeeXVatWrdjeJIlEApVKhdjYWNy6dQtdu3Ytw09UOr179y7yfK9evfDJJ59UUDRERERU0YyamLVv316YgH/58mUhMVMnXpq9ZcD/esNu3ryJnJwcWFlZITc3F2FhYQCAV199FZaWlkXec+7cufjuu+/w8uVLTJkyBX/88YfeT3Fq2rhxIzp37lxsOR8fH8TFxRlcv5qFhQW8vLwwYsQIDB482KD12YgKcySj6CVhtkx7E041bSooGiIiUjNqYtahQwehB+vKlSsYO3YsgP/1gKkn/Ku1aNEC1tbWyM7OxvXr19GlSxeEh4fj5cuXQn3FGTVqFHJycvDjjz8iOzsbkyZNwp9//qm1HIWxaK78L5VKYWNjA0dHx2KTTSJD5aLwB2QAwNrGBra2thUUDRERqRltuQwAsLe3R7NmzQD8r5dM8wnL/D1m6t4jzfKaC87qu7Ds+PHj8fHHHwv3++CDD7Q2KzcWzZX/X3nlFdSrV49JGVER1D8TvLy8YGFhYexwiAzC9ku6GDUxA6C1ov29e/dw5coVKJVK2NjYoEWLFgXK519oVj0fzcLCAm3atNH7vlOnTsWECRMAAOnp6Rg3bhzu3btXmo9CRBVMIpHA0tISlpaWHOYnk8P2S7qIJjED8nq/1D1gbdq00bkemToxu3HjBnJzc3Ht2jUAecOcNjaGzYn58ssvERAQACDvSdCxY8fi8ePHJfocRERERKUlqsTs0qVLQg9Y/mFMNfUCsllZWdi9ezdevHhRoB5DzJ49G2+//TYAIDExEe+99x6ePn1aorqIqGIplUqkpqYiNTWVO2KQyWH7JV2Mnpi5uLgIm4aHhoYiIiICQOGJmZ2dHdzd3QEAf/zxh/C+IRuXa5JIJPj222/h6+sLAIiLi8PYsWPx7NmzEtVHRBVHLpcjJiYGMTExkMvlxg6HyCBsv6SL0RMz4H+9XQkJCcjNzYW5uXmR88XUSZt62FEqlRaayOlDKpViyZIl6NevHwDgwYMHeP/995GSklLiOomIiIgMJarETK158+awtrYutHz+JMzd3R01atQoVQxmZmZYtmwZevXqBSBvT87x48fj+fPnpaqXiIiISF+iSMzyD0MW1/uV/3xJhzHzs7CwwMqVK/Haa68BAG7duoUJEyYgMzOzTOonIiIiKopEpVKpjB0EGU9ubi7Cw8MBAHUaucFvyX4jR0RicGj2MLjYi3+BWc326+XlxXX/yKSw/Zq+8vg3FEWPGRERERExMSMiIiISDaPulUlEVBoSiUTYyoYrp5OpYfslXZiYEZHJsrCwQOvWrY0dBlGJsP2SLkzMSOBgZ4VDs4cZOwwSAccahS9XQ0RE5YeJGQnMpFK42FsZOwwiIqIqi4kZCbhyCpkauVwu7G1bt25dmJvzRxqZDrZf0oVPZZKAe7WRqVEqlUhMTERiYiI3gSaTw/ZLujAxIyIiIhIJJmZEREREIsHEjIiIiEgkmJgRERERiQQTMxLwiSAiIiLjYmJGAm4JQkREZFzsIiGBQqlEQlqmscOgSsCxhjXMpOX/d59UKoW9vb1wTGRK2H5JFyZmJEjJyIHfkv3GDoMqgUOzh8HF3rbc72Nubg43N7dyvw9ReWD7JV2YohMRERGJBBMzIiIiIpHgUCYRmSyZTIZ79+4BANzc3GBhYWHkiIj0x/ZLujAxIyKTpVKpkJmZKRwTmRK2X9KFQ5lEREREImEyPWZZWVnYt28fTp48icjISKSlpUGlUsHOzg6urq5wd3dH27Zt0aNHD9StW7fIup48eYK///4b586dw8OHD5GSkgKlUomaNWvC3d0d7du3h6+vLxo0aFDg2tDQUIwZMwYAMHXqVHz88cfFxj5jxgzs2bMHAHDixAnUr19f6/zu3bsxc+ZMAMCiRYvg7++v19eEiIiIKheTSMyuXbuGzz//HE+ePClwLjU1FampqYiIiMDu3bvh6OiIs2fP6qzn5cuXWLZsGbZs2YLc3NwC5xMTE5GYmIiQkBCsXLkS/fv3x9dff11sokdERERUFkSfmN2/fx/jx48XxuF9fHzQr18/NGnSBBYWFkhNTUVkZCTOnTuH0NDQQutJSUnBlClTcP36dQCAra0t3nzzTXTp0gV16tSBubk5kpKScPXqVRw7dgwPHjzA4cOH0aZNG4wdO7YCPikRERFVdaJPzJYvXy4kZYUN83Xr1g3jx49HSkoKDh8+XOC8UqnEZ599JiRl3t7e+OGHH1C7du0CZX18fPD5559j//79+PHHH8v2wxAREREVQdSJmUKhwOnTpwEArVq1KnbulYODA0aNGlXg/Y0bNwq9ad27d0dgYGCRG3ZLpVIMGTIEXbp0wf3790vxCYiIiIj0J+rELCUlBTk5OQCARo0alaiO3NxcrF27FgBQrVo1LFy4sMikTFOdOnVQp06dEt2XiMqfmZmZ8DCNmZmZkaMhMgzbL+ki6sRMc7E99SJ8hgoJCUFiYiIAoH///nBxcSmT2IiqKksoii2TnZWFTAPWyrS1Ldm+mmZmZvyeJpPF9ku6iDoxs7e3h6urK+Li4hAZGYk1a9bggw8+gFSq//Jrly5dEo579epVDlESVS397R4VW2bD778YVOf8+fNLGg4RUaUi6sQMAEaPHo0lS5YAAJYtW4atW7fCx8cH7dq1g5eXl861xjRFRkYKxy1btizT2JKTkxEdHV1suefPn5fpfYmIiKhyEn1iNnbsWNy9exe7du0CAMTFxSEoKAhBQUEAAEdHR3Tq1Am+vr7w9vaGRCLRuj4tLU041vUUZmls2bIFW7ZsKdM6iUh/ubm5CA8PBwB4eXnB0tLSyBER6Y/tl3QR/ZZMUqkUCxcuxLp169CjR48CE/eTkpJw6NAhTJkyBW+99RYePdIeZlEvtQEA1tbWFRIzERERUUmIvsdMrVu3bujWrRsyMjJw5coVhIeHIyIiApcvX8aLFy8AABEREXj33Xexe/duODs7A9CeVJydnQ07O7syi6kkWzIRmbojGQ2LLbNl2ptwqmlTAdEQEVUuJpOYqdnZ2aFnz57o2bMngLyu4AMHDmDJkiVIT0/Hs2fP8PPPP+OHH34AkPcAgVpSUlKZJmZEVVEuin+s39rGpsRPWhIRVWWiH8osjqWlJYYNG4Zly5YJ7x07dgxKpRIA4OnpKbx/69atCo+PiIiISF8mn5ip9ejRQ9hsPD09XZj037FjR6FMcHCwESIjIiIi0k+lScwACPPKNHXv3l14/8iRI0hISKjosIiIiIj0UmkSs+zsbNy9exdA3jy0WrVqAcgb6hw/fjwA4OXLl5g1axYUiuJXLgeAhIQEnD9/vnwCJiIiIspH1IlZZmYmhg8fjlOnTglzxnRRKpVYsGCBsDSGj4+P1npmY8aMQefOnQHkbdH00UcfISUlpdD6VCoVDhw4AH9/f0RFRZXRpyGismZubg4PDw94eHjovQcukViw/ZIuom8JYWFhmDx5MlxcXNCnTx+0adMG9erVg52dHZ4/f45bt25h165dwgr81atXx6effqpVh1QqxYoVKzB58mTcuHEDp06dQp8+feDr64suXbqgTp06MDc3R1JSEq5fv46jR48iJibGGB8XV65c0aucj4+P1hOnRFWRVCrlk9Zksth+SRdRJ2bm5uZwcnLCs2fPkJCQgE2bNmHTpk2Flm/cuDGWLVuG+vXrFzjn4OCAoKAgLF26FFu2bEFmZia2bt2KrVu36qxLIpHA19cXAwYMKLPPo4+dO3di586dxZbbu3cvEzMiIqJKRtSJWbVq1fDvv//i+vXrOHfuHG7cuIH79+8jOTkZL1++hLW1NZydneHp6YnevXvjjTfeKHJLi2rVqmH27Nl4//33cfDgQZw/fx4PHjxASkoKVCoVatasCXd3d3Ts2BG+vr5wdXWtwE9LRIZSKpXIysoCANjY2EAqFfXsDCItbL+ki0SlUqmMHQQZj+ZebXUaucFvyX4jR0SVwaHZw+BiX/4LzHKvQTJlbL+mrzz+DZmeExEREYkEEzMiIiIikWBiRkRERCQSop78TxXLwc4Kh2YPM3YYVAk41rA2dghERCaJiRkJzKRSuNhbGTsMIiKiKotDmSTgA7pERETGxcSMBHK53NghEBERVWkcyiSBhYWFsUMgMoilpSXat29v7DCISoTtl3RhjxkRERGRSDAxIyIiIhIJDmWSQKFQGDsEIoMoFAokJSUBABwdHWFmZmbkiIj0x/ZLujAxI4FSqTR2CEQGUSgUiI2NBQDUqlWLv9jIpLD9ki4cyiSBuTnzdCIiImNiYkYCiURi7BCIiIiqNHaRkEChVCIhLdPYYejFsYY1zKT8u4KIiCoXJmYkSMnIgd+S/cYOQy+HZg+Di72tscMgIiIqU+xyICIiIhIJJmZEREREIsGhTCIyWRKJBLa2tsIxkSlh+yVdmJgRkcmysLCAp6enscMgKhG2X9LFpBKzixcvIiAgQHi9ZcsWtGvXzqA6Hj16hMOHD+P8+fN49OgRUlNTIZfLUb16ddSvXx8tWrRAr1690K1bt0I39d69ezdmzpxp0H1nzpyJsWPHar0XEBCAixcvFigrlUpRvXp1NGjQAJ06dcI777yDRo0aGXQ/IiIiMj0mlZjt2bNH6/XevXv1TsyeP3+OxYsXY9++fZDL5QXOJycnIzk5GTdu3MCWLVtQu3ZtTJgwAWPGjKnw1ZiVSiXS09ORnp6OiIgIBAUFYebMmRg1alSFxkFEREQVy2QSs5ycHPzzzz8AABsbG2RlZeHIkSOYM2cOLC0ti7z28ePHmDBhAu7fvw8AsLe3x6BBg9ChQwc4OzvD2toaKSkpiImJQUhICM6dO4fk5GQsXrwYb775JpycnAqt+7PPPkPv3r2Ljb+oOgDgwIEDwrFSqUR8fDyOHDmCPXv2QCaTYcGCBXB1dUWvXr2KvRdRVSGXy/Hw4UMAQKNGjbh7BZkUtl/SxWRawbFjx5CZmbf46Zw5czBr1iykp6fj5MmT6N+/f6HXZWVlYdKkSUJS9vbbb+Prr7+GnZ1dgbI9evTAe++9h8ePH+OXX37B7t27i43LxcUF7u7uJfxU/5O/Dk9PT/Tq1QstW7bE999/D5VKhZUrVzIxI9KgVCqRlpYGAGjQoIFxgyEyENsv6WIyy2Xs3bsXAODh4YFhw4ahSZMmWu8XZunSpbh37x4A4N1338WCBQt0JmWaGjRogEWLFmH16tWoVq1aqWMvjVGjRsHV1RUAcPPmTSQnJxs1HiIiIio/JpGYJSYm4vz58wAAPz8/rf+HhIQgJSVF53VJSUnYuXMnAKBu3br4+uuvDbqvj48PatSoUdKwy4RUKkWrVq2E10+ePDFiNERERFSeTCIxO3DgABQKBaRSKXx9fQEAvr6+kEgkkMlkOHjwoM7rDh06hJcvXwIAhg8fDisrqwqLuSxpzjtQKpVGjISIiIjKk0kkZvv27QMAdOrUCS4uLgDyhhvbtm0LoPDhzEuXLgnHPXr0KN8gy1F0dLRw7OzsbMRIjM8SClhCgeysLGRmZmr9R0REZOpEP/n/9u3biIqKAvC/4Us1Pz8/XL16FTdv3sTdu3fxyiuvaJ1XXyeVSsttEb+EhAStxKkwJX1AIDg4GHfu3AGQl4zWrVu3RPVUFv3tHgEANvz+S4Fz8+fPr+hwiIiIypToEzN1b5iVlRX69eundW7AgAH44YcfIJPJsHfvXnz55Zda51NTUwEA1atXL3JJjWfPngll86tZs6bQS6fLihUrsGLFimI/hzpJ1IfmchkrV64U3v/ggw/0roOIiIhMj6gTM7lcLswf8/b2LvA0pb29PXr27Injx4/jwIED+PzzzyGV/m90Vj28ZW1tXeR91qxZg40bN+o8N3ToUCxevLg0H0MvHh4eRZ4fMWIE3nnnnXKPg8iUSKVSYXhf83ufyBSw/ZIuok7MQkJCkJSUBKDgMKaan58fjh8/jvj4eISGhqJr167COVtbWzx//hzZ2dnlFuOiRYvg7+9fLnVbW1ujXbt2GD16NHx8fMrlHkSmzNzcnOs/kcli+yVdRJ2YqYcx7e3tC5287+3tjRo1auD58+fYu3evVmJmb2+P58+f48WLF8jNzS10OHP27NmYPXu28Do2Nlav1fzLkubK/2ZmZrC1tYWTk1OFbwcldkcyGgIAtkx7E041bYwcDRERUdkSbWL24sULnDx5EgCQlpamtZZXYY4ePYr58+fDxibvF7anpycePXoEpVKJqKgoeHl5lWvMpVEWuwdUBbnIS1StbWxga2tr5GiIiIjKlmgHtQ8fPiysQaavrKwsHD16VHjdsWNH4fjMmTNlFhsRiYNMJkNYWBjCwsIgk8mMHQ6RQdh+SRfR9piphzGdnJwwc+bMYsv/+OOPiI+Px759+zBkyBAAwMCBA/HTTz8hNzcXO3bswPjx442+xRIRlR2VSiX8QlOpVEaOhsgwbL+kiygTs8ePH+Pq1asAgH79+mHQoEHFXnP9+nVs3LgRFy5cQEJCAlxcXODo6Ihhw4Zhy5YtePLkCX766SfMmTOnvMMnIiIiKhFRDmXu27dP+Osh/9plhVGXUyqVwk4BAPDll18KG54HBQXhm2++KXaV+OfPn5ckbCIiIqJSEWWPmTqxql27Njp06KDXNe3atYOTkxOePXuG/fv3Y+LEiQAAOzs7/Pbbb5g4cSIePHiALVu24MiRI3jzzTfRoUMHODs7w8bGBhkZGXj48CHOnTuHY8eOCfUWt7+mviv/29nZoV69enp9FiIiIqqaRJeYXblyBY8e5W2706dPH70X3ZNKpejbty82b96MO3fuICIiQniSs1GjRti+fTsWLVqE/fv3IzU1FUFBQQgKCiq0vtq1a+ODDz5AQEBAkffVd+X/3r1745dfCm4jRERERKQmusRMcxhS32FMtTfeeAObN28GkPfwgOYSGzVr1sTixYsxZcoUHDp0CBcuXMDDhw+RlpYGhUKBGjVqoG7dumjVqhW6d++Onj17wsLComw+FBEREZEeJCo+ClKl5ebmIjw8HABQp5Eb/JbsN3JE+jk0exhc7LmOWVWn2X69vLyK3BOXSGzYfk1fefwbiq7HjIhIX+bm5mjatKlwTGRK2H5JF7YEIjJZUqkUtWrVMnYYRCXC9ku6iHK5DCIiIqKqiD1mRGSyNFdOt7CwgEQiMXJERPpj+yVdmJiRwMHOCodmDzN2GHpxrGFt7BBIBGQyGSdPk8li+yVdmJiRwEwqhYt90QvqEhERUfnhHDMScOUUIiIi42JiRgK5XG7sEIiIiKo0JmZEREREIsHEjIiIiEgkmJgRERERiQQTMyIiIiKR4HIZJOBebWRqLCws4OXlJRwTmRK2X9KFv4lJwFWnydRIJBIuykkmi+2XdOFQJhEREZFIsMeMBAqlEglpmYWed6xhDTMpc3kSD6VSifT0dABAzZo1IWX7JBPC9ku6MDEjQUpGDvyW7C/0/KHZw+Bib1uBEREVTS6XIyYmBgD3GiTTw/ZLujA9JyIiIhIJJmZEREREIsHEjIiIiEgkmJgRERERiUSVnPyflZWFffv24eTJk4iMjERaWhpUKhXs7Ozg6uoKd3d3tG3bFj169EDdunW1rp0xYwb27Nlj0P327t2L5s2bC6+nTp2KY8eOAQDWr1+Prl27Fnn9iRMn8OGHHwIAhgwZgiVLlhh0fyIiIjINVS4xu3btGj7//HM8efKkwLnU1FSkpqYiIiICu3fvhqOjI86ePVvmMcybNw+hoaF4/vw55s2bhwMHDsDKykpn2RcvXuCbb74BANSuXRszZ84s83iIiIhIHKpUYnb//n2MHz8emZl5a3X5+PigX79+aNKkCSwsLJCamorIyEicO3cOoaGhxda3du1aODs7F1uuUaNGWq+dnZ0xffp0zJ07F48ePcLPP/+Mr7/+Wue1P/74IxITEwEAc+bMgb29fbH3I6oqJBKJsJUNd64gU8P2S7pUqcRs+fLlQlK2aNEi+Pv7FyjTrVs3jB8/HikpKTh8+HCR9TVu3Bj169cvUSzDhw/HgQMHcPHiRWzYsAGDBg1Cq1attMqEhoZix44dAPKSyIEDB5boXkSVlYWFBVq3bm3sMIhKhO2XdKkyk/8VCgVOnz4NAGjVqpXOpEyTg4MDRo0aVW7xSCQSfP/996hWrRoUCgVmz54NuVwunM/JycHcuXOFuW/q4UwiIiKqvKpMYpaSkoKcnBwABYcWjaVRo0aYOnUqACAyMhJ//PGHcG7lypV4+PAhAGD69OlwcXExSoxERERUcapMYqYexweAe/fuGTESbePGjUOLFi0AAP/9738RExODiIgIrF+/HgDQsWNHjBgxwogREomXXC7H48eP8fjxY60eZyJTwPZLulSZxMze3h6urq4A8nqn1qxZA6VSaeSoAHNzc3z//fcwMzNDbm4u5syZgzlz5kChUKBatWpYsGCBaCaFZmdlCXP0iMRAqVQiMTERiYmJovh+JjIE2y/pUqUm/48ePVpYA2zZsmXYunUrfHx80K5dO3h5eaFBgwYG1ffgwQNkZWUVWcba2rrYelu2bImxY8di7dq1uHLlivD+1KlT0aRJE4NiKk8bfv8FADB//nwjR0JERFQ5VanEbOzYsbh79y527doFAIiLi0NQUBCCgoIAAI6OjujUqRN8fX3h7e1dbE/V+PHji71np06dhPqL8sknn+DQoUN4+vQpAKBFixYYN25csdcRERFR5VFlhjIBQCqVYuHChVi3bh169OgBc3PtvDQpKQmHDh3ClClT8NZbb+HRo0cVFtvDhw+RlJQkvHZwcCgQHxEREVVuVfI3f7du3dCtWzdkZGTgypUrCA8PR0REBC5fvowXL14AACIiIvDuu+9i9+7dhS4ie+LEiRKvY6ZJvVyGTCYT3gsJCcGBAwfg6+tb6vqJiIjINFSpHrP87Ozs0LNnT0ydOhW//vorzp07h4ULF6JmzZoAgGfPnuHnn38u9zg2btyI8PBwAHnDo+pEcOHChUhNTS33++vrvQkf4ssvvzR2GERERJVWlU7M8rO0tMSwYcOwbNky4b1jx46V69Myjx8/FpI/d3d3TJs2DXPnzgWQt/aamDYst7axga2trbHDICIiqrSYmOnQo0cP1K1bFwCQnp6OtLS0crvXvHnzkJ2dDalUih9++AEWFhZ444030KdPHwDAnj17cP78+XK7P5Epk0qlsLe3h729PaRS/jgj08L2S7qwJRRCn83JS2vXrl04d+4cACAgIEBrz7R58+bBzs4OQN7yFC9fviz3eIhMjbm5Odzc3ODm5saHZcjksP2SLkzMdMjOzsbdu3cB5M1Dq1WrVpnfIykpSRimdHV1xWeffaZ13sXFBV988QWAvCc2AwMDyzwGIiIiEpcqk5hlZmZi+PDhOHXqVJFzxpRKJRYsWCCscO/j41MuK+9/9913SE9PBwB8++23sLGxKVBm5MiRaNeuHQBg3bp1iIyMLPM4iIiISDyqVN9pWFgYJk+eDBcXF/Tp0wdt2rRBvXr1YGdnh+fPn+PWrVvYtWsXoqOjAQDVq1fHp59+Wmh9+qz8DwBOTk5avW7Hjx/HP//8AwAYPHgwevToofM6iUSCBQsWYMiQIZDJZJgzZw62b9/OuQhE/08mkwl737q5uWntiUskdmy/pEuVSczMzc3h5OSEZ8+eISEhAZs2bcKmTZsKLd+4cWMsW7asyHXK9Fn5HwBmzpyJsWPHAgBevHiBb7/9FkDeIrIzZ84s8tpXXnkFkyZNQmBgIMLDwxEUFIT33ntPr/sSVXYqlUro3VapVEaOhsgwbL+kS5VJzKpVq4Z///0X169fx7lz53Djxg3cv38fycnJePnyJaytreHs7AxPT0/07t0bb7zxBiwtLcs8jh9//BGJiYkAgFmzZuk1f23SpEk4fPgw7t27hxUrVqBv376oV69emcdGRERExiVRMU2v0nJzc4XFbes0coPfkv2Flj00exhc7LmOGYmHZvv18vIqlz+miMoL26/pK49/Q05WIiIiIhIJJmZEREREIsHEjIiIiEgkmJgRERERiUSVeSqTiudgZ4VDs4cVet6xhnUFRkNUPDMzM2FJGzMzMyNHQ2QYtl/ShYkZCcykUrjYWxk7DCK9mZmZwcXFxdhhEJUI2y/pwqFMIiIiIpFgYkZEREQkEhzKJIFMJuMCh2RSuEAnmTK2X9KFPWZEREREIsHEjIiIiEgkmJgRERERiQQTMyIiIiKRYGJGAnNzPgtCRERkTEzMSCCRSIwdAhERUZXGLhISKJRKJKRllmmdjjWsYSZl/k9ERKQPJmYkSMnIgd+S/WVa56HZw+Bib1umdRKpmZubw8PDQzgmMiVsv6QLWwIRmSypVAo7Oztjh0FUImy/pAvHmIiIiIhEgj1mRGSylEolsrKyAAA2NjaQcj4jmRC2X9KFrYCITJZcLkdUVBSioqIgl8uNHQ6RQdh+SZdK1WMWGhqKMWPGCK9tbGxw7tw5WFtbF3ldTk4OunXrhoyMDOG9jRs3onPnzkVed/HiRQQEBAivt2zZgnbt2hkcpyYrKyvY29vD09MTffv2hZ+fHze2JSIiqiIqdY9ZVlYWjh8/Xmy5EydOaCVl+tqzZ4/W67179xpcR345OTmIj49HcHAwZs+eDX9/f8TGxpa6XiIiIhK/SpuYVatWDQCwb9++Ysuqy6iv0UdOTg7++ecfAHk9cwBw5MgR5ObmGhTnyJEjceDAAeG/9evXY/bs2ahTpw4A4M6dO5gyZQoUCoVB9RIREZHpqbSJmY+PDwDg3LlzePbsWaHlkpOTcfbsWQBA79699a7/2LFjyMzMW4x1zpw5AID09HScPHnSoDhr164Nd3d34b+uXbtizJgx+Pvvv+Hq6goAiI6OxrFjxwyql4iIiExPpU3MunXrBicnJygUCvz999+Fljt48CDkcjmcnJzw2muv6V2/etjSw8MDw4YNQ5MmTbTeLy07OztMmTJFeH3u3LkyqZeIiIjEq9ImZmZmZhg0aBCAoocz1efefPNNmJmZ6VV3YmIizp8/DwDw8/PT+n9ISAhSUlJKHLcm9YrQABAfH18mdRIREZF4VdrEDAAGDx4MALh16xbu3LlT4Pzdu3dx8+ZNrbL6OHDgABQKBaRSKXx9fQEAvr6+kEgkkMlkOHjwYBlED1hYWAjH3K6DiIio8qvUiVmLFi3QrFkzALp7zdTvubu7o3nz5nrXq76uU6dOcHFxAQA0aNAAbdu2BVB2w5n37t0TjtXzzUyFJRSwhALZWVnIzMwU/iMqS5aWlmjfvj3at2/PZWXI5LD9ki6VOjED/tcTdvDgQahUKuF9lUqFAwcOaJXRx+3btxEVFQXgf8OXaurXN2/exN27d0sVt0KhwNq1a4XX/fr1K1V9Fa2/3SP0t3uEDb//gqVLlwr/ERERUeEqfWLm5+cHqVSKp0+fIjQ0VHg/NDQUT58+1RqO1Ie6N8zKyqpAsjRgwABh+LGkvWYpKSk4f/48Ro8ejVu3bgHIS8o6dOhQovqIiIjIdFT6xMzFxUVYwV9zOFN93KVLF2E4sjhyuVyYP+bt7Q07Ozut8/b29ujZsyeAvHloSqWy2DoDAwPh4eEh/Ne1a1eMHTsWV69ehbW1Nd5//30sW7ZMr/iIqhqFQoGEhAQkJCRwrT8yOWy/pEulT8wAYMiQIQCAo0ePIicnR2txWEOGMUNCQpCUlASg4DCmmvr9+Ph4rR66kvD09ERAQIDWQwBE9D8KhQKxsbGIjY3lLzYyOWy/pEuVeNSvb9+++Oabb5CRkYETJ05ApVIhMzMTNjY2eOONN/SuRz08aW9vjx49eugs4+3tjRo1auD58+fYu3cvunbtWmSdI0eOxLvvvgsg75s0Pj4e//zzD/bt24dr164hICAAO3fuhIODg95xisGRjIYAgC3T3oRTTRsjR0NERGQaqkRiZmtriz59+uDAgQPYt2+f8BBAnz59hO2UivPixQthVf+0tDS0atWq2GuOHj2K+fPnF3kP9cr/as2bN4e3tzc6d+6MGTNmIC4uDrNnz8bq1av1ilMscpG3Jpy1jQ1sbW2NHA0REZFpqBJDmcD/hjPPnj0rrKKvfk8fhw8fxsuXLw26Z1ZWFo4ePWrQNWpDhw4VHi44efKksKAtERERVV5VoscMALp27QonJydh30xnZ+dihxk1qYcxnZycMHPmzGLL//jjj4iPj8e+ffsMSgA1TZs2DcePH4dCocDy5csNipeIiIhMT5VJzMzMzDB48GBs3LgRQN6kf6lUvw7Dx48f4+rVqwDylq5Qb/VUlOvXr2Pjxo24cOECEhIS9H7yU1OTJk0wYMAAHDx4EDdu3MDZs2fRrVs3g+shIiIi01BlhjIBYPr06QgPD0d4eDi+/PJLva/TnJem70Kv6nJKpbLIvTqLM2nSJEgkEgAwuXlmREREZJgqlZiVlDqxql27tt4LvbZr1w5OTk4AgP3795f43u7u7vDx8QEAXLp0CZcvXy5xXUSVjUQiga2tLWxtbYU/YIhMBdsv6cLErBhXrlzBo0ePAOQ9xanv8KdUKkXfvn0BAHfu3EFERESJY5g8ebJwzF4zov+xsLCAp6cnPD09ud4fmRy2X9KFiVkxNIchDd2vUnONtNJsbN66dWthbllISAjCwsJKXBcRERGJl0SlubM3VTm5ubkIDw8HANRp5Aa/JSUfdtXl0OxhcLHnOmZERFT5aP4O9fLygqWlZanrrDJPZRJR5SOXy/Hw4UMAQKNGjWBuzh9pZDrYfkkXDmUSkclSKpVIS0tDWloalEqlscMhMgjbL+nCxIyIiIhIJJiYEREREYkEB7RJ4GBnhUOzh5VpnY41rMu0PiIiosqMiRkJzKRSuNhbGTsMIiKiKotDmSTgyilERETGxcSMBHK53NghEBERVWkcyiQB92ojUyOVSuHs7CwcE5kStl/ShYkZCbi4IZkac3NzNGjQwNhhEJUI2y/pwhSdiIiISCSYmBERERGJBMeuSCCTycpkA1aiiiKTyXD79m0AQPPmzWFhYWHkiIj0x/ZLujAxIyKTpVKpIJPJhGMiU8L2S7pwKJMEfCqTiIjIuJiYkcDMzMzYIRAREVVpTMyIiIiIRIKJGREREZFIMDEjIiIiEgkmZkREREQiweUyiMhkmZubo2nTpsIxkSlh+yVdTK4lhIaGYsyYMTrPWVlZwd7eHp6enujbty/8/PyKXDDVw8PDoHt7enpi3759Wu/t3r0bM2fOBAAsWrQI/v7+BtXp4+ODuLi4Au+bm5vDzs4OTZs2xWuvvYa3334bLi4uBtVtKC6XQaZGKpWiVq1axg6DqETYfkmXSjWUmZOTg/j4eAQHB2P27Nnw9/dHbGysscMqEblcjrS0NFy9ehWBgYEYOHAgjh07ZuywiIiIqByZXI+ZppEjR+Ldd98VXicnJ+POnTtYu3Yt4uPjcefOHUyZMgV79+4tco2uVq1aYdGiRcXez8rKqkzi1sXZ2Rlr164VXsvlcsTFxWHXrl04deoUMjIyMG3aNGzfvh0tWrQolxi48jSZGs2V0y0sLNjrSyaF7Zd0MenErHbt2nB3d9d6r2vXrvD394efnx/i4uIQHR2NY8eOoX///oXWY2NjU6CeimZhYVEghhYtWqBv375YvHgx/vzzT8hkMqxevRqrVq0yUpRE4iKTyRAeHg4A8PLy4l6vZFLYfkmXSjWUqWZnZ4cpU6YIr8+dO2fEaErv448/FnrrQkJCoFQqjRwRERERlYdKmZgB2hP74+PjjRhJ6dna2sLNzQ0AkJWVhbS0NOMGREREROWi0iZmFhYWwnFleAxZ8/Owx4yIiKhyqrSJ2b1794RjV1dXI0ZSenK5HDExMQDyEjR7e3vjBkRERETlwvS7knRQKBRaTzj269evyPJZWVmIjo4utt46deqgRo0apY7PUNu2bcPz588BAO3bt68UPYBERERUUKX6DZ+SkoKoqCisXLkSt27dApCXlHXo0KHI6yIiIuDr61ts/SVZQLak1Mtl7N69G7///rvw/vjx4yvk/kRERFTxTDoxCwwMRGBgoM5z1tbWeOedd/DFF19UcFQlExcXV+ROBBKJBJ9++ilef/31CoyKiIiIKpJJJ2ZF8fT0REBAgNak+cJ06tQJQUFBFRCV4ezs7NClSxe8//77xfb8EVU1FhYW8PLyEo6JTAnbL+li0omZ5sr/CoUC8fHx+Oeff7Bv3z5cu3YNAQEB2LlzJxwcHIwcafHyr/xvZmaG6tWrw8nJqcJWg+aq02RqJBIJF+Ukk8X2S7qYdGKWf+X/5s2bw9vbG507d8aMGTMQFxeH2bNnY/Xq1UaMUj+6Vv4nIiKiqqVSLpcxdOhQ4UnMkydP4vz580aOyDRwr0wyNUqlEqmpqUhNTeX6fmRy2H5Jl0qZmAHAtGnThI3Lly9fbuRoiKg8qNf4i4mJgVwuN3Y4RAZh+yVdKm1i1qRJEwwYMAAAcOPGDZw9e9bIEREREREVrdImZgAwadIkYUK7KcwzIyIioqrNpCf/F8fd3R0+Pj44ceIELl26hMuXL+tcckLflf8B4JVXXoFUqjufvXLlil51+Pj4cFslIiIiKqBSJ2YAMHnyZJw4cQJAXq+Z5pIUavqu/A8Aly5dKnRbpp07d2Lnzp3F1rF3714mZkRERFRApR7KBIDWrVujW7duAICQkBCEhYUZOSIiIiIi3SQqrpFQpeXm5iI8PBwA0KpVK1SrVs3IERHpT7P9enl5cbFOMilsv6avPP4NK/1QJhFVXhKJRNjKhjtXkKlh+yVdmJiRgD8YyNRYWFigdevWxg6DqETYfkmXSj/HjIiIiMhUMDEjIiIiEgkOZZKAz4GQqZHL5Xj69CkAoG7dujA35480Mh1sv6QLWwEJFAqFsUMgMohSqURiYiIAwMXFxcjREBmG7Zd04VAmCdhjRkREZFxMzIiIiIhEgokZERERkUgwMSMiIiISCSZmRERERCLBxIyIiIhIJLhcBgm4hg6ZGqlUCnt7e+GYyJSw/ZIu/E1MAu6VSabG3Nwcbm5uxg6DqETYfkkXpugkUCiVUCiVxg6DiIioymJiRoKUjBwkPc82dhhERERVFocyichkyWQy3Lt3DwDg5uYGCwsLI0dEpD+2X9KFiRkRmSyVSoXMzEzhmMiUsP2SLhzKJCIiIhIJJmZEREREIsHEjIiIiEgkTH6OWW5uLo4ePYp///0XYWFhSE1NRUZGBuzs7ODq6govLy/069cPXbp0KXYBv9OnT+P48eO4cuUKkpKSkJ2dDQcHBzRs2BCvv/46/Pz84OLiUuj1gwYNwt27d2Fvb48LFy4UuS7YkCFDcPv2bQDAhx9+iE8//bTQshcvXkRAQAAA4IMPPsD06dOL/BxERERkmkw6MTt69CgWL16MuLi4AufS0tKQlpaGmzdvYuvWrWjcuDFmzpyJXr16FSh7584dzJ07F9euXStwLj4+HvHx8bh48SJ++eUXTJw4EZMnT9aZdLVv3x53795FWloa7t69i2bNmumM+8WLF4iKihJeX758ucjPqXm+Y8eORZYlIiIi02Wyidl///tfrFy5UnjdrVs3+Pj4wM3NDTVq1EB6ejru37+PkydP4ty5c3jw4AGWL19eIDG7fPkypkyZgufPnwMAWrZsiaFDh8LDwwM2NjZISEjAmTNnsHfvXmRlZWHFihW4c+cOlixZUuDR5o4dO2Lbtm1CvYUlZlevXoVSqYSZmRkUCgXCwsIgk8kKfVT6ypUrAPK27Gjfvn2Jvl5EREQkfiaZmO3atUtIymrXro0VK1agU6dOBcq99tprGDVqFKKjo7Fo0SKkpKRonY+Pj8dHH32E58+fQyKRYPr06Rg3bpxWb1irVq3Qu3dvjB8/Hh999BGioqLw999/o27dugWGFDV7sy5fvoyRI0fqjF/dA/b6668jNDQUWVlZCA8PR7t27QqUVSgUQk+eh4cHqlevrs+XiKhKMDMzQ/369YVjIlPC9ku6mNzk/4SEBCxYsAAAYGNjg6CgIJ1JmSZ3d3esXbsW48aN03p/7ty5SEtLAwB8+umnGD9+fKHzwho0aIA///wTzs7OAIC1a9fixo0bWmXq1KkDV1dXAEUPT6rPderUCa+++mqR5W/fvi2sc9OhQ4eiPiZRlWNmZgYXFxe4uLjwFxuZHLZf0sXkErP169cjOztv26BPPvlE7w1gpVIpBg8eLLyOjIzEv//+CyAvcZs4cWKxddSuXRszZ84EkLcY4B9//FGgjLrXLD4+HrGxsQXOv3z5EuHh4QDyEi310KR6uDI/zi8jIiKqOkwqMVOpVNizZw+AvN6y4cOHl7gudT0AMHr0aL3/WunXr5/wZObx48eRnp6udV6zV0tXL9iNGzcgk8lgbW2NFi1aCImZet5ZfpoJGxMzIiKiys2kErM7d+4gNTUVQN4TkHZ2diWu69KlS8Kxt7e33teZmZkJDxAolcoCT3Lmn2eWn/q91q1bw9zcHK+++irMzMzw/PlzREdHFyivTsyaNm0KBwcHveMsiZzsbGRnZSEzM7PAf0RilJubiytXruDKlSvIzc01djhEBmH7JV1MavJ/ZGSkcNyyZctS1aVOgpycnIR5Y/pq0aKFVkyaT3o2btwYTk5OePbsmc7ETJ1oqXvWbG1t4enpiZs3b+Ly5cvw9PQUysbExCA5ORlAxfSW7dy8ETk5OTrPzZ8/v9zvT0REVNWZVI+ZeqI+kDffq6RevHgBmUwGAHB0dDT4es1rNGNSUw9P3r9/X0isAO0nLDWXvVAf50/kNIcxOfGfiIio8jOpxExzSM3a2rpM6rGxsTH4es1rMjIyCpwvbDjz1q1byMzMhJmZGdq0aSO8X1hixon/REREVYtJJWa2trbCsfrJzNLWk5WVZfD1mtfomudW2AMA6h4wT09PrRjU5Z89e4aHDx8WuNbV1RV169Y1OE4iIiIyLSY1x8ze3l441hwiNJSdnR3Mzc0hl8uRlJRk8PWa12jGpObu7o4aNWrg+fPnWsOR6kQr/+r9jo6OaNSoER4+fIjLly+jUaNGSEhIEJbbqKhhzLfeHQMzM3M41TS8F5GIiIhKz6R6zDQnxt+8ebPE9UgkEnh4eADI66V69uyZQdffunVLZ0xqmlsnRUZGCsOd6iRN17ZK+YczjTGMaWVtDWsbG9ja2hb4j4iIiMqfSSVmzZo1Q61atQDkJTm65nfpS7MX6uTJk3pfp1Qqcfr0aQB5CZiubZQ061coFLh69Sru3bsnbAmlqwdMXY86IdNczoMT/4mIiKoGk0rMJBIJhg4dCiBvnteOHTtKXJe/v79wvGnTJigUCr2u++effxAfHw8A6NOnD2rUqKGznGYydeXKFSHhatSokc4nQdU9Zo8ePUJiYqLQu+bk5IQmTZroFRtRVWNubg4PDw94eHjA3NykZmYQsf2STiaVmAHA2LFjhScyV65ciXv37ul1nVKpxP79+4XXnp6e6N69OwAgKioKa9euLbaOlJQULF68GEBekvjBBx8UWrZly5ZCnJcuXRJ6wHQNYwLaC8geP34cd+7cKbI8EeX1WtvZ2cHOzg5Sqcn9OKMqju2XdDG5luDi4oK5c+cCyOs1CwgIwMWLF4u85u7du/jggw8KJF8LFiwQerz+85//YP369VCpVDrriIuLw/vvvy/0lo0fP17YgFwXCwsLYUmM8PBwhIaGAig60VIPZ65bt06Ig8tkEBERVR0m2Xc6bNgwxMfHY+XKlUhOTkZAQAC6d+8OHx8fuLm5oUaNGkhPT8f9+/dx+vRpnDlzBgqFosBE/Xr16iEwMBAffvghMjIysGjRIhw4cABDhw6Fh4cHrK2tkZiYiDNnzmDv3r3CMhkDBw7EZ599VmycHTp0wPnz55Gbm4vExEQARSdm7du3x/Hjx/H48WOtOohIN6VSKXxf2tjYsNeBTArbL+likokZAHz00Udo1qwZFi9ejLi4OISEhCAkJKTQ8s2aNcP06dMLvN+5c2ds3rwZc+fOxY0bNxAREYGIiAiddVhbW2PChAn48MMPIZFIio0xf1JVu3btIueL5U/aatasKTw9SkQFyeVyREVFAQC8vLxgaWlp5IiI9Mf2S7qYbGIGAG+88QZ69eqFf/75B//++y/Cw8ORkpKCzMxM2NnZwdXVFa+++ir69euHzp07F5pMeXh4YPv27QgODsaxY8dw9epVPHv2DDk5OahVqxYaN26MHj16YPDgwXBxcdE7vjZt2sDCwkLY/qmwJzjVWrRoAWtra2Hx3Hbt2umVABIREVHlYNKJGQBYWlrC19cXvr6+pa6rV69eWhuSl5aVlVWhvW+6WFhY4Pr162V2fyIiIjItHNAmIiIiEgkmZkREREQiwcSMiIiISCSYmBERERGJhMlP/qey42Bnxce1iYiIjIiJGQnMpFKYcYFDMiGWlpbctoxMFtsv6cLfwkREREQiwcSMiIiISCQ4lEkChUJh7BCIDKJQKJCUlAQAcHR0hJmZmZEjItIf2y/pwsSMBEql0tghEBlEoVAgNjYWAFCrVi3+YiOTwvZLunAok4iIiEgkmJgRERERiQQTMxKYm3Nkm4iIyJiYmJFAIpEYOwQiIqIqjV0kJFAolUhIy9R5zrGGNRefJSIiKmdMzEiQkpEDvyX7dZ47NHsYXOxtKzgiIiKiqoWJGRGZLIlEAltbW+GYyJSw/ZIuTMyIyGRZWFjA09PT2GEQlQjbL+nCSUNEREREIsHEjIiIiEgkOJRJRCZLLpfj4cOHAIBGjRpxLT4yKWy/pIuoW0FoaCjGjBmj85yVlRUcHBzQvHlzDBgwAAMGDCi2UT969Aj79+9HSEgI4uLikJqaCltbWzg7O6Nz587o378/OnToUGxcAQEBuHjxos5z5ubmqF69Otzc3NCzZ0+8/fbbsLe3L7SuGTNmYM+ePQXel0gksLOzQ7169dC+fXu8/fbbaN68ebGxEVUlSqUSaWlpAIAGDRoYNxgiA7H9ki6iTsyKkpOTgydPnuDJkyc4ceIENmzYgNWrV8PJyalA2dzcXCxduhSbN2+GTCbTOpeWloa0tDRER0cjKCgIPXv2xHfffYc6deqUKC65XI7U1FRcvnwZly9fxvr167Fq1Sq0b9/eoHpUKhVevHiBqKgoREVFYevWrZg4cSKmTZtWoriIiIhI/EwmMRs5ciTeffdd4XVWVhYiIiKwbt06xMXFITw8HB9++CG2b9+u9dhxRkYGPvzwQ4SGhgIA7O3tMXToUHTt2hW1a9fGixcvEBERgV27duH+/fs4ffo0RowYgXXr1sHNza3YuA4cOKD1WiaT4fHjx9i3bx9OnjyJ5ORkTJkyBUeOHIGDg0ORda1duxbOzs4A8v6SSk5ORnBwMDZv3gy5XI5ff/0Vzs7OGDVqlN5fNyIiIjIdJpOY1a5dG+7u7lrvtWnTBr6+vhg+fDgePnyIsLAwnDp1Cj4+PkKZ2bNnC0lZ586dsXz5ctSuXVurnq5du2Ls2LFYtmwZ/vzzT8THx2Py5MnYs2cP7Ozsiowrf0wA0LJlS/Tv3x9ff/019u7di/T0dOzYsQOTJk0qsq7GjRujfv36Wu9169YNXbt2xZQpUwAAgYGBeOedd2BmZlZkXURERGR6TP6pzJo1a2LixInC6zNnzgjHhw4dwpEjRwAAnp6e+O233wokZWoWFhaYMWMGhg8fDiBvPtqyZctKFdv48eOF4/Dw8BLX4+PjI8x9S0lJwc2bN0sVFxEREYmTySdmANC6dWvh+MmTJ8Lx77//LhwvWLAA1tbWxdY1Y8YMODo6AgB27dqFlJSUEsel2fuVm5tb4nqAwj8jERERVR6VIjHTfBpToVAAACIjI3Hr1i0AQNu2bbUSm6LY2dnB398fAPDy5UscPHiwxHFpJlB169YtcT2A9mdUKpWlqksfllBo/ZedlYXMzMwC/xEREVHZMZk5ZkWJjo4WjtWT5y9fviy85+3tbVB9Pj4+WLNmjVBPYUt2FGft2rXCce/evUtUh5quz1ie+ts90nq94fdfdJabP39+ucdCVBipVCp8P0illeLvTKpC2H5JF5NPzORyOf7880/hdadOnQDk9ZiptWjRwqA6mzdvDqlUCqVSiaioqCLLaiZMQN5TmXFxcdi/fz+OHTsGABg4cCBef/11g2LQFBkZKcyds7a2hpeXV4nrIqpMzM3Nuf4TmSy2X9LFZBMz9XIZq1atwvXr1wEArq6uGDhwIAAgNTVVKKtrbbOiWFlZwdbWFi9evBAW/yuMr69voeeaNGmCiRMnYujQoQbdH8hbxywpKQnBwcFYtmyZMEQbEBCAatWqGVwfERERiZ/JJGaBgYEIDAws9Hzt2rXx3//+F5aWlgCgNf/JxsbG4PvZ2NjgxYsXyMjIMDzY/3f//n1s27YN9evXF3ryilLccGevXr3wySeflDgeIiIiEjeTScwKU79+ffTr1w/jx4/XWgrD1tZWOM7KyjK4XvU1xa1jln+oU6lUIjU1FVeuXMF///tfXL9+HePGjcPSpUvRv39/g+OwsLCAl5cXRowYgcGDB2stnluejmQ01Hq9ZdqbcKppeIJLVJ5kMhlu374NIG8KgoWFhZEjItIf2y/pYjKJmebK/xKJBNWqVUOtWrVQvXp1neVr1aolHD979gyenp563ysnJ0focStqn0tdpFIpateujTfeeAPdunWDv78/Hjx4gFmzZqFLly5F1qe58r9UKoWNjQ0cHR2FXsCKlAvtBWytbWy0kl0iMVCpVMI2ayqVysjREBmG7Zd0MZnHQNQr/7u7u6NZs2Zo2LBhoUkZAHh4eAjH6r9I9HX79m1hSQrNegxla2uLkSNHAsgbWv3nn3+KLN+4cWPhM77yyiuoV6+eUZIyIiIiMg6TScwM1bFjR+H45MmTBl2rWV6znpJo2rSpcJz/CU4iIiIiTZU2MfP09ETz5s0BANeuXdN7S6SMjAzs2bMHAFCtWjUMGjSoVHGon6YE8pb2ICIiIipMpU3MAGDChAnC8bx585CTk1PsNT/++COePXsGABg2bBgcHBxKFUNERIRwXNrV/4mIiKhyq9SJ2aBBg/DGG28AAG7duoUpU6YUuvelTCbDjz/+iG3btgEAGjZsiC+++KJU94+Li8OmTZuE1z179ixVfURERFS5mcxTmSW1cOFCpKam4tKlSzh37hwGDBgAf39/dOnSBY6OjsjIyEBERAR27tyJmJgYAECdOnWwevXqYpfKAArOG1MqlUhLS8Ply5cRFBQkLFDr6+srDK0SERER6VLpE7Pq1atj7dq1+Omnn7BlyxakpaVh3bp1WLdunc7yPXr0wIIFC/Qedixq5X+1gQMHYuHChQbFTUTFMzc3Fx6wMTev9D/OqJJh+yVdqkRLqFatGubMmYOAgADs378fISEhiIuLQ1paGmxtbeHs7IyOHTtiwIABpX4KUyKRwMbGBnXr1kWbNm0wZMiQUtdJRLpJpVKtNQuJTAnbL+kiUXFVuyotNzdXeGK1TiM3+C3Zr7PcodnD4GLPBWaJiIjUNH+Henl5lcnao1Wix4yIKifNldMtLCwqbMsyorLA9ku6VOqnMomocpPJZAgPD0d4eLjwC47IVLD9ki5MzIiIiIhEgokZERERkUhwjhkJHOyscGj2MJ3nHGtYV3A0REREVQ8TMxKYSaVwsbcydhhERERVFocyScCVU4iIiIyLiRkJ5HK5sUMgIiKq0piYEREREYkE55iRgHu1kamxsLCAl5eXcExkSth+SRf+JiYBV50mUyORSMpkCxQiY2D7JV04lElEREQkEuwxIwGfyiRTo1QqkZ6eDgCoWbMmpFL+rUmmg+2XdGErIAGHMsnUyOVyxMTEICYmhk8Vk8lh+yVdmJgRERERiQQTMyIiIiKRYGJGREREJBJMzIiIiIhEgokZERERkUgwMSMiIiISCa5jRkQmSyKRCFvZcLkXMjVsv6RLuSZmoaGhGDNmjM5zVlZWsLe3h6enJ/r27Qs/Pz+9t6aIjY1Fnz59hAVRly5dCl9fX4Pje/LkCf7++2+cO3cODx8+REpKCpRKJWrWrAl3d3e0b98evr6+aNCggc7rk5OTERYWhrCwMISHhyM8PBxpaWkAgKFDh2Lx4sXFxjBjxgzs2bOnwPsSiQR2dnaoV68e2rdvj7fffhvNmzc3+DMSVWYWFhZo3bq1scMgKhG2X9LFaD1mOTk5iI+PR3x8PIKDg7F+/Xr8+uuvqF+/frHX7t27V2uV+r179xqUmL18+RLLli3Dli1bkJubW+B8YmIiEhMTERISgpUrV6J///74+uuvUbduXa1yr732mt73NJRKpcKLFy8QFRWFqKgobN26FRMnTsS0adPK7Z5ERERkXBWWmI0cORLvvvuu8Do5ORl37tzB2rVrER8fjzt37mDKlCnYu3cvzMzMiqxr//79AAAbGxtkZWXh/PnzSExMhLOzc7FxpKSkYMqUKbh+/ToAwNbWFm+++Sa6dOmCOnXqwNzcHElJSbh69SqOHTuGBw8e4PDhw2jTpg3Gjh1baL316tVD06ZNERISUvwXoxBr164VPoNSqURycjKCg4OxefNmyOVy/Prrr3B2dsaoUaNKfA8iIiISrwpLzGrXrg13d3et97p27Qp/f3/4+fkhLi4O0dHROHbsGPr3719oPVevXsXDhw8BALNmzcL8+fOhUChw4MABjB8/vsgYlEolPvvsMyEp8/b2xg8//IDatWsXKOvj44PPP/8c+/fvx48//qizvo8++gheXl7w8vKCo6MjYmNj0bt37yJjKErjxo0L9Bh269YNXbt2xZQpUwAAgYGBeOedd4pNXomqArlcjqdPnwIA6tatC3NzTpsl08H2S7oY/alMOzs7IekAgHPnzhVZfu/evQCAWrVqYciQIejatSsAYN++fcXea+PGjQgNDQUAdO/eHYGBgTqTMjWpVIohQ4Zg9+7d8PDwKHD+k08+gbe3NxwdHYu9d2n4+PigQ4cOAPJ6/G7evFmu9yMyFUqlUph6oFQqjR0OkUHYfkkXoydmALSSnvj4+ELL5ebm4siRIwCAAQMGwMLCAn5+fgCAqKgo3L59u8hr165dCwCoVq0aFi5cqPdfJ3Xq1BESQGPRnCD65MkTI0ZCRERE5UUUiZn6cWEARSZLJ0+eRHp6OgAICVnfvn1hY2MD4H+9abqEhIQgMTERANC/f3+4uLiUNuwKpfl1Ka+/rLKyspCZmYnMzMxyqZ+IiIiKJorE7N69e8Kxq6troeXUiVfDhg3Rtm1bAHkPAKjndR08eBAKhULntZcuXRKOe/XqVcqIK150dLRwrM9DDiXx22+/YenSpVi6dGm51E9ERERFM3piplAohCFGAOjXr5/OcikpKcITj/mXxlD3niUlJRX6VGRkZKRw3LJly1LFXNEiIyNx5swZAIC1tTW8vLyMHBERERGVB6MlZikpKTh//jxGjx6NW7duAchLytST3PM7ePAgZDIZgP8lYmrdunWDk5MTgMKHM9ULvwIocsK/WKhUKjx79gw7duzA2LFjhZ7AgIAAVKtWzcjRERERUXmosGdzAwMDERgYqPOctbU13nnnHXzxxReFXq9OuFq3bo3GjRtrnTMzM8PAgQOxYcMGnDx5EhkZGbCzs9MqozlvytraumQfopwVt9RGr1698Mknn1RQNERERFTRRLFoiqenJwICArQeAtB0584dYYmI/L1lan5+ftiwYQNycnJw+PBhDB8+XOu8ra2tcJydnV0gcRMrCwsLeHl5YcSIERg8eHC57qc2adKkQv8NiMRIKpXC3t5eOCYyJWy/pItRVv5XKBSIj4/HP//8g3379uHatWsICAjAzp074eDgUOBadW+Zubk5Bg0apLP+Vq1awc3NDffu3cO+ffsKJGbqxg/kzUUTY2KmufK/VCqFjY0NHB0d9d5DtLRsbGwq7F5EZcHc3Bxubm7GDoOoRNh+SRejrfzfvHlzeHt7o3PnzpgxYwbi4uIwe/ZsrF69Wus6pVKJAwcOAMhbJVmf9cQuX76M2NhYrVX0PT09hcVrb926VWA4VAx0rfxPREREVYfR+06HDh0qPIl58uRJnD9/Xuv8+fPnkZCQYFCdKpWqwE4AHTt2FI6Dg4NLFiwRERFRORLFHLNp06bh+PHjUCgUWL58uVavmHoY09LSEgsXLix2HP6PP/7ArVu3sH//fnz00UfC+927d4ezszMSExNx5MgRfPHFFya3yCwRaZPJZMI6iG5ubpwjSSaF7Zd0EUVi1qRJEwwYMAAHDx7EjRs3cPbsWXTr1g2ZmZk4fvw4gLwlMfKvX6bLs2fPcOvWLTx48ADXrl0TFqK1tLTE+PHjsWjRIrx8+RKzZs3CmjVr9NoMPCEhATExMUbflomItKlUKuGJa5VKZeRoiAzD9ku6GH0oU23SpEnCE4fqeWZHjx5FVlYWgMIXns2vX79+Qj351zQbM2YMOnfuDCBvi6aPPvoIKSkphdalUqlw4MAB+Pv7IyoqyqDPQ0RERGQoUfSYAYC7uzt8fHxw4sQJXLp0CZcvXxYSKwsLC/j4+OhVT926deHl5YWwsDAcPnwYs2fPFp40lEqlWLFiBSZPnowbN27g1KlT6NOnD3x9fdGlSxfUqVMH5ubmSEpKwvXr13H06FHExMQUeq/Lly/j0aNHwuvU1FTh+OHDh9i9e7dWeX9/f32/HERERFQFiSYxA4DJkyfjxIkTAIBVq1bh4sWLAIDOnTujZs2aetfTr18/hIWFIT09HadOndLqbXNwcEBQUBCWLl2KLVu2IDMzE1u3bsXWrVt11iWRSODr64sBAwYUOLdz507s2bNH53VXr17F1atXtd5jYkZERERFEc1QJpC3qn+3bt0AABcuXIBSqQSg/zCmmmZ5XVs0VatWDbNnz8bRo0fxxRdf4LXXXkO9evVgZWWFatWqwdnZGd27d8e0adNw4sQJ/PTTT3xQgIiIiMqdRMUZh1Vabm4uwsPDAQBeXl5cYJZMCtsvmTK2X9NXHv+GouoxIyIiIqrKRDXHjIjIEGZmZsJuGfosfUMkJmy/pAsTMyIyWWZmZpz/SSaL7Zd04VAmERERkUgwMSMiIiISCQ5lkoAP6JKp4VNtZMrYfkkX9piRQC6XGzsEIiKiKo2JGREREZFIMDEjIiIiEgkmZkREREQiwcSMiIiISCSYmBERERGJBBMzIiIiIpHgOmYk4F5tZGrMzc3h4eEhHBOZErZf0oUtgQRSKTtQybRIpVLY2dkZOwyiEmH7JV34m5iIiIhIJNhjRgKlUmnsEIgMolQqkZWVBQCwsbFhry+ZFLZf0oWtgAQKhcLYIRAZRC6XIyoqClFRUdxSjEwO2y/pwsSMiIiISCSYmBERERGJBBMzIiIiIpFgYkZEREQkEkzMiIiIiESCy2VUcSqVSjiWyWTIzc01YjREhpHJZDqPiUwB26/p0/x30/x9WhoSVVnVRCYpMzMTkZGRxg6DiIjIpHl6esLW1rbU9XAok4iIiEgk2GNWxSmVSmRnZwPI20RXIpEYOSIiIiLToFKphMWBra2ty2T3BiZmRERERCLBoUwiIiIikWBiRkRERCQSTMyIiIiIRIKJGREREZFIMDEjIiIiEgkmZkREREQiwcSMiIiISCSYmBERERGJBBMzIiIiIpFgYkZEREQkEkzMiIiIiESCiRkRERGRSDAxIyIiIhIJJmZEREREIsHEjIiIiEgkmJgRERERiQQTMyIiIiKRYGJGREREJBLmxg6AihcXF4egoCAEBwcjPj4elpaWaNCgAQYMGIBRo0bB2tq6TO5z+vRpbN++HeHh4UhJSYGDgwO8vLzw9ttvo2fPnnrVIZfLsWPHDhw4cAAxMTHIysqCs7MzXnvtNQQEBKBZs2ZlEiuZDlNovzNmzMCePXv0us+JEydQv379sgiZTEB5tl+lUomYmBiEhYUhLCwM4eHhiIqKgkwmAwBs3LgRnTt31ru+7Oxs/PXXXzhy5AgeP36M3Nxc1KlTB7169UJAQABcXV1LHCtVHIlKpVIZOwgq3MmTJzF9+nRkZGToPN+4cWOsWbMGjRo1KvE9lEol5s6di507dxZaZvjw4fjuu+8glRbeyZqSkoKJEyciPDxc53lLS0vMmzcPw4cPL3GsZFpMpf0yMSNdyrv97tmzBzNmzCj0vCGJ2cOHDzFx4kQ8ePBA53k7OzssXboU3t7eJQmVKhB7zETs1q1bmDZtGnJycmBjY4NJkyahc+fOyMnJwaFDh7B9+3Y8ePAAEydOxK5du2BnZ1ei+yxfvlz4pdaiRQt88MEHaNCgAR4/fow//vgDt27dwo4dO+Dg4IDPP/9cZx0KhQJTp04VkrI33ngDw4cPh729PW7cuIHVq1cjOTkZ8+bNg7Ozs949cGS6TKn9qjk7O2Pt2rVFlnFxcSlRnGRaKqL9avaLWFhYwN3dHTKZDNHR0QbVk5GRoZWUvf322xg4cCCsrKwQGhqK3377DRkZGZg2bRq2bNmC5s2bGxwrVSAVida7776rcnd3V7Vo0UJ19erVAud///13lbu7u8rd3V21cuXKEt0jJiZG1aJFC5W7u7vK399flZ2drXU+KytL5e/vL8Tx4MEDnfXs2LFDiOWbb74pcP7Bgweqdu3aqdzd3VV9+/ZVyWSyEsVLpsOU2u/XX3+tcnd3V3l7e5coDqp8KqL93rhxQ7Vx40bVtWvXVDk5OSqVSqVauXKlUO+FCxf0qmfFihXCNb///nuB81euXBG+T0aPHl2iWKnicPK/SIWFheHy5csAgGHDhqFt27YFyowbNw5ubm4A8rq81fMSDLFhwwbI5XIAwNy5c2FlZaV13traGnPnzgWQN39s/fr1OutZt24dAMDe3h5fffVVgfONGjXCpEmTAOR1uR87dszgWMl0mFr7JdJUUe23devWCAgIQJs2bVCtWrUSxSqTyRAUFAQAcHNzw7hx4wqUadeuHYYNGwYAuHjxIsLCwkp0L6oYTMxE6vjx48Kx+hsqP6lUiiFDhgAAnj9/jtDQUIPuoVKpcOLECQBA06ZN0aZNG53l2rRpgyZNmgDIm1+jyjct8f79+7h37x4AoH///oVOhh06dKhwrPn5qPIxpfZLlF9FtN+yEhoaihcvXgAAhgwZUug8Sn9/f+GYP3/FjYmZSF25cgUAYGNjg5YtWxZarmPHjsLx1atXDbpHbGwsEhMTC9SjS6dOnQAACQkJiI2N1RmrZjldnJyc0Lhx4xLFSqbFlNovUX4V0X7Lir4/f1u1aiX80cyfv+LGxEyk1D1QDRs2hLl54c9oNG3atMA1+rp7967Oeoq7T0xMjM5YDann6dOnyMrK0jtWMi2m1H41paamYvTo0ejcuTNatWqF7t27Y/z48fjrr7+QnZ1tUHxkuiqi/ZYVfX/+mpubo2HDhgWuIfFhYiZCL1++RGpqKgCgTp06RZatWbMmbGxsAADx8fEG3UezfHH30Tz/9OnTQusp7om1unXrAsgbhjI0XjINptZ+NWVlZeHSpUtIS0uDTCbDs2fPEBISggULFqBfv37saagCKqr9lhX1fW1sbFCjRo0iy6p//qakpCA3N7fcY6OS4XIZIpSZmSkcq7/pi2JtbY2srCyDe6AMuY/mvLH899Gsx9bWtsT1UOVgau0XACQSCdq0aQNvb2+0aNECjo6OePnyJaKjo7Fz506EhYUhISEB48ePx6ZNm9CiRQuDYiXTUVHtt6yo49U3Vs3rLC0tyy0uKjkmZiL08uVL4djCwqLY8upvrpycnHK7j+Y3cP77lFU9VDmYWvsFgJkzZ+rsbWjbti3efvttrFixAr/++iuysrIwZ84c7Nq1CxKJxKB4yTRUVPstK+p4DYlV8zoSHw5lipDmY9P6PIKt7pLOv1RAWd5Hs9s7/33Kqh6qHEyt/QIocghIIpFg2rRp6Nq1KwDg5s2bHNKsxCqq/ZYVdbyGxKp5HYkPEzMR0hwO1Kd7XD0pWZ+u7JLeR3Pic/77aNajOQxgaD1UOZha+9XXiBEjhONLly6VqA4Sv4pqv2VFHa8hsWpeR+LDxEyEqlWrBnt7ewDFTyhNT08XviGLm6ian2b54u6jeV49gVRXPQkJCUXWo554LZFIDI6XTIOptV99vfLKK8Jxce2cTFdFtd+yor5vVlYWnj9/XmRZ9c9fBwcHzi8TMSZmIqX+JfDo0SNhZXNdNB/9V69Cbeg98tdT3H3yP5KteV9966lbty57zCoxU2q/+uKcsqqjItpvWdH3569cLsfjx48LXEPiw8RMpNq3bw8g76+gmzdvFlpOc0ilXbt2Bt2jfv36cHZ2LlBPUfdxcXFB/fr1dcYK5G33UZhnz54Jm+waGiuZFlNqv/rSXDdNfV+qnCqi/ZYVfX/+RkRECL17/PkrbkzMRKpPnz7C8a5du3SWUSqV2Lt3L4C8ycudO3c26B4SiQS9e/cGkPeX1vXr13WWu379uvCXWO/evQv0HDRp0kT4C+zIkSOFLsS5Z88e4Vjz81HlY0rtV1/btm0TjovbaYBMW0W037LSqVMnVK9eHQCwd+/eQrcc2717t3DMn7/ixsRMpFq3bo0OHToAyPvBcO3atQJl1q1bJ6zgPGbMmAKPS4eGhsLDwwMeHh6YMWOGzvu89957MDMzAwAsWLCgwCPfOTk5WLBgAYC8laPfe+89nfWoN85NS0vDTz/9VOD8o0eP8NtvvwHI29C8b9++uj84VQqm1H6vX78ubO2ki0qlwvLly3Hu3DkAgKenp1YvBVU+FdV+y4KlpSUCAgIA5K3ov3bt2gJlrl27JiSYnTp1QuvWrcstHio9rmMmYrNnz8bIkSORk5ODcePGYfLkyejcuTNycnJw6NAh4S/4xo0b4/333y/RPZo0aYLx48djzZo1iIiIwMiRIzFhwgQ0aNAAjx8/xu+//45bt24BAMaPHy/sdZnf0KFDsWvXLly9ehWbNm1CUlIShg8fjpo1ayIsLAy//PILMjIyIJVKMXv27CK3OaHKwVTa75kzZ7BmzRr06NED3bp1g5ubG2rUqIHc3FxERUVh165duHHjBoC8BToXLFjA+WZVQEW0X0C7JwsAbt++LRyfOXMGcXFxwuuGDRsKCaOm8ePH49ChQ3jw4AF++uknPHr0CAMHDoSVlRVCQ0Px66+/Qi6Xw8rKCrNmzSpxrFQxJKrC+j1JFE6ePInp06cjIyND5/nGjRtjzZo1aNSoUYFzoaGhGDNmDIC8xGnx4sU661AqlcKimYV56623sGDBAkilhXeypqSkYOLEiQgPD9d53tLSEvPmzcPw4cMLrYMqF1Nov6tWrUJgYGCxn6VevXpYunQpe8uqkIpovx4eHnrHU1Q9Dx8+xMSJE4V5vPnZ2dlh6dKl8Pb21vt+ZBzsthA5Hx8f7N+/Hxs3bkRwcDASEhJgYWGBhg0bon///hg9erTWNhslIZVKsXDhQvTr1w/btm1DeHg4UlNTUatWLXh5eWHEiBHo2bNnsfU4ODhg69at2L59Ow4ePIh79+4hOzsbzs7O6Nq1K8aMGYNmzZqVKlYyLabQfv39/VG7dm1cv34dUVFRSE5ORlpaGszNzVGrVi20aNEC3t7e8PX15aKcVUxFtN+y0qhRI+zZswebNm3CkSNH8OjRI8hkMtSpUwc9e/bEmDFj4OrqauwwSQ/sMSMiIiISCU7+JyIiIhIJJmZEREREIsHEjIiIiEgkmJgRERERiQQTMyIiIiKRYGJGREREJBJMzIiIiIhEgokZERERkUgwMSMiIiISCSZmRERERCLBxIyIiIhIJJiYEREREYkEEzMiIiIikWBiRkRERCQSTMyIiIiIRIKJGREREZFIMDEjIqoAT548wbx589CnTx94eXnBw8MDHh4eOH78uLFDIyqRGTNmwMPDAz4+PsYOpVIxN3YARKUVGhqKMWPGAACmTp2Kjz/+2MgREWl78uQJ/P39kZqaauxQDKL5vZWflZUV7O3t4enpib59+8LPzw+WlpZ6161QKHD06FGcOHEC4eHhSEpKgkwmg6OjI5o2bYpevXrBz88PNWrUMCjm3NxcHD16FP/++y/CwsKQmpqKjIwM2NnZwdXVFV5eXujXrx+6dOkCqZR9EyQ+TMyICADg4eEBgMlteVi9ejVSU1Nhbm6Ozz77DB07doSNjQ0AoF69ekaOrmRycnIQHx+P+Ph4BAcHY/369fj1119Rv379Yq+9fPky5s+fj7t37xY4FxcXh7i4OJw5cwarVq3CF198gbfffluvmI4ePYrFixcjLi6uwLm0tDSkpaXh5s2b2Lp1Kxo3boyZM2eiV69eetVtyvi9bVqYmBERlbNz584BAHr37o0JEyYYOZqSGTlyJN59913hdXJyMu7cuYO1a9ciPj4ed+7cwZQpU7B3716YmZkVWs+RI0cwffp05ObmAgA6deoEPz8/NGnSBJaWlnjy5AlOnjyJv//+G2lpaZg7dy7u37+Pr7/+usj4/vvf/2LlypXC627dusHHxwdubm6oUaMG0tPTcf/+fZw8eRLnzp3DgwcPsHz58iqRmJWXxYsXY/HixcYOo9JhYkZEVM4SEhIAAE2aNDFyJCVXu3ZtuLu7a73XtWtX+Pv7w8/PD3FxcYiOjsaxY8fQv39/nXXcunULX375JWQyGSwsLLBo0SL4+vpqlWndujX69++P999/H5MnT0Z8fDzWrVuHBg0aaCWGmnbt2iUkZbVr18aKFSvQqVOnAuVee+01jBo1CtHR0Vi0aBFSUlJK8qUgKlccYCciKmcymQwAYG5e+f4WtrOzw5QpU4TX6t7B/JRKJb766ivha7Fw4cICSZmm5s2bY/369cKQ75IlS/DkyZMC5RISErBgwQIAgI2NDYKCgnQmZZrc3d2xdu1ajBs3rugPR2QEle+nBFE+mhOYN27ciE6dOmHnzp3YvXs3YmJiIJPJ0LRpU4wePRpDhgwRrsvNzcWuXbuwZ88ePHjwADKZDB4eHhgzZgwGDhyo816xsbHo3bs3AGDRokXw9/fH4cOHsW3bNkRFRSEzMxP16tVD3759MWHChGInNufm5mLHjh04cuQI7ty5g4yMDNSsWRMtWrTAm2++CV9f30InMM+YMQN79uyBq6srTp48icTERGzYsAHBwcF48uQJsrKysHHjRsycOVNrTk5gYCACAwO16ho6dKjWkEViYiKOHTuG0NBQREZGIjExEXK5HLVq1UKrVq3g6+uL/v37Fxpb/n+Tzp0749ChQ8LXKSsrC3Xr1kXv3r0xceJE2NvbF/l1AoDTp0/jwIEDuHbtGpKTk6FQKODk5ISWLVvC29sbAwYMgLW1tc5rb968iW3btiE0NBSJiYlQqVRwcXFBly5dMHbs2BL1dO3evRszZ87Uei//1zb/1xUAUlJSsGHDBpw+fRqxsbF4+fIlnJyc0L59e4wYMQIdOnQo9J4+Pj6Ii4sT6o2IiMBff/2FixcvIjExETKZDFFRUQZ/luKo5zABQHx8vM4yJ0+exJ07dwAAr7/+Ovz8/Iqtt0mTJpgyZQqWLVuGnJwcbNy4ETNmzNAqs379emRnZwMAPvnkE7i5uekVs1QqxeDBg/Uqq6kif55oevbsGf766y+cOXMGsbGxyMrKQu3atdGmTRuMGDECr732WoFr1O1Brbjv7fyfrWPHjti9ezf27duHe/fuISUlBUOGDBHK5/8ZU5iMjAxs374dwcHBuHfvHtLT02FpaYmGDRuiffv2GDhwINq3b1/s16CqYGJGVYpcLseUKVNw6tQprffDw8Px9ddfIyIiAnPmzEF6ejo++ugjXLp0SavctWvXcO3aNTx69AiTJ08u9n6zZs3Crl27tN67f/8+1qxZg71792L9+vWF/iKJjY3FhAkTEBMTo/V+UlIS/v33X/z777/Ytm0bfvnll2ITl+vXr2Py5Mll8lSgQqFAz549oVQqC5xLTEzEyZMncfLkSezcuROrVq2Cra1tkfUplUpMnz4d+/fv13r/wYMHWLt2LY4fP45NmzbByclJ5/WpqamYNm0azp8/X+CceiL50aNHAQD+/v4F7r1kyRJs2LABKpWqwP0fPHiAnTt3Yt68eRgxYkSRn6MshISE4NNPP0VGRobW++rPsX//fowaNQpz5swp9onCLVu24Pvvv4dcLi/PkAEAFhYWwnFhvYJ79+4Vjt977z29637nnXcQGBiIly9fYs+ePfj6668hkUgAACqVCnv27AGQ11s2fPjwEkRfchX182T//v2YP38+srKytN6Pj4/HkSNHcOTIEbz11lv49ttvy6xX9uXLlxg/fnyhPaD6OnfuHD7//PMCP3tkMhlu376N27dv46+//iqXPxhMFRMzqlJ+/vln3LhxA76+vvD19YWjoyMePHiAVatW4f79+wgKCoKPjw+CgoJw7do1jBw5En379oW9vT1u376Nn3/+GYmJiVi5ciV69+6NZs2aFXqvzZs3Izw8HK1bt8bYsWPRqFEjJCcnY8+ePTh8+DASExPxwQcf4MCBA7Czs9O6NjMzE2PHjsXjx48BAH369MGwYcPg7OyM2NhYbNq0CRcvXsSVK1cwefJkbNq0qdAJ15mZmfj444/x8uVLTJ48Gd26dYOVlRWio6Ph5OSEtWvXQiaTCcNK+Sd5A0DNmjWFY3UC06VLF7z++utwd3eHg4MDMjMz8fjxY+zYsQPXrl3D2bNn8d1332HJkiXF/ptcu3YNffr0wZAhQ1CvXj0kJSVh8+bNCA4OxsOHD7Fo0SL85z//KXBtdnY2xowZg+joaABAy5YtMWLECDRr1gyWlpaIj4/HpUuXcOjQIZ33XrBgATZv3gwA6NixI4YOHYoGDRrAysoKUVFR2LBhA+7cuYN58+bB0dFR6A3VR58+fdCqVSsAKPRrq/l1vX37NiZPnizMvxo1ahR8fHxgbW2N27dvY82aNcK/vbW1NaZPn17ovcPDw7F//37UqVMH48ePR6tWrSCXy3HlyhW94zfEvXv3hGNXV1edZS5fvgwAsLa2RteuXfWuu0aNGujYsSNCQkKQlpaGu3fvCt93d+7cEX7ht2/fvsD3UXmriJ8nhw4dwldffQWVSoUGDRpg9OjRcHNzg4ODA+Li4rBz506cPn0aO3fuhJ2dnVYvraHf25qWLl2KqKgo+Pj4wN/fX/i+zMzM1Pvrc+HCBUyYMAFyuRxmZmYYPHgwevfujbp16+Lly5e4d+8e/v333wKJbZWnIjJxFy5cULm7u6vc3d1VK1euLPK8u7u7av369QXKJCYmqtq2batyd3dXdenSReXh4aE6duxYgXK3b99WeXp6qtzd3VULFiwocP7x48da95owYYJKJpMVKBcYGCiUWbJkSYHzixcvFs4vX768wHmlUqn64osvhDKbNm0qUObrr78Wzrdp00Z1+/btAmU0FfU1zH/vBw8eFFnm559/Vrm7u6s8PDxU9+/fL3A+/7/JL7/8ovM+48aNU7m7u6tatGihSk5OLlBm4cKFQh3ffvutSqlU6ozn5cuXqmfPnmm9FxISIly7fft2ndfl5OSoxowZo3J3d1d5e3vr/LfUhz5f22HDhqnc3d1VzZs3V505c6bA+bS0NNXAgQNV7u7uKk9PT1V0dHSBMt7e3sK93nzzTVV6enqJ4lUr7ntLpVKp5HK5asiQIUK5S5cuFSgTHx8vnH/77bcNjmPp0qXC9fv37xfe37dvn/D+f/7zH4PrLYmK/HmSnJysat++vcrd3V01c+bMQtvff/7zH6Fd3Lt3r8B5fb+38382XT97NKl/xnh7exc4l5OTo+revbvK3d1d9eqrr6ouXLhQaD1Pnjwp8j5VDSf/U5Xy6quv6hxGcXJyQt++fQHkzfEZMGAA+vTpU6Ccp6enMBeiuN4HS0tLLFiwQOfQwpQpU4Qn3Hbt2iUsHQDkzUXZuXMnAKBZs2Y61x2SSCT45ptvhCHMTZs2FRnLBx98AE9PzyLL6EsikaBRo0ZFlvnoo49Qq1YtqFSqIueeAHm9XLqGcSQSCcaOHQsgb8jo2rVrWuefP3+Obdu2CXXMnj1bGOLKz9LSEo6OjlrvrVmzBgDQr1+/QofAqlWrhnnz5gHIG04MDQ0t8rOUVFhYGMLDwwEAw4cPR/fu3QuUqVmzJr777jsAeUOw6p6+wsyfP9/gxVkNkZKSgvPnz2P06NG4desWgLyvpa45cJrDWPn/HfRRu3Zt4TgtLU3nsWaZilLeP0+2bNmCFy9ewMXFBd98802hw5Qff/wxXFxcoFQqsW/fvtJ8JEHjxo1LtebZ3r17kZiYCACYNm0aOnfuXGjZunXrlvg+lRETM6pSippkq5m4DBo0qNhy6mHGwnTr1g0uLi46z0mlUmFicFpamvCLDQAiIiLw/PlzAHkTcwsborSzs8OAAQMAAHfv3hV+COqiz0TrklIqlUhISEBMTAyio6MRHR2Ne/fuoU6dOgCAyMjIIq/39fUtNKFq2bKlcJz/633hwgVh0veYMWOKXDsrv4yMDFy8eBFAXjJRFDc3N9SqVQsACiSHZUVzHs9bb71VaLn27f+vvfuPibr+4wD+PAwFhnJeSGA1ysS6JAixsEmJxHRjqV3MDWJnoP1aOgrcSmvlVq2MrVWQwtq0SI0jHNPE2Q0BT2HWIBin5LyhhOC6BRgXv/LkvO8f7D77HPeDO7iDj9+ej78+270/Hz7c53Ofe937/X693onCnER3c3+ioqLcJglMxddffy0sI/Xwww/jqaeeQk5ODlpaWhAcHIzc3Fx8/vnnTvcVz5mzZVl6QzxPUXws8bCaq8QOf/L388T2oyYlJcXtqgp33XUXHn/8cQC+u0fT09O9+kxNZBueDAkJ8bhAMI3jHDP6T3GXXTd//nxh+4EHHpi03WRzLR577DG3r8fFxQnbBoNBeLDaMteA8V/k7sTHx6O8vFzYLyIiwqFNSEgI7r//frfH8ZbVasVPP/2Eo0ePQq/X499//3XZdrKEgyVLlrh8TZzUMPH9Fgez3mZ0/f7770LyQkFBAQoKCjzar6+vz6u/4ynbNQ8MDIRSqXTbNj4+HleuXEFXVxfMZrPTL2xxluRMeOSRR6BWq+2SAMTEc78mTmD3hPjai48lDthsQfpM8ufzxGKxCD9qKioqhN7hyfjqHp3uPXTp0iUA4z+uZiNovpMxMKP/lKCgIJevibPcPGnnLCtRbLKhFVfDMyaTSdhWKBRujyEeFhLvJ+br4aybN29ix44dOHv2rEft3QVtgOfXZOL7LQ74nAWk7vT393vV3may/2WqbNdfLpdPmlVnu+ZWqxX//POP06FBfwxhiieOWywWGI1GaLVaHD9+HK2trVCr1Th69KjTe9bW4whMLXAQXy9xsC7enuo1nQ5/Pk9MJtOUMmp9dY+6SgrwlO3z6SqbmlxjYEbkJ66G52b6GNMZjnCmpKRECMqefPJJvPjii1i+fDnCw8MRFBQkfNFkZ2cLmXhSI/4S/PDDD5GQkODRftP9spopvr7mgGPlf6VSibVr1yIpKQm7du3C9evX8d5776GkpMRh34iICCxcuBB///03Ll++DIvF4tU5intHxUOE4u329nZv/yVJs1gswvbmzZtdLiY/kateS29xgffZw8CMyE8m6xlw1Qsg/vLv7+93O1wi/hszETRYrVYhMWHlypUoKytz+QB31YPnK+JemL/++sur4Vrx+x0UFOSw1NBMs53PwMAAxsbG3Paa2a65TCbz6+R+T6lUKtTX10Or1aKurg7nz593KIchk8mQmJiI06dPY3R0FOfPn3ea4ODM4OCgEODL5XIsXbpUeC0mJkYI+H777TcMDQ3NeMkMf5lYoma271FvLVy4EEajEb29vbN9KncchsREfmLLsvPkdXH9IvF2W1ub22Po9Xqn+/nLwMCA8KB1V9l/eHgYnZ2dfj0XcWKAtz1zSqVS6I1saWnx6XlNhe3a2YpuumO75tHR0W4nhM+k/Px8oQfsiy++cNpGpVIJ24cOHfL42BUVFcLwnEqlsutFlslkwnFHRkZQWVnp9blL1dy5c4X7Qgr3qLceffRRAOPJTLMx/+9OxsCMyE8aGxtdZkrevn1bqFgeFhZmF2TExsYKPSHHjh1zOZdtaGgIp06dAgAsXbrU63lWE82bNw8A7Ep3TCQeXnH3sK2srPR7xfmkpCQhw+/QoUN25zYZhUIhJFtUV1fP+mLW4uV0Jq4UIdba2oqOjg6HfWbbgw8+KGQIt7W1obGx0aFNamqqkFF65swZnDx5ctLjdnV1Yd++fQDGezadDefl5OQIk8uLiorsit26c/v2bYfVJqQmNTUVAHD16lWcO3duysfx5LPta7ZzHx0d9ThxgcYxMCPyE7PZjA8++MBpwPDNN98I1eozMjLsej7mzp0rlEwwGAzYv3+/w/5WqxUfffSRMME2Ozt72udrm6R77do1l20UCoUQNFZXVzt90Ov1enz11VfTPp/JLFiwQFgmqb29HZ988onDsko2t27dcpgcblt4e2hoCHl5eUKJEmfMZjOOHDmCmzdv+ujs7cXFxQmrBFRWVjpdXmpwcFCoqRYQEICsrCy/nMtUvfbaa0JvlrN5ZgEBASgsLBTmQO3atcvligzAeJmVnJwcIYvznXfeweLFix3a3XPPPXj//fcBjPeaqdVqoRSKKx0dHXj55Zdx4MABz/65WbJlyxbhx8fu3bvtMradOXPmjNPyNJ58tn1t48aNQrmgL7/80u01cbW+6n8V55gR+UlsbCzq6+uRlZVltyTTsWPHhN6CyMhIvPHGGw77bt++HTU1Neju7kZxcTEMBgNeeOEFLFq0CD09PcLC1ACQkJDgk3UcExIS0NPTg7q6Omg0GqxYsUL4pR0aGoq7774bAQEB2LBhA44cOYLLly8jKysLubm5iI6OxtDQEHQ6HX744QeEhIQgIiICf/zxx7TPy50333wTjY2NMBgMOHz4MFpbW5GZmYlly5YhMDAQRqMRzc3NOHnyJN566y27tTLXrFmDLVu24Pvvv0dTUxPS09ORmZmJxMREyOVyjIyM4Nq1a2hubkZNTQ1MJhOef/554T3xtY8//hibN2/GrVu38Morr0CtVmPt2rV2SzLZal1t3bpVcnOOli1bhtTUVNTW1qKpqQnNzc0OtdRiY2NRWFiIt99+G2azGfn5+dBoNNiwYQOWLFmCwMBA/Pnnn6irq0N1dbXQ67p161aHpYTEMjIyYDQaUVRUhP7+fqjVaiQnJwu9dAsWLIDJZEJnZyd0Oh3OnTsHi8Xis6LL/hIeHo7PPvsMeXl56O3tRUZGBlQqFZ555hlERkZibGwMRqMRer0eWq0W3d3dKC0tdfi/PPls+9q8efNQWFiIbdu2YXR0FLm5udi4cSPS0tIQGRkJs9mMq1evQqfToa6uDhcvXvT5OdypGJgR+Ul2djaamppQVVWF/Px8h9dt61SK6x3ZhIaG4rvvvhMWMddqtdBqtQ7tVqxYgZKSEp9k4W3btg1arRZmsxl79uyxe02lUmHv3r0AxucTtbS04NKlS7h48SJ27txp11Yul6O4uBhFRUV+D8yCg4NRVlaGvLw8NDU1ob29Xeg98cS7776LsLAwlJSUoLe3F8XFxS7bhoSE+CXb0UapVKK0tFRYxPzgwYM4ePCgQ7vs7GyH91wqXn/9ddTW1gIY7zVz1iOVnp6ORYsWYc+ePbhy5Qp+/fVXlysqyOVyFBQUePTDY/v27YiJicHevXtx/fp1NDQ0oKGhwWX7mJgYt+uNSsW6deuwf/9+7N69GwMDA9BoNNBoNE7bBgQEOK0Z5uln29dWrVqF0tJS7Ny5EyaTCVVVVaiqqvLL3/p/wsCMyI8+/fRTrF69GhUVFTAYDBgZGcHixYuRlpaGV1991W0m5X333Yfjx4+jsrISP//8MwwGA4aHhxEWFgalUiksnOyrtHalUomKigocOHAALS0t6OvrczpUOX/+fJSXl+Pbb7/FqVOn0NXVhTlz5iAqKgpr1qzBSy+9JFT9nwkKhQKHDx9GTU0NTpw4gba2Nty4cQMymQwRERFYvnw50tLSnFb4l8lk2LFjBzZt2gSNRoNffvkFPT09GBwcRFBQEKKioqBUKpGcnIy0tDS39ah8ITk5GTU1NSgrK4NOp0N3dzfMZjPCw8ORmJiIzMxMn1f096W4uDisXr0ajY2NaGhogF6vtyukbPPEE0/gxIkT0Gq1qK2txYULF9DX14exsTEoFAo89NBDSElJwaZNm7zKPF23bh1SUlKg1Wpx9uxZXLhwATdu3MDw8DBCQ0Nx7733Ij4+HuvXr0dSUpJPytHMBFtP5I8//gidToeOjg6YTCbMmTMH4eHhiImJwapVq7B+/Xqnyxt5+tn2h6effhqnT59GeXk56uvr0dnZicHBQQQHByM6OhorV67Ec889NyPncqeQWV1NyiAir/X09ODZZ58FMB6UiYfOiIiIJsPJ/0REREQSwcCMiIiISCIYmBERERFJBAMzIiIiIolgYEZEREQkEczKJCIiIpII9pgRERERSQQDMyIiIiKJYGBGREREJBEMzIiIiIgkgoEZERERkUQwMCMiIiKSCAZmRERERBLBwIyIiIhIIhiYEREREUkEAzMiIiIiiWBgRkRERCQRDMyIiIiIJIKBGREREZFEMDAjIiIikggGZkREREQSwcCMiIiISCIYmBERERFJBAMzIiIiIolgYEZEREQkEf8Df6RQChOJkcoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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YmBERERGJBAszIiIiIpFgYUZEREQkEizMiIiIiETC3NQBkGlptVo8fvwYAGBubg6FQmHiiIiIiKRBp9NBrVYDAGxsbKBUVry/i4VZDff48WNERkaaOgwiIiJJ8/T0hJ2dXYXb4VAmERERkUiwx6yGMzf/v38Cbm5usLW1NWE0VBqVSiX0cHp6esLCwsLEEVFJmC9pYb6kQyy5MozD8OdpRbAwq+EM55RZWlrC0tLShNFQaZRKJRwcHAAAVlZWlfaNgKoG8yUtzJd0iDFXlTVH2/SfhERDDP+wqWTm5uZwc3MzdRhkJOZLWpgv6ZBzrjjHjIiIiEgkWJgRERERiQTHrkigUqk4x0zkVCoVbt68CSD/Zg1OThY35ktamC/pkHOuWJgRSYhOp0N2drawTeLGfEkL8yUdcs4VhzKJiIiIRIKFGREREZFIsDAjIiIiEgkWZkREREQiwcKMiIiISCRYmBERERGJBJfLIIFSyTpd7MzMzNC4cWNhm8SN+ZIW5ks65JwrFmYkkNs/bjkyMzODi4uLqcMgIzFf0sJ8SYecc8UuEiIiIiKRYGFGREREJBIcyiQBn5Upfnl5eQgPDwcAeHl5MV8ix3xJC/MlHXLOFXvMiIiIiESCPWYkMDfnPwexs7CwgJeXl7BN4sZ8SQvzRWLAn8QkUCgUpg6BSqFQKGTVZS93zJe0MF8kBizMSKDRapGQnm3qMIiISGYca9vAjGtlGoWFGQlSs3Lhv2S3qcMgIiKZ2Rs4Ci4OdqYOQxJYvhIRERGJBAszIiIiIpHgUCYRERFJirm5OTw8PIRtOZHXpyEiIiLZUyqVsLe3N3UYVYJDmUREREQiIfoes8zMTOzevRvHjh3DzZs3kZKSAnNzczg6OqJdu3bo378/Bg0aBDMzM6PbTE1Nxd9//43//vsPMTExSEtLQ15eHurUqYOWLVuiU6dOGDZsGNzd3UttS6vV4ujRozh+/DguXryI5ORkPHr0CHZ2dnBxcRFi7NOnT5ELFk6YMAFnz54t9L5SqUStWrXQpEkTdO3aFS+//DKaNWtm9GckIiKSK61Wi5ycHACAra0tlDJaikOh0+l0pg6iONu2bcPSpUuRnp5e4nHPPPMMPv/8c3Tu3LnE47RaLX799VesWrUK2dmlr9fl4+ODTz/9FM8880yR+8+ePYsFCxYgOjq61LacnZ0xY8YMBAQEFHi/uMLsaRYWFpg9ezbGjRtX6rFlYfi8sQbN3LhcBhERVbrKXi5DLM/KrIo4RNtjtmTJEqxZswZA/sS+oUOHwtfXF66ursjLy8Pt27exZ88enDlzBjdu3MBrr72Gb775BoMHDy6yvdzcXHzwwQc4evQogPxCZ+jQoejZsycaN24Ma2trpKSkIDw8HIcOHcL169fx33//YevWrQgMDCzU3o4dOzB//nyoVCoAQIcOHTBw4EC0bt0aDg4OyMzMxL1793D8+HEcO3YMiYmJWLhwYaHCzFBISIiwrdVqER8fj/379yMoKAgqlQoLFiyAq6sr+vbtW94vKxEREYmYKAuzjRs3CkVZgwYN8Ouvv6J169YFjuncuTNGjx6NkJAQzJ49G3l5eZg5cyaaNWtW6FgA+Oyzz4Si7Nlnn8V3332Hxo0bFzquT58+eOedd3D06FEsXLiwyPhOnz6NOXPmQKvVwtbWFosWLSqyIOzevTtGjx6N+/fvY+nSpThx4kSJn/vpoVNPT0/07dsXbdu2xZdffgmdTodly5axMCMiIpIp0Q3KxsXFYcmSJQDyx43Xrl1bZKGl5+fnJxRQeXl5+Pjjj/H06OzBgwcRFBQEAPDw8MDatWuLLMoM9evXDzt27ED37t0LvP/48WPMnDkTWq0WSqUSv/32W7G9dHqNGzfG999/jzlz5pR4XHHGjRsHV1dXAMDVq1eRkpJSrnaIiIhI3ERXmK1btw5PnjwBALz11lto0aJFqef4+/ujd+/eAIDo6GihZ0zv119/FbYXLVoEW1tbo2KpXbs2+vfvX+C9HTt2ICkpCUB+wdS1a1ej2gKAESNGGH2sIaVSiXbt2gmvHzx4UK52iIiISNxEVZjpdDrs2rULAGBtbY2XX37Z6HNfffVVYXvnzp3CdlRUFCIiIgDkD3+2bdu2QjHq21YoFJg4cWKF2ioLwwX0tFpttV2XiIiIqo+o5pjFxMQId2B27twZtWrVMvrcnj17wtraGrm5uTh//rzw/rlz54TtPn36VCi+rKwsXL9+HQDQokULNG3atELtlYXhnZ/Ozs7Vdl0iIqLiWEJj1HGPc3KQXXjFqGLZ2dXcB56LqjCLjIwUttu0aVOmc83MzODp6YlLly4hNTUVCQkJcHFxKdCm4XBgeURFRQm9VRXteSuLY8eOISYmBgDQpEkTNGzYsNquTUREVJzB9neNOm7d7z+Xqd358+eXJxxZEFVhlpaWJmw7OjqW+fz69esL2+np6XBxcSmwBlq9evUqFJ9hW4bXqgqGy2UsW7ZMeP+NN96o0usSERGJnaWlJby9vU0dRpUQVWFmuOirsRP0DRmek5WVVSltGjJsy8bGpkJtFUX/QNbijBkzpkzz7oiIiEhaRFWYGY4p6x+1UBaG5+gfblrRNg0ZtvX48eMKtWUsGxsbdOrUCePHj4evr2+1XJOIiIhMQ1SFWd26dYXt5OTkMp9vuL6Xg4NDgb+f3l8ehm2VJ77SGK78b2ZmBjs7Ozg5OZXpOaBERETVZX+WcTfBbZ7xApzqVGzUypBGoxF+Djs6Osrq56SoCjPDobxr166V6VyNRoOoqCgA+XPJXFxcAOSvnq939epV+Pj4VCg+pVIJrVZb5viMYcxD04mIiMQiD8YVRDa2tpV6p6VGo8H9+/cB5HfqyKkwE9U6Zu7u7kKvVFhYGDIzM40+99SpU8LwouHDzLt06SJsHz9+vELx2dvbC08huH37Nu7cuVOh9oiIiIgMiaowUygUGD58OID8h45v27bN6HP//PNPYXvkyJHCtoeHh7BMRlhYGK5evVqhGPUPIdfpdNiwYUOF2iIiIiIyJKrCDAAmTpwIS0tLAMCKFSuM6pX6+++/cezYMQD5vW79+vUrsH/atGnC9uzZs42+CeDRo0c4cuRIgfdGjRoFJycnAPkPWz979qxRbQFAcHCw0ccSERFRzSO6wqxx48b4+OOPAeTfRTlp0qQCi8Q+be/evfjkk08AABYWFvj666+hUCgKHDNw4EDhOZVRUVGYNGkS4uLiSozj+PHjePHFF3H69OkC79vY2OCbb74R5ppNnToV//zzT4ltPXjwAB9++CG+/PLLEo8jIiKimk1Uk//1JkyYgLt372L9+vV48OABRo0ahWHDhsHX1xeNGjWCWq3GrVu3sGfPHqFwsrS0xDfffCPMAXvaZ599hoyMDBw9ehSXL1/G4MGDMWTIEPTq1Quurq6wtrZGcnIyrl27hoMHD5Y45NmjRw98+eWXmD9/PnJycvDee++hQ4cOGDRoEFq3bo06deogMzMT9+7dw4kTJ3DkyBHk5eWV6RFTREREVPOIsjADgMDAQLRs2RI//PAD0tPTsWvXLuEB509zc3PDF198UWDS/9NsbGzw888/45dffsHq1auRnZ1dYpsA0LdvX7zyyitF7hs1ahSaNGmCBQsWIDo6GpcuXcKlS5eKbatRo0b46KOPit1PREREJNrCDADGjh2LYcOGYffu3Th27Bhu3LiB1NRUmJmZwdHREV5eXujfvz8GDx5s1K2ySqUSb7/9NsaOHYu///4b//33H6Kjo5GWlgaVSoU6deqgZcuW6NSpE/z9/eHm5lZie127dsWuXbtw9OhRHDt2DBcvXkRycjIyMzNha2uLBg0aCDH26dMH5uai/nITERFJgkKhEJbfeHr6ktQpdDqdztRBkOnk5eUhPDwcANCgmRv8l+w2cURERCQ3ewNHwcWh8tYxEwvDn6FeXl7CzYsVIbrJ/0REREQ1FQszIiIiIpHgpCciIiKSFLVaLaxz2qxZM1nN4WaPGREREUmKVqtFeno60tPTodVqTR1OpZJPiUkVVs/eGnsDR5k6DCIikhnH2jamDkEyWJiRwEyphIuDtanDICIiqrFYmJGAK6eIn06ng0qlApD/CDK5rd8jN8yXtDBfJAacY0YCtVpt6hCoFCqVCuHh4QgPDxd+gJB4MV/SwnyRGLAwIyIiIhIJFmZEREREIsE5ZiTgfArxUyqVcHZ2FrZJ3JgvaWG+pEPOuWJhRgI5LdAnV+bm5mjSpImpwyAjMV/SwnxJh5xzJa8yk4iIiEjCWJgRERERiQTHrkig0WqRkJ5t6jBqHMfaNjAzco6ESqXC9evXAQCtW7eGhYVFVYZGFcR8SQvzJR1yzhULMxKkZuXCf8luU4dR4+wNHAUXBzujjjVcAJMLAosf8yUtzJd0yDlXHMokIiIiEgkWZkREREQiwcKMiIiISCRYmBERERGJBAszIiIiIpGo9sJMo9GgU6dO8PDwwMiRI0s8VqfToVu3bvDw8ICHhwe2b99e4vFBQUHCsZs2bRLenzBhgvB+adatWycc26NHD0RGRgr7li9fLuwLDQ0ttS0A8PX1hYeHB3x9fYvcP2vWLKFNwz+enp7o3Lkz/P398fnnnwu3BRMREZF8VXthZmZmho4dOwIAIiMjkZWVVeyxMTExSE9PF16fP3++xLbDwsKE7S5dupQ5tlWrVmHhwoUAACcnJ2zYsAGenp5lbqcy6HQ6ZGZmIioqCps2bUJAQAC+//57k8RC4mFubo6WLVuiZcuWfISWBDBf0sJ8SYecc2WST9OlSxecPHkSWq0WFy5cwHPPPVfkcfpCy8zMDBqNpkDhVdLxdevWxTPPPFOmmH755Rf88MMPAAAXFxesW7cOLVq0KFMbFbV69WrhoaxarRYpKSk4duwYNm3aBLVajV9//RXOzs4YN25ctcZF4qFUKlG3bl1Th0FGYr6khfmSDjnnyiRzzDp37ixsl1Rs6fcNHjwYAHD37l0kJCQUeWxKSgpiY2MBAN7e3lAoFEbHs3z5cqEoa9SoEf78889qL8oAoHnz5nB3d4e7uzs8PT3h4+ODwMBALF++XDhmxYoV0Gg01R4bERERVT2TFGbt27eHlZUVAOMKs0GDBqFp06YAih/OLO8w5nfffYcVK1YAABo3bow///xTuJZY+Pr6CsVsamoqrl69auKIyFR0Oh3y8vKQl5cnu9Wu5Yj5khbmSzrknCuTFGaWlpZo3749ACA8PBx5eXmFjrl3757QO+bt7Q1vb28AxRdyhu8b9siVZMmSJfjtt98A5PdWbdy4Ea6ursZ/kGqk/3oBwIMHD0wYCZmSSqVCeHg4wsPDhceRkHgxX9LCfEmHnHNlsuUy9MVTXl4eLl++XGi/vtBq3rw5HB0dhcLs3LlzRban70mzt7dH69atS73+l19+iTVr1gAAWrZsiQ0bNqBBgwZl/yDVxHByo1arNWEkVB6W0BT753FODrKzs0v9Q0RE8meyWxm6dOmCX375BUB+Efb08KO+MOvUqVOBv2NiYpCRkYE6deoIx2ZlZQnLWnTs2BFmZmbFXlen0+Hzzz/H5s2bAQDu7u74448/4OjoWEmfrGpER0cL2/obBEg6BtvfLXbfut9/NqqN+fPnV1Y4REQkUiYrzDp06ABzc3Oo1eoihyf17+l71tzc3FC3bl2kpaXhwoUL6Nevn3DsxYsXhQnxpc0vmzt3Lv766y8AQOvWrbFmzRrUq1evzPHfv3/fqDtCKqOLNTIyEidOnAAA2NjYwMvLq8JtEhERkfiYrDCzs7ND69atER4eLhRW+p6up++w1OvUqRMOHz6MsLCwAoVZWeaX6YsyNzc3rFu3rkDPW1l8+umn5TrPWDqdDsnJyTh27BiWLl0qFJ4TJkwQbpwgIiIieTHpqmxdunRBeHg4srOzcf36dbRr1w7A/80jc3R0RPPmzYXjvb29hcLMkH5+mZWVVam9SQqFAjqdDvfv38e1a9fQo0ePSvxEFdO/f/8S9/ft2xfvvfdeNUVDRERE1c2khZm3t7cwAT8sLEwozPSFl2FvGfB/vWFXr15Fbm4urK2tkZeXhytXrgAAnn32WVhaWpZ4zblz5+KLL77AkydPMH36dKxatcrouzgNrV+/Ht26dSv1OF9fX8TFxZW5fT0LCwt4eXlhzJgxGD58eJnWZyPx2J9V/BIsm2e8AKc6ttUYDRERiZVJC7POnTsLPVjnz5/HpEmTAPxfD5h+wr9emzZtYGNjg8ePH+PSpUvo3r07wsPD8eTJE6G90owbNw65ubn4+uuv8fjxY0ydOhV//PFHgeUoTMVw5X+lUglbW1s4OjqWWmyS+OWh+BtSbGxtYWdnV43REBGRWJm0MHNwcECrVq0QHR0t9JIZ3mH5dI+Zvvfo7NmzCAsLQ/fu3QssOGvswrKTJ0/G48ePsXz5cmRlZeGNN97A+vXrTfZcTL3mzZujcePGJo2BxE3/f0C/TeLGfEkL8yUdcs6VydYx0zNc0f7mzZs4f/48tFotbG1t0aZNm0LHP73QrH4+moWFBTp06GD0dd955x1MmTIFAJCRkYHXX38dN2/erMhHIapyCoUClpaWsLS05LC2BDBf0sJ8SYeccyWawgzIH8LU94B16NChyPXI9IXZ5cuXkZeXh4sXLwLIH+a0tS3bPJ3//e9/mDBhAoD8O0EnTZqEe/fuletzEBEREVWUqAqzc+fOCT1gTw9j6ukXkM3JycHOnTuRmZlZqJ2yCAwMxEsvvQQASExMxKuvvoqHDx+Wqy2iqqbVapGWloa0tDQ+AUICmC9pYb6kQ865Mnlh5uLiIjw0PDQ0FBEREQCKL8zs7e3h7u4OAFi1apXwflkeXG5IoVDg888/h5+fHwAgLi4OkyZNQlJSUrnaI6pKarUat27dwq1bt6BWq00dDpWC+ZIW5ks65JwrkxdmwP/1diUkJCAvLw/m5uYlzhfTF236YUelUllsIWcMpVKJJUuWYNCgQQCA2NhYvPbaa0hNTS13m0RERERlJarCTK9169awsbEp9vinizB3d3fUrl27QjGYmZlh6dKl6Nu3L4D8Z3JOnjwZjx49qlC7RERERMYSRWH29DBkab1fT+8v7zDm0ywsLLBs2TL07NkTAHDt2jVMmTIF2dnZldI+ERERUUkUOp1OZ+ogyHTy8vIQHh4OAGjQzA3+S3abOKKaZ2/gKLg4GLfArGG+vLy8uPiwyDFf0sJ8SYdYclUVcYiix4yIiIiIWJgRERERiYZJH8lERGWjUCiEx4/IbbVrOWK+pIX5kg4554qFGZGEWFhYoH379qYOg4zEfEkL8yUdcs4VCzMS1LO3xt7AUaYOo8ZxrF380jBERFSzsDAjgZlSCRcHa1OHQUREVGOxMCMBV04RP7VaLTzLtWHDhjA3539hMWO+pIX5kg4554p3ZZJAbs8bkyOtVovExEQkJibK7sG9csR8SQvzJR1yzhULMyIiIiKRYGFGREREJBIszIiIiIhEgoUZERERkUjI5zYGqjA53dUiVxYWFvDy8hK2SdyYL2lhvkgM+JOYBHJ7rIUcKRQKWFpamjoMMhLzJS3MF4kBCzMSaLRaJKRnmzoMIiIiAPlPRjFTFp51pVQq4eDgIGzLCQszEqRm5cJ/yW5Th0FERAQA2Bs4Ci4OdoXeNzc3h5ubmwkiqnryKjOJiIiIJIyFGREREZFIcCiTiIiIJEWlUuHmzZsAADc3N1ndRcvCjIiIiCRFp9MhOztb2JYTDmUSERERiYRkesxycnKwa9cuHDlyBJGRkUhPT4dOp4O9vT1cXV3h7u6Ojh07onfv3mjYsGGJbT148AB///03Tp06hTt37iA1NRVarRZ16tSBu7s7vL294efnhyZNmhQ6NzQ0FBMnTgQAvPPOO3j33XdLjX3WrFkICgoCABw+fBiNGzcusH/nzp2YPXs2AGDRokUICAgw6mtCRERE8iKJwuzixYv48MMP8eDBg0L70tLSkJaWhoiICOzcuROOjo7477//imznyZMnWLp0KTZv3oy8vLxC+xMTE5GYmIiTJ09i2bJlGDx4MD755JNSCz0iIiKiyiD6wuz27duYPHmyMJbs6+uLQYMGoUWLFrCwsEBaWhoiIyNx6tQphIaGFttOamoqpk+fjkuXLgEA7Ozs8MILL6B79+5o0KABzM3NkZycjAsXLuDgwYOIjY3Fvn370KFDB0yaNKkaPikRERHVdKIvzL7//nuhKCtumM/HxweTJ09Gamoq9u3bV2i/VqvFBx98IBRl/fr1w1dffYX69esXOtbX1xcffvghdu/eja+//rpyPwwRERFRCURdmGk0Ghw/fhwA0K5du1LnXtWrVw/jxo0r9P769euF3rRevXphxYoVJT6wW6lUYsSIEejevTtu375dgU9AREREZDxRF2apqanIzc0FADRr1qxcbeTl5WH16tUAACsrKyxcuLDEosxQgwYN0KBBg3Jdl4iIiKqGmZmZcCOdmZmZiaOpXKIuzAwXjNMvJFdWJ0+eRGJiIgBg8ODBcHFxqZTYiIiIqGiW0FRKO49zcpBdzNqx9vb2ACB04BjDzq7wczfFRtSFmYODA1xdXREXF4fIyEisXLkSb7zxRpmeJH/u3Dlhu2/fvlUQJRERERkabH+3UtpZ9/vPldKO3vz58yu1vaog6sIMAMaPH48lS5YAAJYuXYotW7bA19cXnTp1gpeXV5FrjRmKjIwUttu2bVupsaWkpCA6OrrU4x49elSp1yUiIiJ5En1hNmnSJNy4cQM7duwAAMTFxWHDhg3YsGEDAMDR0RFdu3aFn58f+vXrB4VCUeD89PR0YbuouzArYvPmzdi8eXOltklEREQ1l+gfyaRUKrFw4UKsWbMGvXv3LjRxPzk5GXv37sX06dPx4osv4u7dgt2n+qU2AMDGxqZaYiYiIiIqD9H3mOn5+PjAx8cHWVlZOH/+PMLDwxEREYGwsDBkZmYCACIiIvDKK69g586dcHZ2BlBwot/jx4+FyYKVoTyPZCIiIpK7/VlNK6WdzTNegFMd20Lvq1QqXLt2DQDQpk2bAjcLSp1kCjM9e3t79OnTB3369AGQvxxGSEgIlixZgoyMDCQlJeHHH3/EV199BSD/BgK95OTkSi3MiIiIqLA8VM4SFja2tkXeSZmXlwcrKysAgK2tLSwtLSvlemIg+qHM0lhaWmLUqFFYunSp8N7Bgweh1WoBAJ6ensL7+uqaiIiISIwkX5jp9e7dW3jYeEZGhjDpv0uXLsIxx44dM0FkRERERMaRTWEGQJhXZqhXr17C+/v370dCQkJ1h0VERERkFNkUZo8fP8aNGzcA5M9Dq1u3LoD8oc7JkycDAJ48eYJPP/0UGo1xKxInJCTg9OnTVRMwERER0VNEXZhlZ2dj9OjROHr0qDBnrCharRYLFiwQlsbw9fUtsJ7ZxIkT0a1bNwD5j2h6++23kZqaWmx7Op0OISEhCAgIQFRUVCV9GiIiIqoM5ubm8PDwgIeHh9HPv5YK0X+aK1euYNq0aXBxccGAAQPQoUMHNGrUCPb29nj06BGuXbuGHTt2CCvw16pVC++//36BNpRKJX744QdMmzYNly9fxtGjRzFgwAD4+fmhe/fuaNCgAczNzZGcnIxLly7hwIEDuHXrlik+Ls6fP2/Ucb6+vgXuOCUiIqoplEqlbFdZEHVhZm5uDicnJyQlJSEhIQEbN27Exo0biz2+efPmWLp0qfDEeUP16tXDhg0b8O2332Lz5s3Izs7Gli1bsGXLliLbUigU8PPzw5AhQyrt8xhj+/bt2L59e6nHBQcHszAjIiKSGVEXZlZWVvj3339x6dIlnDp1CpcvX8bt27eRkpKCJ0+ewMbGBs7OzvD09ET//v3x/PPPl7iWiZWVFQIDA/Haa69hz549OH36NGJjY5GamgqdToc6derA3d0dXbp0gZ+fH1xdXavx0xIREZExtFotcnJyAOSvY6ZUinpmVpkodDqdztRBkOnk5eUhPDwcANCgmRv8l+w2cURERET59gaOgotD0QvM6n92eXl5mWyB2aqIQz4lJhEREZHEsTAjIiIiEgkWZkREREQiIerJ/1S96tlbY2/gKFOHQUREBABwrG1j6hCqHQszEpgplXBxsDZ1GERERDUWCzMS8AZd8dPpdFCpVAAACwuLAk+4IPFhvqSF+SIx4BwzEqjValOHQKVQqVQIDw9HeHi48AOExIv5khbmi8SA65jVcGJZC4aIiEhquI4ZERERkYyxMCMiIiISCU7+J4FGozF1CFQKjUaD5ORkAICjoyPMzMxMHBGVhPmSFuZLOuScKxZmJNBqtaYOgUqh0Whw//59AEDdunVl9c1IjpgvaWG+pEPOueJQJgnMzVmnExERmRILMxJwzR4iIiLTYhcJCTRaLRLSs00dRiGOtW1gpuTvEEREJH8szEiQmpUL/yW7TR1GIXsDR8HFwc7UYRAREVU5dkMQERERiQQLMyIiIiKR4FAmkYQoFArY2dkJ2yRuzJe0MF/SIedcsTAjkhALCwt4enqaOgwyEvMlLcyXdMg5V5IqzM6ePYsJEyYIrzdv3oxOnTqVqY27d+9i3759OH36NO7evYu0tDSo1WrUqlULjRs3Rps2bdC3b1/4+PjAwsKiyDZ27tyJ2bNnl+m6s2fPxqRJkwq8N2HCBJw9e7bQsUqlErVq1UKTJk3QtWtXvPzyy2jWrFmZrkdERETSI6nCLCgoqMDr4OBgowuzR48eYfHixdi1axfUanWh/SkpKUhJScHly5exefNm1K9fH1OmTMHEiROrfUVhrVaLjIwMZGRkICIiAhs2bMDs2bMxbty4ao2DiIiIqpdkCrPc3Fz8888/AABbW1vk5ORg//79mDNnDiwtLUs89969e5gyZQpu374NAHBwcMCwYcPQuXNnODs7w8bGBqmpqbh16xZOnjyJU6dOISUlBYsXL8YLL7wAJyenYtv+4IMP0L9//1LjL6kNAAgJCRG2tVot4uPjsX//fgQFBUGlUmHBggVwdXVF3759S70WyZdarcadO3cAAM2aNePTGkSO+ZIW5ks65JwryXySgwcPIjs7f/HTOXPm4NNPP0VGRgaOHDmCwYMHF3teTk4Opk6dKhRlL730Ej755BPY29sXOrZ379549dVXce/ePfz888/YuXNnqXG5uLjA3d29nJ/q/zzdhqenJ/r27Yu2bdviyy+/hE6nw7Jly1iY1XBarRbp6ekAgCZNmpg2GCoV8yUtzJd0yDlXklkuIzg4GADg4eGBUaNGoUWLFgXeL863336LmzdvAgBeeeUVLFiwoMiizFCTJk2waNEi/PLLL7Cysqpw7BUxbtw4uLq6AgCuXr2KlJQUk8ZDREREVUcShVliYiJOnz4NAPD39y/w98mTJ5GamlrkecnJydi+fTsAoGHDhvjkk0/KdF1fX1/Url27vGFXCqVSiXbt2gmvHzx4YMJoiIiIqCpJojALCQmBRqOBUqmEn58fAMDPzw8KhQIqlQp79uwp8ry9e/fiyZMnAIDRo0fD2tq62mKuTIZj51qt1oSREBERUVWSRGG2a9cuAEDXrl3h4uICIH+4sWPHjgCKH848d+6csN27d++qDbIKRUdHC9vOzs4mjKTqWUJT6M/jnBxkZ2cX+4eIiEguRD/5//r164iKigLwf8OXev7+/rhw4QKuXr2KGzdu4JlnnimwX3+eUqmssoXoEhISChROxSnvDQLHjh1DTEwMgPxitGHDhuVqRyoG298t9N66338u8Zz58+dXVThERETVSvSFmb43zNraGoMGDSqwb8iQIfjqq6+gUqkQHByM//3vfwX2p6WlAQBq1apV4pIaSUlJwrFPq1OnjtBLV5QffvgBP/zwQ6mfQ18kGsNwuYxly5YJ77/xxhtGt0FERETSI+rCTK1WC/PH+vXrV+huSgcHB/Tp0weHDh1CSEgIPvzwQyiV/zc6qx/msrGxKfE6K1euxPr164vcN3LkSCxevLgiH8MoHh4eJe4fM2YMXn755SqPg8RNqVQKw9mG/9ZJnJgvaWG+pEPOuRJ1YXby5EkkJycDKDyMqefv749Dhw4hPj4eoaGh6NGjh7DPzs4Ojx49wuPHj6ssxkWLFiEgIKBK2raxsUGnTp0wfvx4+Pr6Vsk1SFrMzc1lt2aPnDFf0sJ8SYeccyXqwkw/jOng4FDs5P1+/fqhdu3aePToEYKDgwsUZg4ODnj06BEyMzORl5dX7HBmYGAgAgMDhdf37983ajX/ymS48r+ZmRns7Ozg5ORU7Y+DMrX9WU0Lvbd5xgtwqmNrgmiIiIiql2gLs8zMTBw5cgQAkJ6eXmAtr+IcOHAA8+fPh61t/g9xT09P3L17F1qtFlFRUfDy8qrSmCuiMp4eIAd5KFyI2tjaws7OzgTREBERVS/RDszu27dPWIPMWDk5OThw4IDwukuXLsL2iRMnKi02IlNRqVS4cuUKrly5ApVKZepwqBTMl7QwX9Ih51yJtsdMP4zp5OSE2bNnl3r8119/jfj4eOzatQsjRowAAAwdOhTffPMN8vLy8Ndff2Hy5Mkmf8QSUUXodDrhm5BOpzNxNFQa5ktamC/pkHOuRFmY3bt3DxcuXAAADBo0CMOGDSv1nEuXLmH9+vU4c+YMEhIS4OLiAkdHR4waNQqbN2/GgwcP8M0332DOnDlVHT4RERFRuYhyKHPXrl1CBfz02mXF0R+n1WqFJwUAwP/+9z/hgecbNmzAZ599Vupq8Y8ePSpP2EREREQVIsoeM31hVb9+fXTu3Nmoczp16gQnJyckJSVh9+7dePPNNwEA9vb2+O233/Dmm28iNjYWmzdvxv79+/HCCy+gc+fOcHZ2hq2tLbKysnDnzh2cOnUKBw8eFNot7fmaxq78b29vj0aNGhn1WYiIiKhmEl1hdv78edy9m/9YngEDBhi9cJxSqcTAgQOxadMmxMTEICIiQriTs1mzZti2bRsWLVqE3bt3Iy0tDRs2bMCGDRuKba9+/fp44403MGHChBKva+zK//3798fPP5f8aCEiIiKq2URXmBkOQxo7jKn3/PPPY9OmTQDybx4wXGKjTp06WLx4MaZPn469e/fizJkzuHPnDtLT06HRaFC7dm00bNgQ7dq1Q69evdCnTx9YWFhUzociIiIiMoJCJ7fbGahM8vLyEB4eDgBo0MwN/kt2mziiwvYGjoKLA9cxAwrmy8vLq8RnwJLpMV/SwnxJh1hyVRVxiK7HjIiKZ25ujpYtWwrbJG7Ml7QwX9Ih51zJ69MQyZxSqUTdunVNHQYZifmSFuZLOuScK1Eul0FERERUE7HHjEhCDFe7trCwgEKhMHFEVBLmS1qYL+mQc65YmJGgnr019gaOMnUYhTjWtjF1CKKhUqlEMeGVjMN8SQvzJR1yzhULMxKYKZVwcSh5QV0iIiKqOpxjRgKunEJERGRaLMxIoFarTR0CERFRjcbCjIiIiEgkWJgRERERiQQLMyIiIiKRYGFGREREJBJcLoMEcnvemBxZWFjAy8tL2CZxY76khfmSDjnnij+JSSCnlZPlSqFQyGohRbljvqSF+ZIOOeeKQ5lEREREIsEeMxJotFokpGcbdaxjbRuYKVnXVzetVouMjAwAQJ06daBkDkSN+ZIW5ks65JwrFmYkSM3Khf+S3UYduzdwFFwc7Ko4InqaWq3GrVu3AMjv+XByxHxJC/MlHXLOlXxKTCIiIiKJY2FGREREJBIszIiIiIhEgoUZERERkUjUyMn/OTk52LVrF44cOYLIyEikp6dDp9PB3t4erq6ucHd3R8eOHdG7d280bNiwwLmzZs1CUFBQma4XHByM1q1bC6/feecdHDx4EACwdu1a9OjRo8TzDx8+jLfeegsAMGLECCxZsqRM1yciIiJpqHGF2cWLF/Hhhx/iwYMHhfalpaUhLS0NERER2LlzJxwdHfHff/9Vegzz5s1DaGgoHj16hHnz5iEkJATW1tZFHpuZmYnPPvsMAFC/fn3Mnj270uMhIiIicahRhdnt27cxefJkZGfnr9Xl6+uLQYMGoUWLFrCwsEBaWhoiIyNx6tQphIaGltre6tWr4ezsXOpxzZo1K/Da2dkZM2fOxNy5c3H37l38+OOP+OSTT4o89+uvv0ZiYiIAYM6cOXBwcCj1eiRfCoVCePwIn9QgfsyXtDBf0iHnXNWowuz7778XirJFixYhICCg0DE+Pj6YPHkyUlNTsW/fvhLba968ORo3blyuWEaPHo2QkBCcPXsW69atw7Bhw9CuXbsCx4SGhuKvv/4CkF9EDh06tFzXIvmwsLBA+/btTR0GGYn5khbmSzrknKsaM/lfo9Hg+PHjAIB27doVWZQZqlevHsaNG1dl8SgUCnz55ZewsrKCRqNBYGAg1Gq1sD83Nxdz584V5r7phzOJiIhIvmpMYZaamorc3FwAhYcWTaVZs2Z45513AACRkZFYtWqVsG/ZsmW4c+cOAGDmzJlwcXExSYxERERUfWpMYaYfiwaAmzdvmjCSgl5//XW0adMGAPDTTz/h1q1biIiIwNq1awEAXbp0wZgxY0wYIYmJWq3GvXv3cO/evQI9rCROzJe0MF/SIedc1ZjCzMHBAa6urgDye6dWrlwJrVZr4qgAc3NzfPnllzAzM0NeXh7mzJmDOXPmQKPRwMrKCgsWLBDdxEZLaPA4JwfZ2dnCH6oeWq0WiYmJSExMFMW/XyoZ8yUtzJd0yDlXNWry//jx44U1wJYuXYotW7bA19cXnTp1gpeXF5o0aVKm9mJjY5GTk1PiMTY2NqW227ZtW0yaNAmrV6/G+fPnhfffeecdtGjRokwxVYfB9nex7vefC7w3f/58E0VDREQkHzWqMJs0aRJu3LiBHTt2AADi4uKwYcMGbNiwAQDg6OiIrl27ws/PD/369Su1p2ry5MmlXrNr165C+yV57733sHfvXjx8+BAA0KZNG7z++uulnkdERETyUWOGMgFAqVRi4cKFWLNmDXr37g1z84J1aXJyMvbu3Yvp06fjxRdfxN27d6sttjt37iA5OVl4Xa9evULxERERkbzVyJ/8Pj4+8PHxQVZWFs6fP4/w8HBEREQgLCwMmZmZAICIiAi88sor2LlzZ7GLyB4+fLjc65gZ0i+XoVKphPdOnjyJkJAQ+Pn5Vbh9IiIikoYaWZjp2dvbo0+fPujTpw8AIC8vDyEhIViyZAkyMjKQlJSEH3/8EV999VWVxrF+/XqEh4cDyB8eDQkJQWJiIhYuXIhevXqhbt26VXr9stqf1RSbZ7wApzq2pg6FiIhIVmrUUGZpLC0tMWrUKCxdulR47+DBg1V6x8e9e/fw448/AgDc3d0xY8YMzJ07F0D+2mtifGB5HsxgY2sLOzs74Q8RERFVHAuzIvTu3RsNGzYEAGRkZCA9Pb3KrjVv3jw8fvwYSqUSX331FSwsLPD8889jwIABAICgoCCcPn26yq5P0qJUKuHg4AAHBwcolfzvK3bMl7QwX9Ih51zJ69NUImMeTl5RO3bswKlTpwAAEyZMKPDcr3nz5sHe3h5A/lIUT548qfJ4SPzMzc3h5uYGNzc33hwiAcyXtDBf0iHnXLEwK8Ljx49x48YNAPnz0KpijldycrIwTOnq6ooPPvigwH4XFxd89NFHAPLv2FyxYkWlx0BERETiUmMKs+zsbIwePRpHjx4tcc6YVqvFggULhNXsfX19q2Tl/S+++AIZGRkAgM8//xy2toUn0o8dOxadOnUCAKxZswaRkZGVHgcRERGJh7z6/0px5coVTJs2DS4uLhgwYAA6dOiARo0awd7eHo8ePcK1a9ewY8cOREdHAwBq1aqF999/v9j2jFn5HwCcnJwK9LodOnQI//zzDwBg+PDh6N27d5HnKRQKLFiwACNGjIBKpcKcOXOwbds22Y2nk/FUKpXwrFc3N7cCz4Al8WG+pIX5kg4556rGFGbm5uZwcnJCUlISEhISsHHjRmzcuLHY45s3b46lS5eWuE6ZMSv/A8Ds2bMxadIkAEBmZiY+//xzAPmLyM6ePbvEc5955hlMnToVK1asQHh4ODZs2IBXX33VqOuS/Oh0OqE3V6fTmTgaKg3zJS3Ml3TIOVc1pjCzsrLCv//+i0uXLuHUqVO4fPkybt++jZSUFDx58gQ2NjZwdnaGp6cn+vfvj+effx6WlpaVHsfXX3+NxMREAMCnn35q1Py1qVOnYt++fbh58yZ++OEHDBw4EI0aNar02IiIiMi0akxhBuTfXtupUydh3lZ5LF68GIsXLy73+QsWLMCCBQvKdI6lpSX27t1b7msSERGRNHCyEhEREZFIsDAjIiIiEgkWZkREREQiwcKMiIiISCRq1OR/Klk9e2vsDRxl1LGOtW2qOBoqipmZmbCEi5mZmYmjodIwX9LCfEmHnHPFwowEZkolXBysTR0GlcDMzAwuLi6mDoOMxHxJC/MlHXLOFYcyiYiIiESChRkRERGRSHAokwQqlapKnnZAlScvLw/h4eEAAC8vL+ZL5JgvaWG+pEPOuWKPGREREZFIsDAjIiIiEgkWZkREREQiwcKMiIiISCRYmJHA3Jz3ghAREZkSCzMSKBQKU4dARERUo7GLhAQarRYJ6dllOsextg3MlKzviYiIKgMLMxKkZuXCf8nuMp2zN3AUXBzsqigiepq5uTk8PDyEbRI35ktamC/pkHOu5PVpiGROqVTC3t7e1GGQkZgvaWG+pEPOueIYFBEREZFIsMeMSEK0Wi1ycnIAALa2tlByfp+oMV/SwnxJh5xzJZ9PQlQDqNVqREVFISoqCmq12tThUCmYL2lhvqRDzrmSVY9ZaGgoJk6cKLy2tbXFqVOnYGNjU+J5ubm58PHxQVZWlvDe+vXr0a1btxLPO3v2LCZMmCC83rx5Mzp16lTmOA1ZW1vDwcEBnp6eGDhwIPz9/WX1cFYiIiIqnqx7zHJycnDo0KFSjzt8+HCBosxYQUFBBV4HBweXuY2n5ebmIj4+HseOHUNgYCACAgJw//79CrdLRERE4ifbwszKygoAsGvXrlKP1R+jP8cYubm5+OeffwDk98wBwP79+5GXl1emOMeOHYuQkBDhz9q1axEYGIgGDRoAAGJiYjB9+nRoNJoytUtERETSI9vCzNfXFwBw6tQpJCUlFXtcSkoK/vvvPwBA//79jW7/4MGDyM7OX4x1zpw5AICMjAwcOXKkTHHWr18f7u7uwp8ePXpg4sSJ+Pvvv+Hq6goAiI6OxsGDB8vULhEREUmPbAszHx8fODk5QaPR4O+//y72uD179kCtVsPJyQk9e/Y0un39sKWHhwdGjRqFFi1aFHi/ouzt7TF9+nTh9alTpyqlXSIiIhIv2RZmZmZmGDZsGICShzP1+1544QWYmZkZ1XZiYiJOnz4NAPD39y/w98mTJ5GamlruuA3pVzUGgPj4+Eppk4iIiMRLtoUZAAwfPhwAcO3aNcTExBTaf+PGDVy9erXAscYICQmBRqOBUqmEn58fAMDPzw8KhQIqlQp79uyphOgBCwsLYVtuj5wgIiKiwmRdmLVp0watWrUCUHSvmf49d3d3tG7d2uh29ed17doVLi4uAIAmTZqgY8eOACpvOPPmzZvCtn6+mRhYQiP8eZyTg+zs7AJ/qOpYWlrC29sb3t7eXEZFApgvaWG+pEPOuZJ9N8zw4cPx7bffYs+ePfjoo4+gUCgAADqdDiEhIcIxxrp+/TqioqIA/N/wpZ6/vz8uXLiAq1ev4saNG3jmmWfKHbdGo8Hq1auF14MGDSp3W5VtsP1dYXvd7z8X2j9//vzqDIeIiEg2ZN1jBuQXS0qlEg8fPkRoaKjwfmhoKB4+fFhgONIY+t4wa2vrQsXSkCFDhOHH8vaapaam4vTp0xg/fjyuXbsGIL8o69y5c7naIyIiIumQfWHm4uIirOBvOJyp3+7evbswHFkatVotzB/r169foSfbOzg4oE+fPgDy56FptdpS21yxYgU8PDyEPz169MCkSZNw4cIF2NjY4LXXXsPSpUuNio/kT6PRICEhAQkJCVzbTgKYL2lhvqRDzrmSfWEGACNGjAAAHDhwALm5uQUWhy3LMObJkyeRnJwMoPAwpp7+/fj4+AI9dOXh6emJCRMmFLgJgGo2jUaD+/fv4/79+7L7ZiRHzJe0MF/SIedcyX6OGQAMHDgQn332GbKysnD48GHodDpkZ2fD1tYWzz//vNHt6IcnHRwc0Lt37yKP6devH2rXro1Hjx4hODgYPXr0KLHNsWPH4pVXXgGQ/w8tPj4e//zzD3bt2oWLFy9iwoQJ2L59O+rVq2d0nFVtf1ZTYXvzjBfgVMfWhNEQERHJR40ozOzs7DBgwACEhIRg165d0Ol0AIABAwYIj1MqTWZmprCqf3p6Otq1a1fqOQcOHMD8+fNLvIZ+5X+91q1bo1+/fujWrRtmzZqFuLg4BAYG4pdffjEqzuqQh/9b783G1hZ2dnYmjIaIiEg+asRQJvB/w5n//fefsIq+/j1j7Nu3D0+ePCnTNXNycnDgwIEynaM3cuRI4eaCI0eOCAvaEhERkXzViB4zAOjRowecnJyE52Y6OzuXOsxoSD+M6eTkhNmzZ5d6/Ndff434+Hjs2rWrTAWgoRkzZuDQoUPQaDT4/vvvyxQvERERSU+NKczMzMwwfPhwrF+/HkD+pH+l0rgOw3v37uHChQsA8peu0D/qqSSXLl3C+vXrcebMGSQkJBh956ehFi1aYMiQIdizZw8uX76M//77Dz4+PmVuh4iIiKShxgxlAsDMmTMRHh6O8PBw/O9//zP6PMN5acYu9Ko/TqvVlvisztJMnTpVWBRXTPPMiIiIqPLVqMKsvPSFVf369Y1e6LVTp05wcnICAOzevbvc13Z3d4evry8A4Ny5cwgLCyt3WyR9CoUCdnZ2sLOzEwp2Ei/mS1qYL+mQc65YmJXi/PnzuHs3/xFEAwYMMHr4U6lUYuDAgQCAmJgYRERElDuGadOmCdvsNavZLCws4OnpCU9PT65vJwHMl7QwX9Ih51yxMCuF4TBkWZ9XabhGWkUebN6+fXthbtnJkydx5cqVcrdFRERE4qXQ6SdPUY2Ul5eH8PBwAECDZm7wX1K2Yde9gaPg4sB1zIiIqOYx/Bnq5eUFS0vLCrdZY+7KJJIDtVqNO3fuAACaNWsGc3P+FxYz5ktamC/pkHOuOJRJJCFarRbp6elIT0+HVqs1dThUCuZLWpgv6ZBzrliYEREREYkECzMiIiIikZDPoCxVWD17a+wNHFWmcxxr21RRNERERDUPCzMSmCmVcHGwNnUYRERENRaHMknAlVOIiIhMi4UZCdRqtalDICIiqtE4lEkCuT1vTI6USiWcnZ2FbRI35ktamC/pkHOuWJiRQE4L9MmVubk5mjRpYuowyEjMl7QwX9Ih51zJq8wkIiIikjAWZkREREQiwbErEqhUqkp5ACtVHZVKhevXrwMAWrduDQsLCxNHRCVhvqSF+ZIOOeeKhRkJOMdM/HQ6HVQqlbBN4sZ8SQvzJR1yzhWHMknAuzKJiIhMi10kJNBotUhIz6726zrWtoGZzG53JiIiKg8WZiRIzcqF/5Ld1X7dvYGj4OJgV+3XJSIiEht2UxARERGJBAszIiIiIpFgYUZEREQkEpxjRiQh5ubmaNmypbBN4sZ8SQvzJR1yzpXkPk1oaCgmTpxY5D5ra2s4ODjA09MTAwcOhL+/f4kLpnp4eJTp2p6enti1a1eB93bu3InZs2cDABYtWoSAgIAytenr64u4uLhC75ubm8Pe3h4tW7ZEz5498dJLL8HFxaVMbZP8KJVK1K1b19RhkJGYL2lhvqRDzrmS1VBmbm4u4uPjcezYMQQGBiIgIAD37983dVjlolarkZ6ejgsXLmDFihUYOnQoDh48aOqwiIiIqApJrsfM0NixY/HKK68Ir1NSUhATE4PVq1cjPj4eMTExmD59OoKDg2FmZlZsO+3atcOiRYtKvZ61tXWlxF0UZ2dnrF69WnitVqsRFxeHHTt24OjRo8jKysKMGTOwbds2tGnTpsriIHEzXO3awsKCiwKLHPMlLcyXdMg5V5IuzOrXrw93d/cC7/Xo0QMBAQHw9/dHXFwcoqOjcfDgQQwePLjYdmxtbQu1U90sLCwKxdCmTRsMHDgQixcvxh9//AGVSoVffvkFy5cvN1GUZGoqlQrh4eEAAC8vLz7bVOSYL2lhvqRDzrmS1VCmnr29PaZPny68PnXqlAmjqbh3331X6K07efIktFqtiSMiIiKiqiDLwgwoOLE/Pj7ehJFUnJ2dHdzc3AAAOTk5SE9PN21AREREVCVkW5hZWFgI23K4ldbw87DHjIiISJ6kX7EU4+bNm8K2q6urCSOpOLVajVu3bgHIL9AcHBxMG1AlsoQGj3NykP1/dSfs7PjcTCIiqplkWZhpNJoCdzgOGjSoxONzcnIQHR1darsNGjRA7dq1KxxfWW3duhWPHj0CAHh7e8uiB1BvsP1drPv95wLvzZ8/30TREBERmZZ8fsIDSE1NRVRUFJYtW4Zr164ByC/KOnfuXOJ5ERER8PPzK7X98iwgW1765TJ27tyJ33//XXh/8uTJ1XJ9IiIiqn6SLsxWrFiBFStWFLnPxsYGL7/8Mj766KNqjqp84uLiSnwSgUKhwPvvv4/nnnuuGqMiIiKi6iTpwqwknp6emDBhQoFJ88Xp2rUrNmzYUA1RlZ29vT26d++O1157rdSeP5I/CwsLeHl5CdskbsyXtDBf0iHnXEm6MDNc+V+j0SA+Ph7//PMPdu3ahYsXL2LChAnYvn076tWrZ+JIS/f0yv9mZmaoVasWnJycZLWi8dP2ZzXF5hkvwKmOralDkQSFQiGrhRTljvmSFuZLOuScK0kXZk+v/N+6dWv069cP3bp1w6xZsxAXF4fAwED88ssvJozSOEWt/F8T5MEMNra2vBOTiIgIMl3HbOTIkcKdmEeOHMHp06dNHBFR5dBqtUhLS0NaWhrXs5MA5ktamC/pkHOuZFmYAcCMGTOEB5d///33Jo6GqHLo17S7desW1Gq1qcOhUjBf0sJ8SYeccyXbwqxFixYYMmQIAODy5cv477//TBwRERERUclkW5gBwNSpU4WJ81KYZ0ZEREQ1m6Qn/5fG3d0dvr6+OHz4MM6dO4ewsLAil5wwduV/AHjmmWegVBZdz54/f96oNnx9fWX1WCUiIiKqHLIuzABg2rRpOHz4MID8XjPDJSn0jF35HwDOnTtX7GOZtm/fju3bt5faRnBwMAszIiIiKkTWQ5kA0L59e/j4+AAATp48iStXrpg4IiIiIqKiSa7HrFu3boiKiirTOWvWrCny/bK2U5SAgIAKPT/zyJEjFY6BiIiI5EFyhRlRTaZQKITHj8j5iRBywXxJC/MlHXLOFQszIgmxsLBA+/btTR0GGYn5khbmSzrknCvZzzEjIiIikgoWZkREREQiwaFMEtSzt8bewFHVfl3H2jbVfk2pUqvVePjwIQCgYcOGMDfnf2ExY76khfmSDjnnSj6fhCrMTKmEi4O1qcOgEmi1WiQmJgIAXFxcTBwNlYb5khbmSzrknCsOZZJAp9OZOgQiIqIajYUZCdRqtalDICIiqtFYmBERERGJBAszIiIiIpFgYUZEREQkEizMiIiIiESCy2WQQE7rwMiVUqmEg4ODsE3ixnxJC/MlHXLOFX8Sk0BuD4KVI3Nzc7i5uZk6DDIS8yUtzJd0yDlXLMxIoNFqkZCeXaE2HGvbwExmv70QERFVFxZmJEjNyoX/kt0VamNv4Ci4ONhVUkREREQ1CwszIglRqVS4efMmAMDNzQ0WFhYmjohKwnxJC/MlHXLOFQszIgnR6XTIzs4WtkncmC9pYb6kQ8654mQgIiIiIpFgYUZEREQkEizMiIiIiERC8nPM8vLycODAAfz777+4cuUK0tLSkJWVBXt7e7i6usLLywuDBg1C9+7dS12E7vjx4zh06BDOnz+P5ORkPH78GPXq1UPTpk3x3HPPwd/fHy4uLsWeP2zYMNy4cQMODg44c+ZMieuCjRgxAtevXwcAvPXWW3j//feLPfbs2bOYMGECAOCNN97AzJkzS/wcREREJE2SLswOHDiAxYsXIy4urtC+9PR0pKen4+rVq9iyZQuaN2+O2bNno2/fvoWOjYmJwdy5c3Hx4sVC++Lj4xEfH4+zZ8/i559/xptvvolp06YVWXR5e3vjxo0bSE9Px40bN9CqVasi487MzERUVJTwOiwsrMTPabi/S5cuJR5LRERE0iXZwuynn37CsmXLhNc+Pj7w9fWFm5sbateujYyMDNy+fRtHjhzBqVOnEBsbi++//75QYRYWFobp06fj0aNHAIC2bdti5MiR8PDwgK2tLRISEnDixAkEBwcjJycHP/zwA2JiYrBkyZJCt+d26dIFW7duFdotrjC7cOECtFotzMzMoNFocOXKFahUqmJv9z1//jyA/MdOeHt7l+vrRUREROInycJsx44dQlFWv359/PDDD+jatWuh43r27Ilx48YhOjoaixYtQmpqaoH98fHxePvtt/Ho0SMoFArMnDkTr7/+eoHesHbt2qF///6YPHky3n77bURFReHvv/9Gw4YNCw0pGvZmhYWFYezYsUXGr+8Be+655xAaGoqcnByEh4ejU6dOhY7VaDRCT56Hhwdq1aplzJeIZMrMzAyNGzcWtkncmC9pYb6kQ865ktzk/4SEBCxYsAAAYGtriw0bNhRZlBlyd3fH6tWr8frrrxd4f+7cuUhPTwcAvP/++5g8eXKx88KaNGmCP/74A87OzgCA1atX4/LlywWOadCgAVxdXQGUPDyp39e1a1c8++yzJR5//fp1Ya2Wzp07l/QxqQYwMzODi4sLXFxcZPfNSI6YL2lhvqRDzrmSXGG2du1aPH78GADw3nvvGf0QU6VSieHDhwuvIyMj8e+//wLIL9zefPPNUtuoX78+Zs+eDSB/QbtVq1YVOkbfaxYfH4/79+8X2v/kyROEh4cDyC+09EOT+uHKp3F+GRERUc0hqcJMp9MhKCgIQH5v2ejRo8vdlr4dABg/frzRFfegQYOEOzMPHTqEjIyMAvsNe7WK6gW7fPkyVCoVbGxs0KZNG6Ew0887e5phwcbCjIiISN4kVZjFxMQgLS0NQP4dkPb29uVu69y5c8J2v379jD7PzMxMuIFAq9UWupPz6XlmT9O/1759e5ibm+PZZ5+FmZkZHj16hOjo6ELH6wuzli1bol69ekbHWR0soSn053FODrKzswv9ocqRl5eH8+fP4/z588jLyzN1OFQK5ktamC/pkHOuJDX5PzIyUthu27ZthdrSF0FOTk7CvDFjtWnTpkBMhnd6Nm/eHE5OTkhKSiqyMNMXWvqeNTs7O3h6euLq1asICwuDp6encOytW7eQkpICQJy9ZYPt7xZ6b93vPxd57Pz586s6HCIiIsmTVI+ZfqI+kD/fq7wyMzOhUqkAAI6OjmU+3/Acw5j09MOTt2/fFgoroOAdlobLXui3ny7kDIcxOfGfiIhI/iRVmBkOidnY2FRKO7a2tmU+3/CcrKysQvuLG868du0asrOzYWZmhg4dOgjvF1eYceI/ERFRzSKpwszOzk7Y1t+ZWdF2cnJyyny+4TlFzXMr7gYAfQ+Yp6dngRj0xyclJeHOnTuFznV1dUXDhg3LHCcRERFJi6TmmDk4OAjbhkOEZWVvbw9zc3Oo1WokJyeX+XzDcwxj0nN3d0ft2rXx6NGjAsOR+kLr6dX7HR0d0axZM9y5cwdhYWFo1qwZEhIShOU2xDqMuT+raaH3Ns94AU51yt4LSURERBLrMTOcGH/16tVyt6NQKODh4QEgv5cqKSmpTOdfu3atyJj0DB+dFBkZKQx36ou0oh6r9PRwphSGMfNgVuiPja0t7OzsCv0hIiKi0kmqMGvVqhXq1q0LIL/IKWp+l7EMe6GOHDli9HlarRbHjx8HkF+AFfUYJcP2NRoNLly4gJs3bwqPhCqqB0zfjr4gM1zOQ6w9ZkRERFS5JFWYKRQKjBw5EkD+PK+//vqr3G0FBAQI2xs3boRGozHqvH/++Qfx8fEAgAEDBqB27dpFHmdYTJ0/f14ouJo1a1bknaD6HrO7d+8iMTFR6F1zcnJCixYtjIqN5M/c3BweHh7w8PCAubmkZiLUSMyXtDBf0iHnXEmqMAOASZMmCXdkLlu2DDdv3jTqPK1Wi927dwuvPT090atXLwBAVFQUVq9eXWobqampWLx4MYD8IvGNN94o9ti2bdsKcZ47d07oAStqGBMouIDsoUOHEBMTU+LxVDMplUrY29vD3t4eSqXk/vvWOMyXtDBf0iHnXEnu07i4uGDu3LkA8nvNJkyYgLNnz5Z4zo0bN/DGG28UKr4WLFgg9Hh99913WLt2LXQ6XZFtxMXF4bXXXhN6yyZPniw8gLwoFhYWwpIY4eHhCA0NBVByoaUfzlyzZo0Qh1jnlxEREVHlk2T/36hRoxAfH49ly5YhJSUFEyZMQK9eveDr6ws3NzfUrl0bGRkZuH37No4fP44TJ05Ao9EUmqjfqFEjrFixAm+99RaysrKwaNEihISEYOTIkfDw8ICNjQ0SExNx4sQJBAcHC8tkDB06FB988EGpcXbu3BmnT59GXl4eEhMTAZRcmHl7e+PQoUO4d+9egTaI9LRarfDv0NbWVna/KcoN8yUtzJd0yDlXkizMAODtt99Gq1atsHjxYsTFxeHkyZM4efJksce3atUKM2fOLPR+t27dsGnTJsydOxeXL19GREQEIiIiimzDxsYGU6ZMwVtvvQWFQlFqjE8XVfXr1y9xvtjTRVudOnWEu0eJAECtViMqKgoA4OXlBUtLSxNHRCVhvqSF+ZIOOedKsoUZADz//PPo27cv/vnnH/z7778IDw9HamoqsrOzYW9vD1dXVzz77LMYNGgQunXrVmwx5eHhgW3btuHYsWM4ePAgLly4gKSkJOTm5qJu3bpo3rw5evfujeHDh8PFxcXo+Dp06AALCwvh8U/F3cGp16ZNG9jY2AiL53bq1MmoApCIiIjkQdKFGQBYWlrCz88Pfn5+FW6rb9++BR5IXlHW1tbF9r4VxcLCApcuXaq06xMREZG0yGdQloiIiEjiWJgRERERiQQLMyIiIiKRYGFGREREJBKSn/xPlaeevTX2Bo6qUBuOtW0qKRoiIqKaR6Erbql7qhHy8vIQHh4OQH5rwRAREVWlqvgZyqFMIiIiIpFgYUZEREQkEpxjRgKNRmPqEKgUGo0GycnJAABHR0eYmZmZOCIqCfMlLcyXdMg5VyzMSKDVak0dApVCo9Hg/v37AIC6devK6puRHDFf0sJ8SYecc8WhTCIiIiKRYGFGREREJBIszEhgbs6RbSIiIlNiYUYChUJh6hCIiIhqNHaRkECj1SIhPduoYx1r28BMybqeiIioMrEwI0FqVi78l+w26ti9gaPg4mBXxRERERHVLCzMiCREoVDAzs5O2CZxY76khfmSDjnnioUZkYRYWFjA09PT1GGQkZgvaWG+pEPOueIkISIiIiKRYGFGREREJBIcyiSSELVajTt37gAAmjVrxrXnRI75khbmSzrknCtRf5LQ0FBMnDixyH3W1taoV68eWrdujSFDhmDIkCGlJubu3bvYvXs3Tp48ibi4OKSlpcHOzg7Ozs7o1q0bBg8ejM6dO5ca14QJE3D27Nki95mbm6NWrVpwc3NDnz598NJLL8HBwaHYtmbNmoWgoKBC7ysUCtjb26NRo0bw9vbGSy+9hNatW5caG8mbVqtFeno6AKBJkyamDYZKxXxJC/MlHXLOlagLs5Lk5ubiwYMHePDgAQ4fPox169bhl19+gZOTU6Fj8/Ly8O2332LTpk1QqVQF9qWnpyM9PR3R0dHYsGED+vTpgy+++AINGjQoV1xqtRppaWkICwtDWFgY1q5di+XLl8Pb27tM7eh0OmRmZiIqKgpRUVHYsmUL3nzzTcyYMaNccREREZH4SaYwGzt2LF555RXhdU5ODiIiIrBmzRrExcUhPDwcb731FrZt21bg1tmsrCy89dZbCA0NBQA4ODhg5MiR6NGjB+rXr4/MzExERERgx44duH37No4fP44xY8ZgzZo1cHNzKzWukJCQAq9VKhXu3buHXbt24ciRI0hJScH06dOxf/9+1KtXr8S2Vq9eDWdnZwD5vw2kpKTg2LFj2LRpE9RqNX799Vc4Oztj3LhxRn/diIiISDokU5jVr18f7u7uBd7r0KED/Pz8MHr0aNy5cwdXrlzB0aNH4evrKxwTGBgoFGXdunXD999/j/r16xdop0ePHpg0aRKWLl2KP/74A/Hx8Zg2bRqCgoJgb29fYlxPxwQAbdu2xeDBg/HJJ58gODgYGRkZ+OuvvzB16tQS22revDkaN25c4D0fHx/06NED06dPBwCsWLECL7/8MszMzEpsi4iIiKRH8ndl1qlTB2+++abw+sSJE8L23r17sX//fgCAp6cnfvvtt0JFmZ6FhQVmzZqF0aNHA8ifj7Z06dIKxTZ58mRhOzw8vNzt+Pr6CnPfUlNTcfXq1QrFRUREROIk+cIMANq3by9sP3jwQNj+/fffhe0FCxbAxsam1LZmzZoFR0dHAMCOHTuQmppa7rgMe7/y8vLK3Q5Q/GckIiIi+ZBFYWZ4N6ZGowEAREZG4tq1awCAjh07FihsSmJvb4+AgAAAwJMnT7Bnz55yx2VYQDVs2LDc7QAFP6NWq61QW8awhKbEP49zcpCdnW3UHyIiIjKOZOaYlSQ6OlrY1k+eDwsLE97r169fmdrz9fXFypUrhXaKW7KjNKtXrxa2+/fvX6429Ir6jFVpsP3dEvev+/1no9uaP39+RcOh/0+pVAr5Vypl8XuVrDFf0sJ8SYeccyX5wkytVuOPP/4QXnft2hVAfo+ZXps2bcrUZuvWraFUKqHVahEVFVXisYYFE5B/V2ZcXBx2796NgwcPAgCGDh2K5557rkwxGIqMjBTmztnY2MDLy6vcbZG0mZuby27NHjljvqSF+ZIOOedKsoWZfrmM5cuX49KlSwAAV1dXDB06FACQlpYmHFvU2mYlsba2hp2dHTIzM4UF7Irj5+dX7L4WLVrgzTffxMiRI8t0fSB/HbPk5GQcO3YMS5cuFYZoJ0yYACsrqzK3R0REROInmcJsxYoVWLFiRbH769evj59++gmWlpYAUGBuk62tbZmvZ2tri8zMTGRlZZU92P/v9u3b2Lp1Kxo3biz05JWktOHOvn374r333it3PERERCRukinMitO4cWMMGjQIkydPLrAUhp2dnbCdk5NT5nb155S2jtnTQ51arRZpaWk4f/48fvrpJ1y6dAmvv/46vv32WwwePLjMcVhYWMDLywtjxozB8OHDCyyeW5X2ZzUtcf/mGS/AqU7ZC16qGJVKhevXrwPIH3K3sLAwcURUEuZLWpgv6ZBzriRTmBmu/K9QKGBlZYW6deuiVq1aRR5ft25dYTspKQmenp5GXys3N1focSvpOZdFUSqVqF+/Pp5//nn4+PggICAAsbGx+PTTT9G9e/cS2zNc+V+pVMLW1haOjo5CL2B1ykPJC9ja2NoWKH6peuh0OuGxYjqdzsTRUGmYL2lhvqRDzrmSzK0M+pX/3d3d0apVKzRt2rTYogwAPDw8hG19VW2s69evC0tSGLZTVnZ2dhg7diyA/KHVf/75p8TjmzdvLnzGZ555Bo0aNTJJUUZERESmIZnCrKy6dOkibB85cqRM5xoeb9hOebRs2VLYfvoOTiIiIiJDsi3MPD090bp1awDAxYsXjX4kUlZWFoKCggAAVlZWGDZsWIXi0N9NCeQv7UFERERUHNkWZgAwZcoUYXvevHnIzc0t9Zyvv/4aSUlJAIBRo0ahXr16FYohIiJC2K7o6v9EREQkb7IuzIYNG4bnn38eAHDt2jVMnz692GdfqlQqfP3119i6dSsAoGnTpvjoo48qdP24uDhs3LhReN2nT58KtUdERETyJpm7Mstr4cKFSEtLw7lz53Dq1CkMGTIEAQEB6N69OxwdHZGVlYWIiAhs374dt27dAgA0aNAAv/zyS6lLZQCF541ptVqkp6cjLCwMGzZsEBao9fPzE4ZWiYiIiIoi+8KsVq1aWL16Nb755hts3rwZ6enpWLNmDdasWVPk8b1798aCBQuMHnYsaeV/vaFDh2LhwoVlipuoKObm5sINJYYPtidxYr6khfmSDjnnSl6fphhWVlaYM2cOJkyYgN27d+PkyZOIi4tDeno67Ozs4OzsjC5dumDIkCEVvgtToVDA1tYWDRs2RIcOHTBixIgKt0mkp1QqC6zRR+LGfEkL8yUdcs6VQie3ldmoTPLy8oQ7Vhs0c4P/kt1Gnbc3cBRcHLjALBER1VyGP0O9vLwqZe3RGtFjRiQXhqtdW1hYVNsjuqh8mC9pYb6kQ865kvVdmURyo1KpEB4ejvDwcOGbEokX8yUtzJd0yDlXLMyIiIiIRIKFGREREZFIcI4ZCerZW2Nv4CijjnWsbVPF0RAREdU8LMxIYKZUwsXB2tRhEBER1VgcyiQBV04hIiIyLRZmJFCr1aYOgYiIqEZjYUZEREQkEpxjRgK5PW9MjiwsLODl5SVsk7gxX9LCfEmHnHPFn8QkkNPKyXKlUCgq5ZEfVD2YL2lhvqRDzrniUCYRERGRSLDHjARardbUIVAptFotMjIyAAB16tSBUsnfrcSM+ZIW5ks65Jwr+XwSqjAWZuKnVqtx69Yt3Lp1i3fRSgDzJS3Ml3TIOVcszEhgZmZm6hCIiIhqNBZmRERERCLBwoyIiIhIJFiYEREREYkECzMiIiIikWBhRkRERCQSXMeMSEIUCoXw+BE+qUH8mC9pYb6kQ865qtLCLDQ0FBMnTixyn7W1NRwcHODp6YmBAwfC39/f6Mcr3L9/HwMGDIBOpwMAfPvtt/Dz8ytzfA8ePMDff/+NU6dO4c6dO0hNTYVWq0WdOnXg7u4Ob29v+Pn5oUmTJkWen5KSgitXruDKlSsIDw9HeHg40tPTAQAjR47E4sWLS41h1qxZCAoKKvS+QqGAvb09GjVqBG9vb7z00kto3bp1mT9jWcjtH7ccWVhYoH379qYOg4zEfEkL8yUdcs6VyXrMcnNzER8fj/j4eBw7dgxr167Fr7/+isaNG5d6bnBwsFCU6V+XpTB78uQJli5dis2bNyMvL6/Q/sTERCQmJuLkyZNYtmwZBg8ejE8++QQNGzYscFzPnj2NvmZZ6XQ6ZGZmIioqClFRUdiyZQvefPNNzJgxo8quSURERKZVbYXZ2LFj8corrwivU1JSEBMTg9WrVyM+Ph4xMTGYPn06goODS13odPfu3QAAW1tb5OTk4PTp00hMTISzs3OpcaSmpmL69Om4dOkSAMDOzg4vvPACunfvjgYNGsDc3BzJycm4cOECDh48iNjYWOzbtw8dOnTApEmTim23UaNGaNmyJU6ePFn6F6MYq1evFj6DVqtFSkoKjh07hk2bNkGtVuPXX3+Fs7Mzxo0bV+5rEBERkXhVW2FWv359uLu7F3ivR48eCAgIgL+/P+Li4hAdHY2DBw9i8ODBxbZz4cIF3LlzBwDw6aefYv78+dBoNAgJCcHkyZNLjEGr1eKDDz4QirJ+/frhq6++Qv369Qsd6+vriw8//BC7d+/G119/XWR7b7/9Nry8vODl5QVHR0fcv38f/fv3LzGGkjRv3rxQj6GPjw969OiB6dOnAwBWrFiBl19+uUpW6TfshSRxUqvVePjwIQCgYcOGMDfnNFExY76khfmSDjnnyuR3Zdrb2wtFBwCcOnWqxOODg4MBAHXr1sWIESPQo0cPAMCuXbtKvdb69esRGhoKAOjVqxdWrFhRZFGmp1QqMWLECOzcuRMeHh6F9r/33nvo168fHB0dS712Rfj6+qJz584A8nv8rl69WqXXI/HSarXCUDufbSp+zJe0MF/SIedcmbwwA1Cg6ImPjy/2uLy8POzfvx8AMGTIEFhYWMDf3x8AEBUVhevXr5d47urVqwEAVlZWWLhwodEVdoMGDYQC0FQMJzk+ePDAhJEQERFRVRFFYaa/5RVAicXSkSNHkJGRAQBCQTZw4EDY2toC+L/etKKcPHkSiYmJAIDBgwfDxcWlomFXK8OvS1X9dpCTk4Ps7OwqaZuIiIhKJ4rC7ObNm8K2q6trscfpC6+mTZuiY8eOAPJvANDP69qzZw80Gk2R5547d07Y7tu3bwUjrn7R0dHCtjE3OZTHypUr8e2331ZJ20RERFQ6kxdmGo1GGGIEgEGDBhV5XGpqqnDH49NLY+h7z5KTk4u9KzIyMlLYbtu2bYVirm6RkZE4ceIEAMDGxgZeXl4mjoiIiIiqgskKs9TUVJw+fRrjx4/HtWvXAOQXZfpJ7k/bs2cPVCoVgP8rxPR8fHzg5OQEoPjhTP3CrwBKnPAvFjqdDklJSfjrr78wadIkoSdwwoQJsLKyMnF0REREVBWq7f7SFStWYMWKFUXus7Gxwcsvv4yPPvqo2PP1BVf79u3RvHnzAvvMzMwwdOhQrFu3DkeOHEFWVhbs7e0LHGM4d8rGxqZ8H6KKlbbURt++ffHee+9VUzRERERU3USx8IenpycmTJhQ4CYAQzExMcISEU/3lun5+/tj3bp1yM3Nxb59+zB69OgC++3s7ITtx48fFyrcxMrCwgJeXl4YM2YMhg8fXqWPTXrzzTeNfiwWmYZSqYSDg4OwTeLGfEkL8yUdcs6VSVb+12g0iI+Pxz///INdu3bh4sWLmDBhArZv34569eoVOlffW2Zubo5hw4YV2X67du3g5uaGmzdvYteuXYUKM30Cgfy5aGIszAxX/lcqlbC1tYWjo2O1FUt2dnYszETO3Nwcbm5upg6DjMR8SQvzJR1yzpXJVv5v3bo1+vXrh27dumHWrFmIi4tDYGAgfvnllwLnabVahISEAMhf6deY9cTCwsJw//79Aqvoe3p6CovXXrt2rdBwqBgUtfI/ERER1Rwm7/8bOXKkcCfmkSNHcPr06QL7T58+jYSEhDK1qdPpCj0JoEuXLsL2sWPHyhcsERERURUSxRyzGTNm4NChQ9BoNPj+++8L9IrphzEtLS2xcOHCUseSV61ahWvXrmH37t14++23hfd79eoFZ2dnJCYmYv/+/fjoo48kt8hsVeOzMsVPpVIJ6/65ubkVOy+TxIH5khbmSzrknCtRFGYtWrTAkCFDsGfPHly+fBn//fcffHx8kJ2djUOHDgHIXxLj6fXLipKUlIRr164hNjYWFy9eFBaitbS0xOTJk7Fo0SI8efIEn376KVauXGnUw8ATEhJw69Ytkz+WiUin0wl3GLOQFj/mS1qYL+mQc65MPpSpN3XqVOGOQ/08swMHDiAnJwdA8QvPPm3QoEFCO0+vaTZx4kR069YNQP4jmt5++22kpqYW25ZOp0NISAgCAgIQFRVVps9DREREVFai6DEDAHd3d/j6+uLw4cM4d+4cwsLChMLKwsICvr6+RrXTsGFDeHl54cqVK9i3bx8CAwOFOw2VSiV++OEHTJs2DZcvX8bRo0cxYMAA+Pn5oXv37mjQoAHMzc2RnJyMS5cu4cCBA7h161ax1woLC8Pdu3eF12lpacL2nTt3sHPnzgLHBwQEGPvlICIiohpINIUZAEybNg2HDx8GACxfvhxnz54FAHTr1g116tQxup1BgwbhypUryMjIwNGjRwv0ttWrVw8bNmzAt99+i82bNyM7OxtbtmzBli1bimxLoVDAz88PQ4YMKbRv+/btCAoKKvK8Cxcu4MKFCwXeY2FGREREJRHNUCaQv6q/j48PAODMmTPQarUAjB/G1DM8vqhHNFlZWSEwMBAHDhzARx99hJ49e6JRo0awtraGlZUVnJ2d0atXL8yYMQOHDx/GN998wxsFiIiIqMopdHKbNUdlkpeXh/DwcAD5i/TyOZziZpgvLy8vLggscsyXtDBf0iGWXFVFHKLqMSMiIiKqyUQ1x4yISmZmZiY8HcKYpV7ItJgvaWG+pEPOuWJhRoKqfEA6VQ4zMzPOd5QQ5ktamC/pkHOuOJRJREREJBIszIiIiIhEgkOZJFCpVLwLSeTEcicSGYf5khbmSzrknCv2mBERERGJBAszIiIiIpFgYUZEREQkEizMiIiIiESChRkRERGRSLAwIyIiIhIJFmZEREREIsF1zEggt+eNyZG5uTk8PDyEbRI35ktamC/pkHOu5PVpqEKUSnagip1SqYS9vb2pwyAjMV/SwnxJh5xzxZ/ERERERCLBHjMSaLVaU4dApdBqtcjJyQEA2NraspdT5JgvaWG+pEPOuZLPJ6EK02g0pg6BSqFWqxEVFYWoqCio1WpTh0OlYL6khfmSDjnnioUZERERkUiwMCMiIiISCRZmRERERCLBwoyIiIhIJFiYEREREYkEl8uo4XQ6nbCtUqmQl5dnwmioNCqVqshtEifmS1qYL+kQS64Mr23487QiFLrKaokkKTs7G5GRkaYOg4iISNI8PT1hZ2dX4XY4lElEREQkEuwxq+G0Wi0eP34MIP9BsAqFwsQRERERSYNOpxMWuLWxsamUJxCwMCMiIiISCQ5lEhEREYkECzMiIiIikWBhRkRERCQSLMyIiIiIRIKFGREREZFIsDAjIiIiEgkWZkREREQiwcKMiIiISCRYmBERERGJBAszIiIiIpFgYUZEREQkEizMiIiIiESChRkRERGRSLAwIyIiIhIJFmZEREREIsHCjIiIiEgkWJgRERERiQQLMyIiIiKRMDd1AFS6uLg4bNiwAceOHUN8fDwsLS3RpEkTDBkyBOPGjYONjU2lXOf48ePYtm0bwsPDkZqainr16sHLywsvvfQS+vTpY1QbarUaf/31F0JCQnDr1i3k5OTA2dkZPXv2xIQJE9CqVatKiVXMpJCv5ORkHD16FGfOnMH169fx8OFDqFQqODg4wMPDA88//zyGDx8Oa2vrSolVzKSQr+JERkZi1KhRUKvVAICRI0di8eLFlRKvGEktVyqVCiEhIdi/fz+io6ORnJwMOzs7ODs7o3379ujVqxeGDBlSKTGLkZTydfHiRWzbtg0XLlxAYmJige+HgwYNwogRI2BpaVkp8ZZGodPpdNVyJSqXI0eOYObMmcjKyipyf/PmzbFy5Uo0a9as3NfQarWYO3cutm/fXuwxo0ePxhdffAGlsvhO1tTUVLz55psIDw8vcr+lpSXmzZuH0aNHlztWsZNCvrZt24bPPvsMGo2mxOs0b94cP/74Izw9Pcsdq9hJIV8ltTtmzBhcuXJFeE/OhZnUchUZGYmZM2ciOjq62GNq1aqFsLCwcscrZlLJl06nw1dffYUNGzaUeK1WrVph5cqVaNSoUbnjNRYLMxG7du0axo4di9zcXNja2mLq1Kno1q0bcnNzsXfvXmzbtg1A/j/wHTt2wN7evlzXWbp0KVauXAkAaNOmDd544w00adIE9+7dw6pVq3Dt2jUAwNSpU/Hhhx8W2YZGo8GECRNw/vx5AMDzzz+P0aNHw8HBAZcvX8Yvv/yClJQUKJVK/Prrr+XuIRAzqeTr559/xo8//ggLCwv069cPPj4+cHNzg52dHe7evYu//voLJ0+eBADUq1cPQUFBaNCgQbliFTOp5Ks469evx1dffYX69esjJSUFgHwLM6nlKjIyEq+++irS09NhZWWF0aNHw8fHBy4uLlCpVIiNjcWJEydw/vx5HDt2rFyxipmU8vXbb7/hu+++AwDY2dnhtddeQ6dOnWBra4vbt2/jjz/+EIprd3d3BAUFwdy8igcbdSRar7zyis7d3V3Xpk0b3YULFwrt//3333Xu7u46d3d33bJly8p1jVu3bunatGmjc3d31wUEBOgeP35cYH9OTo4uICBAiCM2NrbIdv766y8hls8++6zQ/tjYWF2nTp107u7uuoEDB+pUKlW54hUzqeTrjz/+0H3zzTe6lJSUYq+zaNEiIdZZs2aVK1axk0q+ivLw4UNdx44ddR4eHrqgoCAhzk8++aRccYqdlHKVm5ure/7553Xu7u66vn376m7fvl3sNZ88eVKuWMVOKvnKy8vTde7cWefu7q5r27at7tq1a4WOUalUutGjRwvx7tu3r1zxlgUn/4vUlStXhC7uUaNGoWPHjoWOef311+Hm5gYg/7dnlUpV5uusW7dOmJ8yd+7cQnOKbGxsMHfuXAD588fWrl1bZDtr1qwBADg4OODjjz8utL9Zs2aYOnUqAODOnTs4ePBgmWMVMynla9KkSfjf//6HevXqFXudDz/8EE5OTgCAgwcPQqvVljlWMZNSvory+eefIzs7GyNHjkTnzp3LHJeUSC1Xq1evRmxsLJRKJX744Qc0b9682GtW15yl6iSlfN28eROPHj0CAPTr1w+tW7cudIy5ubnwswsALl26VOZYy4qFmUgdOnRI2B41alSRxyiVSowYMQIA8OjRI4SGhpbpGjqdDocPHwYAtGzZEh06dCjyuA4dOqBFixYAgMOHD0P31Oj37du3cfPmTQDA4MGDi53QOXLkSGHb8PPJgZTyZQxLS0t06tQJAJCZmYn09PQytyFmUs7X/v37ceTIkWJ/CZIbKeVKo9Fgy5YtAICePXvi2WefLVMcciClfBkWhI0bNy72ek2bNi3ynKrCwkyk9HO1bG1t0bZt22KP69Kli7B94cKFMl3j/v37SExMLNROUbp27QoASEhIwP3794uM1fC4ojg5OQm/PZY1VrGTUr6MlZeXJ2yXZVK6FEg1X5mZmfjyyy8BADNnzkTdunXLFJMUSSlXFy9eREJCAgDA19dXeP/Jkye4e/cuEhISSr3pRuqklK/mzZtDoVAIbRbn7t27wra+0KtK8vpuKyP6HqimTZuWONGwZcuWhc4x1o0bN4psp7Tr3Lp1q8hYy9LOw4cPkZOTY3SsYielfBlDpVIJXfaOjo5wcHAocxtiJtV8ffPNN0hKSkLnzp2L7Y2QGynlynCYy93dHbGxsXj33Xfh7e2NgQMH4rnnnkO3bt3w8ccfF/hhLydSyletWrUwbNgwAMDRo0cRGRlZ6Hy1Wi3cYGB4fFViYSZCT548QVpaGgCUejdcnTp1YGtrCwCIj48v03UMjy/tOob7Hz58WGw7Li4uJbbTsGFDAPld0WWNV6ykli9jbNu2TfhMgwcPLvP5YibVfJ0/fx7btm2DhYUFPvvsM+E3fTmTWq4MC4xbt25h5MiROHDgQIHhr8zMTOzatQsjRozAqVOnyhSn2EktXwAwe/ZstG3bFiqVCuPGjcOKFStw6tQpXLp0CUFBQRg1ahQuXboEGxsbLFmypFp6qbnArAhlZ2cL2/p/uCWxsbFBTk5OmXugynIdw3ljT1/HsB07O7tytyNVUstXae7du4fvv/9euM6bb75ZpvPFTor5ysvLw7x586DT6TBp0qQasVAzIL1cGc7F/Oqrr/DkyRNMmjQJr7zyCho2bIiEhARs3rwZa9asQXZ2Nt5//33s2rWrWtbGqg5SyxeQPyKwceNGbNu2DStXrsTy5csL7FcoFBg9ejRee+014YaFqsYeMxF68uSJsG1hYVHq8fo7e3Jzc6vsOoZ3Dz19ncpqR6qklq+SPH78GO+88w4yMzMB5N/tVFovqNRIMV8rV67EjRs34OrqirfffrtMcUiZ1HL1+PHjAm2+9957mD17Npo1ayasev/xxx9jxowZAPInvv/2229lilXMpJYvvTNnzmD37t1ITk4utE9/o0FQUFCBebdViYWZCFlZWQnbxtwBov/HUtbH55TlOob/IJ++TmW1I1VSy1dx1Go13n//fWGexdixYxEQEFCmGKVAavm6deuW8MN77ty5lfYYGymQWq4M26lbty6mTJlSZBuTJ08WlqPZv39/ue6cFiOp5QvIX3Zj+vTpiIiIQJcuXfDHH3/g/PnzCA8Px969e/H6668jIyMDv//+O1599dUCvXVVhYWZCBkOBxrTxav/Lc2YruPyXsfwN8Gnr2PYTmn/aEtqR6qklq+i6HQ6zJo1C8ePHwcADBkyBPPmzStTfFIhpXzpdDrMnz8feXl5GDhwIPr161emGKROSrl6up2uXbsWu06Zubk5unfvDiB/+PPevXtlilespJavyMhILF68GDqdDj179sS6devQs2dP2Nvbw9LSEm5ubvjkk0/wxRdfAMi/e/Tpoc6qwDlmImRlZQUHBwekp6eXOikyIyND+IdZ1sfmGB5f2nUM9+sn8BfVTkJCQokLl+onXyoUCtk85kdq+SrK559/jpCQEADAc889h2+++UZ2S2ToSSlfly5dwtmzZwEAHTt2xN9//13o3NTUVGH7/v37wjGtWrWCu7t7mWIWGynl6ul2Svt/Z7g/NTW1wFpZUiW1fO3cuVNYPPvdd9+FmZlZkW28+OKL+P333xEbG4ugoCB88sknVXrzDQszkXrmmWcQFhaGu3fvQq1WF3vbseHtv2WdmPjMM88U2U5p13n69mTD6966davI1ZOfbqdhw4ay6TEDpJWvp33zzTfYvHkzgPw1gZYvX27U/BApk0q+DIdhvv7661Kvee7cOZw7dw4A8M4770i+MAOkkysABW7KKG29MsP9xRUEUiSlfBneRVvSmmtA/rM4Y2NjkZ6ejpSUFDg6OpYl5DKR56/EMuDt7Q0gv5v26tWrxR6n/yYMQFip3ViNGzeGs7NzoXZKuo6Li0uhFZL1sQIQfrsvSlJSEmJjY8sVq9hJKV+Gfv75Z6xatQoA4OXlhV9//VU2c/9KItV81URSypXh47FKW9jZcPhSTjfYSClfhkWj/vFOxTHcX9WFNAszkRowYICwvWPHjiKP0Wq1CA4OBgDUrl0b3bp1K9M1FAoF+vfvDyD/t4ringF26dIl4beO/v37F+rCbdGihfAbz/79+wuM6RsKCgoStg0/nxxIKV9669atw48//gggfzHMVatWwd7evkwxSZVU8tWtWzdERUWV+Ef/aBog/7Fn+vfffffdMsUrVlLJFQA0adIEbdq0AQCEhoYKdzc/LSsrS1jDrGnTpkKRIQdSypdhoaZ/vmdRVCoVLl68CCB/kdmqXnCbhZlItW/fXvjta8eOHcI/CkNr1qwRumInTpxYaPgpNDQUHh4e8PDwwKxZs4q8zquvvipU/wsWLCh0O3Fubi4WLFgAIP+3i1dffbXIdl5//XUA+RNZv/nmm0L77969K9xZ1qxZMwwcOLDoDy5RUsvXjh07sGjRIgD5jyX5448/ZLe6f0mklq+aTGq50t+JmZOTg4ULFxZ5zOLFi5GVlQUAePnll4s8RqqklC/Dm2mWLl0q5ORpy5cvR1JSEgCgT58+Vb64M+eYiVhgYCDGjh2L3NxcvP7665g2bRq6deuG3Nxc7N27F1u3bgWQ/4P1tddeK9c1WrRogcmTJ2PlypWIiIjA2LFjMWXKFDRp0gT37t3D77//jmvXrgHIv8Vb/6zLp40cORI7duzAhQsXsHHjRiQnJ2P06NGoU6cOrly5gp9//hlZWVlQKpUIDAws8VEdUiWVfB06dAhz586FTqeDvb09AgMDkZqaWmAS+dMaN24sqzmBgHTyRdLK1dChQxEcHIzjx49j586dSExMxNixY9GoUSM8fPgQW7duFe5+btOmDcaPH1+ueMVMKvnq1asXunfvjjNnziAqKgrDhw/HxIkT0b59e1hZWeHOnTvYsWMHTpw4ASD/rs7qWEdQoZPLAioydeTIEcycObPYSr558+ZYuXIlmjVrVmhfaGgoJk6cCCC/cFq8eHGRbWi1WsyZM6fYbmcg/66UBQsWlHinXmpqKt58802Eh4cXud/S0hLz5s3D6NGji21D6qSQr1mzZhUYVjbG+vXryzzcIAVSyFdp7t+/LwzrlBSH1EkpV9nZ2Xjvvfdw8uTJYo/x8vLCL7/8IqxnJjdSyVdGRgbeffddhIaGlvh56tWrh6VLl6Jnz54lHlcZ5NdtITO+vr7YvXs31q9fj2PHjiEhIQEWFhZo2rQpBg8ejPHjx1d4wUmlUomFCxdi0KBB2Lp1K8LDw5GWloa6devCy8sLY8aMQZ8+fUptp169etiyZQu2bduGPXv24ObNm3j8+DGcnZ3Ro0cPTJw4UfaPkpFSvoj5khIp5crOzg6rVq3C3r17ERQUhMjISKSnp6NWrVpo3bo1hg0bhhEjRsjqbsynSSVfderUwbp163D48GHs2bMH4eHhSE5OhkajQa1atfDMM8/gueeew+jRo6ttugd7zIiIiIhEgpP/iYiIiESChRkRERGRSLAwIyIiIhIJFmZEREREIsHCjIiIiEgkWJgRERERiQQLMyIiIiKRYGFGREREJBIszIiIiIhEgoUZERERkUiwMCMiIiISCRZmRERERCLBwoyIiIhIJFiYEREREYkECzMiIiIikWBhRkRERCQSLMyIiKrBgwcPMG/ePAwYMABeXl7w8PCAh4cHDh06ZOrQiMpl1qxZ8PDwgK+vr6lDkRVzUwdAVFGhoaGYOHEiAOCdd97Bu+++a+KIiAp68OABAgICkJaWZupQysTw/9bTrK2t4eDgAE9PTwwcOBD+/v6wtLQ0um2NRoMDBw7g8OHDCA8PR3JyMlQqFRwdHdGyZUv07dsX/v7+qF27dplizsvLw4EDB/Dvv//iypUrSEtLQ1ZWFuzt7eHq6govLy8MGjQI3bt3h1LJvgkSHxZmRAQA8PDwAMDitir88ssvSEtLg7m5OT744AN06dIFtra2AIBGjRqZOLryyc3NRXx8POLj43Hs2DGsXbsWv/76Kxo3blzquWFhYZg/fz5u3LhRaF9cXBzi4uJw4sQJLF++HB999BFeeuklo2I6cOAAFi9ejLi4uEL70tPTkZ6ejqtXr2LLli1o3rw5Zs+ejb59+xrVtpTx/7a0sDAjIqpip06dAgD0798fU6ZMMXE05TN27Fi88sorwuuUlBTExMRg9erViI+PR0xMDKZPn47g4GCYmZkV287+/fsxc+ZM5OXlAQC6du0Kf39/tGjRApaWlnjw4AGOHDmCv//+G+np6Zg7dy5u376NTz75pMT4fvrpJyxbtkx47ePjA19fX7i5uaF27drIyMjA7du3ceTIEZw6dQqxsbH4/vvva0RhVlUWL16MxYsXmzoM2WFhRkRUxRISEgAALVq0MHEk5Ve/fn24u7sXeK9Hjx4ICAiAv78/4uLiEB0djYMHD2Lw4MFFtnHt2jX873//g0qlgoWFBRYtWgQ/P78Cx7Rv3x6DBw/Ga6+9hmnTpiE+Ph5r1qxBkyZNChSGhnbs2CEUZfXr18cPP/yArl27FjquZ8+eGDduHKKjo7Fo0SKkpqaW50tBVKU4wE5EVMVUKhUAwNxcfr8L29vbY/r06cJrfe/g07RaLT7++GPha7Fw4cJCRZmh1q1bY+3atcKQ75IlS/DgwYNCxyUkJGDBggUAAFtbW2zYsKHIosyQu7s7Vq9ejddff73kD0dkAvL7LkH0FMMJzOvXr0fXrl2xfft27Ny5E7du3YJKpULLli0xfvx4jBgxQjgvLy8PO3bsQFBQEGJjY6FSqeDh4YGJEydi6NChRV7r/v376N+/PwBg0aJFCAgIwL59+7B161ZERUUhOzsbjRo1wsCBAzFlypRSJzbn5eXhr7/+wv79+xETE4OsrCzUqVMHbdq0wQsvvAA/P79iJzDPmjULQUFBcHV1xZEjR5CYmIh169bh2LFjePDgAXJycrB+/XrMnj27wJycFStWYMWKFQXaGjlyZIEhi8TERBw8eBChoaGIjIxEYmIi1Go16tati3bt2sHPzw+DBw8uNranc9KtWzfs3btX+Drl5OSgYcOG6N+/P9588004ODiU+HUCgOPHjyMkJAQXL15ESkoKNBoNnJyc0LZtW/Tr1w9DhgyBjY1NkedevXoVW7duRWhoKBITE6HT6eDi4oLu3btj0qRJ5erp2rlzJ2bPnl3gvae/tk9/XQEgNTUV69atw/Hjx3H//n08efIETk5O8Pb2xpgxY9C5c+dir+nr64u4uDih3YiICPz55584e/YsEhMToVKpEBUVVebPUhr9HCYAiI+PL/KYI0eOICYmBgDw3HPPwd/fv9R2W7RogenTp2Pp0qXIzc3F+vXrMWvWrALHrF27Fo8fPwYAvPfee3BzczMqZqVSieHDhxt1rKHq/H5iKCkpCX/++SdOnDiB+/fvIycnB/Xr10eHDh0wZswY9OzZs9A5+n8PeqX93376s3Xp0gU7d+7Erl27cPPmTaSmpmLEiBHC8U9/jylOVlYWtm3bhmPHjuHmzZvIyMiApaUlmjZtCm9vbwwdOhTe3t6lfg1qChZmVKOo1WpMnz4dR48eLfB+eHg4PvnkE0RERGDOnDnIyMjA22+/jXPnzhU47uLFi7h48SLu3r2LadOmlXq9Tz/9FDt27Cjw3u3bt7Fy5UoEBwdj7dq1xf4guX//PqZMmYJbt24VeD85ORn//vsv/v33X2zduhU///xzqYXLpUuXMG3atEq5K1Cj0aBPnz7QarWF9iUmJuLIkSM4cuQItm/fjuXLl8POzq7E9rRaLWbOnIndu3cXeD82NharV6/GoUOHsHHjRjg5ORV5flpaGmbMmIHTp08X2qefSH7gwAEAQEBAQKFrL1myBOvWrYNOpyt0/djYWGzfvh3z5s3DmDFjSvwcleHkyZN4//33kZWVVeB9/efYvXs3xo0bhzlz5pR6R+HmzZvx5ZdfQq1WV2XIAAALCwthu7heweDgYGH71VdfNbrtl19+GStWrMCTJ08QFBSETz75BAqFAgCg0+kQFBQEIL+3bPTo0eWIvvyq6/vJ7t27MX/+fOTk5BR4Pz4+Hvv378f+/fvx4osv4vPPP6+0XtknT55g8uTJxfaAGuvUqVP48MMPC33vUalUuH79Oq5fv44///yzSn5hkCoWZlSj/Pjjj7h8+TL8/Pzg5+cHR0dHxMbGYvny5bh9+zY2bNgAX19fbNiwARcvXsTYsWMxcOBAODg44Pr16/jxxx+RmJiIZcuWoX///mjVqlWx19q0aRPCw8PRvn17TJo0Cc2aNUNKSgqCgoKwb98+JCYm4o033kBISAjs7e0LnJudnY1Jkybh3r17AIABAwZg1KhRcHZ2xv3797Fx40acPXsW58+fx7Rp07Bx48ZiJ1xnZ2fj3XffxZMnTzBt2jT4+PjA2toa0dHRcHJywurVq6FSqYRhpacneQNAnTp1hG19AdO9e3c899xzcHd3R7169ZCdnY179+7hr7/+wsWLF/Hff//hiy++wJIlS0rNycWLFzFgwACMGDECjRo1QnJyMjZt2oRjx47hzp07WLRoEb777rtC5z5+/BgTJ05EdHQ0AKBt27YYM2YMWrVqBUtLS8THx+PcuXPYu3dvkddesGABNm3aBADo0qULRo4ciSZNmsDa2hpRUVFYt24dYmJiMG/ePDg6Ogq9ocYYMGAA2rVrBwDFfm0Nv67Xr1/HtGnThPlX48aNg6+vL2xsbHD9+nWsXLlSyL2NjQ1mzpxZ7LXDw8Oxe/duNGjQAJMnT0a7du2gVqtx/vx5o+Mvi5s3bwrbrq6uRR4TFhYGALCxsUGPHj2Mbrt27dro0qULTp48ifT0dNy4cUP4fxcTEyP8wPf29i70/6iqVcf3k7179+Ljjz+GTqdDkyZNMH78eLi5uaFevXqIi4vD9u3bcfz4cWzfvh329vYFemnL+n/b0LfffouoqCj4+voiICBA+H+ZnZ1t9NfnzJkzmDJlCtRqNczMzDB8+HD0798fDRs2xJMnT3Dz5k38+++/hQrbGk9HJHFnzpzRubu769zd3XXLli0rcb+7u7tu7dq1hY5JTEzUdezYUefu7q7r3r27zsPDQ3fw4MFCx12/fl3n6empc3d31y1YsKDQ/nv37hW41pQpU3QqlarQcStWrBCOWbJkSaH9ixcvFvZ///33hfZrtVrdRx99JByzcePGQsd88sknwv4OHTrorl+/XugYQyV9DZ++dmxsbInH/Pjjjzp3d3edh4eH7vbt24X2P52Tn3/+ucjrvP766zp3d3ddmzZtdCkpKYWOWbhwodDG559/rtNqtUXG8+TJE11SUlKB906ePCmcu23btiLPy83N1U2cOFHn7u6u69evX5G5NIYxX9tRo0bp3N3dda1bt9adOHGi0P709HTd0KFDde7u7jpPT09ddHR0oWP69esnXOuFF17QZWRklCtevdL+b+l0Op1ardaNGDFCOO7cuXOFjomPjxf2v/TSS2WO49tvvxXO3717t/D+rl27hPe/++67MrdbHtX5/SQlJUXn7e2tc3d3182ePbvYf3/fffed8O/i5s2bhfYb+3/76c9W1PceQ/rvMf369Su0Lzc3V9erVy+du7u77tlnn9WdOXOm2HYePHhQ4nVqGk7+pxrl2WefLXIYxcnJCQMHDgSQP8dnyJAhGDBgQKHjPD09hbkQpfU+WFpaYsGCBUUOLUyfPl24w23Hjh3C0gFA/lyU7du3AwBatWpV5LpDCoUCn332mTCEuXHjxhJjeeONN+Dp6VniMcZSKBRo1qxZice8/fbbqFu3LnQ6XYlzT4D8Xq6ihnEUCgUmTZoEIH/I6OLFiwX2P3r0CFu3bhXaCAwMFIa4nmZpaQlHR8cC761cuRIAMGjQoGKHwKysrDBv3jwA+cOJoaGhJX6W8rpy5QrCw8MBAKNHj0avXr0KHVOnTh188cUXAPKHYPU9fcWZP39+mRdnLYvU1FScPn0a48ePx7Vr1wDkfy2LmgNnOIz1dB6MUb9+fWE7PT29yG3DY6pLVX8/2bx5MzIzM+Hi4oLPPvus2GHKd999Fy4uLtBqtdi1a1dFPpKgefPmFVrzLDg4GImJiQCAGTNmoFu3bsUe27Bhw3JfR45YmFGNUtIkW8PCZdiwYaUepx9mLI6Pjw9cXFyK3KdUKoWJwenp6cIPNgCIiIjAo0ePAORPzC1uiNLe3h5DhgwBANy4cUP4JlgUYyZal5dWq0VCQgJu3bqF6OhoREdH4+bNm2jQoAEAIDIyssTz/fz8ii2o2rZtK2w//fU+c+aMMOl74sSJJa6d9bSsrCycPXsWQH4xURI3NzfUrVsXAAoVh5XFcB7Piy++WOxx3t7ewpzEkub+NGzYsMSbBMpjxYoVwmOkPDw8/l979x8Tdf3HAfx5GAoM5byQ0GqUieuSIMTCJiUi042ldjE3iJ2B9mvpKHArrZVbtTK2VsES1qZFahzhmCbObgh4CrMGwTgl5w0lBNctwLj4lScn3z/Y57PPcT+4gzv4+O35+Ouz3fvz4cN9Pve5173f79frjaeffhrZ2dlobm5GcHAwcnJy8PnnnzvdVzpnTsiy9IZ0nqL0WNJhNVeJHf7k7+eJ8KMmOTnZ7aoK99xzD5544gkAvrtH09LSvPpMTSQMT4aEhHhcIJjGcY4Z/ae4y66bP3++uP3QQw9N2m6yuRaPP/6429djY2PFbZPJJD5Yhcw1YPwXuTtxcXEoKysT94uIiHBoExISggcffNDtcbw1NjaGn376CceOHYPRaMS///7rsu1kCQdLly51+Zo0qWHi+y0NZr3N6Pr999/F5IX8/Hzk5+d7tF9vb69Xf8dTwjUPDAyEWq122zYuLg5Xr15FZ2cnrFar0y9saZbkTHj00Ueh1WrtkgCkpHO/Jk5g94T02kuPJQ3YhCB9JvnzeWKz2cQfNeXl5WLv8GR8dY9O9x66fPkygPEfV7MRNN/NGJjRf0pQUJDL16RZbp60c5aVKDXZ0Iqr4RmLxSJuq1Qqt8eQDgtJ95Py9XDWrVu3sGvXLpw7d86j9u6CNsDzazLx/ZYGfM4CUnf6+vq8ai+Y7H+ZKuH6K5XKSbPqhGs+NjaGf/75x+nQoD+GMKUTx202G8xmM/R6PU6cOIGWlhZotVocO3bM6T0r9DgCUwscpNdLGqxLt6d6TafDn88Ti8UypYxaX92jrpICPCV8Pl1lU5NrDMyI/MTV8NxMH2M6wxHOFBcXi0HZU089hRdffBErVqxAeHg4goKCxC+arKwsMRNPbqRfgh9++CHi4+M92m+6X1YzxdfXHHCs/K9Wq7Fu3TokJiZiz549uHHjBt577z0UFxc77BsREYGFCxfi77//xpUrV2Cz2bw6R2nvqHSIULrd1tbm7b8kazabTdzeunWry8XkJ3LVa+ktLvA+exiYEfnJZD0DrnoBpF/+fX19bodLpH9jJoKGsbExMTFh1apVKC0tdfkAd9WD5yvSXpi//vrLq+Fa6fsdFBTksNTQTBPOp7+/H6Ojo257zYRrrlAo/Dq531MajQZ1dXXQ6/Wora3FhQsXHMphKBQKJCQk4MyZMxgZGcGFCxecJjg4MzAwIAb4SqUSy5YtE1+Ljo4WA77ffvsNg4ODM14yw18mlqiZ7XvUWwsXLoTZbEZPT89sn8pdhyExkZ8IWXaevC6tXyTdbm1tdXsMo9HodD9/6e/vFx+07ir7Dw0NoaOjw6/nIk0M8LZnTq1Wi72Rzc3NPj2vqRCunVB00x3hmkdFRbmdED6T8vLyxB6wL774wmkbjUYjbh8+fNjjY5eXl4vDcxqNxq4XWaFQiMcdHh5GRUWF1+cuV3PnzhXvCznco9567LHHAIwnM83G/L+7GQMzIj9paGhwmSl5584dsWJ5WFiYXZARExMj9oQcP37c5Vy2wcFBnD59GgCwbNkyr+dZTTRv3jwAsCvdMZF0eMXdw7aiosLvFecTExPFDL/Dhw/bndtkVCqVmGxRVVU164tZS5fTmbhShFRLSwva29sd9pltDz/8sJgh3NraioaGBoc2KSkpYkbp2bNncerUqUmP29nZia+//hrAeM+ms+G87OxscXJ5YWGhXbFbd+7cueOw2oTcpKSkAACuXbuG8+fPT/k4nny2fU0495GREY8TF2gcAzMiP7Farfjggw+cBgzffPONWK0+PT3drudj7ty5YskEk8mEAwcOOOw/NjaGjz76SJxgm5WVNe3zFSbpXr9+3WUblUolBo1VVVVOH/RGoxFfffXVtM9nMgsWLBCXSWpra8Mnn3zisKyS4Pbt2w6Tw4WFtwcHB5GbmyuWKHHGarXi6NGjuHXrlo/O3l5sbKy4SkBFRYXT5aUGBgbEmmoBAQHIzMz0y7lM1WuvvSb2ZjmbZxYQEICCggJxDtSePXtcrsgAjJdZyc7OFrM433nnHSxZssSh3X333Yf3338fwHivmVarFUuhuNLe3o6XX34ZBw8e9OyfmyXbtm0Tf3zs3bvXLmPbmbNnzzotT+PJZ9vXNm/eLJYL+vLLL91eE1frq/5XcY4ZkZ/ExMSgrq4OmZmZdksyHT9+XOwtiIyMxBtvvOGw786dO1FdXY2uri4UFRXBZDLhhRdewKJFi9Dd3S0uTA0A8fHxPlnHMT4+Ht3d3aitrYVOp8PKlSvFX9qhoaG49957ERAQgE2bNuHo0aO4cuUKMjMzkZOTg6ioKAwODsJgMOCHH35ASEgIIiIi8Mcff0z7vNx588030dDQAJPJhCNHjqClpQUZGRlYvnw5AgMDYTab0dTUhFOnTuGtt96yWytz7dq12LZtG77//ns0NjYiLS0NGRkZSEhIgFKpxPDwMK5fv46mpiZUV1fDYrHg+eefF98TX/v444+xdetW3L59G6+88gq0Wi3WrVtntySTUOtq+/btsptztHz5cqSkpKCmpgaNjY1oampyqKUWExODgoICvP3227BarcjLy4NOp8OmTZuwdOlSBAYG4s8//0RtbS2qqqrEXtft27c7LCUklZ6eDrPZjMLCQvT19UGr1SIpKUnspVuwYAEsFgs6OjpgMBhw/vx52Gw2nxVd9pfw8HB89tlnyM3NRU9PD9LT06HRaPDss88iMjISo6OjMJvNMBqN0Ov16OrqQklJicP/5cln29fmzZuHgoIC7NixAyMjI8jJycHmzZuRmpqKyMhIWK1WXLt2DQaDAbW1tbh06ZLPz+FuxcCMyE+ysrLQ2NiIyspK5OXlObwurFMprXckCA0NxXfffScuYq7X66HX6x3arVy5EsXFxT7JwtuxYwf0ej2sViv27dtn95pGo8H+/fsBjM8nam5uxuXLl3Hp0iXs3r3brq1SqURRUREKCwv9HpgFBwejtLQUubm5aGxsRFtbm9h74ol3330XYWFhKC4uRk9PD4qKily2DQkJ8Uu2o0CtVqOkpERcxPzQoUM4dOiQQ7usrCyH91wuXn/9ddTU1AAY7zVz1iOVlpaGRYsWYd++fbh69Sp+/fVXlysqKJVK5Ofne/TDY+fOnYiOjsb+/ftx48YN1NfXo76+3mX76Ohot+uNysWGDRtw4MAB7N27F/39/dDpdNDpdE7bBgQEOK0Z5uln29dWr16NkpIS7N69GxaLBZWVlaisrPTL3/p/wsCMyI8+/fRTrFmzBuXl5TCZTBgeHsaSJUuQmpqKV1991W0m5QMPPIATJ06goqICP//8M0wmE4aGhhAWFga1Wi0unOyrtHa1Wo3y8nIcPHgQzc3N6O3tdTpUOX/+fJSVleHbb7/F6dOn0dnZiTlz5mDx4sVYu3YtXnrpJbHq/0xQqVQ4cuQIqqurcfLkSbS2tuLmzZtQKBSIiIjAihUrkJqa6rTCv0KhwK5du7BlyxbodDr88ssv6O7uxsDAAIKCgrB48WKo1WokJSUhNTXVbT0qX0hKSkJ1dTVKS0thMBjQ1dUFq9WK8PBwJCQkICMjw+cV/X0pNjYWa9asQUNDA+rr62E0Gu0KKQuefPJJnDx5Enq9HjU1Nbh48SJ6e3sxOjoKlUqFRx55BMnJydiyZYtXmacbNmxAcnIy9Ho9zp07h4sXL+LmzZsYGhpCaGgo7r//fsTFxWHjxo1ITEz0STmamSD0RP74448wGAxob2+HxWLBnDlzEB4ejujoaKxevRobN250uryRp59tf3jmmWdw5swZlJWVoa6uDh0dHRgYGEBwcDCioqKwatUqPPfcczNyLncLxZirSRlE5LXu7m6sX78ewHhQJh06IyIimgwn/xMRERHJBAMzIiIiIplgYEZEREQkEwzMiIiIiGSCgRkRERGRTDArk4iIiEgm2GNGREREJBMMzIiIiIhkgoEZERERkUwwMCMiIiKSCQZmRERERDLBwIyIiIhIJhiYEREREckEAzMiIiIimWBgRkRERCQTDMyIiIiIZIKBGREREZFMMDAjIiIikgkGZkREREQywcCMiIiISCYYmBERERHJBAMzIiIiIplgYEZEREQkEwzMiIiIiGSCgRkRERGRTPwPJGeUQtuf1J4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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G2ysoKMDbb7+NQ4cOASgqqIYOHYonn3wSTZs2hYODAzIyMhAbG4sDBw7g8uXL+Pvvv/Hbb78hPDy8RHtbt27F/PnzoVQqAQCdOnXCwIED0bZtW7i4uODhw4e4ffs2jhw5gsOHDyMtLQ0LFy4sUQDqi4yMFLY1Gg1SUlKwd+9ebN++HUqlEgsWLICXlxf69etX3i8rERERUc0sADdu3CgUf40aNcKqVavQtm1bg326dOmC0aNHIzIyErNnz0ZhYSFmzJiBFi1alNgXAD744AOh+Hv88cfxxRdfoGnTpiX269u3L9544w0cOnQICxcuNBrf8ePHMWfOHGg0Gjg5OWHRokVGC88ePXpg9OjRuHPnDpYuXYqjR4+Wet3Fh5z9/PzQr18/tG/fHh9//DG0Wi2WL1/OApCIiIgqpMYNdCcnJ2PJkiUAisbi165da7Sg0wkODhYKtcLCQrz33nsoPqq9f/9+bN++HQDg6+uLtWvXGi3+9AUGBmLr1q3o0aOHwfv5+fmYMWMGNBoN5HI5vvvuO5O9jjpNmzbFsmXLMGfOnFL3M2X8+PHw8vICAFy6dAkZGRnlaoeIiIgIqIEF4Lp16/Do0SMAwGuvvYZWrVqVeUxISAj69OkDAEhISBB6+nRWrVolbC9atAhOTk5mxVKvXj3079/f4L2tW7fi3r17AIoKs27dupnVFgCMGDHC7H31yeVydOjQQXh99+7dcrVDREREBNSwAlCr1WLnzp0AAAcHBzz//PNmH/vCCy8I29u2bRO24+PjcfHiRQBFw8bt27evUIy6tmUyGSZOnFihtiyhvzilRqOptvMSERFR7VOj7gFMTEwUZvx26dIFdevWNfvYJ598Eg4ODigoKMDp06eF90+ePCls9+3bt0Lx5eTk4PLlywCAVq1aoXnz5hVqzxL6M409PDyq7bxERERVxR5qi4/Jz8tDbslV1czi7Fx5s4fFrkYVgFeuXBG227VrZ9GxNjY28PPzw7lz55CZmYnU1FR4enoatKk/jFoe8fHxQu9bRXsSLXH48GEkJiYCAJo1a4bGjRtX27mJiIiqymDFLYuPWffDt+U+3/z588t9bG1TowrArKwsYdvNzc3i411dXYXt7OxseHp6Gqwh2LBhwwrFp9+W/rmqgv4yMMuXLxfef/nll6v0vERERFJib2+PgIAAa4dR7WpUAai/OLO5EzX06R+Tk5NTKW3q02/L0dGxQm0Zo3sYtSljxoyx6L5IIiIiImNqVAGoPzaveyyLJfSP0T3YuaJt6tNvKz8/v0JtmcvR0RGdO3fGhAkTEBQUVC3nJCIiotqtRhWADRo0ELbT09MtPl5/fTwXFxeD/xf/vDz02ypPfGXRfxKIjY0NnJ2d4e7ubtFzjomIiMRib47lkyk3TR8G9/oVG9HTp1arhd/pbm5ukvmdW6MKQP0h0Li4OIuOVavViI+PB1B0r5+npyeAoqdp6Fy6dAm9evWqUHxyuRwajcbi+MxR/EkgREREtVkhLC+2HJ2cKnU2r1qtxp07dwAUdURJpQCsUesA+vj4CL1sp06dwsOHD80+9tixY8KwbJcuXYT3u3btKmwfOXKkQvEpFArhqSTXr1/HzZs3K9QeERERkTXUqAJQJpNh+PDhAICCggJs3rzZ7GN//vlnYXvkyJHCtq+vr7D8y6lTp3Dp0qUKxRgaGgqgaNHqDRs2VKgtIiIiImuoUQUgAEycOBH29vYAgBUrVpjVy/bHH3/g8OHDAIp6EQMDAw0+nzp1qrA9e/ZssyeDPHjwAFFRUQbvjRo1Cu7u7gCAjRs34sSJE2a1BQA7duwwe18iIiKiqlLjCsCmTZvivffeA1A0a3fSpEkGizkXt3v3bsycORMAYGdnh08//RQymcxgn4EDBwrP4Y2Pj8ekSZOQnJxcahxHjhzBs88+i+PHjxu87+joiM8++0y4F/DVV1/Fn3/+WWpbd+/exTvvvIOPP/641P2IiIiIqkONmgSiExYWhlu3bmH9+vW4e/cuRo0ahWeeeQZBQUFo0qQJVCoVrl27hl27dgkFmr29PT777DPhHr3iPvjgA9y/fx+HDh3C+fPnMXjwYAwZMgS9e/eGl5cXHBwckJ6ejri4OOzfv7/UoeKePXvi448/xvz585GXl4e33noLnTp1wqBBg9C2bVvUr18fDx8+xO3bt3H06FFERUWhsLDQokfbEREREVWVGlkAAkB4eDhat26NL7/8EtnZ2di5cyd27txpdF9vb2989NFHBpM/inN0dMS3336LlStXYvXq1cjNzS21TQDo168fxo0bZ/SzUaNGoVmzZliwYAESEhJw7tw5nDt3zmRbTZo0wbvvvmvycyIiIqLqUmMLQAAYO3YsnnnmGURERODw4cO4evUqMjMzYWNjAzc3N/j7+6N///4YPHiwWdO25XI5Xn/9dYwdOxZ//PEH/v77byQkJCArKwtKpRL169dH69at0blzZ4SEhMDb27vU9rp164adO3fi0KFDOHz4MM6ePYv09HQ8fPgQTk5OaNSokRBj3759YWtbo7/cREREkiOTyYRlZYrfQlabybRardbaQZD1FBYWIjY2FgDQqIU3QpZEWDkiIiIi03aHj4KnS+WtA1gR+r9D/f39hUmsYlDjJoEQERERUdViAUhEREQkMbwpjYiIiCRLpVIJaw63aNFCMvfrsweQiIiIJEuj0SA7OxvZ2dnQaDTWDqfaSKPMJbM0VDhgd/goa4dBRERkkls9R2uHUCuwACSBjVwOTxcHa4dBREREVYwFIAm4IpD4aLVaKJVKAEWPQpTSGla1BXMofswhiRHvASSBSqWydghkIaVSidjYWMTGxgq/gEhcmEPxYw5JjFgAEhEREUkMC0AiIiIiieE9gCTgfSviI5fL4eHhIWyT+DCH4scciptU88cCkARSWfyyNrG1tUWzZs2sHQZVAHMofsyhuEk1f9IpdYmIiIgIAAtAIiIiIsnhmB8J1BoNUrNzrR0GUY3iVs8RNlV4X5BSqcTly5cBAG3btoWdnV2VnYuqBnMoblLNHwtAEmTmFCBkSYS1wyCqUXaHj4Kni3OVta+/iDAXYxcn5lDcpJo/DgETERERSQwLQCIiIiKJYQFIREREJDEsAImIiIgkhgUgERERkcRUewGoVqvRuXNn+Pr6YuTIkaXuq9Vq0b17d/j6+sLX1xdbtmwpdf/t27cL+/7yyy/C+2FhYcL7ZVm3bp2wb8+ePXHlyhXhs6+//lr4LCYmpsy2ACAoKAi+vr4ICgoy+vmsWbOENvX/8/PzQ5cuXRASEoIPP/xQmKJOREREVFHVXgDa2NjgiSeeAABcuXIFOTk5JvdNTExEdna28Pr06dOltn3q1Clhu2vXrhbH9uOPP2LhwoUAAHd3d2zYsAF+fn4Wt1MZtFotHj58iPj4ePzyyy8IDQ3FsmXLrBILEVUdW1tbtG7dGq1bt+bjGEWKORQ3qebPKlfatWtXREdHQ6PR4MyZM3jqqaeM7qcr6GxsbKBWqw0KvNL2b9CgAR577DGLYlq5ciW+/PJLAICnpyfWrVuHVq1aWdRGRa1evVp4ILVGo0FGRgYOHz6MX375BSqVCqtWrYKHhwfGjx9frXERUdWRy+Vo0KCBtcOgCmAOxU2q+bPKPYBdunQRtksr6nSfDR48GABw69YtpKamGt03IyMDN27cAAAEBARAJpOZHc/XX38tFH9NmjTBzz//XO3FHwC0bNkSPj4+8PHxgZ+fH3r16oXw8HB8/fXXwj4rVqyAWq2u9tiIiIio9rBKAdixY0fUqVMHgHkF4KBBg9C8eXMApoeByzv8+8UXX2DFihUAgKZNm+Lnn38WzlVTBAUFCUVzZmYmLl26ZOWIiKiyaLVaFBYWorCwUFJPIahNmENxk2r+rFIA2tvbo2PHjgCA2NhYFBYWltjn9u3bQm9fQEAAAgICAJguGPXf1+9hLM2SJUvw3XffASjqfdu4cSO8vLzMv5BqpPt6AcDdu3etGAkRVSalUonY2FjExsYKj6MicWEOxU2q+bPaMjC6Iq2wsBDnz58v8bmuoGvZsiXc3NyEAvDkyZNG29P1DCoUCrRt27bM83/88cdYs2YNAKB169bYsGEDGjVqZPmFVBP9G1M1Go0VIyESL3uoLf4vPy8Pubm5Fv9HRFSTWW26S9euXbFy5UoARcVe8WFbXQHYuXNng/8nJibi/v37qF+/vrBvTk6OsFzLE088ARsbG5Pn1Wq1+PDDD7Fp0yYAgI+PD3766Se4ublV0pVVjYSEBGFbN1GEiCwzWHHL4mPW/fBtuc41f/78ch1HRFQdrFYAdurUCba2tlCpVEaHdXXv6XoKvb290aBBA2RlZeHMmTMIDAwU9j179qwwMaKs+//mzp2L33//HQDQtm1brFmzBg0bNrQ4/jt37pg1a6gyupOvXLmCo0ePAgAcHR3h7+9f4TaJiIhIuqxWADo7O6Nt27aIjY0VCjhdz13xGb06nTt3xsGDB3Hq1CmDAtCS+/90xZ+3tzfWrVtn0JNoiffff79cx5lLq9UiPT0dhw8fxtKlS4UCNywsTJhAQ0RERFQeVl3xsGvXroiNjUVubi4uX76MDh06APjffX5ubm5o2bKlsH9AQIBQAOrT3f9Xp06dMnvHZDIZtFot7ty5g7i4OPTs2bMSr6hi+vfvX+rn/fr1w1tvvVVN0RAREVFtZdUCMCAgQJiIcerUKaEA1BV4+r1/wP969y5duoSCggI4ODigsLAQFy5cAAA8/vjjsLe3L/Wcc+fOxUcffYRHjx5h2rRp+PHHH82eNaxv/fr16N69e5n7BQUFITk52eL2dezs7ODv748xY8Zg+PDhFq1vSESG9uZYvsTTpunD4F7fqQqiISKyHqsWgF26dBF65E6fPo1JkyYB+F+Pnm7ih067du3g6OiI/Px8nDt3Dj169EBsbCwePXoktFeW8ePHo6CgAJ9++iny8/Px6quv4qeffjJYZsVa9J8EIpfL4eTkBDc3tzKLWiIyTyFMTxAzxdHJCc7OzlUQDRGR9VhtGRgAcHFxQZs2bQD8r9dPf0Zv8R5AXW+Y/v76C0ObuwD05MmT8eabbwrne/nll4VzWpP+k0Aee+wxNGnShMUfUS2n+7nm7+8POzs7a4dD5cAciptU82fVAhCAwRMukpKScPr0aWg0Gjg5OaFdu3Yl9i++ILTufkE7Ozt06tTJ7PO+8cYbeOWVVwAA9+/fx0svvYSkpKSKXAoRkcVkMhns7e1hb2/PWzxEijkUN6nmr8YUgEBRb56uR69Tp05G1/PTFYDnz59HYWEhzp49C6BoeNjJybL7dP773/8iLCwMQNHM40mTJuH27dvlug4iIiIisahRBeDJkyeFHr3iw786uoWe8/LysG3bNjx8+LBEO5YIDw/Hc889BwBIS0vDCy+8gH///bdcbRERWUqj0SArKwtZWVl8yo9IMYfiJtX8Wb0A9PT0RPPmRTPzYmJicPHiRQCmC0CFQgEfHx8AwI8//ii8b+79f8XJZDJ8+OGHCA4OBgAkJydj0qRJuHfvXrnaIyKyhEqlwrVr13Dt2jWoVCprh0PlwByKm1TzZ/UCEPhf711qaioKCwtha2tb6v18uuJQN1wrl8tNFozmkMvlWLJkCQYNGgQAuHHjBl588UVkZmaWu00iIiKimqpGFYA6bdu2haOjo8n9ixd7Pj4+qFevXoVisLGxwdKlS9GvXz8ARc8cnjx5Mh48eFChdomIiIhqmhpRABYfvi2rN6/45+Ud/i3Ozs4Oy5cvx5NPPgkAiIuLwyuvvILc3NxKaZ+IiIioJpBptVqttYMg6yksLERsbCwAoFELb4QsibByREQ1y+7wUfB0qbqFoPW/B/39/bn2pwgxh+JWkfyJOfc1ogeQiIiIiKoPC0AiIiIiibHqs4CJiKROJpMJj5+S0lMIahPmUNykmj8WgEREVmRnZ4eOHTtaOwyqAOZQ3KSaPxaAJGiocMDu8FHWDoOoRnGrZ3pJKiIisWIBSAIbuRyeLg7WDoOIiIiqGAtAEnBFIPHRarVQq9UAihYzl9L9K7WFSqUSnj/euHFj2Nryx7LYMIfiJtX8cRYwCaT0DMTaQqlU4vz58zh//jyUSqW1w6Fy0Gg0SEtLQ1pamqQeRF+bMIfiJtX8sQAkIiIikhgWgEREREQSwwKQiIiISGJYABIRERFJjDSmupBZpDLzqTaxs7ODv7+/sE1ERGQO/sYnAZcQER+ZTAZ7e3trh0FERCLDApAEao0Gqdm51g6DyKrc6jnCRl59d8fI5XK4uLgI2yQ+zKG4STV/LABJkJlTgJAlEdYOg8iqdoePgqeLc7Wdz9bWFt7e3tV2Pqp8zKG4STV/0il1iYiIiAgAC0AiIiIiyeEQMBGRFSmVSiQlJQEAvL29OZtbhJhDcZNq/lgAEhFZkVarRW5urrBN4sMciptU88chYCIiIiKJEU0PYF5eHnbu3ImoqChcuXIF2dnZ0Gq1UCgU8PLygo+PD5544gn06dMHjRs3LrWtu3fv4o8//sCxY8dw8+ZNZGZmQqPRoH79+vDx8UFAQACCg4PRrFmzEsfGxMRg4sSJAIA33ngDb775Zpmxz5o1C9u3bwcAHDx4EE2bNjX4fNu2bZg9ezYAYNGiRQgNDTXra0JERERUHqIoAM+ePYt33nkHd+/eLfFZVlYWsrKycPHiRWzbtg1ubm74+++/jbbz6NEjLF26FJs2bUJhYWGJz9PS0pCWlobo6GgsX74cgwcPxsyZM8ssKImIiIjEpMYXgNevX8fkyZOF8fmgoCAMGjQIrVq1gp2dHbKysnDlyhUcO3YMMTExJtvJzMzEtGnTcO7cOQCAs7Mzhg0bhh49eqBRo0awtbVFeno6zpw5g/379+PGjRvYs2cPOnXqhEmTJlXDlRIRERFVjxpfAC5btkwo/kwNj/bq1QuTJ09GZmYm9uzZU+JzjUaDt99+Wyj+AgMD8cknn8DV1bXEvkFBQXjnnXcQERGBTz/9tHIvhoiIiKgGqNEFoFqtxpEjRwAAHTp0KPPeuIYNG2L8+PEl3l+/fr3QO9i7d2+sWLECtramL10ul2PEiBHo0aMHrl+/XoErICIiIqp5anQBmJmZiYKCAgBAixYtytVGYWEhVq9eDQCoU6cOFi5cWGrxp69Ro0Zo1KhRuc5LRGQOGxsbYWKYjY2NlaOh8mAOxU2q+avRBaD+Yoy6RRotFR0djbS0NADA4MGD4enpWSmxEVHNYg91pbSTn5eH3EpaB9bZuexnCtvY2PDnksgxh+Im1fzV6ALQxcUFXl5eSE5OxpUrV/D999/j5Zdfhlxu/vKFJ0+eFLb79etXBVESUU0wWHGrUtpZ98O3ldIOAMyfP7/S2iIiqkw1ugAEgAkTJmDJkiUAgKVLl+LXX39FUFAQOnfuDH9/f6Nr9em7cuWKsN2+fftKjS0jIwMJCQll7vfgwYNKPS8RERFRRdT4AnDSpEm4evUqtm7dCgBITk7Ghg0bsGHDBgCAm5sbunXrhuDgYAQGBkImkxkcn52dLWwbm/VbEZs2bcKmTZsqtU0ikpbCwkLExsYCAPz9/WFvb2/liMhSzKG4STV/Nf5RcHK5HAsXLsSaNWvQp0+fEhM40tPTsXv3bkybNg3PPvssbt0yHAbSLSEDAI6OjtUSMxEREVFNVuN7AHV69eqFXr16IScnB6dPn0ZsbCwuXryIU6dO4eHDhwCAixcvYty4cdi2bRs8PDwAGN6EnZ+fD4VCUWkxledRcERUNfbmNK+UdjZNHwb3+k6V0hYRUU0lmgJQR6FQoG/fvujbty+Aoq7byMhILFmyBPfv38e9e/fw1Vdf4ZNPPgFQNJFEJz09vVILQCKqOQpROcs3ODo5mTV7l4hIzGr8EHBZ7O3tMWrUKCxdulR4b//+/dBoNAAAPz8/4f24uLhqj4+IiIiophF9AajTp08fNG7cGABw//59YfJH165dhX0OHz5shciIiIiIapZaUwACEO7709e7d2/h/b179yI1NbW6wyIiIiKqUWpNAZifn4+rV68CKLpPsEGDBgCKhognT54MAHj06BHef/99qNXmPTEgNTUVx48fr5qAiYiIiKykRheAubm5GD16NA4dOiTc02eMRqPBggULhCVfgoKCDNYDnDhxIrp37w6g6NFwr7/+OjIzM022p9VqERkZidDQUMTHx1fS1RARlWRrawtfX1/4+vqa/ZxyqlmYQ3GTav5q/JVeuHABU6dOhaenJwYMGIBOnTqhSZMmUCgUePDgAeLi4rB161bhiRx169bFf/7zH4M25HI5vvzyS0ydOhXnz5/HoUOHMGDAAAQHB6NHjx5o1KgRbG1tkZ6ejnPnzmHfvn24du2aNS4Xp0+fNmu/oKAggxnORCROcrmcqxOIHHMoblLNX40uAG1tbeHu7o579+4hNTUVGzduxMaNG03u37JlSyxduhRNmzYt8VnDhg2xYcMGfP7559i0aRNyc3Px66+/4tdffzXalkwmQ3BwMIYMGVJp12OOLVu2YMuWLWXut2PHDhaAREREVC41ugCsU6cO/vrrL5w7dw7Hjh3D+fPncf36dWRkZODRo0dwdHSEh4cH/Pz80L9/fzz99NOlPsKlTp06CA8Px4svvohdu3bh+PHjuHHjBjIzM6HValG/fn34+Piga9euCA4OhpeXVzVeLRFJkUajQV5eHgDAyckJcnmNvjOHjGAOxU2q+ZNptVqttYMg69F/BmKjFt4IWRJh5YiIrGt3+Ch4ulTfQtBSfQ5pbcIciltF8ifm3EujzCUiIiIiAQtAIiIiIolhAUhEREQkMTV6EghVr4YKB+wOH2XtMIisyq2eo7VDICKqciwASWAjl8PTxcHaYRAREVEVYwFIAk4IFx+tVgulUgkAsLOzM3gCDhERkSm8B5AEKpXK2iGQhZRKJWJjYxEbGysUgkRERGXhOoASJ+Y1jIiIiKxJzL9D2QNIREREJDEsAImIiIgkhpNASKBWq60dAllIrVYjPT0dAODm5gYbGxsrR0SWYg7FjzkUN6nmjwUgCTQajbVDIAup1WrcuXMHANCgQQPJ/OCqTZhD8WMOxU2q+eMQMAlsbfn3ABERkRSwACQB15AjIiKSBnb5kECt0SA1O9fgPbd6jrCR8+8EIiKi2oQFIAkycwoQsiTC4L3d4aPg6eJspYiIiIioKrBrh4iIiEhiWAASERERSQyHgIlETCaTwdnZWdgm8WEOxY85FDep5o8FIJGI2dnZwc/Pz9phUAUwh+LHHIqbVPMnqgLwxIkTCAsLE15v2rQJnTt3tqiNW7duYc+ePTh+/Dhu3bqFrKwsqFQq1K1bF02bNkW7du3Qr18/9OrVC3Z2dkbb2LZtG2bPnm3ReWfPno1JkyYZvBcWFoYTJ06U2Fcul6Nu3bpo1qwZunXrhueffx4tWrSw6HxEREREpoiqANy+fbvB6x07dphdAD548ACLFy/Gzp07oVKpSnyekZGBjIwMnD9/Hps2bYKrqyteeeUVTJw4sdpXBddoNLh//z7u37+PixcvYsOGDZg9ezbGjx9frXEQERFR7SSaArCgoAB//vknAMDJyQl5eXnYu3cv5syZA3t7+1KPvX37Nl555RVcv34dAODi4oJnnnkGXbp0gYeHBxwdHZGZmYlr164hOjoax44dQ0ZGBhYvXoxhw4bB3d3dZNtvv/02+vfvX2b8pbUBAJGRkcK2RqNBSkoK9u7di+3bt0OpVGLBggXw8vJCv379yjwXSYdKpcLNmzcBAC1atODTXESIORQ/5lDcpJo/0Vzl/v37kZtbtEjxnDlz8P777+P+/fuIiorC4MGDTR6Xl5eHV199VSj+nnvuOcycORMKhaLEvn369MELL7yA27dv49tvv8W2bdvKjMvT0xM+Pj7lvKr/Kd6Gn58f+vXrh/bt2+Pjjz+GVqvF8uXLWQCSAY1Gg+zsbABAs2bNrBsMlQtzKH7MobhJNX+iWQZmx44dAABfX1+MGjUKrVq1MnjflM8//xxJSUkAgHHjxmHBggVGiz99zZo1w6JFi7By5UrUqVOnwrFXxPjx4+Hl5QUAuHTpEjIyMqwaDxEREYmfKArAtLQ0HD9+HAAQEhJi8P/o6GhkZmYaPS49PR1btmwBADRu3BgzZ8606LxBQUGoV69eecOuFHK5HB06dBBe371714rREBERUW0gigIwMjISarUacrkcwcHBAIDg4GDIZDIolUrs2rXL6HG7d+/Go0ePAACjR4+Gg4NDtcVcmfTvR9BoNFaMhIiIiGoDURSAO3fuBAB069YNnp6eAIqGaZ944gkApoeBT548KWz36dOnaoOsQgkJCcK2h4dHtZ3XHmrk5+UhNzdXuP+SiIiIxK/GTwK5fPky4uPjAfxv2FcnJCQEZ86cwaVLl3D16lU89thjBp/rjpPL5VW2yGNqaqpBgWZKeSeKHD58GImJiQCKit7GjRuXq53yGKy4hXU/fCu8nj9/frWdm4iIiKpOjS8Adb17Dg4OGDRokMFnQ4YMwSeffAKlUokdO3bgv//9r8HnWVlZAIC6deuWulTMvXv3hH2Lq1+/vtDraMyXX36JL7/8sszr0BWj5tBfBmb58uXC+y+//LLZbRARERGZUqMLQJVKJdzfFxgYWGL2rouLC/r27YsDBw4gMjIS77zzDuTy/41q64YtHR0dSz3P999/j/Xr1xv9bOTIkVi8eHFFLsMsvr6+pX4+ZswYPP/881UeB4mLXC4XbgvQ/7dP4sEcih9zKG5SzV+NLgCjo6ORnp4OoOTwr05ISAgOHDiAlJQUxMTEoGfPnsJnzs7OePDgAfLz86ssxkWLFiE0NLRK2nZ0dETnzp0xYcIEBAUFVck5SNxsbW0ltW5VbcQcih9zKG5SzV+NLgB1w78uLi4mJ3EEBgaiXr16ePDgAXbs2GFQALq4uODBgwd4+PAhCgsLTQ4Dh4eHIzw8XHh9584ds57uUZn0nwRiY2MDZ2dnuLu7V/tj6PTtzWmOTdOHwb2+k9ViICIiospXYwvAhw8fIioqCgCQnZ1tsBaeKfv27cP8+fPh5FRUsPj5+eHWrVvQaDSIj4+Hv79/lcZcEZXxNJHKVggbODo5wdnZ2dqhEBERUSWqsYPde/bsEdbwM1deXh727dsnvO7atauwffTo0UqLjaimUCqVuHDhAi5cuAClUmntcKgcmEPxYw7FTar5q7E9gLrhX3d3d8yePbvM/T/99FOkpKRg586dGDFiBABg6NCh+Oyzz1BYWIjff/8dkydPtvqj3Ygqk1arFX5gabVaK0dD5cEcih9zKG5SzV+NLABv376NM2fOAAAGDRqEZ555psxjzp07h/Xr1+Off/5BamoqPD094ebmhlGjRmHTpk24e/cuPvvsM8yZM6eqwyciIiKq0WrkEPDOnTuFKrz42n+m6PbTaDTCk0MA4L///S9atWoFANiwYQM++OCDMp9q8eDBg/KETURERCQKNbIHUFfAubq6okuXLmYd07lzZ7i7u+PevXuIiIjAlClTAAAKhQLfffcdpkyZghs3bmDTpk3Yu3cvhg0bhi5dusDDwwNOTk7IycnBzZs3cezYMezfv19ot6znB5v7JBCFQoEmTZqYdS1EREREVanGFYCnT5/GrVu3AAADBgwwe1FGuVyOgQMH4pdffkFiYiIuXrwozBxu0aIFNm/ejEWLFiEiIgJZWVnYsGEDNmzYYLI9V1dXvPzyywgLCyv1vOY+CaR///749ttvy9yPiIiIqKrVuAJQf/jW3OFfnaeffhq//PILgKJJJPpLx9SvXx+LFy/GtGnTsHv3bvzzzz+4efMmsrOzoVarUa9ePTRu3BgdOnRA79690bdvX9jZ2VXORRERERHVIDKtlKa8UAmFhYWIjY0FADRq4Y2QJREGn+8OHwVPF64DWFPp58/f37/UZ15TzcQcih9zKG4VyZ+Yc1/jegCJyHy2trZo3bq1sE3iwxyKH3MoblLNn3SulKgWksvlaNCggbXDoApgDsWPORQ3qeavRi4DQ0RERERVhz2ARCKmv4K9nZ0dZDKZlSMiSzGH4scciptU88cCkAQNFQ7YHT7K4D23eo5WiobMoVQqRXsDMhVhDsWPORQ3qeaPBSAJbORyeLqUvvA1ERERiR/vASQBVwQiIiKSBhaAJFCpVNYOgYiIiKoBC0AiIiIiiWEBSERERCQxLACJiIiIJIYFIBEREZHEcBkYEkjpGYi1hZ2dHfz9/YVtEh/mUPyYQ3GTav74G58EUln9vDaRyWSSWbS0tmIOxY85FDep5o9DwEREREQSwx5AEqg1GqRm51bpOdzqOcJGzr87KotGo8H9+/cBAPXr14ecX1vRYQ7FjzkUN6nmjwUgCTJzChCyJKJKz7E7fBQ8XZyr9BxSolKpcO3aNQDSeoZlbcIcih9zKG5SzZ80ylwiIiIiErAAJCIiIpIYFoBEREREEsMCkIiIiEhiJDkJJC8vDzt37kRUVBSuXLmC7OxsaLVaKBQKeHl5wcfHB0888QT69OmDxo0bGxw7a9YsbN++3aLz7dixA23bthVev/HGG9i/fz8AYO3atejZs2epxx88eBCvvfYaAGDEiBFYsmSJRecnIiIi0ie5AvDs2bN45513cPfu3RKfZWVlISsrCxcvXsS2bdvg5uaGv//+u9JjmDdvHmJiYvDgwQPMmzcPkZGRcHBwMLrvw4cP8cEHHwAAXF1dMXv27EqPh4iIiKRFUgXg9evXMXnyZOTmFq11FxQUhEGDBqFVq1aws7NDVlYWrly5gmPHjiEmJqbM9lavXg0PD48y92vRooXBaw8PD8yYMQNz587FrVu38NVXX2HmzJlGj/3000+RlpYGAJgzZw5cXFzKPB9Jh0wmEx5dxCe5iBNzKH7MobhJNX+SKgCXLVsmFH+LFi1CaGhoiX169eqFyZMnIzMzE3v27Cm1vZYtW6Jp06blimX06NGIjIzEiRMnsG7dOjzzzDPo0KGDwT4xMTH4/fffARQVq0OHDi3Xuaj2srOzQ8eOHa0dBlUAcyh+zKG4STV/kpkEolarceTIEQBAhw4djBZ/+ho2bIjx48dXWTwymQwff/wx6tSpA7VajfDwcKhUKuHzgoICzJ07V7g3UTcMTERERFRRkikAMzMzUVBQAKDkkKy1tGjRAm+88QYA4MqVK/jxxx+Fz5YvX46bN28CAGbMmAFPT0+rxEhERES1j2QKQN34PgAkJSVZMRJDL730Etq1awcA+Oabb3Dt2jVcvHgRa9euBQB07doVY8aMsWKEVJOpVCrcvn0bt2/fNuhBJvFgDsWPORQ3qeZPMgWgi4sLvLy8ABT1tn3//ffQaDRWjgqwtbXFxx9/DBsbGxQWFmLOnDmYM2cO1Go16tSpgwULFtSKm1LtoYY91MjPy0Nubq7wH1WMRqNBWloa0tLSasS/Z7Iccyh+zKG4STV/kpoEMmHCBGENvaVLl+LXX39FUFAQOnfuDH9/fzRr1syi9m7cuIG8vLxS93F0dCyz3fbt22PSpElYvXo1Tp8+Lbz/xhtvoFWrVhbFVFMNVtwCAKz74VuD9+fPn2+NcIiIiCRNUgXgpEmTcPXqVWzduhUAkJycjA0bNmDDhg0AADc3N3Tr1g3BwcEIDAwss+dt8uTJZZ6zW7duQvuleeutt7B79278+++/AIB27drhpZdeKvM4IiIiIktJZggYAORyORYuXIg1a9agT58+sLU1rH/T09Oxe/duTJs2Dc8++yxu3bpVbbHdvHkT6enpwuuGDRuWiI+IiIioMkiywujVqxd69eqFnJwcnD59GrGxsbh48SJOnTqFhw8fAgAuXryIcePGYdu2bSYXez548GC51wHUp1sGRqlUCu9FR0cjMjISwcHBFW6fiIiISJ8kC0AdhUKBvn37om/fvgCAwsJCREZGYsmSJbh//z7u3buHr776Cp988kmVxrF+/XrExsYCKBpWjoyMRFpaGhYuXIjevXujQYMGVXr+6rA3pzkAYNP0YXCv72TlaIiIiKRNUkPAZbG3t8eoUaOwdOlS4b39+/dX6ayg27dv46uvvgIA+Pj4YPr06Zg7dy6AorULdZNWxK4QNiiEDRydnODs7Cz8R0RERNWPBaARffr0QePGjQEA9+/fR3Z2dpWda968ecjPz4dcLscnn3wCOzs7PP300xgwYAAAYPv27Th+/HiVnZ/ETS6Xw8XFBS4uLpDL+e0sRsyh+DGH4ibV/EnnSi1k6r6/yrR161YcO3YMABAWFmbwLMJ58+ZBoVAAKFoq5dGjR1UeD4mPra0tvL294e3tzUlDIsUcih9zKG5SzR8LQCPy8/Nx9epVAEX3CVbFPXjp6enC8K6Xlxfefvttg889PT3x7rvvAiiaIbxixYpKj4GIiIikSTIFYG5uLkaPHo1Dhw6Vek+fRqPBggULhKdUBAUFVcmTOD766CPcv38fAPDhhx/CyankxIixY8eic+fOAIA1a9bgypUrlR4HERERSY90+joBXLhwAVOnToWnpycGDBiATp06oUmTJlAoFHjw4AHi4uKwdetWJCQkAADq1q2L//znPybbM+dJIADg7u5u0It44MAB/PnnnwCA4cOHo0+fPkaPk8lkWLBgAUaMGAGlUok5c+Zg8+bNkrpHgUqnVCqFZ1t7e3sbPPOaxIE5FD/mUNykmj/JFIC2trZwd3fHvXv3kJqaio0bN2Ljxo0m92/ZsiWWLl1a6jp/5jwJBABmz56NSZMmAQAePnyIDz/8EEDRYs+zZ88u9djHHnsMr776KlasWIHY2Fhs2LABL7zwglnnpdpPq9UKvdVardbK0VB5MIfixxyKm1TzJ5kCsE6dOvjrr79w7tw5HDt2DOfPn8f169eRkZGBR48ewdHRER4eHvDz80P//v3x9NNPw97evtLj+PTTT5GWlgYAeP/99826v/DVV1/Fnj17kJSUhC+//BIDBw5EkyZNKj02IiIikgbJFIBA0VTvzp07C/fVlcfixYuxePHich+/YMECLFiwwKJj7O3tsXv37nKfk4iIiEgfbyYjIiIikhgWgEREREQSwwKQiIiISGJYABIRERFJjKQmgVDpGiocsDt8VJWew62eY5W2LzU2NjbCUkU2NjZWjobKgzkUP+ZQ3KSaPxaAJLCRy+Hp4mDtMMgCNjY28PT0tHYYVAHMofgxh+Im1fxxCJiIiIhIYlgAEhEREUkMh4BJoFQqq+TpJ1R1CgsLERsbCwDw9/dn/kSIORQ/5lDcpJo/9gASERERSQwLQCIiIiKJYQFIREREJDEsAImIiIgkhgUgCWxtOSeIiIhIClgAkkAmk1k7BCIiIqoG7PIhgVqjQWp2rlXO7VbPETZy/j1CRERUHVgAkiAzpwAhSyKscu7d4aPg6eJslXOLma2tLXx9fYVtEh/mUPyYQ3GTav6kc6VEtZBcLodCobB2GFQBzKH4MYfiJtX8ccyNiIiISGLYA0gkYhqNBnl5eQAAJycnyHkfpegwh+LHHIqbVPMnjaskqqVUKhXi4+MRHx8PlUpl7XCoHJhD8WMOxU2q+atVPYAxMTGYOHGi8NrJyQnHjh2Do6NjqccVFBSgV69eyMnJEd5bv349unfvXupxJ06cQFhYmPB606ZN6Ny5s8Vx6nNwcICLiwv8/PwwcOBAhISESObB1ERERFQ9anUPYF5eHg4cOFDmfgcPHjQo/sy1fft2g9c7duywuI3iCgoKkJKSgsOHDyM8PByhoaG4c+dOhdslIiIi0qm1BWCdOnUAADt37ixzX90+umPMUVBQgD///BNAUU8jAOzduxeFhYUWxTl27FhERkYK/61duxbh4eFo1KgRACAxMRHTpk2DWq22qF0iIiIiU2ptARgUFAQAOHbsGO7du2dyv4yMDPz9998AgP79+5vd/v79+5GbW7Ro8pw5cwAA9+/fR1RUlEVxurq6wsfHR/ivZ8+emDhxIv744w94eXkBABISErB//36L2iUiIiIypdYWgL169YK7uzvUajX++OMPk/vt2rULKpUK7u7uePLJJ81uXzfc6+vri1GjRqFVq1YG71eUQqHAtGnThNfHjh2rlHaJiIiIam0BaGNjg2eeeQZA6cPAus+GDRsGGxsbs9pOS0vD8ePHAQAhISEG/4+OjkZmZma549anW5kcAFJSUiqlTSIiIqJaWwACwPDhwwEAcXFxSExMLPH51atXcenSJYN9zREZGQm1Wg25XI7g4GAAQHBwMGQyGZRKJXbt2lUJ0QN2dnbCtpQeT0NERERVq1YXgO3atUObNm0AGO8F1L3n4+ODtm3bmt2u7rhu3brB09MTANCsWTM88cQTACpvGDgpKUnY1t0PWJvYQy38l5+Xh9zcXIP/qGz29vYICAhAQEAAlwsSKeZQ/JhDcZNq/mp9t9Lw4cPx+eefY9euXXj33Xchk8kAAFqtFpGRkcI+5rp8+TLi4+MB/G/YVyckJARnzpzBpUuXcPXqVTz22GPljlutVmP16tXC60GDBpW7rZpqsOKWsL3uh29LfD5//vzqDIeIiEgyanUPIFBUlMnlcvz777+IiYkR3o+JicG///5rMIxrDl3vnoODQ4mibMiQIcKwbXl7ATMzM3H8+HFMmDABcXFxAIqKvy5dupSrPSIiIqLian0B6OnpKTzRQ38YWLfdo0cPYRi3LCqVSri/LzAwEAqFwuBzFxcX9O3bF0DRfYIajabMNlesWAFfX1/hv549e2LSpEk4c+YMHB0d8eKLL2Lp0qVmxUfSo1arkZqaitTUVK4VKVLMofgxh+Im1fzV+gIQAEaMGAEA2LdvHwoKCgwWcbZk+Dc6Ohrp6ekASg7/6ujeT0lJMehxLA8/Pz+EhYUZTAYh0qdWq3Hnzh3cuXNHUj+4ahPmUPyYQ3GTav5q/T2AADBw4EB88MEHyMnJwcGDB6HVapGbmwsnJyc8/fTTZrejG9Z1cXFBnz59jO4TGBiIevXq4cGDB9ixYwd69uxZaptjx47FuHHjABT9I0xJScGff/6JnTt34uzZswgLC8OWLVvQsGFDs+MUi705zYXtTdOHwb2+kxWjISIikg5JFIDOzs4YMGAAIiMjsXPnTmi1WgDAgAEDhMe4leXhw4fCUz6ys7PRoUOHMo/Zt28f5s+fX+o5dE8C0Wnbti0CAwPRvXt3zJo1C8nJyQgPD8fKlSvNilNMCvG/dRcdnZzg7OxsxWiIiIikQxJDwMD/hoH//vtv4akauvfMsWfPHjx69Miic+bl5WHfvn0WHaMzcuRIYZJJVFSUsPA0ERERUUVJogcQAHr27Al3d3fhucAeHh5lDs/q0w3/uru7Y/bs2WXu/+mnnyIlJQU7d+60qNDUN336dBw4cABqtRrLli2zKF4iIiIiUyRTANrY2GD48OFYv349gKLJH3K5eR2gt2/fxpkzZwAULcmie8Rcac6dO4f169fjn3/+QWpqqtkzjfW1atUKQ4YMwa5du3D+/Hn8/fff6NWrl8XtEBEREemTzBAwAMyYMQOxsbGIjY3Ff//7X7OP079v0NwFmXX7aTSaUp9FXJZXX31VWLy6Nt4HSERERNVPUgVgeekKOFdXV7MXZO7cuTPc3d0BABEREeU+t4+PD4KCggAAJ0+exKlTp8rdFtU+MpkMzs7OcHZ2Fv5QIHFhDsWPORQ3qeaPBWAZTp8+jVu3ih5ZNmDAALOHjeVyOQYOHAgASExMxMWLF8sdw9SpU4Vt9gKSPjs7O/j5+cHPz4/rRYoUcyh+zKG4STV/LADLoD98a+nzePXXGCzvo+EAoGPHjsK9f9HR0bhw4UK52yIiIiKSaXU3t5EkFRYWIjY2FgDQqIU3QpaUf7i6InaHj4KnC9cBJCIi8dD/Herv7w97e3srR2Q+ycwCJqqNVCoVbt68CQBo0aIFbG35LS02zKH4MYfiJtX8cQiYSMQ0Gg2ys7ORnZ0NjUZj7XCoHJhD8WMOxU2q+WMBSERERCQxLACJiIiIJEYaA91kloYKB+wOH2WVc7vVc7TKeYmIiKSIBSAJbORyeLo4WDsMIiIiqmIcAiYBVwQiIiKSBhaAJFCpVNYOgYiIiKoBh4BJIKVnINYWcrkcHh4ewjaJD3MofsyhuEk1fywASSCVxS9rE1tbWzRr1szaYVAFMIfixxyKm1TzJ51Sl4iIiIgAsAAkIiIikhyO+ZFAqVSK6kHWVJSzy5cvAwDatm0LOzs7K0dElmIOxY85FDep5o8FIAl4D6D4aLVaKJVKYZvEhzkUP+ZQ3KSaPw4Bk4CzgImIiKSBXT4kUGs0SM3Otcq53eo5wkZC0++JiIisiQUgCTJzChCyJMIq594dPgqeLs5WOTcREZHUsMuFiIiISGJYABIRERFJDAtAIiIiIonhPYBEImZra4vWrVsL2yQ+zKH4MYfiJtX8ie5KY2JiMHHiRKOfOTg4wMXFBX5+fhg4cCBCQkJKXdjY19fXonP7+flh586dBu9t27YNs2fPBgAsWrQIoaGhFrUZFBSE5OTkEu/b2tpCoVCgdevWePLJJ/Hcc8/B09PTorap9pPL5WjQoIG1w6AKYA7FjzkUN6nmr1YNARcUFCAlJQWHDx9GeHg4QkNDcefOHWuHVS4qlQrZ2dk4c+YMVqxYgaFDh2L//v3WDouIiIhqAdH1AOobO3Ysxo0bJ7zOyMhAYmIiVq9ejZSUFCQmJmLatGnYsWMHbGxsTLbToUMHLFq0qMzzOTg4VErcxnh4eGD16tXCa5VKheTkZGzduhWHDh1CTk4Opk+fjs2bN6Ndu3ZVFgeJi/4K9nZ2dlzMW4SYQ/FjDsVNqvkTdQHo6uoKHx8fg/d69uyJ0NBQhISEIDk5GQkJCdi/fz8GDx5ssh0nJ6cS7VQ3Ozu7EjG0a9cOAwcOxOLFi/HTTz9BqVRi5cqV+Prrr60UJdU0SqUSsbGxAAB/f38+y1mEmEPxYw7FTar5q1VDwDoKhQLTpk0TXh87dsyK0VTcm2++KfQ+RkdHQ6PRWDkiIiIiErNaWQAChhM8UlJSrBhJxTk7O8Pb2xsAkJeXh+zsbOsGRERERKJWawtAOzs7Ybs2TOvWvx72ABIREVFFiL8yMiEpKUnY9vLysmIkFadSqXDt2jUARYWgi4uLdQOqZPZQIz8vD7n/X+M6O/OZwERERFWpVhaAarXaYEbtoEGDSt0/Ly8PCQkJZbbbqFEj1KtXr8LxWeq3337DgwcPAAABAQG1okdT32DFLaz74Vvh9fz5860YDRERUe1XqyqJzMxMxMfHY/ny5YiLiwNQVPx16dKl1OMuXryI4ODgMtsvz0LP5aVbBmbbtm344YcfhPcnT55cLecnIiKi2kvUBeCKFSuwYsUKo585Ojri+eefx7vvvlvNUZVPcnJyqU8mkclk+M9//oOnnnqqGqMiIiKi2kjUBWBp/Pz8EBYWZjB5wpRu3bphw4YN1RCV5RQKBXr06IEXX3yxzJ5Mkh47Ozv4+/sL2yQ+zKH4MYfiJtX8iboA1H8SiFqtRkpKCv7880/s3LkTZ8+eRVhYGLZs2YKGDRtaOdKyFX8SiI2NDerWrQt3d/davyr53pzm2DR9GNzrO1k7FNGRyWSSWbS0tmIOxY85FDep5k/UBWDxJ4G0bdsWgYGB6N69O2bNmoXk5GSEh4dj5cqVVozSPMaeBCIVhbCBo5MTZ/8SERFVk1q5DuDIkSOFmb9RUVE4fvy4lSMiqhoajQZZWVnIysri+pAixRyKH3MoblLNX60sAAFg+vTpsLGxAQAsW7bMytEQVQ3dGpHXrl2DSqWydjhUDsyh+DGH4ibV/NXaArBVq1YYMmQIAOD8+fP4+++/rRwRERERUc1QawtAAHj11VeFCRRiuA+QiIiIqDqIehJIWXx8fBAUFISDBw/i5MmTOHXqlNGlVMx9EggAPPbYY5DLjdfNp0+fNquNoKCgWvc4NyIiIhKPWl0AAsDUqVNx8OBBAEW9gPpLreiY+yQQADh58qTJx8Ft2bIFW7ZsKbONHTt2sAAkIiIiq6nVQ8AA0LFjR/Tq1QsAEB0djQsXLlg5IiIiIiLrEl0PYPfu3REfH2/RMWvWrDH6vqXtGBMaGlqh5wNHRUVVOAYiIiIiS4iuACSi/5HJZMKji2r7E2NqK+ZQ/JhDcZNq/lgAEomYnZ0dOnbsaO0wqAKYQ/FjDsVNqvmr9fcAEhEREZEhFoBEREREEsMhYBI0VDhgd/goq5zbrZ6jVc4rdiqVCv/++y8AoHHjxrC15be02DCH4scciptU8yeNqySz2Mjl8HRxsHYYZAGNRoO0tDQAgKenp5WjofJgDsWPORQ3qeaPQ8Ak0Gq11g6BiIiIqgELQBKoVCprh0BERETVgAUgERERkcSwACQiIiKSGBaARERERBLDApCIiIhIYrgMDAmksvZRbSKXy+Hi4iJsk/gwh+LHHIqbVPPH3/gkkNJDsGsLW1tbeHt7WzsMqgDmUPyYQ3GTav5YAJJArdEgNTtXeO1WzxE2EvpriIiISCpYAJIgM6cAIUsihNe7w0fB08XZihERERFRVWABSCRiSqUSSUlJAABvb2/Y2dlZOSKyFHMofsyhuEk1fywAiURMq9UiNzdX2CbxYQ7FjzkUN6nmjzd4EREREUkMC0AiIiIiiWEBSERERCQxor8HsLCwEPv27cNff/2FCxcuICsrCzk5OVAoFPDy8oK/vz8GDRqEHj16lLnA45EjR3DgwAGcPn0a6enpyM/PR8OGDdG8eXM89dRTCAkJgaenp8njn3nmGVy9ehUuLi74559/Sl1Xb8SIEbh8+TIA4LXXXsN//vMfk/ueOHECYWFhAICXX34ZM2bMKPU6iIiIiEoj6gJw3759WLx4MZKTk0t8lp2djezsbFy6dAm//vorWrZsidmzZ6Nfv34l9k1MTMTcuXNx9uzZEp+lpKQgJSUFJ06cwLfffospU6Zg6tSpRou7gIAAXL16FdnZ2bh69SratGljNO6HDx8iPj5eeH3q1KlSr1P/865du5a6LxEREVFZRFsAfvPNN1i+fLnwulevXggKCoK3tzfq1auH+/fv4/r164iKisKxY8dw48YNLFu2rEQBeOrUKUybNg0PHjwAALRv3x4jR46Er68vnJyckJqaiqNHj2LHjh3Iy8vDl19+icTERCxZsqTEVPGuXbvit99+E9o1VQCeOXMGGo0GNjY2UKvVuHDhApRKpcmp56dPnwZQ9IiagICAcn29iIiIiHREWQBu3bpVKP5cXV3x5Zdfolu3biX2e/LJJzF+/HgkJCRg0aJFyMzMNPg8JSUFr7/+Oh48eACZTIYZM2bgpZdeMujd69ChA/r374/Jkyfj9ddfR3x8PP744w80bty4xFCsfu/cqVOnMHbsWKPx63r0nnrqKcTExCAvLw+xsbHo3LlziX3VarXQM+nr64u6deua8yUiibCxsUHTpk2FbRIf5lD8mENxk2r+RDcJJDU1FQsWLAAAODk5YcOGDUaLP30+Pj5YvXo1XnrpJYP3586di+zsbADAf/7zH0yePNnkfXvNmjXDTz/9BA8PDwDA6tWrcf78eYN9GjVqBC8vLwClD+vqPuvWrRsef/zxUve/fPmysD5Rly5dSrtMkiAbGxt4enrC09NTUj+4ahPmUPyYQ3GTav5EVwCuXbsW+fn5AIC33nrL7Ac4y+VyDB8+XHh95coV/PXXXwCKCsQpU6aU2Yarqytmz54NoGixyB9//LHEPrpewJSUFNy5c6fE548ePUJsbCyAooJON6SrG+Ytjvf/ERERUWUTVQGo1Wqxfft2AEW9f6NHjy53W7p2AGDChAlmV/2DBg0SZgIfOHAA9+/fN/hcv5fOWK/e+fPnoVQq4ejoiHbt2gkFoO6+wOL0C0MWgERERFQZRFUAJiYmIisrC0DRjFuFQlHutk6ePClsBwYGmn2cjY2NMJFEo9GUmDlc/D7A4nTvdezYEba2tnj88cdhY2ODBw8eICEhocT+ugKwdevWaNiwodlxVpQ91MjPy0Nubq7wH9U8hYWFOH36NE6fPo3CwkJrh0PlwByKH3MoblLNn6gmgVy5ckXYbt++fYXa0hVb7u7uwn195mrXrp1BTPozi1u2bAl3d3fcu3fPaAGoK+h0PYXOzs7w8/PDpUuXcOrUKfj5+Qn7Xrt2DRkZGQCqv/dvsOIW1v3wrcF78+fPr9YYiIiIqGqIqgdQN2EDKLofr7wePnwIpVIJAHBzc7P4eP1j9GPS0Q3rXr9+XSjgAMMZvfrLuei2ixeM+sO/nABCRERElUVUBaD+MKSjo2OltOPk5GTx8frH5OTklPjc1DBwXFwccnNzYWNjg06dOgnvmyoAOQGEiIiIqoKoCkBnZ2dhWzcTuKLt5OXlWXy8/jHG7kM0NRFE16Pn5+dnEINu/3v37uHmzZsljvXy8kLjxo0tjpOIiIjIGFHdA+ji4iJs6w+tWkqhUMDW1hYqlQrp6ekWH69/jH5MOj4+PqhXrx4ePHhgMIyrK+iKP83Dzc0NLVq0wM2bN3Hq1Cm0aNECqampwjIy1hj+3ZvTHJumD4N7fct7SImIiKhmE1UPoP4EiUuXLpW7HZlMBl9fXwBFvW737t2z6Pi4uDijMenoP7LtypUrwjCxrhg09ji34sPA1h7+LYQNHJ2c4OzsLPxHREREtYOoCsA2bdqgQYMGAIqKKWP335lLv1ctKirK7OM0Gg2OHDkCoKjQM/b4Nv321Wo1zpw5g6SkJOFRdMZ69HTt6Ao//WVqOAGEiIiIKpOoCkCZTIaRI0cCKLoP7/fffy93W6GhocL2xo0boVarzTruzz//REpKCgBgwIABqFevntH99Iu206dPC4VdixYtjM481vUA3rp1C2lpaUJvobu7O1q1amVWbCQ9tra28PX1ha+vL2xtRXVHB/0/5lD8mENxk2r+RFUAAsCkSZOEGcDLly9HUlKSWcdpNBpEREQIr/38/NC7d28AQHx8PFavXl1mG5mZmVi8eDGAomL05ZdfNrlv+/bthThPnjwp9OgZG/4FDBd6PnDgABITE0vdnwgo6oVWKBRQKBSQy0X37UxgDmsD5lDcpJo/0V2pp6cn5s6dC6CoFzAsLAwnTpwo9ZirV6/i5ZdfLlHkLViwQOjB++KLL7B27VpotVqjbSQnJ+PFF18Uev8mT56Mxx9/3OQ57ezshKVeYmNjERMTA6D0gk43DLxmzRohDi7/QkRERJVNlH2do0aNQkpKCpYvX46MjAyEhYWhd+/eCAoKgre3N+rVq4f79+/j+vXrOHLkCI4ePQq1Wl1iwkaTJk2wYsUKvPbaa8jJycGiRYsQGRmJkSNHwtfXF46OjkhLS8PRo0exY8cOYfmXoUOH4u233y4zzi5duuD48eMoLCxEWloagNILwICAABw4cAC3b982aIPIFI1GI/y7dHJyktRfr7UFcyh+zKG4STV/oiwAAeD1119HmzZtsHjxYiQnJyM6OhrR0dEm92/Tpg1mzJhR4v3u3bvjl19+wdy5c3H+/HlcvHgRFy9eNNqGo6MjXnnlFbz22muQyWRlxli8eHN1dS31fr7ixWH9+vWF2cpExqhUKsTHxwMA/P39YW9vb+WIyFLMofgxh+Im1fyJtgAEgKeffhr9+vXDn3/+ib/++guxsbHIzMxEbm4uFAoFvLy88Pjjj2PQoEHo3r27yaLN19cXmzdvxuHDh7F//36cOXMG9+7dQ0FBARo0aICWLVuiT58+GD58ODw9Pc2Or1OnTrCzsxMeO2dqxrBOu3bt4OjoKCxy3blzZ7MKTSIiIiJLiLoABAB7e3sEBwcjODi4wm3169cP/fr1q3hQ/8/BwcFkb6IxdnZ2OHfuXKWdn4iIiMgYaQx0ExEREZGABSARERGRxLAAJCIiIpIYFoBEREREEiP6SSBUeRoqHLA7fJTw2q2eoxWjISIioqoi05p69AVJQmFhIWJjYwFIa/0jIiKiihLz71AOARMRERFJDAtAIiIiIonhPYAkUKvV1g6BLKRWq5Geng4AcHNzg42NjZUjIksxh+LHHIqbVPPHApAEGo3G2iGQhdRqNe7cuQMAaNCggWR+cNUmzKH4MYfiJtX8cQiYiIiISGJYABIRERFJDAtAEtja8o4AIiIiKWABSAKZTGbtEIiIiKgasAAkgVqjQWp2LtScDEJERFSrsQAkQWZOAYZ+shXpD/KtHQoRERFVId70RSRiMpkMzs7OwjaJD3MofsyhuEk1fywAiUTMzs4Ofn5+1g6DKoA5FD/mUNykmj8OARMRERFJDAtAIiIiIonhEDCRiKlUKty8eRMA0KJFC67lKELMofgxh+Im1fzV6KuMiYnBxIkTjX7m4OCAhg0bom3bthgyZAiGDBlSZtJu3bqFiIgIREdHIzk5GVlZWXB2doaHhwe6d++OwYMHo0uXLmXGFRYWhhMnThj9zNbWFnXr1oW3tzf69u2L5557Di4uLibbmjVrFrZv317ifZlMBoVCgSZNmiAgIADPPfcc2rZtW2ZsJC0ajQbZ2dkAgGbNmlk3GCoX5lD8mENxk2r+anQBWJqCggLcvXsXd+/excGDB7Fu3TqsXLkS7u7uJfYtLCzE559/jl9++QVKpdLgs+zsbGRnZyMhIQEbNmxA37598dFHH6FRo0blikulUiErKwunTp3CqVOnsHbtWnz99dcICAiwqB2tVouHDx8iPj4e8fHx+PXXXzFlyhRMnz69XHERERER6YimABw7dizGjRsnvM7Ly8PFixexZs0aJCcnIzY2Fq+99ho2b95sMI07JycHr732GmJiYgAALi4uGDlyJHr27AlXV1c8fPgQFy9exNatW3H9+nUcOXIEY8aMwZo1a+Dt7V1mXJGRkQavlUolbt++jZ07dyIqKgoZGRmYNm0a9u7di4YNG5ba1urVq+Hh4QGg6C+SjIwMHD58GL/88gtUKhVWrVoFDw8PjB8/3uyvGxEREVFxoikAXV1d4ePjY/Bep06dEBwcjNGjR+PmzZu4cOECDh06hKCgIGGf8PBwofjr3r07li1bBldXV4N2evbsiUmTJmHp0qX46aefkJKSgqlTp2L79u1QKBSlxlU8JgBo3749Bg8ejJkzZ2LHjh24f/8+fv/9d7z66qulttWyZUs0bdrU4L1evXqhZ8+emDZtGgBgxYoVeP7552FjY1NqW0RERESmiH4WcP369TFlyhTh9dGjR4Xt3bt3Y+/evQAAPz8/fPfddyWKPx07OzvMmjULo0ePBlB0v+DSpUsrFNvkyZOF7djY2HK3ExQUJNybmJmZiUuXLlUoLiIiIpI20ReAANCxY0dh++7du8L2Dz/8IGwvWLAAjo6OZbY1a9YsuLm5AQC2bt2KzMzMcsel35tXWFhY7nYA09dIREREZKlaUQDqz/5Vq9UAgCtXriAuLg4A8MQTTxgUUKVRKBQIDQ0FADx69Ai7du0qd1z6hVrjxo3L3Q5geI0ajaZCbZUlPy8Pubm5VXoOIiIisp5aUQAmJCQI27pJFKdOnRLeCwwMtKg9/XsI9dux1OrVq4Xt/v37l7sdwPg1VpV1P3yLzz//vErPQZVDLpfDw8MDHh4ekMtrxbez5DCH4scciptU8yeaSSCmqFQq/PTTT8Lrbt26ASjqAdRp166dRW22bdsWcrkcGo0G8fHxpe6rX5gBRbOAk5OTERERgf379wMAhg4diqeeesqiGPRduXJFuLfR0dER/v7+5W6LahdbW1tJrVtVGzGH4scciptU8yfaAlC3DMzXX3+Nc+fOAQC8vLwwdOhQAEBWVpawr7G1AUvj4OAAZ2dnPHz4UFgc0pTg4GCTn7Vq1QpTpkzByJEjLTo/ULQOYHp6Og4fPoylS5cKQ9thYWGoU6eOxe0RERER6YimAFyxYgVWrFhh8nNXV1d88803sLe3BwCDe9icnJwsPp+TkxMePnyInJwcy4P9f9evX8dvv/2Gpk2bCj2TpSlrmLhfv3546623yh0PERERESCiAtCUpk2bYtCgQZg8ebLBEi/Ozs7Cdl5ensXt6o4pax3A4kPEGo0GWVlZOH36NL755hucO3cOL730Ej7//HMMHjzY4jjs7Ozg7++PMWPGYPjw4QaLXFeVF155De71LS+aqfoplUpcvnwZQNGtC3Z2dlaOiCzFHIofcyhuUs2faApA/SeByGQy1KlTBw0aNEDdunWN7t+gQQNh+969e/Dz8zP7XAUFBUIPYmnP8TVGLpfD1dUVTz/9NHr16oXQ0FDcuHED77//Pnr06FFqe/pPApHL5XBycoKbm5vQq1ldHJ2cDApoqrm0Wq3weEOtVmvlaKg8mEPxYw7FTar5E810F92TQHx8fNCmTRs0b97cZPEHAL6+vsK2rrI31+XLl4WlVvTbsZSzszPGjh0LoGhI+s8//yx1/5YtWwrX+Nhjj6FJkybVXvwRERFR7SeaAtBSXbt2FbajoqIsOlZ/f/12yqN169bCdvEZw0RERETWUGsLQD8/P7Rt2xYAcPbsWbMfxZaTk4Pt27cDAOrUqYNnnnmmQnHoZu8CRUvWEBEREVlbrS0AAeCVV14RtufNm4eCgoIyj/n0009x7949AMCoUaPQsGHDCsVw8eJFYbuiTwMhIiIiqgy1ugB85pln8PTTTwMA4uLiMG3aNJPP9lUqlfj000/x22+/AQCaN2+Od999t0LnT05OxsaNG4XXffv2rVB7RERERJVBNLOAy2vhwoXIysrCyZMncezYMQwZMgShoaHo0aMH3NzckJOTg4sXL2LLli24du0aAKBRo0ZYuXJlmUvAACXv69NoNMjOzsapU6ewYcMGYSHp4OBgYUiaiIiIyJpqfQFYt25drF69Gp999hk2bdqE7OxsrFmzBmvWrDG6f58+fbBgwQKzh2tLexKIztChQ7Fw4UKL4iYyh62trTDRyNa21n8710rMofgxh+Im1fxJ4krr1KmDOXPmICwsDBEREYiOjkZycjKys7Ph7OwMDw8PdO3aFUOGDKnwrF+ZTAYnJyc0btwYnTp1wogRIyrcJpEpcrncYM1LEh/mUPyYQ3GTav5kWimtekglFBYWCjOkG7XwRsiSCOwOHwVPFy4ETUREVBr936H+/v6iWrtXEj2ARLWV/gr2dnZ21fKoQKpczKH4MYfiJtX81epZwES1nVKpRGxsLGJjY4UfYCQuzKH4MYfiJtX8sQAkIiIikhgWgEREREQSwwKQBA0VDtgdPgpu9RytHQoRERFVIU4CIYGNXA5PFwdrh0FERERVjD2AJOCKQERERNLAApAEKpXK2iEQERFRNWABSERERCQxvAeQBFJ6BmJtYWdnB39/f2GbxIc5FD/mUNykmj/+xieBVFY/r01kMpmoHj1EJTGH4scciptU88chYCIiIiKJYQ8gCTgLWHw0Gg3u378PAKhfvz7kcv5NJzbMofgxh+Im1fxJ4yrJLBwCFh+VSoVr167h2rVrnMUtUsyh+DGH4ibV/LEHkARqjQap2bkG77nVc4SNRP4aIiIikgoWgCTIzClAyJIIg/d2h4+Cp4uzlSIiIiKiqsCuHSIiIiKJYQFIREREJDEsAImIiIgkhgUgERERkcRwEgiRiMlkMuHRRVzGR5yYQ/FjDsVNqvmr0gIwJiYGEydONPqZg4MDXFxc4Ofnh4EDByIkJMTsR7HcuXMHAwYMEBYu/vzzzxEcHGxxfHfv3sUff/yBY8eO4ebNm8jMzIRGo0H9+vXh4+ODgIAABAcHo1mzZkaPz8jIwIULF3DhwgXExsYiNjYW2dnZAICRI0di8eLFZcYwa9YsbN++vcT7MpkMCoUCTZo0QUBAAJ577jm0bdvW4muk2s3Ozg4dO3a0dhhUAcyh+DGH4ibV/FmtB7CgoAApKSlISUnB4cOHsXbtWqxatQpNmzYt89gdO3YYPLVix44dFhWAjx49wtKlS7Fp0yYUFhaW+DwtLQ1paWmIjo7G8uXLMXjwYMycORONGzc22O/JJ580+5yW0mq1ePjwIeLj4xEfH49ff/0VU6ZMwfTp06vsnERERCQN1VYAjh07FuPGjRNeZ2RkIDExEatXr0ZKSgoSExMxbdo07NixAzY2NqW2FRFRtFadk5MT8vLycPz4caSlpcHDw6PMODIzMzFt2jScO3cOAODs7Ixhw4ahR48eaNSoEWxtbZGeno4zZ85g//79uHHjBvbs2YNOnTph0qRJJttt0qQJWrdujejo6LK/GCasXr1auAaNRoOMjAwcPnwYv/zyC1QqFVatWgUPDw+MHz++3OcgIiIiqrYC0NXVFT4+Pgbv9ezZE6GhoQgJCUFycjISEhKwf/9+DB482GQ7Z86cwc2bNwEA77//PubPnw+1Wo3IyEhMnjy51Bg0Gg3efvttofgLDAzEJ598AldX1xL7BgUF4Z133kFERAQ+/fRTo+29/vrr8Pf3h7+/P9zc3HDnzh3079+/1BhK07JlyxI9oL169ULPnj0xbdo0AMCKFSvw/PPPl1kkkzSoVCr8+++/AIDGjRvD1pa39YoNcyh+zKG4STV/Vp8FrFAohOIGAI4dO1bq/jt27AAANGjQACNGjEDPnj0BADt37izzXOvXr0dMTAwAoHfv3lixYoXR4k9HLpdjxIgR2LZtG3x9fUt8/tZbbyEwMBBubm5lnrsigoKC0KVLFwBFPZiXLl2q0vOReGg0GuGWBY1GY+1wqByYQ/FjDsVNqvmzegEIwKC4SklJMblfYWEh9u7dCwAYMmQI7OzsEBISAgCIj4/H5cuXSz129erVAIA6depg4cKFZlf5jRo1EgpNa9G/QfXu3btWjISIiIjErkYUgLrp1wBKLcqioqJw//59ABAKv4EDB8LJyQnA/3oHjYmOjkZaWhoAYPDgwfD09Kxo2NVK/+tSlX+h2ENt8F9+Xh5yc3NL/EdERETiVSMGupOSkoRtLy8vk/vpCrzmzZvjiSeeAFA0EaR///6IjIzErl278N577xm9P+7kyZPCdr9+/Son8GqUkJAgbJsz2aW8BituGbxe98O3RvebP39+lcVAREREVcvqPYBqtVoYmgWAQYMGGd0vMzNTmGFbfMkXXW9genq6yVm4V65cEbbbt29foZir25UrV3D06FEAgKOjI/z9/a0cEREREYmZ1QrAzMxMHD9+HBMmTEBcXByAouJPN9mhuF27dkGpVAL4X8Gn06tXL7i7uwMwPQysW6AZQKkTP2oKrVaLe/fu4ffff8ekSZOgVqsBAGFhYahTp46VoyMiIiIxq7Yh4BUrVmDFihVGP3N0dMTzzz+Pd9991+TxusKuY8eOaNmypcFnNjY2GDp0KNatW4eoqCjk5ORAoVAY7KN/35qjo2P5LqKKlbWETL9+/fDWW29VUzRERERUW9WIewD9/PwQFhZmMBlEX2JiorD0SfHeP52QkBCsW7cOBQUF2LNnD0aPHm3wubOzs7Cdn59fokCsqezs7ODv748xY8Zg+PDhVf6cwr05zQ1eb5o+DO71nar0nFR+crkcLi4uwjaJD3MofsyhuEk1f1Z5EoharUZKSgr+/PNP7Ny5E2fPnkVYWBi2bNmChg0bljhW1/tna2uLZ555xmj7HTp0gLe3N5KSkrBz584SBaAuuUDRvYI1sQDUfxKIXC6Hk5MT3NzczH5GcmUohOEEGkcnJ4PimWoWW1tbeHt7WzsMqgDmUPyYQ3GTav6s9iSQtm3bIjAwEN27d8esWbOQnJyM8PBwrFy50uA4jUaDyMhIAEWrdZuzHt+pU6dw584dg6dq+Pn5CYtMx8XFlRhGrgmMPQmEiIiIqLJZva9z5MiRwszfqKgoHD9+3ODz48ePIzU11aI2tVptiSeDdO3aVdg+fPhw+YIlIiIiqgVqxD2A06dPx4EDB6BWq7Fs2TKDXj7d8K+9vT0WLlxY5vj8jz/+iLi4OEREROD1118X3u/duzc8PDyQlpaGvXv34t133xXdYtBExSmVSmEdTW9vb5P30VLNxRyKH3MoblLNX40oAFu1aoUhQ4Zg165dOH/+PP7++2/06tULubm5OHDgAICipV6Kr/9nzL179xAXF4cbN27g7NmzwoLR9vb2mDx5MhYtWoRHjx7h/fffx/fff2900ejiUlNTce3aNas/Do6oOK1WK8xw12q1Vo6GyoM5FD/mUNykmj+rDwHrvPrqq8IMV919gPv27UNeXh4A0wtEFzdo0CChneJrAk6cOBHdu3cHUPRouNdffx2ZmZkm29JqtYiMjERoaCji4+Mtuh4iIiKimqpG9AACgI+PD4KCgnDw4EGcPHkSp06dEgo4Ozs7BAUFmdVO48aN4e/vjwsXLmDPnj0IDw8XZtHK5XJ8+eWXmDp1Ks6fP49Dhw5hwIABCA4ORo8ePdCoUSPY2toiPT0d586dw759+3Dt2jWT5zp16hRu3frfo9OysrKE7Zs3b2Lbtm0G+4eGhpr75SAiIiKqMjWmAASAqVOn4uDBgwCAr7/+GidOnAAAdO/eHfXr1ze7nUGDBuHChQu4f/8+Dh06ZNB72LBhQ2zYsAGff/45Nm3ahNzcXPz666/49ddfjbYlk8kQHByMIUOGlPhsy5Yt2L59u9Hjzpw5gzNnzhi8xwKQiIiIaoIaMwQMFD3lo1evXgCAf/75BxqNBoD5w786+vsbezRcnTp1EB4ejn379uHdd9/Fk08+iSZNmsDBwQF16tSBh4cHevfujenTp+PgwYP47LPPOGGEiIiIag2ZVkp3PFIJhYWFiI2NBQA0auGNkCURBp/vDh8FTxcuBF1T6efP39+/WhcNp8rBHIofcyhuFcmfmHNfo3oAiYiIiKjq1ah7AInIMjY2NsLTY8xZ0ohqHuZQ/JhDcZNq/lgAEomYjY0N708VOeZQ/JhDcZNq/lgAkqChwgG7w0cZvOdWz9FK0RAREVFVYQFIAhu5HJ4uDtYOg4iIiKoYC0AScEK4+Ih5BhoVYQ7FjzkUN6nmj7OASaBSqawdAhEREVUDFoBEREREEsMCkIiIiEhiWAASERERSQwLQCIiIiKJYQFIREREJDEsAImIiIgkhusAkkBKz0CsLWxtbeHr6ytsk/gwh+LHHIqbVPMnnSulMsnl7BAWG7lcDoVCYe0wqAKYQ/FjDsVNqvnjb3wiIiIiiWEPIAk0Go21QyALaTQa5OXlAQCcnJzYiytCzKH4MYfiJtX8SeMqySxqtdraIZCFVCoV4uPjER8fz0f5iRRzKH7MobhJNX8sAImIiIgkhgUgERERkcSwACQiIiKSGBaARERERBLDApCIiIhIYrgMjMRptVphW6lUorCw0IrRkKWUSqXRbRIP5lD8mENxq0j+9PfX/30qBjKt2CKmSpWbm4srV65YOwwiIiJR8/Pzg7Ozs7XDMBuHgImIiIgkhj2AEqfRaJCfnw+g6CHYMpnMyhERERGJg1arFRaPdnR0FNVTRFgAEhEREUmMeEpVIiIiIqoULACJiIiIJIYFIBEREZHEsAAkIiIikhgWgEREREQSwwKQiIiISGJYABIRERFJDAtAIiIiIolhAUhEREQkMSwAiYiIiCSGBSARERGRxLAAJCIiIpIYFoBEREREEsMCkIiIiEhiWAASERERSQwLQCIiIiKJYQFIREREJDEsAImIiIgkxtbaAUhdcnIyNmzYgMOHDyMlJQX29vZo1qwZhgwZgvHjx8PR0bFSznPkyBFs3rwZsbGxyMzMRMOGDeHv74/nnnsOffv2NasNlUqF33//HZGRkbh27Rry8vLg4eGBJ598EmFhYWjTpo1Z7WRmZmLDhg04cOAAkpOTAQBeXl4YMGAAJk6ciAYNGpT7Oq1BDDlMT0/HoUOH8M8//+Dy5cv4999/oVQq4eLiAl9fXzz99NMYPnw4HBwcSm3H19fXrFi7deuGDRs2mH1t1iSG/M2aNQvbt2836zwHDx5E06ZNS92nuq65utT0HG7btg2zZ8+26FwjR47E4sWLS7zP70HLaDQaXLt2DRcuXMCFCxcQGxuL+Ph4KJVKAMD69evRvXt3s9vLz8/Hzz//jL179+L27dsoLCxEo0aN0K9fP4SFhcHLy8usdmrC96BMq9Vqq/wsZFRUVBRmzJiBnJwco5+3bNkS33//PVq0aFHuc2g0GsydOxdbtmwxuc/o0aPx0UcfQS433SGcmZmJKVOmIDY21ujn9vb2mDdvHkaPHl1qPOfPn8frr7+Oe/fuGf3c3d0d3377LTp27FhqOzWFGHK4efNmfPDBB1Cr1aWep2XLlvjqq6/g5+dncp/a9stHDPkDKrcArI5rrk5iyGF5CsB33nkHr776aon3+T1ome3bt2PWrFkmP7ekALx58yamTJmCGzduGP1coVDg888/R2BgYKnt1JTvQRaAVhIXF4exY8eioKAATk5OePXVV9G9e3cUFBRg9+7d2Lx5M4Cifwhbt26FQqEo13mWLl2K77//HgDQrl07vPzyy2jWrBlu376NH3/8EXFxcQCAV199Fe+8847RNtRqNcLCwnD69GkAwNNPP43Ro0fDxcUF58+fx8qVK5GRkQG5XI5Vq1aZ/Ev433//RWhoKDIzM2Fra4tJkyYJ3yiHDh3C2rVroVKp4Orqim3btqFRo0bluubqIpYcfvvtt/jqq69gZ2eHwMBA9OrVC97e3nB2dsatW7fw+++/Izo6GgDQsGFDbN++3eTXXvfLZ+zYsRg3bpzJmB0dHdGsWbNyXW91EUv+gP8VgB4eHli9enWp52vVqhXs7Oyses3VRSw5fPDgAVJSUso8z5tvvokbN25ALpfj0KFDRr8P+T1oGf3i287ODj4+PlAqlUhISABgfgGYk5ODUaNGCcXfc889h6FDh8LBwQExMTH47rvvkJeXB0dHR2zatAlt27a12jWbTUtWMW7cOK2Pj4+2Xbt22jNnzpT4/IcfftD6+PhofXx8tMuXLy/XOa5du6Zt166d1sfHRxsaGqrNz883+DwvL08bGhoqxHHjxg2j7fz+++9CLB988EGJz2/cuKHt3Lmz1sfHRztw4ECtUqk02s6MGTOEdnbv3l3i8z/++EP4fObMmeW44uollhz+9NNP2s8++0ybkZFh8jyLFi0SYp01a5bJ/Sp6PTWJWPKn1Wq1M2fO1Pr4+GgDAwPLFYdOdVxzdRJTDsty9epVIdYXXnjB5H5iyk9ZqiN/58+f165fv1579uxZbUFBgVar1WqXL18utPvPP/+Y1c6XX34pHPPDDz+U+Pz06dPCv5MJEyaYbKcmfQ9yEogVXLhwAadOnQIAjBo1Ck888USJfV566SV4e3sDKPoLRXe/giXWrVsHlUoFAJg7d26J+7scHR0xd+5cAEX3961du9ZoO2vWrAEAuLi44L333ivxeYsWLYShips3b2L//v0l9rl37x4iIyMBAL1798aQIUNK7DN06FD07t0bALBz506Tw8Q1gZhyOGnSJPz3v/9Fw4YNTZ7nnXfegbu7OwBg//790Gg0FscqJmLKX2WprmuuLrUthzt27BC2R4wYUa42xKS68texY0eEhYWhU6dOqFOnTrliVSqVwnC6t7c3XnrppRL7dO7cGaNGjQIAnDhxAhcuXCixT037HmQBaAUHDhwQtnX/YIqTy+XCD4EHDx4gJibGonNotVocPHgQANC6dWt06tTJ6H6dOnVCq1atABTdO6QtdkfA9evXkZSUBAAYPHiwyRtTR44cKWzrX59OVFSUUFSYumYACA0NBVB0z01UVJTJ/axNTDk0h729PTp37gwAePjwIbKzsy1uQ0xqW/7MUR3XXJ1qUw41Go3wB7KTkxOefvppi44XIzH9e4yJicHDhw8BFBXnpu7V1f3+Aoz/Hqxp18wC0Ap099I5OTmhffv2Jvfr2rWrsH3mzBmLznHnzh2kpaWVaMeYbt26AQBSU1Nx584do7Hq72eMu7s7WrZsaTJW/XZKi6ci11ydxJRDcxUWFgrbpU0Iqg1qY/7KUh3XXJ1qUw5jYmLw77//Aii6x9rJycmi48VITP8ezf092KFDB6GTpLTfgzXlmrkMjBXoetSaN28OW1vTKWjdunWJY8x19epVo+2UdZ5r164Z3Disf15z2rlx4wb+/fdf5OXlGfwQ08VTt25dYajRGA8PDygUCuTk5Fh8zdVJTDk0h1KpxLlz5wAAbm5ucHFxKXX/vXv3Ys+ePUhOToZcLoe7uzueeOIJjBw5Ej169LDo3NYg1vxlZWVhwoQJSExMRG5urrCMT2BgIEaNGlXq0hHVcc3VSaw5NKY8w7/8Hqw+5v4etLW1RfPmzREfH2801pp2zbX7z/wa6NGjR8jKygKAMme51q9fXyiizJlBpk9//7LOo/+57q9QY+14enqW2k7jxo0BFA2bFI83NTXVrFj027H0mquL2HJojs2bNwvXNHjw4DL3v3r1KpKSklBQUIC8vDzcvHkTO3bswAsvvIDXX39dGC6picScv7y8PJw8eRLZ2dlQKpW4d+8eoqOjsWDBAgwaNMhkb0F1XXN1EXMOi8vLy8O+ffsAFP3sM7d44/dg9dGd18nJCfXq1St1X93vr8zMTINRlZp4zewBrGa5ubnCtjnd/I6OjsjLy0NeXl6VnUe/16D4efTbcXZ2rnA75l5z8XPXJGLLYVlu376NZcuWCeeZMmVKqecJCgpCz5490apVKzg7OyMzMxMnTpzAr7/+iuzsbBw4cACvvfYa1qxZY3I5EmsSY/5kMhk6deqEwMBAtGvXDm5ubnj06BESEhKwZcsWXLhwAampqZg8eTI2btyIdu3alTsWXTzluebqIsYcmrJv3z5h/+HDh0Mmk5V5Hn4PVq/y/P7SHWdvb2/QhiXtVPU1swCsZo8ePRK2zfnG1P3jKSgoqLLz6M5h7DyV3Y4l16x/7ppEbDksTX5+Pt544w2ht2Du3Lml9vT+9ddfRv8C7tWrF8LCwvDKK68gLi4OJ06cwKZNmzBx4kSzY6kuYszf7NmzjX7dn3jiCTz33HP48ssvsWrVKuTl5WHOnDnYunWrQSFRXddcXcSYQ1MiIiKE7eHDh5e5P78Hq195fn/pH1d8u6ZcM4eAq5n+NHRzpnfrupDLekRXRc6j301d/DyV3Y4l11zeKftVTWw5NEWlUuE///kPrly5AqBoYVn9WWzGlDb84ebmhuXLlws/3H7++Wez4qhuYsxfaV93mUyG6dOno2fPngCAS5culRgKrq5rri5izKExqampOH78OADg8ccfL/M+Q4Dfg9ZQnt9f+scV364p18wCsJrpD6Oa07Wbn58PwLwu4/KeR3cOY+fRb6esIVlz2rHkmssacrYWseXQGK1Wi1mzZuHIkSMAgCFDhmDevHkWxWdMs2bN8OSTTwIoWhNSd+9nTVIb8mfMmDFjhO2TJ0+WOxb9eGrqbNTaksOIiAhheSxzev/Mwe/Bylee31/6xxXfrinXzAKwmtWpU0eYYVnWzZ33798X/qFY+lg0/f3LOo/+57obWI21U9YPEt2NzzKZrES8umFFc25o1bVTUx8FJ7YcGvPhhx8K64499dRT+Oyzzypt6RfdIqZA2f9mrKE25M+Yxx57TNgu/nWvrmuuLrUlh7rhXzs7OzzzzDMWxVYafg9WLt158/Ly8ODBg1L31f3+atiwocFwcE28ZhaAVqD7QX3r1i1hhXljrl27Jmzrf0Nbco7i7ZR1nuJDEPrnNbedxo0bl/irRRfPw4cPS33CR1pamvCAbEuvuTqJKYfFffbZZ9i0aROAovWmvv7660q9Ubysm9hrAjHnz5Syvu7Vcc3VSew5vHTpkvA82sDAwDKXXrIEvwcrl7m/B1UqFW7fvl3iGJ2ads0sAK0gICAAQNFfE5cuXTK5n/4wju4pDeZq2rQpPDw8SrRT2nk8PT3RtGlTo7ECRY+3MeXevXvCQ7KNxarfTmnxVOSaq5OYcqjv22+/xY8//ggA8Pf3x6pVqyr9HhP9davKWjrIWsSav9Lor1mnO6++6rjm6iT2HOqv/VdZw786/B6sXOb+Hrx48aLQc1fa78Gacs0sAK1gwIABwvbWrVuN7qPRaIQfEPXq1UP37t0tOodMJkP//v0BFP01oVvkt7hz584Jf23079+/xF+OrVq1Ev4C2bt3r8H9Dfq2b98ubOtfn05QUJAwxGjqmgFg27ZtAIqeRBEUFGRyP2sTUw511q1bh6+++goA4OPjgx9//BEKhcKimMpy+/Zt/P333wCKFjutqb98xJi/svz222/CtrGnVlTHNVcnMedQpVLhjz/+AAA0aNAAffv2tSiu0vB7sPJ169YNdevWBVBUuJt61J/u9xdg/PdgTbtmFoBW0LFjR3Tp0gVA0T+Cs2fPlthnzZo1wl9xEydOLDFEFxMTA19fX/j6+mLWrFlGz/PCCy/AxsYGALBgwYIS08kLCgqwYMECAEUrmL/wwgtG29E9+Do7OxufffZZic9v3bqF7777DgDQokULDBw4sMQ+7u7uCA4OBgBER0dj7969JfbZs2cPoqOjART9RVzaE0OsTWw53Lp1KxYtWgQAaNmyJX766SeLh5yioqJKHbZIT0/HW2+9JcxwGzdunEXtVycx5e/cuXPC48iM0Wq1WLZsGY4dOwYA8PPzM+ixqMxrrknElMPijh49ioyMDADAsGHDzP4683vQ8vxVBnt7e4SFhQEo6l1dvXp1iX3Onj0rFHXdunVDx44dS+xT074HuQ6glYSHh2Ps2LEoKCjASy+9hKlTp6J79+4oKCjA7t27hb/mW7ZsiRdffLFc52jVqhUmT56M77//HhcvXsTYsWPxyiuvoFmzZrh9+zZ++OEHxMXFAQAmT54sPMu3uJEjR2Lr1q04c+YMNm7ciPT0dIwePRr169fHhQsX8O233yInJwdyuRzh4eEmH3Ezffp0HD16FJmZmXj33Xdx8eJF9OvXDwBw+PBh/PTTTwCKbp59++23y3XN1UksOTxw4ADmzp0LrVYLhUKB8PBwZGZmIjMz0+R5mzZtWuI+zo8//hhKpRKDBg1Cp06d4OXlBQcHB2RlZSEmJga//fabsNJ9QEAAxo8fX65rri5iyd/Ro0fx/fffo0+fPujVqxe8vb1Rr149FBYWIj4+Hlu3bsX58+cBFC0eu2DBApM9UNVxzdVJLDksrjyPfgP4PVhe+j1zAHD58mVh++jRo0hOThZeN2/eXCjS9E2ePBm7d+/GjRs38Nlnn+HWrVsYOnQoHBwcEBMTg1WrVkGlUsHBwQHvv/++1a/ZHDKtqb5MqnJRUVGYMWOGMOmhuJYtW+L7779HixYtSnwWExMjLPA5cuRILF682GgbGo1GWBjWlGeffRYLFiwodRZoZmYmpkyZgtjYWKOf29vbY968eRg9erTJNgDg/PnzeP31101OBHF3d8c333yDxx9/vNR2agox5HDWrFkGQ/TmWL9+fYmhh6CgIIMflKYMGjQIH3/8cZmPTKoJxJC/r7/+GitWrCjzWpo0aYLPP//caO+fvopcc00khhzqe/DgAXr37o1Hjx6hTZs22LVrV6n76+P3oCFz8+fr62t2PKW1c/PmTUyZMkW43704hUKBzz//HIGBgaWeo6Z8D7IH0IqCgoIQERGB9evX4/Dhw0hNTYWdnR2aN2+OwYMHY8KECaU+3N0ccrkcCxcuxKBBg/Dbb78hNjYWWVlZaNCgAfz9/TFmzBiz7j9p2LAhfv31V2zevBm7du1CUlIS8vPz4eHhgZ49e2LixIlo06ZNme08/vjjwjUfPHgQd+7cAVDU49S/f3+88MILaNCgQYWuuTqJKYcVtXjxYpw4cQLnzp3D7du3kZ2djZycHDg5OaFRo0bCg+ifeOKJKo+lsoghf6GhoXB1dcW5c+cQHx+PjIwMZGdnw9bWFg0aNEC7du0QGBiI4OBgsxZPr45rrk5iyKG+vXv3Ck+FCAkJsSgOfg9aV4sWLbB9+3Zs3LgRe/fuxa1bt6BUKtGoUSP07dsXEydOhJeXV5nt1JRrZg8gERERkcRwEggRERGRxLAAJCIiIpIYFoBEREREEsMCkIiIiEhiWAASERERSQwLQCIiIiKJYQFIREREJDEsAImIiIgkhgUgERERkcSwACQiIiKSGBaARERERBLDApCIiIhIYlgAEhEREUkMC0AiIiIiiWEBSERERCQxLACJiIiIJIYFIBFRNbh79y7mzZuHAQMGwN/fH76+vvD19cWBAwesHRpRucyaNQu+vr4ICgqydihUDrbWDoCoomJiYjBx4kQAwBtvvIE333zTyhERGbp79y5CQ0ORlZVl7VAsov+9VZyDgwNcXFzg5+eHgQMHIiQkBPb29ma3rVarsW/fPhw8eBCxsbFIT0+HUqmEm5sbWrdujX79+iEkJAT16tWzKObCwkLs27cPf/31Fy5cuICsrCzk5ORAoVDAy8sL/v7+GDRoEHr06AG5nH0gJF0sAIkIAODr6wuARXRVWLlyJbKysmBra4u3334bXbt2hZOTEwCgSZMmVo6ufAoKCpCSkoKUlBQcPnwYa9euxapVq9C0adMyjz116hTmz5+Pq1evlvgsOTkZycnJOHr0KL7++mu8++67eO6558yKad++fVi8eDGSk5NLfJadnY3s7GxcunQJv/76K1q2bInZs2ejX79+ZrUtZvzeJmNYABIRVbFjx44BAPr3749XXnnFytGUz9ixYzFu3DjhdUZGBhITE7F69WqkpKQgMTER06ZNw44dO2BjY2Oynb1792LGjBkoLCwEAHTr1g0hISFo1aoV7O3tcffuXURFReGPP/5AdnY25s6di+vXr2PmzJmlxvfNN99g+fLlwutevXohKCgI3t7eqFevHu7fv4/r168jKioKx44dw40bN7Bs2TJJFIBVZfHixVi8eLG1w6ByYgFIRFTFUlNTAQCtWrWyciTl5+rqCh8fH4P3evbsidDQUISEhCA5ORkJCQnYv38/Bg8ebLSNuLg4/Pe//4VSqYSdnR0WLVqE4OBgg306duyIwYMH48UXX8TUqVORkpKCNWvWoFmzZgYFqL6tW7cKxZ+rqyu+/PJLdOvWrcR+Tz75JMaPH4+EhAQsWrQImZmZ5flSENUKvAGCiKiKKZVKAICtbe37m1uhUGDatGnCa11vZ3EajQbvvfee8LVYuHBhieJPX9u2bbF27VphqHzJkiW4e/duif1SU1OxYMECAICTkxM2bNhgtPjT5+Pjg9WrV+Oll14q/eKIarHa99OIqBj9G9nXr1+Pbt26YcuWLdi2bRuuXbsGpVKJ1q1bY8KECRgxYoRwXGFhIbZu3Yrt27fjxo0bUCqV8PX1xcSJEzF06FCj57pz5w769+8PAFi0aBFCQ0OxZ88e/Pbbb4iPj0dubi6aNGmCgQMH4pVXXinzBvfCwkL8/vvv2Lt3LxITE5GTk4P69eujXbt2GDZsGIKDg03eyD5r1ixs374dXl5eiIqKQlpaGtatW4fDhw/j7t27yMvLw/r16zF79myDe6ZWrFiBFStWGLQ1cuRIg6GetLQ07N+/HzExMbhy5QrS0tKgUqnQoEEDdOjQAcHBwRg8eLDJ2IrnpHv37ti9e7fwdcrLy0Pjxo3Rv39/TJkyBS4uLqV+nQDgyJEjiIyMxNmzZ5GRkQG1Wg13d3e0b98egYGBGDJkCBwdHY0ee+nSJfz222+IiYlBWloatFotPD090aNHD0yaNKlcPXfbtm3D7NmzDd4r/rUt/nUFgMzMTKxbtw5HjhzBnTt38OjRI7i7uyMgIABjxoxBly5dTJ4zKCgIycnJQrsXL17Ezz//jBMnTiAtLQ1KpRLx8fEWX0tZdPeYAUBKSorRfaKiopCYmAgAeOqppxASElJmu61atcK0adOwdOlSFBQUYP369Zg1a5bBPmvXrkV+fj4A4K233oK3t7dZMcvlcgwfPtysffVV588Tfffu3cPPP/+Mo0eP4s6dO8jLy4Orqys6deqEMWPG4MknnyxxjO7fg05Z39vFr61r167Ytm0bdu7ciaSkJGRmZmLEiBHC/sV/xpiSk5ODzZs34/Dhw0hKSsL9+/dhb2+P5s2bIyAgAEOHDkVAQECZXwOqXCwASVJUKhWmTZuGQ4cOGbwfGxuLmTNn4uLFi5gzZw7u37+P119/HSdPnjTY7+zZszh79ixu3bqFqVOnlnm+999/H1u3bjV47/r16/j++++xY8cOrF271uQvrDt37uCVV17BtWvXDN5PT0/HX3/9hb/++gu//fYbvv322zILpHPnzmHq1KmVMgtVrVajb9++0Gg0JT5LS0tDVFQUoqKisGXLFnz99ddwdnYutT2NRoMZM2YgIiLC4P0bN25g9erVOHDgADZu3Ah3d3ejx2dlZWH69Ok4fvx4ic90Ewr27dsHAAgNDS1x7iVLlmDdunXQarUlzn/jxg1s2bIF8+bNw5gxY0q9jsoQHR2N//znP8jJyTF4X3cdERERGD9+PObMmVPmDNZNmzbh448/hkqlqsqQAQB2dnbCtqlezh07dgjbL7zwgtltP//881ixYgUePXqE7du3Y+bMmZDJZAAArVaL7du3Ayjq/Rs9enQ5oi+/6vp5EhERgfnz5yMvL8/g/ZSUFOzduxd79+7Fs88+iw8//LDSepkfPXqEyZMnm+zRNdexY8fwzjvvlPjZo1QqcfnyZVy+fBk///xzlfxhQqVjAUiS8tVXX+H8+fMIDg5GcHAw3NzccOPGDXz99de4fv06NmzYgKCgIGzYsAFnz57F2LFjMXDgQLi4uODy5cv46quvkJaWhuXLl6N///5o06aNyXP98ssviI2NRceOHTFp0iS0aNECGRkZ2L59O/bs2YO0tDS8/PLLiIyMhEKhMDg2NzcXkyZNwu3btwEAAwYMwKhRo+Dh4YE7d+5g48aNOHHiBE6fPo2pU6di48aNJm+8z83NxZtvvolHjx5h6tSp6NWrFxwcHJCQkAB3d3esXr0aSqVSGI4rfrM/ANSvX1/Y1hVKPXr0wFNPPQUfHx80bNgQubm5uH37Nn7//XecPXsWf//9Nz766CMsWbKkzJycPXsWAwYMwIgRI9CkSROkp6fjl19+weHDh3Hz5k0sWrQIX3zxRYlj8/PzMXHiRCQkJAAA2rdvjzFjxqBNmzawt7dHSkoKTp48id27dxs994IFC/DLL78AALp27YqRI0eiWbNmcHBwQHx8PNatW4fExETMmzcPbm5uQu+uOQYMGIAOHToAgMmvrf7X9fLly5g6dapwf9z48eMRFBQER0dHXL58Gd9//72Qe0dHR8yYMcPkuWNjYxEREYFGjRph8uTJ6NChA1QqFU6fPm12/JZISkoStr28vIzuc+rUKQCAo6MjevbsaXbb9erVQ9euXREdHY3s7GxcvXpV+L5LTEwUCouAgIAS30dVrTp+nuzevRvvvfcetFotmjVrhgkTJsDb2xsNGzZEcnIytmzZgiNHjmDLli1QKBQGvc6Wfm/r+/zzzxEfH4+goCCEhoYK35e5ublmf33++ecfvPLKK1CpVLCxscHw4cPRv39/NG7cGI8ePUJSUhL++uuvEgU0VRMtkcj9888/Wh8fH62Pj492+fLlpX7u4+OjXbt2bYl90tLStE888YTWx8dH26NHD62vr692//79Jfa7fPmy1s/PT+vj46NdsGBBic9v375tcK5XXnlFq1QqS+y3YsUKYZ8lS5aU+Hzx4sXC58uWLSvxuUaj0b777rvCPhs3biyxz8yZM4XPO3XqpL18+XKJffSV9jUsfu4bN26Uus9XX32l9fHx0fr6+mqvX79e4vPiOfn222+Nnuell17S+vj4aNu1a6fNyMgosc/ChQuFNj788EOtRqMxGs+jR4+09+7dM3gvOjpaOHbz5s1GjysoKNBOnDhR6+Pjow0MDDSaS3OY87UdNWqU1sfHR9u2bVvt0aNHS3yenZ2tHTp0qNbHx0fr5+enTUhIKLFPYGCgcK5hw4Zp79+/X654dcr63tJqtVqVSqUdMWKEsN/JkydL7JOSkiJ8/txzz1kcx+effy4cHxERIby/c+dO4f0vvvjC4nbLozp/nmRkZGgDAgK0Pj4+2tmzZ5v89/fFF18I/y6SkpJKfG7u93bxazP2s0ef7mdMYGBgic8KCgq0vXv31vr4+Ggff/xx7T///GOynbt375Z6HqoanARCkvL4448bHX5yd3fHwIEDARTdgzVkyBAMGDCgxH5+fn7CvSpl9abY29tjwYIFRodkpk2bJsyo3Lp1q7AkBlB0r9CWLVsAAG3atDG6bpdMJsMHH3wgDP1u3Lix1Fhefvll+Pn5lbqPuWQyGVq0aFHqPq+//joaNGgArVZb6r1BQFGvnbHhL5lMhkmTJgEoGmo7e/aswecPHjzAb7/9JrQRHh4uDA0WZ29vDzc3N4P3vv/+ewDAoEGDTA4d1qlTB/PmzQNQNAwbExNT6rWU14ULFxAbGwsAGD16NHr37l1in/r16+Ojjz4CUDR0reu5NGX+/PkWL6JsiczMTBw/fhwTJkxAXFwcgKKvpbF7FPWH/4rnwRyurq7CdnZ2ttFt/X2qS1X/PNm0aRMePnwIT09PfPDBByaHd9988014enpCo9Fg586dFbkkQcuWLSu0ZuCOHTuQlpYGAJg+fTq6d+9uct/GjRuX+zxUfiwASVJKu9lav0B65plnytxPNzxrSq9eveDp6Wn0M7lcLtwgnp2dLfwCBYCLFy/iwYMHAIpu0DY1tKtQKDBkyBAAwNWrV4UftsaYc8N9eWk0GqSmpuLatWtISEhAQkICkpKS0KhRIwDAlStXSj0+ODjYZOHWvn17Ybv41/uff/4Rbv6fOHFiqWvPFZeTk4MTJ04AKCpaSuPt7Y0GDRoAQIkitLLo32f17LPPmtwvICBAuGe0tHuzGjduXOpkkfJYsWKF8Pg6X19f9OzZE5MmTcKZM2fg6OiIF198EUuXLjV6rP49jbpZvZbQv49Uvy394UhTE3yqUlX/PNH98dSvX79Sn7Jia2uLTp06Aai8f6NDhw616HuqON2wrpOTk9kLeVP14j2AJCmlzeasW7eusN2yZcsy9yvrXhh/f/9SP+/YsaOwnZCQIPwA182UBIp6GErz+OOPY9OmTcJxHh4eJfZxcnJCs2bNSm3HUlqtFhEREdiyZQsuXLiAgoICk/uWNfGkdevWJj/Tn9xS/OutXzRbOoMwLu7/2rv7mKauNw7g32JQNCi1IkO3hLiJWSfRIG6YyKYyoomZL5WYwEh9ne8GByabbnEm27I5/tmETMgSnUycRQzRiHENAlYhamAQUGZsEEQwa8bL6HiLlcrvD9KTW/tCCy3wy/1+/mrSc29Pe2/b555zn/P8JZJY0tPTkZ6e7tF27e3tXr2Op2zHPDAwEGq12m3bxYsX4/Hjx2hubobFYnEaGEizcsfC22+/Da1Wa5cMIiW9N+/VRAZPSI+9dF/SwNB2MTCW/Pl7YrVaxcVTfn6+GO0ejq/O0dGeQw8fPgQwdBE3HsE5DY8BIMlKUFCQy+ekWZWetHOWBSs13JSUq2kts9ksHqtUKrf7kE6nSbeT8vU04PPnz3Hw4EHcunXLo/bugkPA82Py6uctDSydBb7udHR0eNXeZrj3MlK2469UKofN4rQd88HBQfz3339Op1T9MfUrTSCwWq0wmUzQ6/W4cuUKampqoNVqcenSJafnrG0EFRhZgCI9XtKLAunjkR7T0fDn74nZbB5RBrevzlFXySGesn0/XWXv0/hjAEjkJ66mNcd6H6OZxnEmOztbBH/vvfcePv74YyxcuBChoaEICgoSf2gpKSki83Oikf7Zfv3114iOjvZou9H+KY4VXx9zwLESiFqtxqpVqxAbG4sjR47g2bNn+PLLL5Gdne2wbVhYGGbOnIl///0Xjx49gtVq9aqP0tFe6dSq9HF9fb23b2lCs1qt4vHmzZvF+nzDcTUK663hlhmi/38MAIn8ZLiRDlejGtIgo6Ojw+00k/Q1xiI4GRwcFAkqS5cuRW5urss/Clcjkr4iHVX6559/vJrmln7eQUFBDiXOxpqtP11dXRgYGHA7Cmg75gqFwq9JHp7SaDQoKyuDXq9HaWkp7ty547DMi0KhQExMDG7cuIH+/n7cuXPHaaKLM93d3eJCQqlUYv78+eK5yMhIEVj++eef6OnpGfOlYPzl1aWXxvsc9dbMmTNhMpnQ1tY23l0hFxjiE/mJLavTk+el639JH9fW1rrdR11dndPt/KWrq0v8oLur9NHb24umpia/9kWaIOLtSKNarRajq9XV1T7t10jYjp1tcVx3bMc8IiLCbWLAWEpLSxMjej/++KPTNhqNRjw+d+6cx/vOz88X05oajcZuVFyhUIj99vX1oaCgwOu+T1STJ08W58VEOEe99c477wAYSmobj/szaXgMAIn8pKKiwmVm7suXL0UFg5CQELtgJioqSozsXL582eW9hj09Pbh+/ToAYP78+V7fB/eqKVOmAIDdkjSvkk5LuftRLygo8HsFitjYWJFReu7cObu+DUelUomkm6KiInR2dvqjix6TlvF6tXKMVE1NDRoaGhy2GW/z5s0TGem1tbWoqKhwaBMfHy8ymG/evIlr164Nu9/m5mb8/PPPAIZGap1Ng27btk0kGWRmZtotSu3Oy5cvHarPTDTx8fEAgMbGRty+fXvE+/Hku+1rtr739/d7nMBCY4sBIJGfWCwWfPXVV04Dk19++UVUr0hMTLQbyZk8ebJYCsRoNOLUqVMO2w8ODuKbb74RN1qnpKSMur+2m7WfPn3qso1KpRLBaVFRkdM/lLq6Opw8eXLU/RnOjBkzRHm2+vp6fPfddw7l3GxevHjhkCSwb98+AEOBdGpqqlh6xxmLxYLz58/j+fPnPuq9vUWLFomqIQUFBU7L2nV3d4s1CQMCApCcnOyXvozUnj17xOics/sAAwICkJGRIe5RO3LkiMsKLcDQ8kHbtm0TWcOff/455s6d69Dutddew7FjxwAMjQJqtVqxxI8rDQ0N+OSTT3D69GnP3tw42bJli7jIOXr0qN0KAc7cvHnT6bJLnny3fW39+vViGayffvrJ7TFxVT+a/Iv3ABL5SVRUFMrKypCcnGxXCu7y5cti9CM8PBz79+932PbAgQMoLi5GS0sLsrKyYDQasWnTJsyePRutra3Iy8sTP6jR0dE+qVMbHR2N1tZWlJaWQqfTYcmSJWLkIDg4GLNmzUJAQADWrVuH8+fP49GjR0hOTsb27dsRERGBnp4eGAwG/P7775g2bRrCwsLw5MmTUffLnUOHDqGiogJGoxF5eXmoqalBUlISFixYgMDAQJhMJlRVVeHatWv49NNP7WoBr1ixAlu2bMFvv/2GyspKrF27FklJSYiJiYFSqURfXx+ePn2KqqoqFBcXw2w2Y+PGjeIz8bVvv/0WmzdvxosXL7Br1y5otVqsWrXKrhScba24HTt2TLh7whYsWID4+HiUlJSgsrISVVVVDmsRRkVFISMjA5999hksFgvS0tKg0+mwbt06vPnmmwgMDMTff/+N0tJSFBUViVHkHTt2OJQwk0pMTITJZEJmZiY6Ojqg1WoRFxcnRh1nzJgBs9mMpqYmGAwG3L59G1ar1WeLo/tLaGgofvjhB6SmpqKtrQ2JiYnQaDT44IMPEB4ejoGBAZhMJtTV1UGv16OlpQU5OTkO78uT77avTZkyBRkZGdi5cyf6+/uxfft2rF+/HgkJCQgPD4fFYkFjYyMMBgNKS0vx4MEDn/eB3GMASOQnKSkpqKysRGFhIdLS0hyet9Xhla4XZhMcHIyzZ89i165daGxshF6vh16vd2i3ZMkSZGdn+yTrc+fOndDr9bBYLDh+/LjdcxqNBidOnAAwdL9XdXU1Hj58iAcPHuDw4cN2bZVKJbKyspCZmen3AHDq1KnIzc1FamoqKisrUV9fL0aDPPHFF18gJCQE2dnZaGtrQ1ZWlsu206ZN80t2rY1arUZOTg4OHTqEnp4enDlzBmfOnHFol5KS4vCZTxR79+5FSUkJgKFRQGcjbGvXrsXs2bNx/PhxPH78GPfu3XNZYUWpVCI9Pd2jC5wDBw4gMjISJ06cwLNnz1BeXo7y8nKX7SMjI93WU54oVq9ejVOnTuHo0aPo6uqCTqeDTqdz2jYgIMDpmnuefrd9bdmyZcjJycHhw4dhNptRWFiIwsJCv7wWeY8BIJEfff/991i+fDny8/NhNBrR19eHuXPnIiEhAbt373abufvGG2/gypUrKCgowB9//AGj0Yje3l6EhIRArVaLAvS+Wq5BrVYjPz8fp0+fRnV1Ndrb251O8U6fPh0XLlzAr7/+iuvXr6O5uRmTJk3CnDlzsGLFCmzdulVUARkLKpUKeXl5KC4uxtWrV1FbW4vOzk4oFAqEhYVh4cKFSEhIcFrxQ6FQ4ODBg9iwYQN0Oh3u3r2L1tZWdHd3IygoCHPmzIFarUZcXBwSEhLcrufmC3FxcSguLkZubi4MBgNaWlpgsVgQGhqKmJgYJCUl+bzChy8tWrQIy5cvR0VFBcrLy1FXV2e34LnNu+++i6tXr0Kv16OkpAT3799He3s7BgYGoFKp8NZbb2HlypXYsGGDV5nOq1evxsqVK6HX63Hr1i3cv38fnZ2d6O3tRXBwMF5//XUsXrwYa9asQWxsrE+WWRoLtpHVixcvwmAwoKGhAWazGZMmTUJoaCgiIyOxbNkyrFmzxmlZNU+/2/7w/vvv48aNG7hw4QLKysrQ1NSE7u5uTJ06FREREVi6dCk++uijMekL2VMMurpphoi81traig8//BDAUPAnnXIkIiKaKJgEQkRERCQzDACJiIiIZIYBIBEREZHMMAAkIiIikhkGgEREREQywyxgIiIiIpnhCCARERGRzDAAJCIiIpIZBoBEREREMsMAkIiIiEhmGAASERERyQwDQCIiIiKZYQBIREREJDMMAImIiIhkhgEgERERkcwwACQiIiKSGQaARERERDLDAJCIiIhIZhgAEhEREckMA0AiIiIimWEASERERCQzDACJiIiIZIYBIBEREZHMMAAkIiIikhkGgEREREQy8z8l9VBcd24D2gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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YZ2pevHix2GPKYohV1yMLGL+IL5Gx2GNGRJVGdHS0wV2Gz69j1bFjR6MXBA0ODsaECROE1+W1kKj+sg5lMfm+VatWwhIUERER6NWrV5HHd+/eXXj6wfbt2+Hn5yeJpzoAhoWqfgFbkISEBOHpFEXRX0KkuMdKFSYiIkLYNmaxXSJTsMeMiCTLlCUPnj17ZrD0hbOzM9q2bVseYZWZI0eOGBSSnTp1KnWbXbp0EbYvX75c7PFvvPGGUABdvXpVWN9NCurXry9sF1V0qdVqLFiwwKhCq1q1asLnTU1NNbk4S0lJEXrMqlatysKMyhwLMyKSrLVr12LSpEk4fPhwkXcZXr58GePGjTMYypo5c6aoi+EWRavV4sCBA5g7d67wXrVq1cpkodvOnTsLE/6vXLkCpVJZ5PGNGzfGpEmThNcrV67E4sWLC52fFxISgjlz5iA6OrrUsRanR48eQu/dhQsXsHz58nw9Z48fP8Z7772HU6dOGXVnqY2NjfCAc5VKhWPHjpkUU3BwsHDH58svv1zso7+ITMU/UUQkWVqtFufOncO5c+dgY2ODZs2aoUGDBqhWrRrUajVSUlIQHh6ebxL/hAkTyuSuyZIq7FmZubm5SElJQUhIiMEEcisrK3z55Zdl8lgoe3t79OnTB/v370dmZibOnz+PHj16FHnORx99hNu3b+PkyZMAgN9++w1//PEHXnrpJdSrVw9WVlZISkpCWFgYHj9+DCCvp628eXh4YMiQIcJDxzdu3IigoCD4+PjA2dkZcXFxuHjxIlQqFRwcHPDpp59i4cKFxbbbr18/rFu3DgAwa9YsBAQEoEGDBlAoFMIxs2fPLvBc/ULOz8+vFJ+OqGAszIhIsmxsbIRtpVKJGzdu4MaNG4Ue7+zsjE8//RRDhw6tgOgKZ8yzMnVq166NxYsXl+mdnv/5z3+wf/9+AMDBgweLLcysra3x448/4vvvv8fGjRuhVCqhUqlw4cIFXLhwId/xVlZWBrkpT59//jmSkpJw9uxZAHk9ZM8/VaFWrVr49ttvC12k9nlvvfUWjhw5gtu3b0OlUuH06dP5jimoMMvJycGpU6cA5N1N2717dxM/DVHxWJgRkWTNmjULfn5++PfffxESEoKYmBjEx8cjMzMTMpkMjo6OqF27Npo3b45u3brB19e3wgqGkpDL5XB0dISrqytatGiBnj174pVXXinzmDt37gxPT09ERUXh0KFD8Pf3R9WqVYuNbebMmXj99dexZ88enDt3Dvfu3UNaWhqsrKzg7OyMpk2bonPnzhg4cGCZPiWgKHZ2dvjll18QFBSEwMBAhIeHIzMzE05OTqhfvz769euHYcOGoXr16sJ6cMWpWrUqdu3ahe3bt+P06dOIiYlBenp6sfPNDh48KKxvN2bMGIMeNqKyItPy2RJERJVOUFAQPvnkEwDA3LlzMXHiRHEDqgRGjhyJkJAQODg44Pjx4wbP+iQqK5z8T0RUCQ0aNAjNmjUDAGzatKnES0NQnuDgYGEZkkmTJrEoo3LDwoyIqBKSy+X49NNPAQCPHj0q9XM4Ld1///tfAHlzy/TvYiUqayzMiIgqqe7du6N3794AgDVr1kjyeZjm4MyZM8L8tU8//RSOjo4iR0SVGeeYEREREUkEe8yIiIiIJIKFGREREZFEsDAjIiIikggWZkREREQSwcKMiIiISCJYmBERERFJBAszIiIiIolgYUZEREQkESzMiIiIiCTCWuwASFwajQbZ2dkAAGtra8hkMpEjIiIiMg9arRa5ubkAADs7O8jlpe/vYmFm4bKzsxERESF2GERERGbN29sbDg4OpW6HQ5lEREREEsEeMwtnbf2/PwIeHh6wt7cXMRrLpFKphF5Lb29vKBQKkSOyPMyBNDAP4mMOTKP/fen/PC0NFmYWTn9OmY2NDWxsbESMxjLJ5XI4OTkBAKpUqVJmf7nJeMyBNDAP4mMOSq6s5mjzGycB/wKKw9raGh4eHmKHYdGYA2lgHsTHHIiPc8yIiIiIJIKFGREREZFEcOyKBCqVinPMRKBSqRATEwMg7wYMTrateMyBNDAP4mMOxMfCjEhkWq0WmZmZwjZVPOZAGpgH8TEH4uNQJhEREZFEsDAjIiIikggWZkREREQSwcKMiIiISCJYmBERERFJBAszIiIiIongchkkkMtZp4vBysoK9erVE7ap4jEH0sA8iI85EB8LMxLwL6E4rKys4O7uLnYYFo05kAbmQXzMgfjYRUJEREQkESzMiIiIiCSCQ5kk4LMyxaFUKhEaGgoA8PHxYQ5EwBxIA/MgPuZAfOwxIyIiIpII9piRwNqafxzEoFAo4OPjI2xTxWMOpIF5EB9zID7+JCaBTCYTOwSLJJPJOFwgMuZAGpgH8TEH4mNhRgK1RoOEtEyxwyAiIjKKSzU7WFWyNThZmJEgJSMHg5fvEzsMIiIioxzwHwF3JwexwyhTlavMJCIiIjJjLMyIiIiIJIKFGREREZFEsDAjIiIikggWZkREREQSIfm7MtPT07Fv3z6cOnUKMTExSE5OhrW1NVxcXPDCCy+gd+/e6NevH6ysrIxuMyUlBX/99Rf++ecfREdHIzU1FUqlEtWrV0eTJk3Qpk0bDBo0CJ6ensW2pdFocPLkSZw+fRpXr15FUlISnj59CgcHB7i7uwsx9ujRo8DF+saPH48LFy7ke18ul6Nq1aqoX78+OnTogNdffx0NGzY0+jMSERGR+ZFptVqt2EEUZufOnVi5ciXS0tKKPK5p06ZYtGgR2rVrV+RxGo0G69atw/r165GZWfx6XV27dsVnn32Gpk2bFrj/woULWLx4MaKioopty83NDTNnzsTw4cMN3i+sMHueQqHA3LlzMXbs2GKPNYX+c9FqNfTgchlERGQ2xF4uozyeLSrZHrPly5dj48aNAPIeFTRw4ED4+vqibt26UCqVuHPnDvbv349///0Xt27dwqRJk7BixQr079+/wPZycnLw4Ycf4uTJkwDyCp2BAweiS5cuqFevHmxtbZGcnIzQ0FAcO3YMN2/exD///IM//vgD/v7++drbvXs3Fi5cCJVKBQBo3bo1+vbti+bNm8PJyQnp6el48OABTp8+jVOnTiExMRFLlizJV5jpCwoKErY1Gg3i4+Nx6NAhBAQEQKVSYfHixahbty569uxZ0q+ViIiIJEyShdm2bduEoqxWrVpYt24dmjdvbnBMu3btMHLkSAQFBWHu3LlQKpWYNWsWGjZsmO9YAPj888+FouzFF1/Et99+i3r16uU7rkePHpgxYwZOnjyJJUuWFBjf+fPnMW/ePGg0Gtjb22Pp0qUFFoSdOnXCyJEjERsbi5UrV+LMmTNFfu7nh069vb3Rs2dPtGzZEl9++SW0Wi1Wr17NwoyIiKiSktzk/7i4OCxfvhwAYG9vj02bNhVYaOn4+fkJBZRSqcSnn36K50dnjx49ioCAAACAl5cXNm3aVGBRpq9Xr17YvXs3OnXqZPB+dnY2Zs2aBY1GA7lcjp9++qnQXjqdevXqYdWqVZg3b16RxxVm7NixqFu3LgDgxo0bSE5OLlE7REREJG2SK8w2b96MZ8+eAQDeeecdNG7cuNhzBg8ejG7dugEAoqKihJ4xnXXr1gnbS5cuhb29vVGxVKtWDb179zZ4b/fu3Xj8+DGAvIKpQ4cORrUFAEOHDjX6WH1yuRwvvPCC8Prhw4claoeIiIikTVKFmVarxd69ewEAtra2eP31140+94033hC29+zZI2xHRkYiLCwMQN7wZ8uWLUsVo65tmUyGCRMmlKotU1hb/2/UWaPRVNh1iYiIqOJIao5ZdHS0cAdmu3btULVqVaPP7dKlC2xtbZGTk4PLly8L71+8eFHY7tGjR6niy8jIwM2bNwEAjRs3RoMGDUrVnin07/x0c3OrsOsSERFVNBuojTouOysLmflXoiqUg4P0H3guqcIsIiJC2G7RooVJ51pZWcHb2xvXrl1DSkoKEhIS4O7ubtCm/nBgSURGRgq9VaXteTPFqVOnEB0dDQCoX78+ateuXWHXJiIiqmj9He8bddzmX340qd2FCxeWJJwKJanCLDU1Vdh2cXEx+XxnZ2dhOy0tDe7u7gZroNWsWbNU8em3pX+t8qC/XMbq1auF9996661yvS4RERGJR1KFmf6ir8ZO0Nenf05GRkaZtKlPvy07O7tStVUQLy+vIvePGjXKpHl3REREZF4kVZjpj/1mZWWZfL7+OY6OjmXSpj79trKzs0vVlrHs7OzQpk0bjBs3Dr6+vhVyTSIiIhKHpAqzGjVqCNtJSUkmn6+/vpeTk5PB/5/fXxL6bZUkvuLor/xvZWUFBwcHuLq6mvQcUCIiInN3KMO4m+t2zHwVrtVLNxomNZIqzPSH8sLDw006V61WIzIyEkDeXDJ3d3cAeavn69y4cQNdu3YtVXxyuRwajcbk+IxhzEPTiYiIKjsljOuQsLO3N4s7LU0hqXXMPD09hV6pS5cuIT093ehzz507Jwwv6j/MvH379sL26dOnSxWfo6Oj8BSCO3fu4N69e6Vqj4iIiEifpAozmUyGIUOGAMh76PjOnTuNPve3334TtocNGyZse3l5CctkXLp0CTdu3ChVjLqHkGu1WmzdurVUbRERERHpk1RhBgATJkyAjY0NAGDNmjVG9Ur99ddfOHXqFIC8XrdevXoZ7J82bZqwPXfuXKNvAnj69ClOnDhh8N6IESPg6uoKIO9h6xcuXDCqLQAIDAw0+lgiIiKyPJIrzOrVq4dPP/0UQN5dlBMnTjRYJPZ5Bw4cwOzZswEACoUCX3/9NWQymcExffv2FZ5TGRkZiYkTJyIuLq7IOE6fPo3//Oc/OH/+vMH7dnZ2WLFihTDXbOrUqTh8+HCRbT18+BAfffQRvvzyyyKPIyIiIssmqcn/OuPHj8f9+/exZcsWPHz4ECNGjMCgQYPg6+uLOnXqIDc3F7dv38b+/fuFwsnGxgYrVqwQ5oA97/PPP8eTJ09w8uRJXL9+Hf3798eAAQPw8ssvo27durC1tUVSUhLCw8Nx9OjRIoc8O3fujC+//BILFy5EVlYW3n//fbRu3Rr9+vVD8+bNUb16daSnp+PBgwc4c+YMTpw4AaVSadIjpoiIiMjySLIwAwB/f380adIE3333HdLS0rB3717hAefP8/DwwBdffGEw6f95dnZ2+PHHH7F27Vps2LABmZmZRbYJAD179sSYMWMK3DdixAjUr18fixcvRlRUFK5du4Zr164V2ladOnXw8ccfF7qfiIiISLKFGQCMHj0agwYNwr59+3Dq1CncunULKSkpsLKygouLC3x8fNC7d2/079/fqLW+5HI53n33XYwePRp//fUX/vnnH0RFRSE1NRUqlQrVq1dHkyZN0KZNGwwePBgeHh5FttehQwfs3bsXJ0+exKlTp3D16lUkJSUhPT0d9vb2qFWrlhBjjx49YG0t6a+biIiIRCbTarVasYMg8SiVSoSGhgIAajX0wODl+0SOiIiIyDgH/EfA3Um8dcz0f4b6+PgINy+WhuQm/xMRERFZKhZmRERERBLBwoyIiIhIIliYEREREUkEbxMkQU1HWxzwHyF2GEREREZxqWYndghljoUZCazkcrg72YodBhERkcViYUYCrpwiDq1WC5VKBSDvsWLPP1KMyh9zIA3Mg/iYA/FxjhkJcnNzxQ7BIqlUKoSGhiI0NFT4B5EqFnMgDcyD+JgD8bEwIyIiIpIIFmZEREREEsE5ZiTgXAJxyOVyuLm5CdtU8ZgDaWAexMcciI+FGQn4kHVxWFtbo379+mKHYdGYA2lgHsTHHIiP5TARERGRRLAwIyIiIpIIjl2RQK3RICEtU+wwiKiScKlmBysT5impVCrcvHkTANC8eXMoFIryCo0KwRyIj4UZCVIycjB4+T6xwyCiSuKA/wi4OzkYfbz+4qZc8FoczIH4OJRJREREJBEszIiIiIgkgoUZERERkUSwMCMiIiKSCBZmRERERBJR4YWZWq1GmzZt4OXlhWHDhhV5rFarRceOHeHl5QUvLy/s2rWryOMDAgKEY7dv3y68P378eOH94mzevFk4tnPnzoiIiBD2/fDDD8K+4ODgYtsCAF9fX3h5ecHX17fA/XPmzBHa1P/P29sb7dq1w+DBg7Fo0SLh9mUiIiKqvCq8MLOyssJLL70EAIiIiEBGRkahx0ZHRyMtLU14ffny5SLbvnTpkrDdvn17k2Nbv349lixZAgBwdXXF1q1b4e3tbXI7ZUGr1SI9PR2RkZHYvn07hg8fjlWrVokSCxFRRbC2tkaTJk3QpEkTPiJOJMyB+ET51tu3b4+zZ89Co9HgypUr6N69e4HH6QotKysrqNVqg8KrqONr1KiBpk2bmhTT2rVr8d133wEA3N3dsXnzZjRu3NikNkprw4YNwsNjNRoNkpOTcerUKWzfvh25ublYt24d3NzcMHbs2AqNi4ioIsjlctSoUUPsMCwacyA+UeaYtWvXTtguqtjS7evfvz8A4P79+0hISCjw2OTkZNy9excA0LZtW8hkMqPj+eGHH4SirE6dOvjtt98qvCgDgEaNGsHT0xOenp7w9vZG165d4e/vjx9++EE4Zs2aNVCr1RUeGxEREZU/UQqzVq1aoUqVKgCMK8z69euHBg0aACh8OLOkw5jffvst1qxZAwCoV68efvvtN+FaUuHr6ysUsykpKbhx44bIERERlT2tVgulUgmlUslV50XCHIhPlMLMxsYGrVq1AgCEhoZCqVTmO+bBgwdC71jbtm3Rtm1bAIUXcvrv6/fIFWX58uX46aefAOT1Vm3btg1169Y1/oNUIN33BQAPHz4UMRIiovKhUqkQGhqK0NBQ4bFAVLGYA/GJtlyGrnhSKpW4fv16vv26QqtRo0ZwcXERCrOLFy8W2J6uJ83R0RHNmzcv9vpffvklNm7cCABo0qQJtm7dilq1apn+QSqI/iRMjUYjYiREZKlsoDbpv+ysLGRmZhr9X1ZWFp49eyb2xyQSlWi3XLRv3x5r164FkFeEPT/8qCvM2rRpY/D/6OhoPHnyBNWrVxeOzcjIEJa1eOmll2BlZVXodbVaLRYtWoQdO3YAADw9PfHrr7/CxcWljD5Z+YiKihK2dTcIEBFVpP6O9006fvMvP5boOq+++mqJziOqDEQrzFq3bg1ra2vk5uYWODype0/Xs+bh4YEaNWogNTUVV65cQa9evYRjr169KkyIL25+2fz58/Hnn38CAJo3b46NGzeiZs2aJscfGxtr1J0rZdEVHBERgTNnzgAA7Ozs4OPjU+o2iYiISHpEK8wcHBzQvHlzhIaGCoWVrqfr+Tssddq0aYPjx4/j0qVLBoWZKfPLdEWZh4cHNm/ebNDzZorPPvusROcZS6vVIikpCadOncLKlSuFwnP8+PHCjRNERERUuYi6elz79u0RGhqKzMxM3Lx5Ey+88AKA/80jc3FxQaNGjYTj27ZtKxRm+nTzy6pUqVJsb5JMJoNWq0VsbCzCw8PRuXPnMvxEpdO7d+8i9/fs2RPvv/9+BUVDREREFU3Uwqxt27bCBPxLly4JhZmu8NLvLQP+1xt248YN5OTkwNbWFkqlEiEhIQCAF198ETY2NkVec/78+fjiiy/w7NkzTJ8+HevXrzf6Lk59W7ZsQceOHYs9ztfXF3FxcSa3r6NQKODj44NRo0ZhyJAhJq3PRkRUlg5lmLaU0I6Zr8K1ur3Rx6tUKoSHh5saFlGlImph1q5dO6EH6/Lly5g4cSKA//WA6Sb867Ro0QJ2dnbIzs7GtWvX0KlTJ4SGhgp38RhTYI0dOxY5OTn4+uuvkZ2djalTp+LXX381WI5CLPor/8vlctjb28PFxaXYYpOIqCIoUfiNVQWxs7eHg4OD8e0rlZyqQRZPtOUyAMDJyQnNmjUD8L9eMv07LJ/vMdP1Hukfr7/grLELy06ePBnvvfeecL233nrL4GHlYtFf+b9p06aoU6cOizIishi6f+N9fHygUCjEDsciMQfiE7UwA2Cwon1MTAwuX74MjUYDe3t7tGjRIt/xzy80q5uPplAo0Lp1a6OvO2PGDLz99tsAgCdPnuDNN99ETExMaT4KERGVgkwmg42NDWxsbDhtQyTMgfgkU5gBeb1fuh6w1q1bF7gema4wu379OpRKJa5evQogb5jT3t74uQwA8Mknn2D8+PEA8u4EnThxIh48eFCiz0FERERUWpIqzC5evCj0gD0/jKmjW0A2KysLe/bsQXp6er52TOHv74/XXnsNAJCYmIg33ngDjx49KlFbRERUchqNBqmpqUhNTeUTTkTCHIhP9MLM3d1deGh4cHAwwsLCABRemDk6OsLT0xMAsH79euF9Ux5crk8mk2HRokXw8/MDAMTFxWHixIl4/PhxidojIqKSyc3Nxe3bt3H79m3k5uaKHY5FYg7EJ3phBvyvtyshIQFKpRLW1tZFzhfTFW26YUe5XF5oIWcMuVyO5cuXo1+/fgCAu3fvYtKkSUhJSSlxm0RERESmklRhptO8eXPY2dkVevzzRZinpyeqVatWqhisrKywcuVK9OzZE0DeMzknT56Mp0+flqpdIiIiImNJojB7fhiyuN6v5/eXdBjzeQqFAqtXr0aXLl0AAOHh4Xj77beRmZlZJu0TERERFUWm1Wq1YgdB4lEqlQgNDQUA1GrogcHL94kcERFVFgf8R8DdybQFZnX/Hvn4+HAdRxEwB6Ypj+9LEj1mRERERMTCjIiIiEgyRH1WJhERkY5MJhMeA8RV58XBHIiPhRkREUmCQqFAq1atxA7DojEH4mNhRoKajrY44D9C7DCIqJJwqVb4skdEVDAWZiSwksvh7mQrdhhEREQWi4UZCbhyiji0Wi3UajWAvIWOOa+j4jEH0pCbmys8q7h27dqwtuaPqIrGHIiP3zgJcnNzUaVKFbHDsDgqlYrrBomMOZAGjUaDxMREAHnPUaaKxxyIj8tlEBEREUkECzMiIiIiiWBhRkRERCQRLMyIiIiIJIKT/0nAu2/EoVAo4OPjI2xTxWMOiEgq+JOYBFwiQBwymYx3AYqMOSAiqWBhRgK1RoOEtEyxwyAyKy7V7GAl56yQsiCXy+Hk5CRsU8VjDsTHwowEKRk5GLx8n9hhEJmVA/4j4O7kIHYYlYK1tTU8PDzEDsOiMQfiYzlMREREJBEszIiIiIgkgkOZREQkCSqVCjExMQAADw8P3iErAuZAfCzMiIhIErRaLTIzM4VtqnjMgfg4lElEREQkEWbTY5aVlYW9e/fixIkTiIiIQFpaGrRaLRwdHVG3bl14enripZdeQrdu3VC7du0i23r48CH++usvnDt3Dvfu3UNKSgo0Gg2qV68OT09PtG3bFn5+fqhfv36+c4ODgzFhwgQAwIwZM/Dee+8VG/ucOXMQEBAAADh+/Djq1atnsH/Pnj2YO3cuAGDp0qUYPny4Ud8JERERVS5mUZhdvXoVH330ER4+fJhvX2pqKlJTUxEWFoY9e/bAxcUF//zzT4HtPHv2DCtXrsSOHTugVCrz7U9MTERiYiLOnj2L1atXo3///pg9e3axhR4RERFRWZB8YXbnzh1MnjxZGPP29fVFv3790LhxYygUCqSmpiIiIgLnzp1DcHBwoe2kpKRg+vTpuHbtGgDAwcEBr776Kjp16oRatWrB2toaSUlJuHLlCo4ePYq7d+/i4MGDaN26NSZOnFgBn5SIiIgsneQLs1WrVglFWWHDfF27dsXkyZORkpKCgwcP5tuv0Wjw4YcfCkVZr1698NVXX8HZ2Tnfsb6+vvjoo4+wb98+fP3112X7YYiIiIiKIOnCTK1W4/Tp0wCAF154odi5VzVr1sTYsWPzvb9lyxahN+3ll1/GmjVrinxgt1wux9ChQ9GpUyfcuXOnFJ+AiIiIyHiSLsxSUlKQk5MDAGjYsGGJ2lAqldiwYQMAoEqVKliyZEmRRZm+WrVqoVatWiW6LhERmcbKykq4OcrKykrkaCwTcyA+SRdm+gvb6Ra8M9XZs2eRmJgIAOjfvz/c3d3LJDYiMl82UJdZW9lZWcgswzU4HRws97mbVlZW/DdaZMyB+CRdmDk5OaFu3bqIi4tDREQEfv75Z7z11lsmPfH+4sWLwnbPnj3LIUoiMjf9He+XWVubf/mxzNoCgIULF5Zpe0RkXiRdmAHAuHHjsHz5cgDAypUr8fvvv8PX1xdt2rSBj49PgWuN6YuIiBC2W7ZsWaaxJScnIyoqqtjjnj59WqbXJSIiospJ8oXZxIkTcevWLezevRsAEBcXh61bt2Lr1q0AABcXF3To0AF+fn7o1asXZDKZwflpaWnCdkF3YZbGjh07sGPHjjJtk4jIUimVSoSGhgIAfHx8YGNjI3JEloc5EJ/kH8kkl8uxZMkSbNy4Ed26dcs3cT8pKQkHDhzA9OnT8Z///Af37xsOUeiW2gAAOzu7ComZiIiIqCQk32Om07VrV3Tt2hUZGRm4fPkyQkNDERYWhkuXLiE9PR0AEBYWhjFjxmDPnj1wc3MDYDiRNjs7G46OjmUWU0keyURE4juU0aDM2tox81W4Vrcvs/aIyLKZTWGm4+joiB49eqBHjx4A8rpdg4KCsHz5cjx58gSPHz/G999/j6+++gpA3g0EOklJSWVamBGReVKi7JYBsLO3t+g7KYmobEl+KLM4NjY2GDFiBFauXCm8d/ToUWg0GgCAt7e38H54eHiFx0dERERkLLMvzHS6desmPGz8yZMnwqT/9u3bC8ecOnVKhMiIiIiIjFNpCjMAwrwyfS+//LLw/qFDh5CQkFDRYREREREZpdIUZtnZ2bh16xaAvHloNWrUAJA31Dl58mQAwLNnz/DZZ59BrTZu1e+EhAScP3++fAImIiIieo6kC7PMzEyMHDkSJ0+eFOaMFUSj0WDx4sXC0hi+vr4G65lNmDABHTt2BJD3iKZ3330XKSkphban1WoRFBSE4cOHIzIysow+DRERFcXa2hpeXl7w8vIy+pnGVLaYA/FJ/lsPCQnBtGnT4O7ujj59+qB169aoU6cOHB0d8fTpU4SHh2P37t3CCvxVq1bFBx98YNCGXC7Hd999h2nTpuH69es4efIk+vTpAz8/P3Tq1Am1atWCtbU1kpKScO3aNRw5cgS3b98W4+Pi8uXLRh3n6+trcMcpEZG5k8vlvHNeZMyB+CRdmFlbW8PV1RWPHz9GQkICtm3bhm3bthV6fKNGjbBy5UrUq1cv376aNWti69at+Oabb7Bjxw5kZmbi999/x++//15gWzKZDH5+fhgwYECZfR5j7Nq1C7t27Sr2uMDAQBZmRERElYykC7MqVarg77//xrVr13Du3Dlcv34dd+7cQXJyMp49ewY7Ozu4ubnB29sbvXv3xiuvvFLk4yOqVKkCf39/TJo0Cfv378f58+dx9+5dpKSkQKvVonr16vD09ET79u3h5+eHunXrVuCnJSKybBqNBllZWQAAe3t7yOWSnm1TKTEH4pNptVqt2EGQePSfi1aroQcGL98nckRE5uWA/wi4O3GB2bLA5zSKjzkwTXl8XyyFiYiIiCSChRkRERGRRLAwIyIiIpIISU/+p4pV09EWB/xHiB0GkVlxqWYndghEVImwMCOBlVwOdydbscMgIiKyWCzMSMAbdMWh1WqhUqkAAAqFwuCpFVQxmAMikgrOMSNBbm6u2CFYJJVKhdDQUISGhgrFAVUs5oCIpILrmFk4rllDRERUMlzHjIiIiKgSY2FGREREJBGc/E8CtVotdggWSa1WIykpCQDg4uICKysrkSOyPMyBNDAP4mMOxMfCjAQajUbsECySWq1GbGwsAKBGjRr8h1AEzIE0MA/iYw7Ex6FMElhbs04nIiISEwszEnDtJiIiInGxi4QEao0GCWmZFXpNl2p2sJLz9wMiIiKAhRnpScnIweDl+yr0mgf8R8DdyaFCr0lERCRV7KogIiIikggWZkREREQSwaFMIpHJZDI4ODgI21TxmANpYB7ExxyIj4UZkcgUCgW8vb3FDsOiMQfSwDyIjzkQn1kVZhcuXMD48eOF1zt27ECbNm1MauP+/fs4ePAgzp8/j/v37yM1NRW5ubmoWrUq6tWrhxYtWqBnz57o2rUrFApFgW3s2bMHc+fONem6c+fOxcSJEw3eGz9+PC5cuJDvWLlcjqpVq6J+/fro0KEDXn/9dTRs2NCk6xEREZH5MavCLCAgwOB1YGCg0YXZ06dPsWzZMuzduxe5ubn59icnJyM5ORnXr1/Hjh074OzsjLfffhsTJkyo8JWPNRoNnjx5gidPniAsLAxbt27F3LlzMXbs2AqNg4iIiCqW2RRmOTk5OHz4MADA3t4eWVlZOHToEObNmwcbG5siz33w4AHefvtt3LlzBwDg5OSEQYMGoV27dnBzc4OdnR1SUlJw+/ZtnD17FufOnUNycjKWLVuGV199Fa6uroW2/eGHH6J3797Fxl9UGwAQFBQkbGs0GsTHx+PQoUMICAiASqXC4sWLUbduXfTs2bPYa5F5yc3Nxb179wAADRs25BMYRMAcSAPzID7mQHxm840fPXoUmZl5i5/OmzcPn332GZ48eYITJ06gf//+hZ6XlZWFqVOnCkXZa6+9htmzZ8PR0THfsd26dcMbb7yBBw8e4Mcff8SePXuKjcvd3R2enp4l/FT/83wb3t7e6NmzJ1q2bIkvv/wSWq0Wq1evZmFWCWk0GqSlpQEA6tevL24wFoo5kAbmQXzMgfjMZrmMwMBAAICXlxdGjBiBxo0bG7xfmG+++QYxMTEAgDFjxmDx4sUFFmX66tevj6VLl2Lt2rWoUqVKqWMvjbFjx6Ju3boAgBs3biA5OVnUeIiIiKj8mEVhlpiYiPPnzwMABg8ebPD/s2fPIiUlpcDzkpKSsGvXLgBA7dq1MXv2bJOu6+vri2rVqpU07DIhl8vxwgsvCK8fPnwoYjRERERUnsyiMAsKCoJarYZcLoefnx8AwM/PDzKZDCqVCvv37y/wvAMHDuDZs2cAgJEjR8LW1rbCYi5L+mP8Go1GxEiIiIioPJlFYbZ3714AQIcOHeDu7g4gb7jxpZdeAlD4cObFixeF7W7dupVvkOUoKipK2HZzcxMxkrKXnZUlzB0kIiKydJKf/H/z5k1ERkYC+N/wpc7gwYNx5coV3LhxA7du3ULTpk0N9uvOk8vl5bZgXkJCgkHhVJiS3iBw6tQpREdHA8grRmvXrl2idqRq8y8/AgAWLlwociRERETik3xhpusNs7W1Rb9+/Qz2DRgwAF999RVUKhUCAwPxySefGOxPTU0FAFStWrXIJTUeP34sHPu86tWrC710Bfnuu+/w3XffFfs5dEWiMfSXy1i9erXw/ltvvWV0G0RERGR+JF2Y5ebmCvPHevXqle9uSicnJ/To0QPHjh1DUFAQPvroI8jl/xud1Q2R2dnZFXmdn3/+GVu2bClw37Bhw7Bs2bLSfAyjeHl5Fbl/1KhReP3118s9Dqp4crlcGKLW//NLFYc5kAbmQXzMgfgkXZidPXsWSUlJAPIPY+oMHjwYx44dQ3x8PIKDg9G5c2dhn4ODA54+fYrs7Oxyi3Hp0qUYPnx4ubRtZ2eHNm3aYNy4cfD19S2Xa5D4rK2tuV6QyJgDaWAexMcciE/ShZluGNPJyanQyfu9evVCtWrV8PTpUwQGBhoUZk5OTnj69CnS09OhVCoLHc709/eHv7+/8Do2Ntao1fzLkv7K/1ZWVnBwcICrq2uFPw6qor3x9jtwrW4vdhhERESSINnCLD09HSdOnAAApKWlGazlVZgjR45g4cKFsLfP+0Hv7e2N+/fvQ6PRIDIyEj4+PuUac2mUxdMDzJGdvT0cHBzEDoOIiEgSJDuAfPDgQWENMmNlZWXhyJEjwuv27dsL22fOnCmz2IjKkkqlQkhICEJCQqBSqcQOxyIxB9LAPIiPORCfZHvMdMOYrq6umDt3brHHf/3114iPj8fevXsxdOhQAMDAgQOxYsUKKJVK/Pnnn5g8ebLoj1giep5WqxX+AdRqtSJHY5mYA2lgHsTHHIhPkoXZgwcPcOXKFQBAv379MGjQoGLPuXbtGrZs2YJ///0XCQkJcHd3h4uLC0aMGIEdO3bg4cOHWLFiBebNm1fe4RMRERGViCSHMvfu3StU6s+vXVYY3XEajUZ4UgAAfPLJJ8IDz7du3YrPP/+82JXmnz59WpKwiYiIiEpFkj1musLK2dkZ7dq1M+qcNm3awNXVFY8fP8a+ffswZcoUAICjoyN++uknTJkyBXfv3sWOHTtw6NAhvPrqq2jXrh3c3Nxgb2+PjIwM3Lt3D+fOncPRo0eFdot7vqaxK/87OjqiTp06Rn0WIiIiskySK8wuX76M+/fvAwD69Olj9AJ3crkcffv2xfbt2xEdHY2wsDDhTs6GDRti586dWLp0Kfbt24fU1FRs3boVW7duLbQ9Z2dnvPXWWxg/fnyR1zV25f/evXvjxx9/NOqzEBERkWWSXGGmPwxp7DCmziuvvILt27cDyLt5QH+JjerVq2PZsmWYPn06Dhw4gH///Rf37t1DWloa1Go1qlWrhtq1a+OFF17Ayy+/jB49ekChUJTNhyIiIiIygkzL2y4smlKpRGhoKACgVkMPDF6+r0Kvf8B/BNydLHsdM/0c+Pj4FPlcVyofzIE0MA/iYw5MUx7fl+R6zIgsjbW1NZo0aSJsU8VjDqSBeRAfcyA+futEIpPL5ahRo4bYYVg05kAamAfxMQfik+RyGURERESWiD1mRCLTX2lboVBAJpOJHJHlYQ6kgXkQH3MgPhZmJKjpaIsD/iMq9Jou1ewq9HpSpFKpONlWZMyBNDAP4mMOxMfCjARWcjncnYpeUJeIiIjKD+eYkYArpxAREYmLhRkJcnNzxQ6BiIjIorEwIyIiIpIIFmZEREREEsHCjIiIiEgiWJgRERERSQSXyyABn4smDoVCAR8fH2GbKh5zIA3Mg/iYA/HxJzEJuMKzOGQyGRdxFBlzIA3Mg/iYA/FxKJOIiIhIIthjRgK1RoOEtMwya8+lmh2s5Kz9i6PRaPDkyRMAQPXq1SHnd1bhmANpYB7ExxyIj4UZCVIycjB4+b4ya++A/wi4OzmUWXuVVW5uLm7fvg2Az6YTC3MgDcyD+JgD8bEUJiIiIpIIFmZEREREEsHCjIiIiEgiWJgRERERSYRFTv7PysrC3r17ceLECURERCAtLQ1arRaOjo6oW7cuPD098dJLL6Fbt26oXbu2wblz5sxBQECASdcLDAxE8+bNhdczZszA0aNHAQCbNm1C586dizz/+PHjeOeddwAAQ4cOxfLly026PhEREZkHiyvMrl69io8++ggPHz7Mty81NRWpqakICwvDnj174OLign/++afMY1iwYAGCg4Px9OlTLFiwAEFBQbC1tS3w2PT0dHz++ecAAGdnZ8ydO7fM4yEiIiJpsKjC7M6dO5g8eTIyM/PW6vL19UW/fv3QuHFjKBQKpKamIiIiAufOnUNwcHCx7W3YsAFubm7FHtewYUOD125ubpg1axbmz5+P+/fv4/vvv8fs2bMLPPfrr79GYmIiAGDevHlwcnIq9npkXmQymfDoEz59QRzMgTQwD+JjDsRnUYXZqlWrhKJs6dKlGD58eL5junbtismTJyMlJQUHDx4ssr1GjRqhXr16JYpl5MiRCAoKwoULF7B582YMGjQIL7zwgsExwcHB+PPPPwHkFZEDBw4s0bVI2hQKBVq1aiV2GBaNOZAG5kF8zIH4LGbyv1qtxunTpwEAL7zwQoFFmb6aNWti7Nix5RaPTCbDl19+iSpVqkCtVsPf3x+5ubnC/pycHMyfP1+Y+6YbziQiIqLKy2IKs5SUFOTk5ADIP7QoloYNG2LGjBkAgIiICKxfv17Yt3r1aty7dw8AMGvWLLi7u4sSIxEREVUciynMdGPmABATEyNiJIbefPNNtGjRAgDw3//+F7dv30ZYWBg2bdoEAGjfvj1GjRolYoRU3nJzc/HgwQM8ePDAoNeUKg5zIA3Mg/iYA/FZTGHm5OSEunXrAsjrnfr555+h0WhEjgqwtrbGl19+CSsrKyiVSsybNw/z5s2DWq1GlSpVsHjxYrOcgGkDNbKzsoQ5fVQ4jUaDxMREJCYmSuLPpCViDqSBeRAfcyA+i5r8P27cOGENsJUrV+L333+Hr68v2rRpAx8fH9SvX9+k9u7evYusrKwij7Gzsyu23ZYtW2LixInYsGEDLl++LLw/Y8YMNG7c2KSYpKK/431s/uVHAMDChQtFjoaIiMg8WFRhNnHiRNy6dQu7d+8GAMTFxWHr1q3YunUrAMDFxQUdOnSAn58fevXqVWxP1eTJk4u9ZocOHYT2i/L+++/jwIEDePToEQCgRYsWePPNN4s9j4iIiCoPixnKBAC5XI4lS5Zg48aN6NatG6ytDevSpKQkHDhwANOnT8d//vMf3L9/v8Jiu3fvHpKSkoTXNWvWzBcfERERVW4W+ZO/a9eu6Nq1KzIyMnD58mWEhoYiLCwMly5dQnp6OgAgLCwMY8aMwZ49ewpdRPb48eMlXsdMn265DJVKJbx39uxZBAUFwc/Pr9TtExERkXmwqB6z5zk6OqJHjx6YMWMG1q1bh3PnzmHJkiWoXr06AODx48f4/vvvyz2OLVu2IDQ0FEDe8KiuEFyyZAlSU1PL/frl4VBGA7zx9jv45JNPxA6FiIjIbFh0YfY8GxsbjBgxAitXrhTeO3r0aLnemfLgwQOh+PP09MTMmTMxf/58AHlrr5nrA8uVsIKdvT0cHBzEDoWIiMhssDArQLdu3VC7dm0AwJMnT5CWllZu11qwYAGys7Mhl8vx1VdfQaFQ4JVXXkGfPn0AAAEBATh//ny5XZ/EJ5fL4eTkBCcnJ8jl/CspBuZAGpgH8TEH4uO3XghjHk5eWrt378a5c+cAAOPHjzd4PtmCBQvg6OgIIG+5iWfPnpV7PCQOa2treHh4wMPDgzd8iIQ5kAbmQXzMgfhYmBUgOzsbt27dApA3D61GjRplfo2kpCRhmLJu3br48MMPDfa7u7vj448/BpB3x+aaNWvKPAYiIiKSFospzDIzMzFy5EicPHmyyDljGo0GixcvFlas9/X1LZeV97/44gs8efIEALBo0SLY29vnO2b06NFo06YNAGDjxo2IiIgo8ziIiIhIOiyqnzIkJATTpk2Du7s7+vTpg9atW6NOnTpwdHTE06dPER4ejt27dyMqKgoAULVqVXzwwQeFtmfMyv8A4OrqatDrduzYMRw+fBgAMGTIEHTr1q3A82QyGRYvXoyhQ4dCpVJh3rx52LlzJ8f9KxmVSiU8v9XDw8Pgua5UMZgDaWAexMcciM9iCjNra2u4urri8ePHSEhIwLZt27Bt27ZCj2/UqBFWrlxZ5Dplxqz8DwBz587FxIkTAQDp6elYtGgRgLxFZOfOnVvkuU2bNsXUqVOxZs0ahIaGYuvWrXjjjTeMui6ZB61WK/TQarVakaOxTMyBNDAP4mMOxGcxhVmVKlXw999/49q1azh37hyuX7+OO3fuIDk5Gc+ePYOdnR3c3Nzg7e2N3r1745VXXoGNjU2Zx/H1118jMTERAPDZZ58ZNX9t6tSpOHjwIGJiYvDdd9+hb9++qFOnTpnHRkREROKymMIMyLsNuE2bNsK8rZJYtmwZli1bVuLzFy9ejMWLF5t0jo2NDQ4cOFDiaxIREZF54GQlIiIiIolgYUZEREQkESzMiIiIiCSChRkRERGRRFjU5H8qWk1HWxzwH1Fm7blUsyuztiozKysrYVkWKysrkaOxTMyBNDAP4mMOxMfCjARWcjncnWzFDsPiWFlZwd3dXewwLBpzIA3Mg/iYA/FxKJOIiIhIIliYEREREUkEhzJJoFKpyuVpB1Q0pVKJ0NBQAICPjw9zIALmQBqYB/ExB+JjjxkRERGRRLAwIyIiIpIIFmZEREREEsHCjIiIiEgiWJiRwNqa94IQERGJiYUZCWQymdghEBERWTR2kZBArdEgIS2zwq/rUs0OVnL+jkBERMTCjAQpGTkYvHxfhV/3gP8IuDs5VPh1pcLa2hpeXl7CNlU85kAamAfxMQfi47dOJDK5XA5HR0exw7BozIE0MA/iYw7Ex/EjIiIiIolgjxmRyDQaDbKysgAA9vb2kHO+XYVjDqSBeRAfcyA+fuNEIsvNzUVkZCQiIyORm5srdjgWiTmQBuZBfMyB+CpVj1lwcDAmTJggvLa3t8e5c+dgZ2dX5Hk5OTno2rUrMjIyhPe2bNmCjh07FnnehQsXMH78eOH1jh070KZNG5Pj1GdrawsnJyd4e3ujb9++GDx4MB8iS0REZCEqdY9ZVlYWjh07Vuxxx48fNyjKjBUQEGDwOjAw0OQ2npeTk4P4+HicOnUK/v7+GD58OGJjY0vdLhEREUlfpS3MqlSpAgDYu3dvscfqjtGdY4ycnBwcPnwYQF7PHAAcOnQISqXSpDhHjx6NoKAg4b9NmzbB398ftWrVAgBER0dj+vTpUKvVJrVLRERE5qfSFma+vr4AgHPnzuHx48eFHpecnIx//vkHANC7d2+j2z969CgyM/MWY503bx4A4MmTJzhx4oRJcTo7O8PT01P4r3PnzpgwYQL++usv1K1bFwAQFRWFo0ePmtQuERERmZ9KW5h17doVrq6uUKvV+Ouvvwo9bv/+/cjNzYWrqyu6dOlidPu6YUsvLy+MGDECjRs3Nni/tBwdHTF9+nTh9blz58qkXSIiIpKuSluYWVlZYdCgQQCKHs7U7Xv11VdhZWVlVNuJiYk4f/48AGDw4MEG/z979ixSUlJKHLc+3erLABAfH18mbRIREZF0VdrCDACGDBkCAAgPD0d0dHS+/bdu3cKNGzcMjjVGUFAQ1Go15HI5/Pz8AAB+fn6QyWRQqVTYv39/GUQPKBQKYZuPxiAiIqr8KnVh1qJFCzRr1gxAwb1muvc8PT3RvHlzo9vVndehQwe4u7sDAOrXr4+XXnoJQNkNZ8bExAjbuvlmlYEN1Ab/ZWdlITMzM99/lsLGxgZt27ZF27ZtuTSKSJgDaWAexMcciK/Sd8MMGTIE33zzDfbv34+PP/4YMpkMAKDVahEUFCQcY6ybN28iMjISwP+GL3UGDx6MK1eu4MaNG7h16xaaNm1a4rjVajU2bNggvO7Xr1+J25Ka/o73DV5v/uXHAo9buHBhRYRDREQkGZW6xwzIK5bkcjkePXqE4OBg4f3g4GA8evTIYDjSGLreMFtb23zF0oABA4Thx5L2mqWkpOD8+fMYN24cwsPDAeQVZe3atStRe0RERGQ+Kn1h5u7uLqzgrz+cqdvu1KmTMBxZnNzcXGH+WK9eveDo6Giw38nJCT169ACQNw9No9EU2+aaNWvg5eUl/Ne5c2dMnDgRV65cgZ2dHSZNmoSVK1caFR+ZJ7VajYSEBCQkJHC9OpEwB9LAPIiPORBfpS/MAGDo0KEAgCNHjiAnJ8dgcVhThjHPnj2LpKQkAPmHMXV078fHxxv00JWEt7c3xo8fb3ATAFU+arUasbGxiI2N5T+EImEOpIF5EB9zIL5KP8cMAPr27YvPP/8cGRkZOH78OLRaLTIzM2Fvb49XXnnF6HZ0w5NOTk7o1q1bgcf06tUL1apVw9OnTxEYGIjOnTsX2ebo0aMxZswYAHl/IeLj43H48GHs3bsXV69exfjx47Fr1y7UrFnT6Dil7lBGA4PXO2a+Ctfq9iJFQ0REJB0WUZg5ODigT58+CAoKwt69e6HVagEAffr0ER6nVJz09HRhVf+0tDS88MILxZ5z5MgRLFy4sMhr6Fb+12nevDl69eqFjh07Ys6cOYiLi4O/vz/Wrl1rVJzmQAnD9eLs7O3h4OAgUjRERETSYRFDmcD/hjP/+ecfYRV93XvGOHjwIJ49e2bSNbOysnDkyBGTztEZNmyYcHPBiRMnhAVtiYiIqPKyiB4zAOjcuTNcXV2F52a6ubkVO8yoTzeM6erqirlz5xZ7/Ndff434+Hjs3bvXpAJQ38yZM3Hs2DGo1WqsWrXKpHiJiIjI/FhMYWZlZYUhQ4Zgy5YtAPIm/cvlxnUYPnjwAFeuXAGQt3SF7lFPRbl27Rq2bNmCf//9FwkJCUbf+amvcePGGDBgAPbv34/r16/jn3/+QdeuXU1uh4iIiMyDxQxlAsCsWbMQGhqK0NBQfPLJJ0afpz8vzdiFXnXHaTSaIp/VWZypU6cKi+JWpnlmRERElJ9FFWYlpSusnJ2djV7otU2bNnB1dQUA7Nu3r8TX9vT0hK+vLwDg4sWLuHTpUonbImmSyWRwcHCAg4ODUIRTxWIOpIF5EB9zID4WZsW4fPky7t/Pe4RQnz59jB7+lMvl6Nu3LwAgOjoaYWFhJY5h2rRpwjZ7zSofhUIBb29veHt7c806kTAH0sA8iI85EB8Ls2LoD0Oa+rxK/TXSSvNg81atWglzy86ePYuQkJASt0VERETSJdPqJk+RRVIqlQgNDQUA1GrogcHLSz7sWlIH/EfA3YnrmBERkXnR/xnq4+MDGxubUrdpMXdlEklVbm4u7t27BwBo2LAhrK3517KiMQfSwDyIjzkQH4cyiUSm0WiQlpaGtLQ0ox58T2WPOZAG5kF8zIH4WJgRERERSQQLMyIiIiKJ4OAxCWo62uKA/4gKv65LNbsKvyYREZEUsTAjgZVcDncnW7HDICIislgcyiQBV04hIiISFwszEuTm5oodAhERkUXjUCYJ+Fw0ccjlcri5uQnbVPGYA2lgHsTHHIiPhRkJuJCgOKytrVG/fn2xw7BozIE0MA/iYw7Ex3KYiIiISCJYmBERERFJBMeuSKBSqcrkAaxkGpVKhZs3bwIAmjdvDoVCIXJEloc5kAbmQXzMgfhYmJGAc8zEodVqoVKphG2qeMyBNDAP4mMOxMehTBLwrkwiIiJxsYuEBGqNBglpmSU616WaHax4azUREVGpsDAjQUpGDgYv31eicw/4j4C7k0MZR0RERGRZ2MVBREREJBEszIiIiIgkgoUZERERkURwjhmRyKytrdGkSRNhmyoecyANzIP4mAPxmd23HhwcjAkTJhS4z9bWFk5OTvD29kbfvn0xePDgIhdM9fLyMuna3t7e2Lt3r8F7e/bswdy5cwEAS5cuxfDhw01q09fXF3Fxcfnet7a2hqOjI5o0aYIuXbrgtddeg7u7u0ltk3mQy+WoUaOG2GFYNOZAGpgH8TEH4qtUQ5k5OTmIj4/HqVOn4O/vj+HDhyM2NlbssEokNzcXaWlpuHLlCtasWYOBAwfi6NGjYodFRERE5cjsesz0jR49GmPGjBFeJycnIzo6Ghs2bEB8fDyio6Mxffp0BAYGwsrKqtB2XnjhBSxdurTY69na2pZJ3AVxc3PDhg0bhNe5ubmIi4vD7t27cfLkSWRkZGDmzJnYuXMnWrRoUW5xUMXTX2lboVBwoV8RMAfSwDyIjzkQn1kXZs7OzvD09DR4r3Pnzhg+fDgGDx6MuLg4REVF4ejRo+jfv3+h7djb2+drp6IpFIp8MbRo0QJ9+/bFsmXL8Ouvv0KlUmHt2rX44YcfRIqSyoNKpUJoaCgAwMfHh88rFQFzIA3Mg/iYA/FVqqFMHUdHR0yfPl14fe7cORGjKb333ntP6K07e/YsNBqNyBERERFReaiUhRlgOLE/Pj5exEhKz8HBAR4eHgCArKwspKWliRsQERERlYtKW5gpFAphuzLc8qv/edhjRkREVDmZf8VSiJiYGGG7bt26IkZSerm5ubh9+zaAvALNyclJ3ICeYwM1srOykPn/taODA5+ZSUREVBKVsjBTq9UGdzj269evyOOzsrIQFRVVbLu1atVCtWrVSh2fqf744w88ffoUANC2bVvJ9QD2d7yPzb/8KLxeuHChiNEQERGZL2n9hC+llJQUREZGYvXq1QgPDweQV5S1a9euyPPCwsLg5+dXbPslWUC2pHTLZezZswe//PKL8P7kyZMr5PpERERU8cy6MFuzZg3WrFlT4D47Ozu8/vrr+Pjjjys4qpKJi4sr8kkEMpkMH3zwAbp3716BUREREVFFMuvCrCje3t4YP368waT5wnTo0AFbt26tgKhM5+joiE6dOmHSpEnF9vyReVIoFPDx8RG2qeIxB9LAPIiPORCfWRdm+iv/q9VqxMfH4/Dhw9i7dy+uXr2K8ePHY9euXahZs6bIkRbv+ZX/raysULVqVbi6ukp+5eVDGQ2wY+arcK1uL3YoZkkmk3ERR5ExB9LAPIiPORCfWRdmz6/837x5c/Tq1QsdO3bEnDlzEBcXB39/f6xdu1bEKI1T0Mr/5kIJK9jZ2/NuTCIiolKqlOuYDRs2TLgT88SJEzh//rzIEREVTqPRIDU1FampqVyjTiTMgTQwD+JjDsRXKQszAJg5c6bw4PJVq1aJHA1R4XTr1N2+fRu5ublih2ORmANpYB7ExxyIr9IWZo0bN8aAAQMAANevX8c///wjckRERERERau0hRkATJ06VZg4bw7zzIiIiMiymfXk/+J4enrC19cXx48fx8WLF3Hp0qUCl5wwduV/AGjatCnk8oLr2cuXLxvVhq+vr+Qeq0RERETiq9SFGQBMmzYNx48fB5DXa6a/JIWOsSv/A8DFixcLfSzTrl27sGvXrmLbCAwMZGFGRERE+VTqoUwAaNWqFbp27QoAOHv2LEJCQkSOiIiIiKhgZtdj1rFjR0RGRpp0zsaNGwt839R2CjJ8+PBSPT/zxIkTpY6BiIiIKgezK8yIKhuZTCY8+kTqT3morJgDaWAexMcciI+FGZHIFAoFWrVqJXYYFo05kAbmQXzMgfgq/RwzIiIiInPBwoyIiIhIIjiUSYKajrY44D+iROe6VLMr42gsR25uLh49egQAqF27Nqyt+deyojEH0sA8iI85EB+/cRJYyeVwd7IVOwyLo9FokJiYCABwd3cXORrLxBxIA/MgPuZAfBzKJIFWqxU7BCIiIovGwowEubm5YodARERk0ViYEREREUkECzMiIiIiiWBhRkRERCQRLMyIiIiIJILLZZCA69WIQy6Xw8nJSdimisccSAPzID7mQHz8SUwCPrBWHNbW1vDw8BA7DIvGHEgD8yA+5kB8LMxIoNZokJCWafJ5LtXsYMXfrIiIiEqNhRkJUjJyMHj5PpPPO+A/Au5ODuUQERERkWVhYUYkMpVKhZiYGACAh4cHFAqFyBFZHuZAGpgH8TEH4mNhRiQyrVaLzMxMYZsqHnMgDcyD+JgD8XFiEBEREZFEsDAjIiIikggWZkREREQSYfZzzJRKJY4cOYK///4bISEhSE1NRUZGBhwdHVG3bl34+PigX79+6NSpU7GL5Z0+fRrHjh3D5cuXkZSUhOzsbNSsWRMNGjRA9+7dMXjwYLi7uxd6/qBBg3Dr1i04OTnh33//LXJdsKFDh+LmzZsAgHfeeQcffPBBocdeuHAB48ePBwC89dZbmDVrVpGfg4iIiMyTWRdmR44cwbJlyxAXF5dvX1paGtLS0nDjxg38/vvvaNSoEebOnYuePXvmOzY6Ohrz58/H1atX8+2Lj49HfHw8Lly4gB9//BFTpkzBtGnTCiy62rZti1u3biEtLQ23bt1Cs2bNCow7PT0dkZGRwutLly4V+Tn197dv377IY4mIiMh8mW1h9t///herV68WXnft2hW+vr7w8PBAtWrV8OTJE9y5cwcnTpzAuXPncPfuXaxatSpfYXbp0iVMnz4dT58+BQC0bNkSw4YNg5eXF+zt7ZGQkIAzZ84gMDAQWVlZ+O677xAdHY3ly5fnu424ffv2+OOPP4R2CyvMrly5Ao1GAysrK6jVaoSEhEClUhV6W/Lly5cB5D0eo23btiX6voiIiEj6zLIw2717t1CUOTs747vvvkOHDh3yHdelSxeMHTsWUVFRWLp0KVJSUgz2x8fH491338XTp08hk8kwa9YsvPnmmwa9YS+88AJ69+6NyZMn491330VkZCT++usv1K5dO9+Qon5v1qVLlzB69OgC49f1gHXv3h3BwcHIyspCaGgo2rRpk+9YtVot9OR5eXmhatWqxnxFZEasrKxQr149YZsqHnMgDcyD+JgD8Znd5P+EhAQsXrwYAGBvb4+tW7cWWJTp8/T0xIYNG/Dmm28avD9//nykpaUBAD744ANMnjy50Hlh9evXx6+//go3NzcAwIYNG3D9+nWDY2rVqoW6desCKHp4UrevQ4cOePHFF4s8/ubNm8KaMu3atSvqY5KZsrKygru7O9zd3fkPoUiYA2lgHsTHHIjP7AqzTZs2ITs7GwDw/vvvG/2wVblcjiFDhgivIyIi8PfffwPIK9ymTJlSbBvOzs6YO3cugLyF99avX5/vGF2vWXx8PGJjY/Ptf/bsGUJDQwHkFVq6oUndcOXzOL+MiIjIcphVYabVahEQEAAgr7ds5MiRJW5L1w4AjBs3zujfDPr16yfcmXns2DE8efLEYL9+r1ZBvWDXr1+HSqWCnZ0dWrRoIRRmunlnz9Mv2FiYERERVW5mVZhFR0cjNTUVQN4dkI6OjiVu6+LFi8J2r169jD7PyspKuIFAo9Hku5Pz+Xlmz9O916pVK1hbW+PFF1+ElZUVnj59iqioqHzH6wqzJk2aoGbNmkbHWZ5soDb4LzsrC5mZmfn+I+MolUpcvnwZly9fhlKpFDsci8QcSAPzID7mQHxmNfk/IiJC2G7ZsmWp2tIVQa6ursK8MWO1aNHCICb9Oz0bNWoEV1dXPH78uMDCTFdo6XrWHBwc4O3tjRs3buDSpUvw9vYWjr19+zaSk5MBSKu3rL/jfYPXm3/5scDjFi5cWBHhEBERVRpm1WOmm6gP5M33Kqn09HSoVCoAgIuLi8nn65+jH5OObnjyzp07QmEFGN5hqb/shW77+UJOfxiTE/+JiIgqP7MqzPSHx+zs7MqkHXt7e5PP1z8nIyMj3/7ChjPDw8ORmZkJKysrtG7dWni/sMKME/+JiIgsi1kVZg4ODsK27s7M0raTlZVl8vn65xQ0z62wGwB0PWDe3t4GMeiOf/z4Me7du5fv3Lp166J27domx0lERETmxazmmDk5OQnb+kOEpnJ0dIS1tTVyc3ORlJRk8vn65+jHpOPp6Ylq1arh6dOnBsORukLr+dX7XVxc0LBhQ9y7dw+XLl1Cw4YNkZCQICy3IbVhzEMZDQxe75j5Klyrm97zSERERIbMqsdMf2L8jRs3StyOTCaDl5cXgLxeqsePH5t0fnh4eIEx6eg/OikiIkIY7tQVaQU9Vun54UwpD2MqYWXwn529PRwcHPL9R0RERKYxq8KsWbNmqFGjBoC8Iqeg+V3G0u+FOnHihNHnaTQanD59GkBeAVbQY5T021er1bhy5QpiYmKER0IV1AOma0dXkOkv5yG1HjMiIiIqH2ZVmMlkMgwbNgxA3jyvP//8s8RtDR8+XNjetm0b1Gq1UecdPnwY8fHxAIA+ffqgWrVqBR6nX0xdvnxZKLgaNmxY4J2guh6z+/fvIzExUehdc3V1RePGjY2KjcyTtbU1vLy84OXlBWtrs5pdUGkwB9LAPIiPORCfWRVmADBx4kThjszVq1cjJibGqPM0Gg327dsnvPb29sbLL78MAIiMjMSGDRuKbSMlJQXLli0DkFckvvXWW4Ue27JlSyHOixcvCj1gBQ1jAoYLyB47dgzR0dFFHk+Vh1wuh6OjIxwdHSGXm91fyUqBOZAG5kF8zIH4zO5bd3d3x/z58wHk9ZqNHz8eFy5cKPKcW7du4a233spXfC1evFjo8fr222+xadMmaLXaAtuIi4vDpEmThN6yyZMnCw8gL4hCoRCWxAgNDUVwcDCAogst3XDmxo0bhTikNr+MiIiIyo9Z9lOOGDEC8fHxWL16NZKTkzF+/Hi8/PLL8PX1hYeHB6pVq4YnT57gzp07OH36NM6cOQO1Wp1von6dOnWwZs0avPPOO8jIyMDSpUsRFBSEYcOGwcvLC3Z2dkhMTMSZM2cQGBgoLJMxcOBAfPjhh8XG2a5dO5w/fx5KpRKJiYkAii7M2rZti2PHjuHBgwcGbVDlptFohD9b9vb2/C1VBMyBNDAP4mMOxGeWhRkAvPvuu2jWrBmWLVuGuLg4nD17FmfPni30+GbNmmHWrFn53u/YsSO2b9+O+fPn4/r16wgLC0NYWFiBbdjZ2eHtt9/GO++8A5lMVmyMzxdVzs7ORc4Xe75oq169unD3KFVeubm5iIyMBAD4+PjAxsZG5IgsD3MgDcyD+JgD8ZltYQYAr7zyCnr27InDhw/j77//RmhoKFJSUpCZmQlHR0fUrVsXL774Ivr164eOHTsWWkx5eXlh586dOHXqFI4ePYorV67g8ePHyMnJQY0aNdCoUSN069YNQ4YMgbu7u9HxtW7dGgqFQnj8U2F3cOq0aNECdnZ2wuK5bdq0MaoAJCIiosrBrAszALCxsYGfnx/8/PxK3VbPnj0NHkheWra2toX2vhVEoVDg2rVrZXZ9IiIiMi8cPCYiIiKSCBZmRERERBLBwoyIiIhIIliYEREREUmE2U/+p7JT09EWB/xHmHyeSzW7coiGiIjI8si0hS11TxZBqVQiNDQUANesISIiMkV5/AzlUCYRERGRRLAwIyIiIpIIzjEjgVqtFjsEi6RWq5GUlAQAcHFxgZWVlcgRWR7mQBqYB/ExB+JjYUYCjUYjdggWSa1WIzY2FgBQo0YN/kMoAuZAGpgH8TEH4uNQJhEREZFEsDAjIiIikggWZiSwtubINhERkZhYmJFAJpOJHQIREZFFYxcJCdQaDRLSMg3ec6lmBys563ciIqKKwMKMBCkZORi8fJ/Bewf8R8DdyUGkiIiIiCwLCzMikclkMjg4OAjbVPGYA2lgHsTHHIiPhRmRyBQKBby9vcUOw6IxB9LAPIiPORAfJw8RERERSQQLMyIiIiKJ4FAmkchyc3Nx7949AEDDhg25npwImANpYB7ExxyIT9LfeHBwMCZMmFDgPltbW9SsWRPNmzfHgAEDMGDAgGL/AN2/fx/79u3D2bNnERcXh9TUVDg4OMDNzQ0dO3ZE//790a5du2LjGj9+PC5cuFDgPmtra1StWhUeHh7o0aMHXnvtNTg5ORXa1pw5cxAQEJDvfZlMBkdHR9SpUwdt27bFa6+9hubNmxcbG5kfjUaDtLQ0AED9+vXFDcZCMQfSwDyIjzkQn6QLs6Lk5OTg4cOHePjwIY4fP47Nmzdj7dq1cHV1zXesUqnEN998g+3bt0OlUhnsS0tLQ1paGqKiorB161b06NEDX3zxBWrVqlWiuHJzc5GamopLly7h0qVL2LRpE3744Qe0bdvWpHa0Wi3S09MRGRmJyMhI/P7775gyZQpmzpxZoriIiIhI+symMBs9ejTGjBkjvM7KykJYWBg2btyIuLg4hIaG4p133sHOnTsNbvHNyMjAO++8g+DgYACAk5MThg0bhs6dO8PZ2Rnp6ekICwvD7t27cefOHZw+fRqjRo3Cxo0b4eHhUWxcQUFBBq9VKhUePHiAvXv34sSJE0hOTsb06dNx6NAh1KxZs8i2NmzYADc3NwB5v7UkJyfj1KlT2L59O3Jzc7Fu3Tq4ublh7NixRn9vREREZD7MpjBzdnaGp6enwXutW7eGn58fRo4ciXv37iEkJAQnT56Er6+vcIy/v79QlHXs2BGrVq2Cs7OzQTudO3fGxIkTsXLlSvz666+Ij4/HtGnTEBAQAEdHxyLjej4mAGjZsiX69++P2bNnIzAwEE+ePMGff/6JqVOnFtlWo0aNUK9ePYP3unbtis6dO2P69OkAgDVr1uD111+HlZVVkW0RERGR+TH7uzKrV6+OKVOmCK/PnDkjbB84cACHDh0CAHh7e+Onn37KV5TpKBQKzJkzByNHjgSQNx9t5cqVpYpt8uTJwnZoaGiJ2/H19RXmvqWkpODGjRuliouIiIikyewLMwBo1aqVsP3w4UNh+5dffhG2Fy9eDDs7u2LbmjNnDlxcXAAAu3fvRkpKSonj0u/9UiqVJW4HKPwzEhERUeVRKQoz/bsx1Wo1ACAiIgLh4eEAgJdeesmgsCmKo6Mjhg8fDgB49uwZ9u/fX+K49Auo2rVrl7gdwPAzajSaUrVlLBuokZ2VhczMTGRmZhZ/AhEREZVKpSjMoqKihG3d5PlLly4J7/Xq1cuk9vTnqOm3Y6oNGzYI27179y5xO0DBn7G89Xe8j82//IhvvvkG33zzTYVc0xLJ5XK4ubnBzc0Ncnml+CtpdpgDaWAexMcciM9sJv8XJjc3F7/++qvwukOHDgDyesx0WrRoYVKbzZs3h1wuh0ajQWRkZJHH6hdMQN5dmXFxcdi3bx+OHj0KABg4cCC6d+9uUgz6IiIihLlzdnZ28PHxKXFbJD3W1tZcL0hkzIE0MA/iYw7EZ7aFmW65jB9++AHXrl0DANStWxcDBw4EAKSmpgrHFrS2WVFsbW3h4OCA9PR0YaG9wvj5+RW6r3HjxpgyZQqGDRtm0vWBvHXMkpKScOrUKaxcuVIYoh0/fjyqVKlicntEREQkfWZTmK1ZswZr1qwpdL+zszP++9//wsbGBgAM5kTZ29ubfD17e3ukp6cjIyPD9GD/3507d/DHH3+gXr16Qk9eUYob7uzZsyfef//9EsdDRERE0mY2hVlh6tWrh379+mHy5MkGS2E4ODgI21lZWSa3qzunuHXMnh/q1Gg0SE1NxeXLl/Hf//4X165dw5tvvolvvvkG/fv3NzkOhUIBHx8fjBo1CkOGDDFYPLe8HcpogB0zX4VrddMLWzKeSqXCzZs3AeQNoysUCpEjsjzMgTQwD+JjDsRnNoWZ/sr/MpkMVapUQY0aNVC1atUCj69Ro4aw/fjxY3h7ext9rZycHKHHrajnXBZELpfD2dkZr7zyCrp27Yrhw4fj7t27+Oyzz9CpU6ci29Nf+V8ul8Pe3h4uLi5CL2BFU8IKdvb2BkUulT2tVis8Kkyr1YocjWViDqSBeRAfcyA+s7nlQrfyv6enJ5o1a4YGDRoUWpQBgJeXl7Ctq/6NdfPmTWFJCv12TOXg4IDRo0cDyBtaPXz4cJHHN2rUSPiMTZs2RZ06dUQryoiIiKjimU1hZqr27dsL2ydOnDDpXP3j9dspiSZNmgjbz9/BSURERKSv0hZm3t7eaN68OQDg6tWrRj8SKSMjAwEBAQCAKlWqYNCgQaWKQ3c3JZC3tAcRERFRYSptYQYAb7/9trC9YMEC5OTkFHvO119/jcePHwMARowYgZo1a5YqhrCwMGG7tKv/ExERUeVWqQuzQYMG4ZVXXgEAhIeHY/r06YU++1KlUuHrr7/GH3/8AQBo0KABPv7441JdPy4uDtu2bRNe9+jRo1TtERERUeVmNndlltSSJUuQmpqKixcv4ty5cxgwYACGDx+OTp06wcXFBRkZGQgLC8OuXbtw+/ZtAECtWrWwdu3aYpfKAPLPG9NoNEhLS8OlS5ewdetWYYFaPz8/YWiViIiIqCCVvjCrWrUqNmzYgBUrVmDHjh1IS0vDxo0bsXHjxgKP79atGxYvXmz0sGNRK//rDBw4EEuWLDEpbrIc1tbWwk0i+g+rp4rDHEgD8yA+5kB8FvGtV6lSBfPmzcP48eOxb98+nD17FnFxcUhLS4ODgwPc3NzQvn17DBgwoNR3YcpkMtjb26N27dpo3bo1hg4dWuo2qXKTy+UG6+5RxWMOpIF5EB9zID6ZlivIWTSlUincsVqroQcGL99nsP+A/wi4O3GBWSIioufp/wz18fEpk7VHLaLHjEjK9FfaVigUFfrYLcrDHEgD8yA+5kB8lfquTCJzoFKpEBoaitDQUOEfRKpYzIE0MA/iYw7Ex8KMiIiISCJYmBERERFJBOeYkaCmoy0O+I8weM+lmp1I0RAREVkeFmYksJLL4e5kK3YYREREFotDmSTgyilERETiYmFGgtzcXLFDICIismgszIiIiIgkgnPMSMDnoolDoVDAx8dH2KaKxxxIA/MgPuZAfPxJTAKu8CwOmUxWJo/xoJJjDqSBeRAfcyA+DmUSERERSQR7zEjAuzLFodFo8OTJEwBA9erVIZfz96WKxhxIA/MgPuZAfPzGScChTHHk5ubi9u3buH37Nu+MFQlzIA3Mg/iYA/Gxx4wEao0GCWmZRh3rUs0OVvxNioiIqEyxMCNBSkYOBi/fZ9SxB/xHwN3JoZwjIiIisizs8iAiIiKSCBZmRERERBLBwoyIiIhIIliYEREREUkEJ/8TiUwmkwmPPuGSJeJgDqSBeRAfcyC+ci3MgoODMWHChAL32drawsnJCd7e3ujbty8GDx5s9GMgYmNj0adPH2FB1G+++QZ+fn4mx/fw4UP89ddfOHfuHO7du4eUlBRoNBpUr14dnp6eaNu2Lfz8/FC/fv0Cz09OTkZISAhCQkIQGhqK0NBQpKWlAQCGDRuGZcuWFRvDnDlzEBAQkO99mUwGR0dH1KlTB23btsVrr72G5s2bm/wZSfoUCgVatWoldhgWjTmQBuZBfMyB+ETrMcvJyUF8fDzi4+Nx6tQpbNq0CevWrUO9evWKPTcwMNBglfrAwECTCrNnz55h5cqV2LFjB5RKZb79iYmJSExMxNmzZ7F69Wr0798fs2fPRu3atQ2O69Kli9HXNJVWq0V6ejoiIyMRGRmJ33//HVOmTMHMmTPL7ZpEREQkrgorzEaPHo0xY8YIr5OTkxEdHY0NGzYgPj4e0dHRmD59OgIDA2FlZVVkW/v25a21ZW9vj6ysLJw/fx6JiYlwc3MrNo6UlBRMnz4d165dAwA4ODjg1VdfRadOnVCrVi1YW1sjKSkJV65cwdGjR3H37l0cPHgQrVu3xsSJEwttt06dOmjSpAnOnj1b/JdRiA0bNgifQaPRIDk5GadOncL27duRm5uLdevWwc3NDWPHji3xNYiIiEi6Kqwwc3Z2hqenp8F7nTt3xvDhwzF48GDExcUhKioKR48eRf/+/Qtt58qVK7h37x4A4LPPPsPChQuhVqsRFBSEyZMnFxmDRqPBhx9+KBRlvXr1wldffQVnZ+d8x/r6+uKjjz7Cvn378PXXXxfY3rvvvgsfHx/4+PjAxcUFsbGx6N27d5ExFKVRo0b5egy7du2Kzp07Y/r06QCANWvW4PXXXy+2eCXzkZubi0ePHgEAateuDWtrTv2saMyBNDAP4mMOxCf6XZmOjo5C0QEA586dK/L4wMBAAECNGjUwdOhQdO7cGQCwd+/eYq+1ZcsWBAcHAwBefvllrFmzpsCiTEcul2Po0KHYs2cPvLy88u1///330atXL7i4uBR77dLw9fVFu3btAOT1+N24caNcr0cVS6PRCMPnGo1G7HAsEnMgDcyD+JgD8YlemAEwKHri4+MLPU6pVOLQoUMAgAEDBkChUGDw4MEAgMjISNy8ebPIczds2AAAqFKlCpYsWWL0bwK1atUSCkCx6E/GfPjwoYiREBERUXmRRB+l7tZcAEUWSydOnMCTJ08AQCjI+vbtK8w1CwwMLPTOxbNnzyIxMREA0L9/f7i7u5dV+BVC/3up6N9ibKDO9152VhYyFQUc/P8cHPgcTSIiIlNJojCLiYkRtuvWrVvocbphzAYNGuCll14CkHcDQO/evREUFIT9+/fj008/LXD+1cWLF4Xtnj17lk3gFSgqKkrYNuYmh7LU3/F+vvc2//JjkecsXLiwvMIhIiKqtEQfylSr1cIQIwD069evwONSUlKEOx6fXxpD13uWlJRU6F2RERERwnbLli1LFXNFi4iIwJkzZwAAdnZ28PHxETkiIiIiKg+iFWYpKSk4f/48xo0bh/DwcAB5RZlukvvz9u/fD5VKBeB/hZhO165d4erqCuB/vWrP0y38CqDICf9SodVq8fjxY/z555+YOHEi1Oq84cTx48ejSpUqIkdHRERE5aHChjLXrFmDNWvWFLjPzs4Or7/+Oj7++ONCz9cVXK1atUKjRo0M9llZWWHgwIHYvHkzTpw4gYyMDDg6Ohock5mZaXA9KSpuqY2ePXvi/fffr6BoiIiIqKJJYo6Zt7c3xo8fb3ATgL7o6GhhiYjne8t0Bg8ejM2bNyMnJwcHDx7EyJEjDfbrT0bPzs7OV7hJlUKhgI+PD0aNGoUhQ4aI8uyyQxkN8r23Y+arcK1uX+GxVEZyuRxOTk7CNlU85kAamAfxMQfiE2Xlf7Vajfj4eBw+fBh79+7F1atXMX78eOzatQs1a9bMd66ut8za2hqDBg0qsP0XXngBHh4eiImJwd69e/MVZro/aEDeXDQpFmb6K//L5XLY29vDxcXF6GeIlhcl8t9MYWdvzzsvy4i1tTU8PDzEDsOiMQfSwDyIjzkQn2gr/zdv3hy9evVCx44dMWfOHMTFxcHf3x9r1641OE+j0SAoKAhA3orExqwndunSJcTGxhqsou/t7S0sXhseHp5vOFQKClr5n4iIiCyH6P2Uw4YNE+7EPHHiBM6fP2+w//z580hISDCpTa1Wm+9JAO3btxe2T506VbJgiYiIiMqRJOaYzZw5E8eOHYNarcaqVasMesV0w5g2NjZYsmRJsWPe69evR3h4OPbt24d3331XeP/ll1+Gm5sbEhMTcejQIXz88cdmt8gsVU4qlUpYy8/Dw6PQuZZUfpgDaWAexMcciE8ShVnjxo0xYMAA7N+/H9evX8c///yDrl27IjMzE8eOHQOQtyTG8+uXFeTx48cIDw/H3bt3cfXqVWEhWhsbG0yePBlLly7Fs2fP8Nlnn+Hnn3826mHgCQkJuH37tuiPZaLKSavVCncNa7VakaOxTMyBNDAP4mMOxCf6UKbO1KlThTsOdfPMjhw5gqysLACFLzz7vH79+gntPL+m2YQJE9CxY0cAeY9oevfdd5GSklJoW1qtFkFBQRg+fDgiIyNN+jxEREREppJEjxkAeHp6wtfXF8ePH8fFixdx6dIlobBSKBTw9fU1qp3atWvDx8cHISEhOHjwIPz9/YW7GuVyOb777jtMmzYN169fx8mTJ9GnTx/4+fmhU6dOqFWrFqytrZGUlIRr167hyJEjuH37dqHXunTpEu7f/9/jilJTU4Xte/fuYc+ePQbHDx8+3Nivg4iIiCyQZAozAJg2bRqOHz8OAPjhhx9w4cIFAEDHjh1RvXp1o9vp168fQkJC8OTJE5w8edKgt61mzZrYunUrvvnmG+zYsQOZmZn4/fff8fvvvxfYlkwmg5+fHwYMGJBv365duxAQEFDgeVeuXMGVK1cM3mNhRkREREWRzFAmkLeqf9euXQEA//77LzQaDQDjhzF19I8v6BFNVapUgb+/P44cOYKPP/4YXbp0QZ06dWBra4sqVarAzc0NL7/8MmbOnInjx49jxYoVvFGAiIiIyp1My9l9Fk2pVCI0NBQAUKuhBwYv32fUeQf8R8DdiQvMlgX9HPj4+Ii+oLAlYg6kgXkQH3NgmvL4viTVY0ZERERkySQ1x4zIEllZWQlPfDBm+RYqe8yBNDAP4mMOxMfCjEhkVlZWnMMoMuZAGpgH8TEH4mNhRoKajrY44D/CqGNdqtmVczRERESWh4UZCazkcrg72YodBhERkcViYUYC3qArDt4FJT7mQBqYB/ExB+LjXZkkyM3NFTsEIiIii8bCjIiIiEgiWJgRERERSQQLMyIiIiKJYGFGREREJBEszIiIiIgkgoUZERERkURwHTMS8Llo4rC2toaXl5ewTRWPOZAG5kF8zIH4+K2TQC5nB6oY5HI5HB0dxQ7DojEH0sA8iI85EB9/EhMRERFJBHvMSKDRaMQOwSJpNBpkZWUBAOzt7dlzKQLmQBqYB/ExB+LjN04CtVotdggWKTc3F5GRkYiMjORjsUTCHEgD8yA+5kB8LMyIiIiIJIKFGREREZFEsDAjIiIikggWZkREREQSwcKMiIiISCK4XIaF02q1wrZKpYJSqRQxGsukUqkK3KaKwxxIA/MgPubANPrfkf7P09KQacuqJTJLmZmZiIiIEDsMIiIis+bt7Q0HB4dSt8OhTCIiIiKJYI+ZhdNoNMjOzgaQ98BamUwmckRERETmQavVCgvx2tnZlcmTEliYEREREUkEhzKJiIiIJIKFGREREZFEsDAjIiIikggWZkREREQSwcKMiIiISCJYmBERERFJBAszIiIiIolgYUZEREQkESzMiIiIiCSChRkRERGRRLAwIyIiIpIIFmZEREREEsHCjIiIiEgiWJgRERERSQQLMyIiIiKJYGFGREREJBEszIiIiIgkgoUZERERkURYix0AFS8uLg5bt27FqVOnEB8fDxsbG9SvXx8DBgzA2LFjYWdnVybXOX36NHbu3InQ0FCkpKSgZs2a8PHxwWuvvYYePXoY1UZubi7+/PNPBAUF4fbt28jKyoKbmxu6dOmC8ePHo1mzZmUSa0UzhxwkJSXh5MmT+Pfff3Hz5k08evQIKpUKTk5O8PLywiuvvIIhQ4bA1ta2TGKtaOaQg8JERERgxIgRyM3NBQAMGzYMy5YtK5N4K5q55UGlUiEoKAiHDh1CVFQUkpKS4ODgADc3N7Rq1Qovv/wyBgwYUCYxVxRzysHVq1exc+dOXLlyBYmJiQb/JvXr1w9Dhw6FjY1NmcRbWci0Wq1W7CCocCdOnMCsWbOQkZFR4P5GjRrh559/RsOGDUt8DY1Gg/nz52PXrl2FHjNy5Eh88cUXkMsL72RNSUnBlClTEBoaWuB+GxsbLFiwACNHjixxrGIwhxzs3LkTn3/+OdRqdZHXadSoEb7//nt4e3uXOFYxmEMOimp31KhRCAkJEd4z18LM3PIQERGBWbNmISoqqtBjqlatikuXLpU43opmLjnQarX46quvsHXr1iKv1axZM/z888+oU6dOieOtbFiYSVh4eDhGjx6NnJwc2NvbY+rUqejYsSNycnJw4MAB7Ny5E0DeX8Tdu3fD0dGxRNdZuXIlfv75ZwBAixYt8NZbb6F+/fp48OAB1q9fj/DwcADA1KlT8dFHHxXYhlqtxvjx43H58mUAwCuvvIKRI0fCyckJ169fx9q1a5GcnAy5XI5169aVuOehoplLDn788Ud8//33UCgU6NWrF7p27QoPDw84ODjg/v37+PPPP3H27FkAQM2aNREQEIBatWqVKNaKZi45KMyWLVvw1VdfwdnZGcnJyQDMszAztzxERETgjTfeQFpaGqpUqYKRI0eia9eucHd3h0qlwt27d3HmzBlcvnwZp06dKlGsFc2ccvDTTz/h22+/BQA4ODhg0qRJaNOmDezt7XHnzh38+uuvQsHs6emJgIAAWFtzEA8AoCXJGjNmjNbT01PbokUL7ZUrV/Lt/+WXX7Senp5aT09P7erVq0t0jdu3b2tbtGih9fT01A4fPlybnZ1tsD8rK0s7fPhwIY67d+8W2M6ff/4pxPL555/n23/37l1tmzZttJ6entq+fftqVSpVieKtaOaSg19//VW7YsUKbXJycqHXWbp0qRDrnDlzShSrGMwlBwV59OiR9qWXXtJ6eXlpAwIChDhnz55dojjFZE55yMnJ0b7yyitaT09Pbc+ePbV37twp9JrPnj0rUaxiMJccKJVKbbt27bSenp7ali1basPDw/Mdo1KptCNHjhTiPXjwYInirYw4+V+iQkJChO71ESNG4KWXXsp3zJtvvgkPDw8Aeb+Vq1Qqk6+zefNmYd7L/Pnz880/srOzw/z58wHkzR/btGlTge1s3LgRAODk5IRPP/003/6GDRti6tSpAIB79+7h6NGjJsda0cwpBxMnTsQnn3yCmjVrFnqdjz76CK6urgCAo0ePQqPRmBxrRTOnHBRk0aJFyMzMxLBhw9CuXTuT45IKc8vDhg0bcPfuXcjlcnz33Xdo1KhRodc0l/lN5pSDmJgYPH36FADQq1cvNG/ePN8x1tbWws8EALh27ZrJsVZWLMwk6tixY8L2iBEjCjxGLpdj6NChAICnT58iODjYpGtotVocP34cANCkSRO0bt26wONat26Nxo0bAwCOHz8O7XOj33fu3EFMTAwAoH///oVOPB02bJiwrf/5pMqccmAMGxsbtGnTBgCQnp6OtLQ0k9uoaOacg0OHDuHEiROF/rJiTswpD2q1Gr///jsAoEuXLnjxxRdNikOqzCkH+gVhvXr1Cr1egwYNCjzH0rEwkyjdXC17e3u0bNmy0OPat28vbF+5csWka8TGxiIxMTFfOwXp0KEDACAhIQGxsbEFxqp/XEFcXV2F31xNjVUM5pQDYymVSmHblAnsYjHXHKSnp+PLL78EAMyaNQs1atQwKSapMac8XL16FQkJCQAAX19f4f1nz57h/v37SEhIKPYmGSkypxw0atQIMplMaLMw9+/fF7Z1hR6xMJMsXQ9UgwYNipwQ2aRJk3znGOvWrVsFtlPcdW7fvl1grKa08+jRI2RlZRkdqxjMKQfGUKlUwnCBi4sLnJycTG6joplrDlasWIHHjx+jXbt2hfZumBNzyoP+kJinpyfu3r2L9957D23btkXfvn3RvXt3dOzYEZ9++qlBYSB15pSDqlWrYtCgQQCAkydPIiIiIt/5ubm5wg0G+scTCzNJevbsGVJTUwGg2DvnqlevDnt7ewBAfHy8SdfRP7646+jvf/ToUaHtuLu7F9lO7dq1AeR1mZsab0UytxwYY+fOncJn6t+/v8nnVzRzzcHly5exc+dOKBQKfP7550LPgbkytzzoFyO3b9/GsGHDcOTIEYOhsvT0dOzduxdDhw7FuXPnTIpTDOaWAwCYO3cuWrZsCZVKhbFjx2LNmjU4d+4crl27hoCAAIwYMQLXrl2DnZ0dli9fbva9ymWJ96ZKUGZmprCt+wtWFDs7O2RlZZncA2XKdfTnjT1/Hf12HBwcStyOlJhbDorz4MEDrFq1SrjOlClTTDpfDOaYA6VSiQULFkCr1WLixIlmu6CyPnPLg/7cya+++grPnj3DxIkTMWbMGNSuXRsJCQnYsWMHNm7ciMzMTHzwwQfYu3evpNfRMrccAHm98tu2bcPOnTvx888/44cffjDYL5PJMHLkSEyaNEm4YYHysMdMgp49eyZsKxSKYo/X3VWUk5NTbtfRv3Pp+euUVTtSYm45KEp2djZmzJiB9PR0AHl3WhXXsykF5piDn3/+Gbdu3ULdunXx7rvvmhSHVJlbHrKzsw3afP/99zF37lw0bNhQWCH/008/xcyZMwHkTZL/6aefTIq1oplbDnT+/fdf7Nu3D0lJSfn26W40CAgIMJj7SizMJKlKlSrCtjF3quj+UJv6qB1TrqP/F+f565RVO1JibjkoTG5uLj744ANhjsfo0aMxfPhwk2IUi7nl4Pbt28IP+Pnz55fZY3HEZm550G+nRo0aePvttwtsY/LkycLyMYcOHSrRnc4VxdxyAOQtuzF9+nSEhYWhffv2+PXXX3H58mWEhobiwIEDePPNN/HkyRP88ssveOONNwx66ywdCzMJ0h8ONKYrWvcbojFd3CW9jv5voc9fR7+d4v5yFdWOlJhbDgqi1WoxZ84cnD59GgAwYMAALFiwwKT4xGROOdBqtVi4cCGUSiX69u2LXr16mRSDlJlTHp5vp0OHDoWuU2ZtbY1OnToByBv+fPDggUnxViRzy0FERASWLVsGrVaLLl26YPPmzejSpQscHR1hY2MDDw8PzJ49G1988QWAvLtHnx/qtGScYyZBVapUgZOTE9LS0oqdvPnkyRPhL5Cpj9jRP7646+jv103gL6idhISEIhc51U0Slclkkn4kkLnloCCLFi1CUFAQAKB79+5YsWKFWSyRoWNOObh27RouXLgAAHjppZfw119/5Ts3JSVF2I6NjRWOadasGTw9PU2KuSKZUx6eb6e4vyf6+1NSUgzW1ZISc8vBnj17hAWs33vvPVhZWRXYxn/+8x/88ssvuHv3LgICAjB79myzv1mmLLAwk6imTZvi0qVLuH//PnJzcwu9PVr/NmVTJ1A2bdq0wHaKu87zt1HrX/f27dsFrvL8fDu1a9eWdI8ZYF45eN6KFSuwY8cOAHnrEf3www9GzU2RGnPJgf6wztdff13sNS9evIiLFy8CAGbMmCHpwgwwnzwAMLjhorj1yvT3F1Y8SIU55UD/ztii1lwD8p7FeffuXaSlpSE5ORkuLi6mhFwpmc+vzxambdu2APK6k2/cuFHocbp/3AEIq7obq169enBzc8vXTlHXcXd3z7eSsy5WAEKvQUEeP36Mu3fvlihWMZhTDvT9+OOPWL9+PQDAx8cH69atk/R8vqKYaw4qG3PKg/6jr4pbiFl/+FLqN8SYUw70i0bd450Ko79f6sVxRWFhJlF9+vQRtnfv3l3gMRqNBoGBgQCAatWqoWPHjiZdQyaToXfv3gDyfvsp7Fll165dE3476t27d76u5saNGwu/mR06dMhg7oG+gIAAYVv/80mVOeVAZ/Pmzfj+++8B5C2uuX79ejg6OpoUk5SYSw46duyIyMjIIv/TPeoGyHs8me799957z6R4xWAueQCA+vXro0WLFgCA4OBg4W7k52VkZAhrmDVo0EAoSKTKnHKgX6jpnu9ZEJVKhatXrwLIW2TWHBa9rggszCSqVatWwm9+u3fvFv7w6tu4caPQZTxhwoR8Q1XBwcHw8vKCl5cX5syZU+B13njjDeG3lMWLF+e77TknJweLFy8GkPdb0BtvvFFgO2+++SaAvEm0K1asyLf//v37wh1rDRs2RN++fQv+4BJibjnYvXs3li5dCiDvkSi//vqr2f9DZ245qKzMLQ+6OzGzsrKwZMmSAo9ZtmwZMjIyAACvv/56gcdIiTnlQP/ml5UrVwrf8/N++OEHPH78GADQo0cPzi/7f5xjJmH+/v4YPXo0cnJy8Oabb2LatGno2LEjcnJycODAAfzxxx8A8n4IT5o0qUTXaNy4MSZPnoyff/4ZYWFhGD16NN5++23Ur18fDx48wC+//ILw8HAAebeX6551+bxhw4Zh9+7duHLlCrZt24akpCSMHDkS1atXR0hICH788UdkZGRALpfD39+/yEeKSIm55ODYsWOYP38+tFotHB0d4e/vj5SUFIMJ58+rV6+e5Of5AeaTg8rOnPIwcOBABAYG4vTp09izZw8SExMxevRo1KlTB48ePcIff/wh3K3cokULjBs3rkTxVjRzycHLL7+MTp064d9//0VkZCSGDBmCCRMmoFWrVqhSpQru3buH3bt348yZMwDy7uqsLOv+lQWZVsqLtxBOnDiBWbNmFfobR6NGjfDzzz+jYcOG+fYFBwdjwoQJAPIKp2XLlhXYhkajwbx58wrtHgfy7p5ZvHhxkXf1paSkYMqUKQgNDS1wv42NDRYsWICRI0cW2oYUmUMO5syZYzBUbIwtW7aYPNQhFnPIQXFiY2OFYaKi4pAyc8pDZmYm3n//fZw9e7bQY3x8fLB27VphPTNzYC45ePLkCd577z0EBwcX+Xlq1qyJlStXokuXLkUeZ0nMo9vCgvn6+mLfvn3YsmULTp06hYSEBCgUCjRo0AD9+/fHuHHjSr2QpVwux5IlS9CvXz/88ccfCA0NRWpqKmrUqAEfHx+MGjUKPXr0KLadmjVr4vfff8fOnTuxf/9+xMTEIDs7G25ubujcuTMmTJhglo+oMaccVFbMgTSYUx4cHBywfv16HDhwAAEBAYiIiEBaWhqqVq2K5s2bY9CgQRg6dKjZTTg3lxxUr14dmzdvxvHjx7F//36EhoYiKSkJarUaVatWRdOmTdG9e3eMHDnS7KdclDX2mBERERFJBCf/ExEREUkECzMiIiIiiWBhRkRERCQRLMyIiIiIJIKFGREREZFEsDAjIiIikggWZkREREQSwcKMiIiISCJYmBERERFJBAszIiIiIolgYUZEREQkESzMiIiIiCSChRkRERGRRLAwIyIiIpIIFmZEREREEsHCjIiIiEgiWJgREVWAhw8fYsGCBejTpw98fHzg5eUFLy8vHDt2TOzQiEpkzpw58PLygq+vr9ihVCrWYgdAVFrBwcGYMGECAGDGjBl47733RI6IyNDDhw8xfPhwpKamih2KSfT/bj3P1tYWTk5O8Pb2Rt++fTF48GDY2NgY3bZarcaRI0dw/PhxhIaGIikpCSqVCi4uLmjSpAl69uyJwYMHo1q1aibFrFQqceTIEfz9998ICQlBamoqMjIy4OjoiLp168LHxwf9+vVDp06dIJezb4Kkh4UZEQEAvLy8ALC4LQ9r165FamoqrK2t8eGHH6J9+/awt7cHANSpU0fk6EomJycH8fHxiI+Px6lTp7Bp0yasW7cO9erVK/bcS5cuYeHChbh161a+fXFxcYiLi8OZM2fwww8/4OOPP8Zrr71mVExHjhzBsmXLEBcXl29fWloa0tLScOPGDfz+++9o1KgR5s6di549exrVtjnj323zwsKMiKicnTt3DgDQu3dvvP322yJHUzKjR4/GmDFjhNfJycmIjo7Ghg0bEB8fj+joaEyfPh2BgYGwsrIqtJ1Dhw5h1qxZUCqVAIAOHTpg8ODBaNy4MWxsbPDw4UOcOHECf/31F9LS0jB//nzcuXMHs2fPLjK+//73v1i9erXwumvXrvD19YWHhweqVauGJ0+e4M6dOzhx4gTOnTuHu3fvYtWqVRZRmJWXZcuWYdmyZWKHUemwMCMiKmcJCQkAgMaNG4scSck5OzvD09PT4L3OnTtj+PDhGDx4MOLi4hAVFYWjR4+if//+BbYRHh6OTz75BCqVCgqFAkuXLoWfn5/BMa1atUL//v0xadIkTJs2DfHx8di4cSPq169vUBjq2717t1CUOTs747vvvkOHDh3yHdelSxeMHTsWUVFRWLp0KVJSUkryVRCVKw6wExGVM5VKBQCwtq58vws7Ojpi+vTpwmtd7+DzNBoNPv30U+G7WLJkSb6iTF/z5s2xadMmYch3+fLlePjwYb7jEhISsHjxYgCAvb09tm7dWmBRps/T0xMbNmzAm2++WfSHIxJB5ftXgug5+hOYt2zZgg4dOmDXrl3Ys2cPbt++DZVKhSZNmmDcuHEYOnSocJ5SqcTu3bsREBCAu3fvQqVSwcvLCxMmTMDAgQMLvFZsbCx69+4NAFi6dCmGDx+OgwcP4o8//kBkZCQyMzNRp04d9O3bF2+//XaxE5uVSiX+/PNPHDp0CNHR0cjIyED16tXRokULvPrqq/Dz8yt0AvOcOXMQEBCAunXr4sSJE0hMTMTmzZtx6tQpPHz4EFlZWdiyZQvmzp1rMCdnzZo1WLNmjUFbw4YNMxiySExMxNGjRxEcHIyIiAgkJiYiNzcXNWrUwAsvvAA/Pz/079+/0Niez0nHjh1x4MAB4XvKyspC7dq10bt3b0yZMgVOTk5Ffk8AcPr0aQQFBeHq1atITk6GWq2Gq6srWrZsiV69emHAgAGws7Mr8NwbN27gjz/+QHBwMBITE6HVauHu7o5OnTph4sSJJerp2rNnD+bOnWvw3vPf7fPfKwCkpKRg8+bNOH36NGJjY/Hs2TO4urqibdu2GDVqFNq1a1foNX19fREXFye0GxYWht9++w0XLlxAYmIiVCoVIiMjTf4sxdHNYQKA+Pj4Ao85ceIEoqOjAQDdu3fH4MGDi223cePGmD59OlauXImcnBxs2bIFc+bMMThm06ZNyM7OBgC8//778PDwMCpmuVyOIUOGGHWsvor890Tf48eP8dtvv+HMmTOIjY1FVlYWnJ2d0bp1a4waNQpdunTJd47uz4NOcX+3n/9s7du3x549e7B3717ExMQgJSUFQ4cOFY5//t+YwmRkZGDnzp04deoUYmJi8OTJE9jY2KBBgwZo27YtBg4ciLZt2xb7HVgKFmZkUXJzczF9+nScPHnS4P3Q0FDMnj0bYWFhmDdvHp48eYJ3330XFy9eNDju6tWruHr1Ku7fv49p06YVe73PPvsMu3fvNnjvzp07+PnnnxEYGIhNmzYV+oMkNjYWb7/9Nm7fvm3wflJSEv7++2/8/fff+OOPP/Djjz8WW7hcu3YN06ZNK5O7AtVqNXr06AGNRpNvX2JiIk6cOIETJ05g165d+OGHH+Dg4FBkexqNBrNmzcK+ffsM3r979y42bNiAY8eOYdu2bXB1dS3w/NTUVMycORPnz5/Pt083kfzIkSMAgOHDh+e79vLly7F582Zotdp817979y527dqFBQsWYNSoUUV+jrJw9uxZfPDBB8jIyDB4X/c59u3bh7Fjx2LevHnF3lG4Y8cOfPnll8jNzS3PkAEACoVC2C6sVzAwMFDYfuONN4xu+/XXX8eaNWvw7NkzBAQEYPbs2ZDJZAAArVaLgIAAAHm9ZSNHjixB9CVXUf+e7Nu3DwsXLkRWVpbB+/Hx8Th06BAOHTqE//znP1i0aFGZ9co+e/YMkydPLrQH1Fjnzp3DRx99lO/fHpVKhZs3b+LmzZv47bffyuUXBnPFwowsyvfff4/r16/Dz88Pfn5+cHFxwd27d/HDDz/gzp072Lp1K3x9fbF161ZcvXoVo0ePRt++feHk5ISbN2/i+++/R2JiIlavXo3evXujWbNmhV5r+/btCA0NRatWrTBx4kQ0bNgQycnJCAgIwMGDB5GYmIi33noLQUFBcHR0NDg3MzMTEydOxIMHDwAAffr0wYgRI+Dm5obY2Fhs27YNFy5cwOXLlzFt2jRs27at0AnXmZmZeO+99/Ds2TNMmzYNXbt2ha2tLaKiouDq6ooNGzZApVIJw0rPT/IGgOrVqwvbugKmU6dO6N69Ozw9PVGzZk1kZmbiwYMH+PPPP3H16lX8888/+OKLL7B8+fJic3L16lX06dMHQ4cORZ06dZCUlITt27fj1KlTuHfvHpYuXYpvv/0237nZ2dmYMGECoqKiAAAtW7bEqFGj0KxZM9jY2CA+Ph4XL17EgQMHCrz24sWLsX37duD/2rv3mKbONw7g3+JEICgXkYvO4FSOVJmI6NDIpiDTyLwhc9EhDG+bm84NzbzMTJ0uTo3TiU6MiU6mDBBmxMtcg6IoRBwIEVRGBVHBjYm4VkClAuf3R9M3p5y2tNhi/fl8/jrpec85b8+tT98rgJEjRyIiIgJ9+/aFnZ0dysrKkJiYiJs3b2Lt2rVwc3NjpaHGCAsLg5+fHwDoPbfC81paWopFixax9ldRUVEIDQ2Fvb09SktLsW/fPnbt7e3t8dVXX+k9dklJCY4fPw5PT0/Mnz8ffn5+aG5uxpUrV4zOvykqKirYcp8+fXSmKSgoAADY29tj9OjRRu+7R48eGDlyJHJycqBQKFBeXs6eu5s3b7If/MDAQNFzZGmd8T75/fffsWLFCvA8j759+2LOnDkYMGAAXF1dce/ePaSnpyM7Oxvp6elwdHTUKqU19dkW2rZtG8rKyhAaGooZM2aw57KxsdHo85OXl4eFCxeiubkZXbp0wbRp0zB+/Hh4eXmhqakJFRUVuHDhgiiwfeXxhLzk8vLyeI7jeI7j+Pj4eIPrOY7jDx48KEpz//59PiAggOc4jh81ahQ/aNAgPjMzU5SutLSU9/X15TmO4zdu3ChaX1VVpXWshQsX8s+ePROl2717N0uzZcsW0frNmzez9Tt27BCtb21t5ZcvX87SJCUlidKsXLmSrR82bBhfWloqSiNk6By2Pfbt27cNptm5cyfPcRw/aNAgvrKyUrS+7TXZs2ePzuPMmzeP5ziOHzx4MF9XVydKs2nTJraPb7/9lm9tbdWZn6amJr62tlbrs5ycHLbtkSNHdG739OlTPiYmhuc4jg8JCdF5LY1hzLmNjIzkOY7jpVIpf/HiRdF6hULBh4eH8xzH8b6+vrxcLhelCQkJYceaPHkyr1QqO5RfjfaeLZ7n+ebmZn769OksXX5+vihNTU0NW//BBx+YnI9t27ax7Y8fP84+z8jIYJ9v377d5P12RGe+T+rq6vjAwECe4zh+9erVeu+/7du3s/uioqJCtN7YZ7vtd9P17hHSvGNCQkJE654+fcoHBwfzHMfx/v7+fF5ent79/P333waP86qhxv/kleLv76+zGqVXr1549913Aajb+EyaNAlhYWGidL6+vqwtRHulD7a2tti4caPOqoVPP/2U9XD77bff2NABgLotSnp6OgDAx8dH57hDEokE69evZ1WYSUlJBvOyYMEC+Pr6GkxjLIlEAm9vb4NpFi9eDBcXF/A8b7DtCaAu5dJVjSORSBAbGwtAXWVUVFSktf7Ro0dITU1l+1izZg2r4mrL1tYWbm5uWp/t27cPADBx4kS9VWDdunXD2rVrAairEy9fvmzwu3RUcXExSkpKAAAzZ85EcHCwKI2TkxM2bNgAQF0Fqynp02fdunUmD85qiocPH+LSpUuYM2cObty4AUB9LnW1gRNWY7W9Dsbo2bMnW1YoFDqXhWk6i6XfJ8nJyaivr4eHhwfWr1+vt5ry888/h4eHB1pbW5GRkfE8X4np16/fc415duzYMdy/fx8AEBcXh6CgIL1pvby8Onyc/0cUmJFXiqFGtsLA5b333ms3naaaUZ8xY8bAw8ND5zobGxvWMFihULAfNgC4du0aHj16BEDdMFdfFaWjoyMmTZoEACgvL2cvQV2MaWjdUa2trfj3339x69YtyOVyyOVyVFRUwNPTEwDw119/Gdx+ypQpegOqIUOGsOW25zsvL481+o6JiTE4dlZbDQ0N+PPPPwGogwlDBgwYABcXFwAQBYfmImzH8/777+tNFxgYyNokGmr74+XlZbCTQEfs3r2bTSM1aNAgjB49GrGxsSgsLIS9vT3mzp2LH374Qee2wjZzml6WphC2UxTuS1itpq9jhyVZ+n2i+VMzbtw4g7MqvPbaaxg2bBgA892j4eHhJj1TbWmqJx0cHIweIJioURsz8kox1Luue/fubLlfv37tpmuvrcWbb75pcP3QoUPZslwuZy9WTc81QP2P3BB/f38kJyez7dzd3UVpHBwc0LdvX4P7MRXP8zh+/DjS09NRXFyMp0+f6k3bXoeD/v37610n7NTQ9nwLg1lTe3TduHGDdV5YtmwZli1bZtR2Dx48MOk4xtJc865du0IqlRpM6+/vj4qKCty5cwcqlUrnD7awl2Rn8PX1RXR0tFYnACFh26+2DdiNIbz2wn0JAzZNkN6ZLPk+aWlpYX9qUlNTWelwe8x1jz7vPVRaWgpA/efqRQTNLzMKzMgrxc7OTu86YS83Y9Lp6pUo1F7Vir7qGaVSyZZdXV0N7kNYLSTcTsjc1VlNTU1YsmQJLly4YFR6Q0EbYPw1aXu+hQGfroDUkLq6OpPSa7T3XTpKc/2dnZ3b7VWnueY8z+PRo0c6qwYtUYUpbDje0tKCmpoayGQyZGRkoKioCNHR0UhPT9d5z2pKHIGOBQ7C6yUM1oXLHb2mz8OS7xOlUtmhHrXmukf1dQowlub51NebmuhHgRkhFqKveq6z9/E81RG6JCQksKDsrbfewocffoghQ4bAzc0NdnZ27IcmKiqK9cSzNsIfwQ0bNiAgIMCo7Z73x6qzmPuaA+KR/6VSKUJCQhAUFIRVq1bh3r17WLNmDRISEkTburu7w8XFBf/99x/KysrQ0tJiUh6FpaPCKkLh8vXr1039SlatpaWFLc+cOVPvZPJt6Su1NBVN8P7iUGBGiIW0VzKgrxRA+ONfV1dnsLpEeIzOCBp4nmcdE0aMGIHExES9L3B9JXjmIiyFuX//vknVtcLzbWdnJ5pqqLNp8qNQKNDc3Gyw1ExzzSUSiUUb9xsrIiIC586dg0wmQ1ZWFi5duiQaDkMikSAwMBBnzpzBkydPcOnSJZ0dHHSpr69nAb6zszMGDhzI1vn4+LCA78qVK2hoaOj0ITMspe0QNS/6HjWVi4sLampqUFtb+6Kz8tKhkJgQC9H0sjNmvXD8IuHy1atXDe6juLhY53aWolAo2IvW0Mj+jY2NqKystGhehB0DTC2Zk0qlrDSysLDQrPnqCM210wy6aYjmmnt7extsEN6Z4uLiWAnYjh07dKaJiIhgy4cOHTJ636mpqax6LiIiQqsUWSKRsP0+fvwYaWlpJufdWtna2rL7whruUVMNHjwYgLoz04to//cyo8CMEAvJzc3V21OytbWVjVju5OSkFWT4+fmxkpBjx47pbcvW0NCA06dPAwAGDhxocjurtrp16wYAWkN3tCWsXjH0sk1LS7P4iPNBQUGsh9+hQ4e08tYeV1dX1tni5MmTL3wya+F0Om1nihAqKipCeXm5aJsX7Y033mA9hK9evYrc3FxRmtDQUNaj9Pz58zh16lS7+71z5w5++uknAOqSTV3VebGxsaxxeXx8vNZgt4a0traKZpuwNqGhoQCAW7du4eLFix3ejzHPtrlp8v7kyROjOy4QNQrMCLEQlUqFtWvX6gwY9u3bx0arj4yM1Cr5sLW1ZUMmyOVy7NmzR7Q9z/PYuHEja2AbFRX13PnVNNK9e/eu3jSurq4saDx58qTOF31xcTF27tz53PlpT48ePdg0SdevX8emTZtE0yppPHv2TNQ4XDPxdkNDA5YuXcqGKNFFpVIhKSkJTU1NZsq9tqFDh7JZAtLS0nROL1VfX8/GVLOxscHs2bMtkpeO+uSTT1hplq52ZjY2Nti6dStrA7Vq1Sq9MzIA6mFWYmNjWS/OlStXonfv3qJ0Hh4e+OabbwCoS82io6PZUCj6lJeXY8GCBdi/f79xX+4FiYmJYX8+Vq9erdVjW5fz58/rHJ7GmGfb3KZOncqGC/rxxx8NXhN986u+qqiNGSEW4ufnh3PnzmH27NlaUzIdO3aMlRZ4enris88+E227ePFiZGZmoqqqCrt27YJcLseMGTPQq1cvVFdXs4mpASAgIMAs8zgGBASguroaWVlZSElJwfDhw9k/bUdHR/Ts2RM2NjaYMmUKkpKSUFZWhtmzZ2Pu3Lnw9vZGQ0MDsrOz8euvv8LBwQHu7u64ffv2c+fLkC+++AK5ubmQy+U4fPgwioqKMGvWLHAch65du6KmpgYFBQU4deoUvvzyS625MseOHYuYmBj88ssvyM/PR3h4OGbNmoXAwEA4Ozvj8ePHuHv3LgoKCpCZmQmlUonp06ezc2Ju3333HWbOnIlnz55h4cKFiI6ORkhIiNaUTJqxrubNm2d1bY44jkNoaCjOnj2L/Px8FBQUiMZS8/Pzw9atW7FixQqoVCrExcUhJSUFU6ZMQf/+/dG1a1f8888/yMrKwsmTJ1mp67x580RTCQlFRkaipqYG8fHxqKurQ3R0NIKDg1kpXY8ePaBUKlFZWYns7GxcvHgRLS0tZht02VLc3NywZcsWLF26FLW1tYiMjERERATeeecdeHp6orm5GTU1NSguLoZMJkNVVRX27t0r+l7GPNvm1q1bN2zduhXz58/HkydPMHfuXEydOhVhYWHw9PSESqXCrVu3kJ2djaysLFy7ds3seXhZUWBGiIVERUUhPz8fR48eRVxcnGi9Zp5K4XhHGo6Ojjh48CCbxFwmk0Emk4nSDR8+HAkJCWbphTd//nzIZDKoVCqsW7dOa11ERAQ2b94MQN2eqLCwEKWlpbh27RqWL1+uldbZ2Rm7du1CfHy8xQMze3t7JCYmYunSpcjPz8f169dZ6Ykxvv76azg5OSEhIQG1tbXYtWuX3rQODg4W6e2oIZVKsXfvXjaJ+YEDB3DgwAFRuqioKNE5txaLFi3C2bNnAahLzXSVSIWHh6NXr15Yt24dKioqcPnyZb0zKjg7O2PZsmVG/fFYvHgxfHx8sHnzZty7dw85OTnIycnRm97Hx8fgfKPWYsKECdizZw9Wr14NhUKBlJQUpKSk6ExrY2Ojc8wwY59tcxs1ahT27t2L5cuXQ6lU4ujRozh69KhFjvX/hAIzQizo+++/x5gxY5Camgq5XI7Hjx+jd+/eCAsLw8cff2ywJ+Xrr7+OjIwMpKWl4Y8//oBcLkdjYyOcnJwglUrZxMnm6tYulUqRmpqK/fv3o7CwEA8ePNBZVdm9e3ckJyfj559/xunTp3Hnzh106dIFXl5eGDt2LD766CM26n9ncHV1xeHDh5GZmYkTJ07g6tWrePjwISQSCdzd3TFkyBCEhYXpHOFfIpFgyZIlmDZtGlJSUpCXl4fq6mrU19fDzs4OXl5ekEqlCA4ORlhYmMHxqMwhODgYmZmZSExMRHZ2NqqqqqBSqeDm5obAwEDMmjXL7CP6m9PQoUMxZswY5ObmIicnB8XFxVoDKWuMHDkSJ06cgEwmw9mzZ1FSUoIHDx6gubkZrq6uGDBgAMaNG4dp06aZ1PN0woQJGDduHGQyGS5cuICSkhI8fPgQjY2NcHR0RJ8+feDv74+JEyciKCjILMPRdAZNSeSRI0eQnZ2N8vJyKJVKdOnSBW5ubvDx8cGoUaMwceJEndMbGftsW8Lbb7+NM2fOIDk5GefOnUNlZSXq6+thb28Pb29vjBgxApMnT+6UvLwsJLy+RhmEEJNVV1dj/PjxANRBmbDqjBBCCGkPNf4nhBBCCLESFJgRQgghhFgJCswIIYQQQqwEBWaEEEIIIVaCAjNCCCGEECtBvTIJIYQQQqwElZgRQgghhFgJCswIIYQQQqwEBWaEEEIIIVaCAjNCCCGEECtBgRkhhBBCiJWgwIwQQgghxEpQYEYIIYQQYiUoMCOEEEIIsRIUmBFCCCGEWAkKzAghhBBCrAQFZoQQQgghVoICM0IIIYQQK0GBGSGEEEKIlaDAjBBCCCHESlBgRgghhBBiJSgwI4QQQgixEhSYEUIIIYRYCQrMCCGEEEKsBAVmhBBCCCFW4n+XIgV5SQ39EQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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WpnUpyN69e4WlyWQyGdavX280GXFuUVFRGDt2rNHgBSDn1rCPjw+cnZ1RoUIFpKen49GjR4iOjs7To9ijRw8sWrTIZPs+Pj7Cdrdu3fLcwtXpdEhPT8fNmzdx9epVGP4ofu211/DTTz+ZXOUDAE6fPo2RI0cKzwjKZDK8/PLL8PLygkqlwsWLF3Hv3j3h+Lp162LDhg35Lrem1WoxduxYHDp0SHjPyckJbdu2RZUqVfD48WNEREQYFYSTJk1CSEhInrYmTpwojLxWKBSoX78+6tevjypVqiArKwtxcXE4d+6cMHUPAMycOdOoR1Pv+PHjGD58uPD65ZdfRqNGjYympBk0aJDw3CcRmYfFHxGVG0+ePEHv3r2RmpoKABg9ejQ+/fRTs8//4IMPhHV0vby8sG3bNmFQgSkpKSlYvnw51q1bZ/aKHwqFAv7+/hg1apTJnjQ9w+LPXDY2NggJCcEnn3xiNCWKKRcuXMAXX3yBBw8eFHhchw4dsGDBgkLX2VWr1ZgzZw42bNhQ4EoclSpVwuTJkxEUFGRy/8yZM/HHH38UeC09BwcHTJw40WThp/f5559j586d+e5fs2ZNsZ87JLJWLP6IqNwYN24c9u3bBwCoX78+tm/fXmDxlltcXBx69+4trN7xwQcf4KOPPir0vNTUVBw5cgQnT57ElStXkJycjJSUFMjlclSuXBmurq5o2LAhmjZtiq5du+aZksUUc4o/e3t7VK5cGS+99BJat26Nfv36Ga2NW5js7Gzs2LEDBw4cQFRUFBITE2FjYwM3Nze0bNkSvXv3xiuvvGJ2e0DO/IJbtmzByZMnERsbi/T0dFSpUgV169ZFp06dEBwcXOj0Lrdu3cKJEydw4cIF3Lx5E48fP0Z6ejoUCgWcnJzQoEED+Pv7o2/fviaf9TOk0+mwc+dO7Nq1C9euXUNycrLRoBQWf0RFx+KPiIiIyIpwwAcRERGRFWHxR0RERGRFWPwRERERWREWf0RERERWhMUfERERkRVh8UdERERkRVj8EREREVkRFn9EREREVoTFHxEREZEVYfFHREREZEVY/BERERFZERZ/RERERFaExR8RERGRFbGxdABkWVqtFpmZmQAAGxsbyGQyC0dEREQkDjqdDmq1GgBgZ2cHuVwcfWos/qxcZmYmoqKiLB0GERGRqPn6+sLBwcHSYZhFHCUqEREREZUK9vxZORub/30LeHl5wd7e3oLRUHGpVCqhB9fX1xdKpdLCEVFxMZfSwVxKQ0F5NNxn+Pu0vBNPpFQmDJ/xs7W1ha2trQWjoeKSy+VwcnICAFSoUEFUP4TIGHMpHcylNJibRzE9M8/vRBLwB5N42djYwMvLy9JhUClgLqWDuZQGKeaRz/wRERERWREWf0RERERWhPf5SKBSqfjMn0ipVCrcunULQM7AHT5YLl7MpXQwl9IgxTyy+COSAJ1Oh/T0dGGbxIu5lA7mUhqkmEfe9iUiIiKyIiz+iIiIiKwIiz8iIiIiK8Lij4iIiMiKsPgjIiIisiIs/oiIiIisCKd6IYFczr8FxEqhUKBmzZrCNokXcykdzKU0SDGPLP5IIJVvamukUCjg4eFh6TCoFDCX0sFcSoMU88iuHiIiIiIrwuKPiIiIyIrwti8JuLaveGVnZyMyMhIA4OfnxzyKGHMpHcylNEgxj+z5IyIiIrIi7PkjgY0Nvx3ESqlUws/PT9gm8WIupYO5pPKKv+1JIJPJLB0CFZNMJpPErQhiLqWEuaTyisUfCTRaDeLT4i0dBhERUZG52LtAIeeUZeZg8UeC5MxkBP8RbOkwiIiIiix0WCjcHd0tHYYocMAHERERkRVh8UdERERkRXjbl4iIiCgfNjY28PHxEbalQBqfgoiIiKgMyOVyODo6WjqMUsXbvkRERERWpNz3/D179gw7duzA4cOHcevWLSQmJsLGxgaurq5o0qQJunbtih49ekChMH94d1JSEnbt2oXjx4/jxo0bSE5ORnZ2NqpUqYL69eujRYsW6N27N7y9vQttS6vV4tChQzhy5AjOnz+PhIQEPH36FA4ODvDw8BBi7NSpk8lJPocMGYJTp07leV8ul6NSpUqoVasW2rRpg7feegt16tQx+zMSERFRyWm1WmRkZAAA7O3tIZeLv99MptPpdJYOIj+bNm3CggULkJKSUuBxL730Er7++mu0atWqwOO0Wi1+/fVXLF++HOnp6YVe39/fH5MnT8ZLL71kcv+pU6cwc+ZMXL9+vdC23N3d8emnnyIoKMjo/fyKv9yUSiUmTZqEwYMHF3psURiuWehZ35NTvRARkSiV1VQvBa3tK9Z1f8ttz993332HlStXAsh5wPL1119HQEAAatSogezsbNy5cwc7d+7Ev//+i5s3b2LYsGGYP38+evbsabK9rKwsfPLJJzh06BCAnGLq9ddfR4cOHVCzZk1UrFgRiYmJiIyMxIEDB3Dt2jUcP34cf/75J6ZMmZKnvS1btmDGjBlQqVQAgGbNmqF79+5o2LAhnJyc8OzZMzx48ABHjhzB4cOHER8fj9mzZ+cp/gyFhYUJ21qtFrGxsdi7dy9CQ0OhUqkwc+ZM1KhRA507dy7ul5WIiIisXLks/tatWycUftWqVcOvv/6Khg0bGh3TqlUrBAcHIywsDJMmTUJ2djbGjx+POnXq5DkWAL766iuh8Hv55Zfxww8/oGbNmnmO69SpEz788EMcOnQIs2fPNhnfyZMnMXXqVGi1Wtjb22POnDkmi8527dohODgYDx8+xIIFC3D06NECP3fu28y+vr7o3LkzGjdujFmzZkGn02HRokUs/oiIiKjYyt2N65iYGHz33XcAcu6tr1q1ymQxpxcYGCgUadnZ2fjyyy+R+072/v37ERoaCgDw8fHBqlWrTBZ+hrp06YItW7agXbt2Ru9nZmZi/Pjx0Gq1kMvl+O9//5tvb6NezZo18eOPP2Lq1KkFHpefwYMHo0aNGgCAK1euIDExsVjtEBEREZW74m/16tV4/vw5AGDs2LGoV69eoef06dMHHTt2BABcv35d6OHT+/XXX4XtOXPmwN7e3qxYKleujK5duxq9t2XLFjx58gRATlHWpk0bs9oCgH79+pl9rCG5XI4mTZoIrx89elSsdoiIiIjKVfGn0+mwfft2AEDFihXx1ltvmX3uu+++K2xv3bpV2I6Ojsbly5cB5Nwqbty4cYli1Lctk8kwdOjQErVVFIYTS2q12hd2XSIiIpKWcvXM340bN4SRva1atUKlSpXMPrdDhw6oWLEisrKycPbsWeH906dPC9udOnUqUXxpaWm4du0aAKBevXqoXbt2idorCsMRxe7uXLiaiIisk1Kbd9o0AMjMyES6rOCZPBwcHMoiJNEpV8VfVFSUsN2oUaMinatQKODr64sLFy4gKSkJcXFx8PDwMGrT8NZpcURHRwu9biXtQSyKw4cP48aNGwCAWrVqwdPT84Vdm4iIqDzpktLF5Purlq4q9NwZM2aUcjTiVK6Kv+TkZGHb1dW1yOe7uLgI2ykpKfDw8DCaI7Bq1aolis+wLcNrlQXDqV4WLVokvP/ee++V6XWJiIjof2xtbdGyZUtLh1GqylXxZzjxsrmDMgwZnpOWllYqbRoybMvOzq5EbZmiXzg6PwMHDizSc5BEREREuZWr4s/wXrx+KZWiMDxHvwhzSds0ZNhWZmZmidoyl52dHVq0aIF33nkHAQEBL+SaREREJF3lqvhzdnYWthMSEop8vuH8d05OTkb/z72/OAzbKk58hTFc4UOhUMDBwQFubm5FWreYiIhIyg45HTL5/upBq+HqUPRHxgqj0WiE3/murq6S+J1croo/w9ueV69eLdK5Go0G0dHRAHKe7fPw8ACQs0qG3pUrV+Dv71+i+ORyObRabZHjM0fuFT6IiIjImEquMvm+nb1dmYzm1Wg0ePjwIYCcTiopFH/lap4/b29voXftzJkzePbsmdnnnjhxQrgV26pVK+H91q1bC9tHjhwpUXyOjo7CaiN37tzBvXv3StQeERER0YtWroo/mUyGvn37AgCysrKwadMms8/9448/hO3+/fsL2z4+PsIUL2fOnMGVK1dKFGNQUBCAnAmp165dW6K2iIiIiF60clX8AcDQoUNha2sLAFiyZIlZvWu7du3C4cOHAeT0HnbpYjwH0OjRo4XtSZMmmT3w4+nTpwgPDzd6b8CAAXBzcwMArFu3DqdOnTKrLQDYtm2b2ccSERERlYVyV/zVrFkTX375JYCc0bkhISFGEzXntnv3bkyYMAEAoFQqMW/ePMhkMqNjunfvLqyrGx0djZCQEMTExBQYx5EjR/Cf//wHJ0+eNHrfzs4O8+fPF579e//99/H3338X2NajR4/w2WefYdasWQUeR0RERFTWytWAD70hQ4bg/v37WLNmDR49eoQBAwagd+/eCAgIQPXq1aFWq3H79m3s3LlTKM5sbW0xf/584Zm83L766iukpqbi0KFDuHjxInr27IlevXrhlVdeQY0aNVCxYkUkJCTg6tWr2L9/f4G3h9u3b49Zs2ZhxowZyMjIwEcffYRmzZqhR48eaNiwIapUqYJnz57hwYMHOHr0KMLDw5GdnV2k5eqIiIiIykK5LP4AYMqUKahfvz4WLlyIlJQUbN++Hdu3bzd5rJeXF7755hujgR652dnZ4ZdffsHSpUuxYsUKpKenF9gmAHTu3Blvv/22yX0DBgxArVq1MHPmTFy/fh0XLlzAhQsX8m2revXq+Pzzz/PdT0RERPQilNviDwAGDRqE3r17Y8eOHTh8+DBu3ryJpKQkKBQKuLq6ws/PD127dkXPnj3NGnotl8vxwQcfYNCgQdi1axeOHz+O69evIzk5GSqVClWqVEH9+vXRokUL9OnTB15eXgW216ZNG2zfvh2HDh3C4cOHcf78eSQkJODZs2ewt7dHtWrVhBg7deoEG5ty/eUmIiKiXGQymTCFTO7HysRKptPpdJYOgiwnOzsbkZGRAADP+p4I/iPYwhEREREVXeiwULg7ur/Qaxr+DvXz8xMGrJZ35W7ABxERERGVHRZ/RERERFaED6ERERER5UOtVgtzDtepU0cSz++z54+IiIgoH1qtFikpKUhJSYFWq7V0OKVC/OUrlRpnO2eEDgu1dBhERERF5mLvYukQRIPFHwkUcsULHylFRERELxaLPxJw1h/x0ul0UKlUAHKWOZTKXFTWiLmUDuaSyis+80cCtVpt6RComFQqFSIjIxEZGSn8siFxYi6lg7mk8orFHxEREZEVYfFHREREZEX4zB8J+DyKeMnlcri7uwvbJF7MpXQwl9IgxTyy+COBFCautFY2NjaoVauWpcOgUsBcSgdzKQ1SzKM0SlgiIiIiMguLPyIiIiIrwvt8JNBoNYhPi7d0GEREJHEu9i5QyBWWDsMsKpUK165dAwA0bNgQSqXSwhGVHIs/EiRnJiP4j2BLh0FERBIXOixUNCtKGU7WLZXFEHjbl4iIiMiKsPgjIiIisiIs/oiIiIisCIs/IiIiIivC4o+IiIjIirzw4k+j0aBFixbw8fFB//79CzxWp9Ohbdu28PHxgY+PDzZv3lzg8aGhocKx69evF94fMmSI8H5hVq9eLRzbvn17REVFCfsWL14s7IuIiCi0LQAICAiAj48PAgICTO6fOHGi0Kbhf76+vmjVqhX69OmDr7/+WhhmTkRERFQSL7z4UygUaN68OQAgKioKaWlp+R5748YNpKSkCK/Pnj1bYNtnzpwRtlu3bl3k2JYvX47Zs2cDANzc3LB27Vr4+voWuZ3SoNPp8OzZM0RHR2P9+vUICgrCjz/+aJFYiIiIrJWNjQ3q16+P+vXrS2YZVIt8itatW+PYsWPQarU4d+4cXn31VZPH6Ys5hUIBjUZjVNwVdLyzszNeeumlIsW0dOlSLFy4EADg4eGB1atXo169ekVqo6RWrFghLB6t1WqRmJiIw4cPY/369VCr1fj111/h7u6OwYMHv9C4iIiIrJVcLoezs7OlwyhVFnnmr1WrVsJ2QQWdfl/Pnj0BAPfv30dcXJzJYxMTE3H37l0AQMuWLSGTycyOZ/HixULhV716dfzxxx8vvPADgLp168Lb2xve3t7w9fWFv78/pkyZgsWLFwvHLFmyBBqN5oXHRkRERNJgkeKvadOmqFChAgDzir8ePXqgdu3aAPK/9VvcW74//PADlixZAgCoWbMm/vjjD+Fa5UVAQIBQMCclJeHKlSsWjoiIiMg66HQ6ZGdnIzs7myt8lIStrS2aNm0KAIiMjER2dnaeYx48eCD08rVs2RItW7YEkH+xaPi+Yc9iQb777jv897//BZDT67Zu3TrUqFHD/A/yAum/XgDw6NEjC0ZCRERkPVQqFSIjIxEZGSks8yZ2FpvqRV+gZWdn4+LFi3n264u5unXrwtXVVSj+Tp8+bbI9fY+go6MjGjZsWOj1Z82ahZUrVwIA6tevj7Vr16JatWpF/yAviOFDplqt1oKREBERFU6pVeb7X2ZGJtLT0wv8j8qOxYattG7dGkuXLgWQU+jlvlWrL/5atGhh9P8bN24gNTUVVapUEY5NS0sTpmRp3rw5FApFvtfV6XT4+uuvsWHDBgCAt7c3fv/9d7i6upbSJysb169fF7b1g0KIiIjKqy4pXfLdt2rpqkLPnzFjRilGQ4YsVvw1a9YMNjY2UKvVJm/l6t/T9xB6eXnB2dkZycnJOHfuHLp0+d831fnz54VBEIU97zdt2jT89ddfAICGDRti5cqVqFq1apHjf/jwoVmjf0qjizgqKgpHjx4FANjZ2cHPz6/EbRIREZF1sljx5+DggIYNGyIyMlIo3vQ9drlH7uq1aNECBw8exJkzZ4yKv6I876cv/Ly8vLB69WqjHsSimDx5crHOM5dOp0NCQgIOHz6MBQsWCMXtkCFDhMEyREREREVl0dkKW7dujcjISKSnp+PatWto0qQJgP891+fq6oq6desKx7ds2VIo/gzpn/erUKFCob1iMpkMOp0ODx8+xNWrV9G+fftS/EQl07Vr1wL3d+7cGR999NELioaIiIikyKLFX8uWLYVBF2fOnBGKP31xZ9jrB/yvV+/KlSvIyspCxYoVkZ2djUuXLgEAXn75Zdja2hZ4zWnTpuGbb77B8+fPMWbMGCxfvtzs0cGG1qxZg7Zt2xZ6XEBAAGJiYorcvp5SqYSfnx8GDhyIvn37Fmn+QiIiIks55HQo332rB62Gq0P5ftZeyixa/LVq1UroiTt79ixCQkIA/K8nTz/IQ69Ro0aws7NDZmYmLly4gHbt2iEyMhLPnz8X2ivM4MGDkZWVhXnz5iEzMxPvv/8+fv/9d6OpVCzFcIUPuVwOe3t7uLq6FlrQEhERlTcqef7PvNvZ28HBweEFRkOGLDbVCwA4OTmhQYMGAP7X22c4cjd3z5++F8zweMNJn82d3HnEiBEYN26ccL333ntPuKYlGa7w8dJLL6F69eos/IiIiCxIX3v4+flBqVRaOpxSYdHiD4DRyhW3bt3C2bNnodVqYW9vj0aNGuU5Pvdkz/rnA5VKJZo1a2b2dT/88EOMHDkSAJCamorhw4fj1q1bJfkoREREJDEymQy2trawtbWVzKNX5ab4A3J68fQ9ec2aNTM5X5+++Lt48SKys7Nx/vx5ADm3hO3t7Yt07S+++AJDhgwBkDPCOCQkBA8ePCjW5yAiIiISg3JV/J0+fVroyct9y1dPP4lzRkYGtm7dimfPnuVppyimTJmCN998EwAQHx+Pd999F48fPy5WW0RERCQtWq0WycnJSE5OlswKWxYv/jw8PFC7dm0AQEREBC5fvgwg/+LP0dER3t7eAIDly5cL75v7vF9uMpkMX3/9NQIDAwEAMTExCAkJwZMnT4rVHhEREUmHWq3G7du3cfv2bajVakuHUyosXvwB/+u1i4uLQ3Z2NmxsbAp8fk9fGOpv0crl8nyLRXPI5XJ899136NGjBwDg7t27GDZsGJKSkordJhEREVF5VK6KP72GDRvCzs4u3+NzF3re3t6oXLlyiWJQKBRYsGABOnfuDCBnDeERI0bg6dOnJWqXiIiIqDwpF8Vf7lu2hfXi5d5f3Fu+uSmVSixatAgdOnQAAFy9ehUjR45Eenp6qbRPREREZGkynU6ns3QQZDnZ2dmIjIwEAHjW90TwH8EWjoiIiKQudFgo3B3dLR2GWQx/T/r5+RnNv1vQvvKsXPT8EREREdGLweKPiIiIyIpYdG1fIiIiovJMJpMJy7pJZYUPFn9ERERE+VAqlWjatKmlwyhVLP5I4GznjNBhoZYOg4iIJM7F3sXSIVg1Fn8kUMgVohl9RURERMXD4o8EnPVHvHQ6HTQaDYCcCcul8lyKNWIupYO5lAa1Wo3Hjx8DADw9PWFjI/7SSfyfgEqNWq1GhQoVLB0GFYNKpRLlXFOUF3MpHcylNGi1WsTHxwMAPDw8LBxN6eBUL0RERERWhMUfERERkRVh8UdERERkRVj8EREREVkRDvgggRRGMFkrpVIJPz8/YZuIiCg//G1PAk5DIF4ymYwjCYmIyCws/kig0WoQnxZv6TCIyi0Xexco5IoyvYZcLoeTk5OwTeLFXEqDFPPI4o8EyZnJCP4j2NJhEJVbocNCy3wVHBsbG3h5eZXpNejFYC6lQYp5lEYJS0RERERmYfFHREREZEV425eIqBxRqVS4desWAMDLy4ujt0WMuZQGKeaRxR8RUTmi0+mQnp4ubJN4MZfSIMU88rYvERERkRURTc9fRkYGtm/fjvDwcERFRSElJQU6nQ6Ojo6oUaMGvL290bx5c3Ts2BGenp4FtvXo0SPs2rULJ06cwL1795CUlAStVosqVarA29sbLVu2RGBgIGrVqpXn3IiICAwdOhQA8OGHH2LcuHGFxj5x4kSEhoYCAA4ePIiaNWsa7d+6dSsmTZoEAJgzZw6CgoLM+poQERERFZUoir/z58/js88+w6NHj/LsS05ORnJyMi5fvoytW7fC1dUVx48fN9nO8+fPsWDBAmzYsAHZ2dl59sfHxyM+Ph7Hjh3DokWL0LNnT0yYMKHQYpKIiIhILMp98Xfnzh2MGDFCuN8eEBCAHj16oF69elAqlUhOTkZUVBROnDiBiIiIfNtJSkrCmDFjcOHCBQCAg4MD3njjDbRr1w7VqlWDjY0NEhIScO7cOezfvx93797Fnj170KxZM4SEhLyAT0pERERU9sp98ffjjz8KhV9+t0T9/f0xYsQIJCUlYc+ePXn2a7VafPLJJ0Lh16VLF3z77bdwcXHJc2xAQAA+++wz7NixA/PmzSvdD0NERERkYeW6+NNoNDhy5AgAoEmTJoU+C1e1alUMHjw4z/tr1qwRegVfeeUVLFmyBDY2+X90uVyOfv36oV27drhz504JPgERERFR+VKui7+kpCRkZWUBAOrUqVOsNrKzs7FixQoAQIUKFTB79uwCCz9D1apVQ7Vq1Yp1XSKi4lAoFMKgMIWibNcRprLFXEqDFPNYros/w4kU9RMsFtWxY8cQHx8PAOjZsyc8PDxKJTYikj6l1ngy18yMTKTL0vMc5+DgUGrXVCgU/DklEcylNEgxj+W6+HNyckKNGjUQExODqKgoLFu2DO+99x7kcvOnJzx9+rSw3blz5zKIkoikqktKF6PXq5auMnncjBkzXkA0RESlo1wXfwDwzjvv4LvvvgMALFiwABs3bkRAQABatGgBPz8/k3PxGYqKihK2GzduXKqxJSYm4vr164Ue9/Tp01K9LhEREVFxlfviLyQkBDdv3sSWLVsAADExMVi7di3Wrl0LAHB1dUWbNm0QGBiILl26QCaTGZ2fkpIibJsa3VsSGzZswIYNG0q1TSKybtnZ2YiMjAQA+Pn5wdbW1sIRUXExl9IgxTyW++Xd5HI5Zs+ejZUrV6Jjx455BmskJCRg9+7dGDNmDP7zn//g/v37Rvv108QAgJ2d3QuJmYiIiKi8Kvc9f3r+/v7w9/dHWloazp49i8jISFy+fBlnzpzBs2fPAACXL1/G22+/ja1bt8Ld3R2A8YPYmZmZcHR0LLWYirO8GxGJxyGnQ0avVw9aDVcHVwtFQ0RUOkRT/Ok5OjqiU6dO6NSpE4Cc7tiwsDB89913SE1NxZMnT/DTTz/h22+/BZAzaEQvISGhVIs/IpI2lVxl9NrO3q5UR/YSEVlCub/tWxhbW1sMGDAACxYsEN7bv38/tFotAMDX11d4/+rVqy88PiIiIqLyRPTFn17Hjh3h6ekJAEhNTRUGerRu3Vo45vDhwxaIjIiIiKj8kEzxB0B4zs/QK6+8Iry/d+9exMXFveiwiIiIiMoNyRR/mZmZuHnzJoCc5wKdnZ0B5NwWHjFiBADg+fPnmDx5MjQajVltxsXF4eTJk2UTMBEREZEFlOviLz09HcHBwTh06JDwDJ8pWq0WM2fOFKZ1CQgIMJrvb+jQoWjbti2AnOXePvjgAyQlJeXbnk6nQ1hYGIKCghAdHV1Kn4aIqHA2Njbw8fGBj4+P2euQU/nEXEqDFPNY7j/FpUuXMHr0aHh4eKBbt25o1qwZqlevDkdHRzx9+hRXr17Fli1bhJU2KlWqhI8//tioDblcjoULF2L06NG4ePEiDh06hG7duiEwMBDt2rVDtWrVYGNjg4SEBFy4cAH79u3D7du3LfFxcfbsWbOOCwgIMBrJTETSIJfLOSuBRDCX0iDFPJbr4s/GxgZubm548uQJ4uLisG7dOqxbty7f4+vWrYsFCxagZs2aefZVrVoVa9euxffff48NGzYgPT0dGzduxMaNG022JZPJEBgYiF69epXa5zHH5s2bsXnz5kKP27ZtG4s/IiIiKrJyXfxVqFAB//zzDy5cuIATJ07g4sWLuHPnDhITE/H8+XPY2dnB3d0dvr6+6Nq1K1577bUCl12pUKECpkyZgmHDhmHnzp04efIk7t69i6SkJOh0OlSpUgXe3t5o3bo1AgMDUaNGjRf4aYmIch5jycjIAADY29tDLi/XT+dQAZhLaZBiHmU6nU5n6SDIcgzXLPSs74ngP4ItHBFR+RU6LBTujnlnFShNUlxH1Foxl9JQUB7FmmPxl69EREREZDYWf0RERERWhMUfERERkRUp1wM+6MVytnNG6LBQS4dBVG652LtYOgQiohJj8UcChVxR5g+zExERkWWx+CMBB36Ll06ng0qlAgAolUqjFW6IiIgM8Zk/EqjVakuHQMWkUqkQGRmJyMhIoQgkIiIyhfP8WTmxzlFERERkaWL9HcqePyIiIiIrwuKPiIiIyIpwwAcJNBqNpUOgYtJoNEhISAAAuLq6QqFQWDgiKi7mUjqYS2mQYh5Z/JFAq9VaOgQqJo1Gg4cPHwIAnJ2dJfHDyVoxl9LBXEqDFPPI4o8ENjb8dhArpVIJPz8/YZvEi7kkorLG3/Yk4Nxw4iWTyUQzyowKxlwSUVlj8UcCjVaD+LR4S4dBRFQmXOxdoJCL/5YdUUmx+CNBcmYygv8ItnQYRERlInRYKJewJAKneiEiIiKyKiz+iIiIiKwIb/sSERGVAZlMBgcHB2GbxEmKeWTxR0REVAaUSiV8fX0tHQaVkBTzKKri79SpUxgyZIjwesOGDWjRokWR2rh//z727NmDkydP4v79+0hOToZarUalSpVQs2ZNNGrUCJ07d4a/v3++c2xt3boVkyZNKtJ1J02ahJCQEKP3hgwZglOnTuU5Vi6Xo1KlSqhVqxbatGmDt956C3Xq1CnS9YiIiIhMEVXxFxoaavR627ZtZhd/T58+xdy5c7F9+3ao1eo8+xMTE5GYmIiLFy9iw4YNcHFxwciRIzF06NAXPpu3VqtFamoqUlNTcfnyZaxduxaTJk3C4MGDX2gcREREJD2iKf6ysrLw999/AwDs7e2RkZGBvXv3YurUqYVOiPrgwQOMHDkSd+7cAQA4OTmhd+/eaNWqFdzd3WFnZ4ekpCTcvn0bx44dw4kTJ5CYmIi5c+fijTfegJubW75tf/LJJ+jatWuh8RfUBgCEhYUJ21qtFrGxsdi7dy9CQ0OhUqkwc+ZM1KhRA507dy70WkREZHlqtRr37t0DANSpU4erKImUFPMomk+wf/9+pKenAwCmTp2KyZMnIzU1FeHh4ejZs2e+52VkZOD9998XCr8333wTEyZMgKOjY55jO3bsiHfffRcPHjzAL7/8gq1btxYal4eHB7y9vYv5qf4ndxu+vr7o3LkzGjdujFmzZkGn02HRokUs/oiIREKr1SIlJQUAUKtWLcsGQ8UmxTyKZqqXbdu2AQB8fHwwYMAA1KtXz+j9/Hz//fe4desWAODtt9/GzJkzTRZ+hmrVqoU5c+Zg6dKlqFChQoljL4nBgwejRo0aAIArV64gMTHRovEQERGRuImi+IuPj8fJkycBAH369DH6/7Fjx5CUlGTyvISEBGzevBkA4OnpiQkTJhTpugEBAahcuXJxwy4VcrkcTZo0EV4/evTIgtEQERGR2Imi+AsLC4NGo4FcLkdgYCAAIDAwEDKZDCqVCjt37jR53u7du/H8+XMAQHBwMCpWrPjCYi5Nhs8XaLVaC0ZCREREYieK4m/79u0AgDZt2sDDwwNAzq3Z5s2bA8j/1u/p06eF7Y4dO5ZtkGXo+vXrwra7O9elJCIqKqVWicyMTKSnpwvPjxNZq3I/4OPatWuIjo4G8L9bvXp9+vTBuXPncOXKFdy8eRMvvfSS0X79eXK5vMwmaIyLizMqzvJT3EEhhw8fxo0bNwDkFLyenp7FaoeIyJp1SemCVUtXCa9nzJhhuWCILKzcF3/6Xr2KFSuiR48eRvt69eqFb7/9FiqVCtu2bcMXX3xhtD85ORkAUKlSpQKng3ny5IlwbG5VqlQRehtNWbhwIRYuXFjo59AXouYwnOpl0aJFwvvvvfee2W0QERERmVKuiz+1Wi08z9elS5c8o3SdnJzQqVMnHDhwAGFhYfjss88gl//vTra+a9/Ozq7A6yxbtgxr1qwxua9///6YO3duST6GWXx8fArcP3DgQLz11ltlHgcREZUOuVwuPKpj+LuJxEWKeSzXxd+xY8eQkJAAIO8tX70+ffrgwIEDiI2NRUREBNq3by/sc3BwwNOnT5GZmVlmMc6ZMwdBQUFl0radnR1atGiBd955BwEBAWVyDSIiKhs2NjaSmRfOmkkxj+W6+NPf8nVycsp3wEaXLl1QuXJlPH36FNu2bTMq/pycnPD06VM8e/YM2dnZ+d76nTJlCqZMmSK8fvjwoVmrdpQmwxU+FAoFHBwc4Obm9sKXliMikqJDToewetBquDq4WjoUIosrt8Xfs2fPEB4eDgBISUkxmusuP/v27cOMGTNgb28PIGeVjPv370Or1SI6Ohp+fn5lGnNJlMYqIUREZJpKroKdvR0cHBwsHQqRxZXbm9d79uwR5ugzV0ZGBvbt2ye8bt26tbB99OjRUouNiIioMCqVCpcuXcKlS5egUqksHQ4VkxTzWG57/vS3fN3c3DBp0qRCj583bx5iY2Oxfft29OvXDwDw+uuvY/78+cjOzsZff/2FESNGWHy5NiIisg46nU4oFnQ6nYWjoeKSYh7LZfH34MEDnDt3DgDQo0cP9O7du9BzLly4gDVr1uDff/9FXFwcPDw84OrqigEDBmDDhg149OgR5s+fj6lTp5Z1+ERERETlVrm87bt9+3ahus49t19+9MdptVphRRAA+OKLL1CvXj0AwNq1a/HVV18VOrv706dPixM2ERERUblXLnv+9MWbi4sLWrVqZdY5LVq0gJubG548eYIdO3Zg1KhRAABHR0f897//xahRo3D37l1s2LABe/fuxRtvvIFWrVrB3d0d9vb2SEtLw71793DixAns379faLew9YDNXeHD0dER1atXN+uzEBEREZWVclf8nT17Fvfv3wcAdOvWzewJFeVyObp3747169fjxo0buHz5sjBCuE6dOti0aRPmzJmDHTt2IDk5GWvXrsXatWvzbc/FxQXvvfcehgwZUuB1zV3ho2vXrvjll1/M+ixEREREZaXcFX+Gt2zNveWr99prr2H9+vUAcgaMGE4PU6VKFcydOxdjxozB7t278e+//+LevXtISUmBRqNB5cqV4enpiSZNmuCVV15Bp06doFQqS+dDEREREZUTMp1Uhq5QsWRnZyMyMhIA4FnfE8F/BFs4IiKishE6LBTuju4v7HqGP1/9/PwKXGOeyq+C8ijWHJe7nj8iIiIpsLGxQf369YVtEicp5lEan4KIiKickcvlcHZ2tnQYVEJSzGO5nOqFiIiIiMoGe/6IiIjKgOHKEEqlEjKZzMIRUXFIMY8s/kjgbOeM0GGhlg6DiKhMuNi7vNDrqVQqUQ4GIGNSzCOLPxIo5IoXOhKOiIiIXjwWfyTgrD/iJcXbEtaKuSSissYBHyRQq9WWDoGKSX9bIjIyUigcSJyYSyIqayz+iIiIiKwIiz8iIiIiK8Lij4iIiMiKsPgjIiIisiIc7UsCqaxZaI2USiX8/PyEbRIv5lI6mEtpkGIe+dueBJxSQrxkMpkkJh4l5lJKmEtpkGIeeduXiIiIyIqw548EGq0G8Wnxlg6j3HKxd4FCrrB0GCZptVqkpqYCAKpUqQK5nH/XiRVzKR3MpTRIMY8s/kiQnJmM4D+CLR1GuRU6LLTcLn+nVqtx+/ZtANJZe9JaMZfSwVxKgxTzKP7ylYiIiIjMxuKPiIiIyIqw+CMiIiKyIiz+iIiIiKyIVQ74yMjIwPbt2xEeHo6oqCikpKRAp9PB0dERNWrUgLe3N5o3b46OHTvC09PT6NyJEyciNDS0SNfbtm0bGjZsKLz+8MMPsX//fgDAqlWr0L59+wLPP3jwIMaOHQsA6NevH7777rsiXZ+IiIhIz+qKv/Pnz+Ozzz7Do0eP8uxLTk5GcnIyLl++jK1bt8LV1RXHjx8v9RimT5+OiIgIPH36FNOnT0dYWBgqVqxo8thnz57hq6++AgC4uLhg0qRJpR4PERERWQ+rKv7u3LmDESNGID09HQAQEBCAHj16oF69elAqlUhOTkZUVBROnDiBiIiIQttbsWIF3N0Ln/qjTp06Rq/d3d0xfvx4TJs2Dffv38dPP/2ECRMmmDx33rx5iI/PmXtv6tSpcHJyKvR6ZH1kMpmw7BBXahE35lI6mEtpkGIerar4+/HHH4XCb86cOQgKCspzjL+/P0aMGIGkpCTs2bOnwPbq1q2LmjVrFiuW4OBghIWF4dSpU1i9ejV69+6NJk2aGB0TERGBv/76C0BOofr6668X61okfUqlEk2bNrV0GFQKmEvpYC6lQYp5tJoBHxqNBkeOHAEANGnSxGThZ6hq1aoYPHhwmcUjk8kwa9YsVKhQARqNBlOmTIFarRb2Z2VlYdq0acKziPpbv0REREQlYTXFX1JSErKysgDkvQ1rKXXq1MGHH34IAIiKisLy5cuFfYsWLcK9e/cAAOPHj4eHh4dFYiQiIiJpsZriT3+/HgBu3bplwUiMDR8+HI0aNQIA/Pzzz7h9+zYuX76MVatWAQBat26NgQMHWjBCEgO1Wo0HDx7gwYMHRj3IJD7MpXQwl9IgxTxaTfHn5OSEGjVqAMjpZVu2bBm0Wq2FowJsbGwwa9YsKBQKZGdnY+rUqZg6dSo0Gg0qVKiAmTNnSuYBU7HLzMgUnhktb7RaLeLj4xEfH18uvq+p+JhL6WAupUGKebSqAR/vvPOOMEfeggULsHHjRgQEBKBFixbw8/NDrVq1itTe3bt3kZGRUeAxdnZ2hbbbuHFjhISEYMWKFTh79qzw/ocffoh69eoVKSYqO6uWrgIAzJgxw7KBEBERlYBVFX8hISG4efMmtmzZAgCIiYnB2rVrsXbtWgCAq6sr2rRpg8DAQHTp0qXQHrcRI0YUes02bdoI7Rfko48+wu7du/H48WMAQKNGjTB8+PBCzyMiIiIqCqu57QsAcrkcs2fPxsqVK9GxY0fY2BjXvgkJCdi9ezfGjBmD//znP7h///4Li+3evXtISEgQXletWjVPfEREREQlZZXVhb+/P/z9/ZGWloazZ88iMjISly9fxpkzZ/Ds2TMAwOXLl/H2229j69at+U7kfPDgwWLP82dIP9WLSqUS3jt27BjCwsIQGBhY4vaJiIiI9Kyq5y83R0dHdOrUCR9++CF+/fVXnDhxArNnz0aVKlUAAE+ePMFPP/1U5nGsWbMGkZGRAHJuJeuLzdmzZyM5ObnMr0/mCRkTgi+++MLSYRAREZWIVRd/udna2mLAgAFYsGCB8N7+/fvLdHTPgwcPhALT29sbn376KaZNmwYgZ25C/QAVsjw7ezs4ODhYOgwiIqISYfFnQseOHeHp6QkASE1NRUpKSplda/r06cjMzIRcLse3334LpVKJ1157Dd26dQMAhIaG4uTJk2V2fZIGuVwOJycnODk5QS7nP2sxYy6lg7mUBinmURqfogzk95xfadqyZQtOnDgBABgyZIjR2oHTp0+Ho6MjgJypRZ4/f17m8ZB42djYwMvLC15eXhwoJHLMpXQwl9IgxTyy+DMhMzMTN2/eBJDzXKCzs3OpXyMhIUG4pVujRg188sknRvs9PDzw+eefA8gZCbxkyZJSj4GIiIisj9UUf+np6QgODsahQ4cKfIZPq9Vi5syZwkoOAQEBZbLCxjfffIPU1FQAwNdffw17e/s8xwwaNAgtWrQAAKxcuRJRUVGlHgcRERFZF2n0X5rp0qVLGD16NDw8PNCtWzc0a9YM1atXh6OjI54+fYqrV69iy5YtuH79OgCgUqVK+Pjjj/Ntz5wVPgDAzc3NqPfwwIED+PvvvwEAffv2RceOHU2eJ5PJMHPmTPTr1w8qlQpTp07Fpk2bJPPMAZUelUolrFnt5eVltJY1iQtzKR3MpTRIMY9WU/zZ2NjAzc0NT548QVxcHNatW4d169ble3zdunWxYMGCAufxM2eFDwCYNGkSQkJCAADPnj3D119/DSBnIudJkyYVeO5LL72E999/H0uWLEFkZCTWrl2Ld99916zrkvXQ6XRCb7VOp7NwNFQSzKV0MJfSIMU8Wk3xV6FCBfzzzz+4cOECTpw4gYsXL+LOnTtITEzE8+fPYWdnB3d3d/j6+qJr16547bXXYGtrW+pxzJs3D/Hx8QCAyZMnm/U84fvvv489e/bg1q1bWLhwIbp3747q1auXemxEREQkfVZT/AE5w7VbtGghPEdXHHPnzsXcuXOLff7MmTMxc+bMIp1ja2uL3bt3F/uaRERERHp8eIyIiIjIirD4IyIiIrIiLP6IiIiIrAiLPyIiIiIrYlUDPqhgznbOCB0Waukwyi0XexdLh5AvhUIhTEukUCgsHA2VBHMpHcylNEgxjyz+SKCQK+DuWPZrGlPpUygU8PDwsHQYVAqYS+lgLqVBinnkbV8iIiIiK8Lij4iIiMiK8LYvCVQqVZmsakJlLzs7G5GRkQAAPz8/5lHEmEvpYC6lQYp5ZM8fERERkRVh8UdERERkRVj8EREREVkRFn9EREREVoTFHwlsbDj+h4iISOpY/JFAJpNZOgQiIiIqY+zqIYFGq0F8WrzZx7vYu0Ahl8ZSN0RERNaCxR8JkjOTEfxHsNnHhw4L5XJw5YSNjQ18fHyEbRIv5lI6mEtpkGIepfEpiKycXC6Ho6OjpcOgUsBcSgdzKQ1SzCOf+SMiIiKyIuz5I5IArVaLjIwMAIC9vT3kcv5dJ1bMpXQwl9IgxTyK/xMQEdRqNaKjoxEdHQ21Wm3pcKgEmEvpYC6lQYp5lFTPX0REBIYOHSq8tre3x4kTJ2BnZ1fgeVlZWfD390daWprw3po1a9C2bdsCzzt16hSGDBkivN6wYQNatGhR5DgNVaxYEU5OTvD19UX37t3Rp08fSSwiTUREROWDpHv+MjIycODAgUKPO3jwoFHhZ67Q0FCj19u2bStyG7llZWUhNjYWhw8fxpQpUxAUFISHDx+WuF0iIiIiQMLFX4UKFQAA27dvL/RY/TH6c8yRlZWFv//+G0BODyMA7N27F9nZ2UWKc9CgQQgLCxP+W7VqFaZMmYJq1aoBAG7cuIExY8ZAo9EUqV0iIiIiUyRb/AUEBAAATpw4gSdPnuR7XGJiIo4fPw4A6Nq1q9nt79+/H+np6QCAqVOnAgBSU1MRHh5epDhdXFzg7e0t/Ne+fXsMHToUu3btQo0aNQAA169fx/79+4vULhEREZEpki3+/P394ebmBo1Gg127duV73M6dO6FWq+Hm5oYOHTqY3b7+Fq+Pjw8GDBiAevXqGb1fUo6OjhgzZozw+sSJE6XSLhEREVk3yRZ/CoUCvXv3BlDwrV/9vjfeeAMKhXlLlcXHx+PkyZMAgD59+hj9/9ixY0hKSip23Ib0M4oDQGxsbKm0SURERNZNssUfAPTt2xcAcPXqVdy4cSPP/ps3b+LKlStGx5ojLCwMGo0GcrkcgYGBAIDAwEDIZDKoVCrs3LmzFKIHlEqlsC2VJWWIiIjIsiRd/DVq1AgNGjQAYLr3T/+et7c3GjZsaHa7+vPatGkDDw8PAECtWrXQvHlzAKV36/fWrVvCtv75v/IkMyNTeO6RLMvW1hYtW7ZEy5YtOTWQyDGX0sFcSoMU8yjp4g/4X4/ezp07odPphPd1Oh3CwsKMjjHHtWvXEB0dDeB/t3r19K+vXLmCmzdvlihujUaDFStWCK979OhRovbKwqqlq/D9999bOgwiIiIqAskXf3369IFcLsfjx48REREhvB8REYHHjx8b3bo1h75Xr2LFinkKsl69egm3aovb+5eUlISTJ0/inXfewdWrVwHkFH6tWrUqVntEREREhiRf/Hl4eAgrdRje+tVvt2vXTrh1Wxi1Wi08z9elSxc4Ojoa7XdyckKnTp0A5DwXqNVqC21zyZIl8PHxEf5r3749QkJCcO7cOdjZ2WHYsGFYsGCBWfGR9dJoNIiLi0NcXBznhBQ55lI6mEtpkGIeJV/8AUC/fv0AAPv27UNWVpbRBM1FueV77NgxJCQkAMh7y1dP/35sbKxRT2Nx+Pr6YsiQIUYDP4hM0Wg0ePjwIR4+fCiZH07WirmUDuZSGqSYR6sYQtq9e3d89dVXSEtLw8GDB6HT6ZCeng57e3u89tprZrejv5Xr5OSEjh07mjymS5cuqFy5Mp4+fYpt27ahffv2BbY5aNAgvP322wByvsFiY2Px999/Y/v27Th//jyGDBmCzZs3o2rVqmbH+aKEjAmBq4OrpcMgIiKiIrCK4s/BwQHdunVDWFgYtm/fLgz86Natm7A0W2GePXsmrN6RkpKCJk2aFHrOvn37MGPGjAKvoV/hQ69hw4bo0qUL2rZti4kTJyImJgZTpkzB0qVLzYrzRbKzt4ODg4OlwyAiIqIisIrbvsD/bv0eP35cWC1D/5459uzZg+fPnxfpmhkZGdi3b1+RztHr37+/MKAkPDxcmFSaiIiIqCSsoucPANq3bw83NzdhnV93d/dCb8ka0t/ydXNzw6RJkwo9ft68eYiNjcX27duLVGQa+vTTT3HgwAFoNBr8+OOPRYqXiIiIyBSrKf4UCgX69u2LNWvWAMgZ6CGXm9fx+eDBA5w7dw5AzrQr+mXjCnLhwgWsWbMG//77L+Li4sweUWyoXr166NWrF3bu3ImLFy/i+PHj8Pf3L3I7RERERHpWc9sXAMaPH4/IyEhERkbiiy++MPs8w+cEzZ1sWX+cVqstcG3hwrz//vuQyWQAUC6f+yMiIiJxsarir7j0xZuLi4vZky23aNECbm5uAIAdO3YU+9re3t4ICAgAAJw+fRpnzpwpdlskXTKZDA4ODnBwcBD+WCBxYi6lg7mUBinmkcVfIc6ePYv79+8DyBkdbO6tYrlcju7duwMAbty4gcuXLxc7htGjRwvb7P0jU5RKJXx9feHr68t5IUWOuZQO5lIapJhHFn+FMLxlW9T1dQ3nECzucm8A0LRpU+FZv2PHjuHSpUvFbouIiIism0ynf5iNrFJ2djYiIyMBAJ71PRH8R7DZ54YOC4W7o3tZhUZERFSuGf4O9fPzg62trYUjMo/VjPYlkjK1Wo179+4BAOrUqQMbG/7TFivmUjqYS2mQYh5525dIArRaLVJSUpCSkgKtVmvpcKgEmEvpYC6lQYp5ZPFHREREZEVY/BERERFZEfHfuKZS42znjNBhoWYf72LvUobREBERUVlg8UcChVzB0btEREQSx9u+JOCsP0RERNLH4o8EarXa0iEQERFRGeNtXxJIZc1CaySXy+Hu7i5sk3gxl9LBXEqDFPPI4o8EUpi40lrZ2NigVq1alg6DSgFzKR3MpTRIMY/SKGGJiIiIyCws/oiIiIisCO/zkUClUolmUWoyplKpcO3aNQBAw4YNoVQqLRwRFRdzKR3MpTRIMY8s/kjAZ/7ES6fTQaVSCdskXsyldDCX0iDFPPK2Lwk42peIiEj62NVDAo1Wg/i0eEuHUe652LtAIVdYOgwiIqJiYfFHguTMZAT/EWzpMMq90GGhXAaPiIhEi7d9iYiIiKwIiz8iIiIiK8Lij4iIiMiK8Jk/IgmwsbFB/fr1hW0SL+ZSOphLaZBiHkX3KSIiIjB06FCT+ypWrAgnJyf4+vqie/fu6NOnT4GTFvv4+BTp2r6+vti+fbvRe1u3bsWkSZMAAHPmzEFQUFCR2gwICEBMTEye921sbODo6Ij69eujQ4cOePPNN+Hh4VGktsl6yOVyODs7WzoMKgXMpXQwl9IgxTxK6rZvVlYWYmNjcfjwYUyZMgVBQUF4+PChpcMqFrVajZSUFJw7dw5LlizB66+/jv3791s6LCIiIhI50fX8GRo0aBDefvtt4XViYiJu3LiBFStWIDY2Fjdu3MCYMWOwbds2KBT5z8vWpEkTzJkzp9DrVaxYsVTiNsXd3R0rVqwQXqvVasTExGDLli04dOgQ0tLS8Omnn2LTpk1o1KhRmcVB4mQ4A71SqeSE3SLGXEoHcykNUsyjqIs/FxcXeHt7G73Xvn17BAUFoU+fPoiJicH169exf/9+9OzZM9927O3t87TzoimVyjwxNGrUCN27d8fcuXPx+++/Q6VSYenSpVi8eLGFoqTySqVSITIyEgDg5+fHNZpFjLmUDuZSGqSYR0nd9tVzdHTEmDFjhNcnTpywYDQlN27cOKHX8dixY9BqtRaOiIiIiMRKksUfYDyYIzY21oKRlJyDgwO8vLwAABkZGUhJSbFsQERERCRaki3+lEqlsC2FodmGn4c9f0RERFRc4q+K8nHr1i1hu0aNGhaMpOTUajVu374NIKcIdHJysmxAVkip/V/xnZmRiXRZutF+BweHFx0SERFRsUiy+NNoNEYjZ3v06FHg8RkZGbh+/Xqh7VarVg2VK1cucXxF9eeff+Lp06cAgJYtW0qiJ1NsuqR0EbZXLV2VZ/+MGTNeYDRERETFJ6kqIikpCdHR0Vi0aBGuXr0KIKfwa9WqVYHnXb58GYGBgYW2X5xJnItLP9XL1q1b8dtvvwnvjxgx4oVcn4iIiKRJ1MXfkiVLsGTJEpP77Ozs8NZbb+Hzzz9/wVEVT0xMTIErjshkMnz88cd49dVXX2BUREREJDWiLv4K4uvriyFDhhgNlMhPmzZtsHbt2hcQVdE5OjqiXbt2GDZsWKE9mGS9lEol/Pz8hG0SL+ZSOphLaZBiHkVd/Bmu8KHRaBAbG4u///4b27dvx/nz5zFkyBBs3rwZVatWtXCkhcu9wodCoUClSpXg5uYmidnExe6Q0yFhe/Wg1XB1cLVgNHnJZDJJTDxKzKWUMJfSIMU8irr4y73CR8OGDdGlSxe0bdsWEydORExMDKZMmYKlS5daMErzmFrhg8oPlVwlbNvZ23F0LxERiZYk5/nr37+/MMI3PDwcJ0+etHBERGVLq9UiOTkZycnJnAdS5JhL6WAupUGKeZRk8QcAn376KRQKBQDgxx9/tHA0RGVLPxfk7du3oVarLR0OlQBzKR3MpTRIMY+SLf7q1auHXr16AQAuXryI48ePWzgiIiIiIsuTbPEHAO+//74wWEIMz/0RERERlTVRD/gojLe3NwICAnDw4EGcPn0aZ86cMTldirkrfADASy+9BLncdM189uxZs9oICAjgEm1ERERkEZIu/gBg9OjROHjwIICc3j/D6VT0zF3hAwBOnz6d7xJvmzdvxubNmwttY9u2bSz+iIiIyCIkfdsXAJo2bQp/f38AwLFjx3Dp0iULR0RERERkOaLr+Wvbti2io6OLdM7KlStNvl/UdkwJCgoq0Xq/4eHhJY6BiIiIyFyiK/6IKC+ZTCYsO8QVYcSNuZQO5lIapJhHFn9EEqBUKtG0aVNLh0GlgLmUDuZSGqSYR8k/80dERERE/8Pij4iIiMiK8LYvCZztnBE6LNTSYZR7LvYulg4hD7VajcePHwMAPD09YWPDf9pixVxKB3MpDVLMo/g/AZUahVwBd0d3S4dBxaDVahEfHw8A8PDwsHA0VBLMpXQwl9IgxTzyti8JdDqdpUMgIiKiMsbijwRqtdrSIRAREVEZY/FHREREZEVY/BERERFZERZ/RERERFaExR8RERGRFeFULySQwtxF1koul8PJyUnYJvFiLqWDuZQGKeaRv+1JIJUFq62RjY0NvLy8LB0GlQLmUjqYS2mQYh5Z/JFAo9UgPi3e0mEUiYu9CxRyhaXDICIiEg0WfyRIzkxG8B/Blg6jSEKHhXJVEiIioiJg8UckASqVCrdu3QIAeHl5QalUWjgiKi7mUjqYS2mQYh5Z/BFJgE6nQ3p6urBN4sVcSgdzKQ1SzKM0hq0QERERkVlY/BERERFZERZ/RERERFZE9M/8ZWdnY9++ffjnn39w6dIlJCcnIy0tDY6OjqhRowb8/PzQo0cPtGvXrtDJGY8cOYIDBw7g7NmzSEhIQGZmJqpWrYratWvj1VdfRZ8+feDh4ZHv+b1798bNmzfh5OSEf//9t8B58/r164dr164BAMaOHYuPP/4432NPnTqFIUOGAADee+89jB8/vsDPQURERJQfURd/+/btw9y5cxETE5NnX0pKClJSUnDlyhVs3LgRdevWxaRJk9C5c+c8x964cQPTpk3D+fPn8+yLjY1FbGwsTp06hV9++QWjRo3C6NGjTRZ2LVu2xM2bN5GSkoKbN2+iQYMGJuN+9uwZoqOjhddnzpwp8HMa7m/dunWBxxIREREVRLTF388//4xFixYJr/39/REQEAAvLy9UrlwZqampuHPnDsLDw3HixAncvXsXP/74Y57i78yZMxgzZgyePn0KAGjcuDH69+8PHx8f2NvbIy4uDkePHsW2bduQkZGBhQsX4saNG/juu+/yDPdu3bo1/vzzT6Hd/Iq/c+fOQavVQqFQQKPR4NKlS1CpVPkOHz979iyAnGVlWrZsWayvFxEREREg0uJvy5YtQuHn4uKChQsXok2bNnmO69ChAwYPHozr169jzpw5SEpKMtofGxuLDz74AE+fPoVMJsP48eMxfPhwo169Jk2aoGvXrhgxYgQ++OADREdHY9euXfD09Mxz+9WwV+7MmTMYNGiQyfj1PXmvvvoqIiIikJGRgcjISLRo0SLPsRqNRuiR9PHxQaVKlcz5EpGVUSgUqFmzprBN4sVcSgdzKQ1SzKPoBnzExcVh5syZAAB7e3usXbvWZOFnyNvbGytWrMDw4cON3p82bRpSUlIAAB9//DFGjBiR73N6tWrVwu+//w5395zVJFasWIGLFy8aHVOtWjXUqFEDQMG3cvX72rRpg5dffrnA469duybML9SqVauCPiZZMYVCAQ8PD3h4eEjmh5O1Yi6lg7mUBinmUXTF36pVq5CZmQkA+Oijj8xebFkul6Nv377C66ioKPzzzz8AcorDUaNGFdqGi4sLJk2aBCBnosfly5fnOUbf+xcbG4uHDx/m2f/8+XNERkYCyCnm9Ldx9bd2c+PzfkRERFSaRFX86XQ6hIaGAsjp9QsOLv46tPp2AOCdd94xu5rv0aOHMOL3wIEDSE1NNdpv2Dtnqjfv4sWLUKlUsLOzQ6NGjYTiT/8cYG6GRSGLPyIiIiopURV/N27cQHJyMoCckbWOjo7Fbuv06dPCdpcuXcw+T6FQCINGtFptnhHCuZ/7y03/XtOmTWFjY4OXX34ZCoUCT58+xfXr1/Mcry/+6tevj6pVq5odpzVQapXIzMhEenq68J+1ys7OxtmzZ3H27FlkZ2dbOhwqAeZSOphLaZBiHkU14CMqKkrYbty4cYna0hdabm5uwnN85mrUqJFRTIYjiOvWrQs3Nzc8efLEZPGnL+b0PYQODg7w9fXFlStXcObMGfj6+grH3r59G4mJiQDY62dKl5QuWLV0ldF7M2bMsEwwREREIiGqnj/94Awg5/m74nr27BlUKhUAwNXVtcjnG55jGJOe/lbunTt3hOINMB65azhli347d7FoeMuXgz2IiIioNIiq+DO8rWdnZ1cq7djb2xf5fMNz0tLS8uzP79bv1atXkZ6eDoVCgWbNmgnv51f8cbAHERERlTZRFX8ODg7Ctn7Eb0nbycjIKPL5hueYeu4wv0Ef+p48X19foxj0xz958gT37t3Lc26NGjXg6elZ5DiJiIiIchPVM39OTk7CtuHt1KJydHSEjY0N1Go1EhISiny+4TmGMel5e3ujcuXKePr0qdGtW30xl3uVDldXV9SpUwf37t3DmTNnUKdOHcTFxQlTxfCWr2mHnA5h9aDVcHUo+q17IiIiayWqnj/DwRBXrlwpdjsymQw+Pj4Acnrbnjx5UqTzr169ajImPcNl2KKiooRbw/pC0NQSbblv/fKWb+FUchXs7O3g4OAg/EdEREQFE1Xx16BBAzg7OwPIKaRMPW9nLsPetPDwcLPP02q1OHLkCICcIs/UkmyG7Ws0Gpw7dw63bt0Slpcz1ZOnb0df9BlORcOePyIiIiotoir+ZDIZ+vfvDyDnubu//vqr2G0FBQUJ2+vWrYNGozHrvL///huxsbEAgG7duqFy5comjzMs2M6ePSsUdXXq1DE5wljf83f//n3Ex8cLvYRubm6oV6+eWbGR9bKxsYGPjw98fHxgYyOqpzkoF+ZSOphLaZBiHkVV/AFASEiIMNJ30aJFuHXrllnnabVa7NixQ3jt6+uLV155BQAQHR2NFStWFNpGUlIS5s6dCyCnEH3vvffyPbZx48ZCnKdPnxZ68kzd8gWMJ3E+cOAAbty4UeDxRIbkcjkcHR3h6OgIuVx0/6zJAHMpHcylNEgxj6L7FB4eHpg2bRqAnN6/IUOG4NSpUwWec/PmTbz33nt5CryZM2cKPXc//PADVq1aBZ1OZ7KNmJgYDBs2TOj1GzFiBF5++eV8r6lUKoXpXCIjIxEREQGg4GJOf+t35cqVQhx83o+IiIhKkyj7LwcMGIDY2FgsWrQIiYmJGDJkCF555RUEBATAy8sLlStXRmpqKu7cuYMjR47g6NGj0Gg0eQZnVK9eHUuWLMHYsWORlpaGOXPmICwsDP3794ePjw/s7OwQHx+Po0ePYtu2bcIUL6+//jo++eSTQuNs1aoVTp48iezsbMTHxwMouPhr2bIlDhw4gAcPHhi1QVQYrVYrfH/a29tL5q9Ta8RcSgdzKQ1SzKMoiz8A+OCDD9CgQQPMnTsXMTExOHbsGI4dO5bv8Q0aNMD48ePzvN+2bVusX78e06ZNw8WLF3H58mVcvnzZZBt2dnYYOXIkxo4dC5lMVmiMuQs3FxeXAp/fy10YVqlSRRiVTFQQtVqN6OhoAICfnx9sbW0tHBEVF3MpHcylNEgxj6It/gDgtddeQ+fOnfH333/jn3/+QWRkJJKSkpCeng5HR0fUqFEDL7/8Mnr06IG2bdvmW7D5+Phg06ZNOHz4MPbv349z587hyZMnyMrKgrOzM+rWrYuOHTuib9++8PDwMDu+Zs2aQalUCkvJ5TcyWK9Ro0aws7MTJrBu0aKFWUUmERERkblEXfwBgK2tLQIDAxEYGFjitjp37ozOnTuXPKj/V7FixXx7EU1RKpW4cOFCqV2fiIiIKDfx37gmIiIiIrOx+CMiIiKyIiz+iIiIiKwIiz8iIiIiKyL6AR9UepztnBE6LNTSYRSJi72LpUMgIiISFZkuvyUtyCpkZ2cjMjISgHTmLyIiInoRxPo7lLd9iYiIiKwIiz8iIiIiK8Jn/kig0WgsHQIVk0ajQUJCAgDA1dUVCoXCwhFRcTGX0sFcSoMU88jijwRardbSIVAxaTQaPHz4EADg7OwsiR9O1oq5lA7mUhqkmEfe9iUiIiKyIiz+iIiIiKwIiz8S2NjwKQAiIiKpY/FHAplMZukQiIiIqIyxq4cEGq0G8Wnxlg5D4GLvAoVc/A/WEhERlScs/kiQnJmM4D+CLR2GIHRYKNwd3S0dBhERkaSw+COSAJlMBgcHB2GbxIu5lA7mUhqkmEcWf0QSoFQq4evra+kwqBQwl9LBXEqDFPPIAR9EREREVoTFHxEREZEV4W1fIglQq9W4d+8eAKBOnTqcs1HEmEvpYC6lQYp5LNefICIiAkOHDjW5r2LFiqhatSoaNmyIXr16oVevXoUm5P79+9ixYweOHTuGmJgYJCcnw8HBAe7u7mjbti169uyJVq1aFRrXkCFDcOrUKZP7bGxsUKlSJXh5eaFTp05488034eTklG9bEydORGhoaJ73ZTIZHB0dUb16dbRs2RJvvvkmGjZsWGhsZJ20Wi1SUlIAALVq1bJsMFQizKV0MJfSIMU8luviryBZWVl49OgRHj16hIMHD2L16tVYunQp3Nzc8hybnZ2N77//HuvXr4dKpTLal5KSgpSUFFy/fh1r165Fp06d8M0336BatWrFikutViM5ORlnzpzBmTNnsGrVKixevBgtW7YsUjs6nQ7Pnj1DdHQ0oqOjsXHjRowaNQqffvppseIiIiIiAkRU/A0aNAhvv/228DojIwOXL1/GypUrERMTg8jISIwdOxabNm0yGoqdlpaGsWPHIiIiAgDg5OSE/v37o3379nBxccGzZ89w+fJlbNmyBXfu3MGRI0cwcOBArFy5El5eXoXGFRYWZvRapVLhwYMH2L59O8LDw5GYmIgxY8Zg7969qFq1aoFtrVixAu7uOfPaabVaJCYm4vDhw1i/fj3UajV+/fVXuLu7Y/DgwWZ/3YiIiIgMiab4c3Fxgbe3t9F7zZo1Q2BgIIKDg3Hv3j1cunQJhw4dQkBAgHDMlClThMKvbdu2+PHHH+Hi4mLUTvv27RESEoIFCxbg999/R2xsLEaPHo3Q0FA4OjoWGFfumACgcePG6NmzJyZMmIBt27YhNTUVf/31F95///0C26pbty5q1qxp9J6/vz/at2+PMWPGAACWLFmCt956CwoFV74gIiKiohP9aN8qVapg1KhRwuujR48K27t378bevXsBAL6+vvjvf/+bp/DTUyqVmDhxIoKDc1a4uH//PhYsWFCi2EaMGCFsR0ZGFrudgIAA4VnEpKQkXLlypURxERERkfUSffEHAE2bNhW2Hz16JGz/9ttvwvbMmTNhZ2dXaFsTJ06Eq6srAGDLli1ISkoqdlyGvXjZ2dnFbgfI/zMSERERFYUkij/DUb4ajQYAEBUVhatXrwIAmjdvblQ8FcTR0RFBQUEAgOfPn2Pnzp3FjsuwSPP09Cx2O4DxZ9RqtSVqSywyMzKRnp5u6TCIiIgkRRLF3/Xr14Vt/YCJM2fOCO916dKlSO0ZPjNo2E5RrVixQtju2rVrsdsBTH9GqVu1dBW+//57S4chCnK5HO7u7nB3d4dcLol/1laLuZQO5lIapJhH0Qz4yI9arcbvv/8uvG7Tpg2AnJ4/vUaNGhWpzYYNG0Iul0Or1SI6OrrAYw2LMiBntG9MTAx27NiB/fv3AwBef/11vPrqq0WKwVBUVJTwLKOdnR38/PyK3RZJk42NjWTmn7J2zKV0MJfSIMU8irb400/1snjxYly4cAEAUKNGDbz++usAgOTkZOFYU3P/FaRixYpwcHDAs2fPhIkd8xMYGJjvvnr16mHUqFHo379/ka4P5Mzzl5CQgMOHD2PBggXC7ewhQ4agQoUKRW6PiIiICBBR8bdkyRIsWbIk3/0uLi74+eefYWtrCwBGz4rZ29sX+Xr29vZ49uwZ0tLSih7s/7tz5w7+/PNP1KxZU+iRLEhht4Y7d+6Mjz76qNjxEBEREYmm+MtPzZo10aNHD4wYMcJoGhcHBwdhOyMjo8jt6s8pbJ6/3LeFtVotkpOTcfbsWfz888+4cOEChg8fju+//x49e/YschxKpRJ+fn4YOHAg+vbtazSBtdSFjAmBq4OrpcMQBZVKhWvXrgHIeWxBqVRaOCIqLuZSOphLaZBiHkVT/Bmu8CGTyVChQgU4OzujUqVKJo93dnYWtp88eQJfX1+zr5WVlSX0HBa0Lq8pcrkcLi4ueO211+Dv74+goCDcvXsXkydPRrt27Qpsz3CFD7lcDnt7e7i6ugq9mdbGzt7OqIin/Ol0OmHpQp1OZ+FoqCSYS+lgLqVBinkUzbAV/Qof3t7eaNCgAWrXrp1v4QcAPj4+wra+YjfXtWvXhOlUDNspKgcHBwwaNAhAzm3ov//+u8Dj69atK3zGl156CdWrV7fawo+IiIjKhmiKv6Jq3bq1sB0eHl6kcw2PN2ynOOrXry9s5x4ZTERERPSiSbb48/X1RcOGDQEA58+fN3t5tbS0NISGhgIAKlSogN69e5coDv0oXSBnWhoiIiIiS5Js8QcAI0eOFLanT5+OrKysQs+ZN28enjx5AgAYMGAAqlatWqIYLl++LGyXdJUPIiIiopKSdPHXu3dvvPbaawCAq1evYsyYMfmu1atSqTBv3jz8+eefAIDatWvj888/L9H1Y2JisG7dOuF1p06dStQeERERUUmJZrRvcc2ePRvJyck4ffo0Tpw4gV69eiEoKAjt2rWDq6sr0tLScPnyZWzevBm3b98GAFSrVg1Lly4tdJoXIO9zfFqtFikpKThz5gzWrl0rTBIdGBgo3IYmIiIishTJF3+VKlXCihUrMH/+fGzYsAEpKSlYuXIlVq5cafL4jh07YubMmWbfoi1ohQ+9119/HbNnzy5S3ERFYWNjIwwusrGR/D9rSWMupYO5lAYp5lEan6IQFSpUwNSpUzFkyBDs2LEDx44dQ0xMDFJSUuDg4AB3d3e0bt0avXr1KvHoXplMBnt7e3h6eqJZs2bo169fidskKoxcLjea25LEi7mUDuZSGqSYR5lOKjMWUrFkZ2cLI6E963si+I9gC0f0P6HDQuHu6G7pMIiIiEwy/B3q5+cnmrl5raLnj0jqDGegVyqVVrUMoNQwl9LBXEqDFPMo6dG+RNZCpVIhMjISkZGRwg8pEifmUjqYS2mQYh5Z/BERERFZERZ/RERERFaEz/yRwNnOGaHDQi0dhsDF3sXSIRAREUkOiz8SKOQKjq4lIiKSON72JQFn/SEiIpI+Fn8kUKvVlg6BiIiIyhiLPyIiIiIrwmf+SCCVNQutkVKphJ+fn7BN4sVcSgdzKQ1SzCN/25NACrOWWyuZTCaaZYWoYMyldDCX0iDFPPK2LxEREZEVYc8fCTjaV7y0Wi1SU1MBAFWqVIFczr/rxIq5lA7mUhqkmEfxfwIqNbztK15qtRq3b9/G7du3OWpb5JhL6WAupUGKeWTPHwk0Wg3i0+KN3nOxd4FCrrBQRERERFTaWPyRIDkzGcF/BBu9FzoslKt+EBERSQhv+xIRERFZERZ/RERERFaExR8RERGRFWHxR0RERGRFOOCDSAJkMpmw7BCn7BE35lI6mEtpkGIey7T4i4iIwNChQ03uq1ixIpycnODr64vu3bujT58+Zi+f8vDhQ3Tr1k2YlPj7779HYGBgkeN79OgRdu3ahRMnTuDevXtISkqCVqtFlSpV4O3tjZYtWyIwMBC1atUyeX5iYiIuXbqES5cuITIyEpGRkUhJSQEA9O/fH3Pnzi00hokTJyI0NDTP+zKZDI6OjqhevTpatmyJN998Ew0bNizyZyTroFQq0bRpU0uHQaWAuZQO5lIapJhHi/X8ZWVlITY2FrGxsTh8+DBWrVqFX3/9FTVr1iz03G3bthmtRrFt27YiFX/Pnz/HggULsGHDBmRnZ+fZHx8fj/j4eBw7dgyLFi1Cz549MWHCBHh6ehod16FDB7OvWVQ6nQ7Pnj1DdHQ0oqOjsXHjRowaNQqffvppmV2TiIiIpO+FFX+DBg3C22+/LbxOTEzEjRs3sGLFCsTGxuLGjRsYM2YMtm3bBoWi4EmFd+zYAQCwt7dHRkYGTp48ifj4eLi7Fz4fXVJSEsaMGYMLFy4AABwcHPDGG2+gXbt2qFatGmxsbJCQkIBz585h//79uHv3Lvbs2YNmzZohJCQk33arV6+O+vXr49ixY4V/MfKxYsUK4TNotVokJibi8OHDWL9+PdRqNX799Ve4u7tj8ODBxb4GERERWbcXVvy5uLjA29vb6L327dsjKCgIffr0QUxMDK5fv479+/ejZ8+e+bZz7tw53Lt3DwAwefJkzJgxAxqNBmFhYRgxYkSBMWi1WnzyySdC4delSxd8++23cHFxyXNsQEAAPvvsM+zYsQPz5s0z2d4HH3wAPz8/+Pn5wdXVFQ8fPkTXrl0LjKEgdevWzdPz6e/vj/bt22PMmDEAgCVLluCtt94qtEAm66JWq/H48WMAgKenJ2xs+DivWDGX0sFcSoMU82jx0b6Ojo5CYQMAJ06cKPD4bdu2AQCcnZ3Rr18/tG/fHgCwffv2Qq+1Zs0aREREAABeeeUVLFmyxGThpyeXy9GvXz9s3boVPj4+efZ/9NFH6NKlC1xdXQu9dkkEBASgVatWAHJ6Lq9cuVKm1yPx0Wq1wuMKWq3W0uFQCTCX0sFcSoMU82jx4g+AUWEVGxub73HZ2dnYu3cvAKBXr15QKpXo06cPACA6OhrXrl0r8NwVK1YAACpUqIDZs2ebXb1Xq1ZNKDItxfBh00ePHlkwEiIiIhKzclH86YdQAyiwIAsPD0dqaioACEVf9+7dYW9vD+B/vYKmHDt2DPHx8QCAnj17wsPDo6Rhv1CGX5cX+ZdHZkYm0tPTX9j1iIiIqGyVi+Lv1q1bwnaNGjXyPU5f3NWuXRvNmzcHkDPoQ/+c3c6dO6HRaEyee/r0aWG7c+fOJYz4xbt+/bqwbc7AltKyaukqfP/99y/sekRERFS2LF78aTQa4XYsAPTo0cPkcUlJScJI2tzTuuh7ARMSEvIdbRsVFSVsN27cuEQxv2hRUVE4evQoAMDOzg5+fn4WjoiIiIjEymLFX1JSEk6ePIl33nkHV69eBZBT+OkHNuS2c+dOqFQqAP8r9vT8/f3h5uYGIP9bv/rJlwEUOMijvNDpdHjy5An++usvhISECD2aQ4YMQYUKFSwcHREREYnVCxuvvGTJEixZssTkPjs7O7z11lv4/PPP8z1fX9Q1bdoUdevWNdqnUCjw+uuvY/Xq1QgPD0daWhocHR2NjjF8bs3Ozq54H6KMFTZNTOfOnfHRRx+9oGiIiIhIisrFZDW+vr4YMmSI0cAPQzdu3BCmN8nd66fXp08frF69GllZWdizZw+Cg4ON9js4OAjbmZmZeYrD8kqpVMLPzw8DBw5E3759X/i6giFjQuDqULZT2VDJyeVyODk5CdskXsyldDCX0iDFPFpkhQ+NRoPY2Fj8/fff2L59O86fP48hQ4Zg8+bNqFq1ap5z9b1+NjY26N27t8n2mzRpAi8vL9y6dQvbt2/PU/zpEwfkPBtYHos/wxU+5HI57O3t4erqavaax2XBzt7OqHCm8snGxgZeXl6WDoNKAXMpHcylNEgxjxZb4aNhw4bo0qUL2rZti4kTJyImJgZTpkzB0qVLjc7TarUICwsDkDPLtjnz7Z05cwYPHz40Wi3D19dXmED66tWreW4dlwemVvggIiIiKk0W77/s37+/MMI3PDwcJ0+eNNp/8uRJxMXFFalNnU6XZ8WP1q1bC9uHDx8uXrBEREREIlcunvn79NNPceDAAWg0Gvz4449GvXv6W762traYPXt2offbly9fjqtXr2LHjh344IMPhPdfeeUVuLu7Iz4+Hnv37sXnn38uuomeifKjUqmE+TK9vLzyfX6Wyj/mUjqYS2mQYh7LRfFXr1499OrVCzt37sTFixdx/Phx+Pv7Iz09HQcOHACQM51L7vn9THny5AmuXr2Ku3fv4vz588Jk0La2thgxYgTmzJmD58+fY/LkyVi2bBkUCkWhbcbFxeH27dsWX+KNKD86nU4Y0a7T6SwcDZUEcykdzKU0SDGPFr/tq/f+++8LI1n1z/3t27cPGRkZAPKf/Dm3Hj16CO3knvNv6NChaNu2LYCc5d4++OADJCUl5duWTqdDWFgYgoKCEB0dXaTPQ0RERFQelYuePwDw9vZGQEAADh48iNOnT+PMmTNC8aZUKhEQEGBWO56envDz88OlS5ewZ88eTJkyRRgtK5fLsXDhQowePRoXL17EoUOH0K1bNwQGBqJdu3aoVq0abGxskJCQgAsXLmDfvn24fft2vtc6c+YM7t+/L7xOTk4Wtu/du4etW7caHR8UFGTul4OIiIioTJSb4g8ARo8ejYMHDwIAFi9ejFOnTgEA2rZtiypVqpjdTo8ePXDp0iWkpqbi0KFDRr2GVatWxdq1a/H9999jw4YNSE9Px8aNG7Fx40aTbclkMgQGBqJXr1559m3evBmhoaEmzzt37hzOnTtn9B6LPyIiIrK0cnPbF8hZvcPf3x8A8O+//0Kr1QIw/5avnuHxppZ7q1ChAqZMmYJ9+/bh888/R4cOHVC9enVUrFgRFSpUgLu7O1555RV8+umnOHjwIObPn8/BIURERCQJMp1Unl6kYsnOzkZkZCQAwLO+J4L/MJ4cO3RYKNwd3S0RGhWBYR79/PwsOjE4lQxzKR3MpTQUlEex5rhc9fwRERERUdkqV8/8EVHxKBQKYXUYc6YvovKLuZQO5lIapJhHFn9EEqBQKPhcqkQwl9LBXEqDFPPI4o8EznbOCB1mPHrZxd7FQtEQERFRWWDxRwKFXMHBHURERBLH4o8EHPgtXmIdcUZ5MZfSwVxKgxTzyNG+JFCr1ZYOgYiIiMoYiz8iIiIiK8Lij4iIiMiKsPgjIiIisiIs/oiIiIisCIs/IiIiIivC4o+IiIjIinCePxJIZc1Ca2RjYwMfHx9hm8SLuZQO5lIapJhHaXwKKhVyOTuCxUoul8PR0dHSYVApYC6lg7mUBinmkb/tiYiIiKwIe/5IoNVqLR0CFZNWq0VGRgYAwN7enr24IsZcSgdzKQ1SzKP4PwGVGo1GY+kQqJjUajWio6MRHR3NZfpEjrmUDuZSGqSYRxZ/RERERFaExR8RERGRFWHxR0RERGRFWPwRERERWREWf0RERERWhFO9WDmdTidsq1QqZGdnWzAaKi6VSmVym8SHuZQO5lIaCsqj4WvD36flnUwnpmip1KWnpyMqKsrSYRAREYmar68vHBwcLB2GWXjbl4iIiMiKsOfPymm1WmRmZgLIWbBaJpNZOCIiIiJx0Ol0wsTPdnZ2oln9g8UfERERkRURR4lKRERERKWCxR8RERGRFWHxR0RERGRFWPwRERERWREWf0RERERWhMUfERERkRVh8UdERERkRVj8EREREVkRFn9EREREVoTFHxEREZEVYfFHREREZEVY/BERERFZERZ/RERERFaExR8RERGRFWHxR0RERGRFWPwRERERWREWf0RERERWhMUfERERkRWxsXQAVLiYmBisXbsWhw8fRmxsLGxtbVGrVi306tULgwcPhp2dXalc58iRI9i0aRMiIyORlJSEqlWrws/PD2+++SY6depkVhtqtRp//fUXwsLCcPv2bWRkZMDd3R0dOnTAkCFD0KBBg1KJVYzEkMeEhAQcOnQI//77L65du4bHjx9DpVLByckJPj4+eO2119C3b19UrFixVGIVKzHkMj9RUVEYMGAA1Go1AKB///6YO3duqcQrRmLLpUqlQlhYGPbu3Yvr168jISEBDg4OcHd3R9OmTfHKK6+gV69epRKzmIgpj+fPn8emTZtw7tw5xMfHG/2M7dGjB/r16wdbW9tSiTc/Mp1OpyvTK1CJhIeHY/z48UhLSzO5v27duli2bBnq1KlT7GtotVpMmzYNmzdvzveY4OBgfPPNN5DL8+8sTkpKwqhRoxAZGWlyv62tLaZPn47g4OBixypWYsjjpk2b8NVXX0Gj0RR4nbp16+Knn36Cr69vsWMVMzHksqB2Bw4ciEuXLgnvWXPxJ7ZcRkVFYfz48bh+/Xq+x1SqVAlnzpwpdrxiJJY86nQ6fPvtt1i7dm2B12rQoAGWLVuG6tWrFzvewrD4K8euXr2KQYMGISsrC/b29nj//ffRtm1bZGVlYffu3di0aROAnG/sLVu2wNHRsVjXWbBgAZYtWwYAaNSoEd577z3UqlULDx48wPLly3H16lUAwPvvv4/PPvvMZBsajQZDhgzB2bNnAQCvvfYagoOD4eTkhIsXL2Lp0qVITEyEXC7Hr7/+WuxeCzESSx5/+eUX/PTTT1AqlejSpQv8/f3h5eUFBwcH3L9/H3/99ReOHTsGAKhatSpCQ0NRrVq1YsUqVmLJZX7WrFmDb7/9Fi4uLkhMTARgvcWf2HIZFRWFd999FykpKahQoQKCg4Ph7+8PDw8PqFQq3L17F0ePHsXZs2dx+PDhYsUqRmLK43//+1/88MMPAAAHBwcMGzYMLVq0gL29Pe7cuYPff/9dKOy9vb0RGhoKG5syukGro3Lr7bff1nl7e+saNWqkO3fuXJ79v/32m87b21vn7e2tW7RoUbGucfv2bV2jRo103t7euqCgIF1mZqbR/oyMDF1QUJAQx927d02289dffwmxfPXVV3n23717V9eiRQudt7e3rnv37jqVSlWseMVILHn8/fffdfPnz9clJibme505c+YIsU6cOLFYsYqZWHJpyuPHj3XNmzfX+fj46EJDQ4U4J0yYUKw4xU5MuczKytK99tprOm9vb13nzp11d+7cyfeaz58/L1asYiWWPGZnZ+tatWql8/b21jVu3Fh39erVPMeoVCpdcHCwEO+ePXuKFa85OOCjnLp06ZLQdT9gwAA0b948zzHDhw+Hl5cXgJy/6FUqVZGvs3r1auHZn2nTpuV5lsvOzg7Tpk0DkPM836pVq0y2s3LlSgCAk5MTvvzyyzz769Spg/fffx8AcO/ePezfv7/IsYqRmPIYEhKCL774AlWrVs33Op999hnc3NwAAPv374dWqy1yrGIlplya8vXXXyM9PR39+/dHq1atihyXlIgtlytWrMDdu3chl8uxcOFC1K1bN99rlvWzYuWJmPJ469YtPH36FADQpUsXNGzYMM8xNjY2wu9JALhw4UKRYzUXi79y6sCBA8L2gAEDTB4jl8vRr18/AMDTp08RERFRpGvodDocPHgQAFC/fn00a9bM5HHNmjVDvXr1AAAHDx6ELteTAnfu3MGtW7cAAD179sz3wdr+/fsL24afT8rElEdz2NraokWLFgCAZ8+eISUlpchtiJWYc7l3716Eh4fn+8eZtRFTLjUaDTZu3AgA6NChA15++eUixSFlYsqjYdFZs2bNfK9Xu3Ztk+eUNhZ/5ZT+2Tl7e3s0btw43+Nat24tbJ87d65I13j48CHi4+PztGNKmzZtAABxcXF4+PChyVgNjzPFzc1N+Iu1qLGKlZjyaK7s7GxhuyiDDcROrLl89uwZZs2aBQAYP348nJ2dixSTFIkpl+fPn0dcXBwAICAgQHj/+fPnuH//PuLi4godpCVVYspj3bp1IZPJhDbzc//+fWFbX0yWBev5yS0y+p602rVrF/jAZ/369fOcY66bN2+abKew69y+fdtkrEVp5/Hjx8jIyDA7VrESUx7NoVKphFsRrq6ucHJyKnIbYiXWXM6fPx9PnjxBq1at8u0dsTZiyqXhrT9vb2/cvXsX48aNQ8uWLdG9e3e8+uqraNu2Lb788kujwsEaiCmPlSpVQu/evQEAhw4dQlRUVJ7z1Wq1MKjE8PiywOKvHHr+/DmSk5MBoNDRlFWqVIG9vT0AIDY2tkjXMTy+sOsY7n/8+HG+7Xh4eBTYjqenJ4CcrvSixis2YsujOTZt2iR8pp49exb5fLESay7Pnj2LTZs2QalU4quvvhJ6HqyZ2HJpWKzcvn0b/fv3x759+4xuCT579gzbt29Hv379cOLEiSLFKVZiyyMATJo0CY0bN4ZKpcLgwYOxZMkSnDhxAhcuXEBoaCgGDBiACxcuwM7ODt99912Z9tJzkudyKD09XdjWf8MWxM7ODhkZGUXuSSvKdQyf48t9HcN2HBwcit2O1Igtj4V58OABfvzxR+E6o0aNKtL5YibGXGZnZ2P69OnQ6XQICQmx6gnWDYktl4bP1X777bd4/vw5QkJC8Pbbb8PT0xNxcXHYsGEDVq5cifT0dHz88cfYvn17mc4RVx6ILY9Azt2SdevWYdOmTVi2bBkWL15stF8mkyE4OBjDhg0TBqmUFfb8lUPPnz8XtpVKZaHH60d3ZWVlldl1DEeQ5b5OabUjNWLLY0EyMzPx4Ycf4tmzZwByRrwV1ssrJWLM5bJly3Dz5k3UqFEDH3zwQZHikDKx5TIzM9OozY8++giTJk1CnTp1hFUsvvzyS3z66acAcgY1/Pe//y1SrGIktjzq/fvvv9ixYwcSEhLy7NMPLgkNDTV6trossPgrhypUqCBsmzPaR/9NUtQlt4pyHcNvxNzXKa12pEZsecyPWq3Gxx9/LDyjMmjQIAQFBRUpRrETWy5v374tFADTpk0rtaWtpEBsuTRsx9nZGSNHjjTZxogRI4RpmPbu3Vus0fxiIrY8AjlTxowZMwaXL19G69at8fvvv+Ps2bOIjIzE7t27MXz4cKSmpuK3337Du+++a9TrWNpY/JVDhrdOzemi1v9laE7Xd3GvY/jXZ+7rGLZT2DdrQe1IjdjyaIpOp8PEiRNx5MgRAECvXr0wffr0IsUnBWLKpU6nw4wZM5CdnY3u3bujS5cuRYpB6sSUy9zttGnTJt95/GxsbNCuXTsAObeKHzx4UKR4xUZseYyKisLcuXOh0+nQoUMHrF69Gh06dICjoyNsbW3h5eWFCRMm4JtvvgGQMyo5923h0sRn/sqhChUqwMnJCSkpKYU+nJqamip8QxZ1qS3D4wu7juF+/aANU+3ExcUVOEmw/iFYmUwm+aXBxJZHU77++muEhYUBAF599VXMnz/fqqZ30RNTLi9cuIBTp04BAJo3b45du3blOTcpKUnYfvjwoXBMgwYN4O3tXaSYxUZMuczdTmH/Zg33JyUlGc0ZJzViy+PWrVuFSfHHjRsHhUJhso3//Oc/+O2333D37l2EhoZiwoQJZTJQi8VfOfXSSy/hzJkzuH//PtRqdb7D2A2Hkxf1AdGXXnrJZDuFXSf3cHfD696+fdvkzOW52/H09JR8zx8grjzmNn/+fGzYsAFAzvxWixcvNuvZGqkSSy4Nbz3Nmzev0GuePn0ap0+fBgB8+OGHki/+APHkEoDRQJ3C5vMz3J9fcSElYsqj4ajtguYkBHLWDr579y5SUlKQmJgIV1fXooRsFuv7E14kWrZsCSCnm/nKlSv5Hqf/oQ1AWHnBXDVr1oS7u3uedgq6joeHR57ZyfWxAhB6HEx58uQJ7t69W6xYxUpMeTT0yy+/YPny5QAAPz8//Prrr5J/RrMwYs0l5SWmXBouxVfYxOyGt3qtYUCWmPJoWJjql4rLj+H+siriWfyVU926dRO2t2zZYvIYrVaLbdu2AQAqV66Mtm3bFukaMpkMXbt2BZDzF0t+6wheuHBB+Iuma9euebqg69WrJ/w1tXfvXqPnHgyFhoYK24afT8rElEe91atX46effgKQM6ns8uXL4ejoWKSYpEgsuWzbti2io6ML/E+/XBWQs+yi/v1x48YVKV6xEksuAaBWrVpo1KgRACAiIkIYcZ9bWlqaMMdf7dq1hYJFysSUR8NiUL8esSkqlQrnz58HkDPRc1lNpM/ir5xq2rSp8Bffli1bhG8GQytXrhS6kocOHZrnllxERAR8fHzg4+ODiRMnmrzOu+++K/xlMXPmzDzD07OysjBz5kwAOX+5vPvuuybbGT58OICcB43nz5+fZ//9+/eF0Yd16tRB9+7dTX9wiRFbHrds2YI5c+YAyFmO6Pfff7eqVTwKIrZcUv7Elkv9CN+MjAzMnj3b5DFz585FWloaAOCtt94yeYzUiCmPhgOvFixYIOQqt8WLF+PJkycAgE6dOpXZxOx85q8cmzJlCgYNGoSsrCwMHz4co0ePRtu2bZGVlYXdu3fjzz//BJDzS3rYsGHFuka9evUwYsQILFu2DJcvX8agQYMwcuRI1KpVCw8ePMBvv/2Gq1evAsiZSkC/Nm9u/fv3x5YtW3Du3DmsW7cOCQkJCA4ORpUqVXDp0iX88ssvSEtLg1wux5QpUwpcikdqxJLHAwcOYNq0adDpdHB0dMSUKVOQlJRkNDggt5o1a1rFs5t6YsklFU5MuXz99dexbds2HDlyBFu3bkV8fDwGDRqE6tWr4/Hjx/jzzz+FEfmNGjXCO++8U6x4xUgseXzllVfQrl07/Pvvv4iOjkbfvn0xdOhQNG3aFBUqVMC9e/ewZcsWHD16FEDOaOGynJ9TppP6ZEAiFx4ejvHjx+f7V0LdunWxbNky1KlTJ8++iIgIDB06FEBOcTZ37lyTbWi1WkydOjXfbnMgZwTSzJkzCxzpmZSUhFGjRiEyMtLkfltbW0yfPh3BwcH5tiFVYsjjxIkTjW7Nm2PNmjVFvo0idmLIZWEePnwo3MoqKA6pE1Mu09PT8dFHH+HYsWP5HuPn54elS5cK8/1ZC7HkMTU1FePGjUNERESBn6dq1apYsGABOnToUOBxJWE93S8iFRAQgB07dmDNmjU4fPgw4uLioFQqUbt2bfTs2RPvvPNOiSdwlcvlmD17Nnr06IE///wTkZGRSE5OhrOzM/z8/DBw4EB06tSp0HaqVq2KjRs3YtOmTdi5cydu3bqFzMxMuLu7o3379hg6dKjVLjElpjxSwZhL6RBTLh0cHLB8+XLs3r0boaGhiIqKQkpKCipVqoSGDRuid+/e6Nevn1WM8s1NLHmsUqUKVq9ejYMHD2Lnzp2IjIxEQkICNBoNKlWqhJdeegmvvvoqgoODy/xxG/b8EREREVkRDvggIiIisiIs/oiIiIisCIs/IiIiIivC4o+IiIjIirD4IyIiIrIiLP6IiIiIrAiLPyIiIiIrwuKPiIiIyIqw+CMiIiKyIiz+iIiIiKwIiz8iIiIiK8Lij4iIiMiKsPgjIiIisiIs/oiIiIisCIs/IiIiIivC4o+IiIjIirD4IyJ6AR49eoTp06ejW7du8PPzg4+PD3x8fHDgwAFLh0ZULBMnToSPjw8CAgIsHQoVkY2lAyAqqYiICAwdOhQA8OGHH2LcuHEWjojI2KNHjxAUFITk5GRLh1Ikhv+2cqtYsSKcnJzg6+uL7t27o0+fPrC1tTW7bY1Gg3379uHgwYOIjIxEQkICVCoVXF1dUb9+fXTu3Bl9+vRB5cqVixRzdnY29u3bh3/++QeXLl1CcnIy0tLS4OjoiBo1asDPzw89evRAu3btIJez/4OsE4s/IgIA+Pj4AGABXRaWLl2K5ORk2NjY4JNPPkHr1q1hb28PAKhevbqFoyuerKwsxMbGIjY2FocPH8aqVavw66+/ombNmoWee+bMGcyYMQM3b97Msy8mJgYxMTE4evQoFi9ejM8//xxvvvmmWTHt27cPc+fORUxMTJ59KSkpSElJwZUrV7Bx40bUrVsXkyZNQufOnc1qW8z4b5tyY/FHRFTGTpw4AQDo2rUrRo4caeFoimfQoEF4++23hdeJiYm4ceMGVqxYgdjYWNy4cQNjxozBtm3boFAo8m1n7969GD9+PLKzswEAbdq0QZ8+fVCvXj3Y2tri0aNHCA8Px65du5CSkoJp06bhzp07mDBhQoHx/fzzz1i0aJHw2t/fHwEBAfDy8kLlypWRmpqKO3fuIDw8HCdOnMDdu3fx448/WkXxV1bmzp2LuXPnWjoMKgYWf0REZSwuLg4AUK9ePQtHUnwuLi7w9vY2eq99+/YICgpCnz59EBMTg+vXr2P//v3o2bOnyTauXr2KL774AiqVCkqlEnPmzEFgYKDRMU2bNkXPnj0xbNgwjB49GrGxsVi5ciVq1aplVHwa2rJli1D4ubi4YOHChWjTpk2e4zp06IDBgwfj+vXrmDNnDpKSkorzpSASPT7wQERUxlQqFQDAxkZ6f287OjpizJgxwmt9L2duWq0WX375pfC1mD17dp7Cz1DDhg2xatUq4fb4d999h0ePHuU5Li4uDjNnzgQA2NvbY+3atSYLP0Pe3t5YsWIFhg8fXvCHI5Io6f0kIsrF8KH1NWvWoE2bNti8eTO2bt2K27dvQ6VSoX79+njnnXfQr18/4bzs7Gxs2bIFoaGhuHv3LlQqFXx8fDB06FC8/vrrJq/18OFDdO3aFQAwZ84cBAUFYc+ePfjzzz8RHR2N9PR0VK9eHd27d8fIkSMLfZg9Ozsbf/31F/bu3YsbN24gLS0NVapUQaNGjfDGG28gMDAw34fWJ06ciNDQUNSoUQPh4eGIj4/H6tWrcfjwYTx69AgZGRlYs2YNJk2aZPSM1JIlS7BkyRKjtvr37290eyc+Ph779+9HREQEoqKiEB8fD7VaDWdnZzRp0gSBgYHo2bNnvrHlzknbtm2xe/du4euUkZEBT09PdO3aFaNGjYKTk1OBXycAOHLkCMLCwnD+/HkkJiZCo9HAzc0NjRs3RpcuXdCrVy/Y2dmZPPfKlSv4888/ERERgfj4eOh0Onh4eKBdu3YICQkpVo/d1q1bMWnSJKP3cn9tc39dASApKQmrV6/GkSNH8PDhQzx//hxubm5o2bIlBg4ciFatWuV7zYCAAMTExAjtXr58GX/88QdOnTqF+Ph4qFQqREdHF/mzFEb/TBkAxMbGmjwmPDwcN27cAAC8+uqr6NOnT6Ht1qtXD2PGjMGCBQuQlZWFNWvWYOLEiUbHrFq1CpmZmQCAjz76CF5eXmbFLJfL0bdvX7OONfQif54YevLkCf744w8cPXoUDx8+REZGBlxcXNCsWTMMHDgQHTp0yHOO/vtBr7B/27k/W+vWrbF161Zs374dt27dQlJSEvr16yccn/tnTH7S0tKwadMmHD58GLdu3UJqaipsbW1Ru3ZttGzZEq+//jpatmxZ6NeASg+LP7IqarUaY8aMwaFDh4zej4yMxIQJE3D58mVMnToVqamp+OCDD3D69Gmj486fP4/z58/j/v37GD16dKHXmzx5MrZs2WL03p07d7Bs2TJs27YNq1atyveX1cOHDzFy5Ejcvn3b6P2EhAT8888/+Oeff/Dnn3/il19+KbQ4unDhAkaPHl0qo001Gg06deoErVabZ198fDzCw8MRHh6OzZs3Y/HixXBwcCiwPa1Wi/Hjx2PHjh1G79+9excrVqzAgQMHsG7dOri5uZk8Pzk5GZ9++ilOnjyZZ59+8MC+ffsAAEFBQXmu/d1332H16tXQ6XR5rn/37l1s3rwZ06dPx8CBAwv8HKXh2LFj+Pjjj5GWlmb0vv5z7NixA4MHD8bUqVMLHam6YcMGzJo1C2q1uixDBgAolUphO7/ezW3btgnb7777rtltv/XWW1iyZAmeP3+O0NBQTJgwATKZDACg0+kQGhoKIKfXLzg4uBjRF9+L+nmyY8cOzJgxAxkZGUbvx8bGYu/evdi7dy/+85//4Ouvvy613uXnz59jxIgR+fbkmuvEiRP47LPP8vzsUalUuHbtGq5du4Y//vijTP4oofyx+COr8tNPP+HixYsIDAxEYGAgXF1dcffuXSxevBh37tzB2rVrERAQgLVr1+L8+fMYNGgQunfvDicnJ1y7dg0//fQT4uPjsWjRInTt2hUNGjTI91rr169HZGQkmjZtipCQENSpUweJiYkIDQ3Fnj17EB8fj/feew9hYWFwdHQ0Ojc9PR0hISF48OABAKBbt24YMGAA3N3d8fDhQ6xbtw6nTp3C2bNnMXr0aKxbty7fh+zT09Mxbtw4PH/+HKNHj4a/vz8qVqyI69evw83NDStWrIBKpRJuweV+sB8AqlSpImzri6R27drh1Vdfhbe3N6pWrYr09HQ8ePAAf/31F86fP4/jx4/jm2++wXfffVdoTs6fP49u3bqhX79+qF69OhISErB+/XocPnwY9+7dw5w5c/DDDz/kOTczMxNDhw7F9evXAQCNGzfGwIED0aBBA9ja2iI2NhanT5/G7t27TV575syZWL9+PQCgdevW6N+/P2rVqoWKFSsiOjoaq1evxo0bNzB9+nS4uroKvbrm6NatG5o0aQIA+X5tDb+u165dw+jRo4Xn4QYPHoyAgADY2dnh2rVrWLZsmZB7Ozs7jB8/Pt9rR0ZGYseOHahWrRpGjBiBJk2aQK1W4+zZs2bHXxS3bt0StmvUqGHymDNnzgAA7Ozs0L59e7Pbrly5Mlq3bo1jx44hJSUFN2/eFP7d3bhxQygqWrZsmeffUVl7ET9Pdu/ejS+//BI6nQ61atXCO++8Ay8vL1StWhUxMTHYvHkzjhw5gs2bN8PR0dGot7mo/7YNff/994iOjkZAQACCgoKEf5fp6elmf33+/fdfjBw5Emq1GgqFAn379kXXrl3h6emJ58+f49at/2vv7oOiqt44gH8XE5FBeRF5sRpM5cqqiYiFDZRgpCP5RmSjEYRvlWkaOpOak5g0hk5poonjjKYpAWKO+DK2g6IojBgGI76QO7yoYG0ixAq+sAL398fOnt9d7t3LLu4izT6fv+7sOffu3b27d5895zznVOLcuXOi4Jl0A56Q/7iioiKe4zie4zg+NTVVtpzjOH7v3r2iOnfv3uWDgoJ4juP48ePH88OHD+dzc3NF9crLy/mAgACe4zg+OTlZVF5TU2P0XAsXLuSfPHkiqrd9+3ZWZ+PGjaLylJQUVr5lyxZReXt7O79ixQpWJz09XVRn5cqVrHzMmDF8eXm5qI6Q3HvY8blv3rwpW2fr1q08x3H88OHD+erqalF5x2uyY8cOyeeZN28ez3EcP2LECL6+vl5UZ8OGDewYX3/9Nd/e3i55Pi0tLXxdXZ3RYwUFBWzfgwcPSu73+PFjPj4+nuc4jo+IiJC8luYw572NiYnhOY7jlUolf/78eVF5Y2MjHxUVxXMcxwcEBPBqtVpUJyIigj3X1KlTea1W26XzNejsu8XzPN/a2srPnDmT1SsuLhbV0Wg0rPy9996z+Dy+++47tv/Ro0fZ4zk5OezxzZs3W3zcrujO+0l9fT0fHBzMcxzHr1692uTnb/PmzexzUVlZKSo397vd8bVJ3XuEDPeYiIgIUdnjx4/5sLAwnuM4PjAwkC8qKjJ5nL/++kv2eYj1UcIHsSuBgYGSXU4DBw7EW2+9BUA/5mrKlCmIjIwU1QsICGBjUzprRXF0dERycrJkN8yiRYtY5uSvv/7Kpr0A9GODDh06BADw9/eXnJdLoVBg3bp1rLs3PT1d9lwWLFiAgIAA2TrmUigU8PPzk62zePFiuLu7g+d52bFAgL61TqrLS6FQICEhAYC+e620tNSo/P79+8jKymLHWLNmDesO7MjR0RGenp5Gj+3atQsAMHnyZJPdhX369MHatWsB6LteL168KPtauqqsrAxXrlwBAMyaNQthYWGiOq6urli/fj0AfXe1ocXSlKSkJIsnSLZEQ0MDLly4gA8++ADXr18HoH8vpcYkCrv8Ol4HcwwYMIBtNzY2Sm4L63QXW99PMjIy0NTUBG9vb6xbt85kl+5nn30Gb29vtLe3Iycn52leEjN48OCnmhPwyJEjuHv3LgAgMTERISEhJuv6+vp2+XlI11DwR+yK3MBqYXD09ttvd1rP0CVrSmhoKLy9vSXLHBwc2GDwxsZG9uMJAFevXsX9+/cB6Adjm+rOdXFxwZQpUwAAFRUV7EYrxZzB9V3V3t6Of/75B1VVVVCr1VCr1aisrISPjw8A4M8//5Tdf9q0aSaDtpEjR7Ltju93UVERG+gfHx8vO7dcR83Nzfj9998B6AMWOUOHDoW7uzsAiAJQaxGOq3r33XdN1gsODmZjROXGYvn6+somhnTF9u3b2ZJ0w4cPx2uvvYaEhASUlJSgb9++mDt3Lr7//nvJfYVjGA3Zu5YQjhsVHkvYBWkqmceWbH0/MfxxCg8Pl1095bnnnsOYMWMAWO8zGhUVZdF3qiNDV66zs7PZk3ST7kNj/ohdkcva7NevH9sePHhwp/U6G/vy8ssvy5aPHj2abavVanbzNmREAvqWBTmBgYHIyMhg+3l5eYnqODs748UXX5Q9jqV4nsfRo0dx6NAhlJWV4fHjxybrdpZkMmTIEJNlwkSWju+3MGC2NFPw+vXrLGFl+fLlWL58uVn73bt3z6LnMZfhmvfu3RtKpVK2bmBgICorK3Hr1i3odDrJoECYfdsdAgICEBcXZ5T4ISQci9cxacEcwmsvPJYwKDT8EehOtryftLW1sT9OWVlZrJW7M9b6jD7tZ6i8vByA/g/cswjMiTwK/ohdcXJyMlkmzJ40p55UtqtQZ91QprqytFot2/bw8JA9hrALTbifkLW7/lpaWrBkyRKcO3fOrPpygSFg/jXp+H4Lg0qpoFdOfX29RfUNOnstXWW4/m5ubp1maxquOc/zuH//vmQ3qi26e4XJAm1tbdBoNFCpVMjJyUFpaSni4uJw6NAhyc+soeUU6FpwIrxewj8Ewu2uXtOnYcv7iVar7VKmtrU+o6YSQcxl+H6aytInzxYFf4TYiKmuzO4+xtN03UhJS0tjgd+rr76K999/HyNHjoSnpyecnJzYj1lsbCzL8OxphD+069evR1BQkFn7Pe0PYnex9jUHxCt8KJVKREREICQkBKtWrcKdO3ewZs0apKWlifb18vKCu7s7/v33X9y4cQNtbW0WnaOwlVfYnSrcvnbtmqUvqUdra2tj27NmzWLz73XGVOurpTqbSoj8t1HwR4iNdNbCYao1Qxhg1NfXy3YtCZ+jOwITnudZMsq4ceOwb98+kz8SploirUXYmnT37l2LuraF77eTk5No2bLuZjifxsZGtLa2yrb+Ga65QqGwaUKHuaKjo3HmzBmoVCrk5eXhwoULoqlcFAoFgoODcerUKTx69AgXLlyQTGqR0tTUxP5EuLm5YdiwYazM39+fBZV//PEHmpubu326F1vpOL3Ss/6MWsrd3R0ajQZ1dXXP+lSIBArtCbERQ/amOeXC+b2E25cvX5Y9RllZmeR+ttLY2Mhu5nIreDx48ADV1dU2PRdhMoilLYxKpZK1qpaUlFj1vLrCcO0ME9/KMVxzPz8/2SSA7pSYmMha8rZs2SJZJzo6mm3v37/f7GNnZWWxrszo6Gij1nCFQsGO+/DhQ2RnZ1t87j2Vo6Mj+1z0hM+opUaMGAFAn8D2LMZjEnkU/BFiI4WFhSYzcNvb29nKBK6urkaBzKhRo1iLzpEjR0yOLWxubsbJkycBAMOGDbN43FtHffr0AQCjaWc6EnZFyd3Qs7Ozbb6yREhICMsc3b9/v9G5dcbDw4Ml2Bw/fhwNDQ22OEWzCZfm6rgijFBpaSkqKipE+zxrL730Ess8v3z5MgoLC0V1Jk6cyDKVz549ixMnTnR63Fu3buHHH38EoG+hler6TEhIYAkFqampRhNOy2lvbxetKtPTTJw4EQBQVVWF8+fPd/k45ny3rc1w7o8ePTI7WYV0Hwr+CLERnU6HtWvXSgYlu3btYqtSxMTEGLXgODo6suk+1Go1duzYIdqf53kkJyezQdWxsbFPfb6Ggdm3b982WcfDw4MFpsePH5f8MSkrK8PWrVuf+nw6079/f7bk2rVr17BhwwbREm0GT548ESUELFq0CIA+iF66dCmbXkeKTqdDeno6WlparHT2xkaPHs1WA8nOzpZcqq6pqYnNOejg4IA5c+bY5Fy66uOPP2atclLj/hwcHLBp0yY2Jm3VqlUmV14B9FMEJSQksOzglStXYtCgQaJ63t7e+OqrrwDoW//i4uLYND6mVFRUYMGCBdi9e7d5L+4ZiY+PZ39wVq9ebTQTgJSzZ89KTq1kznfb2qZPn86muvrhhx9kr4mp9aCJ7dCYP0JsZNSoUThz5gzmzJljtLzbkSNHWKuHj48PPv30U9G+ixcvRm5uLmpqarBt2zao1Wq88847GDhwIGpra3HgwAF2Mw0KCrLKurNBQUGora1FXl4eMjMzMXbsWNZi4OLiggEDBsDBwQHTpk1Deno6bty4gTlz5mDu3Lnw8/NDc3Mz8vPz8csvv8DZ2RleXl64efPmU5+XnGXLlqGwsBBqtRoHDhxAaWkpZs+eDY7j0Lt3b2g0Gly6dAknTpzA559/brS274QJExAfH4+ff/4ZxcXFiIqKwuzZsxEcHAw3Nzc8fPgQt2/fxqVLl5CbmwutVouZM2ey98TavvnmG8yaNQtPnjzBwoULERcXh4iICKPl3Qxzwc2bN6/HjQHjOA4TJ07E6dOnUVxcjEuXLonmGhw1ahQ2bdqEL774AjqdDomJicjMzMS0adMwZMgQ9O7dG3///Tfy8vJw/Phx1no8b9480bJkQjExMdBoNEhNTUV9fT3i4uIQFhbGWhv79+8PrVaL6upq5Ofn4/z582hra7PaxOe24unpiY0bN2Lp0qWoq6tDTEwMoqOj8cYbb8DHxwetra3QaDQoKyuDSqVCTU0Ndu7cKXpd5ny3ra1Pnz7YtGkT5s+fj0ePHmHu3LmYPn06IiMj4ePjA51Oh6qqKuTn5yMvLw9Xr161+jkQ0yj4I8RGYmNjUVxcjMOHDyMxMVFUblhXVzgfmIGLiwv27t2LhQsXoqqqCiqVCiqVSlRv7NixSEtLs0p25/z586FSqaDT6ZCUlGRUFh0djZSUFAD68V0lJSUoLy/H1atXsWLFCqO6bm5u2LZtG1JTU20e/PXt2xf79u3D0qVLUVxcjGvXrrFWIHN8+eWXcHV1RVpaGurq6rBt2zaTdZ2dnW2SRWugVCqxc+dOLFu2DM3NzdizZw/27NkjqhcbGyt6z3uKTz75BKdPnwagb/2TalmLiorCwIEDkZSUhMrKSly8eNHkyilubm5Yvny5WX9uFi9eDH9/f6SkpODOnTsoKChAQUGByfr+/v6y6yP3FJMmTcKOHTuwevVqNDY2IjMzE5mZmZJ1HRwcJOfUM/e7bW3jx4/Hzp07sWLFCmi1Whw+fBiHDx+2yXMRy1DwR4gNffvttwgNDUVWVhbUajUePnyIQYMGITIyEh999JFshu4LL7yAnJwcZGdn47fffoNarcaDBw/g6uoKpVLJFpO31pQMSqUSWVlZ2L17N0pKSnDv3j3Jbt1+/fohIyMDP/30E06ePIlbt26hV69e8PX1xYQJE/Dhhx+y1T26g4eHBw4cOIDc3FwcO3YMly9fRkNDAxQKBby8vDBy5EhERkZKruShUCiwZMkSzJgxA5mZmSgqKkJtbS2amprg5OQEX19fKJVKhIWFITIyUna+NmsICwtDbm4u9u3bh/z8fNTU1ECn08HT0xPBwcGYPXu21VfusKbRo0cjNDQUhYWFKCgoQFlZmdFk5gavvPIKjh07BpVKhdOnT+PKlSu4d+8eWltb4eHhgaFDhyI8PBwzZsywKKN50qRJCA8Ph0qlwrlz53DlyhU0NDTgwYMHcHFxwfPPP4/AwEBMnjwZISEhVplKqTsYWlQPHjyI/Px8VFRUQKvVolevXvD09IS/vz/Gjx+PyZMnSy6VZu532xZef/11nDp1ChkZGThz5gyqq6vR1NSEvn37ws/PD+PGjcPUqVO75VzI/yl4U4NkCCEWq62txZtvvglAH/gJuxkJIYSQnoASPgghhBBC7AgFf4QQQgghdoSCP0IIIYQQO0LBHyGEEEKIHaHgjxBCCCHEjlC2LyGEEEKIHaGWP0IIIYQQO0LBHyGEEEKIHaHgjxBCCCHEjlDwRwghhBBiRyj4I4QQQgixIxT8EUIIIYTYEQr+CCGEEELsCAV/hBBCCCF2hII/QgghhBA7QsEfIYQQQogdoeCPEEIIIcSOUPBHCCGEEGJHKPgjhBBCCLEjFPwRQgghhNgRCv4IIYQQQuwIBX+EEEIIIXaEgj9CCCGEEDtCwR8hhBBCiB2h4I8QQgghxI78D1lfVtg6HUhcAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for idx, row in df.iterrows():\n",
+    "    fig, ax = plt.subplots(figsize=(3,3))\n",
+    "\n",
+    "    barplot_feat_imp(\n",
+    "        row[cols_order], row[std_cols_order], ax=ax,\n",
+    "        color=palette[0] if row[\"model_name\"] not in baselines else palette[2],\n",
+    "    )\n",
+    "    ax.set_title(row[\"model_name\"])\n",
+    "\n",
+    "    ax.set_xlim(left=0, right=ax.get_xlim()[-1])\n",
+    "\n",
+    "    save_fig(fig, f\"feat-imp_{idx}\")\n",
+    "    plt.plot()\n",
+    "    # plt.close(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3a6beb5-b44b-4f81-ae1a-d027afb2c5f4",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/readme.rst.txt b/_sources/readme.rst.txt
new file mode 100644
index 0000000..a1c8bea
--- /dev/null
+++ b/_sources/readme.rst.txt
@@ -0,0 +1,5 @@
+README.md
+=========
+
+.. include:: ../README.md
+   :parser: myst_parser.sphinx_
diff --git a/_sources/source/folktexts.acs.rst.txt b/_sources/source/folktexts.acs.rst.txt
new file mode 100644
index 0000000..241f599
--- /dev/null
+++ b/_sources/source/folktexts.acs.rst.txt
@@ -0,0 +1,53 @@
+folktexts.acs package
+=====================
+
+Submodules
+----------
+
+folktexts.acs.acs\_columns module
+---------------------------------
+
+.. automodule:: folktexts.acs.acs_columns
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.acs.acs\_dataset module
+---------------------------------
+
+.. automodule:: folktexts.acs.acs_dataset
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.acs.acs\_questions module
+-----------------------------------
+
+.. automodule:: folktexts.acs.acs_questions
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.acs.acs\_tasks module
+-------------------------------
+
+.. automodule:: folktexts.acs.acs_tasks
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.acs.acs\_thresholds module
+------------------------------------
+
+.. automodule:: folktexts.acs.acs_thresholds
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: folktexts.acs
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/source/folktexts.classifier.rst.txt b/_sources/source/folktexts.classifier.rst.txt
new file mode 100644
index 0000000..950c0cf
--- /dev/null
+++ b/_sources/source/folktexts.classifier.rst.txt
@@ -0,0 +1,37 @@
+folktexts.classifier package
+============================
+
+Submodules
+----------
+
+folktexts.classifier.base module
+--------------------------------
+
+.. automodule:: folktexts.classifier.base
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.classifier.transformers\_classifier module
+----------------------------------------------------
+
+.. automodule:: folktexts.classifier.transformers_classifier
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.classifier.web\_api\_classifier module
+------------------------------------------------
+
+.. automodule:: folktexts.classifier.web_api_classifier
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: folktexts.classifier
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/source/folktexts.cli.rst.txt b/_sources/source/folktexts.cli.rst.txt
new file mode 100644
index 0000000..2d7a7c4
--- /dev/null
+++ b/_sources/source/folktexts.cli.rst.txt
@@ -0,0 +1,61 @@
+folktexts.cli package
+=====================
+
+Submodules
+----------
+
+folktexts.cli.download\_models module
+-------------------------------------
+
+.. automodule:: folktexts.cli.download_models
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.cli.eval\_feature\_importance module
+----------------------------------------------
+
+.. automodule:: folktexts.cli.eval_feature_importance
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.cli.experiments module
+--------------------------------
+
+.. automodule:: folktexts.cli.experiments
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.cli.launch\_experiments\_htcondor module
+--------------------------------------------------
+
+.. automodule:: folktexts.cli.launch_experiments_htcondor
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.cli.rerun\_experiment module
+--------------------------------------
+
+.. automodule:: folktexts.cli.rerun_experiment
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.cli.run\_acs\_benchmark module
+----------------------------------------
+
+.. automodule:: folktexts.cli.run_acs_benchmark
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: folktexts.cli
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/source/folktexts.rst.txt b/_sources/source/folktexts.rst.txt
new file mode 100644
index 0000000..bb0bd44
--- /dev/null
+++ b/_sources/source/folktexts.rst.txt
@@ -0,0 +1,103 @@
+folktexts package
+=================
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   folktexts.acs
+   folktexts.classifier
+   folktexts.cli
+
+Submodules
+----------
+
+folktexts.benchmark module
+--------------------------
+
+.. automodule:: folktexts.benchmark
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.col\_to\_text module
+------------------------------
+
+.. automodule:: folktexts.col_to_text
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.dataset module
+------------------------
+
+.. automodule:: folktexts.dataset
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.evaluation module
+---------------------------
+
+.. automodule:: folktexts.evaluation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.llm\_utils module
+---------------------------
+
+.. automodule:: folktexts.llm_utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.plotting module
+-------------------------
+
+.. automodule:: folktexts.plotting
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.prompting module
+--------------------------
+
+.. automodule:: folktexts.prompting
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.qa\_interface module
+------------------------------
+
+.. automodule:: folktexts.qa_interface
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.task module
+---------------------
+
+.. automodule:: folktexts.task
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+folktexts.threshold module
+--------------------------
+
+.. automodule:: folktexts.threshold
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: folktexts
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/source/modules.rst.txt b/_sources/source/modules.rst.txt
new file mode 100644
index 0000000..d3bc6a4
--- /dev/null
+++ b/_sources/source/modules.rst.txt
@@ -0,0 +1,7 @@
+folktexts
+=========
+
+.. toctree::
+   :maxdepth: 4
+
+   folktexts
diff --git a/_static/PUMS_Data_Dictionary_2018.pdf b/_static/PUMS_Data_Dictionary_2018.pdf
new file mode 100644
index 0000000..a36c774
Binary files /dev/null and b/_static/PUMS_Data_Dictionary_2018.pdf differ
diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 0000000..8141580
--- /dev/null
+++ b/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/_static/basic.css b/_static/basic.css
new file mode 100644
index 0000000..f316efc
--- /dev/null
+++ b/_static/basic.css
@@ -0,0 +1,925 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 230px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 360px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+a:visited {
+    color: #551A8B;
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/_static/binder_badge_logo.svg b/_static/binder_badge_logo.svg
new file mode 100644
index 0000000..327f6b6
--- /dev/null
+++ b/_static/binder_badge_logo.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="109" height="20"><linearGradient id="b" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="a"><rect width="109" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#a)"><path fill="#555" d="M0 0h64v20H0z"/><path fill="#579aca" d="M64 0h45v20H64z"/><path fill="url(#b)" d="M0 0h109v20H0z"/></g><g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="110"><image x="5" y="3" width="14" height="14" xlink:href="data:image/png;base64,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"/> <text x="415" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="370">launch</text><text x="415" y="140" transform="scale(.1)" textLength="370">launch</text><text x="855" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="350">binder</text><text x="855" y="140" transform="scale(.1)" textLength="350">binder</text></g> </svg>
\ No newline at end of file
diff --git a/_static/broken_example.png b/_static/broken_example.png
new file mode 100644
index 0000000..4fea24e
Binary files /dev/null and b/_static/broken_example.png differ
diff --git a/_static/check-solid.svg b/_static/check-solid.svg
new file mode 100644
index 0000000..92fad4b
--- /dev/null
+++ b/_static/check-solid.svg
@@ -0,0 +1,4 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>
diff --git a/_static/clipboard.min.js b/_static/clipboard.min.js
new file mode 100644
index 0000000..54b3c46
--- /dev/null
+++ b/_static/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.8
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},i="";return"string"==typeof t?(e=t,n="rtl"===document.documentElement.getAttribute("dir"),(o=document.createElement("textarea")).style.fontSize="12pt",o.style.border="0",o.style.padding="0",o.style.margin="0",o.style.position="absolute",o.style[n?"right":"left"]="-9999px",n=window.pageYOffset||document.documentElement.scrollTop,o.style.top="".concat(n,"px"),o.setAttribute("readonly",""),o.value=e,o=o,r.container.appendChild(o),i=c()(o),a("copy"),o.remove()):(i=c()(t),a("copy")),i};function r(t){return(r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==r(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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\ No newline at end of file
diff --git a/_static/copy-button.svg b/_static/copy-button.svg
new file mode 100644
index 0000000..9c074da
--- /dev/null
+++ b/_static/copy-button.svg
@@ -0,0 +1,5 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>
diff --git a/_static/copybutton.css b/_static/copybutton.css
new file mode 100644
index 0000000..f1916ec
--- /dev/null
+++ b/_static/copybutton.css
@@ -0,0 +1,94 @@
+/* Copy buttons */
+button.copybtn {
+    position: absolute;
+    display: flex;
+    top: .3em;
+    right: .3em;
+    width: 1.7em;
+    height: 1.7em;
+	opacity: 0;
+    transition: opacity 0.3s, border .3s, background-color .3s;
+    user-select: none;
+    padding: 0;
+    border: none;
+    outline: none;
+    border-radius: 0.4em;
+    /* The colors that GitHub uses */
+    border: #1b1f2426 1px solid;
+    background-color: #f6f8fa;
+    color: #57606a;
+}
+
+button.copybtn.success {
+    border-color: #22863a;
+    color: #22863a;
+}
+
+button.copybtn svg {
+    stroke: currentColor;
+    width: 1.5em;
+    height: 1.5em;
+    padding: 0.1em;
+}
+
+div.highlight  {
+    position: relative;
+}
+
+/* Show the copybutton */
+.highlight:hover button.copybtn, button.copybtn.success {
+	opacity: 1;
+}
+
+.highlight button.copybtn:hover {
+    background-color: rgb(235, 235, 235);
+}
+
+.highlight button.copybtn:active {
+    background-color: rgb(187, 187, 187);
+}
+
+/**
+ * A minimal CSS-only tooltip copied from:
+ *   https://codepen.io/mildrenben/pen/rVBrpK
+ *
+ * To use, write HTML like the following:
+ *
+ * <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
+ */
+ .o-tooltip--left {
+  position: relative;
+ }
+
+ .o-tooltip--left:after {
+    opacity: 0;
+    visibility: hidden;
+    position: absolute;
+    content: attr(data-tooltip);
+    padding: .2em;
+    font-size: .8em;
+    left: -.2em;
+    background: grey;
+    color: white;
+    white-space: nowrap;
+    z-index: 2;
+    border-radius: 2px;
+    transform: translateX(-102%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+}
+
+.o-tooltip--left:hover:after {
+    display: block;
+    opacity: 1;
+    visibility: visible;
+    transform: translateX(-100%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+    transition-delay: .5s;
+}
+
+/* By default the copy button shouldn't show up when printing a page */
+@media print {
+    button.copybtn {
+        display: none;
+    }
+}
diff --git a/_static/copybutton.js b/_static/copybutton.js
new file mode 100644
index 0000000..2ea7ff3
--- /dev/null
+++ b/_static/copybutton.js
@@ -0,0 +1,248 @@
+// Localization support
+const messages = {
+  'en': {
+    'copy': 'Copy',
+    'copy_to_clipboard': 'Copy to clipboard',
+    'copy_success': 'Copied!',
+    'copy_failure': 'Failed to copy',
+  },
+  'es' : {
+    'copy': 'Copiar',
+    'copy_to_clipboard': 'Copiar al portapapeles',
+    'copy_success': '¡Copiado!',
+    'copy_failure': 'Error al copiar',
+  },
+  'de' : {
+    'copy': 'Kopieren',
+    'copy_to_clipboard': 'In die Zwischenablage kopieren',
+    'copy_success': 'Kopiert!',
+    'copy_failure': 'Fehler beim Kopieren',
+  },
+  'fr' : {
+    'copy': 'Copier',
+    'copy_to_clipboard': 'Copier dans le presse-papier',
+    'copy_success': 'Copié !',
+    'copy_failure': 'Échec de la copie',
+  },
+  'ru': {
+    'copy': 'Скопировать',
+    'copy_to_clipboard': 'Скопировать в буфер',
+    'copy_success': 'Скопировано!',
+    'copy_failure': 'Не удалось скопировать',
+  },
+  'zh-CN': {
+    'copy': '复制',
+    'copy_to_clipboard': '复制到剪贴板',
+    'copy_success': '复制成功!',
+    'copy_failure': '复制失败',
+  },
+  'it' : {
+    'copy': 'Copiare',
+    'copy_to_clipboard': 'Copiato negli appunti',
+    'copy_success': 'Copiato!',
+    'copy_failure': 'Errore durante la copia',
+  }
+}
+
+let locale = 'en'
+if( document.documentElement.lang !== undefined
+    && messages[document.documentElement.lang] !== undefined ) {
+  locale = document.documentElement.lang
+}
+
+let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
+if (doc_url_root == '#') {
+    doc_url_root = '';
+}
+
+/**
+ * SVG files for our copy buttons
+ */
+let iconCheck = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_success']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>`
+
+// If the user specified their own SVG use that, otherwise use the default
+let iconCopy = ``;
+if (!iconCopy) {
+  iconCopy = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_to_clipboard']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>`
+}
+
+/**
+ * Set up copy/paste for code blocks
+ */
+
+const runWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+
+const codeCellId = index => `codecell${index}`
+
+// Clears selected text since ClipboardJS will select the text when copying
+const clearSelection = () => {
+  if (window.getSelection) {
+    window.getSelection().removeAllRanges()
+  } else if (document.selection) {
+    document.selection.empty()
+  }
+}
+
+// Changes tooltip text for a moment, then changes it back
+// We want the timeout of our `success` class to be a bit shorter than the
+// tooltip and icon change, so that we can hide the icon before changing back.
+var timeoutIcon = 2000;
+var timeoutSuccessClass = 1500;
+
+const temporarilyChangeTooltip = (el, oldText, newText) => {
+  el.setAttribute('data-tooltip', newText)
+  el.classList.add('success')
+  // Remove success a little bit sooner than we change the tooltip
+  // So that we can use CSS to hide the copybutton first
+  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
+}
+
+// Changes the copy button icon for two seconds, then changes it back
+const temporarilyChangeIcon = (el) => {
+  el.innerHTML = iconCheck;
+  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
+}
+
+const addCopyButtonToCodeCells = () => {
+  // If ClipboardJS hasn't loaded, wait a bit and try again. This
+  // happens because we load ClipboardJS asynchronously.
+  if (window.ClipboardJS === undefined) {
+    setTimeout(addCopyButtonToCodeCells, 250)
+    return
+  }
+
+  // Add copybuttons to all of our code cells
+  const COPYBUTTON_SELECTOR = 'div.highlight pre';
+  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
+  codeCells.forEach((codeCell, index) => {
+    const id = codeCellId(index)
+    codeCell.setAttribute('id', id)
+
+    const clipboardButton = id =>
+    `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
+      ${iconCopy}
+    </button>`
+    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
+  })
+
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
+
+
+var copyTargetText = (trigger) => {
+  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
+
+  // get filtered text
+  let exclude = '.linenos';
+
+  let text = filterText(target, exclude);
+  return formatCopyText(text, '', false, true, true, true, '', '')
+}
+
+  // Initialize with a callback so we can modify the text before copy
+  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
+
+  // Update UI with error/success messages
+  clipboard.on('success', event => {
+    clearSelection()
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
+    temporarilyChangeIcon(event.trigger)
+  })
+
+  clipboard.on('error', event => {
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
+  })
+}
+
+runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/_static/copybutton_funcs.js b/_static/copybutton_funcs.js
new file mode 100644
index 0000000..dbe1aaa
--- /dev/null
+++ b/_static/copybutton_funcs.js
@@ -0,0 +1,73 @@
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+export function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
diff --git a/_static/css/badge_only.css b/_static/css/badge_only.css
new file mode 100644
index 0000000..88ba55b
--- /dev/null
+++ b/_static/css/badge_only.css
@@ -0,0 +1 @@
+.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions .rst-other-versions .rtd-current-item{font-weight:700}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}#flyout-search-form{padding:6px}
\ No newline at end of file
diff --git a/_static/css/fonts/Roboto-Slab-Bold.woff b/_static/css/fonts/Roboto-Slab-Bold.woff
new file mode 100644
index 0000000..6cb6000
Binary files /dev/null and b/_static/css/fonts/Roboto-Slab-Bold.woff differ
diff --git a/_static/css/fonts/Roboto-Slab-Bold.woff2 b/_static/css/fonts/Roboto-Slab-Bold.woff2
new file mode 100644
index 0000000..7059e23
Binary files /dev/null and b/_static/css/fonts/Roboto-Slab-Bold.woff2 differ
diff --git a/_static/css/fonts/Roboto-Slab-Regular.woff b/_static/css/fonts/Roboto-Slab-Regular.woff
new file mode 100644
index 0000000..f815f63
Binary files /dev/null and b/_static/css/fonts/Roboto-Slab-Regular.woff differ
diff --git a/_static/css/fonts/Roboto-Slab-Regular.woff2 b/_static/css/fonts/Roboto-Slab-Regular.woff2
new file mode 100644
index 0000000..f2c76e5
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diff --git a/_static/css/fonts/fontawesome-webfont.eot b/_static/css/fonts/fontawesome-webfont.eot
new file mode 100644
index 0000000..e9f60ca
Binary files /dev/null and b/_static/css/fonts/fontawesome-webfont.eot differ
diff --git a/_static/css/fonts/fontawesome-webfont.svg b/_static/css/fonts/fontawesome-webfont.svg
new file mode 100644
index 0000000..855c845
--- /dev/null
+++ b/_static/css/fonts/fontawesome-webfont.svg
@@ -0,0 +1,2671 @@
+<?xml version="1.0" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<svg>
+<metadata>
+Created by FontForge 20120731 at Mon Oct 24 17:37:40 2016
+ By ,,,
+Copyright Dave Gandy 2016. All rights reserved.
+</metadata>
+<defs>
+<font id="FontAwesome" horiz-adv-x="1536" >
+  <font-face 
+    font-family="FontAwesome"
+    font-weight="400"
+    font-stretch="normal"
+    units-per-em="1792"
+    panose-1="0 0 0 0 0 0 0 0 0 0"
+    ascent="1536"
+    descent="-256"
+    bbox="-1.02083 -256.962 2304.6 1537.02"
+    underline-thickness="0"
+    underline-position="0"
+    unicode-range="U+0020-F500"
+  />
+<missing-glyph horiz-adv-x="896" 
+d="M224 112h448v1312h-448v-1312zM112 0v1536h672v-1536h-672z" />
+    <glyph glyph-name=".notdef" horiz-adv-x="896" 
+d="M224 112h448v1312h-448v-1312zM112 0v1536h672v-1536h-672z" />
+    <glyph glyph-name=".null" horiz-adv-x="0" 
+ />
+    <glyph glyph-name="nonmarkingreturn" horiz-adv-x="597" 
+ />
+    <glyph glyph-name="space" unicode=" " horiz-adv-x="448" 
+ />
+    <glyph glyph-name="dieresis" unicode="&#xa8;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="copyright" unicode="&#xa9;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="registered" unicode="&#xae;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="acute" unicode="&#xb4;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="AE" unicode="&#xc6;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="Oslash" unicode="&#xd8;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="trademark" unicode="&#x2122;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="infinity" unicode="&#x221e;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="notequal" unicode="&#x2260;" horiz-adv-x="1792" 
+ />
+    <glyph glyph-name="glass" unicode="&#xf000;" horiz-adv-x="1792" 
+d="M1699 1350q0 -35 -43 -78l-632 -632v-768h320q26 0 45 -19t19 -45t-19 -45t-45 -19h-896q-26 0 -45 19t-19 45t19 45t45 19h320v768l-632 632q-43 43 -43 78q0 23 18 36.5t38 17.5t43 4h1408q23 0 43 -4t38 -17.5t18 -36.5z" />
+    <glyph glyph-name="music" unicode="&#xf001;" 
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\ No newline at end of file
diff --git a/_static/custom.js b/_static/custom.js
new file mode 100644
index 0000000..a0626c9
--- /dev/null
+++ b/_static/custom.js
@@ -0,0 +1,14 @@
+// File: custom.js
+
+/**
+ * This function will make sure all links in the document open as separate tabs
+ * instead of opening on the current user window.
+ */
+window.onload = function () {
+    let links = document.getElementsByTagName('a');
+    for (let l of links) {
+        if (l.classList.contains("reference") && l.classList.contains("external")) {
+            l.target = '_blank'; // Open links in a new tab
+        }
+    }
+};
diff --git a/_static/doctools.js b/_static/doctools.js
new file mode 100644
index 0000000..4d67807
--- /dev/null
+++ b/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
new file mode 100644
index 0000000..c1f8f23
--- /dev/null
+++ b/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '0.0.25',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/_static/feat-imp_meta-llama--Meta-Llama-3-70B-Instruct.png b/_static/feat-imp_meta-llama--Meta-Llama-3-70B-Instruct.png
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diff --git a/_static/fonts/Lato/lato-bold.eot b/_static/fonts/Lato/lato-bold.eot
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diff --git a/_static/fonts/Lato/lato-bolditalic.eot b/_static/fonts/Lato/lato-bolditalic.eot
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diff --git a/_static/fonts/Lato/lato-italic.eot b/_static/fonts/Lato/lato-italic.eot
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diff --git a/_static/fonts/Lato/lato-regular.eot b/_static/fonts/Lato/lato-regular.eot
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index 0000000..3bf9843
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diff --git a/_static/fonts/RobotoSlab/roboto-slab-v7-regular.eot b/_static/fonts/RobotoSlab/roboto-slab-v7-regular.eot
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e=n.closest("li");e.siblings("li.current").removeClass("current").attr("aria-expanded","false"),e.siblings().find("li.current").removeClass("current").attr("aria-expanded","false");var t=e.find("> ul li");t.length&&(t.removeClass("current").attr("aria-expanded","false"),e.toggleClass("current").attr("aria-expanded",(function(n,e){return"true"==e?"false":"true"})))}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:n.exports.ThemeNav,StickyNav:n.exports.ThemeNav}),function(){for(var n=0,e=["ms","moz","webkit","o"],t=0;t<e.length&&!window.requestAnimationFrame;++t)window.requestAnimationFrame=window[e[t]+"RequestAnimationFrame"],window.cancelAnimationFrame=window[e[t]+"CancelAnimationFrame"]||window[e[t]+"CancelRequestAnimationFrame"];window.requestAnimationFrame||(window.requestAnimationFrame=function(e,t){var i=(new Date).getTime(),o=Math.max(0,16-(i-n)),r=window.setTimeout((function(){e(i+o)}),o);return 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\ No newline at end of file
diff --git a/_static/js/versions.js b/_static/js/versions.js
new file mode 100644
index 0000000..4958195
--- /dev/null
+++ b/_static/js/versions.js
@@ -0,0 +1,228 @@
+const themeFlyoutDisplay = "hidden";
+const themeVersionSelector = true;
+const themeLanguageSelector = true;
+
+if (themeFlyoutDisplay === "attached") {
+  function renderLanguages(config) {
+    if (!config.projects.translations.length) {
+      return "";
+    }
+
+    // Insert the current language to the options on the selector
+    let languages = config.projects.translations.concat(config.projects.current);
+    languages = languages.sort((a, b) => a.language.name.localeCompare(b.language.name));
+
+    const languagesHTML = `
+      <dl>
+        <dt>Languages</dt>
+        ${languages
+          .map(
+            (translation) => `
+        <dd ${translation.slug == config.projects.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${translation.urls.documentation}">${translation.language.code}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return languagesHTML;
+  }
+
+  function renderVersions(config) {
+    if (!config.versions.active.length) {
+      return "";
+    }
+    const versionsHTML = `
+      <dl>
+        <dt>Versions</dt>
+        ${config.versions.active
+          .map(
+            (version) => `
+        <dd ${version.slug === config.versions.current.slug ? 'class="rtd-current-item"' : ""}>
+          <a href="${version.urls.documentation}">${version.slug}</a>
+        </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return versionsHTML;
+  }
+
+  function renderDownloads(config) {
+    if (!Object.keys(config.versions.current.downloads).length) {
+      return "";
+    }
+    const downloadsNameDisplay = {
+      pdf: "PDF",
+      epub: "Epub",
+      htmlzip: "HTML",
+    };
+
+    const downloadsHTML = `
+      <dl>
+        <dt>Downloads</dt>
+        ${Object.entries(config.versions.current.downloads)
+          .map(
+            ([name, url]) => `
+          <dd>
+            <a href="${url}">${downloadsNameDisplay[name]}</a>
+          </dd>
+        `,
+          )
+          .join("\n")}
+      </dl>
+    `;
+    return downloadsHTML;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const flyout = `
+      <div class="rst-versions" data-toggle="rst-versions" role="note">
+        <span class="rst-current-version" data-toggle="rst-current-version">
+          <span class="fa fa-book"> Read the Docs</span>
+          v: ${config.versions.current.slug}
+          <span class="fa fa-caret-down"></span>
+        </span>
+        <div class="rst-other-versions">
+          <div class="injected">
+            ${renderLanguages(config)}
+            ${renderVersions(config)}
+            ${renderDownloads(config)}
+            <dl>
+              <dt>On Read the Docs</dt>
+              <dd>
+                <a href="${config.projects.current.urls.home}">Project Home</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.builds}">Builds</a>
+              </dd>
+              <dd>
+                <a href="${config.projects.current.urls.downloads}">Downloads</a>
+              </dd>
+            </dl>
+            <dl>
+              <dt>Search</dt>
+              <dd>
+                <form id="flyout-search-form">
+                  <input
+                    class="wy-form"
+                    type="text"
+                    name="q"
+                    aria-label="Search docs"
+                    placeholder="Search docs"
+                    />
+                </form>
+              </dd>
+            </dl>
+            <hr />
+            <small>
+              <span>Hosted by <a href="https://about.readthedocs.org/?utm_source=&utm_content=flyout">Read the Docs</a></span>
+            </small>
+          </div>
+        </div>
+    `;
+
+    // Inject the generated flyout into the body HTML element.
+    document.body.insertAdjacentHTML("beforeend", flyout);
+
+    // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
+    document
+      .querySelector("#flyout-search-form")
+      .addEventListener("focusin", () => {
+        const event = new CustomEvent("readthedocs-search-show");
+        document.dispatchEvent(event);
+      });
+  })
+}
+
+if (themeLanguageSelector || themeVersionSelector) {
+  function onSelectorSwitch(event) {
+    const option = event.target.selectedIndex;
+    const item = event.target.options[option];
+    window.location.href = item.dataset.url;
+  }
+
+  document.addEventListener("readthedocs-addons-data-ready", function (event) {
+    const config = event.detail.data();
+
+    const versionSwitch = document.querySelector(
+      "div.switch-menus > div.version-switch",
+    );
+    if (themeVersionSelector) {
+      let versions = config.versions.active;
+      if (config.versions.current.hidden || config.versions.current.type === "external") {
+        versions.unshift(config.versions.current);
+      }
+      const versionSelect = `
+    <select>
+      ${versions
+        .map(
+          (version) => `
+        <option
+  value="${version.slug}"
+  ${config.versions.current.slug === version.slug ? 'selected="selected"' : ""}
+              data-url="${version.urls.documentation}">
+              ${version.slug}
+          </option>`,
+        )
+        .join("\n")}
+    </select>
+  `;
+
+      versionSwitch.innerHTML = versionSelect;
+      versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+    }
+
+    const languageSwitch = document.querySelector(
+      "div.switch-menus > div.language-switch",
+    );
+
+    if (themeLanguageSelector) {
+      if (config.projects.translations.length) {
+        // Add the current language to the options on the selector
+        let languages = config.projects.translations.concat(
+          config.projects.current,
+        );
+        languages = languages.sort((a, b) =>
+          a.language.name.localeCompare(b.language.name),
+        );
+
+        const languageSelect = `
+      <select>
+        ${languages
+          .map(
+            (language) => `
+              <option
+                  value="${language.language.code}"
+                  ${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
+                  data-url="${language.urls.documentation}">
+                  ${language.language.name}
+              </option>`,
+          )
+          .join("\n")}
+       </select>
+    `;
+
+        languageSwitch.innerHTML = languageSelect;
+        languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+      }
+      else {
+        languageSwitch.remove();
+      }
+    }
+  });
+}
+
+document.addEventListener("readthedocs-addons-data-ready", function (event) {
+  // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
+  document
+    .querySelector("[role='search'] input")
+    .addEventListener("focusin", () => {
+      const event = new CustomEvent("readthedocs-search-show");
+      document.dispatchEvent(event);
+    });
+});
\ No newline at end of file
diff --git a/_static/jupyterlite_badge_logo.svg b/_static/jupyterlite_badge_logo.svg
new file mode 100644
index 0000000..5de36d7
--- /dev/null
+++ b/_static/jupyterlite_badge_logo.svg
@@ -0,0 +1,3 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg width="91px" height="20px" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" xmlns:serif="http://www.serif.com/" style="fill-rule:evenodd;clip-rule:evenodd;stroke-linejoin:round;stroke-miterlimit:2;"><g transform="matrix(1,0,0,1,-17102,-9779)"><g id="lite-badge-launch" transform="matrix(0.0930728,0,0,0.0635416,15477.2,9173.48)"><rect x="17457.1" y="9529.53" width="977.729" height="314.755" style="fill:none;"/><clipPath id="_clip1"><rect x="17457.1" y="9529.53" width="977.729" height="314.755"/></clipPath><g clip-path="url(#_clip1)"><g transform="matrix(1.03361,0,0,1,-586.737,-1.01863e-10)"><g transform="matrix(2.43224,0,0,2.42119,-20084.1,-14912.4)"><path d="M15802.4,10095L15447.7,10095C15440.6,10095 15434.8,10103.8 15434.8,10114.5L15434.8,10205.5C15434.8,10216.3 15440.6,10225 15447.7,10225L15802.4,10225L15802.4,10095Z" style="fill:rgb(85,85,85);"/></g><g transform="matrix(-0.76361,0,0,2.42119,30189.2,-14912.4)"><path 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diff --git a/_static/language_data.js b/_static/language_data.js
new file mode 100644
index 0000000..367b8ed
--- /dev/null
+++ b/_static/language_data.js
@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, if available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/_static/minus.png b/_static/minus.png
new file mode 100644
index 0000000..d96755f
Binary files /dev/null and b/_static/minus.png differ
diff --git a/_static/nbsphinx-broken-thumbnail.svg b/_static/nbsphinx-broken-thumbnail.svg
new file mode 100644
index 0000000..4919ca8
--- /dev/null
+++ b/_static/nbsphinx-broken-thumbnail.svg
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+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: lightcoral; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: crimson; }
+    }
+  </style>
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diff --git a/_static/nbsphinx-code-cells.css b/_static/nbsphinx-code-cells.css
new file mode 100644
index 0000000..a3fb27c
--- /dev/null
+++ b/_static/nbsphinx-code-cells.css
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+/* remove conflicting styling from Sphinx themes */
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt *,
+div.nbinput.container div.input_area pre,
+div.nboutput.container div.output_area pre,
+div.nbinput.container div.input_area .highlight,
+div.nboutput.container div.output_area .highlight {
+    border: none;
+    padding: 0;
+    margin: 0;
+    box-shadow: none;
+}
+
+div.nbinput.container > div[class*=highlight],
+div.nboutput.container > div[class*=highlight] {
+    margin: 0;
+}
+
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt * {
+    background: none;
+}
+
+div.nboutput.container div.output_area .highlight,
+div.nboutput.container div.output_area pre {
+    background: unset;
+}
+
+div.nboutput.container div.output_area div.highlight {
+    color: unset;  /* override Pygments text color */
+}
+
+/* avoid gaps between output lines */
+div.nboutput.container div[class*=highlight] pre {
+    line-height: normal;
+}
+
+/* input/output containers */
+div.nbinput.container,
+div.nboutput.container {
+    display: -webkit-flex;
+    display: flex;
+    align-items: flex-start;
+    margin: 0;
+    width: 100%;
+}
+@media (max-width: 540px) {
+    div.nbinput.container,
+    div.nboutput.container {
+        flex-direction: column;
+    }
+}
+
+/* input container */
+div.nbinput.container {
+    padding-top: 5px;
+}
+
+/* last container */
+div.nblast.container {
+    padding-bottom: 5px;
+}
+
+/* input prompt */
+div.nbinput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nbinput.container div.prompt pre > code {
+    color: #307FC1;
+}
+
+/* output prompt */
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
+    color: #BF5B3D;
+}
+
+/* all prompts */
+div.nbinput.container div.prompt,
+div.nboutput.container div.prompt {
+    width: 4.5ex;
+    padding-top: 5px;
+    position: relative;
+    user-select: none;
+}
+
+div.nbinput.container div.prompt > div,
+div.nboutput.container div.prompt > div {
+    position: absolute;
+    right: 0;
+    margin-right: 0.3ex;
+}
+
+@media (max-width: 540px) {
+    div.nbinput.container div.prompt,
+    div.nboutput.container div.prompt {
+        width: unset;
+        text-align: left;
+        padding: 0.4em;
+    }
+    div.nboutput.container div.prompt.empty {
+        padding: 0;
+    }
+
+    div.nbinput.container div.prompt > div,
+    div.nboutput.container div.prompt > div {
+        position: unset;
+    }
+}
+
+/* disable scrollbars and line breaks on prompts */
+div.nbinput.container div.prompt pre,
+div.nboutput.container div.prompt pre {
+    overflow: hidden;
+    white-space: pre;
+}
+
+/* input/output area */
+div.nbinput.container div.input_area,
+div.nboutput.container div.output_area {
+    -webkit-flex: 1;
+    flex: 1;
+    overflow: auto;
+}
+@media (max-width: 540px) {
+    div.nbinput.container div.input_area,
+    div.nboutput.container div.output_area {
+        width: 100%;
+    }
+}
+
+/* input area */
+div.nbinput.container div.input_area {
+    border: 1px solid #e0e0e0;
+    border-radius: 2px;
+    /*background: #f5f5f5;*/
+}
+
+/* override MathJax center alignment in output cells */
+div.nboutput.container div[class*=MathJax] {
+    text-align: left !important;
+}
+
+/* override sphinx.ext.imgmath center alignment in output cells */
+div.nboutput.container div.math p {
+    text-align: left;
+}
+
+/* standard error */
+div.nboutput.container div.output_area.stderr {
+    background: #fdd;
+}
+
+/* ANSI colors */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
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+.ansi-magenta-intense-fg { color: #A03196; }
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+.ansi-cyan-fg { color: #60C6C8; }
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+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-default-inverse-fg { color: #FFFFFF; }
+.ansi-default-inverse-bg { background-color: #000000; }
+
+.ansi-bold { font-weight: bold; }
+.ansi-underline { text-decoration: underline; }
+
+
+div.nbinput.container div.input_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
+div.nboutput.container div.output_area:not(.rendered_html) > img{
+    padding: 5px;
+    margin: 0;
+}
+
+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
+}
+
+/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
+.prompt .copybtn,
+/* ... and 'sphinx_immaterial' theme */
+.prompt .md-clipboard.md-icon {
+    display: none;
+}
+
+/* Some additional styling taken form the Jupyter notebook CSS */
+.jp-RenderedHTMLCommon table,
+div.rendered_html table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+.jp-RenderedHTMLCommon thead,
+div.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon td,
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.jp-RenderedHTMLCommon th,
+div.rendered_html th {
+  font-weight: bold;
+}
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
+div.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+.jp-RenderedHTMLCommon tbody tr:hover,
+div.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+
diff --git a/_static/nbsphinx-gallery.css b/_static/nbsphinx-gallery.css
new file mode 100644
index 0000000..365c27a
--- /dev/null
+++ b/_static/nbsphinx-gallery.css
@@ -0,0 +1,31 @@
+.nbsphinx-gallery {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+    gap: 5px;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.nbsphinx-gallery > a {
+    padding: 5px;
+    border: 1px dotted currentColor;
+    border-radius: 2px;
+    text-align: center;
+}
+
+.nbsphinx-gallery > a:hover {
+    border-style: solid;
+}
+
+.nbsphinx-gallery img {
+    max-width: 100%;
+    max-height: 100%;
+}
+
+.nbsphinx-gallery > a > div:first-child {
+    display: flex;
+    align-items: start;
+    justify-content: center;
+    height: 120px;
+    margin-bottom: 5px;
+}
diff --git a/_static/nbsphinx-no-thumbnail.svg b/_static/nbsphinx-no-thumbnail.svg
new file mode 100644
index 0000000..9dca758
--- /dev/null
+++ b/_static/nbsphinx-no-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: #ccc; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: #999; }
+    }
+  </style>
+  <path d="M 65.365328,74.550032 H 67.52154 V 88.67217 h 7.760238 v 1.814634 h -9.91645 z m -6.447298,0 h 2.156214 V 90.486804 H 58.91803 Z m -9.955303,2.124192 -2.924765,7.931025 h 5.860208 z m -1.216872,-2.124192 h 2.444423 l 6.073692,15.936772 h -2.241606 l -1.451709,-4.088268 h -7.183824 l -1.451707,4.088268 H 41.66149 Z m -21.264514,0 h 2.903417 L 36.45116,87.882271 V 74.550032 h 2.092171 V 90.486804 H 35.639914 L 28.573506,77.154569 v 13.332235 h -2.092165 z m 53.454813,-9.781663 v 5.838858 h 3.458479 q 1.739919,0 2.572513,-0.71518 0.843272,-0.725854 0.843272,-2.209586 0,-1.494406 -0.843272,-2.198912 -0.832594,-0.71518 -2.572513,-0.71518 z m 0,-6.554037 v 4.803447 h 3.191622 q 1.579803,0 2.348355,-0.587087 0.779225,-0.597763 0.779225,-1.814636 0,-1.206199 -0.779225,-1.803962 -0.768552,-0.597762 -2.348355,-0.597762 z m -2.156212,-1.771939 h 5.507949 q 2.465772,0 3.800065,1.024736 1.334288,1.024735 1.334288,2.914092 0,1.462383 -0.683157,2.327003 -0.683157,0.864621 -2.006771,1.078107 1.590471,0.341579 2.46577,1.43036 0.885967,1.078107 0.885967,2.700605 0,2.134866 -1.451708,3.298368 -1.451709,1.163502 -4.130963,1.163502 h -5.72144 z m -18.86154,0 h 3.212969 l 4.066921,10.845118 4.088269,-10.845118 H 73.49953 V 72.379166 H 71.396692 V 58.385121 L 67.287075,69.315634 H 65.120183 L 61.010567,58.385121 v 13.994045 h -2.092165 z m -16.246333,0 h 2.166892 v 9.681616 q 0,2.561839 0.928668,3.693318 0.928667,1.120804 3.010159,1.120804 2.070819,0 2.999486,-1.120804 0.928667,-1.131479 0.928667,-3.693318 v -9.681616 h 2.166886 v 9.948474 q 0,3.116904 -1.547776,4.70738 -1.537104,1.590474 -4.547263,1.590474 -3.020833,0 -4.568615,-1.590474 -1.537104,-1.590476 -1.537104,-4.70738 z m -16.192953,0 h 2.156213 v 6.532689 h 7.83496 v -6.532689 h 2.156213 v 15.936773 h -2.156213 v -7.589448 h -7.83496 v 7.589448 h -2.156213 z m -15.563168,0 h 13.481671 v 1.814636 H 18.740226 V 72.379166 H 16.573339 V 58.257029 H 10.915948 Z M 65.497127,39.809967 q -2.34835,0 -3.736011,1.750588 -1.376987,1.750588 -1.376987,4.771419 0,3.010157 1.376987,4.760745 1.387661,1.750588 3.736011,1.750588 2.348349,0 3.714662,-1.750588 1.376986,-1.750588 1.376986,-4.760745 0,-3.020831 -1.376986,-4.771419 -1.366313,-1.750588 -3.714662,-1.750588 z m 0,-1.750588 q 3.351735,0 5.358506,2.252281 2.006772,2.241606 2.006772,6.020314 0,3.768034 -2.006772,6.020315 -2.006771,2.241606 -5.358506,2.241606 -3.36241,0 -5.379856,-2.241606 -2.006771,-2.241607 -2.006771,-6.020315 0,-3.778708 2.006771,-6.020314 2.017446,-2.252281 5.379856,-2.252281 z m -22.821688,0.288206 h 2.903414 L 52.64525,51.679807 V 38.347585 h 2.092166 V 54.28434 H 51.834001 L 44.767605,40.952119 V 54.28434 h -2.092166 z" />
+</svg>
diff --git a/_static/no_image.png b/_static/no_image.png
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index 0000000..8c2d48d
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diff --git a/_static/plus.png b/_static/plus.png
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diff --git a/_static/pygments.css b/_static/pygments.css
new file mode 100644
index 0000000..84ab303
--- /dev/null
+++ b/_static/pygments.css
@@ -0,0 +1,75 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #9C6500 } /* Comment.Preproc */
+.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #E40000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #008400 } /* Generic.Inserted */
+.highlight .go { color: #717171 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #687822 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #767600 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
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+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
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+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
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diff --git a/_static/searchtools.js b/_static/searchtools.js
new file mode 100644
index 0000000..b08d58c
--- /dev/null
+++ b/_static/searchtools.js
@@ -0,0 +1,620 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms, anchor)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      "Search finished, found ${resultCount} page(s) matching the search query."
+    ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString, anchor) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    for (const removalQuery of [".headerlink", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent) return docContent.textContent;
+
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  _parseQuery: (query) => {
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          const score = Math.round(Scorer.title * queryLower.length / title.length);
+          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
+          normalResults.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score + boost,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/_static/sg_gallery-binder.css b/_static/sg_gallery-binder.css
new file mode 100644
index 0000000..420005d
--- /dev/null
+++ b/_static/sg_gallery-binder.css
@@ -0,0 +1,11 @@
+/* CSS for binder integration */
+
+div.binder-badge {
+  margin: 1em auto;
+  vertical-align: middle;
+}
+
+div.lite-badge {
+  margin: 1em auto;
+  vertical-align: middle;
+}
diff --git a/_static/sg_gallery-dataframe.css b/_static/sg_gallery-dataframe.css
new file mode 100644
index 0000000..fac74c4
--- /dev/null
+++ b/_static/sg_gallery-dataframe.css
@@ -0,0 +1,47 @@
+/* Pandas dataframe css */
+/* Taken from: https://github.com/spatialaudio/nbsphinx/blob/fb3ba670fc1ba5f54d4c487573dbc1b4ecf7e9ff/src/nbsphinx.py#L587-L619 */
+html[data-theme="light"] {
+  --sg-text-color: #000;
+  --sg-tr-odd-color: #f5f5f5;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+}
+html[data-theme="dark"] {
+  --sg-text-color: #fff;
+  --sg-tr-odd-color: #373737;
+  --sg-tr-hover-color: rgba(30, 81, 122, 0.2);
+}
+
+table.dataframe {
+  border: none !important;
+  border-collapse: collapse;
+  border-spacing: 0;
+  border-color: transparent;
+  color: var(--sg-text-color);
+  font-size: 12px;
+  table-layout: fixed;
+  width: auto;
+}
+table.dataframe thead {
+  border-bottom: 1px solid var(--sg-text-color);
+  vertical-align: bottom;
+}
+table.dataframe tr,
+table.dataframe th,
+table.dataframe td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+table.dataframe th {
+  font-weight: bold;
+}
+table.dataframe tbody tr:nth-child(odd) {
+  background: var(--sg-tr-odd-color);
+}
+table.dataframe tbody tr:hover {
+  background: var(--sg-tr-hover-color);
+}
diff --git a/_static/sg_gallery-rendered-html.css b/_static/sg_gallery-rendered-html.css
new file mode 100644
index 0000000..93dc2ff
--- /dev/null
+++ b/_static/sg_gallery-rendered-html.css
@@ -0,0 +1,224 @@
+/* Adapted from notebook/static/style/style.min.css */
+html[data-theme="light"] {
+  --sg-text-color: #000;
+  --sg-background-color: #ffffff;
+  --sg-code-background-color: #eff0f1;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+  --sg-tr-odd-color: #f5f5f5;
+}
+html[data-theme="dark"] {
+  --sg-text-color: #fff;
+  --sg-background-color: #121212;
+  --sg-code-background-color: #2f2f30;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+  --sg-tr-odd-color: #1f1f1f;
+}
+
+.rendered_html {
+  color: var(--sg-text-color);
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul:not(.list-inline),
+.rendered_html ol:not(.list-inline) {
+  padding-left: 2em;
+}
+.rendered_html ul {
+  list-style: disc;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin-top: 0;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+}
+.rendered_html ol {
+  list-style: decimal;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin-top: 0;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: var(--sg-text-color);
+  background-color: var(--sg-text-color);
+}
+.rendered_html pre {
+  margin: 1em 2em;
+  padding: 0px;
+  background-color: var(--sg-background-color);
+}
+.rendered_html code {
+  background-color: var(--sg-code-background-color);
+}
+.rendered_html p code {
+  padding: 1px 5px;
+}
+.rendered_html pre code {
+  background-color: var(--sg-background-color);
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  color: var(--sg-text-color);
+  font-size: 100%;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: var(--sg-text-color);
+  font-size: 12px;
+  table-layout: fixed;
+}
+.rendered_html thead {
+  border-bottom: 1px solid var(--sg-text-color);
+  vertical-align: bottom;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html tbody tr:nth-child(odd) {
+  background: var(--sg-tr-odd-color);
+}
+.rendered_html tbody tr:hover {
+  color: var(--sg-text-color);
+  background: var(--sg-tr-hover-color);
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+.rendered_html .alert {
+  margin-bottom: initial;
+}
+.rendered_html * + .alert {
+  margin-top: 1em;
+}
+[dir="rtl"] .rendered_html p {
+  text-align: right;
+}
diff --git a/_static/sg_gallery.css b/_static/sg_gallery.css
new file mode 100644
index 0000000..9bcd33c
--- /dev/null
+++ b/_static/sg_gallery.css
@@ -0,0 +1,367 @@
+/*
+Sphinx-Gallery has compatible CSS to fix default sphinx themes
+Tested for Sphinx 1.3.1 for all themes: default, alabaster, sphinxdoc,
+scrolls, agogo, traditional, nature, haiku, pyramid
+Tested for Read the Docs theme 0.1.7 */
+
+/* Define light colors */
+:root, html[data-theme="light"], body[data-theme="light"]{
+  --sg-tooltip-foreground: black;
+  --sg-tooltip-background: rgba(250, 250, 250, 0.9);
+  --sg-tooltip-border: #ccc transparent;
+  --sg-thumb-box-shadow-color: #6c757d40;
+  --sg-thumb-hover-border: #0069d9;
+  --sg-script-out: #888;
+  --sg-script-pre: #fafae2;
+  --sg-pytb-foreground: #000;
+  --sg-pytb-background: #ffe4e4;
+  --sg-pytb-border-color: #f66;
+  --sg-download-a-background-color: #ffc;
+  --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e);
+  --sg-download-a-border-color: 1px solid #c2c22d;
+  --sg-download-a-color: #000;
+  --sg-download-a-hover-background-color: #d5d57e;
+  --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
+  --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
+}
+@media(prefers-color-scheme: light) {
+  :root[data-theme="auto"], html[data-theme="auto"], body[data-theme="auto"] {
+    --sg-tooltip-foreground: black;
+    --sg-tooltip-background: rgba(250, 250, 250, 0.9);
+    --sg-tooltip-border: #ccc transparent;
+    --sg-thumb-box-shadow-color: #6c757d40;
+    --sg-thumb-hover-border: #0069d9;
+    --sg-script-out: #888;
+    --sg-script-pre: #fafae2;
+    --sg-pytb-foreground: #000;
+    --sg-pytb-background: #ffe4e4;
+    --sg-pytb-border-color: #f66;
+    --sg-download-a-background-color: #ffc;
+    --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e);
+    --sg-download-a-border-color: 1px solid #c2c22d;
+    --sg-download-a-color: #000;
+    --sg-download-a-hover-background-color: #d5d57e;
+    --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
+    --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
+  }
+}
+
+html[data-theme="dark"], body[data-theme="dark"] {
+  --sg-tooltip-foreground: white;
+  --sg-tooltip-background: rgba(10, 10, 10, 0.9);
+  --sg-tooltip-border: #333 transparent;
+  --sg-thumb-box-shadow-color: #79848d40;
+  --sg-thumb-hover-border: #003975;
+  --sg-script-out: rgb(179, 179, 179);
+  --sg-script-pre: #2e2e22;
+  --sg-pytb-foreground: #fff;
+  --sg-pytb-background: #1b1717;
+  --sg-pytb-border-color: #622;
+  --sg-download-a-background-color: #443;
+  --sg-download-a-background-image: linear-gradient(to bottom, #443, #221);
+  --sg-download-a-border-color: 1px solid #3a3a0d;
+  --sg-download-a-color: #fff;
+  --sg-download-a-hover-background-color: #616135;
+  --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1);
+  --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25);
+}
+@media(prefers-color-scheme: dark){
+  html[data-theme="auto"], body[data-theme="auto"] {
+    --sg-tooltip-foreground: white;
+    --sg-tooltip-background: rgba(10, 10, 10, 0.9);
+    --sg-tooltip-border: #333 transparent;
+    --sg-thumb-box-shadow-color: #79848d40;
+    --sg-thumb-hover-border: #003975;
+    --sg-script-out: rgb(179, 179, 179);
+    --sg-script-pre: #2e2e22;
+    --sg-pytb-foreground: #fff;
+    --sg-pytb-background: #1b1717;
+    --sg-pytb-border-color: #622;
+    --sg-download-a-background-color: #443;
+    --sg-download-a-background-image: linear-gradient(to bottom, #443, #221);
+    --sg-download-a-border-color: 1px solid #3a3a0d;
+    --sg-download-a-color: #fff;
+    --sg-download-a-hover-background-color: #616135;
+    --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1);
+    --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25);
+  }
+}
+
+.sphx-glr-thumbnails {
+  width: 100%;
+  margin: 0px 0px 20px 0px;
+
+  /* align thumbnails on a grid */
+  justify-content: space-between;
+  display: grid;
+  /* each grid column should be at least 160px (this will determine
+  the actual number of columns) and then take as much of the
+  remaining width as possible */
+  grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+  gap: 15px;
+}
+.sphx-glr-thumbnails .toctree-wrapper {
+  /* hide empty toctree divs added to the DOM
+  by sphinx even though the toctree is hidden
+  (they would fill grid places with empty divs) */
+  display: none;
+}
+.sphx-glr-thumbcontainer {
+  background: transparent;
+  -moz-border-radius: 5px;
+  -webkit-border-radius: 5px;
+  border-radius: 5px;
+  box-shadow: 0 0 10px var(--sg-thumb-box-shadow-color);
+
+  /* useful to absolutely position link in div */
+  position: relative;
+
+  /* thumbnail width should include padding and borders
+  and take all available space */
+  box-sizing: border-box;
+  width: 100%;
+  padding: 10px;
+  border: 1px solid transparent;
+
+  /* align content in thumbnail */
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 7px;
+}
+.sphx-glr-thumbcontainer p {
+  position: absolute;
+  top: 0;
+  left: 0;
+}
+.sphx-glr-thumbcontainer p,
+.sphx-glr-thumbcontainer p a {
+  /* link should cover the whole thumbnail div */
+  width: 100%;
+  height: 100%;
+}
+.sphx-glr-thumbcontainer p a span {
+  /* text within link should be masked
+  (we are just interested in the href) */
+  display: none;
+}
+.sphx-glr-thumbcontainer:hover {
+  border: 1px solid;
+  border-color: var(--sg-thumb-hover-border);
+  cursor: pointer;
+}
+.sphx-glr-thumbcontainer a.internal {
+  bottom: 0;
+  display: block;
+  left: 0;
+  box-sizing: border-box;
+  padding: 150px 10px 0;
+  position: absolute;
+  right: 0;
+  top: 0;
+}
+/* Next one is to avoid Sphinx traditional theme to cover all the
+thumbnail with its default link Background color */
+.sphx-glr-thumbcontainer a.internal:hover {
+  background-color: transparent;
+}
+
+.sphx-glr-thumbcontainer p {
+  margin: 0 0 0.1em 0;
+}
+.sphx-glr-thumbcontainer .figure {
+  margin: 10px;
+  width: 160px;
+}
+.sphx-glr-thumbcontainer img {
+  display: inline;
+  max-height: 112px;
+  max-width: 160px;
+}
+
+.sphx-glr-thumbcontainer[tooltip]::before {
+  content: "";
+  position: absolute;
+  pointer-events: none;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  z-index: 97;
+  background-color: var(--sg-tooltip-background);
+  backdrop-filter: blur(3px);
+  opacity: 0;
+  transition: opacity 0.3s;
+}
+
+.sphx-glr-thumbcontainer[tooltip]:hover::before {
+  opacity: 1;
+}
+
+.sphx-glr-thumbcontainer[tooltip]:hover::after {
+  -webkit-border-radius: 4px;
+  -moz-border-radius: 4px;
+  border-radius: 4px;
+  color: var(--sg-tooltip-foreground);
+  content: attr(tooltip);
+  padding: 10px 10px 5px;
+  z-index: 98;
+  width: 100%;
+  max-height: 100%;
+  position: absolute;
+  pointer-events: none;
+  top: 0;
+  box-sizing: border-box;
+  overflow: hidden;
+  display: -webkit-box;
+  -webkit-box-orient: vertical;
+  -webkit-line-clamp: 6;
+}
+
+.sphx-glr-script-out {
+  color: var(--sg-script-out);
+  display: flex;
+  gap: 0.5em;
+}
+.sphx-glr-script-out::before {
+  content: "Out:";
+  /* These numbers come from the pre style in the pydata sphinx theme. This
+   * turns out to match perfectly on the rtd theme, but be a bit too low for
+   * the pydata sphinx theme. As I could not find a dimension to use that was
+   * scaled the same way, I just picked one option that worked pretty close for
+   * both. */
+  line-height: 1.4;
+  padding-top: 10px;
+}
+.sphx-glr-script-out .highlight {
+  background-color: transparent;
+  /* These options make the div expand... */
+  flex-grow: 1;
+  /* ... but also keep it from overflowing its flex container. */
+  overflow: auto;
+}
+.sphx-glr-script-out .highlight pre {
+  background-color: var(--sg-script-pre);
+  border: 0;
+  max-height: 30em;
+  overflow: auto;
+  padding-left: 1ex;
+  /* This margin is necessary in the pydata sphinx theme because pre has a box
+   * shadow which would be clipped by the overflow:auto in the parent div
+   * above. */
+  margin: 2px;
+  word-break: break-word;
+}
+.sphx-glr-script-out + p {
+  margin-top: 1.8em;
+}
+blockquote.sphx-glr-script-out {
+  margin-left: 0pt;
+}
+.sphx-glr-script-out.highlight-pytb .highlight pre {
+  color: var(--sg-pytb-foreground);
+  background-color: var(--sg-pytb-background);
+  border: 1px solid var(--sg-pytb-border-color);
+  margin-top: 10px;
+  padding: 7px;
+}
+
+div.sphx-glr-footer {
+  text-align: center;
+}
+
+div.sphx-glr-download {
+  margin: 1em auto;
+  vertical-align: middle;
+}
+
+div.sphx-glr-download a {
+  background-color: var(--sg-download-a-background-color);
+  background-image: var(--sg-download-a-background-image);
+  border-radius: 4px;
+  border: 1px solid var(--sg-download-a-border-color);
+  color: var(--sg-download-a-color);
+  display: inline-block;
+  font-weight: bold;
+  padding: 1ex;
+  text-align: center;
+}
+
+div.sphx-glr-download code.download {
+  display: inline-block;
+  white-space: normal;
+  word-break: normal;
+  overflow-wrap: break-word;
+  /* border and background are given by the enclosing 'a' */
+  border: none;
+  background: none;
+}
+
+div.sphx-glr-download a:hover {
+  box-shadow: inset 0 1px 0 var(--sg-download-a-hover-box-shadow-1), 0 1px 5px var(--sg-download-a-hover-box-shadow-2);
+  text-decoration: none;
+  background-image: none;
+  background-color: var(--sg-download-a-hover-background-color);
+}
+
+div.sphx-glr-sidebar-item img {
+  max-height: 20px;
+}
+
+.sphx-glr-example-title:target::before {
+  display: block;
+  content: "";
+  margin-top: -50px;
+  height: 50px;
+  visibility: hidden;
+}
+
+ul.sphx-glr-horizontal {
+  list-style: none;
+  padding: 0;
+}
+ul.sphx-glr-horizontal li {
+  display: inline;
+}
+ul.sphx-glr-horizontal img {
+  height: auto !important;
+}
+
+.sphx-glr-single-img {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+
+.sphx-glr-multi-img {
+  max-width: 42%;
+  height: auto;
+}
+
+div.sphx-glr-animation {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+div.sphx-glr-animation .animation {
+  display: block;
+}
+
+p.sphx-glr-signature a.reference.external {
+  -moz-border-radius: 5px;
+  -webkit-border-radius: 5px;
+  border-radius: 5px;
+  padding: 3px;
+  font-size: 75%;
+  text-align: right;
+  margin-left: auto;
+  display: table;
+}
+
+.sphx-glr-clear {
+  clear: both;
+}
+
+a.sphx-glr-backref-instance {
+  text-decoration: none;
+}
diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js
new file mode 100644
index 0000000..8a96c69
--- /dev/null
+++ b/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/genindex.html b/genindex.html
new file mode 100644
index 0000000..c801754
--- /dev/null
+++ b/genindex.html
@@ -0,0 +1,992 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Index &mdash; folktexts 0.0.25 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css?v=d2d258e8" />
+
+  
+      <script src="_static/jquery.js?v=5d32c60e"></script>
+      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="_static/documentation_options.js?v=5d650c58"></script>
+      <script src="_static/doctools.js?v=9a2dae69"></script>
+      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script src="_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="_static/copybutton.js?v=f281be69"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script src="_static/custom.js?v=882eb7ae"></script>
+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="#" />
+    <link rel="search" title="Search" href="search.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            folktexts
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul>
+<li class="toctree-l1"><a class="reference internal" href="readme.html">Readme file</a></li>
+<li class="toctree-l1"><a class="reference internal" href="source/modules.html">API reference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks.html">Example notebooks</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">folktexts</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Index</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+
+<h1 id="index">Index</h1>
+
+<div class="genindex-jumpbox">
+ <a href="#A"><strong>A</strong></a>
+ | <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#J"><strong>J</strong></a>
+ | <a href="#K"><strong>K</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#O"><strong>O</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#Q"><strong>Q</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#U"><strong>U</strong></a>
+ | <a href="#V"><strong>V</strong></a>
+ | <a href="#W"><strong>W</strong></a>
+ 
+</div>
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark.ACS_DATASET_CONFIGS">ACS_DATASET_CONFIGS (folktexts.benchmark.Benchmark attribute)</a>
+</li>
+      <li><a href="source/folktexts.acs.html#folktexts.acs.acs_dataset.ACSDataset">ACSDataset (class in folktexts.acs.acs_dataset)</a>
+</li>
+      <li><a href="source/folktexts.acs.html#folktexts.acs.acs_tasks.ACSTaskMetadata">ACSTaskMetadata (class in folktexts.acs.acs_tasks)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.llm_utils.add_pad_token">add_pad_token() (in module folktexts.llm_utils)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.answer_keys">answer_keys (folktexts.qa_interface.MultipleChoiceQA property)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.DirectNumericQA.answer_probability">answer_probability (folktexts.qa_interface.DirectNumericQA attribute)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.prompting.apply_chat_template">apply_chat_template() (in module folktexts.prompting)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.threshold.Threshold.apply_to_column_data">apply_to_column_data() (folktexts.threshold.Threshold method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.threshold.Threshold.apply_to_column_name">apply_to_column_name() (folktexts.threshold.Threshold method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.batch_size">batch_size (folktexts.benchmark.BenchmarkConfig attribute)</a>, <a href="source/folktexts.html#id0">[1]</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark">Benchmark (class in folktexts.benchmark)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig">BenchmarkConfig (class in folktexts.benchmark)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.evaluation.bootstrap_estimate">bootstrap_estimate() (in module folktexts.evaluation)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.check_task_columns_are_available">check_task_columns_are_available() (folktexts.task.TaskMetadata method)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.check_webAPI_deps">check_webAPI_deps() (folktexts.classifier.web_api_classifier.WebAPILLMClassifier static method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.Choice">Choice (class in folktexts.qa_interface)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.choice_to_key">choice_to_key (folktexts.qa_interface.MultipleChoiceQA property)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.choices">choices (folktexts.qa_interface.MultipleChoiceQA attribute)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.cols_to_text">cols_to_text (folktexts.task.TaskMetadata attribute)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.QAInterface.column">column (folktexts.qa_interface.QAInterface attribute)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.col_to_text.ColumnToText">ColumnToText (class in folktexts.col_to_text)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.evaluation.compute_best_threshold">compute_best_threshold() (in module folktexts.evaluation)</a>
+</li>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.eval_feature_importance.compute_feature_importance">compute_feature_importance() (in module folktexts.cli.eval_feature_importance)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.compute_risk_estimates_for_dataframe">compute_risk_estimates_for_dataframe() (folktexts.classifier.base.LLMClassifier method)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.compute_risk_estimates_for_dataset">compute_risk_estimates_for_dataset() (folktexts.classifier.base.LLMClassifier method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark.configs_dict">configs_dict (folktexts.benchmark.Benchmark property)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.context_size">context_size (folktexts.benchmark.BenchmarkConfig attribute)</a>, <a href="source/folktexts.html#id1">[1]</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.correct_order_bias">correct_order_bias (folktexts.benchmark.BenchmarkConfig attribute)</a>, <a href="source/folktexts.html#id2">[1]</a>
+
+      <ul>
+        <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.correct_order_bias">(folktexts.classifier.base.LLMClassifier property)</a>
+</li>
+      </ul></li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.create_answer_keys_permutations">create_answer_keys_permutations() (folktexts.qa_interface.MultipleChoiceQA class method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.create_question_from_value_map">create_question_from_value_map() (folktexts.qa_interface.MultipleChoiceQA class method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.create_task_with_feature_subset">create_task_with_feature_subset() (folktexts.task.TaskMetadata method)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.custom_prompt_prefix">custom_prompt_prefix (folktexts.classifier.base.LLMClassifier property)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.data">data (folktexts.dataset.Dataset property)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.Choice.data_value">data_value (folktexts.qa_interface.Choice attribute)</a>, <a href="source/folktexts.html#id9">[1]</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset">Dataset (class in folktexts.dataset)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.default_config">default_config() (folktexts.benchmark.BenchmarkConfig class method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.DEFAULT_INFERENCE_KWARGS">DEFAULT_INFERENCE_KWARGS (folktexts.classifier.base.LLMClassifier attribute)</a>
+</li>
+      <li><a href="source/folktexts.acs.html#folktexts.acs.acs_columns.describe_income_to_poverty_ratio_pct">describe_income_to_poverty_ratio_pct() (in module folktexts.acs.acs_columns)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.description">description (folktexts.task.TaskMetadata attribute)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.direct_numeric_qa">direct_numeric_qa (folktexts.task.TaskMetadata attribute)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.DirectNumericQA">DirectNumericQA (class in folktexts.qa_interface)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.encode_row">encode_row (folktexts.classifier.base.LLMClassifier property)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.prompting.encode_row_prompt">encode_row_prompt() (in module folktexts.prompting)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.prompting.encode_row_prompt_chat">encode_row_prompt_chat() (in module folktexts.prompting)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.prompting.encode_row_prompt_few_shot">encode_row_prompt_few_shot() (in module folktexts.prompting)</a>
+</li>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.Experiment.env_vars">env_vars (folktexts.cli.experiments.Experiment attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.evaluation.evaluate_binary_predictions">evaluate_binary_predictions() (in module folktexts.evaluation)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.evaluation.evaluate_binary_predictions_fairness">evaluate_binary_predictions_fairness() (in module folktexts.evaluation)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.evaluation.evaluate_predictions">evaluate_predictions() (in module folktexts.evaluation)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.evaluation.evaluate_predictions_bootstrap">evaluate_predictions_bootstrap() (in module folktexts.evaluation)</a>
+</li>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.Experiment.executable_path">executable_path (folktexts.cli.experiments.Experiment attribute)</a>
+</li>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.Experiment">Experiment (class in folktexts.cli.experiments)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.feature_subset">feature_subset (folktexts.benchmark.BenchmarkConfig attribute)</a>, <a href="source/folktexts.html#id3">[1]</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.features">features (folktexts.task.TaskMetadata attribute)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.few_shot">few_shot (folktexts.benchmark.BenchmarkConfig attribute)</a>, <a href="source/folktexts.html#id4">[1]</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.filter">filter() (folktexts.dataset.Dataset method)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.fit">fit() (folktexts.classifier.base.LLMClassifier method)</a>
+</li>
+      <li><a href="source/folktexts.acs.html#folktexts.acs.acs_tasks.ACSTaskMetadata.folktables_obj">folktables_obj (folktexts.acs.acs_tasks.ACSTaskMetadata attribute)</a>
+</li>
+      <li>
+    folktexts
+
+      <ul>
+        <li><a href="source/folktexts.html#module-folktexts">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.acs
+
+      <ul>
+        <li><a href="source/folktexts.acs.html#module-folktexts.acs">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.acs.acs_columns
+
+      <ul>
+        <li><a href="source/folktexts.acs.html#module-folktexts.acs.acs_columns">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.acs.acs_dataset
+
+      <ul>
+        <li><a href="source/folktexts.acs.html#module-folktexts.acs.acs_dataset">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.acs.acs_questions
+
+      <ul>
+        <li><a href="source/folktexts.acs.html#module-folktexts.acs.acs_questions">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.acs.acs_tasks
+
+      <ul>
+        <li><a href="source/folktexts.acs.html#module-folktexts.acs.acs_tasks">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.acs.acs_thresholds
+
+      <ul>
+        <li><a href="source/folktexts.acs.html#module-folktexts.acs.acs_thresholds">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.benchmark
+
+      <ul>
+        <li><a href="source/folktexts.html#module-folktexts.benchmark">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.classifier
+
+      <ul>
+        <li><a href="source/folktexts.classifier.html#module-folktexts.classifier">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.classifier.base
+
+      <ul>
+        <li><a href="source/folktexts.classifier.html#module-folktexts.classifier.base">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.classifier.transformers_classifier
+
+      <ul>
+        <li><a href="source/folktexts.classifier.html#module-folktexts.classifier.transformers_classifier">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.classifier.web_api_classifier
+
+      <ul>
+        <li><a href="source/folktexts.classifier.html#module-folktexts.classifier.web_api_classifier">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.cli
+
+      <ul>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    folktexts.cli.download_models
+
+      <ul>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli.download_models">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.cli.eval_feature_importance
+
+      <ul>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli.eval_feature_importance">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.cli.experiments
+
+      <ul>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli.experiments">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.cli.launch_experiments_htcondor
+
+      <ul>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli.launch_experiments_htcondor">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.cli.rerun_experiment
+
+      <ul>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli.rerun_experiment">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.cli.run_acs_benchmark
+
+      <ul>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli.run_acs_benchmark">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.col_to_text
+
+      <ul>
+        <li><a href="source/folktexts.html#module-folktexts.col_to_text">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.dataset
+
+      <ul>
+        <li><a href="source/folktexts.html#module-folktexts.dataset">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.evaluation
+
+      <ul>
+        <li><a href="source/folktexts.html#module-folktexts.evaluation">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.llm_utils
+
+      <ul>
+        <li><a href="source/folktexts.html#module-folktexts.llm_utils">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.plotting
+
+      <ul>
+        <li><a href="source/folktexts.html#module-folktexts.plotting">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.prompting
+
+      <ul>
+        <li><a href="source/folktexts.html#module-folktexts.prompting">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.qa_interface
+
+      <ul>
+        <li><a href="source/folktexts.html#module-folktexts.qa_interface">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.task
+
+      <ul>
+        <li><a href="source/folktexts.html#module-folktexts.task">module</a>
+</li>
+      </ul></li>
+      <li>
+    folktexts.threshold
+
+      <ul>
+        <li><a href="source/folktexts.html#module-folktexts.threshold">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.Experiment.get_all_experiments">get_all_experiments() (folktexts.cli.experiments.Experiment class method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.DirectNumericQA.get_answer_from_model_output">get_answer_from_model_output() (folktexts.qa_interface.DirectNumericQA method)</a>
+
+      <ul>
+        <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_answer_from_model_output">(folktexts.qa_interface.MultipleChoiceQA method)</a>
+</li>
+        <li><a href="source/folktexts.html#folktexts.qa_interface.QAInterface.get_answer_from_model_output">(folktexts.qa_interface.QAInterface method)</a>
+</li>
+      </ul></li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_answer_from_text">get_answer_from_text() (folktexts.qa_interface.MultipleChoiceQA method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_answer_key_from_value">get_answer_key_from_value() (folktexts.qa_interface.MultipleChoiceQA method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.get_data_split">get_data_split() (folktexts.dataset.Dataset method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.get_features_data">get_features_data() (folktexts.dataset.Dataset method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.llm_utils.get_model_folder_path">get_model_folder_path() (in module folktexts.llm_utils)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.llm_utils.get_model_size_B">get_model_size_B() (in module folktexts.llm_utils)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.Choice.get_numeric_value">get_numeric_value() (folktexts.qa_interface.Choice method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.DirectNumericQA.get_question_prompt">get_question_prompt() (folktexts.qa_interface.DirectNumericQA method)</a>
+
+      <ul>
+        <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_question_prompt">(folktexts.qa_interface.MultipleChoiceQA method)</a>
+</li>
+        <li><a href="source/folktexts.html#folktexts.qa_interface.QAInterface.get_question_prompt">(folktexts.qa_interface.QAInterface method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.get_row_description">get_row_description() (folktexts.task.TaskMetadata method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.get_sensitive_attribute_data">get_sensitive_attribute_data() (folktexts.dataset.Dataset method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.get_target">get_target() (folktexts.task.TaskMetadata method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.get_target_data">get_target_data() (folktexts.dataset.Dataset method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.get_task">get_task() (folktexts.task.TaskMetadata class method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.get_test">get_test() (folktexts.dataset.Dataset method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.col_to_text.ColumnToText.get_text">get_text() (folktexts.col_to_text.ColumnToText method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.get_train">get_train() (folktexts.dataset.Dataset method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.get_val">get_val() (folktexts.dataset.Dataset method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_value_to_text_map">get_value_to_text_map() (folktexts.qa_interface.MultipleChoiceQA method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.Experiment.hash">hash() (folktexts.cli.experiments.Experiment method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.inference_kwargs">inference_kwargs (folktexts.classifier.base.LLMClassifier property)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.download_models.is_bf16_compatible">is_bf16_compatible() (in module folktexts.cli.download_models)</a>
+
+      <ul>
+        <li><a href="source/folktexts.html#folktexts.llm_utils.is_bf16_compatible">(in module folktexts.llm_utils)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="J">J</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.Experiment.job_bid">job_bid (folktexts.cli.experiments.Experiment attribute)</a>
+</li>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.Experiment.job_cpus">job_cpus (folktexts.cli.experiments.Experiment attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.Experiment.job_gpu_memory_gb">job_gpu_memory_gb (folktexts.cli.experiments.Experiment attribute)</a>
+</li>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.Experiment.job_gpus">job_gpus (folktexts.cli.experiments.Experiment attribute)</a>
+</li>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.Experiment.job_memory_gb">job_memory_gb (folktexts.cli.experiments.Experiment attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="K">K</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.key_to_choice">key_to_choice (folktexts.qa_interface.MultipleChoiceQA property)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.Experiment.kwargs">kwargs (folktexts.cli.experiments.Experiment attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.launch_experiment_job">launch_experiment_job() (in module folktexts.cli.experiments)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier">LLMClassifier (class in folktexts.classifier.base)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.load_from_disk">load_from_disk() (folktexts.benchmark.BenchmarkConfig class method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.llm_utils.load_model_tokenizer">load_model_tokenizer() (in module folktexts.llm_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.download_models.main">main() (in module folktexts.cli.download_models)</a>
+
+      <ul>
+        <li><a href="source/folktexts.cli.html#folktexts.cli.eval_feature_importance.main">(in module folktexts.cli.eval_feature_importance)</a>
+</li>
+        <li><a href="source/folktexts.cli.html#folktexts.cli.launch_experiments_htcondor.main">(in module folktexts.cli.launch_experiments_htcondor)</a>
+</li>
+        <li><a href="source/folktexts.cli.html#folktexts.cli.run_acs_benchmark.main">(in module folktexts.cli.run_acs_benchmark)</a>
+</li>
+      </ul></li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark.make_acs_benchmark">make_acs_benchmark() (folktexts.benchmark.Benchmark class method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark.make_benchmark">make_benchmark() (folktexts.benchmark.Benchmark class method)</a>
+</li>
+      <li><a href="source/folktexts.acs.html#folktexts.acs.acs_tasks.ACSTaskMetadata.make_folktables_task">make_folktables_task() (folktexts.acs.acs_tasks.ACSTaskMetadata class method)</a>
+</li>
+      <li><a href="source/folktexts.acs.html#folktexts.acs.acs_dataset.ACSDataset.make_from_task">make_from_task() (folktexts.acs.acs_dataset.ACSDataset class method)</a>
+</li>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.launch_experiments_htcondor.make_llm_clf_experiment">make_llm_clf_experiment() (in module folktexts.cli.launch_experiments_htcondor)</a>
+</li>
+      <li><a href="source/folktexts.acs.html#folktexts.acs.acs_tasks.ACSTaskMetadata.make_task">make_task() (folktexts.acs.acs_tasks.ACSTaskMetadata class method)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.model">model (folktexts.classifier.transformers_classifier.TransformersLLMClassifier property)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark.model_name">model_name (folktexts.benchmark.Benchmark property)</a>
+
+      <ul>
+        <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.model_name">(folktexts.classifier.base.LLMClassifier property)</a>
+</li>
+      </ul></li>
+      <li>
+    module
+
+      <ul>
+        <li><a href="source/folktexts.html#module-folktexts">folktexts</a>
+</li>
+        <li><a href="source/folktexts.acs.html#module-folktexts.acs">folktexts.acs</a>
+</li>
+        <li><a href="source/folktexts.acs.html#module-folktexts.acs.acs_columns">folktexts.acs.acs_columns</a>
+</li>
+        <li><a href="source/folktexts.acs.html#module-folktexts.acs.acs_dataset">folktexts.acs.acs_dataset</a>
+</li>
+        <li><a href="source/folktexts.acs.html#module-folktexts.acs.acs_questions">folktexts.acs.acs_questions</a>
+</li>
+        <li><a href="source/folktexts.acs.html#module-folktexts.acs.acs_tasks">folktexts.acs.acs_tasks</a>
+</li>
+        <li><a href="source/folktexts.acs.html#module-folktexts.acs.acs_thresholds">folktexts.acs.acs_thresholds</a>
+</li>
+        <li><a href="source/folktexts.html#module-folktexts.benchmark">folktexts.benchmark</a>
+</li>
+        <li><a href="source/folktexts.classifier.html#module-folktexts.classifier">folktexts.classifier</a>
+</li>
+        <li><a href="source/folktexts.classifier.html#module-folktexts.classifier.base">folktexts.classifier.base</a>
+</li>
+        <li><a href="source/folktexts.classifier.html#module-folktexts.classifier.transformers_classifier">folktexts.classifier.transformers_classifier</a>
+</li>
+        <li><a href="source/folktexts.classifier.html#module-folktexts.classifier.web_api_classifier">folktexts.classifier.web_api_classifier</a>
+</li>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli">folktexts.cli</a>
+</li>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli.download_models">folktexts.cli.download_models</a>
+</li>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli.eval_feature_importance">folktexts.cli.eval_feature_importance</a>
+</li>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli.experiments">folktexts.cli.experiments</a>
+</li>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli.launch_experiments_htcondor">folktexts.cli.launch_experiments_htcondor</a>
+</li>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli.rerun_experiment">folktexts.cli.rerun_experiment</a>
+</li>
+        <li><a href="source/folktexts.cli.html#module-folktexts.cli.run_acs_benchmark">folktexts.cli.run_acs_benchmark</a>
+</li>
+        <li><a href="source/folktexts.html#module-folktexts.col_to_text">folktexts.col_to_text</a>
+</li>
+        <li><a href="source/folktexts.html#module-folktexts.dataset">folktexts.dataset</a>
+</li>
+        <li><a href="source/folktexts.html#module-folktexts.evaluation">folktexts.evaluation</a>
+</li>
+        <li><a href="source/folktexts.html#module-folktexts.llm_utils">folktexts.llm_utils</a>
+</li>
+        <li><a href="source/folktexts.html#module-folktexts.plotting">folktexts.plotting</a>
+</li>
+        <li><a href="source/folktexts.html#module-folktexts.prompting">folktexts.prompting</a>
+</li>
+        <li><a href="source/folktexts.html#module-folktexts.qa_interface">folktexts.qa_interface</a>
+</li>
+        <li><a href="source/folktexts.html#module-folktexts.task">folktexts.task</a>
+</li>
+        <li><a href="source/folktexts.html#module-folktexts.threshold">folktexts.threshold</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.multiple_choice_qa">multiple_choice_qa (folktexts.task.TaskMetadata attribute)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA">MultipleChoiceQA (class in folktexts.qa_interface)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.col_to_text.ColumnToText.name">name (folktexts.col_to_text.ColumnToText property)</a>
+
+      <ul>
+        <li><a href="source/folktexts.html#folktexts.dataset.Dataset.name">(folktexts.dataset.Dataset property)</a>
+</li>
+        <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.name">(folktexts.task.TaskMetadata attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.DirectNumericQA.num_forward_passes">num_forward_passes (folktexts.qa_interface.DirectNumericQA attribute)</a>
+
+      <ul>
+        <li><a href="source/folktexts.html#folktexts.qa_interface.MultipleChoiceQA.num_forward_passes">(folktexts.qa_interface.MultipleChoiceQA attribute)</a>
+</li>
+        <li><a href="source/folktexts.html#folktexts.qa_interface.QAInterface.num_forward_passes">(folktexts.qa_interface.QAInterface attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.numeric_risk_prompting">numeric_risk_prompting (folktexts.benchmark.BenchmarkConfig attribute)</a>, <a href="source/folktexts.html#id5">[1]</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.Choice.numeric_value">numeric_value (folktexts.qa_interface.Choice attribute)</a>, <a href="source/folktexts.html#id10">[1]</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.threshold.Threshold.op">op (folktexts.threshold.Threshold attribute)</a>, <a href="source/folktexts.html#id12">[1]</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.eval_feature_importance.parse_feature_importance">parse_feature_importance() (in module folktexts.cli.eval_feature_importance)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark.plot_results">plot_results() (folktexts.benchmark.Benchmark method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.population_filter">population_filter (folktexts.benchmark.BenchmarkConfig attribute)</a>, <a href="source/folktexts.html#id6">[1]</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.predict">predict() (folktexts.classifier.base.LLMClassifier method)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.predict_proba">predict_proba() (folktexts.classifier.base.LLMClassifier method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="Q">Q</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.QAInterface">QAInterface (class in folktexts.qa_interface)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.llm_utils.query_model_batch">query_model_batch() (in module folktexts.llm_utils)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.llm_utils.query_model_batch_multiple_passes">query_model_batch_multiple_passes() (in module folktexts.llm_utils)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.col_to_text.ColumnToText.question">question (folktexts.col_to_text.ColumnToText property)</a>
+
+      <ul>
+        <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.question">(folktexts.task.TaskMetadata property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.plotting.render_evaluation_plots">render_evaluation_plots() (in module folktexts.plotting)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.plotting.render_fairness_plots">render_fairness_plots() (in module folktexts.plotting)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark.results">results (folktexts.benchmark.Benchmark property)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark.results_dir">results_dir (folktexts.benchmark.Benchmark property)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark.results_root_dir">results_root_dir (folktexts.benchmark.Benchmark property)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.reuse_few_shot_examples">reuse_few_shot_examples (folktexts.benchmark.BenchmarkConfig attribute)</a>, <a href="source/folktexts.html#id7">[1]</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark.run">run() (folktexts.benchmark.Benchmark method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.sample_n_train_examples">sample_n_train_examples() (folktexts.dataset.Dataset method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.plotting.save_fig">save_fig() (in module folktexts.plotting)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark.save_results">save_results() (folktexts.benchmark.Benchmark method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.save_to_disk">save_to_disk() (folktexts.benchmark.BenchmarkConfig method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.seed">seed (folktexts.benchmark.BenchmarkConfig attribute)</a>, <a href="source/folktexts.html#id8">[1]</a>
+
+      <ul>
+        <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.seed">(folktexts.classifier.base.LLMClassifier property)</a>
+</li>
+        <li><a href="source/folktexts.html#folktexts.dataset.Dataset.seed">(folktexts.dataset.Dataset property)</a>
+</li>
+      </ul></li>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.sensitive_attribute">sensitive_attribute (folktexts.task.TaskMetadata attribute)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.sensitive_attribute_value_map">sensitive_attribute_value_map() (folktexts.task.TaskMetadata method)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.set_fit_request">set_fit_request() (folktexts.classifier.base.LLMClassifier method)</a>
+
+      <ul>
+        <li><a href="source/folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_fit_request">(folktexts.classifier.transformers_classifier.TransformersLLMClassifier method)</a>
+</li>
+        <li><a href="source/folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_fit_request">(folktexts.classifier.web_api_classifier.WebAPILLMClassifier method)</a>
+</li>
+      </ul></li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.set_inference_kwargs">set_inference_kwargs() (folktexts.classifier.base.LLMClassifier method)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.set_predict_proba_request">set_predict_proba_request() (folktexts.classifier.base.LLMClassifier method)</a>
+
+      <ul>
+        <li><a href="source/folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_predict_proba_request">(folktexts.classifier.transformers_classifier.TransformersLLMClassifier method)</a>
+</li>
+        <li><a href="source/folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_predict_proba_request">(folktexts.classifier.web_api_classifier.WebAPILLMClassifier method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.set_predict_request">set_predict_request() (folktexts.classifier.base.LLMClassifier method)</a>
+
+      <ul>
+        <li><a href="source/folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_predict_request">(folktexts.classifier.transformers_classifier.TransformersLLMClassifier method)</a>
+</li>
+        <li><a href="source/folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_predict_request">(folktexts.classifier.web_api_classifier.WebAPILLMClassifier method)</a>
+</li>
+      </ul></li>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.set_question">set_question() (folktexts.task.TaskMetadata method)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.set_score_request">set_score_request() (folktexts.classifier.base.LLMClassifier method)</a>
+
+      <ul>
+        <li><a href="source/folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_score_request">(folktexts.classifier.transformers_classifier.TransformersLLMClassifier method)</a>
+</li>
+        <li><a href="source/folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_score_request">(folktexts.classifier.web_api_classifier.WebAPILLMClassifier method)</a>
+</li>
+      </ul></li>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.download_models.setup_arg_parser">setup_arg_parser() (in module folktexts.cli.download_models)</a>
+
+      <ul>
+        <li><a href="source/folktexts.cli.html#folktexts.cli.eval_feature_importance.setup_arg_parser">(in module folktexts.cli.eval_feature_importance)</a>
+</li>
+        <li><a href="source/folktexts.cli.html#folktexts.cli.launch_experiments_htcondor.setup_arg_parser">(in module folktexts.cli.launch_experiments_htcondor)</a>
+</li>
+        <li><a href="source/folktexts.cli.html#folktexts.cli.rerun_experiment.setup_arg_parser">(in module folktexts.cli.rerun_experiment)</a>
+</li>
+        <li><a href="source/folktexts.cli.html#folktexts.cli.run_acs_benchmark.setup_arg_parser">(in module folktexts.cli.run_acs_benchmark)</a>
+</li>
+      </ul></li>
+      <li><a href="source/folktexts.html#folktexts.col_to_text.ColumnToText.short_description">short_description (folktexts.col_to_text.ColumnToText property)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.subsample">subsample() (folktexts.dataset.Dataset method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.subsampling">subsampling (folktexts.dataset.Dataset property)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.target">target (folktexts.task.TaskMetadata attribute)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.target_threshold">target_threshold (folktexts.task.TaskMetadata attribute)</a>
+</li>
+      <li><a href="source/folktexts.acs.html#folktexts.acs.acs_dataset.ACSDataset.task">task (folktexts.acs.acs_dataset.ACSDataset property)</a>
+
+      <ul>
+        <li><a href="source/folktexts.html#folktexts.benchmark.Benchmark.task">(folktexts.benchmark.Benchmark property)</a>
+</li>
+        <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.task">(folktexts.classifier.base.LLMClassifier property)</a>
+</li>
+        <li><a href="source/folktexts.html#folktexts.dataset.Dataset.task">(folktexts.dataset.Dataset property)</a>
+</li>
+      </ul></li>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata">TaskMetadata (class in folktexts.task)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.test_size">test_size (folktexts.dataset.Dataset property)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.qa_interface.Choice.text">text (folktexts.qa_interface.Choice attribute)</a>, <a href="source/folktexts.html#id11">[1]</a>
+
+      <ul>
+        <li><a href="source/folktexts.html#folktexts.qa_interface.QAInterface.text">(folktexts.qa_interface.QAInterface attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/folktexts.html#folktexts.threshold.Threshold">Threshold (class in folktexts.threshold)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.threshold">threshold (folktexts.classifier.base.LLMClassifier property)</a>
+</li>
+      <li><a href="source/folktexts.cli.html#folktexts.cli.experiments.Experiment.to_dict">to_dict() (folktexts.cli.experiments.Experiment method)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.tokenizer">tokenizer (folktexts.classifier.transformers_classifier.TransformersLLMClassifier property)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.track_cost_callback">track_cost_callback() (folktexts.classifier.web_api_classifier.WebAPILLMClassifier method)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.train_size">train_size (folktexts.dataset.Dataset property)</a>
+</li>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier">TransformersLLMClassifier (class in folktexts.classifier.transformers_classifier)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="U">U</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.benchmark.BenchmarkConfig.update">update() (folktexts.benchmark.BenchmarkConfig method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.task.TaskMetadata.use_numeric_qa">use_numeric_qa (folktexts.task.TaskMetadata property)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.dataset.Dataset.val_size">val_size (folktexts.dataset.Dataset property)</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.threshold.Threshold.valid_ops">valid_ops (folktexts.threshold.Threshold attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.html#folktexts.threshold.Threshold.value">value (folktexts.threshold.Threshold attribute)</a>, <a href="source/folktexts.html#id13">[1]</a>
+</li>
+      <li><a href="source/folktexts.html#folktexts.col_to_text.ColumnToText.value_map">value_map (folktexts.col_to_text.ColumnToText property)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="W">W</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier">WebAPILLMClassifier (class in folktexts.classifier.web_api_classifier)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
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+      <li class="breadcrumb-item active">Welcome to <code class="code docutils literal notranslate"><span class="pre">folktexts</span></code>’ documentation!</li>
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+           <div itemprop="articleBody">
+             
+  <section id="welcome-to-folktexts-documentation">
+<h1>Welcome to <code class="code docutils literal notranslate"><span class="pre">folktexts</span></code>’ documentation!<a class="headerlink" href="#welcome-to-folktexts-documentation" title="Link to this heading"></a></h1>
+<p>The <code class="code docutils literal notranslate"><span class="pre">folktexts</span></code> package enables you to benchmark and evaluate
+LLM-generated risk scores.</p>
+<p>We encode unrealizable tabular prediction tasks as natural language text tasks,
+and prompt LLMs for the probability of a target variable being true.
+The correct solutions for each task often require expressing uncertainty, as the
+target variable is not uniquely determined by the input features.</p>
+<p>Folktexts is compatible with any huggingface transformer model and models
+available through web APIs (e.g., OpenAI API).</p>
+<p>Five tabular data tasks are provided out-of-the-box, using the American
+Community Survey as a data source: <cite>ACSIncome</cite>, <cite>ACSMobility</cite>, <cite>ACSTravelTime</cite>,
+<cite>ACSEmployment</cite>, and <cite>ACSPublicCoverage</cite>. These tasks follow the same name,
+feature columns, and target columns as those put forth by <a class="reference external" href="https://arxiv.org/abs/2108.04884">Ding et al. (2021)</a>
+in the <a class="reference external" href="https://github.com/socialfoundations/folktables">folktables</a> python package.</p>
+<p>Full code available on the <a class="reference external" href="https://github.com/socialfoundations/folktexts">GitHub repository</a>,
+including various <a class="reference external" href="https://github.com/socialfoundations/folktexts/tree/main/notebooks">jupyter notebook examples</a> .</p>
+<p>Check out the following sub-pages:</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="readme.html">Readme file</a></li>
+<li class="toctree-l1"><a class="reference internal" href="source/modules.html">API reference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks.html">Example notebooks</a></li>
+</ul>
+</div>
+</section>
+<section id="citing">
+<h1>Citing<a class="headerlink" href="#citing" title="Link to this heading"></a></h1>
+<p>The <code class="code docutils literal notranslate"><span class="pre">folktexts</span></code> package is the basis for the following <a class="reference external" href="https://arxiv.org/abs/2407.14614">publication</a>:</p>
+<div class="highlight-bib notranslate"><div class="highlight"><pre><span></span><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">cruz2024evaluating</span><span class="p">,</span>
+<span class="w">   </span><span class="na">title</span><span class="p">=</span><span class="s">{Evaluating language models as risk scores}</span><span class="p">,</span>
+<span class="w">   </span><span class="na">author</span><span class="p">=</span><span class="s">{Andr\&#39;{e} F. Cruz and Moritz Hardt and Celestine Mendler-D\&quot;{u}nner}</span><span class="p">,</span>
+<span class="w">   </span><span class="na">booktitle</span><span class="p">=</span><span class="s">{The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track}</span><span class="p">,</span>
+<span class="w">   </span><span class="na">year</span><span class="p">=</span><span class="s">{2024}</span><span class="p">,</span>
+<span class="w">   </span><span class="na">url</span><span class="p">=</span><span class="s">{https://openreview.net/forum?id=qrZxL3Bto9}</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>All additional supplementary materials are available in the <a class="reference external" href="https://github.com/socialfoundations/folktexts">GitHub repository</a>.</p>
+</section>
+<section id="indices">
+<h1>Indices<a class="headerlink" href="#indices" title="Link to this heading"></a></h1>
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+<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
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+<li class="toctree-l2"><a class="reference internal" href="notebooks/minimal-example.html">Evaluate model calibration using folktexts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/detailed-example.html">Example: Run ACS benchmark task</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/custom-dataset-example.html">Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a different data source</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/custom-acs-task-example.html">Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a custom ACS task</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/paper-plots-and-tables.html">Render paper plots and tables</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/paper-plots-and-tables.html#Plots-comparing-different-prompting-schemes">Plots comparing different prompting schemes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/minimal-example_web-API-model.html">Evaluate model calibration using folktexts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/parse-acs-results.html">Fetch and parse ACS benchmark results under a given directory</a></li>
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+  <div>Evaluate model calibration using folktexts</div>
+</a>
+<a class="reference internal" href="notebooks/detailed-example.html">
+  <div><img alt="" src="./_images/notebooks_detailed-example_31_7.png"></div>
+  <div>Example: Run ACS benchmark task</div>
+</a>
+<a class="reference internal" href="notebooks/custom-dataset-example.html">
+  <div><img alt="" src="./_images/notebooks_custom-dataset-example_24_5.png"></div>
+  <div>Run folktexts benchmark with a different data source</div>
+</a>
+<a class="reference internal" href="notebooks/custom-acs-task-example.html">
+  <div><img alt="" src="./_images/notebooks_custom-acs-task-example_30_5.png"></div>
+  <div>Run folktexts benchmark with a custom ACS task</div>
+</a>
+<a class="reference internal" href="notebooks/paper-plots-and-tables.html">
+  <div><img alt="" src="./_images/notebooks_paper-plots-and-tables_87_1.png"></div>
+  <div>Render paper plots and tables</div>
+</a>
+<a class="reference internal" href="notebooks/minimal-example_web-API-model.html">
+  <div><img alt="" src="./_images/notebooks_minimal-example_web-API-model_12_5.png"></div>
+  <div>Evaluate model calibration using folktexts</div>
+</a>
+<a class="reference internal" href="notebooks/parse-acs-results.html">
+  <div><img alt="" src="./_static/nbsphinx-no-thumbnail.svg"></div>
+  <div>Fetch and parse ACS benchmark results under a given directory</div>
+</a>
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+             
+  <section id="example-notebooks-using-folktexts">
+<h1>Example notebooks using <code class="docutils literal notranslate"><span class="pre">folktexts</span></code><a class="headerlink" href="#example-notebooks-using-folktexts" title="Link to this heading"></a></h1>
+<table class="docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>File</p></th>
+<th class="head"><p>Category</p></th>
+<th class="head"><p>Description</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><a class="reference internal" href="minimal-example.html"><span class="std std-doc">minimal-example.ipynb</span></a></p></td>
+<td><p>Usage example</p></td>
+<td><p><strong>[Start here]</strong> A minimal example of how to use <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> to evaluate model calibration and other statistics.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="detailed-example.html"><span class="std std-doc">detailed-example.ipynb</span></a></p></td>
+<td><p>Usage example</p></td>
+<td><p>Detailed example of running an ACS benchmark on a given LLM.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="custom-acs-task-example.html"><span class="std std-doc">custom-acs-task-example.ipynb</span></a></p></td>
+<td><p>Usage example</p></td>
+<td><p>Example of how to define and benchmark LLMs on a custom ACS task.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="custom-dataset-example.html"><span class="std std-doc">custom-dataset-example.ipynb</span></a></p></td>
+<td><p>Usage example</p></td>
+<td><p>Example of using <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> with a different dataset (the <a class="reference external" href="https://meps.ahrq.gov/mepsweb/index.jsp">MEPS</a> dataset).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="minimal-example_web-API-model.html"><span class="std std-doc">minimal-example_web-API-model.ipynb</span></a></p></td>
+<td><p>Usage example</p></td>
+<td><p>A minimal example of how to use <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> with models hosted through a web API.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="paper-plots-and-tables.html"><span class="std std-doc">paper-plots-and-tables.ipynb</span></a></p></td>
+<td><p>Plotting and analysis</p></td>
+<td><p>Generate paper plots and tables using benchmark results.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="parse-acs-results.html"><span class="std std-doc">parse-acs-results.ipynb</span></a></p></td>
+<td><p>Parsing results</p></td>
+<td><p>Aggregates and parses all ACS benchmark results saved under a given directory.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="parse-feature-importance-results.html"><span class="std std-doc">parse-feature-importance-results.ipynb</span></a></p></td>
+<td><p>Parsing results</p></td>
+<td><p>Aggregates, parses, and plots results relating to feature importance.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="parse-acs-results_using-feature-subsets.html"><span class="std std-doc">parse-acs-results_using-feature-subsets.ipynb</span></a></p></td>
+<td><p>Parsing results</p></td>
+<td><p>Aggregates, parses and plots results relating ablations on subsets of features.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
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+<li class="toctree-l2"><a class="reference internal" href="minimal-example.html">Evaluate model calibration using folktexts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="detailed-example.html">Example: Run ACS benchmark task</a></li>
+<li class="toctree-l2"><a class="reference internal" href="custom-dataset-example.html">Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a different data source</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a custom ACS task</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Define-custom-task-objects-appropriately:">Define custom task objects appropriately:</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Load-LLM-from-huggingface-or-from-local-disk">Load LLM from huggingface or from local disk</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Run-benchmark-on-the-custom-task">Run benchmark on the custom task</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="paper-plots-and-tables.html">Render paper plots and tables</a></li>
+<li class="toctree-l2"><a class="reference internal" href="paper-plots-and-tables.html#Plots-comparing-different-prompting-schemes">Plots comparing different prompting schemes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="minimal-example_web-API-model.html">Evaluate model calibration using folktexts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parse-acs-results.html">Fetch and parse ACS benchmark results under a given directory</a></li>
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+             
+  <section id="Run-folktexts-benchmark-with-a-custom-ACS-task">
+<h1>Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a custom ACS task<a class="headerlink" href="#Run-folktexts-benchmark-with-a-custom-ACS-task" title="Link to this heading"></a></h1>
+<p>This notebook describes how to define a custom prediction task based on American Community Survey (ACS) data.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">folktexts</span>
+<span class="n">folktexts</span><span class="o">.</span><span class="n">__version__</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;0.0.22&#39;
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+</pre></div>
+</div>
+</div>
+<p><strong>Note:</strong> Change the <code class="docutils literal notranslate"><span class="pre">DATA_DIR</span></code> variable to the ACS data path on your file system (if any, otherwise will attempt to download data).</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">DATA_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;/fast/groups/sf&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;data&quot;</span>
+</pre></div>
+</div>
+</div>
+<section id="Define-custom-task-objects-appropriately:">
+<h2>Define custom task objects appropriately:<a class="headerlink" href="#Define-custom-task-objects-appropriately:" title="Link to this heading"></a></h2>
+<p>In this example, we’ll try to predict whether someone is or ever was part of the Military, based on a series of demographic features.</p>
+<p>We must define objects of the following classes: - <code class="docutils literal notranslate"><span class="pre">Threshold</span></code>: Used to binarize the target column (not needed if target is already binary); - <code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA</span></code>: Defines a question and answer scheme for the target column; - <code class="docutils literal notranslate"><span class="pre">TaskMetadata</span></code>: Defines which columns to use as features, and which to use as the prediction target;</p>
+<p>Select the target column (among those defined in <code class="docutils literal notranslate"><span class="pre">folktexts.acs.acs_columns</span></code>):</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">TARGET_COLUMN</span> <span class="o">=</span> <span class="s2">&quot;MIL&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Define threshold to be applied to target column:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.threshold</span> <span class="kn">import</span> <span class="n">Threshold</span>
+<span class="n">target_threshold</span> <span class="o">=</span> <span class="n">Threshold</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="s2">&quot;!=&quot;</span><span class="p">)</span>
+
+<span class="c1"># data[&quot;MIL&quot;] != 4 means &quot;Is on active duty in the military or was in the past&quot;</span>
+<span class="c1"># data[&quot;MIL&quot;] == 4 means &quot;Never served in the military&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Define question and answer interface used for prompting:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.qa_interface</span> <span class="kn">import</span> <span class="n">MultipleChoiceQA</span>
+<span class="kn">from</span> <span class="nn">folktexts.qa_interface</span> <span class="kn">import</span> <span class="n">Choice</span>
+
+<span class="n">target_column_qa</span> <span class="o">=</span> <span class="n">MultipleChoiceQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">target_threshold</span><span class="o">.</span><span class="n">apply_to_column_name</span><span class="p">(</span><span class="n">TARGET_COLUMN</span><span class="p">),</span>
+    <span class="n">text</span><span class="o">=</span><span class="s2">&quot;Has this person ever served in the military?&quot;</span><span class="p">,</span>
+    <span class="n">choices</span><span class="o">=</span><span class="p">(</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;Yes, this person is now on active duty or was in the past&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;No, this person has never served in the military&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Define task metadata:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.acs</span> <span class="kn">import</span> <span class="n">ACSTaskMetadata</span>
+
+<span class="n">task</span> <span class="o">=</span> <span class="n">ACSTaskMetadata</span><span class="o">.</span><span class="n">make_task</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;ACSMilitary&quot;</span><span class="p">,</span>
+    <span class="n">description</span><span class="o">=</span><span class="s2">&quot;predict if a person has ever served in the military&quot;</span><span class="p">,</span>
+    <span class="n">features</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;AGEP&quot;</span><span class="p">,</span> <span class="s2">&quot;SCHL&quot;</span><span class="p">,</span> <span class="s2">&quot;MAR&quot;</span><span class="p">,</span> <span class="s2">&quot;POBP&quot;</span><span class="p">,</span> <span class="s2">&quot;WKHP&quot;</span><span class="p">,</span> <span class="s2">&quot;SEX&quot;</span><span class="p">,</span> <span class="s2">&quot;RAC1P&quot;</span><span class="p">,</span> <span class="s2">&quot;ST&quot;</span><span class="p">,</span> <span class="s2">&quot;CIT&quot;</span><span class="p">,</span> <span class="s2">&quot;DIS&quot;</span><span class="p">,</span> <span class="s2">&quot;PINCP&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+    <span class="n">target</span><span class="o">=</span><span class="n">TARGET_COLUMN</span><span class="p">,</span>
+    <span class="n">target_threshold</span><span class="o">=</span><span class="n">target_threshold</span><span class="p">,</span>
+    <span class="n">sensitive_attribute</span><span class="o">=</span><span class="s2">&quot;RAC1P&quot;</span><span class="p">,</span>
+    <span class="n">multiple_choice_qa</span><span class="o">=</span><span class="n">target_column_qa</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Load ACS dataset for the custom task:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="kn">from</span> <span class="nn">folktexts.acs.acs_dataset</span> <span class="kn">import</span> <span class="n">ACSDataset</span>
+<span class="n">acs_dataset</span> <span class="o">=</span> <span class="n">ACSDataset</span><span class="o">.</span><span class="n">make_from_task</span><span class="p">(</span>
+    <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+    <span class="n">cache_dir</span><span class="o">=</span><span class="n">DATA_DIR</span><span class="p">,)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Loading ACS data...
+CPU times: user 38 s, sys: 19.3 s, total: 57.3 s
+Wall time: 57.4 s
+</pre></div></div>
+</div>
+<p><strong>Note:</strong> This dataset will contain all samples in the ACS PUMS file, you can now filter the <code class="docutils literal notranslate"><span class="pre">acs_dataset.data</span></code> if you want to use only a portion of the data (e.g, only people in California, or only people above 18 years old).</p>
+<p>As an example, we’ll filter the ACS data to contain only people above 18 years old.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original number of samples: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">acs_dataset</span><span class="o">.</span><span class="n">data</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="n">acs_dataset</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">acs_dataset</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">acs_dataset</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;AGEP&quot;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">18</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Parsed number of samples: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">acs_dataset</span><span class="o">.</span><span class="n">data</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Original number of samples: 3236107
+Parsed number of samples: 2580544
+</pre></div></div>
+</div>
+<p><strong>Optionally,</strong> you can subsample the dataset to have faster but noisier results.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">acs_dataset</span><span class="o">.</span><span class="n">subsample</span><span class="p">(</span><span class="mf">0.01</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;folktexts.acs.acs_dataset.ACSDataset at 0x14584395d510&gt;
+</pre></div></div>
+</div>
+</section>
+<section id="Load-LLM-from-huggingface-or-from-local-disk">
+<h2>Load LLM from huggingface or from local disk<a class="headerlink" href="#Load-LLM-from-huggingface-or-from-local-disk" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">MODEL_NAME_OR_PATH</span> <span class="o">=</span> <span class="s2">&quot;meta-llama/Meta-Llama-3-8B&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">load_model_tokenizer</span>
+<span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">load_model_tokenizer</span><span class="p">(</span><span class="n">MODEL_NAME_OR_PATH</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "e9adceb50efa487fa02fd702a7ef8bb1", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+CPU times: user 4.39 s, sys: 6.29 s, total: 10.7 s
+Wall time: 13.1 s
+</pre></div></div>
+</div>
+<p>Create <code class="docutils literal notranslate"><span class="pre">LLMClassifier</span></code> object.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.classifier</span> <span class="kn">import</span> <span class="n">TransformersLLMClassifier</span>
+
+<span class="n">llm_clf</span> <span class="o">=</span> <span class="n">TransformersLLMClassifier</span><span class="p">(</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+    <span class="n">context_size</span><span class="o">=</span><span class="mi">600</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p><strong>Validation:</strong> Check that model prompts with the custom task make sense, or if anything needs to be adjusted.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_sample</span><span class="p">,</span> <span class="n">_y_sample</span> <span class="o">=</span> <span class="n">acs_dataset</span><span class="o">.</span><span class="n">sample_n_train_examples</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">encode_row</span><span class="p">(</span><span class="n">X_sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">question</span><span class="o">=</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">question</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+The following data corresponds to a survey respondent. The survey was conducted among US residents in 2018. Please answer the question based on the information provided. The data provided is enough to reach an approximate answer.
+
+Information:
+- The age is: 50 years old.
+- The highest educational attainment is: Regular high school diploma.
+- The marital status is: Married.
+- The place of birth is: Nevada.
+- The usual number of hours worked per week is: 50 hours.
+- The sex is: Male.
+- The race is: White.
+- The resident state is: Nevada.
+- The citizenship status is: Born in the United States.
+- The disability status is: No disability.
+- The yearly income is: $70,000.
+
+Question: Has this person ever served in the military?
+A. Yes, this person is now on active duty or was in the past.
+B. No, this person has never served in the military.
+Answer:
+</pre></div></div>
+</div>
+</section>
+<section id="Run-benchmark-on-the-custom-task">
+<h2>Run benchmark on the custom task<a class="headerlink" href="#Run-benchmark-on-the-custom-task" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.benchmark</span> <span class="kn">import</span> <span class="n">Benchmark</span>
+<span class="n">bench</span> <span class="o">=</span> <span class="n">Benchmark</span><span class="p">(</span><span class="n">llm_clf</span><span class="o">=</span><span class="n">llm_clf</span><span class="p">,</span> <span class="n">dataset</span><span class="o">=</span><span class="n">acs_dataset</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">bench</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">results_root_dir</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "f1f553e570e043d0aa2567237fafc552", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+CPU times: user 4min 25s, sys: 5.32 s, total: 4min 31s
+Wall time: 4min 30s
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;threshold&#39;: 0.5,
+ &#39;n_samples&#39;: 2581,
+ &#39;n_positives&#39;: 259,
+ &#39;n_negatives&#39;: 2322,
+ &#39;model_name&#39;: &#39;Meta-Llama-3-8B&#39;,
+ &#39;accuracy&#39;: 0.8082138705927935,
+ &#39;tpr&#39;: 0.305019305019305,
+ &#39;fnr&#39;: 0.694980694980695,
+ &#39;fpr&#39;: 0.13565891472868216,
+ &#39;tnr&#39;: 0.8643410852713178,
+ &#39;balanced_accuracy&#39;: 0.5846801951453114,
+ &#39;precision&#39;: 0.20050761421319796,
+ &#39;ppr&#39;: 0.15265401007361487,
+ &#39;log_loss&#39;: 0.6253456831680403,
+ &#39;brier_score_loss&#39;: 0.21635791438469001,
+ &#39;accuracy_ratio&#39;: 0.79638671875,
+ &#39;accuracy_diff&#39;: 0.20361328125,
+ &#39;fpr_ratio&#39;: 0.0,
+ &#39;fpr_diff&#39;: 1.0,
+ &#39;fnr_ratio&#39;: 0.0,
+ &#39;fnr_diff&#39;: 1.0,
+ &#39;precision_ratio&#39;: 0.0,
+ &#39;precision_diff&#39;: 1.0,
+ &#39;balanced_accuracy_ratio&#39;: 0.4435516271595018,
+ &#39;balanced_accuracy_diff&#39;: 0.5282227307398932,
+ &#39;tpr_ratio&#39;: 0.0,
+ &#39;tpr_diff&#39;: 1.0,
+ &#39;ppr_ratio&#39;: 0.0,
+ &#39;ppr_diff&#39;: 1.0,
+ &#39;tnr_ratio&#39;: 0.0,
+ &#39;tnr_diff&#39;: 1.0,
+ &#39;equalized_odds_ratio&#39;: 0.0,
+ &#39;equalized_odds_diff&#39;: 1.0,
+ &#39;roc_auc&#39;: 0.6900338877083063,
+ &#39;ece&#39;: 0.3593739652181054,
+ &#39;ece_quantile&#39;: 0.3593739652181053,
+ &#39;predictions_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/ACSMilitary_subsampled-0.01_seed-42_hash-1434010626.test_predictions.csv&#39;,
+ &#39;config&#39;: {&#39;numeric_risk_prompting&#39;: False,
+  &#39;few_shot&#39;: None,
+  &#39;reuse_few_shot_examples&#39;: False,
+  &#39;batch_size&#39;: None,
+  &#39;context_size&#39;: None,
+  &#39;correct_order_bias&#39;: True,
+  &#39;feature_subset&#39;: None,
+  &#39;population_filter&#39;: None,
+  &#39;seed&#39;: 42,
+  &#39;model_name&#39;: &#39;Meta-Llama-3-8B&#39;,
+  &#39;model_hash&#39;: 1160994164,
+  &#39;task_name&#39;: &#39;ACSMilitary&#39;,
+  &#39;task_hash&#39;: 4147552870,
+  &#39;dataset_name&#39;: &#39;ACSMilitary_subsampled-0.01_seed-42_hash-1434010626&#39;,
+  &#39;dataset_subsampling&#39;: 0.01,
+  &#39;dataset_hash&#39;: 1434010626},
+ &#39;benchmark_hash&#39;: 190963433,
+ &#39;results_dir&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433&#39;,
+ &#39;results_root_dir&#39;: &#39;/lustre/home/acruz/folktexts/notebooks&#39;,
+ &#39;current_time&#39;: &#39;2024.08.30-14.57.43&#39;,
+ &#39;plots&#39;: {&#39;roc_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/roc_curve.pdf&#39;,
+  &#39;calibration_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/calibration_curve.pdf&#39;,
+  &#39;score_distribution_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/score_distribution.pdf&#39;,
+  &#39;score_distribution_per_label_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/score_distribution_per_label.pdf&#39;,
+  &#39;roc_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/roc_curve_per_subgroup.pdf&#39;,
+  &#39;calibration_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/calibration_curve_per_subgroup.pdf&#39;}}
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">bench</span><span class="o">.</span><span class="n">plot_results</span><span class="p">();</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_custom-acs-task-example_30_0.png" src="../_images/notebooks_custom-acs-task-example_30_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_custom-acs-task-example_30_1.png" src="../_images/notebooks_custom-acs-task-example_30_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_custom-acs-task-example_30_2.png" src="../_images/notebooks_custom-acs-task-example_30_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_custom-acs-task-example_30_3.png" src="../_images/notebooks_custom-acs-task-example_30_3.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_custom-acs-task-example_30_4.png" src="../_images/notebooks_custom-acs-task-example_30_4.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_custom-acs-task-example_30_5.png" src="../_images/notebooks_custom-acs-task-example_30_5.png" />
+</div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="custom-dataset-example.html" class="btn btn-neutral float-left" title="Run folktexts benchmark with a different data source" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="paper-plots-and-tables.html" class="btn btn-neutral float-right" title="Render paper plots and tables" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+</footer>
+        </div>
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+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
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+  </script> 
+
+</body>
+</html>
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diff --git a/notebooks/custom-acs-task-example.ipynb b/notebooks/custom-acs-task-example.ipynb
new file mode 100644
index 0000000..403cdd8
--- /dev/null
+++ b/notebooks/custom-acs-task-example.ipynb
@@ -0,0 +1,590 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run `folktexts` benchmark with a custom ACS task\n",
+    "\n",
+    "This notebook describes how to define a custom prediction task based on \n",
+    "American Community Survey (ACS) data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.0.22'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import folktexts\n",
+    "folktexts.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** Change the `DATA_DIR` variable to the ACS data path on your file system (if any, otherwise will attempt to download data)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define custom task objects appropriately:\n",
+    "\n",
+    "In this example, we'll try to predict whether someone is or ever was part of the Military, based on a series of demographic features.\n",
+    "\n",
+    "We must define objects of the following classes:\n",
+    "- `Threshold`: Used to binarize the target column (not needed if target is already binary);\n",
+    "- `MultipleChoiceQA`: Defines a question and answer scheme for the target column;\n",
+    "- `TaskMetadata`: Defines which columns to use as features, and which to use as the prediction target;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select the target column (among those defined in `folktexts.acs.acs_columns`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TARGET_COLUMN = \"MIL\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define threshold to be applied to target column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.threshold import Threshold\n",
+    "target_threshold = Threshold(4, \"!=\")\n",
+    "\n",
+    "# data[\"MIL\"] != 4 means \"Is on active duty in the military or was in the past\"\n",
+    "# data[\"MIL\"] == 4 means \"Never served in the military\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define question and answer interface used for prompting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.qa_interface import MultipleChoiceQA\n",
+    "from folktexts.qa_interface import Choice\n",
+    "\n",
+    "target_column_qa = MultipleChoiceQA(\n",
+    "    column=target_threshold.apply_to_column_name(TARGET_COLUMN),\n",
+    "    text=\"Has this person ever served in the military?\",\n",
+    "    choices=(\n",
+    "        Choice(\"Yes, this person is now on active duty or was in the past\", 1),\n",
+    "        Choice(\"No, this person has never served in the military\", 0),\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define task metadata:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.acs import ACSTaskMetadata\n",
+    "\n",
+    "task = ACSTaskMetadata.make_task(\n",
+    "    name=\"ACSMilitary\",\n",
+    "    description=\"predict if a person has ever served in the military\",\n",
+    "    features=[\n",
+    "        \"AGEP\", \"SCHL\", \"MAR\", \"POBP\", \"WKHP\", \"SEX\", \"RAC1P\", \"ST\", \"CIT\", \"DIS\", \"PINCP\",\n",
+    "    ],\n",
+    "    target=TARGET_COLUMN,\n",
+    "    target_threshold=target_threshold,\n",
+    "    sensitive_attribute=\"RAC1P\",\n",
+    "    multiple_choice_qa=target_column_qa,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load ACS dataset for the custom task:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "CPU times: user 38 s, sys: 19.3 s, total: 57.3 s\n",
+      "Wall time: 57.4 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.acs.acs_dataset import ACSDataset\n",
+    "acs_dataset = ACSDataset.make_from_task(\n",
+    "    task=task,\n",
+    "    cache_dir=DATA_DIR,)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** This dataset will contain all samples in the ACS PUMS file, you can\n",
+    "now filter the `acs_dataset.data` if you want to use only a portion of the data \n",
+    "(e.g, only people in California, or only people above 18 years old).\n",
+    "\n",
+    "As an example, we'll filter the ACS data to contain only people above 18 years old."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original number of samples: 3236107\n",
+      "Parsed number of samples: 2580544\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Original number of samples: {len(acs_dataset.data)}\")\n",
+    "\n",
+    "acs_dataset.data = acs_dataset.data[acs_dataset.data[\"AGEP\"] >= 18]\n",
+    "print(f\"Parsed number of samples: {len(acs_dataset.data)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Optionally,** you can subsample the dataset to have faster but noisier results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<folktexts.acs.acs_dataset.ACSDataset at 0x14584395d510>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "acs_dataset.subsample(0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load LLM from huggingface or from local disk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAME_OR_PATH = \"meta-llama/Meta-Llama-3-8B\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e9adceb50efa487fa02fd702a7ef8bb1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4.39 s, sys: 6.29 s, total: 10.7 s\n",
+      "Wall time: 13.1 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.llm_utils import load_model_tokenizer\n",
+    "model, tokenizer = load_model_tokenizer(MODEL_NAME_OR_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create `LLMClassifier` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.classifier import TransformersLLMClassifier\n",
+    "\n",
+    "llm_clf = TransformersLLMClassifier(\n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    task=task,\n",
+    "    batch_size=10,\n",
+    "    context_size=600,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Validation:** Check that model prompts with the custom task make sense, or if anything needs to be adjusted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following data corresponds to a survey respondent. The survey was conducted among US residents in 2018. Please answer the question based on the information provided. The data provided is enough to reach an approximate answer.\n",
+      "\n",
+      "Information:\n",
+      "- The age is: 50 years old.\n",
+      "- The highest educational attainment is: Regular high school diploma.\n",
+      "- The marital status is: Married.\n",
+      "- The place of birth is: Nevada.\n",
+      "- The usual number of hours worked per week is: 50 hours.\n",
+      "- The sex is: Male.\n",
+      "- The race is: White.\n",
+      "- The resident state is: Nevada.\n",
+      "- The citizenship status is: Born in the United States.\n",
+      "- The disability status is: No disability.\n",
+      "- The yearly income is: $70,000.\n",
+      "\n",
+      "Question: Has this person ever served in the military?\n",
+      "A. Yes, this person is now on active duty or was in the past.\n",
+      "B. No, this person has never served in the military.\n",
+      "Answer:\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_sample, _y_sample = acs_dataset.sample_n_train_examples(n=1)\n",
+    "print(llm_clf.encode_row(X_sample.iloc[0], question=llm_clf.task.question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run benchmark on the custom task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.benchmark import Benchmark\n",
+    "bench = Benchmark(llm_clf=llm_clf, dataset=acs_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1f553e570e043d0aa2567237fafc552",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4min 25s, sys: 5.32 s, total: 4min 31s\n",
+      "Wall time: 4min 30s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 2581,\n",
+       " 'n_positives': 259,\n",
+       " 'n_negatives': 2322,\n",
+       " 'model_name': 'Meta-Llama-3-8B',\n",
+       " 'accuracy': 0.8082138705927935,\n",
+       " 'tpr': 0.305019305019305,\n",
+       " 'fnr': 0.694980694980695,\n",
+       " 'fpr': 0.13565891472868216,\n",
+       " 'tnr': 0.8643410852713178,\n",
+       " 'balanced_accuracy': 0.5846801951453114,\n",
+       " 'precision': 0.20050761421319796,\n",
+       " 'ppr': 0.15265401007361487,\n",
+       " 'log_loss': 0.6253456831680403,\n",
+       " 'brier_score_loss': 0.21635791438469001,\n",
+       " 'accuracy_ratio': 0.79638671875,\n",
+       " 'accuracy_diff': 0.20361328125,\n",
+       " 'fpr_ratio': 0.0,\n",
+       " 'fpr_diff': 1.0,\n",
+       " 'fnr_ratio': 0.0,\n",
+       " 'fnr_diff': 1.0,\n",
+       " 'precision_ratio': 0.0,\n",
+       " 'precision_diff': 1.0,\n",
+       " 'balanced_accuracy_ratio': 0.4435516271595018,\n",
+       " 'balanced_accuracy_diff': 0.5282227307398932,\n",
+       " 'tpr_ratio': 0.0,\n",
+       " 'tpr_diff': 1.0,\n",
+       " 'ppr_ratio': 0.0,\n",
+       " 'ppr_diff': 1.0,\n",
+       " 'tnr_ratio': 0.0,\n",
+       " 'tnr_diff': 1.0,\n",
+       " 'equalized_odds_ratio': 0.0,\n",
+       " 'equalized_odds_diff': 1.0,\n",
+       " 'roc_auc': 0.6900338877083063,\n",
+       " 'ece': 0.3593739652181054,\n",
+       " 'ece_quantile': 0.3593739652181053,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/ACSMilitary_subsampled-0.01_seed-42_hash-1434010626.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': False,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'Meta-Llama-3-8B',\n",
+       "  'model_hash': 1160994164,\n",
+       "  'task_name': 'ACSMilitary',\n",
+       "  'task_hash': 4147552870,\n",
+       "  'dataset_name': 'ACSMilitary_subsampled-0.01_seed-42_hash-1434010626',\n",
+       "  'dataset_subsampling': 0.01,\n",
+       "  'dataset_hash': 1434010626},\n",
+       " 'benchmark_hash': 190963433,\n",
+       " 'results_dir': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433',\n",
+       " 'results_root_dir': '/lustre/home/acruz/folktexts/notebooks',\n",
+       " 'current_time': '2024.08.30-14.57.43',\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-190963433/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "bench.run(results_root_dir=\".\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bench.plot_results();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/custom-dataset-example.html b/notebooks/custom-dataset-example.html
new file mode 100644
index 0000000..2853bc9
--- /dev/null
+++ b/notebooks/custom-dataset-example.html
@@ -0,0 +1,737 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+  <section id="Run-folktexts-benchmark-with-a-different-data-source">
+<h1>Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a different data source<a class="headerlink" href="#Run-folktexts-benchmark-with-a-different-data-source" title="Link to this heading"></a></h1>
+<p>This notebook describes how to use the <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> pipeline to run a benchmark on a different tabular dataset (distinct from the ACS tasks provided out-of-the-box).</p>
+<p>In this example, we have mapped 11 columns of the <a class="reference external" href="https://meps.ahrq.gov/mepsweb/index.jsp">Medical Expenditure Panel Survey (MEPS)</a> dataset (survey panels 19, 20, and 21).</p>
+<p>Data codebook available <a class="reference external" href="https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_codebook.jsp?PUFId=H192">at this link</a>.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">folktexts</span>
+
+<span class="n">folktexts</span><span class="o">.</span><span class="n">__version__</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;0.0.22&#39;
+</pre></div></div>
+</div>
+<p>Define mappings for each dataset column using <code class="docutils literal notranslate"><span class="pre">ColumnToText</span></code>: each value of each column should be mapped to a meaningful text representation.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.col_to_text</span> <span class="kn">import</span> <span class="n">ColumnToText</span>
+
+<span class="c1"># AGE</span>
+<span class="n">meps_age_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;AGE&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="si">}</span><span class="s2"> years old&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># REGION: Census region</span>
+<span class="n">meps_region_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;REGION&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;US region&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Northeast&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Midwest&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;South&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;West&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># SEX</span>
+<span class="n">meps_sex_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;SEX&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;sex&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Male&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Female&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># MARRY / MARRY31X</span>
+<span class="n">meps_marital_status</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;MARRY&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;marital status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Married&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Widowed&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Divorced&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Separated&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;Never married&quot;</span><span class="p">,</span>
+        <span class="mi">6</span><span class="p">:</span> <span class="s2">&quot;Inapplicable - Under 16 years old&quot;</span><span class="p">,</span>
+        <span class="mi">7</span><span class="p">:</span> <span class="s2">&quot;Married during current survey round&quot;</span><span class="p">,</span>
+        <span class="mi">8</span><span class="p">:</span> <span class="s2">&quot;Widowed during current survey round&quot;</span><span class="p">,</span>
+        <span class="mi">9</span><span class="p">:</span> <span class="s2">&quot;Divorced during current survey round&quot;</span><span class="p">,</span>
+        <span class="mi">10</span><span class="p">:</span> <span class="s2">&quot;Separated during current survey round&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># HONRDC: Honorably discharged from military</span>
+<span class="n">meps_education_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;HONRDC&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;honorably discharged status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Yes, honorably discharged from military&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Not part of military or not honorably discharged&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Inapplicable - Under 16 years old&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Inapplicable - Now on active duty&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># RTHLTH / RTHLTH31: Perceived health status</span>
+<span class="n">meps_health_status_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;RTHLTH&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;self-rated health status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Excellent&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Very good&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Good&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Fair&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;Poor&quot;</span><span class="p">,</span>
+        <span class="o">-</span><span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Inapplicable - Missing data&quot;</span><span class="p">,</span>
+        <span class="o">-</span><span class="mi">7</span><span class="p">:</span> <span class="s2">&quot;Inapplicable - Refused to answer&quot;</span><span class="p">,</span>
+        <span class="o">-</span><span class="mi">8</span><span class="p">:</span> <span class="s2">&quot;Inapplicable - Don&#39;t know&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># MNHLTH / MNHLTH31: Perceived mental health status</span>
+<span class="n">meps_mental_health_status_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;MNHLTH&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;self-rated mental health status&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Excellent&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Very good&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Good&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Fair&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;Poor&quot;</span><span class="p">,</span>
+        <span class="o">-</span><span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Inapplicable - Missing data&quot;</span><span class="p">,</span>
+        <span class="o">-</span><span class="mi">7</span><span class="p">:</span> <span class="s2">&quot;Inapplicable - Refused to answer&quot;</span><span class="p">,</span>
+        <span class="o">-</span><span class="mi">8</span><span class="p">:</span> <span class="s2">&quot;Inapplicable - Don&#39;t know&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># POVCAT / POVCAT15: Poverty category</span>
+<span class="n">meps_poverty_category_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;POVCAT&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;poverty category&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Poor&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Near poor&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Low income&quot;</span><span class="p">,</span>
+        <span class="mi">4</span><span class="p">:</span> <span class="s2">&quot;Middle income&quot;</span><span class="p">,</span>
+        <span class="mi">5</span><span class="p">:</span> <span class="s2">&quot;High income&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># INSCOV / INSCOV15: Insurance coverage</span>
+<span class="n">meps_insurance_coverage_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;INSCOV&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;insurance coverage&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Private insurance&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Public insurance&quot;</span><span class="p">,</span>
+        <span class="mi">3</span><span class="p">:</span> <span class="s2">&quot;Uninsured&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+<span class="p">)</span>
+
+<span class="c1"># DIABDX: Diabetes diagnosis</span>
+<span class="n">meps_diabetes_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;DIABDX&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;diabetes diagnosis&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Yes, diagnosed with diabetes&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;No, not diagnosed with diabetes&quot;</span><span class="p">,</span>
+        <span class="o">-</span><span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Inapplicable - Under 17 years old&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;Inapplicable - Under 17 years old&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># HIBPDX: High blood pressure diagnosis</span>
+<span class="n">meps_high_blood_pressure_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="s2">&quot;HIBPDX&quot;</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;high blood pressure diagnosis&quot;</span><span class="p">,</span>
+    <span class="n">value_map</span><span class="o">=</span><span class="p">{</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Yes, diagnosed with high blood pressure&quot;</span><span class="p">,</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;No, not diagnosed with high blood pressure&quot;</span><span class="p">,</span>
+        <span class="o">-</span><span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;Inapplicable - Under 17 years old&quot;</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">missing_value_fill</span><span class="o">=</span><span class="s2">&quot;Inapplicable - Under 17 years old&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Define the target column mapping and the question to prompt the LLM with by instantiating a <code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA</span></code> object:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.qa_interface</span> <span class="kn">import</span> <span class="n">MultipleChoiceQA</span><span class="p">,</span> <span class="n">Choice</span>
+
+<span class="n">TARGET_COL</span> <span class="o">=</span> <span class="s2">&quot;UTILIZATION&quot;</span>
+
+<span class="n">utilization_qa</span> <span class="o">=</span> <span class="n">MultipleChoiceQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">TARGET_COL</span><span class="p">,</span>
+    <span class="n">text</span><span class="o">=</span><span class="s2">&quot;Was is this person&#39;s estimated number of doctor visits in the past year?&quot;</span><span class="p">,</span>
+    <span class="n">choices</span><span class="o">=</span><span class="p">(</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;More than 10 doctor visits (high health-care utilization)&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+        <span class="n">Choice</span><span class="p">(</span><span class="s2">&quot;10 or fewer doctor visits (low health-care utilization)&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="c1"># UTILIZATION: Number of doctor visits</span>
+<span class="n">meps_utilization_col</span> <span class="o">=</span> <span class="n">ColumnToText</span><span class="p">(</span>
+    <span class="n">TARGET_COL</span><span class="p">,</span>
+    <span class="n">short_description</span><span class="o">=</span><span class="s2">&quot;doctor visits&quot;</span><span class="p">,</span>
+    <span class="n">question</span><span class="o">=</span><span class="n">utilization_qa</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Optionally, you can define a <strong>numeric question prompt that asks for a verbalized probability</strong> by instantiating a <code class="docutils literal notranslate"><span class="pre">DirectNumericQA</span></code> object:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.qa_interface</span> <span class="kn">import</span> <span class="n">DirectNumericQA</span>
+
+<span class="n">utilization_numeric_qa</span> <span class="o">=</span> <span class="n">DirectNumericQA</span><span class="p">(</span>
+    <span class="n">column</span><span class="o">=</span><span class="n">TARGET_COL</span><span class="p">,</span>
+    <span class="n">text</span><span class="o">=</span><span class="p">(</span>
+        <span class="s2">&quot;What is the probability that this person has high health-care utilization? &quot;</span>
+        <span class="s2">&quot;(i.e., more than 10 doctor visits per year)&quot;</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Define the prediction task be instantiating a <code class="docutils literal notranslate"><span class="pre">TaskMetadata</span></code> object:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Helper dict to access ColumnToText objects by column name</span>
+<span class="n">meps_columns_map</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">object</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="n">col_mapper</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">col_mapper</span>
+    <span class="k">for</span> <span class="n">col_mapper</span> <span class="ow">in</span> <span class="nb">globals</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">col_mapper</span><span class="p">,</span> <span class="n">ColumnToText</span><span class="p">)</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.task</span> <span class="kn">import</span> <span class="n">TaskMetadata</span>
+
+<span class="n">meps_task</span> <span class="o">=</span> <span class="n">TaskMetadata</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;health-care utilization&quot;</span><span class="p">,</span>
+    <span class="n">description</span><span class="o">=</span><span class="p">(</span>
+        <span class="s2">&quot;predict whether an individual had low or high healthcare utilization &quot;</span>
+        <span class="s2">&quot;in the past year by their number of doctor visits&quot;</span>
+    <span class="p">),</span>
+    <span class="n">features</span><span class="o">=</span><span class="p">[</span><span class="n">col</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">meps_columns_map</span><span class="o">.</span><span class="n">values</span><span class="p">()</span> <span class="k">if</span> <span class="n">col</span><span class="o">.</span><span class="n">name</span> <span class="o">!=</span> <span class="n">TARGET_COL</span><span class="p">],</span>
+    <span class="n">target</span><span class="o">=</span><span class="n">TARGET_COL</span><span class="p">,</span>
+    <span class="n">cols_to_text</span><span class="o">=</span><span class="n">meps_columns_map</span><span class="p">,</span>
+    <span class="n">sensitive_attribute</span><span class="o">=</span><span class="s2">&quot;SEX&quot;</span><span class="p">,</span>
+    <span class="n">multiple_choice_qa</span><span class="o">=</span><span class="n">utilization_qa</span><span class="p">,</span>
+    <span class="n">direct_numeric_qa</span><span class="o">=</span><span class="n">utilization_numeric_qa</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p><strong>Set</strong> whether to use numeric risk prompting or the default multiple-choice Q&amp;A prompting:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">meps_task</span><span class="o">.</span><span class="n">use_numeric_qa</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="c1"># meps_task.use_numeric_qa = True</span>
+</pre></div>
+</div>
+</div>
+<p>Load MEPS data and instantiate the <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> object:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span>
+
+<span class="n">DATA_PATH</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">folktexts</span><span class="o">.</span><span class="vm">__file__</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">parent</span> <span class="o">/</span> <span class="s2">&quot;notebooks&quot;</span> <span class="o">/</span> <span class="s2">&quot;data&quot;</span> <span class="o">/</span> <span class="s2">&quot;meps.csv&quot;</span>
+<span class="n">meps_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">DATA_PATH</span><span class="p">)</span>
+
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">meps_df</span><span class="p">,</span>
+    <span class="n">task</span><span class="o">=</span><span class="n">meps_task</span><span class="p">,</span>
+    <span class="n">test_size</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span>
+    <span class="n">val_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">subsampling</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>   <span class="c1"># NOTE: Optional, for faster but noisier results!</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p><strong>Optionally,</strong> you can subsample the data to get faster but noisier results, with:</p>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span><span class="o">.</span><span class="n">subsample</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)</span>  <span class="c1"># Keeps only 10% of the data</span>
+</pre></div>
+</div>
+<p>Load huggingface LLM:</p>
+<p><strong>Note:</strong> Update <code class="docutils literal notranslate"><span class="pre">MODEL_NAME_OR_PATH</span></code> as needed.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">MODEL_NAME_OR_PATH</span> <span class="o">=</span> <span class="s2">&quot;meta-llama/Meta-Llama-3-8B&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.classifier</span> <span class="kn">import</span> <span class="n">TransformersLLMClassifier</span>
+<span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">load_model_tokenizer</span>
+
+<span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">load_model_tokenizer</span><span class="p">(</span><span class="n">MODEL_NAME_OR_PATH</span><span class="p">)</span>
+
+<span class="n">llm_clf</span> <span class="o">=</span> <span class="n">TransformersLLMClassifier</span><span class="p">(</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">task</span><span class="o">=</span><span class="n">meps_task</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">context_length</span><span class="o">=</span><span class="mi">800</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "e35aa130a0844ed9910d21349c8c5c44", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<p>Example LLM prompt for this task:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_sample</span><span class="p">,</span> <span class="n">_y_sample</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">sample_n_train_examples</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">encode_row</span><span class="p">(</span><span class="n">X_sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">question</span><span class="o">=</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">question</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+The following data corresponds to a survey respondent. The survey was conducted among US residents in 2018. Please answer the question based on the information provided. The data provided is enough to reach an approximate answer.
+
+Information:
+- The age is: 37 years old.
+- The US region is: West.
+- The sex is: Female.
+- The marital status is: Never married.
+- The honorably discharged status is: Not part of military or not honorably discharged.
+- The self-rated health status is: Good.
+- The self-rated mental health status is: Good.
+- The poverty category is: Poor.
+- The insurance coverage is: Private insurance.
+- The diabetes diagnosis is: No, not diagnosed with diabetes.
+- The high blood pressure diagnosis is: No, not diagnosed with high blood pressure.
+
+Question: Was is this person&#39;s estimated number of doctor visits in the past year?
+A. More than 10 doctor visits (high health-care utilization).
+B. 10 or fewer doctor visits (low health-care utilization).
+Answer:
+</pre></div></div>
+</div>
+<section id="Run-benchmark-on-MEPS-data">
+<h2>Run benchmark on MEPS data<a class="headerlink" href="#Run-benchmark-on-MEPS-data" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.benchmark</span> <span class="kn">import</span> <span class="n">BenchmarkConfig</span><span class="p">,</span> <span class="n">Benchmark</span>
+<span class="n">bench</span> <span class="o">=</span> <span class="n">Benchmark</span><span class="p">(</span><span class="n">llm_clf</span><span class="o">=</span><span class="n">llm_clf</span><span class="p">,</span> <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">bench</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">results_root_dir</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "1a6560c0c807478387e3a96afddf1214", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+CPU times: user 2min 45s, sys: 5.43 s, total: 2min 51s
+Wall time: 2min 50s
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;threshold&#39;: 0.5,
+ &#39;n_samples&#39;: 1963,
+ &#39;n_positives&#39;: 354,
+ &#39;n_negatives&#39;: 1609,
+ &#39;model_name&#39;: &#39;Meta-Llama-3-8B&#39;,
+ &#39;accuracy&#39;: 0.630667345899134,
+ &#39;tpr&#39;: 0.7033898305084746,
+ &#39;fnr&#39;: 0.2966101694915254,
+ &#39;fpr&#39;: 0.3853325046612803,
+ &#39;tnr&#39;: 0.6146674953387197,
+ &#39;balanced_accuracy&#39;: 0.6590286629235971,
+ &#39;precision&#39;: 0.286536248561565,
+ &#39;ppr&#39;: 0.44268976057055526,
+ &#39;log_loss&#39;: 0.6640880361799258,
+ &#39;brier_score_loss&#39;: 0.23555848017340178,
+ &#39;fnr_ratio&#39;: 0.7005952380952382,
+ &#39;fnr_diff&#39;: 0.1007343124165554,
+ &#39;fpr_ratio&#39;: 0.9499246987951807,
+ &#39;fpr_diff&#39;: 0.019768133174791924,
+ &#39;ppr_ratio&#39;: 0.9785014080325124,
+ &#39;ppr_diff&#39;: 0.009620674692892017,
+ &#39;precision_ratio&#39;: 0.7380730854374539,
+ &#39;precision_diff&#39;: 0.086496795041585,
+ &#39;tpr_ratio&#39;: 0.8681980959035723,
+ &#39;tpr_diff&#39;: 0.1007343124165554,
+ &#39;balanced_accuracy_ratio&#39;: 0.9408797810568554,
+ &#39;balanced_accuracy_diff&#39;: 0.04048308962088176,
+ &#39;tnr_ratio&#39;: 0.968370986920333,
+ &#39;tnr_diff&#39;: 0.01976813317479187,
+ &#39;accuracy_ratio&#39;: 0.9913632326092872,
+ &#39;accuracy_diff&#39;: 0.0054705390193721115,
+ &#39;equalized_odds_ratio&#39;: 0.7005952380952382,
+ &#39;equalized_odds_diff&#39;: 0.1007343124165554,
+ &#39;roc_auc&#39;: 0.7183612307886781,
+ &#39;ece&#39;: 0.30890763123834214,
+ &#39;ece_quantile&#39;: 0.3089076312383417,
+ &#39;predictions_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/health-care utilization_subsampled-0.1_seed-42_hash-1494059445.test_predictions.csv&#39;,
+ &#39;config&#39;: {&#39;numeric_risk_prompting&#39;: False,
+  &#39;few_shot&#39;: None,
+  &#39;reuse_few_shot_examples&#39;: False,
+  &#39;batch_size&#39;: None,
+  &#39;context_size&#39;: None,
+  &#39;correct_order_bias&#39;: True,
+  &#39;feature_subset&#39;: None,
+  &#39;population_filter&#39;: None,
+  &#39;seed&#39;: 42,
+  &#39;model_name&#39;: &#39;Meta-Llama-3-8B&#39;,
+  &#39;model_hash&#39;: 2852898721,
+  &#39;task_name&#39;: &#39;health-care utilization&#39;,
+  &#39;task_hash&#39;: 3442708664,
+  &#39;dataset_name&#39;: &#39;health-care utilization_subsampled-0.1_seed-42_hash-1494059445&#39;,
+  &#39;dataset_subsampling&#39;: 0.1,
+  &#39;dataset_hash&#39;: 1494059445},
+ &#39;benchmark_hash&#39;: 1075925211,
+ &#39;results_dir&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211&#39;,
+ &#39;results_root_dir&#39;: &#39;/lustre/home/acruz/folktexts/notebooks&#39;,
+ &#39;current_time&#39;: &#39;2024.08.30-14.54.40&#39;,
+ &#39;plots&#39;: {&#39;roc_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve.pdf&#39;,
+  &#39;calibration_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve.pdf&#39;,
+  &#39;score_distribution_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution.pdf&#39;,
+  &#39;score_distribution_per_label_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution_per_label.pdf&#39;,
+  &#39;roc_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve_per_subgroup.pdf&#39;,
+  &#39;calibration_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve_per_subgroup.pdf&#39;}}
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">bench</span><span class="o">.</span><span class="n">plot_results</span><span class="p">();</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_custom-dataset-example_24_0.png" src="../_images/notebooks_custom-dataset-example_24_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_custom-dataset-example_24_1.png" src="../_images/notebooks_custom-dataset-example_24_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_custom-dataset-example_24_2.png" src="../_images/notebooks_custom-dataset-example_24_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_custom-dataset-example_24_3.png" src="../_images/notebooks_custom-dataset-example_24_3.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_custom-dataset-example_24_4.png" src="../_images/notebooks_custom-dataset-example_24_4.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_custom-dataset-example_24_5.png" src="../_images/notebooks_custom-dataset-example_24_5.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">bench</span><span class="o">.</span><span class="n">results</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;threshold&#39;: 0.5,
+ &#39;n_samples&#39;: 1963,
+ &#39;n_positives&#39;: 354,
+ &#39;n_negatives&#39;: 1609,
+ &#39;model_name&#39;: &#39;Meta-Llama-3-8B&#39;,
+ &#39;accuracy&#39;: 0.630667345899134,
+ &#39;tpr&#39;: 0.7033898305084746,
+ &#39;fnr&#39;: 0.2966101694915254,
+ &#39;fpr&#39;: 0.3853325046612803,
+ &#39;tnr&#39;: 0.6146674953387197,
+ &#39;balanced_accuracy&#39;: 0.6590286629235971,
+ &#39;precision&#39;: 0.286536248561565,
+ &#39;ppr&#39;: 0.44268976057055526,
+ &#39;log_loss&#39;: 0.6640880361799258,
+ &#39;brier_score_loss&#39;: 0.23555848017340178,
+ &#39;fnr_ratio&#39;: 0.7005952380952382,
+ &#39;fnr_diff&#39;: 0.1007343124165554,
+ &#39;fpr_ratio&#39;: 0.9499246987951807,
+ &#39;fpr_diff&#39;: 0.019768133174791924,
+ &#39;ppr_ratio&#39;: 0.9785014080325124,
+ &#39;ppr_diff&#39;: 0.009620674692892017,
+ &#39;precision_ratio&#39;: 0.7380730854374539,
+ &#39;precision_diff&#39;: 0.086496795041585,
+ &#39;tpr_ratio&#39;: 0.8681980959035723,
+ &#39;tpr_diff&#39;: 0.1007343124165554,
+ &#39;balanced_accuracy_ratio&#39;: 0.9408797810568554,
+ &#39;balanced_accuracy_diff&#39;: 0.04048308962088176,
+ &#39;tnr_ratio&#39;: 0.968370986920333,
+ &#39;tnr_diff&#39;: 0.01976813317479187,
+ &#39;accuracy_ratio&#39;: 0.9913632326092872,
+ &#39;accuracy_diff&#39;: 0.0054705390193721115,
+ &#39;equalized_odds_ratio&#39;: 0.7005952380952382,
+ &#39;equalized_odds_diff&#39;: 0.1007343124165554,
+ &#39;roc_auc&#39;: 0.7183612307886781,
+ &#39;ece&#39;: 0.30890763123834214,
+ &#39;ece_quantile&#39;: 0.3089076312383417,
+ &#39;predictions_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/health-care utilization_subsampled-0.1_seed-42_hash-1494059445.test_predictions.csv&#39;,
+ &#39;config&#39;: {&#39;numeric_risk_prompting&#39;: False,
+  &#39;few_shot&#39;: None,
+  &#39;reuse_few_shot_examples&#39;: False,
+  &#39;batch_size&#39;: None,
+  &#39;context_size&#39;: None,
+  &#39;correct_order_bias&#39;: True,
+  &#39;feature_subset&#39;: None,
+  &#39;population_filter&#39;: None,
+  &#39;seed&#39;: 42,
+  &#39;model_name&#39;: &#39;Meta-Llama-3-8B&#39;,
+  &#39;model_hash&#39;: 2852898721,
+  &#39;task_name&#39;: &#39;health-care utilization&#39;,
+  &#39;task_hash&#39;: 3442708664,
+  &#39;dataset_name&#39;: &#39;health-care utilization_subsampled-0.1_seed-42_hash-1494059445&#39;,
+  &#39;dataset_subsampling&#39;: 0.1,
+  &#39;dataset_hash&#39;: 1494059445},
+ &#39;benchmark_hash&#39;: 1075925211,
+ &#39;results_dir&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211&#39;,
+ &#39;results_root_dir&#39;: &#39;/lustre/home/acruz/folktexts/notebooks&#39;,
+ &#39;current_time&#39;: &#39;2024.08.30-14.54.41&#39;,
+ &#39;plots&#39;: {&#39;roc_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve.pdf&#39;,
+  &#39;calibration_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve.pdf&#39;,
+  &#39;score_distribution_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution.pdf&#39;,
+  &#39;score_distribution_per_label_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution_per_label.pdf&#39;,
+  &#39;roc_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve_per_subgroup.pdf&#39;,
+  &#39;calibration_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve_per_subgroup.pdf&#39;}}
+</pre></div></div>
+</div>
+<hr class="docutils" />
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="detailed-example.html" class="btn btn-neutral float-left" title="Example: Run ACS benchmark task" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="custom-acs-task-example.html" class="btn btn-neutral float-right" title="Run folktexts benchmark with a custom ACS task" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
+
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diff --git a/notebooks/custom-dataset-example.ipynb b/notebooks/custom-dataset-example.ipynb
new file mode 100644
index 0000000..edd1013
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+++ b/notebooks/custom-dataset-example.ipynb
@@ -0,0 +1,763 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run `folktexts` benchmark with a different data source\n",
+    "\n",
+    "This notebook describes how to use the `folktexts` pipeline to run a benchmark on a different\n",
+    "tabular dataset (distinct from the ACS tasks provided out-of-the-box).\n",
+    "\n",
+    "In this example, we have mapped 11 columns of the [Medical Expenditure Panel Survey (MEPS)](https://meps.ahrq.gov/mepsweb/index.jsp) dataset (survey panels 19, 20, and 21).\n",
+    "\n",
+    "Data codebook available [at this link](https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_codebook.jsp?PUFId=H192)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.0.22'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import folktexts\n",
+    "\n",
+    "folktexts.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define mappings for each dataset column using `ColumnToText`: each value of each\n",
+    "column should be mapped to a meaningful text representation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.col_to_text import ColumnToText\n",
+    "\n",
+    "# AGE\n",
+    "meps_age_col = ColumnToText(\n",
+    "    \"AGE\",\n",
+    "    short_description=\"age\",\n",
+    "    value_map=lambda x: f\"{int(x)} years old\",\n",
+    ")\n",
+    "\n",
+    "# REGION: Census region\n",
+    "meps_region_col = ColumnToText(\n",
+    "    \"REGION\",\n",
+    "    short_description=\"US region\",\n",
+    "    value_map={\n",
+    "        1: \"Northeast\",\n",
+    "        2: \"Midwest\",\n",
+    "        3: \"South\",\n",
+    "        4: \"West\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# SEX\n",
+    "meps_sex_col = ColumnToText(\n",
+    "    \"SEX\",\n",
+    "    short_description=\"sex\",\n",
+    "    value_map={\n",
+    "        1: \"Male\",\n",
+    "        2: \"Female\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# MARRY / MARRY31X\n",
+    "meps_marital_status = ColumnToText(\n",
+    "    \"MARRY\",\n",
+    "    short_description=\"marital status\",\n",
+    "    value_map={\n",
+    "        1: \"Married\",\n",
+    "        2: \"Widowed\",\n",
+    "        3: \"Divorced\",\n",
+    "        4: \"Separated\",\n",
+    "        5: \"Never married\",\n",
+    "        6: \"Inapplicable - Under 16 years old\",\n",
+    "        7: \"Married during current survey round\",\n",
+    "        8: \"Widowed during current survey round\",\n",
+    "        9: \"Divorced during current survey round\",\n",
+    "        10: \"Separated during current survey round\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# HONRDC: Honorably discharged from military\n",
+    "meps_education_col = ColumnToText(\n",
+    "    \"HONRDC\",\n",
+    "    short_description=\"honorably discharged status\",\n",
+    "    value_map={\n",
+    "        1: \"Yes, honorably discharged from military\",\n",
+    "        2: \"Not part of military or not honorably discharged\",\n",
+    "        3: \"Inapplicable - Under 16 years old\",\n",
+    "        4: \"Inapplicable - Now on active duty\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# RTHLTH / RTHLTH31: Perceived health status\n",
+    "meps_health_status_col = ColumnToText(\n",
+    "    \"RTHLTH\",\n",
+    "    short_description=\"self-rated health status\",\n",
+    "    value_map={\n",
+    "        1: \"Excellent\",\n",
+    "        2: \"Very good\",\n",
+    "        3: \"Good\",\n",
+    "        4: \"Fair\",\n",
+    "        5: \"Poor\",\n",
+    "        -1: \"Inapplicable - Missing data\",\n",
+    "        -7: \"Inapplicable - Refused to answer\",\n",
+    "        -8: \"Inapplicable - Don't know\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# MNHLTH / MNHLTH31: Perceived mental health status\n",
+    "meps_mental_health_status_col = ColumnToText(\n",
+    "    \"MNHLTH\",\n",
+    "    short_description=\"self-rated mental health status\",\n",
+    "    value_map={\n",
+    "        1: \"Excellent\",\n",
+    "        2: \"Very good\",\n",
+    "        3: \"Good\",\n",
+    "        4: \"Fair\",\n",
+    "        5: \"Poor\",\n",
+    "        -1: \"Inapplicable - Missing data\",\n",
+    "        -7: \"Inapplicable - Refused to answer\",\n",
+    "        -8: \"Inapplicable - Don't know\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# POVCAT / POVCAT15: Poverty category\n",
+    "meps_poverty_category_col = ColumnToText(\n",
+    "    \"POVCAT\",\n",
+    "    short_description=\"poverty category\",\n",
+    "    value_map={\n",
+    "        1: \"Poor\",\n",
+    "        2: \"Near poor\",\n",
+    "        3: \"Low income\",\n",
+    "        4: \"Middle income\",\n",
+    "        5: \"High income\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# INSCOV / INSCOV15: Insurance coverage\n",
+    "meps_insurance_coverage_col = ColumnToText(\n",
+    "    \"INSCOV\",\n",
+    "    short_description=\"insurance coverage\",\n",
+    "    value_map={\n",
+    "        1: \"Private insurance\",\n",
+    "        2: \"Public insurance\",\n",
+    "        3: \"Uninsured\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# DIABDX: Diabetes diagnosis\n",
+    "meps_diabetes_col = ColumnToText(\n",
+    "    \"DIABDX\",\n",
+    "    short_description=\"diabetes diagnosis\",\n",
+    "    value_map={\n",
+    "        1: \"Yes, diagnosed with diabetes\",\n",
+    "        2: \"No, not diagnosed with diabetes\",\n",
+    "        -1: \"Inapplicable - Under 17 years old\",\n",
+    "    },\n",
+    "    missing_value_fill=\"Inapplicable - Under 17 years old\",\n",
+    ")\n",
+    "\n",
+    "# HIBPDX: High blood pressure diagnosis\n",
+    "meps_high_blood_pressure_col = ColumnToText(\n",
+    "    \"HIBPDX\",\n",
+    "    short_description=\"high blood pressure diagnosis\",\n",
+    "    value_map={\n",
+    "        1: \"Yes, diagnosed with high blood pressure\",\n",
+    "        2: \"No, not diagnosed with high blood pressure\",\n",
+    "        -1: \"Inapplicable - Under 17 years old\",\n",
+    "    },\n",
+    "    missing_value_fill=\"Inapplicable - Under 17 years old\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the target column mapping and the question to prompt the LLM with by instantiating a `MultipleChoiceQA` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.qa_interface import MultipleChoiceQA, Choice\n",
+    "\n",
+    "TARGET_COL = \"UTILIZATION\"\n",
+    "\n",
+    "utilization_qa = MultipleChoiceQA(\n",
+    "    column=TARGET_COL,\n",
+    "    text=\"Was is this person's estimated number of doctor visits in the past year?\",\n",
+    "    choices=(\n",
+    "        Choice(\"More than 10 doctor visits (high health-care utilization)\", 1),\n",
+    "        Choice(\"10 or fewer doctor visits (low health-care utilization)\", 0),\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# UTILIZATION: Number of doctor visits\n",
+    "meps_utilization_col = ColumnToText(\n",
+    "    TARGET_COL,\n",
+    "    short_description=\"doctor visits\",\n",
+    "    question=utilization_qa,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optionally, you can define a **numeric question prompt that asks for a verbalized probability** by instantiating a `DirectNumericQA` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.qa_interface import DirectNumericQA\n",
+    "\n",
+    "utilization_numeric_qa = DirectNumericQA(\n",
+    "    column=TARGET_COL,\n",
+    "    text=(\n",
+    "        \"What is the probability that this person has high health-care utilization? \"\n",
+    "        \"(i.e., more than 10 doctor visits per year)\"\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the prediction task be instantiating a `TaskMetadata` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper dict to access ColumnToText objects by column name\n",
+    "meps_columns_map: dict[str, object] = {\n",
+    "    col_mapper.name: col_mapper\n",
+    "    for col_mapper in globals().values()\n",
+    "    if isinstance(col_mapper, ColumnToText)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.task import TaskMetadata\n",
+    "\n",
+    "meps_task = TaskMetadata(\n",
+    "    name=\"health-care utilization\",\n",
+    "    description=(\n",
+    "        \"predict whether an individual had low or high healthcare utilization \"\n",
+    "        \"in the past year by their number of doctor visits\"\n",
+    "    ),\n",
+    "    features=[col.name for col in meps_columns_map.values() if col.name != TARGET_COL],\n",
+    "    target=TARGET_COL,\n",
+    "    cols_to_text=meps_columns_map,\n",
+    "    sensitive_attribute=\"SEX\",\n",
+    "    multiple_choice_qa=utilization_qa,\n",
+    "    direct_numeric_qa=utilization_numeric_qa,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Set** whether to use numeric risk prompting or the default multiple-choice Q&A prompting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meps_task.use_numeric_qa = False\n",
+    "# meps_task.use_numeric_qa = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load MEPS data and instantiate the `Dataset` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.dataset import Dataset\n",
+    "\n",
+    "DATA_PATH = Path(folktexts.__file__).parent.parent / \"notebooks\" / \"data\" / \"meps.csv\"\n",
+    "meps_df = pd.read_csv(DATA_PATH)\n",
+    "\n",
+    "dataset = Dataset(\n",
+    "    data=meps_df,\n",
+    "    task=meps_task,\n",
+    "    test_size=0.4,\n",
+    "    val_size=0,\n",
+    "    subsampling=0.1,   # NOTE: Optional, for faster but noisier results!\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Optionally,** you can subsample the data to get faster but noisier results, with:\n",
+    "```py\n",
+    "dataset.subsample(0.1)  # Keeps only 10% of the data\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load huggingface LLM:\n",
+    "\n",
+    "**Note:** Update `MODEL_NAME_OR_PATH` as needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAME_OR_PATH = \"meta-llama/Meta-Llama-3-8B\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e35aa130a0844ed9910d21349c8c5c44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from folktexts.classifier import TransformersLLMClassifier\n",
+    "from folktexts.llm_utils import load_model_tokenizer\n",
+    "\n",
+    "model, tokenizer = load_model_tokenizer(MODEL_NAME_OR_PATH)\n",
+    "\n",
+    "llm_clf = TransformersLLMClassifier(\n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    task=meps_task,\n",
+    "    batch_size=20,\n",
+    "    context_length=800,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example LLM prompt for this task:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following data corresponds to a survey respondent. The survey was conducted among US residents in 2018. Please answer the question based on the information provided. The data provided is enough to reach an approximate answer.\n",
+      "\n",
+      "Information:\n",
+      "- The age is: 37 years old.\n",
+      "- The US region is: West.\n",
+      "- The sex is: Female.\n",
+      "- The marital status is: Never married.\n",
+      "- The honorably discharged status is: Not part of military or not honorably discharged.\n",
+      "- The self-rated health status is: Good.\n",
+      "- The self-rated mental health status is: Good.\n",
+      "- The poverty category is: Poor.\n",
+      "- The insurance coverage is: Private insurance.\n",
+      "- The diabetes diagnosis is: No, not diagnosed with diabetes.\n",
+      "- The high blood pressure diagnosis is: No, not diagnosed with high blood pressure.\n",
+      "\n",
+      "Question: Was is this person's estimated number of doctor visits in the past year?\n",
+      "A. More than 10 doctor visits (high health-care utilization).\n",
+      "B. 10 or fewer doctor visits (low health-care utilization).\n",
+      "Answer:\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_sample, _y_sample = dataset.sample_n_train_examples(n=1)\n",
+    "print(llm_clf.encode_row(X_sample.iloc[0], question=llm_clf.task.question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run benchmark on MEPS data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.benchmark import BenchmarkConfig, Benchmark\n",
+    "bench = Benchmark(llm_clf=llm_clf, dataset=dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a6560c0c807478387e3a96afddf1214",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/99 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 45s, sys: 5.43 s, total: 2min 51s\n",
+      "Wall time: 2min 50s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 1963,\n",
+       " 'n_positives': 354,\n",
+       " 'n_negatives': 1609,\n",
+       " 'model_name': 'Meta-Llama-3-8B',\n",
+       " 'accuracy': 0.630667345899134,\n",
+       " 'tpr': 0.7033898305084746,\n",
+       " 'fnr': 0.2966101694915254,\n",
+       " 'fpr': 0.3853325046612803,\n",
+       " 'tnr': 0.6146674953387197,\n",
+       " 'balanced_accuracy': 0.6590286629235971,\n",
+       " 'precision': 0.286536248561565,\n",
+       " 'ppr': 0.44268976057055526,\n",
+       " 'log_loss': 0.6640880361799258,\n",
+       " 'brier_score_loss': 0.23555848017340178,\n",
+       " 'fnr_ratio': 0.7005952380952382,\n",
+       " 'fnr_diff': 0.1007343124165554,\n",
+       " 'fpr_ratio': 0.9499246987951807,\n",
+       " 'fpr_diff': 0.019768133174791924,\n",
+       " 'ppr_ratio': 0.9785014080325124,\n",
+       " 'ppr_diff': 0.009620674692892017,\n",
+       " 'precision_ratio': 0.7380730854374539,\n",
+       " 'precision_diff': 0.086496795041585,\n",
+       " 'tpr_ratio': 0.8681980959035723,\n",
+       " 'tpr_diff': 0.1007343124165554,\n",
+       " 'balanced_accuracy_ratio': 0.9408797810568554,\n",
+       " 'balanced_accuracy_diff': 0.04048308962088176,\n",
+       " 'tnr_ratio': 0.968370986920333,\n",
+       " 'tnr_diff': 0.01976813317479187,\n",
+       " 'accuracy_ratio': 0.9913632326092872,\n",
+       " 'accuracy_diff': 0.0054705390193721115,\n",
+       " 'equalized_odds_ratio': 0.7005952380952382,\n",
+       " 'equalized_odds_diff': 0.1007343124165554,\n",
+       " 'roc_auc': 0.7183612307886781,\n",
+       " 'ece': 0.30890763123834214,\n",
+       " 'ece_quantile': 0.3089076312383417,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/health-care utilization_subsampled-0.1_seed-42_hash-1494059445.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': False,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'Meta-Llama-3-8B',\n",
+       "  'model_hash': 2852898721,\n",
+       "  'task_name': 'health-care utilization',\n",
+       "  'task_hash': 3442708664,\n",
+       "  'dataset_name': 'health-care utilization_subsampled-0.1_seed-42_hash-1494059445',\n",
+       "  'dataset_subsampling': 0.1,\n",
+       "  'dataset_hash': 1494059445},\n",
+       " 'benchmark_hash': 1075925211,\n",
+       " 'results_dir': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211',\n",
+       " 'results_root_dir': '/lustre/home/acruz/folktexts/notebooks',\n",
+       " 'current_time': '2024.08.30-14.54.40',\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "bench.run(results_root_dir=\".\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+979OJ6O9evXCmjVrMH/+fDRv3rzSM4WKokCSJCxZsqTCJO56tx0AygbST5w4EV26dMHQoUMRFhYGg8GAxYsXO5y1u//++x1+OIwePRpjxoxxqj0VWbRoEd5880307dsX48aNQ0hICGRZxowZMyr8bCuLw/Xic/LkSQwePBhNmjTBxIkTUa9ePZhMJmzbtg3vvvuudp+5V155BevXr9fef/vtt1/zvnqtW7dG7dq18dlnn6Fz5844e/Ys7rzzTodlVqxYgbZt22LVqlVo0aJFub6cmpqKTz75BIcOHXJIrCpi/yH2448/MtG6STHRIuHFx8cDAP78808AZYmJoig4fvx4pQOC7b+yT5w44TBI1Wq14tSpU9fdedq3c/jwYbRr106X+z19+umnkCQJHTp0uOZyAQEB6N69O7p37w6r1YoxY8Zg0aJFePzxx+Hn5+fxumVkZDh8ZhkZGVAURUtG7NPdrz5zZD+LeCVX6hYREeEwA9PuxIkT2us3qnnz5li+fLlDmX3WmDNuvfVWRERE4IcffsAzzzxT6XINGzaEqqqoX79+pRMd7Cr7jL744gs0aNAACxYscFhm3rx5Dsu99tprDrNCb3RQ9hdffIG2bdtixowZDuW5ubkOZ3du1Ndffw2r1Yq33nrLIbZXX1YdNmwY7r33Xu1vZ+4VZ7VatUuPtWvXLhfz5s2bAyibgHHl7Rvs7GcPr7zMWRn7Mq5crqTqhWO0SBi7du2q8BezfTyV/RJSly5dIMsyFi5cWO7u6vb3t2/fHiaTCe+//77DOteuXYvLly+jU6dO163PPffcgz/++AMfffRRudeKiopuaMf69ttv4/vvv0f37t2vee+dCxcuOPxtNpvRtGlTqKqqHQzs9/tydkzL9XzwwQcOf9vHqtxxxx0AgKCgIISGhuKnn35yWK6iafmu1K1Tp07Yu3cv9uzZo5UVFBTgo48+QmRkpEvjzCoTEhKC9u3bO/xz5X5ikiThueeew+jRo8vdH+1K3bp1g8FgwIIFC8p9p9W/ZsnZ+fv7V/j52M+EXfn+X3/9FWlpaQ7L3XrrrQ7tudFEy2AwlKvzli1b8Mcff9zQeivaDuDYvsuXL2PdunUOy0VHRzu0z/7Dq6CgoNzlRqAsUbx06ZK2nJ+fX7mY25Mr+7izqxP8TZs2QZZlxMbGXrcd33zzDYD/JW908+EZLRLGyy+/jMLCQnTt2hVNmjRBSUkJfvnlF2zZsgWRkZHaYPWoqCiMGDEC//73v9G/f39069YNZrMZ+/btwy233ILx48ejVq1aePzxx7FgwQIMGzYMqamp+P3337Fq1SokJCQ4/EKuzH333YctW7bgxRdfxO7du9G6dWvYbDacOHECW7duxdKlS5GQkHDNdZSWlmqPbrFarTh9+jS+/vpr/Pbbb2jbti2mT59+zfcPHToU4eHhaN26NcLCwnDixAmsXLkSnTp10u4oHxcXBwD417/+he7du8NkMqFz585OXZ6qyKlTpzBixAh07NgRaWlp+PTTT9GzZ0+HA0m/fv3w9ttv47nnnkN8fDx++umnCs9GuVK34cOHY9OmTXjssccwcOBAhISEYMOGDTh16hTmz59f6aUob+vSpQu6dOlyzWUaNmyIJ598Eq+//jpOnz6NLl26IDAwEKdOncJXX32FBx54AEOHDgVQ9hlt3rwZM2fOREJCAgICApCamoq///3v+M9//oNRo0bh73//O06dOoU1a9YgOjrapSR/586dDme8rmxHRbdR+fvf/46FCxdi0qRJSE5OxpEjR/DZZ595/PYFHTp0gMlkwogRI/DQQw8hPz8fH3/8McLCwpCVlXXd92dkZGDw4MHo3r07mjRpAlmWsX//fnz66aeIjIwsd4m4IkOHDsV3332HAQMGYMCAAahZsya+/fZbfPfdd+jXr1+5W1n88ccfWn8uKSnB4cOH8eGHHyI0NJSXDW9iTLRIGBMmTMDWrVuxbds2fPjhhygpKUFERAT69++PkSNHOlwyGDduHOrXr4+VK1fiX//6F/z9/REbG+twlmHMmDGoVasWVq5ciZkzZyIkJAQPPPAAnn76aaduRGg/a/buu+9i48aN+PLLL+Hv74/69etj4MCB170kBJQlVxMmTABQduaiVq1aiI+Px6hRo9C1a9frJg8PPvggPvvsMyxfvhwFBQWoW7cuBg4ciCeeeEJbJjExEePGjcOaNWuwfft2KIqC//73v24nWnPnzsWbb76J119/HUajEQ8//LDWBrtRo0YhJycHX3zxBbZs2YI77rgDS5cuLTdxwJW6hYeHY82aNXjttdewcuVKFBcXIzY2FosWLcLf//53t9riS8OHD0ejRo3w7rvvYuHChQDKxvV16NBBuykpAPTv3x+HDh3CJ598gnfffReRkZFITU1Fnz59tHtYff/994iOjsZrr72GrVu34ocffnC6Htu3b9duUnulyMjIChOtESNGoLCwEJ999hk2b96Mli1bYvHixXj99dfd+BQq16RJE8ybNw9z587F7NmzER4ejn/84x+oVasWJk+efN3316lTB3fddRd27dqFDRs2oKSkBJGRkRgwYABGjBjh1GXO2267TRtzt3r1aly8eBGRkZF46qmnMGzYsHLLHzp0SOsLsiwjNDQU3bp1w7hx48olZXTzkNRrjQwlIiIiIrdVjXPtRERERNUQEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItIJEy0iIiIinTDRIiIiItKJ0dcVqC4URUFpaSlkWYYkSb6uDhERETlBVVUoigKj0QhZ9vz5JyZaHlJaWop9+/b5uhpERETkhoSEBJjNZo+vl4mWh9iz4ISEBBgMBh/XxvNsNhvS09PRqFGjatm+6opxEw9jJibGTTz2mDVo0AAHDx7U5WwWwETLY+yXCw0GQ7XtZJcvX67W7auuGDfxMGZiYtzEY48ZAN2G/XAwPBEREZFOmGgRERER6YSJFjlFkiRERUVxRqVgGDfxMGZiYtzE462YcYwWOUWWZYSHh/u6GuQixk08jJmYGDfx2GNms9n03Y6ua6dqw2az4cCBA7p/IcmzGDfxMGZiYtzE462YMdEipxUVFfm6CuQGxk08jJmYGDfxeCNmTLSIiIiIdMJEi4iIiEgnTLTIKbIsIzo6Wrc755I+GDfxMGZiYtzE462YcdYhOUWSJISEhPi6GuQixk08jJmYGDfx2GPGwfBUJdhsNuzZs4czagTDuImHMRMT4yYeb8WMiRY5TVEUX1eB3MC4iYcxExPjJh5vxIyJFhEREZFOmGgRERER6YSJFjlFlmW0bNmSM2oEw7iJR5ZlxMXFMWaCYV8Tj7dixm8EOc1sNvu6CuQGxs0zFEX1ynYkSYLFYvHIg269VWcqw74mHm/EjLd3IKcoioK0tDQkJSXBYDD4ujrkJMbNc2RZwvyvj+L0hULdt1VQUICAgIAbWkdkqD/GpDbzUI3oetjXxGOPWUJCgq7bYaJFROSk0xcKkZ6dr+s2VBXIy8tDUJAKD5zUIiIf46VDIiIiIp0w0SIiIiLSCS8dklNkWUZSUhJn1AiGcROPJAFBQUG8bCgY9jXx2GOmqvpOGuE3gpxmtVp9XQVyA+MmHlXlHcZFxL4mHm/EjIkWOUVRFBw8eJCPmBAM4yYeVQXy8wug849s8jD2NfF4K2ZMtIiIiIh0wkSLiIiISCdMtMhpHOQpJsZNPBwILyb2NfF4I2acdUhOMRgMSE5O9nU1yEWMm3jssw5JLOxr4rHHzGaz6bodpt/kFFVVcenSJd2nwZJnMW5iKi3Vd8dPnse+Jh5vxYyJFjlFURQcO3aMM2oEw7iJR1WBwsJCzjoUDPuaeLwVMyZaRERERDphokVERESkEyZa5DSLxeLrKpAbGDfxcPaamNjXxOONmHHWITnFYDAgLi7O19UgFzFu4pEkIDAwwNfVIBexr4nHHjPOOqQqQVEUnD9/ngM9BcO4CUgFSkpKAA6GFwr7mni8FTMmWuQUVVWRkZHBqcuCYdzEowIoKipmniUY9jXxeCtmTLSIiIiIdMJEi4iIiEgnTLTIacHBwb6uArmBcROP0WjwdRXIDexr4vFGzDjrkJxiMBjQrFkzX1eDXMS4iUeSAH9/f19Xg1zEviYee8w465CqBEVRcObMGc6oEQzjJiAVsBZbOetQMOxr4vFWzJhokVNUVcXZs2c5o0YwjJt4VADFVivzLMGwr4nHWzFjokVERESkEyZaRERERDphokVOkSQJYWFhkCTJ11UhFzBu4pEAmExGMGJiYV8Tj7dixlmH5BRZltGoUSNfV4NcxLgJSOLDiUXEviYee8w465CqBEVRkJ6ezhk1gmHcBKQCRUVFnHUoGPY18XgrZky0yCmqqiI7O5szagTDuIlHBVBSUso8SzDsa+LxVsyYaBERERHphIkWERERkU6YaJFTJElCvXr1OKNGMIybeCQAfmYzZx0Khn1NPN6KGWcdklNkWUZERISvq0EuYtwEJAFmP7Ova0EuYl8Tjz1mnHVIVYLNZsPRo0d1/0KSZzFu4lFVoLCwEBxTLRb2NfF4K2ZMtMhpubm5vq4CuYFxE09pKQ/WImJfE483YsZEi4iIiEgnTLSIiIiIdMJEi5wiSRKioqI4o0YwjJt4JAAWix9nHQqGfU083ooZZx2SU2RZRnh4uK+rQS5i3AQkASaTyde1IBexr4nHHjMOhqcqwWaz4cCBA5xRIxjGTTyqCuTnF3DWoWDY18TjrZgx0SKnFRUV+boK5AbGTTx8MLGY2NfE442Y+TTRWrx4Mfr27Yvk5GS0a9cOTzzxBE6cOOGwTHFxMaZNm4a2bdsiOTkZY8aMwfnz5x2WOXPmDIYPH45WrVqhXbt2mD17NkpLSx2W2b17N3r37o34+Hh07doVn3zySbn6fPDBB0hNTUVCQgL69euHvXv3er7RREREdNPwaaL1ww8/YMCAAfjoo4+wfPlylJaWYujQoSgoKNCWmTFjBr755hvMnTsX77//Pv7880+MHj1ae91ms+Hxxx9HSUkJ1qxZg1mzZmH9+vWYN2+etkxmZiYef/xxtG3bFhs3bsQjjzyCKVOmYPv27doymzdvxsyZMzFq1CisX78ezZs3x9ChQ5Gdne2dD4OIiIiqHUlVq85IgJycHLRr1w4rV67EbbfdhsuXL6Ndu3aYM2cO7r77bgDA8ePH0b17d3z44YdISkrCtm3bMGLECGzfvl0biLh69WrMmTMHO3fuhNlsxmuvvYZt27bh888/17b11FNPITc3F++88w4AoF+/fkhISMALL7wAoOzUfadOnTBw4EAMHz78unW32WxIS0tDQkICDAYDgLIZDbIsQ1EUXPkx28uvvi5cWbksy5AkqcJye12dKTcYDFBVtcLyq+t4dbmqqrh8+TKCg4MrXF7ENl1dx+rYJpvNhtzcXNSoUQOSJFWLNvkqTpIkYeK6vfj9fP5ff5e9fvUeVJIAqMDVO9aKyiX7/ziUq7DZbDAajeXX/df/VLjNq+rSODwQs/omVvi5V+c4+apN9n1kjRo1IMtytWjTlapLnK6Wl5cHi8WC/fv3IykpSTt+e1KVmnV4+fJlAEBISAgAYP/+/SgpKUH79u21ZZo2bYqIiAikpaUhKSkJaWlpiImJcZjtkZKSgqlTp+LYsWNo2bIl0tLS0K5dO4dtpaSkYMaMGQAAq9WKAwcO4PHHH9del2UZ7du3x549e1xqw759+7T/DgsLQ6NGjXDy5EmHM2P16tVDREQETpw44XBX2qioKISHh+Pw4cMO142jo6MREhKCvXv3OnwJW7ZsCbPZjLS0NIc6JCUlwWq14uDBgw7tSU5ORm5uLo4dO6aVWywWxMXFIScnBxkZGVp5cHAwmjVrhnPnzuHs2bNsk6BtyszMrHZt8kWcSkpKYLFYUFBQgLy8PEgSEBQUhNJSGwoLCx3aGhgYgJLSEhQVFWvlRqMB/v7+sFqtKLZatXKTyQiLxYKi4iKUlPxvuIOf2QwYgaKiQoe7xFssfjCZTCgoKHCou7+/P4xGA/Lz87RkqyCgLPtSFOWmiRPbxDa506b9+/dDT1XmjJaiKBg5ciRyc3OxevVqAMBnn32GSZMmlfsQ7r//frRt2xb//Oc/8fzzz+PMmTPamSmg7DlhSUlJePvtt9GpUyfcdddd6NOnj0MitW3bNgwfPhy//vorLl26hDvuuANr1qxBcnKytsyrr76KH3/8ER9//PF161/dz2jZbDbs378fCQkJMJlM1aJNV9exOrappKQE+/btQ3x8PAwGQ7VoU7U/o6WqyM/PR2BQEK7GM1pV97tn30fGx8fDaDRWizZdqbrE6UqKomD//v1o3rw5Dh48WP3PaE2bNg1Hjx7FqlWrfF2VG2IwGMoFyv7FqmhZb5dLklRheWV1vLJcVVXt7+rSJmfKRW+Tqqrlvpeit0mvOl6r3O7qextWeK9DCRXfcNSJchWSlnRVdh9FV8or+9yB6hknX7bJ3tfsy1WHNnmz3BdtUhRFl+TKoT66rt1J06dPx7fffov33nsPdevW1crDw8NRUlJS7qGP2dnZqF27trbM1bMQ7X9fb5mgoCBYLBaEhobCYDCUG/ienZ3NG9ARERGR23yaaKmqiunTp+PLL7/Ee++9hwYNGji8Hh8fD5PJhJ07d2plJ06cwJkzZ5CUlASg7HrvkSNHHJKkHTt2ICgoCNHR0doyu3btclj3jh07tHWYzWbExcU5bEdRFOzcudPhUiIRERGRK3yaaE2bNg2ffvopXn/9dQQGBiIrKwtZWVnaQLsaNWqgb9++mDVrFnbt2oX9+/dj8uTJSE5O1pKklJQUREdHY8KECTh8+DC2b9+OuXPnYsCAATCbzQCAhx56CJmZmXj11Vdx/PhxfPDBB9iyZQsGDx6s1eXRRx/FRx99hPXr1+P48eOYOnUqCgsL0adPH29/LFWSLMto2bJlpadkqWpi3MQjSUBgYECllwepamJfE4+3YubTMVr2Qe8DBw50KJ85c6aW4EyePBmyLGPs2LGwWq1ISUnBiy++qC1rMBiwaNEiTJ06FQ8++CD8/f3Ru3dvjB07VlumQYMGWLx4MWbOnIkVK1agbt26ePnll9GxY0dtme7duyMnJwfz5s1DVlYWWrRogaVLl/LS4RXsiSuJhXETjyTxYC0i9jXxmM3mcgPtPa3KzDoUnX3WoV6zFnyturevumLcPGviur1Iz87XdRuqWnZvn6CgoBs6q9UorGzWIXkH+5p4rrxbwL59+3SLHX82EREREemEiRYRERGRTphoEREREemkytywlKo2WZaRlJTEGTWCYdzEY3+8D2cdioV9TTz2mOk9VJ3fCHKa9YpntJE4GDfxqKpy/YWoymFfE483YsZEi5yiKAoOHjxY7jlUVLUxbuJRVSA/v6Dc8wypamNfE4+3YsZEi4iIiEgnTLSIiIiIdMJEi5zGQZ5iYtzEw4HwYmJfE483YsZZh+QUg8HAB2wLiHETj33WIYmFfU089pjZbDZdt8P0m5yiqiouXbqk+zRY8izGTUylpfru+Mnz2NfE462YMdEipyiKgmPHjnFGjWAYN/GoKlBYWMhZh4JhXxOPt2LGRIuIiIhIJ0y0iIiIiHTCRIucZrFYfF0FcgPjJh7OXhMT+5p4vBEzzjokpxgMBsTFxfm6GuQixk08kgQEBgb4uhrkIvY18dhjxlmHVCUoioLz589zoKdgGDcBqUBJSQnAwfBCYV8Tj7dixkSLnKKqKjIyMjh1WTCMm3hUAEVFxcyzBMO+Jh5vxYyJFhEREZFOmGgRERER6YSJFjktODjY11UgNzBu4jEaDb6uArmBfU083ogZZx2SUwwGA5o1a+brapCLGDfxSBLg7+/v62qQi9jXxGOPGWcdUpWgKArOnDnDGTWCYdwEpALWYitnHQqGfU083ooZEy1yiqqqOHv2LGfUCIZxE48KoNhqZZ4lGPY18XgrZky0iIiIiHTCRIuIiIhIJ0y0yCmSJCEsLAySJPm6KuQCxk08EgCTyQhGTCzsa+LxVsw465CcIssyGjVq5OtqkIsYNwFJfDixiNjXxGOPGWcdUpWgKArS09M5o0YwjJuAVKCoqIizDgXDviYeb8WMiRY5RVVVZGdnc0aNYBg38agASkpKmWcJhn1NPN6KGRMtIiIiIp0w0SIiIiLSCRMtcookSahXrx5n1AiGcROPBMDPbOasQ8Gwr4nHWzHjrENyiizLiIiI8HU1yEWMm4AkwOxn9nUtyEXsa+Kxx4yzDqlKsNlsOHr0qO5fSPIsxk08qgoUFhaCY6rFwr4mHm/FjIkWOS03N9fXVSA3MG7iKS3lwVpE7Gvi8UbMmGgRERER6YSJFhEREZFOmGiRUyRJQlRUFGfUCIZxE48EwGLx46xDwbCvicdbMeOsQ3KKLMsIDw/3dTXIRYybgCTAZDL5uhbkIvY18dhjxsHwVCXYbDYcOHCAM2oEw7iJR1WB/PwCzjoUDPuaeLwVMyZa5LSioiJfV4HcwLiJhw8mFhP7mni8ETMmWkREREQ6YaJFREREpBMmWuQUWZYRHR0NWeZXRiSMm3gkCfD39wcnr4mFfU083ooZZx2SUyRJQkhIiK+rQS5i3MRkNBp8XQVyEfuaeOwx42B4qhJsNhv27NnDGTWCYdzEo6pAXl4eZx0Khn1NPN6KGRMtchpnQomJcRMPkywxsa+JxxsxY6JFREREpBMmWkREREQ6YaJFTpFlGS1btuSMGsEwbuKRJCAwMICzDgXDviYeb8WM3whymtls9nUVyA2Mm3gkibtmEbGviccbMWNvJqcoioK0tDQO9hQM4yYezjoUE/uaeLwVMyZaRERERDphokV0BUUR7zSCiHUmIrpZ8M7wRFeQZQnzvz6K0xcKfV0Vp0SG+mNMajNfV4OIiCrBRIucIssykpKSbooZNacvFCI9O9/X1fCImylu1YUkAUFBQZx1KBj2NfHYY6bqPCCS3whymtVq9XUVyA2Mm3hUlQOqRcS+Jh5vxIyJFjlFURQcPHiQM2oEw7iJR1WB/PwCzjoUDPuaeLwVMyZaRERERDphokVERESkEyZa5DQO8hQT4yYeDoQXE/uaeLwRM846JKcYDAYkJyf7uhrkIsZNPPZZhyQW9jXx2GNms9l03Q7Tb3KKqqq4dOmS7tNgybMYNzGVluq74yfPY18Tj7dixkSLnKIoCo4dO8YZNYJh3MSjqkBhYSFnHQqGfU083oqZTxOtH3/8ESNGjEBKSgpiY2Px1VdfObw+ceJExMbGOvwbOnSowzIXL17E+PHj0bp1a7Rp0waTJ09Gfr7jzSYPHz6M/v37IyEhAZ06dcKSJUvK1WXLli24++67kZCQgF69emHbtm2ebzARERHdVHyaaBUUFCA2NhYvvvhipct07NgR33//vfbvjTfecHj9mWeewbFjx7B8+XIsWrQIP/30E1544QXt9by8PAwdOhQRERH45JNPMGHCBCxYsAAffvihtswvv/yC8ePH4/7778eGDRtw5513YtSoUThy5IjnG01EREQ3DZ8Ohu/UqRM6dep0zWXMZjNq165d4WvHjx/H9u3bsXbtWiQkJAAApkyZguHDh2PChAmoU6cOPv30U5SUlGDGjBkwm81o1qwZDh06hOXLl+PBBx8EAKxYsQIdO3bEsGHDAABPPvkkduzYgZUrV2L69OkutenKQXWSJEGWZSiK4nAN2F5+9QC8ysplWYYkSRWWAyh32rOycoPBAFVVKyy/uo5Xl9tsNvj5+UFRlAqXF7FNV9dRURRtO6oKSAAgodwlnErL/5oppmu5ClR0RelabfLz89M+5+oSJ19896S/AmPftKtxqqjc/l1yKFdVyLLjtq5e3tnvTFlZ+c+9OsfJV22y7yNtNlu1adOVqmObFEWBxWLRfTB8lZ91+MMPP6Bdu3YIDg7G3/72Nzz55JMIDQ0FAOzZswfBwcFakgUA7du3hyzL2Lt3L7p27Yq0tDS0adMGZrNZWyYlJQVLlizBpUuXEBISgrS0NAwePNhhuykpKeUuZTpj37592n+HhYWhUaNGOHnyJLKzs7XyevXqISIiAidOnEBubq5WHhUVhfDwcBw+fBhFRUVaeXR0NEJCQrB3716HL2HLli1hNpuRlpbmUIekpCRYrVYcPHhQK5NlGcnJycjNzcWxY8e0covFgri4OOTk5CAjI0MrDw4ORrNmzXDu3DmcPXtWKz99+nS1a5M9TufOnUNERAQKCgqQl5cHP7MZZj8ziooKHQYnWyx+MJlMKCgocKi7v78/jEYD8vPzHA54gYEBkCQZeXl5Dm0KCgqCqirIzy/QyuwzzkpLbSgs/N+DrWVZRmBgAEpKS1BUVKyVF9cwAACys7Nx8uTJcm06ffo0iouLte9ldYiTL757JSUlsFgs2nfD1TgZjQb4+/vDarWi+IpHfphMRlgsFhQVF6GkpFQr9zObIUllY7Xc/e4VBJRlX4qi3DRxqgpt2rdvX7VrE1D94mRvU1xcHPbv3w89SWoVmSIRGxuLhQsXokuXLlrZpk2bYLFYUL9+fWRmZuKNN95AQEAAPvzwQxgMBixatAjr16/HF1984bCudu3aYcyYMejfvz+GDBmC+vXrO5yZOnbsGHr06IHNmzejadOmiI+Px6xZs9CzZ09tmQ8++AALFy7Ejh07nKq/zWZDWloaEhISYDCUHfyq0y8GRVFw4cIF1KpVC0ajsVq06eo62s9oTVy3F7+fzxfijFbj8EDM6ptYaZtKS0uRk5OD0NBQ7TOvDnHy1Rkt+3ej7O+y1z1+RgsqSktLYTKayq/jr/9x5jtj/27wjJZ32mTfR4aGhsJgMFSLNl2pusTpSqqq4uLFiwgKCsKBAweQlJSkHb89qUqf0erRo4f23/bB8F26dNHOclVFBoOhXKAquyFaZQHVs1ySpArLK6vjleWZmZkICwu75vKitamy8itvGFnZzSN9Ui79dbC9SmVtkiRJi9uVn1F1iZOn63itcrur4+JKnJwpV1UJRUXFMAaZPPKdqexzB6pnnHzZJntfsy9XHdrkzXJvt8lmsyEjI8PhqpgehLq9Q4MGDRAaGqqdQgwPD0dOTo7DMqWlpbh06ZI2ris8PBznz593WMb+d3h4eKXLZGdna68TERERuUOoROvcuXO4ePGilkTZr+leeX11165dUBQFiYmJAMquB//0008oKSnRltmxYwcaN26MkJAQbZldu3Y5bGvHjh1ISkrSuUVERERUnfk00crPz8ehQ4dw6NAhAMCpU6dw6NAhnDlzBvn5+Zg9ezbS0tJw6tQp7Ny5E0888QSioqLQsWNHAEDTpk3RsWNHPP/889i7dy9+/vlnvPTSS+jRowfq1KkDAOjVqxdMJhOee+45HD16FJs3b8aKFSvw6KOPavUYNGgQtm/fjmXLluH48eOYP38+9u/fj4cfftj7H0oVFhwc7OsqkBsYN/EYjZ4fJ0L6Y18Tjzdi5tMxWvv378egQYO0v2fOnAkA6N27N6ZOnYojR45gw4YNuHz5Mm655RZ06NAB48aNc5hBOGfOHLz00kt45JFHIMsyunXrhilTpmiv16hRA++88w6mT5+OPn36IDQ0FE888YR2awcAaN26NebMmYO5c+fijTfeQKNGjbBw4ULExMR44VMQg8FgQLNmzXxdDXIR4yYeSSqbRUhiYV8Tjz1m1fr2Dm3btsVvv/1W6evvvPPOdddRs2ZNvP7669dcpnnz5li1atU1l7nnnntwzz33XHd7NytFUXDu3DnUrVu30oGGVPUwbgJSAavVWvaDspJB71T1sK+Jxx6zyu7V6Sn8NpBTVFXF2bNn+cBUwTBu4lEBFFutFd6Ylqou9jXxeCtmTLSIiIiIdMJEi4iIiEgnTLTIKZIkISwsTHveG4mBcROPhLJH8zBiYmFfE4+3YuZWopWZmenpelAVJ8syGjVqxEGegmHcBCSVPeONmZZY2NfE462YubX2rl27YuDAgdi4cSOKi4uv/wYSnqIoSE9PL/ccKqraGDcBqSh7aC/HVAuFfU083oqZW4nW+vXrERsbi1mzZqFDhw544YUXsHfvXk/XjaoQVVWRnZ3NGTWCYdzEowIoKSllniUY9jXxeCtmbiVaLVq0wJQpU7B9+3bMmDEDf/75J/r374+ePXti+fLl5Z4/SERERHQzuqELk0ajEd26dcO8efPwzDPPICMjA7Nnz0anTp0wYcIE/Pnnn56qJxEREZFwbujO8Pv27cO6deuwefNm+Pv7Y8iQIbj//vvxxx9/YMGCBXjiiSewdu1aT9WVfEiSJNSrV48zagTDuIlHAuBnNnMsvGDY18TjrZi5lWgtX74cn3zyCX7//Xfccccd2lks+8j9Bg0aYNasWUhNTfVoZcl3ZFlGRESEr6tBLmLcBCQBZj/z9ZejKoV9TTz2mFXJZx2uXr0affv2Re/evXHLLbdUuEytWrXwyiuv3FDlqOqw2Ww4ceIEmjRpAoPB4OvqkJMYN/GoKlBUVAiLxR88OSIO9jXx2GMWFRWl63bcSrT+85//XHcZs9mM3r17u7N6qqJyc3N9XQVyA+MmntJSfX9hkz7Y18TjjZi5NRh+3bp12LJlS7nyLVu2YP369TdcKSIiIqLqwK1E6+2330ZoaGi58rCwMCxatOiGK0VERERUHbiVaJ05cwb169cvVx4REYGzZ8/ecKWo6pEkCVFRUZxRIxjGTTwSAIvFj7MOBcO+Jh5vxcytRCssLAy//fZbufLDhw+jZs2aN1onqoJkWUZ4eDif4yUYxk1AEmAymfisQ8Gwr4nHWzFza+09evTAK6+8gl27dsFms8Fms2Hnzp2YMWMGevTo4ek6UhVgs9lw4MAB3afBkmcxbuJRVSA/vwB8kotY2NfE462YuTXrcNy4cTh9+jQGDx4Mo7FsFYqi4L777sNTTz3l0QpS1VFUVOTrKpAbGDfx8MHEYmJfE483YuZWomU2mzF37lz8/vvvOHz4MCwWC2JiYhAZGenp+hEREREJ64YewdO4cWM0btzYU3UhIiIiqlbcSrRsNhs++eQT7Nq1C9nZ2eVOc69YscIjlaOqQ5ZlREdHc6CnYBg38UgS4O/Pu8KLhn1NPN6KmVuJ1iuvvIL169ejU6dOaNasGaez3gQkSUJISIivq0EuYtzEZDTyES6iYV8Tjz1mVXIw/KZNmzB37lx06tTJ0/WhKspms2Hv3r1ITEzkc7wEwriJp2zWYR4CA4N4Vksg7GvisccsLi5O1+24db7MZDKhYcOGnq4LVXGcCSUmxk08vLWDmNjXxOONmLmVaA0ZMgQrVqyAyr0BERERUaXcunT4888/Y/fu3fjuu+/QrFkz7V5adgsWLPBI5YiIiIhE5laiFRwcjK5du3q6LlSFybKMli1bckaNYBg38UgSEBgYwPFZgmFfE4+3YuZWojVz5kxP14MEYDabfV0FcgPjJh5J4sFaROxr4jGbzboPg3K7N5eWlmLHjh1Ys2YN8vLyAAB//PEH8vPzPVY5qjoURUFaWhoHewqGcROPqgJ5eXkcEC8Y9jXxeCtmbp3ROn36NIYNG4azZ8/CarWiQ4cOCAoKwpIlS2C1WjF9+nRP15OIqhFFUSHLvDZGRNWf2zcsjY+Px8aNG9G2bVutvGvXrnj++ec9Vjkiqp5kWcL8r4/i9IVCX1fFKUkNauKh23lLGyJynduzDlevXl3uenRkZCT++OMPj1SMiKq30xcKkZ4txlCDiJr+vq4CEQnKrTFaiqJUeE3z3LlzCAwMvOFKUdUjyzKSkpI4o0YwjJt4JAkICrrxu8KH+JugKOIN9BKxzgD7moi8FTO3zmh16NAB7733Hl566SWtLD8/H/Pnz+djeaoxq9UKi8Xi62qQixg38aiqcsMzDwP9jMJdoo0M9ceY1Ga+robb2NfEY7VaYTKZdN2GW4nWxIkTMXToUHTv3h1WqxXPPPMM0tPTERoaijfeeMPTdaQqQFEUHDx4EElJSXyOl0AYN/GUPeuwwCNntQCxLtGKjH1NPPaYJSQk6LodtxKtunXrYuPGjdi0aRN+++03FBQU4P7770evXr2YzRMRERH9xa1ECwCMRiPuu+8+T9aFiIiIqFpxK9HasGHDNV//v//7P3dWS1UcB3mKiXETDx+/Iyb2NfF4I2Zu30frSqWlpSgsLITJZIK/vz8TrWrIYDAgOTnZ19UgFzFu4rHPOiSxsK+Jxx4zm82m63bcSrR+/PHHcmXp6emYOnUqhg4desOVoqpHVVXk5uYiODgYEn9uC4NxE1NpqQ1GIwdUi4R9TTz2mOl9WyqPnTNr1KgRxo8fX+5sF1UPiqLg2LFjfI6XYBg38agqUFhYyGcdCoZ9TTzeiplHL04ajUb8+eefnlwlERERkbDcunT43//+1+FvVVWRlZWFDz74AK1bt/ZIxYiIiIhE51aiNWrUKIe/JUlCrVq18Le//Q3PPvusRypGVQ/vkSYmxk08nL0mJvY18XgjZm4lWocPH/Z0PaiKMxgMiIuL83U1yEWMm3gkCQgMDPB1NchF7GviscdM71mH/NlETlEUBefPn+dAT8EwbgJSgZKSEoCD4YXCviYeb8XMrTNaM2fOdHrZSZMmubMJqmJUVUVGRgZCQ0N9XRVyAeMmHhVAUVExjEEm8CYB4mBfE489ZlXyWYcHDx7EoUOHUFpaisaNGwMou4+WLMto2bKlthzvJUJEREQ3M7cSrdTUVAQGBmL27NkICQkBAFy6dAmTJk1CmzZtMGTIEI9WkoiIiEhEbo3RWrZsGcaPH68lWQAQEhKCJ598EsuWLfNY5ahqCQ4O9nUVyA2Mm3h4V3gxsa+Jxxsxc+uMVl5eHnJycsqV5+TkID8//4YrRVWPwWBAs2bNfF0NchHjJh5JAvz9/X1dDXIR+5p47DGrkrMOu3btikmTJuE///kPzp07h3PnzuGLL77Ac889h27dunm6jlQFKIqCM2fOcEaNYBg3AamAtdjKWYeCYV8Tj7di5tYZrWnTpmH27NkYP348SktLAZRlhvfffz8mTJjg0QpS1aCqKs6ePYs6der4uirkAsZNPCqAYqsVJrOZsw4Fwr4mHnvMwsPDdd2OW4mWv78/pk6digkTJuDkyZMAgIYNGyIggDfZIyIiIrK7oRuWZmVlISsrC40aNUJAQABUPm6eiIiISOPWGa0LFy7gySefxO7duyFJEv7zn/+gQYMGmDx5MkJCQjBx4kRP15N8TJIkhIWF8d5ogmHcxCMBMJmMvGwoGPY18XgrZm6d0Zo5cyaMRiO+/fZbhwcydu/eHdu3b/dY5ajqkGUZjRo14sNuBcO4CUj660G3PF4LhX1NPN6KmVtr/3//7//hn//8J+rWretQ3qhRI5w5c8YjFaOqRVEUpKenc0aNYBg3AalAUVERZx0Khn1NPN6KmVuJVkFBgcOZLLuLFy/CbDbfcKWo6lFVFdnZ2RyHJxjGTTwqgJKSUuZZgmFfE4+3YuZWotWmTRts2LDBoUxRFCxduhRt27b1RL2IiIiIhOfWYPh//vOfGDx4MPbv34+SkhK89tprOHbsGC5duoTVq1d7uo5EREREQnIr0YqJicEXX3yBlStXIjAwEAUFBejatSsGDBiAW265xdN1pCpAkiTUq1ePM2oEw7iJRwLgx5uVCod9TTzeipnLiVZJSQmGDRuGadOmYeTIkXrUiaogWZYRERHh62qQixg3AUmA2Y9jXUXDviYee8yq3LMOTSYTfvvtNz3qQlWYzWbD0aNHdf9CkmcxbuJRVaCwsBAcUy0W9jXxeCtmbg2Gv/fee7F27dob3viPP/6IESNGICUlBbGxsfjqq68cXldVFW+++SZSUlKQmJiIwYMHIz093WGZixcvYvz48WjdujXatGmDyZMnIz8/32GZw4cPo3///khISECnTp2wZMmScnXZsmUL7r77biQkJKBXr17Ytm3bDbevusnNzfV1FcgNjJt4Skt5sBYR+5p4vBEzt8Zo2Ww2rF69Gjt27EB8fDz8/f0dXp80aZJT6ykoKEBsbCz69u2L0aNHl3t9yZIleP/99zFr1izUr18fb775JoYOHYrNmzfDz88PAPDMM88gKysLy5cvR0lJCSZPnowXXngBr7/+OgAgLy8PQ4cORbt27TBt2jQcOXIEkydPRnBwMB588EEAwC+//ILx48fj6aefRufOnfHZZ59h1KhR+OSTTxATE+POR0RERETkWqKVmZmJyMhIHDlyBC1btgQA/P777w7LuDKorFOnTujUqVOFr6mqihUrVmDkyJHo0qULAODVV19F+/bt8dVXX6FHjx44fvw4tm/fjrVr1yIhIQEAMGXKFAwfPhwTJkxAnTp18Omnn6KkpAQzZsyA2WxGs2bNcOjQISxfvlxLtFasWIGOHTti2LBhAIAnn3wSO3bswMqVKzF9+nRXPiIiIiIijUuJVrdu3fD999/j/fffB1CWkEyZMgXh4eEer9ipU6eQlZWF9u3ba2U1atRAq1atsGfPHvTo0QN79uxBcHCwlmQBQPv27SHLMvbu3YuuXbsiLS0Nbdq0cbiRakpKCpYsWYJLly4hJCQEaWlpGDx4sMP2U1JSyl3KdMaV13olSYIsy1AUxeGGaPbyq68LV1YuyzIkSaqwHEC5u9pWVm4wGKCqaoXlV9fx6nJFUdCgQQNtmerQpqvrqCiKth1V/esJKBLKjZWptPyv3xi6lqsV3zC8sjapqooGDRpon09ViZPBYPjfixW0qaK22j/3ysr1jpNWXdWJ5W+oTSosFr+y4hts09V1vt7y+rXJtTjZbLYqu4+obL9n30cqiiLcfq867sudaZOqqoiKitL9zvAuJVpXN/S7775DYWGhRytkl5WVBQAICwtzKA8LC8P58+cBAOfPn0etWrUcXjcajQgJCdHef/78edSvX99hGXtieP78eYSEhOD8+fPlksUrt+OKffv2OayjUaNGOHnyJLKzs7XyevXqISIiAidOnHC4PhwVFYXw8HAcPny47BEcf4mOjkZISAj27t3r8IVo2bIlzGYz0tLSHOqQlJQEq9WKgwcPamWyLCM5ORm5ubk4duyYVm6xWBAXF4ecnBxkZGRo5cHBwWjWrBnOnTuHs2fPauUFBQXVrk32OJ07dw4REREoKChAXl4e/MxmmP3MKCoqdBgzY7H4wWQyoaCgwKHu/v7+MBoNyM/PczhoBAYGQJJk5OXlObQpKCgIqqogP79AK5OksvLSUptD35JlGYGBASgpLUFRUbFWXlyjLGHJzs7GyZMny7Xp1KlTyM7ORmZmZpWJk7+/P1q2bKlt5+o2GY0G+Pv7w2q1othq1cpNJiMsFguKiotQUlKqlXsrTkDZo3Hy8vJcjpM7bYIEFBW636aiokDtdT2+e3rEqaCgLNP77bffEBkZWeX2Ec7s9zIzM4Xa71XHfbmrbdq/fz/05NYYLTs+aqC8hIQE7de6/TJqw4YN0aBBA20Ze3mTJk0c3msvb968uUO5/RdAYmJiheVJSUnlyi0WS7lyoOwLWlF5rVq1EBoaWq68bt26qFOnDmw2G44cOYLIyMhq06ar62h/dmdAQACCglTtPkYWi+MYRHt5QECAY/lfLwQGBlVYHhRUvlyS5HLlQNlBrKJyk9EEY5BJ+9vPr+xRWGFhYQ4/OuxtioyMRF5eHmJiYmAwGKpUnCwWC5CfX65NdmazGaYrzkRr8fCz4K8hmo7lOsfJXuegoP8lCc7GyaU2qSoKCgtgMptvqE1XvleP755LbYJzcbK3KzY2tkruI66137PvI2NiYmA0lh1aRdjvVcd9ubNtUhQFBw4cQNOmTR0SP09zKdGSJMlrN2OrXbs2gLJf6lfeBDU7O1sLdHh4OHJychzeV1paikuXLmnvDw8PL3dmyv63/SxWRctkZ2e7dUnUYDA4XhbB/75wFS3r7XJJkiosr6yOV5YXFxdrf1eXNlVWfuXXvLKvvE/Kpf8dmK50rTYVFxeX+15WpThV1iZXy/WOR2WvuxInZ8pVSFAU1a06+uK758k4hfiboCiqx7+reuwjHNcvw2CA1tfsy4m23/N0HV0t90WbioqKKq2jp7h86XDixInaeCer1YqpU6eWm3W4YMGCG65Y/fr1Ubt2bezcuRMtWrQAUHb6+9dff8U//vEPANBONe7fvx/x8fEAgF27dkFRFC27TkpKwty5c1FSUgKTqezX2I4dO9C4cWOEhIRoy+zatcthnNaOHTsqzKyJiEgfgX5GyLKE+V8fxekL+gxL8bTIUH+MSW0G3j6LKuNSotW7d2+Hv++9994b2nh+fr7DuJJTp07h0KFDCAkJQUREBAYNGoS33noLUVFR2u0dbrnlFm0WYtOmTdGxY0c8//zzmDZtGkpKSvDSSy+hR48e2unEXr16YeHChXjuuefw2GOP4ejRo1ixYoXDLSgGDRqEgQMHYtmyZejUqRM2b96M/fv3c8YhEZEPnL5QiPTs/OsvSCQAlxKtmTNnenTj+/fvx6BBg8qtv3fv3pg1axYee+wxFBYW4oUXXkBubi5uvfVWLF26VLuHFgDMmTMHL730Eh555BHIsoxu3bphypQp2us1atTAO++8g+nTp6NPnz4IDQ3FE088od3aAQBat26NOXPmYO7cuXjjjTfQqFEjLFy4kPfQuoIsy4iOjq70lCxVTYybeCSpbHA7H5knFvY18XgrZjc0GP5GtW3b9pqP85EkCePGjcO4ceMqXaZmzZrazUkr07x5c6xateqay9xzzz245557rl3hm5gkSdqlVhIH4yYmo1HfMSPkeexr4rHHrEo+goduPjabDXv27OFzvATDuIlHVcvGo3JSt1jY18TjrZgx0SKn6X1TN9IH4yYeJlliYl8TjzdixkSLiIiISCdMtIiIiIh0wkSLnCLLMlq2bMkZNYJh3MQjSfbHAfm6JuQK9jXxeCtm/EaQ0658MDeJg3ETj/2RPyQW9jXxeCNm7M3kFEVRkJaWxsGegmHcxMNZh2JiXxOPt2LGRIuIiIhIJ0y0iIiIiHTCRIuIiIhIJz59BA+JQ5ZlJCUlcUaNYBg38UgSEBQUxFmHgmFfE489ZqrOAyL5jSCnWa1WX1eB3MC4iUdVOaBaROxr4vFGzJhokVMURcHBgwc5o0YwjJt4VBXIzy/grEPBsK+Jx1sxY6JFREREpBMmWkREREQ6YaJFTuMgTzExbuLhQHgxsa+Jxxsx46xDcorBYEBycrKvq0EuYtzEY591SGJhXxOPPWY2m03X7TD9JqeoqopLly7pPg2WPItxE1Npqb47fvI89jXxeCtmTLTIKYqi4NixY5xRIxjGTTyqChQWFnLWoWDY18TjrZgx0SIiIiLSCRMtIiIiIp0w0SKnWSwWX1eB3MC4iYez18TEviYeb8SMsw7JKQaDAXFxcb6uBrmIcROPJAGBgQG+rga5iH1NPPaYcdYhVQmKouD8+fMc6FnFhPiboCgcNV2tqEBJSQnAsAqF+0jxeCtmPKNFTlFVFRkZGQgNDfV1VegKgX5GyLKE+V8fxekLhRUuU1BQgICAqnOGJKlBTTx0e0NfV6PKUgEUFRXDGGQC71sqDu4jxWOPWUJCgq7bYaJFVA2cvlCI9Oz8cuWqCuTl5SEoSK0ydxuPqOnv6yoQEXkNLx0SERER6YSJFjktODjY11UgNxiNBl9XgVzEmImJ+0jxeCNmvHRITjEYDGjWrJmvq0EukiTA35+X6kTCmImJ+0jx2GPGWYdUJSiKgjNnznBGjWhUwFps5Qw2kTBmQuI+UjzeihkTLXKKqqo4e/YsH5gqGBVAsdXKY7ZAGDMxcR8pHm/FjIkWERERkU6YaBERERHphIkWOUWSJISFhUGqKjdjIqdIAEwmI298KRDGTEzcR4rHWzHjrENyiizLaNSoka+rQa6S+KBb4TBmQuI+Ujz2mHHWIVUJiqIgPT2dM2pEowJFRUWcwSYSxkxI3EeKx1sxY6JFTlFVFdnZ2ZxRIxgVQElJKY/ZAmHMxMR9pHi8FTMmWkREREQ6YaJFREREpBMmWuQUSZJQr149zqgRjATAz2zmDDaBMGZi4j5SPN6KGWcdklNkWUZERISvq0GukgCzn9nXtSBXMGZC4j5SPPaYcdYhVQk2mw1Hjx7V/QtJnqWqQGFhITg+VxyMmZi4jxSPt2LGRIuclpub6+sqkBtKS7njFw1jJibuI8XjjZgx0SIiIiLSCRMtIiIiIp0w0SKnSJKEqKgozqgRjATAYvHjDDaBMGZi4j5SPN6KGWcdklNkWUZ4eLivq0GukgCTyeTrWpArGDMhcR8pHnvMOBieqgSbzYYDBw5wRo1gVBXIzy/gDDaBMGZistlsOH78OPeRAvHWcY1ntMhpRUVFvq4CuYEPuRUPYyaOEH8TFEWFwWBA06ZNfV0dlyiKClm+uS91euO4xkSLiIjITYF+RsiyhPn/PYqjZ7IREBDg6yo5JTLUH2NSm/m6GjcFJlpEREQ36PTFQhz/8zKCglRwPDxdiWO0yCmyLCM6OhqyzK+MSCQJ8Pf3545fIIyZuBg3sXjruMajJjlFkiSEhIRw6rKAjEaDr6tALmLMxMS4icVbxzUmWuQUm82GPXv2cEaNYFQVyMvL4ww2gTBm4mLcxOKt4xoTLXIaZ0KJiTt+8TBmYmLcxOON4xoTLSIiIiKdMNEiIiIi0gkTLXKKLMto2bIlZx0KRpKAwMAAzoQSCGMmLsZNLN46rvGoSU4zm82+rgK5QZLYzUXDmImJcROPN45r/FaQUxRFQVpaGgfEC4Yz2MTDmImLcROLt45rTLSIiIiIdMJEi4iIiEgnTLSIiIiIdMJEi5wiyzKSkpJcmp2hKBys4GuSBAQFBXEmlEAYM3ExbmJx57jmDqOua6dqxWq1wmKxOL28LEuY//VRnL5QqGOtPCepQU08dHtDX1fD41RV4WwowTBmYmLcxGO1WmEymXTdRpVOtObPn48FCxY4lDVu3Bhbt24FABQXF2PWrFnYvHkzrFYrUlJS8OKLLyI8PFxb/syZM5g6dSp2796NgIAA/N///R/Gjx8Po/F/Td+9ezdmzZqFo0ePol69ehg5ciT69OnjnUYKQlEUHDx4EElJSTAYnH9w6ukLhUjPztexZp4TUdPf11XwOFUF8vML+EtbIIyZuBg3sdiPawkJCbpup0onWgDQrFkzLF++XPv7yoP8jBkzsG3bNsydOxc1atTASy+9hNGjR2PNmjUAyh4Y+fjjjyM8PBxr1qzBn3/+iWeffRYmkwlPP/00ACAzMxOPP/44HnroIcyZMwc7d+7ElClTULt2bXTs2NG7jSUiIqJqpconWgaDAbVr1y5XfvnyZaxbtw5z5sxBu3btAJQlXt27d0daWhqSkpLw/fff49ixY1i+fDnCw8PRokULjBs3DnPmzMHo0aNhNpuxZs0a1K9fHxMnTgQANG3aFD///DPeffddJlpERER0Q6r8xeSMjAykpKTgzjvvxPjx43HmzBkAwP79+1FSUoL27dtryzZt2hQRERFIS0sDAKSlpSEmJsbhUmJKSgry8vJw7NgxbRl7onblMvZ1uMpms2n/7DdBUxSlwvIry65Vrv51B7yKylVVdbocQKXlV9exonJJklxq05VU1fGfV8pV58pRwZj9K8vLraOych+0qbLP+H9tUiH99WJVaVP5D9vFODlbdw+36erXr7n8DbQJ6l8xu8E2VVRnr/YnN+JUEW/2J3fbZH9R0jbkhbrfaJv+Ul2OT+4ec2VZLne88rQqfUYrMTERM2fOROPGjZGVlYWFCxdiwIAB+Oyzz3D+/HmYTCYEBwc7vCcsLAxZWVkAgPPnzzskWQC0v6+3TF5eHoqKilwa/A0A+/btc6hLo0aNcPLkSWRnZ2vl9erVQ0REBE6cOIHc3FytPCoqCuHh4Th8+DCKioq08ujoaISEhGDv3r0Od7Bt2bIlzGZzuaQwKSkJVqsVBw8e1MpkWUZycjJyc3O1JBMALBYL4uLikJOTg4yMDK08ODgYzZo1w7lz53D27Fmt/PTp0061yd/fHy1btkRpaSkAoKCgwKHu/v7+MBoNyM93vJNy2bPCZOTl5Tm0KSgoCKqqID+/QCuzz84qLbWhsPB/A+5lWUZgYABKSktQVFSslRuNBvj7+8NqtaLYatXKTSYjLBYLSkrKyoqKipCXlwc/sxlmPzOKigpRWvq/jmix+MFkMlWJNpVYawAASktLHdZvb1NxcTFUAHn5ZePkqkKbiooCAZSNsQTgcpyKiotQUlKqlXurTcD/vhue/u5V1CZJAgoL3W+T/XMG4JP+5E6ciooKtc+5tNRW5fYRFbXJrqjof32tKu0jKmuTNbjs8H/1Pl7k45M7x9zk5GTs378feqrSiVanTp20/27evDlatWqFzp07Y8uWLS4nQN6SkJCgjSOT/hoR2bBhQzRo0EBbxl7epEkTh/fay5s3b+5Qbp96mpiYWGF5UlJSuXKLxVKuHCj7glZUXqtWLYSGhpYrr1u3LurUqQNVVXH58mUtsXW2TWWTDooREBBwVVvL/j8wMKjC8qCg8uWSJJcrL9uGocJyk9EEY1D52SRmsxmmK3aQ9rMHJlNZmcViQVCQTSu3WBwHydvLq0KbTGa/v5Y3Oixvr6OfnwVGY+lf30mpSrTJvm0/Pz8gr9TlOFn8LPDzK193vdtUto2y78bVbbrajbVJ1X5h30ibrnyvL/qTO3Gyv2axWGDMt5Vrk73uVa1NZXX2g7+/BQaDQetpVWEfUVmbzH/tO+z7+P9tU7zj09V1dPb4BACXLl1Cs2bNdE22qnSidbXg4GAtW23fvj1KSkqQm5vrcFYrOztbG9MVHh6OvXv3Oqzj/PnzAOCwjL3symXKDgiuJ3MGg6HcrLzK7tFR2ew9PcslSaqwvLI62sttNhtOnDihdQJX21TZLBxdyyXHHeF1yytZn0/qXln5Nepe2XoKC4vKzYQSok0ulutd98pe93SbVFWqMGbO1JFx8k2b7C/a42ZfSIQ2VYfjk7PlV6/bZrPh2LFjus86rPJjtK6Un5+PzMxM1K5dG/Hx8TCZTNi5c6f2+okTJ3DmzBktGUhKSsKRI0ccTiHu2LEDQUFBiI6O1pbZtWuXw3Z27NhRYVZNRERE5IoqnWjNnj0bP/zwA06dOoVffvkFo0ePhizL6NmzJ2rUqIG+ffti1qxZ2LVrF/bv34/JkycjOTlZS5JSUlIQHR2NCRMm4PDhw9i+fTvmzp2LAQMGwPzXKdSHHnoImZmZePXVV3H8+HF88MEH2LJlCwYPHuy7hhMREVG1UKUvHZ47dw5PP/00Ll68iFq1auHWW2/FRx99hFq1agEAJk+eDFmWMXbsWIcbltoZDAYsWrQIU6dOxYMPPgh/f3/07t0bY8eO1ZZp0KABFi9ejJkzZ2LFihWoW7cuXn75Zd7aoQJVdVwcXZvej5cgz2PMxMS4iccbx7UqnWj961//uubrfn5+ePHFFx2Sq6tFRkZiyZIl11xP27ZtsWHDBneqeNMwGAyIi4vzdTXIRZJUNqOJxMGYiYtxE4v9uKb37R2YfpNTFEXB+fPnHabvkgBUoKSk5Kr7/VCVxpgJi3ETi7eOa0y0yCmqqiIjI0O7OR2JQcX/7u9DYmDMxMW4icVbxzUmWkREREQ6YaJFREREpBMmWuS0qx93RGIwGiu+YSBVXYyZmBg38XjjuFalZx1S1WEwGNCsWTNfV4NcJEllz1cjcTBm4mLcxGI/rnHWIVUJiqLgzJkznHUoGhWwFls5E0okjJmwGDexeOu4xkSLnKKqKs6ePctZh4JRARRbrdz3C4QxExfjJhZvHdeYaBERERHphIkWERERkU6YaJFTJElCWFgYJEnydVXIBRIAk8kIRk0cjJm4GDexeOu4xlmH5BRZltGoUSNfV4NcJfFh4MJhzITFuInFflzjrEOqEhRFQXp6OmcdikYFioqKOBNKJIyZsBg3sXjruMZEi5yiqiqys7M561AwKoCSklLu+wXCmImLcROLt45rTLSIiIiIdMJEi4iIiEgnTLTIKZIkoV69epx1KBgJgJ/ZzJlQAmHMxMW4icVbxzXOOiSnyLKMiIgIX1eDXCUBZj+zr2tBrmDMhMW4icV+XOOsQ6oSbDYbjh49qvsXkjxLVYHCwkJwDoM4GDNxMW5i8dZxjYkWOS03N9fXVSA3lJYyORYNYyYmxk083jiuMdEiIiIi0gkTLSIiIiKdMNEip0iShKioKM46FIwEwGLx40wogTBm4mLcxOKt4xpnHZJTZFlGeHi4r6tBrpIAk8nk61qQKxgzYTFuYrEf1zgYnqoEm82GAwcOcNahYFQVyM8v4EwogTBm4mLcxOKt4xoTLXJaUVGRr6tAbuCDwMXDmImJcROPN45rTLSIiIiIdMJEi4iIiEgnTLTIKbIsIzo6GrLMr4xIJAnw9/cHJ4uKgzETF+MmFm8d13jUJKdIkoSQkBDe3kFARqPB11UgFzFmYmLcxOKt4xoTLXKKzWbDnj17OOtQMKoK5OXlcSaUQBgzcTFuYvHWcY2JFjmNM2rExB2/eBgzMTFu4vHGcY2JFhEREZFOmGgRERER6YSJFjlFlmW0bNmSsw4FI0lAYGAAZ0IJhDETF+MmFm8d13jUJKeZzWZfV4HcIEns5qJhzMTEuInHG8c1fivIKYqiIC0tjQPiBcMZbOJhzMTFuInFW8c1JlpEREREOmGiRURERKQTJlpEREREOjH6ugIkBlmWkZSUxFmHgpEkICgoiDOhBMKYiYtxE4v9uKbqPLCOR01ymtVq9XUVyA2qygkMomHMxMS4iccbxzUmWuQURVFw8OBBzjoUjKoC+fkFnAklEMZMXIybWLx1XGOiRURERKQTJlpEREREOmGiRU7jQHgxcXCueBgzMYkUtxB/ExRFvOucnq6zN45rnHVITjEYDEhOTvZ1NchF9hlsJA7GTFwixS3QzwhZljD/66M4faHQ19VxSmSoP8akNvPY+uzHNZvN5rF1VoSJFjlFVVXk5uYiODgYkkg/2wilpTYYjQZfV4NcwJiJScS4nb5QiPTsfF9Xwyfsx7XAwEBdt8NrQeQURVFw7NgxzjoUjKoChYWFnAklEMZMXIybWLx1XGOiRURERKQTJlpEREREOmGiRU6zWCy+rgK5gbNFxcOYiYlxE483jmscDE9OMRgMiIuL83U1yEWSBAQGBvi6GuQCxkxcjJtY7Mc1vWcdMv0mpyiKgvPnz3MwvGhUoKSkBOAAXXEwZsJi3MTireMaEy1yiqqqyMjI0P0p5+RZKoCiomLu+wXCmImLcROLt45rTLSIiIiIdMJEi4iIiEgnTLTIacHBwb6uArlBtDtVE2MmKsZNPN44rnHWITnFYDCgWTPPPWOKvEOSAH9/f19Xg1zAmImLcROL/bjGWYdUJSiKgjNnznDWoWhUwFps5UwokTBmwmLcxOKt4xoTLXKKqqo4e/YsZx0KRgVQbLVy3y8QxkxcjJtYvHVcY6JFREREpBMmWkREREQ6YaJFTpEkCfXq1YMkSb6uCrlAAmAyGcGoiYMxExfjJhZJkhAWFqb7cY2zDgWhKCpk2XddWJZlRERE+Gz75CaJDwMXDmMmLMZNLLIso1GjRrrPOmSidZUPPvgA77zzDrKystC8eXM8//zzSExM9HW1IMsS5n99FKcvFPqsDlZrMcxmP6eWTWpQEw/d3lDnGtF1qUBRcREsfhbwp7YgGDNhFRUxbiJRFAUnT55EZGSkrtthonWFzZs3Y+bMmZg2bRpatWqF9957D0OHDsXWrVsRFhbm6+rh9IVCpGfn+2Tbqgrk5eUhKCgIzpxljajJ+8lUBSqAkpJS+Plx3y8KxkxcjJtYVFVFdna27ldrOEbrCsuXL8cDDzyAvn37Ijo6GtOmTYPFYsG6det8XTUiIiISEM9o/cVqteLAgQN4/PHHtTJZltG+fXvs2bPnuu+334fDarXCYCh7DIMkSZBlGYqiONynw15+9XXhysplWYYkSWhY0wKj5KO7tKgqCoMk+Ptb4Mzvtbo1TLDZbL6ts4uqZ53/ipvFAqdORXpB9fycPUjrazd2Vlircyg/Zz39r85+KKlbo0r1tWsR8bOOCLHAZrPBZrPBYDBUemx19phrv1Gp1WoFAN3upyWpvAMlAOCPP/7AHXfcgTVr1iA5OVkrf/XVV/Hjjz/i448/vub7rVYr9u3bp3c1iYiISAcJCQkwm80eXy/PaHmI0WhEQkKCdvaJiIiIqj5VVaEoCoxGfVIiJlp/CQ0NhcFgQHZ2tkN5dnY2wsPDr/t+WZZ1yYSJiIhIXBwM/xez2Yy4uDjs3LlTK1MUBTt37nS4lEhERETkLJ7RusKjjz6KZ599FvHx8UhMTMR7772HwsJC9OnTx9dVIyIiIgEx0bpC9+7dkZOTg3nz5iErKwstWrTA0qVLnbp0SERERHQ1zjokIiIi0gnHaBERERHphIkWERERkU6YaBERERHphIkWERERkU6YaN3EPvjgA6SmpiIhIQH9+vXD3r17nXrfpk2bEBsbiyeeeMKhXFVVvPnmm0hJSUFiYiIGDx6M9PR0HWp+8/J0zCZOnIjY2FiHf0OHDtWj6jc1V+L2ySeflItJQkKCwzLsa/rzdMzY17zD1X1kbm4upk2bhpSUFMTHx+Ouu+7Ctm3bbmid5ah0U9q0aZMaFxenrl27Vj169Kg6ZcoUtU2bNur58+ev+b7MzEy1Y8eOav/+/dWRI0c6vLZ48WL11ltvVb/88kv10KFD6ogRI9TU1FS1qKhIz6bcNPSI2bPPPqsOHTpU/fPPP7V/Fy9e1LMZNx1X47Zu3Tq1devWDjHJyspyWIZ9TV96xIx9TX+uxq24uFjt06eP+thjj6k//fSTmpmZqe7evVs9dOiQ2+usCBOtm9T999+vTps2TfvbZrOpKSkp6uLFiyt9T2lpqfrggw+qH330kfrss886HLQVRVE7dOigLl26VCvLzc1V4+Pj1c8//1yfRtxkPB0zVVUrLCPPcjVu69atU2+99dZK18e+pj9Px0xV2de8wdW4rVq1Sr3zzjtVq9XqsXVWhJcOb0JWqxUHDhxA+/bttTJZltG+fXvs2bOn0vctXLgQYWFh6NevX7nXTp06haysLId11qhRA61atbrmOsk5esTM7ocffkC7du1w11134cUXX8SFCxc8WvebmbtxKygoQOfOndGpUyeMHDkSR48e1V5jX9OXHjGzY1/Tjztx+/rrr5GUlITp06ejffv26NmzJxYtWgSbzeb2OivCO8PfhC5cuACbzYawsDCH8rCwMJw4caLC9/z0009Yu3YtNmzYUOHrWVlZ2jquXuf58+dvvNI3OT1iBgAdO3ZE165dUb9+fWRmZuKNN97AY489hg8//BAGg8GTTbgpuRO3xo0bY8aMGYiNjcXly5exbNkyPPTQQ9i0aRPq1q3LvqYzPWIGsK/pzZ24ZWZmYteuXejVqxfefvttnDx5EtOmTUNpaSlGjx7t1jorwkSLrisvLw8TJkzASy+9hFq1avm6OuQEZ2PWo0cP7b/tA3S7dOmi/fIm70tOTnZ4kH1ycjK6d++ONWvW4Mknn/RdxahSzsSMfa3qUVUVYWFheOmll2AwGBAfH48//vgD77zzDkaPHu2x7TDRugmFhobCYDAgOzvboTw7O7vC5zpmZmbi9OnTGDlypFamKAoAoGXLlti6dStq166treOWW25xWGfz5s31aMZNRY+YNWzYsNz7GjRogNDQUGRkZHDn7wGuxq0iJpMJLVq0wMmTJwGAfU1nesSsIuxrnuVO3GrXrg2j0ehwRrFJkybIysqC1Wr1yHcB4O0dbkpmsxlxcXHYuXOnVqYoCnbu3Onwq8yuSZMm+Oyzz7BhwwbtX2pqKtq2bYsNGzagbt26qF+/PmrXru2wzry8PPz6668VrpNco0fMKnLu3DlcvHhRO5jTjXE1bhWx2Ww4cuSIFhP2NX3pEbOKsK95ljtxa926NU6ePKn9CAWA9PR01K5dG2az2SPfBYBntG5ajz76KJ599lnEx8cjMTER7733HgoLC9GnTx8AwIQJE1CnTh2MHz8efn5+iImJcXh/cHAwADiUDxo0CG+99RaioqJQv359vPnmm7jlllvQpUsX7zWsGvN0zPLz87FgwQLcddddCA8PR2ZmJl577TVERUWhY8eO3m1cNeZK3ABgwYIFSEpKQlRUFHJzc/HOO+/gzJkz2oQGSZLY13Tm6Zixr3mHq3H7xz/+gZUrV+KVV17Bww8/jIyMDCxevBgDBw50ep3OYKJ1k+revTtycnIwb948ZGVloUWLFli6dKl2OvTs2bOQZddOeD722GMoLCzECy+8gNzcXNx6661YunQp/Pz89GjCTcfTMTMYDDhy5Ag2bNiAy5cv45ZbbkGHDh0wbtw4mM1mvZpx03E1brm5uXj++eeRlZWFkJAQxMXFYc2aNYiOjtaWYV/Tl6djxr7mHa7GrV69enjnnXcwc+ZM3HvvvahTpw4GDRqExx57zOl1OkNSVVX1XDOJiIiIyI5jtIiIiIh0wkSLiIiISCdMtIiIiIh0wkSLiIiISCdMtIiIiIh0wkSLiIiISCdMtIiIiIh0wkSLiIiISCdMtIjIZyZOnIgnnnhC+3vgwIF45ZVXvF6P3bt3IzY2Frm5uTe0nk8++QRt2rTx+LJEJC4+goeIHEycOBHr168HAJhMJtSrVw/33XcfRowYAaNR313G/Pnznd7G7t27MWjQIPz444/acxz1lJqaitOnTwMALBYLGjZsiEGDBmnPswPKHtfRqVMn3eqQmZmJuXPnYvfu3bh06RJCQ0MRFxeHZ555Bk2bNtVtu0TkPiZaRFROx44dMXPmTFitVmzbtg3Tp0+HyWTC448/Xm5Zq9Xqsee11axZ0yPr0cvYsWPxwAMPoKioCFu2bMGUKVNwyy23aMmVxWKBxWLRZdslJSUYMmQIGjdujAULFqB27do4d+4cvvvuO1y+fFmXbdq3azKZdFs/UXXHS4dEVI7ZbEbt2rURGRmJ/v37o3379vj6668B/O9y31tvvYWUlBTcfffdAMoe2Dpu3Di0adMGt99+O0aOHIlTp05p67TZbJg5cybatGmDtm3b4tVXX8XVj1q9+tKh1WrFa6+9hk6dOiE+Ph5du3bFxx9/jFOnTmHQoEEAgNtuuw2xsbGYOHEiAEBRFCxevBipqalITEzEvffei61btzpsZ9u2bbjrrruQmJiIgQMHameqricwMBC1a9dGgwYNMHz4cNSsWRM7duzQXr/6cuDhw4cxcOBAJCcno3Xr1ujTpw/27dtX4bpzcnLQp08fjBo1Clartdzrx44dw8mTJ/Hiiy8iKSkJkZGRuPXWW/HUU08hKSlJW+7cuXN4+umncfvttyMpKQl9+vTBr7/+qr2+atUqdOnSBfHx8bjrrruwYcMGh+3ExsZi1apVGDFiBJKSkrBo0SIAwFdffYXevXsjISEBd955JxYsWIDS0lKnPjeimxnPaBHRdfn5+eHixYva3zt37kRQUBCWL18OoOysx9ChQ5GUlIQPPvgARqMR//73vzFs2DB8+umnMJvNWLZsGdavX48ZM2agadOmWLZsGb788kv87W9/q3S7EyZMQFpaGqZMmYLmzZvj1KlTuHDhAurVq4f58+djzJgx2Lp1K4KCgrQzSYsXL8ann36KadOmoVGjRvjxxx/xz3/+E7Vq1cLtt9+Os2fPYvTo0RgwYAAeeOAB7N+/H7Nnz3bp81AUBV9++SUuXbp0zbM9zzzzDFq0aIGpU6fCYDDg0KFDFS5/9uxZPProo0hKSsIrr7wCg8FQbplatWpBlmV88cUXeOSRRypcJj8/Hw8//DDq1KmDf//736hduzYOHDgARVEAAF9++SVmzJiBSZMmoX379vj2228xefJk1K1b1yEOCxYswPjx4/Hcc8/BYDDgp59+wrPPPospU6agTZs2OHnyJJ5//nkAwOjRo1367IhuNky0iKhSqqpi586d+P777/Hwww9r5QEBAXj55Ze1S4YbN26Eoih45ZVXIEkSAGDmzJm47bbb8MMPPyAlJQXvvfcehg8fjm7dugEApk2bhu+//77Sbf/+++/YsmULli9fjvbt2wMAGjRooL0eEhICAAgLC9PGaFmtVixevBjLly9HcnKy9p6ff/4ZH374IW6//XasXr0aDRs21M6ANWnSBEeOHMGSJUuu+3nMmTMHb775JqxWK0pLS1GzZk2HMVpXO3PmDIYOHaqNn2rUqFG5ZU6cOIEhQ4agS5cueO6557TP72p16tTBlClT8Nprr2HBggWIj49H27Ztce+992qfy+eff46cnBysXbtWuwwbFRWlreOdd95B7969MWDAAABA48aNkZaWhmXLljkkWj179kTfvn21vydPnozhw4ejd+/eAMo+03HjxuG1115jokV0HUy0iKicb7/9FsnJySgpKYGqqujZsyfGjBmjvR4TE+MwLuvw4cM4efIkWrdu7bCe4uJinDx5EpcvX0ZWVhZatWqlvWY0GhEfH1/u8qHdoUOHYDAYcNtttzld74yMDBQWFmLIkCEO5SUlJWjRogUA4Pjx40hMTHR4/cpLb9cydOhQ9OnTB1lZWXj11VfRv39/h0Tmao8++iimTJmCjRs3on379rj77rvRsGFD7fWioiIMGDAAPXv2xHPPPXfd7Q8YMAD33XcffvjhB/z666/YunUrFi9ejLfeegsdOnTAoUOH0LJly0rHup04cQIPPvigQ1nr1q2xYsUKh7L4+HiHvw8fPoxffvlFu4wIlF0KLi4uRmFhIfz9/a9bd6KbFRMtIiqnbdu2mDp1KkwmE2655ZZyMwGvPrAWFBQgLi4Oc+bMKbeuWrVquVUHdwaVFxQUACi7fFinTh2H1zwxYD80NBRRUVGIiorCm2++iV69eiE+Ph7R0dEVLj9mzBj07NkT27Ztw3fffYd58+bhX//6F7p27arVyX4Jb9iwYeXqXJGgoCCkpqYiNTUVTz75JIYOHaolWp4aiB8QEODwd0FBAcaMGaOdjbySn5+fR7ZJVF1xMDwRlePv74+oqChEREQ4dbuFuLg4ZGRkICwsTEtE7P9q1KiBGjVqoHbt2g6DsktLS3HgwIFK1xkTEwNFUfDjjz9W+Lp9rJPNZtPKmjZtCrPZjDNnzpSrR7169bRlrh6QfmW9nFWvXj10794dr7/++jWXa9y4MQYPHoxly5ahW7duWLdunfaaLMt49dVXERcXh0GDBuGPP/5wqQ6SJKFJkyZaghkbG4tDhw45jKe7UpMmTfDLL784lP3yyy+VJop2LVu2xO+//17uM42KioIs8zBCdC3sIUR0w3r16oXQ0FCMHDkSP/30EzIzM7F79268/PLLOHfuHABg0KBBWLJkCb766iscP34c06ZNu+YNQuvXr4/evXtj8uTJ+Oqrr7R1bt68GQAQGRkJSZLw7bffIicnB/n5+QgKCsKQIUMwc+ZMrF+/HidPnsSBAwfw/vvva/cGe+ihh5Ceno7Zs2fjxIkT+Oyzz7TXXDVo0CB88803Fc4kLCoqwvTp07F7926cPn0aP//8M/bt21fuflcGgwFz5sxBbGwsHnnkEWRlZVW4rUOHDmHkyJHYunUrjh07hoyMDHz88cdYt24d7rzzTgBAjx49EB4ejlGjRuHnn39GZmYmvvjiC+zZswcAMGzYMKxfvx6rVq1Ceno6li9fji+//LLcpdarjRo1Chs3bsSCBQtw9OhRHD9+HJs2bcK//vUvdz42opsKLx0S0Q3z9/fHypUrMWfOHIwePRr5+fmoU6cO2rVrh6CgIADAkCFDkJWVhWeffRayLKNv377o2rXrNe8BNXXqVLzxxhuYOnUqLl68iIiICO1eXnXq1MGYMWPw+uuvY9KkSfi///s/zJo1C08++SRq1aqFxYsX49SpU6hRowZatmyJESNGAAAiIiIwf/58zJw5EytXrkRiYiKeeuopTJ482eV2R0dHo0OHDpg3b165wfSyLOPixYt49tlncf78eYSGhqJbt24YO3ZsufUYjUa88cYbeOqpp/DII4/g/fffR1hYmMMyderUQWRkJBYuXIhTp05BkiRERkZizJgxGDx4MABosztnz56N4cOHw2azoWnTpnjxxRcBAF26dMHkyZOxbNkyzJgxA5GRkZgxYwbatm17zXZ27NgRixYtwsKFC7FkyRIYjUY0adLkmhMBiKiMpFY2EpWIiIiIbggvHRIRERHphIkWERERkU6YaBERERHphIkWERERkU6YaBERERHphIkWERERkU6YaBERERHphIkWERERkU6YaBERERHphIkWERERkU6YaBERERHp5P8DEaCQPaSmxPwAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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UVi4mynYJijAyMpJ58qK/vz/CwsIwfvx4PH78GNOnT2dMJ6ojZeePlKXsA11FXLt2Db/99hsSEhLE5l+UfaD9/Pmz2H1DT09P7vtYeeS5J5W/f4jOLc3+5csX+p758uVLbNmyBXfu3KEbUCJEdTIvLw/5+fm0ncfjSTRc2rdvD6B0cpuvry/8/f2hr6+P0aNHAwAyMjIkfNHT08PDhw8xffp0HD9+nB4C69GjBwwMDLB9+3YMHToU9vb29HH6+vrw9vamP3fp0gWtW7fG0KFD6YAvK6wMuBYWFmKTECoiNTUV48ePR9OmTbFkyRJYWlpCW1sbN27cwP79+2Vq/VSHsk9zIs6cOYMlS5agR48emDRpEszMzMDj8RAaGlppC7MyZG1lyjvYrwgfKpoUwTRRoUGDBvDy8kJUVBSmT5+OJ0+e4P3791i4cCGdhvr/sdFFixZJjDOJUMTs4KqcR9YZ7m3atMFff/2FK1eu4J9//sHJkydx4MABrFy5EgEBARUeW9WJJhcuXEBQUJCY7fnz51XKi6mOV5fqTKApT3p6Onx9fcVsBw8elHjVThr+/v7YvHkzli1bBmNjY6nj6qI6sn79enoCTllk+c09ePAAM2bMQJs2bfDTTz+hfv360NbWxqlTpxAZGUmnmz17Nu7du0d/Hjx4MNauXStTeZiQ954krSzSZqeLtMnJycHo0aNhYGCAOXPmoHHjxtDV1UVcXBw2btxI349///13bN++nT7e2toaV69elep/48aN4eTkhHPnztEBt1OnTmJpgoODMWTIEBw/fhxmZmYS8018fHywbds2PH78WCzgMuHi4oJ69erh/v37FaYrD+sCLgB0794dx48fx+PHjyt98fzq1avg8/n47bffYGVlRdurM51fKBQiLS1N7An+9evXACC1FVCWixcvwsbGBtu3bxcLTiEhIWLp5Omitba2hlAoREpKitig/6dPn5CTkyOTX7LQuHFj3Lp1C58/f5bayhW18srPGBS1fOShb9++WLlyJZKTk+l33rp3705/L3pqNjAwEHsKlZeUlBSxzxRFISUlhZ6kpqjzSMPY2BhDhw7F0KFDkZeXh9GjR2Pbtm10wDUyMmK88UnTtHx5gNIWmKgedOrUCWFhYRJpRL+R1NRUugUBlE4WeffunUyrm4nyeP36tcQkltevX4v9DhVdLibq168vUVZ5eg+srKzQqlUr3Lt3DyNHjpT6Op6ojpiZmVVaR6T9ti9evAhdXV3s27dPrAV+6tQpsXSLFy8W+30xva0hD7Lek6rLvXv38PnzZ2zfvh1t2rSh7eUXrRg0aBBat25Nf5bl4bWwsFCsR6D8NRcF0czMTMaGVklJidi/lSEQCMRa4bLAujFcAJg8eTL09fWxbNkyfPr0SeL71NRUHDhwAMD/nsSoMrNEv379KlGB5UU0rV+U95EjR6CtrS3TLDkmn54+fYonT56IpRONE8ky1b1r164AQJdbhKjSib6vLr169QJFUWJPnyJE5TEwMICJiQkePHgg9r0sr2KVp3fv3uDxeDh//jyio6PRrVs3sZaki4sLGjdujN9//51xmEHWMZbTp0+LdW9FR0cjIyMDXbp0Ueh5mCi/uk3dunXRuHFjsZuHjY0NkpOTxc6TmJiIR48eMeZ5+fJlsXGz2NhYPH36lC6PhYUFvL29xf5E5TQ2NsaJEyfEbjznzp2rdLhBhIuLC8zMzHDs2DGxMty4cQOvXr0Sm2mr6HIxoaurK1HWyoYYyjN37lwEBgZWuBxi586dYWBggNDQULEuWRFlyyjtt83j8cDhcMRa+G/fvsWVK1fE0rm4uIiVp7IWWWXIek+qLqIWcNnz8Pl8iXuDjY2NWPlEwbekpISxHsbGxuLFixdwcXGhbeWvueihxM7ODp8+fZJodIl6EMq+gyuNO3fuID8/X+5hH1a2cBs3boyNGzdi3rx58PPzo1ea4vP5ePz4MaKjozFkyBAAQMeOHaGtrY3p06djxIgRyMvLw59//gkzMzOJPn5Z0dXVxc2bN7F48WK4ubnh5s2buH79OqZPny4xzsBEt27dcOnSJcyaNQvdunXD27dvcezYMdjb24s9Menp6cHe3h5RUVGws7ODsbExmjdvDgcHB4k8W7RogcGDB+P48ePIyclBmzZt8OzZM0RERKBHjx5irZXq0L59ewwcOBCHDh1CSkoKOnfuDKFQiIcPH6Jdu3Z0d05AQAB2796NpUuXwsXFBQ8ePKB7AeTBzMwM7dq1Q1hYGPLy8uDn5yf2PZfLxZo1azBlyhT4+/tjyJAhaNCgAT5+/Ii7d+/CwMAAu3btqvQ8RkZGGDVqFIYMGUK/FmRra4vhw4cr9DxM9OvXD23btoWzszOMjY3x7NkzXLx4kdYSAIYNG4b9+/dj0qRJGDZsGDIzM+k6w/QA0LhxY4wcORIjR44En8/HwYMHYWxsLPGKVXl0dHQwe/ZsrF69GuPGjUPfvn3x7t07hIeHS4zrSkNbWxsLFy5EUFAQRo8ejX79+tGvBVlbW2P8+PEqL1d1adu2Ldq2bVthGgMDA6xYsQKLFi3CkCFD4OfnB1NTU7x//x43btxAq1atsHz5cgCAs7MzAGDNmjXo1KkTeDwe+vXrh65duyIsLAyTJ0+Gv78/MjMz8ccff6Bx48Zydfn/+++/jMtStm3bVmyypAhZ70nVxdPTE0ZGRliyZAnGjBkDDoeDM2fOVPjaXFny8/PRrVs39O3bF82bN0edOnXw4sULhIeHo169ehKrgjHx7bffIjw8HNOnT8eYMWNgZWWF+/fvIzIyEh07doS7u7tY+q9fv+LMmTMASlu1r1+/xtGjR6Gnpyf3an+sDLgA4Ovri7Nnz2Lfvn24cuUKjh49Ch0dHTg6OmLJkiX0jbJp06YICQnBli1bsG7dOpibm2PkyJEwNTXFDz/8UKVz83g87N27FytWrMCGDRtQt25dBAYGYtasWTIdP2TIEHz69AnHjx/HrVu3YG9vjw0bNiA6OlpsXAYo/UGuXr0awcHBKC4uRmBgIGPAFaVt1KgRIiIicPnyZZibm2PatGmMM4qrQ3BwMBwdHXHy5EmsX78e9erVg4uLi1j3/qxZs5CVlYWLFy8iKioKXbp0wd69e2XqASiPn58fbt++jbp16zK21Nu1a4fjx49j586dOHz4MPLz81G/fn24ubnhm2++kekc06dPx/Pnz7F7927k5eWhQ4cO+Omnn8RmoyriPEyMGTMGV69exT///AM+nw8rKyvMnTsXkyZNotM0a9YM69atQ0hICIKDg2Fvb4/169cjMjJSos4ApV1yXC4XBw4cQGZmJtzc3PDjjz/K1PU4evRoUBSFsLAwrFu3Di1atMBvv/2GNWvWyDwuPWTIEOjp6WHPnj3YuHEj9PX10aNHD3z//fdi7+CqslyqoH///rCwsMDu3buxb98+8Pl8ei6CqBEAlPYUjRkzBufPn8fZs2dBURT69euHDh064Oeff8aePXvwyy+/oFGjRli4cCHevXsnV8B9+vQpnj59KmH/7rvvGAOuPPek6mBiYoJdu3Zh3bp12LJlCwwNDTFgwAB06NBBrL5LQ09PD8OGDcPdu3dx8eJFFBUVwcLCAv369cOMGTNkmqjYtGlTnDp1Clu2bMHZs2fx6dMnWFhYYOLEiZgzZ45E+g8fPmDRokUASocCjIyM0KZNGwQGBkqdzyENDiXrowWBUAO5e/cuxo4di61bt6JPnz7qdkdjEQqF6NChA3r27Ik1a9ao/Pxv376Fr68vFi1aJNONmUDQRFg5hksgEJRHUVGRRBff6dOn8fnz50q7VQkEgnRY26VMIBCUw5MnTxAcHIw+ffrA2NgY8fHxOHnyJBwcHEgvAIFQDUjAJRAIYlhbW6Nhw4Y4dOgQvnz5AiMjIwwcOBALFy5UyE5XBEJthYzhEggEAoGgAsgYLoFAIBAIKoAEXAKBQCAQVECtG8MVCoUoKSkBl8ut1u42BAKBQGA3FEVBKBRCS0tL6jrQiqTWBdySkhI8e/ZM3W4QCAQCQUNwdXVVyYTAWhdwRU8xrq6u1doxRyAQ4NmzZ9XOp6ZBdJEO0YYZoot0iDbMKEoXUT6qaN0CtTDgirqReTxetS4Ul8uFvb09tLW1Sdd0GYgu0iHaMEN0kQ7RhhlF66IqbWtdwFUUojU1CeIQXaRDtGGG6CIdog0zbNWFzFKuIgKBAI8fP1boRtk1AaKLdIg2zBBdpEO0YYatupCAWw2YNjEmEF0qgmjDDNFFOkQbZtioC+lSZoCiKJSUlFT49CT6rrCwkExmKAPRRTqaoA2Px4OWlhYZDyQQ1AAJuOXg8/lIT0+vdNNliqKgpaWFlJQUcvMqA9FFOpqijb6+PiwtLcm6yASCiiEBtwxCoRCvX78Gj8eDlZUVdHR0pN4YKYoCRVHgcDgksJSB6CIddWtDURT4fD4yMjLw+vVrNG/eXGWvQ1QEl8uFk5OTRviiaRBtmGGrLiTgloHP50MoFMLGxgb6+voVpi275wMJLP+D6CIdTdCmTp060NbWRkpKCvh8PvT09NTiR3lIa1s6RBtm2KiLWh8P7t+/j+nTp6NTp05wdHTE5cuXKz3m7t27GDx4MFxcXNCzZ0+Eh4cr3C9Zn5oq63aurRBdpKMJ2mhaq0AoFOLJkyesnASjbIg2zLBVF7X+8vLz8+Ho6IiffvpJpvRpaWmYNm0a2rVrhzNnzmDcuHFYtmwZbt68qWRPCQQCgUCoHmrtUu7atSu6du0qc/pjx46hUaNGWLJkCQCgWbNmePjwIfbv34/OnTsry00CgUAgEKqNZvUtVcKTJ0/QoUMHMVunTp3w5MkTufMSCAT0n6hbQigU0hNbRH8AJGxlx+JkTS/PX0XnVId927Zt8Pb2hqOjI/76669KfZdHl7L2svmro6x37tyBo6Mjvnz5AoqicOrUKXh5edHfh4SEYODAgdU6Z1W1kcdevhwVpS37OxC9tiTNLhQKZbKX/T0x2cvnIc0u8pPJzuSjNDvby8SUnu1lqs51Onv2LF6+fKnQMqkKVk2a+vTpE8zNzcVs5ubmyM3NRWFhoVwTQMruGGRmZgY7Ozukp6ejuLgYBQUFEAqF0NbWho6ODoqKiugLD5QO1ou2cyooKKDturq60NLSQkFBgdgNtk6dOuBwOBLjd/r6+qAoSiwPDocDfX19CAQCFBUV0XYul4s6deqgpKQEfD6ftvN4POjp6WHRokU4e/YsAEBLSwuWlpYYNGgQJkyYIHbOisqkra2NwsJCsUr49u1bbN++HZs3b4aLiwsMDQ2Rn59fYZn09PQqLNOuXbtw/fp1nDhxQqJMRUVFKCoqgp6eHoqLi1FcXEzno6WlBV1dXfD5fJSUlFS5TNKuk4iCggJoaWmhW7duaNu2LYDSG0FxcTGEQiHy8/OrdJ10dXWhra0tpo2oTP/88w8mTZqEGzduoF69etUqk8ifgoIC1KtXT+I6FRUVgaIoFBUVIT4+Xsx3T09P5OTkICkpibbr6enB2dkZWVlZSElJoe2GhoZo3rw5Pnz4gPT0dNou+j2lpqYiMzOTtltaWsLKygrJycnIycmh7Y0bN4aHhwcSExNRWFhI2+3t7WFkZITY2Fixsjo5OUFHR0fiQdvDwwN8Pl8jymRrawtzc/Nql8nd3R2Ojo5i9yu2l6k61+ns2bNYtWoVHB0dce/ePfz333/48OFDlctkY2MDVcKqgKtIyu4yIZoxamlpiTdv3qBOnTpiwVtXV5cxDx0dHcYJKHXq1GFMzzTzWXTjLg+Px2O0a2lpQUtL8rJxuVx07twZv/zyC/h8Pv7++2+sWrUKWlpamDZtmkR6aWUSlVsgEIDD4eDdu3cAgL59+zLOrJU2m7uiMmlra4PL5dLnKlsmXV1d2jdtbW1oa2tL5KOjo8M4Q7GyMpVH2nWqU6cO9PX1oa+vD1NTUwCl10nkd9myVXad+Hy+mK88Ho+xTKLyi85dnTKJjhE9FAHi10O0F7Suri48PDwk8jA0NGS0m5qawsTERMLesGFDNGjQgP4sOmfjxo3Fbmgie9OmTSXy4PP5cHR0FKtjot+Wm5ubWFqRvbyPojqlCWUS2Vu0aMHou6xl4nA44HK5cHd3l/j9sbVM1blO9evXR2hoKHx8fEBRFBo2bIiGDRtWuUxCoRBpaWkS51MWrOpSNjc3x6dPn8Rsnz59goGBgdyvN4h2C+LxeHTFEN2Iyv4BoFsIolaCyJ6Tk4O8vDzw+Xyx9KK0FPW/dy5LSkqQn5+PoqIisfyZ0hYXF0v4UdYXaXYdHR1YWFigUaNGGDVqFLy9vXHt2jU6z/Xr16NLly7w8PDA8OHDce/ePTqPiIgIeHl54erVq+jXrx/c3NywdOlSzJgxAwDQsmVLtGjRgk5/8uRJ+Pn5wc3NDX379sUff/xB+1JYWIgPHz5gwYIFaNeuHTw9PTFkyBDExsYiIiICO3bsQGJiIlq0aAFHR0dERERIlG/s2LFYvXq1WDmzs7Ph4uKCO3fuSNXg2rVrGDZsGNzc3NC+fXvMmjWLTnPmzBkMHToUrVq1QseOHbFw4UJkZWVJ1TgiIgJt2rSR+P748ePo1q0b3N3dMXfuXOTm5tLfBwUFYebMmdi1axc6d+5MP6iIzu3l5YVOnTqJnfvt27cYN24cAKBt27Zo0aIFgoKCAJS2rHfv3g1fX1+4u7tj4MCBiI6OFvPp77//Rp8+feDm5oaxY8fi/fv3UutK+XKW/R2UfQBlsnO5XJnsZX9PTHamPOLj4yXOW35nr7J2Jh+l2dVRJml2ectEURQSEhIkvmNzmeS9Trm5ubSPjRs3RmxsLLZt20a3hBVRJlXBqoDr4eGBO3fuiNlu377N+ESkaAwMDGBgYCAW8Lds2YJ69eohMDBQLK2FhQUMDAyQmppK23bs2AEDAwNMmjRJLK2dnR0MDAyQkJBA2/bv368Qn3V1deku2VWrVuHx48f49ddfcfbsWfTp0weTJ0/Gmzdv6PSFhYXYs2cP1qxZg8jISCxbtgzBwcEAgFu3buHWrVsAgLNnz2Lr1q2YN28eLly4gPnz5yMkJAQREREASh84xowZg48fP2Lnzp04c+YMJk+eDKFQCD8/P0ycOBHNmzen8/Tz85PwPSAgAJGRkWLdsmfPnoWFhQXat2/PWN7r168jMDAQXbt2xenTp3HgwAGxp+6SkhJ89913OHv2LHbs2IF3797RE/BkJTU1FVFRUdi1axf27t2LhIQErFixQixNTEwMXr9+jbCwMISGhoqd+9ixY9i+fbvYuS0tLbFt2zYAQHR0NG7duoWlS5cCAEJDQ3H69GmsXLkS58+fx/jx4/H999/j3r17AID09HQEBgaie/fuOH36NAICArBp0ya5ykQgaCrh4eGws7PD3bt3aZuFhYUaPaoeau1SzsvLEwtKb9++RUJCAoyMjGBlZYVNmzbh48ePWL9+PQBgxIgROHLkCNavX4+hQ4fizp07iIqKom9qhFIoikJMTAxu3bqF0aNH4/379wgPD8e1a9foLqVJkybh5s2bCA8Px/z58wEAxcXFWLFihViXkaGhIQCgfv36tG3btm1YsmQJevXqBaB0HCQpKQnHjx/HoEGDEBUVhaysLJw8eRLGxsYASsd/ROjr64PH44nlWZ5evXph9erVuHz5Mh2Qw8PDMWTIEKmLRuzatQt+fn6YM2cObStblmHDhtH/t7GxwdKlSzFs2DDk5eWhbt26FSj6P4qKirB+/Xpax2XLlmHatGlYsmQJXR59fX2sWbNGrCt52LBhoCgK+fn50NfXlzi3aKsxMzMzWnM+n4/Q0FCEhYXB09OT9vvhw4c4fvw42rZti6NHj6Jx48Z08G7atClevHiBPXv2yFQeAkFToSgKf/zxBzIzMxESEoIjR46o26Vqo9aA+++//2Ls2LH0Z1FravDgwVi7di0yMjLEBvltbGwQGhqK4OBgHDx4EA0bNsSaNWtU8kpQbm4uAPGxsHnz5uH777+XGJP777//AIiPEc6aNQtTpkyhu05EiFqYZdOOHz++Sj5ev34dnp6eKC4uBkVR8Pf3x+zZs3Hv3j0IBAL06dNHLD2fz6cDIlA6Zuro6FjhOfLz85GamoqlS5fixx9/pO0lJSWoV68eAODFixdo2bKlWN7yoquriwEDBuDUqVPw8/NDXFwcXr58id9++03qMQkJCQgICJD6/b///ovt27cjMTGRnsELlLYS7e3tZfLL0tJSbBzM09OTXhJUFHAdHBwkxpj//fdfbNu2DYmJicjJyZHp3CkpKSgoKMDEiRPF7MXFxWjZsiUA4NWrVxJjZ6ro8VE0mrYYhyZRW7XhcDgICwtDmzZtsGDBAonv2aiLWgNuu3bt8Pz5c6nfr127lvGY06dPK9ErZsq3gDgcjtSAwtRakjYBSFraqtCuXTusWLEC2trasLCwoCfi5Ofng8fj4dSpUxIBv+wDhJ6eXqVLDorGsVevXg13d3ex70Rj4AYGBgpZujAgIACDBg3Chw8fEB4ejvbt28Pa2lpq+orG8fPz8zFp0iR06tQJGzduhImJCdLT0zFp0iSxmdCKoPxkrLLn3rRpk8znFmkdGhoqFuQBdi5rJw0ej0e34Ani1DZtYmNjcfXqVcydOxcAUK9ePSxevFgiHVt1qbWzlKuL6H2xspMG1E2dOnXEum5FtGzZEgKBAFlZWfDy8qrWOczNzWFhYYG0tDQMGDBA4nuKomBvb48///wTnz9/Znwo0dbWlun9N0dHR7i4uODEiROIjIwUa1Ez4eDggJiYGAwdOlTiu+TkZHz+/BkLFy6EpaUlgNJWp7ykp6fj48ePdAB88uQJuFwumjRpIvUY0bkXLFgACwsL8Hg8iXOLHrLKvgLUrFkz6Ojo4P379/TrSeVp1qwZrl69KmZ7+vSp3OVSJxRFIScnB4aGhhrzW9IUapM2b9++RYcOHZCfn49mzZqhf//+UtOyVRf2tck1iLLvX2oyTZo0Qf/+/bFo0SJcunQJaWlpiI2NRWhoKK5fvy53fnPmzMHu3btx8OBBvH79Gs+fP8epU6cQFhYGAOjRowfMzc0xa9YsPHz4EGlpabh48SIeP34MALC2tqbH67OyssQmRpUnICAAu3fvBkVR6NmzZ4V+BQYG4vz58wgJCcGrV6/w/Plz7N69GwBgZWUFbW1tHDp0CGlpabhy5Qp27twpd9l1dXWxZMkSJCYm4sGDB1izZg369u1b4Xi06NyHDx/Gq1evcPXqVYlzW1tbg8Ph4Pr168jKykJeXh4MDAwwceJEBAcHIyIiAqmpqYiLi8OhQ4foCWojRozAmzdvsG7dOiQnJ+PcuXP0d2xBKBQiKSmJdeviqoLapE2jRo0wbdo09OrVCx07dqwwLVt1IQG3lhAcHIxBgwZh7dq16Nu3L2bOnIlnz57RrT15CAgIwJo1axAeHo7+/ftjzJgxiIiIQKNGjQCUttb27dsHMzMzTJ06Ff3798fu3bvp7uzevXujc+fOGDt2LDp06IDIyEip5+rXrx+0tLTQr18/qe+kimjXrh22bt2Kq1evYuDAgRg3bhy9YICpqSnWrl2L6Oho+Pn5Yc+ePYxdVZXRuHFj9OzZE1OmTMHEiRNlWgu87LmHDRuG3bt3S5y7QYMGmD17NjZt2gRvb2+sXr0aADB37lzMnDkToaGh8PPzw+TJk3H9+nVaaysrK2zbtg1XrlzBwIEDcezYMcybN0/uchEI6uDTp0/Iy8ujP69fvx4XLlyg33+vaXAopqV2pCAUCnHv3j08ePAA79+/R2FhIUxNTdGyZUt4e3tX6eatagQCAZ48eQIPDw+J8czCwkK8fv0aTZo0qfS93rIzTtnUpaFsFK3L27dv0bNnT5w8eRLOzs4K8FB9aEqdkaeeq4KKfpO1nZqszaNHjzB48GB07NgRR44ckes3oShdVK2vTC3cwsJC7Ny5E127dsXUqVNx8+ZNfP36FVwuFykpKdi2bRt8fX0xZcqUKq1rzFbYOEtOFShCl+LiYmRkZGDLli1wd3dnfbAVQeoMM5oQ+DWVmqpNbm4u3r9/jwcPHogtwygrbNRFpklTvXv3hoeHB9asWQNvb2/GWbTv3r1DZGQk5s+fj+nTp2P48OEKd1aT4HA4UpcGrM0oSpdHjx5h7NixsLOzQ0hIiAI8Uz+kzjDD4/FqzAOVoqnJ2nTp0gXh4eHo3Lmz3K8QslUXmbqUX716hWbNmsmUYXFxMdLT09G4ceNqO6cMFNmlXFJSAi0tLdKlXAaii3Q0RRtN61IWCoXIysqCqakp6QEoR03S5sOHD/juu+8QEhIi8ZqbvChKF43sUpY12AKlE2Y0Ndgqmopm19ZmiC7SIdpIQlEUUlJSGHduqu3UJG3Gjh2LEydOYPLkydXOi626KOyRKT8/H/fv31dUdgQCgUCoQWzbtg3t27fHxo0b1e2K2lBYwE1NTRVbppFAIBAItZeioiI8fPiQ/uzo6Ijbt29XunxsTYbdgwJqpqZN01cURBfpEG2YEW3YQJCEjdpkZmaiW7du6Natm9hOaIqcu8BGXWRe2lHa0nIiyi5JVxvgcDgaMeFE0yC6SIdowwyPx0Pz5s3V7YZGwlZtDA0NoaenBy0tLaSnp9ObbSgKtuoic8Dl8/kYOXIkHBwcGL9/9+4dduzYoTDHNB2KolBcXAxtbW0yG7cMRBfpEG2YEQqF+PDhAxo2bMj6mbiKhk3aiCYwcTgcaGtr4/jx4/j69atck25lhU26lEXmgNuiRQs0bNgQgwcPZvw+MTGxVgVcAPTNU4zPaUB+BS9x65sBxjbKdayavH37Fr6+vjh9+nSVnkwZdSEAINowQVEU0tPTq/2qSE2ELdoUFBRgxowZ8PDwoHf6sbCwUNpm8WzRpTwyB9xu3brh69evUr83MjLCoEGDFOETe/mcBmxvDZRUsKmBli4Q+FDhQXfJkiWIiIjAN998g1WrVol9t3LlSvzxxx/0PsMEAoGgSMLDw3HgwAEcO3YMw4cPh5WVlbpd0khkDrjTp0+v8HtLS0t6A/laS35mxcEWKP0+P1MprVxLS0tcuHABP/zwAz1WWFRUhMjISPIDIBAISmPUqFG4f/8++vfvT+41FcCezm91w8+T+NMSFpX+v7hQofmCn1f5cQw4OTnB0tISly5dom2XLl2CpaWlWNfw33//jZEjR8LLywvt2rXDtGnTkJqaWmHeL168wOTJk+Hp6Qlvb298//33yMrKYkwr2vieIAnRRhIOhwMzMzMyrs2ApmpDURT++OMPFBcXAyj1c8uWLfD19VXJ+TVVl8ogv35Z+UX8qY0DgN4srnkv4Ns/q5bvFlfmMd8VX6qU3dChQxEeHk5vDn/q1CkMGTIE9+7do9MUFBRgwoQJcHR0RH5+PrZu3YpZs2bhzJkzjBMQcnJyMG7cOAQEBCAoKAhFRUXYuHEj5s6di4MHD4ql5XA4lW6jV1sh2jDD5XJhZ2enbjc0Ek3VZvr06di9ezfu3r2LrVu3qvz8mqpLZZAWbg1jwIABePjwId69e4d3797h0aNHdPAV0bt3b/Tq1Qu2trZo2bIlfvnlF7x48QJJSUmMeR4+fBhOTk6YP38+mjVrBicnJ/zyyy+4e/cuXr9+LZaWoigUFRWxbsk1VUC0YUYoFOLNmzes20xcFWiqNn5+ftDW1oa9vb1azq+pulQGaeHKyg/vxT6W7m1aAH39OuBwqyHj3GfVdEwcU1NTdOvWDREREaAoCt26dZPYzPnNmzcICQnB06dPkZ2dTQeA9PR0xte+EhMTcffuXXh6ekp8l5qaiiZNmojZSkpKoKOjo8BS1RyINpJQFIXMzEzY2Gj27H11oEnaFBUV0T00AwcORFJSktrWzdckXeSBBFxZ0akr/pmigBIOoKMPVGccoXy+CmDo0KH0TOWffvpJ4vvp06fD2toaa9asgYWFBYRCIfz9/enxmPLk5+eje/fuWLhwocR39evXV6zzBAJBo6AoCuvWrcOhQ4cQExNDr/BUWzapUSSkS7kG0rlzZxQXF6OkpASdOnUS+y47OxuvX7/GjBkz0KFDBzRr1gxfvlQ8Xuzs7IyXL1/C2toatra2Yn/6+vrKLAqBQFAznz9/xo4dOxAfH48//vhD3e6wmioF3KCgIPz6669its2bNyMoKEghTrEFiQUM9M1K37OtCC3d0nRKhMfjISoqChcuXJBYu9fIyAjGxsY4fvw4UlJSEBMTU+m7uaNGjcKXL18wf/58xMbGIjU1FTdv3kRQUBDjkp5kYQfpEG0k4XA4sLS0ZN2MU1WgCdqYmJggPDwcoaGhlb4eqio0QZeqUKUu5bdv30pM/Pj48SPS09MV4hQb4HA4kmNxxjali1powEpTBgYGjHYul4tff/0Va9asgb+/P5o0aYJly5ZhzJgxUvNq0KABjh49io0bN2LSpEng8/mwsrJC586dJWY1M+pCAEC0kQaXyyXvbkpBXdpERkaiXr166Nq1KwCgTZs2aNOmjcr9kAZb6wyHqmVTJgUCAZ48eQIPDw+J1l9hYSFev36NJk2aVLrIvGjGqa6uLuuespQJ0UU6mqKNPPVcFQgEAiQnJ6Np06ZkN6VyqEOb06dPY/Dgwahfvz4eP34Ma2trlZxXHhSlS0XxQBmQSVPVoLbtkCQrRBfpEG2YycnJUbcLGouqtenVqxe9wI0mT4pkY52Reww3IiIC169fpz+vX78eXl5eGDFiBN69e6dI3wgEAoGgAv777z/6//r6+rh58ya2b99OhkAUjNwBd9euXfS7WI8fP8Yff/yB77//HsbGxmQtZQKBQGAZ4eHhaNasGQ4fPkzb6tZV/OuKhCoE3A8fPsDW1hYAcPnyZfTq1QvffPMNFixYgAcPHijcQU2GPP0xQ3SRDtFGEg6HA1tbWzLmz4AqtHn8+DFyc3Nx9OhR1qyCxtY6I3fA1dfXx+fPnwEA//zzD7y9vQEAurq6KCqqZKecGoRok2W2XXBlQ3SRDtGGGS6XC3Nzc1ZtJK4qVKHNihUrsHv3bpw5c4Y1dZOtdUZub729vbFs2TIsXboUb968oaeNixZGqC1QFIWCggLWPBGqCqKLdIg2zAgEAsTFxZEJZQwoQ5unT59izpw59DrEPB4PU6ZMYdVOVmytM3IH3J9++gkeHh7IyspCSEgITExMAABxcXHo16+fwh3UZNi2cLaqILpIh2jDTGFhNba4rOEoUpucnBx0794d27Ztw7Zt2xSWrzpgY52R+5HG0NAQy5cvl7DPmTNHIQ4RCAQCQTkYGhpiw4YNOHHiRIWL3RCUg9wt3L///ltsctSRI0cwcOBALFiwoNI1eQkEAoGgWjIyMsRe2Zw0aRKioqIkdhEjKB+5A+6GDRuQl5cHAHj+/DnWrl2Lrl274u3bt5WuyVvTYNpMPD03HfGZ8VL/0nNr/vKXIl18fHywf/9+9TqjYZAN6CXhcrmwt7dn3QQYVVBdbZ4+fQovLy8MHjxYrAuW7Vqztc7I3aX89u1bNGvWDABw6dIldO/eHfPnz0dcXBymTp2qcAc1FQ6HIzHJID03Hf6n/cEX8KUep8PTQeSgSFgaWCrUnyVLliAiIkLCfunSJfo1LlXApAuhFKINMxwOB0ZGRup2QyOprjaGhobIzc2Frq4uPn78qNJ7gTJha52R+/FAW1ubflK6ffs2OnbsCKB0F5rc3FzFeqfBlG5Any824zS7KLvCYAsAfAEf2UXZSvGpc+fOuHXrlthfo0aNlHIuaTDpQiiFaMOMQCDA48ePWTfjVBVUV5smTZogOjoa9+7dqzHBFmBvnZE74LZq1QrBwcHYsWMHnj17hm7dugEA3rx5g4YNGyraP40hvzgf+cXiN0u+gI/84vxKg6ws+Qqp/81eLRYybwRfGTo6Oqhfv77YH4/Hw+XLlzF48GC4urrC19cX27dvR0lJCX2co6Mjjh07hmnTpsHd3R19+/bF48ePkZKSgjFjxsDDwwMjRoxAamoqfUxqaipmzJgBb29veHp6YujQobh9+zYASA0oOTk5WLp0Kdq3b49WrVph7NixSExMrFJZ2QoJtsyQ2dvSkUebDx8+oE+fPnj8+DFta9OmDYyNjZXgmXphY52RO+AuX74cWlpauHjxIn766Sc0aNAAQOlkqs6dOyvcQU2h3R/t0O6PdmKt08PPD6P90fb45e4vVc63z6k+aPdHOyR/TqZtZ5LOVMvXsjx48ACLFy/G2LFjceHCBaxatQrh4eHYtWuXWLqdO3di4MCBOH36NJo2bYoFCxZg+fLlmDp1Kk6dOgWKorBq1So6fX5+Prp27Yr9+/cjIiICnTt3xvTp0/H+/Xupvnz33XfIzMzEnj17EB4eDmdnZ4wbN45eSIVAIFSPoKAgXLx4ERMmTCAPdxqI3ANKVlZWCA0NlbD/8MMPCnGIUHWuX78OT09P+nPnzp2Rk5ODqVOnYvDgwQAAGxsbfPfdd9iwYQMCAwPptEOGDIGfnx8AYMqUKfjmm28wc+ZM+iFq7NixCAoKotO3aNECLVq0oD/PnTsXly9fxtWrVzFkyBAJ3x48eIDY2FjExMTQyxsuXrwYly9fxsWLF/HNN98oUAkCoXayefNmZGZmYsOGDaxZNao2Ua0ZHEVFRSguFu/+lLbxOdu5O+ouAKCOVh3aNsV9Cia4TYA2T7vK+UYPjQYA6Gn9b1/SgfYDq5RXu3btsGLFCvpznTp1MGDAADx69EisRSsQCFBUVISCggLUqVNaHkdHR/p7MzMzAICDg4OYraioCLm5uTAwMEBeXh62b9+O69evIyMjAwKBAIWFhUhPT6fzLMvz58+Rn5+Pdu3aidkLCwvFuqprOkza1Ha4XC6cnJxYN+NUFVSmTVFREa5cuUI/LJuYmODs2bOqdFEtsLXOyB1w8/PzsXHjRkRFRTF2BSYkJCjCL41DX1tfwqajpQMd6FTrSZIpX21u1QJ4nTp1JCZG5OfnY/bs2ejVq5dE+rKvqGhr/++covIw2UTjJuvWrcPt27exePFiNG7cGHp6epgzZw6Ki4sZ9cjLy0P9+vVx6NAhie/q1asnTzFZDWl1MEM2dZCONG3y8/Ph4+ODe/fu4fz58+jbt6+KPVMvbKwzVXoP986dO1ixYgV0dHSwZs0azJ49GxYWFli3bp0yfNRY8vPz1e1CpTg5OeH169ewtbWV+KvO0+Hjx48xePBg9OzZE46OjjA3N6dfrmfSxdnZGZ8+fQKPx5Pwoza9gM+GOqNqhEIhnjx5wspJMMqmIm309fXh4eEBIyMj1rX0qgtb64zcV+natWv46aef0Lt3b/B4PHh5eWHmzJmYN28ezp07pwwfWYOJrgl0eBU/denwdGCia6Iij4BZs2bhzJkz2L59O16+fIlXr17h/Pnz+PXXX6uVr62tLf766y8kJCQgMTERCxYsqLDye3t7w8PDA7NmzcKtW7fw9u1bPHr0CL/++iuePXtWLV8IhNoCRVFibxhs3boVjx8/Ru/evdXoFUFW5O5S/vLlC2xsbACUjteKlnNs3bo1Vq5cqVjvWIalgSUiB0VW+J6tia6Jwhe9qIjOnTtj165d2LFjB/bs2QMtLS00bdoUAQEB1cp3yZIl+OGHHzBixAiYmJhgypQp9ApkTHA4HOzevRtbtmxBUFAQsrOzYW5uDi8vL5ibm1fLFwKhNlBQUIAZM2ZAS0sLe/bsAYfDga6uLuzs7NTtGkFWKDnx9/en7t69S1EURY0bN45au3YtRVEUdeDAAapz587yZkcdPnyY6t69O+Xi4kINGzaMevr0aYXpw8LCqF69elGurq5Uly5dqJ9//pkqLCyU+XwlJSXUgwcPqJKSEonvCgoKqPj4eKqgoKDSfIRCIZWbm0sJhUKZz10bILpIR1O0kaeeq4KKfpO1nbLa/P333xSXy6W4XG6l98majqLqjKrrntxdykOHDqUXK5g6dSqOHDkCV1dXBAcHY9KkSXLldeHCBQQHB2PWrFmIiIhAixYtMGnSJGRmZjKmP3fuHDZt2oTAwEBcuHABP//8My5cuIDNmzfLWwyFoK8vOeGJQHSpCKKNJFwuFx4eHrVuHFIWymrTuXNnbN26FX/99Rfc3NzU7ZpaYWudkbtLefz48fT/vb29ERUVhbi4ODRu3FjsvUxZCAsLw/DhwzF06FAAwMqVK3H9+nWcOnWKcV3mx48fo1WrVujfvz8AoFGjRvD398fTp0/lLYZCoCiKzDplgOgiHaINM3w+H3p6epUnrEVQFIXQ0FD4+/vD2toaAMTena/tsLHOVHsldWtra7oyyAOfz0dcXBymTZtG27hcLry9vcWWJSuLp6cnzp49i9jYWLi5uSEtLQ03btzAwIHyv7dadg1ODocDLpcLoVAIiqLoP9F3lJQVW8q+x1o2L2npZUVaHppml4Y8umiaXR6qkrc6tCmPKA1FURJr0fJ4PFAUJTEBjsfj0b+Pyuzlf0/l7eXPKRQKER8fD1dXV/B4PNrO5XLB4XAk0otaNeV9lGZXR5mk2eUp0+LFi7Fp0ybs378f165dE3sNhq1lqsguT5kEAgHi4+Ph5uYm1sqtSt1TJTIF3IMHD8qc4dixY2VKl52dDYFAQC+yIMLMzAzJycmMx/Tv3x/Z2dkYNWoUPVtvxIgRmD59usz+iSg7M9bMzAx2dnZIT09HcXExCgoKIBQKoa2tDR0dHRQVFYldKB0dHXrXl4KCAtquq6sLLS0tFBQUiF3sOnXqgMPhSLwSoq+vD4qixPLgcDjQ19enF6cQweVyUadOHZSUlIDP/9/azTweD3p6eiguLhZbhERLSwu6urrg8/lisxorKpNoY4qylVDeMomCSU0qk6Kuk+i957L5qKNMRUVFoCgKRUVFiI+PF/Pd09MTOTk5SEpKou16enpwdnZGVlYWUlJSaLuhoSGaN2+ODx8+ID39f9tOin5PqampYsNDlpaWsLKyQnJyMnJycmi7aBLmy5cvxbaQs7e3h5GREWJjY8XK6uTkBB0dHTx58gRl8fDwAJ/P14gy2drawtzcHImJiVUuk5eXF4yMjNClSxfExcXRPSNsLpOirpPo3f2PHz/i48ePVS6TqO6pCg4lwyOxj4+PbJlxOLhy5YpMaT9+/IguXbrg2LFjYssRrl+/Hvfv38eff/4pcczdu3cxf/58zJ07F25ubkhNTcXPP/+MgIAAzJo1S6bzCgQCPHnyROxpWvT0k5+fjzdv3sDOzo4OHBW1GvLz8+kbWlkNansLVx5dNM0uD1XJWx3alKegoAApKSmws7OTWDxAXS3c2NhY0sIFkJWVJbbRQFZWFlJTUyW0YVOZlNXCffbsmUJauLGxsfDw8BDTV1nI1MK9evWqwk9sYmICHo8nMUEqMzNT6msiW7duxYABA+hXWhwdHZGfn4/ly5djxowZcg2g83g8CYF1dXXB4XBQUFAgNrmFacxNNBYn+iuLIsbopOWhafbyVEUXTbPLgzx5q1Obsoha2Do6Oow3GQ6Hw2iX9vuS1y4tb6bfpLT08trVUSZ57BRFYe3atdi0aRPu3r1L7zluamqKt2/fMmqj6WWqil2eMnG5XLreyJK+Il9Uhdp2w9bR0YGzszNiYmLQo0cPAKVPGzExMRg9ejTjMYWFhRJCigSsbmtFlJexsTH+++8/AKVdiRXdwLhcrlh3IqEUoot01KkN9f/78f73338wNjZW+81HBI/HE+vlqo0UFxfj7NmzyMzMxLFjx7B06VIARBtpsFUXuQPu7Nmz4erqKjGLeM+ePXj27BlCQkJkzmvChAlYvHgxXFxc4ObmhgMHDqCgoIDebWbRokVo0KABFixYAADo3r07wsLC4OTkRHcpb926Fd27d1fYzUO0p68o6FaEUChk3bR0VUB0kY4maGNsbKxRe1dTFIWcnBwYGhrW2hncOjo6OHnyJC5duoQJEybQdqINM2zVRe6Ae//+fcap6V26dEFYWJhcefn5+SErKwshISHIyMhAy5YtsXfvXrpLOT09XezmNGPGDHA4HGzZsgUfP36Eqakpunfvjnnz5slbDKlwOBxYWlrCwsJCYieksggEAiQmJqJFixYa01LQBIgu0tEEbbS1tTXuugiFQiQlJalsHE1TiIyMxKdPn+hXLa2trcWCLVB7takMtupSpd2Cyu4iQ2ekpYXc3Fy5HRg9erTULuTyO8toaWkhMDBQJe+iSRtPEiEafNfT02PVBVc2RBfpEG0IIm7duoX+/ftDW1sbHh4e8PDwULdLBBUgd9+Wg4MDLly4IGG/cOEC7O3tFeIUgUAg1GQ6duyIYcOGYdq0aXByclK3OwQVIXcLd+bMmZg9ezbS0tLQvn17AEBMTAzOnz+PrVu3KtxBTYZtq5yoCqKLdIg2zNQGXV69egU7OzvweDxwOBwcPXqUfp+/ImqDNlWBjbrI9B5uea5fv45du3YhMTERurq6cHR0RGBgINq2basMHxWK6D1ctvX9EwgE9hIREYGxY8ciMDAQwcHB6naH8P+oOh5U6bWgbt26oVu3bgp2hV0IhUJkZWXB1NRU7bNONQmii3SINszUBl2KioqQm5uLe/fuobi4mHEeDBO1QZuqwFZd2OOphkFRFFJSUhTy/m9NgugiHaINM7VBlxEjRuDs2bO4ePGizMEWqB3aVAW26kICLoFAICiYp0+fYtCgQcjLy6Nt/fv3l2nMllBzIQGXQCAQFEhJSQmGDBmCM2fOYNmyZep2h6BBkIBbDQwNDdXtgkZCdJEO0YaZmqSLlpYW9u/fD39/f/z444/Vzq8maaNI2KhLlWYpA6X72b59+xaNGzdmVTcJmaVMIBAUTUZGBt6/fw93d3d1u0KQA1XHA7lbuAUFBfjhhx/g4eEBf39/en/F1atXY/fu3Qp3UFMRCoV4//69yjcw1nSILtIh2jDDdl0SExPRunVr+Pn54cOHDwrNm+3aKAu26iJ3wN20aRMSExNx8OBBekNtAOjQoQPjClQ1FYqikJ6ezrpZcsqG6CIdog0zbNfF2toa9erVQ926dcU2N1cEbNdGWbBVF7n7gq9cuYJff/1VYu3P5s2bIzU1VVF+EQgEgsZSdtenevXqITIyEiYmJmKbxxMI5ZG7hZuVlQUzMzMJe0FBAau2SSIQCISq8OHDB3Tv3h2nTp2ibU2aNCHBllApcgdcFxcXXL9+XcL+559/1qodLzgcDszMzMhDRjmILtIh2jDDNl127dqFv//+G9999x0KCwuVei62aaMq2KqL3F3K8+bNw5QpU5CUlASBQICDBw/i1atXePz4scR2ejUZLpcLOzs7dbuhcRBdpEO0YYZtuixbtgzp6elYsGCB0hfQZ5s2qoKtusjdwvXy8sKZM2cgEAjg4OCAf/75B6ampjh27BhcXFyU4aNGIhQK8ebNG9bNklM2RBfpEG2Y0XRdioqKsG/fPnqCjpaWFkJDQ+Hg4KD0c2u6NuqCrbpU6QXaxo0bY82aNYr2hVVQFIXMzEzY2Nio2xWNgugiHaINM5qsi1AoRM+ePXHz5k3k5ORg3rx5Kj2/JmujTtiqi9wt3PHjxyM8PBy5ubnK8IdAIBA0Bi6Xi4CAABgbG6Nly5bqdofAcuQOuPb29ti8eTM6duyIOXPm4PLlyyguLlaGbwQCgaByKIpCfn4+/TkwMBCJiYno06ePGr0i1ATkDrjLli3D33//jR07dkBfXx+LFy9Gx44d8eOPP+LevXvK8FEj4XA4sLS0ZN0sOWVDdJEO0YYZTdKloKAAEyZMQJ8+feiGBIfDQYMGDdTijyZpo0mwVZcqr6UsoqioCFevXsWuXbvw4sULJCQkKMo3pUDWUiYQCNJISkpC69atkZubi7/++gs+Pj7qdomgRDR+LeWyZGRk4OjRo9izZw+eP38OV1dXRfml8QgEArx8+RICgUDdrmgURBfpEG2Y0SRd7O3t8ccff2hMsNUkbTQJtuoi9yzl3NxcXLx4EZGRkbh37x4aNWqE/v37Y8uWLWjcuLEyfNRYFL1uak2B6CIdog0z6tKFoiiEhISgZ8+ecHJyAgD069dPLb5Ig9QZZtioi9wB19vbG4aGhvDz88P8+fNrVauWQCDULNatW4egoCA4ODjg8ePH0NfXV7dLhBqM3AH3t99+Q4cOHeiFuwkEAoGtTJw4EXv27MGcOXNQp04ddbtDqOHIHXA7duyoDD9YB4fDga2tLetmySkboot0iDbMqFqXtLQ0esEECwsLxMXFKX2JxqpC6gwzbNVFpoA7ePBg7N+/H0ZGRhg0aFCFhYyIiFCYc5oMl8uFubm5ut3QOIgu0iHaMKMqXSiKwtq1a/HTTz/h4sWL6N69OwBobLAFSJ2RBlt1kSng+vr6QkdHh/4/254qlIFAIEBiYiJatGhBXi8qA9FFOkQbZlSpS0JCAoqLixEdHU0HXE2G1Blm2KqLTAE3MDCQ/v/s2bOV5gzbUPbWXGyF6CIdog0zqtCFw+EgNDQU/fr1wzfffKP08ykKUmeYYaMucs988vX1RXZ2toQ9JycHvr6+CnGKQCAQFMG5c+ewdOlS+nOdOnVYFWwJNQu5J029e/eOcUskPp+Pjx8/KsQpAoFAqC4vX77EoEGDIBQK0aFDB/j7+6vbJUItR+aAe+XKFfr/N2/eRL169ejPQqEQMTExsLa2Vqx3GgyXy4W9vT15PaocRBfpEG2YUZYuzZs3x9KlS5GdnY1evXopNG9VQeoMM2zVRea1lFu0aFF6AIeD8odoaWnB2toaS5Ys0fiJCGQtZQKh5pKQkAArKysYGRkBKJ2ZTCZ5EqShsWspJyYmIjExEZaWlrh9+zb9OTExEf/++6/YNPvagEAgwOPHj1m3lqeyIbpIh2jDjKJ0iYyMRNu2bTFu3Dh62IvtwZbUGWbYqovcY7hXr15Vhh+shGksm0B0qQiiDTOK0KVBgwYoLi5GTk4O8vLyxIa92AypM8ywUReZAu7BgwfxzTffQFdXFwcPHqww7dixYxXiGIFAIFRG2S7jNm3a4MaNG2jdujW0tORuSxAISkemWrl//370798furq62L9/v9R0HA6HBFwCgaASnj59iunTp+PEiRP0Uo3t2rVTs1cEgnSqvQE921DUIDlFUSgsLISenh7rx4kUCdFFOkQbZqqiC0VR6Ny5M/755x8MGzYMf/75p5K9VA+kzjCjKF00dtKUNAQCARISEvDlyxdF+MMqRMtdEsQhukiHaMOMvLpwOBwcPnwYI0aMQGhoqJK80gxInWGGjbrIHXB//vln+mlSIBDg22+/xeDBg9GtWzfcvXtX4Q5qKkKhEE+ePGHlwL0yIbpIh2jDjKy6ZGRk4NKlS/RnOzs7HD16FKampsp2UW2QOsMMW3WRO+BevHiRfif32rVrePfuHaKiojBu3Dj8+uuvCneQQCAQ0tLS4OXlhYEDB+Lx48fqdodAqBJyB9zs7GzUr18fAHDjxg306dMHTZo0wdChQ/HixQuFO0ggEAhWVlZwcXGBjY0NdHV11e0OgVAl5A645ubmSEpKgkAgwM2bN+kN6QsLC8nKTQQCQWEUFxfTXYY8Hg9HjhzB/fv34eTkpGbPCISqIXfAHTJkCObOnQt/f39wOBx4e3sDKJ2i37RpU4U7qKlwuVx4eHiwbi1PZUN0kQ7RhhkmXT58+AAfHx/88ssvtM3Y2JhesrG2QOoMM2zVRe63w2fPno3mzZvjw4cP6NOnDz1TjMfjYcqUKQp3UJPh8/nQ09NTtxsaB9FFOkQbZsrrcvnyZdy6dQv//vsvZsyYATMzMzV6p15InWGGjbpU6fGgT58+GD9+PBo2bEjbBg8ejB49esid15EjR+Dj4wNXV1cEBAQgNja2wvQ5OTlYuXIlOnXqBBcXF/Tu3Rs3btyQ+7zVRSgUIj4+nnWz5JQN0UU6RBtmmHQZPXo0fvnlF9y9e7dWB1tSZ5hhqy5VWv/s3r17+P333/Hq1SsAQLNmzTB58mR4eXnJlc+FCxcQHByMlStXwt3dHQcOHMCkSZMQHR3N+CPj8/mYMGECzMzMsHXrVjRo0ADv37+HoaFhVYpBIBA0BD6fj7Vr12LBggV0qyUoKEjNXhEIikXuFu6ZM2cwYcIE6OnpYcyYMRgzZgz09PQwfvx4nDt3Tq68wsLCMHz4cAwdOhT29vZYuXIl9PT0cOrUKcb0p06dwpcvX7Bjxw60bt0ajRo1Qtu2benXlAgEAjtZtGgRli1bhsDAQHW7QiAoDblbuLt27cL333+P8ePH07axY8ciLCwMO3fuRP/+/WXKh8/nIy4uDtOmTaNtXC4X3t7eUt+zu3r1Kjw8PLBq1SpcuXIFpqam8Pf3x5QpU+SeIV12WycOhwMulwuhUCi216/IXn4LKNFSYhwOR+w7LpcrYRPZAcndLaTZeTweKIpitJf3UZq9KmVisstbJtFi8uXTs7lMirpOIp+qW/c0qUyKuE5CoRCjRo1CYmIihgwZQn/P5jKR35Nyr5NAIKB9Z/JRnrqnSuQOuGlpaYz73vr4+GDz5s0y55OdnQ2BQCDRdWxmZobk5GSp575z5w769++P3bt3IzU1FStXrkRJSYncT8bPnj0TO6ednR1SU1ORmZlJ2y0tLWFlZYXk5GTk5OTQdltbW5ibm0NXV1csH3t7exgZGSE2NlbsQjo5OUFHRwdPnjwR88HDwwN8Ph/x8fG0jcvlwtPTEzk5OUhKSqLtenp6cHZ2RlZWFlJSUmi7oaEhPYktPT292mVKTExEYWFhtcrUsmVLMV1qQpkUdZ0aNmyokLqnSWWqynX68uULPn/+DBMTE9ja2mLGjBnw8vICl8ulfWVbmcjvSbXXydPTE+/fv69WmUSbXqgKuTcv6NmzJyZNmoQRI0aI2Y8ePYqwsDCxpdcq4uPHj+jSpQuOHTsGT09P2r5+/Xrcv3+fcTHy3r17o6ioCFeuXKFbtGFhYdi3bx9u3bol03lFi1W7urrSeVTlSY/D4eDz58+oV68e3eIlT+Sl+eTk5MDAwEBsUXE2l0lR14nL5eLLly9i2rC9TFW5Trm5uZg5cyZu3rxJT4rKzc1F3bp1xeoMm8pEfk+qvU4URSEvLw8GBgaMPsrTwo2NjVXZ5gVyt3AnTJiANWvWICEhgQ6Ujx49QkREBJYuXSpzPiYmJuDxeGJPIQCQmZkJc3NzxmPq168PLS0tMWGaNm2KjIwM8Pl8uRaz5vF4EgJLe6eL6UIIBAIkJyczXihpF04eO4fDYbRL81FeuyJ8ZLILBAK8evWKURe2lqkiuzxlqkgbtpapKvbi4mLcvn0bqampuHnzJgYNGoSkpCSpNz02lIn8nlR7nQQCQYV1Rt4yqQq5A+6oUaNQv359/P7774iOjgZQGvR+/fVXuV4L0tHRgbOzM2JiYujjhEIhYmJiMHr0aMZjWrVqhcjISAiFQlrQN2/eoH79+qzcOYJAqI2YmJggIiICGRkZ8PHxkWh1EAg1FbkCLkVRSElJgZ2dHQ4dOgQtrSq9VUQzYcIELF68GC4uLnBzc8OBAwdQUFCAIUOGACidudigQQMsWLAAADBy5EgcPnwYP//8M0aPHo2UlBSEhoZizJgx1fKDQCAoD4qi8Ouvv6Jly5bo27cvAMDV1VXNXhEIqkfmiJmWloaZM2fSA9oNGzZESEhItX44fn5+yMrKQkhICDIyMtCyZUvs3buX7lJOT08X6xqwtLTEvn37EBwcjAEDBqBBgwYYO3as2la4YtsqJ6qC6CKd2qhNWFgYFixYAGNjYyQkJIgtmCOiNuoiK0QbZtioi8yTpubMmYOXL19i1qxZ0NXVxb59+1BUVISIiAhl+6hQRJOmVDVITiDUdoqKitCrVy8MHz4cM2fOFJv8QyCoE1XHA5lbuA8fPsTWrVvp1aTc3d3RtWtX5OfnQ19fX2kOaipCoRBZWVkwNTVl3QLayoToIp3apM2zZ8/g4uICDocDXV1dXLt2TWqZa5Mu8kK0YYatusjsaWZmJuzs7OjPFhYW0NPTk5hlXFsQjWfL+VZVjYfoIp3aos26devg7u6O3bt307aKboq1RZeqQLRhhq26yNzC5XA4yM/PR25urpgtLy9PzFb+vSgCgVD7oChKbLEGAoEgR8ClKAq9e/eWsA0ePJj+P4fDQUJCgmI9JBAIrGLRokXw8PCQuF8QCLUdmQPuwYMHlekHKyG7FDFDdJFOTdTm3LlzOHjwII4dOwYejwcOhyN3sK2JuigKog0zbNRF7qUd2Q6ZpUwgKI5Pnz6hSZMmyM3Nxc6dOzFjxgx1u0QgyIyq44FMk6by8/PlylTe9GxEKBTi/fv3rNsAWdkQXaRTE7UxNzfH3r17ERgYiEmTJlUpj5qoi6Ig2jDDVl1kCri9evXC7t278d9//0lNQ1EU/vnnH0yePBmHDh1SmIOaCkVRSE9PZ90sOWVDdJFOTdEmPj4er169oj9/88032LZtW5WXV60puigDog0zbNVFpjHcgwcP4tdff8W2bdvQokULuLi4wMLCArq6uvjy5QtevXqFJ0+egMfjYerUqRI7CREIhJrBlStXMGjQINjZ2SEmJoa8lUAgyIFMAbdp06bYtm0b3r9/j+joaDx48ACPHz9GYWEhTExM4OTkhNWrV6NLly5kXJRAqMG0bNkSBgYGsLCwQFFREQm4BIIcyLX7gJWVFSZOnIiJEycqyx/WwOFwYGZmRpapKwfRRTps1UYgENAP0lZWVrh58ybs7OyqvXmJCLbqogqINsywVRf2rImlYXC5XNjZ2bFqWTFVQHSRDhu1efr0Kb2Npgh7e3uFBVuAnbqoCqINM2zVhV3eahBCoRBv3rxh3Sw5ZUN0kQ4btdm0aROeP3+ORYsWKW2CCht1URVEG2bYqgsJuFWEoihkZmaybpacsiG6SIeN2uzcuRMzZ87EmTNnlNZ9x0ZdVAXRhhm26kICLoFAoMnIyMDvv/9OfzYwMMCOHTtgamqqRq8IhJqB4gZiCAQCq/n8+TO8vLyQmpoKQ0NDDBs2TN0uEQg1CrlbuH///TcePHhAfz5y5AgGDhyIBQsW4MuXLwp1TpPhcDiwtLRk3Sw5ZUN0kY6ma2NsbIyAgAA0b94cTk5OKjuvpuuiTog2zLBVF7kD7oYNG5CXlwcAeP78OdauXYuuXbvi7du3WLt2rcId1FS4XC6srKxYN0tO2RBdpKOJ2vD5fLGlWNeuXYv79++rNOBqoi6aAtGGGbbqIre3b9++RbNmzQAAly5dQvfu3TF//nwsX74cf//9t8Id1FQEAgFevnwJgUCgblc0CqKLdDRNmw8fPsDX1xcTJ06kJ59oaWnByMhIpX5omi6aBNGGGbbqIvcYrra2NgoLCwEAt2/fxqBBgwAARkZGYhvR1wZycnLU7YJGQnSRjiZp8+rVK9y5cwf6+vpITk6mH6TVgSbpomkQbZhhoy5yB9xWrVohODgYrVq1wrNnz7BlyxYAwJs3b9CwYUNF+0cgEJREx44dcfDgQbRu3VqtwZZAqC3I3aW8fPlyaGlp4eLFi/jpp5/QoEEDAKWTqTp37qxwBwkEgmIoKirCokWL8OHDB9o2cuRIODg4qNErAqH2QDagryJCoRBZWVkwNTVl3cC9MiG6SEfd2owfPx4HDhxAly5dcP36dY2Z4aluXTQZog0zitJFIzegL09qaip+/fVXzJ8/H5mZmQCAGzdu4OXLlwp1TpPhcrkwNzcnP4JyEF2ko25tfvjhBzRp0gQ//PCDxgRbQP26aDJEG2bYqovc3t67dw/9+/dHbGwsLl26RL9S8Pz5c2zbtk3hDmoqAoEAcXFxrJslp2yILtJRtTYURYltFO/g4IDnz5+jd+/eKjm/rJA6Ix2iDTNs1UXugLtp0ybMnTsXYWFh0NbWpu3t27fHkydPFOmbxiOarU0Qh+giHVVpU1hYiPHjx8Pd3R1xcXG0vexvVpMgdUY6RBtm2KiL3AH3xYsX6NGjh4Td1NQU2dnZCnGKQCBUDy0tLbx9+xaFhYW4f/++ut0hEAiowmtB9erVQ0ZGBmxsbMTsCQkJ9IxlAoGgXrS0tHDs2DHEx8eja9eu6naHQCCgCgG3X79+2LhxI7Zu3QoOhwOhUIiHDx9i3bp19CIYtQEulwt7e3vWDdorG6KLdJSpDUVR2Lx5M3g8HubOnQsAqF+/vkKDbXpuOrKLpPdimeiawNLAUu58SZ2RDtGGGbbqIvdrQXw+H6tWrUJERAQEAgG0tLQgEAjg7++PtWvXqmRqdXVQ9TRwAkEVREdHo2/fvuByuYiNjYWzs7NC80/PTYf/aX/wBXypaXR4OogcFFmloEsgqANVxwO5W7g6OjpYs2YNZs6ciZcvXyIvLw9OTk6ws7NTgnuai0AgQGxsLNzc3EjgLgPRRTrK1KZ3796YPHkyPDw8lLLxQHZRdoXBFgD4Aj6yi7LlDrikzkiHaMMMW3WRO+A+ePAAXl5esLKygpWVlTJ8Yg1CoVDdLmgkRBfpKFKbf/75B23atIGOjg44HA727NmjsLxVDakz0iHaMMNGXeTuAB8/fjx8fHywefNmJCUlKcMnAoFQCVu2bEHnzp2xYMECdbtCIBBkpEob0E+cOBH37t2Dv78/Bg4ciL1794qtz0ogEJRL06ZNQVEU+Hw+K5/0CYTaSLXWUk5LS0NkZCTOnz+P5ORkeHl54eDBg4r0T+EoapCcoigUFhZCT09Po5bJUzdEF+lUVxuKosSOe/ToEVq1aqVIF6USnxmPbyK/qTTdcf/jcDKTbwyZ1BnpEG2YUZQurFhLWYSNjQ2mTp2KBQsWwMHBoda9YK+jo6NuFzQSoot0qqrN2bNn0a5dO3z58oW2qSrYqgJSZ6RDtGGGjbpUOeA+fPgQK1asQKdOnbBgwQI0b94coaGhivRNoxEKhXjy5AnpzisH0UU6VdWmsLAQc+bMwf3797FhwwYleac+SJ2RDtGGGbbqIvcs5U2bNuH8+fP477//0LFjRyxduhS+vr6oU6eOMvwjEGo9enp6OHnyJI4cOYLly5erxQcTXRPo8HQqfQ/XRNdEhV4RCOxC7oB7//59TJo0CX379oWpqakyfCIQaj3x8fHIyspCp06dAABeXl7w8vJSmz+WBpaIHBSplJWmCITagtwB99ixY8rwg0Ag/D93795Fjx49oKenh4cPH6Jx48bqdglAadAlAZVAqDoyBdwrV66gS5cu0NbWxpUrVypM6+vrqxDHNB0ulwsPDw/WreWpbIgu0pFVGzc3NzRv3hwmJiYaN1QT9ykOKTkpcDV3hY2hTeUHyACpM9Ih2jDDVl1kCrizZs3CP//8AzMzM8yaNUtqOg6Hg4SEBIU5p+nw+Xzo6emp2w2Ng+giHWna5OfnQ19fHwBQp04dXLx4ESYmJtDSkrsTSqlEJkficMJhjHUai+/bfK+wfEmdkQ7Rhhk26iLT40FiYiLMzMzo/0v7q03BVigUIj4+nnWz5JQN0UU60rR5+vQpXFxcxN5hr1+/vsYFWwCwNrCGp4UnXMxdFJYnqTPSIdoww1Zd5G6Pnz59Gny+5ExFPp+P06dPK8InAqFWcebMGbx+/Rrr1q1DSUmJut2pkNFOo3Gw70H0bdJX3a4QCKxD7oAbFBSEr1+/Stjz8vIQFBSkEKcIhNrEsmXLsGrVKty8eVMjW7UEAkExyB1wyy8vJ+Ljx4+oV69elZw4cuQIfHx84OrqioCAAMTGxsp03Pnz5+Ho6IiZM2dW6bzVhW0D9qqC6CIdLpeLjIwMrFy5ku4O43K5+PHHHzX+NTuBUIBqrARbIaTOSIdowwwbdZH5cXrQoEHgcDjgcDgYN26c2JO4QCDA27dv0blzZ7kduHDhAoKDg7Fy5Uq4u7vjwIEDmDRpEqKjo+lxYybevn2LdevWqe3dRB6PB09PT7WcW5MhukiHx+PBxcUFzs7OePnyJbS1tfHDDz+o2y2Z+fPFn9j5ZCeGNB+Cua3nKixfUmekQ7Rhhq26yBxwe/ToAQBISEhAp06dULduXfo7bW1tWFtbo1evXnI7EBYWhuHDh2Po0KEAgJUrV+L69es4deoUpk6dyniMQCDAwoULMXv2bDx8+BA5OTlyn7e6UBSFnJwcGBoakkXFy0B0kQ5FUcjPz0dQUBCCg4MxaNAgdbskF0mfk5BdlA0Kim3lkjojHaINM2zVReaAGxgYCACwtraGn58fdHV1q31yPp+PuLg4TJs2jbZxuVx4e3vj8ePHUo/bsWMHzMzMEBAQgIcPH1bp3AKBgP4/h8MBl8uFUCgU6zIT2cumFdkpikJSUhJcXV3pXSa4XC44HI5EelHXR/kZddLsPB4PFEUx2sv7KM1elTIx2eUtE5MubC9Tda8Tn8/H58+fYWFhgaSkJHz77bcICAhAnTp1IBQKWVOmea3mYVDTQainU48+jyKuk1AoZKwz5PdEfk/SfBcIBEhKSoKbm5tY13JV6p4qkXuGxuDBgxV28uzsbAgEAomuYzMzMyQnJzMe8+DBA5w8ebLaM6KfPXsmdj47OzukpqYiMzOTtltaWsLKygrJyclirWhbW1uYmJhI5GNvbw8jIyPExsaKXUgnJyfo6OjgyZMnYj54eHiAz+cjPj6etnG5XHh6eiInJwdJSUm0XU9PD87OzsjKykJKSgptNzQ0RPPmzfHhwwekp6dXq0zm5uZITExEYWFhlcvk6uoqoQvby1Sd6/Tp0ycsWbIEJSUliImJkdCGTWUqyClAUVoRilCET/iksOtkY1O6gMbLly/Vdp1EaFrdI78n5jKJ5gt9/PgRHz9+rHKZRHVPVci0H27btm0RHR0NU1NTtGnTpsIm/L1792Q++cePH9GlSxccO3ZMrD9+/fr1uH//Pv7880+x9Lm5uRgwYAB++ukndO3aFQCwZMkS5OTkYOfOnTKdU7T/Ydknxqq2cMvnQ57IS5/Inz59Sp7I/9/HN2/eoG3btigpKcFff/0FHo9X7bqn7jJVZq9KKyM2Npa0cBl8J78n6S3cZ8+eKaSFGxsbq7L9cGVq4QYFBcHAwID+v6L6zE1MTMDj8cSeRAAgMzMT5ubmEunT0tLw7t07zJgxg7aJLo6TkxOio6NlXneWx+NJCCxt1hvThRAIBNDT02PMR9qFk8fO4XAY7dJ8lNeuCB+Z7BXpwtYyVWSvrEzNmjXD6dOn0aBBAzRr1gyJiYnVrnvKtjOV6XnWc1xLuwb3+u7oYNVB7DtFXCdpdUZe36XZ2Vr3yO9Juo96enrgcrlyaaCKoFoRMrVwlUlAQADc3Nzw448/AigNoN26dcPo0aMlJk0VFRWJdSsAwJYtW5CXl4elS5fCzs6u0k2JRS1cVT3REGoXRUVFmD9/PiZNmlSjNog/HH8Y6+6vg29jX2zpvkXd7hAICkHV8UDuMdy4uDhoaWnB0dERAHD58mWEh4fD3t4egYGBlQa88kyYMAGLFy+Gi4sL3NzccODAARQUFGDIkCEAgEWLFqFBgwZYsGABdHV14eDgIHa8oaEhAEjYlY1QKERWVhZMTU1Z+T6Ysqjtuixfvhw7d+5EdHQ0EhISxH4PbNamiVETDGg2AB4WHgrPm826KBuiDTNs1UVuT5cvX443b94AKO3inTdvHurUqYPo6Ghs2LBBbgf8/PywePFihISEYODAgUhISMDevXvpLuX09HRkZGTIna+yoSgKKSkpSlsIgK3Udl2CgoLQtm1b7Ny5U+Lhk83adLTuiJ87/YwAhwCF581mXZQN0YYZtuoidwv3zZs3aNmyJQAgKioKbdu2xaZNm/Dw4UPMnz8fS5culduJ0aNHY/To0YzfHTp0qMJj165dK/f5CARFQVEUHjx4gDZt2gAAjI2NcefOHVa9G0ggEFRDlZZ2FE1UiomJQZcuXQCUTrvOzs5WrHcEggYjEAgwfvx4tGvXDufPn6ftNS3YFgmK8JUvuX46gUCQD7kDrouLC3777TecPn0a9+/fR7du3QCULrXINLO4JiMaPyaIU1t04fF4qFu3LrhcLlJTU2U6ho3a3Eu/B++j3ph8cbLSzsFGXVQF0YYZNuoid5fyDz/8gO+//x6XL1/G9OnTYWtrCwC4ePEiK9e2rCo8Hg/NmzdXtxsaR23QpewGHlu2bMG4cePQrl27So9jqzZvc98CAIz1jJWSP1t1UQVEG2bYqovCXgsqKioCl8uFtra2IrJTGoqaBi4UCvHhwwc0bNiQVbPklE1N1oWiKGzatAkvXrxAaGio3F3HbNYmh5+DguICNKjbQOF5s1kXZUO0YUZRuqj6taAqe/rvv//izJkzOHPmDOLi4qCrq6vxwVaRUBSF9PR01s2SUzY1WZfY2FgsXrwYe/bswdWrV+U+ns3aGOoYKiXYAuzWRdkQbZhhqy5ydylnZmZi7ty5uH//Pt2HnpOTg3bt2uHXX3/V+D09CYSq4u7ujg0bNkBXVxc+Pj7qdodAILAMuVu4q1evRn5+Ps6fP4979+7h3r17iIyMRG5uLtasWaMMHwkEtfHXX3/h06dP9Of58+dj1qxZNW4msjQ+5H3A0ltLcTj+sLpdIRBYj9wt3Js3byIsLAzNmjWjbfb29vjpp58wceJEhTqnyXA4HJiZmdWaG6+s1CRddu/ejenTp8PX1xdRUVHQ0pL75yIGG7VJyEzA2Vdn8SL7BUY7jQY+pwH5mdIP0DcDjOXbgYWNuqgKog0zbNVF7juIUChkHKvV0tJS+d6C6oTL5cLOzk7dbmgcNUkXb29v6Ovro2nTpgqp22zUxtbIFrM8ZsFI16g02G5vDZQUST9ASxcIfChX0GWjLqqCaMMMW3WRu0u5ffv2+Pnnn8X2IPz48SOCg4PRoUOHCo6sWQiFQrx586ZWPWTIAtt14fP59P9dXFzw7NkzhIaGyr1GOBNs1KapUVNMd5+OkS1GlrZsKwq2QOn3FbWAGWCjLqqCaMMMW3Wp0lrKubm58PX1RY8ePdCjRw/4+voiNzeX3vGnNkBRFDIzM1k3S07ZsFmXs2fPonnz5mIbYDdp0kRh+bNZG2VCdJEO0YYZtuoid5eypaUlIiIicPv2bSQnJwMo3fPT29tb4c4RCKpCKBRi/fr1SE1Nxbp167Bnzx51u6R2SoQleJ79HE2NmqKOVh11u0MgsJ4qzQLhcDjo2LEjOnbsqGh/CAS1wOVyceLECezYsQMrVqxQtzsaQerXVIyIHIG62nURMzIG7JqeQiBoHlUKuDExMdi/fz9evXoFoLSFO27cuFrVyuVwOLC0tGTdLDllwyZd4uLiEBsbi5EjRwIArKys8PPPPyvtfGzSBgAyCzJhomsCawNrpfrMNl1UCdGGGbbqInfAPXLkCH755Rf07t0bY8eOBQA8ffoUU6dORVBQEL799luFO6mJcLlcWFlZqdsNjYMtuiQmJqJdu3bg8/mws7NTyYQ/tmgjok3DNvh7xN/IL85X6nnYposqIdoww1Zd5A64oaGhCAoKkti/tlWrVti1a1etCbgCgQDJyclo2rSpStbgZAts0cXR0RF+fn7IzMyEvb29Ss7JFm3Ko6+tr9T82aqLKiDaMMNWXeSepfz161d07txZwt6xY0fk5uYqxCm2kJOTo24XNBJN1SU7OxsCgQBAaZfU/v37cfHiRdSvX19lPmiqNjKhb1b6nm1FaOmWppMTVuuiZIg2zLBRF7kDro+PD/766y8J+5UrV+i9cQkETePp06do3bo1li5dStv09fWrvXpUTYUv4GNc1DisjFmJwpLCUqOxTemiFs5DSj+37A9MvSH+J+eiFwRCbULuu02zZs2wa9cu3Lt3Dx4eHgBKb2aPHj3ChAkTcPDgQTqtaIyXQFA3z58/x+vXr3Hy5EksXboU9erVU7dLGs2bnDd49N8jvMx+ieXtl//vC6NGwPtHpf93HwVYeajFPwKBjcgdcE+ePAlDQ0MkJSWJLRBQr149nDx5kv7M4XBqdMDlcDiwtbVl3Sw5ZaOpugwfPhz5+fkYMGCA2oKtpmrDRAP9BljfZT2+8r+K+/vpJZD9BuDpAE26KORcbNJF1RBtmGGrLgrbgJ4tqHrDYYJ6yMjIwI8//oiNGzfCwMBA3e7UHG5vBy4tBZp2B8aeVrc3BEK1YM0G9LUdgUCAuLg4ehIOoRRN0IWiKPj7+yM0NBRz5sxRmx/l0QRtqs3Li6X/OvRWWJY1QhclQbRhhq26kIBbDQoLC9Xtgkaibl04HA42bdoEZ2dnLFy4UK2+lEfd2sjK9bTrSMpOQomw5H/G4kLg7cPS/zfvpdDzsUUXdUC0YYaNupApmoQaAZ/PR0pKCpo3bw4A6NSpE54+fUqGDapAQUkB5lydAwoUrg+/DrM6//+aj7YesPA5kBIDmDWrOBMCgSABaeESWE9GRgZ8fX3RtWtXpKen03YSbKvGl6IvcK3vCpt6Nv8LtiJ06wEOim3dEgi1BZkCbmBgIL2oxenTp8X2DK2tcLlc2Nvbg8slzyxlUYcuderUQXZ2NvLy8vDy5UuVnVde2FJnGtZtiCN+R3B+8HmVnI8tuqgDog0zbNVFJm+vX7+O/PzS9VSDgoLw9etXpTrFBjgcDoyMjFg3LV3ZqEMXAwMDnD59Gvfv30eXLop5VUUZsK3OiPmZHguEdgVublbKedikiyoh2jDDVl1kGsNt0qQJNm/ejHbt2oGiKERFRUl91WLQoEGK9E9jEQgEiI2NhZubG+m6LIMqdCkqKsLs2bPRs2dPBAQEAIDK1kOuDqyuMy8vAulPAEPFLxjPal2UDNGGGbbqIlPAXblyJdauXYvr16+Dw+Fgy5YtjE8WHA6n1gRcoHTTcoIkytYlNDQUe/bswfHjx9GjRw+YmJgo9XyKhA11ZmTkSNTVroufvH+CTb3/X6bxxaXSfxU8O1kEG3RRF0QbZtioi0wBt1WrVjhx4gQAoEWLFrh48SLMzORfoJxAUAQzZ87ErVu3MGnSJFYFWzaQV5yHfzP/BQAY6hj+vzETeHu/9P/Ne6rJMwKB/cj9WtCVK1dgamqqDF8IBEZEwxh9+/YFh8OBlpYW/QBIUCy6PF0c7XcUKTkpMNI1KjW+ugqAAhq4lK6lTCAQqoTcAdfa2ho5OTk4efIkXr16BaB0/GzYsGG1akF4LpcLJycn1s2SUzbK0GXKlCnYt28fNm7ciAULFigsX1XDhjqjxdWCi7kLXMxd/mcUrS6lpNYtG3RRF0QbZtiqi9zePnv2DD179sT+/fvx5csXfPnyBfv370ePHj0QFxenDB81Fh0dHXW7oJEoWhdPT0/weLwasZUe6+qMUAAkXS79f3PFLedYHtbpokKINsywURe5Ny8YNWoUbG1tsXr1avoGWFJSgmXLliEtLQ1HjhxRiqOKQlGLVZNNEJhRpL6i4ymKQkJCApycnBTlplpQV51Jz01HdlG21O9NdE1gaWAJADiffB51tOrAq6FX6RhufhZwYSHw7mHpXrc8xT/0kN+SdIg2zLD1Pi73r+fff/8VC7YAoKWlhcmTJ2Po0KEKdY5Q+6AoCps2bUJkZCQuXboEHR0dcDgc1gdbdZGemw7/0/7gC6QvVqPD00HkoEhYGlhi04NNyCjIwGG/w3Cv7w7omwLDfgcoCmDZO48EgqYhd5eygYGB2PJ5ItLT01G3bl2FOEWovbx//x6rV6/GjRs3yMQoBZBdlF1hsAUAvoCP7KJslAhL0M6yHVqYtkAzo3JrJZNgSyBUG7lbuH5+fli6dCkWL14MT09PAMCjR4+wfv169OvXT+EOEmoX1tbWOHLkCFJTU/Htt9+q251ahRZXC8Gdg/9nKMgGctIBi5Yk4BIICkDugLto0SL6X9FehFpaWhg5cqTGbYWmTLhcLjw8PFg3S07ZVEWX6Oho2NjYwNnZGQDg7++vLPfUCuvqTMI54OxswLEfMPIPpZ2GdbqoEKINM2zVRe6Aq6Ojg2XLlmHBggVITU0FADRu3Bh16tRRuHOaDp/Ph56enrrd0Djk0eXo0aP49ttvYW9vj/v378PIyEjJ3qkXTa4zJcISaHHL3BJe/P/rQJbuSj+3Juuibog2zLBRlyo/HtSpUweOjo5wdHSslcFWKBQiPj6elcuLKRN5denRowdsbGzg4+PDuh+PvGh6nZn611T0OtkLMe9jgBI+kHy99Aslb8en6bqoE6INM2zVhf0vNhJYR05ODgwNS5cNrF+/Ph4+fAhzc3M1e0VIyk5CdlF26QpTqbcBfi5Q1wJoqPwWLoFQG2BXBziB9Zw9exZ2dna4evUqbSPBVv0kf0nGxq4bsaLDChQLihEf/yfSebzS1aVYNk5GIGgqpIVbDdg2YK8qKtIlIiIC2dnZ2L59O3x8fFTolWag6jpjomsCHZ5Opa8GBd0MkrDpNLJCZOO2sFSWc2UgvyXpEG2YYaMucq80xXbIyi3qpaCgANu3b8fcuXOhra2tbndqBem56dj7bC/+fvs3prlPg5OZE5K/JDMG2fIc77kPTlZtVeAlgaB6NH6lKQB48+YN7t69i8zMTIlB68DAQIU4pulQFEWPRTLtDVxbKa9LXFwczpw5gx9++AFA6WS777//Xs1eqgd11RlLA0v82OFHiJ6t5Tq3roGSvPof5LckHaINM2zVRe42+YkTJ+Dn54eQkBBcvHgRly9fpv+uXLlSJSeOHDkCHx8fuLq6IiAgALGxsRWef9SoUWjTpg3atGmD8ePHV5heWQiFQiQlJbFulpyyKavLf//9hw4dOmDp0qU4evSoul1TO+quMxwORyNvTurWRZMh2jDDVl3kbuH+9ttvmDt3LqZOnaoQBy5cuIDg4GCsXLkS7u7uOHDgACZNmoTo6GjGTe7v3r2Lfv36oVWrVtDR0cHevXsxceJEnD9/Hg0aNFCITwTFYGFhgfnz5+PmzZvo0aOHut2ptbz58ga2hrYaGWwJhNqE3C3cL1++oG/fvgpzICwsDMOHD8fQoUNhb2+PlStXQk9PD6dOnWJMv2nTJnz77bdo2bIlmjVrhjVr1kAoFCImJkZhPhGqTmZmJnJzc+nPy5cvx8WLF1G/fn01elV7ef3lNfqf7o9BZwahRFiibncIhFqN3C3cPn364NatWxg5cmS1T87n8xEXF4dp06bRNi6XC29vbzx+/FimPAoKClBSUiL3CkWiZSmB0q42LpcLoVCIsnPIRPayaUV2ANDV1RX7jsvlgsPhSKQXzaYr3/0hzc7j8UBRFKO9vI/S7FUpE5NdnjI9ffoUQ4cOhZ2dHaKiosTyBsDKMlVkl/c6AZJ1RtllepH1AtpcbTTQbwAO9b/vZZ0rKfySDoGxo1Kvk1AohJ6ensZcJ02qexRFSdQZtpdJEddJIBBAT09PIm1V6p4qkTvg2traYuvWrXj69CkcHBwkNgUfO3aszHllZ2dDIBBIdB2bmZkhOTlZpjw2btwICwsLeHt7y3xeAHj27JnY+ezs7JCamorMzEzabmlpCSsrKyQnJyMnJ4e229rawtzcHBwORywfe3t7GBkZITY2VuxCOjk5QUdHB0+ePBHzwcPDA3w+H/Hx8bSNy+XC09MTOTk5SEpKou16enpwdnZGVlYWUlJSaLuhoSGaN2+ODx8+iO3iVNUyJSYmorCwsEplSkhIQHp6OjgcDq5fv063atlcJkVfJ1NTU4XUPVnL1MWpC24E3MCtx7fEyqVrowtZ4JwYjVe+h+HQxlep18nZ2RlxcXEac500qe41a9ZMrM7UhDIp4jo5Ozvj/fv31SqTjY0NVIncrwVV9O4kh8ORa+LUx48f0aVLFxw7dozeeQgA1q9fj/v37+PPP/+s8Pjdu3dj7969OHjwIFq0aCHTOUXTwF1dXelp4FVt4X769AkmJib0Ext5IgcuXbqEZs2aoUmTJmLvybG5TIq6ThwOB5mZmTA2NqbzVFeZPuZ/RP/T/uALiyENHSGFyLfvYTHhMniNWintOlEUhc+fP8PIyEiszpDfUylZWVlidYbtZVLEdRIKhfjy5QuMjY3F5iZUpYUbGxurua8FlV0hqLqYmJiAx+OJPYkApeOAla0+tG/fPuzevRthYWEyB9uy8Hg8CYGlvUjNdCEEAgHS0tJgZmYm8b20CyePncPhMNql+SivXRE+/vfff5g1axY2bdqExo0bAwB69eqFJ0+egMvlShzDhjLJa5enTAKBAKmpqTA1Na1W3ZPVTlEUfTMqn96qnhUivdcj+/gIxnwAwEQghKVAQK80pazrJBAIkJKSIvWmV1t+T0z2iuoMW8tUkV2eMqWkpNAxRJb0FfmiKqq10lSV3usrg46ODpydnRETE0PPYhVNgBo9erTU4/bs2YNdu3Zh3759cHV1rdK5CdVn6tSpOHPmDD59+oRr166p2x1COX649QO+8r9ihvsMOJs7S3xvWccclnzpLVwCgaBYqhRwT58+jX379uHNmzcAADs7O0yaNAmDBg2SO68JEyZg8eLFcHFxgZubGw4cOICCggIMGTIEQOm+uw0aNMCCBQsAlHYjh4SEYNOmTbC2tkZGRgYAQF9fH3Xr1q1KcQhVZOvWrfj06RN27NihblcI5eAL+LiaehX5JfmY4T5D3e4QCARUIeCGhYVh69at+PbbbzF37lwAwMOHD7FixQp8/vwZ48ePlys/Pz8/ZGVlISQkBBkZGWjZsiX27t1Ldymnp6eLdQ8cO3YMxcXFmDNnjlg+gYGBmD17trzFqRaiHW9qC3w+Hw8fPkSHDh0AlE6iuHnzpkQPR23TRR5UpY02VxuH/A7h9rvbaGnWUiXnrA6kzkiHaMMMG3Wp0qSpOXPmSLRmIyIisG3bNoWO8SoDspZy1fjy5Qv69euHhw8f4p9//kGrVq3U7RKhurx/AuzuWnm6qTcAKw9le0MgqBxVxwO5F77IyMgQm1EswtPTk+7erQ0IhUK8f/+edUuLVZV69erBxMQEOjo6+PTpk9R0tU0XeSDaMEN0kQ7Rhhm26iJ3wLW1tRVb1EDEhQsXYGdnpwifWAFFUUhPT5d5AQE2QlEUXT4ul4tDhw7hwYMH6NWrV4XH1HRdqoqqtEnKTsLmh5vxNONpxQn1zQCtSt7H1dItTadESJ2RDtGGGbbqIvcY7uzZszFv3jzcv3+f7lZ89OgR7ty5gy1btijaP4KaKCoqQmBgIGxsbLB8+XIAgLGxMYyNjdXrGKFS/kr5C2H/hiHlSwq2+myVntDYBgh8CMRFADc3AS38gLbTxNPom5WmIxAI1UbugNu7d2+cOHEC+/fvpxe5aNq0Kf788084OTkp3EGCeoiOjsbevXvB4/EwevRoNG3aVN0uEWTEw8ID/Zr2Q7dG3SpPbGwDfE4BCj8DOvXIWC2BoESq9FqQi4sLNm7cqGhfWAWHw4GZmVmN3YFl4MCBCAoKQrdu3eQKtjVdl+qgKm06WHVAB6sOsh/w+u/Sf5t0UY5DlUDqjHSINsywVReZZinn5ubCwMCA/n9FiNJpKmSWMjMUReHIkSMYOnQo6tSpo253CKoi5z2wuSXA4QKLXgN1jNXtEYGgMjRylnKbNm3o5Re9vLzozd/L/onstQWhUIg3b96wbpacNObNm4cxY8ZgxowZ1ZqIUNN0USSq0Ob2u9vIKsyS/YDXN0v/tXRXW7AldUY6RBtm2KqLTF3KBw4coLe/O3jwoFIdYgsURSEzM1Plu00oC39/f/z2229wd3evVj41TRdFomxt8ovzEXg1ECXCElwYcgGN6jWq/KDXN0r/bSLD+7hKgtQZ6RBtmGGrLjIF3LZt29L/b9SoESwtLSX6zkXTtAnsoaCggO4+7tGjB5KTk2Ftba1mrwhV5UP+B9gb2yO3OBfWBjJcR4pS+/gtgVCbkPs9XF/f0n0xy/P582f4+voqxCmCcqEoChs3boSLi4vYIhYk2LKL9Nx0xGfG03+FJYVY4b0CazquQUJWAtJzK3kAFhQDLkMAay+gsRyTrAgEQpWQe5Zy2S2/ypKfnw9dXdk2ta4JcDgcxpY+G8jLy0NoaCiSk5Nx6NAhzJs3T2F5s1kXZaNIbdJz0+F/2h98AV9qGh2eDiIHRcLSwJI5gZYO0HNVtX2pLqTOSIdowwxbdZE54AYHBwMoLeiWLVvEZrIKBALExsZWaV9atsLlcmFlZaVuN6qEgYEBTp8+jb///hvTp09XaN5s1kXZKFKb7KLsCoMtULpjUHZRtvSAqyGQOiMdog0zbNVF5oAbHx8PoLSF++LFC2hra9Pf6ejooEWLFpg4caLiPdRQBAIBkpOT0bRpU1a8XiRajrNv374AAGdnZzg7S+6RWl3Yposq0ShthEIg+Spg0x7QVe+rfBqli4ZBtGGGrbrIHHAPHToEAAgKCsLSpUs1/n1bVZCTk6NuF2QiOjoa/fr1g6GhIR49eqT0VaPYoos60BhtMhKAw0MBPaPS92+56r1paYwuGgjRhhk26iL3GO4PP/yAkpISCfvnz5+hpaVFArEG4uPjgw4dOsDV1ZVMjCKUkvz/rwNZe6k92BIItQW5ZynPmzcP58+fl7BHRUUpdPINoXqU3UlDR0cHly9fxq5du2rVxDZCBYheB2qqvvdvCYTahtwBNzY2Fu3bt5ewt23bFrGxsQpxig1wOBzY2tpq5Cy5M2fOwNHREbt27aJtqlquUZN1UTcao42gBEj5p/T/GvD+rcboooEQbZhhqy5yB1w+n8/YpVxSUoLCwkKFOMUGuFwuzM3NweXKLaHSefHiBb5+/YpTp06pfOkzTdZF3ShSm2rtA5r+BCjKAfSMgYZu1falupA6Ix2iDTNs1UVub11dXXHixAkJ+7Fjx5Qy61VTEQgEiIuLg0AgULcrEixcuBBhYWGIiopSeYXUZF3UjSK1MdUzhRan4ikYOjwdmOiaSH4hWs7RrpNGjN+SOiMdog0zbNVF7klTc+fOxYQJE5CYmIgOHUpXp4mJicGzZ8/w+++/K9xBTUZTWvRxcXHYunUrdu7cCS0tLXA4HIwfP15t/miKLpqIorSxNLDEhSEX8CH/A3R5zOPyJrompe/gfk4D8jP/90VCZOm/ZvbA+ycasck8qTPSIdoww0Zd5A64rVu3xvHjx7F3715ERUVBV1cXjo6O+Pnnn2FnZ6cEFwkVUVBQAF9fX3z8+BG2trZYunSpul0iKJHMgkzU1a4LPS09WBpYVr6oxec0YHtroKRI8rt/tpT+aekCgQ/VHnQJhJpOlTagb9myJTZt2qRoXwhVoE6dOggJCcGePXswbdo0dbtDUCLFwmLMuz4P+cX52NxtMxobNq78oPxM5mBblpKi0nQk4BIISqVKAVdEUVERiouLxWy15T1cLpcLe3t7tQzaZ2ZmIicnB02aNAEADB8+HAEBARoxY0+dumg61dUm7WsaUnJSKl3SkW2QOiMdog0zbNVF7oBbUFCADRs2ICoqCp8/f5b4PiEhQRF+aTwcDofeI1iVxMXFwd/fH3Xr1sWdO3foBxxNCLaA+nRhA9XVpqlRU/zZ/0+8/vJattYtSyB1RjpEG2bYqovcjwfr16/HnTt3sGLFCujo6GDNmjWYPXs2LCwssG7dOmX4qJEIBAI8fvxY5bPkTExMUFhYiIKCAnz48EGl55YFdenCBhShjYW+BdpZtlOgV+qH1BnpEG2YYasucrdwr127hnXr1qFdu3YICgqCl5cXbG1tYWVlhXPnzmHAgAHK8FMjUfU7rgBgZWWF6Oho2NjYwNTUVOXnlwV16MIWpGpTfibx/8MXFmPJs98w3nEk3Jr2VLJ36oPUGekQbZhhoy5yB9wvX77AxqZ0coWBgQG+fPkCoHT28sqVKxXrHQH//fcfxowZg59++gne3t4AAHd3dzV7RVAoFcwk3mdsiL9MjPE4/Q6ijM5Cz8xevryrs0AGgUBQKHIH3EaNGuHt27ewsrJC06ZNERUVBTc3N1y7dg316tVTho+1mtWrV+PSpUt48+YN4uPjWbUVFUFG8jORTpUgW0db4qu2BYV4rFsAv9w86BXlypevUADc3KAgJwkEQnWRO+AOHToUiYmJaNu2LaZOnYrp06fj8OHDKCkpwZIlS5Tho0bC5XLh5OSk9FlywcHBeP/+PdasWcOKYKsqXdiING3SCz7Bv5EV+FzpE98e6umhXcEnyLWV/Mu/gETJjUYk0NItXfxCTZA6Ix2iDTNs1UXugFt2BSNvb29ERUUhLi4OjRs3RosWLRTpm8ajo6Oj8Dz5fD4uXLiAQYMGASjttj916pTCz6NMlKFLTYFJm+zi3AqDLQDwuRxkF+fKF3Ad+wDdlwJ1TIBGbaSn04CVpkidkQ7Rhhk26iLX40FxcTHGjRuHN2/e0DZra2v06tWr1gVboVCIJ0+eKHTgns/nw8fHB4MHD2Zcr5oNKEOXmoJKtCkuBIq+/u9z10VA2ymAlYf0PzUHW1JnpEO0YYatusjVwtXW1sbz58+V5UutR0dHB97e3nj27Bnq1q2rbncImkbW69J/pcxoRkkhcGlZ6djt+EhAh9QhAkGTkLtLecCAATh58iQWLlyoDH9qHRRFoaSkBNrapRNmfvnlF8ycOZOsS02Q5PrPwIurQNodoLLVpl7fABz9VOMXgUCQCbkDrkAgwNGjR3H79m24uLhIbGweFBSkMOdqOoWFhQgMDMTXr19x7NgxcDgcaGlpkWBLkAIHeHMTgAyv+tSzUro3BAJBPuQOuC9evICTkxMA4PXr12LfacrygqqAy+XCw8OjWrPk4uLicPDgQQgEAty7dw/t2rF/BSFF6FJTkaqNnoxL1PlvATKSgZsbFe6bOiF1RjpEG2bYqovMATctLQ2NGjXCoUOHlOkPq+Dz+dDT06vy8a1bt0ZoaCisra1rRLAVUV1dajKM2tRrINvB1p6AuXONC7gAqTMVQbRhho26yPx40KtXL2RlZdGf586di0+fPinFKTYgFAoRHx8v1yw5iqIQGhqK9PR02jZhwgT06tVLGS6qharoUluQpo2Jrgl0eBW/4qDD04GJroky3VMbpM5Ih2jDDFt1kbmFS5VbIu7GjRtYsGCBwh2qyaxYsQKrVq3CwYMHcf36dXqiFKF2Y2lgichBkcguypaaxkTXpHSz+ZyPKvSMQCAokmrth0uQj9GjR2PHjh0ICAiAlhaRnvA/LA0sSwMqgUCosch81+dwOLVqUpQsyDJgn5mZCTOz0mXzmjdvjuTkZBgaGirbNbXCtokMqoRowwzRRTpEG2bYqAuHKt9XLIUWLVqgS5cu9HJa165dQ/v27SVeC9q+fbvivVQgAoEAT548gYeHh1LXJqYoChs3bsSaNWvwzz//wMXFRWnnItQiKthZiEZLFwh8qPYVpAgETUdV8UCEzC3cwYMHi32uTfveMkFRFHJycmBoaMjY8hcKhbh48SJycnJw/PjxWhNwK9OlNqMQbYxtSoMp00pTIjRgbWR5IHVGOkQbZtiqi8wBNzg4WJl+sA6hUIikpCSpT0Y8Hg/Hjh3DuXPnxDZ8qOlUpkttRmHaGNuwKqBWBqkz0iHaMMNWXdjXCa7BREVFYceOHfRnc3NzTJgwgVVPYAQCgUBQDmSqrIJ49OgR+vXrBw6HA09PT3h7e6vbJQKBQCBoEBrRwj1y5Ah8fHzg6uqKgIAAxMbGVpg+KioKffr0gaurK/r3748bN26oyFNxyq5y4unpifHjx2PKlClo3bq1WvzRFNi2+osqIdowQ3SRDtGGGTbqIvMsZWVx4cIFLFq0CCtXroS7uzsOHDiA6OhoREdH06/TlOXRo0cYPXo05s+fj+7du+PcuXPYu3cvwsPD4eDgUOn5FDkr7dWrV7CxsaFnbgsEAlaNJxAIBEJtRtWzlNXewg0LC8Pw4cMxdOhQ2NvbY+XKldDT08OpU6cY0x88eBCdO3fG5MmT0axZM8ydOxdOTk44fPiwSv0+c+YMPDw8MHfuXNpGgm3pZIZPnz6xbsk1VUC0YYboIh2iDTNs1UWtY7h8Ph9xcXGYNm0abeNyufD29sbjx48Zj3ny5InErN9OnTrh8uXLcp1bIBDQ/+dwOOByuRAKhWJLWIrsZdOK7ACQm5uLZ8+eIS8vD3p6euByueBwOBLpRS9ol68c0uw8Hg8URTHay/sozV6VMjHZ5S0TRVFISUmBoaGh2AMIm8ukqOsEQEIbtpdJEddJKBQy1hk2l4n8npR7nQQCAVJSUmBkZFTtuqdK1Bpws7OzIRAIJLqOzczMkJyczHjMp0+fYG5uLpFe3o0Unj17Jna8nZ0dUlNTkZn5v/cbLS0tYWVlheTkZOTk5NB2W1tb+Pv7IyQkBG3btkViYiIAwN7eHkZGRoiNjRW7kE5OTtDR0cGTJ0/EfPDw8ACfz0d8fDxt43K58PT0RE5ODpKSkmi7np4enJ2dkZWVhZSUFNpuaGiI5s2b48OHD2KbIlSlTObm5khMTERhYSFtl7dMrq6uEvqyvUyKuk5NmzaV0IbtZVLEdbKxKX3F6eXLlzWmTOT3pNzrVK9ePQDAx48f8fHj/9YXr2rdUxVqHcP9+PEjunTpgmPHjsHT05O2r1+/Hvfv38eff/4pcYyLiwvWrl0Lf39/2nbkyBHs2LEDt2/frvScoj57V1fXarUyKIqSyIc8kZc+kT99+lRMF7aXSZEtXEXUPU0qk6JauLGxsRJ1hs1lIr8n5bdwnz17Bjc3N7ElHqta9zRupSllYGJiAh6PJ/YkApSuP1y+FSvC3NxcojVbUXpp8Hg8CYGlrc3JdCEEAgHdzVP+e2kXTh47h8NhtEvzUV67InxkslekC1vLVJFdnjJVpA1by1QVO1Pe0nSRll5eO1vrHvk9SffR0NAQXC5XLg3UPc9GrZOmdHR04OzsjJiYGNomFAoRExMj1uIti4eHB+7cuSNmu337Njw8PJTpqgQ8Hg/NmzdX+wXUNIgu0iHaMEN0kQ7Rhhm26qL2WcoTJkzAiRMnEBERgVevXmHFihUoKCjAkCFDAACLFi3Cpk2b6PRjx47FzZs38fvvv+PVq1fYtm0b/v33X4wePVqlfguFQrx//551s+SUDdFFOkQbZogu0iHaMMNWXdS+0pSfnx+ysrIQEhKCjIwMtGzZEnv37qW7iNPT08W6B1q1aoWNGzdiy5Yt2Lx5M+zs7LBjxw6Z3sFVJBRFIT09HQ0aNFDpeTUdoot0iDbMEF2kQ7Rhhq26qD3gAqUbs0troR46dEjC1rdvX/Tt21fZbhEIBAKBoDDU3qVMIBAIBEJtgATcKsLhcGBmZkZ2AioH0UU6RBtmiC7SIdoww1ZdNKJLmY1wuVzY2dmp2w2Ng+giHaINM0QX6RBtmGGrLqSFW0WEQiHevHnDullyyoboIh2iDTNEF+kQbZhhqy4k4FYRiqKQmZnJuJpQbYboIh2iDTNEF+kQbZhhqy4k4BIIBAKBoAJq3Riu6Imo/Jqa8iI6vrr51DSILtIh2jBDdJEO0YYZRekiOl5VLWW1b0Cvavh8vtjOGwQCgUCo3bi6ukJHR0fp56l1AVcoFKKkpITe4YJAIBAItRPR7kRaWlpSNzxQJLUu4BIIBAKBoA7IpCkCgUAgEFQACbgEAoFAIKgAEnAJBAKBQFABJOASCAQCgaACSMAlEAgEAkEFkIBLIBAIBIIKIAGXQCAQCAQVQAIugUAgEAgqgATcCjhy5Ah8fHzg6uqKgIAAxMbGVpg+KioKffr0gaurK/r3748bN26oyFPVIo8uJ06cwKhRo9CmTRu0adMG48ePr1RHNiNvnRFx/vx5ODo6YubMmUr2UD3Iq0tOTg5WrlyJTp06wcXFBb179ya/p/9n//796N27N9zc3NC1a1f88ssvKCoqUpG3quH+/fuYPn06OnXqBEdHR1y+fLnSY+7evYvBgwfDxcUFPXv2RHh4uAo8lROKwMj58+cpZ2dn6uTJk9TLly+pZcuWUV5eXtSnT58Y0z98+JBq2bIltWfPHiopKYn69ddfKWdnZ+r58+cq9ly5yKvL/PnzqcOHD1Px8fFUUlIStWTJEqp169bUhw8fVOy58pFXGxFpaWlU586dqVGjRlEzZsxQkbeqQ15dioqKqCFDhlBTpkyhHjx4QKWlpVF3796lEhISVOy58pFXm7Nnz1IuLi7U2bNnqbS0NOrmzZtUx44dqV9++UXFniuX69evU5s3b6YuXbpEOTg4UH/99VeF6VNTUyl3d3cqODiYSkpKog4dOkS1bNmS+vvvv1XksWyQgCuFYcOGUStXrqQ/CwQCqlOnTlRoaChj+u+++46aOnWqmC0gIID68ccfleqnqpFXl/KUlJRQnp6eVEREhJI8VB9V0aakpIT65ptvqBMnTlCLFy+ukQFXXl3++OMPytfXl+Lz+apyUW3Iq83KlSupsWPHitmCg4OpESNGKNVPdSJLwF2/fj3Vr18/MdvcuXOpiRMnKtM1uSFdygzw+XzExcXB29ubtnG5XHh7e+Px48eMxzx58gQdOnQQs3Xq1AlPnjxRpqsqpSq6lKegoAAlJSUwMjJSlptqoara7NixA2ZmZggICFCFmyqnKrpcvXoVHh4eWLVqFby9veHv749du3bVuC3qqqKNp6cn4uLi6G7ntLQ03LhxA127dlWJz5oKW+6/tW4/XFnIzs6GQCCAmZmZmN3MzAzJycmMx3z69Anm5uYS6T99+qQ0P1VNVXQpz8aNG2FhYSF2k6kJVEWbBw8e4OTJkzh9+rQKPFQPVdElLS0Nd+7cQf/+/bF7926kpqZi5cqVKCkpQWBgoCrcVglV0aZ///7Izs7GqFGjQFEUSkpKMGLECEyfPl0VLmssTPdfc3Nz5ObmorCwEHp6emryTBzSwiWojN27d+PChQvYvn07dHV11e2OWsnNzcWiRYuwevVqmJqaqtsdjYKiKJiZmWH16tVwcXGBn58fpk+fjmPHjqnbNbVz9+5dhIaG4qeffkJ4eDi2b9+OGzduYMeOHep2jSADpIXLgImJCXg8HjIzM8XsmZmZEk9RIszNzSVasxWlZyNV0UXEvn37sHv3boSFhaFFixbKdFMtyKtNWloa3r17hxkzZtA2oVAIAHByckJ0dDQaN26sXKdVQFXqTP369aGlpQUej0fbmjZtioyMDPD5fJVsFK4KqqLN1q1bMWDAAHoIwtHREfn5+Vi+fDlmzJihkj1dNRGm+++nT59gYGCgMa1bgLRwGdHR0YGzszNiYmJom1AoRExMDDw9PRmP8fDwwJ07d8Rst2/fhoeHhzJdVSlV0QUA9uzZg507d2Lv3r1wdXVVhasqR15tmjZtinPnzuH06dP0n4+PD9q1a4fTp0+jYcOGqnRfaVSlzrRq1Qqpqan0AwgAvHnzBvXr168xwRaomjaFhYUSQVX0YELV4q3NWXP/VfOkLY3l/PnzlIuLCxUeHk4lJSVRP/74I+Xl5UVlZGRQFEVR33//PbVx40Y6/cOHDyknJydq3759VFJSEhUSElJjXwuSR5fQ0FDK2dmZio6Opv777z/6Lzc3V11FUBryalOemjpLWV5d3r9/T3l6elKrVq2ikpOTqWvXrlEdOnSgdu7cqa4iKA15tQkJCaE8PT2pyMhIKjU1lbp16xbVo0cP6rvvvlNTCZRDbm4uFR8fT8XHx1MODg5UWFgYFR8fT717946iKIrauHEj9f3339PpRa8FrVu3jkpKSqIOHz6ska8FkS5lKfj5+SErKwshISHIyMhAy5YtsXfvXrqrJz09XexJs1WrVti4cSO2bNmCzZs3w87ODjt27ICDg4O6iqAU5NXl2LFjKC4uxpw5c8TyCQwMxOzZs1Xqu7KRV5vagry6WFpaYt++fQgODsaAAQPQoEEDjB07FlOmTFFXEZSGvNrMmDEDHA4HW7ZswcePH2Fqaoru3btj3rx56iqCUvj3338xduxY+nNwcDAAYPDgwVi7di0yMjKQnp5Of29jY4PQ0FAEBwfj4MGDaNiwIdasWYPOnTur3PeK4FBULe6HIBAIBAJBRdS+x20CgUAgENQACbgEAoFAIKgAEnAJBAKBQFABJOASCAQCgaACSMAlEAgEAkEFkIBLIBAIBIIKIAGXQCAQCAQVQAIugUAgEAgqgARcAitYsmQJZs6cSX8eM2YMfv75Z5X7cffuXTg6OiInJ0fl53779i0cHR2RkJBQrXzKa8lEeX19fHywf/9++rOjoyMuX75cLT+kERMTg759+6ps/1tZyiKLZspGmZorg7///hsDBw4UWxO7tkMCroaxZMkSODo6Yvny5RLfrVy5Eo6OjliyZIkaPNMstm3bhu+++06mtOoMkmylMn1v3bqFLl26AFDcg4CIDRs2YMaMGfSi/OHh4XB0dISjoyNatGiBLl26ICgoSGKXnaoiS1mWLl2KtWvXKuR8NYU1a9ZgyJAhcHFxwcCBAyW+79KlC7S0tHD27Fk1eKeZkICrgVhaWuLChQsoLCykbUVFRYiMjISVlZUaPasefD5fYXkZGxvj/9o776ioji+Of0EUlSWhZEUQBYTQly4qLk1CB0MJHpRiguTIGrErgihFDQJiFFCaCopyTBQQESS2Q7HEyEFsgRgDh04kKPwosoC83x8c3uEBC6sxSpL3+Yudct+dO7vcmTszbxgMxjuT96Hp6+v70CpQmMi+f9fNPaWlpaitrYW1tTUlncFg4ObNmygqKsKePXtQXFyM7du3v5Nn8tMWUVFRfPTRR+/kef8mXF1dYWdnxzPfxcUF6enp71GjyQ3tcCch6urqkJaWxpUrV8i0K1euQFpaGmpqapSyAwMDSEpKwtKlS6GlpYVly5ahoKCAzH/9+jWCgoLIfGtra5w8eZIiYyhcdvz4cbDZbCxcuBBhYWHjOoG4uDh8/vnnOHv2LExNTaGtrY0NGzago6NjlNyEhASw2WzY2NgAGHwh+4YNG2BgYABDQ0NwOBzU19dTdI6IiICBgQEWLlyIqKioUVePjQx59vb2Ijo6GqamptDU1ISlpSXOnTuH+vp68iXoCxYsoEQIJrIdABQVFcHa2hpaWlrw8vJCQ0MDT5sMoaKigoyMDPj6+kJLSwsWFhYUuUOzqPz8fHh6eoLFYiE3NxcDAwOIj4+HiYkJOWsoLi4eJb+qqgru7u5gsVhwcHDAzz//TLHdRP09RHx8PBYtWgQ9PT3s3r2bMiCaKGQ/PLxpYWEBAHBycoKKigq8vLxw7949aGhooKWlhVJv3759WLlyJU+5+fn5MDIygrCwMCVdQEAATCYTUlJSMDU1hZeXF27fvo2enp4J7dbb24vw8HCw2WywWCyYm5sjKSmJ77YA1JDy999/DzabPSpUyuFwEBgYSH6+du0anJ2dwWKxYGFhgfj4ePT39/NsOwCcP38e9vb20NTUBJvNRnh4OM+y0dHRsLa2hra2NiwsLHDo0CHKb7ayshJeXl7Q1dWFnp4eXFxc8OjRIwBAQ0MD/Pz8sGDBAujo6MDe3h5FRUXj6jaS4OBgeHh4YO7cuTzLmJub4/Hjx6itrX0j2f9W6NuCJimurq7IysrCsmXLAACZmZlwcXGh/HMFgKSkJFy8eBFhYWGQl5fHvXv3sG3bNkhISMDQ0BADAwOYPXs2Dh8+DDExMdy/fx+7d+8Gk8mkjEzv3r0LJpOJkydPora2Fps2bYKamhqWL1/OU8fa2lpcvnwZiYmJ6OzsxM6dOxEaGoqYmBiyzJ07d8BgMJCamgpgcCa3evVq6Ojo4MyZMxASEsLRo0fh6+uLixcvYtq0aThx4gSys7Px7bffQlFRESdOnMDVq1exaNEinrps374d5eXlCA4OhqqqKurr6/Hy5UtIS0sjLi4O/v7+KCgooFxIPZHtmpqasG7dOnh4eGD58uV4/PgxIiMj+eq/w4cPY+vWrdi5cydycnKwefNmfPrpp1BUVCTLHDhwADt27ICamhqEhYVx6tQppKamIjw8HGpqasjMzMTatWtx6dIlyMvLk/WioqIQFBQEJSUlpKamws/PD9evX4e4uDjf/X3nzh0ICwsjPT0dDQ0NCAwMhLi4+FvdOnPu3Dm4ubkhLS0NSkpKmDp1KsTExCArK4ucnBz4+voCGOz73NxcbNu2jaes0tJSODg4TPjM6dOnY2BgAP39/Th79uy4dktPT8eNGzdw6NAhSEtLo6mpCc3NzXy3ZSQ2NjbYs2cP7t69i8WLFwMA2traUFJSgpSUFLIdAQEBCA4OhoGBAWpra7Fr1y4AgzdljUVGRgb279+PLVu2wMTEBB0dHSgrK+NpAxEREURERGDWrFl4+vQpdu3aBREREfJWpa1bt0JNTQ2hoaGYMmUKKioqyPaEh4ejr68Pp0+fxsyZM/Hs2TPMnDmTlL106VI4Ozv/5Ru9ZGRk8Mknn6C0tBTz5s37S7L+FXzg6wFpRjB0J2prayuhqalJ1NfXE/X19QSLxSJaW1sJDodDBAQEEARBEFwul9DW1ibKysooMoKCgojNmzfzfEZYWBjh7+9Peaa5uTnR399Ppq1fv57YuHEjTxmxsbGEmpoa0dzcTKYVFRURqqqqxPPnz0m5RkZGBJfLJctcuHCBsLa2JgYGBsg0LpdLaGlpESUlJQRBEMSSJUuIlJQUMr+vr48wMTGh3BXr6elJ7N27lyAIgqiqqiKUlZWJW7dujanrTz/9RCgrKxPt7e2UZ05ku5iYGMLOzo6SHx0dPUrWSJSVlYndu3dT0tzc3IiQkBCCIAiirq6OUFZWJtLS0ihl2Gw2kZCQQElzdXUlQkNDKfWSkpLI/CHbJCcn89RnrP42NDQkuru7ybSMjAxCR0eHeP36NUEQVPsSBEGYm5sTqamplDZevXqVotcvv/xCeW5ycjJha2tLfv7xxx8JHR0doquri6eu+vr6RHZ2NiUtMzOT0NfXJz9XV1cTVlZWhIuLC0EQE9ttz549hLe3N+U7Nxx+2jLyrmIOh0MEBgaSn8+ePUuw2WzSfqtWrSISExMpMi5cuEAsWbKEZ9vZbDZx8OBBnvnD9RyLY8eOEc7OzuRnXV1dIisra8yyDg4ORFxcHE9Z3t7eRHp6Os/84cTGxhLLli3jme/k5DTus/5L0DPcSYqEhATMzMyQnZ0NgiBgZmYGCQkJSpmamhq8evUKPj4+lPS+vj5K6PnMmTPIzMxEY2MjuFwu+vr6oKqqSqmjpKREblIBBte1nj59Oq6O0tLSkJKSIj/r6upiYGAA1dXVYDKZAABlZWXK+lhlZSVqa2uhp6dHkcXlclFbW4uOjg60tLRAW1ubzBMSEoKmpuaosPIQFRUVmDJlChYsWDCuvsPhx3a///47tLS0KPk6Ojp8ydfV1R1Vb+RGHE1NTfLvzs5OPH/+fJRd9PT0UFlZyVP2kG2qqqrINH76W0VFBTNmzKDI7O7uRlNTE+bMmcNXGyfCxcUFhw8fRnl5OXR0dJCVlQVbW1vKTGokPT09o8LJANDR0UF+v7hcLvT19bF3716+7Obs7AwfHx/Y2NjA2NgYZmZmYLPZf6ltjo6O2LVrF0JDQzFt2jTk5ubC3t6evLu2srISZWVlSExMJOu8fv0aXC4Xr169otgeAFpbW/H8+XNyxswP+fn5OHXqFOrq6tDd3Y3+/n7KuvtXX32F4OBg5OTkwMjICDY2NuQs09vbG6Ghobh58yaMjIxgZWVF+Y7wWoZ4G4SFhfHq1at3Ju+fDO1wJzGurq7kGk5ISMio/O7ubgCDodHhjg8A6eTy8vIQGRmJgIAA6OrqQkREBMePH8eDBw8o5YWEqF8FAQEBng7uTRj5j6W7uxsaGho4cODAqLIjBxT8MhQifhP4sd3fzXiO523ht7/fB5KSkjA3N0dWVhZkZWVRUlKCU6dOjVtHXFx8zN3kIiIiyM7OhqCgIJhMJtnnnZ2dE+qhoaGB69evo7i4GLdv38bGjRthZGSE2NjYt2sYBkOuwcHBKCwsBIvFQmlpKWX9tru7G/7+/rCyshpVd6wBxVhp43H//n1s3boV/v7+YLPZEBUVRV5eHrl0AwD+/v5wcHBAUVERiouLERsbi++++w6WlpZwc3MDm81GYWEhbt26heTkZAQEBJBr1u+S9vb2t/5t/9ugHe4kxtjYGH19fRAQEBhzRK6oqIhp06ahsbERhoaGY8ooKyuDrq4uPDw8yLR3tYGhqakJf/zxB+mwysvLISgoCAUFBZ51NDQ0cPnyZUhKSvLcBctkMvHgwQNyxtrf348nT55AXV19zPLKysoYGBjAvXv3YGRkNCp/aN1q+LlOfmynqKiIGzduUNL4dVzl5eVwcnKi1Bu54W04DAYDs2bNQllZGUWfsrKyUbPs8vLyUbYZ6l9++/vXX39FT08P6bjKy8sxc+ZMSEtL89W+4Yxl3yG++OILbNmyBVJSUpg7dy709fXHlaWuro5nz56NShcUFIScnNyodH7txmAwYGdnBzs7O1hbW8PX1xdtbW0QExPjuy3DERYWhpWVFXJzc1FTUwMFBQVoaGhQ2lFdXT2mzmPBYDAwZ84c3LlzZ9y9CkPcv38fMjIy4HA4ZFpjY+OocgoKClBQUMCXX36JzZs3IzMzE5aWlgAGI1QrVqzAihUrEBMTgx9++OGdO1wul4u6ujqev93/GrTDncRMmTIFly9fJv8eCYPBgI+PDyIiIkAQBPT19cmNFgwGA87OzpCTk8OFCxdQUlJCbmJ59OgRZGVl/7J+wsLC2LFjBwICAtDZ2Ym9e/fC1taWDCePhaOjI44fPw4Oh4MNGzZASkoKjY2NuHr1Knx9fTF79mx4e3sjJSUF8vLyUFBQQFpa2rhnaGVlZeHs7IygoCAEBwdDRUUFjY2NaG1thZ2dHebMmQMBAQEUFhbC1NQUwsLCfNnO3d0dJ06cQGRkJNzc3PDkyRNkZ2fzZZuCggJoampCX18fubm5ePjw4YQv6li9ejXi4uIwb948qKqqIisrC5WVlaOiARkZGZCXl8f8+fNx8uRJtLe3w9XVFQD47u/e3l7s3LkTHA4HDQ0NiIuLg6enJxkSfRMkJSUxffp0lJSUYPbs2RAWFoaoqCiAwUEjg8FAQkIC1q9fP6EsNpvNt42HmMhuqampYDKZUFNTg6CgIAoKCsBkMsc85jNeW0bi6OiINWvW4LfffiM3Nw7xzTffwM/PDzIyMrC2toagoCAqKyvx9OlTnhvT/P39ERISAklJSZiYmKCrqwtlZWVjOkE5OTk0NTUhLy8PLBYLhYWFlJdi9PT0ICoqCtbW1pCVlUVzczMePXpEzrj37dsHExMTyMvL43//+x/u3r1L2dC3atUqWFpawtPTk6fda2pq0N3djZaWFvT09JBLJkODWWBwIDd16lS+l2L+7dAOd5Iz0VnTjRs3QkJCAklJSaivr4eoqCjU1dXh5+cHAHB3d0dFRQU2bdoEAQEB2NvbY+XKlWMeN3lT5s2bB0tLS3z99ddob2+HmZnZmKHv4cyYMQOnT5/GgQMHsG7dOnR1dUFKSgqLFy8m2+rj44OWlhYEBARAUFAQrq6usLS0pBw5GkloaCgOHjyI0NBQtLW1QUZGBmvWrAEASElJwd/fHzExMQgMDISTkxP2798/oe1kZGQQFxeHiIgInD59GlpaWti0aROCgoImtI2/vz/y8/MRFhYGJpOJmJgYKCkpjVvH29sbnZ2d2L9/P168eAFFRUUcPXqUskMZALZs2YLk5GRUVFRATk4OCQkJZMiO3/5evHgx5OTk4OHhgd7eXjg4OLz1jlQhISEEBwfjyJEjiI2NhYGBAXn2UlBQEM7OzkhKSqLM+Hnh6OiI6OhoVFVVYf78+Xw9fyK7iYiI4NixY6ipqYGgoCBYLBaSk5PHHFyM15aRLFq0CB9//DGqq6vh6OhIyTM2NkZiYiKOHDmClJQUCAkJYf78+XBzc+PZDmdnZ3C5XKSlpSEqKgpiYmLkUbqRWFhYYNWqVQgPD0dvby/MzMzA4XAQHx8PYNDubW1tCAgIwJ9//glxcXFYWVmRg56BgQGEh4ejubkZDAYDxsbGlJB4XV0dXr58ydvoGDwWNPzUxFD/Xr9+nRzg5eXlwdHRcdTS0n8VAeJdLNTR/OeIi4vDtWvXkJOT86FVmXSoqKjgyJEj+Oyzzz60KpOCoKAgvHjxgrKBaDwiIyPR1dU17hlUmsnPixcvYGtri/Pnz497Vve/BP3iCxoamr+Fjo4OlJaW4tKlS2+0NsjhcCAjI0O/g/cfTkNDA0JCQmhnOww6pExDQ/O3sHbtWjx8+BDu7u5YsmQJ3/U++ugjMqxP88+FxWKBxWJ9aDUmFXRImYaGhoaG5j1Ah5RpaGhoaGjeA7TDpaGhoaGheQ/QDpeGhoaGhuY9QDtcGhoaGhqa9wDtcGloaGhoaN4DtMOloaGhoaF5D9AOl4aGhoaG5j1AO1waGhoaGpr3wP8BMl782ROtdB4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bench.plot_results();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 1963,\n",
+       " 'n_positives': 354,\n",
+       " 'n_negatives': 1609,\n",
+       " 'model_name': 'Meta-Llama-3-8B',\n",
+       " 'accuracy': 0.630667345899134,\n",
+       " 'tpr': 0.7033898305084746,\n",
+       " 'fnr': 0.2966101694915254,\n",
+       " 'fpr': 0.3853325046612803,\n",
+       " 'tnr': 0.6146674953387197,\n",
+       " 'balanced_accuracy': 0.6590286629235971,\n",
+       " 'precision': 0.286536248561565,\n",
+       " 'ppr': 0.44268976057055526,\n",
+       " 'log_loss': 0.6640880361799258,\n",
+       " 'brier_score_loss': 0.23555848017340178,\n",
+       " 'fnr_ratio': 0.7005952380952382,\n",
+       " 'fnr_diff': 0.1007343124165554,\n",
+       " 'fpr_ratio': 0.9499246987951807,\n",
+       " 'fpr_diff': 0.019768133174791924,\n",
+       " 'ppr_ratio': 0.9785014080325124,\n",
+       " 'ppr_diff': 0.009620674692892017,\n",
+       " 'precision_ratio': 0.7380730854374539,\n",
+       " 'precision_diff': 0.086496795041585,\n",
+       " 'tpr_ratio': 0.8681980959035723,\n",
+       " 'tpr_diff': 0.1007343124165554,\n",
+       " 'balanced_accuracy_ratio': 0.9408797810568554,\n",
+       " 'balanced_accuracy_diff': 0.04048308962088176,\n",
+       " 'tnr_ratio': 0.968370986920333,\n",
+       " 'tnr_diff': 0.01976813317479187,\n",
+       " 'accuracy_ratio': 0.9913632326092872,\n",
+       " 'accuracy_diff': 0.0054705390193721115,\n",
+       " 'equalized_odds_ratio': 0.7005952380952382,\n",
+       " 'equalized_odds_diff': 0.1007343124165554,\n",
+       " 'roc_auc': 0.7183612307886781,\n",
+       " 'ece': 0.30890763123834214,\n",
+       " 'ece_quantile': 0.3089076312383417,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/health-care utilization_subsampled-0.1_seed-42_hash-1494059445.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': False,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'Meta-Llama-3-8B',\n",
+       "  'model_hash': 2852898721,\n",
+       "  'task_name': 'health-care utilization',\n",
+       "  'task_hash': 3442708664,\n",
+       "  'dataset_name': 'health-care utilization_subsampled-0.1_seed-42_hash-1494059445',\n",
+       "  'dataset_subsampling': 0.1,\n",
+       "  'dataset_hash': 1494059445},\n",
+       " 'benchmark_hash': 1075925211,\n",
+       " 'results_dir': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211',\n",
+       " 'results_root_dir': '/lustre/home/acruz/folktexts/notebooks',\n",
+       " 'current_time': '2024.08.30-14.54.41',\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/Meta-Llama-3-8B_bench-1075925211/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bench.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/detailed-example.html b/notebooks/detailed-example.html
new file mode 100644
index 0000000..fe2861e
--- /dev/null
+++ b/notebooks/detailed-example.html
@@ -0,0 +1,1146 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Example: Run ACS benchmark task &mdash; folktexts 0.0.25 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=d2d258e8" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
+  
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="../_static/documentation_options.js?v=5d650c58"></script>
+      <script src="../_static/doctools.js?v=9a2dae69"></script>
+      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="../_static/copybutton.js?v=f281be69"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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+<li class="toctree-l1"><a class="reference internal" href="../readme.html">Readme file</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../source/modules.html">API reference</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../notebooks.html">Example notebooks</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="minimal-example.html">Evaluate model calibration using folktexts</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Example: Run ACS benchmark task</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Set-important-local-paths">Set important local paths</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Construct-LLM-Classifier">Construct LLM Classifier</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Load-Dataset">Load Dataset</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Load-and-run-ACS-Benchmark">Load and run ACS Benchmark</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="custom-dataset-example.html">Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a different data source</a></li>
+<li class="toctree-l2"><a class="reference internal" href="custom-acs-task-example.html">Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a custom ACS task</a></li>
+<li class="toctree-l2"><a class="reference internal" href="paper-plots-and-tables.html">Render paper plots and tables</a></li>
+<li class="toctree-l2"><a class="reference internal" href="paper-plots-and-tables.html#Plots-comparing-different-prompting-schemes">Plots comparing different prompting schemes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="minimal-example_web-API-model.html">Evaluate model calibration using folktexts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parse-acs-results.html">Fetch and parse ACS benchmark results under a given directory</a></li>
+</ul>
+</li>
+</ul>
+
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+            <a href="../_sources/notebooks/detailed-example.ipynb.txt" rel="nofollow"> View page source</a>
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+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Example:-Run-ACS-benchmark-task">
+<h1>Example: Run ACS benchmark task<a class="headerlink" href="#Example:-Run-ACS-benchmark-task" title="Link to this heading"></a></h1>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">folktexts</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">folktexts</span><span class="o">.</span><span class="n">__version__</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+folktexts.__version__=&#39;0.0.21&#39;
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">logging</span>
+<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">()</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Set-important-local-paths">
+<h2>Set important local paths<a class="headerlink" href="#Set-important-local-paths" title="Link to this heading"></a></h2>
+<p><strong>NOTE:</strong> Can be ignored if you haven’t previously downloaded the model, just use <code class="docutils literal notranslate"><span class="pre">load_model_tokenizer</span></code> with the model’s name on Huggingface.</p>
+<p>Set your root directory (<strong>change as appropriate</strong>):</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ROOT_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;/fast/groups/sf&quot;</span><span class="p">)</span>
+<span class="n">ROOT_DIR</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+PosixPath(&#39;/fast/groups/sf&#39;)
+</pre></div></div>
+</div>
+<p>Directory where LLMs are saved (<strong>change as appropriate</strong>):</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">MODELS_DIR</span> <span class="o">=</span> <span class="n">ROOT_DIR</span> <span class="o">/</span> <span class="s2">&quot;huggingface-models&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Directory where data is saved or will be saved to (<strong>change as appropriate</strong>):</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">DATA_DIR</span> <span class="o">=</span> <span class="n">ROOT_DIR</span> <span class="o">/</span> <span class="s2">&quot;data&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Other configs:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">MODEL_NAME</span> <span class="o">=</span> <span class="s2">&quot;meta-llama/Meta-Llama-3-8B-Instruct&quot;</span>
+<span class="c1"># MODEL_NAME = &quot;google/gemma-2b&quot;    # Smaller model that is faster to run</span>
+
+<span class="n">TASK_NAME</span> <span class="o">=</span> <span class="s2">&quot;ACSIncome&quot;</span>
+
+<span class="n">RESULTS_ROOT_DIR</span> <span class="o">=</span> <span class="n">ROOT_DIR</span> <span class="o">/</span> <span class="s2">&quot;folktexts-results&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">load_model_tokenizer</span><span class="p">,</span> <span class="n">get_model_folder_path</span>
+<span class="n">model_folder_path</span> <span class="o">=</span> <span class="n">get_model_folder_path</span><span class="p">(</span><span class="n">model_name</span><span class="o">=</span><span class="n">MODEL_NAME</span><span class="p">,</span> <span class="n">root_dir</span><span class="o">=</span><span class="n">MODELS_DIR</span><span class="p">)</span>
+<span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">load_model_tokenizer</span><span class="p">(</span><span class="n">model_folder_path</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+INFO:root:Loading model &#39;/lustre/fast/fast/groups/sf/huggingface-models/meta-llama--Meta-Llama-3-8B-Instruct&#39;
+INFO:accelerate.utils.modeling:We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set `max_memory` in to a higher value to use more memory (at your own risk).
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "f0a08cdd63fa4bf4815578e005a80497", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+INFO:root:Moving model to device: cuda
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results_dir</span> <span class="o">=</span> <span class="n">RESULTS_ROOT_DIR</span> <span class="o">/</span> <span class="n">Path</span><span class="p">(</span><span class="n">model_folder_path</span><span class="p">)</span><span class="o">.</span><span class="n">name</span>
+<span class="n">results_dir</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">results_dir</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+PosixPath(&#39;/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct&#39;)
+</pre></div></div>
+</div>
+<section id="Construct-LLM-Classifier">
+<h3>Construct LLM Classifier<a class="headerlink" href="#Construct-LLM-Classifier" title="Link to this heading"></a></h3>
+<p><strong>NOTE:</strong> Also compatible with models hosted through a web API by using the <code class="docutils literal notranslate"><span class="pre">WebAPILLMClassifier</span></code> class instead of <code class="docutils literal notranslate"><span class="pre">TransformersLLMClassifier</span></code>.</p>
+<p>Load prediction task (which maps tabular data to text):</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.acs</span> <span class="kn">import</span> <span class="n">ACSTaskMetadata</span>
+<span class="n">task</span> <span class="o">=</span> <span class="n">ACSTaskMetadata</span><span class="o">.</span><span class="n">get_task</span><span class="p">(</span><span class="n">TASK_NAME</span><span class="p">,</span> <span class="n">use_numeric_qa</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+INFO:root:Changing Q&amp;A mode for task &#39;ACSIncome&#39; to multiple-choice.
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.classifier</span> <span class="kn">import</span> <span class="n">TransformersLLMClassifier</span>
+<span class="n">llm_clf</span> <span class="o">=</span> <span class="n">TransformersLLMClassifier</span><span class="p">(</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
+    <span class="n">context_size</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Load-Dataset">
+<h3>Load Dataset<a class="headerlink" href="#Load-Dataset" title="Link to this heading"></a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="kn">from</span> <span class="nn">folktexts.acs</span> <span class="kn">import</span> <span class="n">ACSDataset</span>
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">ACSDataset</span><span class="o">.</span><span class="n">make_from_task</span><span class="p">(</span><span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">DATA_DIR</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Loading ACS data...
+CPU times: user 58 s, sys: 27.5 s, total: 1min 25s
+Wall time: 1min 26s
+</pre></div></div>
+</div>
+<p>Optionally, subsample to quickly get approximate results:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span><span class="o">.</span><span class="n">subsample</span><span class="p">(</span><span class="mf">0.01</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dataset</span><span class="o">.</span><span class="n">subsampling</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+INFO:root:Train size: 13316, Test size:  1665, Val size:   1665;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+dataset.subsampling=0.01
+</pre></div></div>
+</div>
+</section>
+<section id="Load-and-run-ACS-Benchmark">
+<h3>Load and run ACS Benchmark<a class="headerlink" href="#Load-and-run-ACS-Benchmark" title="Link to this heading"></a></h3>
+<p><strong>Note:</strong> Helper constructors exist at <code class="docutils literal notranslate"><span class="pre">Benchmark.make_acs_benchmark</span></code> and <code class="docutils literal notranslate"><span class="pre">Benchmark.make_benchmark</span></code> that avoid the above boilerplate code.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.benchmark</span> <span class="kn">import</span> <span class="n">BenchmarkConfig</span><span class="p">,</span> <span class="n">Benchmark</span>
+
+<span class="n">bench</span> <span class="o">=</span> <span class="n">Benchmark</span><span class="p">(</span><span class="n">llm_clf</span><span class="o">=</span><span class="n">llm_clf</span><span class="p">,</span> <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+INFO:root:
+** Benchmark initialization **
+Model: meta-llama--Meta-Llama-3-8B-Instruct;
+Task: ACSIncome;
+Hash: 516109582;
+
+</pre></div></div>
+</div>
+<p>Here’s an example prompt for the current prediction task:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_sample</span><span class="p">,</span> <span class="n">_y_sample</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">sample_n_train_examples</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">encode_row</span><span class="p">(</span><span class="n">X_sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">question</span><span class="o">=</span><span class="n">llm_clf</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">question</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+The following data corresponds to a survey respondent. The survey was conducted among US residents in 2018. Please answer the question based on the information provided. The data provided is enough to reach an approximate answer.
+
+Information:
+- The age is: 37 years old.
+- The class of worker is: Owner of non-incorporated business, professional practice, or farm.
+- The highest educational attainment is: Regular high school diploma.
+- The marital status is: Married.
+- The occupation is: Painters and paperhangers.
+- The place of birth is: New Jersey.
+- The relationship to the reference person in the survey is: The reference person itself.
+- The usual number of hours worked per week is: 40 hours.
+- The sex is: Male.
+- The race is: White.
+
+Question: What is this person&#39;s estimated yearly income?
+A. Below $50,000.
+B. Above $50,000.
+Answer:
+</pre></div></div>
+</div>
+<p>Optionally, you can fit the model’s threshold on a few data samples.</p>
+<p>This is generally quite fast as it is <em>not fine-tuning</em>; it only changes one parameter: the <code class="docutils literal notranslate"><span class="pre">llm_clf.threshold</span></code>.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">X_sample</span><span class="p">,</span> <span class="n">y_sample</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">sample_n_train_examples</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
+<span class="n">llm_clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_sample</span><span class="p">,</span> <span class="n">y_sample</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "c0e4d1784cc8416eb3f30a9b542754b5", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
+WARNING:root:Setting meta-llama--Meta-Llama-3-8B-Instruct threshold to 0.7918923636748516.
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+CPU times: user 7.57 s, sys: 382 ms, total: 7.95 s
+Wall time: 9 s
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<style>#sk-container-id-1 {
+  /* Definition of color scheme common for light and dark mode */
+  --sklearn-color-text: black;
+  --sklearn-color-line: gray;
+  /* Definition of color scheme for unfitted estimators */
+  --sklearn-color-unfitted-level-0: #fff5e6;
+  --sklearn-color-unfitted-level-1: #f6e4d2;
+  --sklearn-color-unfitted-level-2: #ffe0b3;
+  --sklearn-color-unfitted-level-3: chocolate;
+  /* Definition of color scheme for fitted estimators */
+  --sklearn-color-fitted-level-0: #f0f8ff;
+  --sklearn-color-fitted-level-1: #d4ebff;
+  --sklearn-color-fitted-level-2: #b3dbfd;
+  --sklearn-color-fitted-level-3: cornflowerblue;
+
+  /* Specific color for light theme */
+  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
+  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
+  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
+  --sklearn-color-icon: #696969;
+
+  @media (prefers-color-scheme: dark) {
+    /* Redefinition of color scheme for dark theme */
+    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
+    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
+    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
+    --sklearn-color-icon: #878787;
+  }
+}
+
+#sk-container-id-1 {
+  color: var(--sklearn-color-text);
+}
+
+#sk-container-id-1 pre {
+  padding: 0;
+}
+
+#sk-container-id-1 input.sk-hidden--visually {
+  border: 0;
+  clip: rect(1px 1px 1px 1px);
+  clip: rect(1px, 1px, 1px, 1px);
+  height: 1px;
+  margin: -1px;
+  overflow: hidden;
+  padding: 0;
+  position: absolute;
+  width: 1px;
+}
+
+#sk-container-id-1 div.sk-dashed-wrapped {
+  border: 1px dashed var(--sklearn-color-line);
+  margin: 0 0.4em 0.5em 0.4em;
+  box-sizing: border-box;
+  padding-bottom: 0.4em;
+  background-color: var(--sklearn-color-background);
+}
+
+#sk-container-id-1 div.sk-container {
+  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
+     but bootstrap.min.css set `[hidden] { display: none !important; }`
+     so we also need the `!important` here to be able to override the
+     default hidden behavior on the sphinx rendered scikit-learn.org.
+     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
+  display: inline-block !important;
+  position: relative;
+}
+
+#sk-container-id-1 div.sk-text-repr-fallback {
+  display: none;
+}
+
+div.sk-parallel-item,
+div.sk-serial,
+div.sk-item {
+  /* draw centered vertical line to link estimators */
+  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
+  background-size: 2px 100%;
+  background-repeat: no-repeat;
+  background-position: center center;
+}
+
+/* Parallel-specific style estimator block */
+
+#sk-container-id-1 div.sk-parallel-item::after {
+  content: "";
+  width: 100%;
+  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
+  flex-grow: 1;
+}
+
+#sk-container-id-1 div.sk-parallel {
+  display: flex;
+  align-items: stretch;
+  justify-content: center;
+  background-color: var(--sklearn-color-background);
+  position: relative;
+}
+
+#sk-container-id-1 div.sk-parallel-item {
+  display: flex;
+  flex-direction: column;
+}
+
+#sk-container-id-1 div.sk-parallel-item:first-child::after {
+  align-self: flex-end;
+  width: 50%;
+}
+
+#sk-container-id-1 div.sk-parallel-item:last-child::after {
+  align-self: flex-start;
+  width: 50%;
+}
+
+#sk-container-id-1 div.sk-parallel-item:only-child::after {
+  width: 0;
+}
+
+/* Serial-specific style estimator block */
+
+#sk-container-id-1 div.sk-serial {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  background-color: var(--sklearn-color-background);
+  padding-right: 1em;
+  padding-left: 1em;
+}
+
+
+/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
+clickable and can be expanded/collapsed.
+- Pipeline and ColumnTransformer use this feature and define the default style
+- Estimators will overwrite some part of the style using the `sk-estimator` class
+*/
+
+/* Pipeline and ColumnTransformer style (default) */
+
+#sk-container-id-1 div.sk-toggleable {
+  /* Default theme specific background. It is overwritten whether we have a
+  specific estimator or a Pipeline/ColumnTransformer */
+  background-color: var(--sklearn-color-background);
+}
+
+/* Toggleable label */
+#sk-container-id-1 label.sk-toggleable__label {
+  cursor: pointer;
+  display: block;
+  width: 100%;
+  margin-bottom: 0;
+  padding: 0.5em;
+  box-sizing: border-box;
+  text-align: center;
+}
+
+#sk-container-id-1 label.sk-toggleable__label-arrow:before {
+  /* Arrow on the left of the label */
+  content: "▸";
+  float: left;
+  margin-right: 0.25em;
+  color: var(--sklearn-color-icon);
+}
+
+#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {
+  color: var(--sklearn-color-text);
+}
+
+/* Toggleable content - dropdown */
+
+#sk-container-id-1 div.sk-toggleable__content {
+  max-height: 0;
+  max-width: 0;
+  overflow: hidden;
+  text-align: left;
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-0);
+}
+
+#sk-container-id-1 div.sk-toggleable__content.fitted {
+  /* fitted */
+  background-color: var(--sklearn-color-fitted-level-0);
+}
+
+#sk-container-id-1 div.sk-toggleable__content pre {
+  margin: 0.2em;
+  border-radius: 0.25em;
+  color: var(--sklearn-color-text);
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-0);
+}
+
+#sk-container-id-1 div.sk-toggleable__content.fitted pre {
+  /* unfitted */
+  background-color: var(--sklearn-color-fitted-level-0);
+}
+
+#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+  /* Expand drop-down */
+  max-height: 200px;
+  max-width: 100%;
+  overflow: auto;
+}
+
+#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+  content: "▾";
+}
+
+/* Pipeline/ColumnTransformer-specific style */
+
+#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+  color: var(--sklearn-color-text);
+  background-color: var(--sklearn-color-unfitted-level-2);
+}
+
+#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+  background-color: var(--sklearn-color-fitted-level-2);
+}
+
+/* Estimator-specific style */
+
+/* Colorize estimator box */
+#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-2);
+}
+
+#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+  /* fitted */
+  background-color: var(--sklearn-color-fitted-level-2);
+}
+
+#sk-container-id-1 div.sk-label label.sk-toggleable__label,
+#sk-container-id-1 div.sk-label label {
+  /* The background is the default theme color */
+  color: var(--sklearn-color-text-on-default-background);
+}
+
+/* On hover, darken the color of the background */
+#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {
+  color: var(--sklearn-color-text);
+  background-color: var(--sklearn-color-unfitted-level-2);
+}
+
+/* Label box, darken color on hover, fitted */
+#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+  color: var(--sklearn-color-text);
+  background-color: var(--sklearn-color-fitted-level-2);
+}
+
+/* Estimator label */
+
+#sk-container-id-1 div.sk-label label {
+  font-family: monospace;
+  font-weight: bold;
+  display: inline-block;
+  line-height: 1.2em;
+}
+
+#sk-container-id-1 div.sk-label-container {
+  text-align: center;
+}
+
+/* Estimator-specific */
+#sk-container-id-1 div.sk-estimator {
+  font-family: monospace;
+  border: 1px dotted var(--sklearn-color-border-box);
+  border-radius: 0.25em;
+  box-sizing: border-box;
+  margin-bottom: 0.5em;
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-0);
+}
+
+#sk-container-id-1 div.sk-estimator.fitted {
+  /* fitted */
+  background-color: var(--sklearn-color-fitted-level-0);
+}
+
+/* on hover */
+#sk-container-id-1 div.sk-estimator:hover {
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-2);
+}
+
+#sk-container-id-1 div.sk-estimator.fitted:hover {
+  /* fitted */
+  background-color: var(--sklearn-color-fitted-level-2);
+}
+
+/* Specification for estimator info (e.g. "i" and "?") */
+
+/* Common style for "i" and "?" */
+
+.sk-estimator-doc-link,
+a:link.sk-estimator-doc-link,
+a:visited.sk-estimator-doc-link {
+  float: right;
+  font-size: smaller;
+  line-height: 1em;
+  font-family: monospace;
+  background-color: var(--sklearn-color-background);
+  border-radius: 1em;
+  height: 1em;
+  width: 1em;
+  text-decoration: none !important;
+  margin-left: 1ex;
+  /* unfitted */
+  border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+  color: var(--sklearn-color-unfitted-level-1);
+}
+
+.sk-estimator-doc-link.fitted,
+a:link.sk-estimator-doc-link.fitted,
+a:visited.sk-estimator-doc-link.fitted {
+  /* fitted */
+  border: var(--sklearn-color-fitted-level-1) 1pt solid;
+  color: var(--sklearn-color-fitted-level-1);
+}
+
+/* On hover */
+div.sk-estimator:hover .sk-estimator-doc-link:hover,
+.sk-estimator-doc-link:hover,
+div.sk-label-container:hover .sk-estimator-doc-link:hover,
+.sk-estimator-doc-link:hover {
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-3);
+  color: var(--sklearn-color-background);
+  text-decoration: none;
+}
+
+div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
+.sk-estimator-doc-link.fitted:hover,
+div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
+.sk-estimator-doc-link.fitted:hover {
+  /* fitted */
+  background-color: var(--sklearn-color-fitted-level-3);
+  color: var(--sklearn-color-background);
+  text-decoration: none;
+}
+
+/* Span, style for the box shown on hovering the info icon */
+.sk-estimator-doc-link span {
+  display: none;
+  z-index: 9999;
+  position: relative;
+  font-weight: normal;
+  right: .2ex;
+  padding: .5ex;
+  margin: .5ex;
+  width: min-content;
+  min-width: 20ex;
+  max-width: 50ex;
+  color: var(--sklearn-color-text);
+  box-shadow: 2pt 2pt 4pt #999;
+  /* unfitted */
+  background: var(--sklearn-color-unfitted-level-0);
+  border: .5pt solid var(--sklearn-color-unfitted-level-3);
+}
+
+.sk-estimator-doc-link.fitted span {
+  /* fitted */
+  background: var(--sklearn-color-fitted-level-0);
+  border: var(--sklearn-color-fitted-level-3);
+}
+
+.sk-estimator-doc-link:hover span {
+  display: block;
+}
+
+/* "?"-specific style due to the `<a>` HTML tag */
+
+#sk-container-id-1 a.estimator_doc_link {
+  float: right;
+  font-size: 1rem;
+  line-height: 1em;
+  font-family: monospace;
+  background-color: var(--sklearn-color-background);
+  border-radius: 1rem;
+  height: 1rem;
+  width: 1rem;
+  text-decoration: none;
+  /* unfitted */
+  color: var(--sklearn-color-unfitted-level-1);
+  border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+}
+
+#sk-container-id-1 a.estimator_doc_link.fitted {
+  /* fitted */
+  border: var(--sklearn-color-fitted-level-1) 1pt solid;
+  color: var(--sklearn-color-fitted-level-1);
+}
+
+/* On hover */
+#sk-container-id-1 a.estimator_doc_link:hover {
+  /* unfitted */
+  background-color: var(--sklearn-color-unfitted-level-3);
+  color: var(--sklearn-color-background);
+  text-decoration: none;
+}
+
+#sk-container-id-1 a.estimator_doc_link.fitted:hover {
+  /* fitted */
+  background-color: var(--sklearn-color-fitted-level-3);
+}
+</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TransformersLLMClassifier(encode_row=functools.partial(&lt;function encode_row_prompt at 0x14802eedf600&gt;, task=ACSTaskMetadata(name=&#x27;ACSIncome&#x27;, description=&quot;predict whether an individual&#x27;s income is above $50,000&quot;, features=[&#x27;AGEP&#x27;, &#x27;COW&#x27;, &#x27;SCHL&#x27;, &#x27;MAR&#x27;, &#x27;OCCP&#x27;, &#x27;POBP&#x27;, &#x27;RELP&#x27;, &#x27;WKHP&#x27;, &#x27;SEX&#x27;, &#x27;RAC1P&#x27;], target=&#x27;PINCP&#x27;, cols_to_text={&#x27;AGEP&#x27;: &lt;folktexts.col_to_text.ColumnToText...
+    128253: AddedToken(&quot;&lt;|reserved_special_token_248|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+    128254: AddedToken(&quot;&lt;|reserved_special_token_249|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+    128255: AddedToken(&quot;&lt;|reserved_special_token_250|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;TransformersLLMClassifier<span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>TransformersLLMClassifier(encode_row=functools.partial(&lt;function encode_row_prompt at 0x14802eedf600&gt;, task=ACSTaskMetadata(name=&#x27;ACSIncome&#x27;, description=&quot;predict whether an individual&#x27;s income is above $50,000&quot;, features=[&#x27;AGEP&#x27;, &#x27;COW&#x27;, &#x27;SCHL&#x27;, &#x27;MAR&#x27;, &#x27;OCCP&#x27;, &#x27;POBP&#x27;, &#x27;RELP&#x27;, &#x27;WKHP&#x27;, &#x27;SEX&#x27;, &#x27;RAC1P&#x27;], target=&#x27;PINCP&#x27;, cols_to_text={&#x27;AGEP&#x27;: &lt;folktexts.col_to_text.ColumnToText...
+    128253: AddedToken(&quot;&lt;|reserved_special_token_248|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+    128254: AddedToken(&quot;&lt;|reserved_special_token_249|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+    128255: AddedToken(&quot;&lt;|reserved_special_token_250|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+})</pre></div> </div></div></div></div></div>
+</div>
+<p>Run benchmark…</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">bench</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">results_root_dir</span><span class="o">=</span><span class="n">results_dir</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+INFO:root:Test data features shape: (1665, 10)
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "48b6fc00f210464b9d0174c7909c0b1e", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+INFO:root:
+** Test results **
+Model: meta-llama--Meta-Llama-3-8B-Instruct;
+         ECE:       24.8%;
+         ROC AUC :  82.8%;
+         Accuracy:  73.8%;
+         Bal. acc.: 74.1%;
+
+INFO:root:Skipping group &#39;American Indian&#39; as it&#39;s too small.
+INFO:root:Skipping group &#39;Alaska Native&#39; as it&#39;s too small.
+INFO:root:Skipping group &#39;American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races&#39; as it&#39;s too small.
+INFO:root:Skipping group &#39;Native Hawaiian and Other Pacific Islander&#39; as it&#39;s too small.
+INFO:root:Skipping group &#39;Some other race alone (non-White)&#39; as it&#39;s too small.
+INFO:root:Skipping group &#39;Two or more races&#39; as it&#39;s too small.
+INFO:root:Skipping group American Indian plot as it&#39;s too small.
+INFO:root:Skipping group Alaska Native plot as it&#39;s too small.
+INFO:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it&#39;s too small.
+INFO:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it&#39;s too small.
+INFO:root:Skipping group Some other race alone (non-White) plot as it&#39;s too small.
+INFO:root:Skipping group Two or more races plot as it&#39;s too small.
+INFO:root:Saving JSON file to &#39;/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/results.bench-3191680725.json&#39;
+INFO:root:Saved experiment results to &#39;/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/results.bench-3191680725.json&#39;
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+CPU times: user 3min 51s, sys: 7.12 s, total: 3min 58s
+Wall time: 4min 23s
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;threshold&#39;: np.float64(0.7918923636748516),
+ &#39;n_samples&#39;: 1665,
+ &#39;n_positives&#39;: 605,
+ &#39;n_negatives&#39;: 1060,
+ &#39;model_name&#39;: &#39;meta-llama--Meta-Llama-3-8B-Instruct&#39;,
+ &#39;accuracy&#39;: 0.7375375375375376,
+ &#39;tpr&#39;: 0.7553719008264462,
+ &#39;fnr&#39;: 0.24462809917355371,
+ &#39;fpr&#39;: 0.27264150943396226,
+ &#39;tnr&#39;: 0.7273584905660377,
+ &#39;balanced_accuracy&#39;: 0.7413651956962419,
+ &#39;precision&#39;: 0.6126005361930295,
+ &#39;ppr&#39;: 0.44804804804804804,
+ &#39;log_loss&#39;: 0.8504629791262521,
+ &#39;brier_score_loss&#39;: np.float64(0.24624856734149014),
+ &#39;ppr_ratio&#39;: 0.0,
+ &#39;ppr_diff&#39;: 0.5961538461538461,
+ &#39;precision_ratio&#39;: 0.0,
+ &#39;precision_diff&#39;: 1.0,
+ &#39;balanced_accuracy_ratio&#39;: 0.0,
+ &#39;balanced_accuracy_diff&#39;: 1.0,
+ &#39;accuracy_ratio&#39;: 0.0,
+ &#39;accuracy_diff&#39;: 1.0,
+ &#39;tnr_ratio&#39;: 0.0,
+ &#39;tnr_diff&#39;: 1.0,
+ &#39;fnr_ratio&#39;: 0.0,
+ &#39;fnr_diff&#39;: 1.0,
+ &#39;fpr_ratio&#39;: 0.0,
+ &#39;fpr_diff&#39;: 1.0,
+ &#39;tpr_ratio&#39;: 0.0,
+ &#39;tpr_diff&#39;: 1.0,
+ &#39;equalized_odds_ratio&#39;: 0.0,
+ &#39;equalized_odds_diff&#39;: 1.0,
+ &#39;roc_auc&#39;: np.float64(0.8282707001403399),
+ &#39;ece&#39;: 0.2475394044687882,
+ &#39;ece_quantile&#39;: 0.24753940446878786,
+ &#39;predictions_path&#39;: &#39;/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/ACSIncome_subsampled-0.01_seed-42_hash-373553086.test_predictions.csv&#39;,
+ &#39;config&#39;: {&#39;numeric_risk_prompting&#39;: False,
+  &#39;few_shot&#39;: None,
+  &#39;reuse_few_shot_examples&#39;: False,
+  &#39;batch_size&#39;: None,
+  &#39;context_size&#39;: None,
+  &#39;correct_order_bias&#39;: True,
+  &#39;feature_subset&#39;: None,
+  &#39;population_filter&#39;: None,
+  &#39;seed&#39;: 42,
+  &#39;model_name&#39;: &#39;meta-llama--Meta-Llama-3-8B-Instruct&#39;,
+  &#39;model_hash&#39;: 1896439497,
+  &#39;task_name&#39;: &#39;ACSIncome&#39;,
+  &#39;task_hash&#39;: 662640833,
+  &#39;dataset_name&#39;: &#39;ACSIncome_subsampled-0.01_seed-42_hash-373553086&#39;,
+  &#39;dataset_subsampling&#39;: 0.01,
+  &#39;dataset_hash&#39;: 373553086},
+ &#39;plots&#39;: {&#39;roc_curve_path&#39;: &#39;/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/roc_curve.pdf&#39;,
+  &#39;calibration_curve_path&#39;: &#39;/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/calibration_curve.pdf&#39;,
+  &#39;score_distribution_path&#39;: &#39;/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/score_distribution.pdf&#39;,
+  &#39;score_distribution_per_label_path&#39;: &#39;/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/score_distribution_per_label.pdf&#39;,
+  &#39;roc_curve_per_subgroup_path&#39;: &#39;/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/roc_curve_per_subgroup.pdf&#39;,
+  &#39;calibration_curve_per_subgroup_path&#39;: &#39;/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/calibration_curve_per_subgroup.pdf&#39;}}
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">bench</span><span class="o">.</span><span class="n">plot_results</span><span class="p">();</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_detailed-example_31_0.png" src="../_images/notebooks_detailed-example_31_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_detailed-example_31_1.png" src="../_images/notebooks_detailed-example_31_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_detailed-example_31_2.png" src="../_images/notebooks_detailed-example_31_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_detailed-example_31_3.png" src="../_images/notebooks_detailed-example_31_3.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+INFO:root:Skipping group &#39;American Indian&#39; as it&#39;s too small.
+INFO:root:Skipping group &#39;Alaska Native&#39; as it&#39;s too small.
+INFO:root:Skipping group &#39;American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races&#39; as it&#39;s too small.
+INFO:root:Skipping group &#39;Native Hawaiian and Other Pacific Islander&#39; as it&#39;s too small.
+INFO:root:Skipping group &#39;Some other race alone (non-White)&#39; as it&#39;s too small.
+INFO:root:Skipping group &#39;Two or more races&#39; as it&#39;s too small.
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_detailed-example_31_5.png" src="../_images/notebooks_detailed-example_31_5.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+INFO:root:Skipping group American Indian plot as it&#39;s too small.
+INFO:root:Skipping group Alaska Native plot as it&#39;s too small.
+INFO:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it&#39;s too small.
+INFO:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it&#39;s too small.
+INFO:root:Skipping group Some other race alone (non-White) plot as it&#39;s too small.
+INFO:root:Skipping group Two or more races plot as it&#39;s too small.
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_detailed-example_31_7.png" src="../_images/notebooks_detailed-example_31_7.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
+<span class="n">pprint</span><span class="p">(</span><span class="n">bench</span><span class="o">.</span><span class="n">results</span><span class="p">,</span> <span class="n">depth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;accuracy&#39;: 0.7375375375375376,
+ &#39;accuracy_diff&#39;: 1.0,
+ &#39;accuracy_ratio&#39;: 0.0,
+ &#39;balanced_accuracy&#39;: 0.7413651956962419,
+ &#39;balanced_accuracy_diff&#39;: 1.0,
+ &#39;balanced_accuracy_ratio&#39;: 0.0,
+ &#39;brier_score_loss&#39;: np.float64(0.24624856734149014),
+ &#39;config&#39;: {...},
+ &#39;ece&#39;: 0.2475394044687882,
+ &#39;ece_quantile&#39;: 0.24753940446878786,
+ &#39;equalized_odds_diff&#39;: 1.0,
+ &#39;equalized_odds_ratio&#39;: 0.0,
+ &#39;fnr&#39;: 0.24462809917355371,
+ &#39;fnr_diff&#39;: 1.0,
+ &#39;fnr_ratio&#39;: 0.0,
+ &#39;fpr&#39;: 0.27264150943396226,
+ &#39;fpr_diff&#39;: 1.0,
+ &#39;fpr_ratio&#39;: 0.0,
+ &#39;log_loss&#39;: 0.8504629791262521,
+ &#39;model_name&#39;: &#39;meta-llama--Meta-Llama-3-8B-Instruct&#39;,
+ &#39;n_negatives&#39;: 1060,
+ &#39;n_positives&#39;: 605,
+ &#39;n_samples&#39;: 1665,
+ &#39;plots&#39;: {...},
+ &#39;ppr&#39;: 0.44804804804804804,
+ &#39;ppr_diff&#39;: 0.5961538461538461,
+ &#39;ppr_ratio&#39;: 0.0,
+ &#39;precision&#39;: 0.6126005361930295,
+ &#39;precision_diff&#39;: 1.0,
+ &#39;precision_ratio&#39;: 0.0,
+ &#39;predictions_path&#39;: &#39;/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/ACSIncome_subsampled-0.01_seed-42_hash-373553086.test_predictions.csv&#39;,
+ &#39;roc_auc&#39;: np.float64(0.8282707001403399),
+ &#39;threshold&#39;: np.float64(0.7918923636748516),
+ &#39;tnr&#39;: 0.7273584905660377,
+ &#39;tnr_diff&#39;: 1.0,
+ &#39;tnr_ratio&#39;: 0.0,
+ &#39;tpr&#39;: 0.7553719008264462,
+ &#39;tpr_diff&#39;: 1.0,
+ &#39;tpr_ratio&#39;: 0.0}
+</pre></div></div>
+</div>
+<hr class="docutils" />
+</section>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="minimal-example.html" class="btn btn-neutral float-left" title="Evaluate model calibration using folktexts" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="custom-dataset-example.html" class="btn btn-neutral float-right" title="Run folktexts benchmark with a different data source" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/notebooks/detailed-example.ipynb b/notebooks/detailed-example.ipynb
new file mode 100644
index 0000000..6210949
--- /dev/null
+++ b/notebooks/detailed-example.ipynb
@@ -0,0 +1,1223 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e85e7f8a-dcfa-489a-9a8e-77225d2e3499",
+   "metadata": {},
+   "source": [
+    "# Example: Run ACS benchmark task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "98180e30-da85-41fb-a360-35b6acacb170",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "folktexts.__version__='0.0.21'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import folktexts\n",
+    "print(f\"{folktexts.__version__=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e5c41432-8ee0-4fbb-b37b-4eea92504382",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7a4b1e7f-a893-488e-96f1-4094857115f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "logging.getLogger().setLevel(logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04d5e737-9b3c-468a-bd68-b1c161f57c84",
+   "metadata": {},
+   "source": [
+    "## Set important local paths\n",
+    "**NOTE:** Can be ignored if you haven't previously downloaded the model, just use `load_model_tokenizer` with the model's name on Huggingface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cf6dfe9-4ddc-448f-afb9-0e4802ae480d",
+   "metadata": {},
+   "source": [
+    "Set your root directory (_**change as appropriate**_):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "048f506d-83a6-4911-b5f3-2fc290ed9fd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/fast/groups/sf')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ROOT_DIR = Path(\"/fast/groups/sf\")\n",
+    "ROOT_DIR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cb6c30e-5af1-4733-8732-788cba086eaf",
+   "metadata": {},
+   "source": [
+    "Directory where LLMs are saved (_**change as appropriate**_):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a7e60a72-e0c4-4fa8-a5d5-402f82098872",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODELS_DIR = ROOT_DIR / \"huggingface-models\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efbc677d-5208-4a8a-aab8-9cb7282ca39f",
+   "metadata": {},
+   "source": [
+    "Directory where data is saved or will be saved to (_**change as appropriate**_):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8040b3cd-da53-4b62-8aae-58b2bb7cba69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = ROOT_DIR / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "985344ec-6c9c-4e8b-8d8e-26f009937a7c",
+   "metadata": {},
+   "source": [
+    "Other configs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4316d94-110f-4014-a823-95300ac87eb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_NAME = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
+    "# MODEL_NAME = \"google/gemma-2b\"    # Smaller model that is faster to run\n",
+    "\n",
+    "TASK_NAME = \"ACSIncome\"\n",
+    "\n",
+    "RESULTS_ROOT_DIR = ROOT_DIR / \"folktexts-results\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7e0f598d-dd37-471f-a0d4-4b3c1ae07167",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Loading model '/lustre/fast/fast/groups/sf/huggingface-models/meta-llama--Meta-Llama-3-8B-Instruct'\n",
+      "INFO:accelerate.utils.modeling:We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set `max_memory` in to a higher value to use more memory (at your own risk).\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0a08cdd63fa4bf4815578e005a80497",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Moving model to device: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "from folktexts.llm_utils import load_model_tokenizer, get_model_folder_path\n",
+    "model_folder_path = get_model_folder_path(model_name=MODEL_NAME, root_dir=MODELS_DIR)\n",
+    "model, tokenizer = load_model_tokenizer(model_folder_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ab088b1f-37ba-4d2e-b5d2-b829787b7fed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_dir = RESULTS_ROOT_DIR / Path(model_folder_path).name\n",
+    "results_dir.mkdir(exist_ok=True, parents=True)\n",
+    "results_dir"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d87da67-3857-421f-b761-81814ea5498d",
+   "metadata": {},
+   "source": [
+    "### Construct LLM Classifier\n",
+    "\n",
+    "**NOTE:** Also compatible with models hosted through a web API by using the `WebAPILLMClassifier` class instead of `TransformersLLMClassifier`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e664c322-4da5-49b3-a755-3ba0a075ee64",
+   "metadata": {},
+   "source": [
+    "Load prediction task (which maps tabular data to text):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7f0b725d-d97a-40a9-a71d-88fb5fefbddf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Changing Q&A mode for task 'ACSIncome' to multiple-choice.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from folktexts.acs import ACSTaskMetadata\n",
+    "task = ACSTaskMetadata.get_task(TASK_NAME, use_numeric_qa=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0cfa6291-5c06-4618-a14d-86e49f2f4969",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.classifier import TransformersLLMClassifier\n",
+    "llm_clf = TransformersLLMClassifier(\n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    task=task,\n",
+    "    batch_size=32,\n",
+    "    context_size=1000,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53f969d6-9d0e-48c2-9b34-a57aef0edb5c",
+   "metadata": {},
+   "source": [
+    "### Load Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "820004cb-25ce-4802-85c3-6ab251881141",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "CPU times: user 58 s, sys: 27.5 s, total: 1min 25s\n",
+      "Wall time: 1min 26s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.acs import ACSDataset\n",
+    "dataset = ACSDataset.make_from_task(task=task, cache_dir=DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8145eddd-09da-4db9-8779-c61b04c3d777",
+   "metadata": {},
+   "source": [
+    "Optionally, subsample to quickly get approximate results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c977cac3-162c-4cde-8594-e6795f62e29a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Train size: 13316, Test size:  1665, Val size:   1665;\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dataset.subsampling=0.01\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.subsample(0.01)\n",
+    "print(f\"{dataset.subsampling=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3b4ce61-ecbc-4491-bd7b-0cfae38c6c10",
+   "metadata": {},
+   "source": [
+    "### Load and run ACS Benchmark\n",
+    "\n",
+    "**_Note:_** Helper constructors exist at `Benchmark.make_acs_benchmark` and `Benchmark.make_benchmark` that avoid the above boilerplate code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "10d362b6-aac8-49dd-84c3-0ce799ffabba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:\n",
+      "** Benchmark initialization **\n",
+      "Model: meta-llama--Meta-Llama-3-8B-Instruct;\n",
+      "Task: ACSIncome;\n",
+      "Hash: 516109582;\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from folktexts.benchmark import BenchmarkConfig, Benchmark\n",
+    "\n",
+    "bench = Benchmark(llm_clf=llm_clf, dataset=dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a648f32-05f4-4057-8624-2a39bfe7ab55",
+   "metadata": {},
+   "source": [
+    "Here's an example prompt for the current prediction task:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a83832fc-47c4-4e14-adc9-6393f197e2c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The following data corresponds to a survey respondent. The survey was conducted among US residents in 2018. Please answer the question based on the information provided. The data provided is enough to reach an approximate answer.\n",
+      "\n",
+      "Information:\n",
+      "- The age is: 37 years old.\n",
+      "- The class of worker is: Owner of non-incorporated business, professional practice, or farm.\n",
+      "- The highest educational attainment is: Regular high school diploma.\n",
+      "- The marital status is: Married.\n",
+      "- The occupation is: Painters and paperhangers.\n",
+      "- The place of birth is: New Jersey.\n",
+      "- The relationship to the reference person in the survey is: The reference person itself.\n",
+      "- The usual number of hours worked per week is: 40 hours.\n",
+      "- The sex is: Male.\n",
+      "- The race is: White.\n",
+      "\n",
+      "Question: What is this person's estimated yearly income?\n",
+      "A. Below $50,000.\n",
+      "B. Above $50,000.\n",
+      "Answer:\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_sample, _y_sample = dataset.sample_n_train_examples(n=1)\n",
+    "print(llm_clf.encode_row(X_sample.iloc[0], question=llm_clf.task.question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e107c703-17d4-4543-8f56-73c7b9583589",
+   "metadata": {},
+   "source": [
+    "Optionally, you can fit the model's threshold on a few data samples.\n",
+    "\n",
+    "This is generally quite fast as it is _not fine-tuning_; it only changes one parameter: the `llm_clf.threshold`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "16f88399-1b9e-4fd3-a1ca-0aa9f2b91080",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0e4d1784cc8416eb3f30a9b542754b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n",
+      "WARNING:root:Setting meta-llama--Meta-Llama-3-8B-Instruct threshold to 0.7918923636748516.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.57 s, sys: 382 ms, total: 7.95 s\n",
+      "Wall time: 9 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TransformersLLMClassifier(encode_row=functools.partial(&lt;function encode_row_prompt at 0x14802eedf600&gt;, task=ACSTaskMetadata(name=&#x27;ACSIncome&#x27;, description=&quot;predict whether an individual&#x27;s income is above $50,000&quot;, features=[&#x27;AGEP&#x27;, &#x27;COW&#x27;, &#x27;SCHL&#x27;, &#x27;MAR&#x27;, &#x27;OCCP&#x27;, &#x27;POBP&#x27;, &#x27;RELP&#x27;, &#x27;WKHP&#x27;, &#x27;SEX&#x27;, &#x27;RAC1P&#x27;], target=&#x27;PINCP&#x27;, cols_to_text={&#x27;AGEP&#x27;: &lt;folktexts.col_to_text.ColumnToText...\n",
+       "\t128253: AddedToken(&quot;&lt;|reserved_special_token_248|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128254: AddedToken(&quot;&lt;|reserved_special_token_249|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128255: AddedToken(&quot;&lt;|reserved_special_token_250|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TransformersLLMClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>TransformersLLMClassifier(encode_row=functools.partial(&lt;function encode_row_prompt at 0x14802eedf600&gt;, task=ACSTaskMetadata(name=&#x27;ACSIncome&#x27;, description=&quot;predict whether an individual&#x27;s income is above $50,000&quot;, features=[&#x27;AGEP&#x27;, &#x27;COW&#x27;, &#x27;SCHL&#x27;, &#x27;MAR&#x27;, &#x27;OCCP&#x27;, &#x27;POBP&#x27;, &#x27;RELP&#x27;, &#x27;WKHP&#x27;, &#x27;SEX&#x27;, &#x27;RAC1P&#x27;], target=&#x27;PINCP&#x27;, cols_to_text={&#x27;AGEP&#x27;: &lt;folktexts.col_to_text.ColumnToText...\n",
+       "\t128253: AddedToken(&quot;&lt;|reserved_special_token_248|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128254: AddedToken(&quot;&lt;|reserved_special_token_249|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128255: AddedToken(&quot;&lt;|reserved_special_token_250|&gt;&quot;, rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "})</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "TransformersLLMClassifier(encode_row=functools.partial(<function encode_row_prompt at 0x14802eedf600>, task=ACSTaskMetadata(name='ACSIncome', description=\"predict whether an individual's income is above $50,000\", features=['AGEP', 'COW', 'SCHL', 'MAR', 'OCCP', 'POBP', 'RELP', 'WKHP', 'SEX', 'RAC1P'], target='PINCP', cols_to_text={'AGEP': <folktexts.col_to_text.ColumnToText...\n",
+       "\t128253: AddedToken(\"<|reserved_special_token_248|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128254: AddedToken(\"<|reserved_special_token_249|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "\t128255: AddedToken(\"<|reserved_special_token_250|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
+       "})"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "X_sample, y_sample = dataset.sample_n_train_examples(n=50)\n",
+    "llm_clf.fit(X_sample, y_sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dcc20d7-732f-450d-b02d-b94c34b41fc8",
+   "metadata": {},
+   "source": [
+    "Run benchmark..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4dae3a36-2b7b-473b-916b-35711e985140",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Test data features shape: (1665, 10)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48b6fc00f210464b9d0174c7909c0b1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/53 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:\n",
+      "** Test results **\n",
+      "Model: meta-llama--Meta-Llama-3-8B-Instruct;\n",
+      "\t ECE:       24.8%;\n",
+      "\t ROC AUC :  82.8%;\n",
+      "\t Accuracy:  73.8%;\n",
+      "\t Bal. acc.: 74.1%;\n",
+      "\n",
+      "INFO:root:Skipping group 'American Indian' as it's too small.\n",
+      "INFO:root:Skipping group 'Alaska Native' as it's too small.\n",
+      "INFO:root:Skipping group 'American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races' as it's too small.\n",
+      "INFO:root:Skipping group 'Native Hawaiian and Other Pacific Islander' as it's too small.\n",
+      "INFO:root:Skipping group 'Some other race alone (non-White)' as it's too small.\n",
+      "INFO:root:Skipping group 'Two or more races' as it's too small.\n",
+      "INFO:root:Skipping group American Indian plot as it's too small.\n",
+      "INFO:root:Skipping group Alaska Native plot as it's too small.\n",
+      "INFO:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it's too small.\n",
+      "INFO:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it's too small.\n",
+      "INFO:root:Skipping group Some other race alone (non-White) plot as it's too small.\n",
+      "INFO:root:Skipping group Two or more races plot as it's too small.\n",
+      "INFO:root:Saving JSON file to '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/results.bench-3191680725.json'\n",
+      "INFO:root:Saved experiment results to '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/results.bench-3191680725.json'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3min 51s, sys: 7.12 s, total: 3min 58s\n",
+      "Wall time: 4min 23s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': np.float64(0.7918923636748516),\n",
+       " 'n_samples': 1665,\n",
+       " 'n_positives': 605,\n",
+       " 'n_negatives': 1060,\n",
+       " 'model_name': 'meta-llama--Meta-Llama-3-8B-Instruct',\n",
+       " 'accuracy': 0.7375375375375376,\n",
+       " 'tpr': 0.7553719008264462,\n",
+       " 'fnr': 0.24462809917355371,\n",
+       " 'fpr': 0.27264150943396226,\n",
+       " 'tnr': 0.7273584905660377,\n",
+       " 'balanced_accuracy': 0.7413651956962419,\n",
+       " 'precision': 0.6126005361930295,\n",
+       " 'ppr': 0.44804804804804804,\n",
+       " 'log_loss': 0.8504629791262521,\n",
+       " 'brier_score_loss': np.float64(0.24624856734149014),\n",
+       " 'ppr_ratio': 0.0,\n",
+       " 'ppr_diff': 0.5961538461538461,\n",
+       " 'precision_ratio': 0.0,\n",
+       " 'precision_diff': 1.0,\n",
+       " 'balanced_accuracy_ratio': 0.0,\n",
+       " 'balanced_accuracy_diff': 1.0,\n",
+       " 'accuracy_ratio': 0.0,\n",
+       " 'accuracy_diff': 1.0,\n",
+       " 'tnr_ratio': 0.0,\n",
+       " 'tnr_diff': 1.0,\n",
+       " 'fnr_ratio': 0.0,\n",
+       " 'fnr_diff': 1.0,\n",
+       " 'fpr_ratio': 0.0,\n",
+       " 'fpr_diff': 1.0,\n",
+       " 'tpr_ratio': 0.0,\n",
+       " 'tpr_diff': 1.0,\n",
+       " 'equalized_odds_ratio': 0.0,\n",
+       " 'equalized_odds_diff': 1.0,\n",
+       " 'roc_auc': np.float64(0.8282707001403399),\n",
+       " 'ece': 0.2475394044687882,\n",
+       " 'ece_quantile': 0.24753940446878786,\n",
+       " 'predictions_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/ACSIncome_subsampled-0.01_seed-42_hash-373553086.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': False,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'meta-llama--Meta-Llama-3-8B-Instruct',\n",
+       "  'model_hash': 1896439497,\n",
+       "  'task_name': 'ACSIncome',\n",
+       "  'task_hash': 662640833,\n",
+       "  'dataset_name': 'ACSIncome_subsampled-0.01_seed-42_hash-373553086',\n",
+       "  'dataset_subsampling': 0.01,\n",
+       "  'dataset_hash': 373553086},\n",
+       " 'plots': {'roc_curve_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "bench.run(results_root_dir=results_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4dfbfa5f-a83d-4391-a2d9-afe0431ccd77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Skipping group 'American Indian' as it's too small.\n",
+      "INFO:root:Skipping group 'Alaska Native' as it's too small.\n",
+      "INFO:root:Skipping group 'American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races' as it's too small.\n",
+      "INFO:root:Skipping group 'Native Hawaiian and Other Pacific Islander' as it's too small.\n",
+      "INFO:root:Skipping group 'Some other race alone (non-White)' as it's too small.\n",
+      "INFO:root:Skipping group 'Two or more races' as it's too small.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Skipping group American Indian plot as it's too small.\n",
+      "INFO:root:Skipping group Alaska Native plot as it's too small.\n",
+      "INFO:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it's too small.\n",
+      "INFO:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it's too small.\n",
+      "INFO:root:Skipping group Some other race alone (non-White) plot as it's too small.\n",
+      "INFO:root:Skipping group Two or more races plot as it's too small.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bench.plot_results();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2f2e7d60-9fe4-4290-a2a9-7ec22b210cdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 0.7375375375375376,\n",
+      " 'accuracy_diff': 1.0,\n",
+      " 'accuracy_ratio': 0.0,\n",
+      " 'balanced_accuracy': 0.7413651956962419,\n",
+      " 'balanced_accuracy_diff': 1.0,\n",
+      " 'balanced_accuracy_ratio': 0.0,\n",
+      " 'brier_score_loss': np.float64(0.24624856734149014),\n",
+      " 'config': {...},\n",
+      " 'ece': 0.2475394044687882,\n",
+      " 'ece_quantile': 0.24753940446878786,\n",
+      " 'equalized_odds_diff': 1.0,\n",
+      " 'equalized_odds_ratio': 0.0,\n",
+      " 'fnr': 0.24462809917355371,\n",
+      " 'fnr_diff': 1.0,\n",
+      " 'fnr_ratio': 0.0,\n",
+      " 'fpr': 0.27264150943396226,\n",
+      " 'fpr_diff': 1.0,\n",
+      " 'fpr_ratio': 0.0,\n",
+      " 'log_loss': 0.8504629791262521,\n",
+      " 'model_name': 'meta-llama--Meta-Llama-3-8B-Instruct',\n",
+      " 'n_negatives': 1060,\n",
+      " 'n_positives': 605,\n",
+      " 'n_samples': 1665,\n",
+      " 'plots': {...},\n",
+      " 'ppr': 0.44804804804804804,\n",
+      " 'ppr_diff': 0.5961538461538461,\n",
+      " 'ppr_ratio': 0.0,\n",
+      " 'precision': 0.6126005361930295,\n",
+      " 'precision_diff': 1.0,\n",
+      " 'precision_ratio': 0.0,\n",
+      " 'predictions_path': '/lustre/fast/fast/groups/sf/folktexts-results/meta-llama--Meta-Llama-3-8B-Instruct/meta-llama--Meta-Llama-3-8B-Instruct_bench-3191680725/ACSIncome_subsampled-0.01_seed-42_hash-373553086.test_predictions.csv',\n",
+      " 'roc_auc': np.float64(0.8282707001403399),\n",
+      " 'threshold': np.float64(0.7918923636748516),\n",
+      " 'tnr': 0.7273584905660377,\n",
+      " 'tnr_diff': 1.0,\n",
+      " 'tnr_ratio': 0.0,\n",
+      " 'tpr': 0.7553719008264462,\n",
+      " 'tpr_diff': 1.0,\n",
+      " 'tpr_ratio': 0.0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "pprint(bench.results, depth=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0b5be1e-4c00-4b48-adf4-3eee02b8d6e1",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/minimal-example.html b/notebooks/minimal-example.html
new file mode 100644
index 0000000..cc90e57
--- /dev/null
+++ b/notebooks/minimal-example.html
@@ -0,0 +1,419 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Evaluate model calibration using folktexts &mdash; folktexts 0.0.25 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=d2d258e8" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
+  
+      <script src="../_static/jquery.js?v=5d32c60e"></script>
+      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+      <script src="../_static/documentation_options.js?v=5d650c58"></script>
+      <script src="../_static/doctools.js?v=9a2dae69"></script>
+      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+      <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+      <script src="../_static/copybutton.js?v=f281be69"></script>
+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+      <script src="../_static/custom.js?v=882eb7ae"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Example: Run ACS benchmark task" href="detailed-example.html" />
+    <link rel="prev" title="Notebooks Gallery" href="../notebooks.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
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+          
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+          </a>
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+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
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+<li class="toctree-l1"><a class="reference internal" href="../readme.html">Readme file</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../source/modules.html">API reference</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../notebooks.html">Example notebooks</a><ul class="current">
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Evaluate model calibration using folktexts</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#1.-Check-folktexts-is-installed">1. Check folktexts is installed</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#2.-Load-Model-from-Huggingface">2. Load Model from Huggingface</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#3.-Create-default-benchmarking-tasks">3. Create default benchmarking tasks</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Benchmark-configuration">Benchmark configuration</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#4.-Run-benchmark">4. Run benchmark</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#4.-Visualize-results">4. Visualize results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="detailed-example.html">Example: Run ACS benchmark task</a></li>
+<li class="toctree-l2"><a class="reference internal" href="custom-dataset-example.html">Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a different data source</a></li>
+<li class="toctree-l2"><a class="reference internal" href="custom-acs-task-example.html">Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a custom ACS task</a></li>
+<li class="toctree-l2"><a class="reference internal" href="paper-plots-and-tables.html">Render paper plots and tables</a></li>
+<li class="toctree-l2"><a class="reference internal" href="paper-plots-and-tables.html#Plots-comparing-different-prompting-schemes">Plots comparing different prompting schemes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="minimal-example_web-API-model.html">Evaluate model calibration using folktexts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parse-acs-results.html">Fetch and parse ACS benchmark results under a given directory</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
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+      <li class="breadcrumb-item active">Evaluate model calibration using folktexts</li>
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+            <a href="../_sources/notebooks/minimal-example.ipynb.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="Evaluate-model-calibration-using-folktexts">
+<h1>Evaluate model calibration using folktexts<a class="headerlink" href="#Evaluate-model-calibration-using-folktexts" title="Link to this heading"></a></h1>
+<p><strong>Prerequisite</strong>: Install <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> package with <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">folktexts</span></code> or follow the setup guide in the <a class="reference external" href="https://github.com/socialfoundations/folktexts">README</a>.</p>
+<p><strong>Summary</strong>: The script loads a language model from Huggingface and demonstrates how to use folktexts to get insights into model calibration, and plot the benchmark results.</p>
+<section id="1.-Check-folktexts-is-installed">
+<h2>1. Check folktexts is installed<a class="headerlink" href="#1.-Check-folktexts-is-installed" title="Link to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">folktexts</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">folktexts</span><span class="o">.</span><span class="n">__version__</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+folktexts.__version__=&#39;0.0.21&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="2.-Load-Model-from-Huggingface">
+<h2>2. Load Model from Huggingface<a class="headerlink" href="#2.-Load-Model-from-Huggingface" title="Link to this heading"></a></h2>
+<p>We use the <em>Mistral 7B (instruct)</em> model for this demo. The workflow can be similarly applied to any model/tokenizer pair.</p>
+<p><strong>Note:</strong> <em>Set</em> <code class="docutils literal notranslate"><span class="pre">model_name_or_path</span></code> to the <strong>model’s name on huggingface</strong> or to the <strong>path to a saved pretrained model</strong>.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">load_model_tokenizer</span>
+
+<span class="c1"># Note: make sure you have the necessary persmissions on Huggingface to download the model</span>
+<span class="c1"># Note: use gpt2 for the demo if you need a smaller model</span>
+
+<span class="n">model_name_or_path</span> <span class="o">=</span> <span class="s2">&quot;mistralai/Mistral-7B-Instruct-v0.2&quot;</span>
+<span class="c1"># model_name_or_path = &quot;gpt2&quot;</span>
+
+<span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">load_model_tokenizer</span><span class="p">(</span><span class="n">model_name_or_path</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "ef6ed53409ed404795c07a024033a991", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+</section>
+<section id="3.-Create-default-benchmarking-tasks">
+<h2>3. Create default benchmarking tasks<a class="headerlink" href="#3.-Create-default-benchmarking-tasks" title="Link to this heading"></a></h2>
+<p>We generate ACSIncome benchmark using folktexts.</p>
+<p><strong>NOTE:</strong> We will subsample the reference data for faster runtime. This should be removed for obtaining reproducible reslts.</p>
+</section>
+<section id="Benchmark-configuration">
+<h2>Benchmark configuration<a class="headerlink" href="#Benchmark-configuration" title="Link to this heading"></a></h2>
+<ul class="simple">
+<li><p>The <code class="docutils literal notranslate"><span class="pre">subsampling</span></code> and <code class="docutils literal notranslate"><span class="pre">numeric_risk_prompting</span></code> key-word arguments are examples of optional benchmark configurations. See this <a class="reference external" href="https://github.com/socialfoundations/folktexts?tab=readme-ov-file#benchmark-options">page for a list of available configs</a>.</p></li>
+</ul>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="kn">from</span> <span class="nn">folktexts.benchmark</span> <span class="kn">import</span> <span class="n">Benchmark</span><span class="p">,</span> <span class="n">BenchmarkConfig</span>
+
+<span class="c1"># Note: This argument is optional. Omit, or set to 1 for reproducible benchmarking on the full data</span>
+<span class="n">subsampling_ratio</span> <span class="o">=</span> <span class="mf">0.01</span>
+
+<span class="n">bench</span> <span class="o">=</span> <span class="n">Benchmark</span><span class="o">.</span><span class="n">make_acs_benchmark</span><span class="p">(</span>
+    <span class="n">model</span><span class="o">=</span> <span class="n">model</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">task_name</span><span class="o">=</span><span class="s2">&quot;ACSIncome&quot;</span><span class="p">,</span>
+    <span class="n">subsampling</span><span class="o">=</span><span class="n">subsampling_ratio</span><span class="p">,</span>
+    <span class="n">numeric_risk_prompting</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING:root:Received non-standard ACS argument &#39;subsampling&#39; (using subsampling=0.01 instead of default subsampling=None). This may affect reproducibility.
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Loading ACS data...
+Using zero-shot prompting.
+CPU times: user 52.4 s, sys: 1min 30s, total: 2min 22s
+Wall time: 2min 26s
+</pre></div></div>
+</div>
+</section>
+<section id="4.-Run-benchmark">
+<h2>4. Run benchmark<a class="headerlink" href="#4.-Run-benchmark" title="Link to this heading"></a></h2>
+<p>Results will be saved in a folder RESULTS_DIR. There is * <code class="docutils literal notranslate"><span class="pre">.json</span></code> file contains evaluated metrics * <code class="docutils literal notranslate"><span class="pre">.cvs</span></code> file contains risk scores of each datapoint * folder called <code class="docutils literal notranslate"><span class="pre">imgs/</span></code> contains figures</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">RESULTS_DIR</span> <span class="o">=</span> <span class="s2">&quot;res&quot;</span>
+<span class="n">bench</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">results_root_dir</span><span class="o">=</span><span class="n">RESULTS_DIR</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "ffde879b1f5e453abb55e7b423379f7f", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)
+WARNING:root:Failed to compute ECE quantile: The smallest edge difference is numerically 0.
+WARNING:root:Skipping group American Indian plot as it&#39;s too small.
+WARNING:root:Skipping group Alaska Native plot as it&#39;s too small.
+WARNING:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it&#39;s too small.
+WARNING:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it&#39;s too small.
+WARNING:root:Skipping group Some other race alone (non-White) plot as it&#39;s too small.
+WARNING:root:Skipping group Two or more races plot as it&#39;s too small.
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;threshold&#39;: 0.5,
+ &#39;n_samples&#39;: 1665,
+ &#39;n_positives&#39;: 605,
+ &#39;n_negatives&#39;: 1060,
+ &#39;model_name&#39;: &#39;mistralai--Mistral-7B-Instruct-v0.2&#39;,
+ &#39;accuracy&#39;: 0.6816816816816816,
+ &#39;tpr&#39;: 0.856198347107438,
+ &#39;fnr&#39;: 0.14380165289256197,
+ &#39;fpr&#39;: 0.4179245283018868,
+ &#39;tnr&#39;: 0.5820754716981132,
+ &#39;balanced_accuracy&#39;: 0.7191369094027756,
+ &#39;precision&#39;: 0.5390218522372529,
+ &#39;ppr&#39;: 0.5771771771771772,
+ &#39;log_loss&#39;: 0.5812465405486003,
+ &#39;brier_score_loss&#39;: np.float64(0.19836672672672676),
+ &#39;tpr_ratio&#39;: 0.0,
+ &#39;tpr_diff&#39;: 1.0,
+ &#39;balanced_accuracy_ratio&#39;: 0.0,
+ &#39;balanced_accuracy_diff&#39;: 1.0,
+ &#39;accuracy_ratio&#39;: 0.0,
+ &#39;accuracy_diff&#39;: 1.0,
+ &#39;fnr_ratio&#39;: 0.0,
+ &#39;fnr_diff&#39;: 1.0,
+ &#39;ppr_ratio&#39;: 0.0,
+ &#39;ppr_diff&#39;: 0.6442307692307693,
+ &#39;precision_ratio&#39;: 0.0,
+ &#39;precision_diff&#39;: 1.0,
+ &#39;tnr_ratio&#39;: 0.0,
+ &#39;tnr_diff&#39;: 1.0,
+ &#39;fpr_ratio&#39;: 0.0,
+ &#39;fpr_diff&#39;: 1.0,
+ &#39;equalized_odds_ratio&#39;: 0.0,
+ &#39;equalized_odds_diff&#39;: 1.0,
+ &#39;roc_auc&#39;: np.float64(0.814200842039607),
+ &#39;ece&#39;: 0.16251051051051124,
+ &#39;ece_quantile&#39;: None,
+ &#39;predictions_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/ACSIncome_subsampled-0.01_seed-42_hash-1233880265.test_predictions.csv&#39;,
+ &#39;config&#39;: {&#39;numeric_risk_prompting&#39;: True,
+  &#39;few_shot&#39;: None,
+  &#39;reuse_few_shot_examples&#39;: False,
+  &#39;batch_size&#39;: None,
+  &#39;context_size&#39;: None,
+  &#39;correct_order_bias&#39;: True,
+  &#39;feature_subset&#39;: None,
+  &#39;population_filter&#39;: None,
+  &#39;seed&#39;: 42,
+  &#39;model_name&#39;: &#39;mistralai--Mistral-7B-Instruct-v0.2&#39;,
+  &#39;model_hash&#39;: 2545663199,
+  &#39;task_name&#39;: &#39;ACSIncome&#39;,
+  &#39;task_hash&#39;: 127998692,
+  &#39;dataset_name&#39;: &#39;ACSIncome_subsampled-0.01_seed-42_hash-1233880265&#39;,
+  &#39;dataset_hash&#39;: 1233880265},
+ &#39;plots&#39;: {&#39;roc_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve.pdf&#39;,
+  &#39;calibration_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve.pdf&#39;,
+  &#39;score_distribution_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution.pdf&#39;,
+  &#39;score_distribution_per_label_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution_per_label.pdf&#39;,
+  &#39;roc_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve_per_subgroup.pdf&#39;,
+  &#39;calibration_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve_per_subgroup.pdf&#39;}}
+</pre></div></div>
+</div>
+</section>
+<section id="4.-Visualize-results">
+<h2>4. Visualize results<a class="headerlink" href="#4.-Visualize-results" title="Link to this heading"></a></h2>
+<p>We can also visualize the results inline:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">bench</span><span class="o">.</span><span class="n">plot_results</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_minimal-example_10_0.png" src="../_images/notebooks_minimal-example_10_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_minimal-example_10_1.png" src="../_images/notebooks_minimal-example_10_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_minimal-example_10_2.png" src="../_images/notebooks_minimal-example_10_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_minimal-example_10_3.png" src="../_images/notebooks_minimal-example_10_3.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_minimal-example_10_4.png" src="../_images/notebooks_minimal-example_10_4.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING:root:Skipping group American Indian plot as it&#39;s too small.
+WARNING:root:Skipping group Alaska Native plot as it&#39;s too small.
+WARNING:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it&#39;s too small.
+WARNING:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it&#39;s too small.
+WARNING:root:Skipping group Some other race alone (non-White) plot as it&#39;s too small.
+WARNING:root:Skipping group Two or more races plot as it&#39;s too small.
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_minimal-example_10_6.png" src="../_images/notebooks_minimal-example_10_6.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;roc_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve.pdf&#39;,
+ &#39;calibration_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve.pdf&#39;,
+ &#39;score_distribution_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution.pdf&#39;,
+ &#39;score_distribution_per_label_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution_per_label.pdf&#39;,
+ &#39;roc_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve_per_subgroup.pdf&#39;,
+ &#39;calibration_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve_per_subgroup.pdf&#39;}
+</pre></div></div>
+</div>
+<hr class="docutils" />
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="../notebooks.html" class="btn btn-neutral float-left" title="Notebooks Gallery" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="detailed-example.html" class="btn btn-neutral float-right" title="Example: Run ACS benchmark task" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "181f6c46",
+   "metadata": {},
+   "source": [
+    "# Evaluate model calibration using folktexts\n",
+    "\n",
+    "**Prerequisite**: Install `folktexts` package with `pip install folktexts` or follow the setup guide in the [README](https://github.com/socialfoundations/folktexts).\n",
+    "\n",
+    "**Summary**: The script loads a language model from Huggingface and demonstrates how to use folktexts to get insights into model calibration, and plot the benchmark results. \n",
+    "\n",
+    "### 1. Check folktexts is installed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f3867376",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "folktexts.__version__='0.0.21'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import folktexts\n",
+    "print(f\"{folktexts.__version__=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2129c830",
+   "metadata": {},
+   "source": [
+    "### 2. Load Model from Huggingface\n",
+    "\n",
+    "We use the _Mistral 7B (instruct)_ model for this demo. The workflow can be similarly applied to any model/tokenizer pair.\n",
+    "\n",
+    "**Note:** _Set_ `model_name_or_path` to the **model's name on huggingface** or to the **path to a saved pretrained model**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3dc758de-b86f-4adb-9ed0-0148831da070",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef6ed53409ed404795c07a024033a991",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from folktexts.llm_utils import load_model_tokenizer\n",
+    "\n",
+    "# Note: make sure you have the necessary persmissions on Huggingface to download the model\n",
+    "# Note: use gpt2 for the demo if you need a smaller model\n",
+    "\n",
+    "model_name_or_path = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
+    "# model_name_or_path = \"gpt2\"\n",
+    "\n",
+    "model, tokenizer = load_model_tokenizer(model_name_or_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25a2a5f7",
+   "metadata": {},
+   "source": [
+    "### 3. Create default benchmarking tasks\n",
+    "\n",
+    "We generate ACSIncome benchmark using folktexts. \n",
+    "\n",
+    "**NOTE:** We will subsample the reference data for faster runtime. This should be removed for obtaining reproducible reslts.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed481a4-5258-468f-8fb5-91ed794d1503",
+   "metadata": {},
+   "source": [
+    "### Benchmark configuration\n",
+    "- The `subsampling` and `numeric_risk_prompting` key-word arguments are examples of optional benchmark configurations. See this [page for a list of available configs](https://github.com/socialfoundations/folktexts?tab=readme-ov-file#benchmark-options)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bbb17808",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Received non-standard ACS argument 'subsampling' (using subsampling=0.01 instead of default subsampling=None). This may affect reproducibility.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "Using zero-shot prompting.\n",
+      "CPU times: user 52.4 s, sys: 1min 30s, total: 2min 22s\n",
+      "Wall time: 2min 26s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.benchmark import Benchmark, BenchmarkConfig\n",
+    "\n",
+    "# Note: This argument is optional. Omit, or set to 1 for reproducible benchmarking on the full data\n",
+    "subsampling_ratio = 0.01\n",
+    "\n",
+    "bench = Benchmark.make_acs_benchmark(\n",
+    "    model= model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    task_name=\"ACSIncome\",\n",
+    "    subsampling=subsampling_ratio,\n",
+    "    numeric_risk_prompting=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f785fc1",
+   "metadata": {},
+   "source": [
+    "### 4. Run benchmark\n",
+    "\n",
+    "Results will be saved in a folder RESULTS_DIR. There is \n",
+    "* `.json` file contains evaluated metrics\n",
+    "* `.cvs` file contains risk scores of each datapoint\n",
+    "* folder called `imgs/` contains figures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "62b90528",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffde879b1f5e453abb55e7b423379f7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/105 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n",
+      "WARNING:root:Failed to compute ECE quantile: The smallest edge difference is numerically 0.\n",
+      "WARNING:root:Skipping group American Indian plot as it's too small.\n",
+      "WARNING:root:Skipping group Alaska Native plot as it's too small.\n",
+      "WARNING:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it's too small.\n",
+      "WARNING:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it's too small.\n",
+      "WARNING:root:Skipping group Some other race alone (non-White) plot as it's too small.\n",
+      "WARNING:root:Skipping group Two or more races plot as it's too small.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 1665,\n",
+       " 'n_positives': 605,\n",
+       " 'n_negatives': 1060,\n",
+       " 'model_name': 'mistralai--Mistral-7B-Instruct-v0.2',\n",
+       " 'accuracy': 0.6816816816816816,\n",
+       " 'tpr': 0.856198347107438,\n",
+       " 'fnr': 0.14380165289256197,\n",
+       " 'fpr': 0.4179245283018868,\n",
+       " 'tnr': 0.5820754716981132,\n",
+       " 'balanced_accuracy': 0.7191369094027756,\n",
+       " 'precision': 0.5390218522372529,\n",
+       " 'ppr': 0.5771771771771772,\n",
+       " 'log_loss': 0.5812465405486003,\n",
+       " 'brier_score_loss': np.float64(0.19836672672672676),\n",
+       " 'tpr_ratio': 0.0,\n",
+       " 'tpr_diff': 1.0,\n",
+       " 'balanced_accuracy_ratio': 0.0,\n",
+       " 'balanced_accuracy_diff': 1.0,\n",
+       " 'accuracy_ratio': 0.0,\n",
+       " 'accuracy_diff': 1.0,\n",
+       " 'fnr_ratio': 0.0,\n",
+       " 'fnr_diff': 1.0,\n",
+       " 'ppr_ratio': 0.0,\n",
+       " 'ppr_diff': 0.6442307692307693,\n",
+       " 'precision_ratio': 0.0,\n",
+       " 'precision_diff': 1.0,\n",
+       " 'tnr_ratio': 0.0,\n",
+       " 'tnr_diff': 1.0,\n",
+       " 'fpr_ratio': 0.0,\n",
+       " 'fpr_diff': 1.0,\n",
+       " 'equalized_odds_ratio': 0.0,\n",
+       " 'equalized_odds_diff': 1.0,\n",
+       " 'roc_auc': np.float64(0.814200842039607),\n",
+       " 'ece': 0.16251051051051124,\n",
+       " 'ece_quantile': None,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/ACSIncome_subsampled-0.01_seed-42_hash-1233880265.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': True,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'mistralai--Mistral-7B-Instruct-v0.2',\n",
+       "  'model_hash': 2545663199,\n",
+       "  'task_name': 'ACSIncome',\n",
+       "  'task_hash': 127998692,\n",
+       "  'dataset_name': 'ACSIncome_subsampled-0.01_seed-42_hash-1233880265',\n",
+       "  'dataset_hash': 1233880265},\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RESULTS_DIR = \"res\"\n",
+    "bench.run(results_root_dir=RESULTS_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20636ce4",
+   "metadata": {},
+   "source": [
+    "### 4. Visualize results\n",
+    "\n",
+    "We can also visualize the results inline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "edd5bf8f-cd70-4276-9c14-f1e0012aee0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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r1q34/fffERwcjN69e2Pw4MFKdzAxMjKSOjcgervfvHkzrl69KvXwEr/Q1EZMTAy2bNmC2NhYlJWVSXxXVFREX1tNxG/MYqytrWFsbCx1D2xtbaWuz8HBAZ06dcLZs2dRXl4OgUAgs725NhYsWIDFixejR48e8PPzQ/fu3TF06FA0b95c6WPIyodCoRD79u3Dn3/+ibS0NAgEAvo7eXmlvLxc6mVBVkni/v37yM7Oltsz1c3NTaLDRN++fcFisbB3716MGDEC3t7edcrLNWtGxGYozrMmJiZYsGABVq9ejS5duqBdu3bo0aMHhg4dKvNaakOWrsrC5XLBZrPldjapDYFAUGveCwsLw4YNG5CdnY3GjRvj/v37yM3NxYABA+htxUbI5/Oljl1RUSH1siSP0tJSTJs2DSUlJfjzzz/VXpjQeQMMCAigq8F69+6NMWPGYP78+Th//jwtljgTJCUlyexJBgBPnjyR2NbT05NOr20fRYiP8fTpU6mu6qpQ/eFRndoy0sCBA+nepz4+Pjh37hz+97//SZQWhUIhunTpUusblbhaVBYrV67EiRMn6M/BwcEK25tqe5OtLV1cTSMLFouFzZs3IzY2FteuXcPNmzfx1VdfISIiAn/99ZdSP5LqJRQxAoEAkyZNQkFBAaZMmQJPT09YWFggOzsbixcvlts5iMvlYuLEifD09MTixYvh4uICY2Nj3LhxA7///rvcfUNCQiQ+r1q1CsHBwVJj2fbt2ydzSMegQYPw7bff4vXr1+jWrZtKL1thYWHo0KEDLl26hNu3b2P37t3YuXMntmzZQrcrKkJWPty+fTt+/vlnjBgxAp999hlsbW3BZrPx448/yr23UVFREuN7gXe/zeqcOXMGbDZbolZBGTp16oT9+/cjOjoa3t7eas3L1a9r4sSJCA0NxeXLl3Hr1i38/PPP+O2337B37174+voqFausHru1ld5qe0bUhczMzFrz3oABA7B+/XqcO3cOEydOxLlz52BtbU0PNwHevbDk5ORIvSzk5OTI7ThYHT6fjzlz5uDJkyfYvXs3I01JOm+A1eFwOPjiiy8wYcIEHDhwgG7sFlf1nT17FjNmzJCZgU+ePAkAdNvhe++9B1tbW0RGRmL69Ol1qoro2bMnduzYgdOnTytlgLa2tjKr/jIyMlQ6b+/evfHdd9/RVRUvXryQaH8AgBYtWqC0tLROXYqnTJki0e7CROlWGQIDAxEYGIh58+bhzJkzWLBgAaKiojBy5Mg6VfM8ffoUL168wOrVqzF06FA6vXovy9q4evUq+Hw+fv31V4l2wnv37incNyIiQuKzl5cXbG1tpdJra8Pu06cPvv/+e8TGxtZpWI2zszPGjh2LsWPHIjc3F8OGDcP27dtpA6yLlhcuXMD777+PH3/8USK9sLAQ9vb2te4XEhIidd014fP5uHjxIoKDg1XuTFNVVQXgXc0Ok3m5RYsWmDx5MiZPnowXL15g6NCh2LNnD9atWwegbrra2NjIrB2p+Yxo0aIFhEIhnj17Bh8fn1qPJyuGRo0a1Zr3mjdvjoCAAJw7dw7jxo3DxYsX0bt3b4maCfH54uPjJcwuOzsbWVlZGDVqlMLrFAqFWLRoEe7cuYNNmzYhODhY4T51QefbAGsi7ha/d+9eVFRUAADMzc0xefJkPH/+XOYD4vr16zhx4gRCQkIQGBhI7zNlyhQ8e/YM69atk/nWeurUKanuz9UJCgpC165dceTIEVy+fFnqez6fj9WrV9OfmzdvjtTUVInqh6SkJPz7779KXz8g+pGEhITg3LlziIyMhLGxsVQpdsCAAXj48CFu3rwptX9hYSH9oJCFl5cXOnfuTP/5+/urFF99KSgokLof4h+duNrF3NwcgPyq1JqIS4TVj01RFPbt26dwX/ELUvV9xdXyiqiuZefOneHs7AxTU1Op9OrtTtWxtLTEDz/8gDlz5sjsIl8bAoFAqrrR0dERzs7OEtVX5ubmKrdhcjgcqXt07tw5hW2Nzs7OUtddkxs3bqCwsFCltk4x165dA/Dugc5EXi4rK6OfPWJatGgBS0tLKV1VyZ/i4xQVFUkMJ3n16hXdA1pM7969wWazsW3bNqnah+r3RVYMivJeWFgYYmNjcezYMeTn50tUfwJAq1at4OnpicOHD0uUTA8ePAgWi0X3RAdEv5Fnz55J5a/ly5cjKioK33//Pfr27ausPCqjVyVAMeHh4fjss89w/PhxuqPD1KlTkZiYiJ07dyI2NhZ9+/aFmZkZYmJicPr0abRs2VLCjADR22FKSgr27NmDe/fuoV+/fnBycsLr169x+fJlxMXF4dChQ3JjWbNmDSZPnozZs2ejZ8+e6NSpE8zNzfHy5UtERUXh1atX9FjADz/8EL///jvCw8Px4YcfIjc3F4cOHYKXl5fKbZFhYWH48ssv8eeffyIkJETqzTY8PBxXr17F9OnTMWzYMPj5+aGsrAxPnz7FhQsXcOXKFakONtrCiRMncPDgQfTu3RstWrRASUkJDh8+DCsrK7oqxszMDF5eXjh37hzc3d1hZ2eHVq1aya1G8fT0RIsWLbB69WpkZ2fDysoKFy5cUOoh1aVLFxgbG2P69On4+OOPUVJSgiNHjsDR0VGqjY8Jhg0bpvI+JSUl6N69O/r164c2bdrAwsIC//zzD+Lj4yV6hfr5+SEqKgqrVq1C27ZtYWFhodBoe/TogW3btmHJkiUICgrC06dPcebMGZXaFmvjzJkzMDExUdjWmZCQgFOnTtHXevfuXVy4cAFBQUFS1c7q5MWLF5g4cSL69+8PLy8vcDgcXL58Ga9fv5aosvXz88PBgwfxyy+/wM3NjW7PlUdYWBjWrVuH2bNnY/z48SgvL8fBgwfh4eFBj90ERO2f06dPxy+//IIxY8agb9++MDExQXx8PJydnenez3WJYcCAAVi9ejVWr14NOzs7mS8pCxcuxIwZMzB58mQMHDgQT58+xYEDBzBy5EiJdslLly5hyZIlWLVqFd3r+/fff8eff/6JoKAgmJmZ0fdQTJ8+fdQ2KYBeGmDfvn3RokUL7Nmzh+5JxeFwsGnTJpw8eRJHjhzBzz//TM8FOmvWLJlzgbLZbKxZswa9evXC4cOHsWfPHhQXF8Pe3h4dO3bEl19+iaCgILmxODg44NChQ/jzzz8RFRWFjRs3orKyEk2bNkVoaCgmTJhAbys24c2bN2PVqlXw8vLCmjVrcPbsWdy/f18lDcS9skpKSiR6f4oxNzfHH3/8gR07duD8+fM4efIkrKys4O7ujjlz5tTaYUMbCA4ORnx8PKKiovD69WtYW1sjICAA69atk3jArlixAsuXL8eqVatQWVmJ2bNnyzVAY2NjbN++HStWrMCOHTtgamqKPn36YOzYsQq7iXt6emLz5s3YtGkTVq9eDScnJ4wePRoODg746quv1Hbt6sTMzAyjR4/G7du3cfHiRVAUhRYtWuD777/HmDFj6O3GjBmDxMREHD9+HL///judd+Uxffp0lJWV4cyZM4iKioKvry927NiB9evX1ytmcU/XHj16KMyjZ8+exdmzZwGIOj25uLggPDxcZs9IddKkSRMMHDgQd+7cwenTp8HhcODp6YlNmzZJmPasWbOQkZGBXbt2oaSkBMHBwQrNx97eHlu3bsVPP/2EtWvXolmzZvjiiy/w8uVLCQMEgM8++wzNmjXD/v37sXHjRpibm0sNC6pLDE2aNEFQUBD+/fdfjBw5kh77WJ2ePXti69at2Lp1K5YvXw4HBwdMmzZNqeEv4tLtw4cP8fDhQ6nvr1y5ojYDZFHyWqQJBAKBQNBT9K4NkEAgEAgEZSAGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSYoAEAoFAMEiIARIIBALBICEGSCAQCASDhBgggUAgEAwSI00H0NAIhUJUVVWBzWaDxWJpOhwCgUAgqAhFURAKhTAyMgKbXfdynEYN8MGDB9i9ezf+++8/5OTkYNu2bejdu7fcfe7du4effvoJycnJcHFxwYwZMzB8+HClz1lVVYX4+Pj6hk4gEAgEDdO2bVuYmJjUeX+NGmBpaSm8vb0xYsQIzJ49W+H2PB4P06ZNw8cff4x169bhzp07+Oabb9CoUSN07dpVqXOK3xbatm0LDocDgUCAFy9ewN3dHRwOp17Xo48QfRRDNJIP0UcxRCP51NRHIBAgPj6+XqU/QMMG2L17d3Tv3l3p7Q8dOoRmzZph8eLFAICWLVsiJiYGv//+u9IGKK725HA4dEYrKiqS+EyQhOijGKKRfIg+itFXjSiKQkWVEHyBEPwq0V9F1bv/8wUCVFQKUSEQQiikZB5DKBSiMCsPLVu2lNCnvs1YOtUGGBsbi06dOkmkhYSE4Mcff1T5WAKBQOJfQCQyRb27ASwWC2w2W2IbeenidkVZ6eLjK5PO4XDoOu6a6TVjrC1dHGN9r0m8rz5dk7rvU828pA/XpM77JI5JKBTSb++6fk2KYlf1mlT9nTF9TRVVFHIKy5BXykd+KR/5JZXILxX95RZXIL+UjxK+APwqISoF7wyNNrpKAfgC6q3BScaoKlWFOXgduRGOA+bCu3Vr+LjaSulUV3TKAF+/fg0nJyeJNCcnJxQXF6O8vBxmZmZKH0tWOyCXy0Vubi792cXFBa6urkhNTUVhYSGd7ubmBicnJyQlJaG8vJxO9/Lygq2tLeLi4iQypq+vL0xMTBAbGytxvsDAQPD5fCQkJNBpbDYbQUFBKCwsREpKCp1uZmYGPz8/5OXl4eXLl3S6jY0NWrVqhaysLGRmZtLpjo6OcHd3r/c1eXp6AgAeP36sN9fE1H2Kj4/Xu2sC1HefsrOz0axZM726JnXdJ29vbwDSzyV1XJOFlTUcXVvgyYt0pKZlo7CCQiFfCAHHFEJjS6S9ykdOURkKK4QoqhCiqJJCRZXskpg6MOawYMJhgwMhjDgsGLNZMOGwYGNlAVBCVJRX0NvyC3MQc+RrVLzOQNXVrXgzKQCxr9iwtrZWSywsquargobw9vZW2AmmX79+GD58OKZNm0an3bhxA1OnTsWjR4+UMkCBQIDY2Fi6DVAoFCI/P582Vn19C6/rNQFAfn4+bG1tJerbdfma1H2fqqqqkJ+fD3t7e7DZbL24JnXeJ/FvzMHBAUZGRnpxTYpiV/WaACAvLw92dnZyf2dCIYWiiiq8KatCXglfVBor4SOvlI+8Ev7bdHFaJfJK+Cgqr5I6lzKYcFhwsDSBvYUJ7C1N4GBhAgcrE9iZG8HewgSWpkYwMWLD3JgDE2MOjFiAiREbpkZsmBixYWYs+t6YDVEahw1jDhtGRhyJaxJT8z7xeDz06tULqamp8PT0xNGjR+Hv70/rGh8fj8DAwHpVGetUCdDJyQmvX7+WSHv9+jWsrKxUKv0B79oAORwOnJ2dFW7b0OksFktmem2NvqqmqxJLzVJ3XY+jTdekrnQWiwVjY2OpPKTr16TO+1TzN6YP18REjI0aNQIA8KuESMoqxEPuGzzivUHamzLkl7ytiiythKCWdjJ5sFgQGZmFMRwsTeg/e4tq/1q9NTlLkeFZmnAYHSqm6D7dunWLNr/r16+jefPmao9BpwwwMDAQf//9t0TaP//8g8DAwDofUyAQICkpCW3atNG7xmd1QPRRDNFIPkSf2qEoCmn5Zfj3ZR6uPnoObikbjzMKwa+S325mbWoEe0txycwY9pYmcLR8V1Kzr2ZyDhYmsDE3BoetW+Oex40bB0DUWdLV1RWPHz9Wex7SqAGWlJSAy+XSn9PS0pCYmAhbW1u4urpi/fr1yM7Oxpo1awAAH3/8MQ4cOIA1a9ZgxIgRuHv3Ls6dO4cdO3bUK47qdfQEaYg+iiEayYfoI6KovBJxaQWI5b3BQ24+Ynlv8LqYL7WdrbkxApvbIaiFHVo2snpnbpYmsLMwhqmRfr5I8Hg8mJub07VOYhMUCASM5CGNGuB///2HCRMm0J9XrVoFABg2bBh++ukn5OTkSDREN2/eHDt27MCqVauwb98+NGnSBCtWrFB6CASBQCA0FFUCIZ5mFyOW9waxvHw85L5BSk4xava6MGKz4ONijaZmVejT3gvt3Rzg7mhhcDNV8Xg89OjRA5aWlrh69arcphd1oVEDfP/99/HkyZNav//pp59k7nPy5EkGoyIQCATVySooFxkd7w1iuW8Qn16AUr50Z5dm9uYIbG5Hl/D8XG1hzBYN8woMdDXIamKx+Ynb/MrKyhrkvDrVBsgEbDYbXl5e9Z5RQF8h+iiGaCQffdSnlF+F+LdVmeK/zALpKjorUyO0a2771vDsEdjcDo2sTaW2oyhK7zRSlprmJ6vDC1N5yOANkMViwdbWVtNhaC1EH8UQjeSj6/oIhRRSXxfjX+5bs+O+wZPsIqnemGwW0LqxNYJa2COouR0C37bfKdP5RNc1qivKmB/AnD4Gb4ACgQBxcXEICAgwyKoHRRB9FEM0ko+u6ZNbXPG2k4rI8B6lvZE5lq6xjenbakxRya5tU1tYmtbtkaprGqkDZc0PYE4fgzdAQHpAJkESoo9iiEby0VZ9KqoEeJxRiFjuG1HbHS8fvDzp9iczYzYCmopKdeLSnYutuVpj0VaNmKKyshKVlZVKj/NjQh9igAQCwSCgKAovc0vpNruH3HwkZBaiUiA9sNzL2Uqio4p3Y2sYcQyvfY5JxMZnbGzMyCB3ZSAGSCAQ9JKC0krEpona7GJ5ojF3+aWVUts5WJqISnVvS3YBzexga26sgYj1Hy6Xi4SEBPTv3x/Au7mGNYXBGyCbzYavr69B9r5SBqKPYohG8mkIfSoFQiRlFr0bhsB7g9ScEqntTDhs+LraIKjF29Jdc3s0dzDX+Jg7Q8hDXC4XPXv2BI/Hw+nTp2kTVAam9DF4AwRQrxWFDQGij2KIRvJRpz4URSGjoFzUbvd2NpX49AJUyJg+zM3R4q3R2SGwhT18XKy1dhYVfc5DYvMTd3jx8/NT+RhM6GPwBigUCt8OQK3frOL6CtFHMUQj+dRXn+KKKsSlveuVGct7g5yiCqntbMyM0O6t2QW1sEe75nZwsNQNU9HnPFTT/OoysTVT+hi8ARIIBO1BIKSQ/KrobelOZHZPXxXJnD6sjYs1PcA8qIUdPBwtwdaxCZ/1HXWYH5MQAyQQCBrjVWE53Wb3kJuP+LQClMiYPqyp3bvpwwJb2MHf1RbmJvpVUtI3Xr16pdXmBxADJBAIDUQZX4DE7IK3vTJFhpchY/owCxMO2jUTGZ24/c7ZRrX1Pgmax8nJCd27dwcArTQ/QItWhG8oxCvCi+uSxSsQi1dqJkhC9FEM0Uga0fRhJW+nDhP1zEzKkp4+jMUCWjtb0+PtAlvYoZWztc6tXVdf9DUPCYVCvH79WuGi44qoqU/N53hdISVAAHw+X+UV5Q0Joo9iDF2jvBI+Hr0t1T3kiVYyL5QxfVgja1OJAeYBzexgVcfpw/QNfchDPB4PmzdvxqpVq2BkZAQ2m11v8xPDhD4Gn/OEQiESEhL0sveVOiD6KMbQNKqoEiAxs4gu2cXy3uBlbqnUdqZGbLRtaot2zWxhK8jHsK6BaOZgqVclHHWhD3mo+tyeALB27Vq1HZspfQzeAAkEgnxieW+Q8qoYjzMK8JD7BgkZheALpMfceTayfDfmrrk92rhYw5jDpqurXO00P+CcwAw1J7aeO3eupkNSCmKABAJBJimvivFjVCKuJr2S+s7OwpieSSWwhR0Cm9nB1oJMH2aIqLKqg7ZBDBDQ6+mH1AHRRzH6pNGbUj42XU7G/rsvUSWkYMRmIaiFHXxdbOilf9wcLVQqzemTPkyhixo1pPkxoY/B9wIlEAgiKgVC7L/7EpsuJ6OgTDRpdG8fZywJ80HLRlYajo6gbQgEArRr1w6PHz9u8JKfup7juvfKoWYoikJBQQEM7D1AaYg+itF1jSiKwtWkbPTb9DeWnklAQVklvBtbY3/4+9j1Scd6m5+u69MQ6KJGHA4HGzZsgJ+fH+Pmx5Q+Bm+AQqEQKSkpBrcYpbIQfRSjyxo9ySrChD33Mfn3aKTmlMDR0gQrh/kjcm4IQlo5qeUcuqxPQ6FLGlU3ob59++LRo0eMl/yY0oe0ARIIBkhucQU2XHqKg/e5EFKiZYImhbhjVk8v2JiRziwE2fB4PIwdOxa7du1C69atAUCnm5KIARIIBkRFlQB7/3mBLVdSUFQhGqg+wL8JlgzwQQtHCw1HR9Bmqnd4mTJlCm7cuKHzw1qIAQI6P/sC0xB9FKPtGlEUhQuPs7HqXCI9aN3P1QbfDfLF+56OjJ9f2/XRBrRZo5q9PQ8cONDg5seEPqQXKIGg5/yXXoDlZxNw73keANF0ZAv7eWNE+2Zk+SCCQrRxnB+ZC1RNCIVC5OXlwcHBQSfH4TAN0Ucx2qrRq8JyrLv4BEdi0kBRoqnJpnbzxPTuLWHZgPNvaqs+2oS2aqQt5seUPgZvgBRF4eXLl7C3t9d0KFoJ0Ucx2qZReaUAu289x7ZrKSh9u7bekHauWDSgDZramTd4PNqmjzairRrNnz9f4+YHMKePwRsggaAvUBSFM3GZWH0uCelvygAAgc3t8O0gX7znpl0PVoJusGPHDgDA+vXrNV7tyQTEAAkEPSCW9wbLzyYg5mU+AMDV1gyLBrTB4ABX0s5HUImSkhJYWloCAOzt7XH48GENR8QcxAAB2NjYaDoErYbooxhNaZRZUIY155/gxMN0AIC5MQczerTEp109YW6iPZ28SB5SjDZoxOVy0bNnT8ydOxefffaZpsORgAl9SC9QAkEHKeVXYfuNVPz29zOUV4pmx/jwvWb4sp83Gttob3d6gvYiNr/U1FS0bNkSjx49okuC2gbpBaomhEIhsrKy0KRJE63qfaUtEH0U05AaCYUUTjxMx5oLScgurAAABLs74NtBvmjbzJbRc9cVkocUo2mNqpufp6cnrl27plXmx5Q+Bm+AFEUhMzMTjRs31nQoWgnRRzENpVH0izwsO5uAuLQCAEBzB3N8NcAH/f2baPWMHCQPKUaTGtU0P20Y51cTpvQxeAMkELQdXl4pfjqfhMi4TACAlakRZod6YWJnd5gZk2p8Qt3RBfNjEmKABIKWUlReiV+uP8PuW8/BrxKCzQI+6tgCX/RpjUbWppoOj6AHnD171mDNDyAGCBaLBUdHR62uQtIkRB/FqFsjgZDCkWge1l18itfFona+Ll6O+GagL3xcNN9TUFVIHlKMpjSaOXMmAGDw4MFabX5M6UN6gRIIWsQ/z15j+dlEJGYWAgA8nSzxVZgPevk4EwMhqIW0tDTY2trC2tpa06HUGbIivJoQCoV48eKFTixEqQmIPopRh0bPX5fg033RGLPzHhIzC2FjZoRvB/ni/Ofd0Nu3sU6bH8lDimkojbhcLrp3747+/fujqKiI0XOpE6b0MXgDpCgKubm5MLCCsNIQfRRTH40Kyiqx4mwC+m68gUsJ2eCwWfikkxtufNkT4SEeMDHS/Z8oyUOKaQiNqnd4ycrKQkFBAWPnUjdM6WPwbYAEgiaoEghx8D4XGy49RX5pJQCgp3cjfD3QB17Ouls1RdBOZPX2bNasmabD0jjEAAmEBubG0xysOJuA5FfFAIBWzlb4ZpAvurdupOHICPqIoQ91kIfBGyCLxYKLi4tOt7EwCdFHMTI1Sk4G9uwBXrwA3N2ByZORYtsEKyITcf1JDgDA3sIYX/T1xuiOzWHE0f2qztogeUgxTGmkL+bHlD4Gb4BsNhuurq6aDkNrIfooRkqjiAhgyhSAxQIoChSLBWr1GvwWNhfX/XvDmMPCxM7umB3aCrbmxpoLvIEgeUgxTGlUWFiIwsJCnTY/gDl99Pe1U0kEAgGSk5MhEAg0HYpWQvRRjIRGycki8xMKAYEAEArBEgjAooRYFfUzxjrycXFed3w90NcgzA8geUgZmNLI398f165d02nzA5jTx+ANEBC9JRFqh+ijGLFGwt27QcmopmFB9Ba7Mu8+PJy0Z5LhhoLkIcWoSyMej4dbt27Rn/39/XXa/MQwkYeIARII9YSiKKTkVWJFZCIuRd2rdawSi6JEbYIEAkPweDz06NED/fr1kzBBgmwMvg2QQKgrKa+KcDo2A6ceZeBlbimAXDSydEIvsADIGK/EYok6xBAIDCA2P3GHFzc3N02HpPUYvAGyWCy4ubmRHmq1QPSRJONNGc48ysCp2AwkZL6rkjEzYqOPb2O06/4FOIOOy/Q/UBQQHt5wwWoJJA8ppr4a1TQ/XW/zqwlTecjgDZDNZsPJyUnTYWgtRB8RWQXl+DEqEacfZdBpRmwWurduhCGBrujt0xiWpm9/Trt3i4zubS9Q+t/duwEvLw1dgeYgeUgx9dFI380PYC4PGbwBCgQCJCUloU2bNmRybBkYuj78KiH23H6OzVeSUcoX9UB738MBQwJdEebvAntLE2mNJk4EQkJEhiceBxgebpDmB5A8pAx11SgrK0vvzQ9gLg8ZvAECQHl5uaZD0GoMVZ+byTn4/vRjpOaUAADat7DDsg/84d/UVmpbKY28vIBVqxoiTJ3AUPOQKtRFIwcHB7Rr1w4A9Nb8xDCRh4gBEgg1SMsvxYqziTj/OAsA4GRlgsUDfDA8qCnYbNKORdAeTExMcOjQIeTl5aFJkyaaDkfnqJMBZmRkICMjA2VlZXBwcECrVq1gYmKi7tgIhAalvFKAnX+nYtv1FJRXCsFhszChkxvm9WkNGzPDGLRO0H54PB4iIiLw7bffgsViwcTEhJhfHVHaANPS0nDw4EFERUUhKytLYlkKY2NjdOjQAaNGjUK/fv3AZuvO8EI2mw0vLy+dirkhMRR9riZlY+mZhLfDGYBgDwcs+8APbZooXoHdUDSqK0QfxSirUfUOLwDw3XffNUR4GoepPKTUivArVqzAiRMnEBISgp49eyIgIADOzs4wMzNDQUEBnj59ipiYGERGRoLD4eDHH39EQECAWgNVF2RFeEJ1uLmlWHb2MS4nvgIAOFub4uuBPhjSzpV02ydoFYbQ21NZ1PUcV6oEaG5ujsuXL8Pe3l7qO0dHR3Tq1AmdOnXC7Nmz8ffffyMrK0trDbAmAoEAcXFxCAgIIIYoA33Vp7xSgF+uP8P2G8/ArxLCiM1CeIgH5vRqBStT1VoG9FUjdUH0UYwijQzd/JjKQ0r90ufPn6/0Abt161bnYDRFbVNXEUTokz4UReFiQjaWn01AWn4ZAKCLlyOWDvGr10K0+qQRExB9FFObRoZufmKYyEOkFyjBYEjNKcbSMwm48VS0Hp+rrRm+GeSLAf5NSHUnQSvh8/no3bu3wZsfU6itRfHZs2fo1auXug5HIKgNiqLwy/UU9Nv0N248zYEJh41ZPVvi8vzuCGtLFmolaC8mJiZYvnw5WrduTcyPAdRWAqysrERGRobiDbUMNpsNX19f0kOtFnRdn/JKARYfi8PJWFHe7OHdCN8P9lPrkkS6rhHTEH0UI0+jUaNGYejQoQY91IypPKS0Aa5SMKtFXl5evYPRFIacsZRBV/XJKarA1D+i8ZD7BkZsFpZ+4IcxwS0YKfHpqkYNBdFHMWKNuFwuPv30U+zatYsu8RH9mNFAaQPct28ffHx8YGkp+825tLRUbUE1JEKhkAyLkIOu6pOYWYgpe6OR/qYMtubG+HVse3T2YmZCZl3VqKEg+ihGrJGDgwPd5jdlyhRcuHBB06FpBUzlIaUNsEWLFvjkk0/wwQcfyPw+MTERw4cPVzmAAwcOYPfu3cjJyUGbNm3w7bffyh1C8fvvv+PgwYPIzMyEvb09+vXrh/nz58PU1FTlcxP0k0sJ2fjs0EOU8gXwdLLErk86wLORlabDIhDkkpWVhVGjRtEdXnbt2qXpkPQepStU/f398fjx41q/Z7FYUGJMvQRRUVFYtWoVZs2ahRMnTqBNmzYIDw9Hbm6uzO3PnDmD9evXY/bs2YiKisLKlSsRFRWFDRs2qHRegn5CURR23HiGqX9Eo5QvQBcvR5yY2YWYH0Hr4XK5mDZtGunt2cAoXQJcvHgx+Hx+rd+3adMGSUlJKp08IiICo0aNwogRIwAAS5cuxfXr13Hs2DFMnTpVavuHDx+iffv2GDx4MACgWbNmGDRoEB49eqTSeQHRwMrq/wKiYnZ1E2exWGCz2RLbyEtns9lgsVgy08XHVyadw+GAoiiZ6TVjrC1dHGN9r0m8r7ZfU6WQwlfH43Hs33QAwNjg5vhusC9MjY0Yv08185Im7pM25z1xTEKhEBwORy+uSVHsqlwTl8tF7969kZ6eDg8PD1y5cgWurq4QCAQ6e03idFmx1/WaANnP7fqgtAE2atRILScUw+fz8fjxY0ybNo1OY7PZ6Ny5Mx4+fChzn6CgIJw+fZqeEYDH4+HGjRu1VsvKIz4+nv6/g4MD2Gw2Xr58KVH6dHFxgaurK1JTU1FY+G71bzc3Nzg5OSEpKUliiQ4vLy/Y2toiLi5O4ib6+vrCxMQEsbGxEjEEBgaCz+cjISFBQoOgoCAUFhYiJSWFTjczM4Ofnx/y8vLw8uVLOt3GxgatWrVCVlYWMjMz6XRHR0e4u7uDy+XW+5oCAwO1+prM7Rrhh0vpuP8iD2wAk4KsMaAFH0UFb2DagPcpPj5eo/dJ2/Peq1ev0LRpU726JnXcp88++wypqanw8PDAli1bkJubS59DV69J3ffpzZs3AN49t62t6z5pRXWUmguUCbKzs9GtWzccOnQIQUFBdPqaNWvw4MEDHDlyROZ++/btw5o1a0BRFKqqqvDxxx9j6dKlSp9XPIdc27Zt6TeOiooKWFhYgKIonXgTasi3OxaLhYqKChgbG0v0ntSWa0p+VYxP98WAl18GK1MjbBndDt1aNZJ7TUyUAMvLy2FmZgYWi6XXb+F1uSaKolBeXg5zc3NSApRxTZmZmZg2bRo2btwIT09PpX5n2n5N8mKvyzUJBAKUlZXRvzGBQID4+PiGmQtUW7h37x527NiB77//HgEBAeByuVi5ciW2bduGWbNmqXQsDodDC5uUlCRXSE2kix+kNaltHIyq6crGIhAIkJCQUKs+mryma09eYe6fD1FUUYUWDhbYM7GDzOnMmL5PLBYLT548kdCooe+TOtPVfZ8EAgGtj7piVDVdW35PYqqqquiOe82aNcPp06cRGxtba5y6cE1M3icAUr8xdaCxkan29vbgcDhSHV5yc3Ph5CS7u/rPP/+MIUOGYOTIkfD29kafPn0wb948/Pbbb2SuQQOCoijsufUc4b8/QFFFFd73cMCpWV3qNZcngdBQ8Hg8tG3bFvv27dN0KAaPxgzQxMQEfn5+uHPnDp0mFApx584diSrR6pSXl0u9rYjfBjRUk0toYCoFQnx14j8sO5sAIQV81KE5/gh/H/aWZKAwQfsRT2ydnJyMFStWoKKiQtMhGTQarQKdNGkSFi1aBH9/fwQEBGDv3r0oKyujxxMuXLgQjRs3plej6NmzJyIiIuDr60tXgf7888/o2bNnvYrFZIom+WiLPm9K+Zix/1/cSc0FiwV8HeaD8BAPrZjLU1s00laIPtKrOly5ckVi/DLRSD5M6FMnA3zw4AHMzMzQtm1bOi0+Ph7l5eXo2LGj0scJCwtDXl4eNm/ejJycHPj4+GDXrl10FWhmZqbERc+YMQMsFgubNm1CdnY2HBwc0LNnT8ybN68ulwFAVIKsrcRJ0B59nuUUY8reaDx/XQJLEw42jw5CL5/Gmg4LgPZopK0QfRQvaUQ0kg9T+tSpF2ibNm3g6emJqKgoOm3AgAF48eIFEhMT1Rqguqm5kjBFUSgsLISNjY1WlCS0DW3Q51bya8w8EIPC8io0tTPH7okd0KaJjUZikYU2aKTNGLo+yqznZ+gaKaKmPupaEb5OZcorV64gIiJCIu3333/H5cuX6xyIphAKhUhJSSGdaGpB0/r8cfclPom4j8LyKrznZo9Ts7tolfkBmtdI2zF0fQ4cOKBwhhdD10gRTOlTpyrQpk2bSqU1bqwd1VEE/aBKIMTyswnYe0c0oHd4UFP8OLwtzIzJZMoE3WLRokUAgLFjx5LpzbQMnRoHSDAMCsoqMfvPf3Ez+TUAYGF/b8zo3pJUDRF0hoyMDDg4ONADtxcvXqzpkAgyUMoAO3bsqPTD5/79+/UKSBOYmZlpOgStpiH1efG6BOF7H+BZTgnMjTnY+FEg+vs3abDz1xWSh+RjSPqI2/y8vb1x/Phxpa/dkDSqC0zoo5QBfvXVV2o/sbbA4XDg5+en6TC0lobU525qLqbvj8Gb0kq42Jph54QO8G9q2yDnrg8kD8nHkPSp3uEFEC0U7urqqnA/Q9KoLjClj1IGOGzYMLWfWFsQCoXIy8ujJ8QmSNJQ+vz1gIuvT/yHKiGFds3tsHP8e3C20Y03YpKH5GMo+sjq7amM+QGGo1FdYUqfOh2Jy+Vi48aN+OKLL+ipzG7cuIHk5GS1BdZQUBSFly9fkplkaoFpfQRCCivOJmDRsXhUCSkMCnDBX1P/pzPmB5A8pAhD0EeZoQ7yMASN6gNT+qhsgPfv38fgwYMRFxeHixcvorS0FIBootItW7aoNTiCflNUXolP90Vj163nAIB5vVtjy+gg0tOToFPU1/wImkNlA1y/fj0+//xzREREwNjYmE7/3//+J7XuE4FQG7y8Unz46x1cTXoFUyM2to4Jwme9W5GengSdIzMzEzk5OcT8dBCVh0E8ffoU69atk0p3cHBAfn6+WoJqaGxstGtgtbahbn2iX+Rh2h8xyC3hw9naFDsndEC75nZqPUdDQ/KQfPRZn+DgYFy+fBkuLi71Mj991kgdMKGPygZobW2NnJwcqRudmJiok4PhORwOWrVqpekwtBZ163P83zQsPhYPvkAIP1cb7PqkA1xszdV2fE1A8pB89FEfHo+H3Nxceo3D4ODgeh1PHzVSJ0zpo3IV6MCBA7Fu3Trk5OSAxWJBKBQiJiYGq1evxtChQ9UeINMIhUJkZGSQKYhqQV36CIUU1pxPwheHH4EvEKK/XxMcmd5J580PIHlIEfqmj7jNLzQ0VG3NPvqmkbphSh+VDXDevHnw9PREjx49UFpaioEDB2LcuHEICgrCjBkz1BpcQ0BRFDIzM0nvq1pQhz4lFVWYvj8Gv1x/BgCY3dMLv4xtDwsT/ZiIiOQh+eiTPtU7vNjb28PR0VEtx9UnjZiAKX1UfgKZmJhgxYoVmDlzJpKTk1FSUgJfX1+4u7urNTCCfpDxpgxT9kYjIbMQJhw2Vn/YFsOCmmk6LAJBZUhvT/2jzq/grq6ucHFxAQDSc48gk4fcfHy6LwaviyvgZGWCHeM74D03e02HRSCoDDE//aROBnjkyBHs3bsXL168AAC4u7vjk08+wciRI9UZW4PAYrHg6OhITLwW6qrP6UcZWHDkEfhVQrRpYo1dn3RAM3sLhqLULCQPyUfX9UlPT2fc/HRdI6ZhSh+VDfDnn3/G77//jnHjxtE9oGJjY/Hjjz8iIyMDn332mVoDZBo2m02qb+Wgqj5CIYVNV5Kx+YpoVqDePs7Y9HEQrEz1o71PFiQPyUfX9bG3t4ebmxsAMFby03WNmIYpfVR+Kh08eBDLly/HoEGD6LRevXrB29sby5cv1zkDFAqF4HK5aNGiBZmDTwaq6MPLK8VXJ+LpZYymdfPEwv5twGHr91styUPy0XV9LCwscPbsWeTn58tcC1Ud6LpGTMOUPiofqaqqCv7+/lLpfn5+EAgEagmqIaEoCrm5uaT3VS2oos9nhx7iZvJrGLFZWDMiAEvCfPTe/ACShxShi/pwuVxs2rSJjtnCwoIx8wN0U6OGhCl9VDbADz74AAcPHpRKP3z4MAYPHqyWoAi6x5OsIvzLfQMA+G3CexjVkXQQIOgmXC4XPXv2xLx58/Dzzz9rOhwCgyhVBbpq1Sr6/ywWC0eOHMHt27fRrl07AEBcXBwyMjJ0ciA8of68KirH9P0xAIC+vo0R2kb3ZgQiEIB35ifu8DJixAhNh0RgEKUMMCEhQeKzeGFCLpcLALCzs4OdnZ1OLofEYrHg4uJCel/VgiJ9KqoEmLD7Pp6/LkFTO3P8MMTwFvUkeUg+uqJPTfNryKEOuqKRpmBKH6UM8I8//lDrSbUJNput9KKVhogifQ5HpyEpqwiOliY4MOV9uNrp/tRmqkLykHx0QR9Nmh+gGxppEqb0MfjuRgKBAMnJyTrZgachkKdPpUCI7W+nN5sT6gV3J8uGDk8rIHlIPtquT1lZGUJDQzU6yF3bNdI0TOlTp8FZ8fHxOHfuHDIzM1FZWSnx3datW9USWENSWFio6RC0mtr0OfFvOtLflMHJyhQfB7do4Ki0C5KH5KPN+pibm+PLL7/EunXrcPXqVY3N8KLNGmkDTOijcgkwMjISo0ePRmpqKi5duoSqqiokJyfj7t27sLa2VnuABO2kSiDEtuspAICp3TzIKu4EnWbatGmIi4sj05sZGCob4Pbt27FkyRJs374dxsbG+Prrr3H+/HkMGDCAnhuUoP+cjcvEy9xS2FsYY+z7bpoOh0BQCR6Ph2HDhuH169d0mrm54bVfGzoqGyCPx0P37t0BiFaGKC0tBYvFwsSJE3H48GG1B8g0LBYLbm5upPdVLcjSRyiksPWaqPQXHuIBSz2e5kwZSB6Sj7bpI57Y+uTJk/j00081HQ4A7dNI22BKH5UN0MbGBiUlJQAAZ2dneuhDYWEhysrK1BpcQ8Bms+Hk5ESmH6oFWfqc+y8LKa+KYWNmhAmd3TUXnJZA8pB8tEmfmqs6bN68WdMhAdAujbQRpvRR+WgdO3bEP//8AwDo378/Vq5ciW+++Qbz589Hp06d1BpcQyAQCPD48WPS+6oWaupDURS2XBW99Ezs4gEbM2NNhqcVkDwkH23RR5uXNNIWjbQVpvRRue7q22+/RUVFBQBgxowZMDY2xr///ou+ffvq5IrwAFBeXq7pELSa6vpcTnyFpKwiWJpwMLmLu+aC0jJIHpKPpvXRZvMTo2mNtB0m9FHZAO3s7Oj/s9lsTJ06VZ3xELSY6qW/8Z3cYWdhouGICATl+OSTT7Ta/AiaQSkDLC4uVvqAVlZWdQ6GoN38nfwacWkFMDNmY0pXD02HQyAoza5duzBlyhTs3buXmB+BRikD7NChg8LeNxRFgcViITExUS2BNRRsNhteXl6k8bkWxPqwWCxsebvI7dj33eBkZarhyLQHkofkoyl9qqqqYGQkesR5enri6tWrDXp+VSB5SD5M6aOUAe7bt0+tJ9UmWCwWbG1tNR2G1iLW559nrxH9Mh8mRmxM7eap6bC0CpKH5KMJfXg8Hvr27YvVq1djyJAhDXruukDykHyY0kcpAwwODlb7ibUFgUCAuLg4BAQEgMMhs5nURKzPlgeiBuiPOjRHYxszDUelXZA8JJ+G1qd6h5dFixYhLCyMLglqKyQPyYcpfUh5G4BQKNR0CFpNwqty3EnNgxGbhek9Wmo6HK2E5CH5NJQ+NXt7Xrx4UevNTwzJQ/JhQh9igASFHE0UdYIa0b4ZmhrgckcE3UAXhjoQtAtigAS5xKUV4GEWHxw2CzN7ktIfQTsh5keoCwZvgGw2G76+vqT3VS38ciMVADCknSvcHA1zvT9FkDwkn4bQZ/v27TptfiQPyYcpfepUOV5VVYX79++Dy+Vi0KBBsLKyQnZ2NqysrGBpqXsPSRMTMqBbFomZhbiUkA0WC5hF2v7kQvKQfJjWZ/ny5QCA6dOn65z5iSF5SD5M6KOynaanp2Pw4MGYOXMmli1bhvz8fADAzp07sXr1arUHyDRCoRCxsbGkAVoG4hUfOjU1g4eThYaj0V5IHpIPU/pkZ2ejqqoKgKiEsHLlSp01P5KH5MOUPiob4MqVK+Hv74/79+/D1PTdYOg+ffrg7t27ag2OoDlSXhUhKj4TADDCR/dK9QT9hsfjoXPnzpgwYQJtggSCqqhcBRoTE4ODBw9KFUebNm2K7OxstQVG0Czbrj0DRQG9fZzhbkfaJQjaQ/UOLwCQm5uLxo0bazgqgi6i8pNNKBTKLIZmZWXpZPsfQZqXuSU4FZsOAJhNen4StAhZvT2J+RHqisoG2KVLF+zdu1ciraSkBFu2bKFXitcl2Gw2AgMDSe+ravxy7RmEFNDDuxHaNbcn+iiA5CH5qEsffR7qQPKQfJjSR+WjLV68GP/++y/CwsLA5/OxYMEChIaGIjs7GwsWLFBrcA0Fn8/XdAhaQ1p+KY79mwYAmBPqBYDoowxEI/nUVx99Nj8xJA/Jhwl9VDbAJk2a4NSpU5g2bRo++eQT+Pj4YMGCBTh58iQcHR3VHiDTCIVCJCQkkN5Xb9lxIxVVQgqdWzriPTcHoo8SEI3kow59kpOTkZ6errfmR/KQfJjSR+VOMBUVFTA1NcUHH3yg1kAImie7sBx/RfMAAHNCW2k4GgLhHaGhoYiKikKrVq30zvwImkPlEmCnTp2waNEi3L59m7yt6Bk7bqSCXyVEBzd7/M/TQdPhEAwcLpeLp0+f0p9DQ0OJ+RHUisoGuHr1apSVlWHmzJno1q0bVq5cifj4eCZiazBIwzPwurgCf95/CQCY06uVxALIRB/FEI3ko6o+XC4XPXv2RI8ePSRMUJ8heUg+TOjDoiiKqsuOxcXFuHDhAiIjI3H37l00b94cgwcPxuzZs9Udo1oRCASIjY1FYGAgWXerGj+dS8L2G8/QrpktTs7qImGABEJDIjY/fe7wQqgf6nqO19lSraysMGLECOzZswenT5+Gubk5tm3bVudANAVFUSgoKEAd3wP0gvwSPv648wIAMDtUsvRH9FEM0Ug+quhjqOZH8pB8mNKnzgZYUVGBqKgozJw5E8OGDUNBQQHCw8PVGVuDIBQKkZKSYtDtmRH/vEAJXwAfFxv09nGW+I7ooxiikXyU1cdQzQ8geUgRTOmjci/Qmzdv4uzZs7h8+TKMjIzQr18/7NmzBx07dlRrYISGobC8EhG3nwMQjfsjVZ8ETZCWlmaw5kfQHCob4OzZs9GjRw+sXr0a3bt3h7GxMRNxERqIff+8QFF5FbycrdDfr4mmwyEYKJaWlnBwEPU8JuZHaChUNsDbt2/DysqKiVg0hpmZmaZD0AglFVXYfUtU+pvd0wtstuzSn6HqowpEI/ko0sfe3h4XL15ESUkJmjVr1kBRaRckD8mHCX2UMsDi4mIJ0ysuLq51W10zRw6HAz8/P02HoREO3HuJ/NJKuDtaYFCAi8xtDFkfZSEayac2fXg8Hi5duoTJkycDEJmgvb19Q4enFZA8JB+m9FHKADt27Ihbt27B0dERHTp0kNlORFEUWCwWEhMT1R4kkwiFQuTl5cHBwcGgxuGUVwrw29+i0t/Mnl4w4si+dkPVRxWIRvKRpU/NJY3EJmiokDwkH6b0UcoA9+7dC1tbWwDAvn371HZybYCiKLx8+dLg3jwP3efidXEFmtqZY1hQ01q3M1R9VIFoJJ+a+tSc2LpPnz4ajlDzkDwkH6b0UcoAg4OD6f83a9YMLi4uUqVAiqKQmZmp1uAIzFBRJcD2G6I37xk9WsK4ltIfgaBuDGFVB4LuoPKTr1evXsjLy5NKf/PmDXr16qVyAAcOHEBoaCjatm2LkSNHIi4uTu72hYWFWLp0KUJCQuDv749+/frhxo0bKp/XkDkak4aswnI0tjHFyA6G2eGA0PAQ8yNoGyr3AhW39dWktLQUpqamKh0rKioKq1atwtKlS9GuXTvs3bsX4eHhOH/+vMyllfh8PiZNmgRHR0f8/PPPaNy4MTIyMmBjY6PqZUhQ3/11iUqBEL9efwYAmNatJUyNFE8jZEj61BWikXzYbDZ69epFzE8OJA/Jhwl9lDbAVatWAQBYLBY2bdoEc3Nz+juBQIC4uDi0adNGpZNHRERg1KhRGDFiBABg6dKluH79Oo4dO4apU6dKbX/s2DEUFBTg0KFD9PjD+naZ5nA4aNXKcJb+OfkwHWn5ZXCyMsHo4BYKtzc0feoC0Ug+HA4HQUFBCA8Px+7du4n5yYDkIfkwpY/SBpiQkABAVAJ8+vSpxAB4ExMTtGnTRqWeXHw+H48fP8a0adPoNDabjc6dO+Phw4cy97l69SoCAwOxbNkyXLlyBQ4ODhg0aBA+/fRTlSdEFQgEAES9i169egUXFxf6+sSwWCyw2Wx6W0XpbDYbLBZLZrr4XMqkczgcUBQlM10oFErNhycrXRxj9XSBkMK2aykAgPAQD5hw3ulQ2zUBQHZ2Nho1aiTR+0pbrkle7A11n6qqqpCdnY3GjRuDzWbrxTWp8z4JhUJkZ2dj4cKFmDNnDiwsLCTi1MVrUhS7qtcEAFlZWXB2dlbqd6YL16TO+1RVVYWsrCz6NyZLv7qgtAH+8ccfAIAlS5bg66+/rvd4v/z8fAgEAqmqTkdHR7prdE14PB7u3r2LwYMH47fffgOXy8XSpUtRVVWl8ioUNZdwatKkCXg8HnJzc+k0FxcXuLq6IjU1FYWFhXS6m5sbnJyckJSUhPLycjrdy8sLtra2iIuLk7iJvr6+MDExQWxsrMQ5AwMDwefz6ZcLQJQ5goKCUFhYiJSUFDrdzMwMfn5+yMvLw8uXL+l0GxsbtGrVCllZWRKdkBwdHeHu7g4ul0tf001uGV7klsLOwhidGwkk4qntmjw9PZGZmYns7GytvCZAe+5Tdna23l0TUPf7dPfuXaxYsQKLFy+GpaUlAFGNTXJyss5eE1P3ydvbG1lZWcjKytKba1L3fcrOzkZ2djYAwNraGuqgzssh1Zfs7Gx069YNhw4dQlBQEJ2+Zs0aPHjwAEeOHJHap1+/fqioqMCVK1foEl9ERAR2796NW7duKXVe8TIabdu2BYfDgUAgQHx8PAIDA8FisbT+Tajub+EUwrbcRvKrYszv0xoze3gqdU0UReHRo0e0Xtp0TYpib6j7VFlZifj4eFojfbim+t6n9PR0usPL6NGjMX/+fAQEBMDY2Fhnr4nJ+6Tq70wXrkmd96myshJxcXEyn9v1WQ5JqRLg7Nmz8dNPP8HKykphSWvr1q1Kndje3h4cDkfizQMAcnNz4eTkJHOfRo0awcjISOKCPT09kZOTAz6fDxMTE6XODYB+UFWntgGWtQnMZDqLxZKZXluMitIvJmQi+VUxrM2M8EkXd6VjEWdqWXrVFntt6eq+pvrEoq706tdUXSN9uSZlYqyZXrO3548//ojc3Fx6O128JkXp9Y2xLr8zbb+muqQruqba9KkrSg2DqF7ctLa2lvunLCYmJvDz88OdO3foNKFQiDt37kiUCKvTvn17cLlciTeEFy9eoFGjRiqZX3VYLBYcHR31ehUEiqKw5aqoWmFiZ3fYmCk/gbkh6FNfiEbvkDXUoUWLFkQfBZA8JB/G9KE0SGRkJOXv708dP36cSklJob799luqQ4cOVE5ODkVRFPXll19S69ato7fPyMiggoKCqGXLllGpqanUtWvXqE6dOlG//PKL0uesqqqioqOjqaqqKrVfj7ZyOSGLclt0lvL99hyVV1yh6XAIegqXy6U8PT0pAJSnpyfF5XI1HRJBT1HXc1zlcYDl5eWgKIoeBpGeno5Lly7By8sLISEhKh0rLCwMeXl52Lx5M3JycuDj44Ndu3bRVaCZmZkSxXMXFxfs3r0bq1atwpAhQ9C4cWNMmDABn376qaqXQSMUCsHlctGiRQu9nIOPoihsflv6G9fJDfaWqpWU9V0fdUA0EuWzUaNGyRznR/RRDNFIPkzpo7IBzpw5E3369MHo0aNRWFiIkSNHwtjYGPn5+Vi8eDHGjBmj0vHGjRuHcePGyfxO3PO0OkFBQTh8+LCqYdcKRVHIzc3V23FJN5Nf4xHvDcyM2ZgS4ql4hxrouz7qgGgkqqLauXMnPv30Uxw+fFhCC6KPYohG8mFKH5Wt9PHjx+jQoQMA4MKFC3BycsK1a9ewevVqmYZF0Cxb35b+Rge3QCNr1WbqIRAUUb093t/fH//88w95iBN0BpUNsLy8nB7Tc+vWLfTt2xdsNhuBgYHIyMhQe4CEunM3NRf3X+TBhMPGtG4tNR0OQc/g8XgICgqSmIuXdOIg6BIqG2CLFi1w+fJlZGZm4tatW+jSpQsA0fAFXVsMFxD9YGWtbqEPbLmaDAAY2aEZmtjWbTVlfdZHXRiiRuLennFxcZg7d67U2K3qGKI+qkI0kg9T+qhsgLNmzcKaNWsQGhqKgIAAesjC7du34ePjo9bgGgI2mw1XV1e9a3iOeZmP2ym5MGKzMKNH3Ut/+qqPOjE0jWoOdTh79qzcazc0feoC0Ug+TOmjcieY/v3747333kNOTo7E5NedOnVC79691RpcQyAQCOgfsjoHWGqarW9Lf8PbN0Uze4s6H0df9VEnhqRRXZY0MiR96grRSD5M6aOyAQKiGVkaNWpEz1vXpEkTBAQEqC2ohqb6fHf6QHxaAa49yQGbBczs4VXv4+mbPkxgCBrVZz0/Q9CnvhCN5MOEPioboFAoxC+//IKIiAiUlpYCACwtLTFp0iTMmDGDFOG1gK3XRKW/Ie1c4e5kqeFoCPrC2rVryXp+BL1CZQPcuHEjjh49ivnz56N9+/YAgJiYGGzduhV8Ph/z5s1Te5AE5UnKKsSFx9lgsYDZofUv/REIYtatWwcA+PLLL4n5EfQClQ3wxIkTWLFiBXr16kWntWnTBo0bN8bSpUt1zgBZLBbc3Nz0pveVeNxfmL8LvJzrv2SIvunDBPqs0evXr+k5GE1MTLB582aVj6HP+qgLopF8mNJH5frKgoICeHpKzyji6emJgoICtQTVkLDZbDg5OelF1e2znGJExovW+5rVUz2lP33Shyn0VSMul4v3338fs2bNklpeRxX0VR91QjSSD1P6qHy0Nm3a4MCBA1LpBw4ckOgVqisIBAI8fvxYbSsMa5Jt11JAUUBvn8bwdbVRyzH1SR+m0EeNuFwuevbsidTUVFy4cEFq2TJV0Ed91A3RSD5M6aNyFeiXX36JadOm4Z9//kFgYCAAIDY2FpmZmdi5c6dag2soqq9urKtwc0txKlY0E88cNbf96YM+TKNPGlU3P3GHl9rW6FQWfdKHKYhG8mFCH5VLgMHBwTh//jz69OmDoqIiFBUVoU+fPjh//jw9Ryih4fn1RgoEQgrdWjdCu+Z2mg6HoKPIMj/S4YWgr6hUAkxLS8M///yDyspKDBw4EK1bt2YqLoIKpL8pw9GYNADAXNLzk1BHiPkRDA2lDfDu3buYPn06XQw1MjLCypUr8cEHHzAWXEPAZrPh5eWl043PO248Q6WAQidPR3Rwd1DrsfVBH6bRF41iY2Px8uVLtZufvujDJEQj+TClj9JH+/nnn9G5c2f8/fffuHfvHkaOHIm1a9eqNRhNwGKxYGtrq7Pdj18VluPQAx4A9bf9AbqvT0OgLxoNGTIEx48fV3vJT1/0YRKikXyY0kdpA0xOTsYXX3wBZ2dn2NraYuHChcjLy0N+fr5aA2poBAIBHj58qLO9r377OxX8KiHec7NHp5aOaj++ruvTEOiyRjweDzwej/48ZMgQtVd76rI+DQXRSD5M6aO0ARYXF8Pe3p7+bG5uDlNTUxQXF6s1IE0gbykXbSa3uAIH7nEBiEp/TL096qo+DYkuaiSe27NHjx4SJsgEuqhPQ0M0kg8T+qjUCebmzZuwtn43uwhFUbhz5w6ePn1Kp1WfIYbALLtvPUdZpQABzWzRvXUjTYdD0CFqTmxNIBgiKhng4sWLpdK+++47+v8sFguJiYn1j4qgkDelfOy78xIAMLsnc6U/gv5Rn1UdCAR9QmkDTEpKYjIOjcFms+Hr66tzva8ibr9AcUUV2jSxRm+fxoydR1f1aUh0SSNNmJ8u6aMpiEbyYUofojYAExMTTYegEkXllYi4/RyAaMUHNpvZ0p+u6aMJdEEjTZb8dEEfTUM0kg8T+ihlgLGxsUofsKysDMnJyXWNp8ERCoWIjY3VqQbofXdeorC8Ci0bWWKAvwuj59JFfRoaXdHIyMgIJiYmDW5+uqKPJiEayYcpfZQywIULFyI8PBznzp2jF8GtSUpKCjZs2IA+ffrg8ePHag2S8I5SfhV233pX+uMwXPoj6A8uLi64evUqafMjEN6iVBtgZGQkDh48iE2bNmHBggVwd3eHs7MzTE1NUVBQgNTUVJSWlqJPnz7YvXs3vL29mY7bYPnzHhd5JXy4OVpgcICrpsMhaDk8Hg93797FyJEjAYhMkEAgiFDKAI2NjTFhwgRMmDAB8fHxiImJQUZGBsrLy+Ht7Y2JEyfi/fffh52dHcPhGjbllQLs+DsVADCzR0sYcUgTLqF2xG1+z5+LagzEJkggEESwqPqsdKmDCAQCxMbGIjAwEBwOBxRFQSgUgs1ma/1Qgr3/vMD3px+jqZ05ri3oARMj5g1Ql/TRFNqokTYNddBGfbQNopF8aupT8zleV0gRAgCfz9d0CArhVwmx/cYzAMD07p4NYn70uXVAH02jTRppk/mJ0SZ9tBWikXyY0MfgDVAoFCIhIUHre18d+zcNmQXlcLY2xcgODfcw0xV9NIk2aaSN5qdN+mgrRCP5MKWPwRugLlApEOKX6ykAgGndW8LMuO5FfoL+kp+fr3XmRyBoM8QAdYDTsRng5ZXB0dIEY4JbaDocgpZiZ2eH4cOHE/MjEJSkXgZYUVGhrjg0ijZPPyQQUth2TVT6m9LVE+YmDV/602Z9tAVt0IjFYmHNmjWIjo7WOvPTBn20HaKRfJjQR+UjCoVCbNu2DV27dkVQUBC9jMqmTZtw5MgRtQfINBwOB0FBQfXqScQkkfGZSH1dAjsLY4zv5Nbg59d2fbQBTWrE4/Ewffp0lJeXAxCZYPVly7QBkocUQzSSD1P6qGyAv/zyC06cOIEvv/wSxsbGdHrr1q1x9OhRtQbXEFAUhYKCAmjjaBChkMK2q6LS3+QuHrAyVWnxDrWgzfpoC5rSSNzhZceOHfj8888b9NyqQPKQYohG8mFKH5UN8NSpU1i+fDmGDBkiUST19vZGamqqWoNrCIRCIVJSUrSy99XFhGw8yS6CtakRPunsrpEYtFkfbUETGtXs7fn111832LlVheQhxRCN5MOUPiobYHZ2Nlq0kO6IQVEUqqqq1BIUQaTnlquiScU/6ewOW3NjBXsQDAVtHOpAIOgiKhugl5cXoqOjpdLPnz8PHx8ftQRFAK4/ycHjjEJYmHAwOcRD0+EQtARifgSC+lC5UWnmzJlYvHgxsrOzQVEULl68iOfPn+PkyZPYsWMHEzEyjpmZmaZDkICiKGx+W/ob9z83OFhqdp0wbdNHG2kIjYRCIT744AOdND+ShxRDNJIPE/rUaS7Q6OhobNu2DUlJSSgtLYWvry9mzZqFkJAQtQeobtQ1hxyT3Ep+jXG778HUiI2bi3rC2Zr8MAgi7t69i5kzZ+LUqVM6Y34EgrpR13O8Tt0KO3TogIiIiDqfVJsQCoXIy8uDg4OD1ozDEbf9jQ5uoXHz00Z9tA2mNaIoip4g+X//+x+io6N16l6QPKQYopF8mNJH5SP16tUL+fn5UumFhYXo1auXWoJqSCiKwsuXL7Wm+/H953m49zwPJhw2pnX31HQ4WqePNsKkRlwuF8HBwfj333/pNF17QJI8pBiikXyY0kflX1J6errMrqh8Ph/Z2dlqCcqQEZf+PuzQDC625hqOhqBJuFwuevbsiejoaEybNo08HAkENaN0FeiVK1fo/9+8eRPW1tb0Z6FQiDt37qBp06bqjc7AeMjNx83k1+CwWZjRvaWmwyFoELH5iTu8HD9+nKwTRyCoGaUNcNasWQBEUy0tXrxY8iBGRmjatKlUuq5gY2Oj6RAAAFvfzvoyLKgpmjtYaDiad2iLPtqMOjWqaX661NuzNkgeUgzRSD5M6KNyL9DQ0FAcPXoUDg4Oag+mIdDWXqD/pRdg0JZbYLOAK/N7wMPJUtMhETSAPpofgaBuNLYi/NWrV3XW/GQhFAqRkZGh8SmIxCs+DG7nqlXmpy36aDPq1OiHH37QO/MjeUgxRCP5MKVPnYZBlJaW4sGDB8jIyEBlZaXEdxMmTFBLYA0FRVHIzMxE48aNNRbD0+winPsvCwAwq6eXxuKQhTboo+2oU6OtW7cCAJYuXaoX5geQPKQMRCP5MKWPygaYkJCAqVOnoqysDGVlZbC1tUV+fj7Mzc3h4OCgcwaoDYjb/gb4N0HrxtYKtiboGwUFBbCxsQGLxYKFhQX27Nmj6ZAIBINA5SrQVatWoWfPnnjw4AFMTU1x+PBhXLt2DX5+fli0aBETMeo1qTnFOBuXAQCYHapdpT8C8/B4PLRv3x7fffcdGeZAIDQwKhtgYmIiJk2aBDabDQ6HAz6fDxcXF3z55ZfYsGEDEzEyCovFgqOjo8a6mP9y/RmEFNCrjTP8XG01EoM8NK2PLlBXjapPbP3nn3+isLCQoQg1C8lDiiEayYcpfVQ2QCMjI3omCkdHR2RkiEovVlZWyMrKUmtwDQGbzYa7u7tGZtfg5ZXixMN0AMCcXq0a/PzKoEl9dIW6aCRrVQdbW+17AVIHJA8phmgkH6b0Uflovr6+iI+PBwB07NgRmzdvxunTp/Hjjz+iVSvtfIjLQygU4sWLFxrpffXrjWcQCCl0beWEwOZ2DX5+ZdCkPrqCqhoZ2pJGJA8phmgkH6b0UdkA582bh0aNGtH/t7GxwQ8//ID8/HwsW7ZMrcE1BBRFITc3t8HbXzILynA0Og0AMCdUe18cNKWPLqGKRoZmfgDJQ8pANJIPU/qo3Au0bdu29P8dHR2xe/dutQZkKOy4kQq+QIj3PRwQ7KE/4yoJ8rlx44ZBmR+BoM2orUL18ePHmDZtmroOp9e8KirHwftcAMBcLW37IzDDuHHj8McffxDzIxC0AJVKgDdv3sQ///wDY2NjjBw5Es2bN8ezZ8+wfv16XLt2TScWxK0Ji8WCi4tLg/a+2nXzOSqqhAhqYYfOLR0b7Lx1QRP66BqKNOLxeDA3N4eTkxMAkQkaEiQPKYZoJB+m9FHaAI8cOYJvv/0Wtra2KCwsxJEjR7B48WKsWLECAwYMwNmzZ9Gype6tYMBms+Hq6tpg58sr4WP/3ZcAgLmhrbQ+wze0PrqIPI3EbX6Wlpa4evUqbYKGBMlDiiEayYcpfZSuAt23bx8WLFiAe/fuYdOmTcjPz8fBgwdx5swZLFu2TCfNDxBNqpqcnAyBQNAg59tz6zlK+QK0bWqLHt6NGuSc9aGh9dFFatOoeoeXkpISlJWVaShCzULykGKIRvJhSh+lDZDH46F///4AgL59+8LIyAhffvklmjRpotaANEFDDUAuKKvE3n9eABDN+qLtpT8x+jpAW53U1MgQe3vKg+QhxRCN5MOEPkobYHl5OczNRSuUs1gsGBsbw9nZWe0B6TN7/3mBoooqeDe2Rh8fMumtvkLMj0DQDVTqBHPkyBFYWIgWahUIBDh+/Djs7e0ltiGTYdfO8X9F4/5m9mwJNls3Sn8E1SDmRyDoDkoboKurKw4fPkx/dnJywqlTpyS2YbFYOmeALBYLbm5ujFdHvioqx4vcUrBYQA9v3Sk5N5Q+ukx1jaqqqlBVVUXMrxokDymGaCQfpvRR2gCvXr2q1hNX58CBA9i9ezdycnLQpk0bfPvttwgICFC4X2RkJL744gv06tULv/zyS53OzWazG6RnXvSLfABAmyY2sDU3Zvx86qKh9NFlqmvk4eGB69evw8jIiJjfW0geUgzRSD5M6aPxmVejoqKwatUqzJo1CydOnECbNm0QHh6O3NxcufulpaVh9erV6NChQ73OLxAI8PjxY8Z7Xz14kQcA6Ohur2BL7aKh9NFlXrx4gR07dtAaeXh4EPOrBslDiiEayYcpfeq0Irw6iYiIwKhRozBixAgAopWwr1+/jmPHjmHq1Kky9xEIBFiwYAHmzJmDmJiYOvUOEgspEAhQXl4OQDThavW55lgsFthstpTotaWz2WywWCyZ6dFvDbB9Czv6e/HM5jUneOVwOKAoSmZ6zRhrSxfHWN9roigK5eXlMq9JVuy6cE3y7pOq18TlchEaGgoejwdXV1eEhYXp/DWp+z6Jf2NCoRAcDkcvrklR7Kpek6q/M124JnXfp+r6qMsINWqAfD5fago1NpuNzp074+HDh7Xut23bNjg6OmLkyJGIiYmp07nFK1pUh8vlSpQ8XVxc4OrqitTUVAmTdXNzg5OTE5KSkmjzBAAvLy/Y2toiLi5O4ia6tWyNxxmi/S2KMxAbmw0ACAwMBJ/PR0JCAr0tm81GUFAQCgsLkZKSQqebmZnBz88PeXl5ePnyJZ1uY2ODVq1aISsrC5mZmXS6o6Mj3N3d631Nnp6eAERT3VW/Jl9fX5iYmCA2NlZCQ124ptruk6rX5OTkhO7du+PFixdo2rQp2Gw2kpKSdPqamLxP2dnZaNasmV5dk7ruk7e3NwDp55IuX5M671N+fr6EPtbW1lAHLEqD049nZ2ejW7duOHToEIKCguj0NWvW4MGDBzhy5IjUPtHR0fjiiy9w8uRJODg4YPHixSgsLFS6DVAgECA2NhZt27al30bj4+MRGBgIFovFyJvQ7We5mLDnAZrZm+PGgu4S2wPa/XZHURQePXpE66Uodl24JnW8sfJ4PPTq1Qupqanw8PDAli1b0KdPH3A4HJ29JnGMTJQA4+PjERAQAGNjY724JkWx16UEqMrvTBeuSZ33qbKyEnFxcTKf29X1UhWNV4GqQnFxMRYuXIjly5fDwaF+KyiIH1RsNhteXl70Taxt2/qkR798AwDo6O4gcx9ZaSwWS2Z6bQtCqpqubOwURcHLywvGxsYy9VFFG225pvqmVzc/T09PXLt2Dba2thIa6do1VUfd90n8GzMyMlJbjKqma3veq8vvTNuvqS7ptV2TkZGRXH3qSp0MkMvl4tixY+DxePj666/h6OiIGzduwNXVVaVFce3t7cHhcKQ6vOTm5srs8cPj8ZCeno4ZM2bQaeK3BV9fX5w/fx4tWrRQ6VpYLBbjK3E/eC7uAKN7yx41hD66RE5ODhnnpyIkDymGaCQfpvRRuRfo/fv3MXjwYMTFxeHixYsoLS0FADx58gRbtmxR6VgmJibw8/PDnTt36DShUIg7d+5IVImK8fT0xJkzZ3Dy5En6LzQ0FO+//z5OnjxZp2nZBAIBHj58yFjvq0qBEA95ovprXesBCjCvj67h6OiI7t27S5gf0Ug+RB/FEI3kw5Q+KpcA169fj88//xyTJk2SMKn//e9/2L9/v8oBTJo0CYsWLYK/vz8CAgKwd+9elJWVYfjw4QCAhQsXonHjxpg/fz5MTU3RunVrif1tbGwAQCpdFWrWOauTxxmFKK8Uws7CGC0bWTF2HiZhUh9dg81mY9euXcjNzUWjRu8mMycayYfooxiikXyY0EdlA3z69CnWrVsnle7g4ED31FGFsLAw5OXlYfPmzcjJyYGPjw927dpFV4FmZmbWWketC4irPzu4OZDpz3QUHo+HzZs3Y9WqVTAyMgKbzZYwPwKBoJuobIDW1tbIycmRavdITExE48Z1m+B53LhxtS4S+scff8jd96effqrTORsKXR0ATxBRfW5PAFi7dq2GIyIQCOpC5aLVwIEDsW7dOuTk5IDFYkEoFCImJgarV6/G0KFDGQiRWdhsNnx9fRkpZVIUheiXolJxBx3sAAMwq4+2U3Ni67lz58rczpA1Ugaij2KIRvJhSh+VjzZv3jx4enqiR48eKC0txcCBAzFu3DgEBQVJ9M7UJUxMTBg57rOcEuSV8GFqxEbbprrbw4spfbQZVVd1MESNVIHooxiikXyY0EdlAzQxMcGKFStw6dIl7NixA2vWrMG5c+ewdu3aeg1I1BRCoRCxsbGMNLCKpz8LbG4HEyPdfLNjUh9tRVXzM0SNVIHooxiikXyY0kflNsDo6Gh06NABrq6ucHV1VWsw+saDF+LhD7pZ/WmICAQCDBgwgIzzIxAMAJWLJRMnTkRoaCg2bNggMWcbQRq6A4wHMUBdgcPhYMOGDfDz8yPmRyDoOSob4N9//43Jkyfj/v37GDRoED744APs2rULWVlZTMSns2QXloObVwo2S7QCBEG7qT4XYt++ffHo0SNifgSCnqOyATo4OGDcuHE4dOgQLl26hP79+9MzsujaavCAqHdRYGCg2nsXVV8A19pMdxbArQlT+mgTPB4P3bp1w5MnT+g0VdqzDUGj+kD0UQzRSD5M6VOvozVv3hxTp07F/Pnz0bp1azx48EBdcTUofD5f7ccUV38G60H1JxP6aAviDi+3bt3ClClTpGbRVxZ91kgdEH0UQzSSDxP61NkAY2Ji8MMPPyAkJATz589Hq1atsGPHDnXG1iAIhUIkJCSovXeR2AA76PgAeKb00QZq9vb8888/6zTTvD5rpA6IPoohGsmHKX3qNBdoZGQkXr16hS5duuDrr79Gr169YG5urtbAdJmi8kokZooWkiQ9QLUTVYc6EAgE/UNlA3zw4AHCw8MxYMCAeq/Jp6/8y30DIQW0cLBAYxszTYdDqAExPwKBANTBAA8dOsREHBpF/R1g9KP6U4y+NczPnz9f7eanbxqpG6KPYohG8mFCH6UM8MqVK+jWrRuMjY1x5coVudv26tVLLYE1FBwOR+bag/Xh3QTYul9CZkIfTSNuq16/fr1azE8fNVInRB/FEI3kw5Q+ShngrFmzcPv2bTg6OmLWrFm1bsdisZCYmKi24BoCiqJQWFgIGxubOnWAqAm/SoiH3DcA9MMA1a2PpigpKYGlpSUAwN7eHocPH1bbsfVFI6Yg+iiGaCQfpvRRqkyZlJQER0dH+v+1/ema+QGi3kUpKSlq6130X0YBKqqEsLcwRstGlmo5piZRtz6agMfjoV27dti0aRMjx9cHjZiE6KMYopF8mNJH5UrVkydPyhyPwefzcfLkSXXEpNO8a/9zIG9yWoC4w8uzZ8+wdetWlJSUaDokAoGgJahsgEuWLEFRUZFUeklJCZYsWaKWoHSZ+8/FE2DrRwcYXaZmb89r167R1aAEAoGgsgFSFCWzZJOdnQ1ra2u1BNXQmJmpZ6iCUEgh5qX+dIARoy59GpKGHuqgixo1JEQfxRCN5MOEPkoPgxg6dChYLBZYLBY++eQTGBm921UgECAtLQ1du3ZVe4BMw+Fw4Ofnp5Zjpb4uRn5pJcyM2fBz1d0FcKujTn0aioY2P13UqCEh+iiGaCQfpvRR2gB79+4NAEhMTERISIhEVZKxsTGaNm2Kvn37qj1AphEKhcjLy4ODg0O9x5mIqz91eQHcmqhTn4YiMjKyQQe566JGDQnRRzFEI/kwpY/SBjh79mwAQNOmTREWFgZTU1O1BaFJKIrCy5cvYW9f/zY7cQeYYD2q/lSnPg3F9OnTAQADBw5skBledFGjhoTooxiikXyY0kflmWCGDRum1gD0iQcv3/UAJTQsaWlpsLW1pduhxSZIIBAItaGUAQYHB+P8+fNwcHBAx44d5Xbvv3//vtqC0yWyCsrByysDmwUEkQVwGxRxm1+TJk1w/vx5ne2MRSAQGhalDHDJkiWwsrKi/69v49tsbGzqfQzx9Ge+rrq9AK4s1KEPU1Tv8AIABQUFGjFAbdZIGyD6KIZoJB8m9FHKAKtXew4fPlztQWgSDoeDVq1a1fs49AB4N/2q/lSXPkwgq7dns2bNGjwObdZIGyD6KIZoJB+m9FG5O83jx4/x5MkT+vPly5cxc+ZMbNiwQSdXNBYKhcjIyKj3FDv3X4gHwOuXAapLH3WjTUsaaatG2gLRRzFEI/kwpY/KBvjdd9/hxYsXAEQPoXnz5sHc3Bznz5/H2rVr1RpcQ0BRFDIzM0FRVJ2PUVheiaQs8QK4+tWLSx36qBttMj9AOzXSJog+iiEayYcpfVQ2wBcvXsDHxwcAcO7cOQQHB2P9+vVYtWoVLl68qNbgdIV/X+aDogA3Rws4kwVwGaewsBBFRUVaYX4EAkF3UXkYBEVRdDH0zp076NGjBwDAxcUF+fn5ag1OV3igp+1/2oqfnx+uXbsGGxsbYn4EAqHOqFwC9Pf3x6+//oqTJ0/iwYMHtAGmpaXByclJ3fExDovFgqOjY716tj542/4X7KFf1Z+AevRRBzweD7du3aI/+/n5aY35aYtG2grRRzFEI/kwpY/KBvjVV18hISEBy5cvx/Tp0+Hm5gYAuHDhgk6uaMxms+Hu7l7n6XUqqgR4xHsDQD8HwNdXH3UgbvPr16+fhAlqC9qgkTZD9FEM0Ug+TOmjchVomzZtcObMGan0hQsX6uTNEwqF4HK5aNGiRZ3i/y9dtACuo6UJPJ30b6md+upTX2p2eBG/cGkTmtZI2yH6KIZoJB+m9Knzkf777z+cOnUKp06dwuPHj2FqagpjY90bAE5RFHJzc+vcu0hc/dnB3V4vqy/qq0990LbenrWhSY10AaKPYohG8mFKH5VLgLm5ufj888/x4MEDemR+YWEh3n//fWzcuBEODvpXDSgP8QB4fRv/p2l0xfwIBILuonIJcPny5SgtLUVkZCTu37+P+/fv4+zZsyguLsaKFSuYiFFrEQqpaiVAYoDqIisri5gfgUBgHJVLgDdv3kRERARatmxJp3l5eeH777/H5MmT1RpcQ8BiseDi4lKn6suUnGIUlFXC3JgDP1f9nMevPvrUFQcHB7Rr1w4AdML8NKGRLkH0UQzRSD5M6aOyAQqFQpltfUZGRjo5jQ+bzYarq2ud9hWP/wtqYQdjjn42XNdHn7piYmKCQ4cOIS8vD02aNGnQc9cFTWikSxB9FEM0kg9T+qj81P7f//6HlStXIjs7m07Lzs7GqlWr0KlTJ7UG1xAIBAIkJydDIBCovO+D5/q//l999FEFHo+HpUuX0i9RJiYmOmF+QMNppKsQfRRDNJIPU/qoXAL87rvvMGPGDPTq1Yt+QGVlZaFVq1Y6ORcoIOrEUxfoAfB6bIBA3fVRlppLGn3//feMno8JmNZI1yH6KIZoJB8m9FHZAF1cXHDixAncuXMHz549AwC0bNkSnTt3Vntw2kzGmzKkvykDh81CIFkAt87U7O2pi+3IBAJBN1HJAKOionDlyhVUVlaiU6dOGD9+PFNxaT30ArguNrAyVfk9ggAy1IFAIGgWpZ/cf/75J5YtWwY3NzeYmZnh0qVL4HK5WLRoEZPxMQ6LxYKbm5vKvYui9XT9v5rUVR9F6JP5MaWRvkD0UQzRSD5M6aN0J5gDBw5g9uzZuHDhAk6dOoWffvoJBw8eVGswmoDNZsPJyUnl6XUe0APg9W8C7OrUVR958Pl89O7dWy/MD2BGI32C6KMYopF8mNJH6aPxeDwMHTqU/jx48GBUVVXh1atXag2ooREIBHj8+LFKvYsKyirxJLsIgH73AAXqpo8iTExMsHz5crRu3VrnzQ9gRiN9guijGKKRfJjSR2kD5PP5sLCweLcjmw1jY2NUVFSoNSBNUF5ertL24gVwPZws0cjalKGotAdV9VGGUaNGIT4+XufNTwwTGukTRB/FEI3kw4Q+KvXe2LRpE8zNzenPlZWV+PXXX2FtbU2nLVmyRH3RaSnvFsDV7+pPdcLlcjFlyhTs3r2bNj0TExMNR0UgEAwZpQ2wY8eOeP78uURaUFAQeDwe/dlQGnAfkAmwVYLL5aJnz55ITU3FlClTcOHCBU2HRCAQCMob4B9//MFkHBqDzWbDy8tL6cbV8koBHvEKAIiWQNJ3VNWnJtXNz9PTE7t27VJzhJqnvhrpO0QfxRCN5MOUPgY/gI3FYsHW1lbp7f9LLwBfIISTlQk89HAB3Jqoqk91apqfPnR4kUV9NDIEiD6KIRrJhyl9DP51QyAQ4OHDh0r3LrpPt/85GESVr6r6iDEU8wPqrpGhQPRRDNFIPkzpY/AGCEClVSyiq60AbyjUZZWPmTNnGoT5idHFlVAaEqKPYohG8mFCH2KAKiAUUvQK8MEepAOMPHbt2oWBAwcahPkRCATdxODbAFXh6asiFJZXwcKEA18X/VwAtz5UVFTA1FQ0LrJJkyY4e/ashiMiEAiE2qlTCTA6OhoLFizARx99RK8LePLkSURHR6s1uIaAzWbD19dXqd5F4uWPglrYwUhPF8CtibL68Hg8tG3bFvv27WugyLQHVfKQIUL0UQzRSD5M6aPy0S5cuIDw8HCYmZkhISEBfD4fAFBcXIwdO3aoNbiGQtkB2dEGOv5PkT7iia2Tk5OxYsUKvZgdSFXIoH75EH0UQzSSDxP6qGyAv/76K5YuXYoVK1bAyOhdDWr79u2RkJCg1uAaAqFQiNjYWKUaWMUrwBuSASrSp+aqDleuXKGrQQ0FVfKQIUL0UQzRSD5M6aOyAT5//hwdOnSQSre2ttbrFY3T35Qho6BctABucztNh6MV6NOSRgQCwfBQ2QCdnJzA5XKl0mNiYvT64Seu/vR3tYElWQCXmB+BQNB5VDbAUaNGYeXKlXj06BFYLBays7Nx+vRprF69GqNHj2YiRq3g/tvqT31f/khZ/vzzT2J+BAJBp1G5KDN16lQIhUJMnDgRZWVlGDduHExMTDB58mSMHz+eiRgZhc1mIzAwUGHvoncrwBvOAHigdn0WLlwIABgzZozBm5+yechQIfoohmgkH6b0UdkAWSwWZsyYgfDwcHC5XJSWlqJly5awtNTdeTH5fD7MzMxq/b6g1HAWwJWFWJ+MjAw4ODjAzMwMLBYLixYt0nRoWoOiPGToEH0UQzSSDxP61NlOTUxM4OXlhYCAgHqb34EDBxAaGoq2bdti5MiRiIuLq3Xbw4cPY8yYMejYsSM6duyIiRMnyt1eEUKhEAkJCXJ7F0W/FFV/ejpZwsnK8Ho4JiQk4OXLl+jatSuGDx9OFu6sgTJ5yJAh+iiGaCQfpvRRuQQ4fvx4uZNAqzoQOioqCqtWrcLSpUvRrl077N27F+Hh4Th//jwcHR2ltr937x4GDhyI9u3bw8TEBLt27cLkyZMRGRmJxo0bq3o5SvHAAOf/rE5WVhZGjRqF1NRUAEBeXh5cXV01HBWBQCDUD5VLgD4+PmjTpg395+XlhcrKSiQkJKB169YqBxAREYFRo0ZhxIgR8PLywtKlS2FmZoZjx47J3H79+vUYO3YsfHx80LJlS6xYsQJCoRB37txR+dzKYsgL4PJ4PEybNk2iwwsxPwKBoA+oXAL86quvZKZv2bIFpaWlKh2Lz+fj8ePHmDZtGp3GZrPRuXNnPHz4UKljlJWVoaqqSuW1osTLaggEArpEKxQKQVEUvQ2LxQJfQCEu7Q0AoH0LW3p7NpsttTQHm80Gi8WSmS4+vjLpHA4HFEXJTK8ZY23p4hhrS68Zo6x0Ho+HXr16IT09HR4eHrhy5QpcXV0hEAh09prEMarzPonzhPh4+nBN6rxPYn2EQiE4HI5eXJOi2FW9JoqiVLpWXbgmdd+n6udQ17JIahvQNmTIEIwcOVKljhH5+fkQCARSVZ2Ojo50dZsi1q1bB2dnZ3Tu3FmleOPj4yXOx+Fw8OLFC+Tm5tLpLi4uSKswQ6WAgp0pG/ncp3jDY8HNzQ1OTk5ISkqSaA/z8vKCra0t4uLiJG6ir68vTExMEBsbKxFDYGAg+Hy+xAw6bDYbQUFBKCwsREpKCp1uZmYGPz8/5OXl4eXLl3S6jY0NWrVqhaysLGRmZkpck7u7O7hcrtQ1ubq6IjU1VWLigprXlJWVhWnTpiE9PR2enp7YunUrcnNz6WPp4jUxfZ/i4+P17poA9d2nnJwcvbsmdd6ndu3a6d01qes+FRQUgKIo+rltbW0NdcCiar4q1JGTJ09i3bp1uHXrltL7ZGdno1u3bjh06BCCgoLo9DVr1uDBgwc4cuSI3P1/++037Nq1C/v27UObNm2UOqdAIEBsbCzatm1Lv3EUFRXBzs4OFEVJvQn9eiMVay88wQD/xtg6OohO1/c31vv376Nfv35o1KgRTp8+DW9vb4m2X128puoxKnOfhEIhKisrJWoIZF2TQCBASUkJLC0twWKxtPqaFKUzcZ8oikJJSQmsrKxICbCWa2KxWCguLoaFhYVSvzNduKb63CdjY2NwOBw6XSAQoLCwENbW1vS54uPjERgYSG9XF1QuAc6ePVviM0VRyMnJwX///YeZM2eqdCx7e3twOByJtw8AyM3NhZOTk9x9d+/ejd9++w0RERFKm191OBwOLWxqamqtQr5r/3OU+r424dWRLn6Q1qS2cTCqpiuKpVOnTrh8+TKcnZ2Rm5tbazy6dE2qpPP5fDx//lypXmcURaGyshLGxsZyO4gZKkQfxRCNpLGzs0OTJk3oz/Ke03VFZQOsWfRksVjw8PDA3LlzERISotKxTExM4Ofnhzt37qB3794AQHdoGTduXK377dy5E9u3b8fu3bvRtm1bVS9BaQRCCjEvxQPg9b8DDI/HQ05ODtq3bw8ACA4OhkAgkHpB0XcoikJmZiY4HA6aN2+ucPAtRVEoKyuDubk5eXjJgOijGKLROyiKQmlpKV69egVAVCXLFCoZoEAgwPDhw9G6dWuVO53UxqRJk7Bo0SL4+/sjICAAe/fuRVlZGYYPHw5ANONI48aNMX/+fACias/Nmzdj/fr1aNq0KXJycgAAFhYWah+M/ySrCEXlVbA04cDHRT11ztqKeG7PvLw8XLlyhTZBQ6SqqgqlpaVwdXWFhYWFwu3F1TbiCQIIkhB9FEM0ksTc3BwA8OrVKzg7OzN2HpUMkMPhYPLkyYiKilKbAYaFhSEvLw+bN29GTk4OfHx8sGvXLroKNDMzU+IN/NChQ6isrMTcuXMljjN79mzMmTOnTjHUNruAeAB8ezd7vV4At+bE1o0aNZL43tBmpxC3Zaiy/hiZwko+RB/FEI0kEb98iquGmXgOqVwF2qpVK6Slpal1/sdx48bVWuX5xx9/SHy+evWq2s4LiEzdz89P5nf0AHg3/a3+VLSqgzx99B1l38RZLBb9xkqQhuijGKKRNNV/f0w9h1R+5fj888+xevVqXLt2Da9evUJxcbHEn64hFArx+vVrqc4OFEW9WwDXQz9ngFFmSaPa9CG8Q9yBQU0dqvUOoo9iiEbyYeo5pHQJcOvWrZg8eTKmTp0KAJgxY4aEQ4sHciYmJqo1QKahKAovX76Evb2kyaXllyGrsBxGeroAbnp6ulLr+dWmD0ESPp8PIyOyTmRtEH0UQzSqHaaeQ0qrvW3bNowePVrluT51FXH7n19TW1iY6F+mtLe3h5ubGwCQ9fwMDG9vb2zbto3uec0U9+7dw4QJE3D//n36wX758mWsXr0aaWlpGDduHHx8fPDjjz8iOjqakRhSU1Mxfvx4XLhwAVZWVoycw9CYN28e2rZti8mTJ2s6lHqj9JNdXDQPDg5mLBht4v5zUftfsJ5OgG1hYYGzZ88iPz8fTZs21XQ4BDWRk5OD7du34/r168jOzoajoyN8fHzwySefoFOnTg0aS1BQEG7dugVra2uUlZUBAL777jsMHz4c48ePh6WlJYyMjNC9e3fGYtiwYQPGjRsn0/z69++PtLQ0XLt2TarjV2hoKCZMmICJEydKpG/ZsgWXL1/GqVOn6DRNaX7u3Dn8/PPPSE9Ph7u7OxYsWKBQy9OnT2PXrl14+fIlrK2t0bVrVyxcuJAuWSUnJ2Pz5s14/Pgx0tPTsWTJEikNZsyYgXHjxmHkyJFqm5FFU6jUBqiv3XNtbGyk0qJf6N8K8FwuFxs3bqRfZiwsLJQyP1n6GBoURaGUX1XrX4UAcr+vy5+q7UFpaWkYPnw47t69i4ULF+LMmTPYtWsX3n//fSxdupQhZWrHxMQEjRo1oidAKCkpQW5uLkJCQtC4cWNYWVnBzMxM5qovqlBZWSkzPSMjA9evX6eHVFUnOjoaFRUV6NevH06cOFHnc6tTc1UGeP/777+YP38+PvzwQ5w8eRK9evXCrFmz8PTp01r3iYmJwaJFi/Dhhx/i7Nmz2LRpE+Lj4/Htt9/S25SVlaFZs2aYP3++1EuBmNatW6N58+Y4ffq08henBph4DqlUt9evXz+FJnj//v16BdTQcDgctGrVSiItv4SP5FeiDj0d3PSjBMjlctGzZ096jtV58+YptZ8sfQwNiqLw4fY79KQIDUUHN3scmd5J6RfPpUuXgsVi4ciRIxLjF1u1aoURI0bUut/atWtx+fJlZGVlwcnJCYMHD8asWbNgbGwMAEhKSsLKlSvx33//gcViwd3dHUuXLkXbtm2Rnp6O5cuXIyYmBpWVlWjatCkWLlyI7t2701WgDx48QGJiIiZMmAAA+OSTTwCIlk5LT0+XqgK9fPkytm3bhpSUFDg7O2PYsGGYPn06XY3q7e2N77//Hn///Tfu3r2L8PBwmUOgzp07B29vb5nLpB07dgyDBg1Cx44dsXLlSrpvg6rUVfOasFgslbr579u3D127dsWUKVMAiDon/vPPP9i/fz+WLVsmc5/Y2Fg0bdqUvg/NmzfHRx99hJ07d9LbBAQEICAgAIBo5Z3a6NmzJyIjIzF27FilY64PTD2HVDLAOXPm6HyRtyZCoRBZWVlo0qQJPQ4n+u2DzrORJRz1YAHc6ubn6emJDz/8UOl9ZeljiGh73cebN29w8+ZNzJs3T+bgfXlvz5aWlli1ahWcnZ3x9OlTfPvtt7C0tMSnn34KAFiwYAF8fHzwww8/gMPhIDExkTbHZcuWobKyEvv374eFhQVSUlKkzk9RFPz9/XHu3DkMGDAAW7ZsQVBQEGxtbZGeni6xbXR0NBYtWoRvvvkGHTp0AJfLpUso1adh3Lp1K+bPn4+vv/661pJTdHQ0/P39pdKLi4tx/vx5HD58GJ6eniguLkZ0dDQ6dOhQq0ayqI/mp0+fxvfffy+RJu5IKGbnzp21xhQbGytVNRkSEoLLly/Xes7AwEBs3LgRN27cQLdu3ZCbm4sLFy7UqQo6ICAA27dvB5/PV2m8bF1h6jmkkgEOHDiw3tUV2oZ42qvqb4ni6s9gPaj+rGl+qnZ4kaWPocFisXBkeieUVcpegkU0dVMZLCzUO42VuTFH6eNxuVxQFAVPT0+Vz1N9Dt9mzZrh+fPniIyMpA0wIyMD4eHhaNmyJQDA3d2d3j4jIwP9+vWDt7c3AMjNW+Jnh62tba3Va1u3bsXUqVMxbNgw+nifffYZ1q5dK2GAgwYNUljCysjIkGmAUVFRcHNzo0sUYWFhOHr0qMoGWB/NQ0ND0a5dO/qzrKnQ5P3mXr9+LTVfsqOjI16/fl3rPu+99x7Wrl2Lzz//HHw+H1VVVejZsye+++47leN3dnZGZWUlcnJyGqQPAVPPIaUNUF/b/2TxNLsIABDQzE6zgdST+pof4R0sFqvW3sAURQFVHFiYGGnsd1Kf8WNRUVHYt28feDweSktLUVVVJdFpZNKkSfjmm29w6tQpdO7cGf3790eLFi0AABMmTMAPP/yAW7duoXPnzujbt2+dJqcXk5SUhH///Rfbt2+n0wQCASoqKmiDACDT2GpSXl4OU1PpGpxjx45hyJAh9OchQ4Zg/Pjx+Oabb1TqKVofza2srCTOJZ7/suZqEOokJSUFK1euxKxZsxASEoKcnBysWbMG33//PX788UeVjiWurq2+LJIuonIvUEMgu7ACAOBiq7tTgJWVlSE0NJSYn4Hg5uYGFoul9DqaYh4+fIgFCxZgzpw5CAkJgbW1NSIjIxEREUFvM2fOHAwaNAg3btzA33//jc2bN2Pjxo3o06cPRo4ciZCQEFy/fh23b9/Gb7/9hkWLFmH8+PF1uo7S0lLMmTMHffv2lfquupkpM0ervb29xHp2gMgEYmNjERcXh3Xr1tHpAoEAUVFRGDVqFABRtbCsiT3ES/IAddccqH8VqJOTk1RpT9EqOjt27ED79u3pdsM2bdrA3NwcY8eOxeeff67SnJsFBQUAoPPjg5U2wKSkJCbj0BgsFguOjo4SGe9VkcgAnW10t/3P3NwcCxcuxNq1a3H16tU6m58sfQjSaHoAs52dHUJCQnDgwAGMHz9eyiAKCwtltkk9fPgQrq6umDFjBp2WkZEhtZ2Hhwc8PDwwceJEfPHFFzh27Bj69OkDQDRb/+jRozF69GisX78ehw8fljJAIyMjVFVVKbwOX19fPH/+nB6jWh98fX0lFlcFgKNHj6Jjx45S1X7Hjx/H0aNHaQP08PDAf//9J3XMhIQEeHh4AKi75oDsKtCayyHJq+4LDAzE3bt3JdoB//nnHwQGBta6T3l5ea1LuqlawHn69CmaNGkCB4eGaSZi6jlkuL0a3sJms+Hu7k43rFYJhMgteWuA1rpbAgSAqVOnIi4url4lv5r6EKRhsVgwNTXV+EvC999/D6FQiJEjR+LChQt48eIFnj17hn379uGjjz6SuY+bmxsyMzMRGRkJLpeLffv2SXSkKC8vx7Jly3Dv3j2kp6cjJiYG8fHxdHvgypUrcfPmTfB4PDx+/Bj37t2jvxOjij6zZs3CqVOnsHXrViQnJ+PZs2eIjIzExo0bVdYjJCQEsbGx9OTmlZWVOHXqFAYOHIjWrVtL/I0cORKPHj1CcnIyAGDixIm4ceMGfv31Vzx79gxPnz7Fxo0bERsbS/eiBOqmOSCqAnVzc6P/3N3d0apVK7i7u9Np8nqFTpgwATdv3sSePXvw7NkzbNmyBf/995/EnMrr16/HwoUL6c89e/bEpUuX8Oeff4LH4yEmJgYrVqxAQEAAbbZ8Ph+JiYlITEwEn89HdnY2EhMTJVaYB0RDKrp06aLC3agfTD2H9G+KExURCoXgcrlo0aIF2Gw2XhfzQVEAh82CoyXzvZvUCY/Hw9y5c7Fz5066KqS+E+zW1IcgDUVRdG84TZpg8+bNcfz4cWzfvh2rV6/Gq1ev4ODgAD8/P/zwww8y9+nVqxc++eQTLFu2DHw+Hz169MCMGTOwdetWAKIHz5s3b7Bo0SK8fv0a9vb26Nu3L70ai1AoxLJly5CVlQUrKyt07doVS5YskTgHRVGoqKhQqpTRtWtXbN++Hdu2bcPOnTthZGQET09PjBw5UmU9unXrBg6Hg3/++Qddu3bF1atX8ebNG7rkWp2WLVuiZcuWOHr0KJYsWYL27dtj586d2LZtGyIiIsBisdC6dWv8/vvvaN26Nb1fXTSXhap5qH379li3bh02bdqEDRs2wN3dHdu2bZOILScnB5mZmfTn4cOHo6SkBAcOHMDq1athbW2N//3vf/jyyy/pbV69eoWhQ4fSn/fs2YM9e/YgODiYXpigoqICly9fxq5du5S+vvrC2HOIMjCqqqqo6OhoqqqqSubnWG4+5bboLPW/Hy9rMkyV4XK5lKenJwWAGjp0qNqOW1MfQ6CsrIxKSEigysrKlNpeKBRSxcXFlFAoZDgy3UST+uzfv5+aPHlyg59XVXQpDx04cICaNGkS4+ep/jtU9NyuKwZfAqwJ3f5nrTvtfzVXddi8ebOmQyIQtIKPPvoIhYWFKC4uJnOBqgljY2N88803mg5DLRADrEF2oahbr7ONbrT/KbOkEYFgqBgZGUl08CHUn7pUR2srBt+ow2Kx4OLiQte7vxIboA6UABvC/GrqQ5CNeGYUgmyIPoohGtUOU88hgy8BstlsuLq60p/FVaCNdaAEOHHiRMZLfjX1IUjDYrEaZDooXYXooxiikXyYeg4ZfAlQIBAgOTmZ7iqdrUMlwF27diE0NJTRas+a+hCkoSgK5eXlBjVZhCoQfRRDNJIPU88hgy8BApCYLULbS4BVVVX0oGsPDw9cuXKF8XPWnE2DIA15QZAP0UcxRCP5MPEcMvgSYE3E06Bp4ywwPB4Pbdu2bfB1uAgEAkEfIQZYDW2eBUbc4SUpKQmLFi1SalopAoFAINSOwRsgi8WiJ7XV1llgavb2vHjxYoPNPVldH0LtkA4M8iH6KIZoVDtMPYcM3gDZbDacnJzAZrPpDjCNrEzBZmvHA1/T4/yq60OQDYvFkpjEWNvx9vaWu3CqulGXPsePH1d5zb76cu/ePXh7eytsf7pz5w4GDBhQ53Y8XctD9WXUqFG4cOGC0tsz9Rwy+KeaQCDA48ePIRAIqnWA0Y72P02bHyCpD0E21NvFTLWhB19OTg6WL1+OXr16wd/fH927d8f06dNx584djcWkTfowxdq1azFjxgyp1RbKy8sRHByM999/H3w+X2o/8ctITY0WL14ssVAxALx8+RJLlixBt27d4O/vj9DQUHzxxReIj49n7sIAHDhwAKGhoWjbti1GjhyJuLg4udsfP34c3t7eEn9t27aV2GbGjBlYv349hEKhUjEw9RwivUDxblFHbZsFZvv27Voxw4uuL3qpLkpKSmSmU28ne64+e39t2wKit9nqk5TXtq2lpaVK8aWlpWH06NGwsbHBwoUL0bp1a1RVVeHWrVtYunQpzp8/r9Lx1ImyDzpdJDo6GlwuF/369ZP67sKFC/Dy8gJFUbh8+TLCwsJqPY48jeLj4zFx4kS0atUKy5Ytg6enJ0pKSnDlyhWsXr0a+/fvV8u11CQqKgqrVq3C0qVL0a5dO+zduxfh4eE4f/48HB0da93PyspKIr/VLNl269YN33zzDf7++2/06NFDqViYeA4RA6yGts0Dunz5cgDA9OnTyfRmWoC8uST79euHc+fO0Z+dnZ1RWloqc9vu3bvj+vXr9Gd3d3epxU0B1ddoW7p0KVgsFo4cOSKxNl2rVq0wYsQIiW3z8/Mxa9Ys3Lp1C40bN8aiRYvQq1cvAKK37W+//RZ3797F69ev4eLigjFjxuCTTz6h91+8eDEKCwvx3nvvISIiApWVlQgLC8NXX31Fz2jC5/Px888/4+zZs8jNzYWLiwumTp1KT6X19OlTrFmzBjExMTA3N0eXLl2wZMkSldaYu3z5MrZt24aUlBQ4Oztj2LBhmD59OoyMjDB//nwIBAJs2rSJ3r6yshIhISFYsmQJhg4dCqFQiJ07d+Kvv/7C69ev4e7ujpkzZ6J///5KxxAVFYXOnTvLXH3+6NGjGDJkCCiKwtGjR+UaYG1QFIUlS5bAzc0Nf/75p0Q1oI+Pj8TyTOomIiICo0aNovPP0qVLcf36dRw7dgxTp06tdT8Wi4VGjRrV+j2Hw0G3bt0QGRmptAEygcFXgVZHPA2aJscAZmdn0z082Ww2Vq5cScyPoJA3b97g5s2bGDt2rMzV0msuzLp161YMGDAAp0+fRrdu3bBgwQK8efMGgKgk0qRJE/z888+IjIzErFmzsHHjRkRFRUkc4969e+Byudi7dy9++uknnDhxAidOnKC/X7hwISIjI/H111/j2LFjWLp0KV2qLSwsxCeffAJfX18cPXoUu3btQm5uLj7//HOlrzk6OhqLFi3ChAkTEBUVhWXLltFLEwHA4MGDce3aNYkS9q1bt1BeXo7evXsDEK2SfvLkSSxduhSRkZGYOHEivvzyS9y/f1+lOPz9/aXSuVwuYmNjMWDAAAwYMADR0dFIT09X+rhiEhMTkZycjMmTJ8tsA6tt0V1AVIsUFBQk90/WAsiA6AXm8ePH6Ny5M53GZrPRuXNnPHz4UG7MpaWl6NmzJ7p3744ZM2bQ6yxWJyAgADExMXKPwzQGXwJks9nw8vKS6ASjqRKguM2vY8eO2L9/v8ZXGQck9TF0iouLZabLKqm9evWq1uPU1PLFixf1igsQPWwpioKnp6dS2w8bNgyDBg0CAHzxxRf4448/EBcXh27dusHY2Jhe7w8QrXkXGxuL8+fPS5RgbG1t8d1334HD4aBly5bo3r077ty5g1GjRuH58+c4d+4cIiIi0KlTJwgEArRu3ZquCtu/fz98fX3xxRdf0Mf78ccf0b17dzx//pxedV0eW7duxdSpUzFs2DA6zs8++wxr167F7NmzERISAnNzc1y6dIle4+7s2bMIDQ2FlZUV+Hw+duzYgYiICAQFBdHHiImJwV9//YXg4GCltMzIyICzs7NU+rFjx9CtWzfY2toCEC3Qe/z4ccyZM0fmcWSVIIF3+UPZe1udjz/+GAMGDJC7jazYAVEtgUAgkKrqdHR0RGpqaq3H8/DwwI8//ghvb28UFRVhz549+PjjjxEZGYkmTZpInDczMxNCoVDh84Wp55Dmn7AahsVi0RlUk7PAVO/wAgC5ubn0Ks2apLo+ho4qbXJMbVsbqlaXent70/+3sLCAlZUV8vLy6LQDBw7g2LFjyMjIQEVFBSorK9GmTRuJY3h5eUl0+mjUqBGePn0KQFRq4XA46NixI1gsltTLXFJSEu7du0cbT3W4XK5SBpiUlIR///2XLvEBourbiooKlJWVwdzcHAMGDMCZM2cwdOhQlJaW4sqVK9iwYQMAUaeSsrIyTJ48WeK4lZWV8PHxUXh+MeXl5VLmJRAIcOLECXz99dd02pAhQ7BmzRrMmjVL6kEuSyN1YGdnBzs7O7UfVx7ikmX1z2FhYTh06JBECd/MzAxCoRB8Pl+i/VwWTD2HDN4ABQIB4uLiEBAQoLFZYGT19tQG8wMk9anZw40gQtyDz9zcXGPd2MVjpOS9mVen5soDLBaL7oQRGRmJ1atXY9GiRQgKCoKlpSV2796NR48eSexT84HNYrFoI67+QJOlj7iKbMGCBVKxyWs7qk5paSnmzJmDvn37Sn0nNqTBgwdj/PjxyM3Nxe3bt2FqaoquXbvS+wOiatCavzdVxuTZ29tLDZO4desWsrOzMW/ePIl0gUCAO3fuoEuXLgBELz9FRUVSGhUWFsLa2hqAqI0YAFJTU+Hr66t0XICoCnTHjh1yt4mMjJQ50bS9vT04HA5yc3Ml0nNzc+Hk5KR0DMbGxvDx8QGXy5VILygogIWFhULzA5h7Dhm8AQKiNg9NzQKjDUMdFKHPPfjUhaa7+NvZ2SEkJAQHDhzA+PHjpdoBCwsL5bYVVefff/9FUFAQxo4dS6fVfHgponXr1hAKhXjw4AE6deokpY+fnx8uXLiApk2b1rnk4+vri+fPn8PNza3Wbdq3b48mTZogKioKf//9N/r370+bf8uWLWFiYoKMjAylqztriyMlJUUi7ejRoxg4cCCmT58ukb59+3YcPXqUNkAPDw88fvwYQ4cOpTUSCARISkqiOwv5+PjAy8sLe/bsQVhYmFTpUd69rU8VqImJCfz8/HDnzh26zVQoFOLOnTsYN26c3GNWRyAQ4OnTp+jevbtE+tOnT1UqaTPxHCIG+BZNzAKjC+ZH0B2+//57jB49GiNHjsTcuXPh7e0NgUCA27dv4+DBgxK9VOXh5uaGkydP4ubNm2jWrBlOnTqF+Ph4NGvWTOlYmjVrhmHDhuGrr77C119/DTc3N+Tl5SEvLw9hYWEYM2YMDh8+jC+++AJTpkyBnZ0dXr58iaioKKxYsUKpt/xZs2Zh+vTpcHV1Rb9+/cBms5GUlISnT59KlLwGDRqEQ4cO4cWLF9i7dy+dbmVlhcmTJ2PVqlWgKArvvfceioqK8O+//8LKyopuW1RESEiIROefvLw8XLt2Db/88gtat24tse0HH3yA2bNn482bN7Czs8OkSZPw9ddfw8PDA+3btwdFUdi/fz8KCwtpA2SxWFi1ahUmTpyIMWPGYMaMGfQwiGvXruH27du1DoOobxXopEmTsGjRIvj7+yMgIAB79+5FWVkZhg8fTm+zcOFCNG7cGPPnzwcgapsNDAyEm5sbCgsLsXv3bmRkZEgtpBsTE0O/CGgKYoBvEbf/NeQsMMnJyUhPTyfmR1ALzZs3p3tBrl69Gq9evYKDgwP8/Pzwww8/KH2cjz/+GImJiZg3bx5YLBYGDhyIMWPG4O+//1Ypnh9++AEbNmzA0qVL8ebNG7i6umLatGkAgMaNG+PgwYNYt24dwsPDwefz4erqiq5duyrd0aFr167Yvn07tm3bhp07d8LIyAienp5SD9ohQ4Zg+/btaNq0Kd577z2J7z7//HM4ODhgx44dSEtLg7W1NXx9faVKbvIYPHgw1q5dS7/Injx5Eubm5ujUqZPUtp06dYKZmRlOnz6NCRMmYNCgQaAoChEREVi/fj3Mzc3h5+eH/fv3S1QzBgQE4NixY9i+fTu++eYb5Ofnw9nZGUFBQfjqq6+UjlVVwsLCkJeXh82bNyMnJwc+Pj7YtWuXRGyZmZkS96ywsBDffvstcnJyYGtrCz8/Pxw6dAheXl70NtnZ2Xj48CHWrl3LWOzKwKI0XXfTwAgEAsTGxiIwMBAcDodeh+tmagGm/hGDds1scWp2SIPFc/XqVbRq1UprzU+sj5mZmcFM01ReXk73RFSmfYKiKFAUBRaLZTAaqYIh6LN69WqUlJRg2bJlddrfEDSqztq1a1FYWEiPdZZF9d+hqampxHOo5nO8rpC+7RDVdb8qejsPKMPtf1wuF0+ePKE/h4aGaq35iSGT9CrGEB5a9UHf9ZkxYwZcXV3r1U6l7xpVx9HREZ999plK+zDxHDJ4AxQKhYiNjUV2gXgQPHM9QLlcLnr27IkePXpImKA2I9aHdISRT22zvhBE6Ls+NjY2mD59er3Gqem7RtWZPHmySj1JmXoOGbwBislmeAyg2Pz+396Zh1VVrQ38x+iEqSGKmgqigTLIQbwWoV5nU7HyXkrtwiURh5ynwNSuoIYaKSpqiGRONzNF00T7MhPlYpRajlDiBAgKKIiEwAH29wedHcfDdJDBOOv3POdR1l57rXe9e+317vXutdd748YNmjZtWuZuHQKBQCCoO4QB/IP0WtwHtLTxEwteBAKB4NlAGMA/SMuunRmgMH4CgUDwbKLzBlBfXx9HR0fuyYtgam4GmJSU9Jc3fir9iL1AK0a4tCtG6KdyhI7Kp7bGIfEdIJCbl8f930uCVdbkDLB58+ZyaJe/ovFTUZW9+nQd1RJ2QdkI/VSO0FHF1MY4pPOP9cXFxZz5+Uqt7ALTsmVLvv32W6Kiov6yxq+4uJirV6+KVaCV8Pjx4/oW4ZlG6KdyhI7Kp7bGIZ03gACZj0uUWhO7wCQlJfHpp5/Kf7ds2VKrLaQEgpokNjYWa2trjc2aa5uIiAicnZ2fqozk5GSsra2Ji4srN099tU/QMBAGEMjMKwKe/htA1d6e3t7eakZQoKNcuwYLF8K4cSX/lhEUtKbx8PBgxYoVtV5PQ2L37t0MHDgQe3t73N3duXjxYoX5IyIisLa2VvvZ29ur5ZEkiXXr1uHq6oqDgwNeXl41EvdRULMIAwhk5pXsBvc0u8A8ubH1kCFDakq8ekcsgKkcjXc327aBjQ189BHs3Vvyr40NfPZZvcinDQUFBTVe5rP6bisyMpLAwECmTZvGgQMHsLGxwdvbWyME0JOYmJgQHR0t/77//nu142FhYezcuZOlS5eyd+9emjRpgre3N/n5+eWW+azq6FmhNsYhnR/ZDAwMaNSyJBxIdWeADTmqg4GBAQqFQsQCrAA9PT2aNm365wB27RpMnAjFxVBUpP6vtzc8ETqnpvDz8+PHH39kx44d8szkzp07AFy5coUxY8bQs2dPxo4dqxY3cMOGDbz22mt8+eWXDBw4EAcHB6BkU+NFixbx0ksv4eTkhKenJ/Hx8fJ58fHxeHh4oFAocHJyYsyYMVy6dElNptOnTzNixAheeeUVJk6cSFpamnysuLiYkJAQ+vXrh52dHa+99lqlG25HRUUxbNgwHBwc8PDwkNtXXbZt28abb77JP/7xD7p27Yq/vz+NGzdm//79FZ6np6eHmZmZ/Cu9q4kkSezYsYOpU6cyePBgbGxsWL16NWlpaRw/frzc8tT6kECN2hqHdN4ASpJEckbJ+4PqrABtyMYPSvTz8OHDeo939ywjSRKFhYV/6ujTT6G8gUxPD8LDa0WORYsWoVAoePPNN+WZSbt27QBYu3Ytfn5+7N+/HwMDA40IAomJiXzzzTeEhIRw8OBBAGbNmsX9+/cJCwsjIiICW1tb/v3vf5OVlQXA/PnzMTc3Z9++fURERODj46MWaDcvL49PP/2UVatWsX37dlJTU1m1apV8fMeOHWzbtg1fX18OHTqEq6sr7777brmuwtTUVKZPn86AAQM4ePAg7u7ufPzxx2p5UlJS5Ijk5f1UEeQLCgq4cuUKLi4u8vn6+vq4uLjw888/V6hrVUDf/v37M3XqVK6Vcm8nJyeTnp6uVm7z5s3p2bNnueVq9CGBGrU1Dun8ZxDFxcXcTssCtN8F5tGjRw3a+EGJfhISEp561/WGTn5+/p/fcd26BeXdqJJUcrwWaN68OUZGRjRu3FiOqq6a6c2ZM0cO+jpp0iQmTZpEfn6+HDldqVSyevVq+bOds2fPcvHiRc6cOSNvQuzr68vx48f55ptveOutt0hJScHb2xsrKyvgz8jlKpRKJf7+/nTs2JHc3FzefvttNm3aJB8PDw/Hx8eHkSNHArBgwQJiY2PZvn07//nPfzTa9/nnn9OpUyf8/PwA6NKlC7/99hthYWFynjZt2sgGvDxatGgBQGZmJkVFRZiamqodNzU1VZshP4mlpSUffvgh1tbWPHr0iE8//ZSxY8dy5MgRzM3NSU9Pl8t5styMjIxyy1XrQwI1amsc0nkDCPDgsWoRjHYzwObNm+Pj40NYWFiDNH6CamJhUfEM8AlDURdYW1vL/1cZx/v379O+fXsA2rdvLxs/gF9//ZXc3Fz69OmjVk5eXp4cHf6dd95h8eLFfPXVV7i4uDB8+HA6deok523SpAmdOnWSn9rNzMzkd2s5OTmkpaXh5OSkVr6Tk5Oam7U0169fl92zKhwdHdX+NjQ0rDBCfE2gmkmW/nvEiBHs2bOH2bNn12rdgppFGEAgM++PzyCqsQuMn58f06ZNo3nz5jUtluCvyoQJsHp12cckqeQ9YB1jaPjnra56z1T6m6omTZqo5f/9998xMzNj586dGmWp+vqMGTMYNWoUUVFRnDp1ivXr17N27Vp5AVjpOlX11raLLyUlRZ5RlsfkyZOZMmUKrVq1wsDAQGPBy/3797WKVGBkZET37t3lB4PSDxht2rRRK9fGxqbK5QpqH503gIVFxTz8wwBWZQaYlJTEwoUL2bx5szwQNHTjJ3aBqRy1FWrdupW85/P2LpnxSdKf/4aHQ6nI2DWNkZFRjXwsbGtrS0ZGBgYGBhV+x2ppaYmlpSVeXl7MnTuX/fv3l7kC+skVfCYmJrRp04bz58/LrlmA8+fPa8zyVFhZWXHixAm1tAsXLqj9rY0L1NjYGFtbW86cOcPgwYOBPzbGOHOGf/3rXxWWUZqioiJ+++03+vfvD8ALL7yAmZkZZ86coXv37kDJjPfChQuMGzeu3HLEauuKqY1xSOcNYObjIiSqtgtM6QUvALt27aoDCesXAwMDbG1t61uMZxo9PT2NGRReXuDqWmLwbt0qcXt6e9eq8QPo0KEDFy5cIDk5maZNm1bbGLq4uODo6Mi0adNYsGABFhYWpKWlERUVxeDBg+nWrRurV69m2LBhvPDCC9y9e5dLly4xdOhQjbJU+nlyhaO3tzcbNmygU6dO2NjYEBERQXx8PEFBQWXKNHbsWHlRjbu7O1euXOHAgQNqebR1gb7zzjv4+vpiZ2eHg4MD27dv5/Hjx4wZM0bO895779G2bVvmzZsHQEhICI6OjnTu3Jns7GzCw8NJSUnB3d1dbq+npyebN2+mc+fOvPDCC6xbt442bdrIhrY8HQnKprbGIZ03gHcflmw/ZGZiXOEuME+u9gwMDKwrEeuV4uJiHjx4wPPPPy+eUMtBtYLP0NBQfZDv2hXquJ9MmDABPz8/Ro4cSV5eXrX7qZ6eHlu2bCE4OJiFCxeSmZlJ69atcXZ2pnXr1ujr65OVlYWvry8ZGRm0atWKoUOHMnPmTI2yylvh6OnpSU5ODitXruTBgwdYWVmxadMmjcU0Ktq3b8+GDRsIDAxk165dODg4MGfOHI0VrdowYsQIHjx4wPr160lPT6d79+5s3bpVzQWampqq1vezs7NZsmQJ6enptGjRAltbW/bs2UPXUg83Pj4+PH78mA8++IDs7Gx69erF1q1b5UVH5elIow8JgNobh/QkHVt3W1RUxC+//CKvJvrmciqTd53HoUMLDs1wLfOchv6pQ0U8qS9dIC8vj5s3b2JpaVklt4skSeTm5orvuMpB6KdyhI40KX0fGhkZqY1DNTUu6fwjfdofYZDK+wRCl42fQCAQNGR03gDezynZ9snURPP9nyRJvPnmm8L4CQQCQQNE5w1gZq4SgOfLWACjp6dHWFgYL730kk4bv+eee66+RXjm0RX3cHUR+qkcoaOKqY1xSOcXwWQ9LjGApiZ/ukCLiorkzmhnZ0dMTIzO+uUNDAzo1q1bfYvxTKOnpyc+FakAoZ/KETqqmNoah8QM8I9I8C2alDwLJCUloVAoOHnypJxHV40flKy+SklJ0cmAuFVdHyZJEgUFBWIfx3IQ+qkcoSNNSuuitsYhnTeAD/4wgC2bGMkLXi5dusTMmTMpKiqqZ+nqH0mSSE1N1akbUzX71yYskFKprC1xGgRCP5UjdKRObm4uULK5Q22NQ8+EC3T37t2Eh4eTnp6OjY0NS5YsKXc3CICjR4+ybt067ty5g4WFBfPnz5d3YdAWlQs0Pyudv49+Q17wcuTIEeGT11EMDQ1p2rQp6enpGBkZVfrdkSRJ5Ofno6+vr9PegvIQ+qkcoaM/UX0SkpaWRsuWLeXPHmqDejeAqoCU/v7+9OzZk+3bt+Pt7c2xY8c0dlOHkq2S5s2bx9y5cxkwYACHDx9m2rRpRERE8OKLL2pdf+bvBRRmp/Pu29NJun1LrPYUoKenR7t27bh58ya3b9+uNL8kSSiVSoyMjHR+8CoLoZ/KETrSpGXLlpibm9dqHfVuAEsHpATw9/fn5MmT7N+/n0mTJmnk37FjB3379mXixIkAzJ49m5iYGHbt2kVAQIBWdecXFvEw4y73Pl9IYdZdYfzKQE9PD1NTU527KY2NjenWrVuV3KDFxcWkpqbSrl07sVtOGQj9VI7QkTpGRkZqHrjaGofq1QCqAlJOnjxZTqssIOUvv/yCl5eXWpqrq2u5kZbLo6ioiPu/K8n+MUI2fidOnKB9+/bydFtPTw99fX2N6Xd56Sr3RVnpgMYL3PLSDQwMkCSpzPTi4mINP3hZ6SoZy0vXpk0WFhYUFRWpHfurt6mq16m0C7SiNpXevutZb1NF6bV1nSwsLBpcmyqSvTpt6tKlS4NrU1myV7VNqvJUhrBjx45IkqQxFj0N9WoAqxOQMiMjQyNUSWWBJsvi0qVLFBRJdB4+kUdNDDixKxhJkvjll1/kPO3ataN9+/bcuHGD7OxsOb1z5860bt2a+Ph48vLy5PSuXbvSokULLl68qHZxe/TogbGxsVrZUBLLrKCggKtXr8pp+vr6KBQKsrOzSUhIkNMbN26Mra0tDx48UHPLPffcc3Tr1o27d++SmpqqphMLCwsSExPVwr1o2yYrKyuysrLIzMxsMG1qiNfpr9Amc3NzOnTo0KDaVFPXycbGhnv37pGZmdlg2lST1ykjI4OkpCQ5vaYi8NTrXqD37t2jX79+7NmzRy3A5OrVq/npp5/48ssvNc6xs7Nj5cqVjBo1Sk7bvXs3GzduJCYmptI6VXvI2dvbY2BgQM7jAn6Nv0IvhUIjXtmz8iRUOr2un+4kSeLChQuyvhpCm2r6OimVSi5duiTrqCG0qSavU1FREZcuXcLBwQEjI6MG0abKZNe2TdreZ3+FNtXkdVIqlVy8eFHWj6pPPe1eoPU6A6xOQMrWrVtrzPa0DWAJyAOVSRNjDP7wK5fney9PwbWZrqenV2Z6eTJqm15VWUq7Ico656/YpppML92m0jpqKG2qioxVTVf93ZDaVFMyVuc+e9bbVJ30ytpUnn6qS70awOoEpHR0dOSHH35Qew8YExODo6NjlepUPemoOtyT/wrUEfqpHKGjihH6qRyho4opb7x+WgdmvYdDioyMxNfXl4CAADkg5dGjRzl69CitW7fWCEZ5/vx5PDw8mDdvHv379ycyMpLQ0NAqfwZRUFDApUuXartZAoFAIKhl7O3tMTauOJB5RdT7ZxCVBaR8Mhilk5MTQUFBBAcHs2bNGiwsLNi4cWOVvwE0NDTE3t5efHAqEAgEf1FU7woNDZ/OhNX7DFAgEAgEgvpAfHEpEAgEAp1EGECBQCAQ6CTCAAoEAoFAJxEGUCAQCAQ6iTCAAoFAINBJhAEUCAQCgU4iDKBAIBAIdBJhAAUCgUCgk+iEAdy9ezcDBw7E3t4ed3d3Ll68WGH+o0ePMnz4cOzt7XFzcyMqKqqOJK0ftNHP3r17GT9+PL1796Z37954eXlVqs+GgLZ9SMWRI0ewtrbm3XffrWUJ6xdt9ZOdnY2/vz+urq7Y2dkxbNgwcZ89wWeffcawYcNwcHCgf//+fPjhh+Tn59eRtHXLTz/9xJQpU3B1dcXa2rpK8V1jY2N54403sLOzY8iQIURERGhfsdTAOXLkiGRrayvt27dPunbtmrR48WLJ2dlZysjIKDP/uXPnpO7du0thYWFSQkKCtHbtWsnW1lb69ddf61jyukFb/cydO1fatWuXdPXqVSkhIUHy8/OTevXqJd29e7eOJa87tNWRiqSkJKlv377S+PHjpalTp9aRtHWPtvrJz8+XxowZI/n4+Ehnz56VkpKSpNjYWCkuLq6OJa87tNXRoUOHJDs7O+nQoUNSUlKSdPr0aemVV16RPvzwwzqWvG44efKktGbNGun//u//pBdffFH69ttvK8yfmJgo9ezZUwoMDJQSEhKknTt3St27d5dOnTqlVb0N3gD+85//lPz9/eW/i4qKJFdXVyk0NLTM/LNmzZImTZqklubu7i4tWbKkVuWsL7TVz5MUFhZKCoVCOnDgQC1JWP9UR0eFhYXSW2+9Je3du1fy9fVt0AZQW/3897//lQYNGiQVFBTUlYj1jrY68vf3lzw9PdXSAgMDpbFjx9aqnM8CVTGAq1evlkaOHKmWNnv2bGnChAla1dWgXaAFBQVcuXIFFxcXOU1fXx8XFxd+/vnnMs/55ZdfePnll9XSXF1dNaIaNwSqo58nefz4MYWFhbRo0aK2xKxXqqujjRs3Ympqiru7e12IWW9URz8nTpzA0dGRgIAAXFxcGDVqFJ988kmDDQVUHR0pFAquXLkiu0mTkpKIioqif//+dSLzs05NjdP1Hg2iNsnMzKSoqAhTU1O1dFNTU27cuFHmORkZGRrBdU1NTTWC8DYEqqOfJwkKCqJNmzZqN3dDojo6Onv2LPv27ePgwYN1IGH9Uh39JCUl8cMPP+Dm5saWLVtITEzE39+fwsJCpk+fXhdi1ynV0ZGbmxuZmZmMHz8eSZIoLCxk7NixTJkypS5EfuYpa5xu3bo1OTk55OXl0bhx4yqV06BngILaZcuWLURGRhISEkKjRo3qW5xngpycHN577z2WLVvG888/X9/iPJNIkoSpqSnLli3Dzs6OESNGMGXKFPbs2VPfoj0zxMbGEhoayn/+8x8iIiIICQkhKiqKjRs31rdoDYoGPQNs1aoVBgYG3L9/Xy39/v37Gk8PKlq3bq0x26so/1+Z6uhHRXh4OFu2bGHbtm3Y2NjUppj1irY6SkpK4s6dO0ydOlVOKy4uBqBHjx4cO3aMTp061a7QdUh1+pCZmRmGhoYYGBjIaV26dCE9PZ2CgoKnCnD6LFIdHa1bt47Ro0fLLnRra2tyc3P54IMPmDp1qlqMVF2krHE6IyMDExOTKs/+oIHPAI2NjbG1teXMmTNyWnFxMWfOnEGhUJR5jqOjIz/88INaWkxMDI6OjrUpar1QHf0AhIWFsWnTJrZu3Yq9vX1diFpvaKujLl26cPjwYQ4ePCj/Bg4cSJ8+fTh48CDm5uZ1KX6tU50+5OTkRGJiovxgAHDr1i3MzMwanPGD6ukoLy9Pw8ipHhgkEcK15sZp7dbn/PU4cuSIZGdnJ0VEREgJCQnSkiVLJGdnZyk9PV2SJElasGCBFBQUJOc/d+6c1KNHDyk8PFxKSEiQ1q9f3+A/g9BGP6GhoZKtra107NgxKS0tTf7l5OTUVxNqHW119CQNfRWotvpJSUmRFAqFFBAQIN24cUP6/vvvpZdfflnatGlTfTWh1tFWR+vXr5cUCoX09ddfS4mJiVJ0dLQ0ePBgadasWfXUgtolJydHunr1qnT16lXpxRdflLZt2yZdvXpVunPnjiRJkhQUFCQtWLBAzq/6DGLVqlVSQkKCtGvXrmp9BtGgXaAAI0aM4MGDB6xfv5709HS6d+/O1q1bZddDamqq2pOWk5MTQUFBBAcHs2bNGiwsLNi4cSMvvvhifTWhVtFWP3v27EGpVDJz5ky1cqZPn86MGTPqVPa6Qlsd6Rra6qddu3aEh4cTGBjI6NGjadu2LZ6envj4+NRXE2odbXU0depU9PT0CA4O5t69ezz//PMMGDCAOXPm1FcTapXLly/j6ekp/x0YGAjAG2+8wcqVK0lPTyc1NVU+3rFjR0JDQwkMDGTHjh2Ym5uzfPly+vbtq1W9epIk5tMCgUAg0D1097FVIBAIBDqNMIACgUAg0EmEARQIBAKBTiIMoEAgEAh0EmEABQKBQKCTCAMoEAgEAp1EGECBQCAQ6CTCAAoEAoFAJxEGUFAuERERODs717cY1cba2prjx49XmMfPz4933323jiR6tggODmbJkiV1UldsbCzW1tZkZ2dXmG/gwIF89tlntSqLtnXU1H1Qlf6oLQkJCfTr14/c3NwaLVdXEAawgePn54e1tbXG7/bt2/UtGhEREbI8NjY29OvXj4ULF2rsml9doqOj6devHwDJyclYW1sTFxenlmfRokWsXLmyRuorjw0bNsjt7N69O/3792fJkiVkZWVpVU5NGuv09HR27NihFl+udF+xs7NjyJAhhISEUFhY+NT1KRQKoqOjad68OVC+Udm3bx9vvfXWU9fXUNi8eTNjx46lZ8+eZeqra9euODo6sm3btnqQ7q9Pg98LVAB9+/aV99ZT8azEqjMxMeHYsWMUFxcTHx/P+++/T1paGuHh4U9dtpmZWaV5VANybdOtWze2bdtGcXEx169f5/333+fRo0cEBwfXSf1P8uWXX6JQKOjQoYNauqqvFBQUEBUVRUBAAEZGRkyePPmp6jM2Nq7S9XhW+uWzglKpZPjw4Tg6OrJv374y84wZM4bFixczefJkDA3FkK4NYgaoA6gGn9I/AwMDtm3bhpubG46OjvTv35+lS5fy+++/l1tOfHw8Hh4eKBQKnJycGDNmDJcuXZKPnz17lvHjx+Pg4ED//v1Zvnx5pa4ZPT09zMzMaNu2Lf3798fDw4OYmBjy8vIoLi4mJCSEfv36YWdnx2uvvcapU6fkcwsKCggICMDV1RV7e3sGDBhAaGiofLy0y2nQoEEAvP7661hbW+Ph4QGoz6q++OILXF1d1cL0QMnGxAsXLpT/Pn78OG+88Qb29vYMGjSoSrMkAwMDuZ0uLi4MHz6cmJgY+XhRURHvv/8+AwcOxMHBgWHDhrF9+3b5+IYNGzhw4ADfffedPEuLjY0FSjZSnjVrFs7Ozvztb39j6tSpJCcnVyhPZGQkAwcO1EhX9ZUOHTowfvx4XFxcOHHiBAAPHz7kvffeo3fv3vTs2ZOJEydy69Yt+dw7d+4wZcoUevfujaOjIyNHjiQqKgpQd4HGxsaycOFCHj16JLdlw4YNgLp7ct68ecyePVtNPqVSKYeWgpKwQqGhobLeRo8ezbFjxyps+5NU9T44fvw4Q4cOxd7eHm9vb7XNmVXHte0XlTFz5ky8vLwq3IzfxcWFhw8f8tNPPz1VXbqIMIA6jJ6eHosWLeLrr79m5cqV/PDDD3z00Ufl5p8/fz7m5ubs27ePiIgIfHx8MDIyAiAxMREfHx+GDh3KoUOHWLt2LefOnWPZsmVaydS4cWOKi4spLCxkx44dbNu2DV9fXw4dOoSrqyvvvvuuPOju3LmTEydOEBwczLFjx/joo480ZjQqvvzySwA+++wzoqOj5QG3NMOHDycrK0s2LABZWVmcPn2a0aNHAyVG3tfXF09PTyIjIwkICCAiIoJPPvmkym1MTk4mOjpa1h2UDOTm5uasW7eOI0eOMG3aNNauXUtkZCQAEyZM4NVXX6Vv375ER0cTHR2NQqFAqVTi7e1Ns2bN2L17N59//jlNmzZl4sSJFBQUlFl/VlYWCQkJ2NnZVSpro0aNUCqVQMnDwuXLl9m8eTNffPEFkiQxadIk+XhAQAAFBQXs2rWLw4cPM3/+fJo2bapRpkKh4P3338fExERuy4QJEzTyubm58f3336sZo+joaPLy8hg8eDAAoaGhHDx4EH9/f44cOYKXlxcLFizgxx9/rLRtKqpyH+Tl5bF582ZWrVrF559/TnZ2tlpkhur0Cw8PD/z8/KosZ3kYGxvTvXt3zp49+9Rl6RrCAOoAJ0+eRKFQyD9VKCMvLy9eeuklXnjhBV5++WVmz57N0aNHyy0nJSUFFxcXrKyssLCw4NVXX5WjwYeGhuLm5oaXlxcWFhY4OTmxaNEiDh48SH5+fpXkvHXrFp9//jl2dnaYmJgQHh6Oj48PI0eOpEuXLixYsAAbGxt5ZpSamkrnzp3p1asXHTp0wNnZmVGjRpVZtsq11rJlS8zMzGjZsqVGnhYtWtCvXz8OHz4sp33zzTe0atWKPn36ABASEsKkSZN444036NixI6+88gqzZs1iz549Fbbtt99+Q6FQ4ODgwKBBg7h27Zpa+B8jIyNmzpyJvb09HTt2ZPTo0YwZM0aezTRr1ozGjRurzeaNjY2JjIykuLiYFStWYG1tjZWVFYGBgaSmppZrBFJTU5EkiTZt2pQrryRJxMTEEB0dTZ8+fbh16xYnTpxg+fLlODs7Y2NjQ1BQEPfu3ZNn2SkpKTg5OWFtbU3Hjh0ZMGAAvXv31ijb2NiY5s2by7N/MzMzmjVrppHP1dWVJk2a8O2338ppX3/9NQMHDsTExISCggJCQ0P58MMP6du3Lx07dmTMmDGMHj2aL774osLrUZqq3AdKpZIPPvgAhUKBnZ0dK1eu5Oeff+bixYtA9fpFu3btquQWrgpt2rQhJSWlRsrSJYTDWAfo06cPS5culf9u0qQJUBJBOTQ0lBs3bpCTk0NRURH5+fk8fvxYzlOad955h8WLF/PVV1/JbrxOnToBJe7RX3/9Vc14SJJEcXExycnJWFlZlSnbo0ePUCgUFBcXk5+fT69evVi+fDk5OTmkpaXh5OSklt/JyYn4+HigJFbYhAkTGD58OH379uXvf/87rq6uT6UrNzc3lixZwtKlSzE2Nubw4cOMHDlSjtUWHx/P+fPn1Z7sK9MbgKWlJZs3byY/P59Dhw4RFxfHv/71L7U8u3fvZv/+/aSkpJCfn49SqZQfMMojPj6exMREDT3l5+eTmJhY5jl5eXlAyezuSVQPS0qlEkmSGDVqFDNmzODMmTMYGhrSs2dPOW+rVq2wtLTk+vXrAHh6erJ06VKio6NxcXFh6NChlcpfEYaGhrz66qscPnyY119/ndzcXL777jvWrFkDwO3bt3n8+LHG7FGpVNK9e/cq11OV+8DQ0BB7e3v5HCsrK5577jmuX7+Og4NDtfrF6tWrtdJHRTRq1IjHjx/XWHm6gjCAOkCTJk3o3LmzWlpycjKTJ09m3LhxzJkzhxYtWnDu3DkWLVqEUqks84adMWMGo0aNIioqilOnTrF+/XrWrl3LkCFDyM3NZezYsfK7tdK0a9euXNmaNWvGgQMH0NfXx8zMjMaNGwOQk5NTabtsbW357rvvOHXqFDExMcyePRsXFxfWr19f6bnlMXDgQBYvXszJkyext7fn7Nmzau//cnNzmTFjBkOHDtU4tyyDosLIyEi+BvPnz2fSpEmEhITI77iOHDnCqlWr8PX1RaFQ0KxZM8LDw7lw4UKF8ubm5mJra0tQUJDGsfIWlLRq1Qooeaf3ZB7Vw5KRkRFt2rTRalGFu7s7rq6unDx5kv/9739s2bIFX1/fMvtEVXFzc8PDw4P79+/zv//9j0aNGslBT1Xvl0NDQ2nbtq3aecbGxlUqvzr3QVlUt1/UFA8fPpQfRgVVRxhAHeXKlStIkoSfn588u6nI/anC0tISS0tLvLy8mDt3Lvv372fIkCH06NGDhIQEDUNbGfr6+mWeY2JiQps2bTh//jx/+9vf5PTz58/j4OCglm/EiBGMGDGCYcOGMXHiRLKysjRcnKr3bUVFRRXK06hRI4YOHcrhw4e5ffs2lpaW2Nraysd79OjBzZs3tW7nk0ydOpV///vfjBs3jrZt23L+/HkUCgVvv/22nOfJGZyRkZHGAh1bW1uOHj2KqakpJiYmVaq7U6dOmJiYcP36dSwtLdWOlfWwBCUznsLCQi5cuCDPNjMzM7l58yZdu3aV87Vr145x48Yxbtw4Pv74Y/bu3VumATQyMqr0WkDJjN/c3JzIyEhOnTrF8OHD5WtpZWWFsbExKSkpan1EG6p6HxQWFnL58mW57924cYPs7GzZs1FT/aK6XLt2jWHDhtVL3X9lxDtAHaVz584olUp27txJUlISBw8erPB9RV5eHgEBAcTGxnLnzh3OnTvHpUuX5AHAx8eHn3/+mYCAAOLi4rh16xbHjx8nICCg2jJ6e3sTFhZGZGQkN27cICgoiPj4eDw9PYGS1Xtff/01169f5+bNmxw7dgwzMzOee+45jbJMTU1p3Lgxp0+fJiMjg0ePHpVbr5ubGydPnmT//v24ubmpHZs2bRpfffUVISEhXLt2jevXr3PkyBHWrl2rVdsUCgXW1tbyqtXOnTtz+fJlTp8+zc2bNwkODlZbYQvQoUMHfv31V27cuMGDBw9QKpW4ubnRqlUrpk6dytmzZ0lKSiI2Npbly5dz9+7dMuvW19fHxcWFc+fOVVleCwsLBg0axJIlSzh79izx8fEsWLCAtm3byitsV6xYwenTp0lKSuLKlSvExsaW6/ru0KEDubm5nDlzhgcPHlTovhs1ahR79uwhJiZG7XqYmJgwYcIEAgMDOXDgAImJiVy5coWdO3dy4MCBKrWrqveBkZERy5Yt48KFC1y+fJmFCxfi6OgoG8Tq9Iv33nuPjz/+uEL5UlJSiIuLIyUlhaKiIuLi4oiLi1NbGJScnMy9e/dwcXGpUpsFfyJmgDqKjY0NCxcuJCwsjDVr1uDs7MzcuXPx9fUtM7++vj5ZWVn4+vqSkZFBq1atGDp0qLygxsbGhp07dxIcHMz48eMB6NixIyNGjKi2jJ6enuTk5LBy5UoePHiAlZUVmzZtwsLCAihxn27dupXbt2+jr6+Pvb09W7ZskZ/kS2NoaMjixYvZuHEj69evx9nZmZ07d5ZZ70svvUSLFi24efOmhgHs27cvn3zyCRs3biQsLAxDQ0O6dOmCu7u71u3z8vLCz88PHx8fxo4dS1xcHHPmzEFPT4+RI0cyfvx4tc8+3nzzTX788Uf+8Y9/kJuby44dO+jTpw+7du0iKCiI6dOn8/vvv9O2bVtefvnlCmeE//znP1myZAkLFiwoU19lERgYyIoVK5gyZQpKpRJnZ2e2bNkiz8iKi4sJCAjg7t27mJiY0LdvXzX3cWmcnJwYO3Yss2fPJisri+nTpzNjxowy844ePZpPPvmEDh060KtXL7Vjs2fP5vnnnyc0NJTk5GSaN29Ojx491D7wr4iq3geNGzfGx8eHefPmce/ePZydnVmxYoV8vDr9IjU1tVLdr1+/Xs2Yv/766wDytYcS9/krr7xS7gpoQfnoSZIk1bcQAoGgbpEkCXd3d7y8vMpdOSt49ikoKGDYsGEEBQVpPBwIKke4QAUCHURPT49ly5bVyDZngvojNTWVyZMnC+NXTcQMUCAQCAQ6iZgBCgQCgUAnEQZQIBAIBDqJMIACgUAg0EmEARQIBAKBTiIMoEAgEAh0EmEABQKBQKCTCAMoEAgEAp1EGECBQCAQ6CTCAAoEAoFAJ/l/etz04vS3XGoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Skipping group American Indian plot as it's too small.\n",
+      "WARNING:root:Skipping group Alaska Native plot as it's too small.\n",
+      "WARNING:root:Skipping group American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races plot as it's too small.\n",
+      "WARNING:root:Skipping group Native Hawaiian and Other Pacific Islander plot as it's too small.\n",
+      "WARNING:root:Skipping group Some other race alone (non-White) plot as it's too small.\n",
+      "WARNING:root:Skipping group Two or more races plot as it's too small.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve.pdf',\n",
+       " 'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve.pdf',\n",
+       " 'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution.pdf',\n",
+       " 'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/score_distribution_per_label.pdf',\n",
+       " 'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/roc_curve_per_subgroup.pdf',\n",
+       " 'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/mistralai--Mistral-7B-Instruct-v0.2_bench-3440007098/imgs/calibration_curve_per_subgroup.pdf'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bench.plot_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b83085bc-9df7-43b0-9282-cfd4c83713b7",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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+
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+<!DOCTYPE html>
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+    <link rel="next" title="Fetch and parse ACS benchmark results under a given directory" href="parse-acs-results.html" />
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+<li class="toctree-l1"><a class="reference internal" href="../readme.html">Readme file</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../source/modules.html">API reference</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../notebooks.html">Example notebooks</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="minimal-example.html">Evaluate model calibration using folktexts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="detailed-example.html">Example: Run ACS benchmark task</a></li>
+<li class="toctree-l2"><a class="reference internal" href="custom-dataset-example.html">Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a different data source</a></li>
+<li class="toctree-l2"><a class="reference internal" href="custom-acs-task-example.html">Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a custom ACS task</a></li>
+<li class="toctree-l2"><a class="reference internal" href="paper-plots-and-tables.html">Render paper plots and tables</a></li>
+<li class="toctree-l2"><a class="reference internal" href="paper-plots-and-tables.html#Plots-comparing-different-prompting-schemes">Plots comparing different prompting schemes</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Evaluate model calibration using folktexts</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#1.-Check-folktexts-is-installed">1. Check folktexts is installed</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#2.-Load-model-API-using-litellm">2. Load model API using <code class="docutils literal notranslate"><span class="pre">litellm</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#3.-Set-OPENAI_API_KEY-(or-key-to-respective-API-provider)">3. Set <code class="docutils literal notranslate"><span class="pre">OPENAI_API_KEY</span></code> (or key to respective API provider)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#3.-Create-default-benchmarking-tasks">3. Create default benchmarking tasks</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Benchmark-configuration">Benchmark configuration</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#4.-Run-benchmark">4. Run benchmark</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#4.-Visualize-results">4. Visualize results</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="parse-acs-results.html">Fetch and parse ACS benchmark results under a given directory</a></li>
+</ul>
+</li>
+</ul>
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+           <div itemprop="articleBody">
+             
+  <section id="Evaluate-model-calibration-using-folktexts">
+<h1>Evaluate model calibration using folktexts<a class="headerlink" href="#Evaluate-model-calibration-using-folktexts" title="Link to this heading"></a></h1>
+<p><strong>Prerequisite</strong>: Install <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> package with optional model API dependencies: <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">'folktexts[apis]'</span></code></p>
+<p><strong>Summary</strong>: The script demonstrates how to use folktexts to get insights into model calibration on a <strong>model hosted through a web API</strong>.</p>
+<section id="1.-Check-folktexts-is-installed">
+<h2>1. Check folktexts is installed<a class="headerlink" href="#1.-Check-folktexts-is-installed" title="Link to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">folktexts</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">folktexts</span><span class="o">.</span><span class="n">__version__</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+folktexts.__version__=&#39;0.0.21&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="2.-Load-model-API-using-litellm">
+<h2>2. Load model API using <code class="docutils literal notranslate"><span class="pre">litellm</span></code><a class="headerlink" href="#2.-Load-model-API-using-litellm" title="Link to this heading"></a></h2>
+<p>We use OpenAI’s <strong>GPT-4o-mini</strong> model for this demo. The workflow can be similarly applied to any compatible model.</p>
+<p><strong>Note:</strong> <em>Set</em> <code class="docutils literal notranslate"><span class="pre">model_name</span></code> to the model’s name. See the <code class="docutils literal notranslate"><span class="pre">litellm</span></code> <a class="reference external" href="https://docs.litellm.ai/docs/providers/openai#openai-chat-completion-models">list of compatible web-API providers and models</a>.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_name</span> <span class="o">=</span> <span class="s2">&quot;openai/gpt-4o-mini&quot;</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="3.-Set-OPENAI_API_KEY-(or-key-to-respective-API-provider)">
+<h2>3. Set <code class="docutils literal notranslate"><span class="pre">OPENAI_API_KEY</span></code> (or key to respective API provider)<a class="headerlink" href="#3.-Set-OPENAI_API_KEY-(or-key-to-respective-API-provider)" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;OPENAI_API_KEY&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;your-key-here&quot;</span>  <span class="c1"># NOTE: Substitute with your key here!</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="3.-Create-default-benchmarking-tasks">
+<h2>3. Create default benchmarking tasks<a class="headerlink" href="#3.-Create-default-benchmarking-tasks" title="Link to this heading"></a></h2>
+<p>We generate ACSIncome benchmark using folktexts.</p>
+<p><strong>NOTE:</strong> We will subsample the reference data for faster runtime. This should be removed for obtaining reproducible reslts.</p>
+</section>
+<section id="Benchmark-configuration">
+<h2>Benchmark configuration<a class="headerlink" href="#Benchmark-configuration" title="Link to this heading"></a></h2>
+<ul class="simple">
+<li><p>The <code class="docutils literal notranslate"><span class="pre">subsampling</span></code> and <code class="docutils literal notranslate"><span class="pre">numeric_risk_prompting</span></code> key-word arguments are examples of optional benchmark configurations. See this <a class="reference external" href="https://github.com/socialfoundations/folktexts?tab=readme-ov-file#benchmark-options">page for a list of available configs</a>.</p></li>
+</ul>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="kn">from</span> <span class="nn">folktexts.benchmark</span> <span class="kn">import</span> <span class="n">Benchmark</span><span class="p">,</span> <span class="n">BenchmarkConfig</span>
+
+<span class="c1"># Note: This argument is optional. Omit, or set to 1 for reproducible benchmarking on the full data</span>
+<span class="n">subsampling_ratio</span> <span class="o">=</span> <span class="mf">0.005</span>
+
+<span class="n">bench</span> <span class="o">=</span> <span class="n">Benchmark</span><span class="o">.</span><span class="n">make_acs_benchmark</span><span class="p">(</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
+    <span class="n">task_name</span><span class="o">=</span><span class="s2">&quot;ACSIncome&quot;</span><span class="p">,</span>
+    <span class="n">subsampling</span><span class="o">=</span><span class="n">subsampling_ratio</span><span class="p">,</span>
+    <span class="n">numeric_risk_prompting</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING:root:Received non-standard ACS argument &#39;subsampling&#39; (using subsampling=0.005 instead of default subsampling=None). This may affect reproducibility.
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Loading ACS data...
+Using zero-shot prompting.
+CPU times: user 52.6 s, sys: 1min 23s, total: 2min 16s
+Wall time: 2min 21s
+</pre></div></div>
+</div>
+</section>
+<section id="4.-Run-benchmark">
+<h2>4. Run benchmark<a class="headerlink" href="#4.-Run-benchmark" title="Link to this heading"></a></h2>
+<p>Results will be saved in a folder RESULTS_DIR. There is * <code class="docutils literal notranslate"><span class="pre">.json</span></code> file contains evaluated metrics * <code class="docutils literal notranslate"><span class="pre">.cvs</span></code> file contains risk scores of each datapoint * folder called <code class="docutils literal notranslate"><span class="pre">imgs/</span></code> contains figures</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">RESULTS_DIR</span> <span class="o">=</span> <span class="s2">&quot;res&quot;</span>
+<span class="n">bench</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">results_root_dir</span><span class="o">=</span><span class="n">RESULTS_DIR</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "2706f59a1b1b4a7492f9d9376ba60cd9", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING:root:Failed to compute ECE quantile: The smallest edge difference is numerically 0.
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;threshold&#39;: 0.5,
+ &#39;n_samples&#39;: 832,
+ &#39;n_positives&#39;: 305,
+ &#39;n_negatives&#39;: 527,
+ &#39;model_name&#39;: &#39;openai/gpt-4o-mini&#39;,
+ &#39;accuracy&#39;: 0.7884615384615384,
+ &#39;tpr&#39;: 0.6885245901639344,
+ &#39;fnr&#39;: 0.3114754098360656,
+ &#39;fpr&#39;: 0.15370018975332067,
+ &#39;tnr&#39;: 0.8462998102466793,
+ &#39;balanced_accuracy&#39;: 0.7674122002053069,
+ &#39;precision&#39;: 0.7216494845360825,
+ &#39;ppr&#39;: 0.34975961538461536,
+ &#39;log_loss&#39;: 0.8249689807687466,
+ &#39;brier_score_loss&#39;: np.float64(0.15596153846153846),
+ &#39;tpr_ratio&#39;: 0.0,
+ &#39;tpr_diff&#39;: 0.782608695652174,
+ &#39;precision_ratio&#39;: 0.0,
+ &#39;precision_diff&#39;: 0.9,
+ &#39;tnr_ratio&#39;: 0.8177339901477833,
+ &#39;tnr_diff&#39;: 0.18226600985221675,
+ &#39;fnr_ratio&#39;: 0.21739130434782608,
+ &#39;fnr_diff&#39;: 0.782608695652174,
+ &#39;ppr_ratio&#39;: 0.0,
+ &#39;ppr_diff&#39;: 0.47619047619047616,
+ &#39;accuracy_ratio&#39;: 0.7,
+ &#39;accuracy_diff&#39;: 0.30000000000000004,
+ &#39;balanced_accuracy_ratio&#39;: 0.5961800818553888,
+ &#39;balanced_accuracy_diff&#39;: 0.33867276887871856,
+ &#39;fpr_ratio&#39;: 0.0,
+ &#39;fpr_diff&#39;: 0.18226600985221675,
+ &#39;equalized_odds_ratio&#39;: 0.0,
+ &#39;equalized_odds_diff&#39;: 0.782608695652174,
+ &#39;roc_auc&#39;: np.float64(0.8337263197187919),
+ &#39;ece&#39;: 0.032091346153846276,
+ &#39;ece_quantile&#39;: None,
+ &#39;predictions_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/ACSIncome_subsampled-0.005_seed-42_hash-305608976.test_predictions.csv&#39;,
+ &#39;config&#39;: {&#39;numeric_risk_prompting&#39;: True,
+  &#39;few_shot&#39;: None,
+  &#39;reuse_few_shot_examples&#39;: False,
+  &#39;batch_size&#39;: None,
+  &#39;context_size&#39;: None,
+  &#39;correct_order_bias&#39;: True,
+  &#39;feature_subset&#39;: None,
+  &#39;population_filter&#39;: None,
+  &#39;seed&#39;: 42,
+  &#39;model_name&#39;: &#39;openai/gpt-4o-mini&#39;,
+  &#39;model_hash&#39;: 920159687,
+  &#39;task_name&#39;: &#39;ACSIncome&#39;,
+  &#39;task_hash&#39;: 127998692,
+  &#39;dataset_name&#39;: &#39;ACSIncome_subsampled-0.005_seed-42_hash-305608976&#39;,
+  &#39;dataset_hash&#39;: 305608976},
+ &#39;plots&#39;: {&#39;roc_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve.pdf&#39;,
+  &#39;calibration_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve.pdf&#39;,
+  &#39;score_distribution_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution.pdf&#39;,
+  &#39;score_distribution_per_label_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution_per_label.pdf&#39;,
+  &#39;roc_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve_per_subgroup.pdf&#39;,
+  &#39;calibration_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve_per_subgroup.pdf&#39;}}
+</pre></div></div>
+</div>
+</section>
+<section id="4.-Visualize-results">
+<h2>4. Visualize results<a class="headerlink" href="#4.-Visualize-results" title="Link to this heading"></a></h2>
+<p>We can also visualize the results inline:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">bench</span><span class="o">.</span><span class="n">plot_results</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_minimal-example_web-API-model_12_0.png" src="../_images/notebooks_minimal-example_web-API-model_12_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_minimal-example_web-API-model_12_1.png" src="../_images/notebooks_minimal-example_web-API-model_12_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_minimal-example_web-API-model_12_2.png" src="../_images/notebooks_minimal-example_web-API-model_12_2.png" />
+</div>
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+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_minimal-example_web-API-model_12_3.png" src="../_images/notebooks_minimal-example_web-API-model_12_3.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_minimal-example_web-API-model_12_4.png" src="../_images/notebooks_minimal-example_web-API-model_12_4.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_minimal-example_web-API-model_12_5.png" src="../_images/notebooks_minimal-example_web-API-model_12_5.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;roc_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve.pdf&#39;,
+ &#39;calibration_curve_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve.pdf&#39;,
+ &#39;score_distribution_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution.pdf&#39;,
+ &#39;score_distribution_per_label_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution_per_label.pdf&#39;,
+ &#39;roc_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve_per_subgroup.pdf&#39;,
+ &#39;calibration_curve_per_subgroup_path&#39;: &#39;/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve_per_subgroup.pdf&#39;}
+</pre></div></div>
+</div>
+<hr class="docutils" />
+</section>
+</section>
+
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+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="paper-plots-and-tables.html" class="btn btn-neutral float-left" title="Render paper plots and tables" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="parse-acs-results.html" class="btn btn-neutral float-right" title="Fetch and parse ACS benchmark results under a given directory" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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diff --git a/notebooks/minimal-example_web-API-model.ipynb b/notebooks/minimal-example_web-API-model.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "181f6c46",
+   "metadata": {},
+   "source": [
+    "# Evaluate model calibration using folktexts\n",
+    "\n",
+    "**Prerequisite**: Install `folktexts` package with optional model API dependencies: `pip install 'folktexts[apis]'`\n",
+    "\n",
+    "**Summary**: The script demonstrates how to use folktexts to get insights into model calibration on a **model hosted through a web API**.\n",
+    "\n",
+    "### 1. Check folktexts is installed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f3867376",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "folktexts.__version__='0.0.21'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import folktexts\n",
+    "print(f\"{folktexts.__version__=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2129c830",
+   "metadata": {},
+   "source": [
+    "### 2. Load model API using `litellm`\n",
+    "\n",
+    "We use OpenAI's _**GPT-4o-mini**_ model for this demo. The workflow can be similarly applied to any compatible model.\n",
+    "\n",
+    "**Note:** _Set_ `model_name` to the model's name. See the `litellm` [list of compatible web-API providers and models](https://docs.litellm.ai/docs/providers/openai#openai-chat-completion-models)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3dc758de-b86f-4adb-9ed0-0148831da070",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_name = \"openai/gpt-4o-mini\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c58aff1e-082d-4f3f-b061-1f1028d3520d",
+   "metadata": {},
+   "source": [
+    "### 3. Set `OPENAI_API_KEY` (or key to respective API provider)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28d73863-f448-4370-b26a-24576e1498f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-key-here\"  # NOTE: Substitute with your key here!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25a2a5f7",
+   "metadata": {},
+   "source": [
+    "### 3. Create default benchmarking tasks\n",
+    "\n",
+    "We generate ACSIncome benchmark using folktexts. \n",
+    "\n",
+    "**NOTE:** We will subsample the reference data for faster runtime. This should be removed for obtaining reproducible reslts.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed481a4-5258-468f-8fb5-91ed794d1503",
+   "metadata": {},
+   "source": [
+    "### Benchmark configuration\n",
+    "- The `subsampling` and `numeric_risk_prompting` key-word arguments are examples of optional benchmark configurations. See this [page for a list of available configs](https://github.com/socialfoundations/folktexts?tab=readme-ov-file#benchmark-options)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bbb17808",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Received non-standard ACS argument 'subsampling' (using subsampling=0.005 instead of default subsampling=None). This may affect reproducibility.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "Using zero-shot prompting.\n",
+      "CPU times: user 52.6 s, sys: 1min 23s, total: 2min 16s\n",
+      "Wall time: 2min 21s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.benchmark import Benchmark, BenchmarkConfig\n",
+    "\n",
+    "# Note: This argument is optional. Omit, or set to 1 for reproducible benchmarking on the full data\n",
+    "subsampling_ratio = 0.005\n",
+    "\n",
+    "bench = Benchmark.make_acs_benchmark(\n",
+    "    model=model_name,\n",
+    "    task_name=\"ACSIncome\",\n",
+    "    subsampling=subsampling_ratio,\n",
+    "    numeric_risk_prompting=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f785fc1",
+   "metadata": {},
+   "source": [
+    "### 4. Run benchmark\n",
+    "\n",
+    "Results will be saved in a folder RESULTS_DIR. There is \n",
+    "* `.json` file contains evaluated metrics\n",
+    "* `.cvs` file contains risk scores of each datapoint\n",
+    "* folder called `imgs/` contains figures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "62b90528",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2706f59a1b1b4a7492f9d9376ba60cd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing risk estimates:   0%|          | 0/52 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to compute ECE quantile: The smallest edge difference is numerically 0.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'threshold': 0.5,\n",
+       " 'n_samples': 832,\n",
+       " 'n_positives': 305,\n",
+       " 'n_negatives': 527,\n",
+       " 'model_name': 'openai/gpt-4o-mini',\n",
+       " 'accuracy': 0.7884615384615384,\n",
+       " 'tpr': 0.6885245901639344,\n",
+       " 'fnr': 0.3114754098360656,\n",
+       " 'fpr': 0.15370018975332067,\n",
+       " 'tnr': 0.8462998102466793,\n",
+       " 'balanced_accuracy': 0.7674122002053069,\n",
+       " 'precision': 0.7216494845360825,\n",
+       " 'ppr': 0.34975961538461536,\n",
+       " 'log_loss': 0.8249689807687466,\n",
+       " 'brier_score_loss': np.float64(0.15596153846153846),\n",
+       " 'tpr_ratio': 0.0,\n",
+       " 'tpr_diff': 0.782608695652174,\n",
+       " 'precision_ratio': 0.0,\n",
+       " 'precision_diff': 0.9,\n",
+       " 'tnr_ratio': 0.8177339901477833,\n",
+       " 'tnr_diff': 0.18226600985221675,\n",
+       " 'fnr_ratio': 0.21739130434782608,\n",
+       " 'fnr_diff': 0.782608695652174,\n",
+       " 'ppr_ratio': 0.0,\n",
+       " 'ppr_diff': 0.47619047619047616,\n",
+       " 'accuracy_ratio': 0.7,\n",
+       " 'accuracy_diff': 0.30000000000000004,\n",
+       " 'balanced_accuracy_ratio': 0.5961800818553888,\n",
+       " 'balanced_accuracy_diff': 0.33867276887871856,\n",
+       " 'fpr_ratio': 0.0,\n",
+       " 'fpr_diff': 0.18226600985221675,\n",
+       " 'equalized_odds_ratio': 0.0,\n",
+       " 'equalized_odds_diff': 0.782608695652174,\n",
+       " 'roc_auc': np.float64(0.8337263197187919),\n",
+       " 'ece': 0.032091346153846276,\n",
+       " 'ece_quantile': None,\n",
+       " 'predictions_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/ACSIncome_subsampled-0.005_seed-42_hash-305608976.test_predictions.csv',\n",
+       " 'config': {'numeric_risk_prompting': True,\n",
+       "  'few_shot': None,\n",
+       "  'reuse_few_shot_examples': False,\n",
+       "  'batch_size': None,\n",
+       "  'context_size': None,\n",
+       "  'correct_order_bias': True,\n",
+       "  'feature_subset': None,\n",
+       "  'population_filter': None,\n",
+       "  'seed': 42,\n",
+       "  'model_name': 'openai/gpt-4o-mini',\n",
+       "  'model_hash': 920159687,\n",
+       "  'task_name': 'ACSIncome',\n",
+       "  'task_hash': 127998692,\n",
+       "  'dataset_name': 'ACSIncome_subsampled-0.005_seed-42_hash-305608976',\n",
+       "  'dataset_hash': 305608976},\n",
+       " 'plots': {'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve.pdf',\n",
+       "  'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve.pdf',\n",
+       "  'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution.pdf',\n",
+       "  'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution_per_label.pdf',\n",
+       "  'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve_per_subgroup.pdf',\n",
+       "  'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve_per_subgroup.pdf'}}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RESULTS_DIR = \"res\"\n",
+    "bench.run(results_root_dir=RESULTS_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20636ce4",
+   "metadata": {},
+   "source": [
+    "### 4. Visualize results\n",
+    "\n",
+    "We can also visualize the results inline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "edd5bf8f-cd70-4276-9c14-f1e0012aee0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TJbof4+Pj8fDhQ7nLD5Q7pKCgIJw7dw5hYWHQ19ev0ort168fHj16xIxVVSQvLw9lZWU15u/m5oaOHTsyf76+vjUe6+vrC2traxw+fBgCgYBJv379Ol6+fIlu3bpVOefgwYPM/2maxsGDB6Gvr8840H79+kEoFOLXX3+tcm5ZWVmtusa4XG6Ve3zu3DmZEaClpaW4ffu2xPgfAAQFBQEA/vrrL4n0itNQKlOx3BWP7dKlC5NmYmJSpXwWFhYS96Njx44wNDRE+/btsW3btip/VlZW8PX1xbZt25jpNV26dIFQKKxiwx9//AGKohgbmjZtKnGdis6QoNmQFqCWEBISgpkzZ+LEiRMYNWoUAGDSpEmIi4vDzp07ERUVhd69e8PIyAiRkZE4c+YMmjdvLuGMgPKWzosXL7Bnzx7cvXuXWQnm7du3uHTpEqKjo3H48GGptqxZswYTJkzA9OnTmZVgjI2NkZSUhPDwcLx584aZC/jJJ5/gjz/+QEhICD755BNkZWXh8OHDcHNzU3gsUhwy/tdffyEoKKhKKy0kJARXrlzBlClTMHToUPj4+KCoqAjPnz/HhQsXcPny5SpdibVBX18f8+bNw6JFizB69GgMGDAAWVlZ2L9/PxwdHTF+/HiJ4w0NDXHz5k0sXLgQ/v7+uHnzJq5du4YpU6Yw9rRt2xaffvopduzYgbi4OHTq1An6+vp4/fo1zp8/j8WLFzPTPeSlW7du2LZtGxYtWoTAwEA8f/4cf//9d5XxyMpERkYiPz+/iiMXz6Pbt28f3r17h5YtW+L+/ft4/fo1gOq7N5OTkzFlyhR07twZUVFROHPmDAYOHAhPT0/mGB8fH0RERGDv3r2ws7NDkyZNmICSyjg4OFRpYQPATz/9BBsbG4mXouDgYLRr1w4bNmxASkoKPDw8cPv2bVy+fBnjxo2TCEIhaCfEAWoJvXv3RrNmzbBnzx6MHDmSWSVi48aNOHXqFI4ePYpNmzahtLQUzZo1w7Rp0zBhwgSYmJhI5MPhcLBmzRr06NEDR44cwZ49e5Cfnw9LS0u0adMG8+fPl4h4qw4rKyscPnwYf/31F8LDw7FhwwaUlpbC0dERwcHBEmM3Yie8efNmrFq1Cm5ublizZg3Onj3LdGfJS3BwMIyMjFBQUCAR/SnG2NgYBw4cwI4dO3D+/HmcOnUKZmZmcHZ2xowZM5jgBWUwbNgwGBkZYefOnVi3bh1MTEzQs2dPzJ8/v4pj5nK52LVrF5YuXYq1a9fC1NQU06dPx7Rp0ySOW758OXx9fXH48GFs2LABXC4Xjo6OGDx4sNSl6WpiypQpKCoqwt9//43w8HB4e3tjx44dWL9+vdTzrl+/Djc3N6aLsCKrV6+GjY0NwsLCcPHiRXTs2BEbNmxA3759q43I3LhxIzZt2oT169dDT08Po0ePxoIFCySOCQ0NxZIlS7Bx40YUFxdj6NChNTpAReBwOPjtt9+wefNmhIeH48SJE3B0dMSCBQswYcKEOudPUH/IWqAEggoJDQ3FhQsX8OjRI1WbIjf9+/dHt27dqjiqmoiLi8OQIUOwdu1aZhx1y5Yt2Lp1KyIiIpTS6iYQagNpARIIBLkRCATo378/+vXrV+33xcXFVYJ09u3bBw6HgzZt2tSHiQSC3BAHSCAQ5MbAwKDaCEsxu3btwpMnT9C+fXtwuVzcuHEDN27cwKefflrt3DYCQZUQB0ggEJRGYGAgbt++jV9//RWFhYWwt7fHjBkzMGXKFFWbRiBUgYwBEggEAkEnIfMACQQCgaCTEAdIIBAIBJ2EOEACgUAg6CTEARIIBAJBJyEOkEAgEAg6CXGABAKBQNBJiAMkEAgEgk5CHCCBQCAQdBLiAAkEAoGgkxAHSCAQCASdhDhAAoFAIOgkxAESCAQCQSchDpBAIBAIOglxgAQCgUDQSYgDJBAIBIJOQhwggUAgEHQS4gAJBAKBoJMQB0ggEAgEnYQ4QAKBQCDoJMQBEggEAkEnIQ6QQCAQCDoJcYAEAoFA0EmIAyQQCASCTkIcIIFAIBB0EuIACQQCgaCTEAdIIBAIBJ2EOEACgUAg6CR6qjagvhGJRCgrKwOHwwFFUao2h0AgEAgKQtM0RCIR9PT0wOHUvh2nUgd4//597N69G0+ePEFmZia2bduGnj17Sj3n7t27+Pnnn5GQkAB7e3tMnToVw4YNk/uaZWVliImJqavpBAKBQFAxfn5+MDAwqPX5KnWAhYWF8PDwwPDhwzF9+nSZx/P5fEyePBmfffYZ1q1bh4iICHz33XewtbVF586d5bqm+G3Bz88PXC4XQqEQr1+/hrOzM7hcbp3Ko40QfWRDNJIO0Uc2RCPpVNZHKBQiJiamTq0/QMUOsGvXrujatavcxx8+fBhNmjRBaGgoAKB58+aIjIzEH3/8IbcDFHd7crlcpqK9f/9e4jNBEqKPbIhG0iH6yIZoJAlN08gvKcPbfAHe5BYhmZ+N5s2bS+hT12EsjRoDjIqKQocOHSTSgoKC8NNPPymcl1AolPgXKB8fpGma+UxRFDgcjsQx0tLF44rVpYvzlyedy+UyfdyV0yvbWFO62Ma6lkl8rjaVSdn3qXJd0oYyKfM+iW0SiUTM27uml0mW7YqWSdHfmSaUqTrbaZpGYakIb/MFyMwrRmZ+Cd7ml+BtvgBZBQJkvv/wOfN9CUrKRCjLy8TbsA2w7vcNPD3c4WlvUeVatUWjHODbt29hY2MjkWZjY4P8/HwUFxfDyMhI7ryqGwfk8XjIyspiPtvb28PBwQGJiYnIy8tj0p2cnGBjY4P4+HgUFxcz6W5ubrCwsEB0dLTETff29oaBgQGioqIkrhcQEACBQICnT58yaRwOB4GBgcjLy8OLFy+YdCMjI/j4+CA7OxtJSUlMeoMGDdCiRQukp6cjLS2NSbe2toazs3Ody+Tq6goAiI2N1ZoysXWfYmJitK5MgPLuU0ZGBpo0aaJVZVLWffLw8ABQ9bmkCWVq3rw5uEamuHEvCjlFZXhXLMK7YiH0za2RU1SGlymZeFcsQu5/6QJJny2VsrxMvDm8CKU56Si7shU54/0RlcGBubm5/JlIgaIrvyqoCA8PD5lBMH369MGwYcMwefJkJu369euYNGkSHj9+LJcDFAqFiIqKYsYARSIRcnJyGMeqrW/htS0TAOTk5MDCwkKiv12Ty6Ts+1RWVoacnBxYWlqCw+FoRZmUeZ/EvzErKyvo6elpRZlk2a5omQAgOzsbDRs2lOt3xnaZKIpCbqEAb/NLkJUv+K+lJkB2gQBv3hfj7XsB3hZIttQUwdSAC2szQ9iaG8DGzBA2pgawbWAEWzNDWJnqw8bMAKW5mRg9bABevXoFV1dXHDt2DL6+voyuMTExCAgIqFOXsUa1AG1sbPD27VuJtLdv38LMzEyh1h/wYQyQy+XCzs5O5rH1nU5RVLXpNQ36KpquiC2VW921zUedyqSsdIqioK+vX6UOaXqZlHmfKv/GtKFMbNhoa2tb7bE1Ha9omSiKQoFAiLf55Y6N6W58X4LM/Irdj+V/xaWKOzUbc0PYmBnC1swQNv85N9v/0iqmmxjIdj1//nmecX7Xrl1D06ZNFbJHHjTKAQYEBODGjRsSaf/++y8CAgJqnadQKER8fDw8PT3J4HM1EH1kQzSSDtFHNrXVqGKgyAdnxr5Tq+jYFHFqijB69GgA5cGSDg4OiI2NVXodUqkDLCgoAI/HYz4nJycjLi4OFhYWcHBwwPr165GRkYE1a9YAAD777DMcPHgQa9aswfDhw3Hnzh2cO3cOO3bsqJMdFfuzCVUh+siGaCQdoo9sxBrJcmoSrbc6OjUbMwPJFhrLTk0WfD4fxsbGTK+T2AkKhUJW6pBKHeCTJ08wduxY5vOqVasAAEOHDsXPP/+MzMxMiUHbpk2bYseOHVi1ahX279+Pxo0bY8WKFXJPgSAQCARVQNM0CgTCSt2OFVtqxUjKyEHRpeu1cmomBtwKjqyarkdz1Tk1eeHz+ejWrRtMTU1x5coVqUMvykKlSrRr1w7Pnj2r8fuff/652nNOnTrFolUEAoEgm+qcmrh1VvuWWinzPxMDbgVHVp1TM4CtmZFaOzV5ETu/xMREuLq6oqioqF6uq9mqKQEOhwM3N7c6ryigrRB9ZEM0ko4m6cOOU/tAdU5N7MxMOEI0s7OEnbl2ODV5qez8qgt4YasO6YbCUqAoChYWFqo2Q20h+siGaCQdVesjj1Or6Nhq69RqHE+rEDhiaqjzj1wJ5HF+AHt1SOfvhlAoRHR0NPz9/UmEWjUQfWRDNJIO2/rkl5ThVsJb3EnMQnGpECKaRk5haZ2cmrE+l2mlVXRqNv+NpSnbqeliHZLX+QHs6aPzDhCoOsmUIAnRRzZEI+koWx9eViGuxGfgcvwb3E3MhkAoO/+KTk1iPI0lp6YoulaHSktLUVpaKvc8Pzb0IQ6QQCCoPWVCER7y3uFyfAauxL1Bwpt8ie+drE3Q3cMONmblW+NYmBjAtlLrjXQ/qhdix6evr8/KJHd5IDWCQCCoJbmFpbj2/A2uxL/BtWeZyC36ECHJ5VBo42yJHp6NEOxlB1cbU7LBtQbA4/Hw9OlT9O3bF8CHtYZVhc47QA6HA29vb42IUFMFRB/ZEI2kI68+NE3jZWY+Lse9weX4N4hMyoFQ9GGNyoYm+ujuYYdgTzt0cbeFhbE+26bXG7pQh3g8Hrp37w4+n48zZ84wTlAe2NJH5x0ggDrtKKwLEH1kQzSSTk36lJQJce9VNi7Hlbf0eNmFEt97NDJHsJcdenjaIbCZJbgc7W3laXMdEjs/ccCLj4+PwnmwoY/OO0CRSISoqKg6ryqurRB9ZEM0kk5lfTLfl+Dqsze4EvcGNxMyUSD4sDOCAZeDDs2t0cPLDt097NDUykSFltcf2lyHKju/2ixszZY+Ou8ACQQCu9A0jcScUty88gJXn7/FY/47ie9tzQ3Rw7O8a7OTmw0JVtEilOH82ITUNAKBoHSKBELcfvEWl+Pf4Gp8BtLzSgB82HTVv4kFgj3t0MOzEXwcGoCjxV2busqbN2/U2vkBxAESCAQlkfKuCFfi3+BKXAb+fZklsUmqIZdCF3db9PRuhO4edrBroNj+nQTNw8bGBl27dgUAtXR+gBrtCF9fiHeEF/cli3dVFu/UTJCE6CMbXdVIKKIRxX9XPiE97g3i099LfO/Y0Bg9vezQ3dMObZ0awthQX6f0UQRtrUMikQhv376Vuem4LCrrU/k5XltICxCAQCBQeEd5XYLoIxtd0SivuBQ3n7/F5fgMXHuWiewCAfMdhwJaNbNED69G6OFlhxZ2ZqAoCjRN/7eXm/ZMW2ADbahDfD4fmzdvxqpVq6CnpwcOh1Nn5yeGDX103gGKRCI8ffpUK6OvlAHRRzbartHrtwW4HP8Gl+MycO9VNsoqzM0zN9JDV3db9PCyQ1d3O1iZVg1V13Z9lIE2aFRxbU8AWLt2rdLyZksfnXeABAJBEqGIRmRSDi4+Tcfl+DdIzCyQ+N7V1vS/qM1GaO1sCX2u9k7eJshH5YWtv/nmG1WbJBfEARIIBBSXlkdtXohNx+W4N8iq0LWpx6HQztUKwZ6NEOxpBxcbUxVaSlA3FNnVQd0gDhDQ6uWHlAHRRzaaqFFecSmuxL3Bhdh0XH+eicIKE9ItjPUR7GmHXt6NENTCBg2M6jZ+p4n61DeaqFF9Oj829NH5KFACQZegaRoPknJw6B4PYdFpElMV7C2M0Nu7Efr4NEYbFyvStUmQilAoRMuWLREbG1vvLT8SBaokaJpGXl4eGjRooFXhx8qC6CMbTdAou0CAEw+Tcfg+Hy8qbCXkZmeGvj6N0cenMXwd2bFfE/RRNZqoEZfLxS+//II5c+bg3LlzrDo/tvTReQcoEonw4sUL0iKsAaKPbNRVI5GIxp3ELBy6z8eFJ+nMprHG+lwMammPUW2bIaBpQ9YfuOqqjzqhSRrRNM3Umd69e+Px48es28yWPjrvAAkEbePN+2Ici0zG/+7zkZT1YXcFP0cLfNa2KQa3dIB5Hcf0CLoJn8/HF198gV27dsHd3R0A1N5hS4M4QAJBCxCKaNxMyMThe3xcistg5uqZGerh4wAHjGrbDL6OFiq2kqDJVAx4mThxIq5fv64x3bU1QRwgoPGrL7AN0Uc2qtIoPbcYRx7w8b/7fKS8K2LSA5s1xKg2zTCwpT1MDFT/Myd1SDbqrFHlaM+DBw/Wu/NjQx8SBUogaBhlQhGuPcvEoXs8XH32BuKFWSyM9TE00BGftW0Kz8YNVGskQWtQx3l+JApUSYhEImRnZ8PKykoj5+GwDdFHNvWlET+7EEce8HHkAR8ZeSVMelsXK4xq2xT9fO1hpK9+L3WkDslGXTVSF+fHlj467wBpmkZSUhIsLS1VbYpaQvSRDZsalQpFuPQ0A4fu83EzIRPi/horUwMMb+WIT9s0g5udmdKvq0xIHZKNumo0d+5clTs/gD19dN4BEgjqyKu3BTh8n4fjkcl4m/9hWbIgNxt81rYpenk3gqGe+rX2CNrFjh07AADr169XebcnGxAHSCCoCSVlQpx/ko7D9/iISPywe7qtuSFGfNQEn7ZpCidrsg4ngV0KCgpgalpezywtLXHkyBEVW8QexAECaNCABAxIg+gjm7po9OLNexy6x8eJh8nIKSwFAFAU0NXdFp+1aYYeXnYavywZqUOyUQeNeDweunfvjm+++QYzZ85UtTkSsKEPiQIlEFRAkUCI8Jg0HLrHw4OkHCbd3sIII1s3xcg2TeHY0FiFFhJ0DbHzS0xMRPPmzfH48WOmJahukChQJSESiZCeno7GjRurVfSVukD0kY0iGj1NzcPh+zycfJSC98VlAAAuh0Kwpx1GtW2Kru524HI0e3JxZUgdko2qNaro/FxdXXH16lW1cn5s6aPzDpCmaaSlpaFRo0aqNkUtIfrIRpZGBSVl+PtxKg7d5+Mx/x2T3sTSGJ+1aYoRrZuiUQP1nQRdV0gdko0qNars/NRhnl9l2NJH5x0ggcAGNE0jJiUXh+7xcSYqBQX/7bWnz6XQ27sxPmvbFJ2a24CjZa09gmahCc6PTYgDJBCUSF5xKU4/SsGhe3w8Tctj0l1sTPFZm6YY/lET2JgZqtBCAuEDZ8+e1VnnBxAHCIqiYG1trfGLurIF0Uc+0kqN8dfxGITFpKG4tHzbIQM9Dvr5NsZnbZqhvauVzmpI6pBsVKXR119/DQAYNGiQWjs/tvQhUaAEQi15VyjAiYcpOHyfh+cZHzaZbWFnhlFtm2FooCMsTQ1UaCGBUJXk5GRYWFjA3Nxc1abUGhIFqiREIhF4PB6aNWtGItSqgegjCU3TuPsqG4fu8XDuSToEZeWtPUM9CgP9HfB5u2Zo1cyStHYqQOqQbOpLI/GYX+PGjXH+/HmNcYJs6aPzDpCmaWRlZal181+VEH3KeZtfguP/bTKb+LaASfe2b4BPWzeBC+ctOrX1I70K1UDqkGzqQ6OKAS8AkJubqzEOkC19dN4BEgg1IRLRuP3yLQ7f4+Ofp+koFZaPFpgacDH4v01m/RwtIBKJEBWVrWJrCYSaqS7as0mTJqo2S+UQB0ggVMOBO0n4/cZL8LM/bDLbsmlDjGrTFANbOsDMkPx0CJqBrk91kIbO/4opioK9vT0Zs6kBXdTnr7s8fH/qCQDA3EivfJPZNs3g7VD9WoS6qJEiEH1kw5ZG2uL82NJH5x0gh8OBg4ODqs1QW3RNn8ikbPxwptz5TenaHDN7tICxgfRxPV3TSFGIPrJhS6O8vDzk5eVptPMD2NNH50OyhEIhEhISIBQKVW2KWqJL+mTkFWPKnw9RKqTRz7cxFvb1kOn8AN3SqDYQfWTDlka+vr64evWqRjs/gD19dN4BAuVvSYSa0QV9SsqEmPJnJDLfl8C9kRnWjWipUHeLLmhUF4g+slGWRnw+H7du3WI++/r6arTzE8NGHSIOkEAAsPRMLB7x3qGBkR5+H9MapiTIhaCB8Pl8dOvWDX369JFwgoTqIQ6QoPMcvJuEQ/f4oChg86hAONuozzYwBIK8iJ1fYmIiGjduDCcnJ1WbpPbovAOkKApOTk4kQq0GtF2fB6+zsfRMLABgfh8PdPOwUzgPbdeorhB9ZFNXjSo6P00PeKkOtuqQzvfzcDgc2NjYqNoMtUWb9cnIK8bUg+VBLwP87DG1a/Na5aPNGikDoo9s6qKRtjs/gL06pPMtQKFQiNjYWBKhVgPaqk9JmRCTD5QHvXg2NseaT/xr/XaprRopC6KPbGqrUXp6utY7P4C9OqTzLUAAKC4uVrUJao226UPTNJacikUU/x0sjPWxY8xHdQ560TaNlA3RRza10cjKygotW7YEAK11fmLYqEPEARJ0jj/v8vC/B3xw/gt6cbImQS8EzcTAwACHDx9GdnY2GjdurGpzNI5adYGmpqbiwYMHuHnzJmJjYyEQCJRtF4HACvdfZ2PZf0EvC/p6oqu7rYotIhAUg8/nY/ny5RBv5WpgYECcXy2RuwWYnJyMQ4cOITw8HOnp6ai4j66+vj5at26NkSNHok+fPhq15xeHw4Gbm5tG2VyfaJM+ablFmPrnQ5SJaAz0t8fkLq5KyVebNGIDoo9s5NWoYsALACxZsqQ+zFM5bNUhuXaEX7FiBU6ePImgoCB0794d/v7+sLOzg5GREXJzc/H8+XNERkYiLCwMXC4XP/30E/z9/ZVqqLIgO8LrJsWlQnz6+x085r+DZ2NznPi6I0wMyAgAQXPQhWhPeVHWc1wud2psbIxLly5h06ZNGDJkCFxdXWFmZgY9PT1YW1ujQ4cOmD59Os6dO4cFCxYgPT291gbVN0KhEI8ePSIRajWgDfrQNI0lp5/g8X9BL7+Paa1U56cNGrEJ0Uc2sjTSdefHVh2S6ykwd+5cuTPs0qVLrY1RFSKRSNUmqDWarE+hoAzL/36KIw+SwaGArZ8Hopm1idKvo8ka1QdEH9nUpJGuOz8xbNQh0gdE0Foe899h1v+i8OptASgKWDLQG51bkKAXguYgEAjQs2dPnXd+bKG0EcWXL1+iR48eysqOQKg1QhGNrVcSMPy3f/HqbQHsLYxwcGI7jO/komrTCASFMDAwwI8//gh3d3fi/FhAaS3A0tJSpKamKiu7eoPD4cDb25tEqNWApunDzy7EnCNRuP86BwAwwN8ePw3xg4WJPmvX1DSN6huij2ykaTRy5EgMGTIEBgYGKrBMPWCrDsntAFetWiX1++zs7Doboyp0uWLJgyboQ9M0TkWlYMmpWLwvKYOZoR6WDfbBsFaO9bIIsyZopEqIPrIRa8Tj8fDVV19h165dTIuP6MeOBnI7wP3798PLywumptWvmlFYWKg0o+oTkUhEpkVIQRP0yS0qxXennuDvx+U9EK2dLLHh0wA0tVJ+sEt1aIJGqoToIxuxRlZWVsyY38SJE3HhwgVVm6YWsFWH5HaAzZo1w7hx4/Dxxx9X+31cXByGDRumsAEHDx7E7t27kZmZCU9PT3z//fdS5xD+8ccfOHToENLS0mBpaYk+ffpg7ty5MDQ0VPjaBM3nTmIW5vwvCqm5xeByKMzq0QJTuzWHHpd0txE0i/T0dIwcOZIJeNm1a5eqTdJ65H5K+Pr6IjY2tsbvKYqCHHPqJQgPD8eqVaswbdo0nDx5Ep6enggJCUFWVla1x//9999Yv349pk+fjvDwcKxcuRLh4eH45ZdfFLouQfMRlInw87l4jNp5B6m5xXC2NsGxKR0wo0cL4vwIGgePx8PkyZNJtGc9I3cLMDQ0VOqan56enoiPj1fo4nv37sXIkSMxfPhwAMCyZctw7do1HD9+HJMmTapy/KNHj9CqVSsMGjQIANCkSRMMHDgQjx8/Vui6AJgJlRUnVopEIgknTlEUOBxOlcmXNaVzOBxQFFVtujh/edK5XC5omq42vbKNNaWLbaxrmcTnqlOZnqfnYfb/HiM2LQ8AMLJ1E/wwyAdGepLa19d9qlyXVHGf1LnuiW0SiUTgcrlaUSZZtitSJh6Ph549eyIlJQUuLi64fPkyHBwcIBQKNbZM4vTqbK9tmYDqn9t1QW4HaGur3PlTAoEAsbGxmDx5MpPG4XDQsWNHPHr0qNpzAgMDcebMGURHR8Pf3x98Ph/Xr1+vsVtWGjExMcz/rayswOFwkJSUJNH6tLe3h4ODAxITE5GXl8ekOzk5wcbGBvHx8RJbdLi5ucHCwgLR0dESN9Hb2xsGBgaIioqSsCEgIAACgQBPnz6V0CAwMBB5eXl48eIFk25kZAQfHx9kZ2cjKSmJSW/QoAFatGiB9PR0pKWlMenW1tZwdnYGj8erc5kCAgLUokxWVla4lU5hRdhTlJTRMDOg8HVrCwxpbQtTQz0kJCSo9D7FxMSo9D6pe9178+YNHB0dtapMyrhPM2fORGJiIlxcXLBlyxZkZWUx19DUMin7Pr179w7Ah+e2ubk5lIFca4GyQUZGBrp06YLDhw8jMDCQSV+zZg3u37+Po0ePVnve/v37sWbNGtA0jbKyMnz22WdYtmyZ3NcVryHn5+fHvHGUlJTAxMQENE1rxJtQfb7dURSFkpIS6OvrS0RT1neZ3uaXIPTEE1x9lgkA6OxmjdXD/dCogZHK75NQKERxcTGMjMpt0ea38NqUiaZpFBcXw9jYmLQAqylTWloaJk+ejA0bNsDV1VWu35m6l0ma7bUpk1AoRFFREfMbEwqFiImJqXNQjEatBHP37l3s2LEDP/zwA/z9/cHj8bBy5Ups27YN06ZNUygvLpfLCBsfHy9VSFWkix+klalpHoyi6fLaIhQK8fTp0xr1qY8yXYnPwIJj0XibL4CBHgehfT0xvqMzOBzJ6Q2quk8UReHZs2cSGtX3fVJmurLrnlAoZPRRlo2KpqvL70lMWVkZE7jXpEkTnDlzBlFRUTXaqQllYvM+AajyG1MGKosWsLS0BJfLrRLwkpWVBRsbm2rP2bRpEwYPHowRI0bAw8MDvXr1wuzZs/H777+TtQa1kCKBEN+feoIJfzzA23wBPBub48z0TpgQ5FLF+REImgKfz4efnx/279+valN0HpU5QAMDA/j4+CAiIoJJE4lEiIiIkOgSrUhxcXGVtxXx24CKenIJLPEkJRcDt9zEgTvlYxkhQS44Na0TPBs3ULFlBELtES9snZCQgBUrVqCkpETVJuk0Ku0C/fLLL7Fw4UL4+vrC398f+/btQ1FRETOfcMGCBWjUqBGzG0X37t2xd+9eeHt7M12gmzZtQvfu3eu2JxRZokkq9amPUETj9xuJ+OXiM5QKadiZG2L9yJZqv4g1qUPSIfpU3dXh8uXLEvOXiUbSYUOfWjnA+/fvw8jICH5+fkxaTEwMiouL0aZNG7nz6d+/P7Kzs7F582ZkZmbCy8sLu3btYrpA09LSJAo9depUUBSFjRs3IiMjA1ZWVujevTtmz55dm2IAKG9B1tTiJNSvPinvijDnf1G4+6p8Wb2+Po2xapgfLE3VexkoUoekQ/SRvaUR0Ug6bOlTqyhQT09PuLq6Ijw8nEnr168fXr9+jbi4OKUaqGwq7yRM0zTy8vLQoEGDelkzUtOoL33OPE7F4pMxeF9cBhMDLpYO8sGI1k004p6QOiQdXddHnv38dF0jWVTWp153hK/M5cuXsXfvXom0P/74A5cuXaq1IapCJBLhxYsXJIimBtjWJ6+4FLP/F4VvDj3C++IyBDRtiPBvOmNkm6Ya8yAgdUg6uq7PwYMHZa7wousayYItfWrVBero6FglrVGjRnU2hqBb3H+djVmHo5DyrggcCpge3AIzgt2gT5YyI2gRCxcuBAB88cUXZHkzNUOj5gEStINSoQibLiXg12svIKKBplbG2PhpAD5yslK1aQSCUkhNTYWVlRUzcTs0NFTVJhGqQS4H2KZNG7m7o+7du1cng1SBkZGRqk1Qa5Spz6u3BZh1+BEeJ+cCAIa3aoKlg71hbsTehrX1AalD0tElfcRjfh4eHjhx4oTcZdcljWoDG/rI5QC//fZbpV9YXeByufDx8VG1GWqLsvShaRqH7/Ox/O+nKCoVwsJYHz8N9cMAf3slWKlaSB2Sji7pUzHgBSjfKNzBwUHmebqkUW1gSx+5HODQoUOVfmF1QSQSITs7m1kQmyCJMvTJLhBg4fFoXHyaAQDo2Nwa60e2hL2FsTJNVRmkDklHV/SpLtpTHucH6I5GtYUtfWqVE4/Hw4YNGzBnzhxmKbPr168jISFBaYbVFzRNIykpiawkUwN11ef680z02XgDF59mwIDLweL+XvgzpJ3WOD+A1CFZ6II+8kx1kIYuaFQX2NJHYQd47949DBo0CNHR0fjnn39QWFgIoHyh0i1btijVOILmUlwqxNIzsRi35x4y35eghZ0ZTk3rhK+6uJJ1PAlaRV2dH0F1KOwA169fj1mzZmHv3r3Q1/8QuNC+ffsq+z4RdJO4tDwM3noLf/z7GgAwvqMz/p4RBG8Hso4nQftIS0tDZmYmcX4aiMLTIJ4/f45169ZVSbeyskJOTo5SjKpvGjQgD2ZpyKuPSERjz+1XWHP+GQRCEWzMDLF2hD+6e9ixbKHqIXVIOtqsT9u2bXHp0iXY29vXyflps0bKgA19FHaA5ubmyMzMrHKj4+LiNHIyPJfLRYsWLVRthtoirz7pucWYd/Qxbr14CwDo6dUIq4f7wdrMUMaZmg+pQ9LRRn34fD6ysrKYPQ7btm1bp/y0USNlwpY+CneBDhgwAOvWrUNmZiYoioJIJEJkZCRWr16NIUOGKN1AthGJREhNTSVLENWAPPqci0lD3003cOvFWxjrc/HTUD/sHPuRTjg/gNQhWWibPuIxv+DgYKUN+2ibRsqGLX0UdoCzZ8+Gq6srunXrhsLCQgwYMACjR49GYGAgpk6dqlTj6gOappGWlkair2pAmj75JWWYd/Qxph58iHeFpfBvYoGwb4LwebtmGrOOpzIgdUg62qRPxYAXS0tLWFtbKyVfbdKIDdjSR+EuUAMDA6xYsQJff/01EhISUFBQAG9vbzg7OyvVMIJ6E5mUg9n/iwIvuxAUBXzdrTlm9XQn63gStBYS7al91HotUAcHB9jbl6/ioUtv+7pOmVCELVdeYOvVFxCKaDg2NMaGTwPQ1oWs40nQXojz005q5QCPHj2Kffv24fXr1wAAZ2dnjBs3DiNGjFCmbfUCRVGwtrYmTrwGKuqTlFWAWf+LwiPeOwDA0EBHLPvYBw00fB3PukLqkHQ0XZ+UlBTWnZ+ma8Q2bOmjsAPctGkT/vjjD4wePZqJgIqKisJPP/2E1NRUzJw5U6kGsg2HwyHdt1LgcDhwcnLCschkLD0TiwKBEOZGelg51A+DW8q3zJO2Q+qQdDRdH0tLSzg5OQEAay0/TdeIbdjSR2EHeOjQIfz4448YOHAgk9ajRw94eHjgxx9/1DgHKBKJwOPx0KxZM7IGXzVk5xdj9l/3cT0xDwDQzsUKv3waAMeG2rOUWV0hdUg6mq6PiYkJzp49i5ycnGr3QlUGmq4R27Clj8I5lZWVwdfXt0q6j48PhEKhUoyqT2iaRlZWFom+qoZbCW/Rb9MtXE/Mgx6HwsK+nvjrq/bE+VWC1CHpaKI+PB4PGzduZGw2MTFhzfkBmqlRfcKWPgq3AD/++GMcOnQIixYtkkg/cuQIBg0apDTDCKqjpEyIteefYdetVwAAB3Mufh3TFgHNSKALQfvh8Xjo3r07s6XRrFmzVGsQgTXkcoCrVq1i/k9RFI4ePYrbt2+jZcuWAIDo6GikpqZq5ER4giTPM97jm0OPEJ/+HgDwedumGOgogJ+jhYotIxDYp6Lzc3V1xfDhw1VtEoFF5HKAT58+lfgs3piQx+MBABo2bIiGDRtq5HZIFEXB3t5e56OvaJrGH/++xqpz8RCUiWBtaoA1n/iju4ct0tPTdV4faZA6JB1N0aey86vPqQ6aopGqYEsfitaxTmehUIioqCgEBASAy+Wq2hy14E1eMeYdi8aN55kAgO4etljzSUvYmuvGUmYEgiqdH0FxlPUc1/lwI6FQiISEBI0M4FEG/8Smo++mm7jxPBOGehz8+LEP9oxvwzg/XddHHohG0lF3fYqKihAcHKxS56fuGqkatvSp1UT4mJgYnDt3DmlpaSgtLZX4buvWrUoxrD7Jy8tTtQn1TqGgDD+efYpD9/gAAB+HBtj0WQDc7MyrHKuL+igK0Ug66qyPsbEx5s+fj3Xr1uHKlSsqa/mps0bqABv6KNwCDAsLw6hRo5CYmIiLFy+irKwMCQkJuHPnDszNqz48CepHTHIuBmy+hUP3+KAoYHJXV5z8ulO1zo9A0AUmT56M6Oho0u2pYyjsALdv345FixZh+/bt0NfXx+LFi3H+/Hn069ePWRuUoL5k5Zdg9O67ePW2APYWRjg4sR0W9fOCgZ7O94YTdAg+n4+hQ4fi7du3TJqxMZnfqmso/NTj8/no2rUrgPKdIQoLC0FRFMaPH48jR44o3UC2oSgKTk5OOhN9tf7ic+QWlcKzsTnOz+yCjs1tpB6va/rUBqKRdNRNH/HC1qdOncJXX32lanMAqJ9G6gZb+ijsABs0aICCggIAgJ2dHTP1IS8vD0VFRUo1rj7gcDiwsbHR+uWHSsqE2HH9JQ7fK5+6svxjX1iYyF7EWlf0qQtEI+mokz6Vd3XYvHmzqk0CoF4aqSNs6aNwbm3atMG///4LAOjbty9WrlyJ7777DnPnzkWHDh2Ualx9IBQKERsbq53RVwkJwKJFeD90BI71HoNDBy9DRAOffNRE7u2LtFofJUE0ko666KPOWxqpi0bqClv6KBwF+v3336OkpAQAMHXqVOjr6+Phw4fo3bu3Ru4IDwDFxcWqNkH57N0LTJwImqJgIqLxKYDPrh1G5Pfr0Hp4f4Wy0kp9lAzRSDqq1kednZ8YVWuk7rChj8IOsGHDhsz/ORwOJk2apEx7CMogIQGYOBEQiUABEE8TpQG0XTEfGDMYcHNToYEEQv0ybtw4tXZ+BNUgVxdofn6+3H8ENWDPHqCawWIKKE/fvbveTSIQVMmuXbvQvXt34vwIEsjVAmzdurXM6BuapkFRFOLi4pRiWH3B4XDg5uamXYPPr1+X34/qvqNp4PVrubPSSn2UDNFIOqrSp6ysDHp65Y84V1dXXLlypV6vrwikDkmHLX3kcoD79+9X6kXVCYqiYGGhZTsdODtX2wIEUJ6uwM7KWqmPkiEaSUcV+vD5fPTu3RurV6/G4MGD6/XatYHUIemwpY/OL4YtFAoRHR0Nf39/7VkcOyEBIg9PULSoaiuQwwGePZN7DFAr9VEyRCPp1Lc+FQNePD09ERMTw7QE1RVSh6RTWR+yGLYSEYlEqjZBqeQ2cca3A2ZCRFGgudxypyf+d/duhQNgtE0fNiAaSae+9Kkc7fnPP/+ovfMTQ+qQdNjQRzNqBkEhzjxOxWGfHsjwa409omggKam82zMkhER/ErQWTZjqQFAviAPUQo7cL9/hIah/B1BBn6vYGgKBfYjzI9QGne8C5XA48Pb21proqycpuYhJyYU+l8LQQMc656dt+rAB0Ug69aHP9u3bNdr5kTokHbb0qVULsKysDPfu3QOPx8PAgQNhZmaGjIwMmJmZwdTUVKkG1gcGBgaqNkFp7L71CgDQz9ceVqbKKZc26cMWRCPpsK3Pjz/+CACYMmWKxjk/MaQOSYcNfRR2pykpKRg0aBC+/vprLF++HDk5OQCAnTt3YvXq1Uo3kG1EIhGioqK0YgA6PbcYfz9OBQBM7OyilDy1SR+2IBpJhy19MjIyUFZWBqC8hbBy5UqNdX6kDkmHLX0UdoArV66Er68v7t27B0NDQya9V69euHPnjlKNIyjGvojXKBPRaOtiBf8mDVVtDoHAGnw+Hx07dsTYsWMZJ0ggKIrCXaCRkZE4dOhQleaoo6MjMjIylGYYQTEKSspw8E4SAGBikHJafwSCOlIx4AUAsrKy0KhRIxVbRdBEFG4BikSiapuh6enpGjn+py0cf5iMvOIyOFuboIcXeRgQtJPqoj2J8yPUFoUdYKdOnbBv3z6JtIKCAmzZsoXZKV6T4HA4CAgI0OjoK6GIxp7/gl8mBLmAy1HersnaoA/bEI2koyx9tHmqA6lD0mFLH4VzCw0NxcOHD9G/f38IBALMmzcPwcHByMjIwLx585RqXH0hEAhUbUKduByXgddZhbAw1scnHzVRev6ark99QDSSTl310WbnJ4bUIemwoY/CDrBx48Y4ffo0Jk+ejHHjxsHLywvz5s3DqVOnYG1trXQD2UYkEuHp06caHX2167/W3+ftmsHEQLlrG2iDPmxDNJKOMvRJSEhASkqK1jo/Uoekw5Y+Cj8tS0pKYGhoiI8//liphhBqR3TyO9x7lQ09DoVxHZxVbQ6BwArBwcEIDw9HixYttM75EVSHwi3ADh06YOHChbh9+zZ5W1EDxBPfB7V0QGMLIxVbQyAoDx6Ph+fPnzOfg4ODifMjKBWFHeDq1atRVFSEr7/+Gl26dMHKlSsRExPDhm31hqYOPKe+K0JYdBoAIITFqQ+aqk99QjSSjqL68Hg8dO/eHd26dZNwgtoMqUPSYUOfWu8HmJ+fjwsXLiAsLAx37txB06ZNMWjQIEyfPl3ZNioVZe0jpQ6sOheHHdcT0d7VCocndVC1OQSCUhA7P20OeCHUDZXvB2hmZobhw4djz549OHPmDIyNjbFt27ZaG6IqaJpGbm4uNG1f4IKSMvx1lwcAmBjkytp1NFWf+oRoJB1F9NFV50fqkHTY0qfWDrCkpATh4eH4+uuvMXToUOTm5iIkJESZttULIpEIL1680LjxzKMP+HhfXAYXG1MEe9qxdh1N1ac+IRpJR159dNX5AaQOyYItfRSOAr158ybOnj2LS5cuQU9PD3369MGePXvQpk0bpRpGqBmhiMae268BlE985yhx4juBoAqSk5N11vkRVIfCDnD69Ono1q0bVq9eja5du0JfX58NuwhSuPg0A7zsQjQ00cfwVnXf849AUDWmpqawsrICAOL8CPWGwg7w9u3bMDMzY8MWlWFkpFnTB3bfKl8E+AsWJr5Xh6bpowqIRtKRpY+lpSX++ecfFBQUoEkT5a9mpAmQOiQdNvSRKwo0Pz+fcXr5+flSj1V356jpUaBR/HcYsu029LkUbi0MRqMG5EdD0Ez4fD4uXryICRMmqNoUgoahrOe4XM2HNm3a4NatW7C2tkbr1q1BUVXHnGiaBkVRiIuLq7UxqkAkEiE7OxtWVlYaMQ+n4sT3+nB+mqaPKiAaSac6fSpvaaTrTpDUIemwpY9cDnDfvn2wsLAAAOzfv19pF1cHaJpGUlISLC0tVW2KTFLfFSE8hv2J7xXRJH1UBdFIOpX1qbywda9evVRsoeohdUg6bOkjlwNs27Yt8/8mTZrA3t6+SiuQpmmkpaUp1TiCJPv+fQ2hiEbH5tbwcbBQtTkEgsLowq4OBM1B4bZkjx49kJ2dXSX93bt36NGjh8IGHDx4EMHBwfDz88OIESMQHR0t9fi8vDwsW7YMQUFB8PX1RZ8+fXD9+nWFr6tp5JeU4a97/01870x2fCdoHsT5EdQNhUMIxWN9lSksLIShoaFCeYWHh2PVqlVYtmwZWrZsiX379iEkJATnz5+vdmslgUCAL7/8EtbW1ti0aRMaNWqE1NRUNGjQQNFiSFDX8+sD8cR3V1tTdHNnb+J7dWiCPqqGaCQdDoeDHj16EOcnBVKHpMOGPnI7wFWrVgEAKIrCxo0bYWxszHwnFAoRHR0NT09PhS6+d+9ejBw5EsOHDwcALFu2DNeuXcPx48cxadKkKscfP34cubm5OHz4MDP/sK4h01wuFy1atKhTHmxTPvG9PPglpJ4nvmuCPqqGaCQdLpeLwMBAhISEYPfu3cT5VQOpQ9JhSx+5HeDTp08BlLcAnz9/LjEB3sDAAJ6engpFcgkEAsTGxmLy5MlMGofDQceOHfHo0aNqz7ly5QoCAgKwfPlyXL58GVZWVhg4cCC++uorhUNhhUIhgPLoojdv3sDe3p4pnxiKosDhcJhjZaVzOBxQFFVtuvha8qRzuVzQNM2kX4hNBz+7CJYm+hjS0qFK/lwuFyKRqFrba0qXt0wAkJGRAVtbW4noq7qWSZrtbJdJ2feprKwMGRkZaNSoETgcjlaUSZn3SSQSISMjAwsWLMCMGTNgYmIiYacmlkmW7YqWCQDS09NhZ2cn1+9ME8qkzPtUVlaG9PR05jdWnX61QW4HeODAAQDAokWLsHjx4jrP98vJyYFQKKzS1Wltbc2ERleGz+fjzp07GDRoEH7//XfweDwsW7YMZWVlCu9CUXkLp8aNG4PP5yMrK4tJs7e3h4ODAxITE5GXl8ekOzk5wcbGBvHx8SguLmbS3dzcYGFhgejoaImb6O3tDQMDA0RFRUlcMyAgAAKBgHm5AMorR2BgIPLy8vDixQsAwOYr5TaNbu+EovxcxCclMcc3aNAALVq0QHp6ukQQkrW1NZydncHj8epUJldXV6SlpSEjI0OpZQLKJ7b6+PggOzsbSfVYJrbuU0ZGhtaVCaj9fbpz5w5WrFiB0NBQmJqaAijvsUlISNDYMrF1nzw8PJCeno709HStKZOy71NGRgYyMjIAAObm5lAGtd4Oqa5kZGSgS5cuOHz4MAIDA5n0NWvW4P79+zh69GiVc/r06YOSkhJcvnyZafHt3bsXu3fvxq1bt+S6rngCpZ+fH7hcLoRCIWJiYhAQEACKotTuTSiK/w7Dt9+BAZfCrdBg2Jga1OvbHU3TePz4MaOXMsoky3a2y6Ts+1RaWoqYmBhGI20oU13vU0pKChPwMmrUKMydOxf+/v7Q19fX2DKxeZ8U/Z1pQpmUeZ9KS0sRHR1d7XOb9Ynw06dPx88//wwzMzOZLa2tW7fKdWFLS0twuVyJNw8AyMrKgo2NTbXn2NraQk9PT6LArq6uyMzMhEAggIGBgVzXBsA8qCpS0wTLmgRmM52iKHC5XOz9t/xNbnCAI+zMa574XpPtdS2TuFJXp5ci+QAfyiSvjWyVSZnpFctUUSNtKZM8NlZOrxzt+dNPPyErK4s5ThPLJCu9rjbW5nem7mWqTbqsMtWkT22RaxpExeamubm51D95MTAwgI+PDyIiIpg0kUiEiIgIiRZhRVq1agUejyfxhvD69WvY2toq5PwqQlEUrK2tq41sVTXJOYU496S8S6S+Jr5XRp31UReIRh+obqpDs2bNiD4yIHVIOqzpQ6uQsLAw2tfXlz5x4gT94sUL+vvvv6dbt25NZ2Zm0jRN0/Pnz6fXrVvHHJ+amkoHBgbSy5cvpxMTE+mrV6/SHTp0oH/99Ve5r1lWVkY/ePCALisrU3p5lM2Ks7G008Kz9Bc776jaFAJBJjwej3Z1daUB0K6urjSPx1O1SQQtRVnPcYXnARYXF4OmaWYaREpKCi5evAg3NzcEBQUplFf//v2RnZ2NzZs3IzMzE15eXti1axfTBZqWlibRPLe3t8fu3buxatUqDB48GI0aNcLYsWPx1VdfKVoMBpFIBB6Ph2bNmqnVGnxlQhH+d58PQHWtP0B99VEniEblY1gjR46sdp4f0Uc2RCPpsKWPwg7w66+/Rq9evTBq1Cjk5eVhxIgR0NfXR05ODkJDQ/H5558rlN/o0aMxevToar8TR55WJDAwEEeOHFHU7BqhaRpZWVlqNy8pOiUXecVlsDDWRxd3W5XZoa76qBNEo/Iuqp07d+Krr77CkSNHJLQg+siGaCQdtvRR2JXGxsaidevWAIALFy7AxsYGV69exerVq6t1WITa8e+LtwCAjs2twSU7vhPUlIrj8b6+vvj333/JQ5ygMSjsAIuLi5k5Pbdu3ULv3r3B4XAQEBCA1NRUpRuoq9wSO0C36iNiCQRVw+fzERgYKLEWLwniIGgSCjvAZs2a4dKlS0hLS8OtW7fQqVMnAOXTF9R9M9zqoCiq2t0tVEmRQIiHSe8AAEEqdoDqqI+6oYsaiaM9o6Oj8c0331SZu1URXdRHUYhG0mFLH4Ud4LRp07BmzRoEBwfD39+fmbJw+/ZteHl5KdW4+oDD4cDBwUGtBp7vv86GQCiCg4URnK1NVGqLOuqjbuiaRpWnOpw9e1Zq2XVNn9pANJIOW/ooHATTt29ffPTRR8jMzJRY/LpDhw7o2bOnUo2rD4RCIfNDVuYEy7pw+7/uz05uNip/I1RHfdQNXdKoNlsa6ZI+tYVoJB229FHYAQLlK7LY2toy69Y1btwY/v7+SjOqvqm43p06IB7/C2qhHuN/6qaPOqILGtVlPz9d0KeuEI2kw4Y+CjtAkUiEX3/9FXv37kVhYSEAwNTUFF9++SWmTp1KmvB1JLtAgKdp5Te6Q/OqeyISCKpi7dq1ZD8/glahsAPcsGEDjh07hrlz56JVq1YAgMjISGzduhUCgQCzZ89WupG6RMTLLNA04NHIXOranwRCfbNu3ToAwPz584nzI2gFCjvAkydPYsWKFejRoweT5unpiUaNGmHZsmUa5wApioKTk5PKx9rE3Kow/qcOqJs+6og2a/T27VtmDUYDAwNs3rxZ4Ty0WR9lQTSSDlv6KNxfmZubC1dX1yrprq6uyM3NVYpR9QmHw4GNjY3adN3++1LsANWj+1Pd9FFHtFUjHo+Hdu3aYdq0aVW211EEbdVHmRCNpMOWPgrn5unpiYMHD1ZJP3jwoERUqKYgFAoRGxurtB2G6wI/uxBJWYXgcii0c1UPB6hO+qgr2qgRj8dD9+7dkZiYiAsXLlTZtkwRtFEfZUM0kg5b+ijcBTp//nxMnjwZ//77LwICAgAAUVFRSEtLw86dO5VqXH1RcXdjVSJu/QU0bQgzw1oF6LKCuuijzmiTRhWdnzjgpaY9OuVFm/RhC6KRdNjQR+EWYNu2bXH+/Hn06tUL79+/x/v379GrVy+cP3+eWSOUUDtuvSh/y1aX8T+C7lGd8yMBLwRtRaFmRnJyMv7991+UlpZiwIABcHd3Z8sunUMkopkFsFW9/BlBNyHOj6BryO0A79y5gylTpjDNUD09PaxcuRIff/wxa8bVBxwOB25ubioffH6W8R5ZBQIY63MR0LShSm2piLroo85oi0ZRUVFISkpSuvPTFn3YhGgkHbb0kTu3TZs2oWPHjrhx4wbu3r2LESNGYO3atUo1RhVQFAULCwuVhx+Llz9r52oFAz31+RGoiz7qjLZoNHjwYJw4cULpLT9t0YdNiEbSYUsfuZ+0CQkJmDNnDuzs7GBhYYEFCxYgOzsbOTk5SjWovhEKhXj06JHKo69uqWn3p7roo85oskZ8Ph98Pp/5PHjwYKV3e2qyPvUF0Ug6bOkjtwPMz8+HpaUl89nY2BiGhobIz89XqkGqQNpWLvWBoEyEe6+yAQAdm6uXAwRUr48moIkaidf27Natm4QTZANN1Ke+IRpJhw19FAqCuXnzJszNzZnPNE0jIiICz58/Z9IqrhBDkI8o/jsUCoSwNjWAZ2Nz2ScQCHWk8sLWBIIuopADDA0NrZK2ZMkS5v8URSEuLq7uVukY4u7PDs2tweGQMQACu9RlVwcCQZuQ2wHGx8ezaYfK4HA48Pb2Vmn0lTpPf1AHfdQdTdJIFc5Pk/RRFUQj6bClD1EbgIGBgcqu/b64FI/47wCo7wR4VeqjKWiCRqps+WmCPqqGaCQdNvSRywFGRUXJnWFRURESEhJqa0+9IxKJEBUVpbIB6HuvsiEU0WhmZYKmViYqsUEaqtZHE9AUjfT09GBgYFDvzk9T9FElRCPpsKWPXA5wwYIFCAkJwblz55hNcCvz4sUL/PLLL+jVqxdiY2OVaqQ2c5ssf0aoJ+zt7XHlyhUy5kcg/IdcY4BhYWE4dOgQNm7ciHnz5sHZ2Rl2dnYwNDREbm4uEhMTUVhYiF69emH37t3w8PBg226t4bYaj/8RNB8+n487d+5gxIgRAMqdIIFAKEcuB6ivr4+xY8di7NixiImJQWRkJFJTU1FcXAwPDw+MHz8e7dq1Q8OGDVk2V7t4874YzzLeAyiPACUQlIl4zO/Vq1cAwDhBAoFQjsJ77vj5+cHPz48NW1QCh8NBQECASqKvIl6Wd3/6ODSAlal6DoCrUh9NQR01qhzw0r59e5XZoo76qBtEI+mwpQ9RG4BAIFDJdW8laEb3p6r00STUSSN1nOenTvqoK0Qj6bChj847QJFIhKdPn9Z79BVN08z4X0c1doCq0keTUCeN1NH5qZM+6grRSDps6aPzDlBVvM4qRGpuMQy4HLRxtpR9AoEgg5ycHLVzfgSCOkMcoIoQL38W2KwhTAwUHoolEKrQsGFDDBs2jDg/AkFO6uQAS0pKlGWHSlHFwLM6L39WGTIwLxt10IiiKKxZswYPHjxQO+enDvqoO0Qj6bChj8I5ikQibNu2DZ07d0ZgYCCzjcrGjRtx9OhRpRvINlwuF4GBgeByufV2TaGIxr//RYB2aqHeDlAV+mgaqtSIz+djypQpKC4uBlDuBCtuW6YOkDokG6KRdNjSR2EH+Ouvv+LkyZOYP38+9PX1mXR3d3ccO3ZMqcbVBzRNIzc3FzRN19s1Y1NzkVtUCnNDPfg7WtTbdWuDKvTRNFSlkTjgZceOHZg1a1a9XlsRSB2SDdFIOmzpo7ADPH36NH788UcMHjxYoknq4eGBxMREpRpXH4hEIrx48aJeo6/Ey5+1c7WGHle9uz1UoY+moQqNKkd7Ll68uN6urSikDsmGaCQdtvRR+OmbkZGBZs2aVUmnaRplZWVKMUrb+bD8GVn9haA46jjVgUDQRBR2gG5ubnjw4EGV9PPnz8PLy0spRmkzxaVC3H+dDYAsgE1QHOL8CATloXD8/ddff43Q0FBkZGSApmn8888/ePXqFU6dOoUdO3awYSPrGBkZ1du1HibloKRMBDtzQ7jZmdXbdetCfeqjqdSHRiKRCB9//LFGOj9Sh2RDNJIOG/oo3ALs2bMntm/fjoiICBgbG2Pz5s14+fIltm/fjk6dOindQLbhcrnw8fGpt+gr8fy/Tm42oCiqXq5ZF+pbH02kvjTicDj49ddfERgYqFHOj9Qh2RCNpMOWPrWagd26dWvs3btXqYaoCpFIhOzsbFhZWdXLPJzbLzVr/7/61kcTYVsjmqaZl6X27dvjwYMHGnUvSB2SDdFIOmzpo3BOPXr0QE5OTpX0vLw89OjRQylG1Sc0TSMpKalewo9zC0sRk/wOANBJQwJg6lMfTYVNjXg8Htq2bYuHDx8yaZr2gCR1SDZEI+mwpY/Cv6SUlJRqQ1EFAgEyMjKUYpS2EpGYBRENuNqawt7CWNXmENQcHo+H7t2748GDB5g8eTJ5OBIISkbuLtDLly8z/7958ybMzc2ZzyKRCBEREXB0dFSudVrGvy81Z/kzgmoROz9xwMuJEyc0YsyYQNAk5HaA06ZNA1C+1FJoaKhkJnp6cHR0rJKuKTRo0KBerlMxAEaTqC99NBllalTZ+WlSwEtNkDokG6KRdNjQR24HGB8fDwAIDg7GsWPHYGVlpXRjVAGXy0WLFi1Yv05abhESMwvAoYD2rpox/gfUnz6ajDI10kbnR+qQbIhG0mFLH4XHAK9cuaI1zg8o775NTU1lfQki8fJnfk0awsJYX8bR6kN96aPJKFOjpUuXapXzA0gdkgeikXTY0qdW0yAKCwtx//59pKamorS0VOK7sWPHKsWw+oKmaaSlpaFRo0asXke8/Fmn5prT+gPqTx9NRpkabd26FQCwbNkyrXB+AKlD8kA0kg5b+ijsAJ8+fYpJkyahqKgIRUVFsLCwQE5ODoyNjWFlZaVxDrA+oGm6wvqfmjX+R2Cf3NxcNGjQABRFwcTEBHv27FG1SQSCTqBwF+iqVavQvXt33L9/H4aGhjhy5AiuXr0KHx8fLFy4kA0bNZ4Xb/Lx5n0JDPU4aOWkXnu1EVQLn89Hq1atsGTJEjLNgUCoZxR2gHFxcfjyyy/B4XDA5XIhEAhgb2+P+fPn45dffmHDRlahKArW1tashpiLoz/bOFvBSF+zljqqD300ndpqVHFh67/++gt5eXksWahaSB2SDdFIOmzpo7AD1NPTY1aisLa2RmpqKgDAzMwM6enpSjWuPuBwOHB2dmZ1dQ1xAIymTX8A6kcfTac2GlW3q4OFhXpvjlxbSB2SDdFIOmzpo3Bu3t7eiImJAQC0adMGmzdvxpkzZ/DTTz9pZBivSCTC69evWYu+KhOKcCex3AFq4vgf2/poA4pqpGtbGpE6JBuikXTY0kdhBzh79mzY2toy/2/QoAGWLl2KnJwcLF++XKnG1Qc0TSMrK4u18ZfHybnILymDhbE+vB00b6Ir2/poA4popGvODyB1SB6IRtJhSx+Fo0D9/PyY/1tbW2P37t1KNUjbiEwq3/y2nYsVuBzSv6/rXL9+XaecH4GgziitQzU2NhaTJ09WVnZaw5OU8sAG/ybaOb5DUIzRo0fjwIEDxPkRCGqAQi3Amzdv4t9//4W+vj5GjBiBpk2b4uXLl1i/fj2uXr2KoKAgtuxkDYqiYG9vz1r0VWxqLgDAx1EzHSDb+mgDsjTi8/kwNjaGjU35GPDo0aPr0zyVQ+qQbIhG0mFLH7kd4NGjR/H999/DwsICeXl5OHr0KEJDQ7FixQr069cPZ8+eRfPmzZVqXH3A4XDg4ODASt6FgjIkvi0AAPho4PgfwK4+2oI0jcRjfqamprhy5QrjBHUJUodkQzSSDlv6yN0Fun//fsybNw93797Fxo0bkZOTg0OHDuHvv//G8uXLNdL5AYBQKERCQgKEQqHS845LywNNA7bmhrAzN1J6/vUBm/poCzVpVDHgpaCgAEVFRSqyULWQOiQbopF02NJHbgfI5/PRt29fAEDv3r2hp6eH+fPno3Hjxko1SBWwNQE5NrU8X18Nbf2J0dYJ2sqkska6GO0pDVKHZEM0kg4b+sjtAIuLi2FsXL6LOUVR0NfXh52dndIN0iaepPw3/uegmeN/hNpBnB+BoBkoFARz9OhRmJiYAChvkp44cQKWlpJrW5LFsD/AtAAdNbsFSJAf4vwIBM1Bbgfo4OCAI0eOMJ9tbGxw+vRpiWMoitI4B0hRFJycnJQeXSQoE+F5xnsAmt0CZEsfbaKiRmVlZSgrKyPOrwKkDsmGaCQdtvSR2wFeuXJFqReuyMGDB7F7925kZmbC09MT33//Pfz9/WWeFxYWhjlz5qBHjx749ddfa3VtDofDSmTe84z3KBXSaGCkhyaWxkrPv75gSx9toqJGLi4uuHbtGvT09Ijz+w9Sh2RDNJIOW/qofOXV8PBwrFq1CtOmTcPJkyfh6emJkJAQZGVlST0vOTkZq1evRuvWret0faFQiNjYWKVHFz39r/vTx8FCo9/q2NJHm3j9+jV27NjBaOTi4kKcXwVIHZIN0Ug6bOlTqx3hlcnevXsxcuRIDB8+HED5TtjXrl3D8ePHMWnSpGrPEQqFmDdvHmbMmIHIyMhaRQeJhRQKhSguLgZQvuBqxbXmKIoCh8OpInpN6RwOBxRFQSgUIiblHQDA296cybPyQq7ilc0rp3O5XNA0XW16ZRtrShfbWNcy0TSN4uLiasuqqWWqeJ/qWiYej4fg4GDw+Xw4ODigf//+Gl8mZd8n8W9MJBKBy+VqRZlk2a5omRT9nWlCmZR9nyrqoyxHqFIHKBAIqiyhxuFw0LFjRzx69KjG87Zt2wZra2uMGDECkZGRtbq2eEeLivB4PImWp729PRwcHJCYmCjhZJ2cnGBjY4P4+HjGeQKAm5sbLCwsEB0djXsJmQAAs9IcFBcXw8DAAFFRURLXCwgIgEAgwNOnT5k0DoeDwMBA5OXl4cWLF0y6kZERfHx8kJ2djaSkJCa9QYMGaNGiBdLT05GWlsakW1tbw9nZuc5lcnV1BVC+1F3Fiunt7a2xZap4n+pSJhsbG3Tt2hWvX7+Go6MjOBwO4uPjNbpMbN6njIwMNGnSRKvKpKz75OHhAaDqc0mTy6TM+5STkyOhj7m5OZQBRatw+fGMjAx06dIFhw8fRmBgIJO+Zs0a3L9/H0ePHq1yzoMHDzBnzhycOnUKVlZWCA0NRV5entxjgEKhEFFRUfDz82PeRmNiYhAQEACKopTyJiQoLUPAj5dQKBDi/MwgeDQujwLVxDdWmqbx+PFjRi9ZtmtCmZTxxsrn89GjRw8kJibCxcUFW7ZsQa9evcDlcjW2TGIb2WgBxsTEwN/fH/r6+lpRJlm216YFqMjvTBPKpMz7VFpaiujo6Gqf2xX1UhSVd4EqQn5+PhYsWIAff/wRVlZWdcpL/KDicDhwc3NjbmJNxyqSzsspRqFACCN9Dlo0asDkq0g+FEVVm17ThpCKpstrC03TcHNzg76+frX6aGKZ6ppe0fm5urri6tWrsLCwkNBI08pUEWXfJ/FvTE9PT2k2Kpqu7nWvNr8zdS9TbdJrKpOenp5UfWpLrRwgj8fD8ePHwefzsXjxYlhbW+P69etwcHBQaFNcS0tLcLncKgEvWVlZ1Ub88Pl8pKSkYOrUqUya+G3B29sb58+fR7NmzRQqC0VRSt+JW7wAtpd9A43fAokNfTSZzMxMMs9PQUgdkg3RSDps6aNwFOi9e/cwaNAgREdH459//kFhYSEA4NmzZ9iyZYtCeRkYGMDHxwcRERFMmkgkQkREhESXqBhXV1f8/fffOHXqFPMXHByMdu3a4dSpU7Valk0oFOLRo0dKjS76EAGq+RPg2dBHk7G2tkbXrl0lnB/RSDpEH9kQjaTDlj4KtwDXr1+PWbNm4csvv5RwUu3bt8eff/6psAFffvklFi5cCF9fX/j7+2Pfvn0oKirCsGHDAAALFixAo0aNMHfuXBgaGsLd3V3i/AYNyp1M5XRFqNznXFee/NcC9NXgCfAVUbY+mgyHw8GuXbuQlZUFW1tbJp1oJB2ij2yIRtJhQx+FHeDz58+xbt26KulWVlZMpI4i9O/fH9nZ2di8eTMyMzPh5eWFXbt2MV2gaWlpNfZRqyM0TTNLoGnyCjCED/D5fGzevBmrVq2Cnp4eOByOhPMjEAiaicIO0NzcHJmZmVXGPeLi4tCoUaNaGTF69OgaNwk9cOCA1HN//vnnWl2TLVLeFeFdYSn0OBTcG5up2hxCHam4ticArF27VsUWEQgEZaFw02rAgAFYt24dMjMzQVEURCIRIiMjsXr1agwZMoQFE9mFw+HA29tbaa1MceuvRSNzGOrVPjxXXVC2PppE5YWtv/nmm2qP02WN5IHoIxuikXTY0kfh3GbPng1XV1d069YNhYWFGDBgAEaPHo3AwECJ6ExNwsDAQGl5xWpRAIwYZeqjKSi6q4MuaqQIRB/ZEI2kw4Y+CjtAAwMDrFixAhcvXsSOHTuwZs0anDt3DmvXrq3ThERVIRKJEBUVpbQB1lhmD0DtcIDK1kcTUNT56aJGikD0kQ3RSDps6aPwGOCDBw/QunVrODg4wMHBQanGaAMf9gAkATCaiFAoRL9+/cg8PwJBB1C4BTh+/HgEBwfjl19+kVizjQC8zS9Bel4xKKp8EjxB8+Byufjll1/g4+NDnB+BoOUo7ABv3LiBCRMm4N69exg4cCA+/vhj7Nq1C+np6WzYp1GIW38u1qYwM9SoVeZ0noprIfbu3RuPHz8mzo9A0HIUdoBWVlYYPXo0Dh8+jIsXL6Jv377Miiyaths8UB5dFBAQoJToIvESaN5aMv4HKFcfdYXP56NLly549uwZk6bIeLYuaFQXiD6yIRpJhy196pRb06ZNMWnSJMydOxfu7u64f/++suyqVwQCgVLyiU3RzgnwytJHHREHvNy6dQsTJ06ssoq+vGizRsqA6CMbopF02NCn1g4wMjISS5cuRVBQEObOnYsWLVpgx44dyrStXhCJRHj69KlSoovELUBfR+1pASpTH3WjcrTnX3/9VauV5rVZI2VA9JEN0Ug6bOlTq7VAw8LC8ObNG3Tq1AmLFy9Gjx49YGxsrFTDNI33xaV4nVW+MLi2tQC1EUWnOhAIBO1DYQd4//59hISEoF+/fnXek0+bEO8A4WBhBCtTMqFVnSHOj0AgALVwgIcPH2bDDpWinACYcgforYWtP20bmJ87d67SnZ+2aaRsiD6yIRpJhw195HKAly9fRpcuXaCvr4/Lly9LPbZHjx5KMay+4HK51e49qCjiLZC0ZQUYMcrSR50Qj1WvX79eKc5PGzVSJkQf2RCNpMOWPnI5wGnTpuH27duwtrbGtGnTajyOoijExcUpzbj6gKZp5OXloUGDBrUKgBDzVEtXgFGWPqqmoKAApqamAABLS0scOXJEaXlri0ZsQfSRDdFIOmzpI1ebMj4+HtbW1sz/a/rTNOcHlEcXvXjxok7RRcWlQiS8yQegfS1AZeijavh8Plq2bImNGzeykr82aMQmRB/ZEI2kw5Y+Cneqnjp1qtr5GAKBAKdOnVKGTRrHs/T3EIpoWJkawN7CSNXmECogDnh5+fIltm7dioKCAlWbRCAQ1ASFHeCiRYvw/v37KukFBQVYtGiRUozSNCpugUS6L9SHytGeV69eZbpBCQQCQWEHSNN0tQ/5jIwMmJubK8Wo+sbIqG6ttidauARaReqqjyqo76kOmqhRfUL0kQ3RSDps6CP3NIghQ4aAoihQFIVx48ZBT+/DqUKhEMnJyejcubPSDWQbLpcLHx+fOuXBbIGkhVMglKFPfVPfzk8TNapPiD6yIRpJhy195HaAPXv2BADExcUhKChIoitJX18fjo6O6N27t9INZBuRSITs7GxYWVnVap5JmVCE+DTt2wVeTF31UQVhYWH1OsldEzWqT4g+siEaSYctfeR2gNOnTwcAODo6on///jA0NFSaEaqEpmkkJSXB0tKyVue/zCxASZkIpgZcOFtr3/hSXfVRBVOmTAEADBgwoF5WeNFEjeoToo9siEbSYUsfhVeCGTp0qFIN0HQqboHE4ZAAGFWRnJwMCwsLZhxa7AQJBAKhJuRygG3btsX58+dhZWWFNm3aSI10vHfvntKM0wQ+RIBq3/ifpiAe82vcuDHOnz+vscFYBAKhfpHLAS5atAhmZmbM/7Ut1L9Bg9qP3T1J0c4l0CpSF33YpmLACwDk5uaqxAGqs0bqANFHNkQj6bChD0XXdgdQDUUoFCIqKgoBAQEK7fpdHSIRjZbL/sH7kjKEf9NZa6dBqCtkVwcCQTdR1nNc4XCa2NhYPHv2jPl86dIlfP311/jll180ckdjkUiE1NTUWi2xw88pxPuSMhhwOWjRyIwF61RPXfRhE3VyfuqqkbpA9JEN0Ug6bOmjsANcsmQJXr9+DaD8ITR79mwYGxvj/PnzWLt2rVKNqw9omkZaWhpq0xAWj/95NDaHPlc7Q5frog9bqJPzA9RTI3WC6CMbopF02NJH4af269ev4eXlBQA4d+4c2rZti/Xr12PVqlX4559/lGqcuhOrpVsgqTt5eXl4//69Wjg/AoGguSg8DYKmaaYZGhERgW7dugEA7O3tkZOTo1Tj1J0nKf9FgGrZFkjqjo+PD65evYoGDRoQ50cgEGqNwi1AX19f/Pbbbzh16hTu37/POMDk5GTY2Ngo2z7WoSgK1tbWCke20jStEy3A2uqjbPh8Pm7dusV89vHxURvnpy4aqStEH9kQjaTDlj4KO8Bvv/0WT58+xY8//ogpU6bAyckJAHDhwgWN3NGYw+HA2dlZ4eV13peU4W1+edCPRyPtnXdWW32UiXjMr0+fPhJOUF1QB43UGaKPbIhG0mFLH4W7QD09PfH3339XSV+wYIFG3jyRSAQej4dmzZopZH96bjEAwMJYH6aGCsuoMdRWH2VROeBF/MKlTqhaI3WH6CMbopF02NKn1jk9efIEp0+fxunTpxEbGwtDQ0Po6+srzbD6gqZpZGVlKRxdlPquCAC0fgPc2uqjDNQt2rMmVKmRJkD0kQ3RSDps6aNw0yUrKwuzZs3C/fv3mZn5eXl5aNeuHTZs2AArKyulGqiuiFuA2u4AVYWmOD8CgaC5KNwC/PHHH1FYWIiwsDDcu3cP9+7dw9mzZ5Gfn48VK1awYaNakvafA2xsYaxiS7SP9PR04vwIBALrKNwCvHnzJvbu3YvmzZszaW5ubvjhhx8wYcIEpRpXH1AUBXt7e4Wji3SlBVhbfeqClZUVWrZsCQAa4fxUoZEmQfSRDdFIOmzpo7ADFIlE1Y716enpaeQyPhwOBw4ODgqfl5YnbgFqtwOsrT51wcDAAIcPH0Z2djYaN25cr9euDarQSJMg+siGaCQdtvRRuAu0ffv2WLlyJTIyMpi0jIwMrFq1Ch06dFCqcfWBUChEQkIChEKhQuel/RcE46DlXaC11UdR+Hw+li1bxrxEGRgYaITzA+pPI02F6CMbopF02NJH4RbgkiVLMHXqVPTo0YN5QKWnp6NFixYauRYoUB7EoyjpubrRAgRqp48iVN7S6IcffmD1emzAtkaaDtFHNkQj6bChj8IO0N7eHidPnkRERARevnwJAGjevDk6duyodOPUlffFpXhfUgZANxwgm1SO9tTEcWQCgaCZKOQAw8PDcfnyZZSWlqJDhw4YM2YMW3apNRn/jf+ZG+nBTIsnwbMNmepAIBBUidxP77/++gvLly+Hk5MTjIyMcPHiRfB4PCxcuJBN+1iHoig4OTkpFF0kngKh7eN/QO30kQdtcn5saaQtEH1kQzSSDlv6yB0Ec/DgQUyfPh0XLlzA6dOn8fPPP+PQoUNKNUYVcDgc2NjYKLS8Tto73Rn/q40+shAIBOjZs6dWOD+AHY20CaKPbIhG0mFLH7lz4/P5GDJkCPN50KBBKCsrw5s3b5RqUH0jFAoRGxurUHRRmo7MAQRqp48sDAwM8OOPP8Ld3V3jnR/AjkbaBNFHNkQj6bClj9wOUCAQwMTE5MOJHA709fVRUlKiVINUQXFxsULHp+eVT4HQhRYgoLg+8jBy5EjExMRovPMTw4ZG2gTRRzZEI+mwoY9CERwbN26EsfGHca/S0lL89ttvMDf/sB3QokWLlGedmqJLY4DKgsfjYeLEidi9ezfj9AwMDFRsFYFA0GXkdoBt2rTBq1evJNICAwPB5/OZz7oygKtLcwCVAY/HQ/fu3ZGYmIiJEyfiwoULqjaJQCAQ5HeABw4cYNMOlcHhcODm5qbQ4KqubIUE1E6filR0fq6urti1a5eSLVQ9ddVI2yH6yIZoJB229NH5SWwURcHCwkLu4wtKypBXrDuT4BXVpyKVnZ82BLxUR1000gWIPrIhGkmHLX10/nVDKBTi0aNHckcXpf83Cd7MUA/mRpq3AbCiKKqPGF1xfkDtNdIViD6yIRpJhy19dN4BAlBoFwtd2QapIrXZ5ePrr7/WCecnRhN3QqlPiD6yIRpJhw19iANUEPH4ny50f9aFXbt2YcCAATrh/AgEgmai82OAiqKLLUB5KSkpgaGhIQCgcePGOHv2rIotIhAIhJqpVQvwwYMHmDdvHj799FNmX8BTp07hwYMHSjWuPuBwOPD29pY7uujDRri6MQdQXn34fD78/Pywf//+erJMfVC0DukaRB/ZEI2kw5Y+Cud24cIFhISEwMjICE+fPoVAIAAA5OfnY8eOHUo1rr5QZEJ2OjMJXndagLL0ES9snZCQgBUrVmjF6kCKQib1S4foIxuikXTY0EdhB/jbb79h2bJlWLFiBfT0PvSgtmrVCk+fPlWqcfWBSCRCVFSU3AOsr94WAACaWJrIOFI7kKVP5V0dLl++zHSD6gqK1iFdg+gjG6KRdNjSR2EH+OrVK7Ru3bpKurm5udbvaFwkEOJ1VrkD9GhsLuNo7UebtjQiEAi6h8IO0MbGBjwer0p6ZGSk1j/8Et68B00D1qYGsDXXrVZOZYjzIxAImo7CDnDkyJFYuXIlHj9+DIqikJGRgTNnzmD16tUYNWoUGzaqDfFp7wGQ1h9QvkEycX4EAkGTUXgaxKRJkyASiTB+/HgUFRVh9OjRMDAwwIQJEzBmzBg2bGQVDoeDgIAAuaKL4tPLHaBn4wZsm6U21KTPggULAACff/65zjs/ReqQLkL0kQ3RSDps6aOwA6QoClOnTkVISAh4PB4KCwvRvHlzmJqaKtWw+kQgEMDISHZU57OM8jFOTx1rAYr1SU1NhZWVFYyMjEBRFBYuXKhq09QGeeuQrkL0kQ3RSDps6FNrd2pgYAA3Nzf4+/vX2fkdPHgQwcHB8PPzw4gRIxAdHV3jsUeOHMHnn3+ONm3aoE2bNhg/frzU42UhEonw9OlTuaKLxF2gnva64wDF+iQlJaFz584YNmwY2bizEorUIV2E6CMbopF02NJH4RbgmDFjpO77p+hE6PDwcKxatQrLli1Dy5YtsW/fPoSEhOD8+fOwtraucvzdu3cxYMAAtGrVCgYGBti1axcmTJiAsLAwNGrUSNHiyE3m+xJkFQhAUUALO91xgACQnp6OkSNHIjExEQCQnZ0NBwcHFVtFIBAIdUPhFqCXlxc8PT2ZPzc3N5SWluLp06dwd3dX2IC9e/di5MiRGD58ONzc3LBs2TIYGRnh+PHj1R6/fv16fPHFF/Dy8kLz5s2xYsUKiEQiREREKHxtRXj23/ifs7UpjA24rF5LneDz+Zg8ebJEwAtxfgQCQRtQuAX47bffVpu+ZcsWFBYWKpSXQCBAbGwsJk+ezKRxOBx07NgRjx49kiuPoqIilJWVKbxXlHhbDaFQyLRoRSIRaJpmjqEoChwOB0KhEE9T3wEAPBqZQSQSMekV4XA4oCiq2nRx/vKkc7lc0DRdbXplG2tKF9surUwVqS6dz+ejR48eSElJgYuLCy5fvgwHBwcIhUKNLZPYRmXeJ3EdEuenDWVS5n0S6yMSicDlcrWiTLJsV7RMNE0rVFZNKJOy71PFayhrWySlLYY9ePBgjBgxQqHAiJycHAiFwipdndbW1kx3myzWrVsHOzs7dOzYUSF7Y2JiJK7H5XLx+vVrZGVlMen29vZwcHBAYmIiIuL4AAALugDZ2dmwsbFBfHy8xHiYm5sbLCwsEB0dLXETvb29YWBggKioKAkbAgICIBAIJFbQ4XA4CAwMRF5eHl68eMGkGxkZwcfHB9nZ2UhKSmLSGzRogBYtWiA9PR1paWkSZXJ2dgaPx6uxTBUXLnBycpIoU3p6OiZPnoyUlBS4urpi69atyMrKYvLSxDKxfZ9iYmK0rkyA8u5TZmam1pVJmfepZcuWWlcmZd2n3Nxc0DTNPLfNzZUzDEXRlV8VasmpU6ewbt063Lp1S+5zMjIy0KVLFxw+fBiBgYFM+po1a3D//n0cPXpU6vm///47du3ahf3798PT01OuawqFQkRFRcHPz49543j//j0aNmwImqZrfBP6eNu/eJKah18/D0BfX3utf2O9d+8e+vTpA1tbW5w5cwYeHh4SY7+aWKaKNspzn0QiEUpLSyV6CKork1AoREFBAUxNTUFRlFqXSVY6G/eJpmkUFBTAzMyMtABrKBNFUcjPz4eJiYlcvzNNKFNd7pO+vj64XC6TLhQKkZeXB3Nzc+ZaMTExCAgIYI6rDQq3AKdPny7xmaZpZGZm4smTJ/j6668VysvS0hJcLlfi7QMAsrKyYGNjI/Xc3bt34/fff8fevXvldn4V4XK5jLCJiYnShaQ4SHiTDwDwdmjI3MCajldGuvhBWpma5sEomi7Llg4dOuDSpUuws7NDVlZWjfZoUpkUSRcIBHj16pVcUWc0TaO0tBT6+vpSA8R0FaKPbIhGVWnYsCEaN27MfJb5nK4FCjvAyk1PiqLg4uKCb775BkFBQQrlZWBgAB8fH0RERKBnz54AwAS0jB49usbzdu7cie3bt2P37t3w8/NTtAgK8zqrACVlIhjrc9HMSnsXwebz+cjMzESrVq0AAG3btoVQKKzygqLt0DSNtLQ0cLlcNG3aVObkW5qmUVRUBGNjY/Lwqgaij2yIRh+gaRqFhYV48+YNgPIuWbZQyAEKhUIMGzYM7u7uCged1MSXX36JhQsXwtfXF/7+/ti3bx+KioowbNgwAOUrjjRq1Ahz584FUN7tuXnzZqxfvx6Ojo7IzMwEAJiYmLA2GV88/8+9kRk4HO2snOK1PbOzs3H58mXGCeoiZWVlKCwshIODA0xMZL/wiLttxAsEECQh+siGaCSJsXH5fqtv3ryBnZ0da9dRyAFyuVxMmDAB4eHhSnOA/fv3R3Z2NjZv3ozMzEx4eXlh165dTBdoWlqaxBv44cOHUVpaim+++UYin+nTp2PGjBm1skHW6gLP0sUrwGjnEmiVF7a2tbWV+F7XVqcQj2Uosv8YWcJKOkQf2RCNJBG/fIq7htl4DincBdqiRQskJycrdf3H0aNH19jleeDAAYnPV65cUdp1gXKn7uPjI/UY8Rqg2rgItqxdHeTRR1uR902coijmjZVQFaKPbIhGVan4+2PrOaTwK8esWbOwevVqXL16FW/evEF+fr7En6YhEonw9u1bqcEOHxbB1i4HKM+WRvLoo+uIAxiUFFCtdRB9ZEM0kg5bzyG5W4Bbt27FhAkTMGnSJADA1KlTJTy0eCJnXFycUg1kG5qmkZSUBEtLy2q/LygpAy+7fIK/NrUAU1JS5NrPT5Y+hHIEAgH09JQ2rVbrIPrIhmhUM2w9h+RWe9u2bRg1apTCa31qOs8zylt/tuaGsDbTnk1wLS0t4eTkBABkPz8dw8PDA9u2bWMir9ni7t27GDt2LO7du8c82C9duoTVq1cjOTkZo0ePhpeXF3766Sc8ePCAFRsSExMxZswYXLhwAWZmZqxcQ9cYOXIkQkJC0KdPH1WbUmfkdoDipnnbtm1ZM0Yd0dbuTxMTE5w9exY5OTlwdHRUtTkEJZGZmYnt27fj2rVryMjIgLW1Nby8vDBu3Dh06NChXm0JDAzErVu3YG5ujqKiIgDAkiVLMGzYMIwZMwampqbQ09ND165dWbPhl19+wejRo6t1fn379kVycjKuXr1aJfArODgYY8eOxfjx4yXSt2zZgkuXLuH06dNMmqo0P3fuHDZt2oSUlBQ4Oztj3rx5MrU8c+YMdu3ahaSkJJibm6Nz585YsGAB07L6559/sH37dvB4PJSVlcHJyQlffvklhgwZwuQxdepUrFq1Cr169dL4wB2FrNfW8NwGDWqO7nymRQ6Qx+Nhw4YNzMuMiYmJXM5Pmj66Ak3TKBSU1fhXIoTU72vzp+h4UHJyMoYNG4Y7d+5gwYIF+Pvvv7Fr1y60a9cOy5YtY0mZmjEwMICtrS2zAEJBQQGysrIQFBSERo0awczMDEZGRtXu+qIIpaWl1aanpqbi2rVrzJSqijx48AAlJSXo06cPTp48WetrK1NzRSZ4P3z4EHPnzsUnn3yCU6dOoUePHpg2bRqeP39e4zmRkZFYuHAhPvnkE5w9exYbN25ETEwMvv/+e+YYCwsLTJ06Ff/73/9w5swZDBs2DN9++y1u3rzJHNOlSxcUFBTgxo0bCpWvrrDxHFKow7lPnz4yneC9e/fqZFB9w+Vy0aJFixq/j/9vCoSHhk+B4PF46N69O7PG6uzZs+U6T5Y+ugBN0/hkewQik3Lq9bqtnSxxdEoHuV88ly1bBoqicPToUYn5iy1atMDw4cNrPG/t2rW4dOkS0tPTYWNjg0GDBmHatGnQ19cHAMTHx2PlypV48uQJKIqCs7Mzli1bBj8/P6SkpODHH39EZGQkSktL4ejoiAULFqBr165MF+j9+/cRFxeHsWPHAgDGjRsHoHzrtJSUlCpdoJcuXcK2bdvw4sUL2NnZYejQoZgyZQrTjerh4YEffvgBN27cwJ07dxASElLtFKhz587Bw8Oj2m3Sjh8/joEDB6JNmzZYuXIlE9ugKLXVvDIURSkU5r9//3507twZEydOBFAenPjvv//izz//xPLly6s9JyoqCo6Ojsx9aNq0KT799FPs3LmTOaZdu3YS54wbNw6nTp1CZGQkOnfuDKD8mdClSxeEhYWhW7ducttcF9h6DinkAGfMmKG0RUjVBZFIhPT0dDRu3LhKc56maa3oAq3o/FxdXfHJJ5/Ifa40fXQJde/7ePfuHW7evInZs2dXO3lf2tuzqakpVq1aBTs7Ozx//hzff/89TE1N8dVXXwEA5s2bBy8vLyxduhRcLhdxcXGMc1y+fDlKS0vx559/wsTEBC9evKhyfZqm4evri3PnzqFfv37YsmULAgMDYWFhgZSUFIljHzx4gIULF+K7775D69atwePxmBZKxWUYt27dirlz52Lx4sU1tpwePHgAX1/fKun5+fk4f/48jhw5AldXV+Tn5+PBgwdo3bp1jRpVR100P3PmDH744QeJNHEgoZidO3fWaFNUVFSV7tmgoCBcunSpxmsGBARgw4YNuH79Orp06YKsrCxcuHChxm5TmqZx584dvHr1CvPmzZP4zt/fX8Jxsg1bzyGFHOCAAQPq3F2hboiXvaruLfHN+xK8KywFhwLc7DRzAL2y81M04EWaProCRVE4OqUDikqr34KlfOmmIpiYKHcZK2N9rtz58Xg80DQNV1dXha9TcQ3fJk2a4NWrVwgLC2McYGpqKkJCQtC8eXMAgLOzM3N8amoq+vTpAw8PDwCQWrfEzw4LC4sqY25itm7dikmTJmHo0KFMfjNnzsTatWslHODAgQNltrBSU1OrdYDh4eFwcnJiWhT9+/fHsWPHFHaAddE8ODgYLVu2ZD5XtxSatN/c27dvq6yXbG1tjbdv39Z4zkcffYS1a9di1qxZEAgEKCsrQ/fu3bFkyRKJ496/f48uXbpAIBCAw+Hghx9+QKdOnSSOsbOzQ1paGrM1HNuw9RyS2wFq6/ifNMStPxcbUxjpa94muHV1foQPUBQFE4Pqfy40TQNlXJgY6Knsd1KX+WPh4eHYv38/+Hw+CgsLUVZWJhE08uWXX+K7777D6dOn0bFjR/Tt2xfNmjUDAIwdOxZLly7FrVu30LFjR/Tu3btWi9OLiY+Px8OHD7F9+3YmTSgUoqSkhHEQAKp1bJUpLi6GoWHVyO3jx49j8ODBzOfBgwdjzJgx+O677xSKFK2L5mZmZhLXEq9/WXk3CGXy4sULrFy5EtOmTUNQUBAyMzOxZs0a/PDDD/jpp5+Y40xNTXHq1CkUFhYiIiICP//8M5o2bSrRPWpkZASRSASBQKDRK0UpHAWqS8Snae4SaEVFRQgODibOT0dwcnICRVFy76Mp5tGjR5g3bx5mzJiBoKAgmJubIywsDHv37mWOmTFjBgYOHIjr16/jxo0b2Lx5MzZs2IBevXphxIgRCAoKwrVr13D79m38/vvvWLhwIcaMGVOrchQWFmLGjBno3bt3le8qOjN51mi1tLSU2M8OKHcCUVFRiI6Oxrp165h0oVCI8PBwjBw5EkC5E6huYQ/xljxA7TUH6t4FamNjU6W1J2sXnR07dqBVq1bMuKGnpyeMjY3xxRdfYNasWcyamxwOh5ki5eXlhZcvX+L333+XcIC5ubkwMTHRaOcHKOAA4+Pj2bRDZVAUBWtr62rfujQ5AtTY2BgLFizA2rVrceXKlVo7P2n6ED6g6gnMDRs2RFBQEA4ePIgxY8ZUcRB5eXnVjkk9evQIDg4OmDp1KpOWmppa5TgXFxe4uLhg/PjxmDNnDo4fP45evXoBKF+tf9SoURg1ahTWr1+PI0eOVHGAenp6KCsrk1kOb29vvHr1inkA1wVvb2+JzVUB4NixY2jTpk2Vbr8TJ07g2LFjjAN0cXHBkydPquT59OlTuLi4AKi95kD1XaCVt0OS1t0XEBCAO3fuSIwD/vvvvwgICKjxnOLi4irjpeLP0ho44pZeRZ4/fw4vL68az1E2bD2HdDeq4T84HA6cnZ2r7ceO0/A1QCdNmoTo6Og6tfyk6UMoh6IoGBoaqvwl4YcffoBIJMKIESNw4cIFvH79Gi9fvsT+/fvx6aefVnuOk5MT0tLSEBYWBh6Ph/3790sEUhQXF2P58uW4e/cuUlJSEBkZiZiYGGY8cOXKlbh58yb4fD5iY2Nx9+5d5jsxiugzbdo0nD59Glu3bkVCQgJevnyJsLAwbNiwQWE9goKCEBUVxSxuXlpaitOnT2PAgAFwd3eX+BsxYgQeP36MhIQEAMD48eNx/fp1/Pbbb3j58iWeP3+ODRs2ICoqiomiBGqnOVDeBerk5MT8OTs7o0WLFnB2dmbSpLWuxo4di5s3b2LPnj14+fIltmzZgidPnkisqbx+/XosWLCA+dy9e3dcvHgRf/31F/h8PiIjI7FixQr4+/szznbHjh24ffs2+Hw+Xr58iT179uDMmTMSXcZA+ZSKyuOCbMLWc0jn190RiUTg8Xho1qyZhLilQhFe/rcJrqZ0gfL5fHzzzTfYuXMn0xVS1wV2a9KH8AGapiEQCGBgYKBSJ9i0aVOcOHEC27dvx+rVq/HmzRtYWVnBx8cHS5curfacHj16YNy4cVi+fDkEAgG6deuGqVOnYuvWrQDKHzzv3r3DwoUL8fbtW1haWqJ3797MbiwikQjLly9Heno6zMzM0LlzZyxatEjiGjRNo6SkRK5hlM6dO2P79u3Ytm0bdu7cCT09Pbi6umLEiBEK69GlSxdwuVz8+++/6Ny5M65cuYJ3794xLdeKNG/eHM2bN8exY8ewaNEitGrVCjt37sS2bduwd+9eUBQFd3d3/PHHH3B3d2fOq43m1aFoHWrVqhXWrVuHjRs34pdffoGzszO2bdsmYVtmZibS0tKYz8OGDUNBQQEOHjyI1atXw9zcHO3bt8f8+fOZYwoLC7Fs2TKkp6fDyMgIrq6uWLt2Lfr3788ck5GRgUePHmHt2rVyl6+usPUcomgdG9wTCoWIiopidhau/FlMQsZ79NpwA6YGXMQs7aP2+wBWXNh6yJAhdZrcW5Ga9NFmiouL8erVK7i4uMg1xlEfAQyajCr1OXjwIK5cuYLdu3fX63UVRZPq0Nq1a5GXl4cff/yR1etU/B3q6+vL9dxWFJ1vAdaEuPvTvbG5Rjk/V1dXbN68WdUmEQhqwaeffoq8vDzk5+eTtUCVhLW1Nb788ktVm6EUiAOsAU3ZBFeeLY0IBF1FT09PIsCHUHcmTJigahOUhs4P6lAUBXt7+yrdDpoQAVofzq8mfQiSiFdGIVQP0Uc2RKOaYes5pPMtQA6HAwcHhyrpcWnqHwE6fvx41lt+NelD+ABFUTAwMFC1GWoL0Uc2RCPpsPUc0vkWoFAoREJCAhMqDQDvi0uR8q58+xZ1bgHu2rULwcHBrHZ7VqcPQRKaplFcXKyTi0XIA9FHNkQj6bD1HNL5FiCAKqtFiDfBbdzACA1N1OutrKysjJl07eLigsuXL7N+zcr6EKpCXhCkQ/SRDdFIOmw8h3S+BVgd6tr9yefz4efnhzNnzqjaFAKBQNB4iAOsBiYAxl59HKA44CU+Ph4LFy6Ua1kpAoFAINSMzjtAiqKYRW3FqFsEaOVoz3/++afe1p6sTh9CVUgAg3SIPrIhGtUMW88hnXeAHA4HNjY2zPI6NE0jTrwLfCPVzwFU9Ty/yvoQqkJRlMQixuqOh4eH1I1TlY2y9Dlx4oTCe/bVlbt378LDw0Pm+FNERAT69etX63E8TatDdWXkyJG4cOGC3Mez9RzS+aeaUChEbGwsU3HTcovxvrgMehwKze1MVWqbqp0fUFUfQlXEm5mqQwRfZmYmfvzxR/To0QO+vr7o2rUrpkyZgoiICJXZpE76sMXatWsxderUKstyFRcXo23btmjXrl2VHRWADy8jlTUKDQ2V2KgYAJKSkrBo0SJ06dIFvr6+CA4Oxpw5cxATE8NewVC+nFxwcDD8/PwwYsQIREdHSz3+xIkT8PDwkPjz8/OTOGbq1KlYv349RCKRXDaw9RwiUaAor6RixN2frramMNRT7dqX27dvV4sVXirqo8sUFBRUmy5e7LniuqE1HQuUv81WXKS8pmNNTRV7AUtOTsaoUaPQoEEDLFiwAO7u7igrK8OtW7ewbNkynD9/XqH8lIm8DzpN5MGDB+DxeOjTp0+V7y5cuAA3NzfQNI1Lly5JLCpdGWkaxcTEYPz48WjRogWWL18OV1dXFBQU4PLly1i9ejX+/PNPpZSlMuHh4Vi1ahWWLVuGli1bYt++fQgJCcH58+dhbW1d43lmZmYS9a1yy7ZLly747rvvcOPGDXTr1k0uW9h4DhEHWAmm+1MNlkATLzY7ZcoUsryZGiBtLck+ffrg3LlzzGc7OzsUFhZWe2zXrl1x7do15rOzs3OVzU0BxTehXrZsGSiKwtGjRyX2pmvRogWGDx8ucWxOTg6mTZuGW7duoVGjRli4cCF69OgBoPxt+/vvv8edO3fw9u1b2Nvb4/PPP8e4ceOY80NDQ5GXl4ePPvoIe/fuRWlpKfr3749vv/2WWdFEIBBg06ZNOHv2LLKysmBvb49JkyYxOzs8f/4ca9asQWRkJIyNjdGpUycsWrQIVlZWcpf50qVL2LZtG168eAE7OzsMHToUU6ZMgZ6eHubOnQuhUIiNGzcyx5eWliIoKAiLFi3CkCFDIBKJsHPnTvzvf//D27dv4ezsjK+//hp9+/aV24bw8HB07Nix2t3njx07hsGDB4OmaRw7dkyqA6wJmqaxaNEiODk54a+//pLoBvTy8pLYnknZ7N27FyNHjmTqz7Jly3Dt2jUcP34ckyZNqvE8iqJga2tb4/dcLhddunRBWFiY3A6QDXS+C7Qyqg6AycjIYCI8ORwOVq5cSZwfQSbv3r3DzZs38cUXX1S7W3rljVm3bt2Kfv364cyZM+jSpQvmzZuHd+/eAShviTRu3BibNm1CWFgYpk2bhg0bNiA8PFwij7t374LH42Hfvn34+eefcfLkSYldSBYsWICwsDAsXrwYx48fx7Jly5hWbV5eHsaNGwdvb28cO3YMu3btQlZWFmbNmiV3mR88eICFCxdi7NixCA8Px/Lly5mtiQBg0KBBuHr1qkQL+9atWyguLkbPnj0BlO9/d+rUKSxbtgxhYWEYP3485s+fj3v37ilkh6+vb5V0Ho+HqKgo9OvXD/369cODBw+QkpIid75i4uLikJCQgAkTJlQ7BlbTprtAeS9SYGCg1L/qNkAGyl9gYmNj0bFjRyaNw+GgY8eOePTokVSbCwsL0b17d3Tt2hVTp05l9lmsiL+/PyIjI6XmwzY63wLkcDhwc3NjKlZ8muocoHjMr02bNvjzzz9Vvss4UFUfXSY/P7/a9Opaam/evKkxn8pavn79uk52AeUPW5qm4erqKtfxQ4cOxcCBAwEAc+bMwYEDBxAdHY0uXbpAX1+f2e8PKN/zLioqCufPn5dowVhYWGDJkiXgcrlo3rw5unbtioiICIwcORKvXr3CuXPnsHfvXnTo0AFCoRDu7u5MV9iff/4Jb29vzJkzh8nvp59+QteuXZktcGSxdetWTJo0CUOHDmXsnDlzJtauXYvp06cjKCgIxsbGuHjxIoYMGQIAOHv2LIKDg2FmZgaBQIAdO3Zg7969CAwMZPKIjIzE//73P7Rt21YuLVNTU2FnZ1cl/fjx4+jSpQssLCwAlG/Qe+LECcyYMaPafKprQQIf6oe897Yin332Gfr16yf1mOpsB8p7CYRCYZWuTmtrayQmJtaYn4uLC3766Sd4eHjg/fv32LNnDz777DOEhYWhcePGEtdNS0uDSCSS+Xxh6zmk+iesiqEoiqmggjIRXmaWP+TqexJ8xYAXAMjKymJ2aVYlFfXRdRQZk2Pr2JpQtLvUw8OD+b+JiQnMzMyQnZ3NpB08eBDHjx9HamoqSkpKUFpaCk9PT4k83NzcJII+bG1t8fz5cwDlrRYul4s2bdqAoqgqL3Px8fG4e/cu43gqwuPx5HKA8fHxePjwIdPiA8q7b0tKSlBUVARjY2P069cPf//9N4YMGYLCwkJcvnwZv/zyC4DyoJKioqIquxuUlpbCy8tL5vXFFBcXV3FeQqEQJ0+exOLFi5m0wYMHY82aNZg2bVqVB3l1GimDhg0bomHDhkrPVxrilmXFz/3798fhw4clWvhGRkYQiUQQCAQy991k6zmk8w5QKBQiOjoa/v7+SHxbgDIRDXNDPTg2rNtO6opQXbSnOjg/QFIfXdkQV1HEEXzGxsYqC2MXz5GS9mZekco7D1AUxQRhhIWFYfXq1Vi4cCECAwNhamqK3bt34/HjxxLnVH5gUxTFOOKKD7Tq9BF3kc2bN6+KbdLGjipSWFiIGTNmoHfv3lW+EzukQYMGYcyYMcjKysLt27dhaGiIzp07M+cD5d2glX9viszJs7S0rDJN4tatW8jIyMDs2bMl0oVCISIiItCpUycA5S8/79+/r6JRXl4ezM3LX8KdnZ0BAImJifD29pbbLqC8C3THjh1SjwkLC6t2oWlLS0twuVxkZWVJpGdlZcHGxkZuG/T19eHl5QUejyeRnpubCxMTE7k2nWbrOaTzDhD4EH0VX2EJtPp6kKnDVAdZaHMEn7JQdYh/w4YNERQUhIMHD2LMmDFVxgHz8vKkjhVV5OHDhwgMDMQXX3zBpFV+eMnC3d0dIpEI9+/fR4cOHaro4+PjgwsXLsDR0bHWLR9vb2+8evUKTk5ONR7TqlUrNG7cGOHh4bhx4wb69u3LOP/mzZvDwMAAqampcnd31mTHixcvJNKOHTuGAQMGYMqUKRLp27dvx7FjxxgH6OLigtjYWAwZMoTRSCgUIj4+ngkW8vLygpubG/bs2YP+/ftXaT1Ku7d16QI1MDCAj48PIiIimDFTkUiEiIgIjB49WmqeFREKhXj+/Dm6du0qkf78+XOFWtpsPIeIA6xAfHr9rgGqCc6PoDn88MMPGDVqFEaMGIFvvvkGHh4eEAqFuH37Ng4dOiQRpSoNJycnnDp1Cjdv3kSTJk1w+vRpxMTEoEmTJnLb0qRJEwwdOhTffvstFi9eDCcnJ2RnZyM7Oxv9+/fH559/jiNHjmDOnDmYOHEiGjZsiKSkJISHh2PFihVyveVPmzYNU6ZMgYODA/r06QMOh4P4+Hg8f/5couU1cOBAHD58GK9fv8a+ffuYdDMzM0yYMAGrVq0CTdP46KOP8P79ezx8+BBmZmbM2KIsgoKCJIJ/srOzcfXqVfz6669wd3eXOPbjjz/G9OnT8e7dOzRs2BBffvklFi9eDBcXF7Rq1Qo0TePPP/9EXl4e4wApisKqVaswfvx4fP7555g6dSozDeLq1au4fft2jdMg6toF+uWXX2LhwoXw9fWFv78/9u3bh6KiIgwbNow5ZsGCBWjUqBHmzp0LoHxsNiAgAE5OTsjLy8Pu3buRmprKlEdMZGQk8yKgKogDrACzC7x9/UyBSEhIQEpKCnF+BKXQtGlTJgpy9erVePPmDaysrODj44OlS5fKnc9nn32GuLg4zJ49GxRFYcCAAfj8889x48YNhexZunQpfvnlFyxbtgzv3r2Dg4MDJk+eDABo1KgRDh06hHXr1iEkJAQCgQAODg7o3Lmz3IEOnTt3xvbt27Ft2zbs3LkTenp6cHV1rfKgHTx4MLZv3w5HR0d89NFHEt/NmjULVlZW2LFjB5KTk2Fubg5vb+8qLTdpDBo0CGvXrmVeZE+dOgVjY2N06NChyrEdOnSAkZERzpw5g7Fjx2LgwIGgaRp79+7F+vXrYWxsDB8fH/z5558S3Yz+/v44fvw4tm/fju+++w45OTmws7NDYGAgvv32W7ltVZT+/fsjOzsbmzdvRmZmJry8vLBr1y4J29LS0iTuWV5eHr7//ntkZmbCwsICPj4+OHz4MNzc3JhjMjIy8OjRI6xdu5Y12+WBolXdd1PPCIVCREVFISAgAFwul9mHy8jICB1/voK03GIcndIBbZzln4tUF65cuYIWLVqorfOrqI+uLNNUXFzMRCLKMz5B0zRomgZFUTqjkSLogj6rV69GQUEBli9fXqvzdUGjiqxduxZ5eXnMXOfqqPg7NDQ0lHgOVX6O1xYS247yvu7colKk5ZavNODeiL0uUB6Ph2fPnjGfg4OD1db5iSGL9MpGFx5adUHb9Zk6dSocHBzqNE6l7RpVxNraGjNnzlToHDaeQzrvAEUiEaKiohCflgsAcGxoDAtjfRln1Q4ej4fu3bujW7duEk5QnRHrQwJhpFPTqi+EcrRdnwYNGmDKlCl1mqem7RpVZMKECQpFkrL1HNJ5ByiG7QAYsfNLTEyEiYlJtat1EAgEAqH+IA7wP56lszcBvqLzIwEvBAKBoB4QB/gfzzLYWQKNOD8CgUBQT3TeAXI4HLRs2RLPM8pbgJ5K3AWCz+drvPPjcDgICAgga4HKgHRpS4foIxuiUc2w9Rwi8wABJGflI7+kDFwOBVdb5W2Ca25uzmztoonOT4w8a/XpOuIQdkL1EH1kQzSSDhvPIZ1/rReJRLgdFQcAsLcwgj5XeZI0bNgQFy9exPXr1zXW+YlEIjx9+pREgcqgqKhI1SaoNUQf2RCNaoat55DOO0AAeFMgBAA0saz7Ath8Ph979uxhPjds2FChJaQIBGVy9+5deHh4VFmsmW1OnDiB1q1b1ymP5ORkeHh4IC4ursZjVFU+gnZAHCCAN4ViB1i3Pnjx2p4hISESTpCgoyQkAIsWAaNGlf9bzaagymbMmDFYuXIl69fRJg4ePIjg4GD4+flhxIgRiI6Olnr8iRMn4OHhIfHn5+cncQxN09i0aROCgoLg7++P8ePHK2XfR4JyIQ4QQGZhebO6Li3Aygtb9+rVS1nmqRwSACObKmM3e/cCnp7A2rXAkSPl/3p6An/8oRL7FEEgECg9T3Ud2woPD8eqVaswbdo0nDx5Ep6enggJCamyBVBlzMzMcOvWLebv6tWrEt/v3LkTBw4cwNKlS3HkyBEYGxsjJCQEJSUlNeaprhqpC2w8h3T+ycblclHCLW/51bYFqM27OnC5XAQGBpK9AKVAURRMTEw+PMASEoCJEwGRCBAKJf8NCQEqbZ2jLEJDQ3Hv3j3s37+faZmkpKQAAGJjYzFs2DC0bNkSn332mcS+gVu2bMHHH3+Mo0ePIjg4GP7+/gDKFzVevHgx2rdvj1atWmHs2LGIj49nzouPj8eYMWMQGBiIVq1aYdiwYYiJiZGw6ebNm+jfvz86deqEiRMn4s2bN8x3IpEIW7duRZcuXeDr64uPP/5Y5oLb169fR58+feDv748xY8Yw5aste/fuxciRIzF8+HC4ublh2bJlMDIywvHjx6WeR1EUbG1tmb+Kq5rQNI39+/dj6tSp6NmzJzw9PbFmzRq8efMGly5dqjE/iTpEkICt55DOO0CappH0tnwKRG02wdVm5weU65Obm6vy/e7UGZqmUVZW9kGjPXuAmh5kFAXs3s2KHYsXL0ZgYCBGjhzJtEzs7e0BABs2bEBoaCiOHz8OLpdbZQcBHo+HCxcuYOvWrTh16hQAYObMmcjKysLOnTtx4sQJ+Pj4YNy4cXj37h0AYN68eWjcuDGOHTuGEydO4KuvvpLYaLe4uBh79uzB6tWrsW/fPqSlpWH16tXM9/v378fevXuxcOFCnDlzBkFBQfj6669r7CpMS0vD9OnT0b17d5w6dQojRozA+vXrJY5JTU1ldiSv6U+8g7xAIEBsbCw6duzInM/hcNCxY0c8evRIqtbiDX27du2KqVOnIqFC93ZycjIyMzMl8jU3N0fLli1rzLdKHSJIwNZzSOenQZSVCZH63yLYinaBvn//XqudH1D+lv7ixYs6r7qu7ZSUlHyYx/X6NVDTD5Wmy79nAXNzc+jr68PIyIjZVV3c0ps9ezaz6eukSZMwadIklJSUMDunl5aWYs2aNcy0nQcPHiA6OhoRERHMIsQLFy7EpUuXcOHCBXz66adITU1FSEgImjdvDuDDzuViSktLsWzZMjRt2hSFhYX44osv8OuvvzLf7969G1999RUGDBgAAJg/fz7u3r2Lffv24YcffqhSvkOHDqFZs2YIDQ0FALi6uuL58+fYuXMnc4ydnR3jwGvCwsICAJCTkwOhUAhra2uJ762trSVayJVxcXHBTz/9BA8PD7x//x579uzBZ599hrCwMDRu3BiZmZlMPpXzffv2bY35StQhggRsPYd03gFm5pegTARwORTsLRSbY2Jubo6vvvoKO3fu1ErnR6glzs7SW4CVHEV94OHhwfxf7ByzsrLg4OAAAHBwcGCcHwA8e/YMhYWFaNeunUQ+xcXFzO7wX375Jb777jucPn0aHTt2RN++fdGsWTPmWGNjYzRr1ox5a7e1tWXG1vLz8/HmzRu0atVKIv9WrVpJdLNW5OXLl0z3rJiAgACJz3p6elJ3iFcG4pZkxc/9+/fH4cOHMWvWLFavTVAuOu8AU96Vz71p3MAQerWYAxgaGopp06bB3Lx+dpEnaAATJgBr1lT/HU2XjwPWM3p6H37q4nGminOqjI0lez8KCgpga2uLAwcOVMlLXNdnzJiBgQMH4vr167hx4wY2b96MDRs2MAFgFa8pvi7bXXypqalMi7ImJk+ejClTpsDS0hJcLrdKwEtWVpZCOxXo6+vDy8uLeTGo+IJhZ2cnka+np6fc+RLYR+cdYHJOuQOUt/uTz+dj0aJF+O2335gHgbY7P7IKjGwkItRatCgf5wsJKW/x0fSHf3fvBirsjK1s9PX1lTJZ2MfHB2/fvgWXy5U6j9XFxQUuLi4YP3485syZg+PHj1cbAV05gs/MzAx2dnZ4+PAh0zULAA8fPqzSyhPTvHlzXLlyRSLt8ePHEp8V6QI1MDCAj48PIiIi0LNnTwDlLwUREREYPXq01DwqIhQK8fz5c3Tt2hUA0KRJE9ja2iIiIgJeXl4Aylu8jx8/xqhRo2rMh0RbS4eN55DOO8DU3PKwZEc5IkArBrwAwJ9//smqbeoAl8uFj4+Pqs1QayiKqtKCwvjxQFBQucN7/bq82zMkhFXnBwCOjo54/PgxkpOTYWJiUmtn2LFjRwQEBGDatGmYP38+nJ2d8ebNG1y/fh09e/ZEixYtsGbNGvTp0wdNmjRBeno6YmJi0Lt37yp5ifWpHOH4//bOPa6JK4vjPx5BVKoiIFCKgKiAJEgQa6UgK1akKmxlS4vswlIVldYHPgGVruADbKkighqUj63ilraKVgTZrbWoFKVFqyKCK4ICggoFipRHArn7B5tZYgIkEYgl9/v58OGTO3fmnnPmzj1zz9yZs3jxYuzbtw9jx46FtbU10tLSUFxcjNjYWKky+fr6MotqfHx8UFhYiFOnTonVkTcE+sEHHyA0NBRsNht2dnb44osv0NLSAm9vb6bOxo0bYWhoiHXr1gEAEhISYG9vDzMzMzQ2NiI5ORlVVVXw8fFh9A0ICMCBAwdgZmaG1157DXv37sWYMWMYR9udjSjS6a9xSOUdYGVdZxLK3laAPr/aMzo6eiDEUzpCoRB1dXUYPXo0vUPtBtEKPk1NTfFBfvx4YID7yaJFixAWFoZ58+ahtbVV4X6qpqaGpKQkxMXFITw8HPX19dDX14ejoyP09fWhrq6OhoYGhIaGora2Frq6unB3d8eqVaskjtXdCseAgAA0NTUhJiYGdXV1sLS0xP79+yUW04h49dVXsW/fPkRHRyMlJQV2dnZYs2aNxIpWeZg7dy7q6uoQHx+Pmpoa2NjY4PDhw2Ih0OrqarG+39jYiIiICNTU1GDkyJGwtbVFamoqxne5uQkKCkJLSws+/vhjNDY2YsqUKTh8+DCz6Kg7G0n0IQqA/huH1IiKrbvt6OjAjRs3mNVE/sl5uHyvFru82Xj/del3joP9VYeeeN5eqkBrayvKyspgYWEhU9iFEILm5mb6Hlc3UPv0DrWRJF2vQxaLJTYO9dW4pPK39I/+9wzQpJtngKrs/CgUCmUwo9IOkBDCrAJ9TUoIlBCC9957jzo/CoVCGYSotAOsaWpDW7sQ6mqAkZR3ANXU1HDo0CG88cYbKu38RozouyTBgxVVCQ8rCrVP71Ab9Ux/jEMqvQimua0zC8QwLU1oa/3/E04dHR1MZ2Sz2cjNzVXZuLyGhgYmTJigbDFeatTU1OirIj1A7dM71EY901/jkErPAEUQImSWi1dUVIDL5SI7O5vZrqrOD+hcfVVVVaWSCXFlXR9GCAGfz6ffcewGap/eoTaSpKst+mscog4QABESEEKYBS8FBQVYtWoVOjo6lC2a0iGEoLq6WqUuTNHsX560QAKBoL/EGRRQ+/QOtZE4zc2dr6ixWKx+G4deihDo8ePHkZycjJqaGlhbWyMiIqLbr0EAwLlz57B37148evQI5ubmWL9+PfMVBkWpqKjArFmzmAUvGRkZNCavomhqamLYsGGoqakBi8Xq9b0jQgja2tqgrq6u0tGC7qD26R1qo/8jeiXk6dOnGDVqFPPaQ3+gdAcoSkgZGRmJyZMn44svvsDixYuRlZUl8TV1oPNTSevWrcPatWsxc+ZMpKen46OPPkJaWhomTpyokAyCxhox56fKC14onSFvY2NjlJWV4eHDh73WJ4RAIBCAxWKp/OAlDWqf3qE2kmTUqFEwMjLq1zaU7gC7JqQEgMjISGRnZ+PkyZNYunSpRP2jR4/CxcUFS5YsAQCEhIQgNzcXKSkpiIqKkrv99sYaVKWGQ1D/mDo/KaipqUFPT0/lLkotLS1MmDBBpjCoUChEdXU1jI2N6ddypEDt0zvURuKwWCyxCFx/jUNKdYCihJTLli1jynpLSHnjxg0EBgaKlTk7O3ebabk7Ojo60CHsQONPaYzzu3DhAl599VVmuq2mpgZ1dXWJ6Xd35aLwhbRyABIPcLsr19DQACFEarlQKJSIg0srF8nYXbk8Opmbm3faq8u2P7pOsp6nriHQnnTq+vmul12nnsr76zyZm5sPOp16kl0RncaNGzfodJImu6w6iY4ncoSmpqYghEiMRS+CUh2gIgkpa2trJVKV9JZoUhoFBQVoaycw81iCpqEauJASB0IIbty4wdQxNjbGq6++itLSUjQ2NjLlZmZm0NfXR3FxMVpbW5ny8ePHY+TIkbh165bYyZ00aRK0tLTEjg105jLj8/m4c+cOU6aurg4ul4vGxkaUlJQw5dra2rC1tUVdXZ1YWG7EiBGYMGECHj9+jOrqajGbmJubo7y8XCzdi7w6WVpaoqGhAfX19YNGp8F4nv4IOhkZGcHExGRQ6dRX58na2hpPnjxBfX39oNGpL89TbW0tKioqmPK+ysCj1G+BPnnyBDNmzEBqaqpYgslPPvkEP//8M7755huJfdhsNmJiYjB//nym7Pjx40hMTERubm6vbYq+IcfhcKChoYGmFj7uFhdiCpcrka/sZbkT6lo+0Hd3hBDcvHmTsddg0Kmvz5NAIEBBQQFjo8GgU1+ep46ODhQUFMDOzg4sFmtQ6NSb7PLqJO919kfQqS/Pk0AgwK1btxj7iPrUi34LVKkzQEUSUurr60vM9uRNYAmAGah0hmpB439x5e5i790ZuD/L1dTUpJZ3J6O85bLK0jUMIW2fP6JOfVneVaeuNhosOskio6zlot+DSae+klGR6+xl10mR8t506s4+iqJUB6hIQkp7e3tcvXpV7Dlgbm4u7O3tZWpTdKcj6nDP/6eIQ+3TO9RGPUPt0zvURj3T3Xj9ogFMpadDyszMRGhoKKKiopiElOfOncO5c+egr68vkYzy+vXr8Pf3x7p16+Dq6orMzEzweDyZX4Pg8/koKCjob7UoFAqF0s9wOBxoaWkpvL/SX4PoLSHl88koHRwcEBsbi7i4OOzevRvm5uZITEyU+R1ATU1NcDgc+sIphUKh/EERPSvU1HwxF6b0GSCFQqFQKMqAvnFJoVAoFJWEOkAKhUKhqCTUAVIoFApFJaEOkEKhUCgqCXWAFAqFQlFJqAOkUCgUikpCHSCFQqFQVBLqACkUCoWikqiEAzx+/Djc3NzA4XDg4+ODW7du9Vj/3Llz8PDwAIfDgaenJy5evDhAkioHeezz9ddfw8/PD1OnTsXUqVMRGBjYqz0HA/L2IREZGRmwsrLChx9+2M8SKhd57dPY2IjIyEg4OzuDzWZjzpw59Dp7js8//xxz5syBnZ0dXF1dsXPnTrS1tQ2QtAPLzz//jOXLl8PZ2RlWVlYy5XfNy8vDggULwGazMXv2bKSlpcnfMBnkZGRkEFtbW3LixAly7949smXLFuLo6Ehqa2ul1r927RqxsbEhhw4dIiUlJWTPnj3E1taW3L17d4AlHxjktc/atWtJSkoKuXPnDikpKSFhYWFkypQp5PHjxwMs+cAhr41EVFRUEBcXF+Ln50eCg4MHSNqBR177tLW1EW9vbxIUFETy8/NJRUUFycvLI0VFRQMs+cAhr43OnDlD2Gw2OXPmDKmoqCCXL18mb775Jtm5c+cASz4wZGdnk927d5N///vfZOLEieS7777rsX55eTmZPHkyiY6OJiUlJeTYsWPExsaGXLp0Sa52B70DfPfdd0lkZCTzu6Ojgzg7OxMejye1/urVq8nSpUvFynx8fEhERES/yqks5LXP87S3txMul0tOnTrVTxIqH0Vs1N7eTt5//33y9ddfk9DQ0EHtAOW1zz//+U8ya9YswufzB0pEpSOvjSIjI0lAQIBYWXR0NPH19e1XOV8GZHGAn3zyCZk3b55YWUhICFm0aJFcbQ3qECifz0dhYSGcnJyYMnV1dTg5OeGXX36Rus+NGzcwffp0sTJnZ2eJrMaDAUXs8zwtLS1ob2/HyJEj+0tMpaKojRITE6GnpwcfH5+BEFNpKGKfCxcuwN7eHlFRUXBycsL8+fNx8ODBQZsKSBEbcblcFBYWMmHSiooKXLx4Ea6urgMi88tOX43TSs8G0Z/U19ejo6MDenp6YuV6enooLS2Vuk9tba1Ecl09PT2JJLyDAUXs8zyxsbEYM2aM2MU9mFDERvn5+Thx4gROnz49ABIqF0XsU1FRgatXr8LT0xNJSUkoLy9HZGQk2tvbsWLFioEQe0BRxEaenp6or6+Hn58fCCFob2+Hr68vli9fPhAiv/RIG6f19fXR1NSE1tZWaGtry3ScQT0DpPQvSUlJyMzMREJCAoYMGaJscV4KmpqasHHjRmzbtg2jR49WtjgvJYQQ6OnpYdu2bWCz2Zg7dy6WL1+O1NRUZYv20pCXlwcej4d//OMfSEtLQ0JCAi5evIjExERlizaoGNQzQF1dXWhoaODXX38VK//1118l7h5E6OvrS8z2eqr/R0YR+4hITk5GUlISjhw5Amtr6/4UU6nIa6OKigo8evQIwcHBTJlQKAQATJo0CVlZWRg7dmz/Cj2AKNKHDAwMoKmpCQ0NDaZs3LhxqKmpAZ/Pf6EEpy8jitho79698PLyYkLoVlZWaG5uxscff4zg4GCxHKmqiLRxura2Fjo6OjLP/oBBPgPU0tKCra0trly5wpQJhUJcuXIFXC5X6j729va4evWqWFlubi7s7e37U1SloIh9AODQoUPYv38/Dh8+DA6HMxCiKg15bTRu3Dikp6fj9OnTzJ+bmxumTZuG06dPw8jIaCDF73cU6UMODg4oLy9nbgwA4MGDBzAwMBh0zg9QzEatra0STk50w0BoCte+G6flW5/zxyMjI4Ow2WySlpZGSkpKSEREBHF0dCQ1NTWEEEI2bNhAYmNjmfrXrl0jkyZNIsnJyaSkpITEx8cP+tcg5LEPj8cjtra2JCsrizx9+pT5a2pqUpYK/Y68Nnqewb4KVF77VFVVES6XS6KiokhpaSn54YcfyPTp08n+/fuVpUK/I6+N4uPjCZfLJWfPniXl5eUkJyeHvPXWW2T16tVK0qB/aWpqInfu3CF37twhEydOJEeOHCF37twhjx49IoQQEhsbSzZs2MDUF70GsWvXLlJSUkJSUlIUeg1iUIdAAWDu3Lmoq6tDfHw8ampqYGNjg8OHDzOhh+rqarE7LQcHB8TGxiIuLg67d++Gubk5EhMTMXHiRGWp0K/Ia5/U1FQIBAKsWrVK7DgrVqzAypUrB1T2gUJeG6ka8trH2NgYycnJiI6OhpeXFwwNDREQEICgoCBlqdDvyGuj4OBgqKmpIS4uDk+ePMHo0aMxc+ZMrFmzRlkq9Cu3b99GQEAA8zs6OhoAsGDBAsTExKCmpgbV1dXMdlNTU/B4PERHR+Po0aMwMjLC9u3b4eLiIle7aoTQ+TSFQqFQVA/VvW2lUCgUikpDHSCFQqFQVBLqACkUCoWiklAHSKFQKBSVhDpACoVCoagk1AFSKBQKRSWhDpBCoVAoKgl1gBQKhUJRSagDpHRLWloaHB0dlS2GwlhZWeH8+fM91gkLC8OHH344QBK9XMTFxSEiImJA2srLy4OVlRUaGxt7rOfm5obPP/+8X2WRt42+ug5k6Y/yUlJSghkzZqC5ublPj6sqUAc4yAkLC4OVlZXE38OHD5UtGtLS0hh5rK2tMWPGDISHh0t8NV9RcnJyMGPGDABAZWUlrKysUFRUJFZn8+bNiImJ6ZP2umPfvn2MnjY2NnB1dUVERAQaGhrkOk5fOuuamhocPXpULL9c177CZrMxe/ZsJCQkoL29/YXb43K5yMnJwSuvvAKge6dy4sQJvP/++y/c3mDhwIED8PX1xeTJk6Xaa/z48bC3t8eRI0eUIN0fn0H/LVAK4OLiwnxbT8TLkqtOR0cHWVlZEAqFKC4uxqZNm/D06VMkJye/8LENDAx6rSMakPubCRMm4MiRIxAKhbh//z42bdqEZ8+eIS4ubkDaf55vvvkGXC4XJiYmYuWivsLn83Hx4kVERUWBxWJh2bJlL9SelpaWTOfjZemXLwsCgQAeHh6wt7fHiRMnpNbx9vbGli1bsGzZMmhq0iFdHugMUAUQDT5d/zQ0NHDkyBF4enrC3t4erq6u2Lp1K37//fduj1NcXAx/f39wuVw4ODjA29sbBQUFzPb8/Hz4+fnBzs4Orq6u2L59e6+hGTU1NRgYGMDQ0BCurq7w9/dHbm4uWltbIRQKkZCQgBkzZoDNZuPPf/4zLl26xOzL5/MRFRUFZ2dncDgczJw5Ezwej9neNeQ0a9YsAMA777wDKysr+Pv7AxCfVX311VdwdnYWS9MDdH6YODw8nPl9/vx5LFiwABwOB7NmzZJplqShocHo6eTkBA8PD+Tm5jLbOzo6sGnTJri5ucHOzg5z5szBF198wWzft28fTp06he+//56ZpeXl5QHo/JDy6tWr4ejoiNdffx3BwcGorKzsUZ7MzEy4ublJlIv6iomJCfz8/ODk5IQLFy4AAH777Tds3LgRU6dOxeTJk7FkyRI8ePCA2ffRo0dYvnw5pk6dCnt7e8ybNw8XL14EIB4CzcvLQ3h4OJ49e8bosm/fPgDi4cl169YhJCRETD6BQMCklgI60wrxeDzGbl5eXsjKyupR9+eR9To4f/483N3dweFwsHjxYrGPM4u2y9svemPVqlUIDAzs8WP8Tk5O+O233/Dzzz+/UFuqCHWAKoyamho2b96Ms2fPIiYmBlevXsWnn37abf3169fDyMgIJ06cQFpaGoKCgsBisQAA5eXlCAoKgru7O86cOYM9e/bg2rVr2LZtm1wyaWtrQygUor29HUePHsWRI0cQGhqKM2fOwNnZGR9++CEz6B47dgwXLlxAXFwcsrKy8Omnn0rMaER88803AIDPP/8cOTk5zIDbFQ8PDzQ0NDCOBQAaGhpw+fJleHl5Aeh08qGhoQgICEBmZiaioqKQlpaGgwcPyqxjZWUlcnJyGNsBnQO5kZER9u7di4yMDHz00UfYs2cPMjMzAQCLFi3C22+/DRcXF+Tk5CAnJwdcLhcCgQCLFy/G8OHDcfz4cXz55ZcYNmwYlixZAj6fL7X9hoYGlJSUgM1m9yrrkCFDIBAIAHTeLNy+fRsHDhzAV199BUIIli5dymyPiooCn89HSkoK0tPTsX79egwbNkzimFwuF5s2bYKOjg6jy6JFiyTqeXp64ocffhBzRjk5OWhtbcVbb70FAODxeDh9+jQiIyORkZGBwMBAbNiwAT/99FOvuomQ5TpobW3FgQMHsGvXLnz55ZdobGwUy8ygSL/w9/dHWFiYzHJ2h5aWFmxsbJCfn//Cx1I1qANUAbKzs8Hlcpk/USqjwMBAvPHGG3jttdcwffp0hISE4Ny5c90ep6qqCk5OTrC0tIS5uTnefvttJhs8j8eDp6cnAgMDYW5uDgcHB2zevBmnT59GW1ubTHI+ePAAX375JdhsNnR0dJCcnIygoCDMmzcP48aNw4YNG2Btbc3MjKqrq2FmZoYpU6bAxMQEjo6OmD9/vtRji0Jro0aNgoGBAUaNGiVRZ+TIkZgxYwbS09OZsn/961/Q1dXFtGnTAAAJCQlYunQpFixYAFNTU7z55ptYvXo1UlNTe9TtP//5D7hcLuzs7DBr1izcu3dPLP0Pi8XCqlWrwOFwYGpqCi8vL3h7ezOzmeHDh0NbW1tsNq+lpYXMzEwIhULs2LEDVlZWsLS0RHR0NKqrq7t1AtXV1SCEYMyYMd3KSwhBbm4ucnJyMG3aNDx48AAXLlzA9u3b4ejoCGtra8TGxuLJkyfMLLuqqgoODg6wsrKCqakpZs6cialTp0ocW0tLC6+88goz+zcwMMDw4cMl6jk7O2Po0KH47rvvmLKzZ8/Czc0NOjo64PP54PF42LlzJ1xcXGBqagpvb294eXnhq6++6vF8dEWW60AgEODjjz8Gl8sFm81GTEwMfvnlF9y6dQuAYv3C2NhYprCwLIwZMwZVVVV9cixVggaMVYBp06Zh69atzO+hQ4cC6MygzOPxUFpaiqamJnR0dKCtrQ0tLS1Mna588MEH2LJlC7799lsmjDd27FgAneHRu3fvijkPQgiEQiEqKythaWkpVbZnz56By+VCKBSira0NU6ZMwfbt29HU1ISnT5/CwcFBrL6DgwOKi4sBdOYKW7RoETw8PODi4oI//elPcHZ2fiFbeXp6IiIiAlu3boWWlhbS09Mxb948JldbcXExrl+/LnZn35vdAMDCwgIHDhxAW1sbzpw5g6KiIvztb38Tq3P8+HGcPHkSVVVVaGtrg0AgYG4wuqO4uBjl5eUSdmpra0N5ebnUfVpbWwF0zu6eR3SzJBAIQAjB/PnzsXLlSly5cgWampqYPHkyU1dXVxcWFha4f/8+ACAgIABbt25FTk4OnJyc4O7u3qv8PaGpqYm3334b6enpeOedd9Dc3Izvv/8eu3fvBgA8fPgQLS0tErNHgUAAGxsbmduR5TrQ1NQEh8Nh9rG0tMSIESNw//592NnZKdQvPvnkE7ns0RNDhgxBS0tLnx1PVaAOUAUYOnQozMzMxMoqKyuxbNkyLFy4EGvWrMHIkSNx7do1bN68GQKBQOoFu3LlSsyfPx8XL17EpUuXEB8fjz179mD27Nlobm6Gr68v82ytK8bGxt3KNnz4cJw6dQrq6uowMDCAtrY2AKCpqalXvWxtbfH999/j0qVLyM3NRUhICJycnBAfH9/rvt3h5uaGLVu2IDs7GxwOB/n5+WLP/5qbm7Fy5Uq4u7tL7CvNoYhgsVjMOVi/fj2WLl2KhIQE5hlXRkYGdu3ahdDQUHC5XAwfPhzJycm4efNmj/I2NzfD1tYWsbGxEtu6W1Ciq6sLoPOZ3vN1RDdLLBYLY8aMkWtRhY+PD5ydnZGdnY0ff/wRSUlJCA0NldonZMXT0xP+/v749ddf8eOPP2LIkCFM0lPR82UejwdDQ0Ox/bS0tGQ6viLXgTQU7Rd9xW+//cbcjFJkhzpAFaWwsBCEEISFhTGzm57CnyIsLCxgYWGBwMBArF27FidPnsTs2bMxadIklJSUSDja3lBXV5e6j46ODsaMGYPr16/j9ddfZ8qvX78OOzs7sXpz587F3LlzMWfOHCxZsgQNDQ0SIU7R87aOjo4e5RkyZAjc3d2Rnp6Ohw8fwsLCAra2tsz2SZMmoaysTG49nyc4OBh///vfsXDhQhgaGuL69evgcrn461//ytR5fgbHYrEkFujY2tri3Llz0NPTg46Ojkxtjx07Fjo6Orh//z4sLCzEtkm7WQI6Zzzt7e24efMmM9usr69HWVkZxo8fz9QzNjbGwoULsXDhQnz22Wf4+uuvpTpAFovV67kAOmf8RkZGyMzMxKVLl+Dh4cGcS0tLS2hpaaGqqkqsj8iDrNdBe3s7bt++zfS90tJSNDY2MpGNvuoXinLv3j3MmTNHKW3/kaHPAFUUMzMzCAQCHDt2DBUVFTh9+nSPzytaW1sRFRWFvLw8PHr0CNeuXUNBQQEzAAQFBeGXX35BVFQUioqK8ODBA5w/fx5RUVEKy7h48WIcOnQImZmZKC0tRWxsLIqLixEQEACgc/Xe2bNncf/+fZSVlSErKwsGBgYYMWKExLH09PSgra2Ny5cvo7a2Fs+ePeu2XU9PT2RnZ+PkyZPw9PQU2/bRRx/h22+/RUJCAu7du4f79+8jIyMDe/bskUs3LpcLKysrZtWqmZkZbt++jcuXL6OsrAxxcXFiK2wBwMTEBHfv3kVpaSnq6uogEAjg6ekJXV1dBAcHIz8/HxUVFcjLy8P27dvx+PFjqW2rq6vDyckJ165dk1lec3NzzJo1CxEREcjPz0dxcTE2bNgAQ0NDZoXtjh07cPnyZVRUVKCwsBB5eXndhr5NTEzQ3NyMK1euoK6ursfw3fz585Gamorc3Fyx86Gjo4NFixYhOjoap06dQnl5OQoLC3Hs2DGcOnVKJr1kvQ5YLBa2bduGmzdv4vbt2wgPD4e9vT3jEBXpFxs3bsRnn33Wo3xVVVUoKipCVVUVOjo6UFRUhKKiIrGFQZWVlXjy5AmcnJxk0pnyf+gMUEWxtrZGeHg4Dh06hN27d8PR0RFr165FaGio1Prq6upoaGhAaGgoamtroaurC3d3d2ZBjbW1NY4dO4a4uDj4+fkBAExNTTF37lyFZQwICEBTUxNiYmJQV1cHS0tL7N+/H+bm5gA6w6eHDx/Gw4cPoa6uDg6Hg6SkJOZOviuamprYsmULEhMTER8fD0dHRxw7dkxqu2+88QZGjhyJsrIyCQfo4uKCgwcPIjExEYcOHYKmpibGjRsHHx8fufULDAxEWFgYgoKC4Ovri6KiIqxZswZqamqYN28e/Pz8xF77eO+99/DTTz/hL3/5C5qbm3H06FFMmzYNKSkpiI2NxYoVK/D777/D0NAQ06dP73FG+O677yIiIgIbNmyQai9pREdHY8eOHVi+fDkEAgEcHR2RlJTEzMiEQiGioqLw+PFj6OjowMXFRSx83BUHBwf4+voiJCQEDQ0NWLFiBVauXCm1rpeXFw4ePAgTExNMmTJFbFtISAhGjx4NHo+HyspKvPLKK5g0aZLYC/49Iet1oK2tjaCgIKxbtw5PnjyBo6MjduzYwWxXpF9UV1f3avv4+HgxZ/7OO+8AAHPugc7w+ZtvvtntCmhK96gRQoiyhaBQKAMLIQQ+Pj4IDAzsduUs5eWHz+djzpw5iI2Nlbg5oPQODYFSKCqImpoatm3b1iefOaMoj+rqaixbtow6PwWhM0AKhUKhqCR0BkihUCgUlYQ6QAqFQqGoJNQBUigUCkUloQ6QQqFQKCoJdYAUCoVCUUmoA6RQKBSKSkIdIIVCoVBUEuoAKRQKhaKSUAdIoVAoFJXkv+P5dLixIdAeAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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P5MmTjaemoqOjs920rUKFCiQnJ9O0aVOL1g0wdOhQunXrZvz6YUelsvajCxcuZDvScuHCBZP9DMzbXypUqEB4eDh169bNdYBnqW3bttGoUSNmzpxp0p6YmPjISat79uyhUaNGJrWUKVMGg8HApUuXTAaKMTExua4nJibG5KhVTEwMBoPhkUcoq1evzvLly03aSpYsyYEDBwCyTS4HuHbtGu3atWPChAkMHDiQsmXLEh4ezt27d00GO+fPnwf+y75Hjx7Uq1fP+H173DtKuA6ZcyPybOjQoRQsWJBJkyZx48aNbN+/ePEiK1euBP77C+n+v4ju3LnD+vXrH6uG1atXG/+tKAqrV6/G3d0919MK98uppqNHjxIREWGyXIECBQDzDnNnzY3I6neWrF8Q98+deBxhYWG0aNGC77//Pse7yqalpTFnzhzj1+XLl+f8+fMmp4ROnTrF4cOHLXrfokWL0rx5c7Zs2cIvv/yCu7s77du3N1nmqaee4siRI8a5IfdLTEwkIyMj1/UHBATQtGlT43/BwcG5LhscHIyPjw9r1qwxucx+z549nDt3jtatW2d7zaP2l6eeegq9Xs8nn3yS7bUZGRl5Ol2h0+myHQnYsmVLtsvVH5Sens4ff/yRbZ9p3rw5QLbbKNx/Kf6D7u/3/cu2bNnS2FawYMFs/StWrJjJ9mjatCmenp40btyYxYsXZ/vP29ub4OBgFi9ebLzFQMuWLdHr9dlqWLFiBRqNxlhD+fLlTd7n/oGOEJaSIzcizypUqMAHH3zA66+/TqdOnYx3KE5LS+PIkSNs3bqVXr16AdCsWTPc3d0ZPnw4zzzzDElJSXz//ff4+Phw/fr1PL2/p6cn+/btY9y4cYSGhrJv3z52797N8OHDs50+yEnr1q359ddfGTFiBK1bt+bSpUusWbOGgIAAk3P/Xl5eBAQEsGXLFipVqkTx4sWpVq0agYGB2dZZvXp1evbsyXfffUdiYiINGjQgMjKSjRs30r59exo3bpynvuZk7ty5DB48mJEjRxrvUFygQAFiYmLYvHkz//77r/FeN3369GHFihUMGTKEPn36EB8fb+yrJfOmADp16sRbb73FN998Q/PmzbMdXRkyZAg7d+5k+PDh9OzZk1q1apGSksLp06fZtm0bO3bsMGv7PIq7uztvvvkmEyZMoH///nTu3Nl4KXjZsmUZOHCgyfLm7C8NGzbkf//7H0uXLuXkyZPG/TY6OpqtW7cyceJE4yXv5mrdujWLFy9mwoQJhIWFcfr0aTZt2pRt/s+DDh06xN27d7MN0rLuE7Ny5Upu3bplvBQ8OjoayPlI46VLlxg+fDgtWrQgIiKCn376iS5dulC9enXjMrVq1SI8PJzly5fj5+dHuXLljJNzH1SmTJlsR8YAZs6cia+vr8mAt23btjRq1Ih58+Zx+fJlgoKC+OOPP9ixYwcvvPCCWZecC2EpGdyIx9KuXTt++uknli1bxo4dO/j222/x8PAw3gQs654ZVapUYcGCBXz88cfMmTMHX19fnn32Wby9vc2+z8WDdDodX3zxBVOmTOH999+nUKFCjBw5khEjRpj1+l69ehnvOfL7778TEBDA+++/z9atW42H3bPMmDGD6dOnM2vWLNLT0xk5cmSOg5usZcuVK8fGjRvZvn07vr6+vPTSSzkewn8c3t7erFmzhm+++YbNmzczb9480tPTKVu2LG3btjWZK1G1alXmzJnDggULmDVrFgEBAcydO5eff/45W18fpW3btnh5eZGUlGRylVSWAgUKsGrVKpYuXcrWrVv54YcfKFy4MJUqVWLUqFEWTxx/mF69euHl5cXnn3/OBx98QMGCBWnfvj1vvfVWtkGXufvLtGnTCA4OZs2aNcybNw+dTkfZsmXp1q2b2fd0ut/w4cNJSUlh06ZNbN68mZo1a7J06VI+/PDDh75uz549BAQE5HgqM+tn6JdffuG3336jadOmzJs3jyeffDLHK5c+/vhj5s+fz4cffoibmxv9+/dn7NixJsuMHz+eyZMn8/HHH5OamkrPnj1zHdxYQqvV8umnn7JgwQI2b97Mhg0bKFu2LGPHjjVeRSiEtcmzpYQQLk/NZ5FZS6dOnWjdunW2QUhuTp48SY8ePXj//feN85YWLlzIokWLCA8Pt8rRMiGchRy5EUIIB5OWlkanTp146qmncvx+ampqtgnPK1euRKvV0qBBAzVKFMKhyeBGCCEcjIeHx0NPY37xxRccP36cxo0bo9Pp2Lt3L3v37uV///tfjvduESK/kcGNEEI4mbCwMP744w8++eQTkpOT8ff3Z9SoUQwfPtzepQnhEGTOjRBCCCFcitznRgghhBAuRQY3QgghhHApMrgRQgghhEuRwY0QQgghXIoMboQQQgjhUmRwI4QQQgiXIoMbIYQQQrgUGdwIIYQQwqXI4EYIIYQQLkUGN0IIIYRwKTK4EUIIIYRLkcGNEEIIIVyKDG6EEEII4VJkcCOEEEIIlyKDGyGEEEK4FBncCCGEEMKlyOBGCCGEEC5FBjdCCCGEcCkyuBFCCCGES5HBjRBCCCFcigxuhBBCCOFSZHAjhBBCCJcigxshhBBCuBQZ3AghhBDCpcjgRgghhBAuRQY3QgghhHApMrgRQgghhEtxs3cBajMYDGRkZKDVatFoNPYuRwghhBBmUBQFg8GAm5sbWu3Dj83ku8FNRkYGkZGR9i5DCCGEEHkQEhKCh4fHQ5fJd4ObrNFeSEgIOp3OquvW6/VER0dTqVIlq69b/EdyVofkrA7JWT2StTpslbNerycyMvKRR20gHw5usk5F6XQ6m+zcd+7csdm6xX8kZ3VIzuqQnNUjWavDljmbM6VEJhQLIYQQwqXI4EYIIYQQLkUGN1ak0WioWLGiXIVlY5KzOiRndUjO6pGs1eEIOee7OTe2pNVq8fX1tXcZLk9yVofkrA7JWT2StTocIWc5cmNFer2eqKgo9Hq9vUtxaZKzOiRndUjO6pGs1eEIOcvgxspSU1PtXUK+IDmrQ3JWh+SsHslaHfbOWQY3QgghhHApMrgRQgghhEuRwY0VabVaAgICzLp7osg7yVkdkrM6JGf1SNbqcISc5WopK9JoNBQrVszeZbg8yVkdkrM6JGf1SNbqcISc7Tp8PXjwIMOHD6d58+YEBQWxffv2R75m//799OzZk+DgYJ544gk2bNigQqXm0ev1HDlyRGbi25jkrA7JWR2Ss3oka3U4Qs52HdwkJycTFBTEu+++a9bysbGxvPTSSzRq1Igff/yRF154gUmTJrFv3z4bV2o+g8Fg7xLyBclZHZKzOiRn9UjW6rB3znY9LdWqVStatWpl9vJr1qyhXLlyjB8/HoCqVaty6NAhVqxYQYsWLWxVphBCCCGciFPNuYmIiKBJkyYmbc2bN2fmzJkWr+v+w2UajQatVovBYEBRlGztDx5ay60967UPtmdNqnpwJJtbu06nQ1GUHNsfrDG3dmv1SavVotFoHK5P8F/OrtInR9tOWevS6/Uu06dH1W6PPmX9W1EUs/vq6H16WO2O0KdHfXY4Y58e1a5mnx712WGtPj2MUw1ubty4ke2Wzr6+vty9e5fU1FS8vLzMXldkZKTx3z4+PlSqVImLFy8SHx9vbPf396dMmTKcP3+exMREY3vFihXx9fXl1KlTJjcqCggIoGbNmkRFRZls+Jo1a+Lh4UFERIRJDXXq1CEtLY0TJ04Y27RaLWFhYSQmJnL27Flju5eXF7Vq1SIhIYGYmBhje9GiRalWrRpXr14lLi7OJn0qVqwYx44dc5g+Xb58GfhvG7pCnxx5O505c8bl+uRo26lKlSoALtUnR9xO0dHRwH+fHa7QJ0fZTn5+fiQnJ3Pjxg1je3R0NIGBgVbr0+nTpzGXRnlw6GgnQUFBLF68mPbt2+e6TMeOHenVqxcvvfSSsW3Pnj0MGzaMo0ePmjW40ev1REREEBISgk6nA6w3itRoNCiKgqIoJg8Mk9G+dfuk1+vR6/XG2lyhT464nbL+r9VqcXNzc4k+Pap2e/Qpa31arTZbLc7ap4fVbs8+ZWRkGPfph312OFOfHGE7/f777/Tr14+OHTvy+eefP/KzI699SktLIzIykjp16hh/f+fGqY7c+Pr6mowKIfNoTuHChS06agOZG+jBcHK7Jj+3EB9szxo45Ra8ueuBzI2ZU3tuNVrabkkt1mq3Vp+AHHdwZ+6TI24nvV5vzPlhNTpTn8ytUc0+PepzI7f1OHKf8tpu6z5pNBrVPzvyw3YyGAzExcURHh7O3bt3KVy48GN9dlhaY47rNntJB1CnTh3++usvk7Y///zTGKAQQgghbO/+Iy5t2rRh48aNHDx40O73t8li18FNUlISJ0+e5OTJkwBcunSJkydPcuXKFQA+/PBDxo4da1z+mWeeITY2lrlz53Lu3DlWr17Nli1bGDhwoD3KF0IIIfKdnTt30qhRI5P5Mt27d6dw4cJ2rMqUXQc3x48fp0ePHvTo0QOAWbNm0aNHDxYsWADA9evXTSYhlS9fnqVLl/Lnn3/SvXt3li9fzowZM+QycCGEEEIFGRkZjBw5koMHDzJ16lR7l5Mrh5lQrBZzzm/n1f2TqO6fUCysS3JWh+SsDslZPZK1dRw9epQlS5bw4YcfUrBgwWzft1XOlvz+dqo5N84gLS3N3iXkC5KzOiRndUjO6pGsLffrr7/y888/G7+uXbs2n376aY4Dmyz2zlkGN1ZkMBg4ceKE3W877eokZ3VIzuqQnNUjWVtuy5YtdOzYkQEDBpjcl+dhHCFnp7oUXAghhBDqad++PY0bN6ZOnTr4+fnZuxyzyeBGCCGEEEZ///039erVQ6PR4O7uzs6dOylQoIC9y7KInJaysofdeE5Yj+SsDslZHZKzeiTrh5s4cSINGjRg6dKlxra8DGzsnbMcubEinU5HWFiYvctweZKzOiRndUjO6pGsH83b2xuAc+fO5XkdjpCzDGGtSFEUbt++ne15IcK6JGd1SM7qkJzVI1nn7P4rm8aMGcO+fft4//3387w+R8hZBjdWZDAYOHv2rMzEtzHJWR2SszokZ/VI1qbS0tIYPXo0HTt2ND6sUqPR0Lx588daryPkLIMbIYQQIh+KjY1l2bJl7N69m+3bt9u7HKuSOTdCCCFEPlS1alWWL1+Oh4cHHTt2tHc5ViVHbqzMy8vL3iXkC5KzOiRndUjO6snPWaempjJmzBhOnTplbOvTpw/dunWz+nvZO2d5tpQQQgiRD4waNYpFixYRGhrK4cOHne53oDxbyk4MBgM3btyQyWo2JjmrQ3JWh+Ssnvye9cSJEwkODmb27Nk2Hdg4Qs4yuLEiRVGIiYmRywxtTHJWh+SsDslZPfkt65SUFLZt22b8unTp0hw9epSnnnrKpu/rCDnL4EYIIYRwMbdu3aJRo0Z07tyZP//809hu7zsHqyV/9FIIIYTIR4oVK0atWrXw8fExuUlffiGXgltZ0aJF7V1CviA5q0NyVofkrB5XzjopKQl3d3c8PDzQaDQsXbqUpKQk/P39Va/F3jnLkRsr0ul0VKtWzelmoDsbyVkdkrM6JGf1uHLWUVFRNGzYkPHjxxvbihYtapeBjSPkLIMbKzIYDFy5ciXfzsRXi+SsDslZHZKzelw563PnznHixAm+++47bt68addaHCFnGdxYkaIoxMXF5ZuZ+PYiOatDclaH5KweV866W7dufPHFFxw5coQSJUrYtRZHyFkGN0IIIYSTOXbsGF26dCExMdHYNmTIEPz8/OxYleOQwY0QQgjhRPR6PU8//TS//PILEydOtHc5DkkGN1ak0Wjw8fFBo9HYuxSXJjmrQ3JWh+SsHlfJWqfT8eWXX9K9e3feffdde5eTjSPkLM+WEkIIIRzc4cOHuXPnDq1atbJ3KXYjz5ayE4PBQHR0tEvOxHckkrM6JGd1SM7qcdasd+3aRZMmTXj66aeJi4uzdzmP5Ag5y+DGihRFIT4+3iVn4jsSyVkdkrM6JGf1OGvWjRs3JigoiCZNmuDp6Wnvch7JEXKWOxQLIYQQDubChQtUrlwZgAIFCrBr1y68vb2dfr6QWuTIjRBCCOFA5s+fT1BQEKtXrza22XuCrrORwY0VaTQa/P39ZQe0MclZHZKzOiRn9ThL1gkJCaSnp7N9+3Z7l5InjpCzXC0lhBBC2JmiKMbBgF6vZ+PGjfTu3dvhB2Jqkqul7ESv13PmzBn0er29S3FpkrM6JGd1SM7qccSsDQYDc+fOpXfv3sYJuDqdjj59+jjtwMYRcrZoQrHBYODAgQP8/fffXLlyhdTUVLy9valRowZNmza1y9NHHc39t8IWtiM5q0NyVofkrB5Hy/rcuXNMnjyZe/fu8fPPP9O1a1d7l2QV9s7ZrMFNamoqX375Jd9++y23b9+mRo0a+Pn54enpSUxMDNu3b+edd96hWbNmjBgxgjp16ti4bCGEEML5VatWjUWLFqEoCl26dLF3OS7DrMFNx44dqVOnDjNmzKBp06a4u7tnW+by5cv8/PPPjBkzhuHDh/P0009bvVghhBDCmRkMBj744AOefvppKlWqBMDQoUPtW5QLMmtC8blz56hatapZK0xPTycuLo4KFSo8dnG2YMsJxQaDgYSEBLy9vdFqZTqTrUjO6pCc1SE5q8cRsn7zzTf58MMPadSoEb///jtubq53uzlb5Wz1CcXmDmwA3N3dHXZgY2tarRZfX1/5gLIxyVkdkrM6JGf1OELWI0eOpEyZMrz00ksue8WuI+RstXdOTk7m4MGD1lqdU9Lr9URFRTnUTHxXJDmrQ3JWh+SsHntkrdfr+fvvv41fV6pUiXPnzjFo0CCnvRrqURxhn7ba4ObixYs8//zz1lqd00pNTbV3CfmC5KwOyVkdkrN61Mw6MTGRJ554gubNm3P06FFju5eXl2o12Iu992k5DiqEEELYQOHChSlYsCBubm5ER0fbu5x8xeyZTA0bNnzo9+WQqhBCiPwuIyMDjUaDTqdDq9WycuVKbty4QVBQkL1Ly1fMHtykpaXx7LPPEhgYmOP3L1++zOLFi61WmDPSarUEBATIxEAbk5zVITmrQ3JWj62zvnTpEv369aNt27ZMmTIFyHzgpY+Pj03ez1E5wj5t9uCmevXqlC5dmp49e+b4/VOnTuX7wY1Go6FYsWL2LsPlSc7qkJzVITmrx9ZZ79u3j3379hEZGcmrr76Kt7e3zd7LkTnCPm32sKp169bcuXMn1+8XK1aMHj16WKMmp6XX6zly5IicorMxyVkdkrM6JGf12DrrZ599lpkzZ3Lw4MF8O7ABx9inzT5yM3z48Id+39/fn1mzZj12Qc7OYDDYu4R8QXJWh+SsDslZPdbM+uLFi0ydOpVFixZRoEABACZMmGC19Tsze+/TrndrRCGEEMLGDAYDTz75JCdPnqRIkSJ8/PHH9i5J3EdmsAkhhBAW0mq1zJ8/n8aNG/Paa6/ZuxzxALOeLeVKbPlsKUVRSE1NxcvLy2XvPOkIJGd1SM7qkJzV87hZX7hwgZs3b1K3bl1jm8FgkCvdHmCrfdrqz5YS5vPw8LB3CfmC5KwOyVkdkrN68pp1eHg4YWFh9OzZk4SEBGO7DGxyZu99WraKFRkMBiIiIuw+kcrVSc7qkJzVITmr53GyrlWrFr6+vpQtW5aUlBQbVOc6HGGflgnFQgghRA4SEhKMl3QXLVqUHTt2UKZMGdzd3e1cmXiUPB25mTBhAvPmzTNp++ijj+QSOCGEEC7hu+++o3LlymzatMnYVrFiRRnYOIk8DW4uXbrEtWvXTNquXbvG5cuXrVKUEEIIYU9//fUXiYmJfPnll/YuReSBXC1lRYqiGGfOy1UPtiM5q0NyVofkrB5Lsk5LS+Pzzz/npZdews1NZnBYwlb7tFwtZUdpaWn2LiFfkJzVITmrQ3JWT25Zf/311wwbNoysv/c9PDwYMWKEDGzyyN77tMWDm40bN7J7927j13PnzqV+/fo888wz+f60lMFg4MSJE3LVg41JzuqQnNUhOasnt6zPnj3LwIED+fzzz/npp5/MXt/lWykcv3w71/8u38qfV1U5wj5t8ZB0yZIlxke5HzlyhG+++YYJEyawa9cuZs2axaJFi6xdoxBCCGEzAQEBzJo1i+TkZLp06WLWay7fSqHtB7u5l5H7L3BPNy0732xN2eIFrFWqMJPFg5urV69SsWJFALZv306HDh343//+R926dRkwYIDVCxRCCCGsSVEUVq1axRNPPEHp0qUBeOuttyxax82ktIcObADuZRi4mZQmgxs7sPi0VMGCBbl16xYAf/zxB02bNgXA09OTe/fuWVzA6tWradu2LSEhIfTt25djx449dPkVK1bQsWNHQkNDadWqFTNnzszT+9qK3K1SHZKzOiRndUjO6tFqtUybNo3nn3+e/v37o9fr7V2SS7L3Pm3xkZumTZsyadIkatSoQXR0NK1atQLgzJkzlC1b1qJ1bd68mVmzZjF16lRq167NypUrGTJkCFu3bsXHxyfb8ps2beLDDz9k5syZhIWFER0dzfjx49FoNA5xjx2dTkdYWJi9y3B5krM6JGd1SM7qycray8uL+fPn07ZtW7lCzQYcYZ+2eHDz7rvv8vHHHxMXF8eCBQsoUaIEAFFRUXTu3NmidS1fvpynn36a3r17AzB16lR2797N+vXrGTZsWLbljxw5Qt26denatSsA5cqVo0uXLhw9etTSbpiM1jUaDVqtFoPBwP1Xxme1Pziyf1j7nTt3KFSokMkPTNYI9sHJVbm163Q646V0D7Y/WGNu7dbqU9alfDm126tPer2exMREihQpgkajcYk+OeJ2UhSFO3fuUKRIEdzc3FyiT4+q3R59UhSFu3fvUrRo0Wy1OGufHla7PfoEEB0dTcWKFblz5w7VqlXj7Nmz+Pj45LlP5h7xURQl27Kuvp0e9dlhrT49jMWDm6JFizJ58uRs7a+++qpF60lLSyMqKoqXXnrJ2KbVamnatClHjhzJ8TVhYWH89NNPHDt2jNDQUGJjY9mzZw/du3e3rBNAZGSk8d8+Pj5UqlSJixcvEh8fb2z39/enTJkynD9/nsTERGN7xYoV8fX15dSpU6Smphrbq1Spwvnz540bLUvNmjXx8PAgIiLCpIY6deqQlpbGiRMnTDIICwsjMTGRs2fPGtu9vLyoVasWCQkJxMTEGNuLFi1KtWrVuHr1KnFxcVbvU0BAAMWKFePYsWMO1af7H1znKn1yxe0kfTKvTwAhISEmn0vO3idH2U6pqam8//777Nixg3Xr1uHr62tcXqPR5KlPhw4fYcXRO5gjKSmJiIhzVu0TOMd2KlKkCIGBgVbr0+nTpx+StCmLb+K3d+9eChYsSP369YHMOTNr164lICCAyZMnU6xYMbPWc+3aNVq2bMmaNWtMDl/NnTuXgwcP8v333+f4uq+++oq5c+eiKAoZGRk888wzTJ061ez6s24CFBISYrwJkLVGkYqicPToUZN1g2P9BePIo31z+5Sens6xY8eMObtCnxxxO+n1eiIjIwkJCcHDw8Ml+vSo2u3Rp6yca9eune0UibP26WG1q92njIwMOnTowB9//MHnn39OaGjoIz87HtanU1cTGbvuGJGXEzHHppHNqOlfxKp9ur/dEbfToz478tqntLQ0IiMjzbqJn8VHbt5//33efPNNAP755x9mz57NoEGD2L9/P7Nnz2bWrFmWrtJs+/fvZ+nSpbz77ruEhoZy8eJF3nvvPRYvXsyIESMsWpdOp8sWTm4ToHIL8cH2rA2S07otWQ9kbsyc2nOr0dJ2S2qxVru1+/Rgzq7QJ1vUaGn7g33K+rcr9elRNdqjT7nVaOnyjtQnS9ut1aes09VZ6/v222+Jjo6mUaNGRERE5OmzIy3DwPztZ/hk11kyDAoFPXQkpz36NEl+3k55/eywtMacWDy4uXTpElWrVgXg119/pU2bNowZM4aoqKgc58nkpkSJEuh0OpNDUgDx8fEmhw3vN3/+fLp160bfvn0BCAoKIjk5mcmTJ/Pyyy/nGpSavLy87F1CviA5q0NyVofkbD23bt3ixRdfpGnTprz++usAlClThjJlyqDX6/OUdUTsLcauO8rpa3cB6FCzFCPaBPD00vBH3uemRCGPvHXEydl7n7Z4cOPu7m48j/fnn3/So0cPAIoVK8bdu3fNXo+Hhwe1atUiPDyc9u3bA5mHv8LDw+nfv3+Or0lNTc11ZOgIj8jS6XTUqlXL3mW4PMlZHZKzOiRn6/rhhx9Yt24dW7ZsYcCAASZ/LFuadUqano9++4dlv1/AoIBPIQ+mdq9F5xB/NBoNO99szc2k3B8zUKKQR768x40j7NMWD27q1q3LrFmzqFu3LpGRkXz88cdA5kz0rJshmWvQoEGMGzeO4OBgQkNDWblyJSkpKfTq1QuAsWPHUqpUKd544w0A2rRpw/Lly6lZs6bxtNT8+fNp06aNRYerbMVgMJCQkIC3t7dDHEVyVZKzOiRndUjO1vXCCy9w7Ngxnn322WxnASzJOvxcPOM3HCMmPhmAnmFleadLTbzvOxJTtniBfDl4eRRH2KctHtxMnjyZqVOnsm3bNt59911KlSoFZE40btGihUXr6tSpEwkJCSxYsIDr169To0YNvvjiC+MOGRcXZxLMyy+/jEaj4eOPP+batWt4e3vTpk0b46FHe1MUhZiYGOPl8cI2JGd1SM7qkJwfT0JCAh988AFTp07F3d0djUbDRx99lOOy5mR9JzWdWVtO8c3+iwD4F/PivZ7BtK1eyib1uyJH2KctHtyUKVOGpUuXZmt/++2381RA//79cz0NtWrVKpOv3dzcGDlyJCNHjszTewkhhHAdBoOBtm3bcvToUQwGA7Nnz36s9e069S9vb4wk7nbm1It+jSow4anqFPFyt0a5QkWP9Sz3e/fukZ6ebtJWuHDhxypICCGEMIdWq2Xy5MmMGzeOp59+Os/rSUhKY9qmKH6IuAJARZ+CzO4VSpOq2e+UL5yDxYOb5ORkPvjgA7Zs2WJ8xtT9Tp48aY26nFbRokXtXUK+IDmrQ3JWh+Rsvhs3bpCQkEBgYCAAvXr1onPnznh6epr1+vuzVhSFXyLjePfHKOKT0tBqYEjzyox5IogCHvafx+nM7L1P5+k+N/v372fKlCmMHTuWyZMnc+3aNb777jvjxN/8SqfTUa1aNXuX4fIkZ3VIzuqQnM0XERFBly5dKFy4MH///bfxTIG5A5v7s76WmMo7Pxzn1xPXAAgsVZg5vUMJqyBznx6XI+zTFk9j3rVrF++++y4dO3ZEp9NRv359XnnlFV5//XU2bdpkixqdhsFg4MqVK9nu3CisS3JWh+SsDsnZfOXKlTP++9q1axa/3mAwcPnyZdYciKH9R3v49cQ13LQaXm1XjU2jmsvAxkocYZ+2eHBz+/ZtypcvD2TOr7l9+zYA9erV4++//7ZudU5GURTi4uIc4p47rkxyVofkrA7J+eHufz6Sr68v27Zt4++//zbeTNYSsQlJvLL2BOM3HOdOagYhZYuxaVRzxjwRiKebnIayFkfYpy0e3JQrV45Lly4BmQ+K3LJlC5B5RKdIkSIPe6kQQghhtt27d1OtWjV27dplbKtVq5bFF64YDAor/rjAUwv+4Oi1NDzdtEx4qjobX2lKDX+Z7+SKLJ5z07t3b06dOkXDhg0ZNmwYw4cP5+uvvyYjI4Px48fbokYhhBD50Ndff82lS5eYOXMmbdq0ydM6zl2/y7h1x/g75iYANXzdWTigMQGlZFDjyiwe3AwcOND476ZNm7JlyxaioqKoUKEC1atXt2ZtTkej0eDj45Ptyb7CuiRndUjO6pCcc7dgwQL8/f3z9Idzut7AZ3vPM3/HGdIyDBTy0DHuySCa+0OlknLLEltyhH1ao+SzE716vZ6IiAizHpkuhBBCPdu3b2f37t3MmDHjsdYTdeU2Y9cdI+pKIgCtAksys1eIPCrByVny+9usIzdfffWV2W/+/PPPm72sqzEYDFy8eJEKFSrIM2JsSHJWh+SsDsk50/nz53nyySfR6/U0btyYLl26WLyO1HQ9i3aeZcmec2QYFIoVcGdyl5r0qlsWjUYjWavEEXI2a3CzYsUKs1am0Wjy9eBGURTi4+ONV5MJ25Cc1SE5q0NyzlSlShXGjRtHfHw87dq1s/j1h2JuMnbdUc5dTwKgU0hppnYLpmSR/+6BI1mrwxFyNmtws3PnTlvXIYQQIp/59ddfadCggfEBizNmzLB4nkZyWgbvb/uHFX9GoyjgW9iTGT1q8WSwvy1KFk5CjssJIYRQ3bx58+jYsSODBw823g/F0oHN72du0GHeXpb/kTmw6VOvHNvHtJSBjbD8aqlRo0YREhLCsGHDTNo///xzIiMjWbBggdWKczYajQZ/f3+56sHGJGd1SM7qyK85t2zZEg8PD8qVK0dGRgbu7uY/eft2SjozfznJd3/HAlC2eAFm9gqhVWDJh74uv2atNkfI2eLBzcGDBxk5cmS29pYtW7J8+XKrFOWstFotZcqUsXcZLk9yVofkrI78lHN8fDw+PplP2q5Xrx6nTp2icuXKFq3jtxPXmPRDJNcS7wHwfJOKjH2yOoU9H/3rLD9lbU+OkLPFp6WSk5NzHGG7ublx9+5dqxTlrPR6PWfOnEGv19u7FJcmOatDclZHfsg5LS2NN954g6CgIGJjY43tlgxs4u/eY+Q3h3nxq7+5lniPyr6FWPtSE6Z1DzZrYAP5I2tH4Ag5Wzy4CQwMZPPmzdnaN2/eTEBAgFWKcmaJiYn2LiFfkJzVITmrw9VzVhSFPXv2EB8fb/EDlhVF4ceIy7T/aA8/H4tDq4GXWlVhy2staFjZ2+JaXD1rR2HvnC0+LfXKK68watQoYmNjady4MQDh4eH88ssvzJ8/3+oFCiGEcG6enp6sXbuWyMhIunfvbvbr4m6nMGnjcXac+heA6qWLMLdPKKHlituoUuEqLB7ctG3blsWLF7NkyRK2bduGp6cnQUFBLF++nIYNG9qiRiGEEE7k3r17vPnmm4SFhTF48GAg8z42VapUMev1iqKw5mAsM385yZ17GbjrNIxqW43hrari4SYX+YpHs3hwA9C6dWtat25t5VKcn0ajoWLFijIT38YkZ3VIzupwxZy//vprFi1aRMGCBenatSslSz78Kqb7xcQnMX59JOHn4wGoU744c/uEEliqyGPX5YpZOyJHyDlPgxuRM61Wi6+vr73LcHmSszokZ3W4Ys6DBg1i165d9OvXz+yBjd6gsPyPC3zw6z+kphvwctfyZocgBjWrjE5rnV+Srpi1I3KEnOX4nhXp9XqioqJkJr6NSc7qkJzV4Qo5p6SksGDBAgwGA5D5y+3rr7+mU6dOZr3+zLU79FnyJzN+OUlquoEmVXzYNrolQ1tUsdrABlwja2fgCDnLkRsrS01NtXcJ+YLkrA7JWR3OnLOiKLRv354///yTO3fuMHHiRLNfm6438OnucyzaeZY0vYEinm683bkGzzQob7NTGs6ctTOxd84yuBFCCJFnGo2GF198kTNnztCgQQOzXxd56TZvrTvKqat3AGhX3Y8ZPYPxL1bAVqWKfCTPg5u0tDQuXbpEhQoVcHOTMZIQQuQXycnJJCQkUK5cOQAGDhxI9+7djQ/AfJjUdD0fbz/D5/vOozcolCjozpRutehWu4xM9BVWY/GoJCUlhenTp/PDDz8AsG3bNsqXL8/06dMpVapUtmdO5SdarZaAgAC0WpnKZEuSszokZ3U4W86nT5+mV69eeHh48Oeff+Ll5QVg1sDmwIUExq8/xvkbSQB0CfVnSrda+Bb2tGnNWZwta2flCDlb/M4ffvghp06d4quvvsLT878dskmTJjneuTg/0Wg0FCtWTP76sDHJWR2SszqcLeeCBQty7do14uLiOH/+vFmvuXsvg8k/HufppeGcv5GEXxFPPhtQj0X96qo2sAHny9pZOULOFg9uduzYweTJk6lfv75Je7Vq1bh48aLVCnNGer2eI0eOyEx8G5Oc1SE5q8MZcs66CgqgXLly/PTTT0RERFCzZs1HvnbP6et0nLeXr8JjAPhf/fL8NqYVHWqVtlm9uXGGrF2BI+Rs8eAmISHB+FTX+6WkpMhoGNMPAWE7krM6JGd1OHLOkZGRhIWFcfDgQWNbkyZNKFWq1ENfdys5jTfWHuWFLw9w+VYK5UoU4OshjZjTJ5RiBbI/fFktjpy1K7F3zhYPboKDg9m9e3e29u+//546depYoSQhhBCOYs6cORw7dowxY8aY/Zqtx+No/9Fe1h++hEYDg5pVYtvoljSvJjfQE+qweELx66+/zosvvsjZs2fR6/V89dVXnDt3jiNHjrBq1Spb1CiEEMJOFi9eTIECBZg5c+Yjl/33Tirv/hjFluNXAahashBz+4RSr6LlT+8W4nFoFEVRLH3RxYsX+eyzzzh16hTJycnUrFmTF198kaCgIFvUaFV6vZ6IiAjq1KmDTqez6roVRSE1NRUvLy85RWdDkrM6JGd1OFrOR44cYc+ePYwePdrs1yiKwobDl5n28wlup6Sj02oY3qoKo9pWw8vdup+zj8PRsnZVtsrZkt/febpBTYUKFZgxY0aeinN1Hh4e9i4hX5Cc1SE5q8NRco6OjqZJkybcu3ePmjVr0qFDh0e+5vKtFN7eEMme09cBqOlflLl9QgkuW8zW5eaJo2Tt6uyds8VzbgYOHMiGDRu4e/euLepxagaDgYiICLtPpHJ1krM6JGd1OFLOlSpVYujQoXTt2pV69eo9dFmDQWHVXzF0+GgPe05fx8NNy1sdg/hxZDOHHdg4UtauzBFytvjITUBAAB999BFTp06lVatWdOvWjVatWuHubr/Z70IIIfLmyJEjVKtWjcKFCwMwb9483NzcHno64cKNJMatP8aBCwkA1KtYgjm9QwnwK6xKzUI8isVHbiZNmsTevXtZvHgxBQsWZNy4cTRr1ox33nmHAwcO2KJGIYQQNvDll1/SqFEjRowYYWxzd3fPdWCToTewdM85nvx4LwcuJFDQQ8eUrjVZ+1ITGdgIh5KnOTdarZbmzZvTvHlzpk6dys6dO1myZAnr1q3j5MmT1q5RCCGEDQQEBKDX67l79y5paWkPnSdx6moiY9cd49il2wA0D/BlVq8QynsXVKtcIcyWp6ulsly/fp1ffvmFn376iRMnThAaGsratWutWZ/V2fpqKYPBgFarlZn4NiQ5q0NyVofaOaekpFCgwH9P3t6ydz/lqgTl+t6FPd3YcOQyn+w6S4ZBoYiXG+90rknf+uWcbr+QfVodtsrZpldL3b17l23btvHzzz9z4MABypUrR9euXfn444+pUKFCnot2FWlpacYHyQnbkZzVITmrQ42cDQYDH330EYsWLeLgwYOULFmSy7dSGP1rAvcy/sj1dRog6y/gJ2qWYkaPYEoVdd59QvZpddg7Z4vn3DRt2pR58+ZRrVo11qxZw7Zt2xg5cqQMbMj88Dhx4oTMxLcxyVkdkrM61Mo5NTWVL7/8kpiYGFasWAHAzaQ07mU8/H0VoJiXGwufDeOzAfWcemAj+7Q6HCFni4/cfPrppzRp0kQeGS+EEE6kYMGCrF27lj/++INhw4ZZ9NpP+tejWYA8OkE4D4sHN82aNbNFHUIIIazIYDAwe/ZsAgMD6dOnD5D5bMDg4GCL12XPB10KkRdmDW569uzJihUrKFasGD169HjoBKGNGzdarThnJEe01CE5q0NyVoctcl6+fDkTJ06kaNGitGjR4pFP8c4vZJ9Wh71zNmtw065dO+Mlgu3atZNZ5rnQ6XSEhYXZuwyXJzmrQ3JWh61yfuGFF/juu+945pln8PPzy3GZm0lpVn9fRyb7tDocIWezBjcjR440/nvUqFE2K8bZKYpCYmIiRYsWlQGgDUnO6pCc1WGtnPV6PevWrePpp59Go9Hg5ubGtm3bclynoiisO3SJqZtOPE7pTkf2aXU4Qs4WHzdq164dN2/ezNaemJhIu3btrFKUszIYDJw9e1Zm4tuY5KwOyVkd1shZURS6du3KM888w6JFi4ztOf1iiU1I5vkvD/DWumPcvZeR5/d0RrJPq8MRcrZ4cHP58uUcC05LS+PatWtWKUoIIYT5NBoNHTt2pFChQpQoUSLHZfQGhS/2nafDvL3sO3MDTzctL7eqiqfbw38NeLppKVFInqQtnIvZV0vt2LHD+O99+/ZRpEgR49cGg4Hw8HDKli1r3eqEEELkKCMjg8TERLy9vQF49dVX6dmzZ473HDt1NZFx6yM5GnsLgEaVvZndO5TKvoXo36TiQ+felCjkQdniBXL9vhCOyOzBTdaD1TQaDePHjzddiZsbZcuWzdaeH8mdL9UhOatDclaHpTlfvnyZfv36YTAY2LVrl/Ep3g8ObO5l6Fm88yyf7D6X+egETzcmdKrBMw3Ko9VmnrIqW7xAvhq8yD6tDnvnbPGzpdq2bcu6deuMfy04G1s+W0oIIdRw9uxZ6tati6Io7Nu3jzp16mRb5lBMAuPWR3L237tA5qMTpncPpnQx+eUunJNNny21c+fOPBfm6gwGAwkJCXh7e9v9Gn9XJjmrQ3JWR15yDggIYO3atVStWpVq1aqZfO/uvQze33qKr/6KQVHAt7AHU7sF0ymkdL6/Qkj2aXU4Qs5mDW6++uor/ve//+Hp6clXX3310GWff/55qxTmjBRFISYmJtcJfcI6JGd1SM7qMCfn2NhYhgwZwrx586hVqxYATz75ZLbldv3zLxM3RHLldioAfeqVY1LnGhQvKBOCQfZptThCzmYNblasWEHXrl3x9PQ0PnAtJxqNJl8PboQQwhbeeustfvvtN1588UX++OOPbEdgEpLSmLYpih8irgBQrkQBZvUKoUW1kvYoVwi7M2twc/+pKDktJYQQ6lqwYAHJycl8/PHHJgMbRVH46egVpm46QUJSGloNDG5WmTEdAinoYfGsAyFcxmPv/Xq9ntOnT1OmTBmKFStmjZqcWtGiRe1dQr4gOatDclbHgzlfuHCBffv2GY+E+/n58dNPP5ksc/lWCpM2RrLrn+sAVC9dhNm9Q6lTvrgqNTsr2afVYe+cLb5a6r333iMwMJC+ffui1+t57rnniIiIoECBAixZsoRGjRrZqlarkKulhBCOLDY2ltDQUBITE9m9ezctWrQw+b7BoPD1/hjmbDlFUpoeD52WUW0DeKlVVTwecUM+IZyZJb+/Lf5J2LZtG9WrVwdg165dXL58mS1btvDCCy8wb948i4tdvXo1bdu2JSQkhL59+3Ls2LGHLp+YmMjUqVNp3rw5wcHBdOzYkT179lj8vrZgMBi4cuWK3NrbxiRndUjO6ngw53LlytG1a1caNmyY7b41Z/+9Q9+l4Uz+MYqkND31K5Zg82vNGdWumgxszCD7tDocIWeLfxpu3rxJyZKZk9T27NnDk08+SeXKlenduzenT5+2aF2bN29m1qxZjBgxgo0bN1K9enWGDBlCfHx8jsunpaUxaNAgLl++zPz589m6dSvTp0+nVKlSlnbDJhRFIS4uDgsPhgkLSc7qkJzVoSgKf//9N6mpmVc4aTQalixZwt69e6lYsSIAaRkGFuw4Q6f5v3Mo5iaFPHRM616LtS81IcCvyMNWL+4j+7Q6HCFni+fc+Pr6cvbsWUqWLMm+ffuYMmUKAKmpqRaf5lm+fDlPP/00vXv3BmDq1Kns3r2b9evXM2zYsGzLr1+/ntu3b7NmzRrc3d2BzL9y8kKv1xv/rdFo0Gq1GAwGk42R1X7/sg9rz3rtg+1Z1/k/OIrNrV2n06EoSo7tD9aYW7u1+qTVatFoNA7XJ/gvZ1fpk6Ntp6x16fV6l+nTo2q3R582bNjAoEGDGDhwIPPnzwfA09MTyPxMiYi9xfj1x/jnWubN+FoHluS9XiGUKeb1/9vJ8fqU1Q6OuZ0e9dnhjH16VLuafXrUZ4e1+vQwFg9uevXqxejRoylZsiQajYamTZsCcPToUapUqWL2etLS0oiKiuKll14ytmm1Wpo2bcqRI0dyfM3OnTupU6cO06ZNY8eOHXh7e9OlSxdefPFFiwdWkZGRxn/7+PhQqVIlLl68aHLUyN/fnzJlynD+/HkSExON7RUrVsTX15dTp04Z/9oCjP2Piooy2fA1a9bEw8ODiIgIkxrq1KlDWloaJ06cMMkgLCyMxMREzp49a2z38vKiVq1aJCQkEBMTY2wvWrQo1apV4+rVq8TFxVm9TwEBARQrVoxjx445TJ8uXboE/LcNXaFPjrydzpw543J9cqTtdOXKFZKSkoiIiODAgQN4eGTekyY1w8COfwuw4s8YDAoU9dAwOKwoLSu6UbZ4AW7fvu2wfXLU7RQdHQ3899nhCn1y5O0UHR1NYGCg1fpkydkhiycUA2zdupWrV6/y5JNPUrp0aQA2btxIkSJFaN++vVnruHbtGi1btmTNmjWEhYUZ2+fOncvBgwf5/vvvs73mySef5PLly3Tt2pV+/fpx8eJFpk6dyoABAxg5cqRZ75s1ISkkJMQ4ILLmKDI2NpayZcua3JVRRvvW7VNGRgYXL16kfPnyxvqcvU+OuJ0MBgOxsbGUL18ed3d3l+jTo2pXq0/p6ekm9a5evZpnn30WN7fMvzf/OHuDiT9EEXszBYDutf2Z1LkG3v//dG5H7JMzbKf09HTjPv2wzw5n6pMjbqdHfXbktU9paWlERkba5vELkPOdMXv27JmXVVlEURR8fHyYPn06Op2O4OBgrl27xrJly8we3GTR6XTZwsntNtG5hZhTe6VKlR76nua2azSaHNtzq9HSdktqsVa7tfrk5uaW41FCZ+6TI24nnU5nkrMr9MncGm3Zp9WrVzN37lz27t1LsWLF0Ol0DBw4EIBbyWnM+OUk6w5lHp0sU8yL93qF0CbIz6H7ZK12W/fJ3d1d9c+O/LidHvezw9Iac1y32Uve58CBAwwfPpwnnniCJ554guHDh/P3339btI4SJUqg0+myTR6Oj4/H19c3x9eULFmSSpUqmXSwSpUqXL9+nbS0NMs7YmUGg4Ho6GiZiW9jkrM6JGfrS05OZtKkSRw7dowFCxYAmTlfuHCBn49epv1He1l36BIaDQxsWolfx7TKcWAj8kb2aXU4Qs4WD25+/PFHBg0ahJeXFwMGDGDAgAF4eXkxcOBANm3aZPZ6PDw8qFWrFuHh4cY2g8FAeHi4yWmq+9WtW5eLFy+aBBYdHU3JkiWN56ntSVEU4uPjZSa+jUnO6pCcra9gwYJ89913vPvuu7z99tsAxN1KYeymc4z8NoIbd+8R4FeYdcObMKVbLQp7yl2GrUn2aXU4Qs4W/+QsWbKEt956y3gYFTIflrl8+XI++eQTunbtava6Bg0axLhx4wgODiY0NJSVK1eSkpJCr169ABg7diylSpXijTfeAODZZ5/l66+/5r333qN///7ExMSwdOlSBgwYYGk3hBBCFStXrsTf358OHToA0LBhQxo2bIjBoPDN/ovM3HySu/cycNNqeKV1VUa0DcDTTW4wKsTjsHhwExsbS5s2bbK1t23blo8++siidXXq1ImEhAQWLFjA9evXqVGjBl988YXxtFRcXJzJOTl/f3+WLVvGrFmz6NatG6VKleL555/nxRdftLQbQghhc6tWrWLgwIH4+vpy/Phx4z25LtxIYvz6Y+y/kABANW935j/XkJpli9uxWiFch8WDG39/f8LDw403l8ry559/4u/vb3EB/fv3p3///jl+b9WqVdnawsLCWLt2rcXvowaNRoO/v3+2J/YK65Kc1SE5P76+ffsyf/58evbsia+vL+l6A5/vO8/H28+QlmGggLuONzpUo2NlL8r6yzOPbE32aXU4Qs4WD24GDRrEjBkzOHnypHFuzOHDh9m4cSMTJ060eoHORKvVUqZMGXuX4fIkZ3VIzpZTFIU9e/bQunVrIPP+H+Hh4bi7u3P88m3GrT9G1JXM+3e0qObLzJ4hlPcuaMeK8xfZp9XhCDlbPLjp168fJUuW5Msvv2Tr1q1A5hVL8+bNM/seN65Kr9dz/vx5qlSpIg/ltCHJWR2Ss2UUReGFF15g1apVrFixghdeeAEAPVo+2HKSL/ZdQG9QKFbAnXe61KR33bLG+45IzuqQrNXhCDlbNLhRFIWYmBgqVarEqlWrjDecEv+5/66KwnYkZ3VIzubTaDRUq1YNnU7HzZs3AQg/F8+EDceIjk8GoHOoP1O61qJkEU+T10rO6pGs1WHvnM0encTGxvLKK68Yb7lcunRpFixYQEhIiM2KE0IIR6YoCikpKRQsmHlq6e2336Zbt25UCqzJhA3H+PZALAClinoyo0cIT9R0jIf8CuHqzB7cvP/++2RkZPD+++/j6enJsmXLmDx5Mhs3brRlfUII4ZBu377Niy++yN27d/n555/RarXodDquupXixY/28O+dewD0a1SB8U9Vp6iXu50rFiL/MHtwc+jQIebPn0/9+vUBqF27Nq1atSI5Odn4V0t+p9FoqFixoszEtzHJWR2S88PFxsayadMmMjIyOHToEBWrhzDlpyg2R14FoLJvIWb1CqFxFZ+HrkdyVo9krQ5HyNnswU18fLzJc5P8/Pzw8vIiPj5eBjf/T6vV5vroCGE9krM6JOeHCw4OZsWKFVSsWJELmtIM/Wgvt1PS0Wk1DGtZhdfaVcPL/dGTKSVn9UjW6nCEnM1+/IJGoyE5OZm7d+8a/9NoNCQlJZm05Wd6vZ6oqKgcnxYurEdyVofkbOrmzZsMGDCA8+fPG9uatO/KouMaxq47xu2UdILLFuXHEc0Y92R1swY2IDmrSbJWhyPkbPaRG0VR6NixY7a2rKeBK4qCRqPh5MmT1q3QyaSmptq7hHxBclaH5Pyfl19+me+++47o6Gh27d7Dij+j+fDX06Sk6/F00zLmiUCGNK+Mm87y5xFLzuqRrNVh75zNHtx89dVXtqxDCCEc2ty5czl//jyjp7xP70//5Oil2wA0ruLN7F6hVPItZOcKhRBZzB7cNGzY0JZ1CCGEQ4mPj+evv/6ic+fOAJQqU5anp69i3I5zZBgUini5MbFTDf7XoLxMUBXCwZg1uLH0iqj8egWVVqslICDA5GGfwvokZ3Xk55wvX75M48aN+ffffwkPD8fgXYlx649x7noSAB1rlWJa92BKFfV67PfKzzmrTbJWhyPkbNY7d+jQgc8++4x///0312UUReGPP/5g6NChOT7wMj/QaDQUK1ZM/oqzMclZHfk55zJlylCvXj0qBgTxxZE79FkSzrnrSfgW9uTT5+qydEB9qwxsIH/nrDbJWh2OkLNZR26++uor5s2bx8KFC6levTrBwcH4+fnh6enJ7du3OXfuHBEREeh0OoYNG8Yzzzxj67odkl6v59ixY4SGhspzS2xIclZHfsv5xo0bFC9eHDc3NzQaDYMnfcR7286x+UzmVaBP1y/HxE41KVbQujfjy28525NkrQ5HyNmswU2VKlVYuHAhV65cYevWrfz9998cOXKE1NRUSpQoQc2aNZk+fTotW7bM9zuMwWCwdwn5guSsjvyS8549e3j22WcZPHgwr49/h2k/n+DHiCsAVPAuyKxeITQLsN19O/JLzo5AslaHvXO26MmXZcqUYfDgwQwePNhW9QghhOri4uKIi4vj+4Mx/PzhHm6lpKPVwJDmlRnzRBAFPPL3H21COBt5rLcQIt9r3rEb7adv4MxdD+6lpFO9dBHm9A6ldvni9i5NCJEHMmXcirRaLTVr1pSZ+DYmOavDlXPesWMH7du3587dJFb8cYEO8/Zy5q4HHjotb3UMYtOo5qoNbFw5Z0cjWavDEXKWIzdW5uHhYe8S8gXJWR2umHNqaiovvPAC/6ZqafveJq4rRQBoUKkEs3qFEuBXWPWaXDFnRyVZq8PeOcvw1YoMBgMRERF2n0jl6iRndbhqzlo3D/q8+yVlhyziulKEwp5uTO8RzHfDmthlYOOqOTsiyVodjpCzHLkRQri8rVu3UrRoUQqUq8H49ZH8cy0dNDraVfdjeo9gyhQvYO8ShRBWZPHgZu/evRQsWJD69esDsHr1atauXUtAQACTJ0+mWLFiVi9SCCHy6vvvv+d/zw2g3FOvoKvRDgXwKeTBu91q0TXUX27oJoQLsvi01Pvvv09SUuYtyP/55x9mz55Nq1atuHTpErNnz7Z6gUII8TiKBjakwkufof3/gU2vsLL8NqYV3WqXkYGNEC7K4iM3ly5domrVqgD8+uuvtGnThjFjxhAVFcWwYcOsXqAz0Wq11KlTR2bi25jkrA5nzvnEiRP4Vwxgxi8nWX/4EhTyoWzxArzXM5jWQX72Ls+EM+fsbCRrdThCzhYPbtzd3UlNTQXgzz//pEePHgAUK1aMu3fvWrU4Z5SWloaXl3WeOSNyJzmrw9lyVhSFt8aO5dOf91O591juZmjQaOCFJpV4q2MQhTwdc5qhs+XszCRrddg7Z4uHVXXr1mXWrFksXryYyMhIWrduDUB0dDSlS5e2dn1OxWAwcOLECZmJb2OSszqcMeerian8oQ2mZPdx3M3QUM2vMOuGN2VKt1oOO7BxxpydlWStDkfI2eLBzeTJk3Fzc2Pbtm28++67lCpVCsicaNyiRQurFyiEEI+Snp7B13/F0OGjvcRpfNFp4LV21fj51ebUq1jC3uUJIVRm8Z8yZcqUYenSpdna3377basUJIQQ5rp37x6vjJvCASWAOwUyjxzXKV+cuX1CCSxVxM7VCSHsJU/HaS9evMj69euJjY1l4sSJ+Pj4sGfPHsqUKUO1atWsXaNTkYlq6pCc1eHIOafrDUxft5/tbg3RuHngqYPxnWryfJNK6LTOdRWUI+fsaiRrddg7Z4vf/cCBA3Tt2pVjx47x66+/kpycDGReFr5w4UKrF+hMdDodYWFh6HTyBGFbkpzV4cg5R166TfdFf/B15B00bh7UKK6w/Y02DGpW2ekGNo6cs6uRrNXhCDlbPLj58MMPGT16NMuXL8fd3d3Y3rhxYyIiIqxZm9NRFIXbt2+jKIq9S3FpkrM6HDHnm4lJdBy3hO6Lf+dEXCLFC7rz0dO12TyuM+W9C9q7vDxxxJxdlWStDkfI2eLBzenTp2nfvn22dm9vb27evGmVopyVwWDg7NmzMhPfxiRndThazn+eu0GTaZv4R1MegwJdQ/3ZPqYVveqWc+qb8Tlazq5MslaHI+Rs8ZybIkWKcP36dcqXL2/SfvLkSeOVU0IIYS23U9KZtfkkaw7GglsRSL7FiEbevNWvrr1LE0I4KIsHN507d+aDDz5g/vz5aDQaDAYDhw4dYs6cOcYb+gkhhDX8dDiGyT9Gcete5uHt/o0r8Eb7dpQoLDdhE0LkzuLBzeuvv860adNo3bo1er2ezp07o9fr6dKlCy+//LItanQqcudLdUjO6rBXzv/eSWXcd4fYdfYWAGWLujPv2fo0rOxtl3psTfZn9UjW6rB3zholjzN+rly5wpkzZ0hKSqJmzZpUqlTJyqXZhl6vJyIigjp16siMeSEcjKIofP/3JWb8coLE1Aww6MmI3MJX4/vRtpXcJFSI/MyS398WH7n5+++/qV+/PmXKlKFMmTJ5LtIVGQwGEhIS8Pb2tvs1/q5MclaH2jnHxCcx9vsI9kffAiCkbDHGti5DtZINXfrRLrI/q0eyVocj5Gzx4GbgwIH4+fnRpUsXunXrRkBAgC3qckqKohATE0OJEnK7d1uSnNWhVs4ZegNf/nGBD7b9Q5pewQ0D4zrVYlCzSrjpXP8XkOzP6pGs1eEIOVs8uNm7dy+bN2/m559/5rPPPiMoKIiuXbvSpUsXl/7rSghhfSeuJDJ+wzGOXboNQEr0Ubwi19P/3d/zxcBGCGEbFn96eHt7079/f9asWcNvv/3Gk08+yQ8//EDbtm15/vnnbVGjEMLFpKbreX/bKbot+p1jl25T1MuNOb1DeLOeB3/v3kqBAgXsXaIQwonl6dlSWcqXL8+wYcOoXr068+fP5+DBg9aqy2kVLVrU3iXkC5KzOmyR84ELCYzfcIzz15MA6FjTj+k9QvAr6gUN3rT6+zkD2Z/VI1mrw94553lwc+jQITZt2sS2bdu4d+8e7dq1Y8yYMdaszenodLp8/+BQNUjO6rB2zndS05mz9RRf/3URACX5Nte3LsLfqwN+RRtY7X2cjezP6pGs1eEIOVs8uPnwww/55Zdf+Pfff2nWrBkTJ06kXbt2chiZzBniV69epXTp0jIT34YkZ3VYM+cdJ68x6YfjxN1OBeCZBuWpo9Gz+oIPo0aNska5Tkv2Z/VI1upwhJwtHtwcPHiQIUOG8NRTT+Ht7Zo31MorRVGIi4uTx1DYmOSsDmvkfOPuPaZuOsGmo1cAKF1Yx0fP1qdpVV8glP/17OrUz4WyBtmf1SNZq8MRcrZ4cLNmzRpb1CGEcCGKorDxyGWm/XyCW8npaIA7f2/kxqU/CX71L+Ny+X1gI4SwDbMGNzt27KBly5a4u7uzY8eOhy7brl07qxQmhHBOl24m8/bG4+w9fR2AGv5FmfhERfp98zIh9erJE5mFEDZn1uBmxIgR/PHHH/j4+DBixIhcl9NoNJw8edJqxTkbjUaDj4+P/DVqY5KzOizNWW9QWPlnNB/8+g/JaXo8dBpeax/IsJZVcNdp+euvv/D395ft9gDZn9UjWavDEXLO87OlnJU8W0oI6zt97Q7j1h/jyMVbAJRxT+boF+P4cdVntG/f3r7FCSFcgiW/vy2exvzDDz+QlpaWrT0tLY0ffvjB0tW5FIPBQHR0tBx2tzHJWR3m5HwvQ8+8307TecE+jly8RRFPN97rGUyta7+RfPUCGzduVLFi5yT7s3oka3U4Qs4WD24mTJjAnTt3srUnJSUxYcIEqxTlrBRFIT4+nnx2MEx1krM6HpXz4Ys36bLgd+bvOEO6XqF9DT9+G9OK5xpVZMH8+Xz99dcsWrRI5aqdj+zP6pGs1eEIOVt8tZSiKDmeR7t27RpFihSxSlFCCMeVdC+D97f9w8rwaBQFfAp5UJezuO3/hdIvfAJAgQIFeO655+xcqRAivzJ7cNOjRw80Gg0ajYYXXngBN7f/XqrX67l06RItWrSwSZFCCMew5/R13t4QyeVbKQD0rluOruXSaNt8BIqi8Fy/fjRv3tzOVQoh8juzBzdZkwJPnjxJ8+bNKVSokPF77u7ulC1blg4dOli/Qiei0WjkahAVSM7quD/nm0lpTP/5BBuOXAagXIkCzOwZQsvAkgC89957+Pn50axZM3uW7JRkf1aPZK0OR8jZ4qulNm7cSKdOnfD09LRVTTYlV0sJYT5FUdh0LI6pP0URn5SGRgMDm1TE8/RvvDjoeXx8fOxdohAin7Dp1VI9e/Z02oGNren1es6cOYNer7d3KS5NclbHpYQknv1kD69+e4T4pDSCShVhw8tNufLLQia8NYaBAwfKxEwrkP1ZPZK1OhwhZ7NOSzVs2JCtW7fi7e1NgwYNHnqo6cCBA1YrzhklJibau4R8QXK2HYNBYfWBi8zZcpK79/S46zSMbFONl1tXxcNNy8svv8y6devo06ePHN63Etmf1SNZq8PeOZs1uJkwYQKFCxc2/tvaH2irV69m2bJlXL9+nerVq/POO+8QGhr6yNf98ssvjBkzhnbt2vHJJ59YtSYh8qNz1+8yYX0kB6ITAAjycWfes/Uh8SoebpkHeuvUqUNMTIzxM0EIIRyNWYObnj17Gv/dq1cvqxawefNmZs2axdSpU6lduzYrV65kyJAhbN269aHn8y9dusScOXOoX7++VesRIj9K1xv4bO955u84Q1qGgYIeOt7sEEj51PO80r8XR48eJSIigooVKwLIwEYI4dAsnnMTFRXFP//8Y/x6+/btvPLKK3z00Uc53rn4UZYvX87TTz9N7969CQgIYOrUqXh5ebF+/fpcX6PX63nzzTcZNWoU5cuXt/g9bUWj0VCxYkU5VG9jkrN1Hbt0i26L/uD9bf+QlmGgdVBJfhvTioFNK1GrZk1SUlLIyMjI18+NsyXZn9UjWavDEXK2+CZ+kydPZtiwYQQFBREbG8vrr79Ohw4d2Lp1KykpKUycONHsdaWlpREVFcVLL71kbNNqtTRt2pQjR47k+rrFixfj4+ND3759OXTokKVdADCZ6KTRaNBqtRgMBpMJklntD06Kyq1dq9Xi6+uLXq83+Z5WmzmGfPBW1Lm163Q6FEXJsf3BGnNrt2afNBpNju326hNAiRIlUBQFvV7vEn2yx3ZKSdPz4W//sPyPaAwKlCjozuSuNelcyw+tVouiKJQqVYpvv/2WjIwMAgICsq3f0fqUU7szbKeso9Tm9tUZ+uSI20lRFLM+O5ypT466nbJyzlqHLfr0MBYPbqKjo6lRowYAW7ZsoWHDhnz44YccOnSIMWPGWDS4uXnzJnq9PtvpJx8fH86fP5/ja/7++2/WrVv32M+xioyMNHm/SpUqcfHiReLj443t/v7+lClThvPnz5tMjqpYsSK+vr6cOnWK1NRUY3uVKlW4cuUKaWlpJhu+Zs2aeHh4EBERYVJDnTp1SEtL48SJE8Y2rVZLWFgYiYmJnD171tju5eVFrVq1SEhIICYmxthetGhRqlWrxtWrV4mLi7N6nwICAihWrBjHjh1zmD7FxMSQkJDgUn1SezslFynP2xsjib2ZuY4WFbwYElaMqr4KrVq1ok2bNvTu3dukTzdu3HDoPjnzdvLw8CAoKMjkc8nZ++SI2+ncuXMmjw9yhT458nYqUqQIgYGBVuvT6dOnMZfF97mpW7cuGzZsoFKlSgwaNIjWrVvzwgsvcOXKFZ588kmOHTtm9rquXbtGy5YtWbNmDWFhYcb2uXPncvDgQb7//nuT5e/evUu3bt149913adWqFQDjx48nMTHR7AnFWdfJh4SEGK+Tt9YoUlEUjh49arJukNG+tfuUnp7OsWPHjDm7Qp/U2k63U9KZveUf1h66BIB/MS+md6tJm+p+ACxatIjRo0fj5+fHP//8w7lz5wgJCcHDw8Nh+/SodkffTnq9nsjISGrXrp3tML6z9ulhtduzT2lpaURGRj7ys8OZ+uSI2ylrn87tsyOvfcrafubc58biIzfBwcF8+umnNGnShIMHDzJlyhQgc4Kvr6+vResqUaIEOp3OZOQGEB8fn+O6YmNjuXz5Mi+//LKxLSvgmjVrsnXrVipUqGDWe+t0umzhZG3MnJY1pz1rg+S0bkvWA5kbM6f23Gq0tN2SWqzVbu0+PZizK/TJFjVmtW+JjGPyT1Fcv3MPgOebVGTsk9Up7Pnfx8CoUaO4cuUKw4YNMz4rLmudjtgnc9udYTvlVqOlyztSnyxtV6tPan525OftlNfPDktrzInFg5u3336bt956i+3btzN8+HDj1RPbtm0zOfpiDg8PD2rVqkV4eLjx8Q4Gg4Hw8HD69++fbfkqVaqwadMmk7aPP/6YpKQkJk6cSOnSpS3tjhAu79/EVN758Tjboq4BULVkIeb0DqV+JW9iYmJ45+OP+eCDD9DpdGi1WubMmQNkn/8hhBDOwuLBTfXq1bMNMADGjh2b6yjsYQYNGsS4ceMIDg4mNDSUlStXkpKSYrzkfOzYsZQqVYo33ngDT09PAgMDTV5ftGhRgGzt9qDVagkICMhTDsJ8krN5FEXhu4OxvLf5JHdSM3DTani5dVVGtAnAy11HWloaLVq0IDY2Fl9f32zz5SRndUjO6pGs1eEIOVs8uMly/Phxzp07B2ROVKpVq1ae1tOpUycSEhJYsGAB169fp0aNGnzxxRfG01JxcXFOsyNqNBqKFStm7zJcnuT8aNE3kpiwIZLw85mnfGuXK8bs3qHU8C9qXMbDw4NZs2axYMECnnvuuWzrkJzVITmrR7JWhyPkbPGE4vj4eEaPHs3BgweNR00SExNp1KgR8+bNw9vb2yaFWostH5yp1+s5duwYoaGh8lBOG5Kcc5ehN7Ds9wt89Ntp7mUY8HLX8maHIAY1q4xOq+H8+fPo9XqqVav232syMnBzy/53juSsDslZPZK1OmyVs00fnDl9+nSSk5P55ZdfOHDgAAcOHODnn3/m7t27zJgxI89Fu4oHZ5AL25Ccs4u6cpuen/zJrC2nuJdhoHmAL7+ObsXQFlXQaTXs2rWLsLAw+vTpQ0pKivF1OQ1sskjO6pCc1SNZq8PeOVt8Wmrfvn0sX76cqlWrGtsCAgJ49913GTx4sFWLE0I8Wmq6ngU7zrB073n0BoWiXm5M6lKTvvXKmVxaXL16dby8vChcuDCJiYkUKFDAjlULIYTtWDy4MRgMuLu7Z1+Rm5vdR2pC5Df7z8czYUMk528kAdA5xJ93u9XEr4gXkHlvqKznQPn7+7Nnzx6qVq2a48+wEEK4CotPSzVu3Jj33nuPa9euGduuXbvGrFmzaNKkiVWLczZarZaaNWs6zQRoZyU5Q2JqOhM3RvK/z/7i/I0kShX15LMB9Vj8XF3jwObbb7+lYsWK/PXXX8bXVa9e3eyBjeSsDslZPZK1Ohwh5zw9W+rll1+mXbt2xvvKXL16lWrVqvH+++9bvUBn4+HhYe8S8oX8nPNvJ67xzg/HuZqYeWv1ZxtWYPxT1SlW4L9Bi6Io/PjjjyQkJLBo0SIaN26cp/fKzzmrSXJWj2StDnvnbPHgxt/fn40bN/Lnn38an/9UtWpVmjZtavXinI3BYLDZlVjiP/k15+t37jFlUxS/HMt8Rksln4LM6hVKk6o+2ZbVaDR89tln1K9fn9GjR+fp/fJrzmqTnNUjWavDEXLO031uNBoNzZo1o1mzZtauRwjxAEVRWH/4MjN+OcGt5HR0Wg0vtqjC6PbV8HL/74Nj1apVnD59munTpwOZN7h888037VW2EELYTZ4GN+Hh4axYscJ4E7+qVavywgsvyNEbIawsNiGZtzdGsu/MDQBqlSnKnN6hBJc1vUHW4cOHef755wHo0KEDLVq0UL1WIYRwFBYPblavXs3MmTPp2LGj8cP06NGjDBs2jAkTJuR4p1MhhGX0BoUVf0bzwbZ/SEnX4+mmZXT7QIa2qIy7Lvskvbp16/LGG29QrFgx+SNDCJHvWXyH4pYtWzJs2LBsD7ZcvXo1S5YsYd++fVYt0NpseYfirMfAZz1aXtiGq+f8z9U7jFt/jIjYWwA0quzN7N6hVPYtZFxGURTWrFlDly5djE/vtjZXz9lRSM7qkazVYaucbXqH4jt37uR4yLtZs2bcvXvX0tW5nLS0NHuXkC+4Ys73MvR89NtpuizcR0TsLYp4ujGzZwjfvtjYZGADmQ+U7devH8OHD8fCv08s4oo5OyLJWT2StTrsnbPFg5u2bdvy22+/ZWvfsWMHrVu3tkZNTstgMHDixAm5maGNuWLOh2Ju0nnB7yzYcYZ0vcITNUvx25hW9GtUAa02+18+PXr0wNPTk5CQEJvV5Io5OyLJWT2StTocIWeL59xUrVqVJUuWcODAAerUqQNkzrk5fPgwgwYN4quvvjIumzUnRwiRs7v3Mvhg2z+sDI9GUcC3sAfTugfzVHBpk8O5iqJw5coVypYtC2QeKb1w4QL+/v72Kl0IIRyWxYObdevWUbRoUc6ePcvZs2eN7UWKFGHdunXGrzUajQxuhHiIXf/8y6SNx7l8K/Mhln3qlWNS5xoUL2h686s7d+4wdOhQ9uzZQ0REhPHmmTKwEUKInFk8uNm5c6ct6nAZcltvdThzzglJaUz/+QQbj1wGoLx3AWb1DKV5Nd8cl9fpdJw4cYL4+Hh+//13+vTpo1qtzpyzM5Gc1SNZq8PeOVt8tZSzs+XVUkI8jKIo/HT0ClM3nSAhKQ2tBgY3q8yYDoEU9HDLtuz9p6VOnjzJ7du38/wYBSGEcHaW/P7O0038RM4URSExMZGiRYvKZYY25Kg5X76Vws2knK8QuH7nHp/tPUf4+QQAqpcuwuzeodQpXzzbsrdu3WLIkCH07duXZ555BoAaNWrYrO7cOGrOrkZyVo9krQ5HyFkGN1ZkMBg4e/asHBWyMUfM+fKtFNp+sJt7GQ+/OsBdq+HVdtV4qVVVPNxyPmz7+eefs2HDBvbs2UPXrl0pVKhQjsvZmiPm7IokZ/VI1upwhJxlcCOEFdxMSnvkwAZgwbN1eCqkzEOXef3114mKimLkyJF2G9gIIYQzM2vGz8iRI4036Pvhhx/sfnMeIZxVee/sg5WEhARmz55tvBmfm5sbK1asoH79+mqXJ4QQLsGswc3u3btJTk4GYMKECdy5c8emRTkzLy8ve5eQL7hKzmlpaTRu3JgJEyYwf/58e5eTjavk7OgkZ/VI1uqwd85mnZaqXLkyH330EY0aNUJRFLZs2ULhwoVzXLZHjx7WrM+p6HQ6atWqZe8yXJ4r5ezh4cHo0aP5+OOPHe4O366UsyOTnNUjWavDEXI261Lww4cPM3v2bC5evMjt27cpVKhQjjOgNRoNBw4csEmh1mLLS8ENBgMJCQl4e3vb/Rp/V+aIOa8Kj+adH6MeudzPo5pT2jOde/fuGe82rCgKKSkpFCxY0NZlWsQRc3ZFkrN6JGt12Cpnq18KXrduXdauXQtA9erV2bZtGz4+Po9fqYtRFIWYmBhKlChh71JcmiPlrCgKX4XHMHXTowc2AFFRUXQc1JeKFSuyZ88e3N3d0Wg0DjewAcfK2ZVJzuqRrNXhCDlbPKTasWMH3t7etqhFCKeSrjcw6YfjvPtTFAYzb4VZvHhxkpKSuHnzJlevXrVtgUIIkU9ZfCl42bJlSUxMZN26dZw7dw6AgIAA+vTpQ5EiRaxeoBCO6FZyGq+sPsyf5+LRaODlVlVZ9vuFh14O7ummJTiwMtu2baNWrVq5zlsTQgjxeCwe3ERGRjJ06FA8PT0JDQ0FYMWKFSxZsoQvv/zS7pOI7K1o0aL2LiFfsGfOZ/+9y9CVB4mOT6aQh475z4TRvmYpnmtcMdsdig8ePMi0adP46KOPaFinFmWLF6Bso0Z2qtxysj+rQ3JWj2StDnvnbPGzpfr160fFihWZPn06bm6ZY6OMjAwmTZpEbGwsq1evtkmh1iLPlhKPY+/p64z45jB3UjMoV6IAX7xQn+qlc/8h7tq1Kz///DO9evVi/fr1KlYqhBCuxZLf3xbPuTl+/DhDhw41Dmwg86ZjQ4cO5fjx45ZX60IMBgNXrlzBYHj0nWpF3tkjZ0VRWPHHBQYuP8Cd1AwaVCrBjyOaPXRgA7B8+XLefPNNVq1apVKl1iP7szokZ/VI1upwhJwtHtwULlyYuLi4bO1xcXH5/lbxiqIQFxdHPnvQuurUzjldb2DiD8eZsukEBgX61ivH10Mb4VPYM9uyv/76Kx9//LHxa19fX95//32HvBrqUWR/VofkrB7JWh2OkLPFc246derExIkTGTduHGFhYUDmfXDmzp1L586drV6gEPZ0Mylz4nD4+cyJw28/VYOhLSrneJ+no0eP0rFjR7RaLQ0bNqRp06Z2qFgIIYTFg5uxY8ca/6/X6zNX4ubGs88+y5tvvmnd6oSwo7P/3mHIyr+J+f+JwwueDaNdjVK5Ll+7dm0GDRqEp6cndevWVbFSIYQQ97N4cOPh4cGkSZN44403uHjxIgAVKlSgQIECVi/O2Wg0Gnx8fHL8q15Yjxo57/7nX0Z9c4Q79zInDi97oQFBpbPf6mDnzp00bdrU+ByVzz//3GUmqsv+rA7JWT2StTocIec83xe5QIECBAUFERQUJAOb/6fVaqlUqZLc1tvGbJmzoih8+fsFBq84yJ17GTSs5M2PI5rlOLCZOXMm7dq1Y8yYMcY2VxnYgOzPapGc1SNZq8MRcpYtbEUGg4Ho6GiZiW9jtso5LcPA2xsjmfZz5sThp+vnPnEYoF69emg0Gtzc3Fxym8v+rA7JWT2StTocIWcZ3FiRoijEx8fLTHwbs0XON5PSGLBsP98eiEWjgUmdazCndygebqY/IomJicZ/d+zYkaioKBYsWOCSfwnK/qwOyVk9krU6HCFn1/tEFsJCZ67dofviP9h/IYHCnm58+UIDhraoYnK+OC0tjdGjRxMaGsrNmzeN7TVq1LBHyUIIIR5CBjciX9v1z7/0+uRPLiYkU967ABteaUqb6n7ZlktJSeHnn38mJiaGTZs22aFSIYQQ5rL4aimA6Oho9u/fT3x8fLZzaiNHjrRKYc5Io9Hg7+8vM/FtzBo5K4rCl39E894vmfNrGlb2Zkn/engX8shx+WLFivHdd99x5coVunbtmuf3dSayP6tDclaPZK0OR8jZ4mdLrV27lilTplCiRAl8fX1NitdoNGzcuNHqRVqTPFtKpGUYmPzjcdYcjAXgf/XLM71HsMn8mtTUVN566y06duxIly5d7FWqEEKI/2fJ72+Lj9x8+umnjB49mmHDhuW5QFel1+s5f/48VapUkYGTDT1OzglJabz89SH2X0hAq4G3O9VgSPPsdxxesGABixYtYs2aNZw/f54iRbJfCu7qZH9Wh+SsHslaHY6Qs8WDm9u3b/PUU0/ZohaXcP/VNMJ28pLzmWuZdxy+mJBMYU83FvYLo01Q9vk1AK+99hq7du3i1VdfzZcDmyyyP6tDclaPZK0Oe+ds8YTiJ598kt9//90WtQhhM7tO/UvP/584XMG7IBtfaWoysElJSWH58uXGSxc9PT3ZsmWLDOSFEMIJWXzkpmLFisyfP5+jR48SGBiIm5vpKp5//nmrFSfE41IUhWW/X2Dm5pMYFGhU2ZtPH5g4nJ6eTvPmzTl8+DCKojB48GA7ViyEEOJxWTy4+e677yhYsCAHDhzgwIEDJt/TaDT5enCj0WioWLGizMS3MXNzTsswMOmHSNb+fQmAZxqUZ1r34Gw35nN3d6dPnz5cunSJChUq2KxuZyP7szokZ/VI1upwhJwtvlrK2cnVUvlD/N17vPz1YQ5EZ04cnti5JoObVTL+sCUlJZGamoqPjw+Qebvw+Ph4SpYsac+yhRBC5MKS39+PdRM/RVHkNtb30ev1REVFodfr7V2KS3tUzv9czbzj8IHoBIp4urFsYAOTK6JOnjxJw4YNefbZZ433adJqtTKweYDsz+qQnNUjWavDEXLO0038fvjhB5YtW0Z0dDQAlSpVYsiQIfTo0cOKpTmn1NRUe5eQL+SW885T13j12wju3suggndBvhxYnwA/06udDAYDFy5c4ObNm8TExFC5cmU1SnZKsj+rQ3JWj2StDnvnbPHgZvny5cyfP5/nnnuO0aNHA3Do0CGmTJnCrVu3GDhwoJVLFOLRFEXhi30XmLnlJIoCjat48+lz9Sjx/xOHFUUxHrmpVasWGzduJCwsDD+/nC8FF0II4bwsHtysWrWKKVOmmByladeuHdWqVWPhwoUyuBGqu5ehZ9LG43x/KHPi8LMNKzC1Wy3jxOGjR4/y4osv8s033xAQEABkPtFbCCGEa7J4zs3169cJCwvL1h4WFsb169etUpSz0mq1BAQEoNXK80ht6f6c4+/eo/8X+/n+0CW0Gni3a01m9jS9Imrs2LEcPHiQMWPG2LFq5yP7szokZ/VI1upwhJwtfueKFSuyZcuWbO2bN2+mUqVK1qjJaWk0GooVKyaXGdpYVs6nr92l++I/OBh9kyJebiwf1JBBzbI/SmHZsmU899xzfPnll3aq2DnJ/qwOyVk9krU6HCFni09LjRo1itdff52DBw9St25dAA4fPsxff/3Fxx9/bO36nIper+fYsWOEhobKZeY2pNfrWbb1APP33yYpTU9Fn4Ise6EBAX6FAThy5AiRkZHGey6VK1eOr7/+2p4lOyXZn9UhOatHslaHI+Rs8eCmY8eOrF27lhUrVrBjxw4AqlSpwvfff0/NmjWtXqCzybq0WNiGoih8vu8Cc/YloABNqvjwyXN1jROHjx8/TuPGjVEUhZo1a1K/fn37FuzkZH9Wh+SsHslaHfbOOU+XggcHB/PBBx9YuxYhHupehp6JG4+zLmvicIPyTOsRjLvuv7OrtWrVokuXLmRkZFClShV7lSqEEMKOzBrc3L17l8KFCxv//TBZywlhTTfu3uPlrw9xMPomWg0Mql2ECd1r4qbTcuzYMWrUqIG7uzsajYavv/4aLy8vOa8uhBD5lFmDmwYNGvD777/j4+ND/fr1c/ylkXUfkZMnT1pcxOrVq1m2bBnXr1+nevXqvPPOO4SGhua47Nq1a/nhhx84c+YMkPmX+pgxY3JdXk1arZaaNWvKTHwrO3U1kSEr/ubyrRSKeLmx6NkwGlYogk6nY8mSJbz66quMGTOG2bNnA1CgQAE7V+waZH9Wh+SsHslaHY6Qs1mDm5UrV1KsWDEAvvrqK6sWsHnzZmbNmsXUqVOpXbs2K1euZMiQIWzdutX43J/77d+/n86dO1O3bl08PDz44osvGDx4ML/88gulSpWyam154eHh8eiFhNm2n7jGa2uOkJSmp5JPQb54oQFVSxYyns/19fUlPT2d8+fPYzAY5EPLymR/VofkrB7JWh32ztniB2deuXIFf3//bEdvFEUhLi6OMmXKWFRA3759CQkJYfLkyUDmJKRWrVoxYMAAhg0b9sjX6/V6GjRowOTJk816/IMtH5wpD+W0HkVRWLr3PHO2nkJRoGnVzInDxQt6kJqaSlRUlDHnPXv20LJlSzkNZWWyP6tDclaPZK0OW+VsyXotnlDcrl074ymq+926dYt27dpZdFoqLS2NqKgoXnrpJWObVquladOmHDlyxKx1pKSkkJGRYTyyZK77H+il0WjQarUYDAaTB4FmtT/48K/c2rNe+2B71tGEB2eP59au0+lQFCXH9gdrzK3dWn3SarVoNBpV+3Qvw8CkH6PYcPgyAP0almdylxq4aTXMnTuXVatW8cknnxhratGiRY41OlKfcmt35O2UtS69Xu8yfXpU7fboU9a/FUUxu6+O3qeH1e4Ifcr6vyv16VHtavbpUZ8d1urTw1g8uLn/GT33S05OxtPT06J13bx5E71en22g5OPjw/nz581axwcffICfnx9Nmza16L0jIyNN3q9SpUpcvHiR+Ph4Y7u/vz9lypTh/PnzJCYmGtsrVqyIr68vp06dMnk4WNbVOVFRUSYbvmbNmnh4eBAREWFSQ506dUhLS+PEiRPGNq1WS1hYGImJiZw9e9bY7uXlRa1atUhISCAmJsbYXrRoUapVq8bVq1eJi4uzep8CAgIoVqwYx44dU6VPt1P1fLD/Dif+TUWngUF1ivBUxXSiIo+h0+mYP38+V65cYfPmzRQsWNAp+uTs2+nMmTMu1ydH206Q+Uvk/s8lZ++TI26nrIc9Z+XsCn1y5O0UHR1NYGCg1fp0+vRpzGX2aalZs2YBmXNu+vbtazJpM+uGPVqtljVr1pj95teuXaNly5asWbPG5JEOc+fO5eDBg3z//fcPff1nn33GF198wVdffUX16tXNes+sw1ohISHGw1rWPHJz9OhRk3WDjPbN7dOpq3d4cdUhrtxKNU4cbh7w38BXo9Hw+++/ExUVRYMGDYw3iHLkPj2q3ZG3k16vJzIykpCQEDw8PFyiT4+q3R59ysq5du3a2f5wdNY+Pax2e/YpLS3NuE8/7LPDmfrkiNvpUZ8dee1T1vaz6mmprNGboiicPn0ad3d34/c8PDyoXr06gwcPNnd1AJQoUQKdTmcycgOIj4/H19f3oa9dtmwZn332GcuXLzd7YHM/nU6XLZzcJqPmFuKD7YqiUKdOHeNOlNf1QObGzKk9txotbbekFmu1P6xPv0ZdZfR3ESSn6ansW4gvXqhPZZ+CzJ49m6ZNm9K6dWsAWrZsSYsWLYyTh+/P2dH6lBNn2k5arda4Pz+sRmfqk7k1qtmn+3PObd6Ys/Upr+227pO7u3uOWTtznxxxOz3uZ4elNebE7MHNqlWrAJgwYQITJ060yv1sPDw8qFWrFuHh4bRv3x7IHCWGh4fTv3//XF/3+eefs2TJEpYtW0ZISMhj12FNaWlpeHl52bsMp6EoCkv2nGfutsyJw80CfFjcL3Pi8IcffsjEiRPx9/fn1KlTFC1a1Pg6yVkdkrM6JGf1SNbqsHfOFl83+/bbb5ORkZGt/datW4+8wV9OBg0axNq1a9m4cSPnzp1jypQppKSk0KtXLyDzic4ffvihcfnPPvuM+fPnM3PmTMqWLcv169e5fv06SUlJFr+3tRkMBk6cOGH32047i9R0PW98f9R4RdSAxhVZMaghxQtmXkI4fPhw6tevz8yZM00GNpKzOiRndUjO6pGs1eEIOVs8ofj111+nTZs2PPfccybtW7ZsYefOnXz++ecWra9Tp04kJCSwYMECrl+/To0aNfjiiy+Mp6Xi4uJMDl2tWbOG9PR0Xn31VZP1jBw5klGjRlnaHWEn1+/c46VVf3P44i10Wg1TutakX8PybN26hc6dOwNQqFAh9u/fn+uhSyGEECInFg9ujh07xoQJE7K1N2zYMM9PBe/fv3+up6GyTodl2blzZ57eQ6jr8q0Ubial5fi989eTeG/zCa4l3qOolxufPFePxpWL07FjR3bs2MH3339Pnz59gNzPyQohhBC5sXhwk5aWluNpqYyMDJPLzvIr+WWcObBp+8Fu7mU8/JBkhRIFWDG4IVVKZs7fatCgAX/99ZdZ9zKQnNUhOatDclaPZK0Oe+ds8buHhISwdu3abO1r1qyhVq1aVinKWel0OsLCwvL9nS9vJqU9cmADMLNnMH73PQZq2rRpHD16lP/9738PfZ3krA7JWR2Ss3oka3U4Qs4WH7kZPXo0gwYN4tSpUzRp0gSA8PBwIiMj+fLLL61eoDNRFIXExESKFi0qjwIww5uvjaBMAT3r169Ho9Hg7u5O1apVH/k6yVkdkrM6JGf1SNbqcIScLT5yU69ePb777jtKly5tnERcoUIFfvrpJ+rXr2+LGp2GwWDg7NmzMhPfTJGRkWzfvt3kTpfmkJzVITmrQ3JWj2StDkfI2eIjNwA1atQwuTxbiLyYNm0a3VvWJSAgwN6lCCGEcCF5GtxkuXfvHunp6SZt1ri5n3Bet1PS+fKPC2Yt27FjRwLKWvbAUyGEEOJRLB7cpKSk8P7777NlyxZu3bqV7fuWPBXcFeXXO1+m6w2s/iuG+TvOcDM5/dEveEz5NWe1Sc7qkJzVI1mrw945WzznZu7cufz1119MmTIFDw8PZsyYwahRo/Dz82POnDm2qNFp6HQ6atWqla9m4iuKwtbjV+kwby9TNp3gZnI65YrbdqfOjznbg+SsDslZPZK1OhwhZ4sHN7t27eLdd9+lY8eO6HQ66tevzyuvvMLrr7/Opk2bbFGj0zAYDNy4cSPfTFaLiL3F/5b+xfCvD3HhRhI+hTyY0SOYb15sjIfu4TPkPd20lCjkkaf3zW8524vkrA7JWT2StTocIWeLT0vdvn2b8uXLA5nza27fvg1kXkU1depU61bnZBRFISYmhhIlSti7FJuKTUjm/W3/8NPRK0DmQOXFFlV4qVUVini5c/LkSa6vHIVe58WqVatyvLy7RCEPyhYvkK3dHPklZ3uTnNUhOatHslaHI+Rs8eCmXLlyXLp0iTJlylClShW2bNlCaGgou3btokiRIraoUTiI2ynpfLLrLMv/iCZNb0CjgZ5hZXmzQxBl7huoBAUF0aBmVe7evUtYJV8qyKRhIYQQKrJ4cNO7d29OnTpFw4YNGTZsGMOHD+frr78mIyOD8ePH26JGYWdpGQa+2W86WbhJFR8mdq5B8P8PXC5evEi5cuXQarVotVrWrl1LoUKFcHd3t2fpQggh8iGLBzcDBw40/rtp06Zs2bKFqKgoKlSoQPXq1a1Zm1MqWrSovUuwGkVR2BZ1jTlbT3HhRhIAAX6FebtTddoE+RnvPPn9998zZMgQJkyYYHyoavHixW1amyvl7MgkZ3VIzuqRrNVh75wtmlCcnp7OCy+8QHR0tLGtbNmydOjQQQY2ZM4Qr1atmkvMxD9y8SZPLw03Thb2LezBez2D2fpaC9pWL2VyS+27d+9y584dfvvtN7Meevm4XClnRyY5q0NyVo9krQ5HyNmiIzfu7u78888/tqrF6RkMBq5evUrp0qXt/kTUvIpNSGbutn/Y9P+Thb3csyYLV6Ww53+7i6IoxgHOwIEDKVy4MD179lRlZ3aFnJ2B5KwOyVk9krU6HCFni9+1W7durFu3zha1OD1FUYiLi0NRFHuXYrHbyenM3HySdh/uYdPRK2g00LtuOXa92Zo3OgSZDGy+/vprWrduzb179wDQaDT07dsXN7fHuuG12Zw5Z2ciOatDclaPZK0OR8jZ4t9Ger2eb7/9lj///JPg4GAKFDC9nDdrzoVwDmkZBr7+K4YFO89w6/8nCzet6sPbnf6bLHy/mzdvMnr0aOLj41m6dCmvvvqq2iULIYQQD2Xx4Ob06dPUrFkTgAsXTJ8hJI+Qdx6Zk4WvMnvLKaLjkwGo5leYtzvVoHVQyVy3ZYkSJfjqq684cOAAI0aMULNkIYQQwixmD25iY2MpV64cq1atsmU9Tk2j0eDj4+Pwg7wjF2/y3i8n+TvmJgC+hT0Y80QQT9cvh5vO9EyloiisXLmS4OBg6tevD0CnTp3o1KmT6nVncZacnZ3krA7JWT2StTocIWez59x06NCBhIQE49ejR4/mxo0bNinKWWm1WipVquSwE9ViE5IZ+c1hen7yJ3/H3MTLXcurbQPY/VYb+jWqkG1gA/DJJ58waNAg/ve//5GYmGiHqrNz9JxdheSsDslZPZK1OhwhZ7Pf+cGJQXv27CElJcXqBTkzg8FAdHS0wz235HZyOu/9coJ2H+7h52NxaDTQt145dr/ZhjEPTBZ+0HPPPUdAQABDhgyhcOHCKladO0fN2dVIzuqQnNUjWavDEXJW5/KWfEJRFOLj443P3rK3tAwDq/6KYcGOM9xOyZws3Cwgc7JwrTI5PxJBURT+/vtvGjRoAGTejC8yMtLuj6+/n6Pl7KokZ3VIzuqRrNXhCDmbPbjRaDRyntJJKIrC1uNXmb31FDH3TxbuXIPWgblPFtbr9fTv3581a9awdetWOnbsCOBQAxshhBDiUcwe3CiKwvjx4/Hw8AAgLS2NKVOmZLsUfNGiRdatUFjk8P9PFj5knCzsyRsdAulbL/tk4QfpdDpKlCiBTqfj/PnzapQrhMgjvV5Penq6vctwKll3UE9NTZW7FNvQ4+Ts4eFhlbk6Zg9uevbsafJ1t27dHvvNXY1Go8Hf398uR7guxiczZ9spfjkWB2TeWXhYy6oMa1nloXNqFEUhPT3dOGj96KOPGDx4sPHKKEdkz5zzE8lZHZbmrCgKV69e5datW7YtzAUpioKnpycxMTGyX9vQ4+Ss1WqpXLmy8XdSXmmUfHarRr1eT0REBHXq1HGJkfvt5HQW7jzDyvBo0vWKcbLwmCeCKF3s4aeTbt26xdChQylSpAjLly9XqWIhxOOIi4vj1q1b+Pn5UbBgQfklLVyGwWDgypUruLu7U6FChWz7tiW/v2VCsRXp9XrOnz9PlSpVbD5wymmycItqvkx4qgY1y5j3NNbIyEg2btyITqdj3LhxTvPwUzVzzs8kZ3VYkrNerzcObHx8fFSq0HUoisK9e/fw9PSUQaENPU7OJUuW5MqVK2RkZODu7p7nGmRwY2W2vheMoihsOX6VOfdNFg4slXln4VYPmSyckxYtWrBo0SLq16/vNAObLI5yzx1XJzmrw9ycs+bYFCxY0JbluLSs+SDCtvKac9bpKL1eL4Ob/OJQzE3e++UEhy/eAqBkEU/eeCKQPmZMFgZISEhg3LhxzJw5k5IlSwLw8ssv27JkIYQNyFEH4aqstW/L4MYJxMQnMXfrP/wSmTlZuIC7jmEtqzCsZRUKPWSy8IP69evHtm3buHHjBhs3brRVuUIIIYRdyT2orUij0VCxYkWrjTxvJacx/ecTtP9oD79EZt5Z+H/1y7P7rda8/kSgRQMbgPfff5+QkBDeeecdq9RnL9bOWeRMclaH5Px4Fi5cSNOmTQkKCmL79u2PXD6vV+GYu35b2b9/P0FBQcZTmBs2bDC5qnXhwoV0797dXuVlk1vOD/bDVuTIjRVptVp8fX0fez33MvSsCo9h4c6zJpOF3+5Ugxr+5k0WBrhx4wYnT56kRYsWAISEhBAREeH0z1WxVs7i4SRndeSHnMePH288Wuzu7o6/vz/du3dn+PDhuLnl/dfQuXPnWLRoEYsXL6Z27doUK5bzndezaDSaR87jWLhwIdu3b+fHH3/Mc11q6NSpE61atVLlvfbv38/zzz/PwYMHKVr00b+DzMnZ1mRwY0V6vZ5Tp05RvXr1PF1doigKmyMzJwtfTMicLBxUqghvd86cLGyJs2fP0rp1a+7evcvhw4epUqUKgNMPbODxcxbmkZzVkV9ybtGiBbNmzSItLY09e/Ywbdo03N3deemllyxel16vR6PRcPHiRQDatWtn1pEvRVFITU3Fy8vL6Y+UeXl5Pfbd49PS0h77fjI5cYScnf83nYNJTU3N0+sOxSTQ+9M/GfHNYS4mJFOyiCdzeoew+bUWFg9sACpWrEjFihUpXbp0nmtyZK7YJ0ckOavDGjknJSWRlJRk8pDjtLQ0kpKSuHfvXo7L3v9gw/T0dJKSkrLVktOyeeHh4UHJkiUpW7Ys/fr1o2nTpuzcudNY55w5c2jRogV16tShb9++7N+/3/jarFMwO3bsoFOnToSEhPD2228zfPhwAKpXr05QUJBx+e+//56nnnqKkJAQnnzySVavXm38nsFg4OrVq4wZM4aGDRtSp04devXqxdGjR9mwYQOLFi3i1KlTBAUFERQUxIYNG7L15fnnn2fatGkmbQkJCQQHBxMeHp5rBjt37qR3796EhITQqFEjRowYYfzeDz/8QK9evQgLC6NZs2a88cYbxMfH57quB09LZVmzZg2tWrWidu3avPbaa9y5c8f4vfHjx/PKK6/w6aef0rx5c5588slHvvelS5d4/vnnAWjQoAFBQUGMHz/emOXSpUtp27YtoaGhdOvWja1btxq/B5kP2e7YsSOhoaEMGDCAy5cv59ona5IjN3YWE5/EnK2n2Bx5FcicLPxSqyq82MKyycIAN2/epHjx4sZDguvWraNIkSIO8zRvIYTtZP2c//vvv8arId9//30mTZrE0KFD+fzzz43L+vn5kZyczIULF6hUqRIAixcv5vXXX6dfv34mg4FKlSpx48YNjh8/Tq1ataxWr6enp/Euy9OmTePs2bPMmzcPPz8/fvvtN4YOHcqmTZuM9aWmpvL5558zY8YMihcvjp+fHw0bNmTChAn8/vvvxvX+9NNPzJ8/n8mTJ1OjRg1OnjzJO++8Q8GCBenRowfJyckMGDCAUqVK8cknn1CyZEmioqIwGAx06tSJM2fOsG/fPuONTYsUKZKt9r59+zJ9+nSTRxL99NNP+Pn50bhx4xz7u3v3bkaOHMnw4cOZO3cu6enp7Nmzx/j9jIwMXnvtNapUqUJ8fDyzZ89m/PjxJtvtUS5evMiWLVtYsmQJd+/eZeLEiUyZMoUPP/zQuEx4eDiFCxc2uXHrw97b39+fhQsXMmrUKLZu3UrhwoWNR4yWLl3KTz/9xNSpU6lUqRIHDx7krbfeokSJEgQHBxMXF8fIkSN57rnnePrppzl+/Dhz5swxuz+PQwY3dnIrOY2FO8/y1f/fWVirgafrl+f1JwIpVdTyQ427du2iX79+TJo0yfjXgL+/v7XLFkKIx6IoCuHh4fz+++/079+fK1eusGHDBnbt2kWpUqUAGDJkCPv27WPDhg2MGTMGyDyyNGXKFJN7cmXN/8gazEHmnJnx48fToUMHAMqXL8/Zs2f57rvv6NGjB1u2bCEhIYF169ZRvHhxIPNId5aCBQui0+lM1vmgDh06MH36dLZv306nTp2AzCMpvXr1yvU0zJIlS+jUqROvvvqqse3+vvTp08f47/LlyzNx4kT69OlDUlIShQoVekii/7l37x5z58415jhp0iReeuklxo8fb+xPwYIFmTFjhsnpqEe9d9ZcJh8fH2PmaWlpLF26lOXLlxMWFmZ87aFDh/juu+8IDg7m22+/pUKFCsYjPVWqVOH06dMWDdjySgY3VqTVagkICHjovJasycILdpwhMTUDgFaBJZnQqTrVS5s/WfhBERERXL16lWXLljF8+HCXPndvTs7i8UnO6rBWznfv3gVMb/D31ltvMXr06GyTdv/9918AkwcfjxgxghdffDHbZ0d0dHS2ZfNi9+7dhIWFkZ6ejqIodOnShVGjRnHgwAH0er3xFEmWtLQ04+ADMici33/qKSfJyclcvHiRiRMnmlwVmpGRYTwCc/bsWWrUqGGybkt5enrSrVs31q9fT6dOnYiKiuLMmTN8+umnub7m5MmT9O3bN9fvHz9+3HhK7Pbt28bTi3FxcQQEBJhVl7+/v3FgAxAWFobBYODChQvGwU1gYGC2eTZ5ee+YmBhSUlIYPHiwSXt6ejo1atTA09OT8+fPExoaavL9OnXqmNWXxyWDGyvSaDS5ztZXFIVfIuOYs/UUsQkpAFQvXYS3O9WgZR7m1Dxo9OjRuLu7M2jQIJce2MDDcxbWIzmrw1o55/TXvYeHR44TRnNa1t3dPccrXMw9avAojRo1YsqUKbi7u+Pn52cccCUnJ6PT6Vi/fn22z677B2rmTE5NTs68EGP69OnUrl3b5HtarRaNRmO153H17duXHj16cPXqVTZs2EDjxo0pW7Zsrss/bPJvcnIyQ4YMoXnz5nzwwQeUKFGCuLg4hgwZYvUnvz84SM3re2dlvXTpUpMBFWTud49zFZw1yODmMV2+lcLNpDQgcwb/mTNnqFatmvGHtEQhD+JupfDe5pMc+f87C/sV8eTNDkH0rlcOnTZvP2S//fYbn332Gd9++y1ubm5oNBpGjhxplT45Or1ez7FjxwgNDXX5gZw9Sc7qyC85FyhQwOT0T5YaNWqg1+tJSEjIcYKsJXx9ffHz8yM2NpZu3bpl+76iKFSqVInvv/+eW7du5Xj0xt3d3azJ00FBQQQHB7N27Vp+/vnnR94/LDAwkPDwcHr37p3te+fPn+fWrVu8+eabxukEx48ff2QND4qLi+PatWvGwUbWrT8qV66c62vMee+sQe/9j1SoWrUqHh4eXLlyhYYNG5osrygKycnJVKlShV27dpl87+jRoxb3Ky9kcPMYLt9Koe0Hu7mX8cAPwm83jP/UasDw/xcvFPTQ8VLLqrzYsjIFPfIe/a1bt+jbty+3b9+mefPmvPbaa3lel7N63Cs3hHkkZ3Xk55wrV65M165dGTt2LOPHj6dGjRrcvHmT8PBwgoKCaN26tUXre/XVV5kxYwZFihShRYsWpKWlcfz4cRITExk4cCBPPvkky5cvZ8SIEYwZMwY/Pz9OnDiBn58fYWFhlC1blkuXLnHy5ElKlSpF4cKFc71cum/fvkybNo2CBQvyxBNPPLSukSNHMnDgQCpUqEDnzp3JyMhgz549DBs2jDJlyuDu7s6qVat49tlnOX36NJ988olF/YbM02Xjx49n3Lhx3L17lxkzZvDUU089dP6QOe9dtmxZNBoNu3fvplWrVnh6elK4cGEGDx7MrFmzUBSFevXqcefOHQ4fPkyhQoXo2LEjzzzzDMuXL2fOnDn07duXqKgo1e6OLyfTH8PNpLTsA5sHGBTQAM82LM/uN1vzWvtqjzWwAShevDhLlixh2LBhDBs27LHWJYQQ9jZr1ix69OjB7Nmzeeqpp3jllVeIjIzM00URffv2ZcaMGWzYsIGuXbsyYMAANm7cSLly5YDMoxDLli3Dx8eHYcOG0bVrVz777DPjUbOOHTvSokULnn/+eZo0acLPP/+c63t17twZNzc3OnfujKen50PratSoEfPnz2fnzp10796dF154gcjISAC8vb2ZPXs2W7dupVOnTnz++eeMGzfO4r5XqFCBJ554ghdffJHBgwcTFBTEu++++9DXmPPepUqVYtSoUXz44Yc0bdqU6dOnA5nTIV555RWWLl1Kp06dGDp0KLt37zZmXaZMGRYuXMiOHTvo3r07a9as4fXXX7e4X3mhUe6/KUI+oNfriYiIoE6dOo99CPj45dt0Wfj7I5db1C+MLqFlHuu9tmzZQsWKFalZs+ZjrccVWHMbitxJzuqwJOfU1FQuXLhA5cqVH/sGbvlR1ukSa827uXTpEk888QTr1q2z6mXyzu5xcn7YPm7Jz4ocuVFBJZ/Hm5C3YsUKOnXqxNNPP22cxJWfabVaatasKVfx2JjkrA7JWV2Pe9UXZF4RdP36dT7++GNq164tA5scWCPnxyE/TU7gqaeeonTp0rRp00Y+AP+fLW4ZLrKTnNUhOavHGkdsDh8+TPPmzYmMjGTq1KlWqMr12PvxFjKh2EGdP3/e+DyoUqVKcfz4cXx8fOxclWMwGAxyukQFkrM6JGd1ZZ0ueRyNGjXin3/+sVJFrskaOT8OOQzgYPR6PW+88QZBQUEmtxSXgY0QQghhHhncOBitVsu1a9fIyMhg79699i5HCCH+r707D2vqSv8A/k1AUEERERWqgksJW5TgVjGCYBFFsQXFoS7YWp2CI3UXF1RALe5jARdc6u44VcFdfpV2RKxKtUhdGUuxsloRVwQSQs7vD5/cMawJQiLx/TyPz2NO7j33Pe8FcnLOufcS0uTQtNRbMDUygKE+v9bLwQ31+TA1qns+nTEGHo8HHo+HLVu2YPz48Rg+fHhDhksIIYS8F6hz8xY+aNMCP80dzN2hmDEGuVzO3eYbeN0B+qBNzavGJRIJ5s6dCz09PWzcuBHA66fQUsemZnw+H05OTrS4upFRnjWD8qxZ2lwH8j7Rdp6pc/OWPmjTguu8MMZQVlam0jNQFH7++WfExsaCx+Nh6tSpdEmhiqRSKd3nQwMoz5pBedYcxSg5aVzazjN9VWhAcrkcd+7cUetW6h4eHli+fDlOnTpFHRsV1SfPRH2UZ82gPGtWaWmptkN4L2g7z9S50bCysjIsWbIEz58/58rCwsLg7e2txagIIe+DvGeluJX3vMZ/ec+0+4EkEAiQlJTU6MdJTU2FQCDAixcvuLKkpCR4enrCzs4OK1euRHx8/Fs/yJNoD01Ladjf/vY3nDhxApmZmfjXv/6l7XAIIe+JGh/0+wZDfT5+mju41nWCb6OwsBBbt27F+fPn8ddff8HMzAx2dnaYNGkSBgwY0CjHrI5IJMLFixfRqlUrrmzp0qXw8/PDxIkTYWRkBH19fbi5uWksJtKwqHPTwOpaFLho0SL8+uuvmDx5soYi0k20+FIzKM+aoYk8q/KgX4lMjqevpI3SucnNzcVnn32G1q1bY/78+bCxsYFMJsPFixcRERGBxMTEBj9mdXg8HgwMDJSelP3q1SsUFRVBLBajQ4cOXPnbroMqLy9Hs2bN3qqOpkrb65roL1cD0tPTg0gkUrrLaElJCX777Tfudf/+/fHHH3/A09NTGyHqhOryTBoe5VkzGiLPjDGUSGW1/isrr1CprrLyijrrqs/zliMiIsDj8XD48GF4eXmha9eu+PDDD/HFF1/g+++/r3aftWvXwsvLC7169cKQIUOwceNGlJeXc+9nZGRg4sSJEIlEcHZ2hp+fH/ek7by8PAQFBaFv375wcnLCiBEjcOHCBbRs2RK//PILNy2VmpoKZ2dnAMCkSZMgEAiQmppa7bRUUlISfH19IRQKMWTIEMTGxkImk3HvCwQCHDx4EEFBQXBycsLWrVvVzpMu4PF4DfZw0vqikZsGxBjDixcv0Lp1a/B4PGRnZ8Pb2xuPHj1Ceno6LC1fPxnc0NBQy5E2bZXzTBoH5Vkz3jbPjDGM2XoZvz542iDxjNl6uc5t+liZ4nDQAJXjffbsGVJSUjBr1qxqLxFu3bp1tfsZGRkhKioK7du3x71797BkyRIYGRlh6tSpAIC5c+fCzs4O4eHh0NPTw927d7mRksjISJSXl2P//v1o2bIlMjMz0aJFC8hkyp0zkUiExMREDBs2DDExMRCJRDAxMUFeXp5SLNeuXUNoaCjCwsLQp08fZGdnY8mSJQCA6dOnc9vFxsZizpw5WLx48Xv7xYAxhoqKCujp6Wntb8c7MXJz4MABeHh4QCgUwt/fHzdu3Kh1+7Nnz2LYsGEQCoXw8fFBcnKyhiKtnVwuR2ZmJnfVQ/v27aGnpwc9PT3k5uZqOTrdUTnPpHFQnjWjIfL8rnc9s7OzwRjjnpenqmnTpsHZ2RmdOnWCh4cHJk+ejLNnz3Lv5+fnw8XFBd27d4e1tTWGDx8OW1tb7j1nZ2cIBAJ07twZ7u7u6Nu3LyQSidIxDAwMuMfbmJiYwNzcvNoHmcbGxuLvf/87fH190blzZwwcOBAzZszAoUOHlLYbOXIkRo8ejc6dO3NfaN9HlfOsaVofuTlz5gyioqIQERGBXr16Yc+ePfjyyy+RmJhY7fOU0tLSMGfOHMyePRvu7u44efIk/vGPfyA+Ph42NjZaaIEyqVTK/b958+ZISEiAkZGR0jwuIYQ0FB6Ph8NBA1Bax7TTnfwXKo3KHAkaAHvL6kdSFFo0U+8beX2msYDXnw979+5FTk4OSkpKIJPJYGxszL3/xRdfICwsDMePH4eLiwuGDRuGLl26AAACAwMRHh6OixcvwsXFBUOHDoVAIKhXHMDrKbC0tDSlqaaKigpIJBKUlpaiRYvX65QcHR3rfQzScLQ+crNr1y6MHTsWo0ePRo8ePRAREYHmzZvj6NGj1W6/d+9eDBo0CFOmTEH37t0xc+ZM2NvbY//+/RqOvKobN25g3LhxSrF069aNOjaEkEbF4/HQ0kC/1n/Nm6k2RdK8mV6ddak71WBlZQUej4esrCyV97l+/Trmzp0LNzc3bN26FQkJCQgKClJacxMSEoJTp05h8ODBuHLlCry9vXHu3DkAgL+/P5KSkvDJJ5/g3r17GDNmDPbt26dW3G8qKSlBSEgIjh07xv07efIkfvjhB6WlBtq+My95TasjN1KpFLdv38ZXX33FlfH5fLi4uOD69evV7pOeno7PP/9cqUwsFqt9b4SKiv99y+HxeODz+ZDL5UrfMBTlb25bW/mJEyfw559/YuXKlQgICIC+vj7XJgBVhp1rKtfT0+Me5VC5vHKMNZU3VJsUj5KorlybbTI0NORi0pU2vWvnqaKigsuzrrSprti10SZFnhXrFGprU0VFBXf86kZDeDyeWuXVaYz6TUxMIBaLceDAAUyYMKFKB+DFixcwMTHhjssYQ1paGiwtLREcHMzVnZ+fz22jOKa1tTWsra0xadIkzJ49G0ePHsXHH38MAOjYsSMCAgIQEBCADRs24PDhw/D396+xrYpjV5cPe3t73L9/nxsZqi1viv+rmy9tlatDlboZY+Dz+UrnSZ1YFL8Ldf2Nr41WOzdPnz5FRUVFleknMzOzGnv4jx8/Rrt27aps//jxY7WOrVhRr9jf2toa2dnZKCoq4sotLCxgaWmJrKwspZs9WVlZoV27dsjIyEBZWRlXHhISAplMBnd3d9y6dYsrt7e3h4GBAdLT05VicHJyglQqxZ07d7gyPp8PkUiEFy9eIDMzkytv3rw5HBwc8OTJEzx48IArb926NT788EM8fPgQBQUFDd6mHj16wMTEBDdu3FD6cNBmm/Ly8iCRSLhzqAttepfP0++//65zbXoXzxOPx1OpTYovTYopkTfbqlgw++b0uJ6eHpo3b45WBjwY6PEhrah5bY+BHh+tDF6PykgkEqUPEwMDAzRr1gxlZWVKsRsaGkJfXx+lpaVKH1YtWrQAj8dDSUkJAGDevHmYPHkyxo4di5CQEFhZWUEmkyE1NRVHjhzhLgWXSCQoKSmBhYUFCgoKcPr0adja2uI///kPzp07B8YY1+6oqCh4eHjA0tISjx49ws2bN+Hl5QWpVIqoqCgMHDgQVlZWKCkpQWpqKqytrSGXy7n9FVc6Kc6R4tiKkRjGGBf/l19+iZkzZ8LCwgJubm7g8Xj4/fffkZmZiXnz5nFtV9ShuGJI3fNUXl6uNDqlr68PQ0NDSKVSpSuzmjVrBgMDgwY/TwotW7YEY0zpTsPqtknxWBF12qQ4LxkZGVy54vfp3r17UBWPvW037i389ddfcHV1xaFDhyASibjyNWvW4OrVqzh8+HCVfRwdHbFq1SqMHDmSKztw4AA2bdqES5cu1XnMiooKpKenQygUcivZG+pbGfC6w2ZiYqJ03wpd+6ap7TbJZDI8efIEpqamXHxNvU3v4nmSy+V4+vQpTE1N0axZM51oU12xa6NNcrkcz549Q9u2bVFZ5TaVlZXhwYMH6NatW7VXXdb1LTnvWSn3oN/qtlc86LexRgQePXrE3cSvsLAQbdu2hYODAyZNmoSPPvoIAoEAsbGx3MjL2rVrcfToUUilUgwePBi9evVCbGwsrl69ivLycoSGhuL69et4/PgxTE1N4enpidDQUBgYGGD58uVISUnBw4cPYWxsjEGDBiE0NBStW7fGr7/+ikmTJuGXX36BiYkJnj9/jn79+mHPnj3o378/ACAhIQHffPMNrl69ysWfkpKCzZs34+7du9DX10e3bt0wZswYjB07FgBga2urFP+7NkKjyZEbxYivYgRH1XokEgmysrJgZWXF3WdI8fsklUpx8+ZNODk51XklmlY7N1KpFE5OToiOjuZ+GAAgNDQUL168wJYtW6rsM3jwYHz++edKU1PR0dFISkrCiRMn6jymonOjSnLU1Zh1k/+hPGsG5Vkz1MlzWVkZ7t+/j65du9KDNutBMRKj7Xuw6Lq3yXNtP+Pq/K5odUGxgYEBHBwccPny/1bwy+VyXL58WWkk501OTk64cuWKUtmlS5fg5OTUmKESQgghpInQ+tVSirtTJiQk4I8//kB4eDhKS0vh5+cHAJg/fz7Wr1/PbR8YGIiUlBR89913+OOPPxATE4Nbt25hwoQJ2moCIYQQQt4hWr/Pjbe3N548eYLo6GgUFhbCzs4OO3bs4BYNFxQUKK1fcXZ2xrp167Bx40Zs2LAB1tbW2LRp0ztxjxug5jttkoZFedYMyrNmUJ41h6ZYNUPbedbqmhttoHUEhJCmitbcEF2nE2tudI1cLkd+fj7drr6RUZ41g/KsGfXJ83v2nbTBMMYglUopf43sbfLcUOeGOjcNiDGGgoIC+sVpZJRnzaA8a4Y6eVbcB6TyPUmI6t683wppPPXNs+I+OW87s6L1NTeEEEJUo6enhzZt2uDRo0cAQJc0q0lxA0DF/YZI46hvnuVyOQoLC9GyZUvuZpX1RZ0bQghpQjp27AgAXAeHqI4xhvLycjRr1ow6N43obfLM5/PRpUuXtz4/1LlpQDweD2ZmZvRL08goz5pBedYMdfPM4/FgYWGB9u3b0xSLmuRyOQoKCmBhYaF0FS5pWG+TZwMDgwY5N3S1FCGEEELeeXS1lJbI5XL8+eefdHVJI6M8awblWTMoz5pDudaMdyHP1LlpQIwxFBUV0dUljYzyrBmUZ82gPGsO5Voz3oU8U+eGEEIIITrlvVtQrOhJVlRUNHjdijobo27yP5RnzaA8awblWXMo15rRWHlW1KfKiNB7t6BYKpXi5s2b2g6DEEIIIfUgFAphYGBQ6zbvXedGLpdDJpPRTZwIIYSQJoQxBrlcDn19/TovF3/vOjeEEEII0W20oJgQQgghOoU6N4QQQgjRKdS5IYQQQohOoc4NIYQQQnQKdW4IIYQQolOoc0MIIYQQnUKdG0IIIYToFOrcEEIIIUSnUOdGTQcOHICHhweEQiH8/f1x48aNWrc/e/Yshg0bBqFQCB8fHyQnJ2so0qZNnTx///33GDduHPr27Yu+ffvi888/r/O8kNfU/XlWOH36NAQCAaZNm9bIEeoGdfP84sULREREQCwWw9HREV5eXvS3QwXq5nn37t3w8vJCz5494ebmhm+++QYSiURD0TZNV69eRVBQEMRiMQQCAZKSkurcJzU1Fb6+vnB0dISnpyfi4+MbP1BGVHb69Gnm4ODAjhw5wn7//XcWFhbG+vTpwx4/flzt9r/++iuzs7Nj27dvZ5mZmeyf//wnc3BwYP/97381HHnTom6eZ8+ezfbv38/u3LnDMjMz2YIFC1jv3r3Zw4cPNRx506JunhVycnLYoEGD2Lhx41hwcLCGom261M2zRCJhfn5+bOrUqezatWssJyeHpaamsrt372o48qZF3TyfOHGCOTo6shMnTrCcnByWkpLCBg4cyL755hsNR960nD9/nm3YsIH98MMPzMbGhp07d67W7bOzs1mvXr1YVFQUy8zMZPv27WN2dnbswoULjRondW7UMGbMGBYREcG9rqioYGKxmMXFxVW7/YwZM9jf//53pTJ/f3+2ZMmSRo2zqVM3z5XJZDImEolYQkJCI0WoG+qTZ5lMxv72t7+x77//noWGhlLnRgXq5vngwYNsyJAhTCqVaipEnaBuniMiIlhgYKBSWVRUFAsICGjUOHWJKp2bNWvWsBEjRiiVzZw5k02ePLkxQ2M0LaUiqVSK27dvw8XFhSvj8/lwcXHB9evXq90nPT0dAwYMUCoTi8VIT09vzFCbtPrkubLS0lLIZDKYmJg0VphNXn3zvGnTJpiZmcHf318TYTZ59cnzTz/9BCcnJ0RGRsLFxQUjR47E1q1bUVFRoamwm5z65FkkEuH27dvc1FVOTg6Sk5Ph5uamkZjfF9r6HNRv1Np1yNOnT1FRUQEzMzOlcjMzM2RlZVW7z+PHj9GuXbsq2z9+/LjR4mzq6pPnytatW4f27dsr/aEjyuqT52vXruHIkSM4duyYBiLUDfXJc05ODq5cuQIfHx9s27YN2dnZiIiIgEwmw/Tp0zURdpNTnzz7+Pjg6dOnGDduHBhjkMlkCAgIQFBQkCZCfm9U9znYrl07FBcXo6ysDM2bN2+U49LIDdEp27Ztw5kzZxAbGwtDQ0Nth6MziouLMX/+fCxfvhxt27bVdjg6jTEGMzMzLF++HI6OjvD29kZQUBAOHTqk7dB0SmpqKuLi4rBs2TLEx8cjNjYWycnJ2LRpk7ZDIw2ARm5UZGpqCj09PRQVFSmVFxUVVemVKrRr167KKE1t25P65Vlh586d2LZtG3bt2gVbW9vGDLPJUzfPOTk5yMvLQ3BwMFcml8sBAPb29khMTESXLl0aN+gmqD4/z+bm5tDX14eenh5X1q1bNxQWFkIqlcLAwKBRY26K6pPnb7/9FqNGjeKmWAUCAUpKSrB06VIEBweDz6fv/g2hus/Bx48fw9jYuNFGbQAauVGZgYEBHBwccPnyZa5MLpfj8uXLEIlE1e7j5OSEK1euKJVdunQJTk5OjRlqk1afPAPA9u3bsXnzZuzYsQNCoVAToTZp6ua5W7duOHnyJI4dO8b98/DwQP/+/XHs2DF07NhRk+E3GfX5eXZ2dkZ2djbXeQSAP//8E+bm5tSxqUF98lxWVlalA6PoUDLGGi/Y94zWPgcbdbmyjjl9+jRzdHRk8fHxLDMzky1ZsoT16dOHFRYWMsYYmzdvHlu3bh23/a+//srs7e3Zzp07WWZmJouOjqZLwVWgbp7j4uKYg4MDS0xMZI8ePeL+FRcXa6sJTYK6ea6MrpZSjbp5zs/PZyKRiEVGRrKsrCz2n//8hw0YMIBt3rxZW01oEtTNc3R0NBOJROzUqVMsOzubXbx4kX388cdsxowZWmpB01BcXMzu3LnD7ty5w2xsbNiuXbvYnTt3WF5eHmOMsXXr1rF58+Zx2ysuBV+9ejXLzMxk+/fv18il4DQtpQZvb288efIE0dHRKCwshJ2dHXbs2MENexYUFCh9E3B2dsa6deuwceNGbNiwAdbW1ti0aRNsbGy01YQmQd08Hzp0COXl5fj666+V6pk+fTpCQkI0GntTom6eSf2om2cLCwvs3LkTUVFRGDVqFDp06IDAwEBMnTpVW01oEtTNc3BwMHg8HjZu3Ii//voLbdu2hbu7O2bNmqWtJjQJt27dQmBgIPc6KioKAODr64tVq1ahsLAQBQUF3PudO3dGXFwcoqKisHfvXnTs2BErVqzAoEGDGjVOHmM0/kYIIYQQ3UFfywghhBCiU6hzQwghhBCdQp0bQgghhOgU6twQQgghRKdQ54YQQgghOoU6N4QQQgjRKdS5IYQQQohOoc4NIYQQQnQKdW4I0aIFCxZg2rRp3OuJEydi5cqVGo8jNTUVAoEAL1680Pixc3NzIRAIcPfu3beqp3Iuq1M5vx4eHti9ezf3WiAQICkp6a3iqMnly5cxfPhwVFRUNEr9lanSFlVy1tgaM+eN4cKFC/jkk0+Unv1F3j3UuSEasWDBAggEAixdurTKexERERAIBFiwYIEWInu3xMTEYMaMGSptq80OSVNVV34vXrwIV1dXAA3X6VJYu3YtgoODuYczxsfHQyAQQCAQwNbWFq6urli4cGGVJ1vXlyptWbx4MVatWtUgx9MVK1asgJ+fHxwdHfHJJ59Ued/V1RX6+vo4ceKEFqIjqqLODdEYCwsLnDlzBmVlZVyZRCLBqVOnYGlpqcXI3o5UKm2wutq0aQNjY+MGq0/bysvLtR2Ckrry21hP3r527Rqys7Ph5eWlVG5sbIyLFy8iOTkZy5cvx4ULFzB//vwGOaYqbWnVqhVat27dIMfTJaNHj4a3t3eN7/v5+WHfvn0ajIioizo3RGPs7e1hYWGBH374gSv74YcfYGFhATs7O6Vt5XI54uLi4OHhgZ49e2LUqFFITEzk3q+oqMCiRYu49728vLBnzx6lOhRD7jt37oRYLEb//v0RERFR6wduTEwMPvnkExw6dAhubm7o1asXZsyYgZcvX1apd8uWLRCLxRg2bBiA1w/mmzFjBvr06YN+/fohODgYubm5SjFHRUWhT58+6N+/P9asWYPKj3arPG0ilUqxdu1auLm5wdHREZ6enjh8+DByc3O5h9f17dtXaeSrrtwBQHJyMry8vNCzZ09MnDgReXl5NeZEQSAQ4ODBg5gyZQp69uyJIUOGKNWrGB04c+YMJkyYAKFQiJMnT0IulyM2Nhaurq7ct+ELFy5UqT8rKwsBAQEQCoUYOXIkfvnlF6Xc1XW+FWJjY/HRRx/B2dkZS5cuVep81jXt9+YUyZAhQwAAn376KQQCASZOnIirV6/CwcEBhYWFSvutXLkS48aNq7HeM2fOwMXFBYaGhkrlPB4P5ubm6NChA9zc3DBx4kRcunQJZWVldeZNKpUiMjISYrEYQqEQ7u7uiIuLU7ktgPK01L///W+IxeIq0y3BwcFYuHAh9zopKQm+vr4QCoUYMmQIYmNjIZPJamw7ABw5cgQjRoyAo6MjxGIxIiMja9x27dq18PLyQq9evTBkyBBs3LhR6Xc2IyMDEydOhEgkgrOzM/z8/HDz5k0AQF5eHoKCgtC3b184OTlhxIgRSE5OrjW2ysLCwjB+/Hh07ty5xm3c3d1x69YtZGdnq1U30Rx6KjjRqNGjRyM+Ph6jRo0CABw9ehR+fn5KH2QAEBcXhxMnTiAiIgLW1ta4evUq5s2bh7Zt26Jfv36Qy+Xo2LEjvv32W7Rp0wbXr1/H0qVLYW5urvSNKzU1Febm5tizZw+ys7Mxa9Ys2NnZYezYsTXGmJ2djbNnz2Lr1q0oLi7G4sWLER4ejvXr13PbXL58GcbGxti1axeA1yMUX375JZycnHDgwAHo6+tj8+bNmDJlCk6cOAEDAwN89913SEhIwDfffIPu3bvju+++w7lz5/DRRx/VGMv8+fORnp6OsLAw2NraIjc3F0+fPoWFhQViYmIQEhKCxMREGBsbo3nz5irlrqCgANOnT8f48eMxduxY3Lp1C6tXr1bp/H377beYO3cuFi9ejOPHj2P27Nn48MMP0b17d26bdevWYcGCBbCzs4OhoSH27t2LXbt2ITIyEnZ2djh69CimTZuGU6dOwdramttvzZo1WLRoEXr06IFdu3YhKCgIP/74I0xNTVU+35cvX4ahoSH27duHvLw8LFy4EKampvV60vPhw4fh7++P3bt3o0ePHmjWrBnatGmDTp064fjx45gyZQqA1+f+5MmTmDdvXo11Xbt2DSNHjqzzmM2bN4dcLodMJsOhQ4dqzdu+ffvw008/YePGjbCwsEBBQQEePnyoclsqGzZsGJYvX47U1FQMGDAAAPDs2TOkpKRg+/btXDtCQ0MRFhaGPn36IDs7G0uWLAEATJ8+vdpjHzx4EKtWrcKcOXPg6uqKly9fIi0trcYcGBkZISoqCu3bt8e9e/ewZMkSGBkZcU9Fnzt3Luzs7BAeHg49PT3cvXuXa09kZCTKy8uxf/9+tGzZEpmZmWjZsiVXt4eHB3x9fRESElLXqaiVpaUl2rVrh2vXrqFLly5vVRdpJIwQDQgNDWXBwcGsqKiIOTo6stzcXJabm8uEQiErKipiwcHBLDQ0lDHGmEQiYb169WJpaWlKdSxatIjNnj27xmNERESwkJAQpWO6u7szmUzGlX399dds5syZNdYRHR3N7Ozs2MOHD7my5ORkZmtryx49esTV6+LiwiQSCbfNsWPHmJeXF5PL5VyZRCJhPXv2ZCkpKYwxxgYOHMi2b9/OvV9eXs5cXV1ZcHAwVzZhwgS2YsUKxhhjWVlZzMbGhv3888/VxnrlyhVmY2PDnj9/rnTMunK3fv165u3trfT+2rVrq9RVmY2NDVu6dKlSmb+/P1u2bBljjLGcnBxmY2PDdu/erbSNWCxmW7ZsUSobPXo0Cw8PV9ovLi6Oe1+Rm23bttUYT3Xnu1+/fqykpIQrO3jwIHNycmIVFRWMMeX8MsaYu7s727Vrl1Ibz507pxTXnTt3lI67bds2Nnz4cO71//3f/zEnJyf26tWrGmPt3bs3S0hIUCo7evQo6927N/f6/v37bOjQoczPz48xVnfeli9fzgIDA5V+5t6kSlsUv5cKwcHBbOHChdzrQ4cOMbFYzOVv0qRJbOvWrUp1HDt2jA0cOLDGtovFYrZhw4Ya338zzurs2LGD+fr6cq9FIhGLj4+vdtuRI0eymJiYGusKDAxk+/btq/H9N0VHR7NRo0bV+P6nn35a67GIdtHIDdGotm3bYvDgwUhISABjDIMHD0bbtm2Vtnnw4AFKS0sxefJkpfLy8nKl6asDBw7g6NGjyM/Ph0QiQXl5OWxtbZX26dGjB7eAE3i9DuHevXu1xmhhYYEOHTpwr0UiEeRyOe7fvw9zc3MAgI2NjdJ6hoyMDGRnZ8PZ2VmpLolEguzsbLx8+RKFhYXo1asX956+vj4cHR2rTE0p3L17F3p6eujbt2+t8b5Jldz98ccf6Nmzp9L7Tk5OKtUvEomq7Fd5kaqjoyP3/+LiYjx69KhKXpydnZGRkVFj3YrcZGVlcWWqnG+BQIAWLVoo1VlSUoKCggJ88MEHKrWxLn5+fvj222+Rnp4OJycnxMfHY/jw4UojBJWVlZVVmZICgJcvX3I/XxKJBL1798aKFStUypuvry8mT56MYcOGYdCgQRg8eDDEYvFbtc3HxwdLlixBeHg4DAwMcPLkSYwYMQJ8/usVDBkZGUhLS8PWrVu5fSoqKiCRSFBaWqqUewAoKirCo0ePuJEgVZw5cwZ79+5FTk4OSkpKIJPJlNZJffHFFwgLC8Px48fh4uKCYcOGcaMngYGBCA8Px8WLF+Hi4oKhQ4cq/YzUNJVZH4aGhigtLW2w+kjDos4N0bjRo0dzc+7Lli2r8n5JSQmA19Mrb3YyAHAditOnT2P16tUIDQ2FSCSCkZERdu7cid9++01pe3195R9xHo9XY2dCHZX/iJeUlMDBwQHr1q2rsm3lzpuqFNNM6lAld42ttg/5+lL1fGuCmZkZ3N3dER8fj06dOiElJQV79+6tdR9TU9Nqr2ozMjJCQkIC+Hw+zM3NuXNeXFxcZxwODg748ccfceHCBVy6dAkzZ86Ei4sLoqOj69cwvJ62CQsLw/nz5yEUCnHt2jWl9TYlJSUICQnB0KFDq+xbXeeturLaXL9+HXPnzkVISAjEYjFatWqF06dPc9O/ABASEoKRI0ciOTkZFy5cQHR0NP75z3/C09MT/v7+EIvFOH/+PH7++Wds27YNoaGh3BqjhvT8+fN6/26TxkedG6JxgwYNQnl5OXg8XrXfNLt37w4DAwPk5+ejX79+1daRlpYGkUiE8ePHc2UNtbivoKAAf/31F9c5SE9PB5/PR9euXWvcx8HBAWfPnoWZmVmNV+OYm5vjt99+40ZiZDIZbt++DXt7+2q3t7GxgVwux9WrV+Hi4lLlfcU6gzfvm6JK7rp3746ffvpJqUzVTkJ6ejo+/fRTpf0qLwZ/k7GxMdq3b4+0tDSleNLS0qqMHqWnp1fJjeL8qnq+//vf/6KsrIzrJKSnp6Nly5awsLBQqX1vqi6/CmPGjMGcOXPQoUMHdO7cGb179661Lnt7e2RmZlYp5/P5sLKyqlKuat6MjY3h7e0Nb29veHl5YcqUKXj27BnatGmjclveZGhoiKFDh+LkyZN48OABunbtCgcHB6V23L9/v9qYq2NsbIwPPvgAly9frnVtmcL169dhaWmJ4OBgriw/P7/Kdl27dkXXrl3x+eefY/bs2Th69Cg8PT0BvB55/eyzz/DZZ59h/fr1+P777xu8cyORSJCTk1Pj7y7RPurcEI3T09PD2bNnuf9XZmxsjMmTJyMqKgqMMfTu3ZtbhGhsbAxfX19YWVnh2LFjSElJ4RZ43rx5E506dXrr+AwNDbFgwQKEhoaiuLgYK1aswPDhw7kpqer4+Phg586dCA4OxowZM9ChQwfk5+fj3LlzmDJlCjp27IjAwEBs374d1tbW6Nq1K3bv3l3rPWo6deoEX19fLFq0CGFhYRAIBMjPz0dRURG8vb3xwQcfgMfj4fz583Bzc4OhoaFKuQsICMB3332H1atXw9/fH7dv30ZCQoJKuUlMTISjoyN69+6NkydP4saNG3XedPDLL79ETEwMunTpAltbW8THxyMjI6PKKNfBgwdhbW2Nbt26Yc+ePXj+/DlGjx4NACqfb6lUisWLFyM4OBh5eXmIiYnBhAkTuGkVdZiZmaF58+ZISUlBx44dYWhoiFatWgF43UE3NjbGli1b8PXXX9dZl1gsVjnHCnXlbdeuXTA3N4ednR34fD4SExNhbm5e7aXdtbWlMh8fH3z11Vf4/fffuYX/Cv/4xz8QFBQES0tLeHl5gc/nIyMjA/fu3atx0XZISAiWLVsGMzMzuLq64tWrV0hLS6u2w2FlZYWCggKcPn0aQqEQ58+fV7rBX1lZGdasWQMvLy906tQJDx8+xM2bN7mRpJUrV8LV1RXW1tZ48eIFUlNTlRa7T5o0CZ6enpgwYUKNeX/w4AFKSkpQWFiIsrIybtpV8cUBeN1pbtasmcrTuUTzqHNDtKKue7nMnDkTbdu2RVxcHHJzc9GqVSvY29sjKCgIABAQEIC7d+9i1qxZ4PF4GDFiBMaNG1ftJcbq6tKlCzw9PTF16lQ8f/4cgwcPrnb67E0tWrTA/v37sW7dOkyfPh2vXr1Chw4dMGDAAK6tkydPRmFhIUJDQ8Hn8zF69Gh4enoqXWZeWXh4ODZs2IDw8HA8e/YMlpaW+OqrrwAAHTp0QEhICNavX4+FCxfi008/xapVq+rMnaWlJWJiYhAVFYX9+/ejZ8+emDVrFhYtWlRnbkJCQnDmzBlERETA3Nwc69evR48ePWrdJzAwEMXFxVi1ahWePHmC7t27Y/PmzUpXSgHAnDlzsG3bNty9exdWVlbYsmULN+yv6vkeMGAArKysMH78eEilUowcObLeV8bo6+sjLCwMmzZtQnR0NPr06cPd24TP58PX1xdxcXFKI1k18fHxwdq1a5GVlYVu3bqpdPy68mZkZIQdO3bgwYMH4PP5EAqF2LZtW7UdudraUtlHH30EExMT3L9/Hz4+PkrvDRo0CFu3bsWmTZuwfft26Ovro1u3bvD396+xHb6+vpBIJNi9ezfWrFmDNm3acLdPqGzIkCGYNGkSIiMjIZVKMXjwYAQHByM2NhbA67w/e/YMoaGhePz4MUxNTTF06FCugymXyxEZGYmHDx/C2NgYgwYNUppWy8nJwdOnT2tOOl5fCv7m1ZuK8/vjjz9ynenTp0/Dx8enyvQ0eXfwWEMsQCBER8TExCApKQnHjx/XdijvHIFAgE2bNuHjjz/WdijvhEWLFuHJkydKi2trs3r1arx69arWe7yQd9+TJ08wfPhwHDlypNZ74RDtopv4EUKIGl6+fIlr167h1KlTaq3lCA4OhqWlJT2TqInLy8vDsmXLqGPzjqNpKUIIUcO0adNw48YNBAQEYODAgSrv17p1a25qkDRdQqEQQqFQ22GQOtC0FCGEEEJ0Ck1LEUIIIUSnUOeGEEIIITqFOjeEEEII0SnUuSGEEEKITqHODSGEEEJ0CnVuCCGEEKJTqHNDCCGEEJ1CnRtCCCGE6JT/B/H3v9jTpGXJAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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7pqkqp0+fxo033qgqW7NmDXr27Kk8rirW1SXLNfWzS72Wmi4mRCRUt27dAAB//PEHgLIEwul04vfff6/2Q8X1Te/o0aNo3769Um61WnHy5En07t37ku/boUMHHDx4EL169WqQ++Vs3rwZkiShT58+NS7n7e2NAQMGYMCAAbBarZg0aRKWL1+ORx55BB4eHvVet8zMTFXMMjMz4XQ6laTBdfai4pkY1zf18typW2hoqGrGncvRo0eV5y9Xly5dsHr1alVZcHBwvdWp4iw3WZaRmZmpzKhyxdXX17dWfbA2vv32W1itVrz55puq+tTmku6uXbtgtVpx/fXXK2WudWRlZVVavrpZfFWVHz9+XDWAv7q+8NJLL6lmMbpidPr0afznP//Bf/7zn0qvGTJkCLp06YLPPvtMqe+xY8cqnc09duyY8nxwcHClbd+lS5cq60RUHY4hokaxa9euKr91ucYhuC5T3HTTTTAYDHj99dcrfft1vb53794wm814//33VevcsGEDLl68WKv7rfy///f/cPbsWXz00UeVnispKbmsG8StWLECP/74IwYMGICwsLBqlzt//rzqscViQXh4OGRZhs1mAwDlfkmXGitSW67p0S5r164FAOVD09fXFwEBAdizZ49quaqmebtTt+TkZKSmpmLv3r1KWVFRET766CO0bdvWrXFQ1fH390fv3r1Vf2q6B4y7ddq0aZNqbM+2bduQk5OjxK5bt27o0KED3nnnnSrHjtXlDsyusxDl+/nFixfxySefXPK127dvR7du3RAUFKSUtW7dGlFRUcrMMZeffvpJNZW+vK+//lo1xT01NRW//fabKtFy9YWKifRVV12l2h6uhOj111+v9GfAgAEAgEWLFilnbrt164bAwECsX79edTuM7du34/fff1dmz3l4eFTa9uUvTRLVBs8QUaN47rnnUFxcjH79+qFz586w2Wz49ddfsXXrVrRt21YZ9NyxY0eMGzcOb7zxBoYPH47+/fvDYrEgLS0NrVq1wtSpU9GyZUs88sgjWLZsGR588EH07dsXx44dw7p16xAXF4dbb731kvW57bbbsHXrVjzzzDPYvXs3rrzySjgcDhw9ehTbtm3D22+/jbi4uBrXYbfb8dlnnwEoOzt16tQpfPvttzh06BB69uyJ+fPn1/j6sWPHIigoCFdeeSUCAwNx9OhRrF27FsnJycr4kdjYWADAv/71LwwYMABmsxk33HBDpUsvtXXy5EmMGzcO1113HVJSUrB582YMGjRI9W36rrvuwooVK/DUU0+hW7du2LNnT5VnUtyp28MPP4wvvvgCDz30EEaOHAl/f39s2rQJJ0+exNKlS6u9dNSQ3K2Tv78/hg8fjqFDhyrT7jt27Ii7774bQNklmeeeew4PPfQQBg0ahKFDh6J169Y4e/Ysdu/eDV9fXyxfvtytOvbp0wdmsxnjxo3DPffcg8LCQnz88ccIDAxETk5Oja/9/vvvK00mAMqmn0+YMAH33nsvhg4divz8fHzwwQeIioqqMpHr0KED7r33Xtx7772wWq1Ys2YNrrjiCjz44IPKMq6+8NxzzyEpKQlGoxEDBw6stm433XRTpbL09HQAZcm5a2yO2WzGtGnTMGvWLNx3330YOHCgMu2+bdu2GD16dI0xIHIHEyJqFNOnT8e2bduwfft2/Pvf/4bNZkNoaCiGDx+O8ePHq+4lMmXKFLRr1w5r167Fv/71L3h5eSE6Olp1V+hJkyahZcuWWLt2LRYuXAh/f3/cfffdeOKJJ1T3IKqO6yzUu+++i88++wxfffUVvLy80K5dO4wcObLaeweVZ7VaMX36dABl35BbtmyJbt264dFHH0W/fv0u+SE/bNgwfP7551i9ejWKiorQpk0bjBw5EhMmTFCWiY+Px5QpU7B+/Xr88MMPcDqd+Oabb+qcEC1ZsgSvvvoqXn75ZZhMJtx3331KG1weffRRnDt3Dl9++SW2bt2K66+/Hm+//XalSxbu1C0oKAjr16/HSy+9hLVr16K0tBTR0dFYvny56h45jcndOo0bNw6HDh3CihUrUFhYiF69euGZZ55R3fW8Z8+e+Pe//4033ngDa9euRVFREYKDgxEfH49hw4a5XcfOnTvjtddew5IlS7Bo0SIEBQXh3nvvRcuWLTF79uxqX3fkyBGcOnVKdRbHpW/fvnjllVewdOlSvPzyywgLC8PChQuxadMmHDlypNLyt99+OwwGA9577z3k5eUhPj4eTz/9NFq1aqUs079/f4wcORJffPEFNm/eDFmWa0yI3DF06FB4enpi5cqVWLx4Mby9vXHTTTfhySefdOvnbIguRZJrM3qMiEindu/ejVGjRuHVV1/FLbfcIro6tbJy5Uq8++67+PHHH2s91uu2225Dy5YtlbE4J0+exI033ojp06dj7NixDVldIk3gGCIiomambdu2mDVrVpXJkM1mg91uV5Xt3r0bBw8exDXXXNNYVSTSHF4yIyJqZlwDlKty9uxZPPDAA7j11lvRqlUrHD16FOvXr1d+QoNIr5gQERHpiL+/P2JjY/Hxxx/j3Llz8Pb2RnJyMqZNm6b8Jh2RHnEMEREREekexxARERGR7jEhIiIiIt1jQkRERES6x4SIiIiIdI8JEREREekeEyIiIiLSPSZEREREpHtMiIiIiEj3mBARERGR7jEhIiIiIt1jQkRERES6x4SIiIiIdI8JEREREekeEyIiIiLSPSZEREREpHtMiIiIiEj3mBARERGR7jEhIiIiIt1jQkRERES6x4SIiIiIdI8JEREREekeEyIiIiLSPSZEREREpHtMiIiIiEj3mBARERGR7jEhIiIiIt1jQkRERES6ZxJdgabA6XTCbrfDYDBAkiTR1SEiIqJakGUZTqcTJpMJBkPN54CYENWC3W5HWlqa6GoQERFRHcTFxcFisdS4jNCE6K233sJ///tfHD16FJ6enkhMTMS0adPQuXNnZZnS0lK88MIL2LJlC6xWK5KSkvDMM88gKChIWSY7Oxtz587F7t274e3tjdtvvx1Tp06FyfR383bv3o0XXngBR44cQUhICMaPH4+hQ4fWqp6urDIuLg5Go7GeWl/G4XDg+PHjCAsLq/d1U80Ye3EYe3EYe3EY+8bncDiQlpZ2ybNDgOCE6KeffsKIESMQFxcHh8OBV155BWPHjsUXX3wBb29vAMCCBQuwfft2LFmyBC1atMCzzz6LiRMnYv369QDKGvvII48gKCgI69evxx9//IEZM2bAbDbjiSeeAACcOHECjzzyCO655x4sXrwYO3fuxJw5cxAcHIzrrrvukvV0XSYzGo0N0okvXrzYYOummjH24jD24jD24jD2YtRmuIvQhGjVqlWqxy+88AJ69eqF/fv34+qrr8bFixfxySefYPHixejVqxeAsgRpwIABSElJQUJCAn788UdkZGRg9erVCAoKQteuXTFlyhQsXrwYEydOhMViwfr169GuXTvMnDkTABAeHo5ffvkF7777bq0SIheHw6H8X5IkGAwGOJ1OyLJcqbz8sjWVu15bsdyVzTqdzlqVG41G5VppxfKKdayuvL7a5BprpfU2uVS1XZtqm5rSdgL+jn1zaVNT2E6u9bj+bQ5tairbqXzsm0ubalOupTbVRFNjiC5evAgA8Pf3BwDs27cPNpsNvXv3VpYJDw9HaGiokhClpKQgKipKdQktKSkJc+fORUZGBmJiYpCSkqIkVOWXWbBggVv1Kz+OKDAwEGFhYcjKykJeXp5SHhISgtDQUBw9ehT5+flKeceOHREUFISDBw+ipKREKXddHty/f7+qs8TExMBisSAlJUVVh4SEBFitVhw4cEApMxgMSExMRH5+PjIyMpRyT09PxMbG4ty5c8jMzFTK/fz8EBkZiTNnzuD06dP13qaIiAj4+/sjNTVV021q3bo1APV2beptairb6ezZs6rYN4c2NbXtlJaW1uzaBDSN7ZSWltbs2gRoczsdPnwYtSXJFVMzQZxOJ8aPH4/8/Hx8+OGHAIDPP/8cs2bNwr59+1TL3nnnnejZsyeefPJJPP3008jOzladbSouLkZCQgJWrFiB5ORk3HzzzRg6dCgeeeQRZZnt27fj4Ycfxm+//QZPT88a6+ZwOJCSkqIaQ1Sf2er58+fh7++vusbZlDPwpvKtAgByc3MREBCg1KGpt6mpbCe73Y5z584psW8ObWoq28lut+P8+fNK7JtDm5rKdnI6nUrsXWNcm3qbalMusk1Wq1VJQC91mVIzZ4jmzZuHI0eOYN26daKrUq2qrvtWN1CrusBXVV7+7NblrEeSpCrLq6uju+Xu1KW+yhu6Ta1atbrsOrpbzu0EmEymKmPflNvUVLaT2WyuFPum3qamsp2MRmOl2Df1NtW2XGttqnLdtV6yAc2fPx/fffcd3nvvPbRp00YpDwoKgs1mU50GA4C8vDwEBwcry+Tm5qqedz2+1DK+vr6XPDvU0BwOB/bv3+/WdU6qH4y9OIy9OIy9OIy9tglNiGRZxvz58/HVV1/hvffeQ/v27VXPd+vWDWazGTt37lTKjh49iuzsbCQkJAAou2Z5+PBh1TXFHTt2wNfXFxEREcoyu3btUq17x44dyjpEK3/NlhoXYy8OYy8OYy8OY69dQhOiefPmYfPmzXj55Zfh4+ODnJwc5OTkKB2mRYsWuOOOO/DCCy9g165d2LdvH2bPno3ExEQlmUlKSkJERASmT5+OgwcP4ocffsCSJUswYsQI5SZM99xzD06cOIEXX3wRv//+Oz744ANs3boVo0ePFtRyIiIi0hKhY4hcg6dHjhypKl+4cKFy08TZs2fDYDBg8uTJqhszuhiNRixfvhxz587FsGHD4OXlhSFDhmDy5MnKMu3bt8dbb72FhQsXYs2aNWjTpg2ee+45t6bcExERUfOlmVlmWuaaZVabUerukmUZ+fn58PPz4++kNTLGXhzGXhzGXhzGvvG58/mtmVlmeiVJknLfJWpcjL04jL04jL04jL22aWKWmZ45HA7s3buXsw4EYOzFYezFYezFYey1jQmRBlS8WRU1HsZeHMZeHMZeHMZeu5gQERERke4xISIiIiLdY0IkmMFgQGxsbLW3I6eGYzAYEBMTw9gLwNiLw9iLw9hrG7eKBlgsHk1uCqbT2Tzu1uC6eSc1PsZeHMZeHMZeuzjtXjCn0wmj0Yil3xzBqT+LRVenVtoGeGFS30jR1bhsTqezwe4vRTVj7MVh7MVh7LWNCZFGnPqzGMfzCkVXg4iISJd4yYyIiIh0jwkRERER6R4TIsE420Acg8GAhIQEbgMBGHtxGHtxGHtt41YhXbNaraKroFuMvTiMvTiMvXYxIRKMt3EXx+l04sCBA9wGAjD24jD24jD22saEiIiIiHSPCRERERHpHhMi0jUObhSHsReHsReHsdcu3phRMN6tVByj0YjExETR1dAlxl4cxl4cxl7bmKoKJsvN4zfBmiJZlnHhwgVuAwEYe3EYe3EYe21jQiQYZxuI43Q6kZGRwW0gAGMvDmMvDmOvbUyIiIiISPeYEBEREZHuMSEiXfP09BRdBd1i7MVh7MVh7LWLs8wE4ywzcYxGI2JjY0VXQ5cYe3EYe3EYe23jGSLBOLhOHKfTidzcXG4DARh7cRh7cRh7bWNCJBinX4ojyzIyMzO5DQRg7MVh7MVh7LWNCRERERHpHhMiIiIi0j0mRKRrfn5+oqugW4y9OIy9OIy9dnGWmWCcZSaO0WhEZGSk6GroEmMvDmMvDmOvbTxDJBhnG4jjdDqRnZ3NbSAAYy8OYy8OY69tTIgE42wDcWRZxunTp7kNBGDsxWHsxWHstY0JEREREekeEyIiIiLSPaEJ0c8//4xx48YhKSkJ0dHR+Prrr1XPR0dHV/nn7bffVpbp27dvpedXrFihWs/BgwcxfPhwxMXFITk5GStXrmyU9tWGJEmiq6BbkiQhMDCQ20AAxl4cxl4cxl7bhM4yKyoqQnR0NO644w5MnDix0vM//vij6vH333+Pp556CjfffLOqfPLkybj77ruVxz4+Psr/CwoKMHbsWPTq1Qvz5s3D4cOHMXv2bPj5+WHYsGH13CL3GQw8SSeKwWBAWFiY6GroEmMvDmMvDmOvbUITouTkZCQnJ1f7fHBwsOrxN998g549e6J9+/aqch8fn0rLumzevBk2mw0LFiyAxWJBZGQk0tPTsXr1ak0kRE6nk0mRIE6nE1lZWejQoQO3QSNj7MVh7MVh7LWtydyHKDc3F9u3b8cLL7xQ6bmVK1fizTffREhICAYNGoTRo0fDZCprWkpKCnr06AGLxaIsn5SUhJUrV+LChQvw9/evdR0cDofyf0mSYDAY4HQ6VTMGXOXll62pXJblsh1DllF+4oHrjGrFyQh1KpeBinMaqiqXXH9VU15x3a5krmKbDAYDJEmqstz1utqUG41GyLJcZXnFuFdXXtN2kmUZeXl5CA0NVe4HVd12aiptcqfviWyTw+FQxb45tKmpbCe73a6KfXNoU1PZTuX7vdlsbhZtqk25ltpUkyaTEG3cuBE+Pj7o37+/qnzkyJGIiYmBv78/9u7di1deeQU5OTmYNWsWgLJEql27dqrXBAUFKc+5kxClpaUp/w8MDERYWBiysrKQl5enlIeEhCA0NBRHjx5Ffn6+Ut6xY0cEBQXh4MGDKCkpUcojIyPh5+eH4pJiFBQUKOU+Pt6QJIOqDAB8fX0hy04UFhYpZZJUVm63O1BcXKyUGwwG+Ph4w2a3oaSkVCk3mYzw8vKC1WpFqdWqlJvNJnh6eqKktAQ2m10p97BYYPGwoKSkGHa7A0XeZdnXhQsXEBAQUKlNERER8Pf3R2pqqmoHiImJgcViQUpKiqpNCQkJsFqtOHDggKruiYmJyM/PR0ZGhlLu6emJ2NhYnDt3DpmZmUq5n58fIiMjcebMGZw+fVopr2k7tW7dGoB6u1a3nZpKm9zpeyLbdPbsWVXsm0Obmtp2SktLa3ZtAprGdkpLS2t2bQK0uZ0OHz6M2pJkjdwQITo6Gq+//jpuuummKp+/5ZZb0KdPHzz99NM1rmfDhg145plnsHfvXlgsFowZMwbt2rXD/PnzlWUyMjIwcOBAbNmyBeHh4Zesm8PhQEpKCuLi4iqdSaiPM0QmkwkzN/yGY3nqJKfseXVdtHCGqFOQD164I75ZnCGqbrs21TY1lW9/NpsNqampSuybQ5uaynay2WxIS0tTYt8c2tRUtpPD4VBizzNEjdMmq9WqJKCX+mWIJnGGaM+ePTh27BiWLFlyyWW7d+8Ou92OkydPonPnzggKCkJubq5qGddj15mi2nIdPMqr7jpwdYGvWK50EElSkpryqpuM4Fa59FdSc5nlFdftantt21qXckmSqiyvLu7ulDudToSEhMBkMlV6vqm2qb7q6G65u20yGo1Vxr4pt6mpbCeTyVQp9k29TU1lO0mSpMTeNdOsqbeptuVaa1OV6671kgJt2LABsbGx6NKlyyWXTU9Ph8FgQGBgIICyU3h79uyBzWZTltmxYwc6derk1uWyhlLdxqWGZzAYEBoaym0gAGMvDmMvDmOvbUK3SmFhIdLT05Geng4AOHnyJNLT05Gdna0sU1BQgG3btuGuu+6q9Pq9e/fi3XffxcGDB3HixAls3rwZCxcuxK233qokO4MHD4bZbMZTTz2FI0eOYMuWLVizZg0eeOCBxmnkJbgz4Ivql8PhwJEjR7gNBGDsxWHsxWHstU3oJbN9+/Zh1KhRyuOFCxcCAIYMGaLMJvviiy8gyzIGDRpU6fUWiwVbtmzBsmXLYLVa0a5dO4wePVqV7LRo0QKrVq3C/PnzMXToUAQEBGDChAmamHJP4pUfhEeNi7EXh7EXh7HXLqEJUc+ePXHo0KEalxk2bFi1yUtsbCw++uijS75Ply5dsG7dujrVkYiIiJo/XsgkIiIi3WNCJBh/00YcSZLQsWNHbgMBGHtxGHtxGHttaxLT7pszzjYQx2AwuH3rBaofjL04jL04jL228dNYMM42EMfhcGD//v3cBgIw9uIw9uIw9trGhIh0rfxt6qlxMfbiMPbiMPbaxYSIiIiIdI8JEREREekeEyLBOKhaHIPBgIiICG4DARh7cRh7cRh7beMsM8E4/VIcSZI08Xt2esTYi8PYi8PYaxvTVME420Ach8OBvXv3chsIwNiLw9iLw9hrGxMi0jWn0ym6CrrF2IvD2IvD2GsXEyIiIiLSPSZEREREpHtMiATjbANxDAYDYmJiuA0EYOzFYezFYey1jVuFdM1isYiugm4x9uIw9uIw9trFhEgwDrATx+l0IiUlhdtAAMZeHMZeHMZe25gQERERke4xISIiIiLdY0JEREREuseESDDONhDHYDAgISGB20AAxl4cxl4cxl7buFVI16xWq+gq6BZjLw5jLw5jr11MiATjbANxnE4nDhw4wG0gAGMvDmMvDmOvbUyIiIiISPeYEBEREZHuMSEiXePgRnEYe3EYe3EYe+0yia6A3hmNRtFV0C2j0YjExETR1dAlxl4cxl4cxl7bmKoKJsuy6CrolizLuHDhAreBAIy9OIy9OIy9tjEhEoyzDcRxOp3IyMjgNhCAsReHsReHsdc2JkRERESke0yIiIiISPeYEJGueXp6iq6CbjH24jD24jD22sVZZoJxlpk4RqMRsbGxoquhS4y9OIy9OIy9tvEMkWAcXCeO0+lEbm4ut4EAjL04jL04jL22MSESjNMvxZFlGZmZmdwGAjD24jD24jD22saEiIiIiHRPaEL0888/Y9y4cUhKSkJ0dDS+/vpr1fMzZ85EdHS06s/YsWNVy/z555+YOnUqrrzySvTo0QOzZ89GYWGhapmDBw9i+PDhiIuLQ3JyMlauXNngbSMiIqKmQ+ig6qKiIkRHR+OOO+7AxIkTq1zmuuuuw8KFC5XHFotF9fy0adOQk5OD1atXw2azYfbs2fjnP/+Jl19+GQBQUFCAsWPHolevXpg3bx4OHz6M2bNnw8/PD8OGDWu4xlGT4OfnJ7oKusXYi8PYi8PYa5fQhCg5ORnJyck1LmOxWBAcHFzlc7///jt++OEHbNiwAXFxcQCAOXPm4OGHH8b06dPRunVrbN68GTabDQsWLIDFYkFkZCTS09OxevVqTSREnGUmjtFoRGRkpOhq6BJjLw5jLw5jr22an3b/008/oVevXvDz88O1116Lxx57DAEBAQCAvXv3ws/PT0mGAKB3794wGAxITU1Fv379kJKSgh49eqjOLCUlJWHlypW4cOEC/P39a10Xh8Oh/F+SJBgMBjidTtUAOVd5+WVrKgdcSZGM8uPsJKns34pj7+pULgMVh/BVVS65/qqmvOK6nU5nlW0yGAyQJKnKctfralNuNBohy3KV5RXjXl15TdsJALKzs9G6dWulDtVtp6bSJnf6nsg22e12nDlzRol9c2hTU9lOdrsdZ8+eVWLfHNrUVLaT0+lUYm8ymZpFm2pTrqU21UTTCdF1112Hfv36oV27djhx4gReeeUVPPTQQ/j3v/8No9GI3NxctGzZUvUak8kEf39/5OTkAAByc3PRrl071TJBQUHKc+4kRGlpacr/AwMDERYWhqysLOTl5SnlISEhCA0NxdGjR5Gfn6+Ud+zYEUFBQTh48CBKSkqU8sjISPj5+aG4uBgFBQVKuY+PNyTJoCoDAF9fX8iyE4WFRUqZJJWV2+0OFBcXK+UGgwE+Pt6w2W0oKSktFyMjvLy8YLVaUWq1KuVmswmenp4oKS2BzWZXyj0sFlg8LCgpKYbd7kCRd1kyceHCBQQEBFRqU0REBPz9/ZGamqraAWJiYmCxWJCSkqJqU0JCAqxWKw4cOKCqe2JiIvLz85GRkaGUe3p6IjY2FufOnUNmZqZS7ufnh8jISJw5cwanT59WymvaTq1bt8bZs2dx9uxZpby67dRU2uRO3xPdpvKxby5takrb6ezZs82uTUDT2E5nz55tdm0CtLmdDh8+jNqSZI3M/4uOjsbrr7+Om266qdplTpw4gZtuugnvvvsuevXqheXLl2Pjxo348ssvVcv16tULkyZNwvDhwzFmzBi0a9cO8+fPV57PyMjAwIEDsWXLFoSHh1+ybg6HAykpKYiLi1MucdVXtirLMkwmE2Zu+A3H8tRJTtnz6rpo4QxRpyAfvHBHfJM/QyTLcrXbtam2qal8+7PZbEhNTVVi3xza1FS2k81mQ1pamhL75tCmprKdHA6HEnuz2dws2lSbcpFtslqtSEtLQ0JCwiWHqGj6DFFF7du3R0BAADIzM9GrVy8EBQXh3LlzqmXsdjsuXLigjDsKCgpCbm6uahnXY9eZotpyHTzKc3WAqpatTbmyESVJSWrKq6rM7XLpr6TmMssrrtvV9tq2tS7lkiRVWV5d3N0pd8W+qu3aVNtUX3V0t7yubaoY++bQptqWi25T+dg3lzbVpo5aaJPrS0B1yzfFNl2qXGttqnLdtV5SA86cOYM///xTSXZcp+H27dunLLNr1y44nU7Ex8cDKDuFt2fPHthsNmWZHTt2oFOnTm5dLmsoUnWZDTU4SZIQGBjIbSAAYy8OYy8OY69tQhOiwsJCpKenIz09HQBw8uRJpKenIzs7G4WFhVi0aBFSUlJw8uRJ7Ny5ExMmTEDHjh1x3XXXAQDCw8Nx3XXX4emnn0Zqaip++eUXPPvssxg4cCBat24NABg8eDDMZjOeeuopHDlyBFu2bMGaNWvwwAMPCGt3edVlu9TwDAYDwsLCuA0EYOzFYezFYey1Tegls3379mHUqFHKY9f9hoYMGYK5c+fi8OHD2LRpEy5evIhWrVqhT58+mDJlimrG2OLFi/Hss8/i/vvvh8FgQP/+/TFnzhzl+RYtWmDVqlWYP38+hg4dioCAAEyYMEETU+6Bv2dqUeNzOp3IyspChw4duA0aGWMvDmMvDmOvbUITop49e+LQoUPVPr9q1apLruOKK65QbsJYnS5dumDdunVu168xaGRMuy7Jsoy8vDy0b99edFV0h7EXh7EXh7HXNqaoREREpHtMiIiIiEj3mBAJxtkG4kiShJCQEG4DARh7cRh7cRh7bWtS9yFqjjiwThyDwYDQ0FDR1dAlxl4cxl4cxl7b+GksmDu/s0L1y+Fw4MiRI9wGAjD24jD24jD22saEiHSt/G/fUONi7MVh7MVh7LWLCRERERHpHhMiIiIi0j0mRIJxtoE4kiShY8eO3AYCMPbiMPbiMPbaxllmgnGWmTgGgwFBQUGiq6FLjL04jL04jL228dNYMM42EMfhcOD333/nNhDA4XBg//79jL0AjL04jL22MSEi3XA61b8bZzQaER4eDqPRKKhGl1axzs1JSUmJ6CroFmMvDmOvXbxkRrphMEhY+u0RnDpfrJQVFRXB29tbYK2q1zbAC5P6RoquBhGRLjAhIl05db4Yx/MKAQCyDBQUFMDXVwbHOBIR6RsvmQnGQdXiSBLg5eXFZEgAg8GAiIgI9n8BGHtxGHtt4xkiwTj9UiyTSbvjh5ozSZLg7+8vuhq6xNiLw9hrG9NUwTjbQBzXJTO5+Y5b1iyHw4G9e/ey/wvA2IvD2GsbEyLSNSZD4jidTtFV0C3GXhzGXruYEBEREZHuMSEiIiIi3WNCJBhnG4gjSYCPjzdnmQlgMBgQExPD/i8AYy8OY69t3Cqka5Kk3V3A38vcJO9UXds6WyyWBq4JVYexF4ex1y5OuxfM6XRq+qcjmrO/b8zoq8mzRD4epirvrq1ltb27ttPpREpKChISEtj/GxljLw5jr21MiIg0rvzdtYmIqGFo93oBERERUSNhQkRERES6x4RIMM42EEeSoNnxQ82dwWBAQkIC+78AjL04jL22cauQrsky7xoritVqFV0F3WLsxWHstYsJkWC8jbs4sgwUFhbx5zsEcDqdOHDgAPu/AIy9OIy9tjEhIiIiIt1jQkRERI2mOd9slJo23oeIdI0DqsXhwFJxRMa+ud5stLbY77WLCZFgvFupOK5ZZtT4jEYjEhMTRVdDl7QQe73ebFQLsafqMVUVTOaIXqHsdofoKuiSLMu4cOEC+78AjL04jL22MSESjLMNxJFloLi4mLPMBHA6ncjIyGD/F4CxF4ex1zahCdHPP/+McePGISkpCdHR0fj666+V52w2G1566SUMHjwYCQkJSEpKwvTp03H27FnVOvr27Yvo6GjVnxUrVqiWOXjwIIYPH464uDgkJydj5cqVjdI+IiIiahqEjiEqKipCdHQ07rjjDkycOFH1XElJCQ4cOIDx48ejS5cuyM/Px/PPP4/x48fj008/VS07efJk3H333cpjHx8f5f8FBQUYO3YsevXqhXnz5uHw4cOYPXs2/Pz8MGzYsIZtIBERETUJQhOi5ORkJCcnV/lcixYtsHr1alXZ008/jbvuugvZ2dkIDQ1Vyn18fBAcHFzlejZv3gybzYYFCxbAYrEgMjIS6enpWL16tdsJkcPx93gTSZJgMBjgdDpV14Nd5eWXralcea0sqy7duGY/VbycU6dyGah4Vaiqcsn1VzXlFdftdDqrbJPBYIAkSVWWu15Xm3Kj0QhZlqssrxj36srLbyfX+8jyX21C2YwXyDLkv0qqa2u9bo/qyqvYTi6qvuH6q5bbSUSbHA7HJbeTh4eH0kfqa3/Sat/TUpscDocq9o3dpvITSRp7f6rLcc+lYl+ty3YqH3s99j0ttKkmTWqWWUFBASRJgp+fn6p85cqVePPNNxESEoJBgwZh9OjRMJnKmpaSkoIePXrAYrEoyyclJWHlypW4cOEC/P39a/3+aWlpyv8DAwMRFhaGrKws5OXlKeUhISEIDQ3F0aNHkZ+fr5R37NgRQUFBOHjwIEpKSpTyqKgotGjRAsUlxSgoKFDKfXy8IUkGVRlQNitKlp0oLCxSylyzpex2B4qL/57KajAY4OPjDZvdhpKSUqXcZDLCy8sLVqsVpeVuI282m+Dp6YmS0hLYbHal3MNigcXDgpKSYtjtDhR5lx0lLly4gICAgEptioiIgL+/P1JTU1U7QExMDCwWC1JSUlRtSkhIgNVqxYEDB1R1T0xMRH5+PjIyMpRyT09PxMbG4ty5c8jMzFTK/fz8EBkZiTNnzuD06dNKuWs7nTlzBqGhoSgqKkJBQYHSJoPBgILCwnLr94DZbEZRUZGq7l5eXjCZjCgsLFAdfBtyO9msLQAAdrtdtf7abicRbXL1jePHjyM8PLza7ZSTk4PS0lJln6qv/UmLfU+rbUpLS2v0Nnl5eSEmJkb5kGrM/amux73S0rJtcejQIRQXF9fLdkpLS9N132vMNh0+fBi1JckaGe4eHR2N119/HTfddFOVz5eWluLee+9Fp06d8PLLLyvlq1evRkxMDPz9/bF371688sorGDp0KGbNmgUAGDNmDNq1a4f58+crr8nIyMDAgQOxZcsWhIeHX7JuDocDKSkpiIuLU77d1Ge2ajQaMfOT33AsV72zA9r6puRad6cgH7xwR3yTPEM085NUHMstVL742Ww2mMwmVys1dYaoT0QQJt8YqdRZWdb1lwbPELn6xqXOENntdpw7dw4BAQFKf+E32sZpk91ux/nz55XYizhDNPOTVBzPK2wSZ4jCAv/u0zW1tTbbyel0KrF3fWnXU98T0Sar1aokoJe6zU2TOENks9kwZcoUyLKMefPmqZ574IEHlP936dIFZrMZzzzzDKZOnao6K1QfjEZjpYBWd5Ot6gJfsfzvjShVeZPA6m4c6Fa5pDrzW+fyiut2tb22ba1LuSRJVZZXF/falJc/iJaUlsLXbK7UtnqJu7vl1W0PN5cXUvcK5a5tVt32kCQJJ06cQGBgoGr7Xu7+VJ/lDdH36ruO7pa7PiQqxl5Um4TsT+6W/6ViG+q6PVyxl/6qtJ76npbaVOW6a72kIDabDY899hiys7PxzjvvXPJGet27d4fdbsfJkycBAEFBQcjNzVUt43ocFBTUMJUmIiKiJkXTCZErGcrMzMS7776LgICAS74mPT0dBoMBgYGBAMquae7Zswc2m01ZZseOHejUqZNb44eIiIio+RKaEBUWFiI9PR3p6ekAgJMnTyI9PR3Z2dmw2WyYPHky9u3bh8WLF8PhcCAnJwc5OTmw/jUgbu/evXj33Xdx8OBBnDhxAps3b8bChQtx6623KsnO4MGDYTab8dRTT+HIkSPYsmUL1qxZo7rURvplMvGnU0SpODmCGg9jLw5jr11CxxDt27cPo0aNUh4vXLgQADBkyBBMnDgR3377LQDgtttuU71uzZo16NmzJywWC7Zs2YJly5bBarWiXbt2GD16tCrZadGiBVatWoX58+dj6NChCAgIwIQJEzRzDyL+lpk4klQ204oan9FoRGRk/f1gJtUeYy8OY69tQhOinj174tChQ9U+X9NzABAbG4uPPvroku/TpUsXrFu3zu36NYby98ehRiYDVqu1bPB9TaMpqd45nU6cOXMGbdq0Yf9vZIy9OIy9tnGLCKaRux7okgyg1Gqt9maI1HBkWcbp06fZ/wVg7MVh7LWNCRERERHpHhMiIiIi0j0mRIJJ1d1pjBqchLLb9nMLND5JklQ3p6PGw9iLw9hrW50SohMnTtR3PXSLA+sEksp+T4cZUeMzGAwICwtj/xeAsReHsde2Om2Vfv36YeTIkfjss89QWlp66RdQtSr+tgs1IhllP2TI8Y2Nzul04vjx4+z/AjD24jD22lanhGjjxo2Ijo7GCy+8gD59+uCf//wnUlNT67tuusDZBuLIAGw2O/MhAWRZRl5eHvu/AIy9OIy9ttUpIeratSvmzJmDH374AQsWLMAff/yB4cOHY9CgQVi9ejXOnTtX3/UkIiIiajCXdSHTZDKhf//+eO211zBt2jRkZmZi0aJFSE5OxvTp0/HHH3/UVz2JiIiIGsxl3ak6LS0Nn3zyCbZs2QIvLy+MGTMGd955J86ePYtly5ZhwoQJ2LBhQ33VtVnibANxJAAeFgvHVAsgSRJCQkLY/wVg7MVh7LWtTgnR6tWr8emnn+LYsWO4/vrrlbNCrpHz7du3xwsvvIC+ffvWa2WbI842EEgCLB4W0bXQJYPBgNDQUNHV0CXGXhzGXtvqlBB9+OGHuOOOOzBkyBC0atWqymVatmyJ559//rIqpwcOh4M/8CqILAMlJcXw9PQCv7A1LofDgaNHj6Jz587s/42MsReHsde2OiVE//3vfy+5jMViwZAhQ+qyeqJGY7c7RFdBt/Lz80VXQbcYe3EYe+2q0/WaTz75BFu3bq1UvnXrVmzcuPGyK0VERETUmOqUEK1YsQIBAQGVygMDA7F8+fLLrhQRERFRY6pTQpSdnY127dpVKg8NDcXp06cvu1J6wtkG4kgAPD09OMtMAEmS0LFjR/Z/ARh7cRh7batTQhQYGIhDhw5VKj948CCuuOKKy62TrnCWmUASYDab+VtmAhgMBgQFBbH/C8DYi8PYa1udtsrAgQPx/PPPY9euXXA4HHA4HNi5cycWLFiAgQMH1ncdmzWHg4N6RZFloLCwCLyLfuNzOBzYv38/+78AjL04jL221WmW2ZQpU3Dq1CmMHj0aJlPZKpxOJ2677TY8/vjj9VpBoobEH1kUp6SkRHQVdIuxF4ex1646JUQWiwVLlizBsWPHcPDgQXh6eiIqKgpt27at7/oRERERNbjL+umOTp06oVOnTvVVFyIiIiIh6pQQORwOfPrpp9i1axfy8vIqXXZYs2ZNvVRODzi4ThxJAry8eJdqEQwGAyIiItj/BWDsxWHsta1OCdHzzz+PjRs3Ijk5GZGRkZxCeBkYO7FMJt4+XwRJkuDv7y+6GrrE2IvD2GtbnRKiL774AkuWLEFycnJ910d3+Ftm4pTNMiuAj48vzxI1MofDgdTUVMTHx7P/NzLGXhzGXtvqdN7ObDajQ4cO9V0XokbHKfficIafOIy9OIy9dtUpIRozZgzWrFkDmZ8mRERE1AzU6ZLZL7/8gt27d+P7779HZGSkci8il2XLltVL5YiIiIgaQ50SIj8/P/Tr16++66JLnG0gjiQBPj7eHD8kgMFgQExMDPu/AIy9OIy9ttUpIVq4cGF914NICEnigUkUi8Uiugq6xdiLw9hrV50/Dex2O3bs2IH169ejoKAAAHD27FkUFhbWW+X0gAPsxJFloKCggAOrBXA6nUhJSWH/F4CxF4ex17Y6nSE6deoUHnzwQZw+fRpWqxV9+vSBr68vVq5cCavVivnz59d3PYmIiIgaTJ3OED3//PPo1q0bfvrpJ3h4eCjl/fr1w65du+qtckRERESNoc6zzD788MNK10Lbtm2Ls2fP1kvFiIiIiBpLnc4QOZ3OKq+BnjlzBj4+PpddKT3hbANxJAnw9eVdqkUwGAxISEhg/xeAsReHsde2Om2VPn364L333lOVFRYWYunSpW79nMfPP/+McePGISkpCdHR0fj6669Vz8uyjFdffRVJSUmIj4/H6NGjcfz4cdUyf/75J6ZOnYorr7wSPXr0wOzZsysN7D548CCGDx+OuLg4JCcnY+XKle41mJotWebgRlGsVqvoKugWYy8OY69ddUqIZs6ciV9//RUDBgyA1WrFtGnT0LdvX5w9exbTpk2r9XqKiooQHR2NZ555psrnV65ciffffx9z587FRx99BC8vL4wdOxalpaXKMtOmTUNGRgZWr16N5cuXY8+ePfjnP/+pPF9QUICxY8ciNDQUn376KaZPn45ly5bh3//+d12aXu8420Ccst8yK+IsMwGcTicOHDjA/i8AYy8OY69tdRpD1KZNG3z22Wf44osvcOjQIRQVFeHOO+/E4MGD4enpWev1JCcnV3tGSZZlrFmzBuPHj8dNN90EAHjxxRfRu3dvfP311xg4cCB+//13/PDDD9iwYQPi4uIAAHPmzMHDDz+M6dOno3Xr1ti8eTNsNhsWLFgAi8WCyMhIpKenY/Xq1Rg2bFhdmk9ERETNTJ0SIgAwmUy47bbb6rMuKidPnkROTg569+6tlLVo0QLdu3fH3r17MXDgQOzduxd+fn5KMgQAvXv3hsFgQGpqKvr164eUlBT06NFDNQA8KSkJK1euxIULF+Dv71/rOjkcDuX/kiTBYDDA6XSqftPNVV5+2ZrKldfKsupMhWtcS8WzF3Uql4GKJ0GqKpdcf1VTXnHdTqezyjYZDAZIklRluet1tSk3Go2QZbnK8opxr668/HZyvY8s/9UmF1mG/FdJdW2t1+1RXXkV26lcFf9e1vVXLbeTiDY5HI5LbifXcmXrqJ/9Sat9T0ttcq3H9W9jt6n8r7w39v5Ul+OeS8W+WpftVD72eux7WmhTTeqUEG3atKnG52+//fa6rFYlJycHABAYGKgqDwwMRG5uLgAgNzcXLVu2VD1vMpng7++vvD43Nxft2rVTLRMUFKQ8505ClJaWpqpHWFgYsrKykJeXp5SHhIQgNDQUR48eRX5+vlLesWNHBAUF4eDBgygpKVHKIyMj4efnh+KSYuUGl4DrJyUMqjKgbBCwLDtRWFiklLkGB9vtDhQXFyvlBoMBPj7esNltKCn5+zKjyWSEl5cXrFYrSstdzzabTfD09ERJaQlsNrtS7mGxwOJhQUlJMex2B4q8y44SFy5cQEBAQKU2RUREwN/fH6mpqaodICYmBhaLBSkpKao2JSQkwGq14sCBA6q6JyYmIj8/HxkZGUq5p6cnYmNjce7cOWRmZirlfn5+iIyMxJkzZ3D69Gml3LWdzpw5g9DQUBQVFaGgoAAeFgvMfyXJBeXGnHl6esBsNqOoqEhVdy8vL5hMRhQWqm/k2JDbyWZtAaDsJqjl11/b7SSiTa6+cfz4cYSHh1e7nVyzUV37VH3tT1rse1ptU1paWqO3ycvLCzExMcqHVGPuT3U97pWWlm2LQ4cOobi4uF62U1pamq77XmO26fDhw6gtSa7DT9ZfffXVqsd2ux3FxcUwm83w8vLCTz/95O4qER0djddff125PPbrr7/i3nvvxQ8//IBWrVopy02ZMgWSJGHJkiVYvnw5Nm7ciC+//FK1rl69emHSpEkYPnw4xowZg3bt2qluFpmRkYGBAwdiy5YtCA8Pv2TdHA4HUlJSEBcXp3y7qe8MfOYnqTiWW1hu+bJ/tfRNybXuTkE+eOGO+CZ5hsgVZy2dTVHKK8S9T0QQJt8YWblvuP7S4BkiV9+ozRkifqPVZ5uMRiNmfpKK43mFTeIMUVjg3326prY2t+3UXNpktVqVBLT82cmq1OkM0c8//1yp7Pjx45g7dy7Gjh1bl1VWEhwcDADIy8tTJUR5eXno0qULgLIzPefOnVO9zm6348KFC8rrg4KClDNKLq7HrjNFtWU0GisFtLrpk9UFvmK5esNWXr66KeFulUsVLhHVsbziul1tr21b61IuSVKV5dXFvTbl5dvhcDhgMlX1vlWupmHLq9sebi4vpO4Vyl3brLrtIUkSLl68CD8/P0jlXni5+1N9ljdE36vvOrpb7vqQyM/PV8VeVJuE7E/ulv+lYhvqsj1kWa4Uez31PS21qcp113rJSwgLC8PUqVPx/PPP18v62rVrh+DgYOzcuVMpKygowG+//YbExEQAUE7D7du3T1lm165dcDqdiI+PB1B2Cm/Pnj2w2WzKMjt27ECnTp3culzWUDjbQBxZBoqLiznLTACn04mMjAz2fwEYe3EYe22r17tDmUwm/PHHH7VevrCwEOnp6UhPTwdQNpA6PT0d2dnZkCQJo0aNwptvvolvvvkGhw4dwvTp09GqVSvlslp4eDiuu+46PP3000hNTcUvv/yCZ599FgMHDkTr1q0BAIMHD4bZbMZTTz2FI0eOYMuWLVizZg0eeOCB+mw6ERERNWF1umT2zTffqB7LsoycnBx88MEHuPLKK2u9nn379mHUqFHK44ULFwIAhgwZghdeeAEPPfQQiouL8c9//hP5+fm46qqr8Pbbb6t+P23x4sV49tlncf/998NgMKB///6YM2eO8nyLFi2watUqzJ8/H0OHDkVAQAAmTJjAKfdERESkqFNC9Oijj6oeS5KEli1b4tprr8WMGTNqvZ6ePXvi0KFD1T4vSRKmTJmCKVOmVLvMFVdcgZdffrnG9+nSpQvWrVtX63qRfvAW+uK4c88yql+MvTiMvXbVKSE6ePBgfddDt9wZ8EX1S5LKpvlS4zMajYiNjRVdDV1i7MVh7LWNX48F4+A6gWSUDbbnoOpG53Q6kZuby/4vAGMvDmOvbXU6Q+Qa61Mbs2bNqstb6EYdbgNF9UQGUFJSCpOvucbptlT/ZFlGZmYmAgICRFdFdxh7cRh7batTQnTgwAGkp6fDbrejU6dOAMruQ2QwGBATE6MsJ1V30wgiIiIiDalTQtS3b1/4+Phg0aJFyr18Lly4gFmzZqFHjx4YM2ZMvVaSiIioOfDy8hJdBapGncYQvfPOO5g6darqxob+/v547LHH8M4779Rb5YgaWlV3qabG4efnJ7oKusXY156/lxlOZ/0MbTAajYiJiWmUyTT1VWc9qdMZooKCgko/mQEA586dQ2G5H8qkS2uKs8xcBwiDoWlfEpUkflsTxWg0IjIyUnQ1dImxd4+PhwkGg4Sl3x7BqfPFl36BBrQN8MKkvtzG7qpTQtSvXz/MmjULM2fOVH4i47fffsOLL76I/v3712sFmzvXj442JU3xAJHQ/grcc00HdaEMWK1WWCyWmn/EiOqd0+nEmTNn0KZNmybX/5s6xr5uTp0vxvG8y/zCz2OOptUpIZo3bx4WLVqEqVOnwm63Ayj71nHnnXdi+vTp9VrB5q4pzzKrlwNEIwm9ovKZIBlAqdUKs8XCY1Mjk2UZp0+fVn5ihxoPYy8OjznaVqeEyMvLC3PnzsX06dORlZUFAOjQoQO8vXmTOyIiImp6Lut8aU5ODnJychAWFgZvb+8mfbaDiIiI9KtOZ4jOnz+Pxx57DLt374YkSfjvf/+L9u3bY/bs2fD398fMmTPru57NFu/VJI4EwGw28dS1AJIkITAwkP1fAMZeHB5ztK1OZ4gWLlwIk8mE7777TvVDdQMGDMAPP/xQb5XTAw5qFEj664cWeXRqdAaDAWFhYez/AjD2AvGYo2l12iP+7//+D08++STatGmjKg8LC0N2dna9VEwv+Js2AslASUkJf8tMAKfTiePHj7P/C8DYC8RjjqbVKSEqKipSnRly+fPPP8umE1KtcdyVODIAm83OY5MAsiwjLy+P/V8Axl4cHnO0rU4JUY8ePbBp0yZVmdPpxNtvv42ePXvWR72IiIiIGk2dBlU/+eSTGD16NPbt2webzYaXXnoJGRkZuHDhAj788MP6riMRERFRg6pTQhQVFYUvv/wSa9euhY+PD4qKitCvXz+MGDECrVq1qu86Nmuc6SGOBMCDN0gTQpIkhISEsP8LwNiLw2OOtrmdENlsNjz44IOYN28exo8f3xB10hXO9BBIAiweHPMmgsFgQGhoqOhq6BJjLxCPOZrm9qex2WzGoUOHGqIuuuRwOERXQbdkGSguLgbHljY+h8OBI0eOsP8LwNiLw2OOttXp9MStt96KDRs21HddiBqd3c4PBVHy8/NFV0G3GHtxeMzRrjqNIXI4HPjwww+xY8cOdOvWDV5e6h/OnDVrVr1UjoiIiKgxuJUQnThxAm3btsXhw4cRExMDADh27JhqGQ7UIyIioqbGrYSof//++PHHH/H+++8DAB577DHMmTMHQUFBDVI5PWACKY4EwNPTgzM+BJAkCR07dmT/F4CxF4fHHG1zKyGqeGfT77//HsXFxfVaIb3hLDOBpLJJAtT4DAYDv0gJwtgLxGOOpl3WpzFv/X75ONNDHFkGCguLOONDAIfDgf3797P/C8DYi8Njjra5lRBJksTTrNSs8AcuxSkpKRFdBd1i7MXhMUe73L5kNnPmTOUHXK1WK+bOnVtpltmyZcvqr4ZEREREDcythGjIkCGqx7feemu9VoaIiIhIBLcSooULFzZUPXSLg6rFkSTAy8sLvArc+AwGAyIiItj/BWDsxeExR9vqdGNGqj8ckyWWyWQUXQVdkiQJ/v7+oquhS4y9WDzmaBe/IgjGmR7iyDJQUFDAGR8COBwO7N27l/1fAMZeHB5ztI0JEekaD0zicLaNOIy9ODzmaBcTIiIiItI9JkRERESke5ofVN23b1+cOnWqUvnw4cPxzDPPYOTIkfjpp59Uzw0bNgzz589XHmdnZ2Pu3LnYvXs3vL29cfvtt2Pq1KkwmcQ3nzM9xJEkwMfHmzM+BDAYDIiJiWH/F4CxF4fHHG0TnxFcwoYNG1SD/44cOYIHHngAt9xyi1J29913Y/Lkycrj8jeKdDgceOSRRxAUFIT169fjjz/+wIwZM2A2m/HEE080TiNIsySJHwqiuG7wSo2PsReHxxzt0vyWadmyJYKDg5U///vf/9ChQwdcc801yjKenp6qZXx9fZXnfvzxR2RkZOCll15C165dkZycjClTpuCDDz6A1WoV0SQVDm4UhzM+xHE6nUhJSWH/F4CxF4fHHG3T/Bmi8qxWKzZv3owHHnhAdf+ezz//HJs3b0ZwcDBuuOEGTJgwQTlLlJKSgqioKNWvOyclJWHu3LnIyMhATExMrd+//JkqSZJgMBjgdDpVP3LrKq84pbW6cuW1sqzaSVzNq7jj1KlcBiruf1WVS66/qimvbieulzq6W+5umyrUV6pQIP9VUl1bRbWpfJ2VZV1/1XI7iWiTw+GA0WistH8AUMpdy5Wto372J4PBAEmSqiwHKn8Bqa7caDRCluUqy2tqU1V111qbXOtx/dvYbTIa/74Pj6aOEe4cO+q6P5U73rueaIg2uWit72lhf6pJk0qIvv76a1y8eFH1EyKDBg1CaGgoWrVqhUOHDmHx4sU4duyY8ntqubm5qmQIgPI4JyfHrfdPS0tT/h8YGIiwsDBkZWUhLy9PKQ8JCUFoaCiOHj2K/Px8pbxjx44ICgrCwYMHVT+sGBkZCT8/PxSXFKOgoEApL7vObFCVAYCvry9k2YnCwiKlTJLKyu12B4qLi5Vyg8EAHx9v2Ow2lJSUKuUmkxFeXl6wWq0oLXeWzGw2wdPTEyWlJbDZ7Eq5h8UCi4cFJSXFsNsdKCnxAQA4HGXLFBUVqTq6l5cXTCYjCgvV34REtslmKysrKSlBQUEBPCwWmP+6bFBQWKgs7+npAbPZrIk22awtAAB2u121/tpuJxFtKvIuO5ofP34c4eHhOHfuHDIzM5Xl/fz8EBkZibNnzwL4e5+qr/0pIiIC/v7+SE1NVbU1JiYGFosFKSkpqjYlJCTAarXiwIEDqu2RmJiI/Px8ZGRklIujJ2JjY6tt05kzZ3D69GmlXOttSktLa/Q2eXl5ISYmRvmQ0tIxorr9yWYtW4fr2FEf+1NBYWGDtsnqV/bRXnH7aaXvAY23Px0+fBi1JckVUzMNGzt2LMxmM5YvX17tMjt37sTo0aPx1VdfoUOHDnj66aeRnZ2NVatWKcsUFxcjISEBK1asQHJy8iXf1+FwICUlBXFxccq3m/o8Q2QymTBzw284lqfeMcqeV9dFC2eI+kQEYfKNkZj5SSqO5xVq5mxKTW3qHf53nY/lFrqaiYKCAvj6+ChvpqUzROXjfCz376RNy2eIOgX54IU74i95hshmsyE1NVXZp7R6NkUL32jru002mw1paWlK7EWcIWqUY0c9HfeqOnZczhmigsJC+Pr4QDI03Bki136otb4nYn+yWq1IS0tDQkKC6uxkVZrMGaJTp05hx44dWLp0aY3Lde/eHQCQmZmJDh06ICgoCKmpqaplcnNzAQDBwcFu1cF18Civupka1QW+YrmyYSWpypkH1c1GcKtcqnTmt07l9VKX+ip3t01VrE9C2bexBou7u+U11N2d5bWwnVz9vLr9w2QyISEhQTngulzu/lSf5ZIkVVleXR3dLRfVJrPZXCn2otqkqWOEG8cOt+viKpekSsechmyT1vqelvanKtdd6yUF+/TTTxEYGIh//OMfNS6Xnp4O4O9kJyEhAYcPH1adYtuxYwd8fX0RERHRYPWlpkGWObBUFC1MatArxl4cHnO0q0kkRE6nE59++iluv/121b2DsrKy8Prrr2Pfvn04efIkvvnmG8yYMQNXX301unTpAqBsAHVERASmT5+OgwcP4ocffsCSJUswYsQITUw95UwPcWQZKCws4owPAZxOJw4cOMD+LwBjLw6POdrWJC6Z7dixA9nZ2bjjjjtU5WazGTt37sSaNWtQVFSEkJAQ9O/fHxMmTFCWMRqNWL58OebOnYthw4bBy8sLQ4YMUd23iIiIiPStSSRESUlJOHToUKXykJAQrF279pKvb9u2LVauXNkQVSMiIqJmoElcMiNqKNUNaKSGx5+OEIexF4fHHO1qEmeImjN3RsBT/XLd84Man9FoRGJiouhq6BJjLw6POdrGrwmCNaHbQDVL5W9gSI1HlmVcuHCB/V8Axl4sHnO0iwmRYJzpIY4sl92kk58Ljc/pdCIjI4P9XwDGXhwec7SNCRERERHpHhMiIiIi0j0mRKRrnG0jjqenp+gq6BZjLw6POdrFWWaCcZaZOJJU9kvU1PiMRiNiY2NFV0OXGHtxeMzRNqaqgnFgo0AyYLPZKv98NDU4p9OJ3Nxc9n8BGHuBeMzRNCZEgnHqqzgygJKSUh6bBJBlGZmZmez/AjD24vCYo21MiIiIiEj3mBARERGR7jEhIl0zmTioXRQ/Pz/RVdAtxl4cHnO0i7PMBOMsM3EkCfDy8hJdDV0yGo2IjIwUXQ1dYuzF4TFH23iGSDDO9BBIBqylVs74EMDpdCI7O5v9XwDGXiAeczSNCZFgnOkhjgyg1GrlsUkAWZZx+vRp9n8BGHtxeMzRNiZEREREpHtMiIiIiEj3mBAJJkmS6CrolgTAbDaBW6DxSZKEwMBA9n8BGHtxeMzRNs4yE4w/9CeQxB+5FMVgMCAsLEx0NXSJsReIxxxN46exYJzpIZAMlJSUcMaHAE6nE8ePH2f/F4CxF4jHHE1jQiQYZ3qIIwOw2ew8NgkgyzLy8vLY/wWQZRlFRUWMvQA85mgbL5kRETVBTqcMg8H90ShGoxExMTENUCOipo0JERFRE2QwSFj67RGcOl/s9muLiorg7e3dALWqWUL7K3DPNR0a/X2JaoMJkWCc6SGOBMDDYuGMDwEkSUJISAj7/2U6db4Yx/MK3XuRDFitVliKZDR25w+9Qt8/W8FjjrYxIRKMs8wEkgCLh0V0LXTJYDAgNDRUdDX0if1eHMZe0/hpLJjD4RBdBd2SZaC4uBgcW9r4HA4Hjhw5wv4vAPu9OIy9tjEhIl2z2/mBLEp+fr7oKugW+704jL12MSEiIiIi3WNCRERERLrHhEgwzrIRRwLg6enBGR8CSJKEjh07sv8LwH4vDmOvbZxlJhhnmQkkAWazWXQtdMlgMCAoKEh0NfSJ/V4cxl7T+GksGGfZiCPLQGFhEWd8COBwOLB//372fwHY78Vh7LWNCRHpGn/gUpySkhLRVdAt9ntxGHvtYkJEREREuqfphGjp0qWIjo5W/bnllluU50tLSzFv3jz07NkTiYmJmDRpEnJzc1XryM7OxsMPP4zu3bujV69eWLRoEex2e2M3hYiIiDRM84OqIyMjsXr1auWx0WhU/r9gwQJs374dS5YsQYsWLfDss89i4sSJWL9+PYCycQqPPPIIgoKCsH79evzxxx+YMWMGzGYznnjiiUZvS1U4qFocSQK8vLzAiU6Nz2AwICIigv1fAPZ7cRh7bdN8QmQ0GhEcHFyp/OLFi/jkk0+wePFi9OrVC0BZgjRgwACkpKQgISEBP/74IzIyMrB69WoEBQWha9eumDJlChYvXoyJEyfCYnHvN2XKDwCVJAkGgwFOpxNyuRFyrvKKg0WrKy//gVB+oJ1rh6k4+K5O5TJQcQxfVeWS669qyqsbCFgvdXS33N02Vaivq9xoNKrj/tdfWmlT+TpXrGNtt5OINjkcDhiNxkr7B+CKuQxfX19lPEV97k+SJFW7n1Ucv1FduauOVZVX16bq6t5QbXJ9OazLdirf7xvyGCH02FFPbapY38vdn1yxb8g2uXB/qlxeE80nRJmZmUhKSoKHhwcSEhIwdepUhIaGYt++fbDZbOjdu7eybHh4OEJDQ5WEKCUlBVFRUarpvUlJSZg7dy4yMjIQExPjVl3S0tKU/wcGBiIsLAxZWVnIy8tTykNCQhAaGoqjR4+qfpqgY8eOCAoKwsGDB1WDSSMjI+Hn54fi4iIUFBQo5T4+3pAkg6oMAHx9fSHLThQWFillklRWbrc7UFxcrJQbDAb4+HjDZrehpKRUKTeZjPDy8oLVakWp1aqUm80meHp6oqS0BDbb35cVPSwWWDwsKCkpht3uQEmJDwDA4ShbpqioSNXRvby8YDIZUVhYoNqpRbbJZisrKykpQUFBATwsFpgtlkp18fT0gNls1kSbbNYWAAC73a5af223k4g2FXmXHc2PHz+O8PBwnDt3DpmZmcryfn5+iIyMRHZ2Ns6ePauU19f+FBERAX9/f6SmpqraGhMTA4vFgpSUFFWbEhISYLVaceDAAdX2SExMRH5+PjIyMsrF0ROxsbHVtunMmTM4ffp0o7TJy8tLOX5p8Rjxd8zUfc917HA6y5bR0jGiujbZrGXrcB07tHSMqK5NVr+yj/aKfVKP+9Phw4dRW5pOiOLj47Fw4UJ06tQJOTk5eP311zFixAh8/vnnyM3Nhdlshp+fn+o1gYGByMnJAQDk5uZWuteJ67FrGXfExcUp38pcN5Tr0KED2rdvryzjKu/cubPqta7yLl26qMpdma6Xpxd8fctnvWX/+vr6VlgPIEmGSuVA2c5RVbnZZIbJt/K9Lyx/JQXKuv/619PDEx4eqFzu6aX612g0ASiFt7d3pToCgI9P5bqLapPZbPmr7p7w9XW4vhCW1cfHR6mca3kttMls8fhreZNq+dpup4rljdEm13uEhYUBAFq2bImAgIBKy7du3Rpnz55V9qn62p9c31Dj4+OrLE9ISKhU7unpWakcKDswV1VeXZvatGmD1q1bV6pjQ7XJxe3t5OODgsJCVb+v72NExXJXv3A9bzCUHUe1dIyork2u/bD8saN8myrWvcY2ybIS+4Zsk+WvOlfXJ/W0P0VFRalOZtRE0wlRcnKy8v8uXbqge/fuuOGGG7B161Z4eno2en2MRqNqDBNQ/RigistVV66czpOkKq8rV3et2a1yqdKZ3zqV10td6qvc3TZVtT7574KK76GpNrm5vBa2k6ufV7d/uMor7lOXuz/VZ7kkSVWWX6pNtS2vr7q7u52UJ8r3ex4j3Dt2uFuXv8plSJUWasg2cX+qvrzKddd6SQ3w8/NTTpkFBQXBZrNV+sXsvLw8ZcxRUFBQpVlnrsdVjUsiIiIifWpSCVFhYSFOnDiB4OBgdOvWDWazGTt37lSeP3r0KLKzs5XTcgkJCTh8+LDqeuOOHTvg6+uLiIiIxq5+lTjLRhxJcl3fF10T/TEYDIiJiWH/F4D9XhzGXts0fcls0aJFuOGGGxAaGoo//vgDS5cuhcFgwKBBg9CiRQvccccdeOGFF+Dv7w9fX18899xzSExMVBKipKQkREREYPr06XjyySeRk5ODJUuWYMSIEW7PMKPmSZL4gSwK90Fx2O/FYey1S9MJ0ZkzZ/DEE0/gzz//RMuWLXHVVVfho48+QsuWLQEAs2fPhsFgwOTJk2G1WpGUlIRnnnlGeb3RaMTy5csxd+5cDBs2DF5eXhgyZAgmT54sqkmVOJ1Ot65xUv2R5bIZOr6+vvzG1sicTqcyG5T9v3Gx34vD2GubphOif/3rXzU+7+HhgWeeeUaVBFXUtm1brFy5sr6rRkRERM0Iz90RERGR7jEhIiIiIt1jQiQYZ9mI47orLK/lNz6DwYCEhAT2fwHY78Vh7LWNRyPSNVl2XnohahDWcj81QI2L/V4cxl67mBAJVvHH7qjxyDJQWFhU7Q9PUsNxOp04cOAA+78A7PfiMPbaxoSIiIiIdI8JEREREekeEyLSNQ5uFIcDqsVhvxeHsdcuTd+YUQ94l15xXDM+qPEZjUYkJiaKroYusd+Lw9hrG7+iCSZzdJ1QdrtDdBV0SZZlXLhwgf1fEPZ7cRh77WJCJBhn2Ygjy0BxcTFnfAjgdDqRkZHB/i8A+704jL22MSEiIiIi3WNCRERERLrHhIh0jTOdxPH09BRdBd1ivxeHsdcuzjITjLPMxJEkwMfHW3Q1dMloNCI2NlZ0NXSJ/V4cxl7bmKoKxkGlAsmAzWYDOMCx0TmdTuTm5rL/i8B+Lw5jr2lMiATjtGNxZAAlJaU8NgkgyzIyMzPZ/wVgvxeHsdc2JkRERESke0yIiIiISPeYEJGumUwc1C6Kn5+f6CroFvu9OIy9dnGWmWCcZSaOJAFeXl6iq6FLRqMRkZGRoquhS+z34jD22sYzRIJxlo1AMmAttXLGhwBOpxPZ2dns/yKw34vD2GsaEyLBOMtGHBlAqdXKY5MAsizj9OnT7P8CsN+Lw9hrGxMiIiIi0j0mRERERKR7TIgEkyRJdBV0SwJgNpvALdD4JElCYGAg+78A7PfiMPbaxllmgvGH/gSS+AOjohgMBoSFhYmuhj6x34vD2GsaP40F4ywbgWSgpKSEMz4EcDqdOH78OPu/COz34jD2msaESDDOshFHBmCz2XlsEkCWZeTl5bH/C8B+Lw5jr21MiIiIiEj3mBARERGR7jEhEoyzbMSRAHhYLJzxIYAkSQgJCWH/F4D9XhzGXts4y0wwzjITSAIsHhbRtdAlg8GA0NBQ0dXQJ/Z7cRh7TeOnsWAOh0N0FXRLloHi4mJwXG/jczgcOHLkCPu/AOz34jD22qbphOitt97CHXfcgcTERPTq1QsTJkzA0aNHVcuMHDkS0dHRqj///Oc/VctkZ2fj4YcfRvfu3dGrVy8sWrQIdru9MZtCGmW38wNZlPz8fNFV0C32e3EYe+3S9CWzn376CSNGjEBcXBwcDgdeeeUVjB07Fl988QW8vb2V5e6++25MnjxZeezl5aX83+Fw4JFHHkFQUBDWr1+PP/74AzNmzIDZbMYTTzzRqO0hIiIibdJ0QrRq1SrV4xdeeAG9evXC/v37cfXVVyvlnp6eCA4OrnIdP/74IzIyMrB69WoEBQWha9eumDJlChYvXoyJEyfCYuH1XCIiIr3TdEJU0cWLFwEA/v7+qvLPP/8cmzdvRnBwMG644QZMmDBBOUuUkpKCqKgoBAUFKcsnJSVh7ty5yMjIQExMTK3fv/x4B0mSYDAY4HQ6VTeXc5VXHBtRXfnfZNV1Zdfkm4rXmutULle+MWpV5ZLrr2rKq7vuXS91dLfc3TZVqK/0V7GnhwfKYi+pltdKm8rXuVKbarmdRLTJ4XDAaDRW2j8AwGg0QpZltG/fXrlTdX3tTwaDAZIkVVkOVL4zfHXlrjpWVV5dm6qre0O1yWg0AnB/OwGyqt835DFC6LGjntpUsb6Xtz/9HXvXyhuiTS7cn2r6zK2sySRETqcTCxYswJVXXomoqCilfNCgQQgNDUWrVq1w6NAhLF68GMeOHcOyZcsAALm5uapkCIDyOCcnx606pKWlKf8PDAxEWFgYsrKykJeXp5SHhIQgNDQUR48eVY2R6NixI4KCgnDw4MGyW7f/JSoqCi1atEBxcTEKCgqUch8fb0iSQVUGAL6+vpBlJwoLi5QySSort9sdKC4uVsoNBgN8fLxhs9tQUlKqlJtMRnh5ecFqtaLUalXKzWYTPD09UVJaApvt7zFWHhYLLB4WlJQUw253oKTEBwDgcJQtU1RUpOroXl5eMJmMKCwsUO3UIttks5WVlZSUoKCgQGmT3WFHSenf6/H09IDZbNZEm2zWFgAAu92uWn9tt5OINhV5lx3Njx8/jvDwcJw7dw6ZmZnK8n5+foiMjMQff/yB06dP48SJEwDqb3+KiIiAv78/UlNTVW2NiYmBxWJBSkqKqk0JCQmwWq04cOCAanskJiYiPz8fGRkZ5eLoidjY2GrbdObMGZw+fVopb8g2eXl5KV/m6tz3Sksb7Bjxd8zUfc917HA6y5bR0jGiujbZrGXrcB076mV/Ki1t0DZZ/co+2vPy8lSfc8HBwQgODsapU6dU9QkJCUFAQACOHz+O0nLHw/bt26NFixY4cuSIqq2dO3eG2WzGoUOHUF50dDRsNptqrK/BYECXLl1QUFCg7O8A4OHhgfDwcFy4cEHZb+x2O7y8vOp1fzp8+DBqq8kkRPPmzcORI0ewbt06VfmwYcOU/0dHRyM4OBijR49GVlYWOnToUK91iIuLU76Vue6f0qFDB7Rv315ZxlXeuXNn1Wtd5V26dFGVuzJdL08v+PqWz3rL/vX19a2wHkCSDJXKgbKdo6pys8kMk6+5UrnFYoG53CVD15chTw9PeHigcrmnl+pfo9EEoFQ1nqt83X18KtddVJvMZstfdfeEr6+j7AuhXJZo+/r4KJVzLa+FNpktHn8tb1ItX9vtVLG8Mdrkeg/XD7e2bNkSAQEBlZYPDg7GuXPnEBUVBaPRWG/7k+sbanx8fJXlCQkJlco9PT0rlQNliU5V5dW1qU2bNmjdunWlOjZUm1zc3k4+PigqLoK3l7eysev7GFGx3NUvXM8bDGXHUS0dI6prk2s/LH/sKN+minWvsU2yrMS+IdsU7O8Lp1NWEqCKqvtsDA8Pr7K8Yp90qeoKi9ForLK8RYsWVZYHBAQo+5PTKUOWyxKv+tqfoqKiVCczatIkEqL58+fju+++w9q1a9GmTZsal+3evTsAIDMzEx06dEBQUBBSU1NVy+Tm5gJAteOOqmM0GpWEyKW6+whVXK66cuV0niSVO6X9t+ruW+dWuVTpzG+dyuulLvVV7m6bqlqfXLYDVhV7TbXJzeW1sJ1c/by6/cNgMKC0tLTSPnW5+1N9lkuSVGV5TW1yp7y+6u7udoIkVe73PEa4d+xwty5/lcv4O/Z1Wk8t6+7jYYLBIGHpt0dw6nxxVa/QnLYBXpjUNxLApY8dVXF3v6mKphMiWZbx7LPP4quvvsL777+vygqrk56eDuDvZCchIQHLly9HXl4eAgMDAQA7duyAr68vIiIiGq7yREREAp06X4zjeYWiq9FkaDohmjdvHv7zn//gjTfegI+Pj3IttEWLFvD09ERWVhY+//xzJCcn44orrsChQ4ewcOFCXH311copvqSkJERERGD69Ol48sknkZOTgyVLlmDEiBGcYUZEREQANJ4QffjhhwDKbr5Y3sKFCzF06FCYzWbs3LkTa9asQVFREUJCQtC/f39MmDBBWdZoNGL58uWYO3cuhg0bBi8vLwwZMkR13yKR+NMd4khS2UDIai8tUIMxGAyIiIhg/xeA/V4cxl7bNJ0QVRzBXlFISAjWrl17yfW0bdsWK1eurK9q1Sv+uKVYJlPtry9T/ZEkqdLtM6jxsN+Lw9hrF7+eCcbfchJHlsumLPN3hRqfw+HA3r172f8FYL8Xh7HXNiZEpGs8MIlT8SZt1HjY78Vh7LWLCRERERHpHhMiIiIi0j0mRIJxlo04kuS6rb7omuiPwWBATEwM+78A7PfiMPbaxqMR6ZokcRcQhfcBE4f9XhzGXru4ZQTjwFJxOONDHKfTiZSUFPZ/AdjvxWHstY0JEREREekeEyIiIiLSPSZEREREpHtMiATjLBtxJAnw9fXljA8BDAYDEhIS2P8FYL8Xh7HXNh6NSNdkmYN6RbFaraKroFvs9+Iw9trFhEgwzrIRR5aBwsIizvgQwOl04sCBA+z/ArDfi8PYaxsTIiIiItI9JkRERESke0yISNc4uFEcDqgWh/1eHMZeu0yiK6B3RqNRdBV0yzXjgxqf0WhEYmKi6GroEvu9OIy9tvErmmAyR9cJZbc7RFdBl2RZxoULF9j/BWG/F4ex1y4mRIJxlo04sgwUFxdzxocATqcTGRkZ7P8CsN+Lw9hrGxMiIiIi0j0mREQkhJeXl+gqEBEpmBCRrnGmU/3y9zLD6bz09QCj0YiYmBjNTCqoTZ2bE/Z7cRh77eIsM8G08oGgR5IE+Ph4i65Gs+LjYYLBIGHpt0dw6nyx6OrUStsAL0zqGym6Go2G/V4cxl7bmBAJ5nQ6+Y1BFBmw2W0wm8wA7w1Sr06dL8bxvMLqF2DsxWHsxWHsNY2fxIJx2rE4MoCSklJwCzQ+xl4cxl4cxl7bmBARERGR7jEhIiIiIt1jQkS6ZjJxULsojL04jL04jL12cVC1YJxlJo4k8V44ojD24jD24jD22sYzRILxpwsEkgFrqRUc4SgAYy8OYy8OY69pTIgE4ywzcWQApVYrj00CMPbiMPbiMPbaxoSIiIiIdI8JEREREekeEyLBJIm3KxVFAmA2m3jDWAEYe3EYe3EYe23jLDPB+LMdAkmAp6en6FroE2MvDmMvDmOvabr6NP7ggw/Qt29fxMXF4a677kJqaqroKnGWmUgyUFJSwhkfIjD24jD24jD2mqabhGjLli1YuHAhHn30UWzcuBFdunTB2LFjkZeXJ7RenGUmjgzAZrPz2CQAYy8OYy8OY69tukmIVq9ejbvvvht33HEHIiIiMG/ePHh6euKTTz4RXTUiIiISTBdjiKxWK/bv349HHnlEKTMYDOjduzf27t17yde7zuJYrVblztKSJMFgMMDpdKrO8rjKHQ6Hah3Vlbte2+EKD5ikpvG9oU0LMxwOBzpc4dnk61zsK8HL07PsFrIa05ziXJmsmdiHXuEJh8Oh2jeNRiNkWa50SdtoNFba56srr69jhMFggCRJlcqNRiM6BNShb8jiYq/0j7rUW5B63Q8bKfZN8dgR6v/3flif+5PVagVQu6sxkqyDazZnz57F9ddfj/Xr1yMxMVEpf/HFF/Hzzz/j448/rvH1VqsVaWlpDV1NIiIiagBxcXGwWCw1LqOLM0SXy2QyIS4uTvmmRkRERNrnOtNrMl063dFFQhQQEACj0VhpAHVeXh6CgoIu+XqDwXDJzJKIiIiaLl0MqrZYLIiNjcXOnTuVMqfTiZ07d6ouoREREZE+6eIMEQA88MADmDFjBrp164b4+Hi89957KC4uxtChQ0VXjYiIiATTTUI0YMAAnDt3Dq+99hpycnLQtWtXvP3227W6ZEZERETNmy5mmRERERHVRBdjiIiIiIhqwoSIiIiIdI8JEREREekeEyIiIiLSPSZEjeCDDz5A3759ERcXh7vuugupqak1Lr9161bccsstiIuLw+DBg7F9+/ZGqmnz407sP/roIwwfPhxXX301rr76aowePfqS24qq526/d/niiy8QHR2NCRMmNHANmy93Y5+fn4958+YhKSkJ3bp1w80338zjTh25G/t3330XN998M+Lj45GcnIwFCxagtLS0kWpLKjI1qC+++EKOjY2VN2zYIB85ckSeM2eO3KNHDzk3N7fK5X/55Re5a9eu8sqVK+WMjAz5X//6lxwbGysfOnSokWve9Lkb+yeeeEJeu3atfODAATkjI0OeOXOmfNVVV8lnzpxp5Jo3fe7G3uXEiRPyddddJw8fPlweP358I9W2eXE39qWlpfLQoUPlhx56SN6zZ4984sQJeffu3XJ6enoj17zpczf2mzdvlrt16yZv3rxZPnHihPzDDz/Iffr0kRcsWNDINSdZlmUmRA3szjvvlOfNm6c8djgcclJSkvzWW29VufyUKVPkhx9+WFV21113yU8//XSD1rM5cjf2FdntdjkxMVHeuHFjA9Ww+apL7O12uzxs2DD5o48+kmfMmMGEqI7cjf26devkG2+8UbZarY1VxWbL3djPmzdPHjVqlKps4cKF8j333NOg9aSq8ZJZA7Jardi/fz969+6tlBkMBvTu3Rt79+6t8jUpKSno1auXqiwpKQkpKSkNWdVmpy6xr6i4uBh2ux3+/v4NVc1mqa6xf/311xEYGIi77rqrMarZLNUl9t9++y0SEhIwf/589O7dG4MGDcLy5cvhcDgaq9rNQl1in5iYiP379yuX1U6cOIHt27cjOTm5UepMarq5U7UI58+fh8PhQGBgoKo8MDAQR48erfI1ubm5le6eHRgYiNzc3AarZ3NUl9hXtHjxYrRq1Up1gKNLq0vs9+zZgw0bNmDTpk2NUMPmqy6xP3HiBHbt2oXBgwdjxYoVyMrKwrx582C32zFx4sTGqHazUJfYDx48GOfPn8fw4cMhyzLsdjvuuecejBs3rjGqTBXwDBFRFVasWIEtW7Zg2bJl8PDwEF2dZq2goADTp0/Hs88+i5YtW4quju7IsozAwEA8++yz6NatGwYMGIBx48Zh/fr1oqvW7O3evRtvvfUWnnnmGXz66adYtmwZtm/fjtdff1101XSJZ4gaUEBAAIxGI/Ly8lTleXl51f6GWlBQUKWzQTUtT1WrS+xdVq1ahRUrVmD16tXo0qVLQ1azWXI39idOnMCpU6cwfvx4pczpdAIAYmJisG3bNnTo0KFhK91M1KXfBwcHw2QywWg0KmWdO3dGTk4OrFYrLBZLg9a5uahL7F999VXceuutymXi6OhoFBUV4Z///CfGjx8Pg4HnLBoTo92ALBYLYmNjsXPnTqXM6XRi586dSExMrPI1CQkJ2LVrl6psx44dSEhIaMiqNjt1iT0ArFy5Em+88QbefvttxMXFNUZVmx13Y9+5c2d8/vnn2LRpk/Knb9++6NmzJzZt2oQ2bdo0ZvWbtLr0+yuvvBJZWVlKEgoAx48fR3BwMJMhN9Ql9iUlJZWSHldiKvNnRhsdzxA1sAceeAAzZsxAt27dEB8fj/feew/FxcUYOnQoAGD69Olo3bo1pk6dCgAYNWoURo4ciXfeeQfJycnYsmUL9u3bh/nz54tsRpPkbuxXrFiB1157DS+//DLatm2LnJwcAIC3tzd8fHyEtaMpcif2Hh4eiIqKUr3ez88PACqV06W52+/vvfderF27Fs8//zzuu+8+ZGZm4q233sLIkSNFNqNJcjf2N9xwA1avXo2YmBjEx8cjKysLr776Km644QbVGTtqHEyIGtiAAQNw7tw5vPbaa8jJyUHXrl3x9ttvK6dQT58+rfqGcOWVV2Lx4sVYsmQJXnnlFYSFheH111/nB0MduBv79evXw2azYfLkyar1TJw4EZMmTWrUujd17sae6o+7sQ8JCcGqVauwcOFC3HrrrWjdujVGjRqFhx56SFQTmix3Yz9+/HhIkoQlS5bg7NmzaNmyJW644QY8/vjjopqga5LM83JERESkc/yKRkRERLrHhIiIiIh0jwkRERER6R4TIiIiItI9JkRERESke0yIiIiISPeYEBEREZHuMSEiIiIi3WNCREQNZubMmZgwYYLyeOTIkXj++ecbvR67d+9GdHQ08vPzL2s9n376KXr06FHvyxKRePzpDiKdmTlzJjZu3AgAMJvNCAkJwW233YZx48bBZGrYQ8LSpUtr/R67d+/GqFGj8PPPPyu/bdaQ+vbti1OnTgEAPD090aFDB4waNUr5JXKg7KcZkpOTG6wOJ06cwJIlS7B7925cuHABAQEBiI2NxbRp0xAeHt5g70tETIiIdOm6667DwoULYbVasX37dsyfPx9msxmPPPJIpWWtVmu9/er5FVdcUS/raSiTJ0/G3XffjZKSEmzduhVz5sxBq1atlCTI09MTnp6eDfLeNpsNY8aMQadOnbBs2TIEBwfjzJkz+P7773Hx4sUGeU/X+5rN5gZbP1FTwUtmRDpksVgQHByMtm3bYvjw4ejduze+/fZbAH9f5nrzzTeRlJSEW265BUDZD1NOmTIFPXr0wDXXXIPx48fj5MmTyjodDgcWLlyIHj16oGfPnnjxxRdR8acSK14ys1qteOmll5CcnIxu3bqhX79++Pjjj3Hy5EmMGjUKAHD11VcjOjoaM2fOBAA4nU689dZb6Nu3L+Lj43Hrrbdi27ZtqvfZvn07br75ZsTHx2PkyJHKmZ9L8fHxQXBwMNq3b4+HH34YV1xxBXbs2KE8X/Ey2MGDBzFy5EgkJibiyiuvxNChQ5GWllblus+dO4ehQ4fi0UcfhdVqrfR8RkYGsrKy8MwzzyAhIQFt27bFVVddhccffxwJCQnKcmfOnMETTzyBa665BgkJCRg6dCh+++035fl169bhpptuQrdu3XDzzTdj06ZNqveJjo7GunXrMG7cOCQkJGD58uUAgK+//hpDhgxBXFwcbrzxRixbtgx2u71WcSNqDniGiIjg4eGBP//8U3m8c+dO+Pr6YvXq1QDKziKMHTsWCQkJ+OCDD2AymfDGG2/gwQcfxObNm2GxWPDOO+9g48aNWLBgAcLDw/HOO+/gq6++wrXXXlvt+06fPh0pKSmYM2cOunTpgpMnT+L8+fMICQnB0qVLMWnSJGzbtg2+vr7KmZm33noLmzdvxrx58xAWFoaff/4ZTz75JFq2bIlrrrkGp0+fxsSJEzFixAjcfffd2LdvHxYtWuRWPJxOJ7766itcuHChxrMn06ZNQ9euXTF37lwYjUakp6dXufzp06fxwAMPICEhAc8//zyMRmOlZVq2bAmDwYAvv/wS999/f5XLFBYW4r777kPr1q3xxhtvIDg4GPv374fT6QQAfPXVV1iwYAFmzZqF3r1747vvvsPs2bPRpk0b1XZYtmwZpk6diqeeegpGoxF79uzBjBkzMGfOHPTo0QNZWVl4+umnAQATJ050K3ZETRUTIiIdk2UZO3fuxI8//oj77rtPKff29sZzzz2nXCr77LPP4HQ68fzzz0OSJADAwoULcfXVV+Onn35CUlIS3nvvPTz88MPo378/AGDevHn48ccfq33vY8eOYevWrVi9ejV69+4NAGjfvr3yvL+/PwAgMDBQGUNktVrx1ltvYfXq1UhMTFRe88svv+Df//43rrnmGnz44Yfo0KGDckapc+fOOHz4MFauXHnJeCxevBivvvoqrFYr7HY7rrjiCtUYooqys7MxduxYZXxPWFhYpWWOHj2KMWPG4KabbsJTTz2lxK+i1q1bY86cOXjppZewbNkydOvWDT179sStt96qxOU///kPzp07hw0bNiiXHzt27KisY9WqVRgyZAhGjBgBAOjUqRNSUlLwzjvvqBKiQYMG4Y477lAez549Gw8//DCGDBkCoCymU6ZMwUsvvcSEiHSDCRGRDn333XdITEyEzWaDLMsYNGgQJk2apDwfFRWlGjd08OBBZGVl4corr1Stp7S0FFlZWbh48SJycnLQvXt35TmTyYRu3bpVumzmkp6eDqPRiKuvvrrW9c7MzERxcTHGjBmjKrfZbOjatSsA4Pfff0d8fLzq+fKXnGoyduxYDB06FDk5OXjxxRcxfPhwVcJR0QMPPIA5c+bgs88+Q+/evXHLLbegQ4cOyvMlJSUYMWIEBg0ahKeeeuqS7z9ixAjcdttt+Omnn/Dbb79h27ZteOutt/Dmm2+iT58+SE9PR0xMTLVjsY4ePYphw4apyq688kqsWbNGVdatWzfV44MHD+LXX39VLp8BZZdAS0tLUVxcDC8vr0vWnaipY0JEpEM9e/bE3LlzYTab0apVq0ozvyp+ABYVFSE2NhaLFy+utK6WLVvWqQ51GZxcVFQEoOyyWevWrVXP1cfA74CAAHTs2BEdO3bEq6++isGDB6Nbt26IiIiocvlJkyZh0KBB2L59O77//nu89tpr+Ne//oV+/fopdXJdunrwwQcr1bkqvr6+6Nu3L/r27YvHHnsMY8eOVRKi+hrQ7e3trXpcVFSESZMmKWf3yvPw8KiX9yTSOg6qJtIhLy8vdOzYEaGhobWaBh8bG4vMzEwEBgYqCYPrT4sWLdCiRQsEBwerBvfa7Xbs37+/2nVGRUXB6XTi559/rvJ511gch8OhlIWHh8NisSA7O7tSPUJCQpRlKg5sLl+v2goJCcGAAQPw8ssv17hcp06dMHr0aLzzzjvo378/PvnkE+U5g8GAF198EbGxsRg1ahTOnj3rVh0kSULnzp2VRDA6Ohrp6emq8V7lde7cGb/++quq7Ndff602oXOJiYnBsWPHKsW0Y8eOMBj4MUH6wJ5ORJc0ePBgBAQEYPz48dizZw9OnDiB3bt347nnnsOZM2cAAKNGjcLKlSvx9ddf4/fff8e8efNqvBFiu3btMGTIEMyePRtff/21ss4tW7YAANq2bQtJkvDdd9/h3LlzKCwshK+vL8aMGYOFCxdi48aNyMrKwv79+/H+++8r91a65557cPz4cSxatAhHjx7F559/rjznrlGjRuF///tflTPHSkpKMH/+fOzevRunTp3CL7/8grS0tEr3CzIajVi8eDGio6Nx//33Iycnp8r3Sk9Px/jx47Ft2zZkZGQgMzMTH3/8MT755BPceOONAICBAwciKCgIjz76KH755RecOHECX375Jfbu3QsAePDBB7Fx40asW7cOx48fx+rVq/HVV19VusRY0aOPPorPPvsMy5Ytw5EjR/D777/jiy++wL/+9a+6hI2oSeIlMyK6JC8vL6xduxaLFy/GxIkTUVhYiNatW6NXr17w9fUFAIwZMwY5OTmYMWMGDAYD7rjjDvTr16/Ge+jMnTsXr7zyCubOnYs///wToaGhyr2QWrdujUmTJuHll1/GrFmzcPvtt+OFF17AY489hpYtW+Ktt97CyZMn0aJFC8TExGDcuHEAgNDQUCxduhQLFy7E2rVrER8fj8cffxyzZ892u90RERHo06cPXnvttUqDsg0GA/7880/MmDEDubm5CAgIQP/+/TF58uRK6zGZTHjllVfw+OOP4/7778f777+PwMBA1TKtW7dG27Zt8frrr+PkyZOQJAlt27bFpEmTMHr0aABQZvMtWrQIDz/8MBwOB8LDw/HMM88AAG666SbMnj0b77zzDhYsWIC2bdtiwYIF6NmzZ43tvO6667B8+XK8/vrrWLlyJUwmEzp37lzjgHKi5kaSqxvxSERERKQTvGRGREREuseEiIiIiHSPCRERERHpHhMiIiIi0j0mRERERKR7TIiIiIhI95gQERERke4xISIiIiLdY0JEREREuseEiIiIiHSPCRERERHp3v8HDfFJot7I3XkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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rUhnxzljLxIkTadKkicF9myMrrTzHMTZzplWrVmzevJktW7awY8cOVqxYweLFi5k6dWqZd4NQ9ndaFhs2bGDKlCl6tsLzxVSK/qDNRUXupIuTlJREjx499GxLliyhTZs2ZjtGWRS2kJ988slSWxXFg35pF+DC8zArK4tRo0bh4eHBCy+8QN26dXF2dub48eN8+OGH92z9b9u2jSFDhpgqxWw8//zz7N27V3o/ePBg3n//fT744ANatmxJ7dq1pQSGjIwMAK5fv05iYmKJC/y96Nixo977WbNm6Wkv7buuSIbnveqvvFRJYoJKpeKVV17h0Ucf5ccff+SZZ54BoEWLFnh5efH7778zduxYg4kGa9asAZCeA2jRogXe3t6sX7+e5557rlzJCXXr1mX79u3cvHmz1NZQYWug+ANqxe8gjeG+++5j6tSpXLhwgQ0bNuDq6qr3XEOdOnUAXSuvffv2Ju+/kMuXL+u9F0WRy5cvS1lB5jpOafj4+DB06FCGDh3K7du3GTVqFJ9++qkUhLy9vQ12BZT2nRbXA7rWXGFrpmPHjixatKhEmcIfeEJCAm3btpXsGo2Ga9eulciSMkThPi5evEi7du30Prt48aLeRcTcugxRo0aNElrLamXWqlWL06dPIwiC3sWksIur+EXwXj4VnjuOjo5mO3f27t3LzZs3WbBgAa1atZLsxmSenTlzhsTERLp06SLZ/P39cXZ2NqjFkK00+6VLl3B1dZWSD0q7IZ00aZLe9aFmzZqA7obh2rVrJW4aQPcIiaenJ/v379c7x4pz4cIFfH19pZvC4nUfFhZm0CdroMpStNu0aUNMTAyLFy8mPz8fAFdXV5588kkuXrzIJ598UmKbf//9l9WrV9OxY0diY2OlbZ566inOnz/Phx9+aDAqr127liNHjpTqS+/evRFFkQULFpT4rHB/Hh4e+Pr6sn//fr3Pf/rpJ6M1F9KnTx9UKhXr169n06ZNdO3aVa/FERUVRd26dfnuu+8MdjMW74YpjTVr1uh1aWzatInr169LfebmOo4hCu/0CnF3d6du3bp6D+jVqVOHCxcu6B3n1KlTHDx40OA+//rrL71xhiNHjnD48GFJT82aNWnfvr3eX6FOHx8fli9fjkajkbZft26d0V0RUVFR+Pv7s2zZMj0NW7du5fz583Tt2tViugzh7OxcQmtZ3bOdO3fm+vXrbNiwQbJpNBqWLl2Km5ub3kXfGJ/8/f1p3bo1v/zyC6mpqSWOV55zpzA4Fv0Nq9Vqo35jW7duJSAgQC+TVaVS0b59e7Zs2aKn5fLly/z3338G93Po0CGOHz8uvU9KSmLLli106NBBusF1dXUFSnYPRkVF6dVHYWCYNm0an332md5fYQr/pEmT+PDDDwHd+dukSRPWrFmjF8zOnDnDjh079AJs8bovDHjWSJU+wTlmzBhefPFFVq1axciRIwF45plnOHnyJN988w3x8fH07t0bFxcXDhw4wG+//UbDhg2ZPXu23n6eeuopzp07x3fffceePXvo06cPAQEB3Lhxg7/++osjR46wbNmyUv1o27YtAwcOZOnSpVy+fJlOnTohCAIHDhygTZs2jBo1CtA9ZPv111/z5ptvEhUVxf79+w3etdwLf39/2rRpw6JFi7h9+3aJKUaUSiUzZszg6aefpl+/fgwZMoTAwEBSUlLYs2cPHh4efPnll/c8jre3Nw8//DBDhgyRUrTr1avHiBEjzHocQzzwwAO0bt2ayMhIfHx8OHr0KH/88Yf0XYJu8PT7779nzJgxDBs2jLS0NJYtW0ZYWJjBoFi3bl1GjhzJyJEjUavVLFmyBB8fnxLp7sVxcnLi+eefZ/r06Tz22GPcd999XLt2jVWrVpX5vEZRHB0dee2115gyZQqjRo3igQcekFK0a9WqxeOPP17pukzhwQcf5JdffmHy5MkcP36cWrVq8ccff3Dw4EHeeOONEn36xvj07rvv8vDDD9O/f39GjBhBnTp1uHHjBvHx8SQnJ/Pbb7+Z5GNcXBze3t5MnjyZ0aNHo1AoWLt2rVHdPVu3bqVz584lWikTJkxg+/btkhZBEPjhhx9o1KgRJ0+eLLGf8PBwxowZo5eiDbqutkIiIyMB+OSTT7j//vtxdHSkW7dupXaRF+86g7s9Kq1atdILnBMnTuTpp5/mwQcfZNiwYVKKtqenp8HEKVugSoNQ7969pTvxESNGoFKpUKlUzJ07lzVr1vDrr78yb948CgoKqFu3LuPHj+fJJ58sUdlKpZI5c+bQo0cPli9fznfffUd2dja+vr60atWK119/nbi4uDJ9mTVrFhEREaxYsYI5c+bg6elJVFSU3naFD9f98ccfbNy4kc6dO/Ptt9+W6J4xhvvvv5+dO3fi7u6ud4dTSJs2bfjll1/4/PPP+eGHH8jJyaFGjRrExMTw4IMPGnWM5557jtOnT/P1119z+/Zt2rVrx7vvvivdyZnrOIYYPXo0f//9Nzt27ECtVhMSEsJLL73EmDFjpDKFNxTz589n1qxZhIWFMWfOHH7//Xe9vvVCBg0ahFKpZPHixaSlpRETE8Pbb79t1F3gqFGjEEWRRYsWMXv2bBo3bswXX3zBjBkzjB7nGjJkCC4uLnzzzTd8+OGHuLm50bNnT15//XW9Z4QqU5exuLi4sHTpUj788ENWr15NdnY2DRo0KDGWYIpPYWFhrFy5kgULFrB69Wpu3ryJn58fTZs2Zfz48Sb76Ovry5dffsns2bOZO3cuXl5eDBgwgHbt2umdN8W5desWhw4d4pFHHinxWVRUFN988w1z5sxh3rx5BAcH88ILL3DhwgWD2XatWrUiNjaWzz77jMTERMLCwpg1a5ZeV2dMTAwvvvgiy5Yt47///kMQBLZs2WKWcdr27dvz7bffMn/+fObPn4+Dg4N0DSvsArU5TErolrEKCp912bhxY1W7Uq3RarVi69atxTfffLNKjl/4TM63335bJcc3RHX06V6sX79ebNq0qd7zRvdi7NixYq9evfRs4eHh4tSpU83tnsw9kCfDkrEL8vPzS3TrrFmzhps3b9K6desq8krGHHh5efHmm29KacjFycvL03t/6dIltm3bJtd7NaFqZ/WUkakk4uPjmTVrFn379sXHx4cTJ06wYsUKwsPD6du3b1W7J1MBDI25FKVnz54MHjyYOnXqcO3aNZYtW4ajo6NZx9xkyo8chGTsglq1ahEUFMTSpUvJzMzE29ubgQMH8tprr5Vr5l8Z66FTp06sX7+e69ev4+TkRGxsLK+88gr169evatdkAIVYvI9CRkZGRkamkpDHhGRkZGRkqgw5CMnIyMjIVBlVOia0b98+Fi5cyLFjx7h+/TqfffbZPed/27NnD++//z5nz54lODiYsWPHmjRflCAIaDQalEplhWbIlpGRkalqRFFEEAQcHBysduXfKg1COTk5REREMHToUKOeBr5y5QrPPvssDz30EB9++CG7du3irbfeokaNGqVOolgcjUYjrZYqIyMjYwtER0dbbYJNlQahLl26GJwtoDSWLVtG7dq1peVjGzZsyIEDB/j++++NDkKFdwuFU2UcPXqU6OjoCq3Kak1otVq70lzd9Rbk5XH11DEUCiX1mzWX7NcvXyI74wa+QSH4BOkmttSo1Vw5oZsDsUFsS6ls2pXLZKVdx7tmEN6BwRw9epTIpk25eqdsvZg4lEqd9vRrV8m8noxXQE38a+umLBJFkUuHdUtR1I1qhsrBEYCMpERupiTi6VeDgLp3lzi4FH8QEYHaTaJwdNbNNp6Zkkx60lXcffyoWT9UKnv5SDyCoKFWRFOcXHUzCmRdTyXtWgJuXj4Eht6deDPh2BG0GjXBjRrj4q6bRuhWeho3Ei7i4uFFcFi4VPbKiWNo1HkENQzHyc2do0ePElqnNmlXLuHs5kFI+N0ZDq6dOoE6L4fA0Ea4eenm18vJyiTlwlmcXNyo1bipVDbxzCnyc7KpUb8hHj66VVNzb2WRfP4MDk4u1Gl6d62hpHNnyMvOwr9Ofbz8dctH5N3OJunsKZQOjtSLaiaVTblwjpysm/jXqotXDd2sE+rcHK6dPoFS6UC9mFipbOrli9zOSMMvuDbegbr11gry87h6eC+K/Fu4BjqQnOlBVHPdZLxHjx612lYQWNmYUHx8fIkpcjp27Eh8fLzR+yjaBadUKgkNDZWmCyrePadQKAxeuEqzK5XKUu2GTpLS7CqVqlS7oS7EsnwvblepVISFhZXYvzVrKqueHB0dCQ0N1Tt2VWm6evwof3/3BVdO3G2J52RlsnbOdNbPm6NXNv6PdaydM52ze3dKtvyc26ydM521H8zQK3v07z9YO2c6Z3Zuk85pQaPRlZ0zHYS7CbAntm1h7ZzpHP/3r7u+i6JUVqtWS/Yzu/5j7ZzpHN68Xk/Tuk/eY+2c6eTeuiXZz+/fw9o50zm4Ya1ePW1c8CFr50wnOz1dsl86fIC1c6azd+2vwN362/zVPNbOmU5mSrL0vSeePMbaOdPZveLuJKZKpZK/v/uctXOmk3b1iqQ59cJZ1s6ZzvafF+t9P1uXfsvaOdNJvXhesqVePM/aOdPZuuQbvbI7li1m7ZzpJJ85JdnSr11h7Zzp/P3dF3r1unvFT6ydM53Ek8ckW1ZqCmvnTGfzl/P1zrF9a1ewds50Lh85JNmz09NYO2c6G+a/jzJhJ8qTa1Hs/ZpDi2axds50zu3frTv3Vo1BPSuCtXM/YtVXX9Jn7yR8XFL0Fmi05qEFq3pO6MaNG9KCVYUEBASQnZ1NXl6eSWvAFO2S8/f3p379+iQkJJCWlibZg4ODCQkJ4cKFC3qz2tarV4+AgABOnTql9zR2WFgY3t7eHDlyRG/tk6ZNm+Lk5FQiWMbGxqJWqzlx4oRkUyqVxMXFkZWVxblz5yS7i4sLkZGRpKen60037+XlRaNGjUhOTiYpKcloTWfPnrU5TaXVU2JiYqVrunHjBif278UzMFjSdGnPdo78tYm09AzS1Lq1hdwcVASFhaMVRL3jKl3dCQoL57ZaI9kLcnMIqBeKo6OjXj3dyi/Q3bn7BXD06FEEQUCrVuMZXAtXV1cEUeTInX1k5ubhGVwLD/8A8vLyOHHiBKIo4BlcCwUKlCqVpCk9OxvP4Frki7oLXGE9eQQGI2i1XL12Dd/AIJKTk7memYlncC1yBd2SGYX15OpfA0dPL06fO0uogyMhISFk5ebjGVwLtUJFfHy8VE8u/jXAyZlzFy+SlJVNWFgYLp6eeAXXRuN4t16aNm1KQJ36aES4mJDAjXzdQnL+bu7UDA1DdHaVyiqVSgLq1EMU4fLVa9y8M0lM7vXrBIWF4+YXoPe9O3h43/ne1ZL9VkoSvnXqUaNWHb1zT+PohH/d+rh6eUnn3u0bqXgFBuHt4wEXt5F46iBkp+J4MwHP4FrgqOsyy1o4BIeE/QS5NMRJkYFisW7FWwXgnV4Pj6C2HE45QdI5Bx7QaFCJeQS5ZHHO2RGlKHJenYj/rVt65561Um2eE4qIiLhnYkKfPn0YMmQIzz77rGTbunUrzzzzDIcPHzYqCGm1WuLj46XuuGPHjhEdHY2joyOCIOhN7aJQKFAqlSUWIyvNXpjsYMgOlFiUqzS7SqWSBhyL24v7WJq90MfidkEQOHbsGJGRkXp3atasqax6EkWRw4cPExUVJem1tCZREPh63OPkZmXy5LxvpO6XqyeOcm7vLsLatKd2k6hyayqrntRqNceOHZP0Wks9VeTc02q1HDt2jJiYGMkfs2oSBRS5GShzbiDcSoHsVLidCvm3oOtkyXfFiidQnN6AQiigOKJCifBGCkoHRxQKBcLyx1CeWKP7zNkLPGqCew1wr4HoEciBmEE8+fdYvJy8+Kf3ElQowb0G525fw8fRh8TziURHRyMIAkePHiU2NrZadjcbg1W1hAqXZyjKjRs38PDwMHklzMIKE0VROqlL61ctrXItaS+re8wQptgFQZC6ICvio6l2S2oq7ZharRZRFA3qNYemjKREDv+5HoVKRdfRYwoL4hsUgqDRkJmajG+QrjVULzqWetGxFdZ0L7shvdW9nipqF0URhUJhfBe6oIXsVJSFASX7+p3Akg3dptz1ffmjcPJ3EHWBsFgnNnSZCNzp3lU5QGEAcvG5E1hqgkcNFO41UaEFha4lpOw9A3pPB/ea7Ew9wI8nf6RNcBsei3wMBdBc0NLQuyHRNaLJcffHx8UHgAiXCLRaLVeFq6VqtTasKgjFxsaybds2PdvOnTulBe5kZCyJKAhcT7iEi4cnXgE1AMjLvsXBjb/h7O5O54cfR3nnotD/5cm4eftI72UqAUEDOTcgO6VkYOn+5t1yv4yGU7+DaGi5cAV0fl0XUACUDlIAwtXvboulMMBo1XfL9pqm+3OvAQ6lLw9yIfMCwR7BuDrollRJvJ3If9f+42b+TR6LfAwAlVLF6oGrrXqsx1iqNAjdvn2bhIQE6f3Vq1c5efIk3t7ehISE8NFHH5GSksKcObpB24ceeogff/yROXPmMHToUHbv3s3GjRv56quvqkqCjB3xx1fzOf7vX7Qf/gjthukWYQxq2IjYPv2oExmt163j4edfVW7aFtoCuF0YWK7rd4X1eEcqFrr/fyjX/QcYGl1QQJdJd4OFyulOAFKAm5/UWtH9LxZYes/Q/bnXAJVj2b56176nnOc2P8eOxB3M7TqXHvV0y313q9ONm3k36Vanm77XdhCAoIqD0LFjx3j00Uel97NmzQJg8ODBvP/++1y/fl1vYLpOnTp89dVXzJo1iyVLlhAUFMSMGTOMTs8ujlKppGnTplad3mgq9qa5PHoFrZbtvywl4Wg8w96aIaULh4Q35vSu/yjIv5vkoFSp6PHkc2b3u7xYRf1q1HcCioHA0vN/d8v9MgpOrit9P12ngMoRpVKJp28AiiQRFEpw8793YOkzE9wC7tpKwyukXBILtAXsSNxBfGo8L7V4SbI38G7A3uS9XM2+KtkCXAN4OuZpo/ddtI6Lj3NZI9UmMaGyKExMiI2NlSrRnmZPKBygtRfN99IriiLpiVe5nZFO3SLPdXz38nNkJF5lwKtv0Kh1ewAK1PkoFEocHO9xR1yFVFn9avLvBpPs63e7xPKydGMfhSx7RNcVVhpv37jb4lj5NBxdDgoVuAcUCyw1oOsb4OSm05yZiFLlgMI9AJRV0wUqiAJKhS74Z6uz6fRLJzSChrWD1hLqrXt2Ki03DWeVMx5OHmXtqkyK1rEgCNL1zFrHh6xqTMjc2EIFmoq9ab6X3kuHD7Jq1rt41wxkzPxvpQt3uyEPgkIhZbEBODoZtwx4VVIp9SuKUDTArXgSjq0svXz3t8HhztP8Tu66/0oHKRus6AC+rsVyJwj1mQl9Z+nGYspo2QmCQPz5ZJ3mKghAh68fZt7Befg4+/Bx148B8HDyoH9of1wcXHBU3r1p8XeteDdt0Tq2Bew6CMnYF8e3buHwnxuI7NqTZr3uA6BW46Y4ubriHRhMQV6u9FR/k07dytqV/SGKkHgQDv8CZzbCuN13A4qg0f1XOpYcuPeocSdj7E4Q6j0T+r6vyx67V5ehR01LqSk3oihyIv0EPs4+1PKoBYCzypl9yftwUbmQr83HWaW7WZnWYVpVumo1yEFIxibJTE3m0uFDNOncXbJlp6eRdO40bj6+UhBycnFl3Lc/SVPVyBTj5hVdl9jhZXDjzF37qQ0QM1z3ut9c6D9PF1ju1QXoUcNSnlYK03dP59czvzImaow01hPhG8Hbbd+mfUh7KQDJGI8chGRsAlEQUNy5sxYFgR/feIXcW1n4htSSyoS37YCbjw/1Y5rrbSsHIAMkH4VNU+DSdqSMMwcXaNwPmo2E0K53y7r5VYWHFkUURf649Af/XPmHKa2nSM/ptAxsye8XfkdT2PpDl8U2ImJEFXlq/dh1EFIqlVKCgr1ga5qvJ1zi70VfImgFRk7TpfIrlEoaxLUkMzUFRCS9vsG18A2udY89Wjflrl+tBnLT73aBOXvBpf90r+t3gmYPQZMB4OJlXofNgLnO6ZyCHNwcdd2xCoWCb45+w5mMM3So1YEBDQcA0LNeT7rX7Y6Lg2kPx5uTonptITvOroMQgFqtNnm2BWvH2jUXbfW4eHhw9cQxUCjIzb6Fq4cnAH3HvSxN22PqvILWjtH1K4q6Fs+RX+DIcqjVHB7+RfeZbz0Y9AXU7wg+dS3rsBmoyDl95dYVJm6dSGpuKpuHbZYy3EaEj+Da7WtE+kdKZZ1U1WO5BGv/DRfFroOQIAicOHHCbjLFwLo1Z11P5b+fF+Pq6UX3J3TzB3r6BXD/hFcJbtRYCkBw90E/a9ZbHozSm5UER3/VjfOkHr9rTzwEBXngeOfiFvuw5R02A6bUsVbQcij1EAqFghaBLQCo6VaTC5kXyNXkcjHzIg19GgLwYOMHLe57eSiq1xaw6yAkY13cTEnm1I6tqBwcaDvkQdy8fQA5k80k/ngTdn9+d8oalRNE3Kcb5wnree9ZAaycn0/9zOx9s2kT1IZv+3wL6LLb5nabSyPfRgS4BtxjDzLmRg5CMtUWTUEBN5MTCaijW1CtblQMbYeOJKxVWykAyZSBoIWL2yAkFlx1C7Th10AXgOq01Y3zRA66+5mN8eelP/n9wu+MbjqaVkGtAOhcuzNfHP6CYI9gadJTgHYh7cralYwFsfsgZCsD9KZgDZrTrl5h9ez/odVoeHLuV9IKnh1GPGLyvqxBrzlxy76E4q81cGwF3EqEBz6GVndm+I4eDg27g19omfuwNpRKJUm3k6jtdXf+th2JO/jnyj8EuQdJQaiuV122PrgVB6V1X/ps6Zy27pqoICqViri4uKp2o1KxFs3eNQMRRRFRFMlIStRbMtoUrEVvhbl5BY6tQHXkV5oUHedx8QFNXpH33ro/G0KLlulXpnP2yFk2D9tMkLtuSewBDQcQ5B5Er7q99MpbewAqek4XX2vJGrHu2qggoiiSlZWFl5eXXcyjBtVXc3riVU7v+o92Q3WzUzs4OTHo9bfxCQzGsQJZQNVVr1nJy4T5cdJaNqLKCRr1QhHzEIT3KXNZAWtDrVWzL3kfqTmpDG40GABHpSMuChccFA4cv3FcCkItAltIyQe2RNFz2haw6yAkCALnzp2zm8wpqJ6a87KzWTr5RTT5+QSHRVC/me5h0hr1GlR439VRb4XIy4TTGyHl+N2JQV28dV1s6tsIUUM5ogklunUn29BbjKM3jvLcX8/h6eRJv4b9cFTqVkQe6TeSjj064utmm+NbRSl6TtsCdh2EZKqO4s/6xPToS2ZqsrQUtkwR8rPhzCY4tgrO/QXafJ29zbN317B56EdQOSJqtWjj46vMVXOy7eo2lp5YSvuQ9jwR9QQAsTViifCNIKZGDDkFOXg767oWQ1xC8HK2jZaBvSEHIZlKRRQEjm39i72rf2X4O+9JK5R2Gf0kyiqagr/akrBbl0595k/Q5N61B0RA1BAoOk+ZladWi6LImYwz1PWqK604mpKTwu6k3WSrs6UgpFKqWDFgRVW6KmNm7D4I2cpTx6ZQpZoVCo7/u4WbKUkc3LCWro8+BWDRAGQ1dVyQpxvXcb7z0G3GZTixVvfaLxQih+iCT82mZU4UajV6i/DUn0+xN3lviRVHb6tv063uvZ8Ds0bNFcGW9Nr1ona22GdeHbl++SJ+teqgctDd8ySfO8PVk8eI7du/Wi8QVylo1HD+bzi+SjczdedXoePLus/ysuC/jyByMAQ3u/cM1VaAWqtm29VtxKfG81qr1yT77L2z+fXMr7zY/EVGNx1dhR5aF7ZwPbPrIKRQKEhPT8fPz8+m8u7LQhCEStX875JvObBhLd0ff4a4vv0tfrziVLZeo9AWwMWtcGw1nFqnSzYopFFveOTXcu+6OurVCBopLbq0FUcz8jJwVjlLE4iaQnXUbEmK6hVF0eqDkF13x4miyOXLl/H1tf2MmkIqW7NvcAiIIumJ1yrleMWpdnUsaHXp1JlX7to8gnQzF0QOhtqtK7T76qT3UOohPtr/EQGuAcztNhfQrTg6KGwQbg5uemvv+LqU39/qpLkysDW9dh2EZMyLVlPA4c2bCGrYiJDwxgBEdetNUMNwAkPDqti7KkAQIGEXXN4BXSbqbEoV1GoBBbnQdKBujKduO53dihFEgSPXjxDgGkBtT13GnquDK4evH8bVwRW1Vi3NQP1uu3er0lWZaoYchGTMxo7lP7Jv7QpCwpvw0LQ5KBQKVA4O9hWABAGu7tON8RxfA9nJOnvTgVAjQvf6gY91z/aobOfnN23XNFaeXVlixdFp7afRLqRdtVkCQab6YTu/gnJiK08dm4I5NQuCVspsa963P2d2b6dp5+66tWqqyUB6pdTxjXNwYJEu8GRdvWt39oYm/UBRpKXj7m9RVyypVxRFfr/wO/9c+Yd32r4jrTjaOqg1f1z6Q6+sQqGQZjWwNPb2O7YlveVKTEhMTCQxMZHc3Fz8/Pxo1KgRTk7WcadjC9kk1YGbyUls+3ERHv7+dH/8WcleNCjZNKIIWvXdKXFObYBluimHcPKExvfrUqobdrP6aXNuqW/h6XR3raYhvw3hbMZZZnacKa04WqDVTRnkaOXPK1kbtnA9M7oldPXqVX7++Wc2bNhAcnIyRWOXo6MjLVu2ZMSIEfTp08dqMlQEQSA5OZmgoCCr8bmimEtz1o1Uzu7dicrRkbZDHsLNS/fkenULQGatY1GE1BO6mQuOr9IlEvR4R/dZWA9o9jA0fkC3Lo9j1TzHYU69CVkJvPzvy9zMv6m34uhDEQ+RkpNCdEC0VLYqg4+9/Y6L6rUFjApCM2bMYPXq1XTs2JEXX3yRmJgYatasiYuLC5mZmZw5c4YDBw4wf/58PvvsM9577z1iYmIs7XuFEUWRpKQkAgMDq9qVSqO8mgvU+dxMTqJG3foA1I1qRvvhj9CodTspAFVHzFLH10/fCTyr4cbpu/bTm+4GIQdnGPxFxZw1A+WuX6GAAykHUClU0rIHge6BXLl1hXxtPpcyLxHqo0unHhExwux+VwR7+x3bml6jgpCrqyt//fWXwZRAf39/2rVrR7t27ZgwYQLbtm0jOTnZKoKQjHHcSLjEqvenAvDE3C9xdNJ1L7UbNrIq3aocvu8Hl/67+17lBGG9dFlt4X2rzq8KIooigiigutNyXXZqGXP2zaFNUBspCDmrnFnQfQGNfBtVKIVaRqYsjGq7vvrqq0bnpHfu3JnevXtXyCmZ6oVPUAgoAAVkJidVtTuWI+MS7PlK1+1WiF8DUDpCoz4w+Ct4/RyM/Amih4GzR5W5WhFm751Nl1+6sP3adsnWpXYX/Fz8qOdVT6+rvXVwazkAyVgUu86OUygU+Pv72+46MwYwRvP1yxc5s2entIqpg5MTQyb/D5/AYBysJAGlkHvqzbyqy2g7vgquHdDZarWA2i11r7tOgV7TrGYJ7KJ6s9RZrD67mouZF/lf+/9JZXI1uWTkZ3Ag9QBd6nQBdCuO/jPiH2ncx5qwt99xUb22MOGN2YLQ+fPneeaZZ9iyZYu5dmlxlEol9evXr2o3KpV7ac69lcVPb76KpkBNrcZNqR+jW8ExoE69SvLQvBjUK2jhwPdw5Be4sueuXaGE+h11nxfiFVIZblaYAm0Bx9OO46hyJLJ+JABKlHx84GMEUeC5Zs9Ji7090uQRBoUNItI/Um8f1hiAwP5+x0X1yiurFqGgoIDExERz7a5SEASBhIQE6tataxdZNWBYc9G0aldPL2J63Ud2eho+gcFV6apZMFjH8T/C+lfulFDoZiyIGgJNBoCndQz25mvzcVA4SGM63x//nvmH5tO7Xm+er/88devWxcPJg4cbP0ygW6DeFDmNfBtVldsWwd5+x0X12gJGB6FZs2aV+Xl6enqFnalsRFEkLS2NOnXqVLUrlUZRzaIgcHjzRvatW8lDU+fg6R8AQNfRY6QF56wdg3Uc+4huVgOfurrXVtLaKeSVf19h65WtLOyzkNiasQA0D2yOj7MPHo4eenontZ5UhZ5WDvb2O7Y1vUYHoSVLltCkSRPc3d0Nfp6Tk2M2p2QqCYWCUzu3knU9lUOb1tH5Ed3CYbYSgPQQtIComypHqYIBn1a1R/ckNSeVn0/9TEZeht6YjiiKqAU1h68floJQXM04tj64FVHQzaosI2MtGB2E6taty2OPPcbAgQMNfn7y5EmGDBliNsdkLI9CoaDr6KdIOn+GmB7Wm258T0QtirVjddMIDfqyWs7ZlqXO4lDKIWq61aSJfxNANynot0e/RaVQ8Xqr13F31N0AjosdxwvNX6C+V31p+8LxHC3WP0YgY18YfcsbFRXF8ePHS/3cGjM1FAoFwcHBdpNVI4oiBzesJe/aZUlzUFg4cX36SQvO2RoKUaDJqXkoj63QPWyaFF/VLgFwM++m3u/ly8NfMuHvCfx65u5aQkHuQYxuOpp3272LgrvnaCPfRjTwbmDwvLW3cxrsT7Ot6TX6yjN58mTUanWpnzdu3JhTp06ZxanKQqlUEhJiXeMBFeHs3p1s++E7VI6ORLZuh0+Q9ScelImgRbl2LG7nfgelAwz//m7qdRUhiiIj14/keNpxvUXdWga2ZNvVbdRwraFXfmKriSbt397OabA/zUX12lV2XI0aNe5dyMrQarVcuHCB0NBQq538zxQatWpHWOt2uAQE4hFge/Wph6CF1c/C0V8RFSqEod+halK5K7teyLzA0hNLUaLk7XZvA7q72MLVQ0+lnZKCULc63ehet3uFj2lv5zTYn+aiem0B2+yDMYGsrKyqdsGi3ExOwrtmIAqlEoVSyQMvTuLw4cM205Q3SNEApHTgQvO3qd+4n0UPmZSdxN7kvTTxb0K4bzgAaq2aFWdW4ObgxpQ2U6Qlrt9q8xY+Lj74ufhJ25uzPmz9nDaEvWm2Jb02mAYlU8jZvTtZPHECO1f8LNlsOvgUknIMTvwGSgeEod9xM7iTWXcviiLXsvWXK18Qv4C3drzFpoubJFu4bzhPRj3J+53eR+Tu+E+oT6heAJKRsWfsviVky+Tfvo0mP5+ks6cQtFqUdtBVAUBwM3joJ9DkQvj9YMaU5TxNHg+sfoDUnFT+HfEv/q66BeraBrflUtYlgj3ujrMpFUpebvGy2Y4tI2OL2HUQUigU1KtXz2ZbB1HdeuHi4Ulo81ZSALJZzYIWbiWDdy3d+0Y9AVAIQrn1Hrl+hO+Pf09Nt5pMbj0ZABcHF7ydvcnIy+DczXNSEOrfsD/9G1bumJMhbLZ+y8DeNBfVa20ZyYaw6+44pVJJQECAzUz1cSvtBn98OZ8Cdb5kC2vVVq8FZGuaASjIhVXPwDfddctsF8FYvafST7H4+GIuZl6UbDmaHDZf3sxfl//S+7HP7TqXnSN30ia4jXl1mAGbrN97YG+abU1vuVTs27ePo0eP6tmOHj3Kvn37zOJUZaHVajl+/LhNpDmKgsCqWe9y7J8/2bpkYanlbEkzAFf2wpcd4dgKyLkBaWf1Pjakt0Ao4GyGfrkFhxbw4f4P2XZ1m2RrVqMZL8S9wAddPtArW9erLi4OVbNy6r2wufo1AnvTbGt6y9UdN3r0aEJDQ9mwYYNkmzhxIpcuXeLkyZNmc64yyMvLq2oXzIJCqaTb48+y9YeFtBowtMyyNqG5IBf+mQm7PgNRAM9gGPQ5NNSlOYuiyLObn2V/yn66+HThw8YfSpu2+6kdWlHLzpE7cXVwBaBz7c6IiNT2qC2Vc3Vw5emYpytXlxmwifo1EXvTbEt6yxWEtmzZgkOxJ+y///57NBqNWZySMQ5B0HI7I0OaeLRuVAyj3vvENud+K8qVfbB2HNw4o3vfbCT0naW35o9CoWBU01HsStqFgKC3uVbU4u7ozpVbV6R06hERI6rdstUyMvZAuYJQrVq1SthsZb3zymTfulXk375Nx4dGS7aVs94lL/sW9z//Gr5Buqeiz+3fw57Vv1AroildH31KKrvs7Ylk30xn1Huf4ObtA9jo5KPFOb5KF4A8AqH/PIi4D9BNhZOWl0ZDn4YAdKrVifWD1nPp9CW9zf8Y+gcBrgFWu36OjIwtYdfZcUqlkrCwsCob4Nu98mfcfXxpfl9/KYikXjxPTuZNNEWmSMrNyiT53BncvLz1ts+5lUlO5k1SLp6nQWwLo45Z1ZrLjaDVzX4N0P1t3QJ0nV4FN93zNqfTTzN+y3gclY4s778cTydPFAoFdbzq4B3hrae3plvNqlBQKVht/VYAe9NcVK8gCPfeoJpjVBBq1aqV0emPe/furZBDlYlCocDb2/veBS1EUMNw0q9d0TuR7pvwKtqCArwC7l4o68XEMmjiO7gV87X3M8/jExSClwlT8FS1ZpPR5MO/s3RdcI/9pgtETm7QZ6ZesSD3IFQKFSqlirTcNDydPAEr1FtB7E0v2J9mW9OrEI1INF+9erXROxw8eHCFHLI0Wq2W+Ph4YmNjAThy5AgxMTF2MecU6PRbjeZrB2HNOLh+J9nl4eUQ3kf6OKcgR5qHDeDCzQsEuQfp2axKrxmwN71gf5qL6gWk65m1ajeqJVTdA0tFsIXmrKlUe82afNg6B7Z/AqIW3GtAv0/0AtB/V//jrR1v8V7H9+hQqwOgmw7HENVer5mxN71gf5ptSW+5OlETEhL45JNPeOWVV0hLSwNg69atnD179h5bluTHH3+ke/fuREdHM3z4cI4cOVJm+e+//54+ffoQExNDly5deO+998jPzy9zGxkrIjEevu4K/32oC0BRQ2HcHig2A/bWq1tJz0tn8fHFVeKmjIyMeTA5CO3du5f+/ftz5MgR/vzzT2lZ79OnT/Ppp6YtmbxhwwZmzZrF+PHjWb16NY0bN2bMmDFSYCvOunXr+Oijj5gwYQIbNmxg5syZbNiwgY8//thUGdWCn9+ZyA9TXiI73bBeu0MU4feXIPUEuAXAiCUw7Dtw9y9R9PVWr/NC3At82qP6L9MtIyNTOiYHoY8++oiXXnqJRYsW4ejoKNnbtm1r8tr2ixYtYsSIEQwdOpSwsDCmTp2Ki4sLK1euNFj+0KFDNG/enP79+1O7dm06duxIv3797tl6Kg2lUknTpk2rLKsm9dJ5Ui6cQ1uJz1dVteYyUShgwKcQNQzG74Gmd5eS/+PSH7y/933pvbPKmadjnsZZ5VzmLqu1Xgtgb3rB/jTbml6TU7TPnDnDhx9+WMLu5+dHRkaG0ftRq9UcP36cZ599VrIplUrat2/PoUOHDG4TFxfHb7/9Jg3KXblyha1btzJw4ECD5ctCq9UiiiIqlQpBEKT/RfM0FAoFSqWyxPQYpdmVSiUKhcKgHUr24w545Q0EQcDZw1NvG5VKhSiKJcob8rE0e6GPhso7OTmh1Wr1Mh7Npak0u0FN2gJUO+ciKJSIHV/V2Wo0hcFf62m6nHWZidsmIogC7UPa0zGko9H1pFAoUKlUenotqgnz1FNFzr2iem1FU1m+F/6OC49lC5rKsouiiJOTE6Io2sTUPSYHIU9PT65fv06dOnX07CdPnjTpgdWMjAy0Wi3+/vpdLf7+/ly4cMHgNv379ycjI4OHH34YURTRaDQ89NBDPPfcc6bK0Jv7zs/PjwYNGpCQkKDXFRgcHExISAgXLlzQW0SqXr16BAQEcOrUKb3pM8LCwvD29ubIkSN6J03Tpk1xcnIq0VKMjY3VBeMTJySbUqkkLi6OrKwszp27Oxmni4sLkZGRpKenc/nyZcnu5eVFo0aNSE5OJikpSbL7+/tTv379EpoCAwNJSUnB09OTW7duWUzTiXtocs08T4Mjc3C9eRaF0oHjisao3WuVqmlQjUGoXFS0D2lvUj35+vqWmOfQUprMWU/lPfeOHj1qc5qMrafo6Gi0Wq1NaSqtngRBIDQ0tNRrpVUhmsj7778vjhw5UkxNTRXj4uLES5cuifv37xd79Oghfvrpp0bvJzk5WQwPDxcPHjyoZ589e7Y4bNgwg9vs3r1bbN++vbh8+XLx1KlT4p9//il26dJFXLBggdHH1Wg04v79+8X8/HwxPz9f3L9/v6hWq0VRFEWtVitqNBrpT6vVStsYYxcEoVS7IAh6tqQL50StVisKgiAWFBSISRfOSZ+JoliifKG9uI+l2Qt9LG5Xq9WSfnNrKssuacrPFbV/zxKFqf6i+K6XKL5fT9QeXi5qCgqksgUFBeLac2vF67evG6WprHoqWt8W02SBeirvuVd4ThfqtQVN9/K9UHNBQYHNaCrLXlRv4etCv60Rk1tCL7/8MtOmTaNr165otVoeeOABtFot/fr1Y+zYsUbvx9fXF5VKVSIJIS0tjYCAAIPbzJs3jwEDBjB8+HAAIiIiyMnJ4Z133mHs2LEm9ZGqii1vUPR/WWXNYd+9chk7lv+Ab3At6jdrzrXTJ0i9eJ4OI0bRduhDAFI3UnFK89FUu0qlMrh/c2stiiL1BKo1YyHpsM7QuB/0+wSlh/4MBvMPzuebo9/QsVZHPuvxWYnpdUypp8LuCkN6zaKpEuqpPPbiem1B073sRbtfi2Otmsqyl6bJ2jA5CDk5OTFjxgzGjRvH2bNnuX37Nk2bNqV+/fom7ycyMpJdu3bRs6duATJBENi1axejRo0yuE1eXl6Jk6CwEkQrWdzp+uWL7Fj+AwAZSdfISLomjVXsWP4DDVu2oUa9BlXpomXIvwWL7oO8THDxgfs/hOhhumSEYvRt0JcfT/5Iy8CWle+njIxMpVLuueNCQkIIDtYtZVzeFQ2feOIJJk2aRFRUFDExMSxevJjc3FyGDBkC6JaHCAwM5NVXdYPW3bp1Y9GiRTRt2pSYmBgSEhKYN28e3bp1K9cdgVKpJDY2tlKzTALq1iewYSNSL55HLDKwqlAqCWzQkIC69S16/KrQDICzJ3SdAhf/0z146nl3/FC8k3xQ37s+AOG+4fwx9A98XHwqfNgq01tF2JtesD/NRfXawkOr5QpCv/76K4sXL+bSpUsA1K9fn8cee0zqJjOW+++/n/T0dObPn8/169dp0qQJ3377rdQdl5SUpHdijR07FoVCwdy5c0lJScHPz49u3brx8ssvl0cGoMvSc3GpvAXKFAoF0d1689f5z/TsoiAQ1a13pSxRXCmatRrYOR/qtoV67XW2Ns/p/opovKW+xeT/JnMw5SC/9v+V2p669XzMEYAKqew6rmrsTS/Yn2Zb0mtyEJo3bx7ff/89o0aNkuZfi4+P57333iMxMZEXX3zRpP2NGjWq1O63pUuX6jvr4MCECROYMGGCqW4bRBAETpw4UanzLomiyNF//kShVEotIdAtwXDsnz+J6dnXooGoUjSnntKt93PtAPiFwtid4OhqsOvNxcGFzPxM1Fo1p9JPSUHIXFRFHVcl9qYX7E9zUb22gMlB6Oeff2b69On069dPsvXo0YOIiAimT59uchCyN24kXCLlvG56o8JAVPg/+fxZbiRcst4xIUELOz+Ff94DbT44e0PniVBsKWytoEWp0D0v4ah05IPOH5ClziLCL6KKHJeRkakqTO5E1Wg0REVFlbBHRkbaxINTlsbN2wfVnZkmfAKD6fnUeGo20C3C1mHEKOsNQNfPwHd94K93dQGoUW8YvxtiR+q1gJJvJ/PUn0+x7PQyyRbsESwHIBkZO8XkltDAgQP5+eefmTJlip59+fLl9O/fv5Stqi+VPZipcnAksnMPrp0+weg581GpHIjp2bdSW0Bm15x6Cr7qfKf146Vbajv2EYPdb/9c+Yf9Kfs5f/M8AxsO1Ft2wVLYy4B1IfamF+xPsy3pNWo9oVmzZkmvtVotq1atIiQkhGbNmgG6NXkSExMZNGgQb7/9tuW8NQNF1xOqyv5jTUEBDkXm3rNqRBF+ehAEDQyYD96lj+uIosgnBz9haKOh1POqV4lOysjYHtXlelYRjGoJFZ0yAnRdb6Bb0gHAx8cHHx+fci3lUJWIokhWVhZeXl6VkpVWlKoKQGbRLGhh/3e653xcfXUtnmHfgZN7idZPQlYCS04sYXLryTgoHVAoFLzS4hUzKDGOqqzjqsDe9IL9aS6q1xYwKggVz1KzFQRB4Ny5c5VyF3Fg/VrSE6/QceRjuHp4WvRYZVFhzTfOwdrxcGW3Lvtt8Jc6u7NHiaJqrZon/niC1JxUarjW4Nlmz5YoY2kqs46rA/amF+xPc1G9toDtdCxWY/JzbrPz1x848tcmzu/bXdXulA9BgF2fw5cddAHIyQPqtNF1xZWCk8qJV1u8SvOazRkYZvpM5zIyMrZPuR5WPXr0KBs3biQpKYmCggK9zxYsWGAWx2wJZzd3Bk98l2P//kVklx5V7Y7ppJ3XtX4SduneN+gCAxeAT90SRc9knMFB4SAttX1/6P30bdC3xPxvMjIyMlCOltD69esZOXIkFy5cYPPmzWg0Gs6ePcvu3bvx9Ky6bqbyUllPHdduGkXfcS+hqAZZLSZpPrcFvuigC0CO7vDAx/DoWoMBaNvVbTy8/mFe3foqeZq709dXdQCylSfLjcXe9IL9abYlvSZfHb788kumTJnCl19+iaOjI2+++SabNm3ivvvuk+aSsxZUKhWRkZEW60fWagrIv7P8eXXBZM0hcbp53+p3gnE7odUYg6nXAE39m+Lh6EGQexD52nwzel1+LF3H1Q170wv2p9nW9JochK5cuUKXLl0A3UzYOTk5KBQKHn/8cZYvX252By2JIAjcuHHDYpMA7l+3mkUvP8vZvTstsv/ycE/NggCnN90d63Hzg6c2w6O/gW/9EsVv5N6QXge4BvDjAz/yWY/P8Hb2toD3pmPpOq5u2JtesD/NtqbX5CDk5eXF7du3AahZs6aUlp2VlUVubq55vbMwoihy+fJliywDIQhaTu/ezu2bGRQUWVmxqilTc8YlWDIAfn4Qjv561+5bH4p1I4qiyKJji+i7si+HUu8ux17Lo1aVd78VxZJ1XB2xN71gf5ptTa/JiQmtWrVi586dRERE0LdvX2bOnMnu3bvZuXMn7dq1s4SPVolSqeLhGR9xavu/NOnUrardKRtBgAPfwZ/vQMFtcHQDzb27085knCFfm8+fl/4krmZcJTgqIyNja5gchN5++23y83UXqLFjx+Lo6MjBgwfp3bu3SSur2gMOjo5EdetV1W6UTcZl+G0CXNyme1+vgy7zzS/UYHFRFKUVLN9q+xbtQ9rTL7SfwbIyMjIy98LkIOTj4yO9ViqVPPPMM+b0p9Ix91PHBep8Eo7GE9q8dbV9elvSfHQFrHsR1Nng4Ao9/wetnynR9QagETR8Hv85uZpcJrWeBIC7ozv9G1b/+QJt5clyY7E3vWB/mm1Jr1FBKDs72+gdeniUfHK+uqJSqWjUqJFZ97lv7Up2rfiJqG696PNc9VvWQk+zR01dAKrbDgZ+Bv4NS90uPjWeb45+A8DAsIE09mtcGe5WGEvUcXXG3vSC/WkuqtcWVi4wKgi1bNnynnf1hd00J0+eNItjlYEgCCQnJxMUFGS2WWmVSiUqBwfqxVTDMRJRREg9RbLWW6e5QWd4bJ2uC05Zdrpny6CWPBvzLGE+YVYTgMAydVydsTe9YH+ai+q1BYwKQkuWLLG0H1WCKIokJSURGBhotn22HfoQTTt3xzOghtn2aRY0alg+GsXF/0jr9BWBgffr7A06G72LCXHmWdG2MrFEHVdn7E0v2J9mW9NrVBBq3bq1pf2wKbxq1KxqF0py8jc4swlUzrhlngHuN2qzy1mX8XH2qTbP/cjIyNgWtt92rQQK8vLY/PUCsq6nVrUrOpKP3X3YVBR170/9rnvbdhw3Q7oavatZe2bR9ZeurL+w3gKOysjI2DvlmsDUVlAoFPj7+1c4i2336l84smUTV08d5/GPPq/arLitH8A/M3TT7TR/FPZ+C6nH737e+AH8tcZp1ggaMvIz0IgaIv0jLei05TBXHVsL9qYX7E9zUb228MCqUSur2hKWWInwRsIltnz3Jc0fGEijVlX4wG7yMd1SCwAKFYjFMmeaDobhi0qd+600rmRdoY5XHTM5KSMjYy5sYWVVu+6OEwSBS5cuVXgOpoC69Rnx7izCWrY1k2flJDBS1wIyFIBqNIbhixBE0WTN1hyAzFXH1oK96QX702xressVhDQaDTt37mTZsmXSM0QpKSnSnHLWgiiKpKWllbtJKwh3L/SFswhUFtdu5nLsWibXbhaZr0+h0HXBFQ9AAG2ehTvNd2M0F2gLEETrP8krWsfWhr3pBfvTbGt6TR4TunbtGk899RRJSUmo1Wo6dOiAh4cH33zzDWq1mmnTplnCz2rJyplvE9q8DTG9+uLo5Fxpx712M5fuH/5LvkbA2UHJ3691pZaPqy4J4eCSki0hhQoOLoUWTxh9jJVnV/Lt0W95MupJHm7ysAVUyMjIyJSjJTRz5kyioqLYu3cvzs53L7y9evVi924rXbq6nNxKu8G/S74h5fzZSjvmtZu57LuYTr5G10rJ1wjsu5iuaxGlHIfEQ3cC0J1WWWFASjyo+9xItl7dSkpOCgVCwb0Ly8jIyJQTk1tCBw4c4Oeff8bJyUnPXqtWLVJSUszmWGWgUCgIDg4udzdabJ/+5GVnVdpzQUVbQEoFCCIoFfDSL/F3W0R12sKV3eDoAr3fg0NLdIGp21sQFIVCEIzSPLfbXHZe20lUQFSlaLMUFa1ja8Pe9IL9aS6q1xa65EwOQoIgGBwQS05Oxt3d3SxOVRZKpZKQkJByb9/8vsqdvDPjtlpqAQl3zr3C//kagawbidRKPqIzDPseIvpCyyd0LaAgXTAxVrOzypludav5EhRGUNE6tjbsTS/Yn+aiem1h7jiTu+M6dOjA4sWL9Wy3b9/m008/lVZctRa0Wi1nz561mor0dXfC2UFXZco7N32F/50dlNQ++Q0U5Ogy5ML76D5QKKQABNanuaLIem0fe9Nsa3pNDkKTJ0/m4MGD3H///ajVal577TW6d+9OSkoKr732miV8tChZWVnl3jY/J4e829kIlXQy1PJx5e/XujL3wVi9ltDcB2P5d2wTPA9/rzN2nVLms0BlaU7KTmLMH2P49cyvpZaxNipSx9aIvekF+9NsS3pN7o4LCgpi7dq1rF+/ntOnT5OTk8OwYcPo378/Li4ulvCx2vLjGy+RkZTIQ1PnUKtx00o5Zi0fV2jgh7ODUsqOa9XAj+Cd74AmF2q3hka9y73/Py79wd7kvQiiwPDw4Wb0XEZGRqYkJgeh/Px8nJ2dGThwoCX8kTGCwhZRxm01vu5O1HLKhQN3uki7v2XyjAhF6VO/DyIidT3rmslbGRkZmdIxedqe5s2b06tXLwYMGEC7du2sbv2OotNcKBQK0tPT8fPzK5cOrUYD6AYKFVX9PSQfhRO/Qfc3yywmCEKFNFsbsl7bx940F9UriqLVT9tjchDavHkz69atY+vWrXh6enLfffcxYMAAoqOjLeWjWbGFuZbMjVbQ8tOpn2gf0p6GPqWvriojI1O9sIXrmcm3Db169WL+/Pns2LGDl19+mfPnz/Pggw/Sp08fFixYYAkfLYZWq+X48ePWnWWSk25ScUOa5x+az5x9c3hnxztoBSv+LgxgE3VsAvamF+xPs63pLXfb1cPDg6FDh/Ldd9/x22+/4erqymeffWZO3yqFvLy8cm+7d+0Ktv7wHZmpyWb0yARST8JHjeH3V8CE4FFc88ONHybIPYiBYQNt8oG/itSxNWJvesH+NNuS3nKvJ5Sfn8+WLVv4/fff+e+//wgICGDMmDHm9K3ac+yfP8lISiSsZVu8a1bBeu//vAfafLh9HZTGN8Uv517mwtkLDG+sy34LdA9kw+ANOKocLeWpjIyMjEFMDkL//fcfv//+O3/99RcODg706dOH7777jlatWlnCv2pNVLfe5N7KwsPPv/IPnhivW7IbBXR7w+jNzt88z7Tz01BcUBBTM4YIvwgAOQDJyMhUCSYHoQkTJtC1a1dmz55Nly5dcHS03ouXUqkkLCys3Bk1rQcOM7NHJvDPTN3/6OFQs4nRm4X5htEhuANOjk74u1ZB8KxkKlrH1oa96QX701xUry2sKWRyENqxYwceHh6W8KXSUSgUeHt7V7UbppOwB87+qZshu+vkMovmanL58eSPjG46GmeVM0qlko97fIyzqvKWnqhKrLaOy4m96QX702xreo26dShcuK7o+9L+rAmtVsuhQ4fKnWWi1RSg1RQgVvbdyD8zdP/jHgH/0lOqRVHkuc3PMe/gPL48/CWg03ziyAmbyay5FxWtY2vD3vSC/Wm2Nb1GtYRatWrF9u3b8ff3p2XLlgYzqERRRKFQcPLkSbM7aUkq0pxd/Nr4Sp+2h5tX4NpBUDlB54llFlUoFDwa+ShXs68SVzNOsttCE94UZL22j71ptiW9RgWhxYsXS82/JUuWWNQhmXvgUwdePKJbM8inTomPd1zbgbujO7E1YwHoUbcH7UPa4+rgWsmOysjIyNwbo4JQ69atpde1a9c2uICUKIokJSWZ17tqziPvzUUUBZxcKvkC7+4PjR8oYV57bi1v7XiLel71WNF/BS4Ougll5QAkIyNTXTE5naRHjx6kp5d8Sv/mzZv06NHDLE5VFkqlkqZNm5Y7q8bZzQ0Xdw+UlTFdhiDoEhLKoHvd7gS5B9GpVidEDM/GVFHN1oas1/axN822ptfk7LjCsZ/i5OTk4OxsfRlXxZcpr7acWgfLH4WI++Ghn0ChID0vnX+v/MuQRkMA8HTyZO3Atbg5upW5K6vRbCZkvbaPvWm2Jb1GB6FZs2YBusHuuXPn4up6t4tHq9Vy5MgRGjdubH4PLYggCBWa/O/gxnXkZWcR1a0XXgE1LeDhHQStbnYEgKBoUCjIzM9k8NrBpOelU8ujFm2C2wDcMwBVVLO1Ieu1fexNc1G9toDRQejEiROAriV05swZvYdUnZycaNy4MU8++aT5PazGxP+xjoykROpFx1k2CB1dAddPgYsPtB0HgLezN73q9eJQ6iG8nLwsd2wZGRkZC2J0EFq6dCkAU6ZM4c0337SZB1YrQuMOXcnJysTdx9dyB9EWwL+zEIHfYu6nq1JB4WNqr7Z8FQeFgzzljoyMjNVi8phQYbecDLQf/rDlDxL/E2RcZEZgCMuvb2fQ/g+Z3mE6IGe9ycjIWD9GBaEJEybw/vvv4+HhwYQJE8osa+qaQj/++CMLFy7k+vXrNG7cmLfffpuYmJhSy2dlZfHJJ5+wefNmbt68Sa1atXjjjTfo0qWLSccFXZZJbGxs9c0y0eTD1jkA9I8YzrqkTYT5hJWaHGIM1V6zmZH12j72prmoXlt4aNWoIOTp6WnwdUXZsGEDs2bNYurUqTRr1ozFixczZswYNm3ahL9/yck11Wo1TzzxBP7+/sybN4/AwEASExPx8ir/mIharcbFxaUiMizCxcyLpCYdoo2DE3iGENvpDf7UvoKPi0+F911dNVsKWa/tY2+abUqvWIUMGzZMnDp1qvReq9WKHTt2FL/66iuD5X/66SexR48eolqtLvcxNRqNuH//flGj0ei9Lg/fvfyc+PHIAeLVUyfK7Y8hDiQfEJsvaS52XtZZzLh9XRRTT5tt3xXVbG3Iem0fe9NszmtYdcDkMaG8vDxEUZRStK9du8bmzZsJCwujY8eORu9HrVZz/Phxnn32WcmmVCpp3749hw4dMrjN33//TWxsLNOmTWPLli34+fnRr18/nn76aZNTM4tO/icIAiqVCkEQEMW7D3kqFAqUSmWJiQIL7YJWK/1ptVqUSiUKhaJE+cJuguJN59LsUQFR1PGsQ023muRqC/D0awharUEfgTJ9L24vPFZpmgz5bg5NKpUKURQN2iuqqax6KqToZ9auyZh6KvxvS5pK873wc1EUS/Xd2jSVZS+q126644oybtw4evXqxciRI8nKymL48OE4OjqSkZHB5MmTefhh4wbrMzIy0Gq1Jbrd/P39uXDhgsFtrly5wu7du+nfvz9ff/01CQkJTJ06FY1Gc8+xquIcPXpUen316lUaNGhAQkICaWlpkj04OJiQkBAuXLhAVlaWZK9Xrx4BAQHEPfwkebm5JGffJjU+nrCwMLy9vTly5IjeydG0aVOcnJyIj4/X8yE2Nha1Ws2RY0fYm7WXdt7tUKlUxDVpyOfeHUhy70jimUQSScTFxYXIyEjS09O5fPmytA8vLy8aNWpEcnKy3rRJ/v7+1K9fv4SmwMBAAC5dusStW7dKaDp16pTe0sHl1VSY0g+6H1JcXBxZWVmcO3dOsptLU1n15Oury1wsWt/Wrqmsejp+/LieXlvQZGw9CYJAQUGBTWkqrZ4Abt26Veq10qowtenUunVr8cyZM6IoiuLy5cvF/v37i1qtVtywYYPYt29fo/eTnJwshoeHiwcPHtSzz549Wxw2bJjBbXr37i126dJFr+n53XffiR06dDD6uIXN1/z8fDE/P188cOCA1L2n1WqlJq5GoxG1Wq20jTF2QRBKtQuCYNCu1qjFEb+NEKO+jxJ/O/ubTtu/s0XxXS9RWDJIr7whH0uzF/pY3K5Wq8WDBw+K+fn5FtNkyC6KYqn2imoqq540Go144MABPb3Wrqmseio8pwv12oKme/leqLmgoMBmNJVlz8/PFw8ePCgWFBSI+fn59tkd5+7uDsD27dvp3bu3lK2RmJho9H58fX1RqVR6dxUAaWlpBAQEGNymRo0aODg46HW9hYaGcv36ddRqtUlTWahUKlQqFc2bN5dspWXXlNbVZw67o8qRbnW7kZyTjJuTGyqlEvZ/B4Ai9pES25Tmo7F2lUpFXFycwbKm+m6qXaFQGLRXVNO9fClax8aUtwZNpdmdnJwM6rVmTfeyF/8d24KmsuxFf8O2sKaQyTmNdevW5a+//iIpKYnt27fToUMHQBc8THmA1cnJicjISHbt2iXZBEFg165dpV4kmzdvTkJCgl4z9tKlS9SoUaNccymJokhmZmaJPmFjObJlE3vXruBW2g2TtjuYcpCk7LtN/THRY1gzcA096vaA5CNwKwkc3aFxv3L5VRYV1WxtyHptH3vTbGt6TQ5C48ePZ86cOXTv3p2YmBgpYOzYsYMmTZqYtK8nnniC5cuXs3r1as6fP8///vc/cnNzGTJENyHnxIkT+eijj6TyI0eO5ObNm8ycOZOLFy/y77//8tVXX/HII4+YKgPQBb1z586Ve3Bv/7pV/PfT92RdTzV6m59O/sTjmx5n6u6p0knkqHTE1+XOrAunN+n+N+wGjuZPwayoZmtD1mv72JtmW9Nrcndc3759adGihfRwaSHt2rWjZ8+eJu3r/vvvJz09nfnz53P9+nWaNGnCt99+K3XHJSUl6TV/g4ODWbhwIbNmzWLAgAEEBgby6KOP8vTTT5sqwyyEtWpHTuZN3ExY771tSFucVE4EugVSIBTgpCrWgjtzJwiF9zGjpzIyMjLVE5ODEOjGZmrUqEFycjIAQUFBZc5yUBajRo1i1KhRBj8rnK+uKHFxcSxfvrxcxzI3nR954p5lstRZnEg7QdvgtgCEeofy++DfCXIPKln4VgokHtS9btTbnK7KyMjIVEtMDkKCIPD555+zaNEicnJyAHB3d+eJJ55g7NixVjd1hiWfOr6WfY1HNzzKrYJbrB64mloetQAMByCAhDvjYyHNwbOUMmbAZp60NhJZr+1jb5ptSa/JQeiTTz5hxYoVvPrqq1JGyoEDB1iwYAFqtZqXX37Z7E5aCpVKRWRkpMX2H+weTB2vOtzIvUFWfpYUhEolchDUOga3r1vMJ0trrm7Iem0fe9NcVK8tZMeZHIRWr17NjBkz9Jbybty4MYGBgUydOtWqgpAgCKSnp+Pn53fPFtzp9NOE+4ajUCh0ayplnGHvnM/JTElmyJSphIQ3RhRFdiTuoF1wO1RKFUqFkg86f4CnkycuDkbeufjU0f1ZCFM02wKyXtvH3jQX1WsLmFxjmZmZhIaGlrCHhoaSmZlpFqcqC1EUuXz58j1THb86/BXD1g1j5PqRLD+9nJHrRzJs3TCuZyaTn3Mb8U6Wyls73mLsX2P54eQP0rY13GoYH4AqAWM12wqyXtvH3jTbml6Tg1Djxo358ccfS9h//PFHq1ve2xhOp59mQbxueYqT6SeZvns6J9NPAvBrzGnavvUSNUMbAhBXMw4HhQMFQoHpB9r8Dvw4HC7+ZzbfZWRkZKo7JnfHvf766zz77LPs3LlTWuM8Pj6epKQkvvnmG3P7V+WE+4YT6R/JyfSTCKKuxSOIAgoUZLtp+TPrPzo46VLThzYaSqugVtTzqmfaQUQRjq+Bm5ehxb0z7mRkZGRsBZNbQq1bt2bTpk306tWLW7ducevWLXr16sWmTZto2bKlJXy0KPdai0ihUDCk0RApABUiIqJAodcHrVAoTA9AANdP6wKQyhlCTV+cz1Qqsv6SNSLrtX3sTbMt6TWpJXT16lV27txJQUEBDzzwAOHh4Zbyq1JQqVQ0atSozDKiKLLq7CoUKBC52werVCjpmNmIAZktyM5Ix8O3AoOEhQ+oNugMTu7l348RGKPZlpD12j72prmoXrvKjtu9ezfPPfecNC25g4MDM2fOZODAgRZzztIIgkBycjJBQUGlZtWcyTjD8bTjejalQokgCvgfvMXf27+kZv3QCgahP3T/K2GWBGM02xKyXtvH3jQX1WsLGF1j8+bNo3379mzbto09e/YwfPhwPvjgA0v6ZnFEUSQpKanMLJMIvwgmxOrWKor0j+Tttm/TxE83R15A03DC23bEpSJLnuekw5XduteVEISM0WxLyHptH3vTbGt6jW4JnT17lmXLllGzZk1AN7noL7/8QkZGhrRwmK3ybLNn6Vqnq/Sc0PDw4ZzJOEOEX0TFd35uC4gC1IwEn7oV35+MjIyMFWF0Syg7O1sv2Li6uuLs7Ex2drZFHKtuRPhFSMtFKxQK8wQgABdv3VhQE/Mv2yAjIyNT3TEpMeG///7Ds0jXkyiK7Nq1izNnzki2ojMpVHcUCgX+/v5ScCmLzZc3M23XNFoEtmBut7nmcyK8t+6vkjBFsy0g67V97E1zUb220CVnUhCaPHlyCds777wjvVYoFJw8ebLiXlUSSqWS+vXrG1W2QFvAzfybZBfcbfn9/M5EMlOTGfjamwSHmallZGFM0WwLyHptH3vTXFSvXWXHnTp1ypJ+VAmCIJCQkEDdunXvmVXTqXYn1g5cqzcFT27WTW5npCNoynkiXDsA3nXAo2b5ti8Hpmi2BWS9to+9aS6q1xaw/RorA1EUSUtLM6pJ6+nkSahPKCEeIZJt4GtvM3r2fGrUb1A+B1Y/Bx820iUnVBKmaLYFZL22j71ptjW9RgWh+Ph4o3eYm5vL2bNny+uPVeFfuw4164fi5OJq+sZp5+HGGVA6QG3rm2lCRkZGxhwYFYQmTpzImDFj2Lhxo7SQXXHOnTvHxx9/TK9evTh+/LjBMtZMQlYCy08vZ+uVrebZ4dk/df/rtddlyMnIyMjYIUaNCa1fv56ff/6ZuXPn8tprr1G/fn1q1qyJs7MzmZmZXLhwgZycHHr16sXChQuJiLCOQXqFQkFwcLBRWTXHbhxj+u7ptAluQ5c6uvndzu7ZSX5uDg1iW+DuY+KzUoVT9YT3NdXtCmGKZltA1mv72JvmonptoUtOIZqo4ujRoxw4cIDExETy8vLw9fWladOmtGnTBh8fHwu5aT60Wi3x8fHExsaiUqmM3u5AygGWHF9CuF8442PHA/DdS8+QkZTIQ1PnUKtxU+OdyMuCOaEgFMDzB8G/oakyZGRkZMp9PatOmLyUQ3R0NNHR0ZbwpdLRarVcuHCB0NDQe1Zgi8AWtAhsoWer3SQKn6AQXDw8TDvwhX90Acg/rNIDkCmabQFZr+1jb5qL6rUFTA5CtkZWVla5t+397Avl21CasLRyu+IKqYhma0TWa/vYm2Zb0mv3QahK6DUN6neCINtoUcrIyMiUF7t+TsgU/k74m94rejNx28SK78w9AGJHQlBUxfclIyMjY8XYdRBSKBTUq1fPqKyaPE0eSbeTSM9Ll2y/zniLhS8+TcqFc5Z006yYotkWkPXaPvam2db0Vqg7Lj8/H2dnZ3P5UukolUoCAgKMKts+pD3LHliGm6ObZLt1I5WbyUlo1GrjD/rb8+DfCJqPBtfKXwLDFM22gKzX9rE3zUX12sLccSa3hARB4LPPPqNTp07ExcVx5coVAObOncuvv/5qdgctiVar5fjx40ZVpI+LD5EBkTTwvjtFzwMvTOShaR8QULe+cQfMvAoHl8Dmd0AQyul1xTBFsy0g67V97E2zrek1OQh9/vnnrF69mtdffx1HR0fJHh4ezooVK8zqXGVQuFx5eQgMDaNWRBOc3dzuXRjuZsXVaQ3u/uU+bkWpiGZrRNZr+9ibZlvSa3IQWrt2LdOnT2fAgAF6M9ZGRERw4cIFszpXnbiWfY1159exK3FX+XcipWZbfhlvGRkZGWvA5CCUkpJicApxURTRaDRmcao6cjj1MG9sf4OFxxZKtguH9nF613/kZGXeewfqHLh4Z965Kno+SEZGRqa6YXIQCgsLY//+/SXsmzZtokmTJmZxqrJQKpWEhYUZtQZJgGsA7UPaE+kfKdn+XfwNv8+dTUbitXsf7OI20OTp1g+qacIUP2bGFM22gKzX9rE3zbam1+TsuHHjxjF58mRSUlIQRZE///yTixcvsmbNGr766itL+GgxFAoF3t7GzWDdOrg1rYNb69mCGobj7uuHkzFjQtKEpX2gClMrTdFsC8h6bR9702xrek0OpT179uTLL79k165duLq6Mn/+fM6fP8+XX35Jhw4dLOGjxdBqtRw6dKjcWSb3P/8aD777PjWMyY4TteDgUuVdcRXVbG3Iem0fe9Nsa3rL9ZxQy5YtWbRokbl9qRKEykqVHvAp9J0NKsd7l7Uwlaa5miDrtX3sTbMt6TW5JdSjRw8yMjJK2LOysujRo4dZnKqObLu6jQFrBvD2jrfLvxMnt2oRhGRkZGSqCyYHoWvXrhmMwmq1mpSUFLM4VR3JVmdzMfMiSbeTJNvqOdNYMvF5Ui/dIzX9VrKFvZORkZGxTozujtuyZYv0+r///sPT01N6LwgCu3btolatWub1zsIolUqaNm1qVJZJm+A2fNfnO7ycvCRbRuJVMpISKSjrwbHsVPiosS4j7ukt4OhqDtfLjSmabQFZr+1jb5qL6rWFbjmjg9D48brVRBUKBZMnT9bfiYMDtWrVKmG3BpycnIwq5+/qj7+r/iwHfZ57iQJ1Pv51Sj43JXH2T0DUdcNVcQAqxFjNtoKs1/axN822pNfoIHTq1CkAunfvzooVK/Dz87OYU5WFIAgVWhrXqCW9pdTs6vGAakU1WxuyXtvH3jQX1WsLmJwd9/fff1vCj2rP+ZvnOZNxhjCfMBr5NjJuI00+nP9H91qeqkdGRkamBOVK0c7JyWHfvn0kJiZSUFCg99mjjz5qFseqG/9c+Yd5B+cxoOEAZnacCcCV40coyM8nOLwxrh6eJTe6vAPU2eARCMGxleuwjIyMjBVgchA6ceIEzzzzDLm5ueTm5uLt7U1GRgaurq74+fnZbBDyd/Gnec3mRPhGSLbN3ywgIymRh6bOMdw1VzhhaaPeYCeDpjIyMjKmYHIQmjVrFt26dWPq1Km0aNGC5cuX4+DgwOuvv251AUipVBIbG2tUVs3gRoMZ3Giwnq1udByiIKJyLOXZH2nW7OoxHgSmabYFZL22j71pLqrXrrLjCjl58iRTp05FqVSiUqlQq9XUqVOH119/nUmTJtG7d29L+Gkx1Go1Li4u5dq255ixpX8oitBlEiQehAady+mdZaiIZmtE1mv72JtmW9Jr8q2Dg4ODdMfh7+9PYmIiAB4eHiQnW9dDmYIgcOLECcvcTSgUEDsS7v8AXLzuXb6SsKjmaois1/axN822ptfkllDTpk05evQo9evXp1WrVsyfP5+MjAzWrl1Lo0ZGZo1ZGbmaXPqt6kegeyDf9fkOFwcXREFAYSfNfxkZGRlLYfJV9OWXX6ZGjRrSay8vL/73v/+RkZHBtGnTzO5gdSD5djKpuamcv3keZ5UzAGs/msmiV8Zy6fBBwxudXAcJu6HAdpbhlZGRkTE3JreEoqOjpdf+/v4sXLiwjNLVH2MGM0M8Qlj2wDIy1Zko7qwFlHzuDLdvZuDoYmAWBFGE316A3HR4+m+o1cLcblcIexnALUTWa/vYm2Zb0qsQRVE0x46OHz/O/Pnzq/3Cdlqt1ixPV9++mUHKxXPUiYzB0clZ/8P0izA/FpSO8MY1cHA2uA8ZGRmZimCu61lVYlI4/e+//5g9ezYff/wxV65cAeD8+fOMGzeOYcOGlXug7Mcff6R79+5ER0czfPhwjhw5YtR269evJyIignHjxpXruKIokpmZSXnisLuPL6FxrUoGINBlxAEERVe7AFQRzdaIrNf2sTfNtqbX6CD066+/8vTTT7Nq1Sq++eYbRowYwdq1a3nooYcICAjg999/55tvvjHZgQ0bNjBr1izGjx/P6tWrady4MWPGjCEtLa3M7a5evcrs2bNp2bKlyccsRBAEzp07d8/guePaDjZe3EhSdlKZ5SSu3QlCtZqX2zdLYaxmW0HWa/vYm2Zb02t0EFqyZAmvvfYae/bsYe7cuWRkZPDzzz+zbt06pk2bRsOGDcvlwKJFixgxYgRDhw4lLCyMqVOn4uLiwsqVK0vdRqvV8tprr/H8889Tp06dch3XFJaeWMrEbRPZnbQbgIMb1nJ48wZu3yy5uB9wNwiFVL8gJCMjI1OdMDox4cqVK/Ttq3vyv3fv3tIsCUFBQeU+uFqt5vjx4zz77LOSTalU0r59ew4dOlTqdp999hn+/v4MHz6cAwcOlOvYRddnFwQBlUqFIAh6TVyFQoFSqSTcN5xcTS51POqg0WjYs3o5OVmZ+Nepj4vn3WeAlEolCkGLmBSPAtAGx4JWKw0iFr9zKc2uUqkQRdGgvbiPpdkLfS9uL9xn8fXpC8sXtyuVShQKhUF7ddFUmu+FSSTF9Vq7JmPqqfC/LWkqzffCz0VRLNV3a9NUlr2oXltoDRkdhPLy8nB11WWCKRQKHB0dqVmzZoUOnpGRgVarxd9ff50ef39/LlwwvFrp/v37WbFiBWvWrKnQsY8ePSq9vnr1Kg0aNCAhIUGvGzA4OJiQkBD6e/eni6ILJMKhKwdo2L4zt5ITSc9TkxIfL5UPCwvDO+8qioIctCpX4q/ehmvxNG3aFCcnJ+KLlAWIjY1FrVZz4sQJyaZUKomLiyMrK4tz585JdhcXFyIjI0lPT+fy5cuS3cvLi0aNGpGcnExS0t3uQn9/f+rXr19CU2BgIC4uLly6dIlbt25J9nr16hEQEMCpU6fIK7JIX1hYGN7e3hw5ckTvhK9Omgrr6cKFC2RlZelp8vX1RaFQ6NW3tWsqq56OHz8O3D2/bUGTsfUkCAIFBQU2pam0enJxceHWrVulXietCaOz4xo3bsxLL72Em5sbAB9++CFjxozB19dXr5wp88elpKTQuXNnli1bRlxcnGSfM2cO+/bt49dff9Urn52dzYABA3j33Xfp0qULAJMnTyYrK4vPP//cqGMWZpNER0dL2SRmvcvRqtFe3Y8iKwkxcrBkB9u+G5U1yZpkTZWvqaCggKNHj1p1dpzRLaGQkBCWL18uvQ8ICGDt2rV6ZRQKhUlByNfXF5VKVSIJIS0tjYCAgBLlr1y5wrVr1xg79u6cbYWV07RpUzZt2kTdumWscloElUqFQqEgPT1dWqCvtNz70irXoN3BGVX9DhXej0KhMGgvzUdj7YIgcOPGDfz8/AxuY5JWE+2W0lTWMQVBICMjw6Bea9VUlr3oOV10n9as6V52QRD0NNuCprLsRX/D1hp4imJ0ELLEYnZOTk5ERkaya9cuevbsCei+4F27djFq1KgS5UNDQ1m3bp2ebe7cudy+fZs333zT5PEpURS5fPlyidZcUXZc28FbO96iTXAb3u/0PpmpKXj6B6C00so3RrMtIeu1fexNs63pLdeidubkiSeeYNKkSURFRRETE8PixYvJzc1lyJAhAEycOJHAwEBeffVVnJ2dCQ8P19vey0uXGFDcbi4SbydyI/cGt9S3EEWRH994mYL8fEbN+gT/2sVaXQV5sPltXVZczAhQWmegkpGRkaksqjwI3X///aSnpzN//nyuX79OkyZN+Pbbb6XuuKSkpCqdouK++vfR1L8pDgoHcrMy0WoKELQavGsaaHWlHIO9X4NbADR7qPKdlZGRkbEyqjwIAYwaNcpg9xvA0qVLy9z2/fffr9CxC1tSpeHh5EGkf6T0fsJ3v5B14zoOTk4lC1+7ky5eq7luKYdqyr002xqyXtvH3jTbkt5qEYSqCpVKZfLyEwqlEu+agYY/lGZKqF4TlhalPJqtGVmv7WNvmovqLZ5lZ43YzlSs5UAQBBITE8t84GvFmRVsvLiRW+pbpZaRKGwJVeOZEozRbEvIem0fe9Nsa3rLFYQSEhL45JNPeOWVV6T06q1bt3L27FmzOmdpRFEkKSmp1IkARVHk/b3vM3HbRDJyM1g9eyr/Ll1I3u3skoXzMiHtjv5qOGdcIffSbGvIem0fe9Nsa3pNDkJ79+6lf//+HDlyhD///JOcnBwATp8+zaeffmp2B6sStaCmR90eNK/ZHNccuHBwH4c2rsPB4MzZ8br/PnXBveQzTjIyMjIyJTF5TOijjz7ipZde4oknntCb5aBt27b88MMPZnWuqnFWOTO782wA8rKz6fPci9y+mYGDo2PJwqkndf+rcVecjIyMTHXD5CB05swZPvzwwxJ2Pz8/MjJKmVW6mqJQKPD399eb6LI0XDw8iOrWq/QCbZ+DyMFQkGNGD82PKZptAVmv7WNvmovqtYUuOZO74zw9Pbl+/XoJ+8mTJwkMLCVrrJqiVCqpX79+qc8hmVzBnoHg18AMnlmOe2m2NWS9to+9abY1vSareOCBB/jwww+5fv06CoUCQRA4cOAAs2fPZtCgQRZw0XIIgsClS5dKzTKZf2g+3ZZ3Y9HRRZzdt4usG6lWf+dxL822hqzX9rE3zbam1+Qg9PLLLxMaGkrXrl3JycnhgQceYNSoUcTFxelNLGoNiKJIWlpaqYElMVs3ZY82I5vfPpzJdy89i6DVlCx4/m/4YSjs+9bCHlece2m2NWS9to+9abY1vSaPCTk5OTFjxgzGjRvH2bNnuX37Nk2bNqV+/foWcK9qmdJ6Co9GPgpJWRyofxZHJ2dUDgaSEi7vhHN/gUcgtKp8P2VkZGSsFZOD0P79+2nZsiUhISGEhIRYwqdqg4+LDz4uPuAPkbPbIZbW/JVmSpAz42RkZGRMweQg9Pjjj1OzZk369evHgAEDCAsLs4RflYJCoSA4ONjorBqFoYFAUYTEO0HICtKzTdVs7ch6bR9701xUry10yZk8JrRt2zaefPJJ9u7dS79+/Rg4cCDffvstycnJlvDPoiiVSkJCQgxmmWTmZ7Lo2CL+uPRH2TvJuAi5GaBygsAoC3lqPsrSbIvIem0fe9Nsa3pNVuHn58eoUaNYtmwZmzdvpm/fvqxZs4bu3bubtKpqdUCr1XL27FmDkwAmZCXw8YGPWfD3B3w17nHWz//A8E4Ku+KCosHBwMza1YyyNNsisl7bx94025reCs2iXadOHZ555hkaN27MvHnz2Ldvn7n8qjSysrIM2l0dXHkg9AG8z+eRnXaOzNRSWnrXrKcrrpDSNNsqsl7bx94025LecgehAwcOsG7dOv744w/y8/Pp0aMHr7zyijl9q1LCfMN4v9P7FLTKI6XNOYTS7jpEAZw8qvXyDTIyMjLVlXLNHbd+/XpSU1Pp0KEDb775Jj169MDV1dUS/lU5ji4u1G5SxljPfe9Dn5kg2EbTWEZGRqYyMTkI7du3jzFjxnDffffh5+dnCZ8qDYVCQb169Qxm1QiigFJh5JCZUqX7swLK0myLyHptH3vTXFSvLWTHKURbUGECWq2W+Ph4YmNjUalKDxyPbnyU6ylXGePYn7bNexluDQkC2EiGioyMjPVh7PWsOmNUS2jLli107twZR0dHtmzZUmbZHj16mMWxykCr1XLq1CkaN25cogITsxNxT8zh8uEtFJxKZOR0A9lxG16FC/9CtzcheljlOF1BytJsi8h6bR9701xUry1gVBAaP348O3bswN/fn/Hjx5daTqFQcPLkSbM5Vxnk5eUZtP/S7xeOH9xOuvNRghqUsn791f2QfgGUFUoyrHRK02yryHptH3vTbEt6jbp6njp1yuBrW8bHyYfWLfpAiz6AgUovyIPbt8CjDtRoBlZyUhQ+W5CXl2c3d40g67Vl7E1zUb2FWEq7o6Ojxb9Tk8eE1qxZw/3334+Tk/6DmWq1mg0bNlT75RyK9qECJfpTRVEkOTmZmzdvlr0jTT5kp4BCBd61LOu0GRFFkYKCAhwdHe1iIFfWa/vYm+aiegGLa/fx8SEoKMhi+zc5CDVp0oTt27fj7++vZ8/IyKB9+/bVvjuuaBBSKpVkZWXh5eUlfcFJSUlcT7+Op68nXu6euDt7GN7R7TS4nQKOHuBbtxIVVAxRFNFqtahUKrv5wcp6bRt701xUL0Bubi6urq5m1y6KIjk5OaSmpuLj40NwcLBZ91+IyYMZoigaFJuSkoKnp6dZnKosFAoF3t7e0nutVsvNmzfx8vNCQy4FNzTke2rxrmlgxdjcAnBQgIcnuLhUotcyMjIyOkRRRBAEXFxcLBKAC5//TE1NpWbNmhbpmjM6CA0aNAiFQoFCoeCxxx7DweHuplqtlqtXr9KpUyezO2hJtFotR44cISYmBpVKRUFBAQAe7h6oc0RAY3jmbICCHN1/R/fKcdZMiKJosTun6ois1/axN81F9VYGbm5ugK7br0qDUM+ePQE4efIkHTt2xN397sXX0dGRWrVq0bt3b7M7aGkMLZHr4eSBi1cAWo0GKKW30tFdNx7kYH2tIDt7NEzWawfYm+bK1GvpwG50EJowYQIAtWrV4v7778fZ2dliTlUXVA5lfD2+9SrPERkZGRkbxeTH/QcPHmzzAUgQS1lB1QZo3Lgx//zzj9HlJ0+ezLhx4yp0zKtXrxIREVHtk1ZkZGQqH6NaQq1bt2bTpk34+fnRqlWrMptne/fuNZtzlkapVNK0aVO9xaFEUSThxkXcFI7U8A3G1d1AsoWgBYUSqln/8/Xr1/nqq6/YunUrycnJeHp6UrduXQYMGMDgwYOlPuTi6fW2jq1Orlsa9qYX7E+zLek1KghNmTIFDw8P6bUtDf4VvyALooCzVomTVoEmNw8MBaHsVN0zQh41wSuk1H1fu5lLxm01vu5O1PKx7Elz5coVRo4ciaenJy+//DIRERE4OTlx+vRpli9fTmBgoDSlki3VnzHIem0fe9NsS3qNCkKDBw+WXg8ZMsRizlQ2giCUeFhVqVTi5xWIQtDg5F5K5ps2HxB1iQmlcO1mLt0//Jd8jYCzg5K/X+tq0UD0v//9D5VKxcqVK6VsFtAtPNizZ0+9gcz8/Hzp9enTp5k5cybx8fG4urrSu3dvJk+erJd4ArBgwQJ++OEH1Go1/fr146233pIC+LZt2/jiiy84e/YsKpWK2NhY3nzzTerWrR7PT+Xk5Oh9J7aOvekF+9NsS3pNHhM6fvw4p0+flt7/9ddfjBs3jo8//hi1Wm1W56oCBQpc3dzxCQjE2bWUStbc0VnKct7Xbuay72I6+Rrd2FK+RmDfxXSu3cy1hMtkZGSwY8cOHnnkkVJPTEN3Tjk5OYwZMwZvb29WrFjB3Llz2blzJ9OnT9crt2vXLs6fP8/SpUv5+OOP2bx5M5999pn0eW5uLk888QQrV67k+++/R6FQMH78eIOZhzIyMjJFMTkIvfPOO1y6dAnQdQG9/PLLuLq6smnTJj74wMBM07aI9k5LQlUyQaOwBfTSL/Eo71z3lQp46Zd4un/4r0UCUUJCAqIo0qBBAz17mzZtiIuLIy4uzmDd/P7776jVambPnk14eDjt2rXjnXfeYe3atdy4cUMq5+TkxHvvvUejRo3o2rUrL7zwAkuWLJGCTJ8+fejduzf16tWjSZMmvPfee5w5c4Zz586ZXauMjIxtYXIQunTpEk2aNAFg48aNtG7dmo8++ohZs2bx559/mt3ByiZPnUt69g1uq28bLiBoQdDoXhtoCWXcVkstIOFOD1jh/3yNQMbtymstrlixgjVr1hAWFmawlXr+/HkiIiL0Wk/NmzdHEAQuXrwo2SIiIvQGQuPi4sjJySEpKQnQnROvvPIKPXr0oHnz5tLYU+HnMjIyMqVRrml7Cu+Ad+3aRdeuXQEIDg4mIyPDrM5ZGqVSKc0hV4g6Nwdteja3NFrcgwyMCWnvXMwVKoNLOPi6O+HsoCRfI6BU6AJQ4X9nByW+7ubPTKtbty4KhUIvcIBuPAjApdi0QuZOsX/uueeoVasWM2bMoGbNmgiCQL9+/aQZKKoaW+k7NxZ70wv2p9mW9JrcEoqKiuKLL75gzZo17Nu3TwpCV69eJSAgwNz+WZziLQSlQgkKcHIuZSaEwiCkMhxMavm48vdrXZn7YKxeS2jug7EWS07w9fWlQ4cO/PDDD+Tk5NyzfGGSQsOGDTl9+rTeNgcPHkSpVOp17Z0+fVpv2vj4+Hjc3NykG4+LFy8yduxY2rVrR8OGDcnMzDSjuoojP01v+9ibZlvSa3IQeuONNzhx4gTTp0/nueeeo1493cwBf/zxB3FxcWZ30JIIgsCJEyf0BtA9vHwJqF0PX5+ahjdSqMDFF1y8St1vLR9XWjXww9lB9/U6Oyhp1cDPotlx7777LlqtlqFDh7JhwwbOnz/PhQsXWLt2LRcuXNCb86kw8Pbv3x8nJycmT57MmTNn2L17N9OnT2fgwIF6NxRqtZo333yTc+fOsXXrVj799FNGjRqFUqnE29sbHx8ffvnlFy5fvsyuXbt4//33LaazPOTmWiYhpLpib3rB/jTbkl6Tu+MaN27MunXrStgnTpyo161lzRRO1GoQZw/d3z0obBFV1nNCdevWZfXq1Xz11Vd89NFHpKSk4OjoSFhYGE8++SQPP/xwiW1cXV1ZuHAhM2fOZNiwYXop2kVp164d9erV45FHHpFStJ9//nlA16X5ySefMGPGDPr160eDBg146623GD16tEX1ysjI2AYmrydUyLFjxzh//jwAYWFhREZGmtUxS1HWonZ5eXlcuHiB0AahJcZRbIXCNULc3Nxs6oG30pD12j72prmoXsDi2vPy8rh48SINGjSwyHXR5JZQWloaL730Evv27cPLS9cllZWVRZs2bfjkk0/w8/Mzu5OWpGjrbe9vK3GoVYsETwUNgsNwVDmW3ECrBqVjtZuyxxTs4YdaFFmv7WNvmm1Jr8n9Z9OnTycnJ4f169ezd+9e9u7dy++//052djYzZsywhI8WQ6VSERcXJ42XJJ49iUqrQBAEXYJCcUQRUk9C0mHd8t5WiEKhsJs7RpD12gP2ptnW9JochP777z/effddGjZsKNnCwsJ499132bZtm1mdszSiKJKZmSllmnR66DGcPTwI9Ak2HIQEDYgCIIKhVpIVIIoiGo3GprJrykLWa/vYm2Zb02tyEBIEAUfHkhdgBwcHq5umRRAEzp07J/ntX6sOzq5uuDqVcpdRmJ6tdNTNom2lFJ07zh6Q9do+9qbZlvSafCVt27YtM2fOJCUlRbKlpKQwa9Ys2rVrZ1bnqh2FXXAOtr2ekoyMjExlYXJiwjvvvMPYsWPp0aMHQUFBACQnJ9OoUSOrnjvu3P49XLl1DXff2txW3zacBXKPB1VlZGRkZEzD5CAUHBzM6tWrpZmVQffkffv27c3uXGVQGGx2/foT2VmZNH3sUbILsvHHv2RhG2kJ2crzXMYi67V97E2zLek1KQht2LCBLVu2UFBQQLt27az+gUSVSkVkZCSiKBLUsBEJV87j5OSCi0P5puyxBhQKhU2tyngvZL22j71pLqrXFpITjA5CP/30E9OmTaNevXq4uLiwefNmEhISmDRpkiX9syiCIJCeno6fnx+9npkgPZTl7exteANXH1CqoLQgZQUUZtY4ODjYTIpnWch6bR9701xUry1gdJvuxx9/ZMKECfzxxx+sXbuW999/n59//tksTvz44490796d6Ohohg8fzpEjR0otu3z5ch5++GFatWpFq1atePzxx8ssXxaiKHL58mXj7ybca4BfKDhZ9wy2RSdtjYiI4K+//iq17J49e4iIiCArK8sivuzatYv77rsPrVZrkf1DyUlqbRm1Wk2vXr04evRoVbtSqdhTHYNt6TU6CF25coVBgwZJ7/v3749GoyE1NbVCDmzYsIFZs2Yxfvx4Vq9eTePGjRkzZgxpaWkGy+/Zs4cHHniAJUuWsGzZMoKDg3nyySf1svVMRaPWjfUIonWlmBfy888/ExcXh0ajkWy3b98mMjKyRJfpnj17aN68OQkJCUbtOy4uju3bt+Pp6QnAqlWraNmypdl8/+CDDxg7dqzeBKugmyqkdevWtGnTxuAPrrTgOXnyZMaNG6dnS0hIYMqUKXTu3JmoqCi6d+/OK6+8YvELtSk3V4V8//339OnTh5iYGLp06cJ7772nl4771VdfMXToUOLi4mjXrh3jxo3jwoUL0udOTk6MHj2ajz76yCKaZGTMjdFBSK1W661hoVQqcXR0rHC++qJFixgxYgRDhw4lLCyMqVOn4uLiwsqVKw2W/+ijj3jkkUdo0qQJDRs2ZMaMGQiCwK5du8rtw7J3J/HNhCd56ZenSL6dTL6h2RAKcnWJCdWwD7ZNmzbk5ORw7NgxybZ//34CAgI4fPiwXh3t2bOHoKAg6tata9S+nZycqFGjhkW6Ofbv309CQgJ9+vQp8dkff/xBWFgYoaGhZbbU7sXRo0cZNWoUly5dYtq0aWzYsIHPPvuM0NBQZs+eXRH3y8TUmyuAdevW8dFHHzFhwgQ2bNjAzJkz2bBhAx9//LFUZu/evTzyyCMsX76cRYsWodFoGDNmjN5yHPfffz8HDhzg7NmzFtMnI2MuTOpUnDt3rt4AYEFBAV988YV0lwwwZcoUo/enVqs5fvw4zz77rGRTKpW0b9+eQ4cOGbWP3NxcNBoN3t6ljOOUQmH3j5urK+lXryBoNVxXiAiigEKhkLropNdp51Dk30L0qg1ufnplio4TKRQKxPzs0g+sUIJjkUFUAyu4KhQKRMeSXX56xyxCaGgoNWrUYM+ePTRr1gzQXax69OjB7t27OXToEG3atJHsrVu31ttPeno648ePZ/v27QQGBjJp0iS6d+8O6ILWY489xt69ezl58qRUvxEREQCMHz+e559/HrVazSeffML69eu5desWjRo14tVXX5WOa8j39evX0759e5ycnEp8tmLFCvr37w/Ar7/+yn333VdiP6Iolqin4p+98cYb1K1blx9++EGvtdWkSRNGjx5doYHd0upDoVCwaNEihg8fzpAhQwD43//+x7///suKFSt45plnDO7n4MGDNG/enH79+gFQu3ZtHnjgAY4cOSId59tvv9U77qxZs2jfvj3Hjh2T6tXX15fmzZuzfv16XnzxRbNpqk72ooiiKNVtdfKxIprK4l56rQ2jg1CrVq1KrNwZFxfHlStXpPem3i1nZGSg1Wrx99dPh/b399frYiiLDz/8kJo1a5qcIl60K6bf27NwETV0UxQg5oto8jXkkIOjoyNOTk7k5+fj8mVHAIoqLHytCe1B/tClODs76wYLPwxDUWB4vQ9tnXbkPaRr5bm5ucG8GBQ5Je+OtW+l6bVglEolrq6uaDQave4plUqFi4sLrVq1YufOnYwaNQqA3bt38/TTT6NWq9m+fTvR0dHk5eVx5MgRhg0bhlqtlgLxggULePXVV5k4cSKLFi3itddeY/369Xh7e0uzSeTl5dG4cWNee+01vvzySzZs2CAteZGTk8P06dO5cOECH3/8MTVq1GDjxo08/fTTLF++nHr16uHm5oZWq9XTtH//fqlbt6imxMRE4uPj+fjjjykoKGDWrFmcO3eOunXr4uzsLJXNz88nJ0e/nrRaLVqtlpycHM6fP8/Zs2eZNWuW3nEL68nR0VGvBeHq6irpWbhwId99952unu+c18V/8Bs2bKBmzZol6kmlUnH8+HEee+wxaf8qlYr27dtz8OBBvWM6ODhImiIjI/ntt9/Yu3cvcXFxpKSksHXrVu6//35pGycnJxwdHcnLy0MQBKk7vPBGMC8vD1EUadKkCXv37kUURUlTUdzc3BBFUW9dmsI5yYrX073OvYKCAr1VdItqKtpFXLyeCimuqXg95ebm6n33ReupOLamqax60mg0NjFzgtFBaOnSpZb0o1x8/fXXbNiwgSVLlpi8ZHV0dDQKhYKUlBSCgoJwcHDAPyeHS5cu4erqqvewqrND2cFVpVIVW2639PJKpdKopXlL7lOHg4ODwayYdu3aMWvWLJycnMjLy+PUqVO0bt2agoICli1bhpubG0eOHEGtVtO8eXOcnJyki+uQIUMYMGAACoWC119/nZ9//plz587RqVMn6XkEFxcXvLy88PPTtQJr1ry76F9iYiK//fYbf//9N4GBgYBuye89e/awYcMGXnnlFYOakpKSqFmzZglN69ato3PnztLCeh07dmTjxo3SGkZOTroUeWdnZ739OTs7o1KppONcvXoV0D3HVvgjL0ppab1ubm6MHj2aAQMGGPy8kJo1axqsp9TUVLRaLSEhIXqfFd5cGapXJycnhg4dyu3btxkzZoyUAfXQQw9Juovi4uKCIAh88sknNG/enMaNG0t2jUZDSEgImzdvljQbOmbhxaw4pp57jo6OBqfycnJykuqqKKX9VktbJqCseoK72WJgO5qKUlyTKIqo1WocHR1xdXW1+gXuqjTHz9fXF5VKVaKfPC0t7Z5LhS9cuJCvv/6aRYsWST9AUyhszqakpBAcHAzoAkTh3X3RC5aiIAee2KjrRgsIL7EvhUKlt7SD4vVzpR5XoVDqLwPxkuHB8dJalaXZi44LZWVlUb9+ffz8/GjdujVvvPEGarWavXv3UqdOHQICAvQ0RkRESK/d3d3x8PAgPT1dr0zx76WoH2fPnkWr1UpdZoWo1Wp8fHwMbgO6u3ZnZ2c9u1arZc2aNbz55puSfcCAAcyZM4cJEyYY9Knod2PoWBqNxuBChWW13H19ffH19S31c2MobXHE0mx79uzh66+/5t133yUmJoaEhARmzpzJ559/zvjx40uUnzZtGufOneOnn37S011QUICLiwt5eXnlGssz9dyrKntRCgoKcHR0rHY+VkRTWdxLrzVRpUHIycmJyMhIdu3aRc+ePQGkJIPCbiVDfPPNN3z55ZcsXLiQ6OjoCvlw/t8/Ea5dxiWuPoczThCpMLA4X0GOLgC5+YOT+713akyZ8pQtg3r16hEUFMSePXvIzMykVatWAAQGBhIcHMzBgwfZs2cPbdu2LbFt8Ts+hUJh0mS0OTk5qFQqVq5cWSLLraxWn6+vb4nU7+3bt5OSksLLL7+sZ9dqtezatYsOHToAumB569atEvvMysqSuqbq168PwKVLl2jevLnRegC+/PJLvvrqqzLLrF+/npCQkBL28t5czZs3jwEDBjB8+HBAd3OQk5MjTZVV9Cn5adOm8e+///LDDz9I02cVJTMz0+rW9pKxT6r8aacnnniCSZMmERUVRUxMDIsXLyY3N1ca0J04cSKBgYG8+uqrgK4Lbv78+Xz00UfUqlWL69evA7qLnbu7aRf0gvx8ruzbyZW9OwgJHsaScz/xbuN3SxZU3+mnNZAsUJ1o06YNe/fuJTMzkzFjxkj2li1bsm3bNo4cOcJDDz1UoWM4OjqWeKanSZMmaLVa0tPTTUrfbtq0KefO6bcaV6xYwQMPPMBzzz2nZ//yyy9ZsWKFFIQaNGjA8ePHGTx4sFRGq9Vy6tQp6SLepEkTwsLCWLp0KYMGDSoRILOysqSFGYvz0EMPlWjZFadol2RRyntzlZeXV2I6luID0KIoMn36dDZv3szSpUupU6eOwX2dPXuWJk2alOm/jEx1oMqD0P333096ejrz58/n+vXrNGnShG+//Va6Y0xKStL7YS5btoyCggJeeOEFvf1MmDDBYN95WYiClsje/RBzb+NTK4Ju6m44q4r17YqiLj0brCIITZs2DY1GQ+vWrSV769atmTZtGgUFBbRp06ZCT1rXqlWLnJwcdu3aRUREBK6urjRo0ID+/fszceJEJk+eTJMmTcjIyJDKdO3a1eC+OnbsyOrVq6X36enp/PPPP3z++eeEh+t3ew4cOJAJEyZw8+ZNfHx8eOKJJ3jzzTdp0KABHTp0IDc3l6VLl5KVlSUFIYVCwXvvvccTTzzBI488wtixYwkNDeX27dv8888/7Nixgx9++MGgbz4+Pvj4+JT7e7rXzRWUvMHq1q0bixYtomnTplJ33Lx58+jWrZsUjKZOncrvv//O559/jru7u3QT5unpKY0/ODg4sH//fpMz46wZW5k9wFhsSq9oZ2g0GnH//v2iRqMp8Vlubq544sQJMTc3965REEQxJ10Ub14VRUFbiZ6azpUrV8Tw8HCxb9++evarV6+K4eHhYp8+fUpsEx4eLm7evFnP1qJFC3HlypWiKIri7t27xfDwcDEzM1P6/J133hFbt24thoeHi/PnzxdFURTVarU4b948sVu3bmJkZKTYoUMHcfz48eKpU6dK9TcjI0OMjo4Wz58/L4qiKC5cuFBs2bKlqFarS5TNz88XW7ZsKS5evFiy/fbbb+LgwYPFuLg4sX379uLTTz8tnjx5ssS2Fy5cECdOnCh27NhRjIyMFLt16ya+8sor4vHjx0v1zRwsXbpU7Nq1qxgZGSkOGzZMjI+P1/t81KhR4qRJk6T3BQUF4qeffir27NlTjI6OFrt06SL+73//0/vuw8PDDf4V1pcoiuLBgwfFli1b6p/HMjaJIAhidna2KAiCxY5h8LpoRhSiaHqi+f79+1m2bBlXrlxh/vz5BAYGsmbNGmrXrm3Wp+ktgVarJT4+ntjYWBQKBQkJCdStWxelUinNHdegQYNSs1qsHfFOZk3R7LiqZPbs2dy+fZtp06ZZZP/VTa+lEUWRF198kSZNmjB27NiqdqdSsMc6LtQLujFZSy73benrosnzgf/xxx+MGTMGFxcXTpw4IeXYZ2dn33Mgt7qRn3Ob5GtX0RSorXbKnvJQ9DmHqmbs2LGEhIRYdFXe6qTX0qjVaho2bMjjjz9e1a5UKvZUx2Bbek0OQl988QVTp05lxowZev2SzZs358SJE2Z1ztL89tFMdsx/n3P7djP0t6E8vP5h1Fq1fqGcdMi/BYLlJti0Z7y8vHjuuedsan2UqsTJyYmnnnrKZlvyMraHyaNbFy9eNNjl5unpabGZliuD9Lx0nAX9Z1YQRci8AqIANRqD0n7WLJGRkZGpDEwOQgEBASQkJFC7dm09+4EDB0pNF62uDH1zOslJyQQHB7OqYBU3sm6gTSvS4tHk6wKQQmnVawgVx9CT4LaMrNf2sTfNtqTX5D6QESNGMHPmTA4fPixNe/Pbb78xe/ZsRo4caQkfLYaDgyO169RB5eCAv6s/9bzr6beECgqfD3LVn+XAilEoFHYzgAuyXnvA3jTbml6TW0LPPPMMgiDw+OOPk5uby6hRo3BycuLJJ5+0uuW+tVotFy5cIDQ0tMSDjECRIFS9nw8yBVEUyc/PLzFdjq0i67V97E1zUb22gMlBSKFQMHbsWMaMGUNCQgI5OTk0bNjQ5NkKqgOHN2/k7LEj3OrdkYOKM4S6hRJM8N0CVjJTgqlYchXT6ois1/axN822pLfcj906OTkRFhZmTl8qnbN7tnP1xDHEMHfmZX5Lz5CePFP7zlovogga65gpQUZGRsZaMTkIjR49uswm75IlSyrkUGUS3q4TCg9vAuuEMSBgABGeEXc/1OQVSUqwjWavjIyMTHXD5MSEJk2a0LhxY+kvLCyMgoICTpw4UWK+r+pOs5730f2xp+kUdx8zO85kRMSIux86OENABPjUs5mkhKtXr9K4cWOjFww0hoiIiAotv21pcnNzmThxIi1atCAiIqLUxwiuXr1KREQEJ0+erGQPzY+h9W4qwuTJkxk3bpxZ91leXn/9db788ssSdnNrru6YS++2bdsYOHCgRR8Wvyfmmv9n/vz54vvvv2+u3VkMk+eOsxImTZqkN5dY69atxSeffFJvLrXCueVOnDhhtuMamnuuslm3bp3YuHFj8X//+1+Jz3788Uexbdu24unTp8XU1NRS59jSaDRiamqqWFBQYGl3jaIsTZVNVlaW3vx1VcXJkyfF1q1bi9nZ2SU+K+v7WrlypdiiRQuD+zR0/m7atEkcNWqU2Lx5czE2Nlbs16+f+Omnn4oZGRlm0WGIjIwM8ZVXXhHj4uLEFi1aiFOmTDGosyipqania6+9JrZr105s1qyZOGjQIHHTpk0m73fIkCHi6tWrSz2Opa+LZntMfcCAAaxcudJcu6sU1Pl5HD18mHx1XlW7YhY6derE9u3b2b59O99//z0ODg4llkSAu0tA/7+9M4+Lefv/+GuapqJQyJYkS4sWTcKV0i2EUtd6EZElZLtZKvslS3ERySXLtaRra6GU7s8WuogKiUJEkUtEScvMNOf3R9/5aJopTdto+jwfD4+Hzud8zuf9+pzPnPc553M+591YKB9mWRwhISGYNWsWoqKiRMIdZ2ZmQltbGz179oS6urrYqWQOhwMmkwl1dfUfZnfiqjRVBflfKOi6qN/S0lLw+Xy0aNGi0pAXDUlQUBCGDRsmsgiKEILTp09j5syZEt+vivj5+WHx4sUwNDTEgQMHEBkZieXLl+PJkyc4d+5cbSVUyrJly5Ceno7Dhw9j3759SEhIwNq1a8XmFdSxp6cnMjIy4Ofnh4iICAwdOhTu7u5CO9dUp9wxY8ZIN3J2XXmz8PBwMnDgwLoqrt4oPxI6uc6LbPvVnizcM4EMPDGQ3Mi4UebxC78S8ukVIQU51M7ZXzlfyVfOV6GeNIfHIV85X0kJr0ToGoK8peV23eaUluUt5hVXK6+keHl5ETc3N6G0u3fvEh0dHfLx40dCyLeRUGJiIuHz+YTH45EVK1YQa2trYmRkRGxtbcmRI0dEyj5z5gyxs7Ojdsdev349daxiT3LXrl1k4MCBYnezFhAcHEwGDx5MDAwMiK2trUgvTEdHhwQHB5M5c+aQ3r17Uzt1iyMzM5MYGxuT/Px8Mn78eBIREUEdmzJlitDocMqUKYQQQqytrUlAQADx8PAgbDabeHl5iR0lPn36lMyePZuw2WxiYmJCJk2aRF69ekUIIeTBgwfExcWF9OvXj5iampLJkyeTlJQUER2nT58m8+bNI8bGxmTo0KHk0qVLlWqpjiZCvvXsr1y5QmxtbYmxsTFZuHAhKSwsJKGhocTKyoqYmZmRDRs2CI34S0pKiK+vL7GwsCC9e/cm48aNI7dv3xYp99KlS2TEiBFEX1+fZGVliTxbpaWlZP/+/WTIkCHEwMCAWFlZkT///JM6vnXrVsouGxsb4ufnJ7Qzur+/P3F0dCTh4eHE2tqamJqaEnd3d/Lly5dK7wmPxyN9+vQhV69erfR+5eXlVXm/xFH++X3w4AHR0dER+xsghNTbaDA9PZ3o6OiQ5ORkKu3atWtEV1eX/PfffyL5BTtnm5iYkPDwcKFdtPv160dOnz4tUblv3rwhOjo61LNdkfoeCUnc7VuwYEFFJ4acnBykpKT8MPPGklLAKUBeSd63WELcEqDwI8D4XBZNFUD/v/sDAK5NuIbWSmURKw8/Oozd93ZjbM+xWGe+jirv59M/o4hXhJixMdBQ0QAAnEw7ia13t8JO2w5bBm2h8g4PHY5PJZ8Q7hiOHmplqw3PpZ/DOJ1xtdL09etXREREQEtLq9K4OHw+Hx06dMCuXbugqqqKe/fuYe3atVBXV4ednR0A4O+//4avry+WLl2KQYMG4cuXL0hKShIpixCCjRs34urVqwgODoaWlpbYa168eBGbN2/GihUrYG5ujtjYWKxcuRIdOnQQivoaEBCApUuXYtWqVeK/4fofYWFhsLKyQosWLeDo6IiQkBA4ODgAAHbv3o1t27bhyZMn2LNnj9A8+l9//YX58+eLPM8C3r17hylTpqBfv344evQoVFRUkJSURG0c+fXrV4waNQqrV6+myps9ezb++ecfqKioCOnw8PCAp6cngoKCsGzZMly9erXKWEVVaRJQXFyMoKAg+Pn54evXr1iwYAEWLFiAFi1aYPfu3cjJycGiRYtgampK1aUgHLifnx/atWuHixcvYtasWYiMjKSi0BYXF+PAgQPYuHEjVFVV0aZNGxH7tm/fjjNnzmDFihXo06cP3r9/j4yMDOq4srIyfHx80K5dOzx9+hRr1qyBsrIyXF1dqTyZmZm4fPky9u3bh/z8fLi7u+PAgQMiEXUFPHnyBF++fIGhoaHY+2VhYVHl/aoOERERaN68OZycnMQer2o0aG9vj+zs7EqP9+nTBwcPHhR77N69e2jZsqVQlGhzc3PIyckhOTkZQ4cOFXueiYkJoqOj0a9fPygpKSEmJgYlJSVULLHqltupUye0bdsWCQkJ6NKlS6Ua6guJnZAgdLIABoMBbW1tLFq0CBYWFnVmWEPguGw1kh88wBSD7iggX9Ga2Rr/FfxXtjIOKFua3YgWJcTGxoLNZgMo295dXV0dgYGBlW4OymKxhIIDampq4v79+4iJiaEarr1792L69OmYNm0alc/Y2FioHB6Ph2XLliE1NRUnTpxA+/btK7Xx0KFDGD16NCZPngygLELq/fv38ddffwk5oZEjR2Ls2LFV6uXz+QgPD6ccgZ2dHXx9fZGVlQVNTU2oqqqiWbNmYLFYIlNxP/30E2bMmEH9/fr1a6Gyg4ODoaKigh07dlBbpGhra1PHBwwYIJR/w4YNMDMzw927d2FtbU2ljx49GiNHjgQALFmyBEFBQUhOTsagQYNqpEkAl8vFunXrqEZj2LBhiIiIQFxcHBgMBgwNDdG/f3/cvn0bdnZ2yM7ORlhYGK5evUrVz8yZM3Hjxg2EhYVhyZIlQuXq6emJta+goADHjh3D2rVrqai2Xbp0EdpPsnxntHPnzsjIyEBUVJSQEyKEwMfHh3LYjo6OuHXrVqVOKDs7G0wmU8QpCu6Xh4dHlferOrx69Qqampo12hJn//79Ve5sXdWGsh8+fBAJxS4vL49WrVpRQQvFsXPnTixevBjW1taQl5eHkpISAgICqA6gJOW2a9euSidan0jkhEpLSzFmzBjo6OigVatW9WVTg6HUXBl6BoZo2bIl2jMYKC7+n/MRfB+k8O37oHineABAM/lvm5hON5iOKfpTIC8nfBtjf40tK7/cfnMT9SZibM+xYMoJ9+pjxsaI5P2lxy810tO/f3+sW7cOAJCXl4cTJ07A1dUVZ86cgYaGBpWv/I8sODgYoaGhyM7ORklJCbhcLtUAffz4Ee/fvxdpcCvi4+MDBQUFnDp1SuShr8iLFy8wYcIEoTRTU1ORpf3ierwV+ffff1FUVAQrKysAQOvWrTFw4ECEhobC3d2dyifOCX+v/NTUVJiZmVXaIH348AE7d+7EnTt38PHjR/D5fBQVFYn8kHV1vy37b968OVRUVJCbm1trTc2aNRPqtbZt2xYaGhpQVlamPmRs27Ytda2nT5+itLQUw4cPF7oeh8MRGpWxWCwhmyvy4sULcDgcoQ5DRaKjo3Hs2DFkZWWhsLAQPB5PaHQIlEXoLZ/Wrl07fPz4sdIyi4uLxW5V8++//6KwsBA2NjYAKr9f1YHU4j1a+d9XQ6CoqIjt27fjy5cv2Lt3Lzp27IjLly/D3d0dwcHBVdZhZeUVFRXVk7VVI5ETYjKZmDFjBqKjo2XCCTEYDPE6yo+E/kdzMR+sspgssJiijZTYvHIssOSqn7cmNGvWTGgazMDAAGZmZjh9+rRQD1NeXh4MBgNRUVHYsmULvLy8wGazoaysjEOHDuHBgwcAUO1tQczNzREVFYW4uDg4OjrWyPaKNG/+/Q+EQ0JC8PnzZ/Tu3ZtK4/P5ePLkCRYtWkQ5HwaDIdJ4NWtW9Y7o3wuF4OXlhc+fP2PVqlXo1KkTFBQUMGHCBJFFFBWdGIPBqHI5bHU1VVxAwWAwqHoVHCt/rcLCQjCZTISGhopMb5a/10pKSlV+B/i9Z+LevXtYtmwZFi5cSE2RRUVF4fDhw0L5xC0AqcoJqKmpoaioSCiYG1B2v/Ly8tCnTx8qreL9UlFRQVFREfh8vlCHRLBcX+AMu3btisTERHC5XIlHQ7WZjivfWRDA4/GQl5cHdXV1kfwMBgPZ2dkIDg5GZGQkNDQ00Lx5c+jr6yMhIQHBwcHw9vaWqNy8vLzvdiDrC4mn43r27InXr183uh2zxZF8+R8kJ9/BF8Nm0OreCzadbP63U0JJ2Z1hNe7QDYLGt+JqIcHquKSkJLDZbGpqDCibqxegoqICDQ0N3Lp1q8qe7+DBg2FjY4OlS5eCyWTC3t6+0rzdunVDUlISNZUDAElJSRLvvvHp0ydcvnwZfn5+Qufy+XxMmjQJcXFx1JRXaWkpCCES7Sumq6uL8PDwShukpKQk/P7779SI5e3bt/j06ZNEGmqjqTLI/1ZOVXSy+vr6KC0tRW5ubq2iH3ft2hVKSkq4ffu22Dbg3r176NSpk1BU17qY5tHX1wcAPH/+nPq/4H7t2LEDmpqa1N5xFe+XtrY2eDweUlNTYWBgQJX56NEjAN+mWR0cHBAUFIS///5baPpZQH5+fqXvhWozHcdms5Gfn4+UlBRqhH779m3w+XyRqW+grI4Fz1rFUT6TyaSceXXLLSkpQVZWFnr16lWpjfWJxE7I3d0dW7ZswW+//QYDAwORHmvFYfePTGrcVbx7nILY4hywijTKnFApFwAB5FgAs3F9AMfhcKi53vz8fBw/fhyFhYVC7yiAbz1OLS0tnD17Fjdu3EDnzp1x7tw5PHz4UChMx8KFC/H777+jTZs2GDRoEL5+/YqkpCSRzWqHDh2KrVu3wtPTE0wmU2TaR8CsWbPg7u4OfX19mJub4+rVq7h48aJIT/l7nDt3DqqqqhgxYoSIc7GyskJISMh3G+yqmDx5MoKCgrBkyRLMnj0bLVq0wP3792FsbIxu3bqha9euiIiIgJGREQoKCrB169ZaB5KrK03iRhTa2tpwcHCAp6cnli9fDn19fXz69Am3bt2Crq4ufv7552rZqKioCFdXV/zxxx9gsVgwNTVFbm4unj17hvHjx0NLSwtv375FVFQUjIyMEBsbWycfM7du3RoGBgZITEyknFD5+1VUVCQU4rr8/erZsycsLCywcuVKLF++nHpPtXnzZtjZ2VHvyHr37o1Zs2Zhy5YtePfuHYYOHYp27dohMzMTJ06cQJ8+fcQ6J6B203Hdu3eHpaUl1qxZg/Xr14PL5WLDhg2wt7enbHv37h2mTZuGrVu3wsjICFpaWtDS0sLvv/+ORYsWoUOHDrh8+TL+/fdfKsJ1dcoFgPv374PFYsHExKTGGmpDtZ1QQEAAZsyYgdmzy/ZWc3NzE/qhCHqajemL8+59fkIesxSmujpo07lrWSKfC0CuUYZvuHHjBrU4RFlZGd26dcOuXbvQv39/sfknTpyI1NRULF68GAwGA/b29nBycsL169epPKNHj0ZJSQmOHDmCrVu3QlVVtVIHM3z4cPD5fHh6ekJOTg62trYieYYMGYKVK1fir7/+wubNm6GhoYHNmzdXamNlhIaGYujQoWJHN7a2tvD09Kzy3cv3UFNTw9GjR/HHH3/A2dkZcnJy0NfXp6Z9Nm3ahDVr1mD06NHo2LEjFi9ejK1bt9b4ekD9a/Lx8cHevXvh6+uL9+/fQ1VVFSYmJtV2QALmzZsHJpMJf39/vH//Hurq6pg4cSKAslHxtGnT4O3tDQ6Hg59//hlubm4ICAiosd0Cxo0bh3PnzmHKlCkAqn+/WrduDT8/P/j7+2Pt2rV4//49OnTogCFDhois6PXw8ICBgQH+/vtvnDx5EoQQaGpqYtiwYUKj97pm27Zt2LBhA6ZNm0b9dgSLU4CyBSMZGRnUexsWi4XAwEBs374d7u7uKCoqQpcuXeDr60uNzqtTLgBERUXBwcHhu1PU9QWDVPNtnL6+PuLi4vD8+fMq8wmWB/6olJaW4v79+5TXF/yfyWSiuLgYGRkZ0O6iCSVFlkzuGUcIQWFhoVCvUZah9coOxcXFGD58OPz8/KhVoIBsaxZHeb0AaqU9NzcXI0aMQEhISKWvWKh2UVu7XsLGV3skJPBVP7qTkQQ5OTn06tVLdPUUU14mHZAAafV4pAWtVzZQUlLCli1bxL57k1XNlVFXet+8eYPff/9dqu/4JXonJIu9DHnWj7FNS0Mii/VYFbRe2aGyaVtZ1iyOutJrZGQk9DGrNJCoBR42bNh3xd+5c6dWBjUkZzasQubjZMSbFWDm2OX4WU4dKCgAvqoASo1/9V9llB/KNwVovbJPU9MsS3olckILFy4U2TGhMUMIHwwCFHOLoMBUALIfAqQjwJHOR1s0NDQ0TQ2JnJC9vb3YvaQaK/a/eeFW4k3YG3SFRusuwP0YQL0joFD3L99oaGhoaESpdigHWZxzbdaiJVqrdkBPdT20UGgBvP/f8nL5pvWSk4aGhkZaVNsJ1WZfpR8VOTk5mJiYlK2OK/kCfH5ZdkBetkdCsjKXXF1ovbJPU9MsS3qrPR2XlpZWn3ZIhcTYC/g39TI6mhji1046KNspQR4Qsx+cLCHpFjaNHVqv7NPUNMuS3jqLrNoYSb50AbxrTxBx+wSQfa8ssZFt1VMTpLVbrrSg9co+TU2zLOlt0k6oo6EB8rspw0C7D1DKARRbyfwoqDJev34NXV3dBtt2afLkyYiMjGyQazUFTpw4ITaUOw3Nj06TdkK2v7rCcZwnPB03ApZLgFmXAMXKoyc2Bu7duwd9fX1qj7/q0rFjR8TFxaFnz571ZNk3Ll++jA8fPojdbTswMBD6+vpit73fvXs3fvlFNNaSOAdKCMGpU6fw66+/YuDAgejbty/GjBmDI0eO1GsvMjs7G7Nnz0bv3r0xYMAAbNmypcrdlYGy3ZynT58OMzMz9O/fH2vWrMHXr1+p42lpaViyZAmsrKxgbGyMESNG4OjRo0JljB07Fo8ePUJCQkK96KKhqS+atBMCKmyFzmA0uk1LKxISEoIpU6bg7t27ePfundg84uaSmUwm1NXVxcZ5qWuCgoIwduxYscHmQkNDMWvWLISGhtbqGh4eHti8eTNsbGxw4MABhIeHY968edROw/VBaWkp5syZAy6Xi5MnT8LX1xfh4eHw9/ev9Jx3795h+vTp6NKlC06fPo0DBw7g2bNnWLFiBZUnJSUFrVu3xh9//IGoqCjMnTsXO3bswPHjx6k8CgoKGDlyJIKCgmTmXYEkNDXNMqWXNDF4PB5JSEggPB6P8Pn8ssTSUkIIIUVFReTx48ekqKhI5DxOURHhFBV9O4cQwuNyCKeoiHA5HPF5/1duWV5uWd6SkmrlrQkFBQXExMSEPH/+nLi7u5O9e/cKHf/8+TNZsmQJ6d+/PzEyMiJDhw4lISEhhBBCsrKyiI6ODnn8+HGZDTweWbFiBbG2tiZGRkbE1taWHDlyRKg8Ly8v4ubmRg4ePEgGDhxI+vXrR9atW0c4Fe5HeT5+/Eh0dXXJ06dPRY7Fx8cTS0tLwuFwiIWFBUlMTBQ67u/vTxwdHUXOq2h7VFQU0dHRIRcvXhTJy+fzSX5+fqX21YbY2Fiip6dHcnJyqLS///6bmJqakpIK9S7g5MmTZMCAAaS0XP2npaURHR0d8vLly0qvtW7dOuLs7CyUdufOHWJgYCD2+aWRTfh8PikoKBBql+qaqtrFuqDpbZxWDl8vJzDe5KGjhTqmFSUAP7kDahZi8/pPGwcAcDsQjOYty6Kx3o0Iw7+ngmBkYwvbOYuovH/OngxeSQlm7T6EVu3K4nbc/ycKsccOQG+gFewXeVB5DyyYgaIv+Zi2bQ/aapZFRX107RKMB4sPl1AVFy5cQLdu3dCtWzc4Ojpi8+bNmDNnDtVr2rVrF54/f459+/ahTZs2yMrK+hbSvAJ8Ph8dOnTArl27oKqqinv37mHt2rVQV1eHnZ0dlS8+Ph7q6uo4evQoMjMzsXjxYujr6+PXX38VW25iYiKaNWuG7t27ixwLCQmBvb09WCwWRo4ciZCQEJiamkp8HyIjI6GtrY0hQ4aAEILS0lIwmUwqyF9Vu36U351ZHA4ODvD29hZ77P79+9DR0UHbtm2pNAsLC6xbtw7p6elig4ZxOBywWCyhUaFgp+LExEShSLnl+fLli1BYbqAsZHlpaSmSkpIwYMAA2eotV0HFOpZ1yuuVBZq0E+JxOGjOkwPjay5Q8Pp/Ae0aLyEhIVR4bUtLS3z58gV37tyhNn3Mzs6Gvr4+evbsiebNm1e5cy6LxcKiRd8cq6amJu7fv4+YmBghJ9SqVSusXbsWTCYT3bt3h5WVFW7dulWpE3rz5g3atGkjMhVXUFCAf/75B6dOnQIAODo6wsnJCatWrYKysrJE9+HVq1dUtEygLHJkdb+rOHv2bJXHqwra+OHDByEHBID6WxBssCI//fQTfH19cfDgQUydOhVFRUXYvn17leckJSXhwoULVPAyAc2aNUOLFi2QmZmJAQMGVKlD1pCkjmUBWdLbpJ3QpCXeSElJxqDnAUABgDaivXMBi46GAADkFb+FeOjrOAZ97H4Bo0KPZN7+4LK8Ct+We5sMs4fx4GFgVGh8XQP+EslrYDVEYi0vXrzAw4cPsWfPnrLy5OVhZ2eHkJAQyglNmjQJixYtQkpKCiwtLTFkyJAqRxrBwcEIDQ1FdnY2SkpKwOVyoaenJ5SnR48eQj0ydXV1PH36tNIyS0pKoKgoGibj/Pnz6NKlC1W+vr4+NDQ0EB0djfHjx1f/RqB2H1ZXNvKoL3r27AlfX1/4+vpix44dkJOTg7OzM9q2bSu2V//06VPMmzcP8+fPpwIYlkdRUbHS0S0NzY9Ik3ZC2hp6yHv/FWqJz8oSWncDPolfycQSE8yJKc8CU150Sbf4vPJginnpX1leSQkJCQGPx4OlpSWVRgiBgoIC1q5dixYtWsDKygpXrlzBxYsXkZCQABcXF0yePBleXl4i5UVFRWHLli3w8vICm82GsrIyDh06hAcPHgjlq7iQgcFgVOkE1NTUkJeXJ9b+Z8+eCU1Z8fl8hIaGUk5IRUUFX758ETlXkCYYpXTt2hUvXryo1IaqqM10XNu2bZGcnCyU9uHDBwBlzrmqMh0cHPDhwwc0a9YMDAYDR44cERmppqenw8XFBRMmTBCJCCogLy8PampqVWqgofmRaNJOCABa8j6CUVpStl9cSw3g0ytpmyQxPB4P586dw/LlyzFw4EChY/Pnz8f58+cxadIkAEDr1q3xyy+/YMKECTAzM8PWrVvFOqGkpCSw2WxMnjyZSsvMzKy1rfr6+vjw4QPy8vLQqlXZu7UnT54gJSUFQUFBVBpQ1qA6Ozvj+fPn6N69O7S1tfHu3TuRaa9Hjx5BUVERnTp1AlDWqC9evBiXLl3C4MGDhab+CCEoKCio9L1QbabjTExMsG/fPnz8+JHa6PfmzZtQUVFBjx49qr4x+DZ1FxISAkVFRaG6fPbsGaZNm4ZRo0Zh8eLFYs/PzMxESUkJ9PX1v3stWUPcSktZRpb0NmkndDbmIHJfJKCkVAWGnXoCjbRiY2NjkZeXh3Hjxok0rra2tggJCcGkSZOwa9cuGBgYoGfPnuBwOIiNjRW7QAAom5Y6e/Ysbty4gc6dO+PcuXN4+PAhOnfuXCtbe/XqBTU1NSQlJcHa2hpAWaNrbGyMvn37iuQ3MjJCSEgIvLy8YGFhAW1tbSxZsgTu7u5QV1fHo0ePsHPnTkydOpWaFhwxYgQuXryIpUuXws3NDQMHDkTr1q3x9OlTHDlyBM7OzhgyRPyUZ22m4ywsLNCjRw94enrCw8MDOTk52LlzJyZPngyF/023Jicnw9PTE0ePHkX79mWLVo4fPw42m43mzZvj5s2b2Lp1K5YuXYqWLcu+WXv69CmmTZsGCwsLTJ8+nXpXxGQy0bp1a+r6CQkJ0NTUhI6OTo01NEYYDEaTiqxaXm9tpp5/FJq0E0r5vxio/MfFo26tYKjeeHuPISEhMDc3F9u7HzZsGA4ePIi0tDSwWCzs2LEDb968gaKiIszMzLBjxw6xZU6cOBGpqalYvHgxGAwG7O3t4eTkhOvXr9fKViaTiTFjxiAyMhLW1tbgcDiIiIiAq6ur2Py2trY4fPgwlixZAhaLhUOHDsHPzw9Lly5Fbm4uOnfujKlTp2L69OnUOQwGA9u3b8epU6cQGhqKffv2gclkQktLC6NGjRL7LqUuYDKZ2LdvH9atW4cJEyagWbNmGD16tNACj6KiImRkZIDL/bYIJjk5Gbt378bXr1/RrVs3rF+/HqNGjaKO//PPP8jNzUVERAQiIiKodA0NDVy5coX6OyoqCuPHjweXy4W8vHyTWCkGlDXEPB6vyWgur1cWYBBZcKUSUFpaivv378PExAQBfy7F5xdPYdW5CD9bOqHY2BkZGRnQ1tamlsnKGoQQKiqjtH6wOTk5GDlyJMLCwqChoVGv1/oR9DYEgum6mJgYMJlMmddbnqZSxwLK6wVQ79qLi4vrtV2UDVdaQxbM2045JDCZAL2qqEFQV1fHpk2b8Pbt23p3Qk2FnJwcbNmyBS1atEBhYaG0zaGhqTZN2gnRSI/K3snQ1Axzc3MAsvGOgKZp0TjfxNcVvGK0Uhb9ZkXWkZUvrasLrVf2aWqaZUlvkx4JbV7lBLl3JdBnK2LMknPSNqdBYDAYMvu+Sxy0XtmnqWkur1cWRr5NeiREijhQ4siDwxD2xbJQsZVBCAGHw5FpjeWh9co+TU1zQ+ut7+s0aSc0tnczDNJJhrVx2bY2LFbZ7gey/mK3/PLgpgCtV/ZpapobUq+gPRS0j3VNk56O68UqAYOZB37brgDK5llVVVXx/v17AJDJJZ+EEJSUlEBOTk7mtImD1iv7NDXN5fUCqDftgqXg79+/h6qqar29h/ohnFBwcDAOHTqEnJwc6OnpYc2aNTA2Nq40/4ULF7Br1y68efMGXbt2xbJly2BlZVVzA8pVXocOHQCAckSyBiEEXC4XLBaryfxgab2yTVPTXF4vgHrXrqqqSrWL9YHUnVB0dDR8fHywfv169O7dG0ePHsXMmTMRExND7b9VnqSkJCxduhRLliyBtbU1IiMjMX/+fISFhUm8XUlkTgk+F2mi7/s3EOyXwGAw0LFjR7Rr104mh/h8Ph9v375Fx44dZWr/qcqg9co+TU1zeb2EEKSlpYnsZl9XsFisel+JJ/UdE8aPHw8jIyOsXbsWQNkNtrKygrOzM2bPni2S393dHUVFRUKxVH799Vfo6elVurtxecrvmLBpwXAo5yqis203TJhZeQhmGhoamh+R8u1ZY122LdWREIfDwaNHjzBnzhwqTU5ODubm5rh3757Yc+7fvw8XFxehNAsLC1y6dEmia5eWlkK5XXN8ZRZCrW3ZV/t8Pl9oJQiDwYCcnBxKS0uFzq0sXTAvKy5dUH510plMJgghYtMr2lhZusDGiumEELx+/RoaGhpCvcbGrKmqegLKgtx17tyZsq2xa6qqnrhcLrKysqCpqQk5OTmZ0PS9euLz+cjKyoKWlhZ13cauqap0Pp+PN2/eQFNTU6ScxohUndCnT59QWloqMu3Wpk2bSuPBiIte2aZNGypuS3V5+PAhfnYsG30JdiLOzMzEx48fqTwdO3ZEp06d8OLFC+Tn51PpWlpaaNu2LdLS0oQCiPXo0QOtWrVCcnKy0EPTq1cvKCgo4P79+0I2mJiYgMPh4PHjx1SanJwc2Gw28vPzkZ6eTqUrKSnBwMAAubm5ePXqW7iJli1bomfPnvjvv//w9u1boXvStWtXEU3t27fHx48fweFwhGLzNGZNVdWTmpoacnNzkZubKzOaqqqnlJQU8Pl8fPr0SWY0VbeeOnfuLHOaKqsnPp+PVq1a1Thu1o+EVKfj3r17h0GDBuHkyZNCwcS2bt2Ku3fv4syZMyLnGBoawtfXFyNHjqTSgoODsWfPHty8efO71xQMX42MjACUOSNjY2OwWKwfopcD1G/Pjc/nIzk5GUZGRkLD98asqap6IoRQ9S3Q29g1VVVPHA4HDx8+pPTKgqbv1VNpaSkePnyI3r17U/Y0dk1VpZfXy+fz8fDhQ3o6rqaoqamByWQK9SwA4OPHjyKjHQFt27YVGfVUlb8i4nwuIUTssFbS9IoPUE3TKxtiS5Jele3iaOyaZLGeaE2SaxK3Qqyxa6qM8sca84e6UnVCCgoKMDAwwK1bt6gNLfl8Pm7duoUpU6aIPcfExAS3b98Wei908+bNsp2wq4Gg4h4+fEillf9/U6Gpaab1yj5NTXN5vVU5qx8dqS/Rnj59Ory8vGBoaAhjY2McPXoURUVFGDNmDADA09MT7du3x9KlSwEAU6dOhbOzM/766y9YWVkhOjoaKSkp1VoZBwDy8vIwMjJqMh+20dDQyC6CacbGHOBO6pbb2dkhNzcX/v7+yMnJgb6+Pg4ePEhNr719+1ZoFZepqSm2bduGnTt3YseOHejatSv27NlT7W+E5OTkqFDLNDQ0NDTSRerfCdHQ0NDQNF1k//NiGhoaGpofFtoJ0dDQ0NBIDdoJ0dDQ0NBIDdoJ0dDQ0NBIDdoJ0dDQ0NBIDdoJ0dDQ0NBIDdoJ0dDQ0NBIDZl3QsHBwbCxsYGRkRHGjx+P5OTkKvNfuHABw4cPh5GRERwcHHDt2rUGsrTukETz6dOn4eTkhL59+6Jv375wcXH57j360ZC0jgVERUVBV1cX8+bNq2cL6xZJ9ebn52P9+vWwsLCAoaEhhg0b1uiea0k1HzlyBMOGDYOxsTGsrKywefNmlJSUNJC1tePu3buYO3cuLCwsoKurW60wNfHx8Rg9ejQMDQ0xdOhQhIWFNYCldQSRYaKiooiBgQEJCQkhz549I6tXryZmZmbkw4cPYvMnJiYSfX19cuDAAZKenk78/PyIgYEBefLkSQNbXnMk1bxkyRJy/Phx8vjxY5Kenk6WL19O+vTpQ/77778GtrxmSKpXQFZWFrG0tCROTk7Ezc2tgaytPZLqLSkpIWPGjCGurq4kISGBZGVlkfj4eJKamtrAltccSTVHREQQQ0NDEhERQbKyssiNGzfIwIEDyebNmxvY8poRGxtLduzYQf7v//6P6OjokIsXL1aZPzMzk/Tu3Zv4+PiQ9PR0EhQURPT19cn169cbyOLaIdNOaNy4cWT9+vXU36WlpcTCwoIEBgaKzf/bb7+R2bNnC6WNHz+erFmzpl7trEsk1VwRHo9H2Gw2CQ8PrycL65aa6OXxeGTChAnk9OnTxMvLq1E5IUn1/v3332Tw4MGEw+E0lIl1jqSa169fT6ZOnSqU5uPjQyZOnFivdtYH1XFCW7duJfb29kJp7u7uZMaMGfVpWp0hs9Nxgqit5ubmVFp1orYOGDBAKM3CwkIk0NSPSk00V6SoqAg8Hg+tWrWqLzPrjJrq3bNnD9q0aYPx48c3hJl1Rk30XrlyBSYmJvD29oa5uTlGjhyJffv2NZqInDXRzGaz8ejRI2rKLisrC9euXYOVlVWD2NzQNPZ2S+obmNYX0ozaKi1qorki27ZtQ7t27YR+9D8qNdGbkJCAkJAQnD17tgEsrFtqojcrKwu3b9+Gg4MD9u/fj8zMTKxfvx48Hg8LFixoCLNrRU00Ozg44NOnT3BycgIhBDweDxMnTsTcuXMbwuQGR1y71bZtWxQUFKC4uBhKSkpSsqx6yOxIiEZy9u/fj+joaAQEBEBRUVHa5tQ5BQUF8PT0xIYNG6iQ7rIOIQRt2rTBhg0bYGhoCDs7O8ydOxcnT56Utmn1Rnx8PAIDA/H7778jLCwMAQEBuHbtGvbs2SNt02jEILMjIWlEbZU2NdEs4NChQ9i/fz8OHz4MPT29+jSzzpBUb1ZWFt68eQM3NzcqTRAMrFevXoiJiUGXLl3q1+haUJP6VVdXh7y8vFDo527duiEnJwccDueHD2tSE827du2Co6MjNd2qq6uLwsJCrF27Fm5ubkKhYWQBce3Whw8foKKi8sOPggAZHgmVj9oqQBC1lc1miz1HELW1PJJEbZU2NdEMAAcOHMCff/6JgwcPwsjIqCFMrRMk1dutWzdERkbi7Nmz1D8bGxv0798fZ8+eRYcOHRrSfImpSf2ampoiMzNTKPLmy5cvoa6u/sM7IKBmmouLi0UcjcAJExmMXNPY2y2ZXh0XFRVFDA0NSVhYGElPTydr1qwhZmZmJCcnhxBCiIeHB9m2bRuVPzExkfTq1YscOnSIpKenE39//0a5RFsSzYGBgcTAwIDExMSQ9+/fU/8KCgqkJUEiJNVbkca2Ok5SvdnZ2YTNZhNvb2/y4sULcvXqVTJgwADy559/SkuCxEiq2d/fn7DZbHL+/HmSmZlJ4uLiyJAhQ8hvv/0mJQWSUVBQQB4/fkweP35MdHR0yOHDh8njx4/JmzdvCCGEbNu2jXh4eFD5BUu0t2zZQtLT08nx48cb1RJtmZ2OAxo+auuPgKSaT548CS6Xi0WLFgmVs2DBAixcuLBBba8Jkupt7Eiqt2PHjjh06BB8fHzg6OiI9u3bY+rUqXB1dZWWBImRVLObmxsYDAZ27tyJd+/eoXXr1rC2tsbixYulJUEiUlJSMHXqVOpvHx8fAMDo0aPh6+uLnJwcvH37ljquqamJwMBA+Pj44NixY+jQoQM2btwIS0vLBre9JtCRVWloaGhopIbsdBFpaGhoaBodtBOioaGhoZEatBOioaGhoZEatBOioaGhoZEatBOioaGhoZEatBOioaGhoZEatBOioaGhoZEatBOioaGhoZEatBP6wQkLC4OZmZm0zagx1QlPvHz58kYXYruu2LlzJ9asWdMg14qPj4euri7y8/OrzGdjY4MjR47Uqy2SXqOufgfVDZctCenp6Rg0aBAKCwvrtNymAu2EGoDly5dDV1dX5N+rV6+kbRrCwsIoe/T09DBo0CCsWLFCZNfimhIXF4dBgwYBAF6/fg1dXV2kpqYK5Vm1ahV8fX3r5HqVsXv3bkqnvr4+rKyssGbNGnz+/FmicurSYebk5ODYsWNCcW7KPyuGhoYYOnQoAgICwOPxan09NpuNuLg4tGjRAkDlDXtISAgmTJhQ6+vJCnv37sXEiRPRu3dvsferR48eMDExweHDh6VgXeNHpveO+5GwtLSk9oAS8KPEtFFRUUFMTAz4fD7S0tKwcuVKvH//HocOHap12erq6t/NI2gU65uePXvi8OHD4PP5eP78OVauXIkvX75g586dDXL9ipw5cwZsNhsaGhpC6YJnhcPh4Nq1a/D29gaLxcKcOXNqdT0FBYVq1ceP8lz+KHC5XAwfPhwmJiYICQkRm2fMmDFYvXo15syZA3l5ulmVBHok1EAIGoDy/5hMJg4fPgwHBweYmJjAysoK69atw9evXystJy0tDc7OzmCz2TA1NcWYMWPw8OFD6nhCQgKcnJxgbGwMKysrbNy48bvTBAwGA+rq6mjfvj2srKzg7OyMmzdvori4GHw+HwEBARg0aBAMDQ3xyy+/4Pr169S5HA4H3t7esLCwgJGREaytrREYGEgdLz/9MXjwYADAqFGjoKurC2dnZwDCo4tTp07BwsJCKPQAULYp5YoVK6i/L126hNGjR8PIyAiDBw+u1miByWRSOs3NzTF8+HDcvHmTOl5aWoqVK1fCxsYGxsbGGDZsGI4ePUod3717N8LDw3H58mVqtBIfHw+gbBPN3377DWZmZujXrx/c3Nzw+vXrKu2Jjo6GjY2NSLrgWdHQ0ICTkxPMzc1x5coVAEBeXh48PT3Rt29f9O7dG7NmzcLLly+pc9+8eYO5c+eib9++MDExgb29Pa5duwZAeDouPj4eK1aswJcvXygtu3fvBiA8VbZ06VK4u7sL2cflcqnwF0BZaIXAwEDqvjk6OiImJqZK7RWp7u/g0qVLsLW1hZGREWbOnCm0kafguKTPxfdYtGgRXFxcqtzI2NzcHHl5ebh7926trtUUoZ2QlGEwGFi1ahXOnz8PX19f3L59G3/88Uel+ZctW4YOHTogJCQEYWFhcHV1BYvFAgBkZmbC1dUVtra2iIiIgJ+fHxITE7FhwwaJbFJSUgKfzwePx8OxY8dw+PBheHl5ISIiAhYWFpg3bx7V8AUFBeHKlSvYuXMnYmJi8Mcff4j07AWcOXMGAHDkyBHExcVRjV55hg8fjs+fP1ONOwB8/vwZN27cgKOjI4AyR+vl5YWpU6ciOjoa3t7eCAsLw759+6qt8fXr14iLi6PuHVDWmHbo0AG7du1CVFQU5s+fDz8/P0RHRwMAZsyYgREjRsDS0hJxcXGIi4sDm80Gl8vFzJkzoaysjODgYJw4cQLNmzfHrFmzwOFwxF7/8+fPSE9Ph6Gh4XdtVVRUBJfLBVDmsFNSUrB3716cOnUKhBDMnj2bOu7t7Q0Oh4Pjx48jMjISy5YtQ/PmzUXKZLPZWLlyJVRUVCgtM2bMEMnn4OCAq1evCjmEuLg4FBcXY8iQIQCAwMBAnD17FuvXr0dUVBRcXFzg4eGBO3fufFebgOr8DoqLi7F3715s2bIFJ06cQH5+vtDO2DV5LpydnbF8+fJq21kZCgoK0NfXR0JCQq3LamrQTqiBiI2NBZvNpv4JQie4uLjgp59+QufOnTFgwAC4u7vjwoULlZaTnZ0Nc3NzdO/eHV27dsWIESOoSKiBgYFwcHCAi4sLunbtClNTU6xatQpnz55FSUlJtex8+fIlTpw4AUNDQ6ioqODQoUNwdXWFvb09unXrBg8PD+jp6VEjhLdv30JLSwt9+vSBhoYGzMzMMHLkSLFlC6Z5VFVVoa6uDlVVVZE8rVq1wqBBgxAZGUml/fPPP1BTU0P//v0BAAEBAZg9ezZGjx4NTU1NDBw4EL/99tt3Q1Y/ffoUbDYbxsbGGDx4MJ49eyYU0oDFYmHRokUwMjKCpqYmHB0dMWbMGKpXr6ysDCUlJaFRrYKCAqKjo8Hn87Fp0ybo6uqie/fu8PHxwdu3byttiN++fQtCCNq1a1epvYQQ3Lx5E3Fxcejfvz9evnyJK1euYOPGjTAzM4Oenh62bduGd+/eUaPN7OxsmJqaQldXF5qamrC2tkbfvn1FylZQUECLFi2oUbC6ujqUlZVF8llYWKBZs2a4ePEilXb+/HnY2NhARUUFHA4HgYGB2Lx5MywtLaGpqYkxY8bA0dERp06dqrI+ylOd3wGXy8XatWvBZrNhaGgIX19f3Lt3D8nJyQBq9lx07NixWlOU1aFdu3bIzs6uk7KaEvTkZQPRv39/rFu3jvq7WbNmAMoiIAYGBuLFixcoKChAaWkpSkpKUFRUROUpz/Tp07F69WqcO3eOmlIShKROS0vDkydPhBpwQgj4fD5ev36N7t27i7Xty5cvYLPZ4PP5KCkpQZ8+fbBx40YUFBTg/fv3MDU1FcpvamqKtLQ0AGUxTmbMmIHhw4fD0tISP//8MywsLGp1rxwcHLBmzRqsW7cOCgoKiIyMhL29PRUzJi0tDUlJSUI93O/dNwDQ1tbG3r17UVJSgoiICKSmpmLKlClCeYKDgxEaGors7GyUlJSAy+V+N9x5WloaMjMzRe5TSUkJMjMzxZ5TXFwMoGyUUxFBh4XL5YIQgpEjR2LhwoW4desW5OXl0bt3byqvmpoatLW18fz5cwDA1KlTsW7dOsTFxcHc3By2tra1CtcuLy+PESNGIDIyEqNGjUJhYSEuX76MHTt2AABevXqFoqIikVEUl8uFvr5+ta9Tnd+BvLy8UOTf7t27o2XLlnj+/DmMjY1r9Fxs3bpVovtRFYqKiigqKqqz8poKtBNqIJo1awYtLS2htNevX2POnDmYNGkSFi9ejFatWiExMRGrVq0Cl8sV+6NZuHAhRo4ciWvXruH69evw9/eHn58fhg4disLCQkycOJF611Kejh07VmqbsrIywsPDIScnB3V1dSoufUFBwXd1GRgY4PLly7h+/Tpu3rwJd3d3mJubw9/f/7vnVoaNjQ1Wr16N2NhYGBkZISEhQeh9UGFhIRYuXAhbW1uRc8U16gJYLBZVB8uWLcPs2bMREBBAvfOIiorCli1b4OXlBTabDWVlZRw6dAgPHjyo0t7CwkIYGBhg27ZtIscqe8mvpqYGoOwdT8U8gg4Li8VCu3btJHrRPX78eFhYWCA2Nhb//vsv9u/fDy8vL7HPRHVxcHCAs7MzPn78iH///ReKiopUwDTB+8bAwEC0b99e6Lzqhg+vye9AHDV9LuqKvLw8qkNIU31oJyRFHj16BEIIli9fTvXyq5qKE6CtrQ1tbW24uLhgyZIlCA0NxdChQ9GrVy+kp6eLOLvvIScnJ/YcFRUVtGvXDklJSejXrx+VnpSUBGNjY6F8dnZ2sLOzw7BhwzBr1ix8/vxZZLpN8P6ltLS0SnsUFRVha2uLyMhIvHr1Ctra2jAwMKCO9+rVCxkZGRLrrIibmxumTZuGSZMmoX379khKSgKbzcbkyZOpPBVHMiwWS2TRhIGBAS5cuIA2bdpARUWlWtfu0qULVFRU8Pz5c2hrawsdE9dhAcp6/jweDw8ePKBGXZ8+fUJGRgZ69OhB5evYsSMmTZqESZMmYfv27Th9+rRYJ8Risb5bF0DZyLdDhw6Ijo7G9evXMXz4cKouu3fvDgUFBWRnZws9I5JQ3d8Bj8dDSkoK9ey9ePEC+fn51Ai/rp6LmvLs2TMMGzZMKtduzNDvhKSIlpYWuFwugoKCkJWVhbNnz1Y5f11cXAxvb2/Ex8fjzZs3SExMxMOHD6kfoaurK+7duwdvb2+kpqbi5cuXuHTpEry9vWts48yZM3HgwAFER0fjxYsX2LZtG9LS0qjww4cPH8b58+fx/PlzZGRkICYmBurq6mjZsqVIWW3atIGSkhJu3LiBDx8+4MuXL5Ve18HBAbGxsQgNDYWDg4PQsfnz5+PcuXMICAjAs2fP8Pz5c0RFRcHPz08ibWw2G7q6utRqPi0tLaSkpODGjRvIyMjAzp07hVYeAoCGhgaePHmCFy9eIDc3F1wuFw4ODlBTU4ObmxsSEhKQlZWF+Ph4bNy4Ef/995/Ya8vJycHc3ByJiYnVtrdr164YPHgw1qxZg4SEBKSlpcHDwwPt27enVh5u2rQJN27cQFZWFh49eoT4+PhKp2E1NDRQWFiIW7duITc3t8qppJEjR+LkyZO4efOmUH2oqKhgxowZ8PHxQXh4ODIzM/Ho0SMEBQUhPDy8Wrqq+ztgsVjYsGEDHjx4gJSUFKxYsQImJiaUU6rJc+Hp6Ynt27dXaV92djZSU1ORnZ2N0tJSpKamIjU1VWixxuvXr/Hu3TuYm5tXSzPNN+iRkBTR09PDihUrcODAAezYsQNmZmZYsmQJvLy8xOaXk5PD58+f4eXlhQ8fPkBNTQ22trbUIgc9PT0EBQVh586dcHJyAlAWf97Ozq7GNk6dOhUFBQXw9fVFbm4uunfvjj///BNdu3YFUDaVd/DgQbx69QpycnIwMjLC/v37qR5teeTl5bF69Wrs2bMH/v7+MDMzQ1BQkNjr/vTTT2jVqhUyMjJEnJClpSX27duHPXv24MCBA5CXl0e3bt0wfvx4ifW5uLhg+fLlcHV1xcSJE5GamorFixeDwWDA3t4eTk5OQkvSf/31V9y5cwdjx45FYWEhjh07hv79++P48ePYtm0bFixYgK9fv6J9+/YYMGBAlSOjcePGYc2aNfDw8BB7v8Th4+ODTZs2Ye7cueByuTAzM8P+/fupkQmfz4e3tzf+++8/qKiowNLSUmgqszympqaYOHEi3N3d8fnzZyxYsAALFy4Um9fR0RH79u2DhoYG+vTpI3TM3d0drVu3RmBgIF6/fo0WLVqgV69eQh/hVkV1fwdKSkpwdXXF0qVL8e7dO5iZmWHTpk3U8Zo8F2/fvv3uvff39xdyqKNGjQIAqu6BsqncgQMHVroylKZyGIQQIm0jaGiaIoQQjB8/Hi4uLpWuKKT58eFwOBg2bBi2bdsm4qBpvg89HUdDIyUYDAY2bNhQJ1vy0EiPt2/fYs6cObQDqiH0SIiGhoaGRmrQIyEaGhoaGqlBOyEaGhoaGqlBOyEaGhoaGqlBOyEaGhoaGqlBOyEaGhoaGqlBOyEaGhoaGqlBOyEaGhoaGqlBOyEaGhoaGqlBOyEaGhoaGqnx/6tfFp/PbdU5AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'roc_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve.pdf',\n",
+       " 'calibration_curve_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve.pdf',\n",
+       " 'score_distribution_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution.pdf',\n",
+       " 'score_distribution_per_label_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/score_distribution_per_label.pdf',\n",
+       " 'roc_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/roc_curve_per_subgroup.pdf',\n",
+       " 'calibration_curve_per_subgroup_path': '/lustre/home/acruz/folktexts/notebooks/res/openai/gpt-4o-mini_bench-551234521/imgs/calibration_curve_per_subgroup.pdf'}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bench.plot_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b83085bc-9df7-43b0-9282-cfd4c83713b7",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/paper-plots-and-tables.html b/notebooks/paper-plots-and-tables.html
new file mode 100644
index 0000000..ad1ea26
--- /dev/null
+++ b/notebooks/paper-plots-and-tables.html
@@ -0,0 +1,3454 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
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+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Render paper plots and tables &mdash; folktexts 0.0.25 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
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+      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
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+<li class="toctree-l1 current"><a class="reference internal" href="../notebooks.html">Example notebooks</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="minimal-example.html">Evaluate model calibration using folktexts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="detailed-example.html">Example: Run ACS benchmark task</a></li>
+<li class="toctree-l2"><a class="reference internal" href="custom-dataset-example.html">Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a different data source</a></li>
+<li class="toctree-l2"><a class="reference internal" href="custom-acs-task-example.html">Run <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> benchmark with a custom ACS task</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Render paper plots and tables</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Run-baseline-ML-classifiers-on-the-benchmark-ACS-tasks">Run baseline ML classifiers on the benchmark ACS tasks</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Prepare-results-table-for-each-task">Prepare results table for each task</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Output-latex-results-tables---colored!">Output latex results tables - colored!</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Render-paper-plots">Render paper plots</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#1st-page-illustrative-plot">1st page illustrative plot</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Score-distributions-for-base/instruct-pairs">Score distributions for base/instruct pairs</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#Calibration-curves">Calibration curves</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Calibration-per-sub-group">Calibration per sub-group</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Score-distribution-per-sub-group">Score distribution per sub-group</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Compute-mean-under-/over-estimation-of-risk-score">Compute mean under-/over-estimation of risk score</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Compute-risk-score-fairness-per-sub-group">Compute risk score fairness per sub-group</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#Plots-comparing-different-prompting-schemes">Plots comparing different prompting schemes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Scatter-plot-of-ECE-vs-AUC-for-a-few-models-(before-and-after-numeric-prompting)">Scatter plot of ECE vs AUC for a few models (before and after numeric prompting)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="minimal-example_web-API-model.html">Evaluate model calibration using folktexts</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parse-acs-results.html">Fetch and parse ACS benchmark results under a given directory</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">folktexts</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../notebooks.html">Notebooks Gallery</a></li>
+      <li class="breadcrumb-item active">Render paper plots and tables</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/notebooks/paper-plots-and-tables.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Render-paper-plots-and-tables">
+<h1>Render paper plots and tables<a class="headerlink" href="#Render-paper-plots-and-tables" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">tqdm.auto</span> <span class="kn">import</span> <span class="n">tqdm</span>
+</pre></div>
+</div>
+</div>
+<p><strong>Note:</strong> <em>Change</em> the following path to the aggregated results file in your local system (can be obtained using the <code class="docutils literal notranslate"><span class="pre">parse-acs-results.ipynb</span></code> notebook).</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ACS_AGG_RESULTS_PATH</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;../results&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;aggregated-results.2024-08.csv&quot;</span>
+<span class="n">ACS_AGG_RESULTS_PATH</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">ACS_AGG_RESULTS_PATH</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">ACS_AGG_RESULTS_PATH</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">results_df</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">results_df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+results_df.shape=(210, 64)
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>accuracy</th>
+      <th>accuracy_diff</th>
+      <th>accuracy_ratio</th>
+      <th>balanced_accuracy</th>
+      <th>balanced_accuracy_diff</th>
+      <th>balanced_accuracy_ratio</th>
+      <th>brier_score_loss</th>
+      <th>ece</th>
+      <th>ece_quantile</th>
+      <th>equalized_odds_diff</th>
+      <th>...</th>
+      <th>name</th>
+      <th>is_inst</th>
+      <th>num_features</th>
+      <th>uses_all_features</th>
+      <th>fit_thresh_on_100</th>
+      <th>fit_thresh_accuracy</th>
+      <th>optimal_thresh</th>
+      <th>optimal_thresh_accuracy</th>
+      <th>score_stdev</th>
+      <th>score_mean</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>penai/gpt-4o-mini__ACSTravelTime__-1__Num</th>
+      <td>0.570906</td>
+      <td>0.315286</td>
+      <td>0.624340</td>
+      <td>0.553706</td>
+      <td>0.070915</td>
+      <td>0.876372</td>
+      <td>0.267584</td>
+      <td>0.149517</td>
+      <td>NaN</td>
+      <td>0.382569</td>
+      <td>...</td>
+      <td>GPT 4o mini (it)</td>
+      <td>True</td>
+      <td>-1</td>
+      <td>True</td>
+      <td>0.250000</td>
+      <td>0.510013</td>
+      <td>0.450000</td>
+      <td>0.570968</td>
+      <td>0.223652</td>
+      <td>0.384149</td>
+    </tr>
+    <tr>
+      <th>penai/gpt-4o-mini__ACSTravelTime__-1__QA</th>
+      <td>0.551154</td>
+      <td>0.119294</td>
+      <td>0.812222</td>
+      <td>0.588927</td>
+      <td>0.113947</td>
+      <td>0.823835</td>
+      <td>0.404025</td>
+      <td>0.393327</td>
+      <td>0.393301</td>
+      <td>0.274655</td>
+      <td>...</td>
+      <td>GPT 4o mini (it)</td>
+      <td>True</td>
+      <td>-1</td>
+      <td>True</td>
+      <td>0.998499</td>
+      <td>0.602202</td>
+      <td>0.970688</td>
+      <td>0.593339</td>
+      <td>0.365205</td>
+      <td>0.772858</td>
+    </tr>
+  </tbody>
+</table>
+<p>2 rows × 64 columns</p>
+</div></div>
+</div>
+<p>Remove <code class="docutils literal notranslate"><span class="pre">gemma-2-*</span></code> results (need to investigate why results are so poor):</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">results_df</span> <span class="o">=</span> <span class="n">results_df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="n">id_</span> <span class="k">for</span> <span class="n">id_</span> <span class="ow">in</span> <span class="n">results_df</span><span class="o">.</span><span class="n">index</span> <span class="k">if</span> <span class="s2">&quot;gemma-2-&quot;</span> <span class="ow">in</span> <span class="n">id_</span><span class="o">.</span><span class="n">lower</span><span class="p">()])</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">results_df</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+results_df.shape=(170, 64)
+</pre></div></div>
+</div>
+<section id="Run-baseline-ML-classifiers-on-the-benchmark-ACS-tasks">
+<h2>Run baseline ML classifiers on the benchmark ACS tasks<a class="headerlink" href="#Run-baseline-ML-classifiers-on-the-benchmark-ACS-tasks" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">DATA_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;/fast/groups/sf&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;data&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ALL_TASKS</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;ACSIncome&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;ACSMobility&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;ACSEmployment&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;ACSTravelTime&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;ACSPublicCoverage&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+<span class="n">model_col</span> <span class="o">=</span> <span class="s2">&quot;config_model_name&quot;</span>
+<span class="n">task_col</span> <span class="o">=</span> <span class="s2">&quot;config_task_name&quot;</span>
+<span class="n">numeric_prompt_col</span> <span class="o">=</span> <span class="s2">&quot;config_numeric_risk_prompting&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>List all baseline classifiers here:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
+<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">HistGradientBoostingClassifier</span>
+<span class="kn">from</span> <span class="nn">xgboost</span> <span class="kn">import</span> <span class="n">XGBClassifier</span>    <span class="c1"># NOTE: requires `pip install xgboost`</span>
+
+<span class="n">baselines</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;LR&quot;</span><span class="p">:</span> <span class="n">LogisticRegression</span><span class="p">(),</span>
+    <span class="s2">&quot;GBM&quot;</span><span class="p">:</span> <span class="n">HistGradientBoostingClassifier</span><span class="p">(),</span>
+    <span class="s2">&quot;XGBoost&quot;</span><span class="p">:</span> <span class="n">XGBClassifier</span><span class="p">(),</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.acs.acs_dataset</span> <span class="kn">import</span> <span class="n">ACSDataset</span>
+<span class="kn">from</span> <span class="nn">folktexts.evaluation</span> <span class="kn">import</span> <span class="n">evaluate_predictions</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+
+<span class="k">def</span> <span class="nf">fit_and_eval</span><span class="p">(</span>
+    <span class="n">clf</span><span class="p">,</span>
+    <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span>
+    <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">s_test</span><span class="p">,</span>
+    <span class="n">fillna</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">save_scores_df_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Fit and evaluate a given classifier on the given data.&quot;&quot;&quot;</span>
+    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">s_test</span><span class="p">)</span>
+
+    <span class="n">train_nan_count</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">fillna</span> <span class="ow">and</span> <span class="n">train_nan_count</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="c1"># Fill NaNs with value=-1</span>
+        <span class="n">X_train</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s2">&quot;columns&quot;</span><span class="p">,</span> <span class="n">value</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">X_test</span> <span class="o">=</span> <span class="n">X_test</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s2">&quot;columns&quot;</span><span class="p">,</span> <span class="n">value</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># Fit on train data</span>
+    <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+    <span class="c1"># Evaluate on test data</span>
+    <span class="n">y_test_scores</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">test_results</span> <span class="o">=</span> <span class="n">evaluate_predictions</span><span class="p">(</span>
+        <span class="n">y_true</span><span class="o">=</span><span class="n">y_test</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(),</span>
+        <span class="n">y_pred_scores</span><span class="o">=</span><span class="n">y_test_scores</span><span class="p">,</span>
+        <span class="n">sensitive_attribute</span><span class="o">=</span><span class="n">s_test</span><span class="p">,</span>
+        <span class="n">threshold</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Optionally, save test scores DF</span>
+    <span class="k">if</span> <span class="n">save_scores_df_path</span><span class="p">:</span>
+        <span class="n">scores_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
+            <span class="s2">&quot;risk_score&quot;</span><span class="p">:</span> <span class="n">y_test_scores</span><span class="p">,</span>
+            <span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="n">y_test</span><span class="p">,</span>
+        <span class="p">})</span>
+        <span class="n">scores_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">save_scores_df_path</span><span class="p">)</span>
+        <span class="n">test_results</span><span class="p">[</span><span class="s2">&quot;predictions_path&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">save_scores_df_path</span>
+
+    <span class="k">return</span> <span class="n">test_results</span>
+
+<span class="k">def</span> <span class="nf">run_baselines</span><span class="p">(</span><span class="n">baselines</span><span class="p">,</span> <span class="n">tasks</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Run baseline classifiers on all acs tasks.&quot;&quot;&quot;</span>
+    <span class="n">baseline_results</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
+
+    <span class="c1"># Prepare progress bar</span>
+    <span class="n">progress_bar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span>
+        <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">tasks</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">baselines</span><span class="p">),</span>
+        <span class="n">leave</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">task</span> <span class="ow">in</span> <span class="n">tasks</span><span class="p">:</span>
+        <span class="n">progress_bar</span><span class="o">.</span><span class="n">set_postfix</span><span class="p">({</span><span class="s2">&quot;task&quot;</span><span class="p">:</span> <span class="n">task</span><span class="p">})</span>
+
+        <span class="c1"># Load ACS task data</span>
+        <span class="n">acs_dataset</span> <span class="o">=</span> <span class="n">ACSDataset</span><span class="o">.</span><span class="n">make_from_task</span><span class="p">(</span><span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">DATA_DIR</span><span class="p">)</span>
+
+        <span class="c1"># Get train/test data</span>
+        <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">acs_dataset</span><span class="o">.</span><span class="n">get_train</span><span class="p">()</span>
+        <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">acs_dataset</span><span class="o">.</span><span class="n">get_test</span><span class="p">()</span>
+
+        <span class="c1"># Get sensitive attribute test data</span>
+        <span class="n">s_test</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">acs_dataset</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">sensitive_attribute</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">s_test</span> <span class="o">=</span> <span class="n">acs_dataset</span><span class="o">.</span><span class="n">get_sensitive_attribute_data</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">y_test</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">clf_name</span><span class="p">,</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">baselines</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="n">progress_bar</span><span class="o">.</span><span class="n">set_postfix</span><span class="p">({</span><span class="s2">&quot;task&quot;</span><span class="p">:</span> <span class="n">task</span><span class="p">,</span> <span class="s2">&quot;clf&quot;</span><span class="p">:</span> <span class="n">clf_name</span><span class="p">})</span>
+
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">baseline_results</span><span class="p">[</span><span class="n">task</span><span class="p">][</span><span class="n">clf_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">fit_and_eval</span><span class="p">(</span>
+                    <span class="n">clf</span><span class="o">=</span><span class="n">clf</span><span class="p">,</span>
+                    <span class="n">X_train</span><span class="o">=</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span>
+                    <span class="n">X_test</span><span class="o">=</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span> <span class="n">s_test</span><span class="o">=</span><span class="n">s_test</span><span class="p">,</span>
+                    <span class="n">fillna</span><span class="o">=</span><span class="p">(</span><span class="n">clf_name</span> <span class="o">==</span> <span class="s2">&quot;LR&quot;</span><span class="p">),</span>
+                    <span class="n">save_scores_df_path</span><span class="o">=</span><span class="n">ACS_AGG_RESULTS_PATH</span><span class="o">.</span><span class="n">parent</span> <span class="o">/</span> <span class="sa">f</span><span class="s2">&quot;baseline_scores.</span><span class="si">{</span><span class="n">clf_name</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">task</span><span class="si">}</span><span class="s2">.csv&quot;</span>
+                <span class="p">)</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="n">err</span><span class="p">)</span>
+            <span class="k">finally</span><span class="p">:</span>
+                <span class="n">progress_bar</span><span class="o">.</span><span class="n">update</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">baseline_results</span>
+</pre></div>
+</div>
+</div>
+<p>Flatten results and add extra columns.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">parse_baseline_results</span><span class="p">(</span><span class="n">baseline_results</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Flatten and parse baseline results.&quot;&quot;&quot;</span>
+    <span class="n">parsed_results_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
+
+    <span class="k">for</span> <span class="n">task</span><span class="p">,</span> <span class="n">task_results</span> <span class="ow">in</span> <span class="n">baseline_results</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+
+        <span class="k">for</span> <span class="n">clf</span><span class="p">,</span> <span class="n">clf_results</span> <span class="ow">in</span> <span class="n">task_results</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="n">parsed_results</span> <span class="o">=</span> <span class="n">clf_results</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+            <span class="n">parsed_results</span><span class="p">[</span><span class="s2">&quot;config_task_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">task</span>
+            <span class="n">parsed_results</span><span class="p">[</span><span class="s2">&quot;config_model_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">clf</span>
+            <span class="n">parsed_results</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">clf</span>
+            <span class="n">parsed_results</span><span class="p">[</span><span class="s2">&quot;num_features&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+            <span class="n">parsed_results</span><span class="p">[</span><span class="s2">&quot;uses_all_features&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+
+            <span class="n">parsed_results_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">parsed_results</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">parsed_results_list</span>
+</pre></div>
+</div>
+</div>
+<p>Check if baseline results were already computed. If so, load csv; otherwise, compute and save.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">BASELINE_RESULTS_PATH</span> <span class="o">=</span> <span class="n">ACS_AGG_RESULTS_PATH</span><span class="o">.</span><span class="n">parent</span> <span class="o">/</span> <span class="p">(</span><span class="s2">&quot;baseline-results.&quot;</span> <span class="o">+</span> <span class="s2">&quot;.&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">baselines</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span> <span class="o">+</span> <span class="s2">&quot;.csv&quot;</span><span class="p">)</span>
+
+<span class="c1"># If saved results exists: load</span>
+<span class="k">if</span> <span class="n">BASELINE_RESULTS_PATH</span><span class="o">.</span><span class="n">exists</span><span class="p">():</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Loading pre-computed baseline results from </span><span class="si">{</span><span class="n">BASELINE_RESULTS_PATH</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">baselines_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">BASELINE_RESULTS_PATH</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="c1"># Compute baseline results</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Computing baseline results and saving to </span><span class="si">{</span><span class="n">BASELINE_RESULTS_PATH</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="c1"># Compute baseline results</span>
+    <span class="n">baseline_results</span> <span class="o">=</span> <span class="n">run_baselines</span><span class="p">(</span><span class="n">baselines</span><span class="p">,</span> <span class="n">tasks</span><span class="o">=</span><span class="n">ALL_TASKS</span><span class="p">)</span>
+
+    <span class="c1"># Parse results</span>
+    <span class="n">parsed_results_list</span> <span class="o">=</span> <span class="n">parse_baseline_results</span><span class="p">(</span><span class="n">baseline_results</span><span class="p">)</span>
+
+    <span class="c1"># Construct DF</span>
+    <span class="n">baselines_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">parsed_results_list</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="n">r</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">parsed_results_list</span><span class="p">])</span>
+
+    <span class="c1"># Save DF to disk</span>
+    <span class="n">baselines_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">BASELINE_RESULTS_PATH</span><span class="p">)</span>
+
+<span class="c1"># Untie indices</span>
+<span class="n">baselines_df</span><span class="p">[</span><span class="s2">&quot;new_index&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[(</span><span class="n">r</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;_&quot;</span> <span class="o">+</span> <span class="n">r</span><span class="p">[</span><span class="n">task_col</span><span class="p">])</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">baselines_df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()]</span>
+<span class="n">baselines_df</span> <span class="o">=</span> <span class="n">baselines_df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;new_index&quot;</span><span class="p">,</span> <span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Show 2 random rows</span>
+<span class="n">baselines_df</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Loading pre-computed baseline results from ../results/baseline-results.GBM.LR.XGBoost.csv
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>threshold</th>
+      <th>n_samples</th>
+      <th>n_positives</th>
+      <th>n_negatives</th>
+      <th>model_name</th>
+      <th>accuracy</th>
+      <th>tpr</th>
+      <th>fnr</th>
+      <th>fpr</th>
+      <th>tnr</th>
+      <th>...</th>
+      <th>equalized_odds_diff</th>
+      <th>roc_auc</th>
+      <th>ece</th>
+      <th>ece_quantile</th>
+      <th>predictions_path</th>
+      <th>config_task_name</th>
+      <th>config_model_name</th>
+      <th>name</th>
+      <th>num_features</th>
+      <th>uses_all_features</th>
+    </tr>
+    <tr>
+      <th>new_index</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>XGBoost_ACSPublicCoverage</th>
+      <td>0.5</td>
+      <td>113829</td>
+      <td>33971</td>
+      <td>79858</td>
+      <td>NaN</td>
+      <td>0.801650</td>
+      <td>0.515175</td>
+      <td>0.484825</td>
+      <td>0.076486</td>
+      <td>0.923514</td>
+      <td>...</td>
+      <td>0.368044</td>
+      <td>0.839742</td>
+      <td>0.004371</td>
+      <td>0.004271</td>
+      <td>/fast/groups/sf/folktexts-results/2024-07-03/b...</td>
+      <td>ACSPublicCoverage</td>
+      <td>XGBoost</td>
+      <td>XGBoost</td>
+      <td>-1</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>GBM_ACSIncome</th>
+      <td>0.5</td>
+      <td>166450</td>
+      <td>61233</td>
+      <td>105217</td>
+      <td>NaN</td>
+      <td>0.813584</td>
+      <td>0.727973</td>
+      <td>0.272027</td>
+      <td>0.136594</td>
+      <td>0.863406</td>
+      <td>...</td>
+      <td>0.630389</td>
+      <td>0.890792</td>
+      <td>0.007721</td>
+      <td>0.007146</td>
+      <td>/fast/groups/sf/folktexts-results/2024-07-03/b...</td>
+      <td>ACSIncome</td>
+      <td>GBM</td>
+      <td>GBM</td>
+      <td>-1</td>
+      <td>True</td>
+    </tr>
+  </tbody>
+</table>
+<p>2 rows × 42 columns</p>
+</div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">all_results_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">results_df</span><span class="p">,</span> <span class="n">baselines_df</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">all_results_df</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+all_results_df.shape=(185, 64)
+</pre></div></div>
+</div>
+</section>
+<section id="Prepare-results-table-for-each-task">
+<h2>Prepare results table for each task<a class="headerlink" href="#Prepare-results-table-for-each-task" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">table_metrics</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;ece&quot;</span><span class="p">,</span> <span class="s2">&quot;brier_score_loss&quot;</span><span class="p">,</span> <span class="s2">&quot;roc_auc&quot;</span><span class="p">,</span> <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="s2">&quot;fit_thresh_accuracy&quot;</span><span class="p">,</span> <span class="s2">&quot;score_stdev&quot;</span><span class="p">]</span> <span class="c1">#, &quot;score_mean&quot;]</span>
+</pre></div>
+</div>
+</div>
+<p>Add model size and model family columns:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">get_model_size_B</span>
+
+<span class="n">all_results_df</span><span class="p">[</span><span class="s2">&quot;model_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span>
+        <span class="n">get_model_size_B</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">],</span> <span class="n">default</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>  <span class="c1"># Set GPT 4o size as the largest (300B)</span>
+        <span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">baselines</span> <span class="k">else</span> <span class="s2">&quot;-&quot;</span>
+    <span class="p">)</span>
+    <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">all_results_df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span>
+<span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">get_model_family</span><span class="p">(</span><span class="n">model_name</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+    <span class="k">if</span> <span class="s2">&quot;gpt&quot;</span> <span class="ow">in</span> <span class="n">model_name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="k">return</span> <span class="s2">&quot;OpenAI&quot;</span>
+    <span class="k">elif</span> <span class="s2">&quot;llama&quot;</span> <span class="ow">in</span> <span class="n">model_name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="k">return</span> <span class="s2">&quot;Llama&quot;</span>
+    <span class="k">elif</span> <span class="s2">&quot;mistral&quot;</span> <span class="ow">in</span> <span class="n">model_name</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">or</span> <span class="s2">&quot;mixtral&quot;</span> <span class="ow">in</span> <span class="n">model_name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="k">return</span> <span class="s2">&quot;Mistral&quot;</span>
+    <span class="k">elif</span> <span class="s2">&quot;gemma&quot;</span> <span class="ow">in</span> <span class="n">model_name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="k">return</span> <span class="s2">&quot;Gemma&quot;</span>
+    <span class="k">elif</span> <span class="s2">&quot;yi&quot;</span> <span class="ow">in</span> <span class="n">model_name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="k">return</span> <span class="s2">&quot;Yi&quot;</span>
+    <span class="k">elif</span> <span class="s2">&quot;qwen&quot;</span> <span class="ow">in</span> <span class="n">model_name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="k">return</span> <span class="s2">&quot;Qwen&quot;</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="s2">&quot;-&quot;</span>
+
+<span class="n">all_results_df</span><span class="p">[</span><span class="s2">&quot;model_family&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">get_model_family</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="n">model_col</span><span class="p">])</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">all_results_df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()]</span>
+<span class="n">all_results_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="n">task_col</span><span class="p">,</span> <span class="s2">&quot;model_family&quot;</span><span class="p">])[</span><span class="s2">&quot;accuracy&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+config_task_name   model_family
+ACSEmployment      -                3
+                   Gemma            8
+                   Llama            8
+                   Mistral         12
+                   OpenAI           2
+                   Yi               4
+ACSIncome          -                3
+                   Gemma            8
+                   Llama            8
+                   Mistral         12
+                   OpenAI           2
+                   Yi               4
+ACSMobility        -                3
+                   Gemma            8
+                   Llama            8
+                   Mistral         12
+                   OpenAI           2
+                   Yi               4
+ACSPublicCoverage  -                3
+                   Gemma            8
+                   Llama            8
+                   Mistral         12
+                   OpenAI           2
+                   Yi               4
+ACSTravelTime      -                3
+                   Gemma            8
+                   Llama            8
+                   Mistral         12
+                   OpenAI           2
+                   Yi               4
+Name: accuracy, dtype: int64
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">model_sort_key</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">task_df</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Sort key for paper table rows.&quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="s2">&quot;gpt&quot;</span> <span class="ow">in</span> <span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="n">key</span> <span class="o">=</span> <span class="mi">11</span>
+    <span class="k">elif</span> <span class="s2">&quot;llama&quot;</span> <span class="ow">in</span> <span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="n">key</span> <span class="o">=</span> <span class="mi">10</span>
+    <span class="k">elif</span> <span class="s2">&quot;mixtral&quot;</span> <span class="ow">in</span> <span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="n">key</span> <span class="o">=</span> <span class="mi">9</span>
+    <span class="k">elif</span> <span class="s2">&quot;mistral&quot;</span> <span class="ow">in</span> <span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="n">key</span> <span class="o">=</span> <span class="mi">8</span>
+    <span class="k">elif</span> <span class="s2">&quot;yi&quot;</span> <span class="ow">in</span> <span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="n">key</span> <span class="o">=</span> <span class="mi">7</span>
+    <span class="k">elif</span> <span class="s2">&quot;gemma&quot;</span> <span class="ow">in</span> <span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="n">key</span> <span class="o">=</span> <span class="mi">6</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="mi">0</span>
+
+    <span class="n">row</span> <span class="o">=</span> <span class="n">task_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
+    <span class="c1"># return (1e5 * key) + (row[&quot;model_size&quot;] * 100) + int(row[&quot;is_inst&quot;] * 10) + (1 if row[numeric_prompt_col] else 0)</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;model_size&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mf">1e9</span><span class="p">)</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;is_inst&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mf">1e6</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1e3</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="n">key</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">latex_colored_float_format</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">all_values</span><span class="p">,</span> <span class="n">higher_is_better</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Map a cell&#39;s value to its colored latex code.</span>
+
+<span class="sd">    Current definition:</span>
+<span class="sd">    - use cyan color gradient for good values;</span>
+<span class="sd">    - use orange color gradient for bad values;</span>
+<span class="sd">    - use no color for anything in between;</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">min_val</span><span class="p">,</span> <span class="n">max_val</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">all_values</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">all_values</span><span class="p">)</span>
+
+    <span class="n">low_pct_val</span><span class="p">,</span> <span class="n">high_pct_val</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">min_val</span> <span class="o">+</span> <span class="p">(</span><span class="n">max_val</span> <span class="o">-</span> <span class="n">min_val</span><span class="p">)</span> <span class="o">*</span> <span class="n">interp_point</span>
+        <span class="k">for</span> <span class="n">interp_point</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">]</span>
+    <span class="p">]</span>
+
+    <span class="c1"># Use rounded value or original value for coloring?</span>
+    <span class="c1"># &gt; Using rounded value for consistency in table</span>
+    <span class="c1"># val = np.round(val, decimals=2)</span>
+
+    <span class="c1"># Use no color for middle 33% of values</span>
+    <span class="k">if</span> <span class="n">low_pct_val</span> <span class="o">&lt;=</span> <span class="n">val</span> <span class="o">&lt;=</span> <span class="n">high_pct_val</span><span class="p">:</span>
+        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">val</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+
+    <span class="k">if</span> <span class="n">val</span> <span class="o">&lt;</span> <span class="n">low_pct_val</span><span class="p">:</span>
+        <span class="n">color</span> <span class="o">=</span> <span class="s2">&quot;orange&quot;</span> <span class="k">if</span> <span class="n">higher_is_better</span> <span class="k">else</span> <span class="s2">&quot;cyan&quot;</span>
+        <span class="n">color_value</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">*</span> <span class="p">(</span>
+            <span class="p">(</span><span class="n">low_pct_val</span> <span class="o">-</span> <span class="n">val</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">low_pct_val</span> <span class="o">-</span> <span class="n">min_val</span><span class="p">))</span>
+
+    <span class="k">elif</span> <span class="n">val</span> <span class="o">&gt;</span> <span class="n">high_pct_val</span><span class="p">:</span>
+        <span class="n">color</span> <span class="o">=</span> <span class="s2">&quot;cyan&quot;</span> <span class="k">if</span> <span class="n">higher_is_better</span> <span class="k">else</span> <span class="s2">&quot;orange&quot;</span>
+        <span class="n">color_value</span> <span class="o">=</span> <span class="mi">100</span> <span class="o">*</span> <span class="p">(</span>
+            <span class="p">(</span><span class="n">val</span> <span class="o">-</span> <span class="n">high_pct_val</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">max_val</span> <span class="o">-</span> <span class="n">high_pct_val</span><span class="p">))</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">val</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Note: halving `color_value` to have softer colors</span>
+    <span class="n">color_value</span> <span class="o">/=</span> <span class="mi">4</span>
+
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="sa">r</span><span class="s2">&quot;\cellcolor{&quot;</span>
+        <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">color</span><span class="si">}</span><span class="s2">!</span><span class="si">{</span><span class="n">color_value</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="o">+</span> <span class="sa">r</span><span class="s2">&quot;} &quot;</span>
+        <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">val</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="p">)</span>
+
+<span class="n">higher_is_better_cols</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;roc_auc&quot;</span><span class="p">,</span> <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="s2">&quot;fit_thresh_accuracy&quot;</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Output-latex-results-tables---colored!">
+<h2>Output latex results tables - colored!<a class="headerlink" href="#Output-latex-results-tables---colored!" title="Link to this heading"></a></h2>
+<p>Set whether to use <strong>Numeric prompting</strong> or <strong>multiple-choice Q&amp;A prompting</strong>!</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># NUMERIC_PROMPTING = False</span>
+<span class="n">NUMERIC_PROMPTING</span> <span class="o">=</span> <span class="kc">True</span>
+</pre></div>
+</div>
+</div>
+<p>Whether to <strong>combine results from both prompting schemes</strong> in the latex tables:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">COMBINE_PROMPTING_RESULTS</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># COMBINE_PROMPTING_RESULTS = False</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">prettify_model_name</span>
+
+<span class="k">for</span> <span class="n">task</span> <span class="ow">in</span> <span class="n">ALL_TASKS</span><span class="p">:</span>
+    <span class="n">task_df</span> <span class="o">=</span> <span class="n">all_results_df</span><span class="p">[</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">task</span><span class="p">]</span>
+
+    <span class="c1"># Sort table rows</span>
+    <span class="n">sorted_df_index</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span>
+        <span class="n">task_df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
+        <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">id_</span><span class="p">:</span> <span class="n">model_sort_key</span><span class="p">(</span><span class="n">id_</span><span class="p">,</span> <span class="n">task_df</span><span class="p">),</span>
+        <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">latex_tab_cols</span> <span class="o">=</span> <span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span> <span class="o">+</span> <span class="n">table_metrics</span>
+
+    <span class="c1"># latex_table = task_df.sort_values([&quot;model_family&quot;, &quot;model_size&quot;, &quot;is_inst&quot;], ascending=False).set_index(model_col)[table_metrics].round(3)</span>
+    <span class="n">latex_table</span> <span class="o">=</span> <span class="n">task_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sorted_df_index</span><span class="p">]</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="n">model_col</span><span class="p">)[</span><span class="n">latex_tab_cols</span><span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+
+    <span class="c1"># Keep rows for a single selected prompting scheme</span>
+    <span class="n">latex_table</span> <span class="o">=</span> <span class="n">latex_table</span><span class="p">[</span>
+        <span class="p">(</span><span class="n">latex_table</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">NUMERIC_PROMPTING</span><span class="p">)</span>
+        <span class="o">|</span> <span class="n">latex_table</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span>
+    <span class="p">]</span>
+
+    <span class="c1"># But optionally join with the columns of the other prompting scheme</span>
+    <span class="k">if</span> <span class="n">COMBINE_PROMPTING_RESULTS</span><span class="p">:</span>
+        <span class="n">cols_to_keep</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;ece&quot;</span><span class="p">,</span> <span class="s2">&quot;brier_score_loss&quot;</span><span class="p">,</span> <span class="s2">&quot;roc_auc&quot;</span><span class="p">,</span> <span class="s2">&quot;accuracy&quot;</span><span class="p">]</span>
+        <span class="n">latex_table</span> <span class="o">=</span> <span class="n">latex_table</span><span class="p">[</span><span class="n">cols_to_keep</span><span class="p">]</span>
+
+        <span class="n">latex_table_other_prompt</span> <span class="o">=</span> <span class="n">task_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sorted_df_index</span><span class="p">]</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="n">model_col</span><span class="p">)</span>
+        <span class="n">latex_table_other_prompt</span> <span class="o">=</span> <span class="n">latex_table_other_prompt</span><span class="p">[</span>
+            <span class="p">(</span><span class="n">latex_table_other_prompt</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span> <span class="o">!=</span> <span class="n">NUMERIC_PROMPTING</span><span class="p">)</span>
+            <span class="o">|</span> <span class="n">latex_table_other_prompt</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span>
+        <span class="p">][</span><span class="n">cols_to_keep</span><span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+
+        <span class="n">latex_table</span> <span class="o">=</span> <span class="n">latex_table</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
+            <span class="n">latex_table_other_prompt</span><span class="p">,</span>
+            <span class="n">how</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span>
+            <span class="n">lsuffix</span><span class="o">=</span><span class="s2">&quot; (num)&quot;</span> <span class="k">if</span> <span class="n">NUMERIC_PROMPTING</span> <span class="k">else</span> <span class="s2">&quot; (mult. choice)&quot;</span><span class="p">,</span>
+            <span class="n">validate</span><span class="o">=</span><span class="s2">&quot;1:1&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="c1"># Fill NaNs</span>
+    <span class="n">latex_table</span> <span class="o">=</span> <span class="n">latex_table</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="s2">&quot;-&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Prettify model names</span>
+    <span class="n">latex_table</span><span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">prettify_model_name</span><span class="p">(</span><span class="n">id_</span><span class="p">)</span> <span class="k">if</span> <span class="n">id_</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">baselines</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">else</span> <span class="n">id_</span>
+        <span class="k">for</span> <span class="n">id_</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">latex_table</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span>
+    <span class="p">]</span>
+    <span class="n">latex_table</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;Model&quot;</span><span class="p">,</span> <span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">set</span><span class="p">(</span><span class="n">latex_table</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> <span class="o">-</span> <span class="p">{</span><span class="s2">&quot;score_stdev&quot;</span><span class="p">,</span> <span class="n">numeric_prompt_col</span><span class="p">,</span> <span class="s2">&quot;model_family&quot;</span><span class="p">}:</span>
+        <span class="n">index_without_baselines</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">latex_table</span><span class="o">.</span><span class="n">index</span> <span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">baselines</span><span class="p">]</span>
+        <span class="n">index_baselines</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">baselines</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+
+        <span class="n">col_data</span> <span class="o">=</span> <span class="n">latex_table</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">index_without_baselines</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="n">new_col_data</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">latex_colored_float_format</span><span class="p">(</span>
+                <span class="n">val</span><span class="o">=</span><span class="n">col_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">id_</span><span class="p">],</span> <span class="n">all_values</span><span class="o">=</span><span class="n">col_data</span><span class="p">,</span>
+                <span class="n">higher_is_better</span><span class="o">=</span><span class="nb">any</span><span class="p">(</span><span class="n">col_name</span> <span class="ow">in</span> <span class="n">col</span> <span class="k">for</span> <span class="n">col_name</span> <span class="ow">in</span> <span class="n">higher_is_better_cols</span><span class="p">),</span>
+            <span class="p">)</span>
+            <span class="k">for</span> <span class="n">id_</span> <span class="ow">in</span> <span class="n">index_without_baselines</span>
+        <span class="p">]</span> <span class="o">+</span> <span class="p">[</span>
+            <span class="n">latex_table</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">id_</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">id_</span> <span class="ow">in</span> <span class="n">index_baselines</span>
+        <span class="p">]</span>
+
+        <span class="c1"># Set compatible dtype</span>
+        <span class="n">latex_table</span> <span class="o">=</span> <span class="n">latex_table</span><span class="o">.</span><span class="n">astype</span><span class="p">({</span><span class="n">col</span><span class="p">:</span> <span class="nb">str</span><span class="p">})</span>
+
+        <span class="c1"># Set new data</span>
+        <span class="n">latex_table</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">index_without_baselines</span> <span class="o">+</span> <span class="n">index_baselines</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_col_data</span>
+
+    <span class="c1"># Drop prompting column of not combining both</span>
+    <span class="n">latex_table</span> <span class="o">=</span> <span class="n">latex_table</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">numeric_prompt_col</span><span class="p">,</span> <span class="n">errors</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Rename columns to be latex compatible</span>
+    <span class="n">latex_table</span> <span class="o">=</span> <span class="n">latex_table</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="n">col</span><span class="p">:</span> <span class="n">col</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">,</span> <span class="s2">&quot; &quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">latex_table</span><span class="o">.</span><span class="n">columns</span><span class="p">})</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;*** </span><span class="si">{</span><span class="n">task</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span><span class="si">}</span><span class="s2"> ***</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="n">latex_table_str</span> <span class="o">=</span> <span class="n">latex_table</span><span class="o">.</span><span class="n">to_latex</span><span class="p">(</span><span class="n">float_format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">latex_table_str</span><span class="p">)</span>
+
+    <span class="n">tables_dir</span> <span class="o">=</span> <span class="n">ACS_AGG_RESULTS_PATH</span><span class="o">.</span><span class="n">parent</span> <span class="o">/</span> <span class="s2">&quot;tables&quot;</span>
+    <span class="n">tables_dir</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">tables_dir</span> <span class="o">/</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">task</span><span class="si">}</span><span class="s2">-table.</span><span class="si">{</span><span class="n">COMBINE_PROMPTING_RESULTS</span><span class="si">=}</span><span class="s2">.</span><span class="si">{</span><span class="n">NUMERIC_PROMPTING</span><span class="si">=}</span><span class="s2">.tex&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f_out</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">latex_table_str</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">f_out</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+*** ACSINCOME ***
+
+\begin{tabular}{lllllllll}
+\toprule
+ &amp; ece (num) &amp; brier score loss (num) &amp; roc auc (num) &amp; accuracy (num) &amp; ece &amp; brier score loss &amp; roc auc &amp; accuracy \\
+Model &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\
+\midrule
+GPT 4o mini (it) &amp; \cellcolor{cyan!25.0} 0.05 &amp; \cellcolor{cyan!25.0} 0.16 &amp; \cellcolor{cyan!20.6} 0.83 &amp; \cellcolor{cyan!24.4} 0.78 &amp; 0.24 &amp; 0.24 &amp; \cellcolor{cyan!17.6} 0.85 &amp; 0.74 \\
+Mixtral 8x22B (it) &amp; 0.11 &amp; \cellcolor{cyan!15.5} 0.17 &amp; \cellcolor{cyan!25.0} 0.84 &amp; \cellcolor{cyan!18.9} 0.77 &amp; 0.21 &amp; \cellcolor{cyan!3.6} 0.22 &amp; \cellcolor{cyan!11.2} 0.85 &amp; \cellcolor{cyan!11.1} 0.76 \\
+Mixtral 8x22B &amp; 0.13 &amp; \cellcolor{cyan!3.6} 0.18 &amp; \cellcolor{cyan!9.6} 0.82 &amp; \cellcolor{cyan!2.4} 0.74 &amp; \cellcolor{cyan!13.2} 0.17 &amp; \cellcolor{cyan!21.6} 0.19 &amp; \cellcolor{cyan!16.5} 0.85 &amp; 0.68 \\
+Llama 3 70B (it) &amp; 0.25 &amp; 0.23 &amp; \cellcolor{cyan!22.1} 0.84 &amp; 0.67 &amp; 0.27 &amp; 0.27 &amp; \cellcolor{cyan!25.0} 0.86 &amp; 0.69 \\
+Llama 3 70B &amp; 0.27 &amp; 0.24 &amp; \cellcolor{cyan!6.7} 0.82 &amp; 0.54 &amp; 0.20 &amp; \cellcolor{cyan!14.9} 0.20 &amp; \cellcolor{cyan!20.8} 0.86 &amp; 0.70 \\
+Mixtral 8x7B (it) &amp; 0.10 &amp; \cellcolor{cyan!15.5} 0.17 &amp; \cellcolor{cyan!22.8} 0.84 &amp; \cellcolor{cyan!12.8} 0.76 &amp; \cellcolor{cyan!16.8} 0.16 &amp; \cellcolor{cyan!25.0} 0.18 &amp; \cellcolor{cyan!19.7} 0.86 &amp; \cellcolor{cyan!25.0} 0.78 \\
+Mixtral 8x7B &amp; \cellcolor{cyan!4.0} 0.07 &amp; \cellcolor{cyan!13.1} 0.17 &amp; \cellcolor{cyan!3.7} 0.81 &amp; \cellcolor{cyan!25.0} 0.78 &amp; \cellcolor{cyan!10.6} 0.17 &amp; \cellcolor{cyan!11.5} 0.21 &amp; 0.83 &amp; 0.65 \\
+Yi 34B (it) &amp; 0.22 &amp; 0.21 &amp; 0.80 &amp; 0.48 &amp; \cellcolor{cyan!1.3} 0.19 &amp; \cellcolor{cyan!18.8} 0.19 &amp; \cellcolor{cyan!19.7} 0.86 &amp; 0.72 \\
+Yi 34B &amp; 0.15 &amp; 0.19 &amp; \cellcolor{cyan!17.7} 0.83 &amp; 0.61 &amp; 0.25 &amp; \cellcolor{cyan!2.5} 0.22 &amp; \cellcolor{cyan!13.3} 0.85 &amp; 0.62 \\
+Llama 3 8B (it) &amp; 0.23 &amp; 0.23 &amp; \cellcolor{cyan!0.1} 0.81 &amp; 0.67 &amp; 0.32 &amp; 0.30 &amp; \cellcolor{cyan!13.3} 0.85 &amp; 0.62 \\
+Llama 3 8B &amp; 0.14 &amp; 0.24 &amp; 0.63 &amp; \cellcolor{orange!3.7} 0.40 &amp; 0.25 &amp; 0.26 &amp; 0.81 &amp; \cellcolor{orange!20.8} 0.38 \\
+Mistral 7B (it) &amp; 0.16 &amp; 0.19 &amp; \cellcolor{cyan!14.7} 0.83 &amp; 0.70 &amp; 0.21 &amp; \cellcolor{cyan!4.7} 0.22 &amp; 0.83 &amp; \cellcolor{cyan!16.5} 0.77 \\
+Gemma 7B (it) &amp; 0.33 &amp; 0.30 &amp; 0.78 &amp; 0.42 &amp; \cellcolor{orange!14.7} 0.61 &amp; \cellcolor{orange!4.7} 0.59 &amp; \cellcolor{cyan!3.8} 0.84 &amp; \cellcolor{orange!25.0} 0.37 \\
+Mistral 7B &amp; \cellcolor{orange!15.7} 0.36 &amp; 0.32 &amp; 0.75 &amp; 0.49 &amp; 0.20 &amp; 0.23 &amp; 0.80 &amp; 0.73 \\
+Gemma 7B &amp; 0.15 &amp; 0.20 &amp; 0.80 &amp; 0.73 &amp; 0.24 &amp; 0.27 &amp; 0.76 &amp; \cellcolor{orange!25.0} 0.37 \\
+Gemma 2B (it) &amp; 0.28 &amp; 0.31 &amp; \cellcolor{orange!25.0} 0.50 &amp; \cellcolor{orange!25.0} 0.37 &amp; \cellcolor{orange!25.0} 0.63 &amp; \cellcolor{orange!25.0} 0.63 &amp; 0.73 &amp; \cellcolor{orange!25.0} 0.37 \\
+Gemma 2B &amp; \cellcolor{orange!25.0} 0.37 &amp; \cellcolor{orange!25.0} 0.37 &amp; \cellcolor{orange!25.0} 0.50 &amp; 0.63 &amp; \cellcolor{cyan!25.0} 0.14 &amp; 0.25 &amp; \cellcolor{orange!25.0} 0.62 &amp; 0.45 \\
+LR &amp; 0.03 &amp; 0.18 &amp; 0.79 &amp; 0.74 &amp; 0.03 &amp; 0.18 &amp; 0.79 &amp; 0.74 \\
+GBM &amp; 0.01 &amp; 0.13 &amp; 0.89 &amp; 0.81 &amp; 0.01 &amp; 0.13 &amp; 0.89 &amp; 0.81 \\
+XGBoost &amp; 0.00 &amp; 0.13 &amp; 0.90 &amp; 0.82 &amp; 0.00 &amp; 0.13 &amp; 0.90 &amp; 0.82 \\
+\bottomrule
+\end{tabular}
+
+
+*** ACSMOBILITY ***
+
+\begin{tabular}{lllllllll}
+\toprule
+ &amp; ece (num) &amp; brier score loss (num) &amp; roc auc (num) &amp; accuracy (num) &amp; ece &amp; brier score loss &amp; roc auc &amp; accuracy \\
+Model &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\
+\midrule
+GPT 4o mini (it) &amp; 0.22 &amp; 0.25 &amp; 0.49 &amp; \cellcolor{cyan!25.0} 0.73 &amp; 0.26 &amp; 0.26 &amp; \cellcolor{cyan!0.6} 0.57 &amp; \cellcolor{cyan!25.0} 0.73 \\
+Mixtral 8x22B (it) &amp; 0.05 &amp; \cellcolor{cyan!17.9} 0.20 &amp; \cellcolor{cyan!25.0} 0.54 &amp; \cellcolor{cyan!25.0} 0.73 &amp; 0.40 &amp; 0.40 &amp; \cellcolor{orange!12.8} 0.51 &amp; 0.39 \\
+Mixtral 8x22B &amp; 0.13 &amp; 0.22 &amp; 0.49 &amp; \cellcolor{cyan!25.0} 0.73 &amp; \cellcolor{cyan!10.2} 0.11 &amp; \cellcolor{cyan!21.2} 0.21 &amp; 0.55 &amp; \cellcolor{cyan!25.0} 0.73 \\
+Llama 3 70B (it) &amp; 0.05 &amp; \cellcolor{cyan!10.7} 0.20 &amp; 0.52 &amp; \cellcolor{cyan!25.0} 0.73 &amp; 0.20 &amp; \cellcolor{cyan!1.5} 0.25 &amp; 0.57 &amp; 0.58 \\
+Llama 3 70B &amp; 0.06 &amp; \cellcolor{cyan!7.1} 0.20 &amp; 0.53 &amp; \cellcolor{cyan!20.7} 0.73 &amp; 0.22 &amp; \cellcolor{cyan!4.8} 0.24 &amp; 0.55 &amp; 0.53 \\
+Mixtral 8x7B (it) &amp; 0.11 &amp; 0.21 &amp; 0.51 &amp; \cellcolor{cyan!25.0} 0.73 &amp; 0.26 &amp; 0.26 &amp; \cellcolor{cyan!12.8} 0.58 &amp; \cellcolor{cyan!25.0} 0.73 \\
+Mixtral 8x7B &amp; 0.24 &amp; 0.25 &amp; \cellcolor{orange!25.0} 0.48 &amp; \cellcolor{cyan!25.0} 0.73 &amp; \cellcolor{cyan!0.8} 0.14 &amp; \cellcolor{cyan!18.9} 0.21 &amp; 0.57 &amp; \cellcolor{cyan!25.0} 0.73 \\
+Yi 34B (it) &amp; 0.23 &amp; 0.25 &amp; 0.50 &amp; \cellcolor{orange!25.0} 0.27 &amp; \cellcolor{cyan!18.2} 0.09 &amp; \cellcolor{cyan!23.1} 0.20 &amp; 0.56 &amp; \cellcolor{cyan!15.4} 0.72 \\
+Yi 34B &amp; 0.15 &amp; 0.23 &amp; 0.52 &amp; 0.44 &amp; \cellcolor{cyan!25.0} 0.07 &amp; \cellcolor{cyan!25.0} 0.20 &amp; 0.57 &amp; \cellcolor{cyan!20.7} 0.73 \\
+Llama 3 8B (it) &amp; 0.11 &amp; 0.21 &amp; 0.49 &amp; \cellcolor{cyan!23.4} 0.73 &amp; 0.15 &amp; \cellcolor{cyan!13.7} 0.22 &amp; 0.56 &amp; \cellcolor{cyan!5.3} 0.70 \\
+Llama 3 8B &amp; 0.14 &amp; 0.21 &amp; 0.51 &amp; \cellcolor{cyan!19.1} 0.72 &amp; \cellcolor{cyan!12.5} 0.10 &amp; \cellcolor{cyan!21.7} 0.20 &amp; 0.55 &amp; \cellcolor{cyan!24.5} 0.73 \\
+Mistral 7B (it) &amp; 0.17 &amp; 0.23 &amp; 0.49 &amp; \cellcolor{cyan!24.5} 0.73 &amp; 0.26 &amp; 0.26 &amp; 0.57 &amp; \cellcolor{cyan!25.0} 0.73 \\
+Gemma 7B (it) &amp; \cellcolor{orange!8.9} 0.25 &amp; 0.26 &amp; \cellcolor{orange!3.1} 0.49 &amp; \cellcolor{cyan!22.3} 0.73 &amp; 0.25 &amp; 0.26 &amp; \cellcolor{cyan!25.0} 0.58 &amp; \cellcolor{cyan!21.8} 0.73 \\
+Mistral 7B &amp; \cellcolor{orange!25.0} 0.27 &amp; \cellcolor{orange!25.0} 0.27 &amp; 0.50 &amp; \cellcolor{cyan!25.0} 0.73 &amp; 0.20 &amp; \cellcolor{cyan!8.6} 0.23 &amp; 0.53 &amp; \cellcolor{cyan!23.4} 0.73 \\
+Gemma 7B &amp; 0.19 &amp; 0.24 &amp; 0.49 &amp; \cellcolor{cyan!25.0} 0.73 &amp; 0.41 &amp; 0.37 &amp; \cellcolor{orange!25.0} 0.50 &amp; \cellcolor{orange!25.0} 0.27 \\
+Gemma 2B (it) &amp; \cellcolor{cyan!25.0} 0.02 &amp; \cellcolor{cyan!25.0} 0.20 &amp; 0.50 &amp; \cellcolor{cyan!25.0} 0.73 &amp; \cellcolor{orange!25.0} 0.73 &amp; \cellcolor{orange!25.0} 0.73 &amp; 0.52 &amp; \cellcolor{orange!25.0} 0.27 \\
+Gemma 2B &amp; \cellcolor{orange!25.0} 0.27 &amp; \cellcolor{orange!25.0} 0.27 &amp; 0.50 &amp; \cellcolor{cyan!25.0} 0.73 &amp; 0.25 &amp; 0.26 &amp; 0.51 &amp; 0.34 \\
+LR &amp; 0.02 &amp; 0.19 &amp; 0.61 &amp; 0.74 &amp; 0.02 &amp; 0.19 &amp; 0.61 &amp; 0.74 \\
+GBM &amp; 0.01 &amp; 0.17 &amp; 0.74 &amp; 0.76 &amp; 0.01 &amp; 0.17 &amp; 0.74 &amp; 0.76 \\
+XGBoost &amp; 0.00 &amp; 0.16 &amp; 0.74 &amp; 0.76 &amp; 0.00 &amp; 0.16 &amp; 0.74 &amp; 0.76 \\
+\bottomrule
+\end{tabular}
+
+
+*** ACSEMPLOYMENT ***
+
+\begin{tabular}{lllllllll}
+\toprule
+ &amp; ece (num) &amp; brier score loss (num) &amp; roc auc (num) &amp; accuracy (num) &amp; ece &amp; brier score loss &amp; roc auc &amp; accuracy \\
+Model &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\
+\midrule
+GPT 4o mini (it) &amp; 0.23 &amp; 0.23 &amp; 0.80 &amp; 0.73 &amp; 0.28 &amp; 0.29 &amp; 0.79 &amp; 0.65 \\
+Mixtral 8x22B (it) &amp; 0.06 &amp; \cellcolor{cyan!21.9} 0.14 &amp; \cellcolor{cyan!24.4} 0.87 &amp; \cellcolor{cyan!16.5} 0.79 &amp; \cellcolor{orange!23.0} 0.38 &amp; \cellcolor{orange!8.0} 0.39 &amp; 0.60 &amp; 0.51 \\
+Mixtral 8x22B &amp; 0.15 &amp; 0.18 &amp; 0.82 &amp; \cellcolor{cyan!20.7} 0.80 &amp; 0.21 &amp; 0.24 &amp; \cellcolor{cyan!25.0} 0.86 &amp; 0.52 \\
+Llama 3 70B (it) &amp; \cellcolor{cyan!0.7} 0.05 &amp; \cellcolor{cyan!25.0} 0.14 &amp; \cellcolor{cyan!25.0} 0.88 &amp; \cellcolor{cyan!25.0} 0.81 &amp; 0.17 &amp; \cellcolor{cyan!17.1} 0.19 &amp; \cellcolor{cyan!23.3} 0.85 &amp; \cellcolor{cyan!15.4} 0.73 \\
+Llama 3 70B &amp; 0.05 &amp; \cellcolor{cyan!16.7} 0.15 &amp; \cellcolor{cyan!13.6} 0.86 &amp; \cellcolor{cyan!5.8} 0.78 &amp; 0.25 &amp; 0.26 &amp; \cellcolor{cyan!2.3} 0.82 &amp; 0.52 \\
+Mixtral 8x7B (it) &amp; 0.07 &amp; \cellcolor{cyan!16.7} 0.15 &amp; \cellcolor{cyan!23.2} 0.87 &amp; \cellcolor{cyan!7.2} 0.78 &amp; 0.22 &amp; 0.24 &amp; \cellcolor{cyan!6.8} 0.82 &amp; \cellcolor{cyan!13.7} 0.73 \\
+Mixtral 8x7B &amp; 0.08 &amp; 0.17 &amp; 0.81 &amp; 0.73 &amp; 0.30 &amp; 0.31 &amp; \cellcolor{cyan!1.1} 0.81 &amp; \cellcolor{orange!24.1} 0.45 \\
+Yi 34B (it) &amp; 0.15 &amp; 0.21 &amp; 0.81 &amp; 0.51 &amp; 0.14 &amp; 0.21 &amp; 0.79 &amp; 0.69 \\
+Yi 34B &amp; 0.13 &amp; 0.23 &amp; 0.66 &amp; 0.50 &amp; 0.08 &amp; 0.23 &amp; 0.70 &amp; 0.62 \\
+Llama 3 8B (it) &amp; 0.08 &amp; 0.17 &amp; 0.82 &amp; 0.77 &amp; 0.07 &amp; \cellcolor{cyan!25.0} 0.19 &amp; 0.79 &amp; \cellcolor{cyan!25.0} 0.74 \\
+Llama 3 8B &amp; 0.15 &amp; 0.23 &amp; 0.75 &amp; \cellcolor{orange!25.0} 0.46 &amp; 0.34 &amp; 0.34 &amp; 0.76 &amp; \cellcolor{orange!25.0} 0.45 \\
+Mistral 7B (it) &amp; \cellcolor{cyan!7.4} 0.04 &amp; 0.19 &amp; 0.79 &amp; 0.69 &amp; \cellcolor{orange!6.6} 0.35 &amp; 0.36 &amp; 0.72 &amp; 0.63 \\
+Gemma 7B (it) &amp; \cellcolor{cyan!8.8} 0.04 &amp; 0.22 &amp; 0.71 &amp; 0.60 &amp; \cellcolor{orange!10.0} 0.36 &amp; 0.38 &amp; 0.59 &amp; 0.58 \\
+Mistral 7B &amp; 0.14 &amp; 0.19 &amp; 0.80 &amp; \cellcolor{cyan!12.9} 0.79 &amp; 0.26 &amp; 0.30 &amp; 0.76 &amp; \cellcolor{orange!25.0} 0.45 \\
+Gemma 7B &amp; \cellcolor{orange!25.0} 0.35 &amp; \cellcolor{orange!25.0} 0.38 &amp; \cellcolor{orange!1.6} 0.50 &amp; 0.51 &amp; 0.15 &amp; 0.25 &amp; 0.65 &amp; \cellcolor{orange!4.1} 0.48 \\
+Gemma 2B (it) &amp; 0.12 &amp; 0.27 &amp; \cellcolor{orange!25.0} 0.46 &amp; \cellcolor{orange!25.0} 0.46 &amp; \cellcolor{orange!25.0} 0.38 &amp; \cellcolor{orange!25.0} 0.41 &amp; \cellcolor{orange!25.0} 0.42 &amp; \cellcolor{orange!16.3} 0.46 \\
+Gemma 2B &amp; \cellcolor{cyan!25.0} 0.01 &amp; 0.23 &amp; 0.57 &amp; 0.53 &amp; \cellcolor{cyan!25.0} 0.01 &amp; 0.24 &amp; 0.63 &amp; 0.54 \\
+LR &amp; 0.02 &amp; 0.15 &amp; 0.86 &amp; 0.78 &amp; 0.02 &amp; 0.15 &amp; 0.86 &amp; 0.78 \\
+GBM &amp; 0.00 &amp; 0.12 &amp; 0.91 &amp; 0.83 &amp; 0.00 &amp; 0.12 &amp; 0.91 &amp; 0.83 \\
+XGBoost &amp; 0.00 &amp; 0.12 &amp; 0.91 &amp; 0.83 &amp; 0.00 &amp; 0.12 &amp; 0.91 &amp; 0.83 \\
+\bottomrule
+\end{tabular}
+
+
+*** ACSTRAVELTIME ***
+
+\begin{tabular}{lllllllll}
+\toprule
+ &amp; ece (num) &amp; brier score loss (num) &amp; roc auc (num) &amp; accuracy (num) &amp; ece &amp; brier score loss &amp; roc auc &amp; accuracy \\
+Model &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\
+\midrule
+GPT 4o mini (it) &amp; 0.15 &amp; 0.27 &amp; 0.58 &amp; 0.57 &amp; 0.39 &amp; 0.40 &amp; 0.65 &amp; 0.55 \\
+Mixtral 8x22B (it) &amp; 0.12 &amp; \cellcolor{cyan!23.7} 0.24 &amp; \cellcolor{cyan!23.5} 0.64 &amp; \cellcolor{cyan!25.0} 0.59 &amp; 0.31 &amp; 0.33 &amp; 0.66 &amp; \cellcolor{cyan!17.1} 0.59 \\
+Mixtral 8x22B &amp; 0.30 &amp; 0.34 &amp; 0.57 &amp; \cellcolor{cyan!2.4} 0.58 &amp; 0.20 &amp; 0.28 &amp; 0.63 &amp; \cellcolor{orange!25.0} 0.44 \\
+Llama 3 70B (it) &amp; 0.12 &amp; \cellcolor{cyan!22.5} 0.24 &amp; \cellcolor{cyan!25.0} 0.64 &amp; 0.53 &amp; 0.15 &amp; \cellcolor{cyan!22.6} 0.24 &amp; \cellcolor{cyan!25.0} 0.70 &amp; \cellcolor{cyan!25.0} 0.60 \\
+Llama 3 70B &amp; 0.08 &amp; \cellcolor{cyan!12.4} 0.25 &amp; 0.52 &amp; 0.46 &amp; 0.09 &amp; \cellcolor{cyan!21.4} 0.24 &amp; 0.67 &amp; 0.55 \\
+Mixtral 8x7B (it) &amp; 0.09 &amp; \cellcolor{cyan!25.0} 0.24 &amp; 0.61 &amp; 0.57 &amp; \cellcolor{orange!25.0} 0.45 &amp; \cellcolor{orange!25.0} 0.45 &amp; 0.66 &amp; 0.52 \\
+Mixtral 8x7B &amp; 0.07 &amp; \cellcolor{cyan!16.2} 0.25 &amp; 0.57 &amp; \cellcolor{cyan!2.4} 0.58 &amp; 0.28 &amp; 0.32 &amp; 0.60 &amp; \cellcolor{orange!25.0} 0.44 \\
+Yi 34B (it) &amp; \cellcolor{cyan!3.7} 0.06 &amp; \cellcolor{cyan!11.2} 0.25 &amp; 0.50 &amp; \cellcolor{orange!25.0} 0.44 &amp; 0.35 &amp; 0.36 &amp; 0.65 &amp; 0.56 \\
+Yi 34B &amp; 0.14 &amp; 0.27 &amp; 0.53 &amp; \cellcolor{orange!25.0} 0.44 &amp; \cellcolor{cyan!4.5} 0.08 &amp; \cellcolor{cyan!25.0} 0.24 &amp; 0.62 &amp; 0.56 \\
+Llama 3 8B (it) &amp; 0.11 &amp; \cellcolor{cyan!12.4} 0.25 &amp; 0.56 &amp; 0.56 &amp; 0.19 &amp; 0.28 &amp; 0.60 &amp; 0.57 \\
+Llama 3 8B &amp; 0.12 &amp; 0.26 &amp; \cellcolor{orange!25.0} 0.48 &amp; \cellcolor{orange!25.0} 0.44 &amp; 0.08 &amp; \cellcolor{cyan!7.1} 0.25 &amp; 0.53 &amp; 0.56 \\
+Mistral 7B (it) &amp; 0.11 &amp; \cellcolor{cyan!11.2} 0.25 &amp; 0.55 &amp; 0.56 &amp; \cellcolor{orange!2.7} 0.41 &amp; 0.42 &amp; 0.59 &amp; 0.57 \\
+Gemma 7B (it) &amp; 0.10 &amp; \cellcolor{cyan!4.9} 0.26 &amp; \cellcolor{orange!4.9} 0.49 &amp; \cellcolor{orange!25.0} 0.44 &amp; \cellcolor{orange!12.0} 0.42 &amp; 0.43 &amp; 0.53 &amp; 0.56 \\
+Mistral 7B &amp; \cellcolor{orange!25.0} 0.44 &amp; \cellcolor{orange!25.0} 0.44 &amp; 0.50 &amp; 0.56 &amp; \cellcolor{cyan!22.5} 0.05 &amp; \cellcolor{cyan!14.2} 0.25 &amp; 0.57 &amp; 0.56 \\
+Gemma 7B &amp; \cellcolor{cyan!25.0} 0.03 &amp; \cellcolor{cyan!13.7} 0.25 &amp; 0.52 &amp; 0.55 &amp; \cellcolor{cyan!25.0} 0.04 &amp; \cellcolor{cyan!19.0} 0.24 &amp; 0.61 &amp; 0.58 \\
+Gemma 2B (it) &amp; 0.19 &amp; 0.28 &amp; 0.50 &amp; 0.56 &amp; 0.34 &amp; 0.36 &amp; \cellcolor{orange!9.3} 0.49 &amp; 0.56 \\
+Gemma 2B &amp; \cellcolor{orange!25.0} 0.44 &amp; \cellcolor{orange!25.0} 0.44 &amp; 0.50 &amp; 0.56 &amp; 0.09 &amp; \cellcolor{cyan!4.7} 0.26 &amp; \cellcolor{orange!25.0} 0.48 &amp; \cellcolor{orange!25.0} 0.44 \\
+LR &amp; 0.04 &amp; 0.24 &amp; 0.58 &amp; 0.56 &amp; 0.04 &amp; 0.24 &amp; 0.58 &amp; 0.56 \\
+GBM &amp; 0.02 &amp; 0.20 &amp; 0.75 &amp; 0.69 &amp; 0.02 &amp; 0.20 &amp; 0.75 &amp; 0.69 \\
+XGBoost &amp; 0.02 &amp; 0.19 &amp; 0.77 &amp; 0.70 &amp; 0.02 &amp; 0.19 &amp; 0.77 &amp; 0.70 \\
+\bottomrule
+\end{tabular}
+
+
+*** ACSPUBLICCOVERAGE ***
+
+\begin{tabular}{lllllllll}
+\toprule
+ &amp; ece (num) &amp; brier score loss (num) &amp; roc auc (num) &amp; accuracy (num) &amp; ece &amp; brier score loss &amp; roc auc &amp; accuracy \\
+Model &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\
+\midrule
+GPT 4o mini (it) &amp; 0.10 &amp; 0.20 &amp; 0.68 &amp; \cellcolor{cyan!17.6} 0.73 &amp; 0.33 &amp; 0.34 &amp; \cellcolor{cyan!25.0} 0.71 &amp; 0.60 \\
+Mixtral 8x22B (it) &amp; \cellcolor{cyan!25.0} 0.04 &amp; \cellcolor{cyan!25.0} 0.18 &amp; \cellcolor{cyan!11.0} 0.71 &amp; \cellcolor{cyan!25.0} 0.75 &amp; 0.24 &amp; 0.25 &amp; \cellcolor{cyan!11.7} 0.70 &amp; \cellcolor{cyan!12.7} 0.72 \\
+Mixtral 8x22B &amp; \cellcolor{orange!15.5} 0.29 &amp; \cellcolor{orange!3.4} 0.29 &amp; 0.54 &amp; \cellcolor{cyan!0.5} 0.70 &amp; 0.32 &amp; 0.30 &amp; 0.59 &amp; \cellcolor{orange!25.0} 0.30 \\
+Llama 3 70B (it) &amp; 0.13 &amp; 0.20 &amp; \cellcolor{cyan!25.0} 0.73 &amp; \cellcolor{cyan!24.4} 0.75 &amp; 0.16 &amp; \cellcolor{cyan!16.2} 0.21 &amp; \cellcolor{cyan!7.0} 0.69 &amp; \cellcolor{cyan!25.0} 0.75 \\
+Llama 3 70B &amp; 0.12 &amp; 0.21 &amp; 0.64 &amp; 0.53 &amp; 0.18 &amp; \cellcolor{cyan!8.3} 0.22 &amp; 0.67 &amp; 0.63 \\
+Mixtral 8x7B (it) &amp; \cellcolor{cyan!0.3} 0.06 &amp; \cellcolor{cyan!18.5} 0.19 &amp; 0.69 &amp; \cellcolor{cyan!22.2} 0.74 &amp; 0.20 &amp; \cellcolor{cyan!7.4} 0.23 &amp; \cellcolor{cyan!12.7} 0.70 &amp; \cellcolor{cyan!23.3} 0.74 \\
+Mixtral 8x7B &amp; 0.20 &amp; 0.25 &amp; 0.56 &amp; 0.70 &amp; 0.41 &amp; 0.37 &amp; 0.57 &amp; \cellcolor{orange!25.0} 0.30 \\
+Yi 34B (it) &amp; 0.22 &amp; 0.24 &amp; 0.57 &amp; \cellcolor{orange!25.0} 0.31 &amp; \cellcolor{cyan!16.7} 0.06 &amp; \cellcolor{cyan!25.0} 0.19 &amp; 0.67 &amp; \cellcolor{cyan!19.4} 0.74 \\
+Yi 34B &amp; 0.09 &amp; 0.20 &amp; 0.67 &amp; 0.64 &amp; \cellcolor{cyan!25.0} 0.04 &amp; \cellcolor{cyan!17.2} 0.21 &amp; 0.59 &amp; \cellcolor{cyan!2.1} 0.70 \\
+Llama 3 8B (it) &amp; 0.17 &amp; 0.22 &amp; 0.64 &amp; 0.68 &amp; 0.11 &amp; \cellcolor{cyan!12.7} 0.21 &amp; 0.59 &amp; \cellcolor{cyan!7.7} 0.71 \\
+Llama 3 8B &amp; 0.20 &amp; 0.25 &amp; 0.51 &amp; \cellcolor{orange!6.2} 0.34 &amp; 0.41 &amp; 0.38 &amp; 0.55 &amp; \cellcolor{orange!25.0} 0.30 \\
+Mistral 7B (it) &amp; 0.07 &amp; 0.20 &amp; 0.67 &amp; 0.65 &amp; 0.30 &amp; 0.30 &amp; 0.61 &amp; \cellcolor{cyan!0.4} 0.70 \\
+Gemma 7B (it) &amp; 0.18 &amp; 0.24 &amp; 0.57 &amp; 0.61 &amp; 0.30 &amp; 0.34 &amp; \cellcolor{orange!14.6} 0.46 &amp; 0.50 \\
+Mistral 7B &amp; \cellcolor{orange!22.1} 0.30 &amp; \cellcolor{orange!18.5} 0.30 &amp; 0.50 &amp; 0.70 &amp; 0.29 &amp; 0.30 &amp; \cellcolor{orange!25.0} 0.45 &amp; \cellcolor{orange!25.0} 0.30 \\
+Gemma 7B &amp; 0.18 &amp; 0.26 &amp; 0.48 &amp; 0.70 &amp; 0.15 &amp; \cellcolor{cyan!3.4} 0.23 &amp; 0.49 &amp; 0.49 \\
+Gemma 2B (it) &amp; 0.24 &amp; \cellcolor{orange!14.2} 0.29 &amp; \cellcolor{orange!25.0} 0.42 &amp; 0.42 &amp; \cellcolor{orange!25.0} 0.70 &amp; \cellcolor{orange!25.0} 0.70 &amp; 0.54 &amp; \cellcolor{orange!25.0} 0.30 \\
+Gemma 2B &amp; \cellcolor{orange!25.0} 0.30 &amp; \cellcolor{orange!25.0} 0.30 &amp; 0.50 &amp; 0.70 &amp; 0.26 &amp; 0.28 &amp; 0.54 &amp; \cellcolor{orange!25.0} 0.30 \\
+LR &amp; 0.03 &amp; 0.19 &amp; 0.70 &amp; 0.72 &amp; 0.03 &amp; 0.19 &amp; 0.70 &amp; 0.72 \\
+GBM &amp; 0.01 &amp; 0.14 &amp; 0.83 &amp; 0.80 &amp; 0.01 &amp; 0.14 &amp; 0.83 &amp; 0.80 \\
+XGBoost &amp; 0.00 &amp; 0.14 &amp; 0.84 &amp; 0.80 &amp; 0.00 &amp; 0.14 &amp; 0.84 &amp; 0.80 \\
+\bottomrule
+\end{tabular}
+
+
+</pre></div></div>
+</div>
+</section>
+<section id="Render-paper-plots">
+<h2>Render paper plots<a class="headerlink" href="#Render-paper-plots" title="Link to this heading"></a></h2>
+<p>Filter by the currently selected prompting scheme:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">all_results_df_original</span> <span class="o">=</span> <span class="n">all_results_df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+<span class="n">all_results_df</span> <span class="o">=</span> <span class="n">all_results_df</span><span class="p">[</span>
+    <span class="p">(</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">NUMERIC_PROMPTING</span><span class="p">)</span>
+    <span class="o">|</span> <span class="p">(</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">())</span>
+<span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">&quot;whitegrid&quot;</span><span class="p">,</span> <span class="n">rc</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;grid.linestyle&quot;</span><span class="p">:</span> <span class="s2">&quot;--&quot;</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rc</span><span class="p">(</span><span class="s2">&quot;figure&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">4.5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+
+<span class="n">palette_name</span> <span class="o">=</span> <span class="s2">&quot;tab10&quot;</span>   <span class="c1"># &quot;colorblind&quot;</span>
+<span class="n">palette</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">(</span><span class="n">palette_name</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">set_palette</span><span class="p">(</span><span class="n">palette_name</span><span class="p">)</span>
+<span class="n">palette</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<svg  width="550" height="55"><rect x="0" y="0" width="55" height="55" style="fill:#1f77b4;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="55" y="0" width="55" height="55" style="fill:#ff7f0e;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="110" y="0" width="55" height="55" style="fill:#2ca02c;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="165" y="0" width="55" height="55" style="fill:#d62728;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="220" y="0" width="55" height="55" style="fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="275" y="0" width="55" height="55" style="fill:#8c564b;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="330" y="0" width="55" height="55" style="fill:#e377c2;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="385" y="0" width="55" height="55" style="fill:#7f7f7f;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="440" y="0" width="55" height="55" style="fill:#bcbd22;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="495" y="0" width="55" height="55" style="fill:#17becf;stroke-width:2;stroke:rgb(255,255,255)"/></svg></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">IMGS_DIR</span> <span class="o">=</span> <span class="n">ACS_AGG_RESULTS_PATH</span><span class="o">.</span><span class="n">parent</span> <span class="o">/</span> <span class="s2">&quot;imgs&quot;</span>
+<span class="n">IMGS_DIR</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">add_prompt_suffix</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+    <span class="n">num_or_multiple_choice</span> <span class="o">=</span> <span class="s2">&quot;numeric-prompt&quot;</span> <span class="k">if</span> <span class="n">NUMERIC_PROMPTING</span> <span class="k">else</span> <span class="s2">&quot;multiple-choice-prompt&quot;</span>
+    <span class="n">save_path</span> <span class="o">=</span> <span class="p">(</span><span class="n">IMGS_DIR</span> <span class="o">/</span> <span class="n">name</span><span class="p">)</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="s2">&quot;.pdf&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">add_prompt_suffix</span><span class="p">:</span>
+        <span class="n">save_path</span> <span class="o">=</span> <span class="n">save_path</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;.</span><span class="si">{</span><span class="n">num_or_multiple_choice</span><span class="si">}</span><span class="s2">.pdf&quot;</span><span class="p">)</span>
+
+    <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_path</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Saved figure to &#39;</span><span class="si">{</span><span class="n">save_path</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span><span class="si">}</span><span class="s2">&#39;&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Load scores distributions for each model (and with varying degrees of information).</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">predictions_path_col</span> <span class="o">=</span> <span class="s2">&quot;predictions_path&quot;</span>
+
+<span class="k">def</span> <span class="nf">load_model_scores_df</span><span class="p">(</span><span class="n">df_row</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Loads csv containing model scores corresponding to the given DF row.&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">predictions_path_col</span> <span class="ow">in</span> <span class="n">df_row</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">pd</span><span class="o">.</span><span class="n">isna</span><span class="p">(</span><span class="n">df_row</span><span class="p">[</span><span class="n">predictions_path_col</span><span class="p">]):</span>
+        <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">df_row</span><span class="p">[</span><span class="n">predictions_path_col</span><span class="p">],</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="k">return</span> <span class="kc">None</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scores_df_map</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="n">id_</span><span class="p">:</span> <span class="n">load_model_scores_df</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">id_</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">all_results_df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">all_results_df</span><span class="p">))</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "465640a48d654dd3999164c4cc8efc3f", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<section id="1st-page-illustrative-plot">
+<h3>1st page illustrative plot<a class="headerlink" href="#1st-page-illustrative-plot" title="Link to this heading"></a></h3>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">example_task</span> <span class="o">=</span> <span class="s2">&quot;ACSIncome&quot;</span>
+
+<span class="c1"># example_model = &quot;Mistral-7B-v0.1&quot;</span>
+<span class="n">example_model</span> <span class="o">=</span> <span class="s2">&quot;Meta-Llama-3-70B&quot;</span>
+
+<span class="n">baseline_model</span> <span class="o">=</span> <span class="s2">&quot;XGBoost&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Data for baseline model</span>
+<span class="n">baseline_row</span> <span class="o">=</span> <span class="n">all_results_df</span><span class="p">[(</span><span class="n">all_results_df</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">baseline_model</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">example_task</span><span class="p">)]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+<span class="c1"># Data for base and instruct models</span>
+<span class="n">example_df</span> <span class="o">=</span> <span class="n">all_results_df</span><span class="p">[</span>
+    <span class="p">(</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">example_task</span><span class="p">)</span>
+    <span class="o">&amp;</span> <span class="p">(</span><span class="n">all_results_df</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">example_model</span><span class="p">)</span>
+    <span class="o">&amp;</span> <span class="p">(</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">NUMERIC_PROMPTING</span><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Sort examples_df to have (base, instruct) ordering</span>
+<span class="n">example_df</span> <span class="o">=</span> <span class="n">example_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">&quot;is_inst&quot;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">example_df</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>accuracy</th>
+      <th>accuracy_diff</th>
+      <th>accuracy_ratio</th>
+      <th>balanced_accuracy</th>
+      <th>balanced_accuracy_diff</th>
+      <th>balanced_accuracy_ratio</th>
+      <th>brier_score_loss</th>
+      <th>ece</th>
+      <th>ece_quantile</th>
+      <th>equalized_odds_diff</th>
+      <th>...</th>
+      <th>num_features</th>
+      <th>uses_all_features</th>
+      <th>fit_thresh_on_100</th>
+      <th>fit_thresh_accuracy</th>
+      <th>optimal_thresh</th>
+      <th>optimal_thresh_accuracy</th>
+      <th>score_stdev</th>
+      <th>score_mean</th>
+      <th>model_size</th>
+      <th>model_family</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Meta-Llama-3-70B__ACSIncome__-1__Num</th>
+      <td>0.543953</td>
+      <td>0.134809</td>
+      <td>0.771466</td>
+      <td>0.636284</td>
+      <td>0.078752</td>
+      <td>0.887756</td>
+      <td>0.237630</td>
+      <td>0.270157</td>
+      <td>NaN</td>
+      <td>0.191080</td>
+      <td>...</td>
+      <td>-1</td>
+      <td>True</td>
+      <td>0.89560</td>
+      <td>0.784722</td>
+      <td>0.8956</td>
+      <td>0.784722</td>
+      <td>0.253299</td>
+      <td>0.638033</td>
+      <td>70</td>
+      <td>Llama</td>
+    </tr>
+    <tr>
+      <th>Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num</th>
+      <td>0.665731</td>
+      <td>0.086680</td>
+      <td>0.880953</td>
+      <td>0.722658</td>
+      <td>0.061832</td>
+      <td>0.919317</td>
+      <td>0.230512</td>
+      <td>0.246092</td>
+      <td>NaN</td>
+      <td>0.232483</td>
+      <td>...</td>
+      <td>-1</td>
+      <td>True</td>
+      <td>0.75718</td>
+      <td>0.735764</td>
+      <td>0.8239</td>
+      <td>0.784848</td>
+      <td>0.290286</td>
+      <td>0.613968</td>
+      <td>70</td>
+      <td>Llama</td>
+    </tr>
+  </tbody>
+</table>
+<p>2 rows × 66 columns</p>
+</div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[26]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ALPHA</span> <span class="o">=</span> <span class="mf">0.7</span>
+<span class="n">N_BINS</span> <span class="o">=</span> <span class="mi">20</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">,</span> <span class="n">ax3</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">),</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">))</span>
+
+<span class="c1">###</span>
+<span class="c1"># ROC barplot</span>
+<span class="c1">###</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">example_df</span><span class="p">,</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;is_inst&quot;</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;roc_auc&quot;</span><span class="p">,</span>
+    <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">alpha</span><span class="o">=</span><span class="n">ALPHA</span><span class="p">,</span>
+    <span class="n">width</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+    <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Add horizontal line and label</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">baseline_row</span><span class="p">[</span><span class="s2">&quot;roc_auc&quot;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">baseline_model</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">xmin</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">xmax</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
+    <span class="n">x</span><span class="o">=</span><span class="mf">1.15</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="n">baseline_row</span><span class="p">[</span><span class="s2">&quot;roc_auc&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="mf">1e-3</span><span class="p">,</span>
+    <span class="n">s</span><span class="o">=</span><span class="n">baseline_model</span><span class="p">,</span>
+    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
+    <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
+    <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
+    <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">,</span>
+    <span class="n">backgroundcolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span>
+    <span class="n">zorder</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">baseline_row</span><span class="p">[</span><span class="s2">&quot;roc_auc&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="mf">9e-2</span><span class="p">)</span>
+<span class="c1"># ax1.set_ylim(0.5, baseline_row[&quot;roc_auc&quot;] + 7e-2)</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;ROC AUC&quot;</span><span class="p">)</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>   <span class="c1"># Remove the legend</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">([</span><span class="s2">&quot;Base&quot;</span><span class="p">,</span> <span class="s2">&quot;Instr.&quot;</span><span class="p">])</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Predictive signal&quot;</span> <span class="o">+</span> <span class="sa">r</span><span class="s2">&quot;$\uparrow$&quot;</span><span class="p">)</span>
+
+<span class="c1">###</span>
+<span class="c1"># Score distribution</span>
+<span class="c1">###</span>
+<span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram_bin_edges</span><span class="p">([],</span> <span class="n">bins</span><span class="o">=</span><span class="n">N_BINS</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">id_</span> <span class="ow">in</span> <span class="n">example_df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">():</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span>
+        <span class="n">scores_df_map</span><span class="p">[</span><span class="n">id_</span><span class="p">][</span><span class="s2">&quot;risk_score&quot;</span><span class="p">],</span>
+        <span class="n">alpha</span><span class="o">=</span><span class="n">ALPHA</span> <span class="o">*</span> <span class="mf">0.9</span><span class="p">,</span>
+        <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;proportion&quot;</span><span class="p">,</span>
+        <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span>
+        <span class="n">zorder</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+        <span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span>
+    <span class="p">)</span>
+
+<span class="c1"># # Draw baseline score distribution</span>
+<span class="c1"># sns.histplot(</span>
+<span class="c1">#     scores_df_map[baseline_row.name][&quot;risk_score&quot;],</span>
+<span class="c1">#     alpha=1,</span>
+<span class="c1">#     bins=bins,</span>
+<span class="c1">#     stat=&quot;proportion&quot;,</span>
+<span class="c1">#     fill=False,</span>
+<span class="c1">#     color=palette[2],</span>
+<span class="c1">#     edgecolor=palette[2],</span>
+<span class="c1">#     hatch=&quot;/&quot;,</span>
+<span class="c1">#     zorder=-1,</span>
+<span class="c1">#     ax=ax2,</span>
+<span class="c1"># )</span>
+
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Score distribution&quot;</span><span class="p">)</span>
+
+<span class="c1">###</span>
+<span class="c1"># Calibration error</span>
+<span class="c1">###</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">example_df</span><span class="p">,</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;is_inst&quot;</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;ece&quot;</span><span class="p">,</span>
+    <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">alpha</span><span class="o">=</span><span class="n">ALPHA</span><span class="p">,</span>
+    <span class="n">width</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+    <span class="n">ax</span><span class="o">=</span><span class="n">ax3</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Add horizontal line and label</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">baseline_row</span><span class="p">[</span><span class="s2">&quot;ece&quot;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">baseline_model</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">xmin</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">xmax</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
+
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;ECE&quot;</span><span class="p">)</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">([</span><span class="s2">&quot;Base&quot;</span><span class="p">,</span> <span class="s2">&quot;Instr.&quot;</span><span class="p">])</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Calibration error&quot;</span> <span class="o">+</span> <span class="s2">&quot;$\downarrow$&quot;</span><span class="p">)</span>
+<span class="c1"># ax3.legend(</span>
+<span class="c1">#     loc=&quot;center left&quot;,</span>
+<span class="c1">#     bbox_to_anchor=(1.05, 0.5),</span>
+<span class="c1"># )</span>
+
+<span class="n">hs</span><span class="p">,</span> <span class="n">ls</span> <span class="o">=</span> <span class="n">ax3</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="n">hs</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">ls</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper center&quot;</span><span class="p">)</span>
+
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;teaser-</span><span class="si">{</span><span class="n">example_model</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/tmp/ipykernel_3388197/4070898515.py:38: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+  ax1.set_xticklabels([&#34;Base&#34;, &#34;Instr.&#34;])
+/tmp/ipykernel_3388197/4070898515.py:90: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+  ax3.set_xticklabels([&#34;Base&#34;, &#34;Instr.&#34;])
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/teaser-Meta-Llama-3-70B.numeric-prompt.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_41_2.png" src="../_images/notebooks_paper-plots-and-tables_41_2.png" />
+</div>
+</div>
+</section>
+<section id="Score-distributions-for-base/instruct-pairs">
+<h3>Score distributions for base/instruct pairs<a class="headerlink" href="#Score-distributions-for-base/instruct-pairs" title="Link to this heading"></a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Plotting score distributions for the following task: </span><span class="si">{</span><span class="n">example_task</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+
+<span class="n">task_df</span> <span class="o">=</span> <span class="n">all_results_df</span><span class="p">[</span>
+    <span class="p">(</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">example_task</span><span class="p">)</span>
+    <span class="o">&amp;</span> <span class="p">(</span>
+        <span class="p">(</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">NUMERIC_PROMPTING</span><span class="p">)</span>
+        <span class="o">|</span> <span class="p">(</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">())</span>                  <span class="c1"># Baseline models will have NaNs in this col.</span>
+    <span class="p">)]</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">task_df</span><span class="p">)</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Plotting score distributions for the following task: ACSIncome.
+len(task_df)=20
+</pre></div></div>
+</div>
+<p>Sort model pairs as wished for the paper plot:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sorted_model_families</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;OpenAI&quot;</span><span class="p">,</span> <span class="s2">&quot;Llama&quot;</span><span class="p">,</span> <span class="s2">&quot;Mistral&quot;</span><span class="p">,</span> <span class="s2">&quot;Yi&quot;</span><span class="p">,</span> <span class="s2">&quot;Gemma&quot;</span><span class="p">,</span> <span class="s2">&quot;-&quot;</span><span class="p">]</span>
+
+<span class="n">model_pairs</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span>
+        <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">],</span> <span class="n">row</span><span class="p">[</span><span class="n">model_col</span><span class="p">])</span>
+        <span class="k">for</span> <span class="n">id_</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">task_df</span><span class="p">[</span><span class="n">task_df</span><span class="p">[</span><span class="s2">&quot;is_inst&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span>
+    <span class="p">],</span>
+    <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">val</span><span class="p">:</span> <span class="p">(</span>
+        <span class="mf">1e6</span> <span class="o">*</span> <span class="n">sorted_model_families</span><span class="o">.</span><span class="n">index</span><span class="p">((</span><span class="n">row</span> <span class="o">:=</span> <span class="n">task_df</span><span class="p">[</span><span class="n">task_df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">val</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">])[</span><span class="s2">&quot;model_family&quot;</span><span class="p">])</span>
+        <span class="o">+</span> <span class="n">row</span><span class="p">[</span><span class="s2">&quot;model_size&quot;</span><span class="p">]</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+<span class="n">model_pairs</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[(&#39;penai/gpt-4o-mini&#39;, &#39;penai/gpt-4o-mini&#39;),
+ (&#39;Meta-Llama-3-8B&#39;, &#39;Meta-Llama-3-8B-Instruct&#39;),
+ (&#39;Meta-Llama-3-70B&#39;, &#39;Meta-Llama-3-70B-Instruct&#39;),
+ (&#39;Mistral-7B-v0.1&#39;, &#39;Mistral-7B-Instruct-v0.2&#39;),
+ (&#39;Mixtral-8x7B-v0.1&#39;, &#39;Mixtral-8x7B-Instruct-v0.1&#39;),
+ (&#39;Mixtral-8x22B-v0.1&#39;, &#39;Mixtral-8x22B-Instruct-v0.1&#39;),
+ (&#39;Yi-34B&#39;, &#39;Yi-34B-Chat&#39;),
+ (&#39;gemma-2b&#39;, &#39;gemma-1.1-2b-it&#39;),
+ (&#39;gemma-7b&#39;, &#39;gemma-1.1-7b-it&#39;)]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">PLOT_THRESHOLD_LINE</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="n">N_PLOTS</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">model_pairs</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>  <span class="c1"># plus 1 for the XGBoost baseline</span>
+
+<span class="n">N_COLS</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">N_ROWS</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">N_PLOTS</span> <span class="o">/</span> <span class="n">N_COLS</span><span class="p">)</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span>
+    <span class="n">nrows</span><span class="o">=</span><span class="n">N_ROWS</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">N_COLS</span><span class="p">,</span>
+    <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">N_ROWS</span><span class="p">),</span>
+    <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">gridspec_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+        <span class="n">hspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">wspace</span><span class="o">=</span><span class="mf">0.06</span><span class="p">,</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="c1"># Plot settings</span>
+<span class="n">plot_config</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+    <span class="n">bins</span><span class="o">=</span><span class="n">N_BINS</span><span class="p">,</span>
+    <span class="n">binrange</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+    <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;percent&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Plot score distribution for model pairs</span>
+<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">base_name</span><span class="p">,</span> <span class="n">instr_name</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model_pairs</span><span class="p">):</span>
+    <span class="n">ax_row</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">//</span> <span class="n">N_COLS</span>
+    <span class="n">ax_col</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">%</span> <span class="n">N_COLS</span>
+
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">ax_row</span><span class="p">,</span> <span class="n">ax_col</span><span class="p">]</span>
+    <span class="n">base_row</span> <span class="o">=</span> <span class="n">task_df</span><span class="p">[</span><span class="n">task_df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">base_name</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">it_row</span> <span class="o">=</span> <span class="n">task_df</span><span class="p">[</span><span class="n">task_df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">instr_name</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="c1"># Get scores</span>
+    <span class="n">base_scores</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">base_row</span><span class="o">.</span><span class="n">name</span><span class="p">][</span><span class="s2">&quot;risk_score&quot;</span><span class="p">]</span>
+    <span class="n">it_scores</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">it_row</span><span class="o">.</span><span class="n">name</span><span class="p">][</span><span class="s2">&quot;risk_score&quot;</span><span class="p">]</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">base_row</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">])</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;risk scores&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ax_col</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;density (%)&quot;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">ax_row</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">70</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mf">0.03</span><span class="p">,</span> <span class="mf">1.03</span><span class="p">)</span>
+
+    <span class="c1"># Render plot</span>
+    <span class="k">if</span> <span class="s2">&quot;gpt&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">instr_name</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+        <span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">base_scores</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Base&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_config</span><span class="p">)</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">it_scores</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Instruction-tuned&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_config</span><span class="p">)</span>
+
+    <span class="c1"># Draw faint vertical line with t^* of each model</span>
+    <span class="n">threshold_col</span> <span class="o">=</span> <span class="s2">&quot;optimal_thresh&quot;</span>
+    <span class="c1"># threshold_col = &quot;fit_thresh_on_100&quot;</span>
+
+    <span class="k">if</span> <span class="n">PLOT_THRESHOLD_LINE</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">base_row</span><span class="p">[</span><span class="n">threshold_col</span><span class="p">],</span> <span class="n">ls</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">it_row</span><span class="p">[</span><span class="n">threshold_col</span><span class="p">],</span> <span class="n">ls</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+    <span class="c1"># Render legend on a few sub-plots</span>
+    <span class="k">if</span> <span class="p">(</span><span class="n">ax_col</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">ax_row</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">ax_col</span> <span class="o">==</span> <span class="n">N_COLS</span><span class="o">-</span><span class="mi">1</span> <span class="ow">and</span> <span class="n">ax_row</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper center&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">PLOT_THRESHOLD_LINE</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
+                <span class="n">x</span><span class="o">=</span><span class="n">base_row</span><span class="p">[</span><span class="n">threshold_col</span><span class="p">]</span> <span class="o">-</span> <span class="mf">5e-2</span><span class="p">,</span>
+                <span class="n">y</span><span class="o">=</span><span class="mi">43</span><span class="p">,</span>
+                <span class="n">s</span><span class="o">=</span><span class="s2">&quot;$t^*$&quot;</span><span class="p">,</span>
+                <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+                <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
+                <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">,</span>
+                <span class="c1"># zorder=-1,</span>
+            <span class="p">)</span> <span class="c1">#.set_bbox(dict(facecolor=&#39;white&#39;, alpha=0.5))</span>
+
+            <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
+                <span class="n">x</span><span class="o">=</span><span class="n">it_row</span><span class="p">[</span><span class="n">threshold_col</span><span class="p">]</span> <span class="o">+</span> <span class="mf">5e-2</span><span class="p">,</span>
+                <span class="n">y</span><span class="o">=</span><span class="mi">43</span><span class="p">,</span>
+                <span class="n">s</span><span class="o">=</span><span class="s2">&quot;$t^*$&quot;</span><span class="p">,</span>
+                <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+                <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
+                <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">,</span>
+                <span class="c1"># zorder=-1,</span>
+            <span class="p">)</span>
+
+
+<span class="c1"># Plot score distribution for XGBoost baseline</span>
+<span class="n">baseline_name</span> <span class="o">=</span> <span class="s2">&quot;XGBoost&quot;</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">baseline_name</span><span class="p">)</span>
+
+<span class="n">baseline_row</span> <span class="o">=</span> <span class="n">task_df</span><span class="p">[</span><span class="n">task_df</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">baseline_name</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">xgboost_scores</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">baseline_row</span><span class="o">.</span><span class="n">name</span><span class="p">][</span><span class="s2">&quot;risk_score&quot;</span><span class="p">]</span>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span>
+    <span class="n">xgboost_scores</span><span class="p">,</span>
+    <span class="n">label</span><span class="o">=</span><span class="n">baseline_name</span><span class="p">,</span>
+    <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">plot_config</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;risk scores&quot;</span><span class="p">)</span>
+
+<span class="c1"># # Remove unused plot?</span>
+<span class="c1"># axes[1, 3].remove()</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;density (%)&quot;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">])</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">])</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+
+<span class="c1"># Save figure</span>
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;score-distribution&quot;</span> <span class="o">+</span> <span class="p">(</span><span class="s2">&quot;_with-threshold&quot;</span> <span class="k">if</span> <span class="n">PLOT_THRESHOLD_LINE</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/score-distribution.numeric-prompt.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_46_1.png" src="../_images/notebooks_paper-plots-and-tables_46_1.png" />
+</div>
+</div>
+</section>
+</section>
+<section id="Calibration-curves">
+<h2>Calibration curves<a class="headerlink" href="#Calibration-curves" title="Link to this heading"></a></h2>
+<p>Base models vs Instruction-tuned models:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">TASK</span> <span class="o">=</span> <span class="s2">&quot;ACSIncome&quot;</span>
+
+<span class="c1"># df = results_df[(results_df[task_col] == TASK) &amp; (results_df[numeric_prompt_col] == NUMERIC_PROMPTING)]</span>
+
+<span class="n">df</span> <span class="o">=</span> <span class="n">all_results_df</span><span class="p">[(</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">TASK</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">NUMERIC_PROMPTING</span><span class="p">)]</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+len(df)=17
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.calibration</span> <span class="kn">import</span> <span class="n">CalibrationDisplay</span>
+<span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">prettify_model_name</span>
+
+<span class="n">base_models_to_plot</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span> <span class="k">if</span> <span class="s2">&quot;gemma&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
+
+<span class="n">base_models_to_plot</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;Meta-Llama-3-8B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Meta-Llama-3-70B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mistral-7B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mixtral-8x7B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mixtral-8x22B-v0.1&quot;</span><span class="p">,</span>
+    <span class="c1"># &quot;Yi-34B&quot;,</span>
+<span class="p">]</span>
+
+<span class="n">instr_models_to_plot</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">df</span><span class="p">[</span>
+        <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="p">)</span>
+        <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">!=</span> <span class="n">m</span><span class="p">)</span>
+    <span class="p">][</span><span class="n">model_col</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">base_models_to_plot</span>
+<span class="p">]</span>
+
+<span class="c1"># Add GPT models if any</span>
+<span class="n">instr_models_to_plot</span> <span class="o">+=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="s2">&quot;gpt&quot;</span> <span class="ow">in</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.33</span><span class="p">),</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Plot base models</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">model_list</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="p">,</span> <span class="p">(</span><span class="n">base_models_to_plot</span><span class="p">,</span> <span class="n">instr_models_to_plot</span><span class="p">)):</span>
+
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model_list</span><span class="p">):</span>
+
+        <span class="n">curr_row</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">curr_scores</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">curr_row</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+
+        <span class="n">disp</span> <span class="o">=</span> <span class="n">CalibrationDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span>
+            <span class="n">y_true</span><span class="o">=</span><span class="n">curr_scores</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">],</span>
+            <span class="n">y_prob</span><span class="o">=</span><span class="n">curr_scores</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">],</span>
+            <span class="n">n_bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+            <span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;quantile&quot;</span><span class="p">,</span>
+            <span class="n">name</span><span class="o">=</span><span class="n">prettify_model_name</span><span class="p">(</span><span class="n">curr_row</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]),</span>
+            <span class="n">linestyle</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;-.&quot;</span><span class="p">,</span> <span class="s2">&quot;:&quot;</span><span class="p">,</span> <span class="s2">&quot;--&quot;</span><span class="p">][</span><span class="n">idx</span> <span class="o">%</span> <span class="mi">3</span><span class="p">],</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">ylabel</span> <span class="o">=</span> <span class="s2">&quot;Fraction of positives &quot;</span> <span class="o">+</span> <span class="sa">r</span><span class="s2">&quot;$\left(\mathrm</span><span class="si">{P}</span><span class="s2">[Y=1]\right)$&quot;</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">)</span>
+
+<span class="n">xlabel</span> <span class="o">=</span> <span class="s2">&quot;Mean predicted probability&quot;</span> <span class="c1"># + r&quot; $(\mathbb{E}[R])$&quot;</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Base models&quot;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Instruction-tuned models&quot;</span><span class="p">)</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+
+<span class="c1"># Legent to the right of the right-most plot</span>
+<span class="n">h</span><span class="p">,</span> <span class="n">l</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+<span class="n">l</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;Perf. calibrated&quot;</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="n">h</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">l</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.1</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">))</span>
+
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;calibration-curves-base-and-instr.pdf&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/calibration-curves-base-and-instr.numeric-prompt.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_50_1.png" src="../_images/notebooks_paper-plots-and-tables_50_1.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[32]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.calibration</span> <span class="kn">import</span> <span class="n">CalibrationDisplay</span>
+<span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">prettify_model_name</span>
+
+<span class="n">base_models_to_plot</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span> <span class="k">if</span> <span class="s2">&quot;gemma&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
+
+<span class="n">base_models_to_plot</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;Meta-Llama-3-70B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mixtral-8x7B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mixtral-8x22B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Yi-34B&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+<span class="n">instr_models_to_plot</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">df</span><span class="p">[</span>
+        <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="p">)</span>
+        <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">!=</span> <span class="n">m</span><span class="p">)</span>
+    <span class="p">][</span><span class="n">model_col</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">base_models_to_plot</span>
+<span class="p">]</span>
+
+<span class="c1"># Add GPT models if any</span>
+<span class="n">instr_models_to_plot</span> <span class="o">+=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="s2">&quot;gpt&quot;</span> <span class="ow">in</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.33</span><span class="p">),</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Plot base models</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">model_list</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="p">,</span> <span class="p">(</span><span class="n">base_models_to_plot</span><span class="p">,</span> <span class="n">instr_models_to_plot</span><span class="p">)):</span>
+
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model_list</span><span class="p">):</span>
+
+        <span class="n">curr_row</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">curr_scores</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">curr_row</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+
+        <span class="n">disp</span> <span class="o">=</span> <span class="n">CalibrationDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span>
+            <span class="n">y_true</span><span class="o">=</span><span class="n">curr_scores</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">],</span>
+            <span class="n">y_prob</span><span class="o">=</span><span class="n">curr_scores</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">],</span>
+            <span class="n">n_bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+            <span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;quantile&quot;</span><span class="p">,</span>
+            <span class="n">name</span><span class="o">=</span><span class="n">prettify_model_name</span><span class="p">(</span><span class="n">curr_row</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]),</span>
+            <span class="n">linestyle</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;-.&quot;</span><span class="p">,</span> <span class="s2">&quot;:&quot;</span><span class="p">,</span> <span class="s2">&quot;--&quot;</span><span class="p">][</span><span class="n">idx</span> <span class="o">%</span> <span class="mi">3</span><span class="p">],</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">ylabel</span> <span class="o">=</span> <span class="s2">&quot;Fraction of positives &quot;</span> <span class="o">+</span> <span class="sa">r</span><span class="s2">&quot;$\left(\mathrm</span><span class="si">{P}</span><span class="s2">[Y=1]\right)$&quot;</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">)</span>
+
+<span class="n">xlabel</span> <span class="o">=</span> <span class="s2">&quot;Mean predicted probability&quot;</span> <span class="c1"># + r&quot; $(\mathbb{E}[R])$&quot;</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Base models&quot;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Instruction-tuned models&quot;</span><span class="p">)</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+
+<span class="c1"># Legent to the right of the right-most plot</span>
+<span class="n">h</span><span class="p">,</span> <span class="n">l</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+<span class="n">l</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;Perf. calibrated&quot;</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="n">h</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">l</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.1</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">))</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;calibration-curves-base-and-instr.large-models.pdf&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/calibration-curves-base-and-instr.large-models.numeric-prompt.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_52_1.png" src="../_images/notebooks_paper-plots-and-tables_52_1.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[33]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.calibration</span> <span class="kn">import</span> <span class="n">CalibrationDisplay</span>
+<span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">prettify_model_name</span>
+
+<span class="n">base_models_to_plot</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span> <span class="k">if</span> <span class="s2">&quot;gemma&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
+
+<span class="n">base_models_to_plot</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;Meta-Llama-3-70B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mixtral-8x7B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mixtral-8x22B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Yi-34B&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+<span class="n">instr_models_to_plot</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">df</span><span class="p">[</span>
+        <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="p">)</span>
+        <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">!=</span> <span class="n">m</span><span class="p">)</span>
+    <span class="p">][</span><span class="n">model_col</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">base_models_to_plot</span>
+<span class="p">]</span>
+
+<span class="c1"># Add GPT models if any</span>
+<span class="n">instr_models_to_plot</span> <span class="o">+=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="s2">&quot;gpt&quot;</span> <span class="ow">in</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">),</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.15</span><span class="p">),</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Plot base models</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">model_list</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="p">,</span> <span class="p">(</span><span class="n">base_models_to_plot</span><span class="p">,</span> <span class="n">instr_models_to_plot</span><span class="p">)):</span>
+
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model_list</span><span class="p">):</span>
+
+        <span class="n">curr_row</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">curr_scores</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">curr_row</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+
+        <span class="n">disp</span> <span class="o">=</span> <span class="n">CalibrationDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span>
+            <span class="n">y_true</span><span class="o">=</span><span class="n">curr_scores</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">],</span>
+            <span class="n">y_prob</span><span class="o">=</span><span class="n">curr_scores</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">],</span>
+            <span class="n">n_bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+            <span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;quantile&quot;</span><span class="p">,</span>
+            <span class="n">name</span><span class="o">=</span><span class="n">prettify_model_name</span><span class="p">(</span><span class="n">curr_row</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]),</span>
+            <span class="n">linestyle</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;-.&quot;</span><span class="p">,</span> <span class="s2">&quot;:&quot;</span><span class="p">,</span> <span class="s2">&quot;--&quot;</span><span class="p">][</span><span class="n">idx</span> <span class="o">%</span> <span class="mi">3</span><span class="p">],</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+            <span class="n">ms</span><span class="o">=</span><span class="mf">3.5</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="n">ylabel</span> <span class="o">=</span> <span class="s2">&quot;Fraction of positives &quot;</span> <span class="o">+</span> <span class="sa">r</span><span class="s2">&quot;$\left(\mathrm</span><span class="si">{P}</span><span class="s2">[Y=1]\right)$&quot;</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">)</span>
+<span class="c1"># axes[1].set_ylabel(ylabel)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+
+<span class="c1"># xlabel = &quot;Mean predicted probability&quot; # + r&quot; $(\mathbb{E}[R])$&quot;</span>
+<span class="n">xlabel</span> <span class="o">=</span> <span class="s2">&quot;Mean risk score&quot;</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Base models&quot;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Instruction-tuned&quot;</span><span class="p">)</span>
+
+<span class="c1"># Set prettier x ticks</span>
+<span class="n">xticks</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">xticks_labels</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;0.0&quot;</span><span class="p">,</span> <span class="s2">&quot;0.25&quot;</span><span class="p">,</span> <span class="s2">&quot;0.5&quot;</span><span class="p">,</span> <span class="s2">&quot;0.75&quot;</span><span class="p">,</span> <span class="s2">&quot;1.0&quot;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">xticks</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">xticks_labels</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">xticks</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">xticks_labels</span><span class="p">)</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+
+<span class="c1"># Legent to the right of the right-most plot</span>
+<span class="k">if</span> <span class="n">NUMERIC_PROMPTING</span><span class="p">:</span>
+    <span class="n">h</span><span class="p">,</span> <span class="n">l</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+    <span class="n">l</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;Perf. calibrated&quot;</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="n">h</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">l</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+
+<span class="c1"># Figure title</span>
+<span class="k">if</span> <span class="n">NUMERIC_PROMPTING</span><span class="p">:</span>
+    <span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;Numeric Prompting&quot;</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;Multiple-choice Prompting&quot;</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">1.1</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;calibration-curves-base-and-instr.large-models.smaller.pdf&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/calibration-curves-base-and-instr.large-models.smaller.numeric-prompt.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_54_1.png" src="../_images/notebooks_paper-plots-and-tables_54_1.png" />
+</div>
+</div>
+</section>
+<section id="Calibration-per-sub-group">
+<h2>Calibration per sub-group<a class="headerlink" href="#Calibration-per-sub-group" title="Link to this heading"></a></h2>
+<p>Plot calibration curves per race sub-group, for each model pair.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[34]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="n">acs_dataset</span> <span class="o">=</span> <span class="n">ACSDataset</span><span class="o">.</span><span class="n">make_from_task</span><span class="p">(</span><span class="n">task</span><span class="o">=</span><span class="n">TASK</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">DATA_DIR</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Loading ACS data...
+CPU times: user 41.3 s, sys: 28.3 s, total: 1min 9s
+Wall time: 1min 9s
+</pre></div></div>
+</div>
+<p>Plot:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[35]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Omit small groups from the plots (results can be too noisy)</span>
+<span class="n">OMIT_GROUPS_BELOW_PCT</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">N_BINS</span> <span class="o">=</span> <span class="mi">10</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[36]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sens_attr_col</span> <span class="o">=</span> <span class="s2">&quot;RAC1P&quot;</span>
+<span class="n">sens_attr_map</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="mi">1</span><span class="p">:</span> <span class="s2">&quot;White&quot;</span><span class="p">,</span>
+    <span class="mi">2</span><span class="p">:</span> <span class="s2">&quot;Black&quot;</span><span class="p">,</span>
+    <span class="mi">6</span><span class="p">:</span> <span class="s2">&quot;Asian&quot;</span><span class="p">,</span>
+    <span class="c1"># 8: &quot;Other&quot;,   # some other race alone</span>
+<span class="p">}</span>
+
+<span class="n">name_to_sens_attr_map</span> <span class="o">=</span> <span class="p">{</span><span class="n">val</span><span class="p">:</span> <span class="n">key</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">sens_attr_map</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+
+<span class="k">def</span> <span class="nf">plot_cali_curve</span><span class="p">(</span><span class="n">model_id</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Plot sub-group calibration curves using sklearn&quot;&quot;&quot;</span>
+
+    <span class="n">curr_row</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">model_id</span><span class="p">]</span>
+    <span class="n">curr_scores_df</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">curr_row</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+
+    <span class="n">y_true</span> <span class="o">=</span> <span class="n">curr_scores_df</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+    <span class="n">y_scores</span> <span class="o">=</span> <span class="n">curr_scores_df</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+
+    <span class="n">sens_attr</span> <span class="o">=</span> <span class="n">acs_dataset</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">curr_scores_df</span><span class="o">.</span><span class="n">index</span><span class="p">][</span><span class="n">sens_attr_col</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">group_val</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">sens_attr</span><span class="p">)):</span>
+        <span class="n">group_filter</span> <span class="o">=</span> <span class="n">sens_attr</span> <span class="o">==</span> <span class="n">group_val</span>
+
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">group_filter</span><span class="p">)</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">group_filter</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.01</span> <span class="o">*</span> <span class="n">OMIT_GROUPS_BELOW_PCT</span><span class="p">:</span>
+            <span class="c1"># print(f&quot;Skipping group={group_val}&quot;)</span>
+            <span class="k">continue</span>
+
+        <span class="n">disp</span> <span class="o">=</span> <span class="n">CalibrationDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span>
+            <span class="n">y_true</span><span class="o">=</span><span class="n">y_true</span><span class="p">[</span><span class="n">group_filter</span><span class="p">],</span>
+            <span class="n">y_prob</span><span class="o">=</span><span class="n">y_scores</span><span class="p">[</span><span class="n">group_filter</span><span class="p">],</span>
+            <span class="n">n_bins</span><span class="o">=</span><span class="n">N_BINS</span><span class="p">,</span>
+            <span class="c1"># strategy=&quot;uniform&quot;,</span>
+            <span class="n">strategy</span><span class="o">=</span><span class="s2">&quot;quantile&quot;</span><span class="p">,</span>
+            <span class="n">name</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">sens_attr_map</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">group_val</span><span class="p">,</span> <span class="n">group_val</span><span class="p">)),</span>
+            <span class="n">linestyle</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;-.&quot;</span><span class="p">,</span> <span class="s2">&quot;:&quot;</span><span class="p">,</span> <span class="s2">&quot;--&quot;</span><span class="p">][</span><span class="n">idx</span> <span class="o">%</span> <span class="mi">3</span><span class="p">],</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="c1"># Set other plot settings</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">curr_row</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">disp</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[37]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.evaluation</span> <span class="kn">import</span> <span class="n">bootstrap_estimate</span>
+
+<span class="k">def</span> <span class="nf">plot_cali_curve_ci</span><span class="p">(</span><span class="n">model_id</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">ci</span><span class="o">=</span><span class="mi">95</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Plot sub-group calibration curves with bootstrap confidence intervals&quot;&quot;&quot;</span>
+
+    <span class="n">curr_row</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">model_id</span><span class="p">]</span>
+    <span class="n">curr_scores_df</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">curr_row</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+
+    <span class="n">y_true</span> <span class="o">=</span> <span class="n">curr_scores_df</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+    <span class="n">y_scores</span> <span class="o">=</span> <span class="n">curr_scores_df</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+
+    <span class="n">sens_attr</span> <span class="o">=</span> <span class="n">acs_dataset</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">curr_scores_df</span><span class="o">.</span><span class="n">index</span><span class="p">][</span><span class="n">sens_attr_col</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+
+    <span class="c1"># Compute bins</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_scores</span><span class="p">))</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">curr_row</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">])</span>
+        <span class="k">return</span>
+
+    <span class="n">y_scores_binned</span><span class="p">,</span> <span class="n">bin_edges</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">qcut</span><span class="p">(</span><span class="n">y_scores</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="n">N_BINS</span><span class="p">,</span> <span class="n">retbins</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">duplicates</span><span class="o">=</span><span class="s2">&quot;drop&quot;</span><span class="p">)</span>
+    <span class="n">bin_names</span> <span class="o">=</span> <span class="n">y_scores_binned</span><span class="o">.</span><span class="n">categories</span>   <span class="c1"># Name used for each bin: &quot;(bin_low_range, bin_high_range)&quot;</span>
+
+    <span class="c1"># To hold plot render artifacts</span>
+    <span class="n">plot_artifacts</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">group_val</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">sens_attr</span><span class="p">)):</span>
+        <span class="n">group_filter</span> <span class="o">=</span> <span class="n">sens_attr</span> <span class="o">==</span> <span class="n">group_val</span>
+
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">group_filter</span><span class="p">)</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">group_filter</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.01</span> <span class="o">*</span> <span class="n">OMIT_GROUPS_BELOW_PCT</span>
+            <span class="ow">or</span> <span class="n">group_val</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">sens_attr_map</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
+        <span class="p">):</span>
+            <span class="c1"># print(f&quot;Skipping group={group_val}&quot;)</span>
+            <span class="k">continue</span>
+
+        <span class="n">group_labels</span> <span class="o">=</span> <span class="n">y_true</span><span class="p">[</span><span class="n">group_filter</span><span class="p">]</span>
+        <span class="n">group_scores</span> <span class="o">=</span> <span class="n">y_scores</span><span class="p">[</span><span class="n">group_filter</span><span class="p">]</span>
+
+        <span class="n">group_cali_points</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="n">bin_names</span><span class="p">:</span>
+            <span class="n">bin_filter</span> <span class="o">=</span> <span class="n">y_scores_binned</span> <span class="o">==</span> <span class="n">b</span>
+
+            <span class="n">group_labels_current_bin</span> <span class="o">=</span> <span class="n">y_true</span><span class="p">[</span><span class="n">group_filter</span> <span class="o">&amp;</span> <span class="n">bin_filter</span><span class="p">]</span>
+            <span class="n">group_scores_current_bin</span> <span class="o">=</span> <span class="n">y_scores</span><span class="p">[</span><span class="n">group_filter</span> <span class="o">&amp;</span> <span class="n">bin_filter</span><span class="p">]</span>
+
+            <span class="n">results</span> <span class="o">=</span> <span class="n">bootstrap_estimate</span><span class="p">(</span>
+                <span class="n">eval_func</span><span class="o">=</span><span class="k">lambda</span> <span class="n">labels</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">sensattr</span><span class="p">:</span> <span class="p">{</span>
+                    <span class="s2">&quot;mean_score&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">scores</span><span class="p">),</span>
+                    <span class="s2">&quot;mean_label&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">labels</span><span class="p">),</span>
+                <span class="p">},</span>
+                <span class="n">y_true</span><span class="o">=</span><span class="n">group_labels_current_bin</span><span class="p">,</span>
+                <span class="n">y_pred_scores</span><span class="o">=</span><span class="n">group_scores_current_bin</span><span class="p">,</span>
+                <span class="n">sensitive_attribute</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="p">)</span>
+
+            <span class="c1"># Store plot point for this bin</span>
+            <span class="n">group_cali_points</span><span class="o">.</span><span class="n">append</span><span class="p">((</span>
+                <span class="n">results</span><span class="p">[</span><span class="s2">&quot;mean_score_mean&quot;</span><span class="p">],</span>    <span class="c1"># x coordinate</span>
+                <span class="n">results</span><span class="p">[</span><span class="s2">&quot;mean_label_mean&quot;</span><span class="p">],</span>    <span class="c1"># y coordinate</span>
+                <span class="p">(</span>                              <span class="c1"># y coordinate c.i. range</span>
+                    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;mean_label_low-percentile&quot;</span><span class="p">],</span>
+                    <span class="n">results</span><span class="p">[</span><span class="s2">&quot;mean_label_high-percentile&quot;</span><span class="p">],</span>
+                <span class="p">),</span>
+            <span class="p">))</span>
+
+        <span class="c1"># Get group label from sensitive attribute map</span>
+        <span class="n">group_label</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">sens_attr_map</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">group_val</span><span class="p">,</span> <span class="n">group_val</span><span class="p">))</span>
+
+        <span class="c1"># Plot this group&#39;s calibration curve w/ conf. intervals!</span>
+        <span class="n">line</span><span class="p">,</span> <span class="n">caps</span><span class="p">,</span> <span class="n">bars</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span>
+            <span class="c1"># x=[mean_score for mean_score, *_ in group_cali_points],</span>
+            <span class="n">x</span><span class="o">=</span><span class="p">[(</span><span class="n">bin_edges</span><span class="p">[</span><span class="n">idx</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">bin_edges</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span> <span class="o">/</span> <span class="mf">2.</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">bin_edges</span><span class="p">))],</span>
+            <span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="n">mean_label</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">mean_label</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">group_cali_points</span><span class="p">],</span>
+            <span class="n">yerr</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">(</span>
+                <span class="p">[</span>
+                    <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">yerr_range</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">_x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">yerr_range</span> <span class="ow">in</span> <span class="n">group_cali_points</span><span class="p">]</span>
+            <span class="p">),</span>
+            <span class="n">fmt</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+            <span class="n">linestyle</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;-.&quot;</span><span class="p">,</span> <span class="s2">&quot;-&quot;</span><span class="p">,</span> <span class="s2">&quot;--&quot;</span><span class="p">][</span><span class="n">idx</span> <span class="o">%</span> <span class="mi">3</span><span class="p">],</span>
+            <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+            <span class="n">label</span><span class="o">=</span><span class="n">group_label</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">plot_artifacts</span><span class="p">[</span><span class="n">group_label</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">line</span><span class="p">,</span> <span class="n">caps</span><span class="p">,</span> <span class="n">bars</span><span class="p">)</span>
+
+    <span class="c1"># Global plot settings</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+        <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">ls</span><span class="o">=</span><span class="s2">&quot;dotted&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Draw custom legend</span>
+    <span class="n">handles</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">group</span><span class="p">,</span> <span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> <span class="ow">in</span> <span class="n">plot_artifacts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">l</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="n">group</span><span class="p">)</span>
+        <span class="n">l</span><span class="o">.</span><span class="n">set_markersize</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>
+
+    <span class="c1"># Perfectly calibrated line</span>
+    <span class="n">perf_cal_line</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">ls</span><span class="o">=</span><span class="s2">&quot;:&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Perf. calibrated&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Confidence interv. marker line</span>
+    <span class="n">conf_int_line</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">lw</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;|&quot;</span><span class="p">,</span> <span class="n">markeredgewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;grey&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;$95\%$ conf. int.&quot;</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="n">handles</span> <span class="o">+</span> <span class="p">[</span><span class="n">conf_int_line</span><span class="p">,</span> <span class="n">perf_cal_line</span><span class="p">])</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">curr_row</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">disp</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[38]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.calibration</span> <span class="kn">import</span> <span class="n">CalibrationDisplay</span>
+
+<span class="c1"># Get results for the current task</span>
+<span class="n">task_df</span> <span class="o">=</span> <span class="n">all_results_df</span><span class="p">[</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">TASK</span><span class="p">]</span>
+
+<span class="c1"># Get all base/instruct model pairs</span>
+<span class="n">base_models_to_plot</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;Meta-Llama-3-8B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Meta-Llama-3-70B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mistral-7B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mixtral-8x7B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mixtral-8x22B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Yi-34B&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+<span class="c1"># NOTE: Gemma models have degenerate distributions that always predict the same score, can&#39;t plot calibration properly...</span>
+
+<span class="n">instr_models_to_plot</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">df</span><span class="p">[</span>
+        <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="p">)</span>
+        <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">!=</span> <span class="n">m</span><span class="p">)</span>
+    <span class="p">][</span><span class="n">model_col</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">base_models_to_plot</span>
+<span class="p">]</span>
+
+<span class="c1"># Add GPT models if any</span>
+<span class="n">instr_models_to_plot</span> <span class="o">+=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="s2">&quot;gpt&quot;</span> <span class="ow">in</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
+<span class="n">base_models_to_plot</span> <span class="o">+=</span> <span class="p">[</span><span class="kc">None</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">instr_models_to_plot</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">base_models_to_plot</span><span class="p">))]</span>
+
+<span class="n">base_it_pairs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">base_models_to_plot</span><span class="p">,</span> <span class="n">instr_models_to_plot</span><span class="p">))</span>
+
+<span class="k">for</span> <span class="n">base_model</span><span class="p">,</span> <span class="n">it_model</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">base_it_pairs</span><span class="p">):</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mf">2.8</span><span class="p">),</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.35</span><span class="p">),</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">axes</span><span class="p">)),</span> <span class="n">axes</span><span class="p">,</span> <span class="p">(</span><span class="n">base_model</span><span class="p">,</span> <span class="n">it_model</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">m</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+            <span class="k">continue</span>
+
+        <span class="n">curr_row</span> <span class="o">=</span> <span class="n">task_df</span><span class="p">[(</span><span class="n">task_df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">task_df</span><span class="p">[</span><span class="s2">&quot;uses_all_features&quot;</span><span class="p">])]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">curr_id</span> <span class="o">=</span> <span class="n">curr_row</span><span class="o">.</span><span class="n">name</span>
+
+        <span class="c1"># # Original sklearn calibration plot:</span>
+        <span class="c1"># plot_cali_curve(curr_id, ax=ax, df=task_df)</span>
+
+        <span class="c1"># # Plot with confidence intervals:</span>
+        <span class="n">plot_cali_curve_ci</span><span class="p">(</span><span class="n">curr_id</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">task_df</span><span class="p">)</span>
+
+        <span class="c1"># Remove left plot legend</span>
+        <span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">get_legend</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">get_legend</span><span class="p">()</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Mean risk score&quot;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Fraction of positives&quot;</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+    <span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;calibration-curves-per-subgroup.</span><span class="si">{</span><span class="n">base_model</span><span class="si">}</span><span class="s2">.pdf&quot;</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "818d39dfd7f14966b96c35233df9c3a9", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-8B.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-70B.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.Mistral-7B-v0.1.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.Mixtral-8x7B-v0.1.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.Mixtral-8x22B-v0.1.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.Yi-34B.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.None.numeric-prompt.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_62_2.png" src="../_images/notebooks_paper-plots-and-tables_62_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_62_3.png" src="../_images/notebooks_paper-plots-and-tables_62_3.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_62_4.png" src="../_images/notebooks_paper-plots-and-tables_62_4.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_62_5.png" src="../_images/notebooks_paper-plots-and-tables_62_5.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_62_6.png" src="../_images/notebooks_paper-plots-and-tables_62_6.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_62_7.png" src="../_images/notebooks_paper-plots-and-tables_62_7.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_62_8.png" src="../_images/notebooks_paper-plots-and-tables_62_8.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[39]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.calibration</span> <span class="kn">import</span> <span class="n">CalibrationDisplay</span>
+
+<span class="c1"># Get results for the current task</span>
+<span class="n">task_df</span> <span class="o">=</span> <span class="n">all_results_df</span><span class="p">[</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">TASK</span><span class="p">]</span>
+
+<span class="c1"># Get all base/instruct model pairs</span>
+<span class="n">base_models_to_plot</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;Meta-Llama-3-8B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Meta-Llama-3-70B&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mistral-7B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mixtral-8x7B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Mixtral-8x22B-v0.1&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Yi-34B&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+<span class="c1"># NOTE: Gemma models have degenerate distributions that always predict the same score, can&#39;t plot calibration properly...</span>
+
+<span class="n">instr_models_to_plot</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">df</span><span class="p">[</span>
+        <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="p">)</span>
+        <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">!=</span> <span class="n">m</span><span class="p">)</span>
+    <span class="p">][</span><span class="n">model_col</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">base_models_to_plot</span>
+<span class="p">]</span>
+
+<span class="c1"># Add GPT models if any</span>
+<span class="n">instr_models_to_plot</span> <span class="o">+=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="s2">&quot;gpt&quot;</span> <span class="ow">in</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
+<span class="n">base_models_to_plot</span> <span class="o">+=</span> <span class="p">[</span><span class="kc">None</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">instr_models_to_plot</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">base_models_to_plot</span><span class="p">))]</span>
+
+<span class="n">base_it_pairs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">base_models_to_plot</span><span class="p">,</span> <span class="n">instr_models_to_plot</span><span class="p">))</span>
+
+<span class="k">for</span> <span class="n">base_model</span><span class="p">,</span> <span class="n">it_model</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">base_it_pairs</span><span class="p">):</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">4.8</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">),</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.15</span><span class="p">),</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">axes</span><span class="p">)),</span> <span class="n">axes</span><span class="p">,</span> <span class="p">(</span><span class="n">base_model</span><span class="p">,</span> <span class="n">it_model</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">m</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+            <span class="k">continue</span>
+
+        <span class="n">curr_row</span> <span class="o">=</span> <span class="n">task_df</span><span class="p">[(</span><span class="n">task_df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">task_df</span><span class="p">[</span><span class="s2">&quot;uses_all_features&quot;</span><span class="p">])]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">curr_id</span> <span class="o">=</span> <span class="n">curr_row</span><span class="o">.</span><span class="n">name</span>
+
+        <span class="c1"># # Original sklearn calibration plot:</span>
+        <span class="c1"># plot_cali_curve(curr_id, ax=ax, df=task_df)</span>
+
+        <span class="c1"># # Plot with confidence intervals:</span>
+        <span class="n">plot_cali_curve_ci</span><span class="p">(</span><span class="n">curr_id</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">task_df</span><span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Mean risk score&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Fraction of positives&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Set prettier x-ticks</span>
+        <span class="n">xticks</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">xticks_labels</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;0.0&quot;</span><span class="p">,</span> <span class="s2">&quot;0.25&quot;</span><span class="p">,</span> <span class="s2">&quot;0.5&quot;</span><span class="p">,</span> <span class="s2">&quot;0.75&quot;</span><span class="p">,</span> <span class="s2">&quot;1.0&quot;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">xticks</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">xticks_labels</span><span class="p">)</span>
+
+    <span class="c1"># Prepare legend</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">NUMERIC_PROMPTING</span><span class="p">:</span>
+        <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">get_legend</span><span class="p">()</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.03</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+
+    <span class="c1"># Figure title</span>
+    <span class="k">if</span> <span class="n">NUMERIC_PROMPTING</span><span class="p">:</span>
+        <span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;Numeric Prompting&quot;</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;Multiple-choice Prompting&quot;</span>
+
+    <span class="c1"># Don&#39;t use suptitle for GPT4 (no base model)</span>
+    <span class="k">if</span> <span class="n">base_model</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">1.1</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">model_title</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">get_title</span><span class="p">()</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">title</span><span class="si">}</span><span class="se">\n</span><span class="si">{</span><span class="n">model_title</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+    <span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;calibration-curves-per-subgroup.</span><span class="si">{</span><span class="n">base_model</span><span class="si">}</span><span class="s2">.smaller.pdf&quot;</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "cd4b354cb3c140238849fe9f583968d2", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-8B.smaller.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-70B.smaller.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.Mistral-7B-v0.1.smaller.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.Mixtral-8x7B-v0.1.smaller.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.Mixtral-8x22B-v0.1.smaller.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.Yi-34B.smaller.numeric-prompt.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/calibration-curves-per-subgroup.None.smaller.numeric-prompt.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_64_4.png" src="../_images/notebooks_paper-plots-and-tables_64_4.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_64_5.png" src="../_images/notebooks_paper-plots-and-tables_64_5.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_64_6.png" src="../_images/notebooks_paper-plots-and-tables_64_6.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_64_7.png" src="../_images/notebooks_paper-plots-and-tables_64_7.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_64_8.png" src="../_images/notebooks_paper-plots-and-tables_64_8.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_64_9.png" src="../_images/notebooks_paper-plots-and-tables_64_9.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_64_10.png" src="../_images/notebooks_paper-plots-and-tables_64_10.png" />
+</div>
+</div>
+</section>
+<section id="Score-distribution-per-sub-group">
+<h2>Score distribution per sub-group<a class="headerlink" href="#Score-distribution-per-sub-group" title="Link to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[40]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sens_attr_data</span> <span class="o">=</span> <span class="n">acs_dataset</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">sens_attr_col</span><span class="p">]</span>
+<span class="n">sens_attr_data</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[40]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+1    2
+2    1
+8    1
+Name: RAC1P, dtype: int64
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[41]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plot settings</span>
+<span class="n">plot_config</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+    <span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">binrange</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+    <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;percent&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="k">for</span> <span class="n">base_model</span><span class="p">,</span> <span class="n">it_model</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">base_it_pairs</span><span class="p">):</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.33</span><span class="p">),</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">axes</span><span class="p">)),</span> <span class="n">axes</span><span class="p">,</span> <span class="p">(</span><span class="n">base_model</span><span class="p">,</span> <span class="n">it_model</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">m</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+            <span class="k">continue</span>
+
+        <span class="n">model_row</span> <span class="o">=</span> <span class="n">task_df</span><span class="p">[(</span><span class="n">task_df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">task_df</span><span class="p">[</span><span class="s2">&quot;uses_all_features&quot;</span><span class="p">])]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">model_id</span> <span class="o">=</span> <span class="n">model_row</span><span class="o">.</span><span class="n">name</span>
+
+        <span class="n">scores_df</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">model_id</span><span class="p">]</span>
+        <span class="n">scores_df</span><span class="p">[</span><span class="s2">&quot;group&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sens_attr_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">scores_df</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+
+        <span class="c1"># sns.histplot(</span>
+        <span class="c1">#     data=scores_df,</span>
+        <span class="c1">#     x=&quot;risk_score&quot;,</span>
+        <span class="c1">#     hue=&quot;group&quot;,</span>
+        <span class="c1">#     ax=ax,</span>
+        <span class="c1">#     **plot_config,</span>
+        <span class="c1"># )</span>
+
+        <span class="k">for</span> <span class="n">group_val</span><span class="p">,</span> <span class="n">group_name</span> <span class="ow">in</span> <span class="n">sens_attr_map</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="n">group_scores_df</span> <span class="o">=</span> <span class="n">scores_df</span><span class="p">[</span><span class="n">sens_attr_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">scores_df</span><span class="o">.</span><span class="n">index</span><span class="p">]</span> <span class="o">==</span> <span class="n">group_val</span><span class="p">]</span>
+            <span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span>
+                <span class="n">group_scores_df</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">],</span>
+                <span class="n">label</span><span class="o">=</span><span class="n">group_name</span><span class="p">,</span>
+                <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+                <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">plot_config</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="c1"># Remove left plot legend</span>
+        <span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="c1"># ax.set_ylim(0, 1)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">model_row</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">])</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+    <span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;score-per-subgroup.</span><span class="si">{</span><span class="n">base_model</span><span class="si">}</span><span class="s2">.pdf&quot;</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "ad627657fb0044bb9f3620c89dc7d648", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/score-per-subgroup.Meta-Llama-3-8B.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/score-per-subgroup.Meta-Llama-3-70B.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/score-per-subgroup.Mistral-7B-v0.1.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/score-per-subgroup.Mixtral-8x7B-v0.1.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/score-per-subgroup.Mixtral-8x22B-v0.1.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/score-per-subgroup.Yi-34B.numeric-prompt.pdf&#39;
+Saved figure to &#39;../results/imgs/score-per-subgroup.None.numeric-prompt.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_67_2.png" src="../_images/notebooks_paper-plots-and-tables_67_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_67_3.png" src="../_images/notebooks_paper-plots-and-tables_67_3.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_67_4.png" src="../_images/notebooks_paper-plots-and-tables_67_4.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_67_5.png" src="../_images/notebooks_paper-plots-and-tables_67_5.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_67_6.png" src="../_images/notebooks_paper-plots-and-tables_67_6.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_67_7.png" src="../_images/notebooks_paper-plots-and-tables_67_7.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_67_8.png" src="../_images/notebooks_paper-plots-and-tables_67_8.png" />
+</div>
+</div>
+</section>
+<section id="Compute-mean-under-/over-estimation-of-risk-score">
+<h2>Compute mean under-/over-estimation of risk score<a class="headerlink" href="#Compute-mean-under-/over-estimation-of-risk-score" title="Link to this heading"></a></h2>
+<p>It’s basocally akin to computing ECE without the absolute…</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[42]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">compute_confidence_bias_metric_helper</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper to compute the under- / over-confidence metric.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">mean_label</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+    <span class="n">mean_score</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">mean_score</span> <span class="o">&lt;</span> <span class="mf">0.5</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">((</span><span class="mi">1</span> <span class="o">-</span> <span class="n">mean_score</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">mean_label</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">(</span><span class="n">mean_score</span> <span class="o">-</span> <span class="n">mean_label</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">compute_confidence_bias_metric</span><span class="p">(</span><span class="n">model_id</span><span class="p">,</span> <span class="n">quantiled_bins</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">scores_df</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">scores_df</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">scores_df</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">model_id</span><span class="p">]</span>
+
+    <span class="n">discretization_func</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">qcut</span> <span class="k">if</span> <span class="n">quantiled_bins</span> <span class="k">else</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span>
+    <span class="n">scores_df</span><span class="p">[</span><span class="s2">&quot;score_bin&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">discretization_func</span><span class="p">(</span><span class="n">scores_df</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">],</span> <span class="mi">10</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
+
+    <span class="n">bias_metric_val</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">scores_df</span><span class="p">))</span> <span class="o">*</span> <span class="nb">sum</span><span class="p">(</span>
+        <span class="nb">len</span><span class="p">(</span><span class="n">bin_data</span><span class="p">)</span> <span class="o">*</span> <span class="n">compute_confidence_bias_metric_helper</span><span class="p">(</span><span class="n">bin_data</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">bin_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">bin_data</span> <span class="o">:=</span> <span class="n">scores_df</span><span class="p">[</span><span class="n">scores_df</span><span class="p">[</span><span class="s2">&quot;score_bin&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">bin_</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">0</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">bias_metric_val</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[43]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">confidence_scores</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="n">m</span><span class="p">:</span> <span class="n">compute_confidence_bias_metric</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">scores_df_map</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "81416b6ae6a041bdacc00c142bcdb7b9", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[44]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">all_results_df</span><span class="p">[</span><span class="s2">&quot;under_over_score&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_results_df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">row</span><span class="p">:</span> <span class="n">confidence_scores</span><span class="p">[</span><span class="n">row</span><span class="o">.</span><span class="n">name</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">all_results_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">&quot;under_over_score&quot;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="s2">&quot;under_over_score&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[44]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Meta-Llama-3-8B-Instruct__ACSPublicCoverage__-1__Num     -0.161384
+Mixtral-8x7B-Instruct-v0.1__ACSPublicCoverage__-1__Num   -0.061690
+Mixtral-8x7B-Instruct-v0.1__ACSEmployment__-1__Num       -0.056597
+Meta-Llama-3-70B-Instruct__ACSPublicCoverage__-1__Num    -0.052221
+gemma-7b__ACSIncome__-1__Num                             -0.033482
+                                                            ...
+gemma-7b__ACSEmployment__-1__Num                          0.341868
+Mistral-7B-v0.1__ACSIncome__-1__Num                       0.355603
+gemma-2b__ACSIncome__-1__Num                              0.367876
+Mistral-7B-v0.1__ACSTravelTime__-1__Num                   0.438141
+gemma-2b__ACSTravelTime__-1__Num                          0.438312
+Name: under_over_score, Length: 100, dtype: float64
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[45]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">TASK</span> <span class="o">=</span> <span class="s2">&quot;ACSIncome&quot;</span>
+<span class="c1"># TASK = &quot;ACSTravelTime&quot;</span>
+<span class="c1"># TASK = &quot;ACSMobility&quot;</span>
+<span class="c1"># TASK = &quot;ACSEmployment&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[46]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plot_df</span> <span class="o">=</span> <span class="n">all_results_df</span><span class="p">[</span>
+    <span class="p">(</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">TASK</span><span class="p">)</span>
+    <span class="o">&amp;</span> <span class="p">(</span><span class="o">~</span><span class="n">all_results_df</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">())</span>
+<span class="p">]</span>
+
+<span class="c1"># Omit Gemma models bc they&#39;re garbage :)</span>
+<span class="n">plot_df</span> <span class="o">=</span> <span class="n">plot_df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="n">id_</span> <span class="k">for</span> <span class="n">id_</span> <span class="ow">in</span> <span class="n">plot_df</span><span class="o">.</span><span class="n">index</span> <span class="k">if</span> <span class="s2">&quot;gemma&quot;</span> <span class="ow">in</span> <span class="n">id_</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">or</span> <span class="s2">&quot;yi&quot;</span> <span class="ow">in</span> <span class="n">id_</span><span class="o">.</span><span class="n">lower</span><span class="p">()])</span>
+
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">plot_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">&quot;under_over_score&quot;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;under_over_score&quot;</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="c1"># y=task_col,</span>
+    <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;is_inst&quot;</span><span class="p">,</span>
+    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">handles</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="n">handles</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Base&quot;</span><span class="p">,</span> <span class="s2">&quot;Instruct&quot;</span><span class="p">])</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;$\mathrm</span><span class="si">{R}</span><span class="s2">_\mathrm</span><span class="si">{bias}</span><span class="s2">$&quot;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Risk score confidence bias&quot;</span><span class="p">)</span>
+
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;under_over_score.</span><span class="si">{</span><span class="n">TASK</span><span class="si">}</span><span class="s2">.pdf&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/under_over_score.ACSIncome.numeric-prompt.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_73_1.png" src="../_images/notebooks_paper-plots-and-tables_73_1.png" />
+</div>
+</div>
+</section>
+<section id="Compute-risk-score-fairness-per-sub-group">
+<h2>Compute risk score fairness per sub-group<a class="headerlink" href="#Compute-risk-score-fairness-per-sub-group" title="Link to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[47]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sens_attr_data</span> <span class="o">=</span> <span class="n">acs_dataset</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">sens_attr_col</span><span class="p">]</span>
+<span class="n">sens_attr_data</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[47]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+1    2
+2    1
+8    1
+Name: RAC1P, dtype: int64
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[48]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">compute_signed_score_bias</span><span class="p">(</span><span class="n">scores_df</span><span class="p">,</span> <span class="n">quantiled_bins</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
+    <span class="n">discretization_func</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">qcut</span> <span class="k">if</span> <span class="n">quantiled_bins</span> <span class="k">else</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span>
+    <span class="n">scores_df</span><span class="p">[</span><span class="s2">&quot;score_bin&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">discretization_func</span><span class="p">(</span><span class="n">scores_df</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">],</span> <span class="n">n_bins</span><span class="p">,</span> <span class="n">duplicates</span><span class="o">=</span><span class="s2">&quot;drop&quot;</span><span class="p">,</span> <span class="n">retbins</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">bin_categories</span> <span class="o">=</span> <span class="n">scores_df</span><span class="p">[</span><span class="s2">&quot;score_bin&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">categories</span>
+
+    <span class="k">return</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">scores_df</span><span class="p">))</span> <span class="o">*</span> <span class="nb">sum</span><span class="p">(</span>
+        <span class="nb">len</span><span class="p">(</span><span class="n">bin_data</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">bin_data</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">-</span> <span class="n">bin_data</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
+        <span class="k">for</span> <span class="n">bin_</span> <span class="ow">in</span> <span class="n">bin_categories</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">bin_data</span> <span class="o">:=</span> <span class="n">scores_df</span><span class="p">[</span><span class="n">scores_df</span><span class="p">[</span><span class="s2">&quot;score_bin&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">bin_</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">0</span>
+    <span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[49]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">compute_signed_subgroup_score_bias</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">group</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+    <span class="n">scores_df</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">model</span><span class="p">]</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">group</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="n">group</span> <span class="o">=</span> <span class="n">name_to_sens_attr_map</span><span class="p">[</span><span class="n">group</span><span class="p">]</span>
+
+    <span class="n">scores_df</span> <span class="o">=</span> <span class="n">scores_df</span><span class="p">[</span><span class="n">sens_attr_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">scores_df</span><span class="o">.</span><span class="n">index</span><span class="p">]</span> <span class="o">==</span> <span class="n">group</span><span class="p">]</span>
+    <span class="c1"># return compute_confidence_metric(model, scores_df=scores_df)</span>
+    <span class="k">return</span> <span class="n">compute_signed_score_bias</span><span class="p">(</span><span class="n">scores_df</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">compute_calibration_fairness</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">priv_group</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">str</span><span class="p">,</span> <span class="n">unpriv_group</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">compute_signed_subgroup_score_bias</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">group</span><span class="o">=</span><span class="n">priv_group</span><span class="p">)</span>
+        <span class="o">-</span> <span class="n">compute_signed_subgroup_score_bias</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">group</span><span class="o">=</span><span class="n">unpriv_group</span><span class="p">)</span>
+    <span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[50]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">calibration_per_subgroup_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span>
+    <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
+        <span class="p">{</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">group</span><span class="si">}</span><span class="s2">_score_bias&quot;</span><span class="p">:</span> <span class="n">compute_signed_subgroup_score_bias</span><span class="p">(</span><span class="n">model_id</span><span class="p">,</span> <span class="n">group</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">group</span> <span class="ow">in</span> <span class="n">sens_attr_map</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
+        <span class="p">},</span>
+        <span class="n">name</span><span class="o">=</span><span class="n">model_id</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="k">for</span> <span class="n">model_id</span> <span class="ow">in</span> <span class="n">task_df</span><span class="o">.</span><span class="n">index</span>
+<span class="p">])</span>
+
+<span class="n">calibration_per_subgroup_df</span><span class="p">[</span><span class="s2">&quot;White_v_Black_score_bias&quot;</span><span class="p">]</span> <span class="o">=</span> \
+    <span class="n">calibration_per_subgroup_df</span><span class="p">[</span><span class="s2">&quot;White_score_bias&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">calibration_per_subgroup_df</span><span class="p">[</span><span class="s2">&quot;Black_score_bias&quot;</span><span class="p">]</span>
+
+<span class="n">calibration_per_subgroup_df</span><span class="p">[</span><span class="s2">&quot;White_v_Asian_score_bias&quot;</span><span class="p">]</span> <span class="o">=</span> \
+    <span class="n">calibration_per_subgroup_df</span><span class="p">[</span><span class="s2">&quot;White_score_bias&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">calibration_per_subgroup_df</span><span class="p">[</span><span class="s2">&quot;Asian_score_bias&quot;</span><span class="p">]</span>
+
+<span class="n">calibration_per_subgroup_df</span><span class="p">[</span><span class="s2">&quot;Asian_v_Black_score_bias&quot;</span><span class="p">]</span> <span class="o">=</span> \
+    <span class="n">calibration_per_subgroup_df</span><span class="p">[</span><span class="s2">&quot;Asian_score_bias&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">calibration_per_subgroup_df</span><span class="p">[</span><span class="s2">&quot;Black_score_bias&quot;</span><span class="p">]</span>
+
+<span class="n">calibration_per_subgroup_df</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/tmp/ipykernel_3388197/3464681156.py:3: SettingWithCopyWarning:
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+  scores_df[&#34;score_bin&#34;] = discretization_func(scores_df[&#34;risk_score&#34;], n_bins, duplicates=&#34;drop&#34;, retbins=False)
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[50]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>White_score_bias</th>
+      <th>Black_score_bias</th>
+      <th>Asian_score_bias</th>
+      <th>White_v_Black_score_bias</th>
+      <th>White_v_Asian_score_bias</th>
+      <th>Asian_v_Black_score_bias</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Mistral-7B-v0.1__ACSIncome__-1__Num</th>
+      <td>0.353045</td>
+      <td>0.329428</td>
+      <td>0.319214</td>
+      <td>0.023617</td>
+      <td>0.033831</td>
+      <td>-0.010215</td>
+    </tr>
+    <tr>
+      <th>gemma-1.1-2b-it__ACSIncome__-1__Num</th>
+      <td>0.260100</td>
+      <td>0.403764</td>
+      <td>0.202488</td>
+      <td>-0.143664</td>
+      <td>0.057612</td>
+      <td>-0.201276</td>
+    </tr>
+    <tr>
+      <th>Yi-34B-Chat__ACSIncome__-1__Num</th>
+      <td>0.210531</td>
+      <td>0.229600</td>
+      <td>0.212839</td>
+      <td>-0.019069</td>
+      <td>-0.002308</td>
+      <td>-0.016761</td>
+    </tr>
+  </tbody>
+</table>
+</div></div>
+</div>
+<p><strong>Note:</strong> This is an example of comparing risk score bias between different sub-groups. A more detailed analysis must be conducted to draw any sort of conclusions regarding prediction fairness of the LLM.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[51]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">GROUP_A</span> <span class="o">=</span> <span class="s2">&quot;Asian&quot;</span>
+<span class="c1"># GROUP_A = &quot;White&quot;</span>
+<span class="n">GROUP_B</span> <span class="o">=</span> <span class="s2">&quot;Black&quot;</span>
+<span class="c1"># GROUP_B = &quot;Asian&quot;</span>
+
+<span class="n">COLUMN</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">GROUP_A</span><span class="si">}</span><span class="s2">_v_</span><span class="si">{</span><span class="n">GROUP_B</span><span class="si">}</span><span class="s2">_score_bias&quot;</span>
+
+<span class="n">plot_df</span> <span class="o">=</span> <span class="n">calibration_per_subgroup_df</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">results_df</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="n">validate</span><span class="o">=</span><span class="s2">&quot;1:1&quot;</span><span class="p">)</span>
+<span class="n">plot_df</span> <span class="o">=</span> <span class="n">plot_df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="n">id_</span> <span class="k">for</span> <span class="n">id_</span> <span class="ow">in</span> <span class="n">plot_df</span><span class="o">.</span><span class="n">index</span> <span class="k">if</span> <span class="s2">&quot;gemma&quot;</span> <span class="ow">in</span> <span class="n">id_</span><span class="o">.</span><span class="n">lower</span><span class="p">()])</span>   <span class="c1"># omit Gemma models bc they&#39;re garbage :)</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">plot_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">COLUMN</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <span class="n">x</span><span class="o">=</span><span class="n">COLUMN</span><span class="p">,</span>
+    <span class="n">y</span><span class="o">=</span><span class="s2">&quot;name&quot;</span><span class="p">,</span>
+    <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;is_inst&quot;</span><span class="p">,</span>
+    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">handles</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="n">handles</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Base&quot;</span><span class="p">,</span> <span class="s2">&quot;Instruct&quot;</span><span class="p">])</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;$\Delta_</span><span class="si">{SCE}</span><span class="s2">$&quot;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span>
+    <span class="s2">&quot;Signed risk score bias</span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="o">+</span> <span class="sa">r</span><span class="s2">&quot;($SCE_{&quot;</span> <span class="o">+</span> <span class="n">GROUP_A</span>
+    <span class="o">+</span> <span class="sa">r</span><span class="s2">&quot;} - SCE_{&quot;</span> <span class="o">+</span> <span class="n">GROUP_B</span>
+    <span class="o">+</span> <span class="sa">r</span><span class="s2">&quot;}$)&quot;</span>
+<span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;score_bias.</span><span class="si">{</span><span class="n">COLUMN</span><span class="si">}</span><span class="s2">.pdf&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/score_bias.Asian_v_Black_score_bias.numeric-prompt.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_80_1.png" src="../_images/notebooks_paper-plots-and-tables_80_1.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[52]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plot_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">COLUMN</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[52]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>White_score_bias</th>
+      <th>Black_score_bias</th>
+      <th>Asian_score_bias</th>
+      <th>White_v_Black_score_bias</th>
+      <th>White_v_Asian_score_bias</th>
+      <th>Asian_v_Black_score_bias</th>
+      <th>accuracy</th>
+      <th>accuracy_diff</th>
+      <th>accuracy_ratio</th>
+      <th>balanced_accuracy</th>
+      <th>...</th>
+      <th>name</th>
+      <th>is_inst</th>
+      <th>num_features</th>
+      <th>uses_all_features</th>
+      <th>fit_thresh_on_100</th>
+      <th>fit_thresh_accuracy</th>
+      <th>optimal_thresh</th>
+      <th>optimal_thresh_accuracy</th>
+      <th>score_stdev</th>
+      <th>score_mean</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Mixtral-8x22B-v0.1__ACSIncome__-1__Num</th>
+      <td>0.054322</td>
+      <td>-0.069436</td>
+      <td>0.130153</td>
+      <td>0.123758</td>
+      <td>-0.075831</td>
+      <td>0.199589</td>
+      <td>0.740583</td>
+      <td>0.148527</td>
+      <td>0.828562</td>
+      <td>0.761428</td>
+      <td>...</td>
+      <td>Mixtral 8x22B</td>
+      <td>False</td>
+      <td>-1.0</td>
+      <td>True</td>
+      <td>0.65</td>
+      <td>0.769300</td>
+      <td>0.45</td>
+      <td>0.740595</td>
+      <td>0.379947</td>
+      <td>0.411113</td>
+    </tr>
+    <tr>
+      <th>Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num</th>
+      <td>0.110399</td>
+      <td>0.055872</td>
+      <td>0.156710</td>
+      <td>0.054527</td>
+      <td>-0.046311</td>
+      <td>0.100837</td>
+      <td>0.767882</td>
+      <td>0.096716</td>
+      <td>0.885938</td>
+      <td>0.770218</td>
+      <td>...</td>
+      <td>Mixtral 8x22B (it)</td>
+      <td>True</td>
+      <td>-1.0</td>
+      <td>True</td>
+      <td>0.65</td>
+      <td>0.769967</td>
+      <td>0.55</td>
+      <td>0.767882</td>
+      <td>0.298323</td>
+      <td>0.474955</td>
+    </tr>
+    <tr>
+      <th>penai/gpt-4o-mini__ACSIncome__-1__Num</th>
+      <td>-0.001300</td>
+      <td>-0.019960</td>
+      <td>-0.000407</td>
+      <td>0.018661</td>
+      <td>-0.000893</td>
+      <td>0.019553</td>
+      <td>0.777393</td>
+      <td>0.080767</td>
+      <td>0.904748</td>
+      <td>0.758922</td>
+      <td>...</td>
+      <td>GPT 4o mini (it)</td>
+      <td>True</td>
+      <td>-1.0</td>
+      <td>True</td>
+      <td>0.65</td>
+      <td>0.777302</td>
+      <td>0.35</td>
+      <td>0.775512</td>
+      <td>0.317359</td>
+      <td>0.363743</td>
+    </tr>
+  </tbody>
+</table>
+<p>3 rows × 70 columns</p>
+</div></div>
+</div>
+</section>
+</section>
+<hr class="docutils" />
+<section id="Plots-comparing-different-prompting-schemes">
+<h1>Plots comparing different prompting schemes<a class="headerlink" href="#Plots-comparing-different-prompting-schemes" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[53]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">### Plot all models / i.t. / base?</span>
+<span class="n">IT_OR_BASE</span> <span class="o">=</span> <span class="s2">&quot;any&quot;</span>
+<span class="c1"># IT_OR_BASE = &quot;it&quot;</span>
+<span class="c1"># IT_OR_BASE = &quot;base&quot;</span>
+
+<span class="c1">### Plot which metric</span>
+<span class="n">METRIC</span> <span class="o">=</span> <span class="s2">&quot;ece&quot;</span>
+<span class="c1"># METRIC = &quot;roc_auc&quot;</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[54]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Prepare data</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">all_results_df_original</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+<span class="n">min_max_per_task</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="n">task</span><span class="p">:</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">values</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">values</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">task</span> <span class="ow">in</span> <span class="n">ALL_TASKS</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">values</span> <span class="o">:=</span> <span class="n">df</span><span class="p">[(</span><span class="n">df</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">task</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="o">~</span><span class="n">df</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">())][</span><span class="n">METRIC</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">0</span>
+<span class="p">}</span>
+
+<span class="c1"># Sort models</span>
+<span class="n">sorted_df_index</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
+    <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">id_</span><span class="p">:</span> <span class="n">model_sort_key</span><span class="p">(</span><span class="n">id_</span><span class="p">,</span> <span class="n">df</span><span class="p">),</span>
+    <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sorted_df_index</span><span class="p">]</span>
+
+<span class="k">if</span> <span class="n">IT_OR_BASE</span> <span class="o">==</span> <span class="s2">&quot;it&quot;</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Plotting IT models&quot;</span><span class="p">)</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;is_inst&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span>
+<span class="k">elif</span> <span class="n">IT_OR_BASE</span> <span class="o">==</span> <span class="s2">&quot;base&quot;</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Plotting BASE models&quot;</span><span class="p">)</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;is_inst&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="kc">False</span><span class="p">]</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Plotting ALL models&quot;</span><span class="p">)</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;is_inst&quot;</span><span class="p">,</span> <span class="s2">&quot;model_size&quot;</span><span class="p">],</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Data for multiple-choice prompting</span>
+<span class="n">df_mult_choice</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span> <span class="o">==</span> <span class="kc">False</span><span class="p">]</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s2">&quot;name&quot;</span><span class="p">,</span> <span class="n">task_col</span><span class="p">],</span> <span class="n">drop</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Data for numeric risk prompting</span>
+<span class="n">df_numeric</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s2">&quot;name&quot;</span><span class="p">,</span> <span class="n">task_col</span><span class="p">],</span> <span class="n">drop</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Sorted list of model names</span>
+<span class="n">model_names</span> <span class="o">=</span> <span class="n">df_numeric</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">n_models</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">model_names</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">model_names</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Plotting ALL models
+model_names=[&#39;Gemma 2B&#39;, &#39;Gemma 7B&#39;, &#39;Mistral 7B&#39;, &#39;Llama 3 8B&#39;, &#39;Yi 34B&#39;, &#39;Mixtral 8x7B&#39;, &#39;Llama 3 70B&#39;, &#39;Mixtral 8x22B&#39;, &#39;Gemma 2B (it)&#39;, &#39;Gemma 7B (it)&#39;, &#39;Mistral 7B (it)&#39;, &#39;Llama 3 8B (it)&#39;, &#39;Yi 34B (it)&#39;, &#39;Mixtral 8x7B (it)&#39;, &#39;Llama 3 70B (it)&#39;, &#39;Mixtral 8x22B (it)&#39;, &#39;GPT 4o mini (it)&#39;]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[55]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">FancyArrowPatch</span>
+
+<span class="c1"># Create a figure and axis</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">ALL_TASKS</span><span class="p">),</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="n">n_models</span> <span class="o">/</span> <span class="mi">3</span><span class="p">),</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">task_i</span><span class="p">,</span> <span class="n">task</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ALL_TASKS</span><span class="p">):</span>
+
+    <span class="c1"># Get current plot axis</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">task_i</span><span class="p">]</span>
+
+    <span class="c1"># Plot arrows for each model</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">model_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model_names</span><span class="p">):</span>
+
+        <span class="n">curr_mult_choice_metric</span> <span class="o">=</span> <span class="n">df_mult_choice</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">model_name</span><span class="p">,</span> <span class="n">task</span><span class="p">][</span><span class="n">METRIC</span><span class="p">]</span>
+        <span class="n">curr_numeric_metric</span> <span class="o">=</span> <span class="n">df_numeric</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">model_name</span><span class="p">,</span> <span class="n">task</span><span class="p">][</span><span class="n">METRIC</span><span class="p">]</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+            <span class="n">curr_mult_choice_metric</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;s&quot;</span><span class="p">,</span>
+            <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Multiple-choice&#39;</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+            <span class="n">curr_numeric_metric</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">,</span>
+            <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Numeric&#39;</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Draw a FancyArrowPatch arrow from before to after fine-tuning</span>
+        <span class="k">if</span> <span class="n">METRIC</span> <span class="o">==</span> <span class="s2">&quot;ece&quot;</span><span class="p">:</span>
+            <span class="n">curr_metric_improved</span> <span class="o">=</span> <span class="n">curr_numeric_metric</span> <span class="o">&lt;=</span> <span class="n">curr_mult_choice_metric</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">curr_metric_improved</span> <span class="o">=</span> <span class="n">curr_numeric_metric</span> <span class="o">&gt;=</span> <span class="n">curr_mult_choice_metric</span>
+
+        <span class="n">arrow</span> <span class="o">=</span> <span class="n">FancyArrowPatch</span><span class="p">(</span>
+            <span class="p">(</span><span class="n">curr_mult_choice_metric</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span> <span class="p">(</span><span class="n">curr_numeric_metric</span><span class="p">,</span> <span class="n">i</span><span class="p">),</span>
+            <span class="n">arrowstyle</span><span class="o">=</span><span class="s1">&#39;-&gt;&#39;</span><span class="p">,</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">if</span> <span class="n">curr_metric_improved</span> <span class="k">else</span> <span class="n">palette</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+            <span class="n">mutation_scale</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
+            <span class="n">lw</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">add_patch</span><span class="p">(</span><span class="n">arrow</span><span class="p">)</span>
+
+        <span class="c1"># Set axes ranges according to overall min/max values</span>
+        <span class="n">lo</span><span class="p">,</span> <span class="n">hi</span> <span class="o">=</span> <span class="n">min_max_per_task</span><span class="p">[</span><span class="n">task</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">METRIC</span> <span class="o">==</span> <span class="s2">&quot;ece&quot;</span><span class="p">:</span>
+            <span class="n">lo</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">lo</span> <span class="o">-</span> <span class="mf">5e-2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="n">hi</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">hi</span> <span class="o">+</span> <span class="mf">5e-2</span><span class="p">,</span> <span class="mf">0.475</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">METRIC</span> <span class="o">==</span> <span class="s2">&quot;roc_auc&quot;</span><span class="p">:</span>
+            <span class="n">lo</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">lo</span> <span class="o">-</span> <span class="mf">5e-2</span><span class="p">,</span> <span class="mf">0.475</span><span class="p">)</span>
+            <span class="n">hi</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">hi</span> <span class="o">+</span> <span class="mf">4e-2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">task</span> <span class="o">==</span> <span class="s2">&quot;ACSIncome&quot;</span> <span class="ow">and</span> <span class="n">METRIC</span> <span class="o">==</span> <span class="s2">&quot;roc_auc&quot;</span><span class="p">:</span>
+            <span class="n">lo</span> <span class="o">=</span> <span class="mf">0.58</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">lo</span><span class="p">,</span> <span class="n">hi</span><span class="p">)</span>
+
+    <span class="c1"># Set y-ticks and y-labels</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_models</span><span class="p">))</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">model_names</span><span class="p">)</span>
+
+    <span class="c1"># # Prettify x-axis ticks</span>
+    <span class="c1"># xticks = ax.get_xticks()</span>
+    <span class="c1"># ax.set_xticks(xticks, [f&quot;{x}&quot; for x in xticks])</span>
+
+    <span class="c1"># Add labels and title</span>
+    <span class="c1"># ax.set_xlabel(&#39;ECE&#39;)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">task</span><span class="p">[</span><span class="mi">3</span><span class="p">:])</span>
+
+    <span class="c1"># Add horizontal line separating instruct and base models</span>
+    <span class="k">if</span> <span class="n">IT_OR_BASE</span> <span class="o">==</span> <span class="s2">&quot;any&quot;</span><span class="p">:</span>
+        <span class="n">n_instruct_models</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_mult_choice</span><span class="p">[(</span><span class="n">df_mult_choice</span><span class="p">[</span><span class="s2">&quot;is_inst&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="kc">True</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df_mult_choice</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">task</span><span class="p">)])</span>
+        <span class="n">line_y</span> <span class="o">=</span> <span class="n">n_instruct_models</span> <span class="o">-</span> <span class="mf">1.5</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">line_y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1">#, label=&quot;instruct / base&quot;)</span>
+
+        <span class="c1"># Add text label near the horizontal line</span>
+        <span class="k">if</span> <span class="n">task_i</span> <span class="o">&gt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">ALL_TASKS</span><span class="p">)</span> <span class="o">-</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">text_kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+                <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">get_yaxis_transform</span><span class="p">(),</span>  <span class="c1"># Use axis coordinates for y-axis</span>
+                <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
+            <span class="p">)</span>
+
+            <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">line_y</span> <span class="o">+</span> <span class="mf">0.1</span><span class="p">,</span> <span class="s1">&#39;i.t.&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">text_kwargs</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">line_y</span> <span class="o">-</span> <span class="mf">0.1</span><span class="p">,</span> <span class="s1">&#39;base&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">text_kwargs</span><span class="p">)</span>
+
+
+
+<span class="c1"># Add legend for marker</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper right&quot;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">0.15</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">),</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Prompting scheme&quot;</span><span class="p">)</span>
+
+<span class="c1"># X-axis label</span>
+<span class="k">if</span> <span class="n">METRIC</span> <span class="o">==</span> <span class="s2">&quot;ece&quot;</span><span class="p">:</span>
+    <span class="n">x_axis_label</span> <span class="o">=</span> <span class="s2">&quot;Expected Calibration Error (ECE)&quot;</span>
+<span class="k">elif</span> <span class="n">METRIC</span> <span class="o">==</span> <span class="s2">&quot;roc_auc&quot;</span><span class="p">:</span>
+    <span class="n">x_axis_label</span> <span class="o">=</span> <span class="s2">&quot;Predictive Power (ROC AUC)&quot;</span>
+
+<span class="n">fig</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.02</span> <span class="k">if</span> <span class="n">IT_OR_BASE</span> <span class="o">==</span> <span class="s2">&quot;any&quot;</span> <span class="k">else</span> <span class="o">-</span><span class="mf">0.04</span><span class="p">,</span> <span class="n">x_axis_label</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">)</span>
+
+<span class="c1"># Save figure</span>
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">METRIC</span><span class="si">}</span><span class="s2">_comparison_diff_prompts.</span><span class="si">{</span><span class="n">IT_OR_BASE</span><span class="si">}</span><span class="s2">-models.pdf&quot;</span><span class="p">,</span> <span class="n">add_prompt_suffix</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Show plot</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/ece_comparison_diff_prompts.any-models.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_85_1.png" src="../_images/notebooks_paper-plots-and-tables_85_1.png" />
+</div>
+</div>
+<section id="Scatter-plot-of-ECE-vs-AUC-for-a-few-models-(before-and-after-numeric-prompting)">
+<h2>Scatter plot of ECE vs AUC for a few models (before and after numeric prompting)<a class="headerlink" href="#Scatter-plot-of-ECE-vs-AUC-for-a-few-models-(before-and-after-numeric-prompting)" title="Link to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[56]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_METRIC</span> <span class="o">=</span> <span class="s2">&quot;ece&quot;</span>
+<span class="n">Y_METRIC</span> <span class="o">=</span> <span class="s2">&quot;roc_auc&quot;</span>
+
+<span class="c1"># Filter out small models</span>
+<span class="n">df_mult_choice</span> <span class="o">=</span> <span class="n">df_mult_choice</span><span class="p">[</span><span class="n">df_mult_choice</span><span class="p">[</span><span class="s2">&quot;model_size&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">50</span><span class="p">]</span>
+<span class="n">df_numeric</span> <span class="o">=</span> <span class="n">df_numeric</span><span class="p">[</span><span class="n">df_numeric</span><span class="p">[</span><span class="s2">&quot;model_size&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">50</span><span class="p">]</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">ALL_TASKS</span><span class="p">),</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ALL_TASKS</span><span class="p">)</span> <span class="o">*</span> <span class="mf">2.6</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">),</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">))</span>
+
+<span class="n">unique_model_names</span> <span class="o">=</span> <span class="n">df_mult_choice</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+<span class="n">markers</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">unique_model_names</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;s&quot;</span><span class="p">,</span> <span class="s2">&quot;o&quot;</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">,</span> <span class="s2">&quot;P&quot;</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">,</span> <span class="s2">&quot;&lt;&quot;</span><span class="p">,</span> <span class="s2">&quot;&gt;&quot;</span><span class="p">]))</span>
+
+<span class="k">for</span> <span class="n">task_i</span><span class="p">,</span> <span class="n">task</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ALL_TASKS</span><span class="p">):</span>
+
+    <span class="c1"># Create a figure and axis</span>
+    <span class="c1"># fig, ax = plt.subplots(figsize=(2.5, 2.5))</span>
+
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">task_i</span><span class="p">]</span>
+
+    <span class="c1"># Plot arrows for each model</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">model_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">df_mult_choice</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">unique</span><span class="p">()):</span>
+
+        <span class="c1"># Get data for each plotting scheme</span>
+        <span class="n">curr_mult_choice_row</span> <span class="o">=</span> <span class="n">df_mult_choice</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">model_name</span><span class="p">,</span> <span class="n">task</span><span class="p">]</span>
+        <span class="n">curr_numeric_row</span> <span class="o">=</span> <span class="n">df_numeric</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">model_name</span><span class="p">,</span> <span class="n">task</span><span class="p">]</span>
+
+        <span class="c1"># Get points to plot</span>
+        <span class="n">curr_mult_choice_point</span> <span class="o">=</span> <span class="p">(</span><span class="n">curr_mult_choice_row</span><span class="p">[</span><span class="n">X_METRIC</span><span class="p">],</span> <span class="n">curr_mult_choice_row</span><span class="p">[</span><span class="n">Y_METRIC</span><span class="p">])</span>
+        <span class="n">curr_numeric_point</span> <span class="o">=</span> <span class="p">(</span><span class="n">curr_numeric_row</span><span class="p">[</span><span class="n">X_METRIC</span><span class="p">],</span> <span class="n">curr_numeric_row</span><span class="p">[</span><span class="n">Y_METRIC</span><span class="p">])</span>
+
+        <span class="c1"># Plot point for multiple-choice prompting</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+            <span class="o">*</span><span class="n">curr_mult_choice_point</span><span class="p">,</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="n">markers</span><span class="p">[</span><span class="n">model_name</span><span class="p">],</span>
+            <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Multiple-choice&#39;</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Plot point for numeric-style prompting</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+            <span class="o">*</span><span class="n">curr_numeric_point</span><span class="p">,</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="n">markers</span><span class="p">[</span><span class="n">model_name</span><span class="p">],</span>
+            <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Numeric&#39;</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">curr_numeric_point</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span>  <span class="n">curr_mult_choice_point</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="n">curr_numeric_point</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span>  <span class="n">curr_mult_choice_point</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+            <span class="n">arrow_color</span> <span class="o">=</span> <span class="n">palette</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">curr_numeric_point</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span>  <span class="n">curr_mult_choice_point</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="n">curr_numeric_point</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span>  <span class="n">curr_mult_choice_point</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+            <span class="n">arrow_color</span> <span class="o">=</span> <span class="n">palette</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">arrow_color</span> <span class="o">=</span> <span class="s2">&quot;grey&quot;</span>
+
+        <span class="c1"># Draw a FancyArrowPatch arrow from before to after fine-tuning</span>
+        <span class="n">arrow</span> <span class="o">=</span> <span class="n">FancyArrowPatch</span><span class="p">(</span>
+            <span class="n">curr_mult_choice_point</span><span class="p">,</span> <span class="n">curr_numeric_point</span><span class="p">,</span>
+            <span class="n">arrowstyle</span><span class="o">=</span><span class="s1">&#39;-&gt;&#39;</span><span class="p">,</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">arrow_color</span><span class="p">,</span>
+            <span class="n">mutation_scale</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
+            <span class="n">lw</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+            <span class="n">ls</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">add_patch</span><span class="p">(</span><span class="n">arrow</span><span class="p">)</span>
+
+    <span class="c1">### Plot configs</span>
+    <span class="c1"># Add labels and title</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">task</span><span class="p">[</span><span class="mi">3</span><span class="p">:])</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;ECE&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">task_i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;AUC&quot;</span><span class="p">)</span>
+
+<span class="c1"># Create custom global legend</span>
+<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <span class="n">Line2D</span>
+
+<span class="c1"># Create a global legend for markers (model names)</span>
+<span class="n">marker_handles</span> <span class="o">=</span> <span class="p">[</span><span class="n">Line2D</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="n">markers</span><span class="p">[</span><span class="n">model</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">,</span> <span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">model</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">unique_model_names</span><span class="p">]</span>
+
+<span class="c1"># Create a global legend for colors (prompting schemes)</span>
+<span class="n">color_handles</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">Line2D</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">,</span> <span class="n">markerfacecolor</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Multiple-choice&quot;</span><span class="p">),</span>
+    <span class="n">Line2D</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">,</span> <span class="n">markerfacecolor</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Numeric&quot;</span><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Position the legends outside the subplots</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="n">marker_handles</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Models&quot;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper left&#39;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.13</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.12</span><span class="p">),</span> <span class="n">ncol</span><span class="o">=</span><span class="nb">min</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">unique_model_names</span><span class="p">)))</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="n">color_handles</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Prompting Scheme&quot;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper right&#39;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.12</span><span class="p">),</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="c1"># Save figure</span>
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="o">=</span><span class="n">fig</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;comparison_diff_prompts.</span><span class="si">{</span><span class="n">IT_OR_BASE</span><span class="si">}</span><span class="s2">-models.pdf&quot;</span><span class="p">,</span> <span class="n">add_prompt_suffix</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Saved figure to &#39;../results/imgs/comparison_diff_prompts.any-models.pdf&#39;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_paper-plots-and-tables_87_1.png" src="../_images/notebooks_paper-plots-and-tables_87_1.png" />
+</div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="custom-acs-task-example.html" class="btn btn-neutral float-left" title="Run folktexts benchmark with a custom ACS task" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="minimal-example_web-API-model.html" class="btn btn-neutral float-right" title="Evaluate model calibration using folktexts" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/notebooks/paper-plots-and-tables.ipynb b/notebooks/paper-plots-and-tables.ipynb
new file mode 100644
index 0000000..ee45e93
--- /dev/null
+++ b/notebooks/paper-plots-and-tables.ipynb
@@ -0,0 +1,4094 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a436d249-9dfc-4d25-9695-549cb440ea18",
+   "metadata": {},
+   "source": [
+    "# Render paper plots and tables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a7df2c18-8ad5-4733-b77e-68afde7064a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import logging\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a247e64e-8d4c-4dc7-beb2-cf70e1492fc2",
+   "metadata": {},
+   "source": [
+    "**Note:** _Change_ the following path to the aggregated results file in your local system \n",
+    "(can be obtained using the `parse-acs-results.ipynb` notebook)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6d4d0fde-5cea-4ed8-b496-00627aac840f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ACS_AGG_RESULTS_PATH = Path(\"../results\") / \"aggregated-results.2024-08.csv\"\n",
+    "ACS_AGG_RESULTS_PATH = Path(ACS_AGG_RESULTS_PATH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ddc248c4-a5c5-4c95-9f2a-3d386c830520",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "results_df.shape=(210, 64)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>penai/gpt-4o-mini__ACSTravelTime__-1__Num</th>\n",
+       "      <td>0.570906</td>\n",
+       "      <td>0.315286</td>\n",
+       "      <td>0.624340</td>\n",
+       "      <td>0.553706</td>\n",
+       "      <td>0.070915</td>\n",
+       "      <td>0.876372</td>\n",
+       "      <td>0.267584</td>\n",
+       "      <td>0.149517</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.382569</td>\n",
+       "      <td>...</td>\n",
+       "      <td>GPT 4o mini (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.510013</td>\n",
+       "      <td>0.450000</td>\n",
+       "      <td>0.570968</td>\n",
+       "      <td>0.223652</td>\n",
+       "      <td>0.384149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>penai/gpt-4o-mini__ACSTravelTime__-1__QA</th>\n",
+       "      <td>0.551154</td>\n",
+       "      <td>0.119294</td>\n",
+       "      <td>0.812222</td>\n",
+       "      <td>0.588927</td>\n",
+       "      <td>0.113947</td>\n",
+       "      <td>0.823835</td>\n",
+       "      <td>0.404025</td>\n",
+       "      <td>0.393327</td>\n",
+       "      <td>0.393301</td>\n",
+       "      <td>0.274655</td>\n",
+       "      <td>...</td>\n",
+       "      <td>GPT 4o mini (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.998499</td>\n",
+       "      <td>0.602202</td>\n",
+       "      <td>0.970688</td>\n",
+       "      <td>0.593339</td>\n",
+       "      <td>0.365205</td>\n",
+       "      <td>0.772858</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 64 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           accuracy  accuracy_diff  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num  0.570906       0.315286   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA   0.551154       0.119294   \n",
+       "\n",
+       "                                           accuracy_ratio  balanced_accuracy  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num        0.624340           0.553706   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA         0.812222           0.588927   \n",
+       "\n",
+       "                                           balanced_accuracy_diff  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num                0.070915   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA                 0.113947   \n",
+       "\n",
+       "                                           balanced_accuracy_ratio  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num                 0.876372   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA                  0.823835   \n",
+       "\n",
+       "                                           brier_score_loss       ece  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num          0.267584  0.149517   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA           0.404025  0.393327   \n",
+       "\n",
+       "                                           ece_quantile  equalized_odds_diff  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num           NaN             0.382569   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA       0.393301             0.274655   \n",
+       "\n",
+       "                                           ...              name  is_inst  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num  ...  GPT 4o mini (it)     True   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA   ...  GPT 4o mini (it)     True   \n",
+       "\n",
+       "                                           num_features  uses_all_features  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num            -1               True   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA             -1               True   \n",
+       "\n",
+       "                                           fit_thresh_on_100  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num           0.250000   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA            0.998499   \n",
+       "\n",
+       "                                           fit_thresh_accuracy  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num             0.510013   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA              0.602202   \n",
+       "\n",
+       "                                           optimal_thresh  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num        0.450000   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA         0.970688   \n",
+       "\n",
+       "                                           optimal_thresh_accuracy  \\\n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num                 0.570968   \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA                  0.593339   \n",
+       "\n",
+       "                                          score_stdev  score_mean  \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__Num    0.223652    0.384149  \n",
+       "penai/gpt-4o-mini__ACSTravelTime__-1__QA     0.365205    0.772858  \n",
+       "\n",
+       "[2 rows x 64 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_df = pd.read_csv(ACS_AGG_RESULTS_PATH, index_col=0)\n",
+    "print(f\"{results_df.shape=}\")\n",
+    "results_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a70081bb-71f2-4dc6-95d4-0919b0eb93b9",
+   "metadata": {},
+   "source": [
+    "Remove `gemma-2-*` results (need to investigate why results are so poor):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f461c5d8-aca5-47ba-b6a6-bc18a441c1ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "results_df.shape=(170, 64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_df = results_df.drop(index=[id_ for id_ in results_df.index if \"gemma-2-\" in id_.lower()])\n",
+    "print(f\"{results_df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b658cc4b-e0b0-4dd0-a4c6-00c5ed79d4bf",
+   "metadata": {},
+   "source": [
+    "## Run baseline ML classifiers on the benchmark ACS tasks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9f02d293-7725-4754-bce8-caa40c93a362",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5299d2a9-3723-44cc-a88b-fbba1d631eef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ALL_TASKS = [\n",
+    "    \"ACSIncome\",\n",
+    "    \"ACSMobility\",\n",
+    "    \"ACSEmployment\",\n",
+    "    \"ACSTravelTime\",\n",
+    "    \"ACSPublicCoverage\",\n",
+    "]\n",
+    "\n",
+    "model_col = \"config_model_name\"\n",
+    "task_col = \"config_task_name\"\n",
+    "numeric_prompt_col = \"config_numeric_risk_prompting\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64b72e20-a5d6-442b-8ba9-f7402f87b77b",
+   "metadata": {},
+   "source": [
+    "List all baseline classifiers here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1749fee2-ea21-41e9-b7f0-9ae5d0868409",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
+    "from xgboost import XGBClassifier    # NOTE: requires `pip install xgboost`\n",
+    "\n",
+    "baselines = {\n",
+    "    \"LR\": LogisticRegression(),\n",
+    "    \"GBM\": HistGradientBoostingClassifier(),\n",
+    "    \"XGBoost\": XGBClassifier(),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "72fb9307-a5c1-4805-8656-cc352cc736d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.acs.acs_dataset import ACSDataset\n",
+    "from folktexts.evaluation import evaluate_predictions\n",
+    "from collections import defaultdict\n",
+    "\n",
+    "def fit_and_eval(\n",
+    "    clf,\n",
+    "    X_train, y_train,\n",
+    "    X_test, y_test, s_test,\n",
+    "    fillna=False,\n",
+    "    save_scores_df_path: str = None,\n",
+    ") -> dict:\n",
+    "    \"\"\"Fit and evaluate a given classifier on the given data.\"\"\"\n",
+    "    assert len(X_train) == len(y_train) and len(X_test) == len(y_test) == len(s_test)\n",
+    "\n",
+    "    train_nan_count = X_train.isna().any(axis=1).sum()\n",
+    "    if fillna and train_nan_count > 0:\n",
+    "        # Fill NaNs with value=-1\n",
+    "        X_train = X_train.fillna(axis=\"columns\", value=-1)\n",
+    "        X_test = X_test.fillna(axis=\"columns\", value=-1)\n",
+    "\n",
+    "    # Fit on train data\n",
+    "    clf.fit(X_train, y_train)\n",
+    "\n",
+    "    # Evaluate on test data\n",
+    "    y_test_scores = clf.predict_proba(X_test)[:, -1]\n",
+    "    test_results = evaluate_predictions(\n",
+    "        y_true=y_test.to_numpy(),\n",
+    "        y_pred_scores=y_test_scores,\n",
+    "        sensitive_attribute=s_test,\n",
+    "        threshold=0.5,\n",
+    "    )\n",
+    "\n",
+    "    # Optionally, save test scores DF\n",
+    "    if save_scores_df_path:\n",
+    "        scores_df = pd.DataFrame({\n",
+    "            \"risk_score\": y_test_scores,\n",
+    "            \"label\": y_test,\n",
+    "        })\n",
+    "        scores_df.to_csv(save_scores_df_path)\n",
+    "        test_results[\"predictions_path\"] = save_scores_df_path\n",
+    "\n",
+    "    return test_results\n",
+    "\n",
+    "def run_baselines(baselines, tasks) -> dict:\n",
+    "    \"\"\"Run baseline classifiers on all acs tasks.\"\"\"\n",
+    "    baseline_results = defaultdict(dict)\n",
+    "\n",
+    "    # Prepare progress bar\n",
+    "    progress_bar = tqdm(\n",
+    "        total=len(tasks) * len(baselines),\n",
+    "        leave=True,\n",
+    "    )\n",
+    "\n",
+    "    for task in tasks:\n",
+    "        progress_bar.set_postfix({\"task\": task})\n",
+    "\n",
+    "        # Load ACS task data\n",
+    "        acs_dataset = ACSDataset.make_from_task(task=task, cache_dir=DATA_DIR)\n",
+    "    \n",
+    "        # Get train/test data\n",
+    "        X_train, y_train = acs_dataset.get_train()\n",
+    "        X_test, y_test = acs_dataset.get_test()\n",
+    "    \n",
+    "        # Get sensitive attribute test data\n",
+    "        s_test = None\n",
+    "        if acs_dataset.task.sensitive_attribute is not None:\n",
+    "            s_test = acs_dataset.get_sensitive_attribute_data().loc[y_test.index]\n",
+    "    \n",
+    "        for clf_name, clf in baselines.items():\n",
+    "            progress_bar.set_postfix({\"task\": task, \"clf\": clf_name})\n",
+    "\n",
+    "            try:\n",
+    "                baseline_results[task][clf_name] = fit_and_eval(\n",
+    "                    clf=clf,\n",
+    "                    X_train=X_train, y_train=y_train,\n",
+    "                    X_test=X_test, y_test=y_test, s_test=s_test,\n",
+    "                    fillna=(clf_name == \"LR\"),\n",
+    "                    save_scores_df_path=ACS_AGG_RESULTS_PATH.parent / f\"baseline_scores.{clf_name}.{task}.csv\"\n",
+    "                )\n",
+    "            except Exception as err:\n",
+    "                logging.error(err)\n",
+    "            finally:\n",
+    "                progress_bar.update()\n",
+    "\n",
+    "    return baseline_results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f359a7a3-af13-4516-a07f-7d09f71f99ac",
+   "metadata": {},
+   "source": [
+    "Flatten results and add extra columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4580a3a8-279d-4286-a8d0-01d228b0d92e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parse_baseline_results(baseline_results) -> list:\n",
+    "    \"\"\"Flatten and parse baseline results.\"\"\"\n",
+    "    parsed_results_list = list()\n",
+    "    \n",
+    "    for task, task_results in baseline_results.items():\n",
+    "    \n",
+    "        for clf, clf_results in task_results.items():\n",
+    "            parsed_results = clf_results.copy()\n",
+    "    \n",
+    "            parsed_results[\"config_task_name\"] = task\n",
+    "            parsed_results[\"config_model_name\"] = clf\n",
+    "            parsed_results[\"name\"] = clf\n",
+    "            parsed_results[\"num_features\"] = -1\n",
+    "            parsed_results[\"uses_all_features\"] = True\n",
+    "    \n",
+    "            parsed_results_list.append(parsed_results)\n",
+    "\n",
+    "    return parsed_results_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70b0dd85-79f0-4a6b-a2e1-72fbeb84d85a",
+   "metadata": {},
+   "source": [
+    "Check if baseline results were already computed. If so, load csv; otherwise, compute and save."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e04b361c-780c-4e04-a817-6b2409db84e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading pre-computed baseline results from ../results/baseline-results.GBM.LR.XGBoost.csv\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>threshold</th>\n",
+       "      <th>n_samples</th>\n",
+       "      <th>n_positives</th>\n",
+       "      <th>n_negatives</th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>tpr</th>\n",
+       "      <th>fnr</th>\n",
+       "      <th>fpr</th>\n",
+       "      <th>tnr</th>\n",
+       "      <th>...</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>predictions_path</th>\n",
+       "      <th>config_task_name</th>\n",
+       "      <th>config_model_name</th>\n",
+       "      <th>name</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>new_index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>XGBoost_ACSPublicCoverage</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>113829</td>\n",
+       "      <td>33971</td>\n",
+       "      <td>79858</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.801650</td>\n",
+       "      <td>0.515175</td>\n",
+       "      <td>0.484825</td>\n",
+       "      <td>0.076486</td>\n",
+       "      <td>0.923514</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.368044</td>\n",
+       "      <td>0.839742</td>\n",
+       "      <td>0.004371</td>\n",
+       "      <td>0.004271</td>\n",
+       "      <td>/fast/groups/sf/folktexts-results/2024-07-03/b...</td>\n",
+       "      <td>ACSPublicCoverage</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>XGBoost</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GBM_ACSIncome</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>166450</td>\n",
+       "      <td>61233</td>\n",
+       "      <td>105217</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.813584</td>\n",
+       "      <td>0.727973</td>\n",
+       "      <td>0.272027</td>\n",
+       "      <td>0.136594</td>\n",
+       "      <td>0.863406</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.630389</td>\n",
+       "      <td>0.890792</td>\n",
+       "      <td>0.007721</td>\n",
+       "      <td>0.007146</td>\n",
+       "      <td>/fast/groups/sf/folktexts-results/2024-07-03/b...</td>\n",
+       "      <td>ACSIncome</td>\n",
+       "      <td>GBM</td>\n",
+       "      <td>GBM</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           threshold  n_samples  n_positives  n_negatives  \\\n",
+       "new_index                                                                   \n",
+       "XGBoost_ACSPublicCoverage        0.5     113829        33971        79858   \n",
+       "GBM_ACSIncome                    0.5     166450        61233       105217   \n",
+       "\n",
+       "                           model_name  accuracy       tpr       fnr       fpr  \\\n",
+       "new_index                                                                       \n",
+       "XGBoost_ACSPublicCoverage         NaN  0.801650  0.515175  0.484825  0.076486   \n",
+       "GBM_ACSIncome                     NaN  0.813584  0.727973  0.272027  0.136594   \n",
+       "\n",
+       "                                tnr  ...  equalized_odds_diff   roc_auc  \\\n",
+       "new_index                            ...                                  \n",
+       "XGBoost_ACSPublicCoverage  0.923514  ...             0.368044  0.839742   \n",
+       "GBM_ACSIncome              0.863406  ...             0.630389  0.890792   \n",
+       "\n",
+       "                                ece  ece_quantile  \\\n",
+       "new_index                                           \n",
+       "XGBoost_ACSPublicCoverage  0.004371      0.004271   \n",
+       "GBM_ACSIncome              0.007721      0.007146   \n",
+       "\n",
+       "                                                            predictions_path  \\\n",
+       "new_index                                                                      \n",
+       "XGBoost_ACSPublicCoverage  /fast/groups/sf/folktexts-results/2024-07-03/b...   \n",
+       "GBM_ACSIncome              /fast/groups/sf/folktexts-results/2024-07-03/b...   \n",
+       "\n",
+       "                            config_task_name  config_model_name     name  \\\n",
+       "new_index                                                                  \n",
+       "XGBoost_ACSPublicCoverage  ACSPublicCoverage            XGBoost  XGBoost   \n",
+       "GBM_ACSIncome                      ACSIncome                GBM      GBM   \n",
+       "\n",
+       "                           num_features  uses_all_features  \n",
+       "new_index                                                   \n",
+       "XGBoost_ACSPublicCoverage            -1               True  \n",
+       "GBM_ACSIncome                        -1               True  \n",
+       "\n",
+       "[2 rows x 42 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "BASELINE_RESULTS_PATH = ACS_AGG_RESULTS_PATH.parent / (\"baseline-results.\" + \".\".join(sorted(baselines.keys())) + \".csv\")\n",
+    "\n",
+    "# If saved results exists: load\n",
+    "if BASELINE_RESULTS_PATH.exists():\n",
+    "    print(f\"Loading pre-computed baseline results from {BASELINE_RESULTS_PATH.as_posix()}\")\n",
+    "    baselines_df = pd.read_csv(BASELINE_RESULTS_PATH, index_col=0)\n",
+    "\n",
+    "# Compute baseline results\n",
+    "else:\n",
+    "    print(f\"Computing baseline results and saving to {BASELINE_RESULTS_PATH.as_posix()}\")\n",
+    "    # Compute baseline results\n",
+    "    baseline_results = run_baselines(baselines, tasks=ALL_TASKS)\n",
+    "\n",
+    "    # Parse results\n",
+    "    parsed_results_list = parse_baseline_results(baseline_results)\n",
+    "\n",
+    "    # Construct DF\n",
+    "    baselines_df = pd.DataFrame(parsed_results_list, index=[r[\"name\"] for r in parsed_results_list])\n",
+    "    \n",
+    "    # Save DF to disk\n",
+    "    baselines_df.to_csv(BASELINE_RESULTS_PATH)\n",
+    "\n",
+    "# Untie indices\n",
+    "baselines_df[\"new_index\"] = [(r[\"name\"] + \"_\" + r[task_col]) for _, r in baselines_df.iterrows()]\n",
+    "baselines_df = baselines_df.set_index(\"new_index\", drop=True)\n",
+    "\n",
+    "# Show 2 random rows\n",
+    "baselines_df.sample(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "07b4ddea-bd88-4bee-87cc-b6aaa6af5630",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "all_results_df.shape=(185, 64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_results_df = pd.concat((results_df, baselines_df))\n",
+    "print(f\"{all_results_df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b657133-120f-4ddb-95ef-b7106b9511fb",
+   "metadata": {},
+   "source": [
+    "## Prepare results table for each task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "317e5744-31e3-48ef-b051-484f79b01a8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_metrics = [\"ece\", \"brier_score_loss\", \"roc_auc\", \"accuracy\", \"fit_thresh_accuracy\", \"score_stdev\"] #, \"score_mean\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9df28300-70ee-47d7-b7ad-23d06ed415cc",
+   "metadata": {},
+   "source": [
+    "Add model size and model family columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "41009307-905b-4498-b98e-972b589f68ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "config_task_name   model_family\n",
+       "ACSEmployment      -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "ACSIncome          -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "ACSMobility        -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "ACSPublicCoverage  -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "ACSTravelTime      -                3\n",
+       "                   Gemma            8\n",
+       "                   Llama            8\n",
+       "                   Mistral         12\n",
+       "                   OpenAI           2\n",
+       "                   Yi               4\n",
+       "Name: accuracy, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from folktexts.llm_utils import get_model_size_B\n",
+    "\n",
+    "all_results_df[\"model_size\"] = [\n",
+    "    (\n",
+    "        get_model_size_B(row[\"name\"], default=300)  # Set GPT 4o size as the largest (300B)\n",
+    "        if row[\"name\"] not in baselines else \"-\"\n",
+    "    )\n",
+    "    for _, row in all_results_df.iterrows()\n",
+    "]\n",
+    "\n",
+    "def get_model_family(model_name) -> str:\n",
+    "    if \"gpt\" in model_name.lower():\n",
+    "        return \"OpenAI\"\n",
+    "    elif \"llama\" in model_name.lower():\n",
+    "        return \"Llama\"\n",
+    "    elif \"mistral\" in model_name.lower() or \"mixtral\" in model_name.lower():\n",
+    "        return \"Mistral\"\n",
+    "    elif \"gemma\" in model_name.lower():\n",
+    "        return \"Gemma\"\n",
+    "    elif \"yi\" in model_name.lower():\n",
+    "        return \"Yi\"\n",
+    "    elif \"qwen\" in model_name.lower():\n",
+    "        return \"Qwen\"\n",
+    "    else:\n",
+    "        return \"-\"\n",
+    "\n",
+    "all_results_df[\"model_family\"] = [get_model_family(row[model_col]) for _, row in all_results_df.iterrows()]\n",
+    "all_results_df.groupby([task_col, \"model_family\"])[\"accuracy\"].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "027d43fb-8290-499c-847f-70e1315a2ae3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_sort_key(name, task_df):\n",
+    "    \"\"\"Sort key for paper table rows.\"\"\"\n",
+    "\n",
+    "    if \"gpt\" in name.lower():\n",
+    "        key = 11\n",
+    "    elif \"llama\" in name.lower():\n",
+    "        key = 10\n",
+    "    elif \"mixtral\" in name.lower():\n",
+    "        key = 9\n",
+    "    elif \"mistral\" in name.lower():\n",
+    "        key = 8\n",
+    "    elif \"yi\" in name.lower():\n",
+    "        key = 7\n",
+    "    elif \"gemma\" in name.lower():\n",
+    "        key = 6\n",
+    "    else:\n",
+    "        return 0\n",
+    "\n",
+    "    row = task_df.loc[name]\n",
+    "    # return (1e5 * key) + (row[\"model_size\"] * 100) + int(row[\"is_inst\"] * 10) + (1 if row[numeric_prompt_col] else 0)\n",
+    "    return (row[\"model_size\"] * 1e9) + int(row[\"is_inst\"] * 1e6) + 1e3 * (1 if row[numeric_prompt_col] else 0) + key\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "871c31ae-6a64-4b8a-9773-f1d430be9b36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def latex_colored_float_format(val, all_values, higher_is_better=True):\n",
+    "    \"\"\"Map a cell's value to its colored latex code.\n",
+    "\n",
+    "    Current definition:\n",
+    "    - use cyan color gradient for good values;\n",
+    "    - use orange color gradient for bad values;\n",
+    "    - use no color for anything in between;\n",
+    "    \"\"\"\n",
+    "    min_val, max_val = np.min(all_values), np.max(all_values)\n",
+    "\n",
+    "    low_pct_val, high_pct_val = [\n",
+    "        min_val + (max_val - min_val) * interp_point\n",
+    "        for interp_point in [0.1, 0.9]\n",
+    "    ]\n",
+    "\n",
+    "    # Use rounded value or original value for coloring?\n",
+    "    # > Using rounded value for consistency in table\n",
+    "    # val = np.round(val, decimals=2)\n",
+    "\n",
+    "    # Use no color for middle 33% of values\n",
+    "    if low_pct_val <= val <= high_pct_val:\n",
+    "        return f\"{val:.2f}\"\n",
+    "\n",
+    "    if val < low_pct_val:\n",
+    "        color = \"orange\" if higher_is_better else \"cyan\"\n",
+    "        color_value = 100 * (\n",
+    "            (low_pct_val - val) / (low_pct_val - min_val))\n",
+    "        \n",
+    "    elif val > high_pct_val:\n",
+    "        color = \"cyan\" if higher_is_better else \"orange\"\n",
+    "        color_value = 100 * (\n",
+    "            (val - high_pct_val) / (max_val - high_pct_val))\n",
+    "\n",
+    "    else:\n",
+    "        raise RuntimeError(f\"{val}\")\n",
+    "\n",
+    "    # Note: halving `color_value` to have softer colors\n",
+    "    color_value /= 4\n",
+    "\n",
+    "    return (\n",
+    "        r\"\\cellcolor{\"\n",
+    "        + f\"{color}!{color_value:.1f}\"\n",
+    "        + r\"} \"\n",
+    "        + f\"{val:.2f}\"\n",
+    "    )\n",
+    "\n",
+    "higher_is_better_cols = {\"roc_auc\", \"accuracy\", \"fit_thresh_accuracy\"}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c1a03d1-eac4-49c3-9243-3291bfa1640e",
+   "metadata": {},
+   "source": [
+    "## Output latex results tables - colored!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61778ae8-af37-404f-946c-dda26fef6836",
+   "metadata": {},
+   "source": [
+    "Set whether to use **_Numeric_ prompting** or **_multiple-choice Q&A_ prompting**!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "eb0096df-e27a-4f10-a42a-63dbeeae0dce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NUMERIC_PROMPTING = False\n",
+    "NUMERIC_PROMPTING = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8461cf3-b042-4ad3-9cf5-ca383ad66514",
+   "metadata": {},
+   "source": [
+    "Whether to **combine results from both prompting schemes** in the latex tables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "40f8b070-8c7b-4935-af5b-4fa4789c1bc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "COMBINE_PROMPTING_RESULTS = True\n",
+    "# COMBINE_PROMPTING_RESULTS = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "477e4f80-2c42-458b-a98c-b32a95c7d67a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*** ACSINCOME ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & \\cellcolor{cyan!25.0} 0.05 & \\cellcolor{cyan!25.0} 0.16 & \\cellcolor{cyan!20.6} 0.83 & \\cellcolor{cyan!24.4} 0.78 & 0.24 & 0.24 & \\cellcolor{cyan!17.6} 0.85 & 0.74 \\\\\n",
+      "Mixtral 8x22B (it) & 0.11 & \\cellcolor{cyan!15.5} 0.17 & \\cellcolor{cyan!25.0} 0.84 & \\cellcolor{cyan!18.9} 0.77 & 0.21 & \\cellcolor{cyan!3.6} 0.22 & \\cellcolor{cyan!11.2} 0.85 & \\cellcolor{cyan!11.1} 0.76 \\\\\n",
+      "Mixtral 8x22B & 0.13 & \\cellcolor{cyan!3.6} 0.18 & \\cellcolor{cyan!9.6} 0.82 & \\cellcolor{cyan!2.4} 0.74 & \\cellcolor{cyan!13.2} 0.17 & \\cellcolor{cyan!21.6} 0.19 & \\cellcolor{cyan!16.5} 0.85 & 0.68 \\\\\n",
+      "Llama 3 70B (it) & 0.25 & 0.23 & \\cellcolor{cyan!22.1} 0.84 & 0.67 & 0.27 & 0.27 & \\cellcolor{cyan!25.0} 0.86 & 0.69 \\\\\n",
+      "Llama 3 70B & 0.27 & 0.24 & \\cellcolor{cyan!6.7} 0.82 & 0.54 & 0.20 & \\cellcolor{cyan!14.9} 0.20 & \\cellcolor{cyan!20.8} 0.86 & 0.70 \\\\\n",
+      "Mixtral 8x7B (it) & 0.10 & \\cellcolor{cyan!15.5} 0.17 & \\cellcolor{cyan!22.8} 0.84 & \\cellcolor{cyan!12.8} 0.76 & \\cellcolor{cyan!16.8} 0.16 & \\cellcolor{cyan!25.0} 0.18 & \\cellcolor{cyan!19.7} 0.86 & \\cellcolor{cyan!25.0} 0.78 \\\\\n",
+      "Mixtral 8x7B & \\cellcolor{cyan!4.0} 0.07 & \\cellcolor{cyan!13.1} 0.17 & \\cellcolor{cyan!3.7} 0.81 & \\cellcolor{cyan!25.0} 0.78 & \\cellcolor{cyan!10.6} 0.17 & \\cellcolor{cyan!11.5} 0.21 & 0.83 & 0.65 \\\\\n",
+      "Yi 34B (it) & 0.22 & 0.21 & 0.80 & 0.48 & \\cellcolor{cyan!1.3} 0.19 & \\cellcolor{cyan!18.8} 0.19 & \\cellcolor{cyan!19.7} 0.86 & 0.72 \\\\\n",
+      "Yi 34B & 0.15 & 0.19 & \\cellcolor{cyan!17.7} 0.83 & 0.61 & 0.25 & \\cellcolor{cyan!2.5} 0.22 & \\cellcolor{cyan!13.3} 0.85 & 0.62 \\\\\n",
+      "Llama 3 8B (it) & 0.23 & 0.23 & \\cellcolor{cyan!0.1} 0.81 & 0.67 & 0.32 & 0.30 & \\cellcolor{cyan!13.3} 0.85 & 0.62 \\\\\n",
+      "Llama 3 8B & 0.14 & 0.24 & 0.63 & \\cellcolor{orange!3.7} 0.40 & 0.25 & 0.26 & 0.81 & \\cellcolor{orange!20.8} 0.38 \\\\\n",
+      "Mistral 7B (it) & 0.16 & 0.19 & \\cellcolor{cyan!14.7} 0.83 & 0.70 & 0.21 & \\cellcolor{cyan!4.7} 0.22 & 0.83 & \\cellcolor{cyan!16.5} 0.77 \\\\\n",
+      "Gemma 7B (it) & 0.33 & 0.30 & 0.78 & 0.42 & \\cellcolor{orange!14.7} 0.61 & \\cellcolor{orange!4.7} 0.59 & \\cellcolor{cyan!3.8} 0.84 & \\cellcolor{orange!25.0} 0.37 \\\\\n",
+      "Mistral 7B & \\cellcolor{orange!15.7} 0.36 & 0.32 & 0.75 & 0.49 & 0.20 & 0.23 & 0.80 & 0.73 \\\\\n",
+      "Gemma 7B & 0.15 & 0.20 & 0.80 & 0.73 & 0.24 & 0.27 & 0.76 & \\cellcolor{orange!25.0} 0.37 \\\\\n",
+      "Gemma 2B (it) & 0.28 & 0.31 & \\cellcolor{orange!25.0} 0.50 & \\cellcolor{orange!25.0} 0.37 & \\cellcolor{orange!25.0} 0.63 & \\cellcolor{orange!25.0} 0.63 & 0.73 & \\cellcolor{orange!25.0} 0.37 \\\\\n",
+      "Gemma 2B & \\cellcolor{orange!25.0} 0.37 & \\cellcolor{orange!25.0} 0.37 & \\cellcolor{orange!25.0} 0.50 & 0.63 & \\cellcolor{cyan!25.0} 0.14 & 0.25 & \\cellcolor{orange!25.0} 0.62 & 0.45 \\\\\n",
+      "LR & 0.03 & 0.18 & 0.79 & 0.74 & 0.03 & 0.18 & 0.79 & 0.74 \\\\\n",
+      "GBM & 0.01 & 0.13 & 0.89 & 0.81 & 0.01 & 0.13 & 0.89 & 0.81 \\\\\n",
+      "XGBoost & 0.00 & 0.13 & 0.90 & 0.82 & 0.00 & 0.13 & 0.90 & 0.82 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n",
+      "*** ACSMOBILITY ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & 0.22 & 0.25 & 0.49 & \\cellcolor{cyan!25.0} 0.73 & 0.26 & 0.26 & \\cellcolor{cyan!0.6} 0.57 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Mixtral 8x22B (it) & 0.05 & \\cellcolor{cyan!17.9} 0.20 & \\cellcolor{cyan!25.0} 0.54 & \\cellcolor{cyan!25.0} 0.73 & 0.40 & 0.40 & \\cellcolor{orange!12.8} 0.51 & 0.39 \\\\\n",
+      "Mixtral 8x22B & 0.13 & 0.22 & 0.49 & \\cellcolor{cyan!25.0} 0.73 & \\cellcolor{cyan!10.2} 0.11 & \\cellcolor{cyan!21.2} 0.21 & 0.55 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Llama 3 70B (it) & 0.05 & \\cellcolor{cyan!10.7} 0.20 & 0.52 & \\cellcolor{cyan!25.0} 0.73 & 0.20 & \\cellcolor{cyan!1.5} 0.25 & 0.57 & 0.58 \\\\\n",
+      "Llama 3 70B & 0.06 & \\cellcolor{cyan!7.1} 0.20 & 0.53 & \\cellcolor{cyan!20.7} 0.73 & 0.22 & \\cellcolor{cyan!4.8} 0.24 & 0.55 & 0.53 \\\\\n",
+      "Mixtral 8x7B (it) & 0.11 & 0.21 & 0.51 & \\cellcolor{cyan!25.0} 0.73 & 0.26 & 0.26 & \\cellcolor{cyan!12.8} 0.58 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Mixtral 8x7B & 0.24 & 0.25 & \\cellcolor{orange!25.0} 0.48 & \\cellcolor{cyan!25.0} 0.73 & \\cellcolor{cyan!0.8} 0.14 & \\cellcolor{cyan!18.9} 0.21 & 0.57 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Yi 34B (it) & 0.23 & 0.25 & 0.50 & \\cellcolor{orange!25.0} 0.27 & \\cellcolor{cyan!18.2} 0.09 & \\cellcolor{cyan!23.1} 0.20 & 0.56 & \\cellcolor{cyan!15.4} 0.72 \\\\\n",
+      "Yi 34B & 0.15 & 0.23 & 0.52 & 0.44 & \\cellcolor{cyan!25.0} 0.07 & \\cellcolor{cyan!25.0} 0.20 & 0.57 & \\cellcolor{cyan!20.7} 0.73 \\\\\n",
+      "Llama 3 8B (it) & 0.11 & 0.21 & 0.49 & \\cellcolor{cyan!23.4} 0.73 & 0.15 & \\cellcolor{cyan!13.7} 0.22 & 0.56 & \\cellcolor{cyan!5.3} 0.70 \\\\\n",
+      "Llama 3 8B & 0.14 & 0.21 & 0.51 & \\cellcolor{cyan!19.1} 0.72 & \\cellcolor{cyan!12.5} 0.10 & \\cellcolor{cyan!21.7} 0.20 & 0.55 & \\cellcolor{cyan!24.5} 0.73 \\\\\n",
+      "Mistral 7B (it) & 0.17 & 0.23 & 0.49 & \\cellcolor{cyan!24.5} 0.73 & 0.26 & 0.26 & 0.57 & \\cellcolor{cyan!25.0} 0.73 \\\\\n",
+      "Gemma 7B (it) & \\cellcolor{orange!8.9} 0.25 & 0.26 & \\cellcolor{orange!3.1} 0.49 & \\cellcolor{cyan!22.3} 0.73 & 0.25 & 0.26 & \\cellcolor{cyan!25.0} 0.58 & \\cellcolor{cyan!21.8} 0.73 \\\\\n",
+      "Mistral 7B & \\cellcolor{orange!25.0} 0.27 & \\cellcolor{orange!25.0} 0.27 & 0.50 & \\cellcolor{cyan!25.0} 0.73 & 0.20 & \\cellcolor{cyan!8.6} 0.23 & 0.53 & \\cellcolor{cyan!23.4} 0.73 \\\\\n",
+      "Gemma 7B & 0.19 & 0.24 & 0.49 & \\cellcolor{cyan!25.0} 0.73 & 0.41 & 0.37 & \\cellcolor{orange!25.0} 0.50 & \\cellcolor{orange!25.0} 0.27 \\\\\n",
+      "Gemma 2B (it) & \\cellcolor{cyan!25.0} 0.02 & \\cellcolor{cyan!25.0} 0.20 & 0.50 & \\cellcolor{cyan!25.0} 0.73 & \\cellcolor{orange!25.0} 0.73 & \\cellcolor{orange!25.0} 0.73 & 0.52 & \\cellcolor{orange!25.0} 0.27 \\\\\n",
+      "Gemma 2B & \\cellcolor{orange!25.0} 0.27 & \\cellcolor{orange!25.0} 0.27 & 0.50 & \\cellcolor{cyan!25.0} 0.73 & 0.25 & 0.26 & 0.51 & 0.34 \\\\\n",
+      "LR & 0.02 & 0.19 & 0.61 & 0.74 & 0.02 & 0.19 & 0.61 & 0.74 \\\\\n",
+      "GBM & 0.01 & 0.17 & 0.74 & 0.76 & 0.01 & 0.17 & 0.74 & 0.76 \\\\\n",
+      "XGBoost & 0.00 & 0.16 & 0.74 & 0.76 & 0.00 & 0.16 & 0.74 & 0.76 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n",
+      "*** ACSEMPLOYMENT ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & 0.23 & 0.23 & 0.80 & 0.73 & 0.28 & 0.29 & 0.79 & 0.65 \\\\\n",
+      "Mixtral 8x22B (it) & 0.06 & \\cellcolor{cyan!21.9} 0.14 & \\cellcolor{cyan!24.4} 0.87 & \\cellcolor{cyan!16.5} 0.79 & \\cellcolor{orange!23.0} 0.38 & \\cellcolor{orange!8.0} 0.39 & 0.60 & 0.51 \\\\\n",
+      "Mixtral 8x22B & 0.15 & 0.18 & 0.82 & \\cellcolor{cyan!20.7} 0.80 & 0.21 & 0.24 & \\cellcolor{cyan!25.0} 0.86 & 0.52 \\\\\n",
+      "Llama 3 70B (it) & \\cellcolor{cyan!0.7} 0.05 & \\cellcolor{cyan!25.0} 0.14 & \\cellcolor{cyan!25.0} 0.88 & \\cellcolor{cyan!25.0} 0.81 & 0.17 & \\cellcolor{cyan!17.1} 0.19 & \\cellcolor{cyan!23.3} 0.85 & \\cellcolor{cyan!15.4} 0.73 \\\\\n",
+      "Llama 3 70B & 0.05 & \\cellcolor{cyan!16.7} 0.15 & \\cellcolor{cyan!13.6} 0.86 & \\cellcolor{cyan!5.8} 0.78 & 0.25 & 0.26 & \\cellcolor{cyan!2.3} 0.82 & 0.52 \\\\\n",
+      "Mixtral 8x7B (it) & 0.07 & \\cellcolor{cyan!16.7} 0.15 & \\cellcolor{cyan!23.2} 0.87 & \\cellcolor{cyan!7.2} 0.78 & 0.22 & 0.24 & \\cellcolor{cyan!6.8} 0.82 & \\cellcolor{cyan!13.7} 0.73 \\\\\n",
+      "Mixtral 8x7B & 0.08 & 0.17 & 0.81 & 0.73 & 0.30 & 0.31 & \\cellcolor{cyan!1.1} 0.81 & \\cellcolor{orange!24.1} 0.45 \\\\\n",
+      "Yi 34B (it) & 0.15 & 0.21 & 0.81 & 0.51 & 0.14 & 0.21 & 0.79 & 0.69 \\\\\n",
+      "Yi 34B & 0.13 & 0.23 & 0.66 & 0.50 & 0.08 & 0.23 & 0.70 & 0.62 \\\\\n",
+      "Llama 3 8B (it) & 0.08 & 0.17 & 0.82 & 0.77 & 0.07 & \\cellcolor{cyan!25.0} 0.19 & 0.79 & \\cellcolor{cyan!25.0} 0.74 \\\\\n",
+      "Llama 3 8B & 0.15 & 0.23 & 0.75 & \\cellcolor{orange!25.0} 0.46 & 0.34 & 0.34 & 0.76 & \\cellcolor{orange!25.0} 0.45 \\\\\n",
+      "Mistral 7B (it) & \\cellcolor{cyan!7.4} 0.04 & 0.19 & 0.79 & 0.69 & \\cellcolor{orange!6.6} 0.35 & 0.36 & 0.72 & 0.63 \\\\\n",
+      "Gemma 7B (it) & \\cellcolor{cyan!8.8} 0.04 & 0.22 & 0.71 & 0.60 & \\cellcolor{orange!10.0} 0.36 & 0.38 & 0.59 & 0.58 \\\\\n",
+      "Mistral 7B & 0.14 & 0.19 & 0.80 & \\cellcolor{cyan!12.9} 0.79 & 0.26 & 0.30 & 0.76 & \\cellcolor{orange!25.0} 0.45 \\\\\n",
+      "Gemma 7B & \\cellcolor{orange!25.0} 0.35 & \\cellcolor{orange!25.0} 0.38 & \\cellcolor{orange!1.6} 0.50 & 0.51 & 0.15 & 0.25 & 0.65 & \\cellcolor{orange!4.1} 0.48 \\\\\n",
+      "Gemma 2B (it) & 0.12 & 0.27 & \\cellcolor{orange!25.0} 0.46 & \\cellcolor{orange!25.0} 0.46 & \\cellcolor{orange!25.0} 0.38 & \\cellcolor{orange!25.0} 0.41 & \\cellcolor{orange!25.0} 0.42 & \\cellcolor{orange!16.3} 0.46 \\\\\n",
+      "Gemma 2B & \\cellcolor{cyan!25.0} 0.01 & 0.23 & 0.57 & 0.53 & \\cellcolor{cyan!25.0} 0.01 & 0.24 & 0.63 & 0.54 \\\\\n",
+      "LR & 0.02 & 0.15 & 0.86 & 0.78 & 0.02 & 0.15 & 0.86 & 0.78 \\\\\n",
+      "GBM & 0.00 & 0.12 & 0.91 & 0.83 & 0.00 & 0.12 & 0.91 & 0.83 \\\\\n",
+      "XGBoost & 0.00 & 0.12 & 0.91 & 0.83 & 0.00 & 0.12 & 0.91 & 0.83 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n",
+      "*** ACSTRAVELTIME ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & 0.15 & 0.27 & 0.58 & 0.57 & 0.39 & 0.40 & 0.65 & 0.55 \\\\\n",
+      "Mixtral 8x22B (it) & 0.12 & \\cellcolor{cyan!23.7} 0.24 & \\cellcolor{cyan!23.5} 0.64 & \\cellcolor{cyan!25.0} 0.59 & 0.31 & 0.33 & 0.66 & \\cellcolor{cyan!17.1} 0.59 \\\\\n",
+      "Mixtral 8x22B & 0.30 & 0.34 & 0.57 & \\cellcolor{cyan!2.4} 0.58 & 0.20 & 0.28 & 0.63 & \\cellcolor{orange!25.0} 0.44 \\\\\n",
+      "Llama 3 70B (it) & 0.12 & \\cellcolor{cyan!22.5} 0.24 & \\cellcolor{cyan!25.0} 0.64 & 0.53 & 0.15 & \\cellcolor{cyan!22.6} 0.24 & \\cellcolor{cyan!25.0} 0.70 & \\cellcolor{cyan!25.0} 0.60 \\\\\n",
+      "Llama 3 70B & 0.08 & \\cellcolor{cyan!12.4} 0.25 & 0.52 & 0.46 & 0.09 & \\cellcolor{cyan!21.4} 0.24 & 0.67 & 0.55 \\\\\n",
+      "Mixtral 8x7B (it) & 0.09 & \\cellcolor{cyan!25.0} 0.24 & 0.61 & 0.57 & \\cellcolor{orange!25.0} 0.45 & \\cellcolor{orange!25.0} 0.45 & 0.66 & 0.52 \\\\\n",
+      "Mixtral 8x7B & 0.07 & \\cellcolor{cyan!16.2} 0.25 & 0.57 & \\cellcolor{cyan!2.4} 0.58 & 0.28 & 0.32 & 0.60 & \\cellcolor{orange!25.0} 0.44 \\\\\n",
+      "Yi 34B (it) & \\cellcolor{cyan!3.7} 0.06 & \\cellcolor{cyan!11.2} 0.25 & 0.50 & \\cellcolor{orange!25.0} 0.44 & 0.35 & 0.36 & 0.65 & 0.56 \\\\\n",
+      "Yi 34B & 0.14 & 0.27 & 0.53 & \\cellcolor{orange!25.0} 0.44 & \\cellcolor{cyan!4.5} 0.08 & \\cellcolor{cyan!25.0} 0.24 & 0.62 & 0.56 \\\\\n",
+      "Llama 3 8B (it) & 0.11 & \\cellcolor{cyan!12.4} 0.25 & 0.56 & 0.56 & 0.19 & 0.28 & 0.60 & 0.57 \\\\\n",
+      "Llama 3 8B & 0.12 & 0.26 & \\cellcolor{orange!25.0} 0.48 & \\cellcolor{orange!25.0} 0.44 & 0.08 & \\cellcolor{cyan!7.1} 0.25 & 0.53 & 0.56 \\\\\n",
+      "Mistral 7B (it) & 0.11 & \\cellcolor{cyan!11.2} 0.25 & 0.55 & 0.56 & \\cellcolor{orange!2.7} 0.41 & 0.42 & 0.59 & 0.57 \\\\\n",
+      "Gemma 7B (it) & 0.10 & \\cellcolor{cyan!4.9} 0.26 & \\cellcolor{orange!4.9} 0.49 & \\cellcolor{orange!25.0} 0.44 & \\cellcolor{orange!12.0} 0.42 & 0.43 & 0.53 & 0.56 \\\\\n",
+      "Mistral 7B & \\cellcolor{orange!25.0} 0.44 & \\cellcolor{orange!25.0} 0.44 & 0.50 & 0.56 & \\cellcolor{cyan!22.5} 0.05 & \\cellcolor{cyan!14.2} 0.25 & 0.57 & 0.56 \\\\\n",
+      "Gemma 7B & \\cellcolor{cyan!25.0} 0.03 & \\cellcolor{cyan!13.7} 0.25 & 0.52 & 0.55 & \\cellcolor{cyan!25.0} 0.04 & \\cellcolor{cyan!19.0} 0.24 & 0.61 & 0.58 \\\\\n",
+      "Gemma 2B (it) & 0.19 & 0.28 & 0.50 & 0.56 & 0.34 & 0.36 & \\cellcolor{orange!9.3} 0.49 & 0.56 \\\\\n",
+      "Gemma 2B & \\cellcolor{orange!25.0} 0.44 & \\cellcolor{orange!25.0} 0.44 & 0.50 & 0.56 & 0.09 & \\cellcolor{cyan!4.7} 0.26 & \\cellcolor{orange!25.0} 0.48 & \\cellcolor{orange!25.0} 0.44 \\\\\n",
+      "LR & 0.04 & 0.24 & 0.58 & 0.56 & 0.04 & 0.24 & 0.58 & 0.56 \\\\\n",
+      "GBM & 0.02 & 0.20 & 0.75 & 0.69 & 0.02 & 0.20 & 0.75 & 0.69 \\\\\n",
+      "XGBoost & 0.02 & 0.19 & 0.77 & 0.70 & 0.02 & 0.19 & 0.77 & 0.70 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n",
+      "*** ACSPUBLICCOVERAGE ***\n",
+      "\n",
+      "\\begin{tabular}{lllllllll}\n",
+      "\\toprule\n",
+      " & ece (num) & brier score loss (num) & roc auc (num) & accuracy (num) & ece & brier score loss & roc auc & accuracy \\\\\n",
+      "Model &  &  &  &  &  &  &  &  \\\\\n",
+      "\\midrule\n",
+      "GPT 4o mini (it) & 0.10 & 0.20 & 0.68 & \\cellcolor{cyan!17.6} 0.73 & 0.33 & 0.34 & \\cellcolor{cyan!25.0} 0.71 & 0.60 \\\\\n",
+      "Mixtral 8x22B (it) & \\cellcolor{cyan!25.0} 0.04 & \\cellcolor{cyan!25.0} 0.18 & \\cellcolor{cyan!11.0} 0.71 & \\cellcolor{cyan!25.0} 0.75 & 0.24 & 0.25 & \\cellcolor{cyan!11.7} 0.70 & \\cellcolor{cyan!12.7} 0.72 \\\\\n",
+      "Mixtral 8x22B & \\cellcolor{orange!15.5} 0.29 & \\cellcolor{orange!3.4} 0.29 & 0.54 & \\cellcolor{cyan!0.5} 0.70 & 0.32 & 0.30 & 0.59 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Llama 3 70B (it) & 0.13 & 0.20 & \\cellcolor{cyan!25.0} 0.73 & \\cellcolor{cyan!24.4} 0.75 & 0.16 & \\cellcolor{cyan!16.2} 0.21 & \\cellcolor{cyan!7.0} 0.69 & \\cellcolor{cyan!25.0} 0.75 \\\\\n",
+      "Llama 3 70B & 0.12 & 0.21 & 0.64 & 0.53 & 0.18 & \\cellcolor{cyan!8.3} 0.22 & 0.67 & 0.63 \\\\\n",
+      "Mixtral 8x7B (it) & \\cellcolor{cyan!0.3} 0.06 & \\cellcolor{cyan!18.5} 0.19 & 0.69 & \\cellcolor{cyan!22.2} 0.74 & 0.20 & \\cellcolor{cyan!7.4} 0.23 & \\cellcolor{cyan!12.7} 0.70 & \\cellcolor{cyan!23.3} 0.74 \\\\\n",
+      "Mixtral 8x7B & 0.20 & 0.25 & 0.56 & 0.70 & 0.41 & 0.37 & 0.57 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Yi 34B (it) & 0.22 & 0.24 & 0.57 & \\cellcolor{orange!25.0} 0.31 & \\cellcolor{cyan!16.7} 0.06 & \\cellcolor{cyan!25.0} 0.19 & 0.67 & \\cellcolor{cyan!19.4} 0.74 \\\\\n",
+      "Yi 34B & 0.09 & 0.20 & 0.67 & 0.64 & \\cellcolor{cyan!25.0} 0.04 & \\cellcolor{cyan!17.2} 0.21 & 0.59 & \\cellcolor{cyan!2.1} 0.70 \\\\\n",
+      "Llama 3 8B (it) & 0.17 & 0.22 & 0.64 & 0.68 & 0.11 & \\cellcolor{cyan!12.7} 0.21 & 0.59 & \\cellcolor{cyan!7.7} 0.71 \\\\\n",
+      "Llama 3 8B & 0.20 & 0.25 & 0.51 & \\cellcolor{orange!6.2} 0.34 & 0.41 & 0.38 & 0.55 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Mistral 7B (it) & 0.07 & 0.20 & 0.67 & 0.65 & 0.30 & 0.30 & 0.61 & \\cellcolor{cyan!0.4} 0.70 \\\\\n",
+      "Gemma 7B (it) & 0.18 & 0.24 & 0.57 & 0.61 & 0.30 & 0.34 & \\cellcolor{orange!14.6} 0.46 & 0.50 \\\\\n",
+      "Mistral 7B & \\cellcolor{orange!22.1} 0.30 & \\cellcolor{orange!18.5} 0.30 & 0.50 & 0.70 & 0.29 & 0.30 & \\cellcolor{orange!25.0} 0.45 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Gemma 7B & 0.18 & 0.26 & 0.48 & 0.70 & 0.15 & \\cellcolor{cyan!3.4} 0.23 & 0.49 & 0.49 \\\\\n",
+      "Gemma 2B (it) & 0.24 & \\cellcolor{orange!14.2} 0.29 & \\cellcolor{orange!25.0} 0.42 & 0.42 & \\cellcolor{orange!25.0} 0.70 & \\cellcolor{orange!25.0} 0.70 & 0.54 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "Gemma 2B & \\cellcolor{orange!25.0} 0.30 & \\cellcolor{orange!25.0} 0.30 & 0.50 & 0.70 & 0.26 & 0.28 & 0.54 & \\cellcolor{orange!25.0} 0.30 \\\\\n",
+      "LR & 0.03 & 0.19 & 0.70 & 0.72 & 0.03 & 0.19 & 0.70 & 0.72 \\\\\n",
+      "GBM & 0.01 & 0.14 & 0.83 & 0.80 & 0.01 & 0.14 & 0.83 & 0.80 \\\\\n",
+      "XGBoost & 0.00 & 0.14 & 0.84 & 0.80 & 0.00 & 0.14 & 0.84 & 0.80 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils import prettify_model_name\n",
+    "\n",
+    "for task in ALL_TASKS:\n",
+    "    task_df = all_results_df[all_results_df[task_col] == task]\n",
+    "\n",
+    "    # Sort table rows\n",
+    "    sorted_df_index = sorted(\n",
+    "        task_df.index.tolist(),\n",
+    "        key=lambda id_: model_sort_key(id_, task_df),\n",
+    "        reverse=True,\n",
+    "    )\n",
+    "\n",
+    "    latex_tab_cols = [numeric_prompt_col] + table_metrics\n",
+    "\n",
+    "    # latex_table = task_df.sort_values([\"model_family\", \"model_size\", \"is_inst\"], ascending=False).set_index(model_col)[table_metrics].round(3)\n",
+    "    latex_table = task_df.loc[sorted_df_index].set_index(model_col)[latex_tab_cols].round(3)\n",
+    "\n",
+    "    # Keep rows for a single selected prompting scheme\n",
+    "    latex_table = latex_table[\n",
+    "        (latex_table[numeric_prompt_col] == NUMERIC_PROMPTING)\n",
+    "        | latex_table[numeric_prompt_col].isna()\n",
+    "    ]\n",
+    "\n",
+    "    # But optionally join with the columns of the other prompting scheme\n",
+    "    if COMBINE_PROMPTING_RESULTS:\n",
+    "        cols_to_keep = [\"ece\", \"brier_score_loss\", \"roc_auc\", \"accuracy\"]\n",
+    "        latex_table = latex_table[cols_to_keep]\n",
+    "\n",
+    "        latex_table_other_prompt = task_df.loc[sorted_df_index].set_index(model_col)\n",
+    "        latex_table_other_prompt = latex_table_other_prompt[\n",
+    "            (latex_table_other_prompt[numeric_prompt_col] != NUMERIC_PROMPTING)\n",
+    "            | latex_table_other_prompt[numeric_prompt_col].isna()\n",
+    "        ][cols_to_keep].round(3)\n",
+    "\n",
+    "        latex_table = latex_table.join(\n",
+    "            latex_table_other_prompt,\n",
+    "            how=\"left\",\n",
+    "            lsuffix=\" (num)\" if NUMERIC_PROMPTING else \" (mult. choice)\",\n",
+    "            validate=\"1:1\",\n",
+    "        )\n",
+    "    \n",
+    "    # Fill NaNs\n",
+    "    latex_table = latex_table.fillna(\"-\")\n",
+    "\n",
+    "    # Prettify model names\n",
+    "    latex_table[\"Model\"] = [\n",
+    "        prettify_model_name(id_) if id_ not in baselines.keys() else id_\n",
+    "        for id_, row in latex_table.iterrows()\n",
+    "    ]\n",
+    "    latex_table.set_index(\"Model\", drop=True, inplace=True)\n",
+    "\n",
+    "    for col in set(latex_table.columns.tolist()) - {\"score_stdev\", numeric_prompt_col, \"model_family\"}:\n",
+    "        index_without_baselines = [name for name in latex_table.index if name not in baselines]\n",
+    "        index_baselines = list(baselines.keys())\n",
+    "\n",
+    "        col_data = latex_table.loc[index_without_baselines, col].copy()\n",
+    "\n",
+    "        new_col_data = [\n",
+    "            latex_colored_float_format(\n",
+    "                val=col_data.loc[id_], all_values=col_data,\n",
+    "                higher_is_better=any(col_name in col for col_name in higher_is_better_cols),\n",
+    "            )\n",
+    "            for id_ in index_without_baselines\n",
+    "        ] + [\n",
+    "            latex_table.loc[id_, col]\n",
+    "            for id_ in index_baselines\n",
+    "        ]\n",
+    "\n",
+    "        # Set compatible dtype\n",
+    "        latex_table = latex_table.astype({col: str})\n",
+    "\n",
+    "        # Set new data\n",
+    "        latex_table.loc[index_without_baselines + index_baselines, col] = new_col_data\n",
+    "\n",
+    "    # Drop prompting column of not combining both\n",
+    "    latex_table = latex_table.drop(columns=numeric_prompt_col, errors=\"ignore\")\n",
+    "\n",
+    "    # Rename columns to be latex compatible\n",
+    "    latex_table = latex_table.rename(columns={col: col.replace(\"_\", \" \") for col in latex_table.columns})\n",
+    "\n",
+    "    print(f\"*** {task.upper()} ***\\n\")\n",
+    "\n",
+    "    latex_table_str = latex_table.to_latex(float_format=\"%.2f\")\n",
+    "    print(latex_table_str)\n",
+    "\n",
+    "    tables_dir = ACS_AGG_RESULTS_PATH.parent / \"tables\"\n",
+    "    tables_dir.mkdir(exist_ok=True)\n",
+    "    with open(tables_dir / f\"{task}-table.{COMBINE_PROMPTING_RESULTS=}.{NUMERIC_PROMPTING=}.tex\", \"w\") as f_out:\n",
+    "        print(latex_table_str, file=f_out)\n",
+    "    \n",
+    "    print(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d4564ed-1e41-4322-8c87-74fc34265bf5",
+   "metadata": {},
+   "source": [
+    "## Render paper plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82cc9102-66a6-4001-9c36-cf88fa8209e6",
+   "metadata": {},
+   "source": [
+    "Filter by the currently selected prompting scheme:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "37b2b004-44e8-46d3-aa45-2cf168b5e638",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_results_df_original = all_results_df.copy()\n",
+    "\n",
+    "all_results_df = all_results_df[\n",
+    "    (all_results_df[numeric_prompt_col] == NUMERIC_PROMPTING)\n",
+    "    | (all_results_df[numeric_prompt_col].isna())\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "efd4cfdf-ae7e-4770-8bd6-0347342517c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1f77b4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff7f0e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2ca02c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d62728;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8c564b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e377c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7f7f7f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bcbd22;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#17becf;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
+      ],
+      "text/plain": [
+       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
+       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
+       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
+       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
+       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
+       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
+       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
+       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"whitegrid\", rc={\"grid.linestyle\": \"--\"})\n",
+    "plt.rc(\"figure\", figsize=(4.5, 3.5), dpi=200)\n",
+    "\n",
+    "palette_name = \"tab10\"   # \"colorblind\"\n",
+    "palette = sns.color_palette(palette_name)\n",
+    "sns.set_palette(palette_name)\n",
+    "palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e93c2236-8546-439c-8927-fde4f8ea7350",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMGS_DIR = ACS_AGG_RESULTS_PATH.parent / \"imgs\"\n",
+    "IMGS_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "def save_fig(fig, name, add_prompt_suffix=True):\n",
+    "    num_or_multiple_choice = \"numeric-prompt\" if NUMERIC_PROMPTING else \"multiple-choice-prompt\"\n",
+    "    save_path = (IMGS_DIR / name).with_suffix(\".pdf\")\n",
+    "    if add_prompt_suffix:\n",
+    "        save_path = save_path.with_suffix(f\".{num_or_multiple_choice}.pdf\")\n",
+    "\n",
+    "    fig.savefig(save_path, bbox_inches=\"tight\")\n",
+    "    print(f\"Saved figure to '{save_path.as_posix()}'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbdedf0b-ccb1-4887-8474-b1e661f0fe01",
+   "metadata": {},
+   "source": [
+    "Load scores distributions for each model (and with varying degrees of information)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "32dcc2e9-b122-4931-a4be-811efd7fa6cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_path_col = \"predictions_path\"\n",
+    "\n",
+    "def load_model_scores_df(df_row: pd.Series) -> pd.DataFrame:\n",
+    "    \"\"\"Loads csv containing model scores corresponding to the given DF row.\"\"\"\n",
+    "    if predictions_path_col in df_row and not pd.isna(df_row[predictions_path_col]):\n",
+    "        return pd.read_csv(df_row[predictions_path_col], index_col=0)\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1f7cfb2d-3195-4659-97b2-8b0ada073f23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "465640a48d654dd3999164c4cc8efc3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scores_df_map = {\n",
+    "    id_: load_model_scores_df(row)\n",
+    "    for id_, row in tqdm(all_results_df.iterrows(), total=len(all_results_df))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a55adb53-12c1-4784-885c-6e0597f255e2",
+   "metadata": {},
+   "source": [
+    "#### 1st page illustrative plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "0c8819b1-0033-4d44-ab1e-a4b55a4ebd65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_task = \"ACSIncome\"\n",
+    "\n",
+    "# example_model = \"Mistral-7B-v0.1\"\n",
+    "example_model = \"Meta-Llama-3-70B\"\n",
+    "\n",
+    "baseline_model = \"XGBoost\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "deb6b51b-d49c-4551-85a9-f73cef23287a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "      <th>model_size</th>\n",
+       "      <th>model_family</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Meta-Llama-3-70B__ACSIncome__-1__Num</th>\n",
+       "      <td>0.543953</td>\n",
+       "      <td>0.134809</td>\n",
+       "      <td>0.771466</td>\n",
+       "      <td>0.636284</td>\n",
+       "      <td>0.078752</td>\n",
+       "      <td>0.887756</td>\n",
+       "      <td>0.237630</td>\n",
+       "      <td>0.270157</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.191080</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.89560</td>\n",
+       "      <td>0.784722</td>\n",
+       "      <td>0.8956</td>\n",
+       "      <td>0.784722</td>\n",
+       "      <td>0.253299</td>\n",
+       "      <td>0.638033</td>\n",
+       "      <td>70</td>\n",
+       "      <td>Llama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num</th>\n",
+       "      <td>0.665731</td>\n",
+       "      <td>0.086680</td>\n",
+       "      <td>0.880953</td>\n",
+       "      <td>0.722658</td>\n",
+       "      <td>0.061832</td>\n",
+       "      <td>0.919317</td>\n",
+       "      <td>0.230512</td>\n",
+       "      <td>0.246092</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.232483</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.75718</td>\n",
+       "      <td>0.735764</td>\n",
+       "      <td>0.8239</td>\n",
+       "      <td>0.784848</td>\n",
+       "      <td>0.290286</td>\n",
+       "      <td>0.613968</td>\n",
+       "      <td>70</td>\n",
+       "      <td>Llama</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 66 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               accuracy  accuracy_diff  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num           0.543953       0.134809   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num  0.665731       0.086680   \n",
+       "\n",
+       "                                               accuracy_ratio  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                 0.771466   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num        0.880953   \n",
+       "\n",
+       "                                               balanced_accuracy  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                    0.636284   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num           0.722658   \n",
+       "\n",
+       "                                               balanced_accuracy_diff  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                         0.078752   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num                0.061832   \n",
+       "\n",
+       "                                               balanced_accuracy_ratio  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                          0.887756   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num                 0.919317   \n",
+       "\n",
+       "                                               brier_score_loss       ece  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                   0.237630  0.270157   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num          0.230512  0.246092   \n",
+       "\n",
+       "                                               ece_quantile  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                    NaN   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num           NaN   \n",
+       "\n",
+       "                                               equalized_odds_diff  ...  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                      0.191080  ...   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num             0.232483  ...   \n",
+       "\n",
+       "                                               num_features  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                     -1   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num            -1   \n",
+       "\n",
+       "                                               uses_all_features  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                        True   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num               True   \n",
+       "\n",
+       "                                               fit_thresh_on_100  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                     0.89560   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num            0.75718   \n",
+       "\n",
+       "                                               fit_thresh_accuracy  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                      0.784722   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num             0.735764   \n",
+       "\n",
+       "                                               optimal_thresh  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                   0.8956   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num          0.8239   \n",
+       "\n",
+       "                                               optimal_thresh_accuracy  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                          0.784722   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num                 0.784848   \n",
+       "\n",
+       "                                               score_stdev  score_mean  \\\n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num              0.253299    0.638033   \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num     0.290286    0.613968   \n",
+       "\n",
+       "                                              model_size  model_family  \n",
+       "Meta-Llama-3-70B__ACSIncome__-1__Num                  70         Llama  \n",
+       "Meta-Llama-3-70B-Instruct__ACSIncome__-1__Num         70         Llama  \n",
+       "\n",
+       "[2 rows x 66 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Data for baseline model\n",
+    "baseline_row = all_results_df[(all_results_df[\"name\"] == baseline_model) & (all_results_df[task_col] == example_task)].iloc[0]\n",
+    "\n",
+    "# Data for base and instruct models\n",
+    "example_df = all_results_df[\n",
+    "    (all_results_df[task_col] == example_task)\n",
+    "    & (all_results_df[\"base_name\"] == example_model)\n",
+    "    & (all_results_df[numeric_prompt_col] == NUMERIC_PROMPTING)\n",
+    "]\n",
+    "\n",
+    "# Sort examples_df to have (base, instruct) ordering\n",
+    "example_df = example_df.sort_values(\"is_inst\", ascending=True)\n",
+    "example_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "fa917ee4-0c17-4857-9bf9-76de9d9a1e6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3388197/4070898515.py:38: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax1.set_xticklabels([\"Base\", \"Instr.\"])\n",
+      "/tmp/ipykernel_3388197/4070898515.py:90: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax3.set_xticklabels([\"Base\", \"Instr.\"])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/teaser-Meta-Llama-3-70B.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2200x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ALPHA = 0.7\n",
+    "N_BINS = 20\n",
+    "\n",
+    "fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(11, 2.5), gridspec_kw=dict(wspace=0.4))\n",
+    "\n",
+    "###\n",
+    "# ROC barplot\n",
+    "###\n",
+    "sns.barplot(\n",
+    "    data=example_df,\n",
+    "    x=\"is_inst\",\n",
+    "    y=\"roc_auc\",\n",
+    "    hue=\"name\",\n",
+    "    alpha=ALPHA,\n",
+    "    width=0.5,\n",
+    "    ax=ax1,\n",
+    ")\n",
+    "\n",
+    "# Add horizontal line and label\n",
+    "ax1.axhline(y=baseline_row[\"roc_auc\"], label=baseline_model, ls=\"--\", color=\"black\", xmin=0.05, xmax=0.95)\n",
+    "ax1.text(\n",
+    "    x=1.15,\n",
+    "    y=baseline_row[\"roc_auc\"] + 1e-3,\n",
+    "    s=baseline_model,\n",
+    "    color=\"black\",\n",
+    "    fontsize=9,\n",
+    "    ha='center',\n",
+    "    va='bottom',\n",
+    "    backgroundcolor='white',\n",
+    "    zorder=-1,\n",
+    ")\n",
+    "\n",
+    "ax1.set_ylim(0, baseline_row[\"roc_auc\"] + 9e-2)\n",
+    "# ax1.set_ylim(0.5, baseline_row[\"roc_auc\"] + 7e-2)\n",
+    "ax1.set_ylabel(\"ROC AUC\")\n",
+    "ax1.set_xlabel(None)\n",
+    "ax1.legend().remove()   # Remove the legend\n",
+    "ax1.set_xticklabels([\"Base\", \"Instr.\"])\n",
+    "ax1.set_title(\"Predictive signal\" + r\"$\\uparrow$\")\n",
+    "\n",
+    "###\n",
+    "# Score distribution\n",
+    "###\n",
+    "bins = np.histogram_bin_edges([], bins=N_BINS, range=(0, 1))\n",
+    "for id_ in example_df.index.tolist():\n",
+    "    sns.histplot(\n",
+    "        scores_df_map[id_][\"risk_score\"],\n",
+    "        alpha=ALPHA * 0.9,\n",
+    "        stat=\"proportion\",\n",
+    "        bins=bins,\n",
+    "        zorder=100,\n",
+    "        ax=ax2,\n",
+    "    )\n",
+    "\n",
+    "# # Draw baseline score distribution\n",
+    "# sns.histplot(\n",
+    "#     scores_df_map[baseline_row.name][\"risk_score\"],\n",
+    "#     alpha=1,\n",
+    "#     bins=bins,\n",
+    "#     stat=\"proportion\",\n",
+    "#     fill=False,\n",
+    "#     color=palette[2],\n",
+    "#     edgecolor=palette[2],\n",
+    "#     hatch=\"/\",\n",
+    "#     zorder=-1,\n",
+    "#     ax=ax2,\n",
+    "# )\n",
+    "\n",
+    "ax2.set_xlabel(None)\n",
+    "ax2.set_title(\"Score distribution\")\n",
+    "\n",
+    "###\n",
+    "# Calibration error\n",
+    "###\n",
+    "sns.barplot(\n",
+    "    data=example_df,\n",
+    "    x=\"is_inst\",\n",
+    "    y=\"ece\",\n",
+    "    hue=\"name\",\n",
+    "    alpha=ALPHA,\n",
+    "    width=0.5,\n",
+    "    ax=ax3,\n",
+    ")\n",
+    "\n",
+    "# Add horizontal line and label\n",
+    "ax3.axhline(y=baseline_row[\"ece\"], label=baseline_model, ls=\"--\", color=\"black\", xmin=0.05, xmax=0.95)\n",
+    "\n",
+    "ax3.set_ylabel(\"ECE\")\n",
+    "ax3.set_xlabel(None)\n",
+    "ax3.set_xticklabels([\"Base\", \"Instr.\"])\n",
+    "ax3.set_title(\"Calibration error\" + \"$\\downarrow$\")\n",
+    "# ax3.legend(\n",
+    "#     loc=\"center left\",\n",
+    "#     bbox_to_anchor=(1.05, 0.5),\n",
+    "# )\n",
+    "\n",
+    "hs, ls = ax3.get_legend_handles_labels()\n",
+    "ax3.legend().remove()\n",
+    "ax2.legend(handles=hs[:2], labels=ls[:2], loc=\"upper center\")\n",
+    "\n",
+    "save_fig(fig, f\"teaser-{example_model}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36a530b0-4e32-4993-9d4d-6cd8fa4330da",
+   "metadata": {},
+   "source": [
+    "#### Score distributions for base/instruct pairs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "dc652329-ac67-48d1-8f2c-632c2acfe483",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting score distributions for the following task: ACSIncome.\n",
+      "len(task_df)=20\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Plotting score distributions for the following task: {example_task}.\")\n",
+    "\n",
+    "task_df = all_results_df[\n",
+    "    (all_results_df[task_col] == example_task)\n",
+    "    & (\n",
+    "        (all_results_df[numeric_prompt_col] == NUMERIC_PROMPTING)\n",
+    "        | (all_results_df[numeric_prompt_col].isna())                  # Baseline models will have NaNs in this col.\n",
+    "    )]\n",
+    "print(f\"{len(task_df)=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42f4f271-b74c-492d-88cf-ce44724c8a76",
+   "metadata": {},
+   "source": [
+    "Sort model pairs as wished for the paper plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f422a80a-b5f4-413f-a4e7-2bde1611d36d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('penai/gpt-4o-mini', 'penai/gpt-4o-mini'),\n",
+       " ('Meta-Llama-3-8B', 'Meta-Llama-3-8B-Instruct'),\n",
+       " ('Meta-Llama-3-70B', 'Meta-Llama-3-70B-Instruct'),\n",
+       " ('Mistral-7B-v0.1', 'Mistral-7B-Instruct-v0.2'),\n",
+       " ('Mixtral-8x7B-v0.1', 'Mixtral-8x7B-Instruct-v0.1'),\n",
+       " ('Mixtral-8x22B-v0.1', 'Mixtral-8x22B-Instruct-v0.1'),\n",
+       " ('Yi-34B', 'Yi-34B-Chat'),\n",
+       " ('gemma-2b', 'gemma-1.1-2b-it'),\n",
+       " ('gemma-7b', 'gemma-1.1-7b-it')]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_model_families = [\"OpenAI\", \"Llama\", \"Mistral\", \"Yi\", \"Gemma\", \"-\"]\n",
+    "\n",
+    "model_pairs = sorted([\n",
+    "        (row[\"base_name\"], row[model_col])\n",
+    "        for id_, row in task_df[task_df[\"is_inst\"] == True].iterrows()\n",
+    "    ],\n",
+    "    key=lambda val: (\n",
+    "        1e6 * sorted_model_families.index((row := task_df[task_df[model_col] == val[0]].iloc[0])[\"model_family\"])\n",
+    "        + row[\"model_size\"]\n",
+    "    )\n",
+    ")\n",
+    "model_pairs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "de08f766-c37d-4c27-9261-684b97c6b921",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/score-distribution.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2400x1200 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "PLOT_THRESHOLD_LINE = False\n",
+    "\n",
+    "N_PLOTS = len(model_pairs) + 1  # plus 1 for the XGBoost baseline\n",
+    "\n",
+    "N_COLS = 5\n",
+    "N_ROWS = math.ceil(N_PLOTS / N_COLS)\n",
+    "\n",
+    "fig, axes = plt.subplots(\n",
+    "    nrows=N_ROWS, ncols=N_COLS,\n",
+    "    figsize=(12, 3 * N_ROWS),\n",
+    "    sharey=True, sharex=False,\n",
+    "    gridspec_kw=dict(\n",
+    "        hspace=0.5,\n",
+    "        wspace=0.06,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Plot settings\n",
+    "plot_config = dict(\n",
+    "    bins=N_BINS,\n",
+    "    binrange=(0, 1),\n",
+    "    stat=\"percent\",\n",
+    ")\n",
+    "\n",
+    "# Plot score distribution for model pairs\n",
+    "for idx, (base_name, instr_name) in enumerate(model_pairs):\n",
+    "    ax_row = idx // N_COLS\n",
+    "    ax_col = idx % N_COLS\n",
+    "\n",
+    "    ax = axes[ax_row, ax_col]\n",
+    "    base_row = task_df[task_df[model_col] == base_name].iloc[0]\n",
+    "    it_row = task_df[task_df[model_col] == instr_name].iloc[0]\n",
+    "\n",
+    "    # Get scores\n",
+    "    base_scores = scores_df_map[base_row.name][\"risk_score\"]\n",
+    "    it_scores = scores_df_map[it_row.name][\"risk_score\"]\n",
+    "\n",
+    "    ax.set_title(base_row[\"name\"])\n",
+    "\n",
+    "    ax.set_xlabel(\"risk scores\")\n",
+    "    if ax_col == 0:\n",
+    "        ax.set_ylabel(\"density (%)\")\n",
+    "\n",
+    "    if ax_row == 0:\n",
+    "        ax.set_ylim(0, 70)\n",
+    "\n",
+    "    ax.set_xlim(-0.03, 1.03)\n",
+    "\n",
+    "    # Render plot\n",
+    "    if \"gpt\" not in instr_name.lower():\n",
+    "        sns.histplot(base_scores, label=\"Base\", color=palette[0], ax=ax, **plot_config)\n",
+    "    sns.histplot(it_scores, label=\"Instruction-tuned\", color=palette[1], ax=ax, **plot_config)\n",
+    "\n",
+    "    # Draw faint vertical line with t^* of each model\n",
+    "    threshold_col = \"optimal_thresh\"\n",
+    "    # threshold_col = \"fit_thresh_on_100\"\n",
+    "\n",
+    "    if PLOT_THRESHOLD_LINE:\n",
+    "        ax.axvline(x=base_row[threshold_col], ls=\"--\", color=palette[0], alpha=0.5)\n",
+    "        ax.axvline(x=it_row[threshold_col], ls=\"--\", color=palette[1], alpha=0.5)\n",
+    "\n",
+    "    # Render legend on a few sub-plots\n",
+    "    if (ax_col == 0 and ax_row == 1) or (ax_col == N_COLS-1 and ax_row == 0):\n",
+    "        ax.legend(loc=\"upper center\")\n",
+    "\n",
+    "        if PLOT_THRESHOLD_LINE:\n",
+    "            ax.text(\n",
+    "                x=base_row[threshold_col] - 5e-2,\n",
+    "                y=43,\n",
+    "                s=\"$t^*$\",\n",
+    "                color=palette[0],\n",
+    "                fontsize=10,\n",
+    "                ha='center',\n",
+    "                va='bottom',\n",
+    "                # zorder=-1,\n",
+    "            ) #.set_bbox(dict(facecolor='white', alpha=0.5))\n",
+    "            \n",
+    "            ax.text(\n",
+    "                x=it_row[threshold_col] + 5e-2,\n",
+    "                y=43,\n",
+    "                s=\"$t^*$\",\n",
+    "                color=palette[1],\n",
+    "                fontsize=10,\n",
+    "                ha='center',\n",
+    "                va='bottom',\n",
+    "                # zorder=-1,\n",
+    "            )\n",
+    "        \n",
+    "\n",
+    "# Plot score distribution for XGBoost baseline\n",
+    "baseline_name = \"XGBoost\"\n",
+    "ax = axes[1, -1]\n",
+    "ax.set_title(baseline_name)\n",
+    "\n",
+    "baseline_row = task_df[task_df[\"name\"] == baseline_name].iloc[0]\n",
+    "xgboost_scores = scores_df_map[baseline_row.name][\"risk_score\"]\n",
+    "\n",
+    "sns.histplot(\n",
+    "    xgboost_scores,\n",
+    "    label=baseline_name,\n",
+    "    color=palette[2],\n",
+    "    ax=ax,\n",
+    "    **plot_config,\n",
+    ")\n",
+    "\n",
+    "ax.set_xlabel(\"risk scores\")\n",
+    "\n",
+    "# # Remove unused plot?\n",
+    "# axes[1, 3].remove()\n",
+    "\n",
+    "axes[1, 4].set_ylabel(\"density (%)\")\n",
+    "axes[1, 4].set_yticks([0, 20, 40, 60])\n",
+    "axes[1, 4].set_yticklabels([0, 20, 40, 60])\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "# Save figure\n",
+    "save_fig(fig, f\"score-distribution\" + (\"_with-threshold\" if PLOT_THRESHOLD_LINE else \"\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1e20b4f-5e3f-4c22-8309-b31f2befa29d",
+   "metadata": {},
+   "source": [
+    "## Calibration curves"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "831d0a85-c162-4f42-8f5e-d57da37da536",
+   "metadata": {},
+   "source": [
+    "Base models vs Instruction-tuned models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "79f2f793-da86-4797-bdf6-b2aee4a20c36",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "len(df)=17\n"
+     ]
+    }
+   ],
+   "source": [
+    "TASK = \"ACSIncome\"\n",
+    "\n",
+    "# df = results_df[(results_df[task_col] == TASK) & (results_df[numeric_prompt_col] == NUMERIC_PROMPTING)]\n",
+    "\n",
+    "df = all_results_df[(all_results_df[task_col] == TASK) & (all_results_df[numeric_prompt_col] == NUMERIC_PROMPTING)]\n",
+    "print(f\"{len(df)=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "e980b891-543f-4c9a-ae24-970af760c64b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-base-and-instr.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "from utils import prettify_model_name\n",
+    "\n",
+    "base_models_to_plot = [m for m in df[\"base_name\"].unique() if \"gemma\" not in m.lower()]\n",
+    "\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-8B\",\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mistral-7B-v0.1\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    # \"Yi-34B\",\n",
+    "]\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(8, 3), gridspec_kw=dict(wspace=0.33), sharey=False)\n",
+    "\n",
+    "# Plot base models\n",
+    "for ax, model_list in zip(axes, (base_models_to_plot, instr_models_to_plot)):\n",
+    "    \n",
+    "    for idx, m in enumerate(model_list):\n",
+    "\n",
+    "        curr_row = df[df[model_col] == m].iloc[0]\n",
+    "        curr_scores = scores_df_map[curr_row.name]\n",
+    "\n",
+    "        disp = CalibrationDisplay.from_predictions(\n",
+    "            y_true=curr_scores[\"label\"],\n",
+    "            y_prob=curr_scores[\"risk_score\"],\n",
+    "            n_bins=10,\n",
+    "            strategy=\"quantile\",\n",
+    "            name=prettify_model_name(curr_row[\"base_name\"]),\n",
+    "            linestyle=[\"-.\", \":\", \"--\"][idx % 3],\n",
+    "            ax=ax,\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlim(0, 1)\n",
+    "    ax.set_ylim(0, 1)\n",
+    "\n",
+    "ylabel = \"Fraction of positives \" + r\"$\\left(\\mathrm{P}[Y=1]\\right)$\"\n",
+    "axes[0].set_ylabel(ylabel)\n",
+    "axes[1].set_ylabel(ylabel)\n",
+    "\n",
+    "xlabel = \"Mean predicted probability\" # + r\" $(\\mathbb{E}[R])$\"\n",
+    "axes[0].set_xlabel(xlabel)\n",
+    "axes[1].set_xlabel(xlabel)\n",
+    "\n",
+    "axes[0].set_title(\"Base models\")\n",
+    "axes[1].set_title(\"Instruction-tuned models\")\n",
+    "\n",
+    "axes[0].legend().remove()\n",
+    "\n",
+    "# Legent to the right of the right-most plot\n",
+    "h, l = axes[1].get_legend_handles_labels()\n",
+    "l[0] = \"Perf. calibrated\"\n",
+    "axes[1].legend(handles=h, labels=l, loc=\"upper left\", bbox_to_anchor=(1.1, 1.1))\n",
+    "\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"calibration-curves-base-and-instr.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1875bb47-5e9d-499a-bb6b-c497513d4046",
+   "metadata": {},
+   "source": [
+    "### Same plot but only with large models (> 30B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "0177214e-8ae2-4a23-a561-0c4626f6a278",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-base-and-instr.large-models.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "from utils import prettify_model_name\n",
+    "\n",
+    "base_models_to_plot = [m for m in df[\"base_name\"].unique() if \"gemma\" not in m.lower()]\n",
+    "\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    \"Yi-34B\",\n",
+    "]\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(8, 3), gridspec_kw=dict(wspace=0.33), sharey=False)\n",
+    "\n",
+    "# Plot base models\n",
+    "for ax, model_list in zip(axes, (base_models_to_plot, instr_models_to_plot)):\n",
+    "    \n",
+    "    for idx, m in enumerate(model_list):\n",
+    "\n",
+    "        curr_row = df[df[model_col] == m].iloc[0]\n",
+    "        curr_scores = scores_df_map[curr_row.name]\n",
+    "\n",
+    "        disp = CalibrationDisplay.from_predictions(\n",
+    "            y_true=curr_scores[\"label\"],\n",
+    "            y_prob=curr_scores[\"risk_score\"],\n",
+    "            n_bins=10,\n",
+    "            strategy=\"quantile\",\n",
+    "            name=prettify_model_name(curr_row[\"base_name\"]),\n",
+    "            linestyle=[\"-.\", \":\", \"--\"][idx % 3],\n",
+    "            ax=ax,\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlim(0, 1)\n",
+    "    ax.set_ylim(0, 1)\n",
+    "\n",
+    "ylabel = \"Fraction of positives \" + r\"$\\left(\\mathrm{P}[Y=1]\\right)$\"\n",
+    "axes[0].set_ylabel(ylabel)\n",
+    "axes[1].set_ylabel(ylabel)\n",
+    "\n",
+    "xlabel = \"Mean predicted probability\" # + r\" $(\\mathbb{E}[R])$\"\n",
+    "axes[0].set_xlabel(xlabel)\n",
+    "axes[1].set_xlabel(xlabel)\n",
+    "\n",
+    "axes[0].set_title(\"Base models\")\n",
+    "axes[1].set_title(\"Instruction-tuned models\")\n",
+    "\n",
+    "axes[0].legend().remove()\n",
+    "\n",
+    "# Legent to the right of the right-most plot\n",
+    "h, l = axes[1].get_legend_handles_labels()\n",
+    "l[0] = \"Perf. calibrated\"\n",
+    "axes[1].legend(handles=h, labels=l, loc=\"upper left\", bbox_to_anchor=(1.1, 1.1))\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"calibration-curves-base-and-instr.large-models.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c69130d-0fe1-42a8-98a5-f071469a18f0",
+   "metadata": {},
+   "source": [
+    "### Smaller to use for both prompting schemes side by side"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "87b843d0-0082-4eca-9bb7-70bf1a53d35c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-base-and-instr.large-models.smaller.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "from utils import prettify_model_name\n",
+    "\n",
+    "base_models_to_plot = [m for m in df[\"base_name\"].unique() if \"gemma\" not in m.lower()]\n",
+    "\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    \"Yi-34B\",\n",
+    "]\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(5, 2.2), gridspec_kw=dict(wspace=0.15), sharey=True)\n",
+    "\n",
+    "# Plot base models\n",
+    "for ax, model_list in zip(axes, (base_models_to_plot, instr_models_to_plot)):\n",
+    "    \n",
+    "    for idx, m in enumerate(model_list):\n",
+    "\n",
+    "        curr_row = df[df[model_col] == m].iloc[0]\n",
+    "        curr_scores = scores_df_map[curr_row.name]\n",
+    "\n",
+    "        disp = CalibrationDisplay.from_predictions(\n",
+    "            y_true=curr_scores[\"label\"],\n",
+    "            y_prob=curr_scores[\"risk_score\"],\n",
+    "            n_bins=10,\n",
+    "            strategy=\"quantile\",\n",
+    "            name=prettify_model_name(curr_row[\"base_name\"]),\n",
+    "            linestyle=[\"-.\", \":\", \"--\"][idx % 3],\n",
+    "            ax=ax,\n",
+    "            ms=3.5,\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlim(0, 1)\n",
+    "    ax.set_ylim(0, 1)\n",
+    "\n",
+    "ylabel = \"Fraction of positives \" + r\"$\\left(\\mathrm{P}[Y=1]\\right)$\"\n",
+    "axes[0].set_ylabel(ylabel)\n",
+    "# axes[1].set_ylabel(ylabel)\n",
+    "axes[1].set_ylabel(\"\")\n",
+    "\n",
+    "# xlabel = \"Mean predicted probability\" # + r\" $(\\mathbb{E}[R])$\"\n",
+    "xlabel = \"Mean risk score\"\n",
+    "axes[0].set_xlabel(xlabel)\n",
+    "axes[1].set_xlabel(xlabel)\n",
+    "\n",
+    "axes[0].set_title(\"Base models\")\n",
+    "axes[1].set_title(\"Instruction-tuned\")\n",
+    "\n",
+    "# Set prettier x ticks\n",
+    "xticks = (0, 0.25, 0.5, 0.75, 1)\n",
+    "xticks_labels = (\"0.0\", \"0.25\", \"0.5\", \"0.75\", \"1.0\")\n",
+    "axes[0].set_xticks(xticks, labels=xticks_labels)\n",
+    "axes[1].set_xticks(xticks, labels=xticks_labels)\n",
+    "\n",
+    "axes[0].legend().remove()\n",
+    "\n",
+    "# Legent to the right of the right-most plot\n",
+    "if NUMERIC_PROMPTING:\n",
+    "    h, l = axes[1].get_legend_handles_labels()\n",
+    "    l[0] = \"Perf. calibrated\"\n",
+    "    axes[1].legend(handles=h, labels=l, loc=\"upper left\", bbox_to_anchor=(1, 1))\n",
+    "else:\n",
+    "    axes[1].legend().remove()\n",
+    "\n",
+    "# Figure title\n",
+    "if NUMERIC_PROMPTING:\n",
+    "    title = \"Numeric Prompting\"\n",
+    "else:\n",
+    "    title = \"Multiple-choice Prompting\"\n",
+    "plt.suptitle(title, y=1.1, fontsize=14)\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"calibration-curves-base-and-instr.large-models.smaller.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90cf83cb-cfc9-472f-92d7-605da9bb2704",
+   "metadata": {},
+   "source": [
+    "## Calibration per sub-group\n",
+    "Plot calibration curves per race sub-group, for each model pair."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b7f29ce1-b64a-4879-9a67-c305d1c4492b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "CPU times: user 41.3 s, sys: 28.3 s, total: 1min 9s\n",
+      "Wall time: 1min 9s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "acs_dataset = ACSDataset.make_from_task(task=TASK, cache_dir=DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19829429-ba87-471b-84cb-7b01829c5616",
+   "metadata": {},
+   "source": [
+    "Plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "98cb0f15-4434-4022-ba38-675b7c38db9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Omit small groups from the plots (results can be too noisy)\n",
+    "OMIT_GROUPS_BELOW_PCT = 4\n",
+    "N_BINS = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "f5a2f2e6-f557-401e-8f85-4d8ea0c0db72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sens_attr_col = \"RAC1P\"\n",
+    "sens_attr_map = {\n",
+    "    1: \"White\",\n",
+    "    2: \"Black\",\n",
+    "    6: \"Asian\",\n",
+    "    # 8: \"Other\",   # some other race alone\n",
+    "}\n",
+    "\n",
+    "name_to_sens_attr_map = {val: key for key, val in sens_attr_map.items()}\n",
+    "\n",
+    "def plot_cali_curve(model_id, ax, df):\n",
+    "    \"\"\"Plot sub-group calibration curves using sklearn\"\"\"\n",
+    "\n",
+    "    curr_row = df.loc[model_id]\n",
+    "    curr_scores_df = scores_df_map[curr_row.name]\n",
+    "\n",
+    "    y_true = curr_scores_df[\"label\"].to_numpy()\n",
+    "    y_scores = curr_scores_df[\"risk_score\"].to_numpy()\n",
+    "\n",
+    "    sens_attr = acs_dataset.data.loc[curr_scores_df.index][sens_attr_col].to_numpy()\n",
+    "\n",
+    "    for idx, group_val in enumerate(np.unique(sens_attr)):\n",
+    "        group_filter = sens_attr == group_val\n",
+    "\n",
+    "        if np.sum(group_filter) < len(group_filter) * 0.01 * OMIT_GROUPS_BELOW_PCT:\n",
+    "            # print(f\"Skipping group={group_val}\")\n",
+    "            continue\n",
+    "\n",
+    "        disp = CalibrationDisplay.from_predictions(\n",
+    "            y_true=y_true[group_filter],\n",
+    "            y_prob=y_scores[group_filter],\n",
+    "            n_bins=N_BINS,\n",
+    "            # strategy=\"uniform\",\n",
+    "            strategy=\"quantile\",\n",
+    "            name=str(sens_attr_map.get(group_val, group_val)),\n",
+    "            linestyle=[\"-.\", \":\", \"--\"][idx % 3],\n",
+    "            ax=ax,\n",
+    "        )\n",
+    "\n",
+    "    # Set other plot settings\n",
+    "    ax.set_title(curr_row[\"name\"])\n",
+    "    return disp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "798752d9-8abb-49d9-b874-24c54c523e93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from folktexts.evaluation import bootstrap_estimate\n",
+    "\n",
+    "def plot_cali_curve_ci(model_id, ax, df, ci=95):\n",
+    "    \"\"\"Plot sub-group calibration curves with bootstrap confidence intervals\"\"\"\n",
+    "\n",
+    "    curr_row = df.loc[model_id]\n",
+    "    curr_scores_df = scores_df_map[curr_row.name]\n",
+    "\n",
+    "    y_true = curr_scores_df[\"label\"].to_numpy()\n",
+    "    y_scores = curr_scores_df[\"risk_score\"].to_numpy()\n",
+    "\n",
+    "    sens_attr = acs_dataset.data.loc[curr_scores_df.index][sens_attr_col].to_numpy()\n",
+    "\n",
+    "    # Compute bins\n",
+    "    if len(np.unique(y_scores)) == 1:\n",
+    "        ax.set_title(curr_row[\"name\"])\n",
+    "        return\n",
+    "\n",
+    "    y_scores_binned, bin_edges = pd.qcut(y_scores, q=N_BINS, retbins=True, duplicates=\"drop\")\n",
+    "    bin_names = y_scores_binned.categories   # Name used for each bin: \"(bin_low_range, bin_high_range)\"\n",
+    "\n",
+    "    # To hold plot render artifacts\n",
+    "    plot_artifacts = {}\n",
+    "\n",
+    "    for idx, group_val in enumerate(np.unique(sens_attr)):\n",
+    "        group_filter = sens_attr == group_val\n",
+    "\n",
+    "        if (\n",
+    "            np.sum(group_filter) < len(group_filter) * 0.01 * OMIT_GROUPS_BELOW_PCT\n",
+    "            or group_val not in sens_attr_map.keys()\n",
+    "        ):\n",
+    "            # print(f\"Skipping group={group_val}\")\n",
+    "            continue\n",
+    "\n",
+    "        group_labels = y_true[group_filter]\n",
+    "        group_scores = y_scores[group_filter]\n",
+    "\n",
+    "        group_cali_points = []\n",
+    "        for b in bin_names:\n",
+    "            bin_filter = y_scores_binned == b\n",
+    "\n",
+    "            group_labels_current_bin = y_true[group_filter & bin_filter]\n",
+    "            group_scores_current_bin = y_scores[group_filter & bin_filter]\n",
+    "\n",
+    "            results = bootstrap_estimate(\n",
+    "                eval_func=lambda labels, scores, sensattr: {\n",
+    "                    \"mean_score\": np.mean(scores),\n",
+    "                    \"mean_label\": np.mean(labels),\n",
+    "                },\n",
+    "                y_true=group_labels_current_bin,\n",
+    "                y_pred_scores=group_scores_current_bin,\n",
+    "                sensitive_attribute=None,\n",
+    "            )\n",
+    "\n",
+    "            # Store plot point for this bin\n",
+    "            group_cali_points.append((\n",
+    "                results[\"mean_score_mean\"],    # x coordinate\n",
+    "                results[\"mean_label_mean\"],    # y coordinate\n",
+    "                (                              # y coordinate c.i. range\n",
+    "                    results[\"mean_label_low-percentile\"],\n",
+    "                    results[\"mean_label_high-percentile\"],\n",
+    "                ),\n",
+    "            ))\n",
+    "\n",
+    "        # Get group label from sensitive attribute map\n",
+    "        group_label = str(sens_attr_map.get(group_val, group_val))\n",
+    "        \n",
+    "        # Plot this group's calibration curve w/ conf. intervals!\n",
+    "        line, caps, bars = ax.errorbar(\n",
+    "            # x=[mean_score for mean_score, *_ in group_cali_points],\n",
+    "            x=[(bin_edges[idx-1] + bin_edges[idx]) / 2. for idx in range(1, len(bin_edges))],\n",
+    "            y=[mean_label for _, mean_label, _ in group_cali_points],\n",
+    "            yerr=np.hstack(\n",
+    "                [\n",
+    "                    np.abs(np.array(yerr_range).reshape(2, 1) - y)\n",
+    "                    for _x, y, yerr_range in group_cali_points]\n",
+    "            ),\n",
+    "            fmt=\"o\", markersize=1,\n",
+    "            linestyle=[\"-.\", \"-\", \"--\"][idx % 3],\n",
+    "            linewidth=2,\n",
+    "            label=group_label,\n",
+    "        )\n",
+    "        plot_artifacts[group_label] = (line, caps, bars)\n",
+    "    \n",
+    "    # Global plot settings\n",
+    "    ax.plot(\n",
+    "        [0,1], [0,1],\n",
+    "        ls=\"dotted\", color=\"black\",\n",
+    "    )\n",
+    "\n",
+    "    # Draw custom legend\n",
+    "    handles = []\n",
+    "    for group, (l, c, b) in plot_artifacts.items():\n",
+    "        l.set_label(group)\n",
+    "        l.set_markersize(0)\n",
+    "        handles.append(l)\n",
+    "\n",
+    "    # Perfectly calibrated line\n",
+    "    perf_cal_line = plt.Line2D([], [], ls=\":\", color=\"black\", label=\"Perf. calibrated\")\n",
+    "\n",
+    "    # Confidence interv. marker line\n",
+    "    conf_int_line = plt.Line2D([], [], lw=0, ms=10, marker=\"|\", markeredgewidth=2, color=\"grey\", label=\"$95\\%$ conf. int.\")\n",
+    "    \n",
+    "    ax.legend(handles=handles + [conf_int_line, perf_cal_line])\n",
+    "    ax.set_title(curr_row[\"name\"])\n",
+    "\n",
+    "    return disp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e1895eb-cdf3-4f14-8424-aaad256821bb",
+   "metadata": {},
+   "source": [
+    "### Plot sub-group calibration (with confidence intervals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "b3802fc0-f903-4666-b4c9-de43d38bf46c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "818d39dfd7f14966b96c35233df9c3a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-8B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-70B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mistral-7B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mixtral-8x7B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mixtral-8x22B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Yi-34B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.None.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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qper8+eefhecwceJE1KxZs8Tyc+bMEdr8zp07iIiIqNTx8/Xs2RNDhgwR7q9Zs0Yv9RIREZkS9qPYj6oq/aiCoztycnIqXZ8+FYzH1EahEBERUflxeisiMrq1a9cKt99//3289957JZYX6wq+yqhKz6lz586ws7NDdnY2lEolIiIiipzGoSwUCgVCQ0OF+927dy/1MbVr10ajRo2EKzmvXr0qXDVZWX369MG+ffsAALdv38azZ89K/fGAiIioKqlKfY6yqkrPif2osqtRo4ZwOzk5udLx6VNSUpJwm31FIiKiqo8jPYjIqB4/fix8SXNycsKUKVNKLJ+RkVHpK+gMrao9J6lUqjN1RUpKSoXrSk5O1llctKyLVBZcPDI9Pb3Cx3/e819aHz9+rLe6iYiIjK2q9TnKoqo9J/ajyq5+/frC7adPn1aqLn0rGE/BkUNERERUNTHpQURGFR8fL9xu2rQp5HJ5ieUvXbpk8nMAV7XnpFQqdb6gV2bxz4KLQAIo848QBY9f2lzg5fH8QpT6mHKDiIjIVFS1PkdZVLXnxH5U2Xl4eAi37927V6m6CtJH/+7u3bvC7VatWlW6PiIiIjIuJj2IyKgKfkl5/otVUbZt22bIcPSiqj2ns2fPQqlUCvebNWtW4bqcnJzg4OAg3P/nn39KfczTp091Filt1KhRhY//vGvXruncr1Wrlt7qJiIiMraq1ucoi6r2nNiPKrv27dsLt2/cuFGpugqysbERbhd8LcoqMTERz549A5C3nkeLFi30FhsREREZB5MeRGRU9evXF77c3r59Gw8fPiy27IEDB3D8+HGxQqswYz+n8syRnJKSgm+++Ua437Zt20oN6ZdIJOjRo4dwf+PGjTpzJBdl2bJlwhWadnZ28PLyqvDxC8rIyMCuXbuE+82aNYO7u7te6iYiIjIFxu5zGIKxnxP7UXkM0Y9q06aNMBVXTEwMEhISKlVfvoLTgBUcKVRWFy9eFG5369YNVlZc+pSIiKiqY9KDiIzKzc0NHTt2BABoNBrMmDFDZ3h5/vYtW7bg448/hkwm07mayxQZ+zl99tlnmDFjBk6dOgWVSlVkGY1Gg5MnT+LVV1/Vie2jjz6q9PHffPNN4fazZ88wceJEXL9+vVC5tLQ0LFiwAEFBQcK2gIAA2NnZVTqG+/fv4+2339aZn7lgXERERObA2H0OQzD2c2I/ynD9KKlUihdeeEG4X5aRLGVRcGTGlStXEBcXV67HF4yjX79+eomJiIiIjIuXMBBRqf744w8cPXq0zOVnzJiBgQMHlrn8Bx98gEmTJkGj0eDatWsYPnw4OnXqhAYNGiArKwsXL14UhpzPmjULO3bswKNHj8r9PMRkzOek0Whw5MgRHDp0CPb29mjZsiXq1asHR0dHKJVKJCQkICoqComJiTqPmzdvns7VhRXVpUsXTJo0CWvXrgUA3Lp1CyNHjkTbtm3RsmVLyOVyPH78GJcuXUJWVpbwOE9PT7z33ntlOsbGjRtx6NAhnW1arRaZmZm4c+cOrl27pjO/9+DBgzFmzJhKPzciIqLyYj+q/NiPMt9+lL+/v5CoOXr0KIYNG1bpOmvWrIlOnTohPDwcubm5GDFiBPr06YOaNWtCKs27zrNBgwZ47bXXCj1Wq9Xir7/+AgDI5XIMGTKk0vEQERGR8THpQUSlSkhIKNfw87IuupivR48eWLBgARYvXgyVSgWlUonz58/j/PnzQhmpVIp3330XU6ZMwY4dO8pVvzEY8zlZW1sLt7OysnD58mVcvny52PINGjTAZ599pnPlXWV9/PHHqF69OpYvXw6FQgEAiIqKQlRUVJHlhwwZgkWLFpX56sSy/nhkZWWFN954AzNnzhS+9BIREYmJ/ajyYz/KfPtRPXr0QL169fDo0SOcPHkSmZmZOuuYVNSnn36KiRMnIjMzE2lpadi/f7/O/m7duhWZ9AgLCxNGtAwYMECYfouIiIiqNiY9iMgkjBs3Dp07d8b69esRGhqK+Ph42NraolatWujevTvGjBmDNm3aGDvMcjHWc1q2bBneeOMNnD9/HpGRkbh37x6ePHmCrKwsWFlZoVq1aqhXrx7atm2L/v37o1evXpDJZHqNQSKRYPLkyRg5ciR2796N0NBQ3Lp1C6mpqVCr1XB0dESDBg3QqVMnjBo1Cq1bt9bLce3t7eHk5ITmzZuja9euGDlyJGrXrq2XuomIiEwV+1H6w36UYftRMpkM48ePxzfffIOsrCzs378fr7zySqXr9fT0xN69e7F582aEhobi4cOHyMrKglqtLvFxBdctef311ysdBxEREZkGibbguFUiIiIiIiIiIgPJyMjAwIEDkZKSglatWmHPnj1GiSM5ORn9+/dHdnY2OnXqhD/++MMocRAREZH+ca4PIiIiIiIiIhKFo6MjJk+eDAC4ceMGTp06ZZQ4Nm/ejOzsbAB5a8MQERGR+WDSg4iIiIiIiIhEM2HCBNSrVw8A8PPPP4t+/LS0NGzevBkA0L9/f3h7e4seAxERERkOkx5EREREREREJBpbW1t88sknAIDw8HAcPnxY1OP/9ttvSElJgbW1tRAHERERmQ+u6UFERERERERERERERGaBIz2IiIiIiIiIiIiIiMgsMOlBRERERERERERERERmwcrYARhLYmIiIiIiEBERgcjISERGRiIlJQUAMGrUKCxZskTvx9y3bx8CAwNx8+ZNpKWloUaNGvDy8kJAQAA6deqk9+MRERERmRP234iIiIiIiKg0Fpv06Nmzp2jHysnJwYwZM3Dy5Emd7XFxcYiLi8P+/fsxffp0vPfee6LFRERERFTVsP9GREREREREpbHYpEdBdevWRdOmTXHmzBmD1P/JJ58IX5i9vb3x+uuvw93dHbdu3cKqVavw4MEDrFixAjVr1sSrr75qkBiIiIiIzAn7b0RERERERFQUi016TJ8+HZ6envD09ESNGjUQGxuLgQMH6v04586dw/79+wEA/fv3x8qVKyGTyQAA7du3x4ABAzBmzBjExcXhu+++g5+fH5ydnfUeBxEREVFVx/4bERERERERlcZiFzKfMWMG+vfvjxo1ahj0OGvXrgUAWFlZ4fPPPxe+MOdzc3PDnDlzAABpaWnYuXOnQeMhIiIiqqrYfyMiIiIiIqLSWGzSQwwZGRk4d+4cAKBHjx6oXbt2keUGDRoER0dHAMDRo0dFi4+IiIiIdLH/RkREREREVLUx6WFAkZGRUCqVAIBu3boVW87a2hodO3Ys9BgiIiIiEhf7b0RERERERFWbxa7pIYbo6GjhdtOmTUss26RJE5w5cwYqlQoxMTFo3ry53uLQaDTIzs4GkDdNg0Qi0VvdRERE5kar1UKlUgEA7OzsIJXyGhFLYgr9N/bdiIiIyo59NyIieh6THgb05MkT4XatWrVKLFunTh3h9uPHj/Wa9MjOzsaNGzf0Vh8REZGlaNWqFRwcHIwdBonIFPpv7LsRERFVDPtuREQEcHorg8rMzBRu29vbl1jWzs5OuJ2VlWWwmIiIiIioeOy/ERERERERVW0c6WFAubm5wm1ra+sSyxbcn5OTo9c4rKz+fZkbNWqk8wW9IJlMJgwD1Wg0UKvVJdYrl8uF22q1GhqNpszlVSoVtFptsWUlEolO3KXNk10w9oJDW4tTcKqI8j7X0mJXqVS4c+cOgLyrTEojlUohk8mE++V5rnyd/j1u/nQkrVq1glwu13vsfJ3+VbC9mzVrBjs7O4P9PfF1Ktze+XWa0ueeOb1Oq1atwi+//IJNmzYB0D2HkmUwhf4b+26FGfozTKvVCqNrmjdvXuLfvil/hlWV14l9N/bd8pnr61RU/83UPvfM5XUKDw/H9OnTsWrVKgDsuxERUR6eDQzIxsZGuK1QKEosW3C/ra2tXuMoOA+0nZ2dSQz1LO1HhMqWL9j2+lZaLAVfS7lcbvDnakhV5XUqqs0NHbslv04F29ve3r5SbcHXqfTyZW1vY37uiV3ekN5//32kpqYK97mWguUxhf4b+276VZZYCr6WdnZ25YrflD7DqsrrxL4b+276Km9IlYm9LG1u7M89Mcsbkre3N7755hvhPvtuREQEcHorgyr4BbW0KQ/yF6sESp9KgYiIiAxn5syZxg6BjIj9NyIioqqle/fuxg6BiIhMDJMeBlS7dm3h9tOnT0ss+/jxY+F2wUUxiYiISFy8QtCysf9GRERERERUtTHpYUDNmjUTbt+9e7fEsvfu3QOQN/9ko0aNDBoXERGRJbt27RqGDBmCpKQkY4dCJoj9NyIiItOSlZWFMWPG4OzZs8YOhYiIqggmPQzI09NTWGDr/PnzxZZTKBS4fPlyoccQERGRfl28eBF9+/bFwYMH8eKLLyIjI8PYIZGJYf+NiIjIdKSkpGDw4MEIDAzE0KFDhXMvERFRSbiQuQE5OjqiR48eOHXqFM6dO4cnT57oTJmQ78iRI8KPLj4+PgaNiV/IDc/a2hpeXl7GDsOisM3FxfYWF9tbf86ePYshQ4YgPT0dABAaGoqRI0di//79OouJ8lxp2Uyt/8b3ozj4WSsutre42N7iY5vrR1JSEgYOHCgkOlJTU+Hr64vTp0+jZcuWQjmeK4mI6Hkc6VEJgYGB8PDwgIeHB1asWFFkmUmTJgEAVCoVvvjiC6jVap39SUlJ+O677wAATk5OePnllw0bNBERkYWqX78+nJycdLZlZmbqLEZN5o/9NyIioqqhWrVqaNCggc42pVKJ1NRUI0VERERVhcWO9Lh48SIePHgg3E9OThZux8TEIDAwUKf86NGjK3ScHj16YOjQodi/fz+OHTuGN998ExMnToS7uztu3bqFX3/9FXFxcQCAOXPmwNnZuULHISIiopI1atQIR44cQd++fZGQkAAfHx/s3r0bjo6Oxg6Nyoj9NyIiIsshl8uxfft2DBkyBCdPnkSdOnVw+PBhtGvXztihERGRibPYpMeuXbuwe/fuIveFhYUhLCxMZ1tFvzQDwFdffYWMjAycPHkSoaGhCA0N1dkvlUoxbdo0vPrqqxU+Rlk9f6Ui6Z9arUZCQgIAoEaNGpDJZEaOyPyxzcXF9hYX21u/WrdujYMHD+LHH3/E6tWrdaa1ysdzpemyxP4b34/i4GetuNje4mJ7i49trj92dnbYu3cv3nrrLSxZsgTNmjUrVIbnSiIiep7FJj3EZGtri9WrVyM4OBi7d+/GjRs3kJaWhho1asDLywvjx49Hp06dRIlFo9GIchxLplarERsbCwBwdXVlB1cEbHNxsb3FxfbWPy8vL2zYsKHY/TxXEmA6/Te+H8XBz1pxsb3FxfYWH9tcv5ycnLBz585i9/NcSUREz7PYpMeSJUuwZMmSStUxevTocl1B6O/vD39//0odk4iIiEqm1WoRERGBDh06GDsU0jP234iIiMzTlStX0L59e0gkEmOHQkREZsBikx5ERERkftRqNaZOnYoNGzYgODgYvr6+xg6JiIiIiEqwfft2jB8/Hu+//z6+//57Jj7KSaPRICMjA2lpaVAoFJzui4iqBJlMBnt7e7i4uMDW1lbv9TPpQURERGZBoVBg/PjxwvQHo0aNwpEjR9CrVy8jR0ZERERERVm9ejWmTp0KrVaL//3vf3B1dcVnn31m7LCqjPT0dDx69AhardbYoRARlYtKpUJubi6Sk5Ph7OyMOnXq6DXpzaQHERERmYU1a9bozPecnZ2NESNG4O7du3BycjJiZERERET0vLt372L69Ok6P9gvWLAA3t7eGDx4sBEjqxqKSnhIJBKuIUNEVYJKpRJup6amwtraGjVq1NBb/Ux6EBERkVmYMmUKzp49iy1btgAArK2tsWbNGiY8iIiIiExQ06ZNsW7dOkyYMEHY9v7778PHx8eIUVUNGo1GJ+Hh6OgINzc32Nvbc3owIqoS1Go1UlJSEB8fDwB49uwZnJycYG1trZf6pXqphYiIiMjIpFIp1q1bB39/fzg4OCAkJAQjRowwdlhEREREVIzx48fjp59+ApA3ymP58uWQSvlTVWkyMjJ0Eh7169eHg4MDEx5EVGXIZDJUr14d1atXF7ZlZGTorX6O9CDSM4lEAgcHB+E2GR7bXFxsb3GxvctHLpdj+/btuH37Ntq3b2/scIioiuBnrbjY3uJie4uPbV4+06dPR9euXdGtWzdjh1JlpKWlCbfd3Nz4PiOiKsvJyQmJiYkAgMzMTLi5uemlXiY9LIxcLjd2CGZPLpejVatWxg7DorDNxcX2Fhfbu/zs7OwqlfDguZJMCd+P4uBnrbjY3uJie4uPbV5+lUl4WOK5UqFQAMhLqtnb2xs5GiKiirOxsYFEIoFWqxU+2/SBYwaJiIioSgkLC8OUKVN0Fj4jIiIiItOUmpqK8ePHIzY21tihmA21Wg0gb3oYjvIgoqpMIpFAJpMByFuvSF840oOIiIiqjFOnTsHf3x9paWlQqVT4/fff+UWPiIiIyEQ9e/YMvr6+CA8PR1hYGE6dOoUaNWoYOywiIjJzHOlhYXhVrOGpVCpER0cjOjqa7S0Strm42N7iYnv/68CBA/D19RXmMF67di3mzJkjLOKoL5bezmRa+H4UBz9rxcX2FhfbW3xs8zyxsbHo06cPwsPDAQDXr1+Hn5+fznoU+mDJbUxEREVj0sPC6PuHISpMo9EgJSUFKSkpeh2WRcVjm4uL7S0utve/7O3tC53HLl++rNd5PwGeK8m08P0oDn7WiovtLS62t/jY5nny52kvKDY2FnFxcXo9Ds+VRET0PCY9iIiIqEro168fdu7cKcz3OWLECOzfvx82NjZGjoyIiIiInlezZk0cPnwYDRs2BAA0atQIZ86c4SLvRERkcEx6EBERUZXh7++P9evX44033sCuXbtga2tr7JCIiIiIqBgNGjTA0aNH0b9/f5w9exbNmzc3dkhERGQBmPQgIiKiKmX8+PFYt24drKysjB0KEREREZWiRYsWOHbsGOrVq2fsUIiqBA8PD3h4eGDFihUVriM0NFSoJzQ0VI/REVUNTHoQERGRSVGr1YiJiTF2GERERERURnfv3jV2CEQGoVar0blzZ3h4eGDUqFElltVqtfD29haSDbt27Sqx/O7du4WyW7du1WfYRBaPSQ8iIiIyGQqFAgEBAfD29sbt27eNHQ4RERERleK3337jj7ZktmQyGTp16gQAuHHjBjIyMoote/v2baSkpAj3L126VGLdFy9eFG537dq1coGW04oVK4SEC5E5YtKDiIiITEJWVhZGjhyJ7du34+nTpxg0aBBiY2ONHRYRERERFWPp0qV45513oFKp8Prrr2Pfvn3GDolI7/ITEhqNBmFhYcWWy09iyGQynfullXd1ddX7ejfe3t64efMmbt68CW9vb73WTVQVMOlhYSQSibFDMHtSqRTu7u5wd3eHVMo/MTGwzcXF9haXJbX3/PnzERISItyPiYmBv78/1Gq1qHHwXEmmhO9HcVjSZ60pYHuLi+0tPktp82PHjuHjjz8W7qvVarz88su4deuWqHHwXEmG1qVLF+F2SYmM/H1+fn4AgAcPHuDp06dFlk1MTMT9+/cBAF5eXnwfE+mZ+Z59qUhc9NXwrKys0KBBAzRo0IDtLRK2ubjY3uKypPb+/PPP0blzZ+G+g4MDli5dKlwpJRZzb2eqWvh+FIclfdaaAra3uNje4rOUNu/fvz9mzpyps+3DDz9EixYtRI3DnNuYTEP79u1hY2MDoGxJD19fXzRs2BBA8VNcGXNqKyJLwKQHERERmQQnJyccPHgQrVq1gqurK44ePQofHx9jh0VERERERZBIJPj+++/xxhtvAAC+++47LF68mFesk9mxtrZG+/btAQCRkZFQKBSFyjx8+FAY1eHl5QUvLy8AxSdJCm4vOJKkKBEREZg9ezb69u2Ldu3aoU+fPvjoo48QHR1d7GNCQ0OFNTtCQ0OF7YGBgfDw8MBPP/0kbMsvV/BfUdMMq9Vq7N69G1OmTEHv3r3Rrl07eHt7Y9y4cVi3bh1ycnJKfB5EYmLSg4iIiExGzZo1cfjwYZw8eRLdu3c3djhEREREVAKpVIrffvsNhw8fxocffmjscIgMJj8xoVAocOXKlUL785MYjRs3Ro0aNYSkx4ULF4qsL38EiKOjI1q3bl3scbds2YJx48Zh//79ePr0KZRKJeLj47F3716MGTOm2Pr1LS4uDqNHj8bcuXNx4sQJPHv2DEqlEikpKQgLC8OSJUswfPhw3Lt3T5R4iErDMYAWRqlUwtra2thhmDWlUonr168DAFq3bg25XG7kiMwf21xcbG9xWWJ7508HYSxKpdJoxyZ6Hvtu4rDEz1pjYnuLi+0tPktrcysrKwwaNMhox2ffjcTQtWtX/PLLLwDyEhzPT0mVn/TIn643///bt28jNTUVzs7OQtmMjAzcuHEDANCpU6dip/M9c+YMIiIi0LJlS7z++uto2bIlcnNzceTIEWzcuBHZ2dn4+OOPcejQoTL3F318fNCuXTts3boV27ZtAwAEBwcXKlerVi3hdnJyMl577TU8fvwY1tbWeOWVV9C1a1fUq1cPWVlZOHv2LDZu3IiYmBi8/fbb2L17N6pVq1ameIgMhUkPIj3TarVCp0ur1Ro5GsvANhcX21tc5tjeZ86cwfnz5zF79mxjh0JEBMA8P2tNGdtbXGxv8ZlbmyckJGDRokX49ttvYWtra+xwqAJG/XzW2CEI/NvXxaTeTUoss/bMPQRHxBXavntaL0OFVaqOHTvCysoKKpWqyCmr8rfljwhp1qwZXF1dkZycjLCwMPTv318oGx4eDrVaDaDk9TwuX76MF154AT/99JNOUqNLly5wcXHBDz/8gLi4OJw8ebLMiUcnJyc4OTmhevXqwraWLVuW+JjFixfj8ePHqFevHjZs2FDoAjVvb2/4+fkhICAADx8+xO+//45Zs2aVKR4iQ2HSg4iIiEQTEhKCMWPGIDs7G3Z2dnj33XeNHRIRERERFSM2NhaDBw/G9evXERMTg127dpn9yBVzFP4gxdghCDo3dC21zKOUbJOKGQAcHBzQunVrREZGCkmL/BEaiYmJuH//PgAI01oBeaM9/vrrL1y8eFEn6VHW9TxsbGzw9ddfFzmKY8KECVi5ciWUSiUuXrxosNFWsbGxCAkJAQB89tlnxY7Ib9OmDV577TX8/vvvCAwMZNKDjI5rehAREZEotm/fjuHDhyM7OxsAMH36dGzdutXIURERERFRUe7cuYPevXsLU3Xt3bsXkyZNgkajMXJkRMaRPyojMzNT+LsA/l23o0aNGmjcuLGwvbjFzPPX87CxsYGnp2exx+vZs6fOiIyCHB0dhWM9fPiwfE+kHE6ePAm1Wg07Ozv07du3xLL57RMfH4+4uMIjdYjExJEeREREJIqnT59CpVIJ97VaLQIDAzFu3DhIJBIjRkZEREREz8vIyEBycrLOtlOnTiE+Ph61a9c2UlRExuPl5YW1a9cCyEtktGvXTridv7+g/FEcUVFRyMnJga2tLRQKBSIiIgAAHTp0KHEtjqZNm5YYT/46IZmZmRV4NmVz9epVAEB2djbatGlT5sclJCSgbt26hgqLqFQc6UFERESimDFjBj7//HPh/oQJE/DHH38w4UFERERkgjp27Ij9+/fDzs4OANCqVSucPXuWCQ+yWF26dBG+u+SP1ih4O3/x8nxt2rSBnZ0dlEolLl++DACIjIxEbm6uUF9J8v/2iiOV5v2sa8jRV4mJiRV6XP7ofiJj4UgPIiIiEs2CBQuQkpIClUqF5cuXCx11IiIiIjI9vXv3RmBgIL744gvs3bsXNWvWNHZIVAGdGroYOwRBPZeSf8jPL2NKMedzcXFBixYtcOvWLWF0R0ZGBm7cuAGg8EgPuVwOT09PnD9/HhcvXkT37t11kiUlLWJuKvIXXHd1dcXGjRvL/Lj69esbKiSiMmHSg4iIiEQjkUiwbNky4TYRERERmTY/Pz8MHjyYF6tUYbun9TJ2COUyqXcTTOrdxNhhFKlLly64desWkpKSEB0djdjYWGg0Gtjb2xc5/ZOXl5eQ9AD+Xf9DLpejY8eOYoZeIS4uLgDyptBq1qyZsHg7kalj0sPC8MPJ8KysrIR5F62s+CcmBra5uNje4qqK7a1UKpGRkQFXV9ci95t6soPnSjIlfD+Koyp+1lZlbG9xsb3FVxXb/NmzZyWO4jDlhAfPlSSmLl26YOvWrQDyprWKjY0FkDcdXFHvxfzRH1euXIFCoUB4eDiAvKmv7O3tRYq6sLJ+J2vTpg327dsHhUKBq1evokOHDgaOjEg/TPesRQZhyh0VcyGVSuHq6gpXV1e2t0jY5uJie4urqrV3VlYWRo4cCV9fX6Snpxs7nAqpCu1MloPvR3FUtc/aqo7tLS62t/iqWpt///33aNGiBcLCwowdSoVUhTYm81FwHY4LFy4IIzeen9oqX6dOnSCTyZCVlYXAwEDhO1Jp63kYWsEF1BUKRbHl+vfvLyRINmzYYPC4iPSFZwYiIiLSi9TUVPj5+eHAgQO4cOECRowYgZycHGOHRURERGQ2Ag4EwHODp86/gAMBFapLq9Xis88+w5w5c5CamgpfX19hbQIiKlqtWrXQsGFDAEBoaCiuXr0KoPikh6OjI1q2bAkA+P3334Xtxl7Pw93dXbj94MGDYss1bdoUfn5+AID9+/dj3bp1Jdb78OFD7Nu3Tz9BElUCkx4WRqvVGjsEs6fVaqFQKKBQKNjeImGbi4vtLa6q1N4TJ07E6dOnhfvHjx/HpEmTjBhRxZh6O5Nl4ftRHFXps9YcsL3FxfYWX1Vp87Vr12Lx4sXC/YSEBAwaNAgpKSnGC6oCTLmNyTzlj9J4+vQpFAoFrKysSlyfIz8h8vDhQwB5o5OKS5KIpVOnTsLtr7/+GhcuXMD9+/cRExODmJgYqFQqYf/nn3+OBg0aAACWLFmC8ePHY+fOnbh8+TKuXbuGv//+G2vXrsWbb76JwYMH4/Dhw6I/H6LnVY3JJUlvVCoVbGxsjB2GWVMqlYiMjAQAeHp66gwZJMNgm4uL7S2uqtTe33zzDf7++288e/YMQN6id++9956Royq/gh18ImNj300cVemz1hywvcXF9hZfVWnzgIAAbN26FceOHRO2zZgxQ1i4uKpg343E1qVLFwQGBgr3W7duDTs7u2LLe3l5YfPmzcL9li1bwsnJyaAxlqZRo0YYMmQIQkJCcObMGZw5c0Zn/19//YX69esDyPtet23bNsycORMXL17UmdarKA4ODgaNnagsmPQgIiIivfDw8MChQ4fQr18/2NnZ4fDhw2jfvr2xwyIiIiKiItja2iIoKAg+Pj64cOECVq1ahbffftvYYRGZvOenpipt1Mbz+409tVW+pUuXol27djh06BDu3buHzMxMaDSaIsvWrFkTW7ZswYkTJ7Bv3z5cvnwZCQkJUKlUqFatGho1aoROnTphwIABJvP8yLIx6UFERER606lTJ4SEhKBmzZpo0aKFscMhIiIiMisKdfELDldEtWrVEBISgr///hvDhg3Ta91E5qphw4a4efNmmcvXqlWrXOXLWnbTpk3F7vP29i61HrlcjsmTJ2Py5Mlljq1fv37o169fmcsTGQuTHkRERKRXPXv2NHYIRERERGYlKScJv0X8hptJZf/htKzc3NyY8CAiIrPChcyJiIioXA4dOoQDBw4YOwwiIiIis5elzMKvV37Fi4EvYvP1zdCi/It2P3r0CN9++y0X/CYiIovBkR5ERERUZjt37kRAQABkMhkOHTqEvn37GjskIiIiIrOjVCux6/YurLqyCok5iRWu586dOxg0aBDu37+PrKwsfP755/oLkoiIyERxpAcRERGVye+//46xY8dCqVQiJycH/v7+CAsLM3ZYRERERGZDo9Ug5F4IRuwZga9Cv6pUwiMiIgK9e/fG/fv3AQBffPEFli9frqdIiYiITBeTHkRERFQqrVaLs2fPQqPRCNvS0tKwbt06I0ZFREREZD7+jvsbY/eNxcenPsbD9IeVru/GjRuIj4/X2fbLL78gOzu70nUTERGZMk5vZWGsrPiSG5pcLoenp6dwmwyPbS4utre4TKW9JRIJfvvtN6SkpCAoKAgAMG3aNLO8WpDnSjIlfD+Kw1Q+ay0F21tcbG/xlbfNoxKi8L+w/yH0cWiFjnfzSXqR21955RUkJydj6tSpAIBOnTrh4MGDsLOzq9BxTBXPlURE9DyeGSyMRCIxdghmTyKRwNra2thhWBS2ubjY3uIypfa2srLCtm3bMHToUHTv3h2LFy82y/OKOT4nqrr4fhSHKX3WWgK2t7jY3uIra5s/SHuAFeErcPD+wTLV27V2V1x+9AhKWVyZY5kyZQqSk5Oxf/9+7Nu3D87OzmV+bFXBcyURET2PSQ8iIiIqM1tbW4SEhPDHEyIiIqIKSshOwK9XfsWft/6ESqsqtbyHqwdmes1Er7q90HXdiHIfb+7cuZg9ezb7b0REZDGY9LAwBediJ8PQaDRITU0FADg7O0Mq5dI5hsY2FxfbW1zGaO+cnBxYWVkVO1WAuX9h5rmSTAnfj+LguU1cbG9xsb3FV1ybZygysC5qHTZd24RsVenratRzrIf3Or2HF5u8CKkkr45GqhjckumWa6SKQUZGBhwdHYuty5z7bzxXEhHR89jbsTBqtdrYIZg9lUqFu3fv4u7du1CpSr9qhyqPbS4utre4xG7vtLQ0+Pn5YfLkyRb7BZLnSjIlfD+Kg+c2cbG9xcX2Fl9xbX7l2RWsjlhdasLDxcYVfdwm4+0mv2JY02FCwgMAvoq3ReS9B8K/iLsxqPNHAry8vAotWm4peK4kIqLnMelBREREAICEhAQMHDgQJ0+exIYNGzB79mxotVpjh0VERERkFnrW7YlutbsVu9/Oyg5DG7wO+eNPcOBsc3y1/zZSs5Q6ZTxqVxNua7RafHAwFyuPx+HWrVvw8/MTRpgQERFZMiY9iIiICBqNBn5+frh48aKwbfny5fjmm2+MGBURERGR+ZBIJJjZeWah7VYSK4xs+go6S77FH4fb4O7TvJELiZkKfHf4ZrH1fX4iFyvOK4T74eHh8Pf354geIiKyeEx6EBEREaRSKb744guddTzc3d0xZMgQI0ZFREREZF48a3piUKNBwn2/xkMwvsFK7D7aDQcjMguV3xwag8jYokdvTPGyRmMXiXBfIpEgICCg2HXZiIiILAWTHkRERAQAGDp0KDZt2gSJRIKGDRvi9OnT6NChg7HDIiIiIjJ5d1PvYnnY8jJNDTqj0wz0rtcbCzv/hsjwYVhxKBkZuUWPztBqgQV7rxZZbz0nKY5OcEBtZ2tYWVlh69atmDJlSqWfCxERUVXH9D8REREJxo4dC61Wi969e6NBgwbGDoeIiIjIpD3NfIpfrvyCoDtBUGvVaFe9HfrU6VPiYxxldWCfNBVzjsaWWn/rOk6YP7Q1JBJJkfubuUlxeGYnxHov5AhdIiKi/8ekBxEREekYN26csUMgIiIiMmlpijSsjVyLzdc3I1edK2xfHr4cPWr1KPIxao0WW0NjsPTQTaTllLzuRjUbK8we3BITujeClazkSTo86zvCkwkPIiIiAZMeRHomkUggl8uF22R4bHNxsb3FZYj2DgoKQuvWreHh4aGX+oiIqjqe28TF9hYX21u/ctW52HZ9G36L/A1pirRC+++l3sO+e/vQSt4KwL9tfvlhCj4LuorIR0Wvz1HQqE71MO/FVnCvZgsAuHv3LsLCwvDSSy/p8ZkQERGZLyY9LEx+Z5cMRy6Xo3379sYOw6KwzcXF9haXvtt73bp1mDx5MurVq4czZ86gYcOGeqvbXPBcSaaE70dx8NwmLra3uNje+qHWqLE3ei9WXl6Jp1lPSyy76uoq7Bu1D3ZWdkjOVODb4Ej8ceEBSlvuo4W7I74c2Q7dm1YXtl29ehWDBw9GfHw8/vzzT4zQx5MxMzxXEhHR85j0ICIishD/+9//MHv2bADAw4cPMWjQIJw+fRru7u5GjoyIiIjINGm1Wpx4eALLw5YjOjW61PJyqRyDGw2GUqXCnrAH+ObgDSRnKUt8jL21DDN9WuDNXk0gLzCVVWhoKIYMGYLk5GQAwCuvvIKQ99piQLVKPSUiIiKzV/LEkERERGQWFAoFtm3bprPt1q1bWLt2rZEiIiIiIjJtYU/DMPHgRMw4PqPUhIcEEvg39UfwqGAMrTcVE9dGYG5gZKkJj6Ht6+CvD1/AO32b6SQ8ACA4OFhIeAB5/bmFe+9CW9qQESIyWaGhofDw8ICHhwdCQ0ONHU4hgYGBQnyxsbHGDoeowjjSw8KoVCpYW1sbOwyzplKp8PjxYwBAnTp1YGXFPzNDY5uLi+0tLn21t7W1NQ4cOIAXXngB165dAwDMnTsX//nPf/QWq7lQqUpeWJRITOy7iYPnNnGxvcXF9i6/O8l3sDxsOU7EnihT+T71+uCDzh+gtl1TLDt8E5v+uQxNKXmJpjUc8MWItujTomaxZb788ks8efIEa9asAQD06tULwa/IIEm6XMZnYhnYdyMxhIaG4vXXXy92v729Pdzd3dG+fXuMHj0aPXr0EDE6InoeezsWhleEGJ5Go0F8fDwAoFatWkaOxjKwzcXF9haXPtu7Ro0aOHz4MHr37o2pU6cy4VEMnivJlPD9KA6e28TF9hYX27vsHmc8xsrLKxF8NxgarabU8u1rtMdMr5noUqsLAsMeISDkBBIyFCU+xlYuxfsDWmBynyawsZKVWFYikWDVqlVITU1FRkYGdu3aBYdtI4Ckcj0ts8dzJZmCrKws3L9/H/fv38fevXsxcuRIfPXVV5DJSv47JyLDYNKDiIjIgtSrVw+RkZFwdHQ0dihEREREJiE1NxW/R/6Orde3QqEpOWkBAI2dGmNm55kY0HAAbj3NwKur/sH5+6VnIga1qYUFw9qggZt9mWOTyWTYvHkzJBJJ3sg/denxEZFhjRs3Dq+99ppwX6vVIjU1FZcvX8b69euRmJiIoKAg1K5dG7NmzTJipESWi0kPIiIiM6NQKEqcDocJDyIiIqJ/xWXEYX3U+lLLudu5Y1rHaRjRfARylMB/91/Hur/vQ13KXFYN3OzwxfC2GNCq6JE2Wq0WSqWy6P6bIgs2D/4Goo8Bd48D8dcKl1Hllho7EelP9erV0bJly0Lbu3XrhgEDBmD06NHIzc3Fpk2bMH36dE5VSmQETHoQERGZkcTERAwZMgTjx4/HjBkzjB0OERERkclrXb01hjQZgpB7IUXuryavhrc838JrrV+DrcwW+yIeY/H+a3iaVnKyQS4FpvRtivcGtoStvOgpbjQaDWbPno07d+5g9+7dkMtkwNNIIPp4XqLjwbnSR3dkPC3T8yQiw2vevDn69euHQ4cOITMzE3fv3kWrVq2MHRaRxWHSg4iIyEw8evQIgwcPxrVr13DhwgW4uLiUuNgeEREREeV5v+P7OHL/CFTafxfFtpZaI6B1AN7yfAvONs64E5+BhXtDcfZOYqn1daptjbc6OWFwj2awLibhoVKp8Pbbb+PQrg0Y1MwK5//THj1rKyDJSihf8GVYf4SIxFOvXj3htkJR/inpLl++jOPHjyMsLAx3795FamoqrK2tUbt2bXTt2hUTJkxA8+bNy1TXyZMnERwcjPDwcCQmJkKtVqNmzZpo27Yt+vfvjyFDhsDOzq5c8T169AiTJk3C/fv3YW9vj59//pkLt5PJYdKDiIjIDKSnp6NPnz64d++esG3SpElwdXWFv7+/ESMjIiIiMn0NnBrgZY+Xse3GNkglUoxoNgLTOk5DbYfayFKo8M3BG/j99F0o1SVPZVXX2RafDPFAbdUTSCSSogspsoCYv/HXL//Bh3Y3se7Dav+/Iw7IqkDw9jUq8CAiMpS4uDjhdt26dcv12MDAQMybN6/QdqVSiejoaERHR2Pnzp349NNPERAQUGw9ycnJmDVrFs6dO1do36NHj/Do0SMcPnwYADB69OgyxxcdHY1JkybhyZMncHFxwW+//Yb27duX+fFEYmHSg4iIyAxUq1YNb731FubPny9sq1u3bpFzzRIRERFVGb/7ALEXdLfV7wpMPlqmh99MuoltN7bh0+6fQi6Vl1h2SvspSMpJwtT2U9HcNe8q6sSMXAz/6SwepWSX+Fi5TILJfZri/QHNYQUNIiMLTDml0fz/lFXH8qat+v8pq3ydADgVPQqkXGy4XhsVI/o4cHkLkHzf2JEYlmtjoGMA0Ky/sSNBdHQ0Tpw4AQDo2LEjatQoX1JSrVbD2dkZAwcORJcuXdCoUSPY29sjPj4eUVFR2LRpE5KTk/Hll1+iadOmRY6wyM7Oxuuvv45bt24BANq2bYtXX30VLVq0gLW1NZ48eYILFy7gwIED5YotIiICb7/9NlJSUuDu7o5169aVecQJkdiY9EBehnPTpk04ceIEnjx5AmtrazRo0ABDhgxBQEBAuYd5FSU2Nhbbtm3DuXPn8ODBA2RnZ8PBwQFNmzZF7969MW7cOFSvXl0Pz6ZkxV5pQnojlUrh4uIi3CbDY5uLi+0trvK09yeffILk5GR8//33aNmyJY4cOYKGDRuKEKV54bnS9LHvRvrGc5u42N7iqurtHSBLQkQT3f5Me00StpTyuEcZj/BT+E/Yf3c/tNCitVtrvNrq1RIfU92uOr574TvdbY42aF3HqcSkR89m1bFoRDs0d89LPqhUKlS3VsL+8T+w2vMzcO8EUN4pq4pi55r3f3Zy5esyIzxXFiP6OLDlJUCjKr1sVRd7AYjaDYz/E2jaz+CHS0xMFBIKAKDVapGeno7w8HBs2LABOTk5qFatWpEjNkrTt29fDBs2rFB/tk2bNujXrx9ef/11BAQE4ObNm1ixYkWRSY8ffvhBiC8gIACfffaZzt9Ju3bt4OPjgw8//BBpaWlliuvcuXOYNm0asrKy0KhRI6xduxb169cv9/MjEovFJz2OHTuGjz76CBkZGcK27OxspKam4urVq9i5cydWr16NRo0aVfgYQUFBWLhwIXJycnS2p6amIjw8HOHh4di0aROWLVuGXr16Vfg4ZWFlZfEvucFZWVmhWbNmxg7DorDNxcX2Fld52lsikWDp0qVwc3PD5MmT4e7ubuDozBPPlaaNfTcyBJ7bxMX2FpeltXdSThJ+i/gN229uh1KjFLb/cuUX+Dfzh73cvtx1LvRvg9O3nyFXpbt2Ri0nG8wf2gbD2teBRJkN3D4KRB+DVfQxNH52vdLPBVIroEH3vKvXmw0A6nQA1voWHvli4XiuLMblLZaR8MinUQHhm0VJemzbtg3btm0rcp9UKsXYsWPxxhtvoEmTJuWuu1atWiXur1atGmbMmIHp06fj0qVLSE5Ohqurq7A/LS0N27dvB5A3wuPTTz8tNjFobW1dppEoR48exaxZs6BQKODh4YE1a9agZs2a5XhWROIzqTODQqFAWloa3NzcRLkC5dq1a5g1axZycnJgb2+PKVOmwNvbGzk5OThw4AB27NiB+/fv45133sGff/4JR8fyDxm9dOkS5s2bB41GA6lUipEjR2LgwIFwd3fH48ePsXv3bhw/fhwpKSmYNm0a9u3bhwYNGhjg2RIRkSWQSCT45JNPjB0GWQj23dh3IyIyFVnKLGy8thHro9YjU5lZaH9iTiI2XduEKR2mlLvuBm72eK9/c3x/JO/KaZlUgjd7NMTs9grYP9wObDwGPPgHUJd/weJCarTMS3A07Q807gXYVCv9MURkMjQaDQ4cOAAbGxvMmTMH1tbWlaovKysLSUlJyM7Ohlabt6aQXP7vVH03btzQGe3xzz//IDs7b2Ta66+/DpmsclPoBQYGYv78+VCr1ejUqRNWr14NJyenStVJJAZRkh6ZmZm4ePEiAKBLly5wcHDQ2Z+UlISFCxfi+PHjUKvVsLe3x8svv4zZs2dX+sOhJP/973+Rk5MDKysrrF27Fp06dRL29ejRA40aNcLSpUtx//59rFu3Du+//365j7F69WpoNHlXg8yfP19nkaH27dvD19cXS5Yswbp165CTk4N169ZhwYIFlX9yRERktvbs2YP+/fuzs0kGw74b+25ERFWFUq3Ertu7sOrKKiTmJJZYdl3UOrzi8QpcbV1LLFeUd15oilOXItDP6irGu0fD+fpZ4JKepqxq2u/fRIcLE+mkJx0D8qZ8spTRHlIroNN4UQ713nvvFepn5uTkICYmBnv27MGGDRuwYcMGXL16FWvWrCn31KtJSUlYv349Dh06hJiYGCHZUZTkZN3p7q5duybc9vLyKtdxn7dhwwZs2rQJWq0WvXv3xk8//aSXaWSJxCBK0uPw4cOYN28eateujWPHjuns02g0ePvtt3Ht2jXhjzgzMxMbNmzAo0ePsGLFCoPEFBERIXyZHzNmjM6X5nyTJk1CYGAgoqOjsXHjRkydOlUnm1oW4eHhAAAXFxedL80FTZ8+HevWrQMAXL58uVz1l5dSqTTojxGU18bR0dEAgGbNmpX7PUPlxzYXF9tbXM+3988//4yZM2eib9++OHjwIDudBqBUKksvZObYd2PfzdLw3CYutre4DN7elVxovKI0Wg0O3T+EFeEr8DD9YanlbWW2GNdqnM5i5rkqNX4/fQ9NazhgiGedwg9SZAIxfwPRx2ETfQw7s/5/yqqyTYFfNKkcaOCtO2WVVA+LmVsw9t2K0ax/3hoX4ZstYyHzTuNFmdqqOLa2tvDw8MDHH3+Mxo0b47PPPsOlS5fw66+/YtasWWWu5+rVq3jrrbeQkpJSpvK5ubk69wsmQSo73fHGjRsBAG5ublixYgW/e1KVIkrS48yZMwAAHx+fQlMfHDhwAFFRUZBIJGjTpg26deuG8+fP49q1azh69ChOnTqFvn376j2mo0f/7YCNGTOmyDL5Uxp8//33SEtLQ2hoKHr37l2u4+SffEta3KdatWpwdXVFcnIyT9ZmQKvVIjMzU7hNhsc2FxfbW1z57a3VarFo0SIsXrwYAHDq1Cm8/PLL2L17N38sIr1j3419N0vDc5u42N7iMsf2TocGY/eNxfWk0tfNkElkGNViFN7t8C7c7f/9AfDM7QQs2HMVdxMyUcvJBn1a1oSjXAo8jQSij+X909OUVdefqXH4rgqXkp2xaOMxNG7ZttJ1EpVJ035GTQRYqpdeegnff/89UlJS8Oeff5Y56aFQKDBz5kykpKRALpdj/PjxGDhwIBo3bgxnZ2fhQpiHDx/Cx8cHgGE/1319fXHo0CEkJSXh448/xg8//MA1dKjKEOWdeuvWLUgkEnTu3LnQvqCgIAB5i+v88ccfsLKyglKpREBAACIjIxEUFGSQL86XLl0CANjb26Nt2+I7HF27dhVuh4WFlfuLc5MmTRAVFYXY2Nhiy2RkZAiZ2IosckREROYvOTkZv/32m862/fv3488//8TYsWONFBWZK/bd2HcjIjJl96RqoAwJj0GNBuH9Tu+jifO/n9WPU7OxeP917I94DACohST0yYzE/VW/ol1OOJBV+SmrtHauSHbtiN+O3cHKkGt4mJb/o2Q8NIu+xubNmyt9DCIyXVKpFI0aNUJKSgqePXtWaLHx4vzzzz94+DBv5NrChQvx8ssvF1mupFEgBY8THx9fqbXnPv74Y9SoUQNbtmzBkSNHMHv2bCxbtoyJD6oSRHmXJiUlASh8xZxSqcTFixchkUgQEBAg/NHI5XKMHTsWERERiIiIMEhM+cN7GzZsWOIfa9OmTQs9pjzGjh2Lzz77DCkpKdi2bRvGjRtXqMzKlSt1yhuSUqmEQlH0lSpWVlbC1ZwajQYqVcnzPhacakGtVkOtVhdbViKR6FyJrFKphPmyiyKVSnVeF6VSWWL2umDsWq221Ksu5XI5JBIJgNKfa3ljf76u0mKXyWQ6C0sV9/rk4+tUWFH16Dt2vk7/KnhfqVQa9O+Jr9O/7e/m5oagoCAMGTJE6OR+/fXXGDt2rNE/98ztdeKV++y7se9m/HPN8wz9GVZQae1o6p9hVeF1Yt/NvPpuVlotpM8/sIyxl+11Kv9VzF7uXni/w/vwqvvvfPb5r9OFGzHIvBqCz6wi0VsaCQ/p/ye5k8p9mH9J5dDU7wpNk37QNu0PhVsr3Lt1G73aZGP3/Tl4GBoKABg8eDBWrVpVqb+notpb89w2S/t7Yt+NTFHB931J7+mC7ty5I9weMmRIseWuXr1a7L6CFwddvHixUkkPAPjss8+gUqmwfft2HDp0CB999BG+++67Si+QTmRooiQ9UlNTAaDQ9BuRkZHIycmBRCJBnz59dPY1btwYAJCQoIeFwZ6Tm5srXJ1Xu3btEss6OzvD3t4eWVlZePLkSbmPNWbMGFy6dAlBQUFYtGgRoqKiMGDAANSsWROPHz/Gnj17hOkapk6dip49e5b/CZVDSV/+PTw84OjoCADIysrCzZs3S6yr4IJICQkJJV4R6eDggFatWgn3Y2JiSsxMu7u763wwX79+vcSOTNOmTYVstlKpRGRkZImxe3p6Ch2r1NRU3L17t9iycrkc7du3F+4/fvwY8fHxxZZ/fmHh6OhoYTh5UerXr49atWoJ90uLna9T2ZT2Orm4uKBZs2bCfb5O/yrP63Tjxg2D/j3xddJ9neRyOb7//nu8//77+PLLLzF79mwAxv/cs6TXyVKw78a+mymdawDDf4YVjP3+/fvIzs4utnxV/gwzpdfpeTzX/Kuq9d08srLgWMzj9fE6pWjL9mMhADSwbYCXa70MT0dP4DGAugA0GuBJBLIigqG98xeGJV3FcOvK/0iudG4CeSvfvHU5GvXC1Zt3816nBAAJtwEAdnZ2+PrrrzFlyhS0atUKO3fuhI2NDRQKRYX/nopq7+zsbDgUuM+/JyLjys7OFvpwtra2ZRrlAegmSrKzs4W/o4I0Gg127txZbB3e3t5CX3jTpk0YPnx4pRIUEokEX3zxhXDcAwcOQCqVYunSpYUuHCEyJaIkPWxtbZGZmYnExESd7fmLUTZs2BA1atQo9BhDKXgyt7e3L7W8nZ0dsrKykJWVVe5jyWQyfPPNN+jfvz9WrVqFnTt3Fvpw8vb2FuVLMxERVX0dOnTAnj17dL4cEukb+27suxERmYInuU/wUFr6SI8a8hoYXWs0ujt3h1QihTz7GZwSLgH3fgbuHgeyElGtkrGo5E5Iq+mFtJpdkFbTC66N2pXpCmonJyf8+uuv8PT0hI2NTSWjIKKqYMWKFcjJyQEA9O7du8xJh/yLiABg9+7deOeddwqV+f777xEVFVVsHU5OTnj11Vexbt06REVF4auvvsL8+fOFkWMFKZVKpKWloXr16iXGJZFI8OWXX0KtViMwMBD79u2DTCbDkiVLmPggkyVK0qNhw4a4fv06zp8/rzOv8pEjRyCRSHTmXs6XP62Cm5ub3uPJzc0Vbpdl8df8qyvyP7DKKzo6GkFBQbh161aR+y9fvoxdu3ahWbNmOldPGEKzZs2K/bGg4PBWe3t7eHp6lrneGjVqlJi5fv7DtVGjRiV2EJ//0GzdunWpQ7rzyeXyUmMv+Lo7OzuXWP752OvUqVPi66RSqZCWlibcb9asWalDhQsqLXa+ToUplUrcuHFDZ1tpr9PzsfN1+ldpr1PB9m7VqpVB/54s9XXSaDTC61Rce5vS5565vU7Vq1ev0LRI5oR9N/bdjH2ueZ6hP8MKTgPTuHHjEqdQM/XPsKrwOrHvZl59N6sweyC56MdX9nU6cfkEtIV/pxO42LhgctvJGNPQDzZxlyC9uxOSeycgTSh5ZEFZaKVyaOt3g6ZpP2ib9IO2liccpTI4Im8AScHXSavV6ow+KKrN9fX3VFR729nZ6dy3tL8n9t1IbImJiYX6ibm5uYiJiUFQUBBOnz4NALCxscGMGTPKXG/v3r1RvXp1JCYm4ocffkBsbCwGDRoEV1dXPHjwADt27MC5c+fQuXNnhIWFFVvPBx98gLNnz+LWrVvYvHkzwsPDMXbsWLRs2RJyuRxPnjzBxYsXsX//fsycOROjR48uNTaJRIL//ve/0Gg0CAoKwp49eyCTyfDVV18VmVAhMjZRkh49e/bEtWvXsHXrVnh5eaFLly4IDAxEZGQkJBIJ+vfvX+gx+cMf3d3d9R5PwasryjL3Y/58lRW5gvHixYuYOnUq0tPTUa9ePXzwwQfo1asXnJ2dkZiYiL/++gs//vgj9u/fjwsXLmDt2rVo0aJFuY9TVnK5XGe+zOJIpdIylcv3/ByfpSnvokdl+YEjn0QiKVfs5X2uhowdgEFj5+tUPL5ORSvtdSo41zDA16kkZX2ucXFxGDFiBJYuXYp+/frp7CvuM5x/T8WryOtU3vjNEftu7Ls9z9z7BAXnp7eysipX/ab2GVYeVe11Kqiqn2vKw+T7biX82FXZ12l219k4GrUJsVLdKa6kWuCdZiMxUeMAx0s7gaA5gLrkdSbKpIYH0Kw/0GwAJI16QWLjWOx6JflUKhXeeecd1KtXD19++WWh/UV9jlfq76mI9pY+t83S/p7YdyOxbdu2Ddu2bSuxjJubG5YuXQoPD48y12tvb49vvvkG06dPR25uLrZv347t27frlOnWrRsWLFiAYcOGFVuPnZ0dNmzYgBkzZuDChQuIiorCZ599VuY4iiOVSvH1119DrVYjODgYgYGBkMlk+PLLL5n4IJMjStLj9ddfxx9//IHMzExMnTpVZ1+zZs0wYMCAQo85ceIEJBIJ2rRpo/d4HBz+ne2yLNMe5M/pW5bpFApSKBSYPXs20tPTUbNmTWzfvh01a9YU9teuXRsBAQHo1q0bxowZg/j4ePznP/9BYGBguY5THhx2ZngymUxY+JULO4mDbS4utrfh3L17F4MGDcLdu3fh7++P48ePo1OnTmxvkfFcyb4b+26Wh+c2cbG9xVWV21sikcANUsRCDRuNBrlSKeoplfj1yTM0vv9j5Q9g5wY07Ze3Lkez/oBz/XI9PDc3F6+99ppwHnB1dcXs2bOrdJtXRTxXkimQy+VwcXFB8+bN8cILL2D06NFwdnYudz19+vTBn3/+idWrV+Off/5BcnIyqlWrhubNm8Pf3x8vvfQS4uLiSq3Hzc0NmzdvxpEjRxAcHIwrV64gKSkJEokE7u7uaNu2LXx8fODr61uu+KRSKb755htoNBrs378fO3fuhFQqxRdffMHEB5kUUZIe7u7u+PXXXzFr1iw8e/ZM2N6gQQMsX7680B/FgwcPcOnSJQAwyFzJNjY2cHFxQUpKSqkLXKampgpfrktbOPN5p06dwtOnTwEA48eP1/nSXFCLFi0wfPhw7Ny5E1FRUbhx44bO0Fh9YofL8GQymcGnuiBdbHNxsb0NIyYmBr1798bjx48BABkZGfDz88OpU6cM8iMyFY/nSvbdAPbdLA3PbeJie4urqrd3HaUCcxKfolOuAiftbNE1Jxf2JUzFVCKpHGjYXRjNgdodgAr+YK7VajFy5EgcPHhQ2Pbhhx/C2dkZb731VpVu86qG50oSg7e3tzCy2dD1tGjRAkuXLi12f/369cscy6BBgzBo0KAyxzd69OhSp7uSyWRYtmwZli1bVuZ6icQmStIDALp06YK//voLYWFhSEhIQM2aNeHl5VXksMv4+HhMmzYNANCrVy+DxNO8eXNcvHgRDx48gEqlKnb45927d4XbzZo1K9cxCj62tB+s2rZtKyySeffuXYN9cSYiItNUr1499OjRQ+eKcVdXV50r3InExL4b+25EREaXGotPnsbBTZ03vdUL2RVYq6mGx78jORr1Amwc9RKaRCLBmDFjdJIeNjY2xSbMiYiISDyiJT2AvHkfu3fvXmq5Ll26oEuXLgaNxcvLCxcvXkRWVhaioqLQoUOHIstduHBBuN25c+dyHaPg1QZqtbqEknnzgBb1OCIisgxWVlbYunUr/P39ceTIEXTo0AGHDh3iVYJkVOy7FY99NyIiA8tJA7a8IiQ8yipJ64izmnb4W9sB7741GQ2btDRQgMDkyZORkpKCjz76CI6Ojti7d2+R617p96BHDVs/ERGRGbDYiQ99fHyE23/++WeRZTQaDYKCggAATk5O8Pb2Ltcx8ufwBPIWxSxJwS/oBR+nb2VZ/JMqR6FQ4NKlS7h06ZLOophkOGxzcbG9DcfGxga7d+/Ge++9h+PHj6NWrVpsbyPgudI0se9GhsTPWnGxvcVVJdtbrQR2vgHER5VaVKGV4Zy6Db5VvophuYvhlfsr3lfOQJBkAK5mVDN4qHPmzMFXX32FY8eOCQmPKtnmVRjPlURE9DyjJD0ePHiAPXv2YM2aNfjpp5+QlJQkegzt27cXrkj8888/ER4eXqjM2rVrER0dDSBvQU+5XK6zPzQ0FB4eHvDw8MDcuXMLPb5Hjx6ws7MDAGzbtq3Y+fZOnjyJI0eOAABq1aqF1q1bV/yJERFRlebg4IAVK1bA1dXV2KEQCdh308W+GxGRAWm1wP4Pgei/ii1yW1MPa1V+eFPxETrm/oZxyvn4WT0CV7VNoYUUfm1r4+iHL+BFzzqihDxv3jx07dpVlGMRERFR6USd3ioqKgpfffUVwsLCdLb7+fnBzc1NuL9lyxb89NNPqFatGvbv31/oC6u+fPrppxg3bhxycnIwadIkTJ06Fd7e3sjJycGBAwewfft2AEDjxo3x5ptvlrt+JycnvP322/jxxx+RmZmJsWPHYsKECejZsyecnZ2RkJCAv/76Czt37oRGowGQt/CZtIILqRERUdVw6NAhDBo0iJ/3ZPLYd2PfjYhIDFnKLLx37D281e4t9Lp3HgjbUGS5m5r6mKT4CI9Q9LoZjarb44vhbdHPw12v8YWFhaFu3bqoXbu2XuslIiIiwxAt6XH8+HF88MEHUCqV0Gq1wnaJRFKo7IgRI/Ddd98hJSUFJ06cwKBBgwwSU5s2bfC///0PH330ETIyMrBs2bJCZRo3bozVq1fD0bFii51NmzYNqamp2LhxI7KysrBq1SqsWrWqUDm5XI5Zs2ZhxIgRFToOERGZPq1Wi8WLF2PBggWYPn06VqxYUeR5kMgUsO/GvhsRkVhWhK/AhScXcOHJBfinZ+JjqRQu/59czhevdcGbio8RhxqFHm9jJcX0/s3xTt+msJXrd52lkydPwt/fH02aNMGJEyc4GpeIiKgKEOWytPj4eMyePRsKhQLNmzfHb7/9VuiKwYIcHR0xYMAAAMCpU6cMGtuAAQOwd+9evPHGG2jcuDHs7Ozg5OSEdu3aYc6cOQgKCkKjRo0qXL9EIsEnn3yCXbt2YezYsWjZsiUcHBwgk8lQrVo1tG3bFm+++SaCg4Px1ltv6fGZERGRKdFqtfjwww+xYMECAMDKlSuF20Smhn039t2IiMRy5dkVbLm+RbgfXM0BI+rXwUEHe+Sn3LO0NpikmFNkwmNgK3ccmfUCZgxsofeEx759++Dn54f09HRERERg6NChyMzM1OsxiIiISP9EGemxfv16ZGdno27dutiyZQucnJxKfYy3tzf279+PqKjSFy6rrHr16mHevHmYN29euR7n7e1d7FzPz2vXrh3atWtXkfCIiMgMREZG4qefftLZtnjxYgwbNqzciy0TGRr7buy7ERGJQaFWYOHZhdBCq7M9SSbDNidHDM7MgkYrwfvK93BV21SnTD0XO3w+vC0GtallkNiUSiXmzJmDnJwcYdu5c+ewZMkSfPnllwY5JhEREemHKCM9Tp8+DYlEgkmTJpXpSzMANG2a16GJjY01ZGhERESiaN++PTZv3qwzNdDKlSuZ8CCTxL4bERGJ4ffI3xGdGl1ou7VGiy+eJUEKYJHqdfyl8fp3H5QYJz+No7NfMFjCA8ibxjAkJAR16vy7GPqoUaMwf/58gx2TiIiI9EOUpEdcXByAvB98yip/HuasrCyDxERERCS2V155BatXr4aVlRU2b96MadOmGTskoiKx70ZERIZ2J/kOfov8rch976akorFKhTWqIdig9tXZ95P8R7xhcxx21vqdyqooTZo0wZEjR+Dm5oY33ngDO3bsgI2NjcGPS0RERJUjyvRWarUaAKB5biGykqSnpwMA7O3tDRKTpZLJDN8xtHRWVlbw8PAQbpPhsc3FxfaunMmTJ2PAgAHCVfGlYXuLj+dK9t1MCd+P4uBnrbjY3uIyxfZWa9RY+PcCqDSqQvta5SowMTUNh9Ve+K8qQGffUOk/GCy7BNTuKlaoaNu2LS5duoSGDRtCKi3bdaOm2ObmjOdKIiJ6nigjPWrUyFts7OHDh2V+TEREBADoDCWlyitrJ40qTiqVwtHREY6OjmxvkbDNxcX2rryyJjwAtrcxsJ3ZdzMlfD+Kg5+14mJ7i8sU23vbjW2ISIgstF2m1eKLhERc0zTFB8rp0BT4ycIJmVgo3yBmmILGjRuXq+1Msc3NGduYiIieJ8qZoUuXLtBqtTh48GCZyisUCmzfvh0SiQTdunUzcHRERET6c//+ffTv3x8xMTHGDoWowth3IyIiQ4lNj8WPF5cVuW9iahqccp0xWTEH2bDV2TffajPcJal6j0ehUOC1117Dn3/+qfe6iYiIyDhESXqMGjUKAHDs2DGcPXu2xLIKhQL/+c9/8ODBA0gkErzyyitihGgxyjNNBVWMRqNBRkYGMjIy2N4iYZuLi+1dvGvXrqFXr144ceIEfHx88PTp00rXyfYWH9uZfTdTwvejOPhZKy62t7hMqb21Wi0WHZuJbK2y0L5GSiUmpKjwhuJjPIOLzr6e0qt4WXZS7/FkZmZi+PDh2LZtG1577TUcOXJEL/WaUptbArYxERE9T5Skh7e3N1588UVotVpMnToVS5cuFaZAAIBHjx4hLCwMv//+O4YNG4aDBw9CIpFg7NixaNGihRghWoz8ObrJcFQqFW7evImbN29CpSo8Ry3pH9tcXGzvokVERKBv377CAtB37tyBr68vUlJSKlUv21t8PFey72ZK+H4UBz9rxcX2Fpcptffe8F9xLuVmkfs+S0jBjNwPcEdbX2e7jUSFr6zWQCLRbyxZWVnw9fXFoUOHAOQl8UeOHIlz585Vum5TanNLwHMlERE9T7QVtZYsWYLMzEycPHkSa9euxdq1ayH5/17L1KlThXJarRYAMHjwYHz66adihUdERFQp9evXR506dZCYmChs02q1yM3NNWJURBXHvhsREZVFgCwJEU0a6mxrr0nClufKJTy7jm+vrASkhbMXL6WlIzBjIv7WtCu0b5bbOTTOrPzo2efZ2dmhXbt2OiMaJRIJsrOz9X4sIiIiEpdoqz1ZW1tj1apVWLRoERo0aACtVlvkv9q1a2PhwoX48ccfIZPJxAqPiIioUtzc3HD48GFhkfIePXrgxIkTqFWrlpEjI6oY9t2IiEhvFJlYEjweaUUkPNxVKjTN9cF2Vd9C+9rWdcJkl0sGCUkikWDlypV49dVXAeT15Y4dO4YBAwYY5HhEREQkHtFGeuR75ZVX8Morr+DOnTu4evUqEhMToVar4erqitatW6Nt27bCVYRERERVSZ06dXD06FEsXLgQv/zyCxwcHIwdElGlse9GRESVolHj2M5XcEimKHL3ZE1TzEsYWWi7TCrBN2PawypEa7DQZDIZNm7cCAcHB8yaNQvt2hUeaUJERERVj+hJj3zNmzdH8+bNjXV4IiIig2jSpAk2btxo7DCI9I59NyIiqoj0kI/w35y7gFXhnx8GqW3xbey7AAonz9/q3QTt6jkbPD5ra2usWbPG4MchIqqMAQMG4NGjRxg1ahSWLFli7HCITJ4oSY/09HRUq1ZNjEMREREZlFarxYULF9CtWzdjh0JkMOy7ERGRXvzzK5bdDUS8U+FzipMWyNJ+iuTcwgmPhm72mOXTMu/O5KOVDiM8PBxt27aFtbV1pesiIgKA8+fPY8KECcL9bdu2oXPnzkaMiIgKEmVNj169emHGjBk4fPgwFIqih7QSERGZOq1Wi48++gje3t5Yu3atscMhMhj23YiIqNJu7MeFEwuxq4iEBwD4134TB2/Li9z31ShP2FnrZ52oAwcOoGfPnhg/fjzUarVe6iQi2r17t879oKAg4wRCREUSZaSHQqHAkSNHcOTIETg6OsLX1xfDhg1D9+7dxTg8ERFRpanVakyZMkWY/uDtt9+Gs7MzxowZY+TIiPSPfTciIqqMJrk5wJ+T8dhGBluNBjlS3estu7m0wc6L7QAoCz32Ja/66N2ihl7i+OOPPzBhwgSoVCrs3LkTzs7OWL16NdeiIqJKycnJwaFDhwAA9vb2yMrKwsGDBzF//nyDjSg7duyYQeolMlcSrVZruFXB/t+CBQtw6NAhpKam5h30/zsY7u7uGDp0KPz9/dG6dWtDh2GxFAoFIiMjAQCenp4c0ktkJgIOBCDiWYTOtvY122PLi1uMFJF5CwoKwqhRo3S22djYIDo6GvXq1TNSVGQIPG+y72ZsfA8SUVUSsK4zIqT/Ji/qKFXYEfcULpq8URUPrWT4okZ1hNrZAgDspNboav0N9ofnFqqrhqM1jsx6Aa4Olf/cS0xMRJMmTZCenq6zff369Zg4cWKl6yfTYYnnzdu3b0OlUsHKygotWrQwdjgWJzg4GHPmzAEAfPXVV/jkk08AAMuXL4efn58xQyOqkgzxmSbK9FaLFi3C2bNn8fPPP+PFF1+EjY0NtFotnj59inXr1mH06NEYOnQoVq1ahdjYWDFCIiIiKpeRI0fiP//5j3BfJpNh9erVTHiQWWLfjYiIKsJRo8HKp8+EhAcANFCp8duTeCx6lohqUmsMa/R2kQkPAFjo31YvCQ8AqF69Onbv3q3zA/jrr7+OgIAAvdRPRJYrfyorDw8PjBkzBk2aNNHZTkTGJ8r0VgBgZWWFAQMGYMCAAcjKysKRI0ewb98+nDt3DiqVCtHR0fjhhx/www8/oGPHjhg+fDj8/Pzg6uoqVohEREQl+vrrr5GSkoL169dj+/btGDFihLFDIjIY9t2IiKg8rLRa/O/pM7RQFp6ySgJglMcr6DNwPlIzrXHtRhT+uZukU2ZgK3cMa19HrzENHDgQf/zxB1566SVMnz4dP/zwA6RSUa79JCIzFR8fj3PnzgEAhg8fLvy/fPlynDlzBklJSXBzcyv28U+fPsWmTZtw9uxZPHjwADk5OXB2dkb16tXRokUL9O7dG4MHD4ajo6PO4wYMGIBHjx5h1KhRWLJkSZFxHTlyBKGhobhx4wbi4+OhUqng6uqKdu3awd/fH35+fsV+BoaGhuL1118HAGzcuBHe3t44cOAAtm/fjps3byIrKwt16tTBwIED8c4778DFxaUizUckGlGmtypJUlISQkJCEBwcjMuXLwvbJRIJZDIZevfujV9//dV4AZqBgkM927RpAzs7OyNHZN7UajUSEhIAADVq1IBMpp8F+Kh4ltrmxpreylLbO59arUZUVBTat28v2vEsub2NITs7G9euXQNgOVMklAf7bobHvpv4+FkrLra3uAzd3gHrOiNCosCihCSMysgsulBzH2DcdkCWd92lVqvFjosP8d/915GWo4KDtQxHZr+Aui6G+by7fPkyOnToINpaHnyPi8sS+26c3sp41qxZg2+//RZSqRQnTpxArVq18PDhQwwaNAharRaffvqpkDx43sWLFzFlyhRkZGSUeIxff/0V/fv319lWUtJDrVajXbt20Gg0Jdbbq1cvrFixAg4ODoX2FUx6rF+/HoGBgdi7d2+R9TRq1AhbtmxBzZo1SzweUVkZ4jNNtJEexXFzc0NAQAACAgIQGxuLffv2Yf/+/cKTPXnypLFDNCulfQBS5anVamGqD1dXV3ZwRcA2F5elt7dMJhMt4QGwvY2B58qSse8mLr4fxcHPWnGxvcUlRnu/nZpWfMKjlifw8noh4QHkJcpf7doQ/Vu544u91+Dd1M1gCQ8A6Nixo8HqLgrf4+LiuZLEtGfPHgBAt27dUKtWLQBAgwYN0KlTJ4SFhSEoKKjIpIdCocCsWbOQkZEBBwcHjBs3Dt7e3qhevTqUSiViY2MRHh6OI0eOlDum/OvZu3fvjr59+6Jly5Zwc3NDZmYmHj58iJ07dyI8PBxnz57FokWL8M0335RY3/LlyxEeHg4fHx+MHDkSdevWRUJCArZu3YoTJ04gJiYGX3/9NZYtW1buWInEYvSkR0H169fH4MGDkZOTg6dPnyItLc3YIRERkYW5ceMGPv/8c6xduxb29vbGDofIpLHvRkRE3TMz8H5yatE7q9UBXtsO2FQrcrd7NVusDOiMykxAkZWVhXfeeQdz585Fu3btKlwPkTkLOKDftWyGNB6C8W3Gl1hm87XNCLkfotfjGnpWg9Jcv34dN2/eBPDv1Fb5hg8fjrCwMERFReHOnTto3ry5zv5Lly4hPj4eAPD9998XGsnRsWNHDBs2DPPmzUNOTk654pLJZDh48CAaNWpUaF+3bt0wZswY/Pjjj1i5ciX27NmDd999F40bNy62vvDwcMycORPvvvuuzva+ffti8uTJOHPmDA4dOlTqVF5ExmQSSY/4+Hjs378fwcHBuH79us4+SxiWSEREpuHSpUvw8/NDQkICUlNTsWfPHp6HiIrAvhsREQEActPxRtIzAECqVAJnTYHkhbUj8NoOwLleqdVUdNqp1NRU+Pv74/Tp0zh27BjOnDmDpk2bVqguInP2/LTIldWhZodSy8Rlxun9uMaWv1C5ra0tfH19dfYNGTIE//3vf6FUKhEUFIQ5c+bo7M+f8g4AunbtWuwxrKysCq3nURqJRFJkwqOg6dOnY+vWrUhOTsaxY8cwadKkYsu2bdsWU6dOLfI4b7zxBs6cOQOVSoXw8HAMHDiwXLESicVoSY+MjAwcPHgQwcHBuHjxIjQajXB1h0QiQdeuXYVFdoiIiAzt9OnTGDp0KNLT0wEABw8exIQJE7B161ZOSUAE9t2IiKgI14NRTaPBE5kMo+vVwYiMvFEfNlotZC+vB+oYbkrQhIQEDB48GOHh4QCAx48fw8fHB2fOnEHdunUNdlwiskwqlQr79u0DAPTv379QYsLFxQUvvPACjh49iuDgYMyePVtn0fCC61/8+eefmDhxosFi1Wg0ePbsGTIzM6FSqYTttWvXRnJyMm7cuFHi4/39/YtNRrdt21a4/fDhQ/0ETGQAoiY9FAoFTpw4geDgYJw6dQoKhQLAv3PPtWrVCv7+/hg2bJgwLx4REZEYXFxcYGWle1qMiYlBZmYmnJycjBQVkXGx70ZERCWK2A4tgMU13JAuk2KzsxOO29vDW6HCFy0GGfTQdnZ2haYiTU5ORlxcHJMeRKR3Z86cEUZrPD+1Vb7hw4fj6NGjePLkCUJDQ9GjRw9hn5eXFxo0aICHDx/iq6++QnBwMAYNGoQuXbrA09Oz0qOltVot9u7di127diEiIqLEKbKSk5NLrKukEXMuLi7C7czMYtZyIjIBoiQ9zp07h+DgYBw5cgQZGRkA/v2yXLduXfj7+8Pf37/QfHdERERi8fT0xIEDB+Dj44PMzEwMHDgQQUFB5R5aTGQO2HcjIqJSpT0G7p7EQQd7nLT/dxHyR3IrBMqtoD4zH6MbzkLr2i6ws9b/qFkHBwfs27cP/fv3x+XLl1G7dm0cPnwYnp6eej8WEVH+1FYuLi7o06dPkWX69+8PJycnpKWlISgoSCfpIZfL8euvv2LGjBmIjo5GZGQkIiMjAeRNl9WlSxeMHDkSL774YrlnGsjNzcV7772HU6dOlal8aWuG2NraFruv4OgVjUZTtgCJjECUpMebb74JiUQifFl2cXGBn58f/P394eXlJUYIRERmR6FWGDsEs9O9e3fs2bMHv//+O9atW1diZ4/InLHvRkREpbr6J3IlWnzr5lrk7hylGm+uuwRnOzm+GuWJ3i1q6D0EFxcXHDx4EBMnTsTKlSvRrFkzvR+DyBy0r6nfqebqOpQ+mqquQ129H9dY0tPTcezYMQBASkoK2rVrV+pjDh8+jIULF+qMSGvevDmCg4Nx/PhxHDt2DBcvXkRMTAxycnJw5swZnDlzBuvWrcNvv/2G6tWrlzm+X375RUh4dOvWDa+99hratm2LGjVqwNbWVkhUBAQE4OLFi+V56kRVlmjTW9nY2GDAgAHw9/dHnz59Ck0hQmQuJBIJHBwchNtkeJba5g/SHhTalqk0/PBSc2/vgQMHmtRibObe3mS62HcjS8LPWnGxvcVlsPaO2I7TdnZIsCp8RbKVFkiPG4LU7HSkZisxfk0oXvKqj09fbA1Xh8pN4fK8WrVq4eDBg3qts7L4HidTs+XFLaIfc3yb8RjfZrzoxzWEkJAQ5ObmlusxWVlZOHz4MEaOHKmzXSaTwcfHBz4+PgCA+Ph4nD59Glu2bEFUVBSioqKwYMECrFy5skzH0Wq12LVrFwCgS5cu2LBhg85ojIJSU1PL9RyIqjJRvr0uWbIEgwYNEk76ZDxyudzYIZg9uVyOVq1aGTsMi2KJbf4o4xGyVFmFttvKDD8yoaq3t1arxZ07d9CiRQtjh1ImVb29qyKeK9l3MyV8P4qDn7XiYnuLyyDtHX8deBKBA+5Fj95wym6AQzfSdbbtuhSL4zfiETKzD9yrla/PeuvWLbRs2bLC4YqN73Fx8VxJhpY/tVXNmjUxb968Ust/++23ePLkCfbs2VMo6fE8d3d3jBkzBsOHD8err76KqKgonDhxAjk5OWWaeSAlJQXPnj0DAPj5+RWb8MjMzMS9e/dKrY/IXIiS9CjtD5yIiMonOiW6yO1ijPSoytRqNd59911s3boVf/31F7y9vY0dEpFJYt+NiIhKFLED6RIJTtrZFdql1QKPHwUU+bDuzaqXO+GxY8cOjB8/HosXL8bHH39coXCJiCrq4cOHCAsLAwD4+vpi6NChpT7m8uXL2LhxI/755x88ffoUtWrVKvUxcrkcXbt2RVRUFFQqFdLS0sqU9FCr1cLt7OzsYsvt3LkTKpWq1PqIzEXR6T8iIjJp91PvF7ldLuNVTsVRKBR47bXX8NtvvyEzMxNDhgzB1atXjR0WERERUdWi0QCRO3HUwR4KaRFTN2lsYGNVeAoVJ1srLPRvU65D/fbbbxg7diyUSiX+85//YPXq1RWNmoioQvbs2SOsc+fr61umx+SX02g02LNnDwAI63cUR6FQ4MKFCwAAe3t7uLm5lelYbm5ucHJyAgDs27cPCkXhtT8jIiKwfPnyMtVHZC6Y9LAwzOoankqlQnR0NKKjo9neIrHENr+XVvSwVBuZjcGPXVXb+4cffsCOHTuE+8nJyRgyZEiJV8OYgqra3lUZ25lMCd+P4uBnrbjY3uLSe3s/OAekPsR+x6KnQJTIcuHZcA2+HNEWDtb/rvcxf2ibco3yiIqKwpQpU4QfGwFg6tSp+Pvvvyseu0j4HhcX25gMKT9pUb16dXTp0qVMj+ncuTNq1qwJANi7dy8A4Ny5c/Dz88OECRPw+++/4/Tp04iKisKlS5fw559/IiAgAFFRUQCAl156qczr6UmlUvj7+wMAbt68iXHjxmHfvn2IjIzEuXPnsGTJEowfPx42NjZo3LhxeZ46UZWm1+mtWrduDSBvoa5r164V2l4Rz9dFlVOww0iGodFokJKSAgBo0KCBcYOxEJbY5vdSi056SCWGz2VX1faeOXMmjh07hkOHDgEArK2tsWLFCtgVMS2DKamq7V2VWdK5kn0302dJ70dj4metuNje4tJ7e0dsxzOZFOdti7/YJlelwoQejTGwdS18FnQV2Uo1Xu5Sv1yHadu2Lb7//nvMnj1b2DZt2jR07969wqGLhe9xcfFcSYZy6dIlPHjwAADg4+NT7HoZz5NKpRg0aBC2bt2K27dvCzMMaDQanD9/HufPny/2sQMHDsSHH35YrjhnzZqFsLAwXL9+HVevXi30eBcXF6xYsQI//vgj7t+/X666iaoqvSY9ijvR8ARERKRfxSU9qHjW1tb4888/4evri8uXLyMoKAg+Pj7GDovIqNh3IyKiclHlAteCEOLgAK2kiKmtnlPXxQ6/T+yCLIUakjKUf96sWbOQnJyML7/8EvPnz8eiRYsqVA8RUUXkj/IAyj61Vb7Bgwdj69atAPIWQp81axY8PDzw999/4/r164iPj0diYiIAoEaNGmjfvj1GjhyJfv36lTvOatWqYdu2bVi3bh1CQkIQExMDmUyGOnXq4IUXXsDEiRNRu3btctdLVJXpNenx3nvvlWs7ERGVX2puKpJykowdRpXk4OCAffv2ITo6Gl5eXsYOh8jo2HcjIqJyuX0YyEnFAbfSF+XNJ5FI4GBT8Z8evvjiC/Tr1w8DBgyocB1ERBWxaNEiLFq0qEKP7dGjB27evKmzbfDgwRg8eHCF6jt27FiJ++3s7DBt2jRMmzat2DKbNm0qdp+3t3eheItT1nJExsSkBxFRFXM/7X6Zyz7NfIrjD49jbKuxhguoinFxcWHCg+j/se9GRETlErEd962sEGVj+HXk8kkkEiY8iIiIqFy4kDkRURVTlqmttFotgqODMWrvKPw39L848+iMCJGZhrCwMMydO5fT8xARERHpU3YycOsQDhSzgHlFpaamYsqUKUhK4khmIiIi0g+9jvQozoULFwAAnp6esLW1LdNjcnNzERERAQDo2rWrwWIjIqpqSkt6JGQnYNG5RTj+8LiwbeHfC7F7xG44WTsZOjyjOn36NIYNG4a0tDRIJBJ8/fXXxg6JqEpi342IiAq5tgdatQIHHKvrrcpnz57Bz88PYWFhuHLlCo4ePQpHR0e91U9ERESWSZSRHhMmTMDEiRMRGxtb5sc8ffpUeBwREf2rpKSHVqvF+APjdRIeABCfFY9vzn9j6NCMKiQkBIMHD0ZaWhoAYMmSJfj222+NHBVR1cS+GxERFRKxA1HW1oiRywvt0mpk5a4uNjYWffv2RVhYGAAgNDQUI0eORG5ubqVDJSIiIssm2vRWFZ1mhNOT6JdEIjF2CGZPKpXC3d0d7u7ukEo5g5wYLK3NS1rTQyKRYFK7SUXu2xu9Fycenqj08U21vbOzs6FQKHS2HTp0CCqVykgR6Yeptrc547kyD/tupoHvR3Hws1ZcbG9x6aW9Ux4AMWex39G+yN05j19GduxrUGW0hFZbts8ttVqN9PR0nW1Xr17FgwcPKhajCeF7XFw8VxIR0fNEmd6qIjQaDQBAJiv/FSNUPCsrk33JzYaVlRUaNGhg7DAsiiW1uVKjxMO0hyWWebnlyzgacxTnHp8rtO+Lc1+gY82OcLF1qXAMptreo0ePxu+//45Jk/KSPiNGjMAff/xR5T/3TLW9zVlVf88YC/tuhsH3ozj4WSsutre49NLekbugBnDQofB6Hlq1NVTpbQGtHKr09pA5XgfcN5VaZaNGjXDkyBH07dsXCQkJwv0WLVpULlYTwPe4uHiuJCKi55nsJQdxcXEAwPk8iYgKiE2PhUpb8sgFiUSCRb0WwVFe+PMzITsBX58333Uu3nzzTSxbtgwTJkzArl27yrwWARFVHvtuRERmSqsFIrbjvK0NEqwKJ7ZV6e0A7b9TXlk53Cxz1a1bt8bBgwfh7e2NM2fOmEXCg4iIiIzPIOnw/C+9z3v27Bns7YseDptPoVDgwYMHWL58OSQSCTs9REQFFLeexw/9fsDARgOF+7UdauPjrh9jwd8LCpU9cO8ABjUaBJ9GPgaL05hmzZoFrVbLYe5E5cC+GxERFetJJPDsBpysrTEoMwsn7eygkP7bz1Kmdfy3rEQJufMVoByzi3p5eeHcuXPsuxEREZHeGCTpMXDgwELbtFqtMOVIeYwYMUIfIdH/UyqVsLa2NnYYZk2pVOL69esA8q5ckhex0B/plyW1eXHreTRxblJo28jmI3Ek5ghOPzpdaN+X/3yJzrU6w83WrdwxGLu91Wo1EhISUKtWrWLLmNOXZmO3tyVSKpXGDkF07LuZLvbdxMHPWnGxvcVV6faO2A4AaKtQYFl8AtIlEhx1sMf/7D2QaK2AOrOZUNSqWhQksmxApTupxOPHj1GnTp1iD2FOfTeA73GxWWLfjYiISmaQ6a20Wq3Ov+K2l/TP2toab731Fl566SVDhEhkMFqtFkqlEkqlkou5isSS2ryokR4yiQwNqhWeM1gikeDznp+jmnW1QvuScpKw+J/FFWovY7a3UqnE+PHj0atXLzx+/FjUYxuLJb2/yXjYdyNLx89acbG9xVWp9taogchdOpuqabUYkqnGswfvITN6DoB/p7ySu1wsVMXatWvRtGlTHDx4sCLhV0l8jxMRERmXQUZ6fP217nzx8+bNg0QiwQcffFDqlbnW1tZwd3dH69at4VDEImlERJasqKRHg2oNIJcVffWYu7075nWbh0/OfFJo35GYIzh0/xD8mvjpPU5DyMrKwssvv4wDBw4AAAYPHoyTJ0/Cza38o1WISBf7bkREVKR7p4CMJ4U2H1J5IRu2QIHf8yVWyZDZRwMAbOV5iZBly5bhww8/BACMHj0ahw8fRu/evQ0fNxEREVk0gyQ9Ro0apXN/3rx5AAAfHx80b97cEIckIjJ7Wq22yKRHY6fGJT5uWNNhOBJzBMcfHi+0b3HoYnSp3QU17GroK0yDmT17tpDwAICrV69i1KhROHHihNlNiUAkNvbdiIioSBE7itwcqC6cuJC7XIJE8m8WZN++fULCAwCys7MxbNgwREREoGHDhvqPlYiIiOj/GWR6q+dt3LgRGzZsQP369cU4HBGRWVJoFPCu443mLs0hl/47sqOo9TwKkkgkWNBjAZxtnAvtS81NxaJzi6rEsPsvvvhC58dXe3t7zJ8/nwkPIgNg342IiKDIAq4HF9qcLHHGGU2757ZqIHe+pLPlxRdfREBAgM626dOno0GDwtOyEhEREemTQUZ6PK9bt25iHIaIyKzZyGywrN8yAIBao0ZcRhzupd1DbYfapT62hl0NzPeej49OfVRo3/GHx7Hv7j78cfMPRDyL0NnXvmZ7bHlxi36eQCXVqlULR48eRa9evZCZmYkDBw6gR48exg6LyCyx70ZERLgVAijSC23erewBdYF1PABA5hANqXWyzjapVIp169YhLS0NwcHBWLp0KebMmWPQkImIiIgAkZIeRESkXzKpDA2cGqCBU9mvlPNt7IvDMYdxJOZIoX1fn/8a9R1N/4ruRo0a4ciRI1AqlWjfvr2xwyEiIiIyX8VMbbW7qKmtnAsvYA4Acrkc27dvx5EjRzB8+HC9hkdERERUHL0mPYKCgoTbI0eOLHJ7RRSsi4iIKkYikWB+9/m49PQSknKSdPalK9IRmxFrpMjKp3Xr1sYOgchssO9GRERFykwA7hzFQysruKtVsPn/mVDvausiUqs7taqTNAfnEw/CNkn578b6XYWbdnZ2THgQERGRqPSa9Jg7dy4kEgkkEonOl9387RXxfF1UOTKZrPRCVClWVlZo2rSpcJsMj21edm62bpjffT5mn5hdaF96EdMXFMXQ7X327FncuXMHEydO1HvdVRHf3+KzpHMl+26mz5Lej8bEz1pxsb3FVaH2jtoNaFSYU7sWHlrJMSgrCy9mZOJkek8AuucHX5trWPxXOj7vZwMrKddaA/geFxvPlURE9Dy9n32LWwy3KiySawmkUlHWrrdoUqkUrq6uxg7DorDNy2dQo0EY0mQIQu6FVOjxhmzvgwcPYvTo0cjNzYWDgwNeeuklgxynKuH7W3yWdq5k3820Wdr70Vj4WSsutre4KtTeEdtxT26FazY2AIDAao4IrOYISY0oyBPqQZniLRQN3rkdN6MUeJyuxe/DbSucNDcnfI+Li+dK07BmzRrExurOHlC/fn289dZbRoqIiCyZXpMef/31V7m2ExGRcXzS7RNceHIBCdkJAABnG2c4WzvjQfoDo8W0c+dOBAQEQKnMmxrhtddeQ7Vq1eDr62u0mIjMHftuRERUSGI0EHsBIS7OhXZp5ZnQGemREoubUVEAgLWXlXCxleC7wTZg2oOIiIiMSa9Jj3r16pVrO4mPV20anlarFX60lcvlvNJJBGzz8nOxdcGC7gsw4/gM9GvQDwt7LMQHxz8oU9LDUO0dEREh1AsASqUS69ats/ikB9/f4rOkcyX7bqbPkt6PxsTPWnGxvcVV7vaO3AUtgP2O9kXUJYMyrZ1wP+3yYZ39O68pMa+PNWpUOuqqje9xcfFcSUT6EhcXh19//RV///03nj59CoVCAQBYuXIlfHx89HKMwMBAzJs3D0DexWb169fXS72ki5NLWhiVSgWb/x+iTIahVCoRGRkJAPD09IS1tbWRIzJ/bPOK6d+wPzb4bUAn907l+iJmqPZetGgRkpOTsXLlSgBAQEAA1q1bp5e6qzK+v8WnUqmMHQKRgH03cfCzVlxsb3GVq721WiBiO6KsrfFALi+0W5XhAWjykiFymQQrP34DE17eDbVGC4/qUhyZYI8a9pxqiO9xcbHvRmLKzc3Frl27cPjwYdy8eRMZGRlwcXFB69atMXLkSAwdOrTEx3t4eJTpON26dcOmTZuK3X/9+nUsX74cly5dQm5uLpo1a4Zx48bhlVdeKbHenJwcvPjii3j06BHWr1+PHj16lCkeSxAXF4fRo0cjOTnZ2KGQHjDpQURUBfz14C/cTr6Nxs6N0cSpCRo5NYKtlW2l6+1cq7MeotMPiUSCH3/8ESkpKXB2dsaKFSs4Py8RERGRmB6FAUnR2O/mUuRuVWpH4fbgNrUxbnRnqN9sjR8O3MCBAHu4O7DvRkTm6+7du5g2bRru3buns/3Zs2d49uwZTp06hcDAQPz4449wcHAwWBwXL17EW2+9hZycHGHbtWvX8Nlnn+HOnTv45JNPin3sL7/8gkePHmHIkCFMeDznl19+QXJyMqysrDBz5kx07doV9vZ5if66desaObrys/QRJaIlPbKzswEAdnZ2Re7ftGkTQkJCkJycjPr162PcuHEYMGCAWOEREZm0IzFHsP/ufuG+BBI0dm6MPSP2mNVwealUivXr10Mmk5nV8yKqith3IyKyQBHboQYQUsSPdVq1DVQZrYX7L3fJ+/FkfPc6GFv3Iayk7LsRkflKTEzEpEmT8PjxYwCAn58fRo0aBXd3d8THx2P37t04ePAgzpw5g9mzZ2PVqlUl1jdu3Di89tprxe4vrg+u0WjwySefICcnB02aNMHs2bPh6uqKwMBABAYGYsOGDfD19YWXl1ehx8bExGDt2rWwt7cXfgynf/39998AgIEDB+Ltt9822HFGjx6N0aNHG6x+yiNK0uPYsWOYPn067O3tcfLkSTg6OursnzdvHoKCggDkzcV4//59nDlzBjNnzsSUKVPECJGIyKTdS9W9kkQLLWSSqpkYUCqVUCgUxV75YmXFQYhExsa+GxGRBVIrgat/ItTWFolWskK7VeltAW3elFe1nWzRp0VNYR8THkRk7lauXCkkPN577z28//77wr42bdqgX79++PHHH7Fy5UqcOHECBw8ehJ+fX7H1Va9eHS1btix3HGFhYYiJiYFcLseaNWuEtfi6du2K+Ph4nDlzBoGBgUUmPb788ksoFArMmDEDtWrVKvexzd3Tp08BAE2aNDFyJKQPoow9PXPmDLRaLQYMGFDoS/PFixexe/duAICtrS3atGkDGxsbaLVa/Pjjj7h165YYIRIRmSytVov7qfcLbW/iXPVOxNnZ2Rg9ejRGjRqF3NxcY4dDRMVg342IyAJFHweyEnCgiAXMAUCZ1lG4/ZJXfciY6CAiC6FWq7F3714AQL169TBt2rQiy02fPl2YBmn16tUGieX69esAgE6dOgkJj3z564nklyno8OHDOH36NJo2bYo33njDILFVdUqlEgAvxDQXoiQ9Ll++DIlEAm9v70L7duzYAQBwd3dHSEgIAgMDERISgjp16kCj0WD79u1ihEhEZLLis+KRpcoqtL2xU2Pxg6mEtLQ0DBkyBPv27cORI0cQEBDARQeJTBT7bkREFihiO3IkEhx1KJz00Kgcoc5sJtx/ycuy5gUnIst2//59pKenAwB69uwJmazwaDgAkMlk6NmzJwAgKioKDx8+1Hss+XHUrFmz0L4aNWoAADIyMnS2Z2dn4+uvvwYALFiwAHK5XO9xAcClS5fw6aefwtfXF507d0a7du3Qt29fTJkyBVu2bEFaWlqRj1MoFNiyZQsmTJiA7t27o127dujVqxfefvtt7NmzBxqNpthjrlixAh4eHsIC8bm5ufj9998xatQodOrUCZ06dcJLL72EzZs3F/n7Q2BgoM7jAeCnn34Stnl4eGDu3LmVbJnijxkbG6v35xQaGgoPDw+dKcwGDhyo85w8PDwQGhqq1+dlakRJXSUlJQEoenjQ6dOnIZFIMH78eNSuXRsAUKdOHYwfPx5Lly7F+fPnxQiRiMhk3Uu7V+T2qjbS45VXXsHJkyeF+3/++Sc++OADrFy50ohREVFR2HcjIrIwuenAjf04ZWeLTGnhayNVae0B5P3I17WRMxrXMNwCvUREpiYlJUW4Xb169RLL5icegLwkQIMGDfQaS7Vq1QDkLZ7+vISEBAAoNFL7l19+QVxcnMEWL8/JycGnn36Kffv2Fdr39OlTPH36FCdOnEBSUpLOtGAAEBsbi7fffht3797V2Z6QkIBTp07h1KlT2L59O37++We4uLiUGEdCQgImT55caKRLZGQkIiMjcebMGfz888+QFnGeM1Xm+JzEImrS4/n522/fvo3k5GRIJBIMHDhQZ1+7du0AAHFxcWKESERksp5fzyNfU+emIkdSOV9++SXOnj0rXHXi7Oxc4sJtRGQ87LsREVmYG/sBVTYOuNUocrcytZNwe2y3xiIFRURkGgr2iZ8fRfG8/JEYAHDnzp1iyx08eBAhISF49OgRpFIpatasiU6dOmHUqFHo3r17sY9r1aoVACA8PBxPnz7VWZtj//79AIDWrVsL2+7duycsXq7vEQtA3sLq06ZNw9mzZwEAjRs3xrhx49CuXTvY2dnh2bNnCA8PR0hISKHHZmZm4o033hBGxPj4+GDMmDFwd3dHbGwstmzZgvPnz+PSpUuYOnUqtmzZUuwoGyBvrZU7d+5gwoQJGDBgAJydnXHv3j38/PPPiI6OxvHjx7Fjxw6MHTtWeIyPj4/wPcbf3x9A4UXmnZ2dK99QFVSR5+Tp6Yng4GD89ddf+OGHHwAAa9asgbu7u07d9eub96hNUZIeMpkMSqUSqampOtsvXboEAHBzc0PTpro/3uW/oTjnu35xXjrDk8vl8PT0FG6T4Zl7mxeX9Gjk1EjkSPJUtL27du2K4OBg+Pn5wdnZGYcPH0aHDh0MFabZMPf3tyniuZJ9N1PC96M4+FkrLra3uMrU3hHbkSqV4JS9XaFdGkV1aHLyfhhxtLHCEM/aBovVXPA9Li6eK8nQGjZsCLlcDqVSiQsXLpRYtuD+/IXPi/J8QiQmJgYxMTEICgqCj48PlixZIozqKMjLywv169dHbGws3nrrLcycORMuLi4ICgrCmTNnAACjRo0Syi9evBhKpRIffPCBMEpbnzZv3iwkPAYNGoRly5bB2tpap0y/fv3wwQcfFBqd8tNPPwkJj3fffRczZ84U9rVr1w6+vr746KOPEBwcjPDwcGzfvr3ECyevXr2KNWvW6EzR27ZtW/Tu3RtDhw5FQkICtm7dqpMgcHJygpOTk049FV1k3hAq8pzs7e3RsmVLXL16VdjWuHFjs09yPE+UsS/5maTnh+KcOHECEokEXl5ehR6Tnxl1dXU1fIAWRCLhYnOGJpFIYG1tDWtra7a3SMy9zYtaxNzdzh2O1o6FC4ugMu3dr18/oTPGhEfZmPv72xSxndl3MyV8P4qDn7XiYnuLq9T2Tn8C3D2Bv+ztoSxivzK1A4C87f4d6sLe+rkfmCcfBT5P1f03+agBnknVwfe4uNjGZGj29vbCj843b94schonANi3bx9u3bol3M/MzCxUxs7ODkOHDsXixYuxZcsWBAUFYe3atZg6daowfdPRo0cxbdo0YWHtgqRSKf773//C2toat2/fxvTp0xEQEICdO3cCACZMmIAuXboAyBtNcubMGYMtXq7RaLBmzRoAQO3atfHNN98USngUjLvgqBSFQoFdu3YBAFq0aFFo2isg72/7888/F9ply5YtJcYzfvz4ItckdHFxwejRowEAt27d0hmNY+rM8TmJRZSkR5cuXaDVarFlyxZhuoSIiAghA9mnT59Cj4mOjgZQ9MI8RESWpKg1Paraeh4F+fn5oUWLFsYOg4hKwL4bEZEFufonoNVgv2PR63So0joKt1/pYllXiRIR5Xv//feFUUVz587Fzz//jLi4OCiVSsTFxeHnn3/G3LlzdUZ35eTkFKrn1KlTWLZsGV5++WV06dIFrVu3Rq9evTBr1izs378fbdq0AQCcP38e27ZtKzKW7t27Y+vWrejXrx+qVasGa2trtGrVCl988QXmz58PAMjKysKSJUsA6C5efvXqVUyZMgVdunRBhw4d8NJLL+HAgQMVapPr16/jyZMnAICXX3650NS4Jbl69f/Yu+/wKKq2gcO/3VTSE0ICJPQSWuhI70WKNClKly52Xyv62rG/iqBYEJGOoPQivaN0lYTeQ0JNIL1tme+PfFlJdtNgM7vZPPd1eRlmzsw8e3Z25uw+c86JNE1sPnDgwDyHrfLy8qJXr15AVu+YW7du5bnP7OGpLKlfvz4AiqJYnDzcXjnia1KLKn0Ahw8fzqpVq4iOjqZbt25UrVqVCxcuoNfr8fX1NZ289zpw4AAajYYaNWoUe3wxMTEsXLiQXbt2cePGDVxdXalUqRK9evVixIgRlClj3sX3fv3xxx+sXbuWo0ePcvv2bZycnAgMDCQsLIxWrVrRv3//Il0kispoNBbbvkUWo9FoGg7E19dXJhNSgSPXeaoulRspN8yWV/Wtqn4w/6+g+t66dSuenp60adPGFuE5HEc+v+2V3Cul7XYvabuVDnKtVZfUt7oKqu/Mo4u46+TEYXc3s20NaSEYM7N6/9UK8qJxJb9ij9cRyDmuLrlXCjU0btyY9957j3feeQedTseMGTOYMWNGjjLu7u68+uqrvP/++4D5/HiA2VBK9woMDGTmzJn06tULnU7HokWLGD16tMWy4eHh/PDDD3nua9asWVy/fp2ePXuaJi8/dOgQ48ePJzMzEz8/P3x9fYmIiODFF1/k+vXrjB8/vsB6uNfJkydNf2f3Limsc+fOmf4uaBSIRo0amRJA586dM5ubIlvu4Xfvde+8HJZ64NgrR3xNalHlzlu/fn1effVVNBoNqampnDx5koyMDJydnZk2bRpeXjmHaElKSmL37t0APPTQQ8Ua244dO+jXrx8///wzly5dIi0tjYSEBCIjI/n8888ZMGAAV65ceeDjJCQk8NRTTzF27FhWrVpFVFQUaWlpJCcnc/nyZTZv3sx7771HVFSUFV5V3gwGQ7HuX4Ber+fixYtcvHgRvV5v63BKBUeu88uJly0ut2VPj/zqe8WKFfTp04c+ffpw/PhxG0XoWBz5/LZXcq+UthtI2620kWutuqS+1ZVffUcd3Ypr7Ek2eXqgWBraKkcvj0oyjFAhyTmuLrlXCrUMHjyY5cuX0717dzw8PEzLnZ2d6dKlCytXrjRNig35JzjyUqlSJdMDhFeuXOHmzZtF3seFCxeYP38+Hh4eTJ06FchKDr755ptkZmYyatQoDhw4wPbt2/n666/RaDRMnz7dNL9GYd29e9f0d1F7e987d2BAQEC+ZQMDAy1ul1t+Dz7dm3wuSdcMR3xNalFttqcnnniC1q1bs3nzZmJjYylXrhx9+vSxmLE6ePCg6SLRuXPnYovp5MmTvPjii6Snp+Ph4cHkyZNp2bIl6enpbNy4keXLl3P58mUmTZrEihUrzL7gF1ZSUhJjx47lxIkTQNbEPg8//DCVK1dGq9Vy48YNDh06xJYtW6z58oQQDsDSfB4A1Xzsb3iruXPnMnHiRIxGI/Hx8fTo0YO9e/fKUFZClFDSdpO2mxDCsUVGRrL9vcd5vikWh7ZSFA36xIYAOGs1DGgSonaIQghhd+rXr88333yDXq/n9u3b6HQ6goODcXPL6i23Zs0aU9n7/S5co0YN0wNFN2/ezDEXRmF88MEH6HQ6nnvuOdPk5ceOHSMqKoqAgADTw00APXr0oFOnTuzcuZN169bx1FNP3VfMD0IS6qI4qJb0AAgLCyMsLKzAct26daNbt27FHs+HH35Ieno6zs7OzJ07lyZNmpjWtW7dmipVqvD5559z+fJlfv75Z4uT6hTGBx98wIkTJ3B1deWrr76ia9euOdaHh4fTvXt33njjDcnMCSFysDSfB9jfnB6KorBy5cocXctv3rzJ999/zxdffGHDyIQQD0LabtJ2E0I4rr17dtOvWiYXXVw55WY+8awhtTqKPmvojC51gijnbT78lRBClFbOzs5UqFDBbHn2QzMADRs2vK99P0gSYOPGjfz5559Ur16dsWPHmpafOnUKyEra5J5svGnTpuzcuZPTp08X6Vj+/v6mv2/fvl2kYW7vHZopLi6OatXy/o0jNjbW4nZC5KfUDix5/Phxjhw5AsCgQYNyfGnONm7cONMHdsGCBeh0uiIf58iRI6Ys7wsvvGD2pfleGo3GNCmSEEIAXEowT3qUcS5DsOe/T3qkZuoxGpUHPtbi3ouJGBOR47/FvRcXaluNRsPy5ctzTG785JNP8tlnnz1wXEIIAdJ2E0IIa5vSpynV/LWU1xv48HYcbVPT0Cr/tin1CY1Nfw9tXskGEQohRMliMBjYunUrABUqVLDYXi2MCxcumP4uSi+PlJQU0+Tlb731Vo5J1ZOSkgDw9vY22y57WXaZwsqeSBswtdML695eMP/880++Ze8dOltGkii80t6DxmZJD0VRiIqK4vjx4xw/fpyoqCgU5cF/tCusbdu2mf4eNGiQxTJarZYBAwYAkJiYyMGDB4t8nMWLs34w9Pb2ZuTIkUUPVAhRqllKelT1qYpWo6X3jD1UfX0D9d7eTPU3NlL19Q0M/Ha/DaLM4uHhwbp162jatClTp07l22+/xcnJyWbxCCGsS9puQgjhYI4vA8BDUeiXnML3N2+zPSqGarcao0+tii4p68esct5udAor2ljtQghRGv32229cu3YNgMcee+y+vg9fvXqV/fuzvtdXrly5SEmPb775hps3b9KzZ0/TvCDZshMbN27cMNsue1lRh4atU6eOqbfLr7/+WqTJtBs0aGCa82T16tU5Ro24V3JyMr///jsANWvWzHMSc2Eue8g1gMzMTBtGYhuqP5q2d+9eFi9ezMGDB0lPT8+xzt3dnZYtWzJy5EjatWtXrHEcPXoUyPqR7t7MZG4tWrQw/X3s2LEixZWZmcn27dsBaNOmjelkMxgM3Lp1C4PBQLly5XKchEIIkc2oGLmSaD4Zb1WfqiiKwuW4VLN1qZm2nSjR19eXffv25TvZlhCiZJG2m7TdhBAOSJ8BJ1aZLQ5082Xe5O9Yffw2yzOucvpGEoOahuLsVGoHiRBCCJP85tf4888/+eijjwCoWrUq48aNMyuzY8cOOnTokGdP4djYWJ577jlTb+Xhw4cXOrbz58+zcOHCHJOX36tOnTpAVq+Kq1evUqlSVg8+g8HAxo0bc5QpLK1Wy/jx45k2bRo3btzgtdde48svvzQbPguyJlK/ffu2qf5cXV0ZPHgwc+fO5ezZs3z77bc888wzObZRFIUPPvjANGH6iBEjihRfccke/jckJIQdO3bYOJq83Tu5/NWrVy3OzZitpLymolAt6ZGZmcnUqVNNHyRLTwampaWxe/dudu/eTe/evfn4448tflCsIburWOXKlfMdluDeE+Le7mWFcfr0aTIyMgCoXbs2ycnJzJgxg9WrV5OYmAiAi4sLLVq04Mknn6Rly5ZFfRlCCAd2PeU6GYYMs+XVfKtxOS6V1EzzceSj76ahKEqxdmPMyMjI9xiS8BDCMUjbTdpuQgjHkZmZmfP6fG4rpMebF6w/kAAfL8a182Js26pExiRS1qt4rutCCFHSPPLII7Ro0YJOnTpRs2ZNXF1duX79Olu3bmXdunUYjUb8/PyYMWOGxYdkpk2bhk6n4+GHH6Zx48aEhITg7u7O3bt3OXjwIMuWLTP9wN+sWbMi/cj//vvvm01efq9mzZpRqVIlrl69yoQJE3j55Zfx8vJi/vz5REVF4ezsTN++fYtcJyNGjGDnzp3s37+frVu30rdvX4YPH06DBg0oU6YMt2/f5p9//mHDhg088sgjOebce/rpp9m6dStXr17l66+/5uzZszz66KOUK1eO6OhoFi1axKFDhwBo0qQJjz32WJHjK83q1q2Lm5sbGRkZzJgxA2dnZypWrIhWm/UgQ3BwMO7u7jaOsviolvR4+eWX2bp1K4qi4OzsTJs2bWjYsKEp63T79m0iIiLYv38/er2ejRs3otfrmTFjhtVjycjIMF1ELF0I7uXr64uHhwepqakWu4Dl594v2oqiMGjQIC5fvpyjjE6n448//uDPP//kP//5D5MmTSrSMYpKp9Pl2aXJ2dnZdOIbjUb0+vyfGL+30WwwGPKdyFOj0eQYS1Cv1+fZdQ2yssX3/qCh0+nyHULj3tgVRSlwDG8XFxfTD7YFvdaixm4wGEzlNRpNgbE7OTnl6PJYUJczeZ/M6XQ6nJ2d0Wg0pvLWjt0W79O52HMWy1byqsTx6HiL61IyDKw6GsUjDf+dUM2a71NSUhKPPvooVatW5cUXX0Sn0xXr56kkvE95sdZrzT6/s5dZit3W1z1He5/uZx4IRyRtN2m7lcQ2wf3Gfu82BoMh3+uSvV/DSsL75Khtt7zY8n1SFIX333+fNWvWMHv27H/bbv8/tFVuuroDUe6pqzrBHqW6TXC/r9VS+83ernuO9D5J202oRa/Xs337dlPP4Nxq1arF//73v3x7TNy6dYuFCxeycOHCPMs8/PDDTJs2rdAPE61fv56DBw9SrVo1nnjiCYtltFotH330EePHj+fy5ctmvSqef/55KleuXKjj5d7vrFmzeO2119i8eTOXL1829XgpiJeXF/PmzWPixIlcvHiRzZs3s3nzZrNyTZs25bvvvpPhs4vIy8uLUaNGMWfOHE6cOGHW+2jBggUO/RCXKkmPXbt2sWXLFjQaDS1btuSjjz4iJCTEYtlr167xxhtvcODAAbZs2cLu3bvp2LGjVeO5d4w5Dw+PAsuXKVOG1NRUUlPNh5LJT0JCgunvH3/8kYyMDNq3b89zzz1HnTp1SE5OZvPmzXzxxRckJSXxxRdfUL16dbp161ak4xRFfk88hoWFmcbvS01N5cyZM/nuq1mzZqa/Y2NjiY6OzrOsp6dnjov+lStXiI+Pz7N8UFCQqasdwKlTp/JtyFSvXh1/f38gq8EWERGRb+zh4eGmm0dCQgIXL17Ms6yLiwsNGzY0/fv69evcunUrz/J+fn45yp8+fTrfcQ1DQ0NzdI8sKHZ5nyy7n/cpe7JbyPps2Nv79EfsHxbLOic4c+xagsV1ANM2nCTYcAt356wvGtZ6n+Lj43nuuec4efIke/bswcvLi3HjxpX69ykvxfF5On36tOlv+TzlrTjfp9JC2m7SdiuNbYLsbaTt9i97fJ+yOfK9xlrvk9FoZPr06SxduhTI+kFrxowZNG9QC9ezm8z2k1GmPJF3y0D8v3Ul71N8nuUL+z5lt9/k85Q3abuJkmLatGns27ePiIgIbt++TUpKCgEBAYSFhdGzZ0/69euXIymX2yeffMKhQ4f4+++/uXr1KvHx8SQnJ+Ph4UH58uVp0qQJAwcOLNIE6MnJyXz66acAvP322/kmSh566CGWLFnC119/zdGjR9HpdNSqVYuxY8fyyCOPFL4icilTpgwzZ87kwIEDrFy5kqNHjxIbG4vBYCAwMJA6derQuXNn+vTpY7ZtaGgoa9as4ddff2XTpk2cPXuWlJQUfH19qVu3Ln379qVv376mRKoompdffpmqVauyevVqzp8/T1JSUr6JZUeiStJj5cqVQNbYcHPmzMn3AlCxYkV+/PFHhg4dyunTp1mxYoXVvzhnD1sA5BtLtuwLRu5xrAty7xftjIwM2rZtyw8//GDKTAYEBDBs2DBq1arFqFGjMBqNfPnll3Tt2rVYh6YRQpQMLhoXyruW51bmLYxkPamlQUMF9wp59vQAuJNmZNXpFIY18LZaLHq9nilTpnDu3L+9T7799lv8/PwIDw+32nGEEPZB2m7SdhNClHyzZs0yJTwAjhw5wtSpU9n2+RgwmD+pfye0K2jkRyUhhMhLnz59LP5wX1gPPfQQDz30kBUjynqaf+/evYUuHx4ezuzZs60aQ7ZWrVrRqlWrIm/n6urKiBEj7mvOjmeffTbHkFl5admyZYEJ2ILWF7VcXh599FEeffTRPNdb8zVpNBqGDBnCkCFD8i33oK/JHmmU/PowWknHjh25desWn376Kf369SvUNuvWreOVV14hODiY3bt3WzWeO3fu0Lp1awB69+7N9OnT8y3fpk0b4uLiqF27NuvWrSv0cX766Sc+++wz079XrVpFvXr1LJZ97rnnTF241qxZU+TJg/KTmZlpehKjTp06ef5YUFK6ChcUu3QVzkneJ+vEbsv3SWfQEZ0SzeXEy9xKvcXgmkNp+uFO0nR5v5euzlo2PduGUP8yVnufFi1axPjx403lypUrx/r162nRooW8TxbI56nkvk8ZGRmmpzLvfcqyNJG2WxZpu8k1zBJ7v4bJ+5RF3ic4e/YsXbp04fbt26Y4ZsyYwTPem9Bc3me2n8xJ+yGwtqqxy/uUN/k8WSZttyznzp1Dr9fj7OxMrVq1bB0OkNWOyt1LJzQ0NMd3SCGEsKQ4rmmq9PS4c+cOADVr1iz0NtmTUGaP32xNnp6epr8LM+xBWloaULjhFPI6TkBAQJ5fmgHat29v+uIcERFh1S/O99JoNIVqAGi12iI1FHI3ggqS3wSklhTmqc5shX2N2Yr6WguKXa/Xc/XqVQAqVKhQpNiBYo3dUd8nvV7P9evXgaw6d3Z2LtbYQd33yRVXwsqEERYYBsDpG4n5JjwAMvVGPt96nu9GNjNbd7/v07hx40hOTub555+nYsWKLF68mKZNm+Z4utnan6fc7Pl9Ksj9vlZL53dutr7u5VbS36eCvsiXBtJ2k7ZbbiWlTWBJYWLPfa0tSvz2dg0rCmm7WeYo71ODBg3YvHkznTp1IiUlhenTpzNleF80X/+XCFdXkrRaWqan4wRQoTGuFRvYNHZwnPepoPabPVz37lXS3ydpuwkhhMhNlaSHh4cHiYmJRRpnMTExEcgaF87a3Nzc8PPzIz4+vsAJLhMSEkxfrguaODO3ChX+nUi4oG3vXV8cPxZkU6FjT6lnNBpN46feO+6pKD6lrc6PX817Po97/R55gz8uxNKmRqDVjv3cc88BUKNGDby9vfN9gkxYR2k7v+2B3Cul7VaYbaXt5ljkWqsuqW/1NGnShNWrV/PPP//Qpk0biPwNgDl+Puzw9CBQb6BnSgq9a3WigaLIUH1WIue4uuReKYQQIjdVkh7VqlXjn3/+YePGjVkNrULYuHGjadviULNmTY4cOUJUVJSp+4wl904Wdu9EX4U9RraCJom594fDojzVIIQoff7JZz6Pe1X0dSdTb/2kxJNPPlng5IVCiJJN2m7SdhNCOI62bdvi4+MDioI24lcStBr2emQlqGOdnVjk68OiqytpuCGaRX3mSOJDCHFfZBgrIYQ9UWWWsi5duqAoCitXrjRNjJmf1atXs2LFCjQaDV27di2WmJo1yxryJTU1lRMnTuRZ7vDhw6a/mzZtWqRjhISEULFiRQBiYmLyffogKirK9Lc8CSKEyE9EjHlPj0AvV3zcs34AdHPW8nzXWmx/qROdwoKKvP/169cTExPzwHEKIUouabtJ200IUXJcvHiRrVu3FliuTOJFtLGn2ebhgc5CYuPwaW96z9zHgj8vF0OUQgghhBDqUSXpMXLkSMqVK4eiKLz55ptMnjyZLVu2cPPmTXQ6HXq9nps3b7JlyxYmT57M1KlTMRqNBAUFMXLkyGKJqVu3bqa/V6xYYbGM0Whk9erVAPj4+NCyZcsiH6dHjx4AJCcn8+eff+ZZbsuWLaa/s7/UCyFEbhl6A6euJ5otb14lgBe61aZPwwpsf6kjL3avTRnXoj95PG/ePPr370+PHj2Ii4uzRshCiBJI2m7SdhNClAwnTpygXbt29OvXjz179uRbNiAmKzGy0cvT4npdYmNOXU9k15nbVo9TCCGEEEJNqiQ9PDw8+OGHH/Dx8UFRFPbs2cPzzz9Pp06daNiwIeHh4XTq1Innn3+ePXv2oCgKvr6+/PDDD8UyLjRAw4YNad68OZD1xfmvv/4yKzN37lwuXLgAwOjRo80m9zp48CBhYWGEhYXx+uuvWzzOmDFjcHNzA+Djjz8mOTnZrMyaNWs4dOgQAJ06dcoxnrQQQtzr9PUkdAbzJ48bVvJlbNuqzBrelFD/ok3cm+2rr75i7NixGI1GTp48Sa9evUhKSnrQkIUQJZC03aTtJoSwf4cOHaJDhw5cv36d9PR0+vbty7FjxywXVgwExOzgppMTh93dzFYb0kJQMssBMLR5aHGGLYQQQghR7FRJegDUq1ePtWvX0qNHD7RaLYqiWPxPq9Xy8MMPs3btWurUqVOsMb355pu4u7uj1+sZN24cP/zwA3///TcHDhzg7bff5vPPPwegatWqjB079r6OUbFiRdPEv2fPnmXw4MGsWLGCyMhIDhw4wAcffMDUqVMB8PLyMv0thBCWHLcwtBVAwxC/Bxp/OTU1le+//z7HssOHDzN37tz73qcQomSTtpu03YQQ9m3evHncuXPH9O/ExERee+01i2W94/7BNT2WTZ4eKBbajLrExgCU9XSlSx0Zsk8IIYQQJZsqE5lnCw4OZubMmdy6dYtDhw5x7tw54uPjAfDz86NWrVo89NBDBAUVfQz6+1GvXj2mT5/OK6+8QnJyMl9++aVZmapVqzJ79my8vLzu+zgTJkwgISGBH3/8kUuXLvHGG2+YlSlbtiyzZs2iatWq930cIYRj0Bl1zD8xn2o+1ajmW41K3pVwccp6Wvn41XiL24SH+j7QMT08PNiyZQvt2rXj6tWrALz22mumH/6EEKWTtN2k7SaEsF8zZ87k+vXrpmH92rRpw6+//mqxbED0NgA2WBjaSlE06BMbATCwSQiuzqo9GymEEEIIUSxUTXpkCwoK4pFHHrHFoc106dKFtWvXsmDBAnbt2sXNmzdxcXGhcuXK9OzZk5EjR1plmIaXXnqJLl26sHTpUo4cOcLt27dxc3OjatWqdOnShVGjRuHt7W2FV5S/B3kSXBSOVqvFz8/P9Lcofo5W5zFJMcw4NsP0byeNE5W8K/FMk2c4Hm1+PaoW6IlvGRez5UVVuXJltm3bRvv27XnxxRfzHPrF0erb3kl9q0/uleak7SZtN0cn11p1SX1bh7OzM0uXLqVPnz44OzuzcuVKPD3NkxpaQwYBN/Zy0cWZU26uZutblG/BI+HtWXbkKkOaV1IjdIcn57i65F4phBAiN42iKOaDwwuHkpmZSUREBADh4eG4upo3dIUQ9mNn1E6e22new+LTdl/wzBwdxlxX7f6NKzLj8SaF3r/RqKDV5v3F4M6dOwQEBBR6f0I4GrlvCluTc1AIURTJycm4uLiY5iMyE7kSfhvLN36+/OBv3jv4/TbvM7DWwGKOUojiUxrvm+fOnUOv1+Ps7EytWrVsHY4QQjyQ4rim2aSnB0BcXBxnz541GyIhMDDQViEJIYRduJR4yeJyXXogRuW62fLwkMINbXXxdjIfrD9Ji6r+PNU575uIJDyEEJZI200IIWxDURSMRiNOTk4W1xc4nN/x5SjABi8Ps1UuWhe6VulqhSiFEEIIIeyHqkkPRVH45ZdfWLJkCefPn7dYpmbNmgwbNozHH39cuoEKIUqlSwnmSQ8njRM34syHKwBoVMkv3/0lpuv4evs5ft5/Gb1RYdeJaJyjjzFp1GPWCFcI4cCk7SaEELalKAovv/wysbGx/Pzzz0W/zqbEwfmtRLi5Eu1iPhxqx9CO+Lj6WClaIYQQQgj7oFrSIy4ujieffJLIyEggq/Fmyfnz5/nggw9YsWIF33//PeXKlVMrxFJBp9OViq6etqTT6bhw4QIANWrUwMXClwthXY5W55cTLpstC/UO5URMqtlyrQbqV8z7i+q2kzd5feVxYpMzTcsUZzfeXLaf0LLe9O7du8jxOVp92zupb/XpdDpbh2AXpO1mH6Ttpg651qpL6rtw9Ho9kydPZu7cuUBWD7uvvvqqaPMXnFwFRj0bPP0tru5dvehtQVEwOcfVJW03IYQQuamS9MjMzGTMmDFcuHABRVEICAigV69ehIeHm4ZEiI2NJTIykt9//524uDhOnDjB2LFjWblypXzREyWKoiikpKSY/hbFz5HqXFEULiZcNFtezacaERcSzJbXCvLGwzXvS7m/p2uOhEc2zwZdeOyp19m40Jv27dsXOUZHqe+SQOpb2IK03URpI9dadUl9F87YsWNZtGiR6d8zZ87E39+fd999t/A7Ob4cPbDJwtBWXi5edAjt8OCBCjNyjgshhBC2pUrSY968eZw/fx6NRsPgwYN544038PAwb3QNGDCAl156iY8//pjly5dz4cIF5s2bx6RJk9QIUwghbO5uxl0SMxPNllf0rMy62BSz5Q1D85/Po1kVfwY0rsjqv6+ZrQtoP1yeyBZCWCRtNyGEsL3HH3+cX375Bb1eD4CTk1PRJve8cwmuHuRQGXfuWJgPpFuVbrg55TH5uRBCCCFECabKwMsbNmxAo9HQpk0bpk2bZvFLc7YyZcrw/vvv07ZtWxRFYcOGDWqEKIQQdsHSfB4AWn2QxeUNC5jPA+D1XnUp4/LvF13FoEd7bjcb3hpKnTp17itOIYRjk7abEELYXp8+fVi4cCEajQY3NzdWrVrFiBEjCr+DiF8B2OBp+Rreu5oMbSWEEEIIx6RK0iMqKgqA4cOHF3qb7LLZ2wohRGmQV9IjOTnA4vJGBfT0ACjv687TnWsA0L5WWXpr/+LPb1+iQe3q9x+oEMKhSdtNCCHsw+OPP86PP/7Ipk2b6Nu3b+E3VBQ4vox0jYZtFpIerviiT6mO0ShDLwkhhBDC8agyvJWrqyvp6elUqFCh0Ntkl5UxoYUQpUleSY9rt3yA5BzLXJw0hJX3LtR+J7SvTv0QXzrVLodG0+pBwxRCODhpuwkhhP0YP3580Te6dgzizrPL04NUrfmzjslxDRj10xEebRLCl481fvAghRBCCCHsiCo9PapVqwbA9evXC71NdtnsbYUQojS4nHjZbJm/mz+nYvRmy+tW8MHN2Xx85k2bNpGenp5jmbuLE53DgtBoNFaLVQjhuKTtJoQQ6jl58iRnzpyx7k6PLwdgYx5DW+kSmwDQuY7lIVSFEEKUfOnp6XzzzTf069ePxo0bExYWRlhYGB9++KGtQ7OKlStXml5TdHS02frXX3+dsLAwunTpYnH77G2//vrr4g61VOjSpQthYWG8/vrrtg4FUCnp8eijj6IoCr/88kuht/nll1/QaDQMGDCg+AITQgg7Y6mnR6hXFa4lpJsttzSJ+ddff02vXr0YNmyYadJLIYQoKmm7CSGEOo4cOUKHDh3o3r279YYHNOghcgVpGg0HyribrTZmBGJMD8HPw4Ue9YOtc0whhHBgBw8eNP1Anvu/Ro0a0blzZ5566inWrVtnN9/DdTodTzzxBF9//TVnzpwhLS3N1iEJoSpVkh5DhgyhXbt27Nu3j3fffZeMjIw8y2ZmZvL++++zd+9e2rZty2OPPaZGiKWG1kLXZmFdTk5OhIaGEhoaipOT+VP4wvocpc4zDBnEJMeYLfd2qmixfMMQP9PfiqLw/vvv89xzzwGwevVqJkyYgNFotHqcjlLfJYXUt/rkXiltN3si56M65FqrLqnvLLt27aJLly7ExcVx9epVunfvzq1btx58xxd3QcptyigKm65e483YOzRO//c6rktsDGgY0DjEYq9h8eDkHFeX3CuFLaWnp3Pt2jW2b9/Oyy+/zOOPP87t27dtHRabNm3ir7/+ArIeaFqwYAHr1q1j3bp1TJo0ycbRiQcxatQowsLCGDVqlK1DsWuqzOlx5MgRxo4dS0JCAsuWLWPbtm306tWL8PBwypYti0ajITY2loiICDZt2kRsbCzh4eGMGzeOI0eO5LnfFi1aqBG+Q5EGV/FzcnIiOFiemFKTo9R5VGIURsU8SdG6Ul0+eLMrEdEJ/BOdQER0PMejE2hY6d+eHtHR0fzvf//Lsd38+fMZOHAg/fv3t2qcjlLfJYXUt/rkXiltN3si56M65FqrLqnvrAdW3n77bZKSkkzLzp49y5tvvsmPP/74YDs/vsz0Z4DRyONJyTyelMxg5TmOe6WhS2wIwJDmoQ92HJEnOcfVJfdKoaZhw4YxfPhw079TU1OJjIxk7ty5xMTEEBERwVNPPcXy5cttOrz0H3/8AUC5cuWYNm1aqfycfPLJJ3zyySe2DkPYiCpJj1GjRuX4oMfFxbFo0aJ8t4mMjMx3wjaNRsPJkyetFqMQQthaXpOYV/OtRpC3O13rutO1btaXJ0VRcpSpVKkS69ev5+GHHzbN5/Hhhx/Sr1+/Qh//ekIa524m06F2uft8BUIIRyFtNyGEKF4ajYaVK1fSsWNH07WxW7duTJ8+/cF2nJEMp9ebLb6h+HMs4yGMGVlPxNev6EP9iuZDpQohhMhf2bJlqV27do5ljRs3pm/fvgwZMoQrV65w/Phxdu7cmedcEmrI7jkoPc5EaaVaH0BFUaz+nxBCOJL8kh65aTQas6dGOnTowG+//YarqyuzZs3ijTfeKNSTJek6AzO3n6PL/3bz7NK/uJuSeX8vQAjhUKTtJoQQxSswMJAtW7ZQtWpVHn30UdavX4+Xl9eD7fT0BtClmi1eY2iD8Z6v/0ObV3qw4wghhMjB19c3x7BRe/futWE0WUPQAri4uNg0DiFsRZWeHgsWLFDjMKIQdDodrq6utg7DoWVmZhIREQFAeHi41LcKHKXOLydeNlvmrHWmopflOT0s6dOnD+fOnaNy5coFllUUhd8jb/DhhlPExGdNapamM/Dl1rN8MKBBnts5Sn2XFFLf6tPpdLYOweak7WY/pO2mDrnWqkvq+18hISHs37+foKAgnJ2t8PX8nqGt7rXG0Nb0t6uTlv6NC9++FEUn57i6pO0m7EXDhg1Nf1+7ds1svcFgYO3atWzatIkTJ04QHx+Pp6cn1atXp0ePHgwbNgx3d3eL+x41ahSHDh3ioYceYuHChVy+fJkFCxawb98+bt68SXp6Otu3b6dr1645tjt06BBhYWGmf4eEhLBjxw4rveIsRqORjRs3snnzZiIiIrhz5w4AFSpUoEGDBnTv3p2uXbuaJWDOnj3Ltm3bOHr0KOfOnePOnTu4uLhQrlw5mjRpwrBhw2jcuPF9x/X666+zatWqQr/mP/74gwULFhAZGUlCQgJBQUF07NiRyZMn5zlk4cqVK5k6dSoA27dvJygoiCVLlrBx40auXLlCfHw8zzzzDM8++yyQdX/Yt28f+/bt459//iEqKorU1FS8vLyoXLkyHTp0YMSIEQQEBOT5erLlfm8h7/c3KSmJJUuWsHPnTi5fvkxycjJ+fn40aNCAAQMG8PDDDxf40Ozu3btZtGgRERERpKWlUb58eTp16sS4cePsckhHVZIeDz30kBqHEUKIEs1ST48q3lVw1ua8VCuKku/NqDAJD4B/ohN4avExs+WLD15hRKvK1CnvU6j9CCEcj7TdhBDCegpqu1WsaKUERNJNuLjTbPEZYygnlSqmf/eoH4yfh/wIL4QQ1nZv8tpgMORYd+3aNaZMmcLp06dzLI+Pj+fYsWMcO3aMpUuX8sMPP1CtmvloD/fatm0br7zyCqmp5j371BYdHc0zzzzDqVOnzNZdvnyZy5cvs379ehYsWEDLli1N6w4ePMjo0aPNttHpdFy5coUrV66wevVqJk2axEsvvVSsrwHgm2++4euvv86xLDo6msWLF7N27Vq+//57mjdvnu8+7t69m2ddZHv77bdzJC6yxcfHEx8fz/Hjx1m0aBHffvstzZo1u78Xk8uff/7JCy+8QHx8fI7lt2/fZufOnezcuZOOHTsyffp0PD09Le7j448/Zt68eTmWXb58mXnz5rF27Vpmz55tlVitSZWkhxBCiPwpimIx6ZF7aKvr168zdOhQZs6cSZMmTR7omI0r+dGjXjBbTt7MsdyowHtrT7JkYkubTrwmhBBCCFHSGQwGJk+eTMOGDXnuueeK92CRK0Axmi1ebWgH/Numk6GthBCieJw9e9b0d1BQkOnvu3fvMnz4cK5fv46rqytDhw6lRYsWhISEkJqayv79+1mwYAFXrlxh4sSJrFq1Cm9vb4vHuHbtGq+88gru7u5MmTKF5s2b4+TkREREBB4eHqxbtw6AqVOnEhkZSYMGDfj4449N21tzuKvY2FiGDRtmmj+kVatWDBw4kOrVqwNZSYMDBw6wefNms20NBgMeHh507NiRVq1aUb16dby8vIiLi+P8+fMsXLiQmJgYZs+eTdWqVRk0aJDV4s5t165dREZGUq1aNSZMmEBYWBjJycn8/vvv/PrrryQlJTF58mTWr19PhQoV8tzPm2++ydmzZxkwYAC9e/cmMDCQ69ev56hzvV5PpUqV6N69O+Hh4VSsWBEnJyeuXbvGH3/8wYoVK0y9Q9avX0/ZsmVN27744ouMGzcuz/cWzN/fo0ePMnHiRHQ6HYGBgYwcOZI6deoQFBTErVu32LhxI2vXrmX37t28/vrrZokfgHnz5pkSHkFBQaZ2TUZGBrt27WL+/Pk8//zzpKWl3U/1FxtJegghhB1IzEzE392ftOQ0FP4d9/7epMelS5fo3r07Fy5c4OGHH2bv3r1mXRmL6r996rHrzG0yDTm/IP95MY5qUzfSpLIfq55qm8fWQgghhBAiLxkZGYwcOZLffvsNAD8/P4tPtVpNxHKLi9cY2pj+ruDrTtuagcUXgxDCLt2+fTvHv/39/fMcUi93WT8/vzx/KI+Njc0xb5uvr2+ew7nFxcVhNP77vdPHxwc3NzeLZe/cuZOjp0R+Ze/evYu/v7/FdWrS6/X8/PPPpn/f23N62rRpXL9+nZCQEObPn0+lSjmTzy1btqRnz56MGDGCq1evMmfOHF588UWLx4mOjiYoKIhly5bl6CnYqFEjANOwSB4eHqb/55543VreffddU8Lj5ZdfZuLEiTnWN2zYkN69e/Paa6+h1+tzrKtTpw67d+/Gx8d8hIn27dszYsQInnzySfbv38+sWbMYMGBAsU3IHhkZSf369Vm4cGGOng6tW7emadOmvPbaayQnJ/PJJ58wY8aMPPdz5swZpk2bxpAhQ0zL6tevn6PMc889R6VKlcweMA0PD+fhhx9m+PDhPP7449y5c4eFCxfywgsvmMoEBwcTHBxc6PdWp9PxyiuvoNPpaN++PV9//TVlypTJEVvnzp1p0aIFb731Flu2bGH//v20bfvvb0BxcXF89dVXQNbQWcuWLaNcuXKm9S1atKBdu3ZMmDDB7D22NdUmMhdCCJE3XzdfNg3axKERh/it72/8r+P/eLrx07QNybrZXLx4kXbt2nHhwgUgqyHavXt3oqKiHui4lct6MKF9/l1nhRBCCCFE0RgMBvr3729KeACMGzeONWvWFM8Bb5+Fa39xwtWFjwL8+dvNFQU4YKzLNf5NcjzapCJOWunJK0RpExQUlOO/M2fO5Fm2atWqOcr+/fffeZatW7dujrJ//PFHnmWbNWuWo+y2bdvyLNu+ffscZdeuXZtn2R49euS5Tg2pqakcOnSIsWPHmuoqJCSE3r17A1lJit9//x2At956yyzhka1evXoMHz4cyJonIj8vvfSS9YZGvE8XL140vYfdunUzS3jcy9PTE19f3xzLAgICLCY8srm6uvLqq68CEBMTk++QUdbw/vvvWxzaacCAAXTo0AHIGlYsd1LwXq1atcqR8LCkcuXK+Y6oERYWZtrH9u3bCxN6njZs2EBMTAxubm589tlnORIe9xo6dKhpPprc596qVatMPThee+21HAmPbK1bty7wdduCJD2EEMKOuDu7ExYQxsNVH2ZozXFkJlclOUNPxYoVzXp1uLq65nhS5n491bkmQd6Wn5oRQgghhBBF5+TkZPqR5N5lxeb/e3ms9fJiqa83oyqWp1doRT7xC0brestU7NEmMoG5EEI8iG+++YawsDDTf02aNDFNMg5QtmxZZs2aZerxsnv3bgwGA2XKlDG7L+TWokULAG7dumVxInTIGr6oV69eVnxF92f37t2mXj5PPPHEA+8vMzOTa9eucf78ec6ePcvZs2dz9CLKPReKNdWuXZsGDRrkuT57aC29Xm96ny3p27dvkY+dkJBAVFQU586dM73u7GTQ+fPn0el0Rd5ntuwJzVu0aGFxYvR7Zc9XkjvJ+eeffwJZvbi6du2a5/bFOfzY/ZLhrYQQwk7tPXeb53/5G40GapTzosH4T7nlO49TmxbSoHY1Nm/eTPny5R/4OF5uzrzWsw4v//YP97QphBBCCCHEA5g6dSp37tzhiy++wMvLizVr1tClSxfrH0hR4Pgy9MAmLw/T4hgXZyh7EQ+/WSSfe4vwch5U8rf8lKcQQogHExoaysMPP8z48eNzzMMQGRkJQFpaGvXq1Sv0/mJjYy325qhatWqeQ32p6eTJk0BWEiZ7aK2iSk1NZeHChWzYsIHz58+bTf5+r7t3797XMQojPDw83/XZvSAga96WPn36WCxX2OHHz5w5w7x589i7d2++PUeMRiOJiYk5zqeiyD739u3bV+jYYmNjzWKFrF5deQ2Jl73excXlgZI01iZJDyGEsFP/XE0Asr7Hnr+VzPlbyRDWm6EBCt/+70Orjls6sEkIc/df4sS1RKvtUwghhBCiNNNoNHz++ecoisJjjz2WY3x3q7p6EOKjOFjGnTsWepPokxqA4kyXqpLwEEKIBzVs2DDTMFQajQY3Nzf8/f3znHg8Li7uvo6T16TQ+Q0JpabsJER+87jkJzo6mjFjxhAdHV2o8hkZGUU+RmEVlFQIDPx3mMj4+Pg8y+UewsuSX3/9lXfffbfQ81+kp6cXqpwld+7cKfI2uY+XkJD1u1RBdeTs7Iyfn1++SRy1SdKjlCnWLtUCyPqgZ2dQ88uCCutx1Do/Hh1vtizQy5UlH8/KdwzI+6HVanB1LtyIh45a3/ZK6lt9cq8U9kTOR3XItVZdpam+NRoNX3zxRfEe5PgyADZYGIscQJfQBG83J0Z1Dnf4+rYXpekctwdyryxY9mTT2fJ7gO7y5cs5/u3n55dn2VOnTplNZJ6Xo0ePmk1knpe9e/eaTWSely1btuS5rjiULVu2SBODZ78Of39/FixYUOjtQkNDLS53lPP91VdfJTo6Go1Gw6OPPkqfPn2oUaMGAQEBuLi4oNFoMBqN1K1bFyDHeWZt1vp9RavN/zeVCxcumBIeZcuWZfz48bRq1YqQkBA8PT1xcXEB4LfffuPNN98EHux1Z597HTp04JVXXrnv/YD16khNcvctZQr6AIoHp9Vq8fLysnUYpYqj1fm+ffto2ao1kdcSzNY1DPWz+c3G0erb3kl9q0/ulcKeyPmoDrnWqsvR6vv48eNUrVrVNk/f6jPhxCrSNBq2e5r35DDqvDGkVqdfyxAC/Qt+AlVYh6Od4/ZO7pUFszT5sDXK3vsEfEGKMkRPQfMP3MuaIyAUh+ykUUpKCjVq1HCYpEV2vSckJJCZmVmk3h4XLlzg6NGjAEyePJkXX3zRYrn8elVYU+4hnfJbn18SsCCrVq1Cr9fj5OTEwoULqVGjhsVy2b0rHpSfnx+3bt1Cp9MVKVF3L19fX27fvl1gHen1etXer8KSO4MQQtgJRVGYNm0a7du359UPvyRdZz5JecPQ4vuyuuqptlz+pE+O/1Y91bbYjieEEEIIUdLt2bOHdu3a0bdv3zyHIilW57dB2l12e5Qh1cIPv/rERoCWoc0rqR+bEEII0zwemZmZpjkWHEH9+vUB0Ol0ZpNfF+T8+fOmv3v37p1nObXqKyIiotDra9Wqdd/HyX7dderUyTPhAdZ73dnnXmRkJJmZmfe1j+xkyenTp/Mdkuv06dN2NZ8HWDnp0aJFC1q2bMnFixdzLD98+DCHDx9+oHHIhHXc25VQFA+j0UhycjLJyclS3yopyXU+YuMIwueH02BeA8r1Ksdbb70FwFcfvMHjgdG827cejzYJoUY5TzSa4k16FFZJru+SSOpbfaWpnqXtZv9K0/loS3KtVZej1PeGDRt4+OGHSUpKYs+ePQwZMkT9L/ymoa08LK7WJTamdrAX1f2cSnx9lySOco6XFFLHwp517tzZNFrD/PnzbRyN9XTs2PG+X9e9Q5fl98DAL7/8cn/BFdHZs2dNE7NbsmLFCiBraLEHmZ8rO2mQmpqaZ5lbt26xY8eOfPeTPZF9QYmMLl26AJCUlMTKlSuLEqpJ69atgaxeNzt37syzXHYd2ROrJj2SkpJITEw0u+GMGjWqSJPTiOJz74VFFA+9Xs+ZM2c4c+ZMoScmEg/GEeo843oGSX8lQfbIVQY9n77yJM18kvnyscZsf6kTx9/pQZsahe8+XFwcob5LEqlv9ZWme6W03exfaTofbUmutepyhPpOT0/nySefzJEc3rBhA19++aWKQSTAmd9J0GrZ52FhaKuMQIzpIQxqUpGzZ8+W6PouaRzhHC9J5F4p7Fn16tXp2bMnkHWf+Pnnn/Mtf/XqVdavX1/scYWFhREWFmb6YbyoqlWrRvfu3QHYtm0bc+bMybNsampqjiGbqlSpYvo7rx/jlyxZwvbt2+8rtvvx1ltvWUxGrFu3jt27dwPQrVs3goKC7vsYVatWBeDKlSscO3bMbH1aWhovvfRSgQ+eZQ9Bd/Xq1Xzn/Bg4cCAVKlQA4NNPP+Xw4cP57vfIkSMcOnTIbB/u7u4AfPzxxxaHuTp06BDLly/Pd9+2YNU5PbRaLYqiWLypF+eEM0IIUdK5V3Sn9me1MeqMZN7MJO1KGq83fJ3w8HBTGW93FxtGKIRwRNJ2E0KI++Pu7s6GDRvo2LGjaQzrAQMG8Pzzz6sXxMm1YMhgi7cnegtzvukSG+PipKV/owrEXExULy4hhBA5vPvuu0RGRnL16lU++eQTtm/fTv/+/alVqxaurq7Ex8dz+vRp9u7dy4EDB+jevTuPPPKIrcMu0DvvvMPff//NrVu3+Pzzz9m7dy8DBw6kevXqaDQaYmJiOHjwIBs3bmTmzJm0bNkSyBp2qXbt2pw9e5Zly5aRmJhI//79KVeuHDdu3GDt2rVs3ryZpk2bWkwOWFuDBg2IjIxk0KBBTJw4kdq1a5OUlMTmzZtZtiyrR6WnpyevvvrqAx2nX79+LFy4EKPRyOTJkxk/fjzNmjXDzc2NyMhI5s+fz+XLlwt83U2bNmXlypXExcXx8ccf069fP7y9vQFwdnYmJCQEAFdXV7766itGjRpFamoqY8aMoXfv3nTr1o3Q0FCMRiO3b9/mxIkTbN26lbNnz/LWW2/l6M0SGBjI888/z6effkpMTAyPPvookydPJjw8nMzMTHbv3s28efMIDg4mLS2NO3fuPFAdWZNVkx4+Pj4kJCQQHR1NnTp1rLlrIYQoFbQuWtxD3akcWplnxjxj63CEEA5O2m5CCHH/GjZsyMaNG+nWrRuDBw/mp59+wtnZql+x8/f/Q1tt9PS0uFqX0JiedYMJ8HQlRr2ohBBC5OLn58fSpUt54YUXOHLkiGko2bx45nFdtzeBgYEsWbKEp556irNnz3LgwAEOHDhQ4HYajYbPPvuMMWPGkJCQwO+//87vv/+eo0zt2rWZMWMG7du3L67wTTp16kSnTp345ptvmDp1qtl6Ly8vvvvuO0JDQx/oOA0bNuTZZ5/l66+/JjExkenTp5uVGTduHLVq1co36dG7d29++OEHrl69yvz583MMLxYSEpJjeKzGjRuzcOFCXnjhBa5fv866detYt25dnvv28vKyGNO1a9dYuHAhN2/e5P3338+x3t/fn6+++ooXXnghv5evOqu2yOrXr88ff/zB9OnTcXFxoVq1ajkafbdv38bDw/JYo/mpWLGiNcMUQgi7kdeT1M2qNlM5EiFEaSRtNyGEeDCtW7fm8OHD1KlTB62FicSLTUI0XN7HDScnjpRxN1ttSAtF0QXKBOZCCGEnypUrx+LFi9m1axfr16/n77//JjY2Fr1ej7e3N1WqVKFJkyZ06dKFFi1a2DrcQqtUqRKrV69m7dq1bNq0iRMnThAfH4+TkxPly5encePG9OjRg+bNm+fYrm7duqxevZoffviBvXv3cuvWLTw9PalcuTK9evVixIgRprkr1PDss8/SuHFjFi1aRGRkJAkJCQQFBdGxY0cmT55M+fLlrXKcZ555hvDwcBYsWEBERASpqamULVuWhg0b8vjjj9O2bdsC59/w9PTkl19+4YcffmD//v1cu3Yt37lRGjduzJYtW1i5ciU7d+7k5MmT3L17F61WS0BAADVq1KBFixb06NGD6tWrW9zHf//7X9q3b8+CBQuIjIwkLS2N8uXL06FDByZMmGC1+rEmjWLFsQt27drFk08+aZrIJlv2IXIvLwyNRpPvZDKiYJmZmURERABQp06dEpMxLqnure/w8HBcXV1tHJHjK8l1PmTdEE7fOW22/KlGTzGl8RQbRFSwklzfJZHUt/pSUlI4fTrrc+nodS5tN/skbTf1ybVWXVLfVrDvK9j2Dj/7evNlgL/Z6vQbjxBg6Mr+17pgNOilvlUm57i6SlPbLdu5c+fQ6/U4OztTq1YtW4cjhBAPpDiuaVZ9FKVTp0689dZbeHl5oSiK6b9s9y4ryn9CCOEoLl++TN++fbl9+zYAGYYMi+Wq+VZTMywhRCklbTchhMhfZmYmY8aMYdeuXbYOJaeIXwHYYCEpqiga9EkNGdQ0FGcnFXufCCGEEELYCasPODpixAgGDRpEZGQkN2/eJDMzk6lTp6LRaHj++ecJDg629iGFEKJEOHXqFN27dycmJoZevXqxY8cOMvSWkx5VfauqG5wQotSStpsQQliWmprK4MGD+f33301DQuQensMmbkTCzUjOu7hwxs38iXZDSg0UvQ9DZGgrIYQQQpRSxTLLmru7e47GYPYkMN26daNmzZrFcUghhLBrf/31F927dycuLg6Ao38dpcdzPdB11VksX9m7sprhCSFKOWm7CSFETklJSfTu3Zt9+/YBkJycTM+ePdmzZw/16tWzbXARywHY6GV5ziVdYmMeqhpAtUAZGk8IIYQQpVOxJD1yy56Ap0yZMmocTuTDxcXF1iE4PFdXV5o1k0mo1VQS6rxcuXJ4eXlxJ+EOfm39COwdSEpwChjNyxoz/ejw6X7CQ3xpGOpHo0q+tKgagLe7fXx+S0J9OxKpb/XJvVLabvZEzkd1yLVWXSWhvj08PMx6uqWnp3Pz5k3bJj2MRjj+Kwqw0dLQVkZn9EkNGNI91LSsJNS3o5E6V5fcK4UQQuSmStJj4cKFahxGCCHsln+wP0/Pe5qfI37Gyccp37KGjArEJmey88xtdp7Jmvvj1ydb06JqgBqhCiGEtN2EEKWek5MTixcvJikpiS1btuDv78/GjRtp1aqVbQO7sg+SrvGPmysxLuZf5/XJdfB09qR3eAUbBCeEEEIIYR9USXrkRa/Xk5iYCICPjw/OzjYNRwghrO5u+l0Wn1rM0tNLScxMLDDhAaC7m/PLtFYD9Sv6FFeIQghRaNJ2E0KUJm5ubqxcuZJRo0bx3nvvER4ebuuQ4PgyADZ4WR66Sp/YmP4NK+LpJtdnIYQQQpReqreELly4wJIlS/jjjz+4cuUKiqIAoNFoqFKlCm3btuXxxx+X8aOLicFgsHUIDs9gMBAbGwtAYGAgTk4F/8gtHow91vmd9Dv8ePxHVpxbQZo+rdDbpV3vhyElLMey2sHeeLjazxdXe6xvRyb1rT65V+YkbTfbkvNRHXKtVVdJqm9PT09Wrlxp6zCy6NLh5FoAYi3UmWJwR58cxtAWoTmWl6T6dhRS5+qSe6UQQojcVP0V7YsvvmDu3LkYjUbTF+ZsiqJw6dIlLl++zNKlSxk/fjz/+c9/1AyvVDAaLUwgIKzKYDAQHR0NgL+/vzRwVWBPda4oCidOnCCoehDLzixDZ7Q8UbnFbQ3u6OPbmC1vGOprzRAfmD3Vd2kg9a0+uVf+S9putifnozrkWqsue6vvkydPUqdOHbRarU3jKNDZTZCR1dtu+q1Yrjs5sdHLg8We5bntloEuqQE1Av1oWtk/x2b2Vt+lgdS5uuReKYQQIjfVkh4ffPABS5YsMX1hrlGjBg0bNqRcuXIA3L59m4iICM6fP4/BYODHH38kNTWV//73v2qFKIQQD0RRFF577TW+/PJLVq1aRb8a/VhxbkWe5esG1CVFn0JUYhQAhvRQi+XCQ/2KI1whhMiXtN2EEKXB77//zqOPPsr48eP5+uuv0Wg0tg4pb8eX5/hnBYOB8QlJlK05i68upnIlNpOh3SrZ92sQQgghhFCBKkmPo0ePsnjxYjQaDTVr1uT999+nadOmFsv+9ddfvPPOO5w9e5bFixfTu3fvPMsKIYS9MBgMTJkyhR9//BGAIUOGsHDdQlZpVmFUcj551Dy4ORPDJ9K6YmtG/j6SKP4/6ZFmOenRKK+eHnO6QfThnMtCW8CEbQ/2YoQQpZ603YQQpcGyZcsYOXIker2eWbNm4e/vzwcffGDrsCxLvQPntpgvD6jOgEf60R/4+2o8lQM8VA9NCCGEEMLeqNJ/d9myrMnWQkNDWbp0ab5fhJs0acLixYupVKkSAL/88osaIQohRKEZFSOXEy7nWLZkyRJTwgMgIyODCYMn0LF8R9OyTqGdWNhrIT/3/Jk2IW3MnsIzWujp4eqkJay8t3VfgBBCFEDabkIIR3ft2jXGjBmDXq83LZs2bRqrVq2yYVT5OLEKLA2b2vAx0GjQaDQ0qexPWS839WMTQgghhLAzqiQ9jhw5gkajYdKkSXh7F/zjnbe3NxMnTkRRFI4cOaJChEIIUTC9Uc/6i+sZtHYQwzcMJzkz2bRu5MiRjB071vRvJycnvv76a55q/hR9qvdhRb8VfN31axoHNc5z/5Z6etSp4I2bs4wBLIRQl7TdhBCOrmLFiixYsCDHQyijRo2ib9++NowqH7mGtjIJH6JuHEIIu5A9T4zBYDCbd00IIUoSRVEwGAwAVp1fTZXhrWJjYwGoW7duobepV68eAHFxccUSkxBCFFaGIYPV51bz84mfiUmOMS1ffnY54xqMA0Cj0TB79mwSExNZt24dy5YtY8CAAQB80v6TAo9h1Huh6P3MltvbJOZCiNJB2m5CiNJg6NChJCYmMnHiRJ555hlmzJhhn5OZ37kEVw+YLw9pDmVrqB+PEMLmXF1dycjIQFEUUlNT8fT0tHVIQghxX7KvZZB1bbMWVZIebm5u6HQ60tLSCr1NdllrvlghhCiK5Mxklp9dzsKTC4lNizVbv/DkQkbUHYGbU9YwAs7OzixevJjjx4/TokWLIh3LmMd8Hg1lEnMhhA1I200IUVpMmDCBunXr0qaN+dCjdiPiN8vLGz6mbhxCCLvh4+NDUlISAHfu3MHDw8N+r2FCCJGPxMRE09/WTOCq8hhLaGjWj3k7duwo9DbZZbPHhxaipNBoNHh6euLp6SmNDpVYu87vpN9h5rGZ9FjRg+lHp1tMeADEpsWy5vyaHMvc3NyKnPAAMFiYzwPss6eHnOPqkvoWtiBtN1HayLVWXfZW323btrWLOCxSFIjIGtoqxwA2Gido8GihdmFv9V0aSJ2L4ubl5WU6t5KTk4mOjiYlJUWGuhJClBgGg4G4uLgcIwV4eXlZbf+q9PTo0KEDp06dYtGiRXTo0IHWrVvnW/7AgQMsWrQIjUZDhw4d1Aix1HBxcbF1CA7PxcWFOnXq2DqMUsVadX49+TrzT85nxdkVpBvSC7XN75d+Z2jY0Ps+5uLeiwEY+/MhdsbezrGujIsTNctZ74JvLXKOq0vqW31yr5S2mz2R81Edcq1Vl5r1nZaWxrPPPst///tfqlatqsoxrer63xB7lhtOTjxRIZieKSn0SU6lVtUu4BlYqF3I+a0+qXN1lcZ7pVarJSQkhJiYGBRFITk5meTkZDQajWm+DyGEsFf3zuORrVy5ciVveKsxY8awePFiUlJSmDhxIkOHDmXQoEHUrVvXNGaq0Wjk1KlTrFixguXLl6PX6/H29mbMmDFqhCiEKMUuJlxkbsRcNlzcgF7RF2obtxQ33uz+Jo9Uf+SBj68oCsejE8yWNwjxwdnJDseVFkI4PGm7CSEcQUJCAv369WPPnj3s3r2bvXv3Ur58eVuHVTT/P4H5Ri8PYlyc+cnPl5/8fKmqjaV/xByG1B6Cr5v99QwWQhQ/b2/vHIkPyPpuqdcX7jutEELYC19fX8qWLWvVfaqS9AgICOCrr75iypQp6HQ6li5dytKlS3FxccHX1xeNRkN8fDw6nQ7Iuki7uLgwY8YM/P391QhRCFEKnYg9wZyIOWyP2o5C4boBp11K4/b62yQeS2T7U9sZ8PWAB44jJj6NuJRMs+UFzudhNOS/Xggh7pO03YQQJd3t27fp2bMnx44dA+D8+fM8/PDD7Nq1q+Rcpwx603weG3ONcX058zYzjs5Em9yCca0b2SI6IYQd8Pb2pnbt2iQnJ5OYmEhmZqbZ09NCCGGPnJyc8PDwwM/PD3d3d6vvX5WkB0C7du1Yvnw5b731FpGRkQBkZmZy+/Zts7INGjRg2rRp0h20GOj1eplgtJjp9XquXLkCQJUqVXB2Vu1jVmoVpc4VReHwjcPMiZjDn9f/LPQxwsqEsfOTndz5645p2eHDh0lNTX3giZYiLPTygELM5xEfZb5MX7hhuR6EnOPqkvpWnzwdl0XabvZB2m7qkGututSobycnJzIzcz5UEhUVxaVLl0pO0uPSLki5xXkXF864mV8H9Ck1OXEVyH8EQjm/bUDqXF2lve2m1Wrx8fHBx8fH1qEIIYTdUPXOW7duXX777TeOHz/On3/+ydmzZ0lIyPqxz9fXl9q1a9O6dWsaNmyoZlilikxqVfyMRiPx8fGATOaqlqLU+TM7nmFP9J5C77tzpc6MDx9Po3KN2OSziX79+qHT6ejSpQurV69+4IQHwD95Jj388t4o6QakWphgPe4i6DPBufh+IJNzXF1S3+qTe+W/pO1me3I+qkOutepSo74DAgLYsmUL7dq14+LFiwQHB7Nly5aSdb26Z2grS3SJjXisd8H1J+e3+qTO1SX3SiGEELnZ5HGDhg0blqzGphDCYdQvW7/ApIeTxole1XoxrsE4avnXMi3v2bMnixcv5pdffmHx4sVW6353PDrebJm3uzNVy1r+ggtA9BHLy3UpsPND6P6eVWITQgiQtpsQomSqUKEC27ZtY9SoUcybN4+aNWvaOqTCy0yBU+tRMB/aCkAxOlPRpQUtqpaQXitCCCGEECqSPpZCiFJleJ3hzDsxjzR9mtk6V60rA2sN5In6TxDqHWpx+yFDhjB48GA0Go1V4jEaFYvDWzUM9c3/GDFH8163/yuo3hFqdHnwAIUQQgghSrBq1aqxd+9eq7XdVHN6I+hS+MfNlRgX86/t+uS6PNasdsl7XUIIIYQQKtDaOgAhhFCTn7sfg2sPzrHMy8WL8Q3Gs3nwZt5s+SZKfP7do6355bLP13tJyjAfg7bASczzS3oArHoSks3H3RdCCCGEcDRRURbmObtHiUwMHF8GwAYvy0OpVnFrx6Cmlh/SEUIIIYQo7STpIYRwCIn6RH67+Rs3Um4UWHZMvTE4a50JcA/guSbPsXnwZl5o9gL+rv48+eSTNG3alNOnT6sQNaRmGiwub5TfJOZGI1z7K/8dJ9+E1VOyygohhBBCOKhff/2VWrVqMXfuXFuHYj3Jt+DCDnTAZk/z4U69XbxZN34C5X2tM9SqEEIIIYSjkeGthBAl2rXka8w9PpeV51eiU3R4n/bmjdZv5LtNsGcw33f7noblGlLGuQwAmZmZjB49mmXLsp6q6969O/v27aNKlSrFEndKhp4lB6OIiTcfZgsgPL+eHnHnISOx4IOc3woHv4fWT91fkEIIIYQQdmzOnDlMnjwZo9HIxIkT8fX1ZdCgQbYO68FFrgTFwIEy7tx1cjJb3b1qd1ydXG0QmBBCCCFEySBJDyFEiXQh/gJzI+ey8eJG9Mq/w0OturCKKU2m4O+e/6SOLSu0zPHvjz76yJTwAIiOjqZnz578888/uLpa70vl3ZRM5v1xmfl/XiY+VWexjLNWQ8X8ntwraGire219G6q0gYqNixaoEEIIIYQdO3z4MBMnTjT922g0Mnz4cA4ePEjjxo1tF5g1/P/QVhvzGNqqT7U+akYjhBBCCFHiSNKjlCmR49mWMFqtlqCgINPfwroibkcwJ2IOO67usLg+3ZDO4lOLeabJM0Xa78svv8ymTZs4ePAgAK6urnz44YdWS3jcTExnzt6LLD4YleeQVtk83ZzvfxLz3Iw6WDEeJu0GN6/Cb5cPOcfVJfWtPrlXCnsi56M65FqrLmvUd4sWLXjttdf49NNPTcsmTJhAw4YNrRKjzcSeg2vHSNVo2O5Rxmx1UJkgmgU3K9Iu5fxWn9S5uuReKYQQIjdJepQyzs7ylhc3Z2dnKlWqZOswHIqiKBy8cZA5EXM4eP1ggeWXnF7C2AZj8XSx/HScJV5eXmzcuJGOHTty8eJFVq9eTffu3R8kbACuxKXw/e6LrDgaTaahcPNrBHm75V+gKEkPyBoO6/fXYMCsom2XBznH1SX1rT65Vwp7IuejOuRaqy5r1ffHH39MfHw8P/zwA2+88QbTpk0r+T9+Hl8OwG6PMqRZ+LG8V7VeOGnNh7zKj5zf6pM6V5fcK4UQQuQmdwYhhN0yKkZ2Ru1kTsQcIuMiC71di+AWJGUmFSnpARAQEMCWLVuIioqiZcuWBW+Qj6t3Uvl88xnWH7+GUSnatl7u+Vya9RlwIyLv9d4VIOm6+fK/F0GNzhA+uGjBCCGEEELYKY1Gw6xZs+jTpw99+/a1dTgPTlEgIivpsSGPoa16V++tZkRCCCGEECWSJD2EEHZHZ9Sx8eJG5kbO5WLCxUJt46Rxok/1PoxrMI4afjXu+9gVKlSgQoUK9739vTZEXC9ywqNANyKzhqzKres70P4/YNDB3J4Qc8S8zPoXIaQZBFSzclBCCCGEELbh5OTkGAkPgOjDcPcy8Vot+8uYz+9WzbcadQPq2iAwIYQQQoiSRZIepYxOp7PqpMzCnE6n49SpUwDUrVsXFxcXG0dUcqTp01h1bhXzTszjeoqF3goWuDm50b96f9q4tKGcWzkqe1bOt/zff//Ntm3bePnll60Rcp4qBXjQv1FFVv4Vk2eZzmHleKpzTVpUDSj8jq8ds7w85P/HdnZygcE/wfftISMxZ5mMRFgxAcZtyip3n+QcV5fUt/p0OguJRSFsRNpu6pBrrbqKUt+JiYm89957fPDBB3h4eKgVorrmdMtKePy/LZ4e6C0M09W7Wu/7Gr5Lzm/1SZ2rS9puQgghcpOkhxBWpiiKqdGlKNZ+zN8xJWYmsuz0MhadWsSd9DuF2sbLxYvH6zzOiLoj8HHyISIiAp1Ol2+d79+/nz59+pCQkICzszMvvPCClV6BZU92qmGW9NBqoHd4BaZ0qkH9ir5F36nF+Tw0ULHxv//0rwqPTM+awNxs+yOw8yPo9k7Rj/3/5BxXl9S3EEIUP7nWqquw9R0bG0uvXr04cuQIJ0+eZM2aNaUiCbjBy3Jy58KlWtCo6PuT81t9UudCCCGEbame9Lh79y5///03V69eJSUlBYPBUOA2zzzzjAqRCSFsZevlrcz8a2ahyga4BzCq3igeC3sMb1dvADIzMwvcbtOmTTz66KOkpaUB8OKLL+Lr68vYsWOLHK/OYOTC7WTqlPfJt1ztYG961Atmy8mbuDhpGNQ0lMkda1AtsGhzjeRgKekRWBvccyVQwgfDhZ1Zc3nktm86VO8I1TvdfxxCiFJD2m5CCFuIjo6mR48epqflN23axKhRo1iyZAlOTkWbyLskuebsxDF386GtDGmVGNj2PjIeQgghhBClkGpJj7i4OD766CM2b95cqC/L9yruL84xMTEsXLiQXbt2cePGDVxdXalUqRK9evVixIgRlClTxurHTEtL45FHHiE6OhqAkJAQduzYYfXjCFES9K3Rl2///pZbabfyLFPRsyJjG4xlQM0BuDubfxEsSFRUlCnhkW3x4sWMGTMGrVZbqH2kZRpYdjiK2XsukqE3su+1LpRxzf9L99Oda1IpwIMJ7atRwfcBryXpCRB71nx5SFPL5Xt9ClcPQNz5XCsUWDkZpuwHz8AHi0kI4bCk7ZaTtN2EUFd8fDw3btzIsWzXrl1cvXqVqlWr2iYoFfzuabmXh6euBa2ql1U5GiGEEEKIkkmVpEdCQgLDhw8nKirK7rp27tixg1deeYXk5GTTsrS0NBISEoiMjOTXX39l9uzZVKlSxarHnTlzpulLsxClnauTK6Prj+Z/R/5ntq6Gbw3Gh4+nZ7WeuGjvfyzcSZMmcffuXV5//XUA+vbty7JlywqV8EhI07HowBXm7rtEXMq/vUqWHY7iibb5TwreqJIfjSr53XfcOVz7y/Ly7Pk8cnPzgsFzs8aJNuTqDZN8A1Y/BcOXwX2MDS2EcGzSdjMnbTch1NWgQQN+//13unbtSkpKCpUrV2br1q2OmfDI/Pd6FqQ3UDcjk1Nu/w7jpSgaHq39CFqttNmEEEIIIQpDlaTH7NmzuXLlCgBt27Zl7Nix1K9fHz8/v/uaiM1aTp48yYsvvkh6ejoeHh5MnjyZli1bkp6ezsaNG1m+fDmXL19m0qRJrFixAi8vL6sdd/78+bi5ueHs7ExKSopV9iuEvco0ZOLqlP/4y4NrD2b28dkkZmZNvh0eGM6E8Al0qtQJraZwPTEK8tprr3H37l1iYmKYO3duoScUPHktkc83nzFbPnvPRYa3rIKrs3XiK5DF+TzIu6cHQIVG0O092DzVfN25zXDwB2j1pHXiE0I4DGm7mR9X2m5CqK9ly5asWbOGl156ifXr1xMaGmrrkKzv2t9w+9+evH1TUumbkspFF2feL9OEw946jJllGfVQfdvFKIQQQghRwqiS9Ni+fTsajYaOHTvy/fffq3HIQvnwww9JT0/H2dmZuXPn0qRJE9O61q1bU6VKFT7//HMuX77Mzz//zLPPPvvAxzQYDPz3v//FYDDw9NNPs2LFCvniLBySoij8ef1Pfor4CT83P77o9EW+5T1dPBlRdwR/3fqLCeETeKj8Q8Xyw9rHH3+MoiiFHtIKoFX1AJpU9uOvqPgcy68lpLPm7xiGNK9k5SjzEHPMfJmTKwQ3yH+7VlPg4k44t8V83da3oEobqNAwq0dI9OGc60NbwIRt9x+zEKJEkrbbv6TtJoRtde3alaNHjzrmPB43ImHhAFDMhxCsrtMTkzyElFshtK7lQai/5WGvhBBCCCGEOVUeT75+/ToAI0aMUONwhXL8+HGOHDkCwKBBg3J8ac42btw4atSoAcCCBQvQ6XQPfNwFCxZw4sQJqlWrxsSJEx94f0LYG6NiZNuVbTy+4XEmb53MoRuH2HplK5cTLhe47ZONnuTHHj/SskLL+054GI1G4uPj81yv0WiKlPDI3ubpTjUtrtt26maR9vVALPX0KB8Ozm75b6fRQP9vwSvYfJ0hE34bB5nyA54Q4l/SdvuXtN2EKH537tzJd71DJjxunYYF/SHtrsXVi/RdOaeEAhqGNa+jbmxCCCGEECWcKkkPD4+sp1ICA+1nwtxt2/59cnnQoEEWy2i1WgYMGABAYmIiBw8efKBjxsTEMHPmTADee+89XF3zH+6nODjkFwY74+zsTPXq1alevTrOzqp0prILOqOO1edXM2DNAF7c9SIn406a1ikozI2cW+A+7ncYq+w6r1SpEuPGjaNz5875Jj5yS87QF1imS50gwoK9Tf9uUtmPOaOb892IPObTsLbEa5B03Xx5XvN55OZVDgb+AFhIJsWdg99fK3QopfUctxWpb/XJvVLabtmk7VZ6yLVWXffW98KFC6levfoDf15LlNjzsKAfpMZaXL3F0Ix39WMA8C3jQo96Fh5cKQI5v9Unda4uuVcKIYTITZWkR+3atYGsL4724ujRrCemPTw8qF8/7/FRW7RoYfr72DELQ8sUwXvvvUdqair9+/enZcuWD7Sv+1XUJ9xF0Wm1Wvz9/fH39y8V9Z2mT2PxqcX0Xtmbt/a/xaWESxbLrbu4jhspN4olBq1Wi7u7O+PGjWPp0qX8/fffPPLII6Smpua5jaIo/HEhllE/HWTo938WOFGvVqvhqc41aF8rkKUTW7FyShu61QtWb0LJPOfzKELSpUZnaPu85XV/LYTUuELtprSd47Ym9a0+qWdpu2WTtlvpIddadWXX97x585gwYQIJCQn06tWLyMhIW4dW/O5chPl9Idlyb+EdhsY8o3sO/f+PRD2gcUXcXR7sB105v9Unda4uqWMhhBC5qXJneOyxx1AUhbVr16pxuEK5cOECAJUrV873yYvq1aubbXM/NmzYwO7du/H19eX111+/7/0IYS8SMxOZfXw2D//2MJ8c+qTAhIbeqGfJqSXFFs/kyZPZsGGD6d/79+9n2LBhZuWMRoWtJ2/y6Hd/MPzHg+w9F8vJ64nsOnu7wGP0a1SRheNb0rpGWfUn8rVG0gOgy3/z3ubulaLtSwjhsKTtJm03IYrbr7/+yn/+8x/Tv+/evUv37t25eVPFoUPVFh8F8/tB0jWLq/cYwpmie4FMXEzLVJs7TgghhBDCgajSz7J3797s3LmT9evXM3v2bCZNmqTGYfOUkZHB3btZY6eWL18+37K+vr54eHiQmprKjRv395R6QkICH330EQAvvfQSAQEB97Ufa8jMzMxznbOzs+kJCaPRiF6f/5A/9w7xYDAYMBjMJ+DLptFocHH5t/Gu1+sxGo15ltdqtTl+0NDpdPk+iX9v7IqiFDiGt4uLi+lH64Jea1Fj12g0plhdXFzQ6/X5xu7k5JSjO25+7xHY/n2KTYtlyZkl/HbuN1L0hZsHoqx7WUaEjeCxOo+ZllnzfVIUhVdeeYWtW7eaPqceHh5MmjTJVJ96g5HfT9zkhz2XOXsr2Wwfs3aco201vzxjsfX75Bx91DxL7eYLATWK9nlyckHX/3uc53RGk5mrHixMoqkAulyvVVEU0+txdnbG1dW12D5PRb0W2Pp9upe1Xmvu+s6ua3u67jna+1RQPKWBtN2k7Vaa2m5arRYnJ6dCzwFj79ewkvA+KYpCt27d6NGjB1u2bDEtHz16NH5+fhbrtMTfa1JvwbxHIOFqjnXxWi17PMpQLqEKk3T/IYN/91uvgg8NQnwf+H2StlsWNT9Pltpv9nbdc6T3SdpuQgghclMl6XH48GEGDx5MdHQ006dPZ8uWLTzyyCNUr16dMmXKFLj9vcMUWENKyr8/1GaPWZ2fMmXKkJqamu9QOfn57LPPiI2NpUmTJgwdOvS+9mEtZ86cyXNdWFgYXl5eAKSmpuZbFqBZs3+fFo+NjSU6OjrPsp6entSp8+8EfFeuXMl3zoWgoCAqVfr3qaZTp07l2yisXr06/v7+QFaDLSIiIt/Yw8PDTQ2rhIQELl68mGdZFxcXGjZsaPr39evXuXXrVp7lfXx8SExMNB3n4sWLOc653EJDQwkO/nec3oJit9X7dCvzFr/H/s7eu3vRKwXPgQFQzqUcvcr1op1fO1wNrhjSDOCetc7a7xPAV199xZQpU1AUhblz51K+fHmO/n2cnZfTWHMmhZspeTeej1yJZ9mOo9QrZ3m8dpu+T4qRxtEWenqENAGtliuXLhXt83QjHa/6z1P9rw/zjQmyvpDZ8vPk5+dnmpQYsp7adoTPkyUl+bpXmt6n0kLabtJ2K23XsEqVKpliKFOmDGlpaXmWL8nXMHt6nwDefvttUlJS2L9/P1OnTmXQoEF5DnFVku81Pto0au19DuJz9qpN1GqYVD6IU26uGJWWpMe55Vg/tHkoUPI+TyX1fSrJnyd5n+Lz3b8QQojSR5Wkx6hRo3IMBXPixAlOnDhRqG01Gg0nT54suGARZGRkmP6+9wmBvGQ3NNLT04t8rMOHD7NixQqcnZ1599131R8SR4gHdDX9Khtub+BQwiGM5P200L1C3ULpXa43LX1b4qRRb1K5mjVrsnbtWnx8fHDz9GHRwSjWnU0hPr1wca8/l5Jn0sOW3JKjcbLUq6aoQ1vd425oV2JvHyEwevMDRCaEcFTSdpO2mxBqcHd3Z9WqVezcuZPWrVvn+yNtSeWccZfKB16CxMs5lqdoNEwJzkp4AGiDtuOqgczYboAGjTaDAU1C1A9YCCGEEMIBaJSCZu+1gnuz8EWl0Wg4deqUFaOBO3fu0Lp1ayBr+Ibp06fnW75NmzbExcVRu3Zt1q1bV+jjZGZm0q9fPy5dusS4ceN47bXXzMp06dKFmJgYQkJC2LFjR9FeSBHiyH4So0aNGnk+IVlSugoXFLutuwrr9XrTORseHp5juCtL7LWrcGJmIlP3TGVPzJ5893+vhoENGVtvLO0qtkOrMZ8yqLjeJ51Ox+nTpwEIrRHG4sPXmP/HJRLSCtcjpaynK2NaV2Z4i1B8ylj+Mc2W75M2YhnO654x3+jxJVCnz/1/njKScfm5K5o7eT81poS2QDd6Y45l99Z3nTp18PDwkK73Fljrteau7+x92tN1z9Hep+TkZNNcEPc+ZVmaSNtN2m6lqe2m1WoxGo2mOq9bt26+88bY+zWsJLxP997bsq+zDnmvSb2D8+KBaG/nTASnajRMKV+OY+7uZptkxHYi83Z3wur+yeYxHwMP/j5J2y2Lmp8nS+03e7vuOdL7JG03IYQQuanS02PBggVqHKbQPD09TX8XZtiD7O7thRlO4V7fffcdly5dokKFCjz77LNFC7KYuLi4FKoBoNVqi9RQyN0IKkh+XyQtKcxTndk0Gk2RYi/qay3O2IFijb0o75O3izc3Ugs3Fnqbim2YED6B5sHNC/1E7P2+T3/88QexsbH069cvx/q4NANrz6SwffVe0nSF69lR0dedyR1rMLR5Jcq4Fq1Hiqrv041/LBf8/54e931OugbA4LkwpzsYLX/p0pD/a733yxjI5yk/1niteV3D5bqXt/t5n4oavyOStpu03XJz9LbbvT/gZc95UFj2dg0rCjXep7i4OObOncvLL7+cbzvR4e41aXfhlyGQK+GRrtHwXLDlhAeAi89xNM4JBJb790dfa75P0nZT//Nk6TpuD9e9e5X090nabkIIIXJTJenx0EMPqXGYQnNzc8PPz4/4+PgCJ7hMSEgwfbkuaOLM3H788UcAWrduzc6dOy2Wyd53amoqGzZsACAgIMD0NKMQtqLRaJgQPoFX97xqeT0aulXpxvjw8dQvW1+VmDZv3szAgQMxGAxs3LiRrl27cik2hW93nmPVsdvoC9lvrXo5T6Z0rEH/xiG4Opv3SLE7MRbm8/CuCN5FuyZZVLEJdHsXtrxpeb0+w/JyIYRDk7abtN2EsIaYmBh69OjByZMnuXPnDh9//LGtQ1JHegIsfBRuHM+xOBN4ISiQg2UsJzyMOj/0qZVx9fsLaGixjBBCCCGEKJgqSQ97VLNmTY4cOUJUVBR6vT7PJyHunSzs3om+CiO7u+rKlStZuXJlvmXv3r3Lf/7zHyDrhwb54izsQfcq3ankXYmrSVdNy5w1zjxS4xHGNRhHNd9qqsXy22+/MXz4cNPn6tHxz9H7pa84eC0TYyGTHeEhvjzVqQY96pfHSVtCxmjXZ8ANCxMFhjS13jFaPQUXd8L5bebrjHl3LRdCCDVJ202IkuXChQt0796dS5cuAfDJJ5/g7+/Pq69afqDGYWQkweIhcO1YjsU64KWgQPZ7lLG4mVHngz65Bq7+Fh52EUIIIYQQRVICHnEuHs2aZQ0Lk5qamu/EnIcPHzb93bSpFX9kFMKGUnWpbLi4Id9xWwGctc6MbTAWAHcnd0bWHcnvg37ng7YfqJrwANi2bRs6nQ63kHqUG/wO/o9/xp8xhUt4tKoewIJxD7H2mbb0Cq9QchIeADciLQ899QCTmJvRamHAd6C10C3cxfKTiEIIoTZpuwlRspw4cYIrV67kWDZr1iwSExNtFJEKMlNgyWNw9WCOxXrg9XJl2eVpecg9o96LxytN48wL84gYE0HEmAgW916sQsBCCCGEEI7JJj09IiMj+eOPPzh37hwJCQkA+Pr6UqtWLdq0aUODBg2KPYZu3brxww8/ALBixQoaNWpkVsZoNLJ69WoAfHx8aNmyZZGOcebMmQLLqDEZphDZEjISWHp6KYtPLSY+I55gj2Cal2+e7zb9a/TnbvpdBtceTIB7gEqR5qQoCkOff5f9bg+RVCa40Nt1qxvElE41aVbFvxijK2aWhrYC6yY9ALyCIKiu2TAMWJiQXghR+kjb7V/SdhOicPr168ecOXMYN24cALVr12br1q34+PgUOAlyiaRLg6XD4Mr+HIsNwH/LlWWLl6fFzYx6DwaWf5+3Hu6sQpBCCCGEEKWDqkmPM2fO8Pbbb3P8+PE8y0yfPp1GjRrx3nvvERYWVmyxNGzYkObNm3PkyBFWrFjBwIEDadKkSY4yc+fO5cKFCwCMHj3abHKsgwcPMnr0aAAGDhzIJ598UmzxWktRJzQTRefi4kJ4eLjpb3twO/U2C04uYPmZ5aTq/50Adk7knAKTHq5OrkxqOKm4Q7TIYFTYFHmDb3ed58S1RChEwkOrgb6NKjKlUw3qlPdRIcpiZjHpoYGKja1/LGe3QhWzx3PckUl9q0/ulf+StpvtyfmoDrnWFo+xY8cSHx/PwoUL2bRpE0FBQYAD1rc+A5aNhEu7cyw2Au8FBrAhj4SHYnCnV+A7fNC7W7GG53D1XQJInatL7pVCCCFyU+3O8Mcff/Dkk0+i0+lMQ+o4Ozvj5+cHQHx8PHq9HoC///6bIUOG8MMPPxTr+Mhvvvkmw4YNIz09nXHjxvHkk0/SsmVL0tPT2bhxI8uWLQOgatWqjB07ttjiUJNGU4KG9SmhNBoNrq6utg4DgKuJV/n5xM+sPr8anYUhkvbH7Odk3Enqla1ng+jylqk3svqvGL7ffYGLsSmF2sbVScvg5qFM7lCdKmUtf7EskXKNBw1AYG1w91U/lv9nT+d4aSD1rT65V2aRtpt9kPNRHXKtLT4vvvgiTz31FG5u/z5c4VD1rc+E5WPM5kZTgI/K+rPK28viZorBjc6+b/J5v17F/jl3qPouIaTO1SX3SiGEELmpkvS4c+cOzz//PJmZmWi1WgYPHsyQIUOoV6+eKSNvMBg4efIkv/76KytWrCAzM5Pnn3+ezZs34+9fPEPT1KtXj+nTp/PKK6+QnJzMl19+aVamatWqzJ49Gy8vy41VIezRmTtn+CnyJzZf3oxRMeZb9qeIn/ii0xcqRZa/1Ew9iw9c5qd9l7mRmFGobTxcnRjZqgrj21Uj2MfB5p9IT4DYs+bLrT20lRBC5CJtNyFEUaSlpVGmjOUJuoEcCQ+HYtDDivFw9vccixXgswA/lvl4W9xMMbrS1us1ZgzsLz/WCiGEEEIUA1WSHgsWLCApKQkXFxe+/fZb2rdvb1bGycmJ8PBwwsPD6dGjB08++SRJSUksWLCA559/vthi69KlC2vXrmXBggXs2rWLmzdv4uLiQuXKlenZsycjR47MtwFf0hiN+f8ALh6c0WjMMd65VqvenAh/3fqLORFz2BO9p9DbxKbFojPocHGyXbfrhFQdC/68zE/7LhGfZmHSbgv8PFwY26YaY9pUwcfdmYSEBO7eTVO9zovVtb8sLw+x7cS8tjzHSyOpb/XJvVLabvZEzkd1yLX2/s2YMYNvvvmGPXv2UKFChUJt4xD1bTTAqklwam2OxQoww9+XRb6Wh1lVjM40d3uJ7wYPRqtVJ+HhEPVdwkidq0vulUIIIXJTJemxe/duNBoNI0eOtPilObd27doxatQofv75Z3bv3l2sX5wBQkJCmDp1KlOnTi3Sdi1btizUhJf5UXsCTIPBoOrxSiO9Xs/FixcBCA8PL/ZuzYqisP/afn48/iPHblkYCikPbUPaMjF8Is2Cbd9rYNVf0Xyx1UKPBguCfdyY2L46wx6qjKdb1iUsMzNT1TpXjVqTmBeR2ud4aSf1rT65V0rbLT/SdnNMcq0tOkVReO+993jvvfcA6NGjB7t37yYgIKDAbUt8fRuNsOZpiFxhtup7Px9+8rM8DKlidCLc5QXmPPa4agkPcID6LoGkztUl90ohhBC5qZL0iI6OBrKezCusLl268PPPP3P16tXiCkuIEmXExhEcv51zItnK3pXxdPHk1J1ThdqHBg09qvZgfIPx1C1btzjCvC9Dm1fi/ZVHMLp45FmmSlkPpnSswcCmIbg5O6kYnQ3FWEhiOblCcAP1YxFClCrSdhNCFOTLL780JTwAIiMj6d27N7t373bc4awgK+Gx/nn4Z6nZqjm+Pnzr72dxM0XRUkf7DAuGjcLZSZ76F0IIIYQoTqokPTIyssbmL8pQA9llMzMziyUmIRxBVFJUoco5a53pX6M/YxuMpYpPlWKOqug83JzpF+bN6ovmT+jUKe/NU51r0rtB+dL3BdFST4/yDcG5mJ4Um7Ct4DJCiFJB2m5CiIKMHDmS77//nvPnz5uWDRgwwLETHooCv78CxxaYrwIuuFh+7YqipYYymcUjx+JS2tqzQgghhBA2oEqLKzAwEIBTpwr3NPq9ZcuWLVssMQlRGpRxLsPoeqPZ9Ogm3m3zrs0SHpn6gsdYfW9UV9y0iunfdQNd+fmJFvz+fHv6NapY+hIeidcg6br5cpnEXAihAmm7CSEKEhwczLZt2wgJCUGj0fDdd9/x+uuv2zqs4qMosPkNODzH4mqNiwd96/4PfXzLXJtpqGwYx7KRk0tPb2UhhBBCCBtT5VfE5s2boygKs2fPJjk5ucDyycnJ/Pjjj2g0Gpo3b65ChEI4Fh9XH6Y0msKWQVt4pcUrBHsGqx6DoijsOxfLsNkHeH3F8QLL+5ZxYVyHmtT21vNF71B+f7k7nesEodGoN96xXclzPg/bTmIuhCgdpO0mhCiMKlWqsHXrVn755ReefPJJW4dTfBQFtr0DB761vN7ZHYYtpU3XAczsNg393XamVRV1Y/ht1NO4u0jCQwghhBBCLaokPR577DEga3zoESNGEBERkWfZiIgIRo0aRVRUVI5thRAFCyoTxMvNX2br4K081fgp/Nz9VI/BaFTYFHmD/rP2M/Kng/x5MY41/1zj6p3UArd9pUcYW97sz6AOjVSI1M7Z6STmQojSQdpuQojCqlu3LkOHDrV1GMVr50ewf4bldU6u8NhiqN4JgO71y/Ndr/cw3OlEufQRrBj1HB6uqowqLYQQQggh/p8qra9mzZoxfPhwlixZwtmzZxk6dCg1a9akUaNGpiEQ4uLi+Oeff3KMCTt8+HCaNZMf+IQoSGXvyoxrMI6+Nfri6lRM8z0U0jc7z/Pl1rM5lhmMCrP3XOSDAQ3Ytm0bFSpUoH79+mbbarWltFeHJZaSHm6+EFBD/ViEEKWOtN2EENmuX7/Ovn37GDJkiK1DsY09n8Oezyyv0zrD0AVQq1uOxZ3Cgljo/h41g7zxdndRIUghhBBCCHEv1R45eeutt3B3d2fevHkYjUbOnTuX40syZA2HA6DVahk3bhwvv/yyWuEJYTUajQYXFxfT38XJ3cmdD9p+QPcq3XHS2keX+UHNQpm5/Rx6o5Jj+bIjV6mRfpbJY4YRGBjIvn37qFatmlWOqWadq8JohGt/my8PaQJa289t4nD1beekvoWtSNtNlCZyrbXs4sWLdO/enUuXLqEoitV6dJSY+t4/A3ZMs7xO4wSDf4awXhZXN6sSUIyBFU2JqW8HInUuhBBC2JZGyf62qpKzZ8+ydOlS/vjjD65cuZJjXZUqVWjTpg3Dhg2jdu3aaobl0DIzM03DUoSHh+PqatueAOL+jNg4guO3c86NER4YzpI+S2wUUd5e/vUffjsabbY84c9lxO9ZCED16tXZt28fFSpUUDs8+3f7LMxqYb68/UvQ9W314xGilJH7Zk7SdlOfnIPCHpw4cYLu3btz/fp1AFxcXFi7di09e/a0cWQqOfAdbDKfmP0vN1fCdAY8Bs6G8ME2CEwIkZvcN4UQQuSm+uCitWvX5p133gGybkyJiYkA+Pj4yI1JiCKy16eGnuxYgxXHoslOqQZ6uaKL2ETUgd9MZS5evMg333zDhx9+aKMo7ZjM5yGEsCPSdhOidFqzZo0p4QGg0+l4/fXX6d69O05O9tHDuNgc/sliwuOAuxtPBwdRs0xF5oQ9jLcNQhNCCCGEEAWz6Yxqrq6uBAYG2jIEIUqMxb0X2/T4MfFp/LjnIhV83ZncMf95JWoGedGzfnkiYhKY3LEGQ5qFkpbcnE6nt3H8eFZvlcmTJ/P++++rEXrJI0kPIYSdkrabEKXH1KlTuXbtGrNmzQKgUaNGbN682fETHscWwob/mC0+4u7Gs8HlyNRqOJlxnRHrx7Gwzxx83XxtEKQQQgghhMiPTZMeQn16vV6eyixmer3e9FRchQoVcHYu2R+zC7eT+X7XBVb9FYPeqODn4cLIVlXwdMv/dX04MBxvd2dcnLLmoHD392fLli20a9eOQYMG8fHHH1utp4qj1bnFpIdPCHiXVz8WCxyuvu2c1Lf69Hq9rUMQwkTabuqQa605jUbDzJkziY+P59KlS2zYsAE/Pz+r7Ntu6/ufX2Dts+aL3Vx5Orgc6ffMrXYp6TTD1j3Bokd+IsDdfubvsMRu69uBSZ2rS9puQgghcpM7bymj8hQupZLRaOTWrVsABAcH2zia+xcZk8C3u87ze+QN7j1t4lN1LD0UxYT21fPdPsDT/Aea4OBgjhw5gq+vdZ+Ic5Q6B0CfATcizJeHNFU/ljw4VH2XAFLf6pN7pbAncj6qQ661lmm1Wn7++WcyMzPx9PS02n7tsr4jV8DqKUDOz9wJVxemBAeRek/CI9vVlPN88ec8Puxs3jPEnthlfTs4qXN1yb1SCCFEblZNeowePRrIeipo/vz5ZsvvR+59CSGKj6IoHLx0h293XWDP2dt5lpuz9xKjWlfBzdl8eAO9Xp/vk0zWTng4nBuRYNSZL5ehrYQQxUDabkIIyL/95uLigouLi8oRqezkWlgxERRjjsVnXFyYXD6IJCfzhAeAJrk5I+uMVyNCIYQQQghRBFZNehw6dAgwn1z50KFDaDSaImXfs8vb60TNQjgSRVHYcfoW3+66wNErdwssfyMxnVXHYnj8oco5liclJTFgwAB69uzJK6+8UlzhOjaZz0MIoSJpuwlRuimKwgcffMC+fftYt24dbm5utg5JfWc2wW/jQDHkWHzBxZlJFYJIyGsOk+TGLOr/BXUryAM9QgghhBD2xqpJjxYtWhRpuRDCtvQGIxsirvPdrgucvpFUqG2ctBr6NapI86r+OZbHxcXRu3dvDh06xI4dO/D392fChAnFEbZjs5j00ECFxmpHIoQoBaTtJkTpZTQaeemll/jqq68AGDFiBL/88kvpmnvg/DZYPsqsl+0VZ2cmlA/mTh4JDyWlAfMemU7DUPuey0MIIYQQorSyaot24cKFRVouhLCNDL2Blcdi+H73Ba7EpRZqG1dnLUObhzK5Qw0qBXjkWJeenk6nTp2IjIw0LZs0aRJ+fn4MHjzYqrE7PEtJj3Jh4O6jfixCCIcnbTchSq+XX37ZlPAAWLFiBZMnT2bOnDmlo8fWxV3wywgwZOZYHO3sxPgKQcRaGMYVINi5CdN6fk7zKoEqBCmEEEIIIe5HKXqMRwiRkqFnycEo5uy7yM3EjEJt4+XmzMhWVRjXripB3u4Wy7i7u/PEE0/w8ssvm5aVLVuW6tXzn+xc5JIWD3HnzJdXtJ9JzIUQQgjhGIYNG8aPP/5IcnKyaVmzZs1KR8Ljyh+wdBjo03MsvuHkxITywdzMo7dLqwqt+KbrN7g5lcJhwIQQQgghShBJeghRCtxNyWTeH5eZ/+dl4lMtTJJtQYCnK2PbVGV066r4ehQ8eeVLL73E3bt3+fDDDwkNDWXr1q3UqVPnQUMvXa79ZXl5iCQ9hBBCCGFdLVq0YN26dfTs2RO9Xs/8+fMZMWKErcMqflcPweIhoMvZ2/m2k5bxFYKIcbH8Fbl5cHNmdpkpCQ8hhBBCiBJAlaRHnTp10Gq1rF27lpo1axZqm6ioKHr06IFWq+XkyZPFHGHpUSqe3LIxrVaLn5+f6W9bupmYzpy9F1l8MIrUTEPBGwAVfN2Z2L46jz9UCQ/Xol0iPvjgA8qUKcPIkSOpUqXK/YR8X+ypzh/ItWOWl9vZJOYOU98lhNS3+uReKW03eyLnozpK67W2U6dO/PrrrwD07dtXteParL5jjsKiQZCZnGNxnFbLhPLBRLlYftCncbnGComQFQAAedtJREFUzOo6izLOZdSI0upK6/ltS1Ln6pJ7pRBCiNxU6+mhKIqq2wnLStXEhDbi7OxMjRo1bBrDlbgUvt99kRVHo8k0GAu1TfVAT57sWIMBTUJwdb6/hrlGo+HNN9+8r20fhD3UuVXEWEh6OLlCcAP1Y8mHw9R3CSH1rT65V2aRtpt9kPNRHaX5WqtmsiObTer7+j+wcCBkJOZYHK/VMrFCEBddLSc86petz7fdvsXDxcPi+pKgNJ/ftiJ1ri65VwohhMjN7u8MkrEXovBOXU/ku10XWH/8GsZC/uZUv6IPT3WqSc8G5XHSFvx527JlCy1atMDf3/8BoxVmLE1iXr4hOLuqH4sQQtwnabsJYV8uX77MzZs3admypa1DsZ2bJ2DBAEhPyLE4UathUvkgzrlabmuF+YfxQ/cf8Hb1ViFIIYQQQghhLXab9Lh79y4AZcqUzC7EQqjp6JU7fLvzAttP3yr0Ng9VC+CpTjXoWLtcoX+gWrBgAePGjeOhhx5i69ateHp63m/IIrfEa5B03Xy5nQ1tJYQQeZG2mxD25+TJk3Tv3p3U1FR2795Nw4YNbR2S+m6fhQX9Ie1OjsUpGg1TgoM45WY54VHNpzqze8zG181XjSiFEEIIIYQVqZr0KOwPq6mpqSxcuBCAypUrF2dIpY5Op8M1jyeZhHXodDouXLgAQI0aNXDJY2zgB6UoCnvOxfLtzvMcvHSn4A3+X+ewcjzVuSYtqgYU6XgzZ87k+eefB+DPP/9k4MCBrFu3Djc320/mqFadFytLvTzALpMeDlHfJYjUt/p0Op2tQ7Ab0nazPWm7qcMRr7WHDx+mV69exMXFAdCjRw/27t1LrVq1bByZivUddwHm94WU22arTrm5ciKPdqwxI5CRVT4iwL1o7WV75Yjnt72TOleXtN2EEELkVixJj65du1pcPm7cuALHWszMzOTOnTsYjUY0Gg2dO3cujhCFKDaKopCSkmL629qMRoXNJ24wa9d5ImMSC94A0Gqgd3gFpnSqQf2KRX9aLSEhgU8//TTHsq1btzJ//nwmTZpU5P1ZW3HXuSpKUNLDIeq7BJH6FmqQtpso7RzxWvvFF1+YEh4AN2/e5KWXXmLt2rU2jCqLKvV993JWwiP5hsXVqalhpF3tj0voYjRavWm5MTOAibU+Y2jT+sUTlw044vlt76TOhRBCCNsqlqRHTEyM2TJFUbh582aR9tO4cWMmTJhgrbCEcAiv/HacFceiC1XWxUnDoKahTO5Yg2qB9z8Ula+vL1u2bKFDhw7cuZPVq+SVV15h4sSJ971PkYulpIe7LwRUVz8WIUSpI203IRzP3LlzuXbtGnv37gWgdevWzJ8/38ZRqST+KszrC4nm1zaAP4z1mZj5HzIyXdFfHUuZSvPRaDMx6vwYVfVjXujcQuWAhRBCCCGENRVL0mPgwIE5/r1q1So0Gg1dunTBx8cn323d3NwoV64cTZs2pVWrVjIZphD/b+C3+/krKr5QZcu4ODG8ZWUmtK9GBV/rjK1ev359Nm3aRNeuXXnjjTd4/fXXrbJfARiNEPOX+fKKTUGrVT8eIUSpI203IRyPh4cH69ato0uXLgQGBrJy5crSMR9b4rWsHh4JURZXH1bqMD7zJTLIGjbOkFqDtKhxuFVYwdBK7/B69zZqRiuEEEIIIYpBsSQ9Pv744xz/XrVqFQAvvvgiNWvWLI5DCiEA3zIujGlTlSfaVCXA0/rjf7do0YIzZ85QoUIFq++71JrTDaIPW15nh0NbCSEck7TdhHBMvr6+bN26FU9PT7uYh63YJd3MSnjcvWRx9V9KLZ7IeIU03HMsN6RVZUTo17zRs54aUQohhBBCiGKmykTmzzzzDAABAY4xEZwQ9qactxsT21djeMsqeLk92MdaUZR8n9KVhIeKJOkhhLARabsJUXJkzxeQV/ut1HyOU2JhQT+IO29xdYRSg9EZr5GCeS/oJ9pU5Y1e9aSnmhBCCCGEg1A16SGEuH+rnmpr+ttoVOg2fTc6g5EnO9ZgUNNQ3F2cHvgYd+7cYeDAgbz55pv06NHjgfcnHlBIU1tHIIQopaTtJkTJoCgKr7zyChqNhs8++6z0/mifegcW9Ifbp3MsNgJRzs6k6UMZkfEaSXiYbTrsocq801cSHkIIIYQQjkSVpIcQwrq0Wg3zxz5EBV93nJ2sM+fD9evX6dGjB5GRkRw5coStW7fSpo2MaWwzPiHgXd7WUQghhBDCThkMBiZPnsxPP/0EgL+/P2+88YaNo7KBtHhYOABuRuZYrAAflvVnrZcXuqjhJOJltumgpqF8OKCBJDyEEEIIIRyMKjPkHjt2jLp169KwYUNu3rxZYPmbN28SHh5OvXr1iIyMLLC8KDytTIpc7JycnAgNDSU0NBQnpwfvfZGXSgEeVkt43Lx5k3bt2pk+b6mpqfTp04fjx49bZf/FTa06LxaK0fJyO+7lUaLruwSS+laf3Cul7WZP5HxUR0m71iqKwqhRo0wJD4A333yT7777zoZRFZ7V6js9ERYNguv/5FisAJ8F+LHcx5t0rQZ95WU4eZzLUaZvo4p8NrghWq3jJzxK2vntCKTO1SX3SiGEELmpcmfYuHEjiqLQqVMngoODCywfHBxM586dMRqNrF+/XoUISw9pcBU/JycngoODCQ4OLjH1HRgYyEMPPZRjmaenJy4uLjaKqGhKYp2b6FItL7fj+TxKdH2XQFLf6pN6lrabPZHzUR0l7Vqr0Wjo2bNnjmVOTk54enraKKKisUp9ZyTD4iEQcyTHYgX4yt+XRb4+pmUarY4ylebj5Jk1/NXD9YP5cmgjnEpBwgNK3vntCKTO1SV1LIQQIjdVkh5Hjx5Fo9HQoUOHQm/TqVMnAA4fPlxMUQkhsjk5ObFgwQJ69+4NQM2aNdm3bx9169a1cWQOLi0+a9JNS+w46SGEcHzSdhPC/o0ePZqZM2cC4OrqyooVKxg9erSNo1JJZiosfRyuHjBb9Z2fL3P9fM2Wa7R6yoQspUMdD74e1hQXK/WYFkIIIYQQ9keVOT2ioqKArB9SC6t69eo5thVCFC8XFxd+/fVX/vOf//Duu+9SvrzMJ1EsFAWi/oRjC+DEatCnWS5XobGaUQkhRA7SdhOiZHj22WdJTU2lRYsWdOnSxdbhqEOXDr8Mh8t7zVbN8fXhO3/zhAeAojhRXZnE7BEdcHWWhIcQQgghhCNTJemRkZEBgJubW6G3cXV1BSAtLY8fBMV90el0proVxSMzM5OIiAgAwsPDS1R9e3h48P3339s6jCIrEXWefBv+WZqV7Ig7l39ZZ3dw98m/jA2ViPp2IFLf6tPpdLYOweak7WY/pO2mjpJ8rX3ttddsHUKR3Xd96zNg+Si4uNNs1QIfb2YE+FncTFG0hOom8suYcbi7lL5hcEry+V1SSZ2rS9puQgghclPlERc/Pz8Arl27Vuhtbty4AYC3t3dxhCREqbVr1y4MBoOtwygdjEY4vw2Wj4Yv68LWtwpOeACU8Sv20IQQIj/SdhPCfpw+fZqYmBhbh2F7Bh38OhbObTFb9Yu3F5+X9be4maJoKJ8xlt9GT8bDVZVn/oQQQgghhI2pkvSoUaMGADt27Cj0Ntu2bQOgWrVqxRKTEKXR119/TefOnZk8eTKKotg6HMeVEAO7PoUZjWDRIDi5BoxFePrIS4YWE0LYlrTdhLAPR44coV27dvTo0YO4uDhbh2M7Bj2smABnNpitWunlyYeBARY3UxQN5dJHsWL003i5ScJDCCGEEKK0UCXp0bFjRxRFYc2aNRw5cqTA8ocPH2bt2rVoNBrTpJhCiPunKAoffPABzz33HAA//fQTr776qiQ+rMmgg1PrYfEQ+KoB7PoIEu5zXHsnF+vGJoQQRSRtNyFsb/fu3XTp0oW4uDhOnjxJr169SEpKsnVY6jMaYPWTcHK12ap1nh68m0fCA8A/9XFWjnoe3zLSthJCCCGEKE1USXo89thj+Pv7YzAYmDhxIosWLTKNFX2vjIwMFixYwKRJk9Dr9fj4+DB8+HA1QhTCoZ0/f55p06blWPa///2vSE/wijzEXYBt78L0+rBsRNaQC4qxcNuWCQCv4GINTwgh7oe03YSwLaPRyAsvvJAjyXH48GHefvttG0ZlA0YjrH0WIn41W7XZowz/LVcWRaOxuKlX8mBWjX4Jf0+ZS0EIIYQQorRRpY+vp6cnX3zxBZMmTSI9PZ0PP/yQL7/8kvr16xMUFATArVu3iIyMJD09HUVRcHZ25ssvv8TLy0uNEIVwaLVq1WLZsmUMHjzYNJ/HtGnT6NKli40jK6F06XBqHRybD5f3Fn376p2g6Wio8wjM6wPJN60eohBCPAhpuwlhW1qtljVr1tCuXTuuXr0KQNeuXfnggw9sHJmKFAU2vAh/LzZbtcOjDK8FBWLMI+HhkdSf1aNfI9DLrbijFEIIIYQQdki1gU3btGnDnDlzePXVV7l16xapqalmwyVkD7UTHBzMZ599RsuWLdUKTwiHN2DAAObOncvYsWOZOXMmTz/9tK1DKnlunoBjC+CfXyA9vmjbeleAxiOgyUgIkPHuhRD2T9puQthW5cqV2bZtG+3ataNdu3YsXboUN7dS8iO+osDvr8LReWar9pRx56WgQAx5JDzcknqzauSbBPu4F3OQQgghhBDCXqk6m1urVq3Ytm0bq1evZufOnZw6dYq7d+8C4O/vT7169ejcuTP9+/fH1VW6IQthbaNHj6ZVq1bUrl3b1qGUHBnJELkiK9kRU/C49jlotFDrYWg2Bmp2ByeZQFMIUbJI200I26pduzYHDhygcuXKODuXknaEosCW/8Kh2WarDri78WJwOfR5JDxcknqwavg7VPQrU9xRCiGEEEIIO6ZRZCZjh5eZmUlERAQA9evXx91dnnoqTkajkdTUVAA8PDzQalWZOqdUs3qdKwrEHINj8yByJWQmF217vyrQdFRWzw6fivmXndMNog/nXBbaAiZsK9oxVSTnuLqkvtWXnp7OiRMnAAgPD5cf84XqpO2mPrnWqivP+lYU2P4+7PvSbJsj7m5MCQ4iXWs54eGU1JkVQz+kRpB3scVdUsn5rT6pc3VJ200IIURupeRxIZFNGlvFT6vV2mw88xs3bvDEE08wa9YsatSoYZMYbMFqdZ56B44vz+rVcetE0bZ1cs2ao6PpaKjWEQr7WbPj5EZebHmOl0ZS3+qTe6WwJ3I+qsNW11qDwcDTTz9Np06dePzxx1U/vq3kWd+7P7WY8FCAr/z980x4aJPasWzwNEl45EHaEuqTOleX3CuFEELkJkkPIRzE5cuX6d69O+fPn6d79+7s3buXkJAQW4dl/xQFLu/LSnScXAOGjKJtHxiWNXxVw8fBs2zxxCiEEEIIh5OZmcmoUaNYvnw5P/30Ez4+PvTu3dvWYdnO3i9g18eW12md6eT+JMfSNuNUJibnuqRWLBn4CWHlfYo/RiGEEEIIUSJI0qOUMRqNtg7B4dmiK/O5c+fo3LkzMTFZXwIvXbpEjx492LNnD2XLOv4P8fdV50k34Z8lWcmOOxeLdkAXD6j/aFavjkoPQR7jSjsq6a6vLqlv9cm9UtgTOR/Vofa1NjMzk/79+7Np0yYA9Ho9gwYNYvPmzXTo0KFYj20PzOr7wLdZw1pZotGiGTSH8fUGELWuHb9dewenMlez1iU1Z0G/T6kf4qtS5CWTtCXUJ3WuLrlXCiGEyE3VpEdmZibr1q1j27ZtnDlzhrt375Kenp7vNhqNhpMnT6oUoeMzGAy2DsHh6fV6zpw5A6g3nmhQUBBBQUGmpAdARkYGycnJpSLpUeg6Nxrg/HY4Nh/ObgKjvmgHqtA4q1dHg0HgXnq/XNviHC/NpL7VJ/fKf0nbzfbkfFSH2tdaFxcXateubUp6QNaPdnfv3i3W49qLe+u7UcZBtFvezKOkBgbOhvoD0QDv9W2O26ZpLLr8DlqDH/Me+YwmlQNUi7ukkraE+qTO1SX3SiGEELmplvS4dOkSTz/9NJcuXULmThfCunx9fdm0aRPt27fn7NmzhIeHs3nzZipUqGDr0OxDfBT8tSjrv8SYgsvfy80XGg7J6tVRoVHxxCeEEHZI2m5CFB+NRsP06dNJSEhg/vz5eHp6smbNGrp27Wrr0FQVeGU9zsfN5/Aw6T8rqx32/zQaDW/2aorXjo9pWqkcLaqWUyFKIYQQQghR0qiS9EhNTWXixIlER0ej1Wrp2rUrAQEBLF++HI1Gw5QpU0hISCAyMpJ//vkHjUZD48aNadu2rRrhCeEQgoKC2Lp1Ky+//DLff/89AQGl/Kk3fSac/R2OzocLO8iaArMIKrfJSnTU6w+uHsUSohBC2CtpuwlR/LRaLXPmzEGr1TJ58mRatmxp65BUVfbqJqrkl/B45CtoMsLique7NCieoIQQQgghhENQJenxyy+/EB0djZOTE3PmzKF169acO3eO5cuXA/Dcc8+Zyp48eZJXX32Vf/75hz59+jBy5Eg1QhTCIVSuXNn0uSq14s5DxBL4eymkxhZtW49AaDwMmoyGcrWLJz4hhCgBpO0mhDqcnZ2ZO3eurcNQnTbyN6r8/bnZ8kStBm+jgqb3/6D5WBtEJoQQQgghHIEqs2nt3LkTjUZDz549ad26db5l69Wrx4IFCwgICOCTTz4hMjJSjRCFKDH++usvW4dgf3RpBFzdQu39z+P6Q2v44+siJDw0UKMrDJkP/zkFPaZJwkMIUepJ200I6zl16lSBc+GUKidW4bTuaTS5euHecnJiWMXyfNaoD0qLCTYKTgghhBBCOAJVkh7nz58HoHv37hbXG43GHP8OCAhg7Nix6PV6Fi9eXOzxCVESKIrCRx99RNOmTfnhhx9sHY59uH4cNryMy8wGVPv7E7zvRBR+W58Q6PgaPP8PjFoJ9QeAs0wwKIQQIG03Iaxl7969tGrVimHDhqHX620dju2d3gArJqBRcl5D4rRaJpQPIsrFhUWJEbz3x4cYc5URQgghhBCisFQZ3iopKQmAihUrmpa5uv7742JqaipeXl45tmnatCkAhw4dUiFCIeyboii8+uqr/O9//wNgypQp+Pr68vjjj9s4MhtIT4TI3+DYAriW1etFU9htNU4Q1guajoGaXUHrVGxhCiFESSZtNyEe3MaNGxk0aBDp6emsXr2a8ePH8/PPP6PVqvLcmf05uwWWjwFjzuRPvFbLxApBXHJ1MS1bcX4Z6fpMPurwLlpNKa0vIYQQQghx31RpQbq7uwOg0fz706S3t7fp7+vXr+e5bWxsEcfkF8IBbd++3ZTwgKwkyKhRo0xP4jo8RYGog7D6afgiDNa/aEp4FIp/Nej6TtbwVY8vhto9JOEhhBD5kLabEA8mKSmJ0aNH5xjWasGCBXz33Xc2jMqGzm+HZSPBqMuxOFGrYVL5IM65mve23XB5FV8cmqVWhEIIIYQQwoGo0tMjNDSUM2fOcOvWLdOygIAAfH19SUxM5NixY9SqVSvHNidOnADAxcUFYT1Sn8XP1dWVZs2aWXWf3bp14+233+b99983Lfviiy+oWbOmVY9jd1Li4PgvWb06bp8u2rZOblCvHzQdDVXaQWl9qrIYFMc5LvIm9a0+uVdK282eSH2qw9rXWm9vb1auXMnDDz9sSnz079+f8ePHW+0YJcalvfDLcDBk5FicrNEwJTiIU26Whxc1pAcTaOysRoQOT9oS6pM6V5fcK4UQQuSmyq+ADRo0ADCb2LJ169YoisJPP/1EfHy8afnVq1eZPXs2Go2GunXrqhGiEHbv3Xff5dlnn8XJyYn58+fz3HPP2Tqk4mE0wsVd8OtY+LIObH6jaAmPoHrQ81N46TQMmgPVOkjCQwghikjabkI8uA4dOvDbb7/h7OzMqFGj+O2330y9qP6vvfsOj6pM+zj+m0kjIXRCkRaKoSMdUQTpVamKSFMsuAgoKiq47FrWyoKu66qgIoJIk2IBpcOKIBqQJZTQIdJ7IKRnzvtH3owJmUkjc2Yy8/1cF9c1Oe25557hnPvMc85zfMbxrdJXg6XUrA9yj7dY9GSlMO0qFuRwNVtSeT1S5y093LaRGVECAADAy5hyp8cdd9yhr7/+WuvXr9dTTz1lnz58+HD9+OOP+uOPP9S9e3e1adNGCQkJ2r59u+Lj42WxWHT//febESLg8SwWi9577z0NHz5crVq1cnc4he/qaWnnPOn3udLlY/lbN6C41Hhg+rM6qrSQLHl+ygcAwAFqN6Bw9O7dW1u2bFGLFi1871keJyKlefdJKdezTE60WDS+Yph2OOkAsiWX1YM13tCznVuaESUAAAC8kCmdHh07dlSrVq2UlpammJgYVa9eXZLUokULPfnkk/rPf/6j2NhYrVmzRlL68wokacCAAbrnnnvMCNFnpKWluTsEr5eWlmYfz7x8+fLy8yu8Z0dYrVbv6vBIS5UOrUkfvurAKsnI5/ezSkup+Qil1e+rC9fSh0wob7MVas6RnSu/48iOfJuPYyW1myfh+2gOV+5rvap2y6tTv0tzB0jJ17JMTpb0dIXy2hbsuMPDL62M+t3yD03udrsJQfoOagnzkXNzcawEANzIlE6P4OBgzZ071+G8cePGqWXLllq8eLEOHjyotLQ01ahRQ/369VP37t3NCM+n2Gw2d4fg9dLS0nTixAlJUpkyZfJV4B4/flyTJ0/Wxx9/nOWBsV7n8jFpx9z0OzuuOX8YrkPFSku3PSA1Gy5VSh/yIC05WSdOHJaU/5wj/27mO478I9/m41hJ7eZJ+D6ao6D72uTkZI0dO1Z/+ctf1KxZM1eGWHSciZLm9JOSYrNMTpH0bIXy+jkk2OFqFYIr6D+dPlXdcuGycNduoaKWMB85NxfHSgDAjUzp9MhN27Zt1bZtW3eHAbhVdHS0unbtqhMnTujMmTNasWKFd437nJokRX+fflfHkY35Xz/8rvThq+r3kQIcnywDAMxB7QZI8fHxGjRokH744QctX75cP/30k+rWrevusNzr3D5pTl8p8UqWyamSXqhQXhuLhzhcrVyxcvq0+6eqWaqm62MEAACA1zOl02P58uWSpJo1a+q2224zo0mgSNmxY4e6d+9uvwV6/fr1GjJkiBYvXix/f4/omyy4c9HpHR3/my8lXMrfusUrSE0flJqPkMrVdk18AIBsqN2AnF29elV9+vTRTz/9JEk6f/68unbtqs2bN9uHg/M5Fw5KX9wrxV/MMjlN0kth5bTGSYdHqcBS+qTbJ3R4AAAAoNCY8mvqiy++KIvFomnTpnHiDDgQEpL9JPDgwYO6cuWKypcv74aIblLydWnPcmnHF9If2/K3rsUq1emS3tER0UPyC3BJiAAA56jdgJwFBgZmuzDl4sWLOnLkiG92elw8LH1xj3T9XJbJNkkvly+rlaHFHa4WYg3Rhx0/1K1lbjUhSAAAAPgKUzo9SpQoobi4OIWHh5vRHFDk1KtXT6tWrdLdd9+ta9euqXXr1lq5cqXKlSvn7tDy59Tv0vYvpKivsz24MlelqqU/p6PZUKlUVdfEBwDIE2o3IGfFihXTN998o86dO+u3335T6dKltXLlSt8c9u3y8fQ7PG54Tpsh6fVyZbS8RKjD1YpZi+m58OdUr2w9E4IEAACALzGl06Nq1aqKjo5WbGxs7gsDPqp58+b6/vvvNXXqVH311VdF50HmCVekqMXpQ1id2ZW/da3+Ur3e6Xd11OooWXnAHwB4Amo3IHclSpTQypUrNXjwYL377rtq0qSJu0MyX+yJ9Ds8rp7IMtmQ9E7Z0lpU0nE9G+wfrGeqPaNaIbVMCBIAAAC+xpROjy5dumjfvn3asGGDb179BORR+/bt1b59e3eHkTvDkGK2pnd07FkupSbkb/1yddIfSn7bECk0zCUhAgAKjtoNyJvy5ctr3bp17g7DPa6eTu/wuHI826yfgovpy1IlHa5m2ALUv/JfdWvxKq6OEAAAAD7KlE6PkSNHasmSJZo/f746duzocSfPJ0+e1Ny5c7Vx40adOXNGgYGBqlatmnr27KmhQ4cqODi4wNtOSEjQTz/9pJ9//lm7d+9WTEyM4uPjFRoaqvDwcLVr104PPPCAwsL44ddbWCwWFS9e3P76RkeOHFGtWkX0qrbrF6SdX6V3dlw8mL91/YtJDfpJLUZK1dtKDnJTULnlHIWLfJuLfMMdqN2o3XxNTvvaI0eOqGbNmuyDM4s7J825V7p0xOHscknVZFxoJUv5zVmmGzZ/1Uh9Uk+26aZTfxyTxLHNDNQS5iPnAAC4l8UwDMOMho4fP67x48fr0KFDGjBggPr06aO6deuqVKlSbi0C1q9fr4kTJyouLs7h/PDwcM2cOVM1atTI97ajo6M1ZMgQxcfH57hcaGioXnvtNfXq1SvfbeRFcnKyoqKiJEmNGzdWYGCgS9pBzgzD0KRJk/T+++9r9erVateunbtDyhubTTqyIf2h5NErJVtK/tav2Di9o6PxfVJwaZeECACFieNmOmo356jdfMePP/6oAQMGaNKkSZoyZYq7w/EM1y9KX/SRzu11OHu/tY7ui39BV1VcgeXWKajCGkmSYfipStITWvrQoyoeZMq1dwB8BMdNAMCNTOn0qF+/vv21YRj5OlG2WCzau9dxQX2z9u7dqyFDhigxMVEhISEaPXq02rRpo8TERK1cuVKLFi2SlH7yvGTJEoWGOn4InzORkZEaOnSopPTnNXTs2FGNGjVS6dKldenSJa1evVqLFy+WzWaTn5+fPvroI3Xo0KHQ3ycFgPulpaVpzJgxmjlzpiSpZMmS2rhxo5o1a+bmyHIQe1LaOU/aMVeKjcnfuoElpMaD0p/VcUuzQr2rAwBcjeMmtZtE7QZp0aJFGjZsmFJS0i/4+Ne//qXx48e7OSo3i7+UfofHmSiHs49Yw9U/frJi9ef/vYCymxRUYZUqJj6qZQ89oZLFAsyKFoCP4LgJALiRKZfY3NivYtLNJbl6/fXXlZiYKH9/f82aNSvLD9Bt27ZVjRo1NHXqVB07dkyff/65xo0bl6/tW61W9ezZU2PHjlWdOnWyzW/Xrp3at2+vsWPHKi0tTf/4xz/Uvn17bn/1QjNnzrR3eEjS1atX1aNHDx04cEClSpVyY2Q3SEuRDqxKv6vj0FrJsOVv/aqt0+/qaNBPCsrfD00AAM9B7Ubt5uuOHDmiBx98UGlpafZpTz31lOrXr6+uXbu6MTI3SoyVvhzgtMMjxq+6Bl1/MUuHhySlXOqgGsFt9PUj99DhAQAAAFOY0ukxduxYM5rJl127dikyMlKSNHDgQIdX3I8aNUpLly7V4cOHNWfOHD3xxBMKCMh7od68eXM1b948x2W6dOmibt26adWqVYqJidHevXvVsGHD/L2ZfEhNTeWqBxdLTU3V8ePpD3SsUaOG/P399eijj+qHH37Qd999Jyn9R5W3337bczo8Lh6Wfp+b/ryOuLP5Wze4bPoDyZsPlyrUz315F3CUc7gO+TYX+TZfamqqu0NwO2o356jdvNON+9patWrp/fff15NPPmlfZtiwYbr77rvdFKGbJV2Tvhwonfrd4eyTflU08PqLuqTsDy+/tUKoFjzcRaVD/vwec2wzF/k2Hzk3F7UbAOBGPtvpsXbtWvvrgQMHOlzGarWqX79+mjZtmq5evapt27a55DkMbdq00apVqyRJMTExLj1x9pQrNb2ZzWbTlStXJEnVqlWTJAUEBGjRokXq2bOntmzZogULFqh///5ujFJSSqK077v0uzqO/ZT/9Wt2SL+ro14fyT+o8OPLB0c5h+uQb3ORb/NxrKR2yw21m/dxtK8dM2aMLl++rL/+9a968skn9f7778tqtboxSjdJvi7Nu1868Vu2WamSLvhV1oDrk3RepbPNr1m+uOY92kblQrPWihzbzEW+zUfOzcWxEgBwo0Lv9Fi+fLmk9Kvg8juOspm2b98uSQoJCcnxRLVVq1b21zt27HDJiXNycrL9tZ+fX6FvH56hWLFi+vbbb7Vr1y7deeed7gvk7N70jo7/LZASr+Rv3dBKUrOhUrPhUtmaLgkPAGAuarf8o3bzHZMnT1aLFi3UvXt33xzGLCVBmv+AFLMl26xVIcH6uEw5nT72pM6qbLb51coG66vH2qhCyWJmRAoAAADYFXqnx4svviiLxaJGjRo5HAv50qVL+uqrryS59yrCw4cPS5KqV6+e462mtWrVyrZOYfv111/tr2vXru2SNjKkpKRkOVHPzN/f3371ms1my/UW0cxDLaSlpWUZ8/hGFosly/ASqampstmcPy/CarVm+VxSUlJyvHojc+yGYdgfOOlMQECA/cQ1t/ea39hv3Fbm2IOCgtSqVatsP5Zk/sHE2eeToUCfU1KctGepjO2zZTm5Pcflb2RYrDLqdFXabcNk1OkiWf0zAvWYz8nRdgr7O2bK5/T/PP3/U+a/U1JSXPr/ic/JyJZvR7G7e7/nbZ9Tbrn0NtRu+Uftlp2n1AQFjT2zG7fdqVOnLPF5+j6s0D6n1EQFLHlIlqP/zTZrfUiwXqhQXmkWi9JqfC1LzGMyUv8c2uqWUsU075HWKhfs5zA/1G7Ubhm89XNyVL952n7Pmz4nX6vdAAC5M31gyYsXL+qDDz6QxWJx24lzUlKSLl++LEmqVKlSjsuWKlVKISEhio+P15kzZwo9lujoaG3atEmSFBER4fIT55xO/uvWrWu/wjM+Pl779+/PcVstWrSwv75w4YJOnDjhdNnixYurXr169r+PHz9uv93XkQoVKmS5DXjfvn05FjK1atVSmTJlJKUXbFFRjh+wmKFx48b2wio2NlZHjhxxumxAQICaNGli//v06dM6d+6cw2WPHTumVatW6bHHHrMXeYcPH9b169edbr9q1aqqWLGi/e/cYs/z52QYCrkSrfrx26TdS6XkOOXr+sTSNXQxvLdOlu+glOAwKUnSnn1ZFvGkz+lGOX1OklS6dOks/9/c9jn9v6L6/yk6Otpl/58kPqcbP6fo6Gj7a0/Z70m+9Tn5Imq3rKjdHPOkmiA/+7DExETNnDlTb7zxhn3asWPHlJCQ4HT7RXkfltfPyWJLUa3Iv6v02V+yzfspuJie/f8OD0nyCzqvkBozFH/8MRmppVWhRJDmPXa7SliTFRWV83vNjGPNn6jdnCuKn1NG/eYp+z3Jtz4nAIBv8smnaWU+mIeEhOS6fHBwsOLj4xUfH1+ocSQnJ+ull16yX8UwYcKEQt0+zBUdHa1x48bp8uXLun79uts+T7/kqyp7cq3KH1+pkGt57ySQJMPqL0v9e6TmI6WaHXTl6FGlUEACANyM2g2uEBcXp2eeeUY7duzQ7t27NX36dI8e4s00tlTV3P6aww6PX4oF6emKYUq9Yagva+BFhdSYocBzz2jeo+1Vs3xxXb6c85XgAAAAgKtYjEJ+4lO9evVksVj03XffORwi4eDBg7rnnntksVi0b98+B1twvdOnT+vuu++WJPXt21fvvPNOjsvffffdOn36tKpXr641a9YUWhx//etftXjxYklS//799dZbbxXatjNLTk62X4lRu3Ztpz8WFJVbhXOL3R23Cm/evFn9+/fX1atX7dOeeOIJ/etf/5LFYnH9rcKGIUvMFll3filr9HeypCXluI0bGeUilNZsuIzG9yug1J9X0BaVzyklJcV+BVXGFVG+dku32UMkZOS7Xr16CgkJ4dZ7BwpzeKvM+c7Yprv3e3mJ3RlP/5zi4uLsV9dnvsrSW1G75R21m+fXBHmN/cKFC7rnnnu0Y8cO+7TmzZvr/fffV7NmzXIcQs3T92E39TnZUuX3zV/kt295tnUjiwXpiYoVlGR1fO+wJfZuzR/4mhpWKZ2+KWo3O3f/f6J2S2f28FY31m/u3u/lNXZHPP1z8rXaDQCQO5+80yMoKMj+Oi9jP2Yc0IsVK7yH8M2YMcN+0ty4cWP97W9/K7Rt5yQgICBPBYDVas1XoXBjEZSbnE4kHclc1OTGYrHkK/b8vldHscfHx2e7Hfi///2vkpKSVKJEiTxvW1L+Yo+/oMCd86Qdc6RL+burQ/7BUqMBUvORslRrLX8HD+f0ts8pJ/mJXcrn5+TF/58yn4xJfE45KYz36mwfzv8n5wryOeU3frgetRu1W2aFsQ8zDEMXL17MMu3AgQP6448/1KpVq3xt39P2YfmR5XOypUnLx0kOOjx2BgXqyRw6PBR7p77s/2eHh8SxJifUbs554+fkaD9O7eYctRsAoDD4ZKdH8eLF7a/zMuxBxpi+eRlOIS8WLFig6dOnS0of13PmzJmFtu3cWBz8sI2b16dPH33xxRcaPny4DMPQHXfcoU8//VTBwcGF18inXaQTv90w0SIpnzdrVW4qNR8hNR4kFStVSMG5l9VqVYUKFeyv4Vrk21zk23wcKz0PtRsKW5UqVbR27Vq1a9dOZ8+eVYUKFfTFF1+oYcOGvrmvtdmk78ZLuxZkm7UnMFBPVKqgeCcdHrarbTT73tfVpFrpPDfHsc1c5Nt85NxcHCsBADfyyU6PoKAglS5dWleuXMn1AZexsbH2k+vcHpyZF99//71eeeUVSeknW59//rnKli1709vNq/xe8YG8Gzp0qGJjY7Vq1SotXLiwUK8udS6PHR5BpaQm96V3dlS+zbUhuYG/v3+WhwjCtci3uci3+ThWeh5qN7hCnTp1tHr1ao0YMUKLFi1SRESEu0NyD8OQVj4n/f5ltln7AwP0eKUKuu7kR1vb1Rb6pNebahVeLl9NcmwzF/k2Hzk3F8dKAMCNfPbIUKdOHUVGRiomJkapqalOD5JHjvw5ZFDt2rVvqs1169bphRdekM1mU1hYmGbPnl0oJ+PwHGPGjNETTzzhmqt5bDmPg+pQ9bbpDyVv0FcKNOeKVAAAXIHaDa7QpEkT7dixw3evxDYM6cdJUuRn2WYdDvDXo5Uq6qqf49ykXbtNH3d/W3fUDnN1lAAAAEC+uKzT46uvvnJ4FdylS5fsrz/44IM8bWvs2LGFFleGFi1aKDIyUvHx8dqzZ49uu83x1e+//fbncELNmzcvcHtbt27V008/rdTUVJUuXVqff/65qlevXuDtwT0Mw9D58+fttyo74rKT5sTYvC0XUl5qOkRqNkIK89ErFgEA+UbtlhW1m/c4d+6ce2o3T2cY0pq/Sds+yjbrmH96h8cVpx0eDfWvTm+rfURFV0cJAAAA5JvLOj3mz5/vdF7GeIv/+c9/8rQtV5w4d+nSRTNmzJAkLVmyxOGJs81m0/LlyyVJJUuWVJs2bQrU1o4dOzRmzBglJyerRIkS+uyzz3TrrbcWOPabkZKSkq+HiOFPaWlpGjt2rFauXKnNmzc7vV05JSVF+/btkyTVr1+/8B6qZs1pOxapdsf0uzrq9pL8feszdlnO4RD5Nhf5Nl9eHpTtjajd/kTt5j2+/vprjRw5UvPnz9e9997rdDmf3NdueF3a8n62yX/4++mRyhV1wd/xg4TT4urpnfZT1bVBlQI37ZP5diPybT5ybi5frd0AAM655LImwzAK7Z+rNGnSRC1btpSUfuL8+++/Z1tm1qxZOnz4sCRpxIgR2QqVbdu2qW7duqpbt65efPFFh+3s27dPo0ePVnx8vEJCQjRjxgw1atSokN8NXC0lJUXDhg3Txx9/rJiYGHXr1k3nz593uKxhGEpJSVFKSkrhfoeDSmSf5hcgtX9eeup/0vBlUsN+PtfhIbkw53CIfJuLfMMM1G5/onbzHp999pkGDx6s+Ph43X///dq4caPTZX1uX7vpHem/U7NNPu2X3uFxzlmHx/Vb9dodU9Wnyc09q8Dn8u1m5Nt85BwAAPcq9Ds95syZU9ibdJmXXnpJQ4YMUWJiokaNGqUnnnhCbdq0UWJiolauXKmFCxdKksLDw/Xwww/ne/sxMTF65JFHdPXqVUnSU089pRIlSujAgQNO1ylXrpzKlcvfgwDhepMnT9aCBQvsf0dHR6tPnz7asmWL/PwcnxQWuv+/yjaLSrdJnV4yp30AgFeidvsTtZv32LRpkx599FH730lJSbrnnnu0fft2331geYbN76Xf5XGDc//f4XHayfNyUq/X1N9avaOBzcJdGx8AAABwkwq906N169aFvUmXadCggd59911NnDhRcXFxmj59erZlwsPDNXPmTIWGhuZ7+5GRkbp48aL97zfffDPXdcaOHatx48bluy241gsvvKDvv/9e0dHRkqSAgAA9//zz5nV4SNKja81rCwDgM6jd/kTt5j3at2+vUaNGadasWfZpw4cPV506ddwYlQfY+qG09u/ZJl+wWvVopQr6I8Dx6WFafA290GyqhrTy8fwBAACgSHDZMz2Kik6dOunbb7/VnDlztHHjRp09e1YBAQGqXr26evTooWHDhik4ONjdYcLNypcvrzVr1qhdu3Y6f/68li1bpm7durk7LAAAfA61G/LCYrFo5syZio2N1ZIlSzRp0iS9/vrr9ufT+KRfP5FWTco22ZA0oWKYjgY6fuZAWkJVjW/4lh5qW9fFAQIAAACFw+c7PSSpSpUqmjRpkiZNyn4SkJM2bdpo//79TucPGDBAAwYMuNnw4CGqVq2qtWvX6vz582rbtq27wwEAwGdRuyEv/Pz8NG/ePN13330aPHiwu8Nxr+1fSCufczjLIik8sbt+D/hFFr/ELPPSEivr8Yi39ER7nmsDAACAosMlDzIHvFWdOnXo8AAAACgigoKC6PDYOV/67inn83tP18TR01U9eYKMtD/vkkpLrKgR4W/o6U63mRAkAAAAUHjo9AAy+d///pdl7GcAAAB4rmvXrunll19WSkqKu0PxTFFfS9+MUfogVg70eFtq9YhKFgvQoofvV+3U52RLLS5bUnkNrvqaXujWwtRwAQAAgMLA8FY+xtQHbxcxP//8s3r37q3Y2FgFBQVp6NChBdqOv7+/atWqZX8N1yPn5iLf5iLf5uNYCU/C99G5CxcuqGfPnoqMjNShQ4c0Z84cWa0Fu6bLK/e1e7+Rlj4uGTbH87u+Kt3+hP3P0CB/LXhooEbMDVCdimH6W6/bXfYMFK/Mtwcj3+Yj5+biWAkAuBFHXx9T0BNBb7dq1Sr1799fCQkJkqSRI0eqZMmSuueee/K9LavVqjJlyhR2iMgBOTcX+TYX+TYfx0p4Er6Pjp08eVLdunXT3r17JUnz5s1TqVKl9MEHHxToh3qv29fu/0H6epRkpDme3+mv0p3Zh7wKDvTTvJF9FeBncelD370u3x6OfJuPnJuLYyUA4EYcGQBJ27dvt3d4SFJaWpo+/PBDGYaToQAAAADgNjExMTp69GiWaYsXL9apU6fcFJEHObhWWjRCsqU6nt/+ean9RKerB/pbXdrhAQAAALganR4+hh/xHZs0aZKeeeYZ+999+vTR0qVLC3TCZxiGkpOTlZycTL5NQs7NRb7NRb7NR57hSfg+Ota2bVstXbpUAQEBkqRq1app8+bNqlKlSoG25zX72iMbpQUPSmnJWSanSnoxrJw2Nr9f6jjZLaFl5jX5LiLIt/nIubnIMQDgRnR6+JjUVCdXfPk4i8Wif/7znxo1apQefPBBLV26VMHBwQXaVkpKiqKiohQVFcVDNU1Czs1Fvs1Fvs3HsRKehO+jcz169NC8efPUoEED/fzzz4qIiCjwtrxiX3tss/TVA1JaUpbJaZJeCiunFaHFNf7yb1p9fK174svEK/JdhJBv85Fzc3GsBADciGd6AP/PYrFo5syZslgsjAkKAABQBNx3333q16+f/Y4PnxWzTZp3v5SakGWyTdLL5ctqZWhxSZKhND236Tm9mfametfu5YZAAQAAANfjl134FJvNpuvXrzud7+fnR4cHAACAB4mLi8txvs93eJzYLs0bJKVkrXENSa+XK6PlJUJvmG7Ti5tf1DcHvzUxSAAAAMA8/LoLn5GSkqIRI0aoT58+WR5aDgAAAM80e/Zs1alTR/v27XN3KJ7p1E7py/5S0tUskw1J75QtrUUlSzhczbAF6LdDPKwcAAAA3olOD/iEhIQEDRw4UPPmzdPGjRs1ePBgxlYFAADwYO+9954efvhhnT17Vl27dtWxY8fcHZJnObtHmttPSozNMtmQ9G6Z0vqyVEmHqxm2AFVPHqdJnXq6PkYAAADADej0gE946KGH9N1339n//u677zR69Gg3RgQAAABn5s6dqwkTJtj/PnnypLp27aorV664LyhPci5a+uJeKeFytlkflS6lz0s76/DwV+Wkv2jhyKEqHsTjHQEAAOCd6PSAT5g0aZJKlSpl/zs4OFiDBw92Y0QAAABwpn///mrTpk2WaQMHDsxSz/msC4ekOfdK8Reyzfq0VEl9VMZxjgzDT2Hxj+nrEQ+pRDEffw4KAAAAvBqdHvAJTZs21YoVKxQcHKxSpUpp9erV6t69u7vDAgAAgAOhoaFauXKlGjVqJEl688039dZbb8li8fHnUFw6Kn1xjxR3NtusOSVL6F9lSztczc/ip6ebvKolDz2qUiF0eAAAAMC7WQzDMNwdBFwrOTlZUVFRkqRGjRopKCjIzRG5z+rVq1WhQgU1bdrUZW0YhmF/XkhAQAAn5yYg5+Yi3+Yi3+ZLSkrS7t27JUmNGzdWYGCgmyOCr6F2+9Pp06e1du1aDR8+3KXtFIl97ZUY6fPeUmxMtlnzS4TqjfJlHa5mtVj19l1vq0fNHq6OMM+KRL69CPk2Hzk3F7UbAOBGDOTqY3y92OrWrZvL27BYLBRZJiPn5iLf5iLf5vP1YyU8i69/HytXruzyDg+pCOxrY0+m3+HhoMNjaWhxpx0eFln02p2veVSHh1QE8u1lyLf5yLm5fP1YCQDIjuGt4FW2bt2qTZs2uTsMAAAA5MHFixf1ySefuDsMz3btTPozPC4fyzbru+Iherl8OaerTmk7RffWvteFwQEAAACehzs9fIzNZnN3CC6zZs0a9evXT35+flq/fr1atmzpljhsNptiY2MlSaVKlZLVSt+iq5Fzc5Fvc5Fv83nzsRJFjzd/H0+dOqVu3bppz549iouL04QJE9wWi8fua+POS1/cK108lG3WqpBg/TWsvAwnFzhPaj1J90Xc5+IAC8Zj8+2lyLf5yLm5vPlYCQAoGI68PiYtLc3dIbjE0qVL1adPH8XHx+vatWvq0aOH9u3b55ZYUlNTdeTIER05ckSpqaluicHXkHNzkW9zkW/zeeuxEkWTt34fjxw5onbt2mnPnj2SpGeeeUazZs1yWzweua+NvyTN6Std2J9t1vqQYL1QIUw2Jx0ez7V8Tg/Wf9DFARacR+bbi5Fv85Fzc3nrsRIAUHB0eqDIMwxD8+fPV3Jysn3axYsXNW3aNDdGBQAAAGd++uknHT16NMu0V199VQkJCW6KyMMkXE7v8Di3J9us7UFBerZCmNKcdHj0qTZKIxuOdHGAAAAAgOei0wNFnsVi0dy5c9W5c2f7tAceeEAffvihG6MCAACAMyNHjtRbb71l//vWW2/Vpk2bFBwc7MaoPETiVenLgdKZXQ5nl7VUVFp8dcfrXu6ioXVHuTA4AAAAwPPxTA94hWLFimn58uXq0qWLmjVrpg8++EB+fn7uDgsAAABOvPDCC7p8+bJWrVqlH3/8URUrVnR3SO6XFCfNGySd3O5w9oWgarr/ygu6GltcwVXnyj/0oH2ecaWD5vSfokZVSpkVLQAAAOCR6PSA1wgNDdW6desUEhIii8XJ/f4AAADwGG+++aamTJmi4sWLuzsU90uOl74aLP2xzeHsy0FV1Cf2eZ1XGcmQEk6MUHDVL+Uful+2K3dq1j2vqHmNsiYHDQAAAHgeOj1QpKSmpspqtcpqdTwyGyfMAAAAniU5OVmBgYEO51ksFuo3SUpJlBYMkY5vdjj7amAl9Yl9XmdU7s+JRoAS/hiu4LI79Gn/sWpTq5zDdQEAAABfwzM9UGQkJiZq0KBBevrpp2UYhrvDAQAAQC7+/e9/6/bbb9eVK1fcHYrnSk2SFg6Tjmx0ODsusIL6XHtBJxWWbV6gX6A+6vuk7rw1+zwAAADAV3GnB4qEa9euqW/fvtqwYYMkqUyZMnrllVfcHJVjFotFAQEB9tdwPXJuLvJtLvINoCgyDEOvvfaa/v73v0uS+vTpo9WrVyskJMTNkTnmtn1tarK0+CHp0BqHs+MDy+vea88rxsj+vBN/q0UfPNhMd9et4OIgCx/HNnORb/ORcwAA3MticMm810tOTlZUVJQkqXHjxk6HF/BUNptN7du3188//5xl+nvvvaennnrKTVEBALxVUT9uoujzhu/gm2++qcmTJ2eZ1r17d33//ffy9+e6K0lSWqr09cPSvm+zzTrm768Fpctrzcmnddionm2+1SK9P6SZ+jS5xYxIAcCjecNxEwBQuBjeCh7ParVq3LhxWa6QKVmypJo3b+7GqAAAAODM/fffr0qVKmWZ1qFDB/n5+bkpIg9jS5OWjXbY4fGHv58eqVxJ80oE6mTlLZLSssy3WKRp999GhwcAAADgBJ0eKBIGDx6sjz/+WJIUFhamjRs36q677nJzVAAAAHCkdu3aWrNmjcqUKSOLxaIPP/xQkyZNYpgXSbLZpG/GSru/zjbrtJ+fHqlcWef800/TAkrtVLEq8yWl2pd5s39j9W9W1axoAQAAgCKHe8t9TGpqapG91fPxxx9XWlqaOnXqpLp167o7HKdSU1N1+vRpSVLlypUZwsEE5Nxc5Ntc5Nt8qampuS8EmKQo126NGjXSDz/8oCNHjmjIkCHuDidHpu1rbTbp+6el/32VbdY5Pz+NqlxJp/2zXpcWUHK3LJZ5Sjg5VK/ee5seaJ19uKuihmObuci3+ci5uajdAAA34sjrY4r6I1z+8pe/uDuEXNlsNp07d06SVLFi9odOovCRc3ORb3ORb/MV9WMlvEtR/z62adNGbdq0cXcYuTJlX2sY0g/PSzu+yDbrgtWqRytX1IkAJ8N/+cVrYvc6GtE23DWxmYxjm7nIt/nIubmK+rESAFD4GN4KHmXDhg2KiYlxdxgAAADIg9OnT2v16tXuDsPzGYa06iXpt0+yzbpsteqxypV0NMDx9WhpCdX02K2v68m7G7o6SgAAAMAr0OkBj7Fs2TL16NFDXbt2tV8VAwAAAM909OhR3XXXXbrnnnu0bt06d4fjuQxDWveK9Mt/ss2KtVr0eKWKOhTopMMjsbKG1nhNz3S5zdVRAgAAAF6DTg94hNmzZ2vQoEFKTk7WgQMH1L17d125csXdYQEAAMCBvXv3ql27djp8+LCSk5PVt29fbdu2zd1heaaNb0mb3802Oc5i0ROVKio6KMDhammJFTXollc1uUdzV0cIAAAAeBU6PeB2ycnJmjZtmmw2m33azp079e9//9uNUQEAAMCZzz//XKdOnbL/ff36dY0fP55x1W/075bSpreyTY63WDSmUgXtDnL8kPq0pDD1qfCyXunTRhaLxdVRAgAAAF6FTg+4XWBgoFatWqXw8HD7tMcee0yTJ092X1AAAABw6q233tL9999v/7tJkyb65ptv+IH+RtezD9maYLFobMUK+r1YkMNVbMnl1K3M3/RW3zvJJwAAAFAAjgePBUx2yy23aO3atWrXrp1GjBiht956i5M8AAAAD+Xn56e5c+fq6tWrio2N1YoVK1SmTBl3h+XxkizS0xXK67dgZx0epdW+xF81bWAHWa3UwgAAAEBB0OnhYzy5I6F27dratWuXwsLC3B3KTbFarSpdurT9NVyPnJuLfJuLfJvPk4+V8D2e/H0MDAzUkiVLZLPZFBoa6u5wborL9rWhlaTEWElSiqRnw8prS0iww0VtKSXVJniy/n1fJ/l5eYcHxzZzkW/zkXNzefKxEgDgHnR6+Bh/f/d+5DabLceir6h3eEjpOa5du7a7w/Ap5Nxc5Ntc5Nt87j5WApl5wvcxp/otJCTE5Ghcw2X72mIlJUmpkp4Nq6BNxYs5XMyWGqpmAS9qxpDu8vfz/h9IObaZi3ybj5ybyxOOlQAAz+L9FTU8RlxcnHr06KE5c+a4OxQAAADkwjAM/eMf/9CwYcOUlpbm7nCKpKG3VFTjmtXVtEZtbQh11uFRXA0tz+uzob0U4AMdHgAAAICr0R0OU1y6dEm9evXStm3btH79epUsWVL9+vVzd1gAAABwwDAMPffcc5o+fbokqWTJkvroo48YQiSfDMOQYQuQxZrieH5asCKMZzVnxL0K8vczOToAAADAO3EpkY9JSXF8wuVKcXFx6tChg7Zt2yZJSktL0+DBg7Vu3TrTYzFDSkqKoqOjFR0d7ZZ8+yJybi7ybS7ybT7yDE/iru/jU089Ze/wkKQZM2Zo8uTJbonFDK7b11qUfKWVwzlGWpDCU57WvBH9VSzAtzo8OLaZi3ybj5ybixwDAG5Epwdcrnjx4urZs2eWaSVLlrQ/2M3bGIah69ev6/r16zIMw93h+ARybi7ybS7yDcAd7rnnHgUGBmaZVrVqVTdF43qu2tdaLJJ/yDElns1aCxu2QFVJGq/5I+9TSKDv3XzPsc1c5Nt85BwAAPei0wMuZ7FY9Pbbb+uxxx6TlH7C/NNPP6lFixZujgwAAACOdO3aVfPnz5fVapWfn5/mzp2rJ5980t1hFTn7z1yTX7FTCii1Q4lnesswLDJsAaoYP0aLRg5RiWIB7g4RAAAA8Dq+d1kR3MJiseijjz5SqVKlNHbsWNWoUcPdIQEAACAHAwYM0KxZs1SmTBnde++97g6nSPMrdlYq86uSzvaRbMW0ePxwlQqhwwMAAABwBTo9YBo/Pz9NnTrV3WEAAAAgj0aOHOnuEIq0upVKaNf59Nd+QedlKfuzGoTVUdnigTmvCAAAAKDAGN4KhWrjxo2Kj493dxgAAADIg2PHjmnv3r3uDsNnWAMvKTAw0d1hAAAAAF6NOz1QaObOnauHH35Y3bt317Jly7I9/BIAAACeY9++feratasMw9DmzZtVs2ZNd4fkdeb1mufuEAAAAACfw50eKBT//ve/NWLECKWlpWnlypUaOXKk0tLS3B0WAAAAHIiMjNRdd92lkydP6tSpU+rSpYtOnz7t7rAAAAAA4KZxp4ePsVoLv5/r/Pnz+vvf/55l2oIFC9SnTx8NHTq00NvzdH5+fqpatar9NVyPnJuLfJuLfJvPFcdKoKBc9X2cMmWKLl68aP/7yJEjmjBhghYsWOCS9jwd+1pzkW9zkW/zkXNzUbsBAG5Ep4ePcUXBFRYWphUrVqhLly7253k899xzevDBBwu9raLAz89PFStWdHcYPoWcm4t8m4t8m48fJ+BJXPV9/Oqrr3T33Xdr165dkqQ2bdroww8/dElbRQH7WnORb3ORb/ORc3NRuwEAbkR3OApF27ZttXz5cgUGBur111/XO++8I4vF4u6wAAAA4ECZMmW0evVq1alTR507d9batWtVtmxZd4cFAAAAADeNOz1QaLp27ap9+/apVq1a7g4FAAAAuahYsaI2bdqksmXLqlixYu4OBwAAAAAKBZ0ePiYlJUWBgYEu2z4dHlJycrKioqIkSY0bN3ZpvpGOnJuLfJuLfJsvJSXF3SEAdq6u3W655RaXbbsoYV9rLvJtLvJtPnJuLmo3AMCNGN4KeXb58mV1795dv/76q7tDAQAAQC4Mw9Dzzz+vqVOnujsUAAAAADANd3ogT86cOaNu3bopKipKkZGR+umnn9SgQQN3hwUAAAAH0tLS9MQTT+jTTz+VJJUuXVqPPfaYm6MCAAAAANfjTg/k6tSpU2rXrp399txLly6pa9euOnr0qJsjAwAAwI0Mw9CDDz5o7/CQpNGjR2vx4sVujAoAAAAAzEGnB3JVrlw51a5dO8s0i8WixMREN0UEAAAAZywWi1q3bp1lmtVqVUJCgpsiAgAAAADz0OmBXAUFBWnp0qW64447JEm1a9fW5s2bVb9+fTdHBgAAAEeeffZZvfTSS5KkwMBAff311xoxYoSbowIAAAAA1+OZHsiT4sWL6/vvv9fYsWP1z3/+U5UrV3Z3SAAAAMjBa6+9pqSkJPXo0UOdO3d2dzgAAAAAYAo6PZBnZcqU0bx589wdBgAAAPLAYrFo6tSp7g4DAAAAAExFp4eP8fPzy3H+r7/+qlatWslisZgUkffx9/dX3bp17a/heuTcXOTbXOTbfLkdKwEz5fZ9PHDggMLCwlSmTBmTIvJO7GvNRb7NRb7NR87NRe0GALgRz/TwMVar84/8ww8/1O23366//vWvJkbkfaxWq0JDQxUaGppjvlF4yLm5yLe5yLf5yDM8SU7fxx07dujOO+9U7969df36dROj8j7sa81Fvs1Fvs1Hzs1FjgEAN+LIABmGoTfeeENPPvmk/fU///lPd4cFAAAAJ3766Sd17NhRFy5c0NatW9W/f38lJSW5OywAAAAAcDvus5R08uRJzZ07Vxs3btSZM2cUGBioatWqqWfPnho6dKiCg4MLpZ1NmzZp0aJFioqK0qVLl1S2bFk1btxY999/vzp06FAobeTGZrNlmxYVFaUpU6ZkmTZx4kTdeeedatu2rSlxeRObzab4+HhJUkhICFedmICcm4t8m4t8m8/RsRKexddrt9TUVI0aNUpXr161T1uzZo1ee+01/eMf/zAlLm/DvtZc5Ntc5Nt85Nxc1G4AgBtZDMMw3B2EO61fv14TJ05UXFycw/nh4eGaOXOmatSoUeA2bDabpkyZoq+//trpMvfdd59effVVlxRDycnJioqKkiTVq1dPxYsXz7bMp59+qscee8z+96uvvqq//vWvPNujADLnu3HjxgoMDHRzRN6PnJuLfJuLfJvv+vXrio6OlkTOPRG1W7o9e/aoffv2unTpkiSpU6dOWr58uUqUKFHo8fgC9rXmIt/mIt/mI+fmonYDANzIpy832Lt3ryZMmKC4uDiFhIRowoQJWrBggWbPnq37779fknTs2DE9/vjjTk+s8+Ldd9+1nzQ3aNBA06dP1+LFizV9+nQ1aNBAkrR48WK99957N/2eCurRRx+1D2n1/vvva8qUKXR4AAAAj0Lt9qeGDRvqxx9/VGhoqPr166cVK1bQ4QEAAAAA8vHhrV5//XUlJibK399fs2bNUrNmzezz2rZtqxo1amjq1Kk6duyYPv/8c40bNy7fbRw9elSzZs2SJDVq1Ejz5s1TsWLFJElNmjRRp06dNGzYMO3evVufffaZBg4ceFNXJt6MZ599Vp07d1bTpk3d0j4AAEBOqN2yatWqlbZu3ap69erJ39+ny3oAAAAAsPPZOz127dqlyMhISdLAgQOznDRnGDVqlGrXri1JmjNnjlJSUvLdzhdffKHU1FRJ0pQpU+wnzRmCg4Ptz9NITU3V7Nmz891GYaLDAwAAeCJqN8caNWpEhwcAAAAAZOKznR5r1661vx44cKDDZaxWq/r16ydJunr1qrZt25avNgzD0Lp16yRJtWrVctqh0LRpU9WsWVOStG7dOrnyMSt/+ctfdObMGZdtHwAAwBV8tXb7+9//rtWrV7ts+wAAAADgbXy202P79u2SpJCQEDVs2NDpcq1atbK/3rFjR77aOHHihM6dO5dtO460bt1aknT27FmdOHEiX+3kx6pVq9StWzddvnzZZW0AAAAUNl+t3ebOnav+/ftry5YtLmsDAAAAALyJz94Lf/jwYUlS9erVcxwSoFatWtnWyatDhw453E5u7Rw5ckTVqlXLV1s5yXz1Ybly5XT27Fk9+OCD+uKLL1S8eHH7PD8/P1mt6f1gNptNaWlpOW43ICDA/jotLU02my3Py6empuZ4VaTFYsnyueQ2PEXm2A3DsA9L4Yy/v7/9Qe35fa+5xZ657ZSUlFxjt1qt8vPzy7JOTvicssvcbsbrwo6dz+lPmdePj4+XYRgu+//E55Q93xl/e9J+z9s+p4SEBPvfrryCH/njy7WbJI0cOVLz589X/fr17fOKwj7MGU/bh2Wen5CQkGP8nr4PKwqfE7UbtVsGb/2cHNVvnrbf86bPidoNAHAjn+z0SEpKst/pUKlSpRyXLVWqlEJCQhQfH5/vYaEyL59bO5nnnz59Ol/t5CZzMTV37lz76z/++KNQ20F20dHR7g7B55Bzc+X3B0XcHPJtvtTUVAUFBbk7DJ9H7fYnjnOul7nzC67Hd9pc1BLmI+fmonYDAEg+OrzV9evX7a9DQkJyXT44OFhS+hUarmono42CtAMAAODNqN0AAAAAAHnls3d6ZMh8W6QzgYGBkqTExESXtZPRRkHayU1wcLDq1asnKestsgAAILvMQ05k/mEb7kPtRu0GAIAz1G4AgBv5ZKdH5lsdcxuLUpKSk5MlScWKFXNZOxltFKSd3Fit1izP7gAAADljWATPQu0GAAByQu0GAMjMJ4e3ynwSmZfhCDIeipWX4RQK2k7mB2/ltx0AAABvRu0GAAAAAMgrn+z0CAoKUunSpSUp1wdcxsbG2k96c3ug5Y0yL59bO5nnV65cOV/tAAAAeDNqNwAAAABAXvlkp4ck1alTR5IUExNjH/vRkSNHjthf165du0Bt3Lid3NqpVatWvtoBAADwdtRuAAAAAIC88NlOjxYtWkhKH7pgz549Tpf77bff7K+bN2+erzaqVq2qChUqZNtOTu1UrFhRVatWzVc7AAAA3o7aDQAAAACQFz7b6dGlSxf76yVLljhcxmazafny5ZKkkiVLqk2bNvlqw2KxqHPnzpLSrwbcuXOnw+V27txpv1qwc+fOslgs+WoHAADA21G7AQAAAADywmc7PZo0aaKWLVtKSj9x/v3337MtM2vWLB0+fFiSNGLECAUEBGSZv23bNtWtW1d169bViy++6LCdkSNHys/PT5L02muvKTExMcv8xMREvfbaa5Ikf39/jRw58ubeGAAAgBeidgMAAAAA5IXPdnpI0ksvvaRixYopNTVVo0aN0owZM7Rz50798ssv+tvf/qapU6dKksLDw/Xwww8XqI2aNWvqkUcekSTt3r1bQ4YM0cqVKxUVFaWVK1dqyJAh2r17tyTpkUceUXh4eKG8NwAAAG9D7QYAAAAAyI2/uwNwpwYNGujdd9/VxIkTFRcXp+nTp2dbJjw8XDNnzlRoaGiB25kwYYIuXryoJUuWaO/evZowYUK2ZQYNGqSnn366wG0AAAB4O2o3AAAAAEBufLrTQ5I6deqkb7/9VnPmzNHGjRt19uxZBQQEqHr16urRo4eGDRum4ODgm2rDarXqjTfeUPfu3bVw4UJFRUXp8uXLKlOmjBo3bqzBgwerQ4cOhfSOAAAAvBe1GwAAAAAgJxbDMAx3BwEAAAAAAAAAAHCzfPqZHgAAAAAAAAAAwHvQ6QEAAAAAAAAAALwCnR4AAAAAAAAAAMAr0OkBAAAAAAAAAAC8Ap0eAAAAAAAAAADAK9DpAQAAAAAAAAAAvAKdHgAAAAAAAAAAwCvQ6QEAAAAAAAAAALyCv7sDQN6dPHlSc+fO1caNG3XmzBkFBgaqWrVq6tmzp4YOHarg4OBCaWfTpk1atGiRoqKidOnSJZUtW1aNGzfW/fffrw4dOhRKG0WBK/OdkJCgn376ST///LN2796tmJgYxcfHKzQ0VOHh4WrXrp0eeOABhYWFFeI78mxmfb8zS0hIUJ8+fXTixAlJUpUqVbR+/fpCb8dTmZnzLVu26Ntvv9X27dt1/vx5+fn5qXz58qpbt65uv/129e3bV8WLFy+09jyRGfk+ceKE5s+fr61btyomJkYJCQkqXry4atWqpXbt2mnIkCEqV65cIbwbz3Xx4kXt2rVLu3btUlRUlKKionTlyhVJUv/+/fXWW28Vepvff/+9li5dqv379+vq1asqX768WrRooaFDh6pZs2aF3h6KDmo3c1G7mY/6zVzUbuaidjMHtRsAwBUshmEY7g4CuVu/fr0mTpyouLg4h/PDw8M1c+ZM1ahRo8Bt2Gw2TZkyRV9//bXTZe677z69+uqrslq9+yYhV+Y7OjpaQ4YMUXx8fI7LhYaG6rXXXlOvXr3y3UZRY8b325G3335bs2bNsv/tSyfNZuU8NjZWkyZN0rp163Jcbvny5apfv/5NteXJzMj38uXL9fe//12JiYlOlyldurSmT5+uO++8s8DteLq6des6nVfYJ86JiYkaP368Nm3a5HC+1WrVk08+qbFjxxZamyg6qN3MRe1mPuo3c1G7mYvazTzUbgAAV6DTowjYu3evhgwZosTERIWEhGj06NFq06aNEhMTtXLlSi1atEhSeuG1ZMkShYaGFqidadOmaebMmZKkBg0a6NFHH1W1atX0xx9/6NNPP9XevXslSaNHj9YzzzxTOG/OA7k635GRkRo6dKgkqXnz5urYsaMaNWqk0qVL69KlS1q9erUWL14sm80mPz8/ffTRR159laZZ329H7Q4aNEj+/v7y9/fX9evXfeak2aycX7t2TSNHjtSePXskSV27dlX37t1VvXp1Wa1WnTlzRr/++qtWr16tjz/+2GtPnM3I9/bt2zVs2DDZbDZZrVb169dPnTt3VoUKFXT69GktW7ZMGzZskCQVK1ZM33//vapVq1ao79NTZD5xvuWWW1SrVi1t3rxZUuGfOD/zzDNasWKFJKlNmzYaMWKEKlSooAMHDmjGjBmKiYmRJL366qsaPHhwobULz0ftZi5qN/NRv5mL2s1c1G7monYDALiEAY/34IMPGhEREUaDBg2MHTt2ZJv/ySefGBEREUZERITx/vvvF6iNI0eOGA0aNDAiIiKMAQMGGAkJCVnmx8fHGwMGDLDHcezYsQK1UxS4Ot/bt283nnrqKePgwYNOl1mzZo1Rt25dIyIiwujSpYths9ny3U5RYcb3+0apqalG//79jYiICOODDz4wOnbsaERERBgdO3YslO17OrNyPnHiRCMiIsJo1KiRsXbtWqfL2Ww2IyUlpcDteDoz8v3444/bt/Hll186XObNN9+0L/PKK68UqJ2i4F//+pexfv164/z584ZhGMYff/xhf98vvPBCobWzZcsW+3ZHjx5tpKamZpl/8eJF4+677zYiIiKMli1bGleuXCm0tuH5qN3MRe1mPuo3c1G7mYvazVzUbgAAV/Du+9y9wK5duxQZGSlJGjhwoMPxJUeNGqXatWtLkubMmaOUlJR8t/PFF18oNTVVkjRlyhQVK1Ysy/zg4GBNmTJFkpSamqrZs2fnu42iwIx8N2/eXO+9957q1KnjdJkuXbqoW7dukqSYmBj7lZrexqzv943mzJmjPXv2qGbNmnrsscduentFiVk5j4yM1DfffCNJevrpp9W5c2eny1osFvn7e+cjpszK9++//y4pfQiEjKuRb/Tkk0/aX+/cuTPfbRQV48ePV8eOHVW+fHmXtpMxtIq/v79efvll+fn5ZZlftmxZPffcc5Kkq1evavHixS6NB56D2s1c1G7mo34zF7WbuajdzEftBgBwBTo9PNzatWvtrwcOHOhwmYzbYaX0g/O2bdvy1YZhGPYxW2vVqqWmTZs6XK5p06aqWbOmJGndunUyvHBkNDPynVdt2rSxv864zdbbuCPfJ0+e1Pvvvy9JeuWVVxQYGHhT2ytqzMr5vHnzJEklSpTQsGHD8h+olzAr3xkn21WrVnW6TIkSJVSmTJksy6Ng4uLitHXrVklS27ZtValSJYfLde3a1T7kRebvArwbtZu5qN3MR/1mLmo3c1G7eSdqNwDwPXR6eLjt27dLkkJCQtSwYUOny7Vq1cr+eseOHflq48SJEzp37ly27TjSunVrSdLZs2d14sSJfLVTFJiR77xKTk62v77xKhRv4Y58v/LKK4qPj1ffvn2z/DjhK8zIeXJysv3HuDvuuENBQUGSpLS0NJ0+fVonTpxQUlJSfkMvksz6jmf8qJnTfjkuLk6XL1/OsjwKJioqyv7jQ8Zx0ZHAwED7j9GZ14F3o3YzF7Wb+ajfzEXtZi5qN+9E7QYAvodODw93+PBhSVL16tVzvIW4Vq1a2dbJq0OHDjncTm7tHDlyJF/tFAVm5Duvfv31V/vrjNunvY3Z+V6xYoU2bdqkUqVK6cUXXyzwdooyM3IeHR1tPzGOiIhQXFycXn/9dd1+++26++671blzZ7Vo0UIPP/ywy6629RRmfccfeOABSdKVK1c0f/58h8v85z//ybY8CibzZ5TbcTPjR4rU1FQdP37cpXHBM1C7mYvazXzUb+aidjMXtZt3onYDAN/jnQNxeomkpCT7lR3Obr/MUKpUKYWEhCg+Pl5nzpzJVzuZl8+tnczzT58+na92PJ1Z+c6L6Ohobdq0SVL6iYc3njibne/Y2Fi98cYbkqRnn31WZcuWLdB2ijKzcp75pMIwDA0cOFDHjh3LskxKSoq2bNmirVu36plnntHjjz+erzaKAjO/4wMHDtT27du1fPlyvfrqq9qzZ486deqksLAwnT59Wt988439Fv0nnnhCd9xxR/7fEOwyf0YVK1bMcdnKlSvbX58+fTrHZwKg6KN2Mxe1m/mo38xF7WYuajfvRe0GAL6HTg8Pdv36dfvrkJCQXJcPDg5WfHy84uPjXdZOcHCw/XV+2/F0ZuU7N8nJyXrppZeUlpYmSZowYUKhbt9TmJ3vd955RxcuXFCzZs10//33F2gbRZ1ZOY+NjbW//uSTT5SUlKS77rpL48ePV7169RQXF6dVq1Zp2rRpunbtmqZNm6ZatWqpS5cu+WrH05n5Hffz89Pbb7+tjh07asaMGVq8eHG2hy+2adOGk+ZCwnETzlC7mYvazXzUb+aidjMXtZv34rgJAL6H4a08WOZxUwMCAnJdPuOBfomJiS5rJ/NDA/PbjqczK9+5efXVV7V7925JUv/+/dWpU6dC3b6nMDPfv/32m5YsWSJ/f3+9/PLLslgs+d6GNzAr55lPDpKSknTnnXdqxowZatKkiQIDA1W2bFkNGTJEH3/8sazW9MPQ9OnTve4Bu2bvUw4fPqzly5frwIEDDufv3LlTX3/9tc6ePVug7eNPmT/b3B6m683HTWRH7WYuajfzUb+Zi9rNXNRu3ovaDQB8D50eHizjAXKS8vQArYyHJxYrVsxl7WR+QGN+2/F0ZuU7JxlX+UhS48aN9be//a3Qtu1pzMp3cnKypkyZIsMwNGLECNWrVy9/gXoRd+xTJOm5555z+EDXli1bqmvXrpLST/r279+fr3Y8nZn7lMjISA0ePFgbNmxQxYoV9c477+jnn3/W7t27tWnTJv3tb39TcHCwVqxYoUGDBungwYP5bgN/yvzZZj4uOuLNx01kR+1mLmo381G/mYvazVzUbt6L2g0AfA+dHh6sePHi9td5ua0yISFBUt5uxS1oOxltFKQdT2dWvp1ZsGCBpk+fLin94WozZ870uhxnZla+P/roIx09elSVK1fWuHHj8hekl3HHPqVs2bJq0KCB02Xvuusu++uoqKh8tePpzMp3cnKynnnmGV27dk1hYWFauHCh+vbtq/LlyysgIECVKlXS0KFD9eWXXyooKEjnzp3TCy+8kL83gyw4bsIZajdzUbuZj/rNXNRu5qJ2814cNwHA9/BMDw8WFBSk0qVL68qVK7k+HC02NtZ+8M7toWs3yrx8bu1knp/5AV/ewKx8O/L999/rlVdekSRVqVJFn3/+udc/qNGsfH/yySeSpLZt22rDhg0Ol8nYdnx8vFasWCEp/YSvbdu2+WrL05mV88z7hvw8YDfjwZHewqx8//e//7UPezBs2DCFhYU5XO7WW2/Vvffeq8WLF2vPnj2Kjo722Stnb1bmz+js2bNq3Lix02UzPzja246byI7azVzUbuajfjMXtZu5qN28F7UbAPgeOj08XJ06dRQZGamYmBilpqbK39/xR3bkyBH769q1a+e7DUfbya2dWrVq5audosCMfN9o3bp1euGFF2Sz2RQWFqbZs2cXysl4UWBGvjNuTV+6dKmWLl2a47KXL1/WM888I0lq3bq1V500ZzB7n5LxUFdnbDab/bWjYRSKOjPynXndnK7MlKSGDRvah2E5cuQIJ84FlPkzyu24efToUUmSv7+/atSo4dK44Bmo3cxF7WY+6jdzUbuZi9rNO1G7AYDvYXgrD9eiRQtJ6Vcw7dmzx+lyv/32m/118+bN89VG1apVVaFChWzbyamdihUrqmrVqvlqpygwI9+Zbd26VU8//bRSU1NVunRpff7556pevXqBt1fUmJ1vmJPzKlWq6JZbbpEknTx5MseHXMbExNhfV6xYMV/tFAVm5DvzDw65/VCRmprqcD3kT+PGje0POP3111+dLpecnKydO3dmWwfejdrNXNRu5qN+Mxe1m7mo3bwTtRsA+B46PTxcly5d7K+XLFnicBmbzably5dLkkqWLKk2bdrkqw2LxaLOnTtLSr/qIeMgf6OdO3far4ro3LmzLBZLvtopCszId4YdO3ZozJgxSk5OVokSJfTZZ5/p1ltvLdC2iioz8r1///5c/1WpUkVS+glfxrS5c+cW7E15OLO+4926dZMkxcXFaevWrU6XW716tf11xkmmNzEj35l/xIy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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x560 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "\n",
+    "# Get results for the current task\n",
+    "task_df = all_results_df[all_results_df[task_col] == TASK]\n",
+    "\n",
+    "# Get all base/instruct model pairs\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-8B\",\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mistral-7B-v0.1\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    \"Yi-34B\",\n",
+    "]\n",
+    "# NOTE: Gemma models have degenerate distributions that always predict the same score, can't plot calibration properly...\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "base_models_to_plot += [None for _ in range(len(instr_models_to_plot) - len(base_models_to_plot))]\n",
+    "\n",
+    "base_it_pairs = list(zip(base_models_to_plot, instr_models_to_plot))\n",
+    "\n",
+    "for base_model, it_model in tqdm(base_it_pairs):\n",
+    "    fig, axes = plt.subplots(ncols=2, figsize=(7, 2.8), gridspec_kw=dict(wspace=0.35), sharey=False)\n",
+    "\n",
+    "    for idx, ax, m in zip(range(len(axes)), axes, (base_model, it_model)):\n",
+    "        if m is None:\n",
+    "            ax.remove()\n",
+    "            continue\n",
+    "\n",
+    "        curr_row = task_df[(task_df[model_col] == m) & (task_df[\"uses_all_features\"])].iloc[0]\n",
+    "        curr_id = curr_row.name\n",
+    "\n",
+    "        # # Original sklearn calibration plot:\n",
+    "        # plot_cali_curve(curr_id, ax=ax, df=task_df)\n",
+    "\n",
+    "        # # Plot with confidence intervals:\n",
+    "        plot_cali_curve_ci(curr_id, ax=ax, df=task_df)\n",
+    "        \n",
+    "        # Remove left plot legend\n",
+    "        if idx == 0:\n",
+    "            ax.get_legend().remove()\n",
+    "        else:\n",
+    "            ax.get_legend().set(loc=\"upper left\", bbox_to_anchor=(1.1, 1))\n",
+    "\n",
+    "        ax.set_xlim(0, 1)\n",
+    "        ax.set_ylim(0, 1)\n",
+    "\n",
+    "        ax.set_xlabel(\"Mean risk score\")\n",
+    "        ax.set_ylabel(\"Fraction of positives\")\n",
+    "\n",
+    "    plt.plot()\n",
+    "    save_fig(fig, f\"calibration-curves-per-subgroup.{base_model}.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef3001ff-8557-4097-b3a9-f506bff952ca",
+   "metadata": {},
+   "source": [
+    "### Smaller figures to plot both prompting schemes side by side"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "5a35436c-7606-4ebf-b021-b3072aa7625c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd4b354cb3c140238849fe9f583968d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-8B.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Meta-Llama-3-70B.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mistral-7B-v0.1.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mixtral-8x7B-v0.1.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Mixtral-8x22B-v0.1.smaller.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.Yi-34B.smaller.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/calibration-curves-per-subgroup.None.smaller.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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WaPq2f/9+NG7cuMC/Nm3aYNy4cThx4oRS+YYNG6rdDW348OFqlTt16pTS+pgxY9RKAHbv3h21atUS1s+dO1foaykvY3l952dnZ4f33nuv2HIWFhYYOXKksC6TyQrUoaGpO8NumzZthOXC6iotLU2p9V+bNm1Qv379Yo/t5OSE/v37qxUHERERkaExIUZEZmHAgAFKX5y3bduGmJgYA0akmcaNG8POzq7Ycvm7K7Vq1Uqt4+fdT6FQqJxB8enTp0p1lveLszryJkri4+Px+vXrYvfR9Bzqunr1qtJ6v379dHKeklAoFJDJZEr/8g7CnsvS0hLDhw/Hjh071Oq+aWNjo5QYKkrelkEWFhbo2rWr2vH7+fkJy5mZmbh3716x+xjD61sVHx8fODg4qFU2f8u727dvq7WfPlhYWKBly5Zqlc3bLbWwugoLC1N6TWqStNZFgpuIiIhIFzioPhGZBbFYjGnTpmHatGkAcr6o//rrr5g3b56BI1OPul358icV1J3NMf9+6enpqFChgtK2/LMa/uc//8GsWbPUOj4AYRbCXImJiSrHG8qruK5qJZV/Vkl1E0WGIhaL4eDgAGdnZzRo0AAtW7ZE//79NeriWaNGDVhZWalV9smTJ8Kyh4eHWsmqXF5eXkrrjx8/LtCyKz9jeH2rkrcbZHEqVaoEV1dXvH37FgAQGRmp9r665uTkBFtbW7XKqlNXz549U1pv0KCB2rFoUpaIiIjIkJgQIyKz0aNHD3h5eQmJnd27d+Pjjz/W+Qxv2qBuQiJ/t7aS7pc/eQWgwLhrqlotaSLv7HSFcXJyKtU5CpOYmCgsi0QiuLq66uQ8JTF48GCdzMSnySQAycnJwrKLi4tG58lfPu+xCmMMr29VKlWqpFa5XC4uLkJCLCkpSaN9dcne3l7tsurUVf7nVJMx/jiGGBEREZkKdpkkIrMhEokwffp0YV0ikWDt2rUGjMi0qJPA0oQ6CTVLS938LpOWliYs29nZqZxx0dyo2zoMUK4fTZIpqsrnPZap0aRlXP7y6enp2g7HaOSfKVKT11ZJZ4UlIiIi0je2ECMis9K5c2e0bNkSISEhAIADBw5g/PjxOuuaZ07yd7lav3493n33XQNFUzqOjo7CckZGBuRyeZlIiqnLwcFBaAWkaWInf3l1x+AyRhkZGSUur2ki0ZTkb22oSdJT3fHbiIiIiAyN3w6IyOzMnDlTWJbJZFi5cqXGx8jbrUjd7leAabcayT+OUP4ulKYkb7cthUIhdHOjHHm7qsbGxmq0b/7yuur2qg9xcXEalc977Zp0IzQ1+e8FL1++VHtfTcoSERERGRITYkRkdtq2bYt33nlHWD9+/LhaM+HllbdrlCatSExpZsv88s7SCRQcZN+U5B/Y25hmBDQGtWvXFpajoqI0eo3nf12YcuvL+/fvq102Pj5eKbFat25dXYRkFBo1aqS0HhYWpva+oaGh2g6HiIiISCeYECMiszRjxgxhWaFQYMWKFRrtn7fVS2Jiotpdhq5fv67ReYxJo0aNlFpWnTlzptQD6xtK27ZtldYPHTpkoEiMU4sWLYRlmUyGM2fOqL3v6dOnhWVbW1s0btxYq7Hp07Vr19Ru1Zn3uoHCZy7NPy6eKb6HateujYoVKwrrJ06cgFQqVWvfw4cP6yosIiIiIq1iQoyIzFLTpk3RrVs3Yf3s2bO4deuW2vvnbS2lUCgQHBxc7D73798Xxi4zRWKxGH369BHWo6OjsW/fPgNGVHKenp5KrcROnTqFu3fvGjAi45L3vQEAW7ZsUatr8OnTpxEVFSWsd+nSRWcTI+hDeno69uzZU2w5uVyObdu2CetisbhAHebKP6aaMc1GqS6RSIQBAwYI67Gxsfjzzz+L3e/y5cu4du2aLkMjIiIi0homxIjIbE2fPl1pLLC9e/eqvW/z5s2V1rdu3Vpk+YyMDMydO1ej+IzRxIkTlWaUmz9/Pm7evKnRMWJjY/HgwQNth6axCRMmCMsymQwzZ87UqEurJmPHmZqGDRsqtaK7desWNm7cWOQ+MTExmD9/vtK20aNH6yQ+fVq9erVSkk+VDRs2KHW77ty5M6pVq6ayrLu7u9K6Jt0NjYm/v7/SvWD58uW4fPlyoeUfP36M2bNn6yM0IiIiIq1gQoyIzJanpyf69u0rrKvb5QcAqlevjjZt2gjrFy9exNKlS1Ue4+HDhxg1ahTCw8OVvkCaoipVquC///2vsJ6RkYHRo0fjt99+K3L2OKlUikuXLmHu3Lno2rVrkV+c9aVfv37o0qWLsB4VFYVhw4bh1KlThe6TlpaGAwcOYNCgQUhJSdFDlIbzxRdfKL1ely5ditWrV0MikRQoGx4ejjFjxuDVq1fCtj59+qBVq1Z6iVVXrK2tkZycjI8++khld2epVIo1a9Zg+fLlSvsUlfipV6+e0iyNv/76K86dO4fMzEztBq9jNWvWxJQpU4T1rKwsfPLJJ/j+++9x584dZGRkIDs7G5GRkVizZg2GDh2K2NhYpe64RERERMbMdPs5EBGpYerUqTh27JhGybBcM2bMwMiRI4WWQhs2bMDRo0fRqVMnVKpUCSkpKbhz5w5u3boFuVwOV1dX+Pv7azxembEZNWoUnj59ioCAAABAdnY2fv75Z/z2229o0aIF6tati/LlyyMrKwvJycl49OgR7t27Z3QzbIpEIixZsgQjR47Ew4cPAeTMgDdlyhRUqVIFPj4+qFy5MiwtLZGYmIgHDx7gzp07Jpe4KClvb2/MmjULixYtApDTIm7NmjXYvn073n33XVStWhUZGRkICwtDSEiIUou52rVr47vvvjNU6FozceJEBAQE4NWrVxg5ciSaNm2KZs2awdHREW/evMH58+cLzKo5e/bsIgfUt7S0xJAhQ7BlyxYAOS3rclsr2traQiz+97fI9evXo3Xr1jq4Mu2YMGECHjx4gCNHjgDIaWm5bds2pe6jeTk7O+Onn36Cr6+vsM3CwkIvsRIRERFpigkxIjJrHh4eGDRokFrjBOXXunVrzJo1C0uXLhW2vXjxAjt37ixQtnLlyvj1118RERFRqniNxddff426deti0aJFyMrKApDTWuzy5ctqtf6ytrbWdYhqcXZ2xo4dO/D555/j/PnzwvbXr1/jwIEDBozMOHz00UewtrbGDz/8ICSN4+PjsX///kL3ad68OX799VeliSdMVbVq1bBixQpMmzYNycnJCA0NLXSWRJFIhBkzZqjVTXTGjBm4c+cObty4obQ9f7JVJpOVPHg9sLCwwLJly1C1alVs2bKlyB8WPD09sWrVKqXB+AHA0dFR12ESERERlQi7TBKR2ZsyZUqJEzSffPIJ1qxZg5o1a6p83MbGBgMHDsT+/fvh5eVVmjCNzvDhw3H69Gl8/PHHcHV1Lba8h4cHRo0ahd27d2PEiBF6iFA95cqVw/r167Fu3Tq0bNlSqYVOflZWVmjXrh2WLl1aZr7IjxgxAocOHUKvXr2KfJ94eHhgwYIF2L59e4Gkhylr37499u7di65duxbamsnLywsBAQGYNGmSWse0t7dHQEAAfv75Z/Tu3RseHh5wcHAo8rVnrCwsLPDFF1/gwIEDGDduHBo0aAAnJyfY2tqiZs2a6Nq1K37++Wfs2bMHHh4eSEhIUNo/b/dRIiIiImMiUpjzqMFERFqiUChw9+5d3L17F/Hx8XBwcECVKlXQtm1bs2gpo47IyEjcv38fCQkJSE5Oho2NDZycnFC9enXUq1dPraSZMUhMTMSNGzfw9u1bJCYmwsLCAs7OzvDw8EDjxo0LzBJYlmRkZCA4OBgvX75EYmIibGxsUKlSJTRp0gS1a9c2dHil9vz5c6XZIRctWoQhQ4YI62/fvsXNmzfx5s0bZGRkwM3NDU2bNkWdOnUMEa5JOnXqlNLYYzt37uS4YkRERGSU2GWSiEgNIpEIXl5eZtcKTBN169YtcuwkU+Hs7KyUFKF/2dnZoVOnToYOw2BcXV3Ro0cPQ4dh0k6ePCksW1paolGjRgaMhoiIiKhwptd2n4iIiIiMTkREBA4fPiyst2vXDra2tgaMiIiIiKhwTIgRERERkUqnTp2CRCIptlxkZCSmTJmiVHb48OG6DI2IiIioVNhlkoiIiIhU+v777yGXy9G7d2907NgRnp6eqFixIiwtLZGUlIT79+/jxIkT2Lt3rzAjLQB07NiRXZOJiIjIqDEhRkRERESFevv2LbZu3YqtW7cK20QiEQqbl6levXpYvHgxRCKRvkIkIiIi0hi7TBIRERGRSnZ2diq3q0qGWVhYYODAgfjrr79MZtZZIiIiKrvYQoyIiIiIVDp48CAuXbqEa9euITw8HM+fP0dCQgKysrJgY2MDZ2dneHh4oE2bNujbty9q1apl6JCJiIiI1CJSFNbenYiIiIiIiIiIyAyxyyQREREREREREZUpTIgREREREREREVGZwoQYERERERERERGVKUyIERERERERERFRmcKEGBERERERERERlSlMiBERERERERERUZnChBgREREREREREZUpTIgREREREREREVGZwoQYERERERERERGVKUyIERERERERERFRmcKEGBERERERERERlSlMiBERERERERERUZnChBgREREREREREZUpTIgREREREREREVGZwoQYERERERERERGVKUyIERlIYGAgPD094enpiS+//NLQ4RARkQZ4DydzdurUKeH1feLECZVlvvzyS6FMYGCg1s69bNkyeHp6wtvbG9HR0Vo7LhERUX6Whg6AyNSMGjUK165dAwB89tlnmDp1qoEjIk08e/YMN27cQHh4OB4+fIgXL14gNjYWWVlZsLW1RcWKFdGoUSN07twZvXv3hoODg07iyMzMxNGjRxEUFIS7d+8iPj4e2dnZcHR0RI0aNdC8eXMMHDgQ3t7exR7L09OzyMfFYjEcHR1RuXJleHl5oWfPnujcuTMsLCy0dTlEJoP3cNPGe7ju7+GZmZlYuHAhAKB58+bo0aOHTs5TmAkTJmDXrl1ISkrCwoUL8dtvv+n1/EREVHYwIUZEZcqAAQOQnp6u8rG0tDSkpaXh2bNnOHHiBH7++Wd8++236Nmzp1ZjuHLlCubOnYuXL18WeCwxMRGJiYkICwtDQEAAevfujQULFsDR0bHE55PL5UhOTkZycjIePnyI/fv3w8vLC0uXLkXdunVLcylERHrFe7ju7+EBAQF48eIFgJykcWkEBgZizpw5AIDBgwdj8eLFxe7j5OSEkSNHYu3atThz5gyuXbuGtm3blioOIiIiVZgQI6Iyy93dHbVr14arqytsbGyQkpKCBw8e4OHDhwCA2NhYTJ8+HQsWLMB7772nlXNevXoVEyZMQHZ2trCtfv36qF27NhwdHfHixQvcuXMHaWlpAICjR4/i5cuX2LZtG6ysrIo9vp+fHypXrqy0TSqVIj4+Hrdu3cLbt28BAOHh4Rg1ahR2794Nd3d3rVwbEZE+8R6u/Xt4amoqNmzYAABo1KgROnXqpLVja2LUqFHYuHEjMjIy8PPPP2PHjh0GiYOIiMwbE2JEVKYMGjQIPj4+aNOmDSpVqqSyzP379zF37lyEh4dDoVBgwYIF6NChA6pVq1aqc8tkMnz99dfCFyl3d3fh2HklJCRgxYoV2LlzJwDg9u3b+PPPPzF27NhizzF69Gj4+PiofEwulyMwMBD/+9//IJFIEBcXhx9//BErV64s1XUREekL7+G6vYfv2rULiYmJAAB/f/8iyy5evFitFl8lUaFCBfTq1Qv79u1DSEgIbty4gVatWunkXEREVHZxUH0iKlPmzZuHXr16FfpFCgAaNmyIjRs3okKFCgCAjIwMHDhwoNTnDg4OVhogeM2aNQW+SAE5XwS+++47dOnSRdh28ODBUp9fLBbjvffew7Rp04RtQUFBQksGIiJjx3u47u7hMpkMf/75JwDA3t4effr00cpxSypvq74tW7YYMBIiIjJXTIgREang7OyMbt26Cev37t0r9TEjIiKE5Tp16qBx48ZFlu/fv7+w/OTJk1KfP1e/fv2E5ezsbDx79kxrxyYiMga8h2vuypUrwthhnTt31tmEBOpq1aoV3NzcAOQk/hISEgwaDxERmR92mSQyAXfu3MGlS5cQEhKCR48eIT4+HhKJBE5OTqhZsyZ8fHwwbNgwtbqD+Pr6Cn/wnj59GtWrV8fTp0+xc+dOXLhwAa9evYJEIoGHhwf69OmDMWPGwM7OTukYjx8/xp9//onr16/jxYsXEIvFqFOnDgYOHIgPP/xQrZmvtHlNulKxYkVhWRu/wOcdCLp8+fLFls9bRqFQlPr8uVxdXQuNi4i0j/dww+A9XDN5W7H5+fkVW/7LL7/Evn37AACLFi3CkCFDVD6Wa9++fQW2AUDbtm0REBBQYLtIJIKfnx+2b98OiUSCo0ePYsSIEWpfDxERUXGYECMycu+99x7CwsJUPhYXF4e4uDjcvHkTf/zxB6ZPn47x48drdPwDBw5g3rx5yMjIUNoeERGBiIgIHD9+HJs3bxb+sP/ll1+wevVqyOVypfK3b9/G7du3cezYMfz+++8FvoDp85q05dGjR8KyNgYtrlq1qrD85MkTSKVSWFoWfhvOHRgayOkCpC2xsbFK6/m/XBGR9vAezns4YPz3cLlcjnPnzgnr7dq1K/UxtaFdu3bYvn07AODs2bNMiBERkVYxIUZk5F69egUAsLa2Rv369VGzZk2UK1cOCoUCb9++xe3bt5GQkACJRIJly5YBgNpfPs6fP4/58+dDLpfDw8MD3t7esLGxQUREhPBl5+7du/j888/xxx9/YN26dcLgvZ6enmjYsCEsLCwQFhYm/OF/7do1LFq0CN9//71Brklbrly5grNnzwrrPXv2LPUxO3XqBGtra2RnZyMxMREbN27EhAkTVJaNiYnBpk2bhPXhw4eX+vy5jhw5IixXrlwZ1atX19qxiUgZ7+G8hwPGfw+/e/eu0CXRw8MDLi4upTpe+/btYW9vj8ePH+PKlSsAcrqZtm/fvkDZWrVqFXqc1q1bC8vXrl0rNglJRESkCX6iEBm57t27o2vXrvDx8YGtrW2Bx2UyGQ4cOID58+cjPT0dK1euRK9evVCjRo1ij71o0SLY2dlh4cKF6NWrl9JjR44cwaxZsyCTyXDx4kVs3rwZK1euhJubG3766Se0bdtWqfymTZuE2aZ2796NCRMmFPpHui6vqaQUCgVSU1MRGRmJI0eOYPv27UILiv79++Odd94p9TkqVqyIyZMnY8WKFQCAn376Cbdv38aYMWNQu3ZtODo64uXLl7hw4QJ+//13xMXFAciZfn7gwIGlPr9CocDBgweF8wPAJ598ApFIVOpjE5FqvIfzHm4K9/Dbt28Ly56enqU+3sCBAzFw4EAEBgYKCbFmzZrh22+/1eg4lSpVgqurK96+fYuMjAw8fPgQjRo1KnV8REREABNiREbvf//7X5GPW1hYYMiQIbC1tcXMmTMhkUiwc+dOzJ49u9hjSyQSrFu3TuUsWX369MHly5exe/duADlfvGxtbbF582bUrVu3QPmxY8fi/PnzuHz5MuRyOY4ePVpoiwBdXpMmfvnllyKnq7e0tMTYsWPx+eefa+2cn376KWxtbbF06VLIZDKcOnUKp06dUlm2QYMG+PjjjzFo0CC1j79161YcP35caZtUKkVCQgJCQ0Px+vVrADljs8yYMQOjR48u8bUQUfF4D+c93BTu4XknDKhdu7ZWjqktdevWxdu3bwEA9+/fZ0KMiIi0hgkxIjPRs2dP2NvbIz09Xfg1tji+vr4qv0jl6tu3r/BlCgCGDRum8otU3vKXL18GgELHl9FESa5JW+rVq4elS5cWO4tYSYwdOxY9e/bEjz/+iKNHj6os4+zsjJ49e+Ldd9/V6NiFfTHLq2nTpvjhhx/QoEEDjY5NRLrDe7h28R6umdyJGgCgSpUqWjuuNlSuXFlYzhsnERFRaTEhRmRC7t+/j3v37uHFixdITU1Fdna20uO53SYePHgAuVwOsVhc5PGKG1Mlf7eJ4srn/eP8+fPnRZbNpe1r0oS3tzf8/f0B5HTxSU5Oxv379/H48WM8evQI77//PoYMGYL//ve/cHR01Np5//nnH/z4448IDw8HADRu3BgNGjSAlZUVXr16hZCQECQmJmL16tXYvHkzli5diq5du2rt/KGhoRg2bBjGjx+PSZMmabVOiahwvIfzHq4NuriH5x2o39nZudTH06YKFSoIy7ktxYiIiLSBCTEiE7Bv3z789ttviIqKUqu8RCJBSkpKsdPCF/frspOTk9J6/fr1iyyf93ypqalFltXVNWmiU6dO6NSpU4Ht4eHh+P7773Hr1i3s2rULd+7cQUBAgFa+UG3btg3z58+HQqFA3bp1sXTpUnh5eSmVSUpKwtKlS7F7926kpKRg6tSp2Lx5s9LgwoXZunUrfHx8lLYpFAqkpKQIY+vs2LFDGNcnMjISP/30U6mvi4gKx3s47+HGfg/PO0tpUTOMGkLeceryz6ZKRERUGmwWQGTEFAoF5syZgy+//FLtLx250tLSii1T3JeD/DM5lStXrsjyFhYWwrJUKlVZRtfXpA1eXl7YsmULmjVrBiBn9q0ff/yx1McNCQnBggULoFAoUKlSJWzZsqXAFykg50vpggUL0L9/fwA5XyTnzZsHhUJRovOKRCI4OTmhRYsW+Oqrr/D7778Lz9WhQ4ewb9++kl8UERWK93BlvIebxj28pHESERGZGibEiIzYrl27EBgYKKx36tQJS5YswcGDB3H9+nWEhYUhIiJC+Ofu7i6UzZ1dqyiazkyljZmsdH1N2mJra4svvvhCWA8MDERSUlKpjvnLL78I1zBmzBi4uroWWX7WrFlCnT969AihoaGlOn+uDh06oHfv3sL6H3/8oZXjEpEy3sN5DzeVe3jeVmGZmZmlPp425Y3H2FqvERGRaWOXSSIjtnHjRmF56tSp+Oyzz4osr69f30vDlK6pZcuWsLOzQ0ZGBiQSCUJDQ1V2z1FHdnY2rl69Kqy3a9eu2H2qVKmCWrVqCa0w7ty5I7R4KK1OnTrh0KFDAICHDx/i7du3xX65IyLNmNL9Tl2mdE28h6vPxcVFWE5ISCh1fNoUHx8vLPNzioiItIktxIiM1KtXr4Q/op2cnDBx4sQiy6emppb6129dM7VrEovFSl2SEhMTS3yshIQEpcGm1R20OO9gwikpKSU+f375v1S8evVKa8cmItO736nD1K6J93D1Va9eXVh+8+ZNqY6lbXnjydvikIiIqLSYECMyUjExMcJynTp1YGVlVWT5GzduGP24H6Z2TRKJROkLVGkGg847KDAAtb8k5j1/ceP/aCL/wMTa6EpFRP8ytfudOkztmngPV1/eGUmfPHlSqmPlpY3PlsePHwvLDRs2LPXxiIiIcjEhRmSk8v4Rqc6sSjt27NBlOFphatd06dIlSCQSYb1u3bolPpaTkxMcHByE9X/++afYfd68eaM0aHWtWrVKfP787t69q7ReuXJlrR2biEzvfqcOU7sm3sPV17RpU2H5/v37pTpWXjY2NsJy3udCXXFxcXj79i2AnPHDipsplYiISBNMiBEZqerVqwtfPh4+fIhnz54VWvbIkSM4c+aMvkIrMUNfkybjoiQmJmLJkiXCupeXV6m6aohEIrRv315Y37p1q9K4KKosX75caF1hZ2eHVq1alfj8eaWmpmLPnj3Cet26deHm5qaVYxNRDkPf73TB0NfEe3gOXdzDGzduLHTvfPr0KWJjY0t1vFx5u5bmbWGoruDgYGG5bdu2BWZOJSIiKg0mxIiMVMWKFdG8eXMAOTNzTZs2TanbQO72bdu24YsvvoCFhYXSL7HGyNDX9M0332DatGk4f/48pFKpyjJyuRznzp3DsGHDlGKbPXt2qc8/duxYYfnt27cYM2YM7t27V6BccnIyvv32W+zfv1/Y5u/vr5XZtaKiojB+/HilMVnyxkVE2mHo+50uGPqaeA/X3T1cLBajc+fOwro6LeDUkbdF1+3bt/Hy5UuN9s8bR5cuXbQSExERUS7+zEJUCjt37sSpU6fULj9t2jR069ZN7fLTp0/HuHHjIJfLcffuXQwYMAAtWrRAjRo1kJ6ejuDgYKErwcyZM7Fr1y68ePFC4+vQJ0Nek1wux8mTJ3H8+HHY29ujQYMGcHd3h6OjIyQSCWJjYxEeHo64uDil/ebMmaPUMqCkWrdujXHjxgmztD148ACDBg2Cl5cXGjRoACsrK7x69Qo3btxAenq6sJ+3t3exM7nl2rp1K44fP660TaFQIC0tDY8ePcLdu3eVxvTp0aMHhg4dWuprIzJFvIdrjvdw872H9+/fX0jinTp1Cv369Sv1MV1dXdGiRQvcvHkTWVlZGDhwIDp16gRXV1eIxTm/y9eoUQMjRowosK9CocDp06cBAFZWVujdu3ep4yEiIsqLCTGiUoiNjdWoW4Gms221b98e3377LRYsWACpVAqJRIJr167h2rVrQhmxWIxPP/0UEydOxK5duzQ6viEY8pqsra2F5fT0dNy6dQu3bt0qtHyNGjXwzTffKP1qXlpffPEFKlWqhJUrVwozloWHhyM8PFxl+d69e+P7779Xu2WBul/uLS0t8dFHH2HGjBnClxKisob3cM3xHm6+9/D27dvD3d0dL168wLlz55CWlqY0blpJffXVVxgzZgzS0tKQnJyMw4cPKz3etm1blQmxkJAQoSWcr6+v0oydRERE2sCEGJGRGz58OFq2bInNmzfj6tWriImJga2tLSpXrox27dph6NChaNy4saHD1Iihrmn58uX46KOPcO3aNYSFheHJkyd4/fo10tPTYWlpiXLlysHd3R1eXl7o2rUr3nnnHVhYWGg1BpFIhE8++QSDBg3Cvn37cPXqVTx48ABJSUmQyWRwdHREjRo10KJFCwwePBiNGjXSynnt7e3h5OSEevXqoU2bNhg0aBCqVKmilWMTUeF4D9ce3sN1ew+3sLDAyJEjsWTJEqSnp+Pw4cP44IMPSn1cb29v/P333/jzzz9x9epVPHv2DOnp6ZDJZEXul3ectNGjR5c6DiIiovxECmOf45uIiIiIiHQuNTUV3bp1Q2JiIho2bIgDBw4YJI6EhAR07doVGRkZaNGiBXbu3GmQOIiIyLyxnwwREREREcHR0RGffPIJAOD+/fs4f/68QeL4888/kZGRASBnLDoiIiJdYEKMiIiIiIgAAKNGjYK7uzsA4JdfftH7+ZOTk/Hnn38CALp27QofHx+9x0BERGUDE2JERERERAQAsLW1xdy5cwEAN2/exIkTJ/R6/vXr1yMxMRHW1tZCHERERLrAMcSIiIiIiIiIiKhMYQsxIiIiIiIiIiIqU5gQIyIiIiIiIiKiMsXS0AEYSlxcHEJDQxEaGoqwsDCEhYUhMTERADB48GAsXrxY6+c8dOgQAgMDERERgeTkZLi4uKBVq1bw9/dHixYttH4+IiIiIiIiIiIqqMwmxDp06KC3c2VmZmLatGk4d+6c0vaXL1/i5cuXOHz4MKZMmYLPPvtMbzEREREREREREZVVZTYhlle1atVQp04dXLx4USfHnzt3rpAM8/HxwejRo+Hm5oYHDx5g3bp1iI6OxurVq+Hq6ophw4bpJAYiIiIiIiIiIspRZhNiU6ZMgbe3N7y9veHi4oLnz5+jW7duWj/PlStXcPjwYQBA165dsXbtWlhYWAAAmjZtCl9fXwwdOhQvX77EsmXL0KtXL5QvX17rcRARERERERERUY4yO6j+tGnT0LVrV7i4uOj0PBs3bgQAWFpa4n//+5+QDMtVsWJFzJo1CwCQnJyM3bt36zQeIiIiIiIiIqKyrswmxPQhNTUVV65cAQC0b98eVapUUVmue/fucHR0BACcOnVKb/EREREREREREZVFTIjpUFhYGCQSCQCgbdu2hZaztrZG8+bNC+xDRERERERERETax4SYDkVGRgrLderUKbJs7dq1AQBSqRRPnz7VaVxERERERERERGVZmR1UXx9ev34tLFeuXLnIslWrVhWWX716hXr16mktDrlcjoyMDAA5Y5mJRCKtHZuIiACFQgGpVAoAsLOzg1hs2r838XODiEi3zO1zg4jIFDEhpkNpaWnCsr29fZFl7ezshOX09HStxpGRkYH79+9r9ZhERKRaw4YN4eDgYOgwSoWfG0RE+mMOnxtERKaIP0XoUFZWlrBsbW1dZNm8j2dmZuosJiIiIiIiIiKiso4txHTIxsZGWM7Ozi6ybN7HbW1ttRqHpeW/T3OtWrWUWqPlZWFhITTXlsvlkMlkRR7XyspKWJbJZJDL5WqXl0qlUCgUhZYViURKcRc30UDe2PM2QS9M3i5Aml5rbuwSiUQYJ65u3bpCGU1jF4vFsLCwULt8WXie8tZtw4YNYWVlVeLnSVuxm8PzJJFIhFY/eV+zRcWur/dTcbHnMpbnSSqVQiQSCc9TRkaGMP5j3nhNFT83CuLnhnrl+bnxL3N4nvi5URA/N4iIzAfvvjqUt+lzcd0gc8dqAYrvXqmpvGO/2NnZGUWT7OJazJW2fN5kpLblxpI3iWlvb19ojLq+Vl0y1POUt26trKxKVCe6jt2Unyeg6Ndsfvp4PxlLeXW8fv0aPXv2xNdff43333+/wOPmMN4WPze0i58b2ivPzw3t4OeG9sqrIzo6Gr1798aSJUvQr1+/Ao+bw+cGEZEpYpdJHapSpYqw/ObNmyLLvnr1SljOO8A+EREZj5cvX6JLly4IDQ3F8OHDsW/fPkOHRERERiwqKgqdO3fG3bt3MXToUBw7dszQIRER0f9jQkyH6tatKyw/fvy4yLJPnjwBkNNkulatWjqNi4iINBcfH4/OnTsjIiICQE4XpWHDhvHLDRERqfT69Wt07twZUVFRAHJaMg4aNAjnzp0zbGBERASACTGd8vb2FsYNuHbtWqHlsrOzcevWrQL7EBGR8ahQoQJ69uyptK1mzZpo3LixgSIiIiJj5ubmBl9fX6VtdevWhaenp4EiIiKivJgQ0yFHR0e0b98eAHDlyhW8fv1aZbmTJ08iNTUVAODn56e3+IiISH0ikQirVq3ChAkTAAANGjTAuXPnULNmTQNHRkRExkgsFmPDhg3w9/cHkPPD95kzZ5SGVSEiIsNhQqwUAgMD4enpCU9PT6xevVplmXHjxgHImVnmu+++KzATTXx8PJYtWwYAcHJyUjlAMxERGQexWIxff/0V3377Lc6ePQt3d3dDh0REREbMwsICmzdvxldffYWgoCC4ubkZOiQiIvp/ZXaWyeDgYERHRwvrCQkJwvLTp08RGBioVH7IkCElOk/79u3Rt29fHD58GEFBQRg7dizGjBkDNzc3PHjwAL/99htevnwJAJg1axbKly9fovOoK++00lQ6lpaWQpN3TpetXaxb3WC9aodYLMZ3332ntM2c763mfG36xveg7rBudYP1qh2WlpZYsGCB0jbeW4mIDK/MfrLt2bOn0NnBQkJCEBISorStpAkxAFi4cCFSU1Nx7tw5XL16FVevXlV6XCwWY/LkyRg2bFiJz6EusZiNArVFLBbD0dHR0GGYJdatbrBe1ff48WNUr14d1tbWapU353urOV+bvvE9qDusW91gvarv0aNH8PDwUDtxyHsrEZHh8U6sB7a2tvj999+xbNkyvPPOO6hUqRKsrKxQtWpV9OvXD9u3b8fUqVMNHSYREQEIDQ1Fu3bt8OGHH0IikRg6HCIiMnLXr19HmzZt8NFHHxUYHoWIiIyXSKFQKAwdBOlWdnY2wsLCAABeXl6wtbU1cETmQS6XIz09HQBgb2/PX/q0iHWrG6zX4t28eRN+fn6Ij48HALz//vvYvn17sb/4Z2ZmIjw8HEDOoMnqtiwzVvzc0A2+B3WHdasbrNfiXblyBb169UJycjIA4KOPPsIff/xRbF2Z2+cGEZEp4qdaGcNfrbRHKpUiIiICERERkEqlhg7HrLBudYP1WrSsrCwMHDhQSIYBwO7du7F48eJi9zXne6s5X5u+8T2oO6xb3WC9Fi0tLQ0DBw4UkmEAsHnzZqxatarYfXlvJSIyvDI7hhgREVFeNjY2CAgIQO/evZGRkQEA6NSpE6ZPn27gyIiIyBg5ODggICAAAwYMQHZ2NgCgR48emDhxooEjKyg7OxupqalIS0tDdnY25HK5oUMiIiqWhYUFbG1t4eTkBAcHB4hEIq0enwkxIiKi/9e5c2ccOnQIffv2Rfv27XHw4EE4ODgYOiwiIjJSPXv2RGBgIAYPHozu3btj7969RtXNXKFQIDY2FrGxsYYOhYhIY1KpFFlZWUhKSoKdnR1q1qyp1e77TIgRERHl4evri7Nnz8Lb2xv29vaGDoeIiIxc3759cfbsWbRq1Qo2NjaGDkfJq1evkJSUpLRNJBLBwsLCQBEREalPJpMhd9j7jIwMREdHo1atWlprKcaEGBERUT4+Pj6GDoGIiExIhw4dDB1CAZmZmUrJsEqVKsHJyQk2NjZa73ZERKQLcrkcqampeP36NWQyGTIyMpCWlgZHR0etHJ+D6hMRUZlz4sQJhIaGGjoMIiIyEQcPHkRERIShw9BIYmKisOzm5gY3NzfY2toyGUZEJkMsFsPJyQlVqlQRtqWkpGjv+Fo7EhERkQk4dOgQ+vfvDz8/P9y9e9fQ4RARkZHbvXs3hgwZAl9fXzx69MjQ4agtPT1dWHZ2djZcIEREpeTo6Cgk83Mnv9IGJsSIiKjM2L9/P4YMGYLs7Gy8ffsWvr6+JveLPxER6c+OHTswfPhwSKVSvHz5Er6+vnjy5Imhw1KLTCYDAFhaWnLMMCIyaWKxWLiP5d7btIFjiJUxVlZWhg7BbFhbW6NVq1aGDsMssW51o6zXq0KhwJo1ayCRSIRtb968wW+//Yaff/65VMc253urOV+bvpX196AusW51o6zXq0wmw6pVq5S+fD179gwbN27E/PnzS3Vs3luJiAyPLcSIiKhMEIlE2LdvH9q3by9s++ijj7Bs2TIDRkVERMbKwsICR44cQYsWLYRtkydPxnfffWfAqIiISFvYQoyIiMqMcuXK4ejRo+jRoweaNm2KdevWQSzmb0NERKRahQoVcPLkSfj6+qJr1674+eefOSg9EZGZYEKsjNFmf9uyTiaTITY2FgDg4uLCsRm0iHWrG6zXHOXLl8fp06dhb2+vtWSYOd9bzfna9I3vQd1h3eoG6zVHpUqVcPHiRaVBnUuL91YiIsNjQqyMkcvlhg7BbMhkMjx//hxAzq+HZfWPRF1g3eoG6/Vfjo6OWj2eOd9bzfna9I3vQd1h3eoG6/Vf5cqV0+rxeG8lIjI89hMhIiKzs3btWixatMjQYRARkYn48ccfsWrVKkOHQaQ2T09PeHp6YvXq1SU+xtWrV4XjXL16VYvREZkGJsSIiMisrFixAp999hnmzp3LAfOJiKhYCxYswH//+19Mnz4dv/76q6HDIRMkk8nQsmVLeHp6YvDgwUWWVSgU8PHxERJRe/bsKbL8vn37hLLbt2/XZthEZR4TYkREZDaWLl2KmTNnCuuzZ8/GihUrDBcQEREZtf/973/45ptvhPXJkydj/fr1BoyITJGFhYUwG+n9+/eRmppaaNmHDx8iMTFRWL9x40aRxw4ODhaW27RpU7pANbR69WohGUdkjpgQIyIis2FnZ1dgW1pamgEiISIiU2Bra1tgGz83qCRyk1VyuRwhISGFlstNcOWOyZc34VVU+QoVKqBevXraCFXg4+ODiIgIREREwMfHR6vHJjIFTIgREZHZ+Oyzz7B8+XJhfcmSJfjqq68MGBERERmzL7/8Et9//72wvmbNGsyYMcNwAZHJat26tbBcVJIr97FevXoBAKKjo/HmzRuVZePi4hAVFQUAaNWqldZmOSWiHJxlkoiIzMrMmTMhkUhgZWWl1H2SiIhIlW+++QZSqRTu7u6YMGGCocMhE9W0aVPY2NggKytLrYRYz549ERYWhujoaNy4cQN9+vQptCyg/+6SRGWBUSXEsrOzkZycjIoVK0IsZuM1Mm4ikQgODg7CMmkP61Y3ylK9fvHFF4YOgaiAsvQe1DfWrW6UpXr97rvvDB0CmThra2s0bdoU169fR1hYGLKzs2Ftba1U5tmzZ0JrsFatWqFVq1aIjo5GcHBwsQmxvC3QVAkNDcXmzZsRHByM+Ph4VKhQAe3atcOkSZNQt25dlftcvXoVo0ePBgBs3bpV6DYZGBiIOXPmKJVVNY7Y6dOnUb16daVtMpkMf//9N44dO4bw8HAkJibCwcEBderUQY8ePTB8+HCVXZWJDEEvCbG0tDThzdy6dWvhgzVXfHw85s2bhzNnzkAmk8He3h7vv/8+Pv/88wI3ESodKysrQ4dgNqysrNCwYUNDh2GWWLe6YU71qlAokJaWBkdHR0OHAsC8763mfG36Zk7vQWPDutUNc6pXuVyOjIyMAt9DDIX3VvPUunVrXL9+HdnZ2bh9+3aBVl2534k9PDzg4uKCVq1aYd++fbh+/brK4+UOuO/o6IhGjRoVet5t27Zh4cKFkEqlwraYmBj8/fffOHnyJNavX6+XFmYvX77Ep59+ivv37yttT0xMREhICEJCQrBjxw6sW7cOtWvX1nk8RMXRS0LsxIkTmDNnDqpUqYKgoCClx+RyOcaPH4+7d+9CoVAAyEmgbdmyBS9evMDq1av1ESIREZkIuVyOqVOn4urVqzh16hScnZ0NHRIRERkxmUyGjz/+GI8ePcKxY8eM5scUMj9t2rTBr7/+CiAn+VVYQqxly5ZK/z98+BBJSUkoX768UDY1NVVILLVo0UIYhD+/ixcvIjQ0FA0aNMDo0aPRoEEDZGVl4eTJk9i6dSsyMjLwxRdf4Pjx42o3NvHz80OTJk2wfft27NixAwBw8ODBAuUqV64sLCckJGDEiBF49eoVrK2t8cEHH6BNmzZwd3dHeno6Ll26hK1bt+Lp06cYP3489u3bh3LlyqkVD5Gu6CUhdvHiRQA5b6z8XSGPHDmC8PBwiEQiNG7cGG3btsW1a9dw9+5dnDp1CufPn8e7776rjzCJiMjIyeVyTJo0CevXrweQMyDtiRMn4OTkZODIiIhIl/yP+CP0bajStqauTbGtz7Yi95NKpRgzZgy2b98OAOjXrx8OHz5sNC3FjMngXy4ZOgRB/6bVMK5j0S2INl58goOhLwts3zf5HV2FVazmzZvD0tISUqlU5ThieXtNAUDdunVRoUIFJCQkICQkBF27dhXK3rx5EzKZDEDR44fdunULnTt3xpo1a5QSXq1bt4azszNWrFiBly9f4ty5c+jevbta1+Hk5AQnJydUqlRJ2NagQYMi91mwYAFevXoFd3d3bNmyBTVq1FB63MfHB7169YK/vz+ePXuGDRs2cKxXMji9JMQePHgAkUgkZMDz2r9/PwDAy8sLO3fuhKWlJSQSCfz9/REWFob9+/czIaZFUqmU3VC1RCqV4unTpwCAWrVqwdLSqIbkM2msW90wh3qdM2eOkAwDcsa+6N+/P86cOWPQsSfzdlEwN/zc0B5zeA8aK9atbphDvU6fPl1IhgHAuXPn8P777+Pw4cMGHRfNGD83bkYnGjoEQcuaFYot8yIxw6hiBgAHBwc0atQIYWFhQkIrt2VX/hkjc7Vs2RKnT59GcHCwUkJM3fHDbGxssGjRIpWf1aNGjcLatWshkUgQHBysdkJMU8+fP8fRo0cB5ExSkT8Zlqtx48YYMWIENmzYgMDAQCbEyOD08u0hPj4eAAoMuJf7xhSJRPD39xc+ZK2srPDhhx9CoVAgNDS0wPGo5HK7pVLpyeVyJCYmIjExEXK53NDhmBXWrW6YQ71+8sknqFq1qrBuaWmJqVOnGnwiFnO+t5rztembObwHjRXrVjfMoV4//fRTuLi4COvW1taYMmWKwScJ4L3VfOW25kpLS8O9e/eE7bnjhLm4uMDDw0PYnpscy9+iLHf8MBsbG3h7exd6vg4dOii15MrL0dFRONezZ880uxANnDt3DjKZDHZ2dsU2Zsmtn5iYGLx8WbCFH5E+6eVnnqSkJAAFB48MCwtDZmYmRCIROnXqpPRY7hs3NjZWHyESEZEJqF+/PoKCgtC5c2ckJCRg165dGDRokKHDIiIiHcqUZiImLaZE+zZp0gSnTp1C165dkZGRgf3796Nnz55ajpDoX61atcLGjRsB5CS5mjRpIiznPp5Xbuuv8PBwZGZmwtbWFtnZ2ULDkGbNmhXZUrtOnTpFxpM7LllaWloJrkY9d+7cAQBkZGSgcePGau8XGxuLatWq6SosomLp5Sf13GlV4+LilLbn3hRq1qyp9MtN3n2IiIjyatiwIYKCgrB//34mw4iIzFxqdioG7h+I1+mvS3yMZs2a4dSpUzh06BCTYaRzrVu3Flog5rbyyrucfxihxo0bw87ODhKJBLdu3QKQ03AkKytLOF5R7Ozsinw8txW9Llt55v+er66MjAwtR0KkGb20EKtZsybu3buHa9euoWPHjsL2kydPQiQSqRwkMLebZcWKFfURIhERmRAvLy94eXkZOgwiItIxR2tHNHNrhpdPSte1StVYxqSsRU1nQ4cgcHcuOsmTW8aYYs7l7OyM+vXr48GDB0IDkLwzRuZvIWZlZQVvb29cu3YNwcHBaNeunVIiragB9Y1F7uD/FSpUwNatW9XeL/+QSkT6ppeEWIcOHXD37l1s374drVq1QuvWrREYGIiwsDCIRCKlwQNzRUREAADc3Nz0ESIRERmRzMxM3L59Gz4+PoYOhYiIDGxGyxk49uQYFCh83K3c8ZqKa01DhTPk7IwlMa5j7WJnojSU1q1b48GDB4iPj0dkZCSeP38OuVwOe3t7lV0KW7VqJSTEgH/HG7OyskLz5s31GXqJODs7A8h5H9atW1eYSIDI2Omly+To0aPh6OiItLQ0TJo0Ca1bt8bChQsB5Ew16+vrW2Cfs2fPQiQSadQHmYiITF9GRgYGDhyIzp074+TJk4YOh4iIDKyaYzW42LkU+nhKSgp69+6NLl264OLFi3qMjEi1vInZGzduCC2+mjdvrjJZlNtq7Pbt28jOzsbNmzcB5HSntLe310PEqqk7+UTud/bs7GxhPDEiU6CXhJibmxt+++03uLi4QKFQCP9q1KiBlStXFnijRUdHCzeNDh066CNEIiIyAmlpaejXrx9OnDiBrKwsDBgwAGfOnDF0WEREpCNSuRQyuazYckmpBZMCEa9TkJycjF69euHChQtIS0tD7969ceXKFV2ESqS2vAmx69evCy2+8neXzNWiRQtYWFggPT0dgYGBSElJKXAcQ8g7mH92dnah5bp27Sp8p9+yZYvO4yLSFr10mQRy3synT59GSEgIYmNj4erqilatWsHSsmAIMTExmDx5MgDgnXdMq+musTP0FNPmRCwWC116cwerJO1g3eqGKdTrmjVrEBQUJKxnZmbC398fkZGRxQ4aa0jmfG8152vTN1N4D5oq1q1u6LJeFQoFLr64iGXBy+DfyB8feH5QZHlRIb/jL126FJcvXxbWU1NTMXLkSNy/f7/ADPfGhPdW81a5cmXUrFkT0dHRuHr1KhISEgAUnhBzdHREgwYNcO/ePWzYsEHYbujxw/IOXxQdHY169eqpLFenTh306tULR48exeHDh+Ht7Y2xY8cWetxnz57h9u3b6Nevn9ZjJtKE3hJiQE6GuV27dsWWa926tcGz4eZKVQKSSsbS0hI1atQwdBhmiXWrG6ZQr7NmzcKtW7ewc+dOADljUhw4cMCok2GAed9bzfna9M0U3oOminWrG7qq14cJD7EseBkuv8xJZK29tRZ9aveBo7VjofvUkj7FA4uC2775Zg9CQ0Px999/AwBcXFywb98+o06GAby3lgWtW7dGdHQ03rx5AyDnOS9qPLBWrVrh3r17ePbsGYCcJHRhCTR9adGihbC8aNEiTJo0Ca6urkJC193dXXgt/+9//8OdO3fw7NkzLF68GKdPn8bAgQNRv359WFtbIzExEffv38eFCxfwzz//oHv37kyIkcHxTkxEREbDwsICAQEBkEqlCAoKwsmTJzkzGBGRmYjNiMUvt37B3od7IVfIhe3xmfFYH7YeM1vNRGqWFMtPPMDIdjVRx/XfBNnCGFt4Su8rHS/CsiGsra2xa9cuDBkyBDdu3MDp06c5CzEZhdyJ5HI1atSoyB/4WrVqhT///FNYb9CgAZycnHQaY3Fq1aqF3r174+jRo7h48WKBMfpOnz4tzBTp7OyMHTt2YMaMGQgODlbqKqqKg4ODTmMnUodBEmLR0dG4efMmYmNjkZGRgREjRqBixYqGCIWIiIyMpaUltm/fjqdPnxbaNJ+IiExHliwLAXcDsCFsA9IkaSrLBNwNQDVxV6w4FodXSZm4+yoJO8a3E1qieFYpBzxX3sezSjkAgI2NDfbu3YuXL1+iTp06Or0WInXl7+5YXGuv/I8burtkrqVLl6JJkyY4fvw4njx5grS0NMjlcpVlXV1dsW3bNpw9exaHDh3CrVu3EBsbC6lUinLlyqFWrVpo0aIFfH19jeb6qGwTKRSKwucv1rLw8HAsXLgQISEhStsPHjyo9KVn27ZtWLNmDcqVK4fDhw8bfZNnY5ednY2wsDAAQMOGDZmN1xKJRIJ79+4ByPnFh69T7WHd6gbrVXfS0tJw/35OywVvb2+lQWhNET83dIPvQd1h3epGaetVoVDgWNQxrLixAi/TXhZbPjuuE7Ji+grrPw5tig/a/H+XzQ1+wPN8rU2qtwE+OaVRTMZCH58bDx8+hFQqhaWlJerXr6/14xMR6ZMu7ml6G3X0zJkzGD58OEJCQpRmmlRl4MCByMzMxLNnz3D27Fl9hUikEYVCAYlEAolEUuhrmUqGdasbxlSvMTExmDdvHmSy4mcWIzIXxvQeNDesW90oTb3eirmFkUdH4ovzXxSbDLMROSHz1WBkxfRS2v7DkXt4m5IFAHiRkIn557L4/BIRkdboJSEWExODzz//HNnZ2ahXrx7Wr19foJVYXo6OjvD19QUAnD9/Xh8hEhGRnrx69QpdunTB999/jwkTJhTa7J6IiEzPi9QXmH1uNkYdHYXQt6FFlrUWW+PjJh/j8JDDcFV0BqA8an5ShgSLjtxDdHQ0Oi8NwbdnszD9GJNiRESkHXoZQ2zz5s3IyMhAtWrVsG3bNrUGB/Tx8cHhw4cRHh6uhwiJiEgfXrx4AV9fXzx48AAAsHHjRlhZWeHXX3/lFPRERCYsNTsVG8I2IOBuALLl2cWW7+3RG9NbTYe7ozsAYMHgJhi3OVipTLs6FTGogS06d+6MqLcZAIDV17JhJQaW9bABPzWIiKg09NJC7MKFCxCJRBg3bpzaM2XkDoj5/PnzYkoSEZGpuHfvHqKiopS2nThxArGxsYYJiIiISkUql2JXxC703dcXf9z5o9hkWFOXpgjoHYAfO/8oJMMAwLdhZfRtWhUAUN7OCj++1xQ7xrdD0vOHePbsmdIxDj+UIjlL+9dCRERli15aiL18mTNuQNOmTdXex9ExZ5rl9PR0ncRERET65+fnhz179mDIkCGQSqWoV68egoKC4OrqaujQiIhIQ5dfXsbS60vxKPFRsWWrOlTFzFYz0cujV6Etguf1bwxHa0vM6ukJ13I2AID+/ftj27ZtGDH8Q8gVQGNXMYJG26O8LduHERFR6eilhVjuoMmajBOTkpICALC3t9dJTEREZBj9+/fHX3/9BS8vL5w9exY1atQwdEhERFQCF19cLDYZZiO2h31qf/zSeSd61+5dZPd4t3K2WPJeUyEZlmvYsGHYMq4xmlcR4+wYe1R21Nu8YEREZMb08mni4uICAAWaOxclNDRnEM6qVavqJCYiIjKcIUOG4NatW3B3dy++MBERGaWJTSeivE15lY+JIYYbOiPu/ky8efYO5h98VKrB8Ee2q4rr4x3g6sBkGBERaYdePlFat24NhUKBY8eOqVU+Ozsbf/31F0QiEdq2bavj6MoWCwuL4guRWiwtLVGnTh3UqVMHlpZ66X1cZrBudUPf9VrcFx9zem7N+d5qztemb7y36Q7rVjeKq9fyNuXxabNPC2yvZd8csmczEXmvNxSycgCAcw/e4u/bL4s8n8rPDbkceH4DSH4BS7Eo/w7qX4yR4b2ViMjw9JIQGzx4MAAgKCgIly5dKrJsdnY2/vvf/yI6OhoikQgffPCBPkIsM8Ri/qqmLWKxGBUqVECFChVYr1rGutUNfdZreHg42rZti8ePH+v0PMbCnF+n5nxt+sZ7m+6wbnVDnXr9wPMDeDh5AADcHTxQNfMz3LkxDKmpBceG/P7gXSSkqR50PyQkBD4+PjkTaqXFAaG7gL3jgWX1gA2+QLKKZFp6XImvzdD4OiUiMjy9/ITm4+ODPn364MiRI5g0aRJGjx6Nnj17Co+/ePECycnJCAkJwa5du/Ds2TOIRCJ8+OGHqF+/vj5CJCIiLbl9+zb8/PwQGxuLrl274vz586hVq5ahwyIiIg2Evg1FVHIUBtQdUGQ5K7EVZracje0hN3H2eh1I5YUneuLSsrHnxnOMf7eO0vZrV//BnDG90LtKBuIWN4O7iwwiqNH6KytZrWshIiJSRW9tyhcvXoy0tDScO3cOGzduxMaNG4VBNSdNmiSUy20q3aNHD3z11Vd6ie3FixcICAjA2bNn8fr1a1hbW6NGjRro3bs3/P39YWdnV+pzPH/+HDt27MCVK1cQHR2NjIwMODg4oE6dOujYsSOGDx+OSpUqaeFqilaasRtImUKhgEQiAQBYWVkVOUgsaYZ1qxv6qNewsDD4+voiPj4eABAdHS0kxapXr6718xkLc763mvO16RvvbbrDutWeV6mvsCJkBY48OQJbS1u0rNQSbvZuhdbr5chYzN8nx5PYekUet6KDNb7t1xgDm1fL2ZAWB0SeRtw/O1Hn0Wmc/hAAbAFI1Q9WbKV+WSPDeysRkeHpLSFmbW2NdevWYdeuXdiwYQOio6NVlqtSpQomTpyI4cOH6yWuoKAgzJ49G6mpqcK2jIwMJCUl4c6dO9i9ezd+//33UrVu2L9/P+bNm4fMzEyl7UlJSbh58yZu3ryJgIAALF++HO+8806Jz6MOqVQKGxub4gtSsSQSCcLCwgAA3t7esLa2NnBE5oN1qxv6qFd3d3fUqFFDSIgBQLVq1eDk5KT1cxkTqVSDL3Amhp8b2sN7m+6wbguxwQ94fl15W/U2wCenChRNk6Thj7A/sPXuVmTJsgAAmdJM/HDuB3xS/ZMC9ZqYno2FR+5hV/DzYsMY2rI6vu7jiQqJ4cC5AODhSeDFDQAKVAKAkk4qX65KCXc0PHP+3CAiMhV6H3X0gw8+wAcffIBHjx7hzp07iIuLg0wmQ4UKFdCoUSN4eXnp7Ve9u3fvYubMmcjMzIS9vT0mTpwIHx8fZGZm4siRI9i1axeioqIwYcIE7N27F46Ojhqf48aNG5gzZw7kcjnEYjEGDRqEbt26wc3NDa9evcK+fftw5swZJCYmYvLkyTh06BBq1Kihg6slItK9ihUr4tSpU+jatSvu3LmDd999F4cPHy7R/ZOIiErH3yIeobVrKm1rKo/HtjzrMrkM+x7tw+qbqxGfGY/8LiVegl8lP3jDG0BOy6a/b7/E/EN3EZuqejww4VwVpVjWIhYNkvcDv5zW/phfFqbbQoyIiAzPYNPw1KtXD/XqFd20Wtd++OEHZGZmwtLSEhs3bkSLFi2Ex9q3b49atWph6dKliIqKwqZNmzB16lSNz/H7779DLpcDAL7++mv4+/sLjzVt2hQ9e/bE4sWLsWnTJmRmZmLTpk349ttvS39xREQG4uLigtOnT+Pbb7/FTz/9BAcHB0OHREREKlx+eRnLgpfhYcLDQssooMDO1zvRT9EPz+LT8c2BOzgb8VZlWRHkaCp6jK4Wt/GB831UTb0L0aVSdg2sWBeo3x2IDAJiH5TuWERERHnoJSGWkpKCcuXK6eNUagsNDUVwcDAAYOjQoUrJsFzjxo1DYGAgIiMjsXXrVkyaNAlWVpr9EnXz5k0AgLOzs1IyLK8pU6Zg06ZNAIBbt25pdHwiImPk5uaG3377zdBhEBGRCo8TH2NZ8DJceHGh2LI2Yhs0sm+MjZefYFVQFDIkMqXHKyAZncRh6GJxG53Ft1FJlJLzQKqKg6nD0hbw6JSTBKvnB1Sqm7N9g18JD0hERKSaXhJi77zzDrp06YJ+/fqhS5cuRjGuw6lT/46dMHToUJVlcrs4/vTTT0hOTsbVq1fRsWNHjc6TO8BrUYNJlytXDhUqVEBCQoJQnojI2L148QLu7u6GDoOIyPxoMPaXpl6IZBjy9xDIFLIiy4kgwsC6A1E77R1suyVCZELk/2/PaQXWRXwbXSxuo5koEmJRaVuB1QHq9wDqdQc83gGsSj+hFRERUXH0khDLzs7GyZMncfLkSTg6OqJnz57o168f2rVrp4/Tq3Tjxg0AgL29Pby8vAot16ZNG2E5JCRE44RY7dq1ER4ejufPCx9wNDU1FQkJCUJ5IiJjd+zYMQwePBgrVqzAxIkTDR0OERGpKU4kB4rJX/lU9cFnzWbi4DUFFlx+ivKKZAxQ1QqspAprBUZERKRHekmIffDBBzh+/DiSkpKQkpKCvXv3Yu/evXBzc0Pfvn3Rv39/NGrUSB+hCCIjc37lqlmzJiwtC6+GOnXqFNhHEx9++CG++eYbJCYmYseOHSpnz1y7dq1SeV2SSCTIzlY9AKqlpSXEYjEAQC6XFzv7Td6WfjKZDDJZ4b80ikQipe6mUqlUGFtNFbFYrPS8SCSSIqenzht73qnXC5N36vDirrWw2POeI++yprFbWFjAwsJCWC/s+clVFp4nVdtL+jxpK3Zzep4A1XWsKnZVz9OhQ4cwfPhwZGdnY9KkSbC0tMTHH38MoOw+T+bcupefGzn4uaGasTxP5va5oVAA+aeYkisUkOY5hnrPk2Ytt2qVq4WZLWZClN4QazceQuPUq9hrpZ1WYA/j5Tj6UILjkXKMmbcKH/iPAfD/z1MRdZP/ecorf50ApvN+MufPDSIiU6GXhNj333+Pb7/9FufPn8ehQ4cQFBSEzMxMvHnzBps2bcKmTZtQp04dDBgwAH379i2ye6E2ZGVlCS2yqlQperrm8uXLw97eHunp6Xj9+rXG5xo6dChu3LiB/fv34/vvv0d4eDh8fX3h6uqKV69e4cCBA0L3zUmTJqFDhw6aX5AGikrqeXp6CjPBpaenIyIioshjtWrVSliOjY0tshWcg4MDGjZsKKw/ffoUiYmJhZZ3c3NTmm3z3r17Rf7hUKdOHVSoUAGA8tTrhck7dXhSUhIeP35caFkrKys0bdpUWH/16hViYmKUyty/f19YdnZ2Rt26//7SGRkZibS0tEKPX716dVSuXFlYLy72svQ85aWN5ymvsvw8Acqv2fyKep6ePn2KYcOGKX0BGD9+POrVq4fOnTvzeTJD/NzIwc8N1YztecrLlO9HcrkMFvkeT09PR0SeY6jzPMUW0y0yl6OFI4ZU7Ike6dZI3bkSnpk34StKBkoxiaNcbI2USs3xEB4YMW8THsb9G8vxMR+jdoPGaNOmjcbPU1756wQw7fcTERHpl95mmbS0tISvry98fX2Rnp6OkydP4tChQ7hy5QqkUikiIyOxYsUKrFixAs2bN8eAAQPQq1cv4Y8gbcr7oWhvb19seTs7O6SnpyM9PV3jc1lYWGDJkiXo2rUr1q1bh927d2P37t1KZXx8fPSSDCMyNQ0bNtR4IgsqnJWVFby9vXHv3r1if9EuTK1atTB27FisX79e2DZ8+HC888472gqTiKjE+Lnxr9dZr/FKXHSrLguIMUjsjglxcaj6aAVEuS3K8jdPU1fFOpDV8cVjcV2kuDSHwsIGUCjg0zsTD//8Uyg2duxYpUQRESm7evUqRo8eDQDYunUrfHx8DByRssDAQMyZMwcAcPr0aZ03aCHSFZGiqDbEehAfH4+jR4/i4MGDSjMsikQiWFhYoGPHjlqfqezVq1fo0qULAGDgwIH48ccfiyzfpUsXvHr1CjVr1sTJkyc1Pl9kZCSWLl2KCxcuqPwSamNjAz8/P/z3v/9V+oVKW7Kzs4Vfu4r6Q7EsdKnIpY2uL9qKnV1fCsfnSTVDP08KhQLffvstfvzxR4wZMwYbNmwQjl9WnyeJRCK0+MnbkshU8XOjIN6PVOPzpKMuk+v9IHqhPKi+3L01pGOOCutFPU8KhQJTzkzB1TdXCz2fX6YUn7+NQY0S/kACAApLWyhqdgAa9IS4fnegUt1CPzf+85//YO3atfj000+xZs0atV+TwvOkYqKB/HUCmM77SR+fGw8fPoRUKoWlpSXq16+v9eNTQXkTWarY29vDzc0NTZs2xZAhQ9C+fftij8OEGFEOXdzT9NZCrDAVK1aEv78//P398fz5cxw6dAiHDx8WLvbcuXNaP6eNjY2wrE7//dwPRltbW43PFRwcjEmTJiElJQXu7u6YPn063nnnHZQvXx5xcXE4ffo0Vq1ahcOHD+P69evYuHGjTj+wrKys1PrAFYvFGn0w5/9jojhFjdumiia/9opEIo1i1/RadRk7AJ3GzuepcHyeVCvseVq8eDHatm2LwYMHC18MAD5P5oifGwXxdV44Pk+F0yQWkYoWWuIirj//8/Q48TFuxd5SWdYrKwuz4xLRKitL7XiUVKyTMxtk/e4QeXSEKN+MkIU9T6tXr0bXrl0xZMgQIWkJaP485VVUneQy5fcTmZ/09HRERUUhKioKf//9NwYNGoSFCxfydUFkIAZPiOVVvXp19OjRQxhfLDk5WSfncXBwEJbV6QaZkZEBQL3ulXllZ2fj888/R0pKClxdXfHXX3/B1dVVeLxKlSrw9/dH27ZtMXToUMTExOC///0vAgMDNTqPJor65ZM0I5fLkZSUBCBnrLm8SQEqHdatbmizXkUiEYYOHaqt0EyeOd9bzfna9I33Nt1h3Sqr41wH+wbuw8RdPfHM8t8v2h8kp+CruARoUjtSsQ3kNdrBsmFviBv0KPGMkPzcUMZ7q/kbPnw4RowYIawrFAokJSXh1q1b2Lx5M+Li4rB//35UqVIFM2fONGCkRGWXUSTEYmJicPjwYRw8eBD37t1TekwXzYdtbGzg7OyMxMTEYgfKT0pKEpJmxQ3An9/58+fx5s0bAMDIkSOVkmF51a9fHwMGDMDu3bsRHh6O+/fvKw1Qq01FdXsgzUilUmEQWHPoImVMWLe6oWm97t+/H927d1f6EYFUM+d7qzlfm77x3qY7rNuCaiS9wb5nL3HB3haLKuWMyTszPlGtZFiUvDKeVGiPJu8OwTOpCxSWtmrV6549e9CvX78S9aooa3hvNX+VKlVCgwYNCmxv27YtfH19MWTIEGRlZSEgIABTpkzhfYvIAAyWEEtNTcWxY8dw8OBBBAcHQy6XC/37RSIR2rRpg/79+6NXr146OX+9evUQHByM6OhooR+qKnlnvck7Y4068u7buHHjIst6eXkJg+0/fvxYZwkxIiJ1rFmzBlOnToWvry8OHjyocQtZIiIyoOSXwE5/2EABv/QMtM/IxFMrSzgWMpZWpsIK/8gb46y8GcLt22L8oO7o4VUF2dnZiFZzZs/Fixdjzpw56NOnDwIDA5WGKNGKT05p93hEBlSvXj106dIFx48fR1paGr//ERmIXhNi2dnZOHv2LA4ePIjz588LY3PlJsIaNmyI/v37o1+/fjoZXD6vVq1aITg4GOnp6QgPD0ezZs1Ulrt+/d/BO1u2bKnROfL2BS/uV6C8A3KyDzkRGdLPP/+Mzz//HAAQFBSEQYMG4e+//+Yv/kREpkCSAewcAaT+2wvCQaFA42zlcXOj5JVxVt4MZ+XN8I+8MbJENhjdrhY29vREOVvNxt2aP38+vv32WwDAkSNHMGzYMOzevZszfhIVwd3dXVgubjIHVW7duoUzZ84gJCQEjx8/RlJSEqytrVGlShW0adMGo0aNQr169dQ61rlz53Dw4EHcvHkTcXFxkMlkcHV1hZeXF7p27YrevXvDzs6u+APl8eLFC4wbNw5RUVGwt7fHL7/8UugkAkSGopeE2JUrV3Dw4EGcPHkSqampAP5NglWrVg39+/dH//791X7DaoOfnx/WrVsHANi7d6/KhJhcLsf+/fsBAE5OThrP7pF3to3g4GB07dq10LJ5E2+cpYOIDCU2NhY//PCD0raTJ08iMDBQaRwMIiIyQgoFcGAK8PKmyodv2Njgpo0DIsstx47If7tneVYuh0VDvdGyZgWNT/nixQssW7ZMaduBAwdw5MgRDBw4UOPjEZUVL1++FJarVaum0b55Z3nMSyKRIDIyEpGRkdi9eze++uor+Pv7F3qchIQEzJw5E1euXCnw2IsXL/DixQucOHECADBkyBC144uMjMS4cePw+vVrODs7Y/369WjatKna+xPpi14SYmPHjoVIJBKSYM7OzujVqxf69++PVq1a6SOEApo2bYrWrVsjODgYe/fuxeDBg9GiRQulMhs3bkRkZCQAYPTo0QV+5co7He7gwYOxePFipcfbt28POzs7ZGRkYMeOHRgwYAA8PT0LxHLu3DmcPHkSAFC5cmU0atRIa9dJRKQJFxcXnDx5Er6+vkhMTAQA/PDDD0yGERGZggs/AXf2Fvpwq6wsOMpqo/e4Xjiw/BykcgWmd6uPCe/WgZVFySYicHd3x7Fjx9CjRw/hh+8VK1YwGUZUhMjISJw9exYA0Lx5c7i4uGi0v0wmQ/ny5dGtWze0bt0atWrVgr29PWJiYhAeHo6AgAAkJCRg/vz5qFOnjsqWWRkZGRg9ejQePHgAIGcIn2HDhqF+/fqwtrbG69evcf36dRw5ckSj2EJDQzF+/HgkJibCzc0NmzZt0mvDFyJN6K3LpI2NDXx9fdG/f3906tRJ46mtdeGrr77C8OHDkZmZiXHjxmHSpEnw8fFBZmYmjhw5gr/++gsA4OHhgbFjx2p8fCcnJ4wfPx6rVq1CWloaPvzwQ4waNQodOnRA+fLlERsbi9OnT2P37t3CTDP/+c9/yvzMSERkWC1atMCJEyfQvXt3fPXVV5g9e7ahQyIiouLcOwQEzVerqLuzHZZ/0ByeVcqhtkvpJ05p3749jhw5gj59+mDJkiWYPHlyqY9JehR5Bri1DUiIMnQkulXBA2juD9QtvNeONsXFxQnJJiCnh1RKSgpu3ryJLVu2IDMzE+XKlVPZ0qs47777Lvr161egG2Pjxo3RpUsXjB49Gv7+/oiIiMDq1atVJsRWrFghxOfv749vvvkGIpFIeLxJkybw8/PDf/7zHyQnJ6sV15UrVzB58mSkp6ejVq1a2LhxI3s/kVHTS1Zq8eLFRjlTWePGjfHzzz9j9uzZSE1NxfLlywuU8fDwwO+//w5HR8cSnWPy5MlISkrC1q1bkZ6ejnXr1gldNfOysrLCzJkz+WsaERmFNm3aICIiQufjORIRUelcfnkZlx4cwOTLAdDkL+1eTTSbPb04nTp1wqNHj/i5YWoizwDb3gPk0uLLmrrn14HwfcDIvUCdLjo/3Y4dO7Bjxw6Vj4nFYnz44Yf46KOPULt2bY2PXdz7rFy5cpg2bRqmTJmCGzduICEhARUq/NslOjk5WWj84eXlha+++kopGZaXtbW1Wi3YTp06hZkzZyI7Oxuenp74448/4OrqqsFVEemfXhJigwYN0sdpSsTX1xd///03tm7dirNnz+LNmzewsrJCzZo10atXL4wcOVLjAQTzEolEmDt3LgYMGIDdu3cjJCQEL168QGZmJuzt7VGzZk20bdsWw4YNK9HNkAxHJBIJ3WgL+wChkmHd6oam9covNUTaxXub7pTVus2UZmLB5e/wLO0ljlcujy/j5OiWnoHcGlAAKE1t8HOjDLi1rWwkw3LJpcDNP/WSECsyDLkcR44cgY2NDWbNmgVra+vidypCeno64uPjkZGRIQxTlHe4n/v37yu1Evvnn3+QkZEBIGdooNJO6hYYGIivv/4aMpkMLVq0wO+//w4nJ6dSHZNIHwzfb9EIuLu7Y86cORo3V/Xx8UFERIRaZZs0aYImTZqUJDyt4mw/2mNlZcXBIXWEdasbeetVoVDgyy+/xIABA9ChQwcDR2b6zPneas7Xpm+8t+lOWa3bP0J/x7O0nIG531haYmZlV7ybnoE5cfGoLpVB5FwTSIxW2sezSjm1j5+3XuVyOWbOnImRI0cabAxgc8J7q/n77LPPMHXqVKVtmZmZePr0KQ4cOIAtW7Zgy5YtuHPnDv744w+NG2HEx8dj8+bNOH78OJ4+fSokwlRJSEhQWr97966wXNr385YtWxAQEACFQoGOHTtizZo1pWpQQqRPHKyKiKiMkcvlmDJlCpYsWYJevXrh6tWrhg6JiIg0FJX0BH+EbSiw/by9HS7b2eGwVQ9cFbdQsafmZDIZxo4dixUrVqB79+64ffu2Vo5LRqC5PyAuQ20kxJZAi5EGO72trS08PT3xxRdfYN68eQCAGzdu4LffftPoOHfu3EHv3r2xbt06REVFFZkMA4CsrCyl9bwJMjc3N43Ond/WrVuhUChQsWJFrF69mskwMilavfvlzo4oEomUss6lmTUx/7GIiKjk5HI5Jk6ciA0bcr5EpaSkoGfPnjh16hRat25t4OiIiEgdCoUCC05MhgQFvwQ3zcxCjSR3jM4eCUmKJYaIq2Ku1Xa4iNQbFDs/qVSKMWPGYPv27QByvkj7+fnhzJkzRtH7gUqpbtecMbVu/lk2BtVvMdLg3SVzvffee/jpp5+QmJiIvXv3YubMmWrtl52djRkzZiAxMRFWVlYYOXIkunXrBg8PD5QvX17ofvns2TP4+fkBQLEJs9Lo2bMnjh8/jvj4eHzxxRdYsWKFUUygR6QOrb5SC3uj6fINSJqRSqWl7qNOOaRSKV69egUAqFq1Km/8WsS61Q2pVIqnT5/iyZMnSttTU1Px+PFjJsRKQSo13/FX+LmhPby36Y651a2/RTxCa9dU2tZUHo9t/7985J+luJr+vMB+YoUC498qMCV7JiT//2d+oPxd9JAHo5dFsMZxSKVSPHnyBE+fPlXanpSUhKioKCbESsGoPjfqdDGaJFFZIhaLUatWLSQmJuLt27cFBr4vzD///INnz54BAObNm4f3339fZbnExMRCj5H3PDExMahRo4ZmwefxxRdfwMXFBdu2bcPJkyfx+eefY/ny5SZ/H6ayQauv0s8++0yj7aR/TE5qj1wuR0xMDAAOIqttrFvdkMvlSExMxPz582FhYYETJ07A0tISO3bswHvvvWfo8EyaOd9bzfna9I33Nt0pS3Wb/Oo2lt7bAlgUHPnkg+R0LE6bjXj8O5h1V/FN9BRrngwDcuo1OTkZS5Yswdy5c3H+/HlYW1sjMDAQffv2LfE1EO+tlCNvYlQmk6m1z6NHj4Tl3r17F1ruzp07hT7m5eUlLAcHB5cqIQYA33zzDaRSKf766y8cP34cs2fPxrJly0o9WD+RrjEhRkRUxlhbW2PXrl0YMWIEJkyYgIEDBxo6JCIiUkdGIlYdGo0464LJMDepFJnZH+O+4t+WZTYiKb6z3ILSTrxpa2uLffv24cMPP8R//vMf9OzZs3QHJCJkZGQgMjISQM57TJ3WYYByEi0jIwOOjo4FysjlcuzevbvQY/j4+MDe3h7p6ekICAjAgAEDSpW8EolE+O6774TzHjlyBGKxGEuXLoVYzGHLyXjx1UlEVAbZ2dnh0KFDTIYREZkIkUKBO3v8sctKdSuS/1brhnkz5+Cbfo3hYJ3zxfazCldRUxyjlfM7Ojri+PHjTIYRacnq1auRmZkJAOjYsaPaCSkPDw9hed++fSrL/PTTTwgPDy/0GE5OThg2bBgAIDw8HAsXLiy01aJEIkFcXFyxcYlEIsyfPx9DhgwBABw6dAhffvkl5HJ5sfsSGYpeOvZev34dAODt7Q1bW1u19snKykJoaCgAoE2bNjqLjYjIXEkkElhaWkJUSNOAwrYTEZHxGZYQh+/LpUNhU3BMv44W5dG95yqIxGJ83LE2+npXxbrzkZjQuxdguVDtc2RnZ8PKyoqfG0RaEBcXhwcPHihty8rKwtOnT7F//35cuHABAGBjY4Np06apfdyOHTuiUqVKiIuLw4oVK/D8+XN0794dFSpUQHR0NHbt2oUrV66gZcuWCAkJKfQ406dPx6VLl/DgwQP8+eefuHnzJj788EM0aNAAVlZWeP36NYKDg3H48GHMmDFDSHQVRSQS4YcffoBcLsf+/ftx4MABWFhYYOHChbx/kFHSS0Js1KhREIvF+Pvvv1GvXj219nnz5o2wH2eZJCLSTGZmJoYOHYoGDRpg+fLl/COEiMiEDUxJRbJIhnsqkmE2CmBu380Q5emWVKW8Leb19ypQtihpaWno378/OnbsiO+//77UMROVdTt27MCOHTuKLFOxYkUsXboUnp6eah/X3t4eS5YswZQpU5CVlYW//voLf/31l1KZtm3b4ttvv0W/fv0KPY6dnR22bNmCadOm4fr16wgPD8c333yjdhyFEYvFWLRoEWQyGQ4ePIjAwEBYWFhg/vz5/HuUjI7epn4o6cCRHHCSiIrjf8QfoW9DlbY1dW2KbX22FbKHeUtPT8egQYNw8uRJHDlyBNbW1li8eLGhwyIiohJolpmFTxOSMLR6VZWPT/AagxoV1PvBuTApKSno27cvLly4gDNnzsDKykorX4yJSJmVlRWcnZ1Rr149dO7cGUOGDEH58uU1Pk6nTp2wd+9e/P777/jnn3+QkJCAcuXKoV69eujfvz/ee+89vHz5stjjVKxYEX/++SdOnjyJgwcP4vbt24iPj4dIJIKbmxu8vLzg5+encVdpsViMJUuWQC6X4/Dhw9i9ezfEYjG+++47JsXIqBjtXKi5fY05MwURkfoUCoWQDMv1448/wtHREf/9738NGBkREWmqilSKlW/eYlGlCkhTMTB1bfuq+Kil+l2tVJHJZOjTpw8uXrwobPv222/h6OiIKVOmlOrYRGWNj48PIiIi9HKc+vXrY+nSpYU+Xr16dbVj6d69O7p37652fEOGDCm2C6WFhQWWL1+O5cuXq31cIn0z2kH1czPaqmbNoJJjRl57xGIxnJ2d4ezszNlTtIx1W3IikQhjxoxReq87OTmhe/furFcdMud7qzlfm77xPag75li3dnI5Vr95i/s21jju6KCyzNcdF8DaomA3Sk1YWFhgzJgxStsqVaqELl26mGW9GgveW4mIDE8nLcQKa5759u1b2NvbF7lvdnY2oqOjsXLlSohEItSvX18XIZZZlpZG2yjQ5FhaWqJu3bqGDsMssW5Lx9/fH1KpFGPHjkX58uVx8uRJtG7dGgBYrzpizvdWc742feO9TXfMrW5FCgUWxMbBQyLF524uKsv0q9MPbau21cr5PvnkE0gkEkyePBmurq44ffo0vL29AfBzQ1d4byUiMjyd3Im7detWYJtCocC4ceM0PtbAgQO1ERIRUZmS20rM29sbLVq0MHQ4RESkgd4pSeiRnoE1zuXxzMqqwOMKmS1u3e6E2x6JaFbDWSvn/PTTT2FpaYl33nkHjRs31soxiYiIjJlOEmKFDYSvyQD5NjY2GDVqFN577z1thUVEVKaMHj3a0CEQEZGmJJkYlJiAJ1aW2OjspLJIVkxv3EtUYNAvl+DvUxOzezREefuCiTNNjR8/vtTHICIiMhU6SYgtWrRIaX3OnDkQiUSYPn06KleuXOh+IpEI1tbWcHNzQ6NGjeDgoHq8BCo5iUQCa+vSjTVBOSQSCSIjIwHkdCewUvELLpUM61Y9sbGxyM7ORrVq1dQqz3rVHYlEYugQdIafG9rD96DumFXdPjoJB4Uc25zKQ6JinClZek1IEtsAABQK4M9/onH1cTyOz3gXYnHR41K9evUKlpaWcHV1VSsUs6pXI2POnxtERKZCJwmxwYMHK63PmTMHAODn54d69Uo3LTSRsVAoFEhLSxOWSXtYt8WLiYlBt27dkJ2djXPnzqFKlSrF7sN6JTIsvgd1x6zq9s5eAMCXcQnwkEixpkJ5YYZJhUKMzNeDkH9erPHv1ik2Gfb8+XP4+vrCzs4OQUFBqFSpUrGhmFW9EhER5aOX0Ry3bt0KIGfqVyIiKp1Xr16hW7duuHfvHoCccRvPnDkDNzc3A0dGRESlkp0GPDgOIOeP9JHJKeielo7vXCrggr09JPEdIM9SbhXculYFvNey6L+xnz59Cl9fXzx+/BgA0L17d5w+fRoVKlTQyWUQERGZAr3Mn9y2bVu0bdsWtra2+jgdEZFZmzhxopAMA4C7d+/i448/NmBERESkFRFHAUm60qbKMhmaJzkjPXocsmK7Kz1mIRZh/qAmxbYOGzt2rJAMA4CbN29i8uTJ2oubiIjIBOklIUZERNrz22+/KXU/r1OnDtasWWPAiIiISCvC96ncfCBlGGRpDQC5jdL2ce94oFFV1QPv57Vx40bUqFFDWG/YsCGWL19euliJiIhMnFa7TO7fv19YHjRokMrtJZH3WERE6lAoFDj65Ch6efSCSMWgxKasWrVqCAoKQufOnWFhYYEzZ86wSzoRkanLTAIeniiw+bHIGeGyxgW2V3GyxXS/Bmod2sPDA2fOnMG7776LChUq4PTp00VOdEVERFQWaDUh9uWXX0IkEkEkEiklsXK3l0T+YxERqeNR4iN8cf4LZEozMbj+4OJ3MDE1atRAUFAQrK2t1Z5lkoiIjNj9w4Asu8Dmg5KuAAr+Hf1t/8ZwtFH/T/m6devi7NmzcHZ2VnuWSSIiInOm9UH1C5uBhjPTEJE+ZUgzAABLg5eio3tHuNqb3x//Hh4ehg6BiIi05U6gys0HZe0LbOvcwBW9mxQ/u3B+9evX13gfIiIic6XVhNjp06c12k76JxZz2DhtsbCwELqpWVhYGDga86LNuk3JTsGia4uwvIvpjZVy7949hIeH47333tPK8fia1R1zvrea87XpG9+DumPydZsWBzw+U2DzPXkNPFIod4m3thTjuwFeKntf3Lp1C8+ePUP//v21EpbJ16sR472ViMjwtJoQc3d312g76R//mNEeCwsLjr+hI+rWrUQuwY/XfkRsemyR5U4+PYnT0afRrWY3bYWoc3fu3EG3bt0QFxeHHTt24P333y/1Mfma1R1zvrea87XpG9+DumPydXvvb0RaAC9tbNEuIxNW/79ZVeuwyV3qwsPFocD2GzduoHv37khNTcW+ffvQt2/fUodl8vVqxHhvJSIyPP40QUQm62XqS+yM2ImXaS+LLfvDPz8gOTtZD1GV3u3bt9GlSxfExMRAJpNhxIgRpZ6chIiIjFh4IHY4lcPkKm7oUtMd37pUxGU7WxyUt1Uq5lHJHpM61y2w+9WrV9GtWzckJCRAIpFgyJAhOH78uL6iJyIiMklMiBGRyXqa/FTtsm8z3uLnGz/rMBrt2bNnD+Li4oR1qVSKn3/+mWMxEhGZo5Q3kEZdxEkHewBAsoUF9pVzxMQqboivvR3Av/f+7wc2ga1VwZZFO3fuRFJSkrCenZ2NlStX8nODiIioCFofVL8wGRk5A1zb2dmpfDwgIABHjx5FQkICqlevjuHDh8PX11df4ZUZEokE1tbWhg7DLGRnZyMsLAwA4O3tzXrVInXrNiopSqPj7nmwB31q90GbKm1KG6JOff/990hISMDatWsBAB07dsTBgwdLPFtvLr5mdUcikRg6BJ3h54b28D2oOyZdt3cP4IaNFeJVdKGTptdG7gyTFRzC8G4D1d0gf/rpJyQmJmLz5s0AAD8/P+zZs4efG0bMnD83iIhMhV4SYkFBQZgyZQrs7e1x7tw5ODo6Kj0+Z84coTuQQqFAVFQULl68iBkzZmDixIn6CJGITFBRLcQcrRyRKkktsP27K99hT/89sLW01WVopSISibBq1SpIJBI8ePAABw8eLHDfJCIiM3FnL47/f+uw/KQpTYXlahVPAPhSZTmxWIwNGzZAKpUiNjYW+/btg62t8X7OERERGQO9dJm8ePEiFAoFfH19C3ypCw4Oxr59+wAAtra2aNy4MWxsbKBQKLBq1So8ePBAHyESkQkqKiFWvVx12FkWbJH6NPkp1oWu02VYWiEWi/Hrr7/iyJEjTIYREZmrpOeQPvsHp1QkxORSB8jS6gAAxHZRsLWKK1AmLwsLC2zatAn79+9nMoyIiEgNekmI3bp1CyKRCD4+PgUe27VrFwDAzc0NR48eRWBgII4ePYqqVatCLpfjr7/+0keIRGSCopKjCmxrV7UdwsaEYXf/3ZjaYqrK/Tbd2YSI+AgdR1d6YrG40G7mRERkBsL34ZqtLRJUdZdMaQIgZ7tV+RC1DmdpaQkbGxttRkhEJsTX1xeenp748kvVrUmJSJleEmLx8fEAgNq1axd47MKFCxCJRBg5ciSqVKkCAKhatSpGjhwJhUKBa9eu6SNEIjIx6ZJ0vEl/U2B7LadawvKIhiPg7eJdoIxMIcO3l7+FVC7VaYzFOXHiBIYNG4bs7GyDxkFERAZyZy9OOBbSXTL5/z+/RFJYOeWM43XgwAGMHj0aUqlhP7+ISD3Xrl2Dp6en8C8kRL3kNhHph14TYg4ODkrbHz58iISEBABAt27dlB5r0qQJAODly5d6iJCITM2zlGcqt3s4eQjLFmIL/K/D/2ApKjhc4t24u9h2b5uuwivWkSNHMGDAAOzatQsffvghB9clIipr4iIheXkTp+wLtgSWSx0hS8/5IdnS8R5EFhmIuZ2C9957DwEBARgzZgxkMpm+IyYiDeUODZQrd9xsIjIOekmIWfx/M/C800EDwI0bNwAAFStWRJ06dZQeK1++PAAgKytLDxESkalR1V0SUG4hBgANKjTAOO9xKsuuubkGz5JVJ9Z06eDBgxg8eLBwf9u3bx/8/f35iz8RUVkSvg/X7GyRVEx3ScvyN5F0LQmXfnsifE5s374dH3/8MeRyuT4jJiINZGZm4vjx4wAAe/uclqDHjh3Tac+AoKAgREREYPHixTo7B5E50UtCzM3NDQBw7949pe1nz56FSCRCq1atCuyTkpICAKhQoYLuAyQik1PYgPp5W4jlmth0ImqXL9hlO1OWiT77+sB7i7fwz/+Iv7ZDLaBixYqwsrJS2mZpqZdJf4mIyFjcCcSxwmaXTM6ZXVJkkQZLxwhYOFpAbCFSKmNpaQmFQqHzMImoZE6ePIm0tDQAwNdffw0gp4FIUFCQIcMiojz0khBr3bo1FAoFtm3bJnSfDA0NxcWLFwEAnTp1KrBPZGQkAMDV1VUfIZYZFip+haSSsbS0FMYDYDJDu9SpW1UJMUuxJao6Vi2w3drCGv9r/z9th1li77zzDg4fPiwMmD969GgEBATo/HXE16zumPO91ZyvTd/4HtQdk6vbmPuQxITjtL2q2SXLQZbuAQCwdLoNkUgGx8aO6DS1DqytrQEAkyZNwu+//67z96fJ1asJ4b3V/OV2j/T09MTQoUOF8bTZbZLIeOjlk23EiBHYt28fnj9/Dj8/P3h4eCAyMhJSqRTly5dH7969C+zzzz//QCQSoW7duvoIscwQi/WSAy0TxGIxHB0dDR2GWVKnblV1maxRrgYsxapvay0rt8Qwz2H4K8I4Zq7t3LkzDh06hL1792LVqlV6+cOYr1ndMed7qzlfm77xPag7Jle34YG4YmeLFIuC7y9pchPk/ma9O+0omj+JznmgURsc2rsCQUFB+OmnnyASiQrsq20mV68mhPdW8xYTE4MrV64AAAYMGCD8v3LlSly8eBHx8fGoWLFiofu/efMGAQEBuHTpEqKjo5GZmYny5cujUqVKqF+/Pjp27IgePXoUeH/6+vrixYsXGDx4sMpukzExMTh58iSuXr2K+/fvIyYmBlKpFBUqVECTJk3Qv39/9OrVq9DX59WrVzF69GgAwNatW+Hj44MjR47gr7/+QkREBNLT01G1alV069YNEyZMgLOzc0mqj0hv9JIQ8/LywhdffIEff/wR6enpuHv3bs7JLS2xYMGCAm/klJQUnDt3DgDQtm1bfYRIRCZGVQux/OOH5Tej5QyceXYGMekxugpLI76+vvD19TV0GEREpE8KBXBnL44X012yjuglmokilR7r168f+vXrp/MQiah0Dh48CJlMBrFYjP79+wMA+vfvj1WrVkEikeDQoUNCYim/4OBgTJw4EampqUrb4+LiEBcXhwcPHuDw4cOoUKECunbtqnZMMpkMnTt3Vjn2YExMDIKCghAUFIQ9e/Zg9erVBSbEy08ul2P27Nn4+++/lbZHRUXhjz/+wKlTp7Bt2zb2+CKjpre2zx999BHat2+P48ePIzY2Fq6urujbt2+BwfSBnMxz7iyTmrzJqXgcfFV75HI50tPTAeQMlMlf+rSnuLpNzExEUlZSgf1UjR+Wl6O1I75p9w2mBk3VWqzFSUxMNJpfx/ia1R1zvrea87XpG9+DumNSdfs6DNlxj3CmZvUCD8klTpBl5Py4M8TiAvTQCKxIJlWvJob3VvN24MABADmNOypXrgwAqFGjBlq0aIGQkBDs379fZUIsOzsbM2fORGpqKhwcHDB8+HD4+PigUqVKkEgkeP78OW7evImTJ09qHFPumIPt2rXDu+++iwYNGqBixYpIS0vDs2fPsHv3bty8eROXLl3C999/jyVLlhR5vJUrV+LmzZvw8/PDoEGDUK1aNcTGxmL79u04e/Ysnj59ikWLFmH58uUax0qkL3odDCB3DILi+Pn5wc/PTw8RlT2colt7pFIpIiIiAADe3t7CuB5UesXVrbozTKrSpUYX9PLohWNRx2BjYYMsme5msv31118xb948nD59Gt7e3jo7j7r4mtUdc763mvO16Rvfg7pjUnV7Zy8u29mp7i6Z4o3c7pKDLC7pObCCTKpeTYwx3lu1PbFQb4/eGNl4ZJFl/rz7J45GHdXqebf12abV42nq3r17wvsmt7tkrgEDBiAkJATh4eF49OgR6tWrp/T4jRs3EBOT05Php59+KtA4pHnz5ujXrx/mzJmDzMxMjeKysLDAsWPHUKtWwb+X27Zti6FDh2LVqlVYu3YtDhw4gE8//RQeHh6FHu/mzZuYMWMGPv30U6Xt7777Lj755BNcvHgRx48fL7Z7KJEhcXRMIjI5hc0wqU5CDAD+2/a/aFChAYKeBeFO7B1thiZYtWoVpk+fDgDo1q0bzp49i8aNG+vkXEREZCIUCiA8EMcdVXeXlPx/d0n5izvIrBADVGSLLNKf0LehWj1eM9dmxZZ5mfZS6+c1tNxB821tbdGzZ0+lx3r37o0ffvgBEokE+/fvx6xZs5Qej42NFZbbtGlT6DksLS01Ht9PJBKpTIblNWXKFGzfvh0JCQkICgrCuHHjCi3r5eWFSZMmqTzPRx99hIsXL0IqleLmzZvo1q2bRrES6YvBPmUVCgWio6MRGhqK0NBQREdHc+poIlJLYQmx4rpM5nKxc8H4puMhFunmFrhr1y4hGQYAb9++ha+vL96+fauT8xERkYl4cQNZSdE4Y29X4CG5pDzkGTUAAPG3T8N3SxqSs/i3MZEpkUqlOHToEICcoX/yJ62cnZ3RuXNnADnjjOXvOpt3vK29e/fqNFa5XI43b97g8ePHePDgAR48eIDIyEhUqVIFAHD//v0i9+/fv3+hk3t4eXkJy8+ePdNe0ERapvcWYhcuXMC2bdtw9erVAs08bW1t4ePjg5EjR6Jjx476Do2ITISqhJi9pT1c7FwMEE1Bffv2RadOnXDhwgVh2+TJkzmoKBFRWXdnLy7a2SFNxVhc0uSc7pJySRbSIy7iu87WcLIx8CBiRKSRixcvCq288neXzDVgwACcOnUKr1+/xtWrV9G+fXvhsVatWqFGjRp49uwZFi5ciIMHD6J79+5o3bq1VrotKxQK/P3339izZw9CQ0OL7HaZkJBQ5LFUjQWeK+/4uWlpaRrHSaQvekuIZWdnY86cOThy5AgAqGwNlpGRgXPnzuHcuXPo06cPFi1axLEKiKiAwmaY1McU9OpwcHDA4cOH0bNnT1y5cgULFy7EnDlzDB0WEREZklwOhO/DiUJml8ztLpnx8B+sGFID0zxf6zM6ItKC3O6Szs7O6NSpk8oyXbt2hZOTE5KTk7F//36lhJiVlRV+++03TJs2DZGRkQgLC0NYWBiAnMYjrVu3xqBBg9CnTx9YWFhoFFtWVhY+++wznD9/Xq3yxY1RZmtrW+hjeSfg4AQSZMz0lhCbNWsWTp48CYVCAUtLS3To0AFNmzYVWky8ffsWYWFhuHTpEqRSKY4cOQKpVIqVK1fqK0QiMhEudi6IzYhFXGacsE3d7pL6Uq5cORw9ehQHDx7EyJFFDyhLRERlQPQVIOUV5qaJ4ZORieMO9rhqZwuZSAS5xBnyzJzukp/1a4Np4hDgORNipF9NXZtq9XjVHKqpVUbb5zWUlJQUBAUFAciZZbxJkybF7nPixAnMmzcP9vb/Jsrr1auHgwcP4syZMwgKCkJwcDCePn2KzMxMXLx4ERcvXsSmTZuwfv16VKpUSe34fv31VyEZ1rZtW4wYMQJeXl5wcXGBra2tkMTy9/dHcHCwJpdOZLL0khA7e/YsTpw4AZFIBB8fHyxcuBDu7u4qy758+RJz587FP//8gxMnTuDcuXNCP2siIgD4rftvAICU7BREJ0fjafJTo+kumVf58uWZDCMiohx3csYDKi+XY0hqGoakpiFBLMYEx6G4le0OQAQXRxt89XFvYNM6w8ZKZZIhZmcc2XhksTNRmoqjR48iK0uz2cvT09Nx4sQJDBo0SGm7hYUF/Pz84OfnBwCIiYkRhh4KDw9HeHg4vv32W6xdu1at8ygUCuzZswcA0Lp1a2zZskWpFVdeSUlJGl0DkSnTS0IsMDAQANCwYUNs2LABVlZWhZatVq0a1q9fjw8++AD379/H3r17dZ4Qe/HiBQICAnD27Fm8fv0a1tbWqFGjBnr37g1/f3/Y2RUc+LSkLl++jL///hs3btzA27dvYWFhARcXF3h6eqJdu3YYOHAgHBwctHY+InNWzrocvFy84OXiVXxhHVAoFDhx4kSBGYSIiIiUyKTA3QMFNlewdsLOqWtxOSoZgSEvUL2CHSwtOLMkkSnK7S7p6uqq1lAZP/74I16/fo0DBw4USIjl5+bmhqFDh2LAgAEYNmwYwsPDcfbsWWRmZhbZdTFXYmKiMLlTr169Ck2GpaWl4cmTJ8Uej8hc6CUhdvv2bYhEIowdO7bIZFguKysrjBs3DrNnz8bt27d1GltQUBBmz56N1NRUYVtGRgaSkpJw584d7N69G7///nuxU9QWJykpCXPmzMHp06cLPJaamoqoqCgcP34cLVq0QKNGjUp1rqKoU/+kHmtra7Rq1crQYZglU6hbhUKB6dOnY/Xq1ViwYAG++uorQ4dULFOoV1NlzvdWc742feN7UHeMvm6jzgPpsQW3Nx4ACysbdKrvik7180y88skp/cVWBKOvVxPGe6t5efbsGUJCQgAAPXv2RN++fYvd59atW9i6dSv++ecfvHnzBpUrVy52HysrK7Rp0wbh4eGQSqVITk5WKyEmk8mE5YyMjELL7d69G1KptNjjEZkLvfwEFR8fDyCnP7S6cmetKG52i9K4e/cuZs6cidTUVNjb22PmzJnYuXMnNm/ejA8++AAAEBUVhQkTJiglzDSVkpKCsWPHCsmw7t27Y9myZdi1axf27NmDNWvWYPTo0cIUt0Rk3ORyOSZPnozVq1cDAL7++mssXbrUwFEREZGxkoftUf1Ak6H6DYSIdOLAgQPCpHHq9hzILSeXy3HgQE4L0tzxwgqTnZ2N69evAwDs7e1RsWJFtc5VsWJFODk5AQAOHTqE7OzsAmVCQ0M5fjeVOXppIWZvb4/k5GQkJiaqvU9ycjIAaLW7Yn4//PADMjMzYWlpiY0bN6JFixbCY+3bt0etWrWwdOlSREVFYdOmTZg6dWqJzjN//nyEh4fD2toaK1asQLdu3ZQe9/b2Rvfu3TF37lyl7D0RGafz58/jt99+U9r25ZdfYsCAAfD09DRQVEREZJSk2ZCF7S/wK/SbNAUyRTVQuj4IRGQMchNalSpVQuvWrdXap2XLlnB1dcXbt2/x999/Y8KECbhy5Qp++eUXtG7dGp07d4anpycqVqyIzMxMREVFYefOnQgPDwcAvPfee7C0VO/rvFgsRv/+/bFt2zZERERg+PDhGDt2LGrVqoXU1FScO3cO27dvh729Pdzc3BAVFVWieiAyNXpJiNWuXRu3b9/GkSNH0KFDB7X2OXLkiLCvLoSGhgqzZwwdOlQpGZZr3LhxCAwMRGRkJLZu3YpJkyZp3Lw5ODhYuEHOmDGjQDIsL5FIpPZNraSYcNMemUyG2Nic7g8uLi4aT31MhTP2uu3SpQtWr14tJMktLCywbds2o0+GGXu9mjJzvrea87XpG9+DumPUdRsZBCtZWoHN6R7dUbt2XQMEpD6jrlcTx3ur+bhx4waio6MBAH5+foWOz5WfWCxG9+7dsX37djx8+BB37twBkNNi7Nq1a7h27Vqh+3br1g3/+c9/NIpz5syZCAkJwb1793Dnzp0C+zs7O2P16tVYtWoVE2JUZuily6Svry8UCgUCAwOFAfaLsn//fuzduxcikajIBFJpnDr179gMQ4eqbq4uFouFAQ6Tk5Nx9epVjc+zbVvObC3lypUzitnm5HK5oUMwGzKZDM+fP8fz58/5R42WmULdfvbZZ1i+fDksLS3x119/YdiwYYYOqVimUK+mypzvreZ8bfrG96DuGHXdhqv+27d2P82+zBqCUderieO91XzkNn4A1O8umatHjx7C8v79+zFu3DisXr0aw4cPR/PmzVGtWjXY2NjAxsYG7u7u6N27N9atW4dffvlFrbHD8ipXrhx27NiB6dOno0GDBrCxsYG9vT3q1q2LcePG4cCBA2jTpo1GxyQydXppITZy5EgEBAQgNjYWX331FY4fP46hQ4eiWbNmqFixIkQiEeLi4nD79m3s3bsX58+fh0KhQOXKlXWWRLpx4waAnO6cXl6Fz1CX96YQEhKCjh07qn2O7OxsYdywDh06wMbGBkDOHxcxMTGQyWRwdXUVthNRyQz+5RJuRicqbWtR0xn/x95dx1ddfw8cf91td92w0TC6RnfDaBQlBAlRUlBRLFTE+irGV7+KASIoSIcIUlLSKR0bNRpGjQ02GIubvz/222WXe+92N25sd+f5ePBg95Pn0/eezzv+erlVjvM9bvfib7zxBj179sxT+4hCCCGKEHUanP6bPwL8qahW0zA9A3eAgNJQrrmzoxNC2MCnn37Kp59+mq95W7RowZkzZ4yGdenSxShRlhdbtmzJcbyPjw8vv/wyL7/8ssVp5s2bZ3Fcs2bNTOK1xNrphHAmh7UhNn36dIYNG0ZycjI7duxgx44dFqfX6/UEBQUxffp0u7Uhdv78eQDKly+fYzXFrMb9s89jrdOnT5ORkQFAtWrVSElJ4YcffmDFihWGNtKyegoZM2YMzZo1y+tmCFGknLt7jsPxh6kQWIEKgRUo4VsChULh1JgkGSaEEMKisxt5oH7Af0uVReWmoLhGS+cHqXSt2I4GCgdV1RBCCCGEWQ5JiAHUqlWLVatW8fnnn7N582aLxa7d3d3p1KkT77//vlVdz+ZHRkaGoffK3Hp2DAoKwtfXl9TUVG7evJmn9WRPoOn1evr27WtSH1utVrNnzx727t3Lm2++yYsvvpindeSVWq0226sIgIeHh6HOu06ny7XLXU9PT8PfWq02x6L0CoXCqP01jUaTY1FxNzc3o0SlWq029NySW+x6vR61Wp1j7Eql0pBIyW1bLcWefR3Z/85r7O7u7kZtclg6PlmKwnEyN1yn07Hj6g4mH5lsGObt7k35gPKo9S8Axu2a6PX6HPelNbFnZGTw8ccf88477xAWFuYyxwnM72NzsTvqerI29oJ6PeW2jwozeW5kkueGeQXlOFl6bjj6fpSamsonn3zCBx98QEhICO4xy9nm64PKLfPcSfBwZ1FQAIvu7KbDuv/xnzZj8fPyMNnWgnacsrbXEnluWFYUnxtCCFFYOCwhBlCiRAl+/PFH4uPj2b9/P2fPnjX0PBkcHEzVqlVp2rQp4eHhdo3jwYOHDZv6+vrmOr2Pjw+pqamkpqbmaT3JycmGv3/99VcyMjJo06YNr732GjVq1CAlJYUNGzbw7bffcv/+fb799lsqVapEp06d8rSevMiplFv16tXx9/cHMr/Q5VbMtVGjRoa/ExISiIuLszitn58fNWrUMHy+fPlyjr2OhoeHU65cOcPnU6dO5fjFoVKlSoSEhACZXzCio6NzjL1OnTqGLyjJyclcuHDB4rRKpZK6desaPt+4cYP4+HijaU6fPm34Ozg4mMqVHzaSe/78eaNz7lFly5Y1Sv7mFntROk7ZJScnc+TSEaNh6dp0ziadpcQD00Z+1aqcl5/bccrIyGD8+PHs2bOH9evXs2LFCqpVq2YYX5iPExifs49y9vWUXWG+nlyFPDcyyXPDvIJ2nLJz9P0oNTWVN954g0OHDrFt2zb+WjKPirEb2BDqZ3Z5a/YVY+PubXzVqRjlAj0K9HECeW5kkeeGEEK4DocmxLKEh4fz5JNPOmPVAIZqjIBVvUZmPbDT09PztJ7sCbSMjAxatWrF9OnTDW+hQkNDGThwIFWrVmXIkCHodDq+++47Onbs6PRqYEIURLdUt0yGeRNC/AM3wHaN06anp/PWW28ZOtI4deoUAwcOZOvWrQQGBtpsPUIIIVzDgwcPGDduHEePHgXg2LFjzJ7wLG/XS2eXb3GT6bUZ4egySuDno6B0gPTcKIQQQjiDQp9TGWIXdefOHVq0aAFAjx49mDx5co7Tt2zZksTERKpVq8bq1autXs/MmTP5+uuvDZ//+usvatWqZXba1157jQ0bNgCZPZVkf4v3uFQqleFtV+XKlS2WiivIRfULatWXrLelNWrUMExTWIrqm1NQjlP2fZv1Flmn09H5z87Epxm/FfZS1yDh3FDDZ093NzrWDOenAfXyHfuVK1fo0KGD0VvcgIAAtm7danjTWxiPU/Z7QfZzNqfYpeqLMUvHKTU11VCSKnvJh8JKnhum5LlhXkE5TpaeG466H509e5aoqCijkkt/DwlEW9eX98NME2IZtzuhSujEqNYRjO9S1WRbC8pxkueGqcL03Dh79iwajQYPDw+qVq1q8+ULIYQj2eOe5pQSYgCJiYnExsaaVJksXtz0S4Ot+fk9LLpuTTXItLQ0wLrqlZbWExoaajEZBtCmTRtDQiw6OtqmCbHslEqlVQ9cNze3PD2YH/0ykZucOjIwx5qSfFkUCkWeYs/rtmbFrlAoDMfY09PTYox5iT1rWdZy1eOUfd9mfWFO16abJMMAHqSEGH1WaXV4uJu2fZKb7LFXqVKFLVu20K5dO27cuEFQUBAbNmwwqvZQGI+Ttefso/M44nqyVkG9nly5LRh5bpiS54ZlBem54cj7Ue3atdm8eTMdOnQgISGBiqVC6VYZXvMzX11Sc68OAP2alDcbY0E5TvLcyJk8N4QQonBzaEJMr9ezePFiFi5cyLlz58xOU6VKFQYOHMiAAQMMb19szcvLi+DgYJKSknJtKD85OdmQNMutAf5HlSpVyvB3bvNmH5/V4L895PVLgbBMqVTaLXFZ1Jnbt1fuXzE7rSbDNIkeWTqzWuOdByp+3HyWYF8lr3eqZjJdTqpWrcqWLVvo06cPc+fOpXHjxnmavyCSc9Z+XPne6srb5mhyDdpPQdi3kZGRbNq0iYEDB7Lx6xdIOfwlu328Tabz0JRGpypBZJlAqpUIcEKk1isI+9VVyb1VCCGcz2EJscTERMaMGUNMTAyAxaLL586d47PPPmPZsmX88ssvhIWF2SWeKlWqcPDgQa5cuWIodmdO9kY+szfQae06suRUjB0wKvqdlzd8QhQVl+5dMjtcpzJNiFUJ92fatvP8vPUc9zM0eCvdeLZJOUoF+eRpnTVq1CA6OlquSSGEEFapV69e5nNjYT9W+PqiMdMm7OjGvWnTvTUp6TlXtRNCCCGEfTkkIaZSqXjhhRc4f/48er2e0NBQunfvTp06dQxVJBMSEoiJiWHdunUkJiZy4sQJhg0bxvLly+1Sp75Ro0YcPHiQ1NRUTpw4Qb169cxOd+DAAcPfDRs2zNM6ypQpQ+nSpbl+/TrXrl1Dr9dbbCz/ypWHpV+y93QjhMh0Ofmy2eHmEmKvLTrCA9XDJHS6Wse3G2P5Xz/T61yn0+VYGlWSYUIIIbLL9bmRfhcubGNDeKjZ8V0julIxKMhe4QkhhBDCSvapk/iI2bNnG6pIPvPMM2zatIkPP/yQXr160bp1a1q3bk2vXr344IMP2LRpE/379wcyu0mePXu2XWLq1KmT4e9ly5aZnUan07FixQoAAgMDadasWZ7X06VLFwBSUlLYu3evxek2btxo+Dt7O0W2llvDn8J6Go2G8+fPc/78edmvNmZu316+ZyYhpndHrw4xGZyqNi2RuexwHCev3zMadvv2bZo3b866detsE3gBJ+es/bjy/nTlbXM0uQbtx1H79vr16zRp0oRt27ZZnujUKpIVev41U12yekh1KgZVtFt8tibnrP3I/hRCCOdzSELs77//RqFQ0LJlSyZNmpRj4/Q+Pj58+umntGrVCr1ez99//22XmOrWrWtoE2jZsmUcOXLEZJpZs2YZen95/vnnTer679u3j+rVq1O9enXee+89s+t54YUX8PLyAuDLL78kJSXFZJqVK1eyf/9+ANq3b2/U9pitFcFORe1Gp9ORlJREUlJSjr0dibwzt2/NJcR0qmI8ehvrHlmSPg3Kmkyr18PXG04bPt+6dYsOHTpw4MABevfuzT///GPbjSiA5Jy1H1e+t7rytjmaXIP244h9e/XqVdq1a8fhw4d54okn2Llzp/kJY5azxdfHbHXJrhFd7RKbvcg5az9ybxVCCOdzSEIsqzrgoEGDrJ4na9rsVQltbeLEiXh7e6PRaBg+fDjTp0/n6NGj/Pvvv3z00Ud88803AERERDBs2LB8raN06dK89tprAMTGxvLMM8+wbNkyYmJi+Pfff/nss8+YMGECAP7+/oa/hRAP6fV6Lt67aDJca6a6ZGSZIN7uWg0vD+PbW7faJfm4Z20gs03D9u3bc+LECQAyMjJ46qmn2L59ux2iF0IIUdjdvHmTdu3aGWo8pKam0qNHD/bt22c84b0bcGkX6/3Mv/ztEtHF3qEKIYQQwkoOaUPM09OT9PT0PJV8yprWHu2HZalVqxaTJ09m/PjxpKSk8N1335lMExERwYwZM/D398/3ekaOHElycjK//vorFy9e5P333zeZplixYkydOpWIiIh8r0cIV5WUkcR91X2T4XoLCbFSQT6MbFORqVvPU79cMB88UZPGEQ/bcgkJCaFp06acPv2wxFjJkiUpX768fTZACCFEoVa8eHEaNmzIxYsPX86ULl2asmUfKZF8ciV33RTsM1NdsmZoTSoEVrB3qEIIIYSwkkMSYhUrVuTYsWPcuHGDWrVqWTXPjRs3DPPaU1RUFKtWrWLu3Lls27aNW7duoVQqKV++PN26deO5557DxydvPdOZ89ZbbxEVFcWiRYs4ePAgt2/fxsvLi4iICKKiohgyZAgBAQW7620hnMVs+2GYb1C/dulAAMa0q0zt0kF0jyxp0pmFm5sbs2bNQq1Ws2jRIipXrsyWLVskISaEEMIsDw8PFi1aRL9+/Vi5ciU1a9Zky5YtlCxZ0njCmGVs9vNBa6a6pJQOE0IIIQoWhyTE+vTpw9GjR1m8eDEdO3a0ap7FixejUCjo1auXfYMjszfICRMm5Lm6YrNmzThz5ozV0zdo0IAGDRrkNTwhirxL9y6ZHa7LME6IlQryprh/Zpt9Ad5KetSxXCrV3d2duXPnUrp0ad544w3KlCljs3iFEEK4HqVSyZIlS3jvvfeYMGEC4eHhxhMkXYG4/WwoGWZ2/sLWfpgQQgjh6hzShli/fv1o3bo1u3bt4pNPPiEjI8PitCqVik8//ZSdO3fSqlUrnn32WUeEKIQowCyXEDP+0VG7dN66sffw8OB///ufJMOEEEJYxcvLi8mTJ5smwwBO/EWimxv7vU2rSyo15Vl/RE1SqsoBUQohhBDCGg4pIXbw4EGGDRtGcnIyS5YsYdOmTXTv3p06depQrFgxFAoFCQkJREdHs379ehISEqhTpw7Dhw/n4MGDFpfbpEkTR4QvhHAycwkxvdYLvda4bb86ZUwTYlevXqV06dK4u7vbLT4hhBCu48qVK5QtWxY3tzy+N45ZxmY/X3RmqkveT6zN52dP0bFmOMG+9msfVwghhBDWc0hCbMiQIUZt+CQmJjJ//vwc54mJiWHEiBEWxysUCk6ePGmzGIuKR9tSEvnn5uZmeEOc5y/NIkeP7ltzVSYz2w8zPp8jywQafT558iQdO3akS5cuzJo1q8gnxeSctR9Xvre68rY5mlyD9mOrfXvkyBE6d+5M//79mTp1qvXnf+J5uHGMDSXNlBwDNPfrUL9cMJXC8t9JkzPIOWs/RfXeOnPmTOLi4oyGlS1bNsfffUIIYS8OSYgB6PV6R61K5MDDw2GH3OV5eHhQrlw5Z4fhkrLvW51ex5V7V0ymMdegfmS2EmLR0dF07NiR27dvM3fuXDw8PPj111+NvtCnqjSkpGsIDzSt3uKK5Jy1H1e+t7rytjmaXIP2Y4t9e/DgQTp37kxSUhLTpk3Dw8ODH374wbrERcxykt0UHPb2MhmlTSuHXh1K34aFr3q+nLP2I/dWIQqv69ev88svv7Bnzx5u3bqFSpVZHX7q1Kl06tTJJutYvny5oY3zzZs3m/ZqLGzCIXfiuXPnOmI1QggXdOvBLTK0pu0OPpoQK+7vRXhA5g+R9PR0unfvzu3btw3jZ82aRfXq1XnnnXfQ6vQsOxTHt/+coXbpIGYNlerXQghRlN2/f5/u3buTlJRkGPbTTz9Ro0YNXn755dwXELOMIJ2ejVev8Y+vLxv8fTni5YVeoUB9rw5KdwVP1i1tvw0QQhQoGRkZ/Pnnn2zcuJEzZ86QkpJCcHAwNWvWpFevXjzxxBM5zl+9enWr1tO0aVPmzZtncfypU6f44YcfOHToEBkZGVSuXJmBAwfSv3//HJebnp5Ojx49uHbtGrNnz6ZFixZWxVMUXL9+nT59+nD37l1nhyJswCEJsaZNmzpiNUIIF5SQlkCAZwD3VfeNhj/aoH6dMoGGt/je3t7MmDGD3r17G97YtGjRgtGjR7M99jZfrj3F6ZuZy7t1L5495xJoWcW0xJkQQoiiISAggF9++YVnn30WrVYLQIcOHXjhhRdyn/nWSbh9CoAwrY5B91MYdD+F5dTlXZ82aO5H0rl6OCF+0naYEEXBhQsXePnll7l48aLR8Nu3b3P79m127NjB8uXL+fHHH/Hz87NbHAcPHmTEiBGkp6cbhp08eZIPP/yQc+fO8f7771ucd9q0aVy7do3u3btLMuwR06ZN4+7du3h4ePD666/TpEkTfH19AShduvC9+CjqJdGkrG4Ro1ar8fSUL2S2oFarOXUq8wtwzZo1USqVTo7IdTy6b3cP2E1SRhKX713mwLUzfLNlF9o045t15CMN6vfo0YOlS5fSt29fmjdvztq1a1l98i7v/xVtsr7P155i9djWuLm5dnsecs7aj1qtdnYIdiPPDduRa9B+bLFv+/bty/z58xk8eDAdO3ZkxYoVhh85OTqx3OzgnapWqNNbAdCnYeH8gSHnrP248nOjKEtMTGT48OHcuHEDgG7dutG7d2/Cw8OJj4/nr7/+Yv369ezatYs333yT6dOn57i8gQMHMmjQIIvjfXx8zA7X6XS8//77pKenU7FiRd58801CQkJYvnw5y5cvZ86cOXTt2pVGjRqZzHv58mVmzZqFr6+vIVEiHtqzZw8AHTt2ZNSoUXZbT58+fejTp4/dli8ySUJMiHzS6/WGLzPSRp5tPbpvFQoFId4hhHiHcOVGOKrbpo0S1y5t2sPkU089xcaNG2natCl+fn48UdebrzecJinV+Evoiev3WHnsGr0bFM4fLNaSc1YI55Jr0H5stW8HDBhA8eLFad26Nd7eVrQvqddDzDKTwRl6Jf/oMn9oBvko6VAjzGSawkDOWSHyZurUqYZk2NixY3n11VcN42rVqkX79u358ccfmTp1Ktu2bWP9+vV069bN4vKKFStGtWrV8hzH4cOHuXz5MkqlkpkzZ1KmTGYbhk2aNCE+Pp5du3axfPlyswmxzz77DJVKxWuvvUaJEiXyvG5Xd+vWLQAqVqzo5EiELUh3MUKIQiXmWrLZ4XXKmibEILPKS1Zx9CAfJa9FVTU73Tfrz5Cu1tomSCGEEIVWp06drEuGAdw4BncumAzeqqtPCpmly3rWK4WXR9Hu5ViIokCr1bJq1SoAypQpY7H9wVdeecVQtW7GjBl2iSWrZGeDBg0MybAsWe2XZU2T3caNG9m5cyeVKlVi6NChdomtsMt6SSAdY7gGSYgJIQoVcwmxQC83SgdZ9+PlueYVqFDMtArM9eR0Zu2+aGYOIYQQrmTjxo1cvnzZNgszUzoMYLX2YZs7rl76WAiR6dKlS9y/n9lGbcuWLXF3N58Id3d3p2XLlgCcOHGCq1ev2jyWrDjCwkxLpxYvntlubkpKitHwtLQ0vvzySwA++ugju1WRPnToEBMnTqRr1640bNiQyMhI2rZty+jRo1mwYAH37t0zO59KpWLBggUMGTKE5s2bExkZSatWrRg1ahQrV65Ep9NZXOdPP/1E9erVDZ0VZGRk8Ntvv9G7d28aNGhAgwYNeOaZZ5g/fz4ajcZk/uXLlxvNDzBlyhTDsOrVq/Pee+895p6xvM64uDibb9O+ffuoXr26UbXYjh07Gm1T9erV2bdvn023q6CRtKYQotDQ6/VmE2LJl2K4eLE6lSpVynUZnh5uvNutBi8vOGwybtrW8zzbuBzF/L1sEq8QQoiCZeXKlfTr149y5cqxffv2x2s8WK+HE3+ZDE7Ve7FFVx+AiGK+NCwfnP91CCEKjey91BYrVizHabOSUpCZICpXrpxNYwkICAAw6nE9S0JCAgD+/sZNkEybNo3r16/brSH99PR0Jk6cyJo1a0zG3bp1i1u3brFt2zbu3LljVNUUIC4ujlGjRnHhgnGJ3ISEBHbs2MGOHTtYsmQJP//8M8HBwTnGkZCQwMiRI01KyEVHRxMdHc2uXbv4+eefcXMrPGWHXHGbHEUSYkKIQiPubhr30k3fcNy7fIIOHaawY8cOKlSokOtyukeWpGH5YA5fSTIafj9DQ9fvd3Dwg862ClkIIUQB8eeffzJw4EA0Gg0XLlwgKiqKbdu25b9XsLgDkGxasmOTriFpZJZa7tOwrKEHZCGEa8veY+Sjpa8elVWCC+DcuXMWp1u/fj3r1q3j2rVruLm5ERYWRoMGDejduzfNmze3OF+NGjUAOHLkCLdu3TJqC+zvv/8GMjvKyHLx4kVDQ/q2LukEmY38v/zyy+zevRuAiIgIBg4cSGRkJD4+Pty+fZsjR46wbt06k3kfPHjA0KFDDSXpOnXqRN++fQkPDycuLo4FCxawf/9+Dh06xJgxY1iwYIHF0nmQ2bbbuXPnGDJkCFFRUQQFBXHx4kV+/vlnzp8/z9atW/njjz8YMGCAYZ5OnToRGRkJQM+ePQHTDg+Cgsw33+II+dmmOnXqsHr1ajZv3sz3338PwMyZMwkPDzdatqv3OikJMSFEoRFtof0w1c1zXLlyhe+//57JkyfnuhyFQsHEJ2rSd9pek3H+3nJbFEIIV6PRaPj888+Nqo2cPXuWadOm8dlnn+VvoTHLuOHuzoKgALqmpBKpUqHg0eqSZSzPL4RwKeXLl0epVKJWqzlw4ECO02Yfn9UIvzmPJssuX77M5cuXWbFiBZ06deKrr74ylAbLrlGjRpQtW5a4uDhGjBjB66+/TnBwMCtWrGDXrl0A9O7d2zD9pEmTUKvVjBs3jpIlS1q1vXkxf/58QzKsc+fOfPfddyY9WLdv355x48aZlGqbMmWKIRn20ksv8frrrxvGRUZG0rVrV8aPH8/q1as5cuQIS5YsybFnzpiYGGbOnEmzZs0Mw2rXrk3r1q154oknSEhIYOHChUbJo8DAQAIDA42Wk98OD+whP9vk6+tLtWrViImJMQyLiIhw+QTYo6TMnBCi0LDUoL7q1nkGDRrEN998Y/WyGlUIJdjHtG2EEF9PM1MLIYQozDw8PFi/fr2h1ATAyJEj+c9//pO/Beq0cGIFG/18mRMUyKAyJelWtjRfhhRnh2dxQE/TiFDKhZq2WSmEcE2+vr6GhMSZM2fMVg0EWLNmDbGxsYbPDx48MJnGx8eHJ554gkmTJrFgwQJWrFjBrFmzGDNmjKFK4KZNm3j55ZcNjbxn5+bmxueff46npydnz57llVdeYfDgwSxduhSAIUOG0LhxYyCzFNquXbvs1pC+Tqdj5syZAJQsWZL//ve/Jsmw7HFnL82mUqn4888/AahatapJVUrIfNH9ySefGPbLggULcoznueeeM0ocZQkODqZPnz4AxMbGGpXiK+hccZscRYpCFDE5FR8VeePh4WFos0p6GbGtrH0bfSea6DvRRARFEOodSsx1M41sqtMY9HRXZs2cmefzu3SwN0lppl8iXJWcs/bjyvdWV942R5Nr0H6s2bclSpRgy5YttG/fno4dOzJlypT8t6dyeQ+k3GR96Yc/3K4rPVgY7IEyaDqqsxPp3bBu/pZdgMg5az9yb3VNr776Kv/++y8ajYb33nuPK1eu0KtXL8LCwrh9+zYrVqzg559/NpQkg8y2tR61Y8cOkxJJAK1atWLIkCGMGjWKkydPsn//fhYtWsTzzz9vMm3z5s1ZuHAhU6ZM4dChQ2RkZFCpUiUGDhxoKCmUmprKV199BRg3pB8TE8NPP/3EoUOHUKvVVK1aleHDh9OjR48875NTp05x8+ZNAPr162dUtTQ3MTExhkb2e/fubfG68ff3p3v37ixatIhz584RHx9vUvUvS1aVR3Nq164NZLZbHBcXZ1SttCBzxW1yFJs+2Zo0aYKbmxuLFi0yatw6q0honTp1rO/GWtiFNKRnO25uboSEhDg7DJeUtW9n75/NgZuZ948AZQApmmC8wiPIiH/CMG3jyiX4/ZtZ+Tq3vZRF68uonLP248r3VlfeNkeTa9B+rN23pUqVYu/evYSEhDxe214xy4jzcCfGy7QTFm1qJZSKQHrUKZX/5RcQcs7aj9xbXVP9+vX5z3/+w8cff4xareaHH37ghx9+MJrG29ubd955h08//RTAbILIXDIsS/Hixfnxxx/p3r07arWa+fPnm02IQebv7+nTp1tc1tSpU7lx4wbdunUzNKS/f/9+RowYgUqlIjg4mKCgIKKjo3njjTe4ceMGI0aMyHU/ZHfy5EnD31ml0qx19uxZw9/16tXLcdp69eqxaNEiw3yWEmI5dcKVvR0wcyX3CipX3CZHsWlC7P79+ygUCpMuT4cMGYKbmxurVq2iSpUqtlylEMKFXU6+bPj7vvo+eN3HTe1jNE2DCsXy/aXyr5dbPVZ8QgghCp/Q0NDHW4BWDadWsdHPfHVIzb26dKlZgiAz1fKFEK7vmWeeoWbNmkybNo3du3eTmpoKZJaybNu2LW+//bZRo/s5Jb8sKVeuHC1btmT79u1cvnzZpOF8a5w/f545c+bg6+vLhAkTgMzqjRMnTkSlUjFkyBAmTpyIQqFg48aNvPbaa0yePJkuXbrkqVfMu3fvGv4OCwvLU4zJyQ+bS8nt3p29587s8z3Kx8fH4rjsvym0Wq01IRYIrrhNjmLThJibmxt6vd6owdIser3elqsS+STHwXb0er2hqLNSqZRepGxIr9czYM0A4tPiTcbpVMWNPkeWcV6PLoWNnLP248r3VlfeNkeTa9B+su/bn3/+maCgIIYNG2aflV3cDqmJbCht+uNTr1eguV+bPg1dozF9OWftR+6trq127dpMmTIFjUbD7du3UavVlChRAq//L1W6cuVKw7RVq1bN1zoqV67M9u3bAfKVEPvss89Qq9W89tprhob0Dx8+zJUrVwgNDeWdd94xXPNdunShffv2bN26ldWrV/Pyyy/nK+bHIfcfYQ82Laubld2Oi4uz5WKFDZlLVor8UavVREdHEx0dbbYxS5F/arWaew/MtBeGaUKsdmlJiFlLzln7ceV7qytvm6PJNWg/Wfv2nXfe4Y033mDEiBHMnz/fPiuL+YsrHh6cNFdd8kFlQr1DaVstb6UgCio5Z+1H7q1Fg4eHB6VKlaJ8+fKGZBjAiRMnDH/XrZu/9gYfJ0G0du1a9u7dS6VKlYxeHpw6dQrITOg92vB9w4YNATh9+nSe1pW92vWjPUjmJnt1v8TExBynTUhIMDufEDmxaQmx2rVrs2fPHiZPnoxSqaRixYpGDXDevn0bX9+897ZTunRpW4YphCgE4g7HQW3T4dkTYn6e7lQqbn3DnEIIIVzXrFmz+Pnnn4HM0jcvvPACHh4eRt3MPzZNBpxabbm65P269KxXGqW7tA8lhDBPq9Xyzz//AJntGjZo0CBfyzl//rzh77yUDnvw4IGhIf0PP/zQ0JA+YOiFMCAgwGS+rGF57akwq1F3gIMHD9K8eXOr581eeu7YsWM5tkF2/Phxs/OJnBX1knc2TYgNGTKE3bt3c+HCBcaMGWM0Tq/XM3z48DwvU6FQGDXEJ4QoGnSeOtzMFGLNnhCrVToQN7eifRMXQgiRKS0tzeizTqfj6tWrtl3Juc2QkcyGYiVNRun1bqjv16Zvw7K2XacQwqX8+eefXL9+HYBnn302Xz2OXr16ld27dwNQvnz5PCXEpkyZwq1bt+jWrRstW7Y0GpeV9MrqFTK7rGH+/v55irVGjRqUKlWKGzdusHTpUoYNG2Z1T5ORkZEEBgZy7949VqxYwbBhw8y2HZySksK6desAqFKlisUG9YWp7CUXVSqVEyNxDpu+vmrfvj0ffvgh/v7+6PV6w78s2Yfl5Z8QougJrGHawKhe54FeHWz4LNUlhRBCZHn55Zd57rnnDJ+/++47xo8fb9uVnFjOJQ8PTnt5mozSPqhClWIliCyT9wayhRCu49atWxbH7d27ly+++AKAiIgIswVGtmzZkmOV2oSEBF577TVDNeZBgwZZHdu5c+eYN2+eUUP62dWoUQPILI2V/YWCVqtl7dq1RtNYy83NzdAz5c2bN3n33XctJl50Op3R/vP09OSZZ54BIDY21lAKODu9Xs9nn31maLx/8ODBeYrPXqpXr0716tWJiopydig5yt7RQW4vkQrLNuWFTUuIQeYJ2LdvX2JiYrh16xYqlYoJEyagUCgYN25cnhv7E0IUTWqdaVslOlUxsufxpUF9IYQQWbK+a4aEhFC1alVeffVV265AlQqn17LB33x1SfW9OvRpUabIVz8Roqh78sknadKkCe3bt6dKlSp4enpy48YN/vnnH1avXo1OpyM4OJgffvjBqHROlkmTJqFWq+natSv169enTJkyeHt7c/fuXfbt28eSJUsMyZ9GjRrlKQH06aefmjSkn12jRo0oV64cV69eZeTIkbz99tv4+/szZ84crly5goeHBz179szzPhk8eDBbt25l9+7d/PPPP/Ts2ZNBgwYRGRmJj48Pt2/f5tixY/z99988+eSTRvfvV155hX/++YerV6/y008/ERsbS58+fQgLCyMuLo758+ezf/9+ABo0aMCzzz6b5/iKspo1a+Ll5UVGRgY//PADHh4elC5d2lASr0SJEnh7ezs5SvuxeUIMwNvb26h+b1b2uVOnTlSpUsUeqxRCuJDTd06j0pu+OfJWKmkXWZKY68lcvZNGHUmICSGEyEahUPC///3PpDFomzi7AdQP2OBnrrqkO9qU2vSq7xq9Swoh8k+j0bB582Y2b95sdnzVqlX53//+l2NJq/j4eObNm8e8efMsTtO1a1cmTZpk9f1uzZo17Nu3j4oVKzJ06FCz07i5ufHFF18wYsQILl26xNixY43Gjxs3jvLly1u1vkeXO3XqVN599102bNjApUuXDCXlcuPv78/s2bMZNWoUFy5cYMOGDWzYsMFkuoYNGzJt2rR8VUEtyvz9/RkyZAi//fYbJ06cMCm1OHfuXJo1a+ak6OzPLgmxRzVp0gQAHx8fR6xOCFGI6PV6MjIy8Pb25kTiCX45+gvb4raZnTbYP4Np/RsBkJSqIsBbaXY6IYQQrkun06FWq82WrLCrmOVcUHpw1syPT+2DKrSIKEfpYPmuK0RRN2nSJHbt2kV0dDS3b9/mwYMHhIaGUr16dbp168ZTTz1l1JD9o7766iv279/P0aNHuXr1KklJSaSkpODr60vJkiVp0KABvXv3zlNj/CkpKfz3v/8F4KOPPsoxida0aVMWLlzITz/9xKFDh1Cr1VStWpVhw4bx5JNPWr8jHuHj48OPP/7Iv//+y/Llyzl06BAJCQlotVqKFy9OjRo16NChA0888YTJvGXLlmXlypUsXbqU9evXExsby4MHDwgKCqJmzZr07NmTnj17mm1fTOTu7bffJiIighUrVnDu3Dnu37+PVqt1dlgOodBLI10uT6VSER0dDWTW+ba2EUORs+z7tU6dOvZ5G+3i9Ho9b731Fv9e+pe6o+uy++buHKcvG1CWdX3WOSg61yPnrP08ePDA0A25K+xbeW7Yh1yDj0+n0/HSSy9x6dIlVq5caajGYfd9m3EfvqnCNH9Pfg4JNhmddr0fX3UZzjONXKtBfTln7ccRz42zZ8+i0Wjw8PAoML3uzZw5k7i4OKNhZcuWNbQxJYQQltjjnuaQEmKWaDQa7t27B0BgYCAeHk4Np0iQfWw7SqWSOnXqGP4WeaPX6xk6YSjb2U7A0wG5JsMAAj2loeLHIees/bjyvdWVt83R5Bp8PFqtllGjRvH7778D0LdvX5YvX46Xl5f99+2ZdaBJZ4NfsMkovc4dj/RIukWaVqUs7OSctR+5twohhPM5/E58/vx5Fi5cyJ49e7h8+bKhF0mFQkGFChVo1aoVAwYMkLbG7EQaerUdhUIhb0ofw4CvB3Cy5kkCCLB6Hg83+fL4OOSctR9Xvre68rY5mlyDj2fcuHGGZBjA2rVrGTx4MEuXLrX/vo1ZxjmlkvNmq0tWo1vNivh7ud4zSs5Z+5F7qxBCOJ9DK9l+++23PPXUUyxcuJCLFy+i0+nQ6/Xo9Xp0Oh0XL15kwYIFPP3003z33XeODE0I4WCjO45Gr5Ua20IIIazzwgsvEBj4sKSwUqnkueees39iIe0unNvMBj9LvUvWpXdD16oqKYQQQhQFDnuV9dlnn7Fw4UJDibDKlStTt25dwsLCALh9+zbR0dGcO3cOrVbLr7/+SmpqKh988IGjQiwSdDqds0NwGTqdjuTkZACCgoKkEcc8imocRfsb7dl+Z7vJOD+lHz7uPiSkJ1i3sN86QdwB42Flm8DITTaI1HXIOWs/rnxvdeVtczS5Bh9PkyZN2LBhA507d0alUrFs2TJDA8923ben1qDXqc0mxPQ6D0KoT6vKxWy3vgJEzln7kXurEEI4n0MSYocOHWLBggUoFAqqVKnCp59+SsOGDc1Oe+TIET7++GNiY2NZsGABPXr0sDityLui0luEI2g0Gi5cuABIQ7P59W77d9m5fCc6Mr8U+iv9GVxzMM9WeZZR60aRgJUJMWEVOWftx5Xvra68bY4m1+Dja968OevWrSMlJYVu3boZhtt138Ys46xSyUVP0za0NA+q0bt+ZTzcXTNRJOes/RTVe6s0ni+EKEgckhBbsmQJkNmDyKJFiwgIsNxmUIMGDViwYAF9+vQhLi6OxYsXS0JMiELmwM0DJKYn0i2iW47TlQsox1NVnmLz5c08V+s5BtccTJBXECqVCgC93h10ShTu6Y4IWwghRCHQunVrx60s5TZc3M4hf/PVJTX36tK7QRnHxSOEEEIIm3FIQuzgwYMoFApefPHFHJNhWQICAhg1ahQfffQRBw8edECEQojHpdfr2X9zP9OOTePQrUOEeIXQtkxbPPQenD59mrp165qdb1zDcYxvMt5sD5La1AjSroxCoUzA3fs6cWk6dp69TcPyIfi5YOPFQgghIC0tjfPnzxMZGensUODUStDrGHg/hTZpaWz082WDny8nvbzQ6zyo7NeUmqWkB2QhhBCiMHJI+e6EhMxqTzVr1rR6nlq1agGQmJhol5iEELah1+vZe30vQ9cPZeTGkRy6dQiAuxl3WXBiAX379qVly5bs2bPH7PzFfYqbTYYB6NJLZ65DXRzN/bpcvlSfITP3czY+xT4bI4QQwqlSU1Pp2bMnbdq04fDhw84OB2KWG/4sq9EyPPk+S67fojPvoLk1kGcaVHZicEIIIYR4HA4pYuHl5YVarSYtLc3qebKmlbYKhCiYshJh045N4+jto2anmbJvCtEbo9Gr9HTr1o1NmzbRtGlTq5b/YeUPmXw7iV0YV5d0d1NQo2TuJU2FEEIULikpKTz55JNs357Z2Urnzp3ZsmUL9erVc05A967DZTMvc8o05rsXhvBxmho3O3dwKYQQQgj7cUgJsbJlM7ui3rJli9XzZE1brlw5u8QkhMgfvV7Prmu7eG7dc4zeNNpiMgxA560jNCoUgPv379O3b18yMjKsXteFJLXJsKrh/ngr3Y0HalVWL1MIIUTBNGnSJEMyDODOnTv069cPjUbjnIBOrAD0psMj+wIQ5KMkwNu0oX0hhBBCFA4OSYi1bdsWvV7P/Pnz2bt3b67T//vvv8yfPx+FQkHbtm0dEKEQIjd6vZ4dcTt4bu1zvLTpJY7fPp7rPG4ZbugyMnuQDAwMZMmSJXh5eVm1vjS1jhv3TXtgiiwTZDrxnYumwzTSEL8QQhQmH3/8MR07djR8Dg0NZfHixXh4OKnNyJhlZgYqoHYvR0cihBBCCDtwSELshRdewN/fH41Gw6hRo/j00085ceIEOp3OMI1Op+PEiRN8+umnjBw5ErVajb+/Py+88IIjQhQizxQKBUqlEqVSiULhunUm9Ho9269uZ9Dfg3hl8yscT8g9ERbqHcobjd5g26BtNPNqRnBwMP/88w8tW7a0ap0KhQIPpZIxTULo36gMkWUCUbpn7uPI0o+0NxZ/GjLumS7kzkXQFc0uzS0pKuesEAWVXIM58/HxYdWqVbRr147ixYuzZcsWq3sat/m+vXsJrpnp2KlCSwgs/fjLLyTknBVCCOHKHPLKLTQ0lO+//56XXnoJtVrNokWLWLRoEUqlkqCgIBQKBUlJSajVmdWj9Ho9SqWSH374gZCQEEeEWGQolVK031aUSqXFnhNdgV6vZ9vVbfxy/BdOJp60ap5Q71CG1R5G/+r98VVmdlG/fPlyLl68aOgowxpKpZLmjerTvNHDYSqNjthb9wkPeKSE2bVD5heiSoHd30Obt6xer6tz9XPWmVz53urK2+Zocg3mztfXlzVr1nDt2jWqV69u9Xw237cn/jI/PLKP7dZRCMg5az9ybxVCCOdzWBn01q1b88cff/Dhhx8SExMDgEql4vbt2ybTRkZGMmnSJGrUqOGo8IQQ/0+v17Pl6hamH5vOqTunrJqnmHcxhkVmJsJ8PHyMxvn4+OQpGWaJp4eb+eqS149Ynmnrl1ClE5RyUoPMQggh8szf3z9PyTC7MFddUuEONZ92fCxCCCGEsAuHNspQs2ZN/vzzT44fP87evXuJjY0lOTkZgKCgIKpVq0aLFi3kTZQQTqLX6xm6fiiH463r6r64T3Gerfgsd7fd5fn+z9u0OkXvn3dz5EqS0bAG5YP56+VWxhPeOGp5ITo1LH8RXtwOSm+bxSaEECL/bt68yeLFixk3blzBrIaXcBZuRjM3MIB7bm50fZBKVbUaKrYF/zBnRyeEEEIIG3FKK6V169aVpJeTaDQaPD09nR2GS9BoNNy4cQOAUqVKOa/RXxtSKBQ0CG+Qa0Is3Cec4XWG0za4LU90fYLo6Gjir8fz3XffPdaPG41Wh4e7GxqNBlWGFT1HajVwMzrnaW6fhi2fQdfP8x2Xq3DFc7agcFoveA4gzw3bkWsQrl27RlRUFLGxsSQmJvLZZ5/ZZLk23bcxy9ED84MCuOHhwfSQICqrVHQqXoIeyReoFFTJJjEXBnLO2o8rPzeEEKKwcEij+qLg0OvNdB8u8kWn0xEfH098fLxRBxGF3Qu1XzCp9pgl3DecCU0nsLbvWjqGdKRbp25ER2cmpL7//nvefffdfJ1j99LV/Lj5LK3+u4X4e+nodDpDm4I5un3aut4k906BizvyHJercdVztiBw5XurK2+boxX1a/Dq1au0a9eO2NhYACZNmsSnn35qk2XbbN/q9RCzjONentzIlvw57+nJ9Nt7eGn1f1Fpis6xK+rnrD3JvVUIIZxPEmJCCCMh3iEMqDHAaFgJ3xJMbDaRdX3WMajmILzcvTh48CCnT582mm7x4sXcuXPH6nUlp6n5YdNZWn+1he/+ieXWvQx+2X7B+mBzqi75qL9egvRk66cXQghhU3v37uXCBeN7/Ny5c7l3z0xPwc4SfxISzrDBz9fsaG1KXTw95OuzEEII4QrkiS5EEaHVafn7wt8kpiXmOu3Q2kPx8fChlF8pPmz+IWv7rGVAjQF4uj+sNvXkk08yb9483NwybyMRERHs2LGDYsWK5br85DQ132+KpfV/tzB5Uyz30h9WG1iw7zK372dYt1E5Naj/qHtxsO5d66cXQghhU/3792fGjBmGz9WrV2f79u0EBgY6MapHxCxDB2w0kxDTa70YULuT42MSQgghhF1IQwBCuDiNTsO6i+uYcXwGl+5dYljtYbzZ+M0c5wn1DuW3Lr9RM7QmSnfL3YIPGjQItVrNF198waZNmyhXrlyOy01OVTNz90V+332R++nm287I0Oj4bfclvuyYmVirU6eO5faLrh81HRZYBlqMhQ0TTMcdWwTVu0Mt6SVMCCGcYeTIkajVaqZOncqmTZsoWbKks0N6SK+HmOUc9/Lklpm2srQptejdIMLxcQkhhB2lp6fz22+/sXHjRq5cuUJaWhoAzz//PBMnTnRydI9v+fLlTJiQ+btg8+bNlC1b1mj8e++9x19//UWZMmXYsmWLyfxZvR6PHTuWV1991f4Bu7ioqCiuXbtG7969+eqrr5wdjiTEILOB13nz5rFt2zZu3ryJp6cn5cqVo3v37gwePBgfH/PtKT2OtLQ0nnzySeLi4gAsXoBC5JdGp2HtxbXMOD6Dy/cuG4YvPrOYoZFDCfUOzXH+umHWdXzxwgsvMGDAALy8vCxOk5yqZuauC/y++xL3M3JvRHbRgTjahRUnwDOHQqxatfkG9Us3gGZjIHad+XbDVr8O5ZpBQAH6ESaEEEXISy+9xPDhw3N8bjjcb50g7gAA60NDzE5SK6gNxfwLUMxCiAJl3759PP/882bHeXt7ExoaSs2aNenevTvdu3cvEJ1UqNVqhg4dypEjeah1IYQLcf5V6GRbtmxh/PjxpKSkGIalpaWRnJxMTEwMS5cuZcaMGVSoUMGm6/3xxx8NyTAhbEmj07Dmwhp+Pf4rV+5fMRmfpkljzok5vNHoDZut09KPmqRUFTN3XWS2lYkwgDZVi/NKu4ook6/mPOHt06A1U7WyVH1wc4Onf4ZpLSHjkbZp0u7AyrEweCk8Ro+YQggh8q9AJcOyyawuafoiVIkvo5t0c3xAQgiXkJ6ezvXr17l+/TqbN29mzpw5TJs2jbCwMKfGtX79ekMyrE+fPvTq1YuQkMyXAln/i8JpyJAh7N+/n6ZNmzJv3jxnh1NgFemE2MmTJ3njjTdIT0/H19eX0aNH06xZM9LT01m7di1//PEHly5d4sUXX2TZsmX4+/vbbL1z5szBy8sLDw8PHjx4YJPliqJNrVOz5vwaZhyfQVxKzsnWRacXMbT2UEK8c3/QnTlzhtGjR7NgwQLKlCljVSx3H/x/ImzPJVKsTIS1rRbGuI5VaVQhBJVKRXR0LgkxS+2HlW6Q+X9wOejxP/jrRdNpzv0Dh36HxsOtik0IIYT1jh8/zrhx4/jjjz+c/mPPav/f498RLy9umym10b1yJ7rUyrlZACGEyDJw4EAGDRpk+JyamkpMTAyzZs3i2rVrREdH8/LLL/PHH3+gcOIL2j179gAQFhbGpEmTcHd3d1oszvLVV18ViKp7wjmKdELs888/Jz09HQ8PD2bNmkWDBg0M41q0aEGFChX45ptvuHTpEr///rtN6gxrtVo++OADtFotr7zyCsuWLXNoQsyZN1xX4+bmRnBwsOFvZ1Fr1aw6v4pfo3/lWso1q+Yp4VuCGw9u5JoQO3nyJFFRUdy6dYuoqCi2b9+eY3svdx+o+G3XBWbvvsQDldaqWNpVC2Ncp6o0LP8wFqv2rbn2wwBK13/4d93+cOZvOLnSdLoNE6FiOyhW2ao4XUFBOWddkSvfW1152xytKFyDhw8fpnPnzty5c4dOnTqxZcsWqzpbeVyPtW91Orh7CcBi75JdI7o+RnSFV1E4Z51F7q2urVixYlSrVs1oWP369enZsyf9+vXj8uXLHD9+nK1btxIVFeWkKCE+Ph6AsmXLFslkmBBF9sl2/PhxDh48CEDfvn2NkmFZhg8fTuXKmT+W586di1qtfuz1zp07lxMnTlCxYkVGjRr12MvLq4JQV91VeHh4ULlyZSpXruyU/arWqlkau5Qn/3qST/Z+YlUyLCIwgi/bfMmKp1dQq1itHKeNiYmhffv23Lp1C4DY2Fg6duxoeHBmd+eBiv+uP03r/25h6tbzViXD2lcP46+XWzJneFOjZBhYuW9vHDUdFlgW/Io//KxQwJPfg7+ZJJ46Ff4aDVrrSrC5Amefs67MlfenK2+bo7n6NXjo0CE6duzInTt3gMzvWp07d+bu3bt2X3e+961eDxsnQmoCWuAfMwmxQM9AWpRqYbtgCxFXP2edSfZn0RQUFMSLLz6svbBz504nRgMqlQoApdJyJ1pCuLIieyfetGmT4e++ffuancbNzY1evXrx7bffcu/ePfbt20fr1q3zvc5r167x448/AvCf//zHcs95QuRApVWx4twKfov+jRsPblg1T6WgSoyuO5quEV1xd7Pu7U+xYsUICQnh9u3bhmH+/v5G521iSga/7rzI3L2XSLWyRFhUjXBe61iV+uWCrZreLK0absaYDs9eOiyLbyg8PQUWPGM6Lu4AfPZIyYWyTWDkJtNphRBC5CgsLIzg4GCSkpIMwwICAgr2D60d/4N/fwbgsLcXCR6mz8iO5Tvm2OOyEELkRd26Dzuuun79usl4rVbLqlWrWL9+PSdOnCApKQk/Pz8qVapEly5dGDhwIN7e3maX/Wi7UZcuXWLu3Lns2rWLW7dukZ6ezubNm+nYsaPRfPv37zf0pgj26fBNp9Oxdu1aNmzYQHR0tOHlSalSpYiMjKRz58507NjR5JkRGxvLpk2bOHToEGfPnuXOnTsolUrCwsJo0KABAwcOpH79+vmOK7deJh+1Z88e5s6dS0xMDMnJyYSHh9OuXTtGjx5NiRIlzM7zaE+X4eHhLFy4kLVr13L58mWSkpKMerFUqVTs2rWLXbt2cezYMa5cuUJqair+/v6UL1+etm3bMnjwYEJDTTtJy9qeLI8eW7B8fO/fv8/ChQvZunUrly5dIiUlheDgYCIjI+nVqxddu3bNtXTr9u3bmT9/PtHR0aSlpVGyZEnat2/P8OHDLe4fZyqyCbFDhw4B4OvrS+3atS1O16RJE8Pfhw8ffqyE2H/+8x9SU1N5+umnadasWb6XI4omlVbF8rPLmRkzk5sPblo1T+WgyoypN4bOFTpbnQjLUqpUKbZs2UK7du04f/48LVu2ZO3atQQFBZGYksGMnReYt/ey1Ymwjv+fCKv3OImwLPGnzDeoby4hBlC1c2Z7YQdnPf66hRBCmFW+fHm2bt1K27ZtuXr1KlFRUaxatQo/Pz9nh2be/l9h6yTDR6kuKYRwhOylA7Va4+/R169f56WXXuL06dNGw5OSkjh8+DCHDx9m0aJFTJ8+nYoVK+a4nk2bNjF+/HhSU1NtF3w+xcXFMXbsWE6dOmUy7tKlS1y6dIk1a9Ywd+5co9/JlnruVKvVXL58mcuXL7NixQpefPFF3nrrLbtuA8CUKVP46aefjIbFxcWxYMECVq1axS+//ELjxo1zXMbdu3ct7ossH330kVFSK0tSUhJJSUkcP36c+fPn8/PPP9OoUaP8bcwj9u7dy+uvv270Ugvg9u3bbN26la1bt9KuXTsmT55s8bn+5ZdfMnv2bKNhly5dYvbs2axatYoZM2bYJFZbcnhC7O7duxw9epSrV6/y4MEDk5uAOWPHjrV5HOfPnwcyv7zlVGS5UqVKJvPkx99//8327dsJCgrivffey/dyHpdarZaSaTaiVqsN50TlypXt+gb8zJ0zvLz5ZeJTTasrmlMluAqj642mS4UuuCnyXzO6TJkybN26lYkTJzJ16lQyFJ58ufYUc/deJk1tXSKsU83MRFjdssFWrzfXfZtbg/rmdJkEF7bBnQtWx+FqHHnOFjW2qFJfUMlzw3aKwjUYERHB1q1b+eyzz5g2bRo+PqY9NtpDnvft8T9g7duGj+kKhdnqksFewTQt1dSmsRYmReGcdRZXfm6InMXGxhr+Dg8PN/x99+5dBg0axI0bN/D09KR///40adKEMmXKkJqayu7du5k7dy6XL19m1KhR/PXXXwQEBJhdx/Xr1xk/fjze3t689NJLNG7cGHd3d6Kjo/H19WX16tUATJgwgZiYGCIjI/nyyy8N89vyWk9ISGDgwIGGZleaN29O7969Db+z4+Li+Pfff9mwYYPJvFqtFl9fX9q1a0fz5s2pVKkS/v7+JCYmcu7cOebNm8e1a9eYMWMGERERFmt+2cK2bduIiYmhYsWKjBw5kurVq5OSksK6detYunQp9+/fZ/To0axZs4ZSpUpZXM7EiROJjY2lV69e9OjRg+LFi3Pjxg2jfa7RaChXrhydO3emTp06lC5dGnd3d65fv86ePXtYtmyZoVTZmjVrjNrqfOONNxg+fLjFYwumx/fQoUOMGjUKtVpN8eLFee6556hRowbh4eHEx8ezdu1aVq1axfbt23nvvfdMkoIAs2fPNiTDwsPDGT16NHXr1iUjI4Nt27YxZ84cxo0bR1paWn52v904LCGWmJjIF198wYYNG6xKgmVn64RYRkaGoT2LnBoIh8x63r6+vqSmpnLzpnWlch6VnJzMF198AcBbb71ltmijo6jVakNd8Ud5eHgYGkzV6XRoNDm3rZT9B5JWq83xuCoUCpOLXKfTWZzezc3NKFGpVqvR/38PULnFrtfrc/2SoVQqDcU9c9tWS7Gr1WpDhwgqlcoQX15jd3d3N2rE0tzxKeldEq0u9+umSlAVRkWOIqpcFG4KNzRqzWMfpxIlSvDV91OZvPUyiw5cJU1t+bhl17FGGK+0r0Rk6UDDdll7nLLv26x9l/04uccdxmx5t1IPE2Km55gSRc+peMx9AoXe8jbo9Ho0Zo6BNccpu4J4Pen1erPnbE6xO+p6yi32LLa4nrKz1XFy5R828tzIVNieG9k5+jiVK1fO8Bb40djsdZxye24YxXxuIx5/jSGr0ocaGB9WjDtmGpTuULYDeo0eFSqXO07y3Mgkz42HsjeTARASEmKx4MCj0wYHB1tMoiQkJBgdg6CgIIsvWhITE42Ob2BgIF5eXmanvXPnjtF5n9O0d+/eJSQk997V7U2j0fD7778bPjdt+jDhPmnSJG7cuEGZMmWYM2cO5coZ92zbrFkzunXrxuDBg7l69Sq//fYbb7zxhtn1xMXFER4ezpIlSyhdurRheL169QAMv0d9fX0N/z/aCYCtfPLJJ4Zk2Ntvv23SjnbdunXp0aMH7777rsn1VKNGDbZv305gYKDJctu0acPgwYMZM2YMu3fvZurUqfTq1ctunQPExMRQu3Zt5s2bZ1RCqkWLFjRs2JB3332XlJQUvvrqK3744QeLyzlz5gyTJk2iX79+hmGP1lh77bXXKFeunEn1xDp16tC1a1cGDRrEgAEDuHPnDvPmzeP11183TFOiRAlKlChh9bFVq9WMHz8etVpNmzZt+Omnn4xeZtWuXZsOHTrQpEkTPvzwQzZu3Mju3btp1aqVYZrExES+//57ILNAxZIlS4x6mW7SpAmtW7dm5MiRud4zHc0hCbHk5GQGDRrElStXcnwgOUr2Xh2zTpSc+Pj4kJqamu/ipl9//TUJCQk0aNCA/v3752sZtpJTKbfq1avj7+8PZHYNfObMmRyXlb14ZkJCAnFxcRan9fPzo0aNGobPWXWlLQkPDzd6CJw6dSrHLw6VKlUyPOTUajXR0dE5xl6nTh3Dgzg5OZkLFyyXGlIqlUZ1/W/cuGHSsHz2Ys3BwcGGzhggc5/n1JNo2bJljepTW4q9U3AnltxcYnZcOe9yPB32NA0DG+KW7MaJ5BOGcdYcp6SkJIKDg02O05FT55l74DobzqdiZc1ImpT2on8tfyqFuKNPvEx04sNxeT1O2WU/TjUu7OXRgroq35J4+j18O2LuOIEXpaoMpvTZeRbXk5qayhkzcVl7nLIU5OsJMCmKn52zr6fs7HU9ZbHncXIV8tzIVBifG1nscZwsPTecfZyyM3ec/BOPU/Xfd1DoMx9qOuDj4sXYZqG6ZBVtFcM6C+NxyiLPjcJxnJwte2kleJgAMCciIsLot9H+/fuNmprJrmbNmiQkJBg+b926lfbt25udtlGjRly+fNnwec2aNTzxxBNmp23Tpg0nT540fP7jjz+MkgzZdenShQMHDpgd5wipqanExMTw008/cfToUSAzcdCjRw8gM4G1bt06AD788EOT6y9LrVq1GDRoEL/99hvLly+3mBCDzIIY2ZNhznDhwgVD292dOnXKsVM5c9XwcitI4unpyTvvvMPTTz/NtWvXOHXqFJGRkY8XdA4+/fRTs3H26tWLv//+mx07drBp0yZu375tlBDKrnnz5hbP0yzly5fPcXz16tXp168fc+bMYfPmzUYJsbz6+++/uXbtGl5eXnz99dcWS3b379+fpUuXcvz4cZYvX26UEPvrr78MJb/effdds9veokUL+vXrx6JFi/Idqz04JCE2Y8YMw42tVatWDBs2jNq1axMcHOyULoczMh62PWRNcdCsB3Z6enqe13XgwAGWLVuGh4cHn3zyiXSxLPItKjSKtbfXcl973zCsWnA1ugZ2pUFAg3xXjdy/fz9vv/0277//vqGYcfz9dKZvv8D8vZfI0FqXxO5auwTdy0M5f/ue4wqdGp/7pl+c04KrY02lrhvVhhAUvw+/5FjzE1hREk8IIYqynTt38v777/PZZ59Z/KFaEPkkn6XK/om46TJLAOmBb0KDWR1gvi0UbXpJKnnVMDtOCCFyMmXKFKZMmWJxfLFixZg6darhd+b27dvRarX4+PjQtm3bHJfdpEkTfvvtN+Lj47l+/brZpJdSqaR79+6PtxE2sH37dkOBmKFDhz728lQqFQkJCaSmphpKEmYvcHP69Gm7JcSqVauW47L79u3Ljh070Gg07N+/3+LzsWfPnnled3JyMsnJyWRkZBi2N6vU3Llz51Cr1fmu5prVuH6TJk1yTUA2btyY48ePG5K6Wfbu3Qtklv58tMOG7Pr27Vs0E2KbN29GoVDQrl07fvnlF0esMkfZi9JaU1w5q+i0pd48cprvww8/RK/X8/zzzxu9mXOWypUrWywVl71ItK+vL3Xq1LF6ucWLF8+xGPKjicAKFSpYfPMBGIqiZ6lZs2auVSqyKJXKXGPPfsMICgrKcfpHYy9VqhQlSpRArVYb3pbWqFHDsMxHY69cuXKOsav0KnbG7aRN2TYAOcYy3HM4Pxz9gZqhNXmp3ku0LdM2T1WQHz1OGzdu5M033yQ9PZ2PPvqIiIqV+OOsjgX7LpOhsa5qZLfaJXmtY1VqlQ60qupLlpyOU/Z9myXrOCluHsdNZ3rd+lcz7vAi6ziZVeZ39LM6otCYJrn91Alm43q0+HVu51hBvp7A+Jx9lDOuJ0vyej056zilpqY+VjuTBZk8Nx5Ok8XZzw1n3o9WrlzJO++8g1qtZsKECVSuXNno+42zjlNOzw0AEs+j3DwRheZhSaFTnkoWBJpvf0ev8SXcoyZNG9a3GHtBPk6PkudGwTtOrvzcEJaVLVuWrl27MmLECKN2n2JiMntPT0tLo1atWlYvLyEhwWxCLCIiwmL1UUfKKsGnVCoN1TXzKjU1lXnz5vH3339z7ty5HH/7ZDWLZA+5XevZS7LGxsZaTIg92uOjJWfOnGH27Nns3LnTpIpydjqdjnv37hmdT3mRde7t2rXL6tiyl/jMihUyn+k5tc9es2ZNlEplgaoy7pCE2I0bNwAYPHiwI1aXq+zFHK2pBplV/M+a6pXZTZs2jYsXL1KqVClDF6rOplQqrWoc2c3NLU+NKD/a/kJucrpQzMlLxluhUOQp9rxuq7nYc9qvlmJPVaey+Mxi5pyYQ3JGMqt7raZcYLkcYxlUaxBVQ6vStmxbwxfEvOz37Mfp1KlT9O3b15Dw1el0vPD8EJ76Zq1VybDukZmJsJqlHtbpd8hxuh1jdrx7WeMeVnI8x0pHQqf/wPp3TeNKvYOn+j745fxQsec5Zu/rCay/FzjjespJXt9+Oeo4FaQHu63Jc8OUs54bljjqPD98+DCDBg0ytP+hVqt59tln+ffff2nQILMNxwJ5nJLjYHE/SDX+Al9Lpeab+ATeCQ9Dly3notd5ok6pTpmqiTmur6AeJ2vIc8MyeW4IWxg4cCCDBg0CMq8JLy8vQkJCLDaCn5iYaHZ4biw1UG6uzS1nyEpQ5dRuXE7i4uJ44YUXrK5qnL0mmK3llnAqXry44e+cqqQHBQXluq6lS5fyySefWN3eVn5qsmW5c+dOnud5dH3JyclA7vvIw8OD4ODgHBN8juaQhJivry8qlcroJHEmLy8vgoODSUpKyrWh/OTkZEPSLLcG+B/166+/Apn1Zbdu3Wp2mqxlp6am8vfffwOZdaVbtGiRp3WJwuOB+gGLTi9i7om53M14+BZjRvQMPmv1WY7z+ip9aVeunU3iqFGjBq+88gqTJ082DHvmmWd4t3cjev/yr8X5etTJTITVKOmkB+31o+aH59TDpDlNX4Stn0PGvUdG6OHB7VwTYkIIUdTUr1+f4cOHG3WbPnjwYKO34gXOgwSY1xuSr5odrb5fj9SMrniVnY/CTY1e5446uR7K4ANAAd4uIezg0bbZcip1eOnSJaPPwcHBFqc9deqUSaP6lhw6dMikUX1Ldu7cadKoviUbN260OM4eihUrlqdG6rO2IyQkhLlz51o9X9myZc0Ot1fD8o72zjvvEBcXh0KhoE+fPjzxxBNUrlyZ0NBQQ+ccOp2OmjVrAti1vXJbNX1krgRudufPnzckw4oVK8aIESNo3rw5ZcqUwc/Pz5Dk//PPP5k4cSLweNudde61bduW8ePH53s5YLt95EgOSYhVq1aN/fv3c+3aNcPJ6mxVqlTh4MGDXLlyBY1GY/FtU/ZGPrM30GmNrDc/y5cvZ/ny5TlOe/fuXd58800gs7cRSYi5nhRVSmYi7ORckjKSTMavPr+aF+u+SLkAy1UYbEmhUPDtt9+iVquZMmUKgwcPZvbs2Xh4eNCxRjibT8dnmxZ6RJbi1Y5VnJcIy3L9iOmw4PLgm8feW93cIKQi3DxmPNzdC4pVyX98Qgjhotzc3Jg2bRpqtZrff/+dF198kWnTpuX65d5p0u/B/L6QYL7NyO3auryhfhm12gPt5VH4lJtDQ9+RzB06olB+qRficVlqBPxxp81LoYi8VPvKrb2j7ApCD5M5yUooPnjwgMqVK7tMQitrvycnJ6NSqfJUSuz8+fMcOnQIgNGjR1vsQCCn0li29Gg1wZzG55Qgzs1ff/2FRqPB3d2defPmWcxBZJXKelzBwcHEx8ejVqvz3dNoUFAQt2/fznUfaTQahx0vaznkG8yzzz6LXq9n1apVjlidVbJ6fElNTeXEiRMWp8veG0nDhg3tHpe9FdgvrYWQu7s7ZcuWpWzZsjk+tFJUKUw/Np1uy7vx45EfzSbDALR6Lb9F/2anaDPdTE5Ho3341k2hUPDjjz8yd+5c5syZY0gMj+tU9f/Hw5N1S7Hh9bZMHdzQYckwi/tWkwG3zFyvpernb0UeZh7KxauCu0PeFTicteesyDtXvre68rY5mitcg25ubvz666/MnTu3QCXDTPatOh0WD4IbR81Of0hXlTHq11H//7thXXp5mnt8w+/9h0syLBtXOGcLqoJy7YiCIavdMJVKZWjTyRVk9VKqVqtNGmLPzblz5wx/Z/XGaY6j9lduPdBmH1+1atV8rydru2vUqJFjgRxbbXfWuRcTE2NoSievshJpp0+fzrGa5+nTpwtcdXGH3Il79OhBz549+eeff4yK2TtTp06dDH8vW7bM7DQ6nY4VK1YAmUVwmzVrlqd1nDlzJtd/ZcqUATK73c0aNm/evPxtlBXky4ztuLu7U6JECUqUKGF2v95X3WfasWl0WdaFKUenkJyRexY/7n4cWjv0cng9KY0PV8TQ9uutrDh63WicQqFgyJAhRttQt2wwE7rXYOPrbZkyqCHVSphv78BeLO7b+JNgpkH9PFeXzInSfFfDriC3c1bknyvvT1feNkdzlWvQ3d2dIUOGFKgf9Eb7Fj38OQwu7TQ77SldOYapxpPGw86SmlcKZeqglni4F5xtKghc5ZwtiGR/iuw6dOhgSMbPmTPHydHYTrt27fK9Xdmrw1pqKw1g8eLF+Qsuj2JjYw2dBJiTlVNwd3enadOm+V5PVkIpp7bO4+PjDb1DWpLVqUJuSa6oqCgA7t+/n2utNkuyarclJSVZbCoKLOddnMkhT/0DBw7wzDPPUL9+fSZPnswzzzzD7Nmz2bFjBwcOHMj1nz3UrVuXxo0bA5kH5sgR02pYs2bNMvT+8vzzz5s0yrlv3z6qV69O9erVee+99+wSpyh87qnu8fPRn+n6Z1d+Pvoz91X3c52ncYnGzOwyk9+6/Ia7m+2+IF1PSuODFdG0/e8W5v17GZVWx5QtZ41KiVkyul1lqjo4EZYri+2H1XdkFEII4fLWrl1b4N7iWkWng1Vj4cxaw6DsLatc0pXgedV73MPfMKxOmSB+fb4x3kpJUAghnKNSpUp069YNgL///pvff/89x+mvXr3KmjVr7B5X1m/drKRJXlWsWJHOnTsDsGnTJn77zXJtmNTUVKNqgBUqVDD8bSlRs3DhQjZv3pyv2PLjww8/NJuoWr16Ndu3bwcyC96Eh4fnex0REREAXL58mcOHD5uMT0tL46233sq1If2sas1Xr17NsY2x3r17U6pUKQD++9//5pp/OXjwIPv37zdZhrd35kumL7/80mzVyf379/PHH3/kuGxncEi9oCFDhhgVPz9x4kSO1RSzUygUOWZiH8fEiRMZOHAg6enpDB8+nDFjxtCsWTPS09NZu3YtS5YsATJPymHDhtklBuE6kjOSmX9qPgtOLuC+OvckGECTkk14qd5LNCnZxObx6PV6Rs45yMkbxo3GX0pMZdWx6/RpaL4RzgLNQtWXfFeZFEIIYWLy5Mm8+eabPPvss8yfPz9fPRI6hV4PGybAsUWGQbfd3XgjPIwPEu8QkuHHc+oJ3OZhW0KVivsxe1gTArzz1hOhEELY2ieffEJMTAxXr17lq6++YvPmzTz99NNUrVoVT09PkpKSOH36NDt37uTff/+lc+fOPPnkk84OO1cff/wxR48eJT4+nm+++YadO3fSu3dvKlWqhEKh4Nq1a+zbt4+1a9fy448/Gmpl1apVi2rVqhEbG8uSJUu4d+8eTz/9NGFhYdy8eZNVq1axYcMGGjZsaDZxZGuRkZHExMTQt29fRo0aRbVq1bh//z4bNmww5A38/Px45513Hms9Tz31FPPmzUOn0zF69GhGjBhBo0aN8PLyIiYmhjlz5nDp0qVct7thw4YsX76cxMREvvzyS5566ilDL6ceHh6Gmmqenp58//33DBkyhNTUVF544QV69OhBp06dKFu2LDqdjtu3b3PixAn++ecfYmNj+fDDD41KwRUvXpxx48bx3//+l2vXrtGnTx9Gjx5NnTp1UKlUbN++ndmzZ1OiRAnS0tLy1bOlvTjsG449e3zIr1q1ajF58mTGjx9PSkoK3333nck0ERERzJgxA39/fzNLKHzUanW+urwVplQqFdHR0aRoUjjifoQlsUtIUadYNW+zks0YU28MjUs2tlt8CoWCShnnOYlpo6c/bTnHU/VKF9iqIVn7FqBOnToPz1mzDepXyHuD+kWUxf0qHluhLEljJXlu2E5huAa//vpr3n33XQCWLFmCh4cHc+bMKfDVu1QqFQnLxlP6zGzDsGQ3BS+WDOecpydDS5XA6+ozxOkfvrUvFeTNvJHNKObv5YSIC4fCcM4WVq783BD5ExwczKJFi3j99dc5ePBgrrWl/Pz8HBhd/hUvXpyFCxfy8ssvExsby7///su//1ru0T6LQqHg66+/5oUXXiA5OZl169axbt06o2mqVavGDz/8QJs2bewVvkH79u1p3749U6ZMYcKECSbj/f39mTZtmsWeP61Vt25dXn31VX766Sfu3bvH5MmTTaYZPnw4VatWzTEh1qNHD6ZPn87Vq1eZM2eOUZXVMmXKGFW5rF+/PvPmzeP111/nxo0brF69mtWrV1tctrncyPDhw7l+/Trz5s3j1q1bfPrpp0bjQ0JC+P7773n99ddz2nyHc0hCLC9dxzpaVFQUq1atYu7cuWzbto1bt26hVCopX7483bp147nnnsPHx3XbFBL5M3jtYI7fPp7n+ZqXas5L9V6iYQn7d9AQHx/PvC9ex7/fVyiLGfdceTnxAUeuJtEkohAlkjQZcMtMadHHqS45clP+5xVCCBdz5coVPvnkE6NhCxYsYMCAAQW+FILbgV+NkmGpCgWvlMhMhgE8cHMjpdwq3K8Fok2pQYivknkjmlImWL7jCSEKjrCwMBYsWMC2bdtYs2YNR48eJSEhAY1GQ0BAABUqVKBBgwZERUXRpInta5jYS7ly5VixYgWrVq1i/fr1nDhxgqSkJNzd3SlZsiT169enS5cuhiaNstSsWZMVK1Ywffp0du7cSXx8PH5+fpQvX57u3bszePBgQ1tZjvDqq69Sv3595s+fT0xMDMnJyYSHh9OuXTtGjx5NyZIlbbKesWPHUqdOHebOnUt0dDSpqakUK1aMunXrMmDAAFq1apVre19+fn4sXryY6dOns3v3bq5fv55jW2z169dn48aNLF++nK1bt3Ly5Enu3r2Lm5sboaGhVK5cmSZNmtClSxcqVapkdhkffPABbdq0Ye7cucTExJCWlkbJkiVp27YtI0eOtNn+sSWFviAW3RI2lf3tXo0aNQrN24SCLK8JsZalW/JSvZeoH17ffkGZsXv3bp569VMCurwGgAI9vRuUZWxUFSqFFdxSj2bfSF87DL92MJ240yfQ2nw3zMKYvOm3nwcPHnD69GnANfatPDfsozBcg5s3b+bJJ580tE3y9ddfM378eCdHlYtjS+CvFw0f1cCrJcLY7Wua7NLr3dDdHMqiwcOoVy7YcTEWUoXhnC2sHPHcOHv2LBqNBg8Pj8fq9U4IIQoCe9zTCmZ9KSFcRKsyrZjXfR7TO0+3aTLsSmIq7/x5jEOX7+a8/latWDb5fTSJV6jqkciWtzvw3bP1C3QyzCJpP0wIIeyuY8eOrFixAi8vL0OzEgXamXWw4iXDRy0wMayY2WQYgF5VnMlPPyXJMCGEEEI4rg0xIYqSNmXaMKbeGOqG1bXpci8nPmDKlnMsP3INrU7PzXsZzB2ec7e+7du1Y1v5ClSpGGHTWBzOXPthID1MCiGEjXXt2pXY2FjKly/v7FBydmkXLB0Kei2Q2Zvkl8VCWOdvvkSjTh3Mh40n07VmZcfFKIQQQogCyykJsZiYGPbs2cPZs2cNXasGBQVRtWpVWrZsSWRkpDPCEuKxtSvbjjH1xhBZ3Lbn8KWEB0zZeo6//j8RlmVH7G0OX7lLw/IhOcxN4U+GAVw/ajosJAJ8ct52IYQQeVfgk2HXj8LCAaB52O38lOAglgQGmJ1cp/Hj5epfM6ChbV9UCSGEEKLwcmhC7MyZM3z00UccP2657aXJkydTr149/vOf/1C9enUHRifE46kSXIUpHafYdJkXEzJLhK04apwIy+6HTWepeGUtTz/9NA0aNLDp+gsMdTrEnzIdXtpFt1cIIexIp9PxwQcf8Nxzz1GrVi1nh5N3t2Nhfh9Q3TcMmhcYwIyQILOT67VePFv2U15p08JREQohhBCiEHBYQmzPnj2MGTMGtVpNVjv+Hh4eBAcHA5CUlIRGowHg6NGj9OvXj+nTp9OihXx5EQXPgh4L7NrQ7IXbKYZEmIU8mMH22NssnruIKVOmsHXrVurUqWOzOAqM+BOgM9M9ubQfJoQQeaLT6Rg9ejS//fYbv//+O9u2bStcLyCTrsK83pCaaBi0yt+Pr4uZLy2s13nQKXQCH3Xt4qgIhRBCCFFIOCQhdufOHcaNG4dKpcLNzY1nnnmGfv36UatWLTw8MkPQarWcPHmSpUuXsmzZMlQqFePGjWPDhg2EhEiVKFtxd3d3dgguw8PDw/AjIus8flzn/z8RttKKRBiAQq/j3vF/0KbcJfF+Ih07dmTbtm2F841/Nib71lx1SZD2w/LIHuesyOTK91ZX3jZHc/Y1qNVqGTFiBHPmzAHg5s2bREVFsX37dqpUqeLwePIs5TbM6wX34gyDtvr68FHxULOT6/VuNPJ5jclP93FQgK7H2eesK5N7qxBCOJ9Dnmxz587l/v37KJVKfv75Z9q0aWMyjbu7O3Xq1KFOnTp06dKFMWPGcP/+febOncu4ceMcEWaR4OYmHYvaipubG/7+tumt8Vx8ClO2nGXVsetWJcI83BT0aVCKvTP/w64NKw3DExMTOX78eKFPiJnsW0sN6peq55iAXIQtz1lhzJXvra68bY7m7GswPT2d2NhYo2Hx8fGcPHmy4CfE0pMzq0kmnjMMOuDtxdthxdEqFGZnqaoYwcx+L6CwMF7kztnnrCuTe6sQQjifQ+7E27dvR6FQ8Nxzz5lNhj2qdevWDBkyBL1ez/bt2x0QoRDOcS4+hXGLj9B58nZWHM09GaZ0VzCoWXm2jW/P1/0asH7ZAsM15e7uzvz58xkwYIADInewG0dNh4VUlAb1hRAiD/z8/Fi3bh1NmjQBQKlU8ueff/LUU085ObJcqNNg0UC4+bAN2pOeSl4tEYbKzXyyq6TmWRYNegUPd0k6CCGEEMI8h5QQi4vLLNoeFRVl9TxRUVH8/vvvXL161V5hFUk6nc7ZIbgMnU5HamoqAL6+vnl603cu/j4/bj7H6uPX0VtRIkzprqB/43K83KEKZYJ9DMP9/Pz4+++/6dmzJ2PHjuWZZ57J83YUREb71tMNN2lQ3yYe55wVOXPle6srb5ujFYRrMCgoiA0bNvDkk0/y/vvv88QTTzg8hjzRqmHpULi82zDokocHL5UM54GF/ReU0Z3lz7+Lt1KqpD2ugnDOuiq5twohhPM5JCGWkZEBgI+PTy5TPpQ1rUqlsktMRZVWq3V2CC5Do9Fw5swZwPpG9c/eus+PW86xxspEmKe7G882KcdL7StTOtj89RMQEMDWrVtdqkpI9n1bt5gaN53GdCJpPyzP8nPOCuu48r3VlbfN0QrKNRgSEsKuXbsK/nNDp4OVr0DsesOgm+7uvFgqnDsW2l9SprRg6cAPCfBWOipKl1ZQzllXJPdWIYRwPockxIoXL86NGzc4deoUkZGRVs1z6lRmiZBixYrZMzQhHCL21n1+2HyWtdE3rE6EDWiamQgrFeSDVqtFr9db/PFS4H/UPAbFjWPmR0gJMSGEsEir1ebYaHeBf27o9bD+PTi+xDDorpsbL5YM54aFxt3dUuoxqc5wivl7OSpKIYQQQhRiDin33LhxY/R6PTNmzCAlJSXX6VNSUvj1119RKBQ0btzYAREKYR9nbt7nlQWH6fr9Dv4+nnsyzNPdjRdaVGD7O+359OlISgX5kJGRQd++ffn0008dE3QBo7h51PwIaVBfCCHMSk1NpUePHnz77bfODiX/tn0F+6cbDcpQKLD4GE2rwUc1X6KEn5QME0IIIYR1HFJC7Nlnn2XVqlXExcUxePBgJk2aRJ06dcxOGx0dzUcffcSVK1dQKBQ8++yzjghRCJs6ffMeP24+y9rom1ZN7+nhxqCm5RnTrjIlg7wNw9PT0+nbty9r165l5cqVKJVK3n//fXuFXSCZLSEWWgm8gxwfjBBCFHAPHjygZ8+ebN26lY0bN+Lh4VH4euv+dxps/8pkcEmtll/j7/FE8WqovBMNw/VpFZje6Tu8k+MdGaUQBZ67uzsajQaNRpNrqVEhhCjIdDqdoaq5Le9lDkmINWrUiEGDBrFw4UJiY2Pp378/VapUoV69eoYqkYmJiRw7doxz5x52pz1o0CAaNWrkiBCFsIlTNzITYeti8pYIe6l9ZUoEehuN0+l09O7dm/XrH7adMnHiRHx8fHjjjTdsGndBpdBmoLh92nSEVJcUQggTGo2GHj16sGPHDsOw119/HR8fH1588UUnRpYHRxdlVpU0x92TkgMXMfZWON8cfRd334voMkoyuf1PNClfguhoSYgJkZ2vr6+hLeekpCRpikYIUWilpKSg///qVnlpmz43DkmIAXz44Yd4e3sze/ZsdDodZ8+eNUp+AYYNdHNzY/jw4bz99tuOCk+Ix3Lq5n2mbb/E+hPWJcK8PNwY1CyzRNijibAsbm5uPP3000YJMX9//yJVjdjn3gUUejONzpaq7/BYhBCioPPw8ODpp582SogFBwfToEEheYlw+u/MRvTNUbjBM79DpXYMqwShvj8yYdsXfNL2NbrUqCidMAlhRnBwMHfv3gUgPj4erVZLYGAgXl5eBb8dQSGEILOQSEpKCjdvPvydHRAQYLPlOywhplAoeOedd+jVqxeLFi1iz549XL582WiaChUq0LJlSwYOHEi1atUcFZoQ+XYpSc0fJ1PYd836RNhzzSswum0lwi0kwrIbM2YMGo2GV199lcDAQNavX0+LFi0eN+xCwy/pjPkRUkJMCCHMevPNN1Gr1bz33nuEhoayadOmwpEQu7gTlg4Dcy9BAJ6aAjWfNHx8ul4ELSr9SHhA7s9SIYoqb29vgoKCSE5OBjJr5CQmJqJQKKT6pBCiUMjqXC6Lj48Pfn5+Nlu+wxJiWapVq8bHH38MgEql4t69ewAEBgZKV86i0Nl5JZ191zJync5b6cZzzSrwYrtKef7yPnbsWBQKBU2bNqVJkyb5DbVQ8k2ONT+iVF3HBiKEEIXIu+++i1KppFOnTtStWwjul9cOw6KBoLXwPO36BTQYbDJYkmFC5K5UqVJ4enpy+/ZtwzC9Xo9Go3FiVEIIkXc+Pj6UL1/epiVcFXp9bv3eicJOpVIRHR0NQJ06dSTxaAO9f97NkStJuU7nrXRjSPMKvNi2MmEB0g18nk1rBbdijIcVqwKvHnJOPEJY4Gr3WVfbHlGA3T4Ds7pB2h3DoLNKJVXV6swPbcdD1AdOCk4I+3H0fValUpGSksKDBw9QqVTodDq7rk8IIWzB3d0dHx8fAgIC8PPzs3l1b4eXEBOiKPBRujOkRQVGtalkVSLszp076HQ6ihcv7oDoColfo0yTYSDthwkhBJntAXl6ehIcHOzsUPIv6QrM622UDNvo68P48OKMSL7Hq5WfQdFhohMDFMJ1eHp6EhoaSmhoqLNDEUKIAkMSYkLYkI/SnedbZibCivtbVyIsISGBzp07A7B582b5opJFlWp+eOn6Dg1DCCEKmhs3btCxY0cCAwPZuHEjgYGBzg4p71Juw9xecO+aYdBeby/eCy+OTqHg1+Agrvr585Veh7tC2joSQgghhO3ZNCH2/PPPA5kN6M+ZM8dkeH48uizxeLRaC43Vijz56+VWaLVaEhISAFhw7C4qLYxqU5FiVibCIPMNf6dOnQxF5rt06cKmTZsK9xt/G9BqtejT75m/QUmD+vmW/ZwtXry4NKhrQ658b3XlbXM0W1yD165dIyoqitjYzDYWe/Towfr16/H397dprHaVngzze8Od84ZBx708GVciDHW2qhDrr/xF8sZ7TO38DUp3ZY6LlPubfch+tR+5twohhPPZNCG2f/9+AJN6nfv370ehUJCX5sqyppcugW1L2guwHa1WS1xcHABj2+ev7YehQ4cakmEAhw4dYsSIESxbtsxmcRZGWq0WRUaK+ZElC0ED0QVU9nM2JCREftjYkCvfW1152xztca9BvV7PwIEDDckwgN27d/PKK68UnpeHqlRYOABuPnz2nVMqeblEGGlubiaT7721mRmH/+KVJv1zXKzc3+xD9qv9yL1VCCGcz6YJMUs94BW1nvGEsNaUKVNo166d4ctm+fLl+eabb5wclfMpbp/GXWOmyqSHN3gXwqpBQghhAwqFghkzZtCuXTvi4+MBqFKlCp9//rmTI7OSVg1Lh8KVPYZB1zzcGV0yjGQLiRaftPY8W+MpBwUohBBCiKLEpgmxefPm5Wm4EEVdpUqV2LJlC+3atcPb25stW7YQERHh7LCcQ6OCU6vg4CyUl3ebn0bp69iYhBCigKlRowZbtmyhffv2FCtWjC1btlC6dGlnh5U7nQ5WvARnNxgGJbi58WLJcOI9zH8d9UhtwvIBX1E8wNtRUQohhBCiCJFG9YVwsqpVq7J161Z8fX0pV66cs8NxvKQrcGg2HJ4LD27nPK2nn0NCEkKIgqx27dps3bqV4sWLU7JkSWeHkzu9Hta9A9FLDYPuKxS8VDKcK0rzbYMpUmuzuPd3lA2R+74QQggh7EMSYkIUANWrV3d2CI6l08H5zXDgNzi7EfRWtqPhHWzXsIQQorCIjIx0dgjW2/oFHPjV8DFdoWBsyTBOe5lvezPUrQb/6/4D1UsGOyhAIYQQQhRFpq2X2kGNGjWoVasW586ds3qeK1euGOYTorA7e/Ys69atc3YYzvcgAXZNhh/rw4JnIHa99ckwAKVUmxFCFA0xMTFs3rzZ2WE8vr1TYcfXho9q4O3w4hz2Nn8/rxlakzXPzqJJRAkHBSiEEEKIosphJcTy0sOkLeYToqA4ffo0UVFRJCYmsmLFCrp37+7skBxLr4er++DATDi5ArQqZ0ckhBAF2rFjx+jUqRMPHjxg7dq1tG/f3tkh5c+RBbDhfcNHHfBRWDG2+/qYnTwiMIJpnaYR4BngoACFEEIIUZQV+CqTCoXC2SEIYZZCocDPz8/wtzknTpwgKirK0BtY7969Wb16NZ07d3ZYnE6TcR+O/wEHZ8GtmDzNqi9WBXVaCp6pN+0UXNFkzTkrhLAfa67Bw4cP07lzZ+7cuQPAE088wfr162nTpo3D4rSJU2tg1VjDRz3wTWgwa/zNtwlWwrcE0ztPp5hPsXytTu5v9iH7VQghhCsrsAmxu3fvAuDjY/4tosgfpYXGa0XeKZVKatSokeM0v//+uyEZBpCRkcFnn31Gp06dXPeL5a2TcHAmHFsCqvvWz+fmATWegMYjUFRsi+fMziAJMZuy5pwV+ePK91ZX3jZHs+YanDFjhiEZBpCamsqXX35ZuBJiF7bDn8OMqsRPDw5kflCg2cmDvYKZ0XkGpf3z31um3N/sQ/ar/ci9VQghnM+hCTFrEwCpqanMmzcPgPLly9szJCHs6uuvv+b27dvMnTsXgGbNmrFq1SrXS4ZpMuDkqsxE2JW9eZs3oDQ0GgoNn4fAUnYJTwghCospU6aQkJDAsmXLAGjXrh1//PGHk6PKg2uHYPEgo+rxiwP8mRoSbHZyHw8fpnWaRqXgSg4KUAghhBAik10SYh07djQ7fPjw4Xh45LxKlUrFnTt30Ol0KBQKOnToYI8QhXAINzc3Zs2ahVqt5sqVK6xdu5bAQPNvyAulu5fh0O9weB6kJuRt3spR0HgEVOsG7mbuCyM32SZGIYQoRDw8PFi0aBH9+vUjJSWFVatW4evr6+ywrBN/GuY/A6oUw6C1fr58USzE7ORKNyU/Rv1IZPFC1GOmEEIIIVyGXRJi165dMxmm1+u5detWnpZTv359Ro4caauwBKDRaPD0NN/NucgbjUbD5cuXAahQoYLFZK+7uztz584lIyPD0A5HoabTwrlNmY3kn91IZsswVvIOhgbPQePhUKyyxcms3bcib2S/2o9Go3F2CHYjzw3bsfYaVCqV/PHHH2i12sLTdMTdyzCvN6Q9rO55xlPJxLBi6M2UitbrFbQLeYPmpZrbZPVyf7MP2a/248rPDSGEKCzs8lTr3bu30ee//voLhUJBVFRUrqVjvLy8CAsLo2HDhjRv3tz1qpY5mfTaaTs6nY6kpCQAypUrl+O0Hh4ehf9LZMptODIvs0RY0pW8zVumMTQZAbV7gzL3H3d52bfCerJf7ceV762uvG2OlpdrsFAlIVPiYV4vuH/daHA1lZohyff5Pdj0u1857RC+6jbIZiHI/c0+ZL/aj9xbhRDC+ezyC/3LL780+vzXX38B8MYbb1ClShV7rFIIp9q+fTtLlixh+vTpuLu7Ozsc29Hr4cq/cOA3OLkSdGrr51X6Qp1nMqtFlq5vtxCFEKIwWr9+PRs2bOCnn37Czc3N2eHkX1oSzOsDdy6YjFIAdRMqolY1Qhm+0TA8JL03S4e+gZeHCz0vhRBCCFHoOKTIytixmd1uh4aGOmJ1QjjUvn37ePvtt0lLS0OlUvH7778X/qRY+j04vgQOzoL4k3mbt3g1aDIS6j4LPsF2CU8IIQqzHTt28N5776FSqdDpdPz888+Fs0S8KhUWPgu3os2O3qerwSvqcWQkeqLV+uNV8i/80jqy4vkP8Pcq5KWmhRBCCFHoOTQhJoSr2bNnD2+//TYqVWZvWvPmzUOpVPLrr78Wzjf+N2Mye4o8/odRo8i5cvOAmj0zS4NFtIbC+MNOCCEcYOvWrbz33ntotVoAfvnlF5RKJT/88EPhSoppVPDH83D1X7Ojo3URjFS9TQaZVT/VSU0J9Yhg+YhnCfUrRNVBhRBCCOGy5PWcEI/BXNIrOTkZrVZbeBJimozM6pAHZlr8YWNRYFloNBQaDoGAknYJTwghXIm5Z0NSUhI6na7wlC7WaWHFGDj3j9nR53WlGKp6l/s87B0z1M+ThS/0o0xIIekxUwghhBAuzyG/2A8fPkzNmjWpW7euVT1N3rp1izp16lCrVi1iYmIcEKEQ+dO8eXO++eYbQ+PHAwYMYPHixSiVSidHZoU7F+Gfj+C7mrB8VN6SYZU7woBFMO4YtBsvyTAhhLBSu3bt+OKLLwzJr+HDhxeuqvZ6PawdDzHLzI6+rg9liGoCiQQZhvl5ujNnWFMqh/k7KkohhBBCiFw5pITY2rVr0ev1tG/fnhIlSuQ6fYkSJejQoQMbN25kzZo1REZGOiBKIfKnVatWLFq0iL///pvp06cX7N4kdVo4uzGzNNi5TUAeejjyCYEGz0GjYVCsst1CFEIIV9exY0fmzJnDnj17Cl+j+lsmZVat/39q4MeQYEYk30Or9WOIagLXKW4Y7+nuxq8vNKZO2SAzCxNCCCGEcB6H/HI/dOgQCoWCtm3bWj1P+/bt2bhxIwcOHLBjZEVPoWqfpIBzc3MjPDwcgHr16tGnTx8nR5SDlHg4PBcOzYHkK3mbt2xTaDICavUCpbddwntU9n1bqH4oFnCyX+3Hle+trrxtjvboc2Pw4MFOjiiP9vwEO/9n+KgF3g8rxnp/P7b7+qK68gLn9WUM490U8NOgBrSsXNzMwmxL7m/2IfvVfuTeKoQQzueQhNiVK5k/wKtUqWL1PJUqVTKaV9hGgS69VMA9ePAAPz8/w2cPDw/KlSvnxIhyodfD5T2Zb/JPrgKd2vp5lX5Qt19mI/ml6tovRgsK/L4tpGS/2o8r31tdedvsrdA9N3JyeB5s/MDwUQ98USyE9f6Z23fR0wNd+b9RXCmFXp2ZAPuqb1261nZMlfpCvW8LMNmv9iP3ViGEcD6HvOrJyMgAwMvLy+p5stpkSktLs0tMQuTFrFmzqFGjBufOnXN2KLlLvwf7f4WfW8DsHpntvFibDAurAd2/gbdOQc8fnJIME0IIV/DTTz8RGRnpGi/2Tq6C1a8ZDfopJIg/AgOMhrl5JuEb8QtuXtd4v0cN+jeWRIoQQgghCi6HvJoIDg4mMTGR69evU7NmTavmuXnzJgABAQG5TCmEfU2fPp0xY8YA0KFDB7Zv324owVig3DieWRrs+FJQP7B+Pjcl1OwJTUZChZYgRfiFEOKxfPfdd7z11lvAw+dG2bJlnRxVPp3fCstGgF5nGDQ3MIBfg823CaZQqOnXuBwvtpW2JoUQQghRsDkkIVa5cmUSExPZsmULHTt2tGqeTZs2AVCxYkV7hlbkqNVqQ+k7kbslS5YYmUIJegAAU8xJREFUkmEAcXFxdOjQgSNHjhAQEMCpU6cAqFmzpnN6llSnw8kVmY3kx+3P27xB5aDRUGj4PPiH2yO6fFOr1c7fty5I9qv9qNV5qJJcyMhzI29mzZplSIYBXLhwgaioKA4dOoS3t3fhugbjDsLiwaBVGQat9Pfjm2IhZifX6zxo6f8OXz3ZzVERGsj9zT5kv9qPKz83hBCisHBIlcl27dqh1+tZuXIlBw8ezHX6AwcOsGrVKhQKBe3bt7d/gEJY0LVrVxo1amQ0bMiQIYSEhKDX61Gr1ajVavT6PPTWaAt3LsDGD+G7mvDX6DwkwxRQpTMMXAzjjkHbtwtcMgxw7r51YbJfhbC/nj17Urt2baNhQ4cOJSAgoHBdg/GnYMEzRiWOt/j68HHxULOT6/Vu1PYYy7Rn+jmlsfBCtW8LEdmvQgghXJlDSog9++yz/PrrryQlJTFq1Cjeeust+vXrZ9KmWEZGBkuWLGHy5MloNBqCgoIYNGiQ3eO7du0a8+bNY9u2bdy8eRNPT0/KlStH9+7dGTx4MD4+PvledlpaGjt37mT37t3ExMRw5coVUlNT8ff3JyIigtatWzNgwADCwsJsuEXCVoKDg9m4cSNRUVEcO3aMTz75hI8++sg5PQPptBC7IbNa5LlNeZvXtxg0eA4aDYNQKXUphBD2EhYWxubNm2nfvj2nT5/mm2++4e2333Z2WHlz9xLM6w1pdw2DDnh7MT6sOFoLz7/y2mHMfW4o7m5S7V4IIYQQhYNDEmJ+fn58++23vPjii6Snp/P555/z3XffUbt2bUNXzvHx8cTExJCeno5er8fDw4PvvvsOf39/u8a2ZcsWxo8fT0pKimFYWloaycnJxMTEsHTpUmbMmEGFChXyvOzTp08zcOBAUlNTTcYlJSVx9OhRjh49yuzZs/nss8/o0aPHY22LsI/Q0FA2bdrE8uXLefHFFx0fwP1bcGQuHJwN9+LyNm+5Zpk9RdZ6GpTedglPCCGEsRIlSrBlyxbWr1/PsGHDnB1O3ty/BXN7wf0bhkEnPJW8WiIMlYVkV7H0fvwxdCxeHu4OClIIIYQQ4vE5rL/fli1b8ttvv/HOO+8QHx9PamqqSfXJrKLYJUqU4Ouvv6ZZs2Z2jenkyZO88cYbpKen4+vry+jRo2nWrBnp6emsXbuWP/74g0uXLvHiiy+ybNmyPCfnUlJSDMmwhg0b0qFDByIjIwkODubOnTts3LiRpUuXkpKSwttvv42fnx/t2rWzx6aKx1S8eHHHJsP0eri8Gw78BqdWg05j/bxKP6j3bGYirGSk/WIUQghhUalSpQpfMiztLszvA3cvGgZdVHrwUslwHriZb2XDL7U7y59/D38vh32lFEIIIYSwCYd+e2nevDmbNm1ixYoVbN26lVOnTnH3bmZx/JCQEGrVqkWHDh14+umnHdKA7+eff056ejoeHh7MmjWLBg0aGMa1aNGCChUq8M0333Dp0iV+//13Xn311Twt383Nje7duzN27FiqVKliMr5169a0bduWsWPHotVqmTRpEm3btnVOdbwiTq/Xs2fPHlq1auXcQNKT4dhiODgLbp/O27zhtaDxcKj7LHgH2ic+IYQQQAF6btiK6gEsfBZuxRgG3XR358WS4dx1N1/yS/mgDcsHfUKon3S6IIQQQojCx+Gv8zw9Penfvz/9+/d39KqNHD9+3FBCrW/fvkbJsCzDhw9n+fLlnD9/nrlz5zJmzJg89a7TsGFDGjZsmOM0nTp1okuXLmzYsIErV65w8uRJk8Z4hX3p9XrGjx/Pt99+y08//cTYsWMdH8SNY5k9RUYvBbVpFVuL3JSZ1SGbjIDyLUCSqUIIYXc6nY6xY8cybdo0Zs6cyfDhw50d0uPRqGDJELi6zzDorpsbL5YM56aH+a+Kbg8asOSZrygd7OuoKIUQQgghbMohvUwWRJs2PWyUvG/fvmancXNzo1evXgDcu3ePffv2mZ3ucWWvGnrlyhW7rEOYp9fref311/n2228BePXVV5k+fbpjVq5Oh6OL4NeOML0tHJ5jfTIsqDx0/BjePAXPzIQKLSUZJoQQDqDT6XjxxReZNm0aACNHjmTevHlOjuox6LSZvRWf32wY9ECh4KWSYVz0NP8SUJ9ag9lPTqZquJRGFkIIIUThVWQbfDh06BAAvr6+OZbIatKkieHvw4cP07p1a5vHolKpDH+7W6iWYCv2Xn5hs2nTJn788UejYa+88godO3Y0W801Ow8PDypVqmT422qJ5zOrRB5dYNSDV+4UULUzNBkJVTqBm+sey3zvW5Ej2a/248r3VlfetvxYuXIlM2fONHzW6/WMHDmSDh06ULZs2RznLXDXoF4Pf78FJ5YbBmUoYFyJME480hN4Fl1aBFM7fk+D8sUcFaVVCty+dRGyX+1H7q1CCOF8RfbJdv78eQDKly+f4wM+60tA9nlsbf/+/Ya/K1eubJd1ZHGz0ChuUdW5c2cmTZrEBx98AGTun9mzZ+eaDMuaNiQkxLoVaTVwdkNmI/nnt+QtSN9i0PB5aDQUQiLyNm8hlad9K6wm+9V+XPne6srblh+9evViwoQJfPnll0BmkmDhwoW5JsOgAF6Dmz+FQ78bPmqAd8OKs8/HfK/E2vSSfNliMu2rlXFQgNYrcPvWRch+tR+5twohhPM5NCGmUqlYvXo1mzZt4syZM9y9e5f09PQc51EoFJw8edKmcWRkZBga8y9ZsmSO0wYFBeHr60tqaio3b960aRwAp0+fZvv27QBUq1bN7gkxtVptcZyHh4fh4azT6dBocu7ZMHvHB1qtFq1Wa3FahUJh1P6aRqNBp9NZnN7Nzc0oUalWqw29kOYWu16vz3E7AZRKpaHzggkTJpCens6XX37J7Nmz6d+/v1GpvceK/f5NtAd+x+3IXBT3r+cY06N0ZZuhazQMXfUnweP/39SrVEX2OOW2rfaO3d3d3ehtbvZzxBw5Ts6J3VnHKbd9VJjJcyNT1nmuUCj47LPPSE9PZ8qUKSxcuJCePXva7rlhRey2OM/d/p2Cx67vjKa7pFSy10IyTKcqxhu1/0v3muUduq1yP7LMFa4nKLrHyZWfG0IIUVg4LCF28eJFXnnlFS5evJjjQ8kRHjx4YPjb1zf3xmB9fHxITU0lNTUPjZ1bQaVSMXHiRMOD8Y033rDp8s05fdpyz4XVq1fH398fgNTUVM6cOZPjsho1amT4OyEhgbi4OIvT+vn5UaNGDcPny5cvk5SUZHH68PBwypUrZ/h86tSpHL84VKpUyfAGU61WEx0dnWPsderUMXxBSU5O5umnnyYyMpIqVaqYzKtUKqlbt67h840bN4iPj7e47OCgICq7X89sJP/0Gtx1OX9RMuLpT3ypKG5HPEV6YCXQAadijSYpSsepRo0a+Pr6olAoSE5O5sKFCxanzfNxCg42SkCfP3/e6N7wqLJly1KiRAnD59xiL4jHKetHyqlTp3L8Av+415McJ9ciz41M2c/ze/fuMWjQIJo3b07FihUf/7nh4PM8YeN3RBz/1mS6Kmo102/cYWiJMmg9MgzDdeoAuvu+RkMfvcm6CtpxkueGefLcKBzHSQghhGM5JCGWmprKqFGjiIuLw83NjY4dOxIaGsoff/yBQqHgpZdeIjk5mZiYGI4dO4ZCoaB+/fp268o8I+Phlzxreo3MemDnVpotrz799FNiYjK7N+/duzdRUVE2Xb6wnkKhsKqaZE7c1SmEXt1Iyatr4N6lvM0cXjuzp8i6/bkaE5v79EXE6dOnjb40i8djzY8UIYR1FAqF3Ut124P7mTVUOD7Z7Di9wh2fuhNplVqFbfcm4aZMRq/1pqXHWAbWqODgSPNHnhu2Jc8NIYQQrkyhd0BxrVmzZvH111/j7u7Ob7/9RosWLTh79iw9e/ZEoVBw6tQpw7QnT57knXfe4cKFC7z//vs899xzNo/nzp07tGjRAoAePXowebL5L4ZZWrZsSWJiItWqVWP16tU2iWH69Ol8911mVYU6deowd+5cq0qr5YdKpTJ8malcubLF9bhyUX2VSsWXX37Jm2++SUBAgE2K6qvVak6fPo1PUixVk7bjcXolCmt7iQT07p7oajwFTYbjnq2XSKlSgWHfwsO3yEW1SoU5+d3W7PeCGjVqWHwhIFVfLLN0nFJTUw3tTLrCj3F5bmS+BPvvf//L+PHj8fX1telzA4yvQYed5+e3oF/QH4XO9JrWo0D79DR0tfuiVCr5cM0ull/7nDahI/nx6Z6Gbbd2Wy2R54ZlBfF6kueGKXluCCGE63BICbGtW7eiUCjo1q2bIRFlSa1atZg7dy5PPfUUX331FfXr1ycyMtKm8fj5+Rn+tqYaZFpaGmBd9UprLF682JAMq1SpEjNmzLBbMuxRSqXSqgeum5tbnh7Mj36ZyE1eeyqypiRfFoVCYRR7RkYGgwYNYvXq1ezcuZN169YZjc/rtnp4eMBvnfCMO0Cj3Cc3FVwBGg9D0WAI7n7FTUbnJRZXOk65yddxyoO8xA6F+ziB9fcCOU6WZT9OrtwWTFF8bqSmpvLMM8/wzz//cPDgQVauXGmUELLFeZ7TfrXLeX71ACx+zmwyDEDR4xs8Ggw0fJ7Usw2tYmbRpVYp3N3MJ8PMkeeGZfLcMK+oHidXfm4IIURh4ZDuTc6dOwdk9uhnzqNveUJDQxk2bBgajYYFCxbYPB4vLy+Cg4MBcm0oPzk52ZA0y60BfmusWbOG//znPwCUKVOG33//ndDQ0MderjAvPT2d3r17G0r27dy5k549ez5+e3B6y28mzVNAtW4w+E947Si0fgPMJMOEEEI414MHD3jyySf5559/ANi4cSN9+/Y1am6h0Ll1EhY8A2oL7Sh1+ACajjIZ3D2ydJ6SYUIIIYQQhYlDEmL3798HoHTp0oZhj76JfVTDhg0B2L9/v11iymov6sqVKzkWdc7eyOfjthWyefNm3n33XXQ6HWFhYcyePdsmSTZhWVxcHAcPHjQatn//fqNquvmSkWLddH5h0OYteP04DFoCVTuDdLMthBAF1qVLlzhy5IjRsN27dxte7hU6dy7CvN6QnmQYlJ69+mPzl6Ht246PSwghhBDCyRzyy9zbO7ML7+zVDQICAgx/37hxw+K8CQkJdokpq8eX1NRUTpw4YXG6AwcOGP7OStLlx969e3n99dfRaDQEBwfz+++/U758+XwvT1inSpUqbNq0yVAKz9/fn/Xr1xv1+JMv7rkU16/QCvrOhDdOQsePIFiOtRBCFAa1a9dm48aNBAYGAhASEsKmTZuoXbu2kyPLh/s3YV4vSHlYGv6cUskTZUux2dcH6g2CLp8b2rAUQgghhChKHJIQK1u2LIBRF8mhoaEEBQUBcPjwYZN5spJUea33b61OnToZ/l62bJnZaXQ6HStWrAAgMDCQZs2a5Wtdhw8f5uWXX0alUhEQEMDMmTOpWrVqvpYl8q5u3bps2rSJiIgI/vnnH1q3bv34C/XwMR2mcIMmo+ClvTBsLdR5BjykgVQhhChsmjRpwoYNG6hYsSKbN2+mcePGzg4p71LvZJYMu3vJMOiahzujS4YR7+HBG+FhLKkeJaWWhRBCCFFkOeRbUFaj+DExMUbDW7RogV6vZ+bMmSQlJRmGX716lRkzZqBQKKhZs6ZdYqpbt67hC+6yZctMqkdAZu+YWb2/PP/88ybJuX379lG9enWqV6/Oe++9Z3Y9p06dYvTo0aSmpuLr68v06dNt3kmAyF2DBg2IjY2lefPmtlngqM2o3r3GhYYTuVllAJqeU+C9q/DE/6BELdusQwghhNM0b96cM2fO0KBBA2eHkneqB7CwP8SfNAxKcHPjxZLhxP9/g+R6BUw68Cm/HJnlrCiFEEIIIZzKIb1MtmzZkj///JMtW7Ywbtw4w/AhQ4awfv16rl69SteuXWnWrBlpaWkcOnSI1NRUFAoF/fv3t1tcEydOZODAgaSnpzN8+HDGjBlDs2bNSE9PZ+3atSxZsgSAiIgIhg0bluflX7lyhREjRnDv3j0Axo0bR0BAALGxsRbnKVasGMWKFcvfBlkhP70KFRZ6vd5it/Bg+9KGSm8/ynZ7HQB3pVKqnNiQUqmkTp06hr+Fbch+tR9Xvre68rY5/LnhiGtQkwFLnoO4h00+3FcoeKlkOFfMrHPq8cn4K0N4LrK3feJxELm/2YfsV/tx5XurEEIUFg65E3fo0IEmTZqg1Wq5cuWKoe2sRo0a8corrzB16lSSk5MNPTrp9XoA+vTpQ8+ePe0WV61atZg8eTLjx48nJSWF7777zmSaiIgIZsyYgb+/f56Xf/DgQRITEw2fv/zyy1znGTt2LK+++mqe12WtnL74F2Z37tyhd+/efPbZZ7Rt29Yh68xrt+LCerJv7UP2q/246r0VXHfbbt26RZ8+fZg8eTJNmzZ1yDrtfg3qtLD8RTi/xTAoXaFgbMkwTnuZX68utRIR3vlrEqIgkfubfch+tR9XvbcKIURh4pCEmI+PD/PmzTM77tVXX6Vx48YsXbqUs2fPotVqqVChAr169aJr1652jy0qKopVq1Yxd+5ctm3bxq1bt1AqlZQvX55u3brx3HPP4eNjpr0oUWAkJCTQqVMnjh07Ro8ePdiwYQOtWrVydlhCCCEKqBs3bhAVFcXp06fp0qULmzdvfvzOVpxNr4c1b8DJFYZBauDt8OIc/v/OjR6lTS/DV62+o3WV0mbHCyGEEEK4MoU+qziWcFkqlYro6Gggs/csbwtfjAujxMRE2rdvb9Q+XUBAAP/880++O0Gwlk6nIzk5GYCgoCDcpGFim5F9ax+yX+0nPT3d0BlMnTp1Cn2JCld+bty8eZO2bdty9uxZw7CQkBC2bt1KvXr17Lpuu16D/3wMu79/uC5gYlgx1vj7mY8lozjj6/7I0OZ1bBeDE8n9zT5kv9qPqz03hBCiMHLIU23FihWsWLGCY8eOOWJ1IgdardbZIdhUYGCgSY+dQUFBdm2HLYtGo+HChQtcuHABjUZj9/UVJbJv7UP2q/242r01O1fbtpCQECpXrmwyLCQkxO7rtts1uGuyUTJMD3wdGmIxGeauC2ZElS9dJhkGcn+zF9mv9uNq91YhhCiMHJIQe++995gwYQLXrl1zxOpEEaJUKlm8eLGhrbkKFSqwY8cOqlSp4uTIhBBCFEReXl4sX76cTp06AVC1alW2b99uaN+00Dk0GzZ9YjTol+BAFgQFmJ082CuYRT1n8WaUjXpdFkIIIYQopBySEAsIyPxSFhER4YjViSLG09OTpUuXMnr0aLZv307FihWdHZIQQogCzMfHh5UrVzJy5Ei2b99O2bJlnR1S/sQsh9WvGw1aGODPzyHBZif39fBlWqdp1Cxe1ex4IYQQQoiixCGN6pctW5bTp08b2iAQwta8vLz45ZdfnB2GEEKIQsLX15dff/3V2WHk37lNmT1K8rAp2L/9fPmyeKjZyZVuSn6I+oHI4pEOClAIIYQQomBzSAmxTp06odfr2bp1qyNWJ1zUzZs30el0zg5DCCFEIXHjxg1csu+gK/tgyRDQqQ2Ddvh480GY+fYz3RRufNP2G5qXkmqSQgghhBBZHJIQe+GFFyhdujSLFi1i7969jlilcDGxsbE0atSI1157zTV/3AghhLCp6Oho6tWrx3vvvedaz42bMbCwH6hTDYMOe3nxVnhxNAqF2Vk+afEJHSt0dFSEQgghhBCFgkMSYv7+/vz+++9UqlSJkSNH8uGHH7Jv3z6SkpJc60uqsItTp07Rrl07rl+/ztSpU3nzzTflvBFCCGHR0aNH6dChA7dv3+brr7/m448/dnZItnHnAszvA+kPm6A446lkbIkw0t3Mf6V7s9Gb9K7a21ERCiGEEEIUGg5pQ6xmzZqGv/V6PX/++Sd//vmnVfMqFApOnjxpr9BEAZeamkrnzp25efOmYdj3339PxYoVee2115wYWea5qVQqDX8L25F9ax+yX0VRkJycTOfOnUlMTDQM++yzz4iIiGD48OFOjOwxr8F7N2BuL0i5ZRgU5+HO6BLh3Hc3nwyrH9ibYZHD8htuoSL3N/uQ/SqEEMKVOSQh9mhpHind4zxZX2oKC19fX7777jsGDhxoaD+sSZMmDBkyxMmRZe7LunXrOjsMlyT71j5kv9pPYbu35kVh27agoCC++eYbhg8fbvi+0bp1a/r16+fkyB7jGky9A/N6Q9Jlo8EhWh1V1GoSPdxNZnFLac4n3cfnN9RCR+5v9iH71X4K271VCCFckUMSYmPHjnXEaoSL6t+/PxqNhiFDhtC0aVPWr19PUFCQs8MSQghRQA0dOhSNRsOoUaNo3749a9aswc/Pz9lh5U9GCizoB7dPmYzy0+sZfK04e8PL4xb4sDS9PqUu857+L5XDAxwZqRBCCCFEoSIJMVEoDBo0iKCgINq0aUNgYKCzwxFCCFHAjRw5krCwMDp37oyvr6+zw8kfTQYsGQzXDpodfVRXmbHqt3hwzRMv3V94Bh9E96Aqv3T9H3XLhjo4WCGEEEKIwsXmCbEVK1YA0KlTJ/z9/W29ePGYNBoNnp6ezg4jX5544glnh2BEo9Fw48YNAEqVKoWHh0Pyy0WC7Fv7kP1qPxqNxtkh2E1hfm48/fTTzg7BSJ6uQZ0Wlo2EC9vMjo7VlWGo6h0e4ANAxo2+kFGGb7uPpE2VUrYOvcCT+5t9yH61H1d+bgghRGFh86fae++9h0KhIDIykipVqpiMv3PnDgsXLgSk5JgzFOT223bv3k358uUpV66cs0Oxik6nIz4+HoASJUo4ORrXIvvWPmS/2k9Bvrc+roK8bdu2baNGjRqULFnS2aFYxeprUK+H1ePg1Cqzo6/qwhiimkAS2atEKvi84xierFPWhhEXHnJ/sw/Zr/ZTkO+tQghRVJjvlsiOEhMTmTJlClOnTnX0qkUBtmXLFrp06UJUVBTXrl1zdjhCCCEKuLVr19K1a1c6duxo+MHuEvR6+OdDODLP7Ojb+iCeU0/gFsZVIj94oibPNCqayTAhhBBCiPxweEJMiEdt3LiRJ554gtTUVM6dO0dUVBQ3b950dlhCCCEKqJUrV9KrVy9UKhUnT56kU6dOJCYmOjss29g1Gfb8ZDRos68PWuCe3pchqglc1huXiHulQ2VGtqnkwCCFEEIIIQo/SYgJp9LpdEyYMIH09HTDsNjYWL755hsnRiWEEKKg0mg0TJgwAbVabRgWHR3NDz/84MSobOTgLNj8H6NBcwIDeL1EGG+Eh/OC+k1O68sbjR/UrDxvd6nuyCiFEEIIIVyCJMSEU7m5ufH3339TvfrDL/P9+vXjq6++cmJUQgghCioPDw/Wr19PRESEYdjzzz/Pxx9/7LygbCFmGax502jQSn8//lcsBICtft6cLrsH3DIM45+oU4rPno5EoVD8X3t3HhZV2fcB/DvDomyJKGIqCpKDiQsuae6JW2qZuDyEobglZWpaWmmv1ZNP2vNammWluJFkieS+JAqoT26YIgniBqKIKCqbsTNw3j94Oc8gDDMDszHz/VwX1zWcuc+5f+eeM+c35z7n3EevoRIRERGZAnaIkcG1bNkS0dHReO655zB58mT88ssvsLKyMnRYRERkpNq2bYvjx4/D1dUVs2bNwtatW2FhYWHosOruZiSwezaA/w6yHW1rg0+bVx0nzNIuCbZtNwIW+RjYoTlW+3WDhZSdYURERER1wWcnk1Fo1aoVTp8+jWbNmjXsgxoiItILNzc3nD9/Hi1atIBU2oDP76WeA8ICgHK5OOnPxo2w2Lk5ymq48svCJg3PuT7E+oDxaGTJfElERERUV+wQMzPGfFtFixYtDB2CRqRSKRwdHcXXpD1sW91gu+qOMe9b68uY161ly5aqCxmRat/BB/HA9n8A8kKxzBVrK8xzcUaJkiu/HPInIjxwNuwa8SecIu7fdIPtqjvGvG8lIjIXOvs19csvv8DJyana9KysLPH1unXr1FrW3LlztRaXubO0NNwP6JCQEEgkEgQGBhosBm2ytLSEh4eHocMwSWxb3WC76o4h9626Zsh1+/HHH9G8eXNMmjTJYDFoU5XvYGYyEDoeKM4V30+xssTbLVsgX0nHg/XfI7F7yodoametj3AbFO7fdIPtqjumnDeIiBoKne2Jf/31V6XvVZ4R+f7779VaFjvEGr5NmzZh9uzZAAArKytMnjzZwBEREZExW7t2LRYsWAALCwtYWlrC19fX0CFpz5N0IHQckP9QnPTAwgKzW7ZAtpJhA6R/D0C432do2aSxnoIkIiIiMm06ufZZEASt/VHD9+OPP+LNN98UP9MpU6YgPDzc0GEREZGR+uqrr7BgwQIAQFlZGfz8/HDw4EHDBqUtBVlAqC+QkypOypZKMbtlCzxQdsVIXneEjluB9s72egqSiIiIyPRp/Qqxbdu2aXuRpEWlpaWwttbvrRa3b9+u8n95eTliY2Mb/C0wpaWlSE5OBgB4eHjwyZhaxLbVDbar7pSWlho6BJ3Rd94QBAEpKSnVYvjrr7/wyiuv6C0OXSjNy4J8yyuwybomTsuXSPB2S2ekWNf8fSzP90TwyFXo2qapvsJskLh/0w22q+6Yct4gImootN4h1rt3b20vkhq4L7/8EiUlJfjmm28AAB9//DGWL19u2KC0QBAE5Ofni69Je9i2usF2pYZAIpHgu+++Q2lpKTZu3AgAWLFiBZYsWWLgyOrP8ocXYFXwWPy/WAK86+KMK40a1Vi+rMANXw38Gv2fc9FXiA0W92+6wXYlIiJTxtEcSeckEglWr14NuVwOZ2dnfPLJJ4YOiYiIjJhUKsX69eshl8vh5eWF999/39Ah1V/x34BCZ5gcwIfOzRFjU/OYYGVFLbG05yqM7tJOTwESERERmRd2iJFeSCQSfPvtt3zENBERqUUqlWLz5s2mkzckFqhcEwHA582dEGVnW2PR8pJmeMvz3wjo3VFv4RERERGZG50Mqk/mSRCEWsdDMJmDGiIi0gqzyhvWthBsm0MAsLqpI/Y41DxAfnmpAya2+hzzX+qh3/iIiIiIzAw7xEgrBEHA0qVLMXbsWBQXFxs6HCIiMnKCIGD+/Pnw8/Mzm8Glp3XshUCXVghxfKbG94WyxvBx/BifjR6o58iIiIiIzA87xKjeBEHAokWL8OWXX+LIkSOYOHEiSkpKDB0WEREZqfLycrz99ttYt24d9uzZg4CAAMjlckOHpXPZJcAl25pHqxDKrdDdejG+GT/atK6MIyIiIjJS7BCjelu6dClWr14t/n/w4EH4+/vzaURERFSjBQsWYMOGDeL/O3fuxMyZMw0YkX5YSexR/HAEBKHqzy9BsICH8A42vz4RFlJ2hhERERHpAzvEzIxUqv2PfNy4cXBwcBD/l0gkePXVV03+DLeFhQXatGmDNm3awMLCwtDhmBS2rW6wXXVHF/tWY6GLdZswYQJsbGzE/y0sLDBmzBit12Ns7uQ+grXTKRQ/HAmhvOJKMUGQoGXxdPwaEAhrS9PdjnSN+zfdYLvqjinnDSKihoJ7YjOjix8zffr0we+//w47OztIpVKEhoZi2rRpWq/H2FhYWMDFxQUuLi78kahlbFvdYLvqjim3py7WbfDgwTh48CAaN24MS0tL7Ny5E//4xz+0Xo8xklgWoJFzFIofjYBQ1gilj4fhtylvw9aaD/6uD+7fdIPtqjtsTyIiw+OvL9KK/v374/Dhw3jw4IHZHNQQEVHd+fj4YN++fSgqKsLYsWMNHY5eeLZ0wOVHgERagkbOESh+PBTdnsuFo621oUMjIiIiMjvsECOtGTRokKFDICKiBmTEiBGGDsFgJNIyNG5xFI0adTV0KERERERmiR1iZqa0tBTW1nU7E11aWopbt27B09NTy1E1TCUlJYiPjwcAdOnSpc7tStWxbXWD7ao7paWlhg5BZ+qTN4qKinD37l106NBBy1E1TFuHbeV3UEe4f9MNtqvumHLeICJqKDiGGKmlpKQEfn5+ePHFF3Hp0iVDh0NEREausLAQ48aNQ//+/ZGYmGjocIiIiIiIqmCHGKlUXFyMiRMnYs+ePcjJycHw4cPFs4VERERPKygowNixYxEREYFHjx5h6NChuH79uqHDIiIiIiISsUOMVFq5ciUOHDgg/p+ZmYmxY8eipKTEgFEREZGx+uSTTxAZGSn+/+DBA/j6+qKsrMyAURERERER/Rc7xEilDz74AEOGDBH/t7Ozw08//cRxJIiIqEaffvop+vbtK/7fpEkThISEwMLCwoBRERERERH9FzvESCVbW1scOHAAAwcOhIODAyIiIvhESSIiUsrBwQG///47XnjhBTRt2hRRUVHo3bu3ocMiIiIiIhLxKZOkFjs7Oxw6dAjJycnw9vY2dDhERGTkmjRpgoiICNy7dw+dO3c2dDhERERERFWwQ4zU5uDgwM4wIiJSW9OmTdG0aVNDh0FEREREVA07xMxMbeO3ZGdnIywsDEFBQZBIJHqMqmGytLSEp6en+Jq0h22rG2xX3THlsbFqW7dHjx5h3759mDVrlh4jarj4HdQdtq1usF11x5TzBhFRQ8HMZmak0pqHjcvMzMSIESMQGxuLjIwMfPrpp3qOrOGRSqWwt7c3dBgmiW2rG2xX3VG2bzUFytbtwYMHGDp0KBITE5GTk4NFixbpObKGh99B3WHb6gbbVXdMOW8QETUU3BMTHj16BB8fH8TGxgIAPvvsM6xYscLAURERkbFKT0/HSy+9hMTERADA4sWLsXbtWgNHRURERESkPnaIAbh37x6+/PJLvPzyy/D29kbv3r0xYcIEbNq0CYWFhVqr5+TJk3jnnXcwaNAgdO7cGYMGDcI777yDkydPaq0OVcrLy2uM6/Lly1Wmff/998jOztZXWA1SeXk58vLykJeXV2O7Ut2xbXWD7ao7ptyeNa3bsWPHcP369SrT1q5di/z8fH2F1SDxO6g7bFvdYLvqDtuTiMjwzP6WyejoaCxevBh5eXnitMLCQuTm5iIhIQHh4eEIDg5Gu3bt6lxHeXk5li1bht9++63K9IyMDGRkZCAyMhKTJk3C559/rvPLp8vKyqpNmzhxItavX4+33noLAODq6oro6GgOhKyCXC4XDwi7dOkCa2trA0dkOti2usF21Z2a9q2moqZ1CwwMRGZmJt5//30AgIeHB6Kjo2FnZ6fv8BoUfgd1h22rG2xX3THlvEFE1FCYdYdYYmIiFi5ciKKiItja2iIoKAh9+vRBUVERDh8+jJ07d+L27duYPXs2du3aVecxFNasWSN2hnXq1AmzZs2Cq6sr7t69i02bNiExMRHh4eFwcnLCe++9p81VVFtQUBDkcjm++uorREdHw93d3SBxEBFRw/Dee++htLQUW7ZsQXR0NFq3bm3okIiIiIiI1GbWHWJffPEFioqKYGlpiS1btqB79+7ie3379kW7du2watUq3L59G1u3bsW8efM0riMlJQVbtmwBAHTu3Bnbt29H48aNAQBdu3aFj48PAgICkJCQgM2bN2PChAn1uhqtPt555x1MmzaNZ/iJiEgtH374IebOncu8QUREREQNjtmOIXb58mVcuHABADBhwoQqnWGVZsyYAQ8PDwDAtm3bUFpaqnE9P/30E+RyOQBg2bJlYmdYJRsbGyxbtgxAxWXpISEhGtehTTyoISIiTTBvEBEREVFDZLYdYpGRkeLrCRMm1FhGKpVi3LhxAIAnT54gJiZGozoEQUBUVBQAoH379vD29q6xnLe3t3iLYlRUFARB0KgeTUyfPh2PHz/W2fKJiMi0zJw5Ezk5OYYOg4iIiIhIq8y2Q+zixYsAAFtbW3h5eSkt98ILL4ivY2NjNaojLS0NDx8+rLacmvTu3RtAxUD7aWlpGtWjiZMnT2L48OHIysrSWR1ERGQ6jh8/jpdffhlPnjwxdChERERERFpjtmOIJScnAwDatm0LS0vlzdC+fftq86grKSmpxuWoqufWrVtwdXXVqK7aKF5x1qxZM6Snp2PSpEkIDQ1FkyZNxPcsLCzEp1yWl5erfPqNlZWV+LqsrEzl46MVy8vl8lqvhJNIJFU+F1W3qyrGLgiCeJuqMpaWlpBIJAA0X9fK2BVjKigoEP/XNHapVAoLCwu1y5vD56S4nMrXdf2ctBW7KXxOyrbZ2mLX1/dJVeyVjPVzKiwsFKfr8ipffXk6b6SkpMDf3x9bt26tcoukMW7nlZg3/sscPifmDeaNp2OvZKyfk6nlDSKihsgsO8SKi4uRnZ0NAGjZsmWtZZs0aQJbW1sUFBTgwYMHGtWjWF5VPYrv379/X6N6VFH8URIaGlqlHm3XZa407Swl9V27ds3QIZgkbrO6I5fL0ahRI0OHUS/K8sbdu3cNEY5J4ndQd5g3dIPbrO6YQt4gImqIzPKWyfz8fPG1ra2tyvI2NjYAKs6M6aqeyjrqUg8REREREREREanPbK8Qq6R4GbMy1tbWAICioiKd1VNZR13qUcXGxgYdO3YEUPUSdSIi0g7FW4QUT3A0VMwbRES6ZWp5g4ioITLLDjHFS5JVjRMAACUlJQCAxo0b66yeyjrqUo8qUqm0ypgvRESkfaZ0uwvzBhGR7plS3iAiaojM8pZJxR/56tyeWDnopTq3V9a1HsWBNTWth4iIiIiIiIiI1GeWHWKNGjWCo6MjAKgcKD83N1fszFI1MP7TFMurqkfx/WeffVajeoiIiIiIiIiISH1m2SEGAM899xwAIDU1tdZHQ9+6dUt87eHhUac6nl6Oqnrat2+vUT1ERERERERERKQ+s+0Q69mzJ4CKWxmvXLmitNyff/4pvu7Ro4dGdbRp0wYtWrSotpza6nFxcUGbNm00qoeIiIiIiIiIiNRnth1iw4YNE1/v2rWrxjLl5eXYu3cvAOCZZ55Bnz59NKpDIpFg6NChACquAIuLi6uxXFxcnHiF2NChQ/k0LyIiIiIiIiIiHTLbDrGuXbuiV69eACo6xC5dulStzJYtW5CcnAwAmDp1KqysrKq8HxMTA09PT3h6euKjjz6qsZ7AwEBYWFgAAJYvX46ioqIq7xcVFWH58uUAKh5tHxgYWL8VIyIiIiIiIiKiWplthxgAfPzxx2jcuDHkcjlmzJiBDRs2IC4uDufOncMnn3yCVatWAQDc3Nwwffr0OtXh7u6OmTNnAgASEhLg7++Pw4cPIz4+HocPH4a/vz8SEhIAADNnzoSbm5tW1o2IiIiIiIiIiGpmaegADKlTp05Ys2YNFi9ejLy8PKxevbpaGTc3NwQHB8Pe3r7O9SxcuBCZmZnYtWsXEhMTsXDhwmplJk6ciAULFtS5DiIiIiIiIiIiUo9Zd4gBgI+PD/bv349t27bhxIkTyMjIgJWVFdq2bYuXX34ZAQEBsLGxqVcdUqkUK1aswMiRIxEWFob4+HhkZ2ejadOm6NKlC/z8/DB48GAtrREREREREREREdVGIgiCYOggiIiIiIiIiIiI9MWsxxAjIiIiIiIiIiLzww4xIiIiIiIiIiIyK+wQIyIiIiIiIiIis8IOMSIiIiIiIiIiMivsECMiIiIiIiIiIrPCDjEiIiIiIiIiIjIr7BAjIiIiIiIiIiKzwg4xIiIiIiIiIiIyK5aGDoDUd+/ePYSGhuLEiRN48OABrK2t4erqilGjRuGNN96AjY2NVuo5efIkdu7cifj4eGRlZcHJyQldunTBP/7xDwwePFgrdeiLLtussLAQf/zxB06fPo2EhASkpqaioKAA9vb2cHNzw4ABA/D666/D2dm51uVMmTIF58+fV6vO69ev1zleQ9Bl++/evRtLlixRq+zKlSsxfvz4OtelT7pqs7S0NAwdOlSjeVq3bo3o6Ohq001tm83MzMTly5dx+fJlxMfHIz4+Hjk5OQAAX19ffPnll1qv8+DBg9i9ezeuX7+OJ0+eoHnz5ujZsyfeeOMNdO/eXWv1MG9ojnnDsJg3NMe8oX+mnDeIiMwJO8QaiOjoaCxevBh5eXnitMLCQuTm5iIhIQHh4eEIDg5Gu3bt6lxHeXk5li1bht9++63K9IyMDGRkZCAyMhKTJk3C559/DqnU+C8u1GWbXbt2Df7+/igoKKj2Xk5ODuLi4hAXF4eQkBAsX74co0ePrte6NET62GZNjbG1mbu7u17qMbR+/frpra6ioiLMnz8fJ0+erDI9PT0d6enpOHToEN555x3MnTu33nUxb2iOecOwjG0f2BAYW5sxb2ifPvMGEZG5YYdYA5CYmIiFCxeiqKgItra2CAoKQp8+fVBUVITDhw9j586duH37NmbPno1du3bB3t6+TvWsWbNGPKjp1KkTZs2aBVdXV9y9exebNm1CYmIiwsPD4eTkhPfee0+bq6h1um6zvLw88aCmR48eGDJkCDp37gxHR0dkZWXh6NGjCA8PR15eHhYtWgQ7OzuVV0l07twZK1eurPM6GxN9bbOVNm/ejBYtWih9v2XLlvVavj7ous1cXFxw4MABleU2bNiAgwcPAgDGjRtXa1lT2mYrtWrVCu3bt8epU6d0svylS5eKBzV9+vTB1KlT0aJFC9y4cQMbNmxAamoqvvvuOzg7O8PPz6/O9TBvaI55w7CYNzTHvGEcTCVvEBGZJYGM3uTJkwWZTCZ06tRJiI2Nrfb+xo0bBZlMJshkMuHbb7+tUx23bt0SOnXqJMhkMmH8+PFCYWFhlfcLCgqE8ePHi3Hcvn27TvXoi67b7OLFi8K7774r3Lx5U2mZY8eOCZ6enoJMJhOGDRsmlJeX11guICBAkMlkQkBAgMZxGCt9bLO7du0Sl3H37t36hmxw+mgzVeRyuTBgwABBJpMJ3bt3r7YfqGRq2+zatWuF6Oho4dGjR4IgCMLdu3fFtv7www+1Vs+ZM2fE5QYFBQlyubzK+5mZmcJLL70kyGQyoVevXkJOTk6d62Le0BzzhmExb2iOecNwTDFvEBGZI+O/f8HMXb58GRcuXAAATJgwocYxAmbMmAEPDw8AwLZt21BaWqpxPT/99BPkcjkAYNmyZWjcuHGV921sbLBs2TIAgFwuR0hIiMZ16Is+2qxHjx745ptv8NxzzyktM2zYMIwYMQIAkJqaisTERI3qaKj0tc2aEmNpszNnzuDhw4cAgJEjR1bbD5iq+fPnY8iQIWjevLlO69myZQsAwNLSEp999hksLCyqvO/k5IRFixYBAJ48eYLw8PA61cO8oTnmDcMyln1gQ2Isbca8YRp5g4jIXLFDzMhFRkaKrydMmFBjGalUKl6i/uTJE8TExGhUhyAIiIqKAgC0b98e3t7eNZbz9vYWx4aIioqCIAga1aMv+mgzdfXp00d8nZqaqpM6jI0xtX9DYSxttnfvXvG1r6+v1pdvzvLy8nD27FkAQN++fZXejjV8+HDxtibF7UITzBuaM5bvIMC8Yej2byiMpc2YN3RHn3mDiMhcsUPMyF28eBEAYGtrCy8vL6XlXnjhBfF1bGysRnWkpaWJZ/cUl1OT3r17A6gYMDktLU2jevRFH22mrpKSEvH102f1TJUxtX9DYQxtlpeXJ3ZwtG7dWuW+gDQTHx8vXp1RuR+tibW1tdi5pDiPJpg3NGcM38FKzBvMG+owhjZj3tAtfeYNIiJzxUH1jVxycjIAoG3btrC0VP5xtW/fvto86kpKSqpxOarquXXrFlxdXTWqSx/00WbqUnzEeOVtC8rcunULkyZNQkpKCoqLi9G0aVN4eXlh5MiRGDNmDKysrHQSo7YZov2XLFmClJQU5OTkwM7ODu3atUPfvn0xefJkuLi41GvZ+mAM22xERAQKCwsBAK+99hokEonKeUxlm9UHxc9L1X7W3d0dp06dglwux507d2q9xa62upg31GcM38FKzBvMG+owhm2WeUO39Jk3iIjMFa8QM2LFxcXIzs4GoPppR02aNIGtrS0A4MGDBxrVo1heVT2K79+/f1+jevRBX22mjmvXrolPBZLJZCoPbB4/fozLly/j77//RklJCTIyMhAdHY0PP/wQ48aN09nBlzYZqv3Pnz+PR48eobS0FDk5Ofjrr7+wfv16DB8+HDt27KjXsnXNWLZZxdteVD0lrJIpbLP6ovh5qTrYfvbZZ8XXmu5nmTc0ZyzfQYB5g3lDPcayzTJv6Ja+8gYRkTnjFWJGLD8/X3xd+WOmNjY2NigoKBAf666LemxsbMTXmtajD/pqM1VKSkrw8ccfo6ysDACwcOFCpWUlEgn69u2LwYMHo2PHjnB0dER+fj6uXLmCsLAwJCcnIykpCVOnTkV4eDhatWql1Vi1Sd/t7+rqiuHDh6N79+7iQUFaWhoiIiIQERGB4uJifPrpp5BIJEb7KHJj2GbT09Px559/AgC6d++Odu3a1VrelLZZfdHXfpZ5Q3PG8B0EmDcA5g11GcM2y7yhe6a0nyUiMlbsEDNixcXF4mt1LiG3trYGABQVFemsnso66lKPPuirzVT5/PPPkZCQAKBikFkfHx+lZdetW4dnnnmm2vRevXph8uTJWLZsGfbs2YPHjx9jxYoVWLdunVZj1SZ9tv/w4cPh6+tb7RaNrl27YvTo0Th+/DjmzZuH0tJSrFy5Ej4+PnB2dta4Hl0zhm12//794mDn6pzlN6VtVl8UP2fF/WhN6rOfZd7QnDF8BwHmDYB5Q13GsM0yb+ievvIGEZE54y2TRqxRo0bia3UGyKwciFfTR15rUo/iYL/G+GhtfbVZbTZs2CA+9rpLly745JNPai1f0w/ESlZWVvjXv/4lPqXt2LFjyMjI0Fqs2qbP9ndwcKh1vJIhQ4Zgzpw5AIDCwkL89ttvGtehD8awze7btw9AxQ/q0aNHqyxvStusvih+zor70ZrUZz/LvKE5Y/gOMm9UYN5QjzFss8wbuqevvEFEZM7YIWbE7OzsxNfqXP5cObCpOpfP17WeyjrqUo8+6KvNlNmxYwdWr14NoGIA1ODg4Hov29LSEhMnThT/Vxxw2dgYuv2f5ufnJx78VN7aYWwM3WaXL1/GrVu3AAA+Pj61HrSoqyFts/qir/0s84bmDP0dZN5g3tCUoduMeUM/TGk/S0RkrNghZsQaNWoER0dHAKoHQs3NzRWTpaoBVp+mWF5VPYrvKw7gaSz01WY1OXjwIP75z38CqHj8+NatW+Hk5FTv5QKo8rQgYz5rasj2r0mzZs3EeIy13QzdZnUZFFkdDWWb1RfFz0tVeygOiKzpfpZ5Q3PMG4Zl6H3g05g3VGPe0A995Q0iInPGDjEjV/njIDU1FXK5XGm5yjN1gOrHtCur4+nlqKpH1SOgDUUfbfa0qKgofPjhhygvL4ezszNCQkK0+mNdnUeZGwtDtH9tGkLbGarNSktLcejQIQAVB4EDBw6s9zIrNYR21yfFz0vVfjYlJQVAxRUTqgaqrgnzhuaYNwyLeUNzzBumT595g4jIXLFDzMj17NkTQMWl0leuXFFaTvGy/h49emhUR5s2bdCiRYtqy6mtHhcXF7Rp00ajevRFH22m6OzZs1iwYAHkcjkcHR2xdetWtG3bts7Lq0lSUpL4uvKzMlb6bv/aZGVliY+mN+Z2M1SbnTx5Ejk5OQCAV155BZaW2nvOSkPaZvWhS5cu4uDXtd0KVFJSgri4uGrzaIJ5Q3PMG4bFvKE55g3Tp8+8QURkrtghZuSGDRsmvt61a1eNZcrLy8XL15955hn06dNHozokEgmGDh0KoOIMVGVSfVpcXJx4hmro0KFGeyZPH21WKTY2FnPmzEFJSQkcHBywefNmdOjQoU7LUkYul1dZjxdeeEGry9c2fba/KmFhYeJTsIy53QzVZoq3vfj6+tZ7eZUa2jarD/b29ujbty+Ais4QZbc5HTt2DHl5eQCqbheaYN7QHPOGYTFvaI55w/TpM28QEZkrdogZua5du6JXr14AKn7wXLp0qVqZLVu2IDk5GQAwderUameGYmJi4OnpCU9PT3z00Uc11hMYGAgLCwsAwPLly6s9srmoqAjLly8HUHE5dmBgYP1WTIf01WZXr15FUFAQCgoKYGtriw0bNqBz584axXru3Dk8efJE6fulpaX4n//5HzHWIUOGGP3YEPpo/7S0NCQmJtYax/Hjx/H9998DqHji0oQJE+q0Pvqgr21WUU5ODk6cOAEAkMlkeP7559WK1RS3WW3YvXu32P7fffddjWVmzJgBoOLA75///CfKysqqvJ+VlYWvvvoKQMXB66RJk+oUC/OG5pg3DIt5Q3PMGw2fMeUNIiJzpb3rnElnPv74Y/j7+6OoqAgzZszAW2+9hT59+qCoqAiHDx9GWFgYAMDNzQ3Tp0+vUx3u7u6YOXMmgoODkZCQAH9/f7z55ptwdXXF3bt3sXHjRvGH5MyZM+Hm5qat1dMJXbdZamoqZs6cKf7Ae/fdd+Hg4IAbN24onadZs2Zo1qxZlWl79+7F22+/DR8fH/Tp0wfu7u6wt7dHfn4+rly5gp07d4q3EDRr1gwff/yxxrEagq7b/969e5g6dSq6d++OIUOGoGPHjuJA1Hfv3kVERAQiIiLEs/wffPABXFxctLeCOqCP77miQ4cOobS0FIBmZ/lNcZu9cOECUlNTxf8rb5cCgDt37mD37t1Vyo8fP75O9fTt2xdjxozBoUOHEB0djenTpyMwMBAtWrTAjRs3sH79eqSnpwMAFi1ahCZNmtSpHoB5oy6YNwyLeUNzzBuGY4p5g4jIHLFDrAHo1KkT1qxZg8WLFyMvL098PLsiNzc3BAcHw97evs71LFy4EJmZmdi1axcSExOxcOHCamUmTpyIBQsW1LkOfdF1m124cAGZmZni/ytXrlQ5z9y5czFv3rxq0wsKCnDw4EEcPHhQ6bwymQxr1qyBq6urxrEagr622UuXLtV4VrySjY0NlixZAj8/vzrXoS/6arNK+/btAwBYWFjg1Vdf1WheU9tmf/vtN+zZs6fG92JjYxEbG1tlWl0PbABgxYoVyMvLw8mTJxETE4OYmJgq70ulUsyZM6fe2yzzhuaYNwyLeUNzzBuGY4p5g4jIHLFDrIHw8fHB/v37sW3bNpw4cQIZGRmwsrJC27Zt8fLLLyMgIAA2Njb1qkMqlWLFihUYOXIkwsLCEB8fj+zsbDRt2hRdunSBn58fBg8erKU10j19tFl9vfnmm+jYsSPi4uKQnJyMrKws5ObmwsrKCs2bN0fnzp0xcuRIDB8+XLw1qaHQZft7eXlh1apViIuLQ0JCAh49eoTs7GzI5XI0adIEzz33HPr27YtJkyZVu7rCmOlrm719+zb++usvAEC/fv3g7Oys9rymvM3qQ+PGjREcHIwDBw5gz549uHbtGp48eYLmzZujZ8+eCAgIQPfu3bVSF/OG5pg3DIt5Q3PMG6ZPn3mDiMjcSITKa8OJiIiIiIiIiIjMAAfVJyIiIiIiIiIis8IOMSIiIiIiIiIiMivsECMiIiIiIiIiIrPCDjEiIiIiIiIiIjIr7BAjIiIiIiIiIiKzwg4xIiIiIiIiIiIyK+wQIyIiIiIiIiIis8IOMSIiIiIiIiIiMivsECMiIiIiIiIiIrPCDjEiIiIiIiIiIjIr7BAjIiIiIiIiIiKzwg4xIiIiIiIiIiIyK+wQIyIiIiIiIiIis8IOMSIiIiIiIiIiMivsECMiIiIiIiIiIrPCDjEiIiIiIiIiIjIr7BAjIr3avXs3PD094enpibS0NK0tNyYmRlxuTEyM1pZLRESGxbxBREREumBp6ACI9C0mJgZTp04V/7e1tcWZM2dgY2NT63xFRUXo378/8vLyxGnbtm1Dnz59dBYrEREZHvMGERERkenhFWJk9goKChAZGamyXFRUVJWDGiIiMk/MG0REREQNHzvEyKw1atQIALBv3z6VZSvLVM5DdTN+/Hhcv34d169fR5s2bQwdDhGRRpg39I95g4iIiHSBHWJk1nx8fAAAZ86cwaNHj5SWy8zMxOnTpwEAQ4cO1UtsRERkfJg3iIiIiEwDO8TIrPXv3x/Ozs4oKyvDoUOHlJY7ePAg5HI5nJ2d0a9fPz1GSERExoR5g4iIiMg0cFB9MmsWFhYYM2YMQkJCsG/fPkybNq3GcpW3vbzyyiuwsLBQe/nnzp3Dnj17cOHCBTx+/BgWFhZo3bo1BgwYgGnTpsHFxUXpvDdu3EBkZCQuXryImzdvIisrC1ZWVnB2dkb37t3h7+8Pb29vpfN/9913WLduHQDg+vXrKC4uRmhoKA4dOoTbt28DADw8PDBu3Di8/vrrsLSs++7A09MTADB37lzMmzcPZ8+exa+//oq//voLjx8/houLC6KjowFUPC1syZIlACrG16np9peEhAT88ssvuHjxIjIyMlBWVgYnJyc0a9YM3bp1w4ABA+Dj4wOJRKJRnLm5uZg9ezbi4uJgaWmJlStXYuzYsRot48mTJ9i+fTtOnDiBW7duoaCgAA4ODnBycoK7uzv69++PESNGoHnz5jXOX1JSgj179iAqKgpXr15FdnY2LC0t0bp1a3h7e+Pll1/GgAEDaly3/Px8bN++HVFRUUhJSUFBQQGcnJzg7e0NX19fDBkyRGncU6ZMwfnz59G7d2+Ehobi9u3b2LZtG06dOoWMjAwUFRVV+zyKi4sRHh6OY8eOISkpCbm5uXBwcICnpyfGjBkDX1/fem03RA0R8wbzBvMG8wYREZkGZiQye6+99hpCQkKQmJiImzdvokOHDlXeT0pKwpUrV8SyV69eVbnM4uJiLFmypMarB27cuIEbN25gx44d+Prrr8XbbxQ9/USzSqWlpbhz5w7u3LmDvXv3Yvbs2Xj//fdVxvP48WPMmjWrWuzx8fGIj4/HqVOn8MMPP0Aqrf9Fo2vWrMH69evrPH9ISAj+/e9/o7y8vMr0Bw8e4MGDB7hy5Qp++eUXxMbGws7OTu3lZmRkYNasWbhx4wYaN26MtWvX4qWXXtIotuTkZEybNg0PHz6sMj07OxvZ2dlITk5GZGQkysvLERAQUG3+q1evYu7cuUhLS6syvbS0FElJSUhKSsJvv/1W4wFfYmIigoKCqtWdkZGBiIgIREREYMSIEfjqq69UjlcUGRmJxYsXo6CgQGmZa9euYc6cObh3716V6VlZWTh79izOnj2LsLAwrF+/XulBHJGpYt5g3lAX8wbzBhERGS92iJHZ69SpEzp06ICbN29i3759WLRoUZX3K8/yy2QyPP/88yoPbARBwPz583HixAkAwJAhQzBq1Ci4urpCKpXi8uXL2Lp1K9LT0zF//nz8+uuv6NKlS5VllJWVwdbWFoMHD8aLL76I9u3bw97eHpmZmUhKSkJoaCju3buH4OBguLm5YcKECbXGNHfuXCQlJWHKlCnw8fFBkyZNkJKSgh9++AHJyck4fvw4du7ciddff13D1qvq6NGjuHHjBmQyGaZNm4YOHTqguLhYrYNBoOLHdOVBTZs2bRAQEICOHTvC0dER+fn5SElJwblz58SrBtSVmpqK6dOnIy0tDfb29li/fj1eeOEFjddv8eLFePjwIaysrDBp0iQMGjQIzZs3hyAIePDgAeLi4pQ+eS45ORmTJ08WDyaGDx+O0aNHw9XVFeXl5UhJScHp06drnD8jIwPTpk1Dbm4uJBIJfH19MWbMGDg6OiIpKQlbt27FtWvXcPToUXz00UdYs2aN0nVIT0/H4sWL0bhxY7z99tvo1asXLCwsEB8fD1tbWwDAnTt3EBAQgL///hv29vZ444030LVrV7Rs2RI5OTmIjo5GWFgY4uPjMWfOHGzfvh1WVlYatydRQ8W8wbyhLuYN5g0iIjJiApGZOXfunCCTyQSZTCbs2rVLEARBCA4OFmQymTB48GChvLxcLFteXi4MHjxYkMlkwsaNGwVBEIRdu3aJ8587d67a8sPCwgSZTCZ4eXkJJ0+erDGGnJwcYcyYMYJMJhNef/31au9nZmYKubm5StehuLhYmD59uiCTyYQhQ4YIcrm8Wplvv/1WjNPLy6vGWLOzs4V+/foJMplMePXVV5XWp0plPTKZTAgMDBSKi4uVllVsv7t371Z575tvvhFkMpng7e0tPHr0SOkynjx5IpSVlVWZpvi5Kq7rtWvXhP79+wsymUx48cUXhYSEhDqtY2pqqrj80NBQpeXKy8uFnJycatN9fX0FmUwmdOzYUTh48KDS+bOysoTCwsIq0+bNmyfWvXPnzmrzFBcXC1OmTBHLnDhxolqZgIAA8f0BAwYI9+7dUxqDn5+fIJPJhHHjxgmZmZk1ljl58qTQsWNHQSaTCWFhYUqXRWQKmDf+i3lDfcwbVTFvEBGRseGg+kQAxo4dC6lUivv37yMmJkacHhMTg/v370MqleLVV19VuRxBELBx40YAFWNvDBo0qMZyTZo0weLFiwEAsbGx4tgslZycnPDMM88orcfa2hoffPABAODevXsqz6QHBASgT58+1aY7Ojpi/PjxACpuyfn7779rXY4qUqkU//rXv2BtbV2n+R8/fgwAcHNzq/V2CgcHB7Vu04mNjcWUKVPw6NEjPPvss9i+fTu8vLzqFJvi0+R69eqltJxEIkGTJk2qTDt16pR4+9SUKVMwZswYpfM3bdoUjRs3Fv/PyMgQz/4PHDgQkyZNqjaPtbU1VqxYIY7Lsn379lrX5f3330erVq1qfO/ChQu4dOkSAODLL7+Ek5NTjeUGDRqEkSNHAqgY34fI3DBvMG+owrxRFfMGEREZG3aIEQFwcXERf/hX3uqi+PrFF1+sdSDjSklJSUhNTQUA8UefMoq3XsTFxdVatqSkBOnp6UhKShLHkhEEQXz/2rVrtc5f20FZ5Q99QRCqjVGiqR49etQ42LG6nJ2dAVS04+XLl+sVyx9//IEZM2YgNzcX7u7u+PXXX9G+fft6xwYAe/bs0WjeytugACAwMFCjec+fP4+ysjIAwMSJE5WWa9OmjfgkO8V5nmZlZYVRo0YpXU5UVBQAwN3dXRz0WpnKbTghIQFyubzWskSmhnmDeUPd2ADmjUrMG0REZEw4hhjR/xs3bhzOnj2Lo0eP4tNPPwUAREREAKgYFFkdCQkJ4ms/Pz+161Y8i1ypoKBAfLpXUlKS0h+qQMXgvLWp7Qe94lnp/Px8NaJVTtUPYVXGjBmD4OBglJSUwN/fHwMHDsTgwYPRs2dPdOjQQe2ng0VERGDnzp0oLS2Fl5cXNm3apPSMtbpcXV3Rq1cvXLhwASEhITh16hRGjBiB3r17w9vbGzY2NkrnTUxMBAC0atUKrVu31qjemzdviq+7detWa9lu3brhP//5DwoLC3H37l24ublVK+Pm5lbr4MmV23BKSoran2dpaSlyc3PRrFkztcoTmQrmDeaN2jBvKMe8QURExoAdYkT/b/jw4fjss8+Ql5eHqKgoCIKA/Px82NraYsSIEWotIzMzs051FxUVVfk/LS0NgYGBap95Ly4urvX92n50K95CUtvBkzpqu11HHR4eHvj666+xbNky5Obm4vjx4zh+/DiAiltCBg4cCD8/v1pvPQH+e+uHtbU1vv/++3of1FRavXo13n33XVy6dEl8utcPP/wAKysrdOvWDa+88grGjx9f7cCh8sBT8WoBdeXk5IivVR04KN4ulJubW2MZVZ9RVlaW+sEpKCwsrNN8RA0Z8wbzhirMG8oxbxARkaGxQ4zo/9nZ2WHYsGE4cOAA9u3bJ95aMmzYMPEpSqooPvJ9/fr1ap/VffoH6wcffIC0tDRIJBKMHz8eY8aMgYeHB5ycnGBlZQWJRILy8nI8//zzAFDlNhhDsrCwqPcyRo4ciX79+uHw4cM4deoULly4gKysLGRnZ2P//v3Yv38/fH19sWLFCqXjwYwYMQJHjx5FSUkJFi5ciE2bNsHe3r7esbm4uGDHjh3iFSF//vknkpKSUFpaigsXLuDChQvYsmULgoOD4e7uXu/6dEHVZ1R5cNuxY0esWrVK7eWqc2sYkalh3qg/5g3mDSIiIkNhhxiRgnHjxuHAgQM4ffp0lWnqcnR0FF87ODhAJpNpHENycjIuXrwIAAgKCsLChQtrLKd4BtjUODg4wM/PT7x9KDk5GVFRUQgNDcXDhw+xZ88ePP/880rHVQkICEC3bt2watUqXLp0CbNnz8bGjRthZ2enlfj69u2Lvn37Aqg4i3/27FmEhYXh3LlzSE1NxcKFC7F3716xfNOmTQHUfIuTKorbVGZmJp599lmlZSsHlwZQbYBmTesrKCio0/ZLZG6YN4wD88Z/MW8QERGph4PqEyno27cvnJ2dIZfLIZfL0aJFC/EHrDoqz7wDFU+qqoukpCTx9ejRo5WWUxx3xtR5eHhg9uzZ2Llzp3jVxe+//17rPLNmzcJ7770HALh48SKCgoJ0cntG06ZNMXr0aPz000/w8fEBAFy9erXKE+A6deoEAEhPT8e9e/c0Wn6HDh3E13/99VetZSsHlLaxsYGrq6tG9VSqjPXu3bt1OhAjMjfMG8aJeaMC8wYREZFy7BAjUmBhYYHXXnsN1tbWsLa2xmuvvabWY9oreXl5oWXLlgCAnTt3qhyjpSaK47HU9kN8x44dGi+7oXv22WfFAX9VDQgNVFwp8e677wIA/vzzTwQFBVUbd0ebFA+CFeOrPOABgJCQEI2W2bt3b/F2lV27diktl56ejjNnzlSbR1OVsQqCgG3bttVpGUTmhHnDuDFvMG8QEREpww4xoqcsXrwY8fHxiI+Px6JFizSaVyqVIigoCEDFmdIPPvgAJSUlSsvn5eXh559/rjKtXbt24uvdu3fXON8vv/wiPubclERGRuLJkydK379//z5u3boFoOJx8eqYM2cO5s6dCwCIiYnBW2+9VacDzqtXr+Lq1atK3xcEQTywkEgkVcYB6tevH7y8vAAAP//8Mw4dOqR0OdnZ2VUOvlxcXDBs2DAAwH/+8x/s2bOn2jwlJSVYunQpSktLAQBvvPGGBmtW1YABA9C1a1cAwObNm3H48OFay1+/fh3R0dF1ro/IFDBvGA7zBvMGERFRXXEMMSIt8/f3x5kzZ3Ds2DEcOXIEiYmJ8PPzQ9euXeHg4IC8vDzcunUL58+fR3R0NKytrREQECDO36lTJ8hkMty4cQNhYWF48uQJXnvtNTg7O+PBgwfYv38/IiIi0KNHjzrfXmOsfvrpJyxatAiDBw/Giy++CA8PDzg4OCA3NxcJCQn4+eefxR/9/v7+ai933rx5KCsrw48//oizZ89izpw5+PHHH2Ftba32Mq5evYolS5agS5cuGDJkCLy8vNC8eXPI5XKkpaVh9+7d4hhCPj4+aNGiRZX5V61ahYkTJ6KgoADvvfcejhw5gtGjR8PV1RXl5eW4c+cOTp8+jYiICBw4cKDKgdvSpUtx7tw55ObmYunSpbh48SJGjx6NZ555Brdu3cKWLVvEg65Ro0Zh8ODBaq9XTb7++mtMmjQJOTk5WLhwIfbv34/Ro0fDzc0NUqkUmZmZuHr1Ko4fP464uDjMmDGjytUMRKQZ5o26Y95g3iAiIqordogRaZlEIsGaNWvwxRdfYMeOHUhNTa31qUtPP9pdIpHgf//3fxEYGIjc3Fz8/vvv1cY9kclkWLt2LQYOHKiTdTCkwsJCHDlyBEeOHKnxfalUinnz5olnv9W1YMEClJWVITg4GKdOncLcuXOxbt06jQ5uAIhXgSjTvXt3fPHFF9Wme3h4IDQ0FHPnzsX9+/dx9OhRHD16VK06W7ZsiZCQEAQFBeHhw4cIDw9HeHh4tXIjRozAv//9b/VXRom2bdtix44dmD9/Pm7cuIHjx4/j+PHjSstra9BpInPFvFE/zBvVMW8QERGpxg4xIh2wsrLCZ599Bn9/f4SHhyMmJgb3799HQUEBbG1t0aZNG3h5eWHQoEEYMmRItfmff/557N27Fxs2bMAff/yBhw8fws7ODm3btsWoUaPwxhtvoFGjRgZYM936+uuvceLECcTExCA5ORmPHz9GdnY2rK2t0bp1a/Tq1Quvv/46OnbsWKflv//++ygrK8PmzZtx8uRJzJ8/H99++61aBzevvPIKmjVrhjNnziA+Ph4ZGRnIzMyEXC5Hs2bN0KlTJ4wePRpjxoxROn5Q586dceTIEYSHhyMyMhI3b95Ebm4urK2t0aZNG3Tv3h2jRo2q8baeTp064ciRI9i+fTsiIyORkpKCwsJCNG3aFN7e3vD19dXq2XZ3d3fs3bsXv//+O44ePYr4+HhkZWWhrKwMjo6OcHd3R8+ePTF8+HDxth4iqjvmjbph3mDeICIiqiuJIAiCoYMgIiIiIiIiIiLSFw6qT0REREREREREZoUdYkREREREREREZFbYIUZERERERERERGaFHWJERERERERERGRW2CFGRERERERERERmhR1iRERERERERERkVtghRkREREREREREZoUdYkREREREREREZFbYIUZERERERERERGaFHWJERERERERERGRW2CFGRERERERERERmhR1iRERERERERERkVtghRkREREREREREZoUdYkREREREREREZFbYIUZERERERERERGaFHWJERERERERERGRW2CFGRERERERERERmhR1iRERERERERERkVtghRkREREREREREZoUdYkREREREREREZFbYIUZERERERERERGaFHWJERERERERERGRW2CFGRERERERERERmhR1iRERERERERERkVtghRkREREREREREZuX/ALOO6xGCP2XjAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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MGjQIJ0+eVClfoUIFrbuheXt7a1Xu9OnTKssDBw7UKgHYpk0blC5dWli+cOGCxvdSWjnl/f0la2tr9OrVK9NyEokE/fr1E5blcnm6a2hq2s6wW7duXeGxpmsVFxen0vqvbt26KFeuXKbHdnBwQOfOnbWKg4iIiMjUmBAjojyhS5cuKjfOO3bswMePH00YkW4qVaoEa2vrTMt92V2pdu3aWh0/7X5KpVLtDIpv3rxRuWZpb5y1kTZREhERgffv32e6j67n0NaNGzdUljt16mSQ82SFUqmEXC5X+Zd2EPZUZmZm8Pb2xq5du7TqvmlpaamSGMpI2pZBEokELVu21Dr+1q1bC48TExPx5MmTTPfJCe9vderXrw9bW1utyn7Z8u7evXta7WcMEokEtWrV0qps2m6pmq7VgwcPVN6TuiStDZHgJiIiIjIEDqpPRHmCWCzG6NGjMXr0aAApN+pr1qzBjBkzTByZdrTtyvdlUkHb2Ry/3C8+Ph4FCxZUWfflrIa//PILxo8fr9XxAQizEKaKiopSO95QWpl1VcuqL2eV1DZRZCpisRi2trZwdHRE+fLlUatWLXTu3FmnLp4lS5aEubm5VmVfvXolPHZzc9MqWZWqcuXKKssvX75M17LrSznh/a1O2m6QmSlUqBBcXFwQFhYGAAgMDNR6X0NzcHCAlZWVVmW1uVZv375VWS5fvrzWsehSloiIiMiUmBAjojyjbdu2qFy5spDY2bt3L3744QeDz/CmD9omJL7s1pbV/b5MXgFIN+6aulZLukg7O50mDg4O2TqHJlFRUcJjkUgEFxcXg5wnK7p3726Qmfh0mQQgJiZGeOzs7KzTeb4sn/ZYmuSE97c6hQoV0qpcKmdnZyEhFh0drdO+hmRjY6N1WW2u1ZevqS5j/HEMMSIiIsot2GWSiPIMkUiEMWPGCMtSqRSrVq0yYUS5izYJLF1ok1AzMzPM7zJxcXHCY2tra7UzLuY12rYOA1Svjy7JFHXl0x4rt9GlZdyX5ePj4/UdTo7x5UyRury3sjorLBEREZGxsYUYEeUpzZs3R61ateDv7w8AOHjwIAYPHmywrnl5yZddrjZs2IBmzZqZKJrssbOzEx4nJCRAoVDki6SYtmxtbYVWQLomdr4sr+0YXDlRQkJClsvrmkjMTb5sbahL0lPb8duIiIiITI13B0SU5/z888/CY7lcjj///FPnY6TtVqRt9ysgd7ca+XIcoS+7UOYmabttKZVKoZsbpUjbVTU8PFynfb8sb6hur8bw6dMnncqnfe66dCPMbb6sC969e6f1vrqUJSIiIjIlJsSIKM+pV68eGjduLCyfOHFCq5nw0krbNUqXViS5aWbLL6WdpRNIP8h+bvLlwN45aUbAnKBMmTLC49evX+v0Hv/yfZGbW18+ffpU67IREREqiVV3d3dDhJQjVKxYUWX5wYMHWu97//59fYdDREREZBBMiBFRnjR27FjhsVKpxLJly3TaP22rl6ioKK27DN26dUun8+QkFStWVGlZde7cuWwPrG8q9erVU1k+fPiwiSLJmWrWrCk8lsvlOHfunNb7njlzRnhsZWWFSpUq6TU2Y7p586bWrTrTPm9A88ylX46Llxs/Q2XKlIGTk5OwfPLkSchkMq32PXLkiKHCIiIiItIrJsSIKE+qVq0aWrVqJSyfP38ed+/e1Xr/tK2llEol/Pz8Mt3n6dOnwthluZFYLMZXX30lLAcFBeHAgQMmjCjrPD09VVqJnT59Go8fPzZhRDlL2s8GAGzdulWrrsFnzpzB69evheUWLVoYbGIEY4iPj8e+ffsyLadQKLBjxw5hWSwWp7uGqb4cUy0nzUapLZFIhC5dugjL4eHh+PvvvzPd7+rVq7h586YhQyMiIiLSGybEiCjPGjNmjMpYYPv379d63xo1aqgsb9u2LcPyCQkJmDx5sk7x5UQ//fSTyoxys2fPxp07d3Q6Rnh4OJ49e6bv0HQ2ZMgQ4bFcLsfPP/+sU5dWXcaOy20qVKig0oru7t272LRpU4b7fPz4EbNnz1ZZN2DAAIPEZ0wrVqxQSfKps3HjRpVu182bN0exYsXUli1evLjKsi7dDXOSvn37qtQFS5YswdWrVzWWf/nyJSZMmGCM0IiIiIj0ggkxIsqzPD090bFjR2FZ2y4/AFCiRAnUrVtXWL58+TIWLVqk9hjPnz9H//798ejRI5UbyNyoSJEimDhxorCckJCAAQMGYO3atRnOHieTyXDlyhVMnjwZLVu2zPDG2Vg6deqEFi1aCMuvX79G7969cfr0aY37xMXF4eDBg+jWrRs+f/5shChN59dff1V5vy5atAgrVqyAVCpNV/bRo0cYOHAgQkNDhXVfffUVateubZRYDcXCwgIxMTH47rvv1HZ3lslkWLlyJZYsWaKyT0aJHw8PD5VZGtesWYMLFy4gMTFRv8EbWKlSpTBixAhhOSkpCT/++CNmzZqFhw8fIiEhAcnJyQgMDMTKlSvRs2dPhIeHq3THJSIiIsrJcm8/ByIiLYwaNQrHjx/XKRmWauzYsejXr5/QUmjjxo04duwYmjZtikKFCuHz5894+PAh7t69C4VCARcXF/Tt21fn8cpymv79++PNmzfYvn07ACA5ORlLly7F2rVrUbNmTbi7u6NAgQJISkpCTEwMXrx4gSdPnuS4GTZFIhEWLFiAfv364fnz5wBSZsAbMWIEihQpgvr166Nw4cIwMzNDVFQUnj17hocPH+a6xEVWVa1aFePHj8e8efMApLSIW7lyJXbu3IlmzZqhaNGiSEhIwIMHD+Dv76/SYq5MmTKYOXOmqULXm59++gnbt29HaGgo+vXrh2rVqqF69eqws7PDhw8fcPHixXSzak6YMCHDAfXNzMzQo0cPbN26FUBKy7rU1opWVlYQi//7LXLDhg2oU6eOAZ6ZfgwZMgTPnj3D0aNHAaS0tNyxY4dK99G0HB0d8ccff8DLy0tYJ5FIjBIrERERka6YECOiPM3NzQ3dunXTapygL9WpUwfjx4/HokWLhHUhISHYvXt3urKFCxfGmjVrEBAQkK14c4qpU6fC3d0d8+bNQ1JSEoCU1mJXr17VqvWXhYWFoUPUiqOjI3bt2oVx48bh4sWLwvr379/j4MGDJowsZ/juu+9gYWGB33//XUgaR0REwNfXV+M+NWrUwJo1a1QmnsitihUrhmXLlmH06NGIiYnB/fv3Nc6SKBKJMHbsWK26iY4dOxYPHz7E7du3VdZ/mWyVy+VZD94IJBIJFi9ejKJFi2Lr1q0Z/rDg6emJ5cuXqwzGDwB2dnaGDpOIiIgoS9hlkojyvBEjRmQ5QfPjjz9i5cqVKFWqlNrtlpaW6Nq1K3x9fVG5cuXshJnjeHt748yZM/jhhx/g4uKSaXk3Nzf0798fe/fuRZ8+fYwQoXbs7e2xYcMGrFu3DrVq1VJpofMlc3NzNGjQAIsWLco3N/J9+vTB4cOH0b59+ww/J25ubpgzZw527tyZLumRmzVs2BD79+9Hy5YtNbZmqly5MrZv346hQ4dqdUwbGxts374dS5cuRYcOHeDm5gZbW9sM33s5lUQiwa+//oqDBw9i0KBBKF++PBwcHGBlZYVSpUqhZcuWWLp0Kfbt2wc3NzdERkaq7J+2+ygRERFRTiJS5uVRg4mI9ESpVOLx48d4/PgxIiIiYGtriyJFiqBevXp5oqWMNgIDA/H06VNERkYiJiYGlpaWcHBwQIkSJeDh4aFV0iwniIqKwu3btxEWFoaoqChIJBI4OjrCzc0NlSpVSjdLYH6SkJAAPz8/vHv3DlFRUbC0tEShQoVQpUoVlClTxtThZVtwcLDK7JDz5s1Djx49hOWwsDDcuXMHHz58QEJCAlxdXVGtWjWULVvWFOHmSqdPn1YZe2z37t0cV4yIiIhyJHaZJCLSgkgkQuXKlfNcKzBduLu7Zzh2Um7h6OiokhSh/1hbW6Np06amDsNkXFxc0LZtW1OHkaudOnVKeGxmZoaKFSuaMBoiIiIizXJf230iIiIiynECAgJw5MgRYblBgwawsrIyYUREREREmjEhRkRERERqnT59GlKpNNNygYGBGDFihEpZb29vQ4ZGRERElC3sMklEREREas2aNQsKhQIdOnRAkyZN4OnpCScnJ5iZmSE6OhpPnz7FyZMnsX//fmFGWgBo0qQJuyYTERFRjsaEGBERERFpFBYWhm3btmHbtm3COpFIBE3zMnl4eGD+/PkQiUTGCpGIiIhIZ+wySURERERqWVtbq12vLhkmkUjQtWtX7NmzJ9fMOktERET5F1uIEREREZFahw4dwpUrV3Dz5k08evQIwcHBiIyMRFJSEiwtLeHo6Ag3NzfUrVsXHTt2ROnSpU0dMhEREZFWREpN7d2JiIiIiIiIiIjyIHaZJCIiIiIiIiKifIUJMSIiIiIiIiIiyleYECMiIiIiIiIionyFCTEiIiIiIiIiIspXmBAjIiIiIiIiIqJ8hQkxIiIiIiIiIiLKV5gQIyIiIiIiIiKifIUJMSIiIiIiIiIiyleYECMiIiIiIiIionyFCTEiIiIiIiIiIspXmBAjIiIiIiIiIqJ8hQkxIiIiIiIiIiLKV5gQIyIiIiIiIiKifIUJMSIiIiIiIiIiyleYECMiIiIiIiIionyFCTEiPQkODoanpyc8PT3h5eVl6nByhdTr5enpaepQiCifYx2uO9bhpKvhw4fD09MTTZo0QXx8vNoyhnhfJScnw8vLC56envj+++/1dlwiIsrdzEwdAJGh9O/fHzdv3lRZt2rVKrRu3VrrYyxYsACbNm1SWTdy5EiMGjVKLzGSYUilUpw9exYnT57Ew4cPERYWhqSkJNja2qJo0aKoVq0aOnbsiAYNGhg9NoVCgTt37uDatWu4d+8eAgMDERERAZlMhgIFCsDd3R3169dHr169ULhwYa2PGxwcjKtXr+LmzZt49uwZQkNDER8fD1tbWxQuXBg1a9ZEp06dUK9ePa2O5+Pjg0mTJmVYxsLCAvb29nBzc0Pt2rXRrVs3uLu7ax0zUUZYh+dfrMPzZh1+8eJFnDlzBkDK59DGxsZg5/qShYUFRo0ahd9++w1Xr17FyZMn0bZtW6Odn4iIciYmxChfOXjwoNY3U3K5HIcOHTJwRNkTHByMVq1aAQCKFy+Os2fPmjgi03v69CnGjx+P58+fp9sWHR2N6OhoPH36FP/88w8aNmyIBQsW6HTTkh2bN2/GX3/9hbCwMLXbw8PDER4ejhs3bmDNmjUYOnQohg0bBolEovGYjx8/xowZM3D//n2121Of87Nnz7Bnzx7Uq1cPCxYsQLFixbL9fJKTk/Hp0yd8+vQJt2/fxsaNG9GnTx/89ttvMDc3z/bxib7EOjzvYx2uKq/U4QqFAgsXLgQAFC1aFD179szW8dImzLdt24b69etnuk+XLl2watUqvH37FosXL4aXlxfMzHgrRESUn/FbgPKVc+fOITo6GgUKFMi07JUrVzT+0Us504sXLzBgwABER0cL60qVKgUPDw8ULFgQ79+/x6NHjxAVFQUAuHbtGvr164d9+/Zp9Z7IrsuXL6u8pywsLFC1alUUK1YM1tbWCAkJwe3bt5GYmAipVIoVK1bg1atXWLRoEcRi9T3cX716le5Gys3NDeXLl0fBggURExODO3fu4P379wCAmzdvonfv3ti5cydKliypVdy2trbo1q1buvUJCQkIDg7G3bt3kZycDIVCgb///hsRERFYunSplleFSHusw/M21uEp8mIdfvjwYSHJ+f3335vkRxOJRIJBgwZh5syZePPmDQ4cOICvv/7a6HEQEVHOwYQY5QseHh548eIFpFIpjhw5gj59+mS6z8GDB9Ptn5ESJUogICAg27FS1s2cOVO4kXJ0dMTMmTPRrl07iEQioUx8fDz++usvrFq1CkqlEkFBQVixYgWmTp1qlBhFIhGaNWuGr7/+Gs2aNYOlpaXK9oiICPz+++84fPgwgJSbiNq1a2f6ni1dujR69eqFrl27pmstoVAo4OPjgzlz5iAhIQEfP37E+PHjsXv3bpVro4mjoyOmT5+ucXtkZCRmzJiBEydOAACOHj2Kbt26oXnz5pkem0gbrMPzB9bhebcO37BhAwDA2to609Zhhvwcdu3aFYsWLUJ8fDw2btyIXr16aXUNiYgob+Kg+pQvfPXVV8KvkWlvkjSJjY3F6dOnAQAVK1ZE+fLlDRofZV9wcLDKeEO///472rdvn+4PXRsbG4waNUrl5uTQoUNQKpUGj7Fx48bw9fXF+vXr0aZNm3Q3UgDg5OSExYsXC92oAGD16tUa43NxccG8efNw7NgxDBkyRG3XIbFYjF69emHRokXCurt37+Ly5ct6eFZAwYIFsXTpUpQrV05Y9++//+rl2EQA6/D8gHV43q3Dr127hmfPngEA2rdvDzs7O70dW1e2trb46quvAACvX7/GhQsXTBYLERGZHhNilC84OTmhadOmAFL+iHz9+nWG5Y8dO4bExEQAUNvNgHKetL8o29jYoGXLlhmW79y5s/A4KipK6IJjSIMGDUKFChUyLScSifDLL78Iy2FhYXj8+LHasvXq1UOPHj0yHKMmVZs2bVCtWjVhWZ83AhKJBO3btxeWAwMD9XZsItbheR/r8Lxbh+/bt0943KFDB70dN6vSxrB//34TRkJERKbGhBjlG2lvinx9fTMsm9oCwczMTOWP7owEBwcL04R7eXml2/727VvUqlVLKLN+/fpMjzlr1iyhfPPmzYWuJD4+PvD09FT5BTokJERlqnJN05bfuHFDWN+/f39h/YULFzBu3Di0bdsWNWvWhKenJ7Zs2aKyr1QqxaVLl7Bw4UL0798fTZo0QdWqVVGtWjU0a9YMP/74I7Zs2YK4uDitrpk+pZ2+3dbWNtObiy/Hm/ny1/u7d++icuXKwrU6evRopjEMHTpUKN+9e3ckJyfr8AxUubu7q8QYEhKS5WOlVatWLeFxcHCwXo6ZytXVVXickJCg12MTsQ5PwTo8Bevw3FGHx8fHC601bW1t0bBhw0z30fTeT7stbWvCAQMGqP3c+Pj4qD1+gwYNYG9vDyBlXMKYmJisPDUiIsoDmBCjfKNly5bCH6f//vuvxu4LwcHB8PPzAwA0adIEhQoV0sv5S5YsqTKGx/Lly/Hw4UON5c+fP48dO3YASOkusXDhQoMMGvz582eMHDkSQ4YMwZEjR/DmzRuVG5NUoaGhaNKkCX788Uf89ddfuHnzJsLCwpCcnIykpCR8+PABly5dwrx58+Dl5YUrV67oPdaMFC1aVHgcERGBiIiIDMunncHM1dUVTk5OKttr1KiB4cOHC8szZsxAaGioxuPt2LED586dA5AyRsrixYthYWGh03P4UtquQnK5PFvHUndMhUKhl2OmSjvYtLOzs16PTcQ6XD3W4azD9cUQdfi1a9eE1pq1atXK9jXVBzMzM9SpUwdASpLY2O91IiLKOTioPuUbFhYW6NChA3bv3o2QkBDcunUL9erVS1fO19dXuNHq2rWrXmPo1q0bLl26hMOHD0MqleKXX37BgQMHYGNjo1IuLCwMkyZNEpZ//PFHlSnF3d3d0bdvX8TFxQktJTTNIpURpVKJCRMm4Ny5cxCJRKhSpQo8PDygVCrx/PnzdAMZp3ZJKVCgADw8PFCsWDHY2NhAKpUiODgY9+7dQ1JSEqKiojBkyBBs375d5ddsQ6pWrRqcnZ0RHh4OuVyOZcuWYdasWWrLxsXFYdWqVcKyt7e32nJDhw7FlStXcPv2bcTExGDChAnYtm1butnCnj9/jgULFgjLv/32G9zd3bP1fD58+KDSBSjtzWJ2pI7jAgBFihTRyzGBlBuz1AGZAQg3G0T6wjo8PdbhKViHZ5+h6vCrV6/q9Zh9+/YFAJw6dQofP34EALRu3Vrt2GsZvYa1a9cWEqBXr17NEV05iYjI+JgQo3ylW7du2L17N4CUmyZ1N1OpA8k6ODiodGfRl//973+4c+cOQkJC8Pr1a8ydOxdz5swRtiuVSvz222/Cr+NVqlTB6NGjVY5RvXp1VK9eHcHBwcLNVGazSKlz584dyGQylC9fHosXL07XPSFtdxErKyv0798fXbp0QZUqVdROIR8bG4tVq1Zh06ZNkMlkmDRpEo4dO6Zxunl9srCwwK+//opff/0VALBnzx68evUKgwcPFqavDw0NhZ+fH1avXi10X2nbti0GDx6s9pgSiQSLFi1C165d8fnzZ9y6dQvr1q3DsGHDhDLJyckYN24ckpKSAACtWrXCt99+m+3nc+DAAeGxg4MDqlSpku1jvnv3DtevXxeWGzVqlO1jAkBMTAxmzZoljAFka2ur1SyARLpiHa6KdTjrcH0wZB1+79494bG6LpC6Sv2MPH/+XEiIDRgwQCXhrI2KFSsKj+/fv5/tuIiIKHdiQozylZo1a8LNzQ2vX7/GiRMnMH36dFhZWQnb/f398ebNGwApg66qm0Equ+zt7bFw4UIMGDAAcrkce/fuRfPmzdGmTRsAwNatW4WZo2xsbLB48WJhdjV9k8lkcHFxwdatW9N1NwGg0rWhePHimU5rb2dnh4kTJyI+Ph67d+/G69evcenSJb1O3Z6Rrl27wszMDJMnT0ZiYiJu3rypMs5IWiVLlkS/fv0wcODADKdcL168OGbOnIlx48YBAFauXInGjRsLAxsvXLhQ+MXexcVF5cY4qz58+KAyPtE333wDM7PsV9fz588Xuu0UK1ZM7ThJ6kRFRaltqZGUlISQkBD4+/sLN5Ourq5Yu3at2l/ribKLdbgq1uGsw7VhqjpcqVSqtGgrW7Zsto+pL2lbj7148QIymUwvrxEREeUuHEOM8p3ULjSxsbHCQK+p0g7UrO+uNmnVqVMHQ4cOFZanTp2KDx8+4OnTp/jjjz+E9ZMnT0aZMmUMFgcADB8+XO2NVHb07NlTeHzt2jW9HjszHTt2xLlz59C3b1+NN0lWVlZo06YN2rRpk+GNVNpjpnZlkslkGD9+POLi4nDhwgX8/fffAFLGdVmwYEG2r6VcLsfEiROFQa2dnZ0xZMiQbB0TSGmtkLY7zLhx47QeyyUuLg47duxI92/fvn24du0akpKSIBaLMXDgQJw8eRKVK1fOdrxEmrAOV8U6nHV4ZkxVh4eFhQmJNkC/XTyzy8XFRWj5KJPJ8OHDBxNHREREpsCfQijf6dq1K5YvXw6lUglfX1906tQJQEq3iWPHjgEASpUqhdq1axs0jhEjRuDq1au4c+cOoqKiMGHCBHz69Eno4tK2bVt8/fXXBo0BAL766iud95FKpbh37x4CAgIQFhaGuLg4lQGD085Q9uTJE73Eqa0nT55g/vz5QreSsmXLolKlSrC1tcXHjx/h7++P6OhobNq0CX///TemT5+u1XWeNm0a/P39ERQUhDdv3mDSpEm4ffu2MFbRd999h8aNG2c7/sWLFws3oCKRCPPnz8/2QNwPHjzAjBkzhOVOnTppPfOethQKhdAyZtasWRxDjAyGdbgq1uGsw/XBEHX4p0+fhMfW1tYGabGZVWZmZrC3txdmfg0PD0fx4sVNHBURERkbE2KU7xQvXhx169bFzZs3cfXqVYSFhcHFxQVnzpwRpt42ZMuCVKljm3Tr1g2xsbG4ceOGsK1w4cKYPXu2wWMoUaIEHB0dtS6fmJiItWvXYvfu3YiMjNRqH23L6cPp06cxduxYSKVSFC5cGPPnz083xkrqc1i7di2Sk5MxdepU2NraZnpTaWdnh8WLF6NPnz6QyWQqv9RXqFBB6I6THTt37sSmTZuE5ZEjR6Jp06bZOubbt28xbNgw4Vd6T09PzJw5U6djFC9eHGfPnk23XiqVIiIiAvfu3cPWrVvh5+eHwMBADBw4EKtWrUKLFi2yFTuROqzD/8M6nHW4NkxVh6ed7TRt1+acwsrKSkiIqZuZlYiI8j52maR8KfVmSS6X49ChQwCAgwcPAkj5RdcYN1NAyhgoaX/1TT3/ggULdLrJySpduoZER0fj22+/xZo1a3S6QUrb0sCQgoKCMGHCBEilUlhaWmLz5s1qBxy2srLC2LFj8dNPPwnrZs6ciYSEhEzPUb16dYwcOTLd8f74449sTyV/5MgRlRtob2/vdOfS1cePHzFo0CCEhYUBSHm/bdy4EXZ2dtk6bipzc3MULlwYbdu2xd9//40ePXoASOl+MnHiRJUZ1oj0iXV4CtbhKViHZ40x6/DUlng5SU6MiYiIjIsJMcqX2rdvD2trawApY858+vQJly5dApAyFXfJkiWNFkuhQoVUll1cXFC9enWjnFuXX2xnzZoldJ0xNzfH119/jdWrV+PEiRPw9/fH48ePERAQgICAAJw5c0bYz1h/cP7111/CL7zdunXLcLp1ABg6dKhwUxEVFSVMv54ZZ2dnlWV3d/dsDxR87tw5TJw4EQqFAkBKdxhdZ5v7UmRkJAYNGoSgoCAAKe+rzZs3w9XVNVvH1UQkEmHSpEnC5yoqKgr79+83yLmIWIenYB3OOlxfDFGH29jYCI/TjiWWU6SNKW2sRESUfzAhRvmSnZ0dWrVqBQAICAjA4sWLIZPJAEAYeNcYIiIiMHHiRJV1Hz9+xNy5c40WgzY+fPiAI0eOAADEYjE2btyIOXPmoFWrVnBzc4OtrS0kEolQ3lgtCtJKvRkGgAYNGmRa3traGjVq1BCWHzx4kOk+r169SvfaPHr0COvWrdM+0C9cu3YNY8aMgVQqBQB4eXlhwYIFwmC/WREbG4sffvgBz58/BwAULFgQW7ZsMXiSwMHBQZi5DTD+YNyUf7AO1w3r8BSswzOm7zo8bfIxISEBiYmJ2TqePkmlUqGLNZA+UUpERPkDE2KUb6W9afLx8QEAWFpaon379kaLYerUqUJXiNKlSws3JHv37k03e5opXbt2TWgl0KxZs0xvVt69e2eMsFR8/PhReKxtV6W05WJjYzMsK5VKMX78eKEFQ9qZ41auXIn79+9rH+z/u337NoYPHy78St2wYUP8+eef2Zr6PT4+HoMHD8ajR48AAPb29ti4cSM8PDyyfExduLi4CI9N8T6g/IN1uPZYh7MO15Y+63BnZ2eVVozv37/P1vH0KSwsTPhMmJmZoXDhwiaOiIiITIEJMcq3GjVqpPKHHwC0atUK9vb2Rjn/7t27hW4pVlZWWLNmDYYNGyZsnzJlSqbTgGsz3bw+pL1RKV++fKblb926Zchw1Eo7e5W2456kLZfZ6758+XI8fPgQQMof+Tt27ED37t0BpIy3Mn78eJ1aVTx48ABDhgwRbs5q1qyJ1atXZ2scm6SkJAwbNgz+/v4AUlpQrFu3DlWqVMnyMXWVtgVAdlpIEGWGdbj2WIezDteWPutwkUik8n579epVto6nT4GBgcJjDw+PbCUxiYgo9+LdCuVbEokk3bTlxupq8/LlS8yfP19YnjhxItzd3TF8+HDUrFkTQMof+hMnTsxw/Ja0NxCp3TUMIe0fxZkNXJyQkCAMbm1MxYoVEx6nne1Nk8TERNy7d09YLl26tMayN2/exMaNG4XluXPnolChQpg6darQheXNmzeYM2eOVrEGBATgxx9/FFo0VK5cGRs2bMjWGCZSqRSjRo3C9evXAQAWFhZYvXo1ateuneVjZkXqGEUA+Is7GRTrcO2xDmcdri191+Fpu2A+ffo028dLlfazk9pdWhdpY0kbIxER5S9MiFG+NmzYMOzbt0/416RJE4OfMzk5GePHjxduSlq2bIk+ffoASLnBW7RoEWxtbQGkdHNJO4X7lxwcHIQbncjISIPdUKUdt+TixYuQy+Uay86fPx/h4eEGiSMjDRs2FB77+vri5cuXGZZfv349Pn/+DCDlV2x1s5kBKTOz/frrr8Jgyf369UPz5s0BpIxjtHjxYuGXZR8fHxw/fjzD87569QqDBg0SWjZ4eHhg48aN2WrVIpfL8csvv+DChQsAUrp/LFu2TONzMpSTJ08iJCREWNZmHCCi7GAdrh3W4azDtWGIOjztc7h9+3a2j5cqbXfZzFpiqpM2FmNfZyIiyjmYEKN8zcHBAVWrVhX+pR1U2FCWLVsmjA3i4uKSboDfkiVLqsxOtXTpUpVfbNOysLAQfhWXSqUGG7OmQYMGwsxTb968wcSJE1UGowVSxm+ZNm0adu/ebZLZmvr37w9zc3MAKS0HBg0aJPzSnlZiYiJWrFiB1atXC+vat2+vcbDiGTNmIDQ0FABQrlw5/Prrryrba9SoodJNasaMGRrHSXn37h2+//574WazdOnS2Lx5M5ycnHR4pqqUSiWmTJmCEydOAEhpCbJw4UJhwHFjUCqVOHr0KCZNmiSsc3BwQM+ePY0WA+VPrMO1wzqcdXhmMRiqDm/YsKEwjpi/vz+Sk5OzfUxAtevviRMndJoNVSaTwc/PD0DKjKuNGzfWS0xERJT7sMM8kRFdu3YNmzdvBpDyi/bcuXPV/iHdrVs3XLhwAUePHoVUKsUvv/wCHx8flcFpU7Vr1w5r164FAEyYMAEHDhxAqVKlhBsLAOlmQdNVgQIFMGjQIKxatQoAcOjQIVy6dAnVqlVD4cKFERYWhps3byI+Ph5mZmaYMWNGts+pq5IlS+K3337D7NmzAQChoaEYOHAg3N3dUblyZVhbWyMsLAy3b99GdHS0sF+JEiUwbdo0tcf08fHBsWPHAKTcuC5evFilm0aqYcOG4cqVK/D39xe6SW3ZsiXd+ECjR48WbswAoGzZssJrl5nq1auja9eu6dbv3LkTBw4cEJZLlSqF27dva/1LfNobd02ioqIwa9asdOtlMhkiIiJw//59lV/oJRIJ5syZk62bRKKciHW44bAOT5HX6nAbGxu0bt0ahw8fRlxcHK5duya00MuONm3aYMmSJVAqlTh//jy6dOmCmjVrCq0zAeCrr75C1apV0+17/fp1oXVhixYt4ODgkO14iIgod2JCjMhIUv/ITttto1mzZhrLz5w5E3fv3sW7d+8QGBiI+fPn43//+1+6cj/++CNOnjyJly9fQiqVCl0u0tLHjc2IESMQEhICX19f4flcvHhRpYyDgwPmzZuHChUqZPt8WdGvXz/Y2dlhzpw5wh+7gYGBKoPnptWgQQMsWLAAhQoVSrctKChIuDEDgF9++UXj80rtJtW1a1fExsbi+vXr2LhxIwYPHqxSLiIiQmX53LlzWj+3+Ph4tTdTXx7z9evXeP36tdbH1eZmKi4uDjt27NDqeEWLFsXs2bPRtGlTrWMgyg1Yhxse6/C8WYf36tULhw8fBgAcO3ZMLwmxMmXKYMiQIVi3bh0A4NmzZ3j27JlKmXLlyqlNiKXtFsuWzERE+Ru7TBIZyfTp04VfYMuXL48JEyZkWN7BwQELFy4UxpfZtWsXzp49m66cvb099u3bh/Hjx6Nu3bpwcnJSaVmgLxKJBAsWLMDatWvRsmVLFCpUCObm5ihUqBCqVKmCMWPG4PDhw2jdurXez62Lbt264dy5c5g+fTpatWqF4sWLw8bGBmZmZnB0dETFihXRu3dv/P3339i6dSuKFCmS7hipM46lzh7WpEkTDBw4MMPzlihRAjNmzBCW//zzT6FbVV4lFovh4OAAd3d3dO7cGX/88QdOnjzJZBjlSazDjYN1uPEYqw5v2LCh0MXx+PHjQrIzu8aNG4f169ejXbt2KFGihNAtOCNxcXE4cuQIAMDNzQ0tWrTQSyxERJQ7iZS6dLonIiIiIiLSwaFDhzB+/HgAwKRJk/Ddd9+ZJI6dO3di5syZAIDZs2fjm2++MUkcRESUM7CFGBERERERGUzHjh1Rrlw5AMCWLVsMNqNqRuRyuTDra6lSpdCjRw+jx0BERDkLE2JERERERGQwYrFYmOEzNDQU+/fvN3oMhw4dwtu3bwEA48ePh5kZh1ImIsrvmBAjIiIiIiKDatasGVq1agUAWLlypTDGmzEkJydj+fLlAIBGjRqhXbt2Rjs3ERHlXBxDjIiIiIiIiIiI8hW2ECMiIiIiIiIionyFCTEiIiIiIiIiIspX8u1okp8+fcL9+/dx//59PHjwAA8ePEBUVBQAoHv37pg/f77ez3n48GH4+PggICAAMTExcHZ2Ru3atdG3b1/UrFlT7+cjIiIiIiIiIqL08m1CrFGjRkY7V2JiIkaPHo0LFy6orH/37h3evXuHI0eOYMSIERg5cqTRYiIiIiIiIiIiyq/ybUIsrWLFiqFs2bK4fPmyQY4/efJkIRlWv359DBgwAK6urnj27BnWrVuHoKAgrFixAi4uLujdu7dBYiAiIiIiIiIiohT5NiE2YsQIVK1aFVWrVoWzszOCg4OFqaD16dq1azhy5AgAoGXLlli1ahUkEgkAoFq1avDy8kLPnj3x7t07LF68GO3bt0eBAgX0HgcREREREREREaXIt4Pqjx49Gi1btoSzs7NBz7Np0yYAgJmZGf73v/8JybBUTk5OGD9+PAAgJiYGe/fuNWg8RERERERERET5Xb5NiBlDbGwsrl27BgBo2LAhihQporZcmzZtYGdnBwA4ffq00eIjIiIiIiIiIsqPmBAzoAcPHkAqlQIA6tWrp7GchYUFatSokW4fIiIiIiIiIiLSPybEDCgwMFB4XLZs2QzLlilTBgAgk8nw5s0bg8ZFRERERERERJSf5dtB9Y3h/fv3wuPChQtnWLZo0aLC49DQUHh4eOgtDoVCgYSEBAApY5mJRCK9HZuIiAClUgmZTAYAsLa2hlicu39v4vcGEZFh5bXvDSKi3IgJMQOKi4sTHtvY2GRY1traWngcHx+v1zgSEhLw9OlTvR6TiIjUq1ChAmxtbU0dRrbwe4OIyHjywvcGEVFuxJ8iDCgpKUl4bGFhkWHZtNsTExMNFhMRERERERERUX7HFmIGZGlpKTxOTk7OsGza7VZWVnqNw8zsv5e5dOnSKq3R0pJIJEJzbYVCAblcnuFxzc3NhcdyuRwKhULr8jKZDEqlUmNZkUikEndmEw2kjT1tE3RN0nYB0vW5psYulUqFceLc3d2FMrrGLhaLIZFItC6fH16ntNe2QoUKMDc3z/LrpK/Y88LrJJVKhVY/ad+zGcVurM9TZrGnyimvk0wmg0gkEl6nhIQEYfzHtPHmVvzeSI/fG9qV5/fGf/LC68TvjfT4vUFElHew9jWgtE2fM+sGmTpWC5B590pdpR37xdraOkc0yc6sxVx2y6dNRupbaixpk5g2NjYaYzT0czUkU71Oaa+tubl5lq6JoWPPza8TkPF79kvG+DzllPLaeP/+Pdq1a4epU6fi66+/Trc9L4y3xe8N/eL3hv7K83tDP/i9ob/y2ggKCkKHDh2wYMECdOrUKd32vPC9QUSUG7HLpAEVKVJEePzhw4cMy4aGhgqP0w6wT0REOce7d+/QokUL3L9/H97e3jhw4ICpQyIiohzs9evXaN68OR4/foyePXvi+PHjpg6JiIj+HxNiBuTu7i48fvnyZYZlX716BSClyXTp0qUNGhcREekuIiICzZs3R0BAAICULkq9e/fmzQ0REan1/v17NG/eHK9fvwaQ0pKxW7duuHDhgmkDIyIiAEyIGVTVqlWFcQNu3rypsVxycjLu3r2bbh8iIso5ChYsiHbt2qmsK1WqFCpVqmSiiIiIKCdzdXWFl5eXyjp3d3d4enqaKCIiIkqLCTEDsrOzQ8OGDQEA165dw/v379WWO3XqFGJjYwEArVu3Nlp8RESkPZFIhOXLl2PIkCEAgPLly+PChQsoVaqUiSMjIqKcSCwWY+PGjejbty+AlB++z507pzKsChERmQ4TYtng4+MDT09PeHp6YsWKFWrLDBo0CEDKzDIzZ85MNxNNREQEFi9eDABwcHBQO0AzERHlDGKxGGvWrMH06dNx/vx5FC9e3NQhERFRDiaRSLBlyxZMmTIFZ8+ehaurq6lDIiKi/5dvZ5n08/NDUFCQsBwZGSk8fvPmDXx8fFTK9+jRI0vnadiwITp27IgjR47g7Nmz+P777zFw4EC4urri2bNnWLt2Ld69ewcAGD9+PAoUKJCl82gr7bTSlD1mZmZCk3dOl61fvLaGweuqH2KxGDNnzlRZl5fr1rz83IyNn0HD4bU1DF5X/TAzM8OcOXNU1rFuJSIyvXz7zbZv3z6Ns4P5+/vD399fZV1WE2IAMHfuXMTGxuLChQu4ceMGbty4obJdLBZj+PDh6N27d5bPoS2xmI0C9UUsFsPOzs7UYeRJvLaGweuqvZcvX6JEiRKwsLDQqnxerlvz8nMzNn4GDYfX1jB4XbX34sULuLm5aZ04ZN1KRGR6rImNwMrKCuvXr8fixYvRuHFjFCpUCObm5ihatCg6deqEnTt3YtSoUaYOk4iIANy/fx8NGjTAt99+C6lUaupwiIgoh7t16xbq1q2L7777Lt3wKERElHOJlEql0tRBkGElJyfjwYMHAIDKlSvDysrKxBHlDQqFAvHx8QAAGxsb/tKnR7y2hsHrmrk7d+6gdevWiIiIAAB8/fXX2LlzZ6a/+CcmJuLRo0cAUgZN1rZlWU7F7w3D4GfQcHhtDYPXNXPXrl1D+/btERMTAwD47rvv8Ndff2V6rfLa9wYRUW7Eb7V8hr9a6Y9MJkNAQAACAgIgk8lMHU6ewmtrGLyuGUtKSkLXrl2FZBgA7N27F/Pnz89037xct+bl52Zs/AwaDq+tYfC6ZiwuLg5du3YVkmEAsGXLFixfvjzTfVm3EhGZXr4dQ4yIiCgtS0tLbN++HR06dEBCQgIAoGnTphgzZoyJIyMiopzI1tYW27dvR5cuXZCcnAwAaNu2LX766ScTR5ZecnIyYmNjERcXh+TkZCgUClOHRESUKYlEAisrKzg4OMDW1hYikUivx2dCjIiI6P81b94chw8fRseOHdGwYUMcOnQItra2pg6LiIhyqHbt2sHHxwfdu3dHmzZtsH///hzVzVypVCI8PBzh4eGmDoWISGcymQxJSUmIjo6GtbU1SpUqpdfu+0yIERERpeHl5YXz58+jatWqsLGxMXU4RESUw3Xs2BHnz59H7dq1YWlpaepwVISGhiI6OlplnUgkgkQiMVFERETak8vlSB32PiEhAUFBQShdurTeWooxIUZERPSF+vXrmzoEIiLKRRo1amTqENJJTExUSYYVKlQIDg4OsLS01Hu3IyIiQ1AoFIiNjcX79+8hl8uRkJCAuLg42NnZ6eX4HFSfiIjynZMnT+L+/fumDoOIiHKJQ4cOISAgwNRh6CQqKkp47OrqCldXV1hZWTEZRkS5hlgshoODA4oUKSKs+/z5s/6Or7cjERER5QKHDx9G586d0bp1azx+/NjU4RARUQ63d+9e9OjRA15eXnjx4oWpw9FafHy88NjR0dF0gRARZZOdnZ2QzE+d/EofmBAjIqJ8w9fXFz169EBycjLCwsLg5eWV637xJyIi49m1axe8vb0hk8nw7t07eHl54dWrV6YOSytyuRwAYGZmxjHDiChXE4vFQj2WWrfpA8cQy2fMzc1NHUKeYWFhgdq1a5s6jDyJ19Yw8vt1VSqVWLlyJaRSqbDuw4cPWLt2LZYuXZqtY+flujUvPzdjy++fQUPitTWM/H5d5XI5li9frnLz9fbtW2zatAmzZ8/O1rFZtxIRmR5biBERUb4gEolw4MABNGzYUFj33XffYfHixSaMioiIciqJRIKjR4+iZs2awrrhw4dj5syZJoyKiIj0hS3EiIgo37C3t8exY8fQtm1bVKtWDevWrYNYzN+GiIhIvYIFC+LUqVPw8vJCy5YtsXTpUg5KT0SURzAhls/os79tfieXyxEeHg4AcHZ25tgMesRraxi8rikKFCiAM2fOwMbGRm/JsLxct+bl52Zs/AwaDq+tYfC6pihUqBAuX76sMqhzdrFuJSIyPSbE8hmFQmHqEPIMuVyO4OBgACm/HubXPxINgdfWMHhd/2NnZ6fX4+XlujUvPzdj42fQcHhtDYPX9T/29vZ6PR7rViIi02M/ESIiynNWrVqFefPmmToMIiLKJRYuXIjly5ebOgwirXl6esLT0xMrVqzI8jFu3LghHOfGjRt6jI4od2BCjIiI8pRly5Zh5MiRmDx5MgfMJyKiTM2ZMwcTJ07EmDFjsGbNGlOHQ7mQXC5HrVq14Onpie7du2dYVqlUon79+kIiat++fRmWP3DggFB2586d+gybKN9jQoyIiPKMRYsW4eeffxaWJ0yYgGXLlpkuICIiytH+97//Ydq0acLy8OHDsWHDBhNGRLmRRCIRZiN9+vQpYmNjNZZ9/vw5oqKihOXbt29neGw/Pz/hcd26dbMXqI5WrFghJOOI8iImxIiIKM+wtrZOty4uLs4EkRARUW5gZWWVbh2/NygrUpNVCoUC/v7+GsulJrhSx+RLm/DKqHzBggXh4eGhj1AF9evXR0BAAAICAlC/fn29HpsoN2BCjIiI8oyRI0diyZIlwvKCBQswZcoUE0ZEREQ52W+//YZZs2YJyytXrsTYsWNNFxDlWnXq1BEeZ5TkSt3Wvn17AEBQUBA+fPigtuynT5/w+vVrAEDt2rX1NsspEaXgLJNERJSn/Pzzz5BKpTA3N1fpPklERKTO0xIH4NLVBeYFzbHWbi3Wbl2Lagpz7Phecysfoi9Vq1YNlpaWSEpK0ioh1q5dOzx48ABBQUG4ffs2vvrqK41lAeN3lyTKD3JUQiw5ORkxMTFwcnKCWMzGa5SziUQi2NraCo9Jf3htDSM/Xddff/3V1CEQpZOfPoPGxmtrGPnlun6Idod9re9gXWIbgCQAQKJUbtqgKNexsLBAtWrVcOvWLTx48ADJycmwsLBQKfP27VuhNVjt2rVRu3ZtBAUFwc/PL9OEWNoWaOrcv38fW7ZsgZ+fHyIiIlCwYEE0aNAAQ4cOhbu7u9p9bty4gQEDBgAAtm3bJnSb9PHxwaRJk1TKqhtH7MyZMyhRooTKOrlcjn///RfHjx/Ho0ePEBUVBVtbW5QtWxZt27aFt7e32q7KRKZglIRYXFyc8GGuU6eO8MWaKiIiAjNmzMC5c+cgl8thY2ODr7/+GuPGjUtXiVD2mJubmzqEPMPc3BwVKlQwdRh5Eq+tYeSl66pUKhEXFwc7OztThwIgb9etefm5GVte+gzmNLy2hpGXrqtCoUBCQkK6+5BTjz8g8H0/KGGGhLffwbrUZojEyQaPh3Vr3lSnTh3cunULycnJuHfvXrpWXan3xG5ubnB2dkbt2rVx4MAB3Lp1S+3xUgfct7OzQ8WKFTWed8eOHZg7dy5kMpmw7uPHj/j3339x6tQpbNiwwSgtzN69e4dhw4bh6dOnKuujoqLg7+8Pf39/7Nq1C+vWrUOZMmUMHg9RZoySEDt58iQmTZqEIkWK4OzZsyrbFAoFBg8ejMePH0OpVAJISaBt3boVISEhWLFihTFCJCKiXEKhUGDUqFG4ceMGTp8+DUdHR1OHREREOZhcLscPP/yAFy9e4Pjx48KPKUcfhGL0rjtQ/v8tkTyhDBLeDoR1yS0A2EKMdFe3bl2sWbMGQEryS1NCrFatWir/P3/+HNHR0ShQoIBQNjY2Vkgs1axZUxiE/0uXL1/G/fv3Ub58eQwYMADly5dHUlISTp06hW3btiEhIQG//vorTpw4oXVjk9atW6NKlSrYuXMndu3aBQA4dOhQunKFCxcWHkdGRqJPnz4IDQ2FhYUFvvnmG9StWxfFixdHfHw8rly5gm3btuHNmzcYPHgwDhw4AHt7e63iITIUoyTELl++DCDlg/VlV8ijR4/i0aNHEIlEqFSpEurVq4ebN2/i8ePHOH36NC5evIhmzZoZI0wiIsrhFAoFhg4dig0bNgBIGZD25MmTcHBwMHFkRESUE8lkMgwcOBA7d+4EAHTq1AlHjhzB6edRGPfPPcgVSpXy8nh3JAT3h8J1uynCNanuq6+YOgRB52rFMKhJxi2INl1+hUP336Vbf2B4Y0OFlakaNWrAzMwMMplM7ThiaXtNAYC7uzsKFiyIyMhI+Pv7o2XLlkLZO3fuQC5PScxm1Lrr7t27aN68OVauXKmS8KpTpw4cHR2xbNkyvHv3DhcuXECbNm20eh4ODg5wcHBAoUKFhHXly5fPcJ85c+YgNDQUxYsXx9atW1GyZEmV7fXr10f79u3Rt29fvH37Fhs3buRYr2RyRkmIPXv2DCKRSMiAp+Xr6wsAqFy5Mnbv3g0zMzNIpVL07dsXDx48gK+vLxNieiSTydgNVU9kMhnevHkDAChdujTMzHLUkHy5Gq+tYeSF6zpp0iQhGQakjH3RuXNnnDt3zqRjT6btopDX8HtDf/LCZzCn4rU1jLxwXceMGSMkwwCkJAWGTEVoqdZQKtXvIzaPhLW5YcdMy4nfG3eCokwdgqBWqYKZlgmJSshRMQOAra0tKlasiAcPHggJrdSWXV/OGJmqVq1aOHPmDPz8/FQSYtqOH2ZpaYl58+ap/a7u378/Vq1aBalUCj8/P60TYroKDg7GsWPHAADTpk1LlwxLValSJfTp0wcbN26Ej48PE2Jkcka5e4iIiACAdAPupX4wRSIR+vbtK3zJmpub49tvv4VSqcT9+/eNEWK+odT0zU86UygUiIqKQlRUFBQKhanDyVN4bQ0jL1zXH3/8EUWLFhWWzczMMGrUKJNPxJKX69a8/NyMLS98BnMqXlvDyAvXddiwYXB2dhaWC9T6CqElNSfDzAtehWURX4hEhq37WLfmXamtueLi4vDkyRNhfeo4Yc7OznBzcxPWpybHvmxRljp+mKWlJapWrarxfI0aNVJpyZWWnZ2dcK63b9/q9kR0cOHCBcjlclhbW2famCX1+nz8+BHv3qVv4UdkTEb5mSc6OhpA+sEjHzx4gMTERIhEIjRt2lRlW+oHNzw83BghEhFRLlCuXDmcPXsWzZs3R2RkJP755x9069bN1GEREVEOVaVKFZw+fTql5U255nBo+SOUACCOh0XBG0j+1AxASgsec6dLsHQ9ApEIAPNVlEW1a9fGpk2bAKQkuapUqSI8Tt2eVmrrr0ePHiExMRFWVlZITk4WGoZUr149w5baZcuWzTCe1HHJ4uLisvBstPPw4UMAQEJCAipVqqT1fuHh4ShWrJihwiLKlFF+Uk+dVvXTp08q61MrhVKlSqn8cpN2HyIiorQqVKiAs2fPwtfXl8kwIiLKVPXq1TF6lS8cWv74/2uUsCq6H5auJ2BTeh1E5hGwKHTuv2QYUTbUqVMHov9/I6W28kr7+MthhCpVqgRra2tIpVLcvXsXQErDkaSkJOF4GbG2ts5we2orekO28vzyPl9bCQkJeo6ESDdGaSFWqlQpPHnyBDdv3kSTJk2E9adOnYJIJFI7SGBqN0snJydjhEhERLlI5cqVUblyZVOHQUREucCqcy+w5d5nYdnc8QbMHR4BACQ2QbAruwRKkSzfJ8NqlnI0dQiC4o4ZJ3lSy+SkmFM5OjqiXLlyePbsmdAAJO2MkV+2EDM3N0fVqlVx8+ZN+Pn5oUGDBiqJtIwG1M8pUgf/L1iwILZt26b1fl8OqURkbEZJiDVq1AiPHz/Gzp07Ubt2bdSpUwc+Pj548OABRCKRyuCBqQICAgAArq6uxgiRiIhykMTERNy7dw/169c3dShERJQLpI7XlLY1jVKpxJ9nnmPZ6efCOrHle1gWPqy6s1gGEYDfPkWgb0xsyroSOT8JoW+mnJ0xKwY1KZPpTJSmUqdOHTx79gwREREIDAxEcHAwFAoFbGxs1HYprF27tpAQA/4bb8zc3Bw1atQwZuhZ4ujoCCDlc+ju7i5MJECU0xmly+SAAQNgZ2eHuLg4DB06FHXq1MHcuXMBpEw16+XllW6f8+fPQyQS6dQHmYiIcr+EhAR07doVzZs3x6lTp0wdDhER5XCfP39Ghw4d0KJFC1y+fBlASjJs0YkAlWQYRMmwKr4TInH6GR6d5HK0jYs3VsiUx6VNzN6+fVto8VWjRg21yaLUVmP37t1DcnIy7ty5AyClO6WNjY0RIlZPpGWzydR79uTkZGE8MaLcwCgJMVdXV6xduxbOzs5QKpXCv5IlS+LPP/9M90ELCgoSKo1GjRoZI0QiIsoB4uLi0KlTJ5w8eRJJSUno0qULzp07Z+qwiIgoh4qJiUH79u1x6dIlxMXFoUOHDrh69SrmHHmC1ecDVcpaFj4MieVHtceZE/YJLvLcOZMm5TxpE2K3bt0SWnx92V0yVc2aNSGRSBAfHw8fHx98/vw53XFMIe1g/snJyRrLtWzZUrin37p1q8HjItIXo3SZBFI+zGfOnIG/vz/Cw8Ph4uKC2rVrw8wsfQgfP37E8OHDAQCNG+euprs5nbZZfsqcWCwWuvSmDlZJ+sFraxi54bquXLkSZ8+eFZYTExPRt29fBAYGZjporCnl5bo1Lz83Y8sNn8HcitfWMHLDdV20aBGuXr0qLMfGxqHvkoNQejRTKWdmfw8WBW+qPcYAuQ2aJiQaNM4vsW7N2woXLoxSpUohKCgIN27cQGRkJADNCTE7OzuUL18eT548wcaNG4X1ph4/LO3wRUFBQfDw8FBbrmzZsmjfvj2OHTuGI0eOoGrVqvj+++81Hvft27e4d+8eOnXqpPeYiXRhtIQYkJJhbtCgQabl6tSpY/JseF6lLgFJWWNmZoaSJUuaOow8idfWMHLDdR0/fjzu3r2L3bt3A0gZk+LgwYM5OhkG5O26NS8/N2PLDZ/B3IrX1jByw3WdNm0a7t+/j3///RcAUKzzmHTJMJF5BKyK+qjdv1KhShgbGpZ+g0Ku91jTYt2a99WpUwdBQUH48OEDgJTXPKPxwGrXro0nT57g7du3AFKS0JoSaMZSs2ZN4fG8efMwdOhQuLi4CAnd4sWLC+/l//3vf3j48CHevn2L+fPn48yZM+jatSvKlSsHCwsLREVF4enTp7h06RKuX7+ONm3aMCFGJseamIiIcgyJRILt27dDJpPh7NmzOHXqVLrpyYmIiFJZWFjgn3/+QY8ePXD79m3MHOaN+ZfCIVMo/7+EHNbFd0EkSUq3r42ZDRbV+Q3mW7qmP3DUG8MGTnle6kRyqSpWrJjhD3y1a9fG33//LSyXL18eDg4OBo0xM6VLl0aHDh1w7NgxXL58WRijL9WZM2eEmSIdHR2xa9cujB07Fn5+fipdRdWxtbU1aOxE2jBJQiwoKAh37txBeHg4EhIS0KdPHzg5OZkiFCIiymHMzMywc+dOvHnzRmPTfCIiolSWlpbYv38/3r17h7Jly6Jo8VCM2uUPhRKwcDkFifVbtftNK9UJpbb1BBKj029MiAKUSoBdGymLvuzumFlrry+3m7q7ZKpFixahSpUqOHHiBF69eoW4uDgoFOrH23NxccGOHTtw/vx5HD58GHfv3kV4eDhkMhns7e1RunRp1KxZE15eXjnm+VH+JlIqlcrMi+nHo0ePMHfuXPj7+6usP3TokMpNz44dO7By5UrY29vjyJEjMDc3N1aIeVJycjIePHgAAKhQoQKz8XoilUrx5MkTACm/+PB9qj+8tobB62o4cXFxePr0KQCgatWqKoPQ5kb83jAMfgYNh9fWMHLzdfW9E4Lxh/fCutRfard3MS+M359pbr0CMytg6gcDRWec743nz59DJpPBzMwM5cqV0/vxiYiMyRB1mtFGxzx37hy8vb3h7++vMtOkOl27dkViYiLevn2L8+fPGytEIp0olUpIpVJIpVKN72XKGl5bw8hJ1/Xjx4+YMWMG5HLDjtFClJPkpM9gXsNraxg56bqGhIRg9uzZWsfRpIIlnMuqHzfMTQ5Mee6X8QEKlNA1RCIiymWMkhD7+PEjxo0bh+TkZHh4eGDDhg3pWomlZWdnBy8vLwDAxYsXjREiEREZSWhoKFq0aIFZs2ZhyJAhGpvdExERASnDrTRv3hzTp0/HmDFjMk2KKZQKTL08FXGyyHTbzJVKLHwfCpvMEmvWBbMTMhER5QJGSYht2bIFCQkJKFasGHbs2IGmTZvCxsYmw33q168PpVKJR48eGSNEIiIygpCQELRo0ULogrNp0yYMHz7c5C0PiIgoZ3r16hWaN2+OwMBAAMCKFSswfvz4DL83tj3ahivvrqjdNi4iChWTpQaJlYiIchejJMQuXboEkUiEQYMGaT1TRtmyZQEAwcHBhgyNiIiM6MmTJ3j9+rXKupMnTyI8PNw0ARERUY728OFDvH3736D4IgtrHAgvjKtPQ9SXD3+IP/3/VLuteXwC+sZ8Tr9BZLRRZIiIKAcxSu3/7t07AEC1atW03sfOzg4AEB8fb5CYiIjI+Fq3bo19+/bBzCxlkmMPDw9cuHABLi4uJo6MiIhyos6dO2PHjh0Qi8UQWdjAbeAiKIpWxrA9j/AwRHV2yNjkWEy4MAEypSzdcVxlMswO+4R0c0aWbgwUrmK4J0BERDmWURJiqYMm6zJOzOfPKb/eZNa1koiIcpfOnTtjz549qFy5Ms6fP4+SJUuaOiQiIsrBevfujTV/bUOZQUugcHIDAMQkytD/rxt4+j5GKDfv5jwEx6bvXSJSKjE/7BMKpr0XkVgAbWYDAw8BZpaGfgpERJQDGSUh5uzsDAAqzZ0zc//+fQBA0aJFDRITERGZTo8ePXD37l0UL17c1KEQEVEOFxWfjAPRpSD/YubHyHgp+m28gRcfYwEAfct0RmlR+uTWkKgY1E1M+m+Fa2Vg8Dmg8WhALDFo7ERElHOZGeMkderUQXBwMI4fP47OnTtnWj45ORl79uyBSCRCvXr1jBBh/iGR8EtfX8zMzISx7lK7f5F+8NoahrGvq1KphEiUrnOKSjx5RV6uW/PyczM21m2Gw2trGDnle8PGwgzFHa3wJDQm3TZxavkXp1HJdwT2xH3A3EIF8a99yvArtRITMTQqtWulCGg0CvCaavJWYaxbiYhMzygtxLp37w4AOHv2LK5cUT/jS6rk5GRMnDgRQUFBEIlE+Oabb4wRYr4hFnPQUH0Ri8UoWLAgChYsyOuqZ7y2hmHM6/ro0SPUq1cPL1++NOh5coq8/D7Ny8/N2Fi3GQ6vrWEY87r6+/ujfv36aifUsjATY1XfWmheXnW8yaIFrPDPoOrwuPU/4O+eQOx72CqV+D08AnM/hqOoTIb5Hz+ltAAoUAr47jDQdrbJk2EA61YiopzAKDVx/fr18dVXX0GpVGLo0KFYtGiR0CUSAEJCQuDv74+NGzeiU6dOOH78OEQiEb799luUK1fOGCESEZGe3Lt3Dy1atICfnx9atmyJN2/emDokIiLKwW7evIlWrVrh1q1b8PLyEibkSsvSTIJ1/WujsUchAECJgtbw7WoFt33tgVsb0pXvHBePI2/foahcDlTvAwy7Arg1MfhzISKi3MNobcrnz5+PuLg4XLhwAZs2bcKmTZuEJtFDhw4VyimVSgBA27ZtMWXKFKPEFhISgu3bt+P8+fN4//49LCwsULJkSXTo0AF9+/aFtbV1ts8RHByMXbt24dq1awgKCkJCQgJsbW1RtmxZNGnSBN7e3ihUqJAenk3GUq8vZZ9SqYRUKgUAmJubZ9g1jHTDa2sYxriuDx48gJeXFyIiIgAAQUFBaNmyJS5evIgSJUpksnfulZfr1rz83IyNdZvh8NoahjGu661bt9CmTRvExKR0h3z+/DlatWqFCxcuwNXVVaWslbkEGwbUwYwD9zCtwDE47F0CKOUaj21u7QR0XgZU6qr3uLOLdSsRkekZLSFmYWGBdevW4Z9//sHGjRsRFBSktlyRIkXw008/wdvb2yhxnT17FhMmTEBsbKywLiEhAdHR0Xj48CH27t2L9evXo3Tp0lk+h6+vL2bMmIHExESV9dHR0bhz5w7u3LmD7du3Y8mSJWjcuHGWz6MNmUwGS0vTNxPPC6RSKR48eAAAqFq1KiwsLEwcUd7Ba2sYxriuxYsXR8mSJYWEGAAUK1YMDg4Oej9XTiKTyUwdgsHwe0N/WLcZDq+tYRjjupYqVQrFihUTEmJAyneJnZ2d2vI2Ma+xKGYC8OR2xgf2aAN0XQnYF9FnuHqTl783iIhyC6OPOvrNN9/gm2++wYsXL/Dw4UN8+vQJcrkcBQsWRMWKFVG5cmWj/ar3+PFj/Pzzz0hMTISNjQ1++ukn1K9fH4mJiTh69Cj++ecfvH79GkOGDMH+/fs1fjFn5Pbt25g0aRIUCgXEYjG6deuGVq1awdXVFaGhoThw4ADOnTuHqKgoDB8+HIcPH0bJkiUN8GyJiAzPyckJp0+fRsuWLfHw4UM0a9YMR44cyVL9SUREeV/hwoVx9uxZNG/eHM+fP0e7du1w4MCB9D00lErA7y/gxFRAlqD5gOY2QNs5QJ1BAFsKEhFRBkw2DY+Hhwc8PDxMdXoAwO+//47ExESYmZlh06ZNqFmzprCtYcOGKF26NBYtWoTXr19j8+bNGDVqlM7nWL9+PRQKBQBg6tSp6Nu3r7CtWrVqaNeuHebPn4/NmzcjMTERmzdvxvTp07P/5IiITMTZ2RlnzpzB9OnT8ccff8DW1tbUIRERUQ5WtGhRbPjnCCZvO4N/Zg1Inwz7/B44OBJ4cQoAsL6AA5Qi4MeoGKjM1Vi8DtBjPVDI3WixExFR7mWUQfU/f/5sjNPo5P79+/Dz8wMA9OzZUyUZlmrQoEFwd0/5Qt22bZswhoIu7ty5AwBwdHRUSYalNWLECOHx3bt3dT4HEVFO4+rqirVr1zIZRkREmXoYEo2x/75CiEVJTDjwGFK54r+Njw8CqxsKyTA/K0usKlgAKws6YkgRV3yUSACRBGg5BRh0gskwIiLSmlESYo0bN8bo0aNx8uRJJCcnG+OUmTp9+rTwuGfPnmrLpHZxBICYmBjcuHFD5/OkJtEyGkza3t4eBQsWVClPRJTThYSEmDoEIiLKRdR9b9x9G4U+G64jMj7lb+ATjz5g7J67kMVFAgeGAv8MABJSxqWMEosx0aUQFP/fFfKmtRV6lSiOi90WAc1/BSQm6/xCRES5kFESYsnJyTh16hTGjBmDxo0bY+rUqbh+/boxTq3R7dspA3Ha2NigcuXKGsvVrVtXeOzv76/zecqUKQMgZZZJTWJjYxEZGalSnogoJzt+/Dg8PDywbt06U4dCRES5gI+PD9zd3fH3338L626/iUC/jTcQk6g6wHz4g7OIWVofuLdLWKcEMM3ZCR/NVJNekWJgzP2VeB/33qDxExFR3mOUn1G++eYbnDhxAtHR0fj8+TP279+P/fv3w9XVFR07dkTnzp1RsWJFY4QiCAwMBJAys42ZmebLULZs2XT76OLbb7/FtGnTEBUVhV27dqmdPXPVqlUq5Q1JKpVqbKVnZmYGsTglR6pQKDKd/SbtTENyuRxyueZpr0UiEczNzYVlmUwmjK2mjlgsVnldpFJphtNTp4097RThmqSdOjyz56op9rTnSPtY19glEgkkkv9GwMisFWV+eJ3Urc/q66Sv2PPS6wRk3Bo1s9fp8OHD8Pb2RnJyMoYOHQozMzP88MMPAPLv65SXW/fyeyMFvzfUyymvE783cvb3xr59+zBgwADI5XIMHDgQEokEZeq3xaAttxCf/F/cFpDiF7N/MFhyFGKZ6rXdZW+H87Y2as8/rOowOJk7ITk5OWuvk5r1CqUSMjWvGb83iIjyDqMkxGbNmoXp06fj4sWLOHz4MM6ePYvExER8+PABmzdvxubNm1G2bFl06dIFHTt2zLB7oT4kJSUJLbKKFMl4KuYCBQrAxsYG8fHxeP9e91+eevbsidu3b8PX1xezZs3Co0eP4OXlBRcXF4SGhuLgwYNC982hQ4eiUaNGuj8hHWSU1PP09BRmgouPj0dAQECGx6pdu7bwODw8PMNWcLa2tqhQoYKw/ObNG0RFRWks7+rqqjLb5pMnTzL8w6Fs2bIq3U5TpwjXJO3U4dHR0Xj58qXGsubm5qhWrZqwHBoaio8fP6qUefr0qfDY0dFRGHsOSLnmcXFxGo9fokQJFC5cWFjOLPb89DqlpY/XKa38/DoBqu/ZL2X0Or158wa9e/dWuQEYPHgwPDw80Lx5c75OeRC/N1Lwe0O9nPY6pcX66D+m/N4ICAhA//79hQScQqHAj1MWo7i3A9LkwlBBFISl5qtQUfw23fEDLMzxh1NBteeubFsZNWU1hXNm6XX68b+hVFJ7kQAA1Lxm/N4gIso7jNJlEkj5NcXLywtLlizB1atXsWDBAjRt2hQSiQRKpRKBgYFYtmwZ2rRpA29vb+zatUtIWulb2i9FGxv1vzSllTrTTXx8vM7nkkgkWLBgAf78809UqFABe/fuxbBhw9CrVy+MGjUKp0+fRv369bF582b8/PPPOh+fKC+rUKGCyi/alD3m5uaoWrVqhq1iM1O6dGl8//33Kuu8vb3RuHHj7IZHRJRt/N7QL318b5QvXx59+vQRlq3K1kHhr2cIyTAxFBgiOYSDFlPVJsPiRSKMd3FGsliUbpuDxAGDSwyGWGS0Wxoio7hx4wY8PT3h6emZpXGsDc3Hx0eIj0leys1EyozaEBtBREQEjh07hkOHDqnMsCgSiSCRSNCkSROsXbtWr+cMDQ1FixYtAABdu3bFwoULMyzfokULhIaGolSpUjh16pTO5wsMDMSiRYtw6dIltc2qLS0t0bp1a0ycOFHll0R9SU5OFn41y+gPxZzcVD8ndn3RV+x5qUsFX6f/5OXXSalUYvr06Vi4cCEGDhyIjRs3Csc39eskFotxffUQvPsYjm/MLgjrFcXrQDbwWIbPNbtdX1JbT6RtSZRb8XsjPdZH6vF14uv0Zeyavjd++eUXbDrhh8I9pkD5/wmsEqIw/GG+BvXFmlufTXd2wgF7O7XbVrRYgUZFVXtX5JbXyRjfG8+fP4dMJoOZmRnKlSun9+NTejdu3MCAAQM0brexsYGrqyuqVauGHj16oGHDhpkeZ9u2bahfv75B4s0qHx8fTJo0CQBw5swZg/fwIgIMU6eZfCoWJycn9O3bF3379kVwcDAOHz6MI0eOCE/2woULmR9ER5aWlsJjbfrvp34xWllZ6XwuPz8/DB06FJ8/f0bx4sWFiQUKFCiAT58+4cyZM1i+fDmOHDmCW7duYdOmTQb9wjI3N9fqC1csFuv0xfzlHxOZ0fWXRl1+7RWJRDrFrutzNWTsAAwaO18nzfg6qafpdZo/fz7q1auH7t27CzcGgOleJ6VSiXMBH7Hi7AvcedcTtkhAW4kfHEUpLYKff4iFZyZxGfp1yq34vZEe6yPN+Dpplt9fp/ZDJuOovT8UShEAJXpJLmKG2TbYixI0HuuofQGNybDvK3+PFqVbZBpPbn6dKO+Jj4/H69ev8fr1a/z777/o1q0b5s6dy/cFkYmYPCGWVokSJdC2bVthfLGYmBiDnMfW1lZ4rE03yISElC9qbbpXppWcnIxx48bh8+fPcHFxwZ49e+Di4iJsL1KkCPr27Yt69eqhZ8+e+PjxIyZOnAgfHx+dzqOLjH4NJN0oFApER0cDSBlrLm1SgLKH19Yw9HldRSIRevbsqa/QskyhUOLk4/dYcfYFHr377zsjDtbYIm+HsWaGq09V48i7dWtefm7GxrrNcHhtDUNf1/Xg3RD8vOcuFEoRnBCDueZ/ob3kVob7vC1SCbPs5IA8fcKsSqEqGFVzVJZiySlYt+Z93t7eKt2FlUoloqOjcffuXWzZsgWfPn2Cr68vihQpwqFziEwkR/y18PHjR2zevBk9evRAx44dsW7dOnz+/BmAbr/SaMvS0hKOjo4AkOlA+dHR0ULSLLMB+L908eJFfPjwAQDQr18/lWRYWuXKlUOXLl0AAI8ePcpw0NLsyqg5PelGJpPh5cuXePnyZabN5Uk3vLaGoet19fX1zXAgYlOSK5Q4eDcE7ZZdxNC//VWSYak2yTrgs9LaOPHk4bo1Lz83Y2PdZji8toah63Xdt28fEhMTVdbt9XuLsXvuQqEEWorv4ITlxEySYSJIG4/BryVKIU5NMszW3BYLmy+EuSR3jxXHujXvK1SoEMqXLy/88/T0RL169TBkyBBs27ZN6LW0ffv2TLvqEpFhmCwhFhsbi3379mHgwIFo2bIlFi5ciMePHwt9/OvWrYvZs2fj8uXLBjm/h4cHACAoKCjDL/i0sxOlnbFGG2n3rVSpUoZlK1eurHY/IiJTWLlyJbp3744uXbpkaUIRQ5HKFfjH7y1aL7mAMbvv4vnHWI1lY2CL7fLWAADPIvbGCpGIKF+aP38+vv76a/Ts2RNJSUkAgJ03gjBh331YKRMxx+wvbLZYBBdRtOaDOJYCvj+GFQUd8fDTY7VFpjeYjpL2JdVuI8otPDw8hDGt4+LieP9HZCJG7TKZnJyM8+fP49ChQ7h48aKQCU9NglWoUAGdO3dGp06dDDK4fFq1a9eGn58f4uPj8ejRI1SvXl1tuVu3/vsFq1atWjqdI21f8Mx+BUqblGMfciIypaVLl2LcuHEAgLNnz6Jbt274999/szSOor4kSuXYdzsYa84HIiRK83gzaTkjCo7ImS3ciIjyktmzZ2P69OkAgKNHj6J3797o/MsfmH3kKWqKnmOJ+WqUEX/I+CA1+wHt5uHKpwfY/Giz2iLdPLrhq7Jf6Tt8IpMoXry48DgrLcTu3r2Lc+fOwd/fHy9fvkR0dDQsLCxQpEgR1K1bF/379xcagWTmwoULOHToEO7cuYNPnz5BLpfDxcUFlStXRsuWLdGhQwdYW+vW6j4kJASDBg3C69evYWNjg9WrV2ucRIDIVIySELt27RoOHTqEU6dOITY25df81CRYsWLF0LlzZ3Tu3FnrD6w+tG7dGuvWrQMA7N+/X21CTKFQwNfXFwDg4OCg8+weaWfb8PPzQ8uWLTWWTZt44ywdRGQq4eHh+P3331XWnTp1Cj4+PirjYBhLQrIcO28GYf3FQHyISdJqnyKSzxgq2o9vJedgJcp84hQiIsq6kJAQLF68WGXduVAxHh55iJ/NDmCkxBcSUQaT2ts4A12WAxU6IjwhHJMvT1ZbzM3BDZPqTdJn6EQm9e7dO+FxsWLFdNo37SyPaUmlUgQGBiIwMBB79+7FlClT0LdvX43HiYyMxM8//4xr166l2xYSEoKQkBCcPHkSANCjRw+t4wsMDMSgQYPw/v17ODo6YsOGDahWrZrW+xMZi1ESYt9//z1EIpGQBHN0dET79u3RuXNn1K5d2xghpFOtWjXUqVMHfn5+2L9/P7p3746aNWuqlNm0aRMCAwMBAAMGDEg3S03a6XC7d++O+fPnq2xv2LAhrK2tkZCQgF27dqFLly7w9PRMF8uFCxdw6tQpAEDhwoVRsWJFvT1PIiJdODs749SpU/Dy8kJUVBQA4Pfffzd6Miw2SYbt195g46WX+BSn3a+mJQpaY3gLD/S8PxiWIddVNyozuBkjIqIsK168OI4fP462bdsiNjYWDg2/QZ3mrbDUfAaqiV9lvHP59kCXFYCdKxRKBSZdmoSIxIh0xSzEFljcfDFszHWb4IoopwoMDMT58+cBADVq1ICzs7NO+8vlchQoUACtWrVCnTp1ULp0adjY2ODjx4949OgRtm/fjsjISMyePRtly5ZV2zIrISEBAwYMwLNnzwCkDOHTu3dvlCtXDhYWFnj//j1u3bqFo0eP6hTb/fv3MXjwYERFRcHV1RWbN282asMXIl0YrcukpaUlvLy80LlzZzRt2lTn6Z4NYcqUKfD29kZiYiIGDRqEoUOHon79+khMTMTRo0exZ88eAICbmxu+//57nY/v4OCAwYMHY/ny5YiLi8O3336L/v37o1GjRihQoADCw8Nx5swZ7N27V5hp5pdffuHMSERkUjVr1sTJkyfRpk0bTJkyBRMmTDDauaPjpdhy9TU2XXmF6ATtWneVdbbFiJYe6FKjGMwVScAlNTdgUW/0HCkREaVq2LAhjhw5gq9nbsJPDRww2Wxyxi10zW2B9vOAWgMAkQgAsPnhZlwPva62+C91foGnU/oflUlPAs8Bd3cAka9NHYlhFXQDavQF3DX32tGnT58+CckmIKWH1OfPn3Hnzh1s3boViYmJsLe3V9vSKzPNmjVDp06d0nVjrFSpElq0aIEBAwagb9++CAgIwIoVK9QmxJYtWybE17dvX0ybNg2i//88AkCVKlXQunVr/PLLL4iJST95kTrXrl3D8OHDER8fj9KlS2PTpk3s/UQ5mlGyUvPnz0ebNm1ga2trjNNprVKlSli6dCkmTJiA2NhYLFmyJF0ZNzc3rF+/HnZ2dlk6x/DhwxEdHY1t27YhPj4e69atE7pqpmVubo6ff/4ZXbt2zdJ5iIj0qW7duggICDD4eI6pPsUm4a/Lr7Dt2hvEJmk3Q5xnYXuM9PLAV1WLQiIWAW9vAr7DgFg149TEfwLkUiCXz0pGRJQTKZVK3I8E9jZ6gWaSBxkXLlEP6L4WKPTfZFXhCeFYc2+N2uItS7aEdwVvfYZLaQWeA3b0AhT5YHbW4FvAowNAv/1A2RYGP92uXbuwa9cutdvEYjG+/fZbfPfddyhTpozOx87s7zN7e3uMHj0aI0aMwO3btxEZGYmCBQsK22NiYoTGH5UrV8aUKVNUkmFpWVhYaNWC7fTp0/j555+RnJwMT09P/PXXX3BxcdHhWREZn1ESYt26dTPGabLEy8sL//77L7Zt24bz58/jw4cPMDc3R6lSpdC+fXv069dP5wEE0xKJRJg8eTK6dOmCvXv3wt/fHyEhIUhMTISNjQ1KlSqFevXqoXfv3lmqDMl0RCKR0I1W0xcIZQ2vrWHoel2NkQz7GJOI9RdfYseNICRItZuCvmrxAhjl5YHWFQtDLBYB0kTg9O/AtZWAUqF2n2gRUEBs+pbJlL+xbjMcXlvD0Oa6KpVK7N+2Av1ezoejJIOJTMRmQItJQOOxgES1Pna2dsbGthvx68VfERoXKqwvbFMYsxrN4mtqSHd35I9kWCqFDLjzt1ESYhmGoVDg6NGjsLS0xPjx42FhYZGt48XHxyMiIgIJCQnCMEVph/t5+vSpSiux69evIyEhZZKiAQMGZHtSNx8fH0ydOhVyuRw1a9bE+vXr4eDgkK1jEhkD7w6QMvbBpEmTdG6uWr9+fQQEBGhVtkqVKqhSpUpWwtOrL8dBo6wzNzfn4JAGwmtrGGmvq1KpxG+//YYuXbqgUaNGRo8lJCoB6y4EYvett0iWqU9ifal26YIY5eWB5uVd/rs5Cr4N+A4Fwp9luO9xB0f0NuANVV6uW/PyczM21m2Gw2trABtbwzz4FlKvqkKpxM9XHdFv/gFhDGBFfCQebBiCXpEngYyqWGdPoMd6oFgNjUVquNbA3s57MfPaTJx6cwpikRjzm86Ho5Wjvp5RjsK6Ne8bOXIkRo0apbIuMTERb968wcGDB7F161Zs3boVDx8+xF9//aVzI4yIiAhs2bIFJ06cwJs3b4REmDqRkZEqy48fPxYeZ3dM761bt2L79u1QKpVo0qQJVq5cma0GJUTGxIQYEVE+o1AoMHLkSKxZswarV6/GqVOndJ5FN6vefIrD6nOB2O8fDJlCu4HuG7kXwkgvDzQsW+i/RJgsCTg/H7iyLF2rsGixGKdtrNEzNg5JImBFQUfcsXNEbz0/FyKi/EKuUGLQv4nYdu8ttrZpg3PnzqGKbSQ+7xmM6tKPGe9cfxjQegZgnvkNcgHLAvij+R/Y+2wvYpJjUKdIHT09A9KoRt+UboT5pZWY2Ayo2c9kp7eysoKnpyd+/fVXuLm5Ydq0abh9+zbWrl2Ln3/+WevjPHz4ED/88IMwAVJmkpJUZ+pOmyBzdXXV+rzqbNu2DQDg5OSEFStWMBlGuYpeE2KpsyOKRCKVrHN2Zk388lhERJR1CoUCP/30EzZu3AgA+Pz5M9q1a4fTp0+jTh3D3Xi8+PgZq84F4uDdEGiZB0MLTxeM8vJA7dJOqhve3QEODAPCnqTb57NIhKFFXPDQ0hJ3rSxx19ISry3MUU3B7jZERFkhUygx0DcBOx+kJEziYyJxdUZLVK8hh2MG+yVYFYb11+t0HsBcJBLhG89vsh4w6ca9ZcqYWnf+zh+D6tfsZ/Lukql69eqFP/74A1FRUdi/f7/WCbHk5GSMHTsWUVFRMDc3R79+/dCqVSu4ubmhQIECQvfLt2/fonXr1gCQYeux7GrXrh1OnDiBiIgI/Prrr1i2bFmOmECPSBt6fadq+qAZ8gNIupHJZNnuo04pZDIZQkNTxrkoWrQoK3494rU1DJlMhjdv3uDVK9VZGGNjY/Hy5UuDJMQev4vBqnMvcPRhKLT9KmhbqTBGeZVD1RIFVDfIkoGLi4BLfwDK9OONxYtEGF7EFQ8tLQEAvvZZmwwlK2SyvPvLOr839Id1m+Hw2hqOTAF8iE2pwGsUEePvHjao7JLxmI/vSnZEsT6rAOuCGZbLz3LU90bZFjkmSZSfiMVilC5dGlFRUQgLC0s38L0m169fx9u3bwEAM2bMwNdff622XEatx9Ke5+PHjyhZsqRuwafx66+/wtnZGTt27MCpU6cwbtw4LFmyhPUw5Qp6fZeOHDlSp/VkfExO6o9CocDHjyndBIw1E19+wWtrGAqFAlFRUZg9ezYkEglOnjwJMzMz7Nq1C7169dLruZ59+IyFxwNw+omaGR/VEImATtWKYURLd1QoomYQ1tD7KTNIfniodv8kETCqsAvuWlmq3a6EYeu+vFy35uXnZmys2wyH19ZwrMxE+NfbBn/fl+K7GuawkGhucRuttEVQw9mo2v4HI0aYO7FuJUA1MSqXaze50IsXL4THHTp00Fju4UP1fzMBKTNLpvLz88tWQgwApk2bBplMhj179uDEiROYMGECFi9enO3B+okMjQkxIqJ8xsLCAv/88w/69OmDIUOGoGvXrno/R3SCVKtkmEQsQrcaxTG8pTvcXdS06JJLU1qEXVyU4fgm5iJzlHWtjpuf1U90ot2fmEREpI6NuQhDamfcUvSqogpE3VejYc3qGZZLkCXA2oxjDBElJCQgMDAQQMrYYtq0DgNUk2gJCQmws0v/95NCocDevXs1HqN+/fqwsbFBfHw8tm/fji5dumQreSUSiTBz5kzhvEePHoVYLMaiRYsgFouzfFwiQ+O7k4goH7K2tsbhw4cNkgwDgLpuTmhQ1knjdnOJCN71SuH8+Bb445vq6pNh7x8CG7yA8/MyHuy3SFWIh5zD5O57MbDSQLVFzDKc/oyIiLIqUWmO3xUDoex/INNkWGhsKL7y+QrbH29nCynK91asWIHExEQAQJMmTbROSLm5uQmPDxw4oLbMH3/8gUePHmk8hoODA3r3Tplu6NGjR5g7d67Gz6RUKsWnT58yjUskEmH27Nno0aMHAODw4cP47bffoFBoN5s4kSkYpWPvrVu3AABVq1aFlZWVVvskJSXh/v37AIC6desaLDYiorxKKpXCzMzsv5kZv6Bpvb6M8iqH6y9vqKyzNBPDu14p/NS8LIoW0NBCQC4DriwFzi8AFFLNJxCbAU3HA01/AcwsIALwS51fcOzRdnwU8Y8vIiJdJScnw9zcPOUnhJh3mZZ/oHDDFIzClO+6o37ZQhmWlSlkmHhpIsITwrHw1kJcD72O2Y1nw8lK848nRLnZp0+f8OzZM5V1SUlJePPmDXx9fXHp0iUAgKWlJUaPHq31cZs0aYJChQrh06dPWLZsGYKDg9GmTRsULFgQQUFB+Oeff3Dt2jXUqlUL/v7+Go8zZswYXLlyBc+ePcPff/+NO3fu4Ntvv0X58uVhbm6O9+/fw8/PD0eOHMHYsWOFRFdGRCIRfv/9dygUCvj6+uLgwYOQSCSYO3euwf/uJMoKoyTE+vfvD7FYjH///RceHh5a7fPhwwdhP84ySUSkm8TERPTs2RPly5fHkiVL9PpHiEKhxMfPSShSIOMfOBq5F0LNUo64ExQFGwsJ+jcojR+aloGrfQb7fXySMlbYuzsZB+FaGei2GihWQ2W1SCTCGZkLtsY8ReOERHhI/z+hVoI/rBARZSQuLg6dO3dGsyaN8L96cUBMiMaycqUIq+VdsUnyDTYOaoTapTPv6rX23lrc+fhf3X4x+CJ6/dsL85vOR72i9fTyHIhykl27dmHXrl0ZlnFycsKiRYvg6emp9XFtbGywYMECjBgxAklJSdizZw/27NmjUqZevXqYPn06OnXqpPE41tbW2Lp1K0aPHo1bt27h0aNHmDZtmtZxaCIWizFv3jzI5XIcOnQIPj4+kEgkmD17NpNilOMYbeqHrDaLZnNqIiLdxMfHo1u3bjh16hSOHj0KCwsLzJ8/P9vHlckVOPIgFCvPvoBYJMKxMU0hFmv+w0YkEmFcm/K4+SoC3zcuAyfbDMafkcuAayuAc3MBebLmciIJ0HQc0OxXwEzD8X48DfUdJ4mISJ3Pnz+jY8eOuH39EsYWvg6IzTWW/WReDINjh+CFZSVs/6E+qpd0zPT4t97fwvr769OtD0sIg88LHybEKN8wNzeHo6MjPDw80Lx5c/To0QMFChTIfMcvNG3aFPv378f69etx/fp1REZGwt7eHh4eHujcuTN69eqFd+8yb+Xp5OSEv//+G6dOncKhQ4dw7949REREQCQSwdXVFZUrV0br1q3Rrl07neITi8VYsGABFAoFjhw5gr1790IsFmPmzJlMilGOkmPnQk3ta8yZKYiItKdUKoVkWKqFCxfCzs4OEydOzNIxpXIFDtwJwepzL/D6U7yw/uTjD2hfpUiG+zYt54Km5VwyPkHYs5RWYSF+GZdzqQB0WwMUr6Vt6ERElAm5XI6vvvoKAf5XcG6gLeoV1/y3d5SoABx/uYlqJ4Mwu04JVC6W+Y18ZGIkfrv4m9rZfkval8TU+lOzFT9RTlK/fn0EBKif4EffxylXrhwWLVqkcXuJEiW0jqVNmzZo06aN1vH16NEj0y6UEokES5YswZIlS7Q+LpGx5dhB9VMz2upmzaCsY0Zef8RiMRwdHeHo6MjZU/SM1zbrRCIRBg4cqPJZd3BwQJs2bbJ8XY8+CMWv++6rJMMAYOW559lrxauQA1dXAGubZJwME4mBJj9DOfg8dsY8RUxyTNbPaSB5uW7Ny8/N2Fi3GQ6vbdZJJBKM6vMVrv2QcTIMAD5IikJiZY//damsVTJMqVRi+pXp+JjwMd02M5EZFjZbCDuL/Pm3PutWIiLTM0gLMU3NM8PCwmBjY5PhvsnJyQgKCsKff/4JkUiEcuXKGSLEfMvMLMc2Csx1zMzM4O7ubuow8iRe2+zp27cvZDIZvv/+exQoUACnTp1CnTp1ACBL1/WrqkWx6EQAgiMTVNY/DInB+YAwtKzgqnuQ4S+Ag8OBtzcyLleoHNB9LVCiDlb4L8eGBxvg+8IX69ush6OVo+7nNZC8XLfm5edmbKzbDIfXNhve3sQ3MesBJ/0nEnc+3YnzwefVbhtTawyqOFfR+zlzC9atRESmZ5CauFWrVunWKZVKDBo0SOdjde3aVR8hERHlK6mtxKpWrYqaNWtm61jmEjGGtXDHlAMP023bdztYt4SYQgHcXAecngnIEjIoKAIajQRaTgHMrbHu3jpseLABAPAk4gkGnRyE9W3Ww9naWcdnQ0REgieHgP0/ArJErYp7FrHX/tCfnuAPvz/UbmtcrDEGVB6g9bGIiIgMwSAJMU1daHTpWmNpaYn+/fujV69e+gqLiChfGTBAfzcbvWqXwPIzz/EhJgkAUNbFFiNaeKBLjWLaHyTiJeA7Agi6mnE5J/eUscJK1QcAbH20FSvvrlQp8jzyOQadGIQNbTagsG1hnZ4LEREBuLEeOPYroGZsLwCAxBKQJ2Xp0PHSeEy4OAFShTTdtkJWhTCnyRyIRezaSkREpmWQhNi8efNUlidNmgSRSIQxY8agcGHNNy4ikQgWFhZwdXVFxYoVYWtra4jw8jWpVAoLiwxmeiOtSaVSBAYGAkjphmZurnlGJtINr612wsPDkZycjGLFtEtKfXldY5IU+OvyK7yPTsSS3jUy3NfSTIKfmrljz623GOnlga+qFoUkgxkmVSgUwK2NwOkZgDQ+g4IioMEwwGsaYJHSvX7X011Y7LdYbek3MW/w+NPjHJEQk0rT3/TlFfze0B/WbYbDa6ud0NBQmEnEcLm7ImUMRw0CzDzh4lQQTh+vZ+k8v9/4HW9i3qRbL4II85rOY+te5O3vDSKi3MIgCbHu3burLE+aNAkA0Lp1a3h4eBjilERGp1QqERcXJzwm/eG1zdzHjx/RqlUrJCcn48KFCyhSJOPZHoH/rmtkghy7A55ij18IEqRyAMDgZmVRsahDhvv3b1ga3zVyg1jbRBgARL4GDo4EXl/KuFxBN6DrasCtsbBq/7P9mHtjrtriIogwp/EctCzVUvtYiEyMdZvh8NpmLjg4GO1bt8TiJrFoX0LzjxMn5bUxOnEkiiTFY4voFdzEH3Q6z+GXh/Fv4L9qtw2qMggNizXU6XhERESGYpTRHLdt2wYgZepXIiLKntDQULRq1QpPnjwBkDJu47lz5+Dqqnksr+6rr+BOUJTG7avOvcDKPrUyPK+5RIfuLUol4LcJODkNkMZlXLbeEKD1/wCL/1oFHwo8hJnXZmrcZUbDGejs3ln7eIiI8rE3b96ge/sWWNXgI5qX0Pzn/zZZG/xPNhAKiPFaaon9LQ+ihXNK8qxq1aqZthYNignC7Guz1W6r5lINI2qOyPqTICIi0jOjdN6vV68e6tWrBysrK2OcjogoT/vpp5+EZBgAPH78GD/88EOG+8gVGbeYOPIgFIFhsXqJD1FBwPZuwJFxGSfDHEsBAw8BXy1SSYadeH0CU69MhVLDuDaT609Gz/I99RMrEVE+8NvQPvjbKwzN3TQnw+ZJvTFd9h0U/3970Ll6MYxoXlbrc0jlUky4OAHxsvStz+zM7bCg6QKYi9mVlYiIcg6OZklElMusXbtWpft52bJlsXLlSo3llUolnN5fzvCYSmVKK7FsUSqB21uB1Y2Al+czLlvnB2DYNaBMM5XV54LO4beLv0GhVKjdbXyd8fCu4J29OImI8pPQe9je4j0quUjUb5dY4GPbVTju2BtASpf4BmWdsPjrajp1kV/mvwyPPz1Wu21GoxkoYc+eIkRElLPotcukr6+v8Lhbt25q12dF2mMREeV3xYoVw9mzZ9G8eXNIJBKcO3cuwy7pm6+8xnlZ1QyP2bZSYXzXyC3rQUWHAP+OAgLPZFyuQEmgywrAPf3YX5dDLuOXC79AppSp3XVkjZEYWHlg1mMkIspvXpwG/hkIs2QNLYAtCwDf7oBrmabYXy0JP2y5hQSpHOv614GlmQTJyXKtTnMp+BK2Pd6mdlvPcj3R3q19Vp8BERGRweg1Ifbbb79BJBJBJBKpJLFS12fFl8ciIiKgZMmSOHv2LCwsLDKcZfLmqwjMPfpE7TYRlOhUvThGtHRHhSIZD6ivkVIJ3N0BHJ8EJMVkXLbWQKDtHMAq/bluhN7A2HNjIVWon3VrcNXB+Kn6T1mLkYgoP/LfDhwaAyg1JLUcSgD99gGuFQEAznaW2DWkAT4nylDAWvuujWHxYZh6Zarabe4F3DGx3kSdQyciIjIGvQ+qr2lmH874Q0SkX25ubhlu/xCTiOE7/CFTM36YA+KwwHo7OngfynoAMaHAodHA85MZl7MvBnRdAXi0VrvZ/4M/Rp0dhSR5ktrtAyoNwKiao7IeJxFRfqJUAufnAxfmay5TuCrQdy/gUFRltY2FGWwsdLs9uPLuCiITI9Ott5RYYmHzhbA2s9bpeERERMai14TYmTPqu8poWk/GJxZz2Dh9kUgkQjc1iUTDuByUJby2KZ48eYJHjx6hV69eOu+bLFNg2N+3ER6bPskkggJ/mq9CMUli1gJTKoH7e4BjvwKJ0RmXrdEPaPc7YO2odvODsAcYfmY4EmQJarf39uyN8XXGZ7mVsbHk5bo1Lz83Y2PdZji8tinu+vvB6cpMlPp0UXOhsi2Bb7apba37JW2uazePbihsUxiTL09GeEK4sH5CnQkoX7C8bk8gH2HdSkRkenpNiBUvXlyn9WR8+fmPRH2TSCQoXLiwqcPIk3htgYcPH6JVq1b49OkTdu3aha+//lqn/ecceQz/oCi128aa7UdLyV2gSF3dA/v8ATg8Fgg4mnE5+6JA5+VA+bYaizyNeIqfTv+EOA0zUfYo1wOT60/O8ckwIG/XrXn5uRkb6zbD4bUF7ty4hIi1nVCjdAaFavQFOv8JSLTrEqntdW1YrCH2dd6HKVem4ErIFbQq1QrfeH6jZeT5E+tWIiLT03uXSSIiyp579+4JyTAA6NOnD8zNzbUeT9HHPxjbrr1Ru62V2B+jJL66B6VUAg/3A0fHAwnpu8aoqO4NtJ8HWBfUWORF5AsMPjkYn5M/q93esWxHTG8wHWIRf0EnIsqM/4WjkOzxRqsMkmHL5T1QuNhE9NYyGaarQtaFsLrVavwT8A86lOmQK37MICKi/I0JMSKiHGbfvn1CMgwAZDIZli5diq5du2Z6g/EwJBqTfB6o3VZa9B5LzFdDLNJxTMfYMODIz8CTTMYbs3VNaXlQ4atMD/kp8RMSZeq7bLYp3QZzGs+BRMxfz4mIMvXxKdzO/AgnV/Wb5RBjkvQH/CNvCfg8xLvoJIxtXc4gCSuxSIxvK3yr9+MSEREZgtESYgkJKePDWFurH1hz+/btOHbsGCIjI1GiRAl4e3vDy8vLWOHlG1KpFBYWFqYOI09ITk7GgwcpiYeqVavyuupRfr+2s2bNQmRkJFatWgUAaNKkCQ4dOpTpzUtUfDKG7biNJJki3TZrkRRrzZeigChet2AeHQCO/ALEf8q4XNWvgQ4LARsnrQ5bv2h9rGm9BiPOjEC87L+YWpRogQVNF8BMnLt+r5FK1c+OmRfwe0N/8nvdZkj59tq+vgLs9oaTWH3X80SRFYYmjcZ5RQ1h3Z9nnuNdVALm9qgKc0nGrXDz7XU1grz8vUFElFsY5Y7j7NmzGDFiBGxsbHDhwgXY2dmpbJ80aRJ8fX0BpMxG+fr1a1y+fBljx47FTz/9ZIwQiYhyDJFIhOXLl0MqleLZs2c4dOhQunrzS3KFEmN238XbCPWD0893PYWK0W+1DyLuE3D0l5SEWEZsnIFOS4FKXbQ/9v+rU6QO1rddj2GnhuGz9DMaF2uMP1r8AXMDdechIspTHu4HDgwF5MlqN382c4J33Dg8VJZNty0wLBZyhRLmbIhLRET5mFEGZ7l8+TKUSiW8vLzS3dT5+fnhwIGUGy4rKytUqlQJlpaWUCqVWL58OZ49e2aMEImIchSxWIw1a9bg6NGjmSbDAODP089w4VmY2m3fN3ZDV/sA7U/+5BCwun7mybDK3YERN7KUDEtV3aU6NrbbiDal22Bpy6WwkLD1ARFRhpRK4MpyYN8gjcmwCGs3dIiboTYZVtbZFhsH1oWVjtmwRFkiXka/zFLIREREOZFREmJ3796FSCRC/fr10237559/AACurq44duwYfHx8cOzYMRQtWhQKhQJ79uwxRohERDmOWCzW2M08rdOPP2D52Rdqt9Vzc8Lkrypqd8L4CGDfD8CefkCc+uQaAMDaCei1Gfh6C2DrrN2xM1CpUCUsabEE1maZP1cionxNIQeOTQROTdNYJKxgLbSMnIxgpUu6bc52FtjyfT042er+48OSO0vQ+1Bv7H+2H0qljmNREpFReHl5wdPTE7/99pupQyHKFYySEIuIiAAAlClTJt22S5cuQSQSoV+/fihSpAgAoGjRoujXrx+USiVu3rxpjBCJiIzu5MmT6N27N5KT1f/Cr41X4XH4ec9dtdtc7S2xsm/NTMeIAQA8PQqsqg883JdxuQqdUlqFVemhe7BERJR10gS8+7MNcHOdxiIfS3ZAsw9jEI30LYttLCTY9F1dlCpko/Opb0Xfwv4X+5EoT8T/rv0PEy5O0DhLMBH95+bNm/D09BT++fv7mzokIkrDKGOIpSbEbG1tVdY/f/4ckZGREIlEaNWqlcq2KlWqAADevXtnjBCJiIzq6NGj6NGjB5KSkiCVSrFnzx6Ym+s2dlZ8sgxDt9/G5yRZum3mEhHW9KsFV3urjA+SEAkc+w24vzvjclaOQMc/gCo9AS1nJvuc/Bkvo1+iukt1rcoTEZEGcZ/waWUbFEsI1FgkrMpgtLzvhQRF+tZbErEIq/rWQrUSjlqfsu/Rvrgfdl/tthOvT+Bh+EMsabEElQpV0vqYRPlN6tBAqXx9fVGrVi0TRUNEXzJKCzGJJGWMgujoaJX1t2/fBgA4OTmhbFnVMQ4KFCgAAEhKSjJChERExnPo0CF0795dqN8OHDiAvn37QiZLn9jSRKlUYuL+Bwj4oP4X+mmdKqF26UxmfEyIAlY3zDwZVr5DSquwqr20TobFSeMw7PQw/HDiB1wNuarVPkREpEbES3z+swEKaUyGiRDZbBY6PG2PuGT1XRnndq+Clp6u+g0rMQI2Zrq3NiPKLxITE3HixAkAgI1Nymfl+PHj2eoZkJmzZ88iICAA8+fPN9g5iPISoyTEXF1TvoCfPHmisv78+fMQiUSoXbt2un0+f065yStYsKDhAyQiMiInJ6d0rcHMzHRrsPvX5Vc4dE99C9oetYqjf4PSmR/k03Pgc6jm7VYFgO7rAO9dgH0RrWNLkCVg5JmRuBd2D0nyJIw8OxIX3l7Qen8iIvp/wbeBjW1gn/xR7WalmRViu/yFnneqIzxW/Y/IY1qVQ++6pfQe2tQGU+FWwE3vxyXKK06dOoW4uDgAwNSpUwGkNBA5e/asKcMiojSMkhCrU6cOlEolduzYIXSfvH//Pi5fvgwAaNq0abp9AgNTfgVzcUk/IChlXWprPco+MzMzYTwAXZMZlLG8fm0bN26MI0eOCAPmDxgwANu3b9f6uV5/+Qnzjj1Vu61SUQfM7V4VIi1bcmlUri0w/AZQ/VutW4UBQJI8CWPPjYXfBz9hnVQhxdhzY3HqzansxZSD5eW6NS8/N2PL63WbKeXJaxtwDNjSEYgPV7tZaV0QyX0PYOCNongZFqe2zDd1SmBs63JZOn1scqzGbZ3LdkYX96zPMEysW/MDX19fAICnpyd69uwpjKedup6ITM8ofzH06dMHBw4cQHBwMFq3bg03NzcEBgZCJpOhQIEC6NChQ7p9rl+/DpFIBHd3d2OEmG+IxUbJgeYLYrEYdnbpB62l7MsP17Z58+Y4fPgw9u/fj+XLl2v9h/H76ESM3OkPuZoxYgpYm2Nd/9qwMldzrOR47QKzdADazwdq9NEpEQYAUrkUv5z/BVffpe8iKVPK8IffH2hWohksJZY6HTc3yMt1a15+bsaWH+o2U8lz1/bWX8DR8YBSoXaz0rE0FH32YfSJGNx+80FtmeblXfB7Fn8giUiMQNDnILXbStmXwpQGU3Q+Jqli3Zq3ffz48f/Yu+vwKI43gOPfu7hABHcnWHB3d5cWd2tLW2gLhUINaGmpQIFCkVJIcHe34BYsCRLcIQIJ8dPfH/nlSLi95C65uwjzeZ4+JTt7O7N7e7t37868w5kzZwDo2rWr7v9//fUXJ0+e5NWrV3h6Gk5t8fLlS3x9fTl16hSPHj0iPj4eNzc38uTJQ7ly5WjcuDFt27bVu+61bNmSp0+f0qNHD8lhkyEhIRw8eJBz585x8+ZNQkJCUKlUeHh4UKVKFbp06UL79u0Nnp/nzp1j8ODBAPj4+FCvXj327NnD+vXruXXrFrGxsRQqVIhWrVoxevRo3N3d03P4BMFqrBIQq1y5MpMmTWL27NnExsZy/fr1xMptbZk5c6beBzkqKgo/v8ThNXXr1rVGEwVBEKyuZcuWtGzZ0uj1E1RqPlrtT1i0fu4JmQz+6ludYp7v5HMJvQXHZkFIUNoVlGkFXeeBW1Gj25REpVHx9Ymv8XsiPTQyr1NeFrdZnCODYYIgCGaj0cCR6XByjuF1CteAfuuZfiSU/UHSwbAqRXKzcEBN42YZfrcJWg3TTk5DpdHPaylDxuxms3Gxc5F4pSAISXbu3IlarUYul9OlSxcAunTpwrx581AqlezatUsXWHrXxYsXGTNmDNHRKXtphoeHEx4eTnBwMLt378bDw4MWLVoY3Sa1Wk2zZs3QaPQD7SEhIRw5coQjR46wadMm5s+frzch3rs0Gg0TJ05kx44dKZY/ePCAf//9l0OHDrF69Wox4kvI0qzWp3zo0KE0aNCA/fv3ExYWRr58+ejUqZNeMn1IjDwnzTJpyodcSJvUBVBIH41GQ2xsYq8bZ2dn8aTPjHLSsY2IiDDb07EZu65z+VGEZNkXrcvTPHnC5Ff34NivELDBYA8DHftc0O4nqDnY5F5hAGqNmqknpxocEunh4MGytssokduIvGbZVE6+tubkfbO2nHRty2pyxLFVKWD7J4nXbUPKtYM+/7HkzAtWnnkouUpRDyeWD62Di0P6vuavur6KE09PSJYVdClI5TyV07VdISVxbc3Ztm/fDiR27ihQoAAAxYoVo0aNGly6dIlt27ZJBsQUCgUTJkwgOjoaFxcX+vXrR7169ciTJw9KpZInT55w+fJlDh40PQ2FVps4uqB+/fo0bdqU8uXL4+npSUxMDI8fP2bjxo1cvnyZU6dOMX36dH799ddUt/fXX39x+fJlWrduTffu3SlcuDBhYWGsWbOGY8eO8fDhQ2bNmsWff/5pclsFwVqsmmQhKbdDWlq3bk3r1q2t0KL3j1qtzuwm5BgqlYpbt24B4O3tjb29fSa3KOfIKcd20aJFfP/99xw+fBhvb+8MbWuT/xNWnZUevtK6Yn4+aVE28Y+Ix3B8NlxeDVojPu+lmkG3BeCevoTLGq2GH8/8yJ77eyTLc9vnZmnbpZRxz9nD33PytTUn75u15ZRrW1aU7Y9tXASsHwgPpANRANQaBh1/Z3vAS4N5JN2d7Vg5vC75czmmqxlB4UHMuWS4d1pep7zp2q6gLyteWwfsGWDW7XUo2YGBlQamus6q66vY+2CvWetd3XG1Wbdnqhs3buiuR0nDJZN07dqVS5cuERQUxJ07dyhbtmyKcn9/f0JCEifR+OOPP/Q6h1SvXp3OnTszZcoU4uPjTWqXjY0N+/bto0QJ/QeUdevWpVevXsybN4+///6b7du389FHH1GyZEmD27t8+TLjx4/no48+SrG8adOmjBw5kpMnT7J///40h4cKQmbKIVlHBUEQspZ58+bx+eefA9CqVSuOHTtGpUqV0rWtwKeRTN0aIFlWMo8zf3xQHXn0CzjxB1xaCWojp/N2LwGDt6erVxgkPmn8+dzPbL2zVbLc1c6VJW2W4OWZ9oMQQRCE91bkE1jdB0KuG16n1XfQ+AtO3wvnq41XJVdxsJXz75DalMmXvlxqMcoYJvlNkhwqmSTDE7YIWdq10Gtm3V61fNXSXOdZzDOz15vZkpLmOzo60q5duxRlHTp04KeffkKpVLJt2za++uqrFOVhYW8n0ahTp47BOmxtbU3OmyiTySSDYcl98sknrFmzhtevX3PkyBGGDx9ucN3KlSszduxYyXqGDh3KyZMnUalUXL58mVatWpnUVkGwlkzrT67Vann06BHXrl3j2rVrPHr0SNeNUxAEITvbsGGDLhgGEBoaSsuWLQkNDTV5Wyq1hk/XXiZBpT+0wsnOhmW9S+J2/AeYVx0uLDU+GAbgmj9DwbDfLv7G+lvrJcudbJ1Y1HoRlfOKoTWCIAgGvQiEZW0MBsO0clvosRiafMnNl1GM8fVHqdb/vpyYR7IGtUqkvxfGzLMzDSbSFwTBOCqVil27dgGJqX/eDVq5u7vTrFkzIDHP2LtDZ5Pn29q8ebNF26rRaHj58iX37t0jODiY4OBg7t69S8GCBQG4eVO6J2qSLl26GAySV6789vvf48ePzddoQTAzq/cQO3HiBKtXr+bcuXN63TwdHR2pV68eAwcOpHHjxtZumiAIgll06tSJJk2acOLE26EvH3/8cbqSitrayPmlpzefrLlMWHSCbrkb0Wyq6E/ZNcNBGWPElmSA+R46zL88H9/rvpJlDjYO/N3qb6rnr262+gRBEHKcu0dh/SBQREkWJ+CAw8ANULo5zyPjGPbfBaLipXtv/dClMu2rFEx3U3bc3cGue7sky7rk60KvAr2y51BUQbCykydP6np5vTtcMknXrl05dOgQL1684Ny5czRo0EBXVqtWLYoVK8bjx4/5+eef2blzJ23atKF27dpm+QxqtVp27NjBpk2buHbtWqrDLl+/fp3qtqRygSdJnj83JsaY76mCkDmsFhBTKBRMmTKFPXsS88xI9QaLi4vDz88PPz8/OnbsyKxZs8SNVxCEbMfFxYXdu3fTrl07zpw5w88//8yUKVPSvb16pfOw+7PGfLTKn+BHzxhus4+PHffiGGzEFwy5XWKy/CcX4YX0MBtTLb66mKUBSyXL7OR2zGsxjzoFDXfzFwRBeO9dXZeYQN/A8MRoWW5cx+yFglV4E69k2H8XeB4p/cN1TNPSDGlYMt1NeRD5gJlnZ0qWVctbje75u6d724LwvkkaLunu7k6TJk0k12nRogW5c+fmzZs3bNu2LUVAzM7Ojn/++YfPPvuMu3fvEhAQQEBAYtoMR0dHateuTffu3enYsSM2NjYmtS0hIYFx48Zx/Phxo9ZPK0eZo6PhXIXJJzYRE0gIWZnVAmJfffUVBw8eRKvVYmtrS8OGDalataqux0RoaCgBAQGcOnUKlUrFnj17UKlU/PXXX9ZqoiAIgtnkypWLvXv3snPnTgYOTD2hrDEKOKrZUOUcivA5OKvfQFq5eGU2UK0fNJsIHiVhmXkmKvkv8D8WXFkgWWYrs+XP5n/SsEhDs9QlCIKQ42i1cOJ3OCIdgAKIcCiM+8eHwK0ICpWGMT7+3Hwh3Yusa7XCfN2+Qrqbo1ArmHR8EnGqOL2yXPa5+KnhT4TdC5N4pZATVc1X1azbK+xS2Kh1zF1vZomKiuLIkSNA4izjVapUSfM1Bw4c4Pvvv8fZ2Vm3rGzZsuzcuZOjR49y5MgRLl68yMOHD4mPj+fkyZOcPHmS//77j6VLl5InTx6j27do0SJdMKxu3br079+fypUrkzdvXhwdHXVBrAEDBnDx4kVTdl0Qsi2rBMSOHTvGgQMHkMlk1KtXj59//pkiRYpIrvvs2TO++eYbzp49y4EDB/Dz89ONsxYEQchO3NzcMh4MU8bDxeVw8k9sY0KNuGjLwLs3NJsMecumubYpVt9YzZ/+0lNn28hsmN1sNs2LNTdrnYIgCDmGWgW7v0ic/MSQUk1x/3AVOLqh0WiZuOkqZ+6FS65av7Qnv/Wpilye/kT3c/zncOPVDcmyHxv+SCGXQoQhAmLvi8yYnXFgpYFpzkSZXezdu5eEhIS0V0wmNjaWAwcO0L179xTLbWxsaN26Na1bJz7QDAkJ0aUeCgoKIigoiO+++46///7bqHq0Wi2bNm0CoHbt2qxcuTJFL67kIiMjTdoHQcjOrBIQ27JlCwAVKlRg2bJl2NnZGVy3cOHCLF26lA8++ICbN2+yefNmiwfEnj59iq+vL8eOHePFixfY29tTrFgxOnTowIABA3BycjJbXadPn2bHjh34+/sTGhqKjY0NefPmxcvLi/r169OtWzdcXFzMVp8gCJaj1Wo5cOCA3gxCZqFSwGUfOP4HRD0z7jUVu0KLbyB/RbM3Z1PwJn45/4tkmQwZPzX+iTYl2pi9XkEQhBwhIRo2DYPbBwyvU/VD6LoAbBPThczef4vtV6Sv/14FcrF4UG0cbE0bMpWcVqtFayC3ZJ/yfWhTog0KhQkTtQjCey5puGS+fPmMSpUxe/ZsXrx4wfbt2/UCYu/Knz8/vXr1omvXrnz44YcEBQVx7Ngx4uPjUx26mCQiIkI3uVP79u0NBsNiYmK4f/9+mtsThJzCKgGxq1evIpPJGDZsWKrBsCR2dnYMHz6ciRMncvWqeXLeGHLkyBEmTpxIdHS0bllcXByRkZEEBgayceNGlixZkuYUtWmJjIxkypQpHD58WK8sOjqaBw8esH//fmrUqEHFiub/MZvEmOMvGMfe3p5atWpldjNypOxwbLVaLZ9//jnz589n5syZTJ061TwbVqvg2jrw+xUijJztq3z7xEBYobSnN08PlUbFlttbDJb/2PBHOpXuZJG6s4ucfG3Nyftmbdnh2pZdZeljGx0Cq/vA8yuG12nyJbT8Vjfzr8+ZB/zjd1dy1YK5HflvWB3cnDL22ZTJZEyuO5m6Bevy3enviExI7BVS1r0sk+pMArL4cc3mxLU1Z3n8+DGXLl0CoF27dnTqlPb3oitXruDj48PZs2d5+fIlBQoUSPM1dnZ21KlTh6CgIFQqFW/evDEqIKZWv821ERenP0Q6ycaNG1GppHMbCkJOZJWA2KtXr4DE8dDGSpq1Iq3ZLTLi+vXrTJgwgfj4eJydnRkzZgz16tUjPj6ePXv2sGHDBh48eMDo0aPZvHmz3rS5xoqKimLYsGEEBQUB0KZNG9q1a0fx4sWRy+W8ePGC8+fPc+BAKk8NBUHIMjQaDZ988gn//PMPANOmTcPe3p6JEydmYKNqovzXk+vM7/BK+keQntItoMVUKJZ2AvsBNq+4Vqp4imVVNa8wZnCErdyWxW0W89Ghj7gamvIhxdR6U+lRrodx7RUEQXjfhN2GVb0g4qF0uUwOnf6A2sN1i/YFvuD7HUGSq+dysGXF8DoUdjff6IWWxVtSKU8lJp+YTGBYIL81/Q1H27R/YAuC8Nb27dt1k8YZO3KgXbt2+Pj4oNFo2L59O6NHj+bixYvky5fPYGcMhULBhQsXAHB2dsbT09Ooujw9PXWJ/Hft2sXQoUP1Jq+7du2ayN8tvHesEhBzdnbmzZs3REREGP2aN2/eAJh1uOK7fvrpJ+Lj47G1tWX58uXUqFFDV9agQQNKlCjBb7/9xoMHD/jvv//49NNP01XPjBkzCAoKwt7enrlz59KqVasU5d7e3rRp04ZvvvkmRfReEISs6fjx47pgWJLJkyfTtWtXvLy8TNuYVgs3dhK7fzq5Im8b95riDaHlVCjZ2LS6MiCXfS4Wt1nMuMPjuPgyMdHqxNoT6Vuhr9XaIAiCkK08Ogtr+0Kc9MPdGIWWmI5/kb/2YN0y/4ev+XzdZSQmY8fORsbiQbWoUDC32Zta0KUg/7b9l5uvblLWw7z5JwXhfbB9+3YA8uTJQ+3atY16Tc2aNcmXLx+hoaHs2LGD0aNHc+bMGRYuXEjt2rVp1qwZXl5eeHp6Eh8fz4MHD1i3bp2uk0Xv3r2xtTXu57xcLqdLly6sXr2aW7du0a9fP4YNG0aJEiWIjo7Gz8+PNWvW4OzsTP78+Xnw4EG6joMgZDdWCYiVKlWKq1evsmfPHho2NG72sT179uheawnXrl3TzZ7Rq1evFMGwJMOHD2fLli3cvXsXHx8fxo4da3L35osXL+oukOPHj9cLhiUnk8mMvqillwi4mY9arSYsLDHRbN68eU2e+lgwLKsf2+bNmzN//nxdkNzGxobVq1ebFgzTahNzyRyZCS+u4Zz2K6BILWg5LbFnmCz9SZTTy8XOhYWtF/L5kc+pU7AOgysPTvtF74mcfG3NyftmbVn92padZblje307bB4FaukE2yExWm7WnE7Txm+vo/dCoxm58gIJKo3ka37vU42GZfNapLkANnIbKuetnGJZljuuOYi4tuYc/v7+PHqUmOaidevWBvNzvUsul9OmTRvWrFnD7du3CQwMBBJHIpw/f57z588bfG2rVq348ssvTWrnhAkTuHTpEjdu3CAwMFDv9e7u7syfP5958+aJgJjw3rBKQKxly5ZcuXKFLVu2ULNmTXr27Jnq+tu2bWPz5s3IZLJUA0gZcejQId2/e/XqJbmOXC6ne/fu/PHHH7x584Zz587RuLFpPTJWr04ckJQrV66MzzZnBhqN9JcswXRqtZonT54A4OHhIb4kmlF2OLbjxo1DqVQyadIk1q1bZ/A6okerhXvHEgNhT42c0rqgN7SYBuXbZUogLDknWycWtl6Irdwqt49sIydfW3Pyvllbdri2ZVdZ6tieWQj7vwEDCetvv9LwtPk8mvcclmK5i4MthdyceB2r1HvN5A4V6FZdeoZ2S8pSxzWHEdfWnCOp8wMYP1wySdu2bVmzZg2Q+Bt4woQJeHl5cfr0aW7cuEFISAjh4YkzzebNm5eqVavSvXt3mjdvbnI7c+XKxdq1a/nvv//Yu3cvDx8+xMbGhkKFCtGsWTOGDBlCwYIFTd6uIGRnVvlFM3DgQHx9fQkLC2Pq1Kns37+fXr16Ua1aNTw9PZHJZISHh3P16lU2b97M8ePH0Wq1FChQwGJBJH9/fyBxOGflypUNrlenztvcPJcuXTIpIKZQKHRJ9Bs2bIiDgwOQ+OUiJCQEtVpNvnz5dMsFQcheJkyYQJcuXYzPj/jwNBz5CR6eNGr1Vy6l8ez0A1ToAkY+bbQGEQwTBEGQoNHAgWlw9m/D6xSti03vn2leRT/3Y4HcjqwfU5+PV1/ixO0w3fLBDUowpmlpS7RYEAQzmD59OtOnT0/Xaxs0aMCtW7dSLGvbti1t27ZN1/aOHDmSarmTkxMff/wxH3/8scF1fH19DZbVq1dPr72GGLueIGQmq+UQW7x4McOGDSMyMpLjx49z/Phxg+trtVrc3NxYvHixxXKI3b2bmLS6ePHiqQ5TTErun/w1xrp58yYJCYld5cuXL090dDR//fUX27Zt0+VIS5opZOzYsdSrV8/U3RAEwdqWtYYnF3R/lgUoWgdGHjL4Ep74w9GZcDf1LylJ7msKcLjAcIaP+QosPIw6uafRTynsUhhZJvdCEwRByHaU8bB1DFzfZnidCp2h1zJK2xn+bpvL0Y7lQ+vw9eZrbLn0lLaVCvB9l8oZvi4fengIL08viuUqlqHtCIIgCEJOYrVfWpUqVWLHjh389NNPHD582OC4eRsbG1q3bs0333xj1NSz6ZGQkKCbvTKtbqFubm44OzsTGxvLixcvTKoneQBNq9XSq1cvvfHYSqWS06dPc+bMGb744gtGjx5tUh2mUiqVKBQKyTJbW1vdmHeNRpPmlLvJZyZRq9Wp5kKQyWQp8q+pVKpUu4rL5fIUgUqlUqmbuSWttmu1WpRK/eEGydnZ2em+XKa1r4banryO5P82te02NjYphiAYen+SvA/vk9Ty9L5PGWl7QkIC33//PZMmTSJfvnwGh4potFpU77xvtra2yF8GwtGfIXivwXYk90Sbl79UPbnk3o4Ngxqg0mhA4nxIz75K0+rOt5uvbjL2yFjalWjH1PpTsbVJPDbW+jyl1vbs8HlK6xhlZ+K+kUjcN6RllfcpU+8bca+x3TgI+ZNzBl9LvbEoWvwAWhvJ6zq83Vc7Gzm/9fKmepHcdK9eCLVKidQ7YOz7dDviNl8f/xp7G3u+a/AdHUp1MHlfk/bXkMz4PBnb9qz6ecrJ9w1BEITswqrjXgoUKMC8efMICQnh/Pnz3L59WzfzpLu7O+XKlaNu3brkz5/fou2IiYnR/dvZOe1U1k5OTsTGxhIbG2tSPZGRkbp/L126lISEBJo0acJnn31GhQoViI6OZv/+/fzxxx9ERUXxxx9/ULp0aVq3bm1SPaZIrZebl5cXrq6uAMTGxqbZzbVWrVq6f4eFhelyTEhxcXGhQoUKur8fPnyY6qyj+fPnp1ixt08xb9y4keoXh9KlS+Ph4QEkfsEICAhIte3e3t66LyiRkZHcu3fP4Lp2dnZUrVpV9/fz588JCQlJsc7Nmzd1/3Z3d6dMmTK6v+/evZvinHtX0aJFUwR/02r7+/Q+JWeO9ym5tN6nhIQEJk6cyOnTp9m3bx/btm2jfPnyktuKjY3lVrJ9cYx6gNezzciDdxu1bwrHPETW+5JRl7x4GKni305leXD7psH10/M+SdFoNAQEBPAk/gm/3P+FaHU0m+5sIjYhlp+b/4yN3CZTPk/JZefPU04h7huJxH1DWlZ7n5KzxvVIGXqHcmcnI495bLghbX+CBp8QcOlSqu1N/j7FxcVR2fE1t29Kz1AJxr1PCZoEfrz7IwqNAoVGwaTjkzjz7Awf5vuQ+Kh4g9t+932ClOfsuzL785Rcdv48CYIgCNaVKYlg8ufPT+fOnTOjagDdMEbAqFkjk27Y8fGGvzhISR5AS0hIoFGjRixevFj3FMrT05N+/fpRrlw5Bg0ahEaj4c8//6RVq1ZiyJIgZKL4+Hi+/PJLzp1LfNp/48YN+vXrx9GjR0ltsnuH6CcUCvbB8+lhZAaSKSentPfgRbl+hJboQs26DdnUUMWN528o5qTEWt+Xnyc8Z/b92USro3XL9jzeAyfhp8Y/WacRgiAI2ZDDqxuUOTkBuwTpoJVCI0Pb/R8cava1csveWvN8Dc8SnqVYtvXOVi48vcC3pb7FQS7y2AqCIAjvL5k2tT7EOdSrV69o0KABAB07dmTOnDmprt+wYUPCw8MpX748O3fuNLqef//9l9mzZ+v+3rp1K5UqVZJc97PPPmP//v1A4kwlyZ+2ZpRCodA97SpTpozBXnHvw5CKJOYa+pL0tLRChQq6dbJLV30pWeV9Sn5sk54iW3NIxaNHj2jRokWKp7i5cuXi6NGj1Lr8dYocYgCaAt5oC1ZFfm0dMm3a06hrHd1RN/gUTa0RYO8CWP59GuJbl2vylMfbS2PDKxdPQuNCJV/3Ve2vGFxpsBj6koyh9yk2NlbXkyp5z4fsStw39In7hrSs8j5Z/b4RfADtxqHIlNI9j17HaRm424bZ60/qJm9Kep8evYqlmIeT3sNPc79PBx8dZPKpyZKvHVBhAF/U+MLgtpP2Nfm1IPk5+y4xZNKwzLxv3L59G5VKha2tLeXKlTP79gVBEKzJEte0TJsqLDw8nODgYL0hk3nz5rV43S4uLrp/GzMMMi4uDjBueKWhejw9PQ0GwwCaNGmiC4gFBASYNSCWnJ2dnVE3XLlcbtKN+d0vE2lJbSIDKcb05Esik8lMarup+5rUdplMpnuP7e3tDbbRlLYnbctYOfV9Sn5sk74wp/d9MlbytpctW5YjR47QrFkznj9/jpubG/v3708c9nBZ/7Xyl4Hw0ohhPA65ocEnyOp/hK2jm8HVLP0+JbknU6M0EAxrVKQR/Sr0s9rnyVhZ9fOUk3PBiPuGPnHfMOy9uG/4r4BdXxh8APIgQkO/3fb8s+lIipnM7e3tCXgSyYdLztKzZhF+7FoFG7n0iICMvk9Po5/y03npXr4VPSvyRe0vsLdJe/vGnrPvvkbcN6S9L/cNQRCE7MKqATGtVsu6detYs2YNd+7ckVynbNmy9OvXj759+6aSCDpjHBwccHd3JyIiIs1E+ZGRkbqgWVoJ+N9VqFAh3b/Tem3y8qSE/5Zg6pcCwTA7OzuLBS7fd1nh2JYrV44jR47Qs2dPfHx8qF27diprp9HR1s4F6o2Bhp+Cs6dZ25kRSgMjs+sVrMfc5nON+rEkJMrJ19acvG/WlhWubTmVVY6tVps4Ucrx2QZXiXUvz+Adb1i6dTNVqlRJUfb4VSzDVlwgVqFm1dlHvHyTwLy+NXCyNz7gaAylRsmk45OIUkbplTnZOjG76Wyjr+/inLUccW0VBEHIfJaJOEkIDw/ngw8+YPr06dy5cwetViv53507d5gxYwZ9+vQhNFS654I5lC1bFkgcGpVaV+fkST6TJ+g0pQ4g1eEGQIqu36Y8iRUEwXIqVKhAQEBAymCYwoTJNWwdocE4Yj7yZ/SzTtyOysQvvwUqGrVazfw1mddyHo62jhZukCAIQjaiUsC2j1MNhlG2Nc4fHeHohet6wbDXMQqG/HeesOi3eWwPXn9J/2VneRWT+hA9Uy28spBrodcky6bVn0ZJt5JmrU8QBEEQsiur9BBTKBQMGTKEu3fvotVq8fT0pEOHDnh7e+uGSIaFhREYGMjevXsJDw8nKCiIYcOGsWXLFouMqa9VqxYXL14kNjaWoKAgqlWrJrnehQtvcwXVrFnTpDqKFClC4cKFefbsGU+fPkWr1RpMlv/o0SPdv5PPdCMIgmVpNJpUe6OmCFArYiA8OO2Nyu2g1lBo8iXaXAX5avUlDlx/yak7YfzWpxodvQuluYnM4J3Xm79b/Y2znWnDwwVBEHK0+DewYRDcO2Z4nRqDoPMcsLHj3ceaGo2WMb7+3AvVzzd2+VEE/568x8R25umFdfb5Wf4N+FeyrEvpLnQt09Us9QiCIAhCTmCVHmIrVqzQDZHs3bs3hw4d4ttvv6V79+40btyYxo0b0717d6ZNm8ahQ4f44IMPgMRpklesWGGRNrVu3Vr3782bN0uuo9Fo2LZtGwC5c+emXr16JtfTtm1bAKKjozlz5ozB9Q4cOKD7d/Lpmc0trcSfgvFUKhV3797l7t274riambWObWhoKPXr12fv3r3GveD4b6BOJeeHzAZqDobPLkGn3yF3IZYcv8fewMSh2TEKNR+vvsSsPTdQqQ0nBLYEjTb1+ip4VmBR60W42rtaqUU5S06+BuTkfbM2cd+wnAwf22Wt4Qe3lP8taw1vnsF/HVIPhjX/BrrOBxvpXsByuYwRTUrhYKv/tbtp+XyMb13e9PZKCI8LZ8qJKWglhvEXz1WcqfWnmrxNcc5ajjiegiAImc8qAbHdu3cjk8lo2LAhM2fOTDU5vZOTE9OnT6dRo0ZotVp2795tkTZVrVpVNwxq8+bNXL6snyl7+fLlutlfBg8erDfW/9y5c3h5eeHl5cXkydKz+AwZMgQHh8QprWfNmkV0dLTeOtu3b+f8+fMANG/ePEXuMXN7DycVtRiNRkNERAQRERGpznYkmM4ax/bly5e0aNGCCxcu0KNHDw4ePJj6C8Juw+kFhsurfgjjLiT+KHIvDsDpO2H8uu+m3qqLj99j5u4bGWm+yZ5HPzdYVta9LEvaLMHNwXCifyF1OfnampP3zdrEfcNyLHJslXGwrA28DJSuU2YD3RZC86/BwAiAJO0qF2TNqPp4OL/9LlmlSG4WDqiJnU3Gv45rtBqmnZpGWFyYXpmt3JbZzWbjYuci8co0tivOWYsR11ZBEITMZ5WAWNJwwP79+xv9mqR1kw8lNLepU6fi6OiISqVi+PDhLF68mCtXrnD27Fm+++47fvvtNwBKlizJsGHD0lVH4cKF+eyzzwAIDg6md+/ebN68mcDAQM6ePcuMGTOYMmUKAK6urrp/C4JgOeHh4TRv3pygoCAAEhIS6Nq1K35+ftIv0Gphz0TQGOgdVqAK9FwCed7mGXwWEce4tZfRSHzf9XC2Y2STUhndDaPtu7+P8PhwybKSuUuytO1SPBw9rNYeQRCEbCHkBrx5IlkUlaClx0Yl5xRlJcul1CrhweaPGlLM04miHk4sH1oHVwfzZC/xve7LyacnJcsm1JxA5TyVJcsEQRAE4X1mlRxi9vb2xMfHm9TzKWldS+QPS1KpUiXmzJnDxIkTiY6O5s8//9Rbp2TJkixZsgRX1/QPIxo5ciSRkZEsXbqU+/fv88033+itkydPHv7++29KliyZ7noEQTCOh4cHdevW5ebNt723ChYsSPHixaVfcGMH3DtqeIN2Tin+jFeq+WiVv2SiZLkM5verSVEP6+Tpuh95n+9Pfy9ZZi+3Z2nbpeR1ymuVtgiCIGQrWukJkZ5Faei4OpY4t7IULVrUpE2WzufKlo8aEZ2gIn8u80xeEhQexNxLcyXLmhRpwqBKg8xSjyAIgiDkNFbpIVaqVGJPiOfPDQ/ZeVfSukmvtZSWLVuyY8cOhg4dSsmSJXFyciJ37txUqVKFr776im3btlGiRIkM1/Pll1+ydu1aunXrRpEiRbC3tydXrlx4e3vz+eefs3//fmrUqGGGPRIEIS1yuZzly5fTr18/IHEGWT8/P+nrjSIG9ukHsVPz484grj6JlCz7qp0XjctZJwAVp4rjS78viVVJz4xZ0q0kBV0KWqUtgiAIOcGjOGfqL4tB4emFn58fRYoUMXkb+XI5UCqv6cMXpUQropnoNxGVRj8fVT6nfMxsPNPghE6CIAiC8L6zSg+xnj17cuXKFdatW0erVq2Mes26deuQyWR0797dso0jcTbIKVOmmDxcsV69ety6dcvo9WvUqCGCXoKQRdjY2ODj40PhwoWZMGGC4R81x383OGRGyvoLj1h7/rFkWbvKBfioWRnJMkv4+dzP3H5922C5o615eicIgiBke8bkcyrRmAI9ltNL8QtTpkwhf/78lm9XKrRaLTPPzeRxlP49R4aMWU1m4enomQktEwRBEITswSo9xPr06UPjxo05efIkP/zwAwkJCQbXVSgUTJ8+nRMnTtCoUSM+/PBDazRREIT3kK2tLb///rvhYFjYHTg93+jtXX0cwbfbgyTLSudz4fc+1az2pH7r7a1su7PNKnUJgiBka2olvLqX+jpVesGgLTi4F2DOnDmSwbAXkfEEPpXuHWwJO+7uYPc96cmnRnqPpF4h02dHFwRBEIT3iVV6iF28eJFhw4YRGRnJ+vXrOXToEB06dMDb25s8efIgk8kICwsjICCAffv2ERYWhre3N8OHD+fixYsGt1unTh1rNF8QhGzs8ePHFC5cGBsbG9NeqNXC3lQS6b8jPDqBj1b5o1Dpz8LlYm/DkkG1yOVoJ/FK89NqtRx5fMQqdQmCIGRryjjYOBTiXhlep9Hn0OoHkBt+jvwmXsnQ/87z6FUsCwfUpLmXZXuPPXzzkJ/O/SRZVi1fNT6q/pFF6xcEQRCEnMAqAbFBgwal6BURHh7OqlWrUn1NYGAgI0aMMFguk8m4fv262dr4vhB5JMxHLpfrnhDLU/mSLJjOXMf2+vXrtGrVirZt27J8+XLTgmI3dsJd44JKKq2Mz9Zd5llkvGT5b32qUTZ/LuPrziCZTMbc5nNZcm0Ji64uQouY2t3ScvK1NSfvm7WJ+4blpOvYJkTB2n7w4IRksRaQdfwd6o5KdTMKlYaxvv7cfBEFwIiVF5nVw5sP6hQzuv2myueUj7Yl2rL97vYUy3PZ5+LXpr9iJzfPAxhxzlrO+3pt/ffff3nyJGUqiqJFi6b6u08QBMFSrHZn02q1Zv9PMJ2trVVioO8FW1tbihUrRrFixcRxNTNzHNuAgACaN2/Oixcv8PHxYfTo0Wg0+r23JCliYJ+BnIKuBfQW/R7eiFN3wiVXH9O0NB29jZ9h11xs5DZ8VP0j/mnzj8ghYwU5+RqQk/fN2sR9w3JMPraxr2BlV4PBsDillmXXndDWGZnqZjQaLZM2XeX03bf3ALVGy6TN15h7KNhi31ed7ZyZ2XgmvzT5BRe7twn6f2z4I0VcTU/0b4g4Zy1HHE9ByL6ePXvGd999R+vWrfH29sbLywsvLy8OHTpktjq2bNmi2+67QWTBfKxyJfbx8bFGNYIgCADEx8fToUMHQkNDdcuWL1+Ol5cXkyZNSnsDJ/6QTqTvWgByF4Hol7pFe9V1+CdCevh2wzJ5mNjOy+T2m1PDwg3Z0HkDg/cN5ln0s0xtiyAIQpbw5jn49oDQG5LFUQlauqyNxe9hFMrmi/j4448Nbuq3A7fYdkX62ron4Dmjm5bG2d5yX7c7le5E1bxVmXh8IpXyVKJNiTYWq0sQBOMlJCSwadMmDhw4wK1bt4iOjsbd3Z2KFSvSvXt3OnXqlOrrvbyM+/5Yt25dfH19DZbfuHGDv/76C39/fxISEihTpgz9+vXjgw8+SHW78fHxdOzYkadPn7JixQoaNGhgVHveB8+ePaNnz568fv06s5simIFVAmJ169a1RjWCIAgAODo6smTJEnr06IFCoQCgQYMGjBkzJu0Xh92BU/Oky9rMgAtLdX/e0RTmK+VYyVULuzkyv18NbG0yf4hJAZcC5HXKKwJigiAIrx+AT7fE/0sIj9XQfnUsF59paOHlwZAhQwxuyvfMAxYduytZViC3AyuG1bVoMCxJsdzF8O3giwYje0ELgmBR9+7d4+OPP+b+/fsploeGhhIaGsrx48fZsmUL8+bNw8XFxcBWMu7ixYuMGDGC+Pi3KT2uX7/Ot99+y507d/jmm28MvnbRokU8ffqUDh06iGDYOxYtWsTr16+xtbVl/Pjx1KlTB2dnZwAKFy6cya0z3ZYtW5gyJXFkzOHDhylatGgmt8i6RF/d94xSqcTe3j6zm5EjKJVKbtxIfLpcsWJF7OyskzD9fWCOY9uxY0c2btxIr169qF+/Pnv27CFXrjTyeKWWSL94Q6j6AVRLnPk2OkHFmAUniQmN0VvV3kbOooG1yOPqYHK7hexJqTRu8oXsSNw3zEfcNyzHqGMbchN8u0PUc4PbOfdUzaXnGtqUtmHbp9VwNvBjdX/QC77bIT2rcC4HW1YMq0thdyeT9yO97Gwscy6Jc9ZycvJ9430WHh7O8OHDef488TrTvn17evToQf78+QkJCWHr1q3s27ePkydP8sUXX7B48eJUt9evXz/69+9vsNzJSfo6o9Fo+Oabb4iPj6dUqVJ88cUXeHh4sGXLFrZs2cLKlStp164dtWrV0nvtw4cPWb58Oc7OzrpAifDW6dOnAWjVqhWjRqWeYzIjevbsSc+ePS22fSGRCIgJQjpptVrdlxmR0868zHVsu3btyoEDB6hbt65xT+AMJdKX2UCn3+H/CXC1Wi1fbbjKXYlgGMCP3SpTrZh7utstCELOJO4blpPmsX16CVb1MjybpI0DqBPoWM6O/QNlNC5ug6OD9EQs/g9f89nay0hVY2cjY/GgWlQslDu9u5KliHNWEEzz999/64Jh48aN49NPP9WVVapUiebNmzNv3jz+/vtvjh07xr59+2jfvr3B7eXJk4fy5cub3I5Lly7x8OFD7Ozs+PfffylSJDG3YJ06dQgJCeHkyZNs2bJFMiA2Y8YMFAoFn332GQUK6OfOfd+9fJmYOqVUqVKZ3BLBHDJ/LI8gCIIFtWjRwrhgmCIG9hvoOl5vDBSorPvzH7977At6Iblq3zrF6Fe3eHqami7HHh8jWhFttfoEQRCynQcnExPoGwqG5asAn1+FHyLhh0ha+8TgOPMNjNRPjnwvNJqRKy+QoJIenvhb72o0LJvXnK0XBCGbUKvV7NixA4AiRYoYzD/4ySef6IbWLVmyxCJtSerZWaNGDV0wLElS/rKkdZI7cOAAJ06coHTp0gwdOtQibcvukh4SiIkxcgYREBMEIVs7evQot2/fzviGTvwBkY/1l7vkh+aTdX+2/vMYv+67KbmJqkXd+KFrZckyS/B/6c/4o+Ppu7svt17dslq9giAI2Ubw/sSeYYoo6fLCNWDYXsid9mzAoVEJDPnvPK9jpYe6fd2+At1rmG+GR4DIhEi+Pv41L2KkH8IIgpB1PHjwgKioxGtNw4YNsbGR7mVqY2NDw4YNAQgKCuLxY4nvnxmU1I58+fLpleXNmxi0j45O+UA1Li6OWbNmAfDdd99ZbIi0v78/U6dOpV27dtSsWZMqVarQtGlTxowZw+rVq3nz5o3k6xQKBatXr2bQoEHUr1+fKlWq0KhRI0aNGsX27dtTnU1+/vz5uhkbIXHSg2XLltGjRw9q1KhBjRo16N27N6tWrUKlUum9PvmMj0kWLFigW+bl5cXkyZP1XpcRac0ymdF9OnfuHF5eXimGxbZq1SrFPnl5eXHu3Dmz7ldWI8KagiBkW/v376d79+7kzZsXPz8/Spcunb4Nhd2B0/Oly9rOBEc3ANQaLY9fxUmu5uliz6KBtXC0k/7yY27hceFM8puEWqvm4ZuHDNgzgKn1ptKjXA+r1C8IgpDlBWyCrWNAo/9DACChYB0cBm8Bx7SHN8YkqBi+4oLBe8Cg+iUY2yyd9yADtFotP5z+gUOPDnHq2SlmNJxBi+ItzFqHIAjmExERoft3njx5Ul03KSgFiQGiYsWKmbUtSXlzk8+4niQsLAwAV1fXFMsXLVrEs2fPLJZIPz4+nqlTp7Jr1y69spcvX/Ly5UuOHTvGq1evUgw1BXjy5AmjRo3i3r17KZaHhYVx/Phxjh8/zvr161m4cCHu7u6ptiMsLIyRI0fq9ZALCAggICCAkydPsnDhQuTy7NN3KCfuk7WIgJggCNnS7t276dmzJwqFgidPntCiRQuOHz9OiRIlTNuQVgt7J4FaoV+WlEj///YFvjA4TGZ+vxoUsVICZbVGzeQTkwmJC9EtS1An8N3p77gSeoUfGvyA7P/5zpKs7rgahUJBQEAAAN7e3iJRuiAIOdfF/2DXBEA679WuYCVT1j9kf9doChdOPSCmUmsYt+YSAU8jJcvbVCrAD10r6113M2pj8EYOPUocthmZEMlnRz+jf4X+fFH7CxxsxKQtgpDVJE/R8W7vq3cl9eACuHPnjsH19u3bx969e3n69ClyuZx8+fJRo0YNevToQf369Q2+rkKFCgBcvnyZly9fpsgFtnv3biBxoowk9+/f1yXSN3dPJ0hM8v/xxx9z6tQpAEqWLEm/fv2oUqUKTk5OhIaGcvnyZfbu3av32piYGIYOHarrSde6dWt69epF/vz5efLkCatXr+b8+fP4+/szduxYVq9ebbB3HiTmdrtz5w6DBg2iZcuWuLm5cf/+fRYuXMjdu3c5evQoGzZsoG/fvrrXtG7dmipVqgDQpUsXQH/CAzc3t4wfqHRKzz55e3uzc+dODh8+zNy5cwH4999/yZ8/f4pt5/RZJ0VATBCEbEer1fLzzz+jULwNYj169Ii5c+cyZ84c0zZ2cxfcPay/XGYDHX9LkUh/yfG7kpso7O5IIyvmjFl8bTFnn5+VLCviWsTsP8oEQRCyE/nZBXDkR4PlawOUDN4Wh0pzh0WLFjFjxgyD62q1WqZtC+ToLf1eFgA1irszr28NbOTmve7efn2b2Rdm6y1fc3MNaq2aafWnmbU+QRAyrnjx4tjZ2aFUKrlw4UKq6yYvT0rCL+XdYNnDhw95+PAh27Zto3Xr1vzyyy+Ss6jXqlWLokWL8uTJE0aMGMH48eNxd3dn27ZtnDx5EoAePd6OKpg5cyZKpZLPP/+cggULGrW/pli1apUuGNamTRv+/PNPvQezzZs35/PPP9fr1bZgwQJdMOyjjz5i/PjxurIqVarQrl07Jk6cyM6dO7l8+TLr169PdWbOwMBA/v33X+rVq6dbVrlyZRo3bkynTp0ICwtjzZo1KYJHuXPnJnfulA9P0jvhgSWkZ5+cnZ0pX748gYGBumUlS5bM8QGwd4k+c4IgZDsymYydO3dSvXp13bL+/fvz22+/mbYhRSzsMzCddN3RULCK7s+z915x9Yl074D8uaz3pP7009P8c/UfybJGRRox0nuk1doiCIKQpWi1FL75L7apBMPW3XFm4NY4VBoYOXIkP/5oeF2AeYfvsO6CdH6fUnld+HdIHZzszTtUPk4Vx0S/iSSoE/TKPBw8GF11tFnrEwTBPJydnXUBiVu3bkkODQTYtWsXwcHBur9jYvRnLXdycqJTp07MnDmT1atXs23bNpYvX87YsWN1QwIPHTrExx9/rEvynpxcLuenn37C3t6e27dv88knnzBgwAA2btwIwKBBg6hduzaQ2Avt5MmTFkukr9Fo+PfffwEoWLAgv/76q8FRCnK5PEVvNoVCwaZNmwAoV66c3lBKSPxd8MMPP+iOy+rVq1Ntz8CBA1MEjpK4u7vTs2dPAIKDg1P04svqcuI+WYvoIfaeSa37qGAaW1tbXc4qMcuIeRlzbD09PTl48CAtW7akZs2a/Pvvv6af36kl0m+RMlBmqHfYzO5VGFjfxGGa6fQy5iWTT0xGKzEEqIBzAWY1noVcZvg5hzhnLScnX1tz8r5Zm/gMWo6tjZzKj1bieDuVH0KNPqfZ6LGUvdCCVq1asWDBglTzqWy4+Jg5h4Ily/K62rNyWF08Xcw/9Hz2hdncjTRwz2k8k/zO+SXLLEGcs5Yjrq0506effsrZs2dRqVRMnjyZR48e0b17d/Lly0doaCjbtm1j4cKFup5kkJhb613Hjx/X65EE0KhRIwYNGsSoUaO4fv0658+fZ+3atQwePFhv3fr167NmzRoWLFiAv78/CQkJlC5dmn79+ul6CsXGxvLLL78AKRPpBwYGMn/+fPz9/VEqlZQrV47hw4fTsWNHk4/JjRs3ePEicWKQPn36GDf7+/8FBgbqkuz36NHD4OfG1dWVDh06sHbtWu7cuUNISIje0L8kSUMepVSunDg5llar5cmTJymGlWZlOXGfrMWsd7Y6deogl8tZu3ZtiuTWSV1Cvb29cXR0NGeVgolEIj3zkcvleHh4ZHYzciRjj21SMn03NzfTz+3wu3B6nnRZ2xm6RPoAt15ESQ6XyeNiT+9a1ulWrNQomXh8Iq8TXuuV2cps+b3Z73g4pn7MxDlrOTn52pqT983axGfQQtQq5Ns/xvHaesPrtPoemnxBIeDMmTN4eHikOrz82K0QpmwJkCxzsrPh3yF1KJ7HOYMN17f/wX42BW+SLBtUaRBNizY1e52pEees5Yhra85UvXp1fvzxR77//nuUSiV//fUXf/31V4p1HB0dmTRpEtOnTweQDBBJBcOS5M2bl3nz5tGhQweUSiWrVq2SDIhB4u/vxYsXG9zW33//zfPnz2nfvr0ukf758+cZMWIECoUCd3d33NzcCAgIYMKECTx//pwRI0akeRySu379uu7fSb3SjJV8Jvlq1aqlum61atVYu3at7nWGAmKpTcKVPA+YVM+9rCon7pO1mPVKHBUVxZs3b/SmPB00aBBDhgyRnC5UEAQhIzw8PEz/UqnVwp6JBhLpN4CqH6ZYtOT4Pf31gCENS1ptVsn5l+ZzOeSyZNmEWhOonr+6VdohCIKQpSjjYcNgSC0Y1vF3aPKF7k9PT89Ug2GBTyP5ePUl1Br93rg2chkLB9SkWjH3jLRa0tPop/x4WnoIZ0XPioyvOd7sdQqCYH69e/dmw4YNtGnTBmfnt4FzW1tbWrZsyZYtW3QJ2iH14JchxYoVo2HDhkBiXrGXL1+avI27d++ycuVKnJ2dmTIlcWSERqNh6tSpKBQKBg0axNmzZzl8+DDz589HJpMxZ84cXT4vY71+/fZhbr58+Ux6bWTk23Qlnp6eqa6bfObO5K97l5OT4Umwkv+mUKvVxjQxS8iJ+2QtZu0hJpfL0Wq1qFT601trtdKz/AjWJd4H89FqtbquznZ2diKRuRklP7YrV64kISGBcePGma+CVBPp/65LpA/wPDKOHVef6q3qZGfDICsNlTz66Cj/Bf0nWda6eGsGVRpk1HbEOWs5OfnampP3zdrEZ9DMEqJhXX+47yddLrOB7oug2ofS5RIev4pl6H8XiFVI/2j4qXsVWlQw/5BFpUbJ18e/Jkqpn9/F2daZ35r9hr2N9WcGFues5Yhra85WuXJlFixYgEqlIjQ0FKVSSYECBXBwSMw7u337dt265cqVS1cdZcqUwc8v8fr37kySxpgxYwZKpZLPPvtMl0j/0qVLPHr0CE9PTyZNmqT7zLdt25bmzZtz9OhRdu7cyccff5yuNmeEuP4IlmDWHmJJ0W3REyzrkgpWCumjVCoJCAggICBAMpmlkH5Jx/b7779n9OjRfPrpp6l29zZJqon0R6VIpA/w36kHKNX6X1o/rFMMDwvkjnnXk6gnTD01VbKsqGtRpjeabvQXBHHOWk5Ovrbm5H2zNvEZNKPYV+Db3WAwLEGl5ViBESYFw17HKBjy33nCovWT2QN81qocfesWT09r07TwykKuhl6VLJtWfxolclvnAcy7xDlrOeLa+n6wtbWlUKFCFC9eXBcMAwgKCtL9u2rVqunadkYCRHv27OHMmTOULl2aYcOG6ZbfuHEDSAzovZv4vmbNmgDcvHnTpLqSD7t+dwbJtCQf7hceHp7qumFhYZKvE4TUmLWHWOXKlTl9+jRz5szBzs6OUqVKpUjAGRoamqLbqLEKFy5szmYKgpANrFu3jt9//13399ixY7Gzs2P48OEZ23BqifSbpwyUvYlXsubcI71V5TIY0bhUxtphBIVawZd+XxKl0O8xYC+358/mf5LLXn+qbUEQhBwt6iX49oCQIMniaIWWbutiOfbwT1a710oxzbwhXRec5JqBmYQB+tQqyoTW6evFkZazz8/yb8C/0u0q05UuZQwnSxYEIftRq9UcPHgQgEKFClGjRo10befu3beTb5jSOywmJkaXSP/bb7/VJdIHdLMQ5sql//0yaZmpMxUmJXUHuHjxIvXr1zf6tcl7z129ejXVHGTXrl2TfJ2Quve9551ZA2KDBg3i1KlT3Lt3j7Fjx6Yo02q16fohK5PJUiTiEwTh/RAbG6u37OHDhxnbaGqJ9NtMByf3FIvWnHtEdIL+E9xOVQtTzNP8yZTfNfvCbK6HS1//JtebTMU8YpYYQRDeMxGPwKcbvJLO7fgqTkuH1bGcf5o45NGYXDdxCjX3Qg0nGm5aPh8/9/S2yI+G8LhwppyYIjl7cIncJZhaT7qHsCAI2demTZt49uwZAB9++GG6Zhx9/Pgxp06dAqB48eImBcQWLFjAy5cvad++vS4PWZKkoFfSrJDJJS1zdXU1qa0VKlSgUKFCPH/+nI0bNzJs2DCjZ5qsUqUKuXPn5s2bN2zbto1hw4ZJ5g6Ojo5m7969AJQtW9ZgQn1BX/KeiwqFRH7lHM6sQyabN2/Ot99+i6urK1qtVvdfkuTLTPlPEIT3z/Dhwxk1apTu7+nTp/Pjj9LJho2i1cLeSYYT6VdL2YNAodLw36n7kpsa09TwTC7msvf+Xtbfkk4S3al0J3qX623xNgiCIGQpocGwvL3BYJjSwYPfwlrogmF//vknEydOTHWT8Uo1o30vSj78AKhSJDcLB9TEzsb8MwJqtBqmnZpGWFyYXpmt3JbZTWfjbGf5hy+CIJhXagnuz5w5w88//wxAyZIlJTuMHDlyJNUhtWFhYXz22We6Ycz9+/c3um137tzB19c3RSL95CpUqAAk9sZK/kBBrVazZ8+eFOsYSy6X62amfPHiBV9//bXBwItGo0lx/Ozt7endO/E7b3BwMAsXLtR7jVarZcaMGbrk/QMGDDCpfZbi5eWFl5cXLVu2zOympCr5RAdpPUTKLvtkCrP2EIPEE7BXr14EBgby8uVLFAoFU6ZMQSaT8fnnn5uc7E8QhPfX6NGj8fT0xN3dncmTJ2dsYzd3w51D+stlcuj4W4pE+gAKtYZeNYuy6uxD3sS//VLSqGweqhSxbF6C+5H3+eH0D5Jlpd1K813979777s2CILxnnl2BVT0hVjqHjNatGLdq/UyvNoWJcSlBuXLl+PTTT1PdZIJKzRhff07c1g9IAdjbyFk+tA6uDmb/ugyA73VfTj49KVn2Ra0vqJSnkkXqFQTBsjp37kydOnVo3rw5ZcuWxd7enufPn3Pw4EF27tyJRqPB3d2dv/76K0XvnCQzZ85EqVTSrl07qlevTpEiRXB0dOT169ecO3eO9evX64I/tWrVMikANH36dL1E+snVqlWLYsWK8fjxY0aOHMlXX32Fq6srK1eu5NGjR9ja2tKli+nDuAcMGMDRo0c5deoUBw8epEuXLvTv358qVarg5OREaGgoV69eZffu3XTu3DnF9fuTTz7h4MGDPH78mPnz5xMcHEzPnj3Jly8fT548YdWqVZw/fx6AGjVq8OGHxueOFKBixYo4ODiQkJDAX3/9ha2tLYULF9b1xCtQoACOjo6Z3ErLscgd3tHRMcX43qToc+vWrSlbtqwlqhQEIQeSyWRMnz5dL6mnyRSxsM9AQK3uaCjorbfY1cGWSe0r8HGLsqw7/4jlJ+/zLDKe0U3LZKwtaYhTxfHFsS+IVekPGXWydeLP5n+KHgOCILxfHp6BNR9Awhvp8rzlUfbdSMLDcGTA77//nuZ9Q6HS8PGqS/gFSyd4zutqz7rRDcifyzI/AoLCg5h7aa5kWdOiTRlYcaBF6hUEwfJUKhWHDx/m8GGJGc1JzG/1+++/p9rTKiQkBF9fX3x9fQ2u065dO2bOnGn09+Rdu3Zx7tw5SpUqxdChQyXXkcvl/Pzzz4wYMYIHDx7ozfL++eefU7y46ZOLyOVy/v77b77++mv279/PgwcPdD3l0uLq6sqKFSsYNWoU9+7dY//+/ezfv19vvZo1a7Jo0aJ0DUF9n7m6ujJo0CCWLVtGUFCQXq9FHx8f6tWrl0mtszzLPPJ6R506dQBwcnKyRnWCIGQjWq2WhIQEyz55OPmngUT6+fQS6b/L1cGWkU1KM6RhSQ7fCKFpubwWamSiuf5zuRNxR7Ls2/rfUsbdsgE5QRCELOX2IVg/EFRx0uWFqsHALWCXG0h9BrIkSrWGcWsucfhmiGS5p4s9a0bVp2x+0/LkGCtGGcMkv0moNPpDovI55WNGoxmiF7AgZGMzZ87k5MmTBAQEEBoaSkxMDJ6ennh5edG+fXu6du2aIpH9u3755RfOnz/PlStXePz4MREREURHR+Ps7EzBggWpUaMGPXr0MCkZf3R0NL/++isA3333XapBtLp167JmzRrmz5+Pv78/SqWScuXKMWzYMDp37mz8gXiHk5MT8+bN4+zZs2zZsgV/f3/CwsJQq9XkzZuXChUq0KJFCzp16qT32qJFi7J9+3Y2btzIvn37CA4OJiYmBjc3NypWrEiXLl3o0qWLZH4xIW1fffUVJUuWZNu2bdy5c4eoqCjUanVmN8sqZFqRpCvHUygUBAQEAIljvo1NYiikLvlx9fb2zngvpveQVqvlyy+/5OLFi+zdu1d3bpr12IbfhYX1pXOHdV8E1Y3Pu2ANL2Je8KXfl1wLvZZiee/yvfm+wfcZ2rY4Zy0nJiZGNw15Tji24r5hGeIzaKKgrbB5FGiUksWaovWRD9wAjm5GH1uVWsNn6y6zJ0A/YTSAu7Mda0bWp1Lh3ObZBwmzL8zG97p+rw8ZMpa1XUbdQnUtVrepxDlrOda4b9y+fRuVSoWtrW2WmXXv33//5cmTJymWFS1aVJdjShAEwRBLXNOs0kPMEJVKxZs3id3fc+fOja1tpjbnvSCOsfnY2dnh7e2t+7dgGq1Wy2effcaCBQsA6NKlC7t27cLZ2dl8x1arhb1fSwfDitWHqn31l2eygi4FWdFuBXMuzdH9YKrgWYHJdTOYQw1xzlpSTr625uR9szbxGTTBJV/Y+RloNZLFe2+rWOovY+0gRxww/tj+cTDYYDAst6Mtq0bUs2gwDGBstbG8jHnJgYcHUiwf6T0ySwXDQJyzliSurYIgCJnP6lfiu3fvsmbNGk6fPs3Dhw91s0jKZDJKlChBo0aN6Nu3r8g1ZiGiC775yGQy8aQ0A6ZMmaILhgEcPXqUXr16sWfPHvMd25u74c5B/eUyOXT6HbJot2o7Gzsm1ZlEzfw1+fXCr/zR7A8cbPSTrppKnLOWk5OvrTl536xNfAaNdOZv2P+NweINQUoGbolDqdnPgAED2Lhxo9HHdkTjUhy5EcKtl1EpludytGXVyHoWnzQFILd9bn5v9jubb2/m1/O/Eq+Op3q+6nxc/WOL120qcc5ajri2CoIgZD6rBsT++OMPli9fjkaj4d2Rmlqtlvv37/PgwQPWrl3LiBEj+OKLL6zZPEEQrGjAgAEsW7aM8PDEnC9yuZxBgwal/wvistbw5ELKZTYGvsTXGSWZSD+raV2iNc2KNsPORjyVFwThPaDVwrFZ4PerwVV8AmDY1jg02sQeSwMHDjTpvpHX1YG1o+szYNk5bjxPHKXg6mCLz/C6VC3qntE9MJpMJqN3+d7UyF+D6WemM6vJLGzloseQIAiCIFiT1e68M2bMYM2aNbpAWJkyZahatSr58uUDIDQ0lICAAO7cuYNarWbp0qXExsYybdo0azXxvaDRSA89EEyn0WiIjIwEwM3NTSRxNJG3tzeHDh2iZcuWvHnzhrVr19KnTx/AjMdWaqikSz5oYbjnQVZjzmCYOGctJydfW3Pyvlmb+AymQqNJ7BV2bpHhdRqMo3y7zjgfaItCoWDz5s26BM+mHFtPF3vWjqrHwH/PcS80hhXD6lCjuIdZd8dYZdzLsLLDykyp2xjinLUccW0VBEHIfFYJiPn7+7N69WpkMhlly5Zl+vTp1KxZU3Ldy5cv8/333xMcHMzq1avp2LGjwXUF070vs0VYg0ql4t69e4BINJte1atX58CBAzx58oTu3bvrllv02LaZDk7ueovDohNYduI+wxqVpEBuC854mYnEOWs5OfnampP3zdrEZ9AAtSoxX9iV1YbXaTENmn5FfZmMvXv3Eh0dTfv27XXFph5bd2d7Vo+sz/2wGKoXczfHXuRI4py1nPf12iqS5wuCkJVY5THP+vXrgcQZRNauXZtqgKtGjRqsXr2aYsWKAbBu3TprNFEQhExSu3btFMEwiypWz2Ai/ZWnH/CP310a/3qEiRuvcvud/DLmplRLz5omCILwXlElwKahqQfDOsyGZhPh/0MjGzdunCIYll5uTnYiGCYIgiAI7zGrBMQuXryITCZj9OjR5MqVK831c+XKxahRo9BqtVy8eNEKLRQEwVIUCgXXrl3L7GYkJtLvKJ1IPyZBhc+ZhwAo1Vo2+j+hzZzjjFx5EZXa/EMaNFoNE45N4MczP5KgTjD79gVBELIFRQys+RBu7JQul8mh+yKoN8akzWq1Wr1ctZlBo9WgMTBLpiAIgiAImc8qAbGwsDAAKlasaPRrKlWqBKBLuC0IQvaTkJBAr169aNiwIadPn87cxtQZCYWqShZtuPiYyDj9HlsyGdjamP8y+V/gf/g98WNT8CYG7hnI4zePzV6HIAhClhYXAb494N5RyeIEFdyr9T1U72/SZrVaLb8fvMMfB4IzPSi29NpSxh0eR3ic+C4rCIIgCFmRVQJiDg4OAMTFxRn9mqR1Ra4CQcie4uLi6N69O7t27SImJob27dtz/vz5zGmMc15oMVWySKXWsOzEfcmyMU1Lm70pF19cZP7l+bq/b766yQe7PuDQw0Nmr0sQBCFLig6FlZ3h8TnJ4hiFls5rY6gzZAZXr141erNarZa1QdEsPfmABUfv8Mu+m5kWFLsccplFVxdx4ukJeu/szdnnZzOlHYIgCIIgGGaVgFjRokUBOHLkiNGvSVo3KZeYIAjZyx9//MG+fft0f0dFRdGrVy8SEiw0RFAVb7jMQCJ9gKh4FdWLuyOXpVxeq4QHtUt6mq99QFhcGJOOT0KtTZlIN1oZzU/nfiJWGWvW+gRBELKciMfwX3t4ESBZ/DpOSxvfWA7dU/Pq1Sv69OmDSqUyatMbr8ew+UaM7u/FfveYufuG1YNikQmRfH38a921PiwujNEHRjPv0jyUGpE/UhAEQRCyCqsExJo2bYpWq2XVqlWcOXMmzfXPnj3LqlWrkMlkNG3a1AotFATB3CZNmkTnzp11f+fOnZv169freoyaVewrCL8jXVa0LlTrZ/ClHi72/N2/Jse+asHgBiVwtEu8LI42c+8wtUbN5OOTCY0L1SuTy+TMbjobZztns9YpCIKQpYTdgeXtDV6vtc55+fpmFc48SQwkeXp6sm7dOmxt054U/Z/j91l/PVpv+b8n7zNj142MtdsEWq2W709/z/OY5ymXo2V54HLuvDZwrxIEQRAEweqsEhAbMmQIrq6uqFQqRo0axfTp0wkKCkKjeZtoVKPREBQUxPTp0xk5ciRKpRJXV1eGDBlijSYKgslkMhl2dnbY2dkhk8nSfsF7xt7enk2bNtG+fXvc3d05ePAgDRs2NOq1Jh3b2Ffg0xWUBoZkd5JOpP+u4nmcmd6tCqcnt2Jap4q0qVjAqLYa659r/3DuhfTwoHHVx1GnYB2z1idFnLOCkLne68/g82uJPcPePJEuz10U2fD9zF17iGbNmpE3b16OHDmS6szkyRVxd9Lr6Qsgl0GN4u7pb7eJNtzawOFHhyXLxtUYR8U8xufTzQre63NWEARByPFkWiv1Iz958iQfffQRSqVSd0O1s7PDzc0NmUxGREQESmViN3KtVoudnR2LFy82+ge0YJhCoSAgIHFogre3t8jLJlhVXFwc9+/f102UYd6NR4BPN3h+RbrcJT9MvG3+ek10+ulpxh4aixb9y22TIk1Y0GoBcplVnk8IFpLTrrM5bX+ETPboHKzuAwmR0uV5ysKgbeCemCYjOjqap0+f4uXlZVI1u6494/N1V1BrEq+1Mhn8+UE1etQompHWGy34dTD9dvVDoVHoldUrVI8lbZaIa72gY43r7O3bt1GpVNja2lKuXDmzb18QBMGaLHFNs9pduXHjxmzYsIEqVaropsNWKBSEhoYSEhKCQqHQLa9SpQobN24UwTBByAGcnJwsEwyLj4RVPQ0HwwDcipi/XhO9iHnB5BOTJYNhBV0K8nPjn8UPJEEQcq67R8C3u+FgWAFvGLZXFwwDcHV1NTkYBtC5amEW9KuBrVyGTAaze1W1WjAsThXHRL+JksEwT0dPZjWeJa71giAIgpDFpJ2UwYwqVqzIpk2buHbtGmfOnCE4OJjIyMQvSG5ubpQvX54GDRpQtWpVazZLEIQMCA8PZ/ny5Xz11VfWG06REAWresNT/9TXk1v1EqdHqVEy0W8irxNe65XZym35vdnvuDu6W79hgiAI1nBjJ2waDmr9IBGAtlg9ZP03GJz0JD06eBdioVzG61gFfWpbb2KmX8//yr3Ie5JlMxvNJJ9zPqu1RRAEQRAE42TKr8WqVauKoFcmUalUYuiLmahUKp4/T0yaW6hQIaOS/uY0ISEhtG7dmoCAAJ49e8aff/5plqBYqsc2ITpx6M2T8xmux9L+8v+LK6FXJMu+rPUl1fJVs2p7xDlrOcbOgpcdifuG+bxXn8Era2D7J6DVSBbvv6PinLom35kpGJb82Lb0su6x3f9gP5tvb5YsG1xpME2KNrFaW8ztvTpnrSwn3zcEQRCyC3FXe89Ye+rxnEyj0RASEgJAgQLmTcCeHbx48YJWrVpx/fp1AObOnYudnR2//vprhoNiBo+tIhbW9oVHac9Wm9mOPDrCyusrJcvalGjDgIoDrNwicc5aUk6+tubkfbO29+YzePYf2Pe1weLN15X03xKHQv0r2Dnz3XffZbjKzDq2T6Of8uPpHyXLKuWpxPia463WFkt4b87ZTCCurYIgCJlPJDMQBCFdLl68yM2bN1MsW7duHa9evbJMhcq4xGDYgxPS5TZ2Rm0mMlbJmnOPiFeqzdi4lB5HPWbayWmSZcVyFePHhj+K2boEQch5tFrwm51qMOy/K0o+3BSH4v+XYB8fH968eWPU5ndefcbNF8ataw1KjZJJxycRpYzSK3O2dea3pr9hZ+S9SRAEQRAE6xMBMUEQ0qVz5874+voilydeRkqWLMnx48fJkyeP+StTxsO6/nDfT7rctSDkrWDUplade8g3WwNo/OtR/j56h8hYpRkbCgnqBL489qXkDyR7uT1/Nv+TXPa5zFqnIAhCptNq4cA0OPqT4XXqjUXdaS7q/3eM8fLyws/Pj9y5c6e5+R1Xn/H5usv0W3KWoGcGEvRb2d+X/+Za6DXJsmn1p1E8d3Ert0gQBEEQBFOIgJggCOnWv39/li9fTvny5Tl+/DglS5Y0fyWqBNgwKHGmMiku+WHITrBzTHNT8Uo1/516AEBYdAK/7b9Fg18OM33nddQa8wxd+O3Cb9x4dUOybEq9KVTwNC5wJwiCkG1o1LDjUzizwPA6zSZD+18YOWo0CxcupHLlyhw7dowiRdKeDXhPwHMmrL+CRguvY5X0X3qOgCeZGxQ78+wMywOXS5Z1LdOVLmW6WLlFgiAI6RMfH8+CBQvo2rUr1atXx8vLCy8vL376KZUHHNnIli1bdPv05MkTvfLJkyfj5eVFy5YtJV+f9Nr58+dbuqnvhZYtW+Ll5cXkyZMzuymAyCEGwNOnT/H19eXYsWO8ePECe3t7ihUrRocOHRgwYABOTk5mrzMuLo7OnTvrPpRFihThyBEDP/gFIQsbMmQIffv2xcHBwezblmmU2G4dAbcPSK/gnBeG7IB85Y3a3tbLTwmLTkixLFah5nZIFDbyjA9h3H1vN+tvrZcs61K6C73K9cpwHYIgCFmKSgFbRsH1bYbXafczNPhE9+dHH33E8OHDjbpvHAh6wWdrL6d4aBEZp6T/srP4DK9LjeIeGWl9uoTHhfPNyW/Qov8gpWTukkytN9XqbRIEIfOdO3eOwYMHS5Y5Ojri6elJxYoV6dChAx06dMgSk1QolUqGDh3K5cuXM7spgpApMv9TmMmOHDnCxIkTiY6O1i2Li4sjMjKSwMBANm7cyJIlSyhRooRZ6503b55khFoQsiNLBMPQqCjlPwP5i5PS5U6eicGw/BWN25xGy9Lj9yTLxjQtk95W6tyLvMePZ6QTK5dxK8O0+tNE3jBBEHIWRWxiD947h6TLZXLoMg9qDtIrMua+cfjGSz5ZcwmVRA/eqHgVJ26HWT0gptFqmHpqKmFxYXpldnI7ZjedjbOds1XbJAhC1hcfH8+zZ8949uwZhw8fZuXKlSxatIh8+fJlarv27dunC4b17NmT7t274+GReF1N+r+QPQ0aNIjz589Tt25dfH19M7s5WdZ7HRC7fv06EyZMID4+HmdnZ8aMGUO9evWIj49nz549bNiwgQcPHjB69Gg2b96Mq6ur2epduXIlDg4O2NraEhMTY5btCoIl3Lp1izFjxrB69WqjhraYhUZFqUs/4WEoGOboDoO3Q4HKb5eNNPCD7P8O3njJvTD9z1rlwrlpVNb4vGcD9gzQyxlTNV9VxlQdgwz9gJeTrRN/Nv9T/EASBCFniY+ENR8anPVXqZER22EubhLBMGMcuxXCR6suoVRLD2cf06w0n7Ysm65tZ4TvdV9OPT0lWfZFrS+omMe4hzSCIORs/fr1o3///rq/Y2NjCQwMZPny5Tx9+pSAgAA+/vhjNmzYkKkPTE+fPg1Avnz5mDlzJjY2NpnWlszyyy+/8Msvv2R2M4RM8l7nEPvpp5+Ij4/H1taW5cuXM3bsWGrUqEGDBg2YMWMGEydOBODBgwf8999/ZqlTrVYzbdo01Go1Y8aMwd3d3SzbNZbooWI+crkcd3d33N3ddYnlc5rr16/TrFkz/Pz8aNmyJS9evLB8pWoVtjs+wfO5gQT6Dm4weBsUqmrSZhf73ZVcPqZZGbN8LpoWbcq6zuso657yB9p3Db6jtHvpDG/fHN6Hczaz5ORra07eN2vLMZ/BmDBY2cVgMCxWCV3WRNN07J+Eh4ebvPkTt0MZ7euPQq2RLB/RuBST21dIcW5a49gmqBNYc2ONZFmzos0YUHGARerNTDnmnM2CxLU1Z8uTJw/ly5fX/Ve9enUGDhzI1q1bdSOPrl27xtGjRzO1nSEhIQAULVr0vQyGCcJ7e2e7du0aFy9eBKBXr17UqFFDb53hw4dTpkziUCofHx+UyozPRufj40NQUBClSpVi1KhRGd6eqbLCWPWcwtbWljJlylCmTJkceVwDAwNp3rw5L1++BCA4OJhWrVrpbpwWoVHD9o+RX98iXe6QGwZthcL6n9fUXHzwikuPIvSWF/VwomOVguloqLRSbqVY02kN3cp0A6BP+T50Lt3ZbNvPqJx+zmamnHw8c/K+WVuO+AxGPoX/OsDzq5LFbxKgrW8M+++quXbtGm3atOH169dGb/703TBGrryIQiUdDBvasCTTOlXUCyZY49g62DiwtvNamhRpkmJ5fqf8zGg0I0cGOHLEOZtFieP5fnJzc2P06NG6v0+cOJGJrQGFQgGAnZ1dprZDEDLLe3slPnTo7fCqXr2kE13L5XK6d+/OH3/8wZs3bzh37hyNGzdOd51Pnz5l3rx5APz444/Y29une1uCYGl58uTBw8OD0NBQ3TJXV1fLnbcaTeIsZdekk9JjnwsGboGitUze9D9+0rnDRjYuha2NeZ8LONk6MbPxTJoWbUqzYs3Mum1BEIRMFX4XfLpD5CPJYrWjB/13qjn1+I1uWa5cuYz+oXXuXjgjVlwkwUAwbEC94nzfpVKmBp48HT35u9Xf+F73Zc6lOag1amY1mYWHo8i1IwiCcapWfTvK4dmzZ3rlarWaHTt2sG/fPoKCgoiIiMDFxYXSpUvTtm1b+vXrh6Oj9Ozq7+aNevDgAT4+Ppw8eZKXL18SHx/P4cOHadWqVYrXnT9/Hi8vL93flpjwTaPRsGfPHvbv309AQACvXr0CoFChQlSpUoU2bdrQqlUrvXtGcHAwhw4dwt/fn9u3b/Pq1Svs7OzIly8fNWrUoF+/flSvXj3d7Zo8eTJbt241ep9Pnz6Nj48PgYGBREZGkj9/fpo1a8aYMWMoUKCA5Gu2bNnClClTADh8+DD58+dnzZo17Nmzh4cPHxIREcG4ceP49NNPgcRA5cmTJzl58iRXr17l0aNHxMbG4urqSvHixWnatCkDBgzA09PT4P4kefe9BcPvb1RUFGvWrOHo0aM8ePCA6Oho3N3dqVKlCt27d6ddu3Zp3oP9/PxYtWoVAQEBxMXFUbBgQZo3b87w4cMNHp/M9N4GxPz9/QFwdnamcuXKBterU6eO7t+XLl3KUEDsxx9/JDY2lm7dulGvXr10b0cQrKFQoUIcOXKEZs2acffuXRo2bMiePXtwc3Mzf2UaDez6HK6sli63c4GBm6BYHenyVNwJiebQjZd6y92d7figTjGTt2estiXbWmzbgiAIVvcyKDEYFmOgl3CuwtgM3s6CvvZca9qUx48f07JlS3bs2IGLi0uam/d/+IphKy4Qp1RLlvetU4wZ3apkiV5YMpmMwZUHU7tgba6EXKFuobqZ3SRBELKR5L0D1eqU17xnz57x0UcfcfPmzRTLIyIiuHTpEpcuXWLt2rUsXryYUqVKpVrPoUOHmDhxIrGxseZrfDo9efKEcePGcePGDb2yBw8e8ODBA3bt2oWPj0+K38mGZu5UKpU8fPiQhw8fsm3bNkaPHs2XX35p0X0AWLBgAfPnz0+x7MmTJ6xevZodO3bwzz//ULt27VS38fr1a4PHIsl3332XIqiVJCIigoiICK5du8aqVatYuHAhtWqZ3llAypkzZxg/fjwREREploeGhnL06FGOHj1Ks2bNmDNnjsH7+qxZs1ixYkWKZQ8ePGDFihXs2LGDJUuWmKWt5mT1gNjr16+5cuUKjx8/JiYmRu8iIGXcuHFmb8fdu4n5hIoXL55ql+XSpd/m/kl6TXrs3r0bPz8/3NzcmDx5crq3k1FKpVL0TDMTpVKpOyfKlCmTI7saFylShKNHjzJ16lT+/vtvcuXKZf5KtFrY8yVc8pEutnNGNmAjFK+frs0bmllycP0SONu/X88E3odzNrOYY0h9ViXuG+aTbT+Djy/A6t4QHyFd7lkaBm0DjxKUzAdHjx5lxowZLFq0CCcnpzQ3f/nRa4Ysv0CsQvo7Ye9aRfm5hzdyueFgWGYc20p5KlEpTyWL15OZsu05mw3k5PuGkLrg4GDdv/Pnz6/79+vXr+nfvz/Pnz/H3t6eDz74gDp16lCkSBFiY2M5deoUPj4+PHz4kFGjRrF161aD382fPXvGxIkTcXR05KOPPqJ27drY2NgQEBCAs7MzO3fuBGDKlCkEBgZSpUoVZs2apXu9OT/rYWFh9OvXT5d2pX79+vTo0UP3O/vJkyecPXuW/fv3671WrVbj7OxMs2bNqF+/PqVLl8bV1ZXw8HDu3LmDr68vT58+ZcmSJZQsWdLgyC9zOHbsGIGBgZQqVYqRI0fi5eVFdHQ0e/fuZePGjURFRTFmzBh27dpFoUKFDG5n6tSpBAcH0717dzp27EjevHl5/vx5imOuUqkoVqwYbdq0wdvbm8KFC2NjY8OzZ884ffo0mzdv1vUq27VrF3nyvJ0gbMKECQwfPtzgewv676+/vz+jRo1CqVSSN29eBg4cSIUKFcifPz8hISHs2bOHHTt24Ofnx+TJk/WCggArVqzQBcPy58/PmDFjqFq1KgkJCRw7doyVK1fy+eefExcXl57DbzFW+zUYHh7Ozz//zP79+40KgiVn7oBYQkKCLp9FwYKp5w9yc3PD2dmZ2NjYdCcUj4yM5Oeffwbgyy+/lOzaaC1KpVI3Vvxdtra2uoSpGo0GlUqV6raS/0BSq9Wpvq8ymUzvQ67RSA+JgMThqskDlUqlEq1Weqapd9uu1WrT/JJhZ2ene8qc1r4aartSqdTNEKpQKHTtM7XtNjY2KZJYGnp/klj7fSpQoADLli2TbFuG3yeFApsDU7DxXy65vkZuj7rPKuxKNkrX+/QiMo4tl5/oretgK2dA3aIplpn6PiWn1Wj1jk1W/DxptVrJc/ZdmfF5SqvtSbLq5ykn/7AR941E7+19485R5Ov7I1NK9zDQ5q+EbNA2yJU4DCLpS3zSU+C07huXHoQx+D9/ohOk29S9emF+7VUVuVyW6vuU/NgmHTtT3iepGYS983izou0Kg23PUu+TuG/otT1JVn2fsuJ9I3maDAAPDw+DHQfeXdfd3d1gECUsLCzFe+Dm5mbwQUt4eHiK9zd37tw4ODhIrvvq1asU531q675+/RoPj8wf2qxSqVJM1la37tsepjNnzuT58+cUKVKElStXUqxYypEM9erVo3379gwYMIDHjx+zbNkyJkyYIFnPkydPyJ8/P+vXr6dw4cK65dWqVQPQ/R51dnbW/b98+fLm2cl3/PDDD7pg2FdffaWXR7tq1ap07NiRr7/+Wu/zVKFCBfz8/MidO7fedps0acKAAQMYO3Ysp06d4u+//6Z79+4WmxwgMDCQypUr4+vrm6KHVIMGDahZsyZff/010dHR/PLLL/z1118Gt3Pr1i1mzpxJnz59dMveHbH22WefUaxYMb1e0d7e3rRr147+/fvTt29fXr16ha+vL+PHj9etU6BAAQoUKGD0e6tUKpk4cSJKpZImTZowf/78FA+zKleuTIsWLahTpw7ffvstBw4c4NSpUzRq1Ei3Tnh4OHPnzgUSO1SsX7+efPny6crr1KlD48aNGTlyZJrXTGuzSkAsMjKS/v378+jRo1RvSNaSdGOHtxeB1Dg5OREbG5vu7qazZ88mLCyMGjVq8MEHH6RrG+aSWi83Ly8vXF1dgcSpgW/dupXqtpJ3zwwLC+PJE/3AQxIXFxcqVKig+ztprLQh+fPnT3ETuHHjRqpfHEqXLq27ySmVSgICAlJtu7e3t+5GHBkZyb170r2IIPFLW/Kx/s+fP9dLLJ+8W7O7u7tuMgZIPObJz7l3FS1aNMV46rTabon3KSIiAnd3d+u+TwoFr9d9RIH7myXX1cjtuFN3JiVLJiYvTs/79OfhuyjV+tecZsUdiA5/QQF309+nWIkfhbGxsXrvW1b+PAF6XfGTy+zPU3LZ8fOU04j7RqL38r5xczfyjUORqaUDBNEelXjWaC7lc71tiynvU+DTSAb9e54YpfR3w0bFHJnapgQ2/+8ZZsz7lJyp79O73r22Z9n3CXHfSC47v0+ZLXlvJXgbAJBSsmTJFL+Nzp8/nyLVTHIVK1YkLCxM9/fRo0dp3ry55Lq1atXi4cOHur937dpFp06dJNdt0qQJ169f1/29YcOGFEGG5Nq2bcuFCxcky6whNjaWwMBA5s+fz5UrV4DEwEHHjh2BxADW3r17Afj222/1Pn9JKlWqRP/+/Vm2bBlbtmwxGBCDxI4YyYNhmeHevXu63N2tW7dOdVI5qWF4aXUksbe3Z9KkSXTr1o2nT59y48YNqlSpkrFGp2L69OmS7ezevTu7d+/m+PHjHDp0iNDQ0BQBoeTq169v8DxNUrx48VTLvby86NOnDytXruTw4cMpAmKm2r17N0+fPsXBwYHZs2cb7Nn9wQcfsHHjRq5du8aWLVtSBMS2bt2q6/n19ddfS+57gwYN6NOnD2vXrk13Wy3BKgGxJUuW6C5sjRo1YtiwYVSuXBl3d/dMyQWRkJCg+7cx3UGTbtjx8fEm13XhwgU2b96Mra0tP/zwQ5bIfSEIyZ0/f56vvvqKb775xqLdjFPQarE58mOqwbC7dWYQlS/1MfipiVWo2X9XP3glA7qUTzufjRSlRsnjqMfpbpMgCEK2cXU9bPsImVa619GRBxoCXbvS1iF9eSVvPH/DwH/PGQyG1S/iwOd13bBNZZikpWWFh7iCIGRPCxYsYMGCBQbL8+TJw99//637nenn54darcbJyYmmTZumuu06deqwbNkyQkJCePbsmWTQy87Ojg4dOmRsJ8zAz89Pdy0dOnRohrenUCgICwsjNjZW15Mw+bX65s2bFguIlS9fPtVt9+rVi+PHj6NSqTh//rzBQG6XLl1MrjsyMpLIyEgSEhJ0+5vUa+7OnTsolcp0D3NNSq5fp06dNAOQtWvX5tq1a7qgbpIzZ84Aib0/352wIblevXq9nwGxw4cPI5PJaNasGf/88481qkxV8q60xnRXTuo6bWg2j9Re9+2336LVahk8eHCKJ3OZpUyZMgZ7xSXvEu3s7Iy3t7fR282bN2+q3ZDfDQSWKFHC4JMPQNcVPUnFihXTHPqSxM7OLs22J79guLm5pbr+u20vVKgQBQoUQKlU6p6WVqhQQbfNd9tepkyZNLvqJ5dW2835Ph04cIAvvviC+Ph4vvvuO0qXLp3iPLXI+6TVwuHp2Jz7W3IdrdwORfdlvFGmrNfU9+nYExWxEj+02lbKT7uG1dL1Pq2+sZoEdYJemdT7kJU/T5DynH1XZnyeDMkun6fY2NgM5ZnMysR94+06SXL8feP8UtjzlcHiHbfUfLAxBo38ezaUrWjyfSP4ZRQDlp0jIlb6O1irCvmY92FV7GzkRr9PyY9tElPfp3eFacIo6VWSXPa5dG1PLtPfp2TEfeOt7PI+5eT7hmBY0aJFadeuHSNGjEiR9ykwMBCAuLg4KlUyPjdhWFiYZECsZMmSBoePWlNSDz47OzvdcE1TxcbG4uvry+7du7lz506qw8OT0iJZQlqf9eQ9WYODgw0GxN6d8dGQW7dusWLFCk6cOKE3RDk5jUbDmzdvUpxPpkg6906ePGl025L3+ExqKyR+90otP3vFihWxs7PLUkPGrRIQe/78OQADBgywRnVpSt7N0ZhhkEnd/4wZXpncokWLuH//PoUKFdJNoZrZ7OzsjEqOLJfLTUqinFp+JSmpfVCkmBLxlslkJrXd1H2Vantqx9XUaL0l2578fbpx4wa9evXSBXw1Gg2DBg2iRIkSNGzYELDQ+3T0Zzj5p3SZ3BbZByuRl24D7wxZMGVflWoNK04/lCwb27ys5HbSantYXBiLri6SLFNoFKm2Lat9nsD4a0FmfJ5Sk1U/T1npxm5u4r6hL8feN7TaxOvz4ekGX786QMXQbbGoNIBayYcffsjZs2epUaMGkPb7dCckmv5Lz/EqRnoYZqsK+Vk0sBb2tvoBGUu/T+96o3hD/339+bXpr1TPX12vPKtej4wh7huGifuGYA79+vWjf//+QOJnwsHBAQ8PD4NJ8MPDw9NVj6EE5VI5tzJDUoAqtbxxqXny5AlDhgwxeqhx8pFg5pZWwClv3ry6f6c2JN3NLe2e1Rs3buSHH34wOt9WekayJXn16pXJr3m3vsjISCDtY2Rra4u7u3uqAT5rs0pAzNnZGYVCkeIkyUwODg64u7sTERGRZqL8yMhIXdAsrQT871q6dCmQOF726NGjkuskbTs2Npbdu3cDiWOlGzRoYFJdgmCqChUq8MknnzBnzhzdst69exvM/2AWfrPB71fpMpkN9F4OFTpBGglt07Lz6jOeR+rfGOqW8qRG8fQlVJ13aR7RymjJMnsbMQOfIAjZyLLW8OSdXDpFakPJRnDKcCJgbZ2RnHgehUqzVLdswIABqebhSu5+WAz9l54lLFr6B0uz8vlYOLCmZDAsszyLecbQfUP5pPonjPAegVyWddomCOb2bm621HodPnjwIMXf7u7uBte9ceOGXlJ9Q/z9/fWS6hty4sQJvaT6hhw4cMBgmSXkyZPHpCT1Sfvh4eGBj4/0zOtSihYtKrncUonlrW3SpEk8efIEmUxGz5496dSpE2XKlMHT01M3OYdGo6FixYqAZYe6myv1kVQP3OTu3r2rC4blyZOHESNGUL9+fYoUKYKLi4suyL9p0yamTp0KZGy/k869pk2bMnHixHRvB8x3jKzJKgGx8uXLc/78eZ4+fao7WTNb2bJluXjxIo8ePUKlUhl82pQ8yWfyBJ3GSHrys2XLFrZs2ZLquq9fv+aLL74AEmcbEQExwdJkMhl//PEHSqWSBQsWMGDAAFasWJGup8ZGOfEHHP3JQGPk0GspVOqW4Wq0Wi1Ljksn5x3TtHS6thkYFsi2O9sMlmfHi78gCEIKEQ/h1EXD5U2+QtZyGgs7aFEoE2dJGz16NIsWLUrzyz3Aw/AY+i05S0iUdDCscdm8LB5UCwfbrPcjTq1Vc/PVTWSIa72QsxlKAp7RdU3pFGHKsK+08h0llxVmmExNUkAxJiaGMmXK5JiAVtJxj4yMRKFIfUTFu+7evYu/vz8AY8aMMTiBQGq9sczp3WGCqZWnFiBOy9atW1GpVNjY2ODr62swBpHUKyuj3N3dCQkJQalUpnumUTc3N0JDQ9M8RiqVymrvl7Gs8pjrww8/RKvVsmPHDmtUZ5SkGV9iY2MJCgoyuF7y2Uhq1qxp8XZZmjFfWgXj2NjYULRoUYoWLZptb1oymYx58+bh4+PDypUrLRcMOzXP8BAcmRx6LIEqbxP6Z+TYHr8dxs0XUXrLy+V3pYVXfolXpE6j1TDr/Cy0ZP/kyjnhnM2qcvK1NSfvm7Vl6c9gTCrDF9pMh1bfgkyGXC5n6dKl+Pj4GB0Me/wqln5LzvLijfSQjgal87B0cG0c7dJ/TDJ6bONU0sOOAAq7FOb7ht+/lw8/svQ5m82Ja6uQXFLeMIVCocvplBMkzVKqVCr1ErGn5c6dO7p/J83GKcVaxyutGWiTl5crVy7d9STtd4UKFVLtkGOu/U469wIDA3WpdEyVFEi7efNmqsM8b968meWGi1vlStyxY0e6dOnCwYMHWbJkiTWqTFPr1q11/9682cBMdxoN27ZtAxK74NarV8+kOm7dupXmf0WKFAESp91NWubr65u+nTKC+DJjPjY2NhQoUIACBQpk6+Mqk8kYNGiQ5fbhzEI4+K2h2qHbQqiacurhjBzbxX7SCWpHNS2NPB2zle2+t5troddMfl1WlFPO2awoJx/PnLxv1pb9PoMy6DwXGn2eYqmNjQ2DBg0y6gf904g4+i09yzOJYewAdUt68u/Q2jjZZ+x4ZOTY7rq3izuv70iW2chs+LXpr+S2zxr5eKwt+52z2Yc4nkJyLVq00AXdV65cmcmtMZ9mzZqle7+SD4c1lCsNYN26delrnImCg4N1kwRISYop2NjYULdu3XTXkxRQSi3XeUhIiG52SEOSJlVIK8jVsmVLAKKiotIc1WZI0ui2iIgIg6miwHDcJTNZJSB24cIFevfuTfXq1ZkzZw69e/dmxYoVHD9+nAsXLqT5nyVUrVqV2rVrA4lvzOXLl/XWWb58uW72l8GDB+sl5Tx37hxeXl54eXkxefJki7RTEMxh3759Fk0yadC5JbB/iuHyrvOhej+zVRfwJJLTd/WTkhbI7UC36voz8KQlRhnDn/4GJgAQBEHIwTQyG1TdF0PtYenexvPIOPotOcuT19I/ZGqV8GD5sDo421slg4cepUbJr+d/ZcqJKQZ7AY+rMU4yob4gCII5lS5dmvbt2wOwe/du/vvvv1TXf/z4Mbt27bJ4u5J+6yYFTUxVqlQp2rRpA8ChQ4dYtmyZwXVjY2NTDAMsUaKE7t+GAjVr1qzh8OHD6Wpbenz77beSgaqdO3fi5+cHJHa8yZ/f9FEpSUqWLAnAw4cPuXTpkl55XFwcX375ZZqJ9JOGNT9+/DjVHGM9evSgUKFCAPz6669pxl8uXrzI+fPn9bbh6OgIwKxZsySHTp4/f54NGzakuu3MYJVvIIMGDUrRzTwoKCjVYYrJyWSyVCOxGTF16lT69etHfHw8w4cPZ+zYsdSrV4/4+Hj27NnD+vXrgcSTctiw9H8hFITMtGTJEsaMGUOXLl3YtGlThmbcMsmFf2FvKokZO8+FmoPMWuXi49K9w4Y1KpWuvDRLri0hLC71sfCCIAjZjib1WatU2NJt9Rty3djKqlW90jWc/uWbePovPcejV9JPuKsXc2fFsDq4OmROMCwsLoyv/L7C/6W/wXVc7VwZXmW4FVslCML77IcffiAwMJDHjx/zyy+/cPjwYbp160a5cuWwt7cnIiKCmzdvcuLECc6ePUubNm3o3LlzZjc7Td9//z1XrlwhJCSE3377jRMnTtCjRw9Kly6NTCbj6dOnnDt3jj179jBv3jzdqKxKlSpRvnx5goODWb9+PW/evKFbt27ky5ePFy9esGPHDvbv30/NmjUlA0fmVqVKFQIDA+nVqxejRo2ifPnyREVFsX//fl3cwMXFhUmTJmWonq5du+Lr64tGo2HMmDGMGDGCWrVq4eDgQGBgICtXruTBgwdp7nfNmjXZsmUL4eHhzJo1i65du+pmObW1tdWNVLO3t2fu3LkMGjSI2NhYhgwZQseOHWndujVFixZFo9EQGhpKUFAQBw8eJDg4mG+//TZFL7i8efPy+eef8+uvv/L06VN69uzJmDFj8Pb2RqFQ4Ofnx4oVKyhQoABxcXHpmtnSUqz2LcSSMz6kV6VKlZgzZw4TJ04kOjqaP//U7wlSsmRJlixZgquraya00PyUSqX1AiI5nEKh0I0V9O+E1AAAiQ9JREFU9/b2zpLH9e+//2bcuHFA4pOLvn37sn79epOnIDeZ/0rY/YXh8o6/p9rrID3H9vGrWPYEPNdb7upgS/96xdNu8zsevnmIz3XjZ/nJDrLDOZtdZbV8COYk7hvmkyU+g/FvIDTYYHEC9rRd8ZrjD9Vwez22trasXLnSpOFdoVEJ9F96lvthMZLlVYu6sXJ4XXI5mu9eZMqxvRp6lS+OfkFIXIjBdQCK5y7+3s8qmSXO2RwqJ983hPRxd3dn7dq1jB8/nosXL6Y5WsrFxcWKrUu/vHnzsmbNGj7++GOCg4M5e/YsZ8+eTfN1MpmM2bNnM2TIECIjI9m7dy979+5NsU758uX566+/aNKkiaWar9O8eXOaN2/OggULmDJFfwSMq6srixYtMjjzp7GqVq3Kp59+yvz583nz5g1z5szRW2f48OGUK1cu1YBYx44dWbx4MY8fP2blypUphqwWKVIkxZDL6tWr4+vry/jx43n+/Dk7d+5k586dBrctFRsZPnw4z549w9fXl5cvXzJ9esr80R4eHsydO5fx48entvtWZ5WAmClTx1pby5Yt2bFjBz4+Phw7doyXL19iZ2dH8eLFad++PQMHDsTJySmzmykIJgsJCeGbb75JsWzr1q1s3ryZvn37Wq7iy6th5+eGy9v/CnVHmbXK1zEKOvx1Ao1E3L1/veLkTsePrtkXZqOS6EVRr1A9lrZZ+l4mVxYEIZtTxMCaD0EpHahSO7jTamkYpx6+zduyevVq+vbta3QvhLDoxGDY3VDpOioVyo3P8Lq4OVn4wYwBG4M3MuvcLJSatIMRtvLM6b0mCML7K1++fKxevZpjx46xa9curly5QlhYGCqVily5clGiRAlq1KhBy5YtqVOnTmY312jFihVj27Zt7Nixg3379hEUFERERAQ2NjYULFiQ6tWr07ZtW11KoyQVK1Zk27ZtLF68mBMnThASEoKLiwvFixenQ4cODBgwQJcryxo+/fRTqlevzqpVqwgMDCQyMpL8+fPTrFkzxowZQ8GCBc1Sz7hx4/D29sbHx4eAgABiY2PJkycPVatWpW/fvjRq1CjNfF8uLi6sW7eOxYsXc+rUKZ49e5ZqLrbq1atz4MABtmzZwtGjR7l+/TqvX79GLpfj6elJmTJlqFOnDm3btqV06dKS25g2bRpNmjTBx8eHwMBA4uLiKFiwIE2bNmXkyJFmOz7mJNNmxa5bglklf7pXoUKFbPM0IavLDk9NT506Rbt27YiJSfxh8v333/P99xacKevqetg6BgzNyNj2J2g4Ls3NmHJsA59GMnaVv2SOGhlwekpLCrmZFtQ+/uQ4nxz+RG+5jcyGTV02UdajrEnbyyqywzmbXcXExHDz5k0gZxxbcd+wjEz9DCrjYe2HcO+YdLncDsae5HDAUzp37qzLTTJ79mwmTkxl+Hsyr2IU9F96VnKmX4AKBXOxdlR9PFzMv99pHdsEdQKzzs1i823jE/pWzVeV1R1Xm7Wd2Y24b1iONe4bt2/fRqVSYWtrm6FZ7wRBELICS1zT3u9+4IKQwzVq1Ig9e/bg7OzMjBkz+OGHHywXDAvYBNvGYjAY1vpHo4Jhpthw8TE9F502mLDZw8XO5GCYUq3ktwu/SZb1rdA32wbDBEF4j6kUsGGw4WAYQP4KkL8CrVq1Ytu2bTg4OOjSShhDq9UyxveiwWBY+QKurB5ZzyLBsLS8iHnBsH3DUg2G2ckzp8eaIAiCIAiZR/QFF4QcrmnTpty4cYPixU3Po2W0oK2wZTRoNdLlLadB4/Fmqy5BpebHnddZc+5RqusVdHM0edurb6zmwZsHess9HDz4qNpHJm9PEAQhU6lVsGUk3N6f+nq2b6+X7dq1Izg42KT7hkwmY1L7Cgxdfp4YhTpFWZl8LqweWZ88rtYb1pLkwosLfOX3Fa/iDSfwbVi4IREJEVwPt8wkToIgCIIgZE2ZEhALDAzk9OnT3L59Wze1qpubG+XKlaNhw4ZUqVIlM5olCDmWRYNhN3bC5pGgVUuXN58CTY3rYWCM55FxfLTqElceRxhcp0BuB/4ZWIsaxT1M2nZYXBj/XPtHsuyzmp/h5uBm0vYEQRAylUYD2z+G69tNfml67ht1SnriM6IeQ5efJyohMQdjqbwurB1Vn3y5rB8MAwgKC0o1GDbSeyTjqo9j8L7BVmyVIAiCIAhZgVUDYrdu3eK7777j2rVrBteZM2cO1apV48cff8TLy8uKrROE7Emr1fLjjz/SrVs3atSoYd3Kb+6BjUNBIvk8AE2+gmZfm606/4evGO3jT3iMwuA69Up5sqB/zXT9+JrrP5cYiWTTFT0r0qNsD5O3JwiCkGm0Wtg1Hq6tlyyOVWp5HqWhjKfxs0cao1YJD3xH1mPwv+dwd7Znzah65M9tem9dcxlSeQiB4YHsf5Cyh5yzrTM/Nf6J1iVaZ1LLBEEQBEHIbFYLiJ0+fZqxY8eiVCpJyuNva2uLu7s7ABEREahUiT+qr1y5Qp8+fVi8eDENGjSwVhMFIdvRarVMmDCBv/76iwULFnD06FG8vb2tU3nwgcScNIaCYY3GJw6VNGPOsjwuDijUBoZlAiMbl2JyhwrY2pieHvFa6DW235XuRTGl3hRs5Ob90SgIgmAxWi3smwyXVkoWx6qg6X8xPI3ScmyIM155zXt9q17MnTX/T55vah5Hc5PJZExvOJ27EXe5E3EHgJK5S/JXi78o7f52lqz3PXm+IAiCILyPrBIQe/XqFZ9//jkKhQK5XE7v3r3p06cPlSpVwtY2sQlqtZrr16+zceNGNm/ejEKh4PPPP2f//v14eJg27EkwzMZG/Kg3F1tbW10vxqTz2Jo0Gg2ffvopCxcuBCA8PJxWrVpx7NgxKlWqZNnK7xyC9QPB0LT1DcZB6x/SHQwzdGxL5nVh7ofVGbHyYor1ne1t+LVXVbpUK5yu+gAiEiLI45iH8PjwFMs7lupIjfxW7nlnIZl9zuZkOfnampP3zdqs8hnUauHQD3BOevh3vMaGNj5v8H+e+HChpU8sfkNdKFvUvM2oUsS6Q8xTO7bOds7MbTGXfrv6UadgHX5q/BOu9q5WbV92Je4bliOurYIgCJnPKrNM+vj4EBUVhZ2dHYsXL2bGjBlUrVo1xY3VxsYGb29vpk+fzuLFi7G1tSUqKgofHx9rNPG9IZeLiUXNRS6X4+rqiqura6YcV4VCwY0bN1IsCw8PT3VIslncOwbrBoA6Qbq83lhoOzNDPcNSO7atKhbgs1Zvp9ktndeFbZ80ylAwDKBp0abs6rGLoZWHYitPvDY52TrxRa0vMrTdrCSzz9mcLCcfz5y8b9Zmlc/g8d/g1FzJIq2tIxMvFeX047c5H0PibLjeejWMPGSZ9lhJWse2RO4SrOm0hjkt5ohgmAnEfcNyxPEUBEHIfFa5Evv5+SGTyRg4cCBNmjRJc/3GjRszaNAgtFotfn5+VmihIGQ/jo6O7Ny5U/eZsrGxYdWqVfTt29dyld4/AWv6gipeurzOSGj/i1mHSUoZ36oczb3y0bZSAbaNa0T5ArnMsl1Xe1e+rP0lW7puoXGRxozyHkUBlwJm2bYgCILFnZoHR3+SLpPbIftwFTNXn6BOnToA2NnZsWnTJrp27WrU5hNUasb4XsQvONRcLc6wKEUUdyPuGrVuSbeSyGUiCCEIgiAIQiKrfCt48uQJAC1btjT6NUnrPn782CJtel9pNIbzLwmm0Wg0REdHEx0dnWnH1cXFhd27d9OsWTPWrVtHv379LFfZw9Ow5gNQxUmX1xoKHX4zSzAsrWMrl8tYOKAm/wysRW5HuwzX965SbqVY1HoRI7xHmH3bmSkrnLM5VU4+njl536zNop/B80vh4LfSZTIb6PMflGuDm5sb+/fvp2HDhmzdupVu3boZtXmFSsNHqy6xP+glo1Ze5OjNEDM2Pn3uvL5Dv939GHtoLGGxYeL6ZgHivmE54ngKgiBkPqsExBISEodWOTkZn1g1aV2FwvBscoLp1Gp12isJRlGpVNy6dYtbt27pJoTIDLly5eLo0aP07t3bcpU8Oger+4AyVrq8xkDoNAcy2P0/PDqBNeceGXVsne1tkcst2xMtp/UkyCrnbE6Uk6+tOXnfrM1in8HLq2DPVwYKZdBzCVTsolvi4eHByZMn6dSpk1GbV6o1jFtziSP/D4Ip1BpG+17k4PWXGW15uu1/sJ/+e/rz8M1DXsS8YNLxSVy/eV1c38xM3DcsR1xbBUEQMp9Vfu3lzZsXQC/fUWqS1s2TJ49F2iQI2YlardbNzipFZskhik8uwqpeoIiWLq/WD7rMy3Aw7MrjCDrPP8k3WwPYFfAiQ9sSBEF4bwRsgu3jDJd3nQ/e+g9MjL1vqNQaPl93mQPvBL+Uai0frfJnf5B1r9cqjYo//f/kK7+viEvWY/nCywtsfLHRqm0RBEEQBCF7s0pArHbt2mi1WpYsWUJ0tIEf1clER0ezdOlSZDIZtWvXtkILBSHrSkhIoFevXkyfPt36lT+9BL49QRElXe7dB7r9DfL0z5Sk1WpZc+4RH/xzhueRibnJpm4L4lGkgRksBUEQhEQ3dsKW0YD0A5PDDh2g5qAMVSGXycjlID003dnehiLuxvf+z6jX8a/56NBH/Bf4n2T5vvB9nI88b7X2CIIgCIKQvVklIPbhhx8CibnEBgwYQEBAgMF1AwICGDRoEI8ePUrxWkF4H8XHx9OzZ0+2b9/ODz/8wM8//2y9yp9fBd/ukBApXV65J3T/J0PBMIDvdwTxzdYAFOq3uTTilBpmn44gRmmZ/Bqp9bYTBEHIFm4fhI3DQCs97OqrA/G0/mY9f/31V4aqkctlzOrpTf96xVMsz+Voy6qR9ahSxC1D2zdWUHgQH+76kLPPzxpcp7RTaco6l7VKewQhO7CxSfyOplKpxBBNQRCyNY1Go7uOJV3bzMHWbFtKRa1atejfvz9r1qwhODiYDz74gLJly1KtWjXdkMjw8HCuXr3KnTt3dK/r378/tWrVskYTBSHL0Wg09OjRg3379umWTZ06FScnJyZMmGDZyl8EgE83iDcQDKvYNTEnjU3GLyFVi7oDD/WWP49W43stivo1M1yFnv+C/iMwLJAva39JEdci5q9AEATBku75wfqBoJHuSfvd0Xj+OJOYg3X8+PE4OTkxevTodFcnl8v4qXsVbOUyfM48xNXBFp/hdf9//ba87Xe2M/3MdBQaw3lle5bpSQeHDtjJzT/RiiBkV87OzrpczhERESIVjSAI2VZ0dLSuU4MpuenTYpWAGMC3336Lo6MjK1asQKPRcPv27RTBL3jba0MulzN8+HC++spQglhByPnkcjndunVLERBzdXW1/DDil9cTg2Fxr6XLvTpB7+VgY54fHb1rFeXK49esOvsoxXLv/Pb0q5LLLHUkFxobyuKri4lVxXL8yXGGVRnG8CrDcbK13rAfQRCEdHt0Ftb2A1W8ZPF5+0bMOL5X97e7uzs1atTIcLUymYwfu1bG1cGWVhXzU6O4R4a3mRalWsnsC7NZd2udwXXs5HZMrTeVLiW7pDoCQRDeR+7u7rx+nfh9LiQkBLVaTe7cuXFwcLBs/llBEAQzSZrt+MWLtzlLc+Uy329EqwXEZDIZkyZNonv37qxdu5bTp0/z8GHKXiElSpSgYcOG9OvXj/Lly1uraYKQZY0dOxaVSsWnn35K7ty52bdvHw0aNLBchaG3wKcrxIZLl5dvD31WmC0YluTbzpUIfPqGK48jABjdpCSt88dhY4FZJOdemkusKnG2zAR1Av9c/Ydtd7bxY8MfaVi4odnrEwRBMJunl/4/42+MdHm9sdRt/wu/2Mxm8uTJeHp6cujQIbMExOD/3+XaVzDLttISGhvKl35fcjnkssF1CjgXYE7zOXjn8xazkguCBEdHR9zc3IiMTOzxHx4eTnh4ODKZzKxDjgRBECzl3cnlnJyccHFxMdv2rRYQS1K+fHm+//57ABQKBW/evAEgd+7c2NvbW7s5gpDljRs3DplMRt26dalTp47lKgq7DSu7QEyodHnZ1vCBD9ia/3PqYGvDooE16bfkLJM7VKRleU+LPOm/GnqVHXd36C1/EfMCZ1tns9cnCIJgNi8CwbcHJLyRLq85BNr/AjIZX3/9NXZ2drRu3ZqqVatat51mcDnkMl8c+4KwuDCD69QuUJvfm/1OHicxBEwQUlOoUCHs7e0JDX37/U6r1aJSqTKxVYIgCKZzcnKiePHiZu3hKtOK7NI5nkKh0AUXvL29ReBR0Bd+F1Z0gqjn0uWlW0C/tWBn2WGFKrUGWxvLzPWh0Wrov7s/QeFBemWdS3dmVpNZFqlXeD/ktOtsTtufbC80GFZ0NPzAouqH0H1Ruic50Wq1rDr7kB41i+LqYPVnpSnase7WOmafn41Ka/jH+qBKg5hQa4LIFyZka9a+zioUCqKjo4mJiUGhUKDRWGbiIkEQBHOysbHBycmJXLly4eLiYvbh3pn3rUcQBJ1Xr16h0WjImzdvJlR+P7FnmKFgWMkm0HdNuoNhO68+w8PZnsbl0t43SwXDIDEps1QwzMnWiQm1LDxJgSAIQnq9upc4lN1QMKxiV+i2MEPBsF/23WSx3z22Xn7KiuF1ye1o/UBTvCqeGWdnSPbiTeJo48gPDX+gU+lOVmyZIOQM9vb2eHp64unpmdlNEQRByDIs9+tTEASjhIWF0apVK9q0acOrV6+sW/nrh4nBsDdPpctLNIL+68He9OGESrWGGbuu8+nay3y69hJPXsdmsLHpF6WIYu6luZJlo6uOJr9zfus2SBAEwRgRj2FlN4MPLE6GuPKm7Zx0z/ir1Wr540Awi/3uAXDpUQSD/j1PZJz07JWW8iz6GYP3Dk41GFbUtSirOq4SwTBBEARBEMzGrD3EBg8eDCQmXV25cqXe8vR4d1tCxqjV6sxuQo6hVqsJC0vMb5I3b950JScNCQmhdevWui7zbdu25dChQ7i7u5uzqdIinyQGwyIfS5cXqw/9N4C96UkLQ6LiGbfmMufvJwb4Xscq+WjVJTaObYCjXdrHyRzHNrl/rv7Dq3j9YGOxXMUYXCn916fsxtzHVXgrJ19bc/K+WZtJn8GoF4k9wyIfSRYfvKuiy9pn1L7WnX379uHq6mpye/46fJsFR1PO+H31cQQDl53Dd0Rd3J2tM1T294u/c+PVDYPljYs05pcmv+Dm4GZwHXF9swxxXC1HXFsFQRAyn1kDYufPnwfQG9d5/vx5ZDIZpqQrS1pfTAlsXiJfgPmo1WqePHkCgIeHR7q+JA4dOjRF8nh/f39GjBjB5s2bzdZOSW+ewYrOEPFQurxoHRiwERxM/4Hl//AVH6++xMs3CSmWBzyN5PvtQfzaO+0Ez+Y4tknuRd5jzY01kmWT6kzC3ub9yY1kzuMqpJSTr605ed+szejPYEwY+HRLHC4p4fhDFd3Xx5KghlOnTvHJJ5+Y/PBwwZHbzD10W7Ls+vPEWX+be1mn9+y0+tMICAvgRcwLvbLRVUfzcbWPsUljSKi4vlmGOK6WI66tgiAImc+sATFDM+BZdGY8QcjGFixYQLNmzXRfNosXL85vv/1m2UqjXiQGw17fly4vXBMGbgbH3CZtVqvV4nPmITN2XUelkQ5+r7/4mN61i1KnpPnzVwzYM4BroddSLPPO601u+9ySyZkbFW5Es6LNzN4OQRCEDIl7Db7dIfSmdLFnZYYtu0OsMnEYetmyZfnpp59MqqLhL4d5FhEvWSaXwdwPq1stGAbg6ejJnOZzGLJ3CAqNAgAXOxd+bvwzLYu3tFo7BEEQBEF4v5g1IObr62vSckF435UuXZojR47QrFkzHB0dOXLkCCVLlrRchdEhicMkX92VLi9UDQZtAUfDw1KkxCnUfLM1gK2XDeQiAxxs5fzUw9siwTBDohRRBIQF6C23ldkyqe4k0QNVEISsJf4NrOoFL/SvWwAU9MZpyE52tHpG8+bNyZMnD0eOHKFw4cJGV7H0+D2DwTCZDP78oDpdqhm/PXOpkrcK0+pP47vT31HarTRzW8yllFspq7dDEARBEIT3h5hlUhAyWbly5Th69CjOzs4UK1bMchVFhyYGw8KCpcsLeMOgbeDkYdJmH4bHMMbXn5svogyuU9TDiX8G1qJKEdMCbRn1LPqZ5PL+FftT2q20VdsiCIKQKkUsrPkQnvpLl+f10l2jK1f24OjRo+TNm5eCBQsaXcXyk/f5aY/hXF2/9a5G9xpFTGy4+fQo1wONVkP7Uu1xsTM9f6UgCIIgCIIpREBMELIALy8vy1YQE56Yj8bAEBzyV4bB28HZtN5bR26+ZPy6K7yJ1x+SmKRZ+Xz81be61ZIzJ5c09CY5T0dPxlYba/W2CIIgGKSMh3X94NFp6XLP0jBkB7jk1S2qUqWKSVX4nHnA9F3XDZYX93Sid62iJm3TWBqtBqVGiYONQ5rr9irfyyJtEARBEARBeJfcGpVUqFCBSpUqcefOnbRX/r9Hjx7pXicI2d3t27fZu3dv5lQe+wp8u0FIkHR5vgqJwTCXPEZvUqPRMudgMMNXXEw1GPZZy7IsH1onU4JhhoyvOZ5c9rkyuxmCIAiJVArYMBjuHZMudysGg3dALuN7gr1r1dmHfLfdwD0A+KlHFY5PskyurjeKN3x65FOmnpxq0uRKgiAIgiAIlma1HmLp/RIkvjwJ2d3Nmzdp2bIl4eHhbNu2jQ4dOliv8rgI8O1hOB9NnnKJP7Rc8xm9yYhYBRPWX+HorVCD6+RytGXuh9VpVbGAiQ22rMp5KtOtbLfMboYgCEIitQq2jITb+yWLn0dreVhvKvXd0z+cft35R0zbFmiwfHq3ygyoVyLd209N8Otgxh8dz+Oox0DiRCdDKg+xSF2CIAiCIAimskoPsYwQSa+FrEomk+Hi4oKLi4vB8zQoKIhmzZrx/PlzFAoFPXr04ODBg9ZpYHwkrOoJz69Il3uWgSE7IZfxQaugZ5F0WXAy1WBYhYK52DmucYaCYcYc2/SYUm8KclmWv+xZjKWOqyAIxknxGUQL2z+G69sl1w2J0dByZQyteo/kxIkT6apvw8XHTNlq4IEI8F3nSgxuUDJd207Lvvv7GLhnoC4YBvCn/5+ce37OIvWJ65tliOMqCIIg5GRZNofY69evAXBycsrkluQsdnZ2md2EHMPOzo4KFSqkus5///1HSEiI7u+EhARmzJhB69atLfvFMiEKVvU2nJzZo2RiMCx3IaM3ueXSE6ZsCSBBpTG4TrfqhZnV0xtn+4xdWow5tqbqWqYr1fJVM+s2sxtLHFchUU6+tubkfbM23WdQq4Wdn8O19ZLrvYrT0tonlpthGiCWWbNm0aRJE5Pq2nLpCV9vvoahjvbTOlVkeGPzz+Ko0qiY6z+XlddX6pVptBom+k1kfef1FHI1/v5jDHF9swxxXC1HXFsFQRAyn1UDYsYGAGJjY/H19QWgePHilmySIFjU7NmzCQ0NxcfHB4B69eqxY8cOCwfDomF1H3hyXrrcvTgM2QVuxs0kplBpmLHrOr5nHxpcx1YuY1qnigxpWDJLPkF2tnVmfM3xmd0MQRCExGDYvslwST9gBKB1yMWvdyoSEHIIgGbNmrFhwwaTqth+5SlfbbxqMBg2uUMFRjYx/0y7r+JfMdFvIudfGLj/ADZyG17FvzJ7QEwQBEEQBMFUFgmItWrVSnL58OHDsbVNvUqFQsGrV6/QaDTIZDJatGhhiSYKglXI5XKWL1+OUqnk0aNH7Nmzh9y5c1uuQkUMrPkQHp2RLncrlhgMMzIfzYvIeD5e7c+lRxH/a+++A5o8/j+AvxMIGxmKOEAFFFTErbj3aB11b62z2lpn66j2a5dttbVql62rzrpnXXXgal04EAVBERyICipTNkme3x/8SEESkkASIHm//gq5e+4ul/Bcnk/uuVOZx8XeEr+NaooWtbTbodKQpjSaAhcbzddJIyLSC0EATn8JBK5Wni6xgWjUXnxdtRnupQ5BamoqDh06BBsbG42rOHzrGWbvCoZcRTBsbk8fvN/RqxiNL9qdV3cw69wsxKbFqszT2KUxlndajso2lXVePxEREZG29BIQe/r0aaHnBEFAXFycVuU0btwYkyZN0lWzCIBUKoWFRdnZ8a88k0qlePw4d9ZUzZo1VQZ7zczMsGXLFmRlZcHW1lZ/DcpOB3YMBx5fUJ5eoTow9hDgpNniyVcexGPa9iC8Ss1WmadlLWf8OrIJKlewKk6LVdK0b9URi8Rwt3fH6Hqjddm8cktX/UqFSaWqd1st7zhu6I7s3Hcwu7BSeaKZJTBiJ1CjFSQAdu/eDZlMptXSEX+HPMesIoJhs7t548POtbVvuBoH7h/A11e+RrZc9XgxzGcY5reYD4mZfm4T4/lNP9iv+mPM4wYRUXmhl1FtwIABBf4+cOAARCIRunTponZ2jKWlJVxcXNC0aVO0atWqTN5+VZ5x107dkcvlSEpKAgC4uxc948rc3Fy/XyJzMoGdI4GH/yhPt6uSu2aYs/pbZARBwB8XHmLJ33chU3VVBWB821pY2KseJGa6X6Rem74tio+TDz5v8zkszHgxD+iuX6kwYz63GvNrM6iLP8Ps/BLlaWIJMOxPwLOj4iltg5An7sRi+o6bKs/bM7rUxsxudbQqU51sWTaWXl2KPRF7VOaxEFtgUetF6F+7v07rfhPPb/rBftUfnluJiEqfXq7Qlywp+IXvwIEDAIDZs2ejdm3d/zJJVNrOnz+PXbt2Yc2aNTAzMzNs5dIsYNdo4MFZ5em2lYFxR4CK6m+RScuSYv6+2zhy+7nKPNYSMywd5Id+jTVbg0wfZHIZzMTq+1liJoFvRV8DtIiIqAhX1wGnFilNEkRmEA3eAHj3KHbxAWFxmLY9CFIVwbCpnbwwu7t3sctXJi4tDh+d/wi3X95WmaeqbVWs7LyS52EiIiIqkwwy73natGkAAGfnsrvGEFFxBQYGYs6cOcjIyEB2djY2btxouKCYNBvY/S4QeUp5uk2l3JlhldTPCnjwMhXv/3kDEXGpKvPUqmiD1WOaoW4VPa6DVoQsWRa23NmCIw+OYEfvHbCRaL6uDhFRqbj5J3BsjtIkuSBgU7I/xtfri+LOhz9zNw4fbLuBHJnyYNiUDp6Y29NHpzPub8TdwMfnPkZ8ZrzKPP5V/fF9h+/hbMXvfkRERFQ2GTQgRmRsLl26hDlz5iA7O3fdlK1bt0IikWDdunUQi3V/K2EBshxg73gg4rjydJuKucGwyuq3Sz95JxYf776F11mq17PoVq8ylg9tDAdrw28TLggCTj4+iRXXV+BZ2jMAwOY7m/FB4w8M3hYiIo2F7AX+Uv0daNKhTGwMPo5g2Uz89NNPWgetzke8xPtbg1QGwya288Anb9fVWTBMEARsv7sdP1z7AVJB9XgxvsF4zGgyA+ZirjdFREREZRe/qRCVgLKgV3JyMmQymX4DYjIpsG8icPeI8nRrJ+DdvwDX+kUXIxew/OQ9/HYuSmUekQj4uLs3pnaqDbHY8Gv6hSeEY8XNFQh6EVTg+Q2hGzCgzgBUsa1i8DYREakVfhjYPxmA8mDVh8cysDE4BwCQlJQEuVyu1eziC/df4b0t15EtkytNH9emFv7Xu57OgmEZ0gwsvrwYhx8cVpnH2twai9suRs9aPXVSJxEREZE+6XkKS66goCDUq1cPDRs21Ginybi4OPj5+aF+/foIDQ01QAuJiqdVq1ZYtmyZYvHj4cOHY+fOnZBI9DiLSiYFDkwGwv5Snm7lAIw5CFTxK7KYhLRsjNt4tchgmKONBJvGt8S0LnUMHgxLyknCHzF/YMyJMYWCYQCQKcvEj0E/GrRNREQauX8K2DMeEGRKk89KumJNUG4ga8KECVrfan8p8hUmbr6GbKnyYNiYVjXxed/6Op0ZNv3M9CKDYTUr1MT2XtsZDCMiIqJywyABsWPHjkEQBHTq1Amurq5q87u6uqJz586Qy+U4ckTFDBiiMqJt27bYsWMHJkyYgK1bt+p3N0m5DDj4ARC6T3m6ZQVgzAGgWuMiiwmJSUbfXy7g3/uvVObxrVYBh6e1Q0dvlxI0WHtZsixsuLMBn9z/BP8m/QtBxewKADj24BiiU6IN2DoiIjUenM/d6ESeozT5mc84VHjrU2zevBlTp07FunXrtAqGXXkQj4mbryNLRTBspH8NfPmOr07XDBOJRBjnOw4iFSuddXTriO29t6O2EzdOIiIiovLDILdM3rhxAyKRCB06dND4mE6dOuHkyZO4du2aHltmenT5BdnUicViVK5cGQDQqFEjDBw4UL8VyuW5a9GE7FaebmEPjN4PVG+mtqj1Fx7gaVKGyvQhzdywuH8DWEkMt2OmIAg49fgUVtxYgaepT9Xmb1ipIea1nIcaFWoYoHXGIf9nVu9r3JkYYz63GvNr07noK8COEYA0U2myvM0MyOq+h8oiERo1aoRRo0ZpVfy1RwmYsOkaMnKUzzwb1twdX/droJcZve2qt8P0JtPx882fFc+JIMIHjT/AlIZTIBaV7jmF5zf9YL/qD8+tRESlzyABsejo3BkctWtr/suhp6dngWNJN/Q6e8nIpaWlwdbWVvG3ubk53N3dDVO5XA4cngHc2q483cIOGL0PcG+hUXHfDPBD6NNkRL1MK/C8xEyEL97xxciWNQz6RS08PhzfXfsON+JuqM1b2aYyZjebjV4evUr9Aqy8Mehn1sQY87nVmF+bTj0NArYNAXLSlKe3nAJx96/gXsxz643HiRi34SrSs5UHwwY3c8OSgX56vb19ot9EhL4KxZknZ2AvscfSDkvRwU3zHzv1iec3/WC/6g/PrUREpc8gV5NZWVkAAEtLS42PyVuTKSND9SwWIkPZsGED6tati8jISMNXLgjA0Y+Am1uVp0tsgFF7gBr+GhdpZ2mONWOawdbivxlgVR2ssHtKa4zyr2mwYNirjFf47OJnGHZkmNpgmJWZFT5o9AEO9z+MPp59GAwjorIjNhTYOgDISlGe3vRd4K2lubuUFMOLlEyM23AVaSqCYQOaVMd3gxrqfa1HsUiMb9p9g+41u2NHnx1lJhhGREREVBwG+WnC0dER8fHxePbsGerVq6fRMbGxsQAAe3t7fTaNSK01a9bg/fffBwB07twZ58+fV8xg1DtBAI7NBW5sVJ5ubg2M3A3UbKN10bUr22PZkEaYui0IrT0r4peRTVDJTvOgdUlkybKwNWwr1t1eh3Rputr8vT17Y1bTWdxRkojKnpcRwNb+QGaS0uSDDyzRfMIcuJXgdrPKFazwYZfaWPr33UJp7zSqhh+GNIKZFsGwUcdG4fbL2wWea+jSENt6bVN7rJ2FHVZ0WqFxXURERERllUECYl5eXoiPj8eZM2fQtWtXjY4JCAgAAHh4eOizaSYnJydHMfuO1Nu1a5ciGAYAMTEx6Ny5M27evAl7e3uEh4cDAOrVq6f7nSUFATi+ALi2Tnm6uRUwYgfg0b7YVfTyq4qN41qgfZ1KMDfT/4wrQRAQEB2A5deXa7ROmJeNFxa1W4RmVdWvi0bq5eTk6Pcza8JycpQvoG4MOG4UIeEBsOUdIO2l0uS9YTkYvjcFnle648aNG7Cysir2/+D7Hb1gLhbh66Phiud6+1XFiqHaBcNUiU2LxZEHR9DHs0+JyyoNPL/pB/tVf4x53CAiKi8Mcs9Rx44dIQgC/vrrL1y/fl1t/mvXruHQoUMQiUTo1KmT/htIpELPnj3RrFnBYMyYMWPg5OQEQRCQk5ODnJwcCILqnRCLRRCAk/8DAn9Xnm5mAQzfBnh1Vpr8OjMHSenZGlXVuW5lgwTDACApKwn/u/A/tcGwytaVMdltMj71+BR+Ff0M0jZToNfPLJGpSXoCbO4HvH6uNPlIRA5G7suATADGjRsHe3v7Ev8PTmrvic/71gcAvOVbBT8Ob6yz8/eL9Bf48tKXuJtQeBZaecDzm36wX4mIyJgZ5Cp42LBhcHJygkwmw3vvvYc///xTsa5YfllZWdiyZQsmT54MqVSKChUqYOTIkXpv39OnT7F06VK89dZbaNy4MVq2bIlBgwZh/fr1JV7DLCMjAydPnsTnn3+OQYMGoUWLFvD19YW/vz+GDRuGX375BS9fKv9lmUqfo6MjTp48iUaNGgEAvvjiCyxevFi/a2wJAnD6S+Dyr8rTxRJg2DagdjelyffjXqPfqouYvuMmZPKy9eXVycoJ7zV8T2V63jph+/vsRxvHNlwnjIjKptexuTPDkpVv/JPl1gaf3qqBHDmwbNkyLFy4UGdVj2/rgQ3jmuPnEU0g0fGPGZmyTMw6OwvJWck6LZeIiIioLDLILZO2trZYvnw5Jk+ejMzMTHzzzTdYsWIFfH19FVs5v3jxAqGhocjMzIQgCDA3N8eKFStgZ2en17adOXMGc+fORWpqquK5jIwMJCcnIzQ0FHv27MHatWtRs2ZNrcu+e/cuRowYgfT0wmskJSUlITg4GMHBwdi0aRMWL16MXr16lei1kH44OzsjICAA+/fvx+TJk/Vf4dlvgQsrlaeJzYGhWwDvHkqTj95+jrl7byE9W4YHL9Ow4tQ9zO1ZV4+N1d6Y+mOwN2JvoVlivTx6YXaz2ahiWwXZ2ZrNbstPk7VviIhKLO0VsKVf7u2SytRoDcvRe3G8dwqOHz+O8ePH67wJXeq66rzMPE9Tn2L+P/OxqusqmInN1B9AREREVE4ZbL/fNm3aYP369Zg3bx5evHiB9PT0QrdP5k3FdnV1xffffw9/f813zSuOsLAwzJ49G5mZmbCxscGUKVPg7++PzMxMHDt2DLt378ajR48wefJk7Nu3T+vgXGpqqiIY1rRpU3Tu3BkNGjSAo6MjEhIScPLkSezZswepqamYM2cObG1t0bFjR328VCqhSpUqGSYYdu474J/vlaeJzIDBG4G6hQOnUpkc35+4h7X/FLxAW3U2Co3cHNHDt+wsRm9pZomPm3+Mj859BADwq+SHeS3moXHlxqXbMCIiVdZ3A2Kuqc9XrWnuRicWtqha1VbrYJggCHrf5TclOwXRKcpntuUJjA3Enfg7aOjSUK9tISIiIipNBguIAUCrVq0QEBCAgwcP4uzZswgPD0diYiIAwMnJCfXr10fnzp3Rr18/gyzg+8033yAzMxPm5ubYsGEDmjRpokhr3bo1atasiWXLluHRo0fYuHEjpk+frlX5YrEYb7/9NqZNm4batWsXSm/Xrh06dOiAadOmQSaT4euvv0aHDh30/mWYChMEAZcuXULbtm1LrxH//ACc+1Z5msgMGPwHUP8dpckpmVIcCn6mNO3j3bfw1zQ7eLrod7YlAITHh6OKbRU4WTkVma9bjW7o5dEL7aq3Q2/P3rw1kojKP1c/YPQ+wKpCsQ5/+CoN8/fdxo/DGqOao7WOG5fr6vOr+PTip0jKSlKZp7J1ZSzvtJzBMCIiIjJ6Br8KtbCwwNChQ/H777/j3LlzuHXrFm7duoVz587ht99+w5AhQwwSDLt9+7ZihtqgQYMKBMPyTJgwAV5eXgCALVu2aL0bTNOmTfHjjz8qDYbl6datG3r0yL39LTo6GmFhYVrVQSUnCALmzp2Ldu3a4ddfVazbpW8XfwLOLFaeJhIDA9cCvgNUHu5sa4HfRjeFxKxwMNXTxRaWEv3e9vIq4xU+v/Q5hh0ZhlXBq9TmF4lE+K7Dd+jr1ZfBMCIq+wR50emVfIAxBwAb52IV/zg+DSPWXsHVhwkYtvYyYhILL7VQElmyLPxw7QdMOjkJsWmxKvM1rdwUu/ru4oxdIiIiMgkmeyUaEBCgeDxo0CClecRiMfr37w8ASElJQWBgoF7akv/W0Ojoom9jIN0SBAGzZs3C8uXLAQDTp0/HmjVrDNuIy6uAU5+pSBQB/X8H/AarLaZpDSd83te3wHMjWtbArimtUV1Psw2yZFn4I+QP9DnQB/vv74cAAXsi9uB+4n291EdEZHByuer1wgBEJsix13YMYOdSrOKfJKRjxNoriE3J/P+/MzBszRU8SdBNUOxewj0MPzIcm8M2Q4DqjVYqWlfE+p7rUcm6kk7qJSIiIirrTDYgduPGDQCAjY0NfH19VeZr0aKF4nFQUJBe2pJ/AXEzM/3O5NF3+eVNQEAAfv755wLPffjhh4iMjFR7rLm5OTw9PeHp6Qlz82LefRy4BjhRxO5j/X4FGg3XuLhR/jUwqKkbLMzF+H5QQywZ6AcrPcwOEwQBAY8D0O9gP/wY9CPSctIUaXJBjmXXlpVoe3ad9C0Vwn7VH2M+txrza9PIqUVARqLSpMdJcnTdkoZR789BTEyM2qLe/B98mpSB4Wuv4FlyZoF8T5MyMGzN5RIFxWRyGTaGbsSIoyMQmaR+TKtuVx0SsaTY9ZU2nt/0g/2qPyZ/biUiKgNMdmSLiooCANSoUaPIAd7T07PQMbp29epVxeO8WzT1RSw22RioUt27d8fXX3+N//3vfwBy+2fTpk1F3uaaRywWw8mp6LWyinRtPfD3PNXpfX8CmozWqkiRSIRvBjTAxHYeqF+teOvYqHM34S6+v/Y9rsWqXmD68vPLOB9zHp3cOxWrjhL3LSnFftUfYz63GvNrUytwLXBZ+a30z17L0WVLGp6lmWHnzu1wc3NTW9yb/4PWEjNUsJbgaVJGobxVHa3hZFu8JSSepj7Fpxc+xY24G8U6vjzi+U0/2K/6Y9LnViKiMsKgAbHs7GwcPnwYAQEBuHfvHhITE5GZmVnkMSKRSOframVlZSkW869Spejd9xwcHGBjY4P09HTExqped6O47t69i/PnzwMAvL299R4QK2odNHNzc8XgLJfLIZVKiywr/1pvMpkMMplMZV6RSASJ5L9fnqVSKeRy1WuyiMXiAoHKnJycImcc5W+7IAhq13uTSCSKzQsWLFiAzMxMLFmyBJs2bcLQoUMLzNrTR9vFN7fC/O+PVZYhfWsZ5H4jgXztCHuegnpV7CGRSIp8n8QAaleyUrwGXb1P8Rnx+D3kdxyMOljkbTdA7k6SMSkxBfoRKNn7pO4zqe/PmJmZWYFfc998bW8y1f8nU32ftF1jsjwx1XFDdP8EzI/Ph7JtbgRBwJ9RDniSmoUdO7ajb9++xRo37CTAprFNMH5zEMKev1Y838jNAetGNYaFSK70M6zqcy4IAo4+Oorvr3+PNGlaoeMU7YOo0HlckAs6HzeU4flINWP+f3oT3yfjHjeIiMoLgwXEHj58iA8//BAPHz4s0a1UupCW9t+XRBsbG7X5ra2tkZ6ejvR03S5ym52djU8//VQxMM6ePVun5Stz9+5dlWk+Pj6ws8vdiTA9PR337t0rsqxmzZopHr969arI20VsbW1Rt25dxd+PHz9GUlKSyvyVK1eGu7u74u/w8PAivzh4enoqfsHMyclBSEhIkW338/NTfEFJTk5Gv3790KBBA9SuXbvQsRKJBA0b/rfb1vPnz/HixQuVZTs6OhYIbEZFRRX4zFWM/hs1b/2g8vjoBtPxUtIM+P92yAUB+8LTsOtOKsY1ssfHfZsa9H16mfASp+JP4dDLQ8iUFx3ABgB/B38MdR2KipkVC/Wltu9T3bp1YWNjA5FIhOTkZDx4oHodH12/T29yc3ODq6ur4m91bS+L/095Fynh4eFFfoEv6f8T3yfjYorjhk3SPXhfmg2RisX0RSIRPujkhlojV8DDw6PE48a33avgszNi3I5JRiM3B3zWwQkP74erzK/sc/5a+hqbn23G9ZTrKo8DgDoOdZCZmYknWU8KPJ+enq54HeXlfVKG44ZyHDfKx/tERESGZZCAWHp6Ot577z3ExMRALBaja9eucHZ2xu7du3O/VH7wAZKTkxEaGopbt25BJBKhcePGaNu2rV7ak5WVpXic/xcnVfIGbHWz2bT11VdfITQ0FAAwYMAAdOnSRaflk+ZEIpFGt0mWlPOTk6h56weIVM2w6vktXlq0UvyZli3HT1eTceN57md28+3X6NgwCZ187fTeVkEQcOXVFfwR9Qde5rxUm9+3oi8GOQ+Cp6Wn2ryaunv3boEvzVQymlykEJk6i/RY1L66EGYy9TPYdTWr297SDFsn+mPp3+H45K16eB4dhTQtJ48k5CTg5uubKtNFEGFCgwkYW2csxh8fX8IWl10cN3SL4wYRERkzkWCA6VobNmzA999/DzMzM6xfvx6tW7fG/fv30bdvX4hEIoSH//craFhYGObNm4cHDx5g4cKFGD1auzWUNJGQkIDWrVsDAHr16oWVK1cWmb9NmzaIj4+Ht7c3Dh8+rJM2rFmzBitWrACQ+yvZli1bNJqtVhzZ2dmKLzNeXl4q6zHmqfrZ2dlYsmQJPvroI9jb2+tkqn5OTo5i5kTdunUVeVS1XXxnP8wOfaByxgG6fwW0namYqn839jWm7byF6ISCa8tUsrPA0Rnt4VrBSm/v072Ee/j+2ve4GntVZd48LtYumNVsFvp49oFMKtPJLRX5+zbvwsZUb6lQprivNf+5IP9ntqi289aXglS9T+np6Yp1Jo3hYtxkx43MZEg294IoPkL5QV0/A9p/bLBxQxVVn/NNYZvwy61fCuWvZlsNS9ovQVPXppDL5Rh9bDRC4gsGOfwq+mFTj00AysH7pATHDY4bb7Y9T1l9n4xt3CAiKo8MMkPs7NmzEIlEeOuttxSBKFXq16+PLVu24J133sHSpUvRuHFjNGjQQKftsbW1VTzW5DbIjIzcgISuAlY7d+5UBMM8PT2xdu1avQXD3iSRSDQacMVisVYD85tfJtTRdqciTWby5RGJRAXanpWVhZEjR+Lw4cP4999/8ffffxdI1/a1Kmt7Uf0qkUiAOweAQx8AqoJhXT8D2s4EkPvF6a/gp5i/7zYycwrnf5WajanbgrBzcitIzHT7PsVnxOPX679i//39kKtq6/+zNLPEWN+xmNhgImwkuZ9fsUTzBWLffJ/U0cX7VBRtPmMA9Np2ff8/AZqfC/g+qZb/fTLmtWBMZtwQA9g/HlAVDGs6Fmj3EQADjRtayCtnYsOJuPj8IoJe/Lcrdv/a/TG/xXzYWeTOLBaLxdjeZ7vGZZe594nnI6U4bqhWVt8nYx43iIjKC4NsbxIZmbvdd/fu3ZWmv/krj7OzM8aPHw+pVIpt27bpvD2WlpZwdHQEALUL5ScnJyuCZuoW4NfEkSNH8OWXXwIAqlevjo0bN8LZ2bnE5ZJymZmZGDBggGJm37///ou+ffvqfD24IoUdAvZOVB0M67QQaJ+7wH6OTI4vDt3BzJ3BSoNhAGAmFqGXX1WYi5Ut9Vx8eyP2os+BPtgbsVdtMOytWm/hUP9DmN5kuiIYRkRUbgkCcGg68OhfpcnXk5yQ1X0JINL+vJuYlo0NFwyzfqqZ2AzftPsGthJbOFk64cdOP2Jx28WKYBgRERER/ccgM8Rev87dOalatWqK5/L/mpKenq5YnDJP06ZNAQBXr6q/Zas4ateujevXryM6OhpSqVTlr035F/ks6Vohp0+fxvz58yGXy+Hi4oJNmzbpJMhGqsXExOD69YILDF+9ehXh4eEFFjnVm7vHgL3jAUHFbQwd5gGd5gMAXqRkYuq2IFx/nKiyuEp2llg1sgn8PSvqvKlmIjOk5qQWmce3oi/mt5yPJpWb6Lx+IqJSc24JcHun0qTgWBn670vEifcewdfXV6tik9KzMWp9IMKep+BZUgbmdtfvTtIA4GbvhhWdVsDbyRuVrCvpvT4iIiKi8sogM8SsrKwAQLFWAADY29srHj9//lzlsa9evdJLm/KCIenp6bhz547KfNeuXVM8zgvSFcfly5cxa9YsSKVSODo6YuPGjahRo0axyyPN1K5dGwEBAYpZeHZ2djh+/LhhgmERJ4Dd7wJyFWtLtJsNdF4IALj2KAG9f7lQZDCsaQ1HHJ3RTi/BMADoV7sf6jnXU5rmYu2Cr9t+je29tzMYRkTG5eafwPnvlCbFpMgx6qgEB/8+rXUwLDkjB2P+uIqw5ykAgPUXHuKbv+8Va6aYIAjYE7EHG0M3apS/TbU2DIYRERERqWGQgJibmxsAFNgi2dnZGQ4ODgCAoKCgQsfkBam0ve9fU926dVM83rdvn9I8crkcBw8eBABUqFAB/v7+xaorKCgIU6dORXZ2Nuzt7fHHH3+gTp06xSqLtNewYUMEBASgVq1aOHXqFNq1a6f/SiMDgF2jAbmK9SHaTAe6fg4BwMaLDzFi7RW8fJ2lPC+Ad1vXxM7JreFawUo/7QUgFokxv+X8As9ZiC3wnt97ODLgCPrV7gexyCCnDCIiw4g6CxyeqTRJamaDyeec8OehM2jevLlWxaZk5uDdDVcR8jS5wPNbrjzB+puvIdciKPYq4xWmnZmGry5/hZ+CfkLoq1Ct2kJEREREyhnk6jZvUfzQ0IJf4lq3bg1BEPDHH38gKSlJ8fyTJ0+wdu1aiEQi1KunfMZKSTVs2FDxBXffvn24ebPwVuUbNmxQ7P7y7rvvFgrOBQYGwsfHBz4+Pvjkk0+U1hMeHo4pU6YgPT0dNjY2WLNmjc43CSD1mjRpgoiICLRq1Ur/lUWdBXaOAmQqdiny/wDovhjpOTLM2hWMLw+HQSpXfnFkJRFjxdBG+KpfA1iY6//ftZlrM/So2QNA7jphhwccxoymM7hOGBEZn7g7qmfxisxgPuJP/BUYhSZNtJsV+zozB2M3XMWtJ0lK02/GZiE1W7OA2Ono0xj410D8E/MPAEAmyLDg3wXIkGaoOZKIiIiI1DHIGmJt2rTB3r17cebMGcyc+d8vsWPGjMHx48fx5MkT9OzZE/7+/sjIyMCNGzeQnp4OkUiEoUOH6q1dn376KUaMGIHMzExMmDAB77//Pvz9/ZGZmYljx45h165dAIBatWph/PjxWpcfHR2NiRMnIiUl93aJmTNnwt7eHhERKnawAlCxYkVUrKifW+KA4u0qVF4IglDgttw36Xq2oUQigZ+fX8GyH/4D7BgBSDOVH9TiPeCtJXgUn473/7yBu7GvVZZfw9kGq0c3Q/1qFUrUTkEQcO7JOdyIu4E5Leaozf9x848xqt4oNHUt/i3CJaW0b6nE2K/6Y8znVqN8bSnPgW1DgawU5el9fwRqd4W2/yVpWVKM33gNN6OTlKa7OVnjz/HNUM3Rusj/wbScNHx39TsciDxQKO1RyiMsv74c/2v1Py1bZ9x4ftMP9qv+GOW5lYionDHImbhz585o0aIFZDIZoqOjFWtnNWvWDB9++CFWrVqF5ORknDp1CgAU62sMHDgQffv21Vu76tevj5UrV2Lu3LlITU3FihUrCuWpVasW1q5dW2jRf01cv34d8fHxir+XLFmi9php06Zh+vTpWtelqaICRuVZQkICBgwYgMWLF6NDhw4GqbPQtuKPLgLbhwGqfrlvNh7otQwB4S8we3cwXmeqWFsMQGcfF/w4rAkcbEr25TMiMQLfX/segc8DAQCd3DuheZWib/2pZlcN1eyqFZlH37Tdsp00w37VH2M9twJG+NqyUoHtQ4GUGOXp7ecATd/Vutj0bCnGb7qmci3I6o7W2PFeK7g7Fz3j9uaLm1jw7wI8TX2qMs+ue7sw3Gc4ajvV1rqdxornN/1gv+qP0Z1biYjKIYMExKytrbF161aladOnT0fz5s2xZ88e3L9/HzKZDDVr1kT//v3Rs2dPvbetS5cuOHToELZs2YJz584hLi4OEokENWrUwFtvvYXRo0fD2tpa7+2g4nv16hW6deuGW7duoVevXjhx4gTatm1r2EZEXwG2DQFy0pWnNxkDWa/l+PFUBH45E6myGJEImNm1DmZ0qQOxuPhflBIyE7Dq5irsvb8XckGueP77a99jR+8dMBObFbtsIqJySybN3fk39rbydL8hQBftZ15lZMswcdN1XH2YoDS9qoMVtr/nX2QwLEeWg99u/YYNoRsKnLffVNGqIr5q+xWDYUREREQlJBKKs90RlSvZ2dkICQkBAPj6+ip2/TQG8fHx6NSpU4H16ezt7XHq1Klib4KgKblcjuTkZJg9D4L9wbEQZau4/bHRSCT1WImZu27jfMRLleVVsDLHT8OboHPdysVuU44sB9vvbsfqW6uRmpOqNM9Xbb7CgDoDil2HIeT1LQA4ODhALOZi/rrAftWfzMxMxWYwfn5+5X5GhVGOG4IAHP0IuL5BaXJqpcawe/8kYG6pVbGZOTJM2nwdFyKV74rtWsESOye3hkclW5X/g1FJUVjw7wKEJ4QXWVfXGl3xWevP4GzlrFUbTQHPb/rBftUfYxs3iIjKI4PMEMvbqdHDwwONGjUyRJWkgkwmK+0m6FSFChVQp06dAgExBwcHva7DlkcqlSI26Bi8L8+FSJqmPJPfUIQ2/wbv/3oJMYmqF0GuV7UCVo9uipoVbYvVlrx1wn64/gOiX0cXmfenoJ/Qo1YP2EqKV5chSKVSPHjwAAC/JOoS+1V/jO3cmp/RvLaLP6kMhkUmm8Fy1K+wK0YwbPLWGyqDYS72ltj+Xit4VMo93775P2guMcf28O1YeWMlsuUqNmIBYCuxxSctP0E/r368zUoFnt/0g/2qP0ZzbiUiKscMEhD75JNPIBKJsHz5cgbESKckEgl27tyJwYMH4/Dhw6hZsybOnj0LDw8Pvdctir2NOlfmwUxVMKzBIOyr8SkWrglEllT17S8Dm1THNwP8YG1RvNsYIxIjsOzaMlx5fkVt3krWlTCz6UxYm/M2YCIyIaH7gYDPlSa9yhDDdvIxVK3jp1WRWVIZPvjzBv5RMfO3kp0FdrznDy8X5WuQxqXH4aurX6k9dzet3BTftPsGbvZuWrWPiIiIiIpmkICYvb09UlNTUatWLUNURybGwsICe/bswcyZM7FgwQLUrFlT/5Wu6wLJ0xsqk2X13sEX4unYuu+OyjzmYhE+61sfY1rVLNYv/qrWCVPGQmyBsb5jMdFvYpmeGUZEpHPRV4AD7ytNypKbASN3omq9VloVmS2V48NtQTh7T3kwzNnWAtvfa4Xale2VpgcmB+LPY3/idY7qnYbNxeaY1ngaxvmO47qPRERERHpgkICYm5sb7t69q1iDgEjXLC0tsXr1asNVmK1iVhiAzNq9MOrlJNyIeaYyj2sFS/w2qima1dR+HZi8dcLW3FpT5MVUnh41e2B2s9mcXUBEpic+CtgxHJBlKUkUwXLkVljW7aFVkTkyOT7cHoSA8BdK051sJNg2yR/eroWDYSnZKVj9ZDWuJBc9K6y2Y20sab8EdZ3ratU2IiIiItKcQQJi3bp1Q3h4OM6ePYvWrVsbokoyQrGxsahcuXLZWNBVpLwNCW5d8faDMYhLV76gPQC09HDGryOboLK9dotUC4KA8zHn8cP1H/A45bHa/PWc62Fei3loXqW5VvUQERmFtFeQbu4P84xE5elvLQXq9taqyByZHDN23MSpsDil6Q7WEvw5yR/1qlYolBYUF4R5/8xDXLryY/OMqT8GM5vOhKWZduuZEREREZF2DBJZGDt2LKpVq4YdO3bg8uXLhqiSjExERASaNWuGGTNmoExsjCqxKfTUY7OaaPtgLOLSVbdvUjsPbJvkr3UwLCIxApNPTcb0M9PVBsMqWVfCV22+wo7eOxgMIyLTlJOBtD/6wTxFxSYjraYCrZTfRqmKVCbHrF3B+Ds0Vml6BStzbJvkD99qDkrTzcRmeJWhfPF9AHC1ccW6Huswr8U8BsOIiIiIDMAgM8Ts7OywceNGzJgxA5MmTcLAgQPRp08f+Pj4wMHBgTsmUZHCw8PRpUsXxMbGYtWqVZBIJFixYkXpfm7eO43sjDQ8P/Y9zFOe4ESGN76KbgBAeZtsLMzw3aCG6NuomtZVRSZGYsjhIRqtE/au77uY5DeJ64QRkemSy5G0cRgcE0KVp9ftA/T4WqsiM3NkmLPnFo7efq403d7SHFsn+qNBdeXBMABo5NIIE3wnYF3oukJpvTx6YaH/QjhYqj6eiIiIiHTLIAGxevXqKR4LgoC9e/di7969Gh0rEokQFhamr6ZRGZeeno7u3bsjNva/X+R//PFHeHh4YMaMGaXYMkBkboE7ru9gyb1XeJIiVZnPo5It1oxppnQ9GU14OXqhVdVWuPTskso83Wt2x0fNPjKadcJEIhEkEoniMekG+5VMQdbR+XB8dl55YvXmwMB1gBaL1D94mYreP/+LjBzlP0rYWZpj88SWaOTuqLasSQ0m4fSD03iQ/gAAYG9hj0WtFuFtj7c1bg8px/ObfrBfiYjImBkkIPbmLW5l4pY3E5X3paa8sLGxwYoVKzBixAjI5bkXIy1atMCYMWNKuWXA62wB8wNe4XWm6mBY9/quWD60ESpYFb/fRSIR5jafi8GHB0MmyAqkGes6YRKJBA0bNiztZhgd9qv+lLdzqzbK1Wu7ug6WN9YqTZI71IB4xE7AovAt76ocuvUMC/bdVhkMs7Uww+YJLdC0hpNG5dlY2uDHHj9i6OGhaFS5Eb5u+zWq2FbRuD2kGs9v+sF+1Z9ydW4lIjJSBgmITZs2zRDVkJEaOnQopFIpxowZg5YtW+L48eNwcCj920qcbS0woa0Hfjp9v1CaSATM6eGDDzp6QSwu+S+qtZ1qY4j3EOy8txMAUNGqImY2nYl3vN6BmRYzHYiIjNa9v4G/5ylNEqwcIR6zH7Bz0aiozBwZFh8Jw7ZAFWuQARCLgI3jWyp2C877sU/dLBoPBw/82etP1HGqA7GKDVqIiIiISP8YEKNyYeTIkXBwcED79u1RoULh3bsMbcBvF3EzOklpmpONBD8Nb4IO3ppdeCVnJaOCRQW1F1FTG09FQHQA+nn1wyS/SbCzsNO22URExunZTWDvBEDJWouCmQVEw7cDlepoVNTDV2n4cFsQwp6nqMxja2GG9WNboKVHbjAsMTMRX13+Co0rN8ZY37Fq6/Bx9tGoLURERESkPzoPiB08eBAA0K1bN9jZ8YK9rJFKpbCwsCjtZhRL7969S7sJavlVd8Dvo5vCzUn9LTk5shzsvLcTv9/6HV+0/gI9avUoMr+TlRP+Hvg3rMy126GyPJJKpXj+PHfx6qpVq8Lc3CCxe6PHftUfqVT1rdPlXZkfN5Kige3DgJx0pcmi/r8DtdpqVNSR28/wyb4QpGapfj/rVrHHqlFN4eWS+x3nn5h/8NnFzxCfGY/zMefRulpreDt5Kz2W/4P6w77VD/ar/hjzuEFEVF7ofFT75JNPIBKJ0KBBA9SuXbtQekJCArZv3w6AM8dKQ1lev+3ixYuoUaMG3N3dS7spah2Y2hbZ2dkICQkBAJhVqoU9Qc+wqE99WEmKvoVREAT8+/RfLLu2DI9SHgEAVtxYgY7uHWFpZlnksaYQDAMAuVyOFy9eAABcXV1LuTXGg/2qP2X53FpSZfm1XTh1BP5hn0OSGqc8Q9fPAL/BasvJzJHhm6Ph2HrlcZH5JI6BiHE4jP7HpGhQqQF8K/pi171divQceQ4++fcT7Oy9ExZmhYOI/B/UH/atfrBf9acsn1uJiEyFwReviI+Px6+//opVq1YZumoqw86cOYMePXqgS5cuePr0aWk3R2v1q9rjmwF+aoNhkYmReD/gfXx4+kNFMAwAnqY+xdawrXpuJRGR8fj7yCHkbBsOSWKk8gxNxwLtPlJbzqNXaRj0+6Uig2FicQ6squ2EVdUDEIlzZ3VEJEYUCIbluZ94H7/e/FWzF0FEREREpYaruVKpO3nyJHr37o309HRERkaiS5cuiI2NLe1m6VRiZiK+ufINBh8ejEvPLinNs+72OrxMf2nglhERlT9/HTyIVxtGoHMtFT9CeHUFei/P3eGkCGfvvkCfXy7gzjPV64X5uNqjUdMTkDgEF3g+W5atun1Rf+F19usi6yYiIiKi0sWAGJUquVyOBQsWIDMzU/FcREQEli1bVoqt0p0ceQ7+DPsTvQ/0xs57OyETZCrzpkvTcTr6tAFbR0RU/kilUsRsm4YxDVWs+uDqBwzdDJhJ1JZVxcEKObLCC/HnGdbcHQc/bAsbG9UBszd1cOuAfe/sg72FvcbHEBEREZHhcWVMKlVisRhHjx5Fp06dcO/ePQDAkCFDsHTp0lJuWckoWyesKD5OPpjfcj5aVGmh/8YREZVj5iG78KGvitlX9tWAkbsAS82CUfWqVsAX7/hiwf6QAs9bS8zwzYAGGNjUDYIgICEzQW1Z1ubWmNtiLgbXGax212AiIiIiKn0MiFGpq1KlCs6cOYOOHTuiZcuW2Lx5c7nexSgqKQrLri3DxWcX1eZ1tnLGjCYz0L92f5iJi15/jIjI5D04BxyeoTzNwh4YtQdwqK5VkcNbuOPKg3j8FfwMAODtaoffRjVF7cr2SMlOweLLixHzOqbIMhpWaohv23+LmhVqalU3EREREZWe8ht1IKNSrVo1XLx4ERUrVoSZWfkMDCVlJuG3W79h973dRd4aCQASsQSj64/GZL/JsLOwM1ALiYjKsbgwYNcYQC4tnCYyy71NskoDrYsViUT4doAfQp4mo3lNJ3z5TgNYW5gh+EUw5v8zH8/Snqk81kxkhimNpuA9v/dgLuZXKiIiIqLyhN/eTExZvo2jcuXKpd0ErYjFYjg6OkIql2JnxE6sDlmt0SLK3Wp0w0fNPoJ7BXcDtLJ8yuvbvMekG+xX/SnL59aSKhOvLeU5sG0IkKViLa++PwK1uxa7eFtLcxyY2hYO1hLI5DKsvrUaq2+tLvLHDQszC2zquQl+Ln4a18P/Qf1h3+oH+1V/ysS5lYjIxOktILZ9+3Y4OzsXej4h4b91OH79VbNtyadNm6azdpm60rwVcdOmTRCJRBg7dmyptUFXRh0bhdsvb2t1jI+TD+a1mIeWVVvqqVXGw9zcHF5eXqXdDKPDftWf8nybtzql+dp+//13uDrZYWDyOiBFxW2L7ecATd9VmvR3yHM8SUzH5A7qP/cO1hLEpsViwb8LcD3uutr83k7eWgXDAP4P6hP7Vj/Yr/pjzOMGEVF5obcz8Y4dO1Sm5f0ismrVKo3KYkCs/Fu/fj0mT54MAJBIJBg5cmQpt8hwnK2cMb3JdAyoPYDrhBERaein4d74ePd9/DXcGvBWsWOk3xCgy/8KPZ0llWHJsbvYdOkRRCKgQTUHtKldqcj6zj85j4UXFiIlW7MdJcUizpYhIiIiKs/0EhATBEFnZXE6cfn3+++/Y+rUqYq/x4wZA4lEgiFDhpRiq/TPXGyOMfXG4L2G78HeQrMdz4iICPjhhx/wye77WNfXCr1VBcNqtgX6rQLe+J4QHZ+OaTuCcDsmGQAgCMCMncE4NrMdKttbqaxTJBJpHAwjIiIiovJP5wGxLVu26LpI0qGcnBxYWFgYtM5Hjx4V+FsulyMoKMioA2Jd3Lvg4+Yfo0aFGqXdlHIpJycHUVFRAAAvLy9IJCouiEkr7Ff9ycnJKe0m6I2hxw1BEPDsYQQOjbBGrzoqPqMV6wDD/gTMLQs8nZolRf/fLiIhLbvA869SszBrZzC2TvSHmVj5D20d3DpgdL3R+DP8zwLPm4nMUMm6EuLS44r/ov4f/wf1h32rH+xX/THmcYOIqLzQeUCsZUuuj0QFLV26FNnZ2fjxxx8BAJ9++ikWL15cuo3SE28nb8xrMQ/+Vf1LuynlmiAISEtLUzwm3WC/UnkgSn2B5Q3vQvRcxYW3rQswei9gU3idUjtLc7zf0RPfHrtbKO1SVDxOhcXirQZVVdY9u9lsXIu9hnuJ9wAA1e2qY2n7pWhcuXGxXsub+D+oP+xb/WC/EhGRMeNqjqR3IpEIK1asgFQqhYuLCz777LPSbpJeVLerjt19dnOdMCKi4noZAWwbBFFStPJ0kRgYsQtwqqWyiPfae+LqwwQEhL9QPGclEeOrfg2KDIYBuTtHft/xeww/Mhyd3DphUetFvOWdiIiIyEgxIEYGIRKJ8PPPPxv1mnAVrSsyGEZEVFyPLwM7hgOZSarzVKwNuDUrshiRSIQfhjRC758v4GlSBrxcbPHbqGbwqaJZYMvTwRN7++6Fu727UY9ZRERERKaOATHSGUEQIJVKVa4vYUwXFtt6bUN2djZCQkIAAH5+fgZfm42IqLxTjBsRR4D9UwBZVtEHWDloVK6jjQV+GdkE2wOj8eU7vrCQCPjxxo/wcvRCX6++ao/n+o9ERERExo8BMdIJQRCwcOFCBAcH4+DBg7C0tFR/EBERmSxBEDBjxgw0ybqM8dUiIYKK9Ykq1gZG7QWcPQAAmTkyWEnUz8ZtWsMJTWs44UnKE8wPmI+QVyGwMbdBI5dGDHgREREREcSl3QAq/wRBwJw5c7B06VIcP34cgwcPRnZ2tvoDiYjIJMnlckz9YAq87q/DhGr3VQfD3FsBE08pgmEn78Si3XdnEPwkSaN6DkcdxpAjQxDyKnc2b7o0HfP/mY8cGXd3IyIiIjJ1DIhRiS1cuBArVqxQ/H3kyBGMGDGCuxEREZFSc2dNQ9eEPzGrVRGzieu9A7x7ELBxRrZUjsVHwjB56w28Ss3Gh9uCkJyuOqiVmp2KBf8uwMILC5GWk1YgLTQ+FL8E/6KjV0JERERE5RUDYiZGLNb9W96/f3/Y2/+3WLFIJELfvn2Nas0wZczMzODm5gY3NzeYmXExfV1i3+oH+1V/9HFuLSt0/trSE/BZrRsYXF/5epMAgFZTgSGbAIk1YhLTMXTNZfxx4aEi+WlSBubsvaX0h5eQlyEYcngIjjw4orL47eHbEZcWV5JXUSz8H9Qf9q1+sF/1x5jHDSKi8oJriJkYfXyZ8ff3x99//42ePXsiIyMDW7ZswahRo3ReT1ljZmYGV1fX0m6GUWLf6gf7VX+M+UJRp68t4SGwbTAcUiJVZBABPb8BWn8IqUyO3YHR+O74XSRnFJ4NdiosDn9ceIhJ7T0BAHJBjg2hG7Dq5ipIBanKJtSqUAvfd/gerraG/1/g/6D+sG/1g/2qP8Y8bhARlRcMiJFOtG3bFseOHUNsbCyGDh1a2s0hIqKy5ukNYPswIO2l8nQzS2DgWgj1++FE6HPM3BmMLKm8yCLvxr6GIAh4mfESCy8sRODzwCLzD6ozCPNazIONxKa4r4KIiIiIjAQDYqQzHTp0KO0mEBFRWXTvOLB3PJCTrjzd2gkYvgNXZN5Y+tsltYvmW5qL8eU7vhjWwh3nY85j0cVFSMpSfYy9xB6ft/kcPWv1LP5rICIiIiKjwoCYicnJyYGFhUWxj33w4AF8fHx03KryKTs7GyEhuTuX+fn5FbtfqTD2rX6wX/UnJ8d4dy0sybiRmZmJ5DM/wvXad4CgYraXYw1E9diMr89IcfbeFbVlelayxapRTeFZ2RJLri7Bjrs7iszfpHITLG2/FNXsqhXnJegU/wf1h32rH+xX/THmcYOIqLxgQIw0kp2djeHDh+Ps2bM4c+YMmjRpUtpNIiKiMiwjPR0HZzbHiOpPVebJrtwQ3zh+gS1bn0OTjYnfaVQN3w70Q1zGY4w4Ohf3E++rzCsWiTGl4RRMbjgZ5mJ+3SEiIiKigvgNkdTKysrCkCFDcPjwYQBA9+7dcfbsWfj5+ZVyy4iIqCxKf52ES/ObYkT1eJV57ju0wZCnk5EUnam2PB9Xe8x7ywedfVyw9/5efH/te2TJslTmr2JbBUvbL0Uz12bFaj8RERERGT8GxEitJUuWKIJhABAfH4933nkH9+7d49R5IiIqKDMZMd+1RrfKqoNhe4WumB83DjIUvcuayDwJli4n8dThJmZdFVAhuAJSslOKPKZ7ze74vPXncLB0KFbziYiIiMg0MCBGas2bNw///PMPzp49CwCwtbXF5s2bGQwjIqKCkp8C24bA2/yZyizLcoZilawfAJHKPA7WEjhVDcQryX6IxFLF80UFw6zMrPBJy08wsM5AiESqyyYiIiIiAgBxaTeAyj4bGxscPnwY7du3h729PU6cOMEdJYmIqKC4O8D6bsCLO0qTcwQzfJT9PlbJ+kNVMMzSXIwPOnnhn3mdUd3tboFgGAA4WzkrPc7HyQe7+uzCIO9BDIYRERERkUY4Q4w0Ymtri6NHjyIqKgqNGzcu7eYQEVFZ8uAcsGsMkKV8BtdrwRof5MzCBfmba08KEJknw9wqDk28sjHFvzO6etZVWU01u2pwsHTAw+SHiudG1xuNWc1mwdLMUgcvhIiIiIhMBQNipDF7e3sGw4iIqKBbO4G/pgHyHKXJsYITxmXPxT0zR5hZ34PYMg5iyxcws4yD2OIFRGa5i+OHZwO3Ei3QFf4qqxKLxPi+w/cYeXQk7C3ssbjtYnRw44xlIiIiItIeA2ImxsxM9QLGiYmJ2LVrF6ZMmcJbTjRgbm4OHx8fxWPSHfatfrBf9aeoc2t5p/K1CQLSjn8F28AV/z0FINbMDJEWEjyQSHBD4ojzkmqQWWyEnZnqXSHzRCVFqc1T17kulnVchoaVGsLFxkXTl1Em8H9Qf9i3+sF+1R9jHjeIiMoLjmwmRixWvmxcfHw8evTogaCgIMTFxeHzzz83cMvKH7FYDDs7u9JuhlFi3+oH+1V/VJ1bjYGy1zbm6Aj0uXUONcQyRFSwR6SFBFESCaIsJEgvlP95EcvnF6RJQAwAutboqmGJZQv/B/WHfasf7Ff9MeZxg4iovGBAjPDy5Ut069YNt2/fBgB88cUXkEgkWLhwYSm3jIiIyqIht/7FO9npGFqtCsItdbfj8NPUp0jPSYeNxEZnZRIRERERKcOAGICnT59i69atOHfuHGJjY2FhYQF3d3e8/fbbGDVqFKytrXVSz/nz57F7926EhIQgISEBzs7O8PPzw9ChQ9GxY0ed1KGOXC5X2q68YFieVatW4YMPPoCTk5NB2lUeyeVypKenA8jdiZO/9OkO+1Y/2K/6o+zcaiwKvbbXceiT9RoQAZ45OToNiIkgwpPXT+Dj7KOzMssS/g/qD/tWP9iv+mPM4wYRUXlh8gGxM2fOYO7cuUhNTVU8l5GRgeTkZISGhmLPnj1Yu3YtatasWew65HI5Fi1ahL179xZ4Pi4uDnFxcQgICMCQIUPw1Vdf6f2LhkwmK/Tc4MGDsXr1arz//vsAAHd3d5w5c4bBMDWkUinu3bsHAPDz84OFhe4uCk0d+1Y/2K/6o+zcaiwKvbbMZIj//x7I2tnKF9JXRywSw83ODV6OXqjtWBuejp6o7VgbtSrUgpW5FQBgW69tJWl2mcT/Qf1h3+oH+1V/jHncICIqL0w6IBYWFobZs2cjMzMTNjY2mDJlCvz9/ZGZmYljx45h9+7dePToESZPnox9+/YVew2FlStXKoJh9evXx6RJk+Du7o4nT55g/fr1CAsLw549e+Ds7IyPPvpIly9RY1OmTIFUKsUPP/yAM2fOwMPDo1TaQUREZVylOnhoLoGHNAdeOUUHxMQiMdzt3eHpkBvw8nL0gpejV4HAFxERERFRaTDpgNg333yDzMxMmJubY8OGDWjSpIkirXXr1qhZsyaWLVuGR48eYePGjZg+fbrWdTx8+BAbNmwAADRo0ADbtm2DlVXuRUDDhg3RpUsXjB49GqGhofjjjz8waNCgEs1GK4kPP/wQ48aNg62tbanUT0RE5YBIhB9926P//cuwl/3/LT+CCPLsiqhoWQNdPBugRfX6qO1YGzUr1GTgi4iIiIjKJJNdCOD27du4fv06AGDQoEEFgmF5JkyYAC8vLwDAli1bkKPml3BlNm/eDKlUCgBYtGiRIhiWx9raGosWLQKQOy1906ZNWtehSwyGERGROq8sbTDD1QXjq7kiO7EF0qMnwtO+If4ZuxlftJ+L3p694ePsw2AYEREREZVZJhsQCwgIUDweNGiQ0jxisRj9+/cHAKSkpCAwMFCrOgRBwOnTpwEAnp6eaNy4sdJ8jRs3VtyiePr0aQiCoFU92hg/fjxevXqlt/KJiMi4TJw4EUlJSQWe29ZrG0LGhiBkbAjuTF+PyEUzcHjET6XTQCIiIiKiYjDZgNiNGzcA5O6Y4+vrqzJfixYtFI+DgoK0qiMmJgYvXrwoVI4yLVu2BJC70H5MTIxW9Wjj/Pnz6N69OxISEvRWBxERGY+zZ8/irbfeQkpKitJ0czMxRCKRgVtFRERERFQyJruGWFRUFACgRo0aMDdX3Q2enp6FjtFUZGSk0nLU1fPgwQO4u7trVVdR8s84q1ixIp49e4YhQ4Zg69atcHBwUKSZmZkpdrmUy+Vqd7+RSCSKxzKZTO320fnzS6XSImfCiUSiAu+LuttV87ddEATFbaqqmJubKy7gtH2teW3P36b09HTF39q2XSwWw8zMTOP8pvA+5S8n73Fx3yddtd0Y3idVn9mi2m6o/yd1bc9TVt+njIwMxfP6nOVrKG+OGw8fPsSIESOwcePGArfWl8XPeR6OG/8xhfeJ4wbHjTfbnqesvk/GNm4QEZVHJhkQy8rKQmJiIgCgSpUqReZ1cHCAjY0N0tPTERsbq1U9+fOrqyd/+vPnz7WqR538X0q2bt1aoB5d12WqtA2Wkubu3r1b2k0wSvzM6o9UKoWlpWVpN6NEVI0bT548KY3mGCX+D+oPxw394GdWf4xh3CAiKo9M8pbJtLQ0xWMbGxu1+a2trQHk/jKmr3ry6ihOPUREREREREREpDmTnSGWJ/80ZlUsLCwAAJmZmXqrJ6+O4tSjjrW1NerWrQug4BR1IiLSjfy3COX/gaO84rhBRKRfxjZuEBGVRyYZEMs/JVndOgEAkJ2dDQCwstJu+3ht6smrozj1qCMWiwus+UJERLpnTLe7cNwgItI/Yxo3iIjKI5O8ZTL/l3xNbk/MW/RSk9sri1tP/oU1ta2HiIiIiIiIiIg0Z5IBMUtLSzg6OgKA2oXyk5OTFcEsdQvjvyl/fnX15E+vWrWqVvUQEREREREREZHmTDIgBgC1a9cGAERHRxe5NfSDBw8Uj728vIpVx5vlqKvH09NTq3qIiIiIiIiIiEhzJhsQa9asGYDcWxnv3LmjMt+1a9cUj5s2bapVHW5ubqhcuXKhcoqqx9XVFW5ublrVQ0REREREREREmjPZgFi3bt0Uj/ft26c0j1wux8GDBwEAFSpUgL+/v1Z1iEQidO3aFUDuDLDg4GCl+YKDgxUzxLp27crdvIiIiIiIiIiI9MhkA2INGzZE8+bNAeQGxG7evFkoz4YNGxAVFQUAePfddyGRSAqkBwYGwsfHBz4+Pvjkk0+U1jN27FiYmZkBABYvXozMzMwC6ZmZmVi8eDGA3K3tx44dW7IXRkRERERERERERTLZgBgAfPrpp7CysoJUKsWECROwZs0aBAcH48qVK/jss8+wbNkyAECtWrUwfvz4YtXh4eGBiRMnAgBCQ0MxYsQIHDt2DCEhITh27BhGjBiB0NBQAMDEiRNRq1Ytnbw2IiIiIiIiIiJSzry0G1Ca6tevj5UrV2Lu3LlITU3FihUrCuWpVasW1q5dCzs7u2LXM3v2bMTHx2Pfvn0ICwvD7NmzC+UZPHgwZs2aVew6iIiIiIiIiIhIMyYdEAOALl264NChQ9iyZQvOnTuHuLg4SCQS1KhRA2+99RZGjx4Na2vrEtUhFovx7bffomfPnti1axdCQkKQmJgIJycn+Pn5YdiwYejYsaOOXhERERERERERERVFJAiCUNqNICIiIiIiIiIiMhSTXkOMiIiIiIiIiIhMDwNiRERERERERERkUhgQIyIiIiIiIiIik8KAGBERERERERERmRQGxIiIiIiIiIiIyKQwIEZERERERERERCaFATEiIiIiIiIiIjIpDIgREREREREREZFJMS/tBpDmnj59iq1bt+LcuXOIjY2FhYUF3N3d8fbbb2PUqFGwtrbWST3nz5/H7t27ERISgoSEBDg7O8PPzw9Dhw5Fx44ddVKHoeizzzIyMvDvv//i4sWLCA0NRXR0NNLT02FnZ4datWqhXbt2GD58OFxcXIosZ8yYMbh69apGdd67d6/Y7S0N+uz//fv3Y8GCBRrlXbJkCQYOHFjsugxJX30WExODrl27anVM9erVcebMmULPG9tnNj4+Hrdv38bt27cREhKCkJAQJCUlAQAGDBiApUuX6rzOI0eOYP/+/bh37x5SUlJQqVIlNGvWDKNGjUKTJk10Vg/HDe1x3ChdHDe0x3HD8Ix53CAiMiUMiJUTZ86cwdy5c5Gamqp4LiMjA8nJyQgNDcWePXuwdu1a1KxZs9h1yOVyLFq0CHv37i3wfFxcHOLi4hAQEIAhQ4bgq6++glhc9icX6rPP7t69ixEjRiA9Pb1QWlJSEoKDgxEcHIxNmzZh8eLF6NWrV4leS3lkiM+ssSlrfebh4WGQekpbmzZtDFZXZmYmZsyYgfPnzxd4/tmzZ3j27BmOHj2KDz/8ENOmTStxXRw3tMdxo3SVtXNgeVDW+ozjhu4ZctwgIjI1DIiVA2FhYZg9ezYyMzNhY2ODKVOmwN/fH5mZmTh27Bh2796NR48eYfLkydi3bx/s7OyKVc/KlSsVFzX169fHpEmT4O7ujidPnmD9+vUICwvDnj174OzsjI8++kiXL1Hn9N1nqampiouapk2bonPnzmjQoAEcHR2RkJCAkydPYs+ePUhNTcWcOXNga2urdpZEgwYNsGTJkmK/5rLEUJ/ZPH/88QcqV66sMr1KlSolKt8Q9N1nrq6uOHz4sNp8a9aswZEjRwAA/fv3LzKvMX1m81SrVg2enp64cOGCXspfuHCh4qLG398f7777LipXroyIiAisWbMG0dHR+OWXX+Di4oJhw4YVux6OG9rjuFG6OG5oj+NG2WAs4wYRkUkSqMwbOXKk4O3tLdSvX18ICgoqlL5u3TrB29tb8Pb2Fn7++edi1fHgwQOhfv36gre3tzBw4EAhIyOjQHp6erowcOBARTsePXpUrHoMRd99duPGDWHmzJnC/fv3VeY5deqU4OPjI3h7ewvdunUT5HK50nyjR48WvL29hdGjR2vdjrLKEJ/Zffv2Kcp48uRJSZtc6gzRZ+pIpVKhXbt2gre3t9CkSZNC54E8xvaZ/emnn4QzZ84IL1++FARBEJ48eaLo6/nz5+usnkuXLinKnTJliiCVSgukx8fHC506dRK8vb2F5s2bC0lJScWui+OG9jhulC6OG9rjuFF6jHHcICIyRWX//gUTd/v2bVy/fh0AMGjQIKVrBEyYMAFeXl4AgC1btiAnJ0frejZv3gypVAoAWLRoEaysrAqkW1tbY9GiRQAAqVSKTZs2aV2HoRiiz5o2bYoff/wRtWvXVpmnW7du6NGjBwAgOjoaYWFhWtVRXhnqM2tMykqfXbp0CS9evAAA9OzZs9B5wFjNmDEDnTt3RqVKlfRaz4YNGwAA5ubm+OKLL2BmZlYg3dnZGXPmzAEApKSkYM+ePcWqh+OG9jhulK6ycg4sT8pKn3HcMI5xg4jIVDEgVsYFBAQoHg8aNEhpHrFYrJiinpKSgsDAQK3qEAQBp0+fBgB4enqicePGSvM1btxYsTbE6dOnIQiCVvUYiiH6TFP+/v6Kx9HR0Xqpo6wpS/1fXpSVPjt48KDi8YABA3RevilLTU3F5cuXAQCtW7dWeTtW9+7dFbc15f9caIPjhvbKyv8gwHGjtPu/vCgrfcZxQ38MOW4QEZkqBsTKuBs3bgAAbGxs4OvrqzJfixYtFI+DgoK0qiMmJkbx617+cpRp2bIlgNwFk2NiYrSqx1AM0Weays7OVjx+81c9Y1WW+r+8KAt9lpqaqghwVK9eXe25gLQTEhKimJ2Rdx5VxsLCQhFcyn+MNjhuaK8s/A/m4bjBcUMTZaHPOG7olyHHDSIiU8VF9cu4qKgoAECNGjVgbq767fL09Cx0jKYiIyOVlqOungcPHsDd3V2rugzBEH2mqfxbjOfdtqDKgwcPMGTIEDx8+BBZWVlwcnKCr68vevbsid69e0MikeiljbpWGv2/YMECPHz4EElJSbC1tUXNmjXRunVrjBw5Eq6uriUq2xDKwmf2xIkTyMjIAAD069cPIpFI7THG8pk1hPzvl7rzrIeHBy5cuACpVIrHjx8XeYtdUXVx3NBcWfgfzMNxg+OGJsrCZ5bjhn4ZctwgIjJVnCFWhmVlZSExMRGA+t2OHBwcYGNjAwCIjY3Vqp78+dXVkz/9+fPnWtVjCIbqM03cvXtXsSuQt7e32gubV69e4fbt23j9+jWys7MRFxeHM2fOYP78+ejfv7/eLr50qbT6/+rVq3j58iVycnKQlJSEW7duYfXq1ejevTt27txZorL1rax8ZvPf9qJul7A8xvCZNZT875e6i+2qVasqHmt7nuW4ob2y8j8IcNzguKGZsvKZ5bihX4YaN4iITBlniJVhaWlpisd5X2aKYm1tjfT0dMW27vqox9raWvFY23oMwVB9pk52djY+/fRTyGQyAMDs2bNV5hWJRGjdujU6duyIunXrwtHREWlpabhz5w527dqFqKgoREZG4t1338WePXtQrVo1nbZVlwzd/+7u7ujevTuaNGmiuCiIiYnBiRMncOLECWRlZeHzzz+HSCQqs1uRl4XP7LNnz3Dt2jUAQJMmTVCzZs0i8xvTZ9ZQDHWe5bihvbLwPwhw3AA4bmiqLHxmOW7onzGdZ4mIyioGxMqwrKwsxWNNppBbWFgAADIzM/VWT14dxanHEAzVZ+p89dVXCA0NBZC7yGyXLl1U5v31119RoUKFQs83b94cI0eOxKJFi3DgwAG8evUK3377LX799VedtlWXDNn/3bt3x4ABAwrdotGwYUP06tULZ8+exfTp05GTk4MlS5agS5cucHFx0boefSsLn9lDhw4pFjvX5Fd+Y/rMGkr+9zn/eVSZkpxnOW5oryz8DwIcNwCOG5oqC59Zjhv6Z6hxg4jIlPGWyTLM0tJS8ViTBTLzFuLVdstrberJv9hvWdxa21B9VpQ1a9Yotr328/PDZ599VmR+ZV8Q80gkEnz99deKXdpOnTqFuLg4nbVV1wzZ//b29kWuV9K5c2dMnToVAJCRkYG9e/dqXYchlIXP7F9//QUg9wt1r1691OY3ps+soeR/n/OfR5UpyXmW44b2ysL/IMeNXBw3NFMWPrMcN/TPUOMGEZEpY0CsDLO1tVU81mT6c97CpppMny9uPXl1FKceQzBUn6myc+dOrFixAkDuAqhr164tcdnm5uYYPHiw4u/8Cy6XNaXd/28aNmyY4uIn79aOsqa0++z27dt48OABAKBLly5FXrRoqjx9Zg3FUOdZjhvaK+3/QY4bHDe0Vdp9xnHDMIzpPEtEVFYxIFaGWVpawtHREYD6hVCTk5MVg6W6BVbflD+/unryp+dfwLOsMFSfKXPkyBF8+eWXAHK3H9+4cSOcnZ1LXC6AArsFleVfTUuz/5WpWLGioj1ltd9Ku8+KsyiyJsrLZ9ZQ8r9f6voj/4LI2p5nOW5oj+NG6Srtc+CbOG6ox3HDMAw1bhARmTIGxMq4vC8H0dHRkEqlKvPl/VIHqN+mXVUdb5ajrh51W0CXFkP02ZtOnz6N+fPnQy6Xw8XFBZs2bdLpl3VNtjIvK0qj/4tSHvqutPosJycHR48eBZB7Edi+ffsSl5mnPPS7IeV/v9SdZx8+fAggd8aEuoWqleG4oT2OG6WL44b2OG4YP0OOG0REpooBsTKuWbNmAHKnSt+5c0dlvvzT+ps2bapVHW5ubqhcuXKhcoqqx9XVFW5ublrVYyiG6LP8Ll++jFmzZkEqlcLR0REbN25EjRo1il2eMpGRkYrHee9VWWXo/i9KQkKCYmv6stxvpdVn58+fR1JSEgCgT58+MDfX3T4r5ekzawh+fn6Kxa+LuhUoOzsbwcHBhY7RBscN7XHcKF0cN7THccP4GXLcICIyVQyIlXHdunVTPN63b5/SPHK5XDF9vUKFCvD399eqDpFIhK5duwLI/QUqb1B9U3BwsOIXqq5du5bZX/IM0Wd5goKCMHXqVGRnZ8Pe3h5//PEH6tSpU6yyVJFKpQVeR4sWLXRavq4Zsv/V2bVrl2IXrLLcb6XVZ/lvexkwYECJy8tT3j6zhmBnZ4fWrVsDyA2GqLrN6dSpU0hNTQVQ8HOhDY4b2uO4Ubo4bmiP44bxM+S4QURkqhgQK+MaNmyI5s2bA8j9wnPz5s1CeTZs2ICoqCgAwLvvvlvol6HAwED4+PjAx8cHn3zyidJ6xo4dCzMzMwDA4sWLC23ZnJmZicWLFwPInY49duzYkr0wPTJUn4WHh2PKlClIT0+HjY0N1qxZgwYNGmjV1itXriAlJUVlek5ODv73v/8p2tq5c+cyvzaEIfo/JiYGYWFhRbbj7NmzWLVqFYDcHZcGDRpUrNdjCIb6zOaXlJSEc+fOAQC8vb1Rr149jdpqjJ9ZXdi/f7+i/3/55ReleSZMmAAg98Lvyy+/hEwmK5CekJCAH374AUDuxeuQIUOK1RaOG9rjuFG6OG5oj+NG+VeWxg0iIlOlu3nOpDeffvopRowYgczMTEyYMAHvv/8+/P39kZmZiWPHjmHXrl0AgFq1amH8+PHFqsPDwwMTJ07E2rVrERoaihEjRuC9996Du7s7njx5gnXr1im+SE6cOBG1atXS1cvTC333WXR0NCZOnKj4gjdz5kzY29sjIiJC5TEVK1ZExYoVCzx38OBBfPDBB+jSpQv8/f3h4eEBOzs7pKWl4c6dO9i9e7fiFoKKFSvi008/1bqtpUHf/f/06VO8++67aNKkCTp37oy6desqFqJ+8uQJTpw4gRMnTih+5Z83bx5cXV119wL1wBD/5/kdPXoUOTk5ALT7ld8YP7PXr19HdHS04u+826UA4PHjx9i/f3+B/AMHDixWPa1bt0bv3r1x9OhRnDlzBuPHj8fYsWNRuXJlREREYPXq1Xj27BkAYM6cOXBwcChWPQDHjeLguFG6OG5oj+NG6THGcYOIyBQxIFYO1K9fHytXrsTcuXORmpqq2J49v1q1amHt2rWws7Mrdj2zZ89GfHw89u3bh7CwMMyePbtQnsGDB2PWrFnFrsNQ9N1n169fR3x8vOLvJUuWqD1m2rRpmD59eqHn09PTceTIERw5ckTlsd7e3li5ciXc3d21bmtpMNRn9ubNm0p/Fc9jbW2NBQsWYNiwYcWuw1AM1Wd5/vrrLwCAmZkZ+vbtq9WxxvaZ3bt3Lw4cOKA0LSgoCEFBQQWeK+6FDQB8++23SE1Nxfnz5xEYGIjAwMAC6WKxGFOnTi3xZ5bjhvY4bpQujhva47hReoxx3CAiMkUMiJUTXbp0waFDh7BlyxacO3cOcXFxkEgkqFGjBt566y2MHj0a1tbWJapDLBbj22+/Rc+ePbFr1y6EhIQgMTERTk5O8PPzw7Bhw9CxY0cdvSL9M0SfldR7772HunXrIjg4GFFRUUhISEBycjIkEgkqVaqEBg0aoGfPnujevbvi1qTyQp/97+vri2XLliE4OBihoaF4+fIlEhMTIZVK4eDggNq1a6N169YYMmRIodkVZZmhPrOPHj3CrVu3AABt2rSBi4uLxsca82fWEKysrLB27VocPnwYBw4cwN27d5GSkoJKlSqhWbNmGD16NJo0aaKTujhuaI/jRuniuKE9jhvGz5DjBhGRqREJeXPDiYiIiIiIiIiITAAX1SciIiIiIiIiIpPCgBgREREREREREZkUBsSIiIiIiIiIiMikMCBGREREREREREQmhQExIiIiIiIiIiIyKQyIERERERERERGRSWFAjIiIiIiIiIiITAoDYkREREREREREZFIYECMiIiIiIiIiIpPCgBgREREREREREZkUBsSIiIiIiIiIiMikMCBGREREREREREQmhQExIiIiIiIiIiIyKQyIERERERERERGRSWFAjIiIiIiIiIiITAoDYkREREREREREZFIYECMig9q/fz98fHzg4+ODmJgYnZUbGBioKDcwMFBn5RIRUeniuEFERET6YF7aDSAytMDAQLz77ruKv21sbHDp0iVYW1sXeVxmZibatm2L1NRUxXNbtmyBv7+/3tpKRESlj+MGERERkfHhDDEyeenp6QgICFCb7/Tp0wUuaoiIyDRx3CAiIiIq/xgQI5NmaWkJAPjrr7/U5s3Lk3cMFc/AgQNx79493Lt3D25ubqXdHCIirXDcMDyOG0RERKQPDIiRSevSpQsA4NKlS3j58qXKfPHx8bh48SIAoGvXrgZpGxERlT0cN4iIiIiMAwNiZNLatm0LFxcXyGQyHD16VGW+I0eOQCqVwsXFBW3atDFgC4mIqCzhuEFERERkHLioPpk0MzMz9O7dG5s2bcJff/2FcePGKc2Xd9tLnz59YGZmpnH5V65cwYEDB3D9+nW8evUKZmZmqF69Otq1a4dx48bB1dVV5bEREREICAjAjRs3cP/+fSQkJEAikcDFxQVNmjTBiBEj0LhxY5XH//LLL/j1118BAPfu3UNWVha2bt2Ko0eP4tGjRwAALy8v9O/fH8OHD4e5efFPBz4+PgCAadOmYfr06bh8+TJ27NiBW7du4dWrV3B1dcWZM2cA5O4WtmDBAgC56+sou/0lNDQU27dvx40bNxAXFweZTAZnZ2dUrFgRjRo1Qrt27dClSxeIRCKt2pmcnIzJkycjODgY5ubmWLJkCd555x2tykhJScG2bdtw7tw5PHjwAOnp6bC3t4ezszM8PDzQtm1b9OjRA5UqVVJ6fHZ2Ng4cOIDTp08jPDwciYmJMDc3R/Xq1dG4cWO89dZbaNeundLXlpaWhm3btuH06dN4+PAh0tPT4ezsjMaNG2PAgAHo3LmzynaPGTMGV69eRcuWLbF161Y8evQIW7ZswYULFxAXF4fMzMxC70dWVhb27NmDU6dOITIyEsnJybC3t4ePjw969+6NAQMGlOhzQ1QecdzguMFxg+MGEREZB45IZPL69euHTZs2ISwsDPfv30edOnUKpEdGRuLOnTuKvOHh4WrLzMrKwoIFC5TOHoiIiEBERAR27tyJ5cuXK26/ye/NHc3y5OTk4PHjx3j8+DEOHjyIyZMn4+OPP1bbnlevXmHSpEmF2h4SEoKQkBBcuHABv/32G8Tikk8aXblyJVavXl3s4zdt2oTvvvsOcrm8wPOxsbGIjY3FnTt3sH37dgQFBcHW1lbjcuPi4jBp0iRERETAysoKP/30Ezp16qRV26KiojBu3Di8ePGiwPOJiYlITExEVFQUAgICIJfLMXr06ELHh4eHY9q0aYiJiSnwfE5ODiIjIxEZGYm9e/cqveALCwvDlClTCtUdFxeHEydO4MSJE+jRowd++OEHtesVBQQEYO7cuUhPT1eZ5+7du5g6dSqePn1a4PmEhARcvnwZly9fxq5du7B69WqVF3FExorjBscNTXHc4LhBRERlFwNiZPLq16+POnXq4P79+/jrr78wZ86cAul5v/J7e3ujXr16ai9sBEHAjBkzcO7cOQBA586d8fbbb8Pd3R1isRi3b9/Gxo0b8ezZM8yYMQM7duyAn59fgTJkMhlsbGzQsWNHtGrVCp6enrCzs0N8fDwiIyOxdetWPH36FGvXrkWtWrUwaNCgIts0bdo0REZGYsyYMejSpQscHBzw8OFD/Pbbb4iKisLZs2exe/duDB8+XMveK+jkyZOIiIiAt7c3xo0bhzp16iArK0uji0Eg98t03kWNm5sbRo8ejbp168LR0RFpaWl4+PAhrly5opg1oKno6GiMHz8eMTExsLOzw+rVq9GiRQutX9/cuXPx4sULSCQSDBkyBB06dEClSpUgCAJiY2MRHByscue5qKgojBw5UnEx0b17d/Tq1Qvu7u6Qy+V4+PAhLl68qPT4uLg4jBs3DsnJyRCJRBgwYAB69+4NR0dHREZGYuPGjbh79y5OnjyJTz75BCtXrlT5Gp49e4a5c+fCysoKH3zwAZo3bw4zMzOEhITAxsYGAPD48WOMHj0ar1+/hp2dHUaNGoWGDRuiSpUqSEpKwpkzZ7Br1y6EhIRg6tSp2LZtGyQSidb9SVRecdzguKEpjhscN4iIqAwTiEzMlStXBG9vb8Hb21vYt2+fIAiCsHbtWsHb21vo2LGjIJfLFXnlcrnQsWNHwdvbW1i3bp0gCIKwb98+xfFXrlwpVP6uXbsEb29vwdfXVzh//rzSNiQlJQm9e/cWvL29heHDhxdKj4+PF5KTk1W+hqysLGH8+PGCt7e30LlzZ0EqlRbK8/PPPyva6evrq7StiYmJQps2bQRvb2+hb9++KutTJ68eb29vYezYsUJWVpbKvPn778mTJwXSfvzxR8Hb21to3Lix8PLlS5VlpKSkCDKZrMBz+d/X/K/17t27Qtu2bQVvb2+hVatWQmhoaLFeY3R0tKL8rVu3qswnl8uFpKSkQs8PGDBA8Pb2FurWrSscOXJE5fEJCQlCRkZGgeemT5+uqHv37t2FjsnKyhLGjBmjyHPu3LlCeUaPHq1Ib9eunfD06VOVbRg2bJjg7e0t9O/fX4iPj1ea5/z580LdunUFb29vYdeuXSrLIjIGHDf+w3FDcxw3CuK4QUREZQ0X1ScC8M4770AsFuP58+cIDAxUPB8YGIjnz59DLBajb9++assRBAHr1q0DkLv2RocOHZTmc3BwwNy5cwEAQUFBirVZ8jg7O6NChQoq67GwsMC8efMAAE+fPlX7S/ro0aPh7+9f6HlHR0cMHDgQQO4tOa9fvy6yHHXEYjG+/vprWFhYFOv4V69eAQBq1apV5O0U9vb2Gt2mExQUhDFjxuDly5eoWrUqtm3bBl9f32K1Lf9ucs2bN1eZTyQSwcHBocBzFy5cUNw+NWbMGPTu3Vvl8U5OTrCyslL8HRcXp/j1v3379hgyZEihYywsLPDtt98q1mXZtm1bka/l448/RrVq1ZSmXb9+HTdv3gQALF26FM7OzkrzdejQAT179gSQu74PkanhuMFxQx2OGwVx3CAiorKGATEiAK6uroov/nm3uuR/3KpVqyIXMs4TGRmJ6OhoAFB86VMl/60XwcHBRebNzs7Gs2fPEBkZqVhLRhAERfrdu3eLPL6oi7K8L/qCIBRao0RbTZs2VbrYsaZcXFwA5Pbj7du3S9SWf//9FxMmTEBycjI8PDywY8cOeHp6lrhtAHDgwAGtjs27DQoAxo4dq9WxV69ehUwmAwAMHjxYZT43NzfFTnb5j3mTRCLB22+/rbKc06dPAwA8PDwUi16rkvcZDg0NhVQqLTIvkbHhuMFxQ9O2ARw38nDcICKisoRriBH9v/79++Py5cs4efIkPv/8cwDAiRMnAOQuiqyJ0NBQxeNhw4ZpXHf+X5HzpKenK3b3ioyMVPlFFchdnLcoRX2hz/+rdFpamgatVU3dF2F1evfujbVr1yI7OxsjRoxA+/bt0bFjRzRr1gx16tTReHewEydOYPfu3cjJyYGvry/Wr1+v8hdrTbm7u6N58+a4fv06Nm3ahAsXLqBHjx5o2bIlGjduDGtra5XHhoWFAQCqVauG6tWra1Xv/fv3FY8bNWpUZN5GjRrhn3/+QUZGBp48eYJatWoVylOrVq0iF0/O+ww/fPhQ4/czJycHycnJqFixokb5iYwFxw2OG0XhuKEaxw0iIioLGBAj+n/du3fHF198gdTUVJw+fRqCICAtLQ02Njbo0aOHRmXEx8cXq+7MzMwCf8fExGDs2LEa//KelZVVZHpRX7rz30JS1MWTJoq6XUcTXl5eWL58ORYtWoTk5GScPXsWZ8+eBZB7S0j79u0xbNiwIm89Af679cPCwgKrVq0q8UVNnhUrVmDmzJm4efOmYnev3377DRKJBI0aNUKfPn0wcODAQhcOeRee+WcLaCopKUnxWN2FQ/7bhZKTk5XmUfceJSQkaN64fDIyMop1HFF5xnGD44Y6HDdU47hBRESljQExov9na2uLbt264fDhw/jrr78Ut5Z069ZNsYuSOvm3fF+9erXGv+q++YV13rx5iImJgUgkwsCBA9G7d294eXnB2dkZEokEIpEIcrkc9erVA4ACt8GUJjMzsxKX0bNnT7Rp0wbHjh3DhQsXcP36dSQkJCAxMRGHDh3CoUOHMGDAAHz77bcq14Pp0aMHTp48iezsbMyePRvr16+HnZ1didvm6uqKnTt3KmaEXLt2DZGRkcjJycH169dx/fp1bNiwAWvXroWHh0eJ69MHde9R3sVt3bp1sWzZMo3L1eTWMCJjw3Gj5DhucNwgIiIqLQyIEeXTv39/HD58GBcvXizwnKYcHR0Vj+3t7eHt7a11G6KionDjxg0AwJQpUzB79myl+fL/Amxs7O3tMWzYMMXtQ1FRUTh9+jS2bt2KFy9e4MCBA6hXr57KdVVGjx6NRo0aYdmyZbh58yYmT56MdevWwdbWVifta926NVq3bg0g91f8y5cvY9euXbhy5Qqio6Mxe/ZsHDx4UJHfyckJgPJbnNTJ/5mKj49H1apVVebNW1waQKEFmrWtLz09vVifXyJTw3GjbOC48R+OG0RERJrhovpE+bRu3RouLi6QSqWQSqWoXLmy4gusJvJ+eQdyd6oqjsjISMXjXr16qcyXf90ZY+fl5YXJkydj9+7dilkXf//9d5HHTJo0CR999BEA4MaNG5gyZYpebs9wcnJCr169sHnzZnTp0gUAEB4eXmAHuPr16wMAnj17hqdPn2pVfp06dRSPb926VWTevAWlra2t4e7urlU9efLa+uTJk2JdiBGZGo4bZRPHjVwcN4iIiFRjQIwoHzMzM/Tr1w8WFhawsLBAv379NNqmPY+vry+qVKkCANi9e7faNVqUyb8eS1FfxHfu3Kl12eVd1apVFQv+qlsQGsidKTFz5kwAwLVr1zBlypRC6+7oUv6L4Pzty7vgAYBNmzZpVWbLli0Vt6vs27dPZb5nz57h0qVLhY7RVl5bBUHAli1bilUGkSnhuFG2cdzguEFERKQKA2JEb5g7dy5CQkIQEhKCOXPmaHWsWCzGlClTAOT+Ujpv3jxkZ2erzJ+amoo///yzwHM1a9ZUPN6/f7/S47Zv367Y5tyYBAQEICUlRWX68+fP8eDBAwC528VrYurUqZg2bRoAIDAwEO+//36xLjjDw8MRHh6uMl0QBMWFhUgkKrAOUJs2beDr6wsA+PPPP3H06FGV5SQmJha4+HJ1dUW3bt0AAP/88w8OHDhQ6Jjs7GwsXLgQOTk5AIBRo0Zp8coKateuHRo2bAgA+OOPP3Ds2LEi89+7dw9nzpwpdn1ExoDjRunhuMFxg4iIqLi4hhiRjo0YMQKXLl3CqVOncPz4cYSFhWHYsGFo2LAh7O3tkZqaigcPHuDq1as4c+YMLCwsMHr0aMXx9evXh7e3NyIiIrBr1y6kpKSgX79+cHFxQWxsLA4dOoQTJ06gadOmxb69pqzavHkz5syZg44dO6JVq1bw8vKCvb09kpOTERoaij///FPxpX/EiBEalzt9+nTIZDL8/vvvuHz5MqZOnYrff/8dFhYWGpcRHh6OBQsWwM/PD507d4avry8qVaoEqVSKmJgY7N+/X7GGUJcuXVC5cuUCxy9btgyDBw9Geno6PvroIxw/fhy9evWCu7s75HI5Hj9+jIsXL+LEiRM4fPhwgQu3hQsX4sqVK0hOTsbChQtx48YN9OrVCxUqVMCDBw+wYcMGxUXX22+/jY4dO2r8upRZvnw5hgwZgqSkJMyePRuHDh1Cr169UKtWLYjFYsTHxyM8PBxnz55FcHAwJkyYUGA2AxFph+NG8XHc4LhBRERUXAyIEemYSCTCypUr8c0332Dnzp2Ijo4uctelN7d2F4lE+P777zF27FgkJyfj77//LrTuibe3N3766Se0b99eL6+hNGVkZOD48eM4fvy40nSxWIzp06crfv3W1KxZsyCTybB27VpcuHAB06ZNw6+//qrVxQ0AxSwQVZo0aYJvvvmm0PNeXl7YunUrpk2bhufPn+PkyZM4efKkRnVWqVIFmzZtwpQpU/DixQvs2bMHe/bsKZSvR48e+O677zR/MSrUqFEDO3fuxIwZMxAREYGzZ8/i7NmzKvPratFpIlPFcaNkOG4UxnGDiIhIPQbEiPRAIpHgiy++wIgRI7Bnzx4EBgbi+fPnSE9Ph42NDdzc3ODr64sOHTqgc+fOhY6vV68eDh48iDVr1uDff//FixcvYGtrixo1auDtt9/GqFGjYGlpWQqvTL+WL1+Oc+fOITAwEFFRUXj16hUSExNhYWGB6tWro3nz5hg+fDjq1q1brPI//vhjyGQy/PHHHzh//jxmzJiBn3/+WaOLmz59+qBixYq4dOkSQkJCEBcXh/j4eEilUlSsWBH169dHr1690Lt3b5XrBzVo0ADHjx/Hnj17EBAQgPv37yM5ORkWFhZwc3NDkyZN8Pbbbyu9rad+/fo4fvw4tm3bhoCAADx8+BAZGRlwcnJC48aNMWDAAJ3+2u7h4YGDBw/i77//xsmTJxESEoKEhATIZDI4OjrCw8MDzZo1Q/fu3RW39RBR8XHcKB6OGxw3iIiIikskCIJQ2o0gIiIiIiIiIiIyFC6qT0REREREREREJoUBMSIiIiIiIiIiMikMiBERERERERERkUlhQIyIiIiIiIiIiEwKA2JERERERERERGRSGBAjIiIiIiIiIiKTwoAYERERERERERGZFAbEiIiIiIiIiIjIpDAgRkREREREREREJoUBMSIiIiIiIiIiMikMiBERERERERERkUlhQIyIiIiIiIiIiEwKA2JERERERERERGRSGBAjIiIiIiIiIiKTwoAYERERERERERGZFAbEiIiIiIiIiIjIpDAgRkREREREREREJoUBMSIiIiIiIiIiMikMiBERERERERERkUlhQIyIiIiIiIiIiEwKA2JERERERERERGRSGBAjIiIiIiIiIiKTwoAYERERERERERGZFAbEiIiIiIiIiIjIpDAgRkREREREREREJuX/AOoZfATnEnBkAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.calibration import CalibrationDisplay\n",
+    "\n",
+    "# Get results for the current task\n",
+    "task_df = all_results_df[all_results_df[task_col] == TASK]\n",
+    "\n",
+    "# Get all base/instruct model pairs\n",
+    "base_models_to_plot = [\n",
+    "    \"Meta-Llama-3-8B\",\n",
+    "    \"Meta-Llama-3-70B\",\n",
+    "    \"Mistral-7B-v0.1\",\n",
+    "    \"Mixtral-8x7B-v0.1\",\n",
+    "    \"Mixtral-8x22B-v0.1\",\n",
+    "    \"Yi-34B\",\n",
+    "]\n",
+    "# NOTE: Gemma models have degenerate distributions that always predict the same score, can't plot calibration properly...\n",
+    "\n",
+    "instr_models_to_plot = [\n",
+    "    df[\n",
+    "        (df[\"base_name\"] == m)\n",
+    "        & (df[model_col] != m)\n",
+    "    ][model_col].iloc[0]\n",
+    "    for m in base_models_to_plot\n",
+    "]\n",
+    "\n",
+    "# Add GPT models if any\n",
+    "instr_models_to_plot += [m[model_col] for _, m in df.iterrows() if \"gpt\" in m[\"name\"].lower()]\n",
+    "base_models_to_plot += [None for _ in range(len(instr_models_to_plot) - len(base_models_to_plot))]\n",
+    "\n",
+    "base_it_pairs = list(zip(base_models_to_plot, instr_models_to_plot))\n",
+    "\n",
+    "for base_model, it_model in tqdm(base_it_pairs):\n",
+    "    fig, axes = plt.subplots(ncols=2, figsize=(4.8, 2.2), gridspec_kw=dict(wspace=0.15), sharey=True)\n",
+    "\n",
+    "    for idx, ax, m in zip(range(len(axes)), axes, (base_model, it_model)):\n",
+    "        if m is None:\n",
+    "            ax.remove()\n",
+    "            continue\n",
+    "\n",
+    "        curr_row = task_df[(task_df[model_col] == m) & (task_df[\"uses_all_features\"])].iloc[0]\n",
+    "        curr_id = curr_row.name\n",
+    "\n",
+    "        # # Original sklearn calibration plot:\n",
+    "        # plot_cali_curve(curr_id, ax=ax, df=task_df)\n",
+    "\n",
+    "        # # Plot with confidence intervals:\n",
+    "        plot_cali_curve_ci(curr_id, ax=ax, df=task_df)\n",
+    "        \n",
+    "        ax.set_xlim(0, 1)\n",
+    "        ax.set_ylim(0, 1)\n",
+    "\n",
+    "        ax.set_xlabel(\"Mean risk score\")\n",
+    "        if idx == 0:\n",
+    "            ax.set_ylabel(\"Fraction of positives\")\n",
+    "\n",
+    "        # Set prettier x-ticks\n",
+    "        xticks = (0, 0.25, 0.5, 0.75, 1)\n",
+    "        xticks_labels = (\"0.0\", \"0.25\", \"0.5\", \"0.75\", \"1.0\")\n",
+    "        ax.set_xticks(xticks, labels=xticks_labels)\n",
+    "\n",
+    "    # Prepare legend\n",
+    "    axes[0].legend().remove()\n",
+    "    if NUMERIC_PROMPTING:\n",
+    "        axes[1].get_legend().set(loc=\"upper left\", bbox_to_anchor=(1.03, 1))\n",
+    "    else:\n",
+    "        axes[1].legend().remove()\n",
+    "\n",
+    "    # Figure title\n",
+    "    if NUMERIC_PROMPTING:\n",
+    "        title = \"Numeric Prompting\"\n",
+    "    else:\n",
+    "        title = \"Multiple-choice Prompting\"\n",
+    "\n",
+    "    # Don't use suptitle for GPT4 (no base model)\n",
+    "    if base_model is not None:\n",
+    "        plt.suptitle(title, y=1.1, fontsize=14)\n",
+    "    else:\n",
+    "        model_title = axes[1].get_title()\n",
+    "        plt.title(f\"{title}\\n{model_title}\")\n",
+    "    \n",
+    "    plt.plot()\n",
+    "    save_fig(fig, f\"calibration-curves-per-subgroup.{base_model}.smaller.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79d97774-23e4-42e9-a52c-8d7d5504eac7",
+   "metadata": {},
+   "source": [
+    "## Score distribution per sub-group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "47bca57c-3e4b-496a-8f1b-880b6ded9639",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    2\n",
+       "2    1\n",
+       "8    1\n",
+       "Name: RAC1P, dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sens_attr_data = acs_dataset.data[sens_attr_col]\n",
+    "sens_attr_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "d768a5b5-7816-41e8-a4da-763986ed994a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad627657fb0044bb9f3620c89dc7d648",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/score-per-subgroup.Meta-Llama-3-8B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Meta-Llama-3-70B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Mistral-7B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Mixtral-8x7B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Mixtral-8x22B-v0.1.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.Yi-34B.numeric-prompt.pdf'\n",
+      "Saved figure to '../results/imgs/score-per-subgroup.None.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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FcW/Qra6u1ubNm1VaWqrq6mpNnjxZmZmZUX3PTZs26a677gq4rqysTGvWrNGaNWs0d+5cPfbYY/rud78bdFtPPvmk/vrXv8rr9ZrPlZaWatWqVVq1apWWLVumP/3pT0pJSYn4fgAAAMD64lEfBgAAgHXFrUH30KFD+v3vf6933nlHbrfbfH7o0KHq16+f+XjRokV6+eWX1a5dO82bNy9iM/t2795dY8eO1ZAhQ9S9e3d16dJFXq9Xhw8f1vLly/Xee+/p6NGj+slPfqJXX31VAwcObLSNf/zjH/rzn/8sSTr99NP14x//WP3791dJSYkWLlyo1atX66OPPtIvf/lLPf744xGJOxRmPU5cdrtdWVlZ5jISF7kCIotzyhqoQ8RHvOvDrRF/m8TF9cA6yJU1kCfr8L02cZ1KYBZLTVwadL/44gvdfPPN+uabb2QYhvl8oAN74sSJevDBB+V2u7Vq1aqQvWXDNXbsWH300UdB11944YV6//33ddttt8nlcunPf/6z2XBbr7y8XL/73e8kSaeddppefvllZWdnm+snTJig2267TR9++KHeeOMNXXnllRo7dmyLYw8lKSnuHa4RRFJSkvLy8uIdBsJArqzB92rBZ19i45yyBs6j2It3fbi14lhOXFwPrINcWQN5sg6b7WSDO9epxGW1H0ZiHu0333yjW2+9VceOHVPnzp31wAMPaNmyZUHLd+rUSeecc44kaeXKlRGJweFwNFlm8uTJ6tu3ryRp3bp1jdYvWrRIx48flyT9/Oc/92vMrX+PX//61+Z7zZ07t6VhAwAAoBVIhPowAAAArCvmDboLFy7UkSNH1LFjR7388suaMWOGzjjjjJCvOfvss2UYhjZt2hSjKOtkZGRIkmpqahqtW7FihSQpMzNT5557bsDXd+vWTePGjZMkffrppzpx4kSUIgUAAIBVWKk+DAAAgMQT877eH374oWw2m66//nqddtppYb2mfgyxAwcORDM0P3v27NH27dslSbm5uX7ramtrzcr08OHDlZycHHQ7Y8aM0apVq1RbW6vNmzfrrLPOilrMLpcrZCyIH5fLpd27d0uS8vLy5HQ64xwRgiFX1mD4LLvdbm5dSmDxOKeWbzmsIydqo7b9TpnJmjKkW9S2Hw8ulyveIbQpVqkPWxH14cRFHcs6yJU1kCfrMAyPucx3l8Tl9XjjHUKzxPwoqq+Ejho1KuzXdOjQQZKi3sO1qqpKxcXF+vDDD/Xss8+ak1Ncd911fuX27dsnj6fuhGzY2NuQ7/rdu3dHvUG3tjbwF9ikpCRzPBCv1+s38UYgvhVhj8dj7m8gNpvN7+Lhdrvl9QY/Eex2u98HmMvl8hs7LlTshmE0+aXT6XSa4881ta/Rjt3hcMjhcMgwDFVUVEiq+0Eg2GvIU3xir89TfexN5Yo8hRe7UfcmIbcv37EimyrbsPx/eDyegH8j8hSf2H3PJ6nuPAp1TkUqT7779fU3VTp8rFo2m2S3n4zF6/WGjN1ms/mNneX1egIell5v42PD6nmiQTe2Erk+DESLbx0r1OcT4o9cWQN5sg7f9JArRErMG3Trhy9ozi8SlZWVkqTU1NSIx7N48WLdd999QdfffPPNuuSSS/yeO3z4sLncrVvoHjq+631fFw31v84FMmDAAGVmZkqq+3vu2LEj5LZGjhxpLpeWlqqoqCho2YyMDA0cONB8vH//fpWXlwctn5OTo169epmPt23bFvKLZG5urjp27Cip7gtnQUFByNjz8/PNL/zHjh3Tnj17gpZ1Op0aNmyY+fjQoUMqKSkJWj4rK8tv4Pndu3ebF9FAevbsqa5du/o9V9/zOxDyFFg88iQFzxV5CqxhnryGEfRHJuk/DVA+DVYut1tGiAarpKQk2QOMgV5aWhrw85U8BRbr88n3PAp0TkUqT+np6Ro0aJAk6WhZmUqOHJfTmazOnTub5Y8dK1d1dXXQbWdkZKh9+w7m46+/Lg3YeJviaSepr99zVs9TqDoEIi/R6sMAAACwlpg36Hbq1EmHDx9WUVGR3xeVULZt2yap7gtxrAwaNEgPPvhgwBh9vyClp6eH3I7v+vqKOAAAANquRKkPHzlyRJs2bdKmTZtUUFCggoIC88epSy+9VI8++miT22iqc4SvRx55RNOnT29JyAAAAFAcGnSHDRumw4cP61//+pcuvPDCJssbhqFFixbJZrM167a0cE2ePFlDhw6VJFVXV6uwsFBvv/223nvvPd1999365S9/qQkTJvi9xneStKbGqfG9NTRUr6BIyMvLC9rA7NsDJD09Xfn5+WFvt3PnzmZPsUBsDW6B7t27t19PtIZ8b2eV6hrPm7r1uJ7T6Wwydt+cdOjQIWT5hrF37949YE/Neg1jz8vLa/KW1oYGDhwY9LghT4HFI09S8FyRp8Aaxm632Zo1jqHzFMeS6ty5s7p06dLoefIUWKzPp4EDB5o9cwOdU5HKk+9+dczOVo0jvdEIHR06ZKl9+9BDLvjq0qVzwCEXOnZo3EPS6nnKy8ujl24MJUp9+Oyzz47YtgAAABA7MW/QveSSS7R8+XItW7ZMM2fONG+PDObRRx/V9u3bZbPZNG3atIjH0759e7Vv3958PGzYMF100UVasmSJ7r33Xt166636n//5H7/eBCkpKeZyU2PO+d5uHO1b5JxOZ1iNJ3a7vVmNLA3H2WtKcwf4bs7g7bZmNhA1d1+jGbvva8iTv0TMU/3rmoqLPIXYft2bhF2+WWV9OByOJv9O5Cm4aJ9PvuWbOqcilSe7Pdjz9kbPheI7/m5Tz7emPCH6Eq0+LEmnnXaacnNztWrVqlPexty5c0P2IG5qqDIAAACEJ+YNupMnT9bYsWO1evVq/fCHP9RPf/pTTZkyxVzvdrtVXFys9evX6/nnn9eGDRtks9l07rnn6swzz4xZnNOmTdNHH32kt99+W3PmzNHEiROVlZUlqW6MvXpNDaPgu76p4RkAAADQ+iVKffi2225Tfn6+8vPz1blzZxUVFWnSpEmnvL0+ffqoZ8+eEYsPAAAAgcW8QVeSnnzySf3whz/U1q1bNWfOHM2ZM8e8tfDSSy/1K2sYhoYPHx7WGF6RNmnSJL399tuqrKzUv/71L3NytOZMdNacCdQAAADQNiRCffjOO++M6PYAAAAQG8279zBC2rdvr3/84x/68Y9/rMzMTBmGEfBfamqqbrzxRi1cuDAuvVuzs7PN5a+++spc7tOnj3krZ6gZqhuu9519OhqaeyspYsfhcKhnz57q2bNns27jRuyRK2vwHZyh4VijSCycU9ZAHSL2rFIfthqO5cTF9cA6yJU1kCfrsPt8X+E6lcAs9rUyLj10pbrJwmbNmqUf//jHWrt2rQoKClRWViaPx6OsrCwNHjxYZ599ttq1axevEFVcXGwu+1agk5OTNWzYMG3YsEEbN25UbW1t0HHw1qxZY76mfvK1aOFDPHE5HI6Qk9kgcZAr6+GzL7FxTlkD51F8WKE+bDUcy4mL64F1kCtrIE+R0ykj/HkNWrp9rlOJy2qN7XFr0K2Xnp6u8ePHa/z48fEOpZF33nnHXO7fv7/fukmTJmnDhg06ceKE3nvvPV100UWNXn/48GF9+umnkqRx48YpMzMzugEDAADAchK5Ptwc9913n/bu3avy8nJlZGSod+/eGjdunK655hoaHQAACWvKUIbHhPXEvUE3HhYvXqyLLrpIKSkpQcvMnz9fK1eulCT17NlTo0aN8lt/xRVX6KmnntLx48f1+OOP6+yzz1bHjh3N9R6PR7/+9a/l8XgkST/60Y+isCcAAABAYqi/M02SysvLVV5eri+++ELPPfecfvnLX+rqq6+OegwulyvouqSkJLP3jdfrldvtDrkt3zvwPB6PWa8PxGazyel0mo/dbre8Xm/Q8na7XUlJJ7+KuVwuGYYRVuyGYYTcT0lyOp3mkERN7Wu0Y3c4HH490mpra0PGTp7iEzt5Co48Bdaa8hRLgf5OkcqT39/YMGQYde9js53seVr3Nw/+d5dsfkPa1W+jkQC5i3aevF5vk8ekby/bUGUDlfd6gh+/iSguDbr149F27tw56FAF9WpqanTkyBFJ0mmnnRaR9//zn/+s3/72tzrvvPM0cuRI9erVSxkZGTpx4oS+/PJLLVu2TOvXr5dUd7DNmTOnUbf4rKws/fznP9cDDzyggwcP6sorr9Qtt9yi/v37q6SkRAsWLNDq1aslSRdffLHGjh0bkdhDcblcTf49ER+1tbUqKCiQJOXn55OnBEaurMG3+uB2u/0qh0gsnFPW0FTlG5EX7/pwJPXq1UvnnnuuRowYYU4CXFRUpOXLl2v58uWqqanRAw88IJvNpquuuiqqsWzfvj3ougEDBph3zFVWVmrHjh0htzVy5EhzubS0VEVFRUHLZmRkaODAgebj/fv3q7y8PGj5nJwc9erVy3y8bdu2kOdhbm6u2XnE5XKZn6vB+H7eHjt2LOS8H06nU8OGDTMfHzp0SCUlJUHLZ2Vl+c0Nsnv3blVUVAQt37NnT3Xt2tXvehAKeQosVnmSFFauyFNg5Kk8aPlEztO8z5dq++EDQcs7HA4lJ5/sEFhdXR28ofM/209KOtkQ2Suzo24cM12SVFhYqLKyMr/ykcpTdna2+vbtK0mqqalWRUWF7HaH3xCiNTXVIRtRnU6nUlJSzceVlZUBG95raqobPRftPFVWVvoNjdpQamqq31xYR48eDfmjR/v27f3upD9aflR9UvuEjD+RxPwb8KpVq3TTTTcpPT1dH3zwQZMV2KqqKl100UWqqanRc889F7GG0fLycr3yyit65ZVXgpbp1q2bHn74YZ199tkB11999dUqKSnRX//6Vx04cEC//OUvG5UZP368Hn744YjEDAAAAOtLlPpwJJx77rm69NJLG01QOWzYMF144YX68MMPdccdd8jlcumRRx7RxIkT1aVLlzhFCwBAY18dL9HO0sKg65OSkpSammY+rqyslNcbvPdnSkqKnM6T13a7N3RPauBUxLxB95133pFhGJo8ebI6dOjQZPmsrCxNmTJFS5Ys0VtvvRWRCuyzzz6rlStXav369dq/f7+OHDmi8vJypaSkqFOnTho0aJC+973v6YILLlBaWlrIbd155536zne+oxdeeEGff/65SktL1b59ew0cOFDTp0/XxRdf3OJ4AQAA0HokQn04UpqasG3ChAm69dZb9cQTT6iqqkqvvvqqfvKTn0Qtnry8PL+eSL587+ZIT09Xfn5+2Nvt3Lmz3/BqDTVs0O7du7dfT7SGGk68MmjQoCZvPa7ndDqbjN33FtUOHTqELN8w9u7du4cc87hh7Hl5eU3eIt7QwIEDg97uTJ4Ci0eepOC5Ik+BkSdr5ikpKUkZGRkhtu5fPi0tNUi5wNv37d3bvXt39ejRo9H712tJnnz/xikpqQH3KSUlVSkpoYdc8BXsmurbi7detPOUnp4e8nxqWD7U8RuofIcOHaSakC9JKDFv0N24caNsNpu+/e1vh/2a73znO1qyZIk2btwYkRhyc3OVm5ur66+/PiLbO/PMM3XmmWdGZFsAAABo3RKhPhxLV111lf70pz/JMAytXbs2qg26TqczrKFd7HZ7s4aAaThuZVOaOxRQc8ZytNlszYq9ufsazdh9X0Oe/CVinupf11Rc5Ck48hRcIuWpbtxYe9PFzHjCL1tX/mTDYVJSUsi/VcTyZAu8T3Wx2BqXDyLovtoabyPaebLb7c06JptTVpLszcxrvMU82oMHD0qS+vTpE/ZrTj/9dEkKOX4LAAAAYAVtrT7cqVMnZWVlSVLIse8AAAAQnpg36NYPvnwqreo1NRbq+wwAAAAE0Bbrww1vawQAAMCpi3mDbv0YFoWFwQecbqi+bP0v+wAAAIBVtbX6cFlZmY4ePSqpbpZzAAAAtEzMG3QHDRokSXrrrbfCfk192TPOOCMqMQEAAACx0tbqwy+//LI58c3o0aPjHA3ixWazKT09PegEOwAAIHwxb9CdNGmSDMPQe++9p7fffrvJ8m+99Zbeffdd2Ww2TZ48OQYRWldzB3xG7CQlJWnAgAEaMGBAsweLR2yRK2vwvXG34Yy5SCycU9ZAHSK2Wkt9uKioSFu3bg1Z5sMPP9Rf/vIXSVJqaqouu+yyqMbEsZy4nE6nBg0apEGDBnE9SHBcu62BPFmIz5cXvrskLpvdWsNDxfysnzZtmp566ikdPHhQP//5z7Vp0ybNnDlT3bt39yt36NAhzZ8/X3//+99ls9nUvXt3XXHFFbEO11L4YEhcdrtdmZmZ8Q4DYSBX1sNnX2LjnLIGzqPYSpT68Lp163TgwAHzcf2wCJK0f/9+LV682K/89OnT/R4fPHhQM2fO1IgRIzRhwgQNHDhQ2dnZkuqGiFi+fLmWL19u9s6955571LVr14jFHwjHsjWQp8TGtdsayFPkpCbZlZkSveax1KSTPzby+Ze4rDbef8wbdJOTk/XnP/9Z1157rSorKzV//nzNnz9f3bt3N8fUKikp0aFDhyRJhmEoPT1df/7zn5WcnBzrcAEAAICISpT68KuvvqrXXnst4Lr169dr/fr1fs81bNCtt2HDBm3YsCHo+6Slpem+++7TVVddderBIja2vSFVlEZn2xmdpUEXR2fbANACPbPTlZzaIWrbz2GoGURBXPrlDxo0SIsWLdIvfvEL8zatr776yq/SWm/IkCH63//9X+Xl5cUjVEvxer3xDgFBeL1eVVZWSpLS09P5VS6BkSvr8Xq95CmBcU5ZA3WI2GsN9eEhQ4boscce08aNG7V582Z9/fXXOnr0qNxutzp06KB+/fpp3LhxuuKKK9SpU6eYxMSx3EIVpdLxQ1HZtGEY5l3HXLsTG9duayBP1sTnX+LyrXtZQdwGWsnLy9PixYv1ySef6MMPP9S2bdvM27w6duyowYMHa+LEiRo3bly8QrQcj8cT7xAQhNvt1o4dOyRJ+fn59DZPYOTKGnwvtVSKEhvnlDVQh4iPeNeHH330UT366KOn/PrMzExNnTpVU6dOjWBULcOxnLi8hqH6m449Hg/X7gTGtdsayFPkVBTvUfHBzVHbfnqPoVL/umW+uyQuw0uDbkhfffWVpLpfkLKysvTtb39b3/72t2MdBgAAABAX1IcBAEgcnpoq1VZ9E9XtA5EW858FJk6cqEmTJunNN9+M9VsDAAAAcUd9GAAAAC0R8wbd1NRUSXW3BABthc1mU3p6utIZDB0AgDaP+jAAAABaIuYNul27dpXEhAVoW5xOpwYNGqRBgwbJZrM1/QIAANBqUR8GAABAS8S8Qbd+fLDPP/881m8NAAAAxB31YQAAALREzCdFmzlzpl577TXNmzdPF198sdlDAYi7bW9IFaVR2bQ3rZPsQy6JyrYBAIC1UB8GAABAS8S8h26fPn30u9/9TlVVVbryyiu1bNky1dbWxjoMoLGKUun4oaj8Myq+jvfeAQCABEF9GAAAAC0Rlx66kpSdna2ioiLdc889+n//7/+pd+/e6tChg+z24G3MNptNCxYsiFWoluN0OuMdAoJw+BzX5CmxJScna+TIkfEOA03wHYk6KSnmlzI0A+eUNXBtii3qw9HDsZy4qA9bB9duayBP1sF3F2uwO2Le57VFYn4krVmzxm9SKMMwVFtbq507dwZ9jc1mk2EYTCYFAAAAy6M+DAAAgJaIeYPu6NGjY/2WAAAAQMKgPgwAAICWiHmD7vPPPx/rt2wzPB5PvENAEF7DMAes9ng8cjgccY0HwXk8HpWW1k2O17lzZ3JlAZxTiY1zyhqoQ8QW9eHo4VhOXNSHrYNrtzWQJ2vi8y9xeb3eeIfQLAze0YpY7eBrSwzDMJe9Xi8f4AnM4/GoqKhIktSxY0dylaAM32XDCFoO8RePc6pTRrKltx8P1CHQWnAsJy7qw9ZBfdgayJN18N3FIiyWGhp0AQBAqzJlaLd4hwAAAAAAUZMQDbqHDx/W119/rerqauXn5ys1NTXeIQEAAAAxQ30YAAAA4Ypbg+6JEyf07LPP6rXXXlNJSYn5/LJly9SvXz/z8Ztvvql3331X7dq100MPPRSPUAEAgIUs3PiGdpcVR237edldNXP4xVHbPtoO6sMAAAA4FXFp0N23b59uvvlmFRYW+o0fYrPZGpX91re+pV/84hcyDEPTpk3TqFGjYhkqAACwmK9OlGrP0a+itv20ZMaoQ8tRHwYAAMCpsjddJLJqamr04x//WAcOHFBqaqpuvPFGPfXUU0HL9+zZU2PHjpUkffDBB7EKEwAAAIgK6sMAAABoiZj30H3ppZe0f/9+paWl6cUXX9SgQYOafM0555yjTz/9VBs3box+gAAAAEAUUR8GAABAS8S8Qffdd9+VzWbTzJkzw6q8StLAgQMlSfv3749maED0+Nw9GehWSiQOm82mjIwMcxlAy3BOAY1RH0abRH3YMrh2WwN5Atq2mDfo7t69W5L0ne98J+zXZGVlSZK++eabaITUajidzniHgCActpOjmyQlxW0uQoTB6XSaX5qRuHyrrJxTiY1zyhqoQ8QW9eHo4VhOXNSHrYNrtzWQJ+vgu4s12B0xH5W2RWIebWVlpSQpPT097NfU1tZK4sAHAACA9VEfBgAAQEvEvEaYlZWl0tJSHTx4UEOGDAnrNTt37pQkdenSJZqhAQAAAFFHfbgV2PaGVFEave1ndJYGXRy97QMAAEuLeYPukCFDtHLlSq1du1bnnXdeWK9ZunSpbDabhg8fHt3gLM7tdis5OTneYSAAr2GY3eHdbje9axKY2+02xyfs3bs3uUpQhs8y51Ri45yyBrfbHe8Q2hTqw9ETs/pwRal0/FD036cVoT5sHVy7rYE8WQffXazB6/XGO4RmifmQC1OmTJFhGHrllVf01VdfNVl+/vz5Wrt2rSTpoosuinZ4lmYYRtOFEBe+uSFPic3r9aq8vFzl5eWW+0AHEhHnlDVwbYot6sPRw7GcuKgPWwfXbmsgT0CEWezSFPMG3e9///saMGCAampq9IMf/EArV670u6DbbDYZhqFNmzbp7rvv1m9/+1vZbDaNGjVK48ePj3W4AAAAQERRHwYAAEBLxLyft91u19/+9jddc801OnjwoG655RalpqbKZqub9+8HP/iBKioqzIkfDMPQ6aefrj/+8Y+xDhUAAACIOOrDAAAAaImY99CVpNNOO01LlizRRRddJLvdrqqqKhmGIcMwVFZWppqaGrOXwgUXXKBFixapU6dO8QgVAAAAiDjqwwAAADhVcRuJOSsrS48//rjuuusuffTRR9q8ebPKysrk8XiUlZWlwYMHa8KECerbt2+8QgQAAACihvowAAAATkXMGnQ/+ugj/etf/9LBgwfl9XqVk5OjsWPH6vzzz9e1114bqzAAAACAuKA+DAAAgEiIeoNuaWmpbrvtNm3atKnRun/+85964okn9Je//EUDBgyIdigAAABAzFEfBgAAQCRFdQxdj8ejn/zkJ/riiy/MMcEa/isqKtKPfvQjlZWVRTOUNqF+Ig0kHt/ckKfEZrfblZOTo5ycHNntcRlmHGhVOKesgWtT9FAfji2O5cRFfdg6uHZbA3kCIsxil6ao9tB9++23VVBQIJvNptNPP10333yzhg0bpqSkJH355Zd67rnntHHjRh05ckTPPfec7r777miG0+olJcVtSGQ0we5TaSVPiS0pKUm9evWKdxhogu+1lnMqsXFOWQPnUfRQH44tjuXERX3YOrh2WwN5sg6+u1iD1X4YiWq0b7/9tiSpR48eWrRokS677DKdccYZ6tu3r6ZMmaIXXnhBo0ePlmEYeuedd6IZCgAAABBz1IcBAAAQaVFt0N22bZtsNpuuv/56tW/fvtF6h8OhO++8U5JUVFSkEydORDMcAAAAIKaoDwMAACDSotqgWz8OWH5+ftAyQ4cONZePHj0azXBaPZfLFe8QEITH8JrLbrc7jpGgKS6XS5s2bdKmTZs4pxKY4bPMOZXYOKesgdxED/Xh2OJYTlzUh62Da7c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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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UNtu2bcPmzZvdEo8le/bsUZaffPJJm51mALh48aKrQ3KY6Wltt912G8LDw12+T9N9lJeXe8V8bUREROT92C+1j/3S+nF2v9R03t68vLwGbcvZTONp166dByMh8l4s6BJRo3HrrbfW+QV38ODBaNasmUfiUalU6N27Nz799FPcdtttyv3bt2+v09ZdIyPy8/OV5S5duthsq9frcejQIVeH5JC8vDyzTv+4cePcst/aF2Hw1AgWIiIiEgv7pfaxX1o/zu6XxsXFKctnzpxp0LZMNTSuiooKs+J9fHx8Q0MiapRY0CWiRkOtVmP06NFm97n6tDZHSJKEgQMHKrcLCwvrtPH19VWWjafkuYJKdf1j395cWVu3bsXly5ddFkt9bNiwAQaDAQDQvHlzDB482C37PXbsmLLs5+eH0NBQt+yXiIiIxMZ+qX3sl9aPs/uliYmJyvLx48cbtC1TDX39nDx5Uvn7duzYEc2bN3dabESNCefQJaJG5cknn8TIkSOV2926dXPZvkpLS+Hr62vWabEmNzdXWbZ05V/TDpkrT3mKjo7GqVOnANSMyLD29ykqKsI777zjsjjqy/S0thEjRsDPz8/l+8zNzTU7DTE5OZkjdImIiMhh7Jfaxn6p41zRL+3Xrx/UajX0ej3+/PNP6HQ6+Pg0vETU0NfPgQMHlOVbb721wfEQNVYcoUtEjUrz5s2RkJCg/HPlBayOHj2KQYMGYd68eUpntDa9Xo9Nmzbhyy+/VO6744476rS76aablOXdu3crFyhwNtMRGZ999hk2bNhQp83Ro0fxwAMPIDc3F4GBgS6Joz6OHDmCrKws5bY7TmtLT0/H9OnTzU5te+ihh1y+XyIiImo82C+1jf1Sx7iqX9qiRQvccsstAGouOJeWltbgbQINf/388ccfyvKdd97plJiIGiOO0CUiaoDLly9j/vz5mD9/Plq3bo34+Hi0bt0aarUaBQUFOHr0qNn8YL1798Zf/vKXOttJTExEZGQkcnNzcfnyZYwYMQK33XYbQkNDlV/fExISzEZ53Ihx48Zh8eLFOHv2LKqrq/Hyyy/js88+Q3x8PPz8/HDixAmkp6cDqJmvasCAAVi4cGGD9tlQpp37jh07omfPng3e5rx58xAUFGR2n8FgwLVr15CZmYmTJ0+arZs2bRpHCBAREZFXY7/U9Rpbv3T06NHYv38/gJppLZzxfBry+iktLVXmJ27ZsqXZfM9EZI4FXSKiG+Tv7w8fHx9lbqjLly/bnNtr2LBhmDNnjtl8YUYqlQpvvPEGnn32WWi1Wly+fNnsdC6gptPb0I6zr68v/u///g+PPvoosrOzAQBZWVlmIw0A4JZbbsGHH36INWvWNGh/DVVdXY3vv/9eue2sURC1/7bW+Pv74/nnn8fDDz/slP0SERERuQL7pa7XGPulI0eOxNy5c1FeXo6ffvoJL730UoOncmjI62f79u2orq4GANx9991OmQKCqLHiu4OI6AbdfPPN+P333/H777/j4MGDyMjIwPnz51FcXAyDwYDg4GBER0cjKSkJY8aMMbvwgCUDBw7EN998gxUrVuDQoUO4ePEiysvLIcuyU+Pu1KkT1q9fjxUrVuDnn3/GmTNnoNVq0bp1a3Tp0gWjRo3CiBEjXHpaoKN27dqF4uJiADWdw7vvvtul+wsMDERoaCi6dOmCfv36YcyYMRbnliMiIiLyJuyXul5j7Jc2a9YM48ePx5dffomcnBz8/vvvThkVe6Ovn7Vr1wKouajglClTGhwHUWMmyc7+RCYiIiIiIiIiIq+Xk5OD4cOHQ6vVYvDgwfj00089EsepU6cwatQoyLKMUaNG4f333/dIHESi4EXRiIiIiIiIiIiaoKioKEycOBFAzZQHtae8cJeFCxdClmX4+Pjgueee80gMRCJhQZeIiIiIiIiIqIl65plnEBwcDFmW8cknn7h9/+fPn1fmJ77vvvvQoUMHt8dAJBoWdImIiIiIiIiImqhWrVrh2WefBQBs2rQJ6enpbt3/hx9+CK1Wi7CwMDz//PNu3TeRqDiHLhEREREREREREZEgOEKXiIiIiIiIiIiISBAs6BIREREREREREREJggVdIiIiIiIiIiIiIkGwoEtEREREREREREQkCBZ0iYiIiIiIiIiIiATBgi4RERERERERERGRIFjQJSIiIiIiIiIiIhIEC7pEREREREREREREgmBBl4iIiIiIiIiIiEgQPp4OwBMefPBB7Nu3r16PWbZsGZKTky2u27VrF9asWYO0tDQUFRUhLCwMCQkJuPfee5GSkuKMkK0yGAyoqKgAAPj4+ECSJJfuj4iIiBofWZah0+kAAAEBAVCp+Js/iYP9YSIiImoo0frDTbKgW18qlQodO3asc7/BYMCsWbPw9ddfm92fl5eHvLw8bN26Fffccw/eeustl70QKioqcPz4cZdsm4iIiJqe+Ph4BAUFeToMIoexP0xERETOJEJ/uEkWdOfMmaP8im/NqVOnMHPmTABA//79ERERUafNf/7zH6WY261bNzzyyCOIjo5GdnY2Fi5ciGPHjmHt2rUICwvDCy+84PwnQkRERERERERERE1KkyzoRkdH222zYcMGZfnuu++us/7MmTNYvHgxAKBHjx5YsWIF/P39AQCJiYkYNGgQHnjgAaSnp2PRokWYMGECOnTo4KRncJ2Pz/UUxsbGIjAw0On7oIbTarXKyJH4+HhoNBoPR0TWMFdiYJ7EwVyJoby8HFlZWQDM+xZEImB/WAw8HoiDuRID8yQO5koMovWHvT9CDzAYDNi4cSMAIDAwEHfddVedNkuXLlXm1pg1a5ZSzDUKCAjArFmzMGnSJOh0OixZsgRvvPGG02M1nSPM19cXvr6+Tt8HNZxKpUJISAgAwM/PT4gPh6aKuRID8yQO5koMxj4NAM4/SsJhf1gMPB6Ig7kSA/MkDuZKDKL1h/kqsmDPnj3Iy8sDAAwfPhwBAQFm62VZxrZt2wAAMTExSEpKsridpKQkdOrUCWfOnMG2bdvwj3/8w6UvCn4oeC8fHx/ExsZ6OgxyAHMlBuZJHMyVGNiHoMaCr2XvxeOBOJgrMTBP4mCuxCBaH8K7L9nmIevXr1eWLU23kJOTg/z8fABAnz59bG6rb9++AGoulJaTk+O8IImIiIiIiIiIiKjJYUG3lrKyMmzduhUA0K5dOyQnJ9dpc+rUKWU5JibG5vZM158+fdpJURIREREREREREVFTJNZ4Yjf4+eefUV5eDgAYPXq0xSkSLl26pCy3adPG5vZM1+fm5jopSsuMcVvi4+MDlaqmfm8wGMzmBrHEdO4xvV4PvV5vta0kSWaTeut0OhgMBqvtVSqV2VB2rVYLWZYdil2WZWi1WpuxazQaJW/2nqurY1er1VCr1dBqtcjKyoLBYECHDh2sToLOPHkmdmOejG1PnjwJAFZzxTx5JnbmyTpvzRNQ80PpuXPnAFjOFfPkmdhr58lWH4Iap9LSUuzatQtpaWlIT09HXl4eioqKUFVVhWbNmqFz58644447MHHiRISGhtrd3qFDh7By5UocPHgQBQUFaN68OeLj4zFu3DiMGjXKDc+ohlar5Ry6XsrYHwZqLl7HiwJ5L+ZKDMyTOJgrMdjry3sbFnRrMZ1uYezYsRbblJWVKcv2rqJrOv+uq78sGT8gLImLi0NwcLASR2Zmps1t9erVS1kuKCiwOV1EUFAQ4uPjldvnzp1DcXGx1fbh4eGIjo5WbmdkZNh848TExChfJLRaLdLS0mzGnpCQoHTkS0pKbI6M1mg0SExMVG7n5uYq02lYEhISYjb3TVZWltnrobaoqChERERAlmWlnfHqlpYwT5a5K09ATVGmoqICgPVcMU+WMU/FVts31TwB5vmxlCvmyTJ358lWH4IapyNHjuCFF16wuK6oqAj79u3Dvn37sGjRIrz77ru4/fbbrW5r3rx5+PTTT81+dCgoKMDu3buxe/dubNy4ER9//DH8/Pyc/jxIHKb9YVs/OJHnMVdiYJ7EwVyRK7Cga+LSpUvYt28fgOsXNLOkqqpKWbb3y4rpCIHKykonRElERERE1HCRkZFITk5G9+7dERkZidatW8NgMODSpUvYvHkztmzZgitXruDJJ5/E119/bfYDidFXX32F+fPnAwDat2+Pxx9/HF26dEF+fj6WLVuGvXv3YufOnXjttdfw/vvvu/spEhERETVKksyfBxQLFixQOppvvvkmJk+ebLHdwoUL8e677wIAPv/8c9xxxx1Wt7lr1y489thjAID/9//+Hx5++GGnxlxdXa2MCIqNjbU6YpintHomduMpraZ5io+P55QL8M48AXAoV8yTZ2Jnnqzz1jwBNWe1GEfmWsoV8+SZ2Gvnqbi4WBmlazqSmBovvV5v9hqwZOvWrXj66acBAEOHDlUKt0bFxcUYMmQIrl27hrZt2+Kbb75BWFiY2T6efvpp7NixAwCwbNkyi9enaKjax4SgoCCn74MazjRP/JzxbsyVGJgncTBXYjD93iJCnjhC18SGDRsA1HxZHDlypNV2pp1Ee9MoGE8JBuxPz9BQGo3GoRecSqWq1wuz9pc+e0y/UDqiPvPHSJJUr9jr+1xdGbvpY5gnc96YJ+Pj7MXFPFnHPFnXlPJk2t5erpgn69yZJ2oaHHnvDBkyBJ06dcKZM2dw4MCBOuvXrl2La9euAQBeeukls2KucR9vvvkmfvnlF+j1eixatMglBV0iIiKipkbl6QC8RVpaGk6dOgUAGDhwIFq0aGG1remFzkwvkGaJ6frIyMgGRklERERE5D7GgQymU44Zbdu2DQAQHByMoUOHWnx8mzZt0L9/fwDAnj17UFpa6qJIiYiIiJoOFnT/yzg6F7B+MTSjzp07K8u2LmhSe31MTMyNBUdERERE5GanT59WTj2s3Y+trq7GkSNHANRce8LWiPO+ffsqj0lPT3dRtERERERNB6dcQM08cz/88AMAICwszOacuEDNlaHDw8ORn5+P/fv322xrXB8REYGoqCjnBOwClZWVKC4uRnl5uc35COnGmM57eObMGWXORWoYtVoNX19fNG/eHMHBwfy7EhERNVBFRQXy8vKwY8cOLFy4UJnredq0aWbtzp49q/QZ7Q1aMF2flZWFfv36OTlqIiIioqaFBV0Av/zyC4qKigAAo0aNsjtPnSRJGDx4MFatWoXTp08jNTUVSUlJddqlpqYqI3QHDx6sXAjFVW6kmCXLMnJzc1FSUuKCiMhIlmX4+fkBqLlAiK0L25DjdDodqqqqcO3aNUiShHbt2qFZs2YN2qZarVZ+fKnP3JzkXsyTOJgrMfAHsaZt3bp1ePXVV62uf+yxxzB69Giz+0ynFTOdjsyS+kxX1lB6vR7V1dUW1/EijJ6J3TjfufF4oNPpmKf/8sY8GZeN71truWKePBM782Sdt+YJqPlb2soV8+SZ2C3lSSQs6KJ+0y0YTZs2DWvWrIFer8fs2bOxYsUK+Pv7K+srKysxe/ZsADUv8NqjGlzhRr4oFxYW1inm1vfCK0SeotfrlQ9oWZZx4cKFBhd11Wo1IiIinBUiuQjzJA7mSgwstpMlXbt2xVtvvYXExMQ668rKypRlexf+NV1v74LCDXXy5Emr6+Li4hAcHKzEkZmZaXNbvXr1UpYLCgqQk5NjtW1QUBDi4+OV2+fOnUNxcbHV9uHh4YiOjlZuZ2Rk2PwSHBMTg9DQUAA1X1CNV0u3xvTq3CUlJTanidNoNGY5zs3NRX5+vtX2ISEhiI2NVW5nZWWZvR5qi4qKQkREhHI8OHjwoM3CPvNkmbvyBNQcE4w5spYr5sky5qnYavummicAOHbsmLJsKVfMk2XuzpOtPoQ3avKVu5KSEuzYsQMA0KVLF3Tv3t2hx3Xq1AnTp0/HggULkJ6ejsmTJ+PRRx9FdHQ0srOz8fnnnytv2unTp6Njx46uego3rLq6GpcvX1Zuh4eHIyQkhF/qSBiyLKO8vBxFRUUoLS1VirpdunThaDMiIiIHDRkyBD169ABQMyghOzsbP/74I7Zs2YIXX3wRr732GgYOHGj2GNOLpJmOnrHEdCRRZWWlEyMnIiIiapok2db44yZg1apVePPNNwEAf/vb3/DII484/FiDwYDXX38d33zzjdU2EydOxOzZs11WXKqurlZ+ATH9pcMRRUVFyMvLAwC0bNkS4eHhLomRyNVkWUZOTo5y5ex27dqhefPmHo6KiEgcDelPUOO1fv16vPLKK5AkCf/6178wfvx4Zd2PP/6IGTNmAADefPNNTJ482ep2srKyMHLkSADAAw88gFmzZjk1TtPXb3x8vNUCM09p9UzstU9ptTbVghHz5JnYmSfrmCfLmCfmqXbsoueprKxMuRisCP3hJj9C1zjdglqtrjM3mD0qlQpz5szBsGHDsHr1aqSlpeHKlSsIDQ1FQkICJk2ahJSUFFeEbZFWq63XC8506DmLX65lMBhQUVEBAAgICODoUSeTJAlhYWFKQffq1as3/JpmUUMMzJM4mCsx2Ot8U9M0duxY7Ny5Ez/++CNmz56NQYMGISQkBEDNKZxG9qZRMF1vb3oGZ3Dkc0alUtXr86j2lz576juFmb1RzqYkSapX7PV9rq6K/UaOB8yTda6Mvb65Yp6sY56sayp5MnI0V8yTde7Ik0iafEH3q6++avA2UlJS3Fq4dRbjr0SSJCkX7CISVWBgICRJgizLdn8BJSIiIscMHjwYP/74I8rLy/Hrr78qAyDqc6Gz+lxAjYiIiIjs4zDBJsw4VF2tVitD3olEJUmS8sukaFenJCIi8lZhYWHK8sWLF5Xljh07KsddWxc0qb3e9GIlRERERHRjWNAlIiIiauIa+ylpdOOM11sAzKdL8PX1Va48nZqaavPsmH379imPMV58jYiIiIhuHAu6RERERERk0U8//aQsd+nSxWzd4MGDAQClpaXYsmWLxcdfunQJe/bsAQD0798fwcHBLoqUiIiIqOlo8nPoEhEREYlg+/ntKKoscsm2Q9WhCEGIS7ZN3mndunX4y1/+YvM6CkuWLMGuXbsAAFFRUejdu7fZ+nvuuQefffYZrl27hvfffx+33norQkNDlfV6vR5vvvmmMhXS9OnTXfBMiLxIxvdAWYFrth3UCug6yjXbJiIi4bCgS0RERCSAosoi5Jfnu2TbkkZiQbeJmT9/Pv7973/jrrvuQq9evRAdHY2goCCUlpbixIkT2LhxIw4dOgSgZkqO2bNn17mKdkhICF566SW88cYbuHDhAu6991488cQT6NKlC/Lz87F06VLs3bsXADBq1CgkJye7/XkSuVVZAXAt19NREBFRE8CCLhERERFRE1RcXIw1a9ZgzZo1Vtu0adMGc+bMwa233mpx/X333Yf8/Hx8+umnOH/+PF577bU6bVJSUjBnzhynxU1ERETU1LGg24jUHjVB3kOSJPj7+yvL7hAXFwcAeOaZZ/Dss8/e0Db27t2LqVOnAgCWLVvWJEbW+Pj4KH87Hx9+RHor5kkczJUYJJV7jk3kPRYuXIhdu3bh0KFDOHfuHAoLC1FcXAw/Pz+0bNkSXbt2xZ133okRI0YgICDA5raee+45DBgwACtWrMDBgwdRUFCA5s2bIz4+HuPHj8eoUe47TZz9Ye/F44E4mCsxME/iYK7EIFofgq+kRkSl4jXuvJXBYECfPn1QVlaGbt264dtvv7XaVpZl9OvXD8XFxQCAf/3rX5g4caLV9t9++y1eeeUVAMAbb7yB+++/36mxNzUqlYoXbBEA8yQO5koM7vqxkbxHTEwMYmJi8NBDDzlle7fccgtuueUWp2yrIdgf9l48HoiDuRID8yQO5koMovUhxIqWSFBqtRo9e/YEABw/fhylpaVW2548eVIp5gLAwYMHbW77wIEDynKfPn0aFmg9zZs3D3FxccqvjURERERERERE5FocoduIGAwGl+9j89FLKCytdvl+3KllsC+GdW/j0n3IsoxevXph9+7dMBgMOHToEO644w6LbY0FWrVaDb1eb1awtdU+NDQUnTt3dmrcycnJyMzMdOo2vZ3BYEB5eTkAIDAwULhf6ZoK5kkczJUYZFn2dAhETuGO/jDdGB4PxMFciYF5EgdzJQbR+hAs6DYier3e5fsoLK1G3tVKl++nsZFlGYmJicrtAwcO2C3oDh8+HD/88APOnz+PvLw8RERE1GlbWFiIs2fPAgB69erFU2adQKfTKUXshIQE+Pr6ejgisoR5EgdzJQbZwIIuNQ7u6A/TjeHxQBzMlRiYJ3EwV2IQrQ/BnwWI3KR79+7w8/MDAJujbo3rhg0bhvbt2wOwPu2CJ6dbICIiIiIiIiIi9+MIXSI38fX1Rffu3XHo0CGkpaWhurq6zi9z2dnZyMvLA1Az4rZXr144f/48Dhw4gJEjR9bZpmlBt3fv3jb3f+TIESxZsgQHDhxAUVERQkND0a9fPzzxxBOIjY21+Ji9e/di6tSpAIBly5YhOTkZALBu3Tq8+uqrZm0tzaO7bds2REVFmd2n1+vx3Xff4aeffsLRo0dRXFyMoKAgxMTE4K677sLkyZPh7+9v87kQERERERERETVVHKFL5EbGqz9XV1fjzz//rLPeWKDt2LEjWrVqhV69egEA9u/fb3F7xpG7wcHB6Nq1q9X9rlixApMnT8YPP/yAvLw8aLVa5Ofn47vvvsOECROsbt/ZLl68iPHjx+OVV17Bzp07cfnyZWi1WhQXF+PQoUOYO3cuxowZgzNnzrglHiIiIiIiIiIi0XCELpEbGQu6QE3xtvY0CcaCrrGd8f8nT55ESUkJWrRoobQtLS3F8ePHAQA9e/aEWq22uM/du3fjyJEj6NKlC6ZOnYouXbqgqqoKW7ZswbJly1BRUYGXX34ZmzdvdngunyFDhqBHjx5YuXIlVq1aBQDYuHFjnXam8/5euXIF999/P3Jzc+Hr64t7770Xffr0Qbt27VBeXo7ffvsNy5Ytw7lz5/Doo4/i22+/RbNmzRyKh4iIiIiIiIioqWBBl8iNEhMT4ePjA51OZ3EeXeN9xukTYmNjERoaiitXruDQoUMYOHCg0vbw4cPKpN225s9NTU1FSkoK5s+fb1aw7d27N0JCQvDhhx/i4sWL2LVrF4YOHerQ82jevDmaN2+Oli1bKvd16dLF5mPefvtt5Obmol27dli6dCmio6PN1icnJ2P48OGYMmUKsrOzsXDhQsycOdOheIiIiIiIiIiImgpOuUDkRoGBgcrUCKYFWQAoLCzE2bNnAUCZagG4Pkq3dgHY0flz/fz88M4771gcffvggw9Co9FY3L4z5eTk4McffwQAzJo1q04x16hbt264//77AdTM00tEREREREREROZY0CVyM2PxtaysDBkZGcr9xnlsW7VqhY4dOyr3G4u7tQuuxvlz/fz8kJCQYHV/t956q9lIWlPBwcHKvrKzs+v3ROph165d0Ov1CAgIwB133GGzrXG0cX5+Pi5evOiymIiIiIiIiIiIRMQpFxoR40hL8j4qlQpBQUEAagq6X3zxBYCaIm2PHj2UZcB8dK6xPQAcPXoUlZWV8Pf3R3V1NY4cOQIAuPnmm23OfRsTE2MzNuO8vGVlZfV9Wg5LT08HAFRUVKBbt24OP66goABt27Z1VVgW+fr61skBeR/mSRzMlRhUav7GT40D+8Pei8cDcTBXYmCexMFciUG0PgR770Ru1rt3b0iSBOD6KFvTZdMLpwE10xAEBARAq9UiNTUVAJCWloaqqiple7YEBATYXK9S1XwMGAwGx59EPRUWFt7Q4yoqKpwcCRERERERERGR2DhCl8jNQkJCcNNNN+HEiRPKqNzS0lIcP34cQN0RuhqNBgkJCdi3bx8OHDiAfv36mRWCbV0QzVsY5woODQ3FsmXLHH5cVFSUq0IiIiIiIiIiIhISC7qNiOkFtsi7yLIMnU4HAPDx8UHv3r1x4sQJFBUVISsrCzk5OTAYDAgMDLQ4JUGvXr2Ugi5wfb5djUaDpKQktz2PGxUSEgKgZlqH2NhYqNVqzwZkg16vR0FBAYCa+Yy9OdamjHkSB3MlBleepUHkTuwPey8eD8TBXImBeRIHcyUG0foQnHKhEeGXMe8lyzKqq6tRXV0NWZbNpkk4ePCgMuI2KSnJ4oe7cdTun3/+ierqahw+fBhAzXQMgYGBbngGlhmnjrDHWKSurq5W5tP1Vnq9Hjk5OcjJyRHuA70pYZ7EwVwJQvZ0AETOwf6w9+LxQBzMlRiYJ3EwV2IQrQ/Bgi6RB5gWdPfv36+MuLU2UXrPnj2hVqtRXl6OdevW4dq1a3W24wmmF2Orrq622m7gwIFK8Xfp0qUuj4uIiIiIiIiIqLFiQZfIAyIiItC+fXsAwN69e5VRq9YKusHBwejSpQsAYOHChcr9np4/Nzw8XFk+f/681XYxMTEYPnw4AOCHH37AF198YXO72dnZ+P77750TJBERERERERFRI8I5dKleWgb72m8kGE89p969e+P8+fPIy8sDUDO3rq35cHv16oWMjAxkZ2cDAFQqldUCsLv07NlTWX7nnXfwxBNPoHXr1spo3Hbt2sHHp+Zj5s0330R6ejqys7Mxd+5cbNu2DXfffTduuukm+Pr6ori4GMePH8evv/6KP/74A0OHDsWoUaM88ryIiIiIiIiIiLwVC7pUL8O6t/F0CI1G7969sW7dOuV2165dERAQYLV9r1698OWXXyq3u3TpgubNm7s0Rns6dOiAESNG4Mcff8Tu3buxe/dus/Xbtm1DVFQUgJoLo61atQozZszAgQMHzKaasCQoKMilsRMRERERERERiYgFXSIPqT1dgr3RtrXXe3q6BaN3330XPXr0wObNm3HmzBmUlZVZnUy8devWWLFiBXbu3Invv/8eqampKCgogE6nQ7NmzdChQwf07NkTgwYN8prnR0RERERERETkTVjQJfKQ9u3bIzMz0+H2ERER9WrvaNvly5dbXZecnGx3OxqNBo888ggeeeQRh2O78847ceeddzrcnoiIiIiIiIiIarCgS+QmKhWvQSgCSZKU6R6McwGT92GexMFcERERwOOBSJgrMTBP4mCuyBVY0G1ENBqNp0MgK1Qqlc35ccl7aDQaxMfHezoMsoN5EgdzJQaVmj86UuPA/rD34vFAHMyVGJgncTBXYhCtD8HeOxEREREREREREZEgWNAlIiIiIiIiIiIiEgSnXGhEdDodfH19PR0GWSDLMqqqqgAAfn5+nDfHi+l0Opw7dw4A0KFDB/j48GPSGzFP4mCuxGAwGDwdApFTsD/svXg8EAdzJQbmSRzMlRh0Op2nQ6gXjtBtRGRZ9nQIZIUsy9Dr9dDr9cyTlzMYDCguLkZxcTELHF6MeRIHcyUIHpqokWA/y3vxeCAO5koMzJM4mCsxiNaHYEGXiIiIiIiIiIiISBAs6BIREREREREREREJggVdIiIiIiIiIiIiIkGwoEtEREREREREREQkCBZ0iYiIiIiIiIiIiATBgi4RERERERERERGRIHw8HQA5jyRJng6BrJAkCRqNRlkm76VSqRAeHq4sk3dinsTBXAmChyZqJNjP8l48HoiDuRID8yQO5koMovUhWNBtRHx8mE5vJUkSfH19PR0GOcDHxwfR0dGeDoPsYJ7EwVyJgV8uqLFgf9h78XggDuZKDMyTOJgrMYjWhxArWhe6ePEivv76a+zcuRMXL15EWVkZwsLC0K5dOyQnJ2PEiBHo0qWL1cfv2rULa9asQVpaGoqKihAWFoaEhATce++9SElJceMzISIiIiIiIiIiosaKBV0Ay5cvxwcffIDy8nKz+y9duoRLly7h4MGDKC0txd///vc6jzUYDJg1axa+/vprs/vz8vKQl5eHrVu34p577sFbb73F0S9ERERERERERETUIE2+oPvpp5/io48+AgB07NgR9957LxISEtCsWTMUFxfj2LFj2LJli9Vi7H/+8x+lmNutWzc88sgjiI6ORnZ2NhYuXIhjx45h7dq1CAsLwwsvvODS56LVanlav5cyGAyorKwEAPj7+7O478W0Wi0yMjIAAF27dlXmPibvwjyJg7kSg0Fv8HQIRE7B/rD34vFAHMyVGJgncTBXYtBqtZ4OoV6adEF3z549SjF37NixePvtt+u8sfr374/p06ejurq6zuPPnDmDxYsXAwB69OiBFStWwN/fHwCQmJiIQYMG4YEHHkB6ejoWLVqECRMmoEOHDi5+VuStZFn2dAjkAFmWlQ9y5sx7MU/iYK6IiAjg8UAkzJUYmCdxMFfkCk12mKDBYMCbb74JAIiPj8e//vUvm7+SWPqlf+nSpdDpdACAWbNmKcVco4CAAMyaNQsAoNPpsGTJEucET0RERERERERERE1Sky3o7t69G2fPngUAPProo/W+mp0sy9i2bRsAICYmBklJSRbbJSUloVOnTgCAbdu28dcYcoq9e/ciLi4OcXFx2Lt3r6fDqWPdunVKfDk5OZ4Oh4iIiIiIiIio0WiyUy789NNPAABJknDnnXcq9xcXF6O4uBghISEICQmx+vicnBzk5+cDAPr06WNzX3379sWZM2eQl5eHnJwcREdHNzh+EtOBAwfw2GOPWV0fGBiI8PBwJCYmYvz48ejfv78boyMiIiIiIiIiIm/XZAu6f/75JwCgXbt2CA4OxsaNG7FgwQKcOHFCaWO8SNqDDz5YZ8qFU6dOKcsxMTE292W6/vTp0yzoklXl5eU4e/Yszp49i++++w5jx47FnDlzoFarPR0aERERERERERF5gSZZ0DUYDDh9+jQAIDQ0FG+//TaWL19ep93Zs2fxP//zP9iyZQsWLFiA5s2bK+suXbqkLLdp08bm/kzX5+bmNjR8q7RarcWLtwGAj48PVKqaGTYMBgN0Oh0MBoMyBYTBYH51a2NboGZ6CUmSapYzvgdKL9fZvnE9AMiQAVszS0iABJP29qahMGlvd9s30F4Kbg10HaXEYiseSZLMnmvtv5u99qbuu+8+TJ482ey+kpIS/Pnnn1iyZAkKCwuxfv16REREYMaMGWbtLMVoL3agbl5d9VxNtyvLssXH1m7vrNgtrdNqtTa3r1arLRbNrV3l0tL7yRbTH4T0ej30er3VtpIkmc3nbXyvWqNSqcymjLH3XE1jN52c3xqNRqPkyd5zdXXszJN13pwn09gtPQ/mybHYAcCgN1h9vpIkmX1O2vq7WGpvkG1/xhMRERERkfdokgXda9euKV/oTpw4gbS0NLRu3Rovv/wyUlJS4Ofnh7S0NLz33ntITU3F4cOH8dprr2H+/PnKNsrKypTlwMBAm/sLCAhQlsvLy538bK7Lysqyui4uLg7BwcFKDJmZmQCufxmtqKgwax8UFKQs6/V65QuuriQX8tWLZm0lSYLG5/qXTr1OZ/OLoVpl/mVfp9XWFF6t8FH7QDJ+6ZRlaHV2vjD7aABjodBggE5v4wszJGhMipY6nc5qURyoKVSYXvyuqqrKZnHA19fX6sX2mjdvXme09k033YTk5GQMGjQI48ePR1VVFb788ks8/PDDZtupqqqqsz17satUKrPXYlVVlc0v/BqNxqxwUllZabOw4efnZ3Eu6srKyjqvL6DmfWEsbOj1eovPyUiSJLP3mVartVg40ev1FtdlZWWZvWdri4qKQkRERJ37jx8/brG9tfeTNb169VKWCwoKbM4rHBQUhPj4eOX2uXPnUFxcbLV9eHi42esoIyPDZlEpJiYGoaGhAGr+jmlpaTZjT0hIUF4HJSUlyo9hlmg0GiQmJiq3c3NzlalpLAkJCUFsbKxym3m6rjHlyTQ/lnLFPFlWO08AUHK1BEVXiyy29/PzM/vh+dq1azZjDwoKMvtcLSsra6K9QiIiIiIi8TTJrrtpcamqqgoBAQFYtmyZ2dQIffr0wdKlSzFp0iQcP34cW7ZswZ9//ombb75ZeZyRtYKdUe2iGDU9kiTBz8/P4fadO3dGSkoKfv75Z5SVleHMmTPo0qWLCyMko/peIJE8g3mipig0NBRaP9s/ahrZug6AJS1atACs1+mJhMFpqryXj4+P8n2Lx3HvxlyJgXkSB3MlBtH6EE3ylVR7PtyJEydanAfX398fM2fOxOOPPw4A2LRpk1LQNS3O2TvF0nTUpOnoTmeLj4+3Wlw2/dAIDAxEQkICzpw5A71eD7VabTZyszbTF7WP2gfwsV3AVvuooZZtvBFqzUBg9wNNMl/W2Nm/aXtJJUEj2WlfKxZbb+La0yfYK9Ia20uSBB8fH7P2Go2mzt/ddPtRUVHKcu3RtZb2Wzv21NRU7Ny5E4cOHcKZM2dQUlICX19ftGnTBn369MEDDzxgNurO1nPdtWsXNm7ciMOHD6OwsBB6vR6tW7dGt27dMHDgQAwfPtzq383f39/i6+vixYuYPn06zp49i8DAQMyfP9/hi8BpNBqLrxu1Wm1xXWxsrN1TxI1UKhUSEhJs7t/S+8lRrVq1Ukb0WVL7NdahQweb826bnjINAF27drV7iriRRqOxG7vpZ0qLFi1stq8de2RkpMURtUa1Y2eermsseQLAPJloSJ6IyDG138fkPVQqlc3PbPIezJUYmCdxMFdiEK0P0SQLusZTO40GDBhgtW3//v3h4+MDnU5ndiql6ZQE9qZRMB0RbG96hoaofYq8NSqVCr6+vlCpVMp0AbZeuKZfKiVJUqYzsNq+ZhJbh9XnS2u9t30DsdQnnvq+4Wv/LW09/uLF61NbtGvXzqytpRhNY1+3bh1effXVOm20Wi2ysrKQlZWFtWvX4u9//zumTJliNYYrV65g5syZ2LNnT511Fy5cwIULF7BlyxZIkoTx48c7/DyzsrLw8MMP49KlSwgJCcHnn39e59RiW2zlydI6e6Poa3PkfWRkfD85yto8sNbU9xfc+jxXSZJc+lxdGTvAPFnDPFkncp4AIKeoHJkFJfV6jMNahaB9/VJLREREREQe0iQLur6+vggLC0NRUc08dLYuaubn54fQ0FBcvnxZaV/7MaYXSLPEdH1kZOSNhm2X3YuLkccYL9TlaI6ysrKwc+dOAEBSUhJatWpVr/3p9Xq0aNECgwcPRu/evdGhQwcEBgYiPz8fR48exfLly3HlyhXMnj0bMTExFkfGVlRUYOrUqThx4gQAoHv37pg0aRJuuukm+Pr64tKlS9i/fz82bdpUr9iOHDmCRx99FMXFxQgPD8cXX3yBzp0712sbrmR6YSPTCxiRd2GexMFcOU+lzoDSKtsXjbtRVToDwIIuNQLsD3svHg/EwVyJgXkSB3MlBtH6EE2yoAvUzFG6b98+ALB5USvg+pWiTUf2mBagbF3UpPZ6S1M7OItOp6vXPK3kPrIso6Kiwmzu5cLCQqVYamxz7do1HD58GEuXLkVlZSWaNWtmcaStPXfccQdGjRpVZ6qDbt264c4778TUqVMxZcoUZGZmYt68eRYLuh9++KES35QpUzBr1iyzA0+PHj0wZMgQvPjii7h69apDce3ZswdPPfUUysvL0aFDByxevNhsaglvYHphI9MLGJF3YZ7EwVwRkTuxP+y9eDwQB3MlBuZJHMyVGHQ61wyccJUmW9Dt06ePUtDNzs5Gt27dLLYrLS3FlStXAMBsjsGoqCiEh4cjPz8f+/fvt7kv4/qIiAivK16R56xatQqrVq2yuE6lUuG+++7DX//6V3Tq1Kne27Y1HyYANGvWDM899xyefvppHDx4EFeuXDGb0+fq1atYvXo1gJqRuX//+9+t/oro6+vr0AjirVu3YubMmaiurkZcXBwWLVqE1q1b1+NZERERERERERGRWDP+OtFdd92lLG/ZssVquy1btijDrnv16qXcL0kSBg8eDKBmBG5qaqrFx6empiojdAcPHsyh9eQQg8GATZs2YdWqVWYX1btR5eXlyMnJwcmTJ3HixAmcOHHCbG7I48ePm7X/448/lLmfp06d2uCrPa5btw7PPfccqqur0bNnT3z55Zcs5hIRERERERER3YAmO0I3Pj4ed9xxB3755Rf88MMPmDBhQp3Tzi9fvowPP/wQQM08JxMmTDBbP23aNKxZswZ6vR6zZ8/GihUr4O/vr6yvrKzE7NmzAdRM1zBt2jTXPikSyjPPPINnn33W7L7KykqcO3cOGzZswNKlS7F06VKkp6dj0aJFdaZPsKeoqAhLlizB5s2bce7cOZvzwRhHoRsdO3ZMWTb9IeNGLF26FMuXL4csyxgwYADmz59f7+dCREREzpeWloZdu3bh0KFDOHXqFIqKiqDRaBAeHo5bbrkFEyZMQO/evW1uw9qFWC155513zC6iSkREREQ3pskWdAHgtddeQ2pqKq5evYrHH38c06ZNQ0pKCvz8/HDkyBEsWLBAuaDZ888/X+c09k6dOmH69OlYsGAB0tPTMXnyZDz66KOIjo5GdnY2Pv/8c6UwNn36dHTs2NHdT5EE4+/vj7i4OLz88svo2LEjZs2ahYMHD+L//u//MHPmTIe3k56ejunTp6O4uNih9qZz+wLmBd7w8HCH92vJsmXLAABhYWGYN28ei7lEREReYMqUKThw4ECd+7VaLc6ePYuzZ89i3bp1GDt2LGbPns35/oiIiIi8SJMu6Hbq1An/+7//i+effx4FBQVYsGABFixYYNZGkiQ88cQTePTRRy1uY+bMmSgsLMQ333yDY8eOWSy6TZw4ETNmzHDFU6BGbOLEiXj//fdRXFyMb775xuGCbnV1NWbMmIHi4mJoNBo88MADGDx4MDp27IgWLVooX8iys7MxZMgQAK69muOwYcOwefNmFBUV4eWXX8aHH35odoFBIiIicr/8/HwANT/cDh8+HL1790ZkZCQMBgNSU1OxePFi5OXlYf369dDpdHj//fftbnPRokU2fwhu06aN0+InIiIiasqafFWld+/e+P777/Hll19i69atyMnJgVarRevWrdG3b188+OCDVi+YBtRcvGrOnDkYNmwYVq9ejbS0NOUCUwkJCZg0aRJSUlLc+IyosVCpVOjQoQOKi4tx+fLlOhcus+aPP/5AdnY2AOCNN97APffcY7GdrdG7pvvJz89HdHR0/YI38fLLL6NVq1ZYsWIFtmzZghdeeAEffPABi7pEREQeFBMTg5kzZ2LYsGF15spPSkrCmDFjMHnyZJw9exbff/897rvvPvTp08fmNjt27MgLABMRERG5ASsqqClePfvss3XmM62PlJQUFm7J6XQ6nbKs1+sdesypU6eU5REjRlhtl56ebnVd9+7dleUDBw40qKALALNmzYJOp8Pq1auxefNm/O1vf8N7773X4IutERER0Y357LPPbK4PCwvDK6+8gieeeAIAsHnzZrsFXSIiIiJyDxZ0GxGOePRekiQhICAAfn5+Dj+moqICWVlZAGrm1nVkdC5gXgSuqKhAcHBwnTYGgwFr1661uo3k5GQEBgaivLwcy5cvx5gxYxpUfJUkCf/85z+V/W7atAkqlQrvvvsuVCrVDW/XFTQaDRISEpRl8k7MkziYKyJxJScnK8vnz5/3YCSOY3/Ye/F4IA7mSgzMkziYKzGI1ofwrkoKNYgkSZ4OgayQJAkqlapeOZo3bx4qKysBAAMGDHC4oGp68b1vv/3WYpv3338fR48etbqN5s2bY9KkSQCAo0ePYs6cOVbn2dVqtSgsLLQblyRJmD17tnJ16++//x6vvPIKDAaD3ce6kyRJ8PX1ha+vL99TXox5EgdzRSSu6upqZdnbfoC1hp8z3ovHA3EwV2JgnsTBXIlBtNyIVX4mzwtq5ekInM9Dz6mwsBAnTpwwu6+qqgrnzp3D+vXr8euvvwIA/Pz88Nxzzzm83QEDBqBly5YoLCzEhx9+iJycHAwdOhShoaE4f/481qxZgz179uCWW27BoUOHrG7n+eefx2+//YYTJ07gyy+/xOHDh3HfffehS5cu0Gg0uHTpEg4cOIAffvgBM2bMUAq1tkiShH/9618wGAxYv349NmzYALVajTlz5gj34UlERNTY7d+/X1mOjY212/7VV1/FmTNnUFxcjKCgIHTo0AH9+/fH/fffj4iICFeGSkRERNSksKDbiLhlpGPXUa7fRyMkyzL0er3ZPLirVq3CqlWrbD4uLCwM7777LuLi4hzeV2BgIP7973/j6aefRlVVFVavXo3Vq1ebtenbty/+8Y9/YNQo6/kMCAjA0qVL8dxzz2H//v04evQoZs2a5XAc1qhUKrzzzjvQ6/XYuHEj1q1bB7VajdmzZ3tFUddgMKCkpAQA0KJFC2FGJDU1zJM4mCsiMRkMBixYsEC5bWtefqN9+/Ypy8XFxSguLsaff/6JL774Aq+99hruu+8+l8Rqqqqqyuo6Hx8f5TPIYDCYTVNlia+vr7Jcux9XmyRJZqfR6nQ6m31zlUpldmqnVqu1ejZU7dhlWYZWq7UZu0ajUfpV9p6rq2NXq9VQq9XK8UCn06FZs2ZWjwei5cl0m7Isw9iblSHDoLf9/UylVkH67yNkWbb9fU42wPR8PVflCaj5uxvPwLOWK9HyZC12Ud9PxliYp+u8NU8AUFlZiWvXrgGwnCvmyTOx186TrT6EN2JBtxFx9KJZ5H6yLKOqqsqhD7eQkBB07twZKSkpGD9+PFq0aFHv/d1+++345ptvsGDBAvzxxx+4cuUKmjVrhs6dO2P06NGYOHEiLl68aHc7YWFh+PLLL7FlyxZs3LgRf/75J4qKiiBJEsLDw9G9e3cMGTIEw4YNq1d8KpUK//73v2EwGPDDDz9g7dq1UKlU+Oc//+nxoq5Op8Pp06cBAAkJCWYHS/IezJM4mCsiMS1ZsgRHjhwBANx1113o0aOH1bbR0dEYOnQoevbsiTZt2gAAcnJysHnzZmzevBlVVVV44403IEmSMqWTq9Q++8lUXFyccm2B8vJyZGZm2txWr169lOWCggLk5ORYbRsUFIT4+Hjl9rlz51BcXGy1fXh4uNlFZzMyMmz2E2NiYpTrKWi1WqSlpdmM3fTztqSkRPkctkSj0SAxMVG5nZubi/z8fKvtQ0JCzEZsZ2VloayszGr7qKgoREREmB0PbBEtT4GBgejatSsAoEpbDf//3m/QG5CXn2cz9ojwCKWYUFlViStXrlht62cIREuT267KE1Bz7LY3b7ZoeTLVGN5PAPNUm7fmCYDN6Q4B5skad+fJVh/CG7GgS+RGvXv3RkZGRoNHqCUnJ9v9kL/pppvw7rvvWl0fFRVldxtGQ4cOxdChQx2Ob/z48XanYFCr1fjggw/wwQcfOLxdIiIicr19+/bh/fffBwC0bNkSb775ptW2Q4cOxbhx4+r8IJuYmIiRI0dix44dePbZZ6HVavHOO+9g0KBBaN26tSvDJyIiImr0JNnW+GPyetXV1covIPHx8QgKCnL4sSdPnoROp4OPjw9uuukmV4VIqDmVoKKiAkDNVAY85dg1nPGaNn1PcTSh92KexMFcOc/c3UtwNC/bJdu+JaIT7gioGWHGPDVtJ0+exJQpU1BSUgI/Pz8sWrQIffr0adA2P/30U3z00UcAgBkzZuDJJ590RqgK08+Z2NhYBAYGWmzHU1o9E7vxlNba31usXeldtDyZTbmw/wtIpZdqlp095UKLtlD3edipsZsyPfXYkVyJlidrsYv6fgKYp9q8NU8AUFZWhuPHjwOwnCvmyTOx185TcXExsrKyAIjRH+YIXSIiIiIiQnZ2Nh5++GGUlJQoZ9I0tJgLAJMmTcLHH38MWZaxf/9+pxd0TWk0Goe+gKlUqnp9Uav9pc8e0y+UjrBW3LTEeLV0R9X3uboydtPHNMY8mY5UlyDVKxZJstNeqjvnZn3cSJ6Mj7OXA9HyZKoxvJ+Mj2OervO2PJm2t5cr5sk6d+ZJBBwmSERERETUxOXl5eGhhx5Cfn4+JEnCnDlzMGTIEKdsu2XLlggJCVH2Q0REREQNw4IuEREREVETVlRUhIcffhjZ2TVTesyaNQtjx4516j48fdFTIiIiosaEBV0iIiIioibq2rVreOSRR3Dq1CkAwIsvvogpU6Y4dR9FRUW4cuUKgJqrZxMRERFRw3AOXSI34cgUMZhOvM6ceS/mSRzMFZH3qqiowGOPPYajR48CAJ544gk89thjTt/P6tWrlYuSOGNOXhITjwfiYK7EwDyJg7kiV2BBtxERbQLnpkSlUlm94jJ5F41Gg8TERE+HQXYwT+Jgroi8U3V1NZ555hkcOnQIADB16lTMnDmzXtvIycnB1atX0a1bN6ttduzYgU8++QQA4O/vjwkTJtx40A5gf9h78XggDuZKDMyTOJgrMYjWh2BBl4iIiIioiXnxxRexe/duAEC/fv0wceJEnDhxwmp7jUaDTp06md134cIFTJ06FT179sTAgQMRHx+PsLAwAEB2djY2b96MzZs3K6NzX375ZURERLjoGRERERE1HSzoEhERERE1MT///LOy/Mcff2DMmDE227dr1w7bt2+3uO7w4cM4fPiw1ccGBATg1VdfxaRJk24sWCIiIiIyw4JuI6LT6eDr6+vpMMgCWZah1WoB1Ixw4bw53kun0yE3NxcAEBkZCR8ffkx6I+ZJHMwVUePVvXt3vPvuu0hNTUV6ejouX76MK1euQKfToUWLFujcuTP69++Pe+65By1btnRLTOwPey8eD8TBXImBeRIHcyUGnU7n6RDqha+iRsR4Oht5H9OCro+PDwu6XsxgMCA/Px8AeFqoF2OexMFcEXmnzMzMBm8jODgYY8aMsTu6153YH/ZePB6Ig7kSA/MkDuZKDKL1IVSeDoCIiIiIiIiIiIiIHMOCLhEREREREREREZEgWNAlIiIiIiIiIiIiEgQLukRERERERERERESCYEGXiIiIiIiIiIiISBAs6BIREREREREREREJwsfTAZDzSJLk6RDICkmSoFarlWXyXiqVCiEhIcoyeSfmSRzMFRG5E/tZ3ovHA3EwV2JgnsTBXIlBtD4EC7qNiI8P0+mtJEmCv7+/p8PAoEGDcOHCBYwbNw5z5871dDheycfHB7GxsZ4Og+xgnsTBXBGRO7E/7L14PBAHcyUG5kkczJUYROtD8KcBIg/at28f4uLilH+HDh3ydEhEREREREREROTFWNAl8qBvv/3W7Pb69es9EwgREREREREREQlBrPHEZJNWq4Wvr6+nwyALDAYDqqqqAAB+fn5QqVSorKzE5s2bAQCBgYEoLy/HTz/9hNdff91ledy+fbtLttuYaLVaZGVlAQBiY2Oh0Wg8HBFZwjyJg7kiIndif9h78XggDuZKDMyTOJgrMWi1Wk+HUC8s6FK9bD+/HUWVRZ4Ow6nC/MMwqP0gl+/HYDCY3d6yZQvKysoAAK+//jpee+01lJSUYPv27Rg+fLjL4yHLZFlW8iLLsoejIWuYJ3EwV0REBPB4IBLmSgzMkziYK3IFFnSpXooqi5Bfnu/pMBoF4/QKcXFxmDBhAj7//HOcOXMG69evZ0GXiIiIiIiIiIgsYkGXyAPy8/OxZ88eAMCYMWOU/3/00UfYvXs3ioqKEBYWZvXxeXl5WL58OX777TecP38elZWVaNGiBVq2bImbbroJAwYMwF133YXg4GCzxw0aNAgXLlzAuHHjMHfuXItxbdmyBXv37sXx48eRn58PnU6H0NBQ9OjRA6NHj8bw4cOhUlmefnvv3r2YOnUqAGDZsmVITk7Gpk2bsHr1amRmZqK8vByRkZEYPHgwHnvsMYSEhNzIn4+IiIiIiIiIqMliQZfIAzZu3Ai9Xg+VSoXRo0cDAEaPHo2PP/4YWq0W33//vVIYre3AgQN4/PHHUVpaanZ/YWEhCgsLceLECfzwww8IDQ3FwIEDHY5Jr9cjJSWlztQQQE2hd/v27di+fTu+/vprzJs3D0FBQTa3ZzAY8Le//Q3fffed2f1nz57FokWLsHXrVqxYsQKtW7d2OEYiIiIiIiIioqaOBV0iD9iwYQMAoG/fvoiIiAAAREdHo2fPnjh06BDWr19vsaBbXV2NmTNnorS0FEFBQZg8eTKSk5PRsmVLaLVa5OTk4PDhw9iyZUu9YzLO5dOvXz/ccccd6NKlC8LCwlBWVobs7GysXbsWhw8fxm+//Ya33noL//73v21u76OPPsLhw4cxZMgQjB07Fm3btkVBQQFWrlyJnTt34ty5c3jnnXfwwQcf1DtWIiIiIiIiIqKmigVdIjfLyMhAZmYmgOvTLRiNGTMGhw4dwtGjR3Hq1Cl07tzZbP3BgweRn18zh/H7779fZwRuUlISRo0ahVdffRWVlZX1ikutVuOnn35Chw4d6qzr27cvJkyYgI8//hiffPIJNmzYgCeffBIdO3a0ur3Dhw9jxowZePLJJ83uv+OOO/DII49g9+7d2Lx5s93pJYiIiIiIiIiI6DrLE2ESkcsYR+f6+/tj2LBhZutGjBgBjUYD4PpF00wVFBQoy3369LG6Dx8fnzrz59ojSZLFYq6pp59+GqGhoZBlGdu3b7fZtnv37njiiScs7uevf/0rAECn0+Hw4cP1ipOIiIiIiIiIqCnjCN1GxNqFqsjzJEmCr68vdDodfvjhBwDAwIED6xRdQ0JCkJKSgq1bt2Ljxo144YUXzPJqOt/sN998g2nTprksZoPBgMuXL6OsrAw6nU65v02bNrhy5QqOHz9u8/GjR4+GJEkW13Xv3l1Zzs7Odk7ATqJWqxEVFaUsk3dinsTBXBGRO7E/7L14PBAHcyUG5kkczJUYROtDsKDbiPCDwXtJkgSNRoPffvtNGWVbe7oFozFjxmDr1q24dOkS9u7di/79+yvrevXqhejoaGRnZ2POnDnYuHEjhg4dit69eyMhIQG+vr4NilOWZXz33Xf4+uuvceTIEZvTNly5csXmtmJiYqyuCwkJUZbLysrqHacrqdVqZV5j8l7MkziYKyJyJ/aHvRePB+JgrsTAPImDuRKDaH0IFnSJ3Mg4jUJISAhuv/12i20GDhyI5s2b4+rVq1i/fr1ZQVej0eD//u//8NxzzyErKwtpaWlIS0sDUDOFQ+/evTF27FiMHDmy3h9GVVVVeOaZZ/DLL7841N7eHL3+/v5W15n+8mUwGBwLkIiIiIiIiIiIWNAlcpdr164p884WFxejR48edh/z888/44033kBgYKByX+fOnbFx40bs2LED27dvx4EDB3Du3DlUVlZi9+7d2L17N7744gt8/vnnaNmypcPx/e///q9SzO3bty/uv/9+dO/eHa1atYK/v79ShJ0yZQoOHDhQn6dOREREREREREROwoJuI6LVaht8yj25hsFgwPr161FVVVWvx5WXl+Pnn3/G2LFjze5Xq9UYMmQIhgwZAgDIz8/Hr7/+ihUrVuDo0aM4evQo/vGPf+CTTz5xaD+yLOPrr78GAPTu3RtLly61On9MSUlJvZ6DaKqrq5VRz86YxoJcg3kSB3NFRO7E/rD34vFAHMyVGJgncTBXYtBqtZ4OoV5Y0CVyk++//x5AzYXNXn31Vbvt/+d//geXLl3Chg0b6hR0awsPD8eECRMwZswYTJo0CUePHsXOnTtRWVlpc+oDo+LiYly+fBkAMHz4cKvF3LKyMpw5c8bu9oiIiIiIiIiIyDVY0CVyg5ycHPz5558AgLvuugt/+ctf7D4mNTUVy5Ytwx9//IG8vDyHJlHXaDTo06cPjh49Cp1Oh6tXrzpU0NXr9cpyRUWF1XZr166FTqezuz0iIiIiIiIiInINy8PwiMipNmzYAFmWAdQUdB0xbNgwADXTNWzYsAEAlPlyramursb+/fsBAIGBgQgLC3NoX2FhYWjevDmAmpHE1dXVddocOXIEH330kUPbIyIiIiIiIiIi12iyI3Tj4uIcate3b18sX77cZptdu3ZhzZo1SEtLQ1FREcLCwpCQkIB7770XKSkpzgiXBPfdd98BqCmc9u7d26HH3HLLLWjdujUuX76M7777Do899hj27NmDTz/9FL1790ZKSgri4uIQFhaGyspKnD17Fl999RWOHj0KAJg4cSJ8fBx7i6tUKowePRorVqxAZmYmJk+ejIceeggdOnRAaWkpdu3ahZUrVyIwMBDh4eE4e/bsDf0diIiIiIiIiIioYZpsQdcZDAYDZs2apVxMyigvLw95eXnYunUr7rnnHrz11ltW5yQVTZi/YyM+ReLq53Tw4EGcP38eADBw4ECHXwsqlQpDhw7FypUrcfLkSaSnpwOoed3t27cP+/bts/rYwYMH48UXX6xXnDNnzsShQ4eQkZGB9PT0Oo8PCQnBvHnz8PHHH7OgS0RERERERETkIU2+oDt58mTcf//9VtcHBARYXfef//xHKeZ269YNjzzyCKKjo5GdnY2FCxfi2LFjWLt2LcLCwvDCCy84PXZPGNR+kKdDEI5xugSgptBaH3fddRdWrlwJAFi/fj1mzpyJuLg4/P7778jIyEB+fj4KCwsBAK1atUJiYiLGjh2LO++8s95xNmvWDKtWrcIXX3yBH3/8EefOnYNarUZkZCRSUlIwbdo0tGnTpt7bJSIiIiIiIiIi52nyBd2WLVuiS5cu9X7cmTNnsHjxYgBAjx49sGLFCuXiU4mJiRg0aBAeeOABpKenY9GiRZgwYQI6dOjg1NhJDG+99RbefPNNmxcbs6Z///7IzMw0u++uu+5yeB7e2rZv325zfUBAAJ566ik89dRTVtvYmoIkOTm5TrzWONqOiIiIiIiIiIiua/IF3Ru1dOlS6HQ6AMCsWbOUYq5RQEAAZs2ahUmTJkGn02HJkiV44403XBqTWq126fbpxkmSpLxGJEnycDRki4+PjzLHtqNzEJP7MU/iYK6IyJ3YH/ZePB6Ig7kSA/MkDuZKDKL1IRrHxK5uJssytm3bBgCIiYlBUlKSxXZJSUno1KkTAGDbtm2QZdmlcTWWeXobI0mSoFaroVarWdD1ciqVCsHBwQgODuZ7yosxT+JgrojInfg54714PBAHcyUG5kkczJUYRMuNWNF6iZycHOTn5wMA+vTpY7Nt3759AdRcKC0nJ8flsREREREREREREVHj1eTHev/000/48ccfceHCBahUKrRu3Ro9e/bEuHHj0K9fP4uPOXXqlLIcExNjc/um60+fPo3o6GjnBG6BwWBw2bapYWRZVvKjUqk4SteLGQwGlJeXAwACAwOF+5WuqWCexMFcEZE7sT/svXg8EAdzJQbmSRzMlRhE60M0+YKuaXEWAM6dO4dz585h/fr1GDJkCObOnYtmzZqZtbl06ZKy3KZNG5vbN12fm5vrhIitq6ystPrB4OPjo6wzGAzQ6XQwGAzKNBC1X7im25Fl2e50EfVpL0mSWUHT3pvGtL0jsdS3vTtil2UZlZWVAAB/f3+rBV1Xxl7f9qLmydI6rVZrc/vG6TAAQKfTKRdsi4+Ph0ajqdPe0vvJFl9fX2VZr9dDr9dbbStJktk+je9Va1Qqldk8TPaeq2nssixDq9XajF2j0Sh5svdcXR0782Sdt+YJACoqKmzminlyLPb/7gSybC1+889s6+2ubx8wbe/aaaGI3MXWZwJ5lumxOyEhwezznLwLcyUG5kkczJUYROtDNNmCbkBAAAYNGoT+/fujU6dOCAoKQlFREfbt24evvvoKxcXF2Lp1K5566iksXrzY7EtVWVmZshwYGGh3P0bGX2RcJSsry+q6uLg4BAcHK3EYP0yMX0YrKirM2gcFBSnLOp0O1dXVVretUqnMnmdVVZXNN4JGozH7AKusrLT5RdLPz0/5gi3Lcp1YawsICFC+1Or1elRVVVltK0mSWQ61Wq3NL+RqtdrsAnhVVVU2iwO+vr51vpAbC7uW+Pv7K4UQg8Fgsy3APNWm1+strsvKyjJ739YWFRWFiIiIOvcfP37cYntr7ydrevXqpSwXFBTYnH4lKCgI8fHxyu1z586huLjYavvw8HCzkf8ZGRk2X8MxMTEIDQ0FUPN3TEtLsxm7aYejpKQEp0+fttpWo9EgMTFRuZ2bm6tMT2NJSEgIYmNjldvM03WNKU+m+bGUK+bJstp5AoCqqkqrf3sfHx/4+1//jK+oqITBYP0z3s/PDxrN9c94bXUVYLtLQ0REREREXqLJFnR/+eUXNG/evM79t912Gx588EE8+uijOHbsGPbt24dVq1Zh6tSpShvTwpOlUWGmahfEiIiIiIiIiIiIiG5Uky3oWirmGrVq1Qoff/wxRowYAa1Wiy+//NKsoOvn56cs2zu90nTEpOnITleIjY21OmLY9BTSwMBAJCQk4MyZM9Dr9VCr1WYjNy091vT0WXv8/Pzsnspvyt7fxbS9JEk2Y63d3t5zq02j0Zj9rWxtGzB/LTjSHrA/5YJR7RG19jBPNduxtC42NtbuKeKW2DqV38j4fnJUq1atlBF9ltT+u3fo0MHm3Nu1p1np2rWr3VPEjTQajd3YTZ9/ixYtbLavHXtkZKTFEbVGtWNnnq5rTHmKj49XRuZam3LBiHm6ztJxws/P3+zMjFqPMLsVEOD4ZzYAaHxtH8+IiIiIiMh7NNmCrj3R0dG49dZbsWvXLpw7dw55eXnKF17TL1P2plEwPe3c3vQMDVX7FHlrVCoVfH19oVKpzC7UZU3tuVTtqW/7+kwI7upYXBm76T4ceZzIz9WTsVtaZ28kvTWOvKeM7ydH1Z5f1B5bPzBYUp/nKklSvWKv73N1Zey1H8c8XedteTJtby9XzJPdnUCSHPssdrTd9fa8WCcRERERkShY0LUhNjYWu3btAgCzgq7phc5ML5Bmien6yMhIF0RJREREREREROR6289vR1FlkUu2HeYfhkHtB7lk20SNDQu6NlgbrdK5c2dl2dYFTWqvj4mJcU5gRERERERERERuVlRZhPxy6xfUJSL3qP/54k1IVlaWsmw6v2BUVBTCw8MBAPv377e5DeP6iIgIREVFuSBKIjKyNdclEREREREREVFjwBG6VmRnZ+O3334DALRv396soCtJEgYPHoxVq1bh9OnTSE1NRVJSUp1tpKamKiN0Bw8e7PL56eo7r6FarYZOp4Ner4fBYLih+WDJMSqVysaFbMgZ9Ho99Ho9AOsXz3KEr68vevXq5aywyEWYJ3EwV0TkTjc6Hzu5Ho8H4mCuxMA8iYO5EoNofYgmWcHbvn07dDqd1fUFBQV47rnnoNVqAQD3339/nTbTpk1TikazZ89GZWWl2frKykrMnj0bQM1FWKZNm+as8J3G37/mCtiyLKO0tNTD0RA1THFxsbLs6gsQEhERERERERF5SpMcofv2229Dq9Vi2LBhSEpKQrt27eDv748rV65g7969WL16Na5cuQIA6NWrF6ZMmVJnG506dcL06dOxYMECpKenY/LkyXj00UcRHR2N7OxsfP755zh27BgAYPr06ejYsaM7n6JDmjdvjpKSEgDXL94WHBzMkbokDFmWUVVVhatXr6KwsFC5PzQ01INRERERERERERG5TpMs6AJAfn4+li9fjuXLl1ttM2zYMLz99tvw9fW1uH7mzJkoLCzEN998g2PHjmHmzJl12kycOBEzZsxwVtg2GU83d1RQUBACAgJQUVEBvV6PCxcuQJKkBp2uTtYZ53d19dQbTYler68zb26LFi3g5+fXoG0WFBQAAFq1asX3g5dinsTBXBGRO9W3P0zuw+OBOJgrMTBP4mCuxCBaH6JJFnTnzp2Lffv2ITU1FdnZ2SguLkZpaSkCAwPRpk0b9OzZE+PGjUPPnj1tbkelUmHOnDkYNmwYVq9ejbS0NFy5cgWhoaFISEjApEmTkJKS4qZnBRgMhnq1lyQJ7du3x/nz51FRUQGgpuhoazoKujGyLCtTeGg0GhZ1XaR169Zo2bJlg7ah1+uRk5MDoGakLw+23ol5EgdzRUTuVN/+MLkPjwfiYK7EwDyJg7kSg2h9iCZZ0O3bty/69u3rtO2lpKS4tXDrTCqVCh06dEBZWRmuXbumjNYl5zIYDErR3N/fn9NaOIlKpYKvry+CgoIQHBxsdTQ9EREREREREVFj0SQLumROkiQEBwcjODjY06E0WtXV1UhLSwNQM/8yC49ERERERERERHQjOEyQiIiIiIiIiIiISBAs6BIREREREREREREJggVdIiIiIiIiIiIiIkFwDl0iIiIiIiIiIrIrp6gcmQUlLtl2XCte14fIUSzoErmBJEkICgpSlsl7MVdiYJ7EwVwRea+0tDTs2rULhw4dwqlTp1BUVASNRoPw8HDccsstmDBhAnr37u3w9nbt2oU1a9YgLS0NRUVFCAsLQ0JCAu69916kpKS48JmQCHg8EAdzJQZP5alSZ0Bplc5l226M+J4iV2BBtxHRaDSeDoGs0Gg0iI+P93QY5ADmSgzMkziYKyLvNGXKFBw4cKDO/VqtFmfPnsXZs2exbt06jB07FrNnz4avr6/VbRkMBsyaNQtff/212f15eXnIy8vD1q1bcc899+Ctt96CSuXaGd/YH/ZePB6Ig7kSA/MkDuZKDKL1IVjQJSIiIiJqYvLz8wEA4eHhGD58OHr37o3IyEgYDAakpqZi8eLFyMvLw/r166HT6fD+++9b3dZ//vMfpZjbrVs3PPLII4iOjkZ2djYWLlyIY8eOYe3atQgLC8MLL7zgludHRERE1JixoEtERERE1MTExMRg5syZGDZsGNRqtdm6pKQkjBkzBpMnT8bZs2fx/fff47777kOfPn3qbOfMmTNYvHgxAKBHjx5YsWIF/P39AQCJiYkYNGgQHnjgAaSnp2PRokWYMGECOnTo4PonSERERNSIufacJ3Irnc4189hQw+l0OmRlZSErK4t58nLMlRiYJ3EwV0Te6bPPPsPIkSPrFHONwsLC8Morryi3N2/ebLHd0qVLlff2rFmzlGKuUUBAAGbNmgWg5vNgyZIlTojeOn7OeC8eD8TBXImBeRIHcyUG0XLDgm4jIsuyp0MgKwwGA4qLi1FcXAyDoXFO9N5YMFdiYJ7EwVwRiSs5OVlZPn/+fJ31sixj27ZtAGpG/CYlJVncTlJSEjp16gQA2LZtm0v7rOwPey8eD8TBXImBeRIHcyUG0foQLOgSEREREVEd1dXVyrKli5nl5OQoc/Famo7BVN++fQHUXCgtJyfHiVESERERNT0s6BIRERERUR379+9XlmNjY+usP3XqlLIcExNjc1um60+fPu2E6IiIiIiaLl4UjYiIiIiIzBgMBixYsEC5PWLEiDptLl26pCy3adPG5vZM1+fm5johQsu0Wq3ZyGJTPj4+ykhjg8Fgd648X19fZVmv10Ov11ttK0kSNBqNclun09k8rValUsHH5/pXMa1Wa/NUT9PYZVmGVqu1GbtGo4EkSQDsP1dXx65Wq+vM1WwrftHyZLpNWZYh/fd+GTIMetunVqvUKkj/fYQsy7ZPxZYNMP0ruiNPxsdZIlqerMXeGN5PxsdZ4qw8mcUpy5DlmuckSdfHCNbEbeuUdUn5O9a0t/B3MXnuBoOh0eTJNHZLz4PvJ8/EbitPImBBl4iIiIiIzCxZsgRHjhwBANx1113o0aNHnTZlZWXKcmBgoM3tBQQEKMvl5eVOirKurKwsq+vi4uIQHBysxJCZmWlzW7169VKWCwoKbE4VERQUhPj4eOX2uXPnUFxcbLV9eHg4oqOjldsZGRk2v0jGxMQgNDQUQM0XzrS0NJuxJyQkKF/4S0pKbI6K1mg0SExMVG7n5uYqU2lYEhISYjZiOysry+y1UFtUVBQiIiLM7jt+/LjV9qLlKTAwEF27dgUAVGmrYbwsoEFvQF5+ns3YI8IjlGJCZVUlrly5YrWtnyEQLU1uuyNPgPVciZYnU43t/QS4Pk9hYWHKXOhVVZUoKyuDSqU2++yvqqq0WZzTaDTw87t+4czy8vI6xbaqqkqz9cbYRc+TaX4s5YrvJ8vcnSdbfQhvxCkXiIiIiIhIsW/fPrz//vsAgJYtW+LNN9+02K6qqkpZNh1BY4npaKLKykobLYmIiIjIHo7QJSIiIiIiAMDJkyfxzDPPQKfTwc/PDx999BFatmxpsa2fn5+ybO80RdNpEPz9/W20bJjY2Firo4VNT8MMDAxEQkKCw9tt1aqVMgLJEtPTiAGgQ4cOZiOcaqt9kbmuXbvaPaXVSKPR2I3dtMDeokULm+1rxx4ZGWlxBKBR7dhjY2PtntJaW3x8vNUfAUTLk+k2/TS+wH9/51CpVYgIt/53NLYx8vfzt92+RYjZTXfkCbCeK9HyZKqxvZ8A1+fJNE4/P38EBQXVae/n5w8/P9tTLpiy9FltOoLXdL3oeYqPj1dG5lrKFd9Plrk7T7GxsUKN0mVBtxGp/WIn76FSqRAeHq4sk/dirsTAPImDuSISR3Z2Nh5++GGUlJRArVbjgw8+QJ8+fay2N/1Cb28ahYqKCmXZ3vQMDeHr62s2GtgalUrlUDsja/NWWmP6BdcR9kY4m5IkqV6x1/e5uip20+OBn5+fQ/sRLU+m38ckSPWKRZLstJfMj6GufI3VN1ei5cmUqO8nYyweyZMkmc2de/1uCbWLtrZY2gZM3kOm/UaR8wTU5MfRXPH9ZJ2r81SfWLwBC7qNSH1f3OQ+Pj4+Nn8FI+/BXImBeRIHc0Ukhry8PDz00EPIz8+HJEmYM2cOhgwZYvMxphc6M71AmiWm6yMjIxsWrA3sD3svHg/EwVyJgXkSB3MlBtH6EBwqQ0RERETUhBUVFeHhhx9GdnY2AGDWrFkYO3as3cd17txZWbZ1UZPa62NiYm4sUCIiIiICwIIuEREREVGTde3aNTzyyCM4deoUAODFF1/ElClTHHpsVFSUcgrp/v37bbY1ro+IiEBUVFQDIiYiIiIiscYTk01arVa4OT+aCq1Wi4yMDAA1k4rXdy4Xch/mSgzMkziYKyLvVVFRgcceewxHjx4FADzxxBN47LHHHH68JEkYPHgwVq1ahdOnTyM1NRVJSUl12qWmpiojdAcPHuzS6z6wP+y9eDwQB3MlBubJebaf346iyiKXbT/ENwRDOtZMY6TT6YQ7tb+psHeBV2/DVxGRG8iyrHw42LrKInkecyUG5kkczBWRd6qursYzzzyDQ4cOAQCmTp2KmTNn1ns706ZNw5o1a6DX6zF79mysWLEC/v7Xr1JeWVmJ2bNnA6iZm27atGnOeQIkHB4PxMFciYF5cp6iyiLkl+e7bPsGvUFZZq7IWVjQJSIiIiJqYl588UXs3r0bANCvXz9MnDgRJ06csNpeo9GgU6dOde7v1KkTpk+fjgULFiA9PR2TJ0/Go48+iujoaGRnZ+Pzzz/HsWPHAADTp09Hx44dXfJ8iIiIiJoSFnSJiIiIiJqYn3/+WVn+448/MGbMGJvt27Vrh+3bt1tcN3PmTBQWFuKbb77BsWPHLI70nThxImbMmNGgmImIiIioBgu6RERERER0w1QqFebMmYNhw4Zh9erVSEtLw5UrVxAaGoqEhARMmjQJKSkpng6TiIiIqNFgQZeIiIiIqInJzMx0+jZTUlJYuCUiIiJyA5WnAyAiIiIiIiIiIiIix7CgS0RERERERERERCQITrnQiKjVak+HQFb4+PggJiZGWSbvxVyJgXkSB3NFRO7E/rD34vFAHMyVGJgncUgqSVnmccp7iZYbvusbEZWKA669lUqlQmhoqKfDIAcwV2JgnsTBXBGRO7E/7L14PBAHcyUG5kkcknS9oMvjlPcSLTdiRUtERERERERERETUhHGEbiMiy7KnQyArZFmGVqsFAGg0GrNf6Mi7MFdiYJ7EwVwRkTuxP+y9eDwQB3MlBuZJHKbHJlmWmSsvJVofgiN0GxGdTufpEMgKrVaLtLQ0pKWlKQdd8k7MlRiYJ3EwV0TkTuwPey8eD8TBXImBeRKHbLheKORxynuJlhu3F3SnTp2KadOm4cKFCw4/Ji8vT3kcEREREZHI2B8mIiIiooZw+5QL+/btgyRJqKiocPgxVVVVyuOIiIiIiETG/jARERERNQSnXCAiIiIiIiIiIiIShBAF3fLycgCAn5+fhyMhIiIiInI/9oeJiIiIyEiIgu4vv/wCAIiIiPBwJERERERE7sf+MBEREREZuXwO3VdffdXi/R9++CGaNWtm87HV1dXIzs5GWloaJElC3759XREiEREREZHLsD9MRERERM7k8oLut99+W+fiDbIsY9u2bQ49XpZlAECLFi3w2GOPOT0+IiIiIiJXYn+YiIiIiJzJ5QXdtm3bmt2+ePEiJElC69at4eNjffeSJMHPzw+tW7dGz549MXnyZJ5iZoetvyd5lkajQUJCgrJM3ou5EgPzJA7mioj9YXdif9h78XggDuZKDMyTOCTV9R91eZzyXqLlxuXRbt++3ex2fHw8AGDx4sXo3Lmzq3ffpNQe+UHeQ5Ik+Pr6ejoMcgBzJQbmSRzMFRH7w+7E/rD34vFAHMyVGJgncZgem3ic8l6i5cbt5ec+ffoAAAICAty9a4e9++67WLhwoXJ72bJlSE5OtvmYXbt2Yc2aNUhLS0NRURHCwsKQkJCAe++9FykpKa4OmYiIiIgEIUJ/mIiIiIi8l9sLusuXL3f3LuslIyMDS5Yscbi9wWDArFmz8PXXX5vdn5eXh7y8PGzduhX33HMP3nrrLahUKidHWzcW8k4GgwElJSUAaua/c/VrgW4ccyUG5kkczBVRXd7eHxYZ+8Pei8cDcTBXYmCexGGcCx+oyRtz5Z1E60PwVWTCWJzV6XRo2bKlQ4/5z3/+oxRzu3Xrhg8++ABr167FBx98gG7dugEA1q5diw8//NBVYSv0er3L90E3RqfT4fTp0zh9+jR0Op2nwyEbmCsxME/iYK6IyJ3YH/ZePB6Ig7kSA/MkDtlwvaDL45T3Ei03XjHjb2lpKcrKyhz649W+qIQzLVu2DGlpaYiJicHQoUPx2Wef2Wx/5swZLF68GADQo0cPrFixAv7+/gCAxMREDBo0CA888ADS09OxaNEiTJgwAR06dHBZ/EREREQkJm/pDxMRERGR9/NYQfe3337DypUrcfDgQeU0AXskScKxY8dcEs/Fixfx0UcfAQD++c9/Yu/evXYfs3TpUuWXsFmzZinFXKOAgADMmjULkyZNgk6nw5IlS/DGG284P3giIiIiEo639YeJiIiISAwemXLh7bffxiOPPILt27ejuLgYsiw7/M9V3nrrLZSXl2PcuHHo27ev3fayLGPbtm0AgJiYGCQlJVlsl5SUhE6dOgEAtm3b5tLnQERERERi8Mb+MBERERGJwe0jdDdu3Igvv/wSAODn54chQ4age/fuHp3Ee9OmTdixYwdCQkLw8ssvO/SYnJwc5OfnA7h+pWJr+vbtizNnziAvLw85OTmIjo5ucMxEREREJCZv7A8TERERkTjcXtBdvXo1ACAyMhJLly5F+/bt3R2CmatXr2LOnDkAgJdeeglhYWEOPe7UqVPKckxMjM22putPnz7Ngi4RERFRE+Zt/WEiIiIiEovbC7qZmZmQJAlPP/20V3Re3333XVy+fBm33HILJk6c6PDjLl26pCy3adPGZlvT9bm5ufUP0kFarRbV1dUW1/n4+CgjPgwGg92rYPr6+irLer3e5gU6JEmCRqNRbut0OhgMBqvtVSoVfHyuv/S0Wq3N0wdNY5dlGVqt1mbsGo0GkiQBsP9cXR27Wq2GWq02u89W/MyTZ2K3lCfj4yxhnjwTO/NknTfnyTR2S8+DeXIs9v/uBLJsLX5J2XZNU+vP07h9wLQ9T+N3J2/rDxMRERGRWNxe0DV+eenWrZu7d13HgQMHsHbtWvj4+OCf//yn2Rche8rKypTlwMBAm20DAgKU5fLy8voH6qCsrCyr6+Li4hAcHKzEkJmZaXNbvXr1UpYLCgqQk5NjtW1QUBDi4+OV2+fOnUNxcbHV9uHh4WajlDMyMmx+CY6JiUFoaCiAmi/XaWlpNmNPSEhQvvCXlJTg9OnTVttqNBokJiYqt3Nzc5WpNCwJCQlBbGyscjsrK8vstVBbVFQUIiIilC/mWq0Wx48ft9qeebLMXXkCYPY5YC1XzJNlzFOx1fZNNU+AeX4s5Yp5sqx2ngCgqqrS6t/ex8cH/v7X+xsVFZUwGKwXu/38/KDRXC+Oa6urANvdGXIib+oPE7mL6Q9V9fneRe7HXImBeRITc0XO4vaCbrt27ZCVleXSwqYjqqurMWvWLMiyjGnTpqFLly71enxVVZWyXGcETS2mo4kqKyvrFyg1CsYv5gcPHvR0KGSHvfczeQfmiYhE5i394caIxwfvZemHKvJOzJUYmCdxqNTX58c3PRuMvItofQi3v5KGDh2KrKws7NmzB71793b37hWfffYZTp8+jbZt2+KZZ56p9+P9/PyUZXunWJpOg+Dv71/vfTkqPj7e6gvQ9EMjMDAQCQkJDm+3VatWyggkS2r/wtShQweb8wTXvthH165d7Z7SaqTRaOzGbvo3aNGihc32tWOPjIw0G1lWW+3YY2Nj7Z56bMpe7MyTZcwT8wQwT7YwT5Y1pjwBgJ+fP4KCgqw9wuxWQIDt/kbt7fv6ua5/QnV5S3+YiIiIiMTk9oLuww8/jA0bNmDp0qUYOXKk2Wmc7pKVlYXPPvsMAPD666/bnTLBEtMvVPZGV1RUVCjLN7IvR2k0GrPRwNaoVCqH2hlZm7fSmvr+4lSfX0EkSapX7PV9rq6MHYBLY2eerGOeLGOerGOerGOeLHN1nv67E0iSyn47wOF25Bne0B8mIiIiInG5vaDbrFkzLFq0CE888QQmT56M559/HqNGjUKLFi3cFsPSpUuh1WoRHR2NyspK/PDDD3XanDx5Uln+448/UFBQAAAYOHAgAgMDzS50ZnqBNEtM10dGRjY0fKt0Ol39vxySW+h0OuWCeJGRkTzNwosxV2JgnsTBXBHV5Q394caK/WHvxeOBOJgrMTBP4jC9KK9Op2OuvJS9izN7G7e/igYPHgygZi7Zq1ev4u2338a//vUvhIaG2p2OQJIkbN26tcExGKdAyM7OxgsvvGC3/aeffqosb9u2DYGBgejcubNyn62LmtReHxMTU99wHcYrVHsvg8GgXHTI1qnN5HnMlRiYJ3EwV0R1eUN/uLFif9h78XggDuZKDMyTQEwOTTxOeS/RcuP2gu6FCxfMbsuyDFmWUVhYaPex3nQ1wKioKISHhyM/Px/79++32da4PiIiAlFRUe4Ij4iIiIi8VGPpDxMRERGRZ7i9oDtu3Dh377KOuXPnYu7cuTbbzJs3D/PnzwcALFu2DMnJyWbrJUnC4MGDsWrVKpw+fRqpqalISkqqs53U1FRlhO7gwYPZCSciIiJq4ryhP0xERERE4nJ7Qfedd95x9y5dZtq0aVizZg30ej1mz56NFStWmJ0mV1lZidmzZwOouRDLtGnTPBUqEREREXmJxtQfJiIiIiL34yWQG6BTp06YPn06ACA9PR2TJ0/Gpk2bkJaWhk2bNmHy5MlIT08HAEyfPh0dO3b0YLREREREREREREQkOl5ar4FmzpyJwsJCfPPNNzh27BhmzpxZp83EiRMxY8YM9wdHREREREREREREjYrHC7qVlZVIT09HQUEBKisrMWTIEAQHB3s6LIepVCrMmTMHw4YNw+rVq5GWloYrV64gNDQUCQkJmDRpElJSUjwdJhERERF5KdH7w0RERETkXh4r6Obm5uKDDz7ATz/9BJ1Op9zfo0cPdO7cWbm9du1arF69Gs2aNcPixYvddlGxZ599Fs8++6zD7VNSUjxeuOUF17yXSqVCSEiIskzeq9HnKuN7oKzAddsPagV0HeW67f9Xo89TI8JcEVnn7f1hEfFv472axPEgqJWY266lSeSqEWCeBGJyaOJxynuJlhuPFHT//PNPPPbYY7h69SpkWVbut/THGzRoEN566y3odDrs3r0bt99+uztDFYqPj8cHXJMVPj4+iI2N9XQY5IBGn6uyAuBarqejaLBGn6dGhLkisoz9Yddgf9h7NYnjgRt+1HaHJpGrRoB5EodpwZ3HKe8lWm7c/jPO1atX8dRTT6GkpAStWrXCG2+8gY0bN1pt37JlS9xxxx0AgF27drkrTCIiIiIil2B/mIiIiIgawu3l52XLlqGwsBChoaFYvXo12rZta/cxt956K7Zt24YjR464IUIiIiIiItdhf5iocdp+ZAmKik65ZNthYZ0xKPGvLtk2ERGJx+0F3R07dkCSJDz00EMOdV4BKHOInT9/3pWhCU+r1cLX19fTYZAFWq0WWVlZAIDY2FhoNBoPR0TWMFdiYJ7EwVwR1cX+sOuwP+y9msLxoOjaReRfyXLNxjWBrtmuBU0hV40B8yQOg96gLOt0OuFO7W8qtFqtp0OoF7e/ioyd0N69ezv8mBYtWgAASktLXRITkavJsoyysjJlmbwXcyUG5kkczBVRXewPU1PE44E4mCsxME9iYq7IWdw+h25VVRWA+k02XF5eDgDw9/d3SUxERERERO7C/jARERERNYTbR+i2bNkSly5dQk5ODhITEx16TEZGBgAgPDzclaEREREReS1/HxWC/VzTdfP1kVyyXbKM/WEiIiIiagi3F3QTExNx6dIl/Prrrxg5cqTd9rIsY+3atZAkqV6npRERERE1JlFhgfD1b+GSbUf6BwJlLtk0WcD+MBERERE1hNunXBg9ejRkWcbGjRuVkQa2zJ07F8ePHwcAjB071sXRERERERG5FvvDRERERNQQbh+hO2TIECQnJ2Pv3r3461//iueffx7Dhg1T1ut0OuTl5eHQoUNYvnw5Dh8+DEmSMHToUNxyyy3uDpeIiIjIK5TlnUbehXSXbLt5VC8goL1Ltk11sT9MRERERA3h9oIuAMybNw9//etfcezYMcyePRuzZ8+GJNXM3TZu3DiztrIsIykpCXPnzvVEqEREREReQV9VgeqKqy7ZtqGqEghwyabJCvaHiYiIiOhGeaSg27x5c3z11Vf45JNPsHLlSly7ds1iu4CAAEyZMgXPPfccfH193RyleFQqt8+gQQ5Sq9WIiopSlsl7MVdiYJ7EwVyJgZdEcz/2h12D/WHvxeOBOJgrMTBPAjHpaPE45b1Ey41HCroA4Ovri5kzZ+Lxxx/H/v37kZaWhqKiIuj1eoSEhKBbt2649dZb0axZM0+FKBx+iHsvtVqNiIgIT4dBDmCuxMA8iYO5IrKO/WHnY3/Ye/F4IA7mSgzMkzhMC4U8Tnkv0XLjsYKuUWBgIFJSUpCSkuLpUIiIiIiI3I79YSIiIiKqD48XdImIiIiIyP0KCwtx5MgRHDlyBGlpaUhLS0NxcTGAmnl8HZmzd926dXj11Vcd2t8777yD8ePHNyRkIq9WWq1D3rUql2w7METnku0SEZGYPFLQvXjxIgCgVatWducCq6qqQmFhIQCgbdu2Lo9NZFqtlnOreanq6mqkpaUBABISEpgnL8ZciYF5EgdzJQbZ0wE0Qd7QH7711ludti1vwf6w92oKxwODXka13uCybbtLU8hVY8A8icNg8rmg1Wqh0Wg8GA1Zo9VqPR1Cvbi9oLt79248+uijCAwMxPbt2+1+6FRUVOAvf/kLqqqq8MUXXyA5OdlNkRIREREROZ839ofbtm2LmJgY7N69+4a3sWjRIoSHh1td36ZNmxveNhERERFd5/aC7k8//QRZljFkyBC0aNHCbvuQkBAMGzYM69evx6ZNm1jQJSIiIiKheUt/+Omnn0ZCQgISEhLQqlUr5OTkYPDgwTe8vY4dOypXXCciIiIi11HZb+JcqampkCQJt912m8OPGTBggPJYIiIiIiKReUt/+LnnnsPAgQPRqlUrp22TiIiIiFzP7QXdCxcuAKj5Bd9R7du3BwDk5OS4IiQiIiIiIrdhf5iIiIiIGsLtBV2drubqnGq12uHHGNtWVbnmiqFERERERO7C/jAREVHToVJfL71JkuTBSKgxcXtBNzQ0FACQnZ3t8GOMbUNCQlwREhERERGR2zTW/vCrr76KAQMGoEePHkhOTsa9996L//znP8jLy/N0aERERESNitsvita1a1dcvnwZmzZtwvDhwx16zKZNmwAAN910kytDIyIiIiJyucbaH963b5+yXFxcjOLiYvz555/44osv8Nprr+G+++5zeQxarRbV1dUW1/n4+EClqhnPYjAYlJHS1vj6+irLer0eer3ealtJkqDRaJTbOp0OBoPBanuVSgUfn+tfxbRaLWRZttreNHZZlqHVam3GrtFolFFg9p6rq2NXq9V1RqPbil+0PNVuc50MWP+z/Dcg5T+Ajb/hf7dmxh15Mj7OEtHyZC32xvB+Mj7OEmflySxOWYYs1zwnSbo+RrAmbluvY8lsdKpxG2ZMnrvBYHBbnnKKynH88hUboUuQYBI7DDafak0c19t3aRWkLFt6TfD95JnYa7+f7MXubdxe0B08eDB27dqFLVu24Mcff8SIESNstt+0aRN+/vlnSJKEIUOGuClKMdXntD1yLx8fH8TFxSnL5L2YKzEwT+JgrsTAk//cq7H1h6OjozF06FD07NkTbdq0AVAz1+/mzZuxefNmVFVV4Y033oAkSZg0aZJLY8nKyrK6Li4uDsHBwQCA8vJyZGZm2txWr169lOWCggKb8xcHBQUhPj5euX3u3DkUFxdbbR8eHo7o6GjldkZGhs0vkjExMcrIbq1Wi7S0NJuxJyQkKF/4S0pKcPr0aattNRoNEhMTldu5ubnIz8+32j4kJASxsbHK7aysLJSVlVltHxUVhYiICOV4kJmZiePHj1ttL1qe2rRpg3bt2gEAZNMKjwyrPy4Y+fr6Kh/ABtkAndZ6YcNgMC/YuCpPgPnx2lquRMuTqcbwfgLcm6ewsDB06tQJAFBVVYmysjKoVGoEBgYq7auqKm0W5zQaDfz8/JXb5eXldYptVVWVZuuNsbs6T6WVVcgrKrHa3sfHB/7+AWax1X5PmvLz84NGc73oerW0XFnOzc1FUVGRWXu+nyxz5/vJ2F4kbv9mNXbsWHz22We4cOECXnrpJRw5cgRTp05FZGSkWbvc3FwsWbIEX375JSRJQmRkJO655x53hysU468i5H1UKpXyAU3ejbkSA/MkDuaKqK7G1B8eOnQoxo0bV2dOwMTERIwcORI7duzAs88+C61Wi3feeQeDBg1C69atPRQteRKPB+Lg90oxME9ETZsk2xp/7CIZGRmYMmUKysvLlc5fZGQkwsPDAQD5+fnIzc0FUDNkOzAwEF9++SW6devm7lC9XnV1tfILiOkvHUREFh1YAlzLdd32m0UCvf/quu0TNWGLt/4TJ7MPu2Tb3dr3R7eQmpGf7E+4hzf2h3NycjB48GAAwLhx4zB37lynbPfTTz/FRx99BACYMWMGnnzySads18i0PxwfH292eqYpntLqmdhrn9Jqb9SqaHkybWP+Oe3cKRc6t78F04e84dTYTTX2PFmLne8nc45MuTD31y+QnncegPOnXOgR0R6v3P6QEq+78jR39xdIv3S+QbHX3r7p+U/dw6OU58UpF7z3/VRWVqaMdhehP+yRcx+7du2KtWvX4m9/+xuOHTsGALh48aJZp9Woe/fu+J//+R+zYdNkma0XNnmWwWBAeXnNaRaBgYH8NdWLMVdiYJ7EwVwRWdaU+sOTJk3Cxx9/DFmWsX//fqcXdE2p1WqHvoCpVKp6fVGzNm+lNfWdYsZaEdoSSZLqFXt9n6urYr+R44HIeQKk+s1nI9luXHutK2M3GAxKodCRXImcJ1HfT4AH8yRJZoXc63ebFzHtsbQN0/eB6fNxdZ5qCraO91Hr0/a/m1dYn3f7+nq+nyxz7We2eNOYemwyu9jYWKxbtw6//fYbduzYgYyMDFy5UjMJdWhoKLp164ZBgwahf//+ngpROLZ+lSHP0ul0yjw4IvzS05QxV2JgnsTBXInB7adrEYCm0x9u2bIlQkJCcOXKFeTl5bl0X+wPey8eD8TBXImBeRKISUfLdOQxeRfR+hBuL+hevHgRQM0vSCEhIbjttttw2223uTsMIiIiIiKPaIr94dpz7BIRkZj8fVQI9nNNKcnfh4VOIke5vaA7aNAgSJKE119/HVOmTHH37omIiIiIPKqp9YeLioqUkcfGOYKJiEhMUWGB8PVv4ZJthwcGumS7RI2R23/+8Pf3B1BzSgARERERUVPT1PrDq1evVuYE7tOnj4ejISIiIhKf20foRkRE4Pz587yAFxERERE1SY2lP5yTk4OrV6+iW7duVtvs2LEDn3zyCYCaQvaECRPcFR4REblAWd5p5F1Id8m2g9r1ALq4ZNNEjY7bC7q33XYbzp8/j4MHDyIpKcnduyciIiIi8ihv6Q8fOHAA58+fV24bp0UAgHPnzmHdunVm7cePH292+8KFC5g6dSp69uyJgQMHIj4+HmFhYQCA7OxsbN68GZs3b1ZG57788suIiIhw1dMhIiI30FdVoLriqsu2TUSOcXtBd+rUqfj222+xePFijBo1ip06IiIiImpSvKU//PXXX+Pbb7+1uO7QoUM4dOiQ2X21C7pGhw8fxuHDh63uJyAgAK+++iomTZp048ESERERkcLtc+h27NgR7733HioqKnDvvfdi48aNqK6udncYREREREQe0Vj6w927d8e7776LKVOm4Oabb0bbtm0REBAAjUaDVq1aoV+/fpg5cya2bdvGYi4RERGRE3lkhC4AhIWFIScnBy+//DL+/ve/o0OHDmjRogVUKus1ZkmSsHTpUneFKhyNRuPpEMgKX19f9OrVy9NhkAOYKzEwT+JgrsQgeTqAJsZb+sNz587F3Llzb/jxwcHBGDNmDMaMGeOUeJyB/WHvxeOBOJgrMTBPApGu97R8fNxehiMHidaHcPsrad++fZBMXsyyLKO6uhonT560+hhJkiDLstnjiIiIiIhExP4wERERETWE2wu6ffr0cfcuiYiIiIi8BvvDRERERNQQbi/oLl++3N27bDL0er2nQyAr9Ho9CgoKAACtWrWCWq32cERkDXMlBuZJHMwVUV3sD7sO+8Pei8cDcTBXYmCeRCIrS3q9nrnyUqL1ITh5RyNiMBg8HQJZodfrkZOTAwAIDQ3lB7gXY67EwDyJg7kSg2y/CZEQ2B/2XjweiIO5EgPzJBCTjpYss9flrUTrQ1i/4gIREREREREREREReRWvGKF76dIlXL58GZWVlUhISIC/v7+nQyIiIiIichv2h4mIiIjIUR4r6JaWlmLhwoX49ttvkZ+fr9y/ceNGdO7cWbn9ww8/4Oeff0azZs3w9ttveyJUIiIiIiKnY3+YiIiIiG6ERwq6Z8+exWOPPYbs7Gyz+UMkSarT9uabb8bf/vY3yLKMsWPHonfv3g3ef2lpKXbt2oW0tDSkp6cjLy8PRUVFqKqqQrNmzdC5c2fccccdmDhxIkJDQ+1u79ChQ1i5ciUOHjyIgoICNG/eHPHx8Rg3bhxGjRrV4HiJiIiIqHHxdH+YiMirZHwPlBW4bvtBrYCu/G5ORI2H2wu6VVVVePzxx3H+/HkEBARgypQp6NOnDx5//HGL7aOiopCcnIw//vgD27dvd0oH9siRI3jhhRcsrisqKsK+ffuwb98+LFq0CO+++y5uv/12q9uaN28ePv30U7PJkwsKCrB7927s3r0bGzduxMcffww/P78Gx01ERERE4vOG/jARkVcpKwCu5Xo6CiIiYbi9oLtq1SqcO3cOAQEBWLlyJbp27Wr3MXfccQf27NmD1NRUp8URGRmJ5ORkdO/eHZGRkWjdujUMBgMuXbqEzZs3Y8uWLbhy5QqefPJJfP3114iPj6+zja+++grz588HALRv3x6PP/44unTpgvz8fCxbtgx79+7Fzp078dprr+H99993WuxEREREJC5v6Q8TERERkZjcXtD9+eefIUkSpk6d6lDnFYBSTD137pxTYkhOTsbOnTutrh85ciS2bt2Kp59+GlqtFvPnz1cKt0bFxcV47733AABt27bF6tWrERYWpqwfOHAgnn76aezYsQPff/897r33XiQnJzslfhKPJEkICgpSlsl7MVdiYJ7EwVwR1eUN/WEid+PxQBzMlRiYJ4EwPeQCbi/oZmVlAQAGDBjg8GNCQkIAAFevXnVKDGq12m6bIUOGoFOnTjhz5gwOHDhQZ/3atWtx7do1AMBLL71kVsw17uPNN9/EL7/8Ar1ej0WLFrm8oKvRaFy6fbpxGo3G4ihv8j7MlRiYJ3EwV2Lg9wz38ob+cGPF/rD34vFAHMyVGJgnkVzvafn4eORSVuQA0foQKnfvsLy8HAAQGBjo8GOqq6sBuP+Fb/y1q6qqqs66bdu2AQCCg4MxdOhQi49v06YN+vfvDwDYs2cPSktLXRQpEREREYlCpP4wEREREXkftxd0jaMLLly44PBjTp48CQBo3bq1K0Ky6PTp0zh+/DgAICYmxmxddXU1jhw5AgBISkqCr6+v1e307dtXeUx6erqLoiUiIiIiUYjSHyYiIiIi7+T2gm737t0BAPv373f4MRs2bIAkSUhKSnJRVDUqKipw9uxZfPHFF3jwwQeh0+kAANOmTTNrd/bsWej1egB1i721ma43nl7nKsZ4yfvodDpkZWUhKyuLefJyzJUYmCdxMFdikD0dQBPjzf1h0fFzxnvxeCAO5koMzJNIrve0mCvvJVpu3H7O1rBhw7Bz506sWbMGDz30ENq2bWuz/ZIlS7B//35IkoS//OUvTo9n3bp1ePXVV62uf+yxxzB69Giz+y5duqQst2nTxub2TdebPs4VqqurrZ6G5+PjA5Wqpn5vMBjsvlBNRx3r9XqlgG2JJElmc43odDoYDAar7VUqlVmcWq0Wsmz9q6Rp7LIsQ6vV2oxdo9Eok8Lbe66ujl2tVkOtVsNgMKC4uBhAzWvC2j6YJ8/EbsyTMRZ7uRItT6bblGVZmcFJhgyD3vq2AUClVkH67yNkWbYZCyTAdIZy5um6pvp+AmqmLbKVK+bJsdiB/34VsBEPTC+IYqudpfbkVt7WH25MbL1nybNMj93R0dGeDYZsYq7EwDwJhIcmIYjWh3B7Qffuu+/GkiVLkJmZiQcffBD/+Mc/cMcddyjrJUmCLMtIS0vD0qVLsWnTJkiShN69eyMlJcVtcXbt2hVvvfUWEhMT66wrKytTlu3NfWa63jhfmqvYGgEcFxeH4OBgJY7MzEyb2+rVq5eyXFBQgJycHKttg4KCzCZjP3funHJgsSQ8PNzsgJORkWHzS3BMTAxCQ0MB1Hy5TktLsxl7QkKC8oW/pKQEp0+fttpWo9GY5Tg3Nxf5+flW24eEhCA2Nla5nZWVZfZ6qC0qKgoRERFm9xmn8rCEebLME3kCrOdKtDwFBgYqV1Gv0lbD/7/3G/QG5OXn2Yw9IjxCKc5VVlXiypUrVtuqVWpEtIhSbhcWFtp8rsxT03g/mebHUq6YJ8tq5wkADHq9Mo9qbSqVCj4mBWCtTgfZRvHax8cHKpPCu8FgvTBOzidKf5iIiIiIvJPbC7oqlQr/+7//i/vvvx8XLlzAE088AX9/f2XEyoMPPoiysjLlC4ssy2jfvj0+/PBDl8QzZMgQ9OjRAwBQWVmJ7Oxs/Pjjj9iyZQtefPFFvPbaaxg4cKDZY0wvkmbvKnimI4kqKyudGDkRERERicjb+sNEREREJBaPXCa3bdu2WL9+PWbPno2ffvoJFRUVyrqioiJlWZIkjBgxAm+++SZatGjhkliaN2+O5s2bK7cTExPxl7/8BevXr8crr7yCp556Cv/6178wfvx4pY2fn5+ybO/0StORNP7+/jZaNlxsbKzVEcOmp5AGBgYiISHB4e22atVKGYFkiVTrlM0OHTrYPOXDeHqqUdeuXe2e0mqk0Wjsxm5aZG/RooXN9rVjj4yMtDgC0Kh27LGxsXZPPa4tPj7e6g8BzJNlnsgTYD1XouXJdJt+Gl/gv79JqdQqRIRb/zsa2xj5+/nbbl/r7O2WLVvafK7MU9N4P8XHxysjcy3linmyrHbsAKBSq21eiNVsX1amYLJGpbL8/iLX8ab+MBERiUeSJLtnDBNR4+WRgi5Qcwrn+++/jxdeeAE7d+5Eeno6ioqKoNfrERISgv/f3p3HR1Xf+x9/zySTHQkhBBDCkigBarggIKK2KFi3tiqiIgWlimhrlYrLo73tA1vFKu2t9nbxV8UVlFpWsbV6WwXl1mpZ5RJkUUBWIRCWQAghM5n5/ZHmMElmJplkzsx8Z17Px0MfJzPf+Z7v8Jlzzmc+c873DBw4UJdddpn69u0bk/Fdf/31+uCDD/TOO+9o5syZGj16tHVH4uzsbKtdS9Mo+D9v987W5XK16oue0+ls9RdCqfl8iC0JNo9vMC2d5ezP4XCENfZw36udY/d/DXFqLB7j1PC6lsZlWpz8i0QOOcIai8MRXnviFFwybU/+7VuKFXFqYR31K2rtgMLqG7ER7/kwACB+uVwua1q1lk40A5B4olbQ/eCDD/SPf/xD+/btk9frVUFBgUaMGKGrrrpKEydOjNYwwjJmzBi98847qq6u1j/+8Q/r5mjh3OgsnBuoAQAAIHGZmA8DAAAg/the0K2oqND3v/99bdiwodlzixcv1m9+8xs988wzKikpsXsoYcvLy7OWv/zyS2u5T58+SklJUV1dXcgbmkhq9Lz/TWUAAACQHEzOhwEAbfe3Tw/ocFXgG5q2V15Wqq4qPduWvgHEP1sLunV1dfre974X8q7Pe/fu1ZQpU/TnP/+5UQE1HpSXn7n7u/90CWlpaRo0aJA++eQTrV+/XrW1tUEvm1y1apX1moabr9kl0Hx7iA9Op1MFBQXWMuIXsTIDcTIHsUKyMz0fNg35cPzieGAOYhU5h6tqVX7cnpuj+3xnahDs++Ic4TGCaduRrQXdd955R2VlZXI4HOrVq5fuuusuDRo0SKmpqfrss8/08ssva/369Tp8+LBefvllPfjgg3YOJ2z/8z//Yy3369ev0XNjxozRJ598oqqqKr377rv6xje+0ez1Bw4c0McffyxJGjlypHJycmwdb7hzAyJ6UlNTQ96IB/GDWJmBOJmDWJnBrPTVLKbnw6YhH45fHA/MQazM4HCcKbaz74t3ZzItYhW/TIuNrT+3vfPOO5KkHj16aOHChRo3bpzOPfdc9e3bV1deeaXmzZun4cOHy+fzNSqe2m3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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot settings\n",
+    "plot_config = dict(\n",
+    "    bins=20,\n",
+    "    binrange=(0, 1),\n",
+    "    stat=\"percent\",\n",
+    ")\n",
+    "\n",
+    "for base_model, it_model in tqdm(base_it_pairs):\n",
+    "    fig, axes = plt.subplots(ncols=2, figsize=(8, 3), gridspec_kw=dict(wspace=0.33), sharey=False)\n",
+    "\n",
+    "    for idx, ax, m in zip(range(len(axes)), axes, (base_model, it_model)):\n",
+    "        if m is None:\n",
+    "            ax.remove()\n",
+    "            continue\n",
+    "\n",
+    "        model_row = task_df[(task_df[model_col] == m) & (task_df[\"uses_all_features\"])].iloc[0]\n",
+    "        model_id = model_row.name\n",
+    "\n",
+    "        scores_df = scores_df_map[model_id]\n",
+    "        scores_df[\"group\"] = sens_attr_data.loc[scores_df.index]\n",
+    "\n",
+    "        # sns.histplot(\n",
+    "        #     data=scores_df,\n",
+    "        #     x=\"risk_score\",\n",
+    "        #     hue=\"group\",\n",
+    "        #     ax=ax,\n",
+    "        #     **plot_config,\n",
+    "        # )\n",
+    "\n",
+    "        for group_val, group_name in sens_attr_map.items():\n",
+    "            group_scores_df = scores_df[sens_attr_data.loc[scores_df.index] == group_val]\n",
+    "            sns.histplot(\n",
+    "                group_scores_df[\"risk_score\"],\n",
+    "                label=group_name,\n",
+    "                ax=ax,\n",
+    "                alpha=0.5,\n",
+    "                **plot_config,\n",
+    "            )\n",
+    "\n",
+    "        # Remove left plot legend\n",
+    "        if idx == 0:\n",
+    "            ax.legend(loc=\"upper left\")\n",
+    "\n",
+    "        ax.set_xlim(0, 1)\n",
+    "        # ax.set_ylim(0, 1)\n",
+    "\n",
+    "        ax.set_title(model_row[\"name\"])\n",
+    "\n",
+    "    plt.plot()\n",
+    "    save_fig(fig, f\"score-per-subgroup.{base_model}.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77754ec9-7368-4edd-bf26-465710a99e73",
+   "metadata": {},
+   "source": [
+    "## Compute mean under-/over-estimation of risk score\n",
+    "It's basocally akin to computing ECE without the absolute..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "323b2ad2-1d01-4a9c-ab88-5f5079a7d014",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_confidence_bias_metric_helper(data):\n",
+    "    \"\"\"Helper to compute the under- / over-confidence metric.\n",
+    "    \"\"\"\n",
+    "    mean_label = data[\"label\"].mean()\n",
+    "    mean_score = data[\"risk_score\"].mean()\n",
+    "\n",
+    "    if mean_score < 0.5:\n",
+    "        return ((1 - mean_score) - (1 - mean_label))\n",
+    "    else:\n",
+    "        return (mean_score - mean_label)\n",
+    "\n",
+    "def compute_confidence_bias_metric(model_id, quantiled_bins=False, scores_df=None):\n",
+    "    if scores_df is None:\n",
+    "        scores_df = scores_df_map[model_id]\n",
+    "\n",
+    "    discretization_func = pd.qcut if quantiled_bins else pd.cut\n",
+    "    scores_df[\"score_bin\"] = discretization_func(scores_df[\"risk_score\"], 10, labels=range(10))\n",
+    "    \n",
+    "    bias_metric_val = (1 / len(scores_df)) * sum(\n",
+    "        len(bin_data) * compute_confidence_bias_metric_helper(bin_data)\n",
+    "        for bin_ in range(10)\n",
+    "        if len(bin_data := scores_df[scores_df[\"score_bin\"] == bin_]) > 0\n",
+    "    )\n",
+    "    return float(bias_metric_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "57d97476-15cf-4eba-a53b-468b1f23f1ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81416b6ae6a041bdacc00c142bcdb7b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "confidence_scores = {\n",
+    "    m: compute_confidence_bias_metric(m)\n",
+    "    for m in tqdm(scores_df_map.keys())\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "66363a5b-9104-4da5-a96f-ad59eb464634",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Meta-Llama-3-8B-Instruct__ACSPublicCoverage__-1__Num     -0.161384\n",
+       "Mixtral-8x7B-Instruct-v0.1__ACSPublicCoverage__-1__Num   -0.061690\n",
+       "Mixtral-8x7B-Instruct-v0.1__ACSEmployment__-1__Num       -0.056597\n",
+       "Meta-Llama-3-70B-Instruct__ACSPublicCoverage__-1__Num    -0.052221\n",
+       "gemma-7b__ACSIncome__-1__Num                             -0.033482\n",
+       "                                                            ...   \n",
+       "gemma-7b__ACSEmployment__-1__Num                          0.341868\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num                       0.355603\n",
+       "gemma-2b__ACSIncome__-1__Num                              0.367876\n",
+       "Mistral-7B-v0.1__ACSTravelTime__-1__Num                   0.438141\n",
+       "gemma-2b__ACSTravelTime__-1__Num                          0.438312\n",
+       "Name: under_over_score, Length: 100, dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_results_df[\"under_over_score\"] = all_results_df.apply(lambda row: confidence_scores[row.name], axis=1)\n",
+    "all_results_df.sort_values(\"under_over_score\", ascending=True)[\"under_over_score\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "a558de58-e4e9-4105-8fbd-4abafe41fca2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TASK = \"ACSIncome\"\n",
+    "# TASK = \"ACSTravelTime\"\n",
+    "# TASK = \"ACSMobility\"\n",
+    "# TASK = \"ACSEmployment\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "987310f0-4078-47d0-b841-b43e83446e4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/under_over_score.ACSIncome.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_df = all_results_df[\n",
+    "    (all_results_df[task_col] == TASK)\n",
+    "    & (~all_results_df[numeric_prompt_col].isna())\n",
+    "]\n",
+    "\n",
+    "# Omit Gemma models bc they're garbage :)\n",
+    "plot_df = plot_df.drop(index=[id_ for id_ in plot_df.index if \"gemma\" in id_.lower() or \"yi\" in id_.lower()])\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(4, 3))\n",
+    "sns.barplot(\n",
+    "    data=plot_df.sort_values(\"under_over_score\", ascending=True),\n",
+    "    x=\"under_over_score\",\n",
+    "    y=\"name\",\n",
+    "    # y=task_col,\n",
+    "    hue=\"is_inst\",\n",
+    "    ax=ax,\n",
+    ")\n",
+    "\n",
+    "handles, _ = plt.gca().get_legend_handles_labels()\n",
+    "plt.legend(handles=handles, labels=[\"Base\", \"Instruct\"])\n",
+    "\n",
+    "plt.ylabel(\"\")\n",
+    "plt.xlabel(r\"$\\mathrm{R}_\\mathrm{bias}$\")\n",
+    "\n",
+    "plt.title(f\"Risk score confidence bias\")\n",
+    "\n",
+    "save_fig(fig, f\"under_over_score.{TASK}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e37f6688-c821-43ee-899a-a6308b723bf3",
+   "metadata": {},
+   "source": [
+    "## Compute risk score fairness per sub-group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "46e74470-cf67-43da-855a-727a37912916",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    2\n",
+       "2    1\n",
+       "8    1\n",
+       "Name: RAC1P, dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sens_attr_data = acs_dataset.data[sens_attr_col]\n",
+    "sens_attr_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "6c50ef72-2027-4cb8-8279-89880376652c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_signed_score_bias(scores_df, quantiled_bins=False, n_bins=10):\n",
+    "    discretization_func = pd.qcut if quantiled_bins else pd.cut\n",
+    "    scores_df[\"score_bin\"] = discretization_func(scores_df[\"risk_score\"], n_bins, duplicates=\"drop\", retbins=False)\n",
+    "    bin_categories = scores_df[\"score_bin\"].dtype.categories\n",
+    "\n",
+    "    return (1 / len(scores_df)) * sum(\n",
+    "        len(bin_data) * (bin_data[\"risk_score\"].mean() - bin_data[\"label\"].mean())\n",
+    "        for bin_ in bin_categories\n",
+    "        if len(bin_data := scores_df[scores_df[\"score_bin\"] == bin_]) > 0\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "a6dfaa3b-1a75-44fc-9f72-3dcb61051223",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_signed_subgroup_score_bias(model, group: int | str) -> float:\n",
+    "    scores_df = scores_df_map[model]\n",
+    "    if isinstance(group, str):\n",
+    "        group = name_to_sens_attr_map[group]\n",
+    "\n",
+    "    scores_df = scores_df[sens_attr_data.loc[scores_df.index] == group]\n",
+    "    # return compute_confidence_metric(model, scores_df=scores_df)\n",
+    "    return compute_signed_score_bias(scores_df)\n",
+    "\n",
+    "\n",
+    "def compute_calibration_fairness(model: str, priv_group: int | str, unpriv_group: int | str) -> float:\n",
+    "    return (\n",
+    "        compute_signed_subgroup_score_bias(model, group=priv_group)\n",
+    "        - compute_signed_subgroup_score_bias(model, group=unpriv_group)\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "2770de00-0ade-4c9a-8a06-684957f2b169",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3388197/3464681156.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  scores_df[\"score_bin\"] = discretization_func(scores_df[\"risk_score\"], n_bins, duplicates=\"drop\", retbins=False)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>White_score_bias</th>\n",
+       "      <th>Black_score_bias</th>\n",
+       "      <th>Asian_score_bias</th>\n",
+       "      <th>White_v_Black_score_bias</th>\n",
+       "      <th>White_v_Asian_score_bias</th>\n",
+       "      <th>Asian_v_Black_score_bias</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mistral-7B-v0.1__ACSIncome__-1__Num</th>\n",
+       "      <td>0.353045</td>\n",
+       "      <td>0.329428</td>\n",
+       "      <td>0.319214</td>\n",
+       "      <td>0.023617</td>\n",
+       "      <td>0.033831</td>\n",
+       "      <td>-0.010215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gemma-1.1-2b-it__ACSIncome__-1__Num</th>\n",
+       "      <td>0.260100</td>\n",
+       "      <td>0.403764</td>\n",
+       "      <td>0.202488</td>\n",
+       "      <td>-0.143664</td>\n",
+       "      <td>0.057612</td>\n",
+       "      <td>-0.201276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Yi-34B-Chat__ACSIncome__-1__Num</th>\n",
+       "      <td>0.210531</td>\n",
+       "      <td>0.229600</td>\n",
+       "      <td>0.212839</td>\n",
+       "      <td>-0.019069</td>\n",
+       "      <td>-0.002308</td>\n",
+       "      <td>-0.016761</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     White_score_bias  Black_score_bias  \\\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num          0.353045          0.329428   \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num          0.260100          0.403764   \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num              0.210531          0.229600   \n",
+       "\n",
+       "                                     Asian_score_bias  \\\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num          0.319214   \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num          0.202488   \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num              0.212839   \n",
+       "\n",
+       "                                     White_v_Black_score_bias  \\\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num                  0.023617   \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num                 -0.143664   \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num                     -0.019069   \n",
+       "\n",
+       "                                     White_v_Asian_score_bias  \\\n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num                  0.033831   \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num                  0.057612   \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num                     -0.002308   \n",
+       "\n",
+       "                                     Asian_v_Black_score_bias  \n",
+       "Mistral-7B-v0.1__ACSIncome__-1__Num                 -0.010215  \n",
+       "gemma-1.1-2b-it__ACSIncome__-1__Num                 -0.201276  \n",
+       "Yi-34B-Chat__ACSIncome__-1__Num                     -0.016761  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calibration_per_subgroup_df = pd.DataFrame([\n",
+    "    pd.Series(\n",
+    "        {\n",
+    "            f\"{group}_score_bias\": compute_signed_subgroup_score_bias(model_id, group)\n",
+    "            for group in sens_attr_map.values()\n",
+    "        },\n",
+    "        name=model_id,\n",
+    "    )\n",
+    "    for model_id in task_df.index\n",
+    "])\n",
+    "\n",
+    "calibration_per_subgroup_df[\"White_v_Black_score_bias\"] = \\\n",
+    "    calibration_per_subgroup_df[\"White_score_bias\"] - calibration_per_subgroup_df[\"Black_score_bias\"]\n",
+    "\n",
+    "calibration_per_subgroup_df[\"White_v_Asian_score_bias\"] = \\\n",
+    "    calibration_per_subgroup_df[\"White_score_bias\"] - calibration_per_subgroup_df[\"Asian_score_bias\"]\n",
+    "\n",
+    "calibration_per_subgroup_df[\"Asian_v_Black_score_bias\"] = \\\n",
+    "    calibration_per_subgroup_df[\"Asian_score_bias\"] - calibration_per_subgroup_df[\"Black_score_bias\"]\n",
+    "\n",
+    "calibration_per_subgroup_df.sample(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1efc1204-874f-4e5e-b1b4-716e5af1d044",
+   "metadata": {},
+   "source": [
+    "**Note:** This is an example of comparing risk score bias between different sub-groups. A more detailed analysis must be conducted to draw any sort of conclusions regarding prediction fairness of the LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "ca1a5631-3070-4d93-9258-b527dac8fe85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/score_bias.Asian_v_Black_score_bias.numeric-prompt.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "GROUP_A = \"Asian\"\n",
+    "# GROUP_A = \"White\"\n",
+    "GROUP_B = \"Black\"\n",
+    "# GROUP_B = \"Asian\"\n",
+    "\n",
+    "COLUMN = f\"{GROUP_A}_v_{GROUP_B}_score_bias\"\n",
+    "\n",
+    "plot_df = calibration_per_subgroup_df.join(results_df, how=\"left\", validate=\"1:1\")\n",
+    "plot_df = plot_df.drop(index=[id_ for id_ in plot_df.index if \"gemma\" in id_.lower()])   # omit Gemma models bc they're garbage :)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(3, 3))\n",
+    "sns.barplot(\n",
+    "    data=plot_df.sort_values(COLUMN, ascending=True),\n",
+    "    x=COLUMN,\n",
+    "    y=\"name\",\n",
+    "    hue=\"is_inst\",\n",
+    "    ax=ax,\n",
+    ")\n",
+    "\n",
+    "handles, _ = plt.gca().get_legend_handles_labels()\n",
+    "plt.legend(handles=handles, labels=[\"Base\", \"Instruct\"])\n",
+    "\n",
+    "plt.ylabel(\"\")\n",
+    "plt.xlabel(\"$\\Delta_{SCE}$\")\n",
+    "\n",
+    "plt.title(\n",
+    "    \"Signed risk score bias\\n\"\n",
+    "    + r\"($SCE_{\" + GROUP_A\n",
+    "    + r\"} - SCE_{\" + GROUP_B\n",
+    "    + r\"}$)\"\n",
+    ")\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"score_bias.{COLUMN}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "bc511e0a-2928-464e-a09b-ebd2a9928b9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>White_score_bias</th>\n",
+       "      <th>Black_score_bias</th>\n",
+       "      <th>Asian_score_bias</th>\n",
+       "      <th>White_v_Black_score_bias</th>\n",
+       "      <th>White_v_Asian_score_bias</th>\n",
+       "      <th>Asian_v_Black_score_bias</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>...</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mixtral-8x22B-v0.1__ACSIncome__-1__Num</th>\n",
+       "      <td>0.054322</td>\n",
+       "      <td>-0.069436</td>\n",
+       "      <td>0.130153</td>\n",
+       "      <td>0.123758</td>\n",
+       "      <td>-0.075831</td>\n",
+       "      <td>0.199589</td>\n",
+       "      <td>0.740583</td>\n",
+       "      <td>0.148527</td>\n",
+       "      <td>0.828562</td>\n",
+       "      <td>0.761428</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Mixtral 8x22B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.769300</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>0.740595</td>\n",
+       "      <td>0.379947</td>\n",
+       "      <td>0.411113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num</th>\n",
+       "      <td>0.110399</td>\n",
+       "      <td>0.055872</td>\n",
+       "      <td>0.156710</td>\n",
+       "      <td>0.054527</td>\n",
+       "      <td>-0.046311</td>\n",
+       "      <td>0.100837</td>\n",
+       "      <td>0.767882</td>\n",
+       "      <td>0.096716</td>\n",
+       "      <td>0.885938</td>\n",
+       "      <td>0.770218</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Mixtral 8x22B (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.769967</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>0.767882</td>\n",
+       "      <td>0.298323</td>\n",
+       "      <td>0.474955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>penai/gpt-4o-mini__ACSIncome__-1__Num</th>\n",
+       "      <td>-0.001300</td>\n",
+       "      <td>-0.019960</td>\n",
+       "      <td>-0.000407</td>\n",
+       "      <td>0.018661</td>\n",
+       "      <td>-0.000893</td>\n",
+       "      <td>0.019553</td>\n",
+       "      <td>0.777393</td>\n",
+       "      <td>0.080767</td>\n",
+       "      <td>0.904748</td>\n",
+       "      <td>0.758922</td>\n",
+       "      <td>...</td>\n",
+       "      <td>GPT 4o mini (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.777302</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>0.775512</td>\n",
+       "      <td>0.317359</td>\n",
+       "      <td>0.363743</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 70 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 White_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                   0.054322   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num          0.110399   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                   -0.001300   \n",
+       "\n",
+       "                                                 Black_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                  -0.069436   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num          0.055872   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                   -0.019960   \n",
+       "\n",
+       "                                                 Asian_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                   0.130153   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num          0.156710   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                   -0.000407   \n",
+       "\n",
+       "                                                 White_v_Black_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                           0.123758   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num                  0.054527   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                            0.018661   \n",
+       "\n",
+       "                                                 White_v_Asian_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                          -0.075831   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num                 -0.046311   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                           -0.000893   \n",
+       "\n",
+       "                                                 Asian_v_Black_score_bias  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                           0.199589   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num                  0.100837   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                            0.019553   \n",
+       "\n",
+       "                                                 accuracy  accuracy_diff  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num           0.740583       0.148527   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num  0.767882       0.096716   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num            0.777393       0.080767   \n",
+       "\n",
+       "                                                 accuracy_ratio  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                 0.828562   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num        0.885938   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                  0.904748   \n",
+       "\n",
+       "                                                 balanced_accuracy  ...  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                    0.761428  ...   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num           0.770218  ...   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                     0.758922  ...   \n",
+       "\n",
+       "                                                               name  is_inst  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                Mixtral 8x22B    False   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num  Mixtral 8x22B (it)     True   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num              GPT 4o mini (it)     True   \n",
+       "\n",
+       "                                                 num_features  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                   -1.0   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num          -1.0   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                    -1.0   \n",
+       "\n",
+       "                                                 uses_all_features  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                        True   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num               True   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                         True   \n",
+       "\n",
+       "                                                 fit_thresh_on_100  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                        0.65   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num               0.65   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                         0.65   \n",
+       "\n",
+       "                                                 fit_thresh_accuracy  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                      0.769300   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num             0.769967   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                       0.777302   \n",
+       "\n",
+       "                                                 optimal_thresh  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                     0.45   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num            0.55   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                      0.35   \n",
+       "\n",
+       "                                                 optimal_thresh_accuracy  \\\n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num                          0.740595   \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num                 0.767882   \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num                           0.775512   \n",
+       "\n",
+       "                                                 score_stdev  score_mean  \n",
+       "Mixtral-8x22B-v0.1__ACSIncome__-1__Num              0.379947    0.411113  \n",
+       "Mixtral-8x22B-Instruct-v0.1__ACSIncome__-1__Num     0.298323    0.474955  \n",
+       "penai/gpt-4o-mini__ACSIncome__-1__Num               0.317359    0.363743  \n",
+       "\n",
+       "[3 rows x 70 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot_df.sort_values(COLUMN, ascending=False).head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "750f97e8-44cc-465b-a2fc-c08b626063d7",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "# Plots comparing different prompting schemes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "972c2e80-6c0e-4f37-be60-039677d2464a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Plot all models / i.t. / base?\n",
+    "IT_OR_BASE = \"any\"\n",
+    "# IT_OR_BASE = \"it\"\n",
+    "# IT_OR_BASE = \"base\"\n",
+    "\n",
+    "### Plot which metric\n",
+    "METRIC = \"ece\"\n",
+    "# METRIC = \"roc_auc\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "f42aa360-45b3-4c6d-a8f7-11f10ebacee0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plotting ALL models\n",
+      "model_names=['Gemma 2B', 'Gemma 7B', 'Mistral 7B', 'Llama 3 8B', 'Yi 34B', 'Mixtral 8x7B', 'Llama 3 70B', 'Mixtral 8x22B', 'Gemma 2B (it)', 'Gemma 7B (it)', 'Mistral 7B (it)', 'Llama 3 8B (it)', 'Yi 34B (it)', 'Mixtral 8x7B (it)', 'Llama 3 70B (it)', 'Mixtral 8x22B (it)', 'GPT 4o mini (it)']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prepare data\n",
+    "df = all_results_df_original.copy()\n",
+    "\n",
+    "min_max_per_task = {\n",
+    "    task: (np.min(values), np.max(values))\n",
+    "    for task in ALL_TASKS\n",
+    "    if len(values := df[(df[task_col] == task) & (~df[numeric_prompt_col].isna())][METRIC]) > 0\n",
+    "}\n",
+    "\n",
+    "# Sort models\n",
+    "sorted_df_index = sorted(\n",
+    "    df.index.tolist(),\n",
+    "    key=lambda id_: model_sort_key(id_, df),\n",
+    "    reverse=True,\n",
+    ")\n",
+    "df = df.loc[sorted_df_index]\n",
+    "\n",
+    "if IT_OR_BASE == \"it\":\n",
+    "    print(\"Plotting IT models\")\n",
+    "    df = df[df[\"is_inst\"] == True]\n",
+    "elif IT_OR_BASE == \"base\":\n",
+    "    print(\"Plotting BASE models\")\n",
+    "    df = df[df[\"is_inst\"] == False]\n",
+    "else:\n",
+    "    print(\"Plotting ALL models\")\n",
+    "    df = df.sort_values(by=[\"is_inst\", \"model_size\"], ascending=False)\n",
+    "\n",
+    "# Data for multiple-choice prompting\n",
+    "df_mult_choice = df[df[numeric_prompt_col] == False].set_index([\"name\", task_col], drop=False)\n",
+    "\n",
+    "# Data for numeric risk prompting\n",
+    "df_numeric = df[df[numeric_prompt_col] == True].set_index([\"name\", task_col], drop=False)\n",
+    "\n",
+    "# Sorted list of model names\n",
+    "model_names = df_numeric[\"name\"].unique().tolist()[::-1]\n",
+    "n_models = len(model_names)\n",
+    "print(f\"{model_names=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "91a7f4e9-6af2-44b4-a8ca-0e17cbcea1ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/ece_comparison_diff_prompts.any-models.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1133.33 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import FancyArrowPatch\n",
+    "\n",
+    "# Create a figure and axis\n",
+    "fig, axes = plt.subplots(ncols=len(ALL_TASKS), figsize=(8, n_models / 3), sharey=True)\n",
+    "\n",
+    "for task_i, task in enumerate(ALL_TASKS):\n",
+    "\n",
+    "    # Get current plot axis\n",
+    "    ax = axes[task_i]\n",
+    "        \n",
+    "    # Plot arrows for each model\n",
+    "    for i, model_name in enumerate(model_names):\n",
+    "\n",
+    "        curr_mult_choice_metric = df_mult_choice.loc[model_name, task][METRIC]\n",
+    "        curr_numeric_metric = df_numeric.loc[model_name, task][METRIC]\n",
+    "        \n",
+    "        ax.scatter(\n",
+    "            curr_mult_choice_metric, i,\n",
+    "            color=palette[0], marker=\"s\",\n",
+    "            label='Multiple-choice' if i == 0 else \"\",\n",
+    "        )\n",
+    "        ax.scatter(\n",
+    "            curr_numeric_metric, i,\n",
+    "            color=palette[1], marker=\"o\",\n",
+    "            label='Numeric' if i == 0 else \"\",\n",
+    "        )\n",
+    "\n",
+    "        # Draw a FancyArrowPatch arrow from before to after fine-tuning\n",
+    "        if METRIC == \"ece\":\n",
+    "            curr_metric_improved = curr_numeric_metric <= curr_mult_choice_metric\n",
+    "        else:\n",
+    "            curr_metric_improved = curr_numeric_metric >= curr_mult_choice_metric\n",
+    "        \n",
+    "        arrow = FancyArrowPatch(\n",
+    "            (curr_mult_choice_metric, i), (curr_numeric_metric, i),\n",
+    "            arrowstyle='->',\n",
+    "            color=palette[2] if curr_metric_improved else palette[3],\n",
+    "            mutation_scale=15,\n",
+    "            lw=1.5,\n",
+    "        )\n",
+    "        ax.add_patch(arrow)\n",
+    "\n",
+    "        # Set axes ranges according to overall min/max values\n",
+    "        lo, hi = min_max_per_task[task]\n",
+    "        if METRIC == \"ece\":\n",
+    "            lo = max(lo - 5e-2, 0)\n",
+    "            hi = min(hi + 5e-2, 0.475)\n",
+    "        elif METRIC == \"roc_auc\":\n",
+    "            lo = max(lo - 5e-2, 0.475)\n",
+    "            hi = min(hi + 4e-2, 1)\n",
+    "\n",
+    "        if task == \"ACSIncome\" and METRIC == \"roc_auc\":\n",
+    "            lo = 0.58\n",
+    "        ax.set_xlim(lo, hi)\n",
+    "    \n",
+    "    # Set y-ticks and y-labels\n",
+    "    ax.set_yticks(range(n_models))\n",
+    "    ax.set_yticklabels(model_names)\n",
+    "\n",
+    "    # # Prettify x-axis ticks\n",
+    "    # xticks = ax.get_xticks()\n",
+    "    # ax.set_xticks(xticks, [f\"{x}\" for x in xticks])\n",
+    "    \n",
+    "    # Add labels and title\n",
+    "    # ax.set_xlabel('ECE')\n",
+    "    ax.set_title(task[3:])\n",
+    "\n",
+    "    # Add horizontal line separating instruct and base models\n",
+    "    if IT_OR_BASE == \"any\":\n",
+    "        n_instruct_models = len(df_mult_choice[(df_mult_choice[\"is_inst\"] == True) & (df_mult_choice[task_col] == task)])\n",
+    "        line_y = n_instruct_models - 1.5\n",
+    "        ax.axhline(line_y, color='black', linestyle='--', linewidth=1) #, label=\"instruct / base\")\n",
+    "\n",
+    "        # Add text label near the horizontal line\n",
+    "        if task_i > len(ALL_TASKS) - 3:\n",
+    "            text_kwargs = dict(\n",
+    "                horizontalalignment='right',\n",
+    "                transform=ax.get_yaxis_transform(),  # Use axis coordinates for y-axis\n",
+    "                fontsize=9, color='black',\n",
+    "            )\n",
+    "\n",
+    "            ax.text(1, line_y + 0.1, 'i.t.', verticalalignment='bottom', **text_kwargs)\n",
+    "            ax.text(1, line_y - 0.1, 'base', verticalalignment='top', **text_kwargs)\n",
+    "        \n",
+    "\n",
+    "\n",
+    "# Add legend for marker\n",
+    "axes[0].legend(loc=\"upper right\", bbox_to_anchor=(-0.15, 0.01), title=\"Prompting scheme\")\n",
+    "\n",
+    "# X-axis label\n",
+    "if METRIC == \"ece\":\n",
+    "    x_axis_label = \"Expected Calibration Error (ECE)\"\n",
+    "elif METRIC == \"roc_auc\":\n",
+    "    x_axis_label = \"Predictive Power (ROC AUC)\"\n",
+    "\n",
+    "fig.text(0.5, 0.02 if IT_OR_BASE == \"any\" else -0.04, x_axis_label, horizontalalignment=\"center\", verticalalignment=\"center\")\n",
+    "\n",
+    "# Save figure\n",
+    "save_fig(fig=fig, name=f\"{METRIC}_comparison_diff_prompts.{IT_OR_BASE}-models.pdf\", add_prompt_suffix=False)\n",
+    "\n",
+    "# Show plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3db3e60-220d-4a15-bc46-c0815b39e6df",
+   "metadata": {},
+   "source": [
+    "## Scatter plot of ECE vs AUC for a few models (before and after numeric prompting)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "e77dd307-0e63-4f1d-b755-9025566e1947",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved figure to '../results/imgs/comparison_diff_prompts.any-models.pdf'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2600x440 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_METRIC = \"ece\"\n",
+    "Y_METRIC = \"roc_auc\"\n",
+    "\n",
+    "# Filter out small models\n",
+    "df_mult_choice = df_mult_choice[df_mult_choice[\"model_size\"] > 50]\n",
+    "df_numeric = df_numeric[df_numeric[\"model_size\"] > 50]\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=len(ALL_TASKS), figsize=(len(ALL_TASKS) * 2.6, 2.2), gridspec_kw=dict(wspace=0.4))\n",
+    "\n",
+    "unique_model_names = df_mult_choice.name.unique()\n",
+    "markers = dict(zip(unique_model_names, [\"s\", \"o\", \"D\", \"P\", \"X\", \"<\", \">\"]))\n",
+    "\n",
+    "for task_i, task in enumerate(ALL_TASKS):\n",
+    "\n",
+    "    # Create a figure and axis\n",
+    "    # fig, ax = plt.subplots(figsize=(2.5, 2.5))\n",
+    "\n",
+    "    ax = axes[task_i]\n",
+    "        \n",
+    "    # Plot arrows for each model\n",
+    "    for i, model_name in enumerate(df_mult_choice.name.unique()):\n",
+    "\n",
+    "        # Get data for each plotting scheme\n",
+    "        curr_mult_choice_row = df_mult_choice.loc[model_name, task]\n",
+    "        curr_numeric_row = df_numeric.loc[model_name, task]\n",
+    "\n",
+    "        # Get points to plot\n",
+    "        curr_mult_choice_point = (curr_mult_choice_row[X_METRIC], curr_mult_choice_row[Y_METRIC])\n",
+    "        curr_numeric_point = (curr_numeric_row[X_METRIC], curr_numeric_row[Y_METRIC])\n",
+    "\n",
+    "        # Plot point for multiple-choice prompting\n",
+    "        ax.scatter(\n",
+    "            *curr_mult_choice_point,\n",
+    "            color=palette[0], marker=markers[model_name],\n",
+    "            label='Multiple-choice' if i == 0 else \"\",\n",
+    "        )\n",
+    "\n",
+    "        # Plot point for numeric-style prompting\n",
+    "        ax.scatter(\n",
+    "            *curr_numeric_point,\n",
+    "            color=palette[1], marker=markers[model_name],\n",
+    "            label='Numeric' if i == 0 else \"\",\n",
+    "        )\n",
+    "\n",
+    "        if curr_numeric_point[0] <=  curr_mult_choice_point[0] and curr_numeric_point[1] >=  curr_mult_choice_point[1]:\n",
+    "            arrow_color = palette[2]\n",
+    "        elif curr_numeric_point[0] >  curr_mult_choice_point[0] and curr_numeric_point[1] <  curr_mult_choice_point[1]:\n",
+    "            arrow_color = palette[3]\n",
+    "        else:\n",
+    "            arrow_color = \"grey\"\n",
+    "\n",
+    "        # Draw a FancyArrowPatch arrow from before to after fine-tuning\n",
+    "        arrow = FancyArrowPatch(\n",
+    "            curr_mult_choice_point, curr_numeric_point,\n",
+    "            arrowstyle='->',\n",
+    "            color=arrow_color,\n",
+    "            mutation_scale=15,\n",
+    "            lw=1,\n",
+    "            ls=\"--\",\n",
+    "        )\n",
+    "        ax.add_patch(arrow)\n",
+    "\n",
+    "    ### Plot configs\n",
+    "    # Add labels and title\n",
+    "    ax.set_title(task[3:])\n",
+    "    ax.set_xlabel(\"ECE\")\n",
+    "    if task_i == 0:\n",
+    "        ax.set_ylabel(\"AUC\")\n",
+    "\n",
+    "# Create custom global legend\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "# Create a global legend for markers (model names)\n",
+    "marker_handles = [Line2D([0], [0], marker=markers[model], color='w', markerfacecolor='black', markersize=8, label=model) for model in unique_model_names]\n",
+    "\n",
+    "# Create a global legend for colors (prompting schemes)\n",
+    "color_handles = [\n",
+    "    Line2D([0], [0], marker='o', color='w', markerfacecolor=palette[0], markersize=8, label=\"Multiple-choice\"),\n",
+    "    Line2D([0], [0], marker='o', color='w', markerfacecolor=palette[1], markersize=8, label=\"Numeric\")\n",
+    "]\n",
+    "\n",
+    "# Position the legends outside the subplots\n",
+    "fig.legend(handles=marker_handles, title=\"Models\", loc='upper left', bbox_to_anchor=(0.13, -0.12), ncol=min(4, len(unique_model_names)))\n",
+    "fig.legend(handles=color_handles, title=\"Prompting Scheme\", loc='upper right', bbox_to_anchor=(0.9, -0.12), ncol=2)\n",
+    "\n",
+    "# Save figure\n",
+    "save_fig(fig=fig, name=f\"comparison_diff_prompts.{IT_OR_BASE}-models.pdf\", add_prompt_suffix=False)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/parse-acs-results.html b/notebooks/parse-acs-results.html
new file mode 100644
index 0000000..c8c8cbd
--- /dev/null
+++ b/notebooks/parse-acs-results.html
@@ -0,0 +1,512 @@
+
+
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+  <title>Fetch and parse ACS benchmark results under a given directory &mdash; folktexts 0.0.25 documentation</title>
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">Fetch and parse ACS benchmark results under a given directory</a></li>
+</ul>
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+             
+  <section id="Fetch-and-parse-ACS-benchmark-results-under-a-given-directory">
+<h1>Fetch and parse ACS benchmark results under a given directory<a class="headerlink" href="#Fetch-and-parse-ACS-benchmark-results-under-a-given-directory" title="Link to this heading"></a></h1>
+<p>Each ACS benchmark run outputs a json file. This script collects all such files under a given root directory, parses them, and aggregates them into a more easily digestable pandas DataFrame.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">tqdm.auto</span> <span class="kn">import</span> <span class="n">tqdm</span>
+</pre></div>
+</div>
+</div>
+<p><strong>[Action required]</strong> Set <code class="docutils literal notranslate"><span class="pre">RESULTS_ROOT_DIR</span></code> to the root results directory path:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">RESULTS_ROOT_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;/fast/groups/sf&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;folktexts-results&quot;</span> <span class="o">/</span> <span class="s2">&quot;2024-08-28_2&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Set the local path to the root data directory (needed only to train baseline ML methods):</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">DATA_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;/fast/groups/sf&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;data&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Important results columns:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_col</span> <span class="o">=</span> <span class="s2">&quot;config_model_name&quot;</span>
+<span class="n">task_col</span> <span class="o">=</span> <span class="s2">&quot;config_task_name&quot;</span>
+<span class="n">numeric_prompt_col</span> <span class="o">=</span> <span class="s2">&quot;config_numeric_risk_prompting&quot;</span>
+
+<span class="n">feature_subset_col</span> <span class="o">=</span> <span class="s2">&quot;config_feature_subset&quot;</span>
+<span class="n">predictions_path_col</span> <span class="o">=</span> <span class="s2">&quot;predictions_path&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Helper function to parse each dictionary containing benchmark results:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">num_features_helper</span><span class="p">,</span>
+    <span class="n">parse_model_name</span><span class="p">,</span>
+    <span class="n">get_non_instruction_tuned_name</span><span class="p">,</span>
+    <span class="n">prettify_model_name</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">parse_results_dict</span><span class="p">(</span><span class="n">dct</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Parses results dict and brings all information to the top-level.&quot;&quot;&quot;</span>
+
+    <span class="c1"># Make a copy so we don&#39;t modify the input object</span>
+    <span class="n">dct</span> <span class="o">=</span> <span class="n">dct</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+    <span class="c1"># Discard plots&#39; paths</span>
+    <span class="n">dct</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;plots&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="c1"># Bring configs to top-level</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">dct</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;config&quot;</span><span class="p">,</span> <span class="p">{})</span>
+    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">config</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">dct</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;config_</span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="c1"># Parse model name</span>
+    <span class="n">dct</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">parse_model_name</span><span class="p">(</span><span class="n">dct</span><span class="p">[</span><span class="n">model_col</span><span class="p">])</span>
+    <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">get_non_instruction_tuned_name</span><span class="p">(</span><span class="n">dct</span><span class="p">[</span><span class="n">model_col</span><span class="p">])</span>
+    <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">prettify_model_name</span><span class="p">(</span><span class="n">dct</span><span class="p">[</span><span class="n">model_col</span><span class="p">])</span>
+
+    <span class="c1"># Is instruction-tuned model?</span>
+    <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;is_inst&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span> <span class="o">!=</span> <span class="n">dct</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="ow">or</span> <span class="s2">&quot;(it)&quot;</span> <span class="ow">in</span> <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+
+    <span class="c1"># Log number of features</span>
+    <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;num_features&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">num_features_helper</span><span class="p">(</span><span class="n">dct</span><span class="p">[</span><span class="n">feature_subset_col</span><span class="p">],</span> <span class="n">max_features_return</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;uses_all_features&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">dct</span><span class="p">[</span><span class="n">feature_subset_col</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">dct</span><span class="p">[</span><span class="s2">&quot;num_features&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">dct</span><span class="p">[</span><span class="n">feature_subset_col</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">dct</span><span class="p">[</span><span class="n">feature_subset_col</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;full&quot;</span>
+
+    <span class="c1"># Assert all results are at the top-level</span>
+    <span class="k">assert</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">dct</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
+    <span class="k">return</span> <span class="n">dct</span>
+<br/></pre></div>
+</div>
+</div>
+<p>Iteratively search the root directory for results files matching the given regex:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">find_files</span><span class="p">,</span> <span class="n">load_json</span>
+
+<span class="c1"># Results file name pattern</span>
+<span class="n">pattern</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;^results.bench-(?P&lt;hash&gt;\d+)[.]json$&#39;</span>
+
+<span class="c1"># Find results files and aggregate</span>
+<span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">file_path</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">find_files</span><span class="p">(</span><span class="n">RESULTS_ROOT_DIR</span><span class="p">,</span> <span class="n">pattern</span><span class="p">)):</span>
+    <span class="n">results</span><span class="p">[</span><span class="n">Path</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">parse_results_dict</span><span class="p">(</span><span class="n">load_json</span><span class="p">(</span><span class="n">file_path</span><span class="p">))</span>
+
+<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">results</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Couldn&#39;t find any results at </span><span class="si">{</span><span class="n">RESULTS_ROOT_DIR</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Found </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">results</span><span class="p">)</span><span class="si">}</span><span class="s2"> benchmark results.&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "b984f67ca7644ba998983af5385ba147", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Found 241 benchmark results.
+</pre></div></div>
+</div>
+<p>Aggregate results into a single DataFrame, generate a unique identifier for each row, and drop potential duplicates:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
+
+<span class="k">def</span> <span class="nf">row_id</span><span class="p">(</span><span class="n">row</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Unique row identifier.&quot;&quot;&quot;</span>
+    <span class="n">numeric_or_multiple_choice</span> <span class="o">=</span> <span class="s2">&quot;Num&quot;</span> <span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="n">numeric_prompt_col</span><span class="p">]</span> <span class="k">else</span> <span class="s2">&quot;QA&quot;</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span><span class="si">}</span><span class="s2">__</span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span><span class="si">}</span><span class="s2">__</span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;num_features&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">__</span><span class="si">{</span><span class="n">numeric_or_multiple_choice</span><span class="si">}</span><span class="s2">&quot;</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">row_id</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Drop duplicates</span>
+<span class="n">len_with_dups</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">,</span> <span class="s2">&quot;is_inst&quot;</span><span class="p">,</span> <span class="s2">&quot;num_features&quot;</span><span class="p">,</span> <span class="n">task_col</span><span class="p">,</span> <span class="n">numeric_prompt_col</span><span class="p">])</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verify_integrity</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">if</span> <span class="n">len_with_dups</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Dropping </span><span class="si">{</span><span class="n">len_with_dups</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="si">}</span><span class="s2"> duplicates!&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+df.shape=(241, 58)
+Dropping 31 duplicates!
+df.shape=(210, 58)
+</pre></div></div>
+</div>
+<p>Load scores DFs and analyze score distribution:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">load_model_scores_df</span><span class="p">(</span><span class="n">df_row</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Loads csv containing model scores corresponding to the given DF row.&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">predictions_path_col</span> <span class="ow">in</span> <span class="n">df_row</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">pd</span><span class="o">.</span><span class="n">isna</span><span class="p">(</span><span class="n">df_row</span><span class="p">[</span><span class="n">predictions_path_col</span><span class="p">]):</span>
+        <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">df_row</span><span class="p">[</span><span class="n">predictions_path_col</span><span class="p">],</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="k">return</span> <span class="kc">None</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.evaluation</span> <span class="kn">import</span> <span class="n">compute_best_threshold</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
+
+<span class="c1"># Number of samples used to fit the one-parameter binarization threshold!</span>
+<span class="n">N</span> <span class="o">=</span> <span class="mi">100</span>
+
+<span class="n">fit_thresh_col</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;fit_thresh_on_</span><span class="si">{</span><span class="n">N</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="n">fit_acc_col</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;fit_thresh_accuracy&quot;</span>
+
+<span class="n">optimal_thres_col</span> <span class="o">=</span> <span class="s2">&quot;optimal_thresh&quot;</span>
+<span class="n">optimal_acc_col</span> <span class="o">=</span> <span class="s2">&quot;optimal_thresh_accuracy&quot;</span>
+
+<span class="n">score_stdev_col</span> <span class="o">=</span> <span class="s2">&quot;score_stdev&quot;</span>
+<span class="n">score_mean_col</span> <span class="o">=</span> <span class="s2">&quot;score_mean&quot;</span>
+
+<span class="n">scores_stats</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">row_id</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)):</span>
+
+    <span class="c1"># Load model scores</span>
+    <span class="n">scores_df</span> <span class="o">=</span> <span class="n">load_model_scores_df</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">scores_df</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Couldn&#39;t find scores for </span><span class="si">{</span><span class="n">row_id</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Extract scores and labels</span>
+    <span class="n">risk_scores</span> <span class="o">=</span> <span class="n">scores_df</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="n">scores_df</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+
+    <span class="c1"># Sample N rows to fit threshold</span>
+    <span class="n">scores_df_sample</span> <span class="o">=</span> <span class="n">scores_df</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="n">N</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+
+    <span class="c1"># Compute optimal threshold on each data sample</span>
+    <span class="n">fit_thr</span> <span class="o">=</span> <span class="n">compute_best_threshold</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">scores_df_sample</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">],</span> <span class="n">y_pred_scores</span><span class="o">=</span><span class="n">scores_df_sample</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">])</span>
+    <span class="n">opt_thr</span> <span class="o">=</span> <span class="n">compute_best_threshold</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">y_pred_scores</span><span class="o">=</span><span class="n">risk_scores</span><span class="p">)</span>
+
+    <span class="c1"># Evaluate accuracy</span>
+    <span class="n">fit_acc</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="p">(</span><span class="n">risk_scores</span> <span class="o">&gt;=</span> <span class="n">fit_thr</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">))</span>
+    <span class="n">opt_acc</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="p">(</span><span class="n">risk_scores</span> <span class="o">&gt;=</span> <span class="n">opt_thr</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">))</span>
+
+    <span class="c1"># Save results</span>
+    <span class="n">scores_stats</span><span class="p">[</span><span class="n">row_id</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="n">fit_thresh_col</span><span class="p">:</span> <span class="n">fit_thr</span><span class="p">,</span>
+        <span class="n">fit_acc_col</span><span class="p">:</span> <span class="n">fit_acc</span><span class="p">,</span>
+        <span class="n">optimal_thres_col</span><span class="p">:</span> <span class="n">opt_thr</span><span class="p">,</span>
+        <span class="n">optimal_acc_col</span><span class="p">:</span> <span class="n">opt_acc</span><span class="p">,</span>
+        <span class="n">score_stdev_col</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">risk_scores</span><span class="p">),</span>
+        <span class="n">score_mean_col</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">risk_scores</span><span class="p">),</span>
+    <span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "64f4e6718312499b84fb8c9637ff10fd", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<p>Update results DF with scores statistics:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scores_stats_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">scores_stats</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">scores_stats</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">scores_stats_df</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="n">results_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">df</span><span class="p">,</span> <span class="n">scores_stats_df</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="s2">&quot;columns&quot;</span><span class="p">)</span>
+<span class="n">results_df</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+scores_stats_df.shape=(210, 6)
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>accuracy</th>
+      <th>accuracy_diff</th>
+      <th>accuracy_ratio</th>
+      <th>balanced_accuracy</th>
+      <th>balanced_accuracy_diff</th>
+      <th>balanced_accuracy_ratio</th>
+      <th>brier_score_loss</th>
+      <th>ece</th>
+      <th>ece_quantile</th>
+      <th>equalized_odds_diff</th>
+      <th>...</th>
+      <th>name</th>
+      <th>is_inst</th>
+      <th>num_features</th>
+      <th>uses_all_features</th>
+      <th>fit_thresh_on_100</th>
+      <th>fit_thresh_accuracy</th>
+      <th>optimal_thresh</th>
+      <th>optimal_thresh_accuracy</th>
+      <th>score_stdev</th>
+      <th>score_mean</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>gemma-2-27b__ACSTravelTime__-1__Num</th>
+      <td>0.539392</td>
+      <td>0.258000</td>
+      <td>0.656000</td>
+      <td>0.504500</td>
+      <td>0.100184</td>
+      <td>0.823204</td>
+      <td>0.388077</td>
+      <td>0.347071</td>
+      <td>NaN</td>
+      <td>0.221264</td>
+      <td>...</td>
+      <td>Gemma 2 27B</td>
+      <td>False</td>
+      <td>-1</td>
+      <td>True</td>
+      <td>0.13000</td>
+      <td>0.514676</td>
+      <td>0.210000</td>
+      <td>0.524692</td>
+      <td>0.311014</td>
+      <td>0.219864</td>
+    </tr>
+    <tr>
+      <th>gemma-1.1-2b-it__ACSEmployment__-1__QA</th>
+      <td>0.462874</td>
+      <td>0.235384</td>
+      <td>0.631881</td>
+      <td>0.427372</td>
+      <td>0.091640</td>
+      <td>0.800718</td>
+      <td>0.408398</td>
+      <td>0.382143</td>
+      <td>0.376308</td>
+      <td>0.212855</td>
+      <td>...</td>
+      <td>Gemma 2B (it)</td>
+      <td>True</td>
+      <td>-1</td>
+      <td>True</td>
+      <td>0.02033</td>
+      <td>0.482054</td>
+      <td>0.007577</td>
+      <td>0.485395</td>
+      <td>0.232183</td>
+      <td>0.216562</td>
+    </tr>
+  </tbody>
+</table>
+<p>2 rows × 64 columns</p>
+</div></div>
+</div>
+<p>Check if any results are missing:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">experiments_per_model_task_pair</span> <span class="o">=</span> <span class="n">results_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="n">model_col</span><span class="p">,</span> <span class="n">task_col</span><span class="p">])</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">results_df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">results_df</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
+        <span class="n">match_</span> <span class="o">=</span> <span class="n">results_df</span><span class="p">[(</span><span class="n">results_df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">m</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">results_df</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">t</span><span class="p">)]</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">match_</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">experiments_per_model_task_pair</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Couldn&#39;t find all results for m=</span><span class="si">{</span><span class="n">m</span><span class="si">}</span><span class="s2">, t=</span><span class="si">{</span><span class="n">t</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">match_</span><span class="p">)</span><span class="si">}</span><span class="s2"> &lt; </span><span class="si">{</span><span class="n">experiments_per_model_task_pair</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Finally, save results DF to the results root directory:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">get_current_timestamp</span>
+<span class="n">results_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">RESULTS_ROOT_DIR</span><span class="p">)</span> <span class="o">/</span> <span class="sa">f</span><span class="s2">&quot;aggregated_results.</span><span class="si">{</span><span class="n">get_current_timestamp</span><span class="p">()</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<hr class="docutils" />
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="minimal-example_web-API-model.html" class="btn btn-neutral float-left" title="Evaluate model calibration using folktexts" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
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+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/notebooks/parse-acs-results.ipynb b/notebooks/parse-acs-results.ipynb
new file mode 100644
index 0000000..6b99e2d
--- /dev/null
+++ b/notebooks/parse-acs-results.ipynb
@@ -0,0 +1,589 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "04c2f12d-f989-4ac9-b90f-3464a8bcca96",
+   "metadata": {},
+   "source": [
+    "# Fetch and parse ACS benchmark results under a given directory\n",
+    "Each ACS benchmark run outputs a json file. This script collects all such files under a given root directory, parses them, and aggregates them into a more easily digestable pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b241208-d10f-43cf-a486-84c54bbf43c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faf6afa4-8648-4d35-9312-65636ea5d0b2",
+   "metadata": {},
+   "source": [
+    "**[Action required]** Set `RESULTS_ROOT_DIR` to the root results directory path:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26089a60-81c0-4736-8ba5-99572ec01398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RESULTS_ROOT_DIR = Path(\"/fast/groups/sf\") / \"folktexts-results\" / \"2024-08-28_2\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d1af7f-d013-40a0-80de-cb9ad65cff6d",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root data directory (needed only to train baseline ML methods):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b18dad87-a1ed-495b-93b6-4c8af4043ddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db7c98c5-2942-4f88-984a-8c9014afe761",
+   "metadata": {},
+   "source": [
+    "Important results columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e96f0c4f-5683-4150-8cca-93a883f20154",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_col = \"config_model_name\"\n",
+    "task_col = \"config_task_name\"\n",
+    "numeric_prompt_col = \"config_numeric_risk_prompting\"\n",
+    "\n",
+    "feature_subset_col = \"config_feature_subset\"\n",
+    "predictions_path_col = \"predictions_path\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4296ffdf-a9af-43f7-bd0c-20f4ae5f9619",
+   "metadata": {},
+   "source": [
+    "Helper function to parse each dictionary containing benchmark results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0cf4a6c8-ec28-4e66-9f6d-8a4e977d7b60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import (\n",
+    "    num_features_helper,\n",
+    "    parse_model_name,\n",
+    "    get_non_instruction_tuned_name,\n",
+    "    prettify_model_name,\n",
+    ")\n",
+    "\n",
+    "def parse_results_dict(dct) -> dict:\n",
+    "    \"\"\"Parses results dict and brings all information to the top-level.\"\"\"\n",
+    "\n",
+    "    # Make a copy so we don't modify the input object\n",
+    "    dct = dct.copy()\n",
+    "\n",
+    "    # Discard plots' paths\n",
+    "    dct.pop(\"plots\", None)\n",
+    "\n",
+    "    # Bring configs to top-level\n",
+    "    config = dct.pop(\"config\", {})\n",
+    "    for key, val in config.items():\n",
+    "        dct[f\"config_{key}\"] = val\n",
+    "\n",
+    "    # Parse model name\n",
+    "    dct[model_col] = parse_model_name(dct[model_col])\n",
+    "    dct[\"base_name\"] = get_non_instruction_tuned_name(dct[model_col])\n",
+    "    dct[\"name\"] = prettify_model_name(dct[model_col])\n",
+    "\n",
+    "    # Is instruction-tuned model?\n",
+    "    dct[\"is_inst\"] = dct[\"base_name\"] != dct[model_col] or \"(it)\" in dct[\"name\"].lower()\n",
+    "\n",
+    "    # Log number of features\n",
+    "    dct[\"num_features\"] = num_features_helper(dct[feature_subset_col], max_features_return=-1)\n",
+    "    dct[\"uses_all_features\"] = (dct[feature_subset_col] is None) or (dct[\"num_features\"] == -1)\n",
+    "\n",
+    "    if dct[feature_subset_col] is None:\n",
+    "        dct[feature_subset_col] = \"full\"\n",
+    "\n",
+    "    # Assert all results are at the top-level\n",
+    "    assert not any(isinstance(val, dict) for val in dct.values())\n",
+    "    return dct\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02009a86-e099-4ec0-8676-8d66190ceddb",
+   "metadata": {},
+   "source": [
+    "Iteratively search the root directory for results files matching the given regex:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aaefa99d-dbd7-40a3-a3c1-7571d3811409",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b984f67ca7644ba998983af5385ba147",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 241 benchmark results.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils import find_files, load_json\n",
+    "\n",
+    "# Results file name pattern\n",
+    "pattern = r'^results.bench-(?P<hash>\\d+)[.]json$'\n",
+    "\n",
+    "# Find results files and aggregate\n",
+    "results = {}\n",
+    "for file_path in tqdm(find_files(RESULTS_ROOT_DIR, pattern)):\n",
+    "    results[Path(file_path).parent.name] = parse_results_dict(load_json(file_path))\n",
+    "\n",
+    "if len(results) == 0:\n",
+    "    raise RuntimeError(f\"Couldn't find any results at {RESULTS_ROOT_DIR}\")\n",
+    "else:\n",
+    "    print(f\"Found {len(results)} benchmark results.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ecc0d33-902d-492a-89c6-d9729fe69fa1",
+   "metadata": {},
+   "source": [
+    "Aggregate results into a single DataFrame, generate a unique identifier for each row, and drop potential duplicates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "94f1900f-fea3-4872-b722-ee1cd3f5f7e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.shape=(241, 58)\n",
+      "Dropping 31 duplicates!\n",
+      "df.shape=(210, 58)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(list(results.values()))\n",
+    "\n",
+    "def row_id(row) -> str:\n",
+    "    \"\"\"Unique row identifier.\"\"\"\n",
+    "    numeric_or_multiple_choice = \"Num\" if row[numeric_prompt_col] else \"QA\"\n",
+    "    return f\"{row[model_col]}__{row[task_col]}__{row['num_features']}__{numeric_or_multiple_choice}\"\n",
+    "\n",
+    "print(f\"{df.shape=}\")\n",
+    "df[\"id\"] = df.apply(row_id, axis=1)\n",
+    "\n",
+    "# Drop duplicates\n",
+    "len_with_dups = len(df)\n",
+    "df = df.drop_duplicates(subset=[\"name\", \"is_inst\", \"num_features\", task_col, numeric_prompt_col])\n",
+    "df = df.set_index(\"id\", drop=True, verify_integrity=True)\n",
+    "\n",
+    "if len_with_dups != len(df):\n",
+    "    print(f\"Dropping {len_with_dups - len(df)} duplicates!\")\n",
+    "    print(f\"{df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97cd24df-3c1f-49c0-bb50-a6fdba22e0fb",
+   "metadata": {},
+   "source": [
+    "Load scores DFs and analyze score distribution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "43ba0fbf-b886-4f2a-8a01-564aa6f0f2c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_model_scores_df(df_row: pd.Series) -> pd.DataFrame:\n",
+    "    \"\"\"Loads csv containing model scores corresponding to the given DF row.\"\"\"\n",
+    "    if predictions_path_col in df_row and not pd.isna(df_row[predictions_path_col]):\n",
+    "        return pd.read_csv(df_row[predictions_path_col], index_col=0)\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "54e22896-1d3d-4d95-bb90-2b453f58f09a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64f4e6718312499b84fb8c9637ff10fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/210 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from folktexts.evaluation import compute_best_threshold\n",
+    "from sklearn import metrics\n",
+    "from scipy import stats\n",
+    "\n",
+    "# Number of samples used to fit the one-parameter binarization threshold!\n",
+    "N = 100\n",
+    "\n",
+    "fit_thresh_col = f\"fit_thresh_on_{N}\"\n",
+    "fit_acc_col = f\"fit_thresh_accuracy\"\n",
+    "\n",
+    "optimal_thres_col = \"optimal_thresh\"\n",
+    "optimal_acc_col = \"optimal_thresh_accuracy\"\n",
+    "\n",
+    "score_stdev_col = \"score_stdev\"\n",
+    "score_mean_col = \"score_mean\"\n",
+    "\n",
+    "scores_stats = dict()\n",
+    "for row_id, row in tqdm(df.iterrows(), total=len(df)):\n",
+    "\n",
+    "    # Load model scores\n",
+    "    scores_df = load_model_scores_df(row)\n",
+    "    if scores_df is None:\n",
+    "        logging.error(f\"Couldn't find scores for {row_id}\")\n",
+    "\n",
+    "    # Extract scores and labels\n",
+    "    risk_scores = scores_df[\"risk_score\"].to_numpy()\n",
+    "    labels = scores_df[\"label\"].to_numpy()\n",
+    "\n",
+    "    # Sample N rows to fit threshold\n",
+    "    scores_df_sample = scores_df.sample(n=N, random_state=42)\n",
+    "\n",
+    "    # Compute optimal threshold on each data sample\n",
+    "    fit_thr = compute_best_threshold(y_true=scores_df_sample[\"label\"], y_pred_scores=scores_df_sample[\"risk_score\"])\n",
+    "    opt_thr = compute_best_threshold(y_true=labels, y_pred_scores=risk_scores)\n",
+    "\n",
+    "    # Evaluate accuracy\n",
+    "    fit_acc = metrics.accuracy_score(labels, (risk_scores >= fit_thr).astype(int))\n",
+    "    opt_acc = metrics.accuracy_score(labels, (risk_scores >= opt_thr).astype(int))\n",
+    "\n",
+    "    # Save results\n",
+    "    scores_stats[row_id] = {\n",
+    "        fit_thresh_col: fit_thr,\n",
+    "        fit_acc_col: fit_acc,\n",
+    "        optimal_thres_col: opt_thr,\n",
+    "        optimal_acc_col: opt_acc,\n",
+    "        score_stdev_col: np.std(risk_scores),\n",
+    "        score_mean_col: np.mean(risk_scores),\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1a58b87-2de8-473a-badb-934951d1bcdc",
+   "metadata": {},
+   "source": [
+    "Update results DF with scores statistics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0ba74c81-c008-4010-a9a3-1b511ae445df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scores_stats_df.shape=(210, 6)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>gemma-2-27b__ACSTravelTime__-1__Num</th>\n",
+       "      <td>0.539392</td>\n",
+       "      <td>0.258000</td>\n",
+       "      <td>0.656000</td>\n",
+       "      <td>0.504500</td>\n",
+       "      <td>0.100184</td>\n",
+       "      <td>0.823204</td>\n",
+       "      <td>0.388077</td>\n",
+       "      <td>0.347071</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.221264</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Gemma 2 27B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.13000</td>\n",
+       "      <td>0.514676</td>\n",
+       "      <td>0.210000</td>\n",
+       "      <td>0.524692</td>\n",
+       "      <td>0.311014</td>\n",
+       "      <td>0.219864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gemma-1.1-2b-it__ACSEmployment__-1__QA</th>\n",
+       "      <td>0.462874</td>\n",
+       "      <td>0.235384</td>\n",
+       "      <td>0.631881</td>\n",
+       "      <td>0.427372</td>\n",
+       "      <td>0.091640</td>\n",
+       "      <td>0.800718</td>\n",
+       "      <td>0.408398</td>\n",
+       "      <td>0.382143</td>\n",
+       "      <td>0.376308</td>\n",
+       "      <td>0.212855</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Gemma 2B (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.02033</td>\n",
+       "      <td>0.482054</td>\n",
+       "      <td>0.007577</td>\n",
+       "      <td>0.485395</td>\n",
+       "      <td>0.232183</td>\n",
+       "      <td>0.216562</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 64 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        accuracy  accuracy_diff  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num     0.539392       0.258000   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA  0.462874       0.235384   \n",
+       "\n",
+       "                                        accuracy_ratio  balanced_accuracy  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num           0.656000           0.504500   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA        0.631881           0.427372   \n",
+       "\n",
+       "                                        balanced_accuracy_diff  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num                   0.100184   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA                0.091640   \n",
+       "\n",
+       "                                        balanced_accuracy_ratio  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num                    0.823204   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA                 0.800718   \n",
+       "\n",
+       "                                        brier_score_loss       ece  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num             0.388077  0.347071   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA          0.408398  0.382143   \n",
+       "\n",
+       "                                        ece_quantile  equalized_odds_diff  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num              NaN             0.221264   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA      0.376308             0.212855   \n",
+       "\n",
+       "                                        ...           name  is_inst  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num     ...    Gemma 2 27B    False   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA  ...  Gemma 2B (it)     True   \n",
+       "\n",
+       "                                        num_features  uses_all_features  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num               -1               True   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA            -1               True   \n",
+       "\n",
+       "                                        fit_thresh_on_100  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num               0.13000   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA            0.02033   \n",
+       "\n",
+       "                                        fit_thresh_accuracy  optimal_thresh  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num                0.514676        0.210000   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA             0.482054        0.007577   \n",
+       "\n",
+       "                                        optimal_thresh_accuracy score_stdev  \\\n",
+       "gemma-2-27b__ACSTravelTime__-1__Num                    0.524692    0.311014   \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA                 0.485395    0.232183   \n",
+       "\n",
+       "                                        score_mean  \n",
+       "gemma-2-27b__ACSTravelTime__-1__Num       0.219864  \n",
+       "gemma-1.1-2b-it__ACSEmployment__-1__QA    0.216562  \n",
+       "\n",
+       "[2 rows x 64 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_stats_df = pd.DataFrame(scores_stats.values(), index=list(scores_stats.keys()))\n",
+    "print(f\"{scores_stats_df.shape=}\")\n",
+    "\n",
+    "results_df = pd.concat((df, scores_stats_df), axis=\"columns\")\n",
+    "results_df.sample(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d04ff64-39c4-43cc-9df2-fa4e099c4e56",
+   "metadata": {},
+   "source": [
+    "Check if any results are missing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f1faf926-de0f-442e-baf7-c3029f5386e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiments_per_model_task_pair = results_df.groupby([model_col, task_col]).nunique().max(axis=None)\n",
+    "\n",
+    "for m in results_df[model_col].unique():\n",
+    "    for t in results_df[task_col].unique():\n",
+    "        match_ = results_df[(results_df[model_col] == m) & (results_df[task_col] == t)]\n",
+    "        if len(match_) < experiments_per_model_task_pair:\n",
+    "            print(f\"Couldn't find all results for m={m}, t={t} ({len(match_)} < {experiments_per_model_task_pair})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6e859fc-9d82-436d-a238-11fd008da44c",
+   "metadata": {},
+   "source": [
+    "Finally, save results DF to the results root directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9725afa2-b6f8-46b1-86e7-57ae82ef05d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import get_current_timestamp\n",
+    "results_df.to_csv(Path(RESULTS_ROOT_DIR) / f\"aggregated_results.{get_current_timestamp()}.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3a6beb5-b44b-4f81-ae1a-d027afb2c5f4",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/parse-acs-results_using-feature-subsets.html b/notebooks/parse-acs-results_using-feature-subsets.html
new file mode 100644
index 0000000..fd0be34
--- /dev/null
+++ b/notebooks/parse-acs-results_using-feature-subsets.html
@@ -0,0 +1,976 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Fetch and parse ACS benchmark results under a given directory &mdash; folktexts 0.0.25 documentation</title>
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+             
+  <section id="Fetch-and-parse-ACS-benchmark-results-under-a-given-directory">
+<h1>Fetch and parse ACS benchmark results under a given directory<a class="headerlink" href="#Fetch-and-parse-ACS-benchmark-results-under-a-given-directory" title="Link to this heading"></a></h1>
+<p>Each ACS benchmark run outputs a json file. This script collects all such files under a given root directory, parses them, and aggregates them into a more easily digestable pandas DataFrame.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">tqdm.auto</span> <span class="kn">import</span> <span class="n">tqdm</span>
+</pre></div>
+</div>
+</div>
+<p>Set the local path to the root results directory:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">RESULTS_ROOT_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;/fast/groups/sf&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;folktexts-results&quot;</span> <span class="o">/</span> <span class="s2">&quot;2024-07-03_feature-subsets&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Set the local path to the root data directory (needed only to train baseline ML methods):</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">DATA_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;/fast/groups/sf&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;data&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Important results columns:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_col</span> <span class="o">=</span> <span class="s2">&quot;config_model_name&quot;</span>
+<span class="n">task_col</span> <span class="o">=</span> <span class="s2">&quot;config_task_name&quot;</span>
+
+<span class="n">feature_subset_col</span> <span class="o">=</span> <span class="s2">&quot;config_feature_subset&quot;</span>
+<span class="n">predictions_path_col</span> <span class="o">=</span> <span class="s2">&quot;predictions_path&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Helper function to parse each dictionary containing benchmark results:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">num_features_helper</span><span class="p">,</span>
+    <span class="n">parse_model_name</span><span class="p">,</span>
+    <span class="n">get_non_instruction_tuned_name</span><span class="p">,</span>
+    <span class="n">prettify_model_name</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">parse_results_dict</span><span class="p">(</span><span class="n">dct</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Parses results dict and brings all information to the top-level.&quot;&quot;&quot;</span>
+
+    <span class="c1"># Make a copy so we don&#39;t modify the input object</span>
+    <span class="n">dct</span> <span class="o">=</span> <span class="n">dct</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+    <span class="c1"># Discard plots&#39; paths</span>
+    <span class="n">dct</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;plots&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="c1"># Bring configs to top-level</span>
+    <span class="n">config</span> <span class="o">=</span> <span class="n">dct</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;config&quot;</span><span class="p">,</span> <span class="p">{})</span>
+    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">config</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">dct</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;config_</span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
+
+    <span class="c1"># Parse model name</span>
+    <span class="n">dct</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">parse_model_name</span><span class="p">(</span><span class="n">dct</span><span class="p">[</span><span class="n">model_col</span><span class="p">])</span>
+    <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">get_non_instruction_tuned_name</span><span class="p">(</span><span class="n">dct</span><span class="p">[</span><span class="n">model_col</span><span class="p">])</span>
+    <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">prettify_model_name</span><span class="p">(</span><span class="n">dct</span><span class="p">[</span><span class="n">model_col</span><span class="p">])</span>
+
+    <span class="c1"># Is instruction-tuned model?</span>
+    <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;is_inst&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;base_name&quot;</span><span class="p">]</span> <span class="o">!=</span> <span class="n">dct</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span>
+
+    <span class="c1"># Log number of features</span>
+    <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;num_features&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">num_features_helper</span><span class="p">(</span><span class="n">dct</span><span class="p">[</span><span class="n">feature_subset_col</span><span class="p">],</span> <span class="n">max_features_return</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">dct</span><span class="p">[</span><span class="s2">&quot;uses_all_features&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">dct</span><span class="p">[</span><span class="n">feature_subset_col</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">dct</span><span class="p">[</span><span class="s2">&quot;num_features&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">dct</span><span class="p">[</span><span class="n">feature_subset_col</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">dct</span><span class="p">[</span><span class="n">feature_subset_col</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;full&quot;</span>
+
+    <span class="c1"># Assert all results are at the top-level</span>
+    <span class="k">assert</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">dct</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
+    <span class="k">return</span> <span class="n">dct</span>
+<br/></pre></div>
+</div>
+</div>
+<p>Iteratively search the root directory for results files matching the given regex:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">find_files</span><span class="p">,</span> <span class="n">load_json</span>
+
+<span class="c1"># Results file name pattern</span>
+<span class="n">pattern</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;^results.bench-(?P&lt;hash&gt;\d+)[.]json$&#39;</span>
+
+<span class="c1"># Find results files and aggregate</span>
+<span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="k">for</span> <span class="n">file_path</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">find_files</span><span class="p">(</span><span class="n">RESULTS_ROOT_DIR</span><span class="p">,</span> <span class="n">pattern</span><span class="p">)):</span>
+    <span class="n">results</span><span class="p">[</span><span class="n">Path</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">parse_results_dict</span><span class="p">(</span><span class="n">load_json</span><span class="p">(</span><span class="n">file_path</span><span class="p">))</span>
+
+<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">results</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Couldn&#39;t find any results at </span><span class="si">{</span><span class="n">RESULTS_ROOT_DIR</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Found </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">results</span><span class="p">)</span><span class="si">}</span><span class="s2"> benchmark results.&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "3293cf3c9da64e4eaab95a251b59aad5", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Found 195 benchmark results.
+</pre></div></div>
+</div>
+<p>Aggregate results into a single DataFrame, and generate a unique identifier for each row:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
+
+<span class="k">def</span> <span class="nf">row_id</span><span class="p">(</span><span class="n">row</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Unique row identifier.&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span><span class="si">}</span><span class="s2">__</span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="n">task_col</span><span class="p">]</span><span class="si">}</span><span class="s2">__</span><span class="si">{</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;num_features&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">row_id</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verify_integrity</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+df.shape=(195, 58)
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>accuracy</th>
+      <th>accuracy_diff</th>
+      <th>accuracy_ratio</th>
+      <th>balanced_accuracy</th>
+      <th>balanced_accuracy_diff</th>
+      <th>balanced_accuracy_ratio</th>
+      <th>brier_score_loss</th>
+      <th>ece</th>
+      <th>ece_quantile</th>
+      <th>equalized_odds_diff</th>
+      <th>...</th>
+      <th>config_population_filter</th>
+      <th>config_reuse_few_shot_examples</th>
+      <th>config_seed</th>
+      <th>config_task_hash</th>
+      <th>config_task_name</th>
+      <th>base_name</th>
+      <th>name</th>
+      <th>is_inst</th>
+      <th>num_features</th>
+      <th>uses_all_features</th>
+    </tr>
+    <tr>
+      <th>id</th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX__7</th>
+      <td>0.630994</td>
+      <td>0.281751</td>
+      <td>0.663274</td>
+      <td>0.500705</td>
+      <td>0.007359</td>
+      <td>0.985495</td>
+      <td>0.215502</td>
+      <td>0.111277</td>
+      <td>0.116464</td>
+      <td>0.031550</td>
+      <td>...</td>
+      <td>None</td>
+      <td>False</td>
+      <td>42</td>
+      <td>591377254</td>
+      <td>ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX</td>
+      <td>Mixtral-8x22B-v0.1</td>
+      <td>Mixtral 8x22B</td>
+      <td>False</td>
+      <td>7</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>Meta-Llama-3-8B__ACSIncome_RAC1P__1</th>
+      <td>0.369300</td>
+      <td>0.286688</td>
+      <td>0.362849</td>
+      <td>0.500000</td>
+      <td>0.000000</td>
+      <td>1.000000</td>
+      <td>0.279789</td>
+      <td>0.220760</td>
+      <td>NaN</td>
+      <td>0.000000</td>
+      <td>...</td>
+      <td>None</td>
+      <td>False</td>
+      <td>42</td>
+      <td>664576746</td>
+      <td>ACSIncome_RAC1P</td>
+      <td>Meta-Llama-3-8B</td>
+      <td>Llama 3 8B</td>
+      <td>False</td>
+      <td>1</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX__7</th>
+      <td>0.477681</td>
+      <td>0.190822</td>
+      <td>0.677577</td>
+      <td>0.508865</td>
+      <td>0.210830</td>
+      <td>0.678362</td>
+      <td>0.392382</td>
+      <td>0.354748</td>
+      <td>0.353959</td>
+      <td>0.294444</td>
+      <td>...</td>
+      <td>None</td>
+      <td>False</td>
+      <td>42</td>
+      <td>591377254</td>
+      <td>ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX</td>
+      <td>gemma-2-27b</td>
+      <td>Gemma 2 27B (it)</td>
+      <td>True</td>
+      <td>7</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+<p>3 rows × 58 columns</p>
+</div></div>
+</div>
+<p>Drop potential duplicates:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">parsed_df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">,</span> <span class="s2">&quot;is_inst&quot;</span><span class="p">,</span> <span class="s2">&quot;num_features&quot;</span><span class="p">,</span> <span class="n">task_col</span><span class="p">])</span>
+<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">parsed_df</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Found </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">parsed_df</span><span class="p">)</span><span class="si">}</span><span class="s2"> duplicates! dropping rows...&quot;</span><span class="p">)</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">parsed_df</span>
+</pre></div>
+</div>
+</div>
+<p>Load scores DFs and analyze score distribution:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">load_model_scores_df</span><span class="p">(</span><span class="n">df_row</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Loads csv containing model scores corresponding to the given DF row.&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">predictions_path_col</span> <span class="ow">in</span> <span class="n">df_row</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">pd</span><span class="o">.</span><span class="n">isna</span><span class="p">(</span><span class="n">df_row</span><span class="p">[</span><span class="n">predictions_path_col</span><span class="p">]):</span>
+        <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">df_row</span><span class="p">[</span><span class="n">predictions_path_col</span><span class="p">],</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="k">return</span> <span class="kc">None</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scores_df_map</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="n">id_</span><span class="p">:</span> <span class="n">load_model_scores_df</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">id_</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">parsed_df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">parsed_df</span><span class="p">))</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "133e0fbc9ecc4262990da41e34a12425", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.evaluation</span> <span class="kn">import</span> <span class="n">compute_best_threshold</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
+
+<span class="c1"># Number of samples used to fit the one-parameter binarization threshold!</span>
+<span class="n">N</span> <span class="o">=</span> <span class="mi">100</span>
+
+<span class="n">fit_thresh_col</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;fit_thresh_on_</span><span class="si">{</span><span class="n">N</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="n">fit_acc_col</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;fit_thresh_accuracy&quot;</span>
+
+<span class="n">optimal_thres_col</span> <span class="o">=</span> <span class="s2">&quot;optimal_thresh&quot;</span>
+<span class="n">optimal_acc_col</span> <span class="o">=</span> <span class="s2">&quot;optimal_thresh_accuracy&quot;</span>
+
+<span class="n">score_stdev_col</span> <span class="o">=</span> <span class="s2">&quot;score_stdev&quot;</span>
+<span class="n">score_mean_col</span> <span class="o">=</span> <span class="s2">&quot;score_mean&quot;</span>
+
+<span class="n">scores_stats</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">row_id</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)):</span>
+
+    <span class="c1"># Load model scores</span>
+    <span class="n">scores_df</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">row_id</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">scores_df</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Couldn&#39;t find scores for </span><span class="si">{</span><span class="n">row_id</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="c1"># Extract scores and labels</span>
+    <span class="n">risk_scores</span> <span class="o">=</span> <span class="n">scores_df</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="n">scores_df</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+
+    <span class="c1"># Sample N rows to fit threshold</span>
+    <span class="n">scores_df_sample</span> <span class="o">=</span> <span class="n">scores_df</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="n">N</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+
+    <span class="c1"># Compute optimal threshold on each data sample</span>
+    <span class="n">fit_thr</span> <span class="o">=</span> <span class="n">compute_best_threshold</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">scores_df_sample</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">],</span> <span class="n">y_pred_scores</span><span class="o">=</span><span class="n">scores_df_sample</span><span class="p">[</span><span class="s2">&quot;risk_score&quot;</span><span class="p">])</span>
+    <span class="n">opt_thr</span> <span class="o">=</span> <span class="n">compute_best_threshold</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">y_pred_scores</span><span class="o">=</span><span class="n">risk_scores</span><span class="p">)</span>
+
+    <span class="c1"># Evaluate accuracy</span>
+    <span class="n">fit_acc</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="p">(</span><span class="n">risk_scores</span> <span class="o">&gt;=</span> <span class="n">fit_thr</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">))</span>
+    <span class="n">opt_acc</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="p">(</span><span class="n">risk_scores</span> <span class="o">&gt;=</span> <span class="n">opt_thr</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">))</span>
+
+    <span class="c1"># Save results</span>
+    <span class="n">scores_stats</span><span class="p">[</span><span class="n">row_id</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="n">fit_thresh_col</span><span class="p">:</span> <span class="n">fit_thr</span><span class="p">,</span>
+        <span class="n">fit_acc_col</span><span class="p">:</span> <span class="n">fit_acc</span><span class="p">,</span>
+        <span class="n">optimal_thres_col</span><span class="p">:</span> <span class="n">opt_thr</span><span class="p">,</span>
+        <span class="n">optimal_acc_col</span><span class="p">:</span> <span class="n">opt_acc</span><span class="p">,</span>
+        <span class="n">score_stdev_col</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">risk_scores</span><span class="p">),</span>
+        <span class="n">score_mean_col</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">risk_scores</span><span class="p">),</span>
+    <span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "2325fd86cbdc436e84da4bc8be53fd98", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<p>Update results DF with scores statistics:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">scores_stats_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">scores_stats</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">scores_stats</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+
+<span class="n">results_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">df</span><span class="p">,</span> <span class="n">scores_stats_df</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="s2">&quot;columns&quot;</span><span class="p">)</span>
+<span class="n">results_df</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>accuracy</th>
+      <th>accuracy_diff</th>
+      <th>accuracy_ratio</th>
+      <th>balanced_accuracy</th>
+      <th>balanced_accuracy_diff</th>
+      <th>balanced_accuracy_ratio</th>
+      <th>brier_score_loss</th>
+      <th>ece</th>
+      <th>ece_quantile</th>
+      <th>equalized_odds_diff</th>
+      <th>...</th>
+      <th>name</th>
+      <th>is_inst</th>
+      <th>num_features</th>
+      <th>uses_all_features</th>
+      <th>fit_thresh_on_100</th>
+      <th>fit_thresh_accuracy</th>
+      <th>optimal_thresh</th>
+      <th>optimal_thresh_accuracy</th>
+      <th>score_stdev</th>
+      <th>score_mean</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4</th>
+      <td>0.567786</td>
+      <td>0.183621</td>
+      <td>0.743500</td>
+      <td>0.636874</td>
+      <td>0.105719</td>
+      <td>0.843667</td>
+      <td>0.273436</td>
+      <td>0.237165</td>
+      <td>0.237165</td>
+      <td>0.630823</td>
+      <td>...</td>
+      <td>Yi 34B (chat)</td>
+      <td>True</td>
+      <td>4</td>
+      <td>False</td>
+      <td>0.531214</td>
+      <td>0.582998</td>
+      <td>0.562204</td>
+      <td>0.597789</td>
+      <td>0.192288</td>
+      <td>0.606465</td>
+    </tr>
+    <tr>
+      <th>Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6</th>
+      <td>0.422379</td>
+      <td>0.183270</td>
+      <td>0.609219</td>
+      <td>0.538078</td>
+      <td>0.085607</td>
+      <td>0.857950</td>
+      <td>0.303729</td>
+      <td>0.306175</td>
+      <td>0.306175</td>
+      <td>0.232553</td>
+      <td>...</td>
+      <td>Yi 34B</td>
+      <td>False</td>
+      <td>6</td>
+      <td>False</td>
+      <td>0.731161</td>
+      <td>0.659718</td>
+      <td>0.679091</td>
+      <td>0.610856</td>
+      <td>0.117468</td>
+      <td>0.675475</td>
+    </tr>
+  </tbody>
+</table>
+<p>2 rows × 64 columns</p>
+</div></div>
+</div>
+<p>Check if any results are missing:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">results_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="n">model_col</span><span class="p">])[</span><span class="n">task_col</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+config_model_name
+Meta-Llama-3-70B                9
+Meta-Llama-3-70B-Instruct       9
+Meta-Llama-3-8B                10
+Meta-Llama-3-8B-Instruct       10
+Mistral-7B-Instruct-v0.2       10
+Mistral-7B-v0.1                10
+Mixtral-8x22B-Instruct-v0.1     9
+Mixtral-8x22B-v0.1              9
+Mixtral-8x7B-Instruct-v0.1      9
+Mixtral-8x7B-v0.1              10
+Yi-34B                         10
+Yi-34B-Chat                    10
+gemma-1.1-2b-it                10
+gemma-1.1-7b-it                10
+gemma-2-27b                    10
+gemma-2-27b-it                 10
+gemma-2-9b                     10
+gemma-2-9b-it                  10
+gemma-2b                       10
+gemma-7b                       10
+Name: config_task_name, dtype: int64
+</pre></div></div>
+</div>
+<p>Finally, save results DF to the results root directory:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">get_current_timestamp</span>
+<span class="n">results_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">RESULTS_ROOT_DIR</span><span class="p">)</span> <span class="o">/</span> <span class="sa">f</span><span class="s2">&quot;aggregated_results.</span><span class="si">{</span><span class="n">get_current_timestamp</span><span class="p">()</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<hr class="docutils" />
+<section id="Plots">
+<h1>Plots<a class="headerlink" href="#Plots" title="Link to this heading"></a></h1>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">&quot;whitegrid&quot;</span><span class="p">,</span> <span class="n">rc</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;grid.linestyle&quot;</span><span class="p">:</span> <span class="s2">&quot;--&quot;</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rc</span><span class="p">(</span><span class="s2">&quot;figure&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">4.5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+
+<span class="n">palette_name</span> <span class="o">=</span> <span class="s2">&quot;tab10&quot;</span>   <span class="c1"># &quot;colorblind&quot;</span>
+<span class="n">palette</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">(</span><span class="n">palette_name</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">set_palette</span><span class="p">(</span><span class="n">palette_name</span><span class="p">)</span>
+<span class="n">palette</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<svg  width="550" height="55"><rect x="0" y="0" width="55" height="55" style="fill:#1f77b4;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="55" y="0" width="55" height="55" style="fill:#ff7f0e;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="110" y="0" width="55" height="55" style="fill:#2ca02c;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="165" y="0" width="55" height="55" style="fill:#d62728;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="220" y="0" width="55" height="55" style="fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="275" y="0" width="55" height="55" style="fill:#8c564b;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="330" y="0" width="55" height="55" style="fill:#e377c2;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="385" y="0" width="55" height="55" style="fill:#7f7f7f;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="440" y="0" width="55" height="55" style="fill:#bcbd22;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="495" y="0" width="55" height="55" style="fill:#17becf;stroke-width:2;stroke:rgb(255,255,255)"/></svg></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">IMGS_DIR</span> <span class="o">=</span> <span class="n">RESULTS_ROOT_DIR</span> <span class="o">/</span> <span class="s2">&quot;imgs&quot;</span>
+<span class="n">IMGS_DIR</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">((</span><span class="n">IMGS_DIR</span> <span class="o">/</span> <span class="n">name</span><span class="p">)</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="s2">&quot;.pdf&quot;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<section id="Score-distributions-of-an-example-model-with-increasing-evidence">
+<h2>Score distributions of an example model with increasing evidence<a class="headerlink" href="#Score-distributions-of-an-example-model-with-increasing-evidence" title="Link to this heading"></a></h2>
+<p>Score distribution should get more discriminative (less Gaussian-shaped) with increasing evidence.,</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># EXAMPLE_MODEL = &quot;Meta-Llama-3-70B-Instruct&quot;</span>
+<span class="c1"># EXAMPLE_MODEL = &quot;Meta-Llama-3-70B&quot;</span>
+<span class="n">EXAMPLE_MODEL</span> <span class="o">=</span> <span class="s2">&quot;Mixtral-8x7B-Instruct-v0.1&quot;</span>
+<span class="c1"># EXAMPLE_MODEL = &quot;Mixtral-8x7B-v0.1&quot;</span>
+<span class="c1"># EXAMPLE_MODEL = &quot;Mistral-7B-v0.1&quot;</span>
+<span class="c1"># EXAMPLE_MODEL = &quot;Mistral-7B-Instruct-v0.2&quot;</span>
+<span class="c1"># EXAMPLE_MODEL = &quot;Yi-34B&quot;</span>
+<span class="c1"># EXAMPLE_MODEL = &quot;Yi-34B-Chat&quot;</span>
+<span class="c1"># EXAMPLE_MODEL = &quot;Meta-Llama-3-8B&quot;</span>
+<span class="c1"># EXAMPLE_MODEL = &quot;Meta-Llama-3-8B-Instruct&quot;</span>
+<span class="c1"># EXAMPLE_MODEL = &quot;Mixtral-8x22B-Instruct-v0.1&quot;</span>
+<span class="c1"># EXAMPLE_MODEL = &quot;Mixtral-8x22B-v0.1&quot;</span>
+</pre></div>
+</div>
+</div>
+<p><strong>TODO?</strong> plot xgboost score distribution as features are added! to show below the LLM score distribution as what true uncertainty quantification looks like…</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get data to plot</span>
+<span class="n">plot_df</span> <span class="o">=</span> <span class="n">results_df</span><span class="p">[</span><span class="n">results_df</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">EXAMPLE_MODEL</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">&quot;num_features&quot;</span><span class="p">)</span>
+<span class="c1"># Skip every other row in plot</span>
+<span class="n">plot_df</span> <span class="o">=</span> <span class="n">plot_df</span><span class="p">[[</span><span class="n">idx</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">plot_df</span><span class="p">))]]</span>
+<span class="c1"># plot_df = plot_df.iloc[[0, 2, 3, 5, 7]]</span>
+
+<span class="c1"># # For current Llama-3-8B-Instruct results</span>
+<span class="c1"># plot_df = plot_df[[False, True, False, True, True, False, True]]</span>
+
+<span class="n">N_PLOTS</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">plot_df</span><span class="p">))</span>
+
+<span class="n">N_COLS</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">N_ROWS</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">N_PLOTS</span> <span class="o">/</span> <span class="n">N_COLS</span><span class="p">)</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span>
+    <span class="n">nrows</span><span class="o">=</span><span class="n">N_ROWS</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">N_COLS</span><span class="p">,</span>
+    <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">N_ROWS</span><span class="p">),</span>
+    <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">gridspec_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
+        <span class="n">hspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">wspace</span><span class="o">=</span><span class="mf">0.06</span><span class="p">,</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="c1"># Plot settings</span>
+<span class="n">plot_config</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+    <span class="n">bins</span><span class="o">=</span><span class="p">(</span><span class="n">N_BINS</span> <span class="o">:=</span> <span class="mi">20</span><span class="p">),</span>
+    <span class="n">binrange</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+    <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;percent&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Plot score distribution for model pairs</span>
+<span class="n">current_featureset</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+
+<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">id_</span><span class="p">,</span> <span class="n">row</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">plot_df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()):</span>
+    <span class="n">ax_row</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">//</span> <span class="n">N_COLS</span>
+    <span class="n">ax_col</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">%</span> <span class="n">N_COLS</span>
+
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">ax_row</span><span class="p">,</span> <span class="n">ax_col</span><span class="p">]</span> <span class="k">if</span> <span class="n">N_ROWS</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">axes</span><span class="p">[</span><span class="n">ax_col</span><span class="p">]</span>
+
+    <span class="c1"># Get scores</span>
+    <span class="n">scores</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">row</span><span class="o">.</span><span class="n">name</span><span class="p">][</span><span class="s2">&quot;risk_score&quot;</span><span class="p">]</span>
+
+    <span class="c1"># Added features</span>
+    <span class="n">added_features</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;config_feature_subset&quot;</span><span class="p">])</span> <span class="o">-</span> <span class="n">current_featureset</span>
+
+    <span class="c1"># Set title with added features compared to last plot</span>
+    <span class="n">title_str</span> <span class="o">=</span> <span class="s2">&quot;+ &quot;</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">current_featureset</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+    <span class="n">title_str</span> <span class="o">+=</span> <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">added_features</span><span class="p">)</span>
+    <span class="n">current_featureset</span> <span class="o">|=</span> <span class="n">added_features</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title_str</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;risk scores&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ax_col</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;density (%)&quot;</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mf">0.03</span><span class="p">,</span> <span class="mf">1.03</span><span class="p">)</span>
+
+    <span class="c1"># Render plot</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">scores</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">1</span> <span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s2">&quot;is_inst&quot;</span><span class="p">]</span> <span class="k">else</span> <span class="mi">0</span><span class="p">],</span> <span class="o">**</span><span class="n">plot_config</span><span class="p">)</span>
+
+    <span class="c1"># Add text box with distribution stats</span>
+    <span class="n">textstr</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span>
+        <span class="sa">r</span><span class="s2">&quot;$\text</span><span class="si">{ROC}</span><span class="s2">=</span><span class="si">%.2f</span><span class="s2">$&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;roc_auc&quot;</span><span class="p">],),</span>
+        <span class="sa">r</span><span class="s2">&quot;$\text</span><span class="si">{ECE}</span><span class="s2">=</span><span class="si">%.2f</span><span class="s2">$&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;ece&quot;</span><span class="p">],),</span>
+        <span class="sa">r</span><span class="s2">&quot;$\sigma=</span><span class="si">%.2f</span><span class="s2">$&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;score_stdev&quot;</span><span class="p">],),</span>
+        <span class="sa">r</span><span class="s2">&quot;$\mu=</span><span class="si">%.2f</span><span class="s2">$&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;score_mean&quot;</span><span class="p">],),</span>
+    <span class="p">])</span>
+
+    <span class="n">props</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">boxstyle</span><span class="o">=</span><span class="s1">&#39;round&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;wheat&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s2">&quot;is_inst&quot;</span><span class="p">]:</span>
+        <span class="c1"># Center-aligned text box</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="n">textstr</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
+            <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">bbox</span><span class="o">=</span><span class="n">props</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="c1"># Right-aligned text box</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="n">textstr</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
+            <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">,</span> <span class="n">bbox</span><span class="o">=</span><span class="n">props</span><span class="p">)</span>
+
+        <span class="c1"># # Left-aligned text box</span>
+        <span class="c1"># ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=9,</span>
+        <span class="c1">#     verticalalignment=&#39;top&#39;, bbox=props)</span>
+
+<span class="c1"># Remove unused axes</span>
+<span class="p">[</span>
+    <span class="p">(</span><span class="n">axes</span><span class="p">[</span><span class="n">N_ROWS</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">idx</span><span class="p">]</span> <span class="k">if</span> <span class="n">N_ROWS</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N_COLS</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">plot_df</span><span class="p">)</span> <span class="o">-</span> <span class="n">N_COLS</span> <span class="o">*</span> <span class="p">(</span><span class="n">N_ROWS</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="p">];</span>
+
+<span class="c1"># Set suptitle</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">plot_df</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="mf">1.05</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;increasing-evidence.</span><span class="si">{</span><span class="n">EXAMPLE_MODEL</span><span class="si">}</span><span class="s2">.pdf&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-acs-results_using-feature-subsets_32_0.png" src="../_images/notebooks_parse-acs-results_using-feature-subsets_32_0.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">prevalence</span> <span class="o">=</span> <span class="n">scores_df_map</span><span class="p">[</span><span class="n">row</span><span class="o">.</span><span class="n">name</span><span class="p">][</span><span class="s1">&#39;label&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Label prevalence: </span><span class="si">{</span><span class="n">prevalence</span><span class="si">:</span><span class="s2">.1%</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Label prevalence: 36.9%
+</pre></div></div>
+</div>
+<ul class="simple">
+<li><p>☐ <strong>TODO:</strong> Make the same plot for XGBoost to have a plot of a calibrated baseline…</p></li>
+</ul>
+</section>
+<section id="Plots-for-XGBoost-baseline">
+<h2>Plots for XGBoost baseline<a class="headerlink" href="#Plots-for-XGBoost-baseline" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># TODO</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Change-in-ECE-and-ROC-with-increasing-evidence">
+<h2>Change in ECE and ROC with increasing evidence<a class="headerlink" href="#Change-in-ECE-and-ROC-with-increasing-evidence" title="Link to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">results_df</span>
+
+<span class="c1"># PLOT_BASE = True</span>
+<span class="n">PLOT_BASE</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="k">if</span> <span class="n">PLOT_BASE</span><span class="p">:</span>
+    <span class="n">models_to_plot</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="s2">&quot;Meta-Llama-3-8B&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Meta-Llama-3-70B&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Mistral-7B-v0.1&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Mixtral-8x7B-v0.1&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Mixtral-8x22B-v0.1&quot;</span><span class="p">,</span>
+    <span class="p">]</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">models_to_plot</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="s2">&quot;Meta-Llama-3-8B-Instruct&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Meta-Llama-3-70B-Instruct&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Mistral-7B-Instruct-v0.2&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Mixtral-8x7B-Instruct-v0.1&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Mixtral-8x22B-Instruct-v0.1&quot;</span><span class="p">,</span>
+    <span class="p">]</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">models_to_plot</span><span class="p">),</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">),</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
+
+<span class="n">num_features</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">10</span><span class="p">]</span>
+
+<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">model_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">models_to_plot</span><span class="p">):</span>
+    <span class="n">this_data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">model_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">model_name</span><span class="p">]</span>
+    <span class="n">vals_ece</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">vals_acc</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">nf</span> <span class="ow">in</span> <span class="n">num_features</span><span class="p">:</span>
+        <span class="n">this_row</span> <span class="o">=</span> <span class="p">(</span><span class="n">this_data</span><span class="p">[</span><span class="n">this_data</span><span class="p">[</span><span class="s2">&quot;num_features&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">nf</span><span class="p">])</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">vals_ece</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">this_row</span><span class="p">[</span><span class="s2">&quot;ece&quot;</span><span class="p">])</span>
+        <span class="n">vals_acc</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">this_row</span><span class="p">[</span><span class="s2">&quot;roc_auc&quot;</span><span class="p">])</span>
+
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">vals_ece</span><span class="p">,</span> <span class="n">vals_acc</span><span class="p">,</span> <span class="s2">&quot;-o&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">this_row</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">])</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.50</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">])</span>
+
+    <span class="k">if</span> <span class="n">PLOT_BASE</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.29</span><span class="p">])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.58</span><span class="p">])</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">vals_ece</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">vals_acc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;*&#39;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">13</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;all features&#39;</span><span class="p">,</span><span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;tab:orange&#39;</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Calibration (ECE)&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">((</span><span class="s2">&quot;Predictive Signal (ROC)&quot;</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span> <span class="k">if</span> <span class="n">PLOT_BASE</span> <span class="k">else</span> <span class="s2">&quot;lower left&quot;</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">idx</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">models_to_plot</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;lower right&quot;</span><span class="p">)</span>
+
+
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;feature-ablation.</span><span class="si">{</span><span class="s1">&#39;.&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">models_to_plot</span><span class="p">)</span><span class="si">}</span><span class="s2">.pdf&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-acs-results_using-feature-subsets_39_0.png" src="../_images/notebooks_parse-acs-results_using-feature-subsets_39_0.png" />
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/notebooks/parse-acs-results_using-feature-subsets.ipynb b/notebooks/parse-acs-results_using-feature-subsets.ipynb
new file mode 100644
index 0000000..2ca3445
--- /dev/null
+++ b/notebooks/parse-acs-results_using-feature-subsets.ipynb
@@ -0,0 +1,1275 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "04c2f12d-f989-4ac9-b90f-3464a8bcca96",
+   "metadata": {},
+   "source": [
+    "# Fetch and parse ACS benchmark results under a given directory\n",
+    "Each ACS benchmark run outputs a json file. This script collects all such files under a given root directory, parses them, and aggregates them into a more easily digestable pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b241208-d10f-43cf-a486-84c54bbf43c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faf6afa4-8648-4d35-9312-65636ea5d0b2",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root results directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26089a60-81c0-4736-8ba5-99572ec01398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RESULTS_ROOT_DIR = Path(\"/fast/groups/sf\") / \"folktexts-results\" / \"2024-07-03_feature-subsets\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d1af7f-d013-40a0-80de-cb9ad65cff6d",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root data directory (needed only to train baseline ML methods):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b18dad87-a1ed-495b-93b6-4c8af4043ddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db7c98c5-2942-4f88-984a-8c9014afe761",
+   "metadata": {},
+   "source": [
+    "Important results columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e96f0c4f-5683-4150-8cca-93a883f20154",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_col = \"config_model_name\"\n",
+    "task_col = \"config_task_name\"\n",
+    "\n",
+    "feature_subset_col = \"config_feature_subset\"\n",
+    "predictions_path_col = \"predictions_path\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4296ffdf-a9af-43f7-bd0c-20f4ae5f9619",
+   "metadata": {},
+   "source": [
+    "Helper function to parse each dictionary containing benchmark results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0cf4a6c8-ec28-4e66-9f6d-8a4e977d7b60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import (\n",
+    "    num_features_helper,\n",
+    "    parse_model_name,\n",
+    "    get_non_instruction_tuned_name,\n",
+    "    prettify_model_name,\n",
+    ")\n",
+    "\n",
+    "def parse_results_dict(dct) -> dict:\n",
+    "    \"\"\"Parses results dict and brings all information to the top-level.\"\"\"\n",
+    "\n",
+    "    # Make a copy so we don't modify the input object\n",
+    "    dct = dct.copy()\n",
+    "\n",
+    "    # Discard plots' paths\n",
+    "    dct.pop(\"plots\", None)\n",
+    "\n",
+    "    # Bring configs to top-level\n",
+    "    config = dct.pop(\"config\", {})\n",
+    "    for key, val in config.items():\n",
+    "        dct[f\"config_{key}\"] = val\n",
+    "\n",
+    "    # Parse model name\n",
+    "    dct[model_col] = parse_model_name(dct[model_col])\n",
+    "    dct[\"base_name\"] = get_non_instruction_tuned_name(dct[model_col])\n",
+    "    dct[\"name\"] = prettify_model_name(dct[model_col])\n",
+    "\n",
+    "    # Is instruction-tuned model?\n",
+    "    dct[\"is_inst\"] = dct[\"base_name\"] != dct[model_col]\n",
+    "\n",
+    "    # Log number of features\n",
+    "    dct[\"num_features\"] = num_features_helper(dct[feature_subset_col], max_features_return=-1)\n",
+    "    dct[\"uses_all_features\"] = (dct[feature_subset_col] is None) or (dct[\"num_features\"] == -1)\n",
+    "\n",
+    "    if dct[feature_subset_col] is None:\n",
+    "        dct[feature_subset_col] = \"full\"\n",
+    "\n",
+    "    # Assert all results are at the top-level\n",
+    "    assert not any(isinstance(val, dict) for val in dct.values())\n",
+    "    return dct\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02009a86-e099-4ec0-8676-8d66190ceddb",
+   "metadata": {},
+   "source": [
+    "Iteratively search the root directory for results files matching the given regex:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aaefa99d-dbd7-40a3-a3c1-7571d3811409",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3293cf3c9da64e4eaab95a251b59aad5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 195 benchmark results.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils import find_files, load_json\n",
+    "\n",
+    "# Results file name pattern\n",
+    "pattern = r'^results.bench-(?P<hash>\\d+)[.]json$'\n",
+    "\n",
+    "# Find results files and aggregate\n",
+    "results = {}\n",
+    "for file_path in tqdm(find_files(RESULTS_ROOT_DIR, pattern)):\n",
+    "    results[Path(file_path).parent.name] = parse_results_dict(load_json(file_path))\n",
+    "\n",
+    "if len(results) == 0:\n",
+    "    raise RuntimeError(f\"Couldn't find any results at {RESULTS_ROOT_DIR}\")\n",
+    "else:\n",
+    "    print(f\"Found {len(results)} benchmark results.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ecc0d33-902d-492a-89c6-d9729fe69fa1",
+   "metadata": {},
+   "source": [
+    "Aggregate results into a single DataFrame, and generate a unique identifier for each row:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "94f1900f-fea3-4872-b722-ee1cd3f5f7e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.shape=(195, 58)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>config_population_filter</th>\n",
+       "      <th>config_reuse_few_shot_examples</th>\n",
+       "      <th>config_seed</th>\n",
+       "      <th>config_task_hash</th>\n",
+       "      <th>config_task_name</th>\n",
+       "      <th>base_name</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX__7</th>\n",
+       "      <td>0.630994</td>\n",
+       "      <td>0.281751</td>\n",
+       "      <td>0.663274</td>\n",
+       "      <td>0.500705</td>\n",
+       "      <td>0.007359</td>\n",
+       "      <td>0.985495</td>\n",
+       "      <td>0.215502</td>\n",
+       "      <td>0.111277</td>\n",
+       "      <td>0.116464</td>\n",
+       "      <td>0.031550</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>42</td>\n",
+       "      <td>591377254</td>\n",
+       "      <td>ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX</td>\n",
+       "      <td>Mixtral-8x22B-v0.1</td>\n",
+       "      <td>Mixtral 8x22B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>7</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Meta-Llama-3-8B__ACSIncome_RAC1P__1</th>\n",
+       "      <td>0.369300</td>\n",
+       "      <td>0.286688</td>\n",
+       "      <td>0.362849</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.279789</td>\n",
+       "      <td>0.220760</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>42</td>\n",
+       "      <td>664576746</td>\n",
+       "      <td>ACSIncome_RAC1P</td>\n",
+       "      <td>Meta-Llama-3-8B</td>\n",
+       "      <td>Llama 3 8B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX__7</th>\n",
+       "      <td>0.477681</td>\n",
+       "      <td>0.190822</td>\n",
+       "      <td>0.677577</td>\n",
+       "      <td>0.508865</td>\n",
+       "      <td>0.210830</td>\n",
+       "      <td>0.678362</td>\n",
+       "      <td>0.392382</td>\n",
+       "      <td>0.354748</td>\n",
+       "      <td>0.353959</td>\n",
+       "      <td>0.294444</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>False</td>\n",
+       "      <td>42</td>\n",
+       "      <td>591377254</td>\n",
+       "      <td>ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX</td>\n",
+       "      <td>gemma-2-27b</td>\n",
+       "      <td>Gemma 2 27B (it)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>7</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 58 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    accuracy  accuracy_diff  \\\n",
+       "id                                                                            \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...  0.630994       0.281751   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                 0.369300       0.286688   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...  0.477681       0.190822   \n",
+       "\n",
+       "                                                    accuracy_ratio  \\\n",
+       "id                                                                   \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...        0.663274   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                       0.362849   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...        0.677577   \n",
+       "\n",
+       "                                                    balanced_accuracy  \\\n",
+       "id                                                                      \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...           0.500705   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                          0.500000   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...           0.508865   \n",
+       "\n",
+       "                                                    balanced_accuracy_diff  \\\n",
+       "id                                                                           \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...                0.007359   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                               0.000000   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...                0.210830   \n",
+       "\n",
+       "                                                    balanced_accuracy_ratio  \\\n",
+       "id                                                                            \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...                 0.985495   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                                1.000000   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...                 0.678362   \n",
+       "\n",
+       "                                                    brier_score_loss  \\\n",
+       "id                                                                     \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...          0.215502   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                         0.279789   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...          0.392382   \n",
+       "\n",
+       "                                                         ece  ece_quantile  \\\n",
+       "id                                                                           \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...  0.111277      0.116464   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                 0.220760           NaN   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...  0.354748      0.353959   \n",
+       "\n",
+       "                                                    equalized_odds_diff  ...  \\\n",
+       "id                                                                       ...   \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...             0.031550  ...   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                            0.000000  ...   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...             0.294444  ...   \n",
+       "\n",
+       "                                                    config_population_filter  \\\n",
+       "id                                                                             \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...                      None   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                                     None   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...                      None   \n",
+       "\n",
+       "                                                    config_reuse_few_shot_examples  \\\n",
+       "id                                                                                   \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...                           False   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                                          False   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...                           False   \n",
+       "\n",
+       "                                                    config_seed  \\\n",
+       "id                                                                \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...           42   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                          42   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...           42   \n",
+       "\n",
+       "                                                    config_task_hash  \\\n",
+       "id                                                                     \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...         591377254   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                        664576746   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...         591377254   \n",
+       "\n",
+       "                                                                              config_task_name  \\\n",
+       "id                                                                                               \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...  ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                                            ACSIncome_RAC1P   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...  ACSIncome_AGEP_COW_MAR_POBP_RAC1P_RELP_SEX   \n",
+       "\n",
+       "                                                             base_name  \\\n",
+       "id                                                                       \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...  Mixtral-8x22B-v0.1   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                    Meta-Llama-3-8B   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...         gemma-2-27b   \n",
+       "\n",
+       "                                                                name  is_inst  \\\n",
+       "id                                                                              \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...     Mixtral 8x22B    False   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                       Llama 3 8B    False   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...  Gemma 2 27B (it)     True   \n",
+       "\n",
+       "                                                   num_features  \\\n",
+       "id                                                                \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...            7   \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                           1   \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...            7   \n",
+       "\n",
+       "                                                    uses_all_features  \n",
+       "id                                                                     \n",
+       "Mixtral-8x22B-v0.1__ACSIncome_AGEP_COW_MAR_POBP...              False  \n",
+       "Meta-Llama-3-8B__ACSIncome_RAC1P__1                             False  \n",
+       "gemma-2-27b-it__ACSIncome_AGEP_COW_MAR_POBP_RAC...              False  \n",
+       "\n",
+       "[3 rows x 58 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(list(results.values()))\n",
+    "\n",
+    "def row_id(row) -> str:\n",
+    "    \"\"\"Unique row identifier.\"\"\"\n",
+    "    return f\"{row[model_col]}__{row[task_col]}__{row['num_features']}\"\n",
+    "\n",
+    "df[\"id\"] = df.apply(row_id, axis=1)\n",
+    "df = df.set_index(\"id\", drop=True, verify_integrity=True)\n",
+    "\n",
+    "print(f\"{df.shape=}\")\n",
+    "df.sample(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fd59aee-3802-4430-a977-243dd44f8f62",
+   "metadata": {},
+   "source": [
+    "Drop potential duplicates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "69ef57de-0059-4c40-9eae-fb4e6b3bf582",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parsed_df = df.drop_duplicates(subset=[\"name\", \"is_inst\", \"num_features\", task_col])\n",
+    "if len(parsed_df) != len(df):\n",
+    "    print(f\"Found {len(df) - len(parsed_df)} duplicates! dropping rows...\")\n",
+    "    df = parsed_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97cd24df-3c1f-49c0-bb50-a6fdba22e0fb",
+   "metadata": {},
+   "source": [
+    "Load scores DFs and analyze score distribution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "43ba0fbf-b886-4f2a-8a01-564aa6f0f2c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_model_scores_df(df_row: pd.Series) -> pd.DataFrame:\n",
+    "    \"\"\"Loads csv containing model scores corresponding to the given DF row.\"\"\"\n",
+    "    if predictions_path_col in df_row and not pd.isna(df_row[predictions_path_col]):\n",
+    "        return pd.read_csv(df_row[predictions_path_col], index_col=0)\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9bc88155-08a3-4cc2-bb8d-aaec96350f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "133e0fbc9ecc4262990da41e34a12425",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/195 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scores_df_map = {\n",
+    "    id_: load_model_scores_df(row)\n",
+    "    for id_, row in tqdm(parsed_df.iterrows(), total=len(parsed_df))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "54e22896-1d3d-4d95-bb90-2b453f58f09a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2325fd86cbdc436e84da4bc8be53fd98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/195 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from folktexts.evaluation import compute_best_threshold\n",
+    "from sklearn import metrics\n",
+    "from scipy import stats\n",
+    "\n",
+    "# Number of samples used to fit the one-parameter binarization threshold!\n",
+    "N = 100\n",
+    "\n",
+    "fit_thresh_col = f\"fit_thresh_on_{N}\"\n",
+    "fit_acc_col = f\"fit_thresh_accuracy\"\n",
+    "\n",
+    "optimal_thres_col = \"optimal_thresh\"\n",
+    "optimal_acc_col = \"optimal_thresh_accuracy\"\n",
+    "\n",
+    "score_stdev_col = \"score_stdev\"\n",
+    "score_mean_col = \"score_mean\"\n",
+    "\n",
+    "scores_stats = dict()\n",
+    "for row_id, row in tqdm(df.iterrows(), total=len(df)):\n",
+    "\n",
+    "    # Load model scores\n",
+    "    scores_df = scores_df_map[row_id]\n",
+    "    if scores_df is None:\n",
+    "        logging.error(f\"Couldn't find scores for {row_id}\")\n",
+    "\n",
+    "    # Extract scores and labels\n",
+    "    risk_scores = scores_df[\"risk_score\"].to_numpy()\n",
+    "    labels = scores_df[\"label\"].to_numpy()\n",
+    "\n",
+    "    # Sample N rows to fit threshold\n",
+    "    scores_df_sample = scores_df.sample(n=N, random_state=42)\n",
+    "\n",
+    "    # Compute optimal threshold on each data sample\n",
+    "    fit_thr = compute_best_threshold(y_true=scores_df_sample[\"label\"], y_pred_scores=scores_df_sample[\"risk_score\"])\n",
+    "    opt_thr = compute_best_threshold(y_true=labels, y_pred_scores=risk_scores)\n",
+    "\n",
+    "    # Evaluate accuracy\n",
+    "    fit_acc = metrics.accuracy_score(labels, (risk_scores >= fit_thr).astype(int))\n",
+    "    opt_acc = metrics.accuracy_score(labels, (risk_scores >= opt_thr).astype(int))\n",
+    "\n",
+    "    # Save results\n",
+    "    scores_stats[row_id] = {\n",
+    "        fit_thresh_col: fit_thr,\n",
+    "        fit_acc_col: fit_acc,\n",
+    "        optimal_thres_col: opt_thr,\n",
+    "        optimal_acc_col: opt_acc,\n",
+    "        score_stdev_col: np.std(risk_scores),\n",
+    "        score_mean_col: np.mean(risk_scores),\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1a58b87-2de8-473a-badb-934951d1bcdc",
+   "metadata": {},
+   "source": [
+    "Update results DF with scores statistics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0ba74c81-c008-4010-a9a3-1b511ae445df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>accuracy_diff</th>\n",
+       "      <th>accuracy_ratio</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>balanced_accuracy_diff</th>\n",
+       "      <th>balanced_accuracy_ratio</th>\n",
+       "      <th>brier_score_loss</th>\n",
+       "      <th>ece</th>\n",
+       "      <th>ece_quantile</th>\n",
+       "      <th>equalized_odds_diff</th>\n",
+       "      <th>...</th>\n",
+       "      <th>name</th>\n",
+       "      <th>is_inst</th>\n",
+       "      <th>num_features</th>\n",
+       "      <th>uses_all_features</th>\n",
+       "      <th>fit_thresh_on_100</th>\n",
+       "      <th>fit_thresh_accuracy</th>\n",
+       "      <th>optimal_thresh</th>\n",
+       "      <th>optimal_thresh_accuracy</th>\n",
+       "      <th>score_stdev</th>\n",
+       "      <th>score_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4</th>\n",
+       "      <td>0.567786</td>\n",
+       "      <td>0.183621</td>\n",
+       "      <td>0.743500</td>\n",
+       "      <td>0.636874</td>\n",
+       "      <td>0.105719</td>\n",
+       "      <td>0.843667</td>\n",
+       "      <td>0.273436</td>\n",
+       "      <td>0.237165</td>\n",
+       "      <td>0.237165</td>\n",
+       "      <td>0.630823</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yi 34B (chat)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.531214</td>\n",
+       "      <td>0.582998</td>\n",
+       "      <td>0.562204</td>\n",
+       "      <td>0.597789</td>\n",
+       "      <td>0.192288</td>\n",
+       "      <td>0.606465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6</th>\n",
+       "      <td>0.422379</td>\n",
+       "      <td>0.183270</td>\n",
+       "      <td>0.609219</td>\n",
+       "      <td>0.538078</td>\n",
+       "      <td>0.085607</td>\n",
+       "      <td>0.857950</td>\n",
+       "      <td>0.303729</td>\n",
+       "      <td>0.306175</td>\n",
+       "      <td>0.306175</td>\n",
+       "      <td>0.232553</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yi 34B</td>\n",
+       "      <td>False</td>\n",
+       "      <td>6</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.731161</td>\n",
+       "      <td>0.659718</td>\n",
+       "      <td>0.679091</td>\n",
+       "      <td>0.610856</td>\n",
+       "      <td>0.117468</td>\n",
+       "      <td>0.675475</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 64 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  accuracy  accuracy_diff  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4     0.567786       0.183621   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6  0.422379       0.183270   \n",
+       "\n",
+       "                                                  accuracy_ratio  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4           0.743500   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6        0.609219   \n",
+       "\n",
+       "                                                  balanced_accuracy  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4              0.636874   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6           0.538078   \n",
+       "\n",
+       "                                                  balanced_accuracy_diff  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                   0.105719   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6                0.085607   \n",
+       "\n",
+       "                                                  balanced_accuracy_ratio  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                    0.843667   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6                 0.857950   \n",
+       "\n",
+       "                                                  brier_score_loss       ece  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4             0.273436  0.237165   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6          0.303729  0.306175   \n",
+       "\n",
+       "                                                  ece_quantile  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4         0.237165   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6      0.306175   \n",
+       "\n",
+       "                                                  equalized_odds_diff  ...  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                0.630823  ...   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6             0.232553  ...   \n",
+       "\n",
+       "                                                           name  is_inst  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4     Yi 34B (chat)     True   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6         Yi 34B    False   \n",
+       "\n",
+       "                                                  num_features  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                4   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6             6   \n",
+       "\n",
+       "                                                  uses_all_features  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                 False   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6              False   \n",
+       "\n",
+       "                                                  fit_thresh_on_100  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4              0.531214   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6           0.731161   \n",
+       "\n",
+       "                                                  fit_thresh_accuracy  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                0.582998   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6             0.659718   \n",
+       "\n",
+       "                                                  optimal_thresh  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4           0.562204   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6        0.679091   \n",
+       "\n",
+       "                                                  optimal_thresh_accuracy  \\\n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4                    0.597789   \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6                 0.610856   \n",
+       "\n",
+       "                                                 score_stdev  score_mean  \n",
+       "Yi-34B-Chat__ACSIncome_COW_POBP_RAC1P_RELP__4       0.192288    0.606465  \n",
+       "Yi-34B__ACSIncome_COW_MAR_POBP_RAC1P_RELP_SEX__6    0.117468    0.675475  \n",
+       "\n",
+       "[2 rows x 64 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_stats_df = pd.DataFrame(scores_stats.values(), index=list(scores_stats.keys()))\n",
+    "\n",
+    "results_df = pd.concat((df, scores_stats_df), axis=\"columns\")\n",
+    "results_df.sample(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d04ff64-39c4-43cc-9df2-fa4e099c4e56",
+   "metadata": {},
+   "source": [
+    "Check if any results are missing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "dbb3b379-7697-4b54-a33d-09d503210f87",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "config_model_name\n",
+      "Meta-Llama-3-70B                9\n",
+      "Meta-Llama-3-70B-Instruct       9\n",
+      "Meta-Llama-3-8B                10\n",
+      "Meta-Llama-3-8B-Instruct       10\n",
+      "Mistral-7B-Instruct-v0.2       10\n",
+      "Mistral-7B-v0.1                10\n",
+      "Mixtral-8x22B-Instruct-v0.1     9\n",
+      "Mixtral-8x22B-v0.1              9\n",
+      "Mixtral-8x7B-Instruct-v0.1      9\n",
+      "Mixtral-8x7B-v0.1              10\n",
+      "Yi-34B                         10\n",
+      "Yi-34B-Chat                    10\n",
+      "gemma-1.1-2b-it                10\n",
+      "gemma-1.1-7b-it                10\n",
+      "gemma-2-27b                    10\n",
+      "gemma-2-27b-it                 10\n",
+      "gemma-2-9b                     10\n",
+      "gemma-2-9b-it                  10\n",
+      "gemma-2b                       10\n",
+      "gemma-7b                       10\n",
+      "Name: config_task_name, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(results_df.groupby([model_col])[task_col].count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6e859fc-9d82-436d-a238-11fd008da44c",
+   "metadata": {},
+   "source": [
+    "Finally, save results DF to the results root directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9725afa2-b6f8-46b1-86e7-57ae82ef05d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import get_current_timestamp\n",
+    "results_df.to_csv(Path(RESULTS_ROOT_DIR) / f\"aggregated_results.{get_current_timestamp()}.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3a6beb5-b44b-4f81-ae1a-d027afb2c5f4",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5d721ce1-f3f3-4a56-a2fa-fb63f652128d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1f77b4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff7f0e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2ca02c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d62728;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8c564b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e377c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7f7f7f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bcbd22;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#17becf;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
+      ],
+      "text/plain": [
+       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
+       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
+       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
+       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
+       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
+       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
+       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
+       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"whitegrid\", rc={\"grid.linestyle\": \"--\"})\n",
+    "plt.rc(\"figure\", figsize=(4.5, 3.5), dpi=200)\n",
+    "\n",
+    "palette_name = \"tab10\"   # \"colorblind\"\n",
+    "palette = sns.color_palette(palette_name)\n",
+    "sns.set_palette(palette_name)\n",
+    "palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "919690a2-8f87-451b-b778-0740127cd135",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMGS_DIR = RESULTS_ROOT_DIR / \"imgs\"\n",
+    "IMGS_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "def save_fig(fig, name):\n",
+    "    fig.savefig((IMGS_DIR / name).with_suffix(\".pdf\"), bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5d64604-8c0e-40f9-8130-fca5fff63194",
+   "metadata": {},
+   "source": [
+    "## Score distributions of an example model with increasing evidence\n",
+    "Score distribution should get more discriminative (less Gaussian-shaped) with increasing evidence.,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2742c1eb-6440-4ffe-b2b2-25886902976e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EXAMPLE_MODEL = \"Meta-Llama-3-70B-Instruct\"\n",
+    "# EXAMPLE_MODEL = \"Meta-Llama-3-70B\"\n",
+    "EXAMPLE_MODEL = \"Mixtral-8x7B-Instruct-v0.1\"\n",
+    "# EXAMPLE_MODEL = \"Mixtral-8x7B-v0.1\"\n",
+    "# EXAMPLE_MODEL = \"Mistral-7B-v0.1\"\n",
+    "# EXAMPLE_MODEL = \"Mistral-7B-Instruct-v0.2\"\n",
+    "# EXAMPLE_MODEL = \"Yi-34B\"\n",
+    "# EXAMPLE_MODEL = \"Yi-34B-Chat\"\n",
+    "# EXAMPLE_MODEL = \"Meta-Llama-3-8B\"\n",
+    "# EXAMPLE_MODEL = \"Meta-Llama-3-8B-Instruct\"\n",
+    "# EXAMPLE_MODEL = \"Mixtral-8x22B-Instruct-v0.1\"\n",
+    "# EXAMPLE_MODEL = \"Mixtral-8x22B-v0.1\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54ae284c-79af-4940-89ff-35327283cff6",
+   "metadata": {},
+   "source": [
+    "**TODO?** plot xgboost score distribution as features are added! to show below the LLM score distribution as what true uncertainty quantification looks like..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9be9c590-589f-4ed1-a3cc-9e83e7bd6034",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2400x600 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get data to plot\n",
+    "plot_df = results_df[results_df[model_col] == EXAMPLE_MODEL].sort_values(\"num_features\")\n",
+    "# Skip every other row in plot\n",
+    "plot_df = plot_df[[idx % 2 == 0 for idx in range(len(plot_df))]]\n",
+    "# plot_df = plot_df.iloc[[0, 2, 3, 5, 7]]\n",
+    "\n",
+    "# # For current Llama-3-8B-Instruct results\n",
+    "# plot_df = plot_df[[False, True, False, True, True, False, True]]\n",
+    "\n",
+    "N_PLOTS = min(10, len(plot_df))\n",
+    "\n",
+    "N_COLS = 5\n",
+    "N_ROWS = math.ceil(N_PLOTS / N_COLS)\n",
+    "\n",
+    "fig, axes = plt.subplots(\n",
+    "    nrows=N_ROWS, ncols=N_COLS,\n",
+    "    figsize=(12, 3 * N_ROWS),\n",
+    "    sharey=True, sharex=False,\n",
+    "    gridspec_kw=dict(\n",
+    "        hspace=0.5,\n",
+    "        wspace=0.06,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Plot settings\n",
+    "plot_config = dict(\n",
+    "    bins=(N_BINS := 20),\n",
+    "    binrange=(0, 1),\n",
+    "    stat=\"percent\",\n",
+    ")\n",
+    "\n",
+    "# Plot score distribution for model pairs\n",
+    "current_featureset = set()\n",
+    "\n",
+    "for idx, (id_, row) in enumerate(plot_df.iterrows()):\n",
+    "    ax_row = idx // N_COLS\n",
+    "    ax_col = idx % N_COLS\n",
+    "\n",
+    "    ax = axes[ax_row, ax_col] if N_ROWS > 1 else axes[ax_col]\n",
+    "\n",
+    "    # Get scores\n",
+    "    scores = scores_df_map[row.name][\"risk_score\"]\n",
+    "\n",
+    "    # Added features\n",
+    "    added_features = set(row[\"config_feature_subset\"]) - current_featureset\n",
+    "\n",
+    "    # Set title with added features compared to last plot\n",
+    "    title_str = \"+ \" if len(current_featureset) > 0 else \"\"\n",
+    "    title_str += \", \".join(added_features)\n",
+    "    current_featureset |= added_features\n",
+    "\n",
+    "    ax.set_title(title_str)\n",
+    "\n",
+    "    ax.set_xlabel(\"risk scores\")\n",
+    "    if ax_col == 0:\n",
+    "        ax.set_ylabel(\"density (%)\")\n",
+    "\n",
+    "    ax.set_xlim(-0.03, 1.03)\n",
+    "\n",
+    "    # Render plot\n",
+    "    sns.histplot(scores, ax=ax, color=palette[1 if row[\"is_inst\"] else 0], **plot_config)\n",
+    "\n",
+    "    # Add text box with distribution stats\n",
+    "    textstr = '\\n'.join([\n",
+    "        r\"$\\text{ROC}=%.2f$\" % (row[\"roc_auc\"],),\n",
+    "        r\"$\\text{ECE}=%.2f$\" % (row[\"ece\"],),\n",
+    "        r\"$\\sigma=%.2f$\" % (row[\"score_stdev\"],),\n",
+    "        r\"$\\mu=%.2f$\" % (row[\"score_mean\"],),\n",
+    "    ])\n",
+    "\n",
+    "    props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
+    "\n",
+    "    if row[\"is_inst\"]:\n",
+    "        # Center-aligned text box\n",
+    "        ax.text(0.5, 0.95, textstr, transform=ax.transAxes, fontsize=9,\n",
+    "            verticalalignment='top', horizontalalignment='center', bbox=props)\n",
+    "    else:\n",
+    "        # Right-aligned text box\n",
+    "        ax.text(0.95, 0.95, textstr, transform=ax.transAxes, fontsize=9,\n",
+    "            verticalalignment='top', horizontalalignment='right', bbox=props)\n",
+    "\n",
+    "        # # Left-aligned text box\n",
+    "        # ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=9,\n",
+    "        #     verticalalignment='top', bbox=props)\n",
+    "\n",
+    "# Remove unused axes\n",
+    "[\n",
+    "    (axes[N_ROWS - 1, idx] if N_ROWS > 1 else axes[idx]).remove()\n",
+    "    for idx in range(N_COLS-1, len(plot_df) - N_COLS * (N_ROWS - 1) - 1, -1)\n",
+    "];\n",
+    "\n",
+    "# Set suptitle\n",
+    "plt.suptitle(plot_df[\"name\"].iloc[0], y=1.05, fontsize=15)\n",
+    "\n",
+    "plt.plot()\n",
+    "\n",
+    "save_fig(fig, f\"increasing-evidence.{EXAMPLE_MODEL}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "06d0f410-3fe5-4dd3-913d-4369aa8cfacf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Label prevalence: 36.9%\n"
+     ]
+    }
+   ],
+   "source": [
+    "prevalence = scores_df_map[row.name]['label'].mean()\n",
+    "print(f\"Label prevalence: {prevalence:.1%}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41dfe9c8-52c3-4e86-ac51-2f73540963bb",
+   "metadata": {},
+   "source": [
+    "- [ ] **TODO:** Make the same plot for XGBoost to have a plot of a calibrated baseline..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6047a8d-2577-4123-833a-a7ead8ca8251",
+   "metadata": {},
+   "source": [
+    "## Plots for XGBoost baseline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0e899105-2370-4ee3-b304-aea95d7d6380",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bea01302-f19e-4322-a092-836f5cf65969",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5606a544-28a0-4805-9878-829fdfbd1fd8",
+   "metadata": {},
+   "source": [
+    "## Change in ECE and ROC with increasing evidence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a2494f73-152a-47f7-a9d6-d15092227b22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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33wGolw8saja6MY/DPn36YMmSJUhPT8fq1avx1ltv6fQ3tAQM7U5EJvHll1+iW7duhXYSBAQEYM2aNXB3dwegHsmlT5ihgly9elUrdOHy5cvzdKID6k7guXPn4vXXXxe2iRG2SCqV4q233tK6SXj8+HFh9ruhFAoFNm7cCABwdHREjx49RMm3uDRnoeaEFSYiy2XK9lsfmmsj1qtXr9AQ5Nu2bRPOC25ubli9enWeDpZq1arh999/F0ZiR0ZGYufOnaKUtU6dOvjtt9+EUcPp6enC2lZi2Lx5s9BhY+oRwS1btkSNGjUAAPv27UNCQoJJy0OWq0ePHsIxo0v7kpqaimPHjgFQR+vRNaIEma/cIUV79eql0+Cmotja2uKDDz7A22+/LWwTM3RpSkoKdu3aBUB9bskdsaSk5Vyra/6GICoOtsulX1RUlNaSbd988w26deuW5ya5o6MjJk6cqNWxsX//fqhUqhIrqy70+b2gj44dO2Lu3LnC8+DgYERGRoqSN6B9X0WsqI3FNWDAAOH75/0ey8X2u/SzhPa7bdu22LNnD1atWoU33ngjTyc6AJQvXx5Lly5Fp06dhG2//PJLgeXz8PDAggULcOjQIYwdOzbfddVz7tMvWbJE2Hbz5k2cPXtWhE+l7l/44YcftJbXE3M5jAsXLgiTMrp16yZqFC19OTk5Cf0QkZGROHXqlMnKIjZ2pBORWStXrpzWyfH27dsG56k58qpWrVqoV69eoel79eolPH748KHB+8+RsyYhoJ758uTJE1HyvXDhgrA2+muvvZYnbGVJa9asGSpVqgRAPWDg5cuXJi0PEZUMY7TfukpKSsLx48eF5/369Ss0/ebNm4XHo0ePhoeHR77pKlWqpBWVZNOmTQaW9D9eXl5o2rSp8LyoEf26yhldDahnjbzxxhui5GuIbt26AQCysrLw999/m7g0ZKnKly8vzD6+efNmkTeoDx06hMzMTAC6zzgj85Y7/GNRbb2+NH8DPHv2DCkpKaLke/DgQSECSn43MEtap06dhNmMe/fuLXXrGVLJYbtc+mneS3F0dESHDh0KTa/ZjiYmJuq8bEdJ0Pf3gr7eeOMNrZniYl3bP3z4ENeuXQMANGzYUOfQw8ZStWpVNG7cGAAQEhLCGccWiu136WcJ7ffo0aMREBBQZDqJRIIpU6YIz+Pj4xEWFpZv2hYtWqB///6wsrIqMt833ngDDRs2FJ6L2QlsZWUl3AcBgPv374uWt2bEk+7du4uWb3FplkGsySfmgKHdifQQEhKCc+fO4fr167h37x4SEhIgk8ng6uqK6tWro2XLlnj77bd1Cl3bsWNHobPz33//RbVq1fDo0SNs3boVZ86cQUxMDGQyGXx8fNCjRw+MGDECDg4OWnk8ePAAGzduxJUrVxAdHQ2pVIpatWqhT58+GDx4sE4nCTE/k7GUL19eeCzGrO2cG1cAdFrjRTONmCPwcnfUaJbLEJozZjp37lxk+pkzZwojsRcsWKAVokzztRy7d+/Osw1QX5zkF3pSIpGgc+fOwozIQ4cOib72L1FR2H6bhtjtt64OHjwoLN9hY2OjNXApt0ePHmnd2CrqJlr//v3x448/AlD/GH3y5IloN7A0zwsZGRmi5Hn58mXExcUBUM8Gd3FxKTT9pUuX8O677wLI265rvqapoNk7OcdHbp07d8bKlSsBqEdiDx8+XLcPQ5RL3759hZvge/bswSeffFJg2pzZNdbW1ujVqxeCgoKKzD8qKkoYEOTl5aV1wz23mJgY7Ny5ExcuXMDDhw+RnJwMQD0q39PTE3Xq1EGzZs3wxhtvaB3rw4cP15odAqijJS1fvjzPPvr166e1HE9+13DJycnYvXs3jh49isePH+PFixdQKBS4cuUKXF1dhfe+ePECJ0+exOXLlxEeHo6nT58iLS0NDg4OqFixIpo0aYIePXroHCrdFHKWrADUs6F0ufGmj/yu1YtqQ3Wh77W6Zh1Zv3691vJRhtSfHM7OzmjdujVOnTqFFy9e4Ny5c6KGmqSyxdTt8pMnT9CnTx/hunPKlCkYO3ZsoXl+/fXXwuDIypUrY9++fXBzc8OuXbswa9YsrbTR0dEFXvdodlIUdD116tQp7N27FyEhIYiPj0d6ejpmzZqFkSNHCu+VyWS4ePEiLly4gODgYDx8+BBJSUmQSCQoV64c/Pz88Oqrr2LgwIElPmhe856Fk5NTkb9Zct9vyX0/5ebNmxg6dKgwgOeHH34oMqLeuHHjhCU96tWrh7/++qvQ0OYF0ef3QnHY2tqiXLlyePbsGQDxru31PYcsW7ZMOCd89NFHWmHeNV/Lcfny5XzreGHXQZ07d8aNGzcAqK/tp02bVvQHIbPD9luN7beaObffAODr6ws3NzchVH10dDTq169frLw0NW3aVFhTPSoqyuD8NOVMMgPEOyekp6cL0SGcnJx0inSleRzkHvyU3zGS3z0gIG8fQo5WrVrBxcUFKSkpOHHiBJKTk7V+h1oqdqQT6eitt95CcHBwvq+9ePECL168wI0bN/DHH39g0qRJGDNmjF757927F19++WWehjQ8PBzh4eE4cuQI1q1bJ5zIfvnlFyxbtgxKpVIrfVBQEIKCgnD48GGsWrUqT+dNSX4msWh2cIixZl+VKlWExw8fPoRcLi80DGRERITwWMwbdM+fP9d6XtAMSH0olUqtEXOtWrUyOE8xtGrVSpjxefLkSXakU4li+1162m9daYaja9++vVaHfm4XL14UHvv4+OQb6kuTp6cnfHx8hFH6Fy9eFK0jXfO8ULFiRVHyzPmhCkCr88WUGjRoIPywCg4OxosXL0RbD57Klg4dOgg3UPbt24dJkyblO7s3KioKV69eBQC8+uqrote3v/76C99++60wM0dTzgyO8PBwHDhwAPv378eWLVtE3X+Oa9euYcqUKYiJiSk03fr167Fw4UIoFIo8r6WkpCAlJQUPHz7Erl270KpVK/z444/CUh3mIiUlRWsJDGPMhtJsk62trUX5GyQlJeH69esA1De7AgMDDc5TDK1atRJ+Q5w8eZId6VRspm6Xvb29MWfOHMyYMQMA8PPPP6NNmzZo0KBBvulPnjwpdMJIpVIsXrxYp8H2+kpJScGsWbPwzz//FJouJiYGffv2LXDmX1xcHOLi4nDmzBn8+uuv+P7779G2bVvRy1sQzXspCQkJSEhIKPQ6W/NeSqVKlfKkbdy4MSZMmICff/4ZgHoJqSZNmmjtR9OmTZuEa1sHBwcsXbq02J0w+vxeKA6ZTCZ08ADGubY3p/s9OU6ePMmOdAvF9jt/bL/Nr/3OoVk/8/tdY2ieue/ZGUozkpZY54QLFy4Iv0GbNm1q8N9UDNbW1mjevDlOnDgBmUyGc+fOmcVMeUOxI51IRzk3pGxtbVGnTh1Ur14dLi4uUKlUiI+PR1BQEF6+fAmZTIalS5cCgM4dF6dPn8a8efOgVCrh4+ODwMBA2NnZITw8XOgoCQsLw+TJk/HHH39g5cqV+OmnnwCoRwoFBATAysoKwcHBwonu8uXLWLBgAb7++muTfCaxXLhwASdPnhSed+3a1eA827VrB1tbW2RnZyMxMRFr1qwpcJTjs2fPsHbtWuH5kCFDDN5/joMHDwqPPT098521p6+wsDAhdLqPj4/BJ+bWrVvD0dERDx48wIULFwCow+HnN8ItZ83b/DRv3lx4fPny5SIHLxCJie136Wm/dfHw4UNhRgSAfEfIatIMqaXrCOZ69eoJHeliheSKjY3VKrdmu2mI8+fPi5anp6cnhg4dCkA7rH3OttwKWptLKpWiSZMmOH36NFQqFc6fP68VOo5IV7a2tujevTu2bt2K6OhoXLlyBS1atMiTbs+ePcIsij59+ohahmPHjmHOnDnCc2dnZzRu3BiVK1eGlZUVUlNTERkZibt370Imk+V5f+fOnVGnTh3cunVLOG8EBgZqhRXM0ahRowLL8ejRI3z77bdISUmBk5MTXnnlFVSqVAlJSUnCzc4cz549E242eXt7w9fXF+XLl4etrS1SUlJw9+5d4Zx08eJFjBo1Ctu2bTOLGzM5Dh06hKysLADqmYS9e/cWfR+a1+qBgYGifP6LFy8Kf/vGjRsbfD0sVv3RPD9onjeI9GUO7XLfvn1x5swZHDhwADKZDFOmTMHu3bvh6OiolS4+Pl5rxuL777+vNejQ19cXQ4cORVpamhABw8nJSe+BOyqVCtOmTcOJEycgkUjQoEED1K5dGyqVChEREVo37dPT04VOGDc3N9SuXRtVq1aFo6MjZDIZoqKiEBQUhKysLCQmJmLs2LHYsGGD1vJAxtSwYUNUrFgRz58/h0KhwI8//ljgb5a0tDSsWLFCeF7QvZRx48bh3LlzuHbtGpKTkzFt2jSsX78eUqn2aqQRERFYtGiR8HzmzJnw9fUt1ufQ9/dCcfz777/CecrOzk6UgVMJCQnCcln29vYGz75s2LAhhg4diri4OGFGY6VKlfJdCqpcuXIF5lOvXj04OjoiPT0d9+7dQ1xcXJGDk8n8sP3Oi+23mjm13zni4uK0Bi0U1IGvr5y1xgF1lAOxKJVKHDlyRHhujvd7gP/u7fzzzz9CRJXOnTvn26YX9h02a9ZMGDhx/vx5dqQTlSVvvPEGOnTogJYtW8Le3j7P6wqFAnv37sW8efOQnp6On376Cd26ddNpltqCBQvg4OCAb7/9Vmu9DEB9A2fq1KlQKBQ4e/Ys1q1bh59++gmVKlXCd999l+eiZu3atULIvu3bt2Ps2LEFdtAa8zMVl0qlQmpqKu7fv4+DBw9i8+bNwgiwXr16iTJar3z58pgwYYIQmve7775DUFAQRowYgZo1a8LZ2RlPnz7FmTNnsGrVKrx48QKAOnSiGBeJKpUK+/fvF/YPqC/6xFgfUTOcUkEhi/TRp08f9OnTB7t27RI60hs1aqR1w1gXFSpUgIeHB+Lj45GRkYGIiAjUrVvX4PIR6YLtd+lpv3WhGerX3d29yFl1Dx8+FB7rGgZf80fagwcP9CtgAWWYPHmycLOtQYMGOoXkKkpaWpoQFUAikcDPz8+g/Hx8fIT2X7MjXd9zAqAOw3z69GkAwK1bt9iRTsXWt29fbN26FYD6+M/vht++ffsAAK6urkJISbFohkQdNmwYpk6dmm9EkbS0NJw+fRqhoaFa20eMGAFAHV41pyP0tdde0wq9qovVq1dDLpdj6NChmDJlila4SJlMphW+0cfHB1988QXeeOONAm9037lzB59//jlCQkJw+/ZtrF69GhMmTNCrTMakucxQu3btRJ1JKJfLsWHDBiGaEiDeYDSxr9XFqj9+fn6wsrKCQqFAZGQkkpKSjDKri8oGU7fLAPDVV1/hxo0biI6ORmRkJL799lvMnz9feF2lUmHmzJlISEgAoL72+vjjj7XyaNSoERo1aoSoqCjh+rJcuXJ6X/fcuHEDcrkcfn5+WLp0aZ5jPye8OKDuHB0+fDh69+6NBg0a5OmMAIDU1FSsWLECa9asgVwux6xZs3Do0KF804rN1tYW06dPx/Tp0wGoI7I8fPgQY8aMgZ+fH9zd3RETE4OrV6/il19+EZbD6tKlS4HtqJWVFZYsWYI+ffogJSUFV65cwcqVKzF+/HghTXZ2tta1cqdOnTB48OBifw59fy/o68yZM/jyyy+F50OGDClwgKk+goODhQ5MX19fgwdjvfbaa3jttddw6dIloSNd83pfV1KpFP7+/sLghODgYHakWyi239rYfptf+51D87eAq6trgZEL9PH06VOtiIVt2rQxOE8ASE5Oxtdffy2EUXdychItUqvYvy1yjpGIiAihI/3dd9/VO7qh5j3/nFD5lo4d6UQ6+uqrrwp93crKCv3794e9vT0+/fRTyGQybN26VaeQRjKZDCtXrsy3ge7RowfOnz+P7du3A1B32tjb22PdunX5jvwZNWoUTp8+jfPnz0OpVOLQoUMFnvCM+Zn08csvvwgzNPNjbW2NUaNGYfLkyaLtc/z48bC3t8eSJUugUChw7Ngx4YdDbn5+fnjvvff0Grm4fv16rZFmgPqm3MuXL3Hr1i3ExsYCUHdsfPLJJwWuN6IvzbVNatasKUqeYvH19RXC2Ny5c4cd6VRi2H6Xrva7MCqVSvhhDwA9e/aEjY1Noe/RHMWsaxQPzaU4NEM2FiQoKCjPaG+VSiXM2ggNDRVuijVt2hQrVqwQ5cd0RESEMJihUqVKotzAE0utWrWEx3fu3DFhScjSNWnSRFhu4ciRI5gzZ47WAKPr16/j0aNHAIDu3bvDzs5OtH2npaUJM8OqVKmC2bNnFzgw0snJCd27dzfaaHy5XI6BAwfme4Mwdzv41ltvFZlfQEAA1q1bh+7duyM+Ph6bN2/GBx98UOR6iiXh8ePHQnh0QL32t76WLVuWZ21KuVyO+Ph43LhxQ4jwZGNjg3nz5ol2o1jzWl2zHTQ1e3t7VK1aFU+ePAGgLmd+N8+JdGHKdjmHi4sLFi9ejHfffRcKhQLbt2/Ha6+9Jsy0/fPPP3H27FkAgKOjI5YuXVrkNWNxyeVyeHh44M8//8x30I9mtAsvLy/Mnj270PycnZ0xY8YMpKenY+vWrYiMjMSZM2dKbEmGPn36wNraGp999hkyMzNx+fJlXL58Od+03t7eGDZsGEaMGFHoxAEvLy/MnTtX+M2wfPlytG3bVoiusXjxYmGWoIeHh1anmr6K83sht3379iEkJERrm0KhQFJSEsLCwoT6DQDvvPOO0HFlKHM9hwDq8uR0pN+5c0en9dvJ/LD91sb227za7xxxcXFYtWqV8HzQoEGiRD3VXPqqatWq6Nixo07vS0xMzHd2f1ZWFqKjo3H9+nVhIEGlSpXw22+/iTLYSKVSac2gN6fzguY9z3v37pWKyLSWXXoiM9S1a1chpFHODN6idOzYsdBRTm+++abQEQMAb7/9dqHhM958800htEdBa+jqozifSSy1a9fGkiVLUK9ePdHzHjVqFLp27YrFixfj0KFD+aYpV64cunbtivbt2+uVd0Gd8poaNmyIb775xuAZgppyRgwC4oagEYPmRYJmOYnMBdtvcRmz/S7IpUuX8PTpU+G5Lp0r6enpwmNdbwRoptN8f0EePHhQ5Mx1FxcXTJ8+HQMHDhQlQgmgXr8uB88JVJr16dMHP/30E1JTU3Hs2DH07NlTeE1z1pnY4SdTU1OFx+XKlRPt2C0OOzs70QdLubi4oHPnztiyZQvi4+Nx7949UWY6GErzOy1Xrhxef/11g/IoyKuvvop58+bpHK1EF5rtsrnN1vP09BQ60tkuk6FM1S5rat68OcaNGyeEp509ezYaNmyIly9f4rvvvhPSffbZZ0YfhD5hwgTR1+AeMGCAMHP0woULJdYRA6h/s7Ru3RrLly/H5s2bhQGhmuzt7fHGG2/gjTfe0On8+Oabb+L06dPYs2cP5HI5pk6dit27d+Pq1avYuHEjAPVEhEWLFhn0tyzO74XcLly4UORvKx8fH3zzzTeihe8FeG1PJYPttza23+bTfgPqQUszZsxAWloaAPVkiIKWa9XH7t27tSbETZ48WedlndLS0rQi9uVHKpVi+PDh+PTTT/ONXlYc8fHxQgc9YF7nBQ8PD0ilUiiVSsjlcsTFxcHLy8vUxTIIO9KJiuHOnTu4ffs2oqOjkZqaqhXKBYBwkrl79y6USmWRs8qKWjc29w2rotJrdsxqXmgXRuzPpI/AwEBhDQ6FQoHk5GTcuXMHDx48wL179zBw4ED0798fM2bMEHU23cWLF7F48WIhxGa9evXg5+cHGxsbxMTE4Pr160hMTMSyZcuwbt06LFmyBB06dBBt/7du3cLbb7+NMWPGYNy4caL8TZ8/fy48LmwNK1Nwd3cXHufMTCcqaWy/S0f7XRDNH/Z+fn46rRuo+cND15Hsmj+oMjMzdS9gIVJSUvDFF1/gn3/+wbx580T5EWQp5wTNchIVR58+ffDzzz9DpVJhz549wg2/7OxsYbBk9erV0axZM1H36+7uDjs7O2RlZSEiIgLXrl0TfR+6atu2bbFCcb948QI3b97E/fv3kZycjIyMDK0bapoz7m7fvm3yjvSc7zhHz549jbZ2+9mzZ9G/f398+umnePvtt0XJM2fJKMC822Veq5OhTNUu5/bhhx/i/PnzuHHjBhITEzFt2jS8ePFCuF7u0qULBg4caNQyAOpIVfqSyWQICgpCeHg44uPjkZaWJsyUAyB0IgAQoqOUlNu3b2PhwoVCCNxatWqhXr16cHJywrNnz3D9+nUkJSVhzZo12LhxI+bMmaPT3/mLL77A9evX8fjxYzx69AizZs3CtWvXhPPSyJEjDV4uqji/F4ojMjISI0eOxJAhQzB16lRRZu5ayrU9zyGWje23Nrbf5tN+A8DSpUuFgUwSiQQLFy40eDmi4OBgreU4evbsKfrSc0qlUoim8PXXX4syyErzd4WDg4NRIkQUl7W1NVxcXIQIjs+fP2dHOlFZsnv3bvz222+IjIzUKb1MJkNKSkqRDXpRM5JdXV21ntepU6fQ9Jr705wpkx9jfSZ9tGvXDu3atcuzPTQ0FF9//TVu3ryJbdu2ISQkBBs2bBClM2bTpk2YN28eVCoVfH19sWTJkjw/oJKSkrBkyRJs374dKSkpmDhxItatW6fTyW79+vV51g9RqVRISUkR1g/esmWLsHbx/fv3tUZVFldGRobwWKwRbmLRDAelWU6iksD2u/S03wVJT0/XGkGs63Icmj82ZDKZTu/JvRZaUfr164eFCxfm2Z6ZmYnY2FhcuHABq1evRlRUFE6fPo2BAwdiy5YtqFatmk7lKYhmW6tLOUuS5jkqKytL9EEeVLZ4eXnhlVdeweXLl3H+/HnEx8fDw8MD//77L5KTkwEYZ9aMra0tOnfujL///htyuRwjRoxAjx490LVrV7zyyit5zgHGpG9HwL1797B06VKcPn1a68ZeYTSXwjCVK1euaM1002fpJU3//vtvnjZWqVQiKSkJ4eHh2LNnD/bs2YOXL19izpw5iI6OFmWZEl6rU1lhqnY5t5z1W/v27YvU1FRcunRJeM3T0xPz5s0zehmqVaumV6dnZmYmfvvtN2zdulVYZqIouqYTw7Fjx/DJJ59AJpPB09MTCxcuzBOhK+cz/Pbbb8jOzsbs2bPh5ORUZIeUs7Mzli5dinfeeQdyuVzr2j4gIMDgdri4vxdyW7BgAfr376+1TaVSIS0tDY8ePcKxY8ewbt06pKenY/369bhz5w7++OMPgwd+mfM5RLM8PIdYNrbf/2H7bT7tNwBs3rwZa9asEZ5/9NFH+d6P0seTJ08wfvx4YYKFv78/5s6dq1ceXl5eOH78eJ7tMpkMCQkJCAoKwp9//omrV6/i/v37GDFiBFasWFGsqFqaNKMjmtv9HkBdppyOdF0iOZo73q0i0oFKpcKsWbMwc+ZMnTsscmiOMitIUR0LudeQcHFxKTS95tqFcrk83zTG/kxiqF+/Pv788080atQIABAWFobFixcbnO/169cxf/58qFQqVKhQAX/++We+Nx/d3Nwwf/58YRSaTCbDl19+mW/YG11IJBK4urqiSZMm+Pzzz7Fq1Srhuzpw4AB2795d/A+Vj+KWk6g0YfutzdLb78IcPXpUuDi3srJC7969dXqfo6Oj8FhzdnphNNNpvl9f9vb28PHxwZAhQ7B3715hpuezZ88wa9asYudrCXiOIrHl3NBTKBTYv38/AGDv3r0A1NdgxrrhN2vWLPj4+ABQXyvu3bsXEyZMQMuWLdGrVy/MnTsXx44dyxMtRGz6hEk8c+YM+vXrhxMnTujciQ4UPcCrJGjOJKxTpw4CAwNFy1sqlcLd3R2tWrXCwoUL8e233wqvrVy5UusGrhjMrR00t/KQ5TNVu5ybt7e31kyznP0vWrSoRGb16tM+JyUlYfDgwfj111/16lwpqWv8x48fY9q0aZDJZLCzs8PatWvzXebK3t4en3zyCT744ANh29y5c3XqYG3UqBE++uijPPl99913BndEF/f3gi4kEgmcnZ1Rv359TJo0CVu3boWTkxMA4PLly1rr+YrB3NpscysPGYbttxrbbzVzaL///vtvrcETQ4YMybMvfT179gyjR48Womh4e3tj9erVok0AsbGxgaenJ7p06YKNGzcKA7DkcjlmzJgh6iBlc2yDzbFMhmBHOpEOtm3bhl27dgnP27Vrh0WLFmH//v24cuUKgoODER4eLvzTDFWhVCqLzF/f9RTFWH/R2J9JLPb29pg+fbrwfNeuXcJopuL65ZdfhM8wYsQIeHh4FJp+6tSpwt/83r17uHXrlkH7z9GmTRt0795deP7HH38YnKfmKGCxwg2LRbM85jZ6mkovtt+lq/0ujGbnyquvvlpk255D8we4riHGNcMVijXD39nZGTNmzBCeX758GcHBwQblaSnnBDs7O85GJ4N169ZNqPN79uzBixcvcObMGQBAs2bN4O3tbZT9enh4YOfOnRg/fjwqVqwobFcqlbh79y42b96MDz/8EK+++ipWrVqlV8e1PnSdhZCQkIBPP/1U6Nj38vLClClTsHnzZpw5cwZBQUG4c+eOcA7RvEFl6pshGRkZOHz4sPC8uDMJddW/f380adJEeK45A6a4zLld1hwkxmt1EoOp2uX8VKhQQeu5h4eHMODT2PSZJfb1118LYX5tbGwwcOBA/PLLLzhy5AiuX7+OsLAwoX3+999/hfeVVPv8xx9/CB3Rffv2ha+vb6Hpx40bJ3RIJCYm4sSJEzrtR/N8CgC+vr6oVatWMUqsrbi/F4rD398fw4cPF55v2LDB4EF15nwO4f2e0oXttxrbb/Nov0+cOIEZM2YI97N69uyJOXPmGJTny5cvMXr0aDx+/BiAul6tXbsWlSpVMijfgkgkEsyaNUs4rhITE7Fz506D8izOpJCSJNYEFHPBO1ZEOtC8aTJx4kSsXr0affv2hZ+fH1xdXfOMqiqp0WSGsKTP1LRpU+FEI5PJDOrIzs7O1ppN0qpVqyLfU7lyZdSoUUN4rrlWpKE0Q9BEREQYvJaU5gVLSYYH0kVCQoLw2Jg/WIk0WVJbpytL+kxitt+FiYmJ0Wrb9elcqVmzpvD46dOnOu8vhxg39XK0aNFC6/vLWfuruDTbWnM7J2iWJ/ePbaLicHZ2RqdOnQAA4eHhWLp0qRDZw9gdrs7Ozvjkk09w+vRpbNu2DdOnT0fnzp211gtNSkrCd999h4kTJ5q0Q3rbtm1ISUkBoA6zuG/fPowdOxbNmjVDpUqVYG9vrzXoy5zOi//8849QHrFnEhbk1VdfFR5fvnzZ4IEQvFanssSU7bKmhIQErcGKgHoWmmbUCXMQFxeHv//+G4A6Qsbq1asxf/58dOrUCT4+PnByctKKXmWK9jmnIw3Q7V6Kg4MDGjduLDzXZZDow4cP83w3oaGhWLlype4FzYchvxeKS/MckpiYaPBayDyHUElh+60ftt9qxmi/L1y4gEmTJgnL8HXs2BGLFi0yaCB+amoq3nvvPURERAAA3N3dsW7dOqMPEHF1dUXDhg2F54be79E8J2RkZJjVACuZTCYsBQGUjns+7EgnKkJMTIwQOtfV1VUrtEl+UlNTjTrjTgyW9pmkUqlWWBVDQp+8fPlSaxSwrqGANG+E5tx8FEPuHxianTPFobnWY1xcnEF5iU2zPJozZImMxdLaOl1Y2mcSs/0uzN69e4XRyW5ubujcubPO79UciR0WFqbTezTTFTWSWx82NjZa5yVdO/YLonlOiI2NNSgvsfGcQMageWMvJ3KHnZ0dunXrViL7t7KyQqNGjfDee+9hxYoVOH/+PDZt2oSOHTsKaf7991+tNQNLmuYNm/HjxxcZutDQdkhMmjMJ27Zta7QZI5o095Genm7weYzX6lTWmLpdBoDZs2cLA9Zr1KghdGZs374dx44dK7FyFOXChQvCQKv27dsX2dFhivb52bNnwmNd76VopitqiRCZTIapU6cKsyY1B7wuX77coEG5hvxeKK7c93uio6MNyo/X9lSS2H7rju23cdrva9euYcKECcLM5tatW+Onn37Ks4SiPtLT0zFmzBiEhoYCUC+/uHr1atSuXbvYeepD87xgaD2oWLGiVtQEczovxMfHC8eEtbU1PD09TVwiw7EjnagImieaWrVqwcbGptD0165dM3nYw6JY2meSyWRaN60MCaObOyyPrh1Mmvsvao1jfeReY8bQsM856+sC6pGAYhEjHPWDBw+ExwEBAQbnR1QUS2vrdGFpn0nM9rswmp0r3bt312v9rZYtWwqPHz58qPU3zk9cXJzW2vS6jObWh+YoYkPDndeuXVvIIz4+3izWN85x//594THPCSSWNm3a5Llp3alTJ1Gv3fQhlUrRvHlz/PLLL2jbtq2w/fjx43nSinGtpQvNNs7Pz6/QtAqFAtevXzd2kXQSFxenNQigX79+JbLfsnKtnpGRoXUzj+0yicXU7fLWrVuFELr29vb49ddfMX78eOH1zz//vMhBLebYPgPAlStXjFmcfNnZ2QmPdR1YpM+9lJ9//lmIAFixYkVs2rRJaO/lcjmmTp1a7JmchvxeKK7cswMNvbY313MIwPs9pRHbb92x/Ra//Q4ODsbYsWOFjvkmTZrgl19+MajtzsrKwvjx44XfNw4ODli5ciUaNGhQ7Dz1Jeb9HolEolXfxDwvGErzfk/t2rUNGvxgLtiRTlQEzZNu7hsp+dmyZYsxiyMKS/tM586dE0K4AIbN/nN1dYWTk5Pw/OLFi0W+J3eniWaYd0Plnv1o6AgtzRAxd+7cMSgvTZoXXJrfha5evHghjCJ1cHBAnTp1RCsbUUEsra3ThaV9JjHb74IEBQVp/WDQt3PFx8dHa/Sx5k22/Gi+7ufnJ2r4rydPnmiFvzL0nODk5CR8NpVKhfDwcIPy02ToeUHzHKV57iIyhJWVFXr16qW1rSTDTxZEIpGgQ4cOwvMXL17kSaN5UygndKYxaN6wKSr837FjxwxedkgsmjMJXV1dhXCjxqZ5rW5nZ6cVpao4jHWtbmj9iYiIEP6+Pj4+cHV1Fa1sVLaZsl1+8OABFi5cKDyfMWMGfH19MWHCBDRp0gSAupNgxowZhQ48NfSaR1ea7XNR1/kZGRnYu3ev0cpSkKpVqwqPNcOkFyQzMxNBQUHC88LupVy+fBmrV68Wnn/77beoUKECZs+eLVxvP3r0CPPnz9e73Ib+XiiunBmPOQy9tg8MDBR+D96/f1+06wVD67hSqcTdu3eF54GBgaKUi0yL7bfu2H6L236Hh4fj/fffFyYC1K9fH7///rtB62zLZDJMnDhR6AewtbXFL7/8gmbNmhU7z+LQXOJDjFnaJdEPUJxzTWm838OOdKIiVKtWTbhQjYiIwJMnTwpMe/DgQZw4caKkilZspv5M+qzllJiYiEWLFgnP69evb1CYKIlEgtatWwvP169fr7WWU36+//574cLMwcFBtJNsamoqduzYITz39fU1OERlvXr1hBt8jx49wvPnzw3KL4dmOJ+iZmvm5+rVq8LjFi1alIqRaGT+TN3WGYOpP5Mp2++C7N69W3hcs2ZNrXW8dPXOO+8Ij//4448C2874+HitNeqHDh2q974K8+eff2o91zxfFVebNm2Ex9euXTM4vxya5wV9wxMrlUrcuHEDgPq8rFlGIkONHz8eO3bsEP5prk8qttTUVK0lgwqjuXxP+fLl87yu2UFrzJDfmoN/8psZnyMhIQELFiwwWjn0lXsmoebNHWOJiYnRCsPfsmVLg2c2tWrVSghLGhQUJFoniKH1R/NanW0yia0k2+Uc2dnZmDp1qtCh0aFDB+F6z8rKCkuWLBEG2F+4cEHr+i43V1dXoZPk5cuXRuuM0WyfT58+DYVCUWDahQsXivZbXx+a16Z79uzRmoWcn1WrVglL4xV2zZeUlITp06cLA3qGDRuG1157DYB6realS5cK9xB27dqFw4cP61VuMX4v6Esmk2HTpk3Cc2dnZ4NnPZYvXx5169YFoO7kyt1RX1yG3u8JCwsTZo3Wrl27VITwJTW237ph+y1e+/3w4UOMHj1amA1fu3ZtrF692qBICAqFAlOmTMGpU6cAqEON//jjjyV+zXv06FGtJT7EiG5ojvd7cpeltPy2YEc6URHKly8vXGQrlUp8/PHHeU42SqUSmzZtwvTp02FlZVUiN3YMYerP9MUXX+Djjz/G6dOnC7x5pFQqcerUKbz99ttaZZs2bZrB+x81apTwOD4+HiNGjNAaEZYjOTkZc+bM0bpxN3ToUDg4OBhchsjISIwZM0brZKRZruKSSqXCBQug24x7XWjOIA8KCtJ7HRfNcrz++uuilImoKKZu64zB1J/J1O13btnZ2Th48KDwvLizSwYNGoTq1asDUA8AGDNmTJ41DKOjozF27FjhB52Pjw/eeuut4hU8l4yMDCxbtgwbNmwQtjVr1kyUkbuas2B1GXmuK83zgr43M0NCQoQf5YGBgahQoYJo5SJydXVFYGCg8C+n09IYQkND0bFjRyxbtgz37t3LN41CocDBgwexceNGYVv79u3zpNM8ps6ePSscI2LTbBNWrlyZ74yY0NBQDBs2DDExMQbN/BDLrVu3tMIDlsRMwpCQELz33ntas4rEuFZ3c3ND06ZNAQBpaWkIDg42OE/A8PrDa3UyppJsl3P8+OOPQiejh4cHvv32W63Xvb29MWfOHOH5Dz/8kO89AUA9ay1nJp5MJjPaurytWrUS7jU8evQIM2bM0IpUBKgHcH3xxRfYunWrSdrn4cOHC0tLZWZmYvTo0fnec8jMzMSyZcvwyy+/CNu6detWYCSnL7/8UhhwVqdOHUyfPl3r9caNG2uFdP7yyy91XgtWrN8L+nj27BkmTZqk1dE9bNiwIpfl0oXmeVys+z3VqlUT6l50dLTeaxnzHFJ6sf3WDdtvcdrvp0+fYtSoUcJAgxo1amDt2rX5DkLWlUqlwueffy4MjpVKpVi8eHGJRbfKKcPBgwcxa9YsYZurqysGDBhgcN6tW7cWlrG9fv26zoO8i6IZMv7IkSN6LRkpl8uFQbo2NjZaS5xZMk4JpFJh69atep0MP/74Y70azEmTJmH06NFQKpUICwtD79690aRJE3h7eyM9PR1Xr14Vwh5++umn2LZtW54b8ObGlJ9JqVTin3/+wZEjR+Do6Ag/Pz94eXnB2dkZMpkMz58/R2hoaJ6wl7NmzRJldl7z5s0xevRoYdTi3bt30bdvX9SvXx9+fn6wsbFBTEwMrl27JoyqBdQ3+z/66COd9rF+/XqtGSyA+sSZlpaGe/fuISwsTOsk1KVLF1FOoADQq1cvofP/2LFj6Nmzp8F5enh4oEmTJrhx4waysrLQp08ftGvXDh4eHsLoTm9vb61ZnTlUKpWwrpGNjQ26d+9ucHmo9GD7rb+y3H7nduLECSQlJQFQ/yDq06dPsfKxsbHBsmXLMGTIEKSnpyMsLAxdu3ZFq1at4OnpidjYWFy6dEkYwe7s7Ixly5bpHF0jKCgIX3/9dZ7tWVlZiI2Nxc2bN7XWL69QoQK++eabYn2W3Fq0aAFPT0/ExcXh0qVLSElJEWVdu65du+Ls2bMAgKVLl+L06dOoU6eOVnjhcePGwc3NLc97NY/53OECiSxNfHw8li9fjuXLl8PDwwMBAQHw8PCAlZWV0CZqzu5q3rw53nzzzTz5NGzYEFWqVEFMTAzi4+PRvXt3tG3bFu7u7sIs6MDAQPTo0cOg8vbr1w9r1qxBZGQksrOzMX36dKxcuRIBAQGws7PD3bt3hfUNAwIC8Oqrr2qFajQFzc5+Hx8fIaSnIZYtW6a13BOgPselpKQgPDwcERERWq+NGDFCtNkUvXr1EtbGPHbsmCifx5D6k5qaKqw/X6FChVJzs4vKrgsXLmDt2rUA1LPovv3223xvwvft2xenTp3CwYMHIZPJMGXKFOzatUu4Ia2pa9eu+O233wCoB4fu3r0b1atX1+oYnTFjhkHldnNzw+jRo7FixQoAwP79+3HmzBk0bNgQnp6eiI+Px+XLl5Geng5ra2t8+eWXBu9TX97e3pg5cybmzZsHQB25Y8SIEfD19UX9+vXh4OCA+Ph4XLt2TbhGB9QdtV988UW+ee7atQuHDh0CoO70Wrp0ab4DgcePH49z587h+vXrQkjndevWFRkpRKzfC5r27dsnnCs1ZWRk4OHDhwgODtYadNykSROMGzfO4P0C6nNITh05duwYPvjgA4PztLKyQqdOnXDgwAEAwLvvvot27dqhSpUqQsepm5tbgZ+B1/YkFrbfxmMJ7ffHH3+sFcWrVq1awndXlEaNGuXbvm/evFkrKkn16tVx7do1nWdvaw7aKEhiYmK+93vkcjkSEhJw69YtrYl0VlZWmD9/vkEDBHI4Ojqic+fOOHDgANLS0nDhwgWtCXbF9cYbbwgRek+ePCncd9T8/dSjR498l/K4ePGiMKj39ddfLzVLRrEjnUqF58+f6xUWRfOEoIvWrVtjzpw5mD9/PuRyOWQyGS5fvozLly8LaaRSKcaPH48PPvgA27Zt0yt/UzDlZ9K8yZ6eno6bN2/i5s2bBab39vbGF198IcqJIMf06dNRoUIF/PTTT8JordDQ0AJDY3Xv3h1ff/21zrPRde0YtLa2xsiRI/HJJ59oraljiNatW8PLywvR0dE4deoU0tLS8twoLI7PP/8cI0aMQFpaGpKTk/H3339rvd6iRYt8O9KvX78uXDB07NjR4LUlqXRh+62/st5+a9L8QdS6dWtUrly52HkFBARgzZo1mDp1KqKioiCTyXDmzJk86by9vbF06VKtEbpFefDgQZGh03I0bdoUCxYsgI+Pj875F0YqlaJ///749ddfIZPJ8M8//6B///4G59uvXz/s27cPV65cgUqlwqVLl/LMeB86dGi+Hek5A83s7OxEGexFZCr29vawtrYWbpbHx8cXuqZ4165d8e233+Z7zSeVSvHll19i4sSJkMlkiI+P14qKBKiPO0M70m1tbfHbb79hzJgxwvIg9+/f15rxDajboh9//NHk58Xs7Gzhxj4g3kzC3H/bgtjb2wsD2MTSo0cPLFy4EOnp6Th8+DCmTp1qcMh4Q+rP8ePHhd9Dffr04RJMZNFybtBrhpjNLwpIjrlz5+LmzZt4+vQp7t+/j4ULF+Krr77Kk+7999/H0aNH8eDBA8hkMiE8rCYxOkU+/PBDREdHC8dvYmIiTp8+rZXG1dUVCxYsQEBAgMH7K45hw4bB2dkZ8+fPF26U53ceydGqVSssWrQo3whEjx8/Fjp1AGDKlCkFfq6ckM59+vRBamoqLl68iNWrV2PMmDGFllfM3ws5Lly4IAxAKsqAAQPw2WefiRLZEFCHpm/WrBmuXbuG4OBgPHnypMCZovqYPHkyLl26hPj4eGRkZODo0aNar3t5eeXbkR4TEyP8Hqxfv77J6iVZPrbfxmfu7XfupVf1WaowPT0934703HlGRkYiMjJS53x16UhPS0vTWsqjMFWqVMG8efPQrl07nctQlLfeekv4vXTo0CFR7r/VrFkTY8eOxcqVKwGoJyHevXtXK02dOnXy7UjXjFgo1qRBc8BfSEQ6GjJkCJo2bYp169bh0qVLePbsGezt7eHp6YlWrVphwIABqFevnqmLqRdTfabvv/8eI0eOxOXLlxEcHIyHDx8iNjZWGJnn4uICLy8v1K9fHx06dEDbtm1FDx8kkUjw/vvvo2/fvti9ezcuXbqEu3fvIikpCQqFAs7OzvD29kaTJk3Qr18/YR0qQzk6OsLV1RW1a9fGK6+8gr59+4ryQ06TlZUVhg0bhkWLFiE9PR1///03Bg0aZHC+gYGB2LdvHzZu3IhLly7hyZMnSE9PL3TtHwBa68C/++67BpeDSF9sv8VjDu13joSEBK2O7r59+xqcZ5MmTbBv3z7s2bMHhw8fxsOHD5GYmIhy5cqhZs2a6NatG/r27SvK4CRAPRPe2dkZXl5eCAwMRPfu3dGyZUtR8tb0zjvvYPXq1ZDJZNi2bZsoHek2NjZYu3YtduzYgaNHjyIiIgKJiYlFrjt3+fJl4Ydrr169RBmFTWQqjRo1wvnz53H+/Hlcu3YNt2/fxuPHj5GYmAilUilcTzZu3Bi9e/cucrmGDh06YOfOndi0aROuX7+Op0+fIj09Xa9QerqoWbMm9uzZg02bNuHo0aN4+PAhZDIZPDw84Ofnh549e6J79+4lEr6zKKdOnRKW1BBrJmFhHB0d4e7uDj8/P7Rq1Qq9e/cWvZ1ycXFB//79sXHjRkRFReH8+fOizAIvbv3Zvn07APVviKFDhxpcDiJTmjNnjjCI28/Pr8ilhVxdXbF48WK8++67UCqV2LJlC9q3b4+OHTtqpXNxccGOHTuwefNmnDp1Cvfv30dKSoro6+1aWVlh0aJF6NatG/766y/cunULycnJcHV1RZUqVdCpUycMGDAAnp6eiIqKEnXf+ujbty86deqEffv24dy5c7hz5w5evnyJ7OxsODs7o0qVKmjYsCF69eqFV155Jd885HI5pk6dKkQBfPXVVzFixIhC91utWjV8+eWXwvf6008/oU2bNqhfv36+6Y3xe6Ew9vb2cHFxQa1atdCkSRP06dMHtWrVEn0/I0aMwLVr16BSqbBjxw58+umnBufp5eWFvXv3YuPGjTh37hwiIyORlpZW4HJeOXbu3CmcZ4r6/ogKw/a7ZFhK+10aSKVSODs7w8PDA/Xq1cPrr7+OLl26aE1QEUPr1q3h5+eHu3fv4vDhw/j8889FiUI4efJkNGvWDDt37hQiT2oue5WftLQ0YeKdj49PqVruQ6IS+1c5ERGZXGpqKjp16oTExEQEBATku/5lSXj58iU6dOiAjIwMNGnSBFu3bjVJOYiIyrovvvhCmFm6e/dukw0e+eSTT3Do0CFYWVlh//798PX1NUk5iIhMKSoqCt26dYNMJkOnTp201qEsSffu3UPPnj2hUqnQs2dPfPfddyYpBxER6U6pVOLNN9/EgwcPULFiRRw/fjzfcMrGJpPJ0LlzZ8TGxsLLywtHjhwRZR14IiLSz/79+zF16lQA6qUVR44caZJybN68GXPnzgUAzJs3T5SJfeZCnDjCRERkVpydnfH+++8DAO7cuZMnnFBJ2bhxozBaTYxR0kREVDwTJkwQRj7//vvvJinD48ePhTCRvXv3Zic6EZVZ1apVw1tvvQVAHVq9oJCaxrZ69WqoVCpYW1vj448/NkkZiIhIP1KpFBMnTgSgXipt165dJinH/v37ERsbCwD46KOP2IlORGQib775JurUqQMAWLdunegRF3ShUCiwZs0aAOq16MWIhGhO2JFORFRKDR8+HF5eXgBgklkuycnJ2LhxIwB1qEljhCsmIiLdVKlSRQi3duTIEdy7d6/Ey/Drr79CoVDAwcGBHTZEVOZ99NFHcHZ2hkqlwooVK0p8/48fPxbWUxw8eDBq1KhR4mUgIqLi6d69Oxo3bgxAPUg2Ozu7RPcvl8uFtXMDAgKMvvQKEREVTCqVYvr06QCAmJgY7Ny5s8TLsH//fjx58gQAMHXqVFhbl65VxdmRTkRUStnb2+Ozzz4DANy4cUOYBVhSfv/9dyQmJsLW1lYoBxERmc748ePh6ekJhUKBpUuXlui+7927Jywz8sEHH6Bq1aolun8iInNTsWJFYUbhwYMHERISUqL7//HHHyGTyVC+fHlMmjSpRPdNRESGkUgk+OKLLyCVShEdHY1NmzaV6P537NiByMhIAOolpKysrEp0/0REpK19+/bo1KkTAGD58uXCGvYlITs7Gz///DMAoE2bNujatWuJ7bukcI10IiIiIiIiIiIiIiIiIiIiDZyRTkREREREREREREREREREpIEd6URERERERERERERERERERBpK14rvxRQdHY0NGzbg5MmTiI2Nha2tLby9vdG9e3cMHToUDg4OBu/jyZMn2LBhA86fP4/o6GioVCpUqlQJbdq0wdChQ1GnTh0RPgkRERERERERERERERERERmqzK+Rfvz4cUybNg2pqan5vu7j44NVq1ahRo0axd7HX3/9hXnz5kEmk+X7uo2NDWbOnIlhw4YVex9ERERERERERERERERERCSOMt2RHhYWhiFDhiAzMxOOjo744IMP0LJlS2RmZuLgwYPYtm0bAHVn+s6dO+Hs7Kz3Pv7++29MnjwZAODi4oJRo0ahVatWsLW1xe3bt7F69Wo8evQIEokE33//PXr06CHqZyQiIiIiIiIiIiIiIiIiIv2U6Y70oUOH4urVq7C2tsbGjRvRpEkTrddXr16NJUuWAAA++ugjTJw4Ua/8MzIy0KlTJ7x48QKOjo7466+/4Ofnp5UmNTUVQ4YMwd27d1GxYkUcPXoUTk5Ohn0wIiIiIiIiIiIiIiIiIiIqNqmpC2Aqt27dwtWrVwEAAwYMyNOJDgCjR4+Gr68vAGD9+vUFhmYvyKlTp/DixQsAwLvvvpunEx0AnJ2dMWvWLADA8+fPsXv3br32QURERERERERERERERERE4iqzHenHjh0THg8YMCDfNFKpFH379gUAJCcn49KlS3rtIyQkRHjcvn37AtO1aNECdnZ2AIAjR47otQ8iIiIiIiIiIiIiIiIiIhJXme1Iv3btGgDA0dER9evXLzDdK6+8Ijy+fv26XvtITEwUHlesWLHAdNbW1nBzcwMA3LhxA3K5XK/9EBERERERERERERERERGReKxNXQBTuX//PgCgevXqsLYu+M9Qq1atPO/RlaOjo/A4JSWlwHQqlQqpqakAAJlMhkePHgkh5cWgVCqRkZEBQN1pL5FIRMubiKgsU6lUwuAnBwcHSKWmG5/Gtp6IyDjY1hMRlX5s64mISj9zaevZzhMRGY8x2voy2ZGelZWFly9fAgAqV65caFo3Nzc4OjoiPT0dsbGxeu1HszP8ypUraNCgQb7pwsLCkJ6eLjyPiYkRtSM9IyMDd+7cES0/IiLKKyAgAE5OTibbP9t6IiLjY1tPRFT6sa0nIir9TNnWs50nIioZYrX1ZTK0e1pamvBYc9Z4QRwcHABAq7NbF+3btxdmu69duxYJCQl50iiVSvzwww8Flo+IiIiIiIiIiIiIiIiIiEpWmZ2RnsPGxqbI9La2tgCAzMxMvfZTpUoVDB48GBs3bkRcXByGDBmCadOmoVWrVrCxscHt27exbNkynD17FjY2NpDJZMXaT1E0Q9f7+voW+JmtrKyEMAdKpRIKhaLQfDXzUSgUUCqVOqeXy+VQqVQFppVIJFrlzvnbFESz7JqhGwqiGTYnKysLd+/eBVDw38eYZZdKpbCystI5fVn9nvT9rCXxPclkMmHJh9x1h9+Tacpe0sdTVlaWUAcKWyakJLCtz4ttvW7pzel7Msc2hG29mrl/T4VhW1826qZMJhNm9hT2tylNddMSvyfAPNsQtvVq5v49FYZtfdmom2zrC09vLt8TYJ5tCNt6NXP/ngpTWtp6tvN58f6NbunN6Xsy1/ajoLae35Npym6K40mzDojV1pfJjnQ7OzvhcVFfHABkZ2cDAOzt7fXe14wZM/DkyROcOnUKkZGR+PDDD/OkadCgAQIDA7FlyxYAED2sjOY6K46OjsLAAFPStwz6ptf8jvWhy9/H2GU3h+8nh7l+T7ooie8pp20ATHts8XsSL72+NC8ETL2mFdt63bGt12au35Mu2NaLl97SvydjYltfOHOsm7r+bSy9burDHL8nXbGtFy+9pX9PxsS2vnDmWDfZ1udljt+TrtjWi5fe0r8nYzKXtp7tvO54/0abuX5Puiip78kc2np+T+KlLw7NOiBWW18mQ7trdlTrEq49IyMDgG5h4HOztbXFb7/9hvnz56Nu3bpaX1yFChUwbtw4bN68WWuUh5ubm977ISIiIiIiIiIiIiIiIiIicZTZGenlypVDYmIiYmNjC02blJQkdLZXrly5WPuTSqUYOHAgBg4ciNTUVLx48QL29vbw8PAQwhQ8evRISO/r61us/RARERERERERERERERERkeHK5Ix0AKhduzYA4PHjx4WuKfDgwQPhsRgd3M7OzqhRowY8PT2FTnSFQoHbt28DALy9vVG+fHmD90NERERERERERERERERERMVTZjvSmzVrBkAd2j00NLTAdFeuXBEeN23a1ChluXTpEhITEwEAPXr0MMo+iIiIiIiIiIiIiIiIiIhIN2W2I71z587C4507d+abRqlUYs+ePQAAV1dXtGzZUvRyqFQqLFu2DABgY2ODgQMHir4PIiIiIiIiIiIiIiIiIiLSXZlcIx0AGjZsiObNm+Pq1avYuXMn+vXrhyZNmmilWbNmDe7fvw8AePfdd2FjY6P1+qVLl/Duu+8CAPr164eFCxfm2c/Lly/h5OQEW1vbPK8pFArMnz8f169fBwCMHTsW3t7eonw+0p2NjQ0CAwOFx0S6Yt0hshw8Xqm4WHeILAOPVTIE6w+RZeCxSoZg/SGyDDxWyRCsP2QMZbYjHQA+//xzDBkyBJmZmRg9ejTGjRuHli1bIjMzEwcPHsRff/0FAPDx8cGoUaOKtY9Lly5h3rx56NGjB1q0aIEqVaogKysL4eHh2LZtm7A2evv27TFu3DjRPhvpTiKR5DvQgagorDtEloPHKxUX6w6RZeCxSoZg/SGyDDxWyRCsP0SWgccqGYL1h4yhTHek16tXDz/88AOmTZuG1NRUfP/993nS+Pj4YNWqVXB2di72fp4/f47169dj/fr1eV6TSCTo378/vvrqKx7gRERERERERERERERERERmoEx3pANAx44dsW/fPqxfvx4nT55EXFwcbGxsUL16dXTr1g3Dhg2Dg4NDsfNv3rw5pk+fjosXL+LBgwd48eIFJBIJKlWqhJYtW2LAgAFo1KiRiJ+I9KVUKpGUlAQAcHNzg1QqNXGJyFKw7hBZDh6vVFysO0SWgccqGYL1h8gy8FglQ7D+EFkGHqtkCNYfMoYy35EOAF5eXpg1axZmzZql1/tatmyJ8PDwQtNUrFgR7733Ht577z1DikhGJJfL8eDBAwBAYGAgIwOQzlh3iCwHj1cqLtYdIsvAY5UMwfpDZBl4rJIhWH+ILAOPVTIE6w8ZA4djEBERERERERERERERERERaWBHOhERERERERERERERERERkQZ2pBMREREREREREREREREREWlgRzoREREREREREREREREREZEGa1MXgIiIiIjMhDwL+KkxkJ0C2LoAk24C1namLhURERERERERERFRieOMdCIiIiJSu38cSHkKZKWo/79/wtQlIiIiIiIiIiIiIjIJzkinMk8ikcDGxkZ4TKQr1h0iy8HjVUehe7Sfh+0B/LuZoiRmg3WHyDLwWCVDsP4QWQYeq2QI1h8iy8BjlQzB+kPGwI50KvNsbGzQsGFDUxeDLBDrDpHl4PGqA3kWEH5Qe9udg+rtZTi8O+sOkWXgsUqGYP0hsgw8VskQrD9EloHHKhmC9YeMgaHdiYiIiEgd1j0rWXtbVhLDuxMREREREREREVGZxI50IiIiIsob1j1HWAHbiYiIiIiIiIiIiEoxhnanMk8ulyMmJgYAUKVKFVhb87Ag3bDuEFkOHq9FyC+se44yHt6ddYfIMvBYJUOw/hAVQp4F/NQYyE4BbF2ASTdNdl3IY5UMwfpDZBl4rJIhWH/IGDgjnco8pVKJZ8+e4dmzZ1AqlaYuDlkQ1h0iy8HjtQj5hXXPUcbDu7PuEFkGHqtkCNYfokLcPw6kPAWyUtT/m/C6kMcqGYL1h8gy8FglQ7D+kDGwI52IiIiorCsorHsOhncnIiIiKptyXyfyupCIiIiIyhB2pBMRERGVYSpZJuS3DxSaRh52ACpZZgmViIiIiIjMQn7L/+Qs+0NEREREVAawI52IiIiojAqJTsKXPyyDtSy10HTWshTM+WE5QqKTSqhkRERERGRy+S3/U8aX/SEiopIhUWTDZllDYEE14Lu6HMRFRCZjbeoCEBEREZGIDkwGwg8VmSxLrkDFdBmmIQOQFJ3t9LSlSFu1AlmONrCztir6Df7dgZ7f61BgIiIiIjJLBS3/E7YH8O9WkiUhIqIyxjX+KiQpMeonWSnqQVw89xCRCbAjnYiIiKi0yE4HgrYAsvQik9oBqKxDB3oOF0kGXJABZOj4hqAtQJf5gK2j7jshIiIiIvOQX1j3HDnh3a3tSrZMRERUZrjHnNLewEFcRGQiDO1OREREVFo8uw3IdO3pNjJZhro8RERERGR58gvrnoPh3YmIyIgkimyUiz2vvTFnEBcRUQnjjHQq86RSKcqVKyc8JtIV6w6R5Sgzx2u1ZsA7fwE7x6hvcJqKnRswYLW6PBauzNQdIgvHY5UMwfpDlI+CwrrnMMHMQB6rZAjWHyLLIJVKUSX9Nqzkadov5Azi4qx0KgTbejIGdqRTmWdtbQ1fX19TF4PEIM8CfmoMZKcAti7ApJtGDTXHukNkOcrU8erXFRh7Atj6DhB/p+T37xEADN4MVCgdf+8yVXeILBiPVTIE6w+RNpUsE4rbBwq9aSgPOwCrnpmQ2NiXWLl4rJIhWH+ILIO1tTWqJFzK/0WGd6cisK0nY+CQDCIqPe4fB1KeAlkp6v8Zao6IyqoKvsD7x4CAniW737q91PstJZ3oRERERGVNSHQSvvxhGaxlqYWms5alYM4PyxESbcIoSEREVPrIs4Dwg/m/xvDuRGQCnJFORKVH7tBzHKVIRGWZnQswaANw9jvg+DcAVEbcmQToOBtoNwWQSIy4HyIiIiLS24HJQPihIpNlyRWomC7DNGQAOlzSTU9birRVK5DlaAM7a6ui3+DfHej5vQ4FJiKiMuv+cSArOf/XGN6diEygxDvSExIS8OTJE8THxyMjIwPW1tZwdXVFlSpVUKNGDVhZ6XDhTSQimUyG+/fvAwB8fX1hY2Nj4hJRseQ3WjFnlKKRwruz7hBZjjJ7vEqlQPtpQOWGxls3PWc9dL8u4udtBsps3SGyMDxWyRCsP1SqZacDQVsAWXqRSe0AVNZjTKSLJAMuyAAydHxD0Bagy3zA1lH3nWjgsUqGYP0hsgzKkF2Fh1HmxCkqBNt6Mgajd6Snp6fj33//xenTp3HlyhXExcUVmNbW1hZ169bFq6++is6dOyMgIMDYxSOCSqVCWlqa8JgsVH6jFY08SpF1h8hylPnj9f/rpmdtHAy7l3dFyzbL3Q92w7aW6lDuZb7uEFkIHqtkCNYfKtWe3QZkuvZ0G5ksQ12eas2K9XYeq2QI1h8i86eSZUJ552ChHenysAOw6pkJiY19iZWLLAfbejIGo62RHhYWhlmzZqFt27aYPn06Dhw4gNjYWKhUqgL/ZWVl4ebNm1ixYgX69euHXr16YevWrcjIMJMLfiIyX7nDuucIK2A7EVFZU8EX2SOP4LCyhSjZHVa2QPbII6W6E52IiIjI4lVrBrzzlzqKkCnZuQHvbCt2JzoREZVuIdFJ+PKHZbCWpRaazlqWgjk/LEdItBEi7hER5UP0GelhYWH46aefcPr0aQD/jfqoWLEiGjZsiPr166NChQpwc3ODm5sbMjMzkZSUhKSkJERGRiI4OBjh4eGQy+WIiIjA3Llz8dNPP+H999/H8OHDYWtrK3aRicjS5RfWPYeRw7sTEVkSF7fyCPceiG7Rlw3O6673IHRzKy9CqYiIjECeBfzUGMhOAWxdgEk3eT1IRGXX/6MTYes7QPydkt+/RwAweDMHYBIRlTUHJgPhh4pMliVXoGK6DNOQAeiwxMj0tKVIW7UCWY42sLPWYalg/+5Az+91KDARUV6idqTPmjULe/fuhVKpBADUq1cPvXv3RpcuXVC1alWd88nOzsbVq1exf/9+/PPPP3j58iWWLl2KzZs3Y9GiRWjevLmYxSYiS5dfWPccRg7vTkRkaQY5B4uSz0CXEFHyISIyivvHgZSn6sdZKbweJCKq4Au8fwzYPQ64c6Dk9lu3F9D3V8DOpeT2SUREppedDgRtAWTpRSa1A1BZhw70HC6SDLggA9A1kHHQFqDLfMDWUfedEBH9n6ih3Xfv3g0rKysMGjQIhw4dwq5duzBy5Ei9OtEB9Vrpbdq0wYIFC3D+/HksWrQINWvWRHR0NC5evChmkYmoNCgorHsOhncnIlJTqVA59rgoWVWO+RfgelNEZK5yXx/yepCISN2ZPWgD0HE2dJryZxAJ0PEL9f7YiU5EVPY8uw3IzGTJXlmGujxERMUg6oz0d955B2PGjEGVKlVEy9PW1hZ9+vRB7969cejQIWG2OxERAKhkmVDcPlBoYyYPOwCrnpmQ2NiXWLmIiMxSbDAkSVGiZCVJegLEhQCVA0XJj4hINPkt+8PlfoiI1KRSoP00oHJDYOcYdRQ3sdm5AQNWA35dxM+biIgsQ7VmwDt/Ge9co6ucc1K1ZqYrAxFZNFFnpM+ZM0fUTnRNEokEPXr0QM+ePY2SPxFZnpDoJHz5wzJYy1ILTWctS8GcH5YjJNqEF21EROYgd8eSoe6InB8RkQgkD0/mXfYnZ7kfIiJS+/+66VnufqJmm+Xup16PnZ3oRET0/3MNPAJMs3+PAJ6TiMhgos5IJ7JEVlZWqFatmvCYTOzAZCD8UJHJsuQKVEyXYRoydIpINz1tKdJWrUCWow3srHX4nv27Az2/LzQJ6w6R5eDx+n93/hY3v/C/gddniJunmWHdIbIMWsfq2ZX5Jwrbw3XSKV9s66nMquCL7JFHcOK7t9FNetng7A4rW6DtyL9g51ZehMLlxWOVDMH6Q2QiFXyB948Bu8cBdw6U3H7r9gL6/srlRcoYtvVkDOxIpzLPysoKnp6epi4GAUB2OhC0BZClF5nUDkBlPZZ0c5FkwAUZgK5L8wRtAbrMB2wdC0zCukNkOXi8AkiKAmJvFZksWlUBK+R98aH1HnhJXhSeOCYISIoG3LxEKqT5Yd0hsgzCsSrPAu4WMCiT4d2pAGzrqSxzcSuPcO+B6BZteEf6Xe9B6GakTnSAxyoZhvWHyITsXIBBG4Cz3wHHvwGgMuLOJEDH2UC7KYBEj5vHVCqwrSdjEDW0e464uDisX78e69evx6VLl/R678WLF4X3xsfHG6N4RGSunt0GZLr2dBuZLENdHiKi0kKHaB8y/17Y12obNis6oXvWAhxUtNAhX4Z3JyIzcv943rDuORjenYgoX4Ocg0XJZ6BLiCj5EBFRKSSVAu2nqddNt3Mzzj7s3IB3tgHtp7ITnYhEY5SO9IULF2LBggVYtWoVfHx89HpvzZo1sWrVKixYsADfffedMYpHROaqWjPjXkzpKueiq1oz05aDiEhMhYV1t7YHev4Im8Eb0DawNgAgGc6YIJuEWbL3oLK2L/i97EgnInMSuqfw18OKeJ2IqKxRqVA59rgoWVWO+RdQGXOWIRERWbz/r5ue5e4narZZ7n5cD52IjEL0jvSoqCgcPnwYADBt2jS9wyh4enpixowZUKlU2LdvH2JiYsQuIpGW7OxsXLt2DdeuXUN2drapi0P/v5iCR4Bp9u8RoPNFF+sOkeUo88drZhIQeTb/1yrVA8aeBJqPAiQS+Fd2gbU0Z+S2BFsUnRDcY486XX4enlHnX0qV+bpDZCGys7Nx7dJ5ZIfuKzSdPOwAVLLMEioVWQq29VSmxQZDkhQlSlaSpCdAnPFmpfNYJUOw/hCZkQq+yB55BIeVOkTB08FhZQtkjzyiXo+dyjS29WQMonek79+/HyqVCjVq1ECfPn2KlUevXr1Qq1YtqFQq7N+/X+QSEpHZq+ALvH8MCOhZsvut20u9X150EVFpE/EPoJTl3f7K+8CY40ClusImO2sr+Hm6aCW7kl5Zne6V9/PmoZQB946JXWIiIr2EPk3Gtn9OwFaRXmg6a1kK5vywHCHRpXcAEBGRXsSOLnSH0YqIiKhoLm7lEe49UJS87noPgotbeVHyIiLKzVrsDK9evQqJRIIuXQwLodGlSxf89ttvuHz5MsaOHStS6YjIYti5AIM2AGe/A45/A8CY4eEkQMfZQLspXD+HiEqn3Ouj25cD+qwA6uY/YKmBlyvCYv5bYzg0OgmwqQm8+R1QqwOw90MgM1E7/wYDxC83EZVtBybnbb/ykSVXoEq6DLORAehwKTc9bSnSVq1AlqMN7Kytin6Df3eg5/c6FJiIyAIVtvxPcYT/Dbw+Q9w8iYioVBrkHCxKPgNdjBcNhYhI9I70iIgIAEDTpk0Nyqdx48Za+RFRGSSVAu2nAZUbAjvHAFlGmDlk5wYMWM31c4iodKvXB4g4CmQlAzXaAv1XAW7VCkzewMsN267+F+Iz5KlG+1u3J1C1MbBrLPDoHGDnCtTtbcTCE1GZlJ0OBG0BZIXPMAcAOwCV9RgL6SLJgAsygAwd3xC0BegyH7B11H0nRESWICkKiL1VZLJoVQWskPfFh9Z74CV5UXjimCAgKRpw8xKpkEREVCqpVKgce1yUrCrH/AuoVJwgRURGIXpo96Qk9Y3WihUrGpRPzvtz8iOiMuz/66ZnufuJmm2Wu5/O66ETEVm0er2BCReA7kuAEfsL7UQHgPpV3bSe33uWioxsxX8b3Kqp8+mxVJ1vPXakE5HInt0GZLr2dBuZLENdHiKi0kaHqB8y/17Ieu8UBo2bg6z3TkHm30uHfBnenYiIihAbDElSVNHpdCBJegLEcVY6ERmH6B3pUqk6S7lcblA+Oe+XcBQREQFABV9kjzyCw8oWomR3WNkC2SOPcD10Iio73KoBLccC0qLDGNet4gKpxiWYUgXcjk3WTiS1AlqMKbJTnoioWKo1A975Sx09yJTs3IB3tqnLQ0RU2hQW1t3aHuj5I2wGb0Ct6t5o7F0Otap7w2bwBqDnj1Ba2RX8XnakExFRUcQ+V9zhuYeIjEP0jnR3d3cAQGxsrEH55Lw/Jz8iIhe38gj3HihKXne9B8HFrbwoeRERlTaOttbw9XDW2hYazShBRFTC/h+VCB4Bptm/RwCjFxFR6ZWZBESezf+1SvWAsSeB5qPyhsmVSIDmoyD94BSeOdTK//0Pz6jzJyIiKkhhg7mKI1zk/IiI/k/0jnQfHx8AwKVLlwzK5+LFiwCAmjVrGlokIipFBjkHi5LPQBeG+yEiKkwDL+1ZoMHsSCciU6jgC7x/DAjoWbL7rdtLvV9GLyKi0iriH0Apy7v9lfeBMceBSnULf3+lunCdeAZ7bXrkfU0pA+4dE6ecRERU+iRFAbG3ikwWraqAz2TvIVpVoeg8Y4KApGgRCkdEpE30jvQ2bdpApVJh//79SEhIKFYeCQkJ2L9/PyQSCVq3bi1yCYm0WVtbw9/fH/7+/rC2tjZ1cagwKhUqxx4XJavKMf8CKpVBebDuEFkOHq/6y92RHhKdXEDK0o11h8gM2LkAgzYAHWcDMPbSXxKg4xfq/dm5GHlfZC7Y1lOZlHt9dPtywNubgDe/A2wcdMrC3tEZNUf8inGyyUhUOWm9prgtfohdHqtkCNYfIjOS+xyUj0zf7sgYfRKDxs1B1nunIPPvVeR7VFxapMxjW0/GIHpH+ptvvglbW1ukpaVh9uzZUCqVer1fpVLh888/R1paGmxsbNCzZwnPPKAyRyqVwtnZGc7OzpBKRT8kSEyxwZAkRYmSlSTpCRBn2Kx01h0iy8HjVX8NqrpqPb8bl4IsucJEpTEd1h0iMyGVAu2nGXfd9Jz10NtPzRvKmEo1tvVUJtXrA9j9/3qvRltg/Dmgrv734BpWKwe/14ege9ZCXFKql+JIVjliT1YzMUsLgMcqGYb1h8iMFBbW3doe6Pkj7IdtQe0a1dHYuxxqVfeGzeANQM8f1a8XIObyLvHLShaFbT0Zg+g1qUqVKhg8eDBUKhVOnDiBcePGIT4+Xqf3Pnv2DGPHjsWJEycgkUgwZMgQVK5cWewiEpGlEntU4R2OUiQiKki9XB3pcqUKd2NTTVQaIqL/+/+66VnufqJmm+Xux/XQiahsqdcbmHAB6L4EGLEfcKtW7Kw+6lAb5avWxJDs2fhCNhJdsxZhamgNXIksXqRKIiIqxTKTgMiz+b9WqR4w9iTQfFTega0SiXr72JPqdPmoGH8JD54wvDsRicsoQzKmTp2KRo0aQaVS4cyZM3jjjTcwc+ZMHDp0CE+ePEFaWhpUKhXS0tLw+PFjHDp0CDNnzkSXLl1w9qy6EW3UqBGmTJlijOIRaVEqlUhNTUVqaqreERSohBU2WrE4wg3Lj3WHyHLweNWfi70NalbUDtEZ8rTsrZPOukNkhir4InvkERxWthAlu8PKFsgeeYTroZdhbOupzHKrBrQcC0itDMrG1lqK7wc1hrWVNTYouiAGFaBSAVO2BSEtSy5SYXmskmFYf4jMRMQ/gFKWd/sr7wNjjkNZ0b/wY7VSXWDMcXX6XGwlCmzbuq5MRtMjNbb1ZAxG6Ui3tbXFb7/9hhYtWkClUiEzMxN79+7F5MmT0aVLFzRv3hz16tVD8+bN0bVrV0yePBl79+5FZmYmVCoVWrZsiV9//RW2trbGKB6RFrlcjvDwcISHh0MuF+8HHoksKQqIvVVksmhVBXwmew/RqgpF5xkTBCQVf5Qi6w6R5eDxWjz1c81KD4kuex3prDtE5snFrTzCvQeKktdd70FwcSsvSl5kmdjWExnOv7ILpnTRjhbyOCEdCw7dFm0fPFbJEKw/RGYi9/ro9uWAtzcBb34H2DjodqzaOKjTv70J6VYuWi8FJJ/Fd0fvGqfsZPbY1pMxGG2RAHd3d/z555+YOXMmKlWqBJVKVeS/SpUqYebMmVi3bh3c3d2NVTQiskS5L7LyIfPvhaz3TmHQuDnIeu8UZP69dMiX4d2JiArSwEt7HeKQp8kmKgkRUV6DnINFyWegS4go+RARlXXvt6uF5jW07+dtvPgYp+/qtuQjERGVAfX6AHb/H7Rfoy0w/hxQt2fx8qrbE9IJ53DLqj4AIFnliEOKFlh1+gHORjwXqcBEVNZZGzNziUSCkSNHYujQoTh79iwuX76M8PBwJCYmIi0tDU5OTihXrhz8/f3RokULvPrqq7CxsTFmkYjIQmWG7Id9QS9a2wPdFsKm2UjUEtbPKQd4bwCurQMOzwTkmfm/N/wg0GKM6OUlIioNGlTV7ki/HZMMmUIJGyujjcUkItKNSoXKscdFyapyzL+ASpV3HUYiItKLlVSCpQMboftPZ5Ah+y+s7vQdt3Dk0/Zwc+A9PyKiMq9eb8CrKXDnIPDKewYvL2JfoQZsRv+Nub/NxWFZU8RAHaV08rabOPxJe5R3YtRjIjKMUTvSc9jY2KBDhw7o0KFDSeyOiEoZVUYirB+fy//FSvWAt9ao18fJTSIBmo8CqrcCdowGnoXlTfPwDJCZBNi75X2NiKiMyx3aPVuuxL1nqahbxbWAdxARlZDYYEiSokTJSpL0BIgLASoHipIfEVFZ5lPRCZ/1CMAXe0OFbbHJmZi7LxTfv93YdAUjIiLz4VYNaDlWtOzqernDu+skxBz4797vs5QszNh5C6uGN4OEA2aJyACcTkREZu/68e2wRj5rmrzyPjDmeP6d6Joq1VWne+X9vK8pZcC9Y+IUlIiolHF3skU1dwetbWVxnXQiMkNiL89zh8v9EBGJZVirGmhXp6LWtl03onE4JNZEJSIiotJuVFsfvObnobXtn7A4bLn8xEQlIqLSwiQd6enp6Xj27BnS09NNsXsisiCJ6dmIv7pHa1sSnJHWbz3w5neAjUP+b8zNxkGd/u1NgH057dd0WH+diKisyh3ePZTrpBORObjzt7j5hYucHxFRGSaRSLBoQEO42GsHwvx8dzCep2aZqFRERFSaSSQSLBnYEBVyhXL/+kAo7j1LNVGpiKg0KJGO9Lt372LRokUYOHAgGjZsiGbNmuG1115Ds2bN0LBhQwwcOBCLFi1CRERESRSHiCzIN3/fxu6s5khWOQIALikDcLXbfjg16lO8DOv2BMafA2q0VT+3cwXq9haptEREpU8DL+0w7pyRTkQmlxQFxN4qMlm0qgI+k72HaFWFovOMCQKSokUoHBERAUDVcg74qld9rW0v0rLx+e5gqFQqE5WKiIhKs0ou9lj8VkOtbZkyJSZtvYFsudJEpSIiS2fUjvS4uDiMGzcOffr0wbp16xASEoLs7GyoVCrhX3Z2NkJCQrBu3Tr07t0b48aNQ1xcnDGLRUQW4ty959h+LQpHlC3QNWsR5shGYHWtn9GxZRPDMnarBozYD/RYCky4ANRjRzoRUUHqe2nPSA+LSYZCyZufRGRCOkQTkvn3QtZ7pzBo3BxkvXcKsV5ddciX4d2JiMTUv6kXutTz1Np2JDQOe25y4BIRERlHp7qeeLd1Da1toU+T8d3RcBOViIgsnXXRSYrn6tWr+Oijj5CUlKQ10rRixYqoWLEiHB0dkZ6ejufPn+P58+fC66dOnUKfPn3wyy+/oGnTpsYqHpHA1tYWzZo1M3UxKJeMbAU+2x0sPI9BBeyyfhP/9GsIiURi+A6kVkCLMQZlwbpDZDl4vBZf7tDu6dkKPHyehtqVnE1UopLFukNkhgoL625tD3RbCJtmI1FLuGYsB+Xorfhu8ef4MPN32Etk+b83/KDB14dkmdjWExmHRCLBt/0DcfXRSySkZQvb5+wNRataFVDFTcel2v6PxyoZgvWHyDKIcax+1qMuLtx/gQiNkO4rTz9Aez8PtK1d0dAikhljW0/GYJQZ6ffu3cOECROETvQ6depg7ty5OHPmDM6ePYs9e/Zg8+bN2LNnD86ePYszZ87gq6++gr+/P1QqFRITEzFu3Djcv3/fGMUjIgvw47938ehFuta2Gd389f6hTUREhvFwsYOnq53WttCnDO9ORCaSmQREns3/tUr1gLEngeajgFwDL6VWUni8/gF6ZX+DO0rv/N//8Iw6fyIiEk1FZzt826+B1raUTDmm77jFEO9ERGQU9jZW+HlIE9haaXd/Td52Ey81BnYREenCKB3ps2fPRnJyMiQSCT755BPs2bMHb7/9Njw8PPJN7+HhgcGDB2P37t2YPHkyJBIJUlJSMHv2bGMUj4jMXEh0Elafeai1rVkNdwxtWaOAdxARkTHlnpXOddKJyGQi/gGU+cwof+V9YMxxoFLdAt86oGk1xNr6oE/2PKyXv5E3gVIG3DsmYmGJiAgAujWogn5NvLS2nYl4jk2XHpuoREREVNrVreKKGd0DtLbFJWdh5i4O5CIi/YjekX7q1CncvHkTEokE06dPx7hx42BlZaVbYaRSjB07FjNmzIBKpcLNmzdx6tQpsYtIpEWhUCAuLg5xcXFQKBSmLk6ZJ1coMXPXLa31d22tpFjYPxBSqQgh3UXEukNkOXi8Gib3Oukh0ckmKknJY90hMjO510e3Lwe8vQmKbosRl5Bc6LHqZGeNQa94Iwu2mCMfhbHZnyJJ5VR4/lQmsK0nMr6vetVHZVd7rW3fHryNRy/SdM6DxyoZgvWHyDKIeayOauODdnW0Q7kfCY3D1itPDMqXzBfbejIG0TvS//nnHwBAgwYNMHLkyGLlMXLkSAQGBmrlR2QsCoUCUVFRiIqKYuNqBtace5ing+bDDrVRx9PFRCUqGOsOkeXg8WqYwNwd6U+TyswIbtYdIjNTrw9g56p+XKMtMP4cULenzsfqiNY+QtT3o8pX0C1rIeLc/7+Gnp0rULe3kT8AmSO29UTG5+Zog0VvNdTalp6twNTtQVoD6QvDY5UMwfpDZBnEPFalUgm+G9gI5Z1stbZ/vT8M9+NTC3gXWTK29WQMonekX79+HRKJBH379jUon759+0KlUuH69eviFIyIzN7jF+n4/p+7WtvqVHLG+Nd9TVQiIiICgAZerlrPUzLleJyQbqLSEFGZVq83MOEC0H0JMGI/4FZNr7dXr+CITgGewvMYVMBQ2edQdl+izrceO9KJiIzlNT8PvNOyuta2K5Ev8cfZByYqERERlXaVXO2xeID2QK4MmQKfbL2JbLnSRKUiIksiekf6ixcvAAD+/v4G5ZPz/ufPnxtcJiIyfyqVCp/tDkam7L8LGIkEWDigIWytRW+qiIhID5Vd7VEh1wjushTenYjMjFs1oOVYQKrbEmK5jWrro/X83vNMnHHvp3enPBER6e/zHnVRvbyj1ralR+7iblyKiUpERESlXed6nhjWSnsgV3B0Er77J9xEJSIiSyJ671RGRgYAwMHBwaB8ct6fmZlpcJmIyPztvB6Ns/e0B86826oGmtVwN1GJiIgoh0QiybtO+tMkE5WGiMgwbXwrwM/TWWvbunMPTVQaIqKyxcnOGksHNhKW2QCAbIUSk7fdhEzBmYFERGQcn/eoh9qVtH8DrDr9AOfvcSInERVO9I708uXLAwDi4uIMyifn/Tn5EVHp9Tw1C/P/DtPaVsXNHtO6BZioRERElFuDqtrh3UOi2ZFORJZJIpFgZJuaWttOhMfjAddJJCIqES1qlsf7r2q3wyHRyVh+/J6JSkRERKWdg60VfhrcGLZW/3WJqVTA5G1BeJmWbcKSEZG5E70jvUaNGgCAs2fPGpTPmTNntPIjotLr6/1hSEyXaW2b37cBnO2sTVQiIiLKrUGuGemhT5OhUqlMVBoiIsP0a+IFNwcbrW3rLzwyUWmIiMqeKV3888wMXH7iHoKjOFiTiIiMo35VN0zvpr0kcWxyJmbtCub9DSIqkOgd6a+99hpUKhV2796N6OjoYuURHR2NPXv2QCKRoH379iKXkIjMyfE7cdgX9FRrW8+GVdCprqeJSkRERPlpUFW7Iz0hLRsxSVyCh4gsk4OtFQa38Nbatv3qE6Rkygp4BxERicnexgrfD2oEK+l/Md4VShUmb7uJTJnChCUjIqLSbHTbmmhXp6LWtsOhsfjryhMTlYiIzJ3oHem9evWCg4MDMjMz8eGHH+Lly5d6vT8xMREffvghMjIyYG9vj969e4tdRCItEokETk5OcHJygkRzkS4yutQsOWbvDtHa5uZggy971TdRifTDukNkOXi8Gs67vANc7LUjhZSF8O6sO0SWoTjH6vBWNaDRf4O0bAW2X40yUgnJnLGtJzKNhtXK4aMOtbW2RTxLxXdHw/NNz2OVDMH6Q2QZjH2sSqUSfDewEdwdtaNTzd0fxqWeSgG29WQMoneke3h44IMPPoBKpUJ4eDj69u2LI0eO6PTeo0ePol+/frhz5w4kEgnGjBkDDw8PsYtIpMXGxgYBAQEICAiAjY1N0W8g0Sw9Eo6nuWYzfv5mXXi42JmoRPph3SGyHDxeDSeRSPLMSg95mmyi0pQc1h0iy1CcY7WauyO61Kuste3PC5FQKhnWsaxhW09kOh91rI0GXq5a21affYjLDxPypOWxSoZg/SGyDCVxrFZytcfitxppbcuQKTBp601ky5VG2SeVDLb1ZAxGWYB43LhxuHv3Lg4ePIhnz57hk08+QeXKldG+fXvUr18fFStWhKOjI9LT0/H8+XOEhYXh1KlTiI2NFfLo1q0bJkyYYIziEZEZuP74Jf68EKm1rY1vBQxsVs00BSIioiIFVnPDhQcvhOdlYUY6EZVuo9r64HDof79DH71Ix8m7z9AxgMsMERGVBBsrKb4f1Bg9l50VOi9UKmDq9iAcmtQOTnZGuXVJRERl3Bv1PDG0ZXVsuvRY2BYcnYTv/7mLmd0DTFgyIjI3RrsaXbx4MSpXroy1a9dCpVIhNjYW27ZtK/Q9KpUKEokEo0aNwuTJk41VNCIysWy5EjN33oJKY7KPnbUU3/YLZMgVIiIzVr+q9mwhdqQTkaVrUbM86lZxxe2Y/yJsrD0XyY50IqIS5Ofpgqld/PDtwTvCtscJ6fj24G180y/QhCUjIqLSbPab9XDpYQLuPfsvpPvK0/fR3q8i2vhWLOSdRFSWiB7aPYe1tTWmT5+OLVu2oFOnTpBIJFCpVAX+k0gk6Ny5MzZv3owZM2Yw7AKVGLlcjvv37+P+/fuQy+WmLk6ZsPLUfdyN015z5tM3/OBT0clEJSoe1h0iy8HjVRwNvLRDuz9LycKz5MwCUpcOrDtElqG4x6pEIsGoNj5a285EPEdEXIrIJSRzxraeyPTee7UWXvFx19q26dJjnLobLzznsUqGYP0hsgwleaw62Frhp8GNYWP138QulQqY/FcQEtOzjbpvMg629WQMRo+P1LhxY6xYsQLJycm4fv067ty5g5cvXyItLQ1OTk5wd3eHv78/mjVrBldX16IzJBKZUqlEYmIiAMDb29u0hSkD7j1LxbLj97S21a/qivdfrWmiEhUf6w6R5eDxKo6aFZzgZGuFtGyFsC30aTIqudqbsFTGxbpDZBkMOVZ7N66KhYfvICHtv5tlf16IxPy+nAVZVrCtJzI9K6kESwc2QvefziBd41pz+o4gHP3kNbg52vBYJYOw/hBZhpI+VutXdcP0rgH45uBtYVtsciZm7QrGL0ObMnqqhWFbT8ZQYgsNubq64vXXX8frr79eUrskIjOjVKowa9ctZCuUwjapBFjYvyGsrYwWIIOIiEQilUpQr6orrkS+FLaFRCehQ0AlE5aKiMgw9jZWGNLCGytO3Be27bwWjWldAuDmyEhpREQlpUYFJ3zWoy5m7wkRtsUlZ+Gr/aH44e3GpisYERGVau+9WhOnI+JxJuK5sO1QSCy2X43CoFfYGUtU1rHniohKzJYrj7U6XwDg/Xa1EFjNrYB3EBGRualfVbvNDnnKddKJyPINa1UDVtL/ZptkyBTYdvWJCUtERFQ2DW1ZHe3qaK9Lu/tGNA6HxJioREREVNpJ/x8VxT3XINqv9ofi4fM0E5WKiMyFWXek//333+jdu7epi0FEIohNysTCg3e0tlUv74hPO/uZqERERFQcuddJD4lONlFJiIjEU8XNAd0bVNba9ueFSCiUKhOViIiobJJIJFj8VkO42GsH0fxsdwhepHK9WiIiMg5PV3ssGtBQa1t6tgKTtt5AtlxZwLuIqCwwu450pVKJ3bt3o3v37pg6dSoiIiKMvs/o6GgsXLgQ3bp1Q+PGjdGiRQsMGDAAq1evRkZGhij7iIqKwpIlS9C/f380b94c9evXR4sWLTB48GAsX74cL168EGU/ROZqzt4QpGTJtbZ92y8QDrZWJioREREVRwMvV63n0YkZeJnGm5pEZPlGtfXReh71MgPHbseZpjBERGVYFTcHfN2nvta2hLRsfLE/DCoVBzgREZFxdKlfGe+0rK617VZUEn48dtdEJSIic2DUNdIVCgVCQ0MRExMDa2treHl5ISAgoMC0O3bswKpVq/D06VMAgEqlgo2NcdekO378OKZNm4bU1FRhW0ZGBpKSkhASEoLt27dj1apVqFGjRrH3sWfPHnz55ZfIzMzU2p6UlIQbN27gxo0b2LBhA77//nu0bdu22PshMleHQ2JwNEz7JuSAptXwaq5wbUREZP5qezjDzlqKLI0R2XuDotHWtyIqu9nDxZ7rCRORZWpa3R0Nq7nhVtR/S1asOxeJrvUrF/IuIiIyhr6NvXA4JBZHQv+7l3DsdjzqOrvhdR8HE5aMiIhKsy/erIeLD17gQfx/Id1/PXUf7ep4oLVvBROWjIhMxWgd6WvWrMHKlSuRnKwd7tPHxwfTp09Hhw4dhG3Hjh3D4sWL8eSJeg06lUoFOzs7DBgwAO+//76xioiwsDB8+umnyMzMhKOjIz744AO0bNkSmZmZOHjwILZt24bIyEiMHTsWO3fuhLOzs977uHbtGmbNmgWlUgmpVIq+ffuiU6dOqFSpEmJiYrB7926cOHECiYmJmDBhAg4cOABvb28jfFoi00jKkOGLvaFa2yo42WL2m3VNVCIiIjKElVSCau4OuK/xo/KrfWHCa13re2JYqxpoXasCJBJJQdkQEZkdiUSCkW18MHlbkLDtwoMXuBObjIDKroW8k4iIxCaRSPBNv0BcjXyJFxrRj/64mYwGlWxNWDIiIirNHGyt8PPgJuj3yznIFOooKCoVMHnbTRya1A7lHHkOIiprjBLaff78+ViyZAmSk5OhUqm0/j18+BATJ07EyZMnAQBff/01Jk6ciCdPnkClUsHBwQGjR4/Gv//+izlz5qBq1arGKCIA4JtvvkFmZiasra2xZs0ajBs3Dk2aNEHr1q0xb948TJs2DQAQGRmJtWvXFmsfq1atglKpnrE1e/ZsLFiwAJ07d0bDhg3RtWtX/Pbbbxg1ahQAIDMzs9j7oeKTSqWoVKkSKlWqBKnU7FY7sHgLD91GfEqW1rYve9eHu5PlX3Sw7hBZDh6v4giJTkLXH09rdaJrUihVOBgci3d+v4SuP55GSHRSvuksCesOkWUQ61h9s2EVVHS209q27lykgaUjc8e2nsg8VXS2wzf9ArW2pctUWH0rgwM2SW9s64ksgzkcqw283DCtq7/WtpikTHy2O5hLjJg5c6g/VPqIXpOCgoKwceNGAOqZ5c2aNcOQIUPw7rvvom3btpBKpZDL5Vi4cCF+/vlnbN68GSqVCo6Ojhg/fjxOnDiB6dOno2JF44Z8vnXrFq5evQoAGDBgAJo0aZInzejRo+Hr6wsAWL9+PWQymd77uXHjBgCgXLlyGDp0aL5pPvzwQ+HxzZs39d4HGcba2hre3t7w9vaGtbVRVzsocy4+eIEtl59obevg74FeDauYqETiYt0hshw8Xg13JiIeg1ZewN241KITA7gbl4pBKy/gTES8kUtmXKw7RJZBrGPVztoKQ3Oti7j7RjReasyGpNKHbT2R+erWoDL6N/HS2nblSSq2Xo02UYnIUrGtJ7IM5nKsvv9qLbStrR3K/WBwLLZfizJRiUgX5lJ/qHQRvSN9x44dAAArKyv8+uuv2LRpE7788kt89tln+OOPP/DXX3+hXLlyePToEX777TcAQNu2bXH48GFMmjQJbm5uYhcpX8eOHRMeDxgwIN80OaHYASA5ORmXLl3Sez85ne/VqlUrMI2Liwvc3d210hNZukyZArN2BWttc7S1wvx+gRw5TkRkYUKik/DBhmtIz1bo9b70bAU+2HCtVMxMJ6KyY2jL6rCx+u96NUuuxNYrTwp5BxERGdOXveujsqu91rZvD95B5PP8oyQREREZSiqV4LuBjVHO0UZr+1f7QvGQ5x+iMkX0jvSbN29CIpFg8ODBWuug5wgMDMTHH38MlUoFpVKJevXqYdWqVfDw8BC7KIW6du0aAMDR0RH169cvMN0rr7wiPL5+/bre+6lZsyYAICqq4JFKqampePnypVZ6Iku37HhEnouK6V394VXOwUQlIiKi4lCpVJi87abeneg50rMVmLItiOHPiMhiVHK1x5uB2hGUNlyIhFyhNFGJiIjKNjcHGyx+q6HWtgyZAlO3B0Gh5DUmEREZR2U3eywaoH3+Sc9W4JOtNyDjbwOiMkP0jvTY2FgAQOvWrQtM065dOwCARCLB8OHDYWVlJXYxinT//n0AQPXq1QsN8VCrVq0879HH4MGDAQCJiYnYsmVLvmlWrFiRJz2VHJlMhlu3buHWrVuMCCCS2zHJWHnqgda2xt7lMLy1j2kKZCSsO0SWg8dr8V148ELncO4FCY9LwcUHCSKVqGSx7hBZBrGP1VFttQc4P03KxNGwOIPzJfPEtp7I/LX388A7LbSjPV599BKrzzwo4B1E2tjWE1kGcztWu9avjCEttJd+CopKwo/H7pqoRFQYc6s/VDqIvkhAaqr6RquXl1eBaTRfy1mDvCRlZWUJM8ArV65caFo3Nzc4OjoiPT1dGCSgjwEDBuDatWvYs2cPvv76a4SGhqJjx47w8PBATEwM9u7dK4SZHzduHNq0aaP/B9JDYY2HtbU1pFL12AqlUgm5XF5oXra2tsJjhUIBhaLgmWoSiQQ2Nv+FQZHL5VAqCx61JZVKtQY4yGSyQmeyaZZdpVIV2Uja2NgI4cUVCoWQPjs7O89+jF12KysrrcEk2dmFr79o7t+TXKHE9B1BkGuMCreWSjC/d11IoAKg/rvr+z0V9VlN8T3JZLIC6465f0/GOp7M8XsqjKHfk7lekLGtV2Nbnz99P+v685GF7ltX688/RDNvlzzbzb0NYVuvZu7fU2HY1peNuqmZNr92Xt+y1/V0RGNvN9x88t/SFGvOPkBn/wr5pgcsu60v620I23o1c/+eCsO2vmzUzWlv1MG/oTGIS/uv7EuPhqNtLXf4eTqXSNnZ1hfM3NsQtvVq5v49FaY0tvVs59XM/f7NjC61cfHBczx8ni5s/+XkfbT2KYc2dSqVye/JXNuPgtr6sno8mev3VBAxvidjtPWid6SrVCpIJJJCZ3nn/CEAdWj1kpaW9l+4aV327+DggPT0dKSnpxeZNjcrKyssWrQIHTp0wMqVK7F9+3Zs375dK03Lli1LpBMdAO7cuVPga/7+/nB2Vv/wSE9PR3h4eKF5NWvWTHj8/PnzQsPXOzk5ISAgQHj+6NEjJCYmFpi+UqVK8Pb2Fp7fvn270AOgVq1aWuvMBwcHF5gWUC8xkHOQpaSkCNvz+/vY2NigYcP/QrjExMTg2bNnBeZdrlw5rQEi9+/f16pzuVWrVg2enp7C86LKbu7f0+7QRARHp2il6+vviKxnD5HkXPzvKSkpCQ8eFDzS3NTfU+66Y+7fk7GOJ3P/nnIz5vdkSmzr1djW50+f7yldpsQ/YQX/HfRxJCwOl64HwdFGOyCSJbUhbOst43vKjW192ambOQr62+hbN9+s7aTVkX71USL2nr6OWu42+aY3p7ppzt+TubchbOst43vKjW192aibTnbWmNjCDV+cSEDOLVuZQoWJm65gQacKsJFKylTdNNfvCTD/NoRtvWV8T7mVxrae7byaJdy/mdDYAbP+TYf8/ycglQr4ZOsN7J3QCl4e6s9alr4nS2g/NOtPWT2eLOF70mSu7bzood0tQVZWlvBYc7RFQXIqXWZmZrH2d//+fezZswd37+Yf7uPmzZvYsWMH4uIYKpAsW1yqHJtDtMP/VnWxwoC6ziYqERERGeJFhhIKkZadVKqAhAyuIUZElqN9LRd4utppbTt4T//B1UREJJ66FW3R299Ja9vDRDl23jZsKSIiIqLC1HK3wZBA7Sh7zzOU+PpQRKEzconI8klUIh/lAQEBkEgkWLNmDWrUqFFguo4dO+qUDgCqVq0qZhGRkJAgrOHeo0cP/PDDD4Wmb9OmDV68eAE/Pz/s379fr31dvXoV48aNQ0pKCry8vDBp0iS0bdsWbm5uePHiBf7991/8/PPPSExMRKVKlbBmzRrUqVOn2J8tP9nZ2cJIj4CAgAIHD5TV8BaZmZkIDQ0FkP/fpzSGtyiIIZ9VpVLh3T8u4cy9F1ppNo1ujld83POUvTSEIZHJZMLIttx1x1y/J4DhYjQZ+j1lZmYKdSC/GXEliW19Xmzr86fPZw2KSsLAVZcL3bc+to9tgUbV3LS2mXsbwrZezdy/p8KwrS8bdVOXv01xyv7rqYf47p//BkTbWElwekp7VHDOWw8sta0H2IawrVcz9++pMGzry0bdzPnbZCtU+OJMCu7F/ze4yUoqwV9jXkGjauVKVd20xO8JMM82hG29mrl/T4UpLW092/m8LOX+jVKpwsg/r+PiwwSt9yx5qyEGNvcuU9+TubYfBbX1ZfV4MtfvqSBihXYXu60XPbR7jtGjRxf6es4XqUu6sLAw0coFqEMt5NAlXHtGRgYA/cPQZ2dnY/LkyUhJSYGHhwf++usveHh4CK9XrlwZQ4cORYsWLTBgwAA8e/YMM2bMwK5du/Tajz5sbGx0qjhSqVSvCpb7gChKYaH/86NL5IAcEolEr7JrLjWgy9/HmGUHoHfZzeV72nvzaZ5O9KEtq6Otn2e+6YvzPemTvqS/p8Lqjjl9T4Dxjydz/p4KU5zvSd/ylBS29XmxrS9YYZ+1nJO9zvnowt3ZvtDPYu5tCNt6NXP/ngrDtr5ollw3Nfehy/t0KfuQltWx7Pg9ZCvUNxNkChV23IjBxE5FD362lLY+P2W5DWFbr2bu31Nh2NYXzZLrJgDYWkmweEAgBq26DLlSffNWoVRh5u4wHJj4KjT3bul101K/J3NvQ9jWq5n791SY0tLWs53Py9zv3/w4uAm6/XQaien/dWZ+tS8Ur/iUh09FpzL1PZl7+1FQ/Slrx5O5f08FMfb3pA+jhHZXqVSi/hObnZ0dypUrBwCIjY0tNG1SUpLQ2V65cmW99nP69GkhXPuwYcO0OtE11alTB7179wYAhIaGFro2CpE5SkjLxtcHtAe8eLraYUb3gALeQURElqCymz2spBJR8rKWSuDpKm7HPBGRsVV0tkPvxtoR0jZcfASZgktVEBGZUoOqrvioY22tbfeepWLpkcLX0iQiIjJEZTd7LOzfUGtbWrYCk/66KfxGSMmUISIuBTefJCIiLgUpmYXPICYi8yb6jPR+/fqJnaVR1K5dG1evXsXjx48hl8sLHGnx4MED4bGvr69e+9B8b7169QpNW79+fWzfvl14X0AAOyDJPKVkyhCblIm0bAWcbK1Q2c0e8w+EISFNOyzH130awNXe/EZ6EhGR7lzsbdC1vicOBhc+8FAXXetXhgvPC0RkgUa28cGOa1HC82cpWTgUEovejcRdgoyIiPTzYYfa+Pf2MwRHJwnb/jj3EJ3reaJVrQomLBkREZVm3RpUxuBXvLH1yhNhW9CTREzbHoQsuRJHw+KgUP43QdRKKkHX+p4Y1qoGWteqIERrJiLLIHpH+oIFC8TO0iiaNWuGq1evIj09HaGhoWjUqFG+6a5cuSI8btq0qV770AwjUNgaCgC0Yv0bK/wA5c/a2hq1atUSHlNeKpUKFx68wIYLj/JcCEglgDJX4IjuDSqja339IjhYItYdIsvB47X4hrWqIUpHemCutdEtBesOkWUw5rHawMsNLXzK43Lkf2shrj33kB3ppQjbeiLLkPtYlUql+H5QI7y57Cyy5epZgCoVMG1HEA5Nag9nOx7P9B+29USWwVKO1Tm96uHywwQ8eJ4mbNtz82m+aRVKFQ4Gx+JgcCz8PJ3x/aDGaOBlmfdIzJ2l1B+yLEYJ7W4JOnfuLDzeuXNnvmmUSiX27NkDAHB1dUXLli312ke1atWEx1evXi00rWaHveb7yPikUinc3d3h7u6utQYLqYVEJ6Hrj6fxzu+XcCgkVqsTHcjbie5oa4W5veuXYAlNh3WHyHLweC2+1rUqwM/T2eB8Fh26gzVnHxpl2R5jYt0hsgzGPlZHtvXRen7jcSJuPkkUfT9kGmzriSxDfsdqHU8XTOvir5XuSUIGvvn7timKSGaMbT2RZbCUY9XR1ho/DW4CfVfDuxuXikErL+BMRLxxClbGWUr9IctSZmtSw4YN0bx5cwDqjvQbN27kSbNmzRrcv38fAPDuu+/CxkY7HOmlS5fg7+8Pf39/zJw5M8/7W7duDQcHBwDAli1bEB6e/zpNp06dwj///AMA8PT0RN26dYv/wYhEdCYiHoNWXsDduFSd3yNXqBAel2LEUhERUUmSSCT4flBjONoaFjFHBeDrA2GYtStYmDFERGQputTzRFU3e61tf56PNE1hiIhIy+hXa6KFT3mtbVsuP8bJ8GcmKhEREZUFEok6bLu+0rMV+GDDNYRoLE1CROarzHakA8Dnn38Oe3t7yOVyjB49GitXrsTNmzdx8eJFzJkzB0uWLAEA+Pj4YNSoUXrn7+rqijFjxgAA0tLSMHjwYHz//fe4ePEibt++jTNnzuCrr77ChAkToFSqbyhPmTKFI2VKmEqlQnZ2NrKzsy1ulpwxhUQn4YMN15CeXfiyBLllK5Rl5kKAdYfIcvB4NUwDLzesHN5M7870/H5Qbr3yBMNWX8KL1CyximdUrDtElsHYx6q1lRTDW/tobTtw6ykexKciIi4FN58kIiIuBSmZMtH3TcbHtp7IMhR0rFpJJVg6sFGea9UZO28hKV3dLqdkythel3Fs64ksg6UcqyqVCpO33YRMUbwypmcrMGVbkFl/RktkKfWHLIuoiwSEhISgQYMGYmapJTMzE9HR0fD19RUlv3r16uGHH37AtGnTkJqaiu+//z5PGh8fH6xatQrOzsULaTphwgQkJSVh/fr1SE9Px8qVK7Fy5co86WxsbPDpp5+iT58+xdoPFZ9MJkNwcDAAIDAwELa2tiYukenlXAjo24meI+dC4PAn7SCR6D8qz1Kw7hBZDh6vhmtXxwPbPmiNydtu6hSpxN/TBUsHNsTxO/H44dhdrdcuRyag9/JzWD2iOepWcTVWkUXBukNkGUriWB38ijd+PHYXWf+PqiFTqNDp+1PQvD9jJZWga31PDGtVA61rVSjV18KlCdt6IstQ2LFavYIjPn+zLj7fHSJsi0vOwvhN1+DmYIOjYXFaS9WxvS572NYTWQZLOVYvPHihVxTX/ITHpeDigwS09q0gUqnIUuoPWRZRpz4PHDgQ48aNEyqqWDIyMvD777+jU6dOOHTokKh5d+zYEfv27cPIkSPh4+MDBwcHuLq6okGDBpg6dSr27NmDGjVqFDt/iUSCzz77DDt27MDgwYPh5+cHJycnWFlZwcXFBfXr18eoUaOwf/9+vPfeeyJ+MqLiE/NCgIiISo8GXm448kl7bBnTCj0CK+eZcW4tleDNwCrYMqYVDn/SDoHVymFS5zr4ZWhT2NtoX3ZGJ2ZgwK/ncTQ0tiQ/AhFRsbk72eI1Pw+tbbknOSiUKhwMjsU7v19C1x9Pl4koTURE5uKdFtXRPlc7ff7+CxwKidXqRAfYXhMRkWE2XnxkVvkQkfGIOiPdxcUFJ0+exKlTp9C0aVP06dMHXbt2hZubW7Hyu3r1Kg4cOIBDhw4hOTkZKpUK7u7uYhYZAODl5YVZs2Zh1qxZer2vZcuWBa57nluDBg2MOlufSExiXghwRB0RUekikUjQ2rcCWvtWQEqmDHHJmUjNUsDZzgqervZwsbfJ854egVVQvbwjxq6/iqdJmcL29GwFPth4DVO7+GPC676cCfQ/9u47Pqoq/R/4505L76RACAkEEiCUQEASEAQbdmXRWGgWpKjrWnfVXV1/a911Ufe762oEUQEXiaKAioCoqECAEFoSIEFCKIH0kD7JZOb+/ogZcjMpM8m0O/N5v16+TM69c+cJc87JZJ57nkNETu2XE2X4+USZ2efnl9QhNS0DafOSMHVYaM8PICKiPhEEAf+YPQZX/vNHNOgMZj+O8zUREVmiVqvD1twSq1xrS24xarW6Tj9LISLnYNVE+rZt2/Cvf/0Ln332GbKysnDgwAG89NJLuOyyy5CYmIjRo0djxIgRCAkJgUolfeq6ujqcOnUK2dnZyM7Oxt69e3HhwgUArWWmExIS8NRTTyElJcWaIRNRB3wjQERE5vLzVJs9x4+KDMCGR6ZgyeosHDhz0dguisAbW/OQX1KLv88eA0+1ZfuwExHZQ05RNRavzoLWgsQM8NsNQ6uzkL44BaMie3eDORERma+8rgktvdgSlfM1ERGZq7haa1LppLf0BhElNVp+fk7kxKyaSA8MDMRf//pXPPjgg3j//fexceNGNDY2YteuXdi9e7fkXE9PTwQEBECr1aK2thYGg/QDCfG3GnmJiYlYsGABrr/+emuGSkRd4BsBIiKylTA/T6xdlIxnv8jGFweKJMc2HjqPwvJ6vD9/AsL9PR0UIRGRKVEU8UT6ITQ063v1+IZmPZ5MP4wtj01l5Q0iIhtqm6+bWyy76akN52siIjJHfS//LuhKXZN1r0dE1mXVPdLbDBgwAC+++CJ+/vlnvPjii5g0aRLUajVEUTT+19jYiOLiYly8eBF6vV5yrH///pg3bx6++uorfPrpp0yiE9kR3wgQEZEteaiUWHbHWDx3w3B0/Hzy8Llq3PKfnThy7qJDYiMi6kxGQQXyS+r6dI28klrsKai0UkRERNQZztdERGQPPhrrVtLz9WBlPiJnZtUV6R35+fnhrrvuwl133YXm5mZkZ2fj4MGDKC4uRlVVFS5evAgPDw8EBwcjODgYcXFxmDBhAiIiImwZFhF1g28EiIjI1gRBwKJpsRga5otH1x5CXVOL8VhJTRPueC8Db9wxFreMHeDAKImIWq3Zc9pq10mJDbHKtYiIyBTnayIisoeIAE8oFYJVqrqqFAKr8hE5OZsm0tvTaDRISkpCUlKSvZ6SiHqBbwSIiMherhweji8emoyFH+/HmcoGY3tTiwGPrj2IEyW1ePzqOCgULK1JRI5Rq9Vha26JVa61JbcYtVodtz0iIrIBztdERGQvfp5qzEwIx+bs4j5fa2ZCBH/fEDk5uyXSiZyVWq3G6NGjjV+7O74RMB/7DpF8cLw6r7hwP2x8eAqWfpJlUkbz3z/8ivySWryZmggfD8e8bWXfIZIHW43V4mqtVW4wBQC9QURJjdal3x/LFed6InnobqxyvqaecK4nkge5jNW5ydFW+fx8bnK0FaKhNnLpPyQvNtkjnUhOBEGARqOBRqOB0HGzVjdlrV/grv5GgH2HSD44Xp1bkI8Gqx+YhDmTBpkc25pbgtnv7sa5qoZOHml77DtE8mCrsVrfrLfatQCgrsm61yPr4FxPJA/djVXO19QTzvVE8iCXsZoyJARx4b59ukZ8uB+ShwRbKSIC5NN/SF6YSCciE9Z4IwAAv5bWWiEaIiJyB2qlAq/MGo2Xbk2AskMp9+PFtbj1P7uwv7Cyi0cTEdmGj0Zp1ev5elj3ekRE1IrzNRER2ZMgCHgzNRHevfz9461RYlnqWCZ7iWSAiXRyewaDAVVVVaiqqoLBYHB0OE6hr28E2jy/MRf/+eEERNE65dWcDfsOkXxwvMrHvJQYrLr/MgR4SUtwVdQ34+7le5C+/6xd42HfIZIHW43ViABPk5t7ekulEBDu72mVa5F1ca4nkofuxirna+oJ53oieZDTWB0VGYC0eUm9+gx94dTBGBUZYIOo3Juc+g/JBxPp5PZaWlpQUFCAgoICtLS0ODocp9GXNwLt/XNbPl76+hgMVtqrzJmw7xDJB8ervEwZ2g8bHp6C2FAfSbtOL+KPnx/By18ftdoemD1h3yGSB1uNVT9PNWYmhFvlWjMTIrjfrpPiXE8kD92NVc7X1BPO9UTyILexOnVYKNIXp1hc3XXtvrOobtTZKCr3Jbf+Q/LARDoRdcnSNwLx4b64e2KUSfvKXafw1OeHodPzLjAiIjLP4H4++PLhKbgiLtTk2Iqdp3D/R5mo0fKPTiKyvbnJ0U51HSIi6hznayIicoRRkQHY+tg0rH0wGTeMjjCpkKJSCEiMCpS0ldU24bXNx+wYJRH1lsrRARCRc2t7I7CnoBKr9xRia26JZBWgSiFgZkIE5iZHI3lIMARBQHyEH1786qjkOl8cKEJNow7/uWc8PNXca4yIiHrm76nGynsn4vVvj2H5L6ckx37KL8Osd3ZhxYKJGNzPp4srEBH1XcqQEMSF+yK/pK7X14gP90PykGArRkVERB1xviYiIkcRBAEpsSFIiQ1BrVaHkhot6pr08PVQItzfE74eKtz/USZ+zCszPubTzLO4JXEAJsf2c2DkRNQTJtKJqEc9vRHoWPLs3imDEeitwVOfHUZLu6T79mOlmP/BPqy4dwL8WSaNiIjMoFQI+PONIzEs3A9//jIbOv2l3ysny+px2zu78N854zFlKP/wJCLbEAQBb6YmIjUtAw3Neosf761RYlnqWAiCdfbuJSKiznG+JiIiZ+Dnqe50i5CXZ43GtW/+hPp2v6Oe+yIbWx6bxoVnRE6Mpd2JyCJ+nmoMDfNDYlQghob5dblv2G3jIvH+/CR4qKTTzL7CStyVtgdltU32CJeIiFxE6oQorH0wGSE+Gkl7daMO81fuw6qMQscERkRuYVRkANLmJcFbY9kHXN4aJdLmJWFUZICNIiMiovY4XxMRkbOKDPTCH68bLmkrrGjA29tPOCgiIjIHE+lEZDNXDg/HmoWT4OcpLX5x9EIN7nhvN85WNjgoMiIikqMJMcHY+MgUjOjvL2nXG0S8sDH3txXrBgdFR0SubuqwUKQvTkFcuK9Z58eH+yF9cQqmDgu1cWRERNQe52siInJW85KjMX5QoKRt+S8FyCmqdkxARNQjJtKJyKYmxgQjfXEK+vl6SNoLKxpw+3u7kVdc66DIiIhIjgYGeePzJSmYmRBucuyTvWcw74O9qKpvdkBkROQORkUGYOtj0/D41cM6Pa5SCLhxdH+sfTAZWx6bypWNREQO0jZfv3jzyE6Pc74mIiJHUCgE/H32GGiUl1JzeoOIP60/ghYuDCBySkykk9sTBAFqtRpqtZr7YNnIiP7+WL80BVHBXpL2kpompKZlIOt0lYMi6xv2HSL54Hh1LT4eKrw7Jwm/v3KoybE9BZW49Z1dOFFinRu12HeI5MGeY1UQBJM9DEcN8Mf2J6bh4AvX4J0545ESG8I5Q0Y41xPJg6VjVRAE+HhIK+RFBXlxvnZTnOuJ5MEdxuqwcD88PEP6eUbu+Rqs2HnKQRG5DnfoP2R/qp5PMd+GDRuseTmj2267zSbXJQIAtVqNMWPGODoMlxcd4oP1SyZj/sp9ON5uFXp1ow5zV+zFe/OScEWcvMqose8QyQfHq+tRKAQ8eW08hoX74enPDqOp5dKd22cqGzDrv7vxf3cn4srhpivXLcG+QyQP9h6rZzpsUZQwIABDw/zs9vxkXZzrieShN2P1dIV0vo6P8Od87aY41xPJg7uM1aXTY/FN9nnkl9QZ2976Lh8zEyIwuJ+PAyOTN3fpP2RfVk2kP/PMM1a/y0MQBCbSiVxEmL8n1i1Kwf0fZ0pWoTfq9Fj4cSbeTE3EzWMHODBCIiKSm1vGDkBMiDceXLUfJTVNxva6phY88PF+PHPdcCyaNoR3IhORVXVMpA8K8XZQJERE1J3CinrJ99Gcr4mIyAloVAq8PnsMZr+7G6LY2tbUYsCzXxzB2geT+RkGkROxeml3URSt/h8RuY4AbzVWP3AZpsdLV5/r9CIe/fQgVu857aDIiIhIrsYMDMSmRy7H2IHSvS1FEXjt2+N48rPD0Or0DoqOiFzR2Q6J9KhgJmaIiJxRxxXpMUykExGRkxg/KAj3To6RtO0pqMS6zLOOCYiIOmXVFenff/+9NS9HZBctLS24cOECAKB///5Qqaw6LKgT3hoVls+fgKc+O4yNh84b20UReH5DDi7WN+ORK4c6/Z137DtE8sHx6vrC/T2xbnEK/rT+iOR3CwB8caAIheX1eG9eEsL8PC26LvsOkTzYc6y26A04V9UoaRvERLqsca4nkgdLx6ooip2sSGe5XHfFuZ5IHtxtrD51bTy25Zag6OKlvy9e2XwMVw4PQ5i/ZZ9fkPv1H7IPq/aiyMhIa16OyC4MBgNKS0sBAOHhfdtHlcynVirwVmoiArzUWJUhXYW+7Lt8VDXo8JcbR0ChcN5kOvsOkXxwvLoHT7USb9+ZiLhwP7yxNU9y7MCZi7jtP7vw/vwJGBUZ0MUVTLHvEMmDPcfqhWotWgzSymlMpMsb53oiebB0rF5s0KFW2yJpi2Ei3W1xrieSB3cbqz4eKrwyaxTu/TDT2FarbcELG3Px3rwkB0YmT+7Wf8g+rF7anYjIXAqFgP93SwL+cNUwk2Mrd53CU58dhk5vcEBkREQkV4Ig4OEZQ/H+vCR4a5SSY+ertbjjvQxszr7goOiIyBV0LOvu66FCkLfaQdEQEVFXOq5GVykEDAjk6j4iInIu0+PDMGucdJHqltxibMnhZxdEzoCJdCJyKEEQ8Pg1cXjx5pEmx744WISla7K4ry0REVns2oQIrF86GZGBXpL2Rp0eD31yAP/afgKiKHbxaCKirp3pZH90Z9+SiIjIHXXcH31gkBdUSn4USkREzuf5m0Yi2EcjaXthYy6qG3UOioiI2vDdIxE5hXunDMbbdyZC1aGU+/ZjpZj/wT7UaPmmgYiILDOivz82PTIFl8UEmxx7a3s+HvnfQTQ282YtIrJMx0T6oGCvLs4kIiJH4v7oREQkF8E+Gvy1w0Kz0tomvLb5mIMiIqI2Vt0j3RxnzpzBDz/8gOPHj6Oqqgparbbb1UCCIODjjz+2Y4RE5Ci3jYtEgJcaSz/JglZ3qaT7vsJK3JW2Bx/ffxlC/TwcGCEREclNiK8H1iychOc35GDd/rOSY99kX8Dpynosnz8B/QOYCCMi85gm0rk/OhGRMzrTYUV6TAjnayIicl63jB2ADQeL8GNembHt08yzuCVxACbH9nNgZETuzW6J9MbGRvztb3/Dxo0bTRLnoiialMJrO4cl8ojcy4zhYVj9wCTc/1EmarUtxvajF2pwx3u7sfqBSYjih5VERGQBjUqB12ePRnyEH17+5igM7d6K5hTV4Jb/7ELavCSMHxTkuCCJSDY67pHORDoRkXPiinQiIpITQRDw8qzRuPbNn1Dfrnrec19kY8tj0+CpVjowOiL3ZZfS7qIo4pFHHsGGDRtgMBgQGBiI4cOHG5PlEyZMwNChQ6FUKo1tgwcPxsSJEzFhwgR7hEhETmRiTDDSF6egn6909XlhRQNuf2838oprHRQZERHJlSAIuP/ywfjwvsvg5ym9l7Sstgl3vb8HXxw456DoiEhOOtsjnYiInE/HPdJj+nG+JiIi5xYZ6IU/Xjdc0lZY0YC3t59wUEREZJdE+rfffotdu3YBAB555BHs2rULf//7343H16xZg6+++gr79u3DM888Ay8vL1RXV+MPf/gDVq9ebY8QyY0pFAoEBgYiMDAQCoVdhgSZYUR/f6xfmoKoDntOltQ0ITUtA1mnqxwU2SXsO0TywfFKba6IC8WGh6dgcD/piqTmFgOeSD+M1789Dn27JevsO0TyYK+xWqPVoapBJ2njinT541xPJA+WjNUarQ4V9c2StkHBXJHuzjjXE8kDxyowLzka4wcFStqW/1KAnKJqxwQkI+w/ZAt26Ulff/01ACAxMRGPPPIIFApFpyXbvb29ce+99+Kjjz5CfX09fv/736OkpMQeIZIbU6lUiI2NRWxsLFQqu+12QGaIDvHB+iWTMTzCT9Je3ajD3BV78VN+WRePtA/2HSL54Hil9mJDfbHhoSm4fKjpHmPv/XQSi1btR622NVHGvkMkD/Yaqx3LugsCEBnk1cXZJBec64nkwZKx2nF/dEGAyY365F441xPJA8cqoFAI+PvsMdAoL6Xv9AYRf1p/BC16gwMjc37sP2QLdkmk5+TkQBAEpKammnX+mDFjcPfdd6Oqqoor0oncXJi/J9YtSkFStHTf2kadHgs/zsRXh887KDIiIpKzAG81PrpvIu6dHGNy7PvjpZj97m6TD2CJiDom0vv7e8JDxb0KiYicTcey7gMCvDhfExGRbAwL98PDM4ZK2nLP12DFzlMOiojIfdklkV5V1VqCOSoqytjW/m4QrVZr8pgrrrgCALBjxw7bBkdETi/AW43VD1yG6fGhknadXsSjnx7E6j2nHRQZERHJmUqpwIu3JODVWaOhUkirJeWX1OHWd3ZiT0GFg6IjImfE/dGJiOShsKJe8j33RyciIrlZOj0WceG+kra3vsvHqfL6Lh5BRLZgl0R6W9Lcx+fSXkTtvy4rMy3P7OfXWsr5woULNo6O3J1Op8Px48dx/Phx6HS6nh9ADuGtUWH5/Am4NXGApF0Ugec35ODf35+AKIpdPNo22HeI5IPjlbpzz6RBWLNwEoK81ZL2qobWrUTe3LiPfYfIydlrnu+YSI8OYWLGFfB9ApE8WDJWT3dIpEeHcH90d8e5nkgeOFYv0agUeH32GLTfJbmpxYBnvzhi98/B5YL9h2zBLon0sLAwAEBlZaWxLTQ0FJ6engCAo0ePmjzm9OnWFaZ6vd4OEZI7E0UR9fX1qK+v5y8gJ6dWKvBWaiLmp0SbHFv2XT5e+voYDAb7vYbsO0TywfFKPUkeEoJNj1xucrd3i0HE/2WU4f92XoCuhe9LiZyVveb5M5WNku8HcUW6S+D7BCJ5sGSsFnYo7R7N+drtca4nkgeOVanxg4KwICVG0ranoBLrMs86JiAnx/5DtmCXRHp8fDwAID8/39gmCALGjBkDAPjf//4nOV+n0+HDDz8EAERHmybMiMh9KRQC/t8tCfjDVcNMjq3cdQpPfXYYOr3BAZEREZHcRQV7Y/3Sybh6RJjJsc2/NmDh6oOobuAdzUTurOMe6SztTkTknLginYiIXMXTM+MRGeglaXtl8zGU1phumUxE1meXRHpycjJEUcQvv/wiaZ89ezZEUcS+ffswb948fPLJJ1i+fDnuuOMO5OTkQBAEXH/99fYIkYhkRBAEPH5NHF68eaTJsS8OFmHpmixodVw1SERElvPzVCNt3gQsnR5rcmx3QSVu++8u/Fpa54DIiMjR9AYR56qkiXSuSCcicj6NzXqU1DRJ2rhHOhERyZWPhwqvzBolaavVtuCFjbkOiojIvdglkX7NNddAEATs3bsXZ89eKjlx6623YurUqRBFEfv378fLL7+MN998E3l5eQCAESNG4L777rNHiEQkQ/dOGYy370yESiFI2rcfK8X8D/ahRstVg0REZDmlQsCfrhuOt+4cC41K+nb5VHk9Zv13F37KL3NQdETkKMU1Wuj00vKATKQTETmfMx2qhwCcr4mISN6mx4dh1rhISduW3GJsybngoIiI3IddEumhoaHIzc3FkSNHEBUVJTn2zjvvYMmSJQgJCYEoihBFEX5+fpgzZw5WrVoFDw8Pe4RIRDJ127hILJ8/AZ5q6XS2r7ASd6XtQVltUxePJCIi6t6scQPxyX0TEOgp/R1Tq23BfR/uwwc7T3HPLSI3cqbDfrs+GiWCfTQOioaIiLpS2KGse7i/B7w1KgdFQ0REZB3P3zTS5O+PFzbmorqRi8mIbMkuiXQAUCgUUCqVJu0ajQaPPfYYdu7ciT179mDXrl3Yu3cvnn/+efj6+torPCKSsRnDw7D6gUnw85T+YXz0Qg3ueG+3yV6WRERE5hobFYB/XBWCIYHS3zEGEXjp66N4Zn02mlsMDoqOiOyps/3RBUHo4mwiInIUk/3Rg7k/OhERyV+wjwZ/7bDVaWltE17bfMxBERG5B7sl0s0RGBiIkJAQfhhBRBabGBOM9MUpCPWTVrEorGjA7e/tRl5xrYMiIyIiuQvxVuLlGSG4flS4ybF1+89izoo9KK9jBRQiV9exVDDLBBMROafCDhVEokM4XxMRkWu4ZewAzIgPlbR9mnkWu0+WOygiItfnVIl0IkdQKpUYOHAgBg4c2GnVBJKPEf398fmSFJMPNUtqmpCaloGs01VWfT72HSL54Hil3mrrO7ExUfj3XYl44po4k3MyC6tw63924diFGgdESESAfeZ5JtJdF98nEMmDuWO141YcMf24Ip041xPJBcdq9wRBwMuzRsNHI/23ee6LbGh1egdF5TzYf8gWmEgnt6dUKhEeHo7w8HBOri4gOsQHny9JwfAIP0l7daMOc1fsxU/5ZVZ7LvYdIvngeKXeat93VCoVHr1qGN6dMx5eamk/KrrYiNnv7sa23GIHRUrk3uwxz5sk0rnC0WXwfQKRPJg7Vjvukc4V6QRwrieSC47VnkUGeuGP1w2XtBVWNODt7SccFJHzYP8hW1D1fIr1VFVVYdOmTcjKysLZs2dRX18Pvb77u2QEQcD27dvtFCERuYIwf0+sW5SC+z/OlKxCb9TpsfDjTLyZmoibxw5wYIRERCRn14/uj0Eh3njw4/04X601tjc067FodRaenhmPh6bHcrsiIhfT2R7pRETkXJpa9Dh/sVHSFhPCFelERORa5iZHY+OhIhw4c9HYtvyXAtw0pj9GRQY4LjAiF2S3RPq3336LF154AXV1dQAAURTNehw/gCSi3gjwVmPNA5Ow9JMs7Mi7tApdpxfx6KcHcbFRh3nJ0Q6MkIiI5CxhQAA2PnI5Fq/eL/nDFQDe2JqH/JJa/H32GHiqeQc0kSuoa2pBRX2zpI2l3YmInM+5qkYYOnzkyAoiRETkapQKAX+fPQY3/t9ONOsNAAC9QcSf1h/BxoenQKVkMWoia7FLIv3w4cN46qmnYDAYIIoiwsLCMHLkSAQEBECh4IAmx2pubkZ2djYAYPTo0dBoNA6OiKzFS6PE8vkT8NRnh7Hx0HljuygCz2/IwcX6Zjxy5dBe37DDvkMkHxyv1Fvd9Z1QPw+sXZSM577IwfoD5ySP23joPArL6/H+/AkI9/e0a8xE7sjW83zH/XYFobWkIrkGvk8gkgdzxurpDmXdg3008PdU2yU+cm6c64nkgWPVfMPC/fDwjKF4a3u+sS33fA1W7DyFJVfEOjAyx2H/IVuwSyJ9+fLl0Ov18PT0xEsvvYSbb77ZHk9LRAS1UoG3UhMR6KXGxxmnJceWfZePqgYd/nLjCCgUrH5BRESW81Ap8c87xiA+whevfXsc7YsuHT5XjVv+sxPvz5uAsVGBDouRiPqu4/7oEf6erDhBROSETne48Yn7oxMRkStbOj0W32SfR35JnbHtre/ycV1CBGL6cWsTImuwy3LwgwcPQhAEPPjgg0yiE5HdKRQCXrwlAY9dPczk2Mpdp/DUZ4eh+60EDhERkaUEQcCiabH4YMEE+HpI71MtqWlCaloGNh0+38WjiUgOuD86EZE8dEykc390IiJyZRqVAq/PHoP2BVebWgx49otss7dXJqLu2SWRXlNTAwCYOnWqPZ6OiMiEIAh47Oo4vHjzSJNjXxwswtI1WdDq9A6IjIiIXMWVw8Px5UOTTVY+NbUY8Ojag1i2LQ+Gjpt2EpEsdFyRzv3RiYicU2GH0u5ckU5ERK5u/KAgLEiJkbRlFFRgXeZZxwRE5GLskkgPDQ21x9MQEfXo3imD8fadiVB1KOW+/Vgp5n+wDzVanYMiIyIiVzAs3A8bHpqClCEhJsf+/cOvWPpJFuqbWhwQGRH1BRPpRETywNLuRETkjp6eGY/IQC9J2yubj6G0RuugiIhch10S6ZMnTwYA5Obm2uPpiIi6ddu4SCyfPwGeaukUuK+wEnem7UFZbZODIiMiIlcQ5KPBqgcuw9zkQSbHtuaWYPa7u3GuqqGTRxKRs+pY2p2JdCIi59OiN5jM19Es7U5ERG7Ax0OFV2aNkrTValvwwkbm5Ij6yi6J9Pvvvx8eHh5YuXIl6uvre34AEZGNzRgehtUPTIKfp3Qv22MXanDHe7tN/vgmIiKyhFqpwMu3jcZLtyZA2aEKyvHiWtz6n13ILKx0UHREZAm9QcS5qkZJG/dIJyJyPheqtWjpsI0O90gnIiJ3MT0+DLPGRUratuQWY0vOBQdFROQa7JJIHzJkCP75z3+itLQU9957L06cOGGPpyUi6tbEmGCkL05BqJ+HpL2wogG3v7cbecW1DoqMiIhcxbyUGKy6/zIEeKkl7RX1zbhn+R6kc88yIqdXUqNFs94gaeOKdCIi59Nxf3Q/TxWCvNVdnE1EROR6nr9pJIJ9NJK2FzbmorqR25kS9Zaq51P67tlnnwUAxMbGIjs7G7fccgvi4uIwZMgQeHp6dvtYQRDw6quv2iNMclMqlQrx8fHGr8m9jOjvj8+XpGDeB/ske1+W1DQhNS0DK++diKTooE4fy75DJB8cr9Rb1ug7U4b2w8aHp+CBjzNxsuzSB7w6vYg/rj+C/JJaPHvDCJOV60RkPlvO8x33R/dSK9HPV9PF2SRHfJ9AJA89jdXCDvujx4T4QBD4/opaca4nkgeO1b4J9tHgrzePxB8+PWRsK61twuvfHsNrvxvjuMDshP2HbMEuPenLL780vnEVBAGiKCI/Px/5+fndPk4URSbSyeYUCgV8fX0dHQY5UHSIDz5fkoL5K/fheLtV6NWNOsxdsRfvzUvCFXGhJo9j3yGSD45X6i1r9Z2Yfj748uEpeHTtQezIK5McW7HzFE6U1uHf94yDvydXTRH1hi3n+Y6J9EHB3kzMuBi+TyCSh57G6uly6Yr0QSGsHkKXcK4nkgeO1b67ZewAbDhYhB/bffawdt9Z3DI2EimxIQ6MzPbYf8gW7FLafcCAAejfv7/xv47fd/Vf23lERLYW5u+JdYtSMKHD6vNGnR4LP87EV4fPOygyIiJyFf6eanywYCIenDrY5NhP+WWY9c4unOrwATAROd7ZDol07o9OROScTFekc74mIiL3IwgCXp41Gj4apaT92S+OQKvTOygqIvmyy4r0H374wR5PQ9QrBoMBDQ2tf2x5e3tDobDL/SXkhAK81Vj9wCQs/SRLslpQpxfx6KcHcbFRh3nJ0cZ29h0i+eB4pd6ydt9RKgT8+caRGBbuhz9/mQ2dXjQeO1lWj9ve2YV37hmPy4f169PzELkbW87zna1IJ9fC9wlE8tDTWD1TKb0hMTrEx26xkfPjXE8kDxyr1hEZ6IU/Xjccf92Ua2wrrGjA29tP4JnrhzswMtti/yFbYC8it9fS0oK8vDzk5eWhpaXF0eGQg3lplFg+fwJuTRwgaRdF4PkNOfj39ycgiq1JD/YdIvngeKXeslXfSZ0QhbUPJpvss1zdqMOCD/dhVUah8fcNEfXMlvO8aSLdy6rXJ8fj+wQieehurBoMIk53skc6URvO9UTywLFqPXOTozF+UKCkbfkvBcgpqnZMQHbA/kO2wEQ6EVEHaqUCb6UmYkFKtMmxZd/l46Wvj8FgYHKDiIj6ZkJMMDY8PAUj+vtL2vUGES9szMVfNuRApzc4KDoiatOxtDv33CUicj4ltVo0tUjfN7G0OxERuTOlQsDfZ4+BWikY2/QGEX9afwQt/KyByGxMpBMRdUKhEPDiLQl47OphJsdW7jqFpz47zOQGERH12cAgb3y+JAUzE8JNjn2y9wzmfbAXVfXNDoiMiACgvqkF5XXSMcjS7kREzqewXHrTk5daiVA/DwdFQ0RE5ByGhfvh4RlDJW2552uwYucpB0VEJD922SP9/PnzFj9GEAR4eHjA19cXGo2m5wcQEVmZIAh47Oo4BHlrJPvJAMAXB4twsaEJCxOU8Gh3Vx8REZGlfDxUeHdOEt7eno//++FXybE9BZW49Z1dWLFgAuLC/RwUIZH7OlvVYNI2MIiJdCIiZ3O6ouP+6N4QBP6tTkRE9ND0odicfQH5JXXGtre+y8d1CRGI6cdtUIh6YpdE+lVXXdWnx0dERGDs2LGYNWsWrrjiCitFRURkngWTYxDorcaT6YfR0q6k+w955bhQocazlwc5MDoiInIFCoWAJ66Nx7BwPzz12WFJadIzlQ343X934193JeKqEaYr14nIds502G83wt8Tnmqlg6IhIqKunO6wDUc0y7oTEREBADQqBV6fPQaz390N8bePtptaDHj2i2z878FJvPGMqAd2Ke0uimKf/rtw4QK2bt2KJUuW4IEHHkB1dbU9wiYiMro1MRLL50+Ap1o6bR4r1+GFHytRXtfkoMiIiMiV3Dx2AD5bkoJwf2kp0rqmFixctR9pP52EKIpdPJqIrO1Mx/3RWdadiMgpdVyRHhPCFXZERERtxg8KwoKUGElbRkEF1mWedUxARDJilxXpr732GgDg008/xeHDh+Hh4YHLL78co0aNQnBwMACgsrISOTk52LlzJ5qbmzFmzBikpqairq4OJ06cwPfff4+qqirs3r0bDz/8MNasWWOP0ImIjGYMD8OaBybh/o8yUaNtMbYXVrfg7hWZWLMwGVH8cJWIiPpozMBAbHrkcixatR+Hz126gVQUgde+PY68klq8Oms0V8US2cHZDol0vtcjInJOHfdIj2YinYiISOLpmfH47mgJii42Gtte2XwMVw4PQ5i/pwMjI3JudlmRPmvWLBw8eBBHjhzBVVddhR9++AHvvPMOli5dijvvvBN33nknli5dinfeeQc//PADZsyYgSNHjuDAgQNYsGABXn75ZezYsQO33XYbRFFEVlYWNm/ebI/QiYgkJsQEY93iFIT6SVcKnq5sxO3v7UZeca2DIiMiIlcS7u+JdYtTcGviAJNjXxwowt3L96C0VuuAyIjcC1ekExE5P1EUO90jnYiIiC7x8VDhlVmjJG212ha8sDHXQRERyYNdEunbt29Heno6EhMT8Z///AchISFdnhsSEoJ33nkHY8eOxRdffGFMmHt4eODVV1/FyJEjAQDffPONPUInN6DRaJCUlISkpCRoNBpHh0MyMKK/Pz5fkmLyQWpJTRNS0zKQdbrKQZERUVc411NvObLveKqVePvORPzxunh03LLs4JmLuPU/u5BTxC2PiADbjVWTRHqIl9WuTc6D7xOI5KGrsVpe14z6Zr3kXCbSqSPO9UTywLFqW9PjwzBrXKSkbUtuMbbkXAAA1Gp1OFFSi0NnL+JESS1qtTpHhNlr7D9kC3ZJpP/vf/+DIAiYP38+hI6fAnZCEAQsWLAAoigiPT3d2K5QKJCamgpRFJGTk2PLkImIuhUd4oPPl6RgeISfpL26UYe5K/bip/wyB0VGRESuRBAEPDR9KNLmJsFbIy3lfqFai9vf243N2RccFB2RazMYRJytapS0cUU6EZHzOVMpXY2uUSrQP4A3PhEREXXm+ZtGIthHmmR+Zn02Fn6cicS/fYdr3voZt72zC9e89TMS//YdHvokC7tPlkMURQdFTORYdkmk5+XlAQCio6PNfkzbufn5+ZL2+Ph4AMDFixetExwRUS+F+Xti3aIUTIgOkrQ36vRY+HEmvjp83kGRERGRq7k2IQJfPDQZA4OkHwprdQY89MkBvL09HwYD/6glsqaSWi2aWwySNu6RTkTkfDrujx4V7AWloueFPERERO4o2EeDv948UtJ2sVGH7cdKoe/wuYLeIGJzdjHuWb4XM9/+mVXxyC3ZJZFeV1cHAKisrDT7MW3n1tdL7yr19PQEAKhUKitFR+5Or9ejpKQEJSUl0Ov1PT+A6Dd6vR7a2kosuzkGV8T1kxzT6UU8+ulBrN5z2kHREVF7nOupt5yp7wyP8MfGh6fgsphgk2Nvbz+B3689iMZm9m9yT7YYq2cqpIkZT7UCob4eVrk2ORdnmuuJqGtdjdWO+6PHhPjYOzSSAc71RPLAsWoft4wdgDEDAyx6TH5JHVLTMvDLCeetxMr+Q7Zgl0R6//79AVi2r/lXX30leWybiooKAEBwsOkHiES9odfrce7cOZw7d46TK1mkre+Ul1zAf+8ei1sTB0iOiyLw/IYc/Pv7Eyx9Q+RgnOupt5yt74T4emDNwkm4a2KUybFvsi/g9vd24/zFxk4eSeTabDFWTfZHD/Y2a6sykh9nm+uJqHNdjdXCDjc+DeL+6NQJzvVE8sCxah+552twoqTO4sc1NOuxeHWW065MZ/8hW7BLIv3KK6+EKIrYsGED1qxZ0+P5q1evxsaNGyEIAq688krJscOHDwMAIiMjbRIrEVFvqJUKvJWaiAUppltYLPsuHy99fYwld4mIyCo0KgVe+91o/PXmkehYtTT3fA1u+c8uHDhT5ZjgiFzI2U4S6URE5Hy4Ip2IiMh8oijiifRDaNT1LtHc0KzHk+mHuXCM3IZd6qMvWrQIGzduRGVlJV555RVs2rQJt9xyCxISEowryysrK5Gbm4tNmzYhOzsbQOuq80WLFkmutXnzZgiCgMmTJ9sjdCIisykUAl68JQFBPhq8vf2E5NjKXadwsaEZf799DNRKu9zDRERELkwQBNw3ZTBiQ33x8P8OoFbbYjxWXteEu9L24PXZo/G78QM7fXytVofiai3qm/Xw0SgREeAJP0+1vcInkoWOK9K5PzoRkXM63WG+juaKdCIioi5lFFQgvxer0dvLK6nFnoJKpMSGWCkqIudll0R6YGAgVq5ciYULF6KsrAzZ2dnGZHlnRFFEv379sGLFCgQGBhrbz549izFjxmDMmDG49tpr7RA5EZFlBEHAY1fHIchbg79uypUc++JgEWq0OvznnvHwVCsdFCEREbmSaXGh2PDwFCz8eD9OlV9ajdWsN+CJ9MPIK6nFH2cOh1IhQBRFZBRUYHXGaWw7WgJ9u0opSoWAmQnhmJscjZQhISxfTYTOS7sTEZFzudjQjIsNOkkbV6QTERF1bc2e01a7DhPp5A7skkgHgPj4eGzevBn//ve/sWHDBtTU1HR6np+fH2699Vb8/ve/R0BAgORYVFQUXnvtNXuES0TUJwsmxyDQW40n0w+jpV2iYvuxUsz/YB9W3DsB/lz5R0REVhAb6osND03BI2sP4JcT5ZJjaT8V4NeSOiyaNgTPb8zp8q5zvUHE5uxibM4uRly4L95MTcSoyIBOzyVyF2cqGyXfM5FOROR8TnfYH12pEBAZ5OWgaIiIiJxbrVaHrbklVrnWltxi1Gp1rG5HLs9uiXSgNUn+3HPP4amnnkJOTg5OnDiB6upqAEBAQACGDh2K0aNHQ6PR2DMsIiKbuDUxEv6eaiz9JAtancHYvq+wEnem7cGq+y9DqJ+HAyMkIiJXEeCtxof3TsTL3xzDR7sLJce+P16KH/JKYe72ZfkldUhNy0DavCRMHRZq/WCJZKChuQXldU2SNibSiYicT2GH/dEHBnlxOzUiIqIuFFdrJdXp+kJvEFFSo2UinVyeXRPpbTQaDcaPH4/x48c74ulNFBUVYfXq1dixYweKi4uh0WgQFRWF66+/HnPmzIGXV+/uZD137hyuuuoqix4TGRmJH374oVfPR0TOZ8bwMKx5YBLu/ygTNe32rz12oQZ3vLcbqx+YxP02iYjIKlRKBV68JQHxEX54fkOOpCKKuUn0Ng3NeixenYX0xSlcmU5u6WyH1egAMDCI79mIiJxNxxXpvOmJiIioa/XNeqter67JutcjckZuf4vmDz/8gFtuuQUffvghTp06hcbGRlRXVyMnJwdvvPEGbrvtNpw+bZ09I8wxePBguz0XtRIEAT4+PvDx8eF+oGQRc/vOhJhgrFucYrL6vLCiAbe/txt5xbW2DpXI7XGup96SY9+5+7JBWLNwEgK9+nbPbEOzHk+mH4ZoaRaeyAGsPVY77o8e5ucBL42yz9cl5yTHuZ7IHXU2Vjsm0rk/OnWFcz2RPHCs2paPlf+m8fVwrr+R2H/IFhyyIt1ZHD16FI8//ji0Wi28vb2xePFiTJo0CVqtFps3b0Z6ejoKCwuxaNEirF+/Hr6+vhZdPzw8HF999VWP56WlpeHrr78GANx22229+VGoD9RqNYYPH+7oMEiGLOk7I/r74/MlKZj3wT7JB7MlNU1ITcvAynsnIik6yFahErk9zvXUW3LtO8lDQvD/bhmFP6w71Kfr5JXUYk9BJVJiQ6wTGJGNWHusdkykc4Wja5PrXE/kbjobq6c7lHaPDuF8TZ3jXE8kDxyrthUR4AmlQrBKeXeVQkC4v6cVorIe9h+yBasm0jds2GD8un1CuH17b9gqufzKK69Aq9VCpVJh5cqVGDdunPFYSkoKoqOj8cYbb6CwsBAffvghfv/731t0fbVajbi4uG7P0ev12LdvHwDAx8cH11xzjeU/CBHJQnSIDz5fkoL5K/fheLtV6NWNOsxdsRfvzUvCFXHci5aIiKxj69Fiq1xnzZ7TTKST2znLRDoRkSwUckU6ERGR2fw81ZiZEI7N2X3/vGBmQgT3Rye3YNVE+jPPPANBECAIgiT53dbeGx2vZS1HjhzB/v37AQCzZ8+WJNHb3H///fjiiy9w8uRJrFq1CkuWLIFabd2JYffu3SgtLQUAzJw5E56eznUHDxFZV5i/J9YtSsEDH2di/+kqY3ujTo+FH2fizdRE3Dx2gAMjJCIiV1Cr1WFrbolVrrUltxi1Wh3/QCa30nFFehQT6URETqeuqQXldU2Stph+nK+JiIi6Mzc52iqJ9LnJ0VaIhsj5WX2PdFEUO91Hsa29N//Zwvbt241fz549u9NzFAqFMYlfU1ODvXv3Wj2O9qv1Z82aZfXrU89aWlpw8uRJnDx5Ei0tLY4Oh2Skt30nwFuN1Q9MwvR46epznV7Eo58exOo9p60dKpHb41xPvSXXvlNcrbVKqTYA0BtElNRorXItIlux9lhlaXf3Ite5nsjddByrHcu6CwIwMIjzNXWOcz2RPHCs2l7KkBDEhVu2jXFH8eF+SB4SbKWIrIf9h2zBqivSv//+e4vaHSkrKwsA4O3tjYSEhC7PmzhxovHrAwcO4PLLL7daDHV1dcZ/m8jISMlzkf0YDAZcvHgRABAVFeXYYEhW+tJ3vDRKLJ8/AU99dhgbD503tosi8PyGHFysb8YjVw7tdTUPIpLiXE+9Jde+U9+st+r16pqsez0ia7PmWDUYRNPS7txz16XJda4ncjcdx+qZDmXd+/t7wlOtdEBkJAec64nkgWPV9gRBwJupiUhNy0BDLz478NYosSx1rFN+bs3+Q7Zg1UR6ZGSkRe2OdPLkSQDAoEGDoFJ1/c8wZMgQk8dYy9atW9HY2AgAuPXWW51y4iEi21ErFXgrNRGBXmp8nCFdhb7su3xUNjTj+RtHQqHg3EBERJbx0Vj3Q2RfD34oTe6jrK4JTS0GSRtXpBMROZ+O+6NHc390IiIis4yKDMB/7hmH+z/ab9HjvDVKpM1LwqjIABtFRuR8rJpIl4umpiZUVbXuTRwREdHtuQEBAfD29kZDQwOKi/u+b0R77cu622If+M7odLouj6lUKigUrdX+DQZDj6UvNBqN8Wu9Xg+9vuu7lwRBkOwv39LSAoPB0OX5CoVCcoODTqfrtsx/+9hFUez25wQAtVptvHGhfRydPc7WsSuVSiiVlz6cbm5u7jZ2d36duvtZHfE6tY+/489iyev03HXDEOSjwdvbT0jaP9xViMq6Jrx620iolZd24uDr5Dzjqad/G0fhXN+Kc33nnP11crY5xFpzPWDf1ynEWwWlQrBKeXeVQkCYn4fxe2d8nbqNn3O9U/VNW84h7Z+jL7GfLKmWfO+hUiDU18Pp+qZcXydnnEPkOte72+vUHc717tM32z9HQVmtpC0qyBM6nc6l+qZcXydnnEM417dy9tepO64413Oeb8XPbzpn65+1oq6p21g7ig/3xbLURIyKDHDa+aOruV7OrxPn+a519jrZYq53y0R6ff2lPZS8vXteWeDl5YWGhgY0NDT0eK65zp8/j8zMTADAuHHjEB0dbbVrd+f48eNdHouPj4evb+veGA0NDcjLy+v2WklJScavy8vLce7cuS7P9fHxwfDhw43fnz592lhiozNhYWGS0hvHjh3rdgAMGTIEQUFBAFoHb3Z2drexjx492jjIamsv/eHV2b+PWq3GmDFjjN9fuHABpaWlXV47MDAQsbGxxu9Pnjwp6XMdDRw4EOHh4cbve4rdXV+n6upqFBQUdHmuo1+njn3H0tfpsauTEOStwV835UraNx6+gHOllXgyJRAeytZfgHyd5DGeHIlzfSvO9Z1z5tfJ2eeQvs719nydzp06gcsGaJBxzrI/jDszcYAHPNstSHf216kjzvXO1TdtOYe06erfxty+mVHYKPk+KtgbCoWA/Hz59E1nfp2cfQ6R01zvzq9TR5zr3advtjl+/DiOnqmUtHk0V+PYsWNu0zed+XVy9jmEc708XqeOXHGu5zzfip/fdM6Wr5Moilj+s/SGNH+NgDqdiPb34ysF4LJIT1wX64W7rkyCh0frTfZymD/a9x+5vk4A5/nu2Gued3gi/cCBA9iyZQvOnDkDoHXfgquuugrJyck2e86mpksfKLa/26IrbZ1Oq9VaLYZNmzYZ79Sw12p0InJuCybHINBbjSfSD0tWEGZdaMJLP1fi2SlB8NEourkCERHRJTNjva2SSL8u1ssK0RDJR0m99K53lnUnInJOxXXS+Trc1+EfcxIREclCTlkz8kulC0cfmhiAhFANKhsNaGwxwEulQLCXAt7q1s+juTUxuStB7G7dfS9otVr897//BQBMnDgRU6dO7fQ8vV6Pv/zlL5Ly5u1dffXVWLZsmckdp9ZQWVmJlJQUAMANN9yAt956q9vzJ0+ejIqKCsTFxeGrr76ySgzXX389CgoKoNFosGvXLvj7+1vlup1pbm423ukxfPjwLm8ecNfyFlqtFrm5rauAO/v3ccXyFl1x5tfJGcuQ6HQ6451tHftOX16n7UeL8cjag9DqpPEPj/DFyvnjEernydepC/YeT1qt1tgHOlslYU+c601xru+cM79OzjiH2Gqut8frZDAYcNM7GThR2vXdwT3x0Six6+lpCPD1curXqTuc652vb9piDjHn38bc2J9en4ONhy8Yv793cgxevCXB6fqmHF8nwDnnEDnP9e70OnWHc7179M32/zYxscOQ9PovkvM2PpSMkf39XapvyvF1ApxzDuFc38rZX6fuuMpcz3neFD+/6Zwtf9bFaw7ix/xy4/cxId7Y8vvJUCi6TpbLYf7oaq6X6+vUMXbO81JdlXa39lxv9Vs19+7di/fffx+CIOCaa67p8rx//vOf+PLLL7s8vn37dvzlL3/BP/7xD2uHCB8fH+PX5pRrb2xsLe1nThl4cxw5csRYTuHKK6+0aRK9I7VabVbHUSgUFnWwjgOiJ+0HmznMqRzQRhAEi2JvG5htz9PTY20ZOwCLY3en18mS8+39OnXXdyyN/eqREVjzwCTc/1EmarSXflkcL67DPR/sx+oHJiEqWPoHnyXc+XXqTm/Gk6Xx2AvnelOc67vmbK+TM88h1pzr7fU6vXXnOKSmZaChues/+rpT36zHwjWHsGLBBAR6t/58zv46dYdzfc/kPIe0fw5zHtdV7EUXpZXI2lakO1vflOvr5OxziBznenO42uvUHc71PZNz32xTUmf6werQ8ACo1dJY5d435fo6Ofscwrm+lbO/Tt1xlbme87wpfn7TNWv9rAVldZIkOgDcf/lgeHp6mH1tOcwfXfUfubxOneE83zVLXydLWL1G8L59+wC01u0fPXp0p+cUFhZi1apVEAQBgiDgxhtvxEcffYTNmzfj73//OwYMGABRFPHVV1/h6NGj1g4RHh4eCAwMBAAUFxd3e251dbUx2R4REWGV52+/Cp9l3R1PoVAgLCwMYWFhkl/URD2xZd+ZEBOMdYtTEOonfQNTWNGA2e/uRl5xbRePJKLOcK6n3pJ73xkVGYC0eUnw1vT+j4n9p6uQmpaBC9WNPZ9M5CDWHKtnKqU3W7O0u+uT+1xP5C7aj9UzVdKbnkL9PODjwdLu1DXO9UTywLFqex/uKpR87++pwuzxAx0TjJWx/5AtWL0n5ebmQhAEzJgxo8tzPv/8c2MphDvuuAPLli1DcnIyhgwZgltvvRWrV682rhq3Vin1joYOHQoAOHPmTLflAdpWjgNAbGxsn59Xp9Phm2++AQCEhIR0Wfqe7EelUiEqKgpRUVEW33FD7s3WfWdEf398viTF5MPb0tompKZlIOt0ldWfk8hVca6n3nKFvjN1WCjSF6cgLtzXrPOjgr3g0yHxnl9Sh9n/3Y1fS3kjFzkna43VxmY9SmubJG2DQphId3WuMNcTuYP2Y/Vch+ohMZyrqQec64nkgWPVti42NOPzrHOStnsmRbvMzWjsP2QLVk+knzlzBgAwduzYLs/5+eefW59cocAjjzxicjwyMhK33347RFHEwYMHrR0iACApKQlAa2n3tj03OpOZmWn8evz48X1+3p9++gkXL14EANx0000czETUregQH3y+JAXDI/wk7dWNOsxdsRc/5Zc5KDIiIpKTUZEB2PrYNKx9MBk3jI6AssO+ZyqFgBtH98faB5Px89Mz8MVDUxDuL62Kcr5ai9vfy8CBM7yRi1zX8eIak7aoICZniIicTWFFveT7QcE+XZxJREREbdbuO4tG3aWt31QKAQsmRzswIiLnZ/VEelVV6wdr4eHhnR6vq6vDiRMnIAgCRo0ahbCwsE7PS05OBnApMW9tV199tfHr9evXd3qOwWAwlmH39/fHpEmT+vy87cu6z5o1q8/XIyLXF+bviXWLUjAhOkjS3qjTY+HHmfjq8HkHRUZERHIiCAJSYkPw3zlJOPTCNdj+xDRseHgKtj8xDQdfuAbvzBmPlNgQCIKA+Ag/rF86GUNCpR9KX2zQ4Z7le/Dj8VIH/RRE1ieKInafLMfSNVmY/e5uk+NPfnYIu0+WQxRFB0RHRESdOV0h3YaDK9KJiIi6p9Mb8PHuQknbDaP7o3+Al2MCIpIJqyfSdTodgK43jc/JyTF+AJGYmNjlddoS8XV1ddYN8DdjxozBhAkTALQm0jtb+b5y5UqcPHkSADB//nyo1WrJ8b179yI+Ph7x8fF45plnenzOixcvYseOHQCAuLg4jBgxoo8/BVmDTqfDkSNHcOTIEWP/JTKHPftOgLcaqx+YhBnxodIY9CIe/fQgVu85bdPnJ5I7zvXUW67ad/w81Rga5ofEqEAMDfODn6fa5JyBQd74fMlkJEYFStq1OgMWrtqP9R3KwRE5Um/Hak5RNWa+/TPuWb4X3+YUw9BJrnxzdjHuWb4XM9/+GTlF1VaMmpyFq871RK6m/VgtLJeuSI/uxxXp1D3O9UTywLFqO5uzL6C4Rro1ygOXD3ZQNLbB/kO2YPVEetve5qWlna9SOXz4sPHrhISELq9jMBisG1gn/vznP8PT0xMtLS24//77kZaWhkOHDmHPnj144YUX8MYbbwAAYmJicN999/X5+b755hvj4OVqdOchiiJ0Oh10Oh1XmZBF7N13vDRKvD9/Am5NHNAhDuD5DTn49/cnOo2jVqvDiZJaHDp7ESdKalGr5ZsIcj+c66m33L3vBPto8L8HJ2F6hxu59AYRT352GGk/nXTLfxdyPr0Zq7+cKENqWgbyS8y7eTu/pA6paRn45QS31nE17j7XE8lF21htaGpG0cVGyTGuSKeecK4nkgeOVdsQRREf7DwlaZsQHYSxHW6clzv2H7IFq2/QPWjQIOTk5GD//v2YNm2ayfFdu3YZvx43blyX16moqADQWlLdVkaOHIm33noLTz/9NOrq6vDmm2+anBMTE4P3338fvr6+fX6+jRs3AgCUSiVuvvnmPl+PiNyPWqnAW6mJCPRS4+MM6Sr0Zd/lo7KhGc/fOBKCAGQUVGB1xmlsO1oCfbvlVUqFgJkJ4ZibHI2UIa0lfImIiLrirVFh+fwJ+NPnR/DFwSLJsde+PY6y2iY8d8MIKBT8fULykVNUjcWrs9DQrO/55HYamvVYvDoL6YtTMCoywEbRERFRd8rq9SYVRKK5RzoREVGX9p+uwpFz0uparrYanchWrJ5InzhxIrKzs/Hpp59izpw5kr3Sjx8/jszMTAiCgJiYGERFRXV5nZycHADo9hxruPLKK7Fp0yasWrUKO3bsQElJCdRqNQYNGoTrrrsOc+fOhZdX3/eIKCwsNK7Gnzx5MkJDQ3t4BBFR5xQKAS/ekoBgHw+8tT1fcuzDXYUoLK/HuapGnCjtfHWV3iBic3YxNmcXIy7cF2+mJvKDYCIi6pZaqcA/7xiLfn4eeP/nAsmxFTtPobyuCf+4fSw0KqsXvCKyOlEU8UT6IYuT6G0amvV4Mv0wtjw2lTckEhE5QHGddP4O9FYjwNt0mxoiIiJq9cEv0tXoA4O8cG1ChIOiIZIXqyfSU1NTsWrVKtTW1uKuu+7Cgw8+iOjoaJw8eRJpaWkQRRGCIOCOO+7o9jq7du2CIAjdln+3lsjISDz77LN49tlnLXrcpEmTkJeXZ9a5MTExZp9LRNQTQRDwh6uHIdBbjb9uypUc+zHP/HKjbSVK0+YlYeow3uBDRERdUygEPHfDCIT6euCVzcckxzYcOo/KBh3enTMePh5W/xODyKoyCirMLufelbySWuwpqERKbIiVoiIiInMV17dIvo8O4Wp0IiKirpytbMC2o8WStnsnx0DJqnJEZrH6kpGYmBgsWbIEoiiiuLgYL730EhYuXIjXXnsNlZWVAFrLv8+ZM6fLa5w5cwYHDhwA0LrCnYiIOrdgcgz+dVciVH1449NWojSnqLrnk4mIyO09OG0I3rpzrMnvnp/zy3DP8j2oqGtyUGRE5lmz53TPJ9nxOkREZJmOK9K5PzoREVHXPtxVKNkSxddDhTsn2rYSNJErsUntxUceeQRPPPEENBoNRFGU/BcfH4/ly5fDw8Ojy8evWLECAKBWqzFlyhRbhEhE5DJuTYzE+/OT0Jd7CNtKlIqi2PPJRETk9maNG4gVCybAS62UtB8+V4073svA2coGB0VG1L1arQ5bc0uscq0tucWo1eqsci0iIjJfx0Q6V6QTERF1rlarQ/r+s5K21AlR8PPklihE5rJZ3cVFixbhjjvuwK5du3D+/HloNBqMHDkSEydO7HEfuVGjRiE+Ph7BwcHw9fW1VYhERC7DU61EX1PgLFFKRESWmB4fhv89OAn3f5SJqoZLycSC8nrMfnc3Pr7/Mozo7+/ACIlMFVdroTdY58ZBvUFESY2WH0IREdnZhTppaXeuSCciIurcusyzqGu69HtTIQD3TYlxXEBEMmTTDQyDgoJw0003Wfy41NRUG0RD1DmVSoUhQ4YYvyYylzP1HWuWKGUinVyRM41Xkhf2ne6NGxSEz5dOxvwP9qHoYqOxvbS2CalpGVgxfwImDeHvFbI9c8dqfbO+y2O9Uddk3euRY3CuJ5IHlUqF6JjBKK2XVhaJZiKdzMC5nkgeOFatR28Q8dHuQknbzIQIRAW77u9N9h+yBZuUdieSE4VCgaCgIAQFBUGh4JAg8zlL32GJUqKeOct4Jflh3+lZbKgv1i+djPhwP0l7rbYF81buw5acYgdFRu7E3LHqo1F2eaw3fD2sez1yDM71RPKgUCigVXiipUNlEZZ2J3NwrieSB45V69mWW4xzVY2StgcuH+ygaOyD/YdsgT2JiEjmbFGilIiIyBIRAZ5IX5KCy2KCJe3NLQY89EkW/rf3jIMiI5KKCPCEUtH9VmPmUikEhPt7WuVaRERkntMVDZLvfT1UCPHROCgaIiIi5/XBzlOS78cODEBSdJCDoiGSLybSye2Joojm5mY0NzdDFK2TjCT34Cx9hyVKiXrmLOOV5Id9x3wBXmqseuAyXDsyXNJuEIHnvszGv7af4L8h2Yy5Y9XPU42ZCeFdHrfEzIQI7o/uIjjXE8mDKIo4WVIjaYsO8YYgWOcGKXJtnOuJ5IFj1ToOnb2I/aerJG33Xz7Y5X9nsv+QLTCRTm5Pp9MhOzsb2dnZ0OlY0prM5yx9hyVKiXrmLOOV5Id9xzKeaiX+O2c87r4syuTYW9vz8cLGXKtVUSFqz5KxOjc52irPaa3rkONxrieSB51Oh/15pyVtMSzrTmbiXE8kDxyrvVOr1eFESS0Onb2IEyW1eO+nk5Lj/QM8ccPo/g6Kzn7Yf8gWVI4OgIiI+qatRKk1EhMsUUpERH2lUirw6qzRCPX1wP/98Kvk2Oo9p1FR34Q3UxPhqeaNW+QYKUNCEBfui/ySul5fIz7cD8lDgns+kYiIrKq4TlpBbVCIt4MiISIicixRFJFRUIHVGaex7WhJt58Nz0+JgVrJdbVEvcGRQ0QkcyxRSkREzkYQBDxxbTz+dmsCOlaO25xdjHs/3IcaLe8OJ8cQBAFvpibCu5dVfbw1SixLHevyZRGJiJxRcb00kR7DRDoREbmhnKJqzHz7Z9yzfC++zSnuNokuABgXFWi32IhcDRPpREQugCVKiYjIGc1PicF/7h4PTYc73/cUVOKutD0ordU6KDJyd6MiA5A2L8niZLq3Rom0eUkYFRlgo8iIiKgroiiiuK5F0hbN0u5ERORmfjlRhtS0DLMrbIkA7v84E7+cKLNtYEQuiol0IiIX0FaitC9YopSIiGzhxjH98dF9E+HrId1V6uiFGsx+dzcKy+sdFBm5u6nDQpG+OMXs91Dx4X5IX5yCqcNCbRwZERF15lR5PZqlC9K5RzoREbmVnKJqLF6dhYaOvxB70NCsx+LVWcgpqrZRZESui4l0IiIXwBKlRETkzCYP7YdPFyWjn6+HpP1sZSNmv7sb2ef4xzw5xqjIAGx9bBrWPpiMG0ZHQKkwfS8U4KXG2geTseWxqVyJTkRkZ6IoYvfJcixdk4Ub/pNhcvz/fZWL3SfLIYpdl7QlIiJyBaIo4on0QxYn0ds0NOvxZPph/s4kshAT6URELoIlSomIyJmNigzA+qUpiO6wl2lFfTPuej8DO0+UOygycneCICAlNgT/nZOEQy9cg9dnj5Ycb9TpMSEmiDccEhHZWcf9Xzvb/vXbnGLcs3wvZr79M1fZERGRS8soqDC7nHtX8kpqsaeg0koREbkHhyXSi4uLkZ2djczMTGi13BuRiMgaLC1RGhPizRKlRERkN9EhPvh8yWSMivSXtNc363HfR/uw6fB5B0VG1MrPU43rEiIkbc0tBuSX1DooIiIi92Tp/q/5JXVITcvg/q9EROSy1uw57VTXIXIXqp5PsZ66ujqsWLECX375JUpLS43tX331FYYOHWr8/ptvvsG2bdvg5+eHl19+2Z4hkhtSq9UYPXq08Wsiczlr32krUbqnoBKr9xRia24J9J3dug/g6hFhXIlObsFZxys5P/Yd6wv188DaB5OxZE0Wdv1aYWzX6UU8uvYgKuqacN+UwQ6MkOTImmM10FuDqGAvnK1sNLblFFUjYQDfM7kqzvVEzqWv+7+mL07h37lkgnM9kTxwrHauVqvD1twSq1xrS24xarU6+Hm63r8v+w/Zgt1WpBcWFuJ3v/sd0tLSUFJSAlEUu9yLYezYsfjuu++wfv167N+/314hkpsSBAEajQYajYblGskiztx3OpYo3f7ENGx4eApSJwyUnPflwfNobjE4KEoi+3Hm8UrOjX3HNvw81Vh570TcOKa/ybH/99VR/GPLce7bRhax9lgdExko+f7IOZYLdmWc64mcB/d/JVvhXE8kDxyrnSuu1na5UMpSeoOIkhrXrBLN/kO2YJdEelNTExYvXowzZ87A09MTCxcuRFpaWpfnDxw4EJMmTQIA/PDDD/YIkYjIZfl5qjE0zA+JUYFYNC1WcqyivhnfH7PO3YxERESW8FAp8e+7xuHeyTEmx/674yT+tP4IWvS82Ysco+NKRu67S0RkH9z/lYiIyFR9L28w60pdk3WvR+TK7JJIX7t2LU6fPg0vLy/873//w1NPPYUrrrii28dMmzYNoiji0KFD9giR3JjBYEBVVRWqqqpgMPDDWjKfHPvO0DBfTIwJkrSt23/WQdEQ2Y8cxys5B/Yd21IoBPz15pF4ema8ybH0/eewZE0WGq38gQG5JmuP1TEDpYn0YxdqWcXHhXGuJ3Ie3P+VbIVzPZE8cKx2zkejtOr1fD2sez1nwf5DtmCXRPq2bdsgCALmz5+PESNGmPWY4cOHAwBOn+YbX7KtlpYWFBQUoKCgAC0tLY4Oh2RErn0ndUKU5Puf88tw/mJjF2cTuQa5jldyPPYd2xMEAQ/PGIq/zx4NRYfKa9uPlWLeB3txsaHZMcGRbFh7rI7qsB96s96A/JLaPl+XnBPneiLnYIv9X4nacK4nkgeO1c5FBHhC2fEP5l5SKQSE+3ta5VrOhv2HbMEuifSTJ08CAC6//HKzHxMYGAgAqKmpsUVIRERu68Yx/eHroTJ+bxCBz7POOTAiIiIi4M6Jg5A2bwI8VNI/UfafrsId72XgQjVv+iL7CfBWIzrEW9KWzfLuREQ2xf1fiYiIOufnqcbMhHCrXGtmQgT8PNVWuRaRO7BLIr2hoQEA4O3t3cOZlzQ3t646UalUPZxJRESW8NaocPPYAZK29P1nYbDSBxZERES9dc3IcHyycBL8PaV/A5worcPs/+7Gr6VcEUz203GfdCbSiYhsi/u/EhERde3yof2scp25ydFWuQ6Ru7BLIr1tdXlRUZHZjzlx4gQAIDQ01BYhERG5tTsnSsu7n6tqxO6TFQ6KhoiI6JIJMcH4fOlkRHQoNXe+Wovb38tA1ukqB0VG7mZ0h0R6DhPpREQ2xf1fiYiIOvfd0RK89PXRPl8nPtwPyUOCrRARkfuwSyI9ISEBAJCZmWn2YzZu3AhBEJCYmGijqIiI3NfYgQEYHuEnaVu3/6yDoiEiIpKKC/fD+ocmIzbUR9J+sUGHOSv24Ifj1tk/lag7Yzok0o9fqEVzi8FB0RARuT7u/0pERCQliiKW/1yARav3o1HXt79FvDVKLEsdC0Gwzu9aIndhl0T6zJkzIYoi0tPTcf78+R7P/+ijj4xJ9xtvvNHW4RERuR1BEJA6QboqfWtOMarqmx0UERERkVRkoBc+XzIZiVGBknatzoAHV2Xh86xzjgmM3EZCh0R6s96A/BJuL0BEZCvc/5WIiOgSnd6A577Mxiubj0HssCOnpfedeWuUSJuXZLJ9FRH1zC6J9FtvvRXx8fFoamrCvHnz8NNPP0FsN/IFQYAoijhy5AiefPJJ/P3vf4cgCJgwYQKuuOIKe4RIROR2Zo2LhEZ56ddAs96ADYfM34KDiIjI1oJ8NPjfg5MwI1663ZPeIOKpzw7jvZ9OSv6uILKmAC81YkK8JW1HzrG8OxGRLVlr31bu/0pERHJW3aDDgpX7sHafaQXR342PxOdLUxAX7mvWteLD/ZC+OAVTh3EbZaLeUNnjSRQKBd59913cc889KCoqwpIlS+Dp6WksITFv3jzU19ejubl1JaQoihg0aBDefvtte4RHbk4QBKjVauPXROaSe98J8tHg2oRwfH3kgrFtXeZZ3Ds5RpY/D1F35D5eyXHYdxzPW6PC+/Mn4E/rj+CLA9Ibvl7/9jjKapvw5xtGQGGlUrAkT7Yaq6MiA1BY0WD8fkdeKSbGBCEiwJMrHV0I53oi55EyJARx4b7IL6nr9TW4/yt1hnM9kTxwrAKF5fW4/+NMFJTVmxx7emY8HpoeC0EQsPWxadhTUInVewqxNbcEesOlm8xVCgEzEyIwNzkayUOC3ebfkv2HbMEuiXQAGDBgADZs2ICXXnoJW7ZsQWNjo/FYZWWl8WtBEHD99dfjxRdfREAAy0yQ7anVaowZM8bRYZAMuULfuWviIEki/XhxLY6cq8bYDmV0ieTOFcYrOQb7jnNQKxVYdsdYhPp5IO2nAsmxD3aeQnldE964fSw0KrsU3CInZIuxKooi/L2kfzJvO1qCbUdLoFQImJkQjrnJ0UgZEsIPaWSOcz2R8xAEAW+mJiI1LQMNzXqLH8/9X6krnOuJ5MHdx+reggosXpOFiw06SbuHSoG37kzEDaP7G9sEQUBKbAhSYkNQq9WhpEaLuiY9fD2UCPd3zxt/3b3/kG3YLZEOAIGBgVi2bBmeeOIJ7NixAzk5OaisrIRer0dgYCBGjhyJGTNmYPDgwfYMi4jIbU2ODcHAIC+cq7p0c9O6/WeZSCciIqcjCAKevX4EQn098PI3xyTHNh46j8r6Zrw3Nwk+Hnb9E4dcVE5RNZ5IP9Tliki9QcTm7GJszi5GXLgv3kxN5H6DRERWMioyAGnzkrB4dZZFyXTu/0pERHL22f6zeO7LbOj00u3LQv08sGL+hG4/r/XzVLtl4pzIHhzyKVNkZCTmzJnjiKcmIqJ2FAoBdyRF4a3t+ca2TYfO4y83joC3hokIIiJyPgunDkE/Xw889dlhtLQrXffLiXLcs3wPVt47ESG+HpLH1Gp1KK7Wor5ZDx+NkmW5qVu/nCizKHmTX1KH1LQMpM1L4r6DRERWMnVYKNIXp3R7U1N78eF+WJY6lkl0IiKSHYNBxBvb8vDujpMmx0b098cHCyZgQKCXAyIjIsBBiXQiZ9LS0oILF1pLW/fv3x8qFYcFmcdV+s4dEwbi7e/zIf6Wi6hrasHm7GLcnjTQsYERWZGrjFeyP/Yd53TbuEgE+WiwdI002Xn4XDVufy8Dq+6/DAODvJBRUIHVGaex7ah0vziW5XY91hqrOUXVFq+ABICGZj0Wr85C+uIUJnFkiHM9kXMaFRnQ7f6vABAV5IV/3D7WrfZ/pd7hXE8kD+42Vhub9Xh83SFsyS02OXbV8DD8393jWHXNAu7Wf8g+7LKJYGpqKtasWSPZC53IWRgMBpSWlqK0tBQGg8HR4ZCMuErfGRDohWkdVk+tyzzjoGiIbMNVxivZH/uO87oiLhT/ezAZwT4aSfup8nrc8p+dmP7PHbhn+V58m1Ns8qF7W1nue5bvxcy3f0ZOUbU9QycbsMZYFUURT6Qf6tWevEBrMv3J9MMQRbHnk8mpcK4ncl5t+7/+d04SMp+ZjlvivCXHQ3w9kBLLm+KoZ5zrieTBncZqSY0WqWkZnSbRF14+GO/Pn8AkuoXcqf+Q/dglkX7kyBG88sormDZtGhYuXIiNGzeioaHBHk9NRERmuGtilOT7zMIqnCzruXweERGRIyVGBeLzJSmI7FDmrqpBh9MV5v290VaW+5cTZbYIkWQko6DCrPLB3ckrqcWeAt5ATkRkC76eKowJl27fkl9SC4OBNzAREZG85J6vxm3v7EJ2h5u6lQoBr84ajb/cNBJKBW8SI3IGdkmkR0dHQxRFtLS0YNeuXXjmmWcwZcoUPPnkk9ixYwf0+t7d8U9ERNZx1YhwhHRY0Ze+/6yDoiEiIjLfkFBffPHQZAyP8Ov1NdrKcnNluntbs+e0U12HiIhMRQdIV+Y1NOtxrqrRQdEQERFZ7rujJbjjvQxcqNZK2v08Vfj4vstwz6RBDoqMiDpjl0T61q1b8fnnn+Pee+9FaGgoRFFEY2MjNm/ejKVLl+Lyyy/H3/72Nxw4cMAe4RARUQcalQK/Gx8paVufdQ46PUvgEBGR8wv398Sni5LhpVb2+hosy+3earU6bM0tscq1tuQWo1ars8q1iIhIKshTAV+NdIXe8eIaB0VDRERkPlEUsfznAixavd9kO6noEG98+dAUXD6sn4OiI6Ku2CWRDgCjRo3CM888g59++gkffvghZs+eDT8/P4iiiKqqKqxduxZz5szBVVddhbfffhsnT560V2hERATgzg7l3cvrmvHD8VIHRUNERGSZoxdq0KjrW6UrluV2X8XVWuitVBpYbxBRUqPt+UQiIrKYIAgmq9LzimsdFA0REZF5dHoDnvsyG69sPoaO925fFhOMLx+agqFhvo4Jjoi6ZbdEehtBEJCSkoJXXnkFO3fuxL///W9ce+210Gg0EEURRUVFSEtLw0033YRZs2bhww8/tHeIRERuaWiYH5KigyRt6zJZ3p2IiOSBZbmpL+qbrbvdWF0Tty8jIrKVQQFqyffHS5hIJyIi51XdoMOClfuwdp/p56yzxw/E6oWXIbjDlptE5DxUPZ9iOxqNBtdccw2uueYa1NXVYevWrfj666+xb98+6PV6HDt2DMePH8d9993nyDCJiNzGnROikHW6yvj9jrxSFFdrERHg6cCoiIiIumeLstx+nuqeTyaX4aPp/bYAnfH1sO71iIjokkFckU5ERDJRWF6P+z/KREF5vcmxp2fG46HpsRAEoZNHEpGzsPuK9K74+vpi9uzZWLlyJV577TX4+/s7OiRyEwqFAoGBgQgMDIRC4TRDgmTAFfvOjWP6Sz5INojA51lclU7y54rjleyDfUceWJab+jpWIwI8oVRY5wMslUJAuD9vQpQTzvVE8tA2VhMipZXUTpXXo6mFlUCoe5zrieTBlcbqnoIK3PbfXSZJdE+1Av+dMx4PzxjKJLqVuVL/Iefh0BXp7eXm5uLrr7/GN998g7KyMkeHQ25EpVIhNjbW0WGQDLli3/HxUOHmsQPwabuS7un7z+Gh6UOhsNKHy0SO4IrjleyDfcf5natqwPoD56x6TZbllp++jlU/TzVmJoRjc3Zxn2OZmRDBigYyw7meSB7axmr4wBZg06WtWPQGEb+W1iFhQIADoyNnx7meSB5cZax+tv8snvsyGzq99IbvUD8PrJg/AWOjAh0TmItzlf5DzsWhifSzZ89i06ZN+Prrr1FYWAgAEMXWicXLywtXX301br75ZgdGSETkflInRkkS6WcqG7CnoAKTh/ZzYFREREStmlr02F9YhR+Pl2JHfhl+La2z+nOwLLd7mpscbZVE+tzkaCtEQ0REXfH1UGFgkBfOVTUa2/KKa5lIJyIihzMYRLyxLQ/v7jhpcmxEf398sGACBgR6OSAyIuotuyfSKysr8c033+Drr7/GkSNHAFxKnqtUKkyZMgU333wzrrrqKnh5cUIhIrK3cVGBiAv3RX7JpcTEuv1nmUgnIiKHOVfVgB15ZdiRV4bdJ8vR0Gy7FeMsy+2+UoaEmLwHslR8uB+ShwRbMSoiIurM8Ag/k0Q6ERGRIzU26/H4ukPYkmt6c+7VI8Lwr7vGwcfDaYpEE5GZ7DJqGxoasG3bNnz11VfYu3cv9PrWD77aEuiJiYm4+eabcf311yM4mB86kH3pdDqcPNl6h1hsbCzUapZhJPO4at8RBAGpE6Lw8jfHjG3f5hTjbw06BHi7xs9I7sdVxyvZHvuOYzS16JF5qgo78my36rwrLMstT9YYq4Ig4M3URKSmZfTqZg1vjRLLUsdyn0MZ4lxPJA/tx+qwUB9sv/QnK44zkU494FxPJA9yHaslNVos/Hg/souqTY4tvHwwnr1hBJTcNtPm5Np/yLnZJZE+efJkNDU1AbiUPB8yZAhuvvlm3HTTTYiKirJHGESdEkUR9fX1xq+JzOXKfed34wfi71uOG/fxaW4xYMOhIiyYHOPYwIh6yZXHK9kW+479nK1swI78MvyUV4rdJyssSmQGeqsxsr8/dp+s6HMcLMstT9Yaq6MiA5A2LwmLV2dZ1AcFAP+6KxGjIllWWI441xPJQ/uxOiw8VHKMK9KpJ5zrieRBjmM1p6gaCz/ej+IaraRdpRDwt1tH4Z5JgxwUmfuRY/8h52eXRLpW2zqBhIWF4cYbb8TNN9+MkSNH2uOpiYioF4J9NLh2ZAS+yb5gbPs08yzmp0RzlRUREVlFU4se+05V/layvRQny+rNfqwgAGMiA3BFfBimx4di7MBAKARg5ts/syw39dnUYaFIX5yCJ9IPmd2fRADZ56pxzcgI2wZHREQAgPhwX8n3xTVaVLOKGhER2dl3R0vw6NqDaNRJb8L191Th3blJmMKtMolkzy6J9N/97ne4+eabkZyczAQMEZFMpE6MkiTSj12oQU5RDUYP5EorIiLqnfarznf9WmHyYUN3Ar3VuCIuFNPjQzF1WCj6+XqYnMOy3GQtoyIDsPWxadhTUInVewqxNbcEesOlFQ0qhYBAbzXK65qNbWk/FyB1YhQGBnk7ImQiIrcSE+INtVIwVlEDgOPFNZg0JMSBURERkbsQRRHLfynAa98eR8eFz9Eh3vhgwUQMDfPt/MFEJCt2SaS/+uqr9ngaIiKyosuH9kNkoBeKLjYa29btP4PRA0c7MCoiIpITa68672lPud6W5fbWKJE2L4lluUlCEASkxIYgJTYEtVodSmq0qGvSw9dDiXB/T1yo1uL6f/1iTLA3tRjw9y15+Pfd4xwcORGR61MrFYgN9ZXsjZ5XUstEOhER2ZxOb8DzG3LwaeZZk2OXxQTjvXlJCPbROCAyIrIFuyTSiYhIfpQKAbcnDcS/vj9hbNt48Dz+fMNIeGmUDoyMiIic2dnKBuzIK8WOvDLsPmnZqvMgbzWm/bbqfNqwUIR0suq8J5aW5Y4P98Oy1LFMolO3/DzV8PNUm7TNmTQIqzJOG9u+OnweC1KiMSGGWwQQEdna8Ag/SSL9OPdJJyIiG6tu0GHpJ1nYfbLC5Njs8QPx6u9GwUPFz02JXAkT6URE1KU7JgzE//1wwliiqLapBd/mXMDvxg90bGBEROQ0tLp2q87zS1Fg6arzgYGY/lvyfIwZq87NYU5Z7pkJEZibHI3kIcEs50699vjVcdhwsAg12hZj29++PooND02Bwgp9mYiIuhYf4Q/gvPH7PCbSiYjIhk6V1+OBjzJRUG76N+8fr4vH0iti+bclkQuyaiJ9/vz5AFpL4H388ccm7b3R8VpERGQ/A4O8cfnQfvjlRLmxbV3mWSbSiYjc3JmKBuzIb111nmHhqvNgHw2mDeuH6fFhmDqsX69WnZujp7LcHVcXE/VGkI8Gj10dh799fdTYduRcNb44WITbk/h+iYjIloZH+Em+P3a+BvnFNegf6MXf80REZFV7CiqwZE0WLjboJO2eagXeSk3E9aP7OygyIrI1qybS9+3bBwAmd93s27cPgiBAFMXOHtaptvN5Bw/ZmlKpxMCBA41fE5nLXfrOXRMHSRLpe09V4lR5PQb383FgVESWcZfxStbHvtNKq9Nj76lK7MgrxU95ZZ3egd+V9qvOZwwPw+jIAKusOrdEZ2W5ybU4cqzOS4nGmr2nJdUY/rHlOK4fFQEfDxaBkwPO9UTy0H6sKhQK1DW1SI436PS49u1foFQImJkQjrnJ0UgZEsLPFgkA53oiuXDGsZq+/yz+/GU2dHppfivUzwMfLJiAMQMDHRMYmXDG/kPyZ9W/6idOnGhRO5EzUCqVCA8Pd3QYJEPu0neuHhmGIG81qtrdcZm+/yz+dN1wB0ZFZBl3Ga9kfe7cd9qvOt99shxancHsx9pr1TlRG0eOVbVSgedvHIn7Pso0tpXWNuHdHSfx1Mx4h8RElnHnuZ5ITtrGak5RNeb9exfyS+o6PU9vELE5uxibs4sRF+6LN1MTMSoywM7RkrPhXE8kD840Vg0GEf/Ymof3fjppcmxkf3+sWDABAwK9HBAZdcWZ+g+5Dqsm0levXm1ROxEROT8PlRK/Gz8QH+w8ZWz7POscnrwmDiqlwoGRERGRNfV11fnYgYGYHh+K6fGOWXVO5EgzhofhirhQ/JRfZmx7/5cC3DkxClHB3g6MjIjItfxyogyLV2ehodm8bWXyS+qQmpaBtHlJmDos1MbRERGRq2hobsET6w5jS26xybGrR4ThX3eNY/UpIjfBkU5ERD26c2KUJJFeVtuEH/PKcM1I3uFHRCRnpyvqsSOvDDvySpFRUGHRqvMQHw2mxYVienwopg4LRbCPxoaREjm/v9w4Ajt/LYfe0FrysbnFgNe3HMc794x3cGRERK4hp6jaoiR6m4ZmPRavzkL64hSuTCcioh6V1Gix8OP9yC6qNjn24NTBeOb6EbxxnMiNMJFObq+5uRnZ2dkAgNGjR0Oj4YfAZB536jtx4X5IjArEobMXjW3rMs8wkU6y4U7jlazL1fqOVqfHnoIK7Mgrw0/5ZThl4arzxKhATI8Lw/T4UIyODICCHx6Qk3CGsTos3A/zkqPx0e5CY9s3Ry5gQUolLhscbPd4yHzO0H+IqHuiKOLxdQctTqK3aWjW48n0w9jy2FTume6mONcTyYOjx2pOUTUWfrwfxTVaSbtKIeCl20bh7ssG2TUesoyj+w+5Jrsk0ocPHw6FQoFNmzZh6NChZj3mzJkzuPbaa6FQKHD06FEbR0hERD25a2KUJJH+Y14ZSmu0CPP3dFxQRETUo8LyeuzIK8WO/DLs6cWq8yviQnFFfCimDQtFEFedE3XrD1cNw5cHi1DdqDO2/e3rXGx6+HLeeEJE1AcZBRU4UWr+DYCdySupxZ6CSqTEhlgpKiIiciXbcovxh08PoVEnvWnL31OFd+cmYcrQfg6KjIgcyW4r0kVRtOvjiIjIum4aOwB/+/qocQWA3iDi8wPn8NB0826QIiIi+7DWqvMZw0MxagBXnRNZIshHg8evHoYXv7p0M3hOUQ0+P3AOqROiHBgZEZG8rdlz2mrXYSKdiIjaE0URy38pwGvfHkfHdFR0iDc+WDARQ8N8HRMcETmc05d2Z7klIiLn4Ouhwk1j+iN9/zljW3rmWSy9IpZzNRGRg7VfdZ5xsgJNLVx1TuQoc5KjsWbvGfxaWmdse2NrHm4Y3R++Hk7/JzgRkdOp1eqwNbfEKtfakluMWq0Ofp5qq1yPiIjkrbnFgBc25uDTzLMmxy4bHIy0uUn8G5nIzTntX/FVVVUAAC8vLwdHQkREbe6cGCVJpBdWNGDvqUokD+Ed/URE9qTV6ZFRUIGf8sqwI68UhRUNZj9W0bbqPL51r3OuOieyLrVSgb/cOAL3fphpbCurbcJ/f/wVf7xuuAMjIyKSp+JqLfQG61Ss1BtElNRomUgnIiJcbGjG0jUHkFFQYXLs9qSBeHXWaGhUCgdERkTOxK6JdHNXLDY0NGD16tUAgEGDBtkyJCIissD4QUEYGuYrWWG1LvMsE+lERHZwqm3VeV7rXueWrDrv56vBtLhQTI8Pw9Sh/XhHPZGNTY8Pw4z4UPyYV2ZsW7HzFO6+bBCigr0dGBkRkfzUN+t7PskCdU3WvR4REcnPqfJ6PPBRJgo62Qrtj9fFswInERnZJJF+1VVXddp+//33Q6Xq/imbm5tRWVkJg8EAQRAwY8YMW4RIRES9IAgC7pwQhVc2HzO2bc6+gBdvSUCAF+/oJyKyprZV5zuOt5ZsP23hqvNxg4Iw/bfkecIAf646J7KzP984Er+c+Bktv62ibG4x4LVvj+G/c5IcHBkRkbz4aJRWvZ6vh3WvR0RE8rKnoAJL1mThYoNO0u6pVuCt1ERcP7q/gyIjImdkk0R6UVGRSZsoiigpsWw/o8TERCxcuNBaYRERkRXMGh+Jv285bvxQuKnFgE2HijAvJcaxgRERuQBrrTqfNqwfAr256pzIkYaG+WJeSjQ+3FVobNucXYy9BRWYxGo+RERmiwjwhFIhWKW8u0ohINzf0wpRERGRHKXvP4s/f5kNnV76OyXMzwMrFkzAmIGBjgmMiJyWTRLps2bNknz/5ZdfQhAEXHnllfD39+/2sR4eHggNDcX48eORnJzM8hlkcyqVCvHx8caviczlrn2nn68HrhkZjm9zio1t6/afZSKdnJq7jlfqO1v3ncZmPfYUVLQmz7nqnKjXnHWe/8NVw/DlwSLJape/fX0Umx65HEqOV6fhrP2HiFr5eaoxMyEcm7OLez65BzMTIrg/upviXE8kD7YaqwaDiH9szcN7P500OTayvz8+uHcC+gd4We35yDE415Mt2KQnvfbaa5Lvv/zySwDA448/jqFDh9riKYl6TaFQwNfX19FhkAy5c99JnRglSaTnFNUgp6gaoyIDHBgVUdfcebxS31i774ii+Nuq8zLsyC/DXotXnXvgirhQTI8PxVSuOicyctZ5PtBbgyeuicMLG3ONbbnna/B51lncOXGQAyOj9py1/xDRJXOTo62SSJ+bHG2FaEiOONcTyYMtxmpDcwseX3cIW3NNKyZfPSIc/7orET4eTLq6As71ZAt2mR0eeeQRAEBwcLA9no6IiGxs2rBQ9A/wxIVqrbEtff9ZJtKJiDrR2KxHRkF5a/I8rwxnKi1bdT5+UBCmx7euOh/Zn6vOieTmnssGYXXGaZworTO2vbE1HzeM7s9VkUREZkoZEoK4cF/kl9T1fHIX4sP9kDyEn00SEbmTkhotHvg4EzlFNSbHFk0bgj9dN5yVooioW3ZNpBM5I4PBgIaG1g+0vb29oVAoHBwRyYU79x2lQsAdSQPxfz/8amzbcLAIz90wAp5qpQMjI+qcO49X6pve9J2Oq873FFSg2YJV56F+7VadDw1FgDcTbUQ9ceZ5XqVU4PmbRmL+yn3GtvK6Jrzz40k8c/1wB0ZGbZy5/xBRK0EQ8M/bx+DO9/egUWf++6o23hollqWO5RaSboxzPZE8WHOs5hRVY+HH+1Fco5W0qxQCXr5tFO66jBWiXA3nerIF1qsgt9fS0oK8vDwAwOjRo6HRsEQqmcfd+84dE6Lw7x9/hSi2fl+jbcGWnGLcNi7SsYERdcLdxyv1nrl9p6+rzpOigzA9PgxXxIVy1TlRLzj7PD8tLhRXDQ/D98dLjW0rd57CPZcNwqAQbwdGRoDz9x8iajU83AdPpwTgH7suQqsXzX6ct0aJtHlJrKDm5jjXE8mDtcbqttxi/OHTQ2jU6SXt/p4qvDc3CZOH9utzrOR8ONeTLdglkX7gwAHMmTMHarUa3333HcLDw7s9v6SkBFdffTX0ej3S09MxatQoe4RJREQWiAr2xpTYftj5a7mxbV3mWSbSicgtiKKIgrZV53ml2HuqkqvOiahbz904Aj/ll6HF0Jr8adYb8OrmY3hvXpKDIyMiko+x4R54aUYw3j+ixYnS+h7PD/PzwMp7JzKJTkTkJkRRxPJfCvDat8eNi3/axIR444N7JyI2lHtoE5H57JJI37x5M0RRxPTp03tMogNAeHg4ZsyYgW3btuHrr79mIp2IyEndOTFKkkjPKKjA6Yp6RIf4ODAqIiLbaGjW45eTJb+VbC/F2cpGsx+rVAgYPyiQq86J3FhsqC8WTI7BBztPGdu25BYj42QFUmJDHBgZEZG8DAlS4+uHx+HAuTqs3lOIrbkl0Bs6X6EeG+rDJDoRkZtobjHg+Q05WLf/rMmxywYHI21uEoJ8uEKZiCxjl0R6VlYWBEHAtGnTzH7M9OnTsW3bNmRmZtowMiIi6otrE8IR6K3GxQadsS19/1k8PZP7fRKR/ImiiJNl9fgqvx4Hi5tw7MsdFq06DzOuOg/D5cP6IcCLq86J3N2jVw7DFwfOoarde6e/fX0UX//+cih5cw0RkdkEQUBKbAhSYkNQq9WhpEaLuiY9Dp6pwv/76qjxvH2FVaiqb2bihIjIxV1saMbSNQeQUVBhcuz2pIF4ddZoaFTcL5uILGeXRPqZM2cAAEOHDjX7MUOGDJE8loiInI+HSonbEiPx0e5CY9vnWefw+NVxUCn55pSI5KehuQW7f63AjvxS7Mgrw7kqy1adJw0KwhXxrSXbR/b3hyAwMUZElwR4q/HEMNXRVQAAbF5JREFUtfF4fkOOse3YhRp8tv8s7rpskAMjIyKSLz9PNfw8W29YjAv3xevfHkfTbzc/6g0ivj9eituTBjoyRCIisqFT5fV44KNMFJSbbvnxx+visfSKWP5tTkS9ZpdEelNTEwDAw8PD7MdoNK13ijY2mv/hJRER2d+dE6MkifSSmib8lF+Gq0b0vJUHEZGjta0635FXip/yy7C3oBLNestXnc8YHoYpQ7nqnIh6dvfEKKzJOI28klpj2z+35eHGMf2NiSAiIuodb40K0+JC8d3REmPb1txiJtKJiFxUxskKLFmThepGnaTdU63A23cm4rpR/R0UGRG5Crsk0gMDA1FRUYHz589jxIgRZj2muLgYAODn52fL0IiIqI9G9PfH2IEBOHyu2ti2LvMsE+lE5LS46pyIHEmlVOD5m0Zi7gd7jW3ldc34z4+/4tnrzft7mYiIujYzIUKSSP85vwwNzS3w1tjlY1AiIrKT9P1n8ecvs6HTi5L2MD8PfLBgIkYPDHBQZETkSuzyDjI2NhYVFRX44YcfcNVVV5n1mO3btwMABg8ebMvQiIjICu6cOAiHz2Ubv//+eClKa7UI8/N0YFRERK1aV53XYUdeGXbklWHfKctWnQd5KjAuwgO3TRqGK4ZHcNU5EfXZ5cP64eoR4dh+7FKi58OdhbjnskGIDvFxYGRERPJ39YgwKBUC9IbWxEpTiwE/55fjulERDo6MiIiswWAQ8fetx5H2U4HJsZH9/fHBvRPQP8DLAZERkSuySyL9iiuuwN69e7Fx40bMmjULEyZM6Pb8zMxMbNq0CYIgYPr06TaPr6ioCKtXr8aOHTtQXFwMjUaDqKgoXH/99ZgzZw68vKw36e7evRubNm1CVlYWysrKoFQq0a9fP8THxyM5ORm33norfHz4wYk9aTQaJCUlOToMkiH2nUtuHtsfL319FI06PYDWfei+OFCEJVfEOjgyolYcr+6nvqkFu09WYEde66rzoosWrjqPDsL0+FBMjwvDiP5+XHVO5OTkOM//+cYR+Cm/1LiCpllvwKubjyFtXvd/L5P1ybH/ELkjc8dqoLcGkwYHY/fJCmPbttxiJtLdHOd6Innoaaw2NLfg8XWHsDW3xOTY1SPC8a+7EuHjwQok7opzPdmCXWaUO++8E8uXL8fFixfx4IMP4sknn8Qdd9xhsmd6U1MT1q1bh7feegstLS0ICAjAPffcY9PYfvjhBzz99NOoq6sztjU2NqK6uho5OTn47LPP8P777yM6OrpPz1NdXY1nn30W33//vcmxuro6FBYWYuvWrRg3bpzZ5e+JiJyFn6caN47pj8+zzhnb0jPPYvG0IUw+EZFdtK06//F4GXbklyLzVJVFq87D/T0wPS4M0+NDMWVYP/hzn2IisrHB/Xxw7+QYLP/llLFta24Jdp8sx+TYfg6MjIhI/mYmREgS6duPlUCnN0CtVDgwKiIi6oviai0WrspETlGNybFF04bgT9cNh1LBzyGJyLrskkj38fHBsmXLsGjRImi1Wrzyyit48803kZCQgLCwMABAaWkpcnJyoNVqIYoiVCoV3nzzTfj6+tosrqNHj+Lxxx+HVquFt7c3Fi9ejEmTJkGr1WLz5s1IT09HYWEhFi1ahPXr1/c6ltraWtx3333Izc0FAFxzzTWYOXMmBg0aBIVCgeLiYuzbtw/btm2z5o9HRGRXd06MkiTSC8rrkVlYhcsGBzswKiJyZdZadT4jPgzDI7jqnIjs75Erh2H9gSJU1jcb2/721VF88+hUfghIRNQH14wMx1835Rq/r9G2YG9BJS4fxhuViIjkKKeoGg98nImSmiZJu0oh4OXbRuGuywY5KDIicnV2q3ExefJkrFixAn/84x9RWlqKhoYG7N+/X3KOKLaWtAsPD8c//vEPTJo0yaYxvfLKK9BqtVCpVFi5ciXGjRtnPJaSkoLo6Gi88cYbKCwsxIcffojf//73vXqel156Cbm5udBoNHj77bdN9okfPXo0rrnmGjz33HPQ6/V9+pnIcnq9HuXl5QCAfv36QalUOjgikgv2HakJ0UEYEuqDgrJ6Y9uaPacR5K1GfbMePholIgI84cdVnuQAHK+uQRRF/Fr6217ndlp1zr5DJA9yHasBXmo8eW0c/vxljrHteHEt1mWexT2T+GGgvci1/xC5G0vG6oBAL4wZGIAj56qNbVtzi5lId2Oc64nkobOxujW3GI99esi4pWQbf08V3pubhMlDObdTK871ZAt23SwiOTkZ27dvx4YNG/Djjz/i2LFjqKqqAgAEBQVh5MiRmDFjBm699VZoNBqbxnLkyBFjIn/27NmSJHqb+++/H1988QVOnjyJVatWYcmSJVCrLUsA7d+/Hxs3bgQAPPbYYyZJ9PYEQYBKxf077E2v1+PcudZVtEFBQZxcyWzsO1KCIODOCVF47dvjxrZNh89j0+Hzxu+VCgEzE8IxNzkaKUNCuPqT7IbjVb7qm1qw69dy7Mgvw08WrjpXGVedtybPe7PqnH2HSB7kPFbvnBCF1Rmncby41ti2bFsebhrbn9tM2Imc+w+RO7F0rM5MiJAk0rcdLcb/uyUBClb8cEuc64nkof1YDQwMxIqdhXh9y3H8tgbTKCbEGyvvnYghobaraEzyw7mebMHuWVuNRoPU1FSkpqba+6kltm/fbvx69uzZnZ6jUChw2223YdmyZaipqcHevXtx+eWXW/Q8n3zyCQDAz88Pc+fO7X3AREQyMLK/f7fH9QYRm7OLsTm7GHHhvngzNRGjIgPsFB0R9VatVofiaq1dqkuIoogTpXXGcu2ZhZXQ6cWeH/ibCH9PTI8PbV11PrQfq2AQkdNTKRV44aaRuGfFXmNbRX0z/vPDr3juhhEOjIyISN5mJoTjja15xu9LappwpKgaiVGBjguKiIjMojOI+PPGo/j8wHmTY5MGB+O9uUkI8rHtYkwiIsABiXRnkZWVBQDw9vZGQkJCl+dNnDjR+PWBAwcsSqQ3Nzfj+++/B9Ba2t7DwwNA610xpaWl0Ov1CA0NNbYTEcnZLyfKsHhNltnn55fUITUtA2nzkjB1WKgNIyOi3hBFERkFFVidcRrbjpZAb7iUzLZ2dYm6phbs/rUcP+aV4ed8y1edT4i5tOo8Ppx7nROR/Ewe2g/XjAzHd0dLjG0f7jqFuy8bhMH9fBwYGRGRfA0N8zPZfmxrbjET6URETq622YA3dl9EblmzybHbkwbi1VmjoVEpHBAZEbkjt02knzx5EgAwaNCgbsupDxkyxOQx5jp+/DiampoAAHFxcairq8O//vUvbNiwATU1NQAAtVqNiRMnYsmSJTbfE56IyFZyiqqxeHUWGpr1PZ/cTkOzHotXZyF9cQpXphM5kZyiajyRfgj5JXWdHu9rdYm+rjrvH9C66vyKuDBMGRrCVedE5BL+fMMI7MgrNc6HOr2IVzcfw/L5ExwcGRGRfM1MiMC7Oy59nrc1txh/um64AyMiIqLuFFbU49nvK3ChzvQzxj9dNxxLrhjCm+eJyK7cMpHe1NRk3Js9IiKi23MDAgLg7e2NhoYGFBcXW/Q87RPvoihi9uzZKCwslJyj0+mwe/duZGRk4IknnsCiRYsseg5L6XS6Lo+pVCooFK13chkMBrS0tHR7rfb72Ov1euj1XSfQBEGQ7C/f0tICg8HQ5fkKhUJyg4NOp4PYcSOULmIXRbHbnxNovYGh7Rdu+zg6e5ytY1cqlZK9OpqbTe+0a8+dX6fuflZHvE7t4+/4s7jT66RSqfBE+iGLk+htGpr1eGLdIXz1cLLJG2FnH089/ds4Cuf6VpzrO9fTz7rr1wo8/Olhs8d0W3WJd+4aiylDQ7p8neqaWpBRUImfT5Tj5xMVuFCtNev6wG97nQ8KxLS4frh6ZH/EhftCEARj7F39e3Kud5/fyd3hXO8+fbP9czgi9r72zQH+aixIHoQVu04bz/nuaAl2HLuAybEhLvM6OeMcwrm+lbO/Tt3hXO8+fbP9c5gT+9XD+0kS6QVl9ThaVImhv+2p6+x9U66vkzPOIZzrWzn769QdV5zrOc+3auuXGScrsGT1flRrpbF7qhV4Y/YozBwZjpaWFpfql3J8nQDnnT+6muv5OjkmdkeMJ1vM9VZNpF911VUAWv/x2+9B3tbeGx2vZQ319ZdKOnl7e/d4vpeXFxoaGtDQ0GDR81RXVxu/Xr58OZqamjB16lQ8+uijGD58OOrq6rB161YsW7YMtbW1WLZsGYYMGYKrr77aouexxPHjx7s8Fh8fD1/f1j8kGhoakJeX1+W5AJCUlGT8ury8HOfOnevyXB8fHwwffumO39OnT+PixYtdnh8WFoaoqCjj98eOHet2AAwZMgRBQUEAWgdvdnZ2t7GPHj3aOMhqa2uN7Z39+6jVaowZM8b4/YULF1BaWtrltQMDAxEbG2v8/uTJk5I+19HAgQMRHh5u/L6n2N31daqurkZBQUGX5zr6derYd9zpdar3iexy1aq58kvr8OkPWRgVJt3qQs7jyZE417fiXN+57l6ngiodnv+xEloLVocDrTfELP3kIF6aEYyrk+IRFBQEURSRe64K637JwcELTThe3owWCy4b4qXAuAgPjO/vgdFhGnirFVCrtYiP8DOew7n+Ev5O7hrnevfpm226+reRQ9+cFmpAuocCNU2XPrx44csj+Oc1Ibhs4qWV6XJ+nZx9DuFcL4/XqSPO9e7TN9uYO9f3UzQg2FOBSu2lefWdbw9jarQnPFUKDIkIwpgRccZjcuqbzvw6OfscwrleHq9TR64413OebzV69GhsOFyC577MRotB+sFBkKcCz14ehAH6UmRnl7pVv3TG10lO80f78cXXqXPO8Dq156zzvFUT6UVFRQBgcpdoW3tv2KJMR1u5dQCSuy260tbptFrzV04BkCTem5qaMGXKFKSlpRnvwAgODsbdd9+NYcOGYd68eTAYDHjzzTdx1VVXsTwJEcnG/zLPWuU6W042miTSich+RFHE/+2rtjiJ3karF/GvfRch+JYj6/xZ/JRXhvMWrDpXKwUkRvpheKCI8REaRPmr+H6IiNyOj1qBe0b54r2sGmPbmZoWbD/ViMsmOjAwIiKZUggCJg7wwNaCRmPbN7824JtfG347Xo7rRtVibnI0UoaEOCpMIiK3ZRBFvLHtBJbvLDQ5NjhQhWenBCHEW2n6QCIiOxHE7tbdW+jZZ581fv3aa6912t4b7a9lDZWVlUhJSQEA3HDDDXjrrbe6PX/y5MmoqKhAXFwcvvrqK7Of54MPPsA//vEP4/dffvklRo4c2em5jz76KLZu3QoA2Lhxo+QOlr5qbm423ukxfPjwLm8ecNfyFk1NTcYy/NHR0Sb/Pq5Y3qIrzvw6OWMZEp1Oh9OnW0tvduw77vA6NbcYcKK0FrPT9sFghd8kSoWAzGemw9fzUqzOPp60Wq3x7sbOVknYE+d6U5zrO9fVz7qnoBLzP8rq9nmsrW2v8+nxYZgytB+81QrO9b9x5vHkjL+Tu8O53j36pk6nw6+//gpRFDud5+0Ru7X6pt4gYtZ7e3C8+FLFnyBvNXY8PQMBXq3xy/V1ApxzDuFc38rZX6fucK53j77Zm7n+0JlKPPy/gyi62PMNnnHhvvjH70ZhZH+/Ls+Rw/v6rrj7HMK5vpWzv07dcZW53l3m+fpmPYqrtahraoGHQkSEv6fkMz+gtbrdU+uzsf1Ymck1kqO88X/3jEegj5ddY+c83zU5zB9dzfV8nRwTu6NKu1t7rrfqivSuEt7WToT3lY+Pj/Frc8q1Nza23rVqThn4rp4nODi4yyQ6AEydOtWYSM/OzrZqIr09tVptVsdRKBQWdbCOA6In7QebOcypHNBGEASLYvfw8Oj2tenIlrEDsCh2d3qdLP1Z7fE6aTQas/qOXF8nvUHEhepGnK1sxNmqBpyrasS5ygbj18U1WljvVqzW56vU6hHs3/Vc62zjydJ47IVzvSnO9V1r+1k/zep9BSFzqZUCJsYEG5Pnw8J8TVadc67vnDONJ2f8nWwuzvU9k2vfVKvVGDFihEWxOHPffOHmBNyzfK/x+6oGHf79/Qn85abW+UiurxPgnHMI53pTzvg6mYtzfc/k2jctnet/OVGGxauz0NDc9Yff7eWX1OGeDzKRNi8JU4eFmvUYZ3xfby53m0M415tyxtfJXK4y17vaPC+KIjIKKrA64zS2HS2Bvt3KG6VCwMyEcGMFkJKaJixctR85RTUm11k8bQj+eN1wKBU9V6mTe7/k/NG53r5O5sz1fJ265k7jyRJWTaTLhYeHBwIDA3Hx4kUUFxd3e251dbUx2R4REWHR8/Tv39/4dU+PbX+8qqrKouchIuoNg0FEeV0TzlY1tCbLK1sT5GerWpPlFy5qTfYlsrW6JvM+4CAi66rV6rA1t8Qm1x4Q4Ikr4sMwPT4UU4b2g6+HW779JCKyyOTYfpiZEC6Zmz/aXYh7Jg3CkFBfB0ZGRCQPOUXVFiXR2zQ067F4dRbSF6dgVGSAjaIjInI9OUXVeCL9EPJL6jo9rjeI2JxdjM3ZxRgU7I26Jh0q66Ura1UKAa/MGoU7Jw6yR8hERGZx208yhw4div379+PMmTNoaWnp8k6LgoIC49exsbEWP0eb7ko/AJCUS7DVXRNE5F5EUURVgw7n2hLlVQ2SZHlRVSOaWrou1eIIvh6c/4gcobhaK7lTvK8SowJww+j+Xa46JyKinv35hpH48XgZmvWt79daDCJe3XwMKxZws3Qiou6Ioogn0g9ZnERv09Csx5Pph7Hlsal8H0tEZAZLK4CcqTStEhzgpca7c8djcmw/a4dHRNQnbptIT0pKwv79+9HQ0IDc3FyMHTu20/MyMzONX48fP96i54iMjMSAAQNw/vx5FBUVQRTFLt+Anzlzxvh1eHi4Rc9DfdPS0iLZN8PS8hXkvpyh79RqdThb2diaLK9qW1X+W7K8sgH1vfzgwBFUCgHh/p6ODoNclDOMV2dm7bnixVtGITEq0KrXdBT2HSJ5cMWxOijEG/dfPhjv/XTS2Lb9WCl+OVGGxKhAFFdrUd+sh49GiYgAT/h5Ol+5Urlwxf5D5IrMHasZBRVdrog0V15JLfYUVCIlNqRP1yHnwbmeyDZ6WwGkvZgQb6y8dyKGhPpyrFKfsP+QLVi1F7VPOlvTxInWv+P+6quvRlpaGgBg/fr1nSbSDQYDNmzYAADw9/fHpEmTLH6ea6+9Fh999BHq6uqQkZGByZMnd3retm3bjF8nJSVZ/DzUewaDARcvXgQAREVFOTYYkhV79B2tTm9cUd4+Wd62T/nFBl3PF+kjL7USUcFeiAryRlSwNwYGeWFgkDeiglv//+wXR7A5u/ttMswxMyGCHwCTzXCu756PxrrVIFypugT7DpE8uOpYfXhGLD7POofyuiZj29I1WWjUGbrdc5IrKC3jqv2HyNWYO1bX7Dltledbs+c0E+kuhHM9kfX1tQII0Pq54xdLJyPY1wMAxyr1DfsP2YJVE+nz5s2z+h/sgiDg6NGjVr0mAIwZMwYTJkzA/v37sX79esyaNQvjxo2TnLNy5UqcPNl69//8+fOhVksTPHv37sX8+fMBALNmzcLrr79u8jwLFizA2rVr0dTUhNdeew1r166Fr690T7uNGzdi3759AIDp06dL9lYnItfW3GLAherGTkuvn61slHxoaisapQIDg7wQGeSFqGBvRAW1Jstbv/ZCsI+m27l9bnK0VRLpc5Oj+3wNIuqdiABPKBWCVcq7s7oEEZH1+Hmq8fTMOPxpfbaxra7J9IPK9ntOxoX74s3URO7tS0RuqVarw9bcEqtca0tuMWq1Ot7wTUTUBWtUAGnU6ZFXUoeU3xLpRETOxup1DUTRevtr2tqf//xn3H333dBqtbj//vuxZMkSTJo0CVqtFps3b8a6desAADExMbjvvvt69RwDBgzAo48+ijfeeAP5+fm4/fbb8eCDDyI+Ph51dXX47rvvsHbtWgCAr68vnn32Wav9fETkeHqDiOIaLc5Vmq4mP1fZgOIaLay4LXGnlAoB/QM8pQny31aTRwV5I8zPAwpF72+CShkSgrhw3z69cY4P90PykOBeP56I+sbPU42ZCeGsLkFE5IQi/D0hCIC5f2rnl9QhNS0DafOSMHVYqG2DIyJyMsXVWqvcHAq0/j1fUqPle1sioi6wAggRuQOrJtJXrVrV5TGdToe3334b2dnZCA4OxvXXX4/Ro0ejX79+AIDy8nLk5OTg22+/RUVFBUaPHo3HH3/cpnsYjBw5Em+99Raefvpp1NXV4c033zQ5JyYmBu+//77JKnJLLFy4ENXV1Vi+fDlOnTqF5557zuSckJAQvPPOO4iJien18xCR/YmiiItNBhw8cxHFdTrj3uRtyfLzFxuh09v+BqNwfw9J6fWoIG8M/K0ce/8AT6iUCps9tyAIeDM1EalpGb0q5eStUWJZ6liWICVyMFaXICJyPjlF1Vj6yQGzk+htGpr1WLw6C+mLU7gynYjcSn0fygt3prMqIERExAogROQ+rJqlvuyyyzptF0URDz74IHJycnD77bfjueeeg7e3t8l5t912G5588km8+uqr+Oyzz/Dhhx9i+fLl1gzRxJVXXolNmzZh1apV2LFjB0pKSqBWqzFo0CBcd911mDt3Lry8vPr8PE8++SSuvPJKrF27Fvv370dZWRk8PDwQExODK6+8EvPmzYOfn58VfiIisiZRFFHdqOu09PqZigacq6xHswEAymwaR4iPpnVv8k5Krw8I9IKn2rH7EY+KDEDavCQsXp1lUTLdW6NE2rwkfsBL5ARYXYKIyLn0dc/JhmY9nkw/jC2PTeUNi0TkNnw01v3b2NfDsX9rExE5K1YAISJ3Ybvl3u18/vnn2LlzJ6ZMmYKXX36523O9vLzw0ksv4fz589i5cyfWrVuHO++806bxRUZG4tlnn7W4rPqkSZOQl5dn9vnjxo0z2YedSC5qtToUV2tR36yHj0aJiABPl3lzU9fUgnO/7UnefjV5W9K8rqnF5jH4eapMEuStq8tb23w87DJd98nUYaFIX5yCJ9IPmZWIiw/3w7LUsUyiEzkJVpcgInIu1thzMq+kFnsKKlkqk4jcRkSAJ5QKwSrJHZVCQLi/pxWiIiJyPawAQkTuwi6ZmS+++AKCIODuu+82+zH33HMPdu3ahS+//NLmiXQi6pwoisgoqMDqjNPYdrRE8oeoUiFgZkI45iZHI2VIiFMnTrQ6feue5FWt+5Sf65Asr2rQ2TwGT7XCpPR6+33KA7xd46aEUZEB2PrYNOwpqMTqPYXYmivtNyqFgJkJEZibHI3kIcFO3W+I3BGrSxAROQ/uOUlEZDk/TzVmJoRbZcuimQkRLrOAgIjI2lgBhIjchV0S6QUFBQCAAQMGmP2Y/v37AwBOnTplk5iI2igUCoSFhRm/plY5RdXdrizWG0Rszi7G5uxixIX74s3URIclUHR6Ay5c1P6WHG8wKcNeWttk8xjUSgGRgdJV5O1Xlof4aNwmaSwIAlJiQ5ASG4JarQ4lNVrUNenh66FEuL/rVDIgeeFcbz5Wl5Bi3yGSB1cbq9xz0r5crf8QuSpzx+rc5GirJNLnJkf3+RrkPDjXE1mXrSqAcKxSX7D/kC3YJZHe3NwMALhw4QJGjhxp1mMuXLggeSyRrahUKkRFRTk6DKfyy4kyi1Yj5pfUITUtA2nzkjB1WKjV49EbRJTWarssvX6huhFW2pKnSwoB6B/g1S5B3m5FebAXwv08oVC4R6LcEn6ean5oS06Bc71lWF3iEvYdInlwtbHKPSfty9X6D5GrMnespgwJQVy4b5+2x4gP90PykOBeP56cD+d6IuuyVQUQjlXqC/YfsgW7JNIHDRqE/Px8fPrpp7jqqqvMesynn34KAOz0RHaWU1RtcUlfAGho1mPx6iykL06xeFWiKIoor2s2ll5vTZBfSpYXXWyETm/jTDmAMD8Pk9LrrfuWe6N/oCfUSt7FRkTug9UliIgch3tOEhH1niAIeDM1EalpGRZ/tgG0blu0LHWsS98sSkRkDawAQkTuwC6J9Ouvvx55eXnYuXMnXnzxRTz77LPw8PDo9Nzm5ma8/vrr+OWXXyAIAm688UZ7hEhEaE1oP5F+qFd/aAKtyfQn0w9jy2NTTf7grG7QdVl6/VxVIxp1tv9wL8hbbVxNPjDICwPblV6PDPSCp5p78RARdYbVJYiI7It7ThIR9c2oyACkzUuyeKGAt0aJtHlJLrttERGRNbECCBG5A7sk0u+77z5s2rQJBQUFWLduHbZv347rr78eo0ePRkhICARBQHl5ObKzs7FlyxaUl5cDAAYPHoz77rvPHiGSG9PpdDh27BgAYMSIEVCr3TdRkFFQ0ac3PgCQV1KLFzflQqVU/FaGvRHnqhpQq22xUpRd8/VQdVl6fWCQN3w9rDvlse8QyQfHK/UW+w6RPLjaWLXVnpPUOVfrP0SuytKxOnVYKNIXp+CJ9ENmfdYRH+6HZaljmUR3UZzriazPFhVAOFapL9h/yBbskkj38PDAqlWrsGjRIhw9ehTl5eVYs2ZNp+eKYusHBSNHjsR7770HjUZjjxDJjYmiCJ1OZ/zana3Zc9oq1/k4wzrX6chTrcDAoM5Lr0cFeyHAS23X0mvsO0TywfFKvcW+QyQPrjZWbbXnJHXO1foPkavqzVgdFRmArY9Nw56CSqzeU4ituSWSm5RUCgEzEyIwNzkayUOCWc7dhXGuJ7INa1cA4VilvmD/IVuwSyIdAPr164fPPvsMn376KdauXYtff/210/NiY2Nx99134+6774ZSyfJzRPZSq9Vha26JQ2NQKwUMCPSSrCZvW2E+MMgLob4e/KOWiIiIiNwC95wkIrIOQRCQEhuClNgQ1Gp1KKnRoq5JD18PJcL9PXmzERFRH7ECCBG5Mrsl0gFAqVRizpw5mDNnDsrKypCfn4/q6moAgL+/P+Li4hAWFmbPkIjoN8XVWquUjuyOIAD9/T1/25u8Xen135Ll4f6tJSyJiIiIiNwd95wkIrI+P081E+dERDbACiBE5KrsmkhvLzQ0FKGhoY56eiLqoL4X+9h0Z+qwfhgzMEBSer1/gBc0KoVVn4eIiIiIyBXZYs9JIiIiIiJbYQUQInJFDkukE5Fz+f/t3Xd8VHW+//H3pJIQWiIECE0iCaBUpdlARFQstLUgAgoroCsi7pVFXfXaFuXeVUFdBRfRsAqoBBYUFpAmRUNVqjRpCSWUJBDSJpn5/ZGb+SWkkORMOzOv5+Phw5M53znnO5PveZ8wnzPfUzPEubdSePW+trqmQS2nbhMAAADwJ86+5yQAAADgDswAAsBXuL2QbrPZlJSUpO3bt+vs2bPKzs7WhAkTSkzpnpeXp4KCAgUGBiokJMTdXQT8UsM6hdOqO2N696AAi6Jr13BCrwAAAAD/xj0nAQAAAADwDLcW0levXq0333xTJ06cKPH4qFGjShTSv/nmG7355psKDw/XunXrFB4e7s5uAn6pVo1g3XlttJbsPGV4W3de25ArDgEAAAAn4Z6TAAAAAAC4n9sK6V9//bVeffVV2e2F/9ivV6+e0tLSyvwH/gMPPKCpU6fq4sWLWrFihfr37++ubsIPBQUFqWXLlo5lf/Zo9+ZOKaQ/2r25E3rj/Rg7gHlwvKK6GDuAOfjDsco9J13HH8YP4As4VmEE4wcwB45VGMH4gSu4ZSQdOXJEr7/+uiSpe/fuevnllxUbG6vWrVuX2T4kJER9+/bVt99+qw0bNlBIh0sFBASoXr16nu6GV+jRMkpx0RGVmjKyPPHRtdS9ZaQTe+W9GDuAeXC8oroYO4A5+Nuxyj0nncvfxg9gVhyrMILxA5gDxyqMYPzAFQLcsZPPP/9c+fn5uuaaazRjxgzFxsZe8Tk33HCDJGnv3r2u7h6A/2OxWPTugx0VHhJYreeHhwTq7w92YCpJAAAAAAAAAAAAmJpbCuk///yzLBaLRowYoZCQkEo9p1mzZpKkkydPurJrgOx2u/Ly8pSXl+e49YA/uy6mjqYPu77KxfTwkEBNH3a9roup46KeeR/GDmAeHK+oLsYOYA4cqzCC8QOYA8cqjGD8AObAsQojGD9wBbcU0k+fPi1J5U7lXpbw8HBJUk5Ojkv6BBSxWq3auXOndu7cKavV6unueIVbWtXX12N6KC46olLt46Nr6esxPXRLq/ou7pl3YewA5sHxiupi7ADmwLEKIxg/gDlwrMIIxg9gDhyrMILxA1dwyz3Si1SlKJ6WliZJioioXCEPgHNdF1NHy569VT//fl6zfz6iZbtPq8D2/6/iCgqw6M5rG+rR7s3VvWUk07kDAAAAAAAAAADAZ7ilkB4dHa2jR4/q+PHjjnufX8nWrVslSU2bNnVl11AFOTk5Sk9PV1ZWlgoKCjzdHaex2WyO5cOHDysgwC0TNZjGVZImdK2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",
+      "text/plain": [
+       "<Figure size 2400x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = results_df\n",
+    "\n",
+    "# PLOT_BASE = True\n",
+    "PLOT_BASE = False\n",
+    "\n",
+    "if PLOT_BASE:\n",
+    "    models_to_plot = [\n",
+    "        \"Meta-Llama-3-8B\",\n",
+    "        \"Meta-Llama-3-70B\",\n",
+    "        \"Mistral-7B-v0.1\",\n",
+    "        \"Mixtral-8x7B-v0.1\",\n",
+    "        \"Mixtral-8x22B-v0.1\",\n",
+    "    ]\n",
+    "else:\n",
+    "    models_to_plot = [\n",
+    "        \"Meta-Llama-3-8B-Instruct\",\n",
+    "        \"Meta-Llama-3-70B-Instruct\",\n",
+    "        \"Mistral-7B-Instruct-v0.2\",\n",
+    "        \"Mixtral-8x7B-Instruct-v0.1\",\n",
+    "        \"Mixtral-8x22B-Instruct-v0.1\",\n",
+    "    ]\n",
+    "\n",
+    "fig, axes = plt.subplots(1,len(models_to_plot), sharey=True, figsize=(12, 2.5), gridspec_kw=dict(wspace=0.1))\n",
+    "\n",
+    "num_features = [2,4,6,8,10]\n",
+    "\n",
+    "for idx, model_name in enumerate(models_to_plot):\n",
+    "    this_data = data[data[model_col] == model_name]\n",
+    "    vals_ece = []\n",
+    "    vals_acc = []\n",
+    "\n",
+    "    for nf in num_features:\n",
+    "        this_row = (this_data[this_data[\"num_features\"] == nf]).iloc[0]\n",
+    "        vals_ece.append(this_row[\"ece\"])\n",
+    "        vals_acc.append(this_row[\"roc_auc\"])\n",
+    "\n",
+    "    ax = axes[idx]\n",
+    "    ax.plot(vals_ece, vals_acc, \"-o\")\n",
+    "    ax.set_title(this_row[\"name\"])\n",
+    "    ax.set_ylim([0.50, 0.9])\n",
+    "\n",
+    "    if PLOT_BASE:\n",
+    "        ax.set_xlim([0.0, 0.29])\n",
+    "    else:\n",
+    "        ax.set_xlim([0.1, 0.58])\n",
+    "\n",
+    "    ax.plot(vals_ece[-1], vals_acc[-1], '*', ms=13, label='all features',color = 'tab:orange')\n",
+    "\n",
+    "    ax.set_xlabel(\"Calibration (ECE)\")\n",
+    "    if idx == 0:\n",
+    "        ax.set_ylabel((\"Predictive Signal (ROC)\"))\n",
+    "\n",
+    "    if idx == 0:\n",
+    "        ax.legend(loc=\"upper left\" if PLOT_BASE else \"lower left\")\n",
+    "    elif idx == len(models_to_plot) - 1:\n",
+    "        ax.legend(loc=\"lower right\")\n",
+    "\n",
+    "\n",
+    "save_fig(fig, f\"feature-ablation.{'.'.join(models_to_plot)}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bde315a6-81fc-4830-8281-e32925829be5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/parse-feature-importance-results.html b/notebooks/parse-feature-importance-results.html
new file mode 100644
index 0000000..c42bc7c
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+             
+  <section id="Fetch-and-parse-feature-importance-results-under-a-given-directory">
+<h1>Fetch and parse feature importance results under a given directory<a class="headerlink" href="#Fetch-and-parse-feature-importance-results-under-a-given-directory" title="Link to this heading"></a></h1>
+<p>This script collects all feature importance results files under a given root directory, parses them, and aggregates them into a more easily digestable pandas DataFrame.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">tqdm.auto</span> <span class="kn">import</span> <span class="n">tqdm</span>
+</pre></div>
+</div>
+</div>
+<p>Set the local path to the root results directory:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">RESULTS_ROOT_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;/fast/groups/sf&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;folktexts-results&quot;</span> <span class="o">/</span> <span class="s2">&quot;2024-07-03&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Set the local path to the root data directory (needed only to train baseline ML methods):</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">DATA_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;/fast/groups/sf&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;data&quot;</span>
+</pre></div>
+</div>
+</div>
+<p>Function to parse and flatten results:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">operator</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">reduce</span>
+
+<span class="k">def</span> <span class="nf">parse_results_dict</span><span class="p">(</span><span class="n">dct</span><span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+    <span class="k">return</span> <span class="n">reduce</span><span class="p">(</span>
+        <span class="n">operator</span><span class="o">.</span><span class="n">or_</span><span class="p">,</span>
+        <span class="p">[{</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">feat</span><span class="si">}</span><span class="s2">_imp_mean&quot;</span><span class="p">:</span> <span class="n">dct</span><span class="p">[</span><span class="n">feat</span><span class="p">][</span><span class="s2">&quot;imp_mean&quot;</span><span class="p">],</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">feat</span><span class="si">}</span><span class="s2">_imp_std&quot;</span><span class="p">:</span> <span class="n">dct</span><span class="p">[</span><span class="n">feat</span><span class="p">][</span><span class="s2">&quot;imp_std&quot;</span><span class="p">]</span>
+        <span class="p">}</span> <span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="n">dct</span><span class="p">],</span>
+    <span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Iteratively search the root directory for results files matching the given regex:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">find_files</span><span class="p">,</span> <span class="n">load_json</span>
+<span class="kn">import</span> <span class="nn">re</span>
+
+<span class="c1"># Results file name pattern</span>
+<span class="n">pattern</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;^feature-importance[.](?P&lt;task&gt;\w+)[.](?P&lt;model&gt;.*?)[.]json$&#39;</span>
+
+<span class="c1"># Find results files and aggregate</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">file_path</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">find_files</span><span class="p">(</span><span class="n">RESULTS_ROOT_DIR</span><span class="p">,</span> <span class="n">pattern</span><span class="p">)):</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="n">Path</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+    <span class="n">results</span><span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;task&quot;</span><span class="p">]][</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;model&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">parse_results_dict</span><span class="p">(</span><span class="n">load_json</span><span class="p">(</span><span class="n">file_path</span><span class="p">))</span>
+
+<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">results</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Couldn&#39;t find any results at </span><span class="si">{</span><span class="n">RESULTS_ROOT_DIR</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "a998789eb64b4cb6b29af43c4d373d03", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<p>Aggregate results into a single DataFrame, and generate a unique identifier for each row:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">llms_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s2">&quot;ACSIncome&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;ACSIncome&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">llms_df</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+llms_df.shape=(20, 20)
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Currently discarding Gemma 2 results as some extra analysis is needed:</span>
+<span class="n">llms_df</span> <span class="o">=</span> <span class="n">llms_df</span><span class="p">[[</span><span class="s2">&quot;gemma-2-&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">id_</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="k">for</span> <span class="n">id_</span> <span class="ow">in</span> <span class="n">llms_df</span><span class="o">.</span><span class="n">index</span><span class="p">]]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">prettify_model_name</span>
+
+<span class="c1"># Add prettified model name</span>
+<span class="n">llms_df</span><span class="p">[</span><span class="s2">&quot;model_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">prettify_model_name</span><span class="p">(</span><span class="n">m</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s2">&quot;--&quot;</span><span class="p">)</span><span class="o">+</span><span class="mi">2</span><span class="p">:])</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">llms_df</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+<span class="n">llms_df</span> <span class="o">=</span> <span class="n">llms_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">&quot;model_name&quot;</span><span class="p">)</span>
+
+<span class="n">llms_df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>AGEP_imp_mean</th>
+      <th>AGEP_imp_std</th>
+      <th>COW_imp_mean</th>
+      <th>COW_imp_std</th>
+      <th>MAR_imp_mean</th>
+      <th>MAR_imp_std</th>
+      <th>OCCP_imp_mean</th>
+      <th>OCCP_imp_std</th>
+      <th>POBP_imp_mean</th>
+      <th>POBP_imp_std</th>
+      <th>...</th>
+      <th>RAC1P_imp_std</th>
+      <th>RELP_imp_mean</th>
+      <th>RELP_imp_std</th>
+      <th>SCHL_imp_mean</th>
+      <th>SCHL_imp_std</th>
+      <th>SEX_imp_mean</th>
+      <th>SEX_imp_std</th>
+      <th>WKHP_imp_mean</th>
+      <th>WKHP_imp_std</th>
+      <th>model_name</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>google--gemma-2b</th>
+      <td>0.030893</td>
+      <td>0.003416</td>
+      <td>0.005972</td>
+      <td>0.001597</td>
+      <td>0.004709</td>
+      <td>0.002058</td>
+      <td>0.033321</td>
+      <td>0.004408</td>
+      <td>0.011186</td>
+      <td>0.003455</td>
+      <td>...</td>
+      <td>0.002855</td>
+      <td>0.004928</td>
+      <td>0.002963</td>
+      <td>0.047764</td>
+      <td>0.002630</td>
+      <td>-0.000595</td>
+      <td>0.002619</td>
+      <td>0.009022</td>
+      <td>0.003113</td>
+      <td>Gemma 2B</td>
+    </tr>
+    <tr>
+      <th>google--gemma-1.1-2b-it</th>
+      <td>0.057288</td>
+      <td>0.001795</td>
+      <td>0.002239</td>
+      <td>0.000988</td>
+      <td>0.003562</td>
+      <td>0.000592</td>
+      <td>0.054559</td>
+      <td>0.001415</td>
+      <td>0.002923</td>
+      <td>0.000761</td>
+      <td>...</td>
+      <td>0.000334</td>
+      <td>0.010951</td>
+      <td>0.000447</td>
+      <td>0.073909</td>
+      <td>0.001346</td>
+      <td>0.003064</td>
+      <td>0.000409</td>
+      <td>0.028131</td>
+      <td>0.001265</td>
+      <td>Gemma 2B (it)</td>
+    </tr>
+  </tbody>
+</table>
+<p>2 rows × 21 columns</p>
+</div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">re</span>
+<span class="n">all_features</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">llms_df</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="p">(</span><span class="n">m</span> <span class="o">:=</span> <span class="n">re</span><span class="o">.</span><span class="n">fullmatch</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;^(?P&lt;name&gt;[a-zA-Z0-9]+)_imp_mean$&quot;</span><span class="p">,</span> <span class="n">col</span><span class="p">))]</span>
+
+<span class="n">feat_imp_cols</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">col</span><span class="si">}</span><span class="s2">_imp_mean&quot;</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">all_features</span><span class="p">]</span>
+<span class="n">feat_imp_std_cols</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">col</span><span class="si">}</span><span class="s2">_imp_std&quot;</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">all_features</span><span class="p">]</span>
+
+<span class="n">all_features</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[&#39;AGEP&#39;, &#39;COW&#39;, &#39;MAR&#39;, &#39;OCCP&#39;, &#39;POBP&#39;, &#39;RAC1P&#39;, &#39;RELP&#39;, &#39;SCHL&#39;, &#39;SEX&#39;, &#39;WKHP&#39;]
+</pre></div></div>
+</div>
+<section id="Get-feature-importance-for-GBM-and-LR">
+<h2>Get feature importance for GBM and LR<a class="headerlink" href="#Get-feature-importance-for-GBM-and-LR" title="Link to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%time</span>
+<span class="kn">from</span> <span class="nn">folktexts.acs.acs_dataset</span> <span class="kn">import</span> <span class="n">ACSDataset</span>
+<span class="n">acs_income_dt</span> <span class="o">=</span> <span class="n">ACSDataset</span><span class="o">.</span><span class="n">make_from_task</span><span class="p">(</span><span class="n">task</span><span class="o">=</span><span class="s2">&quot;ACSIncome&quot;</span><span class="p">,</span> <span class="n">cache_dir</span><span class="o">=</span><span class="n">DATA_DIR</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Loading ACS data...
+CPU times: user 43.1 s, sys: 26.9 s, total: 1min 10s
+Wall time: 1min 19s
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">acs_income_dt</span><span class="o">.</span><span class="n">get_train</span><span class="p">()</span>
+<span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">acs_income_dt</span><span class="o">.</span><span class="n">get_test</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
+<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">HistGradientBoostingClassifier</span>
+<span class="kn">from</span> <span class="nn">xgboost</span> <span class="kn">import</span> <span class="n">XGBClassifier</span>    <span class="c1"># NOTE: requires `pip install xgboost`</span>
+
+<span class="n">baselines</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;GBM&quot;</span><span class="p">:</span> <span class="n">HistGradientBoostingClassifier</span><span class="p">(),</span>
+    <span class="s2">&quot;LR&quot;</span><span class="p">:</span> <span class="n">LogisticRegression</span><span class="p">(),</span>
+    <span class="s2">&quot;XGBoost&quot;</span><span class="p">:</span> <span class="n">XGBClassifier</span><span class="p">(),</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">parse_feat_imp_results</span><span class="p">(</span><span class="n">results</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+    <span class="n">parsed_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">feat</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">tolist</span><span class="p">()):</span>
+        <span class="n">parsed_dict</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">feat</span><span class="si">}</span><span class="s2">_imp_mean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;importances_mean&quot;</span><span class="p">][</span><span class="n">idx</span><span class="p">]</span>
+        <span class="n">parsed_dict</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">feat</span><span class="si">}</span><span class="s2">_imp_std&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="s2">&quot;importances_std&quot;</span><span class="p">][</span><span class="n">idx</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">parsed_dict</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.inspection</span> <span class="kn">import</span> <span class="n">permutation_importance</span>
+<span class="n">baseline_feat_imp_results</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+
+<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">baselines</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+    <span class="c1"># Fit classifier</span>
+    <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+    <span class="c1"># Compute permutation importance</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">permutation_importance</span><span class="p">(</span>
+        <span class="n">estimator</span><span class="o">=</span><span class="n">clf</span><span class="p">,</span>
+        <span class="n">X</span><span class="o">=</span><span class="n">X_test</span><span class="p">,</span>
+        <span class="n">y</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span>
+        <span class="n">scoring</span><span class="o">=</span><span class="s2">&quot;roc_auc&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">baseline_feat_imp_results</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">r</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "9b9ef16acef94f42a75dda6f3e91873a", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/home/acruz/miniconda3/envs/folktexts/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">baselines_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span>
+    <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">parse_feat_imp_results</span><span class="p">(</span><span class="n">r</span><span class="p">)</span> <span class="o">|</span> <span class="p">{</span><span class="s2">&quot;model_name&quot;</span><span class="p">:</span> <span class="n">name</span><span class="p">},</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">baseline_feat_imp_results</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+<span class="p">])</span>
+<span class="n">baselines_df</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>AGEP_imp_mean</th>
+      <th>AGEP_imp_std</th>
+      <th>COW_imp_mean</th>
+      <th>COW_imp_std</th>
+      <th>SCHL_imp_mean</th>
+      <th>SCHL_imp_std</th>
+      <th>MAR_imp_mean</th>
+      <th>MAR_imp_std</th>
+      <th>OCCP_imp_mean</th>
+      <th>OCCP_imp_std</th>
+      <th>...</th>
+      <th>POBP_imp_std</th>
+      <th>RELP_imp_mean</th>
+      <th>RELP_imp_std</th>
+      <th>WKHP_imp_mean</th>
+      <th>WKHP_imp_std</th>
+      <th>SEX_imp_mean</th>
+      <th>SEX_imp_std</th>
+      <th>RAC1P_imp_mean</th>
+      <th>RAC1P_imp_std</th>
+      <th>model_name</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>GBM</th>
+      <td>0.057407</td>
+      <td>0.000544</td>
+      <td>0.006616</td>
+      <td>0.000135</td>
+      <td>0.038793</td>
+      <td>0.000252</td>
+      <td>0.002003</td>
+      <td>0.000039</td>
+      <td>0.061977</td>
+      <td>0.000385</td>
+      <td>...</td>
+      <td>0.000078</td>
+      <td>0.013170</td>
+      <td>0.000146</td>
+      <td>0.075559</td>
+      <td>0.000622</td>
+      <td>0.013678</td>
+      <td>0.000182</td>
+      <td>0.000804</td>
+      <td>0.000042</td>
+      <td>GBM</td>
+    </tr>
+    <tr>
+      <th>LR</th>
+      <td>0.002650</td>
+      <td>0.000024</td>
+      <td>-0.000173</td>
+      <td>0.000084</td>
+      <td>-0.000614</td>
+      <td>0.000018</td>
+      <td>0.022415</td>
+      <td>0.000363</td>
+      <td>0.092150</td>
+      <td>0.000710</td>
+      <td>...</td>
+      <td>0.000185</td>
+      <td>0.049616</td>
+      <td>0.000824</td>
+      <td>0.071947</td>
+      <td>0.000480</td>
+      <td>0.003057</td>
+      <td>0.000042</td>
+      <td>0.000509</td>
+      <td>0.000119</td>
+      <td>LR</td>
+    </tr>
+    <tr>
+      <th>XGBoost</th>
+      <td>0.054982</td>
+      <td>0.000185</td>
+      <td>0.008279</td>
+      <td>0.000207</td>
+      <td>0.035391</td>
+      <td>0.000434</td>
+      <td>0.002507</td>
+      <td>0.000076</td>
+      <td>0.075997</td>
+      <td>0.000635</td>
+      <td>...</td>
+      <td>0.000098</td>
+      <td>0.013292</td>
+      <td>0.000332</td>
+      <td>0.072216</td>
+      <td>0.000690</td>
+      <td>0.012949</td>
+      <td>0.000112</td>
+      <td>0.001207</td>
+      <td>0.000072</td>
+      <td>XGBoost</td>
+    </tr>
+  </tbody>
+</table>
+<p>3 rows × 21 columns</p>
+</div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">llms_df</span><span class="p">,</span> <span class="n">baselines_df</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+df.shape=(19, 21)
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">get_current_timestamp</span>
+<span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">RESULTS_ROOT_DIR</span><span class="p">)</span> <span class="o">/</span> <span class="sa">f</span><span class="s2">&quot;feature_importance.</span><span class="si">{</span><span class="n">get_current_timestamp</span><span class="p">()</span><span class="si">}</span><span class="s2">.csv&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+<section id="Plots">
+<h1>Plots<a class="headerlink" href="#Plots" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">IMGS_DIR</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">RESULTS_ROOT_DIR</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;imgs&quot;</span>
+<span class="n">IMGS_DIR</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">((</span><span class="n">IMGS_DIR</span> <span class="o">/</span> <span class="n">name</span><span class="p">)</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="s2">&quot;.pdf&quot;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">&quot;whitegrid&quot;</span><span class="p">,</span> <span class="n">rc</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;grid.linestyle&quot;</span><span class="p">:</span> <span class="s2">&quot;--&quot;</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rc</span><span class="p">(</span><span class="s2">&quot;figure&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">4.5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
+
+<span class="n">palette_name</span> <span class="o">=</span> <span class="s2">&quot;tab10&quot;</span>
+<span class="c1"># palette_name = &quot;colorblind&quot;</span>
+<span class="n">palette</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">(</span><span class="n">palette_name</span><span class="p">)</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">set_palette</span><span class="p">(</span><span class="n">palette_name</span><span class="p">)</span>
+<span class="n">palette</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<svg  width="550" height="55"><rect x="0" y="0" width="55" height="55" style="fill:#1f77b4;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="55" y="0" width="55" height="55" style="fill:#ff7f0e;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="110" y="0" width="55" height="55" style="fill:#2ca02c;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="165" y="0" width="55" height="55" style="fill:#d62728;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="220" y="0" width="55" height="55" style="fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="275" y="0" width="55" height="55" style="fill:#8c564b;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="330" y="0" width="55" height="55" style="fill:#e377c2;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="385" y="0" width="55" height="55" style="fill:#7f7f7f;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="440" y="0" width="55" height="55" style="fill:#bcbd22;stroke-width:2;stroke:rgb(255,255,255)"/><rect x="495" y="0" width="55" height="55" style="fill:#17becf;stroke-width:2;stroke:rgb(255,255,255)"/></svg></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">barplot_feat_imp</span><span class="p">(</span><span class="n">row</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">,</span> <span class="n">stdev_values</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
+
+    <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span><span class="p">[:</span><span class="n">name</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)]</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">row</span><span class="o">.</span><span class="n">items</span><span class="p">()]</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">row</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span>
+        <span class="n">x</span><span class="o">=</span><span class="n">row</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+        <span class="n">y</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span>
+        <span class="n">xerr</span><span class="o">=</span><span class="n">stdev_values</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span>  <span class="c1"># 2 stdev ~&gt; 95% c.i.</span>
+        <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">capsize</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Mean feature importance (ACSIncome)&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Importance for ROC metric&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">feat_imp_cols_sorted</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">feat_imp_cols</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="n">feat_imp_std_cols_sorted</span> <span class="o">=</span> <span class="p">[</span><span class="n">col</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;_imp_mean&quot;</span><span class="p">,</span> <span class="s2">&quot;_imp_std&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">feat_imp_cols_sorted</span><span class="p">]</span>
+<span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">feat_imp_cols_sorted</span><span class="p">,</span> <span class="n">feat_imp_std_cols_sorted</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[(&#39;OCCP_imp_mean&#39;, &#39;OCCP_imp_std&#39;),
+ (&#39;SCHL_imp_mean&#39;, &#39;SCHL_imp_std&#39;),
+ (&#39;WKHP_imp_mean&#39;, &#39;WKHP_imp_std&#39;),
+ (&#39;AGEP_imp_mean&#39;, &#39;AGEP_imp_std&#39;),
+ (&#39;RELP_imp_mean&#39;, &#39;RELP_imp_std&#39;),
+ (&#39;MAR_imp_mean&#39;, &#39;MAR_imp_std&#39;),
+ (&#39;SEX_imp_mean&#39;, &#39;SEX_imp_std&#39;),
+ (&#39;COW_imp_mean&#39;, &#39;COW_imp_std&#39;),
+ (&#39;POBP_imp_mean&#39;, &#39;POBP_imp_std&#39;),
+ (&#39;RAC1P_imp_mean&#39;, &#39;RAC1P_imp_std&#39;)]
+</pre></div></div>
+</div>
+<p>Mean feature importance among all models:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">barplot_feat_imp</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">feat_imp_cols_sorted</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">df</span><span class="p">[</span><span class="n">feat_imp_std_cols_sorted</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">()[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">save_fig</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">(),</span> <span class="s2">&quot;mean-feat-imp&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_30_0.png" src="../_images/notebooks_parse-feature-importance-results_30_0.png" />
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_feat_imp_subplots</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
+    <span class="n">N_COLS</span> <span class="o">=</span> <span class="mi">4</span>
+    <span class="n">N_ROWS</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">/</span> <span class="n">N_COLS</span><span class="p">)</span>
+
+    <span class="n">cols_order</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">feat_imp_cols_sorted</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">std_cols_order</span> <span class="o">=</span> <span class="p">[</span><span class="n">col</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;_imp_mean&quot;</span><span class="p">,</span> <span class="s2">&quot;_imp_std&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">cols_order</span><span class="p">]</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">N_ROWS</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">N_COLS</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mf">2.8</span> <span class="o">*</span> <span class="n">N_ROWS</span><span class="p">),</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">):</span>
+        <span class="n">ax_row</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">//</span> <span class="n">N_COLS</span>
+        <span class="n">ax_col</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">%</span> <span class="n">N_COLS</span>
+
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">ax_row</span><span class="p">,</span> <span class="n">ax_col</span><span class="p">]</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">m</span><span class="p">]</span>
+
+        <span class="n">barplot_feat_imp</span><span class="p">(</span>
+            <span class="n">row</span><span class="p">[</span><span class="n">cols_order</span><span class="p">],</span> <span class="n">stdev_values</span><span class="o">=</span><span class="n">row</span><span class="p">[</span><span class="n">std_cols_order</span><span class="p">],</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">m</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">baselines</span> <span class="k">else</span> <span class="n">palette</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;model_name&quot;</span><span class="p">])</span>
+
+    <span class="c1"># Set xlim</span>
+    <span class="n">max_xlim</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">ax_i</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">()[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">ax_row</span> <span class="ow">in</span> <span class="n">axes</span> <span class="k">for</span> <span class="n">ax_i</span> <span class="ow">in</span> <span class="n">ax_row</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="n">max_xlim</span><span class="p">)</span>
+
+    <span class="c1"># Remove extra axes</span>
+    <span class="p">[</span><span class="n">axes</span><span class="p">[</span><span class="n">N_ROWS</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">N_COLS</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">((</span><span class="n">N_ROWS</span> <span class="o">*</span> <span class="n">N_COLS</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">))];</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">fig</span>
+</pre></div>
+</div>
+</div>
+<p>Feature importance for each model, plus baselines:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[38]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plot_feat_imp_subplots</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="s2">&quot;all-feat-imps&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_33_0.png" src="../_images/notebooks_parse-feature-importance-results_33_0.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">get_model_size_B</span>
+<span class="n">large_models_filter</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([(</span><span class="n">get_model_size_B</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">default</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">40</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">])</span>
+<span class="c1"># baselines_filter = np.array([(m in baselines) for m in df.index])</span>
+<span class="n">baselines_filter</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([(</span><span class="n">m</span> <span class="o">==</span> <span class="s2">&quot;XGBoost&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+ERROR:root:Could not infer model size from name &#39;GBM&#39;
+ERROR:root:Could not infer model size from name &#39;LR&#39;
+ERROR:root:Could not infer model size from name &#39;XGBoost&#39;
+</pre></div></div>
+</div>
+<p>Plot feature importance among large models (&gt;= 40B):</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[32]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plot_feat_imp_subplots</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">large_models_filter</span> <span class="o">|</span> <span class="n">baselines_filter</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;Feature importance among large models&quot;</span><span class="p">)</span>
+
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="s2">&quot;feat-imps-large-models&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_36_0.png" src="../_images/notebooks_parse-feature-importance-results_36_0.png" />
+</div>
+</div>
+<p>Feature importance averaged among all large models:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[33]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cols_order</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">large_models_filter</span><span class="p">][</span><span class="n">feat_imp_cols_sorted</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="n">std_cols_order</span> <span class="o">=</span> <span class="p">[</span><span class="n">col</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;_imp_mean&quot;</span><span class="p">,</span> <span class="s2">&quot;_imp_std&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">cols_order</span><span class="p">]</span>
+
+<span class="n">barplot_feat_imp</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">large_models_filter</span><span class="p">][</span><span class="n">cols_order</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">df</span><span class="p">[</span><span class="n">large_models_filter</span><span class="p">][</span><span class="n">std_cols_order</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">()[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Mean feature importance</span><span class="se">\n</span><span class="s2">(model size &gt; 50B; ACSIncome)&quot;</span><span class="p">)</span>
+<span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="s2">&quot;mean-feat-imp-large-models&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_38_0.png" src="../_images/notebooks_parse-feature-importance-results_38_0.png" />
+</div>
+</div>
+<p>Plot feature importance for each model separately:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+
+    <span class="n">barplot_feat_imp</span><span class="p">(</span>
+        <span class="n">row</span><span class="p">[</span><span class="n">cols_order</span><span class="p">],</span> <span class="n">row</span><span class="p">[</span><span class="n">std_cols_order</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+        <span class="n">color</span><span class="o">=</span><span class="n">palette</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s2">&quot;model_name&quot;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">baselines</span> <span class="k">else</span> <span class="n">palette</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+    <span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">&quot;model_name&quot;</span><span class="p">])</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">()[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="n">save_fig</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;feat-imp_</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+    <span class="c1"># plt.close(fig)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_0.png" src="../_images/notebooks_parse-feature-importance-results_40_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_1.png" src="../_images/notebooks_parse-feature-importance-results_40_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_2.png" src="../_images/notebooks_parse-feature-importance-results_40_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_3.png" src="../_images/notebooks_parse-feature-importance-results_40_3.png" />
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+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_4.png" src="../_images/notebooks_parse-feature-importance-results_40_4.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_5.png" src="../_images/notebooks_parse-feature-importance-results_40_5.png" />
+</div>
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+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_6.png" src="../_images/notebooks_parse-feature-importance-results_40_6.png" />
+</div>
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+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_7.png" src="../_images/notebooks_parse-feature-importance-results_40_7.png" />
+</div>
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+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
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+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_8.png" src="../_images/notebooks_parse-feature-importance-results_40_8.png" />
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+<div class="nboutput docutils container">
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+<img alt="../_images/notebooks_parse-feature-importance-results_40_9.png" src="../_images/notebooks_parse-feature-importance-results_40_9.png" />
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+<img alt="../_images/notebooks_parse-feature-importance-results_40_10.png" src="../_images/notebooks_parse-feature-importance-results_40_10.png" />
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+<img alt="../_images/notebooks_parse-feature-importance-results_40_11.png" src="../_images/notebooks_parse-feature-importance-results_40_11.png" />
+</div>
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+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_12.png" src="../_images/notebooks_parse-feature-importance-results_40_12.png" />
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+<div class="nboutput docutils container">
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+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_13.png" src="../_images/notebooks_parse-feature-importance-results_40_13.png" />
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+<div class="nboutput docutils container">
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+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_14.png" src="../_images/notebooks_parse-feature-importance-results_40_14.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
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+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_parse-feature-importance-results_40_15.png" src="../_images/notebooks_parse-feature-importance-results_40_15.png" />
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+<div class="nboutput nblast docutils container">
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+</div>
+</div>
+<hr class="docutils" />
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
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+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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diff --git a/notebooks/parse-feature-importance-results.ipynb b/notebooks/parse-feature-importance-results.ipynb
new file mode 100644
index 0000000..9d40549
--- /dev/null
+++ b/notebooks/parse-feature-importance-results.ipynb
@@ -0,0 +1,1253 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "04c2f12d-f989-4ac9-b90f-3464a8bcca96",
+   "metadata": {},
+   "source": [
+    "# Fetch and parse feature importance results under a given directory\n",
+    "\n",
+    "This script collects all feature importance results files under a given root directory, parses them, and aggregates them into a more easily digestable pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3b241208-d10f-43cf-a486-84c54bbf43c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faf6afa4-8648-4d35-9312-65636ea5d0b2",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root results directory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26089a60-81c0-4736-8ba5-99572ec01398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RESULTS_ROOT_DIR = Path(\"/fast/groups/sf\") / \"folktexts-results\" / \"2024-07-03\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d1af7f-d013-40a0-80de-cb9ad65cff6d",
+   "metadata": {},
+   "source": [
+    "Set the local path to the root data directory (needed only to train baseline ML methods):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b18dad87-a1ed-495b-93b6-4c8af4043ddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = Path(\"/fast/groups/sf\") / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db7c98c5-2942-4f88-984a-8c9014afe761",
+   "metadata": {},
+   "source": [
+    "Function to parse and flatten results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eeded803-6ba7-49b2-81b6-836549f3e10b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import operator\n",
+    "from functools import reduce\n",
+    "\n",
+    "def parse_results_dict(dct: dict) -> dict:\n",
+    "    return reduce(\n",
+    "        operator.or_,\n",
+    "        [{\n",
+    "            f\"{feat}_imp_mean\": dct[feat][\"imp_mean\"],\n",
+    "            f\"{feat}_imp_std\": dct[feat][\"imp_std\"]\n",
+    "        } for feat in dct],\n",
+    "    )\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02009a86-e099-4ec0-8676-8d66190ceddb",
+   "metadata": {},
+   "source": [
+    "Iteratively search the root directory for results files matching the given regex:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "aaefa99d-dbd7-40a3-a3c1-7571d3811409",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a998789eb64b4cb6b29af43c4d373d03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from utils import find_files, load_json\n",
+    "import re\n",
+    "\n",
+    "# Results file name pattern\n",
+    "pattern = r'^feature-importance[.](?P<task>\\w+)[.](?P<model>.*?)[.]json$'\n",
+    "\n",
+    "# Find results files and aggregate\n",
+    "from collections import defaultdict\n",
+    "results = defaultdict(dict)\n",
+    "\n",
+    "for file_path in tqdm(find_files(RESULTS_ROOT_DIR, pattern)):\n",
+    "    m = re.match(pattern, Path(file_path).name)\n",
+    "    results[m[\"task\"]][m[\"model\"]] = parse_results_dict(load_json(file_path))\n",
+    "\n",
+    "if len(results) == 0:\n",
+    "    raise RuntimeError(f\"Couldn't find any results at {RESULTS_ROOT_DIR}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ecc0d33-902d-492a-89c6-d9729fe69fa1",
+   "metadata": {},
+   "source": [
+    "Aggregate results into a single DataFrame, and generate a unique identifier for each row:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5e89e632-a448-4b0e-8d8e-621f6c74bed7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "llms_df.shape=(20, 20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "llms_df = pd.DataFrame(results[\"ACSIncome\"].values(), results[\"ACSIncome\"].keys())\n",
+    "print(f\"{llms_df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c374c14e-0e81-4813-9983-309ce6215648",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Currently discarding Gemma 2 results as some extra analysis is needed:\n",
+    "llms_df = llms_df[[\"gemma-2-\" not in id_.lower() for id_ in llms_df.index]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cbe9d8e6-8af1-4663-a976-6c9d5020cc20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AGEP_imp_mean</th>\n",
+       "      <th>AGEP_imp_std</th>\n",
+       "      <th>COW_imp_mean</th>\n",
+       "      <th>COW_imp_std</th>\n",
+       "      <th>MAR_imp_mean</th>\n",
+       "      <th>MAR_imp_std</th>\n",
+       "      <th>OCCP_imp_mean</th>\n",
+       "      <th>OCCP_imp_std</th>\n",
+       "      <th>POBP_imp_mean</th>\n",
+       "      <th>POBP_imp_std</th>\n",
+       "      <th>...</th>\n",
+       "      <th>RAC1P_imp_std</th>\n",
+       "      <th>RELP_imp_mean</th>\n",
+       "      <th>RELP_imp_std</th>\n",
+       "      <th>SCHL_imp_mean</th>\n",
+       "      <th>SCHL_imp_std</th>\n",
+       "      <th>SEX_imp_mean</th>\n",
+       "      <th>SEX_imp_std</th>\n",
+       "      <th>WKHP_imp_mean</th>\n",
+       "      <th>WKHP_imp_std</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>google--gemma-2b</th>\n",
+       "      <td>0.030893</td>\n",
+       "      <td>0.003416</td>\n",
+       "      <td>0.005972</td>\n",
+       "      <td>0.001597</td>\n",
+       "      <td>0.004709</td>\n",
+       "      <td>0.002058</td>\n",
+       "      <td>0.033321</td>\n",
+       "      <td>0.004408</td>\n",
+       "      <td>0.011186</td>\n",
+       "      <td>0.003455</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.002855</td>\n",
+       "      <td>0.004928</td>\n",
+       "      <td>0.002963</td>\n",
+       "      <td>0.047764</td>\n",
+       "      <td>0.002630</td>\n",
+       "      <td>-0.000595</td>\n",
+       "      <td>0.002619</td>\n",
+       "      <td>0.009022</td>\n",
+       "      <td>0.003113</td>\n",
+       "      <td>Gemma 2B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>google--gemma-1.1-2b-it</th>\n",
+       "      <td>0.057288</td>\n",
+       "      <td>0.001795</td>\n",
+       "      <td>0.002239</td>\n",
+       "      <td>0.000988</td>\n",
+       "      <td>0.003562</td>\n",
+       "      <td>0.000592</td>\n",
+       "      <td>0.054559</td>\n",
+       "      <td>0.001415</td>\n",
+       "      <td>0.002923</td>\n",
+       "      <td>0.000761</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000334</td>\n",
+       "      <td>0.010951</td>\n",
+       "      <td>0.000447</td>\n",
+       "      <td>0.073909</td>\n",
+       "      <td>0.001346</td>\n",
+       "      <td>0.003064</td>\n",
+       "      <td>0.000409</td>\n",
+       "      <td>0.028131</td>\n",
+       "      <td>0.001265</td>\n",
+       "      <td>Gemma 2B (it)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         AGEP_imp_mean  AGEP_imp_std  COW_imp_mean  \\\n",
+       "google--gemma-2b              0.030893      0.003416      0.005972   \n",
+       "google--gemma-1.1-2b-it       0.057288      0.001795      0.002239   \n",
+       "\n",
+       "                         COW_imp_std  MAR_imp_mean  MAR_imp_std  \\\n",
+       "google--gemma-2b            0.001597      0.004709     0.002058   \n",
+       "google--gemma-1.1-2b-it     0.000988      0.003562     0.000592   \n",
+       "\n",
+       "                         OCCP_imp_mean  OCCP_imp_std  POBP_imp_mean  \\\n",
+       "google--gemma-2b              0.033321      0.004408       0.011186   \n",
+       "google--gemma-1.1-2b-it       0.054559      0.001415       0.002923   \n",
+       "\n",
+       "                         POBP_imp_std  ...  RAC1P_imp_std  RELP_imp_mean  \\\n",
+       "google--gemma-2b             0.003455  ...       0.002855       0.004928   \n",
+       "google--gemma-1.1-2b-it      0.000761  ...       0.000334       0.010951   \n",
+       "\n",
+       "                         RELP_imp_std  SCHL_imp_mean  SCHL_imp_std  \\\n",
+       "google--gemma-2b             0.002963       0.047764      0.002630   \n",
+       "google--gemma-1.1-2b-it      0.000447       0.073909      0.001346   \n",
+       "\n",
+       "                         SEX_imp_mean  SEX_imp_std  WKHP_imp_mean  \\\n",
+       "google--gemma-2b            -0.000595     0.002619       0.009022   \n",
+       "google--gemma-1.1-2b-it      0.003064     0.000409       0.028131   \n",
+       "\n",
+       "                         WKHP_imp_std     model_name  \n",
+       "google--gemma-2b             0.003113       Gemma 2B  \n",
+       "google--gemma-1.1-2b-it      0.001265  Gemma 2B (it)  \n",
+       "\n",
+       "[2 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from utils import prettify_model_name\n",
+    "\n",
+    "# Add prettified model name\n",
+    "llms_df[\"model_name\"] = [prettify_model_name(m[m.find(\"--\")+2:]) for m in llms_df.index]\n",
+    "llms_df = llms_df.sort_values(\"model_name\")\n",
+    "\n",
+    "llms_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b83b99c-de78-4792-b712-305929207a94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['AGEP', 'COW', 'MAR', 'OCCP', 'POBP', 'RAC1P', 'RELP', 'SCHL', 'SEX', 'WKHP']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "all_features = [m[\"name\"] for col in llms_df.columns if (m := re.fullmatch(r\"^(?P<name>[a-zA-Z0-9]+)_imp_mean$\", col))]\n",
+    "\n",
+    "feat_imp_cols = [f\"{col}_imp_mean\" for col in all_features]\n",
+    "feat_imp_std_cols = [f\"{col}_imp_std\" for col in all_features]\n",
+    "\n",
+    "all_features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba2fe6d9-5a91-4424-bf09-5584caf3574b",
+   "metadata": {},
+   "source": [
+    "## Get feature importance for GBM and LR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f7f90d13-0b1c-467f-99ce-62b902958111",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading ACS data...\n",
+      "CPU times: user 43.1 s, sys: 26.9 s, total: 1min 10s\n",
+      "Wall time: 1min 19s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from folktexts.acs.acs_dataset import ACSDataset\n",
+    "acs_income_dt = ACSDataset.make_from_task(task=\"ACSIncome\", cache_dir=DATA_DIR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "401a71fd-fbc9-496f-92c2-1fa721873d41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, y_train = acs_income_dt.get_train()\n",
+    "X_test, y_test = acs_income_dt.get_test()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c316b332-0c67-421e-9321-d718d91fd838",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
+    "from xgboost import XGBClassifier    # NOTE: requires `pip install xgboost`\n",
+    "\n",
+    "baselines = {\n",
+    "    \"GBM\": HistGradientBoostingClassifier(),\n",
+    "    \"LR\": LogisticRegression(),\n",
+    "    \"XGBoost\": XGBClassifier(),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5876f670-bc7a-4733-8281-05d4fc150465",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parse_feat_imp_results(results) -> dict:\n",
+    "    parsed_dict = dict()\n",
+    "    for idx, feat in enumerate(X_train.columns.tolist()):\n",
+    "        parsed_dict[f\"{feat}_imp_mean\"] = results[\"importances_mean\"][idx]\n",
+    "        parsed_dict[f\"{feat}_imp_std\"] = results[\"importances_std\"][idx]\n",
+    "\n",
+    "    return parsed_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "10e14028-aa11-47d5-bd15-dbaa4cc92875",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b9ef16acef94f42a75dda6f3e91873a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/acruz/miniconda3/envs/folktexts/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.inspection import permutation_importance\n",
+    "baseline_feat_imp_results = dict()\n",
+    "\n",
+    "for name, clf in tqdm(baselines.items()):\n",
+    "    # Fit classifier\n",
+    "    clf.fit(X_train, y_train)\n",
+    "\n",
+    "    # Compute permutation importance\n",
+    "    r = permutation_importance(\n",
+    "        estimator=clf,\n",
+    "        X=X_test,\n",
+    "        y=y_test,\n",
+    "        scoring=\"roc_auc\",\n",
+    "    )\n",
+    "\n",
+    "    baseline_feat_imp_results[name] = r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b3543466-3b2e-4221-be63-49e1e7bd5e7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AGEP_imp_mean</th>\n",
+       "      <th>AGEP_imp_std</th>\n",
+       "      <th>COW_imp_mean</th>\n",
+       "      <th>COW_imp_std</th>\n",
+       "      <th>SCHL_imp_mean</th>\n",
+       "      <th>SCHL_imp_std</th>\n",
+       "      <th>MAR_imp_mean</th>\n",
+       "      <th>MAR_imp_std</th>\n",
+       "      <th>OCCP_imp_mean</th>\n",
+       "      <th>OCCP_imp_std</th>\n",
+       "      <th>...</th>\n",
+       "      <th>POBP_imp_std</th>\n",
+       "      <th>RELP_imp_mean</th>\n",
+       "      <th>RELP_imp_std</th>\n",
+       "      <th>WKHP_imp_mean</th>\n",
+       "      <th>WKHP_imp_std</th>\n",
+       "      <th>SEX_imp_mean</th>\n",
+       "      <th>SEX_imp_std</th>\n",
+       "      <th>RAC1P_imp_mean</th>\n",
+       "      <th>RAC1P_imp_std</th>\n",
+       "      <th>model_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>GBM</th>\n",
+       "      <td>0.057407</td>\n",
+       "      <td>0.000544</td>\n",
+       "      <td>0.006616</td>\n",
+       "      <td>0.000135</td>\n",
+       "      <td>0.038793</td>\n",
+       "      <td>0.000252</td>\n",
+       "      <td>0.002003</td>\n",
+       "      <td>0.000039</td>\n",
+       "      <td>0.061977</td>\n",
+       "      <td>0.000385</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000078</td>\n",
+       "      <td>0.013170</td>\n",
+       "      <td>0.000146</td>\n",
+       "      <td>0.075559</td>\n",
+       "      <td>0.000622</td>\n",
+       "      <td>0.013678</td>\n",
+       "      <td>0.000182</td>\n",
+       "      <td>0.000804</td>\n",
+       "      <td>0.000042</td>\n",
+       "      <td>GBM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LR</th>\n",
+       "      <td>0.002650</td>\n",
+       "      <td>0.000024</td>\n",
+       "      <td>-0.000173</td>\n",
+       "      <td>0.000084</td>\n",
+       "      <td>-0.000614</td>\n",
+       "      <td>0.000018</td>\n",
+       "      <td>0.022415</td>\n",
+       "      <td>0.000363</td>\n",
+       "      <td>0.092150</td>\n",
+       "      <td>0.000710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000185</td>\n",
+       "      <td>0.049616</td>\n",
+       "      <td>0.000824</td>\n",
+       "      <td>0.071947</td>\n",
+       "      <td>0.000480</td>\n",
+       "      <td>0.003057</td>\n",
+       "      <td>0.000042</td>\n",
+       "      <td>0.000509</td>\n",
+       "      <td>0.000119</td>\n",
+       "      <td>LR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>XGBoost</th>\n",
+       "      <td>0.054982</td>\n",
+       "      <td>0.000185</td>\n",
+       "      <td>0.008279</td>\n",
+       "      <td>0.000207</td>\n",
+       "      <td>0.035391</td>\n",
+       "      <td>0.000434</td>\n",
+       "      <td>0.002507</td>\n",
+       "      <td>0.000076</td>\n",
+       "      <td>0.075997</td>\n",
+       "      <td>0.000635</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000098</td>\n",
+       "      <td>0.013292</td>\n",
+       "      <td>0.000332</td>\n",
+       "      <td>0.072216</td>\n",
+       "      <td>0.000690</td>\n",
+       "      <td>0.012949</td>\n",
+       "      <td>0.000112</td>\n",
+       "      <td>0.001207</td>\n",
+       "      <td>0.000072</td>\n",
+       "      <td>XGBoost</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         AGEP_imp_mean  AGEP_imp_std  COW_imp_mean  COW_imp_std  \\\n",
+       "GBM           0.057407      0.000544      0.006616     0.000135   \n",
+       "LR            0.002650      0.000024     -0.000173     0.000084   \n",
+       "XGBoost       0.054982      0.000185      0.008279     0.000207   \n",
+       "\n",
+       "         SCHL_imp_mean  SCHL_imp_std  MAR_imp_mean  MAR_imp_std  \\\n",
+       "GBM           0.038793      0.000252      0.002003     0.000039   \n",
+       "LR           -0.000614      0.000018      0.022415     0.000363   \n",
+       "XGBoost       0.035391      0.000434      0.002507     0.000076   \n",
+       "\n",
+       "         OCCP_imp_mean  OCCP_imp_std  ...  POBP_imp_std  RELP_imp_mean  \\\n",
+       "GBM           0.061977      0.000385  ...      0.000078       0.013170   \n",
+       "LR            0.092150      0.000710  ...      0.000185       0.049616   \n",
+       "XGBoost       0.075997      0.000635  ...      0.000098       0.013292   \n",
+       "\n",
+       "         RELP_imp_std  WKHP_imp_mean  WKHP_imp_std  SEX_imp_mean  SEX_imp_std  \\\n",
+       "GBM          0.000146       0.075559      0.000622      0.013678     0.000182   \n",
+       "LR           0.000824       0.071947      0.000480      0.003057     0.000042   \n",
+       "XGBoost      0.000332       0.072216      0.000690      0.012949     0.000112   \n",
+       "\n",
+       "         RAC1P_imp_mean  RAC1P_imp_std  model_name  \n",
+       "GBM            0.000804       0.000042         GBM  \n",
+       "LR             0.000509       0.000119          LR  \n",
+       "XGBoost        0.001207       0.000072     XGBoost  \n",
+       "\n",
+       "[3 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "baselines_df = pd.DataFrame([\n",
+    "    pd.Series(parse_feat_imp_results(r) | {\"model_name\": name}, name=name) for name, r in baseline_feat_imp_results.items()\n",
+    "])\n",
+    "baselines_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "02560b20-98b7-427a-994e-71f3e1a84c03",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "df.shape=(19, 21)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.concat((llms_df, baselines_df), axis=0)\n",
+    "print(f\"{df.shape=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "36c8f364-eaa6-4bee-ac15-4d03c5314e4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import get_current_timestamp\n",
+    "df.to_csv(Path(RESULTS_ROOT_DIR) / f\"feature_importance.{get_current_timestamp()}.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f4c4ba2-00f9-45cd-b9e0-b9ce3e0edc9d",
+   "metadata": {},
+   "source": [
+    "# Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "53df0763-7d81-4edd-8e6b-f444546b9d82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IMGS_DIR = Path(RESULTS_ROOT_DIR) / \"imgs\"\n",
+    "IMGS_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "def save_fig(fig, name):\n",
+    "    fig.savefig((IMGS_DIR / name).with_suffix(\".pdf\"), bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e3ca551a-2d21-4eb6-a573-a504687e0d1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1f77b4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff7f0e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2ca02c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d62728;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8c564b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e377c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7f7f7f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bcbd22;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#17becf;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
+      ],
+      "text/plain": [
+       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
+       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
+       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
+       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
+       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
+       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
+       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
+       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
+       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
+       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"whitegrid\", rc={\"grid.linestyle\": \"--\"})\n",
+    "plt.rc(\"figure\", figsize=(4.5, 3.5), dpi=200)\n",
+    "\n",
+    "palette_name = \"tab10\"\n",
+    "# palette_name = \"colorblind\"\n",
+    "palette = sns.color_palette(palette_name)\n",
+    "sns.set_palette(palette_name)\n",
+    "palette"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8a97c7b0-7021-4727-8a8c-cf10806d573a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def barplot_feat_imp(row: pd.Series, stdev_values=None, ax=None, **kwargs):\n",
+    "    if ax is None: ax = plt.gca()\n",
+    "\n",
+    "    labels = [name[:name.find(\"_\")] for name, val in row.items()]\n",
+    "    sns.barplot(x=row, y=labels, ax=ax, **kwargs)\n",
+    "\n",
+    "    ax.errorbar(\n",
+    "        x=row.values,\n",
+    "        y=labels,\n",
+    "        xerr=stdev_values * 2,  # 2 stdev ~> 95% c.i.\n",
+    "        fmt='none', c='grey', capsize=0,\n",
+    "    )\n",
+    "\n",
+    "    ax.set_title(\"Mean feature importance (ACSIncome)\")\n",
+    "    ax.set_xlabel(\"Importance for ROC metric\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d71958d8-eaf8-4448-8f32-d88c017182f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('OCCP_imp_mean', 'OCCP_imp_std'),\n",
+       " ('SCHL_imp_mean', 'SCHL_imp_std'),\n",
+       " ('WKHP_imp_mean', 'WKHP_imp_std'),\n",
+       " ('AGEP_imp_mean', 'AGEP_imp_std'),\n",
+       " ('RELP_imp_mean', 'RELP_imp_std'),\n",
+       " ('MAR_imp_mean', 'MAR_imp_std'),\n",
+       " ('SEX_imp_mean', 'SEX_imp_std'),\n",
+       " ('COW_imp_mean', 'COW_imp_std'),\n",
+       " ('POBP_imp_mean', 'POBP_imp_std'),\n",
+       " ('RAC1P_imp_mean', 'RAC1P_imp_std')]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feat_imp_cols_sorted = df[feat_imp_cols].mean().sort_values(ascending=False).index.tolist()\n",
+    "feat_imp_std_cols_sorted = [col.replace(\"_imp_mean\", \"_imp_std\") for col in feat_imp_cols_sorted]\n",
+    "list(zip(feat_imp_cols_sorted, feat_imp_std_cols_sorted))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b2372ab-fc18-443a-bf26-b979b9e3c03e",
+   "metadata": {},
+   "source": [
+    "Mean feature importance among all models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "62b9793d-a99b-40c9-b091-da1067e5f96d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "barplot_feat_imp(df[feat_imp_cols_sorted].mean(), df[feat_imp_std_cols_sorted].mean())\n",
+    "ax = plt.gca()\n",
+    "ax.set_xlim(left=0, right=ax.get_xlim()[-1])\n",
+    "save_fig(plt.gcf(), \"mean-feat-imp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "68cfa49f-499d-4320-8511-408a8671e791",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_feat_imp_subplots(df):\n",
+    "    N_COLS = 4\n",
+    "    N_ROWS = math.ceil(len(df) / N_COLS)\n",
+    "\n",
+    "    cols_order = df[feat_imp_cols_sorted].mean().sort_values(ascending=False).index.tolist()\n",
+    "    std_cols_order = [col.replace(\"_imp_mean\", \"_imp_std\") for col in cols_order]\n",
+    "    \n",
+    "    fig, axes = plt.subplots(nrows=N_ROWS, ncols=N_COLS, figsize=(12, 2.8 * N_ROWS), sharey=True, sharex=True)\n",
+    "    \n",
+    "    for idx, m in enumerate(df.index):\n",
+    "        ax_row = idx // N_COLS\n",
+    "        ax_col = idx % N_COLS\n",
+    "    \n",
+    "        ax = axes[ax_row, ax_col]\n",
+    "        row = df.loc[m]\n",
+    "        \n",
+    "        barplot_feat_imp(\n",
+    "            row[cols_order], stdev_values=row[std_cols_order],\n",
+    "            ax=ax,\n",
+    "            color=palette[0] if m not in baselines else palette[2],\n",
+    "        )\n",
+    "        \n",
+    "        ax.set_title(row[\"model_name\"])\n",
+    "\n",
+    "    # Set xlim\n",
+    "    max_xlim = max(ax_i.get_xlim()[-1] for ax_row in axes for ax_i in ax_row)\n",
+    "    axes[0,0].set_xlim(left=0, right=max_xlim)\n",
+    "\n",
+    "    # Remove extra axes\n",
+    "    [axes[N_ROWS-1, N_COLS - 1 - i].remove() for i in range((N_ROWS * N_COLS) - len(df))];\n",
+    "\n",
+    "    plt.plot()\n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a70eb7d-1e7e-4dc8-9a0e-ee79bcc6d6cf",
+   "metadata": {},
+   "source": [
+    "Feature importance for each model, plus baselines:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9f64cbcb-3a1d-4831-9d9e-46e4081d0e9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2400x2800 with 19 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_feat_imp_subplots(df)\n",
+    "save_fig(fig, \"all-feat-imps\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "2827713d-0828-4e69-9618-0c3304c6a29c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR:root:Could not infer model size from name 'GBM'\n",
+      "ERROR:root:Could not infer model size from name 'LR'\n",
+      "ERROR:root:Could not infer model size from name 'XGBoost'\n"
+     ]
+    }
+   ],
+   "source": [
+    "from folktexts.llm_utils import get_model_size_B\n",
+    "large_models_filter = np.array([(get_model_size_B(m, default=-1) >= 40) for m in df.index])\n",
+    "# baselines_filter = np.array([(m in baselines) for m in df.index])\n",
+    "baselines_filter = np.array([(m == \"XGBoost\") for m in df.index])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68160fcf-3c16-46ec-8222-7a4ddf105503",
+   "metadata": {},
+   "source": [
+    "Plot feature importance among large models (>= 40B):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "085d4f05-85ce-4047-9671-859a1c307aea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2400x1120 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_feat_imp_subplots(df[large_models_filter | baselines_filter])\n",
+    "plt.suptitle(\"Feature importance among large models\")\n",
+    "\n",
+    "save_fig(fig, \"feat-imps-large-models\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "078abc0b-cb4e-4434-bf65-185da6459f7c",
+   "metadata": {},
+   "source": [
+    "Feature importance averaged among all large models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "b232fa0a-30ec-4dd6-8bb8-d501ff69c89b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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e2LhxI2rUqJFjP3t7eyxYsEAoIzY2Frt3787nlRKZDpMnIjIaTVc87enINTTJj/Z+Ym3dulXYnjVrVr4DmgcPHow6deoAAC5cuICEhIQCHU+bpaUlvL29AUDodifGN998AwsL/T2jJRKJziD+kJAQg+MzxOnTp4VZEXv06KG3S5E2Z2dnfPrppwCyTnKPHDlSqOMPGTJE2L506VKhyipqTZo0gY+PT577mPv9LShLS0vMmjUr18dbtmyJKlWqCLednJwwadKkXPfv1auXcPL/8OHDfJMSAKhWrRrGjh2b6+POzs747LPPhNt79+4VTuA1IiMjcfXqVeH2d999l2dC88UXXwgJWWpqKg4ePJhvnMOHD0ft2rXz3c/UDPkMffzxx/Dw8Mj18QEDBgjfWY8ePdL7dzt+/LgwyYetrS2WLVuW6/dcXhQKhdDN2MrKCgsXLsx3Up4ZM2YIFywCAgIKfEwiY+GYJyIymgEDBmDdunVQq9Xw9fXF+++/DyDryrbmhLBGjRpo2bKl6DIzMzMRGBgIIOsKZLdu3UQ9r23btnj48CHUajWuX7+e5yDz5ORk3Lx5Ew8ePEBiYiLS09OFVgMg6wRQ4969e/Dy8srz2NWrV0ejRo3y3Kdhw4bCtr7Z8kzp/Pnzwrbmb5Qf7TEGwcHBGDNmTK77qlQqhIaGIiwsDNHR0UhNTdWZRl3bvXv3REZdPPTv3z/ffUz9/hpbq1atcrTOZFe/fn1hrF63bt3yTEqsra1Ro0YNREZGQq1W4/nz53pnm9P2/vvv53sS7uPjg+XLl0OpVOLVq1d4+PChTovH5cuXhe0GDRrofMb0sbW1xfvvv48dO3YAAIKCgvDJJ5/k+Rwxf39jMMVnqE+fPnk+rpmx9NGjR7n+3f79919hu3///gWamU9baGgoXr9+DQBo3769qMmDXF1dUadOHURGRiIiIgIpKSlmm2CFSjcmT0RkNFWrVkXr1q1x5coVBAYGIjY2Fs7Ozjh16pSwgGtBW53Cw8ORnp4OIKvL29KlS0U9T7s1J7duUNHR0fj+++9x7NgxvbPu6SOmFUvMwr/ag8XFXJk3phs3bgjbx48f17lanxvtWctevnypd5/MzEzs2LED27ZtE931rDCtguaQX1IMmO79NZX69evnu4+Dg4Ownd/03gBQrlw5YVtM/W7evLmoMmvXro0HDx4AyEoatJMn7SRCTHkA0KJFCyF5unv3bp77WlpamnxRb1N+hozxvXTz5k1hW1/XabG0y4mOjsaiRYtEPU/zO6JWqxEdHc3kicyCyRMRGdWAAQNw5coVKJVKBAQEYOzYsUKffIlEUuDk6dWrV8J2YmKizoxyYukbp3T37l2MHj1a1BgmbZrZ3/Ii5gdd+yp7bleUTUX7PTVkvJXmBEabXC7HlClTcOHChQKVJeb9LE7EXGk3xftrSgWtr6ao35UrV853H81+muQpPj5e5zHt29rdDPNStWpVYTu/JMTBwcGgLmpimfozJObvpj3WSt/fTdNaBKBASzdkp/0Z0UyTXlAF/e4mMhYmT0RkVH369MGSJUuQkZEBX19fDBgwQOjq0bJlywL/4GZfp8UQ2deEksvl+Pzzz4Uf3woVKuDjjz9G+/btUbNmTZQrVw7W1tZC/3rtBXCzj7PQp7AL35paYVu6sr+fQNaAe81Jn0QiQd++fdGrVy+4ubnB1dUVZcqU0Tkx03QHEvN+FidiJjoxxftrSgWtr6ao32KnZ7e1tRW2sycNmhbq7PuJPW5+SYip12Mz9WfIGH837fdI7Husjym+14mKCpMnIjIqe3t7dO/eHQcPHkR4eDi+//574QrmwIEDC1ye9g+0u7s7/P39Cx3jsWPH8OzZMwBZ/ej37t0LFxeXXPcvaa0j+bGxsRFOXg4cOJDv2JD8yOVyoesTAKxYsSLPv3VRd1PMi/bYNmMx9vtbGmRkZIjaTztByr7ul/Z3hfZ+Yo+rbx2xolJSPkN2dnbCRSex77E+2knryJEjMW/evELHRlRUONseERmd9o/+/v37AWRdsc9vwLI+2gOJ4+LiCh0boDs71aeffppn4gRAGCj/rtB+TzWLUxbG7du3hROp+vXr55skm+r9zK/LkT7GuAKenbHf39JA7Dgv7XFA2Res1u5SKbY87claxCyAbSrF5TOUH+26rbkAZQjtCUqM9b1OVFSYPBGR0XXo0AHOzs4693Xv3t2gwb0NGjQQZvZ6/fq1sFZUYWj3txcziFrMgP+SpGnTpsL29evXC11ecXk/tVsOkpOT8+3O9OLFC5NcwTf2+1saaE8gkJvk5GSdmS+zt+g1aNBA2NaetCMv2n8fY7YQFrSLXHH5DOWnWbNmwrb27IYF1aRJE2H7xo0bJa77LpVuTJ6IyOhkMpmwNpKGIV32gKxxBtrTOP/111+FCQ0AdNYTyb6Yb3ahoaFFvg6TqXXt2lXY3rdvH96+fVuo8rRPFPPrfqVSqfDPP/+IKld7fJGYliR7e3thtrCMjAw8evQoz/1NtZ6Ssd/f0uDQoUP5jmEJCAgQ9nF2dhbWctPQ/p64e/cuwsLC8iwvIyNDZ0IP7ecXlnbdVSgU+e5vqs+QsXXu3FnYPnToUI5JO8Rq2bKlMINjdHQ0Tp8+bZT4iIoCkyciMokpU6Zg7969wr9OnToZXNaECROE7Z07dwrrPomhr9uU9qQVef1oZ2Rk4LvvvhN9rJKid+/eqFmzJoCs92fBggWir/ympaXlGOug/X5evXo1z65wf/zxR74ntRra0ybHxMSIeo72Fe0DBw7kul90dDQ2bdokqsyCMvb7Wxo8efIE27Zty/XxuLg4/PLLL8LtDz74IEfrTt26ddG6dWvh9uLFi/NMXH766Sdh9jh7e3vRa3KJoV13tVuVcmOqz5Cx9erVS5ihMD09Hd98841Bs4VaWVkJC0MDwMKFC0V/xgF29SPzYvJERCbh4OAAT09P4Z9MJjO4rDZt2mDQoEEAslogJk6ciI0bN+Y6kcPbt29x8uRJTJkyBVOmTMnxuPZCuwcOHMCWLVtyXPV+/Pgxxo4dizt37hRqVqniSCaTYcGCBcLfZP/+/Zg4cSIiIyNzfc69e/ewevVqdO3aNcdYh4YNG8LV1RVA1hiiadOm5TgRksvlWLt2LdasWSP6/dRef+jo0aOinqN9Arx161YcO3Ysxz43b97EiBEjkJSUpDNOyliM/f6WBpaWlvj+++/x559/5pjEIzIyEmPGjBESHScnJ4wePVpvOV988YXwvl+7dg2ff/65zvTaQFZdXLNmjU6yNnXqVKNOGFGtWjVhUoTnz5/j9u3bee5vqs+QsVlYWODbb78VEtczZ85g3LhxudbtZ8+eYe3atfD19c3x2JgxY4TPeExMDIYMGYIjR47kOolLfHw8/v77bwwaNAibN282zgsiMgBn2yOiEmHRokWIjY3FhQsXoFAo8MMPP2DDhg1o0qQJqlSpAisrKyQnJ+PJkyeIiIgQFr3Vt6hpp06d0Lp1a1y9ehVqtRorV67Erl270KhRI9jb2+Px48e4ceMGlEolXF1dMWrUKKxevbqoX7JJdejQAQsWLMCCBQugVCpx/vx5/Pvvv6hXrx7c3d1hZ2eHN2/eIDY2FmFhYXl2z5FKpZg2bRq++eYbAMDFixfRp08fNG/eHFWqVEFiYiKuXLkizNK1aNEifPXVV/nG2Lt3b/z9998Asrpr3rlzBw0bNtSZqWvo0KGoUaOGcLt///7YsmULwsLCoFAo8J///AeNGjWCh4cHVCoVwsPDhcVQP//8c+zfv19n0gBjMeb7WxrMnDkTy5Ytw7Jly7Blyxa0bNkStra2iIqKQnBwsHBCbWFhgWXLlum07Ghr0aIFvvzyS6xatQpA1sl9165d0bZtW1SuXBlJSUkICgpCYmKi8JyePXvmmowZSiaTCbOOAsCoUaPQuXNnVK5cWUjuypUrh8mTJwMw3WfIFLp164YvvvgCa9asAZA19ql///7w8PBAvXr1YGtri6SkJISHhwtdZzVLPWizs7PDhg0bMHr0aDx79gyxsbGYPn06ypcvj2bNmsHJyQlqtRpJSUl48OABHj9+LNQDY3axJCooJk9EVCJYWVlh06ZNWL9+PbZu3YqMjAxkZGQgKCgo1+dYWlrqDHDW9tNPP2HixIm4c+cOgKwrpNmv+NerVw9r167N96pxSfXRRx+hRo0amD9/PqKioqBWqxEREYGIiIhcn1O/fn2UK1cux/1DhgzBkydP8NtvvwHI6tJz8eJFnX3KlCmDb775Bt7e3qJO/Dp27Ij3339fOAG9desWbt26pbNP165ddZInCwsLrF+/HmPGjMHTp08BAHfu3BH+zkDW+JJJkybhs88+E2aDNAVjvr/vuu7du8PKygpLly5FdHQ0Dh06lGMfBwcHLFu2DF26dMmzrHHjxsHBwQErVqxAamoq5HK5sNacNplMhuHDh2P27NkmWbvqiy++QFBQEGJjY5GRkYHjx4/rPF61alUheQJM8xkylYkTJ6JatWpYunQp4uLioFarce/ePdy7d0/v/rm1lFWvXh379u3D/PnzcezYMajVaiQkJODMmTO5HtvBwUHUpBpEpsLkiYhKDJlMhmnTpmHkyJHw9fVFYGAgIiMjkZCQgMzMTNjZ2aFq1apwc3ND27Zt0aVLF53pi7U5OTlhz549+O9//4tDhw4hIiICGRkZqFixImrXro1+/frB29sbNjY272zyBGRdwT18+DBOnjyJs2fP4tatW4iLi0Nqaiqsra3h5OSEOnXqoHnz5njvvfd0ZjTLbsaMGejcuTN27dqF4OBgxMfHw87ODpUqVULnzp3xwQcfoFatWgWK7/vvv0fXrl1x6NAh3Lt3DwkJCflOwFC9enX4+/tj586dOH78OKKioiCXy+Hi4oJWrVph6NChOjPimZIx39933dChQ9GqVSvs2bMHgYGBwrTk1apVQ7du3TBixIh8lxXQ+PDDD9G9e3f897//xfnz5xEVFYWkpCShPnbo0AFDhgxBvXr1TPZ6qlatCj8/P+zcuRMXL15EVFQU0tLS8hwjZIrPkKn069cPXbt2ha+vL86fP4/w8HDEx8dDqVSiXLlyqF27Nlq2bInevXvnOZOho6Mj1q5di/v37+PQoUMICgrCs2fPkJiYCKlUCgcHB9SoUQMNGzZEhw4d0LFjR1GLVROZikTN+SGJiIioiHl5eQldJk+dOoVq1aqZOSIiovxxwggiIiIiIiIRmDwRERERERGJwOSJiIiIiIhIBCZPREREREREIjB5IiIiIiIiEoHJExERERERkQicqpyIiIiIiEgEtjwRERERERGJwOSJiIiIiIhIBCZPREREREREIjB5IiIiIiIiEoHJE1ERWrp0Kdzd3dG0aVNER0ebO5xia/bs2XB3d4e7uzv2799fZMfVHNPd3b3IjpmfkSNHCjEFBQWZOxwiIiqEjz/+GO7u7ujXrx8UCoW5wyEDWJg7AKLS4v79+/jrr78AAKNGjUKlSpXMHBERiVHQZFomk+Hu3bui93/x4gX27duHM2fO4MWLF0hPT4eLiwsaNmwIb29v9OjRAxKJJN9yvLy88Pz581wfl0gksLOzg5OTExo0aIAePXqgV69esLKyEh1rUUhNTUWnTp2QkZEBIOv9PHPmDFxdXQtV7uvXr3Hu3DlcvHgR9+/fR3x8PJKSklCmTBmUK1cO9erVQ5MmTdCzZ0/Rf/PQ0FAEBATg1q1bePLkCZKTkyGRSGBra4tKlSqhZs2aaNy4MVq0aIFmzZrBwkL/adf+/fsxZ84cAEDVqlVx+vTpQr1WKr6+/PJLjBw5EpGRkdi5cyfGjBlj7pCogJg8ERWR1atXIzMzE7a2thg7dqy5wyGiYuC///0vli1bhvT0dJ37nz59iqdPn+LYsWPo0KEDVq1aBWdn50IdS61WIzU1FampqYiKisKRI0dQo0YNrFy5Ei1atChU2cZ05MgRIXECAKVSCX9/f0yYMMGg8hITE7Fp0ybs2rULb968yfG4QqFAamoqnj9/jnPnzuHnn39Go0aNMGPGDHTu3FlvmdHR0fjuu+9w7tw5vY/L5XIkJiYiLCwMx44dAwCULVsWQUFBkMlkBr0Oeje0adMGrVu3xtWrV/Hrr79iyJAhcHBwMHdYVABMnoiKQHBwMM6fPw8A+PDDD1G+fHkzR0REhhg+fHi++0il4nrE7927F/PmzRNuOzg4oF27drC3t0dERARCQkIAAIGBgRg3bhx2794NOzs7UWW3b98ederU0blPqVQiMTERoaGhePbsGQDgyZMnGDduHLZv3w5PT09RZZuar6+v3vsMSZ7CwsIwZcoUvHjxQuf+2rVro06dOqhQoQLevHmDuLg43LlzB8nJyQCAO3fuYPz48fjxxx/Rr18/nec+f/4cI0aM0CnTwcEBTZo0gYuLCywtLZGYmIhHjx4hMjISSqUSAJCSkgIurUkAMGHCBFy9ehXJycnYvHkzZsyYYe6QqACYPBEVgd9//x1AVreZYcOGmTkaKkl27Nhh7hBIy3fffWeUch4+fIgFCxYIt729vbFo0SLY2toK9126dAnTpk1DUlISwsPDsWTJEixfvlxU+T4+Phg8eHCuj58+fRpff/01UlJSkJ6ejoULF2Lv3r0Gvx5jefr0KYKDgwFkJaEymQwKhQIPHjxASEhIgRK827dv49NPPxVa9SwtLfHRRx9h7NixqFatWo79MzMzERwcjK1bt+LMmTMAoLel6ptvvhESJ3t7e8ydOxfe3t6wtLTMsW9qairOnDmDgwcPChfQiN577z1Uq1YNz549w19//YVJkybpfPapeOOEEUQmFhUVhbNnzwIAWrdujVq1apk1HiIyv7Vr1wqDxVu0aIFVq1blOHlq3749Vq9eLdz28/NDZGSkUY7v5eWFhQsXCrdDQkIQFRVllLILw9fXV2idadu2Lby8vHQeEysxMRHTp08XEqdy5cph+/bt+O677/QmTgBgYWGBtm3b4rfffsOuXbtQuXLlHPvcvn0bly9fBpB1Mey3337D4MGD9SZOQFZy5e3tjY0bN+L48ePsskcAsurOkCFDAADJyck4cOCAmSOigmDyRGRi+/fvF04G+vbta+ZoiIonX19fnDt3Tuji9C6Li4vD8ePHhdszZ87Mtatfly5d0KFDBwBZ3e52795ttDh69uypM1nEgwcPjFa2IdRqtU6CNGDAAPj4+Ai3Dx48CLlcLqqs1atXC5NnyGQybNy4sUDjulq1aoX9+/ejQYMGOvdfvHhR2G7atClat24tuszq1auLmviDSgft84F9+/aZMRIqKHbbIzKxgIAAYbtHjx757q89y1N4eDgA4N69e9izZw8uX76MV69eAQDq1auHgQMH4uOPP84xg1NISAh27dqFW7du4eXLlyhTpgzc3Nzw4Ycf6pyM5EehUMDf3x+nTp3CvXv38Pr1a1hYWMDZ2RktW7ZE//790bFjR9HlAcDJkyexb98+hIaGIiEhARUqVEDdunUxYMAAvP/++7nORpUXtVqNkydP4uTJk7h58ybi4uIgl8tRoUIFNGrUCD179oS3t7dBZRfW7du34evrixs3buDZs2dIS0uDhYUFHBwcUKVKFTRo0ABt27ZF165d9XbbGDlyJK5cuQIA2L59O9q2bavzeH4zrOVm+fLleXbrioyMhJ+fHwIDA/HixQskJyfD3t4e1atXR6dOnfDJJ58UevYzbZcvX8aBAwfg7OwMb29vDBgwAB4eHkYrvzg5ffo0VCoVAKBWrVr5ntQPGjQIgYGBAIBTp07pjJMqDCsrKzg6OgrfKdqTNJjDtWvXhLFYNjY2wkyAjo6OSExMRGJiIs6dO4eePXvmWU5MTAz8/PyE26NGjULz5s0LHE+FChVQoUKFHGVrVKlSpcBlFpb25/3UqVOoVq0aoqOjsWfPHpw+fRovXrxAZmYmKlWqhI4dO2Ls2LGoWrWq6PKVSiWOHTuGs2fP4tatW4iPj0d6ejrs7e1Ro0YNNG3aFN27d0e7du3yTASN9duh7/U+fvwYe/bswb///ouXL19CoVCgVq1a6NevHz799FPY2NjolPHw4UPs3LkTV69exfPnzyGVSlGnTh0MGDAAn3zySYFaA435vVi7dm3Uq1cPDx48wJ07dxAZGYm6deuKjoXMh8kTkQmFhYUJfePr1KkDFxeXApfx+++/48cff8xxRf727du4ffs2Tp8+jQ0bNsDKygpKpRKLFy/OcXU6IyMDV65cwZUrV3D27FmsXr063x+MW7du4auvvsKTJ0907n/79i3S0tIQFRWFffv2oWPHjvj+++9znGRkl5aWhhkzZuSYnSomJgYxMTEIDAzEP//8g7Vr1+b3lugICwvD7Nmzce/evRyPRUdHIzo6GqdOncLGjRuxfv161KtXr0DlGyozMxOLFi3C33//neMxpVKJ2NhYxMbG4tatW9izZw8mT55cLAYNy+VyLFmyBHv37s1R5xISEpCQkIDbt29jy5YtmDlzJkaMGGHU48fGxmLLli3YsmULPDw8MHDgQHh7e8PJycmoxzEn7fW6sifD+mjv8+LFCzx+/Bg1a9YsdBwKhQJJSUnCbXO/x9pdl7p37y5MjtGvXz9hmYcDBw7kmzzt3btX6BIplUoxatQoo8Wo3UKoSfTM6eTJk5g9ezZSUlJ07n/06BEePXqEvXv3Yu3atejatWu+ZV27dg1z587V231Tk7zevn0bO3bswIQJE/DVV1/pLcfYvx3a/Pz8MH/+/ByJfnh4OMLDw3Hs2DFs27YN5cqVAwD8+uuv+Pnnn4WLFdox3rp1C0ePHsWmTZtyJFzZmep7sV27dkKL75kzZ5g8lRBMnohMSHO1GMjqBlJQe/bswffffw8gq0WqQYMGkEqluH37tvCFe+HCBSxZsgSLFi3CwoUL8ffff0MqlcLT0xN169aFSqXSuaJ76NAheHh4YOLEibke9+rVq5gwYYLwAyWRSNCkSRPUrVsXCoVCWNMEyOrGMnToUOzevTvXH0GFQoFJkybh6tWrwn3Ozs5o1aoV7Ozs8OTJEwQHByM4OBhTp07NdUyCvjgnT56M1NRUAFkDwhs3boxatWrBwsICz58/R3BwMN6+fYtHjx7hk08+wd9//10kP1CrVq3SSZxcXV3RpEkTVKhQASqVComJiXjw4AEePXpUqOMMHDgQiYmJ+e6XmJiIQ4cOCbf1XTVOT0/HuHHjcP36deG+GjVqoFGjRnBwcEBSUhKuX7+OV69e4c2bN1i8eDFSU1MxefLkQr0GIGuCg9jYWFy6dEk4OQkLC8OKFSuwevVqdOrUCQMHDkT37t1RpkyZQh/PUFevXsXt27cRFxcHmUyG8uXLw8PDA82bNxc94Ft73FLDhg3z3d/V1RVOTk6Ii4sTnm+M5OnUqVN4+/YtAKBMmTJ5TsagvQ6R5rliP6diZGRk4OjRo8LtAQMGCNsDBw4Ukqfz588jPj4+zxNuzZgkAGjZsqVRW4hq1KghbIeGhuLSpUto37690coviEuXLmH+/PlQKpWoUqUKmjVrBnt7ezx79gxXrlxBZmYm3rx5g+nTpyMgIADVq1fPtaxDhw5h1qxZOou21qpVCw0bNkTZsmWRmpqKiIgIPHjwACqVSqg32Rn7t0Pb+fPnsXjxYqhUKtSqVQuenp4oU6YMwsPDhZkp7969iy+++AKbN2/Gxo0bhYtx7u7u8PDwgEwmQ0hICCIiIgAAV65cwfLly7Fo0aJcj2vK78WWLVti586dALLOF8aPH5/vc8j8mDwRmdCtW7eE7YIutAkAS5cuhbOzM9asWZPjCvWWLVuwcuVKAFn9pevUqSMkBj/88INOlyelUomVK1fizz//BABs2LABI0aM0Huyl5SUhC+//FL48atVqxbWrFmDxo0b6+zn7++Pb7/9Fm/evEFUVBS++eYb/Pbbb3pfx6ZNm4TESSKRYPr06ZgwYYJO69ejR48wffp03Lx5E3fu3Mn3vYmNjcW0adOExGngwIH48ssvc7TuxcXFYcGCBThx4gRSUlIwffp0+Pr6mnTgdkJCAnbt2gUga7zF0qVLMXDgQL0Jy6tXr3Ds2DFYW1sbdKz//Oc/+e6jUCh01harU6eO3i6kCxcuFE4QatWqhUWLFuWod0qlEn///TeWL18OuVyOdevWoW3btgZ1i9LWoUMHdOjQAbGxsTh48CD8/PyE1kSlUolz587h3LlzKFu2LPr27YsBAwYYdEGisHK7omxjY4MhQ4bg//7v/1CxYsU8y9BOmMWe2FeuXFlInh4+fKgzkYIh/v33X8yfP1+4PXToUNjb2xeqzMI4ceIE0tLSAGRdWNHu0tW0aVPUqlULUVFRUCgUOHjwYK6tSZoTdI0mTZoYNc5u3bph5cqVUKlUUKlUmDJlCsaMGYOBAwcaJaEtiEWLFqFMmTJYsGABfHx8dL5fIiIiMG7cOMTExCAjIwO//vprrjM13r17F3PmzBESp4YNG2LBggVo2rRpjn1jY2Ph7++foyUHMM1vh7bly5fDxsYGy5YtQ58+fXQeO3z4ML766isolUpcuHAB27Ztw9q1a+Hi4oI1a9agTZs2Ovtv3boVK1asAJC11trEiRNzvRhgyu9F7TF1ISEhUKvVHBdXAnDCCCIT0oxZApBjzRUxJBIJtm7dqrdrz9ixY4WB5JmZmVi+fDkqVqyIHTt25BgrIpPJMGvWLNSuXRtA1pU0zQyA2f35559Cv/5y5cph27ZtOX78gKyWAk2rGJDV5UC7ZUkjJSVFmKodAKZOnYrJkyfnSF5q166NrVu3wtnZWefqZ25+/PFHvH79GkDWuKCVK1fq7Rbp5OSEtWvXol27dgCA+/fvC4tWmsrNmzeRmZkJIKvL0aBBg3L9QXRxccHIkSPx4YcfmiyeRYsWCeOmHB0d8dtvv6Fs2bI6+1y7dk0YrF+jRg3s3r1bb72TyWQYNmyYMFObUqnEL7/8YrRYnZ2dMWbMGPj6+iIgIADjx49HpUqVhMdTUlLwzz//YPjw4ejRowd+/vlnPH361GjHN1RGRgZ27tyJAQMG4ObNm7nu9+bNG53pr8V2ldPeT7urXW78/f2xaNEinX/z58/H9OnT0atXL4wfP15osRw2bBi+/vprUXGYivZEEf3798/x/aA9VjOvWfdev36t0ypSv359o8UIADVr1tRZ60uTmPTq1QteXl6YMWMGNm/ejGvXrome3MJQCoUCa9euxYABA3J8v9SvX1+nNeXo0aPCd1J2ixcvFt6zxo0bY+fOnXoTJyDr8zlu3Di9a24Z+7cjO4VCgfXr1+dInICs71ntMZzLly+HpaUltm3bliNxAoAxY8YIv58qlQpHjhzRe0xTfy/WrFlTGIubnJycYz0yKp6YPBGZiFqt1vkiNGRw/ccff5znj3///v11bk+aNCnXq94ymUxndh9NNwdtarUa//zzj3D7//7v//RO16vRs2dPvPfee8JtfTOBBQQECFciK1WqlGd3wQoVKohqSYmPj4e/vz+ArB/zmTNn5rm/TCbTGU+kea6paFrDABSoP78pbNu2TfibWlpaYt26dXqvkG/dulXYnjVrVr5xDx48WLggcOHCBSQkJBgx6ixubm6YOXMmzpw5g23btmHw4ME6i8Q+ffoU69evR48ePTBs2DD8888/OcZ+GIOVlRX69u2LFStW4ODBgwgODkZoaCguXryIjRs3onfv3sLJa2xsLCZNmpRrd0zN1NkaYrsgardMZi9Dn0uXLmHXrl06//bs2YMjR47g8ePHALKuou/atQvz58836xTaMTExuHTpknBbu8uehnbLyp07d3D//n29ZWXvwpr9IoExzJ49G0OHDs1x//Pnz3H48GGsWrUKw4cPR8uWLTF58mScPHnS6DEAQNeuXXW+f7Pr0qULnJ2dAWTVGX3T3N+6dUtoVZFIJFi5cqXohZi1meK3IzsvLy8h4dEn++/hxx9/nGcXbe399f0eAqb/XpRKpcLfCIBBk/9Q0WPyRGQiKSkpOldAy5cvX+Ayevfunefjbm5uOrf1XZHTpp2I6RvsHBkZidjYWABZCYe+k5jstFtMNK0b2rQHx/fr109namR9+vXrl+uaKRqBgYFC61TPnj1FnYA2bdpU6Kao3XfdFLRPGk6cOCG0kBW18+fPY9WqVcLt+fPn671qmpmZKYzPs7e3R7du3USVrylLrVab9D2VSqVo3749li9fjsDAQKxZswbvvfeezuyJwcHB+Pbbb9GxY0dhYhJjTXt+/vx5/PTTTxg0aBDq168Pe3t7WFpawsnJCV27dsW6deuwYcMGoR4mJibqrKGkLftYkfw+D/r207dwqyGioqIwevRoLF26NNcxLBqDBw8WBuWHh4cbdbyTn5+f0A2sfv36eseBVa9eHS1bthRu57Yujqbrn4YhiUB+LCwssGDBAuzZswc9evTI9ftKLpfjzJkz+Oyzz/DJJ58YvVUhv+97iUSi011c34n5v//+K2y3b9/e4Al1TPHbkV1+v4fZu8YX5PdT3+9hUX0vap8baLrmUvHGMU9EJpJ9NiBDxrRkT46y08woBGRdYc2vdcvR0VHY1m4d0bh7966wXbt2bVEJn/Y0y7GxsYiJidGJQ3sWvGbNmuVbnr29Pdzc3PIc96TdLSo8PDzPwb76JCUlIT093WQrujdt2hSVK1fGy5cv8eLFC/Tv3x+DBw+Gl5cXmjRpIvqEuTAePHiAGTNmCAnE6NGjc+0aGB4eLrRmWFhYYOnSpaKOoX21Njo6upARi2NtbY33338f77//Pl6/fi2Mj9LUl7dv3+Lw4cM4fPiw3qndDSHmc9CtWzfMmzcP3377LYCslp/Q0NAc3ZayJ/piu3Zp7yfmu0TfVPRqtRppaWl4/PgxTp48iW3btiE9PR3bt29HWFgYNm/eXCR1MzvtRCivpRQGDBiAa9euAchq0f7qq69ytJhlT5ayJ1PG1Lx5c/zyyy9ISUnB1atXERwcjDt37uDu3bs5ulbeuHEDH330Efbt22e0Kf7FjKPN7ztf+7u0MJ8VU/x2ZJff76GDg4PO7fy6bGr/fup7b4rqe7GgrcpkfkyeiIqIZqHcgsivy4n2iYOY7ina++vr/x4fHy9six3I7uTkhDJlyghXrhMSEnR+ALXLzKsbh7bKlSvnmTxp1qUBIMzSV1DJyckmS54sLS2xatUqTJo0Cenp6UhISMDmzZuxefNmlClTBo0bN0br1q3x3nvvoUWLFkYfIJyQkKAzC2GXLl0wa9asXPfXfj8TExOFyS4KQsw4HGOrWLEiPv30UwwZMgQbNmzA5s2bDfqcGcsHH3yADRs2CC0M58+fz5E8Za9z+bX4aGi3NhlabyUSCezt7dGoUSM0atQIffr0wdChQ5GWloYrV65g06ZNmDp1qkFlG+r27dt4+PAhgKwWxrySpz59+mDx4sWQy+WIjY3FhQsX0KVLF519tJMFACbpxpld2bJl4eXlJUzioVarcffuXfj7++Pvv/8WLqTFxsZi/vz5oiZHEEPMBB/arWL6vvO1W8Xzmo0vP6b47cguv9ebfR2/gvx+6ntviup70ZzfWWQYdtsjMpHs60aIPUnSVpCTamOcgGtf9cpv3Qtt2vtmv9JrSJn57WeME6LcBk8bS5s2beDv74+BAwfqXFl8+/YtgoOD8dtvv2HYsGHo06ePUcdEKBQK/Oc//xEmUahfvz5++OEHnfVpsjPG+2msLnJiKRQKnDlzBjNmzEDHjh3xxx9/6JyE2NjYFPnscVKpVJiYBIDeMSbW1tY69UFsNx3t/bSvmBeGu7s7Ro4cKdzesWOHySc5yE671al169Y6k4Nk5+DgoNN1St/EERUrVtRp3dMs6VCUJBIJGjVqhDlz5mD//v06Y1rOnDljtAlOjPGdr/19XZiLSab47ciuoK+3sO9PUX0vap8bmOqCHhkXW56ITKRs2bI5rqqZe/KA/Gh/cWfvdpgX7X2zd5uxtbUVfoTElpnffto/uHPmzMHo0aNFRlq0qlevjpUrV2L+/PlCC9n169dx69YtoSUhKioKn332GWbPno0xY8YU+pgLFiwQxg+UL18eGzZsyDeJ0P67u7u7m3xCjcK4efMm/P39cfjw4RyDsSUSCVq3bo2BAweid+/eZpl6W/tEObf1t2rXri10ZxU7Dubly5fCtiEzd+amU6dOQktIYmIi7t27l+tMa8Yml8tx+PBh4XZQUFCBlnQ4deoUUlJSdFoYLC0t0bRpU+EzcPv2beMFbIA6depg9uzZ+PLLL4X7goODC9XKY0za39eF6TJmit8Ocyuq70XtVjtzL1RN4jB5IjIRiUSCqlWrCl1SoqOji/3q4drJnfbJWl6yTw2cva97hQoVhOTp5cuXotZdye/Y2j8wmkHKxZmtrS06d+6Mzp07A8jqgnX27Fn88ssvwqxha9asQb9+/Qo1HmLr1q3Yu3cvgKyTyPXr14s6SdOeobE4Dlh+/Pgx/P39ERAQIMwUp61WrVrw8fHBwIEDUbVqVTNE+D9irsDXrVtXSJ60x4rkJiYmRufvYszvEe1kD8iaVKCokqczZ86IWuA5N5rxbR9//LHO/W3bthWSp+DgYLx8+VJ0l2FT0HzuNbS7g5mb9mdf36QJYpnit8PciuJ7UaVS6ZRt7u8vEofd9ohMSPsqam5TFxcn2rNcPXz4UNSJjfZsQs7OzjlO/rUXAcxr/RuNtLQ0YfX33GgnYKaeOc8UrK2t0adPH+zYsUNIBBUKhc7MVwV17tw5nZn1Fi9eLHoR2QYNGggTBbx+/VpvglLU4uPjsXPnTnz00Ufo1asX1q9frxOXg4MDPv74Y+zevRvHjh3DZ599VixOPLQnSNG37higOzBfzCxj2mvgVKlSxaiLsWafuS+v7p3Gpt1lz9nZGU2bNhX1T/vvrG/WvQ8//FAY66NUKrF9+3bTv5g8ZJ8kxByTcuRGexKfy5cvG1yOKX47zK0ovhejoqKELuQODg7F4juM8sfkiciEtE/yw8LCzBiJOHXr1hWuRCuVSlHdFDQtHYD+2Zq07zty5Ei+C+AePnw433EXnTt3FgYH37hxo0S8t/o4OjrqzDhl6JTmERER+OKLL4TpnsePH49BgwaJfr61tbXOWJ2//vrLoDgK682bNzh8+DAmT56Mzp07Y/Hixbh165bwuIWFBbp27YqffvoJFy9exKJFi3TeP3OLjIzEjRs3hNv6FucEstar0SQpjx49yveignaC0L1798IHqiX7xCxFdQL7+vVrnYsF3333Hf755x9R/9atWyc878aNG4iKisrxGrQnnvjzzz916pFY8fHxOsmwobJ/P4mdUKEoaK+1dOnSJb3j9MQwxW+HuRXF96J23fD09DR6+WQaTJ6ITEh7QT9DZoQrahKJBB999JFw+5dffhFWjNfn1KlTOHv2rHD7k08+ybGPt7e30H3p5cuX+P3333MtLyEhQefEKDfaJ0dqtRpff/213qlm9VGpVDp9zE2hIAvGandxMWRMXHx8vM7Met27d9cZXyHWhAkThO2dO3cK65uIYYyuk9u3b0eHDh0wY8YMnDlzRmdCjwYNGmDOnDk4f/48Nm7ciL59+xbZ1XuxU11nZGRgzpw5wgDx8uXL5+iupeHk5ISePXsKt1evXp3rjFsXLlzAhQsXAGTNDqbvM2YohUKhM4OYvb19jtkBTSUgIEDninvXrl1FP7dx48Y64770TRwxc+ZMoaueUqnExIkTC5RAXbt2DYMHD86RPO3fvx9Hjx4VPUOaUqnEzz//LNy2tLRE+/btRcdhak2aNBEuQGi+Sw2Z3t0Uvx3Fgam/F7XPC/JaAJiKFyZPRCbk4eEhXGV8+PBhserrnptPP/1UuPqcmJiITz/9VO/V10OHDumcpHfr1g2tW7fOsV/ZsmUxfvx44fa6deuwadOmHLMQRUVFYezYsXj16lW+i+QCwPTp04UrneHh4fjggw+Ek0x9oqOjsW3bNvTp00dnkLop7Ny5EwMGDMBff/2V6w9oWloafvzxR2FNEJlMhk6dOhXoOHK5HJ9//rkwVsHd3R2rV682qOtVmzZthNaqzMxMTJw4ERs3bsz1ROrt27c4efIkpkyZgilTphT4eNndvXtX51jOzs4YO3Ys/P394evri9GjR+uMQSgqXl5eWLt2bZ5X5IODg/Hxxx/rnJxPmzYtzwHw06ZNE+r5tWvXMGvWrBwD9i9fvqzzGRswYIDBi5hm9+rVK0ybNk2n5WnEiBG5fvb2798Pd3d34V9hxscAuglP7969C5wMe3t7C9v+/v45kpny5ctj7dq1wqD/xMREDB8+HEuWLNG7WCyQVe+vXLmCyZMnY/jw4XrH7jx8+BDTpk1Dv3798Pvvv+daFpA109/EiRN1vpc++eQTUctKFKV58+YJ739oaChGjBiRa6IZGxuLzZs3448//sjxmLF/O4oDU38vaneVFLsIL5kfJ4wgMjFvb29s3LgRAHDy5EkMGzbMzBHlrVy5clizZg0mTJiAjIwMPHr0CIMGDULTpk1Rt25dKBQK3Lp1S6f/d61atbBs2bJcy5w4cSIuXryI69evQ61WY82aNdi+fTvatGkDW1tbPH78GMHBwVAqlWjatCmqV6+OgwcP5hmnq6srfv31V0ycOBEJCQl49OgRxo0bB1dXVzRp0gQVKlSAQqFAQkICIiIiCn2yV1BhYWFYuHAhFi1ahBo1aqB+/fooX748MjMzERsbi+vXr+ucLE+YMKHAg9pv3rwpLBoKZA02XrNmjajnDhgwIMfEAIsWLRLWz1EoFPjhhx+wYcMGNGnSBFWqVIGVlRWSk5Px5MkTRERECN0rGzVqVKC4c2NtbY3u3btjwIAB6NSpU44FUM0hMTERv/76K3799Ve4uLjA3d0dTk5OsLKyQlJSEu7cuZNj6unhw4dj6NCheZZbt25dzJ8/H/PmzQMA+Pn54ezZs2jXrh3s7OwQGRmpcwLr7u6OuXPnio7b398foaGhOe7XfKZDQkJ0WveaN2+OyZMniy6/MMLCwnROqrUTIbG8vb2xdu1aAFmTXAQFBel0sQKyFqv+66+/MHnyZERHR0OhUGDHjh3YsWMH6tSpgzp16qB8+fJ4+/Yt4uLicOfOnRzr8uSWAD98+BDff/89vv/+e1SqVAnu7u6oUKGCUC8iIiJyJNzNmzfHjBkzCvxaTa1Ro0ZYunQp5syZg8zMTNy9excfffQRateujYYNG8Le3h6pqal48OABIiIioFKpMGrUqBzlmOK3ozgw1fdiVFSUMJV+w4YNi/2EUvQ/TJ6ITGzw4MHYtGkT1Go1jhw5UuyTJyBrvZVt27bhq6++wtOnT6FWq3Hz5k29YzM6dOiANWvW5NnlzMrKCr///jumT58ujHOIjY3FoUOHdPZr3rw51q1bhx9++EFUnE2aNMG+ffswd+5cXLp0CUDWzGQnTpzI9TlOTk5GHXCvj/YJl1qtxuPHj3MdbGxpaYnJkycbtDhp9qvtp0+fFv3cxo0b50ierKyssGnTJqxfvx5bt25FRkYGMjIyEBQUlGs5lpaWOoPODTVixAjMmzfPLNOLi/Xq1as8W4/LlSuHmTNn4sMPPxRVnma/ZcuWIT09HUlJSTh27FiO/dq3b49Vq1YV6L25dOmS8JnIz5AhQ/DNN9/kuT5P9rpWmMRWu9WpcuXKuY4Ny0v16tXRvHlzYYyZr69vjuQJyOryeeDAAWzatAm7d+8WJsh4+PChMBOqPi1btsSMGTNytIg0a9YM9erV01k/Kjo6GtHR0bmWZWFhgWHDhmHGjBnFdh0fHx8fODs7Y968ecKFpkePHuU60VFur8PYvx3Fgam+F48cOSJsDxkyxFjhUhFg8kRkYrVq1ULXrl1x5swZXLlyBVFRUahVq5a5w8pXs2bNcPjwYfj7++PkyZMICwvD69evYWFhAWdnZ7Rs2RL9+/cX3dXM3t4ef/zxB44fP479+/cjNDQUiYmJKF++POrWrQtvb2/4+PiI6rKnrWrVqti2bRtu3LiBo0eP4urVq4iOjkZycjJkMhkcHR1Rs2ZNNG7cGJ06dUKbNm1yrERvbGPHjkWvXr0QGBiIGzduIDw8HM+fP0daWhokEgkcHBxQp04dtGvXrlhMr61NJpNh2rRpGDlyJHx9fREYGIjIyEgkJCQgMzMTdnZ2qFq1Ktzc3NC2bVt06dLFKCc/RTXWpqCOHTuGmzdvCn/H+Ph4JCQkID09Hba2tqhQoQIaN26M9u3bo3///gVaIBTISqA6duyIvXv34syZM3jx4gXS09Ph7OyMRo0awcfHBz169DDKgqhAVute2bJlUadOHTRv3hwDBgwQtW5UeHi4sN26dWuDp/7OzMxEQECAcLt///4GvzZvb28heTp27Bi+/fZbvS1FFSpUwOzZszFhwgScOXMGgYGBuH//PuLj45GUlARra2s4Ojqifv36aNasGXr37o3atWvrPWaPHj3Qo0cPPHnyBEFBQbhx4wYePnyIZ8+eITk5GUqlUqgXbm5uaNmyJfr165frzIvFSfv27XH06FEcOnQIZ8+eRWhoKF6/fg2FQgF7e3vUrFkTzZo1Q8+ePfOcydPYvx3FgbG/F9VqNfbt2wcgq2t7QSb4IfOTqMWOeiQig12/fl3oxjNq1KgCdb8hIjI3Hx8fIYHatWuX6GnwiSinc+fOYeLEiQCAyZMnF8vunJQ7ThhBVARatGghTAm7d+/eAs3GRkRkTvHx8cJizu+99x4TJ6JC0ky44eDggHHjxpk5GiooJk9ERWTmzJmwsLBAeno6tmzZYu5wiIhECQoKglqthkQi4RVyokK6evWqsDj2lClT4ODgYOaIqKCYPBEVETc3N2GyiO3bt+e5BgYRUXGhmU65d+/eaNiwoZmjISrZNDOi1q1bFyNHjjRzNGQIjnkiIiIiIiISgS1PREREREREIjB5IiIiIiIiEoHJExERERERkQhMnoiIiIiIiERg8kRERERERCQCkyciIiIiIiIRmDwRERERERGJwOSJiIiIiIhIBCZPREREREREIliYOwAyL5VKhYyMDACAhYUFJBKJmSMiIiIiIioctVqNzMxMAICNjQ2kUuO0GTF5KuUyMjIQFhZm7jCIiIiIiEzCw8MDdnZ2RimL3faIiIiIiIhEYMtTKWdh8b8qULduXdja2poxGiopFAqF0GLp4eEBS0tLM0dEJQHrDRmKdYcMwXpTumn//bXPdwuLyVMppz3GydLSElZWVmaMhkoi1hsyBOsNGYp1hwzBelO6GXNMP7vtERERERERicDkiYiIiIiISAQmT0RERERERCIweSIiIiIiIhKByRMREREREZEITJ6IiIiIiIhEkKjVarW5gyDzkcvlCAkJAQB4enpyGk8iIiIiKvFMdY7LliciIiIiIiIRmDwRERERERGJYGHuAKj4UCqV5g6BSgilUom4uDgAgJOTE2QymZkjopKA9YYMxbpDhmC9IVNg8kQClUpl7hCohFAqlXj27BkAoHz58vxBIlFYb8hQrDtkCNYbMgV22yMiIiIiIhKByRMREREREZEITJ6IiIiIiIhEYPJEREREREQkApMnIiIiIiIiEZg8ERERERERicCpyomowCQSCezs7IRtIjFYb8hQrDtkCNYbMgUmTySwtLQ0dwhUQlhaWsLDw8PcYVAJw3pDhmLdIUOw3pApsNseERERERGRCEyeiIiIiIiIRGC3PRKo1Wpzh0AlhFqthlKpBADIZDL2JSdRWG/IUKw7ZAjWGzIFJk8k4JcKiSWRSGBhwa8PKhjWGzIU6w4ZgvWGTIE1igRKlQoxiWnmDoOIiIiIipiTgw1kUo7oyQ+TJxLEp76Bz0p/c4dBREREREXs8NwhcHW0M3cYxR7TSyIiIiIiIhGYPBEREREREYnA5ImIiIiIiEgEJk9EREREREQiMHkiIiIiIiISgckTERERERGRCEyeiIiIiIiIRCj26zylpKTA398fZ8+eRWRkJF6/fg0LCws4OTmhcePG6N69O3r37g2ZTCa6zPj4eBw6dAgXL15EREQEEhISIJfLUa5cOdSpUwctWrRA//794ebmlm9ZKpUKZ86cwblz53Djxg3ExcUhOTkZdnZ2cHV1FWLs0qULLC0tczx/5MiRuHLlSo77pVIpypYti+rVq6NNmzb45JNPULNmTdGvkYiIiIiIjEuiVqvV5g4iN//88w/WrFmDxMTEPPerV68eFi5ciFatWuW5n0qlwm+//YY//vgDaWlp+R6/Y8eO+Oabb1CvXj29j1+5cgWLFy/G/fv38y3LxcUFM2bMwODBg3Xuzy15ys7S0hJz5szB8OHD8923IORyOUJCQgAAlWrW5SK5RERERKXQu7ZIrvY5rqenJ6ysrIxSbrFteVq5ciW2bNkCALCwsEC/fv3g5eWFqlWrQi6X49GjRzh48CAuX76MBw8eYMyYMVi9ejX69Omjt7w3b95g+vTpOHPmDICsZKRfv37o0KEDqlWrBmtra7x+/RohISE4efIk7t27h4sXL+Lvv//G3Llzc5S3b98+zJ8/HwqFAgDQrFkz9OzZEw0aNICjoyNSUlLw9OlTnDt3DmfPnsWrV6+wbNmyHMmTtoCAAGFbpVIhOjoaR48exYEDB6BQKLB48WJUrVoVXbt2NfRtJSIiIiIiAxXL5GnXrl1C4lSpUiX89ttvaNCggc4+rVq1wocffoiAgADMmTMHcrkcM2fORM2aNXPsCwALFiwQEqemTZvihx9+QLVq1XLs16VLF0ydOhVnzpzBsmXL9MZ36dIlzJs3DyqVCra2tli+fLnepK1du3b48MMP8ezZM6xZswb//vtvnq87ezdBDw8PdO3aFY0aNcKSJUugVquxbt06Jk9ERERERGZQ7CaMeP78OVauXAkAsLW1xbZt2/QmQxre3t5CkiOXy/H1118je0/EEydO4MCBAwAAd3d3bNu2TW/ipK1bt27Yt28f2rVrp3N/RkYGZs6cCZVKBalUio0bN+ba2qVRrVo1/Pjjj5g3b16e++Vm+PDhqFq1KgDgzp07eP36tUHlEBERERGR4Ypd8vTnn3/i7du3AID/+7//Q+3atfN9jo+PDzp37gwAuH//vtDCpPHbb78J28uXL4etra2oWBwcHNC9e3ed+/bt24fY2FgAWUlNmzZtRJUFAAMHDhS9rzapVIrGjRsLt1+8eGFQOUREREREZLhilTyp1Wr4+fkBAKytrfHJJ5+Ifu6nn34qbO/fv1/YDg8PR2hoKICsrn6NGjUqVIyasiUSCUaNGlWosgrCwuJ/PSxVKlWRHZeIiIiIiLIUqzFPERERwsx6rVq1QtmyZUU/t0OHDrC2tsabN28QHBws3H/16lVhu0uXLoWKLzU1Fffu3QMA1K5dGzVq1ChUeQWhPaOfi4tLkR2XiIiIiIofKyiNWl5GejrScq6qUyh2du/O7H0axSp5CgsLE7YbNmxYoOfKZDJ4eHjg5s2biI+PR0xMDFxdXXXK1O76Zojw8HCh1aewLVgFcfbsWURERAAAqlevjsqVKxfZsYmIiIio+Olj/8So5f35+69GLQ8A5s+fb/Qyza1YJU8JCQnCtpOTU4GfX7FiRWE7MTERrq6uOmtEVahQoVDxaZelfSxT0J6qfN26dcL948ePN+lxiYiIiIhIv2KVPGkvXCt2Ugdt2s9JTU01SpnatMuysbEpVFn6uLu75/n4xx9/XKBxYEREREREZDzFKnnS7heZnp5e4OdrP8fe3t4oZWrTLisjI6NQZYllY2ODFi1aYMSIEfDy8iqSYxIRERERUU7FKnkqX768sB0XF1fg52uvf+To6Kjzf/bHDaFdliHx5ScgIEDYlslksLOzg7OzM2QymdGPRUREREQl19FU405ctnvG+3AuV7heWqVBsUqetLut3b17t0DPVSqVCA8PB5A1tsnV1RUA4OHhIexz584ddOzYsVDxSaVSqFSqAscnhpubm9HLJCIiIqJ3jxzGvbhuY2v7Ts6OZ2zFap0nNzc3oXXn2rVrSElJEf3cwMBAoStdq1athPtbt24tbJ87d65Q8dnb26NBgwYAgEePHuHx48eFKo+IiIiIiEqOYpU8SSQSDBgwAADw5s0b/PPPP6Kfu3PnTmF70KBBwra7u7swRfm1a9dw586dQsU4ePBgAFkL+u7YsaNQZRERERERUclRrJInABg1ahSsrKwAAOvXrxfVunPo0CGcPXsWQFbrVbdu3XQenzx5srA9Z84c0RNHJCcn4/Tp0zr3DRkyBM7OzgCAXbt24cqVK6LKAgBfX1/R+xIRERERUfFS7JKnatWq4euvvwaQNTve6NGjdRa6ze7w4cOYNWsWAMDS0hKrVq2CRCLR2adnz54YOHAggKyFbkePHo3nz5/nGce5c+fwwQcf4NKlSzr329jYYPXq1cLYp0mTJuHYsWN5lvXixQt88cUXWLJkSZ77ERERERFR8VWsJozQGDlyJJ48eYLt27fjxYsXGDJkCPr37w8vLy9UqVIFmZmZePjwIQ4ePCgkN1ZWVli9erUwJim7BQsWICkpCWfOnMGtW7fQp08f9O3bF506dULVqlVhbW2NuLg43L17FydOnMize1/79u2xZMkSzJ8/H+np6fjPf/6DZs2aoXfv3mjQoAHKlSuHlJQUPH36FP/++y9Onz4NuVyOsmXLmuT9IiIiIiIi0yuWyRMAzJ07F3Xq1MFPP/2ExMRE+Pn5wc/PT+++devWxaJFi3QmisjOxsYGv/76KzZs2IDNmzcjLS0tzzIBoGvXrhg2bJjex4YMGYLq1atj8eLFuH//Pm7evImbN2/mWlaVKlXw5Zdf5vo4EREREREVb8U2eQKAoUOHon///vD398fZs2fx4MEDxMfHQyaTwcnJCZ6enujevTv69Okjai0kqVSKzz77DEOHDsWhQ4dw8eJF3L9/HwkJCVAoFChXrhzq1KmDFi1awMfHB3Xr1s2zvDZt2sDPzw9nzpzB2bNncePGDcTFxSElJQW2traoVKmSEGOXLl1gYVGs324iIiIiIsqDRK1Wq80dBJmPXC5HSEgIAKBSzbrwWelv5oiIiIiIqKgdnjsEro7vzjpP2ue4np6ewoR0hVXsJowgIiIiIiIqjpg8ERERERERicDkiYiIiIiISAQmT0RERERERCIweSIiIiIiIhKByRMREREREZEITJ6IiIiIiIhE4KqtJKhgb43Dc4eYOwwiIiIiKmJODjbmDqFEYPJEAplUCldHa3OHQURERERULDF5IoFarTZ3CFRCqNVqKBQKAIClpSUkEomZI6KSgPWGDMW6Q4ZgvSFT4JgnEmRmZpo7BCohMjMzERYWhrCwMNYbEo31hgzFukOGYL0hU5Co2dxQqsnlcoSEhAAAPD09YWVlZeaIiIiIiIgKx1TnuGx5IiIiIiIiEoHJExERERERkQicMIIEmZmZ7LZHomRmZuLly5cAgMqVK8PCgl8llD/WGzIU6w4ZgvWGTIG1iAQc/kZiqVQqvHr1CgDg6upq5miopGC9IUOx7pAhWG/IFNhtj4iIiIiISAS2PJGAzdkklqWlJTw9PYVtIjFYb4iIqKTj2TIJuHgciSWRSDg+jgqM9YaIiEo6Jk8kUKpUiElMM3cYREQlnpODDWRS9ownInrXMHkiQXzqG/is9Dd3GEREJd7huUPg6mhn7jCIiMjIeFmMiIiIiIhIBLY8ERERUbEmlUrh6OgobBOJwXpDpsDkiYiIiIo1CwsL1K1b19xhUAnDekOmwDSciIiIiIhIBCZPREREREREIrDbHhERERVrCoUCkZGRAIC6detykWUShfWGTIHJExERERVrarUaaWlpwjaRGKw3ZApF3m1PqVSiRYsWcHd3x6BBg/LcV61Wo23btnB3d4e7uzv27t2b5/4HDhwQ9v3rr7+E+0eOHCncn58///xT2Ld9+/YICwsTHvv555+Fx4KCgvItCwC8vLzg7u4OLy8vvY/Pnj1bKFP7n4eHB1q1agUfHx8sXLgQ9+7dE3U8IiIiIiIyjSJPnmQyGZo3bw4ACAsLQ2pqaq77RkREIDExUbgdHBycZ9nXrl0Ttlu3bl3g2P744w8sW7YMAODs7IwdO3bAw8OjwOUYg1qtRkpKCsLDw/HXX39h8ODB+PHHH80SCxERERERmanbXuvWrXHhwgWoVCpcv34d7733nt79NMmQTCaDUqnUSY7y2r98+fKoV69egWLasGEDfvrpJwCAq6sr/vzzT9SuXbtAZRTW5s2b4eLiAgBQqVR4/fo1zp49i7/++guZmZn47bff4OLiguHDhxdpXEREREREZKbZ9lq1aiVs55UQaR7r06cPAODJkyeIiYnRu+/r168RFRUFAGjZsiUkEonoeH7++WchcapSpQp27txZ5IkTANSqVQtubm5wc3ODh4cHOnbsiLlz5+Lnn38W9lm/fj2USmWRx0ZEREREVNqZJXlq0qQJypQpA0Bc8tS7d2/UqFEDQO5d9wztsvfDDz9g/fr1AIBq1aph586dwrGKCy8vLyHhjI+Px507d8wcERERERFR6WOW5MnKygpNmjQBAISEhEAul+fY5+nTp0IrU8uWLdGyZUsAuSdb2vdrt2zlZeXKldi4cSOArFafXbt2oWrVquJfSBHSvF8A8OLFCzNGQkRERERUOpltkVxNgiOXy3Hr1q0cj2uSoVq1asHJyUlInq5evaq3PE2LlL29PRo0aJDv8ZcsWYItW7YAAOrUqYMdO3agUqVKBX8hRcTC4n/D01QqlRkjISIiKloymQzVqlVDtWrVIJPJzB0OlRCsN2QKZlvnqXXr1tiwYQOArEQpe1c7TfLUokULnf8jIiKQlJSEcuXKCfumpqYKU4o3b948zw+IWq3GwoULsXv3bgCAm5sbtm7dCicnJyO9MtO4f/++sK2ZVIKIiIzPCoUfV5qRno40I63HaWdnZ5yCSjCZTAZXV1dzh0ElDOsNmYLZkqdmzZrBwsICmZmZerviae7TtFDVrVsX5cuXR0JCAq5fv45u3boJ+964cUOYRCG/8U7ffvst/vvf/wIAGjRogC1btqBChQoFjv/Zs2coX758vvspFIoCl51dWFgY/v33XwCAjY0NPD09C10mERHp18f+SaHL+PP3X40QSZb58+cbrSwiIiocsyVPdnZ2aNCgAUJCQoTkR9NilH3mPI0WLVrg1KlTuHbtmk7yVJDxTprEqW7duvjzzz91WrAK4ptvvjHoeWKp1WrExcXh7NmzWLNmjZAcjhw5Uphsg4iIiIiIio7Zkicgq5UoJCQEaWlpuHfvHho3bgzgf+OanJycUKtWLWH/li1bCsmTNs14pzJlyuTbKiORSKBWq/Hs2TPcvXsX7du3N+IrKpzu3bvn+XjXrl3xn//8p4iiISIiKh7kcjlCQkIAAJ6enrCysjJzRFQSsN6QKZhtwghAt1VJOyHSbGs/DvyvVenOnTt48+YNgKwPxu3btwEATZs2zfeD8e233wIA3r59iylTpuS78G5utm/fjvDw8Hz/FXb2PktLS7Ro0QIrV67Eb7/9BktLI3WiJyIiIiKiAjFry1OrVq2ElqDg4GCMHj0awP9akjSTRGg0bNgQNjY2yMjIwM2bN9GuXTuEhITg7du3Qnn5GT58ON68eYNVq1YhIyMDkyZNwtatW3WmAjeXzZs3C5NBSKVS2NrawsnJiVdKiIiK0NHUwq/1t3vG+3AuZ2uEaIiIqDgxa/Lk6OiI+vXr4/79+0ILkPbMedlbniwtLeHp6YkrV67g2rVraNeunc6iuWIXxx03bhwyMjLw888/IzU1FePHj8f27dvh4eFhpFdmmFq1aqFatWpmjYGIqLSTo/BTGtvY2nKWPCKid5BZu+0B/2stio+PR2RkJIKDg6FSqWBra4uGDRvm2D/7Yrma8VGWlpZo1qyZ6ONOnToVEyZMAAAkJSVh7NixiIyMLMxLISIiIiKid1ixSZ6ArO56mpakZs2a6V2vSZM83bp1C3K5HDdu3ACQ1aXP1rZgXSS++uorjBw5EkDWDH+jR4/G06dPDXodRERERET0bitWydPVq1eFlqTsXfY0NIvgpqenY//+/UhJSclRTkHMnTsXH330EQDg1atX+PTTT/Hy5UuDyiIiIiIioneX2ZMnV1dX1KiRNTg3KCgIoaGhAHJPnuzt7eHm5gYA+OOPP4T7xY53yk4ikWDhwoXw9vYGADx//hyjR49GbGysQeUREREREdG7yezJE/C/VqOYmBjI5XJYWFjkOX5Jk1hputhJpdJcky0xpFIpVq5cid69ewMAoqKiMGbMGMTHxxtcJhERERmHhYUF3N3d4e7uDgsLs851RSUI6w2ZQrFKnjQaNGgAGxubXPfPnii5ubnBwcGhUDHIZDKsWbMGXbt2BQBERERg3LhxSE5OLlS5REREVDhSqRT29vawt7eHVFosTl2oBGC9IVMoFjUpe5e7/FqRsj9uaJe97CwtLbFu3Tp06NABAHD37l1MmDABaWlpRimfiIiIiIhKLolarVabOwgyH7lcjpCQEABApZp14bPS38wRERGVfIfnDoGrI9d5MhaVSoX09HQAgK2tLVsRSBTWm9JN+xzX09MTVlZWRimXtYiIiIiKtczMTISHhyM8PByZmZnmDodKCNYbMgUmT0RERERERCIweSIiIiIiIhKByRMREREREZEITJ6IiIiIiIhEYPJEREREREQkApMnIiIiIiIiESzMHQAVHxXsrXF47hBzh0FEVOI5OdiYOwQiIjIBJk8kkEmlcHW0NncYREREOqysrNCyZUtzh0ElDOsNmQK77REREREREYnA5ImIiIiIiEgEdtsjgVKpNHcIVEIolUrExcUBAJycnCCTycwcEZUErDdkKNYdMgTrDZkCkycSqFQqc4dAJYRSqcSzZ88AAOXLl+cPEonCekOGYt0hQ7DekCmw2x4REREREZEITJ6IiIiIiIhEYPJEREREREQkApMnIiIiIiIiEThhBAksLFgdSBxLS0t4enoK20RisN4QEVFJx7NlEkgkEnOHQCWERCKBlZWVucOgEob1hoiISjomTyRQqlSISUwzdxhEREXCycEGMil7r5cEEokEdnZ2wjaRGKw3ZApMnkgQn/oGPiv9zR0GEVGRODx3CFwd7cwdBolgaWkJDw8Pc4dBJQzrDZkCL7kRERERERGJwOSJiIiIiIhIBHbbIyIiomItMzMTjx8/BgDUrFmTs8OSKKw3ZApseSIiIqJiTaVSITExEYmJiVCpVOYOh0oI1hsyBSZPREREREREIjB5IiIiIiIiEoHJExERERERkQglZuRceno6/Pz8cPr0aYSFhSExMRFqtRr29vaoWrUq3Nzc0Lx5c3Tu3BmVK1fOs6wXL17g0KFDCAwMxOPHjxEfHw+VSoVy5crBzc0NLVu2hLe3N6pXr57juUFBQRg1ahQAYOrUqfj888/zjX327Nk4cOAAAODUqVOoVq2azuP79+/HnDlzAADLly/H4MGDRb0nRERERERUdEpE8nTjxg188cUXePHiRY7HEhISkJCQgNDQUOzfvx9OTk64ePGi3nLevn2LNWvWYPfu3ZDL5Tkef/XqFV69eoULFy5g3bp16NOnD2bNmpVvMkZERERERO++Yp88PXr0COPGjUNaWhoAwMvLC71790bt2rVhaWmJhIQEhIWFITAwEEFBQbmWEx8fjylTpuDmzZsAADs7O7z//vto164dKlWqBAsLC8TFxeH69es4ceIEoqKicOTIETRr1gyjR48ugldKRERERETFWbFPnn788UchccqtS1vHjh0xbtw4xMfH48iRIzkeV6lUmD59upA4devWDUuXLkXFihVz7Ovl5YUvvvgC/v7+WLVqlXFfDBERERWYVCqFi4uLsE0kBusNmUKxTp6USiXOnTsHAGjcuHG+Y4EqVKiA4cOH57h/+/btQqtUp06dsH79+jwXSpNKpRg4cCDatWuHR48eFeIVEBERUWFZWFjoHYdMlBfWGzKFYp08xcfH482bNwCyVoY2hFwux+bNmwEAZcqUwbJly0SvMF2pUiVUqlTJoOMSEREREdG7pVi3YVpaWgrbkZGRBpVx4cIFvHr1CgDQp08fuLq6GiU2IiIiIiIqXYp1y5OjoyOqVq2K58+fIywsDJs2bcL48eML1G/16tWrwnbXrl1NECUREZmaFZRGLzMjPR1plvnvZyg7OzvTFV7KKBQK3Lt3DwDQoEEDnYurRLlhvSFTKNbJEwCMGDECK1euBACsWbMGe/bsgZeXF1q0aAFPT898+7KGhYUJ240aNTJqbK9fv8b9+/fz3S85OdmoxyUiKm362D8xepl//v6r0cvUNn/+fJOWX5qo1WooFAphm0gM1hsyhWKfPI0ePRoPHjzAvn37AADPnz/Hjh07sGPHDgCAk5MT2rRpA29vb3Tr1g0SiUTn+YmJicK2vtn1CmP37t3YvXu3UcskIiIiIqLiqViPeQKyZr5btmwZtmzZgs6dO+eY7CEuLg6HDx/GlClT8MEHH+DJE92rk5ppzgHAxsamSGImIiIiIqJ3T7FvedLo2LEjOnbsiNTUVAQHByMkJAShoaG4du0aUlJSAAChoaEYNmwY9u/fL8zrr93nPCMjA/b29kaLaerUqfj888/z3W/27Nk4cOCA0Y5LRERERERFr8QkTxr29vbo0qULunTpAiBrKvKAgACsXLkSSUlJiI2Nxdq1a7F06VIAWZNOaMTFxRk1eSIioqJxNLWG0cvcPeN9OJezNXq5RET07ipxyVN2VlZWGDJkCFxcXDB+/HgAwIkTJ7B48WJIpVJ4eHggMDAQAHD37l3UqlXLjNESEZEh5JAZvUwbW1vOiEdERAVS7Mc8idW5c2dUrlwZAJCUlCRMFNG6dWthn7Nnz5ohMiIiIiIiehe8M8kTAGGck7ZOnToJ9x89ehQxMTFFHRYREREVgoWFBerUqYM6derkmDiKKDesN2QK70zylJGRgQcPHgDIGhdVvnx5AFnd+saNGwcAePv2Lb755hsoleIWW4yJicGlS5dMEzARERGJIpVKUb58eZQvXx5S6Ttz6kImxnpDplCsa1JaWho+/PBDnDlzBiqVKtf9VCoVFi9eLExL7uXlpbPe06hRo9C2bVsAwIULF/DZZ58hPj4+1/LUajUCAgIwePBghIeHG+nVEBERERFRSVbs2zBv376NyZMnw9XVFT169ECzZs1QpUoV2NvbIzk5GXfv3sW+fftw//59AEDZsmUxbdo0nTKkUil++uknTJ48Gbdu3cKZM2fQo0cPeHt7o127dqhUqRIsLCwQFxeHmzdv4vjx43j48KE5Xi6Cg4NF7efl5aUzkyAREdG7Sq1WQ6FQAAAsLS11LpAS5Yb1hkyhWCdPFhYWcHZ2RmxsLGJiYrBr1y7s2rUr1/1r1aqFNWvWoFq1ajkeq1ChAnbs2IHvv/8eu3fvRlpaGvbs2YM9e/boLUsikcDb2xt9+/Y12usRY+/evdi7d2+++/n6+jJ5IiKiUkGhUCAkJAQA4OnpCSsrKzNHRCUB6w2ZQrFOnsqUKYPz58/j5s2bCAwMxK1bt/Do0SO8fv0ab9++hY2NDVxcXODh4YHu3bujV69eeX4wypQpg7lz52LMmDE4ePAgLl26hKioKMTHx0OtVqNcuXJwc3ND69at4e3tjapVqxbhqyUiIiIiouJMolar1eYOgsxHLpcLV2Uq1awLn5X+Zo6IiKhoHJ47BK6OXOepJND+rWILAonFelO6mervX6wnjCAiIiIiIioumDwRERERERGJwOSJiIiIiIhIBCZPREREREREIjB5IiIiIiIiEqFYT1VOREREZGlpCU9PT2GbSAzWGzIFJk9ERERUrEkkEk4zTQXGekOmwOSJBBXsrXF47hBzh0FEVCScHGzMHQIREZUwTJ5IIJNK4epobe4wiIiIdKhUKiQlJQEAypUrB6mUQ7Ypf6w3ZAqsRSRQq9XmDoFKCLVaDblcDrlcznpDorHekKEyMzPx8OFDPHz4EJmZmeYOh0oI1hsyBSZPJOAXC4mlUCgQEhKCkJAQKBQKc4dDJQTrDRERlXRMnoiIiIiIiERg8kRERERERCQCkyciIiIiIiIRmDwRERERERGJwOSJiIiIiIhIBK7zREQFJpFIYGlpKWwTicF6Q4Zi3SFDsN6QKUjUXGyjVJPL5QgJCQEAeHp6wsrKyswREREREREVjqnOcdltj4iIiIiISAR22yOBUqVCTGKaucMwGScHG8ikvF5ARERERIZh8kSC+NQ38Fnpb+4wTObw3CFwdbQzdxjvhMzMTLx8+RIAULlyZVhY8KuE8sd6Q4Zi3SFDsN6QKbAWEVGBqVQqvHr1CgDg6upq5miopGC9IUOx7pAhWG/IFNiHiYiIiIiISAQmT0RERERERCIweSIiIiIiIhKByRMREREREZEITJ6IiIiIiIhEYPJEREREREQkQomaqvzKlSsYOXKkcHv37t1o0aJFgcp48uQJjhw5gkuXLuHJkydISEhAZmYmypYti2rVqqFhw4bo2rUrOnbsCEtLS71l7N+/H3PmzCnQcefMmYPRo0fr3Ddy5EhcuXIlx75SqRRly5ZF9erV0aZNG3zyySeoWbNmgY5HZEpSqRSOjo7CNpEYrDdkKNYdMgTrDZlCiUqeDhw4oHPb19dXdPKUnJyMFStWwM/PD5mZmTkef/36NV6/fo1bt25h9+7dqFixIiZMmIBRo0ZBJpMZJX6xVCoVkpKSkJSUhNDQUOzYsQNz5szB8OHDizQOotxYWFigbt265g6DShjWGzIU6w4ZgvWGTKHEJE9v3rzBsWPHAAC2trZIT0/H0aNHMW/ePFhZWeX53KdPn2LChAl49OgRAMDR0RH9+/dHq1at4OLiAhsbG8THx+Phw4e4cOECAgMD8fr1a6xYsQLvv/8+nJ2dcy17+vTp6N69e77x51UGAAQEBAjbKpUK0dHROHr0KA4cOACFQoHFixejatWq6Nq1a77HIiIiIiIi4ysxydOJEyeQlpYGAJg3bx6++eYbJCUl4fTp0+jTp0+uz0tPT8ekSZOExOmjjz7CrFmzYG9vn2Pfzp0749NPP8XTp0/x66+/Yv/+/fnG5erqCjc3NwNf1f9kL8PDwwNdu3ZFo0aNsGTJEqjVaqxbt47JExERERGRmZSYDqC+vr4AAHd3dwwZMgS1a9fWuT8333//PSIjIwEAw4YNw+LFi/UmTtqqV6+O5cuXY8OGDShTpkyhYy+M4cOHo2rVqgCAO3fu4PXr12aNhwgAFAoFwsLCEBYWBoVCYe5wqIRgvSFDse6QIVhvyBRKRPL06tUrXLp0CQDg4+Oj8/+FCxcQHx+v93lxcXHYu3cvAKBy5cqYNWtWgY7r5eUFBwcHQ8M2CqlUisaNGwu3X7x4YcZoiLKo1WqkpaUhLS0NarXa3OFQCcF6Q4Zi3SFDsN6QKZSI5CkgIABKpRJSqRTe3t4AAG9vb0gkEigUChw8eFDv8w4fPoy3b98CAD788ENYW1sXWczGZGHxv96VKpXKjJEQEREREZVeJSJ58vPzAwC0adMGrq6uALK61jVv3hxA7l33rl69Kmx37tzZtEGa0P3794VtFxcXM0ZSMlhBqfdfRnq6cAVK3z8iIiIiorwU+wkj7t27h/DwcAD/66qn4ePjg+vXr+POnTt48OAB6tWrp/O45nlSqRQeHh4miS8mJkYnucmNoZNKnD17FhEREQCyEsbKlSsbVE5p0sf+id77//z91zyfN3/+fFOEQ0RERETviGKfPGlalaytrdG7d2+dx/r27YulS5dCoVDA19cXX331lc7jCQkJAICyZcvmOZ15bGyssG925cqVE1q79Pnpp5/w008/5fs6NImcGNpTla9bt064f/z48aLLICIiIiIi4yrWyVNmZqYwnqlbt245ZslzdHREly5dcPLkSQQEBOCLL77QWUFa0xXLxsYmz+Ns2rQJ27dv1/vYoEGDsGLFisK8DFHc3d3zfPzjjz/GJ598YvI4iIiIiIhIv2KdPF24cAFxcXEAcnbZ0/Dx8cHJkycRHR2NoKAgtG/fXnjMzs4OycnJyMjIMFmMy5cvx+DBg01Sto2NDVq0aIERI0bAy8vLJMcgIiIiIiJxinXypOmy5+jomOuED926dYODgwOSk5Ph6+urkzw5OjoiOTkZKSkpkMvluXbdmzt3LubOnSvcfvbsGbp37268FyJCQECAsC2TyWBnZwdnZ2fIZLIijeNdcDS1ht77d894H87lbIs4mneTTCZDtWrVhG0iMVhvyFCsO2QI1hsyhWKbPKWkpOD06dMAgMTERJ21jnJz/PhxzJ8/H7a2WSfIHh4eePLkCVQqFcLDw+Hp6WnSmAvD0AklKCc59H9B2tjaws7OroijeTfJZLI8xwIS6cN6Q4Zi3SFDsN6QKRTbqcqPHDkirNEkVnp6Oo4fPy7cbt26tbD977//Gi02IiIiIiIqfYpty5Omy56zszPmzJmT7/6rVq1CdHQ0/Pz8MHDgQABAv379sHr1asjlcvz3v//FuHHjUKZMGRNGTURERERE76pimTw9ffoU169fBwD07t0b/fv3z/c5N2/exPbt23H58mXExMTA1dUVTk5OGDJkCHbv3o0XL15g9erVmDdvnqnDJ3rnyeVyhISEAAA8PT3zXAqASIP1hgzFukOGYL0hUyiW3fb8/PygVqsBIMfaTrnR7KdSqeDn5yfc/9VXX6F27doAgB07dmDBggXCFOa5SU5ONiRsIiIiIiJ6hxXLlidN8lOxYkW0atVK1HNatGgBZ2dnxMbGwt/fHxMnTgQA2NvbY+PGjZg4cSKioqKwe/duHD16FO+//z5atWoFFxcX2NraIjU1FY8fP0ZgYCBOnDghlGttbZ3ncWNiYnD//v1847O3t0eVKlVEvRYiIiIiIip+il3yFBwcjCdPngAAevToobPobV6kUil69uyJv/76CxEREQgNDRVm6KtZsyb++ecfLF++HP7+/khISMCOHTuwY8eOXMurWLEixo8fj5EjR+Z53J9++gk//fRTvvF1794dv/76q6jXQkRERERExU+xS560u9yJ7bKn0atXL/z1118Asiac0J7evFy5clixYgWmTJmCw4cP4/Lly3j8+DESExOhVCrh4OCAypUro3HjxujUqRO6dOkCS0tL47woIiIiIiIq8SRqzeAiKpW0B1NWqlkXPiv9zRyR6RyeOwSujlznyRg4CJcMwXpDhmLdIUOw3pRupvr7F8sJI4iIiIiIiIobJk9EREREREQiFLsxT0RU/FlYWMDd3V3YJhKD9YYMxbpDhmC9IVNgTSKiApNKpbC3tzd3GFTCsN6QoVh3yBCsN2QK7LZHREREREQkAlueiKjAVCoV0tPTAQC2trai12Oj0o31hgzFukOGYL0hU2AtIqICy8zMRHh4OMLDw5GZmWnucKiEYL0hQ7HukCFYb8gU2PJEggr21jg8d4i5wzAZJwcbc4dARERERCUYkycSyKRSuDpamzsMIiIiIqJiid32SKBWq80dAhERERFRscXkiQTsD0xERERElDsmT0RERERERCIweSIiIiIiIhKByRMREREREZEIEjVnCSjV5HI5QkJCAACenp6wsrIyc0RERERERIVjqnNctjwRERERERGJwOSJiIiIiIhIBC6SSwL24CSxlEol4uLiAABOTk6QyWRmjohKAtYbMhTrDhmC9YZMgckTCSQSiblDoBJCqVTi2bNnAIDy5cvzB4lEYb0hQ7HukCFYb8gUmDyRQKlSISYxzdxhFJiTgw1kUvZAJSIiIiLTYvJEgvjUN/BZ6W/uMArs8NwhcHW0M3cYRERERPSO4+V6IiIiIiIiEZg8ERERERERicDkiYiIiIiISAQmT0RERERERCJwwggiKjCJRAI7Ozthm0gM1hsyFOsOGYL1hkyByRMRFZilpSU8PDzMHQaVMKw3ZCjWHTIE6w2ZArvtERERERERiVAqW57S09Ph5+eH06dPIywsDImJiVCr1bC3t0fVqlXh5uaG5s2bo3PnzqhcubLOc2fPno0DBw4U6Hi+vr5o0KCBcHvq1Kk4ceIEAGDbtm1o3759ns8/deoU/u///g8AMHDgQKxcubJAxyciIiIiosIrdcnTjRs38MUXX+DFixc5HktISEBCQgJCQ0Oxf/9+ODk54eLFi0aP4bvvvkNQUBCSk5Px3XffISAgANbW1nr3TUlJwYIFCwAAFStWxJw5c4weD1FBZWZm4vHjxwCAmjVrwsKi1H2VkAFYb8hQrDtkCNYbMoVSVYsePXqEcePGIS0tDQDg5eWF3r17o3bt2rC0tERCQgLCwsIQGBiIoKCgfMvbvHkzXFxc8t2vZs2aOrddXFwwc+ZMfPvtt3jy5AnWrl2LWbNm6X3uqlWr8OrVKwDAvHnz4OjomO/xiExNpVIhMTERAFC9enXzBkMlBusNGYp1hwzBekOmUKqSpx9//FFInJYvX47Bgwfn2Kdjx44YN24c4uPjceTIkTzLq1WrFqpVq2ZQLB9++CECAgJw5coV/Pnnn+jfvz8aN26ss09QUBD++9//AshK9Pr162fQsYiIiIiIqPBKzYQRSqUS586dAwA0btxYb+KkrUKFChg+fLjJ4pFIJFiyZAnKlCkDpVKJuXPnIjMzU3j8zZs3+Pbbb4WxWJque0REREREZB6lJnmKj4/HmzdvAOTsRmcuNWvWxNSpUwEAYWFh+OOPP4TH1q1bJ/TTnTlzJlxdXc0SIxERERERZSk1yZOlpaWwHRkZacZIdI0dOxYNGzYEAPzyyy94+PAhQkNDsW3bNgBA69at8fHHH5sxQiIiIiIiAkpR8uTo6IiqVasCyGrl2bRpE1QqlZmjAiwsLLBkyRLIZDLI5XLMmzcP8+bNg1KpRJkyZbB48WKuiq2HFZTCv4z0dKSlpeX4R0RERERkTKVqwogRI0YIayStWbMGe/bsgZeXF1q0aAFPT88Cz8QSFRWF9PT0PPexsbHJt9xGjRph9OjR2Lx5M4KDg4X7p06ditq1axcoptKij/0TYfvP33/Vu8/8+fOLKhwiIiIiKgVKVfI0evRoPHjwAPv27QMAPH/+HDt27MCOHTsAAE5OTmjTpg28vb3RrVu3fFt8xo0bl+8x27RpI5Sfl//85z84fPgwXr58CQBo2LAhxo4dm+/ziMxBKpUK0/RLpaWmAZsKifWGDMW6Q4ZgvSFTKFXJk1QqxbJly9C/f39s3boVly5d0pnhLi4uDocPH8bhw4fRuHFj/Pjjj6hRo0aRxPb48WPExcUJtytUqMDF3KjYsrCw4JoZVGCsN2Qo1h0yBOsNmUKpPDvv2LEjOnbsiNTUVAQHByMkJAShoaG4du0aUlJSAAChoaEYNmwY9u/fn+tCuKdOnTJ4nSdtmqnKFQqFcN+FCxcQEBAAb2/vQpdPRERERESFVyqTJw17e3t06dIFXbp0AQDI5XIEBARg5cqVSEpKQmxsLNauXYulS5eaNI7t27cjJCQEQFZXwICAALx69QrLli1Dp06dUL58eZMevyQ6mvq/FsHdM96HczlbM0ZDRERERKUBO4BqsbKywpAhQ7BmzRrhvhMnTph0Vr6nT59i7dq1AAA3NzfMmDED3377LYCstak0E1yQLjlkwj8bW1vY2dnl+Eemo1AocPv2bdy+fVunxZQoL6w3ZCjWHTIE6w2ZApMnPTp37ozKlSsDAJKSkpCYmGiyY3333XfIyMiAVCrF0qVLYWlpiV69eqFHjx4AgAMHDuDSpUsmOz6RIdRqNRQKBRQKBdRqtbnDoRKC9YYMxbpDhmC9IVNg8pSL3MY5GdO+ffsQGBgIABg5ciSaNGkiPPbdd9/B3t4eQNaU22/fvjV5PERERERElDsmT3pkZGTgwYMHALLGRZlizFFcXJzQJa9q1aqYPn26zuOurq748ssvAWTNxLd+/Xqjx0BEREREROKVmuQpLS0NH374Ic6cOZPnGCaVSoXFixcjLS0NAODl5ZXvek+GWLRoEZKSkgAACxcuhK1tzgkPhg4dihYtWgAAtmzZgrCwMKPHQURERERE4pSq2fZu376NyZMnw9XVFT169ECzZs1QpUoV2NvbIzk5GXfv3sW+fftw//59AEDZsmUxbdq0XMuLiopCenp6vsd1dnbWab06efIkjh07BgAYMGAAOnfurPd5EokEixcvxsCBA6FQKDBv3jz8888/XOiNiIiIiMgMSk3yZGFhAWdnZ8TGxiImJga7du3Crl27ct2/Vq1aWLNmTZ7rOI0bN07UsefMmYPRo0cDAFJSUrBw4UIAWQvhzpkzJ8/n1qtXD5MmTcL69esREhKCHTt24NNPPxV1XCIiIiIiMp5SkzyVKVMG58+fx82bNxEYGIhbt27h0aNHeP36Nd6+fQsbGxu4uLjAw8MD3bt3R69evWBlZWX0OFatWoVXr14BAL755htR46kmTZqEI0eOIDIyEj/99BN69uyJKlWqGD02IiIiIiLKnUTNuRtLNblcLizQW6lmXfis9DdzRAV3eO4QuDpyXaeipFKphDF75cqVY1dSEoX1hgzFukOGYL0p3bTPcT09PY3WKFJqWp6IyHikUqlJZqGkdxvrDRmKdYcMwXpDpsAUnIiIiIiISAS2PBFRgWlWbQcAS0tLk0znT+8e1hsyFOsOGYL1hkyBLU9EVGAKhQIhISEICQkRfpiI8sN6Q4Zi3SFDsN6QKTB5IiIiIiIiEoHJExERERERkQhMnoiIiIiIiETghBEkqGBvjcNzh5g7jAJzcrAxdwhEREREVAoweSKBTCqFq6O1ucMgIiIiIiqW2G2PBGq12twhEBEREREVW0yeSJCZmWnuEIiIiIiIii122yOBhQWrA4ljaWkJT09PYZtIDNYbMhTrDhmC9YZMgWfLJODK2ySWRCKBlZWVucOgEob1hgzFukOGYL0hU2C3PSIiIiIiIhHY8kQClUpl7hCohFCpVEhKSgIAlCtXDlIpr8NQ/lhvyFCsO2QI1hsyBdYiEiiVSnOHQCVEZmYmHj58iIcPH3KiERKN9YYMxbpDhmC9IVNg8kRERERERCQCkycScLY9IiIiIqLcMXkiAWfbIyIiIiLKHZsaSKBUqRCTmGbuMERzcrCBjIM/iYiIiKiIMHkiQXzqG/is9Dd3GKIdnjsEro525g6DiIiIiEoJXrYnIiIiIiISgS1PRFRgEokElpaWwjaRGKw3ZCjWHTIE6w2ZApMnIiowS0tLNGnSxNxhUAnDekOGYt0hQ7DekCmw2x4REREREZEITJ6IiIiIiIhEYLc9IiqwzMxMvHz5EgBQuXJlLrBMorDekKFYd8gQrDdkCqxFRFRgKpUKr169AgC4urqaORoqKVhvyFCsO2QI1hsyhXcqeQoKCsKoUaOE27a2tggMDISNjU2ez3vz5g06duyI1NRU4b7t27ejbdu2eT7vypUrGDlypHB79+7daNGiRYHj1GZtbQ1HR0d4eHigZ8+e8PHxgZWVVb5lEhERERGRab3TY57S09Nx8uTJfPc7deqUTuIk1oEDB3Ru+/r6FriM7N68eYPo6GicPXsWc+fOxeDBg/Hs2bNCl0tERERERIXzziZPZcqUAQD4+fnlu69mH81zxHjz5g2OHTsGIKuFCwCOHj0KuVxeoDiHDh2KgIAA4d+2bdswd+5cVKpUCQAQERGBKVOmQKlUFqhcIiIiIiIyrnc2efLy8gIABAYGIjY2Ntf9Xr9+jYsXLwIAunfvLrr8EydOIC0tDQAwb948AEBSUhJOnz5doDgrVqwINzc34V/79u0xatQoHDp0CFWrVgUA3L9/HydOnChQuUREREREZFzvbPLUsWNHODs7Q6lU4tChQ7nud/DgQWRmZsLZ2RkdOnQQXb6mi567uzuGDBmC2rVr69xfWPb29pgyZYpwOzAw0CjlEhERERGRYd7Z5Ekmk6F///4A8u66p3ns/fffh0wmE1X2q1evcOnSJQCAj4+Pzv8XLlxAfHy8wXFrc3d3F7ajo6ONUiYRERERERnmnU2eAGDAgAEAgLt37yIiIiLH4w8ePMCdO3d09hUjICAASqUSUqkU3t7eAABvb29IJBIoFAocPHjQCNEDlpaWwjbXJqDiRCqVwtHREY6OjpBK3+mvETIi1hsyFOsOGYL1hkzhna5JDRs2RP369QHob33S3Ofm5oYGDRqILlfzvDZt2gjrBlSvXh3NmzcHYLyue5GRkcK2ZvxTaWcFpfAvIz0daWlpOv+oaFhYWKBu3bqoW7cuE3sSjfWGDMW6Q4ZgvSFTeOdr0oABA/D999/j4MGD+PLLLyGRSAAAarUaAQEBwj5i3bt3D+Hh4QD+11VPw8fHB9evX8edO3fw4MED1KtXz+C4lUolNm/eLNzu3bu3wWW9S/rYPxG2//z91xyPz58/vyjDISIiIqJS5J1ueQKyEhqpVIqXL18iKChIuD8oKAgvX77U6XonhqZVydraOkdC07dvX6GrnaGtT/Hx8bh06RJGjBiBu3fvAshKnFq1amVQeUREREREZBzvfPLk6uqKtm3bAtDtuqfZbteundD1Lj+ZmZnCeKZu3brB3t5e53FHR0d06dIFQNa4KJVKlW+Z69evh7u7u/Cvffv2GD16NK5fvw4bGxuMGTMGa9asERUfUVFRKBQICwtDWFgYFAqFucOhEoL1hgzFukOGYL0hU3jnkycAGDhwIADg+PHjePPmjc4CtwXpsnfhwgXExcUByNllT0Nzf3R0tE5LlyE8PDwwcuRInYkjiIoDtVotjDNTq9XmDodKCNYbMhTrDhmC9YZM4Z0f8wQAPXv2xIIFC5CamopTp04JHyZbW1v06tVLdDmarniOjo7o3Lmz3n26desGBwcHJCcnw9fXF+3bt8+zzKFDh2LYsGEAssY5RUdH49ixY/Dz88ONGzcwcuRI7N27FxUqVBAd57vsaGoNYXv3jPfhXM7WjNEQERERUWlSKpInOzs79OjRAwEBAfDz8xOuPvTo0QO2tuJOvlNSUnD69GkAQGJiIho3bpzvc44fP4758+fneYyKFSvCzc1NuN2gQQN069YNbdu2xezZs/H8+XPMnTsXGzZsEBXnu06O/63FZWNrCzs7OzNGQ0RERESlSanotgf8r+vexYsXERgYqHOfGEeOHMHbt28LdMz09HQcP368QM/RGDRokDAhxenTp4VFeYmIiIiIyDxKRcsTALRv3x7Ozs6IjY0FALi4uOTbpU6bpsues7Mz5syZk+/+q1atQnR0NPz8/AqUpGmbMWMGTp48CaVSiR9//LFA8RIRERERkXGVmuRJJpNhwIAB2L59O4CsiSLErjb99OlTXL9+HUDWtOH9+/fP9zk3b97E9u3bcfnyZcTExIie0U9b7dq10bdvXxw8eBC3bt3CxYsX0bFjxwKXQ0REREREhVdquu0BwMyZMxESEoKQkBB89dVXop+nPU5K7GK1mv1UKpXOFOkFNWnSJGFhX457IiIiIiIyn1KVPBlKk/xUrFhR9GK1LVq0gLOzMwDA39/f4GO7ubnBy8sLAHD16lVcu3bN4LKIjEUmk6FatWqoVq0aZDJZ/k8gAusNGY51hwzBekOmwOQpH8HBwXjy5AmArNn5xHb1k0ql6NmzJwAgIiICoaGhBscwefJkYZutT1QcyGQyuLq6wtXVlT9IJBrrDRmKdYcMwXpDpsDkKR/aXe7EdtnT0F5DSjPhhCGaNGkijHW6cOECbt++bXBZRERERERkGImaSy6XanK5HCEhIQCASjXrwmel4V0Mi9rhuUPg6sh1noiIiIhIl/Y5rqenJ6ysrIxSbqmZbY+IjMdUX0j0bmO9IUOx7pAhWG/IFNhtj4iIiIiISAQmT0RERERERCIweSIiIiIiIhKByRMREREREZEITJ6IiIiIiIhEYPJEREREREQkAqcqJ0EFe2scnjvE3GGI5uRgY+4QiIiIiKgUYfJEAplUCldHa3OHQSWAhYUF3N3dhW0iMVhvyFCsO2QI1hsyBdYkIiowqVQKe3t7c4dBJQzrDRmKdYcMwXpDpsAxT0RERERERCKw5YkEKpXK3CFQCaFSqZCeng4AsLW1hVTK6zCUP9YbMhTrDhmC9YZMgbWIBEql0twhUAmRmZmJ8PBwhIeHIzMz09zhUAnBekOGYt0hQ7DekCkweSIiIiIiIhKByRMREREREZEITJ6IiIiIiIhEYPJEREREREQkApMnEnABOSIiIiKi3DF5IoFEIjF3CERERERExRabGkigVKkQk5hmlmM7OdhAxvUXiIiIiKgYk6jVarW5gyDzkcvlCAkJAQBUqlkXPiv9zRLH4blD4OpoZ5ZjExEREdG7Rfsc19PTE1ZWVkYpl5f6iYiIiIiIRGDyREREREREJALHPBFRgSmVSsTFxQEAnJycIJPJzBwRlQSsN2Qo1h0yBOsNmQKTJyIqMKVSiWfPngEAypcvzx8kEoX1hgzFukOGYL0hU2C3PSIiIiIiIhGYPBEREREREYnA5ImIiIiIiEiEEjfmKSgoCKNGjdL7mLW1NRwdHeHh4YGePXvCx8cnzznd3d3dC3RsDw8P+Pn56dy3f/9+zJkzBwCwfPlyDB48uEBlenl54fnz5znut7CwgL29PerUqYMOHTrgo48+gqura4HKJiIiIiIi43mnWp7evHmD6OhonD17FnPnzsXgwYOFgYIlTWZmJhITE3H9+nWsX78e/fr1w4kTJ8wdFhERERFRqVXiWp60DR06FMOGDRNuv379GhEREdi8eTOio6MRERGBKVOmwNfXN88ZVho3bozly5fnezxra2ujxK2Pi4sLNm/eLNzOzMzE8+fPsW/fPpw5cwapqamYMWMG/vnnHzRs2NBkcRARERERkX4lOnmqWLEi3NzcdO5r3749Bg8eDB8fHzx//hz379/HiRMn0KdPn1zLsbW1zVFOUbO0tMwRQ8OGDdGzZ0+sWLECW7duhUKhwIYNG/Dzzz+bKUqiLBKJBHZ2dsI2kRisN2Qo1h0yBOsNmcI71W1Pw97eHlOmTBFuBwYGmjGawvv888+FVq8LFy5ApVKZOSIq7SwtLeHh4QEPDw9YWlqaOxwqIVhvyFCsO2QI1hsyhXcyeQJ0J4OIjo42YySFZ2dnh7p16wIA0tPTkZiYaN6AiIiIiIhKoXc2edK+wmBhUaJ7JwLQfT1seSIiIiIiKnolP6vIRWRkpLBdtWpVM0ZSeJmZmXj48CGArCTK0dHRvAEVghWUeu/PSE9HWh4t6po+y1Q8ZGZm4vHjxwCAmjVrvhMXKMj0WG/IUKw7ZAjWGzKFd7IWKZVKnZnrevfunef+6enpuH//fr7lVqpUCQ4ODoWOr6D+/vtvJCcnAwBatmxZoj/8feyf6L3/z99/zfN58+fPN0U4ZCCVSiV0H61evbp5g6ESg/WGDMW6Q4ZgvSFTKLln4XrEx8cjPDwc69atw927dwFkJU6tWrXK83mhoaHw9vbOt3xDFsE1lGaq8v379+P3338X7h83blyRHJ+IiIiIiHSV6ORp/fr1WL9+vd7HbGxs8Mknn+DLL78s4qgM8/z5c51JLrKTSCSYNm0a3nvvvSKMioiIiIiINEp08pQXDw8PjBw5UtTUlG3atMGOHTuKIKqCs7e3R7t27TBmzJh8W9CIiIiIiMh0SnTyNHToUAwbNgxA1jin6OhoHDt2DH5+frhx4wZGjhyJvXv3okKFCmaONH8uLi4647RkMhnKli0LZ2fnd2pht6OpNfTev3vG+3AuZ1vE0RARERERiVeik6eKFSvCzc1NuN2gQQN069YNbdu2xezZs/H8+XPMnTsXGzZsMGOU4lhaWuq8lneVHDK999vY2nJGPSIiIiIq1t7JdZ4GDRokzLB3+vRpXLp0ycwRERERERFRSfdOJk8AMGPGDMhkWa0cP/74o5mjIXq3SKVSuLi4wMXFBVLpO/s1QkbGekOGYt0hQ7DekCm8szWpdu3a6Nu3LwDg1q1buHjxopkjInp3WFhYoHr16qhevXqJXneMihbrDRmKdYcMwXpDpvDOJk8AMGnSJGGyhZIw7omIiIiIiIqvdzoNd3Nzg5eXF06dOoWrV6/i2rVreqf7Tk9Px/3790WVWa9evVybfoODg0WV4eXlBUdHR1H7EhERERFR8fBOJ08AMHnyZJw6dQpAVuuT9nTgGqGhofD29hZV3tWrV+Hg4KD3sb1792Lv3r35luHr68vkiUo0hUKBe/fuAcia5VLMempErDdkKNYdMgTrDZnCO91tDwCaNGmCjh07AgAuXLiA27dvmzkiopJPrVZDoVBAoVBArVabOxwqIVhvyFCsO2QI1hsyhRLX8tS2bVuEh4cX6DlbtmzRe39By9Fn8ODBGDx4sMHPP336dKFjICIiIiIi03vnW56IiIiIiIiMgckTERERERGRCEyeiIiIiIiIRGDyREREREREJAKTJyIiIiIiIhFK3Gx7RGR+FhYWqPP/7d15eEzn/z/+50wWsiCSCLGG1CRBbEFstQS1tEHinaoqVUqpVqv9KKpo611LW11QvLVRS+1LCFVrbClCCAmyIIJEI5JISCLJTGZ+f+Q75zfJzCRnRsZk9Pm4Ltd1Muc+93nN5DUxr7nvc58WLYRtIjGYN2Qs5g4Zg3lDpsBMIiKDSaVS1K1b19xhkIVh3pCxmDtkDOYNmQKLJxI4O9bEgTkjzHJu19p2ZjkvEREREZFYLJ5IYCWVor5TTXOHQRZAfdd2ALCxsYFEIjFzRGQJmDdkLOYOGYN5Q6bABSNIoFKpzB0CWQi5XI64uDjExcUJ/zERVYZ5Q8Zi7pAxmDdkCiyeSKBQKMwdAhERERFRtcXiiYiIiIiISAQWT0RERERERCKweCIiIiIiIhKBxRMREREREZEILJ6IiIiIiIhEYPFEREREREQkAm+SSwJra6YDiWNjYwNfX19hm0gM5g0Zi7lDxmDekCnw0zIJeOdtEksikcDW1tbcYZCFYd6QsZg7ZAzmDZkCiycSlCiVeJCT/0x9uNa2g5WUs0GJiIiI6MXD4okE2XmFGLok/Jn6ODBnBOo7OVRRRFRdKZVK5ObmAgDq1KkDKQtmEoF5Q8Zi7pAxmDdkCswiIjKYQqFAcnIykpOToVAozB0OWQjmDRmLuUPGYN6QKbB4IiIiIiIiEoHFExERERERkQgsnoiIiIiIiERg8URERERERCQCiyciIiIiIiIRWDwRERERERGJYPH3eSouLsbhw4dx6tQpxMbG4tGjR8jLy4OjoyMaNWoEX19fDBw4EF27dq10ff+TJ0/i6NGjuHjxIjIzM/H06VM4OzujadOm6NWrF4YOHYr69evrPf7VV1/FzZs34eTkhHPnzkEikehtO3z4cMTHxwMA3n//fXz00Ud6254/fx5jxowBALz77ruYMWNGhc+DyNQkEglsbGyEbSIxmDdkLOYOGYN5Q6Zg0cXT4cOHsXjxYqSlpWnty8nJQU5ODq5du4atW7fCw8MDs2fPRp8+fbTa3rhxA3PnzkVMTIzWvvT0dKSnp+P8+fNYuXIlJk2ahMmTJ+t8E/r5+eHmzZvIycnBzZs30bJlS51xP3nyBImJicLP0dHRFT5Pzf2dO3eusC3R82BjY4O2bduaOwyyMMwbMhZzh4zBvCFTsNji6ZdffsGyZcuEn3v06IGAgAB4enqidu3ayM3Nxe3btxEREYEzZ84gJSUFP/74o1bxFB0djSlTpuDx48cAgNatWyMoKAheXl6wt7fHgwcPcPr0aezZswcFBQX46aefcOPGDSxZskT4NkOtc+fO2LZtm9CvvuLp0qVLUCqVsLKyQklJCWJjYyGXy7X6U7t48SIAQCqVws/Pz6jXi4iIiIiIno1FFk+7du0SCicXFxf89NNP6NKli1a77t27Y/To0UhKSsKiRYuQnZ1dZn96ejqmTp2Kx48fQyKRYMaMGRg/fnyZUaU2bdqgX79+mDBhAqZOnYrExET8+eefcHd315o+pzkqFB0djVGjRumMXz2S1KtXL0RFRaGgoABxcXHo2LGjVtuSkhJhRMzLywu1atUS8xIREREREVEVs7gFIx48eIAFCxYAAOzt7bFx40adhZMmmUyG0NBQjB8/vszjc+fORU5ODgDgo48+woQJE/TOiW3SpAl+//13uLm5AQBCQ0Nx5cqVMm0aNGiARo0aAah4Kp56X5cuXdCuXbsK28fHxyM/Px8A0KlTp4qeJtFzo1AocO/ePdy7dw8KhcLc4ZCFYN6QsZg7ZAzmDZmCxRVP69atw9OnTwEA06ZNg6enp6jjpFIphg0bJvyckJCAU6dOASgtriZNmlRpHy4uLpg9ezYAQKVS4bffftNqox59Sk9PR2pqqtb+oqIixMXFASgthtTT8NRT88rj9U5UHSmVSmRkZCAjIwNKpdLc4ZCFYN6QsZg7ZAzmDZmCRRVPKpUKYWFhAEpHnUJCQozuS90PALz11luwsrISddzAgQOFFfeOHj2K3NzcMvs1R4d0jSZduXIFcrkcdnZ2aNWqlVA8qa+DKk+zqGLxRERERERkPhZVPN24cQOPHj0CULqynaOjo9F9XbhwQdju27ev6OOsrKyERSeUSqXWCn3lr3sqT/1Y27ZtYW1tjXbt2sHKygqPHz9GUlKSVnt18dSiRQs4OzuLjvN5sEWJ1r+nBQXIz8/X+Y+IiIiIyJJZ1IIRCQkJwnbr1q2fqS91oVKvXj3hOiaxWrVqVSYmzRX8PDw8UK9ePTx8+FBn8aQuhtQjVA4ODvD29sa1a9cQHR0Nb29voW1ycjKysrIAVM9Rp0GOd7UeW//rSr3t58+fb8pwiIiIiIhMyqJGntSLOwCl1x8Z68mTJ5DL5QAAV1dXg4/XPEYzJjX1VLzbt28LxQ9QduU8zSXH1dvliy3NKXtcLIKIiIiIyLwsqnjSnPplZ2dXJf3Y29sbfLzmMXl5eVr79U3du379OvLz82FlZYX27dsLj+srnrhYBBERERFR9WFRxZODg4OwrV5x71n7KSgoMPh4zWN0XXelb9EI9UiSt7d3mRjU7R8+fIg7d+5oHduoUSO4u7sbHCcREREREVUdi7rmycnJSdjWnA5nKEdHR1hbW0OhUCAzM9Pg4zWP0YxJTSaToXbt2nj8+HGZqXfqYkhzyh5QOg2wWbNmuHPnDqKjo9GsWTM8ePBAWOq8uk7ZO5jXVOuxLdNfQ706ho/mkWWRSqVC7kulFvUdDJkR84aMxdwhYzBvyBQsKpM0F1O4du2a0f1IJBJ4eXkBKB3tefjwoUHHX79+XWdMalKpVCiQEhIShKl96kKqfPGk+Zi6wLKEKXvFsNL6Z2dvDwcHB53/6MVhbW0NT09PeHp6wtraor6DITNi3pCxmDtkDOYNmYJFFU8tW7ZE3bp1AZQWIrquNxJLczQnIiJC9HFKpRInT54EUFokdezYscL+S0pKcOnSJdy6dQvZ2dla51ZT96MumjSXUq+uI09ERERERP8mFlU8SSQSBAUFASi97mjHjh1G9xUcHCxsb9q0CSUlJaKOO3ToENLT0wEA/fv3R+3atXW20yx4Ll68KBRFzZo107nCn3rk6e7du8jIyBBGqerVq4fmzZuLio2IiIiIiEzHooonABg3bpyw0t6yZctw69YtUccplUqEh4cLP3t7e6Nnz54AgMTERISGhlbaR3Z2NhYvXgygtJB799139bZt3bq1EOeFCxeEkSRdU/aAsjfBPXr0KG7cuFFheyJzksvlSEhIQEJCgrDsP1FlmDdkLOYOGYN5Q6ZgccVT/fr1MXfuXAClo09jxozB+fPnKzzm5s2bePfdd7UKpAULFggjRz/88APWrVsHlUqls4+0tDS88847wqjThAkT0K5dO73ntLGxEZYjj4uLQ1RUFICKiyH11L21a9cKcVTX653o302lUiE/Px/5+fl63zNE5TFvyFjMHTIG84ZMwSKvnhsxYgTS09OxbNkyZGVlYcyYMejZsycCAgLg6emJ2rVrIzc3F7dv38bJkydx+vRplJSUaC3u0LBhQ6xYsQLvv/8+8vLysGjRIuzbtw9BQUHw8vKCnZ0dMjIycPr0aezZs0dYonzIkCH4+OOPK42zU6dOOHv2LIqLi5GRkQGg4uLJz88PR48exb1798r0QURERERE5meRxRMATJ06FS1btsTixYuRlpaGyMhIREZG6m3fsmVLzJgxQ+txf39/bN68GXPnzsWVK1dw9epVXL16VWcfdnZ2mDhxIt5//31IJJJKYyxf+Li4uFR4/VL5wqpOnTrCqoBERERERGReFls8AcArr7yCPn364NChQzh16hTi4uKQnZ2N/Px8ODo6olGjRmjXrh0GDhwIf39/vQWPl5cXtm/fjhMnTuDIkSO4dOkSHj58iMLCQtStWxceHh54+eWXMWzYMNSvX190fO3bt4eNjY0wz1bfynxqrVq1gp2dnXAD4I4dO4oq0oiIiIiIyPQsungCAFtbWwQGBiIwMPCZ++rTpw/69Onz7EH9PzVr1tQ7iqWLjY0NLl++XGXnJyIiIiKiqmNxC0YQERERERGZA4snIiIiIiIiESx+2h4RPX9WVlZo3LixsE0kBvOGjMXcIWMwb8gUWDwRkcGsrKwMWjyFCGDekPGYO2QM5g2ZAqftERERERERicDiiYiIiIiISARO2yMigxUXFyMuLg4A4OvrC1tbWzNHRJaAeUPGYu6QMZg3ZAosnkjg7FgTB+aMeKY+XGvbVVE0RERERETVC4snElhJpajvVNPcYRARERERVUu85okEKpXK3CEQEREREVVbLJ5IoFAozB0CEREREVG1xeKJiIiIiIhIBBZPREREREREIrB4IiIiIiIiEoGr7ZHAysrK3CGQhbC2toaXl5ewTSQG84aMxdwhYzBvyBSYSSSQSjkQSeJIpVI4OjqaOwyyMMwbMhZzh4zBvCFT4KdlIiIiIiIiETjyRALe54nEUiqVKCgoAADY29tz1JJEYd6QsZg7ZAzmDZkCs4gEEonE3CGQhVAoFEhMTERiYiLvD0aiMW/IWMwdMgbzhkyBI08kKFEq8SAn36BjXGvbwYrf5BARERHRvwCLJxJk5xVi6JJwg445MGcE6js5mCgiIiIiIqLqg0MGREREREREIrB4IiIiIiIiEoHFExERERERkQgsnoiIiIiIiERg8URERERERCQCV9sjIoPZ2trCz8/P3GGQhWHekLGYO2QM5g2ZAkeeiIiIiIiIRKjWI09RUVEYO3aszn01a9aEs7MzfHx8MHjwYAwePBjW1hU/nbt37yI8PByRkZFIS0vDo0eP4ODgADc3N/j7+2PQoEHo1KlTpXGNGTMG58+f17nP2toatWrVgqenJ3r37o3XX38dTk5OevuaNWsWwsLCtB6XSCRwdHREw4YN4efnh9dffx0+Pj6VxkZERERERKZRrYunihQWFuL+/fu4f/8+jh07hvXr12PVqlWoV6+eVtvi4mJ8//332Lx5M+RyeZl9OTk5yMnJQVJSEjZu3IjevXvj66+/RoMGDYyKS6FQ4NGjR4iOjkZ0dDTWrVuH5cuXGzxsrFKp8OTJEyQmJiIxMRFbt27FpEmTMH36dKPiIqpKJSUlyMzMBAC4urrCysrKzBGRJWDekLGYO2QM5g2ZgsUUT6NGjcKbb74p/FxQUICrV69i7dq1SEtLQ1xcHN5//31s374dEolEaJeXl4f3338fUVFRAAAnJycEBQWhW7ducHFxwZMnT3D16lXs2rULt2/fxsmTJzFy5EisXbsWnp6elca1b9++Mj/L5XLcu3cPe/fuRUREBLKysjBlyhQcPHgQzs7OFfYVGhoKNzc3AIBSqURWVhZOnDiBzZs3Q6FQYPXq1XBzc8Po0aNFv25EplBSUoLU1FQAQN26dfkfEonCvCFjMXfIGMwbMgWLKZ5cXFwgk8nKPNa+fXsEBgYiJCQEd+7cQWxsLI4fP46AgAChzZw5c4TCyd/fHz/++CNcXFzK9NOtWzeMGzcOS5cuxe+//4709HRMnjwZYWFhcHR0rDCu8jEBQOvWrTFo0CDMnDkTe/bsQW5uLnbs2IH33nuvwr48PDzQuHHjMo/16NED3bp1w5QpUwAAK1aswBtvvME/AEREREREz5nFLxhRp04dTJo0Sfj59OnTwvaBAwdw8OBBAIC3tzf+97//aRVOajY2Npg1axZCQkIAlF4ftXTp0meKbcKECcJ2XFyc0f0EBAQI12JlZ2fj2rVrzxQXEREREREZzuKLJwBo27atsH3//n1h+9dffxW2FyxYADs7u0r7mjVrFlxdXQEAu3btQnZ2ttFxaY4iFRcXG90PoP85EhERERHR8/FCFE+aq+yVlJQAABISEnD9+nUAQIcOHcoUHxVxdHREcHAwAKCoqAj79+83Oi7NIsfd3d3ofoCyz1GpVD5TX0REREREZLgXonhKSkoSttULLkRHRwuP9e3b16D+NK+Z0uzHUKGhocJ2v379jO4H0P0czcEWJWX+PS0oQH5+fpl/REREREQvIotZMEIfhUKB33//Xfi5S5cuAEpHntRatWplUJ8+Pj6QSqVQKpVITEyssK1mUQOUrraXlpaG8PBwHDlyBAAwZMgQ9OrVy6AYNCUkJAjXctnZ2cHX19fovp7VIMe7ZX5e/+tKrTbz589/XuEQERERET03Fls8qZcqX758OS5fvgwAaNSoEYYMGQIAePTokdBW172fKlKzZk04ODjgyZMnyMnJqbBtYGCg3n3NmzfHpEmTEBQUZND5gdL7PGVmZuLEiRNYunSpMB1xzJgxqFGjhsH9EVUliUQCBwcHYZtIDOYNGYu5Q8Zg3pApWEzxtGLFCqxYsULvfhcXF/zyyy+wtbUFgDLTx+zt7Q0+n729PZ48eYK8vDzDg/1/bt++jW3btqFx48bCiFhFKpva16dPH0ybNs3oeIiqio2NDby9vc0dBlkY5g0Zi7lDxmDekClYTPGkT+PGjTFw4EBMmDChzDLk6m8agNJRKkOpj6nsPk/lp/UplUo8evQIFy9exC+//ILLly9j/Pjx+P777zFo0CCD47CxsYGvry9GjhyJYcOG8ZsTIiIiIiIzsZjiadSoUXjzzTcBlA691qhRA3Xr1kWtWrV0tq9bt66w/fDhQ4O+eSgsLBRGrpycnAyKUyqVwsXFBa+88gp69OiB4OBgpKSk4PPPP0fXrl0r7C80NFRYDEIqlcLe3h6urq7CaFp1cDCvaZmft0x/DfXqGD6yR0RERERkaSymeHJxcYFMJhPd3svLS9iOj4/Hyy+/LPrY+Ph4YTlwzX4M5eDggFGjRmHRokXIz8/HoUOHMHLkSL3tPTw8ytwbqjoqhlWZn+3s7cuM8tG/g0KhwJ07dwAAzZo1K7OUPpE+zBsyFnOHjMG8IVN4IZYq16Vz587CdkREhEHHarbX7McYLVq0ELbLr8xHZKmUSiVycnKQk5PD+46RaMwbMhZzh4zBvCFTeGGLJ29vb/j4+AAAYmJiEBcXJ+q4vLw8hIWFAQBq1KiBV1999ZniUK+SB5R+A0JERERERJbphS2eAGDixInC9rx581BYWFjpMd9++y0ePnwIABgxYgScnZ2fKYarV68K2+7u7s/UFxERERERmc8LXTy9+uqreOWVVwAA169fx5QpU5Cdna2zrVwux7fffott27YBAJo2bYpPP/30mc6flpaGTZs2CT/37t37mfojIiIiIiLzeeGvnFu4cCEePXqECxcu4MyZMxg8eDCCg4PRtWtXuLq6Ii8vD1evXsXOnTuRnJwMAGjQoAFWrVpV6TLlgPZ1TOr5tdHR0di4caNwk93AwEBhGiEREREREVmeF754qlWrFkJDQ/Hdd99hy5YtyMnJwdq1a7F27Vqd7V9++WUsWLBA9BS7wMDAStsMGTIECxcuNChuIiIiIiKqXl744gkoXfjhiy++wJgxYxAeHo7IyEikpaUhJycHDg4OcHNzQ+fOnTF48OBnXl1PIpHA3t4e7u7uaN++PYYPH/7MfRIRERERkflJVCqVytxBkPkUFxcLKxE2aOaJoUvCDTr+wJwRqO/E+zz92ygUCvzzzz8AShdC4b0zSAzmDRmLuUPGYN78u2l+xvX19YWtrW2V9MssIiKDWVtbo0mTJuYOgywM84aMxdwhYzBvyBRe6NX2iIiIiIiIqgqLJyIiIiIiIhE4bY+IDCaXyxEfHw8A8PHxgY2NjZkjIkvAvCFjMXfIGMwbMgUWT0RkMJVKBblcLmwTicG8IWMxd8gYzBsyBU7bIyIiIiIiEoHFExERERERkQictkcCZ8eaODBnhEHHuNa2M1E0RERERETVC4snElhJpajvVNPcYRARERERVUuctkcCXkxJRERERKQfiycSKBQKc4dARERERFRtcdoeCaysrMwdAlkIa2trtGjRQtgmEoN5Q8Zi7pAxmDdkCswkEkilHIgkcaRSKerWrWvuMMjCMG/IWMwdMgbzhkyBn5aJiIiIiIhE4MgTCbhgBImledd2GxsbSCQSM0dEloB5Q8Zi7pAxmDdkChx5IgEXjCCx5HI54uLiEBcXJ/zHRFQZ5g0Zi7lDxmDekCmweCIiIiIiIhKBxRMJuBINEREREZF+LJ5IwLnARERERET6caiBBCVKJR7k5Gs97lrbDlZcxpyIiIiI/uVYPJEgO68QQ5eEaz1+YM4I1HdyMENERERERETVB4cTiIiIiIiIRGDxREREREREJAKn7RGRwWxsbODr6ytsE4nBvCFjMXfIGMwbMgUWT0RkMIlEAltbW3OHQRaGeUPGYu6QMZg3ZAqctkdERERERCQCR56IyGBKpRK5ubkAgDp16kDKpexJBOYNGYu5Q8Zg3pApMIuIyGAKhQLJyclITk6GQqEwdzhkIZg3ZCzmDhmDeUOmYNKRp6ioKIwdO1bnvpo1a8LJyQne3t4YMGAAhg4dKnpeampqKvr37w+VSgUA+P777xEYGGhwfPfv38eff/6JM2fO4M6dO8jOzoZSqUSdOnUgk8ng5+eHwMBANGnSROfxWVlZiI2NRWxsLOLi4hAXF4ecnBwAQFBQEBYvXlxpDLNmzUJYWJjW4xKJBI6OjmjYsCH8/Pzw+uuvw8fHx+DnSEREREREVcNs0/YKCwuRnp6O9PR0nDhxAuvWrcPq1avRuHHjSo/ds2ePUDipfzakeCoqKsLSpUuxZcsWFBcXa+3PyMhARkYGIiMjsWzZMgwaNAgzZ86Eu7t7mXbdu3cXfU5DqVQqPHnyBImJiUhMTMTWrVsxadIkTJ8+3WTnJCIiIiIi/Z5b8TRq1Ci8+eabws9ZWVm4ceMGQkNDkZ6ejhs3bmDKlCnYs2cPrKysKuwrPDwcAGBvb4+CggKcPXsWGRkZcHNzqzSO7OxsTJkyBZcvXwYAODg44LXXXkPXrl3RoEEDWFtbIzMzE5cuXcKRI0eQkpKCv/76C+3bt8e4ceP09tuwYUO0aNECkZGRlb8YeoSGhgrPQalUIisrCydOnMDmzZuhUCiwevVquLm5YfTo0Uafg4iIiIiIjPPciicXFxfIZLIyj3Xr1g3BwcEYOnQo0tLSkJSUhCNHjmDQoEF6+7l06RLu3LkDAPj8888xf/58lJSUYN++fZgwYUKFMSiVSnz88cdC4dS3b1988803cHFx0WobEBCATz75BOHh4fj222919jd16lT4+vrC19cXrq6uSE1NRb9+/SqMoSIeHh5aI289evRAt27dMGXKFADAihUr8MYbb1RaYBIRERERUdUy+4IRjo6OQmEAAGfOnKmw/Z49ewAAdevWxfDhw9GtWzcAwN69eys914YNGxAVFQUA6NmzJ1asWKGzcFKTSqUYPnw4du/eDS8vL63906ZNQ9++feHq6lrpuZ9FQEAAOnXqBKB05OzatWsmPR8REREREWkze/EEoExhkp6errddcXExDh48CAAYPHgwbGxsMHToUABAYmIi4uPjKzw2NDQUAFCjRg0sXLgQ1tbiBt4aNGggFGnm0rZtW2H7/v37ZoyEiIiIiOjfqVoUTzY2NsJ2RQVNRESEsF6/umgaMGAA7O3tAfz/o1K6REZGIiMjAwAwaNAg1K9f/1nDfq40XxelUmnGSIhKV4O0sbGBjY0NJBKJucMhC8G8IWMxd8gYzBsyhWpxk9xbt24J240aNdLbTl0cNW3aFB06dABQumhEv379sG/fPuzfvx+fffaZzuuBLly4IGz36dOnagJ/jpKSkoRtMQtjGMsWJVqPPS0oQL6NjsYoXXCD/n1sbGzKjIYSicG8IWMxd8gYzBsyBbMXTyUlJcJ0OgAYOHCgznbZ2dnCSnbllyUfOnQo9u3bh8zMTERGRqJ3795axyckJAjbrVu3rorQn5uEhAScPn0aAGBnZwdfX1+TnWuQ412tx9b/ulJv+/nz55ssFiIiIiKi6sRs0/ays7Nx9uxZvPXWW7h+/TqA0sJJvTBCefv374dcLgfw/0/ZU+vRowfq1asHQP/UPfXNawFUuEhEdaFSqfDw4UPs2LED48aNQ0lJ6YjQmDFjUKNGDTNHR0RERET07/PcRp5WrFiBFStW6NxnZ2eHN954A59++qne49VFUdu2beHh4VFmn5WVFYYMGYL169cjIiICeXl5cHR0LNMmPz+/zPmqo8qWOe/Tpw+mTZv2nKIh0k+hUOCff/4BALi7u4tefIX+3Zg3ZCzmDhmDeUOmUC0WjPD29saYMWPKLByh6caNG8Ly3OVHndTUjxcWFuKvv/7S2q95bc7Tp0+fNeTnxsbGBh07dsSSJUuwevVqva8R0fOkVCqRkZGBjIwMLmBCojFvyFjMHTIG84ZM4bmV4KNGjcKbb74JoPQ6p/T0dBw6dAh79+5FTEwMxowZg507d8LZ2VnrWPWok7W1NV599VWd/bdp0waenp64desW9u7di5CQkDL7nZychO3MzEytkanqIDQ0VFgMQiqVwt7eHq6urrC1tX1uMRzMa6r12Jbpr6FeHfvnFgMRERERUXX03IonFxcXyGQy4WcfHx/07dsX/v7+mDVrFtLS0jBnzhysWrWqzHFKpRL79u0DUDr8KuZ+S9HR0UhNTUXjxo2Fx7y9vYUb8F6/fl1r6l914OHhUSZmcyiG9kqFdvb2XFWPiIiIiP71zD5tLygoSFhhLyIiAmfPni2z/+zZs3jw4IFBfapUKuzdu7fMY507dxa2T5w4YVywRERERET0r1UtrpybPn06jh49ipKSEvz4449lRpfUU/ZsbW2xcOFCSKUV13u//fYbrl+/jvDwcEydOlV4vGfPnnBzc0NGRgYOHjyITz/91OJulEtEREREROZTLYqn5s2bY/Dgwdi/fz+uXLmCv//+Gz169EB+fj6OHj0KoHQ58vL3d9Ll4cOHuH79OlJSUhATEyPcTNfW1hYTJkzAokWLUFRUhM8//xxr1qzReUPd8h48eIDk5GRRUwaJiIiIiOjFZPZpe2rvvfceJBIJAAjXPR0+fBgFBQUA9N88t7yBAwcK/ZS/59PYsWPh7+8PAIiMjMTUqVORnZ2tty+VSoV9+/YhODgYiYmJBj0fIiIiIiJ6sVSLkScAkMlkCAgIwLFjx3DhwgVER0cLxY+NjQ0CAgJE9ePu7g5fX1/Exsbir7/+wpw5c4TV6qRSKX766SdMnjwZV65cwfHjx9G/f38EBgaia9euaNCgAaytrZGZmYnLly/j8OHDSE5O1nuu6Oho3L17V/j50aNHwvadO3ewe/fuMu2Dg4PFvhxE1ZpUKhVWsKxsKi2RGvOGjMXcIWMwb8gUqk3xBACTJ0/GsWPHAADLly/H+fPnAQD+/v6oU6eO6H4GDhyI2NhY5Obm4vjx42VGrZydnbFx40Z8//332LJlC/Lz87F161Zs3bpVZ18SiQSBgYEYPHiw1r6dO3ciLCxM53GXLl3CpUuXyjzG4oleFNbW1vD09DR3GGRhmDdkLOYOGYN5Q6ZQrcrwtm3bokePHgCAc+fOCTc0EztlT02zffmpewBQo0YNzJkzB4cPH8ann36K7t27o2HDhqhZsyZq1KgBNzc39OzZE9OnT8exY8fw3XffcXEJIiIiIqJ/OYlKpVKZOwgyn+LiYsTFxQEAGjTzxNAl4VptDswZgfpOvM8TEREREVkGzc+4vr6+wmU8z6paTdsjIssgl8tx69YtAICnpydsbGzMHBFZAuYNGYu5Q8Zg3pApsHgiIoOpVCrk5+cL20RiMG/IWMwdMgbzhkyhWl3zREREREREVF2xeCIiIiIiIhKBxRMREREREZEILJ6IiIiIiIhEYPFEREREREQkAlfbI4GzY00cmDNC63HX2nZmiIaIiIiIqHph8UQCK6kU9Z1qmjsMsgBWVlZo3LixsE0kBvOGjMXcIWMwb8gUWDwRkcGsrKxQv359c4dBFoZ5Q8Zi7pAxmDdkCrzmiYiIiIiISAQWT0RERERERCJw2h4J5HI5bG1tzR0GWYDi4mLExcUBAHx9fZk3JArzhozF3CFjMG/IFDjyREREREREJAKLJyIiIiIiIhFYPBEREREREYnA4omIiIiIiEgEFk9EREREREQisHgiIiIiIiISgcUTERERERGRCLzPEwmsrKzMHQJZCGtra3h5eQnbRGIwb8hYzB0yBvOGTIGZRAKplAORJI5UKoWjo6O5wyALw7whYzF3yBjMGzIFflomIiIiIiISgSNPJFAqleYOgSyEUqlEQUEBAMDe3p6jliQK84aMxdwhYzBvyBSYRSQoKSkxdwhkIRQKBRITE5GYmAiFQmHucMhCMG/IWMwdMgbzhkyBxRMREREREZEILJ6IiIiIiIhEYPFEREREREQkAosnIiIiIiIiEVg8ERERERERicClyv/lVCqVsC2Xy1FcXGzGaMhSyOVyndtEFWHekLGYO2QM5s2/m+bvXPPz7rOSqKqyN7I4+fn5SEhIMHcYREREREQm4e3tDQcHhyrpi9P2iIiIiIiIRODI07+cUqnE06dPAQDW1taQSCRmjoiIiIiI6NmoVCrh5sh2dnaQSqtmzIjFExERERERkQictkdERERERCQCiyciIiIiIiIRWDwRERERERGJwOKJiIiIiIhIBBZPREREREREIrB4IiIiIiIiEoHFExERERERkQgsnoiIiIiIiERg8URERERERCQCiyciIiIiIiIRWDwRERERERGJwOKJiIiIiIhIBBZPREREREREIrB4IiIiIiIiEoHFExERERERkQgsnoiIiIiIiERg8URERERERCQCiyciIiIiIiIRrM0dAFUuLS0NGzduxIkTJ5Ceng5bW1s0adIEgwcPxujRo2FnZ1cl5zl58iS2b9+OuLg4ZGdnw9nZGb6+vnj99dfRu3dvUX0oFArs2LED+/btQ3JyMgoKCuDm5obu3btjzJgxaNmyZZXESpWzhLzJzMzE8ePHce7cOcTHx+Off/6BXC6Hk5MTvLy88Morr2DYsGGoWbNmlcRKlbOEvNEnISEBI0aMgEKhAAAEBQVh8eLFVRIvVc7Sckcul2Pfvn04ePAgkpKSkJmZCQcHB7i5uaFt27bo2bMnBg8eXCUxk36WlDcxMTHYvn07Ll26hIyMjDL/Xw0cOBDDhw+Hra1tlcRL1ZdEpVKpzB0E6RcREYEZM2YgLy9P534PDw+sWbMGzZo1M/ocSqUSc+fOxc6dO/W2CQkJwddffw2pVP9gZXZ2NiZNmoS4uDid+21tbTFv3jyEhIQYHSuJYwl5s337dnz55ZcoKSmp8DweHh74+eef4e3tbXSsJI4l5E1F/Y4cORKxsbHCYyyenh9Ly52EhATMmDEDSUlJetvUqlUL0dHRRsdLlbOUvFGpVPjmm2+wcePGCs/VsmVLrFmzBg0bNjQ6Xqr+WDxVY9evX8eoUaNQWFgIe3t7vPfee/D390dhYSEOHDiA7du3Ayj947Jr1y44OjoadZ6lS5dizZo1AIBWrVrh3XffRZMmTXDv3j389ttvuH79OgDgvffewyeffKKzj5KSEowZMwYXL14EALzyyisICQmBk5MTrly5glWrViErKwtSqRSrV682+ptlqpyl5M3KlSvx888/w8bGBn379kWPHj3g6ekJBwcH3L17Fzt27EBkZCQAwNnZGWFhYWjQoIFRsVLlLCVv9NmwYQO++eYbuLi4ICsrCwCLp+fF0nInISEBb7/9NnJyclCjRg2EhISgR48eqF+/PuRyOVJSUnD69GlcvHgRJ06cMCpWqpwl5c3//vc//PDDDwAABwcHvPPOO+jYsSPs7e1x+/Zt/P7770IhLpPJEBYWBmtrTu56Yamo2nrzzTdVMplM1apVK9WlS5e09v/6668qmUymkslkqmXLlhl1juTkZFWrVq1UMplMFRwcrHr69GmZ/QUFBarg4GAhjpSUFJ397NixQ4jlyy+/1NqfkpKi6tixo0omk6kGDBigksvlRsVLlbOUvPn9999V3333nSorK0vveRYtWiTEOmvWLKNiJXEsJW90+eeff1QdOnRQeXl5qcLCwoQ4Z86caVScZBhLyp3CwkLVK6+8opLJZKo+ffqobt++rfecRUVFRsVK4lhK3hQXF6s6deqkkslkqtatW6uuX7+u1UYul6tCQkKEeP/66y+j4iXLwAUjqqnY2FhhusCIESPQoUMHrTbjx4+Hp6cngNJvXeVyucHnWb9+vXB9wNy5c7WuLbGzs8PcuXMBlF7PtG7dOp39rF27FgDg5OSEzz77TGt/s2bN8N577wEA7ty5gyNHjhgcK1XOkvJm3Lhx+L//+z84OzvrPc8nn3yCevXqAQCOHDkCpVJpcKxUOUvKG12++uor5OfnIygoCJ06dTI4LjKepeVOaGgoUlJSIJVK8dNPP8HDw0PvOXntiulYUt7cunULjx8/BgD07dsXPj4+Wm2sra2FzzgAcPnyZYNjJcvB4qmaOnr0qLA9YsQInW2kUimGDx8OAHj8+DGioqIMOodKpcKxY8cAAC1atED79u11tmvfvj2aN28OADh27BhU5WZ63r59G7du3QIADBo0SO/FnUFBQcK25vOjqmNJeSOGra0tOnbsCAB48uQJcnJyDO6DKmfJeXPw4EFERETo/eKGTMuScqekpARbt24FAHTv3h3t2rUzKA6qOpaUN5pFW+PGjfWer2nTpjqPoRcPi6dqSn3tkL29PVq3bq23XefOnYXtS5cuGXSO1NRUZGRkaPWjS5cuXQAADx48QGpqqs5YNdvpUq9ePeFbPkNjJXEsKW/EKi4uFrYNWUCAxLPUvHny5An++9//AgBmzJiBunXrGhQTPTtLyp2YmBg8ePAAABAQECA8XlRUhLt37+LBgweVLmBDVcOS8sbDwwMSiUToU5+7d+8K2+pijF5M/CRSTalHcpo2bVrhRYctWrTQOkasmzdv6uynsvMkJyfrjNWQfv755x8UFBSIjpXEsaS8EUMulwvTH1xdXeHk5GRwH1Q5S82b7777Dg8fPkSnTp30fntNpmVJuaM5lUomkyElJQUffvgh/Pz8MGDAAPTq1Qv+/v747LPPynwQpqpnSXlTq1YtvPrqqwCA48ePIyEhQet4hUIhLEqh2Z5eTCyeqqGioiI8evQIACpdXaxOnTqwt7cHAKSnpxt0Hs32lZ1Hc/8///yjt5/69etX2I+7uzuA0uF0Q+Olilla3oixfft24TkNGjTI4OOpcpaaNxcvXsT27dthY2ODL7/8UvhmmJ4fS8sdzQ/fycnJCAoKwuHDh8tMsXry5An27t2L4cOH48yZMwbFSeJYWt4AwOzZs9G6dWvI5XKMHj0aK1aswJkzZ3D58mWEhYVhxIgRuHz5Muzs7LBkyRKOgr/guI5iNZSfny9sq/9oVMTOzg4FBQUGj+QYch7N65jKn0ezHwcHB6P7oWdjaXlTmXv37uHHH38UzjNp0iSDjidxLDFviouLMW/ePKhUKowbN4433zYTS8sdzWsmv/nmGxQVFWHcuHF488034e7ujgcPHmDLli1Yu3Yt8vPz8dFHH2Hv3r28Z08Vs7S8AUpnPmzatAnbt2/HmjVrsHz58jL7JRIJQkJC8M477wiLXNCLiyNP1VBRUZGwbWNjU2l79YpAhYWFJjuP5qpD5c9TVf3Qs7G0vKnI06dP8cEHH+DJkycASldJqmxUk4xjiXmzZs0a3Lx5E40aNcLUqVMNioOqjqXlztOnT8v0OW3aNMyePRvNmjWDra0tmjRpgs8++wzTp08HULpIwf/+9z+DYqXKWVreqJ07dw7h4eHIzMzU2qdenCIsLKzMdbr0YmLxVA3VqFFD2BazYov6jVp+Cc6qPI/mH4Py56mqfujZWFre6KNQKPDRRx8J88pHjRqF4OBgg2Ik8Swtb5KTk4UPtHPnztW7uieZnqXljmY/devWxcSJE3X2MWHCBOEWCQcPHjRqpVDSz9LyBihd8nzKlCm4evUqOnfujN9//x0XL15EXFwcDhw4gPHjxyM3Nxe//vor3n777TKjXvTiYfFUDWlOfRMzTK3+Nk3M8Lex59H8xq78eTT7qewPRkX90LOxtLzRRaVSYdasWTh58iQAYPDgwZg3b55B8ZFhLClvVCoV5s+fj+LiYgwYMAB9+/Y1KAaqWpaUO+X76dKli977OFlbW6Nr164ASqf63bt3z6B4qWKWljcJCQlYvHgxVCoVunfvjvXr16N79+5wdHSEra0tPD09MXPmTHz99dcASlcFLD+tj14svOapGqpRowacnJyQk5NT6QWSubm5wh+Fyi6ILE+zfWXn0dyvXvRBVz8PHjyo8Kan6gsxJRKJwfFSxSwtb3T56quvsG/fPgBAr1698N1333F5chOzpLy5fPkyzp8/DwDo0KED/vzzT61js7Ozhe3U1FShTcuWLSGTyQyKmSpmSblTvp/K/h5p7s/Ozi5zDx96NpaWN7t37xZu0P7hhx/CyspKZx//+c9/8OuvvyIlJQVhYWGYOXMmF7J5QbF4qqZeeuklREdH4+7du1AoFHqX8tRcUtPQixRfeuklnf1Udp7yS35qnjc5OVnn3bfL9+Pu7s6RJxOwpLwp77vvvsOWLVsAlN6TY/ny5aLmw9Ozs5S80Zxa8+2331Z6zgsXLuDChQsAgA8++IDFkwlYSu4AKLOwSGX3c9Lcr+/DMhnPkvJGc5XGiu5JBQCtWrVCSkoKcnJykJWVBVdXV0NCJgvBr3SrKT8/PwClQ83Xrl3T2079wQAAOnbsaNA5GjduDDc3N61+KjpP/fr1te6wrY4VgPCtsC4PHz5ESkqKUbGSOJaUN5pWrlyJ3377DQDg6+uL1atX85q458hS84bMz5Jyp1OnTsJ2ZTft1pyqx8Vqqp4l5Y1mYadQKCrsR3M/i+4XF4unaqp///7C9q5du3S2USqV2LNnDwCgdu3a8Pf3N+gcEokE/fr1A1D6rYvmDQQ1Xb58WfhWpl+/flrD0M2bNxe+ETp48GCZucOawsLChG3N50dVx5LyRm39+vX4+eefAZTeuPK3336Do6OjQTHRs7GUvPH390diYmKF/44dOya0DwoKEh7/8MMPDYqXxLGU3AGAJk2aoFWrVgCAqKgoYTXP8vLy8oR7PDVt2lT4AE5Vx5LyRrOYio6O1ns+uVyOmJgYAKU3yuVN3V9cLJ6qqbZt2wrfku3atUt4Q2pau3atMJw8duxYrSlOUVFR8PLygpeXF2bNmqXzPG+//bbw7ciCBQu0lugsLCzEggULAJR++/L222/r7Gf8+PEASi+u/e6777T23717V1ghq1mzZhgwYIDuJ07PxNLyZteuXVi0aBEAwMPDA7///jv/wzEDS8sbqj4sLXfUK+wVFBRg4cKFOtssXrwYeXl5AIA33nhDZxt6NpaUN5oL0yxdulTIjfKWL1+Ohw8fAgB69+7N651eYLzmqRqbM2cORo0ahcLCQowfPx6TJ0+Gv78/CgsLceDAAWzbtg1A6YfOd955x6hzNG/eHBMmTMCaNWtw9epVjBo1ChMnTkSTJk1w7949/Prrr7h+/TqA0uVbPTw8dPYTFBSEXbt24dKlS9i0aRMyMzMREhKCOnXqIDY2FitXrkReXh6kUinmzJmjd34zPTtLyZujR49i7ty5UKlUcHR0xJw5c5CdnV3mgv/yGjduzGvlTMRS8oaqH0vKnSFDhmDPnj04efIkdu/ejYyMDIwaNQoNGzbEP//8g23btgmrfbZq1QpvvfWWUfFS5Swlb3r27ImuXbvi3LlzSExMxLBhwzB27Fi0bdsWNWrUwJ07d7Br1y6cPn0aQOlqfbz/3ItNouINDKq1iIgIzJgxQ+83HR4eHlizZg2aNWumtS8qKgpjx44FUFrcLF68WGcfSqUSX3zxhd6hc6B0FZkFCxZUuPJZdnY2Jk2ahLi4OJ37bW1tMW/ePISEhOjtg6qGJeTNrFmzykzlFGPDhg0GT90g8SwhbyqTmpoqTNWpKA6qWpaUO/n5+Zg2bRoiIyP1tvH19cWqVauE+z2RaVhK3uTm5uLDDz9EVFRUhc/H2dkZS5cuRffu3StsR5aNX/9XcwEBAQgPD8eGDRtw4sQJPHjwADY2NmjatCkGDRqEt95665lvEimVSrFw4UIMHDgQ27ZtQ1xcHB49eoS6devC19cXI0eORO/evSvtx9nZGVu3bsX27duxf/9+3Lp1C0+fPoWbmxu6deuGsWPHllntiEzHkvKGqg/mDRnLknLHwcEBv/32Gw4cOICwsDAkJCQgJycHtWrVgo+PD1599VUMHz6cF/w/B5aSN3Xq1MH69etx7Ngx7N+/H3FxccjMzERJSQlq1aqFl156Cb169UJISAinnv8LcOSJiIiIiIhIBC4YQUREREREJAKLJyIiIiIiIhFYPBEREREREYnA4omIiIiIiEgEFk9EREREREQisHgiIiIiIiISgcUTERERERGRCCyeiIiIiIiIRGDxREREREREJAKLJyIiIiIiIhFYPBEREREREYnA4omIiIiIiEgEFk9EREREREQisHgiIiIiIiISgcUTERERERGRCCyeiIiIiIiIRGDxREREZIT79+9j3rx56N+/P3x9feHl5QUvLy8cPXrU3KERGWXWrFnw8vJCQECAuUMhqraszR0AEVF5UVFRGDt2LADggw8+wIcffmjmiIjKun//PoKDg/Ho0SNzh2IQzfdWeTVr1oSTkxO8vb0xYMAADB06FLa2tqL7LikpweHDh3Hs2DHExcUhMzMTcrkcrq6uaNGiBfr06YOhQ4eidu3aBsVcXFyMw4cP49SpU4iNjcWjR4+Ql5cHR0dHNGrUCL6+vhg4cCC6du0KqZTfCRORabF4IiJ6QXl5eQFgAWoKq1atwqNHj2BtbY2PP/4YnTt3hr29PQCgYcOGZo7OOIWFhUhPT0d6ejpOnDiBdevWYfXq1WjcuHGlx0ZHR2P+/Pm4efOm1r60tDSkpaXh9OnTWL58OT799FO8/vrromI6fPgwFi9ejLS0NK19OTk5yMnJwbVr17B161Z4eHhg9uzZ6NOnj6i+LRnf20Tmw+KJiIjIQGfOnAEA9OvXDxMnTjRzNMYZNWoU3nzzTeHnrKws3LhxA6GhoUhPT8eNGzcwZcoU7NmzB1ZWVnr7OXjwIGbMmIHi4mIAQJcuXTB06FA0b94ctra2uH//PiIiIvDnn38iJycHc+fOxe3btzFz5swK4/vll1+wbNky4ecePXogICAAnp6eqF27NnJzc3H79m1ERETgzJkzSElJwY8//vivKJ5MZfHixVi8eLG5wyCq1lg8ERERGejBgwcAgObNm5s5EuO5uLhAJpOVeaxbt24IDg7G0KFDkZaWhqSkJBw5cgSDBg3S2cf169fxf//3f5DL5bCxscGiRYsQGBhYpk3btm0xaNAgvPPOO5g8eTLS09Oxdu1aNGnSpEzxpmnXrl1C4eTi4oKffvoJXbp00WrXvXt3jB49GklJSVi0aBGys7ONeSmIiETj5GAiIiIDyeVyAIC19Yv3HaSjoyOmTJki/KweZStPqVTis88+E16LhQsXahVOmnx8fLBu3TpheuOSJUtw//59rXYPHjzAggULAAD29vbYuHGjzsJJk0wmQ2hoKMaPH1/xkyMiekYv3l99InrhaV70vmHDBnTp0gU7d+7E7t27kZycDLlcjhYtWuCtt97C8OHDheOKi4uxa9cuhIWFISUlBXK5HF5eXhg7diyGDBmi81ypqano168fAGDRokUIDg7GX3/9hW3btiExMRH5+flo2LAhBgwYgIkTJ1Z6MXxxcTF27NiBgwcP4saNG8jLy0OdOnXQqlUrvPbaawgMDNR70fusWbMQFhaGRo0aISIiAhkZGVi/fj1OnDiB+/fvo6CgABs2bMDs2bPLXCOyYsUKrFixokxfQUFBZabnZGRk4MiRI4iKikJCQgIyMjKgUChQt25dtGnTBoGBgRg0aJDe2Mr/Tvz9/XHgwAHhdSooKIC7uzv69euHSZMmwcnJqcLXCQBOnjyJffv2ISYmBllZWSgpKUG9evXQunVr9O3bF4MHD4adnZ3OY69du4Zt27YhKioKGRkZUKlUqF+/Prp27Ypx48YZNWK0e/duzJ49u8xj5V/b8q8rAGRnZ2P9+vU4efIkUlNTUVRUhHr16sHPzw8jR45Ep06d9J4zICAAaWlpQr9Xr17FH3/8gfPnzyMjIwNyuRyJiYkGP5fKqK+pAYD09HSdbSIiInDjxg0AQK9evTB06NBK+23evDmmTJmCpUuXorCwEBs2bMCsWbPKtFm3bh2ePn0KAJg2bRo8PT1FxSyVSjFs2DBRbTU9z78nmh4+fIg//vgDp0+fRmpqKgoKCuDi4oL27dtj5MiR6N69u9Yx6nxQq+y9Xf65de7cGbt378bevXtx69YtZGdnY/jw4UL78n9j9MnLy8P27dtx4sQJ3Lp1C7m5ubC1tUXTpk3h5+eHIUOGwM/Pr9LXgMgSsXgiIoumUCgwZcoUHD9+vMzjcXFxmDlzJq5evYovvvgCubm5mDp1Ki5cuFCmXUxMDGJiYnD37l1Mnjy50vN9/vnn2LVrV5nHbt++jTVr1mDPnj1Yt26d3g97qampmDhxIpKTk8s8npmZiVOnTuHUqVPYtm0bVq5cWWlxcfnyZUyePLlKVnsrKSlB7969oVQqtfZlZGQgIiICERER2LlzJ5YvXw4HB4cK+1MqlZgxYwbCw8PLPJ6SkoLQ0FAcPXoUmzZtQr169XQe/+jRI0yfPh1nz57V2qdefODw4cMAgODgYK1zL1myBOvXr4dKpdI6f0pKCnbu3Il58+Zh5MiRFT6PqhAZGYmPPvoIeXl5ZR5XP4/w8HCMHj0aX3zxRaUrxW3ZsgX//e9/oVAoTBkyAMDGxkbY1je6tmfPHmH77bffFt33G2+8gRUrVqCoqAhhYWGYOXMmJBIJAEClUiEsLAxA6ahTSEiIEdEb73n9PQkPD8f8+fNRUFBQ5vH09HQcPHgQBw8exH/+8x989dVXVTa6WVRUhAkTJugdSRTrzJkz+OSTT7T+9sjlcsTHxyM+Ph5//PGHSYp6ouqAxRMRWbSff/4ZV65cQWBgIAIDA+Hq6oqUlBQsX74ct2/fxsaNGxEQEICNGzciJiYGo0aNwoABA+Dk5IT4+Hj8/PPPyMjIwLJly9CvXz+0bNlS77k2b96MuLg4tG3bFuPGjUOzZs2QlZWFsLAw/PXXX8jIyMC7776Lffv2wdHRscyx+fn5GDduHO7duwcA6N+/P0aMGAE3NzekpqZi06ZNOH/+PC5evIjJkydj06ZNei/Sz8/Px4cffoiioiJMnjwZPXr0QM2aNZGUlIR69eohNDQUcrlcmEJVfmEAAKhTp46wrS4yunbtil69ekEmk8HZ2Rn5+fm4d+8eduzYgZiYGPz999/4+uuvsWTJkkp/JzExMejfvz+GDx+Ohg0bIjMzE5s3b8aJEydw584dLFq0CD/88IPWsU+fPsXYsWORlJQEAGjdujVGjhyJli1bwtbWFunp6bhw4QIOHDig89wLFizA5s2bAQCdO3dGUFAQmjRpgpo1ayIxMRHr16/HjRs3MG/ePLi6ugqjimL0798fbdq0AQC9r63m6xofH4/JkycL1wONHj0aAQEBsLOzQ3x8PNasWSP87u3s7DBjxgy9546Li0N4eDgaNGiACRMmoE2bNlAoFLh48aLo+A1x69YtYbtRo0Y620RHRwMA7Ozs0K1bN9F9165dG507d0ZkZCRycnJw8+ZN4X1348YN4UO5n5+f1vvI1J7H35MDBw7gs88+g0qlQpMmTfDWW2/B09MTzs7OSEtLw86dO3Hy5Ens3LkTjo6OZUY7DX1va/r++++RmJiIgIAABAcHC+/L/Px80a/PuXPnMHHiRCgUClhZWWHYsGHo168f3N3dUVRUhFu3buHUqVNaxSfRC0VFRFTNnDt3TiWTyVQymUy1bNmyCvfLZDLVunXrtNpkZGSoOnTooJLJZKquXbuqvLy8VEeOHNFqFx8fr/L29lbJZDLVggULtPbfu3evzLkmTpyoksvlWu1WrFghtFmyZInW/sWLFwv7f/zxR639SqVS9emnnwptNm3apNVm5syZwv727dur4uPjtdpoqug1LH/ulJSUCtv8/PPPKplMpvLy8lLdvn1ba3/538nKlSt1nmf8+PEqmUymatWqlSorK0urzcKFC4U+vvrqK5VSqdQZT1FRkerhw4dlHouMjBSO3b59u87jCgsLVWPHjlXJZDJV3759df4uxRDz2o4YMUIlk8lUPj4+qtOnT2vtz8nJUQ0ZMkQlk8lU3t7eqqSkJK02ffv2Fc712muvqXJzc42KV62y95ZKpVIpFArV8OHDhXYXLlzQapOeni7sf/311w2O4/vvvxeODw8PFx7fu3ev8PgPP/xgcL/GeJ5/T7KyslR+fn4qmUymmj17tt78++GHH4S8uHXrltZ+se/t8s9N198eTeq/MX379tXaV1hYqOrZs6dKJpOp2rVrpzp37pzefu7fv1/heYgsGReMICKL1q5dO51ThurVq4cBAwYAKL3mZPDgwejfv79WO29vb2FufmXf4tva2mLBggU6p9FMmTJFWLls165dwrLNQOm1ETt37gQAtGzZUud9WSQSCb788kthut6mTZsqjOXdd9+Ft7d3hW3EkkgkaNasWYVtpk6dirp160KlUlV4LQRQOlqka8qSRCLBuHHjAJROj4qJiSmz//Hjx9i2bZvQx5w5c4TpXOXZ2trC1dW1zGNr1qwBAAwcOFDvdK8aNWpg3rx5AEqnzkVFRVX4XIwVGxuLuLg4AEBISAh69uyp1aZOnTr4+uuvAZRON1SPmOkzf/58g28wa4js7GycPXsWb731Fq5fvw6g9LXUdU2W5pSt8r8HMVxcXITtnJwcnduabZ4XU/892bJlC548eYL69evjyy+/1Dsl78MPP0T9+vWhVCqxd+/eZ3lKAg8Pj2e6J9SePXuQkZEBAJg+fTr8/f31tnV3dzf6PETVHYsnIrJoFV2YrVlcvPrqq5W2U0+p06dHjx6oX7++zn1SqVS4mDwnJ0f48AkAV69exePHjwGUXsytbzqeo6MjBg8eDAC4efOm8EFFFzEX5xtLqVTiwYMHSE5ORlJSEpKSknDr1i00aNAAAJCQkFDh8YGBgXqLntatWwvb5V/vc+fOCQsFjB07tsJ7C5WXl5eH8+fPAyj9wF8RT09P1K1bFwC0CriqonldyX/+8x+97fz8/IRr5Cq6FsXd3b3ChSWMsWLFCnh5eQn/unXrhnHjxuHSpUuws7PDO++8g6VLl+o8VvMaLvXqeYbQvG5Osy/NKWT6FgMxJVP/PVF/8dCnTx/Y2trq7cPa2hrt27cHUHU5OmTIEIPeU+Wpp+LZ29uLvskx0YuI1zwRkUWraNW0WrVqCdseHh6Vtqts7r+vr2+F+9u2bStsJyUlCR9+1CuSAaXfbFekXbt22LJli3Ccm5ubVht7e3s0adKkwn4MpVKpEB4ejp07dyI2NhaFhYV621a2SEWLFi307tNcCKP8661ZcBq6Utf169eFBS8++eQTfPLJJ6KOy8zMNOg8Yql/5zY2NvDx8amwbbt27XDr1i3cuXMHxcXFOj9Ua65+9zx4e3tjzJgxZRaO0KR5LVL5RQ/E0Pzda/alWVSpC+nnyZR/T0pKSoQvHrZt2yaMslamqnL0WXMoPj4eQOkXIOYobImqCxZPRGTRatasqXef5uplYtrpWm1OU2XTiPRNRcrNzRW2nZ2dK+xDcwqU5nGaqnrqVlFRET744AOcOnVKVPuKCitA/O+k/OutWZTpKhorkpWVZVB7tcqei7HUv38nJ6dKV0tT/85VKhUeP36scxqcKabraS42UFJSgvT0dBw6dAh79+5FTEwMxowZg507d+rMWfXIHWDch3vN35dmQa25bezv9FmY8u9Jbm6uUSslVlWO6ltIQiz1+1PfKplE/xYsnoiIRNI3Fe159/EsU290WbVqlVA4denSBW+++SZat24NV1dX1KxZU/gwOHr0aGGFtepG84Pq119/jQ4dOog67lk/UD4vVf07B0qLffV1ekDpTWz79u0Lf39/zJo1C2lpaZgzZw5WrVqldaybmxvq1q2LR48eITExESUlJQbFqDnKqDkdTnP72rVrhj6laq2kpETYDgkJEe6/VBl9o3+GqmwpfCISh8UTEZFIlX3Dru/bdM0P6FlZWRVODdI8x/P4YK9SqYTFLDp16oT169fr/ZClbySsqmiOZmRkZBg0NVHz9a5Zs2aZosAc1PHk5ORAoVBUOPqk/p1LJBKTLgghVlBQEI4fP45Dhw4hIiICZ8+e1VqKXCKRwM/PD0ePHsXTp09x9uxZnYti6PLkyROhCHdycsJLL70k7GvZsqVQlF28eBF5eXnPfblyUyl/ewBz56ih6tati/T0dDx8+NDcoRCZFb+GICISSb16mpj9mvd30dy+cuVKhX3ExsbqPM5UcnJyhA9DgwYN0ls45efn4/bt2yaNRXMxCUNHuHx8fIRRvUuXLlVpXMZQ/+7UNw6tiPp33qxZswoXEXiepk+fLowk/fjjjzrbBAUFCdsbN24U3fe2bduEqWhBQUFlRmMlEonQb0FBAXbs2GFw7NWVra2tkBfVIUcN1apVKwClC+CY43o0ouqCxRMRkUh///233hXwlEolwsLCAJR+w6xZCLRp00YYUdizZ4/ea6vy8vLw119/AQBeeuklg6/7Ka9GjRoAUGbZ9PI0pxJV9IFox44dRl2vYQh/f39h5baNGzeWia0yzs7OwgId+/fvR3Z2tilCFK179+7C9q5du/S2i4mJwc2bN7WOMbfmzZsLKz9euXIFf//9t1abgIAAYaXAEydO4M8//6y03zt37uCXX34BUDpCqGvq2rhx44QFCZYtW1bmhr0VUSqVCA8PF9XWXAICAgAAycnJOH36tNH9iHlvVzV17E+fPhW92AXRi4jFExGRSMXFxZg3b57OD/Vr1qxBUlISAGDEiBFlRhBsbW2F5aqTkpKwcuVKreNVKhUWLFggXJQ9evToZ45XfWH33bt39bZxdnYWCrv9+/fr/DAWGxuLn3/++ZnjqUzt2rUxcuRIAKXXuyxcuBAqlUpnW7lcrrWgwJQpUwCUFqHTpk0TlofXpbi4GJs2bUJRUVEVRV9W27Zt0aZNGwClhefZs2e12jx58kS455RUKsWoUaNMEoux3nvvPWFUSNd1T1KpFN9++61wTc6sWbNw4MABvf0lJCRg3Lhxwup8M2fORMOGDbXa1a9fH3PnzgVQOvo0ZswYYRl6fW7evIl3330XoaGh4p6cmYwdO1b4gmD27NllVuLU5cSJEzpvDSDmvV3Vhg4dKtyq4aeffqrwd5Kenv68wiJ67njNExGRSG3atMHx48cxatQojBs3Ds2aNUNWVhb27NkjfOveoEEDvP/++1rHTp06FUeOHMG9e/ewfPlyJCUlITg4GPXq1UNqair++OMP4cNIhw4dhCLiWXTo0AGpqamIiIjA1q1b0bFjR+Eba0dHR7i4uEAqlSIwMBCbNm1CYmIiRo0ahXfeeQfNmjVDXl4eTp48ic2bN8Pe3h5ubm5ISUl55rgq8tFHH+Hvv/9GUlIS/vjjD8TExOCNN96ATCaDjY0N0tPTER0djT///BMff/wxgoODhWN79+6NsWPHYsOGDbhw4QKGDBmCN954A35+fnByckJBQQHu3r2L6OhoHDlyBLm5uRg+fLjwmlS1//73vwgJCYFcLsfEiRMxZswY9O3bF3Z2doiPj8eaNWuEewGNHz++2l0DI5PJEBAQgGPHjuHChQuIjo7WutdUmzZt8O233+Kzzz5DcXExpk+fjq1btyIwMBAtWrSAjY0N/vnnH0RERGD//v3C6OX48eOFlf50GTFiBNLT07Fs2TJkZWVhzJgx6NmzpzDaVbt2beTm5uL27ds4efIkTp8+jZKSkiq7cbSpuLq6YsmSJZg2bRoePnyIESNGICgoCL169UKDBg2gUCiQnp6O2NhYHDp0CPfu3cPq1au1npeY93ZVq1GjBr799ltMmDABT58+xTvvvIOhQ4eif//+aNCgAYqLi5GcnIyTJ08iIiICV69erfIYiKoDFk9ERCKNHj0aFy5cwO7duzF9+nSt/fXq1UNoaGiZ+8GoOTo6Yt26dZg4cSKSk5Nx6NAhHDp0SKtdx44dsWrVqipZXW3ChAk4dOgQiouLMX/+/DL7goKCsHjxYgCl17dcunQJ8fHxuHr1Kj799NMybZ2cnLB8+XIsW7bM5MWTnZ0d1q9fj2nTpuHChQu4du2aMAohxueff446depg1apVePjwIZYvX663rb29vUlWsVPz8fHB6tWr8dFHHyEvLw9r167F2rVrtdqNHj1a6zWvLiZPnoxjx44BKB190jWyM2TIENSrVw/z58/HrVu3EBUVhaioKJ39OTk54ZNPPhH15cDUqVPRsmVLLF68GGlpaYiMjERkZKTe9i1btsSMGTNEPjPzeeWVV7By5UrMnj0bOTk52Lp1K7Zu3aqzrVQq1XlPJbHv7arWtWtXrF69Gp9++ilyc3Oxe/du7N692yTnIqquWDwRERlg0aJF6NGjB7Zt24akpCQUFBSgYcOG6N+/PyZNmlThCnmNGzfG3r17sWPHDhw8eBBJSUnIz89HnTp14OPjg8DAQAQGBlbZksI+Pj7Ytm0bQkNDcenSJWRmZuqcllerVi1s2bIFv//+O/766y/cuXMHVlZWcHd3R+/evfH222+jQYMGVRKTGM7Ozvjjjz9w5MgR7Nu3D1euXEF2djYkEgnc3NzQunVr9O/fHwMHDtQ6ViKR4IMPPsCwYcOwdetWnDt3DqmpqXjy5Alq1qwJd3d3+Pj4oGfPnujfv3+F9+upCj179sSRI0ewfv16nDx5Evfu3UNxcTFcXV3h5+eHN954Q2s0pzpp27YtevTogb///huRkZGIjY0tczNotc6dO2Pfvn04dOgQjh07hri4OGRmZkKhUMDZ2Rmenp7o06cPhg0bZtCKgq+88gr69OmDQ4cO4dSpU4iLi0N2djby8/Ph6OiIRo0aoV27dhg4cCD8/f2r5FYAz4N6RG/79u04efIkbt68idzcXFhZWcHV1RUtW7ZE165dMXDgQLi7u2sdL/a9bQovv/wyjh49ii1btuD48eO4ffs2njx5Ajs7OzRr1gydOnXCa6+99lxiITIHiUrfhHIiIkJqair69esHoLRw0pwmRkRERP8uXDCCiIiIiIhIBBZPREREREREIrB4IiIiIiIiEoHFExERERERkQgsnoiIiIiIiETgantEREREREQicOSJiIiIiIhIBBZPREREREREIrB4IiIiIiIiEoHFExERERERkQgsnoiIiIiIiERg8URERERERCQCiyciIiIiIiIRWDwRERERERGJwOKJiIiIiIhIBBZPREREREREIrB4IiIiIiIiEoHFExERERERkQgsnoiIiIiIiERg8URERERERCQCiyciIiIiIiIRWDwRERERERGJwOKJiIiIiIhIBBZPREREREREIrB4IiIiIiIiEuH/A/aBOTJqNyDCAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 900x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cols_order = df[large_models_filter][feat_imp_cols_sorted].mean().sort_values(ascending=False).index.tolist()\n",
+    "std_cols_order = [col.replace(\"_imp_mean\", \"_imp_std\") for col in cols_order]\n",
+    "\n",
+    "barplot_feat_imp(df[large_models_filter][cols_order].mean(), df[large_models_filter][std_cols_order].mean())\n",
+    "ax = plt.gca()\n",
+    "ax.set_xlim(left=0, right=ax.get_xlim()[-1])\n",
+    "\n",
+    "plt.title(\"Mean feature importance\\n(model size > 50B; ACSIncome)\")\n",
+    "save_fig(fig, \"mean-feat-imp-large-models\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70515b7b-80f7-4f2d-a015-273fc1a5e31d",
+   "metadata": {},
+   "source": [
+    "Plot feature importance for each model separately:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "e5cd171b-ce3f-44ae-9afe-6dd6831d6e59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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RkJBQYhu6LPVQGTSLHnd391KPv3TpUlWGUyTNOwt1fSC55nGl5V3zEVqOjo7YsWOHsJyF5hIa5aE5jDl06NBqmYifm5ur9bo6FgEm88P/q4hIZxYWFoUeUF5dw5gPHz7E8uXLhddz5syBm5sb3nvvPXTo0AFAftEwZ86cEudoaRYjMpmsyuLV/KVd2iT5ly9faj3rsbo0bNhQ2NYcFixObm4ubty4Ibxu2rRpscdevHgRGzduFF4vXboU9erVw/z584X5iI8fPy7X4rI3btzAo0ePhNflWbusPG7fvq312sXFpVquS+aFhRkRlcnMmTOxd+9e4Utz3k1VkUql+Pzzz4UCp3///hg/fjyA/y2hob5j7sKFC9i8eXOxbdWqVUsomtLS0qqsONO8GeKff/7RWrOsoOXLlyM5OblK4ihJ9+7dhe3g4GA8fPiwxOM3bNiAzMxMAPk9j0XdwQnk3436xRdfCEONEydOFJ5koF5Cw9Iyf7Wm/fv348iRI2WKOygoSNhu3ry51ry3qiKTybBjxw7htUQiQdu2bav8umR+WJgRUZnUqlULXl5ewld1rIL+ww8/CL0VTk5OhSaTN27cWOuZl99//32xE7Otra2Fnh6ZTFZl89K6desm3MX6+PFjzJkzR1jzTS0rKwsLFizArl27Cj2UvToEBAQIjy/Kzc3FO++8Iywyqyk3Nxc//fQT1q1bJ7zn6+tb7J24ixYtwvPnzwEAr7zySqFHGLVv315rCHrRokWIj4/XKWapVIpDhw4Jr6ujtywxMREff/yxVo/ZxIkTq+yxX2TeuMAsERm0Cxcu4LfffgOQ30uzdOnSItcSGzlyJE6fPo1Dhw5BJpPhs88+w/79+4tc3mLIkCH45ZdfAACzZ89GUFAQmjRpovWLtuCdn2VVu3ZtvPPOO/j5558BAKGhoThz5gzatWsHFxcXJCUl4eLFi8jJyYGlpSUWLVpU4WuWVePGjTF37lxhYd7nz5/jrbfegpubG9q0aQNbW1skJSXhypUryMjIEM5r1KgRFixYUGSb+/fvF54GYW1tjVWrVhW5Sv7MmTNx7tw5XL16VRiC3rJlS6lzAE+ePCnEIhaLK2X9vKKelQnkDzE/evQIERERwh3IQP7zYzUfD0ZUmViYEZHBUv/C1hwS69OnT7HHL168GNevX8ezZ8/w4MEDLF++HF999VWh46ZOnYqjR4/i4cOHkMlkOH36dKFjKqNIev/99xEXFydMVE9PT8c///yjdUytWrWwbNkyeHp6Vvh65TFx4kRIJBJ88803wjDlgwcP8ODBgyKP79atG1asWFFoLTkgf8FazacvfPbZZ8V+LvUQ9IgRI5CVlYV///0XGzduxLRp00qMV3MYs3v37qhfv36pn7E0Fy5c0Pn5pGPGjMGXX34p9IYSVTYWZkRksBYuXCjcZenu7o7Zs2eXeHytWrWwcuVKTJo0CUqlEjt37kSfPn3g4+OjdVzNmjWxd+9e/PHHHzh9+jQePHiAzMzMSp9vZmFhgRUrVsDX1xe7d+/GzZs38eLFC9SqVQsNGjTAgAEDMGbMGLi4uCA2NrZSr10WI0eOxIABAxASEoJz584hMjISaWlpkEqlkEgkaNCgAdq1awc/Pz907ty5yDbUd1mql8bo1asX3nrrrRKv26hRIyxatEjI648//ogePXoU+7zL1NRUrbXXqvrGExsbG9SsWRMtWrRAhw4dMGLEiEpZGJeoJCJVdS0xTUREREQl4uR/IiIiIgPBwoyIiIjIQLAwIyIiIjIQLMyIiIiIDAQLMyIiIiIDwcKMiIiIyECwMCMiIiIyECzMiIiIiAwECzMiIiIiA8HCjIiIiMhAsDAjIiIiMhAszIiIiIgMBAszIiIiIgNhqe8ASL+USiVevnwJALC0tIRIJNJzRERERMZBpVJBLpcDAGxtbSEWV7y/i4WZmXv58iUiIyP1HQYREZFR8/T0hL29fYXb4VAmERERkYFgj5mZs7T83/8Cbm5usLOz02M0VNVkMpnQQ+rp6QkrKys9R0RVifk2L8x39dP8nmv+Pq0IFmZmTnNOmbW1NaytrfUYDVU1sVgMBwcHAECNGjUq7QcJGSbm27ww3/pVWXO0mTUS8B+x6bO0tISbm5u+w6BqwnybF+bbNHCOGREREZGBYGFGREREZCA4dkUCmUzGOWYmTiaT4cGDBwDyb/bg5GDTxnybF+bbNLAwIzIjKpUK2dnZwjaZNubbvDDfpoFDmUREREQGgoUZERERkYFgYUZERERkIFiYERERERkIFmZEREREBoKFGREREZGB4HIZJBCLWaebOgsLCzRq1EjYJtPGfJsX5ts0sDAjAf8hmz4LCwu4uLjoOwyqJsy3eWG+TQO7SIiIiIgMBAszIiIiIgPBoUwS8FmZpk8qlSIiIgIA4OXlxXybOObbvDDfpoE9ZkREREQGgj1mJLC05P8Ops7KygpeXl7CNpk25pvI+PA3MQlEIpG+Q6AqJhKJOLxhRphvIuPDwowECqUSCenZ+g6DiMisOdayhQXXlTRbLMxIkJqVC/8VIfoOg4jIrB0KHAMXB3t9h0F6wpKciIiIyECwMCMiIiIyEBzKJCIiMgGWlpbw8PAQtsk4MXNEREQmQCwWQyKR6DsMqiAOZRIREREZCIPvMcvMzERISAhOnTqFBw8eICUlBZaWlnB0dETbtm0xYMAADBkyBBYWFjq3mZqair/++gvnzp1DdHQ00tLSIJVKUbt2bbRo0QIdO3bE8OHD4e7uXmpbSqUSJ0+exOnTp3Ht2jUkJyfjxYsXsLe3h4uLixBj3759i1zgMSAgABcvXiz0vlgsRs2aNdG4cWN06dIFb775Jpo2barzZyQiIvOiVCqRk5MDALCzs4OYS24YJZFKpVLpO4ji7NmzB6tXr0Z6enqJx7Vs2RKLFy9Gp06dSjxOqVTil19+wcaNG5GdXfp6XT179sSXX36Jli1bFrn/4sWLWLJkCe7du1dqW87Ozpg1axZGjx6t9X5xhVlBVlZWmDdvHiZMmFDqsWWh+Wy1+k3duFwGEZGelXe5DD4rs/pVxffcYHvMVqxYgc2bNwPIn8Q4bNgw+Pj4wNXVFVKpFI8ePcLBgwfx77//4v79+3j77bfx3XffwdfXt8j2cnNz8cknn+DkyZMA8gudYcOGoUePHmjUqBFsbGyQkpKCiIgIHD9+HHfv3sW5c+ewe/duBAYGFmpv3759WLRoEWQyGQCgffv2GDRoEFq1agUHBwdkZmbi6dOnOH36NE6dOoXExEQsXbq0UGGmKTQ0VNhWKpWIj4/HkSNHEBQUBJlMhiVLlsDV1RX9+vUr77eViIiIDJhBFmY7duwQirL69evjl19+QatWrbSO6dSpE8aOHYvQ0FDMmzcPUqkUs2fPRtOmTQsdCwBfffWVUJS9+uqr+M9//oNGjRoVOq5v37744IMPcPLkSSxdurTI+C5cuID58+dDqVTCzs4Oy5YtK7Ig7NatG8aOHYvY2FisXr0aZ86cKfFzFxw69fT0RL9+/dCmTRt88803UKlUWLNmDQszIiIiE2VwA9BxcXFYsWIFgPwx8i1bthRZaKn5+fkJBZRUKsUXX3yBgqOzx44dQ1BQEADAw8MDW7ZsKbIo09S/f3/s27cP3bp103r/5cuXmD17NpRKJcRiMX799ddie+nUGjVqhO+//x7z588v8bjiTJgwAa6urgCA27dvIyUlpVztEBERkWEzuMJs69atyMvLAwC89957aN68eann+Pv7o3fv3gCAe/fuCT1jar/88ouwvWzZMtjZ2ekUS61atTBgwACt9/bt24ekpCQA+QVTly5ddGoLAEaOHKnzsZrEYjHatm0rvH727Fm52iEiIiLDZlCFmUqlwoEDBwAANjY2ePPNN3U+96233hK29+/fL2xHRUXh1q1bAPKHP9u0aVOhGNVti0QiTJo0qUJtlYXmYoFKpbLarktERETVx6DmmEVHRwt3YHbq1Ak1a9bU+dwePXrAxsYGubm5uHLlivD+pUuXhO2+fftWKL6srCzcvXsXANC8eXM0adKkQu2Vheadn87OztV2XSIiKh9rKMp13sucHGQXXl2pVDKZDHl5eahRo0a5rkuGwaAKs8jISGG7devWZTrXwsICnp6euH79OlJTU5GQkAAXFxetNjWHA8sjKipK6K2qaM9bWZw6dQrR0dEAgMaNG6NBgwbVdm0iIiofX8mTcp239b/rKnTd1157rULnk34ZVGGWlpYmbDs6Opb5/Hr16gnb6enpcHFx0VoDrW7duhWKT7MtzWtVBc3lMtasWSO8P3Xq1Cq9LhERGTdvb299h0AVYFCFmeair7pO0NekeU5WVlaltKlJsy1bW9sKtVUU9cNni/PGG2+Uad4dERERGReDKszs7f+30rH6sRJloXmO+kGuFW1Tk2ZbL1++rFBburK1tUXHjh0xceJE+Pj4VMs1iYiISD8MqjCrU6eOsJ2cnFzm8zXX93JwcND6b8H95aHZVnniK43myv8WFhawt7eHk5NTmZ4DSkREhuFIVvluENs56zU41S77CI9CoRDmWDs6OvJ3h5EyqMJMcyjvzp07ZTpXoVAgKioKQP5cMhcXFwD5q+er3b59Gz179qxQfGKxGEqlsszx6UKXh6YTEZFxkKJ8hZGtnZ3WCI3O15NKhQ6IOnXqsDAzUga1jpm7u7vQK3X58mVkZmbqfO758+eF4UXNh5l37txZ2D59+nSF4pNIJMJTCB49eoTHjx9XqD0iIiIiTQZVmIlEIowYMQJA/kPH9+zZo/O5v//+u7A9atQoYdvDw0NYJuPy5cu4fft2hWJUP4RcpVJh+/btFWqLiIiISJNBFWYAMGnSJFhbWwMA1q5dq1Ov1F9//YVTp04ByO9169+/v9b+GTNmCNvz5s3T+SaAFy9eICwsTOu9MWPGwMnJCUD+w9YvXryoU1sAEBwcrPOxREREZH4MrjBr1KgRvvjiCwD5d1FOnjxZa5HYgg4dOoQ5c+YAAKysrLBy5UqIRCKtYwYNGiQ8pzIqKgqTJ09GXFxciXGcPn0a//d//4cLFy5ovW9ra4vvvvtOmGv27rvv4u+//y6xrWfPnuHTTz/FN998U+JxREREZN4MavK/WkBAAJ48eYJt27bh2bNnGDNmDIYPHw4fHx80bNgQcrkcDx8+xMGDB4XCydraGt99950wB6ygr776ChkZGTh58iRu3LgBX19fDB06FL169YKrqytsbGyQnJyMO3fu4NixYyUOeXbv3h3ffPMNFi1ahJycHHz00Udo3749hgwZglatWqF27drIzMzE06dPcebMGYSFhUEqlZbpEVNERERkfgyyMAOAwMBAtGjRAj/88APS09Nx4MAB4QHnBbm5ueHrr7/WmvRfkK2tLdatW4f169dj06ZNyM7OLrFNAOjXrx/Gjx9f5L4xY8agcePGWLJkCe7du4fr16/j+vXrxbbVsGFDfPbZZ8XuJyIiIjLYwgwAxo0bh+HDhyMkJASnTp3C/fv3kZqaCgsLCzg6OsLLywsDBgyAr6+vTrcFi8VivP/++xg3bhz++usvnDt3Dvfu3UNaWhpkMhlq166NFi1aoGPHjvD394ebm1uJ7XXp0gUHDhzAyZMncerUKVy7dg3JycnIzMyEnZ0d6tevL8TYt29fWFoa9LebiIiMmEgkEpbZKDilh4yHSKVSqfQdBOmPVCpFREQEAKB+Uzf4rwjRc0RERObtUOAYuDiUfR0zqn6av0O9vLyEmxcrwuAm/xMRERGZKxZmRERERAaCk56IiIhMgFwuF9b+bNq0Kec1Gyn2mBEREZkApVKJ9PR0pKenQ6lU6jscKieW0ySoK7HBocAx+g6DiMisOday1XcIpEcszEhgIRbDxcFG32EQERGZLRZmJODKKaZPpVJBJpMByH+EGdc6Mm3MN5Hx4RwzEsjlcn2HQFVMJpMhIiICERERwi9sMl3MN5HxYWFGREREZCBYmBEREREZCM4xIwHnn5g+sVgMZ2dnYZtMG/NtXphv08DCjARcjND0WVpaonHjxvoOg6oJ821emG/TwJKaiIiIyECwMCMiIiIyEBy7IoFCqURCera+wyAi0pljLVtYcD4VgPzlUe7evQsAaNWqFaysrPQcEZUHCzMSpGblwn9FiL7DICLS2aHAMXBxsNd3GAZBc0FhLhhuvPhnBhEREZGBYGFGREREZCBYmBEREREZCBZmRERERAaChRkRERGRgaj2wkyhUKBjx47w8PDAqFGjSjxWpVKha9eu8PDwgIeHB/bu3Vvi8UFBQcKxf/zxh/B+QECA8H5ptm7dKhzbvXt3REZGCvt++uknYV94eHipbQGAj48PPDw84OPjU+T+uXPnCm1qfnl6eqJTp07w9/fH4sWLhVugiYiIyHRVe2FmYWGBDh06AAAiIyORlZVV7LHR0dFIT08XXl+5cqXEti9fvixsd+7cucyxbdy4EUuXLgUAODk5Yfv27fD09CxzO5VBpVIhMzMTUVFR+OOPPzB69Gh8//33eomFiIgMn6WlJVq0aIEWLVrwEXtGTC+Z69y5M86ePQulUomrV6+iT58+RR6nLrQsLCygUCi0Cq+Sjq9Tpw5atmxZppjWr1+PH374AQDg4uKCrVu3onnz5mVqo6I2bdokPIBWqVQiJSUFp06dwh9//AG5XI5ffvkFzs7OmDBhQrXGRUREhk8sFqNOnTr6DoMqSC9zzDp16iRsl1Rsqff5+voCAJ48eYKEhIQij01JSUFMTAwAwNvbGyKRSOd4fvrpJ6Eoa9iwIX7//fdqL8oAoFmzZnB3d4e7uzs8PT3Rs2dPBAYG4qeffhKOWbt2LRQKRbXHRkRERFVPL4VZu3btUKNGDQC6FWZDhgxBkyZNABQ/nFneYcz//Oc/WLt2LQCgUaNG+P3334VrGQofHx+hmE1NTcXt27f1HBERERkalUoFqVQKqVTKlf+NmF4KM2tra7Rr1w4AEBERAalUWuiYp0+fCr1j3t7e8Pb2BlB8Iaf5vmaPXElWrFiBX3/9FUB+b9WOHTvg6uqq+wepRurvFwA8e/ZMj5EQEZEhkslkiIiIQEREhPBoJjI+epsd2KlTJ1y6dAlSqRQ3btwo1MulLrSaNWsGR0dHeHt7IygoCJcuXSqyPXVPmkQiQatWrUq9/jfffIPt27cDAFq0aIGtW7cK87sMkeZETqVSqcdIiIh0Y42qn3bxMicH2dX4rG57ez6Xk6qW3gqzzp07Y/369QDyi7DiCrOOHTtq/Tc6OhoZGRmoXbu2cGxWVpawrEWHDh1gYWFR7HVVKhUWL16MnTt3AgDc3d3x22+/wdHRsZI+WdW4d++esG3IBSQRkZqv5EmVX2Prf9dV+TU0LVq0qFqvR+ZHb4VZ+/btYWlpCblcXuTwpPo99bCkm5sb6tSpg7S0NFy9ehX9+/cXjr127ZowIb60+WULFizAn3/+CQBo1aoVNm/ejLp165Y5/tjYWJ3ufqmM7uTIyEicOXMGAGBrawsvL68Kt0lERESGR2+Fmb29PVq1aoWIiAihsFL3dBW8w1KtY8eOOHHiBC5fvqxVmJVlfpm6KHNzc8PWrVu1et7K4ssvvyzXebpSqVRITk7GqVOnsHr1aqHwDAgIEG6cICIiItOi1xXoOnfujIiICGRnZ+Pu3bto27YtAAjzyBwdHdGsWTPheG9vb6Ew06SeX1ajRo1Se5NEIhFUKhViY2Nx584ddO/evRI/UcUMGDCgxP39+vXDRx99VE3REBERUXXTa2Hm7e2NzZs3A8jv9VIXZurCS7O3DPhfb9jt27eRm5sLGxsbSKVS3Lx5EwDw6quvwtrausRrLliwAF9//TXy8vIwc+ZMbNy4Uee7ODVt27YNXbt2LfU4Hx8fxMXFlbl9NSsrK3h5eeGNN97AiBEjyrQ+GxGRPh3Jqvqlh3bOeg1Ote2q/DpE1UWvhVmnTp2EHqwrV65g8uTJAP7XA6ae8K/WunVr2Nra4uXLl7h+/Tq6deuGiIgI5OXlCe2VZsKECcjNzcXKlSvx8uVLvPvuu/jtt9+0lqPQF82V/8ViMezs7ODo6FhqsUlEZIikKP5GrMpia2fHOyXJpOhlHTM1BwcHvPLKKwD+10umeYdlwR4zde+R5vGaC87qurDslClT8OGHHwrXmzp1qtbDyvVFc+X/li1bomHDhizKiIhIJ+rfkV5eXrCyqsY1RKhS6bUwA6C1ov2DBw9w5coVKJVK2NnZoXXr1oWOL7jQrHo+mpWVFdq3b6/zdT/44ANMmzYNAJCRkYF33nkHDx48qMhHISIi0huRSARra2tYW1tz2osRM5jCDMjv/VL3gLVv377I9cjUhdmNGzcglUpx7do1APnDnHZ2ZZtn8PnnnyMgIABA/p2gkydPxtOnT8v1OYiIiIgqyqAKs0uXLgk9YAWHMdXUC8jm5ORg//79yMzMLNROWQQGBuL1118HACQmJuKtt97C8+fPy9UWERGRviiVSqSlpSEtLY1PiDFiei/MXFxchIeGh4eH49atWwCKL8wkEgnc3d0BABs3bhTeL8uDyzWJRCIsXrwYfn5+AIC4uDhMnjwZSUlJ5WqPiIhIH+RyOR4+fIiHDx9CLpfrOxwqJ70XZsD/ersSEhIglUphaWlZ4nwxddGmHnYUi8XFFnK6EIvFWLFiBYYMGQIAiImJwdtvv43U1NRyt0lERERUVgZVmKm1atUKtra2xR5fsAhzd3dHrVq1KhSDhYUFVq9ejX79+gHIfybnlClT8OLFiwq1S0RERKQrgyjMCg5Dltb7VXB/eYcxC7KyssKaNWvQo0cPAMCdO3cwbdo0ZGdnV0r7RERERCURqVQqlb6DIP2RSqWIiIgAANRv6gb/FSF6joiISHeHAsfAxYELzALaP8+9vLy4DmY1qIrvuUH0mBERERERCzMiIiIig6HXZ2USERFR5RCJRMKjmLjyv/FiYUZERGQCrKys0K5dO32HQRXEwowEdSU2OBQ4Rt9hEBHpzLFW8UsrERkjFmYksBCL4eJgo+8wiIiIzBYLMxJw5RTTp1KpoFAoAOQvqsx5KKaN+TYvcrlceNZzgwYNYGnJX/HGiHdlkoDPVjN9MpkMN27cwI0bNyCTyfQdDlUx5tu8KJVKJCYmIjExkQ8xN2IszIiIiIgMBAszIiIiIgPBwoyIiIjIQLAwIyIiIjIQvGWDBLyDx/RZWVnBy8tL2CbTxnybF+bbNPA3MQl4K73pE4lEsLa21ncYVE2Yb/PCfJsGFmYkUCiVSEjP1ncYRERkZhxr2cJCzNlVAAsz0pCalQv/FSH6DoOIiMzMocAxcHGw13cYBoHlKREREZGBYGFGREREZCBYmBEREREZCBZmRERERAaChRkRERGRgTCauzJzcnJw4MABhIWFITIyEunp6VCpVJBIJHB1dYW7uzs6dOiA3r17o0GDBiW29ezZM/z11184f/48Hj9+jNTUVCiVStSuXRvu7u7w9vaGn58fGjduXOjc8PBwTJo0CQDwwQcf4MMPPyw19rlz5yIoKAgAcOLECTRq1Ehr//79+zFv3jwAwLJlyzB69GidvidERERkWoyiMLt27Ro+/fRTPHv2rNC+tLQ0pKWl4datW9i/fz8cHR1x7ty5ItvJy8vD6tWrsXPnTkil0kL7ExMTkZiYiLNnz2LNmjXw9fXFnDlzSi30iIiIiCqDwRdmjx49wpQpU5Cdnb/wqY+PD4YMGYLmzZvDysoKaWlpiIyMxPnz5xEeHl5sO6mpqZg5cyauX78OALC3t8drr72Gbt26oX79+rC0tERycjKuXr2KY8eOISYmBocPH0b79u0xefLkavikREREZO4MvjD7/vvvhaKsuGG+nj17YsqUKUhNTcXhw4cL7Vcqlfjkk0+Eoqx///749ttvUa9evULH+vj44NNPP0VISAhWrlxZuR+GiIiIqAQGXZgpFAqcPn0aANC2bdtS517VrVsXEyZMKPT+tm3bhN60Xr16Ye3atSU+sFssFmPkyJHo1q0bHj16VIFPQERERKQ7gy7MUlNTkZubCwBo2rRpudqQSqXYtGkTAKBGjRpYunRpiUWZpvr166N+/frlui4RERFRWRl0YWZlZSVsP3jwoFxtnD17FomJiQAAX19fuLi4VEpsRERE5sIaiipt/2VODrKtSj+uouztDf95nAZdmDk4OMDV1RVxcXGIjIzEhg0bMHXqVIjL8AT6S5cuCdv9+vWrgiiJiIhMm6/kSZW2v/W/66q0fbVFixZVy3UqwqALMwCYOHEiVqxYAQBYvXo1du3aBR8fH3Ts2BFeXl5FrjWmKTIyUthu06ZNpcaWkpKCe/fulXrcixcvKvW6REREZJoMvjCbPHky7t+/j3379gEA4uLisH37dmzfvh0A4OjoiC5dusDPzw/9+/eHSCTSOj89PV3YLuouzIrYuXMndu7cWaltEhERkfky+EcyicViLF26FJs3b0bv3r0LTdxPTk7GoUOHMHPmTPzf//0fnjzR7m5VL7UBALa2ttUSMxEREVF5GHyPmVrPnj3Rs2dPZGVl4cqVK4iIiMCtW7dw+fJlZGZmAgBu3bqF8ePHY//+/XB2dgagPdHv5cuXkEgklRZTeR7JREREZGyOZDWp0vZ3znoNTrXtqvQaxsJoCjM1iUSCvn37om/fvgDyl8MIDQ3FihUrkJGRgaSkJPz444/49ttvAeTfQKCWnJxcqYUZERGROZDCokrbt7WzM4o7JquDwQ9llsba2hpjxozB6tWrhfeOHTsGpVIJAPD09BTev3PnTrXHR0RERKQroy/M1Hr37i08bDwjI0OY9N+5c2fhmFOnTukhMiIiIiLdmExhBkCYV6apV69ewvtHjhxBQkJCdYdFREREpBOTKcxevnyJ+/fvA8ifh1anTh0A+UOdU6ZMAQDk5eXhyy+/hEKh2wrGCQkJuHDhQtUETERERFSAQRdm2dnZGDt2LE6ePCnMGSuKUqnEkiVLhKUxfHx8tNYzmzRpErp27Qog/xFN77//PlJTU4ttT6VSITQ0FKNHj0ZUVFQlfRoiIiKikhn8XZk3b97EjBkz4OLigoEDB6J9+/Zo2LAhJBIJXrx4gTt37mDfvn3CCvw1a9bExx9/rNWGWCzGDz/8gBkzZuDGjRs4efIkBg4cCD8/P3Tr1g3169eHpaUlkpOTcf36dRw9ehQPHz7Ux8fFlStXdDrOx8dH645TIiIiMn4GXZhZWlrCyckJSUlJSEhIwI4dO7Bjx45ij2/WrBlWr16NRo0aFdpXt25dbN++HatWrcLOnTuRnZ2NXbt2YdeuXUW2JRKJ4Ofnh6FDh1ba59HF3r17sXfv3lKPCw4OZmFGRERkYgy6MKtRowb++ecfXL9+HefPn8eNGzfw6NEjpKSkIC8vD7a2tnB2doanpycGDBiAwYMHw9rausT2AgMD8fbbb+PgwYO4cOECYmJikJqaCpVKhdq1a8Pd3R2dO3eGn58fXF1dq/HTEhERkbkTqVQqlb6DIP2RSqWIiIgAANRv6gb/FSF6joiIiMzNocAxcHEwvgVmNX+Henl5ldg5pCuDnvxPREREZE5YmBEREREZCBZmRERERAbCoCf/U/WqK7HBocAx+g6DiIjMjGMtW32HYDBYmJHAQiyGi4ONvsMgIiIyWyzMSMAbdE2fSqWCTCYDAFhZWWk9IYNMD/NtXphv08A5ZiSQy+X6DoGqmEwmQ0REBCIiIoQf4GS6mG/zwnybBvaYkcDKykrfIVAVs7a2hre3t77DoGrCfJsX5ts0sMeMiIiIyECwMCMiIiIyEBzKJIFCodB3CFTFFAoFkpOTAQCOjo6wsLDQc0RUlZhv88J8mwYWZiRQKpX6DoGqmEKhQGxsLACgTp06/MFt4phv88J8mwYOZZLA0pJ1OhERkT6xMCMB17whIiLSL3aRkEChVCIhPVvfYVQrx1q2sBDz7xMiIjIMLMxIkJqVC/8VIfoOo1odChwDFwd7fYdBREQEgEOZRERERAaDhRkRERGRgeBQJpEZEYlEsLe3F7bJtDHf5oX5Ng0szIjMiJWVFTw9PfUdBlUT5tu8MN+mwagKs4sXLyIgIEB4vXPnTnTs2LFMbTx58gSHDx/GhQsX8OTJE6SlpUEul6NmzZpo1KgRWrdujX79+qFnz57FPtR7//79mDdvXpmuO2/ePEyePFnrvYCAAFy8eLHQsWKxGDVr1kTjxo3RpUsXvPnmm2jatGmZrkdERETGx6gKs6CgIK3XwcHBOhdmL168wPLly3HgwAHI5fJC+1NSUpCSkoIbN25g586dqFevHqZNm4ZJkyZV++rJSqUSGRkZyMjIwK1bt7B9+3bMmzcPEyZMqNY4iIiIqHoZTWGWm5uLv//+GwBgZ2eHnJwcHDlyBPPnz4e1tXWJ5z59+hTTpk3Do0ePAAAODg4YPnw4OnXqBGdnZ9ja2iI1NRUPHz7E2bNncf78eaSkpGD58uV47bXX4OTkVGzbn3zyCQYMGFBq/CW1AQChoaHCtlKpRHx8PI4cOYKgoCDIZDIsWbIErq6u6NevX6nXIiqOXC7H48ePAQBNmzbl0x5MHPNtXphv02A0WTt27Biys/MXP50/fz6+/PJLZGRkICwsDL6+vsWel5OTg3fffVcoyl5//XXMmTMHEomk0LG9e/fGW2+9hadPn2LdunXYv39/qXG5uLjA3d29nJ/qfwq24enpiX79+qFNmzb45ptvoFKpsGbNGhZmVCFKpRLp6ekAgMaNG+s3GKpyzLd5Yb5Ng9EslxEcHAwA8PDwwJgxY9C8eXOt94uzatUqPHjwAAAwfvx4LFmypMiiTFPjxo2xbNkyrF+/HjVq1Khw7BUxYcIEuLq6AgBu376NlJQUvcZDREREVccoCrPExERcuHABAODv76/137NnzyI1NbXI85KTk7F3714AQIMGDTBnzpwyXdfHxwe1atUqb9iVQiwWo23btsLrZ8+e6TEaIiIiqkpGUZiFhoZCoVBALBbDz88PAODn5weRSASZTIaDBw8Wed6hQ4eQl5cHABg7dixsbGyqLebKpDlPQKlU6jESIiIiqkpGMcfswIEDAIAuXbrAxcUFQP5wY4cOHXD16lUEBwdj0qRJhc67dOmSsN27d+/qCbYK3Lt3T9h2dnbWYySGxRqKCrfxMicH2UWvilIm6kUdiYiIKsLgC7O7d+8iKioKwP+GL9X8/f1x9epV3L59G/fv30fLli219qvPE4vFVbboXkJCglbhVJzy3iBw6tQpREdHA8gvRhs0aFCudkyRr+RJhdvY+t91lRAJsGjRokpph4iIzJvBF2bqyf02NjYYMmSI1r6hQ4fi22+/hUwmQ3BwMD7//HOt/WlpaQCAmjVrlrikRlJSknBsQbVr1xZ66Yryww8/4Icffij1c6iLRF1oLpexZs0a4f2pU6fq3AYREREZH4MuzORyuTB/rH///oXupnRwcEDfvn1x/PhxhIaG4tNPP4VY/L9pc+rlNWxtbUu8zoYNG7Bt27Yi940aNQrLly+vyMfQiYeHR4n733jjDbz55ptVHgeZNrFYLAyHa/5bIdPEfJsX5ts0GHRhdvbsWSQnJwMoPIyp5u/vj+PHjyM+Ph7h4eHo3r27sM/e3h4vXrzAy5cvqyzGZcuWYfTo0VXStq2tLTp27IiJEyfCx8enSq5B5sXS0pLrG5kR5tu8MN+mwaALM/UwpoODQ7GT9/v3749atWrhxYsXCA4O1irMHBwc8OLFC2RmZkIqlRY7nBkYGIjAwEDhdWxsrE6r+VcmzZX/LSwsYG9vDycnp2p/HJQxOZLVpMJt7Jz1Gpxq21VCNERERBVnsIVZZmYmwsLCAADp6elaa3kV5+jRo1i0aBHs7PJ/0Xp6euLJkydQKpWIioqCl5dXlcZcEZXx9ABzI0XFi1ZbOzveUUlERAbDYAehDx8+LKxBpqucnBwcPXpUeN25c2dh+8yZM5UWG5GxkslkuHnzJm7evAmZTKbvcKiKMd/mhfk2DQbbY6YexnRycsK8efNKPX7lypWIj4/HgQMHMHLkSADAsGHD8N1330EqleLPP//ElClT9P6IJSJ9UqlUwg9slUql52ioqjHf5oX5Ng0GWZg9ffoUV69eBQAMGTIEw4cPL/Wc69evY9u2bfj333+RkJAAFxcXODo6YsyYMdi5cyeePXuG7777DvPnz6/q8ImIiIjKxSCHMg8cOCBU+wXXLiuO+jilUik8KQAAPv/8c+GB59u3b8dXX30lLKNRnBcvXpQnbCIiIqIKMcgeM3VhVa9ePXTq1Emnczp27AgnJyckJSUhJCQE06dPBwBIJBL8+uuvmD59OmJiYrBz504cOXIEr732Gjp16gRnZ2fY2dkhKysLjx8/xvnz53Hs2DGh3dKer6nryv8SiQQNGzbU6bMQERGReTK4wuzKlSt48iT/UTsDBw7UeZE8sViMQYMG4Y8//kB0dDRu3bol3MnZtGlT7NmzB8uWLUNISAjS0tKwfft2bN++vdj26tWrh6lTpyIgIKDE6+q68v+AAQOwbl3lPP6HiIiITJPBFWaaw5C6DmOqDR48GH/88QeA/JsHNJfYqF27NpYvX46ZM2fi0KFD+Pfff/H48WOkp6dDoVCgVq1aaNCgAdq2bYtevXqhb9++sLKqhKdbExEREelIpOKtG2ZNKpUiIiICAFC/qRv8V4ToOaLqdShwDFwczGcdM818e3l5lfgMWTJ+zLd5Yb6rX1V8zw2ux4yIqo6lpSVatGghbJNpY77NC/NtGpg5IjMiFotRp04dfYdB1YT5Ni/Mt2kwyOUyiIiIiMwRe8yIzIjmyuBWVlYQiUR6joiqEvNtXphv08DCjAR1JTY4FDhG32FUK8datvoOoVrJZDJODjYjzLd5Yb5NAwszEliIxXBxKHlBXSIiIqo6nGNGAq6cQkREpF8szEggl8v1HQIREZFZY2FGREREZCBYmBEREREZCBZmRERERAaChRkRERGRgeByGSTgs9VMn5WVFby8vIRtMm3Mt3lhvk0DfxOTgKtEmz6RSMRFJ80I821emG/TwKFMIiIiIgPBHjMSKJRKJKRn6zsMnTnWsoWFmH9blIVSqURGRgYAoHbt2hDz+2fSmG/zwnybBhZmJEjNyoX/ihB9h6GzQ4Fj4OJgr+8wjIpcLsfDhw8B8Fl65oD5Ni/Mt2lgOU1ERERkIFiYERERERkIFmZEREREBoKFGREREZGBMMvJ/zk5OThw4ADCwsIQGRmJ9PR0qFQqSCQSuLq6wt3dHR06dEDv3r3RoEEDrXPnzp2LoKCgMl0vODgYrVq1El5/8MEHOHbsGABgy5Yt6N69e4nnnzhxAu+99x4AYOTIkVixYkWZrk9ERETGwewKs2vXruHTTz/Fs2fPCu1LS0tDWloabt26hf3798PR0RHnzp2r9BgWLlyI8PBwvHjxAgsXLkRoaChsbGyKPDYzMxNfffUVAKBevXqYN29epcdDREREhsGsCrNHjx5hypQpyM7OX6vLx8cHQ4YMQfPmzWFlZYW0tDRERkbi/PnzCA8PL7W9TZs2wdnZudTjmjZtqvXa2dkZs2fPxoIFC/DkyRP8+OOPmDNnTpHnrly5EomJiQCA+fPnw8HBodTrERVHJBIJj2rhkx5MH/NtXphv02BWhdn3338vFGXLli3D6NGjCx3Ts2dPTJkyBampqTh8+HCJ7TVr1gyNGjUqVyxjx45FaGgoLl68iK1bt2L48OFo27at1jHh4eH4888/AeQXkcOGDSvXtYjUrKys0K5dO32HQdWE+TYvzLdpMJvJ/wqFAqdPnwYAtG3btsiiTFPdunUxYcKEKotHJBLhm2++QY0aNaBQKBAYGAi5XC7sz83NxYIFC4S5b+rhTCIiIjJdZlOYpaamIjc3F0DhoUV9adq0KT744AMAQGRkJDZu3CjsW7NmDR4/fgwAmD17NlxcXPQSIxEREVUfsynM1OPuAPDgwQM9RqLtnXfeQevWrQEAP//8Mx4+fIhbt25hy5YtAIDOnTvjjTfe0GOEZErkcjmePn2Kp0+favXQkmlivs0L820azKYwc3BwgKurK4D83qkNGzZAqVTqOSrA0tIS33zzDSwsLCCVSjF//nzMnz8fCoUCNWrUwJIlS8x+Eqc1FEV+vczJQXZ2dolfpE2pVCIxMRGJiYkG8f8/VS3m27ww36bBrCb/T5w4UVgDbPXq1di1axd8fHzQsWNHeHl5oXHjxmVqLyYmBjk5OSUeY2trW2q7bdq0weTJk7Fp0yZcuXJFeP+DDz5A8+bNyxSTKfKVPCny/a3/XVfquYsWLarscIiIiKqMWRVmkydPxv3797Fv3z4AQFxcHLZv347t27cDABwdHdGlSxf4+fmhf//+pfZUTZkypdRrdunSRWi/JB999BEOHTqE58+fAwBat26Nd955p9TziIiIyHSYzVAmAIjFYixduhSbN29G7969YWmpXZcmJyfj0KFDmDlzJv7v//4PT54U3VNTFR4/fozk5GThdd26dQvFR0RERKbNLH/z9+zZEz179kRWVhauXLmCiIgI3Lp1C5cvX0ZmZiYA4NatWxg/fjz2799f7CKyJ06cKPc6ZprUy2XIZDLhvbNnzyI0NBR+fn4Vbp+IiIiMg1kWZmoSiQR9+/ZF3759AQBSqRShoaFYsWIFMjIykJSUhB9//BHffvttlcaxbds2REREAMgfHg0NDUViYiKWLl2KXr16oU6dOlV6fUN3JKtJke/vnPUanGrbVXM0REREVceshjJLY21tjTFjxmD16tXCe8eOHavSu1uePn2KH3/8EQDg7u6OWbNmYcGCBQDy117jA8sBKSyK/LK1s4O9vX2JX0RERMaEhVkRevfujQYNGgAAMjIykJ6eXmXXWrhwIV6+fAmxWIxvv/0WVlZWGDx4MAYOHAgACAoKwoULF6rs+mRexGIxHBwc4ODgALGY//xNHfNtXphv08DMFUOXh5NX1L59+3D+/HkAQEBAgNYzzhYuXAiJRAIgf8mHvLy8Ko+HTJ+lpSXc3Nzg5ubGm0vMAPNtXphv08DCrAgvX77E/fv3AeTPQ6uKOV7JycnCMKWrqys++eQTrf0uLi747LPPAOTfsbl27dpKj4GIiIgMi9kUZtnZ2Rg7dixOnjxZ4pwxpVKJJUuWCKvG+/j4VMnK+19//TUyMjIAAIsXL4adXeFJ7OPGjUPHjh0BAJs3b0ZkZGSlx0FERESGw6z6Om/evIkZM2bAxcUFAwcORPv27dGwYUNIJBK8ePECd+7cwb59+3Dv3j0AQM2aNfHxxx8X254uK/8DgJOTk1av2/Hjx/H3338DAEaMGIHevXsXeZ5IJMKSJUswcuRIyGQyzJ8/H3v27OHcASo3mUwmPCvWzc1N6xmyZHqYb/PCfJsGsynMLC0t4eTkhKSkJCQkJGDHjh3YsWNHscc3a9YMq1evLnGdMl1W/geAefPmYfLkyQCAzMxMLF68GED+IrLz5s0r8dyWLVvi3Xffxdq1axEREYHt27fjrbfe0um6RAWpVCqhN1ilUuk5GqpqzLd5Yb5Ng9kUZjVq1MA///yD69ev4/z587hx4wYePXqElJQU5OXlwdbWFs7OzvD09MSAAQMwePBgWFtbV3ocK1euRGJiIgDgyy+/1Gn+2rvvvovDhw/jwYMH+OGHHzBo0CA0bNiw0mMjIiIi/TKbwgzIv5W4Y8eOwryt8li+fDmWL19e7vOXLFmCJUuWlOkca2trHDp0qNzXJCIiIuPAyUpEREREBoKFGREREZGBYGFGREREZCBYmBEREREZCLOa/E8lqyuxwaHAMfoOQ2eOtWz1HYLRsbCwEJaAsbCw0HM0VNWYb/PCfJsGFmYksBCL4eJgo+8wqApZWFjAxcVF32FQNWG+zQvzbRo4lElERERkIFiYERERERkIDmWSQCaTVcnTDshwSKVSREREAAC8vLyYbxPHfJsX5ts0sMeMiIiIyECwMCMiIiIyECzMiIiIiAwECzMiIiIiA8HCjASWlrwXhIiISJ9YmJFAJBLpOwQiIiKzxi4SEiiUSiSkZ+s7jBI51rKFhZh/TxARkWliYUaC1Kxc+K8I0XcYJToUOAYuDvb6DsNoWVpawsPDQ9gm08Z8mxfm2zQwc0RmRCwWQyKR6DsMqibMt3lhvk0Dx4SIiIiIDAR7zIjMiFKpRE5ODgDAzs4OYs7XM2nMt3lhvk0Ds0ZkRuRyOaKiohAVFQW5XK7vcKiKMd/mhfk2DSbVYxYeHo5JkyYJr+3s7HD+/HnY2tqWeF5ubi569uyJrKws4b1t27aha9euJZ538eJFBAQECK937tyJjh07ljlOTTY2NnBwcICnpycGDRoEf39/PoiWiIjITJh0j1lOTg6OHz9e6nEnTpzQKsp0FRQUpPU6ODi4zG0UlJubi/j4eJw6dQqBgYEYPXo0YmNjK9wuERERGT6TLcxq1KgBADhw4ECpx6qPUZ+ji9zcXPz9998A8nvmAODIkSOQSqVlinPcuHEIDQ0VvrZs2YLAwEDUr18fABAdHY2ZM2dCoVCUqV0iIiIyPiZbmPn4+AAAzp8/j6SkpGKPS0lJwblz5wAAAwYM0Ln9Y8eOITs7fzHW+fPnAwAyMjIQFhZWpjjr1asHd3d34at79+6YNGkS/vrrL7i6ugIA7t27h2PHjpWpXSIiIjI+JluY9ezZE05OTlAoFPjrr7+KPe7gwYOQy+VwcnJCjx49dG5fPWzp4eGBMWPGoHnz5lrvV5REIsHMmTOF1+fPn6+UdomIiMhwmWxhZmFhgeHDhwMoeThTve+1116DhYWFTm0nJibiwoULAAB/f3+t/549exapqanljluTegVnAIiPj6+UNomIiMhwmWxhBgAjRowAANy5cwfR0dGF9t+/fx+3b9/WOlYXoaGhUCgUEIvF8PPzAwD4+flBJBJBJpPh4MGDlRA9YGVlJWzz8RpERESmz6QLs9atW+OVV14BUHSvmfo9d3d3tGrVSud21ed16dIFLi4uAIDGjRujQ4cOACpvOPPBgwfCtnq+mTmwhqLYr5c5OcjOzi72i0pmbW0Nb29veHt7cxkWM8B8mxfm2zSYfDfMiBEjsGrVKhw8eBCfffYZRCIRAEClUiE0NFQ4Rld3795FVFQUgP8NX6r5+/vj6tWruH37Nu7fv4+WLVuWO26FQoFNmzYJr4cMGVLutoyNr+RJsfu2/nddiecuWrSossMhIiKqNibdYwbkF0tisRjPnz9HeHi48H54eDieP3+uNRypC3VvmI2NTaFiaejQocLwY3l7zVJTU3HhwgVMnDgRd+7cAZBflHXq1Klc7REREZHxMPnCzMXFRVjBX3M4U73drVs3YTiyNHK5XJg/1r9/f0gkEq39Dg4O6Nu3L4D8eWhKpbLUNteuXQsPDw/hq3v37pg8eTKuXr0KW1tbvP3221i9erVO8RGVRqFQICEhAQkJCVwbzwww3+aF+TYNJl+YAcDIkSMBAEePHkVubq7W4rBlGcY8e/YskpOTARQexlRTvx8fH6/VQ1cenp6eCAgI0LoJgKgiFAoFYmNjERsbyx/cZoD5Ni/Mt2kw+TlmADBo0CB89dVXyMrKwokTJ6BSqZCdnQ07OzsMHjxY53bUw5MODg7o3bt3kcf0798ftWrVwosXLxAcHIzu3buX2Oa4ceMwfvx4APn/qOLj4/H333/jwIEDuHbtGgICArB3717UrVtX5ziN3ZGsJsXu2znrNTjVtqvGaIiIiKqPWRRm9vb2GDhwIEJDQ3HgwAGoVCoAwMCBA4XHKZUmMzNTWNU/PT0dbdu2LfWco0ePYtGiRSVeQ73yv1qrVq3Qv39/dO3aFXPnzkVcXBwCAwOxfv16neI0BVIUv56crZ0d7O3tqzEaIiKi6mMWQ5nA/4Yzz507J6yir35PF4cPH0ZeXl6ZrpmTk4OjR4+W6Ry1UaNGCTcXhIWFCQvaEhERkekyix4zAOjevTucnJyE52Y6OzuXOsyoST2M6eTkhHnz5pV6/MqVKxEfH48DBw6UqQDUNGvWLBw/fhwKhQLff/99meIlIiIi42M2hZmFhQVGjBiBbdu2Acif9C8W69Zh+PTpU1y9ehVA/tIV6kc9leT69evYtm0b/v33XyQkJOh856em5s2bY+jQoTh48CBu3LiBc+fOoWfPnmVuh4iIiIyD2QxlAsDs2bMRERGBiIgIfP755zqfpzkvTdeFXtXHKZXKEp/VWZp3331XWBTXnOaZERERmSOzKszKS11Y1atXT+eFXjt27AgnJycAQEhISLmv7e7uDh8fHwDApUuXcPny5XK3RSQSiWBvbw97e3uh4CfTxXybF+bbNLAwK8WVK1fw5En+I4IGDhyo8/CnWCzGoEGDAADR0dG4detWuWOYMWOGsM1eM6oIKysreHp6wtPTk+vjmQHm27ww36aBhVkpNIchy/q8Ss010iryYPN27doJc8vOnj2LmzdvlrstIiIiMlwilXryFJklqVSKiIgIAED9pm7wX1H+YdfqcChwDFwcuI4ZERHpn+bvUC8vL1hbW1e4TbO5K5OI8p/3+vjxYwBA06ZNYWnJHwGmjPk2L8y3aeBQJpEZUSqVSE9PR3p6OpRKpb7DoSrGfJsX5ts0sDAjIiIiMhAszIiIiIgMBAegSVBXYoNDgWP0HUaJHGvZ6jsEIiKiKsPCjAQWYjFcHGz0HQYREZHZ4lAmCbhyChERkX6xMCOBXC7XdwhERERmjUOZJOCz1UyfWCyGs7OzsE2mjfk2L8y3aWBhRgIuRmj6LC0t0bhxY32HQdWE+TYvzLdpYElNREREZCBYmBEREREZCI5dkUAmk1XKA1jJcMlkMty9excA0KpVK1hZWek5IqpKzLd5Yb5NAwszEnCOmelTqVSQyWTCNpk25tu8MN+mgUOZJOBdmURERPrFLhISKJRKJKRnV8u1HGvZwoK3cxMREWlhYUaC1Kxc+K8IqZZrHQocAxcH+2q5FhERkbFglwURERGRgWBhRkRERGQgWJgRERERGQjOMSMyI5aWlmjRooWwTaaN+TYvzLdpMLrMhYeHY9KkSUXus7GxgYODAzw9PTFo0CD4+/uXuGCqh4dHma7t6emJAwcOaL23f/9+zJs3DwCwbNkyjB49ukxt+vj4IC4urtD7lpaWkEgkaNGiBXr06IHXX38dLi4uZWqbqCCxWIw6deroOwyqJsy3eWG+TYNJDWXm5uYiPj4ep06dQmBgIEaPHo3Y2Fh9h1Uucrkc6enpuHr1KtauXYthw4bh2LFj+g6LiIiIqpDR9ZhpGjduHMaPHy+8TklJQXR0NDZt2oT4+HhER0dj5syZCA4OhoWFRbHttG3bFsuWLSv1ejY2NpUSd1GcnZ2xadMm4bVcLkdcXBz27duHkydPIisrC7NmzcKePXvQunXrKouDTJvmyuBWVlZcVNjEMd/mhfk2DUZdmNWrVw/u7u5a73Xv3h2jR4+Gv78/4uLicO/ePRw7dgy+vr7FtmNnZ1eonepmZWVVKIbWrVtj0KBBWL58OX777TfIZDKsX78eP/30k56iJGMnk8kQEREBAPDy8uKzUU0c821emG/TYFJDmWoSiQQzZ84UXp8/f16P0VTchx9+KPTWnT17FkqlUs8RERERUVUwycIM0J7YHx8fr8dIKs7e3h5ubm4AgJycHKSnp+s3ICIiIqoSJluYWVlZCdumcNuw5udhjxkREZFpMv6KpRgPHjwQtl1dXfUYScXJ5XI8fPgQQH6B5uDgoN+AAFhDUaHzX+bkINuq9ON0YW/PZ24SEZFpMMnCTKFQaN3hOGTIkBKPz8nJwb1790ptt379+qhVq1aF4yur3bt348WLFwAAb29vg+gB9JU8qdD5W/+7rpIiARYtWlRpbREREemT/n/DV6LU1FRERUVhzZo1uHPnDoD8oqxTp04lnnfr1i34+fmV2n55FpAtL/VyGfv378d///tf4f0pU6ZUy/WJiIio+hl1YbZ27VqsXbu2yH22trZ488038dlnn1VzVOUTFxdX4pMIRCIRPv74Y/Tp06caoyIiIqLqZNSFWUk8PT0REBCgNWm+OF26dMH27durIaqyk0gk6NatG95+++1Se/6ISmNlZQUvLy9hm0wb821emG/TYNSFmebK/wqFAvHx8fj7779x4MABXLt2DQEBAdi7dy/q1q2r50hLV3DlfwsLC9SsWRNOTk4GuXrzkawmFTp/56zX4FTbrpKiIV2JRCIuOmlGmG/zwnybBqMuzAqu/N+qVSv0798fXbt2xdy5cxEXF4fAwECsX79ej1HqpqiV/w2ZFMU/4koXtnZ2vJuSiIioAJNcx2zUqFHCnZhhYWG4cOGCniMiMgxKpRJpaWlIS0vjenhmgPk2L8y3aTDJwgwAZs2aJTy4/Pvvv9dzNESGQb0m3sOHDyGXy/UdDlUx5tu8MN+mwWQLs+bNm2Po0KEAgBs3buDcuXN6joiIiIioZCZbmAHAu+++K0ycN4Z5ZkRERGTejHryf2nc3d3h4+ODEydO4NKlS7h8+XKRS07ouvI/ALRs2RJicdH17JUrV3Rqw8fHxyAeq0RERESGxaQLMwCYMWMGTpw4ASC/10xzSQo1XVf+B4BLly4V+1imvXv3Yu/evaW2ERwczMKMiIiICjHpoUwAaNeuHXr27AkAOHv2LG7evKnniIiIiIiKZnQ9Zl27dkVUVFSZztm8eXOR75e1naKMHj26Qs/PDAsLq3AMREREZBqMrjAjovITiUTCo1oM8YkSVLmYb/PCfJsGFmZEZsTKygrt2rXTdxhUTZhv88J8mwaTn2NGREREZCxYmBEREREZCA5lkqCuxAaHAsdUy7Uca9lWy3VIm1wux/PnzwEADRo0gKUlfwSYMubbvDDfpoFZI4GFWAwXBxt9h0FVSKlUIjExEQDg4uKi52ioqjHf5oX5Ng0cyiSBSqXSdwhERERmjYUZCeRyub5DICIiMmsszIiIiIgMBAszIiIiIgPBwoyIiIjIQLAwIyIiIjIQXC6DBFzzxvSJxWI4ODgI22TamG/zwnybBv4mJgEfemv6LC0t4ebmpu8wqJow3+aF+TYNLMxIoFAqkZCeXepxjrVsYcG/xoiIiCodCzMSpGblwn9FSKnHHQocAxcH+2qIiIiIyLywMCMyIzKZDA8ePAAAuLm5wcrKSs8RUVVivs0L820aWJgRmRGVSoXs7Gxhm0wb821emG/TwIlCRERERAaChRkRERGRgWBhRkRERGQgjH6OmVQqxdGjR/HPP//g5s2bSEtLQ1ZWFiQSCVxdXeHl5YUhQ4agW7dupS64d/r0aRw/fhxXrlxBcnIyXr58ibp166JJkybo06cP/P394eLiUuz5w4cPx/379+Hg4IB///23xHXBRo4cibt37wIA3nvvPXz88cfFHnvx4kUEBAQAAKZOnYrZs2eX+DmIiIjIOBl1YXb06FEsX74ccXFxhfalp6cjPT0dt2/fxq5du9CsWTPMmzcP/fr1K3RsdHQ0FixYgGvXrhXaFx8fj/j4eFy8eBHr1q3D9OnTMWPGjCKLLm9vb9y/fx/p6em4f/8+XnnllSLjzszMRFRUlPD68uXLJX5Ozf2dO3cu8VgiIiIyXkZbmP38889Ys2aN8Lpnz57w8fGBm5sbatWqhYyMDDx69AhhYWE4f/48YmJi8P333xcqzC5fvoyZM2fixYsXAIA2bdpg1KhR8PDwgJ2dHRISEnDmzBkEBwcjJycHP/zwA6Kjo7FixYpCtyJ37twZu3fvFtotrjC7evUqlEolLCwsoFAocPPmTchksmJvbb5y5QqA/EdseHt7l+v7RURERIbPKAuzffv2CUVZvXr18MMPP6BLly6FjuvRowcmTJiAe/fuYdmyZUhNTdXaHx8fj/fffx8vXryASCTC7Nmz8c4772j1hrVt2xYDBgzAlClT8P777yMqKgp//fUXGjRoUGhIUbM36/Llyxg3blyR8at7wPr06YPw8HDk5OQgIiICHTt2LHSsQqEQevI8PDxQs2ZNXb5FREWysLBAo0aNhG0ybcy3eWG+TYPRTf5PSEjAkiVLAAB2dnbYvn17kUWZJnd3d2zatAnvvPOO1vsLFixAeno6AODjjz/GlClTip0X1rhxY/z2229wdnYGAGzatAk3btzQOqZ+/fpwdXUFUPLwpHpfly5d8Oqrr5Z4/N27d4V1aTp16lTSxyQqlYWFBVxcXODi4sIf3GaA+TYvzLdpMLrCbMuWLXj58iUA4KOPPtL5ga1isRgjRowQXkdGRuKff/4BkF+4TZ8+vdQ26tWrh3nz5gHIX7xv48aNhY5R95rFx8cjNja20P68vDxEREQAyC+01EOT6uHKgji/jIiIyHwYVWGmUqkQFBQEIL+3bOzYseVuS90OAEycOFHnvy6GDBki3Jl5/PhxZGRkaO3X7NUqqhfsxo0bkMlksLW1RevWrYXCTD3vrCDNgo2FGRERkWkzqsIsOjoaaWlpAPLvgJRIJOVu69KlS8J2//79dT7PwsJCuIFAqVQWupOz4DyzgtTvtWvXDpaWlnj11VdhYWGBFy9e4N69e4WOVxdmLVq0QN26dXWOs7JYQ1Ho62VODrKzs4v8IsMmlUpx5coVXLlyBVKpVN/hUBVjvs0L820ajGryf2RkpLDdpk2bCrWlLoKcnJyEeWO6at26tVZMmnd6NmvWDE5OTkhKSiqyMFMXWuqeNXt7e3h6euL27du4fPkyPD09hWMfPnyIlJQUAPrrLfOVPCn03tb/riv2+EWLFlVlOERERCbNqHrM1BP1gfz5XuWVmZkJmUwGAHB0dCzz+ZrnaMakph6efPTokVBYAdp3WGoue6HeLljIaQ5jcuI/ERGR6TOqwkxzqMzW1rZS2rGzsyvz+ZrnZGVlFdpf3HDmnTt3kJ2dDQsLC7Rv3154v7jCjBP/iYiIzItRFWb29vbCtvrOzIq2k5OTU+bzNc8pap5bcTcAqHvAPD09tWJQH5+UlITHjx8XOtfV1RUNGjQoc5xERERkXIxqjpmDg4OwrTlEWFYSiQSWlpaQy+VITk4u8/ma52jGpObu7o5atWrhxYsXWsOR6kKr4Or9jo6OaNq0KR4/fozLly+jadOmSEhIEJbb0Ocw5pGsJoXe2znrNTjVLntPIxEREZXMqHrMNCfG3759u9ztiEQieHh4AMjvpUpKSirT+Xfu3CkyJjXNRydFRkYKw53qIq2oxyoVHM40lGFMKSwKfdna2cHe3r7ILyIiIio/oyrMXnnlFdSpUwdAfpFT1PwuXWn2QoWFhel8nlKpxOnTpwHkF2BFPUZJs32FQoGrV6/iwYMHwiOhiuoBU7ejLsg0l/PgxH8iIiLzYFSFmUgkwqhRowDkz/P6888/y93W6NGjhe0dO3ZAoVDodN7ff/+N+Ph4AMDAgQNRq1atIo/TLKauXLkiFFxNmzYt8k5QdY/ZkydPkJiYKPSuOTk5oXnz5jrFRlQaS0tLeHh4wMPDA5aWRjWTgcqB+TYvzLdpMKrCDAAmT54s3JG5Zs0aPHjwQKfzlEolQkJChNeenp7o1asXACAqKgqbNm0qtY3U1FQsX74cQH6ROHXq1GKPbdOmjRDnpUuXhB6wooYxAe0FZI8fP47o6OgSjycqD7FYDIlEAolEArHY6P75Uxkx3+aF+TYNRpc5FxcXLFiwAEB+r1lAQAAuXrxY4jn379/H1KlTCxVfS5YsEXq8/vOf/2DLli1QqVRFthEXF4e3335b6C2bMmWK8ADyolhZWQlLYkRERCA8PBxAyYWWejhz8+bNQhxcJoOIiMh8GGVf55gxYxAfH481a9YgJSUFAQEB6NWrF3x8fODm5oZatWohIyMDjx49wunTp3HmzBkoFIpCE/UbNmyItWvX4r333kNWVhaWLVuG0NBQjBo1Ch4eHrC1tUViYiLOnDmD4OBgYZmMYcOG4ZNPPik1zk6dOuHChQuQSqVITEwEUHJh5u3tjePHj+Pp06dabRBVFqVSKfx/bGdnx7+qTRzzbV6Yb9NglIUZALz//vt45ZVXsHz5csTFxeHs2bM4e/Zssce/8sormD17dqH3u3btij/++AMLFizAjRs3cOvWLdy6davINmxtbTFt2jS89957EIlEpcZYsKiqV69eifPFChZttWvXFu4eJaoMcrkcUVFRAAAvLy9YW1vrOSKqSsy3eWG+TYPRFmYAMHjwYPTr1w9///03/vnnH0RERCA1NRXZ2dmQSCRwdXXFq6++iiFDhqBr167FFlMeHh7Ys2cPTp06hWPHjuHq1atISkpCbm4u6tSpg2bNmqF3794YMWIEXFxcdI6vffv2sLKyEh7/VNwdnGqtW7eGra2tsHhux44ddSoAiYiIyDQYdWEGANbW1vDz84Ofn1+F2+rXr5/WA8krysbGptjet6JYWVnh+vXrlXZ9IiIiMi4cgCYiIiIyECzMiIiIiAwECzMiIiIiA8HCjIiIiMhAGP3kf6o8dSU2OBQ4ptTjHGvZVkM0RERE5oeFGQksxGK4ONjoOwyqQtbW1nzMlxlhvs0L820aOJRJREREZCBYmBEREREZCA5lkkChUOg7BKpiCoUCycnJAABHR0dYWFjoOSKqSsy3eWG+TQMLMxIolUp9h0BVTKFQIDY2FgBQp04d/uA2ccy3eWG+TQOHMomIiIgMBAszIiIiIgPBwowElpYc2SYiItInFmYkEIlE+g6BiIjIrLGLhAQKpRIJ6dnF7nesZQsLMWt5IiKiqsLCjASpWbnwXxFS7P5DgWPg4mBfjRERERGZFxZmRGZEJBLB3t5e2CbTxnybF+bbNLAwIzIjVlZW8PT01HcYVE2Yb/PCfJsGThgiIiIiMhAszIiIiIgMBIcyicyIXC7H48ePAQBNmzbl2nUmjvk2L8y3aTDorIWHh2PSpElF7rOxsUHdunXRqlUrDB06FEOHDi31f8InT54gJCQEZ8+eRVxcHNLS0mBvbw9nZ2d07doVvr6+6NSpU6lxBQQE4OLFi0Xus7S0RM2aNeHm5oa+ffvi9ddfh4ODQ7FtzZ07F0FBQYXeF4lEkEgkaNiwIby9vfH666+jVatWpcZGVBKlUon09HQAQOPGjfUbDFU55tu8MN+mwaALs5Lk5ubi2bNnePbsGU6cOIGtW7di/fr1cHJyKnSsVCrFqlWr8Mcff0Amk2ntS09PR3p6Ou7du4ft27ejb9+++Prrr1G/fv1yxSWXy5GWlobLly/j8uXL2LJlC3766Sd4e3uXqR2VSoXMzExERUUhKioKu3btwvTp0zFr1qxyxUVERESGz2gKs3HjxmH8+PHC65ycHNy6dQubN29GXFwcIiIi8N5772HPnj1atwlnZWXhvffeQ3h4OADAwcEBo0aNQvfu3VGvXj1kZmbi1q1b2LdvHx49eoTTp0/jjTfewObNm+Hm5lZqXKGhoVqvZTIZnj59igMHDiAsLAwpKSmYOXMmjhw5grp165bY1qZNm+Ds7Awg/y+flJQUnDp1Cn/88Qfkcjl++eUXODs7Y8KECTp/34iIiMh4GE1hVq9ePbi7u2u91759e/j5+WHs2LF4/Pgxbt68iZMnT8LHx0c4JjAwUCjKunbtiu+//x716tXTaqd79+6YPHkyVq9ejd9++w3x8fGYMWMGgoKCIJFISoyrYEwA0KZNG/j6+mLOnDkIDg5GRkYG/vzzT7z77rslttWsWTM0atRI672ePXuie/fumDlzJgBg7dq1ePPNN2FhYVFiW0RERGR8jP6uzNq1a2P69OnC6zNnzgjbhw4dwpEjRwAAnp6e+PXXXwsVZWpWVlaYO3cuxo4dCyB/Ptrq1asrFNuUKVOE7YiIiHK34+PjI8x9S01Nxe3btysUFxERERkmoy/MAKBdu3bC9rNnz4Tt//73v8L2kiVLYGtrW2pbc+fOhaOjIwBg3759SE1NLXdcmr1fUqm03O0AxX9GIiIiMh0mUZhp3o2pUCgAAJGRkbhz5w4AoEOHDlqFTUkkEglGjx4NAMjLy8PBgwfLHZdmAdWgQYNytwNof0alUlmhtkpiDUWxXy9zcpCdnV3sFxEREVWM0cwxK8m9e/eEbfXk+cuXLwvv9e/fv0zt+fj4YMOGDUI7xS3ZUZpNmzYJ2wMGDChXG2pFfcaq4Ct5Uuy+rf9dV+K5ixYtquxwqJKJxWLh/x+x2CT+LqMSMN/mhfk2DUZfmMnlcvz222/C6y5dugDI7zFTa926dZnabNWqFcRiMZRKJaKioko8VrNgAvLvyoyLi0NISAiOHTsGABg2bBj69OlTphg0RUZGCnPnbG1t4eXlVe62yLxZWlpyfSMzwnybF+bbNBhtYaZeLuOnn37C9evXAQCurq4YNmwYACAtLU04tqi1zUpiY2MDe3t7ZGZmCov1FcfPz6/Yfc2bN8f06dMxatSoMl0fyF/HLDk5GadOncLq1auFIdqAgADUqFGjzO0RERGR4TOawmzt2rVYu3Ztsfvr1auHn3/+GdbW1gCgNefJzs6uzNezs7NDZmYmsrKyyh7s//fo0SPs3r0bjRo1EnrySlLacGe/fv3w0UcflTseIiIiMmxGU5gVp1GjRhgyZAimTJmitRSGvb29sJ2Tk1PmdtXnlLaOWcGhTqVSibS0NFy5cgU///wzrl+/jnfeeQerVq2Cr69vmeOwsrKCl5cX3njjDYwYMUJr8dyqcCSrSbH7ds56DU61y17kkuGQyWS4e/cugPwheysrKz1HRFWJ+TYvzLdpMJrCTHPlf5FIhBo1aqBOnTqoWbNmkcfXqVNH2E5KSoKnp6fO18rNzRV63Ep6zmVRxGIx6tWrh8GDB6Nnz54YPXo0YmJi8OWXX6Jbt24ltqe58r9YLIadnR0cHR2FXsDqIEXxC9fa2tlpFbxkfFQqlfBYMpVKpedoqKox3+aF+TYNRnPbhnrlf3d3d7zyyito0qRJsUUZAHh4eAjb6r8gdHX37l1hSQrNdsrK3t4e48aNA5A/tPr333+XeHyzZs2Ez9iyZUs0bNiwWosyIiIi0i+jKczKqnPnzsJ2WFhYmc7VPF6znfJo0aKFsF3wDk4iIiIiTSZbmHl6eqJVq1YAgGvXrun8SKSsrCwEBQUBAGrUqIHhw4dXKA713ZRA/tIeRERERMUx2cIMAKZNmyZsL1y4ELm5uaWes3LlSiQlJQEAxowZg7p161Yohlu3bgnbFV39n4iIiEybSRdmw4cPx+DBgwEAd+7cwcyZM4t99qVMJsPKlSuxe/duAECTJk3w2WefVej6cXFx2LFjh/C6b9++FWqPiIiITJvR3JVZXkuXLkVaWhouXbqE8+fPY+jQoRg9ejS6desGR0dHZGVl4datW9i7dy8ePnwIAKhfvz7Wr19f6lIZQOF5Y0qlEunp6bh8+TK2b98uLFDr5+cnDK0SERERFcXkC7OaNWti06ZN+O6777Bz506kp6dj8+bN2Lx5c5HH9+7dG0uWLNF52LGklf/Vhg0bhqVLl5YpbqKqYGlpKdyQYmlp8v/8zR7zbV6Yb9NgFpmrUaMG5s+fj4CAAISEhODs2bOIi4tDeno67O3t4ezsjM6dO2Po0KEVvgtTJBLBzs4ODRo0QPv27TFy5MgKt0lUWcRisdYaf2TamG/zwnybBpGKq9CZNalUKtyxWr+pG/xXhBR77KHAMXBx4AKzREREgPbvUC8vr0pZe9QsesyIKJ/myuBWVlZV/ogv0i/m27ww36bBpO/KJCJtMpkMERERiIiIEH6Ak+livs0L820aWJgRERERGQgWZkREREQGgnPMSFBXYoNDgWOK3e9Yy7YaoyEiIjI/LMxIYCEWw8XBRt9hEBERmS0OZZKAK6cQERHpFwszEsjlcn2HQEREZNZYmBEREREZCM4xIwGfrWb6rKys4OXlJWyTaWO+zQvzbRr4m5gEXCXa9IlEokp5ZAgZB+bbvDDfpoFDmUREREQGgj1mJOBdmaZPqVQiIyMDAFC7dm2IxfzbzJQx3+aF+TYNzBoJOJRp+uRyOR4+fIiHDx/yLlwzwHybF+bbNLAwI4FCqYRCqdR3GERERGaLhRkJUrNykfzipb7DICIiMlsszIiIiIgMBAszIiIiIgPBwoyIiIjIQLAwIyIiIjIQXMeMyIyIRCLhUS1cHsX0Md/mhfk2DVVamIWHh2PSpElF7rOxsYGDgwM8PT0xaNAg+Pv76/woidjYWAwcOFBYEHXVqlXw8/Mrc3zPnj3DX3/9hfPnz+Px48dITU2FUqlE7dq14e7uDm9vb/j5+aFx48ZFnp+SkoKbN2/i5s2biIiIQEREBNLT0wEAo0aNwvLly0uNYe7cuQgKCir0vkgkgkQiQcOGDeHt7Y3XX38drVq1KvNnJNJkZWWFdu3a6TsMqibMt3lhvk2D3nrMcnNzER8fj/j4eJw6dQpbtmzBL7/8gkaNGpV6bnBwsNYq9cHBwWUqzPLy8rB69Wrs3LkTUqm00P7ExEQkJibi7NmzWLNmDXx9fTFnzhw0aNBA67gePXrofM2yUqlUyMzMRFRUFKKiorBr1y5Mnz4ds2bNqrJrEhERkX5VW2E2btw4jB8/XnidkpKC6OhobNq0CfHx8YiOjsbMmTMRHBwMCwuLEtsKCQkBANjZ2SEnJwcXLlxAYmIinJ2dS40jNTUVM2fOxPXr1wEA9vb2eO2119CtWzfUr18flpaWSE5OxtWrV3Hs2DHExMTg8OHDaN++PSZPnlxsuw0bNkSLFi1w9uzZ0r8Zxdi0aZPwGZRKJVJSUnDq1Cn88ccfkMvl+OWXX+Ds7IwJEyaU+xpERERkuKqtMKtXrx7c3d213uvevTtGjx4Nf39/xMXF4d69ezh27Bh8fX2Lbefq1at4/PgxAODLL7/EokWLoFAoEBoaiilTppQYg1KpxCeffCIUZf3798e3336LevXqFTrWx8cHn376KUJCQrBy5coi23v//ffh5eUFLy8vODo6IjY2FgMGDCgxhpI0a9asUI9hz5490b17d8ycORMAsHbtWrz55pulFq9ERZHL5Xj+/DkAoEGDBrC05DRTU8Z8mxfm2zTo/a5MiUQiFB0AcP78+RKPDw4OBgDUqVMHI0eORPfu3QEABw4cKPVa27ZtQ3h4OACgV69eWLt2bZFFmZpYLMbIkSOxf/9+eHh4FNr/0UcfoX///nB0dCz12hXh4+ODTp06Acjv8bt9+3aVXo9Ml1KpFIbqlXz8lsljvs0L820a9F6YAdAqeuLj44s9TiqV4siRIwCAoUOHwsrKCv7+/gCAqKgo3L17t8RzN23aBACoUaMGli5dqvNfE/Xr1xcKQH3RnND57NkzPUZCREREVcUgCjP17b0ASiyWwsLCkJGRAQBCQTZo0CDY2dkB+F9vWlHOnj2LxMREAICvry9cXFwqGna10vy+VNVfQrkvX+JlTg6ys7O1voiIiKh6GMQA9IMHD4RtV1fXYo9TF15NmjRBhw4dAOTfADBgwACEhobi4MGD+OKLL4qcf3Xp0iVhu1+/fpUTeDW6d++esK3LTQ7lsfePbcjNzS30/qJFi6rkekRERKRN7z1mCoVCGGIEgCFDhhR5XGpqqnDHY8GlMdS9Z8nJycXeFRkZGSlst2nTpkIxV7fIyEicOXMGAGBrawsvLy89R0RERERVQW+FWWpqKi5cuICJEyfizp07APKLMvUk94IOHjwImUwG4H+FmFrPnj3h5OQEoPjhTPXCrwBKnPBvKFQqFZKSkvDnn39i8uTJUCgUAICAgADUqFFDz9ERERFRVai2ocy1a9di7dq1Re6ztbXFm2++ic8++6zY89UFV7t27dCsWTOtfRYWFhg2bBi2bt2KsLAwZGVlQSKRaB2jOVfK1ta2fB+iipW21Ea/fv3w0UcfVVM0REREVN0MYo6Zp6cnAgICtG4C0BQdHS0sEVGwt0zN398fW7duRW5uLg4fPoyxY8dq7be3txe2X758WahwM1RWVlbw8vLCG2+8gREjRlTp88/+b/wkWFhYwqm2XZVdg/RLLBbDwcFB2CbTxnybF+bbNOhl5X+FQoH4+Hj8/fffOHDgAK5du4aAgADs3bsXdevWLXSuurfM0tISw4cPL7L9tm3bws3NDQ8ePMCBAwcKFWbq/1mB/LlohliYaa78LxaLYWdnB0dHR52fIVpRNra2sLS00ipiybRYWlrCzc1N32FQNWG+zQvzbRr0tvJ/q1at0L9/f3Tt2hVz585FXFwcAgMDsX79eq3zlEolQkNDAeSvaqzLemKXL19GbGys1ir6np6ewuK1d+7cKTQcagiKWvmfiIiIzIfe+zpHjRol3IkZFhaGCxcuaO2/cOECEhISytSmSqUq9CSAzp07C9unTp0qX7BEREREVcgg5pjNmjULx48fh0KhwPfff6/VK6YexrS2tsbSpUtLHTffuHEj7ty5g5CQELz//vvC+7169YKzszMSExNx5MgRfPbZZ0a3yCxRRclkMmHdQDc3t2LndZJpYL7NC/NtGgyiMGvevDmGDh2KgwcP4saNGzh37hx69uyJ7OxsHD9+HED+khgF1y8rSlJSEu7cuYOYmBhcu3ZNWIjW2toaU6ZMwbJly5CXl4cvv/wSGzZs0Olh4AkJCXj48KHeH8tEVFEqlUq4Q1mlUuk5GqpqzLd5Yb5Ng96HMtXeffdd4Y5D9Tyzo0ePIicnB0DxC88WNGTIEKGdgmuaTZo0CV27dgWQ/4im999/H6mpqcW2pVKpEBoaitGjRyMqKqpMn4eIiIiorAyixwwA3N3d4ePjgxMnTuDSpUu4fPmyUFhZWVnBx8dHp3YaNGgALy8v3Lx5E4cPH0ZgYKBwV6NYLMYPP/yAGTNm4MaNGzh58iQGDhwIPz8/dOvWDfXr14elpSWSk5Nx/fp1HD16FA8fPiz2WpcvX8aTJ0+E12lpacL248ePsX//fq3jR48ereu3g4iIiMyQwRRmADBjxgycOHECAPDTTz/h4sWLAICuXbuidu3aOrczZMgQ3Lx5ExkZGTh58qRWb1vdunWxfft2rFq1Cjt37kR2djZ27dqFXbt2FdmWSCSCn58fhg4dWmjf3r17ERQUVOR5V69exdWrV7XeY2FGREREJTGYoUwgf1X/nj17AgD+/fdfKJVKALoPY6ppHl/UI5pq1KiBwMBAHD16FJ999hl69OiBhg0bwsbGBjVq1ICzszN69eqFWbNm4cSJE/juu+94owARERFVOZGKMwTNmlQqRUREBACgflM3WFpawcWBC8yaKs18e3l5VdvixaQfzLd5Yb6rX1V8zw2qx4yIiIjInBnUHDMiqloWFhbC0yV0WSqGjBvzbV6Yb9PAwozIjFhYWHC+pBlhvs0L820aWJiRoK7EhnMSiIiI9IhzzEhgIRbDopRHXhEREVHVYY8ZCXiDrunjXVvmhfk2L8y3aWD3CAnkcrm+QyAiIjJrLMyIiIiIDAQLMyIiIiIDwcKMiIiIyECwMCMiIiIyECzMiIiIiAwECzMiIiIiA8F1zEjAZ6uZPktLS3h4eAjbZNqYb/PCfJsGZo4EYq76b/LEYjEkEom+w6BqwnybF+bbNPA3MREREZGBYI8ZCZRKpb5DoCqmVCqRk5MDALCzs2MvqYljvs0L820amDUSKBQKfYdAVUwulyMqKgpRUVF8BJcZYL7NC/NtGliYERERERkIFmZEREREBoKFGREREZGBYGFGREREZCBYmBEREREZCC6XYeZUKpWwLZPJIJVK9RgNVTWZTFbkNpkm5tu8MN/VT/P7rPn7tCJEqspqiYxSdnY2IiMj9R0GERGRUfP09IS9vX2F2+FQJhEREZGBYI+ZmVMqlXj58iWA/IfeikQiPUdERERkHFQqlbCYr62tbaU8bYGFGREREZGB4FAmERERkYFgYUZERERkIFiYERERERkIFmZEREREBoKFGREREZGBYGFGREREZCBYmBEREREZCBZmRERERAaChRkRERGRgWBhRkRERGQgWJgRERERGQgWZkREREQGgoUZERERkYFgYUZERERkIFiYERERERkIFmZEREREBoKFGREREZGBYGFGREREZCAs9R0AlS4uLg7bt2/HqVOnEB8fD2trazRu3BhDhw7FhAkTYGtrWynXOX36NPbs2YOIiAikpqaibt268PLywuuvv46+ffvq1IZcLseff/6J0NBQPHz4EDk5OXB2dkaPHj0QEBCAV155pVJiNWXGkO/k5GScPHkS//77L+7evYvnz59DJpPBwcEBHh4eGDx4MEaMGAEbG5tKidWUGUO+ixMZGYkxY8ZALpcDAEaNGoXly5dXSrymyNhyLZPJEBoaiiNHjuDevXtITk6Gvb09nJ2d0a5dO/Tq1QtDhw6tlJjpf0QqlUql7yCoeGFhYZg9ezaysrKK3N+sWTNs2LABTZs2Lfc1lEolFixYgL179xZ7zNixY/H1119DLC6+kzU1NRXTp09HREREkfutra2xcOFCjB07ttyxmjpjyPeePXvw1VdfQaFQlHidZs2a4ccff4Snp2e5YzV1xpDvktp94403cPPmTeE9FmbFM7ZcR0ZGYvbs2bh3716xx9SsWROXL18ud7xUNBZmBuzOnTsYN24ccnNzYWdnh3fffRddu3ZFbm4uDh06hD179gDI/we9b98+SCSScl1n9erV2LBhAwCgdevWmDp1Kho3boynT59i48aNuHPnDgDg3XffxaefflpkGwqFAgEBAbhy5QoAYPDgwRg7diwcHBxw48YNrF+/HikpKRCLxfjll1/K/Re6KTOWfK9btw4//vgjrKys0L9/f/Ts2RNubm6wt7fHkydP8Oeff+Ls2bMAgLp16yIoKAj169cvV6ymzFjyXZxt27bh22+/Rb169ZCSkgKAhVlxjC3XkZGReOutt5Ceno4aNWpg7Nix6NmzJ1xcXCCTyRATE4MzZ87gypUrOHXqVLlipRKoyGCNHz9e5e7urmrdurXq6tWrhfb/97//Vbm7u6vc3d1Va9asKdc1Hj58qGrdurXK3d1dNXr0aNXLly+19ufk5KhGjx4txBETE1NkO3/++acQy1dffVVof0xMjKpjx44qd3d31aBBg1Qymaxc8ZoyY8n3b7/9pvruu+9UKSkpxV5n2bJlQqxz584tV6ymzljyXZTnz5+rOnTooPLw8FAFBQUJcc6ZM6dccZo6Y8p1bm6uavDgwSp3d3dVv379VI8ePSr2mnl5eeWKlUrGyf8G6ubNm0IX8ZgxY9ChQ4dCx7zzzjtwc3MDkP/Xq0wmK/N1tm7dKswPWbBgQaE5Qba2tliwYAGA/PljW7ZsKbKdzZs3AwAcHBzwxRdfFNrftGlTvPvuuwCAx48f49ixY2WO1ZQZU74nT56Mzz//HHXr1i32Op9++imcnJwAAMeOHYNSqSxzrKbMmPJdlMWLFyM7OxujRo1Cp06dyhyXOTG2XG/atAkxMTEQi8X44Ycf0KxZs2KvaW1tXeY4qXQszAzU8ePHhe0xY8YUeYxYLMbIkSMBAC9evEB4eHiZrqFSqXDixAkAQIsWLdC+ffsij2vfvj2aN28OADhx4gRUBUa/Hz16hAcPHgAAfH19i53AOmrUKGFb8/ORceVbF9bW1ujYsSMAIDMzE+np6WVuw5QZc76PHDmCsLCwYv8II23GlGuFQoFdu3YBAHr06IFXX321THFQ5WBhZqDUc7Xs7OzQpk2bYo/r3LmzsH316tUyXSM2NhaJiYmF2ilKly5dAAAJCQmIjY0tMlbN44ri5OQk/PVV1lhNnTHlW1dSqVTYLsukcnNgrPnOzMzEN998AwCYPXs26tSpU6aYzJEx5fratWtISEgAAPj4+Ajv5+Xl4cmTJ0hISCj1ph+qOP60NFDqHqgmTZrA0rL4VU1atGhR6Bxd3b9/v8h2SrvOw4cPi4y1LO08f/4cOTk5Osdq6owp37qQyWS4fv06AMDR0REODg5lbsOUGWu+v/vuOyQlJaFTp07F9v6QNmPKtfrfLAC4u7sjJiYGH374Iby9vTFo0CD06dMHXbt2xRdffIEnT56UKUbSHQszA5SXl4e0tDQAKPVuttq1a8POzg4AEB8fX6braB5f2nU09z9//rzYdlxcXEpsp0GDBgDyu97LGq+pMrZ862LPnj3CZ/L19S3z+abMWPN95coV7NmzB1ZWVvjqq68gEonKFI85MrZcaxaEDx8+xKhRo3D06FGtOW+ZmZk4cOAARo4cifPnz5cpTtINCzMDlJ2dLWyr/6GWRD2nq6w9UGW5jua8sYLX0WzH3t6+3O2YK2PLd2mePn2K77//XrjO9OnTy3S+qTPGfEulUixcuBAqlQqTJ0/mQtE6MrZca84F/fbbb5GTk4PJkyfj6NGjiIiIwPHjxzFlyhSIRCJkZ2fj448/xrNnz8oUK5WOhZkBysvLE7atrKxKPV59Z0xubm6VXUfz7puC16msdsyVseW7JC9fvsQHH3yAzMxMAPl3h5XWi2pujDHfGzZswP379+Hq6or333+/THGYM2PL9cuXL7Xa/OijjzBv3jw0bdpUeErBF198gVmzZgHIv1Hh119/LVOsVDoWZgaoRo0awrYut02rJ1mX9fE3ZbmO5kTugteprHbMlbHluzhyuRwff/wxIiMjAQDjxo3D6NGjyxSjOTC2fD98+FD45btgwYJKe2yQOTC2XGu2U6dOHUybNq3INqZMmSIsh3PkyJFy3blNxWNhZoA0hwN16dJW/5WjS1d5ea+j+ZdUwetotqPZpV7WdsyVseW7KCqVCnPnzsXp06cBAEOHDsXChQvLFJ+5MKZ8q1QqLFq0CFKpFIMGDUL//v3LFIO5M6ZcF2ynS5cuxa5TZmlpiW7dugHIH/58+vRpmeKlkvEh5gaoRo0acHBwQHp6eqmTQDMyMoR/iGV97I3m8aVdR3O/egJ/Ue0kJCSUuPCoerKpSCTiY3r+P2PLd1EWL16M0NBQAECfPn3w3XffcYmMYhhTvq9fv46LFy8CADp06IC//vqr0LmpqanCdmxsrHDMK6+8And39zLFbGqMKdcF2ynt373m/tTUVDRp0kSnWKl0LMwMVMuWLXH58mU8efIEcrm82NusNW93Vq8cXZZrFNVOadcpeDu25nUfPnyIVq1aldpOgwYN2GOmwZjyXdB3332HnTt3AshfQ+mnn37SaT6NOTOWfGsOe61cubLUa166dAmXLl0CAHzwwQdmX5gBxpNrAFo3dZS2XpnmfgsLC53iJN3wT1oD5e3tDSC/W/r27dvFHqf+IQhAWGldV40aNYKzs3Ohdkq6jouLCxo1alRkrACEv66LkpSUhJiYmHLFauqMKd+a1q1bh40bNwIAvLy88Msvv3DuoA6MNd9UdsaUa83Ha5W2sLTm8CVv8KlcLMwM1MCBA4Xtffv2FXmMUqlEcHAwAKBWrVro2rVrma4hEokwYMAAAPl/RWkuLqjp+vXrwl9ZAwYMKLR+UfPmzYW/8I4cOaI1h0FTUFCQsK35+ci48q22detW/PjjjwDyF6PcuHEjJBJJmWIyV8aS765duyIqKqrEL/WjgID8x66p3//www/LFK+pMpZcA0Djxo3RunVrAEB4eLhwd3VBWVlZwhpmTZo0EYpCqhwszAxUu3bthL9e9u3bh2vXrhU6ZvPmzcKCgJMmTSo0fBQeHg4PDw94eHhg7ty5RV7nrbfeErqhlyxZUuj26dzcXCxZsgRA/oTPt956q8h23nnnHQD5E0G/++67QvufPHki3NnVtGlTDBo0qOgPbqaMLd/79u3DsmXLAADNmjXDb7/9xtX9y8DY8k3lZ2y5Vt+JmZOTg6VLlxZ5zPLly5GVlQUAePPNN4s8hsqPc8wMWGBgIMaNG4fc3Fy88847mDFjBrp27Yrc3FwcOnQIu3fvBpD/i/Htt98u1zWaN2+OKVOmYMOGDbh16xbGjRuHadOmoXHjxnj69Cn++9//4s6dOwDyb5FWP+uyoFGjRmHfvn24evUqduzYgeTkZIwdOxa1a9fGzZs3sW7dOmRlZUEsFiMwMLDER5OYK2PJ9/Hjx7FgwQKoVCpIJBIEBgYiNTVVaxJ4QY0aNeKcwgKMJd9UccaU62HDhiE4OBinT5/G/v37kZiYiHHjxqFhw4Z4/vw5du/eLdx93bp1a0ycOLFc8VLxRCouQGLQwsLCMHv2bOGvk4KaNWuGDRs2oGnTpoX2hYeHY9KkSQDyC6fly5cX2YZSqcT8+fOL7WYHgP/7v//DkiVLSrzTLjU1FdOnT0dERESR+62trbFw4UKMHTu22DbMnTHke+7cuVrD0rrYtm1bmYdnzIEx5Ls0sbGxwjBaSXGYO2PKdXZ2Nj766COcPXu22GO8vLywfv16YT0zqjzstjBwPj4+CAkJwbZt23Dq1CkkJCTAysoKTZo0ga+vLyZOnFjhBR/FYjGWLl2KIUOGYPfu3YiIiEBaWhrq1KkDLy8vvPHGG+jbt2+p7dStWxe7du3Cnj17cPDgQTx48AAvX76Es7MzunfvjkmTJvFRLqUwpnxTxTHf5sOYcm1vb4+NGzfi0KFDCAoKQmRkJNLT01GzZk20atUKw4cPx8iRI3k3ZhVhjxkRERGRgeDkfyIiIiIDwcKMiIiIyECwMCMiIiIyECzMiIiIiAwECzMiIiIiA8HCjIiIiMhAsDAjIiIiMhAszIiIiIgMBAszIiIiIgPBwoyIiIjIQLAwIyIiIjIQLMyIiIiIDAQLMyIiIiIDwcKMiIiIyECwMCMiIiIyECzMiIiIiAwECzMiomrw7NkzLFy4EAMHDoSXlxc8PDzg4eGB48eP6zs0onKZO3cuPDw84OPjo+9QTIqlvgMgqqjw8HBMmjQJAPDBBx/gww8/1HNERNqePXuG0aNHIy0tTd+hlInmv62CbGxs4ODgAE9PTwwaNAj+/v6wtrbWuW2FQoGjR4/ixIkTiIiIQHJyMmQyGRwdHdGiRQv069cP/v7+qFWrVplilkqlOHr0KP755x/cvHkTaWlpyMrKgkQigaurK7y8vDBkyBB069YNYjH7JsjwsDAjIgCAh4cHABa3VWH9+vVIS0uDpaUlPvnkE3Tu3Bl2dnYAgIYNG+o5uvLJzc1FfHw84uPjcerUKWzZsgW//PILGjVqVOq5ly9fxqJFi3D//v1C++Li4hAXF4czZ87gp59+wmeffYbXX39dp5iOHj2K5cuXIy4urtC+9PR0pKen4/bt29i1axeaNWuGefPmoV+/fjq1bcz4b9u4sDAjIqpi58+fBwAMGDAA06ZN03M05TNu3DiMHz9eeJ2SkoLo6Ghs2rQJ8fHxiI6OxsyZMxEcHAwLC4ti2zly5Ahmz54NqVQKAOjSpQv8/f3RvHlzWFtb49mzZwgLC8Nff/2F9PR0LFiwAI8ePcKcOXNKjO/nn3/GmjVrhNc9e/aEj48P3NzcUKtWLWRkZODRo0cICwvD+fPnERMTg++//94sCrOqsnz5cixfvlzfYZgcFmZERFUsISEBANC8eXM9R1J+9erVg7u7u9Z73bt3x+jRo+Hv74+4uDjcu3cPx44dg6+vb5Ft3LlzB59//jlkMhmsrKywbNky+Pn5aR3Trl07+Pr64u2338aMGTMQHx+PzZs3o3HjxlqFoaZ9+/YJRVm9evXwww8/oEuXLoWO69GjByZMmIB79+5h2bJlSE1NLc+3gqhKcYCdiKiKyWQyAIClpen9LSyRSDBz5kzhtbp3sCClUokvvvhC+F4sXbq0UFGmqVWrVtiyZYsw5LtixQo8e/as0HEJCQlYsmQJAMDOzg7bt28vsijT5O7ujk2bNuGdd94p+cMR6YHp/ZQgKkBzAvO2bdvQpUsX7N27F/v378fDhw8hk8nQokULTJw4ESNHjhTOk0ql2LdvH4KCghATEwOZTAYPDw9MmjQJw4YNK/JasbGxGDBgAABg2bJlGD16NA4fPozdu3cjKioK2dnZaNiwIQYNGoRp06aVOrFZKpXizz//xJEjRxAdHY2srCzUrl0brVu3xmuvvQY/P79iJzDPnTsXQUFBcHV1RVhYGBITE7F161acOnUKz549Q05ODrZt24Z58+ZpzclZu3Yt1q5dq9XWqFGjtIYsEhMTcezYMYSHhyMyMhKJiYmQy+WoU6cO2rZtCz8/P/j6+hYbW8GcdO3aFYcOHRK+Tzk5OWjQoAEGDBiA6dOnw8HBocTvEwCcPn0aoaGhuHbtGlJSUqBQKODk5IQ2bdqgf//+GDp0KGxtbYs89/bt29i9ezfCw8ORmJgIlUoFFxcXdOvWDZMnTy5XT9f+/fsxb948rfcKfm8Lfl8BIDU1FVu3bsXp06cRGxuLvLw8ODk5wdvbG2+88QY6depU7DV9fHwQFxcntHvr1i38/vvvuHjxIhITEyGTyRAVFVXmz1Ia9RwmAIiPjy/ymLCwMERHRwMA+vTpA39//1Lbbd68OWbOnInVq1cjNzcX27Ztw9y5c7WO2bJlC16+fAkA+Oijj+Dm5qZTzGKxGCNGjNDpWE3V+fNEU1JSEn7//XecOXMGsbGxyMnJQb169dC+fXu88cYb6NGjR6Fz1P8/qJX2b7vgZ+vcuTP279+PAwcO4MGDB0hNTcXIkSOF4wv+jClOVlYW9uzZg1OnTuHBgwfIyMiAtbU1mjRpAm9vbwwbNgze3t6lfg/MBQszMityuRwzZ87EyZMntd6PiIjAnDlzcOvWLcyfPx8ZGRl4//33cenSJa3jrl27hmvXruHJkyeYMWNGqdf78ssvsW/fPq33Hj16hA0bNiA4OBhbtmwp9hdJbGwspk2bhocPH2q9n5ycjH/++Qf//PMPdu/ejXXr1pVauFy/fh0zZsyolLsCFQoF+vbtC6VSWWhfYmIiwsLCEBYWhr179+Knn36Cvb19ie0plUrMnj0bISEhWu/HxMRg06ZNOH78OHbs2AEnJ6ciz09LS8OsWbNw4cKFQvvUE8mPHj0KABg9enSha69YsQJbt26FSqUqdP2YmBjs3bsXCxcuxBtvvFHi56gMZ8+exccff4ysrCyt99WfIyQkBBMmTMD8+fNLvaNw586d+OabbyCXy6syZACAlZWVsF1cr2BwcLCw/dZbb+nc9ptvvom1a9ciLy8PQUFBmDNnDkQiEQBApVIhKCgIQH5v2dixY8sRfflV18+TkJAQLFq0CDk5OVrvx8fH48iRIzhy5Aj+7//+D4sXL660Xtm8vDxMmTKl2B5QXZ0/fx6ffvppoZ89MpkMd+/exd27d/H7779XyR8MxoqFGZmVH3/8ETdu3ICfnx/8/Pzg6OiImJgY/PTTT3j06BG2b98OHx8fbN++HdeuXcO4ceMwaNAgODg44O7du/jxxx+RmJiINWvWYMCAAXjllVeKvdYff/yBiIgItGvXDpMnT0bTpk2RkpKCoKAgHD58GImJiZg6dSpCQ0MhkUi0zs3OzsbkyZPx9OlTAMDAgQMxZswYODs7IzY2Fjt27MDFixdx5coVzJgxAzt27Ch2wnV2djY+/PBD5OXlYcaMGejZsydsbGxw7949ODk5YdOmTZDJZMKwUsFJ3gBQu3ZtYVtdwHTr1g19+vSBu7s76tati+zsbDx9+hR//vknrl27hnPnzuHrr7/GihUrSs3JtWvXMHDgQIwcORINGzZEcnIy/vjjD5w6dQqPHz/GsmXL8J///KfQuS9fvsSkSZNw7949AECbNm3wxhtv4JVXXoG1tTXi4+Nx6dIlHDp0qMhrL1myBH/88QcAoHPnzhg1ahQaN24MGxsbREVFYevWrYiOjsbChQvh6Ogo9IbqYuDAgWjbti0AFPu91fy+3r17FzNmzBDmX02YMAE+Pj6wtbXF3bt3sWHDBiH3tra2mD17drHXjoiIQEhICOrXr48pU6agbdu2kMvluHLlis7xl8WDBw+EbVdX1yKPuXz5MgDA1tYW3bt317ntWrVqoXPnzjh79izS09Nx//594d9ddHS08Avf29u70L+jqlYdP08OHTqEL774AiqVCo0bN8bEiRPh5uaGunXrIi4uDnv37sXp06exd+9eSCQSrV7asv7b1rRq1SpERUXBx8cHo0ePFv5dZmdn6/z9+ffffzFt2jTI5XJYWFhgxIgRGDBgABo0aIC8vDw8ePAA//zzT6HC1uypiIzcv//+q3J3d1e5u7ur1qxZU+J+d3d31ZYtWwodk5iYqOrQoYPK3d1d1a1bN5WHh4fq2LFjhY67e/euytPTU+Xu7q5asmRJof1Pnz7Vuta0adNUMpms0HFr164VjlmxYkWh/cuXLxf2f//994X2K5VK1WeffSYcs2PHjkLHzJkzR9jfvn171d27dwsdo6mk72HBa8fExJR4zI8//qhyd3dXeXh4qB49elRof8GcrFu3rsjrvPPOOyp3d3dV69atVSkpKYWOWbp0qdDG4sWLVUqlssh48vLyVElJSVrvnT17Vjh3z549RZ6Xm5urmjRpksrd3V3Vv3//InOpC12+t2PGjFG5u7urWrVqpTpz5kyh/enp6aphw4ap3N3dVZ6enqp79+4VOqZ///7CtV577TVVRkZGueJVK+3flkqlUsnlctXIkSOF4y5dulTomPj4eGH/66+/XuY4Vq1aJZwfEhIivH/gwAHh/f/85z9lbrc8qvPnSUpKisrb21vl7u6umjdvXrH///3nP/8R/r948OBBof26/tsu+NmK+tmjSf0zpn///oX25ebmqnr16qVyd3dXvfrqq6p///232HaePXtW4nXMDSf/k1l59dVXixxGcXJywqBBgwDkz/EZOnQoBg4cWOg4T09PYS5Eab0P1tbWWLLk/7V370FRlW8cwL+LcR2Ui8jFajAVc4MgxMIGSiRGJgqNiBmIIFC7YhQ4U5qTONaUMdNUUMI4o0VKQBADiWM7CIjCoEEwLKC5wyUEJ5JLLNdcufz+2Nl3zrK7h13YxeOv5/PXGfY9h7N7ds8++77v87wfax1aeOutt1iG288//8xKBwDKuShFRUUAAA8PD611h0QiEQ4fPsyGMHNzc3nPZc+ePdi4cSNvG32JRCK4u7vztklKSoKDgwNmZ2d5554Ayl4ubcM4IpEICQkJAJRDRk1NTWqPj4yMoKCggB3j4MGDbIhrLgsLCzg5Oan97fjx4wCA0NBQnUNglpaWOHToEADlcOKVK1d4n8tCSaVStLS0AACioqIQGBio0cbOzg5HjhwBoByCVfX06ZKWlmZwcVZDDA0Noa6uDq+88gquXr0KQPlaapsDxx3Gmnsd9LFy5Uq2PTw8rHWb22apmPp+kpeXh9HRUbi4uODw4cM6hynfeecduLi4YGZmBqWlpYt5SsyaNWsWVfOspKQEt27dAgCkpKTA399fZ1s3N7cF/5//RxSYkf8Uvkm23MDlueeem7edaphRl4CAALi4uGh9zMzMjE0MHh4eZl9sANDa2oqRkREAyom5uoYobW1t8eyzzwIA2tvb2U1QG30mWi/UzMwM/v77b3R2dkImk0Emk6GjowOurq4AgD/++IN3//DwcJ0BlaenJ9ue+3pfvnyZTfqOj4/nrZ0119jYGH777TcAymCCz7p16+Dg4AAAGsGhsXDn8bz00ks62/n5+bE5iXxzf9zc3HiTBBbim2++YctIPfzww3jyySeRkJCAxsZGWFtbIzExEV988YXWfblz5lRZlobgzlPkHos7rKYrscOUTH0/Uf2oCQoK4l1V4b777sNjjz0GwHjv0bCwMIM+U3OphidtbGz0LhBMlGiOGflP4cuuW758Odtes2bNvO3mm2vx6KOP8j7u7e3NtmUyGbuxqjLXAOUvcj4+Pj7Iy8tj+zk7O2u0sbGxwYMPPsh7HEPNzs7il19+QVFREaRSKf7991+dbedLOFi7dq3Ox7hJDXNfb24wa2hG19WrV1nyQmpqKlJTU/Xab2BgwKD/oy/VNTc3N4dYLOZt6+Pjg46ODnR3d0OhUGj9wuZmSS6FjRs3Ii4uTi0JgIs792vuBHZ9cK8991jcgE0VpC8lU95Ppqen2Y+agoIC1js8H2O9Rxf7Hrp27RoA5Y+ruxE038soMCP/KVZWVjof42a56dNOW1Yi13xDK7qGZ+RyOdt2dHTkPQZ3WIi7H5exh7Nu376NvXv34uLFi3q15wvaAP2vydzXmxvwaQtI+QwODhrUXmW+57JQqutvb28/b1ad6prPzs5iZGRE69CgKYYwuRPHp6en0dfXB4lEgtLSUjQ1NSEuLg5FRUVa37OqHkdgYYED93pxg3Xu9kKv6WKY8n4il8sXlFFrrPeorqQAfak+n7qyqYluFJgRYiK6hueW+hiLGY7QJisriwVlTzzxBF5++WV4enrCyckJVlZW7IsmNjaWZeIJDfdL8MiRI/D19dVrv8V+WS0VY19zQLPyv1gsxrZt2+Dv74/9+/fj5s2bOHjwILKysjT2dXZ2hoODA/755x9cv34d09PTBp0jt3eUO0TI3W5razP0KQna9PQ0246KitK5mPxcunotDUULvN89FJgRYiLz9Qzo6gXgfvkPDg7yDpdw/8dSBA2zs7MsMWHz5s3IycnReQPX1YNnLNxemFu3bhk0XMt9va2srDSWGlpqqvMZHh7G1NQUb6+Z6pqLRCKTTu7XV0REBKqqqiCRSFBZWYm6ujqNchgikQh+fn44f/48JicnUVdXpzXBQZvR0VEW4Nvb22P9+vXsMQ8PDxbw/f777xgbG1vykhmmMrdEzd1+jxrKwcEBfX196O/vv9uncs+hkJgQE1Fl2enzOLd+EXe7ubmZ9xhSqVTrfqYyPDzMbrR8lf3Hx8fR1dVl0nPhJgYY2jMnFotZb2RjY6NRz2shVNdOVXSTj+qau7u7804IX0opKSmsB+zLL7/U2iYiIoJtnzp1Su9jFxQUsOG5iIgItV5kkUjEjjsxMYHCwkKDz12oLCws2PtCCO9RQz3yyCMAlMlMd2P+372MAjNCTKS2tlZnpuTMzAyrWG5nZ6cWZHh5ebGekJKSEp1z2cbGxnDu3DkAwPr16w2eZzWXpaUlAKiV7piLO7zCd7MtLCw0ecV5f39/luF36tQptXObj6OjI0u2KCsru+uLWXOX05m7UgRXU1MT2tvbNfa52x566CGWIdzc3Iza2lqNNsHBwSyj9MKFCzh79uy8x+3u7sa3334LQNmzqW04LyEhgU0uz8jIUCt2y2dmZkZjtQmhCQ4OBgB0dnbi0qVLCz6OPp9tY1Od++TkpN6JC0SJAjNCTEShUODQoUNaA4bjx4+zavWRkZFqPR8WFhasZIJMJsOxY8c09p+dncXHH3/MJtjGxsYu+nxVk3Rv3Lihs42joyMLGsvKyrTe6KVSKb7++utFn898VqxYwZZJamtrw6effqqxrJLKnTt3NCaHqxbeHhsbQ3JyMitRoo1CoUBubi5u375tpLNX5+3tzVYJKCws1Lq81OjoKKupZmZmhpiYGJOcy0K98cYbrDdL2zwzMzMzpKenszlQ+/fv17kiA6Ass5KQkMCyOD/44AOsXr1ao52Liws++ugjAMpes7i4OFYKRZf29nbs2bMHJ06c0O/J3SXx8fHsx8eBAwfUMra1uXDhgtbyNPp8to1tx44drFzQV199xXtNdK2v+l9Fc8wIMREvLy9UVVUhJiZGbUmmkpIS1lvg6uqKt99+W2PfpKQklJeXo6enB5mZmZDJZHjxxRexatUq9Pb2soWpAcDX19co6zj6+vqit7cXlZWVyM/Px6ZNm9gvbVtbW6xcuRJmZmYIDw9Hbm4url+/jpiYGCQmJsLd3R1jY2Oorq7Gjz/+CBsbGzg7O+PPP/9c9Hnxeffdd1FbWwuZTIbTp0+jqakJ0dHR2LBhA8zNzdHX14eGhgacPXsW7733ntpamVu3bkV8fDx++OEH1NfXIywsDNHR0fDz84O9vT0mJiZw48YNNDQ0oLy8HHK5HC+88AJ7TYztk08+QVRUFO7cuYPXXnsNcXFx2LZtm9qSTKpaV7t27RLcnKMNGzYgODgYFRUVqK+vR0NDg0YtNS8vL6Snp+P999+HQqFASkoK8vPzER4ejrVr18Lc3Bx//fUXKisrUVZWxnpdd+3apbGUEFdkZCT6+vqQkZGBwcFBxMXFITAwkPXSrVixAnK5HF1dXaiursalS5cwPT1ttKLLpuLk5ITPP/8cycnJ6O/vR2RkJCIiIvD000/D1dUVU1NT6Ovrg1QqhUQiQU9PD7KzszWelz6fbWOztLREeno6du/ejcnJSSQmJmLHjh0ICQmBq6srFAoFOjs7UV1djcrKSrS2thr9HO5VFJgRYiKxsbGor69HcXExUlJSNB5XrVPJrXekYmtri++//54tYi6RSCCRSDTabdq0CVlZWUbJwtu9ezckEgkUCgXS0tLUHouIiMDRo0cBKOcTNTY24tq1a2htbcW+ffvU2trb2yMzMxMZGRkmD8ysra2Rk5OD5ORk1NfXo62tjfWe6OPDDz+EnZ0dsrKy0N/fj8zMTJ1tbWxsTJLtqCIWi5Gdnc0WMT958iROnjyp0S42NlbjNReKN998ExUVFQCUvWbaeqTCwsKwatUqpKWloaOjA1euXNG5ooK9vT1SU1P1+uGRlJQEDw8PHD16FDdv3kRNTQ1qamp0tvfw8OBdb1Qotm/fjmPHjuHAgQMYHh5Gfn4+8vPztbY1MzPTWjNM38+2sW3ZsgXZ2dnYt28f5HI5iouLUVxcbJL/9f+EAjNCTOizzz5DQEAACgoKIJPJMDExgdWrVyMkJASvv/46byblAw88gNLSUhQWFuLXX3+FTCbD+Pg47OzsIBaL2cLJxkprF4vFKCgowIkTJ9DY2IiBgQGtQ5XLly9HXl4evvvuO5w7dw7d3d1YtmwZ3NzcsHXrVrz66qus6v9ScHR0xOnTp1FeXo4zZ86gubkZQ0NDEIlEcHZ2hqenJ0JCQrRW+BeJRNi7dy927tyJ/Px8XL58Gb29vRgdHYWVlRXc3NwgFosRGBiIkJAQ3npUxhAYGIjy8nLk5OSguroaPT09UCgUcHJygp+fH6Kjo41e0d+YvL29ERAQgNraWtTU1EAqlaoVUlZ5/PHHcebMGUgkElRUVKClpQUDAwOYmpqCo6Mj1q1bh6CgIOzcudOgzNPt27cjKCgIEokEFy9eREtLC4aGhjA+Pg5bW1vcf//98PHxQWhoKPz9/Y1SjmYpqHoif/rpJ1RXV6O9vR1yuRzLli2Dk5MTPDw8sGXLFoSGhmpd3kjfz7YpPPXUUzh//jzy8vJQVVWFrq4ujI6OwtraGu7u7ti8eTOef/75JTmXe4VoVtekDEKIwXp7e/HMM88AUAZl3KEzQgghZD40+Z8QQgghRCAoMCOEEEIIEQgKzAghhBBCBIICM0IIIYQQgaDAjBBCCCFEICgrkxBCCCFEIKjHjBBCCCFEICgwI4QQQggRCArMCCGEEEIEggIzQgghhBCBoMCMEEIIIUQgKDAjhBBCCBEICswIIYQQQgSCAjNCCCGEEIGgwIwQQgghRCAoMCOEEEIIEQgKzAghhBBCBIICM0IIIYQQgaDAjBBCCCFEICgwI4QQQggRCArMCCGEEEIEggIzQgghhBCBoMCMEEIIIUQgKDAjhBBCCBEICswIIYQQQgTif/3ajvK16MI3AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for idx, row in df.iterrows():\n",
+    "    fig, ax = plt.subplots(figsize=(3,3))\n",
+    "\n",
+    "    barplot_feat_imp(\n",
+    "        row[cols_order], row[std_cols_order], ax=ax,\n",
+    "        color=palette[0] if row[\"model_name\"] not in baselines else palette[2],\n",
+    "    )\n",
+    "    ax.set_title(row[\"model_name\"])\n",
+    "\n",
+    "    ax.set_xlim(left=0, right=ax.get_xlim()[-1])\n",
+    "\n",
+    "    save_fig(fig, f\"feat-imp_{idx}\")\n",
+    "    plt.plot()\n",
+    "    # plt.close(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3a6beb5-b44b-4f81-ae1a-d027afb2c5f4",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/objects.inv b/objects.inv
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+
+
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+             
+
+   <h1>Python Module Index</h1>
+
+   <div class="modindex-jumpbox">
+   <a href="#cap-f"><strong>f</strong></a>
+   </div>
+
+   <table class="indextable modindextable">
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-f"><td></td><td>
+       <strong>f</strong></td><td></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-1" style="display: none" alt="-" /></td>
+       <td>
+       <a href="source/folktexts.html#module-folktexts"><code class="xref">folktexts</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.acs.html#module-folktexts.acs"><code class="xref">folktexts.acs</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.acs.html#module-folktexts.acs.acs_columns"><code class="xref">folktexts.acs.acs_columns</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.acs.html#module-folktexts.acs.acs_dataset"><code class="xref">folktexts.acs.acs_dataset</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.acs.html#module-folktexts.acs.acs_questions"><code class="xref">folktexts.acs.acs_questions</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.acs.html#module-folktexts.acs.acs_tasks"><code class="xref">folktexts.acs.acs_tasks</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.acs.html#module-folktexts.acs.acs_thresholds"><code class="xref">folktexts.acs.acs_thresholds</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.html#module-folktexts.benchmark"><code class="xref">folktexts.benchmark</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.classifier.html#module-folktexts.classifier"><code class="xref">folktexts.classifier</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.classifier.html#module-folktexts.classifier.base"><code class="xref">folktexts.classifier.base</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.classifier.html#module-folktexts.classifier.transformers_classifier"><code class="xref">folktexts.classifier.transformers_classifier</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.classifier.html#module-folktexts.classifier.web_api_classifier"><code class="xref">folktexts.classifier.web_api_classifier</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.cli.html#module-folktexts.cli"><code class="xref">folktexts.cli</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.cli.html#module-folktexts.cli.download_models"><code class="xref">folktexts.cli.download_models</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.cli.html#module-folktexts.cli.eval_feature_importance"><code class="xref">folktexts.cli.eval_feature_importance</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.cli.html#module-folktexts.cli.experiments"><code class="xref">folktexts.cli.experiments</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.cli.html#module-folktexts.cli.launch_experiments_htcondor"><code class="xref">folktexts.cli.launch_experiments_htcondor</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.cli.html#module-folktexts.cli.rerun_experiment"><code class="xref">folktexts.cli.rerun_experiment</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.cli.html#module-folktexts.cli.run_acs_benchmark"><code class="xref">folktexts.cli.run_acs_benchmark</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.html#module-folktexts.col_to_text"><code class="xref">folktexts.col_to_text</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.html#module-folktexts.dataset"><code class="xref">folktexts.dataset</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.html#module-folktexts.evaluation"><code class="xref">folktexts.evaluation</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.html#module-folktexts.llm_utils"><code class="xref">folktexts.llm_utils</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.html#module-folktexts.plotting"><code class="xref">folktexts.plotting</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.html#module-folktexts.prompting"><code class="xref">folktexts.prompting</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.html#module-folktexts.qa_interface"><code class="xref">folktexts.qa_interface</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.html#module-folktexts.task"><code class="xref">folktexts.task</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/folktexts.html#module-folktexts.threshold"><code class="xref">folktexts.threshold</code></a></td><td>
+       <em></em></td></tr>
+   </table>
+
+
+           </div>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">Readme file</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#book-folktexts">:book: folktexts   <!-- omit in toc --></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#installing">Installing</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#basic-setup">Basic setup</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#example-usage">Example usage</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#benchmark-features-and-options">Benchmark features and options</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#evaluating-feature-importance">Evaluating feature importance</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#faq">FAQ</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#citation">Citation</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#license-and-terms-of-use">License and terms of use</a></li>
+</ul>
+</li>
+</ul>
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+             
+  <section id="readme-md">
+<h1>README.md<a class="headerlink" href="#readme-md" title="Link to this heading"></a></h1>
+<section id="book-folktexts">
+<h2>:book: folktexts   <!-- omit in toc --><a class="headerlink" href="#book-folktexts" title="Link to this heading"></a></h2>
+<p><img alt="Tests status" src="https://github.com/socialfoundations/folktexts/actions/workflows/python-tests.yml/badge.svg" />
+<img alt="PyPI status" src="https://github.com/socialfoundations/folktexts/actions/workflows/python-publish.yml/badge.svg" />
+<img alt="Documentation status" src="https://github.com/socialfoundations/folktexts/actions/workflows/python-docs.yml/badge.svg" />
+<img alt="PyPI version" src="https://badgen.net/pypi/v/folktexts" />
+<img alt="PyPI - License" src="https://img.shields.io/pypi/l/folktexts" />
+<img alt="Python compatibility" src="https://badgen.net/pypi/python/folktexts" /></p>
+<blockquote>
+<div><p>This package is the basis for our NeurIPS’24 paper titled <a class="reference external" href="https://arxiv.org/abs/2407.14614">“Evaluating language models as risk scores”</a></p>
+</div></blockquote>
+<p>Folktexts is a python package to evaluate statistical properties of LLMs as classifiers.
+It enables computing and evaluating classification <em>risk scores</em> for tabular prediction tasks using LLMs.</p>
+<p>Several benchmark tasks are provided based on data from the American Community Survey.
+Namely, each prediction task from the popular
+<a class="reference external" href="https://github.com/socialfoundations/folktables">folktables</a> package is made available
+as a natural-language prompting task.</p>
+<p>Package documentation can be found <a class="reference external" href="https://socialfoundations.github.io/folktexts/">here</a>.</p>
+<p><strong>Table of contents:</strong></p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#installing"><span class="std std-ref">Installing</span></a></p></li>
+<li><p><a class="reference internal" href="#basic-setup"><span class="xref myst">Basic setup</span></a></p></li>
+<li><p><a class="reference internal" href="#example-usage"><span class="xref myst">Example usage</span></a></p></li>
+<li><p><a class="reference internal" href="#benchmark-features-and-options"><span class="xref myst">Benchmark features and options</span></a></p></li>
+<li><p><a class="reference internal" href="#evaluating-feature-importance"><span class="xref myst">Evaluating feature importance</span></a></p></li>
+<li><p><a class="reference internal" href="#faq"><span class="std std-ref">FAQ</span></a></p></li>
+<li><p><a class="reference internal" href="#citation"><span class="std std-ref">Citation</span></a></p></li>
+<li><p><a class="reference internal" href="#license-and-terms-of-use"><span class="xref myst">License and terms of use</span></a></p></li>
+</ul>
+<section id="installing">
+<h3>Installing<a class="headerlink" href="#installing" title="Link to this heading"></a></h3>
+<p>Install package from <a class="reference external" href="https://pypi.org/project/folktexts/">PyPI</a>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">folktexts</span>
+</pre></div>
+</div>
+</section>
+<section id="basic-setup">
+<h3>Basic setup<a class="headerlink" href="#basic-setup" title="Link to this heading"></a></h3>
+<blockquote>
+<div><p>You’ll need to go through these steps to run the benchmark tasks.</p>
+</div></blockquote>
+<ol class="arabic simple">
+<li><p>Create conda environment</p></li>
+</ol>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">create</span> <span class="o">-</span><span class="n">n</span> <span class="n">folktexts</span> <span class="n">python</span><span class="o">=</span><span class="mf">3.11</span>
+<span class="n">conda</span> <span class="n">activate</span> <span class="n">folktexts</span>
+</pre></div>
+</div>
+<ol class="arabic simple" start="2">
+<li><p>Install folktexts package</p></li>
+</ol>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">folktexts</span>
+</pre></div>
+</div>
+<ol class="arabic simple" start="3">
+<li><p>Create models dataset and results folder</p></li>
+</ol>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mkdir</span> <span class="n">results</span>
+<span class="n">mkdir</span> <span class="n">models</span>
+<span class="n">mkdir</span> <span class="n">data</span>
+</pre></div>
+</div>
+<ol class="arabic simple" start="4">
+<li><p>Download transformers model and tokenizer</p></li>
+</ol>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">download_models</span> <span class="o">--</span><span class="n">model</span> <span class="s1">&#39;google/gemma-2b&#39;</span> <span class="o">--</span><span class="n">save</span><span class="o">-</span><span class="nb">dir</span> <span class="n">models</span>
+</pre></div>
+</div>
+<ol class="arabic simple" start="5">
+<li><p>Run benchmark on a given task</p></li>
+</ol>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">run_acs_benchmark</span> <span class="o">--</span><span class="n">results</span><span class="o">-</span><span class="nb">dir</span> <span class="n">results</span> <span class="o">--</span><span class="n">data</span><span class="o">-</span><span class="nb">dir</span> <span class="n">data</span> <span class="o">--</span><span class="n">task</span> <span class="s1">&#39;ACSIncome&#39;</span> <span class="o">--</span><span class="n">model</span> <span class="n">models</span><span class="o">/</span><span class="n">google</span><span class="o">--</span><span class="n">gemma</span><span class="o">-</span><span class="mi">2</span><span class="n">b</span>
+</pre></div>
+</div>
+<p>Run <code class="docutils literal notranslate"><span class="pre">run_acs_benchmark</span> <span class="pre">--help</span></code> to get a list of all available benchmark flags.</p>
+</section>
+<section id="example-usage">
+<h3>Example usage<a class="headerlink" href="#example-usage" title="Link to this heading"></a></h3>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># Load transformers model</span>
+<span class="kn">from</span> <span class="nn">folktexts.llm_utils</span> <span class="kn">import</span> <span class="n">load_model_tokenizer</span>
+<span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">load_model_tokenizer</span><span class="p">(</span><span class="s2">&quot;gpt2&quot;</span><span class="p">)</span>   <span class="c1"># using tiny model as an example</span>
+
+<span class="kn">from</span> <span class="nn">folktexts.acs</span> <span class="kn">import</span> <span class="n">ACSDataset</span>
+<span class="n">acs_task_name</span> <span class="o">=</span> <span class="s2">&quot;ACSIncome&quot;</span>     <span class="c1"># Name of the benchmark ACS task to use</span>
+
+<span class="c1"># Create an object that classifies data using an LLM</span>
+<span class="kn">from</span> <span class="nn">folktexts</span> <span class="kn">import</span> <span class="n">TransformersLLMClassifier</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">TransformersLLMClassifier</span><span class="p">(</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+    <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">task</span><span class="o">=</span><span class="n">acs_task_name</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># NOTE: You can also use a web-hosted model like GPT4 using the `WebAPILLMClassifier` class</span>
+
+<span class="c1"># Use a dataset or feed in your own data</span>
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">ACSDataset</span><span class="o">.</span><span class="n">make_from_task</span><span class="p">(</span><span class="n">acs_task_name</span><span class="p">)</span>   <span class="c1"># use `.subsample(0.01)` to get faster approximate results</span>
+
+<span class="c1"># You can compute risk score predictions using an sklearn-style interface</span>
+<span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_test</span><span class="p">()</span>
+<span class="n">test_scores</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+
+<span class="c1"># Optionally, you can fit the threshold based on a few samples</span>
+<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">dataset</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">100</span><span class="p">])</span>    <span class="c1"># (`dataset[...]` will access training data)</span>
+
+<span class="c1"># ...in order to get more accurate binary predictions with `.predict`</span>
+<span class="n">test_preds</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+
+<span class="c1"># If you only care about the overall metrics and not individual predictions,</span>
+<span class="c1"># you can simply run the following code:</span>
+<span class="kn">from</span> <span class="nn">folktexts.benchmark</span> <span class="kn">import</span> <span class="n">Benchmark</span><span class="p">,</span> <span class="n">BenchmarkConfig</span>
+<span class="n">bench</span> <span class="o">=</span> <span class="n">Benchmark</span><span class="o">.</span><span class="n">make_benchmark</span><span class="p">(</span>
+    <span class="n">task</span><span class="o">=</span><span class="n">acs_task_name</span><span class="p">,</span> <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span>
+    <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
+    <span class="n">numeric_risk_prompting</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>    <span class="c1"># See the full list of configs below in the README</span>
+<span class="p">)</span>
+<span class="n">bench_results</span> <span class="o">=</span> <span class="n">bench</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">results_root_dir</span><span class="o">=</span><span class="s2">&quot;results&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="benchmark-features-and-options">
+<h3>Benchmark features and options<a class="headerlink" href="#benchmark-features-and-options" title="Link to this heading"></a></h3>
+<p>Here’s a summary list of the most important benchmark options/flags used in
+conjunction with the <code class="docutils literal notranslate"><span class="pre">run_acs_benchmark</span></code> command line script, or with the
+<code class="docutils literal notranslate"><span class="pre">Benchmark</span></code> class.</p>
+<table class="docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head text-left"><p>Option</p></th>
+<th class="head text-left"><p>Description</p></th>
+<th class="head text-center"><p>Examples</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td class="text-left"><p><code class="docutils literal notranslate"><span class="pre">--model</span></code></p></td>
+<td class="text-left"><p>Name of the model on huggingface transformers, or local path to folder with pretrained model and tokenizer. Can also use web-hosted models with <code class="docutils literal notranslate"><span class="pre">&quot;[provider]/[model-name]&quot;</span></code>.</p></td>
+<td class="text-center"><p><code class="docutils literal notranslate"><span class="pre">meta-llama/Meta-Llama-3-8B</span></code>, <code class="docutils literal notranslate"><span class="pre">openai/gpt-4o-mini</span></code></p></td>
+</tr>
+<tr class="row-odd"><td class="text-left"><p><code class="docutils literal notranslate"><span class="pre">--task</span></code></p></td>
+<td class="text-left"><p>Name of the ACS task to run benchmark on.</p></td>
+<td class="text-center"><p><code class="docutils literal notranslate"><span class="pre">ACSIncome</span></code>, <code class="docutils literal notranslate"><span class="pre">ACSEmployment</span></code></p></td>
+</tr>
+<tr class="row-even"><td class="text-left"><p><code class="docutils literal notranslate"><span class="pre">--results-dir</span></code></p></td>
+<td class="text-left"><p>Path to directory under which benchmark results will be saved.</p></td>
+<td class="text-center"><p><code class="docutils literal notranslate"><span class="pre">results</span></code></p></td>
+</tr>
+<tr class="row-odd"><td class="text-left"><p><code class="docutils literal notranslate"><span class="pre">--data-dir</span></code></p></td>
+<td class="text-left"><p>Root folder to find datasets in (or download ACS data to).</p></td>
+<td class="text-center"><p><code class="docutils literal notranslate"><span class="pre">~/data</span></code></p></td>
+</tr>
+<tr class="row-even"><td class="text-left"><p><code class="docutils literal notranslate"><span class="pre">--numeric-risk-prompting</span></code></p></td>
+<td class="text-left"><p>Whether to use verbalized numeric risk prompting, i.e., directly query model for a probability estimate. <strong>By default</strong> will use standard multiple-choice Q&amp;A, and extract risk scores from internal token probabilities.</p></td>
+<td class="text-center"><p>Boolean flag (<code class="docutils literal notranslate"><span class="pre">True</span></code> if present, <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise)</p></td>
+</tr>
+<tr class="row-odd"><td class="text-left"><p><code class="docutils literal notranslate"><span class="pre">--use-web-api-model</span></code></p></td>
+<td class="text-left"><p>Whether the given <code class="docutils literal notranslate"><span class="pre">--model</span></code> name corresponds to a web-hosted model or not. <strong>By default</strong> this is False (assumes a huggingface transformers model). If this flag is provided, <code class="docutils literal notranslate"><span class="pre">--model</span></code> must contain a <a class="reference external" href="https://docs.litellm.ai">litellm</a> model identifier (<a class="reference external" href="https://docs.litellm.ai/docs/providers/openai#openai-chat-completion-models">examples here</a>).</p></td>
+<td class="text-center"><p>Boolean flag (<code class="docutils literal notranslate"><span class="pre">True</span></code> if present, <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise)</p></td>
+</tr>
+<tr class="row-even"><td class="text-left"><p><code class="docutils literal notranslate"><span class="pre">--subsampling</span></code></p></td>
+<td class="text-left"><p>Which fraction of the dataset to use for the benchmark. <strong>By default</strong> will use the whole test set.</p></td>
+<td class="text-center"><p><code class="docutils literal notranslate"><span class="pre">0.01</span></code></p></td>
+</tr>
+<tr class="row-odd"><td class="text-left"><p><code class="docutils literal notranslate"><span class="pre">--fit-threshold</span></code></p></td>
+<td class="text-left"><p>Whether to use the given number of samples to fit the binarization threshold. <strong>By default</strong> will use a fixed $t=0.5$ threshold instead of fitting on data.</p></td>
+<td class="text-center"><p><code class="docutils literal notranslate"><span class="pre">100</span></code></p></td>
+</tr>
+<tr class="row-even"><td class="text-left"><p><code class="docutils literal notranslate"><span class="pre">--batch-size</span></code></p></td>
+<td class="text-left"><p>The number of samples to process in each inference batch. Choose according to your available VRAM.</p></td>
+<td class="text-center"><p><code class="docutils literal notranslate"><span class="pre">10</span></code>, <code class="docutils literal notranslate"><span class="pre">32</span></code></p></td>
+</tr>
+</tbody>
+</table>
+<p>Full list of options:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">usage</span><span class="p">:</span> <span class="n">run_acs_benchmark</span> <span class="p">[</span><span class="o">-</span><span class="n">h</span><span class="p">]</span> <span class="o">--</span><span class="n">model</span> <span class="n">MODEL</span> <span class="o">--</span><span class="n">results</span><span class="o">-</span><span class="nb">dir</span> <span class="n">RESULTS_DIR</span> <span class="o">--</span><span class="n">data</span><span class="o">-</span><span class="nb">dir</span> <span class="n">DATA_DIR</span> <span class="p">[</span><span class="o">--</span><span class="n">task</span> <span class="n">TASK</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">few</span><span class="o">-</span><span class="n">shot</span> <span class="n">FEW_SHOT</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">batch</span><span class="o">-</span><span class="n">size</span> <span class="n">BATCH_SIZE</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">context</span><span class="o">-</span><span class="n">size</span> <span class="n">CONTEXT_SIZE</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">fit</span><span class="o">-</span><span class="n">threshold</span> <span class="n">FIT_THRESHOLD</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">subsampling</span> <span class="n">SUBSAMPLING</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">seed</span> <span class="n">SEED</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">use</span><span class="o">-</span><span class="n">web</span><span class="o">-</span><span class="n">api</span><span class="o">-</span><span class="n">model</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">dont</span><span class="o">-</span><span class="n">correct</span><span class="o">-</span><span class="n">order</span><span class="o">-</span><span class="n">bias</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">numeric</span><span class="o">-</span><span class="n">risk</span><span class="o">-</span><span class="n">prompting</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">reuse</span><span class="o">-</span><span class="n">few</span><span class="o">-</span><span class="n">shot</span><span class="o">-</span><span class="n">examples</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">use</span><span class="o">-</span><span class="n">feature</span><span class="o">-</span><span class="n">subset</span> <span class="n">USE_FEATURE_SUBSET</span><span class="p">]</span>
+                         <span class="p">[</span><span class="o">--</span><span class="n">use</span><span class="o">-</span><span class="n">population</span><span class="o">-</span><span class="nb">filter</span> <span class="n">USE_POPULATION_FILTER</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">logger</span><span class="o">-</span><span class="n">level</span> <span class="p">{</span><span class="n">DEBUG</span><span class="p">,</span><span class="n">INFO</span><span class="p">,</span><span class="n">WARNING</span><span class="p">,</span><span class="n">ERROR</span><span class="p">,</span><span class="n">CRITICAL</span><span class="p">}]</span>
+
+<span class="n">Benchmark</span> <span class="n">risk</span> <span class="n">scores</span> <span class="n">produced</span> <span class="n">by</span> <span class="n">a</span> <span class="n">language</span> <span class="n">model</span> <span class="n">on</span> <span class="n">ACS</span> <span class="n">data</span><span class="o">.</span>
+
+<span class="n">options</span><span class="p">:</span>
+  <span class="o">-</span><span class="n">h</span><span class="p">,</span> <span class="o">--</span><span class="n">help</span>            <span class="n">show</span> <span class="n">this</span> <span class="n">help</span> <span class="n">message</span> <span class="ow">and</span> <span class="n">exit</span>
+  <span class="o">--</span><span class="n">model</span> <span class="n">MODEL</span>         <span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="n">Model</span> <span class="n">name</span> <span class="ow">or</span> <span class="n">path</span> <span class="n">to</span> <span class="n">model</span> <span class="n">saved</span> <span class="n">on</span> <span class="n">disk</span>
+  <span class="o">--</span><span class="n">results</span><span class="o">-</span><span class="nb">dir</span> <span class="n">RESULTS_DIR</span>
+                        <span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="n">Directory</span> <span class="n">under</span> <span class="n">which</span> <span class="n">this</span> <span class="n">experiment</span><span class="s1">&#39;s results will be saved</span>
+  <span class="o">--</span><span class="n">data</span><span class="o">-</span><span class="nb">dir</span> <span class="n">DATA_DIR</span>   <span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="n">Root</span> <span class="n">folder</span> <span class="n">to</span> <span class="n">find</span> <span class="n">datasets</span> <span class="n">on</span>
+  <span class="o">--</span><span class="n">task</span> <span class="n">TASK</span>           <span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="n">Name</span> <span class="n">of</span> <span class="n">the</span> <span class="n">ACS</span> <span class="n">task</span> <span class="n">to</span> <span class="n">run</span> <span class="n">the</span> <span class="n">experiment</span> <span class="n">on</span>
+  <span class="o">--</span><span class="n">few</span><span class="o">-</span><span class="n">shot</span> <span class="n">FEW_SHOT</span>   <span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="n">Use</span> <span class="n">few</span><span class="o">-</span><span class="n">shot</span> <span class="n">prompting</span> <span class="k">with</span> <span class="n">the</span> <span class="n">given</span> <span class="n">number</span> <span class="n">of</span> <span class="n">shots</span>
+  <span class="o">--</span><span class="n">batch</span><span class="o">-</span><span class="n">size</span> <span class="n">BATCH_SIZE</span>
+                        <span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="n">The</span> <span class="n">batch</span> <span class="n">size</span> <span class="n">to</span> <span class="n">use</span> <span class="k">for</span> <span class="n">inference</span>
+  <span class="o">--</span><span class="n">context</span><span class="o">-</span><span class="n">size</span> <span class="n">CONTEXT_SIZE</span>
+                        <span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="n">The</span> <span class="n">maximum</span> <span class="n">context</span> <span class="n">size</span> <span class="n">when</span> <span class="n">prompting</span> <span class="n">the</span> <span class="n">LLM</span>
+  <span class="o">--</span><span class="n">fit</span><span class="o">-</span><span class="n">threshold</span> <span class="n">FIT_THRESHOLD</span>
+                        <span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="n">Whether</span> <span class="n">to</span> <span class="n">fit</span> <span class="n">the</span> <span class="n">prediction</span> <span class="n">threshold</span><span class="p">,</span> <span class="ow">and</span> <span class="n">on</span> <span class="n">how</span> <span class="n">many</span> <span class="n">samples</span>
+  <span class="o">--</span><span class="n">subsampling</span> <span class="n">SUBSAMPLING</span>
+                        <span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="n">Which</span> <span class="n">fraction</span> <span class="n">of</span> <span class="n">the</span> <span class="n">dataset</span> <span class="n">to</span> <span class="n">use</span> <span class="p">(</span><span class="k">if</span> <span class="n">omitted</span> <span class="n">will</span> <span class="n">use</span> <span class="nb">all</span> <span class="n">data</span><span class="p">)</span>
+  <span class="o">--</span><span class="n">seed</span> <span class="n">SEED</span>           <span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="n">Random</span> <span class="n">seed</span> <span class="o">--</span> <span class="n">to</span> <span class="nb">set</span> <span class="k">for</span> <span class="n">reproducibility</span>
+  <span class="o">--</span><span class="n">use</span><span class="o">-</span><span class="n">web</span><span class="o">-</span><span class="n">api</span><span class="o">-</span><span class="n">model</span>   <span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="n">Whether</span> <span class="n">use</span> <span class="n">a</span> <span class="n">model</span> <span class="n">hosted</span> <span class="n">on</span> <span class="n">a</span> <span class="n">web</span> <span class="n">API</span> <span class="p">(</span><span class="n">instead</span> <span class="n">of</span> <span class="n">a</span> <span class="n">local</span> <span class="n">model</span><span class="p">)</span>
+  <span class="o">--</span><span class="n">dont</span><span class="o">-</span><span class="n">correct</span><span class="o">-</span><span class="n">order</span><span class="o">-</span><span class="n">bias</span>
+                        <span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="n">Whether</span> <span class="n">to</span> <span class="n">avoid</span> <span class="n">correcting</span> <span class="n">ordering</span> <span class="n">bias</span><span class="p">,</span> <span class="n">by</span> <span class="n">default</span> <span class="n">will</span> <span class="n">correct</span> <span class="n">it</span>
+  <span class="o">--</span><span class="n">numeric</span><span class="o">-</span><span class="n">risk</span><span class="o">-</span><span class="n">prompting</span>
+                        <span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="n">Whether</span> <span class="n">to</span> <span class="n">prompt</span> <span class="k">for</span> <span class="n">numeric</span> <span class="n">risk</span><span class="o">-</span><span class="n">estimates</span> <span class="n">instead</span> <span class="n">of</span> <span class="n">multiple</span><span class="o">-</span><span class="n">choice</span> <span class="n">Q</span><span class="o">&amp;</span><span class="n">A</span>
+  <span class="o">--</span><span class="n">reuse</span><span class="o">-</span><span class="n">few</span><span class="o">-</span><span class="n">shot</span><span class="o">-</span><span class="n">examples</span>
+                        <span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="n">Whether</span> <span class="n">to</span> <span class="n">reuse</span> <span class="n">the</span> <span class="n">same</span> <span class="n">samples</span> <span class="k">for</span> <span class="n">few</span><span class="o">-</span><span class="n">shot</span> <span class="n">prompting</span> <span class="p">(</span><span class="ow">or</span> <span class="n">sample</span> <span class="n">new</span> <span class="n">ones</span> <span class="n">every</span> <span class="n">time</span><span class="p">)</span>
+  <span class="o">--</span><span class="n">use</span><span class="o">-</span><span class="n">feature</span><span class="o">-</span><span class="n">subset</span> <span class="n">USE_FEATURE_SUBSET</span>
+                        <span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="n">Optional</span> <span class="n">subset</span> <span class="n">of</span> <span class="n">features</span> <span class="n">to</span> <span class="n">use</span> <span class="k">for</span> <span class="n">prediction</span><span class="p">,</span> <span class="n">comma</span> <span class="n">separated</span>
+  <span class="o">--</span><span class="n">use</span><span class="o">-</span><span class="n">population</span><span class="o">-</span><span class="nb">filter</span> <span class="n">USE_POPULATION_FILTER</span>
+                        <span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="n">Optional</span> <span class="n">population</span> <span class="nb">filter</span> <span class="k">for</span> <span class="n">this</span> <span class="n">benchmark</span><span class="p">;</span> <span class="n">must</span> <span class="n">follow</span> <span class="n">the</span> <span class="nb">format</span> <span class="s1">&#39;column_name=value&#39;</span> <span class="n">to</span> <span class="nb">filter</span> <span class="n">the</span> <span class="n">dataset</span> <span class="n">by</span> <span class="n">a</span> <span class="n">specific</span> <span class="n">value</span><span class="o">.</span>
+  <span class="o">--</span><span class="n">logger</span><span class="o">-</span><span class="n">level</span> <span class="p">{</span><span class="n">DEBUG</span><span class="p">,</span><span class="n">INFO</span><span class="p">,</span><span class="n">WARNING</span><span class="p">,</span><span class="n">ERROR</span><span class="p">,</span><span class="n">CRITICAL</span><span class="p">}</span>
+                        <span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="n">The</span> <span class="n">logging</span> <span class="n">level</span> <span class="n">to</span> <span class="n">use</span> <span class="k">for</span> <span class="n">the</span> <span class="n">experiment</span>
+</pre></div>
+</div>
+</section>
+<section id="evaluating-feature-importance">
+<h3>Evaluating feature importance<a class="headerlink" href="#evaluating-feature-importance" title="Link to this heading"></a></h3>
+<p>By evaluating LLMs on tabular classification tasks, we can use standard feature importance methods to assess which features the model uses to compute risk scores.</p>
+<p>You can do so yourself by calling <code class="docutils literal notranslate"><span class="pre">folktexts.cli.eval_feature_importance</span></code> (add <code class="docutils literal notranslate"><span class="pre">--help</span></code> for a full list of options).</p>
+<p>Here’s an example for the Llama3-70B-Instruct model on the ACSIncome task (<em>warning: takes 24h on an Nvidia H100</em>):</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">folktexts</span><span class="o">.</span><span class="n">cli</span><span class="o">.</span><span class="n">eval_feature_importance</span> <span class="o">--</span><span class="n">model</span> <span class="s1">&#39;meta-llama/Meta-Llama-3-70B-Instruct&#39;</span> <span class="o">--</span><span class="n">task</span> <span class="n">ACSIncome</span> <span class="o">--</span><span class="n">subsampling</span> <span class="mf">0.1</span>
+</pre></div>
+</div>
+<div style="text-align: center;">
+<img src="docs/_static/feat-imp_meta-llama--Meta-Llama-3-70B-Instruct.png" alt="feature importance on llama3 70b it" width="50%">
+</div>
+<p>This script uses sklearn’s <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.inspection.permutation_importance.html#sklearn.inspection.permutation_importance"><code class="docutils literal notranslate"><span class="pre">permutation_importance</span></code></a> to assess which features contribute the most for the ROC AUC metric (other metrics can be assessed using the <code class="docutils literal notranslate"><span class="pre">--scorer</span> <span class="pre">[scorer]</span></code> parameter).</p>
+</section>
+<section id="faq">
+<h3>FAQ<a class="headerlink" href="#faq" title="Link to this heading"></a></h3>
+<ol class="arabic">
+<li><p><strong>Q:</strong> Can I use <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> with a different dataset?</p>
+<p><strong>A:</strong> <strong>Yes!</strong> Folktexts provides the whole ML pipeline needed to produce risk scores using LLMs, together with a few example ACS datasets. You can easily apply these same utilities to a different dataset following the <a class="reference internal" href="notebooks/custom-dataset-example.html"><span class="std std-doc">example jupyter notebook</span></a>.</p>
+</li>
+<li><p><strong>Q:</strong> How do I create a custom prediction task based on American Community Survey data?</p>
+<p><strong>A:</strong> Simply create a new <code class="docutils literal notranslate"><span class="pre">TaskMetadata</span></code> object with the parameters you want. Follow the <a class="reference internal" href="notebooks/custom-acs-task-example.html"><span class="std std-doc">example jupyter notebook</span></a> for more details.</p>
+</li>
+<li><p><strong>Q:</strong> Can I use <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> with closed-source models?</p>
+<p><strong>A:</strong> <strong>Yes!</strong> We provide compatibility with local LLMs via <a class="reference external" href="https://github.com/huggingface/transformers">🤗 transformers</a> and compatibility with web-hosted LLMs via <a class="reference external" href="https://github.com/BerriAI/litellm">litellm</a>. For example, you can use <code class="docutils literal notranslate"><span class="pre">--model='gpt-4o'</span> <span class="pre">--use-web-api-model</span></code> to use GPT-4o when calling the <code class="docutils literal notranslate"><span class="pre">run_acs_benchmark</span></code> script. <a class="reference external" href="https://docs.litellm.ai/docs/providers/openai#openai-chat-completion-models">Here’s a complete list</a> of compatible OpenAI models. Note that some models are not compatible as they don’t enable access to log-probabilities.
+Using models through a web API requires installing extra optional dependencies with <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">'folktexts[apis]'</span></code>.</p>
+</li>
+<li><p><strong>Q:</strong> Can I use <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> to fine-tune LLMs on survey prediction tasks?</p>
+<p><strong>A:</strong> The package does not feature specific fine-tuning functionality, but you can use the data and Q&amp;A prompts generated by <code class="docutils literal notranslate"><span class="pre">folktexts</span></code> to fine-tune an LLM for a specific prediction task.</p>
+ <!-- **A:** Yes. Although the package does not feature specific fine-tuning functionality, you can use the data and Q&A prompts generated by `folktexts` to fine-tune an LLM for a specific prediction task. Follow the [example jupyter notebook](notebooks/finetuning-llms-example.ipynb) for more details. In the future we may bring this functionality into the main package implementation. -->
+</li>
+</ol>
+</section>
+<section id="citation">
+<h3>Citation<a class="headerlink" href="#citation" title="Link to this heading"></a></h3>
+<div class="highlight-bib notranslate"><div class="highlight"><pre><span></span><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">cruz2024evaluating</span><span class="p">,</span>
+<span class="w">    </span><span class="na">title</span><span class="p">=</span><span class="s">{Evaluating language models as risk scores}</span><span class="p">,</span>
+<span class="w">    </span><span class="na">author</span><span class="p">=</span><span class="s">{Andr\&#39;{e} F. Cruz and Moritz Hardt and Celestine Mendler-D\&quot;{u}nner}</span><span class="p">,</span>
+<span class="w">    </span><span class="na">booktitle</span><span class="p">=</span><span class="s">{The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track}</span><span class="p">,</span>
+<span class="w">    </span><span class="na">year</span><span class="p">=</span><span class="s">{2024}</span><span class="p">,</span>
+<span class="w">    </span><span class="na">url</span><span class="p">=</span><span class="s">{https://openreview.net/forum?id=qrZxL3Bto9}</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</section>
+<section id="license-and-terms-of-use">
+<h3>License and terms of use<a class="headerlink" href="#license-and-terms-of-use" title="Link to this heading"></a></h3>
+<p>Code licensed under the <a class="reference internal" href="#LICENSE"><span class="xref myst">MIT license</span></a>.</p>
+<p>The American Community Survey (ACS) Public Use Microdata Sample (PUMS) is
+governed by the U.S. Census Bureau <a class="reference external" href="https://www.census.gov/data/developers/about/terms-of-service.html">terms of service</a>.</p>
+</section>
+</section>
+</section>
+
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+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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diff --git a/searchindex.js b/searchindex.js
new file mode 100644
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--- /dev/null
+++ b/searchindex.js
@@ -0,0 +1 @@
+Search.setIndex({"alltitles": {"1. Check folktexts is installed": [[6, "1.-Check-folktexts-is-installed"], [7, "1.-Check-folktexts-is-installed"]], "1st page illustrative plot": [[8, "1st-page-illustrative-plot"]], "2. Load Model from Huggingface": [[6, "2.-Load-Model-from-Huggingface"]], "2. Load model API using litellm": [[7, "2.-Load-model-API-using-litellm"]], "3. Create default benchmarking tasks": [[6, "3.-Create-default-benchmarking-tasks"], [7, "3.-Create-default-benchmarking-tasks"]], "3. Set OPENAI_API_KEY (or key to respective API provider)": [[7, "3.-Set-OPENAI_API_KEY-(or-key-to-respective-API-provider)"]], "4. Run benchmark": [[6, "4.-Run-benchmark"], [7, "4.-Run-benchmark"]], "4. Visualize results": [[6, "4.-Visualize-results"], [7, "4.-Visualize-results"]], ":book: folktexts   <!-- omit in toc -->": [[12, "book-folktexts"]], "Basic setup": [[12, "basic-setup"]], "Benchmark configuration": [[6, "Benchmark-configuration"], [7, "Benchmark-configuration"]], "Benchmark features and options": [[12, "benchmark-features-and-options"]], "Calibration curves": [[8, "Calibration-curves"]], "Calibration per sub-group": [[8, "Calibration-per-sub-group"]], "Change in ECE and ROC with increasing evidence": [[10, "Change-in-ECE-and-ROC-with-increasing-evidence"]], "Citation": [[12, "citation"]], "Citing": [[0, "citing"]], "Compute mean under-/over-estimation of risk score": [[8, "Compute-mean-under-/over-estimation-of-risk-score"]], "Compute risk score fairness per sub-group": [[8, "Compute-risk-score-fairness-per-sub-group"]], "Construct LLM Classifier": [[5, "Construct-LLM-Classifier"]], "Define custom task objects appropriately:": [[3, "Define-custom-task-objects-appropriately:"]], "Evaluate model calibration using folktexts": [[6, null], [7, null]], "Evaluating feature importance": [[12, "evaluating-feature-importance"]], "Example notebooks using folktexts": [[2, null]], "Example usage": [[12, "example-usage"]], "Example: Run ACS benchmark task": [[5, null]], "FAQ": [[12, "faq"]], "Fetch and parse ACS benchmark results under a given directory": [[9, null], [10, null]], "Fetch and parse feature importance results under a given directory": [[11, null]], "Get feature importance for GBM and LR": [[11, "Get-feature-importance-for-GBM-and-LR"]], "Indices": [[0, "indices"]], "Installing": [[12, "installing"]], "License and terms of use": [[12, "license-and-terms-of-use"]], "Load Dataset": [[5, "Load-Dataset"]], "Load LLM from huggingface or from local disk": [[3, "Load-LLM-from-huggingface-or-from-local-disk"]], "Load and run ACS Benchmark": [[5, "Load-and-run-ACS-Benchmark"]], "Module contents": [[13, "module-folktexts"], [14, "module-folktexts.acs"], [15, "module-folktexts.classifier"], [16, "module-folktexts.cli"]], "Notebooks Gallery": [[1, null]], "Output latex results tables - colored!": [[8, "Output-latex-results-tables---colored!"]], "Plots": [[10, "Plots"], [11, "Plots"]], "Plots comparing different prompting schemes": [[8, "Plots-comparing-different-prompting-schemes"]], "Plots for XGBoost baseline": [[10, "Plots-for-XGBoost-baseline"]], "Prepare results table for each task": [[8, "Prepare-results-table-for-each-task"]], "README.md": [[12, null]], "Render paper plots": [[8, "Render-paper-plots"]], "Render paper plots and tables": [[8, null]], "Run baseline ML classifiers on the benchmark ACS tasks": [[8, "Run-baseline-ML-classifiers-on-the-benchmark-ACS-tasks"]], "Run benchmark on MEPS data": [[4, "Run-benchmark-on-MEPS-data"]], "Run benchmark on the custom task": [[3, "Run-benchmark-on-the-custom-task"]], "Run folktexts benchmark with a custom ACS task": [[3, null]], "Run folktexts benchmark with a different data source": [[4, null]], "Scatter plot of ECE vs AUC for a few models (before and after numeric prompting)": [[8, "Scatter-plot-of-ECE-vs-AUC-for-a-few-models-(before-and-after-numeric-prompting)"]], "Score distribution per sub-group": [[8, "Score-distribution-per-sub-group"]], "Score distributions for base/instruct pairs": [[8, "Score-distributions-for-base/instruct-pairs"]], "Score distributions of an example model with increasing evidence": [[10, "Score-distributions-of-an-example-model-with-increasing-evidence"]], "Set important local paths": [[5, "Set-important-local-paths"]], "Submodules": [[13, "submodules"], [14, "submodules"], [15, "submodules"], [16, "submodules"]], "Subpackages": [[13, "subpackages"]], "Welcome to folktexts\u2019 documentation!": [[0, null]], "folktexts": [[17, null]], "folktexts package": [[13, null]], "folktexts.acs package": [[14, null]], "folktexts.acs.acs_columns module": [[14, "module-folktexts.acs.acs_columns"]], 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"folktexts.cli.experiments module": [[16, "module-folktexts.cli.experiments"]], "folktexts.cli.launch_experiments_htcondor module": [[16, "module-folktexts.cli.launch_experiments_htcondor"]], "folktexts.cli.rerun_experiment module": [[16, "module-folktexts.cli.rerun_experiment"]], "folktexts.cli.run_acs_benchmark module": [[16, "module-folktexts.cli.run_acs_benchmark"]], "folktexts.col_to_text module": [[13, "module-folktexts.col_to_text"]], "folktexts.dataset module": [[13, "module-folktexts.dataset"]], "folktexts.evaluation module": [[13, "module-folktexts.evaluation"]], "folktexts.llm_utils module": [[13, "module-folktexts.llm_utils"]], "folktexts.plotting module": [[13, "module-folktexts.plotting"]], "folktexts.prompting module": [[13, "module-folktexts.prompting"]], "folktexts.qa_interface module": [[13, "module-folktexts.qa_interface"]], "folktexts.task module": [[13, "module-folktexts.task"]], "folktexts.threshold module": [[13, "module-folktexts.threshold"]]}, "docnames": 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+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">folktexts</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
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+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="modules.html">folktexts</a></li>
+          <li class="breadcrumb-item"><a href="folktexts.html">folktexts package</a></li>
+      <li class="breadcrumb-item active">folktexts.acs package</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/source/folktexts.acs.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="folktexts-acs-package">
+<h1>folktexts.acs package<a class="headerlink" href="#folktexts-acs-package" title="Link to this heading"></a></h1>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
+</section>
+<section id="module-folktexts.acs.acs_columns">
+<span id="folktexts-acs-acs-columns-module"></span><h2>folktexts.acs.acs_columns module<a class="headerlink" href="#module-folktexts.acs.acs_columns" title="Link to this heading"></a></h2>
+<p>Module to hold ACS column mappings from values to natural text.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.acs.acs_columns.describe_income_to_poverty_ratio_pct">
+<span class="sig-prename descclassname"><span class="pre">folktexts.acs.acs_columns.</span></span><span class="sig-name descname"><span class="pre">describe_income_to_poverty_ratio_pct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ratio_pct</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/acs/acs_columns.html#describe_income_to_poverty_ratio_pct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.acs.acs_columns.describe_income_to_poverty_ratio_pct" title="Link to this definition"></a></dt>
+<dd><p>Describe the income-to-poverty ratio in natural language.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-folktexts.acs.acs_dataset">
+<span id="folktexts-acs-acs-dataset-module"></span><h2>folktexts.acs.acs_dataset module<a class="headerlink" href="#module-folktexts.acs.acs_dataset" title="Link to this heading"></a></h2>
+<p>Module to access ACS data using the folktables package.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.acs.acs_dataset.ACSDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.acs.acs_dataset.</span></span><span class="sig-name descname"><span class="pre">ACSDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_acs_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsampling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/acs/acs_dataset.html#ACSDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.acs.acs_dataset.ACSDataset" title="Link to this definition"></a></dt>
+<dd><p>Bases: <a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset" title="folktexts.dataset.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a></p>
+<p>Wrapper for ACS folktables datasets.</p>
+<p>Construct a Dataset object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>pd.DataFrame</em>) – The dataset’s data in pandas DataFrame format.</p></li>
+<li><p><strong>task</strong> (<a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata" title="folktexts.task.TaskMetadata"><em>TaskMetadata</em></a>) – The metadata for the prediction task.</p></li>
+<li><p><strong>test_size</strong> (<em>float</em><em>, </em><em>optional</em>) – The size of the test set, as a fraction of the total dataset size,
+by default 0.1.</p></li>
+<li><p><strong>val_size</strong> (<em>float</em><em>, </em><em>optional</em>) – The size of the validation set, as a fraction of the total dataset
+size, by default 0.1.</p></li>
+<li><p><strong>subsampling</strong> (<em>float</em><em>, </em><em>optional</em>) – Whether to use sub-sampling, and which fraction of the data to keep.
+By default will not use sub-sampling (<cite>subsampling=None</cite>).</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – The random state seed, by default 42.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.acs.acs_dataset.ACSDataset.make_from_task">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">make_from_task</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">survey_year</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'2018'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">horizon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'1-Year'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">survey</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'person'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/acs/acs_dataset.html#ACSDataset.make_from_task"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.acs.acs_dataset.ACSDataset.make_from_task" title="Link to this definition"></a></dt>
+<dd><p>Construct an ACSDataset object from a given ACS task.</p>
+<p>Can customize survey sample parameters (survey year, horizon, survey type).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>task</strong> (<em>str</em><em> | </em><a class="reference internal" href="#folktexts.acs.acs_tasks.ACSTaskMetadata" title="folktexts.acs.acs_tasks.ACSTaskMetadata"><em>ACSTaskMetadata</em></a>) – The name of the ACS task or the task object itself.</p></li>
+<li><p><strong>cache_dir</strong> (<em>str</em><em> | </em><em>Path</em><em>, </em><em>optional</em>) – The directory where ACS data is (or will be) saved to, by default
+uses DEFAULT_DATA_DIR.</p></li>
+<li><p><strong>survey_year</strong> (<em>str</em><em>, </em><em>optional</em>) – The year from which to load survey data, by default DEFAULT_SURVEY_YEAR.</p></li>
+<li><p><strong>horizon</strong> (<em>str</em><em>, </em><em>optional</em>) – The time horizon of survey data to load, by default DEFAULT_SURVEY_HORIZON.</p></li>
+<li><p><strong>survey</strong> (<em>str</em><em>, </em><em>optional</em>) – The name of the survey unit to load, by default DEFAULT_SURVEY_UNIT.</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – The random seed, by default DEFAULT_SEED.</p></li>
+<li><p><strong>**kwargs</strong> – Extra key-word arguments to be passed to the Dataset constructor.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.acs.acs_dataset.ACSDataset.task">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">task</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="#folktexts.acs.acs_tasks.ACSTaskMetadata" title="folktexts.acs.acs_tasks.ACSTaskMetadata"><span class="pre">ACSTaskMetadata</span></a></em><a class="headerlink" href="#folktexts.acs.acs_dataset.ACSDataset.task" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-folktexts.acs.acs_questions">
+<span id="folktexts-acs-acs-questions-module"></span><h2>folktexts.acs.acs_questions module<a class="headerlink" href="#module-folktexts.acs.acs_questions" title="Link to this heading"></a></h2>
+<p>A collection of instantiated ACS column objects and ACS tasks.</p>
+</section>
+<section id="module-folktexts.acs.acs_tasks">
+<span id="folktexts-acs-acs-tasks-module"></span><h2>folktexts.acs.acs_tasks module<a class="headerlink" href="#module-folktexts.acs.acs_tasks" title="Link to this heading"></a></h2>
+<p>A collection of ACS prediction tasks based on the folktables package.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.acs.acs_tasks.ACSTaskMetadata">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.acs.acs_tasks.</span></span><span class="sig-name descname"><span class="pre">ACSTaskMetadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols_to_text</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensitive_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multiple_choice_qa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">direct_numeric_qa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_numeric_qa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">folktables_obj</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/acs/acs_tasks.html#ACSTaskMetadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.acs.acs_tasks.ACSTaskMetadata" title="Link to this definition"></a></dt>
+<dd><p>Bases: <a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata" title="folktexts.task.TaskMetadata"><code class="xref py py-class docutils literal notranslate"><span class="pre">TaskMetadata</span></code></a></p>
+<p>A class to hold information on an ACS prediction task.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.acs.acs_tasks.ACSTaskMetadata.folktables_obj">
+<span class="sig-name descname"><span class="pre">folktables_obj</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">BasicProblem</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#folktexts.acs.acs_tasks.ACSTaskMetadata.folktables_obj" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.acs.acs_tasks.ACSTaskMetadata.make_folktables_task">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">make_folktables_task</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/acs/acs_tasks.html#ACSTaskMetadata.make_folktables_task"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.acs.acs_tasks.ACSTaskMetadata.make_folktables_task" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference internal" href="#folktexts.acs.acs_tasks.ACSTaskMetadata" title="folktexts.acs.acs_tasks.ACSTaskMetadata"><code class="xref py py-class docutils literal notranslate"><span class="pre">ACSTaskMetadata</span></code></a></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.acs.acs_tasks.ACSTaskMetadata.make_task">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">make_task</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensitive_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multiple_choice_qa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">direct_numeric_qa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">folktables_obj</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/acs/acs_tasks.html#ACSTaskMetadata.make_task"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.acs.acs_tasks.ACSTaskMetadata.make_task" title="Link to this definition"></a></dt>
+<dd><p>Create an ACS task object from the given parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference internal" href="#folktexts.acs.acs_tasks.ACSTaskMetadata" title="folktexts.acs.acs_tasks.ACSTaskMetadata"><code class="xref py py-class docutils literal notranslate"><span class="pre">ACSTaskMetadata</span></code></a></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-folktexts.acs.acs_thresholds">
+<span id="folktexts-acs-acs-thresholds-module"></span><h2>folktexts.acs.acs_thresholds module<a class="headerlink" href="#module-folktexts.acs.acs_thresholds" title="Link to this heading"></a></h2>
+<p>Threshold instances for ACS / folktables tasks.</p>
+</section>
+<section id="module-folktexts.acs">
+<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-folktexts.acs" title="Link to this heading"></a></h2>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="folktexts.html" class="btn btn-neutral float-left" title="folktexts package" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
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+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../readme.html">Readme file</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="modules.html">API reference</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="folktexts.html">folktexts package</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="folktexts.html#subpackages">Subpackages</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html">folktexts.acs package</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">folktexts.classifier package</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html">folktexts.cli package</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#submodules">Submodules</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.benchmark">folktexts.benchmark module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.col_to_text">folktexts.col_to_text module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.dataset">folktexts.dataset module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.evaluation">folktexts.evaluation module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.llm_utils">folktexts.llm_utils module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.plotting">folktexts.plotting module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.prompting">folktexts.prompting module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.qa_interface">folktexts.qa_interface module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.task">folktexts.task module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.threshold">folktexts.threshold module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts">Module contents</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../notebooks.html">Example notebooks</a></li>
+</ul>
+
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+      <li class="breadcrumb-item active">folktexts.classifier package</li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="folktexts-classifier-package">
+<h1>folktexts.classifier package<a class="headerlink" href="#folktexts-classifier-package" title="Link to this heading"></a></h1>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
+</section>
+<section id="module-folktexts.classifier.base">
+<span id="folktexts-classifier-base-module"></span><h2>folktexts.classifier.base module<a class="headerlink" href="#module-folktexts.classifier.base" title="Link to this heading"></a></h2>
+<p>Module containing the base class for all LLM risk classifiers.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.classifier.base.</span></span><span class="sig-name descname"><span class="pre">LLMClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prompt_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encode_row</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">correct_order_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">inference_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/classifier/base.html#LLMClassifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ClassifierMixin</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ABC</span></code></p>
+<p>An interface to produce risk scores and class predictions with an LLM.</p>
+<p>Creates an LLMClassifier object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>model_name</strong> (<em>str</em>) – The model name or ID.</p></li>
+<li><p><strong>task</strong> (<a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata" title="folktexts.task.TaskMetadata"><em>TaskMetadata</em></a><em> | </em><em>str</em>) – The task metadata object or name of an already created task.</p></li>
+<li><p><strong>custom_prompt_prefix</strong> (<em>str</em><em>, </em><em>optional</em>) – A custom prompt prefix to supply to the model before the encoded
+row data, by default None.</p></li>
+<li><p><strong>encode_row</strong> (<em>Callable</em><em>[</em><em>[</em><em>pd.Series</em><em>]</em><em>, </em><em>str</em><em>]</em><em>, </em><em>optional</em>) – The function used to encode tabular rows into natural text. If not
+provided, will use the default encoding function for the task.</p></li>
+<li><p><strong>threshold</strong> (<em>float</em><em>, </em><em>optional</em>) – The classification threshold to use when outputting binary
+predictions, by default 0.5. Must be between 0 and 1. Will be
+re-calibrated if <cite>fit</cite> is called.</p></li>
+<li><p><strong>correct_order_bias</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to correct ordering bias in multiple-choice Q&amp;A questions,
+by default True.</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – The random seed - used for reproducibility.</p></li>
+<li><p><strong>**inference_kwargs</strong> – Additional keyword arguments to be used at inference time. Options
+include <cite>context_size</cite> and <cite>batch_size</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.DEFAULT_INFERENCE_KWARGS">
+<span class="sig-name descname"><span class="pre">DEFAULT_INFERENCE_KWARGS</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'batch_size':</span> <span class="pre">16,</span> <span class="pre">'context_size':</span> <span class="pre">600}</span></em><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.DEFAULT_INFERENCE_KWARGS" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.compute_risk_estimates_for_dataframe">
+<span class="sig-name descname"><span class="pre">compute_risk_estimates_for_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/classifier/base.html#LLMClassifier.compute_risk_estimates_for_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.compute_risk_estimates_for_dataframe" title="Link to this definition"></a></dt>
+<dd><p>Compute risk estimates for a specific dataframe (internal helper function).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>df</strong> (<em>pd.DataFrame</em>) – The dataframe to compute risk estimates for.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>risk_scores</strong> – The risk estimates for each row in the dataframe.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.compute_risk_estimates_for_dataset">
+<span class="sig-name descname"><span class="pre">compute_risk_estimates_for_dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/classifier/base.html#LLMClassifier.compute_risk_estimates_for_dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.compute_risk_estimates_for_dataset" title="Link to this definition"></a></dt>
+<dd><p>Computes risk estimates for each row in the dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>dataset</strong> (<a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset" title="folktexts.dataset.Dataset"><em>Dataset</em></a>) – The dataset to compute risk estimates for.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>results</strong> – The risk estimates for each data type in the dataset (usually “train”,
+“val”, “test”).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict[str, np.ndarray]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.correct_order_bias">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">correct_order_bias</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.correct_order_bias" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.custom_prompt_prefix">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">custom_prompt_prefix</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.custom_prompt_prefix" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.encode_row">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">encode_row</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Series</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.encode_row" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_pos_cost</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_neg_cost</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/classifier/base.html#LLMClassifier.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.fit" title="Link to this definition"></a></dt>
+<dd><p>Uses the provided data sample to fit the prediction threshold.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.inference_kwargs">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">inference_kwargs</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">dict</span></em><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.inference_kwargs" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.model_name">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">model_name</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.model_name" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions_save_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/classifier/base.html#LLMClassifier.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.predict" title="Link to this definition"></a></dt>
+<dd><p>Returns binary predictions for the given data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> | <code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>]</span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.predict_proba">
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions_save_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/classifier/base.html#LLMClassifier.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.predict_proba" title="Link to this definition"></a></dt>
+<dd><p>Returns probability estimates for the given data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>pd.DataFrame</em>) – The DataFrame to compute risk estimates for.</p></li>
+<li><p><strong>predictions_save_path</strong> (<em>str</em><em> | </em><em>Path</em><em>, </em><em>optional</em>) – If provided, will save the computed risk scores to this path in
+disk. If the path exists, will attempt to load pre-computed
+predictions from this path.</p></li>
+<li><p><strong>labels</strong> (<em>pd.Series</em><em> | </em><em>np.ndarray</em><em>, </em><em>optional</em>) – The labels corresponding to the provided data. Not required to
+compute predictions. Will only be used to save alongside predictions
+to disk.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>risk_scores</strong> – The risk scores for the given data.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.seed">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">seed</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.seed" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_neg_cost</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_pos_cost</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#folktexts.classifier.base.LLMClassifier" title="folktexts.classifier.base.LLMClassifier"><span class="pre">LLMClassifier</span></a></span></span><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.set_fit_request" title="Link to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>false_neg_cost</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">false_neg_cost</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><strong>false_pos_cost</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">false_pos_cost</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.set_inference_kwargs">
+<span class="sig-name descname"><span class="pre">set_inference_kwargs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/classifier/base.html#LLMClassifier.set_inference_kwargs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.set_inference_kwargs" title="Link to this definition"></a></dt>
+<dd><p>Set inference kwargs for the model.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.set_predict_proba_request">
+<span class="sig-name descname"><span class="pre">set_predict_proba_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions_save_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#folktexts.classifier.base.LLMClassifier" title="folktexts.classifier.base.LLMClassifier"><span class="pre">LLMClassifier</span></a></span></span><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.set_predict_proba_request" title="Link to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">data</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p></li>
+<li><p><strong>labels</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">labels</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p></li>
+<li><p><strong>predictions_save_path</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">predictions_save_path</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.set_predict_request">
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions_save_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#folktexts.classifier.base.LLMClassifier" title="folktexts.classifier.base.LLMClassifier"><span class="pre">LLMClassifier</span></a></span></span><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.set_predict_request" title="Link to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">predict</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">data</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+<li><p><strong>labels</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">labels</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+<li><p><strong>predictions_save_path</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">predictions_save_path</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#folktexts.classifier.base.LLMClassifier" title="folktexts.classifier.base.LLMClassifier"><span class="pre">LLMClassifier</span></a></span></span><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.set_score_request" title="Link to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.task">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">task</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata" title="folktexts.task.TaskMetadata"><span class="pre">TaskMetadata</span></a></em><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.task" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.classifier.base.LLMClassifier.threshold">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">threshold</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#folktexts.classifier.base.LLMClassifier.threshold" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-folktexts.classifier.transformers_classifier">
+<span id="folktexts-classifier-transformers-classifier-module"></span><h2>folktexts.classifier.transformers_classifier module<a class="headerlink" href="#module-folktexts.classifier.transformers_classifier" title="Link to this heading"></a></h2>
+<p>Module for using huggingface transformers models as classifiers.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.classifier.transformers_classifier.TransformersLLMClassifier">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.classifier.transformers_classifier.</span></span><span class="sig-name descname"><span class="pre">TransformersLLMClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prompt_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encode_row</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">correct_order_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">inference_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/classifier/transformers_classifier.html#TransformersLLMClassifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.classifier.transformers_classifier.TransformersLLMClassifier" title="Link to this definition"></a></dt>
+<dd><p>Bases: <a class="reference internal" href="#folktexts.classifier.base.LLMClassifier" title="folktexts.classifier.base.LLMClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">LLMClassifier</span></code></a></p>
+<p>Use a huggingface transformers model to produce risk scores.</p>
+<p>Creates an LLMClassifier based on a huggingface transformers model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>model</strong> (<em>AutoModelForCausalLM</em>) – The torch language model to use for inference.</p></li>
+<li><p><strong>tokenizer</strong> (<em>AutoTokenizer</em>) – The tokenizer used to train the model.</p></li>
+<li><p><strong>task</strong> (<a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata" title="folktexts.task.TaskMetadata"><em>TaskMetadata</em></a><em> | </em><em>str</em>) – The task metadata object or name of an already created task.</p></li>
+<li><p><strong>custom_prompt_prefix</strong> (<em>str</em><em>, </em><em>optional</em>) – A custom prompt prefix to supply to the model before the encoded
+row data, by default None.</p></li>
+<li><p><strong>encode_row</strong> (<em>Callable</em><em>[</em><em>[</em><em>pd.Series</em><em>]</em><em>, </em><em>str</em><em>]</em><em>, </em><em>optional</em>) – The function used to encode tabular rows into natural text. If not
+provided, will use the default encoding function for the task.</p></li>
+<li><p><strong>threshold</strong> (<em>float</em><em>, </em><em>optional</em>) – The classification threshold to use when outputting binary
+predictions, by default 0.5. Must be between 0 and 1. Will be
+re-calibrated if <cite>fit</cite> is called.</p></li>
+<li><p><strong>correct_order_bias</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to correct ordering bias in multiple-choice Q&amp;A questions,
+by default True.</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – The random seed - used for reproducibility.</p></li>
+<li><p><strong>**inference_kwargs</strong> – Additional keyword arguments to be used at inference time. Options
+include <cite>context_size</cite> and <cite>batch_size</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.classifier.transformers_classifier.TransformersLLMClassifier.model">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">model</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">AutoModelForCausalLM</span></em><a class="headerlink" href="#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.model" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_neg_cost</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_pos_cost</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#folktexts.classifier.transformers_classifier.TransformersLLMClassifier" title="folktexts.classifier.transformers_classifier.TransformersLLMClassifier"><span class="pre">TransformersLLMClassifier</span></a></span></span><a class="headerlink" href="#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_fit_request" title="Link to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>false_neg_cost</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">false_neg_cost</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><strong>false_pos_cost</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">false_pos_cost</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_predict_proba_request">
+<span class="sig-name descname"><span class="pre">set_predict_proba_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions_save_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#folktexts.classifier.transformers_classifier.TransformersLLMClassifier" title="folktexts.classifier.transformers_classifier.TransformersLLMClassifier"><span class="pre">TransformersLLMClassifier</span></a></span></span><a class="headerlink" href="#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_predict_proba_request" title="Link to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">data</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p></li>
+<li><p><strong>labels</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">labels</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p></li>
+<li><p><strong>predictions_save_path</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">predictions_save_path</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_predict_request">
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions_save_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#folktexts.classifier.transformers_classifier.TransformersLLMClassifier" title="folktexts.classifier.transformers_classifier.TransformersLLMClassifier"><span class="pre">TransformersLLMClassifier</span></a></span></span><a class="headerlink" href="#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_predict_request" title="Link to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">predict</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">data</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+<li><p><strong>labels</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">labels</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+<li><p><strong>predictions_save_path</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">predictions_save_path</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#folktexts.classifier.transformers_classifier.TransformersLLMClassifier" title="folktexts.classifier.transformers_classifier.TransformersLLMClassifier"><span class="pre">TransformersLLMClassifier</span></a></span></span><a class="headerlink" href="#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_score_request" title="Link to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.classifier.transformers_classifier.TransformersLLMClassifier.tokenizer">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">tokenizer</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">AutoTokenizer</span></em><a class="headerlink" href="#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.tokenizer" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-folktexts.classifier.web_api_classifier">
+<span id="folktexts-classifier-web-api-classifier-module"></span><h2>folktexts.classifier.web_api_classifier module<a class="headerlink" href="#module-folktexts.classifier.web_api_classifier" title="Link to this heading"></a></h2>
+<p>Module for using a language model through a web API for risk classification.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.classifier.web_api_classifier.WebAPILLMClassifier">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.classifier.web_api_classifier.</span></span><span class="sig-name descname"><span class="pre">WebAPILLMClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prompt_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encode_row</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">correct_order_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_api_rpm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">inference_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/classifier/web_api_classifier.html#WebAPILLMClassifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.classifier.web_api_classifier.WebAPILLMClassifier" title="Link to this definition"></a></dt>
+<dd><p>Bases: <a class="reference internal" href="#folktexts.classifier.base.LLMClassifier" title="folktexts.classifier.base.LLMClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">LLMClassifier</span></code></a></p>
+<p>Use an LLM through a web API to produce risk scores.</p>
+<p>Creates an LLMClassifier object that uses a web API for inference.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>model_name</strong> (<em>str</em>) – The model ID to be resolved by <cite>litellm</cite>.</p></li>
+<li><p><strong>task</strong> (<a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata" title="folktexts.task.TaskMetadata"><em>TaskMetadata</em></a><em> | </em><em>str</em>) – The task metadata object or name of an already created task.</p></li>
+<li><p><strong>custom_prompt_prefix</strong> (<em>str</em><em>, </em><em>optional</em>) – A custom prompt prefix to supply to the model before the encoded
+row data, by default None.</p></li>
+<li><p><strong>encode_row</strong> (<em>Callable</em><em>[</em><em>[</em><em>pd.Series</em><em>]</em><em>, </em><em>str</em><em>]</em><em>, </em><em>optional</em>) – The function used to encode tabular rows into natural text. If not
+provided, will use the default encoding function for the task.</p></li>
+<li><p><strong>threshold</strong> (<em>float</em><em>, </em><em>optional</em>) – The classification threshold to use when outputting binary
+predictions, by default 0.5. Must be between 0 and 1. Will be
+re-calibrated if <cite>fit</cite> is called.</p></li>
+<li><p><strong>correct_order_bias</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to correct ordering bias in multiple-choice Q&amp;A questions,
+by default True.</p></li>
+<li><p><strong>max_api_rpm</strong> (<em>int</em><em>, </em><em>optional</em>) – The maximum number of requests per minute allowed for the API.</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – The random seed - used for reproducibility.</p></li>
+<li><p><strong>**inference_kwargs</strong> – Additional keyword arguments to be used at inference time. Options
+include <cite>context_size</cite> and <cite>batch_size</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.web_api_classifier.WebAPILLMClassifier.check_webAPI_deps">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">check_webAPI_deps</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/classifier/web_api_classifier.html#WebAPILLMClassifier.check_webAPI_deps"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.check_webAPI_deps" title="Link to this definition"></a></dt>
+<dd><p>Check if litellm dependencies are available.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_fit_request">
+<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_neg_cost</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_pos_cost</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#folktexts.classifier.web_api_classifier.WebAPILLMClassifier" title="folktexts.classifier.web_api_classifier.WebAPILLMClassifier"><span class="pre">WebAPILLMClassifier</span></a></span></span><a class="headerlink" href="#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_fit_request" title="Link to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>false_neg_cost</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">false_neg_cost</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+<li><p><strong>false_pos_cost</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">false_pos_cost</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_predict_proba_request">
+<span class="sig-name descname"><span class="pre">set_predict_proba_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions_save_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#folktexts.classifier.web_api_classifier.WebAPILLMClassifier" title="folktexts.classifier.web_api_classifier.WebAPILLMClassifier"><span class="pre">WebAPILLMClassifier</span></a></span></span><a class="headerlink" href="#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_predict_proba_request" title="Link to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">data</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p></li>
+<li><p><strong>labels</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">labels</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p></li>
+<li><p><strong>predictions_save_path</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">predictions_save_path</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict_proba</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_predict_request">
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions_save_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#folktexts.classifier.web_api_classifier.WebAPILLMClassifier" title="folktexts.classifier.web_api_classifier.WebAPILLMClassifier"><span class="pre">WebAPILLMClassifier</span></a></span></span><a class="headerlink" href="#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_predict_request" title="Link to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">predict</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">data</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+<li><p><strong>labels</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">labels</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+<li><p><strong>predictions_save_path</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">predictions_save_path</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#folktexts.classifier.web_api_classifier.WebAPILLMClassifier" title="folktexts.classifier.web_api_classifier.WebAPILLMClassifier"><span class="pre">WebAPILLMClassifier</span></a></span></span><a class="headerlink" href="#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_score_request" title="Link to this definition"></a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sample_weight</strong> (<em>str</em><em>, </em><em>True</em><em>, </em><em>False</em><em>, or </em><em>None</em><em>,                     </em><em>default=sklearn.utils.metadata_routing.UNCHANGED</em>) – Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>self</strong> – The updated object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.classifier.web_api_classifier.WebAPILLMClassifier.track_cost_callback">
+<span class="sig-name descname"><span class="pre">track_cost_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">completion_response</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_time</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_time</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/classifier/web_api_classifier.html#WebAPILLMClassifier.track_cost_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.track_cost_callback" title="Link to this definition"></a></dt>
+<dd><p>Callback function to cost of API calls.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-folktexts.classifier">
+<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-folktexts.classifier" title="Link to this heading"></a></h2>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="folktexts.acs.html" class="btn btn-neutral float-left" title="folktexts.acs package" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
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+
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+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
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+
+</body>
+</html>
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diff --git a/source/folktexts.cli.html b/source/folktexts.cli.html
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+
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+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+<li class="toctree-l2 current"><a class="reference internal" href="folktexts.html">folktexts package</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="folktexts.html#subpackages">Subpackages</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html">folktexts.acs package</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html">folktexts.classifier package</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">folktexts.cli package</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#submodules">Submodules</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.benchmark">folktexts.benchmark module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.col_to_text">folktexts.col_to_text module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.dataset">folktexts.dataset module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.evaluation">folktexts.evaluation module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.llm_utils">folktexts.llm_utils module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.plotting">folktexts.plotting module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.prompting">folktexts.prompting module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.qa_interface">folktexts.qa_interface module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.task">folktexts.task module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts.threshold">folktexts.threshold module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#module-folktexts">Module contents</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../notebooks.html">Example notebooks</a></li>
+</ul>
+
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+      <li class="breadcrumb-item active">folktexts.cli package</li>
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+            <a href="../_sources/source/folktexts.cli.rst.txt" rel="nofollow"> View page source</a>
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+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="folktexts-cli-package">
+<h1>folktexts.cli package<a class="headerlink" href="#folktexts-cli-package" title="Link to this heading"></a></h1>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
+</section>
+<section id="module-folktexts.cli.download_models">
+<span id="folktexts-cli-download-models-module"></span><h2>folktexts.cli.download_models module<a class="headerlink" href="#module-folktexts.cli.download_models" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.download_models.is_bf16_compatible">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.download_models.</span></span><span class="sig-name descname"><span class="pre">is_bf16_compatible</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/download_models.html#is_bf16_compatible"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.download_models.is_bf16_compatible" title="Link to this definition"></a></dt>
+<dd><p>Checks if the current environment is bfloat16 compatible.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.download_models.main">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.download_models.</span></span><span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/download_models.html#main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.download_models.main" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.download_models.setup_arg_parser">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.download_models.</span></span><span class="sig-name descname"><span class="pre">setup_arg_parser</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/download_models.html#setup_arg_parser"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.download_models.setup_arg_parser" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">ArgumentParser</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-folktexts.cli.eval_feature_importance">
+<span id="folktexts-cli-eval-feature-importance-module"></span><h2>folktexts.cli.eval_feature_importance module<a class="headerlink" href="#module-folktexts.cli.eval_feature_importance" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.eval_feature_importance.compute_feature_importance">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.eval_feature_importance.</span></span><span class="sig-name descname"><span class="pre">compute_feature_importance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">llm_clf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scorer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/eval_feature_importance.html#compute_feature_importance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.eval_feature_importance.compute_feature_importance" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.eval_feature_importance.main">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.eval_feature_importance.</span></span><span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/eval_feature_importance.html#main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.eval_feature_importance.main" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.eval_feature_importance.parse_feature_importance">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.eval_feature_importance.</span></span><span class="sig-name descname"><span class="pre">parse_feature_importance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">columns</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/eval_feature_importance.html#parse_feature_importance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.eval_feature_importance.parse_feature_importance" title="Link to this definition"></a></dt>
+<dd><p>Parse the results dictionary of sklearn’s permutation_importance.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.eval_feature_importance.setup_arg_parser">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.eval_feature_importance.</span></span><span class="sig-name descname"><span class="pre">setup_arg_parser</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/eval_feature_importance.html#setup_arg_parser"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.eval_feature_importance.setup_arg_parser" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">ArgumentParser</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-folktexts.cli.experiments">
+<span id="folktexts-cli-experiments-module"></span><h2>folktexts.cli.experiments module<a class="headerlink" href="#module-folktexts.cli.experiments" title="Link to this heading"></a></h2>
+<p>General constants and helper classes to run the main experiments on htcondor.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.Experiment">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.cli.experiments.</span></span><span class="sig-name descname"><span class="pre">Experiment</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">executable_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env_vars=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job_cpus=4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job_gpus=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job_memory_gb=62</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job_gpu_memory_gb=30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">job_bid=25</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/experiments.html#Experiment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.experiments.Experiment" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>A generic experiment to run on the cluster.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.Experiment.env_vars">
+<span class="sig-name descname"><span class="pre">env_vars</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">''</span></em><a class="headerlink" href="#folktexts.cli.experiments.Experiment.env_vars" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.Experiment.executable_path">
+<span class="sig-name descname"><span class="pre">executable_path</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></em><a class="headerlink" href="#folktexts.cli.experiments.Experiment.executable_path" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.Experiment.get_all_experiments">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_all_experiments</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/experiments.html#Experiment.get_all_experiments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.experiments.Experiment.get_all_experiments" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.Experiment.hash">
+<span class="sig-name descname"><span class="pre">hash</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/experiments.html#Experiment.hash"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.experiments.Experiment.hash" title="Link to this definition"></a></dt>
+<dd><p>Generate a hexadecimal hash that uniquely identifies the experiment’s arguments.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.Experiment.job_bid">
+<span class="sig-name descname"><span class="pre">job_bid</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">25</span></em><a class="headerlink" href="#folktexts.cli.experiments.Experiment.job_bid" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.Experiment.job_cpus">
+<span class="sig-name descname"><span class="pre">job_cpus</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">4</span></em><a class="headerlink" href="#folktexts.cli.experiments.Experiment.job_cpus" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.Experiment.job_gpu_memory_gb">
+<span class="sig-name descname"><span class="pre">job_gpu_memory_gb</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">30</span></em><a class="headerlink" href="#folktexts.cli.experiments.Experiment.job_gpu_memory_gb" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.Experiment.job_gpus">
+<span class="sig-name descname"><span class="pre">job_gpus</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">0</span></em><a class="headerlink" href="#folktexts.cli.experiments.Experiment.job_gpus" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.Experiment.job_memory_gb">
+<span class="sig-name descname"><span class="pre">job_memory_gb</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">62</span></em><a class="headerlink" href="#folktexts.cli.experiments.Experiment.job_memory_gb" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.Experiment.kwargs">
+<span class="sig-name descname"><span class="pre">kwargs</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></em><a class="headerlink" href="#folktexts.cli.experiments.Experiment.kwargs" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.Experiment.to_dict">
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/experiments.html#Experiment.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.experiments.Experiment.to_dict" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.experiments.launch_experiment_job">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.experiments.</span></span><span class="sig-name descname"><span class="pre">launch_experiment_job</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exp</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/experiments.html#launch_experiment_job"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.experiments.launch_experiment_job" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="module-folktexts.cli.launch_experiments_htcondor">
+<span id="folktexts-cli-launch-experiments-htcondor-module"></span><h2>folktexts.cli.launch_experiments_htcondor module<a class="headerlink" href="#module-folktexts.cli.launch_experiments_htcondor" title="Link to this heading"></a></h2>
+<p>Launch htcondor jobs for all ACS benchmark experiments.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.launch_experiments_htcondor.main">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.launch_experiments_htcondor.</span></span><span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/launch_experiments_htcondor.html#main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.launch_experiments_htcondor.main" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.launch_experiments_htcondor.make_llm_clf_experiment">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.launch_experiments_htcondor.</span></span><span class="sig-name descname"><span class="pre">make_llm_clf_experiment</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">executable_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env_vars_str</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/launch_experiments_htcondor.html#make_llm_clf_experiment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.launch_experiments_htcondor.make_llm_clf_experiment" title="Link to this definition"></a></dt>
+<dd><p>Create an experiment object to run.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference internal" href="#folktexts.cli.experiments.Experiment" title="folktexts.cli.experiments.Experiment"><code class="xref py py-class docutils literal notranslate"><span class="pre">Experiment</span></code></a></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.launch_experiments_htcondor.setup_arg_parser">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.launch_experiments_htcondor.</span></span><span class="sig-name descname"><span class="pre">setup_arg_parser</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/launch_experiments_htcondor.html#setup_arg_parser"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.launch_experiments_htcondor.setup_arg_parser" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">ArgumentParser</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-folktexts.cli.rerun_experiment">
+<span id="folktexts-cli-rerun-experiment-module"></span><h2>folktexts.cli.rerun_experiment module<a class="headerlink" href="#module-folktexts.cli.rerun_experiment" title="Link to this heading"></a></h2>
+<p>Helper script to re-run a single experiment locally.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.rerun_experiment.setup_arg_parser">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.rerun_experiment.</span></span><span class="sig-name descname"><span class="pre">setup_arg_parser</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/rerun_experiment.html#setup_arg_parser"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.rerun_experiment.setup_arg_parser" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">ArgumentParser</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-folktexts.cli.run_acs_benchmark">
+<span id="folktexts-cli-run-acs-benchmark-module"></span><h2>folktexts.cli.run_acs_benchmark module<a class="headerlink" href="#module-folktexts.cli.run_acs_benchmark" title="Link to this heading"></a></h2>
+<p>Runs the LLM calibration benchmark from the command line.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.run_acs_benchmark.main">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.run_acs_benchmark.</span></span><span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/run_acs_benchmark.html#main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.run_acs_benchmark.main" title="Link to this definition"></a></dt>
+<dd><p>Prepare and launch the LLM-as-classifier experiment using ACS data.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.cli.run_acs_benchmark.setup_arg_parser">
+<span class="sig-prename descclassname"><span class="pre">folktexts.cli.run_acs_benchmark.</span></span><span class="sig-name descname"><span class="pre">setup_arg_parser</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/cli/run_acs_benchmark.html#setup_arg_parser"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.cli.run_acs_benchmark.setup_arg_parser" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">ArgumentParser</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-folktexts.cli">
+<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-folktexts.cli" title="Link to this heading"></a></h2>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="folktexts.classifier.html" class="btn btn-neutral float-left" title="folktexts.classifier package" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../notebooks.html" class="btn btn-neutral float-right" title="Notebooks Gallery" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Social Foundations of Computation, at MPI-IS.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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diff --git a/source/folktexts.html b/source/folktexts.html
new file mode 100644
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--- /dev/null
+++ b/source/folktexts.html
@@ -0,0 +1,1852 @@
+
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
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+    <link rel="next" title="folktexts.acs package" href="folktexts.acs.html" />
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+</head>
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+<li class="toctree-l1 current"><a class="reference internal" href="modules.html">API reference</a><ul class="current">
+<li class="toctree-l2 current"><a class="current reference internal" href="#">folktexts package</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#subpackages">Subpackages</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html">folktexts.acs package</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html">folktexts.classifier package</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html">folktexts.cli package</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#submodules">Submodules</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#module-folktexts.benchmark">folktexts.benchmark module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.benchmark.Benchmark"><code class="docutils literal notranslate"><span class="pre">Benchmark</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.benchmark.BenchmarkConfig"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-folktexts.col_to_text">folktexts.col_to_text module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.col_to_text.ColumnToText"><code class="docutils literal notranslate"><span class="pre">ColumnToText</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-folktexts.dataset">folktexts.dataset module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.dataset.Dataset"><code class="docutils literal notranslate"><span class="pre">Dataset</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-folktexts.evaluation">folktexts.evaluation module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.evaluation.bootstrap_estimate"><code class="docutils literal notranslate"><span class="pre">bootstrap_estimate()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.evaluation.compute_best_threshold"><code class="docutils literal notranslate"><span class="pre">compute_best_threshold()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.evaluation.evaluate_binary_predictions"><code class="docutils literal notranslate"><span class="pre">evaluate_binary_predictions()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.evaluation.evaluate_binary_predictions_fairness"><code class="docutils literal notranslate"><span class="pre">evaluate_binary_predictions_fairness()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.evaluation.evaluate_predictions"><code class="docutils literal notranslate"><span class="pre">evaluate_predictions()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.evaluation.evaluate_predictions_bootstrap"><code class="docutils literal notranslate"><span class="pre">evaluate_predictions_bootstrap()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-folktexts.llm_utils">folktexts.llm_utils module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.llm_utils.add_pad_token"><code class="docutils literal notranslate"><span class="pre">add_pad_token()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.llm_utils.get_model_folder_path"><code class="docutils literal notranslate"><span class="pre">get_model_folder_path()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.llm_utils.get_model_size_B"><code class="docutils literal notranslate"><span class="pre">get_model_size_B()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.llm_utils.is_bf16_compatible"><code class="docutils literal notranslate"><span class="pre">is_bf16_compatible()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.llm_utils.load_model_tokenizer"><code class="docutils literal notranslate"><span class="pre">load_model_tokenizer()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.llm_utils.query_model_batch"><code class="docutils literal notranslate"><span class="pre">query_model_batch()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.llm_utils.query_model_batch_multiple_passes"><code class="docutils literal notranslate"><span class="pre">query_model_batch_multiple_passes()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-folktexts.plotting">folktexts.plotting module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.plotting.render_evaluation_plots"><code class="docutils literal notranslate"><span class="pre">render_evaluation_plots()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.plotting.render_fairness_plots"><code class="docutils literal notranslate"><span class="pre">render_fairness_plots()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.plotting.save_fig"><code class="docutils literal notranslate"><span class="pre">save_fig()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-folktexts.prompting">folktexts.prompting module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.prompting.apply_chat_template"><code class="docutils literal notranslate"><span class="pre">apply_chat_template()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.prompting.encode_row_prompt"><code class="docutils literal notranslate"><span class="pre">encode_row_prompt()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.prompting.encode_row_prompt_chat"><code class="docutils literal notranslate"><span class="pre">encode_row_prompt_chat()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.prompting.encode_row_prompt_few_shot"><code class="docutils literal notranslate"><span class="pre">encode_row_prompt_few_shot()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-folktexts.qa_interface">folktexts.qa_interface module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.qa_interface.Choice"><code class="docutils literal notranslate"><span class="pre">Choice</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.qa_interface.DirectNumericQA"><code class="docutils literal notranslate"><span class="pre">DirectNumericQA</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.qa_interface.MultipleChoiceQA"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.qa_interface.QAInterface"><code class="docutils literal notranslate"><span class="pre">QAInterface</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-folktexts.task">folktexts.task module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.task.TaskMetadata"><code class="docutils literal notranslate"><span class="pre">TaskMetadata</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-folktexts.threshold">folktexts.threshold module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#folktexts.threshold.Threshold"><code class="docutils literal notranslate"><span class="pre">Threshold</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-folktexts">Module contents</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../notebooks.html">Example notebooks</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">folktexts</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="modules.html">folktexts</a></li>
+      <li class="breadcrumb-item active">folktexts package</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/source/folktexts.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="folktexts-package">
+<h1>folktexts package<a class="headerlink" href="#folktexts-package" title="Link to this heading"></a></h1>
+<section id="subpackages">
+<h2>Subpackages<a class="headerlink" href="#subpackages" title="Link to this heading"></a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="folktexts.acs.html">folktexts.acs package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.acs.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.acs.html#module-folktexts.acs.acs_columns">folktexts.acs.acs_columns module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.acs.html#folktexts.acs.acs_columns.describe_income_to_poverty_ratio_pct"><code class="docutils literal notranslate"><span class="pre">describe_income_to_poverty_ratio_pct()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.acs.html#module-folktexts.acs.acs_dataset">folktexts.acs.acs_dataset module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.acs.html#folktexts.acs.acs_dataset.ACSDataset"><code class="docutils literal notranslate"><span class="pre">ACSDataset</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html#folktexts.acs.acs_dataset.ACSDataset.make_from_task"><code class="docutils literal notranslate"><span class="pre">ACSDataset.make_from_task()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html#folktexts.acs.acs_dataset.ACSDataset.task"><code class="docutils literal notranslate"><span class="pre">ACSDataset.task</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.acs.html#module-folktexts.acs.acs_questions">folktexts.acs.acs_questions module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.acs.html#module-folktexts.acs.acs_tasks">folktexts.acs.acs_tasks module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.acs.html#folktexts.acs.acs_tasks.ACSTaskMetadata"><code class="docutils literal notranslate"><span class="pre">ACSTaskMetadata</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html#folktexts.acs.acs_tasks.ACSTaskMetadata.folktables_obj"><code class="docutils literal notranslate"><span class="pre">ACSTaskMetadata.folktables_obj</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html#folktexts.acs.acs_tasks.ACSTaskMetadata.make_folktables_task"><code class="docutils literal notranslate"><span class="pre">ACSTaskMetadata.make_folktables_task()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html#folktexts.acs.acs_tasks.ACSTaskMetadata.make_task"><code class="docutils literal notranslate"><span class="pre">ACSTaskMetadata.make_task()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.acs.html#module-folktexts.acs.acs_thresholds">folktexts.acs.acs_thresholds module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.acs.html#module-folktexts.acs">Module contents</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="folktexts.classifier.html">folktexts.classifier package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.classifier.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.classifier.html#module-folktexts.classifier.base">folktexts.classifier.base module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier"><code class="docutils literal notranslate"><span class="pre">LLMClassifier</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.DEFAULT_INFERENCE_KWARGS"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.DEFAULT_INFERENCE_KWARGS</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.compute_risk_estimates_for_dataframe"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.compute_risk_estimates_for_dataframe()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.compute_risk_estimates_for_dataset"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.compute_risk_estimates_for_dataset()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.correct_order_bias"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.correct_order_bias</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.custom_prompt_prefix"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.custom_prompt_prefix</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.encode_row"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.encode_row</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.fit"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.fit()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.inference_kwargs"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.inference_kwargs</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.model_name"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.model_name</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.predict"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.predict()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.predict_proba()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.seed"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.seed</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.set_fit_request"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.set_fit_request()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.set_inference_kwargs"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.set_inference_kwargs()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.set_predict_proba_request"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.set_predict_proba_request()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.set_predict_request"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.set_predict_request()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.set_score_request"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.set_score_request()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.task"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.task</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier.threshold"><code class="docutils literal notranslate"><span class="pre">LLMClassifier.threshold</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.classifier.html#module-folktexts.classifier.transformers_classifier">folktexts.classifier.transformers_classifier module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier"><code class="docutils literal notranslate"><span class="pre">TransformersLLMClassifier</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.model"><code class="docutils literal notranslate"><span class="pre">TransformersLLMClassifier.model</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_fit_request"><code class="docutils literal notranslate"><span class="pre">TransformersLLMClassifier.set_fit_request()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_predict_proba_request"><code class="docutils literal notranslate"><span class="pre">TransformersLLMClassifier.set_predict_proba_request()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_predict_request"><code class="docutils literal notranslate"><span class="pre">TransformersLLMClassifier.set_predict_request()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.set_score_request"><code class="docutils literal notranslate"><span class="pre">TransformersLLMClassifier.set_score_request()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.transformers_classifier.TransformersLLMClassifier.tokenizer"><code class="docutils literal notranslate"><span class="pre">TransformersLLMClassifier.tokenizer</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.classifier.html#module-folktexts.classifier.web_api_classifier">folktexts.classifier.web_api_classifier module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier"><code class="docutils literal notranslate"><span class="pre">WebAPILLMClassifier</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.check_webAPI_deps"><code class="docutils literal notranslate"><span class="pre">WebAPILLMClassifier.check_webAPI_deps()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_fit_request"><code class="docutils literal notranslate"><span class="pre">WebAPILLMClassifier.set_fit_request()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_predict_proba_request"><code class="docutils literal notranslate"><span class="pre">WebAPILLMClassifier.set_predict_proba_request()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_predict_request"><code class="docutils literal notranslate"><span class="pre">WebAPILLMClassifier.set_predict_request()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.set_score_request"><code class="docutils literal notranslate"><span class="pre">WebAPILLMClassifier.set_score_request()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.web_api_classifier.WebAPILLMClassifier.track_cost_callback"><code class="docutils literal notranslate"><span class="pre">WebAPILLMClassifier.track_cost_callback()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.classifier.html#module-folktexts.classifier">Module contents</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="folktexts.cli.html">folktexts.cli package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.cli.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli.download_models">folktexts.cli.download_models module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.download_models.is_bf16_compatible"><code class="docutils literal notranslate"><span class="pre">is_bf16_compatible()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.download_models.main"><code class="docutils literal notranslate"><span class="pre">main()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.download_models.setup_arg_parser"><code class="docutils literal notranslate"><span class="pre">setup_arg_parser()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli.eval_feature_importance">folktexts.cli.eval_feature_importance module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.eval_feature_importance.compute_feature_importance"><code class="docutils literal notranslate"><span class="pre">compute_feature_importance()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.eval_feature_importance.main"><code class="docutils literal notranslate"><span class="pre">main()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.eval_feature_importance.parse_feature_importance"><code class="docutils literal notranslate"><span class="pre">parse_feature_importance()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.eval_feature_importance.setup_arg_parser"><code class="docutils literal notranslate"><span class="pre">setup_arg_parser()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli.experiments">folktexts.cli.experiments module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.Experiment"><code class="docutils literal notranslate"><span class="pre">Experiment</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.Experiment.env_vars"><code class="docutils literal notranslate"><span class="pre">Experiment.env_vars</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.Experiment.executable_path"><code class="docutils literal notranslate"><span class="pre">Experiment.executable_path</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.Experiment.get_all_experiments"><code class="docutils literal notranslate"><span class="pre">Experiment.get_all_experiments()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.Experiment.hash"><code class="docutils literal notranslate"><span class="pre">Experiment.hash()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.Experiment.job_bid"><code class="docutils literal notranslate"><span class="pre">Experiment.job_bid</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.Experiment.job_cpus"><code class="docutils literal notranslate"><span class="pre">Experiment.job_cpus</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.Experiment.job_gpu_memory_gb"><code class="docutils literal notranslate"><span class="pre">Experiment.job_gpu_memory_gb</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.Experiment.job_gpus"><code class="docutils literal notranslate"><span class="pre">Experiment.job_gpus</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.Experiment.job_memory_gb"><code class="docutils literal notranslate"><span class="pre">Experiment.job_memory_gb</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.Experiment.kwargs"><code class="docutils literal notranslate"><span class="pre">Experiment.kwargs</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.Experiment.to_dict"><code class="docutils literal notranslate"><span class="pre">Experiment.to_dict()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.experiments.launch_experiment_job"><code class="docutils literal notranslate"><span class="pre">launch_experiment_job()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli.launch_experiments_htcondor">folktexts.cli.launch_experiments_htcondor module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.launch_experiments_htcondor.main"><code class="docutils literal notranslate"><span class="pre">main()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.launch_experiments_htcondor.make_llm_clf_experiment"><code class="docutils literal notranslate"><span class="pre">make_llm_clf_experiment()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.launch_experiments_htcondor.setup_arg_parser"><code class="docutils literal notranslate"><span class="pre">setup_arg_parser()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli.rerun_experiment">folktexts.cli.rerun_experiment module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.rerun_experiment.setup_arg_parser"><code class="docutils literal notranslate"><span class="pre">setup_arg_parser()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli.run_acs_benchmark">folktexts.cli.run_acs_benchmark module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.run_acs_benchmark.main"><code class="docutils literal notranslate"><span class="pre">main()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html#folktexts.cli.run_acs_benchmark.setup_arg_parser"><code class="docutils literal notranslate"><span class="pre">setup_arg_parser()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli">Module contents</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
+</section>
+<section id="module-folktexts.benchmark">
+<span id="folktexts-benchmark-module"></span><h2>folktexts.benchmark module<a class="headerlink" href="#module-folktexts.benchmark" title="Link to this heading"></a></h2>
+<p>A benchmark class for measuring and evaluating LLM calibration.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.benchmark.</span></span><span class="sig-name descname"><span class="pre">Benchmark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">llm_clf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">BenchmarkConfig(numeric_risk_prompting=False,</span> <span class="pre">few_shot=None,</span> <span class="pre">reuse_few_shot_examples=False,</span> <span class="pre">batch_size=None,</span> <span class="pre">context_size=None,</span> <span class="pre">correct_order_bias=True,</span> <span class="pre">feature_subset=None,</span> <span class="pre">population_filter=None,</span> <span class="pre">seed=42)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/benchmark.html#Benchmark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.benchmark.Benchmark" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Measures and evaluates risk scores produced by an LLM.</p>
+<p>A benchmark object to measure and evaluate risk scores produced by an LLM.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>llm_clf</strong> (<a class="reference internal" href="folktexts.classifier.html#folktexts.classifier.base.LLMClassifier" title="folktexts.classifier.base.LLMClassifier"><em>LLMClassifier</em></a>) – A language model classifier object (can be local or web-hosted).</p></li>
+<li><p><strong>dataset</strong> (<a class="reference internal" href="#folktexts.dataset.Dataset" title="folktexts.dataset.Dataset"><em>Dataset</em></a>) – The dataset object to use for the benchmark.÷</p></li>
+<li><p><strong>config</strong> (<a class="reference internal" href="#folktexts.benchmark.BenchmarkConfig" title="folktexts.benchmark.BenchmarkConfig"><em>BenchmarkConfig</em></a><em>, </em><em>optional</em>) – The configuration object used to create the benchmark parameters.
+<strong>NOTE</strong>: This is used to uniquely identify the benchmark object for
+reproducibility; it <strong>will not be used to change the benchmark
+behavior</strong>. To configure the benchmark, pass a configuration object
+to the Benchmark.make_benchmark method.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark.ACS_DATASET_CONFIGS">
+<span class="sig-name descname"><span class="pre">ACS_DATASET_CONFIGS</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'horizon':</span> <span class="pre">'1-Year',</span> <span class="pre">'seed':</span> <span class="pre">42,</span> <span class="pre">'subsampling':</span> <span class="pre">None,</span> <span class="pre">'survey':</span> <span class="pre">'person',</span> <span class="pre">'survey_year':</span> <span class="pre">'2018',</span> <span class="pre">'test_size':</span> <span class="pre">0.1,</span> <span class="pre">'val_size':</span> <span class="pre">0.1}</span></em><a class="headerlink" href="#folktexts.benchmark.Benchmark.ACS_DATASET_CONFIGS" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark.configs_dict">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">configs_dict</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">dict</span></em><a class="headerlink" href="#folktexts.benchmark.Benchmark.configs_dict" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark.make_acs_benchmark">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">make_acs_benchmark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task_name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_api_rpm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">BenchmarkConfig(numeric_risk_prompting=False,</span> <span class="pre">few_shot=None,</span> <span class="pre">reuse_few_shot_examples=False,</span> <span class="pre">batch_size=None,</span> <span class="pre">context_size=None,</span> <span class="pre">correct_order_bias=True,</span> <span class="pre">feature_subset=None,</span> <span class="pre">population_filter=None,</span> <span class="pre">seed=42)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/benchmark.html#Benchmark.make_acs_benchmark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.benchmark.Benchmark.make_acs_benchmark" title="Link to this definition"></a></dt>
+<dd><p>Create a standardized calibration benchmark on ACS data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>task_name</strong> (<em>str</em>) – The name of the ACS task to use.</p></li>
+<li><p><strong>model</strong> (<em>AutoModelForCausalLM</em><em> | </em><em>str</em>) – The transformers language model to use, or the model ID for a webAPI
+hosted model (e.g., “openai/gpt-4o-mini”).</p></li>
+<li><p><strong>tokenizer</strong> (<em>AutoTokenizer</em><em>, </em><em>optional</em>) – The tokenizer used to train the model (if using a transformers
+model). Not required for webAPI models.</p></li>
+<li><p><strong>data_dir</strong> (<em>str</em><em> | </em><em>Path</em><em>, </em><em>optional</em>) – Path to the directory to load data from and save data in.</p></li>
+<li><p><strong>max_api_rpm</strong> (<em>int</em><em>, </em><em>optional</em>) – The maximum number of API requests per minute for webAPI models.</p></li>
+<li><p><strong>config</strong> (<a class="reference internal" href="#folktexts.benchmark.BenchmarkConfig" title="folktexts.benchmark.BenchmarkConfig"><em>BenchmarkConfig</em></a><em>, </em><em>optional</em>) – Extra benchmark configurations, by default will use
+<cite>BenchmarkConfig.default_config()</cite>.</p></li>
+<li><p><strong>**kwargs</strong> – Additional arguments passed to <cite>ACSDataset</cite> and <cite>BenchmarkConfig</cite>.
+By default will use a set of standardized configurations for
+reproducibility.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>bench</strong> – The ACS calibration benchmark object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#folktexts.benchmark.Benchmark" title="folktexts.benchmark.Benchmark">Benchmark</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark.make_benchmark">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">make_benchmark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_api_rpm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">BenchmarkConfig(numeric_risk_prompting=False,</span> <span class="pre">few_shot=None,</span> <span class="pre">reuse_few_shot_examples=False,</span> <span class="pre">batch_size=None,</span> <span class="pre">context_size=None,</span> <span class="pre">correct_order_bias=True,</span> <span class="pre">feature_subset=None,</span> <span class="pre">population_filter=None,</span> <span class="pre">seed=42)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/benchmark.html#Benchmark.make_benchmark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.benchmark.Benchmark.make_benchmark" title="Link to this definition"></a></dt>
+<dd><p>Create a calibration benchmark from a given configuration.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>task</strong> (<a class="reference internal" href="#folktexts.task.TaskMetadata" title="folktexts.task.TaskMetadata"><em>TaskMetadata</em></a><em> | </em><em>str</em>) – The task metadata object or name of the task to use.</p></li>
+<li><p><strong>dataset</strong> (<a class="reference internal" href="#folktexts.dataset.Dataset" title="folktexts.dataset.Dataset"><em>Dataset</em></a>) – The dataset to use for the benchmark.</p></li>
+<li><p><strong>model</strong> (<em>AutoModelForCausalLM</em><em> | </em><em>str</em>) – The transformers language model to use, or the model ID for a webAPI
+hosted model (e.g., “openai/gpt-4o-mini”).</p></li>
+<li><p><strong>tokenizer</strong> (<em>AutoTokenizer</em><em>, </em><em>optional</em>) – The tokenizer used to train the model (if using a transformers
+model). Not required for webAPI models.</p></li>
+<li><p><strong>max_api_rpm</strong> (<em>int</em><em>, </em><em>optional</em>) – The maximum number of API requests per minute for webAPI models.</p></li>
+<li><p><strong>config</strong> (<a class="reference internal" href="#folktexts.benchmark.BenchmarkConfig" title="folktexts.benchmark.BenchmarkConfig"><em>BenchmarkConfig</em></a><em>, </em><em>optional</em>) – Extra benchmark configurations, by default will use
+<cite>BenchmarkConfig.default_config()</cite>.</p></li>
+<li><p><strong>**kwargs</strong> – Additional arguments for easier configuration of the benchmark.
+Will simply use these values to update the <cite>config</cite> object.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>bench</strong> – The calibration benchmark object.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#folktexts.benchmark.Benchmark" title="folktexts.benchmark.Benchmark">Benchmark</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark.model_name">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">model_name</span></span><a class="headerlink" href="#folktexts.benchmark.Benchmark.model_name" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark.plot_results">
+<span class="sig-name descname"><span class="pre">plot_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_plots</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/benchmark.html#Benchmark.plot_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.benchmark.Benchmark.plot_results" title="Link to this definition"></a></dt>
+<dd><p>Render evaluation plots and save to disk.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>show_plots</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to show plots, by default True.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>plots_paths</strong> – The paths to the saved plots.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict[str, str]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark.results">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">results</span></span><a class="headerlink" href="#folktexts.benchmark.Benchmark.results" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark.results_dir">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">results_dir</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Path</span></em><a class="headerlink" href="#folktexts.benchmark.Benchmark.results_dir" title="Link to this definition"></a></dt>
+<dd><p>Get the results directory for this benchmark.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark.results_root_dir">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">results_root_dir</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Path</span></em><a class="headerlink" href="#folktexts.benchmark.Benchmark.results_root_dir" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results_root_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/benchmark.html#Benchmark.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.benchmark.Benchmark.run" title="Link to this definition"></a></dt>
+<dd><p>Run the calibration benchmark experiment.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>results_root_dir</strong> (<em>str</em><em> | </em><em>Path</em>) – Path to root directory under which results will be saved.</p></li>
+<li><p><strong>fit_threshold</strong> (<em>int</em><em> | </em><em>bool</em><em>, </em><em>optional</em>) – Whether to fit the binarization threshold on a given number of
+training samples, by default 0 (will not fit the threshold).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The benchmark metric value. By default this is the ECE score.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark.save_results">
+<span class="sig-name descname"><span class="pre">save_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results_root_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/benchmark.html#Benchmark.save_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.benchmark.Benchmark.save_results" title="Link to this definition"></a></dt>
+<dd><p>Save the benchmark results to disk.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>results_root_dir</strong> (<em>str</em><em> | </em><em>Path</em><em>, </em><em>optional</em>) – Path to root directory under which results will be saved. By default
+will use <cite>self.results_root_dir</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.benchmark.Benchmark.task">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">task</span></span><a class="headerlink" href="#folktexts.benchmark.Benchmark.task" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.benchmark.</span></span><span class="sig-name descname"><span class="pre">BenchmarkConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">numeric_risk_prompting</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">few_shot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reuse_few_shot_examples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">correct_order_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_subset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">population_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/benchmark.html#BenchmarkConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>A dataclass to hold the configuration for risk-score benchmark.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.numeric_risk_prompting">
+<span class="sig-name descname"><span class="pre">numeric_risk_prompting</span></span><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.numeric_risk_prompting" title="Link to this definition"></a></dt>
+<dd><p>Whether to prompt for numeric risk-estimates instead of multiple-choice
+Q&amp;A, by default False.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool, optional</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.few_shot">
+<span class="sig-name descname"><span class="pre">few_shot</span></span><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.few_shot" title="Link to this definition"></a></dt>
+<dd><p>Whether to use few-shot prompting with a given number of examples, by
+default None.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int | None, optional</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.reuse_few_shot_examples">
+<span class="sig-name descname"><span class="pre">reuse_few_shot_examples</span></span><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.reuse_few_shot_examples" title="Link to this definition"></a></dt>
+<dd><p>Whether to reuse the same samples for few-shot prompting (or sample new
+ones every time), by default False.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool, optional</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.batch_size">
+<span class="sig-name descname"><span class="pre">batch_size</span></span><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.batch_size" title="Link to this definition"></a></dt>
+<dd><p>The batch size to use for inference.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int | None, optional</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.context_size">
+<span class="sig-name descname"><span class="pre">context_size</span></span><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.context_size" title="Link to this definition"></a></dt>
+<dd><p>The maximum context size when prompting the LLM.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int | None, optional</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.correct_order_bias">
+<span class="sig-name descname"><span class="pre">correct_order_bias</span></span><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.correct_order_bias" title="Link to this definition"></a></dt>
+<dd><p>Whether to correct the ordering bias in multiple-choice Q&amp;A when
+prompting the LLM, by default True.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool, optional</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.feature_subset">
+<span class="sig-name descname"><span class="pre">feature_subset</span></span><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.feature_subset" title="Link to this definition"></a></dt>
+<dd><p>Whether to use a subset of the standard feature set for the task. The
+list should contain the names of the columns of features to use.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>list[str] | None, optional</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.population_filter">
+<span class="sig-name descname"><span class="pre">population_filter</span></span><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.population_filter" title="Link to this definition"></a></dt>
+<dd><p>Optional population filter for this benchmark; must follow the format
+<cite>{“column_name”: “value”}</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict | None, optional</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.seed">
+<span class="sig-name descname"><span class="pre">seed</span></span><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.seed" title="Link to this definition"></a></dt>
+<dd><p>Random seed – to set for reproducibility.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>int, optional</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id0">
+<span class="sig-name descname"><span class="pre">batch_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code> <span class="pre">|</span> <code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#id0" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id1">
+<span class="sig-name descname"><span class="pre">context_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code> <span class="pre">|</span> <code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#id1" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id2">
+<span class="sig-name descname"><span class="pre">correct_order_bias</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">True</span></em><a class="headerlink" href="#id2" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.default_config">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">default_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">changes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/benchmark.html#BenchmarkConfig.default_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.default_config" title="Link to this definition"></a></dt>
+<dd><p>Returns the default configuration with optional changes.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id3">
+<span class="sig-name descname"><span class="pre">feature_subset</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code><span class="pre">[</span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code><span class="pre">]</span> <span class="pre">|</span> <code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#id3" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id4">
+<span class="sig-name descname"><span class="pre">few_shot</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code> <span class="pre">|</span> <code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#id4" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.load_from_disk">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load_from_disk</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/benchmark.html#BenchmarkConfig.load_from_disk"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.load_from_disk" title="Link to this definition"></a></dt>
+<dd><p>Load the configuration from disk.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id5">
+<span class="sig-name descname"><span class="pre">numeric_risk_prompting</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#id5" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id6">
+<span class="sig-name descname"><span class="pre">population_filter</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code> <span class="pre">|</span> <code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#id6" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id7">
+<span class="sig-name descname"><span class="pre">reuse_few_shot_examples</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#id7" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.save_to_disk">
+<span class="sig-name descname"><span class="pre">save_to_disk</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/benchmark.html#BenchmarkConfig.save_to_disk"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.save_to_disk" title="Link to this definition"></a></dt>
+<dd><p>Save the configuration to disk.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id8">
+<span class="sig-name descname"><span class="pre">seed</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">42</span></em><a class="headerlink" href="#id8" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.benchmark.BenchmarkConfig.update">
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">changes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/benchmark.html#BenchmarkConfig.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.benchmark.BenchmarkConfig.update" title="Link to this definition"></a></dt>
+<dd><p>Update the configuration with new values.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference internal" href="#folktexts.benchmark.BenchmarkConfig" title="folktexts.benchmark.BenchmarkConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">BenchmarkConfig</span></code></a></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-folktexts.col_to_text">
+<span id="folktexts-col-to-text-module"></span><h2>folktexts.col_to_text module<a class="headerlink" href="#module-folktexts.col_to_text" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.col_to_text.ColumnToText">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.col_to_text.</span></span><span class="sig-name descname"><span class="pre">ColumnToText</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">short_description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">question</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">connector_verb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'is:'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_value_fill</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'N/A'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_value_map_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/col_to_text.html#ColumnToText"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.col_to_text.ColumnToText" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Maps a single column’s values to natural text.</p>
+<p>Constructs a <cite>ColumnToText</cite> object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>name</strong> (<em>str</em>) – The column’s name.</p></li>
+<li><p><strong>short_description</strong> (<em>str</em>) – A short description of the column to be used before different
+values. For example, short_description=”yearly income” will result
+in “The yearly income is […]”.</p></li>
+<li><p><strong>value_map</strong> (<em>dict</em><em>[</em><em>int</em><em> | </em><em>str</em><em>, </em><em>str</em><em>] </em><em>| </em><em>Callable</em><em>, </em><em>optional</em>) – A map between column values and their textual meaning. If not
+provided, will try to infer a mapping from the <cite>question</cite>.</p></li>
+<li><p><strong>question</strong> (<a class="reference internal" href="#folktexts.qa_interface.QAInterface" title="folktexts.qa_interface.QAInterface"><em>QAInterface</em></a><em>, </em><em>optional</em>) – A question associated with the column. If not provided, will try to
+infer a multiple-choice question from the <cite>value_map</cite>.</p></li>
+<li><p><strong>connector_verb</strong> (<em>str</em><em>, </em><em>optional</em>) – Which verb to use when connecting the column’s description to its
+value; by default “is”.</p></li>
+<li><p><strong>missing_value_fill</strong> (<em>str</em><em>, </em><em>optional</em>) – The value to use when the column’s value is not found in the
+<cite>value_map</cite>, by default “N/A”.</p></li>
+<li><p><strong>use_value_map_only</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to only use the <cite>value_map</cite> for mapping values to text,
+or whether natural language representation should be generated using
+the <cite>connector_verb</cite> and <cite>short_description</cite> as well.
+By default (False) will construct a natural language representation
+of the form:
+<cite>“The [short_description] [connector_verb] [value_map.get(val)]”.</cite></p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.col_to_text.ColumnToText.get_text">
+<span class="sig-name descname"><span class="pre">get_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/col_to_text.html#ColumnToText.get_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.col_to_text.ColumnToText.get_text" title="Link to this definition"></a></dt>
+<dd><p>Returns the natural text representation of the given data value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.col_to_text.ColumnToText.name">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">name</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><a class="headerlink" href="#folktexts.col_to_text.ColumnToText.name" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.col_to_text.ColumnToText.question">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">question</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="#folktexts.qa_interface.QAInterface" title="folktexts.qa_interface.QAInterface"><span class="pre">QAInterface</span></a></em><a class="headerlink" href="#folktexts.col_to_text.ColumnToText.question" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.col_to_text.ColumnToText.short_description">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">short_description</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><a class="headerlink" href="#folktexts.col_to_text.ColumnToText.short_description" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.col_to_text.ColumnToText.value_map">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">value_map</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Callable</span></em><a class="headerlink" href="#folktexts.col_to_text.ColumnToText.value_map" title="Link to this definition"></a></dt>
+<dd><p>Returns the value map function for this column.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-folktexts.dataset">
+<span id="folktexts-dataset-module"></span><h2>folktexts.dataset module<a class="headerlink" href="#module-folktexts.dataset" title="Link to this heading"></a></h2>
+<p>General Dataset functionality for text-based datasets.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.dataset.</span></span><span class="sig-name descname"><span class="pre">Dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsampling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/dataset.html#Dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.dataset.Dataset" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Construct a Dataset object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>pd.DataFrame</em>) – The dataset’s data in pandas DataFrame format.</p></li>
+<li><p><strong>task</strong> (<a class="reference internal" href="#folktexts.task.TaskMetadata" title="folktexts.task.TaskMetadata"><em>TaskMetadata</em></a>) – The metadata for the prediction task.</p></li>
+<li><p><strong>test_size</strong> (<em>float</em><em>, </em><em>optional</em>) – The size of the test set, as a fraction of the total dataset size,
+by default 0.1.</p></li>
+<li><p><strong>val_size</strong> (<em>float</em><em>, </em><em>optional</em>) – The size of the validation set, as a fraction of the total dataset
+size, by default 0.1.</p></li>
+<li><p><strong>subsampling</strong> (<em>float</em><em>, </em><em>optional</em>) – Whether to use sub-sampling, and which fraction of the data to keep.
+By default will not use sub-sampling (<cite>subsampling=None</cite>).</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – The random state seed, by default 42.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.data">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">data</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">DataFrame</span></em><a class="headerlink" href="#folktexts.dataset.Dataset.data" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.filter">
+<span class="sig-name descname"><span class="pre">filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">population_feature_values</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/dataset.html#Dataset.filter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.dataset.Dataset.filter" title="Link to this definition"></a></dt>
+<dd><p>Filter dataset rows in-place.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.get_data_split">
+<span class="sig-name descname"><span class="pre">get_data_split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/dataset.html#Dataset.get_data_split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.dataset.Dataset.get_data_split" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Series</span></code>]</span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.get_features_data">
+<span class="sig-name descname"><span class="pre">get_features_data</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/dataset.html#Dataset.get_features_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.dataset.Dataset.get_features_data" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.get_sensitive_attribute_data">
+<span class="sig-name descname"><span class="pre">get_sensitive_attribute_data</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/dataset.html#Dataset.get_sensitive_attribute_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.dataset.Dataset.get_sensitive_attribute_data" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">Series</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.get_target_data">
+<span class="sig-name descname"><span class="pre">get_target_data</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/dataset.html#Dataset.get_target_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.dataset.Dataset.get_target_data" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">Series</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.get_test">
+<span class="sig-name descname"><span class="pre">get_test</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/dataset.html#Dataset.get_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.dataset.Dataset.get_test" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.get_train">
+<span class="sig-name descname"><span class="pre">get_train</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/dataset.html#Dataset.get_train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.dataset.Dataset.get_train" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.get_val">
+<span class="sig-name descname"><span class="pre">get_val</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/dataset.html#Dataset.get_val"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.dataset.Dataset.get_val" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.name">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">name</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><a class="headerlink" href="#folktexts.dataset.Dataset.name" title="Link to this definition"></a></dt>
+<dd><p>A unique name for this dataset.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.sample_n_train_examples">
+<span class="sig-name descname"><span class="pre">sample_n_train_examples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reuse_examples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/dataset.html#Dataset.sample_n_train_examples"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.dataset.Dataset.sample_n_train_examples" title="Link to this definition"></a></dt>
+<dd><p>Return a set of samples from the training set.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n</strong> (<em>int</em>) – The number of example rows to return.</p></li>
+<li><p><strong>reuse_examples</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to reuse the same examples for consistency. By default will
+sample new examples each time (<cite>reuse_examples=False</cite>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>X, y</strong> – The features and target data for the sampled examples.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple[pd.DataFrame, pd.Series]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.seed">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">seed</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><a class="headerlink" href="#folktexts.dataset.Dataset.seed" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.subsample">
+<span class="sig-name descname"><span class="pre">subsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subsampling</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/dataset.html#Dataset.subsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.dataset.Dataset.subsample" title="Link to this definition"></a></dt>
+<dd><p>Subsamples this dataset in-place.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.subsampling">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">subsampling</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#folktexts.dataset.Dataset.subsampling" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.task">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">task</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="#folktexts.task.TaskMetadata" title="folktexts.task.TaskMetadata"><span class="pre">TaskMetadata</span></a></em><a class="headerlink" href="#folktexts.dataset.Dataset.task" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.test_size">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">test_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#folktexts.dataset.Dataset.test_size" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.train_size">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">train_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#folktexts.dataset.Dataset.train_size" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.dataset.Dataset.val_size">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">val_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">float</span></em><a class="headerlink" href="#folktexts.dataset.Dataset.val_size" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-folktexts.evaluation">
+<span id="folktexts-evaluation-module"></span><h2>folktexts.evaluation module<a class="headerlink" href="#module-folktexts.evaluation" title="Link to this heading"></a></h2>
+<p>Module to map risk-estimates to a variety of evaluation metrics.</p>
+<p class="rubric">Notes</p>
+<p>Code based on the <cite>error_parity.evaluation</cite> module,
+at: <a class="reference external" href="https://github.com/socialfoundations/error-parity/blob/main/error_parity/evaluation.py">https://github.com/socialfoundations/error-parity/blob/main/error_parity/evaluation.py</a></p>
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.evaluation.bootstrap_estimate">
+<span class="sig-prename descclassname"><span class="pre">folktexts.evaluation.</span></span><span class="sig-name descname"><span class="pre">bootstrap_estimate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eval_func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred_scores</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensitive_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">confidence_pct</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/evaluation.html#bootstrap_estimate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.evaluation.bootstrap_estimate" title="Link to this definition"></a></dt>
+<dd><p>Computes bootstrap estimates of the given evaluation function.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>eval_func</strong> (<em>Callable</em><em>[</em><em>[</em><em>np.ndarray</em><em>, </em><em>np.ndarray</em><em>, </em><em>np.ndarray</em><em>]</em><em>, </em><em>dict</em><em>[</em><em>str</em><em>, </em><em>float</em><em>]</em><em>]</em>) – The evaluation function to run for each bootstrap sample. Must follow
+the signature <cite>eval_func(y_true, y_pred_scores, sensitive_attribute)</cite>.</p></li>
+<li><p><strong>y_true</strong> (<em>np.ndarray</em>) – The true labels.</p></li>
+<li><p><strong>y_pred_scores</strong> (<em>np.ndarray</em>) – The predicted scores.</p></li>
+<li><p><strong>sensitive_attribute</strong> (<em>np.ndarray</em><em>, </em><em>optional</em>) – Optionally, provide the sensitive attribute data to compute fairness
+metrics, by default None.</p></li>
+<li><p><strong>k</strong> (<em>int</em><em>, </em><em>optional</em>) – How many bootstrap samples to draw, by default 200.</p></li>
+<li><p><strong>confidence_pct</strong> (<em>float</em><em>, </em><em>optional</em>) – The confidence interval to use, in percentage, by default 95.</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – The random seed, by default 42.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>results</strong> – A dictionary containing bootstrap estimates for a variety of metrics.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict[str, float]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.evaluation.compute_best_threshold">
+<span class="sig-prename descclassname"><span class="pre">folktexts.evaluation.</span></span><span class="sig-name descname"><span class="pre">compute_best_threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred_scores</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_pos_cost</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_neg_cost</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/evaluation.html#compute_best_threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.evaluation.compute_best_threshold" title="Link to this definition"></a></dt>
+<dd><p>Computes the binarization threshold that maximizes accuracy.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y_true</strong> (<em>np.ndarray</em>) – The true class labels.</p></li>
+<li><p><strong>y_pred_scores</strong> (<em>np.ndarray</em>) – The predicted risk scores.</p></li>
+<li><p><strong>false_pos_cost</strong> (<em>float</em><em>, </em><em>optional</em>) – The cost of a false positive error, by default 1.0</p></li>
+<li><p><strong>false_neg_cost</strong> (<em>float</em><em>, </em><em>optional</em>) – The cost of a false negative error, by default 1.0</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>best_threshold</strong> – The threshold value that maximizes accuracy for the given predictions.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.evaluation.evaluate_binary_predictions">
+<span class="sig-prename descclassname"><span class="pre">folktexts.evaluation.</span></span><span class="sig-name descname"><span class="pre">evaluate_binary_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/evaluation.html#evaluate_binary_predictions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.evaluation.evaluate_binary_predictions" title="Link to this definition"></a></dt>
+<dd><p>Evaluates the provided binary predictions on common performance metrics.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y_true</strong> (<em>np.ndarray</em>) – The true class labels.</p></li>
+<li><p><strong>y_pred</strong> (<em>np.ndarray</em>) – The binary predictions.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A dictionary with key-value pairs of (metric name, metric value).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.evaluation.evaluate_binary_predictions_fairness">
+<span class="sig-prename descclassname"><span class="pre">folktexts.evaluation.</span></span><span class="sig-name descname"><span class="pre">evaluate_binary_predictions_fairness</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensitive_attribute</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_groupwise_metrics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_group_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.04</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/evaluation.html#evaluate_binary_predictions_fairness"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.evaluation.evaluate_binary_predictions_fairness" title="Link to this definition"></a></dt>
+<dd><p>Evaluates fairness of the given predictions.</p>
+<p>Fairness metrics are computed as the ratios between group-wise performance
+metrics.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y_true</strong> (<em>np.ndarray</em>) – The true class labels.</p></li>
+<li><p><strong>y_pred</strong> (<em>np.ndarray</em>) – The discretized predictions.</p></li>
+<li><p><strong>sensitive_attribute</strong> (<em>np.ndarray</em>) – The sensitive attribute (protected group membership).</p></li>
+<li><p><strong>return_groupwise_metrics</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to return group-wise performance metrics (bool: True) or only
+the ratios between these metrics (bool: False), by default False.</p></li>
+<li><p><strong>min_group_size</strong> (<em>float</em><em>, </em><em>optional</em>) – The minimum fraction of samples (as a fraction of the total number of
+samples) that a group must have to be considered for fairness
+evaluation, by default 0.04. This is meant to avoid evaluating metrics
+on very small groups which leads to noisy and inconsistent results.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A dictionary with key-value pairs of (metric name, metric value).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.evaluation.evaluate_predictions">
+<span class="sig-prename descclassname"><span class="pre">folktexts.evaluation.</span></span><span class="sig-name descname"><span class="pre">evaluate_predictions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred_scores</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensitive_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'best'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/evaluation.html#evaluate_predictions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.evaluation.evaluate_predictions" title="Link to this definition"></a></dt>
+<dd><p>Evaluates predictions on common performance and fairness metrics.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y_true</strong> (<em>np.ndarray</em>) – The true class labels.</p></li>
+<li><p><strong>y_pred_scores</strong> (<em>np.ndarray</em>) – The predicted scores.</p></li>
+<li><p><strong>sensitive_attribute</strong> (<em>np.ndarray</em><em>, </em><em>optional</em>) – The sensitive attribute data. Will compute fairness metrics if provided.</p></li>
+<li><p><strong>threshold</strong> (<em>float</em><em> | </em><em>str</em><em>, </em><em>optional</em>) – The threshold to use for binarizing the predictions, or “best” to infer
+which threshold maximizes accuracy.</p></li>
+<li><p><strong>model_name</strong> (<em>str</em><em>, </em><em>optional</em>) – The name of the model to be used on the plots, by default None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>results</strong> – A dictionary with key-value pairs of (metric name, metric value).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.evaluation.evaluate_predictions_bootstrap">
+<span class="sig-prename descclassname"><span class="pre">folktexts.evaluation.</span></span><span class="sig-name descname"><span class="pre">evaluate_predictions_bootstrap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred_scores</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensitive_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'best'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">confidence_pct</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/evaluation.html#evaluate_predictions_bootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.evaluation.evaluate_predictions_bootstrap" title="Link to this definition"></a></dt>
+<dd><p>Computes bootstrap estimates of classification metrics for the given predictions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y_true</strong> (<em>np.ndarray</em>) – The true labels.</p></li>
+<li><p><strong>y_pred_scores</strong> (<em>np.ndarray</em>) – The score predictions.</p></li>
+<li><p><strong>sensitive_attribute</strong> (<em>np.ndarray</em><em>, </em><em>optional</em>) – The sensitive attribute data. Will compute fairness metrics if provided.</p></li>
+<li><p><strong>threshold</strong> (<em>float</em><em> | </em><em>str</em><em>, </em><em>optional</em>) – The threshold to use for binarizing the predictions, or “best” to infer
+which threshold maximizes accuracy, by default “best”.</p></li>
+<li><p><strong>k</strong> (<em>int</em><em>, </em><em>optional</em>) – How many bootstrap samples to draw, by default 200.</p></li>
+<li><p><strong>confidence_pct</strong> (<em>float</em><em>, </em><em>optional</em>) – How large of a confidence interval to use when reporting lower and upper
+bounds, by default 95 (i.e., 2.5 to 97.5 percentile of results).</p></li>
+<li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em>) – The random seed, by default 42.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>results</strong> – A dictionary containing bootstrap estimates for a variety of metrics.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict[str, float]</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-folktexts.llm_utils">
+<span id="folktexts-llm-utils-module"></span><h2>folktexts.llm_utils module<a class="headerlink" href="#module-folktexts.llm_utils" title="Link to this heading"></a></h2>
+<p>Common functions to use with transformer LLMs.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.llm_utils.add_pad_token">
+<span class="sig-prename descclassname"><span class="pre">folktexts.llm_utils.</span></span><span class="sig-name descname"><span class="pre">add_pad_token</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/llm_utils.html#add_pad_token"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.llm_utils.add_pad_token" title="Link to this definition"></a></dt>
+<dd><p>Add a pad token to the model and tokenizer if it doesn’t already exist.</p>
+<p>Here we’re using the end-of-sentence token as the pad token. Both the model
+weights and tokenizer vocabulary are untouched.</p>
+<p>Another possible way would be to add a new token <cite>[PAD]</cite> to the tokenizer
+and update the tokenizer vocabulary and model weight embeddings accordingly.
+The embedding for the new pad token would be the average of all other
+embeddings.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.llm_utils.get_model_folder_path">
+<span class="sig-prename descclassname"><span class="pre">folktexts.llm_utils.</span></span><span class="sig-name descname"><span class="pre">get_model_folder_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'/tmp'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/llm_utils.html#get_model_folder_path"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.llm_utils.get_model_folder_path" title="Link to this definition"></a></dt>
+<dd><p>Returns the folder where the model is saved.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.llm_utils.get_model_size_B">
+<span class="sig-prename descclassname"><span class="pre">folktexts.llm_utils.</span></span><span class="sig-name descname"><span class="pre">get_model_size_B</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/llm_utils.html#get_model_size_B"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.llm_utils.get_model_size_B" title="Link to this definition"></a></dt>
+<dd><p>Get the model size from the model name, in Billions of parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.llm_utils.is_bf16_compatible">
+<span class="sig-prename descclassname"><span class="pre">folktexts.llm_utils.</span></span><span class="sig-name descname"><span class="pre">is_bf16_compatible</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/llm_utils.html#is_bf16_compatible"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.llm_utils.is_bf16_compatible" title="Link to this definition"></a></dt>
+<dd><p>Checks if the current environment is bfloat16 compatible.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.llm_utils.load_model_tokenizer">
+<span class="sig-prename descclassname"><span class="pre">folktexts.llm_utils.</span></span><span class="sig-name descname"><span class="pre">load_model_tokenizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_name_or_path</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/llm_utils.html#load_model_tokenizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.llm_utils.load_model_tokenizer" title="Link to this definition"></a></dt>
+<dd><p>Load a model and tokenizer from the given local path (or using the model name).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>model_name_or_path</strong> (<em>str</em><em> | </em><em>Path</em>) – Model name or local path to the model folder.</p></li>
+<li><p><strong>kwargs</strong> (<em>dict</em>) – Additional keyword arguments to pass to the model <cite>from_pretrained</cite> call.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The loaded model and tokenizer, respectively.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple[AutoModelForCausalLM, AutoTokenizer]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.llm_utils.query_model_batch">
+<span class="sig-prename descclassname"><span class="pre">folktexts.llm_utils.</span></span><span class="sig-name descname"><span class="pre">query_model_batch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">text_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/llm_utils.html#query_model_batch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.llm_utils.query_model_batch" title="Link to this definition"></a></dt>
+<dd><p>Queries the model with a batch of text inputs.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>text_inputs</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – The inputs to the model as a list of strings.</p></li>
+<li><p><strong>model</strong> (<em>AutoModelForCausalLM</em>) – The model to query.</p></li>
+<li><p><strong>tokenizer</strong> (<em>AutoTokenizer</em>) – The tokenizer used to encode the text inputs.</p></li>
+<li><p><strong>context_size</strong> (<em>int</em>) – The maximum context size to consider for each input (in tokens).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>last_token_probs</strong> – Model’s last token <em>linear</em> probabilities for each input as an
+np.array of shape (batch_size, vocab_size).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.array</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.llm_utils.query_model_batch_multiple_passes">
+<span class="sig-prename descclassname"><span class="pre">folktexts.llm_utils.</span></span><span class="sig-name descname"><span class="pre">query_model_batch_multiple_passes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">text_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_passes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">digits_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/llm_utils.html#query_model_batch_multiple_passes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.llm_utils.query_model_batch_multiple_passes" title="Link to this definition"></a></dt>
+<dd><p>Queries an LM for multiple forward passes.</p>
+<p>Greedy token search over multiple forward passes: Each forward pass takes
+the highest likelihood token from the previous pass.</p>
+<p>NOTE: could use model.generate in the future!</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>text_inputs</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – The batch inputs to the model as a list of strings.</p></li>
+<li><p><strong>model</strong> (<em>AutoModelForCausalLM</em>) – The model to query.</p></li>
+<li><p><strong>tokenizer</strong> (<em>AutoTokenizer</em>) – The tokenizer used to encode the text inputs.</p></li>
+<li><p><strong>context_size</strong> (<em>int</em>) – The maximum context size to consider for each input (in tokens).</p></li>
+<li><p><strong>n_passes</strong> (<em>int</em><em>, </em><em>optional</em>) – The number of forward passes to run.</p></li>
+<li><p><strong>digits_only</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to only sample for digit tokens.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>last_token_probs</strong> – Last token <em>linear</em> probabilities for each forward pass, for each text
+in the input batch. The output has shape (batch_size, n_passes, vocab_size).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.array</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-folktexts.plotting">
+<span id="folktexts-plotting-module"></span><h2>folktexts.plotting module<a class="headerlink" href="#module-folktexts.plotting" title="Link to this heading"></a></h2>
+<p>Module to plot evaluation results.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.plotting.render_evaluation_plots">
+<span class="sig-prename descclassname"><span class="pre">folktexts.plotting.</span></span><span class="sig-name descname"><span class="pre">render_evaluation_plots</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred_scores</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">imgs_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_plots</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/plotting.html#render_evaluation_plots"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.plotting.render_evaluation_plots" title="Link to this definition"></a></dt>
+<dd><p>Renders evaluation plots for the given predictions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.plotting.render_fairness_plots">
+<span class="sig-prename descclassname"><span class="pre">folktexts.plotting.</span></span><span class="sig-name descname"><span class="pre">render_fairness_plots</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred_scores</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensitive_attribute</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_results</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_value_map</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_size_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.04</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">imgs_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_plots</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/plotting.html#render_fairness_plots"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.plotting.render_fairness_plots" title="Link to this definition"></a></dt>
+<dd><p>Renders fairness plots for the given predictions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.plotting.save_fig">
+<span class="sig-prename descclassname"><span class="pre">folktexts.plotting.</span></span><span class="sig-name descname"><span class="pre">save_fig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fig</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">imgs_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pdf'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/plotting.html#save_fig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.plotting.save_fig" title="Link to this definition"></a></dt>
+<dd><p>Helper to save a matplotlib figure to disk.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-folktexts.prompting">
+<span id="folktexts-prompting-module"></span><h2>folktexts.prompting module<a class="headerlink" href="#module-folktexts.prompting" title="Link to this heading"></a></h2>
+<p>Module to map risk-estimation questions to different prompting techniques.</p>
+<p>e.g.,
+- multiple-choice Q&amp;A vs direct numeric Q&amp;A;
+- zero-shot vs few-shot vs CoT;</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.prompting.apply_chat_template">
+<span class="sig-prename descclassname"><span class="pre">folktexts.prompting.</span></span><span class="sig-name descname"><span class="pre">apply_chat_template</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_prompt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system_prompt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chat_prompt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'If</span> <span class="pre">had</span> <span class="pre">to</span> <span class="pre">select</span> <span class="pre">one</span> <span class="pre">of</span> <span class="pre">the</span> <span class="pre">options,</span> <span class="pre">my</span> <span class="pre">answer</span> <span class="pre">would</span> <span class="pre">be'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/prompting.html#apply_chat_template"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.prompting.apply_chat_template" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.prompting.encode_row_prompt">
+<span class="sig-prename descclassname"><span class="pre">folktexts.prompting.</span></span><span class="sig-name descname"><span class="pre">encode_row_prompt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">row</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">question</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prompt_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_task_description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/prompting.html#encode_row_prompt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.prompting.encode_row_prompt" title="Link to this definition"></a></dt>
+<dd><p>Encode a question regarding a given row.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.prompting.encode_row_prompt_chat">
+<span class="sig-prename descclassname"><span class="pre">folktexts.prompting.</span></span><span class="sig-name descname"><span class="pre">encode_row_prompt_chat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">row</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">question</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">chat_template_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/prompting.html#encode_row_prompt_chat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.prompting.encode_row_prompt_chat" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="folktexts.prompting.encode_row_prompt_few_shot">
+<span class="sig-prename descclassname"><span class="pre">folktexts.prompting.</span></span><span class="sig-name descname"><span class="pre">encode_row_prompt_few_shot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">row</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_shots</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">question</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reuse_examples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prompt_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/prompting.html#encode_row_prompt_few_shot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.prompting.encode_row_prompt_few_shot" title="Link to this definition"></a></dt>
+<dd><p>Encode a question regarding a given row using few-shot prompting.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>row</strong> (<em>pd.Series</em>) – The row that the question will be about.</p></li>
+<li><p><strong>task</strong> (<a class="reference internal" href="#folktexts.task.TaskMetadata" title="folktexts.task.TaskMetadata"><em>TaskMetadata</em></a>) – The task that the row belongs to.</p></li>
+<li><p><strong>n_shots</strong> (<em>int</em><em>, </em><em>optional</em>) – The number of example questions and answers to use before prompting
+about the given row, by default 3.</p></li>
+<li><p><strong>reuse_examples</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to reuse the same examples for consistency. By default will
+resample new examples each time (<cite>reuse_examples=False</cite>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>prompt</strong> – The encoded few-shot prompt.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-folktexts.qa_interface">
+<span id="folktexts-qa-interface-module"></span><h2>folktexts.qa_interface module<a class="headerlink" href="#module-folktexts.qa_interface" title="Link to this heading"></a></h2>
+<p>Interface for question-answering with LLMs.</p>
+<ul class="simple">
+<li><p>Create different types of questions (direct numeric, multiple-choice).</p></li>
+<li><p>Encode questions and decode model outputs.</p></li>
+<li><p>Compute risk-estimate from model outputs.</p></li>
+</ul>
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.qa_interface.Choice">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.qa_interface.</span></span><span class="sig-name descname"><span class="pre">Choice</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">text</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">numeric_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#Choice"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.Choice" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Represents a choice in multiple-choice Q&amp;A.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.qa_interface.Choice.text">
+<span class="sig-name descname"><span class="pre">text</span></span><a class="headerlink" href="#folktexts.qa_interface.Choice.text" title="Link to this definition"></a></dt>
+<dd><p>The text of the choice. E.g., “25-34 years old”.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.qa_interface.Choice.data_value">
+<span class="sig-name descname"><span class="pre">data_value</span></span><a class="headerlink" href="#folktexts.qa_interface.Choice.data_value" title="Link to this definition"></a></dt>
+<dd><p>The categorical value corresponding to this choice in the data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.qa_interface.Choice.numeric_value">
+<span class="sig-name descname"><span class="pre">numeric_value</span></span><a class="headerlink" href="#folktexts.qa_interface.Choice.numeric_value" title="Link to this definition"></a></dt>
+<dd><p>A meaningful numeric value for the choice. E.g., if the choice is “25-34
+years old”, the numeric value could be 30. The choice with the highest
+numeric value can be used as a proxy for the positive class. If not
+provided, will try to use the <cite>choice.value</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float, optional</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id9">
+<span class="sig-name descname"><span class="pre">data_value</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></em><a class="headerlink" href="#id9" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.qa_interface.Choice.get_numeric_value">
+<span class="sig-name descname"><span class="pre">get_numeric_value</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#Choice.get_numeric_value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.Choice.get_numeric_value" title="Link to this definition"></a></dt>
+<dd><p>Returns the numeric value of the choice.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id10">
+<span class="sig-name descname"><span class="pre">numeric_value</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#id10" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id11">
+<span class="sig-name descname"><span class="pre">text</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></em><a class="headerlink" href="#id11" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.qa_interface.DirectNumericQA">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.qa_interface.</span></span><span class="sig-name descname"><span class="pre">DirectNumericQA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">column</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_forward_passes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">answer_probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#DirectNumericQA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.DirectNumericQA" title="Link to this definition"></a></dt>
+<dd><p>Bases: <a class="reference internal" href="#folktexts.qa_interface.QAInterface" title="folktexts.qa_interface.QAInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">QAInterface</span></code></a></p>
+<p>Represents a direct numeric question.</p>
+<p class="rubric">Notes</p>
+<p>For example, the prompt could be ”
+Q: What is 2 + 2?
+A: ”
+With the expected answer being “4”.</p>
+<p>If looking for a direct numeric probability, the answer prompt will be
+framed as so: ”
+Q: What is the probability, between 0 and 1, of getting heads on a coin flip?
+A: 0.”
+So that we can extract a numeric answer with at most 2 forward passes.
+This is done automatically by passing the kwarg <cite>answer_probability=True</cite>.</p>
+<p>Note that some models have multi-digit tokens in their vocabulary, so we
+need to correctly assess which tokens in the vocabulary correspond to valid
+numeric answers.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.qa_interface.DirectNumericQA.answer_probability">
+<span class="sig-name descname"><span class="pre">answer_probability</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">True</span></em><a class="headerlink" href="#folktexts.qa_interface.DirectNumericQA.answer_probability" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.qa_interface.DirectNumericQA.get_answer_from_model_output">
+<span class="sig-name descname"><span class="pre">get_answer_from_model_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">last_token_probs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer_vocab</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#DirectNumericQA.get_answer_from_model_output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.DirectNumericQA.get_answer_from_model_output" title="Link to this definition"></a></dt>
+<dd><p>Outputs a numeric answer inferred from the model’s output.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>last_token_probs</strong> (<em>np.ndarray</em>) – The last token probabilities of the model for the question.
+The first dimension must correspond to the number for forward passes
+as specified by <cite>num_forward_passes</cite>.</p></li>
+<li><p><strong>tokenizer_vocab</strong> (<em>dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em>]</em><em>,</em>) – The tokenizer’s vocabulary.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>answer</strong> – The numeric answer to the question.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float | int</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Eventually we could run a search algorithm to find the most likely
+answer over multiple forward passes, but for now we’ll just take the
+argmax on each forward pass.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.qa_interface.DirectNumericQA.get_question_prompt">
+<span class="sig-name descname"><span class="pre">get_question_prompt</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#DirectNumericQA.get_question_prompt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.DirectNumericQA.get_question_prompt" title="Link to this definition"></a></dt>
+<dd><p>Returns a question and answer key.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.qa_interface.DirectNumericQA.num_forward_passes">
+<span class="sig-name descname"><span class="pre">num_forward_passes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">2</span></em><a class="headerlink" href="#folktexts.qa_interface.DirectNumericQA.num_forward_passes" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.qa_interface.</span></span><span class="sig-name descname"><span class="pre">MultipleChoiceQA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">column</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_forward_passes=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">choices=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_answer_keys_source=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#MultipleChoiceQA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA" title="Link to this definition"></a></dt>
+<dd><p>Bases: <a class="reference internal" href="#folktexts.qa_interface.QAInterface" title="folktexts.qa_interface.QAInterface"><code class="xref py py-class docutils literal notranslate"><span class="pre">QAInterface</span></code></a></p>
+<p>Represents a multiple-choice question and its answer keys.</p>
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA.answer_keys">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">answer_keys</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA.answer_keys" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA.choice_to_key">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">choice_to_key</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#folktexts.qa_interface.Choice" title="folktexts.qa_interface.Choice"><span class="pre">Choice</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA.choice_to_key" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA.choices">
+<span class="sig-name descname"><span class="pre">choices</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code><span class="pre">[</span><a class="reference internal" href="#folktexts.qa_interface.Choice" title="folktexts.qa_interface.Choice"><code class="xref py py-class docutils literal notranslate"><span class="pre">Choice</span></code></a><span class="pre">]</span></em><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA.choices" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA.create_answer_keys_permutations">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_answer_keys_permutations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">question</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#MultipleChoiceQA.create_answer_keys_permutations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA.create_answer_keys_permutations" title="Link to this definition"></a></dt>
+<dd><p>Yield questions with all permutations of answer keys.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>question</strong> (<em>Question</em>) – The template question whose answer keys will be permuted.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>permutations</strong> – A generator of questions with all permutations of answer keys.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Iterator[Question]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA.create_question_from_value_map">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_question_from_value_map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">column</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value_map</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attribute</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#MultipleChoiceQA.create_question_from_value_map"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA.create_question_from_value_map" title="Link to this definition"></a></dt>
+<dd><p>Constructs a question from a value map.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference internal" href="#folktexts.qa_interface.MultipleChoiceQA" title="folktexts.qa_interface.MultipleChoiceQA"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultipleChoiceQA</span></code></a></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA.get_answer_from_model_output">
+<span class="sig-name descname"><span class="pre">get_answer_from_model_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">last_token_probs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer_vocab</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#MultipleChoiceQA.get_answer_from_model_output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA.get_answer_from_model_output" title="Link to this definition"></a></dt>
+<dd><p>Decodes the model’s output into an answer for the given question.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>last_token_probs</strong> (<em>np.ndarray</em>) – The model’s last token probabilities for the question. The first
+dimension corresponds to the number of forward passes as specified
+by <cite>self.num_forward_passes</cite>.</p></li>
+<li><p><strong>tokenizer_vocab</strong> (<em>dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em>]</em><em>,</em>) – The tokenizer’s vocabulary.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>answer</strong> – The answer to the question.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA.get_answer_from_text">
+<span class="sig-name descname"><span class="pre">get_answer_from_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">text</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#MultipleChoiceQA.get_answer_from_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA.get_answer_from_text" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference internal" href="#folktexts.qa_interface.Choice" title="folktexts.qa_interface.Choice"><code class="xref py py-class docutils literal notranslate"><span class="pre">Choice</span></code></a></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA.get_answer_key_from_value">
+<span class="sig-name descname"><span class="pre">get_answer_key_from_value</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#MultipleChoiceQA.get_answer_key_from_value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA.get_answer_key_from_value" title="Link to this definition"></a></dt>
+<dd><p>Returns the answer key corresponding to the given data value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA.get_question_prompt">
+<span class="sig-name descname"><span class="pre">get_question_prompt</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#MultipleChoiceQA.get_question_prompt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA.get_question_prompt" title="Link to this definition"></a></dt>
+<dd><p>Returns a question and answer key.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA.get_value_to_text_map">
+<span class="sig-name descname"><span class="pre">get_value_to_text_map</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#MultipleChoiceQA.get_value_to_text_map"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA.get_value_to_text_map" title="Link to this definition"></a></dt>
+<dd><p>Returns the map from choice data value to choice textual representation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code>[<code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code>]</span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA.key_to_choice">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">key_to_choice</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#folktexts.qa_interface.Choice" title="folktexts.qa_interface.Choice"><span class="pre">Choice</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA.key_to_choice" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.qa_interface.MultipleChoiceQA.num_forward_passes">
+<span class="sig-name descname"><span class="pre">num_forward_passes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">1</span></em><a class="headerlink" href="#folktexts.qa_interface.MultipleChoiceQA.num_forward_passes" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.qa_interface.QAInterface">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.qa_interface.</span></span><span class="sig-name descname"><span class="pre">QAInterface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">column</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_forward_passes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#QAInterface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.QAInterface" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">ABC</span></code></p>
+<p>An interface for a question-answering system.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.qa_interface.QAInterface.column">
+<span class="sig-name descname"><span class="pre">column</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></em><a class="headerlink" href="#folktexts.qa_interface.QAInterface.column" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.qa_interface.QAInterface.get_answer_from_model_output">
+<span class="sig-name descname"><span class="pre">get_answer_from_model_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">last_token_probs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer_vocab</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#QAInterface.get_answer_from_model_output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.QAInterface.get_answer_from_model_output" title="Link to this definition"></a></dt>
+<dd><p>Decodes the model’s output into an answer for the given question.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>last_token_probs</strong> (<em>np.ndarray</em>) – The model’s last token probabilities for the question. The first
+dimension corresponds to the number of forward passes as specified
+by <cite>self.num_forward_passes</cite>.</p></li>
+<li><p><strong>tokenizer</strong> (<em>dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em>]</em>) – The tokenizer’s vocabulary.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>answer</strong> – The answer to the question.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.qa_interface.QAInterface.get_question_prompt">
+<span class="sig-name descname"><span class="pre">get_question_prompt</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/qa_interface.html#QAInterface.get_question_prompt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.qa_interface.QAInterface.get_question_prompt" title="Link to this definition"></a></dt>
+<dd><p>Returns a question and answer key.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.qa_interface.QAInterface.num_forward_passes">
+<span class="sig-name descname"><span class="pre">num_forward_passes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></em><a class="headerlink" href="#folktexts.qa_interface.QAInterface.num_forward_passes" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.qa_interface.QAInterface.text">
+<span class="sig-name descname"><span class="pre">text</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></em><a class="headerlink" href="#folktexts.qa_interface.QAInterface.text" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-folktexts.task">
+<span id="folktexts-task-module"></span><h2>folktexts.task module<a class="headerlink" href="#module-folktexts.task" title="Link to this heading"></a></h2>
+<p>Definition of a generic TaskMetadata class.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.task.</span></span><span class="sig-name descname"><span class="pre">TaskMetadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols_to_text</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensitive_attribute</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multiple_choice_qa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">direct_numeric_qa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_numeric_qa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/task.html#TaskMetadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.task.TaskMetadata" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>A base class to hold information on a prediction task.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.check_task_columns_are_available">
+<span class="sig-name descname"><span class="pre">check_task_columns_are_available</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">available_cols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/task.html#TaskMetadata.check_task_columns_are_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.task.TaskMetadata.check_task_columns_are_available" title="Link to this definition"></a></dt>
+<dd><p>Checks if all columns required by this task are available.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>available_cols</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – The list of column names available in the dataset.</p></li>
+<li><p><strong>raise</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to raise an error if some columns are missing, by default True.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>all_available</strong> – True if all required columns are present in the given list of
+available columns, False otherwise.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.cols_to_text">
+<span class="sig-name descname"><span class="pre">cols_to_text</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code><span class="pre">[</span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code><span class="pre">,</span> <a class="reference internal" href="#folktexts.col_to_text.ColumnToText" title="folktexts.col_to_text.ColumnToText"><code class="xref py py-class docutils literal notranslate"><span class="pre">ColumnToText</span></code></a><span class="pre">]</span></em><a class="headerlink" href="#folktexts.task.TaskMetadata.cols_to_text" title="Link to this definition"></a></dt>
+<dd><p>A mapping between column names and their textual descriptions.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.create_task_with_feature_subset">
+<span class="sig-name descname"><span class="pre">create_task_with_feature_subset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">feature_subset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/task.html#TaskMetadata.create_task_with_feature_subset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.task.TaskMetadata.create_task_with_feature_subset" title="Link to this definition"></a></dt>
+<dd><p>Creates a new task with a subset of the original features.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.description">
+<span class="sig-name descname"><span class="pre">description</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#folktexts.task.TaskMetadata.description" title="Link to this definition"></a></dt>
+<dd><p>A description of the task, including the population to which the task pertains to.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.direct_numeric_qa">
+<span class="sig-name descname"><span class="pre">direct_numeric_qa</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="#folktexts.qa_interface.DirectNumericQA" title="folktexts.qa_interface.DirectNumericQA"><code class="xref py py-class docutils literal notranslate"><span class="pre">DirectNumericQA</span></code></a></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#folktexts.task.TaskMetadata.direct_numeric_qa" title="Link to this definition"></a></dt>
+<dd><p>The direct numeric question and answer interface for this task.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.features">
+<span class="sig-name descname"><span class="pre">features</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code><span class="pre">[</span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code><span class="pre">]</span></em><a class="headerlink" href="#folktexts.task.TaskMetadata.features" title="Link to this definition"></a></dt>
+<dd><p>The names of the features used in the task.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.get_row_description">
+<span class="sig-name descname"><span class="pre">get_row_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">row</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/task.html#TaskMetadata.get_row_description"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.task.TaskMetadata.get_row_description" title="Link to this definition"></a></dt>
+<dd><p>Encode a description of a given data row in textual form.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.get_target">
+<span class="sig-name descname"><span class="pre">get_target</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/task.html#TaskMetadata.get_target"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.task.TaskMetadata.get_target" title="Link to this definition"></a></dt>
+<dd><p>Resolves the name of the target column depending on <cite>self.target_threshold</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.get_task">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_task</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_numeric_qa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/task.html#TaskMetadata.get_task"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.task.TaskMetadata.get_task" title="Link to this definition"></a></dt>
+<dd><p>Fetches a previously created task by its name.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>name</strong> (<em>str</em>) – The name of the task to fetch.</p></li>
+<li><p><strong>use_numeric_qa</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to set the retrieved task to use verbalized numeric Q&amp;A
+instead of the default multiple-choice Q&amp;A prompts. Default is False.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>task</strong> – The task object with the given name.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#folktexts.task.TaskMetadata" title="folktexts.task.TaskMetadata">TaskMetadata</a></p>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>ValueError</strong> – Raised if the task with the given name has not been created yet.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.multiple_choice_qa">
+<span class="sig-name descname"><span class="pre">multiple_choice_qa</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="#folktexts.qa_interface.MultipleChoiceQA" title="folktexts.qa_interface.MultipleChoiceQA"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultipleChoiceQA</span></code></a></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#folktexts.task.TaskMetadata.multiple_choice_qa" title="Link to this definition"></a></dt>
+<dd><p>The multiple-choice question and answer interface for this task.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.name">
+<span class="sig-name descname"><span class="pre">name</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></em><a class="headerlink" href="#folktexts.task.TaskMetadata.name" title="Link to this definition"></a></dt>
+<dd><p>The name of the task.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.question">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">question</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="#folktexts.qa_interface.QAInterface" title="folktexts.qa_interface.QAInterface"><span class="pre">QAInterface</span></a></em><a class="headerlink" href="#folktexts.task.TaskMetadata.question" title="Link to this definition"></a></dt>
+<dd><p>Getter for the Q&amp;A interface for this task.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.sensitive_attribute">
+<span class="sig-name descname"><span class="pre">sensitive_attribute</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#folktexts.task.TaskMetadata.sensitive_attribute" title="Link to this definition"></a></dt>
+<dd><p>The name of the column used as the sensitive attribute data (if provided).</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.sensitive_attribute_value_map">
+<span class="sig-name descname"><span class="pre">sensitive_attribute_value_map</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/task.html#TaskMetadata.sensitive_attribute_value_map"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.task.TaskMetadata.sensitive_attribute_value_map" title="Link to this definition"></a></dt>
+<dd><p>Returns a mapping between sensitive attribute values and their descriptions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.set_question">
+<span class="sig-name descname"><span class="pre">set_question</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">question</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/task.html#TaskMetadata.set_question"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.task.TaskMetadata.set_question" title="Link to this definition"></a></dt>
+<dd><p>Sets the Q&amp;A interface for this task.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.target">
+<span class="sig-name descname"><span class="pre">target</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></em><a class="headerlink" href="#folktexts.task.TaskMetadata.target" title="Link to this definition"></a></dt>
+<dd><p>The name of the target column.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.target_threshold">
+<span class="sig-name descname"><span class="pre">target_threshold</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="#folktexts.threshold.Threshold" title="folktexts.threshold.Threshold"><code class="xref py py-class docutils literal notranslate"><span class="pre">Threshold</span></code></a></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#folktexts.task.TaskMetadata.target_threshold" title="Link to this definition"></a></dt>
+<dd><p>The threshold used to binarize the target column (if provided).</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="folktexts.task.TaskMetadata.use_numeric_qa">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">use_numeric_qa</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#folktexts.task.TaskMetadata.use_numeric_qa" title="Link to this definition"></a></dt>
+<dd><p>Getter for whether to use numeric Q&amp;A instead of multiple-choice Q&amp;A prompts.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-folktexts.threshold">
+<span id="folktexts-threshold-module"></span><h2>folktexts.threshold module<a class="headerlink" href="#module-folktexts.threshold" title="Link to this heading"></a></h2>
+<p>Helper function for defining binarization thresholds.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="folktexts.threshold.Threshold">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">folktexts.threshold.</span></span><span class="sig-name descname"><span class="pre">Threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/threshold.html#Threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.threshold.Threshold" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>A class to represent a threshold value and its comparison operator.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.threshold.Threshold.value">
+<span class="sig-name descname"><span class="pre">value</span></span><a class="headerlink" href="#folktexts.threshold.Threshold.value" title="Link to this definition"></a></dt>
+<dd><p>The threshold value to compare against.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float | int</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.threshold.Threshold.op">
+<span class="sig-name descname"><span class="pre">op</span></span><a class="headerlink" href="#folktexts.threshold.Threshold.op" title="Link to this definition"></a></dt>
+<dd><p>The comparison operator to use. One of ‘&gt;’, ‘&lt;’, ‘&gt;=’, ‘&lt;=’, ‘==’, ‘!=’.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.threshold.Threshold.apply_to_column_data">
+<span class="sig-name descname"><span class="pre">apply_to_column_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/threshold.html#Threshold.apply_to_column_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.threshold.Threshold.apply_to_column_data" title="Link to this definition"></a></dt>
+<dd><p>Applies the threshold operation to a pandas Series or scalar value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code> | <code class="xref py py-class docutils literal notranslate"><span class="pre">Series</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="folktexts.threshold.Threshold.apply_to_column_name">
+<span class="sig-name descname"><span class="pre">apply_to_column_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">column_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/folktexts/threshold.html#Threshold.apply_to_column_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#folktexts.threshold.Threshold.apply_to_column_name" title="Link to this definition"></a></dt>
+<dd><p>Standardizes naming of thresholded columns.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id12">
+<span class="sig-name descname"><span class="pre">op</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></em><a class="headerlink" href="#id12" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="folktexts.threshold.Threshold.valid_ops">
+<span class="sig-name descname"><span class="pre">valid_ops</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-data docutils literal notranslate"><span class="pre">ClassVar</span></code><span class="pre">[</span><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code><span class="pre">]</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'!=':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">ne&gt;,</span> <span class="pre">'&lt;':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">lt&gt;,</span> <span class="pre">'&lt;=':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">le&gt;,</span> <span class="pre">'==':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">eq&gt;,</span> <span class="pre">'&gt;':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">gt&gt;,</span> <span class="pre">'&gt;=':</span> <span class="pre">&lt;built-in</span> <span class="pre">function</span> <span class="pre">ge&gt;}</span></em><a class="headerlink" href="#folktexts.threshold.Threshold.valid_ops" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="id13">
+<span class="sig-name descname"><span class="pre">value</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code> <span class="pre">|</span> <code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></em><a class="headerlink" href="#id13" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-folktexts">
+<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-folktexts" title="Link to this heading"></a></h2>
+</section>
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diff --git a/source/modules.html b/source/modules.html
new file mode 100644
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+++ b/source/modules.html
@@ -0,0 +1,350 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
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+  <title>folktexts &mdash; folktexts 0.0.25 documentation</title>
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+<li class="toctree-l1"><a class="reference internal" href="../readme.html">Readme file</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="folktexts.html">folktexts package</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../notebooks.html">Example notebooks</a></li>
+</ul>
+
+        </div>
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+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">folktexts</li>
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+            <a href="../_sources/source/modules.rst.txt" rel="nofollow"> View page source</a>
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+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="folktexts">
+<h1>folktexts<a class="headerlink" href="#folktexts" title="Link to this heading"></a></h1>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="folktexts.html">folktexts package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#subpackages">Subpackages</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.acs.html">folktexts.acs package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html#module-folktexts.acs.acs_columns">folktexts.acs.acs_columns module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html#module-folktexts.acs.acs_dataset">folktexts.acs.acs_dataset module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html#module-folktexts.acs.acs_questions">folktexts.acs.acs_questions module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html#module-folktexts.acs.acs_tasks">folktexts.acs.acs_tasks module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html#module-folktexts.acs.acs_thresholds">folktexts.acs.acs_thresholds module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.acs.html#module-folktexts.acs">Module contents</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.classifier.html">folktexts.classifier package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#module-folktexts.classifier.base">folktexts.classifier.base module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#module-folktexts.classifier.transformers_classifier">folktexts.classifier.transformers_classifier module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#module-folktexts.classifier.web_api_classifier">folktexts.classifier.web_api_classifier module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.classifier.html#module-folktexts.classifier">Module contents</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.cli.html">folktexts.cli package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli.download_models">folktexts.cli.download_models module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli.eval_feature_importance">folktexts.cli.eval_feature_importance module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli.experiments">folktexts.cli.experiments module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli.launch_experiments_htcondor">folktexts.cli.launch_experiments_htcondor module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli.rerun_experiment">folktexts.cli.rerun_experiment module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli.run_acs_benchmark">folktexts.cli.run_acs_benchmark module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.cli.html#module-folktexts.cli">Module contents</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#module-folktexts.benchmark">folktexts.benchmark module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark"><code class="docutils literal notranslate"><span class="pre">Benchmark</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark.ACS_DATASET_CONFIGS"><code class="docutils literal notranslate"><span class="pre">Benchmark.ACS_DATASET_CONFIGS</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark.configs_dict"><code class="docutils literal notranslate"><span class="pre">Benchmark.configs_dict</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark.make_acs_benchmark"><code class="docutils literal notranslate"><span class="pre">Benchmark.make_acs_benchmark()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark.make_benchmark"><code class="docutils literal notranslate"><span class="pre">Benchmark.make_benchmark()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark.model_name"><code class="docutils literal notranslate"><span class="pre">Benchmark.model_name</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark.plot_results"><code class="docutils literal notranslate"><span class="pre">Benchmark.plot_results()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark.results"><code class="docutils literal notranslate"><span class="pre">Benchmark.results</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark.results_dir"><code class="docutils literal notranslate"><span class="pre">Benchmark.results_dir</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark.results_root_dir"><code class="docutils literal notranslate"><span class="pre">Benchmark.results_root_dir</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark.run"><code class="docutils literal notranslate"><span class="pre">Benchmark.run()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark.save_results"><code class="docutils literal notranslate"><span class="pre">Benchmark.save_results()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.Benchmark.task"><code class="docutils literal notranslate"><span class="pre">Benchmark.task</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.numeric_risk_prompting"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.numeric_risk_prompting</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.few_shot"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.few_shot</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.reuse_few_shot_examples"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.reuse_few_shot_examples</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.batch_size"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.batch_size</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.context_size"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.context_size</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.correct_order_bias"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.correct_order_bias</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.feature_subset"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.feature_subset</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.population_filter"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.population_filter</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.seed"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.seed</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id0"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.batch_size</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id1"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.context_size</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id2"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.correct_order_bias</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.default_config"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.default_config()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id3"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.feature_subset</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id4"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.few_shot</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.load_from_disk"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.load_from_disk()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id5"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.numeric_risk_prompting</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id6"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.population_filter</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id7"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.reuse_few_shot_examples</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.save_to_disk"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.save_to_disk()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id8"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.seed</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.benchmark.BenchmarkConfig.update"><code class="docutils literal notranslate"><span class="pre">BenchmarkConfig.update()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#module-folktexts.col_to_text">folktexts.col_to_text module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.col_to_text.ColumnToText"><code class="docutils literal notranslate"><span class="pre">ColumnToText</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.col_to_text.ColumnToText.get_text"><code class="docutils literal notranslate"><span class="pre">ColumnToText.get_text()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.col_to_text.ColumnToText.name"><code class="docutils literal notranslate"><span class="pre">ColumnToText.name</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.col_to_text.ColumnToText.question"><code class="docutils literal notranslate"><span class="pre">ColumnToText.question</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.col_to_text.ColumnToText.short_description"><code class="docutils literal notranslate"><span class="pre">ColumnToText.short_description</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.col_to_text.ColumnToText.value_map"><code class="docutils literal notranslate"><span class="pre">ColumnToText.value_map</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#module-folktexts.dataset">folktexts.dataset module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset"><code class="docutils literal notranslate"><span class="pre">Dataset</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.data"><code class="docutils literal notranslate"><span class="pre">Dataset.data</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.filter"><code class="docutils literal notranslate"><span class="pre">Dataset.filter()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.get_data_split"><code class="docutils literal notranslate"><span class="pre">Dataset.get_data_split()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.get_features_data"><code class="docutils literal notranslate"><span class="pre">Dataset.get_features_data()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.get_sensitive_attribute_data"><code class="docutils literal notranslate"><span class="pre">Dataset.get_sensitive_attribute_data()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.get_target_data"><code class="docutils literal notranslate"><span class="pre">Dataset.get_target_data()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.get_test"><code class="docutils literal notranslate"><span class="pre">Dataset.get_test()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.get_train"><code class="docutils literal notranslate"><span class="pre">Dataset.get_train()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.get_val"><code class="docutils literal notranslate"><span class="pre">Dataset.get_val()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.name"><code class="docutils literal notranslate"><span class="pre">Dataset.name</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.sample_n_train_examples"><code class="docutils literal notranslate"><span class="pre">Dataset.sample_n_train_examples()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.seed"><code class="docutils literal notranslate"><span class="pre">Dataset.seed</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.subsample"><code class="docutils literal notranslate"><span class="pre">Dataset.subsample()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.subsampling"><code class="docutils literal notranslate"><span class="pre">Dataset.subsampling</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.task"><code class="docutils literal notranslate"><span class="pre">Dataset.task</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.test_size"><code class="docutils literal notranslate"><span class="pre">Dataset.test_size</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.train_size"><code class="docutils literal notranslate"><span class="pre">Dataset.train_size</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.dataset.Dataset.val_size"><code class="docutils literal notranslate"><span class="pre">Dataset.val_size</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#module-folktexts.evaluation">folktexts.evaluation module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.evaluation.bootstrap_estimate"><code class="docutils literal notranslate"><span class="pre">bootstrap_estimate()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.evaluation.compute_best_threshold"><code class="docutils literal notranslate"><span class="pre">compute_best_threshold()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.evaluation.evaluate_binary_predictions"><code class="docutils literal notranslate"><span class="pre">evaluate_binary_predictions()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.evaluation.evaluate_binary_predictions_fairness"><code class="docutils literal notranslate"><span class="pre">evaluate_binary_predictions_fairness()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.evaluation.evaluate_predictions"><code class="docutils literal notranslate"><span class="pre">evaluate_predictions()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.evaluation.evaluate_predictions_bootstrap"><code class="docutils literal notranslate"><span class="pre">evaluate_predictions_bootstrap()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#module-folktexts.llm_utils">folktexts.llm_utils module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.llm_utils.add_pad_token"><code class="docutils literal notranslate"><span class="pre">add_pad_token()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.llm_utils.get_model_folder_path"><code class="docutils literal notranslate"><span class="pre">get_model_folder_path()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.llm_utils.get_model_size_B"><code class="docutils literal notranslate"><span class="pre">get_model_size_B()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.llm_utils.is_bf16_compatible"><code class="docutils literal notranslate"><span class="pre">is_bf16_compatible()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.llm_utils.load_model_tokenizer"><code class="docutils literal notranslate"><span class="pre">load_model_tokenizer()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.llm_utils.query_model_batch"><code class="docutils literal notranslate"><span class="pre">query_model_batch()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.llm_utils.query_model_batch_multiple_passes"><code class="docutils literal notranslate"><span class="pre">query_model_batch_multiple_passes()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#module-folktexts.plotting">folktexts.plotting module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.plotting.render_evaluation_plots"><code class="docutils literal notranslate"><span class="pre">render_evaluation_plots()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.plotting.render_fairness_plots"><code class="docutils literal notranslate"><span class="pre">render_fairness_plots()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.plotting.save_fig"><code class="docutils literal notranslate"><span class="pre">save_fig()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#module-folktexts.prompting">folktexts.prompting module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.prompting.apply_chat_template"><code class="docutils literal notranslate"><span class="pre">apply_chat_template()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.prompting.encode_row_prompt"><code class="docutils literal notranslate"><span class="pre">encode_row_prompt()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.prompting.encode_row_prompt_chat"><code class="docutils literal notranslate"><span class="pre">encode_row_prompt_chat()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.prompting.encode_row_prompt_few_shot"><code class="docutils literal notranslate"><span class="pre">encode_row_prompt_few_shot()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#module-folktexts.qa_interface">folktexts.qa_interface module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.Choice"><code class="docutils literal notranslate"><span class="pre">Choice</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.Choice.text"><code class="docutils literal notranslate"><span class="pre">Choice.text</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.Choice.data_value"><code class="docutils literal notranslate"><span class="pre">Choice.data_value</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.Choice.numeric_value"><code class="docutils literal notranslate"><span class="pre">Choice.numeric_value</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id9"><code class="docutils literal notranslate"><span class="pre">Choice.data_value</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.Choice.get_numeric_value"><code class="docutils literal notranslate"><span class="pre">Choice.get_numeric_value()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id10"><code class="docutils literal notranslate"><span class="pre">Choice.numeric_value</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id11"><code class="docutils literal notranslate"><span class="pre">Choice.text</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.DirectNumericQA"><code class="docutils literal notranslate"><span class="pre">DirectNumericQA</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.DirectNumericQA.answer_probability"><code class="docutils literal notranslate"><span class="pre">DirectNumericQA.answer_probability</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.DirectNumericQA.get_answer_from_model_output"><code class="docutils literal notranslate"><span class="pre">DirectNumericQA.get_answer_from_model_output()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.DirectNumericQA.get_question_prompt"><code class="docutils literal notranslate"><span class="pre">DirectNumericQA.get_question_prompt()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.DirectNumericQA.num_forward_passes"><code class="docutils literal notranslate"><span class="pre">DirectNumericQA.num_forward_passes</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA.answer_keys"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA.answer_keys</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA.choice_to_key"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA.choice_to_key</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA.choices"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA.choices</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA.create_answer_keys_permutations"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA.create_answer_keys_permutations()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA.create_question_from_value_map"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA.create_question_from_value_map()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_answer_from_model_output"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA.get_answer_from_model_output()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_answer_from_text"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA.get_answer_from_text()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_answer_key_from_value"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA.get_answer_key_from_value()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_question_prompt"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA.get_question_prompt()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA.get_value_to_text_map"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA.get_value_to_text_map()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA.key_to_choice"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA.key_to_choice</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.MultipleChoiceQA.num_forward_passes"><code class="docutils literal notranslate"><span class="pre">MultipleChoiceQA.num_forward_passes</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.QAInterface"><code class="docutils literal notranslate"><span class="pre">QAInterface</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.QAInterface.column"><code class="docutils literal notranslate"><span class="pre">QAInterface.column</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.QAInterface.get_answer_from_model_output"><code class="docutils literal notranslate"><span class="pre">QAInterface.get_answer_from_model_output()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.QAInterface.get_question_prompt"><code class="docutils literal notranslate"><span class="pre">QAInterface.get_question_prompt()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.QAInterface.num_forward_passes"><code class="docutils literal notranslate"><span class="pre">QAInterface.num_forward_passes</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.qa_interface.QAInterface.text"><code class="docutils literal notranslate"><span class="pre">QAInterface.text</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#module-folktexts.task">folktexts.task module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata"><code class="docutils literal notranslate"><span class="pre">TaskMetadata</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.check_task_columns_are_available"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.check_task_columns_are_available()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.cols_to_text"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.cols_to_text</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.create_task_with_feature_subset"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.create_task_with_feature_subset()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.description"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.description</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.direct_numeric_qa"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.direct_numeric_qa</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.features"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.features</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.get_row_description"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.get_row_description()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.get_target"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.get_target()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.get_task"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.get_task()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.multiple_choice_qa"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.multiple_choice_qa</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.name"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.name</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.question"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.question</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.sensitive_attribute"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.sensitive_attribute</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.sensitive_attribute_value_map"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.sensitive_attribute_value_map()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.set_question"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.set_question()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.target"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.target</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.target_threshold"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.target_threshold</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.task.TaskMetadata.use_numeric_qa"><code class="docutils literal notranslate"><span class="pre">TaskMetadata.use_numeric_qa</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#module-folktexts.threshold">folktexts.threshold module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="folktexts.html#folktexts.threshold.Threshold"><code class="docutils literal notranslate"><span class="pre">Threshold</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.threshold.Threshold.value"><code class="docutils literal notranslate"><span class="pre">Threshold.value</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.threshold.Threshold.op"><code class="docutils literal notranslate"><span class="pre">Threshold.op</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.threshold.Threshold.apply_to_column_data"><code class="docutils literal notranslate"><span class="pre">Threshold.apply_to_column_data()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.threshold.Threshold.apply_to_column_name"><code class="docutils literal notranslate"><span class="pre">Threshold.apply_to_column_name()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id12"><code class="docutils literal notranslate"><span class="pre">Threshold.op</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#folktexts.threshold.Threshold.valid_ops"><code class="docutils literal notranslate"><span class="pre">Threshold.valid_ops</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="folktexts.html#id13"><code class="docutils literal notranslate"><span class="pre">Threshold.value</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="folktexts.html#module-folktexts">Module contents</a></li>
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