From 0016827b7cc0c27a0e64a28cefaa631cc3e0ee1e Mon Sep 17 00:00:00 2001
From: anantnawal <67642890+anantnawal@users.noreply.github.com>
Date: Mon, 28 Oct 2024 18:27:37 +0100
Subject: [PATCH 1/3] feat: migrate existing code for notebook to the changes
in the GA Vertex GenAI evaluation release (#1346)
# Description
feat: migrate existing code for notebook
evaluation_quality_and_explainability_with_rapideval.ipynb to the
changes in the GA Vertex GenAI evaluation release, and renamed notebook
accordingly.
---
...quality_and_explainability_with_eval.ipynb | 708 ++++++++++++++++++
...ty_and_explainability_with_rapideval.ipynb | 690 -----------------
2 files changed, 708 insertions(+), 690 deletions(-)
create mode 100644 gemini/evaluation/enhancing_quality_and_explainability_with_eval.ipynb
delete mode 100644 gemini/evaluation/legacy/enhancing_quality_and_explainability_with_rapideval.ipynb
diff --git a/gemini/evaluation/enhancing_quality_and_explainability_with_eval.ipynb b/gemini/evaluation/enhancing_quality_and_explainability_with_eval.ipynb
new file mode 100644
index 00000000000..f7a32b31d09
--- /dev/null
+++ b/gemini/evaluation/enhancing_quality_and_explainability_with_eval.ipynb
@@ -0,0 +1,708 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ijGzTHJJUCPY"
+ },
+ "outputs": [],
+ "source": [
+ "# Copyright 2024 Google LLC\n",
+ "#\n",
+ "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+ "# you may not use this file except in compliance with the License.\n",
+ "# You may obtain a copy of the License at\n",
+ "#\n",
+ "# https://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing, software\n",
+ "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+ "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+ "# See the License for the specific language governing permissions and\n",
+ "# limitations under the License."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VEqbX8OhE8y9"
+ },
+ "source": [
+ "# Enhancing quality and explainability with Vertex AI Evaluation\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ce84bd67392c"
+ },
+ "source": [
+ "| | |\n",
+ "|-|-|\n",
+ "| Author(s) | [Anant Nawalgaria](https://github.com/anantnawal) |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CkHPv2myT2cx"
+ },
+ "source": [
+ "## Overview\n",
+ "\n",
+ "### Vertex Gen AI Evaluation API\n",
+ "\n",
+ "The [Vertex Gen AI Evaluation Service](https://cloud.google.com/vertex-ai/generative-ai/docs/models/evaluation-overview) which can be accessed both through its SDK and web API interfaces, lets you evaluate your large language models (LLMs), both pointwise and pairwise, across several metrics.\n",
+ "\n",
+ "It is primarily used ad-hoc in the initial experimental phase for evaluating which set of prompts and models work well for a use case. However, as described in detail in the corresponding blog, this notebook will show some sample code on dummy data of how you can use \n",
+ "Evaluation to enhance the quality of the response generated by the LLMs by combining the pairwise and pointwise capabilities of Gen AI Evaluation elegantly at the time of generation. It would also then return a human readable explanation to help understand the quality evaluation of the response. Note that although this notebook only demonstrates this workflow on text, it can be extended to any modality once an evaluation mechanism is available for that modality.\n",
+ "\n",
+ "For more information about generative AI on Vertex AI please see [Generative AI on Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) documentation."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DrkcqHrrwMAo"
+ },
+ "source": [
+ "### Objectives\n",
+ "\n",
+ "In this tutorial, you will learn how to combine the Vertex AI Gemini API with the Gen AI Eval API service for Python to improve generation quality & explainability of the responses.\n",
+ "You will complete the following tasks:\n",
+ "\n",
+ "- Install the Vertex AI SDK for Python\n",
+ "- Use the Vertex AI Gemini API to interact with each model\n",
+ " - Gemini 1.5 Pro (`gemini-1.5-pro`) model:\n",
+ " - Generate multiple responses for a given instruction and context\n",
+ " - Use the pairwise and pointwise capabilities of eval to select the best response and also return a human readable explanation for it. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "C9nEPojogw-g"
+ },
+ "source": [
+ "### Costs\n",
+ "\n",
+ "This tutorial uses billable components of Google Cloud:\n",
+ "\n",
+ "- Vertex AI\n",
+ "\n",
+ "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "r11Gu7qNgx1p"
+ },
+ "source": [
+ "## Getting Started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "No17Cw5hgx12"
+ },
+ "source": [
+ "### Install the required libraries for Python"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "tFy3H3aPgx12"
+ },
+ "outputs": [],
+ "source": [
+ "%pip install --upgrade --user --quiet google-cloud-aiplatform[evaluation]\n",
+ "%pip install --upgrade --user bigframes -q\n",
+ "%pip install --quiet --upgrade nest_asyncio"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "j7UyNVSiyQ96"
+ },
+ "source": [
+ "### Restart current runtime\n",
+ "\n",
+ "To use the newly installed packages in this Jupyter runtime, it is recommended to restart the runtime. Run the following cell to restart the current kernel.\n",
+ "\n",
+ "The restart process might take a minute or so."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "YmY9HVVGSBW5"
+ },
+ "outputs": [],
+ "source": [
+ "import IPython\n",
+ "\n",
+ "app = IPython.Application.instance()\n",
+ "app.kernel.do_shutdown(True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5b21ea7cdbf7"
+ },
+ "source": [
+ "#### Set your project ID and region"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "14cc8984ed02"
+ },
+ "outputs": [],
+ "source": [
+ "PROJECT_ID = \"[your-project-id]\"\n",
+ "LOCATION = \"us-central1\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ea53274ede4d"
+ },
+ "source": [
+ "After the restart is complete, continue to the next step."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EXQZrM5hQeKb"
+ },
+ "source": [
+ "
\n",
+ "⚠️ Wait for the kernel to finish restarting before you continue. ⚠️\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dmWOrTJ3gx13"
+ },
+ "source": [
+ "### Authenticate your notebook environment (Colab only)\n",
+ "\n",
+ "If you are running this notebook on Google Colab, run the following cell to authenticate your environment. This step is not required if you are using [Vertex AI Workbench](https://cloud.google.com/vertex-ai-workbench)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "NyKGtVQjgx13"
+ },
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "\n",
+ "# Additional authentication is required for Google Colab\n",
+ "if \"google.colab\" in sys.modules:\n",
+ " # Authenticate user to Google Cloud\n",
+ " from google.colab import auth\n",
+ "\n",
+ " auth.authenticate_user()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DF4l8DTdWgPY"
+ },
+ "source": [
+ "### Define Google Cloud project information (Colab only)\n",
+ "\n",
+ "If you are running this notebook on Google Colab, specify the Google Cloud project information to use. In the following cell, you specify your project information, import the Vertex AI package, and initialize the package. This step is not required if you are using [Vertex AI Workbench](https://cloud.google.com/vertex-ai-workbench)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Nqwi-5ufWp_B"
+ },
+ "outputs": [],
+ "source": [
+ "if \"google.colab\" in sys.modules:\n",
+ " # Define project information\n",
+ " # Initialize Vertex AI\n",
+ " import vertexai\n",
+ "\n",
+ " vertexai.init(project=PROJECT_ID, location=LOCATION)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jXHfaVS66_01"
+ },
+ "source": [
+ "### Import libraries & initialize project variables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "lslYAvw37JGQ"
+ },
+ "outputs": [],
+ "source": [
+ "import functools\n",
+ "import uuid\n",
+ "from functools import partial\n",
+ "\n",
+ "import nest_asyncio\n",
+ "import pandas as pd\n",
+ "from google.cloud import aiplatform\n",
+ "from vertexai.evaluation import EvalTask, MetricPromptTemplateExamples\n",
+ "from vertexai.generative_models import GenerationConfig, GenerativeModel\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "39ce45a03542"
+ },
+ "source": [
+ "## Defining functions for ranking using evaluations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4437b7608c8e"
+ },
+ "source": [
+ "This section defines the various helper functions to perform pairwise and pointwise evaluations, as well as the logic to combine them \n",
+ "to select the best response and return associated quality metrics and explanation."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "afc4054f1f70"
+ },
+ "source": [
+ "This function simplifies AutoSXS comparisons between pairs of responses. It uses the Gen AI Evaluation Service API in Vertex AI and works well with Python's max() or sorted() functions. This lets you easily find the best response or rank a list of responses using pairwise comparisons. For other tasks, like summarization SxS, you can find a full list of metrics on the website mentioned below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ac10531efd0c"
+ },
+ "outputs": [],
+ "source": [
+ "experiment_name = \"qa-quality\"\n",
+ "\n",
+ "\n",
+ "def pairwise_greater(\n",
+ " instructions: list,\n",
+ " context: str,\n",
+ " project_id: str,\n",
+ " location: str,\n",
+ " experiment_name: str,\n",
+ " baseline: str,\n",
+ " candidate: str,\n",
+ ") -> tuple:\n",
+ " \"\"\"\n",
+ " Takes Instructions, Context and two different responses.\n",
+ " Returns the response which best matches the instructions/Context for the given\n",
+ " quality metric ( in this case question answering).\n",
+ " More details on the web API and different quality metrics which this function\n",
+ " can be extended to can be found on\n",
+ " https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/evaluation\n",
+ " \"\"\"\n",
+ " eval_dataset = pd.DataFrame(\n",
+ " {\n",
+ " \"instruction\": [instructions],\n",
+ " \"context\": [context],\n",
+ " \"response\": [candidate],\n",
+ " \"baseline_model_response\": [baseline],\n",
+ " }\n",
+ " )\n",
+ "\n",
+ " eval_task = EvalTask(\n",
+ " dataset=eval_dataset,\n",
+ " metrics=[\n",
+ " MetricPromptTemplateExamples.Pairwise.QUESTION_ANSWERING_QUALITY,\n",
+ " ],\n",
+ " experiment=experiment_name,\n",
+ " )\n",
+ " results = eval_task.evaluate(\n",
+ " prompt_template=\"{instruction} \\n {context}\",\n",
+ " experiment_run_name=\"gemini-qa-pairwise-\" + str(uuid.uuid4()),\n",
+ " )\n",
+ " result = results.metrics_table[\n",
+ " [\n",
+ " \"pairwise_question_answering_quality/pairwise_choice\",\n",
+ " \"pairwise_question_answering_quality/explanation\",\n",
+ " ]\n",
+ " ].to_dict(\"records\")[0]\n",
+ " choice = (\n",
+ " baseline\n",
+ " if result[\"pairwise_question_answering_quality/pairwise_choice\"] == \"BASELINE\"\n",
+ " else candidate\n",
+ " )\n",
+ " return (choice, result[\"pairwise_question_answering_quality/explanation\"])\n",
+ "\n",
+ "\n",
+ "def greater(cmp: callable, a: str, b: str) -> int:\n",
+ " \"\"\"\n",
+ " A comparison function which takes the comparison function, and two variables as input\n",
+ " and returns the one which is greater according to the logic defined inside the cmp function.\n",
+ " \"\"\"\n",
+ " choice, explanation = cmp(a, b)\n",
+ "\n",
+ " if choice == a:\n",
+ " return 1\n",
+ " return -1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "602441b87ef7"
+ },
+ "source": [
+ "The below function performs the pointwise evaluation of the provided set of responses, with respect to the provided metric, instruction and context."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "6863af9c235f"
+ },
+ "outputs": [],
+ "source": [
+ "def pointwise_eval(\n",
+ " instruction: str,\n",
+ " context: str,\n",
+ " responses: list[str],\n",
+ " eval_metrics: list[object] = [\n",
+ " MetricPromptTemplateExamples.Pointwise.QUESTION_ANSWERING_QUALITY,\n",
+ " MetricPromptTemplateExamples.Pointwise.GROUNDEDNESS,\n",
+ " ],\n",
+ " experiment_name: str = experiment_name,\n",
+ ") -> object:\n",
+ " \"\"\"\n",
+ " Takes the instruction, context and a variable number of corresponding generated responses, and returns the pointwise evaluation metrics\n",
+ " for each of the provided metrics. For this example the metrics are Q & A related, however the full list can be found on the website:\n",
+ " https://cloud.google.com/vertex-ai/generative-ai/docs/models/online-pipeline-services\n",
+ " \"\"\"\n",
+ "\n",
+ " instructions = [instruction] * len(responses)\n",
+ "\n",
+ " contexts = [context] * len(responses)\n",
+ "\n",
+ " eval_dataset = pd.DataFrame(\n",
+ " {\n",
+ " \"instruction\": instructions,\n",
+ " \"context\": contexts,\n",
+ " \"response\": responses,\n",
+ " }\n",
+ " )\n",
+ "\n",
+ " eval_task = EvalTask(\n",
+ " dataset=eval_dataset, metrics=eval_metrics, experiment=experiment_name\n",
+ " )\n",
+ " results = eval_task.evaluate(\n",
+ " prompt_template=\"{instruction} \\n {context}\",\n",
+ " experiment_run_name=\"gemini-qa-pointwise-\" + str(uuid.uuid4()),\n",
+ " )\n",
+ " (results.metrics_table.columns)\n",
+ " return results"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "971acda8dd12"
+ },
+ "source": [
+ "This function integrates pairwise and pointwise logic to enhance response selection and evaluation. Here's the process:\n",
+ "\n",
+ "**Current Workflow**:\n",
+ "\n",
+ "1. Pairwise Comparison: Compares responses in pairs to identify the best one based on user-defined metrics.\n",
+ "2. Pointwise Evaluation: Assesses the quality of the chosen response, providing human-readable explanations to build trust.\n",
+ "\n",
+ "**Alternative Workflow**:\n",
+ "\n",
+ "1. Pointwise Evaluation: Evaluate each response individually based on the Pointwise scores or likelihood/logprobs of the response, filtering out those that don't meet specific quality criteria or selecting the top K responses.\n",
+ "2. Pairwise Comparison: Ranks the remaining high-quality responses using pairwise methods to determine the best one(s).\n",
+ "\n",
+ "**Key Points**\n",
+ "\n",
+ "- Combines pairwise and pointwise approaches for robust response selection.\n",
+ "- Offers flexibility with two possible workflows to suit different needs.\n",
+ "- Prioritizes response quality and provides explanations to support user confidence."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "0ac1c136a334"
+ },
+ "outputs": [],
+ "source": [
+ "def rank_responses(instruction: str, context: str, responses: list[str]) -> tuple:\n",
+ " \"\"\"\n",
+ " Takes the instruction, context and a variable number of responses as input, and returns the best performing response as well as its associated\n",
+ " human readable pointwise quality metrics for the configured criteria in the above functions.\n",
+ " The process consists of two steps:\n",
+ " 1. Selecting the best response by using Pairwise comparisons between the responses for the user specified metric ( e.g. Q & A)\n",
+ " 2. Doing pointwise evaluation of the best response and returning human readable quality metrics and explanation along with the best response.\n",
+ " \"\"\"\n",
+ " cmp_f = partial(\n",
+ " pairwise_greater, instruction, context, PROJECT_ID, LOCATION, experiment_name\n",
+ " )\n",
+ " cmp_greater = partial(greater, cmp_f)\n",
+ "\n",
+ " pairwise_best_response = max(responses, key=functools.cmp_to_key(cmp_greater))\n",
+ " pointwise_metric = pointwise_eval(instruction, context, [pairwise_best_response])\n",
+ " qa_metrics = pointwise_metric.metrics_table[\n",
+ " [\n",
+ " col\n",
+ " for col in pointwise_metric.metrics_table.columns\n",
+ " if (\"question_answering\" in col) or (\"groundedness\" in col)\n",
+ " ]\n",
+ " ].to_dict(\"records\")[0]\n",
+ "\n",
+ " return pairwise_best_response, qa_metrics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BY1nfXrqRxVX"
+ },
+ "source": [
+ "### Load the Gemini 1.5 Pro model\n",
+ "\n",
+ "Here we load the model, and assign a temperature value in the range `0.3` to `1.0` and configure it to generate multiple responses. A higher temperature value is critical, even for use cases where creativity is less important like Q & A: since de-correlated responses would mean if the model gets it wrong with the top choice for one response, it has a possibility of getting it right with one of the other responses"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "e761e89b9065"
+ },
+ "outputs": [],
+ "source": [
+ "generation_model = GenerativeModel(\"gemini-1.5-pro-002\")\n",
+ "generation_config = GenerationConfig(\n",
+ " temperature=0.4, max_output_tokens=512, candidate_count=num_responses\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8669160b92b9"
+ },
+ "source": [
+ "### Prompt Gemini\n",
+ "Now we prompt Gemini to generate multiple slightly de-correlated responses based on the above configuration. Multiple responses will be generated in single call."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3fb5e3ae9faf"
+ },
+ "outputs": [],
+ "source": [
+ "instruction_qa = \"Please answer the following question based on the context provided. Question: what is the correct process of fixing your tires?\"\n",
+ "context_qa = (\n",
+ " \"Context:\\n\"\n",
+ " + \"the world is a magical place and fixing tires is one of those magical tasks. According to the Administration and Association (TIA), the only method to properly repair a tire puncture is to fill the injury with a repair stem and back the stem with a repair patch. This is commonly known as a combination repair or a patch/plug repair.\"\n",
+ ")\n",
+ "prompt_qa = instruction_qa + \"\\n\" + context_qa + \"\\n\\nAnswer:\\n\"\n",
+ "responses = [\n",
+ " candidate.text\n",
+ " for candidate in generation_model.generate_content(\n",
+ " contents=prompt_qa,\n",
+ " generation_config=generation_config,\n",
+ " ).candidates\n",
+ "]\n",
+ "\n",
+ "prompt_qa"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "c54c44f3f0b5"
+ },
+ "source": [
+ "Here we use the `rank_responses()` function to fetch the best selected response as well as its associated quality metrics."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "20230a87d5c1"
+ },
+ "outputs": [],
+ "source": [
+ "best_response, metrics = rank_responses(instruction_qa, context_qa, responses)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6b5b63a55144"
+ },
+ "source": [
+ "Now we print the various generated responses:\n",
+ "1. The raw responses generated by Gemini\n",
+ "2. The best performing response\n",
+ "3. Its associated pointwise quality metrics and explanation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7ba246f23c1f"
+ },
+ "outputs": [],
+ "source": [
+ "for ix, response in enumerate(responses, start=1):\n",
+ " print(f\"Response no. {ix}: \\n {response}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "8321faa562f0"
+ },
+ "outputs": [],
+ "source": [
+ "print(best_response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "88458eafb3f9"
+ },
+ "outputs": [],
+ "source": [
+ "metrics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2871493acdd3"
+ },
+ "source": [
+ "## Cleaning Up\n",
+ "\n",
+ "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
+ "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
+ "\n",
+ "Otherwise, you can delete the individual resources you created in this tutorial."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "86bbf7dc97ff"
+ },
+ "outputs": [],
+ "source": [
+ "aiplatform.init(project=PROJECT_ID, location=LOCATION)\n",
+ "experiment = aiplatform.Experiment(experiment_name)\n",
+ "experiment.delete()"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "name": "enhancing_quality_and_explainability_with_rapideval.ipynb",
+ "toc_visible": true
+ },
+ "environment": {
+ "kernel": "python3",
+ "name": "tf2-cpu.2-11.m114",
+ "type": "gcloud",
+ "uri": "gcr.io/deeplearning-platform-release/tf2-cpu.2-11:m114"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (Local)",
+ "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": "3.10.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/gemini/evaluation/legacy/enhancing_quality_and_explainability_with_rapideval.ipynb b/gemini/evaluation/legacy/enhancing_quality_and_explainability_with_rapideval.ipynb
deleted file mode 100644
index 6f83bfd3c23..00000000000
--- a/gemini/evaluation/legacy/enhancing_quality_and_explainability_with_rapideval.ipynb
+++ /dev/null
@@ -1,690 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "ijGzTHJJUCPY"
- },
- "outputs": [],
- "source": [
- "# Copyright 2024 Google LLC\n",
- "#\n",
- "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
- "# you may not use this file except in compliance with the License.\n",
- "# You may obtain a copy of the License at\n",
- "#\n",
- "# https://www.apache.org/licenses/LICENSE-2.0\n",
- "#\n",
- "# Unless required by applicable law or agreed to in writing, software\n",
- "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
- "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
- "# See the License for the specific language governing permissions and\n",
- "# limitations under the License."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "VEqbX8OhE8y9"
- },
- "source": [
- "# Enhancing quality and explainability with Vertex AI Rapid Evaluation\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ce84bd67392c"
- },
- "source": [
- "| | |\n",
- "|-|-|\n",
- "| Author(s) | [Anant Nawalgaria](https://github.com/anantnawal) |"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CkHPv2myT2cx"
- },
- "source": [
- "## Overview\n",
- "\n",
- "### Vertex AI Rapid Evaluation API\n",
- "\n",
- "The [Rapid Evaluation Service](https://cloud.google.com/vertex-ai/generative-ai/docs/models/rapid-evaluation) which can be accessed both through its SDK and web API interfaces, lets you evaluate your large language models (LLMs), both pointwise and pairwise, across several metrics. It is part of the [Vertex AI Evaluation Service](https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval), which also allows you to evaluate models and prompts in an offline fashion.\n",
- "\n",
- "Rapid Evaluation is primarily used ad-hoc in the initial experimental phase for evaluating which set of prompts and models work well for a use case. However, as described in detail in the corresponding blog, this notebook will show some sample code on dummy data of how you can use Rapid Evaluation to enhance the quality of the response generated by the LLMs by combining the pairwise and pointwise capabilities of Rapid Evaluation elegantly at the time of generation. It would also then return a human readable explanation to help understand the quality evaluation of the response. Note that although this notebook only demonstrates this workflow on text, it can be extended to any modality once an evaluation mechanism is available for that modality.\n",
- "\n",
- "For more information about generative AI on Vertex AI please see [Generative AI on Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) documentation."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "DrkcqHrrwMAo"
- },
- "source": [
- "### Objectives\n",
- "\n",
- "In this tutorial, you will learn how to combine the Vertex AI Gemini API with the Rapid Eval API service for Python to improve generation quality & explainability of the responses.\n",
- "You will complete the following tasks:\n",
- "\n",
- "- Install the Vertex AI SDK for Python\n",
- "- Use the Vertex AI Gemini API to interact with each model\n",
- " - Gemini 1.5 Pro (`gemini-1.5-pro`) model:\n",
- " - Generate multiple responses for a given instruction and context\n",
- " - Use the pairwise and pointwise capabilities of Rapid eval to select the best response and also return a human readable explanation for it. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "C9nEPojogw-g"
- },
- "source": [
- "### Costs\n",
- "\n",
- "This tutorial uses billable components of Google Cloud:\n",
- "\n",
- "- Vertex AI\n",
- "\n",
- "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "r11Gu7qNgx1p"
- },
- "source": [
- "## Getting Started"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "No17Cw5hgx12"
- },
- "source": [
- "### Install the required libraries for Python"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "tFy3H3aPgx12"
- },
- "outputs": [],
- "source": [
- "%pip install --upgrade --user --quiet google-cloud-aiplatform[rapid_evaluation]\n",
- "%pip install --upgrade --user bigframes -q\n",
- "%pip install --quiet --upgrade nest_asyncio"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "j7UyNVSiyQ96"
- },
- "source": [
- "### Restart current runtime\n",
- "\n",
- "To use the newly installed packages in this Jupyter runtime, it is recommended to restart the runtime. Run the following cell to restart the current kernel.\n",
- "\n",
- "The restart process might take a minute or so."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "YmY9HVVGSBW5"
- },
- "outputs": [],
- "source": [
- "import IPython\n",
- "\n",
- "app = IPython.Application.instance()\n",
- "app.kernel.do_shutdown(True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5b21ea7cdbf7"
- },
- "source": [
- "#### Set your project ID and region"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "14cc8984ed02"
- },
- "outputs": [],
- "source": [
- "PROJECT_ID = \"[your-project-id]\"\n",
- "LOCATION = \"us-central1\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ea53274ede4d"
- },
- "source": [
- "After the restart is complete, continue to the next step."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "EXQZrM5hQeKb"
- },
- "source": [
- "
\n",
- "⚠️ Wait for the kernel to finish restarting before you continue. ⚠️\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "dmWOrTJ3gx13"
- },
- "source": [
- "### Authenticate your notebook environment (Colab only)\n",
- "\n",
- "If you are running this notebook on Google Colab, run the following cell to authenticate your environment. This step is not required if you are using [Vertex AI Workbench](https://cloud.google.com/vertex-ai-workbench)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "NyKGtVQjgx13"
- },
- "outputs": [],
- "source": [
- "import sys\n",
- "\n",
- "# Additional authentication is required for Google Colab\n",
- "if \"google.colab\" in sys.modules:\n",
- " # Authenticate user to Google Cloud\n",
- " from google.colab import auth\n",
- "\n",
- " auth.authenticate_user()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "DF4l8DTdWgPY"
- },
- "source": [
- "### Define Google Cloud project information (Colab only)\n",
- "\n",
- "If you are running this notebook on Google Colab, specify the Google Cloud project information to use. In the following cell, you specify your project information, import the Vertex AI package, and initialize the package. This step is not required if you are using [Vertex AI Workbench](https://cloud.google.com/vertex-ai-workbench)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Nqwi-5ufWp_B"
- },
- "outputs": [],
- "source": [
- "if \"google.colab\" in sys.modules:\n",
- " # Define project information\n",
- " # Initialize Vertex AI\n",
- " import vertexai\n",
- "\n",
- " vertexai.init(project=PROJECT_ID, location=LOCATION)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "jXHfaVS66_01"
- },
- "source": [
- "### Import libraries & initialize project variables"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "lslYAvw37JGQ"
- },
- "outputs": [],
- "source": [
- "import functools\n",
- "from functools import partial\n",
- "import uuid\n",
- "\n",
- "from google.cloud import aiplatform\n",
- "import nest_asyncio\n",
- "import pandas as pd\n",
- "from vertexai.preview.evaluation import EvalTask\n",
- "from vertexai.preview.evaluation.metrics import PairwiseQuestionAnsweringQuality\n",
- "from vertexai.preview.generative_models import GenerationConfig, GenerativeModel\n",
- "\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "39ce45a03542"
- },
- "source": [
- "## Defining functions for ranking using evaluations"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4437b7608c8e"
- },
- "source": [
- "This section defines the various helper functions to perform pairwise and pointwise evaluations, as well as the logic to combine them \n",
- "to select the best response and return associated quality metrics and explanation."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "afc4054f1f70"
- },
- "source": [
- "This function simplifies AutoSXS comparisons between pairs of responses. It uses the Rapid Evaluation Web API and works well with Python's max() or sorted() functions. This lets you easily find the best response or rank a list of responses using pairwise comparisons. For other tasks, like summarization SxS, you can find a full list of metrics on the website mentioned below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "ac10531efd0c"
- },
- "outputs": [],
- "source": [
- "experiment_name = \"qa-quality\"\n",
- "\n",
- "\n",
- "def pairwise_greater(\n",
- " instructions: list,\n",
- " context: str,\n",
- " project_id: str,\n",
- " location: str,\n",
- " experiment_name: str,\n",
- " baseline: str,\n",
- " candidate: str,\n",
- ") -> tuple:\n",
- " \"\"\"\n",
- " Takes Instructions, Context and two different responses.\n",
- " Returns the response which best matches the instructions/Context for the given\n",
- " quality metric ( in this case question answering).\n",
- " More details on the web API and different quality metrics which this function\n",
- " can be extended to can be found on\n",
- " https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/evaluation\n",
- " \"\"\"\n",
- " eval_dataset = pd.DataFrame(\n",
- " {\n",
- " \"instruction\": [instructions],\n",
- " \"context\": [context],\n",
- " \"response\": [candidate],\n",
- " \"baseline_model_response\": [baseline],\n",
- " }\n",
- " )\n",
- " pairwise_qa_quality = PairwiseQuestionAnsweringQuality(use_reference=False)\n",
- " eval_task = EvalTask(\n",
- " dataset=eval_dataset, metrics=[pairwise_qa_quality], experiment=experiment_name\n",
- " )\n",
- " results = eval_task.evaluate(\n",
- " experiment_run_name=\"gemini-qa-pairwise-\" + str(uuid.uuid4())\n",
- " )\n",
- " result = results.metrics_table[\n",
- " [\n",
- " \"pairwise_question_answering_quality/pairwise_choice\",\n",
- " \"pairwise_question_answering_quality/confidence\",\n",
- " \"pairwise_question_answering_quality/explanation\",\n",
- " ]\n",
- " ].to_dict(\"records\")[0]\n",
- " choice = (\n",
- " baseline\n",
- " if result[\"pairwise_question_answering_quality/pairwise_choice\"] == \"BASELINE\"\n",
- " else candidate\n",
- " )\n",
- " return (\n",
- " choice,\n",
- " result[\"pairwise_question_answering_quality/explanation\"],\n",
- " result[\"pairwise_question_answering_quality/confidence\"],\n",
- " )\n",
- "\n",
- "\n",
- "def greater(cmp: callable, a: str, b: str) -> int:\n",
- " \"\"\"\n",
- " A comparison function which takes the comparison function, and two variables as input\n",
- " and returns the one which is greater according to the logic defined inside the cmp function.\n",
- " \"\"\"\n",
- " choice, explanation, confidence = cmp(a, b)\n",
- "\n",
- " if choice == a:\n",
- " return 1\n",
- " return -1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "602441b87ef7"
- },
- "source": [
- "The below function performs the pointwise evaluation of the provided set of responses, with respect to the provided metric, instruction and context."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "6863af9c235f"
- },
- "outputs": [],
- "source": [
- "def pointwise_eval(\n",
- " instruction: str,\n",
- " context: str,\n",
- " responses: list[str],\n",
- " eval_metrics: list[str] = [\n",
- " \"question_answering_quality\",\n",
- " \"question_answering_helpfulness\",\n",
- " \"question_answering_relevance\",\n",
- " ],\n",
- " experiment_name: str = experiment_name,\n",
- ") -> object:\n",
- " \"\"\"\n",
- " Takes the instruction, context and a variable number of corresponding generated responses, and returns the pointwise evaluation metrics\n",
- " for each of the provided metrics. For this example the metrics are Q & A related, however the full list can be found on the website:\n",
- " https://cloud.google.com/vertex-ai/generative-ai/docs/models/online-pipeline-services\n",
- " \"\"\"\n",
- "\n",
- " instructions = [instruction] * len(responses)\n",
- "\n",
- " contexts = [context] * len(responses)\n",
- "\n",
- " eval_dataset = pd.DataFrame(\n",
- " {\n",
- " \"instruction\": instructions,\n",
- " \"context\": contexts,\n",
- " \"response\": responses,\n",
- " }\n",
- " )\n",
- "\n",
- " eval_task = EvalTask(\n",
- " dataset=eval_dataset, metrics=eval_metrics, experiment=experiment_name\n",
- " )\n",
- " results = eval_task.evaluate(\n",
- " experiment_run_name=\"gemini-qa-pointwise-\" + str(uuid.uuid4())\n",
- " )\n",
- " return results"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "971acda8dd12"
- },
- "source": [
- "This function integrates pairwise and pointwise logic to enhance response selection and evaluation. Here's the process:\n",
- "\n",
- "**Current Workflow**:\n",
- "\n",
- "1. Pairwise Comparison: Compares responses in pairs to identify the best one based on user-defined metrics.\n",
- "2. Pointwise Evaluation: Assesses the quality of the chosen response, providing human-readable explanations to build trust.\n",
- "\n",
- "**Alternative Workflow**:\n",
- "\n",
- "1. Pointwise Evaluation: Evaluates each response individually, filtering out those that don't meet specific quality criteria.\n",
- "2. Pairwise Comparison: Ranks the remaining high-quality responses using pairwise methods to determine the best one(s).\n",
- "\n",
- "**Key Points**\n",
- "\n",
- "- Combines pairwise and pointwise approaches for robust response selection.\n",
- "- Offers flexibility with two possible workflows to suit different needs.\n",
- "- Prioritizes response quality and provides explanations to support user confidence."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "0ac1c136a334"
- },
- "outputs": [],
- "source": [
- "def rank_responses(instruction: str, context: str, responses: list[str]) -> tuple:\n",
- " \"\"\"\n",
- " Takes the instruction, context and a variable number of responses as input, and returns the best performing response as well as its associated\n",
- " human readable pointwise quality metrics for the configured criteria in the above functions.\n",
- " The process consists of two steps:\n",
- " 1. Selecting the best response by using Pairwise comparisons between the responses for the user specified metric ( e.g. Q & A)\n",
- " 2. Doing pointwise evaluation of the best response and returning human readable quality metrics and explanation along with the best response.\n",
- " \"\"\"\n",
- " cmp_f = partial(\n",
- " pairwise_greater, instruction, context, PROJECT_ID, LOCATION, experiment_name\n",
- " )\n",
- " cmp_greater = partial(greater, cmp_f)\n",
- "\n",
- " pairwise_best_response = max(responses, key=functools.cmp_to_key(cmp_greater))\n",
- " pointwise_metric = pointwise_eval(instruction, context, [pairwise_best_response])\n",
- " qa_metrics = pointwise_metric.metrics_table[\n",
- " [\n",
- " col\n",
- " for col in pointwise_metric.metrics_table.columns\n",
- " if (\"question_answering\" in col) or (\"groundedness\" in col)\n",
- " ]\n",
- " ].to_dict(\"records\")[0]\n",
- "\n",
- " return pairwise_best_response, qa_metrics"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "BY1nfXrqRxVX"
- },
- "source": [
- "### Load the Gemini 1.5 Pro model\n",
- "\n",
- "Here we load the model, and assign a temperature value in the range `0.2` to `0.6`. A higher temperature value is critical This is important even for use cases where creativity is less important like Q & A: since de-correlated responses would mean if the model gets it wrong with the top choice for one response, it has a possibility of getting it right with one of the other responses"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "e761e89b9065"
- },
- "outputs": [],
- "source": [
- "generation_model = GenerativeModel(\"gemini-1.5-pro-001\")\n",
- "generation_config = GenerationConfig(\n",
- " temperature=0.4, max_output_tokens=512, candidate_count=1\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "8669160b92b9"
- },
- "source": [
- "### Prompt Gemini\n",
- "Now we prompt Gemini to generate multiple slightly de-correlated responses based on the above configuration. Multiple responses can also be generated in single call."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "3fb5e3ae9faf"
- },
- "outputs": [],
- "source": [
- "instruction_qa = \"Please answer the following question based on the context provided. Question: what is the correct process of fixing your tires?\"\n",
- "context_qa = (\n",
- " \"Context:\\n\"\n",
- " + \"the world is a magical place and fixing tires is one of those magical tasks. According to the Administration and Association (TIA), the only method to properly repair a tire puncture is to fill the injury with a repair stem and back the stem with a repair patch. This is commonly known as a combination repair or a patch/plug repair.\"\n",
- ")\n",
- "prompt_qa = instruction_qa + \"\\n\" + context_qa + \"\\n\\nAnswer:\\n\"\n",
- "responses: list[str] = []\n",
- "num_responses = 5\n",
- "for i in range(num_responses):\n",
- " responses.append(\n",
- " generation_model.generate_content(\n",
- " contents=prompt_qa,\n",
- " generation_config=generation_config,\n",
- " ).text\n",
- " )\n",
- "\n",
- "prompt_qa"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "c54c44f3f0b5"
- },
- "source": [
- "Here we use the `rank_responses()` function to fetch the best selected response as well as its associated quality metrics."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "20230a87d5c1"
- },
- "outputs": [],
- "source": [
- "best_response, metrics = rank_responses(instruction_qa, context_qa, responses)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6b5b63a55144"
- },
- "source": [
- "Now we print the various generated responses:\n",
- "1. The raw responses generated by Gemini\n",
- "2. The best performing response\n",
- "3. Its associated pointwise quality metrics and explanation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "7ba246f23c1f"
- },
- "outputs": [],
- "source": [
- "for ix, response in enumerate(responses, start=1):\n",
- " print(f\"Response no. {ix}: \\n {response}\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "8321faa562f0"
- },
- "outputs": [],
- "source": [
- "print(best_response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "88458eafb3f9"
- },
- "outputs": [],
- "source": [
- "metrics"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "2871493acdd3"
- },
- "source": [
- "## Cleaning Up\n",
- "\n",
- "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
- "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
- "\n",
- "Otherwise, you can delete the individual resources you created in this tutorial."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "86bbf7dc97ff"
- },
- "outputs": [],
- "source": [
- "aiplatform.init(project=PROJECT_ID, location=LOCATION)\n",
- "experiment = aiplatform.Experiment(experiment_name)\n",
- "experiment.delete()"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "name": "enhancing_quality_and_explainability_with_rapideval.ipynb",
- "toc_visible": true
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
From e6233eac9e3193114517e6a593c1e742dc87625d Mon Sep 17 00:00:00 2001
From: Roy Arsan
Date: Tue, 29 Oct 2024 15:41:02 -0500
Subject: [PATCH 2/3] feat: Add e2e vector search outlier detection NB (#1348)
# Description
Add notebook to demonstrate building a real-world outlier detection
using Gemini and BigQuery vector search.
Notebook also demonstrates how to tune hyperparameters for vector search
and evaluate performance using publicly available HDFS logs labelled
dataset.
- [X] Follow the [`CONTRIBUTING`
Guide](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/CONTRIBUTING.md).
- [X] You are listed as the author in your notebook or README file.
- [X] Your account is listed in
[`CODEOWNERS`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/.github/CODEOWNERS)
for the file(s).
- [X] Make your Pull Request title in the
specification.
- [X] Ensure the tests and linter pass (Run `nox -s format` from the
repository root to format).
- [X] Appropriate docs were updated (if necessary)
@kazunori279 @holtskinner
---------
Co-authored-by: Holt Skinner
Co-authored-by: Holt Skinner <13262395+holtskinner@users.noreply.github.com>
---
.github/actions/spelling/allow.txt | 27 +
embeddings/README.md | 5 +-
...-vector-search-log-outlier-detection.ipynb | 1424 ----
...-search-outlier-detection-audit-logs.ipynb | 1448 +++++
...-search-outlier-detection-infra-logs.ipynb | 5782 +++++++++++++++++
5 files changed, 7261 insertions(+), 1425 deletions(-)
delete mode 100644 embeddings/use-cases/outlier-detection/bq-vector-search-log-outlier-detection.ipynb
create mode 100644 embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-audit-logs.ipynb
create mode 100644 embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb
diff --git a/.github/actions/spelling/allow.txt b/.github/actions/spelling/allow.txt
index 3d6875030d3..e1e9fa7d5d7 100644
--- a/.github/actions/spelling/allow.txt
+++ b/.github/actions/spelling/allow.txt
@@ -113,7 +113,11 @@ Glickman
Gmb
GmbH
Googlers
+Hadoop
+HBox
HDFC
+HDFS
+hdfs
HIDPI
HMO
HREDRAW
@@ -136,6 +140,7 @@ Hyperdisk
ICICI
ICML
INFOPLIST
+IOMGR
IVF
Isner
JPY
@@ -171,6 +176,7 @@ LMPA
LOOKBACK
LRESULT
LSTATUS
+LSTM
LSum
LTRB
LUVBPTK
@@ -179,6 +185,8 @@ LangGraph
Lego
Leung
Llion
+Loghub
+loghub
Logrus
Lottry
MLB
@@ -250,6 +258,7 @@ RAGAS
RLHF
RMSE
RNNs
+RNN
ROOTSPAN
RRF
RTN
@@ -269,6 +278,7 @@ SEK
SEO
SIMONE
SKUs
+SNE
SNB
SPII
SPLADE
@@ -316,6 +326,8 @@ Traceloop
Trapp
Tribbiani
Tricyle
+TSNE
+tsne
UDFs
USERDATA
Unimicron
@@ -403,6 +415,7 @@ bigframes
bigquery
bitcoin
blogs
+bml
boundings
bpa
bpd
@@ -419,9 +432,11 @@ chromadb
cicd
cimg
claude
+clf
clickable
clsx
cmap
+cnf
codebase
codebases
codefile
@@ -437,6 +452,8 @@ colvis
colwidth
constexpr
corpuses
+countplot
+COUNTIF
csa
cse
ctd
@@ -453,6 +470,7 @@ deepeval
dente
descgen
deskmates
+dfs
dino
diy
docai
@@ -606,6 +624,7 @@ linted
linting
llm
llms
+logparser
logprobs
lparam
lxml
@@ -621,8 +640,10 @@ memes
metadatas
mgrs
miranda
+millis
morty
moviepy
+mpld
mpn
mrr
multitool
@@ -630,6 +651,7 @@ mwouts
nbconvert
nbfmt
nbformat
+nbviewer
ncols
ndarray
neering
@@ -715,6 +737,7 @@ ramen
rapideval
rarians
ratelimit
+rbf
redef
repreve
reranked
@@ -732,6 +755,7 @@ rsp
saaagesh
scann
scattergl
+scikit
seaborn
selectbox
sft
@@ -758,6 +782,7 @@ stuffie
subviews
subword
supima
+svm
sxs
tabular
tagline
@@ -780,6 +805,7 @@ toself
toset
tqdm
traceloop
+treeah
tritan
tseslint
tsv
@@ -833,3 +859,4 @@ youtube
ytd
yticks
zaxis
+Zhu
diff --git a/embeddings/README.md b/embeddings/README.md
index 81a62c1f5ac..10383691e1b 100644
--- a/embeddings/README.md
+++ b/embeddings/README.md
@@ -14,4 +14,7 @@ This repository explores various techniques and use-cases for embedding in Machi
## Use Cases
-- **[use-cases/outlier-detection/bq-vector-search-log-outlier-detection.ipynb](use-cases/outlier-detection/bq-vector-search-log-outlier-detection.ipynb):** Shows how to utilize vector search and BigQuery to identify outlier log entries based on their semantic similarity to other logs.
+### Outlier Detection
+
+- **[bq-vector-search-outlier-detection-audit-logs.ipynb](use-cases/outlier-detection/bq-vector-search-outlier-detection-audit-logs.ipynb):** Shows how to detect and investigate anomalies in audit logs using BigQuery vector search and Cloud Audit logs as an example dataset.
+- **[bq-vector-search-outlier-detection-infra-logs.ipynb](use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb):** Demonstrates building a real-world outlier detection using Gemini and BigQuery vector search. Also shows how to tune hyperparameters and evaluate performance using a public HDFS logs dataset.
diff --git a/embeddings/use-cases/outlier-detection/bq-vector-search-log-outlier-detection.ipynb b/embeddings/use-cases/outlier-detection/bq-vector-search-log-outlier-detection.ipynb
deleted file mode 100644
index 67206dcf00d..00000000000
--- a/embeddings/use-cases/outlier-detection/bq-vector-search-log-outlier-detection.ipynb
+++ /dev/null
@@ -1,1424 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "7d9bbf86da5e"
- },
- "outputs": [],
- "source": [
- "# Copyright 2023 Google LLC\n",
- "#\n",
- "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
- "# you may not use this file except in compliance with the License.\n",
- "# You may obtain a copy of the License at\n",
- "#\n",
- "# https://www.apache.org/licenses/LICENSE-2.0\n",
- "#\n",
- "# Unless required by applicable law or agreed to in writing, software\n",
- "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
- "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
- "# See the License for the specific language governing permissions and\n",
- "# limitations under the License."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "e95c369b3b9d"
- },
- "source": [
- "# Log Anomaly Detection & Investigation with Text Embeddings + BigQuery Vector Search\n",
- "\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "61bf5ba500f8"
- },
- "source": [
- "| | |\n",
- "|-|-|\n",
- "| Author: | [Roy Arsan](https://github.com/arsan) |"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "TGE5SpdQrFUD"
- },
- "source": [
- "## Overview\n",
- "Security and IT operations teams are tasked with analyzing an increasing amount of logs such as [Cloud Audit Logs](https://cloud.google.com/logging/docs/audit). At the same time, they are faced with a number of challenges, including shortage of talent, increased toil, and increased cyber threats. By leveraging AI for log analytics, cloud administrators, security analysts, and threat hunters can keep up with these challenges:\n",
- "- Proactively and intelligently identify cloud threats or misconfigurations\n",
- "- Reduce the time and effort during a threat or incident investigation.\n",
- "\n",
- "In this notebook, we will demonstrate **Generative AI-enabled outliers detection and incident investigation** using your Cloud Audit logs in your own project.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "-kHvlYPt9OsY"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "38b2358a2636"
- },
- "source": [
- "### Objective\n",
- "\n",
- "Using [Vertex AI](https://cloud.google.com/vertex-ai/docs) and [BigQuery](https://cloud.google.com/bigquery/docs), you will:\n",
- "\n",
- "* Summarize and transform your logs from a verbose deeply nested complex structured payload into a natural language text summary.\n",
- "* Create embeddings for each textual log summary directly in BigQuery using [`textembedding-gecko` Generative AI model](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings) from Vertex AI.\n",
- "* Create a fully-managed BigQuery vector index to search all logs summaries without operational overhead.\n",
- "* Search directly in BigQuery to find any past actions that are semantically similar to suspected current actions.\n",
- "* Visualize findings in an interative table and timechart for rapid investigation & confirmation of such potential outliers."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "3b1ffd5ab768"
- },
- "source": [
- "## Getting Started"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hsX82WHAR0Gk"
- },
- "source": [
- "### Prerequisite\n",
- "If you haven't already done so, the only requirement is to [upgrade your existing log bucket](https://cloud.google.com/logging/docs/buckets#upgrade-bucket) to use Log Analytics which provides you with a linked BigQuery dataset with your own queryable logs data. This is a **one-click step without incurring any additional costs**. By default, Cloud Audit Admin Activity logs are enabled, ingested and stored in every project's `_Required` bucket without any charges.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Y0PJAOju-yxx"
- },
- "source": [
- "### Install packages and SDKs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "CuonDAxt-ewW"
- },
- "outputs": [],
- "source": [
- "# Install mpld3 package for interactive rendering of matplotlib\n",
- "%pip install mpld3\n",
- "# Install Vertex AI and BigQuery SDKs\n",
- "%pip install google-cloud-aiplatform google-cloud-bigquery --upgrade --user"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "RFcQnSibyn4F"
- },
- "source": [
- "### Restart runtime\n",
- "\n",
- "To use the newly installed packages in this Jupyter runtime, you must restart the runtime. You can do this by running the cell below, which will restart the current kernel."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "rGUqrj9jyxOd"
- },
- "outputs": [],
- "source": [
- "# Restart kernel after installs so that your environment can access the new packages\n",
- "import IPython\n",
- "\n",
- "app = IPython.Application.instance()\n",
- "app.kernel.do_shutdown(True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "02d6dfc513c3"
- },
- "source": [
- "
\n",
- "⚠️ The kernel is going to restart. Please wait until it is finished before continuing to the next step. ⚠️\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7Eg9Mmp1xEAK"
- },
- "source": [
- "### Authenticate your notebook environment\n",
- "\n",
- "Authenticating your notebook environment\n",
- "- If you are using **Colab** to run this notebook, run the cell below and continue.\n",
- "- If you are using **Vertex AI Workbench**, check out the setup instructions [here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "RjHTFGJbxXXN"
- },
- "outputs": [],
- "source": [
- "import sys\n",
- "\n",
- "if \"google.colab\" in sys.modules:\n",
- " from google.colab import auth\n",
- "\n",
- " auth.authenticate_user()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "I0DdmYrZ-lsY"
- },
- "outputs": [],
- "source": [
- "# For debug only\n",
- "!gcloud config list --format 'value(core.account)'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "fKvFT-9d_GlP"
- },
- "source": [
- "### Import libraries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Xrz9wjvP-mE-"
- },
- "outputs": [],
- "source": [
- "from google.cloud import bigquery\n",
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "M_-tnDXOwY3i"
- },
- "outputs": [],
- "source": [
- "from google.colab import data_table\n",
- "\n",
- "data_table.enable_dataframe_formatter()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "RgTWVU2z_9KS"
- },
- "source": [
- "### Set Google Cloud region and project ID"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "wH0Qwzzt_Wq0"
- },
- "outputs": [],
- "source": [
- "PROJECT_ID = \"[project-id]\" # @param {type:\"string\"}\n",
- "LOCATION = \"US\" # @param {type:\"string\"}\n",
- "REGION = \"us-central1\" # @param {type:\"string\"}\n",
- "\n",
- "# Set the project id for gcloud CLI and BigQuery client libraries\n",
- "!gcloud config set project {PROJECT_ID}\n",
- "bq = bigquery.Client(project=PROJECT_ID)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "BFwOxRrMA4vc"
- },
- "source": [
- "## Prepare log data and convert to text"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ShreV-cE0X53"
- },
- "source": [
- "You'll preprocess the raw logs that reside in your BigQuery linked dataset (or `SOURCE_DATASET`) into a summary table in your new BigQuery dataset (`PROCESSED_DATASET`). This new dataset will contain the aggregated logs (daily user actions are coalesced) which will also be converted into a simple natural language text. It will also contain the text embeddings and vector index for semantic search.\n",
- "\n",
- "Make sure you have **BigQuery Data Viewer** role over `SOURCE_DATASET` dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "NcQguptKCPba"
- },
- "outputs": [],
- "source": [
- "SOURCE_DATASET = \"[linked-dataset]\" # @param {type:\"string\"}\n",
- "PROCESSED_DATASET = \"[new-dataset]\" # @param {type:\"string\"}\n",
- "\n",
- "TABLE_NAME = \"nb_admin_actions_summary\" # @param {type:\"string\"}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "_Zb53qoUBf-K"
- },
- "source": [
- "### Create BigQuery dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "tqpblzhADUn_"
- },
- "outputs": [],
- "source": [
- "!bq --location={LOCATION} mk --dataset {PROJECT_ID}:{PROCESSED_DATASET}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pgnAhPHwCu1S"
- },
- "source": [
- "### Define BigQuery User-Defined Functions (UDFs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "b9jABImgMQht"
- },
- "source": [
- "The following UDF helper function converts a structured log entry payload into a natural language text explaining the log entry activity of who did what, when, on which resource and from which ip and user agent. All of these dimensions are important facets to include in the text content and as a result, to consider in the embeddings vector space."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "SWSjMkDkDx3D"
- },
- "outputs": [],
- "source": [
- "# Produce user-friendly text content from an audit log entry's parameters\n",
- "UDF_NAME = \"stringifyAdminLogEntry\"\n",
- "\n",
- "sql = f\"\"\"\n",
- "CREATE OR REPLACE FUNCTION `{PROJECT_ID}.{PROCESSED_DATASET}.{UDF_NAME}`(\n",
- " day DATE,\n",
- " principal_email STRING,\n",
- " action STRING,\n",
- " resource_type STRING,\n",
- " resource_id STRING,\n",
- " container_type STRING,\n",
- " container_id STRING,\n",
- " channel STRING,\n",
- " ip STRING,\n",
- " counter INT64\n",
- ")\n",
- "RETURNS STRING\n",
- "AS (\n",
- " \"On \" || CAST(day AS STRING) || \", principal \" || principal_email ||\n",
- " \" ran operation \" || action || \" over \" || resource_type || \" \" ||\n",
- " resource_id || \" in \" || container_type || \" \" || container_id ||\n",
- " \" using \" || channel || \" from ip \" || ip || \" \" || counter || \" time(s)\"\n",
- ");\n",
- "\"\"\"\n",
- "\n",
- "query_job = bq.query(sql)\n",
- "print(query_job.result()) # Wait for the job to complete.\n",
- "print(f\"Created UDF {PROJECT_ID}.{PROCESSED_DATASET}.{UDF_NAME}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ATM61oqTMmfe"
- },
- "source": [
- "The following UDF extracts the resource ID that was acted on per the audit log entry. In the audit log entry, The resource ID is specified in a different resource label field depending on the resource type. That's why this UDF is needed to normalize that resource ID field."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "NYQsa3Lo2-qn"
- },
- "outputs": [],
- "source": [
- "# Deduce resource ID from a log entry resource field\n",
- "UDF_NAME = \"getResourceId\"\n",
- "\n",
- "sql = f\"\"\"\n",
- "CREATE OR REPLACE FUNCTION `{PROJECT_ID}.{PROCESSED_DATASET}.{UDF_NAME}`(\n",
- " type STRING,\n",
- " labels JSON\n",
- ")\n",
- "RETURNS STRING\n",
- "AS (\n",
- " COALESCE(\n",
- " JSON_VALUE(labels.email_id), # service_account\n",
- " JSON_VALUE(labels.pod_id), # container\n",
- " JSON_VALUE(labels.instance_id), # gce_instance, spanner_instance, redis_instance, ...\n",
- " JSON_VALUE(labels.subnetwork_id),# gce_subnetwork,\n",
- " JSON_VALUE(labels.network_id), # gce_network, gce_network_region, ...\n",
- " JSON_VALUE(labels.topic_id), # pubsub_topic\n",
- " JSON_VALUE(labels.subscription_id), # pubsub_subscription\n",
- " JSON_VALUE(labels.endpoint_id), # aiplatform.googleapis.com/Endpoint\n",
- " JSON_VALUE(labels.job_id), # dataflow_step\n",
- " JSON_VALUE(labels.dataset_id), # bigquery_dataset\n",
- " JSON_VALUE(labels.project_id),\n",
- " JSON_VALUE(labels.organization_id),\n",
- " JSON_VALUE(labels.id),\n",
- " \"other\")\n",
- ");\n",
- "\"\"\"\n",
- "\n",
- "query_job = bq.query(sql)\n",
- "print(query_job.result()) # Wait for the job to complete.\n",
- "print(f\"Created UDF {PROJECT_ID}.{PROCESSED_DATASET}.{UDF_NAME}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "YqGoF8lfNEmL"
- },
- "source": [
- "The following UDF deduces where a user or system action occurred from per the audit log entry. For example, an action may have occurred through the Cloud Console, or using gcloud CLI, or via Terraform script or another unknown client or channel."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "PfRcocm65-rk"
- },
- "outputs": [],
- "source": [
- "# Deduce channel from a log entry request user agent\n",
- "UDF_NAME = \"getChannelType\"\n",
- "\n",
- "sql = f\"\"\"\n",
- "CREATE OR REPLACE FUNCTION `{PROJECT_ID}.{PROCESSED_DATASET}.{UDF_NAME}`(\n",
- " caller_supplied_user_agent STRING\n",
- ")\n",
- "RETURNS STRING\n",
- "AS (\n",
- " CASE\n",
- " WHEN caller_supplied_user_agent LIKE \"Mozilla/%\" THEN 'Cloud Console'\n",
- " WHEN caller_supplied_user_agent LIKE \"google-cloud-sdk gcloud/%\" THEN 'gcloud CLI'\n",
- " WHEN caller_supplied_user_agent LIKE \"google-api-go-client/% Terraform/%\" THEN 'Terraform'\n",
- " ELSE 'other'\n",
- " END\n",
- ");\n",
- "\"\"\"\n",
- "\n",
- "query_job = bq.query(sql)\n",
- "print(query_job.result()) # Wait for the job to complete.\n",
- "print(f\"Created UDF {PROJECT_ID}.{PROCESSED_DATASET}.{UDF_NAME}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hWCB3WeUBiDN"
- },
- "source": [
- "### Build summary table of admin actions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "5lUGTp-MhqoJ"
- },
- "outputs": [],
- "source": [
- "LOOKBACK_WINDOW_DAYS = 360 # @param {type:\"slider\", min:30, max:1080, step:30}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "GPhiu82VBx6P"
- },
- "outputs": [],
- "source": [
- "TABLE_ID = f\"{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}\"\n",
- "\n",
- "job_config = bigquery.QueryJobConfig(\n",
- " destination=TABLE_ID, write_disposition=\"WRITE_TRUNCATE\"\n",
- ")\n",
- "\n",
- "sql = f\"\"\"\n",
- "SELECT\n",
- " {PROCESSED_DATASET}.stringifyAdminLogEntry(\n",
- " day, principal_email, action, resource_type, resource_id, container_type,\n",
- " container_id, channel, ip, counter) AS content,\n",
- " *\n",
- "FROM (\n",
- " SELECT\n",
- " EXTRACT(DATE FROM timestamp) AS day,\n",
- " IFNULL(proto_payload.audit_log.authentication_info.principal_email, \"unknown\") as principal_email,\n",
- " IFNULL(proto_payload.audit_log.method_name, \"unknown\") as action,\n",
- " IFNULL(resource.type, \"unknown\") as resource_type,\n",
- " {PROCESSED_DATASET}.getResourceId(resource.type, resource.labels) AS resource_id,\n",
- " -- proto_payload.audit_log.resource_name as resource_name,\n",
- " SPLIT(log_name, '/')[SAFE_OFFSET(0)] as container_type,\n",
- " SPLIT(log_name, '/')[SAFE_OFFSET(1)] as container_id,\n",
- " {PROCESSED_DATASET}.getChannelType(proto_payload.audit_log.request_metadata.caller_supplied_user_agent) AS channel,\n",
- " IFNULL(proto_payload.audit_log.request_metadata.caller_ip, \"unknown\") as ip,\n",
- " COUNT(*) counter,\n",
- " -- ANY_VALUE(resource) as resource, -- for debugging\n",
- " -- ANY_VALUE(proto_payload) as proto_payload -- for debugging\n",
- " FROM `{PROJECT_ID}.{SOURCE_DATASET}._AllLogs`\n",
- " WHERE\n",
- " -- log_id = \"cloudaudit.googleapis.com/activity\" AND\n",
- " timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL {LOOKBACK_WINDOW_DAYS} DAY)\n",
- " GROUP BY\n",
- " day, principal_email, action, resource_type, resource_id, container_type, container_id, channel, ip, log_name\n",
- " ORDER BY\n",
- " day DESC, principal_email, action\n",
- ")\n",
- "\"\"\"\n",
- "\n",
- "# Start the query and save results in new table\n",
- "query_job = bq.query(sql, job_config=job_config)\n",
- "result = query_job.result() # Wait for the job to complete.\n",
- "\n",
- "print(f\"{result.total_rows} admin action records loaded to table {TABLE_ID}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "PXSx722dNf4x"
- },
- "source": [
- "Let's peak into some of these summarized daily user actions:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Ao2gLpW6NoCI"
- },
- "outputs": [],
- "source": [
- "result.to_dataframe().head(10)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "1194baac6a52"
- },
- "source": [
- "Below are example daily user actions summarizing what action was taken by whom, on what resource, and from where:"
- ]
- },
- {
- "attachments": {
- "4c5c1883-5ab6-4fca-9b4f-c7b1584d50e3.png": {
- "image/png": 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376avzciZlMMWSJE3NjbttddeXdJsRUZVX9EFwkH5MBjaG7uo2VWNHDvqqKO2/Z4Hertp2aDJdna+tcFHwdnWzpnfHEVx+OGHe8MlSgerUVBq6LxZPcnZ21XEalFWGSM4oUzxAlBu+EYIKzF5qSjx7Jy0FZk5nghyEIIXq0Th0yxZBWCVmnZmNouewgsBISAEhIAQEAJCQAgIASFQhQDHhJ5zzjn++0IsEL3jjjsaUTj1hh0wLB5lZf0999zj/ZjwQj9h0agZfNDR2iHpPu2g1/1xb7nlFn+sHLo3xKLbdonj5PgeJbo9uzw4GYmJyJxBM5UH9HQWMmPkfOWVVxpHX9adlKUMGObhjWGU4/k44hJjPt/1bIY6kZdm0lObaQYthe0UAkzushDBxgV2a5txpNSm4zmycE6NsYax6O677/afhcLwwHjDsZh2Mlun8m981H4MCV37GwLU3dxc9SSTTOIOPPBAP15zRDPPEGMrhkvaGvPaGC9pw7TltdZay4fBIMjRzyxUY1wt7dwkfY5dp50yZ81iopNOOsnvBLUjbfm2LmM7uyfZqckxsHxKLUcYISGOjsXAwhx8Sc7kW5kYajkphN3hduw9p0dcfPHFfgEen0LgtAV2b3JcbiukvqIV1AZOnIHa3sI3xCcbWQjBZjoWqJpuF4Zp9n4gt5uWDZqAwurLyy67zBssWf3BB8kxSKKE820NVo3R+eK32Wabefcq8BGa+G4GHzlHidl9993dr3/9a3fGGWe4o48+2nf4nL1Nh2xb9XM8CUNnykoYOnbOJm+FKKtICAgBISAEhIAQEAJCQAgIASHQHQhgiOSEGyZ/mDhmAtl2zfHtTCaCTjzxxMZ3kPiuIN8XZGfn1FNP7Y8oQg9D/2mXpPu0i2D3xed7qejEn3/+uddtzTjRTorLL7+8P8aYxcjozHwbD10+R7k8rL322n4R8iGHHOK/vceEbbhrJMfP3JlDYKKTiRx2aTKRy84Srs0QE8Tt5qWZ9AirNtMsYgrfLgLMbbEI5uCDD/bfpmO3NjuzWPxSatPxHFls2Nh444393Nspp5ziNxSwsKHUH7RbDuKr/XQCRfHoaQRGG2207Fw1i804cvbss8/233RmrOYTZoyvEH6nnXaaO+KII/yGHhbx8B1riG9O8i29E044wY9/GCrDb0n7QMEf7RNexx13nD/lAEPisssu60NssskmPg9nnXWWN5TAy9IJWHS5Rf7EQImssdNOOzmMLCU5E4Mn/QXz88zV28mKzOtzWuMNN9zg5/c5StO+/90lwSYe1Fc0AdYACzpQ21v4mmh3fDeTNsMiBfukRBimlfuB2m6GDFWUf7Cz9lsBBusxClVO0bBjZ5vlzVG2rBazc/mJz7ZbFPXwHOU6fMkfEwPh6rM68RRGCAgBISAEhIAQEAJCQAgIASFQBwEmnNrRq0gDfYeJsJTewm63UDeyPOHON4qYmGbCTNR3EWi1jrBzEYMjxgsMhejgqbrQTsk5vpJfbidWnTyQL3jk8obBBWNlTOzO3HLLLRvOzCEwedXK0XTGpCovFk5XIdBbCLTaH4T55WhL5shSVGrTVXNk8KX95dpyKj25CYG+jkAn2lyqjKW5atoa41mK8MMgmBrr4Ml4nPJL8UrNoVs45EQWLNmpgxgrOWExpn333dfv4MQ9nMtPyZmcxrD//vv7sZsxnMVOKfmhymYQ50HPAwcBtbeR/cus097Ct057gzT+hqj8795waXmHprGic80ZMwlT8jMeqat1tKEfHX0rlBs8WuGlOEJACAgBISAEhIAQEAJCQAgIge5AoKTvmAIXpvvoo4/6lbzsyll00UVDL90PUAQ4UrU7CL51eefCVRlA2B22xx57DJP9eM4gfh4mQg2HqrzUYKEgQqDPI5AzZpLxUpuumiMr8e3zoCiDQqCHESjJbqW2VvIr8UwVLzWHbuFi+ZFvaq688srm3bjadzdxsHG4rpyZMmbCp8pmQBiREGgGgVLbKLWpkl+JZypvnW5vYRpxew39dP8/BIb7363uhIAQEAJCQAgIASEgBISAEBACQqAZBHbddddmgnc0LKufxxhjDLftttv6nXsdZS5mHUGgE/WDnb8cS9ybRoZO5IEJII6WjX8TTTRRR7AWEyHQHxDoRJ/QH8qpPAqBvoKA2lzXN8E3PeNxmOeUISUnZ2IcQi7pxNH3XXOnp/6OgNpb1zfYTHvrGlNPJQTaPnK2xFx+QkAICAEhIASEgBAQAkJACAiBgYoASvu6667rv3nUXUcrDVTsBkO5wvpBeVVHBsNbVxmFQB6BsE9Qf5DHST5CoFMIqM11CknxEQLVCKi9VWOkEO0hYAsvtEOzPRwVWwgIASEgBISAEBACQkAICIFBiIAp7Xw7SCQEYgRUP2JE9CwEBjcC6hMG9/tX6XseAbW5nsdcKQ5eBNTeBu+7742Sy6DZG6grTSEgBISAEBACQkAICAEhIAT6LQKh0s73K0VCIERA9SNEQ/dCQAioT1AdEAI9i4DaXM/irdQGNwJqb4P7/fdG6WXQ7A3UlaYQEAJCQAgIASEgBISAEBAC/RKBWGnnWSQEDAHVD0NCVyEgBEBAfYLqgRDoWQTU5noWb6U2uBFQexvc77+3Si+DZm8hr3SFgBAQAkJACAgBISAEhIAQ6FcIpJR2vqEpEgIgoPqheiAEhECIgPqEEA3dC4HuR0BtrvsxVgpCwBBQezMkdO1pBGTQ7GnElZ4QEAJCQAgIASEgBISAEBAC/Q6BnNKub2j2u1fZLRlW/egWWMVUCPRbBNQn9NtXp4z3UwTU5vrpi1O2+yUCam/98rUNmEyPQElGHHHEAVMgFUQICAEhIASEgBAQAkJACAgBIdBpBIYbbjg3//zze7bcx894SK/qNOr9h19cH+JnK4nqiCGhqxAY2AjEfUD8TOnVHwzsOqDS9SwCcRuLn8mN2lzPvhOlNnARiNtX/EzJ1d4G7vvv7ZINefXVV3+YbLLJejsfSl8ICAEhIASEgBAQAkJACAgBIdBvEXjttdec9Kp++/p6JOOqIz0CsxIRAv0CAfUH/eI1KZMDCAG1uQH0MlWUPo+A2luff0X9OoM6crZfvz5lXggIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgMbAT8kbMffPDBwC6lSicEhIAQEAJCQAgIASEgBISAEOhGBD788EM32mijdWMKYt3fEVAd6e9vUPkXAp1DQP1B57AUJyFQBwG1uTooKYwQ6AwCam+dwVFcuiIwzjjjeIcRhgwZ4kYeeeSuvnoSAkJACAgBISAEhIAQEAJCQAgIgdoIjDLKKNKraqM1OAOqjgzO965SC4EUAuoPUqjITQh0HwJqc92HrTgLgRgBtbcYET13EoERfvjhB60k7iSi4iUEhIAQEAJCQAgIASEgBITAoEMAxV07NAfda2+qwKojTcGlwEJgQCOg/mBAv14Vrg8ioDbXB1+KsjRgEVB7G7Cvtk8UTN/Q7BOvQZkQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkIghYAMmilU5CYEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhECfQEAGzT7xGpQJISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEUgiMkHKUmxDoCQQ++eSTnkimo2nwzVmjZu8tXu46ZMiQLl7xc+gZ+oX5CMNwX/Kr4x/zC5/DPITu7dyPM8447URXXCEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYAAiIIPmAHyp/aVIVca2vliOMM9V96F/nbIQPjQSxs8pHqU0Un4ptxTfOm4pXmH+6/BQmN5FgHeod9a770CpCwEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRANQJtGzS/+OIL949//MO99NJLbpFFFnEzzDBDdaqZEJ9//rm77rrr3GijjeYWX3xxN8ooozRCfvfdd97vxRdfdDPOOKNbaqmlukzEl+Iak6+++spddtll7qc//ambdNJJzbnL9cMPP3TXX3+9++CDD9yCCy7oZp999i7+PBDmySef9P7DeAYOb731lrv22mvdzDPP7Oaee2433HD/O+H39ttvd6+//noQ2rlVV13Vl72LY4sPTz31lOe/5JJLtsjhP9HOOusst9pqq7kf/ehHLfP55z//6TbYYAP3+OOPd+GRMoh1CdAHH+I827NdybLd29WKET/jHhuTCBO7WfzcNcWXsLF7/Jzj1467pdFMGSwO6X7zzTduxBFHTGbhvffec99//72bYIIJkv4DwZG+5eqrr3brr79+ZXFOPPFE9/DDD7tjjz22MmwcAMz33ntvd/LJJ7tf/vKXbr/99ouDdNtzM2Xstkz0AOObbrrJTTHFFG766afvgdSUhBDoikCnZICuXKuf6spH1Zz6Z4jewr0voTXLLLM4ZMc555yzmK0f//jHjn6yHb2hmEA/9UTfueuuu9yrr77qll9+eTfuuOM2StJTulBVOmSoblsv6WfdrQs1gBsgN2BpMjPX8J4i2nN8313FL8n6YV7i/JifXXP+pXzHacfPxE25hTzD9Ou4EyYXJ4zPfVXacXh7tnh107F4XMcff/zGI2345ZdfdlNOOaUbfvjhG+66GXwIMEf35Zdf+vmzwVd65+WMwayPPf30027ppZd2r7zySuXrl1xWCVGPBhgscyY9CqoSEwJCYEAh8D8LWwvFQkCabLLJ3FFHHeUeeeQRt+KKK7rNN9+8trAfJnn66ae7aaed1t1yyy2Oe4TycOBdb731PG8MmmuuuabbcccdG9Gr4lrAQw45xK277rru0UcfNacuVwaNeeaZxxEOIxz3F110USPMrbfe6jbaaCM34YQT+jI3PBI32267rVtooYXcM88843bYYQc377zzemOMBcWYcPTRR3v+pMEPg2uniDKCZTv07bffuvPOO89hmG2Hvv76a2/wjnmgrPW3n5XB8s2zKZ3mZlfzyz2bezPXOH3ixuk0w6+3w2KgDPPAAomQUMgPPfRQt9hii3kj39Zbb+1WX31194c//MHhN9DozTff9G2uTtk4shkjbx2iD0KhMXrwwQfd8ccf7+677z637777mnOPXJspY49kqJsSufHGG4dZxNFNSfUJtm+88YbbbLPNBmS77BMAV2TihBNOcFdccUUjVCdkgAazGjfNyEc12PV6kLjPrJuhTuHeavp189md4ZDT68izr732ml/EVJWX2267zR188MFVwQaEP7rHNNNM4/7yl7/48Xmqqabq0q57QhcCyFI6zbT1Kv2su3WhAVEpEoUwuRkvu4+vJb84bLPPlqVSvDhMmB+7t2vMJ+du4ar8CdepMJYm11hnCf3i+2bChnGJ10xcC88V+te//uVlMeYd9tlnH7fyyiv7uYt7773X++tvcCDAAuFzzz3XG7Kmnnpqxzjan6kdHaMT+lg76fc27tQFFkjVobpyWaxz1OGtMM0j0Kk5E72v5rFXDCEgBPoHAkOGDnA/YJRsljB2sfqanXe77767j84EOwI0xkYMDyExuTHSSCMlVywyeT/66KO7888/3xsriIcyzU5MhHF2Ic0111x+8n7sscd2CBWstHrhhRf8TstSXMsDuwMxuH766adewFtuueXMq3E97rjj3Jlnnunuvvtun092c+66667uueee82HWWWcdRzyMlEzYkN8UPfDAA27++ed3CAUTTzyxA6tJJpnEXXjhhW6JJZbwUVgxeemll/qdmykeJTeMPiOPPHKXHZ8WnrRQasA6RaX3gB98qwjFizywkzZF+I066qgNL5Qodop+9tlnDTduMCCHBN/eJlsZm8pHnD97titx7N6uoVuKZ+wWpp+7j+OEz2G6uFc9h3E7cR/muS6/MI/syg53A++0005+cQOTu2OMMUaD5SmnnOKN5Pvvv3/DjRtWoLLDM7cauVTHaTv0RXXaQIlPXP+7ZDB4IC3aam5HahDU9yFx3g477DB3//33d+mHcjzHG288d/nll7tFF13UsyUu/RQLFmJiB0Cubaf6lxjzXB7idOLnEm7wJO3w3ZBuuIs/5mfPVeVh53y4e97ioQDinqpLKRwsXulKXYdvqn/GjzKFfafxKmFaxTOXHrxL2FjauWuMPwZzxmzSG2GE/OEP+INpCnPKyS+FD/noVF1jLEJuMCrVPUu33brGeyKdXNsqvYs6+WWxFfj/7ne/s2Ilr6V0milrzLyufBTGK/WjVfmM/Uu8munbLX9xn2nuvEPqQrNjHfnLycCpvJfST/URlr+4XZp7VT+R63vALtdHGm+utFvaNtjQtm6++Wa3wAILNIKkcIMviyHZ0WkEj7jfPe2009zZZ5/teVo4rqU+MwzX0/fI/q3oVeSTk1pY3Hn44Yf7bJ900km+7ExG95QuVEqH3Rt12zp1oko/a0cX6un32sn0Wq0jpkdR98MfeeM5vPqHwN2eW7022+fF6Vj+cLf7+Br6WXwLw3MqD7Fb/Gx8StcwDQuXcqvjR5hm8lAnbN28fPTRR37xJwsJOB3KCGMGehSyP4skQorH0tCPdOmTczJZLJuEccP70jjC2INsXweHkOdAum+1PyhhwO5c5uc23HBD/97XXnttt9VWW5WieL+UPGKRSnWFMLF/Th4hLDIB7z2lC+AfU0nHiNON4+aeS/ljMX5Y70vpU5acXIaeT/0PeVl+SvpQShayeKV3ZGG4Uj7mGDhVbtZZZ230uxamrlyWCpfTOegzSDeHh6Xd29dOtDnDNzVPQPlK76nVOmu4lepVKt3c+0JOK81FldpIKh3LX7vlMz66DgwEOtHeBgYSKkV3INDyDk2Meo899phXbC1jY445pt89eckll3inK6+80k9obLPNNm6cccZx0003nbv44osteONKh3jGGWd4Y6E5EtYMXkx4IJhjzIQwDnJEFQN0VVzC01lvueWW7phjjuliLMEvpIceesgfm2tC9cILL+yef/55h6EWwoC56aabdjn+KYxv9xyJSdkxZkJM7nLsIIoGxMBBw6YcuPFcRYblWmut5XEAayZ4jDjSlqMjcWdH25FHHumFWPwtbu49PPvss34HHMdaMQEVviOETTsqljR23nlnn2/SWWaZZdy7775rWXAYhCn75JNP7leEPvHEEw2/1A1CT/iLw4R+pfs4XukZPiWqm06Kj7mFV7snTe5zP8tTHD50D/3M3a7Gt9lni9fq1dKzayt8wrh2z5X2ikKx5557emMmCxg4DppFCewERwmyHUkI/iyiwOhJX4MSh/JgbStXx9955x0fjjTGGmssH58FGfQZKbrhhhv8UdPUc+r/Pffc0whWt/4j5G233Xa+H2FykF1tlAnCQMlENtRM3nI8UcDIK0dor7DCCu7AAw/0eFJe+mn86OMgdl2wcAT8mIy96qqrvDt/cf8C7rgxsUl4jND0RxiY4cmPNFIUlhF/jpzDEEN/ya54e6dvv/12492QBu+Wd0N/xoIWxgPSt3EiTouFKeBLOI4PX2ONNXzfSDjGDRR7+DChuttuuzUUPeoSO+wnmmgiPxmLn9WjGIff/OY3bo899mgkze4advBTb0mPI32NwJ4+n/4R/rxfI3bL4k4fvMoqqzR23ubeq8Ur8Sz50bbAhvTAhmMyIY5Bp93YWIUbJw2wuAei/L///e/9DiLaCyceEJaFQPPNN58PA24YM2KiLCyA4vQFJuwPOOCABubgRful7lOXCGfvtZ26FuaBeoNCTzugLjHBkKt7xMuVNeRp91VtpzRukgcWh/EukHMwJkDN5Bd5CfmEo6M5ohIKZQCec+8cv7pl5Qj+sF/guOtf/OIXsKgtHxE21x/jV8KSBWbUOfoJ2jXtl08eWD8EDhdccAFsKvtPjGj050Z77bWXYwFNqs8kDG0DbKm/pG3pWHy7hrhXyV6p8SSXfjt1tdQX5PqeUh9pZbUr/RwyIbggI4fjZ13ccv0u/QLjJQY9xpWPP/7YJ5vLt+Wpv14Zl8ITaNhZYycx9JQuVEoHXOvqQlX6WSu6UH99r53MdyhnG1/coPBq4SxMu1fjF17r8rR8Ed7u42vsZ+mEaZibxbU4pecwfnxv/MzdnlP8Un4WL76GYeP7ZsJaXItjz+EVP3tmLOS4b2QJ2h+yGDo88iWLI1gsblSSkQmTGzeQiZHRkAfo89lRjuybotI48u9//9vrUcj+GFkPOuigFAu5FRBggT+Go1Cf+PnPf+4/RYJuQ1+90kordVkMmmOHvEK9QRblJLTwnZbkMvRGZCf0RuQAFu6j5yGnoScsvvjiftcw6ZpMlJvLysllOR0jJz/HZQz1MervFlts4fNJ/jgByuRu4iHDoaNQHuo5O+Ry6ef0ftPfWUCA3ILcyAYNo5I+lJOFiIu+TlnQs2abbTafL+MZXo2HzYlwkl5IdeWyXLiUzgH/gSqXhdhxD76tzDkRt26dJSwUzplU1atc/Ui9r9L8QlUbKelwzZbvP6XUvxCoh4DJOrrmbQoDEZuq2tGyQZPBH6MiAlNICDJMMKGo8qMjZlcjRkEmjFKT3OxYYOWYreZhgpTVhQg8EBPb4Qpu3BAOOAq1Ki5hGWCZ+GKiuEQIhuG3fBB0oFBQLMU3PzBhN6gRQiHGV9ud+f777/sJH74DiuCIQoCQVyKwhA9CIxM6GC0RyFAWICaEMfhw3BlhmFBiwIVK74H4TNLxwzj517/+1W2yySaOPELwoGFApAHmpIOxEiMMhmiIvDHRza4v3hcTtHWUk1Kj84xr/JV4xH6wi93C5xrJNfAwXs3GSYUnD0a5e/wtr2HYMLyFMf/4OY4fhrOwMb84TPhs/JqJUzc+E/AoAxC7FWi/d955p98FjhvCJEoWxPHKfK+Wvod6jLHT6n+dOo6hiXbFN3zZDUI9jon6jyGVek0f9bOf/cyveCZcM/Wfvg0DEv0NbYlVtDY5TltlNZwRZaiTtxxP+jP6LwyC4EP/yyQFR/ZiiMDv//7v/7yShNKLIZi+GoUOpYnJdSjuX8gnC1qYeKUf4DhAVgGDPSvBOcr2z3/+s9/JbmWxa1hG3g19P2HBFCMX79X6Ha4Y3jDIMvmCUgb+vB8ULYT30MBiaXAlbyijTAafeuqpvu7YRPs555zjmExhpz1jGbvlMZZDv/3tb30/B2/Kj+HXlN0YB8Yo+kDKBGF8ozwYBQlr6REGgy/HHr300kvenSMFLQ7lZ8LexjSOiYJy7xW/Es+SH+XhfXHMOe+fcYl3z7ugHVu7IQ0IdysHZaKNgT9KPhNlfAOPlZ+8c4gyMLbF9Ktf/crzBlPeJfkww+cuu+zi2wE4448xE2Mx1E5dC/MAH8Yt8ko9pUy5uke8XFlDntxTT6raTm7cpO0jK3DMPe0IYxBtE2omvyiXTGQyYUIfBlE+e5eld07YumWlPYULntjhbQZN+NShUn9chSVGaI7To81TR/geOgvP2NVGvwV2GD1tPKL8uf6TftbaLfmGN79UnwmWjD+kR19E+owFhm9Y7hB3+OdkYDBPjSep9Ev9JGmX3l+pL6C/yvU9pT4yLC/vgT6bSXTqMN9cJz9QM7jl+l1kUgznTDrSX7HgoZTvMG/98Z6+BH3FiG9lM7ZCPaULldKxfNW5VulnrehCddIdTGFMBqfM1u/Z1dwsTCeuMbbGM3YPn8P8hO7xfd1wFi8OX/Vs8exaFR7/OAxxzb10tTRS1zBeyj92y4VPuTMes0CQvnKmmWZyf/vb3/xcAYvSmG/AyITsBpVk5NK4Qb/OZyuuueYaz4tv8zG/k6LSOMJYzUI4ZHQWh6Kb2OK6FC+5DYsA8g0GQ9OD0GMZHxmHmyFkY+oOOjc8OKYYmR2qksuQT3iHyKEs1udzUchK1CH0OgxvjOEQMlFpLisnl6V0jJL87BML/pBJqLcQV9oFxkjKSnvh1CeI+sdcFnoO5WAugc9DpdKv0vuRD5EXwQT9iPqN3gqV9KGcLEQ8PhHDAkTGToysLIBivI6JuRF0fnQt9B2MqkZ15bJSuJTOMZDlMsPOrq3OOTVTZy0t2kw8L5SrV7n6kXpfpfmFUhsp6XCtlM/KqasQEAJCwBBgc2H4M/fctWWDJgMoE80xsQqQVR/W+f7kJz/xx8iyHZ9v37Gzk4n8HNFxs9OQVfjsDIGYZIqPpKCQdLghpeIyecwkORP0MTGJjaDCDyEmTsd2asbpxHwQVowPxpCQEDqY9GYy2wykKPmcZc4RkEwGYURAuEE5KRG7PJm8Y9ckuy1RTmwSmXi//vWv/aoyMwyHvHLvgQlpJvY5ioZ4c8wxh5+wZLBMEQIv+WfSjTyg0EAYMFGgqBMMnKxuYxK1RFXlLcXtLr9m82R1pLvy0y7fuuUhXBjWnkO3dvMSxy/xpk5OOumk/vg6FgPQXlAeaCd2tAeKHIoWxCQ+xgmUY4xJNgGIX506Tlz4YoihvWJooW1Zu8ZoA9EuOEKZK/0URjDKUar/cT/D7jz4kx79IbvGzAjhE4n+UnmLgvgdf83wjOOzUwiljrTAjxW+9L8ck20U9y+EZ0c35TCjBmHoH+irOHYbA3SJ6MsYDzBIoDyyUIWxJdzdTV5Ig4kYVg8zec4uSPo/Jn///ve/D5MEEzYYEjCUsWKYiR121Box8U4dw4CGsZcdKSjgvEtWsWO44Sgt0iVvZuwkfogD5eeIReuHMfAwdsVEX7/++uv7dw0+GF5Q8iGM2Rij4INyjSEZZRsq1ZUSz5If6bFKn7yDDUeUggWKbx2iDYI9uGGMq/NdHsZQjLSUh9X4rC5mrGTMYdxmMgG+LAbihzETf445gjpR1+DDOMj7YSdonbpXp6x12k5u3CQuq9spH3WWSST6AmSoVvLrIyX+6rzzOmWlHPQJvBcmZpjUZMdrM1Tqj+tgCUbUIdoRfTV1ifpMW0XGpB9hoYJRnf7TwuauGNlZwEF69E/smuOdWbvPxcM9J3vhlxtP8Aupnbpa1Rfk+p5cHxnmi3smzpAbWeDDaSS8A5NB6+JWp98N0y31mWG4/n7PJDD6BTu8oVhHwa0ndKFcOriHVNKFUvoZbbgVXShMU/ddEQjl6vC+a6jWn+CZ4ptys1Tq6kl1wxnf3riWyhnmp9PhQt7cp/jjZvMm9BnImSwCRNYzbFnAyELKKhm5NG6QPrIfO9iQpZjDYLEecovpTFyRZ0vjCP04Rh4WIyFPMI5gKCj1I6Qt6ooA+i4LFCFkKBYpMhfXDKHjUk9Y3I4stcnQhe2cbIYeVEcuQ0+jP0c2Q2dCzkZfQ6/BuHr11Vc3slM1l9UIWHFTJT+XooMZeiGyFfkzHY/yIt+i59NmOH0CA2OKSnq/hWduDQyQF6nf6FklfYg2XNJBaU/I0C8NnduEH4sl0cViYm4O2Y7PgKHThzuz68pldcNZ2oNFLqO8rc45tVNnDWeuqXqFe936QdjS/AL+uTZSpcOVdFr4ioRAdyBAf63fwMGg2TqS/9BVBScEkrvuusuvUA+/Acfq+amGHh2CYBOTfVfLVm/H/jyzzR0jH0enGfENFtsxaG4IykzGhhTHRTDA6MYKJgQUJrlYWcIEOiuWWD3FM4SAwdEMoSHPJhVxLxGKuvFBcDEiPoLdxhtv3GXHJthgDDFi0p1dR2CHgFSXUFzYKWPfxasbL3wPGB+ZWA/fFwJtHSLeHXfc4Sekeafbb7+9NxBgzATblMJVh29fD2OKYVw+3HGLr5QnF8fKav5h2Pi+9BzmxdK38Dk//I3C9M2t7rXVuGE+LS2EdI4vYnUkBiYIARAjD0I5RvNQUaMdQygJ7ExOUamOwy8mhDGEVVa9WrtmIpH0mcBmRws7KFAamTAgPAs4cvU/7mfohzCK0dcw2Y3xDuNsHbK8xWHb4QkvBFTD23hzFCnGgTqEUgjZlXv6QnCrInbfYiTBSGk7/sM6G8ZnojzsY0kvNZ5goGQcsuN7Qx7cc4Qh9Y+xgTrElUkZxgbGFo6RtX6S8DE2uEHwYJcVq9/ZOcQ4w6RATBhOmRwwQunHOAJheMUoaEZM3JhQYFxhtWOurpR4lvwwQoVGV8Y3+nImkTiWphli7E/hH/Ngsoq2xBFlRijjEDt6GStZdWzEOEgdQFmPyeqYXfGvW9diXs3UvVxZm2074bhJPeVdhd/0xlhGn5WiZvIbxi+98zCc3efKSvtkoQKTjrxTjPTht0gtfula6o+bxZL3HsouVifoc1OU6z9TYUM3+mjqI+NQSCxIYZFZXbI+hTZTGk9S/Jp59+H7K/UFpb4n10fGeWPij7EiRXVxa7bfLeU7XvyYyld/cMOggF7AziXeJ9RTulDddGIcc7oQ4WL9DLdO6ULwEvVtBEJ53+7jKyUI3XjOyYKEC6nqOQxr95ZW3WfChemk8hb6G9/UtW44SzOVVoovchRyNwspbSE3/SvyNYTxA4MmE99TFWTk0rgRp4uRlH6XRT+mMxGGPJfGEXQ7+jjkXQw+LKwjTqkfidPWs/OnR3BiF7oM750Fmc0Si/qR7Yx4nxg1oVbkslhPC+uFZxr8tTqXVZKfQ10jSCp5y/hqmzBoP4y9nKCDfItRkE0R8XwjjEp6fyohZD5kv5I+hF9JB+W0oT/+8Y/+81gs+mZctQXFYZrMzeUMsXXlsrrhLN3BIJdZWVudc6JPzul8zdRZywdXq1fc160fhG1m3ihsIyUdrjvKR15FQsAQaEZ2sji6DnwEWjZoMhnJ5C0TZeEErq3yagU6JmwYhOFBB23EKkB2+iDoUpFJF+Mfk8JGqbhM0GKwYGWYrQ7jme+8scoeQSAkjBSUxwhhCaEuPKrB/MIruxI5OiUkFAqESiZm995779DLHz/LgICxE2KSyyYsugSseMCgzK6KdghDCsfUgqmtqmcSnUnunDHA0mNQRhAFI3YmkR/cKA84hhP0FqcvX5vtJAlvdZJy2X18NT+upTRCv9w9PFJk4Ukb4jm8xy33jF9dsnSqwpfCWT7gYeHMDeM/xy/SfqxNoGgtPtS4CNF+EeIhFj7QN0AY9VGm2Y0TU6mOx2F5pu6y05O6zc+IlZvssj799NP9UTXsLFhkkUW8YbJU/+M8cXQNhgE7IogV1PR7dcjyFofliJxWecILHC0/xhujMrttupNYAUo//Nxzz7mphk6uQBwx2C5huGByBMUQQ2NMTPBgiMRYSP1idxGTq0y+0J+xQjZnxIx58X6pn9QNvodK/JgoG+OJHUVOf4vxnhXCjB/sDE4dx156ryWeJb+phxrMGD/NOMPEFv02xkwbd0uTD3HZ6jwzmUZbZ2LEDEBMvmDIZBwHM9qvjel8z5q8sHiBndndQZ2qe822nXDcpLxMoGAgjIn6EVI7+S298zCNOvf00Zy+wGKH8PuxdeISptQfN4tl3TQtXNh/Iu/YJJb5565WfzEupSa2cvFK7qXxxAyzFr+dd1/qC0p9DwsNUn2k7Ra0vDEJakfDm5td6+KGrNlMv1vKt6Xdn6/oOyx6ZKELZTWiffSELoShviody1N4Ja+xLoR/Sj/DndM3OqELwUv0HwQYZ02eDu87hY/J7DG/nHsYzsKQP8tbeCWs+cX3IZ/w3niaW/yMe8otxIgwdZ8Ja5Tia37htW64ME58bzxy+SQ8YdC/MYpwcoHp8KFBkzaHAYS2WpKRS+NGnDfkdxaFs0DTvqVuYUrjCAaj8DvczNlAuX7EeOraFQFkEvQQFoaix/GZpWaJseXv0Wk3GLRYYNzdclk4l9WMXFaSn5stv4VHL0GmQVdjQSWnxaDXxfox4Ut6v/FLXU0uSulDtNucLEQb4whfDJp/+tOf/PtiMSRtNW53NidiC2fDfFj6VfJs3XDGm3ab02UtzEC5Gr7x/A7lK+k43VFnDdNm6gdxSvMLxjN17a3ypfIiNyEgBIQACAw781oTFzo0dkRhrOPoElbUYYhkZVNsKKzDkuPomETG8BhPGLETilVNhMHQgUGB705yBCGUi8tkKDsYwx9unJufyiPfSGLFIOE5AoXz/+0YrTplsDAoHBgaWaHGahVTRMwfIQFjJ4oFuPGNCRQPjMQYafgWUYo4XhPjI3EoM7tWQ2NyKk6VGxhisMWIAMas2EEpye3M4hgLBk0UD94DRz5CvA8mp8ENIwIY864ImyNwSf1y4XPuKR45N3iU/MI0cuHMPeQV3pt/s9eQR+6+xDOME4bDHTK3/zz979nc61zrxrVwqWuYjvmbGxN4fI+FHeAoyRD31lY4/gUDDERfY4sCll12Wd8mMBZQ72gfRnXquB35yoQ334EgDzHRJlj0QBvE+MVRLtR7qJn6T1tHKcFoxIQC7QgDV47q5K2KJ0ZgFLUc0c+x+5Sy02ZR3niuu1s7x9fcmbRknIiJvp53z25VdlVhJOb9tWJQI74dm8rRtbyfI444wvdLpM/4ZIQhBmMmfR47yjEGY/jEkMCKXIzq9HH05fTPYVzjYVcMgdQLjs5KKTeEwx0+TBRTvn333bfxzSGOAqLcGBkhjHkYuaHSey3xLPlhkGKMIT36bCabKT87BxkLGIfsKHHwxNBYhziGFrJ6Fr4PcGUSjXGGcYsxgjrHohcMl3w7kiPKmIijnvD9XCa7bEdbnfSbDdOpulen7eTGTY66os+jL6PdsdCKupnaYViVX95dasc5uJTeebO4sciB72hSLzDk1yG+r8TkGFTqj+tgWSe9MEyu/+SoXL4xRHtkwpe+L6SwzyTPTESwOx9DJP0GC+NY/AUhM9KWmqHSeAKfMP2qd19Kt9QXlPqeXB9JWmF/jnzOLk1kd2RTvgdv41kVbpbvqn6XvoW2AX+olG/j2V+vTHIyCckYzIKXkHpKF6pKJ8xT1X1OPyNeSReq4iv//yBgsnOIB25G5m9Xc69ztTjhNYyXcg/d4nuLG7unngmbco/d4nDxM+FTFPKJ41Q9h3Hr3Ifp1wmfC1PKl6WBToEBCmMM8wkspGNxgunnLBBDxquSkUvjBmlx1Dj8GQ/R99GZUqdZ5cYRZGvGVORF+nXyyLG1dRcZWXl1/Q8CjIksJmEeyz5vVIVNOI6jSyOLYtTk3SCTcvICxrVOy2WluaySXBbrGCX5OdQ/qnAI/ZEDWchM3ca4OMssszTwjNNvRu8P0yjpQ1WyEHotcwb0EbRjZENrd6EcikGWT13R/pFxzz///EYW6splVeFinWMgy2UN8P570+qcU6nOxmm08lyqH/H7Ks0vlNKmXsArNW/c3eUr5Ut+QkAIDF4EWjZoAhmTvuxAZDcME/ys9mDStpljUw16VoIz2cskDhOc9mNlOkIDK88475tJZ455ZIcAAz9Uimv861xR4pnkZgcJggwTC4ceemidqF3CYFRhgppvGrDazMpC2SAGEQwzHOfB8S8cOcsqRQQUJrk4/jBF4IDAiiLCirETTzyxy/GLqThVbrw3JqDghbDGbhHygqCUIgwPrMKaauiKMIReO5IEYZjJTRQpjN3kkQlaVmc2SzllLufeDH94pCjFOwwX++MXu3Xnc7PpNRu+2bx3B394GmFkoh1BGM2Z3MNwxPfTUIZoq/QJtsuMurfkkkv6voh2YhPNxK9Tx1GkmVxjlSPHccarHeHDkUi0a1bmoYjzDQPaDf1QM/V/xx139JMN9GUojAigGElzVCdvVTxZYIGysddeeyWTwWhMn8qxqOSLPolnFjd0gth9CHYx0ScyjqAcoJhhUAZb+zZqHL70TH9NvcBYDLFriJXK9GtMEGN0pJ5DpMnED370Z/TJdkwThjX67amH7mSkTtQZ0zDIUT/iCWif2NA/xigUdb6XSloomUwCQcTlx6pglAS+W2G7Q0vvtcSz5Ieh0NKjfBhrWchjR0RhbMU4y7GDHFlEX1+HLO/gRnuN3we4Mn4wRrCwCLnBxjrGWdIjLnWRNotRszupU3WvTtvJjZtMliAn0O7AjyOH+O6Q7U4Py1+VX75hdOaZZ/pd42E87qveeRy+9Ez/Sp+MkZSj4uoQfTXfvoVK/XEdLOukF4bJ9Z+0AWROJv9oe3E9D/tM8kydxmhHm6GPYsKLeoycw6p0W0wRpl26L40nxAvTr3r3pXRKfUGp7yn1kWF/Tn2gra655pp+Yg0DFhhBJdziPJf63cUWW8zLm4wRGDZL+Y759rdn+l/KyOkPpjtwZRITrHtCF6pKpxlMqX853a6kCzWTxmALG8rrVvbYzZ7N367mXudqccJrGC/lHrpxT3ijOG74bOFiNz3/Z+GvYWl4hM/hPXMmjMv00cijyPz0z8i3TEQjcxnWJRm5NG6Qnukv9PV8O49jOVOUG0fIAwsOkYORt/mWO7oVfYWoeQTQfRhvkfXqUjiOo5vw3Xp0WWQiPsFx8803+zGo03IZ40tuLqskl8U6Rkl+jvWPupjQTtBHkbmYy8LAu88++/jocfrN6P1x+iV9KCcL0a6Zr2N3JnNszNexOQM9PZZDqQ/MiTAfi56LvGpUVy6rChfrHANZLjPs7NrqnFOpzhrvVq+l+gHP+H2V5hdKeaBe5OaNu7N8pTzJTwgIgcGNwJChBqcfmGBshzBssUrPVgm1w6sUl3QYlFMfwC7Fa9aP1UysZkRg726iPAh3Rgj4HD/HYBESq8ZQTDB8sLuFAaXTu1fIC4KsKTth+twj8KK0YFRiJ2aYbwsLbky+1MlbKGBZ/PjKOzfK5cv8O3FtNY0wn3XuU3mN0656DnmEaeIePof3sV/Io5n7OG/NxA3DhnnjHoXBCIUaYZyJXVbwsuLSFjGkjhJl1wxhMAYw6YdCZkcYGc+4jrOjCYWcCUQmS0mHtlVFGM1Sq2Cbqf+0IfrMHJat5K3Ekz6atpkyloTljfuk0K/Ve3BlpW+OUMbYlVkH+xwP3OFjRhb4UV7eFX05Bgu+12OGS8LzvsAjhQm7jKiT7eaJdIwYW1iBnhoryTu7j1P1qvReSzxLfqRHGVN5odyk2coYSP0hHu87fB+GAZgzPvBuYiI/xLN2Hvt3xzN57ETdI2+ptlNn3CQuRs86sk0pv9YHYhxOE6K6eQAAQABJREFUUemdp8J30i1VF+L+OEwvhWXoX3Vft//M9eXwT/WZhKfNWD9DuFTZcK9LuTzE6ZfefVVaVX1Bru9J9ZGp/pyxl3aU6k/IWwq3VJ5L/W5cJ8Ajl+8U755yQ+5oV68q5ZX+GSzq9BclPlV+PZUO+YjfbVXe+rt/q3WE9sh7sR84xPeGDe7dRSm5NeWWSz/MW537HJ9Umim3XHzcw/RTzzk33OtSs3mK+Vbl0fxZXMNOO4wY1BWTX7lHrgrHrToycmrc4BhOCINoXbklNY5YGVP6nPkNlmur/UE7+KTGceoRY3Vqfoe02u2n685l5WQiy4PpGFb+VD1sRy4jLrpaSpYGgzB96nbdeS/Lr12JW9KHeB/Whi0OV9oMcxZhv5IqbyxDhjy4ryuX5cKldI6+KpfFZW+3zVnZm5lzCvMQ1llOTEotpGe+gu9iNkup+gEPy3NYr0vzC1Xp0hZy88Zh+ar4yH/gI9Buexv4CKmE7SAwQjuRLS4Dam4Sw8J04ko63a3Ak0+Ei1YmclspYyw0MinEsWYl6q68xXnJ5QGFKBc2JXjl+ISCWBjGFDPcUmFSbmH8du+7i7/xDctXt4wWN1c2/EO+4XN4b+mFYXM8U+5V+TD+qbi4xemGeYt5s1OYI0+Jw3ciWCWKMMY3DzkeMySOK8KAyfFGHGGE4X377bcPg/j7XL3F03aoDRMp4ZAyOhGsmfqPMlKX6uatxDMUYEvpljAqxSv5lYyZxKNPaQa7XFrhZA1HYy4+dOcuq1gvvfRSb5xgl2RIpTRZNd5pYmzJjZXkPVevSu+1xLPkR3q5vNAWWx1nwvoTvg/Dsqcxt3RzV/JYylMuXso9LHvsTzol/7qyTSm/TKqkDMWWF+Lm3rmF6a4racdUwqPkF/Opei71n7k2B89Un5kKnypbVZ5C/xTPVPqldx/yS91X9QW5PKTaRqo/zy0Msbzk+Ju/XUv9blwnwKMuX+M/EK70z3X7i3bK21PpkMf43baT74EeN5SVkY/tOXffXXhYuiH/lFvon7onjukG8T3hzS+Mm0sn5x7Gje/DNPGLn3NuMZ/Ucyv5yfEJcUjlkXh8X4/j/dGFOAEG4sQaxv24L68jI5fGDXjX7YfitIlr1KqsafF1bQ2B1DhOvSr1xSW/ZnNReu+lcT2Vh1Q9bEcuI25K/qOMcfqlul2FSSluSRZKYZcqb64Mlq8SzhaGay5cSucYDHJZJ+acwjrLqXjMb3WKUvUD3qn3VZpfqMpP3BbC8GH5QnfdCwEhIAQ6jUBHDJqdztRg5pf6tid4cPyFfa+yt/AhffLRKUopeqFCXkonFbcUvhm/VngTx5TN3L3loYp/7B8/G592rmEe6/CpykOVv6Vh4Qwr3HN5YRKa73jw/TmM/Bwhwwpjjs2IDZocz8aR13yTDwWBb7zVOfoa4Y4jenpyR5hhUXXty3mryntf8OeoWI4E5hhOjhBioienlPWF/CoPAxOBTo+bAxOlzpdK/WfnMRVHISAEeheBUF4O5Wm77+ncxenGz7n8EC7UA8JwsV8zPEM+4b3xaDbNMLzxCPl24j7kG6YX8yZc6B8+2z0nKPHJHI5aP/jgg/1CvqmHHuW/3377+c8dhDxblZE5Gj/Mc8hT90KghEBfmMsq5U9+QqAOAq3OOdXhrTBCQAgIASHQHAIdOXK2uSQVWgj8BwGOOQgpVNRC99R9dytTzfJP5T10C+9T5TG3ON342cLlrmE64T3h42fjkXOvk3adMJZOfA3TtXut6IpR0rMQEAJCQAgIASHQXxDQ0Ur95U31Xj5brSMcg4i8bD9KYPJzfG22dBY/Fa8k66f8Um4x3zi9quc4vj2X0sr5xWkZr5R7ys3Ct3rN5Svml0o7dgufOWpW1P8QaLU/6H8lVY6FQN9AQG2ub7wH5WJwIKD2Njjec2+VUjs0ewt5pTsMAnUVvGEiDnVoJ26Kn7l1iq/xCRVPS4Or+YduJfc4HM/wzvFJhTc34sT5qsOnThhLI3UN0w3vU2HlJgSEgBAQAkJACAgBISAEBjsCJn+b3G/XEi6xnB+HNZ6xe/icC9Ose538Gs9Svi1MmMfwvso/DMs94eP0jEfsHsetejY+VeFC/1x+2s1LmIbuhYAQEAJCQAgIASEgBIRAf0RABs3++NYGSJ5bUe6aKbopgmE6pgSGbnV45sLn+OGei5NKLxc25x7zaDa9MH4ubintkl/IW/dCQAgIASEgBISAEBACQkAItIeAyd45uT1259nI4tpzu9cUv5RbKp264VJxQ7e6fCyc4WHPIa+qe+JY/KqwoX8raYXxq+5bzVcVX/kLASEgBISAEBACQkAICIG+jIAMmn357QzwvOWUvJzCmAtfgimOEz/Hcav864Qn/yk+OXfjmYpT8gtxCuOae+wWPsM3Fc7Ss2scx9y5lvzCcHXvO82vbroKJwSEgBAQAkJACAgBISAE+gMCyMumU8RX8l9Hvo/LaXFid3suyegpv5Sb8Qqvlm7d8GFc7puJVxXWsIzT4LnklwqfcqtKP4zTLi4hL90LASEgBISAEBACQkAICIGBhoAMmgPtjfaj8ow++uj9KLfKqhAQAkJACAgBISAEhIAQEAJCoOcRGHnkkXs+UaUoBISAEBACQkAICAEhIASEgBDoYwgM18fyo+wIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBBoIyKDZgEI3QkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAI9DUEZNDsa29E+RECQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQqCBgAyaDSh0IwSEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEQF9DQAbNvvZGlB8hIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIAQaCMig2YBCN0JACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACPQ1BGTQ7GtvRPkRAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkKggcAIjTvdCIFBhMDHH3+cLe0PP/yQ9eu0x5AhQ5pimcpbHbc4TPycykSct/C5zj08CTfmmGOm2MstgwDvJsQ3E0zOAwQB3vdDDz3k5p577myJHnjgAe+vepGFSB5CQAgIASEgBIRADyIgebUHwVZSQkAICAEhIASEgBAQAkJACDQQ0A7NBhRdb5566il3yy23dHUcYE9nnXWWKxn2uru43333nTv99NPdV1991d1JJfmjiKd+BE65d9qtlXRScVJunc5rjl+YdnhPeHv2Nx346wttcr/99nP8uoPuv/9+t8IKK7iRRhrJffTRR92RRJLnhx9+6M4555ykXycd+8L7ozwvvfSSIy91qTvx+f77791OO+3krr322mJ27rvvPrfjjjs6wouEgBAQAkJACAgBIRAjUFevO/HEE912220XR6/9fN5557nZZpvNLbLIIrXjdCJgd8pjnchfp3j0FXm5U+URHyEgBISAEBACQkAICAEh0GkEZNDMIProo492xKC5ww47uKeffjqTSu85f/vttw6F9K233uq1THzyySc+D+0ab0444QR3xRVXNFUOM7gRKWWsy7mnwrbi1t38W8lT3TipvMdu4TP3naBOtclm8nLbbbe5gw8+uBGFutpufW0wi272339/t+CCC7rPPvvMjTXWWJFv9z2++eabvh2ywKA7qRPvL34fdfP7zTffuHPPPdctvfTSbuqpp3bwydEbb7zhNttsM2d4dCc+f/vb39xdd93ldtttt1x2vPu2227rXnvtNXf55ZcXw8lTCAgBISAEhIAQGHwINKPXoX+99957tUCKdSwWoW6xxRbusMMOc3fccUctHp0K1J3yWKfy2Ak+nZCXO5GPnuTRV+dLehIDpSUEhIAQEAJCQAgIASFQH4GOGDQ///zz+in+NyTGky+++CIbr8Sz5Affr7/+Osm3Ks0w0s9//nN3wAEHhE7+/ssvvxzGzRxSOw3PPvts9/bbb1uQxrVUdgLl0ulU+UYYYQR3zTXXuOmmm66RJ7vJpW3+dqUMuR1DKb/4vY099tju+uuvdxNOOKGx9FfiUs4UpTDGIPD4448PEzwV1gLB334pN0vfwnTqmkvL+Ju/Xc09vOIXPqfu4zBVzykeoVscH7+UW8rdB2zhL27HuTbJe7Z042TiOhf7h8+psC+88IK74YYbwmCN+1I7SfGyiBgsYyL/V199tfvZz37md2iG/kxSxViU+GOIi+t+Ka8zzjiju+qqq9zwww8fJutIN+bTJUDwkGrv5m35z70/Cxdf4RlT7n2AXyq8xX/99dcdxkN2Qy6xxBLmnLyya/20005r1KkcPs3gnMofbowxxx133DDvPIU7OzQJTzyREBACQkAICAEhMHgRiGWenF6HzFCSGUMEY574xTrWv/71L/9ZhGWWWcYNN1zXaYRSOiYLhukhm9riMdy5ZwFajnLyWCrfIY84XyWZuBQv9OM+ls3NPyXzmR/XOD+hH/c5eTklGxK+lF4Vpjme8C35VZWB+DlK4Z+bLwl5UM4c5pQz5wcP/OPypPIRpqd7ISAEhIAQEAJCQAgIgb6LQFdNpMl83nrrrW6uueZy44wzjptsssn8pDgsfvOb37g99tijwe2f//ynN1qZoHn88ce7ySef3I077rhulVVWaawSZZcKE/rsoME9JnbOLLbYYu5HP/qRT4/jclZeeeVGsAMPPNBNMskknjc7Wt55552GXy7NRoDo5sgjj3QbbrihdyVfrEZlIpydU+Th4YcfbsTA4MHOqgkmmMCh4N1zzz1+VybPH3zwgT9GkrxBt99+u0Mhm3jiid0888zTZWch31D7/e9/76aZZhqfzrrrrttlN1gny0deRh555IYhsCptwkNXXnmlW2CBBdxaa63lMEjyfUSUEAi8OS6T9zLGGGN4JRj3Y445xv34xz/27xuc7LjHTz/91Id/5ZVXCOYwOiy//PJu/PHH97wvuOAC787fs88+63GnzpD+xRdf7P0wyJ5//vn+GFDiQs8//7xbY4013EQTTeSPRDrzzDO9e/yHYhT+8A+fuQ8p9mvm2fjEcczdriX/0I/w8XPKLQ4TP8dx8DeysGEYu7erhbdr7M5zTBhm9tlnn4YzRu1ZZ5218Uzbm3baaX0b32CDDRwrsqGwTVo93GabbXz/Qz2wOkHYu+++29c56ih1jvpw6KGH4jUM5fqxrbfe2tdldvPRlu145pdfftktvvjiyb6A9j3nnHP6uj7zzDM3+gnySxk5Upa2wcSOEbukqfMsDlhooYXcr3/9a+9Fm/zlL3/p25jlPdeWWDRB29tzzz09HqQBzqQ7xRRT+Pa0zjrrOI7riomjbscbbzzvbG0YPvR1xoeJgJjsHaT6AsLG+Q/f3+yzz94YLwh77LHHul/84hfcZvvI3Puo07dPOeWUvp9YaaWVfL/nE0r80VfMN9983of+4+abb3YhPq3gnMsffB955BE3xxxzNHKSGkvMk7rB+2F8EQkBISAEhIAQEAKDDwEWQSGTokejAz/xxBMNEGK9buedd/Z6Mboa+um7777bCBve5HjGOhaL35ZaailvkCMPnPQD5eRo/GJZkBN1cEMmZf4AnR49klNK4MkPGTRFoTyGf0mnZn6CRWzMT1B+dHjio3sj3yLHY5xNUdV8Q05PgVdO5ovnOEpzJaG8DE/kbXQKdOA111zTMa9ilEsPgyP6OHHQwTl5BL3bqMSz5JfTQ9Dh409mILdedtllPsncXAqnWPHO4/kSy6ddc3MgzC2hH6DHUJfQG03XaUVmt/R0FQJCQAgIASEgBISAEOjbCLRs0MRoxOTw7rvv7ji6BuEbowGCKRPcZ5xxRmP3HsrL2muv7QVd7v/85z/7I/+YyB9ttNH8UYDAxKQ5x9dgoEodIYoQPfroozt26bAj78knn2ys5CQ9FKIbb7zRfyMNXn/5y188+qU0c6+H+LZKlHt296DwYdTAIHnKKaf4qAjSm2++uTvooIO88RGDLMemzDDDDH7yGWWN76OhnLEyEBwoP8dWstsGIdzSgdc//vEP98ADD3gjDmW86aabfDqdLh9MSdcMUaW0fQb++4fRBUUKJREsMLRg7LVVj/DECPLqq696w+Mll1zij+y86KKLPB4olNQbwpO25QGMMWL/9Kc/9UoNSjO44k9YjJX8UMb/+te/uk022cS9//773tCJUoyB7LrrrvO53Hfffb2yjD8Giq222mqYXbJWbisbzzm3lJ/Fq3s1Hs2kEcYJ45m7pW3PFiZ+Jpy55eKYeyms+YXX8D6XBmFCwpgXGvR497ZKFmM3Rn3aAYZuVn8fffTRPnrYJqmHTEwst9xyvv/Za6+9ukyAYMxDqeVIrVNPPdXdeeedXVaBW35K/Rj1DEMbCxgwJKEoQ0ygMIEQ9wUYOldccUV3yCGH+EmDTTfd1P3hD3/wccgvk04bbbSRr8OspDfCcAZ/6N577230W7RJ+jqOnqK91WlLTFowKYBB96ijjvL9Ev0JEw2UlXYVE3kz/PGjzbEwhPLRptitCI+YqvqCOP/h++PdhAZoJsUwaJb6yNT7aKVvj8sRPvN++F4lxPjExF2ID3UcfOriXMofdX3eeed1I444ok8PvFJjifcc+kdbwFjeF48wtzzqKgSEgBAQAkJACHQPAuhfyMjIZBhrkAnQP41Mp+IZmQI5BjkS+RNjFrpkTCWeLCYNdSwMqCy8Qh5GbmXhbUmOtnyEsiwy1WOPPeYX3ZEnPuvAAmL0O3RHZDD05BdffDHOahd5rCQvEhF/dADKwJH96BTomGCADs43QJHxU1SabyjpKSWZDxk4nOMozZWE8jLvDvmYI36Ry3kHv/rVr3y2S+mdfvrpfgExxlnwR0exhcIlniW/OnpIiCfvgLJAXFNzKan5kpAH96U5kF122cWXjYXmyMcYMzEWQ83K7D6S/oSAEBACQkAICAEhIAT6BQItGzRRaDDsmaGS41FYiXfhhRd6Q9Yoo4zSmBhmshqlB0KYRjnCH8EcIygCrhFKzfzzz++NYuZmV74fxs5L23FkPPHHb/311/e7oDCSYkhAWYGq0vSBKv7I10wzzeR396y22mr+qFSLggEBQwRXdlSx4wwhOiZWzqJYrLrqql6xYlcWCinKgxFKCqspMYRiHLFvvXV3+Ug/l7blza7TTz+9W3jhhT0W7JAjv2YEIAzKBQYRJurBfr311vN1ghWyv/vd79y///1vb3AxflxRQlBwUfDAhF261C/4YtglDobiUUcd1e9owhgC3ilCQSbOSy+95MOi0INnjuJ3xXPsFsY1/zrXMJ7dWzx7Dq/mxzVFsXvVMzzqhKmTloUxfnZNpWFhm71yHCtHR9GehgwZ4k4++WT329/+NsnmJz/5iVt99dX9Ualcn3nmGd++2NGJsZN6SJ2j3bIyPUWlfiwVHrdcXwAvVoRTb8kDkw4YBO1YJvJLn2W7IXP8Q3d2a7JblXpfpy3RD3N0LCvQWc3NggmOdKaNspDg73//e8g+e0+/Dh8MerRJdiqmqKovCPMfxmdyhrGCd42x9cEHH/S7V+v0kSGfTvTtIb+693VxLuWPCSYMmiFVjSXsNCWeSAgIASEgBISAEBhcCGDAxJDIoioMdciHGJlyhKyFTozRCH2NT43E1CzPOH4dOTqWBZGTOW0HOZM8QoQhr8iVzAOwELFEdeRFZDV4cioSujcyOLIxcxDo8nzqIUWl+YaSnlKS+UgnnOOg/Lm5kjBPLGwmLIt6wWuToQt6t9xyS0c+SukxX4LsThz0I06JsYW/JZ4lP9Kro9OH+Q/vc/pTGCZ1n5sDwTjOAnPmL5CP+WHMZCFmeAxxXZk9lbbchIAQEAJCQAgIASEgBPomAv/bJtRk/jAYsdIxJI52ZAUkhgh2mnDMCKs4ESoRxiEMDRjpQiMmwjZCaYnYBYrhj6MKU8TKPAR8I5QXJuKhUprx9z8sfumKoc52NHG8iq0mxdDKDkSMqRg2UnT44Yf7iXyMDAjeUGgYCuOQDitsoZ4sH+mFafNcIgzZrBZGQY0JQ0VoeGb3JoZmVsty5JAR7xYcMHCHhBKGMkocrkZ2vKw9h1d25P3xj390iyyyiJt00kkdx9yYwhyGa+U+965yvAhPe0hRyS8XPnSvk5c6YUKenbhvtlxhmtQJFFd2V7NAgcUP7GTGMFki2/FIe8E4PtVUU9UyHJb6sVJ65hf2BRwfSjtl16gRhnkmnTpBpbaE0TYmjKBMlhgx+WP9ibnVudKX2SRIVXjrCxZddNFiUPo+JqxuueUWb9DkXbP7Hmqmj+x0317MdMazhHMpf4xp4fupM5bQB7KzQCQEhIAQEAJCQAgMLgTQPbfffnsva2LMDHdkViGBHsUOwVjfbocnabYrRyObQnblHtkoPMkFtxQ1Iy/CM5S5SI8dhDFVzTeU9JSSzBenU5orCcOyIxR52Yh5C4yaUCk9dmRiJGahHAsq0bNZbAyVeJb8SnoIxxM3Q6H+VBUvNwfCjl4WjYbzCRjo0UNZzIx8HlNJZo/D6lkICAEhIASEgBAQAkKg7yLQ8g5NvkmBIB0Sx0SaUYsdPhwBi3GC7zaY4ZBVpawaRAGyH4YA8w/5hfcYNFDGELRThAEDPkYYUTmSFmo1TeNVurJCkm9xnD70aBd2Fi655JLekJZSkjC0YFzjSF0MuhhtqsptafdW+Sz90vWuu+7yxx6lwmDQCb9Rwu5JFJNY8cHAi3LH9+GsXnDl2E4UMI7dDFdboqhxlFBMHGnDhD8GTRQdji3CWBLuII3jxM85AyTh8Cv5N8srDt/Mc518pMKk3FLphuFy9xavyt/CcWUSwRYEhO7cc+QUijvHNGMIRFGl/2iGMMCxo5cd4FVU1Y9VxQ/9WUSBUh3WX+5zizDCuHXu67alOryaCcMEhn1XsipeqS+I42688cZ+ZwE7NW3RQ7N9ZHf27XF+W3ku5Y/3ad+HhXedsYQJKeKJhIAQEAJCQAgIgcGFACceIWfxO+ecc/xnX+oigO7ForNY72yHJ2l3Uo6uWxbCNSsv1uVdNd9Q0lNKMl8q/dxcSRgWfJHDQ0IWRI8qpcfRxCwepK6cdNJJXRb2lniW/Ep6CIvyoNT8R5j3Vu5zcyDMHVCfOeHJ6KGHHvKfOOIbsyIhIASEgBAQAkJACAiBgYtAywZNvhWJkYjvN2BE4ttsPNvOOQxW7FjkI+4I7EYcVXLCCSc0jFwInnzrrQ5xXAzfsWQSmBWUGAaNSAPljh19rOrkO4p8Vw9qJ03jn7uyI5VyYnBj9wxHCI499tiN4OwQRPmBUJIw/LBaklW14EBe6wj/vVW+RkGCG76XQnlZ5Xvuuef6b1naDtwgmL/FaHHWWWf5941xitW0KFgYp0PiiEwMQhggmdhnJ9n+++/vv4uB3wQTTOC/nYk731VhtSnYQ/jZNwh55h1gYAbrOeaYw+POztAcES40yBEufo7jWpyqaxzPnnP8Q34W1q7mZ89cYz65MLG7PZeulg5hjHL3KX9zi6/sYuaIKgzPGKmZTDGiP2HChW+g8G3JWWaZxS8YMP86V45ypg4cccQR3iCKYdS+RRvHr+rHOKqVhQrxivaYD88cX8XucxZx0CcSb4899vBtPRW+Wbe6balZvqnwtiOTSRTeydJLL50K5r+dVLcviBlwTDBHRzMRsfjii3vvqj4yfh/d0beTBmT9tn9o8a+Uv6mGLsIJvxFVNZaQBRYRMaEkEgJCQAgIASEgBAYXAuiX6FLowCzaQydGj0TmTBFHzOLHqTjoRXx6IKYqnrGOFcevkqPj8M0+s3gVA15MVfJiHL70jE7Ot+eNSvMNJT2lJPMZ7/CamysJw5AXdAs+GcHOQ3QMTsTCkFdKj89boGcxx8AiT96/LQwu8Sz5lfQQ6gk6tx1rDJ6hoTEsU+o+nC/Bn5OfqOtQbg4EIyoLn48++mh/2gvzQ3z/lIXMdnKPZ6A/ISAEhIAQEAJCQAgIgQGHQMtHzvKNC77bscUWW3gDFKsEecbQZISQidEuPA4RNyZu2YXFcbSsrmMnZx1iZyeKk31Dk+84mnDOEbcY2vhGBEfKYBRBeIfaSdMzKPyxI5UycsQi6bKy8cQTT2wcncN3HVA4+KYDR2cecsghbtlll/XK6EYbbeSNeOQ7PE4mlVxvlS+VF5TfXXfd1RuwUTYoL2VHwY4JpeKll17y5eN4GXbPYcwiPAqkEcZgjOIoSxg92cXHe2OFJUcSX3nllV5B2Xvvvf3Ky2OPPdYrTsTn2xgo6Uz2880VwvKdDnbDUse4hvWSOKFhzo5lNbf4mfBG5mfPda/GOw6fcydc7Gdpm3v8TJyUW8k99OPeeHMfUuje7H3Ih3sMmuxaRHmFwmOGN9hgA2/8xo+dz4Sru+DBM/vv32GHHebWXHNNv1OXbwfRZ4T5trBV/dhiiy3mMWXiJDQ+WfzwivH1oosu8kdfM4FEHcZAT73vBJXaUqePIb3xxht9n8VEEt9dyu3QzPUFdcpLXNotCxxo4xDvvdRHxu+jO/p2JmXoPzAc8j55bpVK+WP8YpKGxRikUTWWvP/++97QX3Wcb6t5VTwhIASEgBAQAkKg7yKAsYkTftA1kWnRMZE3OZEGWSomFtCinyNnIHPYUaVhuCqesY4VxuW+So6Owzf7zCkeLHy8/fbbu0Stkhe7BK544DQVdBOOKUX3KM03lPSUksyXywJx4rmSMCzzGnwTkvcEcWoR38Y0Y15uTmXHHXf0czTowSz0RN62xYolniW/kh5C3lhMvssuu7jtttvOLyxl4V5dCudL+HTPUkst5S699FJ/Lc2BHHrooV5PQWZH7wInjJoiISAEhIAQEAJCQAgIgYGNwJChStAPKEbtELupmJxuhlDAOH4UxaEusTsPAZ70ENC32morH5WjVIxYqcoxLKkdeXGaGMV22mkni9q4YqBLKX2NAJkbjAqp8rDjkHybYYN8sGIy/CZkhuUwzs2Ub5jIHXBgZSo7X9mVxepgylBnFSRlxvgcvhfigxfKWHg0DDgSzowcYbZ598SJjVPUDQyk9h0+4sDfVvCGPLiHT4rMIBj75dzjcFXPcb4tfM49l27KPeUG/5x7zi+Vl9gtfM7dW9lyfQPtn2+ZpN4z9YV2HL5P41fnSvuizVGX6CtQqg866CDHRESOSv1YyS/Fj0mkccYZJ+XVtluqLbXN9L8MmPRikQm7S2k77EzN9VOt9gV18lrVR8bvg/DNjidV+SAN6k58PFtVvJR/Ln977rmnN+Iy+RNSaixhXMKoznHaIiEgBISAEOibCLAbrl29qm+WTLnqFALt1hEWkyLjlvQvDFMspF155ZX9gtOcLG5lKvFM6VgWL7zGslno1849MlRKV4BnlbxYN90wjTrzDYTP6Sn4dVomRZdDNky9x1J6LDZGpw51NcOkxLPkR3qxTh/yJE3k52YpnC8hjfidl+ZAyA/yOkbN/kbt9gf9rbzKrxDobQTU5nr7DSj9wYSA2ttgets9X9aWd2iGWU0J16F/6h4hNWX8S4U1N3ZdsZtxnXXW8d+TYGdnfJQkyl1oNLO4XOM0mUSOJ5LD8M3e58oTG2XIR85IUJVmM+Wr4tWufzPKCmUO3wsGE1YZszPJdupZfnI44p+rayj2/EIq5S+n2DXjHqbV7H0qnRSPVDiUzNg95ZbiF7vFfGJ/e47DlZ5jP+MRX8P6EPtRX+J2E4cpPXOcKceY8k1LVviiJK+wwgqlKNm6RaRcvcsx7C5jJunFbSmXh3bd2UVdl0ptrS6PMFxVHxm/D8KX+o2Qd937OI268VLhcvnbfvvtHbtO+fZyeJJBXBYEQRbusPtcJASEgBAQAkJACAxeBJrRIZE/6sgzJZ4pHSuFfp10UvGq3ChDjvAr5T0XL3YP06gz30D4nJ6CXyzHxek1+4xulcO3lB6LE3NU4lnyI72cDke8VnWCEE/SiKk0B8ICWZEQEAJCQAgIASEgBITA4EGg5W9o9gZEHB3KdyMwZCLUPv300/4Ind7Iy2BNE+Nj6hsszeLx8MMP++9s8C47sQOq2fRRuOr84FsnXLNhWuXbarxm89dOePIIGY//PPXsP9/Txch75pln+uOnX3jhBcf3ZERlBJi04kipOiucO9UXlHM0sH35Tuz111/vj2svlZR6zLFrfGdYJASEgBAQAkJACAiBEgLoavGC0VJ4+f0PAc03/A8L3QkBISAEhIAQEAJCQAgIgb6IQEeOnO2LBVOehIAQEAJCQAgIASEgBISAEBACPYWAjlbqKaT7bzqqI/333SnnQqDTCKg/6DSi4icEygiozZXxka8Q6CQCam+dRFO8YgT61Q7NOPN6FgJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYGAjIIPmwH6/Kp0QEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQ6NcIyKDZr1+fMi8EhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEBjYCMmgO7Per0gkBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBfo2ADJr9+vUp80JACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJgYCMgg+bAfr8qnRAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBDo1wiMQO4/+OCDfl0IZV4ICAEhIASEgBAQAkJACAgBIdCbCHz44YdutNFG680sKO0+joDqSB9/QcqeEOhBBNQf9CDYSkoIDEVAbU7VQAj0HAJqbz2H9WBKaZxxxvHFHWHIkCFu5JFHHkxlV1mFgBAQAkJACAgBISAEhIAQEAIdRWCUUUaRXtVRRAceM9WRgfdOVSIh0CoC6g9aRU7xhEBrCKjNtYabYgmBVhBQe2sFNcWpi8AIP/zwg1YS10VL4YSAEBACQkAICAEhIASEgBAQAgkEUNy1QzMBjJwaCKiONKDQjRAY9AioPxj0VUAA9DACanM9DLiSG9QIqL3lX/8333zjvv76ax+AjYbtEHa9Zqk30kzlMcxH1b35gx33/sjZFFO5CQEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEh0B4CGCHNEGlXOJrRrsQ9DF8KV/JL8SilnQof8q/yD8OG6RDPnnP3xCWM+VtaMmiGqOpeCAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACHQQAYxyZpgL2abcQv/cfbPxzIgY8muWRyl87BemF/qZoZJ82H0YNsyf3Vs4GTQNEV2FgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEQDcg8P333ze4VhnxGgH/exMaBWM/e7YwKd7mZ2FTYcwvvMbxzC/nnvO39IgX34duFj++EkcGzRgVPQsBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBDiGA0S40AtYx4pF0GCfMSs49F8eMiMYjjB/75XiEcYxPLqz5G2+LyzP35m7hjE/K3cLIoGlI6NorCHz88cfZdK2CpwKUKnUqfCfc4vyEz3ZvV9IL7y390C1Xhtg9fjZepWuYjoVLuZlfJ6/t5He88cbrZFbEqwIB6kQr76uCrbyFgBAQAkJACAgBISAEOozA559/7jkiv5lcH18JYG4dTt6zqyM3xumHz6n70M3yHLuV0k35pdxyvHGP0yuFNb/ctZR2Lk677tKh2kVQ8YWAEBACQkAICAEh0DMIIHfGsqc9p+RI84tzV9c95mnxYnf4m1+cVvgch4mfw7DhvYWzdHnmPr6Gcew+jIPb8DvvvPO+P/rRj8y/6esXX3zhbr/9dnfrrbe60Ucf3Y0//vhN87AIKGlXXnmle/nll93kk0/uRhjhf/bW7777zl177bXuhhtucBjBpp566i4T8aW4xv+rr75yF198sRt33HHdmGOOac5drh9++KG74oor3J133ulGHHFEN/HEE3fx54Ew//znP92Pf/zjYfxCh7feesun980337hJJpmkS34tXJ08Wdhmrk899ZR75JFHPE7NxIvDnnXWWZ7HyCOPHHvVfgarpZde2m233XbDxPnyyy+HcevLDtb4LI/2HF/xN7c4bO7Z3Otc44Ycx4nTxj/lFsbDP/6F/nXvq9IJ+Vh65kabG3744e2xy/W9995zn332me9nungMoAf6lksuucTNNttslaU68cQT3amnnupWWmmlyrBxAHDfe++93frrr+/70yWWWCIO0m3PzZSx2zLRA4xvuukm9+233zpNMPUA2EpiGAQ6JQMMw7jCoa58VMGm33r3Fu59CbBZZpnFLbjggl7uLeULGZrxqx29ocS/t/w++eQT145ehb6DDnLXXXe5ySabzI066qiNovSULlSVDhmq29Y7pZ81QBgAN+3UEXTKUHaO7w0e3LuDTP+oyzvMR+re3OxqfONn3FNuuKfylHIjrFGKV8qN8Dl345W6EqeVeCleVW6WFvMwRrThl156yfdFww03nDnr2gcRaKc/qFMc6sGbb7454MbaOmUnzGDXx55++mk355xzuqFzvpWQDVS5LC54d7e5OL1WnwfLnEmr+Che/0CgU+3t/vvv9zqR6UWPP/64u+WWW9xjjz3W5ffKK6+46aabrgHO119/7W1V00wzTcONePfee2+XePAhLHYbCP3lqquu8rLUFFNM0cU2ZYxStpz333/f25PCfGEzQ6eD3n33XXfNNdf49OFbJa9aWjmZMuVucqHFtau5x3HM3cLVuaZ4xPGMb3gNw4Q8wnsLk3IzP3Dj15aEi4DEiznqqKO88WzFFVd0m2++eUsC/Omnn+6mnXZaXym5Z4KDymi03nrred4vvviiW3PNNd2OO+5oXq4qrgU85JBD3LrrruseffRRc+pyZdCYZ555HOEwwnF/0UUXNcJgtN1oo43chBNO6Mvc8EjcbLvttm6hhRZyzzzzjNthhx3cvPPO68LzkS1KVZ4sXLNXykhDbYeYjD/vvPMchtl2iI6BupKisHL3h3vKkMqnudvVGl8YNvQL3Uv3cRyeIYvzn6f/PYfudl/nSt1Mhcu5p8J2wo3OPiQU8kMPPdQttthi3vi29dZbu9VXX9394Q9/cPgNNELhpM3VKRvCAUbeOkQfhEJj9OCDD7rjjz/e3XfffW7fffc15x65NlPGHslQNyVy4403OgStwUJvvPGG22yzzWrV3cGCSU+W84QTTvDCs6XZCRnAeNW5NiMf1eHX22HiPrNufjqFe6vp181nd4ZDTkfBq6LXXnvNYZypottuu80dfPDBVcEGhD+6Bwr3X/7yFz8+TzXVVF3adU/oQgBZSqeZtt4p/WxAvNwOFcJkbdjZvV1Dt/De/Nu9tsIzjBPex3mJ/eLnOHzpuU5cwkAhn/g59GvlvtP8UnkI9TTS+9e//uVlMeYd9tlnH7fyyiv7uQsm7kSDBwHG1nPPPdcvKGcTAONof6Z2dIxO6GPtpN/buFMXXn311VrZqCuXxTpHLeYK1DQCnZoz0ftqGnpF6CMIYPw74ogj3EwzzeTmm28+9/rrrzdyRr+MkTP8YZdi0wfE3Ofuu+/uN6itttpqjXjcPPvss13iwYNFHywohYiLYfO6665zZ599tptooomSNpGULefhhx92W2yxhbcjYUvih2wG0cfOPvvs7rTTTnOXXXaZO+aYY7rIoSk5z9yIb/e5KzJhKBfmwuFeN1yJR8kvxz92Lz3XKfOQoQPcD2YtJkJdwtjFap8NNtjAVxTiMcGOAI2xEcNDSExujDTSSEkLNJP3rCo8//zzvbGCeCjTM844oxfGqRRzzTWXn7wfe+yxHZUXa/YLL7zgJp100mJcywMTyxhcP/30Uy/gLbfccubVuB533HHuzDPPdHfffbfPJ5Vs1113dc8995wPs8466zjiYaRkwob8puiBBx5w888/v6+w7PAEKxrEhRde6MKdUHXylOKP0Ycdk6kVl6RFpQDrFJXeA351dmJSccnDaKONlkrC+9nKCQKgRC255JJ+d10c4YMPPoidevW5tEKCcodkz3bFz+7tGrqFcXP3YfrhPeHj5xSPMF38q55jHha+TlpxXJ5biWdpEn+UUUbpsrNhp5128osbmNwdY4wxCOLplFNO8Uby/fff35z8lR2/pV2epTpO26EvqtMGSnxoG2H975LB4IG0aKvkt4pSeTvssMP8QBz2Qzme7BK8/PLL3aKLLuqTIi79FMbTmFiNlGvbqf4lxjyXhzid+LmEGzxJO3w3pEt9qaKq8tCPpvpSFEDcUzuGUzhU5QN/6jp8U/0zfpQpVXdKmFbxzKVHfkrY4F+iGH8EP8Zs0gtPV4h54A+mKcwpJ78UPvDpVF2Ld3qX6p6l225d4z2Vxs3Su6iTXxZbgf/vfve7GPIuz6V0milrF6ZDH+rKR2G8Uj9alc/Yv8Qr1X+G+Ujdx32mheEdUheaHevIX04GTuW9lH6qj7D8xe3S3Kv6iVzfA3a5PtJ4c6Xd0rbBBnn+5ptvdgsssEAjSAo3+HKSCDs6jeAR97sofSiT8Ayp1GeG4Xr6HmW1Fb2KfK666qp+cefhhx/us33SSSf5sjMZ3VO6UCkddm/UbevUiZJuZ++lVV3I4vfHazt1hPGAuh/+wIDn8OofAnd7buVa1d+l/C0/YXrmlrsS1vzCeKX7OO34uRTX/OI04+dcOHNPXevmo064XH5IN/T76KOP/OJPFhLMPPPMjWxhzECPQvZnkURI8Vga+sGbPjknk8WySRg3vC+NI4w9yPZ1cAh5DqT7dvqDHA6cdsZE7oYbbujf+9prr+222mqrXPCGe0oeMc9SXSFM7J+TRwiLTMB7T+kC+MdU0jHidOO4uedS/liMH9b7UvqUJSeXoedT/0Nelp+SPpSShSxe6R1ZGK6UjzmGJ5980s0666xd+gr868plqXA5nYM+g3RzeJBuX6BOtDnDNzVPQBlL76nVOmvYlepVKt3c+0JOK81FldpIKh3LX7vlMz66DgwE2mlvzF+iA2EbwqDJwuFQbwwRov+ZY445HDrUUkst5Zgnpg/EbrTlllsmbREWH/sSuzrZUDfOOOP48ZN+2PQxNsVh5wp3uuf0F+ZXL7jgAj/3avzt+pvf/Ma988473uaEGyfzsXHHqI4sFMp9xKt6Nt7xNZVWyS1Op07aqTjEC9OJ7+2Za51fyzs0MeqxjRbF1ohjXNk9yYuBOD6WCY1tttnGVwwqCUe+xkSHeMYZZ3hjofkRlh2TEBMeCOYYMyGMgzPMMIMfoKviEp7OmkqMBbx0DNRDDz3kFllkkQbACy+8sHv++ee9oRY+GA423XRTf2QtzzmaYIIJfNntuFomd6effnqHomFUN08W3rBca621PA5gzQSP0dxzz+1++ctf+qN02dF25JFHeiEWf4ubew+sUKAhcRQv7yt8RwibNFaINGjE4E/6yyyzjN8y7T2H/mEQpuwcF8yK0CeeeMK8mrpS8Zv9lRJI8cqFT4U1tzAObjGZW3iN73lO/YyX+fEc3qeeLU7KL4xr91zDsKG73RtPe666Wni7VoVP+Ydx7Z4r7RWFYs899/TGTBYwXH/99X5RAjvBUYI4HhpiwGERBUZPG4RQHhDSoFwdZ0AhHGmMNdZYPj4LMmifKeLIa47So55T/++5555GsLr1HyGP45dpb0wOsquNhRYQq4PsiNJm8pbjiQJGXlk4sMIKK7gDDzzQl5Xy0k/jRx8HseuChSPgx2QsRywYxf0LuONG/094+lX6IwQHePIjjRSFZcSfI8sxxNBfsive3unbb7/deDekwbvl3dCfIZgwHpC+jRNxWixMAV/C8c7WWGMNv9uXcIwbKPbwmXLKKd1uu+3WEAaoS+ywZyUWk7H4WT2KcUAo2WOPPRpJs7uGHfzUW9I7+eSTG35gT99J/wh/3q8Ru2Vxp06sssoqjZ23ufdq8Uo8S360LbAhPbDhmEyIVW+0h3Cs4qQBFvdAlP/3v/+930FEe0G4IywLgRAyIXCLDQ+4UxYWQHH6AqcxHHDAAQ3MwYv2S92nLhHO3ms7dY10jag3KPS0A+oSEwy5ukecXFmNX3itajulcZM8sDjs/9k7DzBbiuLtD4p/MYs5exGzmBWzYMKECbMoYs6IWZAPFRVFMYsoZjCjGDBHzDlnxZwz5qzf/kbetW7RXd1zztm9u3ernme3p1N1zzsdqqu6+/AtkHMwJkBT6ou8hHxy0EEHDTe4wQ3G/FYGIKD2zYnrfVd2FNpx4bDDDhtuc5vbwKJbPiJtbTwmLsKSDWa0OcYJ+jX996Mf/ejyOAQOLCCg1vjJYojxXHTAAQcMbKApjZmkoW+ALe2XslWO8su1uLdkr9J8Uit/nrYajQW1sScaI/WuchnnkAnBBRnZzp+9uNXGXcYF5ksWs8wr+t31Wr1Vp/XqMi/ZG2g4WaObGFZrLRSVA669a6FFrs/W6/dciXpLjra8CYOsW0pn80x5Fi/r2vw2XM82nmfCI9fG6Vm8Su7IbOmf4mp+hZfcVl7Fyy3xqIUpj3d9eh9f8itPLU7hyMT8VAyyBP0PWYw1PPIlmyNQCIoiGZk0tXkDmRgZDXmAMZ8T5ci+JYrmkR/84AfjOgrZHyPrwQcfXGKRYQECKGAxHNn1xK1vfesB2Yy1DWM117rbzaA1dsgrtBtkUXR59ptGchnrRmQn1o3IAWzcZ52HnMY6Ydddd10+mSKZqKbLqslltTVGTX7272jXY7RfTs5QT+qH/ktyN/mQ4Vij8D60c07I1cqvrfslf7KBALkFuZHT0qJoPVSThcjLep13YZ3FT9NQrxKJh3QinFiy1CuX1dKV1hzw31rlMosdz+A7i86JvL1tlrSQ1Zm02lWtfZS+V6RfaPWRaA039f3++5b5PxGoI8DNfJzQxJjYImQedHYcpIL4eS30dKylWsT4hS0FnSOEEZY5QsTcyJgoimw5zM3o2dA1ScerfPTp3XbbTd7RiIoMJ5I8J7cU7sPwK33Ey6fxfstHZfSGiZdNb+ti+fk0pby1NEprec9s0GTyx6iIwGQJAQEFE42JPz4apxo5vYnCqKTk5jQQO8e0mwcFKbsLEXggFNveEo9wwFWorbzkp4Gi+EJRHBGNj3cSqRFbQVFxkQsmnAYVIRSyO8qezuytk3iAJXwQGlHoYLREIGOxAKEQxuDDrgXS0MmYcKHoO5AfJR1/HOl+3vOeN+y9994Ddz9D8FCDoQwwpxyMlXRQDNEQdUPRzakvvhcK2p7FiW2Ueh4ZTvynvCW3xKqUjrAesunsc0/eWpqIj4/D7/8sX5vePpPG+5XP86ulU3q5yif/VFf5S+WhgGcxALE7hv7LNQCa0BAmWWRBXK/Mb+wy9tCOMXaq/fe0cQxN9CuuFeA0CO3YE+0fQyrtmjHqlre85bjjmXRT2j9jGwYkxhv6ErtopRynr7IbTsQ79NStxpPxjPGLyRl8GH9RUnBlL4YI4nbcccdxkcSil928jNUs6Fg0oVyH/PhCPdnQguKVcYDrANkFDPbsBOcq2yc/+cnjSXa9i1z7jnwbxn7SgilGLr6rxh1cBAIMsihfWJSBP98HoQLh3RpYVAYudWMxijKY3xql7UjR/opXvGJAmcJJe+ayY445ZjSWk2+//fYbxzl48/4YfrXY9TgwRzEG8k4QxjfeB6MgaVUeaTD4cu3R9773vTGcKwWVh/dHYa85jWuioNp3JS7iGcXxPnwvrr/g+zMv8e35FvRD9RvKgAjXe/BO9DHwZ5GPoozfpmHnJ98c4h3YFefp/ve//8gbTPmW1EOGz4c+9KFjPwBn4jFmYiyG5mlrtg7wYd6irrRT3qnW9shXe1fLk2faSavv1OZN+j6yAteV0I8wBtE3oSn1ZXGJIhOFCWMYxPvpW0bfnLS970p/shue2IEogyZ8eigaj1tYsjDgyhb6PG3kmte85sDGM061MW6BHUZPzSe8f238ZJxVv6Xe8OavNGaCJfMP5TEWUT5zgfC1721xh39NBgbz0nxSKj8aJyk7+n7RWMB4VRt7ojHSvi/fgTEbJTptmCt9qA80BbfauItMinIWpSPjFRseonrbuq3HZ8YS1isirk1iboVWay0UlaN69biLXJ/1lLcR0mjzEe8q+VnvrXFPrk2jtPO4KkeueMkfubZOUTrF9aRvpZka79N7P3UjrOdP71Fybf5SfClMeWycwnBFzMdsEGSs5Hq2N7/5zaOugE1pKOMwMiG7QZGMHM0bjOv8bAW//wSv6173uqN+R3WwbjSPMFezEQ4Znc2hrE1s+7Z88rmMAPINBkOtg1jHMj/6q/XKuf8XimxM22HNDQ82pSOzQy25DPmEb4gcigKY0y3ISrQh1nUY3pjDIWSiSJdVk8tKa4xIfh4LM/+QSWi3EC79AmMk70p/4dYniPaHLot1Du+BLuFZz3pWcY3TWvcjHyIvggnrI9q3lOHReqgmC1E/fiKGDYjo6DBmsgGK+doTuhHW/Ky1WO9gVBX1ymVRutKaY2uWy4Sd3Fl1TlParMqiz3i9UK1d1dpH6XtF+oWoj0RruFneT++ZbiKwCATQb2GQ1wm/Xp7MBeia2UAr2meffUY9HPMi4zf6FQ7IiSJbDmM9uhc2fbHRh01CrJEhbhe1sg6HJyAry40BJ/6TrGfDeLbp7bPifFgpv01jn8XD55E/SluKI8z/iZcty+a1zzatfVaamQ2aTKAomj2xC5BdHxp8L37xi4/XyHIcHws7JztR5NeIgRvrOLvwORkCoWTyV1LQUBlwLZXyojxGSY6C3hNKbAQV/mhYvhx1Bl+O54OwIj4YQywhdKD0RpktA2lUJ5vXP3PKE+UdO+04bcniREpk0j7gAQ8Yd5XJMGzz174DCmkU+1xFQz6OaaOwZLIsEQIvSgqUbtSBBQ2EAZMFFG2CiZPdbXTkeck3fjXcefnW8qu8Wvx6Dp+K3RQspvIGxygPbZIBn+vr2AzA3f8sHlgE6GoPFnIstCAmGYwTLI4xJkkBSFxPGycvfDHE0F8xtNC31K8x2kD0C65QxmWc4sQo7xG1fz/OcDoP/pTHeMipMRkhxkLcv1LdXJLxxN8Unj4/J4VY1FEW+LHDl/GXa7JFfnwhPSe6eQ8ZNUjD+MBYxbXbuodePLzLWMZ8gEGCxSMbVZhb7Olu6kIZKGLYPYzynFOQjH8of4877jjPdlSyYEjAUIYggWKHE7UihAraGAY0jL2cSGEBzrdkFzuGG67SolzqxncWWRx4f9qoxmEMPMxdnrguY8899xy/NfhgeGGRD2HMxhgFHwQqDMkstqGorUQ8ozjKY5c+dQcbrigFCxa+PUQfBHtwwxiHIbZFzKEYaXkfduOzu5i5kjmHeRtlAnzZDMQfxkziueYIWkRbgw/zIN+Hk6A9ba/nXXv6Tm3eJC+723k/FIMokRgLkKFmqe+YqfCv55v3vCvvwZjAd0FYR6nJidcpFI3HPViCEW2IfsRYTVuiPdNXkTEZR9ioIOoZP5W25mJkZwMH5TE+sdOTb6Z+X8tHeE32Iq42nxBnaZ622hoLamNPbYy09eIZxRlyIxt8uI2EbyAZtBe3nnHXlhuNmTbden9GCcz6Qtcb+TUK77caa6FaOR7faC00dX3meaf/vwggl3iysnTt2eeZxQ9vy188SmGKi1ytseXatMM5dEMAAEAASURBVKUwG9/zvAgePeWU0syKSS+vEn/CpDdhzEDOZBMgsp6wYAMjGymRNyIZOZo3qCOyHyfYkKXYCMlmPeQWrZlwkWejeYRxHCUhm5GQJ5hHMBRE40gJn40exnqXDYoQMhSbFNHFTSHWuLQTNrcjS+29tLFd1/P1yGWs05DJkM1YMyFns15jXYNx9a1vfetydVq6rOWEjYeW/BxlBzPWhchW1E9rPMZX5FvW+fQZbp/AwFiiaN2v9OjWwAB5kfbNOitaD9GHozUo/QkZ+ntLuk34sVlSp4lUJi66OWQ7NoCzprcns3vlst50KnejyGW876w6p3narHDGLbUrwnvbB2kj/QLxtT7SWsNFa1r4JiUCK4UAN22y6Zn5aCph4EefgYwk4uYIdAxszCKe9Ty6aYgxuGZfIp41MQdzGKPRlaDXlG4PvQTGUG5QYA2tcPKtNSrJmouqY8Q7ivPlk3ZbH9jrRyD5yEc+Mu5Qt78BR0OiASDYeNLvamn3to/Hj1WdHd9cnSbCcq0TgwpDUEYZa8nn5QUxurGDCQEFJRc7S1Cgs2OJ3VP4IQQMrmawhjwpFQmPiIW6+CC4iMiPYHfnO995+cRmq04o9HuJhQsnZfS7eL357HfA+Ihi3X4vBNoeIt+HPvShUSHNN2UnAwYCjJlgO6Uxlsqr5Ve4FmilvCsZRrm2Dv5Z8XKpi30u1Y34Gtk4+2zTqw6E2bJqzzav8viwXn+tTq38tm5KywTB9UXsjsTABCEAYuRBKMdobhdq9GOIRQInk0sUtXH4eUIYQ1hl16v6NYpEykeBzYkWTlCwaERhQHo2cNTavx9nGIcwijHWMDkyyWGc7SHVzaedhye8EFCFt3hzFSnGgR5iUQjJ5ZmxENxaxI4ojCQYKXXi37Znmx9FuR1jKa80n2CgZB7S9b2WB8/swKL9MTfQhnBRyjA3MLdwPYXGSdJ7bAiD4MEpK3a/c3KIeQalgCfGRburi0U/xhEIpRJGQSvQoFBgXmG3Y62tRDyjOAQra3RlfmMsR4nEtTRTiLm/hL/ngbKKvsQVZSIW4xAnepkr2XUsQlFAG0AQ9KQ2Jpf43rbmeU1pe7V3ndp37LxJO+Vb2d/03mHJWMaYVaIp9bX5o29u0+m59q70TzYqoHTkm2Kk185C5W250Xg8FUu+u5Vd1CYYc0tUGz9LaW0YYzTtkXnIEhtS2GTWSxpT6DPRfFLiN+Xb2+8XjQXR2FMbI33dUPwxV5SoF7ep425Ub7/5sVSv9RCGQYHbCDi5xPeEVmst1FuOx7G2FiLd1PXZlLWQr8fW7GeeRGm53sjK+/bZv4eNs8+ldDaMtJa838bp2fNv+cknvjUZVfEqo+b2plOZvjxfV5VD+0DuZiOlNnIzviJfQxg/MGjShjYFMnI0b6gsuSgAGXfZ9KM1E3HUOZpHWNsxxiHvYvBhYx15onFEZab7PwS4PYIbu1jL8N3ZkDmV2NSPbCfie2LUhGaRy/w6zbaLkan5N6suK5Kf7VrDFFV8ZH7VIQz6D3MvN+gg32IURGnt9Y0witb9pYKQ+ZD9ovUQcdEalNuGnvjEJ44/j4VinXlVG4ptmejmaobYXrmsN53K3Qhymd51Vp0TY3JtzTelzaoeuGpXPPe2D9JO0RvZPhKt4Vbi/ahrUiLQgwAnKdkQb+efnnyMy0960pOWNwYpD/MqNhFOPiObcKCCMZfT/ZF9ifWLXRejeyA9B8bYXIIugXGezfzoKYnzMqH3q05WFrRyoH0mrfcrv3dtOfZZPHx6+ZVW9bHl2WfLR2ltmOcnvnIVH7mkndmgiTIS5S2KMqvA1S6vqOBaHAobJmF4MECL2AXISR+AoNKUi+EUpbColBcFLQYLdoZpdxh+fueNXfY0SksYKXgfEcISQp29qkFx1uVUIlenWGJBgVCJYpb7m0VT66R8JReDMqcq5iEMKVxTC6baVY8SHSV3zRig8piUEUTBiJNJ1IcwlHbgaBX0ymPd1slXNeaoA1h+i3hWmS1eSqc2qToSbsN8ul6+pFNe5fF+hdu0vh6Ks+GEyc/zFIrqYPnU0vlylU7hGP+5fpH+IyUeC61dl4yLEP1Xu2PY+MDYAGHUZzHNrhdPURv3afHTdjnpSdvmT8TOTU5Zv3Rplw6TGadG+c1dDJNR+/d14uocDAO6Iogd1Ix7PaS6+bRckTMrT3iBo+oj3hiVOW2zksQOUMbh448/fti0pFyBuGJwXkJ4QDmCwICh0RMKHgyRGAtpX+ykwjCC8oXxjB2yNSOm58X3pX3SNvg9VPJ74t2YT3QVOeMtxnt2CDN/cDK4dB179F0jnlEcBjPmTxlnUGwxbmPM1LwbKR/8u/X4UabR11GMyACE8gVDJvM4mNF/Naezy466sHmBk9krQYtqe1P7jp03eV8UKBgIPdE+LM1T3+ib2zJ6nhmjuX2BzQ7292N78pImGo+nYtlbptLZ8RN5R0osxddctV+MSyXFVi1fFB7NJzLMKv883z4aC6Kxh40GpTFSpwVVN5SguhpeYXJ7cUPWnDLuRvVW2evZZb3DDRBsdOFdRfSP1VgLYahvlaM6WZe6+rUQ8YtYn9lyNvIz82akrGGelTxtnxeFmWR2y68UZuP1rHTUT3VTmNIoDr99Vrx3fX7vJ30pzGJEmpbfpuG5xJPwEk1JW8pPmHiongrzftbfGEU4VaA1vDVocuMNBhD6aiQjR/OGryPyO+t5Nmjqt9SVJppHMBhxCl0GGfQjUG0cEc90N0cAmYR1CBtDWcdx8mMqMbcc5267waDFBuOVlsusLmuKXBbJz1PfX+kZX5FpWKuxoRLlOOs6vz4mfbTuF7+SK7motB6i39ZkIfoYV/hi0ET5zvdiMyR91fc76US0cdbWQ+W35NnedOJNv62tZZVma3GFr9fv8H7RGmcl2qwwndI+yBPpF8Sz5G6p9yvVJcMSASHARi0MhtrIpfAelxsOGL+kn1IeTu5Lz4EMxglO1sA9thxuGSO99FpebkeXwh/Eb0tDkvNGT+Wf0kj2s3773MtPxShvLZ+NJ02rfJvep1WZvizlsa6ebR49E6f4k2pelarhMqBxIgpjHVeXsKMOQyQ7m7yhsMFqjOY6OpTIGB69woiTUFjPSYOhA4MCdxJzBSFUy4sylBOM9o8wjBGlOrLLjR2DpOcKFO7/1zVaY0Gd//hwGBppvHQsgU32Vp0w0vBbRCXiek2Mj2DNO3Nq1RqTS3laYWCIwRYjAhizY4dFSe1kFtdYMGnSmfkOXPkI8T1QToMbRgQw5luRdl5Sg7U4zsvT51cZPhy/4qyrdDaM51L6WlgtXDx9vPcrncq18bVnpbV5pzzDF2rl+W+qk/73+ZRC4Sjw+D0WToCzSIZ4ZhFOu+f6F+1OZ6zhBDTEjyvTJzAW0O54FvW0cV35isKb34GgDp7oE2x6oA9i/OIqF9o9NKX9o2RgUYLRCIUC/QgDV4166tbiiRGYhVqNGOc4fcq702dZvOHvPa1d46twdvQxT3hirOfbYxTmVBVGYr7fLAY18uvaVHZH8X34EXHGJcpnfhIhoGDMZMzjRDnGYAyfGBLYkYtRnTGOsZyrJmxe8ZCLIZB2wdVZpcUN6QiHD4pi3o/dXvymNMRuLd4bIyOEMQ8jNxR914hnFIcAxRxDeWCDspn35+QgcwGblXSVOHhiaOwhrqGF1M7s9wBXlFbMM8xbzBG0OTa9YLjktyO5ogxFHO2EazpQdulEW0/5U9Msqu319J3avMlVV4x5jGX0OzZa0TZLJwxb9eXblU6cg0v0zafixiYHFg20Cwz5PcTvK6Ecg6LxuAfLnvJsmtr4yUKD3xiiP6LwZeyzZMdM6syihNP5GCIZN9gYx+YvCJmRvjSFovkEPrb81rePyo3GgmjsqY2RlGXHc+RzTmkiuzNHszDTfNbCTfVujbuMLfQN+ENRvcVzvbooOVFCMgez4cXSaq2FWuXYOrWeF7U+a5WzUeL9OCXZ2b4/YSLFy1U4rsLk2rhSPOkslfIprOQqr49TWXKJt88+vfX7dN4vXiND88/y8M+eh7L5dL3+efOrnFK9bBzxrCkwQGGMQZ/ARjo2J2h9zgYxZLyWjBzNG5TDNWnwZz5kvc+aqXSbVW0eQbZmTkVeZFynjlxb27vJiDok/Q8B5kQ2k6DH0s8b/S+2/GTncdbSyKIYyfg2yKScMMG4tmi5LNJlRXKZX2NE8rNdf5TfvhzK+MpGZto2xsWddtppGU9f/pR1vy0tWg+1ZCHWtegM6Pf0Y2RD9Tsrh2KQ5aeu6P/IuFxtKOqVy1rp/Jpja5bLhJ3cWXVOUZsV73ncqH347xXpF6I60C7gVdIbr/T7RfXKuI2NADokbkKjbU4h5A82ifATVZ6YAxlHkZ/QW6HHY8NLy5YDH+wpnO5Hn4mOgXEbuUqEzofNPNjS0PsxpkOS6SLXp7N++ywehFlSeM0lrY+z+UvxPn2v3/LSs1x42GfPc4w88d/MBk3yo/TlBCKnYVDws9uDj03YVGInOMpelDgoOPXHznSEBnaecd83SmeueeSEABM/FOWdUg8W8Si5sdAjyKBYOPTQQ6ewGNNiVEFBjcWf3WZ6F96tRSi5uP6wROCAwMpChB1jRxxxRLhTt8TDh/HdUEDBC2GN0yLsmERQKhGGB3YxbNq0aRR6dSUJwjDKTRZSGLupI52V3Zk14poZ3zitv5aPcJtuEc+2LM/PxunZpiHM+nkuhSmNjbPPireuj8dfoloe0tbibPjUZ893an7Sex5jwIn/MDLRjyCM5ij3MBzx+2kshuirjAk6ZUbbu/a1rz2ORfQTKZrJ39PGWUizG5FdjlzH6Xc7wocrkejX7MxjIc5vGNBvGIemtP999913VDYwljFZIoBiJK1RT91aPNlgwWJDRjRfFkZjxlSEAerFxI6fzQ2LIE4fgp0nxkTmERYHLMwwKIOtfhvVp4/8jNe0C4zFEKeG2KnMuIaCGKOj2h1lovghjvEMwV7XNCEUMW7vsHSSkTbRM6dhkKN9eAW06sscxUKd30ulLBaZKIEg8vKHkIQgxu9W6HRo9F0jnlEchkKVx/thrGUjj3aPYWzFOMu1g1xZxFjfQ6o7uNFf/fcAV+YP5giEQeQGzXXMs5RHXtoifRaj5krSotpeT9+pzZsoS5ATJIRz5RC/O6TT6fb9W/XlNyOOPPLI8dS4zcdz65v79JGf8ZUxGSMpc3gPMVZzVQsUjcc9WPaUZ9PUxk/6ADInyj/6nm/ndsykzrRpjHb0GcYoFF60Y+QcdqVrM4UtO3qO5hPy2fJb3z4qJxoLorEnGiPteE57oK/uscceo2INAxYYQRFuvs7RuLvLLruM8iZzBIbNqN6e73rzM/7yjtz+oLUDLothsF6NtVCrnCmY0v5qa7spfDLtfxFATrHytnDxYfIrXq7CcT3ZuFI86W0a5S+F2Tj7rLSE6bnkYkgpha9EWKsuiypznnJ8Xu9XHdGZMC8zRiOPIvMzPtNuUEQjc5EWimTkaN4gr9YvjPX8dh6KuxLV5hHqwIZD5GDkbX7LnbUVY0XSdARY+zDfIuv1kp3HWZtwooS1LDIRP8Hx3ve+d5yDFi2XMb/UdFmRXObXGJH87NcfvZjQT1iPInOhy8LAe+CBB47ZfflT1v2+/Gg9VJOF6Nfo6zidiY4NfR2HM1inezmU9oBOBH0s61zkVVGvXNZK59ccW7NcJuzkzqpzitqseM/qRu0Dnv57RfqFqA60i5reeCXfL6pTxm1sBNjMjb4PG85UQv+FjqZ0Ix1zImMrOmnkKeYF5JYe0qEJ9FocGti0pEtjw5GIcRxbDpvCsG9JhutxI/m4FEeYwuX2lLMaaRZWnyWD039QMM5D7Obig2iX0Dy8oryUw6Rc+gHsKN/UOHYzYY1HYF9toqNw/RyThSV2jbEwwfDBLgEmlEWfXgFbBFkacIkQeFm0YFTiJCZCqSdwQ/nSWzcJWXzbRZCve8TXp/Xlt+JJ7/nLL7eUxpfj/b5c7/fpvT8q28b5fL3+qfWJ+Nr68MyCQcSCGmEcxS47aNhxqU0M9AHfPzk1QxqMASj9WJDpCiPx9G2cSZAFOQpElKWUQ99qEUaz0i7YKe2fPsSYWcNzlrpFPBmj6ZslY4l9XzAq9W2bZuozuDJp1QiBgV1MPdjXeBAOHxlZ4Mf78q1oKwgT/F6PDJek53uBRwkTThnRJuetE+WImFvYgV6aK6k7p49L7Sr6rhHPKI7yeMdSXXhvyvR9TO8RubQf8vG97fdQHjBnfuDbeKI+5FM/9/Er4aeOi2h71K3Ud3rmTfIiUPfINlF9NQZiHC5R9M1L6RcZVmoLfjy25ZWwtPGt597xszaWw780ZpKePqNxhnSldyO8l2p18OVH375VVmssqI09pTGyNJ4z99KPSuMJdSvhVqpzNO76NgEetXqXeK9WGHLHvOuqqK6Mz2DRM15EfFpxq1VOqx5bY/w8bYRxgW/DH+SfhZfi5V+kW5JbS2G1Mm3dep5rfEpllsJq+RVeq0MpXmFT3FnqZPnb+hFe87O5hpN2GDEYuyW/8oxcZeetHhm5NG9wDSeEQbRXbinNIyOTpX+l9ZziNoo7z3gwK0aleZx2xVxdWwP6OXhq2b26rJpMRHnUQWsMlV9qh/PIZeRlrVaSpX35tO0pei/VGZe80XqI76E+bPPRZ9BZ2HGl9L5ehrQ8eO6Vy2rpSmuOtSqX+Xeft8/p3afonGwdbJvlxqTSRnr0Ffwu5lQqtQ94qM62XUf6hVa59IWa3ti+X4tPxm/9CMzb37YkQvQbdFO9tg1bV+Qs5lo2btWIuWaW2ymsHGifKcf7a2UTbueRkr8W5sto+eETkepRcgmz4fLL3TZi3BsHs5oSo5dHTzrKWekFPPWgwc6iyO15h1YalEJcaxbRStWtJsD6urAgqqUtCV4+f8nPt10J6uUbpYvianUmjzq2fa6lt+G+PO+3af0zZU5J7/O3/D28a2mER60Mn4+Twlx5Sj5+J4Jdokwq/OYh12Na4roiDJhcb8QVRhje99lnH5tkfK61WyJ1Qu0kmQoBJaMTyaa0fxYjvdRbt4inFWCjciOMonxRXGTMJB9jyhTsamVZZQ27rXZdOrnLLtZjjjlmNE5wStJSVGYkfFgeU56ZW2pzJXWvtavou0Y8ozjKq9WFvjjrPGPbj/0ewmm1MVe5NZc6RnWq5SuF23f38ZQTxffKNlF9UaqUDMWqC3lr31xpVsqlbE8RHlGc59PyR+Nnrc/BszRmltKX3q1VJxtf4lkqP/r2ll/puTUW1OpQ6hul8by2MUR1qfFXvNxo3PVtAjx6+Yr/1uAyPveOF/O872qVM08dN2JeKytbmb/2vFIY2XqojFKY4mouebQ+8M/kUZzNXyunFm7ztp5tHZS2FKa4lrsSdarVh6vTuN6ftRA3wEDcWMO878fyHhk5mjfg3TsO+bLJK5pV1lT+dGdDoDSP0678PGu5R3E2Xc9z9N2jeb1Uh1I7nEcuI29J/uO9fPlR227hEOWNZKESdqX3rb2D6hXhrDS4tXSlNcdGkMsWoXOybZZb8dBvLYpK7QPepe8V6Rda9fF9waa372fD8zkRWG8IRHqV1rv0bNBn3i3JiTasJgcrXGm9n/opzNZV6WcJUx7xEP+WX/m8q3yE+2f5cf2zwnAXYtD0FUv/7AiUftsTbhxz1u9Vzs59vpyUTz0WSWqcJZ7qID4uyuPTTvW3eCtedZOfcgiz/qllK32JRylM6ae48FHde/P1lD0ljS0/qg9KaH7Hg9+fw8jPFTJMDFyb4Q2aXM/Gldf8Jh8LBH7jrefqayYprujpmXB68VpUurVct0W940ry4apYrgTmGk6uEELRU1uUrWQ9kvfGRmAl5s2NjWjf2+f42YdTpkoEEoH1iYDk7kWtPWZBQXWweUthNp7nSPb3cT38xNOXY/2ebxSnMv16xebh2cbjVz6ee8nn8TzFx9ff+vXMDUr8ZA5XrR9yyCHjRr4dlq48O+igg8afOxAv3FllZK7G93W2fPM5EaghsBZ0WbW6ZXgi0IvArDqnXv6ZLhFIBDYWAshUkVzl4yQnKrzmB0WlqSFaii+FtfLX6tDLy6bTs3X9M/7lsEVcOVt7wQxPBFoIcC0CpE7QSl+KV2Muxc0aNgtP/w4tf61utbJr4eLjyyO8N0w8rNsqrxVveflnXy/5c0eXRyr9iUAikAgkAolAIrBeEFjPVyutF4zXez3naSO6lllyc82dipH4lPJF8n4prhRm+ZbK8mHeb/P751p5tfAa71J4KcyXP9Vfq5flUyvXh1s/V80mrT8E5hkP1t/bZo0TgS2PQPa5Lf8NsgYbB4Hsb/VvzensWU9oW/mPEry/XupJY0pyaSnM5yyVWQrz+Up+W56erVt7zhOaJTQzbNURUANdVMGL5NfipU7bStf7bi0+Pr5WPuE+7SLqMCtPWzY8VG/Cvd+mzedEIBFIBBKBRCARSAQSgUQgEfjfjmvJ+XIjbKzMXUrXI9vX0kwNL5Xvw8SzVm/F+3zW35PGp/fliYcPt/laz+LRSmfjlceXS7gN837LI58TgUQgEUgEEoFEIBFIBNYmAshwkvfk1mpqZT/SKL3C5Vd+hctvXZ9WcbVwxXvXpld5pTCfT36b1ocpLnKJS4OmkEt3iyCgBqrC1RHkj1yfN0o7JW4qX59e72DDCbP+nvr0pi+lq5VXC/f1KfEkTS3c509/IpAIJAKJQCKQCCQCiUAikAgsDgHk8Jos78PxixYtv5f4lcJUvnV709k8/nkKD5vWY+T51vzwsHjW0vlwW7aPS38ikAgkAolAIpAIJAKJwMZEABmx9JvWJTSsPGnl0Z7wEj+F2fwKkxvFkaZVj1Z+lYNr05aeFebdNGhaFPN5iyOgBrrSFVnJcixvdXIbttLvBn9bnurgw1v1sDxs2lq4TZPPiUAikAgkAolAIpAIJAKJQCKwWASQw5HtSy4lSe6fIq8rT62mEa9SXCmsxFvl9qb3PKbmi9ILU18G/iiulL4UFpXt08+Li+eX/kQgEUgEEoFEIBFIBBKBtYXAFNlQNVceyYo+XP5ZXPHuyau0vfVQOuUrlWHjSs8Kk5sGzRKKGbZqCJzudKdbtbKyoEQgEUgEEoFEIBFIBBKBRCARSATWIwLbbbfdeqx21jkRSAQSgUQgEUgEEoFEIBFIBE5EYNtttx34S5odgZPNnjVzJgKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKwsgikQXNl8U3uiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAiMAcCadCcA7zMmggkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAiuLQBo0Vxbf5J4IJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAJzIJAGzTnAy6yJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKwsgikQXNl8U3uiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAiMAcC286RN7OucQT++Mc/dtfwP//5TzVtLc6HT/WrQJ9P4b3uNttsc5KkpbCTJCoERHUpxbXKacWXeJbCqGotnLhWOaQpkc0XPZ/hDGcoZc+wRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBFYcgTRorjjEa7uAmpGsFs7b+Djrt88+rY/z8fi3NJXqSJ2icGsInKX+Jd4+zPt9OTa+tz6ks/k8T/w9aUr5tsawP/zhDyd5rQi/KO4kjAoBre/Yio/Kr8X58JbfV9vXyfpLzzbM8iL8tKc9rQ3K5wABvlMNyyBbRq1TBPjen/3sZ4fLX/7y1Tf45Cc/Oey8887V+IxIBBKBRCARSATmQSBlj3nQy7yJQCKQCCQCiUAikAgkAolAIjArAnnlbAW5r3/968P73ve+Suz6CGah2fqzb2LT2nCeS3EKU1r8Pq3SlNx///vfm/EtpZklrFQH1VHuFL6tetbiS/UoldubrpR3JcJK9SHshBNOGF7xilfwuMXoZz/72fCGN7xhi5Q/Betam+jlwQtGaVvxUd5anK9zy1/iQx6fr5ROYT6tPizxi6C1MI4fdNBBA38rQZ/61KeGG97whsP//d//Db/73e9Woogiz9UaC9bC9/va1742vOQlLxm+853vFLEoBa4kPvSZBz3oQcPb3/72UtHLYU972tOG17zmNcv+fEgEEoFEIBHYuhE46qijht///vfNlzziiCOG+93vfs10tQSvetWrhktd6lLD1a9+9VqSFQlfybl1RSo8I9O1IPvMWPXMlggkAolAIpAIJAKJQCKQCKwKAmnQrMD8pS99aSEGzQc+8IHDN77xjUopayNYxoVSbWpx1uBg0yjch/nwkl95el3V16YnzPt9GPEim1bhPWE+zbx+X8eWf97yfH5fHvEKGx+W/mFMRInxr3/9S0Gr7n7ve98bXv/6189d7iz90mNmMfJxVNCH9fj1YlHaXt696aKy5o3zBkvPL6qjsJjHXdQ4PqUOz3/+84c3velNy1kwNK6UsfFxj3vccJWrXGX405/+NKzmtdCrNRYs4vv577H8YRoPfDOwvdvd7jYcf/zxw7Wuda3h4Q9/eDHXBz7wgeGQQw5ZjltJfN785jcPH/nIR6p1USVQWO+3337DD3/4QwWlmwgkAolAIrCVIvDPf/5zlNF//vOfN9+QG0d+/etfN9ORwM+hf/vb34a73/3uw1Of+tThQx/6UBePRSVaybl1UXVcBJ9FyD6LqMdq8phlXbaa9cuyEoFEIBFIBBKBRCARSATWFgILMWj++c9/nvxWKLb/8pe/VPNFPKM4+P79738v8m2VaTPd+ta3Hh7/+MfboPH5r3/960nCFMAiz9PLX/7y4Re/+IUPDt+dxLVyprwfae2frUQtXGkUL791iROVnhVmeZSelQ5eiu9xbdlK78O8X+m8W0pHWCmdD8MvsnFRmOKsq7wKs34944psmH1uxSutT4ffxvGsMOte9KIXHd7ylrcMJz/5ycd4/cNoVetzSiMXw0uNGA/gJcJw6vle+cpXHuhTnsirevu4Kf2ylNbzE1ZyidezdZXPhvlnpZHr462/J41NzzPkw2rhtbSl/POE1crx4finEMo8237IO8s4Hs0vvj6ltBibvvKVr/iko782rhNZ4iUmpX7DN3jrW9863PKWtxxPaCotbk9fsumpl92owPM//vEPm2Sz59pYwDfo6UMwK72TClH9a99P6bxLPk+17wF+pfTKz6lMlL4f/ehHhyc84QnjichnPvOZ40l1pZHL6c13vetd8g41fKbgXKofYcglz33uc0/yzT3upz/96Yd73OMew7Oe9azleuVDIpAIJAKJwNaBgJ+/tt122+Ftb3vbcKELXWizF2TeiOQLm9jzJM7PoZ///OfHK+6vd73rDSc72eZqhKgc5AMv00+ZE6lLbW4t1Zv0Il+vSBZTHlyfz8bx7N9H8aX5W3G4Lb412cfP8+IZldeS52o84R3Ftd5BdSu5Jfxr+hKbn/esYV5aM9q8xPv3KdXD5snnRCARSAQSgUQgEUgEEoG1i8DmK5GJ9Xz/+98/XO5ylxu233774dznPvdo7IDFQx7ykOGRj3zkMrfPfe5zw9nOdrZlIfTwww8fznOe8wxnOtOZhpvc5CbLu0Q5/YBydocddhjDlxmc+PDTn/502GWXXQYUdZTHdTm77777cjKUjuc85zlH3ve9732HX/7yl8txtTKXE7iHZzzjGcOd7nSnMZR6sRuVExqcgqEOX/jCF5ZzoMjkJMdZz3rWgQXexz/+8fFUJv7f/va345WA1A364Ac/OC7IznGOcwxXuMIVNjvJw+9hPfrRjx4ucIELjOXc/va33+xkz7zvx0JAf8uVLzyQpkZRXCuPL1v+qTyVz5bneXi/TWufPa+av8ZP6W28wkrlKM66Sqcw/PbZ+gmvkfKUXJvHxlveei65hHG95ZnPfGYex77FNZfs0Kadn+UsZxkOPPDAMc7/o//c8Y53HC596UsPZzzjGcf2zZgA0UfhQ1/mNxNRnLBYvfe97z2WRV8nL9dMQe95z3vG/jN6lv795Cc/GW5wgxuM5cPbXrH4rW99a+yrjDMYQl/3utdV++W3v/3t4Ra3uMVw9rOffbxG68gjj1QRy65wU4D8uJD122elL7k2Xe1Z+Xy8wr1bS1cKj8Lga+O938bZZ5vOPts0PNs4+6y4McGJ/3baaafNDEYHHHDAeO0m0Sgo7nWvew3nO9/5hvOf//zjyTXxmDKOf+xjHxvOe97zjm2U8Zz2cOihh9pqLD8/5znPGdPStkjL9WAQisRXv/rV4xWztEvR97///WHXXXctzh/MCZe97GXH+fASl7jE8txy7LHHDpe85CXH+YO+gTJQxAkM+hwG3Kte9arDAx7wgMl9iVOkzDm3u93txjmcvoZCiVOf9Gn+HvWoR6nIzdzSWEBa5kfquu+++25mIFXmqWOB/X6MH2yoEB122GHDbW5zm9Fbm1dr36NHHrja1a42vPSlL1VxY/sqGWwZqxi/OKUJZlz3Z/GZBeda/eD7xS9+cbjMZS6zXK+S/KHI2972tqNBM5V2QiTdRCARSATWNwJsaGGuYR3NGvirX/3q8gud8pSnXN5Qxfz+4Ac/eFwXn+50pxvXp7/61a+W09qHGk8/hzIHX+c61xkNctSBW1ugmj6AOOpxz3vec6AOyFSzzInwgezcir829xOHfoLr2dEXUDZrePKz9kZWQSbDOFuilr4B2eSCF7zgyJs1CidHRbX52+s4Il2JlX3gizyIfIjMucceewxaQxFXKw+DI7IJeZBt73rXuw5//OMfyTJSxDOKq8m/rMf8zx8gn+pnQmq6FG6xoi15fYnqKbemA4nWjGwop07Ip+iqJJ/yfqwZWDciA2uNqbLSTQQSgUQgEUgEEoFEIBFY+wjMbNDEAHDjG994eMQjHjGeYkD4RgGMYHqrW91qeNnLXrZ8WobFC4o1hEqen/zkJ4/KP5Sypz71qYdXvvKVI1LsnuP6GhTCpPOEEH2a05xm/C0rTsDw21Y6RUJ5KGPf/e53D1xLCa9nP/vZI4uoTF+G/OQXb5655o0FH8pKdoi+6EUvGpMiSHMl3cEHHzwaHzHIcm3KRS5ykVHBjADNb10hTKN4Bwfen+vsOGmBMlTlwIvTIJ/+9KfHxRHviAEHmuX9pNQfGUz4t80221RT1+JUltwqgxMjSukIq/2V+HkeLb/n0UpfiifM/lmeCleYz6/wqW6Nj8qrxdtyamlLeX2Y/BhPrGKcdssC9Mc//vHwyU9+cjzFxILWE/3nM5/5zLhznEX/da973QGDlAg+LDJ/9KMfjYbHhz70oQMGIDYNMJ6w0GThD1EH7bCFLxsirnnNa471QNFCX4QfaTAo8YcC53nPe96w9957j4tmjKi2X8L3MY95zKh0+c1vfjNgzMRAVjpZTVphyTNU8yt8Fve/nP/73+avhfekUd5SWh8mv8+DX3El18aXnhVmXT3DT2SfCaPd2dOXGJZk4ON3XX/wgx8M3/3ud8c2c8wxxwzvfOc7R1a94ziJMcKhmOIathe/+MXDhz/84aJRjiuPuV706KOPHsd4lGTMhbQ5jOgoRzDuv+Md7xjrwD+Ubiid/PxBO7/RjW40POUpTxkVTXe5y12Gxz72sWM+3hdF5V577TW2YU5fiDC8a8POJz7xieW5bmpf+vKXvzy+N0ou3olNPPQX+iJ9mrkKXD2VxgI2E/F+vDenG5nHPE0dC+z349uwKUGEIhWDZjSvlr5HrzxwxStecVR8qjzGBcYusLfE2IJxlY1OfBMMwxYfnqfgHNUPwzn1OsUpTjFWoSZ/qH5sDqP8vHZWiKSbCCQCicD6RQBDFhtfmV+RUdkMxfpThAwg+Yn5gXU2NwggSzDPs5b0FPH0cygGVDbRMM8x37HxNtIHUBb1oA5co4qBceqcaOtr59Zo7icP8chzvAPrFOZB1gtgwBqc3wBl7i5RpG9gHuYbsF6HJ6dUdRNCNH8jz1gdR6QrsbIP3w5Zhw2kyFh8g/vf//5jtaPy2JBFXTHOgj/ypjZ9RjyjuEj+pd3R/izxDXgXCLekSynpSywPniMdSLRmVJ0w6LJeZeMiN23QZ+hDrFlpv3aznC87/YlAIpAIJAKJQCKQCCQCaxOBmQ2aLGgw7MlQyfUo7MR77WtfOxoltttuu1EhymujeGTRAyFMo/AlHsEcIygCrgiF6pWudKXRwKEwuW984xsHTl5qV514Ek/cnnvuOZ5owUiKUpjFCtQqc0zU+Ee9Lnaxiw3sfr3ZzW62rDAnG0IySmVcdqGiTNeC0rIlLwuLm970puMiiBM2LEhZPIhYpLCbEoMLim5OfUCzvl+pHiorcmuGS/KU4hQmN+Jdi6vlrYV7Pj6d9/v0kX9W3CKei4xbjfr1lIHxnr7MWMCpIRaLJaIts0uaE55sgmDjgb2uiAUpBhGus2WzAP2A03b8YczEQOKvlMLYiaEApQD9COU99cAQw2YAjFzU71SnOtVYN4wh9NESoeghD5sheA+UQPRBS+DhMbH+UrzNP+W5xqsW7nmX0k0Js/zIJ7LPCrOujbfPSqMw7/p4+Xtcvh3fGgM434wTbCitSlQbxzG0s3OfdshOfsZ6TtuXiPnkDne4wzjPkXb//fcfy6+1fXjUymUe5RQB7ZY6oKjCIKi+cfGLX3ycO3UyulQfHzalL1Eup5fpdzrtyGlP5tALX/jC41yMYbeHkAXgw+kN+uR73/veYrbesUBGOzGhfsgXjAMoodgkccMb3nCck1vzqnjgziIPoPhCgfmCF7zAsup+noJzVD+Ukhg0LUXyB3Pgpk2bRkWmzZPPiUAikAgkAusPAQyYGBIx0GCo45Rg9LvyzJvM5xiN7nOf+4wbC/1bT+Xp80f6AKVFrqCuyOPQlDlRPLzbs6ZGN8H7cysSa2/0Bpx0ZN3CWp5r+0sU6Ru4Lh85hHU/c+wLX/jC8feq4RPN38RbHQeyV01XQloRG5tJywZNZKy9lzZncp089YjKQ1+CHEaeb37zm6N+RBvtIp5RHOVNlX/1Hrg1WdimKT3XdCAYuHvWjLQDcOB0Lt+fzeTcHIa+hQ2Jxx13XKnYDEsEEoFEIBFIBBKBRCARWMMI/O/Ix8RKovz3SmOu3mO3IgI+J6W4ZoRdnAj+COMQSmOMdNaIiZCJUBoRv2WF4Y9r50qEMhsBX8TiBaUqFJXpf/9D+SMXRatOqnHqVCdZMLRyWgdjKkrqEj3taU8blbII0hhrICn3fXrKYWcrNMv7eX5T/dYg6OtIHGFy4W2fS2XZePts0xLeQz5dy1/i6etg/fZZeX0ZCreuTWOflcaHWVyJk7/2XOOj8Mj1ZZM2ClOc3Ig3cfQFtdco7Y477jjuaGYM2bSkbLeEoQJjDldUiVC2gAtGRkuMB4SzKcISC3cUGGxowBXZK0AVJpcTeU984hOHq1/96sO5znWu8dpSGXiUxrv6Vj58kX7KKOFfC6fsUr1KYaV6+nTeX8rTEwaf0nuQ18dNLZMrteDNqVpOF+JiNG+RHccxgtIWewyHGNLsZhpOF9PWOAHQQ75cxvbrX//6y1kxzKOonJem9CXKQjloXZ5Rtukk7BjZ+Y/5T4qzKEs0Fvh8zJdsdnrf+943GjTZwMSNDdCUeXWqPHD88cePm4vYmMUcPy+1cI7qhxzENxH1yB8Y3dk8lpQIJAKJQCKwvhFg7bnPPvuMa0IMhPZEZuvNkFM4IejX2/PwpMxIH9CqE/GtOTHiMWXuZ+608yflcoLQU0vfwPoEAxu3QCGHsEmbG5eYa6P525eD3FrTldi0nLJE9hGht9h7yagJReVxIhNDMpue2BzHmomNo1DEM4qL5F//261jQcE/KwsHycaomg6E20SiNaMM6JY/Yb4d9KxdLY98TgQSgUQgEUgEEoFEIBHY8gjMbNC0v5mp1+D6lZvf/Oajl9Ma/D4Fv1PB7zbIcMiuUk5w1H6bS7y8y0KBxRiCNjw8oYxGKc3pDwgjKqetOGkza5m+jJKfHZK8I1e7cCXt85///NEowgJCizTl40QZvyGBgpT6QlxX10OzvB9X6ngjgvfXyvZGBZtPcYRZY4R9LvG18faZtPgjsuXbdD685Vdelaf0Jb+PU96Sq7SKs377rHi5imuVr3TkU1rx8K5N6+Pk92nk967SL9Kl/XP1EH2TfmoJQz9jBScv2ZQAffaznx2NpfxeEMZKEWmpL79Zy/hgiSs+ud4K/lrQsrjH+OSNVtSFvoJB80lPetK4UxcDE31u5513tmw3e6bs1rfYLMMMHn0Pn7UWTrpSvUphnmcpr8/n/Z4H8SWqhZfSEubT8w21icTn4XQeSiEUN8wPXPuNIZvfjuolDHCc8sTwwyaciDA48rtLMqRzQhRly1RlDmXQxlGOYaTzxPw1D7X6kv0NpnnKKeVF6RX1HeWJxgKlse6d73zn8TQKG6f0O91T59Up8gCnZnfbbbdRccl1s6tBUf1oe/Yq35b8wdiGsnkRhtjVePcsIxFIBBKBRKCOABtr+K15ZA6uoWeutRuE6zn/uzGWm5S0HlfaeXjCo6UPUDmLdqfO/b3lt/QNbJzDwIi+gY2W3CKDnoNrS6P5u1R+TVdi04Lvce4EIcbJC1zgAmF53CrBRjBdp8pVqxi0oYhnFBfJv2ywgkpG4jFijn+sx0o6Hm2Kq60ZuXI4KRFIBBKBRCARSAQSgURg60Rg5itnURpzMpHfb0BphsCMX6egUO6iRONH3BHYRVxVgtEPhTCEsQIhu4e4LgajIUpGdlDyW5siyuC31DB4cKKE38TT7/TNU6b411wUq7wnxhOU6FwHx4/MizjtxeIHYpGEoh5jJ7tqwYG69gj/s7yfNQrwbP2qX81VeusqrcLw+2eF1VzSK87nlb/k+jxKUwu38WOB7p/icS0PJVO8jVNYyfXprN8+l/L6MJvePpPO+31epWmlq8UrXK7lR9is9P73v3/8LUwU8PRhjAMYFz2xIOZ3BPk9Gk6Y0df5LRt2QdvfECQfVwdhEOJ3/uDLDtvHPe5x4/WKxJ31rGcdNwwQzu8AskOZ/grZfomffsumBN6XK2fpq75+JSxsGM/WD99ZqcarFu7LKaUr1a2Wzqb1aeQvuaqH4vDzLNKz3FK8jVM+rhblt4gZLzFgMu+IMGxhzOQ7c2IBBU7LKKm8crn+mzbw9Kc/fdztzQlJ/X6x0sjFqHbUUUeNcxg7wzkhQJlsuIFod/p9S+WpuVx5xo0F7PZnHuXqZN7H/w5RLX8UPqUvRXx643QiEwUr36dmAOwdC0rlsmGKq6NRXrFhCmrNq/579MoDGLe5iuxhD3vYZjLMWKj7x7VlfDt/8sUl6/JG9du0adNmv2nakj8Y9+gXtM+kRCARSAQSgfWNAOtL5A7WwMxRyNPIRcgPJXrb2942xnGDBDIuc5qnFk8/h/r8LX2ATz/Vj1EKA56n1tzv00d+ZC77swGRvgH5BsPwCSecMP6m9k477TRubIZ/NH+Xyq/pSmxa6oKciFGTTZTIi9yIhWE6Ko/Nm2zWRMfAhj2+vzaRRjyjuEj+pZ2wfqLNQeCJrNZLfl3G5i3aOlTTgay2nNv7LpkuEUgEEoFEIBFIBBKBRGDlEZjZoMnva/G7HVzzivKY3+3Cj9FAhGGC38ngJJaIMP7Y3Yjwy+8p+Ktrlda7/B4EuyExILIrj3Kl/OaEDkrvS17ykuMJLHbysdCD5inT18H7eT8Mk9e4xjVG4wq/2XfEEUcsn87ktwBZcGBcRVh/ylOeMp74YCGG4heDDD9I36JZ3w98hJHKUFiv28pHfC+vRadjQWd5bsm6RGXPGmffjWf/vj5+kX5bZ57nIfoqC3BOjfEbLFzPVKNDDz10vEpyh6XTSIwzbBTAqOmJcDZS8Bs48KUvoWDgJCdxxx577NgXMTZwCu6www4bF9vwsf2SK69Jy+lMDFuk5SS1HctUtsXXh3m/0ipcrsIjV2lxbTob3oqz8cpX41UK92HW33r2ZSu9wuUSLrLPCpPLGM5mFU7DM3dsWjLsiBhTUdbwndkdjxLnjne8o6K73ac+9anD4YcfPvLhunLmmVKdMK5rDqPdYfjkN6R0hRW/1XPkkUeOJ/VbhaMIO/roo8d5lPmQK9X5fR+u4loE9falRZTF7+KCByczuZK3dkJzyljg64XiFYUsSjX6LdSaV/336JUHXv7yl48brpjTUZrpj5MYnnbZZZdR0ci8jmFzHorqd7WrXW08oSmDeUv+4MQyeWhbSYlAIpAIJALrGwGMTWzm4RQd14cis2LM5OrNErGBFrkImQljmK4qtWlbPP0cavPy3KMP8Hmm+PntbH7z0FNr7vfpIz83GfCTMRgAoUjfgHzJzQ2UzzfA0HjggQeO+aL5e0xQ+EceryuxydBrvOQlLxl/AxQZmJ9U4LcxZcwjf0mnsu+++446E8lcbNpj8zUU8YziWvIvm8nZyM7PAey3336byer2nUrPdl1Gm+Y2LxmZIx3Iasq5pXpnWCKQCCQCiUAikAgkAonAlkFgm6VF0H9YGM1DGBHsqcQeXgirXNWHcN5LnDRAgKc8BHR+Kw16wQtesMyCnapcTehPV5HAl4mB40EPetByXj1gkCwt+hRfc1kIld6H02PUW0pq6sGOSYwuU2nK+7GDV1RSzCtuquuv2ZRfLvz07F0b58tVWhveqreP937Lq/Zsy7XPpPf+Gg/Cfdktv+Vly7HPpGn5LR89+7IVLtfHW3/peWr/Vjm4XMcEYTBCubL99tuP/tY/dhJjwLVXN7/zne8cFQ1+EwB9jz4vI4flzXhBv7TvRbzvl4Sx4127vvFbkqKj53v4NJZP9OzraNNGcTadfS7VoxSmPLW4WrjylVxfX/nlksc+e39pLK2NseRlJzdjrMZZwqYQYzLjNGUwv6AAPPjgg6vGUcZx2mhprmGuYse/fuOxpx5T+kYPP5um1Jds/DzPGNcwZGLIo+9wSrE2t806FvTUL5pXS9/DywM9ZbTSzCIL1XjW6sd1/ShS+e1YS6W+gWF5r732Gjd72bT5nAgkAiuHAKfh5l1XrVztkvNaQGDeNoK8g7ziby6x74Zhis2Du++++3iisyXHRzxLc6gtS8+LnAPFE5f5sCTfK27WNXWtjB59A3VC31CS82rzty1v6jNyOPN86TtG5aELQE718jblRzyjOMqryb/ko0zk6Klk12WU4b95pANZSTl36ntMTT/veDC1vEyfCGx0BLLPbfQWkO+/mghkf1tNtDdeWdsu4pVLwnWLL0JqSWEd5cMggiHjdre73Xi6kRMU/lpAFnclBTN8fZkoBL1SMCq/FVd7H7/YoR41hW+rjCnvBy+/gPH+Vnk+noWK5eH9Pr385CEtZJ8Vr3Drj55tHZSuFKa4XtfXzfsjPrZ8+6w8pTDFWdeXWfLb9FOfS/WwYbXnqeWU0vcaM8mr374UH65P5KonThx5qvU90tXGJ98vSduzALf4lNp/KQzevWT5+zxRnE1r+5oP7+Vh883y7Mux/tpzq5zoO886pqpMrjPl5AMndI855pjR4M2p/xoxjtfmGhSN/E2hKX1jCl/S+r40NX9vep1S7Um/6PeN5tXS9/DyQE+dW2lqY00rXym+Vr999tln4ETota997c1uv/B9g99lRbmsnwEolZFhiUAikAgkAusPgSnyDnNJz9wU8SzNoSXUesop5WuF8Q41Ii6qey2fD7dl9OgbSF9aR8CXOD8n+/Km+pGba/hG5bHRrEYRzyiO8mryL/l61lKlOlk8KcNTpANZLTnX1yn9iUAikAgkAolAIpAIJAJbBoGZr5zdEtXlGkiurcSQiVD7jW98o2jc2BJ1W4tlsqiAcPXn/QrvcefJ6/lz8m6W61Nr+SJetTyqk89b8vswm7fFn7S1/JaPnkvpe8qw+e2z8sq1caWyfDz+eYhrNPldlXmJ385j1/T+++8/L6uZ8pdwWe0wKt4qs5amFt7iR3yr/XoePr332/S1ehG+msRvMGMI5rpYfh/pO9/5znh97WrWYT2WhaKTa8jsSeraeyxqLKjx3wjhZz/72QdOqnPFf0QY5UlDX0tKBBKBRCAR2FgIcD07p/mTpiOQ+obpmGWORCARSAQSgUQgEUgEEoFEYDURWMiVs6tZ4SyrHwGubhG1lJo+Xie8lL/k+jTW3/Nsedr0NtzXy8bpuZamFl4qqzdMZUauL9f7o7y+Ht6vvLVw4nvLK6XzYfJHO3xVp63dtVc4866lb1AKm4qLMI/y1dLUyq+F+zJ60rXSlOrmw1p+6pVtzn+d9CcCiUAikAisdQTyaqW1/oW2fP2yjWz5b5A1SATWCgI5HqyVL5H12CgIZJ/bKF8633MtIJD9bS18ha23Dgu5cnbrhWd9v5m9umV9v0nWPhHY8gikgW3Lf4OsQSKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAonAxkRgXV05uzE/Ub51IpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIrBxEUiD5sb99vnmiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCicCaRyANmmv+E2UFE4FEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoGNi0AaNDfut883TwQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSATWPAJp0FzznygrmAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAhsXAS25dV/+9vfblwE8s0TgUQgEUgEEoFEIBFIBBKBRCARmBOBE044YTj1qU89J5fMvjUjkG1ka/66+W6JwDQEcjyYhlemTgTmRSD73LwIZv5EoB+B7G/9WGXKfgS23377MfG222yzzXDKU56yP2emTAQSgUQgEUgEEoFEIBFIBBKBRCAR2AyB7bbbLtdVmyGSHo9AthGPSPoTgY2LQI4HG/fb55tvGQSyz20Z3LPUjYlA9reN+d1X6623/c9//pM7iVcL7SwnEUgEEoFEIBFIBBKBRCARSAS2SgRYuOcJza3y0y7spbKNLAzKZJQIrHsEcjxY958wX2CdIZB9bp19sKzuukYg+9u6/nxrvvL5G5pr/hNlBROBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBjYtAGjQ37rfPN08EEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgE1jwC2675GmYFE4FEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoEtiMA//vGP4e9//3u1BvzEY41qcbXwGp9tttlmsyj55RKpZ+va51oaMVZa/Cc72do5F5kGTX2hdBOBRCARSAQSgUQgEVhjCPzhD3/oqlEk/FohtIvZiYlmzefLiOpWiusNUzm+nlP98LF5TnOa04h1uolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAILCOAzmKq3mLe9FZnQUXErxTuw0hPmPLghxRWcv+bYm3+T4Pm2vwuWatEIBFIBBKBRCARSASWEfCC53LE0kMURzofXxJuLT89k683rfJ415et+FJ4b5jAeS3PAABAAElEQVR4yFU+1bXkV5zyeJc8rTQ+z9bq/+Mf/1h8NeHqI2vhPl3kt9jr2bo9z/BXOvu8lnaSRhhkXCKQCCQCiUAikAgkAolAIpAIJAKJwPpAwK+DvV9vUQovhZG+Fu7j7LqXPPjlqtzIVf4peSJ+qx0391nRv/zlL8N73/ve4UUvetHwjW98Y676//nPfx6OOeaY4R3veMfw17/+dTNe//rXv4a3vvWtw3Oe85zhPe95z0k+cJRXjP72t78Nr371q4ef/OQnCjqJe8IJJwyvec1rhuc973nDF77whZPEE0Caj33sY8U4G/jzn/98eOlLXzp86lOfGv7973/bqIGjye9+97uH173udUPv6YvNGDQ8X//614f3ve99jVTt6KOOOmr4/e9/304YpPjc5z43XOISlwhSrP2oH/zgBwNtbC3R3nvvPfB9ktY2AovoQ/O84Tvf+c7h+te/fpXFec973rnH7irziRGrgdVOO+00MCa1aC3h0qprb/z1rne94V3velcxOfPaK17ximJcBm5ZBBAw+YP07N0ozqfFj0xi/0ppFKZ08ve6tXxRuOVdSqcw6/o8kV9x5NdzyR3BXvq3KFlK/GZxP/vZzw6f+MQnZsk6c54SJrWwFpbks99Lz55fDx9eSPlKzwrDXa/EeueDH/zg8MpXvnL4zW9+s9lrrNZaqFUOlepdC0Xrs5VeC20GXnpWDQF0A9///vdXrbyego444ojhfve7X0/STJMIbJUI/PrXvx7Ofe5zn2Re0cuuJb0Cur5vfvObqtqKuIwHz3/+85u81xIuzcpmghVH4KCDDhr4q9Fq6DJqZWd4IpAIrD4CWpfa9a4Na4X3pvXp5K+tnxUuV+nlgpSeca1/9Lh/SuOCt7h3LoPm9773vVEweuYznzl88YtfHG50oxsNd7vb3UZgpr4Zhr8LXvCCoxGO57Oc5SzDD3/4w2U2d7jDHUbe3/3ud4c99thj2HfffZfjWnmV8ClPecpw+9vffvjSl76koM1cFudXuMIVBtKh8Ob56KOPXk7z/ve/f9hrr72Gs53tbAPvHNF973vf4apXveoojD3wgQ8crnjFKy4bNT//+c8P5zvf+UZj1Ic+9KFhxx13HD760Y9G7CbH8Y7zGjT/+c9/Dq961asGDLPzEHdK01bWG/3yl78cHvKQh4ztAPc2t7nN+IeSaS0Q9VtrRta1gMuWrMNPf/rT4a53veuAMhBaVB/y7/SBD3xgOOSQQ3xw0c/mkGgTx49//ONxg0Ux8yoGrhRW/hWYQ9jc0qJeXKZ8i1aZKx3/s5/9bEDRWCLiGO/VdktpMmzLIFATNm34vM8IuzWBd17ePfl92d4Pj1JYKbw3XVQvfelFyFLiNavLprieTXQRfz83RWmJi7CZEse3mPd7+Pqo7tRjayPWHhe4wAWGZz/72cMnP/nJYdOmTcOb3vSm5ddcjbUQhUXlTFkLReuz1VgLLQOXD6uCwBve8Ibhpje96XCd61xn2H///Ycb3/jGoxER+WJLE5uHMegkJQJbEwJT5nbmYtaDNTl/LekV2PT/la98ZUU/FeNBz6GCXlymfIsVfbFkvqII/O53vxv4K9Fq6TJKZWdYIpAIbDkEorVx7zq4tWZWfImfDbPPvl4gZMOEmMLkVzrr2ri19DyzQZMB+yY3ucnwiEc8Ynjzm988GvhYnH7kIx8p7nZCgQxQJUKwuve97z0897nPHU9gcoqSBdFLXvKSMTknJTnJyE75pz71qeNpItJi8GzlVXkIRS94wQuGM53pTAo6iYuhCkMqyiN2bL32ta8d9ttvv+V0hF3rWtcajVzLgYWHT3/602N+sDj44IPHndac8EPxDWGIwDh25JFHjrix6KNuvYRCmoZaIr4LBsRb3/rWw+Mf//iTJIm+g1fyb7vttsPb3va24UIXutBmfPiOkSGtpjDfjInzqN4K5ruyc7tGf/rTnzaL8nW3kVF9wLH0I768HxjyvT/+8Y+Phu23vOUtw8te9rLRz7OnCBPSRvG8b6ke5CPOn1gmvERgRvoeijCL6gpvH99bP1uvqd88aruWb4Ql6aL+E+HXU19OMzNuaayr9SHwqvVh6th61+985zvFk3a8e9RvonenXFGEQ9Ru4K93Fy+5rbJrWFGXGlZRPVUubqsPRfW2fErl1b4FOMC3h3x/6snT4t/i6eMvetGLDoxrJz/5yTcr3qezkVGcTVd6jjBvtZUSPx/meZS+nfJEbbrVV8Wj5Pp5KmrLER58a/unsmwYzyIf3usnf2/a1U7HGBCV2Yonr30/+yy+NmxMvPRvFllKecUv6ic+rtTe7nWve222iU98aTMl4n28POHnJpvPpxV/4WLdKM6mW/RzqVz7DpS3NdCjH/3o4Za3vOUocz7jGc8Y1zxPe9rTxldbrbVQVA4V6V0LMe9Ga7t510Jbw/ee5x38/FIbDyjDy6/4a3JVS46syfsYM1/+8pcPnIRkky43PnCz0p577jmOXz5fSy4jvjQ2CTM/diocN4qz6aakjebvVl15d9KIeIZfUiIwLwLR3B6NCbTBSPZVvZhba3yicSSSuf14pLKsy/xwi1vcwgaNMmBtTIjquRmTJU80PkT1tnxK5UXfIirT8s3n9YWAn4druozo+/v5YQoCUXvt6Wetsuj7VlYotXvxiMaD1hwpHiW3VIfaOBDhUeKdYesLAd+PanMTb+XbTdQ+6YO07Rp5+dWnI+/Uvx69hXiW0pbClN671NeGeT9xnkphPs1a8c9s0OQaiC9/+cvD7W53u+V3Od3pTjeennz9618/hh177LHDla985eE+97nPsP3224+GMQyTnhCoMBTZaxExonFiEuL0J1eWnvGMZxz95zznOYeLXOQiw9e+9rVRGIvykoFB9B73uMdoLD396U8/8ij941qvq1/96su/v3O1q11t+Pa3v728ewtD613ucpfQKArfs571rAPvfo5znGMshsntwhe+8PJuHhZ7nAIVnfrUpx5QikckLG91q1uNOIA1C0fR5S9/+eGe97znQPihhx46oAi5053uNEYrb+07fOtb3xp22WWX8b34XvYbnfKUp1zeIUcZD37wgwfwpxyuL/zVr36lKowGad79POc5z7D77rsPX/3qV5fjag++3gxUXAGC4ZkrHzntpt9y4j0ueclLDje84Q2H0572tOMCne+DwHv2s599uNSlLjUaiVUWV3ahpOc7cNpWu9zZafd///d/o6KI+mLEPvDAA5VtdMEOvHgPFPzslP/whz88gMeznvWssS1hpIbYkQd+tC2ucqH+5BOxm/1yl7vc2AeIt8ZQJmQUPmc+85nH/He84x2X2z35aStgTdun7dEvSic1mMA5FQwOpznNaYaHP/zhmwkgqgsuONKfwJjTzvb6zaiuvMODHvSg8R2pE8ZejP9ge4YznGHYddddBzY1tGjqN+8ZQygzwlJ9oNZ/Ivx668sGhZ133nl8fb4DV3FDtg/96Ec/Gsc5vjcYsuFBgqLqWOunI7Olf7QX2hgbJGi/LKCifkM++hCn02lHlEtdSxThEPU1dvze4AY3GPsSZXBtN6S+Rn3ps2zyuPSlL71ZHzjssMPGDR6kt1jxTvQH+ien99mgock1qid8LL3whS/crA8xH4hq9Va83Fp5pW9BnsMPP3wcB+ljbPypnQqo9bc3vvGN46l+lU/bps2of9X40+9Rhu+www5jucpvXRSMGsOve93rjt+IePoyZYgYZxiD+Z5XucpVxnGWuQUCN8Y84kjDdUyMJRDzP3OniHmc8ZZxEqph7tsK4zc3GWjcJu8jH/nIcZ7juUSeB+2t9u3IH7XpqK9yQ4SdM7jWmbkJKs1TUVuu4TEyK/zzAilJbJj6SCFrM8jzkV8Z5e91a/kI9zx8WMnvw8SDcMj6S88K8+5/c/8vP/GiKbKU8uBG8lKpn0bt7TGPecywzz77LLOv9X8SPOEJTxj7N3IYMgF9ojY38W6MrcgljLW1U1QlvGwY5Vq/fSZOVAr3YfghuXq2/jGB+RfFmWTr4tEbrxnLNX+s1looKgcQe9dCrbXdLGuhdfERV7CSpfmlttahGl5+5ZvQxphbz3/+849rBfWfSI5kzLr73e8+yv3I+8z/9mdZWCMhH7AeZ83129/+dnj7298+zvfcVkTeAw44YBmZaG0TyfIwqMlNxLXWY6QRgdtlL3vZcS3Emsi+j9LgRvN3VFdkF/BHT4IOhDUi6/bHPe5xo+yO/P6oRz3KFpXPicAkBGpzO5v+I33ME5/4xHHdjr6AW8TsushWoCZrRONIJHP78Yjbr5DrRegjuAWNfoVuh/WbqCTbKK5WT8XL5ep+xj6ta+ztYVG9lV9uqbzat+gdZ8Q73fWBAIdsaM+M7chpn/nMZ5YrbnUZ0ZxEO2c+Rj9Cu0dXst122418WBuydrYnQZlL2TgERe3V9zP0hw996EPHfPxDD8lNINI/LUec+FBaSxNVaveER+NBNEdG+oJaHWrjQIQHdUxavwiU1sy1Oa7UbqL2iS4fewZyKzfioJcVteRepcPl50GQpSVPEya/wqx/SpjSwtOT5yk/6ZTPh1m/5ye/8sq/1t2ZDZoI/hgVWRBZwsjBzkwGSf5QkmKo5EoHFjMl4R2D3m1ve9vhVKc61ciKwZtrijA+QL/4xS8GfvfMEkpurkJt5SUPAzACDIrliJh0eCcRizaIzjGFwITrd0UIaBhfwQbCoMAkxXWzXCvFqVOrHFU+64IlfDAoYcBg4cgCkU4KMWFgFOV6NNIgnDK4Q9F3ID+GCP4wTvLboSin9bs98FCjpgwwpxyMlRhJWLxC1I2d5ZxO5XuxSLSDwpio8M/Xm++OkMC3oBx+g0XGEd6Dcrn2l7piKEbRx8RNfREmEQwon/eiTT35yU8ehQGMIRgfhAkuC26ulWQhzgSJ8ABRJhiTnzIRqnkXsMGgC2Egl/DC77qyKKC+nATmW6scFOZcucRJZvoACkT46fdmETAoj/5EGMp/BHyIZ4xbYAzWnFqGn77HmOjEf5wk5tsQDx+EhNLiHPw4HUyb4x0xvN7//vcfubTqCqYo1jCAgxsnpK95zWuO9aPPYlDGONWiqd+8ZwyhzAjLVv+J8OutL+2StgTxLbhyC1IfYmcQxvjLXOYyo+KYK6E5ma2NCVE/HRmd+I92CM4olBibUJJE/YZsTNh3vvOdx3bE6Qrab2m3UYRDra8x1jC20hboUwjKXD2uPoCLsI7CHuM0inO7aYJrTmmTkLDimXaJn/aMcY02LSNxVE/yiuhD9Ht+z4I+dLOb3Wx5x3+r3uKBWyuv9C1QYjHuYHDWHFW6pjrqb7vttts4luj3Y5grmPNoOxF/3om0KJytIdC+C32XMVZjyYtf/OIxmvZn2wRGO74VfZ40bOiAP8S8RbtDGcD3YTymn0Dw0fMYsPRP37WFuW0rGEW59pCbEiDGPU58MH5GZHnQ3mrfDh61Nt3qq8TzJ+K9hB3v7+epWltu4SH+vLv+FIbrw+RflKuyLD+FtdxSnqlhKqOUT3G4Pl5+G6dnXIg0IpteYXL5RrQpqHeMJi19oCYvwdP20572pjpE/R9ZgfmEq9rom5RDXy3NTYwByGzI68zlJzvZycbNWtQdEiYWJxs+JjLp5LdpLI8aH6VRPu/6fKVybNh6f0ZeZL0iwvCjzYmrtRaKylG9etzW+myWtVBPuVtzGj+/0MejtY6XX5lDkQe5ep81wjHHHDOwIQeK5EjK4TYmlEisHdgs+qIXvWgZamQE5B5kPcYUNvFyuw2boSgPeZg1EtRa20SyfCQ3wTtajxEvYt3FOp3NxciGKHwf+9jHKnoztzZ/kyiqK9+Kjd/IUpTBiTP6MvIS8jDrBTDjWyQlArMgUJrbe/QxGGHQm2BsQ8dS+o3uSNaIxpFI5vbjEXo+5Bb6CkSZjGfoyEjLuAPVZBvionoSbwnjLX2Qd+c2JY1JpInqbXnUyit9iynjjC0jn9c+AvQz1qfoYViXctueCHldsms0J6HTeNe73jXqypG72FwoWZ/8ehZf9HDqE1F79f2Mm2boQ8qrfobcXyPKtrqbWrsnfzQetOZI6mrJYufrEI0DER6Wfz6vPwRot3bN3JrjfLuJ2ufDHvaw8TAKui5kVuwBrI8hyo3kXovkcccdt9m6Wf0fV39KL7/SEN4Ks/GlZ8tb/Lxry7PpI35Kt9bd+kjWqDkDLyczPLErnN2MUu5d/OIXH25+85uPp9xwUdBirKoRQg0nDTlNxE4UCGWPH3S32Wab5YFZvEp5UepgzMKQ5ImFHKft+KPx+nIoA9IE4PPLj4JYfHRNruIQmhDYUDLJQKo4FoZPf/rTx9097JJvEQtElLzs/MHQxekfGVDI+4AHPGDcbS/DsOVX+w4Y4FhsIuSRD4U5xgYMEyXC8ICSAsMvdeBKWggDJpM6bQKDF++DAaKHbL05UcAEz6lI2gqnXt7xjncss+E9aBs6ScQikXfgO1N3FIgI6mBEO8MIiLIOYwBtFkWziHdmJxQLc/Lq5CMupw0hFvjscuTEFCef1A7ZxSFjA3HUW7v+qJ8IQQX+EtARKmjXCEG0VxQCLJYxgvOHMZM2xDF5diFjJCQPxlsEJoQLTwxEGMYwPnD1Bdjx3lJS2PTvec97RqMSBmzS7b1kvMa4Rb6oruJBXfj+7GShDN6Vnc3giLGI0189NPWbt8aQFpbUqdZ/evCbUt/a+9P2aKsYt1HkXelKVxqVKDIoka/WT2s8Fd7Tb/jmpzjFKcZ2wjjNxG2phUOtr2HQQmnDOEcfY6cibd6OTQi0nAqkfMYQ2j9tnE0EbAzA0GuJMRcjIO9FW+PEK/2CMapVT8uHPkTfpu/Sh2hHGh976g2vKeWRHuUAO/LpEyj+6LcIRJ6i/kYfY9GtfIwxnFaHWvzJR9sqjRXkZ35lLmIMw9jLKQpPnNRiIwHfjbZ6sYtdbNzBpnTsECXvuc51rnE8sCfSlabk9mBu2wo4Mh8xPnFqiI0T2ihQ4q8w8eCbR2NjrU339FWVVXLtPBW15R48SvwJo11a8n4bN+szPD1fhflwyqjFKdzWY0qYzafnUvnE2XD7XMrn471feaw7ZYyuyUvws/10SnuL+j/jBNc7IjcxhqCgR4YoEX2KMRhFJrIuJyFQCETk8Sn5fVjEz8Ypn3dtGp6J15+P29r8bFxifaGNdH6NwvuuxlqoVo7HO1oLldZnyj91LaR8G9W180vPWsfKr8x5rPkwZrJWYl5lMxrUkiMZs5AFKBN5364xWLexDmS+Q95iXGH8QQ5BFqKdsn5C0RStbVqyfCQ38Q7Reox4EXy4dQZZFXkHGYa1JvoLS9H83aorfODPxirWW9q4x/dgfGZNgqzGZrGkRGBRCPToY5DfMRqij2Dtps2itg6RrFEbR5ibI5kb/nY8om8wPmi9yAZXq0NRfSLZJqqn8uNqXYMCm3GJvnnta197TNJTb/HqLY/0veOMeKe7fhCgne64447jehoZjU3qyNWeojmJOYd5gT7LHNE6eCPePe3V9jNuY8JwiH4TOZI2rPlIPEuu1tLobqJ2XxsPeubIUrk2zNahNg704GF55vP6Q8CumXvmONtuau0TFGjXGDE5dIROEp0hfVlUkntpb7L74KLrY7OBJ9JZwt8TRh6fzvIpPdv0evau8hGuP4Wtd3fbWV8AQVzXuTHQibgSD0UpwronlIuQ341h03G8l92bBx100HIwp98wDFqi8aBoteTz8rEwunFqD8UNuyBpcBi62CHJwkoNkEULAo415GlhU1MMq2wGbPFBMBORH6MPJ6PsiU3FI7hBGFzZ2aXf2FR8y0UQ5UTkNa5xjVbSzeLtd8D4iMLLfi8MHz1EPnZM8P58U65EY5GMMRNs1ZF6eCkN34hJmO/Aoh1FH4aQGmEM4uoUrgpGuU4b0CTN7w5hOOEaB50krtVJuwEph++AgA9hUMAAAGGswbgCcV0JO4UZAKkj12iWCAOWlAWKZ8cyRlYGTtoIJ0xFDNLUEcMshh7Stoj2TH/g2hZ9W/L4cgnDcM8CWoSCAaMmFNV1TOD+0dZte0fJoX7gkobeKd9c7+fHkBaWpQqo/yCQ9uIHnyn1teVivGAjABsRRFzXy+KvRLV3LaWdWqcrXvGK49ghwz08W+2o1tdo/7RZf3IOgz/zhCf6Im0QgYHvhgKeMd4S4bQl268QMiDier8XSrVaH4rqba9MbeFi680zhkDGEBkjCWOhwjipDRGEtfobpxM5bc8fxl34QhH/McGEfygn7clLZUXJyTyujSMKx2V+pi0zVk2lCPNSW+GbM59w4oy8zJOM1b3U+na1Nj21r0b1idoyxtpa37FtMOK/JeOouzZ+8byapHJ9mbVwn24R/iljtJWXfNlT2lvU/5G/2KAkQl6RLKMwucgdKAc4wckYzOYBZFEvVyv9Ily1kdX8Rouo95bgwbfhZhA2HmmNtVprod5yPC61tRDp/PrM5p13LWR5bcTnKWsdrrSj/7EuZh2MyyY7aIocSZvUxmUMpGw6Yx3JuoW1GNfZQZxElBzBRiqM3tHahvkyWhdFclNrPTZW6MR/yDiMl/anbtiMx5rYyp3R/M27RXW15fHMGsm6PLOGQsGclAgsCgH65RR9DBugOH3lKZI1auNIS+b2ZTAWcaMPmyS59QVdHUZOT5FsE9XTrrn4WSnGJfQ3nqbUOyrP8+0dZ3y+9K8vBNgkztqUW/Js+23NSay/WxsJS0hMaa/kp25siuYgD3I48/XUNXzU7mvjQWs+L71bFFYbByinVy8U8c+49YHA1Dmu1j55WzadsNZCT8cmF+RZrVU9Glbutfpu0nNgopbP80n/4hGY2aCJohHBg4WJHbx1AmyWqrIow0AGD6u05Ag+gz4NBeGHcjGcyrhEWaW8LJw4NcipMZ0cw8/vV2CcYoFtiRN2vI8IQQRhiBN6EXEqkR1flligcGKOCeP//b//Z6PG6w9RJmGwgzAgcvUXRlU6VC9hUOZ03zyEsZAJGEx1cokrMjDiahFa48/EglEIjFBIUB/CUPKBo1Xo13j4cHDA4KHfmXzmM585tgmfDj94cWIHg+aTnvSk4bil494sTjctKeJZOPN9jz/++NFPeq6H7CG+nRRIvI9+Gw0jowyjOj2K8g9FJYbC0oll/5uZlM91TAx8GFrBjtNtassI3LR9hCOMPj3X9vKd4INhrGTEtO9MfcDJEoYChPyorjb9op+nfPNa2S0s+Y6e1H+m4AePWetLX0NZgtDFwg1CQLTKkzFwhn9T64Shzyq+KTLCIeprYM+4jOLVK8MZb0vEJg9OcGPYt7+forTiifJKhh2UbyiPEMR72zt9iOuVS6QySvW26SNcbDo9Mw5wirB0vbrS4Lb6G5s02FHMVdoYHqUg7OVvy6o9M/Ywf/t5h5MLCHW2rYoHygAM84wbKC49MX4xn5QowrzWVliEYfijvNJNC6VyFBZ9u6hNt/oqikkpc1VWzdU7l9qy4lptkP7lBWUfhh/y6Wr16g0XX5s+CiuV7+sKr1oYcZaHyrJhpCmR0irO+wm3YfbZx4nHolwrL3merfZm00f9f9OS7IPcqg109MPvLd1ewfjhifGU8ZG0yDbcDkFfk+zl0+MHL/sdWn7Pw+Nt432c9yst4f5PcVuLy3qHjXRsjLFyJXPGaqyFkG9b5ZSwpq5+LUS60vqMcK6CX8RaCF4bldgMMWWtgyIUAwIbR1m3cJoSQyQnTKbKkcKcuVRrdmRcxhWIOZJ1peQL1pa04Whtw5wYrYsiuam1HlN9cVlz0fbsTnwbr2fN0aX5Gx5RXbkWLSkRWG0Epupj0KftvPPOJ6lmJGtgzC+NIyiOe9dnKpAbrHbddddRb4MMQn5PkWwT1dPyYV3Dpg2MTH6tGq0VLA+ee8sjbe84Q9qk9YsA62UMHNLr6k1acxLrfHSwpY2HmlOt4UR8p7RX5WHTIidAkZ/3PvEgg+J63Kjd18YDdNTRHMm6qKYvKNWpNg6gH5k67pT4Z9j6QGDqHBfJvWzo5TYcHYbCdtQi+pBd6yADY1fSpjXlJ120ZiYdaSCbDr/CS3GEWbJpCZffu8pDuP4U1uuKZ2/61Up3Uqmhs2QUMJwcwViH0pWduRgi2VXsDYU9LDmBgkEGw6MXNPjtSU5kkQZj00uXrgFFqcrpJqiWF6MQJxjtH2Fcb1SqIwM9lnrSI/BwV7euKux5B6WhUTKIs/MSBaz/+CgpKB9jHAs+6o/SnkUfp19Y5JeIE4FMfGBNHk6tWmNyKU8rDAwx2GLsA2OEPSZkDA0l4opZOi4dnu/A7/1AfA+MDeCGEhyM+VaknUJM0uDCBI6AQBnRZMdJM9KAMUZ22g7GWFzCOOnJaVF+N5D6lAQDXz+Uf/pdBX67FeUO78WVl7wj74QxRkpDTuHyvvQD0vH7dSLaD1epsPuRfCgL8WPERljhd1s4rs7uIvJz1z6DK0ZhjJO0xWOPPXb85ril6ywYQBFU+C0Yvgvtj/vC6Y8QCgZ4Q9SV9odRk3T0V06wIQhEdR0zr9C/qd+8VI0WluSp9Z8Wfr68qL46fYmy2BNtddOSwpnfv6UdIchxFTXX+E4lymHyZCyAojoRz++60h5og5xapr2xGcFSC4daX2MMYdHGbwHRPhlH2DSC8qpGGPQxUmFQYyHribogXDAuMc4xptA+2STRqqdt78wdGG+ZV8CKPqTxpLferfL8t+BkJeMNSgKITQpszPDU6m/MBygMmEvs4qOXvy9Pfq655juxAOG5dMUNV3TzvbkSnbYKphpP4MNpXL4NRkjGVHstOHMZ7Y1xk/am0zfk68WctCIWemqzMm4rruW2vl2tTbf6Ku0KWYG5ivZk39HXKWrLU/BgPvOyRBSmOLm+XiW/0lpX6UphirNuLV0p3IbxLLLhpTAbb599WvyK17NNY59Jt2iqyUu+nFZ7s+mj/o9ikLmfvoe885jHPGb87Xry+7kJmYQ5AJmTTSLIOvZnEYSbx0XhqlPN7/MpvXdL+ZVmCg/lwVU+uTZurT9jOGHMQ1b0hujVWgu1ypmCYW19Bo9oLTSljI2cdupahw1kGDOR07j9gE2a2mDXkiNrOLMZmM2jEDeeMP8z97MZC9kNYkzatCT/slk0Wtu0ZPmW3BStx8aKnPiPK3Npf6yBqCvyNNiwXuRPP0ESzd+tutry8jkRWCkE/Nzeo4/hinnaPZvP6QO77bbbSaoXyRq1caQlc5+kkKWAC13oQuNP9nDtHzJMiSLZJqonv4vGZkiIdSoyN3o55HbWNdrQMKXeUXn+W0TjTOk9M2z9IMC6FD0H62luOmEjs+ZS+xbRnIQuEJmdOROdnP0JGHSz6DX1017MSehMoCntVXVh0wLyAjrHkgFV6Wpu1O5r40Frjoz0BaV61MaBWfAo8c+w9YFAzxxn36TWPulzzAvobNERspGTG8F6N6zbMrBh+fUs8T5Mfr8+teGtuFpahatc73q+ilc+7xIvKuVV3FpwZzZoUnkMKJxAZNcTOzzZ3cnAPPUYO7xQ3LLg4cQHA6D+2H1Kw2Xi4Pc9mCz4XQ4MSgxgrbxjgs5/LOJZ6DHAouBBsYDxYSphqEChffTRR4+7U/UuOs3CaUIEOBRILP5QGGM8hTAkcgVQicCBHQEom9nFdsQRR2x25WcpTyuM74aSH14IYuxYZacCk2iJMBCyS4eFKQOBFO1M2Bgm2D2LsZs6IiyjNJ9C++6773hagO+MoQ1FH0bcEqHwp+7gSXnUHUMxBlmwpn0ipDOBI7QzaGHUahFXH0oA5ncmeEfejV3mlIUSEEGDbwfRLjnhwKKeMOqujg8v2iqn4QjnTm/82sVF++J6rx2WrjoiLd8DoyaEchEhCcEDIy3GL9pSiRBQ2AkNH3YU237ISTHaI8RpUwyzfC/4c9UUv50B31ZdS+UuImzKN4/Ki7AkX9R/Ivx8mVF9aRfct8538CddGK9orwinpOM3DejHnFacSrvsssvY/2jbKGKiOsGb747RnfTUD2OUTmTbsms4RH2NNsu7YjSk7dHPUJKzeaRGfAs2Q/Du8C4RdWG8YUyBF/OMxsZaPeFj2zvl0J/22GOPsQ+hXKWO0JR6R+X5b8EmBf44haD2UDo53dPf2KiAYIUBWNTLX+m9S9tk/MZFqQm/EnGy9fDDDx/nBcYgxjeNaxhZGduZC1Aw6kQ7fBiLmaNZPDHm2VsPpmCuOqFwZRdr1FaUtuTWvl3Uplt9FVmBk9XMM7wvc2FEtbbci4dwpwye9acy5beu4uTauNqz0uL6NIrz4SV/Le0s4SX+iwijLuJjn22Y6jurW5OXPL9We7Ppo/6PHM3vETPeY5zgtCbjPqSxiH7PeM0NIshHtGFktuOWNjkdeOCBtqjlZ2GCK1KY99twPUduLT95IJ9X6b2r9D58vfkxQjOnM+Zp7YDLxj3ms9VYC7XKmYJptLaL1kJTytjIaaeudVgXsVmU9R7zN+MEYwHUkiNrODOXwofNZ5e4xCXG9TO8KQd5E/kHGYz1D9Ra20SyfEtuitZjtv6sv1mjszZjbGQ+p+7IMqz9mePZsATV5m/ioroSn5QIrDQCfm7v0cew0YDf0EVOp2/qVLWtayRrRONITea2vP0zZXHdn9/Eo3SRbBPVk/kSPY6ITZrouBj/MI5ww5aot95Ref5bROOMyk13fSKArhg9BnMfugUM8iWK5iSMoOgQaCfw8zpT5EH4oifkatpNS2t3UW97VXpkZHROrA+4ZWkqRe0+Gg+iOTLSF5TqF40DU/Eo8c+w9YFAzxxn36TWPukTzAmsk9GJ7r///uPP+GGPmkLIwBj8RaV1a0+Y0sBHz/O6lpd9hm+LetK0eKxW/DZLg+d/UBjPQyjz2KHS+q3JecogL+WgKEcIW0liZztGRpSxK0kY/ChLhlnKomOxYwzDhyV20x9wwAGjcQ9BFEWofr/JppvnGWxZaNYaMJMgv7G0++67jycRUXp4AjeUL/PWjZOOtKdaXXy5YIKxxqcHY04QgVcvUTYGTIw+GFQxKPBOnGLke2EEsPzwEw9+tBkZXfxvIxJfwox6sVsQ/rYtEE7dCcflNBTXKnB6FKVkieBDP7H1Y9cJPCyRhsV6rT5RXS2fRT5P/ea1sktY9vafEn61cqL6qi143MWL9kr7rsUrXcv13ymqE7z4YWxOjrf6Z4RDra/BnzbFe9WMlKSZSowp1Jc+5qlUz1J7Z8c9fag2R/XWu1Se6uS/BWMPeDOmtsjnbaUnfgp/zw+M+I61/k968AJzsGFc27S0kOIKbCk/SUN7Y9xlrMRIYo345Gecsb+zSx5RL+bUlQ02/N4lu6shFnd8U0ssjDh5XKPo20VtOuqrfF97pV6tbIVHbTnCg7gSgW+JauGltFGYn0+VthZOfK3sWrh41uJr4cpXcm397DNprd8++7h55b8eealU91J7Y5MGYwSKOFHU/5FTkFtK4x18eDfNPfAhLX3KEvUokf8e3k+eUliJlw2z34Jn+UvPCvNprB/e1q9nW+ZKPXNTxrzrqqhu4Mt3XOm10GqVQxv0a6Ho/beGuEW3ETBkzrWyf4QTcxHGO7sZSelbcqTSWZfTmKxbX/Oa14xjC/xVF8YSP55SV8Yg3NrahjmbNH5dpHJLclPvekw8cNl44vsSeHo5Npq/W3W15eVzIuARWMR44Od22isyfG29R19h3FU/9XWSn75QW8tE40gkc4v3VDeSbWr19H2ZeQ2Zurb+6a13rTzeyX8LwkrjDOFJWwaBRfQ5as63jnSnPXMSOnTWkmymYb1L2xLRXpmT/Ryq+N72SnpuVmSTMJuXIG7C4oS2Jw6S1IyeUbtvjQe1+bylL/D1i8aBKXh4vulfOQQW1d9sDVtznE3Lc9Q+S3Kqzx/5WUfz54n+W6JauNK24pXOunadW3omTH/k07N1S+EK8y751gptu4iK8EIlxckieFselOMXHTZ+Uc8If7WJY1FlwIfFkl8woaTl6saIVqpuNeHO14U619K2BGPPq+ZHST6FaphQ16l1omxOUXKtIrs12F3MiV2EFq4v0clM1Y+TTJz85DQVp5E4SWmvZ1S6GmbEl07LUR5XCqPM5P04acb1FJuWDAs1KvGR4tLmoS9F9YniLJ9FPk/95rWySxjYtLW2QppWXssnqm8Lv6gOtozWsy8nqhO8esfpCIeo7j3Gu9Y7+fio/5bqWWrvNcWdyuqtd6k88fDfgrGnl6/PK56RO4W/5wNGrTI5Fbrr0sYOTr4fc8wx46YlTn9ZitpbTQmp/D3YcA0PJ5MwDlAPEYu/qRR9u6hNR3G9/Ul1jdpyhMdUoVHpZxGIVVfxkN+6UZxNZ5/JE9WnFm/LauW35enZ5rfPxLf84jGPG8lLJb6+vXFlNbcoaKOU8kT9Hxm21jZ9v4ePN2ZShsdG5fa45I2+lefhy8KvMOsqXGHwUZh4Kk6uwrcml3dbjbXQapVDG+QvaXYEwC+aXzznKG00r3s+8u+1116jMnafffYZb1PgRg3GAH5H0t/a1Lu2ieZsyvVjGWG96zHSikp9qdQeI8xadVVZ6SYCK4WA7w9Re6UOGDtLG0V9/egLNfk0KmMl+kQk29Tq6fsy85rHyr5zb71r5cGrxL80zthy83l9IlD61vZNeuakkgwuHrRXvy5QHG5Pe+Vnp/hpGH4SC6OpiBPMUylq97OOBy19ga9jNA704OH5pX99IhC1t9IbRemjPlbi5cPop/x5Iqy0Hq6FK7/nVeJBWp/Ohym+5BKmcFuuD1cauUq7ltzNj22tpZpt0LpwZWrpyg2uFdLvVW4paCifemwE2rRkNHzDG94w3qvNdRFcf8txcn5j1BPfjHgMmUyyXJPM1RHzEkIShgSuv+U6T64b5jcE1/KAMu87r1T+tdB/Vurdkm8isBIIcG01AhQnL7hmm5PhXK1TIjZ5lK7VLaWdEsbii53h/N7PRh73MEDz/v6P8FocaRVvXc9DfpumxlNplKfkKk2Jh+JK+QhTfCmvj7dpS+kVr7J8Gu9XOrlT2mkp7aLkpUMOOWT8bR42WK0mCYeWW8NR+NfixdfHe7/SWRccSv4SPqRLSgQSgZVHgBscbnSjG43XInONHtcmH3XUUcu/W64arOTaZqXWY6p7uolAIpAIJAKJQC8CU+YkjCp77rlnL+vudJ/4xCfGnztCr6nfee3OnAkTgUSgiQBrzdr6NQonTn92Xeuflca7pXQ+bKpfL0u+EtXCS2lXK2whV86uVmWznEQgEUgEEoFEIBFIBDYSAvZEam2XXi18ETjNI7z21qsnXZSmVsdSuA/zfjCLdkwvAtO1zsO2OV/X2neohfv8Nb/9Dnq2bs8zvJXOPtuwWvmLCl+Jq5UWVbfkszYQyDayNr5D1iIRWAsI5HiwFr5C1mEjIZB9biN97XzXLY3A1t7fuFqaP1FrPdyKFx+5pfTRutbG+Wf5rdvzrLrYtArb0u5Crpzd0i+R5ScCiUAikAgkAolAIrA1IiDhkXfTsxduFW7f36exca3nEr9WnlK85RPVx6YTH5++lEZprVtLVwovhVleG/WZXaAef2EhzHy8wknn45S35Np8xFu/nnHts9KVwlSG4uRPNxFIBBKBRCARSAQSgUQgEUgEEoFEIBFYBAJ2jQo/u/4srYdtvMovpVNcKb3i5JbS2DA9l9xSmPhaV+ls2Fp4ToPmWvgKWYdEIBFIBBKBRCARSAQKCJQESIXNKwAXiguDVG6UqFanUt5aWviX0kfl1uJKfEphtfwbMRx8hFHtGxEfxc2Cm8okr57lKkx+785SXuZJBBKBRCARSAQSgUQgEUgEEoFEIBFIBKYiwHpUa1Kf14bX1szksek8j1n8ll/pWWG97ix1WK08adBcLaSznEQgEUgEEoFEIBFIBCYiIGGzlE1xkZBcyjclTGX05rHpW/WyaUv8W/lLeVo8o/gorlTW1hpmcdBz6VsoDhxK8VPwsbzIZ/16rrm19FPKz7SJQCKQCCQCiUAikAgkAolAIpAIJAKJQC8C3Gwkqq2HtYZVOu/W8vl0JX+Ntw+Xv+bC28fZsFLZWzosDZpb+gtk+YlAIpAIJAKJQCKQCPx/9s4Ebrti/OOTSnuKkCRvRJYSKdIfRUKFyr63yU6yJCTJUqkkW8nSLtqUSAtSkV2SfavIvhRlaeH85zt13a5n3jlzznnu53neZ/ldn899z3bNNTO/MzNnZq6ZOS0I2MCyJTh5t/FMZnDcJquWfltYLmtofvL4ben08e+S1RXeJ435wjMZLHycIc/Zx/P4eX+zmwlfH7uXJ7sQEAJCQAgIASEgBISAEBACQkAICIGpQID5aNucdCrmw5PNo88TMry7ZC/55fEmm5fpjCeF5nSiK9lCQAgIASEgBISAEBgDgeWWW26M2IoqBIYjcNvb3nZ4JMUQAkJACAgBISAEhIAQEAJCQAgIASGwABBYeumlAz/RkkHgf2djl0z6SlUICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEh0IqAFJqt0ChACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBJY2AFJpL+gkofSEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBFoRkEKzFRoFCAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhsKQRkEJzST8BpS8EhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhEArAsu0hhQC/v3vf0/wbZomudtMAi0st+duz5eH4S7RUkstNcHb3GYS2McO30orrYQhEgKzBoHrrrsu5aVP28h5ximEbzNeTpu/5+myD5HRVaau8FJe2uKU8uX9vB25uTtPq5RO7pe7cxm4+/D4eG38bf4+bl6m3A1vya8ku+Tn0zJ7Lq/LbfG8maeVuz1vye7T9HZ4ca+yyiqlaPITAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICIEZRGCQQtPyZQvGXSb8OU8uo+S2OBbWZsKXL0C38cJncr29jV/+CwuBIXVpppCx+kp63m7p53652/jGNa2NTZf8Wv6GpDmEN08zj+v7CLMbDsTN+XN55vZ83t4lI+c1eV1mKV7Jr0tOKdxwyMNK8kt+eTzc8HlcSzxdfnlaubsW35cptxNviKxaOtMddv3114ff/OY3Yf311y8mxeaI3/72t63hxUjyFAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACMwiBAZdOcviri3wlkwLN/O///1v4jd3m9mXry3+VPiP+0yOP/748Pe//31cMcX4P/vZz8LRRx8dSINFa6NLL7003P/+9zfnlJobbLBBQH4b/eEPfwinnnpqOOqoo8Jll13WxjZj/jvvvHPCZ2iClGPXXXcNK664Yjj//POHRh+Lv6vO+HptCeV+NbcP62snnSG8li8z2+J2hU9FPEvDzDaZNX8fN7dbPPNvM40vN+HP/UrucfrDUholv1K6k/WbLvl95fbly8vn47XZiePpP//5T7jooovCxz/+8fDXv/7VB1XtP/7xj8MXv/jFKs9kA6+99tqw7bbbht///vetIsj3TjvtFL7//e+38ihACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhMJsRGKTQtILYIq83czvutl9twT4Pw53/vNyc34eV7JQh97dyTda8+eabw0knnRRQjk01vepVrwrbbLNN+MlPfhI++clPhnvf+97h61//ekrmxhtvDFdeeeVUJ5nkXXHFFeGGG24oyj799NPDPe5xj3DhhRemvGy++ebhAx/4QJF3pjz/9Kc/hX/+85+Dk+O5/fGPf0zKgK222mpw/MlGGFJnfH0lvZrbhw21l2S3+eX+lhb+JbLwNtPHKfFYeCmszc/iYLbxeH/P18fu43p7W1zP02Yf2p+1yZlqf1+mPrKH8veRCU+bXO/v7W1yPU/NTpgRGzzo9973vveFb3zjG2HRokXhzDPPtOAJJn3jQQcdNPK7/PLLp02huf/++4dNN900bLHFFqP0cstqq60WjjzyyPDMZz4zvU/zcLmFgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACsx2BwVfO+kVlCmcLxiW7+Xkzt+duk48/lLtv8e33768QbIvRhyePi6JvueWWG3kvs8wy4eyzzx65vYXvji6//PLeK9nxX3bZZcPSSy+9WJh5HHfcceHkk08OnO5ZddVVk/e73/3ucMABB4QzzjjD2BYzkX3b29423OY2w/TVnOK56aabivm1RMgLi+IoVR/0oAcl7xe96EXhsY99bNhll13SSUfjRR4/8lIicCSMZ5BTjrEPR3HJicouou6AxQorrNDKyqnM7bffPtzudrebwIPCEeWSz3tXunl427MnobY6Q57/9a9/pfLldb/mroVNKFiLI28HJTdR83RaxHV658+8JrcWNtk8mUyfD++H3cLa7KVCmow8X338TZ7nNT9v+nx5/9xeklPyIx4yfZh3e3ueRu72Mrw95+ty52nihnKZXe48Ti7Hp2N2z2N25LzlLW8JT3nKUwJ9MMTpeOz0Hzn98pe/DOedd154/etfn4Ke9rSnBX455e8B+kv6Hd4NOdE306f7dwabaE444YTixhY2vPj+64EPfGBYc801w2c/+9nwxCc+MRcvtxAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQmBWIzBI42WLx96s2Qkr/UCk5J/Lmoy7JNfSM9Pk5m6+McYC8N/+9jeCEnH68FOf+lSy/+IXvwg77rhjuPOd7xwe8IAHBBSORig4f/CDHyTngx/84LT4zWkeFGXPetazRjJZlH7xi18cVl555bD66qunBW/SZBE7p7POOivssMMOI2Um4VwbuNZaaxWvt7366qvD4x73uHCHO9whrLLKKuGd73znSC4nPffdd99REueee27YcMMNR+6PfOQjKQ4neVBMsrBeIq5NXHvttUfKTHge8pCHhJe//OXpFCluFtIpI/lAEfvc5z43cC0iRJk222yz8JKXvCSV/173ule6ujYFxj+UAA972MPCHe94x7D11luHr33taxYULrjggrDxxhuneHe9613DZz7zmVFYbjniiCNSPm9/+9unxfu//OUvOUt4+MMfHsABbLhiF+LZvfCFL0xYHHLIIcmvli752XPPPQP5AfNHPepR4Zvf/GbYZJNN0rPfcsstw3e/+90kJ//L68yrX/3qcJe73CXJoeyQr88W3/x8eCnMh1scb1ocz2d+xtfmNv820+KbmfOZvzeNx/th95SH5eGe1+y1OD7M82P3snO7dxuv9yvZ8TP/kt37WV5KZhuf+Ztpcc2N2UY5j7nhN3tb/Dzc3JaWuXMzD7e0zITfKI9r7jw8d3sZhJXiWRwzfRxvZ+MGfYXRuuuuG0r9Cn3fy172snSCnX6Mq8jf8573hOc973kpKqc66WfYGMI7gD4SpeTb3va21O8R501vepMlkzaZvPSlL03vnZVWWinstddeo379xBNPTH2N3+BBWuutt17qk+h7/VW0vD8++MEPjmTLIgSEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEJgrCAxSaPpC+YVev0ic24nTlzeP692WtvmZTHP7dHxY7u/DLK73Q+HoiZOCptzbb7/90kI0305DmckCN9eVQsQzOSj0LrnkkvCtb30rLSaj6Pz85z+f+FAIfu5zn0vhf/7zn9OJmzzNxBj/Lr744pBfg4qSkAVpO7FpvJwo5GpaTuGwgE06H/7wh9NCOTyE8zOiTJwOglA2sgjPNx2vv/76dOKIMpSIMj3hCU9YLOjtb3/7SMn52te+Nlx11VXp25pclYv817zmNSkOilsUfiher7vuurDPPvuMFu9Jc7fddkuKWJTKnIbaY489UjyUydttt11SABOPRXuUy8jPCYXBu971rqRQ4AQTi/188y6nL3/5ywlfrpC0b8uRB05XcUUkisqudKkfKDX4zinfN/31r38dHvnIR4Zjjz02KbFRfLddx5vXGfJK2j/84Q/Tc7D65PPt/bwdHu/G7t1ehtlLPHmcLncuqyQTHvPP5Vl8z+P9vL0W1/N5WUPi5DJMTsnfwqxcnsenaXZvluy5PHj6/Hw8n4c2/5pMi2/5K8koxbd4xl9z52F5WrkMS8/H8/ZSuJfpeb29xOP9vN3i0f+ss8465kzfDzYl5cgzWrjalTbPFbBchU1fTX9r/Tx9IP0NylH6W66mRQ7vAzalcJ0t/RfXfkNveMMb0nXm9EX0d6eddtrou8W8W7hu1ogT9Jwk5f1DX8SJzve+970WHO573/uGb3/72yO3LEJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICIG5gsBghaZf6PV2CuzdZvem2dt4DTTP5/1yf+82e5eZp23y+5gsPrOAfGX8biWKQxRQnLApEScWOR1I+LbbbpuUa/Cdc8454RnPeEY61cjJzNKCuMlDUcZp0D6EEoy8ccUhJwUf+tCHppOWH/vYxzqjcwKR8qBA5CpUToW2XdOKMpdThG3EYv1HP/rRdGLz7ne/e+CHMvPoo49OV6kS7373u19Kg6sTSeunP/1p+N3vfpdEXnPNNek6W0xOSnKCkmfKyc373Oc+CTtw4/pGTs9yJW9OfGuU009c9cvpKBSfn/70p3O2VvcrXvGKdMIJDPqkS15QmnKd45Oe9KR0IpfrKEmf6yi54rEPPf3pT09y1l9//XSCNY9jdTv3x10LK/F7v6Fxh/L7tLCPGz+X593Ink75Pq0uey0fPmycPHs54+QnjztErsXN4+Ru4+tj5nFzd5uMvnz+Ktk2WTV/FJZsYOBU9WSIvoyT6vSBtHuIfod+hO8k03+z4YLysDGFk5b/+Mc/Ej99DP0ihIKTvtsIHq6s5kpwysjJexSiRpzuR8kKn0gICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEwlxAYrNDMC5cvIOfunB93jacWNlRWzu9le3vO1+ZGOXi3u90tXVXKlaIoJ0vfOsvjw2MnHr/61a+mU545T8nNaSCUlH2IhW0UYShRjcgjp3q6iMVvrnntQ1ytetlll7Wycm0v35LkSkUjvrUJ3iiAc0KBCoEPikpOJ3HKiLKjDOTEIgvz4MDJR0/kmVNIOXEClBO0nALlx1WOpF261jePm7uHpEtclJj+m6lcK8spzqHEyc6ZoppyJw/DnfuRzzb/UhlK8Ut8k/Ebko8+8qczr33SH4cnz3sNm5x3MunmMnL3ZGQOjTNOmj6ut+d54PvFXOf9iU98olf/n8fP3fY9ZjMJpw/hRD0bPdiUsffee4/6MzZZcAIe4iS7j0e/S/44xc/VtWysQcFpZBtVOOUuEgJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACMwlBMZWaE51YdsWkvEvhZX8JpsnFGpQSQHFlYEsHh9wwAHpWkCuBHzOc56TFHBD0nvEIx4xut60Kx5XCZ5//vkT2PgeI9fO5gvSnJrk2lMWv41Q7G200UbJyaK3XTFr4WZyGujSSy81Z9VE0ca3K+0aXphZMCd9lLV3utOd0jWHdoUr4d/5zneSspJvb9aIU0N8c/SYY45JV/k++tGPTspjngeKVMrjiWsVOemUE0pnvjmHMtJ+3/ve91K+ct4u95B0u2QNCS8pjfO67t3eTjq4cz+fvoXnPN7t7SbTyyjZTe7QMJNv8fO08/CSfO/n5ZRklXjNz/jNxD+34/Y/i+tNH8f7j2P3aZbsuWzj8f7m581SuPnBl1PJD57cP3fXz28ZBwAAQABJREFU5Hje3O7dXgb+eZj59fE3HjNLZfDpcWqSk+NcIU4/M91EX8+1sZzStL4Mk28EQ5zm9BtFuLaW/pxrzXkfsLlk1113HWXz5z//eVLC0k+LhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAnMJgbEVmn4hmILnbgPD+2Mf4jYZ3izJINzL9fx5WImPEy1c33f22WenqCjovGIOBSPKNuLCx9WuK6+8cp5M1f3Yxz42fc8RhRUncErfdjQBXB+L8pDvYUJccbj//vun70qStifytmjRonDIIYekRWzkn3DCCelqVvge9ahHpetTUcqigDzppJNG0QnjlCZXo3KK8ayzzppwqmfEGC1cfUiZ+YYlSk0UqIceemg6HcqJSZTCu+yyS/puG6c1+Z7n+9///qT8tdOYXp63c9qSk5l8v5KrFynTaqutlli4DpfTm3wfk3TBBffjH/94LyLZn/3sZ4cPfehDAeUvhEL18MMPT/ahf0PSHSo756feUTaeM/WsRG31Hl4L83Xb+3m7l537m9t4cneeFuE5WRxvdvHUwmthtTQsnufJ7TkPbniM2uwWjmkyze7DvJ/JMtPCfPzcz8J8nFy+uT2v5/f+xuvNUrj5GZ+5MSHvNj/vn5gKfD6e5+myW3jJLMmEr+Sf++E2yu3ejSLxqU99aup7+BZljTgpz/eVJ3Mq3MtlIwrXZx988MGpX+Ck+4knnjj6JvM973nPCafU6R+5ipvvFnNd+QYbbJA2iZhMribnJD9KUpEQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJgLiEwpauafvHX7GYCCvY+buMx/pJpIFuYyTfTyzA/H8fsubnffvuFd7zjHWGllVZK3x5btGhRYuFbZyj6DjzwwLDGGmukK1X32muv9D3IXEbNveWWWwZOHm688cZJWWdXB5bicFUr337cbbfdUn5Q9qHoe+UrX7kYOwvf5A+lGIrZxzzmMelkzk477ZR4UVpyWpNvqJF/f+UuSkOUjk9+8pOTshIla9sJHnDhlBIL5yhVWbgnTb4pZ4RSFb511103nSAiz8jvIk5boojkFCvfo9xjjz3CUUcdla5U5CTSaaedFnbfffew6qqrhte+9rXJzfc4c0Khyo+TSmDBd0rz62rzOG3uIem2yejrz3dDOfVFneP5WN22uuzlWBh+Zvd83q/LXpLh4+ThPh3CIM+f29vCU0T3V4pnwXkY7hKV+Pr6Ic94+9iNF9PI7N70dpNrJmEW7v1MnpnGN8T08ixeya+UvvlZPHPX4nveoXYvt83eR6aP28af83h3bsdtxLsBJeXDH/7wtHGDzRv8SpsPtthii9SG6SOJMw69973vTd80pj+lX0ahybsB2mabbVI/aPLZcMKmGfp5Ts1/6UtfCvvuu68Fp1OebNIQCQEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBOYaAktdffXVDddq9iGur4NQtphCrGY33hTp1j+LV/Mr8Xh+b2fR2qjL7sOJ491cdWpE+lzpiuKsRJxKZKHaxy/xtfnxvcibbropXf138sknB05iciqvRpy4QUnY55ud5I9TlKVTONdff31aHEdBmxN54nrXvqdOrT6gsCwRJ0HJg//GW4mv5MdJUv9MPA9Y2MlN75/bOe1Iedvk5Pxd7r7pdskphW+44Ybh7W9/e3jCE56Q6h7lI70a5e0kd9filsLy+tzlLskwv1pecrkWp69Zk12Tkcdry4f3b7O3pePTMHubaTIs3Nxtps9LG4/3b5Pb5l+SX/IjDe9fklfy83kr2b1Mb8/TK8XFb2iatTR8WNt7oC0f3n8q+wz6U8qY97ebbbZZOPLII9OtAZY2fR9XjPPOMMJ9//vff3Q1uPnLFAJCQAgIgalDgPF833nV1KUqSXMJAdWRufS0lFchML0IqD+YXnwlXQjkCKjN5YjILQSmDwG1t+nDVpJDWGayILDgy+KqLfx6e5vMPjzENdltcry/pW9+3m12M0s85udN+GuL2LUwL6dk53QjCsy999478L0zlFilE5d53D4KPItTy19NWYmytI/C1NLJF9bN38wVVljBrIPNmhKyLxYobWtyhmaqb7pD5Xp+8mzp5PXW+GhHUFu48Q01c3m5u0+aQ/JWkt+V5759iMmx/NTynufDu729JsPSMX7LZ25avsy0cHO3mSa3LXyIP7Isvz5eKY2Sn4+DvcTTlkYe17tzOV1ui2tlyfktvM3M+bvcbXJq/taWazx9w9r6U66j5RT/RRddNFJg0o94ZSZpHHTQQen7n22n7/vmQ3xCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBJYHAIIUmC762AO9NMt4Wli8ST3Uhc/ne3Wa3PPhw85tuk+sK3/KWt6SrZFEInnrqqaPrA6c7bcmfvQhst9126ZpIn0Pqpylrcn/vxm7tMfcf4s7bQ+5GVslvSBqedypleblm74tJKR/ez9tNdm56nr7p5jJqbi+/xufDiFOqP/D0ldeXz6dr9lr6xuPNPK3cbbzmb2Uzt4UPMfO4uXuIrCXFyxXdKCvPOeec0HadLN8npmxsphEJASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAE5iICg66c5co7yBaS+5oGjPGb28yh/hYPM1+ArrnzMC+ndqrR88kuBGYKAa4O7kttbahv/FLbKPmZvDxsnPRzWZaGNycjv2+cUvp9/Wp59Omb3UyLl7vN35ulvPjwLnufNNpkzGTaeVq5uy2P4/iX0sj9aqfax0lbcYWAEBACQmD+IaCrlebfM53qEqmOTDWikicE5i4C6g/m7rNTzucmAmpzc/O5KddzEwG1t7n53OZKrged0Gy78m6uFFb5FAJzCQEp2efS01JehYAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiB6ULgNtMlWHKFgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAuMiIIXmuAgqvhAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAtOGgBSa0watBAsBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBITAuAlJojoug4gsBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBITBtCCyD5GuuuWbaEpBgISAEhIAQEAJCQAgIASEgBITAfEfg2muvDSuuuOJ8L6bKNwYCqiNjgKeoQmCeIaD+YJ49UBVn1iOgNjfrH5EyOI8QUHubRw9zFhVl9dVXT7lZZqmllgrLLbfcLMqasiIEhIAQEAJCQAgIASEgBISAEJhbCCy//PKaV82tRzbjuVUdmXHIlaAQmLUIqD+YtY9GGZunCKjNzdMHq2LNSgTU3mblY5k3mVqmaRrtJJ43j1MFEQJCQAgIASEgBISAEBACQmBJIMDEXSc0lwTycydN1ZG586yUUyEw3QioP5huhCVfCExEQG1uIh5yCYHpREDtbTrRlWx9Q1N1QAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgVmLgBSas/bRKGNCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAhIoak6IASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEwKxFQArNWftolDEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASWEQRCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYCEjcP3114emaYoQlPxLfsXIAzyXWmqpCdzm9ib23L3ccstNiDcfHWOf0PzXv/4VvvCFL4SPfvSj4Sc/+clYGP3zn/8Mp59+ejjnnHPCv//97wmy/vOf/4TPfvaz4f3vf3/4/Oc/v1ilqsU1QTfccEP4xCc+EX7729+a12LmtddeGz75yU+GI488Mlx22WWLheMBz1e/+tVimPf8wx/+EI455pjwzW9+M/z3v//1QeGmm24K559/fjj11FPDddddNyFsKhw//vGPwxe/+MWxRR1//PHh73//+1hyLr300nD/+99/LBlLOvKvfvWrQB2bTbTzzjsHno9odiMwFW1otpTwbne729j9vC/LdPYNG2ywQUB+Tvvvv3/g10bz6XlZGfu8H+G95JJL0juS9+RZZ50VrrnmGhMx5eZUvaPIGO/QT33qU6k//OlPfzrleeWdf+KJJ06pXMYQ4MwP3BmfDKGpbouk/fOf/3yUJ8YmV1555ZAsDeKdDkwHZWAKmP/yl7+Eu971ruGvf/1rURpj1emoj8XEZsiTcT51TzR9CDDfueiii8LHP/7xxerWTM2FutKh9H3nQrX3z3TPhabvKUlyDQHWBq666qoay4yHHXXUUeFlL3vZjKerBIXAfERgOtYgpmNcC/a0+w996EOLPYZzzz03PO5xj1vM3zzm4xiuNr6wcmNqPuDRmGhfiOsIbW1oIjJyCYGpR6CrvU1lim3vIFNQYnb90Pl08UwmvCYXDLxMwwS/hUBjKTRZ7GIx5/DDDw/f+973wrbbbht22223xZSNfYBE8bfeeuslJRz2NdZYI/z6178eRX32s5+dZF9xxRXhyU9+cnjVq141CuuKa4wHH3xweNaznhUuv/xy85pgMjnfZJNNAnwshGM/5ZRTRjwXXHBBeP7znx/udKc7pTKPAgqWl770pWHzzTdPi1l77LFH2HTTTUdKze9+97thnXXWSYuvF198cbjnPe+ZFjQLYibtRRnHVWjefPPN4aSTTgooZsehG2+8cVoXRsfJWy3un/70p/Ca17wm1QPMpz/96enHItNsIPI325SsswGXJZmH3/3ud2HXXXcNTBigqWpDS7JMPu3f/OY3aTOG9xvHPp19A++KkpLob3/7W+BXovn2vChj3/cjvB/+8IfDO97xjnDhhReGk08+Ob2bHvOYxwQUN+MSMg866KCRmKl4RyGM9/W97nWv8LnPfS7wjt54443DEUccMUpnKiy///3v07vQ2vVUyPRYU4a11147bdjqK7tvW8xxr8n/8pe/HF70ohel53/eeeeFrbbaKrAx4Gtf+1otWq+wvG+cDkx7ZWQKmZhcsEGurV6wae0HP/jBFKa45EWhgLr66qt7ZWRI3eslsMCU16sCy5zyYu5xj3vcI7zvfe8L3/jGN8KiRYvCmWeeOSrDTMyFSKyWzpC5UO39MxNzoRFwsswIAmwsetKTnpTeHW984xvDdtttl5QJ9PdLmtj4NBVjmSVdDqUvBGYDAtOxBtF3XDu0/LT70uEBDk/UDjnMtzFc1/jC46r5gEdjon2hrSNQ+rY2NBGZW1yse497yKkk1/uxQcGPjX2Y7PMLgVp7m+qStr2DvLJwttrBIs/bVOMza+XFhYlY9uEUFzWauNDVHHjggaPI8SRfs/766zdxMXHkZ5Y4aGji4o85J5hxEbmJx2GbOBEa+UfFY/PWt741ueOkt7nNbW7TxNMiyR0HH80yyyzTxFNzTVdcE/j973+/iUrE5va3v30TT4Ca9wTzAx/4QPPQhz50lM94WrSJysYRzzOe8YzmYx/7WLP33ns32NsonshM+Y0LLYkFrKKCtokKxuR+5jOf2bzyla8cRT/ssMOauNNt5O6yRCVWExfQimykFRfxi2F41p4DYX2I5/iPf/yjlZX8eYqLoc2KK67ovRaz5/nmuUZlx2J85hGPfps1mbW85/nxEcGxhBfl22KLLZp4Sqkhb0bIesUrXpH8zc/MGibw1MIpbykfxCMs7nbGOoHiBoImniSe4Adm8PehGma1vCI7Dy/lrysPQ595re76tGpYwldrPzX8+uQ3njpjK8yEOuPzZnbwamvD8PQta/4cTL6ZtfBaHtrqXLzGoImKKBOfzBpmExijI69zbX1DLd+5TO/2+abPiafgfHCyv/rVr274QXk/kjwLf7X8gGPfNpeLrmGX17c8bh83+aq9W3MZvIde//rXj7zJQ9xo1Lz5zW8e+WFBblt/NYHROXh3PvrRj3Y+ZWut7tN2/TiCMQF1Mi46jITFSXh6/8bJ18gPS+0Z9q0HEwRGR16ffXieVx+GfZdddpmANWMxxihxw0zOmvosX24Y+rbFNtyRRx49HX300U1UCHuvJio4m4c97GET/HDU8FyMOXr07Rvb3snIrLWXPM2h+euSX2qPf/zjH1N/j0mbqNUHy19XfzHZuoj8WpnHkYtsyzf9/y1zJnz/R3ldIqSt7oEVz7KN2vqWUp3tW6/a0poO/8nOq8jLE5/4xGbPPfccZSueKmse+chHJvdMzYVq6ZCRvnOhrvfPuHOhEUhz0DJOHbHi5m261AaNN++/cLeNQbvaJ31BiZgzxw3Hjc19jecrX/lKqjN5PNLJ/SwOJuFtfQHhtf6uFHbIIYcU5+4lXuTnRJ9Vw6yWV+s/TWYXxsYnc2EgMBX9QV7HcuS66nktvBRWWoMovaPzfJi7NF4qjWspV1u7M1ltpuWbd1bcOLgY2xlnnJHWMgmg/+yTTg1n2nWtH14sA86jCzsri4sy2FobX+TCjp4F84E8T1PpHqfNLaR1BKt3pTbU9h5jrT3eMrLY46q1jVJ/YAKIR/vw9LznPa+JG7C9l+yzGIGZaG/5mDiHw+qy+ZfqXOkdBH889Db6sfbkf/GWpsb/WH+ajp9Pw9K3fEWlb4MeLm7cSWublJV2w/tqIdCkT2hyjVZUEoY4IR0pa1dZZZV0evK0005LflxZt9lmm4WXvOQlYfXVV0+nKLjGLKc4CQjHHnvshGsfOHHBiUmI059cWbraaqsl913ucpcQFafhRz/6UTqBU4tLhDhACbvvvns6/bDqqqsmGaW/73znO+HhD3/46O7h//u//wu/+MUvRru6uBouLkKG2FGXoo/87njHO6br+tZcc83kF5Wv4d73vvfoVBDX3nAiwyguvIdf/vKX5iyahuVTn/rUhANYn3DCCSPeBz/4weGFL3xhwD9O2sJ73vOeEDv7FG5x257Dz372sxCVd6lcPC//jLh32U4YkEZ8iQfwJ52tt946/PnPfx7l4YMf/GCg7Jw0ecITnhB++MMfjsLaLHm+Y+NLu3nBmGPfnHaLHdSoHBtuuGHYZpttwsorr5xOv/F8dtxxx3DnO985POABDwjHHXfcKCmu7LrPfe4TeA6ctrWdPOwqvO1tbxsOPfTQlF9OA++7776jeFjADrwox9JLL512yrNbDTze+973prrENbQQJwTAj7rFiWWuZSCekZ0aog0Q/pnPfMaCQpwghxe/+MXhDne4Q4r/3Oc+d1TvYaKugDV1n7pHuyhddxwXJwKngsFhpZVWCnvttdfoRPAosVst1AfaExhz2tlfy1nLKyef4kJbKgN5etSjHpWuUwbb293udmHLLbcM7LjvoqHPvE8fQpo1LK0NtLWfGn5980vde8hDHpKKz3PgKm7It6H4Qk/9HM8bDN/5zneOnpPlsa2dJmG3/nXVOa7mpv3wjKMyInC9p1EtD/DMdJ2zfGHW6h8nxTg1ZrTPPvuk+mjuj3zkIxPaCv1+G4EHz5U2ue6664Zvf/vbI1b/vGo4U984zUZfxKl92v3yyy+f5LDjlz7GnwTlxD6nF6Ah9Y12/9rXvjbF44/+gxM8cXA/8uMEedxoM3LTpskTO5K73o+jSAUL7y76W7stodbGamWmjwMfTmzxjuAa8yHvKGQ//vGPTzc30BdyLTwExuDvCZzBKA7gkjfvgQc96EGpLdDv2VXytDf/PuG6e25SMKKstFP6NK6Nx25Ue2e25dXitpm8G7gBguvujfrKaqtPJdyRzQlW3tP0D3GBo3pqhfc8V08Z1fqWtjZa6hs9pl3v5LbyWZ682Za/tjbCc67Jz/t/n5bZDzjggPQO5N3LzmTrexib0C9BpMHzoL7S78SNA6n+Wjvm2mH6bMYjvFO5Bo3bTyBuJPFjFK5Jo+4a9a3jfBqCtmAE7vR3/vlamJmGjeXb8mThbeOsUt2rje+QR5/ATS20BcZC/lRXqc6W6pXla66avFP8DTS8n9gZD83UXKiWDvnoOxfqmttNZi5E+guZ8vdWXFBM1xOX5jrglPdfPBPqGLcE3f3ud09zBXtX1tonc4oXvOAFadzPeJ85j71LSYfTxMw9GW8w54qLLenWBN5hvJOJy5jNqDbOrI0ziF8bJ9bGbJa2mW39poWbCS70R/TN9E9vf/vbR+OLWl6Zc4I/6yT0+cwRmbe/7W1vS+MgxkJvetObLBmZQmBSCHSNLWrthQTbxkyEsd7AuIRxN3NJxjS08xKV3tElvtr42fPX5qqsM7JGZ8R6IfMB2j/09a9/PfVxlu8rK59PYI2J29vgZV7u15GGjOE4MUZ82jXy6GO/9a1vpfy0jY1TYPxrw45+9ylPeUqapzJWj0rGzjFcVNK2zmVq4wvLS83M5wO1utVW5tK4zc8HaunPprD5to6QY1trQ21jBU5lUv95/7N2wI1PUNs8gbDaOm7bHJi1UMahXEXK2oBo/iNQa2+1+fPrXve61B8zr6aPp1+G38Zl1CVbU6qhyDjZfsbn3WbHhLx7Kuxeptm9id2T5cP7zWv7ZDXmnCLgNGZOccE5ncZjlxM7nzhZyclLdnHEF3ETFXt5lMXcaJs5qcCuTohdlZzY9MTpufhS9F7JnsfFM17dNNqVGa9vaj2hyW6zOMkayURzHx9+EzvbkR+Wd73rXSN5EwJaHFFR2sTJTNLse5bYwTdROZYwiff0+6DF7GAZB2sNp0/IV1QeppM3trsgDhyaeD1cEweKSSMPZnaKtPYciB8Xx5uoWEnx4kJ4Exfl0s4CMuF3KpBGvIYq7YqNHUsTFU3p2cBHGTmFGgeN6VnHiWvznOc8h6Cm7RQWYXm+OSXLKR52FvDDHhcEYR3VJ+peVKQmvzjJbOLkMO2qI+9RodDEK3ITRnFS3cQJZeKL1xE2uOPks7FTFXEimXYuRMV4qqfxmHnipQzxmsVkpx5vv/326Ud54mJl8o9K34bdyFC8VqmJL+4mfi8m7diIir7msY99bAqLC4XNCius0MQXb9ppHK9xTKeLwQ/itGd8GSfcSDdez9REJW4KY/cF7Yc4ccKS0uN5WLvwuyPjYm3aGc3uDeTwTHkmOUXldMIIPGiTcYGziYsNia0rr/e73/0adkTxXOKkIZ1eBm9OP7MDhLLERYs8ycXck3nmffqQGpZd7aeG35D88lzpM3heRtaG8ENWnKCkXTS0C/qjuACUWGvt1GSZWatz1M2orEinE9mt84Y3vCHVB9p6Vx666pyVhXzUMLN8YtbqnO8buuofp+Wpt0acJHz5y1+enOQ7bjxoKDtlpL6Q17YTmnFTRkN6vC84DWPtFWG+jDWczz777PT8aGe0JXYK0l4h3qvUA+QbcfItXmGenDXs8voWvx2d+lbSgGhj3BLgKQ7OUv9GfwW95S1vGWHj+UrvRx+en9CkL+HdZe/aWhvrKnN+WqvvO4pygx34cgKC9ybvKPpyCDn0Q1Hp28SNVr44qS+ElxOc1At2aMcFisRj7c3eJ/Rr9NVxQpbCeSfbeMXX09o7syuvPnP5CU3C6Nu5fcJ2gNfK7etprT7luFNu+h3GCuzoi1epp3EI6R8dx2ikaUS74j0TPyWQvGp9Cwy1Npr3jR7TrndyrXyWV8xa/mptpCY/b48+Pct3nFSnusnzi5tZiu9obgZhXBsXCRIvbZg+wtpsXPhuGJNQD2l39OFx01VKLm4GSP24pU2fwMlpCN6+dTwqMFMfFTebpbjxe8EN6dYoKk9TfYkKi5Rv3ifkG6IttI2zCM/rXm18xzgsLvin8Rv1n7EGaUG1OpvXqxRhCf9Ndl5VyvbTnva00U04MzUX6pvO0LlQ2/tnyFyohNFc9JtsHcnfW11tMO+/GPMz5yAeO8h9n11rn5xIiQuVDX0HceMG2jTmN+zjQnman+KmH2Q+GDdhpD6PuRGnK+zd2zXOrI0zusaJtTGbH3PU+k0rk5m8q3lPMu+JmyyaBz7wgaNbIWp5ZY7IvJ1xKGMD1iLoO7mhiX6esQbv+7ih2ZKSuUARmGx/AFy1sUVXe6mNmZAdN+mm+QZzSdo+7dpuZvNrELV3NHKM6Dv6rDl1zVVZF4kL1CY2rb3Qtmwdh9vW6As4rcLYgr6r7YQm8zbmlswpGBPRZu1Uiy9jDWfWXpBDm2dMx/wBN+M9yPezuP38tYad9buMlylLnzFcbS5D2p78+ML7Y++aD3TVrVqZ83Gbnw/k+Zgu9zhtjucy39YRcpxrbag2VkBO3Cw7OqHZNUZpW8ftmk8zR2BdQDQ3EJjO9labP8cNok3c8JfGb/SNrOXzrjjxxBPTuAwzbtoZnQD2ayseWcat/PwpSez5SUz0E/mPfnvoL5dhbkvP8kGemFvxazul6csxX+2TPqEZF3LSrq1YKSYQu/7ZuRE7sOQflSBhhx12SKfcMDnZaTuoJkS81cFudU4asruJXZ1Q7NRCHBjcynGLESvcaBe8BZTixklL2k3Jzu+cOJHBaTt+8eW6WDqkAcXBSR51gpv7lk1OHABMCIsVLnAqjF2Z7Gr1xInGeN1s2rnJrs8u4pQnO9LYUc8pLk5XsCvWKE6s0u7RuChrXiOz7Tlw+pKThpwqIF6cqKUTmrGBjOJ6C9+R5EQpJ2TJQ1zUT8Hs+o+NNdUJdt9RHjup6+OX7D7fnDLkZBunIqkrnEKIg8hRNMpB3bDTMuysoww8Z/LO9z7ZCQtG1DO+5cLpInbWUmf9qVHKzIkqdjYT104+YnLaEOL7P+w2igPOtEvO6mFcEE75g4cw8s2uD3Z/kD8jTpQhPyqX0+7BOHhM9Zrv01FfOTERlTJpp/Td427puLCaduDFwXTahcypU+JwUoqdgvlpJNKJnVP69h07iGNnnbCj3JziyIkP3HPikR1NYLzzzjun08vEq+XV5JAXnn9cxEzYUlZOPYFjVPyGuNBqrFVz6DPv6kO6sCQzbe2nD35D8ttWcOoedZWTOezijIPFdOo2LvqOorS10xHDrZZanWOnEd+/4jmTTpzUpTZOve7KwwXxG4QzWed8ufrUP8/v7eSbNsxOVtoK9aXUD1oc6m2cbKU+mVPnfG+YNpBTDWf6JfpD+j7aErtn+9DQ+sY3LDkFwPPjXcjzJV1PPGvaoL0P+Pax74fgLb0fvQyz0yexE5t2zekg+iTqU582ZjImY7bVfXZ8chsE71H6cPJEn2plJX9xMSJ9B+e+971vAC/e5xB1KiroEj+nvTg5z3NjjAL59wn9GifzP/3pT6cwnj03BORUe2d25TWXlbt5h/CM48A0fX+kVm6L26c+GS8m9YedidQXTsryXrEyE06aPH9Oq/LeBBe7UaLWtxB3XCq9k4eUr5a/tjbSR77v/0tl5DQipwMYs1JP7XS+56XeMQ7gFD+81DUj6mbcGJFuwABvxgI2/jWeNnNIHed50jdStyFOEO20005topM/YzzqC/WBfNMXGPUZZxkvZm18R//LuIdd4Yy9OdkaFZopeled9WnMJ3tcNArML3hHQUtqLkTapTkX/p5qc6Ha+2foXMinuRDt/r3Vpw36/os5E3M+TlcyV+I0brzSOMFYa58w0GfxjiVNxvt+jkGfx1iI+TLjMNpx/GxMmrPwrqH+MH+Kiorq3KZrnNE1TqyN2Xxd6eo3jZfyxE1Pqe9i3sPtK8zzGV925RUZjFV49zBGtHEbz4N+njkJ8wBu/xEJgckiUBtbdLWX2piJcQkn57ghhrkkbZ8TeiXq+46ujZ+93K65qufN7ZZvTuawZkIbjJvjc7aRm/6UNZFll102ncRmHTMq2EbhZqnhzBx0o402SuNp+jm+P0+/14e6sKPfpZ+gLH3GcH3nMvn4opTX2nygq26V5M0nv/m2juCfTVcb6horeFldY5S2ddxx59M+D7LPfQTa2luf+TNrP4zf6BuZozOOYy7LuIw5MXqCK664ohMk1gtqlIfjzv1q8X1YW1yTZyZxvN3LWEj2ZSZbWAbicRdmmpwwCDDiejaUPVSanFhohlicbSOuV2Axj2PkRnH3eUAx6ImFOAZYnvK4PGCUblyxwEIJlZXrdlB0RU13yjtuiEkLgwWvyLNFz5ISyafLpMbk+AEM8VEssWAUd3n5KMnOojPE9TVcNcd1fEOIBS+us3jEIx4xJFpa8CcCzwHlIwoM/7wY2PUh4l188cVpUoesuOs0TZJRZjJpnUwD4xkx2eM5MMhkUMsVt22EMojr3rgqeK211krXJ9mk8d3vfndAccjViywQQm15YqHO6iXPgQ4PYqEXBTvEYjodIoTikmsSuNKRPDKQLRGDd1sssHAWi+k8uUqBOsKVREYsCJBHFLNcgwlvF1GfaQ9cOWltjDh5uvix0M/A2IiB985RqQnV8poYsj/quq/vDFqsHWSsVeeQZ27ls2dlgruwND5vWvtBsdUXP+IPya9Pj8EZGwHYiGDEFQgf/vCHzTnBbCtrV52j3nhlFv0XbZXFRjYd1PIw03XOF3ho/fNxWTzr01Z8HLMzeaX9x2/DpYUn8+/CmUUoW3C3OH3MIe0VeeQNxRobcOgbGIjRT3hisTCeoktX2nKtGe87FtE85e9HH+btKGa58pC+iWulWQhg0Bd311X7Kz8O8PImY/d1n/6VcqN488SGF7tyig0o/LhGnPcBZaePZbGWhVuu7zRCIcp7r0QMcLlWmx+LmCzm5FR7Z8bd7p15zeV5N9fy0D/wnqF+dZWbuEPrE2XiHeeVmDxfxjEQ+HB1FsTYCQUcC99QrW9JDFP4Z+/kIeWr5a+tjQyR36d4bMKy6+09PxsSqFclQpnJe8i/G0p8Jb+hdZyNT/GbuGkTG3WATXU1YoxX6+eGjLNq707GQSgj4immEHfQJiUqY2PG+V11tpb/uRoGFlxJz8Kq9a0zNRfqm06ObdtcCL7a+2fcuVCej4XmHtIGufqdvpC+nXkwJpvsoFr7zDGlTtrGZRSkjEuYR9IHMxfjWnyIcYNtQGVTL+PQ2jizayxfGyd2jdl8GWr9pp/PkR/mNd6PzXNWttoczqeHnTmSN7Ezh2KjgkgITBaB2tii1l5IrzZmYp7Nep6131r+au9o2whO/Nr42csfOl/2cRlP0f+wTjMZYvMWa2vMKTzVcGaDpc1HfJw+9hp2pfh9xnBdc5nS+KKUVm0+0FW3SvLmq998WEfwz6arDQ0ZKyC3NkZpW8ftM/f3eZZ94SDg2xv6oSHzZ8ZcXudh4zL0FnOBGL+LFkdg0gpNBvQsmjIY8gundgJs8aS6fejwWDxBBotZRnxPxxb3eJCki+LUlEvwleLaAj6nxuzkGAv6fL8C5RQTbE+cjqA8Rkx4GIhxQq9GfF+AnWCemKAw6GDhmcUjT/FKi6TEQmEHoUCMVwQmpSoLi30JhTKn+8YhlIUs5IOpnWiK1wMl5W7XIJaFYpRCYMSCBPnBj8VocPQLpn3zCA4oH+07k3yviTpRIpTQfKOOBex4BUr40pe+lBauGYAzceb5xisxAm7If5ssebT88exsAYny2LeqUDKaYtROj7LYhrIIRSH1IKf8m5mEc3c3O0JQtIIdO+CsLjOIoO7TWaN4ZEGri3hOyEExVlJi+vjkB5w8MWhg8F/Lq+efavuQZ96WdheWPMecrP0MwQ8Zk80vbY3JHMpTlE4QExm/UJI8O/666hwTEPpHUwCx+43yc088E9RaHma6zvmidtU/+idbQPPxsJNvlFmTIRbjWLCy/thkdOHMJgr6Ttv8YPEw7f1VUvAPrW/I44QUikbefzvfugEBf0+c/toyTsDp+1CA0icYld6PFpabtCX6NIhvXrE7m/dvVxuzzUClMudpDHGTLuVmYZ9n4ol8cVqCvo/y0o/yPmDnsY0RUJRwAjenK+Op/px4puz24xsv7Ea3RVnPV3tn1vLqZbTZWWRgZzfUV9bQ+sR7ijTavt2FMsOe/0EHHZTGJ2zKIj+1voU819oo4ZOhIeXryl+pjQyR3yf/1Dv7lrLnZ+MZ73rykBOniPmOun83eB4mXG19H3V+SB1nvMnYlFtFWIBjrFEjy3epn2PRccg4q/buRLlCP87mP8ZanLymH2Ms2FVna/mfi2H0a2ykY+OBH1fyjpyJuRDj2650SriS13wuBF/b+2eq5kKlvCwUv6FtkA0cbH5i4yjzFk5TsrjDKeBa+6zhyVzMxjyML2nHEH0W7wSb1zK3pA7Xxpm8Z2rzoto4sWvM5stQ6zc9n72HWbw3hQV9FYpMZNTyyrfMRUJguhGwd3RpbFFrL+SrNmZi/Mv8qG1c4svV9x1dGz8zFjPqmi/T37BuVSLGUyhc2OCQzxlK/LkfG2R333333DsdHmgbwzHmq81Ba2PjvthZhvqM4Wpzmbbxhcn3Zm0+0FW3amX2acwH+3xZR7Bn0dWGhowVamOU2jquvXtLc3/Lp8yFiYBvb4zFavPnqUSItSg2mrdRHo4bqsXJZVmc3B+3D8vtuM3P7GaWZM03v/+teA4sGYMNdpujrGMBkJ25KCJZkMsVhX1EcxqCRUkUj/kAhFMiLMTDg7LpmHgNKDuvON0EtcVloYZdVv6HH9cblfLIgjFXKMDPQCh+2250hWGfMhgPFRdFI7sAuOrWKpiFs0hB+ijjmPCRfyZKTPo4ncokv0ScCGQBHayJw64Er0wuxenyA0MUtij7wJhBIAv7nG4pEdeP8QJCWcxziN98TGw8DzoVcGPwC8Y8K3iHEANacGFRnA6LNNoGrchlJx08YIySnbrDiTRM/FhkZ9cFJ07IT5/Fdhay6Ryh+P2ZdLUs5WLhnDJSJq7TtVO3nMKlvLQD+PhQtRGLBezc48QG8Vicw82AlAF5/DZLWlhkFzDx3//+96fTCSiFUU5SF88666z0zDFL12Ky2InCg2sBeS7Uv3gneGqP5IMFBmRD5JX6h1ITPtorJ9uYlNfymiJP09/QZ17KRheWxGlrP1345enV8msnbFjwyIm6uigq1+N3fFI9QsnIVdRc4zuUanUO5QN9CAvr1FcWE1HEo6ToysNM1zlf7q76xwfmuVqUdsyinD8FxTuCSSjvD/pH2orvN2hHYGHEyW0W12hPnAhCwWNKZuPBrOFMG6adxe+zpLZE3ozoU+mP7EpudvYyEYaG1jfiMFmmT0MJYYqFeI/+hCu0UVizKSd+U2KC0qTt/Yhc+kby1kacuqd89BNdbaxWZuTTNthIxPMZQryjWDiM32tLz4v3FJuSeH7cBkAbQoHJe4f+neub6PtZjOFaPPo78k//S/qcZG/bicc7mMVe3t9tiuPaO7OW1xLW9MHki/6fsQITN5SIUE2Wx6+rPuW4s3Ob9yH9A8QmGjYOlYj+gLZhG2tqfQvxa2201jeW0ja/rvIZH2ZX/kptZIh8n5a3cz0qz5GNXNS1+E1eH5zs+NEW6ffpw7AbUb/Z/EZfRB2mPlAXjOjfGJsyNqJfsxNthA+t4ywycSMIY3cUm13EOIc2xgY/8u3HN13jrLzu1d6d9OdskGMMzGkvxl72mYZanZ1sveoq95IKR3FCH89YkbGop5maC3Wl4/PUZa+9f2pzoS65Cr8Fga42mOPE+w9lJu9R3p+MDW3sU2ufuRzvZtzB5lGInfKMGegP2RjEexlCSc74l82itXFm1zija5xYG7OljNz6V+s3/bua9wO3/jBHZs5NecgDG3a78urTk10ITBcCtbFFV3upjZm43pQ5I7c4MH9iLYF1vhLV3tF+rlIbP3u5XXNV1szoZ+hX6Gv8mIjxFOkwpma8RL79pka/JkKayKFcyGFuyPiLsUhONZxtDmrXcLMR329Cq42Na9jlecBdGsP5PguetrlMbXxBvHyujJ9RPh/oqlu1MneN2xhLU+eYL3IIgGfDep7Noy1PS9KcD+sIbfh1taGusQK3NNg6WNcYhbZeWsftmgOz5sAhJdHCQKCtvXXNn6caHdaX+Bl1ueEznj6myfWmxTM/3JCZ5t/m58PnrX2cj7TGxY0m7sZNH0aOg/4mDgDSh7XjIl2iuLDTxEUJc6YPWkcgm3gyYuRnlqj8Sx/QjpOdxv/iDtLEEhc7mjjpauKLvImLHU28r96iNl1xR4zREidU6QPq3s/s8cXZxF0n6YPgpBN3kTZx0dSCR2ZcWG3iTriRO7fEXZmo75uolJpQlqgETqyxA274SG1Urjax02/ilT9NVKClsLholMqYywRL4ser85p4/VsD3vEFMGIDZ3iMotJklMeu5xAHDumDuXEnVpIdFXcmpomNpYlK1OQmjah8SfmgbFtssUUTF4hTGHUhLgCnvMfdvk1U2CYMeNZxYa7Br0R5vuGP30BLzwBs4i659LFe4ublwC8uxjdxUp4+QM2zjadz8E4UB+HpA/ZghZzY6aWPAJNnno/lHeaoGG7iwkuKF5UhqV7jiItK6TnF3UrpY+9x4t5E5UITFwUSL39x8NtEpWB61vEF28TTl+k5GQN1F38wiNd9JpkWFgdoTVRGpvLGl376WHFUslhwc8QRR4zqY5xUp7oeTxam8LjQ2MRJdrLHgUN6NrSdOAlp4kAyfZSYQJ7Zfvvtl/j4i4uTDWnxi1dHN/GU4Cislteo6G7iJH7EG6+ha+KVziN3HMCn5zDyaLGM88yjwnhUr3LxNSy72k8NvyH5JU/xexfpOVGPIN+G4mnYJp6mSXWB5xQVBYmHv7x+18paq3Nx4N/EK7tTvaFNx927TVxsGqVTywNMtTrny1LDrG+dy/uGWv0j3/EEYsIu7mhtokKxiYqnUbloC3GBKfVBtCneE/QP9E28O+KENfHGkwipb4zfiEz9KP2MbwO+jDWco+KhiSeI0nuL9h8XCZPdMsTzpG+i3dNX0tZOOeWUFFzDLq9vJi9ev9hEpYc5m7iJIj3bkUe0REVVQ1/lqfZ+5F1F/xiVCCkKfXi8fs5HT/Vh3XXXbShvrY0RqVZm4oI5eMQTWM2QdxTvRcpOXN5/PEOeKxQnuE2caKdyRAVI8+QnP7mJyp8Uxl/cfJL6ROLxDoiLrCksb28WAXlxg0f6gLv55fW09s5sy2uOddzMkvoy3kW8F6LyuIk7WS3JZLbJItDX01p9ynEHt3333Te1Ffog+vWo1EzpHR3f/Xn9iQq4Ji6OpLz16VtqbdT3jR7TrndyrXwp47f+deUPtlIbqclva4/IsnwzHuB9Sr2J35YZ1U3/jo4KhOYFL3hB4qFfjsrE9PzJM8T7k3ECz4R2yHOIO/5TWDxl38RFr1T/aQPxStbUt6TA+De0jkdF0mJ13GTlJvlm/ETZKGO8sjLl2/jaxlmE53WvNr6Li3+pP2fsTTtlrGvj/1qdJR1fr3AvaRpnXhWVmalt+3kQdtomNBNzoa50UkbiX9dcCL7a+6c2F7I05qs52TpSem/V2mDefzGmYY5FH0Q7ox+Ki/4J5lr75J3Lz4h3LPMho7iBq4mKi4b5C/Nj5qvxFoAmnjRO4xTe/VFxYOzVcWbXOKM2TqyN2fyYg4y09Zv5u5rxSty8m+oyfSDzd/pFqJbX+JmAJm4kTHz8Mc7ifR8364784oahJl65N3LLsjARmGx/AFpdY4tae+kaM7HewFyKeQJ1mflr3HSXHpIf39Te0flcpTZ+9uParrlq3NjYMOZnTBQ3Iqa2FTd1p7xFJVjKN+8f5g60s7jpO4X5+Sn9KX1XvNEozR2ZQ/r26MvYhXPckJ7G1cz1mEchK260TWnW5q817PJ+NwmLf/kYLu+z4CvNZWrjC/Lh58pd8wHSqNWtWpmJ68dtfj7AWizj4LhJOPWV4BgPWzTx0EtaqyPuVNA4bY7nwnturq8j1HCstaHaWAGZrMfQ9hgDQLUxSm0dtzYHJl5UjDdxc0NKQ3+zG4HpbG+1+TPjtXjT2Agc+hXWtIzo9xiX2XzTv4OMB5Pxm/0Y9/lf3OzW+B9j4an+efmWtuUnbnZu4ma7pGdj7Zg1A8bicVPIaHzvyzIf7XzfYuxy8fIBwOkm0uGBTjfFnU4NlWO6iUbEIo6neJqqiR+v9V7J7iex5I08TjXRQMC4jWxiTL7hLRENaCryRsOs5SVPG0xK/OTVKwjzeCU3abOYGE9SpmAm+7boSNlyeSz0Q2ACH4uW/HJqwww+Op28LuCPH4No2hdKbzpdU34TnhNy8vxZ3j0vWNXyUwvzcqbSPvSZt6VdwrJv+ynh15ZOLb9WF9riUl9Lz6WNP/fvU+eo+7V+uS0PS6rO+TLW6h8v8DaytpKHg0WJSKfUbxhvH5wZOPAsGXiz6O8J2eDcRkPqG4MyJgWe2srlebrsk5FRamOWTleZa8/WZLSZPPu2/FJvbWG2FH86xg61+lPKa1veS/n1fiVZPtzstfqU405eam3JZJZM4tb6lppc8jHZvq9WPp/Prvx5Xm/vK9/HMTt9Rf7utTAz4aG/wIyn6NOGNgvDpO0QDrGByRSaySP+gXmtDvWt4yy0xxtJTGzDQlyuQPNKNBjJF/1richTrex53au9O5FlGORpEdZWt8apV3k647qnYl5VywP1pO+zrsnpCpupdHiupfFvV/7mcvhU15GuNphjRXtta8+19pnLMXe80ahhsdz6dt8flMZAfcaZtXEG6eb9Cn59xmzweSq1JfDMiTKZ/DysK685v9xCwCMwbn/QNbYgrVJ7sTxQ30tjOtop7wF7v7KZlLFLGyGn9I4utSdkIruL2uaqxKuN+y3fuXzro3J/yt+1ftWFM32qzUNQtJpC09IqYWNhbdhZuDfzMRxhJYx9nD72ycqo1a1ama1e5Xnz+Wiz53GGusdtc5ZeVz22d4aVtbRGyJh3SawjoNwvjf1NudPWhqzstbFCPmfgOfpxgckwk3be1h9Qh3w9sDjWHs0tc/YiMN3tjbpjc8fS/HkoMnnboH7yoy76H+06/zGmnOpfnoblgTzRDvnxDgMD2hljUt5FC2VuNelvaEbFyoiiNjtd8znymCYL6cRTFtMk/X9iue7TruD5n+/U27gSgp+nOJhKV+t5v9w+XXnr+wF18tzGGwdweXYn5Y67YAfFa8OEvA7NE2lzpSzfIHzjG9+YrkfiCrTYeQSuTuFKJU98N4HrTLn2lWsyuJ6tdC1LG2bI4gqRnEiPK4X51hnl4zparrmMJ1Fz1pG7JCcqWUbhZqEt1fJTCzMZU20OfeZt6Zcw8LxtdQWerrheTi2/XfjV8uDTaLP3qXPUfa5fbqNSHpZknfP5rOFn1xB6frPHgfno+7fmh5n3sxZWSweePjjHkw4mbjGTdlbC2Rj71Deuiz7nnHPSVY9cF+SprVyep8s+GRm1fHeVuQvzWn5rzz7uIK9FnZaxQ60spbxOBmsKVZJVKmztueR5JS995eZpEbfWt9Tk5vnIZdfctfL5eF3587ze3le+j2P2uIt89B058/Mm3y2Ku/bTlcfxRGiIJ64DVzp7ou3U+pMa5sjpGh/HSU66bp7vUzJOMYqnLgO/GtXyBd61cVb+zGvvTmS1pUVYW93K06iVZa6HUU+6nvVUlHGm0uG58hNNHgHwq7XBXHKNt9Y+cznm5hprxif0aTvFzx7ETaHpKv54cihdp218mH3HmV39canN9xmz+bxgL7WlUn2sYdaV1zxNuYXAVCHQZ2xBWqX2YnmgvpfGF/HGqRBv3Ujf6Y5KtPTZB64SbSPklN7RpfZUy4+XX5tD1cb9vL9KaZTWREivVH6fjz44t81BTU4JGwtrw87CMdvGcISVMMZ/CE1WRglnS7dW5rZ4Ph9tdpO/pM22Mli++ryT2sa8yKAe19pAn3dP2zoC13jWqK0NWZzaWCEvE8+x9g6tlbGtDnXNuyyfMucPAm3tjbqa17lxSp23jagsLIoj3agsLYZNlSdpeDJ3ycTPfj7OfLcvHY/N71frROY7ALOtfHwXgHvBc6Ihxd0pIV4VlAfNmJtvWsZreAMfAp/vRIeJ8jCeukrfteJbDHwPj28a8W02T3xrAUUn3zzi27Lxys6w4YYbepZJ2eN1DeHRj350+MIXvpC+u8VH6lGw+sHdpAQvwEizof1MJeyqc1OJZrusITjHXVDpmxFshJhKov2zkMB3DvkGoEgICIG5hwDfmYunG9I4Iu6oTN+B5VuJbRSvTkztPV5f1sYy2D/ukA3x+vmw5557jr4BPliIInQiwHhD86pOmBY0w3ysI8xXUBbwLXO+cx2vbkxzI769xkK/0XTObYaM2Sw/MoXAkkZgnP5g6NhiSFnjlatpozabtFFsxutjixsAhsicq7xDcY6frEprdva9yKkot8ZwU4HiLTLGaXNDcjHknaR1hCHIincuITBT7Q1MpmP+zIEzr0A0paGZpGt2M0t+FtZlMpYu8bT557w+7YWgN1iKI8ALQUHFgxUJASEgBISAEBACQkAICAEhIASmA4H4Ha8FsfFvOrBbKDJVRxbKk1Y5hUA3AuoPujEShxCYSgTU5qYSTckSAnUE5np743S8UduJzDZ/izeuidLSk7m9mdtx+w2FPv58sk/JlbPzCRCVRQgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYGEhYIpCSm32XIHZ5j8OUiYzl+H9zY5Zsudx56NbCs35+FRVJiEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgd4I1L7/2luIGKcNgdtMm2QJFgJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAiMiYAUmmMCqOhCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAhMHwJSaE4ftpIsBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBITAmAhIoTkmgIouBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBITA9CEgheb0YSvJQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAIjImAFJpjAqjoQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAITB8CUmhOH7aSLASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEwJgISKE5JoCKLgSEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEwPQhIIXm9GEryUJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACIyJgBSaYwKo6EJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACEwfAssMFf3Pf/4zRWmapmoSaDyJscffUksttRhXyW8xphaPUvolv5boY3nX8m1hq6yyylhpzMXI1113XWe2Z+oZ+YzYMzG/Wh7awtr8kTkkLM+L5cn797ETD76VV17ZRMhc4AhQD33dWeBwqPhCYNIIzLa2VMpPyW/SBZ4FEb/5zW+GTTfdtDUn3/rWt8KDH/xg9XGtCClACAgBISAEhIAQEAJCQAgIASEgBISAEBACcxuBwQpNFsigvuYQeKZy8c3y59Mv+fnwqbT3Kcvxxx8ftt9++7DqqqtOZdK9Zf3nP/8J5OFZz3pWWG655XrHmyrGtufR5j9V6eZyUPCU0iz5WdxSWMmvxk9YKY75jat4aiuX5Wmo+eMf/zj89re/DY9+9KOHRu3k/zZs9MEAAEAASURBVMc//hG++tWvhp/85Cdh/fXXD1tttdVoUXrnnXdO7uc973mdcoYwnHDCCeG8884Lxx13XDEa7ePSSy8N3/jGN8Kaa64ZHv/4x4cVV1yxyDsTnuTluc99bvjBD34wOLmTTjopHHDAAYFNFF/5ylcGx59shMnWmb/97W8pSWsLOLy95E4RevyV2lXJr4eoVpah8vKytQouBAyNm+fNu9vsebJ5mkPdXfLy8DZ3nm4bn/f3ZcS/y21x87S63BavTxp5HixuLY08zOKYaTL7mvkGq2uvvTb86Ec/Cg972MNMZKc5nWOak08+ORx11FHh85//fGs+6KvJw2GHHRZucxtdQNIKlAKEgBAQAkJACAgBISAEhIAQEAJCQAgIASEwRxGY1IqPLaR5E3vt99///jfkvxr/uGE8j3FljBO/LX3vj5LhD3/4A15LhDgtSR5MeTDZTHzoQx8KZ555Zq/o42A6HXHJdJtcK1ApvBSv5Edc6n1JRpt/ibePX1v6Vo5xzcsvvzx88YtfHFfMYvF/+tOfhrXWWiscffTR4Ze//GXYaaedwtOf/vQR35/+9KdgJ8NHnlNgQYn65z//uSiJ9P7v//4vvPa1rw2//vWvwzve8Y5w3/ved+y2Ukysp+eNN94Yrrzyyp7c/2O74YYbwgte8IJw6KGHhosvvvh/ATNgm2ydKdV3slvyH+KXyzAIvAzz62sSNycvr4/d4vfhzXksLmYelrtzHu9us+cycHvekrsUp81vnH4wz0dbGn39a/JqYcjvCm/LQy1eLaxNXp+8mFxvYocuuOCC8PznPz/c6U53Cocffvgtni3/e+yxR9qEQvDNN9+cxhPTMaZhI83uu+8ePvrRj7bk5Bbvl770peE3v/lNOOOMM6p8ChQCQkAICAEhIASEgBAQAkJACAgBISAEhIAQmJsIDFZo+sUyilxbVLOwyS5YluS3wWxp1cySvBr/VIfl6Z999tnhXve612JF+ve//72YX8njX//6V1KW9Q3LlUKrrbZaOPfcc9PCpZeBXMpeIpQjOXHiq3RqrMTrMTU53s/Szf2mw036feSOm8+2dNr8++TJeNpkeH/skyEUaJ6e9rSnhbe//e3eK9l5zuSnRHmdy3lIg5OPyD3xxBOT0u1nP/tZqpff/va3c/YJ7i7ZE5ido0+8V7ziFWHttddOClxONpKXDTfcMBxzzDFO0i3WLnlt7RkFAH1jiTgdmuNf4sOv1l4J/+53v5tOoW299daLnVpCqeupK13KAo8R9ptuusmci5ltdabWdyGkq377+ma8XaaXa/Etjs+4+fU1c7kWz8us2Y0f08j7DbG3xS/545en2cZnvD4vJT8Lr4UZj5mWZpdp/Lk5JK1x4/q0vD2X6905X+72vCW752+z+3glHvPDhOAvmfixQelRj3pUeM1rXpN4an+ccv/jH/+YWJZZZpnQNqbJ+8i2PhFBOS9+733ve8N+++0X7n73u+McUWmM8apXvSq9T6yMI2ZZhIAQEAJCQAgIASEgBISAEBACQkAICAEhIATmPAKDFZqU2BaKSotoFl4KG+KXy8ENeRnefkvoxPAav487lfa2fOR5wc01r6YI5LtPb3nLW8I97nGPcLvb3S5dA1s6OXnWWWeFzTbbLDz1qU8NKCS5Jo5FRYiTbLe97W3Dy172svTdRLta8v3vf3+4293uFm5/+9un6+O4BhK6/vrrEz8n0CBOQaBcWmONNZLsT37yk8mfP5RMW2yxRZJB+qeeemoKQyH7iU98Iuy///4pLp6/+MUvwo477hjufOc7hwc84AETrvQsYU2c3L/kl/NM1o1saGj8UpzcDzeUyy75weOp5DY5Pr639wn3aWBnwXffffcdeaPURlln9J73vCest9564a53vWu64vT3v/99CsLfrn21eviSl7wkrL766kkxb3UCZq6Ppc5RR7mykPpwyCGHWBIjkysNr7jiirDDDjuM/LjW9QMf+ECqRyNPZ2mrz9Rf6r9vN5tvvnn41Kc+lWJTji233DJd8UzZ2q4uRMlInUYZ568tfPOb3zxBNieZNt5441R+5H3mM58Z5bLWnlmEf9GLXhTWWWedtEC/1157pfpCZJSYL37xi8Md7nCHlE+umOXqxxLV2qvxkyeu70VJcMc73jGdoOLZ8by32Wab1E+gWK2ly+lryvPMZz4zlZUrsulz3va2tyWZyH3Tm95kSU4wS3Wm1HdNiHSrw9dtvHz7sDDvZzwWdquYkZH7e7e3jyKMaTGZZnpx5ofpyfy9X81u/LmZx/HhFoYfZGG5PQVm4W1+JiuX4WX7MPMfYlraZvq45ldLw3gsnufNw+AxMn4z83jm7+NYXM9r4cZvPOY2M49Tiud5TY6ZPszH9eGWhjctnL5vl112Se9688tNrgan3V9zzTWpH+EEO+THNPSNe+65Z3qPME5BSco3MDfZZJM0xqEvZrOF0UUXXRQe9KAHpXTvf//7h8suuywF0TfRj/irb2tjDPp8xkJf+9rXTLRMISAEhIAQEAJCQAgIASEgBISAEBACQkAICIF5gsCkFJqU3RbKDAdz54tp3m28Fj8P825vb+PP/Ylj1BbfwktxLU5f08syu4/b5cfJJvghFu0uueSS8K1vfSugeEHRWVK4oGzhW3osFP79739PSiKuk7STCshceeWVw9VXX50Un6eddlo46KCDwimnnJIW+VhU3G677RI/aVseOGn1xCc+MTzykY9Mi5QoQnbbbbcUjmwUnfy4ovPII48MfNfwr3/9a1J0ouhAQXbOOeeksnCSAgUI4XyfEOWNneJIDLf+DcHK807Wbmnn8c2/zfT8xtPHz/NgN8r9zU242c3M43ges5tJnNyePLI/FFj8jHj2dmIGZTeKdeoiim4UepyOgfxpPOohi9OPe9zjAlcX77PPPhOUWihNUcb95S9/CR/72MfCl7/85Qkn+yxtFrTvfe97J+Wn+WGiOPXXzlpYn/psvJjUXfINoQilbaBApX2RtxJx7S0KQNqCp4c+9KEjRTAL6rSj17/+9an8LLijtGWhH6q1Z06i/upXv0r5YNH+9NNPTydSiccVt1dddVVazEcWyszSSalae0WO0ROe8IT0nVCUkCzy871cnt0Pf/jDdK0k7ZmTVbV04f/+97+fFOFsgqA/4fkQl36G79a9613vSuWxdM3M60yt77I4mFaXzW7utnaR+/t4Fhc/KHff4nvLfy3M82G3NHMz5zO35zM/b7al7ePldh+/r92n4+0+fps/PD4Mu7m77MZnMow/N30+crvnbQvL/c1tcb3b2wn3lPNbmOfzdsItTm76uBbHeCzMTAs3N6b383bP08c+Tlwvn+8c05+wmeVzn/vcqO+38QS89L30sWyG4hpY3if0qccee2zaGMJmJzauQPR52267bTj44IPTRisUqm9961tTGPHoTzfYYIPk5q82xuCdhWLU+uJRJFmEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBOY8AoMVmn5BDHubu+Rv/GYaeiW3DzO7mcaPWSLvb7zGZ27PY2FDTS+rJM/CvdwSn4W//OUvT6cTWCRkce/CCy+0oAkmCiC+78dpCE7IcfISpYIRyom73OUuYdlllw2csnz2s5+dlJucknjjG9+YlCmcoPPE4h9KC05PoXxcd911w33uc58kF+UPChi+l7XCCiuEBz7wgemEJqczSoTSgzh87w9evqlFmSDDpIRD7me8pTQm49cmz/zbTJ+W8fTx8zzYS3Fzntydx8Gdk/fL7d6dxyu5uYKU60C//vWvp2tKP/KRj4Q3vOENJdZwv/vdL52sXHrppZPJtzB/97vfJYU8yk7qIXWOb09y3WmJUNhxkrMv9a3PJXkoQzm9bCdHn/GMZ5TYRqcw+YZcG5133nmpfSCDU6Gc5uRk0MknnzyK0taeaR+0J5SZtIvvfe97aaEfxSHfiCMeVyvyQ5nJt0V5Jp5q7dXztdl5dig3OQnaJ136Ak5m86xN0cy1vJympT9C2YvSuou6+q6u+D58SN2u8dbCfHp97VMpbxxZxB0nfqm8bfK8f5sdeYT58LY0jC83S/wmty0s9/fpe3vOV3LX+IeEeV5v70qzxIuf/Urxl6Qf/SJ9xJprrhme9KQnpT6H05fLL7982H777cNnP/vZlD36U0500s+woYuNGGyQYmMJ4wgUqGxGMaqNMeCh72RjikgICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYH4hMFihObT4pQU4k1ELMx7MvnxdvF1yCLefT7+vvUu+yenDhzKSUwl9CEUKJztLxLf/UEQYsSjIyQhOTHjixBb54pQZp+74oURBScUJC+KwMGnEac173vOe5pxgciIPpdHDH/7wdL0cC5OUp0Y5Jrm7Fndo2FTKLskq+Q3NYx/+6UiHk7VnnHFG+OAHP5iuFES5livTSnnjlB9EnUVBt2jRoqQsK/F6PxTvKD/tFKUPK9n71uc8LopTTp9utNFGedBibq5KhrgOt41Q2OcnOLkSkdNEJfLtGaUq18ratbPvfve7U/vgClkW8HkGRpw04jmzKcBTrb16vj72Iekij40U3sSOgsKf+k0MPf5qfVdX9KWWWqqLZRRe4/Vh3j6K3GKBN+cv+bVE7+Wdy+8V6Vamqc7LkLRLvJPpr6wMXTh0hfv8DOH18brs0yXXp1tKAz/7ed6SvRS/xDcdfvQR/IzoR+xmCd4ZbPCwscdTnvKUtKmKsQfKSz/2IH7XGAN+f/W4pSlTCAgBISAEhIAQEAJCQAgIASEgBISAEBACQmBuIzDtCs3ZBE/XYp4tCnbxtZWpb7y+fG3p5P58KxPFR4k4aem/U8XiIAuHfPvSE6fRyBffnUJZYz+ufkPpdPnll09QbHFFXOnKThRTLCQecMAB6TpKrqJ8znOeM+EEqU93Ptrbnm+bfxcGk43XJpeFZLtiNufhGlFO23GNIIvJKNh23XXXnK3q5qQNJxC5ErmLOCkIH/XLE6cAS9/crNVnTkpCtkju5fEtT07/1JSUxk9954rW/IQ01yNycgjim5koYj1xTS9pdBGnWLnOmetXzz///PRdy/e9732BNsh1iZyUNvrOd76TToCuvfba5pXMWnudwNjDMSTdHuIGsdT6rpKgvC3gzv18vFK452+zexlddkvDyyrF8XwlXgvP45p/buZ8fd0+bbObiQxLpyTP8/nwNv+hPPBb+rlM75+H1eJZHiy+uS2OuUsyLczMXEYex8LNP3d7OSW7+XnTZJlf7jZ/My1N3MZrpvF0hXm+mbBzgpONHDbuMJMNKJy25H1i1GeMwYYP3hUiISAEhIAQEAJCQAgIASEgBISAEBACQkAICIH5hcBghWa+MDbU7eHzcdvs8PswH79kz3m73CUZff2Q7X95PAsz/9xt/kNNvt+HAoirIj/+8Y+nb1X6U5he3k477RSOP/74pNREOcVpMBYIOXHpaZNNNknXwqGA5NpRTtq97W1vS9+2IuyOd7xj+nYm/nyDEEWUnUYjjO9pGW266abhmGOOSdhw5SzXjtp1cWBQotx/qrDK02qTa/65WYrv/Yzf+2Hv8m8L93JKPPgZeXubX4mH76hyxR+KZ05fnnTSSRY9nHnmmenqVE40clKR75bd7na3G4X3sayxxhqBOnDYYYclhSiK0dL3YJGFfE7jvPOd70wncfgG29lnn52uIkTpl1OtPlMPqW/Eh7hW2SsHSYeTp1xpyHc//fWwPh2uVSU/fDv0ynjdISfLkMO3I+17lsjimmfwYoGdb87i5uRyF+29996B61ppS+utt15qjyhQUciygYB0OTVJPvnuJxsC7ASsya61V+Ppaw5Jt6/MNr6+fZevt9hrbtIynty0fHh/7+fjen/P32Y3/jYzj5fztYW3+XfF9/E8r/fHDpmf2ZPnrf5mbzNNBuHebvzez9uNH7/az+R0mbkMz5+H4fZk4fh5u/GYnzctLI9jPD7ceEp+8Bt5u/fL/S0N8/fu3I4c8zN7bno5Pgz7EFprrbXSt3SHxCnxcv0sG0i4HYD+lGvv6Sd5H6yzzjpp8xTjF6PaGAMeNpusK4WmwSVTCAgBISAEhIAQEAJCQAgIASEgBISAEBAC8waBwQpNK7lfMPN+3u4XzYzfm563zW4yfLiXkYfDl/uV3F6GyfamD2+ze36ze97cz9yY8E2WOG32ute9LqA44vTcUUcdNeEqNy8XZQhKEk7dcRILxRLKLH/1G/xc0YZShm9awceJCZRanAwj7Kyzzkrp8L1OTlJwWg3lEcR3so477rh0xSzKIHgPPPDAlD9499prr6QATczxr4QRYeZvfN7Pwsz0PDW78Zvpec0Ps41KPObn45hfLsv7m70tnoWbaXzmxoTMXbInhozH/MxEocnJFxajqUP+KsjnPve54bGPfWwK46Til770pbDvvvta1N7moYceGo444oj0fVe+y8qitOU/F8I3IlF6o5BEscdVrCjq7burnr+rPu+3337hHe94R1hppZXStz8XLVo0is71uZyOJC+cUK59u5Nv077whS9MOKGMZwF99913D2AH8S1IvsmJH3nme6G4UfR30cEHH5xOHNGWyAPfsQR3iFOp5H3duBhPGrQ9lJo51dprztvH3TfdPrJqPEP6LqvnVm9qbh9WsluefBh+uI0szNyY5uf5LNyHlezGZ2YXjw+3OJjev2Zvi1Py93Jzu3fn6Zks8zdeM/E3e7LEP+O1MPPPTc83xG5yhsYx/lJ88/Om8WMamR9us9fMnC93l2SbPHghc9fMLj4L9yb2oUSfyje699lnn6FRJ/CzseWUU05J/SnvAd5PfGuTq7rpJx/xiEeEL3zhCylO1xjjr3/9axq7EEckBISAEBACQkAICAEhIASEgBAQAkJACAgBITC/EFjq6quvbrg+sS9xZSmEEsR+3m12b2Lvonwhz/P7MO9fsnvljIXnfrnb+LrMIfko8eZ+LO4PJU6EsXjICU2u6kSxkZ/eKsnk1AOn8eykJDzE5/QdJy39lZac3IOPhcOcUHISJy8Lp804TYEyxgj5KKo8L/HbqPZcamFt8nJ/n4++YW3plvxLfj6drnDPm9vzvHt3m91ktNUz2vIKK6xQfM7UF66l9c/T5PUxufaVk3/UJRR+ixYtSqceTXFXksE3GMlTW359nFJ9tnBw5gQm6ZaorQ6XeJHFAjmL6h5nz4u8Pnn2cbBz4ogF+9L3ZWmrXD9r36vM43p3rb16vj72Ien2ked5hvRd1k+U2kzJz6fTZffP0duJl7u7ZE1HeN/y9eXzeczL593e7uOU7D7tofaSPO83JB/E8+l7OV32PJ3cXYufp5m7a3EJy9PK3Xn8mnzCSvG9n9nbTNLjXT0Z4kYH+vpSPzYZeddcc81im1nOPffc8OEPfziceuqpE0SWxhhs8OIWCa69FwmBJYEA36gfMq9aEnlUmksWAdWRJYu/UhcCswkB9Qez6WkoLwsBAbW5hfCUVcbZgoDa22x5EvMzH8sMLRYLYra4ZotjfkHN7GaafItjbovb193Gh7+X7eWaf+7n3SZ3iNk3fomv5Dckbc/bprDxPGZHOemVmVzp9ulPfzqdiuOknqfaFaNtihsWNPl5KuWvrfx5fTE5pWdoYZM12/JQkpfztuWnLf8lmZPxy/NRc+dhben5+pDzUF8mq8xE1g477BC23HLLdJr39NNPT9cYb7PNNnkyE9wo5tvq1wTG6Mjrsw+n/KW6Zzx904AfWZygrNEQeV4OmxHaCEVzX6q1174yjG9IuhZnMmbt+Xh5eV2einaWy+xy+/zkdusPcn/cudwST+7Xt3x9+XL5uPN8ebe3l+Li59Meam+Tmfv3yUceZ6i7lEbJb6jcPvxt6ZT8PcYl2aU48Hl/s5vpw71fSX6X3zjviZLs0sl8bg3gKu4TTjhhdJqduHk/woQJxSe3RIiEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBOYfApO6cpYFMFsEK9nNb4gJtJ4/d1tY6RFYmDfb4nueydr7yi7xWf5JezKE8nG77babTNQJcS677LL0rUEWCDkJNlNUwpy0h/iXeIf4taXXJaMtXpt/Lg+c+eX+Xe6hcfL84J5pOvHEE5Pig6uIN9988/DLX/6yUzE403lUejOLwJC+q6tNzIZw0Kvloys8j9uXvy9fLt/ctfiEQcbrzVtC/hfm+drsPv502SfTrw7tU6cr77lcj2MeNhXuknzzw5ytRNk5nXnJJZdUs8j7hm8jc2W+SAgIASEgBISAEBACQkAICAEhIASEgBAQAkJg/iEw+MrZ+QeBSiQEhIAQEAJCQAgIASEgBISAEBgPAV2tNB5+CyG26shCeMoqoxDoh4D6g344iUsITBUCanNThaTkCIFuBNTeujESx+QRmLmjeZPPo2IKASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASGwQBGQQnOBPngVWwgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAjMBQSk0JwLT0l5FAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAILFAEpNBfog1exhYAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhMBcQEAKzbnwlJRHISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBIbBAEViGcl9zzTULtPgqthAQAkJACAgBISAEhIAQEAJCYHwErr322rDiiiuOL0gS5i0CqiPz9tGqYEJgMALqDwZDpghCYCwE1ObGgk+RhcAgBNTeBsEl5p4IrL766olzmaWWWiost9xyPaOJTQgIASEgBISAEBACQkAICAEhIARyBJZffnnNq3JQ5J6AgOrIBDjkEAILGgH1Bwv68avwSwABtbklALqSXLAIqL0t2Ec/IwVfpmka7SSeEaiViBAQAkJACAgBISAEhIAQEALzFQEm7jqhOV+f7tSUS3VkanCUFCEwHxBQfzAfnqLKMJcQUJubS09LeZ3rCKi9zfUnOLvzr29ozu7no9wJASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgQWNgBSaC/rxq/BCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYHYjIIXm7H4+yp0QEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQWNAISKG5oB+/Ci8EhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEZjcCy8zu7Cl3QkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQmB6Ebj++utD0zTFREr+Jb9i5AGeSy211Ii7ZMfPfjCa3XjNTdgyy8wvFeDSr371q/dbddVVKduk6F//+le46KKLwgUXXBBWWmmlsMYaa0xKDpH++c9/hrPOOitcddVVYe21154A9n/+85/wuc99Lpx33nnh73//e1h33XXTg7LEanGN54YbbginnnpquP3tbx9WWWUV855gXnvtteHMM88MX/7yl8Oyyy4b1lxzzQnhOOC59NJLw93udrfFwrzHH/7wh5TeTTfdFO5yl7tMyC9+X/ziF0dylltuOR91bPuPf/zj8L3vfS/hNI6w448/PskYJ39g9ZjHPCa87GUvGycrSzTur371q1QfqBOzhXbeeedw3XXXhY022mi2ZEn5KCAwFW2oIHaJeNHnbbfddmP18z7j09k3bLDBBuFhD3tY6nt9mvvvv3+48MILwxZbbOG9R/b59LysUH3ejz/72c/C+eefH77//e8v9vvNb34T1ltvPRO3mNmF6ZB6c/PNN6f377nnnhuwr7XWWhPenYslPgmPqX7Gv/jFL9LYBOx++9vfhjvc4Q5h+eWX752z6ejLP/WpT4XLLrtssWdJHldfffXQNu77y1/+Eu51r3uFXXbZJaywwgqLleGoo44KH/vYx1I/sFjgLPLYeuut0/jtnve852K5Ygx32mmnhQc84AGLhc1lD8ZYV199ddhkk03mcjHmfN4Zl7W1rz6FY77DHOQrX/lKuOtd7zqhHc7UXKgrHcrRdy5Ue/9M91yoD95LgmfcOrIk8jwkTdYGeBeuttpqQ6JNK+9ceXdNKwgSPisRmIv9wXSMW4fMFYY8yLaxEfMMwp73vOcVxX3+859P8xDG9POFauMLKyPv9jPOOKM4f2AOce973zssvfTSxj7B7MJ0OurNhAz0dIzT5rrmvFM9x+xZpGlla2tD05qohM8bBKazvU0lSG3vIHRIUK6ozN0lnhRxCv58Wt6OaFNaWjJeeWnhxoN5m9vMr0taxyrNlVdemSbbhx9+eFKebbvttmG33XZb7GEbuDXzmGOOSQumKPmwoxj99a9/PYry7Gc/O8m+4oorwpOf/OTwqle9ahTWFdcYDz744PCsZz0rXH755eY1weQFzkIQfCy0Yz/llFNGPChtn//854c73elOgTLX6KUvfWnYfPPNw09/+tOwxx57hE033TT897//TVG++93vhnXWWSfwwrv44osDC26XXHJJTdzgMMoIluMQi8knnXRSQDE7Dt14442BujLX6E9/+lN4zWtek+oB5tOf/vT0+/jHPz4rikL+WCgSzR4Efve734Vdd901MGGApqoNzZYSothiAXKqaDr7Bt4VNgDx+f3b3/4W+JVovj0vytj3/UjdRdHL74Mf/GDgHWbub3/72yW4Rn41TGHqW294Z6HA5P36ox/9KDz1qU9N7Yl6MlU0Hc8Y5cOLXvSihNexxx6bFGWMFUirD/Xty/P+pSYbZYg9v5122ikcd9xxIzdKyzZinMJCtPVhOR+Tklr8nH9JuX//+98HFtVLRBhjm7YyluLMBT+eC8+ni4bUoy5ZtXDGvj/5yU9qLArLEGDucY973CO8733vC9/4xjfCokWL0iZLY5uJuRBp1dIZMheqvX9mYi5kuMmcGQTYSPOkJz0pbLXVVuGNb3xj2vjCQih97pKmufLuWtI4KX0h0AeBvuPWPrKMp+9cwfj7mm1jo3//+99pvNsmh02eP/jBD9qC55x/1/jCCsTY2eYPKDZZO0W5a361uU0XptNRbyzfM2XW5rzTMcecqXLV0mlrQ6U4MzH2/9CHPjRhbFzKh/zmBwK19jbVJWx7B6FAnO0/sMjzONX4zFp5cTd3LPtwiovaTTwB0xx44IGjyPHkZLP++us3RxxxxMjPLPEF18SFMnNOMGPn38QTgE2cCI3848uzeetb35rccdLbRE1yc8011yR3XGxr4lHZJp6aa7rimsC4o6iJSsQmns5szjnnHPOeYH7gAx9oHvrQh47yefrppzdR2TjiecYzntHEkwnN3nvv3WBvo29+85spv3HRKLGAVVTQNnGxNrmf+cxnNq985StH0Q877LAm7lgaubssUYnVxIW4IhtpxUX8YhietedAWB/iOf7jH/9oZSV/nr72ta81K664ovdazJ7nm+caF7EX4zOPePTbrMms5T3Pj48IjiW8KF88wdXEE8MNeTNC1ite8Yrkb35m1jCBpxZOeUv5IB5hcXCJdQLFDQTNkUceOcEPzODvQzXManlFdh5eyl9XHoY+81rd9WnVsISv1n5q+PXJbzwZzX0EE+qMz5vZwautDcPTt6z5czD5ZtbCa3loq3NxV08TN0uY+GTWMJvAGB15nWvrG2r5zmV6t883fc5Xv/pVH5zs8VaChh+U9yPJs/BXyw849m1zuegadnl9y+P2cZOv2ru1TQbvuc0226wYjEzy7akL0771Jm4iat785jePRMcFyCaeEmw++9nPjvyw0IbbxhO19j1BSOagTG1tspYeYuKifbPxxhuPJFIndtxxxyYqOUd+Zik981JfTvlI11Otf6nVUcY98XYLL2pkz9P44x//mPowTJ513mYPOeSQ4vinlv4osWip9TueL7fX5JfaMePTuBCTxNTi+nRqfLO5nVu+GZfGDXm+SOkZ5u21Vo/y+uCF5XXBh1kevB/1Lt7g4r0WhH2y8yrAeeITn9jsueeeI5ziqbLmkY98ZHLP1Fyolg4Z6TsX6nr/jDsXGoE0By3j1BEr7v+3dybQ1t1UHT8gFiooDlS0IC2KyiCTgKIU+6FLoIJSF+IE0ioUQXGpCygWSlspbQUssrAiCNgKrUCpTMUKKoIDKlpRRHGmqDiC4CyCety/0P9lv/0lOefcd9/73nvfzlr3ZtrZSf4nydnJTnLiuNfru1GewN9639Xe8coTm7GwZpgz24bjUXNf0djmmtJmYjryiWFKg018a15EfG3MIRxTi9vuu6snI0yVNb4/pjD+WC3y/2hBYBPjQWxjEbtan/A0vfha3Fy51efh3TV5ojZXoF6tscrzq7lV7ppsBD0yIrIihvFzTj49nOnXvXG4ZNT4q8n8nlR18WFL3T35osVL8iLzsZqJ9Z3CdLvtplaGdcK20+em5ry18vSeX69N1Xj5sNqcUvHxva/wJbbKXetDrfdYS/aPbcWXozYeKJ50cc5vp6rHCy64QCRp73EEdqO/RZk4QqK2rPBam6u9g6C3Q2+rH/oo//vgBz84+p8p/8ep3wc+8IGR3xSdj/d5KH+Vy5S+I3o4xmlwoK70G8YW6okszY/xgnHhoJm1T2hy8pBrB2xCulLWco0rpye5ygvD9bG2KDo87nGPK1eccY0ZV75GYwAPnGp4wAMesIqClhOTGK5OvdOd7rS6uobrW01xWk5wTKUlvQkowxlnnDFccskl3Wug3vGOdwwnnXTS6tjufe5zn4Gr5NjViXnFK15RrmGzgbr4W3/HHXdcqbuuq+WeYq5nYIcBhmtvOAUqYwvvw3ve8x55q7aw5MQKV/iA9eWXX76ivcc97jE85jGPKeE2aRue+9znrq7QUNrWc+CqQVPelat4eV7+GXHVrHankYe9xMsVjuTPlW7WGVdl4FQPdee64Ac/+MHDu9/97lVcyxHLbZ2vXP8Bxhz75rSbdcySnHrc+c53Hk455ZThZje7WTn9wvOxhePhlre8ZTkVwykUGa5Cvv3tb1+uneO0LVcJY9gddswxxwwXX3xxKS+ngc855xwlKzbXj4AX9eBaDXbKcwoHPJ73vOeVtsQ1tBhOO4AfV4xxPRi7kUknw252W+wufYD4N7zhDYoabGAZHvvYx5YrCkn/iEc8YtXuIaKtgDXPnCsA6RempFmll8MGp3KiChy4+vnMM89cnQgWjWxwpD+BMf2VHXsyvbJSB1toK3WkTPe73/0GU96XE6w3v/nNh0OHDg3suJ8yS5/5nDGEPHtYqg+0+k8Pv7nlpe19yZd8Sak+z+HNb35zcfs+ZC/0Ms5xfQ0YXnjhhavnpDK2+mlhdv3fVJtjrKP/8Iy5dtUmJKvkvTJAtNttblUwc/TaH1fIcuW4zNlnn13ao/wvfvGLt/QVxv2WAQ+eK1dvcn25P4Hon1cPZ9obp/IYizi1T7/XNaOccGOM0ZhPOTixz+kFzJL2Rr9/4hOfWNLxx/jBCR4T7ldhnCC3jTYrP32aMvHu6r1bVwlmOHpjM8l7mHr2vbrzzjRhckUOtrQJXXcLrg984APLDQ6Mia985SsLrcZ0ngFpGOu5LpVwmYc97GGDbVoqXv+MqRfjLu8B8nnGM56xumWilZ94tmzaAf34RS960WBCZSHr1TvysU1h5T1K/7UFiHIisjW+8J67+93vXvo64zrXzM4xU+/riy66aGBc533CbttWf5qbf2vcYfc3N1jI0K8YH/UuafGvyQPiIdsU4St5hWvv1R54b/krxHinMl7SphgvkSmQozC70c+f9axnlVs7JKOQL/0Zma5n3v72t5d0Kre/CaPVX3vtqCYvkT/jLrgg3yH32UaUVbFqz4dTmdDahKvIa7bosKJPRx8B3in+BhreTzahLYl2ay7Uy4eCzJ0LTc3P1pkL9dE7+LG1cY8+2Oq7UX7lmdDGuCXohBNOKHMFW9gowLXGDCKZUzz60Y8ucj/vBeY8/l3DHIkxE3mD9zh9n8/E8A5F9iEtMptMT87syfKk78mJPZlNecuujV2K83ZPRuiVlfEc/Fkn0TXvzNvPP//8Mj4yRj71qU/1WaU7EViMALIl6wjIvrSzJz/5yWX+oTlCr7+QWW++2JONYkFrcmukwd9bc/L0LZkRGtYZWaOTYb2QORf9H9OTjZRGNmtMnEBEjmJe7teRkAWZW2KmcObEGOnp1/BjjL322mtL2qn5aws7xt2HPvShZZ7KXODSSy8t8n5han/ItMxn/vzP/1xB5arYlkzdky9WDGY6enOIHqaRfavukW6v+XtzXj/H7L2TdmN+wXt5zjpCxLfXh1qyQkv278kovXXc1hyctVDkUK7+ZU0gzcFHoNffuOWSOTzrOKz3o0/STZpPetKTynhMOGM84zL0kstoS1pL6qGInKyf6LxfbmyM99fckUeNxod5nnJ7W/xaNrwOtDGBweq43Ni1m+U0ZkzJKQBOxrDLiV06nKzk5CW7OOxFPJpiLyY5zI+2mdOU7OrE2GA8cmLTG5tMjSZ0+KDijmkJtKubVicK7Pqm5glNdg3ZJGvFE422PfzRBttVGA5bfFrx2xLR8JiidDQhs2j2PYkN8KMpxwomdpWDjzrMDZYmrI2mVCuadhMkyskb7S5gh5ld8TOaoFg08mDGbhpM7zmQ3hbHR1OslHS2ED7aAmbZMUBav1OBPOwaqqL1t4GlnOAhHwx15BSqLaiVZ20T1/HhD394iWudwiIylptTsl/5lV9Z8mB3AW4TJgsf1YO2x64GjC1EjzY5LO2NsttC8mhX5BaMbFI92oSy0NnEesRvwsOoEyg2kSw7F+xqw9JO7Zh5oaUOtvhZ3LTjhzzkIeVHfWxht4Sb0ndkNzLGrlUaTck62rdfy44NU/SN97///UucCZnlhJG9eMvOiCuvvLKcLgY/DKc97WVccCNf+z7haErcEsfuC/oPaUyQLvnxPNQv/C43U2iUndHs3oAPz5RnEo0ppwtG4EGffMlLXjLaYkMhmyrrHe94x5EdUTwXE9DK6WXw5vQzO0Coiy1axCwP86/zzOeMIT0sp/pPD78l5eW5MmbwvGTUhwiDl01Qyi4a+gXjkS0AFVK17zl17bU52qZtKiinE9mtc9ZZZ5X2QF+fKsNUm1NdKHAPM9Udu9fm/Ngw1f44LU+7lbFJ+/j4xz++eCm3bTwYqTt1BEPK2jqhaZtiRvLjfcFpGPVXmPk69nC+5ppryvOjn9GX2ClIf8XwXqUdwF+GE3x2hXnx9rCL7Y3TiYyt5IGhj3FLgDcmnJXxjfEKc+65566w8XS196OPl7t2QrM3NrNbdS6mvbrbFezlPce7C7ffkUn9wRCcbVG2nPziXeXHdPqWTYBKGOMq9cCwc82+fzzaNfbF75+xTfRGu068jGl2Ld54t7vdbbRrpgrerfwKE/cXT2gSZRO+1Tsbf6/efixnHGBc4F1OuSkbcgImji+M9WBAeWn3nM7jZGg0phjdckKz977W+9EmigVn3je2QWP13vGyxdz8e+MO7xNO4dpEuBQbWUhyYo+/xksvD/h604/AlTZ/3XXXFVlCN4r4cYc0thGl9CnGS9uIVfqbaHernzOeIV9haPeM4eTdM9wowhjFjkzkGFtIW53Q7PXX2I54N7TkJfoaZeGGEcpli08FL8rVez7E0+6Qc482s+68qoaTbcRY3YSzW3OhufksnQu13j9L5kI1jPZj2LptJI57vb4LLlGeQObn3Ug6dn57uao3ZvCOZ3xhfCStbbYoMr+wt4XyMj/FzzsDmcU2ipa5PHMjxg69m6bkzJ4sPyUn9mS2dd5d1KclIxDXKytzRObtyKHIL6xFIBdyQxPvPd553DZlG5phleYoRmDd8QDIuHWMNTNTQBSZjfkB7Yz5wFR/6c0X4d2TjebKrfCRYeyYs+bUkxnhxboIN7rIMKZQZ63j9GQjpcHWeMrckrmEfQqq9FnWNDC+jj2cWXth/kefB3fkWPw8E4wfZ/H7+WtP5te4i8yKnGcKzMKXcRhDeW3TRHHrrydTi0a2ly8UFm3Ji+Qv05tDLMG0V3fltVP2dvrckjlv7520W/OLOesIEedeH+rJCvDxsv+UjNJax+3N+ckjT2iCwv4xO9nfGANZ02fsY17KnNUOLRVwbIPoaBv+yhoP8azl86644oorilyGbcrQ1XqTXx/y6PKO0c+flMTtT1Hq5KW3GbeX/nx671ZeKgNlYm7FjzWjqVOavOd4vx40s/YJTVv0KtpwaxRbjDWi8l0/G8BKuClBhlNPPbWccsPmZKd2UG1JeL3HJj1lVzq7m9jVibFB7bCPl1qDO+zEQC2tNexy4oITi9GY4FFO23Hizl7Yh+VDHhgTTmLSLX7uW4YHv0tt95Q31uDKd8DYlcmuVm/Y3WTXzZYddTqB4uOjm1Oe7Ehj5w+nPzi9wa5YGZtYlRMmnEyJpvUcOH3JSUNOYJDOFnPLCU3rIJFF8fMdSU6UckKWMtjLuIRzQoSdYuyQYPcd9dFJ3SojF+jLzXfbONnGqUjaCicyTYhcUVMP2oZOV7ALjDrwnCk73/tkhyIY0c74lgvfYmXXBm3WnxqlzpykYWczadmJiMHmtCGG7/+w286ErrJLzgTUEm4LzqV8eIij3Oz6YPcH5ZPhZAP8bYG+7B7klBDt2oTxcsLKFhcGU8qUndIn2G5pW/QubciE6bIL+S53uctAGk75slOQHZjR2KBUTgJxysgG64Id9ebD7NHwDQROPLKjCYxPP/30cnqZdL2yig9l4fmzA5s8qCungsDRFL8DJ2LmmKXPfGoMoe/3sKRMrf4zB78l5W3Vn7ZHW2X3LLs4TVgsu+ZM6bJK0uqnK4LrHb02x04jvn/FcyYfE6ZLH6ddT5XhLXYabjfbnK/XnPbn6b2bctOH2clKX6G91MZBpaHd2gSzjMk2MSnfG6YPRNPDmXGJ8ZCxj77E7tk5Zml742SZCSBlXOJdyPMlX2941vRBvQ/4PqAfh6CtvR89jyn31Ng8B9OpunNDAt9/Ro6whYThDne4Q7ntgLKx85NbIThlzVjOySXGVtUZGk6ycoODKS/LDQX6BjZtixPl8PWGdzvfRKZujGmcrOYdTtuZk5/nFd3wID/KOlVvn5bny85BnqcJpmXcf/3rX+9JVm7qxcl5cOAbZdwMQLtk92zPzHlfs+OfHe+8r8BcJ84937n598Yd3ifciKA60ue4mQEzxT/KA75suDnhiNzF+5r62KJVJCm4cWKTtsN4SZvjFKLMbvVznrktLpZ3OCfkON3Nd+hahudNudl9ilxAG7ANYCvyqf66IjTHlLyEPMhubWwwRbagTU89H59HupcjYItG5fvDvKMwR2ouRN61ORfh3vTmQr33z9K5kM/zaHT7cW+q74KPl1+ZMzHn43QlcyXGGrvSuMA4NWYwTjM+kifyvp9j8H7gvcI7FTmM8cI+G1PGJt5ltB/mT7aQ0p3bTMnyjDmtORWV6MlspZLX/80du3oywlRZyYqyIp8hI0pu43nw3mNOwjyA23/SJALrIoCMwhoDNwQhs9FPZab6S2++KBmjJRspD+y5cuvcNaeezOjzrblV7pZsFNMwnrImwryBdRTkdlMgRrIiW7dwZg5617vetcjrjHPIbox7c8wUdjxPxgnkPNaymN8yzmE4BX7aaadtyaYnU3vCKF/4uCn31BxiLqZTdZ8qx5GMnzPnpXy9d9JuzS/mrCN4LKf60JSs4HlNySitddztzsF9GdK9/xFo9Tfaqm2wKDdIMvaxli79kWrN2ghrPMSznsFaD+ukyGWMp+gJbOOzyLs2c19vvN+7ocEfw3zanruVVvxkK58er6Mh7kbrVhJB3HZhlskJQoAM14SxeESjiYaFZgyLsy3D9QqmZS7HyEXDlWcoBr1hoY/FJ29iWh42SjeuWEBBRGPluh0UXabtLmXHj2HSgrDgFXlaFKwpkXy+TGrExwswpEfpg7DB4mw0LDpjuOLukY98ZPnYdqTp+emwXGdx3/vet0d2WJx/DigfUWD454VgN8eQzk7RlIVynqntOi2TZJSZTFp9Z5vDDxqeEZM9ngMCEUItC9QtgzKIq/FYCD/++OPLtUiaND7nOc8pikOuXmSAw7TKxCRA7ZKPnjPgYVhk1bVvLEwzIGJQXHJNAlc6UkYE2ZpBeNdigeK5uo3Bk6sUaCNcSSSDkEgZUcxyDSa0U4b2TH/gijo9W9LEfAlDcY9gLIPgfbopNTG9shaC8Edb9+0doUX9IJB2vUueueqnZyXGU1iKztvqPyi25uJH+iXl9fkhnLERgI0IMlyBwLWUNdOq61Sbo914ZRbjF32VxUY2HfTKsNttztd7afvzaVk8m9NXfBq5mbzS/1GksfAkM4Uzi1B2+lXks+0l/RWmlA0FDxtwGBsQxBgnvGGx8FGPelS50parq3nf+bpAG9+PPv0c95J238J0Tt2RLewkRZEBuFIGYROsab/Un40d3rDxhTTRoGDmmjve9WDHQkU0jBuMWX78RjGOQbnUys9fdRV5ym+nUspV8rzH5tRb6VBS8Q6Sgo9whG7kjGhYjGZh2l/Xj6KX97qvU0zHhrMl72s2FvnrUMVvbv5TYx8TC64z54eCGQwwPf4qw1ybhXjaEbKeN+SBzKpNUj4O9271c9odspOdti0yBTIhfb9lmMBx9TSySM0s6a+kb8lLlIFNA7oWl40BKCpQpPeeT6/91cqbYVsRYBGMK+lZWNUca7fmQnPz2Vrij22akQzoZUPoeu+f7c6FYjmONn+r79Zw4Fp25AXmxbwbsdlkh1kyZtAmtXEZBSlyCfNIZEjmYoxNGNuVvxpb2VzCe7wnZ07J8j05cUpmKwW6/m/u2NWTEahbbw7n88PNHMnbuOknbFRIkwisiwCbVZGdaqbXX6DvzReZZ/dkI59fT27VRnDo5645TcmMPu/onpKNIn30c10ra2uHDh3aEtXDGRlpzrxgC8PrPT3savTMZZ72tKeVgwjMEzgYEU1LphZdTb5Q3Bx76RyihWmv7r7dzCnTkaRpzXmn3km7Nb+Ys47g8ZvqQ0tkBfj2ZJTWOi5rq9uZg/v6pPtgIeD7G/oh1nH92mqvtshcXuchuQy9xX4wyO9LzTppluZxpOnXVmiy+MKiKcKQXzjVCbB1KsaAh4IMHn4hh+8OMugzsPFQyBfFqZRL5FVLqwV8To3p5BgL+ny/AuUUE2xvOGFHfWSY8PBC5YRez3AqkZ1g3jBBQehg4RnBwxu7IqIosVjoxKBAtCsCy0IbC5dzDQplTvdtx6AsZCEfTHWiiYVYlCCtBT7lx0IqSiEwYkGC8hCGIgYc/YKs0kzZ4IDyUd+Z5A5s2kTNsDDJKQYUmnY93PDWt761LOwigDNx5vnadSsDfswLXvCCYk/98ey0gER9WMzFoGSUYlSnR1GqoyxCUUg7iCZ+M5N47u5mkR5FK9hx6khtGSGCts9gjeKRBa0pw3OCD4qxmhLTp6c84OQNQgOLD72yevpNu5c881beU1jyHKNR/1mCHzzWLS99jckcylOUThiE+aULv1NtDoUG46MUP+x+o/7cE88EtVeG3W5zBYTr/6baH+OTFtB8OtyUm2/irmNYjGMRVuOxeEzhrNOE2vygdNh6f2lx18ctbW+k5fQWCjref9qA4HniZufwIZuAM/ahAGVMkKm9HxU3117S7luY9urOd+L41jU3HTD+IXBSD75nw+5nfd+aBf64mYn3ejTQgAmn4Xmf8J2WaBg3wJRFHy1GsMjLIqXiavlFPjU/txegGKDfISjPHaN5j7C7e863tXhvsCHGrgOtFaEZtvR9zXii7wN7pnPznxr7eLbssuRZo3jUYniP/3vtVoYlhvesTsr4dCjmaK9+XPbxu9nPae8o0nkn124V8eWi3CwqsGAS+wN0S/ori4cteYmT64wpl9ltFZyk4ztRYMKms97z8WVN9zIEmO+wkY6NDV6u5B25G3Mh5NupfGo1qs2FoGu9fzY1F6qV5WgJ6/XdGgZsrmHzExtHmbdwmpJ3LaeAl4wZnjdzMck8yJfaNIm8htymeS1zS9pwT86ckuV7cuKUzObLPHfskhxQkxHg0ZvD8S3zNInATiPApnbkG+TdaHr9BdrefBE5rCcb+bzmyq1z15ymZEbGG9atamZKNqql8WFskGUuEk0PZ+Tj3hy0N3+di53Kw5oh64vMlZi3MF+KpiVTQ9eSLyKPnn/pHKKF6dK698p0JONac96pd9Juzi/mrCMIw6k+tERW6MkovXVcvXvXnYOrLmkfPAR8f2O9xq7tb87hN1171ozQR9VMjMOPadHXeBCmdK34SAN97yf6OXx7ee7luI+veC4sJcIGO8JQ1rEQxekBFJHs+omKwjms2ZWPQgbFY1ycYUc4C/HQoGxiYYVd6ZxuwrTS8pJnl5X/EcaiTK2MLBhzPQf0LBLZt+1WVxjOqYNoaLgoGtkFwKJUbEAsUpA/yjgmfJQfoYRJH6dTmeTXDCcCUT6CNWnYleCVybU0U2FgiMIWZR8Ys0DGwj4nCGuGRVpeQCiLeQ72zcdCxvNgUAE3FgbBmGcF7RLDgje4oAhgwCKPltAKX3Z9QQPGKNlpOyhjsQlj0Y3FZBbhKE9NwRDLx4IqgyPGvj9TrpalXlxFSB2pE9fp6tQtp3CpL/0AOk4VybBYwM49TreQjoV1/AikCOR8qBuhlF3ApL/kkksGu9+7KIVRTtIWr7766vLMsWvXYrK7BEHFvp+2OsVkd4KX/kg5WGCAN4ay0v5QatJO6a+cbGNS3itrSbxDf0ufea0YU1iSptV/pvCL+fXKqx1CKEWioa2eaMp1Tp/RjlAychU11/guNb02x4lwxhCUEOTDYiKKeBTvU2XY7Tbn6z3V/uw7teXKSPoxi3L+xBjvCCZMvD8YH+krftygH4GFDEouFtfoT/Zdo6JAkpJZNNg9nOnD9DP7dl3pS/46S8ZUxiNdyc3OXhYcMEvbG2mYLDOmMVZIgYoCEMWHDIozNuXYNya3LGy03o+kY2ykbHNMr92Tfg6mvbrDnzHfvrtVxjGeIxtjKB9jMu8qFhA5KcZz433F5iSeY8uwS5nNRrzbUcpEQ3k40c/7j/cp7y7aIZtxpvKLbYrxlDGedw5tk7Jx5S1ja6/esUyUmfcV/RfDJhd93D6OL1z7x3jOOE7ebNTipP7UTsM572v7dnXhyTOAv31nNha1XDs4J/+pcQfZh0V25CavsF+3fioo15DTVjhJg5t2EA1XeFE+drkzRvC+RJaV2c1+Tt+mHyEPSMGuckSbvkAbRcZkrKPcXrHd66+xHfXkJWRBxhVkTxQfYKVToVPPh5NatXdhrEv6P44AihPaAbIi4543uzUXmsrHl2nK3Xv/9OZCU3wz/mMI9PpuDSPeDygzeX9yIhzZULJPb8yo8VIY4wObRzHslEcm4n3EBhXeqRiU5Mi/bBbtyZlTsvyUnNiT2UpBrv/rjV1eLurJCFNl9fmlOxHYKQSQzRhnmVMyP8ItM9VfevPFKdlIeWD35FY/V5m75jQlMyIfMc4wrjDW6JQ/ZZmSjfyaCPTwQeaDj+SveGUhdD2cNQfVNdxsxPcbcHvz1x525BsNylFu8GD9VTfP+DEL+pZM3ZMvSBfnNYTVzNQcYi6mvbozl9K8mbmh5nrMSfx8vla+3QibM+elHL130m7OL2rrCC2cpvrQlKzgZf8pGYW+XlvHnZqDs85S28zcqlOG728EWv2NtsrhMdbyWONn/owSfCeN1+vg7vkph2jm2rHsPp0eI+StAAAuJklEQVT4yfZ5x3Q1/1L6Go89Gbadj7Sa4DTaYuFoys3RhP7RBICRD2vL2CLYaAohecsHrQ2E8sHWVeD1DlP+lQ9d22Rn9D/bQVoobIFwtEnXaC/y0a62Ge3ExorFVNoVoTlsQlU+oO7D5LYF1NF2nZQPgpOP7SId7QWq6JVtC6qj7YRb+aPDdmWiuh/tpOKWuoATxgbgkY/UmnJ1tEF/tCt/RlOglTg7FVrqWDzuDyxJb1fLjfbdk4K3vQBWFOAMjYwpTVZlnHoOJhyUD+baaZLC2xR3YjNawx9tIav4ycOUL6Uc1O3kk08ebQG1xNEWbCGylN0WvUZT2BYM7BTFaAPLSFjNxHJDb3e9l2cANrZLrnysl7SxHoTZYvdok/LyAWqerZ3WJLgYW6AcTSAvWMHHBr3yEWDKzPNR2SE2xfBok4CSzpQhpV3jsUWl8pxst1L52LtN3EcTCkZbFCi0/NngOZpSsDxre8GOdvqyPCcR0HYJBwO77rPwVJwJbKMpI0t97aVfPlZsC7CKHu1U0ao92sL7SFu3k4Ul3n+s3hYNy7Oh71BnE55HwjA8s/POO6+4+TMl2khe/OyqxtFOCa7iemU1RfdoC/0rWrtuc7QrnVd+E+DLc1gFNBzbeeamMF61q8i+h+VU/+nht6S8lMm+d1GeE+0I4/uQKaBGPl5NW+A52QncQsNfbN+9uvbanE3Kxqc//eml3dCnbVfpaItNq3x6ZYCo1+Z8XXqYzW1zcWzotT/KbScQC3a2q3O0icBoCpBVvUwpteor9CneE4wPjE28O2zCWmjtJEIZG+3br2VsYJzxfcDXsYezCU6jnagq7y36vy0SFrcKxPNkbOJZM1bS10zBVaJ72MX2Jn52NfloQpu8o22iKM92FWAOU4SNjFXe9N6PvKt4d/MxcW/sCpgyJvqw3ti8BNNe3U2pONoEvWBG27WFz1JPlYP3IxiAKe9B8uX51sZ00tgkv/QzU3SLRbH9M7aT96NtzCl9ljGRd7Mt9ha6Vn6xTfEu5p3Cj7GeMcCuDt2SZ6/efiyHtyl1S1tmjGDcNaXmilccX2xzTakjePCOs0XkFa0cpsAabbOWvAWz1vtaWPKOAw9TyI72vZSSBgZetsA/J3/opsYdW7goeSEfedPiH8dLnwY3/ciu1S3YIK/QlnjWmDju8E5lvKAv2MaPMkY/85nPLLS73c9tYazITyXziT+77aGUmz5Ov7CTvaNtbCqpev0VgtiOWvIStLbZcLQNASUvxhe924hrPR/iGJMpm502xnvUmO3Mq0yZWWQGPw/Cfen1cvluzIV4UL189CCn5kLQ9d4/vbmQ8jio9rptpDbu9fpulCeQaZhj8X7lfcH71jZEFJh7YwbvWn4yvE+YD8nYBq7RFBcj8xfmx8xX6fd20rjIKchbtsgt8q6c2ZPlYdCTE3sy29x3V5SLejJCr6x21X15n6jSyFnICLY4r6AyZiNvpTm6EVh3PAA15FX7vEKRn+jX9GnaGXNBTK+/TM0Xe7LRXLk1zlXmrjlNyYy2abDIJcwHbENcqbNtti917slGfn7KeMrYZTcalfUr26Qw+v7o6ziFs21IL3I7cz27xru4baNtKU9v/tqT+eO4W5jZn20G2iIvxzELuppM3ZMv4rxGebEOS3tiTUIG2tYcYgmmvbrbJsSy3kaeyPKsN9FemeuY4lhFWdveTp/jufCe2+/rCD3wen2oJyvAM8r+PRmlt47bmoOTB+mY29rmBrxp9jgCO9nfWH9m7YU1E8Yl5ql2Yr4gwpqOfQd6hQ56KruZYOVnDGJ8k77Jrw+tiMyB/KYfcp//sXblf8jCm/55/spb5bGDYKNtHixjNOO03cxXZHHb+FHke9tcM7KWwTuMdTF+B82wc33bdUIR6F9022bYYEA+PNCdNjxoGsdOGzoRjcwbO0012sdrfVBx+0ksZduJxkgHAeOW0cSYckNbM0xmN1E2OmavLDFvMKnRU1avIIzpan7yZjC0k5Qlmsm+JgbULfJjkMCACXRMLPhF08IMOgad2BYIJ4wBiP6F0ptBV8pv4qOBTyyfyu5pwapXnl6c57NJ99Jn3sq7huXc/lPDr5VPr7xqC620tNfac2nRx/A5bY623xuXW2U4Um3O17HX/niBt4z6SowHi5ohn9q4Ido5OCM48CwRvFH+eANvcG6ZJe0NoYxJgTetenmaKfdSHr12T15TmKo8vbqDJ/m0DG1gablbvBTOuKnnrTDZtfzWzb9Xb+WHDf9WW6+NL+vIRtS5974Gj/g+8WX07rn5t8Ydz6vmnss/pqUt9cYT6Bnz9E6Eng0Tdsp9C6vd6OfkbVfcb9lgwWJdVG5pcxwFVLm3FNZ5ev01tiPaXO95t9oj2bWeD7gxLh9NZhPzqh5ePPMW3r10S+N2Kx/aXU3+XVre/US/6TYy1XcjNvTzVr/sjRmRj/x2o9HIYjljGMaPIzUZaI6cybuy1y5q47re4RrbWvMxlRu71pfAMxrqJP4xbqqskT79iYBHYLvjAe2Sdy02sgsbNaOp9RfR0N5r88U5spF4YMOnJifU+hPl4R0zZXoyI+XThozIpyUbaYyK9NS/Jw9DP4UzY6rKg+wmhabyqmGjuBZ2ivc2myXsxhEftJE5Ue05bckkeBgTe5jNwRSWrbqrPDxLtRWFhaIs9m63zynDqXasd0bvnbQb8wvKG9cRUO7H+QV+KXdafUh178kKUfbnuXm5QDxk08/1jBUmm35Te+7qj6JLe+8isNP9jbZDm8NwAEgKzXURiX2D9smPthh/9G3/Q6bc9M/z9/lTJvohP8ZbMKCfIZPyLuIdST/h3cSP8brWl9bFaa+kW/sbmqZYWRnTZpdrPlcBO+QgH9uZs0PcP86Wb0DqCp6Ph27exZUQ/Lyxhlau1PNh0b1TZdM1YjG/6KfMLVoT4CL5Wn47GbIoXQsTyrq0TORtpw7KNwif8pSnlOuR7PRGuQ6Yq1O4UskbvpvAdaZc+8pVGFwj6q+sE20LM+K5QiQaG7DKlcJ8S436cR0t11PYSdRIuvLX+HDlYTT0pV55enGR16b8S595K98aBp621VagmUrr+fTKO4Vfrww+j5Z7Tpuj7XP9csvUynAk25wvZw+/2tWhSmuC+Or7twrDjuOs4nr5QDMHZ9sRLXaH2fSzGs4inNPeuC76jW98Y7nKmuuCvGnVy9NMuZfy6LV78prCVOXp1Z0xq5dPrw2I/1K7956o5bcUN5WnV2/RYMO/li9xNYzXkY16dSYfrtTjN8fMzb/XH3r5zOUfedCWanh5Or5pfci+P8v3SG2hqFxTyxVh3ux0P+eKLjvpW74xRzlkbGIiZ9Weepf3+lHEhTbXaxOt9kjBWs+nh1u1Qhk4icBuzYV2Kx/a3brj6SRYRwnBVN+NMPT6eW/MiHzk5wpE5BM7STOcZp89sE2h5Sp+OzlUrgMTHfZcOXPqXRnHL3jPkdmg86Y2dtXaYw+zqbL6/NKdCGwSAb6JaCcCy+cG7CaQwW5zKf0w5lHrL6Khvdfmi3NkI/HAhk9NTqj1p155PM+ezMiV0C3Tko1qayLwqNXf856Dc2sOKj41bBTXwk7x2MiDfDLoCU94Qllr8nE1jH38HPdSHr0xkfymMFWZWnVXeXiWMgqT/0jbU+14zjupJydT914fmPPuaa0jcI1nz7T6kNL0ZIVYJ55br7306tjqN0vmqCpz2vsbgVZ/o63GNredmsa+YcrC7bDbVlo//sFI/ppNmH4xU9HH8IPgP1zTcRBqtY/rwLc143dzqI7ttlt9r/JIVY/vZVKOo8GcaErD17zmNeX7T3bdXvnOJHf9X2ffGI2GZ8Z3KFFkogznA9hT37+KPGp+Bm0WV+2qkvJtQL4Fx7fdDvKAVMNhE2F7of9soh7ikW1OSOysvQRnhHG+f7tpw7dB+RYn45G+fbfpPJJfInA0I8C3cG2XYfnmG99OslsQBr4RUzM71c/59rbtrBz4dmm+42vIZ1gikAjsZQTsEwqDXdFYvkN38sknDyeddFL5nrvtEt9S7J2c2yyR2bYUKj2JwD5FgH527rnnDny/kY1RV111VVFwbqI6S2SjTeS3l3ksxZn54KbnbHb9ddncjtK69p3PvYzf0Vq2Je+knZpf5DrC0dr6js56Mzba1d8brbyUhDWbTTI+HH8M8/Fz3OIR+UR/ixeV93ECg7CDaG7AEeBb3epWB7FuWadEIBFIBBKBRCARSAQSgUQgEUgEdgUB+45XOWm7K5llJvsSgWwj+/KxZaETgR1BIMeDHYE1mSYCTQSyzzWhyYhEYOMI7Pf+5m9LYgN0zbTCa7TrhEVlpPzejm7vJ0/599pJ93Xw8Gk2cuWsZ5juRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRGA/ISBFIGWWOyowW+Hbqad4Rh4+XG7sltuXWzSR5372p0JzPz+9LHsikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCicC2EagpARXWUmxuO9MKA+WpKPllE45b/pat9AfFToXmQXmSWY9EIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoG1EJBisJaYuKjUrNFtN6xWBh8m95S93XLsxfSp0NyLTyXLlAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAisGsISEnYytDHb0K56fnNyRMapZE9Fdbiux/DU6G5H59aljkRSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRGBjCNz4xjfeGK9ktHkEUqG5eUyTYyKQCCQCicAaCHz0ox8tqTaxu2mN7FdJtpO/3xm1YrgHHDtdru1gtgfgySJcj8AxxxyTWCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAnsSgVRo7snHkoVKBBKBRODoQ+AjH/lIqXRNOVYLiwhFmu0o8SKvmFfLP5Un8evyJs8p/q1y+fAej1bcVJmn4sl/Do0v5067W3WdynfT9aiVo5dHL86Xvca3FqY0xKVCU2iknQgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIrDXEJit0Pzwhz+8pexxQc37vXtLoj3mqZWzt9i36eLf9KY33TTL5JcIJAKJwL5FoDYmzw2rVbqWtka3m2HbLdN20uv95nkoTBj4OIX17B59Ly7yXEIb0y7x+/rW8vTx4lujmxMnGm8v5e/T1soRwzx/H6dwhck/xd/HpzsRSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgETiSCMxWaPpCakGMsDlun3apu7boNpeHL9tOppnLG7rt1GdJPkmbCCQCicB+RiCO39FP3WphU3Xe6TFYZVonH6XdqTpE/pSxFjaVP/ExndK0wntplLZl93i20iwJ989qKq9afC2slv9SvGt8a2E+r1q8z1d1FZ38nke6E4FEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBvYjAbIWmFr9UCe+XW3aNRmFzbL/AFnnOSR9pNsEj8lzi33R9luS9F2n/4z/+47BiTT2jqfjDGO5QQK8cvThfnLl0Po1vQ4TLL9uHKZ3i8iSwEEl7ryNQ6xs+zLt9XVrholFfwN+i9TRKF+1WWuhietHGcM9TND5sjnvddJ435fJ8VE4f5ulb7hZ9Kxw+vbhWPj583fSxzp7nXHcr71a4+NbyroWJ3tuRd/R72uj2eUQ3tOJFXJpEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRGAvIzBboekroQWwaEOjsOj26Xtuv+BWo+stuvm8a2mn4mtpthOmsu52vtsp826kbeHRCq+VaQmtnkONz5ywWl61MM9ru/GeV+wT0Q9tDIt+zy/dicBeR8D3H++m3NHv6xLj6Ae9NIrv0Xj+PffSvD2vmNbH7YQ7jg81fy/fWnnnhsG3RtvLrxa3Dg/S+GcO31j3Wl4xrJZ3LSym8/4l+Ube0e/5encrDx+OGzOXp+ef7kQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEYDcRWKTQ9Ateck/ZSysDPy2w+bQKU34+LrprNLWwmG7T/lZdNp3PfuPXexa9OOo5FV/DYlPPoZZ3LUxlWDdO6adsvyjtaVvhnibdRycCm+oLO4We+oxs5eP93q142b040chu9RO9a0Tn7Sn+Pu0Urecr9zpplHZdO+IQ/T2+sbzRT9q5Yb18anE1vjW6nQiLeUf/Tue5Tn56rrIpY3TvRLk3yfO3f/u3h3vd615Nltdee+1wj3vcoypDNhNlRCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCicC+QeCGc0uqBTTsdX//93//N+jX4kF5YlwtLNJM+TfBYyqPGN/Lk7iXvexlw7/+67/iPCLmf//3f4fLLrts+O///u9dzT/itNN+KreJPGp8amGbyKvFYyq/GI9/U+aP//iPh1/6pV/aFLstfLiG+Bd/8ReHH/uxHys29Zc5/fTTS1+Rf1P25ZdfPjzykY9ssqN/sED+/Oc/f3j1q189/Od//meTdjcifvd3f3e4053utFZWL3/5y4e73OUuw0knnbRW+nUTLW0zavfkJ3e058SpvDGt/DUeU2lIO2XEX/a69Eq/EzZlavGNcbH8MV2NPtLU/DFdjWYqbBM8WnlM1buVbrvhPt/t8vLpa1j5sOj+h3/4hyIboEBEZptrdlKmufLKK4ezzjqrW5Tf+q3fGr7v+75vUZm7DDMyEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEYE8hMFuhSalZIGvZfvGs5mZRzIdHv4/zbuXnw6bc66SZ4rk0vlcGxaFkYOHwSJl/+7d/GyjDv/zLv2yrCC984QuH173udYt5LMUU+mh6PETbo1GcaL2tOG8rfiqMeIynW8c9h0eLRuHYmzDvete7dkSh+ad/+qfD8ccfP1x66aXDe97znuG0004bvvEbv3FV5Pe///07okxEifqBD3xglY93oLy8z33uMzzxiU8c/vqv/3q44IILhjvc4Q7b7is+j6Xuj3zkI8N73/vepcnKhoVHP/rRw8UXXzz86q/+6uL020mwtM34PkK+8ns3YdEvuqm4WnxhZn/iIX/LFl3L9ulaNApv0frwuW7x7Nnw8vHeH93erzSEYeTHnvJ72k27Y96b4F/jGcOm/LEcU/TEY3y6nj/G+XRy12gUJhva6P6u7/qu4cu//MsHxuXv/d7vLSciW0pN4v/kT/6k8Pif//mfIk/shEzzt3/7t8MZZ5wxvOQlLyl5tf4o+9/8zd8Mr33ta1skGZ4IJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKwjxGYrdD0C1/U1/ujG/86P/FV2uhXPoTXjNKJzvtbYZFmE37KVuOjMivummuuGT7/8z9fwSv7wx/+8Mrdc/zXf/1X8yRCLS6eMPvUT/3U4U1vetPwmZ/5mVuyIS1lrJnaac63ve1twx/+4R8eRl6jhUj19+5emOJq9mGZuoAavQ9zpKsyzY2fW/YaHXm0wn3+0e3TeLfofFh0419qUKB587CHPWx4xjOe4YOKm+esOsXI2OZiPHk88IEPLHyvuOKKonT7sz/7s9Iuf+d3fieSb/FP8d5C7Dxz0n3P93zPcOtb37oocC+66KKBstz5zncup5Ycq+Kc4tfqzygAWooCTodG/GO+8vf6KzS/93u/V65g/Oqv/urhhjfcOuSj1PVmKl/qAo0M7o9+9KPyHma32kxtfPKJp9q02pvo5tjiL9opv+iirXQt29O3aHy46Gthiptj99IrTny8v+Wu0bbC4KE4bO/37khTCBf8+fRyt/gr3tuRdsq/ibRTecT4uXm20hGOER+5a7YPk5sNSrzTL7zwwuFXfuVXhr/6q78afvmXf5nowwyn3P/xH/+xhN/oRjcaWjJNHCNbYyKMIi1hz3ve84bzzjtvOOGEE/CuTE3G4IQm7ynqnyYRSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUTgYCGwdXV7Yd3igpkWkLzdoiErxdXsGK+i1WgVFmmiHzqM6Ddt1/JTnjFf/De+8Y1XikC++3TuuecOn/u5nzvc/OY3H77lW76lehrs6quvHu5973sP3/AN3zCgkPzkT/7kgUVFDCfZjjnmmOG7v/u7h5vd7GZlUZLwSy65ZPicz/mc4dM//dOHL/uyLxu4BhLz7//+74WeE2gYTkGgXLrFLW5ReL/yla8s4fyhZDr55JMLD/K/6qqrShwK2Ve84hXD05/+9JKWwL/4i78Yvv7rv3645S1vWa65fOlLX1po9edxnxvm02zXXctzimctDWExXSusFx7j8MuIP/6euxVPmmhY8D3nnHNWwSi1UdbJPPe5zx1ud7vbDbe61a2GRzziEcPf//3flyjCv+3bvq241Q4f97jHDZ/2aZ9WFPNqExD8xm/8RmlztFHaHO3hh3/4h0ta//dHf/RHw3XXXTeceuqpq+BP+qRPKlfP0o5qptWeab+0f3/imJNGr3nNawob6nHo0KHhUz7lU0rduOK2ZlAy0qZRxnkF4NOe9rQtvN/ylrcMX/zFX1zqD1ZveMMbVux6/ZlF+O/8zu8cbnOb25QF+jPPPLM8WxKjxHzsYx87fMZnfEYpJ/j/8z//84qvd/T6q+go01d91VcVJcFxxx1XTlDx7Hjep5xyShknUKz28uX0NfX55m/+5lJX8GPMOf/88wd48nvqU5+qLLfYtTZTG7uUyLdxwnz7jW7vV/qYJob7NLij39Nvwq08fD4qo+J8PrUwH19zK423I12Mkx863DLe7cNa4Z7Gu0WPLTfx8q9rK4/Iy4fLrTzkr6WJNNGvtDF8yq90S/KEp4z418I8T9F7W2kVJh4tW/xEz/jwWZ/1WcWLkvILvuALtox7RHAqk37/oQ99qIwjnGDHeJmGsfH7v//7y1iLnHK/+91v4Arbe97znkXGYSxms4UMytO73/3uRcbgau13vvOdJYqxiXGE94hMT8ZgzEcW+s3f/E2Rp50IJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCJwQBBYpNCMC2LCwC+gRRrFKZw0PiyGe55y+zQ+LLrFV+FTfs9XtC1bPHu2T+vpFB7DONlEHIZFu1//9V8v3+xD8cKJx5rCBWUL39JjoZDvb6Ik4jpJnVSAJ8rM973vfUXx+TM/8zPDM5/5zOFVr3pVWeRjUfFBD3pQoSdvlYGTVl/7tV87fMVXfEVZpEQR8qhHParEwxtFJz+u6HzBC14w8F3DD37wg0XRiaIDBdkb3/jGUhdOUqAAIR5lJsobneIoBO6vhY1wcaSHtRulnWO3+Pjwnlt5iCb6Ce+FEeeNaGW34hTu6TwvuWVD791K720UWPxkePY6MYOyG8U6bRFFNwo9Tsdg/Gk82iGL0w94wAMGri4+++yztyi1UJqijPunf/qn4Sd/8ieHX/u1X9tysk95s6DNgjkKd29QnPprZxU3pz2LFpu2S7kxKELpGyhQ6V+UrWa49pZTQvQFb770S790pQhmQZ1+9OQnP7nUnwV3lLa6frHXnzmJyqknysGiPd/nRKmM4Yrbv/zLvyzh8EKZ+YQnPMEXo7h7/dUTP/jBDx5+/ud/vihHWeRnowTP7t3vfnf5fij9GaVFL1/o/+AP/qB8m45NEIwnPB/SMs7w3bpnPetZpT4+b9yxzfTGLp+WNuzbccutNKIXXfSLDls0PkzuXpxovO3zmUrraT0PuXvpfdroVvrt2D5v727x9DQ1N2Ex3PvFV3S1ONHU7BZ9jV8tDJ6eh3crbm66mNanx+2NpxX/GO9pFOfDvFvxNXsuXUz7NV/zNasg+iubTpAbvPnCL/zCIk+wmeXnfu7nVmO/5AloGXsZY9kMxTWwvE8YU3/qp36qKEj5pi/fTMYw5pHvs5/97LLR6tu//duHH/zBHyxxpGM8/aIv+qLi568nY/DOQjGqsXiVKB2JQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQC+x6BRQpNX9vaYpnCoq10hCtOYdg+rOUWvY/3YTF8jj/SiF/Nhjb+anQKq/GuhYke+/GPf3w5ncAiIYt7rWveUADxfT9OQ3BCjpOXKBVkUE589md/9vCJn/iJA6csv/Vbv7UoNzkl8ZSnPKUoUzhB5w2LfygtOD2F8vG2t73tcPvb377wRfmDAobvZR177LHD3e52t3JCk9MZNYPSgzR87w9avqlFnbwRlnPDprDzfGruWn7QtcI9j5h39IuPT9MK64XH9D3aqTLU4mv8YxhXkHId6Nvf/vZyTemLX/zi4ayzzopkxX/HO96xnKz8hE/4hGLzzbW/+7u/Kyc6UXbSDmlzfHuS605rBoVdVGbW6BQ2tz2L3tsoQzm9TH6cHP2mb/omH71y64RnvIp5RWAOlIT0D3hwKpTTnJwMuvLKK1dkrf5M/6A/ocykX/z+7/9+WehHccg34kjH1Yr8UGbybVGeiTe9/urpWm6eHcpNToLOyZe6cjKbZy1FM9fycpqW8QhlL0rrKTM1dk2lr8Wv29ZrvG5wgxvUgpth0CuN7CbxREQvfS9ugu2uRbeeQy2csBge/btW8A1n5Ovh3cqmFqa4TdrbbTNsSEIe4DQ2t0asYxgXGSM48fl1X/d1Zczh9OVNbnKT4SEPecjwsz/7s4Ut4yknOhln2NDFRgw2SLGxBDkCBSqbUWSmZAzGTjampEkEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBE4WAisrdBcCoNfxPPupXwi/SZ5Rd6b9i9ZYEQZyamEOQZFyrXXXlsl5dt/KCJkWBTkZAQnJrzhxBZYcsqMU3f8UKKgpOKEBWlYmJThtObnfd7nybvF5kQeSqOTTjqpXC/HwiT1kdlPz0xlPlL2kjYTy7gOzpysfe1rXzs8//nPL1cKolyLyrSYD35O+WFosyjoTjzxxKIsK4GdPxTvKD91irJDWqLmtufIB8Upp0/vete7xqjD/FyVjOFkUsugsI8nOLkSkdNENeP7M0pVrpXVtbPPec5zSv/gClkW8HkGMpw04jmyKcCbXn/1dHPcS/KFHxspvI0bBYU/9VsIZvz1xq5W8tgn8Mcw0vbCxdun827Fz7Wn0qos3q7x7vHxaeWu8eiFef5z3D1e24nzeYtPLUxx0W7REt6Li3zW8Xv+3i1eKoNshcuupVFcy/ZpWnx92khPnMKi7ePEg3EIBeRpp51WNlcpfDs2YwQ/GcYR3SzBO4MNHpI9HvrQh5ZNVcgeKC+97EH6KRkDem1MUX5pJwKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCOx/BBYpNLUQRrW9WzDUwnwc8fopHLuVrhYew2r8ejyVb+Sj8JatfLzdoiU88o/+XtqlcW9729vKFWu1dLe1k5b+O1UsDrJwyLcvveE0GmXku1Moa/Tj6jeUTu9617u2KLa4Iq52ZSeKKRYSL7roonIdJVdRPvzhD99yglQY+vxx1zCqhcV0c/0tXq1w8Y3x0Q9dK6wXXovzecZ47/dupYl2i4aFZF0xG9NwjSin7bhGkMVkFra/4zu+I5J1/Zy04QQiVyJPGU4KQkf78oZTgLVvbvbaMyclMVok9/w4kcnpn56SUvS0d74TGU9Icz0iJ4cwfDMTRaw3XNNLHlOGU6xc58x1jr/wC79Qvmv5oz/6owN9kOsSOSkt8453vKOcAL31rW+toGL3+usWwhmeJfnOYLeIpDV20XZ9+/VuMojxPmwqztNGt/eLT8uGtmd8uhqdj8ct48MV1rI9bXSTphamcPGERmFye794iF5x8iuN7Fa86GWLr2yFR7sVr3DsaHyc3J7Gh3k3NPLXbPFQnKdXXMv2aVrpRCPb84ph8tfsyB8ahXm75mZjAteFs5mC7wbvhuEEJxs5JHfIZgMKpy15n8jMkTHY8MG7Ik0ikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAonAwUJgkUJTVdfimPzYPkxubLk9rdwx3vt9OoX7MPGQLRr5sT19K17hU7bnW3PH9KJRePQTvq7h+30ogLgq8qd/+qfLtyr9KUzPlxMWL3vZy4pSE+UUp8FYIOTEpTf3vOc9y7VwKCC5dpSTdueff375thVxxx13XPl2JuF8+w9FlE6jEcf3+WTuda97DZdddlnBnytnuXbUXxcnOmHjsVCYaLAVNsfu0bd4+vCWW3krPvoJVxi2Nz58Kk60tfQKEw9P23Irjbf5HhpX/KF45vTly1/+8lX06173unJ1KicaOanId8uWXjd4i1vcYqAN/MiP/EhRiKIYrX0Plkzhz2mcCy+8sJzE4Rts11xzTbmKEKVfNL32TDukvZEew7XKXjlIPpw85UpDvvvpr4f1+XCtKuXh26HvtesOOSEJH74dqe9ZwotrnsGLBXa+OYufk8tT5gd+4AcGrmulL93udrcr/REFKgpZNhCQL6cmKSff/WRDgE7Ainevv4pmrr0k37k8W3RTY5fattLX2rWnUfyU3eNHnE8faWtxMY2nkVt8erQtGoWL1xxbaWSTxhvPg3D55ZbtwwmTUTg2Rn65Zft4wjCi9fbHYupxS+ha/D2P6PZpYjnkr9mej+fhw1tu8VN89BMejWhlEy/3lF2jVZhseEQ3p/I5Scl3gRVfiCp/xx9/fPmWbiVqURDXz7KBhNsBGE+59p5xkvfBbW5zm7J5CvlFZkrGYLPJbVOhKbjSTgQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUTgwCAwW6GphS3ZIIBbP+9vuUUrGzqM97fcnk403i6MHC/iYpro9+m3627xjuH4MSrfx3zL/jlt9qQnPWlAccTpuZ/4iZ/YcpWb54YyBCUJp+44iYViCWWWv/oNeq5oQynDN62g48QESi1OhhF39dVXl3z4XicnKTithvIIw3eyXvrSl5YrZlEGQftDP/RDpXzQnnnmmUUBWojtT1jLjx3D5I+2T1NzQx9N5OFpanGtMPj6uOifw7eWxvNc4va8orvmJwyDQpOTLyxG04b81bSczLn//e9f4jip+Na3vnU455xzPpZwwf/FF188/PiP/3j5vivfYWNR2uPjWfGNSJTeKCRR7HEVK4r6+N1V0ky15/POO2+44IILhpve9Kbl258nnnjiKisW6jkdSVk4odz7diffpn3MYx5TcEIZzwL6GWecUbCDId+C5JuchFFmvheKH0X/lHn2s59dThzRlygD37EEdwynUin7bW0xnjzoeyg1o+n110g7xz833zm8ejRzxq7YB+AXwzbtr+URw6J/SRlIWzM1HtDVwmthnmctfmnYnLw9jdyyyc+7Y/4l8vo/xSlM/v1gxzpG/36oQ6uML3zhC4dXvepV5XvZbHbgx7uiZhhT+Ub32WefXYueHcbGFvJkPOU9wPuJb21yVTfj5H3ve9/hzW9+c+E3JWPw7U9kF9KkSQQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEThYCNzgfe9738j1iVNGu+Ol/MCObu+Hn/xTvFlYk2m5FY/taVp51MJrYZ7vdt2+XJ5XK3zpyTd4ciKMxUNOaHJVJ4qNeHrL5y03px44jedPSpKeMnDS0l9pyck96Fg4jIaFQtLEOnHajNMUKGNk4I+iKtIS3noWS8OV15Qdy+Dpe3Giq5WrFgZ9K7zHS3E1O5bP+72btDU/Crea4frhY489tvqcaS9cS+ufZ41HK4xrX1kIpy2RP4pFTj1KcVdLx1WHlAml15SptWelAX9OYLbq3WrDSu9teLFAzqJ6xFZ08JtTZtHLZkxlwd5/X1Zx9FWun9X3KhVes3v9tUbfC1uSb49PLW7O2EVdoqn1p1pYTBf98flN+UnvadbJM5Zhjn9pPnPpfV1i3XpxvswxL/llQ9tyi4+PJyzmLbqlduTbS1/LsxbW4xHzi/5a2loetbBaWsLm5KG0nm/N7cNII7+XEcSrZ3OjA2N9bRzrpWvFfehDHzpsM8ub3vSm4UUvetFw1VVXbUlWkzHY4MUtElx7nyYROBII8I36OfOqI1G2zHNvIJBtZG88hyxFIrAXEMjxYC88hSzD0YRA9rmj6WlnXY80AtnfjvQTONj532hp9Vj0YlFNi1/eHXmJNoZ7v/gozPu9uxavsJo9J+9aunXDamUVr1pcLUz0c+2WwqaWHuWkX6jkSrfXv/715TREPH3RU7S2FDc6yeHzbpVvnborzZIFXZVFaeX3di/O0y1xw7NXzphnpI3xPu8Yt9Tvefn24MNx017WVWaS/tRTTx0OHTpUTvO++tWvLtcYn3LKKUQ1DYr5VvuKiWJ79vFg0mp70M3NA1p4cYKyZ5bw83zYjNAyKJrnml5/nctDdEvyVZp17Nbzie0Z3oTV+kgM65Uj8p3yi1ekU/hO2rX69vLzZaxh4uPFJ4ZN+ZXO2+QV0/n4lntJmhbtVD1r8ZSnxW8qvMaPND7c8/Dh6+Q7hV3k7+l9OWLePs67lb4WpriWvZ33RI1n7WQ+twZwFffll1++ZVNMHEeYMKH45JaINIlAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAIHD4HZCk0tdLGQJneEg3DFa8FNYZEWf+Tj/d7t08Zw/MrL04m/j1NaHxbTzPGLzxRti64VPsWPeJSPD3rQg+aQdmne+c53lm8NskDISbDdNNSfX+05CJtaHGVUfKu8pJuiUdq5dMo3lknpY7jolQ92jUbx4iN/za7RxLDoh08trMZ/02FXXHFFuaKYq4hZjOa0zCYVb5sub/LbeQTmjF2t9kp47EORVvExPNYsxkc/9L0w5RP5zvF7vj0+ouvR1PJTulqcD/N03i2aWpjiZEOj8s1xK92UPTdv8VEZ5Meew6NFV0vrw3x+CvdhLb6Eyyid/LJb4Uv5t/h5/t4NffSLx16wKRunM/mGce+UP+8bvo3MlflpEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBA4eArOvnOX6Sb+oJnfLBirFTcEWF9KiX+lb4VN5zS2H8tmu3Sunj9v0yYbtlnu30nNFnUzv2fTilH4d2z+DddL3ytWL6+WldHPKVqPphR2t7ayHd8btTQS4uno3Ta3f7Gb+S/PSOLE0nejn1LdH08u/F6f8d9OO5enVa6fKRRmORL6xPnPKIJo5V9hH/ulPBBKBjyOQVyt9HIt01RHINlLHJUMTgaMRgRwPjsannnU+kghknzuS6GfeRxsC2d+Otie+u/WdfUKTYmnBq1dEaLSQ6N21NDV+c8MiP6VT3j6+F+fp1nWLfy/9HJpe+oMU57GQu/fcfN1rdD6+5VY+rXiFi66XT49GceInu8cPmlY6pW/F18JrYeKTdiKwlxHg6uo0iUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQERgtkLzJje5SUyb/kRgLQRqCjcf1lP+ebq1Mg+JWvxieK1Mc2iUXaRV+Lp2jV8tbF3+mS4RSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBvYLAbIXmXilwlmP/I3Dsscfu/0pkDRKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBHYFgRvuSi6ZSSKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCayDw/yDpjZ2zJBKcAAAAAElFTkSuQmCC"
- }
- },
- "cell_type": "markdown",
- "metadata": {
- "id": "ce36b3ed8e7d"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pxhwaND4_IvS"
- },
- "source": [
- "## Generate text embeddings for logs"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gjy7jR0T_eAL"
- },
- "source": [
- "### Create BigQuery Cloud resource connection"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hpQqsysu_zCr"
- },
- "source": [
- "You need to create a Cloud resource connection to enable BigQuery Dataframes to interact with Vertex AI services"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "dbWYeyEzii71"
- },
- "outputs": [],
- "source": [
- "CONN_NAME = \"bqdf-llm-embeddings\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "5_ko_ngACnPd"
- },
- "outputs": [],
- "source": [
- "!bq mk --connection --location={LOCATION} --project_id={PROJECT_ID} \\\n",
- " --connection_type=CLOUD_RESOURCE {CONN_NAME}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hS0kzr2gD0-h"
- },
- "source": [
- "Retrieve the connection service account ID"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "IG3KaOQb_doE"
- },
- "outputs": [],
- "source": [
- "!bq show --connection {PROJECT_ID}.{LOCATION}.{CONN_NAME}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "D143kdK7Hjpf"
- },
- "source": [
- "Copy the service account ID in the following parameter:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "ryD1ufggF6Fj"
- },
- "outputs": [],
- "source": [
- "# Copy the value of `serviceAccountId` field in last cell's output\n",
- "CONN_SA_ID = \"[bq-connection-service-account]\" # @param {type:\"string\"}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pa3lCS2hHqv-"
- },
- "source": [
- "Give that service account access to Vertex AI"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "h-SoTNgKHpvg"
- },
- "outputs": [],
- "source": [
- "!gcloud projects add-iam-policy-binding {PROJECT_ID} --member='serviceAccount:{CONN_SA_ID}' --role='roles/aiplatform.user' --condition=None --no-user-output-enabled"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "8HJgiz91IBHz"
- },
- "source": [
- "### Define the LLM model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "b32791b4d884"
- },
- "source": [
- "Let's create a remote model in BigQuery using the above BigQuery connection. The remote endpoint is set as `textembeddings-gecko` LLM model in Vertex AI."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "4ff3c629535e"
- },
- "outputs": [],
- "source": [
- "LLM_ENDPOINT = \"textembedding-gecko\"\n",
- "MODEL_NAME = \"embedding_model\"\n",
- "\n",
- "sql = f\"\"\"\n",
- "CREATE OR REPLACE MODEL `{PROJECT_ID}.{PROCESSED_DATASET}.{MODEL_NAME}`\n",
- "REMOTE WITH CONNECTION `{PROJECT_ID}.{LOCATION}.{CONN_NAME}`\n",
- "OPTIONS (ENDPOINT = '{LLM_ENDPOINT}');\n",
- "\"\"\"\n",
- "\n",
- "# Start the query\n",
- "query_job = bq.query(sql)\n",
- "print(query_job.result()) # Wait for the job to complete.\n",
- "print(f\"Created remote model {PROJECT_ID}.{PROCESSED_DATASET}.{MODEL_NAME}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "AXw5FKWu9WoJ"
- },
- "source": [
- "### Generate embedding for each log summary"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "AUeODs3ejZpO"
- },
- "source": [
- "The following code generates text embedding for each past action in your admin actions summary table. All records including their embeddings are saved into a new table with suffix `_embeddings`.\n",
- "\n",
- " CAUTION: This may take several minutes and incur Vertex AI costs depending on how many rows in your table."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "936b956c5a56"
- },
- "outputs": [],
- "source": [
- "TABLE_ID = f\"{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_embeddings\"\n",
- "\n",
- "job_config = bigquery.QueryJobConfig(\n",
- " destination=TABLE_ID, write_disposition=\"WRITE_TRUNCATE\"\n",
- ")\n",
- "\n",
- "sql = f\"\"\"\n",
- "SELECT\n",
- " * EXCEPT (ml_embed_text_status, statistics),\n",
- "FROM ML.GENERATE_TEXT_EMBEDDING(\n",
- " MODEL `{PROJECT_ID}.{PROCESSED_DATASET}.{MODEL_NAME}`,\n",
- " TABLE `{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}`,\n",
- " STRUCT(TRUE AS flatten_json_output)\n",
- ")\n",
- "\"\"\"\n",
- "\n",
- "# Start the query and save results in new table\n",
- "query_job = bq.query(sql, job_config=job_config)\n",
- "results = query_job.result() # Wait for the job to complete.\n",
- "\n",
- "print(f\"{results.total_rows} embedded admin actions loaded to table {TABLE_ID}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6ef250380afb"
- },
- "source": [
- "Let's peak into some of the embedded admin actions:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "f7117e41dc5e"
- },
- "outputs": [],
- "source": [
- "results.to_dataframe().head(2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "da26c0b996bf"
- },
- "source": [
- "## Search logs with BigQuery Vector Search"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "qGvFqZd6q9pG"
- },
- "source": [
- "### Create a vector index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "M1TENz6mrAuN"
- },
- "outputs": [],
- "source": [
- "sql = f\"\"\"\n",
- "CREATE OR REPLACE VECTOR INDEX `my_vector_index`\n",
- "ON `{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_embeddings`(text_embedding)\n",
- "OPTIONS(distance_type='COSINE', index_type='IVF');\n",
- "\"\"\"\n",
- "\n",
- "query_job = bq.query(sql)\n",
- "print(query_job.result()) # Wait for the job to complete.\n",
- "print(\n",
- " f\"Created Vector index for {PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_embeddings\"\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ullFyiSloDu5"
- },
- "source": [
- "Inspect vector index progress, and total storage (chargeable) from `INFORMATION_SCHEMA.VECTOR_INDEXES` view:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "M3Qi1hVsoHf7"
- },
- "outputs": [],
- "source": [
- "sql = f\"\"\"\n",
- "SELECT\n",
- " table_name,\n",
- " index_name,\n",
- " coverage_percentage,\n",
- " unindexed_row_count,\n",
- " total_logical_bytes,\n",
- " total_storage_bytes\n",
- "FROM `{PROJECT_ID}.{PROCESSED_DATASET}.INFORMATION_SCHEMA.VECTOR_INDEXES`\n",
- "WHERE index_status = 'ACTIVE';\n",
- "\"\"\"\n",
- "query_job = bq.query(sql)\n",
- "results = query_job.result() # Wait for the job to complete\n",
- "results.to_dataframe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gR6FcfBEfZFt"
- },
- "source": [
- "### Vector search across existing embeddings"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pRF7JGg_H2Xg"
- },
- "source": [
- "Pick a couple of recent suspicious actions from the summary table with embeddings, and save into a new table of actions to investigate:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "782xJrFxh3fy"
- },
- "outputs": [],
- "source": [
- "TABLE_ID = f\"{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_test_actions\"\n",
- "\n",
- "job_config = bigquery.QueryJobConfig(\n",
- " destination=TABLE_ID, write_disposition=\"WRITE_TRUNCATE\"\n",
- ")\n",
- "\n",
- "# Search for any N destructive actions (containing 'delete') over the last week\n",
- "sql = f\"\"\"\n",
- "SELECT\n",
- " *\n",
- "FROM `{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_embeddings`\n",
- "WHERE\n",
- " day > DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY) AND\n",
- " lower(action) LIKE \"%delete%\"\n",
- "LIMIT 2\n",
- "\"\"\"\n",
- "\n",
- "# Start the query and save results in new table\n",
- "query_job = bq.query(sql, job_config=job_config)\n",
- "results = query_job.result() # Wait for the job to complete.\n",
- "\n",
- "print(\n",
- " f\"{results.total_rows} suspicious admin action records loaded to table {TABLE_ID}\"\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "dS340NF0gSfa"
- },
- "source": [
- "As part of your investigation, let's confirm whether each of these actions is an anomaly or not. We can do so using a semantic search across all past actions to find out if a similar activity has occurred in the past."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "XkQkZLnMxIaK"
- },
- "outputs": [],
- "source": [
- "sql = f\"\"\"\n",
- "SELECT\n",
- " query.content as suspicious_action,\n",
- " base.content as past_similar_action,\n",
- " distance,\n",
- " base.day as day,\n",
- " base.counter as counter,\n",
- "FROM VECTOR_SEARCH(\n",
- " TABLE `{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_embeddings`, 'text_embedding',\n",
- " TABLE `{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_test_actions`,\n",
- " top_k => 5\n",
- ")\n",
- "WHERE query.content != base.content -- remove exact dups\n",
- "\"\"\"\n",
- "\n",
- "# Start the query and save results in an in-memory dataframe\n",
- "query_job = bq.query(sql)\n",
- "results = query_job.result() # Wait for the job to complete\n",
- "df = results.to_dataframe()\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "zgJ9IDVHp8vX"
- },
- "source": [
- "#### Visualize results"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pNMlbSqu4PoO"
- },
- "source": [
- "In a table:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5c29f5876f95"
- },
- "source": [
- "First, create helper functions to:\n",
- "- Reshape the dataframe to group by suspicious action and list associated similar actions,\n",
- "- Infer likelihood of equivalency (`likelihood`) for each pair given nearest neighbor distance (`distance`),\n",
- "- Apply custom table styling to highlight \"very similar\" actions (d < 0.01) that are deemed equivalent"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "oaF4oLxI8v3h"
- },
- "outputs": [],
- "source": [
- "def similarity(d):\n",
- " if d < 0.01:\n",
- " return \"Very similar\"\n",
- " elif d < 0.04:\n",
- " return \"Similar\"\n",
- " return \"Somewhat similar\"\n",
- "\n",
- "\n",
- "def reshape(df):\n",
- " df2 = df.copy().query(\"distance > 0.000000000001\")\n",
- " df2[\"suspected\"] = True\n",
- " df2[\"likelihood\"] = df2[\"distance\"].apply(similarity)\n",
- " df2.sort_values(by=\"day\", inplace=True)\n",
- "\n",
- " pivoted = df2.pivot(\n",
- " index=[\"suspicious_action\", \"past_similar_action\"],\n",
- " columns=\"suspected\",\n",
- " values=[\"day\", \"distance\", \"likelihood\"],\n",
- " )\n",
- " return pivoted\n",
- "\n",
- "\n",
- "def highlight_similar_actions(styler):\n",
- " styler.highlight_between(\n",
- " left=0,\n",
- " right=0.01,\n",
- " axis=1,\n",
- " subset=[\"distance\"],\n",
- " props=\"color:white; font-weight:bold; background-color:darkblue;\",\n",
- " )\n",
- " return styler"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "f052e822aa1d"
- },
- "outputs": [],
- "source": [
- "reshape(df).style.pipe(highlight_similar_actions)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4c6a1269aeb2"
- },
- "source": [
- "The results will be rendered in a table as follows:"
- ]
- },
- {
- "attachments": {
- "7a699ddf-ba3c-436d-a938-33505258c2c9.png": {
- "image/png": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {
- "id": "aabbd61af7df"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "673408ff9b26"
- },
- "source": [
- "In this example, the search returned similar past action by the same service account on several occasions in the past, so the suspected action is deemed **not an anomaly**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "NfLG816P4bxA"
- },
- "source": [
- "In a timechart:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5FKlNvhU33or"
- },
- "source": [
- "First, create helper function to plot the dataframe of past actions grouped by a given suspicious action:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "YHJmyNqGh958"
- },
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "import mpld3\n",
- "from mpld3 import plugins\n",
- "\n",
- "\n",
- "def plot_actions_over_time(df):\n",
- " mpld3.enable_notebook()\n",
- "\n",
- " # Set CSS to style our custom labels\n",
- " css = \"\"\"\n",
- " p {\n",
- " font-size: 14px;\n",
- " color: blue;\n",
- " background-color: white;\n",
- " }\n",
- " \"\"\"\n",
- "\n",
- " fig, ax = plt.subplots(figsize=(12, 8))\n",
- "\n",
- " df[\"day\"] = pd.to_datetime(df[\"day\"])\n",
- " for i, ts in df.groupby(\"suspicious_action\"):\n",
- " points = ax.plot(\n",
- " ts.day, ts.distance, \"o\", label=str(i), mec=\"k\", ms=15, mew=1, alpha=0.6\n",
- " )\n",
- " labels = list(ts.past_similar_action.apply(lambda x: f\"
{x}
\"))\n",
- " tooltip = plugins.PointHTMLTooltip(\n",
- " points[0], labels, voffset=10, hoffset=10, css=css\n",
- " )\n",
- " plugins.connect(fig, tooltip)\n",
- "\n",
- " ax.legend(loc=\"upper left\", bbox_to_anchor=(-0.1, 1.1))\n",
- " ax.set_xlabel(\"time\")\n",
- " ax.set_ylabel(\"distance\")\n",
- " plt.show()\n",
- "\n",
- " mpld3.disable_notebook()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "R7ABHBfO3OJG"
- },
- "outputs": [],
- "source": [
- "plot_actions_over_time(df)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ea954aabc228"
- },
- "source": [
- "The results will be rendered in a timechart as follows, where past closest matches of each suspected action are displayed in a separate line chart. The y-axis represents the distance of these nearest neighbors to the suspected action, where 0 is exact match."
- ]
- },
- {
- "attachments": {
- "cbe85182-03c5-4aa0-936b-a94252b91d64.png": {
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"
- }
- },
- "cell_type": "markdown",
- "metadata": {
- "id": "afee4a455a64"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "f293b5bcedec"
- },
- "source": [
- "In this example, the 'orange' dots are not close enough matches and therefore the suspected action of 'DeleteRoutine' is likely an anomaly that needs to be further investigated."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "3Egnvumyot88"
- },
- "source": [
- "### Vector search of a new log entry"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "WOWRvRf4iT2F"
- },
- "source": [
- "Let's pick a seemingly suspicious destructive action from your recent admin activity log stream"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Iz9R9gUTnJ-c"
- },
- "outputs": [],
- "source": [
- "inner_sql = f\"\"\"\n",
- "SELECT\n",
- " EXTRACT(DATE FROM timestamp) AS day,\n",
- " IFNULL(proto_payload.audit_log.authentication_info.principal_email, \"unknown\") as principal_email,\n",
- " IFNULL(proto_payload.audit_log.method_name, \"unknown\") as action,\n",
- " IFNULL(resource.type, \"unknown\") as resource_type,\n",
- " {PROCESSED_DATASET}.getResourceId(resource.type, resource.labels) AS resource_id,\n",
- " -- proto_payload.audit_log.resource_name as resource_name,\n",
- " SPLIT(log_name, '/')[SAFE_OFFSET(0)] as container_type,\n",
- " SPLIT(log_name, '/')[SAFE_OFFSET(1)] as container_id,\n",
- " {PROCESSED_DATASET}.getChannelType(proto_payload.audit_log.request_metadata.caller_supplied_user_agent) AS channel,\n",
- " IFNULL(proto_payload.audit_log.request_metadata.caller_ip, \"unknown\") as ip,\n",
- " COUNT(*) counter,\n",
- "FROM `{PROJECT_ID}.{SOURCE_DATASET}._AllLogs`\n",
- "WHERE\n",
- " log_id = \"cloudaudit.googleapis.com/activity\" AND\n",
- " timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 DAY) AND\n",
- " lower(proto_payload.audit_log.method_name) LIKE \"%delete%\"\n",
- "GROUP BY\n",
- " day, principal_email, action, resource_type, resource_id, container_type, container_id, channel, ip, log_name\n",
- "ORDER BY\n",
- " day DESC,\n",
- " counter DESC\n",
- "LIMIT 2\n",
- "\"\"\"\n",
- "\n",
- "# Start the query and save results in an in-memory dataframe\n",
- "query_job = bq.query(inner_sql)\n",
- "results = query_job.result() # Wait for the job to complete\n",
- "results.to_dataframe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "029c09ceb983"
- },
- "source": [
- "In the following singular SQL command, 1) convert the suspected log into a text string summary, 2) generate the text embeddings 2) and do vector search across all past actions to find out similar activity (if any) has occurred in the past:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "CEqc2MSSpkv6"
- },
- "outputs": [],
- "source": [
- "sql = f\"\"\"\n",
- "SELECT\n",
- " query.content as suspicious_action,\n",
- " base.content as past_similar_action,\n",
- " distance,\n",
- " base.day as day,\n",
- " base.counter as counter\n",
- "FROM VECTOR_SEARCH(\n",
- " TABLE `{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_embeddings`,\n",
- " 'text_embedding',\n",
- " (SELECT\n",
- " text_embedding, content\n",
- " FROM ML.GENERATE_TEXT_EMBEDDING(\n",
- " MODEL `{PROJECT_ID}.{PROCESSED_DATASET}.embedding_model`,\n",
- " (SELECT\n",
- " {PROCESSED_DATASET}.stringifyAdminLogEntry(\n",
- " day, principal_email, action, resource_type, resource_id, container_type,\n",
- " container_id, channel, ip, counter) AS content\n",
- " FROM ({inner_sql})),\n",
- " STRUCT(TRUE AS flatten_json_output)\n",
- " )),\n",
- " top_k => 5\n",
- ")\n",
- "WHERE query.content != base.content -- remove potential dups\n",
- "\"\"\"\n",
- "\n",
- "# Start the query and save results in an in-memory dataframe\n",
- "query_job = bq.query(sql)\n",
- "results = query_job.result() # Wait for the job to complete\n",
- "df = results.to_dataframe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "VrX4CSU57F-b"
- },
- "source": [
- "#### Visualize results"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Q4mphXYM7JoF"
- },
- "source": [
- "In a table:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "8_jWjpjP7JBH"
- },
- "outputs": [],
- "source": [
- "reshape(df).style.pipe(highlight_similar_actions)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ouYmslmU8SFV"
- },
- "source": [
- "In a timechart:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "0XT6wkTK8gs1"
- },
- "outputs": [],
- "source": [
- "plot_actions_over_time(df)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "959b92998af7"
- },
- "source": [
- "## Summary\n",
- "\n",
- "In this notebook, you were able to:\n",
- "- Process your Google Cloud audit logs into a summary table for easier, faster and cost-effective log analysis\n",
- "- Convert logs from semi-structured deeply nested JSON payload into a concise textual log summary \n",
- "- Generate embeddings for each log summary using Vertex AI, and store them in a BigQuery table alongside the log content\n",
- "- Create a vector index in BigQuery to semantically search all historical log summaries using embeddings column\n",
- "- Search logs using BigQuery `VECTOR_SEARCH` SQL command to detect & investigate suspicious or anomalous activity\n",
- "- Visualize findings in a table and a timechart to highlight past similar or equivalent actions, if any"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9633a0a9a970"
- },
- "source": [
- "## Cleanup\n",
- "\n",
- "To clean up all Google Cloud resources used in this notebook, you can delete the Google Cloud project you used for the tutorial.\n",
- "\n",
- "Otherwise, you can delete the individual resources you created in this tutorial, namely the BigQuery dataset `PROCESSED_DATASET` with the processed data:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "554bae6af364"
- },
- "outputs": [],
- "source": [
- "# Delete the created BigQuery dataset\n",
- "!bq rm -r -f {PROJECT_ID}:{PROCESSED_DATASET}"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "collapsed_sections": [
- "WN2LVDh0-c_l"
- ],
- "name": "bq-vector-search-log-outlier-detection.ipynb",
- "toc_visible": true
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-audit-logs.ipynb b/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-audit-logs.ipynb
new file mode 100644
index 00000000000..f8aa14819ad
--- /dev/null
+++ b/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-audit-logs.ipynb
@@ -0,0 +1,1448 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7d9bbf86da5e"
+ },
+ "outputs": [],
+ "source": [
+ "# Copyright 2023 Google LLC\n",
+ "#\n",
+ "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+ "# you may not use this file except in compliance with the License.\n",
+ "# You may obtain a copy of the License at\n",
+ "#\n",
+ "# https://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing, software\n",
+ "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+ "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+ "# See the License for the specific language governing permissions and\n",
+ "# limitations under the License."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e95c369b3b9d"
+ },
+ "source": [
+ "# Log Anomaly Detection & Investigation with Text Embeddings + BigQuery Vector Search\n",
+ "\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "61bf5ba500f8"
+ },
+ "source": [
+ "| | |\n",
+ "|-|-|\n",
+ "| Author: | [Roy Arsan](https://github.com/rarsan) |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TGE5SpdQrFUD"
+ },
+ "source": [
+ "## Overview\n",
+ "Security and IT operations teams are tasked with analyzing an increasing amount of logs such as [Cloud Audit Logs](https://cloud.google.com/logging/docs/audit). At the same time, they are faced with a number of challenges, including shortage of talent, increased toil, and increased cyber threats. By leveraging AI for log analytics, cloud administrators, security analysts, and threat hunters can keep up with these challenges:\n",
+ "- Proactively and intelligently identify cloud threats or misconfigurations\n",
+ "- Reduce the time and effort during a threat or incident investigation.\n",
+ "\n",
+ "In this notebook, we will demonstrate **Generative AI-enabled outliers detection and incident investigation** using your Cloud Audit logs in your own project.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-kHvlYPt9OsY"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "38b2358a2636"
+ },
+ "source": [
+ "### Objective\n",
+ "\n",
+ "Using [Vertex AI](https://cloud.google.com/vertex-ai/docs) and [BigQuery](https://cloud.google.com/bigquery/docs), you will:\n",
+ "\n",
+ "* Summarize and transform your logs from a verbose deeply nested complex structured payload into a natural language text summary.\n",
+ "* Create embeddings for each textual log summary directly in BigQuery using [`textembedding-gecko` Generative AI model](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings) from Vertex AI.\n",
+ "* Create a fully-managed BigQuery vector index to search all logs summaries without operational overhead.\n",
+ "* Search directly in BigQuery to find any past actions that are semantically similar to suspected current actions.\n",
+ "* Visualize findings in an interative table and timechart for rapid investigation & confirmation of such potential outliers."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3b1ffd5ab768"
+ },
+ "source": [
+ "## Getting Started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hsX82WHAR0Gk"
+ },
+ "source": [
+ "### Prerequisite\n",
+ "If you haven't already done so, the only requirement is to [upgrade your existing log bucket](https://cloud.google.com/logging/docs/buckets#upgrade-bucket) to use Log Analytics which provides you with a linked BigQuery dataset with your own queryable logs data. This is a **one-click step without incurring any additional costs**. By default, Cloud Audit Admin Activity logs are enabled, ingested and stored in every project's `_Required` bucket without any charges.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Y0PJAOju-yxx"
+ },
+ "source": [
+ "### Install packages and SDKs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "CuonDAxt-ewW"
+ },
+ "outputs": [],
+ "source": [
+ "# Install mpld3 package for interactive rendering of matplotlib\n",
+ "%pip install mpld3\n",
+ "# Install Vertex AI and BigQuery SDKs\n",
+ "%pip install google-cloud-aiplatform google-cloud-bigquery --upgrade --user"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RFcQnSibyn4F"
+ },
+ "source": [
+ "### Restart runtime\n",
+ "\n",
+ "To use the newly installed packages in this Jupyter runtime, you must restart the runtime. You can do this by running the cell below, which will restart the current kernel."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "rGUqrj9jyxOd"
+ },
+ "outputs": [],
+ "source": [
+ "# Restart kernel after installs so that your environment can access the new packages\n",
+ "import IPython\n",
+ "\n",
+ "app = IPython.Application.instance()\n",
+ "app.kernel.do_shutdown(True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "02d6dfc513c3"
+ },
+ "source": [
+ "
\n",
+ "⚠️ The kernel is going to restart. Please wait until it is finished before continuing to the next step. ⚠️\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7Eg9Mmp1xEAK"
+ },
+ "source": [
+ "### Authenticate your notebook environment\n",
+ "\n",
+ "Authenticating your notebook environment\n",
+ "- If you are using **Colab** to run this notebook, run the cell below and continue.\n",
+ "- If you are using **Vertex AI Workbench**, check out the setup instructions [here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "RjHTFGJbxXXN"
+ },
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "\n",
+ "if \"google.colab\" in sys.modules:\n",
+ " from google.colab import auth\n",
+ "\n",
+ " auth.authenticate_user()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "I0DdmYrZ-lsY"
+ },
+ "outputs": [],
+ "source": [
+ "# For debug only\n",
+ "!gcloud config list --format 'value(core.account)'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fKvFT-9d_GlP"
+ },
+ "source": [
+ "### Import libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Xrz9wjvP-mE-"
+ },
+ "outputs": [],
+ "source": [
+ "from google.cloud import bigquery\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "M_-tnDXOwY3i"
+ },
+ "outputs": [],
+ "source": [
+ "from google.colab import data_table\n",
+ "\n",
+ "data_table.enable_dataframe_formatter()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RgTWVU2z_9KS"
+ },
+ "source": [
+ "### Set Google Cloud region and project ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "wH0Qwzzt_Wq0"
+ },
+ "outputs": [],
+ "source": [
+ "PROJECT_ID = \"[project-id]\" # @param {type:\"string\"}\n",
+ "LOCATION = \"US\" # @param {type:\"string\"}\n",
+ "REGION = \"us-central1\" # @param {type:\"string\"}\n",
+ "\n",
+ "# Set the project id for gcloud CLI and BigQuery client libraries\n",
+ "!gcloud config set project {PROJECT_ID}\n",
+ "bq = bigquery.Client(project=PROJECT_ID)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BFwOxRrMA4vc"
+ },
+ "source": [
+ "## Prepare log data and convert to text"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ShreV-cE0X53"
+ },
+ "source": [
+ "You'll preprocess the raw logs that reside in your BigQuery linked dataset (or `SOURCE_DATASET`) into a summary table in your new BigQuery dataset (`PROCESSED_DATASET`). This new dataset will contain the aggregated logs (daily user actions are coalesced) which will also be converted into a simple natural language text. It will also contain the text embeddings and vector index for semantic search.\n",
+ "\n",
+ "Make sure you have **BigQuery Data Viewer** role over `SOURCE_DATASET` dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "NcQguptKCPba"
+ },
+ "outputs": [],
+ "source": [
+ "SOURCE_DATASET = \"[linked-dataset]\" # @param {type:\"string\"}\n",
+ "PROCESSED_DATASET = \"[new-dataset]\" # @param {type:\"string\"}\n",
+ "\n",
+ "TABLE_NAME = \"nb_admin_actions_summary\" # @param {type:\"string\"}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_Zb53qoUBf-K"
+ },
+ "source": [
+ "### Create BigQuery dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "tqpblzhADUn_"
+ },
+ "outputs": [],
+ "source": [
+ "!bq --location={LOCATION} mk --dataset {PROJECT_ID}:{PROCESSED_DATASET}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pgnAhPHwCu1S"
+ },
+ "source": [
+ "### Define BigQuery User-Defined Functions (UDFs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "b9jABImgMQht"
+ },
+ "source": [
+ "The following UDF helper function converts a structured log entry payload into a natural language text explaining the log entry activity of who did what, when, on which resource and from which ip and user agent. All of these dimensions are important facets to include in the text content and as a result, to consider in the embeddings vector space."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "SWSjMkDkDx3D"
+ },
+ "outputs": [],
+ "source": [
+ "# Produce user-friendly text content from an audit log entry's parameters\n",
+ "UDF_NAME = \"stringifyAdminLogEntry\"\n",
+ "\n",
+ "sql = f\"\"\"\n",
+ "CREATE OR REPLACE FUNCTION `{PROJECT_ID}.{PROCESSED_DATASET}.{UDF_NAME}`(\n",
+ " day DATE,\n",
+ " principal_email STRING,\n",
+ " action STRING,\n",
+ " resource_type STRING,\n",
+ " resource_id STRING,\n",
+ " container_type STRING,\n",
+ " container_id STRING,\n",
+ " channel STRING,\n",
+ " ip STRING,\n",
+ " counter INT64\n",
+ ")\n",
+ "RETURNS STRING\n",
+ "AS (\n",
+ " \"On \" || CAST(day AS STRING) || \", principal \" || principal_email ||\n",
+ " \" ran operation \" || action || \" over \" || resource_type || \" \" ||\n",
+ " resource_id || \" in \" || container_type || \" \" || container_id ||\n",
+ " \" using \" || channel || \" from ip \" || ip || \" \" || counter || \" time(s)\"\n",
+ ");\n",
+ "\"\"\"\n",
+ "\n",
+ "query_job = bq.query(sql)\n",
+ "print(query_job.result()) # Wait for the job to complete.\n",
+ "print(f\"Created UDF {PROJECT_ID}.{PROCESSED_DATASET}.{UDF_NAME}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ATM61oqTMmfe"
+ },
+ "source": [
+ "The following UDF extracts the resource ID that was acted on per the audit log entry. In the audit log entry, The resource ID is specified in a different resource label field depending on the resource type. That's why this UDF is needed to normalize that resource ID field."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "NYQsa3Lo2-qn"
+ },
+ "outputs": [],
+ "source": [
+ "# Deduce resource ID from a log entry resource field\n",
+ "UDF_NAME = \"getResourceId\"\n",
+ "\n",
+ "sql = f\"\"\"\n",
+ "CREATE OR REPLACE FUNCTION `{PROJECT_ID}.{PROCESSED_DATASET}.{UDF_NAME}`(\n",
+ " type STRING,\n",
+ " labels JSON\n",
+ ")\n",
+ "RETURNS STRING\n",
+ "AS (\n",
+ " COALESCE(\n",
+ " JSON_VALUE(labels.email_id), # service_account\n",
+ " JSON_VALUE(labels.pod_id), # container\n",
+ " JSON_VALUE(labels.instance_id), # gce_instance, spanner_instance, redis_instance, ...\n",
+ " JSON_VALUE(labels.subnetwork_id),# gce_subnetwork,\n",
+ " JSON_VALUE(labels.network_id), # gce_network, gce_network_region, ...\n",
+ " JSON_VALUE(labels.topic_id), # pubsub_topic\n",
+ " JSON_VALUE(labels.subscription_id), # pubsub_subscription\n",
+ " JSON_VALUE(labels.endpoint_id), # aiplatform.googleapis.com/Endpoint\n",
+ " JSON_VALUE(labels.job_id), # dataflow_step\n",
+ " JSON_VALUE(labels.dataset_id), # bigquery_dataset\n",
+ " JSON_VALUE(labels.project_id),\n",
+ " JSON_VALUE(labels.organization_id),\n",
+ " JSON_VALUE(labels.id),\n",
+ " \"other\")\n",
+ ");\n",
+ "\"\"\"\n",
+ "\n",
+ "query_job = bq.query(sql)\n",
+ "print(query_job.result()) # Wait for the job to complete.\n",
+ "print(f\"Created UDF {PROJECT_ID}.{PROCESSED_DATASET}.{UDF_NAME}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YqGoF8lfNEmL"
+ },
+ "source": [
+ "The following UDF deduces where a user or system action occurred from per the audit log entry. For example, an action may have occurred through the Cloud Console, or using gcloud CLI, or via Terraform script or another unknown client or channel."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "PfRcocm65-rk"
+ },
+ "outputs": [],
+ "source": [
+ "# Deduce channel from a log entry request user agent\n",
+ "UDF_NAME = \"getChannelType\"\n",
+ "\n",
+ "sql = f\"\"\"\n",
+ "CREATE OR REPLACE FUNCTION `{PROJECT_ID}.{PROCESSED_DATASET}.{UDF_NAME}`(\n",
+ " caller_supplied_user_agent STRING\n",
+ ")\n",
+ "RETURNS STRING\n",
+ "AS (\n",
+ " CASE\n",
+ " WHEN caller_supplied_user_agent LIKE \"Mozilla/%\" THEN 'Cloud Console'\n",
+ " WHEN caller_supplied_user_agent LIKE \"google-cloud-sdk gcloud/%\" THEN 'gcloud CLI'\n",
+ " WHEN caller_supplied_user_agent LIKE \"google-api-go-client/% Terraform/%\" THEN 'Terraform'\n",
+ " ELSE 'other'\n",
+ " END\n",
+ ");\n",
+ "\"\"\"\n",
+ "\n",
+ "query_job = bq.query(sql)\n",
+ "print(query_job.result()) # Wait for the job to complete.\n",
+ "print(f\"Created UDF {PROJECT_ID}.{PROCESSED_DATASET}.{UDF_NAME}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hWCB3WeUBiDN"
+ },
+ "source": [
+ "### Build summary table of admin actions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5lUGTp-MhqoJ"
+ },
+ "outputs": [],
+ "source": [
+ "LOOKBACK_WINDOW_DAYS = 360 # @param {type:\"slider\", min:30, max:1080, step:30}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "GPhiu82VBx6P"
+ },
+ "outputs": [],
+ "source": [
+ "TABLE_ID = f\"{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}\"\n",
+ "\n",
+ "job_config = bigquery.QueryJobConfig(\n",
+ " destination=TABLE_ID, write_disposition=\"WRITE_TRUNCATE\"\n",
+ ")\n",
+ "\n",
+ "sql = f\"\"\"\n",
+ "SELECT\n",
+ " {PROCESSED_DATASET}.stringifyAdminLogEntry(\n",
+ " day, principal_email, action, resource_type, resource_id, container_type,\n",
+ " container_id, channel, ip, counter) AS content,\n",
+ " *\n",
+ "FROM (\n",
+ " SELECT\n",
+ " EXTRACT(DATE FROM timestamp) AS day,\n",
+ " IFNULL(proto_payload.audit_log.authentication_info.principal_email, \"unknown\") as principal_email,\n",
+ " IFNULL(proto_payload.audit_log.method_name, \"unknown\") as action,\n",
+ " IFNULL(resource.type, \"unknown\") as resource_type,\n",
+ " {PROCESSED_DATASET}.getResourceId(resource.type, resource.labels) AS resource_id,\n",
+ " -- proto_payload.audit_log.resource_name as resource_name,\n",
+ " SPLIT(log_name, '/')[SAFE_OFFSET(0)] as container_type,\n",
+ " SPLIT(log_name, '/')[SAFE_OFFSET(1)] as container_id,\n",
+ " {PROCESSED_DATASET}.getChannelType(proto_payload.audit_log.request_metadata.caller_supplied_user_agent) AS channel,\n",
+ " IFNULL(proto_payload.audit_log.request_metadata.caller_ip, \"unknown\") as ip,\n",
+ " COUNT(*) counter,\n",
+ " -- ANY_VALUE(resource) as resource, -- for debugging\n",
+ " -- ANY_VALUE(proto_payload) as proto_payload -- for debugging\n",
+ " FROM `{PROJECT_ID}.{SOURCE_DATASET}._AllLogs`\n",
+ " WHERE\n",
+ " -- log_id = \"cloudaudit.googleapis.com/activity\" AND\n",
+ " timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL {LOOKBACK_WINDOW_DAYS} DAY)\n",
+ " GROUP BY\n",
+ " day, principal_email, action, resource_type, resource_id, container_type, container_id, channel, ip, log_name\n",
+ " ORDER BY\n",
+ " day DESC, principal_email, action\n",
+ ")\n",
+ "\"\"\"\n",
+ "\n",
+ "# Start the query and save results in new table\n",
+ "query_job = bq.query(sql, job_config=job_config)\n",
+ "result = query_job.result() # Wait for the job to complete.\n",
+ "\n",
+ "print(f\"{result.total_rows} admin action records loaded to table {TABLE_ID}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PXSx722dNf4x"
+ },
+ "source": [
+ "Let's peak into some of these summarized daily user actions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Ao2gLpW6NoCI"
+ },
+ "outputs": [],
+ "source": [
+ "result.to_dataframe().head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1194baac6a52"
+ },
+ "source": [
+ "Below are example daily user actions summarizing what action was taken by whom, on what resource, and from where:"
+ ]
+ },
+ {
+ "attachments": {
+ "4c5c1883-5ab6-4fca-9b4f-c7b1584d50e3.png": {
+ "image/png": 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376avzciZlMMWSJE3NjbttddeXdJsRUZVX9EFwkH5MBjaG7uo2VWNHDvqqKO2/Z4Hertp2aDJdna+tcFHwdnWzpnfHEVx+OGHe8MlSgerUVBq6LxZPcnZ21XEalFWGSM4oUzxAlBu+EYIKzF5qSjx7Jy0FZk5nghyEIIXq0Th0yxZBWCVmnZmNouewgsBISAEhIAQEAJCQAgIASFQhQDHhJ5zzjn++0IsEL3jjjsaUTj1hh0wLB5lZf0999zj/ZjwQj9h0agZfNDR2iHpPu2g1/1xb7nlFn+sHLo3xKLbdonj5PgeJbo9uzw4GYmJyJxBM5UH9HQWMmPkfOWVVxpHX9adlKUMGObhjWGU4/k44hJjPt/1bIY6kZdm0lObaQYthe0UAkzushDBxgV2a5txpNSm4zmycE6NsYax6O677/afhcLwwHjDsZh2Mlun8m981H4MCV37GwLU3dxc9SSTTOIOPPBAP15zRDPPEGMrhkvaGvPaGC9pw7TltdZay4fBIMjRzyxUY1wt7dwkfY5dp50yZ81iopNOOsnvBLUjbfm2LmM7uyfZqckxsHxKLUcYISGOjsXAwhx8Sc7kW5kYajkphN3hduw9p0dcfPHFfgEen0LgtAV2b3JcbiukvqIV1AZOnIHa3sI3xCcbWQjBZjoWqJpuF4Zp9n4gt5uWDZqAwurLyy67zBssWf3BB8kxSKKE820NVo3R+eK32Wabefcq8BGa+G4GHzlHidl9993dr3/9a3fGGWe4o48+2nf4nL1Nh2xb9XM8CUNnykoYOnbOJm+FKKtICAgBISAEhIAQEAJCQAgIASHQHQhgiOSEGyZ/mDhmAtl2zfHtTCaCTjzxxMZ3kPiuIN8XZGfn1FNP7Y8oQg9D/2mXpPu0i2D3xed7qejEn3/+uddtzTjRTorLL7+8P8aYxcjozHwbD10+R7k8rL322n4R8iGHHOK/vceEbbhrJMfP3JlDYKKTiRx2aTKRy84Srs0QE8Tt5qWZ9AirNtMsYgrfLgLMbbEI5uCDD/bfpmO3NjuzWPxSatPxHFls2Nh444393Nspp5ziNxSwsKHUH7RbDuKr/XQCRfHoaQRGG2207Fw1i804cvbss8/233RmrOYTZoyvEH6nnXaaO+KII/yGHhbx8B1riG9O8i29E044wY9/GCrDb0n7QMEf7RNexx13nD/lAEPisssu60NssskmPg9nnXWWN5TAy9IJWHS5Rf7EQImssdNOOzmMLCU5E4Mn/QXz88zV28mKzOtzWuMNN9zg5/c5StO+/90lwSYe1Fc0AdYACzpQ21v4mmh3fDeTNsMiBfukRBimlfuB2m6GDFWUf7Cz9lsBBusxClVO0bBjZ5vlzVG2rBazc/mJz7ZbFPXwHOU6fMkfEwPh6rM68RRGCAgBISAEhIAQEAJCQAgIASFQBwEmnNrRq0gDfYeJsJTewm63UDeyPOHON4qYmGbCTNR3EWi1jrBzEYMjxgsMhejgqbrQTsk5vpJfbidWnTyQL3jk8obBBWNlTOzO3HLLLRvOzCEwedXK0XTGpCovFk5XIdBbCLTaH4T55WhL5shSVGrTVXNk8KX95dpyKj25CYG+jkAn2lyqjKW5atoa41mK8MMgmBrr4Ml4nPJL8UrNoVs45EQWLNmpgxgrOWExpn333dfv4MQ9nMtPyZmcxrD//vv7sZsxnMVOKfmhymYQ50HPAwcBtbeR/cus097Ct057gzT+hqj8795waXmHprGic80ZMwlT8jMeqat1tKEfHX0rlBs8WuGlOEJACAgBISAEhIAQEAJCQAgIge5AoKTvmAIXpvvoo4/6lbzsyll00UVDL90PUAQ4UrU7CL51eefCVRlA2B22xx57DJP9eM4gfh4mQg2HqrzUYKEgQqDPI5AzZpLxUpuumiMr8e3zoCiDQqCHESjJbqW2VvIr8UwVLzWHbuFi+ZFvaq688srm3bjadzdxsHG4rpyZMmbCp8pmQBiREGgGgVLbKLWpkl+JZypvnW5vYRpxew39dP8/BIb7363uhIAQEAJCQAgIASEgBISAEBACQqAZBHbddddmgnc0LKufxxhjDLftttv6nXsdZS5mHUGgE/WDnb8cS9ybRoZO5IEJII6WjX8TTTRRR7AWEyHQHxDoRJ/QH8qpPAqBvoKA2lzXN8E3PeNxmOeUISUnZ2IcQi7pxNH3XXOnp/6OgNpb1zfYTHvrGlNPJQTaPnK2xFx+QkAICAEhIASEgBAQAkJACAiBgYoASvu6667rv3nUXUcrDVTsBkO5wvpBeVVHBsNbVxmFQB6BsE9Qf5DHST5CoFMIqM11CknxEQLVCKi9VWOkEO0hYAsvtEOzPRwVWwgIASEgBISAEBACQkAICIFBiIAp7Xw7SCQEYgRUP2JE9CwEBjcC6hMG9/tX6XseAbW5nsdcKQ5eBNTeBu+7742Sy6DZG6grTSEgBISAEBACQkAICAEhIAT6LQKh0s73K0VCIERA9SNEQ/dCQAioT1AdEAI9i4DaXM/irdQGNwJqb4P7/fdG6WXQ7A3UlaYQEAJCQAgIASEgBISAEBAC/RKBWGnnWSQEDAHVD0NCVyEgBEBAfYLqgRDoWQTU5noWb6U2uBFQexvc77+3Si+DZm8hr3SFgBAQAkJACAgBISAEhIAQ6FcIpJR2vqEpEgIgoPqheiAEhECIgPqEEA3dC4HuR0BtrvsxVgpCwBBQezMkdO1pBGTQ7GnElZ4QEAJCQAgIASEgBISAEBAC/Q6BnNKub2j2u1fZLRlW/egWWMVUCPRbBNQn9NtXp4z3UwTU5vrpi1O2+yUCam/98rUNmEyPQElGHHHEAVMgFUQICAEhIASEgBAQAkJACAgBIdBpBIYbbjg3//zze7bcx894SK/qNOr9h19cH+JnK4nqiCGhqxAY2AjEfUD8TOnVHwzsOqDS9SwCcRuLn8mN2lzPvhOlNnARiNtX/EzJ1d4G7vvv7ZINefXVV3+YbLLJejsfSl8ICAEhIASEgBAQAkJACAgBIdBvEXjttdec9Kp++/p6JOOqIz0CsxIRAv0CAfUH/eI1KZMDCAG1uQH0MlWUPo+A2luff0X9OoM6crZfvz5lXggIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgMbAT8kbMffPDBwC6lSicEhIAQEAJCQAgIASEgBISAEOhGBD788EM32mijdWMKYt3fEVAd6e9vUPkXAp1DQP1B57AUJyFQBwG1uTooKYwQ6AwCam+dwVFcuiIwzjjjeIcRhgwZ4kYeeeSuvnoSAkJACAgBISAEhIAQEAJCQAgIgdoIjDLKKNKraqM1OAOqjgzO965SC4EUAuoPUqjITQh0HwJqc92HrTgLgRgBtbcYET13EoERfvjhB60k7iSi4iUEhIAQEAJCQAgIASEgBITAoEMAxV07NAfda2+qwKojTcGlwEJgQCOg/mBAv14Vrg8ioDbXB1+KsjRgEVB7G7Cvtk8UTN/Q7BOvQZkQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkIghYAMmilU5CYEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhECfQEAGzT7xGpQJISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEUgiMkHKUmxDoCQQ++eSTnkimo2nwzVmjZu8tXu46ZMiQLl7xc+gZ+oX5CMNwX/Kr4x/zC5/DPITu7dyPM8447URXXCEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYAAiIIPmAHyp/aVIVca2vliOMM9V96F/nbIQPjQSxs8pHqU0Un4ptxTfOm4pXmH+6/BQmN5FgHeod9a770CpCwEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRANQJtGzS/+OIL949//MO99NJLbpFFFnEzzDBDdaqZEJ9//rm77rrr3GijjeYWX3xxN8ooozRCfvfdd97vxRdfdDPOOKNbaqmlukzEl+Iak6+++spddtll7qc//ambdNJJzbnL9cMPP3TXX3+9++CDD9yCCy7oZp999i7+PBDmySef9P7DeAYOb731lrv22mvdzDPP7Oaee2433HD/O+H39ttvd6+//noQ2rlVV13Vl72LY4sPTz31lOe/5JJLtsjhP9HOOusst9pqq7kf/ehHLfP55z//6TbYYAP3+OOPd+GRMoh1CdAHH+I827NdybLd29WKET/jHhuTCBO7WfzcNcWXsLF7/Jzj1467pdFMGSwO6X7zzTduxBFHTGbhvffec99//72bYIIJkv4DwZG+5eqrr3brr79+ZXFOPPFE9/DDD7tjjz22MmwcAMz33ntvd/LJJ7tf/vKXbr/99ouDdNtzM2Xstkz0AOObbrrJTTHFFG766afvgdSUhBDoikCnZICuXKuf6spH1Zz6Z4jewr0voTXLLLM4ZMc555yzmK0f//jHjn6yHb2hmEA/9UTfueuuu9yrr77qll9+eTfuuOM2StJTulBVOmSoblsv6WfdrQs1gBsgN2BpMjPX8J4i2nN8313FL8n6YV7i/JifXXP+pXzHacfPxE25hTzD9Ou4EyYXJ4zPfVXacXh7tnh107F4XMcff/zGI2345ZdfdlNOOaUbfvjhG+66GXwIMEf35Zdf+vmzwVd65+WMwayPPf30027ppZd2r7zySuXrl1xWCVGPBhgscyY9CqoSEwJCYEAh8D8LWwvFQkCabLLJ3FFHHeUeeeQRt+KKK7rNN9+8trAfJnn66ae7aaed1t1yyy2Oe4TycOBdb731PG8MmmuuuabbcccdG9Gr4lrAQw45xK277rru0UcfNacuVwaNeeaZxxEOIxz3F110USPMrbfe6jbaaCM34YQT+jI3PBI32267rVtooYXcM88843bYYQc377zzemOMBcWYcPTRR3v+pMEPg2uniDKCZTv07bffuvPOO89hmG2Hvv76a2/wjnmgrPW3n5XB8s2zKZ3mZlfzyz2bezPXOH3ixuk0w6+3w2KgDPPAAomQUMgPPfRQt9hii3kj39Zbb+1WX31194c//MHhN9DozTff9G2uTtk4shkjbx2iD0KhMXrwwQfd8ccf7+677z637777mnOPXJspY49kqJsSufHGG4dZxNFNSfUJtm+88YbbbLPNBmS77BMAV2TihBNOcFdccUUjVCdkgAazGjfNyEc12PV6kLjPrJuhTuHeavp189md4ZDT68izr732ml/EVJWX2267zR188MFVwQaEP7rHNNNM4/7yl7/48Xmqqabq0q57QhcCyFI6zbT1Kv2su3WhAVEpEoUwuRkvu4+vJb84bLPPlqVSvDhMmB+7t2vMJ+du4ar8CdepMJYm11hnCf3i+2bChnGJ10xcC88V+te//uVlMeYd9tlnH7fyyiv7uYt7773X++tvcCDAAuFzzz3XG7Kmnnpqxzjan6kdHaMT+lg76fc27tQFFkjVobpyWaxz1OGtMM0j0Kk5E72v5rFXDCEgBPoHAkOGDnA/YJRsljB2sfqanXe77767j84EOwI0xkYMDyExuTHSSCMlVywyeT/66KO7888/3xsriIcyzU5MhHF2Ic0111x+8n7sscd2CBWstHrhhRf8TstSXMsDuwMxuH766adewFtuueXMq3E97rjj3Jlnnunuvvtun092c+66667uueee82HWWWcdRzyMlEzYkN8UPfDAA27++ed3CAUTTzyxA6tJJpnEXXjhhW6JJZbwUVgxeemll/qdmykeJTeMPiOPPHKXHZ8WnrRQasA6RaX3gB98qwjFizywkzZF+I066qgNL5Qodop+9tlnDTduMCCHBN/eJlsZm8pHnD97titx7N6uoVuKZ+wWpp+7j+OEz2G6uFc9h3E7cR/muS6/MI/syg53A++0005+cQOTu2OMMUaD5SmnnOKN5Pvvv3/DjRtWoLLDM7cauVTHaTv0RXXaQIlPXP+7ZDB4IC3aam5HahDU9yFx3g477DB3//33d+mHcjzHG288d/nll7tFF13UsyUu/RQLFmJiB0Cubaf6lxjzXB7idOLnEm7wJO3w3ZBuuIs/5mfPVeVh53y4e97ioQDinqpLKRwsXulKXYdvqn/GjzKFfafxKmFaxTOXHrxL2FjauWuMPwZzxmzSG2GE/OEP+INpCnPKyS+FD/noVF1jLEJuMCrVPUu33brGeyKdXNsqvYs6+WWxFfj/7ne/s2Ilr6V0milrzLyufBTGK/WjVfmM/Uu8munbLX9xn2nuvEPqQrNjHfnLycCpvJfST/URlr+4XZp7VT+R63vALtdHGm+utFvaNtjQtm6++Wa3wAILNIKkcIMviyHZ0WkEj7jfPe2009zZZ5/teVo4rqU+MwzX0/fI/q3oVeSTk1pY3Hn44Yf7bJ900km+7ExG95QuVEqH3Rt12zp1oko/a0cX6un32sn0Wq0jpkdR98MfeeM5vPqHwN2eW7022+fF6Vj+cLf7+Br6WXwLw3MqD7Fb/Gx8StcwDQuXcqvjR5hm8lAnbN28fPTRR37xJwsJOB3KCGMGehSyP4skQorH0tCPdOmTczJZLJuEccP70jjC2INsXweHkOdAum+1PyhhwO5c5uc23HBD/97XXnttt9VWW5WieL+UPGKRSnWFMLF/Th4hLDIB7z2lC+AfU0nHiNON4+aeS/ljMX5Y70vpU5acXIaeT/0PeVl+SvpQShayeKV3ZGG4Uj7mGDhVbtZZZ230uxamrlyWCpfTOegzSDeHh6Xd29dOtDnDNzVPQPlK76nVOmu4lepVKt3c+0JOK81FldpIKh3LX7vlMz66DgwEOtHeBgYSKkV3INDyDk2Meo899phXbC1jY445pt89eckll3inK6+80k9obLPNNm6cccZx0003nbv44osteONKh3jGGWd4Y6E5EtYMXkx4IJhjzIQwDnJEFQN0VVzC01lvueWW7phjjuliLMEvpIceesgfm2tC9cILL+yef/55h6EWwoC56aabdjn+KYxv9xyJSdkxZkJM7nLsIIoGxMBBw6YcuPFcRYblWmut5XEAayZ4jDjSlqMjcWdH25FHHumFWPwtbu49PPvss34HHMdaMQEVviOETTsqljR23nlnn2/SWWaZZdy7775rWXAYhCn75JNP7leEPvHEEw2/1A1CT/iLw4R+pfs4XukZPiWqm06Kj7mFV7snTe5zP8tTHD50D/3M3a7Gt9lni9fq1dKzayt8wrh2z5X2ikKx5557emMmCxg4DppFCewERwmyHUkI/iyiwOhJX4MSh/JgbStXx9955x0fjjTGGmssH58FGfQZKbrhhhv8UdPUc+r/Pffc0whWt/4j5G233Xa+H2FykF1tlAnCQMlENtRM3nI8UcDIK0dor7DCCu7AAw/0eFJe+mn86OMgdl2wcAT8mIy96qqrvDt/cf8C7rgxsUl4jND0RxiY4cmPNFIUlhF/jpzDEEN/ya54e6dvv/12492QBu+Wd0N/xoIWxgPSt3EiTouFKeBLOI4PX2ONNXzfSDjGDRR7+DChuttuuzUUPeoSO+wnmmgiPxmLn9WjGIff/OY3bo899mgkze4advBTb0mPI32NwJ4+n/4R/rxfI3bL4k4fvMoqqzR23ubeq8Ur8Sz50bbAhvTAhmMyIY5Bp93YWIUbJw2wuAei/L///e/9DiLaCyceEJaFQPPNN58PA24YM2KiLCyA4vQFJuwPOOCABubgRful7lOXCGfvtZ26FuaBeoNCTzugLjHBkKt7xMuVNeRp91VtpzRukgcWh/EukHMwJkDN5Bd5CfmEo6M5ohIKZQCec+8cv7pl5Qj+sF/guOtf/OIXsKgtHxE21x/jV8KSBWbUOfoJ2jXtl08eWD8EDhdccAFsKvtPjGj050Z77bWXYwFNqs8kDG0DbKm/pG3pWHy7hrhXyV6p8SSXfjt1tdQX5PqeUh9pZbUr/RwyIbggI4fjZ13ccv0u/QLjJQY9xpWPP/7YJ5vLt+Wpv14Zl8ITaNhZYycx9JQuVEoHXOvqQlX6WSu6UH99r53MdyhnG1/coPBq4SxMu1fjF17r8rR8Ed7u42vsZ+mEaZibxbU4pecwfnxv/MzdnlP8Un4WL76GYeP7ZsJaXItjz+EVP3tmLOS4b2QJ2h+yGDo88iWLI1gsblSSkQmTGzeQiZHRkAfo89lRjuybotI48u9//9vrUcj+GFkPOuigFAu5FRBggT+Go1Cf+PnPf+4/RYJuQ1+90kordVkMmmOHvEK9QRblJLTwnZbkMvRGZCf0RuQAFu6j5yGnoScsvvjiftcw6ZpMlJvLysllOR0jJz/HZQz1MervFlts4fNJ/jgByuRu4iHDoaNQHuo5O+Ry6ef0ftPfWUCA3ILcyAYNo5I+lJOFiIu+TlnQs2abbTafL+MZXo2HzYlwkl5IdeWyXLiUzgH/gSqXhdhxD76tzDkRt26dJSwUzplU1atc/Ui9r9L8QlUbKelwzZbvP6XUvxCoh4DJOrrmbQoDEZuq2tGyQZPBH6MiAlNICDJMMKGo8qMjZlcjRkEmjFKT3OxYYOWYreZhgpTVhQg8EBPb4Qpu3BAOOAq1Ki5hGWCZ+GKiuEQIhuG3fBB0oFBQLMU3PzBhN6gRQiHGV9ud+f777/sJH74DiuCIQoCQVyKwhA9CIxM6GC0RyFAWICaEMfhw3BlhmFBiwIVK74H4TNLxwzj517/+1W2yySaOPELwoGFApAHmpIOxEiMMhmiIvDHRza4v3hcTtHWUk1Kj84xr/JV4xH6wi93C5xrJNfAwXs3GSYUnD0a5e/wtr2HYMLyFMf/4OY4fhrOwMb84TPhs/JqJUzc+E/AoAxC7FWi/d955p98FjhvCJEoWxPHKfK+Wvod6jLHT6n+dOo6hiXbFN3zZDUI9jon6jyGVek0f9bOf/cyveCZcM/Wfvg0DEv0NbYlVtDY5TltlNZwRZaiTtxxP+jP6LwyC4EP/yyQFR/ZiiMDv//7v/7yShNKLIZi+GoUOpYnJdSjuX8gnC1qYeKUf4DhAVgGDPSvBOcr2z3/+s9/JbmWxa1hG3g19P2HBFCMX79X6Ha4Y3jDIMvmCUgb+vB8ULYT30MBiaXAlbyijTAafeuqpvu7YRPs555zjmExhpz1jGbvlMZZDv/3tb30/B2/Kj+HXlN0YB8Yo+kDKBGF8ozwYBQlr6REGgy/HHr300kvenSMFLQ7lZ8LexjSOiYJy7xW/Es+SH+XhfXHMOe+fcYl3z7ugHVu7IQ0IdysHZaKNgT9KPhNlfAOPlZ+8c4gyMLbF9Ktf/crzBlPeJfkww+cuu+zi2wE4448xE2Mx1E5dC/MAH8Yt8ko9pUy5uke8XFlDntxTT6raTm7cpO0jK3DMPe0IYxBtE2omvyiXTGQyYUIfBlE+e5eld07YumWlPYULntjhbQZN+NShUn9chSVGaI7To81TR/geOgvP2NVGvwV2GD1tPKL8uf6TftbaLfmGN79UnwmWjD+kR19E+owFhm9Y7hB3+OdkYDBPjSep9Ev9JGmX3l+pL6C/yvU9pT4yLC/vgT6bSXTqMN9cJz9QM7jl+l1kUgznTDrSX7HgoZTvMG/98Z6+BH3FiG9lM7ZCPaULldKxfNW5VulnrehCddIdTGFMBqfM1u/Z1dwsTCeuMbbGM3YPn8P8hO7xfd1wFi8OX/Vs8exaFR7/OAxxzb10tTRS1zBeyj92y4VPuTMes0CQvnKmmWZyf/vb3/xcAYvSmG/AyITsBpVk5NK4Qb/OZyuuueYaz4tv8zG/k6LSOMJYzUI4ZHQWh6Kb2OK6FC+5DYsA8g0GQ9OD0GMZHxmHmyFkY+oOOjc8OKYYmR2qksuQT3iHyKEs1udzUchK1CH0OgxvjOEQMlFpLisnl6V0jJL87BML/pBJqLcQV9oFxkjKSnvh1CeI+sdcFnoO5WAugc9DpdKv0vuRD5EXwQT9iPqN3gqV9KGcLEQ8PhHDAkTGToysLIBivI6JuRF0fnQt9B2MqkZ15bJSuJTOMZDlMsPOrq3OOTVTZy0t2kw8L5SrV7n6kXpfpfmFUhsp6XCtlM/KqasQEAJCwBBgc2H4M/fctWWDJgMoE80xsQqQVR/W+f7kJz/xx8iyHZ9v37Gzk4n8HNFxs9OQVfjsDIGYZIqPpKCQdLghpeIyecwkORP0MTGJjaDCDyEmTsd2asbpxHwQVowPxpCQEDqY9GYy2wykKPmcZc4RkEwGYURAuEE5KRG7PJm8Y9ckuy1RTmwSmXi//vWv/aoyMwyHvHLvgQlpJvY5ioZ4c8wxh5+wZLBMEQIv+WfSjTyg0EAYMFGgqBMMnKxuYxK1RFXlLcXtLr9m82R1pLvy0y7fuuUhXBjWnkO3dvMSxy/xpk5OOumk/vg6FgPQXlAeaCd2tAeKHIoWxCQ+xgmUY4xJNgGIX506Tlz4YoihvWJooW1Zu8ZoA9EuOEKZK/0URjDKUar/cT/D7jz4kx79IbvGzAjhE4n+UnmLgvgdf83wjOOzUwiljrTAjxW+9L8ck20U9y+EZ0c35TCjBmHoH+irOHYbA3SJ6MsYDzBIoDyyUIWxJdzdTV5Ig4kYVg8zec4uSPo/Jn///ve/D5MEEzYYEjCUsWKYiR121Box8U4dw4CGsZcdKSjgvEtWsWO44Sgt0iVvZuwkfogD5eeIReuHMfAwdsVEX7/++uv7dw0+GF5Q8iGM2Rij4INyjSEZZRsq1ZUSz5If6bFKn7yDDUeUggWKbx2iDYI9uGGMq/NdHsZQjLSUh9X4rC5mrGTMYdxmMgG+LAbihzETf445gjpR1+DDOMj7YSdonbpXp6x12k5u3CQuq9spH3WWSST6AmSoVvLrIyX+6rzzOmWlHPQJvBcmZpjUZMdrM1Tqj+tgCUbUIdoRfTV1ifpMW0XGpB9hoYJRnf7TwuauGNlZwEF69E/smuOdWbvPxcM9J3vhlxtP8Aupnbpa1Rfk+p5cHxnmi3smzpAbWeDDaSS8A5NB6+JWp98N0y31mWG4/n7PJDD6BTu8oVhHwa0ndKFcOriHVNKFUvoZbbgVXShMU/ddEQjl6vC+a6jWn+CZ4ptys1Tq6kl1wxnf3riWyhnmp9PhQt7cp/jjZvMm9BnImSwCRNYzbFnAyELKKhm5NG6QPrIfO9iQpZjDYLEecovpTFyRZ0vjCP04Rh4WIyFPMI5gKCj1I6Qt6ooA+i4LFCFkKBYpMhfXDKHjUk9Y3I4stcnQhe2cbIYeVEcuQ0+jP0c2Q2dCzkZfQ6/BuHr11Vc3slM1l9UIWHFTJT+XooMZeiGyFfkzHY/yIt+i59NmOH0CA2OKSnq/hWduDQyQF6nf6FklfYg2XNJBaU/I0C8NnduEH4sl0cViYm4O2Y7PgKHThzuz68pldcNZ2oNFLqO8rc45tVNnDWeuqXqFe936QdjS/AL+uTZSpcOVdFr4ioRAdyBAf63fwMGg2TqS/9BVBScEkrvuusuvUA+/Acfq+amGHh2CYBOTfVfLVm/H/jyzzR0jH0enGfENFtsxaG4IykzGhhTHRTDA6MYKJgQUJrlYWcIEOiuWWD3FM4SAwdEMoSHPJhVxLxGKuvFBcDEiPoLdxhtv3GXHJthgDDFi0p1dR2CHgFSXUFzYKWPfxasbL3wPGB+ZWA/fFwJtHSLeHXfc4Sekeafbb7+9NxBgzATblMJVh29fD2OKYVw+3HGLr5QnF8fKav5h2Pi+9BzmxdK38Dk//I3C9M2t7rXVuGE+LS2EdI4vYnUkBiYIARAjD0I5RvNQUaMdQygJ7ExOUamOwy8mhDGEVVa9WrtmIpH0mcBmRws7KFAamTAgPAs4cvU/7mfohzCK0dcw2Y3xDuNsHbK8xWHb4QkvBFTD23hzFCnGgTqEUgjZlXv6QnCrInbfYiTBSGk7/sM6G8ZnojzsY0kvNZ5goGQcsuN7Qx7cc4Qh9Y+xgTrElUkZxgbGFo6RtX6S8DE2uEHwYJcVq9/ZOcQ4w6RATBhOmRwwQunHOAJheMUoaEZM3JhQYFxhtWOurpR4lvwwQoVGV8Y3+nImkTiWphli7E/hH/Ngsoq2xBFlRijjEDt6GStZdWzEOEgdQFmPyeqYXfGvW9diXs3UvVxZm2074bhJPeVdhd/0xlhGn5WiZvIbxi+98zCc3efKSvtkoQKTjrxTjPTht0gtfula6o+bxZL3HsouVifoc1OU6z9TYUM3+mjqI+NQSCxIYZFZXbI+hTZTGk9S/Jp59+H7K/UFpb4n10fGeWPij7EiRXVxa7bfLeU7XvyYyld/cMOggF7AziXeJ9RTulDddGIcc7oQ4WL9DLdO6ULwEvVtBEJ53+7jKyUI3XjOyYKEC6nqOQxr95ZW3WfChemk8hb6G9/UtW44SzOVVoovchRyNwspbSE3/SvyNYTxA4MmE99TFWTk0rgRp4uRlH6XRT+mMxGGPJfGEXQ7+jjkXQw+LKwjTqkfidPWs/OnR3BiF7oM750Fmc0Si/qR7Yx4nxg1oVbkslhPC+uFZxr8tTqXVZKfQ10jSCp5y/hqmzBoP4y9nKCDfItRkE0R8XwjjEp6fyohZD5kv5I+hF9JB+W0oT/+8Y/+81gs+mZctQXFYZrMzeUMsXXlsrrhLN3BIJdZWVudc6JPzul8zdRZywdXq1fc160fhG1m3ihsIyUdrjvKR15FQsAQaEZ2sji6DnwEWjZoMhnJ5C0TZeEErq3yagU6JmwYhOFBB23EKkB2+iDoUpFJF+Mfk8JGqbhM0GKwYGWYrQ7jme+8scoeQSAkjBSUxwhhCaEuPKrB/MIruxI5OiUkFAqESiZm995779DLHz/LgICxE2KSyyYsugSseMCgzK6KdghDCsfUgqmtqmcSnUnunDHA0mNQRhAFI3YmkR/cKA84hhP0FqcvX5vtJAlvdZJy2X18NT+upTRCv9w9PFJk4Ukb4jm8xy33jF9dsnSqwpfCWT7gYeHMDeM/xy/SfqxNoGgtPtS4CNF+EeIhFj7QN0AY9VGm2Y0TU6mOx2F5pu6y05O6zc+IlZvssj799NP9UTXsLFhkkUW8YbJU/+M8cXQNhgE7IogV1PR7dcjyFofliJxWecILHC0/xhujMrttupNYAUo//Nxzz7mphk6uQBwx2C5huGByBMUQQ2NMTPBgiMRYSP1idxGTq0y+0J+xQjZnxIx58X6pn9QNvodK/JgoG+OJHUVOf4vxnhXCjB/sDE4dx156ryWeJb+phxrMGD/NOMPEFv02xkwbd0uTD3HZ6jwzmUZbZ2LEDEBMvmDIZBwHM9qvjel8z5q8sHiBndndQZ2qe822nXDcpLxMoGAgjIn6EVI7+S298zCNOvf00Zy+wGKH8PuxdeISptQfN4tl3TQtXNh/Iu/YJJb5565WfzEupSa2cvFK7qXxxAyzFr+dd1/qC0p9DwsNUn2k7Ra0vDEJakfDm5td6+KGrNlMv1vKt6Xdn6/oOyx6ZKELZTWiffSELoShviody1N4Ja+xLoR/Sj/DndM3OqELwUv0HwQYZ02eDu87hY/J7DG/nHsYzsKQP8tbeCWs+cX3IZ/w3niaW/yMe8otxIgwdZ8Ja5Tia37htW64ME58bzxy+SQ8YdC/MYpwcoHp8KFBkzaHAYS2WpKRS+NGnDfkdxaFs0DTvqVuYUrjCAaj8DvczNlAuX7EeOraFQFkEvQQFoaix/GZpWaJseXv0Wk3GLRYYNzdclk4l9WMXFaSn5stv4VHL0GmQVdjQSWnxaDXxfox4Ut6v/FLXU0uSulDtNucLEQb4whfDJp/+tOf/PtiMSRtNW53NidiC2fDfFj6VfJs3XDGm3ab02UtzEC5Gr7x/A7lK+k43VFnDdNm6gdxSvMLxjN17a3ypfIiNyEgBIQACAw781oTFzo0dkRhrOPoElbUYYhkZVNsKKzDkuPomETG8BhPGLETilVNhMHQgUGB705yBCGUi8tkKDsYwx9unJufyiPfSGLFIOE5AoXz/+0YrTplsDAoHBgaWaHGahVTRMwfIQFjJ4oFuPGNCRQPjMQYafgWUYo4XhPjI3EoM7tWQ2NyKk6VGxhisMWIAMas2EEpye3M4hgLBk0UD94DRz5CvA8mp8ENIwIY864ImyNwSf1y4XPuKR45N3iU/MI0cuHMPeQV3pt/s9eQR+6+xDOME4bDHTK3/zz979nc61zrxrVwqWuYjvmbGxN4fI+FHeAoyRD31lY4/gUDDERfY4sCll12Wd8mMBZQ72gfRnXquB35yoQ334EgDzHRJlj0QBvE+MVRLtR7qJn6T1tHKcFoxIQC7QgDV47q5K2KJ0ZgFLUc0c+x+5Sy02ZR3niuu1s7x9fcmbRknIiJvp53z25VdlVhJOb9tWJQI74dm8rRtbyfI444wvdLpM/4ZIQhBmMmfR47yjEGY/jEkMCKXIzq9HH05fTPYVzjYVcMgdQLjs5KKTeEwx0+TBRTvn333bfxzSGOAqLcGBkhjHkYuaHSey3xLPlhkGKMIT36bCabKT87BxkLGIfsKHHwxNBYhziGFrJ6Fr4PcGUSjXGGcYsxgjrHohcMl3w7kiPKmIijnvD9XCa7bEdbnfSbDdOpulen7eTGTY66os+jL6PdsdCKupnaYViVX95dasc5uJTeebO4sciB72hSLzDk1yG+r8TkGFTqj+tgWSe9MEyu/+SoXL4xRHtkwpe+L6SwzyTPTESwOx9DJP0GC+NY/AUhM9KWmqHSeAKfMP2qd19Kt9QXlPqeXB9JWmF/jnzOLk1kd2RTvgdv41kVbpbvqn6XvoW2AX+olG/j2V+vTHIyCckYzIKXkHpKF6pKJ8xT1X1OPyNeSReq4iv//yBgsnOIB25G5m9Xc69ztTjhNYyXcg/d4nuLG7unngmbco/d4nDxM+FTFPKJ41Q9h3Hr3Ifp1wmfC1PKl6WBToEBCmMM8wkspGNxgunnLBBDxquSkUvjBmlx1Dj8GQ/R99GZUqdZ5cYRZGvGVORF+nXyyLG1dRcZWXl1/Q8CjIksJmEeyz5vVIVNOI6jSyOLYtTk3SCTcvICxrVOy2WluaySXBbrGCX5OdQ/qnAI/ZEDWchM3ca4OMssszTwjNNvRu8P0yjpQ1WyEHotcwb0EbRjZENrd6EcikGWT13R/pFxzz///EYW6splVeFinWMgy2UN8P570+qcU6nOxmm08lyqH/H7Ks0vlNKmXsArNW/c3eUr5Ut+QkAIDF4EWjZoAhmTvuxAZDcME/ys9mDStpljUw16VoIz2cskDhOc9mNlOkIDK88475tJZ455ZIcAAz9Uimv861xR4pnkZgcJggwTC4ceemidqF3CYFRhgppvGrDazMpC2SAGEQwzHOfB8S8cOcsqRQQUJrk4/jBF4IDAiiLCirETTzyxy/GLqThVbrw3JqDghbDGbhHygqCUIgwPrMKaauiKMIReO5IEYZjJTRQpjN3kkQlaVmc2SzllLufeDH94pCjFOwwX++MXu3Xnc7PpNRu+2bx3B394GmFkoh1BGM2Z3MNwxPfTUIZoq/QJtsuMurfkkkv6voh2YhPNxK9Tx1GkmVxjlSPHccarHeHDkUi0a1bmoYjzDQPaDf1QM/V/xx139JMN9GUojAigGElzVCdvVTxZYIGysddeeyWTwWhMn8qxqOSLPolnFjd0gth9CHYx0ScyjqAcoJhhUAZb+zZqHL70TH9NvcBYDLFriJXK9GtMEGN0pJ5DpMnED370Z/TJdkwThjX67amH7mSkTtQZ0zDIUT/iCWif2NA/xigUdb6XSloomUwCQcTlx6pglAS+W2G7Q0vvtcSz5Ieh0NKjfBhrWchjR0RhbMU4y7GDHFlEX1+HLO/gRnuN3we4Mn4wRrCwCLnBxjrGWdIjLnWRNotRszupU3WvTtvJjZtMliAn0O7AjyOH+O6Q7U4Py1+VX75hdOaZZ/pd42E87qveeRy+9Ez/Sp+MkZSj4uoQfTXfvoVK/XEdLOukF4bJ9Z+0AWROJv9oe3E9D/tM8kydxmhHm6GPYsKLeoycw6p0W0wRpl26L40nxAvTr3r3pXRKfUGp7yn1kWF/Tn2gra655pp+Yg0DFhhBJdziPJf63cUWW8zLm4wRGDZL+Y759rdn+l/KyOkPpjtwZRITrHtCF6pKpxlMqX853a6kCzWTxmALG8rrVvbYzZ7N367mXudqccJrGC/lHrpxT3ijOG74bOFiNz3/Z+GvYWl4hM/hPXMmjMv00cijyPz0z8i3TEQjcxnWJRm5NG6Qnukv9PV8O49jOVOUG0fIAwsOkYORt/mWO7oVfYWoeQTQfRhvkfXqUjiOo5vw3Xp0WWQiPsFx8803+zGo03IZ40tuLqskl8U6Rkl+jvWPupjQTtBHkbmYy8LAu88++/jocfrN6P1x+iV9KCcL0a6Zr2N3JnNszNexOQM9PZZDqQ/MiTAfi56LvGpUVy6rChfrHANZLjPs7NrqnFOpzhrvVq+l+gHP+H2V5hdKeaBe5OaNu7N8pTzJTwgIgcGNwJChBqcfmGBshzBssUrPVgm1w6sUl3QYlFMfwC7Fa9aP1UysZkRg726iPAh3Rgj4HD/HYBESq8ZQTDB8sLuFAaXTu1fIC4KsKTth+twj8KK0YFRiJ2aYbwsLbky+1MlbKGBZ/PjKOzfK5cv8O3FtNY0wn3XuU3mN0656DnmEaeIePof3sV/Io5n7OG/NxA3DhnnjHoXBCIUaYZyJXVbwsuLSFjGkjhJl1wxhMAYw6YdCZkcYGc+4jrOjCYWcCUQmS0mHtlVFGM1Sq2Cbqf+0IfrMHJat5K3Ekz6atpkyloTljfuk0K/Ve3BlpW+OUMbYlVkH+xwP3OFjRhb4UV7eFX05Bgu+12OGS8LzvsAjhQm7jKiT7eaJdIwYW1iBnhoryTu7j1P1qvReSzxLfqRHGVN5odyk2coYSP0hHu87fB+GAZgzPvBuYiI/xLN2Hvt3xzN57ETdI2+ptlNn3CQuRs86sk0pv9YHYhxOE6K6eQAAQABJREFUUemdp8J30i1VF+L+OEwvhWXoX3Vft//M9eXwT/WZhKfNWD9DuFTZcK9LuTzE6ZfefVVaVX1Bru9J9ZGp/pyxl3aU6k/IWwq3VJ5L/W5cJ8Ajl+8U755yQ+5oV68q5ZX+GSzq9BclPlV+PZUO+YjfbVXe+rt/q3WE9sh7sR84xPeGDe7dRSm5NeWWSz/MW537HJ9Umim3XHzcw/RTzzk33OtSs3mK+Vbl0fxZXMNOO4wY1BWTX7lHrgrHrToycmrc4BhOCINoXbklNY5YGVP6nPkNlmur/UE7+KTGceoRY3Vqfoe02u2n685l5WQiy4PpGFb+VD1sRy4jLrpaSpYGgzB96nbdeS/Lr12JW9KHeB/Whi0OV9oMcxZhv5IqbyxDhjy4ryuX5cKldI6+KpfFZW+3zVnZm5lzCvMQ1llOTEotpGe+gu9iNkup+gEPy3NYr0vzC1Xp0hZy88Zh+ar4yH/gI9Buexv4CKmE7SAwQjuRLS4Dam4Sw8J04ko63a3Ak0+Ei1YmclspYyw0MinEsWYl6q68xXnJ5QGFKBc2JXjl+ISCWBjGFDPcUmFSbmH8du+7i7/xDctXt4wWN1c2/EO+4XN4b+mFYXM8U+5V+TD+qbi4xemGeYt5s1OYI0+Jw3ciWCWKMMY3DzkeMySOK8KAyfFGHGGE4X377bcPg/j7XL3F03aoDRMp4ZAyOhGsmfqPMlKX6uatxDMUYEvpljAqxSv5lYyZxKNPaQa7XFrhZA1HYy4+dOcuq1gvvfRSb5xgl2RIpTRZNd5pYmzJjZXkPVevSu+1xLPkR3q5vNAWWx1nwvoTvg/Dsqcxt3RzV/JYylMuXso9LHvsTzol/7qyTSm/TKqkDMWWF+Lm3rmF6a4racdUwqPkF/Opei71n7k2B89Un5kKnypbVZ5C/xTPVPqldx/yS91X9QW5PKTaRqo/zy0Msbzk+Ju/XUv9blwnwKMuX+M/EK70z3X7i3bK21PpkMf43baT74EeN5SVkY/tOXffXXhYuiH/lFvon7onjukG8T3hzS+Mm0sn5x7Gje/DNPGLn3NuMZ/Ucyv5yfEJcUjlkXh8X4/j/dGFOAEG4sQaxv24L68jI5fGDXjX7YfitIlr1KqsafF1bQ2B1DhOvSr1xSW/ZnNReu+lcT2Vh1Q9bEcuI25K/qOMcfqlul2FSSluSRZKYZcqb64Mlq8SzhaGay5cSucYDHJZJ+acwjrLqXjMb3WKUvUD3qn3VZpfqMpP3BbC8GH5QnfdCwEhIAQ6jUBHDJqdztRg5pf6tid4cPyFfa+yt/AhffLRKUopeqFCXkonFbcUvhm/VngTx5TN3L3loYp/7B8/G592rmEe6/CpykOVv6Vh4Qwr3HN5YRKa73jw/TmM/Bwhwwpjjs2IDZocz8aR13yTDwWBb7zVOfoa4Y4jenpyR5hhUXXty3mryntf8OeoWI4E5hhOjhBioienlPWF/CoPAxOBTo+bAxOlzpdK/WfnMRVHISAEeheBUF4O5Wm77+ncxenGz7n8EC7UA8JwsV8zPEM+4b3xaDbNMLzxCPl24j7kG6YX8yZc6B8+2z0nKPHJHI5aP/jgg/1CvqmHHuW/3377+c8dhDxblZE5Gj/Mc8hT90KghEBfmMsq5U9+QqAOAq3OOdXhrTBCQAgIASHQHAIdOXK2uSQVWgj8BwGOOQgpVNRC99R9dytTzfJP5T10C+9T5TG3ON342cLlrmE64T3h42fjkXOvk3adMJZOfA3TtXut6IpR0rMQEAJCQAgIASHQXxDQ0Ur95U31Xj5brSMcg4i8bD9KYPJzfG22dBY/Fa8k66f8Um4x3zi9quc4vj2X0sr5xWkZr5R7ys3Ct3rN5Svml0o7dgufOWpW1P8QaLU/6H8lVY6FQN9AQG2ub7wH5WJwIKD2Njjec2+VUjs0ewt5pTsMAnUVvGEiDnVoJ26Kn7l1iq/xCRVPS4Or+YduJfc4HM/wzvFJhTc34sT5qsOnThhLI3UN0w3vU2HlJgSEgBAQAkJACAgBISAEBjsCJn+b3G/XEi6xnB+HNZ6xe/icC9Ose538Gs9Svi1MmMfwvso/DMs94eP0jEfsHsetejY+VeFC/1x+2s1LmIbuhYAQEAJCQAgIASEgBIRAf0RABs3++NYGSJ5bUe6aKbopgmE6pgSGbnV45sLn+OGei5NKLxc25x7zaDa9MH4ubintkl/IW/dCQAgIASEgBISAEBACQkAItIeAyd45uT1259nI4tpzu9cUv5RbKp264VJxQ7e6fCyc4WHPIa+qe+JY/KqwoX8raYXxq+5bzVcVX/kLASEgBISAEBACQkAICIG+jIAMmn357QzwvOWUvJzCmAtfgimOEz/Hcav864Qn/yk+OXfjmYpT8gtxCuOae+wWPsM3Fc7Ss2scx9y5lvzCcHXvO82vbroKJwSEgBAQAkJACAgBISAE+gMCyMumU8RX8l9Hvo/LaXFid3suyegpv5Sb8Qqvlm7d8GFc7puJVxXWsIzT4LnklwqfcqtKP4zTLi4hL90LASEgBISAEBACQkAICIGBhoAMmgPtjfaj8ow++uj9KLfKqhAQAkJACAgBISAEhIAQEAJCoOcRGHnkkXs+UaUoBISAEBACQkAICAEhIASEgBDoYwgM18fyo+wIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBBoIyKDZgEI3QkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAI9DUEZNDsa29E+RECQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQqCBgAyaDSh0IwSEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEQF9DQAbNvvZGlB8hIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIAQaCMig2YBCN0JACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACPQ1BGTQ7GtvRPkRAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkKggcAIjTvdCIFBhMDHH3+cLe0PP/yQ9eu0x5AhQ5pimcpbHbc4TPycykSct/C5zj08CTfmmGOm2MstgwDvJsQ3E0zOAwQB3vdDDz3k5p577myJHnjgAe+vepGFSB5CQAgIASEgBIRADyIgebUHwVZSQkAICAEhIASEgBAQAkJACDQQ0A7NBhRdb5566il3yy23dHUcYE9nnXWWKxn2uru43333nTv99NPdV1991d1JJfmjiKd+BE65d9qtlXRScVJunc5rjl+YdnhPeHv2Nx346wttcr/99nP8uoPuv/9+t8IKK7iRRhrJffTRR92RRJLnhx9+6M4555ykXycd+8L7ozwvvfSSIy91qTvx+f77791OO+3krr322mJ27rvvPrfjjjs6wouEgBAQAkJACAgBIRAjUFevO/HEE912220XR6/9fN5557nZZpvNLbLIIrXjdCJgd8pjnchfp3j0FXm5U+URHyEgBISAEBACQkAICAEh0GkEZNDMIProo492xKC5ww47uKeffjqTSu85f/vttw6F9K233uq1THzyySc+D+0ab0444QR3xRVXNFUOM7gRKWWsy7mnwrbi1t38W8lT3TipvMdu4TP3naBOtclm8nLbbbe5gw8+uBGFutpufW0wi272339/t+CCC7rPPvvMjTXWWJFv9z2++eabvh2ywKA7qRPvL34fdfP7zTffuHPPPdctvfTSbuqpp3bwydEbb7zhNttsM2d4dCc+f/vb39xdd93ldtttt1x2vPu2227rXnvtNXf55ZcXw8lTCAgBISAEhIAQGHwINKPXoX+99957tUCKdSwWoW6xxRbusMMOc3fccUctHp0K1J3yWKfy2Ak+nZCXO5GPnuTRV+dLehIDpSUEhIAQEAJCQAgIASFQH4GOGDQ///zz+in+NyTGky+++CIbr8Sz5Affr7/+Osm3Ks0w0s9//nN3wAEHhE7+/ssvvxzGzRxSOw3PPvts9/bbb1uQxrVUdgLl0ulU+UYYYQR3zTXXuOmmm66RJ7vJpW3+dqUMuR1DKb/4vY099tju+uuvdxNOOKGx9FfiUs4UpTDGIPD4448PEzwV1gLB334pN0vfwnTqmkvL+Ju/Xc09vOIXPqfu4zBVzykeoVscH7+UW8rdB2zhL27HuTbJe7Z042TiOhf7h8+psC+88IK74YYbwmCN+1I7SfGyiBgsYyL/V199tfvZz37md2iG/kxSxViU+GOIi+t+Ka8zzjiju+qqq9zwww8fJutIN+bTJUDwkGrv5m35z70/Cxdf4RlT7n2AXyq8xX/99dcdxkN2Qy6xxBLmnLyya/20005r1KkcPs3gnMofbowxxx133DDvPIU7OzQJTzyREBACQkAICAEhMHgRiGWenF6HzFCSGUMEY574xTrWv/71L/9ZhGWWWcYNN1zXaYRSOiYLhukhm9riMdy5ZwFajnLyWCrfIY84XyWZuBQv9OM+ls3NPyXzmR/XOD+hH/c5eTklGxK+lF4Vpjme8C35VZWB+DlK4Z+bLwl5UM4c5pQz5wcP/OPypPIRpqd7ISAEhIAQEAJCQAgIgb6LQFdNpMl83nrrrW6uueZy44wzjptsssn8pDgsfvOb37g99tijwe2f//ynN1qZoHn88ce7ySef3I077rhulVVWaawSZZcKE/rsoME9JnbOLLbYYu5HP/qRT4/jclZeeeVGsAMPPNBNMskknjc7Wt55552GXy7NRoDo5sgjj3QbbrihdyVfrEZlIpydU+Th4YcfbsTA4MHOqgkmmMCh4N1zzz1+VybPH3zwgT9GkrxBt99+u0Mhm3jiid0888zTZWch31D7/e9/76aZZhqfzrrrrttlN1gny0deRh555IYhsCptwkNXXnmlW2CBBdxaa63lMEjyfUSUEAi8OS6T9zLGGGN4JRj3Y445xv34xz/27xuc7LjHTz/91Id/5ZVXCOYwOiy//PJu/PHH97wvuOAC787fs88+63GnzpD+xRdf7P0wyJ5//vn+GFDiQs8//7xbY4013EQTTeSPRDrzzDO9e/yHYhT+8A+fuQ8p9mvm2fjEcczdriX/0I/w8XPKLQ4TP8dx8DeysGEYu7erhbdr7M5zTBhm9tlnn4YzRu1ZZ5218Uzbm3baaX0b32CDDRwrsqGwTVo93GabbXz/Qz2wOkHYu+++29c56ih1jvpw6KGH4jUM5fqxrbfe2tdldvPRlu145pdfftktvvjiyb6A9j3nnHP6uj7zzDM3+gnySxk5Upa2wcSOEbukqfMsDlhooYXcr3/9a+9Fm/zlL3/p25jlPdeWWDRB29tzzz09HqQBzqQ7xRRT+Pa0zjrrOI7riomjbscbbzzvbG0YPvR1xoeJgJjsHaT6AsLG+Q/f3+yzz94YLwh77LHHul/84hfcZvvI3Puo07dPOeWUvp9YaaWVfL/nE0r80VfMN9983of+4+abb3YhPq3gnMsffB955BE3xxxzNHKSGkvMk7rB+2F8EQkBISAEhIAQEAKDDwEWQSGTokejAz/xxBMNEGK9buedd/Z6Mboa+um7777bCBve5HjGOhaL35ZaailvkCMPnPQD5eRo/GJZkBN1cEMmZf4AnR49klNK4MkPGTRFoTyGf0mnZn6CRWzMT1B+dHjio3sj3yLHY5xNUdV8Q05PgVdO5ovnOEpzJaG8DE/kbXQKdOA111zTMa9ilEsPgyP6OHHQwTl5BL3bqMSz5JfTQ9Dh409mILdedtllPsncXAqnWPHO4/kSy6ddc3MgzC2hH6DHUJfQG03XaUVmt/R0FQJCQAgIASEgBISAEOjbCLRs0MRoxOTw7rvv7ji6BuEbowGCKRPcZ5xxRmP3HsrL2muv7QVd7v/85z/7I/+YyB9ttNH8UYDAxKQ5x9dgoEodIYoQPfroozt26bAj78knn2ys5CQ9FKIbb7zRfyMNXn/5y188+qU0c6+H+LZKlHt296DwYdTAIHnKKaf4qAjSm2++uTvooIO88RGDLMemzDDDDH7yGWWN76OhnLEyEBwoP8dWstsGIdzSgdc//vEP98ADD3gjDmW86aabfDqdLh9MSdcMUaW0fQb++4fRBUUKJREsMLRg7LVVj/DECPLqq696w+Mll1zij+y86KKLPB4olNQbwpO25QGMMWL/9Kc/9UoNSjO44k9YjJX8UMb/+te/uk022cS9//773tCJUoyB7LrrrvO53Hfffb2yjD8Giq222mqYXbJWbisbzzm3lJ/Fq3s1Hs2kEcYJ45m7pW3PFiZ+Jpy55eKYeyms+YXX8D6XBmFCwpgXGvR497ZKFmM3Rn3aAYZuVn8fffTRPnrYJqmHTEwst9xyvv/Za6+9ukyAYMxDqeVIrVNPPdXdeeedXVaBW35K/Rj1DEMbCxgwJKEoQ0ygMIEQ9wUYOldccUV3yCGH+EmDTTfd1P3hD3/wccgvk04bbbSRr8OspDfCcAZ/6N577230W7RJ+jqOnqK91WlLTFowKYBB96ijjvL9Ev0JEw2UlXYVE3kz/PGjzbEwhPLRptitCI+YqvqCOP/h++PdhAZoJsUwaJb6yNT7aKVvj8sRPvN++F4lxPjExF2ID3UcfOriXMofdX3eeed1I444ok8PvFJjifcc+kdbwFjeF48wtzzqKgSEgBAQAkJACHQPAuhfyMjIZBhrkAnQP41Mp+IZmQI5BjkS+RNjFrpkTCWeLCYNdSwMqCy8Qh5GbmXhbUmOtnyEsiwy1WOPPeYX3ZEnPuvAAmL0O3RHZDD05BdffDHOahd5rCQvEhF/dADKwJH96BTomGCADs43QJHxU1SabyjpKSWZDxk4nOMozZWE8jLvDvmYI36Ry3kHv/rVr3y2S+mdfvrpfgExxlnwR0exhcIlniW/OnpIiCfvgLJAXFNzKan5kpAH96U5kF122cWXjYXmyMcYMzEWQ83K7D6S/oSAEBACQkAICAEhIAT6BQItGzRRaDDsmaGS41FYiXfhhRd6Q9Yoo4zSmBhmshqlB0KYRjnCH8EcIygCrhFKzfzzz++NYuZmV74fxs5L23FkPPHHb/311/e7oDCSYkhAWYGq0vSBKv7I10wzzeR396y22mr+qFSLggEBQwRXdlSx4wwhOiZWzqJYrLrqql6xYlcWCinKgxFKCqspMYRiHLFvvXV3+Ug/l7blza7TTz+9W3jhhT0W7JAjv2YEIAzKBQYRJurBfr311vN1ghWyv/vd79y///1vb3AxflxRQlBwUfDAhF261C/4YtglDobiUUcd1e9owhgC3ilCQSbOSy+95MOi0INnjuJ3xXPsFsY1/zrXMJ7dWzx7Dq/mxzVFsXvVMzzqhKmTloUxfnZNpWFhm71yHCtHR9GehgwZ4k4++WT329/+NsnmJz/5iVt99dX9Ualcn3nmGd++2NGJsZN6SJ2j3bIyPUWlfiwVHrdcXwAvVoRTb8kDkw4YBO1YJvJLn2W7IXP8Q3d2a7JblXpfpy3RD3N0LCvQWc3NggmOdKaNspDg73//e8g+e0+/Dh8MerRJdiqmqKovCPMfxmdyhrGCd42x9cEHH/S7V+v0kSGfTvTtIb+693VxLuWPCSYMmiFVjSXsNCWeSAgIASEgBISAEBhcCGDAxJDIoioMdciHGJlyhKyFTozRCH2NT43E1CzPOH4dOTqWBZGTOW0HOZM8QoQhr8iVzAOwELFEdeRFZDV4cioSujcyOLIxcxDo8nzqIUWl+YaSnlKS+UgnnOOg/Lm5kjBPLGwmLIt6wWuToQt6t9xyS0c+SukxX4LsThz0I06JsYW/JZ4lP9Kro9OH+Q/vc/pTGCZ1n5sDwTjOAnPmL5CP+WHMZCFmeAxxXZk9lbbchIAQEAJCQAgIASEgBPomAv/bJtRk/jAYsdIxJI52ZAUkhgh2mnDMCKs4ESoRxiEMDRjpQiMmwjZCaYnYBYrhj6MKU8TKPAR8I5QXJuKhUprx9z8sfumKoc52NHG8iq0mxdDKDkSMqRg2UnT44Yf7iXyMDAjeUGgYCuOQDitsoZ4sH+mFafNcIgzZrBZGQY0JQ0VoeGb3JoZmVsty5JAR7xYcMHCHhBKGMkocrkZ2vKw9h1d25P3xj390iyyyiJt00kkdx9yYwhyGa+U+965yvAhPe0hRyS8XPnSvk5c6YUKenbhvtlxhmtQJFFd2V7NAgcUP7GTGMFki2/FIe8E4PtVUU9UyHJb6sVJ65hf2BRwfSjtl16gRhnkmnTpBpbaE0TYmjKBMlhgx+WP9ibnVudKX2SRIVXjrCxZddNFiUPo+JqxuueUWb9DkXbP7Hmqmj+x0317MdMazhHMpf4xp4fupM5bQB7KzQCQEhIAQEAJCQAgMLgTQPbfffnsva2LMDHdkViGBHsUOwVjfbocnabYrRyObQnblHtkoPMkFtxQ1Iy/CM5S5SI8dhDFVzTeU9JSSzBenU5orCcOyIxR52Yh5C4yaUCk9dmRiJGahHAsq0bNZbAyVeJb8SnoIxxM3Q6H+VBUvNwfCjl4WjYbzCRjo0UNZzIx8HlNJZo/D6lkICAEhIASEgBAQAkKg7yLQ8g5NvkmBIB0Sx0SaUYsdPhwBi3GC7zaY4ZBVpawaRAGyH4YA8w/5hfcYNFDGELRThAEDPkYYUTmSFmo1TeNVurJCkm9xnD70aBd2Fi655JLekJZSkjC0YFzjSF0MuhhtqsptafdW+Sz90vWuu+7yxx6lwmDQCb9Rwu5JFJNY8cHAi3LH9+GsXnDl2E4UMI7dDFdboqhxlFBMHGnDhD8GTRQdji3CWBLuII3jxM85AyTh8Cv5N8srDt/Mc518pMKk3FLphuFy9xavyt/CcWUSwRYEhO7cc+QUijvHNGMIRFGl/2iGMMCxo5cd4FVU1Y9VxQ/9WUSBUh3WX+5zizDCuHXu67alOryaCcMEhn1XsipeqS+I42688cZ+ZwE7NW3RQ7N9ZHf27XF+W3ku5Y/3ad+HhXedsYQJKeKJhIAQEAJCQAgIgcGFACceIWfxO+ecc/xnX+oigO7ForNY72yHJ2l3Uo6uWxbCNSsv1uVdNd9Q0lNKMl8q/dxcSRgWfJHDQ0IWRI8qpcfRxCwepK6cdNJJXRb2lniW/Ep6CIvyoNT8R5j3Vu5zcyDMHVCfOeHJ6KGHHvKfOOIbsyIhIASEgBAQAkJACAiBgYtAywZNvhWJkYjvN2BE4ttsPNvOOQxW7FjkI+4I7EYcVXLCCSc0jFwInnzrrQ5xXAzfsWQSmBWUGAaNSAPljh19rOrkO4p8Vw9qJ03jn7uyI5VyYnBj9wxHCI499tiN4OwQRPmBUJIw/LBaklW14EBe6wj/vVW+RkGCG76XQnlZ5Xvuuef6b1naDtwgmL/FaHHWWWf5941xitW0KFgYp0PiiEwMQhggmdhnJ9n+++/vv4uB3wQTTOC/nYk731VhtSnYQ/jZNwh55h1gYAbrOeaYw+POztAcES40yBEufo7jWpyqaxzPnnP8Q34W1q7mZ89cYz65MLG7PZeulg5hjHL3KX9zi6/sYuaIKgzPGKmZTDGiP2HChW+g8G3JWWaZxS8YMP86V45ypg4cccQR3iCKYdS+RRvHr+rHOKqVhQrxivaYD88cX8XucxZx0CcSb4899vBtPRW+Wbe6balZvqnwtiOTSRTeydJLL50K5r+dVLcviBlwTDBHRzMRsfjii3vvqj4yfh/d0beTBmT9tn9o8a+Uv6mGLsIJvxFVNZaQBRYRMaEkEgJCQAgIASEgBAYXAuiX6FLowCzaQydGj0TmTBFHzOLHqTjoRXx6IKYqnrGOFcevkqPj8M0+s3gVA15MVfJiHL70jE7Ot+eNSvMNJT2lJPMZ7/CamysJw5AXdAs+GcHOQ3QMTsTCkFdKj89boGcxx8AiT96/LQwu8Sz5lfQQ6gk6tx1rDJ6hoTEsU+o+nC/Bn5OfqOtQbg4EIyoLn48++mh/2gvzQ3z/lIXMdnKPZ6A/ISAEhIAQEAJCQAgIgQGHQMtHzvKNC77bscUWW3gDFKsEecbQZISQidEuPA4RNyZu2YXFcbSsrmMnZx1iZyeKk31Dk+84mnDOEbcY2vhGBEfKYBRBeIfaSdMzKPyxI5UycsQi6bKy8cQTT2wcncN3HVA4+KYDR2cecsghbtlll/XK6EYbbeSNeOQ7PE4mlVxvlS+VF5TfXXfd1RuwUTYoL2VHwY4JpeKll17y5eN4GXbPYcwiPAqkEcZgjOIoSxg92cXHe2OFJUcSX3nllV5B2Xvvvf3Ky2OPPdYrTsTn2xgo6Uz2880VwvKdDnbDUse4hvWSOKFhzo5lNbf4mfBG5mfPda/GOw6fcydc7Gdpm3v8TJyUW8k99OPeeHMfUuje7H3Ih3sMmuxaRHmFwmOGN9hgA2/8xo+dz4Sru+DBM/vv32GHHebWXHNNv1OXbwfRZ4T5trBV/dhiiy3mMWXiJDQ+WfzwivH1oosu8kdfM4FEHcZAT73vBJXaUqePIb3xxht9n8VEEt9dyu3QzPUFdcpLXNotCxxo4xDvvdRHxu+jO/p2JmXoPzAc8j55bpVK+WP8YpKGxRikUTWWvP/++97QX3Wcb6t5VTwhIASEgBAQAkKg7yKAsYkTftA1kWnRMZE3OZEGWSomFtCinyNnIHPYUaVhuCqesY4VxuW+So6Owzf7zCkeLHy8/fbbu0Stkhe7BK544DQVdBOOKUX3KM03lPSUksyXywJx4rmSMCzzGnwTkvcEcWoR38Y0Y15uTmXHHXf0czTowSz0RN62xYolniW/kh5C3lhMvssuu7jtttvOLyxl4V5dCudL+HTPUkst5S699FJ/Lc2BHHrooV5PQWZH7wInjJoiISAEhIAQEAJCQAgIgYGNwJChStAPKEbtELupmJxuhlDAOH4UxaEusTsPAZ70ENC32morH5WjVIxYqcoxLKkdeXGaGMV22mkni9q4YqBLKX2NAJkbjAqp8rDjkHybYYN8sGIy/CZkhuUwzs2Ub5jIHXBgZSo7X9mVxepgylBnFSRlxvgcvhfigxfKWHg0DDgSzowcYbZ598SJjVPUDQyk9h0+4sDfVvCGPLiHT4rMIBj75dzjcFXPcb4tfM49l27KPeUG/5x7zi+Vl9gtfM7dW9lyfQPtn2+ZpN4z9YV2HL5P41fnSvuizVGX6CtQqg866CDHRESOSv1YyS/Fj0mkccYZJ+XVtluqLbXN9L8MmPRikQm7S2k77EzN9VOt9gV18lrVR8bvg/DNjidV+SAN6k58PFtVvJR/Ln977rmnN+Iy+RNSaixhXMKoznHaIiEgBISAEOibCLAbrl29qm+WTLnqFALt1hEWkyLjlvQvDFMspF155ZX9gtOcLG5lKvFM6VgWL7zGslno1849MlRKV4BnlbxYN90wjTrzDYTP6Sn4dVomRZdDNky9x1J6LDZGpw51NcOkxLPkR3qxTh/yJE3k52YpnC8hjfidl+ZAyA/yOkbN/kbt9gf9rbzKrxDobQTU5nr7DSj9wYSA2ttgets9X9aWd2iGWU0J16F/6h4hNWX8S4U1N3ZdsZtxnXXW8d+TYGdnfJQkyl1oNLO4XOM0mUSOJ5LD8M3e58oTG2XIR85IUJVmM+Wr4tWufzPKCmUO3wsGE1YZszPJdupZfnI44p+rayj2/EIq5S+n2DXjHqbV7H0qnRSPVDiUzNg95ZbiF7vFfGJ/e47DlZ5jP+MRX8P6EPtRX+J2E4cpPXOcKceY8k1LVviiJK+wwgqlKNm6RaRcvcsx7C5jJunFbSmXh3bd2UVdl0ptrS6PMFxVHxm/D8KX+o2Qd937OI268VLhcvnbfvvtHbtO+fZyeJJBXBYEQRbusPtcJASEgBAQAkJACAxeBJrRIZE/6sgzJZ4pHSuFfp10UvGq3ChDjvAr5T0XL3YP06gz30D4nJ6CXyzHxek1+4xulcO3lB6LE3NU4lnyI72cDke8VnWCEE/SiKk0B8ICWZEQEAJCQAgIASEgBITA4EGg5W9o9gZEHB3KdyMwZCLUPv300/4Ind7Iy2BNE+Nj6hsszeLx8MMP++9s8C47sQOq2fRRuOr84FsnXLNhWuXbarxm89dOePIIGY//PPXsP9/Txch75pln+uOnX3jhBcf3ZERlBJi04kipOiucO9UXlHM0sH35Tuz111/vj2svlZR6zLFrfGdYJASEgBAQAkJACAiBEgLoavGC0VJ4+f0PAc03/A8L3QkBISAEhIAQEAJCQAgIgb6IQEeOnO2LBVOehIAQEAJCQAgIASEgBISAEBACPYWAjlbqKaT7bzqqI/333SnnQqDTCKg/6DSi4icEygiozZXxka8Q6CQCam+dRFO8YgT61Q7NOPN6FgJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYGAjIIPmwH6/Kp0QEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQ6NcIyKDZr1+fMi8EhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEBjYCMmgO7Per0gkBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBfo2ADJr9+vUp80JACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJgYCMgg+bAfr8qnRAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBDo1wiMQO4/+OCDfl0IZV4ICAEhIASEgBAQAkJACAgBIdCbCHz44YdutNFG680sKO0+joDqSB9/QcqeEOhBBNQf9CDYSkoIDEVAbU7VQAj0HAJqbz2H9WBKaZxxxvHFHWHIkCFu5JFHHkxlV1mFgBAQAkJACAgBISAEhIAQEAIdRWCUUUaRXtVRRAceM9WRgfdOVSIh0CoC6g9aRU7xhEBrCKjNtYabYgmBVhBQe2sFNcWpi8AIP/zwg1YS10VL4YSAEBACQkAICAEhIASEgBAQAgkEUNy1QzMBjJwaCKiONKDQjRAY9AioPxj0VUAA9DACanM9DLiSG9QIqL3lX/8333zjvv76ax+AjYbtEHa9Zqk30kzlMcxH1b35gx33/sjZFFO5CQEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEh0B4CGCHNEGlXOJrRrsQ9DF8KV/JL8SilnQof8q/yD8OG6RDPnnP3xCWM+VtaMmiGqOpeCAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACHQQAYxyZpgL2abcQv/cfbPxzIgY8muWRyl87BemF/qZoZJ82H0YNsyf3Vs4GTQNEV2FgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEQDcg8P333ze4VhnxGgH/exMaBWM/e7YwKd7mZ2FTYcwvvMbxzC/nnvO39IgX34duFj++EkcGzRgVPQsBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBDiGA0S40AtYx4pF0GCfMSs49F8eMiMYjjB/75XiEcYxPLqz5G2+LyzP35m7hjE/K3cLIoGlI6NorCHz88cfZdK2CpwKUKnUqfCfc4vyEz3ZvV9IL7y390C1Xhtg9fjZepWuYjoVLuZlfJ6/t5He88cbrZFbEqwIB6kQr76uCrbyFgBAQAkJACAgBISAEOozA559/7jkiv5lcH18JYG4dTt6zqyM3xumHz6n70M3yHLuV0k35pdxyvHGP0yuFNb/ctZR2Lk677tKh2kVQ8YWAEBACQkAICAEh0DMIIHfGsqc9p+RI84tzV9c95mnxYnf4m1+cVvgch4mfw7DhvYWzdHnmPr6Gcew+jIPb8DvvvPO+P/rRj8y/6esXX3zhbr/9dnfrrbe60Ucf3Y0//vhN87AIKGlXXnmle/nll93kk0/uRhjhf/bW7777zl177bXuhhtucBjBpp566i4T8aW4xv+rr75yF198sRt33HHdmGOOac5drh9++KG74oor3J133ulGHHFEN/HEE3fx54Ew//znP92Pf/zjYfxCh7feesun980337hJJpmkS34tXJ08Wdhmrk899ZR75JFHPE7NxIvDnnXWWZ7HyCOPHHvVfgarpZde2m233XbDxPnyyy+HcevLDtb4LI/2HF/xN7c4bO7Z3Otc44Ycx4nTxj/lFsbDP/6F/nXvq9IJ+Vh65kabG3744e2xy/W9995zn332me9nungMoAf6lksuucTNNttslaU68cQT3amnnupWWmmlyrBxAHDfe++93frrr+/70yWWWCIO0m3PzZSx2zLRA4xvuukm9+233zpNMPUA2EpiGAQ6JQMMw7jCoa58VMGm33r3Fu59CbBZZpnFLbjggl7uLeULGZrxqx29ocS/t/w++eQT145ehb6DDnLXXXe5ySabzI066qiNovSULlSVDhmq29Y7pZ81QBgAN+3UEXTKUHaO7w0e3LuDTP+oyzvMR+re3OxqfONn3FNuuKfylHIjrFGKV8qN8Dl345W6EqeVeCleVW6WFvMwRrThl156yfdFww03nDnr2gcRaKc/qFMc6sGbb7454MbaOmUnzGDXx55++mk355xzuqFzvpWQDVS5LC54d7e5OL1WnwfLnEmr+Che/0CgU+3t/vvv9zqR6UWPP/64u+WWW9xjjz3W5ffKK6+46aabrgHO119/7W1V00wzTcONePfee2+XePAhLHYbCP3lqquu8rLUFFNM0cU2ZYxStpz333/f25PCfGEzQ6eD3n33XXfNNdf49OFbJa9aWjmZMuVucqHFtau5x3HM3cLVuaZ4xPGMb3gNw4Q8wnsLk3IzP3Dj15aEi4DEiznqqKO88WzFFVd0m2++eUsC/Omnn+6mnXZaXym5Z4KDymi03nrred4vvviiW3PNNd2OO+5oXq4qrgU85JBD3LrrruseffRRc+pyZdCYZ555HOEwwnF/0UUXNcJgtN1oo43chBNO6Mvc8EjcbLvttm6hhRZyzzzzjNthhx3cvPPO68LzkS1KVZ4sXLNXykhDbYeYjD/vvPMchtl2iI6BupKisHL3h3vKkMqnudvVGl8YNvQL3Uv3cRyeIYvzn6f/PYfudl/nSt1Mhcu5p8J2wo3OPiQU8kMPPdQttthi3vi29dZbu9VXX9394Q9/cPgNNELhpM3VKRvCAUbeOkQfhEJj9OCDD7rjjz/e3XfffW7fffc15x65NlPGHslQNyVy4403OgStwUJvvPGG22yzzWrV3cGCSU+W84QTTvDCs6XZCRnAeNW5NiMf1eHX22HiPrNufjqFe6vp181nd4ZDTkfBq6LXXnvNYZypottuu80dfPDBVcEGhD+6Bwr3X/7yFz8+TzXVVF3adU/oQgBZSqeZtt4p/WxAvNwOFcJkbdjZvV1Dt/De/Nu9tsIzjBPex3mJ/eLnOHzpuU5cwkAhn/g59GvlvtP8UnkI9TTS+9e//uVlMeYd9tlnH7fyyiv7uQsm7kSDBwHG1nPPPdcvKGcTAONof6Z2dIxO6GPtpN/buFMXXn311VrZqCuXxTpHLeYK1DQCnZoz0ftqGnpF6CMIYPw74ogj3EwzzeTmm28+9/rrrzdyRr+MkTP8YZdi0wfE3Ofuu+/uN6itttpqjXjcPPvss13iwYNFHywohYiLYfO6665zZ599tptooomSNpGULefhhx92W2yxhbcjYUvih2wG0cfOPvvs7rTTTnOXXXaZO+aYY7rIoSk5z9yIb/e5KzJhKBfmwuFeN1yJR8kvxz92Lz3XKfOQoQPcD2YtJkJdwtjFap8NNtjAVxTiMcGOAI2xEcNDSExujDTSSEkLNJP3rCo8//zzvbGCeCjTM844oxfGqRRzzTWXn7wfe+yxHZUXa/YLL7zgJp100mJcywMTyxhcP/30Uy/gLbfccubVuB533HHuzDPPdHfffbfPJ5Vs1113dc8995wPs8466zjiYaRkwob8puiBBx5w888/v6+w7PAEKxrEhRde6MKdUHXylOKP0Ycdk6kVl6RFpQDrFJXeA351dmJSccnDaKONlkrC+9nKCQKgRC255JJ+d10c4YMPPoidevW5tEKCcodkz3bFz+7tGrqFcXP3YfrhPeHj5xSPMF38q55jHha+TlpxXJ5biWdpEn+UUUbpsrNhp5128osbmNwdY4wxCOLplFNO8Uby/fff35z8lR2/pV2epTpO26EvqtMGSnxoG2H975LB4IG0aKvkt4pSeTvssMP8QBz2Qzme7BK8/PLL3aKLLuqTIi79FMbTmFiNlGvbqf4lxjyXhzid+LmEGzxJO3w3pEt9qaKq8tCPpvpSFEDcUzuGUzhU5QN/6jp8U/0zfpQpVXdKmFbxzKVHfkrY4F+iGH8EP8Zs0gtPV4h54A+mKcwpJ78UPvDpVF2Ld3qX6p6l225d4z2Vxs3Su6iTXxZbgf/vfve7GPIuz6V0milrF6ZDH+rKR2G8Uj9alc/Yv8Qr1X+G+Ujdx32mheEdUheaHevIX04GTuW9lH6qj7D8xe3S3Kv6iVzfA3a5PtJ4c6Xd0rbBBnn+5ptvdgsssEAjSAo3+HKSCDs6jeAR97sofSiT8Ayp1GeG4Xr6HmW1Fb2KfK666qp+cefhhx/us33SSSf5sjMZ3VO6UCkddm/UbevUiZJuZ++lVV3I4vfHazt1hPGAuh/+wIDn8OofAnd7buVa1d+l/C0/YXrmlrsS1vzCeKX7OO34uRTX/OI04+dcOHNPXevmo064XH5IN/T76KOP/OJPFhLMPPPMjWxhzECPQvZnkURI8Vga+sGbPjknk8WySRg3vC+NI4w9yPZ1cAh5DqT7dvqDHA6cdsZE7oYbbujf+9prr+222mqrXPCGe0oeMc9SXSFM7J+TRwiLTMB7T+kC+MdU0jHidOO4uedS/liMH9b7UvqUJSeXoedT/0Nelp+SPpSShSxe6R1ZGK6UjzmGJ5980s0666xd+gr868plqXA5nYM+g3RzeJBuX6BOtDnDNzVPQBlL76nVOmvYlepVKt3c+0JOK81FldpIKh3LX7vlMz66DgwE2mlvzF+iA2EbwqDJwuFQbwwRov+ZY445HDrUUkst5Zgnpg/EbrTlllsmbREWH/sSuzrZUDfOOOP48ZN+2PQxNsVh5wp3uuf0F+ZXL7jgAj/3avzt+pvf/Ma988473uaEGyfzsXHHqI4sFMp9xKt6Nt7xNZVWyS1Op07aqTjEC9OJ7+2Za51fyzs0MeqxjRbF1ohjXNk9yYuBOD6WCY1tttnGVwwqCUe+xkSHeMYZZ3hjofkRlh2TEBMeCOYYMyGMgzPMMIMfoKviEp7OmkqMBbx0DNRDDz3kFllkkQbACy+8sHv++ee9oRY+GA423XRTf2QtzzmaYIIJfNntuFomd6effnqHomFUN08W3rBca621PA5gzQSP0dxzz+1++ctf+qN02dF25JFHeiEWf4ubew+sUKAhcRQv7yt8RwibNFaINGjE4E/6yyyzjN8y7T2H/mEQpuwcF8yK0CeeeMK8mrpS8Zv9lRJI8cqFT4U1tzAObjGZW3iN73lO/YyX+fEc3qeeLU7KL4xr91zDsKG73RtPe666Wni7VoVP+Ydx7Z4r7RWFYs899/TGTBYwXH/99X5RAjvBUYI4HhpiwGERBUZPG4RQHhDSoFwdZ0AhHGmMNdZYPj4LMmifKeLIa47So55T/++5555GsLr1HyGP45dpb0wOsquNhRYQq4PsiNJm8pbjiQJGXlk4sMIKK7gDDzzQl5Xy0k/jRx8HseuChSPgx2QsRywYxf0LuONG/094+lX6IwQHePIjjRSFZcSfI8sxxNBfsive3unbb7/deDekwbvl3dCfIZgwHpC+jRNxWixMAV/C8c7WWGMNv9uXcIwbKPbwmXLKKd1uu+3WEAaoS+ywZyUWk7H4WT2KcUAo2WOPPRpJs7uGHfzUW9I7+eSTG35gT99J/wh/3q8Ru2Vxp06sssoqjZ23ufdq8Uo8S360LbAhPbDhmEyIVW+0h3Cs4qQBFvdAlP/3v/+930FEe0G4IywLgRAyIXCLDQ+4UxYWQHH6AqcxHHDAAQ3MwYv2S92nLhHO3ms7dY10jag3KPS0A+oSEwy5ukecXFmNX3itajulcZM8sDjs/9k7DzBbiuLtD4p/MYs5exGzmBWzYMKECbMoYs6IWZAPFRVFMYsoZjCjGDBHzDlnxZwz5qzf/kbetW7RXd1zztm9u3ernme3p1N1zzsdqqu6+/AtkHMwJkBT6ou8hHxy0EEHDTe4wQ3G/FYGIKD2zYnrfVd2FNpx4bDDDhtuc5vbwKJbPiJtbTwmLsKSDWa0OcYJ+jX996Mf/ejyOAQOLCCg1vjJYojxXHTAAQcMbKApjZmkoW+ALe2XslWO8su1uLdkr9J8Uit/nrYajQW1sScaI/WuchnnkAnBBRnZzp+9uNXGXcYF5ksWs8wr+t31Wr1Vp/XqMi/ZG2g4WaObGFZrLRSVA669a6FFrs/W6/dciXpLjra8CYOsW0pn80x5Fi/r2vw2XM82nmfCI9fG6Vm8Su7IbOmf4mp+hZfcVl7Fyy3xqIUpj3d9eh9f8itPLU7hyMT8VAyyBP0PWYw1PPIlmyNQCIoiGZk0tXkDmRgZDXmAMZ8T5ci+JYrmkR/84AfjOgrZHyPrwQcfXGKRYQECKGAxHNn1xK1vfesB2Yy1DWM117rbzaA1dsgrtBtkUXR59ptGchnrRmQn1o3IAWzcZ52HnMY6Ydddd10+mSKZqKbLqslltTVGTX7272jXY7RfTs5QT+qH/ktyN/mQ4Vij8D60c07I1cqvrfslf7KBALkFuZHT0qJoPVSThcjLep13YZ3FT9NQrxKJh3QinFiy1CuX1dKV1hzw31rlMosdz+A7i86JvL1tlrSQ1Zm02lWtfZS+V6RfaPWRaA039f3++5b5PxGoI8DNfJzQxJjYImQedHYcpIL4eS30dKylWsT4hS0FnSOEEZY5QsTcyJgoimw5zM3o2dA1ScerfPTp3XbbTd7RiIoMJ5I8J7cU7sPwK33Ey6fxfstHZfSGiZdNb+ti+fk0pby1NEprec9s0GTyx6iIwGQJAQEFE42JPz4apxo5vYnCqKTk5jQQO8e0mwcFKbsLEXggFNveEo9wwFWorbzkp4Gi+EJRHBGNj3cSqRFbQVFxkQsmnAYVIRSyO8qezuytk3iAJXwQGlHoYLREIGOxAKEQxuDDrgXS0MmYcKHoO5AfJR1/HOl+3vOeN+y9994Ddz9D8FCDoQwwpxyMlXRQDNEQdUPRzakvvhcK2p7FiW2Ueh4ZTvynvCW3xKqUjrAesunsc0/eWpqIj4/D7/8sX5vePpPG+5XP86ulU3q5yif/VFf5S+WhgGcxALE7hv7LNQCa0BAmWWRBXK/Mb+wy9tCOMXaq/fe0cQxN9CuuFeA0CO3YE+0fQyrtmjHqlre85bjjmXRT2j9jGwYkxhv6ErtopRynr7IbTsQ79NStxpPxjPGLyRl8GH9RUnBlL4YI4nbcccdxkcSil928jNUs6Fg0oVyH/PhCPdnQguKVcYDrANkFDPbsBOcq2yc/+cnjSXa9i1z7jnwbxn7SgilGLr6rxh1cBAIMsihfWJSBP98HoQLh3RpYVAYudWMxijKY3xql7UjR/opXvGJAmcJJe+ayY445ZjSWk2+//fYbxzl48/4YfrXY9TgwRzEG8k4QxjfeB6MgaVUeaTD4cu3R9773vTGcKwWVh/dHYa85jWuioNp3JS7iGcXxPnwvrr/g+zMv8e35FvRD9RvKgAjXe/BO9DHwZ5GPoozfpmHnJ98c4h3YFefp/ve//8gbTPmW1EOGz4c+9KFjPwBn4jFmYiyG5mlrtg7wYd6irrRT3qnW9shXe1fLk2faSavv1OZN+j6yAteV0I8wBtE3oSn1ZXGJIhOFCWMYxPvpW0bfnLS970p/shue2IEogyZ8eigaj1tYsjDgyhb6PG3kmte85sDGM061MW6BHUZPzSe8f238ZJxVv6Xe8OavNGaCJfMP5TEWUT5zgfC1721xh39NBgbz0nxSKj8aJyk7+n7RWMB4VRt7ojHSvi/fgTEbJTptmCt9qA80BbfauItMinIWpSPjFRseonrbuq3HZ8YS1isirk1iboVWay0UlaN69biLXJ/1lLcR0mjzEe8q+VnvrXFPrk2jtPO4KkeueMkfubZOUTrF9aRvpZka79N7P3UjrOdP71Fybf5SfClMeWycwnBFzMdsEGSs5Hq2N7/5zaOugE1pKOMwMiG7QZGMHM0bjOv8bAW//wSv6173uqN+R3WwbjSPMFezEQ4Znc2hrE1s+7Z88rmMAPINBkOtg1jHMj/6q/XKuf8XimxM22HNDQ82pSOzQy25DPmEb4gcigKY0y3ISrQh1nUY3pjDIWSiSJdVk8tKa4xIfh4LM/+QSWi3EC79AmMk70p/4dYniPaHLot1Du+BLuFZz3pWcY3TWvcjHyIvggnrI9q3lOHReqgmC1E/fiKGDYjo6DBmsgGK+doTuhHW/Ky1WO9gVBX1ymVRutKaY2uWy4Sd3Fl1TlParMqiz3i9UK1d1dpH6XtF+oWoj0RruFneT++ZbiKwCATQb2GQ1wm/Xp7MBeia2UAr2meffUY9HPMi4zf6FQ7IiSJbDmM9uhc2fbHRh01CrJEhbhe1sg6HJyAry40BJ/6TrGfDeLbp7bPifFgpv01jn8XD55E/SluKI8z/iZcty+a1zzatfVaamQ2aTKAomj2xC5BdHxp8L37xi4/XyHIcHws7JztR5NeIgRvrOLvwORkCoWTyV1LQUBlwLZXyojxGSY6C3hNKbAQV/mhYvhx1Bl+O54OwIj4YQywhdKD0RpktA2lUJ5vXP3PKE+UdO+04bcniREpk0j7gAQ8Yd5XJMGzz174DCmkU+1xFQz6OaaOwZLIsEQIvSgqUbtSBBQ2EAZMFFG2CiZPdbXTkeck3fjXcefnW8qu8Wvx6Dp+K3RQspvIGxygPbZIBn+vr2AzA3f8sHlgE6GoPFnIstCAmGYwTLI4xJkkBSFxPGycvfDHE0F8xtNC31K8x2kD0C65QxmWc4sQo7xG1fz/OcDoP/pTHeMipMRkhxkLcv1LdXJLxxN8Unj4/J4VY1FEW+LHDl/GXa7JFfnwhPSe6eQ8ZNUjD+MBYxbXbuodePLzLWMZ8gEGCxSMbVZhb7Olu6kIZKGLYPYzynFOQjH8of4877jjPdlSyYEjAUIYggWKHE7UihAraGAY0jL2cSGEBzrdkFzuGG67SolzqxncWWRx4f9qoxmEMPMxdnrguY8899xy/NfhgeGGRD2HMxhgFHwQqDMkstqGorUQ8ozjKY5c+dQcbrigFCxa+PUQfBHtwwxiHIbZFzKEYaXkfduOzu5i5kjmHeRtlAnzZDMQfxkziueYIWkRbgw/zIN+Hk6A9ba/nXXv6Tm3eJC+723k/FIMokRgLkKFmqe+YqfCv55v3vCvvwZjAd0FYR6nJidcpFI3HPViCEW2IfsRYTVuiPdNXkTEZR9ioIOoZP5W25mJkZwMH5TE+sdOTb6Z+X8tHeE32Iq42nxBnaZ622hoLamNPbYy09eIZxRlyIxt8uI2EbyAZtBe3nnHXlhuNmTbden9GCcz6Qtcb+TUK77caa6FaOR7faC00dX3meaf/vwggl3iysnTt2eeZxQ9vy188SmGKi1ytseXatMM5dEMAAEAASURBVKUwG9/zvAgePeWU0syKSS+vEn/CpDdhzEDOZBMgsp6wYAMjGymRNyIZOZo3qCOyHyfYkKXYCMlmPeQWrZlwkWejeYRxHCUhm5GQJ5hHMBRE40gJn40exnqXDYoQMhSbFNHFTSHWuLQTNrcjS+29tLFd1/P1yGWs05DJkM1YMyFns15jXYNx9a1vfetydVq6rOWEjYeW/BxlBzPWhchW1E9rPMZX5FvW+fQZbp/AwFiiaN2v9OjWwAB5kfbNOitaD9GHozUo/QkZ+ntLuk34sVlSp4lUJi66OWQ7NoCzprcns3vlst50KnejyGW876w6p3narHDGLbUrwnvbB2kj/QLxtT7SWsNFa1r4JiUCK4UAN22y6Zn5aCph4EefgYwk4uYIdAxszCKe9Ty6aYgxuGZfIp41MQdzGKPRlaDXlG4PvQTGUG5QYA2tcPKtNSrJmouqY8Q7ivPlk3ZbH9jrRyD5yEc+Mu5Qt78BR0OiASDYeNLvamn3to/Hj1WdHd9cnSbCcq0TgwpDUEYZa8nn5QUxurGDCQEFJRc7S1Cgs2OJ3VP4IQQMrmawhjwpFQmPiIW6+CC4iMiPYHfnO995+cRmq04o9HuJhQsnZfS7eL357HfA+Ihi3X4vBNoeIt+HPvShUSHNN2UnAwYCjJlgO6Uxlsqr5Ve4FmilvCsZRrm2Dv5Z8XKpi30u1Y34Gtk4+2zTqw6E2bJqzzav8viwXn+tTq38tm5KywTB9UXsjsTABCEAYuRBKMdobhdq9GOIRQInk0sUtXH4eUIYQ1hl16v6NYpEykeBzYkWTlCwaERhQHo2cNTavx9nGIcwijHWMDkyyWGc7SHVzaedhye8EFCFt3hzFSnGgR5iUQjJ5ZmxENxaxI4ojCQYKXXi37Znmx9FuR1jKa80n2CgZB7S9b2WB8/swKL9MTfQhnBRyjA3MLdwPYXGSdJ7bAiD4MEpK3a/c3KIeQalgCfGRburi0U/xhEIpRJGQSvQoFBgXmG3Y62tRDyjOAQra3RlfmMsR4nEtTRTiLm/hL/ngbKKvsQVZSIW4xAnepkr2XUsQlFAG0AQ9KQ2Jpf43rbmeU1pe7V3ndp37LxJO+Vb2d/03mHJWMaYVaIp9bX5o29u0+m59q70TzYqoHTkm2Kk185C5W250Xg8FUu+u5Vd1CYYc0tUGz9LaW0YYzTtkXnIEhtS2GTWSxpT6DPRfFLiN+Xb2+8XjQXR2FMbI33dUPwxV5SoF7ep425Ub7/5sVSv9RCGQYHbCDi5xPeEVmst1FuOx7G2FiLd1PXZlLWQr8fW7GeeRGm53sjK+/bZv4eNs8+ldDaMtJa838bp2fNv+cknvjUZVfEqo+b2plOZvjxfV5VD+0DuZiOlNnIzviJfQxg/MGjShjYFMnI0b6gsuSgAGXfZ9KM1E3HUOZpHWNsxxiHvYvBhYx15onFEZab7PwS4PYIbu1jL8N3ZkDmV2NSPbCfie2LUhGaRy/w6zbaLkan5N6suK5Kf7VrDFFV8ZH7VIQz6D3MvN+gg32IURGnt9Y0witb9pYKQ+ZD9ovUQcdEalNuGnvjEJ44/j4VinXlVG4ptmejmaobYXrmsN53K3Qhymd51Vp0TY3JtzTelzaoeuGpXPPe2D9JO0RvZPhKt4Vbi/ahrUiLQgwAnKdkQb+efnnyMy0960pOWNwYpD/MqNhFOPiObcKCCMZfT/ZF9ifWLXRejeyA9B8bYXIIugXGezfzoKYnzMqH3q05WFrRyoH0mrfcrv3dtOfZZPHx6+ZVW9bHl2WfLR2ltmOcnvnIVH7mkndmgiTIS5S2KMqvA1S6vqOBaHAobJmF4MECL2AXISR+AoNKUi+EUpbColBcFLQYLdoZpdxh+fueNXfY0SksYKXgfEcISQp29qkFx1uVUIlenWGJBgVCJYpb7m0VT66R8JReDMqcq5iEMKVxTC6baVY8SHSV3zRig8piUEUTBiJNJ1IcwlHbgaBX0ymPd1slXNeaoA1h+i3hWmS1eSqc2qToSbsN8ul6+pFNe5fF+hdu0vh6Ks+GEyc/zFIrqYPnU0vlylU7hGP+5fpH+IyUeC61dl4yLEP1Xu2PY+MDYAGHUZzHNrhdPURv3afHTdjnpSdvmT8TOTU5Zv3Rplw6TGadG+c1dDJNR+/d14uocDAO6Iogd1Ix7PaS6+bRckTMrT3iBo+oj3hiVOW2zksQOUMbh448/fti0pFyBuGJwXkJ4QDmCwICh0RMKHgyRGAtpX+ykwjCC8oXxjB2yNSOm58X3pX3SNvg9VPJ74t2YT3QVOeMtxnt2CDN/cDK4dB179F0jnlEcBjPmTxlnUGwxbmPM1LwbKR/8u/X4UabR11GMyACE8gVDJvM4mNF/Naezy466sHmBk9krQYtqe1P7jp03eV8UKBgIPdE+LM1T3+ib2zJ6nhmjuX2BzQ7292N78pImGo+nYtlbptLZ8RN5R0osxddctV+MSyXFVi1fFB7NJzLMKv883z4aC6Kxh40GpTFSpwVVN5SguhpeYXJ7cUPWnDLuRvVW2evZZb3DDRBsdOFdRfSP1VgLYahvlaM6WZe6+rUQ8YtYn9lyNvIz82akrGGelTxtnxeFmWR2y68UZuP1rHTUT3VTmNIoDr99Vrx3fX7vJ30pzGJEmpbfpuG5xJPwEk1JW8pPmHiongrzftbfGEU4VaA1vDVocuMNBhD6aiQjR/OGryPyO+t5Nmjqt9SVJppHMBhxCl0GGfQjUG0cEc90N0cAmYR1CBtDWcdx8mMqMbcc5267waDFBuOVlsusLmuKXBbJz1PfX+kZX5FpWKuxoRLlOOs6vz4mfbTuF7+SK7motB6i39ZkIfoYV/hi0ET5zvdiMyR91fc76US0cdbWQ+W35NnedOJNv62tZZVma3GFr9fv8H7RGmcl2qwwndI+yBPpF8Sz5G6p9yvVJcMSASHARi0MhtrIpfAelxsOGL+kn1IeTu5Lz4EMxglO1sA9thxuGSO99FpebkeXwh/Eb0tDkvNGT+Wf0kj2s3773MtPxShvLZ+NJ02rfJvep1WZvizlsa6ebR49E6f4k2pelarhMqBxIgpjHVeXsKMOQyQ7m7yhsMFqjOY6OpTIGB69woiTUFjPSYOhA4MCdxJzBSFUy4sylBOM9o8wjBGlOrLLjR2DpOcKFO7/1zVaY0Gd//hwGBppvHQsgU32Vp0w0vBbRCXiek2Mj2DNO3Nq1RqTS3laYWCIwRYjAhizY4dFSe1kFtdYMGnSmfkOXPkI8T1QToMbRgQw5luRdl5Sg7U4zsvT51cZPhy/4qyrdDaM51L6WlgtXDx9vPcrncq18bVnpbV5pzzDF2rl+W+qk/73+ZRC4Sjw+D0WToCzSIZ4ZhFOu+f6F+1OZ6zhBDTEjyvTJzAW0O54FvW0cV35isKb34GgDp7oE2x6oA9i/OIqF9o9NKX9o2RgUYLRCIUC/QgDV4166tbiiRGYhVqNGOc4fcq702dZvOHvPa1d46twdvQxT3hirOfbYxTmVBVGYr7fLAY18uvaVHZH8X34EXHGJcpnfhIhoGDMZMzjRDnGYAyfGBLYkYtRnTGOsZyrJmxe8ZCLIZB2wdVZpcUN6QiHD4pi3o/dXvymNMRuLd4bIyOEMQ8jNxR914hnFIcAxRxDeWCDspn35+QgcwGblXSVOHhiaOwhrqGF1M7s9wBXlFbMM8xbzBG0OTa9YLjktyO5ogxFHO2EazpQdulEW0/5U9Msqu319J3avMlVV4x5jGX0OzZa0TZLJwxb9eXblU6cg0v0zafixiYHFg20Cwz5PcTvK6Ecg6LxuAfLnvJsmtr4yUKD3xiiP6LwZeyzZMdM6syihNP5GCIZN9gYx+YvCJmRvjSFovkEPrb81rePyo3GgmjsqY2RlGXHc+RzTmkiuzNHszDTfNbCTfVujbuMLfQN+ENRvcVzvbooOVFCMgez4cXSaq2FWuXYOrWeF7U+a5WzUeL9OCXZ2b4/YSLFy1U4rsLk2rhSPOkslfIprOQqr49TWXKJt88+vfX7dN4vXiND88/y8M+eh7L5dL3+efOrnFK9bBzxrCkwQGGMQZ/ARjo2J2h9zgYxZLyWjBzNG5TDNWnwZz5kvc+aqXSbVW0eQbZmTkVeZFynjlxb27vJiDok/Q8B5kQ2k6DH0s8b/S+2/GTncdbSyKIYyfg2yKScMMG4tmi5LNJlRXKZX2NE8rNdf5TfvhzK+MpGZto2xsWddtppGU9f/pR1vy0tWg+1ZCHWtegM6Pf0Y2RD9Tsrh2KQ5aeu6P/IuFxtKOqVy1rp/Jpja5bLhJ3cWXVOUZsV73ncqH347xXpF6I60C7gVdIbr/T7RfXKuI2NADokbkKjbU4h5A82ifATVZ6YAxlHkZ/QW6HHY8NLy5YDH+wpnO5Hn4mOgXEbuUqEzofNPNjS0PsxpkOS6SLXp7N++ywehFlSeM0lrY+z+UvxPn2v3/LSs1x42GfPc4w88d/MBk3yo/TlBCKnYVDws9uDj03YVGInOMpelDgoOPXHznSEBnaecd83SmeueeSEABM/FOWdUg8W8Si5sdAjyKBYOPTQQ6ewGNNiVEFBjcWf3WZ6F96tRSi5uP6wROCAwMpChB1jRxxxRLhTt8TDh/HdUEDBC2GN0yLsmERQKhGGB3YxbNq0aRR6dSUJwjDKTRZSGLupI52V3Zk14poZ3zitv5aPcJtuEc+2LM/PxunZpiHM+nkuhSmNjbPPireuj8dfoloe0tbibPjUZ893an7Sex5jwIn/MDLRjyCM5ij3MBzx+2kshuirjAk6ZUbbu/a1rz2ORfQTKZrJ39PGWUizG5FdjlzH6Xc7wocrkejX7MxjIc5vGNBvGIemtP999913VDYwljFZIoBiJK1RT91aPNlgwWJDRjRfFkZjxlSEAerFxI6fzQ2LIE4fgp0nxkTmERYHLMwwKIOtfhvVp4/8jNe0C4zFEKeG2KnMuIaCGKOj2h1lovghjvEMwV7XNCEUMW7vsHSSkTbRM6dhkKN9eAW06sscxUKd30ulLBaZKIEg8vKHkIQgxu9W6HRo9F0jnlEchkKVx/thrGUjj3aPYWzFOMu1g1xZxFjfQ6o7uNFf/fcAV+YP5giEQeQGzXXMs5RHXtoifRaj5krSotpeT9+pzZsoS5ATJIRz5RC/O6TT6fb9W/XlNyOOPPLI8dS4zcdz65v79JGf8ZUxGSMpc3gPMVZzVQsUjcc9WPaUZ9PUxk/6ADInyj/6nm/ndsykzrRpjHb0GcYoFF60Y+QcdqVrM4UtO3qO5hPy2fJb3z4qJxoLorEnGiPteE57oK/uscceo2INAxYYQRFuvs7RuLvLLruM8iZzBIbNqN6e73rzM/7yjtz+oLUDLothsF6NtVCrnCmY0v5qa7spfDLtfxFATrHytnDxYfIrXq7CcT3ZuFI86W0a5S+F2Tj7rLSE6bnkYkgpha9EWKsuiypznnJ8Xu9XHdGZMC8zRiOPIvMzPtNuUEQjc5EWimTkaN4gr9YvjPX8dh6KuxLV5hHqwIZD5GDkbX7LnbUVY0XSdARY+zDfIuv1kp3HWZtwooS1LDIRP8Hx3ve+d5yDFi2XMb/UdFmRXObXGJH87NcfvZjQT1iPInOhy8LAe+CBB47ZfflT1v2+/Gg9VJOF6Nfo6zidiY4NfR2HM1inezmU9oBOBH0s61zkVVGvXNZK59ccW7NcJuzkzqpzitqseM/qRu0Dnv57RfqFqA60i5reeCXfL6pTxm1sBNjMjb4PG85UQv+FjqZ0Ix1zImMrOmnkKeYF5JYe0qEJ9FocGti0pEtjw5GIcRxbDpvCsG9JhutxI/m4FEeYwuX2lLMaaRZWnyWD039QMM5D7Obig2iX0Dy8oryUw6Rc+gHsKN/UOHYzYY1HYF9toqNw/RyThSV2jbEwwfDBLgEmlEWfXgFbBFkacIkQeFm0YFTiJCZCqSdwQ/nSWzcJWXzbRZCve8TXp/Xlt+JJ7/nLL7eUxpfj/b5c7/fpvT8q28b5fL3+qfWJ+Nr68MyCQcSCGmEcxS47aNhxqU0M9AHfPzk1QxqMASj9WJDpCiPx9G2cSZAFOQpElKWUQ99qEUaz0i7YKe2fPsSYWcNzlrpFPBmj6ZslY4l9XzAq9W2bZuozuDJp1QiBgV1MPdjXeBAOHxlZ4Mf78q1oKwgT/F6PDJek53uBRwkTThnRJuetE+WImFvYgV6aK6k7p49L7Sr6rhHPKI7yeMdSXXhvyvR9TO8RubQf8vG97fdQHjBnfuDbeKI+5FM/9/Er4aeOi2h71K3Ud3rmTfIiUPfINlF9NQZiHC5R9M1L6RcZVmoLfjy25ZWwtPGt597xszaWw780ZpKePqNxhnSldyO8l2p18OVH375VVmssqI09pTGyNJ4z99KPSuMJdSvhVqpzNO76NgEetXqXeK9WGHLHvOuqqK6Mz2DRM15EfFpxq1VOqx5bY/w8bYRxgW/DH+SfhZfi5V+kW5JbS2G1Mm3dep5rfEpllsJq+RVeq0MpXmFT3FnqZPnb+hFe87O5hpN2GDEYuyW/8oxcZeetHhm5NG9wDSeEQbRXbinNIyOTpX+l9ZziNoo7z3gwK0aleZx2xVxdWwP6OXhq2b26rJpMRHnUQWsMlV9qh/PIZeRlrVaSpX35tO0pei/VGZe80XqI76E+bPPRZ9BZ2HGl9L5ehrQ8eO6Vy2rpSmuOtSqX+Xeft8/p3afonGwdbJvlxqTSRnr0Ffwu5lQqtQ94qM62XUf6hVa59IWa3ti+X4tPxm/9CMzb37YkQvQbdFO9tg1bV+Qs5lo2btWIuWaW2ymsHGifKcf7a2UTbueRkr8W5sto+eETkepRcgmz4fLL3TZi3BsHs5oSo5dHTzrKWekFPPWgwc6iyO15h1YalEJcaxbRStWtJsD6urAgqqUtCV4+f8nPt10J6uUbpYvianUmjzq2fa6lt+G+PO+3af0zZU5J7/O3/D28a2mER60Mn4+Twlx5Sj5+J4Jdokwq/OYh12Na4roiDJhcb8QVRhje99lnH5tkfK61WyJ1Qu0kmQoBJaMTyaa0fxYjvdRbt4inFWCjciOMonxRXGTMJB9jyhTsamVZZQ27rXZdOrnLLtZjjjlmNE5wStJSVGYkfFgeU56ZW2pzJXWvtavou0Y8ozjKq9WFvjjrPGPbj/0ewmm1MVe5NZc6RnWq5SuF23f38ZQTxffKNlF9UaqUDMWqC3lr31xpVsqlbE8RHlGc59PyR+Nnrc/BszRmltKX3q1VJxtf4lkqP/r2ll/puTUW1OpQ6hul8by2MUR1qfFXvNxo3PVtAjx6+Yr/1uAyPveOF/O872qVM08dN2JeKytbmb/2vFIY2XqojFKY4mouebQ+8M/kUZzNXyunFm7ztp5tHZS2FKa4lrsSdarVh6vTuN6ftRA3wEDcWMO878fyHhk5mjfg3TsO+bLJK5pV1lT+dGdDoDSP0678PGu5R3E2Xc9z9N2jeb1Uh1I7nEcuI29J/uO9fPlR227hEOWNZKESdqX3rb2D6hXhrDS4tXSlNcdGkMsWoXOybZZb8dBvLYpK7QPepe8V6Rda9fF9waa372fD8zkRWG8IRHqV1rv0bNBn3i3JiTasJgcrXGm9n/opzNZV6WcJUx7xEP+WX/m8q3yE+2f5cf2zwnAXYtD0FUv/7AiUftsTbhxz1u9Vzs59vpyUTz0WSWqcJZ7qID4uyuPTTvW3eCtedZOfcgiz/qllK32JRylM6ae48FHde/P1lD0ljS0/qg9KaH7Hg9+fw8jPFTJMDFyb4Q2aXM/Gldf8Jh8LBH7jrefqayYprujpmXB68VpUurVct0W940ry4apYrgTmGk6uEELRU1uUrWQ9kvfGRmAl5s2NjWjf2+f42YdTpkoEEoH1iYDk7kWtPWZBQXWweUthNp7nSPb3cT38xNOXY/2ebxSnMv16xebh2cbjVz6ee8nn8TzFx9ff+vXMDUr8ZA5XrR9yyCHjRr4dlq48O+igg8afOxAv3FllZK7G93W2fPM5EaghsBZ0WbW6ZXgi0IvArDqnXv6ZLhFIBDYWAshUkVzl4yQnKrzmB0WlqSFaii+FtfLX6tDLy6bTs3X9M/7lsEVcOVt7wQxPBFoIcC0CpE7QSl+KV2Muxc0aNgtP/w4tf61utbJr4eLjyyO8N0w8rNsqrxVveflnXy/5c0eXRyr9iUAikAgkAolAIrBeEFjPVyutF4zXez3naSO6lllyc82dipH4lPJF8n4prhRm+ZbK8mHeb/P751p5tfAa71J4KcyXP9Vfq5flUyvXh1s/V80mrT8E5hkP1t/bZo0TgS2PQPa5Lf8NsgYbB4Hsb/VvzensWU9oW/mPEry/XupJY0pyaSnM5yyVWQrz+Up+W56erVt7zhOaJTQzbNURUANdVMGL5NfipU7bStf7bi0+Pr5WPuE+7SLqMCtPWzY8VG/Cvd+mzedEIBFIBBKBRCARSAQSgUQgEfjfjmvJ+XIjbKzMXUrXI9vX0kwNL5Xvw8SzVm/F+3zW35PGp/fliYcPt/laz+LRSmfjlceXS7gN837LI58TgUQgEUgEEoFEIBFIBNYmAshwkvfk1mpqZT/SKL3C5Vd+hctvXZ9WcbVwxXvXpld5pTCfT36b1ocpLnKJS4OmkEt3iyCgBqrC1RHkj1yfN0o7JW4qX59e72DDCbP+nvr0pi+lq5VXC/f1KfEkTS3c509/IpAIJAKJQCKQCCQCiUAikAgsDgHk8Jos78PxixYtv5f4lcJUvnV709k8/nkKD5vWY+T51vzwsHjW0vlwW7aPS38ikAgkAolAIpAIJAKJwMZEABmx9JvWJTSsPGnl0Z7wEj+F2fwKkxvFkaZVj1Z+lYNr05aeFebdNGhaFPN5iyOgBrrSFVnJcixvdXIbttLvBn9bnurgw1v1sDxs2lq4TZPPiUAikAgkAolAIpAIJAKJQCKwWASQw5HtSy4lSe6fIq8rT62mEa9SXCmsxFvl9qb3PKbmi9ILU18G/iiulL4UFpXt08+Li+eX/kQgEUgEEoFEIBFIBBKBtYXAFNlQNVceyYo+XP5ZXPHuyau0vfVQOuUrlWHjSs8Kk5sGzRKKGbZqCJzudKdbtbKyoEQgEUgEEoFEIBFIBBKBRCARSATWIwLbbbfdeqx21jkRSAQSgUQgEUgEEoFEIBFIBE5EYNtttx34S5odgZPNnjVzJgKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKwsgikQXNl8U3uiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAiMAcCadCcA7zMmggkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAiuLQBo0Vxbf5J4IJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAJzIJAGzTnAy6yJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKwsgikQXNl8U3uiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAiMAcC286RN7OucQT++Mc/dtfwP//5TzVtLc6HT/WrQJ9P4b3uNttsc5KkpbCTJCoERHUpxbXKacWXeJbCqGotnLhWOaQpkc0XPZ/hDGcoZc+wRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBFYcgTRorjjEa7uAmpGsFs7b+Djrt88+rY/z8fi3NJXqSJ2icGsInKX+Jd4+zPt9OTa+tz6ks/k8T/w9aUr5tsawP/zhDyd5rQi/KO4kjAoBre/Yio/Kr8X58JbfV9vXyfpLzzbM8iL8tKc9rQ3K5wABvlMNyyBbRq1TBPjen/3sZ4fLX/7y1Tf45Cc/Oey8887V+IxIBBKBRCARSATmQSBlj3nQy7yJQCKQCCQCiUAikAgkAolAIjArAnnlbAW5r3/968P73ve+Suz6CGah2fqzb2LT2nCeS3EKU1r8Pq3SlNx///vfm/EtpZklrFQH1VHuFL6tetbiS/UoldubrpR3JcJK9SHshBNOGF7xilfwuMXoZz/72fCGN7xhi5Q/Betam+jlwQtGaVvxUd5anK9zy1/iQx6fr5ROYT6tPizxi6C1MI4fdNBBA38rQZ/61KeGG97whsP//d//Db/73e9Woogiz9UaC9bC9/va1742vOQlLxm+853vFLEoBa4kPvSZBz3oQcPb3/72UtHLYU972tOG17zmNcv+fEgEEoFEIBHYuhE46qijht///vfNlzziiCOG+93vfs10tQSvetWrhktd6lLD1a9+9VqSFQlfybl1RSo8I9O1IPvMWPXMlggkAolAIpAIJAKJQCKQCKwKAmnQrMD8pS99aSEGzQc+8IHDN77xjUopayNYxoVSbWpx1uBg0yjch/nwkl95el3V16YnzPt9GPEim1bhPWE+zbx+X8eWf97yfH5fHvEKGx+W/mFMRInxr3/9S0Gr7n7ve98bXv/6189d7iz90mNmMfJxVNCH9fj1YlHaXt696aKy5o3zBkvPL6qjsJjHXdQ4PqUOz3/+84c3velNy1kwNK6UsfFxj3vccJWrXGX405/+NKzmtdCrNRYs4vv577H8YRoPfDOwvdvd7jYcf/zxw7Wuda3h4Q9/eDHXBz7wgeGQQw5ZjltJfN785jcPH/nIR6p1USVQWO+3337DD3/4QwWlmwgkAolAIrCVIvDPf/5zlNF//vOfN9+QG0d+/etfN9ORwM+hf/vb34a73/3uw1Of+tThQx/6UBePRSVaybl1UXVcBJ9FyD6LqMdq8phlXbaa9cuyEoFEIBFIBBKBRCARSATWFgILMWj++c9/nvxWKLb/8pe/VPNFPKM4+P79738v8m2VaTPd+ta3Hh7/+MfboPH5r3/960nCFMAiz9PLX/7y4Re/+IUPDt+dxLVyprwfae2frUQtXGkUL791iROVnhVmeZSelQ5eiu9xbdlK78O8X+m8W0pHWCmdD8MvsnFRmOKsq7wKs34944psmH1uxSutT4ffxvGsMOte9KIXHd7ylrcMJz/5ycd4/cNoVetzSiMXw0uNGA/gJcJw6vle+cpXHuhTnsirevu4Kf2ylNbzE1ZyidezdZXPhvlnpZHr462/J41NzzPkw2rhtbSl/POE1crx4finEMo8237IO8s4Hs0vvj6ltBibvvKVr/iko782rhNZ4iUmpX7DN3jrW9863PKWtxxPaCotbk9fsumpl92owPM//vEPm2Sz59pYwDfo6UMwK72TClH9a99P6bxLPk+17wF+pfTKz6lMlL4f/ehHhyc84QnjichnPvOZ40l1pZHL6c13vetd8g41fKbgXKofYcglz33uc0/yzT3upz/96Yd73OMew7Oe9azleuVDIpAIJAKJwNaBgJ+/tt122+Ftb3vbcKELXWizF2TeiOQLm9jzJM7PoZ///OfHK+6vd73rDSc72eZqhKgc5AMv00+ZE6lLbW4t1Zv0Il+vSBZTHlyfz8bx7N9H8aX5W3G4Lb412cfP8+IZldeS52o84R3Ftd5BdSu5Jfxr+hKbn/esYV5aM9q8xPv3KdXD5snnRCARSAQSgUQgEUgEEoG1i8DmK5GJ9Xz/+98/XO5ylxu233774dznPvdo7IDFQx7ykOGRj3zkMrfPfe5zw9nOdrZlIfTwww8fznOe8wxnOtOZhpvc5CbLu0Q5/YBydocddhjDlxmc+PDTn/502GWXXQYUdZTHdTm77777cjKUjuc85zlH3ve9732HX/7yl8txtTKXE7iHZzzjGcOd7nSnMZR6sRuVExqcgqEOX/jCF5ZzoMjkJMdZz3rWgQXexz/+8fFUJv7f/va345WA1A364Ac/OC7IznGOcwxXuMIVNjvJw+9hPfrRjx4ucIELjOXc/va33+xkz7zvx0JAf8uVLzyQpkZRXCuPL1v+qTyVz5bneXi/TWufPa+av8ZP6W28wkrlKM66Sqcw/PbZ+gmvkfKUXJvHxlveei65hHG95ZnPfGYex77FNZfs0Kadn+UsZxkOPPDAMc7/o//c8Y53HC596UsPZzzjGcf2zZgA0UfhQ1/mNxNRnLBYvfe97z2WRV8nL9dMQe95z3vG/jN6lv795Cc/GW5wgxuM5cPbXrH4rW99a+yrjDMYQl/3utdV++W3v/3t4Ra3uMVw9rOffbxG68gjj1QRy65wU4D8uJD122elL7k2Xe1Z+Xy8wr1bS1cKj8Lga+O938bZZ5vOPts0PNs4+6y4McGJ/3baaafNDEYHHHDAeO0m0Sgo7nWvew3nO9/5hvOf//zjyTXxmDKOf+xjHxvOe97zjm2U8Zz2cOihh9pqLD8/5znPGdPStkjL9WAQisRXv/rV4xWztEvR97///WHXXXctzh/MCZe97GXH+fASl7jE8txy7LHHDpe85CXH+YO+gTJQxAkM+hwG3Kte9arDAx7wgMl9iVOkzDm3u93txjmcvoZCiVOf9Gn+HvWoR6nIzdzSWEBa5kfquu+++25mIFXmqWOB/X6MH2yoEB122GHDbW5zm9Fbm1dr36NHHrja1a42vPSlL1VxY/sqGWwZqxi/OKUJZlz3Z/GZBeda/eD7xS9+cbjMZS6zXK+S/KHI2972tqNBM5V2QiTdRCARSATWNwJsaGGuYR3NGvirX/3q8gud8pSnXN5Qxfz+4Ac/eFwXn+50pxvXp7/61a+W09qHGk8/hzIHX+c61xkNctSBW1ugmj6AOOpxz3vec6AOyFSzzInwgezcir829xOHfoLr2dEXUDZrePKz9kZWQSbDOFuilr4B2eSCF7zgyJs1CidHRbX52+s4Il2JlX3gizyIfIjMucceewxaQxFXKw+DI7IJeZBt73rXuw5//OMfyTJSxDOKq8m/rMf8zx8gn+pnQmq6FG6xoi15fYnqKbemA4nWjGwop07Ip+iqJJ/yfqwZWDciA2uNqbLSTQQSgUQgEUgEEoFEIBFY+wjMbNDEAHDjG994eMQjHjGeYkD4RgGMYHqrW91qeNnLXrZ8WobFC4o1hEqen/zkJ4/KP5Sypz71qYdXvvKVI1LsnuP6GhTCpPOEEH2a05xm/C0rTsDw21Y6RUJ5KGPf/e53D1xLCa9nP/vZI4uoTF+G/OQXb5655o0FH8pKdoi+6EUvGpMiSHMl3cEHHzwaHzHIcm3KRS5ykVHBjADNb10hTKN4Bwfen+vsOGmBMlTlwIvTIJ/+9KfHxRHviAEHmuX9pNQfGUz4t80221RT1+JUltwqgxMjSukIq/2V+HkeLb/n0UpfiifM/lmeCleYz6/wqW6Nj8qrxdtyamlLeX2Y/BhPrGKcdssC9Mc//vHwyU9+cjzFxILWE/3nM5/5zLhznEX/da973QGDlAg+LDJ/9KMfjYbHhz70oQMGIDYNMJ6w0GThD1EH7bCFLxsirnnNa471QNFCX4QfaTAo8YcC53nPe96w9957j4tmjKi2X8L3MY95zKh0+c1vfjNgzMRAVjpZTVphyTNU8yt8Fve/nP/73+avhfekUd5SWh8mv8+DX3El18aXnhVmXT3DT2SfCaPd2dOXGJZk4ON3XX/wgx8M3/3ud8c2c8wxxwzvfOc7R1a94ziJMcKhmOIathe/+MXDhz/84aJRjiuPuV706KOPHsd4lGTMhbQ5jOgoRzDuv+Md7xjrwD+Ubiid/PxBO7/RjW40POUpTxkVTXe5y12Gxz72sWM+3hdF5V577TW2YU5fiDC8a8POJz7xieW5bmpf+vKXvzy+N0ou3olNPPQX+iJ9mrkKXD2VxgI2E/F+vDenG5nHPE0dC+z349uwKUGEIhWDZjSvlr5HrzxwxStecVR8qjzGBcYusLfE2IJxlY1OfBMMwxYfnqfgHNUPwzn1OsUpTjFWoSZ/qH5sDqP8vHZWiKSbCCQCicD6RQBDFhtfmV+RUdkMxfpThAwg+Yn5gXU2NwggSzDPs5b0FPH0cygGVDbRMM8x37HxNtIHUBb1oA5co4qBceqcaOtr59Zo7icP8chzvAPrFOZB1gtgwBqc3wBl7i5RpG9gHuYbsF6HJ6dUdRNCNH8jz1gdR6QrsbIP3w5Zhw2kyFh8g/vf//5jtaPy2JBFXTHOgj/ypjZ9RjyjuEj+pd3R/izxDXgXCLekSynpSywPniMdSLRmVJ0w6LJeZeMiN23QZ+hDrFlpv3aznC87/YlAIpAIJAKJQCKQCCQCaxOBmQ2aLGgw7MlQyfUo7MR77WtfOxoltttuu1EhymujeGTRAyFMo/AlHsEcIygCrgiF6pWudKXRwKEwuW984xsHTl5qV514Ek/cnnvuOZ5owUiKUpjFCtQqc0zU+Ee9Lnaxiw3sfr3ZzW62rDAnG0IySmVcdqGiTNeC0rIlLwuLm970puMiiBM2LEhZPIhYpLCbEoMLim5OfUCzvl+pHiorcmuGS/KU4hQmN+Jdi6vlrYV7Pj6d9/v0kX9W3CKei4xbjfr1lIHxnr7MWMCpIRaLJaIts0uaE55sgmDjgb2uiAUpBhGus2WzAP2A03b8YczEQOKvlMLYiaEApQD9COU99cAQw2YAjFzU71SnOtVYN4wh9NESoeghD5sheA+UQPRBS+DhMbH+UrzNP+W5xqsW7nmX0k0Js/zIJ7LPCrOujbfPSqMw7/p4+Xtcvh3fGgM434wTbCitSlQbxzG0s3OfdshOfsZ6TtuXiPnkDne4wzjPkXb//fcfy6+1fXjUymUe5RQB7ZY6oKjCIKi+cfGLX3ycO3UyulQfHzalL1Eup5fpdzrtyGlP5tALX/jC41yMYbeHkAXgw+kN+uR73/veYrbesUBGOzGhfsgXjAMoodgkccMb3nCck1vzqnjgziIPoPhCgfmCF7zAsup+noJzVD+Ukhg0LUXyB3Pgpk2bRkWmzZPPiUAikAgkAusPAQyYGBIx0GCo45Rg9LvyzJvM5xiN7nOf+4wbC/1bT+Xp80f6AKVFrqCuyOPQlDlRPLzbs6ZGN8H7cysSa2/0Bpx0ZN3CWp5r+0sU6Ru4Lh85hHU/c+wLX/jC8feq4RPN38RbHQeyV01XQloRG5tJywZNZKy9lzZncp089YjKQ1+CHEaeb37zm6N+RBvtIp5RHOVNlX/1Hrg1WdimKT3XdCAYuHvWjLQDcOB0Lt+fzeTcHIa+hQ2Jxx13XKnYDEsEEoFEIBFIBBKBRCARWMMI/O/Ix8RKovz3SmOu3mO3IgI+J6W4ZoRdnAj+COMQSmOMdNaIiZCJUBoRv2WF4Y9r50qEMhsBX8TiBaUqFJXpf/9D+SMXRatOqnHqVCdZMLRyWgdjKkrqEj3taU8blbII0hhrICn3fXrKYWcrNMv7eX5T/dYg6OtIHGFy4W2fS2XZePts0xLeQz5dy1/i6etg/fZZeX0ZCreuTWOflcaHWVyJk7/2XOOj8Mj1ZZM2ClOc3Ig3cfQFtdco7Y477jjuaGYM2bSkbLeEoQJjDldUiVC2gAtGRkuMB4SzKcISC3cUGGxowBXZK0AVJpcTeU984hOHq1/96sO5znWu8dpSGXiUxrv6Vj58kX7KKOFfC6fsUr1KYaV6+nTeX8rTEwaf0nuQ18dNLZMrteDNqVpOF+JiNG+RHccxgtIWewyHGNLsZhpOF9PWOAHQQ75cxvbrX//6y1kxzKOonJem9CXKQjloXZ5Rtukk7BjZ+Y/5T4qzKEs0Fvh8zJdsdnrf+943GjTZwMSNDdCUeXWqPHD88cePm4vYmMUcPy+1cI7qhxzENxH1yB8Y3dk8lpQIJAKJQCKwvhFg7bnPPvuMa0IMhPZEZuvNkFM4IejX2/PwpMxIH9CqE/GtOTHiMWXuZ+608yflcoLQU0vfwPoEAxu3QCGHsEmbG5eYa6P525eD3FrTldi0nLJE9hGht9h7yagJReVxIhNDMpue2BzHmomNo1DEM4qL5F//261jQcE/KwsHycaomg6E20SiNaMM6JY/Yb4d9KxdLY98TgQSgUQgEUgEEoFEIBHY8gjMbNC0v5mp1+D6lZvf/Oajl9Ma/D4Fv1PB7zbIcMiuUk5w1H6bS7y8y0KBxRiCNjw8oYxGKc3pDwgjKqetOGkza5m+jJKfHZK8I1e7cCXt85///NEowgJCizTl40QZvyGBgpT6QlxX10OzvB9X6ngjgvfXyvZGBZtPcYRZY4R9LvG18faZtPgjsuXbdD685Vdelaf0Jb+PU96Sq7SKs377rHi5imuVr3TkU1rx8K5N6+Pk92nk967SL9Kl/XP1EH2TfmoJQz9jBScv2ZQAffaznx2NpfxeEMZKEWmpL79Zy/hgiSs+ud4K/lrQsrjH+OSNVtSFvoJB80lPetK4UxcDE31u5513tmw3e6bs1rfYLMMMHn0Pn7UWTrpSvUphnmcpr8/n/Z4H8SWqhZfSEubT8w21icTn4XQeSiEUN8wPXPuNIZvfjuolDHCc8sTwwyaciDA48rtLMqRzQhRly1RlDmXQxlGOYaTzxPw1D7X6kv0NpnnKKeVF6RX1HeWJxgKlse6d73zn8TQKG6f0O91T59Up8gCnZnfbbbdRccl1s6tBUf1oe/Yq35b8wdiGsnkRhtjVePcsIxFIBBKBRKCOABtr+K15ZA6uoWeutRuE6zn/uzGWm5S0HlfaeXjCo6UPUDmLdqfO/b3lt/QNbJzDwIi+gY2W3CKDnoNrS6P5u1R+TVdi04Lvce4EIcbJC1zgAmF53CrBRjBdp8pVqxi0oYhnFBfJv2ywgkpG4jFijn+sx0o6Hm2Kq60ZuXI4KRFIBBKBRCARSAQSgURg60Rg5itnURpzMpHfb0BphsCMX6egUO6iRONH3BHYRVxVgtEPhTCEsQIhu4e4LgajIUpGdlDyW5siyuC31DB4cKKE38TT7/TNU6b411wUq7wnxhOU6FwHx4/MizjtxeIHYpGEoh5jJ7tqwYG69gj/s7yfNQrwbP2qX81VeusqrcLw+2eF1VzSK87nlb/k+jxKUwu38WOB7p/icS0PJVO8jVNYyfXprN8+l/L6MJvePpPO+31epWmlq8UrXK7lR9is9P73v3/8LUwU8PRhjAMYFz2xIOZ3BPk9Gk6Y0df5LRt2QdvfECQfVwdhEOJ3/uDLDtvHPe5x4/WKxJ31rGcdNwwQzu8AskOZ/grZfomffsumBN6XK2fpq75+JSxsGM/WD99ZqcarFu7LKaUr1a2Wzqb1aeQvuaqH4vDzLNKz3FK8jVM+rhblt4gZLzFgMu+IMGxhzOQ7c2IBBU7LKKm8crn+mzbw9Kc/fdztzQlJ/X6x0sjFqHbUUUeNcxg7wzkhQJlsuIFod/p9S+WpuVx5xo0F7PZnHuXqZN7H/w5RLX8UPqUvRXx643QiEwUr36dmAOwdC0rlsmGKq6NRXrFhCmrNq/579MoDGLe5iuxhD3vYZjLMWKj7x7VlfDt/8sUl6/JG9du0adNmv2nakj8Y9+gXtM+kRCARSAQSgfWNAOtL5A7WwMxRyNPIRcgPJXrb2942xnGDBDIuc5qnFk8/h/r8LX2ATz/Vj1EKA56n1tzv00d+ZC77swGRvgH5BsPwCSecMP6m9k477TRubIZ/NH+Xyq/pSmxa6oKciFGTTZTIi9yIhWE6Ko/Nm2zWRMfAhj2+vzaRRjyjuEj+pZ2wfqLNQeCJrNZLfl3G5i3aOlTTgay2nNv7LpkuEUgEEoFEIBFIBBKBRGDlEZjZoMnva/G7HVzzivKY3+3Cj9FAhGGC38ngJJaIMP7Y3Yjwy+8p+Ktrlda7/B4EuyExILIrj3Kl/OaEDkrvS17ykuMJLHbysdCD5inT18H7eT8Mk9e4xjVG4wq/2XfEEUcsn87ktwBZcGBcRVh/ylOeMp74YCGG4heDDD9I36JZ3w98hJHKUFiv28pHfC+vRadjQWd5bsm6RGXPGmffjWf/vj5+kX5bZ57nIfoqC3BOjfEbLFzPVKNDDz10vEpyh6XTSIwzbBTAqOmJcDZS8Bs48KUvoWDgJCdxxx577NgXMTZwCu6www4bF9vwsf2SK69Jy+lMDFuk5SS1HctUtsXXh3m/0ipcrsIjV2lxbTob3oqz8cpX41UK92HW33r2ZSu9wuUSLrLPCpPLGM5mFU7DM3dsWjLsiBhTUdbwndkdjxLnjne8o6K73ac+9anD4YcfPvLhunLmmVKdMK5rDqPdYfjkN6R0hRW/1XPkkUeOJ/VbhaMIO/roo8d5lPmQK9X5fR+u4loE9falRZTF7+KCByczuZK3dkJzyljg64XiFYUsSjX6LdSaV/336JUHXv7yl48brpjTUZrpj5MYnnbZZZdR0ci8jmFzHorqd7WrXW08oSmDeUv+4MQyeWhbSYlAIpAIJALrGwGMTWzm4RQd14cis2LM5OrNErGBFrkImQljmK4qtWlbPP0cavPy3KMP8Hmm+PntbH7z0FNr7vfpIz83GfCTMRgAoUjfgHzJzQ2UzzfA0HjggQeO+aL5e0xQ+EceryuxydBrvOQlLxl/AxQZmJ9U4LcxZcwjf0mnsu+++446E8lcbNpj8zUU8YziWvIvm8nZyM7PAey3336byer2nUrPdl1Gm+Y2LxmZIx3Iasq5pXpnWCKQCCQCiUAikAgkAonAlkFgm6VF0H9YGM1DGBHsqcQeXgirXNWHcN5LnDRAgKc8BHR+Kw16wQtesMyCnapcTehPV5HAl4mB40EPetByXj1gkCwt+hRfc1kIld6H02PUW0pq6sGOSYwuU2nK+7GDV1RSzCtuquuv2ZRfLvz07F0b58tVWhveqreP937Lq/Zsy7XPpPf+Gg/Cfdktv+Vly7HPpGn5LR89+7IVLtfHW3/peWr/Vjm4XMcEYTBCubL99tuP/tY/dhJjwLVXN7/zne8cFQ1+EwB9jz4vI4flzXhBv7TvRbzvl4Sx4127vvFbkqKj53v4NJZP9OzraNNGcTadfS7VoxSmPLW4WrjylVxfX/nlksc+e39pLK2NseRlJzdjrMZZwqYQYzLjNGUwv6AAPPjgg6vGUcZx2mhprmGuYse/fuOxpx5T+kYPP5um1Jds/DzPGNcwZGLIo+9wSrE2t806FvTUL5pXS9/DywM9ZbTSzCIL1XjW6sd1/ShS+e1YS6W+gWF5r732Gjd72bT5nAgkAiuHAKfh5l1XrVztkvNaQGDeNoK8g7ziby6x74Zhis2Du++++3iisyXHRzxLc6gtS8+LnAPFE5f5sCTfK27WNXWtjB59A3VC31CS82rzty1v6jNyOPN86TtG5aELQE718jblRzyjOMqryb/ko0zk6Klk12WU4b95pANZSTl36ntMTT/veDC1vEyfCGx0BLLPbfQWkO+/mghkf1tNtDdeWdsu4pVLwnWLL0JqSWEd5cMggiHjdre73Xi6kRMU/lpAFnclBTN8fZkoBL1SMCq/FVd7H7/YoR41hW+rjCnvBy+/gPH+Vnk+noWK5eH9Pr385CEtZJ8Vr3Drj55tHZSuFKa4XtfXzfsjPrZ8+6w8pTDFWdeXWfLb9FOfS/WwYbXnqeWU0vcaM8mr374UH65P5KonThx5qvU90tXGJ98vSduzALf4lNp/KQzevWT5+zxRnE1r+5oP7+Vh883y7Mux/tpzq5zoO886pqpMrjPl5AMndI855pjR4M2p/xoxjtfmGhSN/E2hKX1jCl/S+r40NX9vep1S7Um/6PeN5tXS9/DyQE+dW2lqY00rXym+Vr999tln4ETota997c1uv/B9g99lRbmsnwEolZFhiUAikAgkAusPgSnyDnNJz9wU8SzNoSXUesop5WuF8Q41Ii6qey2fD7dl9OgbSF9aR8CXOD8n+/Km+pGba/hG5bHRrEYRzyiO8mryL/l61lKlOlk8KcNTpANZLTnX1yn9iUAikAgkAolAIpAIJAJbBoGZr5zdEtXlGkiurcSQiVD7jW98o2jc2BJ1W4tlsqiAcPXn/QrvcefJ6/lz8m6W61Nr+SJetTyqk89b8vswm7fFn7S1/JaPnkvpe8qw+e2z8sq1caWyfDz+eYhrNPldlXmJ385j1/T+++8/L6uZ8pdwWe0wKt4qs5amFt7iR3yr/XoePr332/S1ehG+msRvMGMI5rpYfh/pO9/5znh97WrWYT2WhaKTa8jsSeraeyxqLKjx3wjhZz/72QdOqnPFf0QY5UlDX0tKBBKBRCAR2FgIcD07p/mTpiOQ+obpmGWORCARSAQSgUQgEUgEEoFEYDURWMiVs6tZ4SyrHwGubhG1lJo+Xie8lL/k+jTW3/Nsedr0NtzXy8bpuZamFl4qqzdMZUauL9f7o7y+Ht6vvLVw4nvLK6XzYfJHO3xVp63dtVc4866lb1AKm4qLMI/y1dLUyq+F+zJ60rXSlOrmw1p+6pVtzn+d9CcCiUAikAisdQTyaqW1/oW2fP2yjWz5b5A1SATWCgI5HqyVL5H12CgIZJ/bKF8633MtIJD9bS18ha23Dgu5cnbrhWd9v5m9umV9v0nWPhHY8gikgW3Lf4OsQSKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAonAxkRgXV05uzE/Ub51IpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIrBxEUiD5sb99vnmiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCicCaRyANmmv+E2UFE4FEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoGNi0AaNDfut883TwQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSATWPAJp0FzznygrmAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAhsXAS25dV/+9vfblwE8s0TgUQgEUgEEoFEIBFIBBKBRCARmBOBE044YTj1qU89J5fMvjUjkG1ka/66+W6JwDQEcjyYhlemTgTmRSD73LwIZv5EoB+B7G/9WGXKfgS23377MfG222yzzXDKU56yP2emTAQSgUQgEUgEEoFEIBFIBBKBRCAR2AyB7bbbLtdVmyGSHo9AthGPSPoTgY2LQI4HG/fb55tvGQSyz20Z3LPUjYlA9reN+d1X6623/c9//pM7iVcL7SwnEUgEEoFEIBFIBBKBRCARSAS2SgRYuOcJza3y0y7spbKNLAzKZJQIrHsEcjxY958wX2CdIZB9bp19sKzuukYg+9u6/nxrvvL5G5pr/hNlBROBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBjYtAGjQ37rfPN08EEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgE1jwC2675GmYFE4FEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoEtiMA//vGP4e9//3u1BvzEY41qcbXwGp9tttlmsyj55RKpZ+va51oaMVZa/Cc72do5F5kGTX2hdBOBRCARSAQSgUQgEVhjCPzhD3/oqlEk/FohtIvZiYlmzefLiOpWiusNUzm+nlP98LF5TnOa04h1uolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAILCOAzmKq3mLe9FZnQUXErxTuw0hPmPLghxRWcv+bYm3+T4Pm2vwuWatEIBFIBBKBRCARSASWEfCC53LE0kMURzofXxJuLT89k683rfJ415et+FJ4b5jAeS3PAABAAElEQVR4yFU+1bXkV5zyeJc8rTQ+z9bq/+Mf/1h8NeHqI2vhPl3kt9jr2bo9z/BXOvu8lnaSRhhkXCKQCCQCiUAikAgkAolAIpAIJAKJwPpAwK+DvV9vUQovhZG+Fu7j7LqXPPjlqtzIVf4peSJ+qx0391nRv/zlL8N73/ve4UUvetHwjW98Y676//nPfx6OOeaY4R3veMfw17/+dTNe//rXv4a3vvWtw3Oe85zhPe95z0k+cJRXjP72t78Nr371q4ef/OQnCjqJe8IJJwyvec1rhuc973nDF77whZPEE0Caj33sY8U4G/jzn/98eOlLXzp86lOfGv7973/bqIGjye9+97uH173udUPv6YvNGDQ8X//614f3ve99jVTt6KOOOmr4/e9/304YpPjc5z43XOISlwhSrP2oH/zgBwNtbC3R3nvvPfB9ktY2AovoQ/O84Tvf+c7h+te/fpXFec973rnH7irziRGrgdVOO+00MCa1aC3h0qprb/z1rne94V3velcxOfPaK17ximJcBm5ZBBAw+YP07N0ozqfFj0xi/0ppFKZ08ve6tXxRuOVdSqcw6/o8kV9x5NdzyR3BXvq3KFlK/GZxP/vZzw6f+MQnZsk6c54SJrWwFpbks99Lz55fDx9eSPlKzwrDXa/EeueDH/zg8MpXvnL4zW9+s9lrrNZaqFUOlepdC0Xrs5VeC20GXnpWDQF0A9///vdXrbyego444ojhfve7X0/STJMIbJUI/PrXvx7Ofe5zn2Re0cuuJb0Cur5vfvObqtqKuIwHz3/+85u81xIuzcpmghVH4KCDDhr4q9Fq6DJqZWd4IpAIrD4CWpfa9a4Na4X3pvXp5K+tnxUuV+nlgpSeca1/9Lh/SuOCt7h3LoPm9773vVEweuYznzl88YtfHG50oxsNd7vb3UZgpr4Zhr8LXvCCoxGO57Oc5SzDD3/4w2U2d7jDHUbe3/3ud4c99thj2HfffZfjWnmV8ClPecpw+9vffvjSl76koM1cFudXuMIVBtKh8Ob56KOPXk7z/ve/f9hrr72Gs53tbAPvHNF973vf4apXveoojD3wgQ8crnjFKy4bNT//+c8P5zvf+UZj1Ic+9KFhxx13HD760Y9G7CbH8Y7zGjT/+c9/Dq961asGDLPzEHdK01bWG/3yl78cHvKQh4ztAPc2t7nN+IeSaS0Q9VtrRta1gMuWrMNPf/rT4a53veuAMhBaVB/y7/SBD3xgOOSQQ3xw0c/mkGgTx49//ONxg0Ux8yoGrhRW/hWYQ9jc0qJeXKZ8i1aZKx3/s5/9bEDRWCLiGO/VdktpMmzLIFATNm34vM8IuzWBd17ePfl92d4Pj1JYKbw3XVQvfelFyFLiNavLprieTXQRfz83RWmJi7CZEse3mPd7+Pqo7tRjayPWHhe4wAWGZz/72cMnP/nJYdOmTcOb3vSm5ddcjbUQhUXlTFkLReuz1VgLLQOXD6uCwBve8Ibhpje96XCd61xn2H///Ycb3/jGoxER+WJLE5uHMegkJQJbEwJT5nbmYtaDNTl/LekV2PT/la98ZUU/FeNBz6GCXlymfIsVfbFkvqII/O53vxv4K9Fq6TJKZWdYIpAIbDkEorVx7zq4tWZWfImfDbPPvl4gZMOEmMLkVzrr2ri19DyzQZMB+yY3ucnwiEc8Ynjzm988GvhYnH7kIx8p7nZCgQxQJUKwuve97z0897nPHU9gcoqSBdFLXvKSMTknJTnJyE75pz71qeNpItJi8GzlVXkIRS94wQuGM53pTAo6iYuhCkMqyiN2bL32ta8d9ttvv+V0hF3rWtcajVzLgYWHT3/602N+sDj44IPHndac8EPxDWGIwDh25JFHjrix6KNuvYRCmoZaIr4LBsRb3/rWw+Mf//iTJIm+g1fyb7vttsPb3va24UIXutBmfPiOkSGtpjDfjInzqN4K5ruyc7tGf/rTnzaL8nW3kVF9wLH0I768HxjyvT/+8Y+Phu23vOUtw8te9rLRz7OnCBPSRvG8b6ke5CPOn1gmvERgRvoeijCL6gpvH99bP1uvqd88aruWb4Ql6aL+E+HXU19OMzNuaayr9SHwqvVh6th61+985zvFk3a8e9RvonenXFGEQ9Ru4K93Fy+5rbJrWFGXGlZRPVUubqsPRfW2fErl1b4FOMC3h3x/6snT4t/i6eMvetGLDoxrJz/5yTcr3qezkVGcTVd6jjBvtZUSPx/meZS+nfJEbbrVV8Wj5Pp5KmrLER58a/unsmwYzyIf3usnf2/a1U7HGBCV2Yonr30/+yy+NmxMvPRvFllKecUv6ic+rtTe7nWve222iU98aTMl4n28POHnJpvPpxV/4WLdKM6mW/RzqVz7DpS3NdCjH/3o4Za3vOUocz7jGc8Y1zxPe9rTxldbrbVQVA4V6V0LMe9Ga7t510Jbw/ee5x38/FIbDyjDy6/4a3JVS46syfsYM1/+8pcPnIRkky43PnCz0p577jmOXz5fSy4jvjQ2CTM/diocN4qz6aakjebvVl15d9KIeIZfUiIwLwLR3B6NCbTBSPZVvZhba3yicSSSuf14pLKsy/xwi1vcwgaNMmBtTIjquRmTJU80PkT1tnxK5UXfIirT8s3n9YWAn4druozo+/v5YQoCUXvt6Wetsuj7VlYotXvxiMaD1hwpHiW3VIfaOBDhUeKdYesLAd+PanMTb+XbTdQ+6YO07Rp5+dWnI+/Uvx69hXiW0pbClN671NeGeT9xnkphPs1a8c9s0OQaiC9/+cvD7W53u+V3Od3pTjeennz9618/hh177LHDla985eE+97nPsP3224+GMQyTnhCoMBTZaxExonFiEuL0J1eWnvGMZxz95zznOYeLXOQiw9e+9rVRGIvykoFB9B73uMdoLD396U8/8ij941qvq1/96su/v3O1q11t+Pa3v728ewtD613ucpfQKArfs571rAPvfo5znGMshsntwhe+8PJuHhZ7nAIVnfrUpx5QikckLG91q1uNOIA1C0fR5S9/+eGe97znQPihhx46oAi5053uNEYrb+07fOtb3xp22WWX8b34XvYbnfKUp1zeIUcZD37wgwfwpxyuL/zVr36lKowGad79POc5z7D77rsPX/3qV5fjag++3gxUXAGC4ZkrHzntpt9y4j0ueclLDje84Q2H0572tOMCne+DwHv2s599uNSlLjUaiVUWV3ahpOc7cNpWu9zZafd///d/o6KI+mLEPvDAA5VtdMEOvHgPFPzslP/whz88gMeznvWssS1hpIbYkQd+tC2ucqH+5BOxm/1yl7vc2AeIt8ZQJmQUPmc+85nH/He84x2X2z35aStgTdun7dEvSic1mMA5FQwOpznNaYaHP/zhmwkgqgsuONKfwJjTzvb6zaiuvMODHvSg8R2pE8ZejP9ge4YznGHYddddBzY1tGjqN+8ZQygzwlJ9oNZ/Ivx668sGhZ133nl8fb4DV3FDtg/96Ec/Gsc5vjcYsuFBgqLqWOunI7Olf7QX2hgbJGi/LKCifkM++hCn02lHlEtdSxThEPU1dvze4AY3GPsSZXBtN6S+Rn3ps2zyuPSlL71ZHzjssMPGDR6kt1jxTvQH+ien99mgock1qid8LL3whS/crA8xH4hq9Va83Fp5pW9BnsMPP3wcB+ljbPypnQqo9bc3vvGN46l+lU/bps2of9X40+9Rhu+www5jucpvXRSMGsOve93rjt+IePoyZYgYZxiD+Z5XucpVxnGWuQUCN8Y84kjDdUyMJRDzP3OniHmc8ZZxEqph7tsK4zc3GWjcJu8jH/nIcZ7juUSeB+2t9u3IH7XpqK9yQ4SdM7jWmbkJKs1TUVuu4TEyK/zzAilJbJj6SCFrM8jzkV8Z5e91a/kI9zx8WMnvw8SDcMj6S88K8+5/c/8vP/GiKbKU8uBG8lKpn0bt7TGPecywzz77LLOv9X8SPOEJTxj7N3IYMgF9ojY38W6MrcgljLW1U1QlvGwY5Vq/fSZOVAr3YfghuXq2/jGB+RfFmWTr4tEbrxnLNX+s1looKgcQe9dCrbXdLGuhdfERV7CSpfmlttahGl5+5ZvQxphbz3/+849rBfWfSI5kzLr73e8+yv3I+8z/9mdZWCMhH7AeZ83129/+dnj7298+zvfcVkTeAw44YBmZaG0TyfIwqMlNxLXWY6QRgdtlL3vZcS3Emsi+j9LgRvN3VFdkF/BHT4IOhDUi6/bHPe5xo+yO/P6oRz3KFpXPicAkBGpzO5v+I33ME5/4xHHdjr6AW8TsushWoCZrRONIJHP78Yjbr5DrRegjuAWNfoVuh/WbqCTbKK5WT8XL5ep+xj6ta+ztYVG9lV9uqbzat+gdZ8Q73fWBAIdsaM+M7chpn/nMZ5YrbnUZ0ZxEO2c+Rj9Cu0dXst122418WBuydrYnQZlL2TgERe3V9zP0hw996EPHfPxDD8lNINI/LUec+FBaSxNVaveER+NBNEdG+oJaHWrjQIQHdUxavwiU1sy1Oa7UbqL2iS4fewZyKzfioJcVteRepcPl50GQpSVPEya/wqx/SpjSwtOT5yk/6ZTPh1m/5ye/8sq/1t2ZDZoI/hgVWRBZwsjBzkwGSf5QkmKo5EoHFjMl4R2D3m1ve9vhVKc61ciKwZtrijA+QL/4xS8GfvfMEkpurkJt5SUPAzACDIrliJh0eCcRizaIzjGFwITrd0UIaBhfwQbCoMAkxXWzXCvFqVOrHFU+64IlfDAoYcBg4cgCkU4KMWFgFOV6NNIgnDK4Q9F3ID+GCP4wTvLboSin9bs98FCjpgwwpxyMlRhJWLxC1I2d5ZxO5XuxSLSDwpio8M/Xm++OkMC3oBx+g0XGEd6Dcrn2l7piKEbRx8RNfREmEQwon/eiTT35yU8ehQGMIRgfhAkuC26ulWQhzgSJ8ABRJhiTnzIRqnkXsMGgC2Egl/DC77qyKKC+nATmW6scFOZcucRJZvoACkT46fdmETAoj/5EGMp/BHyIZ4xbYAzWnFqGn77HmOjEf5wk5tsQDx+EhNLiHPw4HUyb4x0xvN7//vcfubTqCqYo1jCAgxsnpK95zWuO9aPPYlDGONWiqd+8ZwyhzAjLVv+J8OutL+2StgTxLbhyC1IfYmcQxvjLXOYyo+KYK6E5ma2NCVE/HRmd+I92CM4olBibUJJE/YZsTNh3vvOdx3bE6Qrab2m3UYRDra8x1jC20hboUwjKXD2uPoCLsI7CHuM0inO7aYJrTmmTkLDimXaJn/aMcY02LSNxVE/yiuhD9Ht+z4I+dLOb3Wx5x3+r3uKBWyuv9C1QYjHuYHDWHFW6pjrqb7vttts4luj3Y5grmPNoOxF/3om0KJytIdC+C32XMVZjyYtf/OIxmvZn2wRGO74VfZ40bOiAP8S8RbtDGcD3YTymn0Dw0fMYsPRP37WFuW0rGEW59pCbEiDGPU58MH5GZHnQ3mrfDh61Nt3qq8TzJ+K9hB3v7+epWltu4SH+vLv+FIbrw+RflKuyLD+FtdxSnqlhKqOUT3G4Pl5+G6dnXIg0IpteYXL5RrQpqHeMJi19oCYvwdP20572pjpE/R9ZgfmEq9rom5RDXy3NTYwByGzI68zlJzvZycbNWtQdEiYWJxs+JjLp5LdpLI8aH6VRPu/6fKVybNh6f0ZeZL0iwvCjzYmrtRaKylG9etzW+myWtVBPuVtzGj+/0MejtY6XX5lDkQe5ep81wjHHHDOwIQeK5EjK4TYmlEisHdgs+qIXvWgZamQE5B5kPcYUNvFyuw2boSgPeZg1EtRa20SyfCQ3wTtajxEvYt3FOp3NxciGKHwf+9jHKnoztzZ/kyiqK9+Kjd/IUpTBiTP6MvIS8jDrBTDjWyQlArMgUJrbe/QxGGHQm2BsQ8dS+o3uSNaIxpFI5vbjEXo+5Bb6CkSZjGfoyEjLuAPVZBvionoSbwnjLX2Qd+c2JY1JpInqbXnUyit9iynjjC0jn9c+AvQz1qfoYViXctueCHldsms0J6HTeNe73jXqypG72FwoWZ/8ehZf9HDqE1F79f2Mm2boQ8qrfobcXyPKtrqbWrsnfzQetOZI6mrJYufrEI0DER6Wfz6vPwRot3bN3JrjfLuJ2ufDHvaw8TAKui5kVuwBrI8hyo3kXovkcccdt9m6Wf0fV39KL7/SEN4Ks/GlZ8tb/Lxry7PpI35Kt9bd+kjWqDkDLyczPLErnN2MUu5d/OIXH25+85uPp9xwUdBirKoRQg0nDTlNxE4UCGWPH3S32Wab5YFZvEp5UepgzMKQ5ImFHKft+KPx+nIoA9IE4PPLj4JYfHRNruIQmhDYUDLJQKo4FoZPf/rTx9097JJvEQtElLzs/MHQxekfGVDI+4AHPGDcbS/DsOVX+w4Y4FhsIuSRD4U5xgYMEyXC8ICSAsMvdeBKWggDJpM6bQKDF++DAaKHbL05UcAEz6lI2gqnXt7xjncss+E9aBs6ScQikXfgO1N3FIgI6mBEO8MIiLIOYwBtFkWziHdmJxQLc/Lq5CMupw0hFvjscuTEFCef1A7ZxSFjA3HUW7v+qJ8IQQX+EtARKmjXCEG0VxQCLJYxgvOHMZM2xDF5diFjJCQPxlsEJoQLTwxEGMYwPnD1Bdjx3lJS2PTvec97RqMSBmzS7b1kvMa4Rb6oruJBXfj+7GShDN6Vnc3giLGI0189NPWbt8aQFpbUqdZ/evCbUt/a+9P2aKsYt1HkXelKVxqVKDIoka/WT2s8Fd7Tb/jmpzjFKcZ2wjjNxG2phUOtr2HQQmnDOEcfY6cibd6OTQi0nAqkfMYQ2j9tnE0EbAzA0GuJMRcjIO9FW+PEK/2CMapVT8uHPkTfpu/Sh2hHGh976g2vKeWRHuUAO/LpEyj+6LcIRJ6i/kYfY9GtfIwxnFaHWvzJR9sqjRXkZ35lLmIMw9jLKQpPnNRiIwHfjbZ6sYtdbNzBpnTsECXvuc51rnE8sCfSlabk9mBu2wo4Mh8xPnFqiI0T2ihQ4q8w8eCbR2NjrU339FWVVXLtPBW15R48SvwJo11a8n4bN+szPD1fhflwyqjFKdzWY0qYzafnUvnE2XD7XMrn471feaw7ZYyuyUvws/10SnuL+j/jBNc7IjcxhqCgR4YoEX2KMRhFJrIuJyFQCETk8Sn5fVjEz8Ypn3dtGp6J15+P29r8bFxifaGNdH6NwvuuxlqoVo7HO1oLldZnyj91LaR8G9W180vPWsfKr8x5rPkwZrJWYl5lMxrUkiMZs5AFKBN5364xWLexDmS+Q95iXGH8QQ5BFqKdsn5C0RStbVqyfCQ38Q7Reox4EXy4dQZZFXkHGYa1JvoLS9H83aorfODPxirWW9q4x/dgfGZNgqzGZrGkRGBRCPToY5DfMRqij2Dtps2itg6RrFEbR5ibI5kb/nY8om8wPmi9yAZXq0NRfSLZJqqn8uNqXYMCm3GJvnnta197TNJTb/HqLY/0veOMeKe7fhCgne64447jehoZjU3qyNWeojmJOYd5gT7LHNE6eCPePe3V9jNuY8JwiH4TOZI2rPlIPEuu1tLobqJ2XxsPeubIUrk2zNahNg704GF55vP6Q8CumXvmONtuau0TFGjXGDE5dIROEp0hfVlUkntpb7L74KLrY7OBJ9JZwt8TRh6fzvIpPdv0evau8hGuP4Wtd3fbWV8AQVzXuTHQibgSD0UpwronlIuQ341h03G8l92bBx100HIwp98wDFqi8aBoteTz8rEwunFqD8UNuyBpcBi62CHJwkoNkEULAo415GlhU1MMq2wGbPFBMBORH6MPJ6PsiU3FI7hBGFzZ2aXf2FR8y0UQ5UTkNa5xjVbSzeLtd8D4iMLLfi8MHz1EPnZM8P58U65EY5GMMRNs1ZF6eCkN34hJmO/Aoh1FH4aQGmEM4uoUrgpGuU4b0CTN7w5hOOEaB50krtVJuwEph++AgA9hUMAAAGGswbgCcV0JO4UZAKkj12iWCAOWlAWKZ8cyRlYGTtoIJ0xFDNLUEcMshh7Stoj2TH/g2hZ9W/L4cgnDcM8CWoSCAaMmFNV1TOD+0dZte0fJoX7gkobeKd9c7+fHkBaWpQqo/yCQ9uIHnyn1teVivGAjABsRRFzXy+KvRLV3LaWdWqcrXvGK49ghwz08W+2o1tdo/7RZf3IOgz/zhCf6Im0QgYHvhgKeMd4S4bQl268QMiDier8XSrVaH4rqba9MbeFi680zhkDGEBkjCWOhwjipDRGEtfobpxM5bc8fxl34QhH/McGEfygn7clLZUXJyTyujSMKx2V+pi0zVk2lCPNSW+GbM59w4oy8zJOM1b3U+na1Nj21r0b1idoyxtpa37FtMOK/JeOouzZ+8byapHJ9mbVwn24R/iljtJWXfNlT2lvU/5G/2KAkQl6RLKMwucgdKAc4wckYzOYBZFEvVyv9Ily1kdX8Rouo95bgwbfhZhA2HmmNtVprod5yPC61tRDp/PrM5p13LWR5bcTnKWsdrrSj/7EuZh2MyyY7aIocSZvUxmUMpGw6Yx3JuoW1GNfZQZxElBzBRiqM3tHahvkyWhdFclNrPTZW6MR/yDiMl/anbtiMx5rYyp3R/M27RXW15fHMGsm6PLOGQsGclAgsCgH65RR9DBugOH3lKZI1auNIS+b2ZTAWcaMPmyS59QVdHUZOT5FsE9XTrrn4WSnGJfQ3nqbUOyrP8+0dZ3y+9K8vBNgkztqUW/Js+23NSay/WxsJS0hMaa/kp25siuYgD3I48/XUNXzU7mvjQWs+L71bFFYbByinVy8U8c+49YHA1Dmu1j55WzadsNZCT8cmF+RZrVU9Glbutfpu0nNgopbP80n/4hGY2aCJohHBg4WJHbx1AmyWqrIow0AGD6u05Ag+gz4NBeGHcjGcyrhEWaW8LJw4NcipMZ0cw8/vV2CcYoFtiRN2vI8IQQRhiBN6EXEqkR1flligcGKOCeP//b//Z6PG6w9RJmGwgzAgcvUXRlU6VC9hUOZ03zyEsZAJGEx1cokrMjDiahFa48/EglEIjFBIUB/CUPKBo1Xo13j4cHDA4KHfmXzmM585tgmfDj94cWIHg+aTnvSk4bil494sTjctKeJZOPN9jz/++NFPeq6H7CG+nRRIvI9+Gw0jowyjOj2K8g9FJYbC0oll/5uZlM91TAx8GFrBjtNtassI3LR9hCOMPj3X9vKd4INhrGTEtO9MfcDJEoYChPyorjb9op+nfPNa2S0s+Y6e1H+m4AePWetLX0NZgtDFwg1CQLTKkzFwhn9T64Shzyq+KTLCIeprYM+4jOLVK8MZb0vEJg9OcGPYt7+forTiifJKhh2UbyiPEMR72zt9iOuVS6QySvW26SNcbDo9Mw5wirB0vbrS4Lb6G5s02FHMVdoYHqUg7OVvy6o9M/Ywf/t5h5MLCHW2rYoHygAM84wbKC49MX4xn5QowrzWVliEYfijvNJNC6VyFBZ9u6hNt/oqikkpc1VWzdU7l9qy4lptkP7lBWUfhh/y6Wr16g0XX5s+CiuV7+sKr1oYcZaHyrJhpCmR0irO+wm3YfbZx4nHolwrL3merfZm00f9f9OS7IPcqg109MPvLd1ewfjhifGU8ZG0yDbcDkFfk+zl0+MHL/sdWn7Pw+Nt432c9yst4f5PcVuLy3qHjXRsjLFyJXPGaqyFkG9b5ZSwpq5+LUS60vqMcK6CX8RaCF4bldgMMWWtgyIUAwIbR1m3cJoSQyQnTKbKkcKcuVRrdmRcxhWIOZJ1peQL1pa04Whtw5wYrYsiuam1HlN9cVlz0fbsTnwbr2fN0aX5Gx5RXbkWLSkRWG0Epupj0KftvPPOJ6lmJGtgzC+NIyiOe9dnKpAbrHbddddRb4MMQn5PkWwT1dPyYV3Dpg2MTH6tGq0VLA+ee8sjbe84Q9qk9YsA62UMHNLr6k1acxLrfHSwpY2HmlOt4UR8p7RX5WHTIidAkZ/3PvEgg+J63Kjd18YDdNTRHMm6qKYvKNWpNg6gH5k67pT4Z9j6QGDqHBfJvWzo5TYcHYbCdtQi+pBd6yADY1fSpjXlJ120ZiYdaSCbDr/CS3GEWbJpCZffu8pDuP4U1uuKZ2/61Up3Uqmhs2QUMJwcwViH0pWduRgi2VXsDYU9LDmBgkEGw6MXNPjtSU5kkQZj00uXrgFFqcrpJqiWF6MQJxjtH2Fcb1SqIwM9lnrSI/BwV7euKux5B6WhUTKIs/MSBaz/+CgpKB9jHAs+6o/SnkUfp19Y5JeIE4FMfGBNHk6tWmNyKU8rDAwx2GLsA2OEPSZkDA0l4opZOi4dnu/A7/1AfA+MDeCGEhyM+VaknUJM0uDCBI6AQBnRZMdJM9KAMUZ22g7GWFzCOOnJaVF+N5D6lAQDXz+Uf/pdBX67FeUO78WVl7wj74QxRkpDTuHyvvQD0vH7dSLaD1epsPuRfCgL8WPERljhd1s4rs7uIvJz1z6DK0ZhjJO0xWOPPXb85ril6ywYQBFU+C0Yvgvtj/vC6Y8QCgZ4Q9SV9odRk3T0V06wIQhEdR0zr9C/qd+8VI0WluSp9Z8Wfr68qL46fYmy2BNtddOSwpnfv6UdIchxFTXX+E4lymHyZCyAojoRz++60h5og5xapr2xGcFSC4daX2MMYdHGbwHRPhlH2DSC8qpGGPQxUmFQYyHribogXDAuMc4xptA+2STRqqdt78wdGG+ZV8CKPqTxpLferfL8t+BkJeMNSgKITQpszPDU6m/MBygMmEvs4qOXvy9Pfq655juxAOG5dMUNV3TzvbkSnbYKphpP4MNpXL4NRkjGVHstOHMZ7Y1xk/am0zfk68WctCIWemqzMm4rruW2vl2tTbf6Ku0KWYG5ivZk39HXKWrLU/BgPvOyRBSmOLm+XiW/0lpX6UphirNuLV0p3IbxLLLhpTAbb599WvyK17NNY59Jt2iqyUu+nFZ7s+mj/o9ikLmfvoe885jHPGb87Xry+7kJmYQ5AJmTTSLIOvZnEYSbx0XhqlPN7/MpvXdL+ZVmCg/lwVU+uTZurT9jOGHMQ1b0hujVWgu1ypmCYW19Bo9oLTSljI2cdupahw1kGDOR07j9gE2a2mDXkiNrOLMZmM2jEDeeMP8z97MZC9kNYkzatCT/slk0Wtu0ZPmW3BStx8aKnPiPK3Npf6yBqCvyNNiwXuRPP0ESzd+tutry8jkRWCkE/Nzeo4/hinnaPZvP6QO77bbbSaoXyRq1caQlc5+kkKWAC13oQuNP9nDtHzJMiSLZJqonv4vGZkiIdSoyN3o55HbWNdrQMKXeUXn+W0TjTOk9M2z9IMC6FD0H62luOmEjs+ZS+xbRnIQuEJmdOROdnP0JGHSz6DX1017MSehMoCntVXVh0wLyAjrHkgFV6Wpu1O5r40Frjoz0BaV61MaBWfAo8c+w9YFAzxxn36TWPulzzAvobNERspGTG8F6N6zbMrBh+fUs8T5Mfr8+teGtuFpahatc73q+ilc+7xIvKuVV3FpwZzZoUnkMKJxAZNcTOzzZ3cnAPPUYO7xQ3LLg4cQHA6D+2H1Kw2Xi4Pc9mCz4XQ4MSgxgrbxjgs5/LOJZ6DHAouBBsYDxYSphqEChffTRR4+7U/UuOs3CaUIEOBRILP5QGGM8hTAkcgVQicCBHQEom9nFdsQRR2x25WcpTyuM74aSH14IYuxYZacCk2iJMBCyS4eFKQOBFO1M2Bgm2D2LsZs6IiyjNJ9C++6773hagO+MoQ1FH0bcEqHwp+7gSXnUHUMxBlmwpn0ipDOBI7QzaGHUahFXH0oA5ncmeEfejV3mlIUSEEGDbwfRLjnhwKKeMOqujg8v2iqn4QjnTm/82sVF++J6rx2WrjoiLd8DoyaEchEhCcEDIy3GL9pSiRBQ2AkNH3YU237ISTHaI8RpUwyzfC/4c9UUv50B31ZdS+UuImzKN4/Ki7AkX9R/Ivx8mVF9aRfct8538CddGK9orwinpOM3DejHnFacSrvsssvY/2jbKGKiOsGb747RnfTUD2OUTmTbsms4RH2NNsu7YjSk7dHPUJKzeaRGfAs2Q/Du8C4RdWG8YUyBF/OMxsZaPeFj2zvl0J/22GOPsQ+hXKWO0JR6R+X5b8EmBf44haD2UDo53dPf2KiAYIUBWNTLX+m9S9tk/MZFqQm/EnGy9fDDDx/nBcYgxjeNaxhZGduZC1Aw6kQ7fBiLmaNZPDHm2VsPpmCuOqFwZRdr1FaUtuTWvl3Uplt9FVmBk9XMM7wvc2FEtbbci4dwpwye9acy5beu4uTauNqz0uL6NIrz4SV/Le0s4SX+iwijLuJjn22Y6jurW5OXPL9We7Ppo/6PHM3vETPeY5zgtCbjPqSxiH7PeM0NIshHtGFktuOWNjkdeOCBtqjlZ2GCK1KY99twPUduLT95IJ9X6b2r9D58vfkxQjOnM+Zp7YDLxj3ms9VYC7XKmYJptLaL1kJTytjIaaeudVgXsVmU9R7zN+MEYwHUkiNrODOXwofNZ5e4xCXG9TO8KQd5E/kHGYz1D9Ra20SyfEtuitZjtv6sv1mjszZjbGQ+p+7IMqz9mePZsATV5m/ioroSn5QIrDQCfm7v0cew0YDf0EVOp2/qVLWtayRrRONITea2vP0zZXHdn9/Eo3SRbBPVk/kSPY6ITZrouBj/MI5ww5aot95Ref5bROOMyk13fSKArhg9BnMfugUM8iWK5iSMoOgQaCfw8zpT5EH4oifkatpNS2t3UW97VXpkZHROrA+4ZWkqRe0+Gg+iOTLSF5TqF40DU/Eo8c+w9YFAzxxn36TWPukTzAmsk9GJ7r///uPP+GGPmkLIwBj8RaV1a0+Y0sBHz/O6lpd9hm+LetK0eKxW/DZLg+d/UBjPQyjz2KHS+q3JecogL+WgKEcIW0liZztGRpSxK0kY/ChLhlnKomOxYwzDhyV20x9wwAGjcQ9BFEWofr/JppvnGWxZaNYaMJMgv7G0++67jycRUXp4AjeUL/PWjZOOtKdaXXy5YIKxxqcHY04QgVcvUTYGTIw+GFQxKPBOnGLke2EEsPzwEw9+tBkZXfxvIxJfwox6sVsQ/rYtEE7dCcflNBTXKnB6FKVkieBDP7H1Y9cJPCyRhsV6rT5RXS2fRT5P/ea1sktY9vafEn61cqL6qi143MWL9kr7rsUrXcv13ymqE7z4YWxOjrf6Z4RDra/BnzbFe9WMlKSZSowp1Jc+5qlUz1J7Z8c9fag2R/XWu1Se6uS/BWMPeDOmtsjnbaUnfgp/zw+M+I61/k968AJzsGFc27S0kOIKbCk/SUN7Y9xlrMRIYo345Gecsb+zSx5RL+bUlQ02/N4lu6shFnd8U0ssjDh5XKPo20VtOuqrfF97pV6tbIVHbTnCg7gSgW+JauGltFGYn0+VthZOfK3sWrh41uJr4cpXcm397DNprd8++7h55b8eealU91J7Y5MGYwSKOFHU/5FTkFtK4x18eDfNPfAhLX3KEvUokf8e3k+eUliJlw2z34Jn+UvPCvNprB/e1q9nW+ZKPXNTxrzrqqhu4Mt3XOm10GqVQxv0a6Ho/beGuEW3ETBkzrWyf4QTcxHGO7sZSelbcqTSWZfTmKxbX/Oa14xjC/xVF8YSP55SV8Yg3NrahjmbNH5dpHJLclPvekw8cNl44vsSeHo5Npq/W3W15eVzIuARWMR44Od22isyfG29R19h3FU/9XWSn75QW8tE40gkc4v3VDeSbWr19H2ZeQ2Zurb+6a13rTzeyX8LwkrjDOFJWwaBRfQ5as63jnSnPXMSOnTWkmymYb1L2xLRXpmT/Ryq+N72SnpuVmSTMJuXIG7C4oS2Jw6S1IyeUbtvjQe1+bylL/D1i8aBKXh4vulfOQQW1d9sDVtznE3Lc9Q+S3Kqzx/5WUfz54n+W6JauNK24pXOunadW3omTH/k07N1S+EK8y751gptu4iK8EIlxckieFselOMXHTZ+Uc8If7WJY1FlwIfFkl8woaTl6saIVqpuNeHO14U619K2BGPPq+ZHST6FaphQ16l1omxOUXKtIrs12F3MiV2EFq4v0clM1Y+TTJz85DQVp5E4SWmvZ1S6GmbEl07LUR5XCqPM5P04acb1FJuWDAs1KvGR4tLmoS9F9YniLJ9FPk/95rWySxjYtLW2QppWXssnqm8Lv6gOtozWsy8nqhO8esfpCIeo7j3Gu9Y7+fio/5bqWWrvNcWdyuqtd6k88fDfgrGnl6/PK56RO4W/5wNGrTI5Fbrr0sYOTr4fc8wx46YlTn9ZitpbTQmp/D3YcA0PJ5MwDlAPEYu/qRR9u6hNR3G9/Ul1jdpyhMdUoVHpZxGIVVfxkN+6UZxNZ5/JE9WnFm/LauW35enZ5rfPxLf84jGPG8lLJb6+vXFlNbcoaKOU8kT9Hxm21jZ9v4ePN2ZShsdG5fa45I2+lefhy8KvMOsqXGHwUZh4Kk6uwrcml3dbjbXQapVDG+QvaXYEwC+aXzznKG00r3s+8u+1116jMnafffYZb1PgRg3GAH5H0t/a1Lu2ieZsyvVjGWG96zHSikp9qdQeI8xadVVZ6SYCK4WA7w9Re6UOGDtLG0V9/egLNfk0KmMl+kQk29Tq6fsy85rHyr5zb71r5cGrxL80zthy83l9IlD61vZNeuakkgwuHrRXvy5QHG5Pe+Vnp/hpGH4SC6OpiBPMUylq97OOBy19ga9jNA704OH5pX99IhC1t9IbRemjPlbi5cPop/x5Iqy0Hq6FK7/nVeJBWp/Ohym+5BKmcFuuD1cauUq7ltzNj22tpZpt0LpwZWrpyg2uFdLvVW4paCifemwE2rRkNHzDG94w3qvNdRFcf8txcn5j1BPfjHgMmUyyXJPM1RHzEkIShgSuv+U6T64b5jcE1/KAMu87r1T+tdB/Vurdkm8isBIIcG01AhQnL7hmm5PhXK1TIjZ5lK7VLaWdEsbii53h/N7PRh73MEDz/v6P8FocaRVvXc9DfpumxlNplKfkKk2Jh+JK+QhTfCmvj7dpS+kVr7J8Gu9XOrlT2mkp7aLkpUMOOWT8bR42WK0mCYeWW8NR+NfixdfHe7/SWRccSv4SPqRLSgQSgZVHgBscbnSjG43XInONHtcmH3XUUcu/W64arOTaZqXWY6p7uolAIpAIJAKJQC8CU+YkjCp77rlnL+vudJ/4xCfGnztCr6nfee3OnAkTgUSgiQBrzdr6NQonTn92Xeuflca7pXQ+bKpfL0u+EtXCS2lXK2whV86uVmWznEQgEUgEEoFEIBFIBDYSAvZEam2XXi18ETjNI7z21qsnXZSmVsdSuA/zfjCLdkwvAtO1zsO2OV/X2neohfv8Nb/9Dnq2bs8zvJXOPtuwWvmLCl+Jq5UWVbfkszYQyDayNr5D1iIRWAsI5HiwFr5C1mEjIZB9biN97XzXLY3A1t7fuFqaP1FrPdyKFx+5pfTRutbG+Wf5rdvzrLrYtArb0u5Crpzd0i+R5ScCiUAikAgkAolAIrA1IiDhkXfTsxduFW7f36exca3nEr9WnlK85RPVx6YTH5++lEZprVtLVwovhVleG/WZXaAef2EhzHy8wknn45S35Np8xFu/nnHts9KVwlSG4uRPNxFIBBKBRCARSAQSgUQgEUgEEoFEIBFYBAJ2jQo/u/4srYdtvMovpVNcKb3i5JbS2DA9l9xSmPhaV+ls2Fp4ToPmWvgKWYdEIBFIBBKBRCARSAQKCJQESIXNKwAXiguDVG6UqFanUt5aWviX0kfl1uJKfEphtfwbMRx8hFHtGxEfxc2Cm8okr57lKkx+785SXuZJBBKBRCARSAQSgUQgEUgEEoFEIBFIBKYiwHpUa1Kf14bX1szksek8j1n8ll/pWWG97ix1WK08adBcLaSznEQgEUgEEoFEIBFIBCYiIGGzlE1xkZBcyjclTGX05rHpW/WyaUv8W/lLeVo8o/gorlTW1hpmcdBz6VsoDhxK8VPwsbzIZ/16rrm19FPKz7SJQCKQCCQCiUAikAgkAolAIpAIJAKJQC8C3Gwkqq2HtYZVOu/W8vl0JX+Ntw+Xv+bC28fZsFLZWzosDZpb+gtk+YlAIpAIJAKJQCKQCPx/9s4Ebrti/OOTSnuKkCRvRJYSKdIfRUKFyr63yU6yJCTJUqkkW8nSLtqUSAtSkV2SfavIvhRlaeH85zt13a5n3jlzznnu53neZ/ldn899z3bNNTO/MzNnZq6ZOS0I2MCyJTh5t/FMZnDcJquWfltYLmtofvL4ben08e+S1RXeJ435wjMZLHycIc/Zx/P4eX+zmwlfH7uXJ7sQEAJCQAgIASEgBISAEBACQkAICIGpQID5aNucdCrmw5PNo88TMry7ZC/55fEmm5fpjCeF5nSiK9lCQAgIASEgBISAEBgDgeWWW26M2IoqBIYjcNvb3nZ4JMUQAkJACAgBISAEhIAQEAJCQAgIASGwABBYeumlAz/RkkHgf2djl0z6SlUICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEh0IqAFJqt0ChACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBJY2AFJpL+gkofSEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBFoRkEKzFRoFCAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhsKQRkEJzST8BpS8EhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhEArAsu0hhQC/v3vf0/wbZomudtMAi0st+duz5eH4S7RUkstNcHb3GYS2McO30orrYQhEgKzBoHrrrsu5aVP28h5ximEbzNeTpu/5+myD5HRVaau8FJe2uKU8uX9vB25uTtPq5RO7pe7cxm4+/D4eG38bf4+bl6m3A1vya8ku+Tn0zJ7Lq/LbfG8maeVuz1vye7T9HZ4ca+yyiqlaPITAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICIEZRGCQQtPyZQvGXSb8OU8uo+S2OBbWZsKXL0C38cJncr29jV/+CwuBIXVpppCx+kp63m7p53652/jGNa2NTZf8Wv6GpDmEN08zj+v7CLMbDsTN+XN55vZ83t4lI+c1eV1mKV7Jr0tOKdxwyMNK8kt+eTzc8HlcSzxdfnlaubsW35cptxNviKxaOtMddv3114ff/OY3Yf311y8mxeaI3/72t63hxUjyFAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACMwiBAZdOcviri3wlkwLN/O///1v4jd3m9mXry3+VPiP+0yOP/748Pe//31cMcX4P/vZz8LRRx8dSINFa6NLL7003P/+9zfnlJobbLBBQH4b/eEPfwinnnpqOOqoo8Jll13WxjZj/jvvvHPCZ2iClGPXXXcNK664Yjj//POHRh+Lv6vO+HptCeV+NbcP62snnSG8li8z2+J2hU9FPEvDzDaZNX8fN7dbPPNvM40vN+HP/UrucfrDUholv1K6k/WbLvl95fbly8vn47XZiePpP//5T7jooovCxz/+8fDXv/7VB1XtP/7xj8MXv/jFKs9kA6+99tqw7bbbht///vetIsj3TjvtFL7//e+38ihACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhMJsRGKTQtILYIq83czvutl9twT4Pw53/vNyc34eV7JQh97dyTda8+eabw0knnRRQjk01vepVrwrbbLNN+MlPfhI++clPhnvf+97h61//ekrmxhtvDFdeeeVUJ5nkXXHFFeGGG24oyj799NPDPe5xj3DhhRemvGy++ebhAx/4QJF3pjz/9Kc/hX/+85+Dk+O5/fGPf0zKgK222mpw/MlGGFJnfH0lvZrbhw21l2S3+eX+lhb+JbLwNtPHKfFYeCmszc/iYLbxeH/P18fu43p7W1zP02Yf2p+1yZlqf1+mPrKH8veRCU+bXO/v7W1yPU/NTpgRGzzo9973vveFb3zjG2HRokXhzDPPtOAJJn3jQQcdNPK7/PLLp02huf/++4dNN900bLHFFqP0cstqq60WjjzyyPDMZz4zvU/zcLmFgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACsx2BwVfO+kVlCmcLxiW7+Xkzt+duk48/lLtv8e33768QbIvRhyePi6JvueWWG3kvs8wy4eyzzx65vYXvji6//PLeK9nxX3bZZcPSSy+9WJh5HHfcceHkk08OnO5ZddVVk/e73/3ucMABB4QzzjjD2BYzkX3b29423OY2w/TVnOK56aabivm1RMgLi+IoVR/0oAcl7xe96EXhsY99bNhll13SSUfjRR4/8lIicCSMZ5BTjrEPR3HJicouou6AxQorrNDKyqnM7bffPtzudrebwIPCEeWSz3tXunl427MnobY6Q57/9a9/pfLldb/mroVNKFiLI28HJTdR83RaxHV658+8JrcWNtk8mUyfD++H3cLa7KVCmow8X338TZ7nNT9v+nx5/9xeklPyIx4yfZh3e3ueRu72Mrw95+ty52nihnKZXe48Ti7Hp2N2z2N25LzlLW8JT3nKUwJ9MMTpeOz0Hzn98pe/DOedd154/etfn4Ke9rSnBX455e8B+kv6Hd4NOdE306f7dwabaE444YTixhY2vPj+64EPfGBYc801w2c/+9nwxCc+MRcvtxAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQmBWIzBI42WLx96s2Qkr/UCk5J/Lmoy7JNfSM9Pk5m6+McYC8N/+9jeCEnH68FOf+lSy/+IXvwg77rhjuPOd7xwe8IAHBBSORig4f/CDHyTngx/84LT4zWkeFGXPetazRjJZlH7xi18cVl555bD66qunBW/SZBE7p7POOivssMMOI2Um4VwbuNZaaxWvt7366qvD4x73uHCHO9whrLLKKuGd73znSC4nPffdd99REueee27YcMMNR+6PfOQjKQ4neVBMsrBeIq5NXHvttUfKTHge8pCHhJe//OXpFCluFtIpI/lAEfvc5z43cC0iRJk222yz8JKXvCSV/173ule6ujYFxj+UAA972MPCHe94x7D11luHr33taxYULrjggrDxxhuneHe9613DZz7zmVFYbjniiCNSPm9/+9unxfu//OUvOUt4+MMfHsABbLhiF+LZvfCFL0xYHHLIIcmvli752XPPPQP5AfNHPepR4Zvf/GbYZJNN0rPfcsstw3e/+90kJ//L68yrX/3qcJe73CXJoeyQr88W3/x8eCnMh1scb1ocz2d+xtfmNv820+KbmfOZvzeNx/th95SH5eGe1+y1OD7M82P3snO7dxuv9yvZ8TP/kt37WV5KZhuf+Ztpcc2N2UY5j7nhN3tb/Dzc3JaWuXMzD7e0zITfKI9r7jw8d3sZhJXiWRwzfRxvZ+MGfYXRuuuuG0r9Cn3fy172snSCnX6Mq8jf8573hOc973kpKqc66WfYGMI7gD4SpeTb3va21O8R501vepMlkzaZvPSlL03vnZVWWinstddeo379xBNPTH2N3+BBWuutt17qk+h7/VW0vD8++MEPjmTLIgSEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEJgrCAxSaPpC+YVev0ic24nTlzeP692WtvmZTHP7dHxY7u/DLK73Q+HoiZOCptzbb7/90kI0305DmckCN9eVQsQzOSj0LrnkkvCtb30rLSaj6Pz85z+f+FAIfu5zn0vhf/7zn9OJmzzNxBj/Lr744pBfg4qSkAVpO7FpvJwo5GpaTuGwgE06H/7wh9NCOTyE8zOiTJwOglA2sgjPNx2vv/76dOKIMpSIMj3hCU9YLOjtb3/7SMn52te+Nlx11VXp25pclYv817zmNSkOilsUfiher7vuurDPPvuMFu9Jc7fddkuKWJTKnIbaY489UjyUydttt11SABOPRXuUy8jPCYXBu971rqRQ4AQTi/188y6nL3/5ywlfrpC0b8uRB05XcUUkisqudKkfKDX4zinfN/31r38dHvnIR4Zjjz02KbFRfLddx5vXGfJK2j/84Q/Tc7D65PPt/bwdHu/G7t1ehtlLPHmcLncuqyQTHvPP5Vl8z+P9vL0W1/N5WUPi5DJMTsnfwqxcnsenaXZvluy5PHj6/Hw8n4c2/5pMi2/5K8koxbd4xl9z52F5WrkMS8/H8/ZSuJfpeb29xOP9vN3i0f+ss8465kzfDzYl5cgzWrjalTbPFbBchU1fTX9r/Tx9IP0NylH6W66mRQ7vAzalcJ0t/RfXfkNveMMb0nXm9EX0d6eddtrou8W8W7hu1ogT9Jwk5f1DX8SJzve+970WHO573/uGb3/72yO3LEJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICIG5gsBghaZf6PV2CuzdZvem2dt4DTTP5/1yf+82e5eZp23y+5gsPrOAfGX8biWKQxRQnLApEScWOR1I+LbbbpuUa/Cdc8454RnPeEY61cjJzNKCuMlDUcZp0D6EEoy8ccUhJwUf+tCHppOWH/vYxzqjcwKR8qBA5CpUToW2XdOKMpdThG3EYv1HP/rRdGLz7ne/e+CHMvPoo49OV6kS7373u19Kg6sTSeunP/1p+N3vfpdEXnPNNek6W0xOSnKCkmfKyc373Oc+CTtw4/pGTs9yJW9OfGuU009c9cvpKBSfn/70p3O2VvcrXvGKdMIJDPqkS15QmnKd45Oe9KR0IpfrKEmf6yi54rEPPf3pT09y1l9//XSCNY9jdTv3x10LK/F7v6Fxh/L7tLCPGz+X593Ink75Pq0uey0fPmycPHs54+QnjztErsXN4+Ru4+tj5nFzd5uMvnz+Ktk2WTV/FJZsYOBU9WSIvoyT6vSBtHuIfod+hO8k03+z4YLysDGFk5b/+Mc/Ej99DP0ihIKTvtsIHq6s5kpwysjJexSiRpzuR8kKn0gICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEwlxAYrNDMC5cvIOfunB93jacWNlRWzu9le3vO1+ZGOXi3u90tXVXKlaIoJ0vfOsvjw2MnHr/61a+mU545T8nNaSCUlH2IhW0UYShRjcgjp3q6iMVvrnntQ1ytetlll7Wycm0v35LkSkUjvrUJ3iiAc0KBCoEPikpOJ3HKiLKjDOTEIgvz4MDJR0/kmVNIOXEClBO0nALlx1WOpF261jePm7uHpEtclJj+m6lcK8spzqHEyc6ZoppyJw/DnfuRzzb/UhlK8Ut8k/Ebko8+8qczr33SH4cnz3sNm5x3MunmMnL3ZGQOjTNOmj6ut+d54PvFXOf9iU98olf/n8fP3fY9ZjMJpw/hRD0bPdiUsffee4/6MzZZcAIe4iS7j0e/S/44xc/VtWysQcFpZBtVOOUuEgJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACMwlBMZWaE51YdsWkvEvhZX8JpsnFGpQSQHFlYEsHh9wwAHpWkCuBHzOc56TFHBD0nvEIx4xut60Kx5XCZ5//vkT2PgeI9fO5gvSnJrk2lMWv41Q7G200UbJyaK3XTFr4WZyGujSSy81Z9VE0ca3K+0aXphZMCd9lLV3utOd0jWHdoUr4d/5zneSspJvb9aIU0N8c/SYY45JV/k++tGPTspjngeKVMrjiWsVOemUE0pnvjmHMtJ+3/ve91K+ct4u95B0u2QNCS8pjfO67t3eTjq4cz+fvoXnPN7t7SbTyyjZTe7QMJNv8fO08/CSfO/n5ZRklXjNz/jNxD+34/Y/i+tNH8f7j2P3aZbsuWzj8f7m581SuPnBl1PJD57cP3fXz28ZBwAAQABJREFU5Hje3O7dXgb+eZj59fE3HjNLZfDpcWqSk+NcIU4/M91EX8+1sZzStL4Mk28EQ5zm9BtFuLaW/pxrzXkfsLlk1113HWXz5z//eVLC0k+LhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAnMJgbEVmn4hmILnbgPD+2Mf4jYZ3izJINzL9fx5WImPEy1c33f22WenqCjovGIOBSPKNuLCx9WuK6+8cp5M1f3Yxz42fc8RhRUncErfdjQBXB+L8pDvYUJccbj//vun70qStifytmjRonDIIYekRWzkn3DCCelqVvge9ahHpetTUcqigDzppJNG0QnjlCZXo3KK8ayzzppwqmfEGC1cfUiZ+YYlSk0UqIceemg6HcqJSZTCu+yyS/puG6c1+Z7n+9///qT8tdOYXp63c9qSk5l8v5KrFynTaqutlli4DpfTm3wfk3TBBffjH/94LyLZn/3sZ4cPfehDAeUvhEL18MMPT/ahf0PSHSo756feUTaeM/WsRG31Hl4L83Xb+3m7l537m9t4cneeFuE5WRxvdvHUwmthtTQsnufJ7TkPbniM2uwWjmkyze7DvJ/JMtPCfPzcz8J8nFy+uT2v5/f+xuvNUrj5GZ+5MSHvNj/vn5gKfD6e5+myW3jJLMmEr+Sf++E2yu3ejSLxqU99aup7+BZljTgpz/eVJ3Mq3MtlIwrXZx988MGpX+Ck+4knnjj6JvM973nPCafU6R+5ipvvFnNd+QYbbJA2iZhMribnJD9KUpEQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJgLiEwpauafvHX7GYCCvY+buMx/pJpIFuYyTfTyzA/H8fsubnffvuFd7zjHWGllVZK3x5btGhRYuFbZyj6DjzwwLDGGmukK1X32muv9D3IXEbNveWWWwZOHm688cZJWWdXB5bicFUr337cbbfdUn5Q9qHoe+UrX7kYOwvf5A+lGIrZxzzmMelkzk477ZR4UVpyWpNvqJF/f+UuSkOUjk9+8pOTshIla9sJHnDhlBIL5yhVWbgnTb4pZ4RSFb511103nSAiz8jvIk5boojkFCvfo9xjjz3CUUcdla5U5CTSaaedFnbfffew6qqrhte+9rXJzfc4c0Khyo+TSmDBd0rz62rzOG3uIem2yejrz3dDOfVFneP5WN22uuzlWBh+Zvd83q/LXpLh4+ThPh3CIM+f29vCU0T3V4pnwXkY7hKV+Pr6Ic94+9iNF9PI7N70dpNrJmEW7v1MnpnGN8T08ixeya+UvvlZPHPX4nveoXYvt83eR6aP28af83h3bsdtxLsBJeXDH/7wtHGDzRv8SpsPtthii9SG6SOJMw69973vTd80pj+lX0ahybsB2mabbVI/aPLZcMKmGfp5Ts1/6UtfCvvuu68Fp1OebNIQCQEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBOYaAktdffXVDddq9iGur4NQtphCrGY33hTp1j+LV/Mr8Xh+b2fR2qjL7sOJ491cdWpE+lzpiuKsRJxKZKHaxy/xtfnxvcibbropXf138sknB05iciqvRpy4QUnY55ud5I9TlKVTONdff31aHEdBmxN54nrXvqdOrT6gsCwRJ0HJg//GW4mv5MdJUv9MPA9Y2MlN75/bOe1Iedvk5Pxd7r7pdskphW+44Ybh7W9/e3jCE56Q6h7lI70a5e0kd9filsLy+tzlLskwv1pecrkWp69Zk12Tkcdry4f3b7O3pePTMHubaTIs3Nxtps9LG4/3b5Pb5l+SX/IjDe9fklfy83kr2b1Mb8/TK8XFb2iatTR8WNt7oC0f3n8q+wz6U8qY97ebbbZZOPLII9OtAZY2fR9XjPPOMMJ9//vff3Q1uPnLFAJCQAgIgalDgPF833nV1KUqSXMJAdWRufS0lFchML0IqD+YXnwlXQjkCKjN5YjILQSmDwG1t+nDVpJDWGayILDgy+KqLfx6e5vMPjzENdltcry/pW9+3m12M0s85udN+GuL2LUwL6dk53QjCsy999478L0zlFilE5d53D4KPItTy19NWYmytI/C1NLJF9bN38wVVljBrIPNmhKyLxYobWtyhmaqb7pD5Xp+8mzp5PXW+GhHUFu48Q01c3m5u0+aQ/JWkt+V5759iMmx/NTynufDu729JsPSMX7LZ25avsy0cHO3mSa3LXyIP7Isvz5eKY2Sn4+DvcTTlkYe17tzOV1ui2tlyfktvM3M+bvcbXJq/taWazx9w9r6U66j5RT/RRddNFJg0o94ZSZpHHTQQen7n22n7/vmQ3xCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBJYHAIIUmC762AO9NMt4Wli8ST3Uhc/ne3Wa3PPhw85tuk+sK3/KWt6SrZFEInnrqqaPrA6c7bcmfvQhst9126ZpIn0Pqpylrcn/vxm7tMfcf4s7bQ+5GVslvSBqedypleblm74tJKR/ez9tNdm56nr7p5jJqbi+/xufDiFOqP/D0ldeXz6dr9lr6xuPNPK3cbbzmb2Uzt4UPMfO4uXuIrCXFyxXdKCvPOeec0HadLN8npmxsphEJASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAE5iICg66c5co7yBaS+5oGjPGb28yh/hYPM1+ArrnzMC+ndqrR88kuBGYKAa4O7kttbahv/FLbKPmZvDxsnPRzWZaGNycjv2+cUvp9/Wp59Omb3UyLl7vN35ulvPjwLnufNNpkzGTaeVq5uy2P4/iX0sj9aqfax0lbcYWAEBACQmD+IaCrlebfM53qEqmOTDWikicE5i4C6g/m7rNTzucmAmpzc/O5KddzEwG1t7n53OZKrged0Gy78m6uFFb5FAJzCQEp2efS01JehYAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiB6ULgNtMlWHKFgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAuMiIIXmuAgqvhAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAtOGgBSa0watBAsBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBITAuAlJojoug4gsBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBITBtCCyD5GuuuWbaEpBgISAEhIAQEAJCQAgIASEgBITAfEfg2muvDSuuuOJ8L6bKNwYCqiNjgKeoQmCeIaD+YJ49UBVn1iOgNjfrH5EyOI8QUHubRw9zFhVl9dVXT7lZZqmllgrLLbfcLMqasiIEhIAQEAJCQAgIASEgBISAEJhbCCy//PKaV82tRzbjuVUdmXHIlaAQmLUIqD+YtY9GGZunCKjNzdMHq2LNSgTU3mblY5k3mVqmaRrtJJ43j1MFEQJCQAgIASEgBISAEBACQmBJIMDEXSc0lwTycydN1ZG586yUUyEw3QioP5huhCVfCExEQG1uIh5yCYHpREDtbTrRlWx9Q1N1QAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgVmLgBSas/bRKGNCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAhIoak6IASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEwKxFQArNWftolDEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASWEQRCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYCEjcP3114emaYoQlPxLfsXIAzyXWmqpCdzm9ib23L3ccstNiDcfHWOf0PzXv/4VvvCFL4SPfvSj4Sc/+clYGP3zn/8Mp59+ejjnnHPCv//97wmy/vOf/4TPfvaz4f3vf3/4/Oc/v1ilqsU1QTfccEP4xCc+EX7729+a12LmtddeGz75yU+GI488Mlx22WWLheMBz1e/+tVimPf8wx/+EI455pjwzW9+M/z3v//1QeGmm24K559/fjj11FPDddddNyFsKhw//vGPwxe/+MWxRR1//PHh73//+1hyLr300nD/+99/LBlLOvKvfvWrQB2bTbTzzjsHno9odiMwFW1otpTwbne729j9vC/LdPYNG2ywQUB+Tvvvv3/g10bz6XlZGfu8H+G95JJL0juS9+RZZ50VrrnmGhMx5eZUvaPIGO/QT33qU6k//OlPfzrleeWdf+KJJ06pXMYQ4MwP3BmfDKGpbouk/fOf/3yUJ8YmV1555ZAsDeKdDkwHZWAKmP/yl7+Eu971ruGvf/1rURpj1emoj8XEZsiTcT51TzR9CDDfueiii8LHP/7xxerWTM2FutKh9H3nQrX3z3TPhabvKUlyDQHWBq666qoay4yHHXXUUeFlL3vZjKerBIXAfERgOtYgpmNcC/a0+w996EOLPYZzzz03PO5xj1vM3zzm4xiuNr6wcmNqPuDRmGhfiOsIbW1oIjJyCYGpR6CrvU1lim3vIFNQYnb90Pl08UwmvCYXDLxMwwS/hUBjKTRZ7GIx5/DDDw/f+973wrbbbht22223xZSNfYBE8bfeeuslJRz2NdZYI/z6178eRX32s5+dZF9xxRXhyU9+cnjVq141CuuKa4wHH3xweNaznhUuv/xy85pgMjnfZJNNAnwshGM/5ZRTRjwXXHBBeP7znx/udKc7pTKPAgqWl770pWHzzTdPi1l77LFH2HTTTUdKze9+97thnXXWSYuvF198cbjnPe+ZFjQLYibtRRnHVWjefPPN4aSTTgooZsehG2+8cVoXRsfJWy3un/70p/Ca17wm1QPMpz/96enHItNsIPI325SsswGXJZmH3/3ud2HXXXcNTBigqWpDS7JMPu3f/OY3aTOG9xvHPp19A++KkpLob3/7W+BXovn2vChj3/cjvB/+8IfDO97xjnDhhReGk08+Ob2bHvOYxwQUN+MSMg866KCRmKl4RyGM9/W97nWv8LnPfS7wjt54443DEUccMUpnKiy///3v07vQ2vVUyPRYU4a11147bdjqK7tvW8xxr8n/8pe/HF70ohel53/eeeeFrbbaKrAx4Gtf+1otWq+wvG+cDkx7ZWQKmZhcsEGurV6wae0HP/jBFKa45EWhgLr66qt7ZWRI3eslsMCU16sCy5zyYu5xj3vcI7zvfe8L3/jGN8KiRYvCmWeeOSrDTMyFSKyWzpC5UO39MxNzoRFwsswIAmwsetKTnpTeHW984xvDdtttl5QJ9PdLmtj4NBVjmSVdDqUvBGYDAtOxBtF3XDu0/LT70uEBDk/UDjnMtzFc1/jC46r5gEdjon2hrSNQ+rY2NBGZW1yse497yKkk1/uxQcGPjX2Y7PMLgVp7m+qStr2DvLJwttrBIs/bVOMza+XFhYlY9uEUFzWauNDVHHjggaPI8SRfs/766zdxMXHkZ5Y4aGji4o85J5hxEbmJx2GbOBEa+UfFY/PWt741ueOkt7nNbW7TxNMiyR0HH80yyyzTxFNzTVdcE/j973+/iUrE5va3v30TT4Ca9wTzAx/4QPPQhz50lM94WrSJysYRzzOe8YzmYx/7WLP33ns32NsonshM+Y0LLYkFrKKCtokKxuR+5jOf2bzyla8cRT/ssMOauNNt5O6yRCVWExfQimykFRfxi2F41p4DYX2I5/iPf/yjlZX8eYqLoc2KK67ovRaz5/nmuUZlx2J85hGPfps1mbW85/nxEcGxhBfl22KLLZp4Sqkhb0bIesUrXpH8zc/MGibw1MIpbykfxCMs7nbGOoHiBoImniSe4Adm8PehGma1vCI7Dy/lrysPQ595re76tGpYwldrPzX8+uQ3njpjK8yEOuPzZnbwamvD8PQta/4cTL6ZtfBaHtrqXLzGoImKKBOfzBpmExijI69zbX1DLd+5TO/2+abPiafgfHCyv/rVr274QXk/kjwLf7X8gGPfNpeLrmGX17c8bh83+aq9W3MZvIde//rXj7zJQ9xo1Lz5zW8e+WFBblt/NYHROXh3PvrRj3Y+ZWut7tN2/TiCMQF1Mi46jITFSXh6/8bJ18gPS+0Z9q0HEwRGR16ffXieVx+GfZdddpmANWMxxihxw0zOmvosX24Y+rbFNtyRRx49HX300U1UCHuvJio4m4c97GET/HDU8FyMOXr07Rvb3snIrLWXPM2h+euSX2qPf/zjH1N/j0mbqNUHy19XfzHZuoj8WpnHkYtsyzf9/y1zJnz/R3ldIqSt7oEVz7KN2vqWUp3tW6/a0poO/8nOq8jLE5/4xGbPPfccZSueKmse+chHJvdMzYVq6ZCRvnOhrvfPuHOhEUhz0DJOHbHi5m261AaNN++/cLeNQbvaJ31BiZgzxw3Hjc19jecrX/lKqjN5PNLJ/SwOJuFtfQHhtf6uFHbIIYcU5+4lXuTnRJ9Vw6yWV+s/TWYXxsYnc2EgMBX9QV7HcuS66nktvBRWWoMovaPzfJi7NF4qjWspV1u7M1ltpuWbd1bcOLgY2xlnnJHWMgmg/+yTTg1n2nWtH14sA86jCzsri4sy2FobX+TCjp4F84E8T1PpHqfNLaR1BKt3pTbU9h5jrT3eMrLY46q1jVJ/YAKIR/vw9LznPa+JG7C9l+yzGIGZaG/5mDiHw+qy+ZfqXOkdBH889Db6sfbkf/GWpsb/WH+ajp9Pw9K3fEWlb4MeLm7cSWublJV2w/tqIdCkT2hyjVZUEoY4IR0pa1dZZZV0evK0005LflxZt9lmm4WXvOQlYfXVV0+nKLjGLKc4CQjHHnvshGsfOHHBiUmI059cWbraaqsl913ucpcQFafhRz/6UTqBU4tLhDhACbvvvns6/bDqqqsmGaW/73znO+HhD3/46O7h//u//wu/+MUvRru6uBouLkKG2FGXoo/87njHO6br+tZcc83kF5Wv4d73vvfoVBDX3nAiwyguvIdf/vKX5iyahuVTn/rUhANYn3DCCSPeBz/4weGFL3xhwD9O2sJ73vOeEDv7FG5x257Dz372sxCVd6lcPC//jLh32U4YkEZ8iQfwJ52tt946/PnPfx7l4YMf/GCg7Jw0ecITnhB++MMfjsLaLHm+Y+NLu3nBmGPfnHaLHdSoHBtuuGHYZpttwsorr5xOv/F8dtxxx3DnO985POABDwjHHXfcKCmu7LrPfe4TeA6ctrWdPOwqvO1tbxsOPfTQlF9OA++7776jeFjADrwox9JLL512yrNbDTze+973prrENbQQJwTAj7rFiWWuZSCekZ0aog0Q/pnPfMaCQpwghxe/+MXhDne4Q4r/3Oc+d1TvYaKugDV1n7pHuyhddxwXJwKngsFhpZVWCnvttdfoRPAosVst1AfaExhz2tlfy1nLKyef4kJbKgN5etSjHpWuUwbb293udmHLLbcM7LjvoqHPvE8fQpo1LK0NtLWfGn5980vde8hDHpKKz3PgKm7It6H4Qk/9HM8bDN/5zneOnpPlsa2dJmG3/nXVOa7mpv3wjKMyInC9p1EtD/DMdJ2zfGHW6h8nxTg1ZrTPPvuk+mjuj3zkIxPaCv1+G4EHz5U2ue6664Zvf/vbI1b/vGo4U984zUZfxKl92v3yyy+f5LDjlz7GnwTlxD6nF6Ah9Y12/9rXvjbF44/+gxM8cXA/8uMEedxoM3LTpskTO5K73o+jSAUL7y76W7stodbGamWmjwMfTmzxjuAa8yHvKGQ//vGPTzc30BdyLTwExuDvCZzBKA7gkjfvgQc96EGpLdDv2VXytDf/PuG6e25SMKKstFP6NK6Nx25Ue2e25dXitpm8G7gBguvujfrKaqtPJdyRzQlW3tP0D3GBo3pqhfc8V08Z1fqWtjZa6hs9pl3v5LbyWZ682Za/tjbCc67Jz/t/n5bZDzjggPQO5N3LzmTrexib0C9BpMHzoL7S78SNA6n+Wjvm2mH6bMYjvFO5Bo3bTyBuJPFjFK5Jo+4a9a3jfBqCtmAE7vR3/vlamJmGjeXb8mThbeOsUt2rje+QR5/ATS20BcZC/lRXqc6W6pXla66avFP8DTS8n9gZD83UXKiWDvnoOxfqmttNZi5E+guZ8vdWXFBM1xOX5jrglPdfPBPqGLcE3f3ud09zBXtX1tonc4oXvOAFadzPeJ85j71LSYfTxMw9GW8w54qLLenWBN5hvJOJy5jNqDbOrI0ziF8bJ9bGbJa2mW39poWbCS70R/TN9E9vf/vbR+OLWl6Zc4I/6yT0+cwRmbe/7W1vS+MgxkJvetObLBmZQmBSCHSNLWrthQTbxkyEsd7AuIRxN3NJxjS08xKV3tElvtr42fPX5qqsM7JGZ8R6IfMB2j/09a9/PfVxlu8rK59PYI2J29vgZV7u15GGjOE4MUZ82jXy6GO/9a1vpfy0jY1TYPxrw45+9ylPeUqapzJWj0rGzjFcVNK2zmVq4wvLS83M5wO1utVW5tK4zc8HaunPprD5to6QY1trQ21jBU5lUv95/7N2wI1PUNs8gbDaOm7bHJi1UMahXEXK2oBo/iNQa2+1+fPrXve61B8zr6aPp1+G38Zl1CVbU6qhyDjZfsbn3WbHhLx7Kuxeptm9id2T5cP7zWv7ZDXmnCLgNGZOccE5ncZjlxM7nzhZyclLdnHEF3ETFXt5lMXcaJs5qcCuTohdlZzY9MTpufhS9F7JnsfFM17dNNqVGa9vaj2hyW6zOMkayURzHx9+EzvbkR+Wd73rXSN5EwJaHFFR2sTJTNLse5bYwTdROZYwiff0+6DF7GAZB2sNp0/IV1QeppM3trsgDhyaeD1cEweKSSMPZnaKtPYciB8Xx5uoWEnx4kJ4Exfl0s4CMuF3KpBGvIYq7YqNHUsTFU3p2cBHGTmFGgeN6VnHiWvznOc8h6Cm7RQWYXm+OSXLKR52FvDDHhcEYR3VJ+peVKQmvzjJbOLkMO2qI+9RodDEK3ITRnFS3cQJZeKL1xE2uOPks7FTFXEimXYuRMV4qqfxmHnipQzxmsVkpx5vv/326Ud54mJl8o9K34bdyFC8VqmJL+4mfi8m7diIir7msY99bAqLC4XNCius0MQXb9ppHK9xTKeLwQ/itGd8GSfcSDdez9REJW4KY/cF7Yc4ccKS0uN5WLvwuyPjYm3aGc3uDeTwTHkmOUXldMIIPGiTcYGziYsNia0rr/e73/0adkTxXOKkIZ1eBm9OP7MDhLLERYs8ycXck3nmffqQGpZd7aeG35D88lzpM3heRtaG8ENWnKCkXTS0C/qjuACUWGvt1GSZWatz1M2orEinE9mt84Y3vCHVB9p6Vx666pyVhXzUMLN8YtbqnO8buuofp+Wpt0acJHz5y1+enOQ7bjxoKDtlpL6Q17YTmnFTRkN6vC84DWPtFWG+jDWczz777PT8aGe0JXYK0l4h3qvUA+QbcfItXmGenDXs8voWvx2d+lbSgGhj3BLgKQ7OUv9GfwW95S1vGWHj+UrvRx+en9CkL+HdZe/aWhvrKnN+WqvvO4pygx34cgKC9ybvKPpyCDn0Q1Hp28SNVr44qS+ElxOc1At2aMcFisRj7c3eJ/Rr9NVxQpbCeSfbeMXX09o7syuvPnP5CU3C6Nu5fcJ2gNfK7etprT7luFNu+h3GCuzoi1epp3EI6R8dx2ikaUS74j0TPyWQvGp9Cwy1Npr3jR7TrndyrXyWV8xa/mptpCY/b48+Pct3nFSnusnzi5tZiu9obgZhXBsXCRIvbZg+wtpsXPhuGJNQD2l39OFx01VKLm4GSP24pU2fwMlpCN6+dTwqMFMfFTebpbjxe8EN6dYoKk9TfYkKi5Rv3ifkG6IttI2zCM/rXm18xzgsLvin8Rv1n7EGaUG1OpvXqxRhCf9Ndl5VyvbTnva00U04MzUX6pvO0LlQ2/tnyFyohNFc9JtsHcnfW11tMO+/GPMz5yAeO8h9n11rn5xIiQuVDX0HceMG2jTmN+zjQnman+KmH2Q+GDdhpD6PuRGnK+zd2zXOrI0zusaJtTGbH3PU+k0rk5m8q3lPMu+JmyyaBz7wgaNbIWp5ZY7IvJ1xKGMD1iLoO7mhiX6esQbv+7ih2ZKSuUARmGx/AFy1sUVXe6mNmZAdN+mm+QZzSdo+7dpuZvNrELV3NHKM6Dv6rDl1zVVZF4kL1CY2rb3Qtmwdh9vW6As4rcLYgr6r7YQm8zbmlswpGBPRZu1Uiy9jDWfWXpBDm2dMx/wBN+M9yPezuP38tYad9buMlylLnzFcbS5D2p78+ML7Y++aD3TVrVqZ83Gbnw/k+Zgu9zhtjucy39YRcpxrbag2VkBO3Cw7OqHZNUZpW8ftmk8zR2BdQDQ3EJjO9labP8cNok3c8JfGb/SNrOXzrjjxxBPTuAwzbtoZnQD2ayseWcat/PwpSez5SUz0E/mPfnvoL5dhbkvP8kGemFvxazul6csxX+2TPqEZF3LSrq1YKSYQu/7ZuRE7sOQflSBhhx12SKfcMDnZaTuoJkS81cFudU4asruJXZ1Q7NRCHBjcynGLESvcaBe8BZTixklL2k3Jzu+cOJHBaTt+8eW6WDqkAcXBSR51gpv7lk1OHABMCIsVLnAqjF2Z7Gr1xInGeN1s2rnJrs8u4pQnO9LYUc8pLk5XsCvWKE6s0u7RuChrXiOz7Tlw+pKThpwqIF6cqKUTmrGBjOJ6C9+R5EQpJ2TJQ1zUT8Hs+o+NNdUJdt9RHjup6+OX7D7fnDLkZBunIqkrnEKIg8hRNMpB3bDTMuysoww8Z/LO9z7ZCQtG1DO+5cLpInbWUmf9qVHKzIkqdjYT104+YnLaEOL7P+w2igPOtEvO6mFcEE75g4cw8s2uD3Z/kD8jTpQhPyqX0+7BOHhM9Zrv01FfOTERlTJpp/Td427puLCaduDFwXTahcypU+JwUoqdgvlpJNKJnVP69h07iGNnnbCj3JziyIkP3HPikR1NYLzzzjun08vEq+XV5JAXnn9cxEzYUlZOPYFjVPyGuNBqrFVz6DPv6kO6sCQzbe2nD35D8ttWcOoedZWTOezijIPFdOo2LvqOorS10xHDrZZanWOnEd+/4jmTTpzUpTZOve7KwwXxG4QzWed8ufrUP8/v7eSbNsxOVtoK9aXUD1oc6m2cbKU+mVPnfG+YNpBTDWf6JfpD+j7aErtn+9DQ+sY3LDkFwPPjXcjzJV1PPGvaoL0P+Pax74fgLb0fvQyz0yexE5t2zekg+iTqU582ZjImY7bVfXZ8chsE71H6cPJEn2plJX9xMSJ9B+e+971vAC/e5xB1KiroEj+nvTg5z3NjjAL59wn9GifzP/3pT6cwnj03BORUe2d25TWXlbt5h/CM48A0fX+kVm6L26c+GS8m9YedidQXTsryXrEyE06aPH9Oq/LeBBe7UaLWtxB3XCq9k4eUr5a/tjbSR77v/0tl5DQipwMYs1JP7XS+56XeMQ7gFD+81DUj6mbcGJFuwABvxgI2/jWeNnNIHed50jdStyFOEO20005topM/YzzqC/WBfNMXGPUZZxkvZm18R//LuIdd4Yy9OdkaFZopeled9WnMJ3tcNArML3hHQUtqLkTapTkX/p5qc6Ha+2foXMinuRDt/r3Vpw36/os5E3M+TlcyV+I0brzSOMFYa58w0GfxjiVNxvt+jkGfx1iI+TLjMNpx/GxMmrPwrqH+MH+Kiorq3KZrnNE1TqyN2Xxd6eo3jZfyxE1Pqe9i3sPtK8zzGV925RUZjFV49zBGtHEbz4N+njkJ8wBu/xEJgckiUBtbdLWX2piJcQkn57ghhrkkbZ8TeiXq+46ujZ+93K65qufN7ZZvTuawZkIbjJvjc7aRm/6UNZFll102ncRmHTMq2EbhZqnhzBx0o402SuNp+jm+P0+/14e6sKPfpZ+gLH3GcH3nMvn4opTX2nygq26V5M0nv/m2juCfTVcb6horeFldY5S2ddxx59M+D7LPfQTa2luf+TNrP4zf6BuZozOOYy7LuIw5MXqCK664ohMk1gtqlIfjzv1q8X1YW1yTZyZxvN3LWEj2ZSZbWAbicRdmmpwwCDDiejaUPVSanFhohlicbSOuV2Axj2PkRnH3eUAx6ImFOAZYnvK4PGCUblyxwEIJlZXrdlB0RU13yjtuiEkLgwWvyLNFz5ISyafLpMbk+AEM8VEssWAUd3n5KMnOojPE9TVcNcd1fEOIBS+us3jEIx4xJFpa8CcCzwHlIwoM/7wY2PUh4l188cVpUoesuOs0TZJRZjJpnUwD4xkx2eM5MMhkUMsVt22EMojr3rgqeK211krXJ9mk8d3vfndAccjViywQQm15YqHO6iXPgQ4PYqEXBTvEYjodIoTikmsSuNKRPDKQLRGDd1sssHAWi+k8uUqBOsKVREYsCJBHFLNcgwlvF1GfaQ9cOWltjDh5uvix0M/A2IiB985RqQnV8poYsj/quq/vDFqsHWSsVeeQZ27ls2dlgruwND5vWvtBsdUXP+IPya9Pj8EZGwHYiGDEFQgf/vCHzTnBbCtrV52j3nhlFv0XbZXFRjYd1PIw03XOF3ho/fNxWTzr01Z8HLMzeaX9x2/DpYUn8+/CmUUoW3C3OH3MIe0VeeQNxRobcOgbGIjRT3hisTCeoktX2nKtGe87FtE85e9HH+btKGa58pC+iWulWQhg0Bd311X7Kz8O8PImY/d1n/6VcqN488SGF7tyig0o/LhGnPcBZaePZbGWhVuu7zRCIcp7r0QMcLlWmx+LmCzm5FR7Z8bd7p15zeV5N9fy0D/wnqF+dZWbuEPrE2XiHeeVmDxfxjEQ+HB1FsTYCQUcC99QrW9JDFP4Z+/kIeWr5a+tjQyR36d4bMKy6+09PxsSqFclQpnJe8i/G0p8Jb+hdZyNT/GbuGkTG3WATXU1YoxX6+eGjLNq707GQSgj4immEHfQJiUqY2PG+V11tpb/uRoGFlxJz8Kq9a0zNRfqm06ObdtcCL7a+2fcuVCej4XmHtIGufqdvpC+nXkwJpvsoFr7zDGlTtrGZRSkjEuYR9IHMxfjWnyIcYNtQGVTL+PQ2jizayxfGyd2jdl8GWr9pp/PkR/mNd6PzXNWttoczqeHnTmSN7Ezh2KjgkgITBaB2tii1l5IrzZmYp7Nep6131r+au9o2whO/Nr42csfOl/2cRlP0f+wTjMZYvMWa2vMKTzVcGaDpc1HfJw+9hp2pfh9xnBdc5nS+KKUVm0+0FW3SvLmq998WEfwz6arDQ0ZKyC3NkZpW8ftM/f3eZZ94SDg2xv6oSHzZ8ZcXudh4zL0FnOBGL+LFkdg0gpNBvQsmjIY8gundgJs8aS6fejwWDxBBotZRnxPxxb3eJCki+LUlEvwleLaAj6nxuzkGAv6fL8C5RQTbE+cjqA8Rkx4GIhxQq9GfF+AnWCemKAw6GDhmcUjT/FKi6TEQmEHoUCMVwQmpSoLi30JhTKn+8YhlIUs5IOpnWiK1wMl5W7XIJaFYpRCYMSCBPnBj8VocPQLpn3zCA4oH+07k3yviTpRIpTQfKOOBex4BUr40pe+lBauGYAzceb5xisxAm7If5ssebT88exsAYny2LeqUDKaYtROj7LYhrIIRSH1IKf8m5mEc3c3O0JQtIIdO+CsLjOIoO7TWaN4ZEGri3hOyEExVlJi+vjkB5w8MWhg8F/Lq+efavuQZ96WdheWPMecrP0MwQ8Zk80vbY3JHMpTlE4QExm/UJI8O/666hwTEPpHUwCx+43yc088E9RaHma6zvmidtU/+idbQPPxsJNvlFmTIRbjWLCy/thkdOHMJgr6Ttv8YPEw7f1VUvAPrW/I44QUikbefzvfugEBf0+c/toyTsDp+1CA0icYld6PFpabtCX6NIhvXrE7m/dvVxuzzUClMudpDHGTLuVmYZ9n4ol8cVqCvo/y0o/yPmDnsY0RUJRwAjenK+Op/px4puz24xsv7Ea3RVnPV3tn1vLqZbTZWWRgZzfUV9bQ+sR7ijTavt2FMsOe/0EHHZTGJ2zKIj+1voU819oo4ZOhIeXryl+pjQyR3yf/1Dv7lrLnZ+MZ73rykBOniPmOun83eB4mXG19H3V+SB1nvMnYlFtFWIBjrFEjy3epn2PRccg4q/buRLlCP87mP8ZanLymH2Ms2FVna/mfi2H0a2ykY+OBH1fyjpyJuRDj2650SriS13wuBF/b+2eq5kKlvCwUv6FtkA0cbH5i4yjzFk5TsrjDKeBa+6zhyVzMxjyML2nHEH0W7wSb1zK3pA7Xxpm8Z2rzoto4sWvM5stQ6zc9n72HWbw3hQV9FYpMZNTyyrfMRUJguhGwd3RpbFFrL+SrNmZi/Mv8qG1c4svV9x1dGz8zFjPqmi/T37BuVSLGUyhc2OCQzxlK/LkfG2R333333DsdHmgbwzHmq81Ba2PjvthZhvqM4Wpzmbbxhcn3Zm0+0FW3amX2acwH+3xZR7Bn0dWGhowVamOU2jquvXtLc3/Lp8yFiYBvb4zFavPnqUSItSg2mrdRHo4bqsXJZVmc3B+3D8vtuM3P7GaWZM03v/+teA4sGYMNdpujrGMBkJ25KCJZkMsVhX1EcxqCRUkUj/kAhFMiLMTDg7LpmHgNKDuvON0EtcVloYZdVv6HH9cblfLIgjFXKMDPQCh+2250hWGfMhgPFRdFI7sAuOrWKpiFs0hB+ijjmPCRfyZKTPo4ncokv0ScCGQBHayJw64Er0wuxenyA0MUtij7wJhBIAv7nG4pEdeP8QJCWcxziN98TGw8DzoVcGPwC8Y8K3iHEANacGFRnA6LNNoGrchlJx08YIySnbrDiTRM/FhkZ9cFJ07IT5/Fdhay6Ryh+P2ZdLUs5WLhnDJSJq7TtVO3nMKlvLQD+PhQtRGLBezc48QG8Vicw82AlAF5/DZLWlhkFzDx3//+96fTCSiFUU5SF88666z0zDFL12Ky2InCg2sBeS7Uv3gneGqP5IMFBmRD5JX6h1ITPtorJ9uYlNfymiJP09/QZ17KRheWxGlrP1345enV8msnbFjwyIm6uigq1+N3fFI9QsnIVdRc4zuUanUO5QN9CAvr1FcWE1HEo6ToysNM1zlf7q76xwfmuVqUdsyinD8FxTuCSSjvD/pH2orvN2hHYGHEyW0W12hPnAhCwWNKZuPBrOFMG6adxe+zpLZE3ozoU+mP7EpudvYyEYaG1jfiMFmmT0MJYYqFeI/+hCu0UVizKSd+U2KC0qTt/Yhc+kby1kacuqd89BNdbaxWZuTTNthIxPMZQryjWDiM32tLz4v3FJuSeH7cBkAbQoHJe4f+neub6PtZjOFaPPo78k//S/qcZG/bicc7mMVe3t9tiuPaO7OW1xLW9MHki/6fsQITN5SIUE2Wx6+rPuW4s3Ob9yH9A8QmGjYOlYj+gLZhG2tqfQvxa2201jeW0ja/rvIZH2ZX/kptZIh8n5a3cz0qz5GNXNS1+E1eH5zs+NEW6ffpw7AbUb/Z/EZfRB2mPlAXjOjfGJsyNqJfsxNthA+t4ywycSMIY3cUm13EOIc2xgY/8u3HN13jrLzu1d6d9OdskGMMzGkvxl72mYZanZ1sveoq95IKR3FCH89YkbGop5maC3Wl4/PUZa+9f2pzoS65Cr8Fga42mOPE+w9lJu9R3p+MDW3sU2ufuRzvZtzB5lGInfKMGegP2RjEexlCSc74l82itXFm1zija5xYG7OljNz6V+s3/bua9wO3/jBHZs5NecgDG3a78urTk10ITBcCtbFFV3upjZm43pQ5I7c4MH9iLYF1vhLV3tF+rlIbP3u5XXNV1szoZ+hX6Gv8mIjxFOkwpma8RL79pka/JkKayKFcyGFuyPiLsUhONZxtDmrXcLMR329Cq42Na9jlecBdGsP5PguetrlMbXxBvHyujJ9RPh/oqlu1MneN2xhLU+eYL3IIgGfDep7Noy1PS9KcD+sIbfh1taGusQK3NNg6WNcYhbZeWsftmgOz5sAhJdHCQKCtvXXNn6caHdaX+Bl1ueEznj6myfWmxTM/3JCZ5t/m58PnrX2cj7TGxY0m7sZNH0aOg/4mDgDSh7XjIl2iuLDTxEUJc6YPWkcgm3gyYuRnlqj8Sx/QjpOdxv/iDtLEEhc7mjjpauKLvImLHU28r96iNl1xR4zREidU6QPq3s/s8cXZxF0n6YPgpBN3kTZx0dSCR2ZcWG3iTriRO7fEXZmo75uolJpQlqgETqyxA274SG1Urjax02/ilT9NVKClsLholMqYywRL4ser85p4/VsD3vEFMGIDZ3iMotJklMeu5xAHDumDuXEnVpIdFXcmpomNpYlK1OQmjah8SfmgbFtssUUTF4hTGHUhLgCnvMfdvk1U2CYMeNZxYa7Br0R5vuGP30BLzwBs4i659LFe4ublwC8uxjdxUp4+QM2zjadz8E4UB+HpA/ZghZzY6aWPAJNnno/lHeaoGG7iwkuKF5UhqV7jiItK6TnF3UrpY+9x4t5E5UITFwUSL39x8NtEpWB61vEF28TTl+k5GQN1F38wiNd9JpkWFgdoTVRGpvLGl376WHFUslhwc8QRR4zqY5xUp7oeTxam8LjQ2MRJdrLHgUN6NrSdOAlp4kAyfZSYQJ7Zfvvtl/j4i4uTDWnxi1dHN/GU4Cislteo6G7iJH7EG6+ha+KVziN3HMCn5zDyaLGM88yjwnhUr3LxNSy72k8NvyH5JU/xexfpOVGPIN+G4mnYJp6mSXWB5xQVBYmHv7x+18paq3Nx4N/EK7tTvaFNx927TVxsGqVTywNMtTrny1LDrG+dy/uGWv0j3/EEYsIu7mhtokKxiYqnUbloC3GBKfVBtCneE/QP9E28O+KENfHGkwipb4zfiEz9KP2MbwO+jDWco+KhiSeI0nuL9h8XCZPdMsTzpG+i3dNX0tZOOeWUFFzDLq9vJi9ev9hEpYc5m7iJIj3bkUe0REVVQ1/lqfZ+5F1F/xiVCCkKfXi8fs5HT/Vh3XXXbShvrY0RqVZm4oI5eMQTWM2QdxTvRcpOXN5/PEOeKxQnuE2caKdyRAVI8+QnP7mJyp8Uxl/cfJL6ROLxDoiLrCksb28WAXlxg0f6gLv55fW09s5sy2uOddzMkvoy3kW8F6LyuIk7WS3JZLbJItDX01p9ynEHt3333Te1Ffog+vWo1EzpHR3f/Xn9iQq4Ji6OpLz16VtqbdT3jR7TrndyrXwp47f+deUPtlIbqclva4/IsnwzHuB9Sr2J35YZ1U3/jo4KhOYFL3hB4qFfjsrE9PzJM8T7k3ECz4R2yHOIO/5TWDxl38RFr1T/aQPxStbUt6TA+De0jkdF0mJ13GTlJvlm/ETZKGO8sjLl2/jaxlmE53WvNr6Li3+pP2fsTTtlrGvj/1qdJR1fr3AvaRpnXhWVmalt+3kQdtomNBNzoa50UkbiX9dcCL7a+6c2F7I05qs52TpSem/V2mDefzGmYY5FH0Q7ox+Ki/4J5lr75J3Lz4h3LPMho7iBq4mKi4b5C/Nj5qvxFoAmnjRO4xTe/VFxYOzVcWbXOKM2TqyN2fyYg4y09Zv5u5rxSty8m+oyfSDzd/pFqJbX+JmAJm4kTHz8Mc7ifR8364784oahJl65N3LLsjARmGx/AFpdY4tae+kaM7HewFyKeQJ1mflr3HSXHpIf39Te0flcpTZ+9uParrlq3NjYMOZnTBQ3Iqa2FTd1p7xFJVjKN+8f5g60s7jpO4X5+Sn9KX1XvNEozR2ZQ/r26MvYhXPckJ7G1cz1mEchK260TWnW5q817PJ+NwmLf/kYLu+z4CvNZWrjC/Lh58pd8wHSqNWtWpmJ68dtfj7AWizj4LhJOPWV4BgPWzTx0EtaqyPuVNA4bY7nwnturq8j1HCstaHaWAGZrMfQ9hgDQLUxSm0dtzYHJl5UjDdxc0NKQ3+zG4HpbG+1+TPjtXjT2Agc+hXWtIzo9xiX2XzTv4OMB5Pxm/0Y9/lf3OzW+B9j4an+efmWtuUnbnZu4ma7pGdj7Zg1A8bicVPIaHzvyzIf7XzfYuxy8fIBwOkm0uGBTjfFnU4NlWO6iUbEIo6neJqqiR+v9V7J7iex5I08TjXRQMC4jWxiTL7hLRENaCryRsOs5SVPG0xK/OTVKwjzeCU3abOYGE9SpmAm+7boSNlyeSz0Q2ACH4uW/HJqwww+Op28LuCPH4No2hdKbzpdU34TnhNy8vxZ3j0vWNXyUwvzcqbSPvSZt6VdwrJv+ynh15ZOLb9WF9riUl9Lz6WNP/fvU+eo+7V+uS0PS6rO+TLW6h8v8DaytpKHg0WJSKfUbxhvH5wZOPAsGXiz6O8J2eDcRkPqG4MyJgWe2srlebrsk5FRamOWTleZa8/WZLSZPPu2/FJvbWG2FH86xg61+lPKa1veS/n1fiVZPtzstfqU405eam3JZJZM4tb6lppc8jHZvq9WPp/Prvx5Xm/vK9/HMTt9Rf7utTAz4aG/wIyn6NOGNgvDpO0QDrGByRSaySP+gXmtDvWt4yy0xxtJTGzDQlyuQPNKNBjJF/1richTrex53au9O5FlGORpEdZWt8apV3k647qnYl5VywP1pO+zrsnpCpupdHiupfFvV/7mcvhU15GuNphjRXtta8+19pnLMXe80ahhsdz6dt8flMZAfcaZtXEG6eb9Cn59xmzweSq1JfDMiTKZ/DysK685v9xCwCMwbn/QNbYgrVJ7sTxQ30tjOtop7wF7v7KZlLFLGyGn9I4utSdkIruL2uaqxKuN+y3fuXzro3J/yt+1ftWFM32qzUNQtJpC09IqYWNhbdhZuDfzMRxhJYx9nD72ycqo1a1ama1e5Xnz+Wiz53GGusdtc5ZeVz22d4aVtbRGyJh3SawjoNwvjf1NudPWhqzstbFCPmfgOfpxgckwk3be1h9Qh3w9sDjWHs0tc/YiMN3tjbpjc8fS/HkoMnnboH7yoy76H+06/zGmnOpfnoblgTzRDvnxDgMD2hljUt5FC2VuNelvaEbFyoiiNjtd8znymCYL6cRTFtMk/X9iue7TruD5n+/U27gSgp+nOJhKV+t5v9w+XXnr+wF18tzGGwdweXYn5Y67YAfFa8OEvA7NE2lzpSzfIHzjG9+YrkfiCrTYeQSuTuFKJU98N4HrTLn2lWsyuJ6tdC1LG2bI4gqRnEiPK4X51hnl4zparrmMJ1Fz1pG7JCcqWUbhZqEt1fJTCzMZU20OfeZt6Zcw8LxtdQWerrheTi2/XfjV8uDTaLP3qXPUfa5fbqNSHpZknfP5rOFn1xB6frPHgfno+7fmh5n3sxZWSweePjjHkw4mbjGTdlbC2Rj71Deuiz7nnHPSVY9cF+SprVyep8s+GRm1fHeVuQvzWn5rzz7uIK9FnZaxQ60spbxOBmsKVZJVKmztueR5JS995eZpEbfWt9Tk5vnIZdfctfL5eF3587ze3le+j2P2uIt89B058/Mm3y2Ku/bTlcfxRGiIJ64DVzp7ou3U+pMa5sjpGh/HSU66bp7vUzJOMYqnLgO/GtXyBd61cVb+zGvvTmS1pUVYW93K06iVZa6HUU+6nvVUlHGm0uG58hNNHgHwq7XBXHKNt9Y+cznm5hprxif0aTvFzx7ETaHpKv54cihdp218mH3HmV39canN9xmz+bxgL7WlUn2sYdaV1zxNuYXAVCHQZ2xBWqX2YnmgvpfGF/HGqRBv3Ujf6Y5KtPTZB64SbSPklN7RpfZUy4+XX5tD1cb9vL9KaZTWREivVH6fjz44t81BTU4JGwtrw87CMdvGcISVMMZ/CE1WRglnS7dW5rZ4Ph9tdpO/pM22Mli++ryT2sa8yKAe19pAn3dP2zoC13jWqK0NWZzaWCEvE8+x9g6tlbGtDnXNuyyfMucPAm3tjbqa17lxSp23jagsLIoj3agsLYZNlSdpeDJ3ycTPfj7OfLcvHY/N71frROY7ALOtfHwXgHvBc6Ihxd0pIV4VlAfNmJtvWsZreAMfAp/vRIeJ8jCeukrfteJbDHwPj28a8W02T3xrAUUn3zzi27Lxys6w4YYbepZJ2eN1DeHRj350+MIXvpC+u8VH6lGw+sHdpAQvwEizof1MJeyqc1OJZrusITjHXVDpmxFshJhKov2zkMB3DvkGoEgICIG5hwDfmYunG9I4Iu6oTN+B5VuJbRSvTkztPV5f1sYy2D/ukA3x+vmw5557jr4BPliIInQiwHhD86pOmBY0w3ysI8xXUBbwLXO+cx2vbkxzI769xkK/0XTObYaM2Sw/MoXAkkZgnP5g6NhiSFnjlatpozabtFFsxutjixsAhsicq7xDcY6frEprdva9yKkot8ZwU4HiLTLGaXNDcjHknaR1hCHIincuITBT7Q1MpmP+zIEzr0A0paGZpGt2M0t+FtZlMpYu8bT557w+7YWgN1iKI8ALQUHFgxUJASEgBISAEBACQkAICAEhIASmA4H4Ha8FsfFvOrBbKDJVRxbKk1Y5hUA3AuoPujEShxCYSgTU5qYSTckSAnUE5np743S8UduJzDZ/izeuidLSk7m9mdtx+w2FPv58sk/JlbPzCRCVRQgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYGEhYIpCSm32XIHZ5j8OUiYzl+H9zY5Zsudx56NbCs35+FRVJiEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgd4I1L7/2luIGKcNgdtMm2QJFgJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAiMiYAUmmMCqOhCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAhMHwJSaE4ftpIsBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBITAmAhIoTkmgIouBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBITA9CEgheb0YSvJQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAIjImAFJpjAqjoQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAITB8CUmhOH7aSLASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEwJgISKE5JoCKLgSEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEwPQhIIXm9GEryUJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACIyJgBSaYwKo6EJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACEwfAssMFf3Pf/4zRWmapmoSaDyJscffUksttRhXyW8xphaPUvolv5boY3nX8m1hq6yyylhpzMXI1113XWe2Z+oZ+YzYMzG/Wh7awtr8kTkkLM+L5cn797ETD76VV17ZRMhc4AhQD33dWeBwqPhCYNIIzLa2VMpPyW/SBZ4FEb/5zW+GTTfdtDUn3/rWt8KDH/xg9XGtCClACAgBISAEhIAQEAJCQAgIASEgBISAEBACcxuBwQpNFsigvuYQeKZy8c3y59Mv+fnwqbT3Kcvxxx8ftt9++7DqqqtOZdK9Zf3nP/8J5OFZz3pWWG655XrHmyrGtufR5j9V6eZyUPCU0iz5WdxSWMmvxk9YKY75jat4aiuX5Wmo+eMf/zj89re/DY9+9KOHRu3k/zZs9MEAAEAASURBVMc//hG++tWvhp/85Cdh/fXXD1tttdVoUXrnnXdO7uc973mdcoYwnHDCCeG8884Lxx13XDEa7ePSSy8N3/jGN8Kaa64ZHv/4x4cVV1yxyDsTnuTluc99bvjBD34wOLmTTjopHHDAAYFNFF/5ylcGx59shMnWmb/97W8pSWsLOLy95E4RevyV2lXJr4eoVpah8vKytQouBAyNm+fNu9vsebJ5mkPdXfLy8DZ3nm4bn/f3ZcS/y21x87S63BavTxp5HixuLY08zOKYaTL7mvkGq2uvvTb86Ec/Cg972MNMZKc5nWOak08+ORx11FHh85//fGs+6KvJw2GHHRZucxtdQNIKlAKEgBAQAkJACAgBISAEhIAQEAJCQAgIASEwRxGY1IqPLaR5E3vt99///jfkvxr/uGE8j3FljBO/LX3vj5LhD3/4A15LhDgtSR5MeTDZTHzoQx8KZ555Zq/o42A6HXHJdJtcK1ApvBSv5Edc6n1JRpt/ibePX1v6Vo5xzcsvvzx88YtfHFfMYvF/+tOfhrXWWiscffTR4Ze//GXYaaedwtOf/vQR35/+9KdgJ8NHnlNgQYn65z//uSiJ9P7v//4vvPa1rw2//vWvwzve8Y5w3/ved+y2Ukysp+eNN94Yrrzyyp7c/2O74YYbwgte8IJw6KGHhosvvvh/ATNgm2ydKdV3slvyH+KXyzAIvAzz62sSNycvr4/d4vfhzXksLmYelrtzHu9us+cycHvekrsUp81vnH4wz0dbGn39a/JqYcjvCm/LQy1eLaxNXp+8mFxvYocuuOCC8PznPz/c6U53Cocffvgtni3/e+yxR9qEQvDNN9+cxhPTMaZhI83uu+8ePvrRj7bk5Bbvl770peE3v/lNOOOMM6p8ChQCQkAICAEhIASEgBAQAkJACAgBISAEhIAQmJsIDFZo+sUyilxbVLOwyS5YluS3wWxp1cySvBr/VIfl6Z999tnhXve612JF+ve//72YX8njX//6V1KW9Q3LlUKrrbZaOPfcc9PCpZeBXMpeIpQjOXHiq3RqrMTrMTU53s/Szf2mw036feSOm8+2dNr8++TJeNpkeH/skyEUaJ6e9rSnhbe//e3eK9l5zuSnRHmdy3lIg5OPyD3xxBOT0u1nP/tZqpff/va3c/YJ7i7ZE5ido0+8V7ziFWHttddOClxONpKXDTfcMBxzzDFO0i3WLnlt7RkFAH1jiTgdmuNf4sOv1l4J/+53v5tOoW299daLnVpCqeupK13KAo8R9ptuusmci5ltdabWdyGkq377+ma8XaaXa/Etjs+4+fU1c7kWz8us2Y0f08j7DbG3xS/545en2cZnvD4vJT8Lr4UZj5mWZpdp/Lk5JK1x4/q0vD2X6905X+72vCW752+z+3glHvPDhOAvmfixQelRj3pUeM1rXpN4an+ccv/jH/+YWJZZZpnQNqbJ+8i2PhFBOS9+733ve8N+++0X7n73u+McUWmM8apXvSq9T6yMI2ZZhIAQEAJCQAgIASEgBISAEBACQkAICAEhIATmPAKDFZqU2BaKSotoFl4KG+KXy8ENeRnefkvoxPAav487lfa2fOR5wc01r6YI5LtPb3nLW8I97nGPcLvb3S5dA1s6OXnWWWeFzTbbLDz1qU8NKCS5Jo5FRYiTbLe97W3Dy172svTdRLta8v3vf3+4293uFm5/+9un6+O4BhK6/vrrEz8n0CBOQaBcWmONNZLsT37yk8mfP5RMW2yxRZJB+qeeemoKQyH7iU98Iuy///4pLp6/+MUvwo477hjufOc7hwc84AETrvQsYU2c3L/kl/NM1o1saGj8UpzcDzeUyy75weOp5DY5Pr639wn3aWBnwXffffcdeaPURlln9J73vCest9564a53vWu64vT3v/99CsLfrn21eviSl7wkrL766kkxb3UCZq6Ppc5RR7mykPpwyCGHWBIjkysNr7jiirDDDjuM/LjW9QMf+ECqRyNPZ2mrz9Rf6r9vN5tvvnn41Kc+lWJTji233DJd8UzZ2q4uRMlInUYZ568tfPOb3zxBNieZNt5441R+5H3mM58Z5bLWnlmEf9GLXhTWWWedtEC/1157pfpCZJSYL37xi8Md7nCHlE+umOXqxxLV2qvxkyeu70VJcMc73jGdoOLZ8by32Wab1E+gWK2ly+lryvPMZz4zlZUrsulz3va2tyWZyH3Tm95kSU4wS3Wm1HdNiHSrw9dtvHz7sDDvZzwWdquYkZH7e7e3jyKMaTGZZnpx5ofpyfy9X81u/LmZx/HhFoYfZGG5PQVm4W1+JiuX4WX7MPMfYlraZvq45ldLw3gsnufNw+AxMn4z83jm7+NYXM9r4cZvPOY2M49Tiud5TY6ZPszH9eGWhjctnL5vl112Se9688tNrgan3V9zzTWpH+EEO+THNPSNe+65Z3qPME5BSco3MDfZZJM0xqEvZrOF0UUXXRQe9KAHpXTvf//7h8suuywF0TfRj/irb2tjDPp8xkJf+9rXTLRMISAEhIAQEAJCQAgIASEgBISAEBACQkAICIF5gsCkFJqU3RbKDAdz54tp3m28Fj8P825vb+PP/Ylj1BbfwktxLU5f08syu4/b5cfJJvghFu0uueSS8K1vfSugeEHRWVK4oGzhW3osFP79739PSiKuk7STCshceeWVw9VXX50Un6eddlo46KCDwimnnJIW+VhU3G677RI/aVseOGn1xCc+MTzykY9Mi5QoQnbbbbcUjmwUnfy4ovPII48MfNfwr3/9a1J0ouhAQXbOOeeksnCSAgUI4XyfEOWNneJIDLf+DcHK807Wbmnn8c2/zfT8xtPHz/NgN8r9zU242c3M43ges5tJnNyePLI/FFj8jHj2dmIGZTeKdeoiim4UepyOgfxpPOohi9OPe9zjAlcX77PPPhOUWihNUcb95S9/CR/72MfCl7/85Qkn+yxtFrTvfe97J+Wn+WGiOPXXzlpYn/psvJjUXfINoQilbaBApX2RtxJx7S0KQNqCp4c+9KEjRTAL6rSj17/+9an8LLijtGWhH6q1Z06i/upXv0r5YNH+9NNPTydSiccVt1dddVVazEcWyszSSalae0WO0ROe8IT0nVCUkCzy871cnt0Pf/jDdK0k7ZmTVbV04f/+97+fFOFsgqA/4fkQl36G79a9613vSuWxdM3M60yt77I4mFaXzW7utnaR+/t4Fhc/KHff4nvLfy3M82G3NHMz5zO35zM/b7al7ePldh+/r92n4+0+fps/PD4Mu7m77MZnMow/N30+crvnbQvL/c1tcb3b2wn3lPNbmOfzdsItTm76uBbHeCzMTAs3N6b383bP08c+Tlwvn+8c05+wmeVzn/vcqO+38QS89L30sWyG4hpY3if0qccee2zaGMJmJzauQPR52267bTj44IPTRisUqm9961tTGPHoTzfYYIPk5q82xuCdhWLU+uJRJFmEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBOY8AoMVmn5BDHubu+Rv/GYaeiW3DzO7mcaPWSLvb7zGZ27PY2FDTS+rJM/CvdwSn4W//OUvT6cTWCRkce/CCy+0oAkmCiC+78dpCE7IcfISpYIRyom73OUuYdlllw2csnz2s5+dlJucknjjG9+YlCmcoPPE4h9KC05PoXxcd911w33uc58kF+UPChi+l7XCCiuEBz7wgemEJqczSoTSgzh87w9evqlFmSDDpIRD7me8pTQm49cmz/zbTJ+W8fTx8zzYS3Fzntydx8Gdk/fL7d6dxyu5uYKU60C//vWvp2tKP/KRj4Q3vOENJdZwv/vdL52sXHrppZPJtzB/97vfJYU8yk7qIXWOb09y3WmJUNhxkrMv9a3PJXkoQzm9bCdHn/GMZ5TYRqcw+YZcG5133nmpfSCDU6Gc5uRk0MknnzyK0taeaR+0J5SZtIvvfe97aaEfxSHfiCMeVyvyQ5nJt0V5Jp5q7dXztdl5dig3OQnaJ136Ak5m86xN0cy1vJympT9C2YvSuou6+q6u+D58SN2u8dbCfHp97VMpbxxZxB0nfqm8bfK8f5sdeYT58LY0jC83S/wmty0s9/fpe3vOV3LX+IeEeV5v70qzxIuf/Urxl6Qf/SJ9xJprrhme9KQnpT6H05fLL7982H777cNnP/vZlD36U0500s+woYuNGGyQYmMJ4wgUqGxGMaqNMeCh72RjikgICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYH4hMFihObT4pQU4k1ELMx7MvnxdvF1yCLefT7+vvUu+yenDhzKSUwl9CEUKJztLxLf/UEQYsSjIyQhOTHjixBb54pQZp+74oURBScUJC+KwMGnEac173vOe5pxgciIPpdHDH/7wdL0cC5OUp0Y5Jrm7Fndo2FTKLskq+Q3NYx/+6UiHk7VnnHFG+OAHP5iuFES5livTSnnjlB9EnUVBt2jRoqQsK/F6PxTvKD/tFKUPK9n71uc8LopTTp9utNFGedBibq5KhrgOt41Q2OcnOLkSkdNEJfLtGaUq18ratbPvfve7U/vgClkW8HkGRpw04jmzKcBTrb16vj72Iekij40U3sSOgsKf+k0MPf5qfVdX9KWWWqqLZRRe4/Vh3j6K3GKBN+cv+bVE7+Wdy+8V6Vamqc7LkLRLvJPpr6wMXTh0hfv8DOH18brs0yXXp1tKAz/7ed6SvRS/xDcdfvQR/IzoR+xmCd4ZbPCwscdTnvKUtKmKsQfKSz/2IH7XGAN+f/W4pSlTCAgBISAEhIAQEAJCQAgIASEgBISAEBACQmBuIzDtCs3ZBE/XYp4tCnbxtZWpb7y+fG3p5P58KxPFR4k4aem/U8XiIAuHfPvSE6fRyBffnUJZYz+ufkPpdPnll09QbHFFXOnKThRTLCQecMAB6TpKrqJ8znOeM+EEqU93Ptrbnm+bfxcGk43XJpeFZLtiNufhGlFO23GNIIvJKNh23XXXnK3q5qQNJxC5ErmLOCkIH/XLE6cAS9/crNVnTkpCtkju5fEtT07/1JSUxk9954rW/IQ01yNycgjim5koYj1xTS9pdBGnWLnOmetXzz///PRdy/e9732BNsh1iZyUNvrOd76TToCuvfba5pXMWnudwNjDMSTdHuIGsdT6rpKgvC3gzv18vFK452+zexlddkvDyyrF8XwlXgvP45p/buZ8fd0+bbObiQxLpyTP8/nwNv+hPPBb+rlM75+H1eJZHiy+uS2OuUsyLczMXEYex8LNP3d7OSW7+XnTZJlf7jZ/My1N3MZrpvF0hXm+mbBzgpONHDbuMJMNKJy25H1i1GeMwYYP3hUiISAEhIAQEAJCQAgIASEgBISAEBACQkAICIH5hcBghWa+MDbU7eHzcdvs8PswH79kz3m73CUZff2Q7X95PAsz/9xt/kNNvt+HAoirIj/+8Y+nb1X6U5he3k477RSOP/74pNREOcVpMBYIOXHpaZNNNknXwqGA5NpRTtq97W1vS9+2IuyOd7xj+nYm/nyDEEWUnUYjjO9pGW266abhmGOOSdhw5SzXjtp1cWBQotx/qrDK02qTa/65WYrv/Yzf+2Hv8m8L93JKPPgZeXubX4mH76hyxR+KZ05fnnTSSRY9nHnmmenqVE40clKR75bd7na3G4X3sayxxhqBOnDYYYclhSiK0dL3YJGFfE7jvPOd70wncfgG29lnn52uIkTpl1OtPlMPqW/Eh7hW2SsHSYeTp1xpyHc//fWwPh2uVSU/fDv0ynjdISfLkMO3I+17lsjimmfwYoGdb87i5uRyF+29996B61ppS+utt15qjyhQUciygYB0OTVJPvnuJxsC7ASsya61V+Ppaw5Jt6/MNr6+fZevt9hrbtIynty0fHh/7+fjen/P32Y3/jYzj5fztYW3+XfF9/E8r/fHDpmf2ZPnrf5mbzNNBuHebvzez9uNH7/az+R0mbkMz5+H4fZk4fh5u/GYnzctLI9jPD7ceEp+8Bt5u/fL/S0N8/fu3I4c8zN7bno5Pgz7EFprrbXSt3SHxCnxcv0sG0i4HYD+lGvv6Sd5H6yzzjpp8xTjF6PaGAMeNpusK4WmwSVTCAgBISAEhIAQEAJCQAgIASEgBISAEBAC8waBwQpNK7lfMPN+3u4XzYzfm563zW4yfLiXkYfDl/uV3F6GyfamD2+ze36ze97cz9yY8E2WOG32ute9LqA44vTcUUcdNeEqNy8XZQhKEk7dcRILxRLKLH/1G/xc0YZShm9awceJCZRanAwj7Kyzzkrp8L1OTlJwWg3lEcR3so477rh0xSzKIHgPPPDAlD9499prr6QATczxr4QRYeZvfN7Pwsz0PDW78Zvpec0Ps41KPObn45hfLsv7m70tnoWbaXzmxoTMXbInhozH/MxEocnJFxajqUP+KsjnPve54bGPfWwK46Til770pbDvvvta1N7moYceGo444oj0fVe+y8qitOU/F8I3IlF6o5BEscdVrCjq7burnr+rPu+3337hHe94R1hppZXStz8XLVo0is71uZyOJC+cUK59u5Nv077whS9MOKGMZwF99913D2AH8S1IvsmJH3nme6G4UfR30cEHH5xOHNGWyAPfsQR3iFOp5H3duBhPGrQ9lJo51dprztvH3TfdPrJqPEP6LqvnVm9qbh9WsluefBh+uI0szNyY5uf5LNyHlezGZ2YXjw+3OJjev2Zvi1Py93Jzu3fn6Zks8zdeM/E3e7LEP+O1MPPPTc83xG5yhsYx/lJ88/Om8WMamR9us9fMnC93l2SbPHghc9fMLj4L9yb2oUSfyje699lnn6FRJ/CzseWUU05J/SnvAd5PfGuTq7rpJx/xiEeEL3zhCylO1xjjr3/9axq7EEckBISAEBACQkAICAEhIASEgBAQAkJACAgBITC/EFjq6quvbrg+sS9xZSmEEsR+3m12b2Lvonwhz/P7MO9fsnvljIXnfrnb+LrMIfko8eZ+LO4PJU6EsXjICU2u6kSxkZ/eKsnk1AOn8eykJDzE5/QdJy39lZac3IOPhcOcUHISJy8Lp804TYEyxgj5KKo8L/HbqPZcamFt8nJ/n4++YW3plvxLfj6drnDPm9vzvHt3m91ktNUz2vIKK6xQfM7UF66l9c/T5PUxufaVk3/UJRR+ixYtSqceTXFXksE3GMlTW359nFJ9tnBw5gQm6ZaorQ6XeJHFAjmL6h5nz4u8Pnn2cbBz4ogF+9L3ZWmrXD9r36vM43p3rb16vj72Ien2ked5hvRd1k+U2kzJz6fTZffP0duJl7u7ZE1HeN/y9eXzeczL593e7uOU7D7tofaSPO83JB/E8+l7OV32PJ3cXYufp5m7a3EJy9PK3Xn8mnzCSvG9n9nbTNLjXT0Z4kYH+vpSPzYZeddcc81im1nOPffc8OEPfziceuqpE0SWxhhs8OIWCa69FwmBJYEA36gfMq9aEnlUmksWAdWRJYu/UhcCswkB9Qez6WkoLwsBAbW5hfCUVcbZgoDa22x5EvMzH8sMLRYLYra4ZotjfkHN7GaafItjbovb193Gh7+X7eWaf+7n3SZ3iNk3fomv5Dckbc/bprDxPGZHOemVmVzp9ulPfzqdiuOknqfaFaNtihsWNPl5KuWvrfx5fTE5pWdoYZM12/JQkpfztuWnLf8lmZPxy/NRc+dhben5+pDzUF8mq8xE1g477BC23HLLdJr39NNPT9cYb7PNNnkyE9wo5tvq1wTG6Mjrsw+n/KW6Zzx904AfWZygrNEQeV4OmxHaCEVzX6q1174yjG9IuhZnMmbt+Xh5eV2einaWy+xy+/zkdusPcn/cudwST+7Xt3x9+XL5uPN8ebe3l+Li59Meam+Tmfv3yUceZ6i7lEbJb6jcPvxt6ZT8PcYl2aU48Hl/s5vpw71fSX6X3zjviZLs0sl8bg3gKu4TTjhhdJqduHk/woQJxSe3RIiEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBOYfApO6cpYFMFsEK9nNb4gJtJ4/d1tY6RFYmDfb4nueydr7yi7xWf5JezKE8nG77babTNQJcS677LL0rUEWCDkJNlNUwpy0h/iXeIf4taXXJaMtXpt/Lg+c+eX+Xe6hcfL84J5pOvHEE5Pig6uIN9988/DLX/6yUzE403lUejOLwJC+q6tNzIZw0Kvloys8j9uXvy9fLt/ctfiEQcbrzVtC/hfm+drsPv502SfTrw7tU6cr77lcj2MeNhXuknzzw5ytRNk5nXnJJZdUs8j7hm8jc2W+SAgIASEgBISAEBACQkAICAEhIASEgBAQAkJg/iEw+MrZ+QeBSiQEhIAQEAJCQAgIASEgBISAEBgPAV2tNB5+CyG26shCeMoqoxDoh4D6g344iUsITBUCanNThaTkCIFuBNTeujESx+QRmLmjeZPPo2IKASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASGwQBGQQnOBPngVWwgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAjMBQSk0JwLT0l5FAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAILFAEpNBfog1exhYAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhMBcQEAKzbnwlJRHISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBIbBAEViGcl9zzTULtPgqthAQAkJACAgBISAEhIAQEAJCYHwErr322rDiiiuOL0gS5i0CqiPz9tGqYEJgMALqDwZDpghCYCwE1ObGgk+RhcAgBNTeBsEl5p4IrL766olzmaWWWiost9xyPaOJTQgIASEgBISAEBACQkAICAEhIARyBJZffnnNq3JQ5J6AgOrIBDjkEAILGgH1Bwv68avwSwABtbklALqSXLAIqL0t2Ec/IwVfpmka7SSeEaiViBAQAkJACAgBISAEhIAQEALzFQEm7jqhOV+f7tSUS3VkanCUFCEwHxBQfzAfnqLKMJcQUJubS09LeZ3rCKi9zfUnOLvzr29ozu7no9wJASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgQWNgBSaC/rxq/BCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYHYjIIXm7H4+yp0QEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQWNAISKG5oB+/Ci8EhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEZjcCy8zu7Cl3QkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQmB6Ebj++utD0zTFREr+Jb9i5AGeSy211Ii7ZMfPfjCa3XjNTdgyy8wvFeDSr371q/dbddVVKduk6F//+le46KKLwgUXXBBWWmmlsMYaa0xKDpH++c9/hrPOOitcddVVYe21154A9n/+85/wuc99Lpx33nnh73//e1h33XXTg7LEanGN54YbbginnnpquP3tbx9WWWUV855gXnvtteHMM88MX/7yl8Oyyy4b1lxzzQnhOOC59NJLw93udrfFwrzHH/7wh5TeTTfdFO5yl7tMyC9+X/ziF0dylltuOR91bPuPf/zj8L3vfS/hNI6w448/PskYJ39g9ZjHPCa87GUvGycrSzTur371q1QfqBOzhXbeeedw3XXXhY022mi2ZEn5KCAwFW2oIHaJeNHnbbfddmP18z7j09k3bLDBBuFhD3tY6nt9mvvvv3+48MILwxZbbOG9R/b59LysUH3ejz/72c/C+eefH77//e8v9vvNb34T1ltvPRO3mNmF6ZB6c/PNN6f377nnnhuwr7XWWhPenYslPgmPqX7Gv/jFL9LYBOx++9vfhjvc4Q5h+eWX752z6ejLP/WpT4XLLrtssWdJHldfffXQNu77y1/+Eu51r3uFXXbZJaywwgqLleGoo44KH/vYx1I/sFjgLPLYeuut0/jtnve852K5Ygx32mmnhQc84AGLhc1lD8ZYV199ddhkk03mcjHmfN4Zl7W1rz6FY77DHOQrX/lKuOtd7zqhHc7UXKgrHcrRdy5Ue/9M91yoD95LgmfcOrIk8jwkTdYGeBeuttpqQ6JNK+9ceXdNKwgSPisRmIv9wXSMW4fMFYY8yLaxEfMMwp73vOcVxX3+859P8xDG9POFauMLKyPv9jPOOKM4f2AOce973zssvfTSxj7B7MJ0OurNhAz0dIzT5rrmvFM9x+xZpGlla2tD05qohM8bBKazvU0lSG3vIHRIUK6ozN0lnhRxCv58Wt6OaFNaWjJeeWnhxoN5m9vMr0taxyrNlVdemSbbhx9+eFKebbvttmG33XZb7GEbuDXzmGOOSQumKPmwoxj99a9/PYry7Gc/O8m+4oorwpOf/OTwqle9ahTWFdcYDz744PCsZz0rXH755eY1weQFzkIQfCy0Yz/llFNGPChtn//854c73elOgTLX6KUvfWnYfPPNw09/+tOwxx57hE033TT897//TVG++93vhnXWWSfwwrv44osDC26XXHJJTdzgMMoIluMQi8knnXRSQDE7Dt14442BujLX6E9/+lN4zWtek+oB5tOf/vT0+/jHPz4rikL+WCgSzR4Efve734Vdd901MGGApqoNzZYSothiAXKqaDr7Bt4VNgDx+f3b3/4W+JVovj0vytj3/UjdRdHL74Mf/GDgHWbub3/72yW4Rn41TGHqW294Z6HA5P36ox/9KDz1qU9N7Yl6MlU0Hc8Y5cOLXvSihNexxx6bFGWMFUirD/Xty/P+pSYbZYg9v5122ikcd9xxIzdKyzZinMJCtPVhOR+Tklr8nH9JuX//+98HFtVLRBhjm7YyluLMBT+eC8+ni4bUoy5ZtXDGvj/5yU9qLArLEGDucY973CO8733vC9/4xjfCokWL0iZLY5uJuRBp1dIZMheqvX9mYi5kuMmcGQTYSPOkJz0pbLXVVuGNb3xj2vjCQih97pKmufLuWtI4KX0h0AeBvuPWPrKMp+9cwfj7mm1jo3//+99pvNsmh02eP/jBD9qC55x/1/jCCsTY2eYPKDZZO0W5a361uU0XptNRbyzfM2XW5rzTMcecqXLV0mlrQ6U4MzH2/9CHPjRhbFzKh/zmBwK19jbVJWx7B6FAnO0/sMjzONX4zFp5cTd3LPtwiovaTTwB0xx44IGjyPHkZLP++us3RxxxxMjPLPEF18SFMnNOMGPn38QTgE2cCI3848uzeetb35rccdLbRE1yc8011yR3XGxr4lHZJp6aa7rimsC4o6iJSsQmns5szjnnHPOeYH7gAx9oHvrQh47yefrppzdR2TjiecYzntHEkwnN3nvv3WBvo29+85spv3HRKLGAVVTQNnGxNrmf+cxnNq985StH0Q877LAm7lgaubssUYnVxIW4IhtpxUX8YhietedAWB/iOf7jH/9oZSV/nr72ta81K664ovdazJ7nm+caF7EX4zOPePTbrMms5T3Pj48IjiW8KF88wdXEE8MNeTNC1ite8Yrkb35m1jCBpxZOeUv5IB5hcXCJdQLFDQTNkUceOcEPzODvQzXManlFdh5eyl9XHoY+81rd9WnVsISv1n5q+PXJbzwZzX0EE+qMz5vZwautDcPTt6z5czD5ZtbCa3loq3NxV08TN0uY+GTWMJvAGB15nWvrG2r5zmV6t883fc5Xv/pVH5zs8VaChh+U9yPJs/BXyw849m1zuegadnl9y+P2cZOv2ru1TQbvuc0226wYjEzy7akL0771Jm4iat785jePRMcFyCaeEmw++9nPjvyw0IbbxhO19j1BSOagTG1tspYeYuKifbPxxhuPJFIndtxxxyYqOUd+Zik981JfTvlI11Otf6nVUcY98XYLL2pkz9P44x//mPowTJ513mYPOeSQ4vinlv4osWip9TueL7fX5JfaMePTuBCTxNTi+nRqfLO5nVu+GZfGDXm+SOkZ5u21Vo/y+uCF5XXBh1kevB/1Lt7g4r0WhH2y8yrAeeITn9jsueeeI5ziqbLmkY98ZHLP1Fyolg4Z6TsX6nr/jDsXGoE0By3j1BEr7v+3dybQ1t1UHT8gFiooDlS0IC2KyiCTgKIU+6FLoIJSF+IE0ioUQXGpCygWSlspbQUssrAiCNgKrUCpTMUKKoIDKlpRRHGmqDiC4CyCety/0P9lv/0lOefcd9/73nvfzlr3ZtrZSf4nydnJTnLiuNfru1GewN9639Xe8coTm7GwZpgz24bjUXNf0djmmtJmYjryiWFKg018a15EfG3MIRxTi9vuu6snI0yVNb4/pjD+WC3y/2hBYBPjQWxjEbtan/A0vfha3Fy51efh3TV5ojZXoF6tscrzq7lV7ppsBD0yIrIihvFzTj49nOnXvXG4ZNT4q8n8nlR18WFL3T35osVL8iLzsZqJ9Z3CdLvtplaGdcK20+em5ry18vSeX69N1Xj5sNqcUvHxva/wJbbKXetDrfdYS/aPbcWXozYeKJ50cc5vp6rHCy64QCRp73EEdqO/RZk4QqK2rPBam6u9g6C3Q2+rH/oo//vgBz84+p8p/8ep3wc+8IGR3xSdj/d5KH+Vy5S+I3o4xmlwoK70G8YW6okszY/xgnHhoJm1T2hy8pBrB2xCulLWco0rpye5ygvD9bG2KDo87nGPK1eccY0ZV75GYwAPnGp4wAMesIqClhOTGK5OvdOd7rS6uobrW01xWk5wTKUlvQkowxlnnDFccskl3Wug3vGOdwwnnXTS6tjufe5zn4Gr5NjViXnFK15RrmGzgbr4W3/HHXdcqbuuq+WeYq5nYIcBhmtvOAUqYwvvw3ve8x55q7aw5MQKV/iA9eWXX76ivcc97jE85jGPKeE2aRue+9znrq7QUNrWc+CqQVPelat4eV7+GXHVrHankYe9xMsVjuTPlW7WGVdl4FQPdee64Ac/+MHDu9/97lVcyxHLbZ2vXP8Bxhz75rSbdcySnHrc+c53Hk455ZThZje7WTn9wvOxhePhlre8ZTkVwykUGa5Cvv3tb1+uneO0LVcJY9gddswxxwwXX3xxKS+ngc855xwlKzbXj4AX9eBaDXbKcwoHPJ73vOeVtsQ1tBhOO4AfV4xxPRi7kUknw252W+wufYD4N7zhDYoabGAZHvvYx5YrCkn/iEc8YtXuIaKtgDXPnCsA6RempFmll8MGp3KiChy4+vnMM89cnQgWjWxwpD+BMf2VHXsyvbJSB1toK3WkTPe73/0GU96XE6w3v/nNh0OHDg3suJ8yS5/5nDGEPHtYqg+0+k8Pv7nlpe19yZd8Sak+z+HNb35zcfs+ZC/0Ms5xfQ0YXnjhhavnpDK2+mlhdv3fVJtjrKP/8Iy5dtUmJKvkvTJAtNttblUwc/TaH1fIcuW4zNlnn13ao/wvfvGLt/QVxv2WAQ+eK1dvcn25P4Hon1cPZ9obp/IYizi1T7/XNaOccGOM0ZhPOTixz+kFzJL2Rr9/4hOfWNLxx/jBCR4T7ldhnCC3jTYrP32aMvHu6r1bVwlmOHpjM8l7mHr2vbrzzjRhckUOtrQJXXcLrg984APLDQ6Mia985SsLrcZ0ngFpGOu5LpVwmYc97GGDbVoqXv+MqRfjLu8B8nnGM56xumWilZ94tmzaAf34RS960WBCZSHr1TvysU1h5T1K/7UFiHIisjW+8J67+93vXvo64zrXzM4xU+/riy66aGBc533CbttWf5qbf2vcYfc3N1jI0K8YH/UuafGvyQPiIdsU4St5hWvv1R54b/krxHinMl7SphgvkSmQozC70c+f9axnlVs7JKOQL/0Zma5n3v72t5d0Kre/CaPVX3vtqCYvkT/jLrgg3yH32UaUVbFqz4dTmdDahKvIa7bosKJPRx8B3in+BhreTzahLYl2ay7Uy4eCzJ0LTc3P1pkL9dE7+LG1cY8+2Oq7UX7lmdDGuCXohBNOKHMFW9gowLXGDCKZUzz60Y8ucj/vBeY8/l3DHIkxE3mD9zh9n8/E8A5F9iEtMptMT87syfKk78mJPZlNecuujV2K83ZPRuiVlfEc/Fkn0TXvzNvPP//8Mj4yRj71qU/1WaU7EViMALIl6wjIvrSzJz/5yWX+oTlCr7+QWW++2JONYkFrcmukwd9bc/L0LZkRGtYZWaOTYb2QORf9H9OTjZRGNmtMnEBEjmJe7teRkAWZW2KmcObEGOnp1/BjjL322mtL2qn5aws7xt2HPvShZZ7KXODSSy8t8n5han/ItMxn/vzP/1xB5arYlkzdky9WDGY6enOIHqaRfavukW6v+XtzXj/H7L2TdmN+wXt5zjpCxLfXh1qyQkv278kovXXc1hyctVDkUK7+ZU0gzcFHoNffuOWSOTzrOKz3o0/STZpPetKTynhMOGM84zL0kstoS1pL6qGInKyf6LxfbmyM99fckUeNxod5nnJ7W/xaNrwOtDGBweq43Ni1m+U0ZkzJKQBOxrDLiV06nKzk5CW7OOxFPJpiLyY5zI+2mdOU7OrE2GA8cmLTG5tMjSZ0+KDijmkJtKubVicK7Pqm5glNdg3ZJGvFE422PfzRBttVGA5bfFrx2xLR8JiidDQhs2j2PYkN8KMpxwomdpWDjzrMDZYmrI2mVCuadhMkyskb7S5gh5ld8TOaoFg08mDGbhpM7zmQ3hbHR1OslHS2ED7aAmbZMUBav1OBPOwaqqL1t4GlnOAhHwx15BSqLaiVZ20T1/HhD394iWudwiIylptTsl/5lV9Z8mB3AW4TJgsf1YO2x64GjC1EjzY5LO2NsttC8mhX5BaMbFI92oSy0NnEesRvwsOoEyg2kSw7F+xqw9JO7Zh5oaUOtvhZ3LTjhzzkIeVHfWxht4Sb0ndkNzLGrlUaTck62rdfy44NU/SN97///UucCZnlhJG9eMvOiCuvvLKcLgY/DKc97WVccCNf+z7haErcEsfuC/oPaUyQLvnxPNQv/C43U2iUndHs3oAPz5RnEo0ppwtG4EGffMlLXjLaYkMhmyrrHe94x5EdUTwXE9DK6WXw5vQzO0Coiy1axCwP86/zzOeMIT0sp/pPD78l5eW5MmbwvGTUhwiDl01Qyi4a+gXjkS0AFVK17zl17bU52qZtKiinE9mtc9ZZZ5X2QF+fKsNUm1NdKHAPM9Udu9fm/Ngw1f44LU+7lbFJ+/j4xz++eCm3bTwYqTt1BEPK2jqhaZtiRvLjfcFpGPVXmPk69nC+5ppryvOjn9GX2ClIf8XwXqUdwF+GE3x2hXnx9rCL7Y3TiYyt5IGhj3FLgDcmnJXxjfEKc+65566w8XS196OPl7t2QrM3NrNbdS6mvbrbFezlPce7C7ffkUn9wRCcbVG2nPziXeXHdPqWTYBKGOMq9cCwc82+fzzaNfbF75+xTfRGu068jGl2Ld54t7vdbbRrpgrerfwKE/cXT2gSZRO+1Tsbf6/efixnHGBc4F1OuSkbcgImji+M9WBAeWn3nM7jZGg0phjdckKz977W+9EmigVn3je2QWP13vGyxdz8e+MO7xNO4dpEuBQbWUhyYo+/xksvD/h604/AlTZ/3XXXFVlCN4r4cYc0thGl9CnGS9uIVfqbaHernzOeIV9haPeM4eTdM9wowhjFjkzkGFtIW53Q7PXX2I54N7TkJfoaZeGGEcpli08FL8rVez7E0+6Qc482s+68qoaTbcRY3YSzW3OhufksnQu13j9L5kI1jPZj2LptJI57vb4LLlGeQObn3Ug6dn57uao3ZvCOZ3xhfCStbbYoMr+wt4XyMj/FzzsDmcU2ipa5PHMjxg69m6bkzJ4sPyUn9mS2dd5d1KclIxDXKytzRObtyKHIL6xFIBdyQxPvPd553DZlG5phleYoRmDd8QDIuHWMNTNTQBSZjfkB7Yz5wFR/6c0X4d2TjebKrfCRYeyYs+bUkxnhxboIN7rIMKZQZ63j9GQjpcHWeMrckrmEfQqq9FnWNDC+jj2cWXth/kefB3fkWPw8E4wfZ/H7+WtP5te4i8yKnGcKzMKXcRhDeW3TRHHrrydTi0a2ly8UFm3Ji+Qv05tDLMG0V3fltVP2dvrckjlv7520W/OLOesIEedeH+rJCvDxsv+UjNJax+3N+ckjT2iCwv4xO9nfGANZ02fsY17KnNUOLRVwbIPoaBv+yhoP8azl86644oorilyGbcrQ1XqTXx/y6PKO0c+flMTtT1Hq5KW3GbeX/nx671ZeKgNlYm7FjzWjqVOavOd4vx40s/YJTVv0KtpwaxRbjDWi8l0/G8BKuClBhlNPPbWccsPmZKd2UG1JeL3HJj1lVzq7m9jVibFB7bCPl1qDO+zEQC2tNexy4oITi9GY4FFO23Hizl7Yh+VDHhgTTmLSLX7uW4YHv0tt95Q31uDKd8DYlcmuVm/Y3WTXzZYddTqB4uOjm1Oe7Ehj5w+nPzi9wa5YGZtYlRMmnEyJpvUcOH3JSUNOYJDOFnPLCU3rIJFF8fMdSU6UckKWMtjLuIRzQoSdYuyQYPcd9dFJ3SojF+jLzXfbONnGqUjaCicyTYhcUVMP2oZOV7ALjDrwnCk73/tkhyIY0c74lgvfYmXXBm3WnxqlzpykYWczadmJiMHmtCGG7/+w286ErrJLzgTUEm4LzqV8eIij3Oz6YPcH5ZPhZAP8bYG+7B7klBDt2oTxcsLKFhcGU8qUndIn2G5pW/QubciE6bIL+S53uctAGk75slOQHZjR2KBUTgJxysgG64Id9ebD7NHwDQROPLKjCYxPP/30cnqZdL2yig9l4fmzA5s8qCungsDRFL8DJ2LmmKXPfGoMoe/3sKRMrf4zB78l5W3Vn7ZHW2X3LLs4TVgsu+ZM6bJK0uqnK4LrHb02x04jvn/FcyYfE6ZLH6ddT5XhLXYabjfbnK/XnPbn6b2bctOH2clKX6G91MZBpaHd2gSzjMk2MSnfG6YPRNPDmXGJ8ZCxj77E7tk5Zml742SZCSBlXOJdyPMlX2941vRBvQ/4PqAfh6CtvR89jyn31Ng8B9OpunNDAt9/Ro6whYThDne4Q7ntgLKx85NbIThlzVjOySXGVtUZGk6ycoODKS/LDQX6BjZtixPl8PWGdzvfRKZujGmcrOYdTtuZk5/nFd3wID/KOlVvn5bny85BnqcJpmXcf/3rX+9JVm7qxcl5cOAbZdwMQLtk92zPzHlfs+OfHe+8r8BcJ84937n598Yd3ifciKA60ue4mQEzxT/KA75suDnhiNzF+5r62KJVJCm4cWKTtsN4SZvjFKLMbvVznrktLpZ3OCfkON3Nd+hahudNudl9ilxAG7ANYCvyqf66IjTHlLyEPMhubWwwRbagTU89H59HupcjYItG5fvDvKMwR2ouRN61ORfh3vTmQr33z9K5kM/zaHT7cW+q74KPl1+ZMzHn43QlcyXGGrvSuMA4NWYwTjM+kifyvp9j8H7gvcI7FTmM8cI+G1PGJt5ltB/mT7aQ0p3bTMnyjDmtORWV6MlspZLX/80du3oywlRZyYqyIp8hI0pu43nw3mNOwjyA23/SJALrIoCMwhoDNwQhs9FPZab6S2++KBmjJRspD+y5cuvcNaeezOjzrblV7pZsFNMwnrImwryBdRTkdlMgRrIiW7dwZg5617vetcjrjHPIbox7c8wUdjxPxgnkPNaymN8yzmE4BX7aaadtyaYnU3vCKF/4uCn31BxiLqZTdZ8qx5GMnzPnpXy9d9JuzS/mrCN4LKf60JSs4HlNySitddztzsF9GdK9/xFo9Tfaqm2wKDdIMvaxli79kWrN2ghrPMSznsFaD+ukyGWMp+gJbOOzyLs2c19vvN+7ocEfw3zanruVVvxkK58er6Mh7kbrVhJB3HZhlskJQoAM14SxeESjiYaFZgyLsy3D9QqmZS7HyEXDlWcoBr1hoY/FJ29iWh42SjeuWEBBRGPluh0UXabtLmXHj2HSgrDgFXlaFKwpkXy+TGrExwswpEfpg7DB4mw0LDpjuOLukY98ZPnYdqTp+emwXGdx3/vet0d2WJx/DigfUWD454VgN8eQzk7RlIVynqntOi2TZJSZTFp9Z5vDDxqeEZM9ngMCEUItC9QtgzKIq/FYCD/++OPLtUiaND7nOc8pikOuXmSAw7TKxCRA7ZKPnjPgYVhk1bVvLEwzIGJQXHJNAlc6UkYE2ZpBeNdigeK5uo3Bk6sUaCNcSSSDkEgZUcxyDSa0U4b2TH/gijo9W9LEfAlDcY9gLIPgfbopNTG9shaC8Edb9+0doUX9IJB2vUueueqnZyXGU1iKztvqPyi25uJH+iXl9fkhnLERgI0IMlyBwLWUNdOq61Sbo914ZRbjF32VxUY2HfTKsNttztd7afvzaVk8m9NXfBq5mbzS/1GksfAkM4Uzi1B2+lXks+0l/RWmlA0FDxtwGBsQxBgnvGGx8FGPelS50parq3nf+bpAG9+PPv0c95J238J0Tt2RLewkRZEBuFIGYROsab/Un40d3rDxhTTRoGDmmjve9WDHQkU0jBuMWX78RjGOQbnUys9fdRV5ym+nUspV8rzH5tRb6VBS8Q6Sgo9whG7kjGhYjGZh2l/Xj6KX97qvU0zHhrMl72s2FvnrUMVvbv5TYx8TC64z54eCGQwwPf4qw1ybhXjaEbKeN+SBzKpNUj4O9271c9odspOdti0yBTIhfb9lmMBx9TSySM0s6a+kb8lLlIFNA7oWl40BKCpQpPeeT6/91cqbYVsRYBGMK+lZWNUca7fmQnPz2Vrij22akQzoZUPoeu+f7c6FYjmONn+r79Zw4Fp25AXmxbwbsdlkh1kyZtAmtXEZBSlyCfNIZEjmYoxNGNuVvxpb2VzCe7wnZ07J8j05cUpmKwW6/m/u2NWTEahbbw7n88PNHMnbuOknbFRIkwisiwCbVZGdaqbXX6DvzReZZ/dkI59fT27VRnDo5645TcmMPu/onpKNIn30c10ra2uHDh3aEtXDGRlpzrxgC8PrPT3savTMZZ72tKeVgwjMEzgYEU1LphZdTb5Q3Bx76RyihWmv7r7dzCnTkaRpzXmn3km7Nb+Ys47g8ZvqQ0tkBfj2ZJTWOi5rq9uZg/v6pPtgIeD7G/oh1nH92mqvtshcXuchuQy9xX4wyO9LzTppluZxpOnXVmiy+MKiKcKQXzjVCbB1KsaAh4IMHn4hh+8OMugzsPFQyBfFqZRL5FVLqwV8To3p5BgL+ny/AuUUE2xvOGFHfWSY8PBC5YRez3AqkZ1g3jBBQehg4RnBwxu7IqIosVjoxKBAtCsCy0IbC5dzDQplTvdtx6AsZCEfTHWiiYVYlCCtBT7lx0IqSiEwYkGC8hCGIgYc/YKs0kzZ4IDyUd+Z5A5s2kTNsDDJKQYUmnY93PDWt761LOwigDNx5vnadSsDfswLXvCCYk/98ey0gER9WMzFoGSUYlSnR1GqoyxCUUg7iCZ+M5N47u5mkR5FK9hx6khtGSGCts9gjeKRBa0pw3OCD4qxmhLTp6c84OQNQgOLD72yevpNu5c881beU1jyHKNR/1mCHzzWLS99jckcylOUThiE+aULv1NtDoUG46MUP+x+o/7cE88EtVeG3W5zBYTr/6baH+OTFtB8OtyUm2/irmNYjGMRVuOxeEzhrNOE2vygdNh6f2lx18ctbW+k5fQWCjref9qA4HniZufwIZuAM/ahAGVMkKm9HxU3117S7luY9urOd+L41jU3HTD+IXBSD75nw+5nfd+aBf64mYn3ejTQgAmn4Xmf8J2WaBg3wJRFHy1GsMjLIqXiavlFPjU/txegGKDfISjPHaN5j7C7e863tXhvsCHGrgOtFaEZtvR9zXii7wN7pnPznxr7eLbssuRZo3jUYniP/3vtVoYlhvesTsr4dCjmaK9+XPbxu9nPae8o0nkn124V8eWi3CwqsGAS+wN0S/ori4cteYmT64wpl9ltFZyk4ztRYMKms97z8WVN9zIEmO+wkY6NDV6u5B25G3Mh5NupfGo1qs2FoGu9fzY1F6qV5WgJ6/XdGgZsrmHzExtHmbdwmpJ3LaeAl4wZnjdzMck8yJfaNIm8htymeS1zS9pwT86ckuV7cuKUzObLPHfskhxQkxHg0ZvD8S3zNInATiPApnbkG+TdaHr9BdrefBE5rCcb+bzmyq1z15ymZEbGG9atamZKNqql8WFskGUuEk0PZ+Tj3hy0N3+di53Kw5oh64vMlZi3MF+KpiVTQ9eSLyKPnn/pHKKF6dK698p0JONac96pd9Juzi/mrCMIw6k+tERW6MkovXVcvXvXnYOrLmkfPAR8f2O9xq7tb87hN1171ozQR9VMjMOPadHXeBCmdK34SAN97yf6OXx7ee7luI+veC4sJcIGO8JQ1rEQxekBFJHs+omKwjms2ZWPQgbFY1ycYUc4C/HQoGxiYYVd6ZxuwrTS8pJnl5X/EcaiTK2MLBhzPQf0LBLZt+1WVxjOqYNoaLgoGtkFwKJUbEAsUpA/yjgmfJQfoYRJH6dTmeTXDCcCUT6CNWnYleCVybU0U2FgiMIWZR8Ys0DGwj4nCGuGRVpeQCiLeQ72zcdCxvNgUAE3FgbBmGcF7RLDgje4oAhgwCKPltAKX3Z9QQPGKNlpOyhjsQlj0Y3FZBbhKE9NwRDLx4IqgyPGvj9TrpalXlxFSB2pE9fp6tQtp3CpL/0AOk4VybBYwM49TreQjoV1/AikCOR8qBuhlF3ApL/kkksGu9+7KIVRTtIWr7766vLMsWvXYrK7BEHFvp+2OsVkd4KX/kg5WGCAN4ay0v5QatJO6a+cbGNS3itrSbxDf0ufea0YU1iSptV/pvCL+fXKqx1CKEWioa2eaMp1Tp/RjlAychU11/guNb02x4lwxhCUEOTDYiKKeBTvU2XY7Tbn6z3V/uw7teXKSPoxi3L+xBjvCCZMvD8YH+krftygH4GFDEouFtfoT/Zdo6JAkpJZNNg9nOnD9DP7dl3pS/46S8ZUxiNdyc3OXhYcMEvbG2mYLDOmMVZIgYoCEMWHDIozNuXYNya3LGy03o+kY2ykbHNMr92Tfg6mvbrDnzHfvrtVxjGeIxtjKB9jMu8qFhA5KcZz433F5iSeY8uwS5nNRrzbUcpEQ3k40c/7j/cp7y7aIZtxpvKLbYrxlDGedw5tk7Jx5S1ja6/esUyUmfcV/RfDJhd93D6OL1z7x3jOOE7ebNTipP7UTsM572v7dnXhyTOAv31nNha1XDs4J/+pcQfZh0V25CavsF+3fioo15DTVjhJg5t2EA1XeFE+drkzRvC+RJaV2c1+Tt+mHyEPSMGuckSbvkAbRcZkrKPcXrHd66+xHfXkJWRBxhVkTxQfYKVToVPPh5NatXdhrEv6P44AihPaAbIi4543uzUXmsrHl2nK3Xv/9OZCU3wz/mMI9PpuDSPeDygzeX9yIhzZULJPb8yo8VIY4wObRzHslEcm4n3EBhXeqRiU5Mi/bBbtyZlTsvyUnNiT2UpBrv/rjV1eLurJCFNl9fmlOxHYKQSQzRhnmVMyP8ItM9VfevPFKdlIeWD35FY/V5m75jQlMyIfMc4wrjDW6JQ/ZZmSjfyaCPTwQeaDj+SveGUhdD2cNQfVNdxsxPcbcHvz1x525BsNylFu8GD9VTfP+DEL+pZM3ZMvSBfnNYTVzNQcYi6mvbozl9K8mbmh5nrMSfx8vla+3QibM+elHL130m7OL2rrCC2cpvrQlKzgZf8pGYW+XlvHnZqDs85S28zcqlOG728EWv2NtsrhMdbyWONn/owSfCeN1+vg7vkph2jm2rHsPp0eI+StAAAuJklEQVT4yfZ5x3Q1/1L6Go89Gbadj7Sa4DTaYuFoys3RhP7RBICRD2vL2CLYaAohecsHrQ2E8sHWVeD1DlP+lQ9d22Rn9D/bQVoobIFwtEnXaC/y0a62Ge3ExorFVNoVoTlsQlU+oO7D5LYF1NF2nZQPgpOP7SId7QWq6JVtC6qj7YRb+aPDdmWiuh/tpOKWuoATxgbgkY/UmnJ1tEF/tCt/RlOglTg7FVrqWDzuDyxJb1fLjfbdk4K3vQBWFOAMjYwpTVZlnHoOJhyUD+baaZLC2xR3YjNawx9tIav4ycOUL6Uc1O3kk08ebQG1xNEWbCGylN0WvUZT2BYM7BTFaAPLSFjNxHJDb3e9l2cANrZLrnysl7SxHoTZYvdok/LyAWqerZ3WJLgYW6AcTSAvWMHHBr3yEWDKzPNR2SE2xfBok4CSzpQhpV3jsUWl8pxst1L52LtN3EcTCkZbFCi0/NngOZpSsDxre8GOdvqyPCcR0HYJBwO77rPwVJwJbKMpI0t97aVfPlZsC7CKHu1U0ao92sL7SFu3k4Ul3n+s3hYNy7Oh71BnE55HwjA8s/POO6+4+TMl2khe/OyqxtFOCa7iemU1RfdoC/0rWrtuc7QrnVd+E+DLc1gFNBzbeeamMF61q8i+h+VU/+nht6S8lMm+d1GeE+0I4/uQKaBGPl5NW+A52QncQsNfbN+9uvbanE3Kxqc//eml3dCnbVfpaItNq3x6ZYCo1+Z8XXqYzW1zcWzotT/KbScQC3a2q3O0icBoCpBVvUwpteor9CneE4wPjE28O2zCWmjtJEIZG+3br2VsYJzxfcDXsYezCU6jnagq7y36vy0SFrcKxPNkbOJZM1bS10zBVaJ72MX2Jn52NfloQpu8o22iKM92FWAOU4SNjFXe9N6PvKt4d/MxcW/sCpgyJvqw3ti8BNNe3U2pONoEvWBG27WFz1JPlYP3IxiAKe9B8uX51sZ00tgkv/QzU3SLRbH9M7aT96NtzCl9ljGRd7Mt9ha6Vn6xTfEu5p3Cj7GeMcCuDt2SZ6/efiyHtyl1S1tmjGDcNaXmilccX2xzTakjePCOs0XkFa0cpsAabbOWvAWz1vtaWPKOAw9TyI72vZSSBgZetsA/J3/opsYdW7goeSEfedPiH8dLnwY3/ciu1S3YIK/QlnjWmDju8E5lvKAv2MaPMkY/85nPLLS73c9tYazITyXziT+77aGUmz5Ov7CTvaNtbCqpev0VgtiOWvIStLbZcLQNASUvxhe924hrPR/iGJMpm502xnvUmO3Mq0yZWWQGPw/Cfen1cvluzIV4UL189CCn5kLQ9d4/vbmQ8jio9rptpDbu9fpulCeQaZhj8X7lfcH71jZEFJh7YwbvWn4yvE+YD8nYBq7RFBcj8xfmx8xX6fd20rjIKchbtsgt8q6c2ZPlYdCTE3sy29x3V5SLejJCr6x21X15n6jSyFnICLY4r6AyZiNvpTm6EVh3PAA15FX7vEKRn+jX9GnaGXNBTK+/TM0Xe7LRXLk1zlXmrjlNyYy2abDIJcwHbENcqbNtti917slGfn7KeMrYZTcalfUr26Qw+v7o6ziFs21IL3I7cz27xru4baNtKU9v/tqT+eO4W5jZn20G2iIvxzELuppM3ZMv4rxGebEOS3tiTUIG2tYcYgmmvbrbJsSy3kaeyPKsN9FemeuY4lhFWdveTp/jufCe2+/rCD3wen2oJyvAM8r+PRmlt47bmoOTB+mY29rmBrxp9jgCO9nfWH9m7YU1E8Yl5ql2Yr4gwpqOfQd6hQ56KruZYOVnDGJ8k77Jrw+tiMyB/KYfcp//sXblf8jCm/55/spb5bGDYKNtHixjNOO03cxXZHHb+FHke9tcM7KWwTuMdTF+B82wc33bdUIR6F9022bYYEA+PNCdNjxoGsdOGzoRjcwbO0012sdrfVBx+0ksZduJxkgHAeOW0cSYckNbM0xmN1E2OmavLDFvMKnRU1avIIzpan7yZjC0k5Qlmsm+JgbULfJjkMCACXRMLPhF08IMOgad2BYIJ4wBiP6F0ptBV8pv4qOBTyyfyu5pwapXnl6c57NJ99Jn3sq7huXc/lPDr5VPr7xqC620tNfac2nRx/A5bY623xuXW2U4Um3O17HX/niBt4z6SowHi5ohn9q4Ido5OCM48CwRvFH+eANvcG6ZJe0NoYxJgTetenmaKfdSHr12T15TmKo8vbqDJ/m0DG1gablbvBTOuKnnrTDZtfzWzb9Xb+WHDf9WW6+NL+vIRtS5974Gj/g+8WX07rn5t8Ydz6vmnss/pqUt9cYT6Bnz9E6Eng0Tdsp9C6vd6OfkbVfcb9lgwWJdVG5pcxwFVLm3FNZ5ev01tiPaXO95t9oj2bWeD7gxLh9NZhPzqh5ePPMW3r10S+N2Kx/aXU3+XVre/US/6TYy1XcjNvTzVr/sjRmRj/x2o9HIYjljGMaPIzUZaI6cybuy1y5q47re4RrbWvMxlRu71pfAMxrqJP4xbqqskT79iYBHYLvjAe2Sdy02sgsbNaOp9RfR0N5r88U5spF4YMOnJifU+hPl4R0zZXoyI+XThozIpyUbaYyK9NS/Jw9DP4UzY6rKg+wmhabyqmGjuBZ2ivc2myXsxhEftJE5Ue05bckkeBgTe5jNwRSWrbqrPDxLtRWFhaIs9m63zynDqXasd0bvnbQb8wvKG9cRUO7H+QV+KXdafUh178kKUfbnuXm5QDxk08/1jBUmm35Te+7qj6JLe+8isNP9jbZDm8NwAEgKzXURiX2D9smPthh/9G3/Q6bc9M/z9/lTJvohP8ZbMKCfIZPyLuIdST/h3cSP8brWl9bFaa+kW/sbmqZYWRnTZpdrPlcBO+QgH9uZs0PcP86Wb0DqCp6Ph27exZUQ/Lyxhlau1PNh0b1TZdM1YjG/6KfMLVoT4CL5Wn47GbIoXQsTyrq0TORtpw7KNwif8pSnlOuR7PRGuQ6Yq1O4UskbvpvAdaZc+8pVGFwj6q+sE20LM+K5QiQaG7DKlcJ8S436cR0t11PYSdRIuvLX+HDlYTT0pV55enGR16b8S595K98aBp621VagmUrr+fTKO4Vfrww+j5Z7Tpuj7XP9csvUynAk25wvZw+/2tWhSmuC+Or7twrDjuOs4nr5QDMHZ9sRLXaH2fSzGs4inNPeuC76jW98Y7nKmuuCvGnVy9NMuZfy6LV78prCVOXp1Z0xq5dPrw2I/1K7956o5bcUN5WnV2/RYMO/li9xNYzXkY16dSYfrtTjN8fMzb/XH3r5zOUfedCWanh5Or5pfci+P8v3SG2hqFxTyxVh3ux0P+eKLjvpW74xRzlkbGIiZ9Weepf3+lHEhTbXaxOt9kjBWs+nh1u1Qhk4icBuzYV2Kx/a3brj6SRYRwnBVN+NMPT6eW/MiHzk5wpE5BM7STOcZp89sE2h5Sp+OzlUrgMTHfZcOXPqXRnHL3jPkdmg86Y2dtXaYw+zqbL6/NKdCGwSAb6JaCcCy+cG7CaQwW5zKf0w5lHrL6Khvdfmi3NkI/HAhk9NTqj1p155PM+ezMiV0C3Tko1qayLwqNXf856Dc2sOKj41bBTXwk7x2MiDfDLoCU94Qllr8nE1jH38HPdSHr0xkfymMFWZWnVXeXiWMgqT/0jbU+14zjupJydT914fmPPuaa0jcI1nz7T6kNL0ZIVYJ55br7306tjqN0vmqCpz2vsbgVZ/o63GNredmsa+YcrC7bDbVlo//sFI/ppNmH4xU9HH8IPgP1zTcRBqtY/rwLc143dzqI7ttlt9r/JIVY/vZVKOo8GcaErD17zmNeX7T3bdXvnOJHf9X2ffGI2GZ8Z3KFFkogznA9hT37+KPGp+Bm0WV+2qkvJtQL4Fx7fdDvKAVMNhE2F7of9soh7ikW1OSOysvQRnhHG+f7tpw7dB+RYn45G+fbfpPJJfInA0I8C3cG2XYfnmG99OslsQBr4RUzM71c/59rbtrBz4dmm+42vIZ1gikAjsZQTsEwqDXdFYvkN38sknDyeddFL5nrvtEt9S7J2c2yyR2bYUKj2JwD5FgH527rnnDny/kY1RV111VVFwbqI6S2SjTeS3l3ksxZn54KbnbHb9ddncjtK69p3PvYzf0Vq2Je+knZpf5DrC0dr6js56Mzba1d8brbyUhDWbTTI+HH8M8/Fz3OIR+UR/ixeV93ECg7CDaG7AEeBb3epWB7FuWadEIBFIBBKBRCARSAQSgUQgEUgEdgUB+45XOWm7K5llJvsSgWwj+/KxZaETgR1BIMeDHYE1mSYCTQSyzzWhyYhEYOMI7Pf+5m9LYgN0zbTCa7TrhEVlpPzejm7vJ0/599pJ93Xw8Gk2cuWsZ5juRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRGA/ISBFIGWWOyowW+Hbqad4Rh4+XG7sltuXWzSR5372p0JzPz+9LHsikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCicC2EagpARXWUmxuO9MKA+WpKPllE45b/pat9AfFToXmQXmSWY9EIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoG1EJBisJaYuKjUrNFtN6xWBh8m95S93XLsxfSp0NyLTyXLlAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAisGsISEnYytDHb0K56fnNyRMapZE9Fdbiux/DU6G5H59aljkRSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRGBjCNz4xjfeGK9ktHkEUqG5eUyTYyKQCCQCicAaCHz0ox8tqTaxu2mN7FdJtpO/3xm1YrgHHDtdru1gtgfgySJcj8AxxxyTWCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAnsSgVRo7snHkoVKBBKBRODoQ+AjH/lIqXRNOVYLiwhFmu0o8SKvmFfLP5Un8evyJs8p/q1y+fAej1bcVJmn4sl/Do0v5067W3WdynfT9aiVo5dHL86Xvca3FqY0xKVCU2iknQgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIrDXEJit0Pzwhz+8pexxQc37vXtLoj3mqZWzt9i36eLf9KY33TTL5JcIJAKJwL5FoDYmzw2rVbqWtka3m2HbLdN20uv95nkoTBj4OIX17B59Ly7yXEIb0y7x+/rW8vTx4lujmxMnGm8v5e/T1soRwzx/H6dwhck/xd/HpzsRSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgETiSCMxWaPpCakGMsDlun3apu7boNpeHL9tOppnLG7rt1GdJPkmbCCQCicB+RiCO39FP3WphU3Xe6TFYZVonH6XdqTpE/pSxFjaVP/ExndK0wntplLZl93i20iwJ989qKq9afC2slv9SvGt8a2E+r1q8z1d1FZ38nke6E4FEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBvYjAbIWmFr9UCe+XW3aNRmFzbL/AFnnOSR9pNsEj8lzi33R9luS9F2n/4z/+47BiTT2jqfjDGO5QQK8cvThfnLl0Po1vQ4TLL9uHKZ3i8iSwEEl7ryNQ6xs+zLt9XVrholFfwN+i9TRKF+1WWuhietHGcM9TND5sjnvddJ435fJ8VE4f5ulb7hZ9Kxw+vbhWPj583fSxzp7nXHcr71a4+NbyroWJ3tuRd/R72uj2eUQ3tOJFXJpEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRGAvIzBboekroQWwaEOjsOj26Xtuv+BWo+stuvm8a2mn4mtpthOmsu52vtsp826kbeHRCq+VaQmtnkONz5ywWl61MM9ru/GeV+wT0Q9tDIt+zy/dicBeR8D3H++m3NHv6xLj6Ae9NIrv0Xj+PffSvD2vmNbH7YQ7jg81fy/fWnnnhsG3RtvLrxa3Dg/S+GcO31j3Wl4xrJZ3LSym8/4l+Ube0e/5encrDx+OGzOXp+ef7kQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEYDcRWKTQ9Ateck/ZSysDPy2w+bQKU34+LrprNLWwmG7T/lZdNp3PfuPXexa9OOo5FV/DYlPPoZZ3LUxlWDdO6adsvyjtaVvhnibdRycCm+oLO4We+oxs5eP93q142b040chu9RO9a0Tn7Sn+Pu0Urecr9zpplHZdO+IQ/T2+sbzRT9q5Yb18anE1vjW6nQiLeUf/Tue5Tn56rrIpY3TvRLk3yfO3f/u3h3vd615Nltdee+1wj3vcoypDNhNlRCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCicC+QeCGc0uqBTTsdX//93//N+jX4kF5YlwtLNJM+TfBYyqPGN/Lk7iXvexlw7/+67/iPCLmf//3f4fLLrts+O///u9dzT/itNN+KreJPGp8amGbyKvFYyq/GI9/U+aP//iPh1/6pV/aFLstfLiG+Bd/8ReHH/uxHys29Zc5/fTTS1+Rf1P25ZdfPjzykY9ssqN/sED+/Oc/f3j1q189/Od//meTdjcifvd3f3e4053utFZWL3/5y4e73OUuw0knnbRW+nUTLW0zavfkJ3e058SpvDGt/DUeU2lIO2XEX/a69Eq/EzZlavGNcbH8MV2NPtLU/DFdjWYqbBM8WnlM1buVbrvhPt/t8vLpa1j5sOj+h3/4hyIboEBEZptrdlKmufLKK4ezzjqrW5Tf+q3fGr7v+75vUZm7DDMyEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEYE8hMFuhSalZIGvZfvGs5mZRzIdHv4/zbuXnw6bc66SZ4rk0vlcGxaFkYOHwSJl/+7d/GyjDv/zLv2yrCC984QuH173udYt5LMUU+mh6PETbo1GcaL2tOG8rfiqMeIynW8c9h0eLRuHYmzDvete7dkSh+ad/+qfD8ccfP1x66aXDe97znuG0004bvvEbv3FV5Pe///07okxEifqBD3xglY93oLy8z33uMzzxiU8c/vqv/3q44IILhjvc4Q7b7is+j6Xuj3zkI8N73/vepcnKhoVHP/rRw8UXXzz86q/+6uL020mwtM34PkK+8ns3YdEvuqm4WnxhZn/iIX/LFl3L9ulaNApv0frwuW7x7Nnw8vHeH93erzSEYeTHnvJ72k27Y96b4F/jGcOm/LEcU/TEY3y6nj/G+XRy12gUJhva6P6u7/qu4cu//MsHxuXv/d7vLSciW0pN4v/kT/6k8Pif//mfIk/shEzzt3/7t8MZZ5wxvOQlLyl5tf4o+9/8zd8Mr33ta1skGZ4IJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKwjxGYrdD0C1/U1/ujG/86P/FV2uhXPoTXjNKJzvtbYZFmE37KVuOjMivummuuGT7/8z9fwSv7wx/+8Mrdc/zXf/1X8yRCLS6eMPvUT/3U4U1vetPwmZ/5mVuyIS1lrJnaac63ve1twx/+4R8eRl6jhUj19+5emOJq9mGZuoAavQ9zpKsyzY2fW/YaHXm0wn3+0e3TeLfofFh0419qUKB587CHPWx4xjOe4YOKm+esOsXI2OZiPHk88IEPLHyvuOKKonT7sz/7s9Iuf+d3fieSb/FP8d5C7Dxz0n3P93zPcOtb37oocC+66KKBstz5zncup5Ycq+Kc4tfqzygAWooCTodG/GO+8vf6KzS/93u/V65g/Oqv/urhhjfcOuSj1PVmKl/qAo0M7o9+9KPyHma32kxtfPKJp9q02pvo5tjiL9opv+iirXQt29O3aHy46Gthiptj99IrTny8v+Wu0bbC4KE4bO/37khTCBf8+fRyt/gr3tuRdsq/ibRTecT4uXm20hGOER+5a7YPk5sNSrzTL7zwwuFXfuVXhr/6q78afvmXf5nowwyn3P/xH/+xhN/oRjcaWjJNHCNbYyKMIi1hz3ve84bzzjtvOOGEE/CuTE3G4IQm7ynqnyYRSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUTgYCGwdXV7Yd3igpkWkLzdoiErxdXsGK+i1WgVFmmiHzqM6Ddt1/JTnjFf/De+8Y1XikC++3TuuecOn/u5nzvc/OY3H77lW76lehrs6quvHu5973sP3/AN3zCgkPzkT/7kgUVFDCfZjjnmmOG7v/u7h5vd7GZlUZLwSy65ZPicz/mc4dM//dOHL/uyLxu4BhLz7//+74WeE2gYTkGgXLrFLW5ReL/yla8s4fyhZDr55JMLD/K/6qqrShwK2Ve84hXD05/+9JKWwL/4i78Yvv7rv3645S1vWa65fOlLX1po9edxnxvm02zXXctzimctDWExXSusFx7j8MuIP/6euxVPmmhY8D3nnHNWwSi1UdbJPPe5zx1ud7vbDbe61a2GRzziEcPf//3flyjCv+3bvq241Q4f97jHDZ/2aZ9WFPNqExD8xm/8RmlztFHaHO3hh3/4h0ta//dHf/RHw3XXXTeceuqpq+BP+qRPKlfP0o5qptWeab+0f3/imJNGr3nNawob6nHo0KHhUz7lU0rduOK2ZlAy0qZRxnkF4NOe9rQtvN/ylrcMX/zFX1zqD1ZveMMbVux6/ZlF+O/8zu8cbnOb25QF+jPPPLM8WxKjxHzsYx87fMZnfEYpJ/j/8z//84qvd/T6q+go01d91VcVJcFxxx1XTlDx7Hjep5xyShknUKz28uX0NfX55m/+5lJX8GPMOf/88wd48nvqU5+qLLfYtTZTG7uUyLdxwnz7jW7vV/qYJob7NLij39Nvwq08fD4qo+J8PrUwH19zK423I12Mkx863DLe7cNa4Z7Gu0WPLTfx8q9rK4/Iy4fLrTzkr6WJNNGvtDF8yq90S/KEp4z418I8T9F7W2kVJh4tW/xEz/jwWZ/1WcWLkvILvuALtox7RHAqk37/oQ99qIwjnGDHeJmGsfH7v//7y1iLnHK/+91v4Arbe97znkXGYSxms4UMytO73/3uRcbgau13vvOdJYqxiXGE94hMT8ZgzEcW+s3f/E2Rp50IJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCJwQBBYpNCMC2LCwC+gRRrFKZw0PiyGe55y+zQ+LLrFV+FTfs9XtC1bPHu2T+vpFB7DONlEHIZFu1//9V8v3+xD8cKJx5rCBWUL39JjoZDvb6Ik4jpJnVSAJ8rM973vfUXx+TM/8zPDM5/5zOFVr3pVWeRjUfFBD3pQoSdvlYGTVl/7tV87fMVXfEVZpEQR8qhHParEwxtFJz+u6HzBC14w8F3DD37wg0XRiaIDBdkb3/jGUhdOUqAAIR5lJsobneIoBO6vhY1wcaSHtRulnWO3+Pjwnlt5iCb6Ce+FEeeNaGW34hTu6TwvuWVD791K720UWPxkePY6MYOyG8U6bRFFNwo9Tsdg/Gk82iGL0w94wAMGri4+++yztyi1UJqijPunf/qn4Sd/8ieHX/u1X9tysk95s6DNgjkKd29QnPprZxU3pz2LFpu2S7kxKELpGyhQ6V+UrWa49pZTQvQFb770S790pQhmQZ1+9OQnP7nUnwV3lLa6frHXnzmJyqknysGiPd/nRKmM4Yrbv/zLvyzh8EKZ+YQnPMEXo7h7/dUTP/jBDx5+/ud/vihHWeRnowTP7t3vfnf5fij9GaVFL1/o/+AP/qB8m45NEIwnPB/SMs7w3bpnPetZpT4+b9yxzfTGLp+WNuzbccutNKIXXfSLDls0PkzuXpxovO3zmUrraT0PuXvpfdroVvrt2D5v727x9DQ1N2Ex3PvFV3S1ONHU7BZ9jV8tDJ6eh3crbm66mNanx+2NpxX/GO9pFOfDvFvxNXsuXUz7NV/zNasg+iubTpAbvPnCL/zCIk+wmeXnfu7nVmO/5AloGXsZY9kMxTWwvE8YU3/qp36qKEj5pi/fTMYw5pHvs5/97LLR6tu//duHH/zBHyxxpGM8/aIv+qLi568nY/DOQjGqsXiVKB2JQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQC+x6BRQpNX9vaYpnCoq10hCtOYdg+rOUWvY/3YTF8jj/SiF/Nhjb+anQKq/GuhYke+/GPf3w5ncAiIYt7rWveUADxfT9OQ3BCjpOXKBVkUE589md/9vCJn/iJA6csv/Vbv7UoNzkl8ZSnPKUoUzhB5w2LfygtOD2F8vG2t73tcPvb377wRfmDAobvZR177LHD3e52t3JCk9MZNYPSgzR87w9avqlFnbwRlnPDprDzfGruWn7QtcI9j5h39IuPT9MK64XH9D3aqTLU4mv8YxhXkHId6Nvf/vZyTemLX/zi4ayzzopkxX/HO96xnKz8hE/4hGLzzbW/+7u/Kyc6UXbSDmlzfHuS605rBoVdVGbW6BQ2tz2L3tsoQzm9TH6cHP2mb/omH71y64RnvIp5RWAOlIT0D3hwKpTTnJwMuvLKK1dkrf5M/6A/ocykX/z+7/9+WehHccg34kjH1Yr8UGbybVGeiTe9/urpWm6eHcpNToLOyZe6cjKbZy1FM9fycpqW8QhlL0rrKTM1dk2lr8Wv29ZrvG5wgxvUgpth0CuN7CbxREQvfS9ugu2uRbeeQy2csBge/btW8A1n5Ovh3cqmFqa4TdrbbTNsSEIe4DQ2t0asYxgXGSM48fl1X/d1Zczh9OVNbnKT4SEPecjwsz/7s4Ut4yknOhln2NDFRgw2SLGxBDkCBSqbUWSmZAzGTjampEkEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBE4WAisrdBcCoNfxPPupXwi/SZ5Rd6b9i9ZYEQZyamEOQZFyrXXXlsl5dt/KCJkWBTkZAQnJrzhxBZYcsqMU3f8UKKgpOKEBWlYmJThtObnfd7nybvF5kQeSqOTTjqpXC/HwiT1kdlPz0xlPlL2kjYTy7gOzpysfe1rXzs8//nPL1cKolyLyrSYD35O+WFosyjoTjzxxKIsK4GdPxTvKD91irJDWqLmtufIB8Upp0/vete7xqjD/FyVjOFkUsugsI8nOLkSkdNENeP7M0pVrpXVtbPPec5zSv/gClkW8HkGMpw04jmyKcCbXn/1dHPcS/KFHxspvI0bBYU/9VsIZvz1xq5W8tgn8Mcw0vbCxdun827Fz7Wn0qos3q7x7vHxaeWu8eiFef5z3D1e24nzeYtPLUxx0W7REt6Li3zW8Xv+3i1eKoNshcuupVFcy/ZpWnx92khPnMKi7ePEg3EIBeRpp51WNlcpfDs2YwQ/GcYR3SzBO4MNHpI9HvrQh5ZNVcgeKC+97EH6KRkDem1MUX5pJwKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCOx/BBYpNLUQRrW9WzDUwnwc8fopHLuVrhYew2r8ejyVb+Sj8JatfLzdoiU88o/+XtqlcW9729vKFWu1dLe1k5b+O1UsDrJwyLcvveE0GmXku1Moa/Tj6jeUTu9617u2KLa4Iq52ZSeKKRYSL7roonIdJVdRPvzhD99yglQY+vxx1zCqhcV0c/0tXq1w8Y3x0Q9dK6wXXovzecZ47/dupYl2i4aFZF0xG9NwjSin7bhGkMVkFra/4zu+I5J1/Zy04QQiVyJPGU4KQkf78oZTgLVvbvbaMyclMVok9/w4kcnpn56SUvS0d74TGU9Icz0iJ4cwfDMTRaw3XNNLHlOGU6xc58x1jr/wC79Qvmv5oz/6owN9kOsSOSkt8453vKOcAL31rW+toGL3+usWwhmeJfnOYLeIpDV20XZ9+/VuMojxPmwqztNGt/eLT8uGtmd8uhqdj8ct48MV1rI9bXSTphamcPGERmFye794iF5x8iuN7Fa86GWLr2yFR7sVr3DsaHyc3J7Gh3k3NPLXbPFQnKdXXMv2aVrpRCPb84ph8tfsyB8ahXm75mZjAteFs5mC7wbvhuEEJxs5JHfIZgMKpy15n8jMkTHY8MG7Ik0ikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAonAwUJgkUJTVdfimPzYPkxubLk9rdwx3vt9OoX7MPGQLRr5sT19K17hU7bnW3PH9KJRePQTvq7h+30ogLgq8qd/+qfLtyr9KUzPlxMWL3vZy4pSE+UUp8FYIOTEpTf3vOc9y7VwKCC5dpSTdueff375thVxxx13XPl2JuF8+w9FlE6jEcf3+WTuda97DZdddlnBnytnuXbUXxcnOmHjsVCYaLAVNsfu0bd4+vCWW3krPvoJVxi2Nz58Kk60tfQKEw9P23Irjbf5HhpX/KF45vTly1/+8lX06173unJ1KicaOanId8uWXjd4i1vcYqAN/MiP/EhRiKIYrX0Plkzhz2mcCy+8sJzE4Rts11xzTbmKEKVfNL32TDukvZEew7XKXjlIPpw85UpDvvvpr4f1+XCtKuXh26HvtesOOSEJH74dqe9ZwotrnsGLBXa+OYufk8tT5gd+4AcGrmulL93udrcr/REFKgpZNhCQL6cmKSff/WRDgE7Ainevv4pmrr0k37k8W3RTY5fattLX2rWnUfyU3eNHnE8faWtxMY2nkVt8erQtGoWL1xxbaWSTxhvPg3D55ZbtwwmTUTg2Rn65Zft4wjCi9fbHYupxS+ha/D2P6PZpYjnkr9mej+fhw1tu8VN89BMejWhlEy/3lF2jVZhseEQ3p/I5Scl3gRVfiCp/xx9/fPmWbiVqURDXz7KBhNsBGE+59p5xkvfBbW5zm7J5CvlFZkrGYLPJbVOhKbjSTgQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUTgwCAwW6GphS3ZIIBbP+9vuUUrGzqM97fcnk403i6MHC/iYpro9+m3627xjuH4MSrfx3zL/jlt9qQnPWlAccTpuZ/4iZ/YcpWb54YyBCUJp+44iYViCWWWv/oNeq5oQynDN62g48QESi1OhhF39dVXl3z4XicnKTithvIIw3eyXvrSl5YrZlEGQftDP/RDpXzQnnnmmUUBWojtT1jLjx3D5I+2T1NzQx9N5OFpanGtMPj6uOifw7eWxvNc4va8orvmJwyDQpOTLyxG04b81bSczLn//e9f4jip+Na3vnU455xzPpZwwf/FF188/PiP/3j5vivfYWNR2uPjWfGNSJTeKCRR7HEVK4r6+N1V0ky15/POO2+44IILhpve9Kbl258nnnjiKisW6jkdSVk4odz7diffpn3MYx5TcEIZzwL6GWecUbCDId+C5JuchFFmvheKH0X/lHn2s59dThzRlygD37EEdwynUin7bW0xnjzoeyg1o+n110g7xz833zm8ejRzxq7YB+AXwzbtr+URw6J/SRlIWzM1HtDVwmthnmctfmnYnLw9jdyyyc+7Y/4l8vo/xSlM/v1gxzpG/36oQ6uML3zhC4dXvepV5XvZbHbgx7uiZhhT+Ub32WefXYueHcbGFvJkPOU9wPuJb21yVTfj5H3ve9/hzW9+c+E3JWPw7U9kF9KkSQQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEThYCNzgfe9738j1iVNGu+Ol/MCObu+Hn/xTvFlYk2m5FY/taVp51MJrYZ7vdt2+XJ5XK3zpyTd4ciKMxUNOaHJVJ4qNeHrL5y03px44jedPSpKeMnDS0l9pyck96Fg4jIaFQtLEOnHajNMUKGNk4I+iKtIS3noWS8OV15Qdy+Dpe3Giq5WrFgZ9K7zHS3E1O5bP+72btDU/Crea4frhY489tvqcaS9cS+ufZ41HK4xrX1kIpy2RP4pFTj1KcVdLx1WHlAml15SptWelAX9OYLbq3WrDSu9teLFAzqJ6xFZ08JtTZtHLZkxlwd5/X1Zx9FWun9X3KhVes3v9tUbfC1uSb49PLW7O2EVdoqn1p1pYTBf98flN+UnvadbJM5Zhjn9pPnPpfV1i3XpxvswxL/llQ9tyi4+PJyzmLbqlduTbS1/LsxbW4xHzi/5a2loetbBaWsLm5KG0nm/N7cNII7+XEcSrZ3OjA2N9bRzrpWvFfehDHzpsM8ub3vSm4UUvetFw1VVXbUlWkzHY4MUtElx7nyYROBII8I36OfOqI1G2zHNvIJBtZG88hyxFIrAXEMjxYC88hSzD0YRA9rmj6WlnXY80AtnfjvQTONj532hp9Vj0YlFNi1/eHXmJNoZ7v/gozPu9uxavsJo9J+9aunXDamUVr1pcLUz0c+2WwqaWHuWkX6jkSrfXv/715TREPH3RU7S2FDc6yeHzbpVvnborzZIFXZVFaeX3di/O0y1xw7NXzphnpI3xPu8Yt9Tvefn24MNx017WVWaS/tRTTx0OHTpUTvO++tWvLtcYn3LKKUQ1DYr5VvuKiWJ79vFg0mp70M3NA1p4cYKyZ5bw83zYjNAyKJrnml5/nctDdEvyVZp17Nbzie0Z3oTV+kgM65Uj8p3yi1ekU/hO2rX69vLzZaxh4uPFJ4ZN+ZXO2+QV0/n4lntJmhbtVD1r8ZSnxW8qvMaPND7c8/Dh6+Q7hV3k7+l9OWLePs67lb4WpriWvZ33RI1n7WQ+twZwFffll1++ZVNMHEeYMKH45JaINIlAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAIHD4HZCk0tdLGQJneEg3DFa8FNYZEWf+Tj/d7t08Zw/MrL04m/j1NaHxbTzPGLzxRti64VPsWPeJSPD3rQg+aQdmne+c53lm8NskDISbDdNNSfX+05CJtaHGVUfKu8pJuiUdq5dMo3lknpY7jolQ92jUbx4iN/za7RxLDoh08trMZ/02FXXHFFuaKYq4hZjOa0zCYVb5sub/LbeQTmjF2t9kp47EORVvExPNYsxkc/9L0w5RP5zvF7vj0+ouvR1PJTulqcD/N03i2aWpjiZEOj8s1xK92UPTdv8VEZ5Meew6NFV0vrw3x+CvdhLb6Eyyid/LJb4Uv5t/h5/t4NffSLx16wKRunM/mGce+UP+8bvo3MlflpEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBA4eArOvnOX6Sb+oJnfLBirFTcEWF9KiX+lb4VN5zS2H8tmu3Sunj9v0yYbtlnu30nNFnUzv2fTilH4d2z+DddL3ytWL6+WldHPKVqPphR2t7ayHd8btTQS4uno3Ta3f7Gb+S/PSOLE0nejn1LdH08u/F6f8d9OO5enVa6fKRRmORL6xPnPKIJo5V9hH/ulPBBKBjyOQVyt9HIt01RHINlLHJUMTgaMRgRwPjsannnU+kghknzuS6GfeRxsC2d+Otie+u/WdfUKTYmnBq1dEaLSQ6N21NDV+c8MiP6VT3j6+F+fp1nWLfy/9HJpe+oMU57GQu/fcfN1rdD6+5VY+rXiFi66XT49GceInu8cPmlY6pW/F18JrYeKTdiKwlxHg6uo0iUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQERgtkLzJje5SUyb/kRgLQRqCjcf1lP+ebq1Mg+JWvxieK1Mc2iUXaRV+Lp2jV8tbF3+mS4RSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBvYLAbIXmXilwlmP/I3Dsscfu/0pkDRKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBHYFgRvuSi6ZSSKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCayDw/yDpjZ2zJBKcAAAAAElFTkSuQmCC"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ce36b3ed8e7d"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pxhwaND4_IvS"
+ },
+ "source": [
+ "## Generate text embeddings for logs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gjy7jR0T_eAL"
+ },
+ "source": [
+ "### Create BigQuery Cloud resource connection"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hpQqsysu_zCr"
+ },
+ "source": [
+ "You need to create a Cloud resource connection to enable BigQuery Dataframes to interact with Vertex AI services"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dbWYeyEzii71"
+ },
+ "outputs": [],
+ "source": [
+ "CONN_NAME = \"bqdf-llm-embeddings\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5_ko_ngACnPd"
+ },
+ "outputs": [],
+ "source": [
+ "!bq mk --connection --location={LOCATION} --project_id={PROJECT_ID} \\\n",
+ " --connection_type=CLOUD_RESOURCE {CONN_NAME}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hS0kzr2gD0-h"
+ },
+ "source": [
+ "Retrieve the connection service account ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "IG3KaOQb_doE"
+ },
+ "outputs": [],
+ "source": [
+ "!bq show --connection {PROJECT_ID}.{LOCATION}.{CONN_NAME}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "D143kdK7Hjpf"
+ },
+ "source": [
+ "Copy the service account ID in the following parameter:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ryD1ufggF6Fj"
+ },
+ "outputs": [],
+ "source": [
+ "# Copy the value of `serviceAccountId` field in last cell's output\n",
+ "CONN_SA_ID = \"[bq-connection-service-account]\" # @param {type:\"string\"}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pa3lCS2hHqv-"
+ },
+ "source": [
+ "Give that service account access to Vertex AI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "h-SoTNgKHpvg"
+ },
+ "outputs": [],
+ "source": [
+ "!gcloud projects add-iam-policy-binding {PROJECT_ID} --member='serviceAccount:{CONN_SA_ID}' --role='roles/aiplatform.user' --condition=None --no-user-output-enabled"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8HJgiz91IBHz"
+ },
+ "source": [
+ "### Define the LLM model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "b32791b4d884"
+ },
+ "source": [
+ "Let's create a remote model in BigQuery using the above BigQuery connection. The remote endpoint is set as `textembeddings-gecko` LLM model in Vertex AI."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4ff3c629535e"
+ },
+ "outputs": [],
+ "source": [
+ "LLM_ENDPOINT = \"textembedding-gecko\"\n",
+ "MODEL_NAME = \"embedding_model\"\n",
+ "\n",
+ "sql = f\"\"\"\n",
+ "CREATE OR REPLACE MODEL `{PROJECT_ID}.{PROCESSED_DATASET}.{MODEL_NAME}`\n",
+ "REMOTE WITH CONNECTION `{PROJECT_ID}.{LOCATION}.{CONN_NAME}`\n",
+ "OPTIONS (ENDPOINT = '{LLM_ENDPOINT}');\n",
+ "\"\"\"\n",
+ "\n",
+ "# Start the query\n",
+ "query_job = bq.query(sql)\n",
+ "print(query_job.result()) # Wait for the job to complete.\n",
+ "print(f\"Created remote model {PROJECT_ID}.{PROCESSED_DATASET}.{MODEL_NAME}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AXw5FKWu9WoJ"
+ },
+ "source": [
+ "### Generate embedding for each log summary"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AUeODs3ejZpO"
+ },
+ "source": [
+ "The following code generates text embedding for each past action in your admin actions summary table. All records including their embeddings are saved into a new table with suffix `_embeddings`.\n",
+ "\n",
+ " CAUTION: This may take several minutes and incur Vertex AI costs depending on how many rows in your table."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "936b956c5a56"
+ },
+ "outputs": [],
+ "source": [
+ "TABLE_ID = f\"{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_embeddings\"\n",
+ "\n",
+ "job_config = bigquery.QueryJobConfig(\n",
+ " destination=TABLE_ID, write_disposition=\"WRITE_TRUNCATE\"\n",
+ ")\n",
+ "\n",
+ "sql = f\"\"\"\n",
+ "SELECT\n",
+ " * EXCEPT (ml_embed_text_status, statistics),\n",
+ "FROM ML.GENERATE_TEXT_EMBEDDING(\n",
+ " MODEL `{PROJECT_ID}.{PROCESSED_DATASET}.{MODEL_NAME}`,\n",
+ " TABLE `{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}`,\n",
+ " STRUCT(TRUE AS flatten_json_output)\n",
+ ")\n",
+ "\"\"\"\n",
+ "\n",
+ "# Start the query and save results in new table\n",
+ "query_job = bq.query(sql, job_config=job_config)\n",
+ "results = query_job.result() # Wait for the job to complete.\n",
+ "\n",
+ "print(f\"{results.total_rows} embedded admin actions loaded to table {TABLE_ID}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6ef250380afb"
+ },
+ "source": [
+ "Let's peak into some of the embedded admin actions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "f7117e41dc5e"
+ },
+ "outputs": [],
+ "source": [
+ "results.to_dataframe().head(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "da26c0b996bf"
+ },
+ "source": [
+ "## Search logs with BigQuery Vector Search"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qGvFqZd6q9pG"
+ },
+ "source": [
+ "### Create a vector index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "M1TENz6mrAuN"
+ },
+ "outputs": [],
+ "source": [
+ "sql = f\"\"\"\n",
+ "CREATE OR REPLACE VECTOR INDEX `my_vector_index`\n",
+ "ON `{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_embeddings`(text_embedding)\n",
+ "OPTIONS(distance_type='COSINE', index_type='IVF');\n",
+ "\"\"\"\n",
+ "\n",
+ "query_job = bq.query(sql)\n",
+ "print(query_job.result()) # Wait for the job to complete.\n",
+ "print(\n",
+ " f\"Created Vector index for {PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_embeddings\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ullFyiSloDu5"
+ },
+ "source": [
+ "Inspect vector index progress, and total storage (chargeable) from `INFORMATION_SCHEMA.VECTOR_INDEXES` view:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "M3Qi1hVsoHf7"
+ },
+ "outputs": [],
+ "source": [
+ "sql = f\"\"\"\n",
+ "SELECT\n",
+ " table_name,\n",
+ " index_name,\n",
+ " coverage_percentage,\n",
+ " unindexed_row_count,\n",
+ " total_logical_bytes,\n",
+ " total_storage_bytes\n",
+ "FROM `{PROJECT_ID}.{PROCESSED_DATASET}.INFORMATION_SCHEMA.VECTOR_INDEXES`\n",
+ "WHERE index_status = 'ACTIVE';\n",
+ "\"\"\"\n",
+ "query_job = bq.query(sql)\n",
+ "results = query_job.result() # Wait for the job to complete\n",
+ "results.to_dataframe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gR6FcfBEfZFt"
+ },
+ "source": [
+ "### Vector search across existing embeddings"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pRF7JGg_H2Xg"
+ },
+ "source": [
+ "Pick a couple of recent suspicious actions from the summary table with embeddings, and save into a new table of actions to investigate:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "782xJrFxh3fy"
+ },
+ "outputs": [],
+ "source": [
+ "TABLE_ID = f\"{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_test_actions\"\n",
+ "\n",
+ "job_config = bigquery.QueryJobConfig(\n",
+ " destination=TABLE_ID, write_disposition=\"WRITE_TRUNCATE\"\n",
+ ")\n",
+ "\n",
+ "# Search for any N destructive actions (containing 'delete') over the last week\n",
+ "sql = f\"\"\"\n",
+ "SELECT\n",
+ " *\n",
+ "FROM `{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_embeddings`\n",
+ "WHERE\n",
+ " day > DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY) AND\n",
+ " lower(action) LIKE \"%delete%\"\n",
+ "LIMIT 2\n",
+ "\"\"\"\n",
+ "\n",
+ "# Start the query and save results in new table\n",
+ "query_job = bq.query(sql, job_config=job_config)\n",
+ "results = query_job.result() # Wait for the job to complete.\n",
+ "\n",
+ "print(\n",
+ " f\"{results.total_rows} suspicious admin action records loaded to table {TABLE_ID}\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dS340NF0gSfa"
+ },
+ "source": [
+ "As part of your investigation, let's confirm whether each of these actions is an anomaly or not. We can do so using a semantic search across all past actions to find out if a similar activity has occurred in the past."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "XkQkZLnMxIaK"
+ },
+ "outputs": [],
+ "source": [
+ "sql = f\"\"\"\n",
+ "SELECT\n",
+ " query.content as suspicious_action,\n",
+ " base.content as past_similar_action,\n",
+ " distance,\n",
+ " base.day as day,\n",
+ " base.counter as counter,\n",
+ "FROM VECTOR_SEARCH(\n",
+ " TABLE `{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_embeddings`, 'text_embedding',\n",
+ " TABLE `{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_test_actions`,\n",
+ " top_k => 5\n",
+ ")\n",
+ "WHERE query.content != base.content -- remove exact dups\n",
+ "\"\"\"\n",
+ "\n",
+ "# Start the query and save results in an in-memory dataframe\n",
+ "query_job = bq.query(sql)\n",
+ "results = query_job.result() # Wait for the job to complete\n",
+ "df = results.to_dataframe()\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zgJ9IDVHp8vX"
+ },
+ "source": [
+ "#### Visualize results"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pNMlbSqu4PoO"
+ },
+ "source": [
+ "In a table:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5c29f5876f95"
+ },
+ "source": [
+ "First, create helper functions to:\n",
+ "- Reshape the dataframe to group by suspicious action and list associated similar actions,\n",
+ "- Infer likelihood of equivalency (`likelihood`) for each pair given nearest neighbor distance (`distance`),\n",
+ "- Apply custom table styling to highlight \"very similar\" actions (d < 0.01) that are deemed equivalent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "oaF4oLxI8v3h"
+ },
+ "outputs": [],
+ "source": [
+ "def similarity(d):\n",
+ " if d < 0.01:\n",
+ " return \"Very similar\"\n",
+ " elif d < 0.04:\n",
+ " return \"Similar\"\n",
+ " return \"Somewhat similar\"\n",
+ "\n",
+ "\n",
+ "def reshape(df):\n",
+ " df2 = df.copy().query(\"distance > 0.000000000001\")\n",
+ " df2[\"suspected\"] = True\n",
+ " df2[\"likelihood\"] = df2[\"distance\"].apply(similarity)\n",
+ " df2.sort_values(by=\"day\", inplace=True)\n",
+ "\n",
+ " pivoted = df2.pivot(\n",
+ " index=[\"suspicious_action\", \"past_similar_action\"],\n",
+ " columns=\"suspected\",\n",
+ " values=[\"day\", \"distance\", \"likelihood\"],\n",
+ " )\n",
+ " return pivoted\n",
+ "\n",
+ "\n",
+ "def highlight_similar_actions(styler):\n",
+ " styler.highlight_between(\n",
+ " left=0,\n",
+ " right=0.01,\n",
+ " axis=1,\n",
+ " subset=[\"distance\"],\n",
+ " props=\"color:white; font-weight:bold; background-color:darkblue;\",\n",
+ " )\n",
+ " return styler"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "f052e822aa1d"
+ },
+ "outputs": [],
+ "source": [
+ "reshape(df).style.pipe(highlight_similar_actions)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4c6a1269aeb2"
+ },
+ "source": [
+ "The results will be rendered in a table as follows:"
+ ]
+ },
+ {
+ "attachments": {
+ "7a699ddf-ba3c-436d-a938-33505258c2c9.png": {
+ "image/png": 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ePHhwV3XZX2mllbKC+uCDD86GBVjg6YHgnXLttddmrw2MHKyjgZcCCvrDDz88kaaJYADYYYcdssHlj3/8Y5psssnS448/nlZbbbVcZ/JAYU4c1q3gGmCI+de//pU9OsKbo0lZreLgHQIPJDxHWB8j2rD//vvn8jrFqa5FQj1ZkBsPFAwbLEDP2hXf+9738sLiZ511Vl47ham8MJ6wFgeGBjxxomzCkTJsm222yQuvd4qDB04T2W677bKRDkML3hZcTzw8vvOd76QFF1wwZ9EkDkaq5ZdfPt8zBx10UL4/+U2QZ9yjTepDnHGGWUJaL83eNBfjSUACEpCABCQgAQlIQAISkIAEJCABCUhAAhIYCwngfcHCyHgdzDzzzFm5jyKaBbTXXHPN3GI8BTB2MPXRbLPNllhg/LrrrstK8DAABJo999wzL6aNIYLFxHtL8ICgTBaexsOBUf8o4VlQGwMKi1qj+KeOdYKnAMYCvB3wjsATA4U6HisYLBZZZJFs2CnT0ma8WVhnAeMITNZff/0cBYU7HhZ4rTC9FYtT423AmhsISnXqRx5MbxQGkViQGsMMSnCE6bAY4Y8nAp4MGCswxAwatsh1CHlxXYiDypspnRZYYIHhjDERt7vbp556KhujWqVba621MisMVq2EONQJJtSVttFGFPoXXXRRvi7cN7POOmsOu+uuu7IBh/sOYw7hLO7NPmwvuOCCVkWlRRddNN+HneJgVMBYgREI/jHNFNcIjx3Kw5AHT4w7eCNh5OKe4LfA+fDcaRKHCnON7rvvvnwdmYqLdTvwFsJAghGrqWjYaErKeBKQgAQkIAEJSEACEpCABCQgAQlIQAISkMCAJIB3A1P7xMLWdRCIg1EgFl4mDmlQAjOFEwpdFMiMomd0PSP0Y7R7XX49DRs6dGg2Mow//shN1oMBgam1yIc2tBIU2rSbEfx1ZUYecS5G5sdxq3zrwsmL8krG1XjEQTDu9HeJ+w7+PeHV2+2HPfcXdWl1TzSJw/1CvJ4sbh9t0rARJNxKQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEhhFBHbcccfsvYD3Afu77757n1BWj6Lmmq0ERimBkTPbjdKqmbkEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggbGDANNBMdqdKaFYm6IvjMAfO8jaioFIQI+NgXjVbbMEJCABCUhAAhKQgAQkIAEJSEACEpCABCQwWgkwLRVTCzGNElNTKRKQQM8JaNjoOTtTSkACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkMJoJjDuay7M4CUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJNBjAho2eozOhBKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCYxuAho2Rjdxy5OABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSKDHBDRs9BidCSUgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEhjdBDRsjG7ilicBCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkECPCWjY6DE6E0pAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJDC6CWjYGN3ELU8CEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIEeE9Cw0WN0JpSABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSGB0E9CwMbqJW54EJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAI9JqBho8foTCgBCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkMDoJqBhY3QTtzwJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAR6TEDDRo/RmVACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIHRTUDDxugmbnkSkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQn0mICGjR6jM6EEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAKjm4CGjdFN3PIkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhLoMQENGz1GZ0IJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgARGNwENG6ObuOVJQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCTQYwLj9zilCSUgAQlIQAISkIAEJCABCUhAAhKQgAQkIIEBQ+DDDz9M4403Xhp//N5RKb733nvpwAMPTBNMMEH66U9/OmA42lAJSGDkCYzz2TAZ+WzMQQISkIAEJCABCUhAAhKQgAQkIAEJSEACEhhbCXz00Ufp//2//5fWWmuttPrqq/dKM1999dW0wgorpAknnDA98MADvZJndzI59thj0wsvvNAyyWabbZYWWWSRluc90ZrAf/7zn/Sb3/ymdYRhZ/baa680xRRTtI3jyVFL4NFHH02nnXZay0KmmWaatMcee7Q8PyZP9I55dUy2wLIlIAEJSEACEpCABCQgAQlIQAISkIAEJNCHCLz//vvpk08+SZNPPnkaZ5xxelwzlMMffPBBzmfccVvPKI/RYejQoS2VxB9//HE+P9lkk6VW+ZAHHhnEqavzP//5z3TmmWemWWedta1h49NPP014Ykw88cTZE6Ou8fDhPOOtqVurOtWl7c0wjCmPPfZYzvL555/P7Z9++unTF77whRy26qqr1hYHK65LO561CT8PbMKoXfr+cI423nrrrbmqXOcnn3wy788+++xdHj9wrBPuD2SSSSapvRfr0pRh3Mfkze9PaU/gzTff7LpOcH/xxRfz9eE6ITPNNFNtBlxTfud4W0000US1cdoFkp5nFr99ngU9EQ0bPaFmGglIQAISkIAEJCABCUhAAhKQgAQkIAEJVAjcc8896dJLL02vvPJKQrHLaOc111wzLbfccjnmVVddlfjDS2GDDTbIYWeffXYiHceEI3gRnHvuuemZZ57JCloU6EsvvXTaaKON8lRQnD/yyCPTdNNNl/O+4IILspJx3nnnTdtvv32adNJJcz4oLYcMGZIwSqDsZXQ83halwv6dd97JZUWcL37xi2nFFVfM9Q4DxxFHHJFuu+22bKy57LLLEkYAZJtttknzzz9/3keRTNtvv/32RJ4oKxdbbLG06aabZo8MIsHl1FNPzekpZ7XVVstpx9S/b3zjG7mulL/ffvtl3uutt15uP2ELLLAAm3TyySenRx55JG277bbZEHLTTTdlngsttFD65je/meP87Gc/69rS9tdffz394Q9/yMr5mGarCaOcyVjwD4X3Pvvsk1vCvbfbbrvl/Z133jlNO+20eR/DAwruX//61wkjHh4cf//739PDDz+cz2+88cb5Xm36u3nqqafShRdemJ577rlsMJtxxhnTJptskgYPHpzz89+IBOaaa66u63TvvfemQw89NPHbjGuHcQmJZw6GDn7T/I7DCIJHBwbP448/PnENyucCU8299dZbaffdd09cDwSD19VXX53w2OIZM/fcc6evfvWrXfdFjtTgn4aNBpCMIgEJSEACEpCABCQgAQlIQAISkIAEJCCBdgQwIhx88MHp2WefzQp7RiJfeeWV2WiBcm/QoEHp/vvvT6effnpW+odhAyX5+eefn1AwhmEDo8Xll1+elbooge+6665sWMDAwVRQb7/9ds5nqqmmyspFlMgoC2+44YY0wwwzJKZQwmOEfC666KK08MIL5/JvvvnmhPKSdDHF0oknnpjOOOOMhKcCCmAUjnfccUcehb3yyivnJjOSO4wZKKDffffdHI63RQiGmL/85S95lDxGmCeeeCIdddRR2RCD0hI55ZRTEuV96Utfyoaas846K5KPke1SSy3VVe4hhxyS9+ebb74RPFIwanDdMFbdeeedmS1eHaH0JSHnERTCGDYw7hCGMSkMG00Y5UzGgn+sxRJTloUHBs1afvnl0yyzzDJcC6+99tr09NNPZ08ZDBMYPLin//3vf+d4TX43KMkPOOCAfH9jMEM5f/HFF2eD1EEHHZSmnHLK4cr04L8EML7GdcLIhHD/Rth/Y6XsxcH9zPMIAyVGTAyrPGfiecDzjucQhtMweJ533nn5Om699dbZsHH33Xfn5+Rrr72W1lhjjfw7wXBInvxOwpga5bbbathoR8dzEpCABCQgAQlIQAISkIAEJCABCUhAAhJoQIBR4hgp5plnnmxYQOnHiH4UrqUCHOVhKBDJNo4jDOU5yl0MCHgP4DWw0kor5TUoYqQ7i3cTH2PKMsssk1DQY5hAOc98+Rg2GPXO1FEo1hlRzXk8KBg5z2hrDBuMwmYfJfzvfve7POqa+l9yySXZMyEMG1tuuWU2bDAaG+8TjpEYgY3i+s9//nM2ZrDFkMJo7l122SWHr7/++nkEPfWhrP333z+3C0V/f5CZZ54588ZbBSZ4zuAVg6EpJK5fHLMljOuJNGEUnjY5wQD6B1+mqjrnnHMSCnDuUwwjeAGEwLJkHMcRdsUVVyT+MGpw3zE9Emu3HHPMMTl88803j6zc9oAABhCYYpDAQPrd7343MV0V9zfXD6lekzIsRxj2j+cNhtPvf//7aauttspTumHAwlMHA2gYRCJ+u62GjXZ0PCcBCUhAAhKQgAQkIAEJSEACEpCABCQggQYEpp566jyCmZHHKPdR9jFtC9M6YVRoKoxYxriAZwUjpBndzh9GEv6QGNXMyOp11103K9jXWWedbNh46KGH8vRVpMezA6MKU1UhGEsQRsijhMT48dJLL+Xy8BbBy4RpZiinHOE+22yzda3fgXGF+pWCgQQPDQTjDtNWIXh0sIYF5xnZTVkYA5iei7pj8Dj88MNz3L78r1yrgSl1mDqnu9KEUU/y7W49+mL8WNMEryamByvvvab15b7jHkPxHt4zTOWG4v36669PGjaakqyPh6EJwxtry6yyyiqZJ4aj7grXAuG5cMIJJ+R9DLRMV8WUfBo2MhL/SUACEpCABCQgAQlIQAISkIAEJCABCUhg9BBgeqV99903r6Hx+OOPp1tuuSVPqYMi74c//GFi/YuQGGXOcTmdE8cYLfbee+9sjGDdCxa4xuCAcYG1CjBghDCCOrwGmHoHQfGIghdlIYJCEqVhCGsOEIbCN+KgWKYMBAMNXiDdEQwotIl8WVA4BO8G6oJEOAYN/pBQaOeDPvwv2DBdWCfjQ1zb6nVtwqgPIxilVQu+3HftjBrBlspU+ca9zLm43/FW+vKXv5yneSNcGTkCcZ3wimln1Ghynbh+cZ14Ns4xxxx5irzu1FCPje7QMq4EJCABCUhAAhKQgAQkIAEJSEACEpCABGoIYJBgmiYUdCjsmIKKKZ2Yd37xxRfPhg0MEQijyhGMC0xhVQpKQUYtMzoaYwD5YNw47rjjsjKxNGwwvREKcxS4xEMwdLA+QXiJUOaOO+6YDSbkzaLWeCBQX6aXQfAyoS4oLvEsYMolzrF4c0h4iRCvKnhxkJb8mQYrlNNvvPFGnr4Go08oRakz7aKelNWfJKYCq6szil48YuCLkQlvgVKaMCrjD8T9Vnyb/G7ifmd6pFjQHUMg91urfAci495ocyuecZ34DSBsmXquFK7Tv/71r/ysZIo9hDU6eHbElFZl/Hb7/zXFtovhOQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSKAtARb4Zu0IFs9FQbfoootm4wIKu/BaiEWTWUAX4wHzzaP8R4iHYPT41a9+lVh0FwU561XgKYBUR6l/9NFHeU2NBx98MK+nQRxGveM5wRbjBEYLDCPUgXiUSV0xNCy44IJ5nnymiDr77LPzeRYAx4jClFalsOA4wnQx/DFPPh4lCIYLjDcYPfBQoZ7Mm8+UQEyDhcKTOCid8SghnDoxrz4Sbc8HffgfXFtJrAcB32uuuSYv2k7caFsTRq3yHijhrfg2+d1g8GPtGe5x7j2MTNyLXA/We1F6j0Cn68Si7Ux3d/zxx3d5dsTvgOnnEDzaMDph1BgyZEg65ZRTup6TTWuqx0ZTUsaTgAQkIAEJSEACEpCABCQgAQlIQAISkEALAij+UdL97W9/y8YNPByeffbZPB8962AgLPLNwsisf3HYYYdlwwIGELw2MFIgGAGYqglPD9atwBMAbwyMENtvv32OE//C6+HAAw/MhgKU63hnICiDv/e976UTTzwxHXXUUdnYggEDYwnTWiGMvGbNCBZY5g+DDNNosYYGC5eXsvrqq2cPFAwalIehZNVVV83eJdSRsmjTWWedldvHSG08M1gMGiMKHiiMpGeBc+qEASim5yIvFJ/hFVKW21/2v/71r+e2YZCCIWxYDDvWNWnCqL+0dXTXs8nvBm+pHXbYISvJucfwSsIzAO+n8OYY3fUeaOVtsMEG2ajHMwJvGa4b6ww98sgjXc83ficYmjBsHHDAAfl3jyFqjTXWyOsBdYfZOMMeGv81B3cnlXElIAEJSEACEpCABCQgAQlIQAISkIAEJCCBLgKo2O677768KG7M948RAeX9Ioss0qW0v/nmm7Oij4SEM8oc7wg8MzBeIExjRF54W2DwYC0KjBFLL710Nnxg8Fh77bXzehgswIsHBp4QTIPFwr4xJQyGFhbyxnDCeerDGhF4V8Soa0ZNY2TACEMcppEaPHhw9jgpDQ3U48Ybb0zPP/98VlpSxnzzzZeVl9QZLw3qgVKf6bEwumBooc7sI3C5Zpg3AwYWDEHLL798zpNzW265ZRcjjke3XHrppdl7Ztlll80cy/JpE8pargGLrNcJxqfrrrsuG6FQpHMd8MqBIW1DmjCqy7u/h9FuPIIQpjeL+yHaxcLf3H8owlutYdLkd4OC/M4778yKc36PGO4WWGCBfD9HWW5bE2BqOO5hjJCsxVMVjHY8IzBgMP1dVTiHpwbPG64xvxUWCee3sdZaa3VNCYahA48twpk2b8YZZ8zPJLyauiMaNrpDy7gSkIAEJCABCUhAAhKQgAQkIAEJSEACEmhDAIUqo5WRWCS7Gh0jAfEYxd9OGO2PUph8Yo0K4odhA+MAilzikOckk0xSmx3n+KvmU0ZuEof4eFdQHgrJsk6RF+dpP23DaFMV2j106NC2damm6S/HtA3lLpxLo1C1/p0YVeN7/F8C3KNNfjdcA6TV7++/ufl/VBGAP8+HMJ7WlRPPAZ4RnZ6DdekJG/Hp0iqm4RKQgAQkIAEJSEACEpCABCQgAQlIQAISkEBbAii0OylUUfo1ERR+TZR+KAfrjAhRBuV1KrNJHPJDWdlOYck5Rny3Evi0O98qXX8Ip22tjEtl/TsxKuO6/38EOt3DEbPT7y/iuR01BJrw743ngIaNUXP9zFUCEpCABCQgAQlIQAISkIAEJCABCUhAAqOEAIuC77333h0NKKOkcDOVgAQk0AcIOBVVH7gIVkECEpCABCQgAQlIQAISkIAEJCABCUhAAk0JfPrpp4m1MRAWSVYkIAEJDDQCGjYG2hW3vRKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCBfkxg3H5cd6suAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpDAACOgYWOAXXCbKwEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhLozwQ0bPTnq2fdJSABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAIDjICGjQF2wW2uBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSKA/E9Cw0Z+vnnWXgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCQwwAho2BtgFt7kSkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggf5MQMNGf7561l0CEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkMMAIaNgYYBfc5kpAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAE+jMBDRv9+epZdwlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpDAACOgYWOAXXCbKwEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhLozwQ0bPTnq2fdJSABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAIDjICGjQF2wW2uBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSKA/E9Cw0Z+vnnWXgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCQwwAho2BtgFt7kSkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggf5MQMNGf7561l0CEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkMMAIaNgYYBfc5kpAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAE+jMBDRv9+epZdwlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpDAACMw/gBrr82VgAQkIAEJSGAMEjj11FPTY489ljbbbLO0yCKL9LgmN9xwQ7riiivSEksskTbZZJMe59MfE3700Ufps88+SxNOOGHb6u+///6JuHvttVeaYoop2sb1pAQkIAEJSEACEuhtAh9++GEab7zx0vjjjx7VU2/1fdr1Vw877LD02muvdaEa2T5tV0Z9YIf+JdeMPua447YeB/3888+nP//5z2maaaZJe+yxRx+ouVWQgAQGKoHWT6qBSsR2S0ACEpCABCQwygi8+OKL6cknn0zvvvvuSJXx5ptv5nxeffXVkcqnPybmQ/Kkk07qWPWnnnoqM/r44487xjWCBCQgAQlIQAIS6E0CDK7Yb7/90vXXX9+b2bbNq7f6Pu36q2+99VZ644030mWXXZZOO+209PTTT7etU386SR/95z//eXruuefaVhvjB3GfffbZtvE8KQEJSGBUExg9ZvNR3Qrzl4AEJCABCUhgBAKMusKAwCi5SSaZZITz3Ql4//330yeffJImn3zyNM4447RMOnTo0OxNMOmkk9bG+fKXv5xWX331NMccc9SeJ5APYT6YJptsspZlLbXUUmnGGWdM0047bct8/vOf/6QPPvgg17ndqLOWGXx+gna/9957meEEE0zQKXrL859++mmCY7t2cc0oi5FydR4ZnMOoMXjw4PStb32rZVmc2H333RNlcs3qJO4P2jTxxBPXRekK43rAodV17YrojgQkIAEJSEACfZ4AfS36bK08Opv0WTr1j/75z3+mM888M80666y57zcyUKLP0qlP26nvQ9+Qfix9HwZ+0IaJJppohKq1669uvfXWiXxeeOGF9O9//zv3tUbI4PMAGNMHrSujmoY+IkKfvV1fu5oujrmm/LXrq3XqY19zzTXp73//e6KNs88+e2Q9wvZLX/pS+v73v9+2XfClTbSnXf85ri3945Hpr49QSQMkIIEBQUDDxoC4zDZSAhKQgAQGEgE+EC699NJ0yy23pLfffjt/JMwyyyx5yqY555wzo7jqqqsSfyussELaYIMNctjZZ5+d7rnnnnxMOMIxeb3yyiv5ww2X8zXXXDMtt9xy+TxTQl1yySVp6aWXzh+IlEn5K664Ytp00027PswOOeSQ4dz2v/a1r6Wpp5465xH/3nnnnXTuuecmPoRRpH/xi1/M+VBefOCdd9556Y477ogkCQNH9cPr5ZdfzvkwkowPzy984QtpySWXTBtuuGE28jCa7/jjj8+GET6AkTvvvDOnmX/++dM222yTw/gYoz4PPvhg/vjH0IBBYfPNN2+pCMgJK/9oC/ncf//9OR8MDSuttFJaZ511hotJHRj9B2s+3LlWMOTjEbnooovS1VdfnWgfH34/+9nPcnh5DbneBxxwQA6Pf/PMM89wRhKuD/lw7fB8YYoI7o+NN944l0m6a6+9Nl1++eVZEUH9r7vuunx94fONb3yj7QdqlOtWAhKQgAQkIIG+QQAl/JFHHpmmm2663Ie74IIL8kCKeeedN22//fZdyvAmfZYm/aMjjjgi3XbbbdlwQN+GqYsQ+lj0JZpKkz5tp74PZR999NG5T0nfhz4WivvTTz89D4DZYYcd0nzzzZer1KS/Gn1pFPGthL4j/baXXnop99kw7tD3jT4d7fr1r3+d+6lMGYox4eGHH87Z0R9bddVVW2U9Qjj9QtLjOYKhhv41eSy88MJdcTv1sRkIRH2ij33sscfmaabIgP5mDIC5995701lnndWVL98FCy20UNcxO+R1/vnnp/vuuy8bNkjLffaVr3wlTTnllDnuL3/5y/xdseuuu2avF+5P+utbbLFFWnDBBYfLzwMJSEAC7Qho2GhHx3MSkIAEJCCBfkiAjw7m/0Vhvcwyy+SPCj7eWNuCDzYU9CjZCeNjIwwbN910U/4QmWuuubLBA6X3wQcfnN3MV1tttfxhduWVV2Zjx4EHHpgGDRqUnnjiiZwPBpBYM4MPOerAR0x8nPARjPfIrbfemhXzGEIWXXTR4eieeOKJ6YwzzkjTTz99NiCgfOcDi1FuK6+8co7LBzf5PP7449kAwgccyv8QRocdfvjh2dhC+SjsUcpjcEEYgcdHJm3nwzoMG9GOtddeu8uwMWTIkHTMMcekueeeO8EEt3ymgcJrojvzCV944YX5g5pRkVwPju+6665ctwUWWCDXi4/Rgw46KLdrlVVWyVMc/OUvf0kYYf73f/83XzOMNLj88zFMu2M6L8JDGGEZ4f/4xz/yR+N3v/vd4QwxMP3Tn/6UDSgYWJhOgev6yCOPpN/97nc57qOPPpoZoQzgQ5MRfigDbrzxxswtDF9RrlsJSEACEpCABPouAZT/9H2mmmqqxDRLjKCnT8a7fYYZZshrn1H7Jn2WJv0j+n1hzKCfEn2T7k6P2aRP26nv8/rrr+e2P/PMM9mwQz+VPi5KdpT0GHvCsNGkv9rpKsOafjJ944022ij3O1kXjjKZ5glhwA6DSOj/0c+COwNfuC54gXRHjjrqqJx+scUWy33o6PMfeuihOW/yatLH5hpF2XCo8zLh+hGPviP3DoOL9txzz+Gqy0ApBhDxjUGd6F/SVgww++yzT247hhgMILSX60O/nr4ng3swisWApuEy9kACEpBADQENGzVQDJKABCQgAQn0ZwKM/MfrgY8pRqTF6H+25YcCCnL+QuI4wlDkY+xgxD8LI/Lhx6gs1rWIqa0wnhCfDxFGYjFyi7mH+YC7+OKLuwwbjNBDOc6ILAwLUUaUTTgLNTKiDOU6I9soF28QDDJh2MDAwoLhf/vb39JDDz00Qj4YbPDq4CORDy2mqsIo8dvf/jZhKIiFxqOtUX4cl/VC2c9HOQxhyYcqYeTXHYERI+8wauA5QvsxOtx8880pDBt84KFggOFuu+2WPTv++Mc/Zq588DHCDy8YFqu8/fbb02yzzdZllInRb9SJj+Iw1sAfA0hVTjjhhGzY2mWXXfLoQQw1e++9d75m1GndddfNRjFYYFj5xS9+kcvnGC8O4mjYqFL1WAISkIAEJNB3CdAH5D2Ocp3+CB6vDCRhwAvrRNDPQ5r0WZr0j7bccsvch6IfgZcvxwjTiHZHmvRpO/V96PvSdhTteJ3ST6R/R9+H/iTeFSGd+qsRr90WQw59RfqxlEffDb6Uu++++3Z5vc4888x5nYpzzjkn9zUxAsCfPnBTwaiDsWq99dZLX/3qV3M/nL47RgfYM+ioSR+bQU/0HzEk0d8lr/CsKadGZdAQ8fjOYO0Uyi+FtPS3+YbAsMIAJwbl7LzzznlaMvgy6AjPY9Ky3WmnnbLR4zvf+U66ZthUWPRLuaaKBCQggSYENGw0oWQcCUhAAhKQQD8igCcFHyF8qDHtAB8QfEhiHGg3x221ibiyY8zAaIGnAh9gM800U1aw8zGMhKEEt3y8MBC8ADBsMFIthDohreb9xf0ehT8fgSjN+dDBEwNDSqm4Z1Qhf63W1sATgpFkGBD4Q1DUY9jgI4wPrqbCxxsf7/zxUcaoNP7CyNI0H6YCYAQcBho8Q2gnwodmCCPZENYfCcMJcxfzcRxTHXAtYgoDOMKqKiguIjyuTRmHj0g+eBG4xFoncKZ+KBAID+HeWWONNfJ1XnzxxbNhI+ofcdxKQAISkIAEJNC3CUSfAOU+73n6FkyJiWGDgSIMPqGP2KTP0qR/xACMWL+DPlv0TbpLqUmftlPfJ8qkfxV9LPq01BEeKNJDOvVXI167LYNrMBw98MAD2RM6PGvxcggvBdITD6FMDCBlfzefaPCP64rhCCMDxgwGtFAe2+ivNelj0+/mGsV3QgwwqlaBOvMXHjjV8xg08ELh2jOVLF4f5EUfFmMSXhn0LeN+xFOaew5PEAZNcS3oq2vYqJL1WAISaEVAw0YrMoZLQAISkIAE+ikBPqYYhYYHwN13352nYsJIgaHhxz/+cdeHJs1jBFtIdXoAlOiMLGMtDlzEmc6JDw0+nn74wx/mqaYibbleBmtjIOWHYsRrtWUEIcLHEh9XCHkyqrA7EoaL+JgmbXw40j4+KEPatZ04rKWBIYA5gvlYxMCBgQgjB9M7NRHS44nCFE54Z/AhHVLyxssFCXbsl3Mjc4yUdf5vSPf+U5/4GK1jFOciV+6B+PgMoxTKD0UCEpCABCQggf5HAEVzDJiIPgd9IxTheAs06bP0Rv+oKbnu9Gk75YkRgzYiMc0Sx/SNelMwaJx88sm5H4zRIYwFlFH2/aK/S1+3J0YN8mPwEZ649NOXX3753MePfluU1Z0+9sj2M6MfDutgTDujD1ntZ4YHD8YpBmXx7WA/kyurSEACTQlo2GhKyngSkIAEJCCBfkKAEU+M/F922WXzBw8jtk466aTsAs8aE3xkxccG7vEIH3WMsiolRoExqh/FO1NQMTUUCn5G7zNiLyTm5OWYjyykOx9pTGGFkJa68BGERwMLTnKORRCbCCPCEObxDYl9uGDkiLbzoRdlMe9yVfAMody11lortx2DBvP+wpKRdaVhoJo2jimbuYaHDh2afvOb32RvE0ayVYU2wr/kyHRVTEPFmhuxQHp8rPb0IxyuTFnAtaRu4bERjMITJ+oXH91x7FYCEpCABCQggf5LgLUTUD7Th6EvgGDoQPnetM/StH80sn0W6takT0u8JhL1aRJ3ZOLQV2YwENOLbrXVVrkvhwd1K2nlhdwqfhnOoCX6ynhbMHUqvFi4u5Tu9LGDUU8NHNEWDBTcaxg0MFTgrYLYzyyvjPsSkEBvENCw0RsUzUMCEpCABCTQhwiwXgNeFawLgZcAo6GYvxfX9PhgwQ0cwaODDyKMH/HRER8zTOvEYoN4DuBOjrcBYZyvrt3APL6s6cAIf/JDGDmGPPnkk13GjvBMYEomPEoYKcdoPObgRXnPgpYYAjg+99xzc56si4GBgQ9w0iFhhMEQQj4I0ylRJop7Firkw5J2nnnmmfk861zwgUUd+YDHaMDcyoxYhAMSbcdwwPQMfORvu+22eVFJ4qHoZ2QjI8uaCJz4uIM76ZgOK4woGG5oEx+BLODOxykfoyxWjtIBIwqj7TBshIRnDEYW2s3oNq4rRiyEPFhgHQnjB2EwwdA0ePDgPA/zKaeckrnAgzbSfvbLsnIm/pOABCQgAQlIYKwhgJKZNTWYVjP6R3gM0B9r0mehH9K0f0TfA6Efwh99GowosXZDE6hN+rSd+j5NyiFOk/5qkzjR14U1/Tn6owysYR05pv2Cd+nFEV4kTetZxsMDgv4eZTGI5o477sj9QuLQZ6YP2qSPHXnGNbvuuutyXxfjBFN2MRUt/UtYI3iIIIRFP5x4fCuwrgdr3nGf0S+nj8k3BlNu0Q9VJCABCfQmgWZf5b1ZonlJQAISkIAEJDBKCaC4R/HP6DCMGnxIosT/2te+1vVBgTGBRQpZJPCwww7LHz2LLrpoTsfHEcLHDR9MKP/5aEE5j0Id5Xe5DgNxMSCEgYSPGfJff/31OZU/6FgMEAlvhYsuuijnyYcd0x6g3GcxwmOOOSb/8WHERxMj0FgQEcG1P0a8hWcDZR188MH5/P77759Y64NFsZkCgHbxAU4+fMCzKCFCWRhKMCJQHh981Jf1JWIeZNqK9weLZVMWRg0+DmHCCDxGxDURRskxfzBrjqAIID1rh2BMgD0GJxZUxAjFlFdMWXXggQfmj1M+gLlmXM8QPghZw4QPV9qNgYUywrBx3HHHdRmRwvhEG+HM9f7JT36Stttuu+wNg/GLevABzkc1fGChSEACEpCABCQwdhLAsMDACvoa9KtY/2DHHXfMjW3SZ/n2t7/duH+E9zBKffoslEe/hEEm3TFsNOnTdur7NPX6pa6d+qtN4tDvY+00vDboY9FHpg6nn356Ovzww3P/DUNBb8gSSyyRB98wwAjG9DPp51HOkCFD8uAkmHfqY0dd6OMyAAeDEteNb4hdd901GzYw0kSfmz4xgvdzhLEwOJ7he+yxR144HA9nDCSsrYGXN/1d6qdIQAIS6E0CGjZ6k6Z5SUACEpCABPoAgRVXXDErsjFCoBxnVD8fq4ygiumh+FBknQw8GxDOoSRn4e5Y2wEjwQ9+8IO8qHTMz4tBg4+T6iKQfLShsMeIwAcdCnI+lhEW8Q5PgxxQ/CunOsJrgXpSb7wbNtxww2yIoW4IHh2bbbZZkXr4XT6WUNBjDCAuH1IYKjAaYBCIfFDyY/zgY5APMxaSpD2kYXoFBMPGFltskQYNG12GYYNRcBgz4IYRhPOMYuskpOEDD2NEjHrj+pAP+cbINY5Zt4OPT7w4uBYwZUH2mDqLsuC455575sUXMTrRlph+i/Ncn6hXlRVlIHiEsDA53iMYvLg/MCSVZVFHps6KqapIx6g7wuCkSEACEpCABCTQ/wjQb6B/gwcufT7e87z/EdZF6NRnadI/Cir0BVnbjf4YI/vpz8w333z5dPRVIm7dlj5Ukz5tp74PfRz6L3j20ucp+zc//elPu6YWbdJfbRIHrr/4xS+yAYk2kIa2xxSuscYJ01QRl35lVfDCoC/cTuhD4yXBenh4Z5AGvhiOGMQD4zCgdOpjRzkMJmKaMga+YIjCm5e+MMJ+tW+ZT3z+L9rHYCLuJbxb6KuyPh95lOvmwYeBVKWhg2tBv51vAUUCEpBAUwLjDJty4f9WDW2ayngSkIAEJCABCfR5AvFRhJKcD7k64aOCrkCr86ThfExvxIdKKXhb/PznP89eFXhTEI+P3nb5lenr9qkTf5RVGj7q4rYL4+OIkWat8oEPcaptquZJHsSjTbBESHvGGWdUo45wzAcg+fNxSB584IbAijzhFQJrjCgYaEqDRpyPLfHIj3hRpzjXdBtltbs/muZlPAlIQAISkIAE+i4BFN8MPEGRjIcqfRv6WmW/JGrftM9S1z+KPMot+REXowr9uu72ocirSZ+2LLMv7MfUpdFPgwESx+3qyNSsV199dbsoeXqr8EahT0i/LvqO8KK8an+8aR+beORR7ae2rVDlJOWHQatJmyvJPZSABCTQiIAeG40wGUkCEpCABCTQ/wjw8cjoqnbCR2YnQfHeSfkfecQHVRz3ZEudmtSrU958jFU/6Mo08GnSLj7G6j7I8PToJBgeELZV5UEdK1h3umbkR7y69JxrKk3Lapqf8SQgAQlIQAIS6B8EWvVtqH3TPku7PEoK5Bf9oQjvTh+KNE36tJF3X9lW+5h1fclWdaXP2IlRmX+1vwuvahhlNe1j90Y/vOn90YqB4RKQgASaENBjowkl40hAAhKQgAQkUEvgwQcfTNcMWz+Dj69YC6M2ooESkIAEJCABCUhAAmOEAFOK4mWLMnynnXYaI3WwUAlIQAISkEBvE9Cw0dtEzU8CEpCABCQwgAjgqo6bOaOyypFjAwiBTZWABCQgAQlIQAJ9mgDTCsW6Fqx5oEhAAhKQgATGBgIaNsaGq2gbJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAIDhMC4A6SdNlMCEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIGxgICGjbHgItoECUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkMBAIaBhY6BcadspAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEhgLCGjYGAsuok2QgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCQwUAho2BsqVtp0SkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggbGAgIaNseAi2gQJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQwEAhoGFjoFxp2ykBCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISGAsIjP/yyy+PBc2wCRKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCYytBKaffvqupo0/7rg6bXTRcEcCEpCABCQgAQlIQAISkIAEJCABCUhAAhKQQB8ksOd3r+6DtRo7qnTI4auPHQ0ZQK0YZ+jQoZ8NoPbaVAlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEAC/Y7A1JMc3u/q3F8q/PrQ7/aXqg7oek488cRd7dddowuFOxKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCfR1Aho2+voVsn4SkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQl0EdCw0YXCHQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABPo6AQ0bff0KWT8JSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAS6CIzfteeOBCQgAQlIQAISkIAEJCABCdQSGDp0aG34Z599Vhs+JgLHGWecbhVb1r1uP8JiS+blfncKq9atehx5tQrvVHbTejWNF/X54he/GLtuJSABCUhAAhKQgAT6EAENG33oYlgVCUhAAhKQgAQkIAEJSKBvEvj0009rK9ZdRXltJr0U2M4o0KqIsv6x32kbeUW8OK7bVutUHpf7Zdq68FZl1YXXhZX5D6T9t99+u6u5dVzjZN25pmGRx6jcVq9pedxqP+pTno+wptuepq2y63RMfapxyjq2OzfZZJOVUd2XgAQkIAEJDBgCGjYGzKW2oRKQgAQkIAEJSEACEpBATwnUGTZ6qvTsaR06pWun/GyXNtrR3W3kGeniuK4eZVi5T5rqceTTuu9Y0QAAQABJREFUaVstt3oc6VuFx/mxefvJJ5/U8q1j3iSsLs7o4Fe9hnEcW+rQar9avzJe9Vx3j8mrHZPyXLlPOdXjVmHtwjmnSEACEpCABAYqAQ0bA/XK224JSEACEpCABCQgAQlIoDGBqmGjN5WjZSWa5FunEI086s7VhZXlxH7dti4syopzcdxqG+XHlnjlft1xq7wIr5bb6bhdXmP7uep9W+VO+6th3T0OhqSrXos4N7LbMt+6/bowyizDm9ahJ2mqeZcMy33ilcflfplHXXhdWJnGfQlIQAISkMBAI6BhY6BdcdsrAQlIQAISkIAEJCCBMUDgww8/zKVONNFEvVb6O++8k77whS/0Sn6MbH/kkUdyXvPNN18ad9xxh8u3VBC3U3y2OzdchsMOUFR2J35d+p6Gka4sO/bZlvsRry4syo5zccy2qoQtj2M/tnXxy7yq+2V5rfZJU56r5jFQjrmvkZJ1tL0a1u64PFful3kF79jGueq2er4uvzJNNX4ct9qSNs5FPtXjCO/utl0+de0ow1rtU4fyXNSpaVjEr25HxTO3WkZ/Ou7N90V/ard1lYAEJDA2E9CwMTZfXdsmAQlIQAISkIAE+jCB9957L910003pySefTP/5z3/S9NNPn5Zddtk0aNCgkar1v/71r3TDDTekl156KU088cRpzjnnTCuttFLej4yfeOKJHOfNN99Mk08+eVp66aXTIossEqfTxx9/nG6++eb0z3/+M6EcmnrqqdPyyy+f84pIKMFvu+229Prrr+c85pprrrTCCiukCSecMKK03VLGo48+mqjLxhtvPFxcyrzmmmvSY489lhV0s802W1pjjTUaKfHvv//+dOedd6a33norTTHFFGnhhRdOSyyxxAiK+nblD1eZXjjgWh988MFZebf33nunSSeddKRzvfzyy9MVV1yR1llnnbTmmmuOdH4sDk4dkcMPPzxNMskkw+WJYaOdUrPdueEy6uWDTspPzrerW3mO/Thuut+pOWX9Yj+2pI392HbKj/NRx3K/LqyaVxmneq7V8WuvvZauuuqq9MILL+Tf9vzzz59WXnnlNN54442Q5Omnn07XXnttDt9uu+26fnMoVAl/5plncntnnHHGtMoqq6RppplmhDyo45AhQxLPpvjtjhBpWMDzzz+fXn311fysYoFznpsTTDDBCFHrpqKqsm53XJ6L/diWhZVh7Ti3OlemL/Nlv0wT+622ZfyI0y6/6rneOi7b02Sfcst4UY+mYRE/tqPimRt598dtb78v+iMD6ywBCUhgbCSgYWNsvKq2SQISkIAEJCABCfRxAijdjzrqqHTrrbeml19+OaE0xsBw3XXXpe233z4ts8wyPWrBU089lf74xz9mgwRKrY8++igbJQj/1re+lRVH7KPAfuihh7LiEcUfhpC99torKxJJd8opp6SLL744G0dQFlK/G2+8Mf3whz9Mc8wxR7r33nuz8hujxPjjj5+ViygpyXuHHXaoVVBFg5577rl09913JwwQGHVQQFUNGyeffHK68MILE0pVPAdgg5GDOlY9CSJftihPTzzxxISClXphMJp11lnTjjvumA0jxGlSPvF6U6jHJZdckuv+ve99r1eyxih0wQUX5OvRG4YN7hWMWQhGn6qEx0adsrQurJp+VB5XlZ/lcbnfqg5R/3Jb3a8ek1eEtco3wqMOsSU89qvbSNNuG+XGlrixH9tIXz2O8KbbP/3pT/k5hbGRZ8WXvvSlbDT88pe/PFwW3DN//vOf87OEE1//+tezIYTfd+Tx/vvv53ritcQz4Ec/+lGqLvyMUfLYY4/Nz4Wtt946GyXLgh5//PH8W2LLswkDywcffJCNJKuttlo24gZT0sV9G3mU5wgrjzvttzpfzYfjOu51YcQNKfOPsGqaOK7bRhhpW+1Hvj3ZlnlW09fVvQxrsk+eZbwooxpWPY545XZUPHPL/Pvbfm+/L/pb++vqy7Ph97//fT612267JQykigQkIIH+RkDDRn+7YtZXAhKQgAQkIAEJjAUEzjrrrHT22Wdno8M3v/nNrNzDyHHppZemd999N80zzzxpqqmmGq6lKBVR4FUVgWWkM844I4/iZ0T1RhttlPDeOPTQQ7NXxeabb57z/Pvf/54NAKuuumpaf/310x133JGoz0knnZQOPPDAbGzAOMAoa5TweGtg6MBosOiii6bvfOc7iXIwdOAtsPbaa6d///vf6ZBDDsmGCBSepGklp59+es4LTw8MPNVR288++2wuDw4/+MEPsqfJEUcckXlRXulZUpaB4vWYY45J99xzTzauLLjggtlQhPIfRSoeH0in8ss8O+1TJtJqeimUvRgMuGb77LNPVtrVeWuQD0pDPGyqgvIFhTDpSqMO7YHd4osvXk2S84o0dUpA7iOkrrwRMvs8IBTEVeVm9TjSR3hd+RGnt7ZlGeyXZcc+ZZXxouzyPPtxHPvtjskjzpf5VcuJ43JLOo4jfZwjn3K/zLfdfuQTW+KW+5G2XXhdufx+Zp555rTLLrvk5whGiuOPPz6tvvrq2SMq8r3yyivTZZddlp9fZRl4Xp177rnZILL77rtn4wh5kO8GG2yQlltuucgip+U3jIEU4RlQyl133ZWOO+64bNBYaqml0gwzzJANmDyr8C6jXuyTbwjPzZCyfd3Zj7ixJb/qfh3rpmF19Yuwah5xzLbcJ36r48grthEvjntrWzIhz/K41X41Xk+OSVOVds/cJs8/DCPx7CZvmPFMxZOtfA5Xy41j0nLvtXvGdorD+yPe+SW/KIMthsMmdWr1vmj1fokyOE8ZvH/qvLSapI93UR23JunxJqT8Vgyiru22df0nril9GYQBJZ0MG1wP3tWt+mC0pUld4QHL6DdEulb5tmuX5yQgAQlo2PAekIAEJCABCUhAAhIYrQRQEvztb3/LSpI999wzbbbZZvkDl+mSHnzwwTyNEkYDDBP7779/Hj2PEeL666/PCgamcWE0NFNXVYXpp5i+Zdttt80jl99+++08+hmvEKZtwViC8pEP+i233DKRL4YCjB14i6BEefHFF7PCkGmxtthii6yYwTPjvvvuy8pDymQamYUWWigtueSSifUYUEDigYKBg3LaGTZQ9Ky33npZOYTBoiqM5Ka+5P21r30tKy5RaJ533nnplltuaWnYoK0oPkiHkgKlJ+1FgYp3Skin8iNe3Ra+Rx99dJpyyimzohavE4QyMeiguOHaPvzww9ngADPWwPj2t7+dDS7EXXfddbMnDErY6aabLs0000z5mqN4GTx4cNdId+LiScN1wQCEYgcjBsYoRqrjlYIylzaSLspdbLHFcvlvvPFGmnbaaRMGrUGfT2+GhwyeI0zjg3Av4S0T53Ngi38oX0KqitHqMfEI43rUnYt8emNbLSOUX2V4hLWrS5xjW92vC4s2lttoT6TnuK4ehFXDSdOdepbllvUrw6v7HCNl/f4b8n//ow4RgiIPI8amm26an1kYKXge8BdGNX4XGBxQDFaNERhXMdSutdZaOQ/uI36TGFTJozRsnHPOOen222/PvwmmvirllVde6ZoiDQ+sBRZYID8P+W3wO2I6PdLj7cEzbZZZZsnt5HdVtqk7+xG3bhsMORf7ZX1jv+5cNSzyr4bX5RFx2Jb7xC3D4rgujwjrzW0dh2gX5bTar57ryTFpqoISGiM3wjOXZ2a751/5bOd5yP2J4YFnK/c/7x/ebzyHMZxhWKsTrgEDFJhmkjowFSRt55m71VZb5fx4vzHdGs9wfg88p/l9hNEexTceiw888EBWpGPA5p3J+bKevONo4zbbbJPjY4zhXcM7hX5GTC1IP6P6vqDu7d4v1Ovqq6/OXnwYC1G681ulnrznYMO7hPd1GD7ow2y44YZdhp92+Xcqn98tUy0y3SX58x7lN46BBm9MBkD89a9/zd6cTO8Y+WFg5dlA36Fd/4n4Rx55ZG4H+3i68i5kUEhVaCvXA9Y8D3nO4R3G9JxIp7oyaAVWPAcxfOBthmGDsujHXXTRRdkgy31AvwwvU0UCEpBAUwIaNpqSMp4EJCABCUhAAhKQQK8QYDQySjsULSgBYi2DeeedNysKUYCgwMewgVcHygrSsEUxg8IB5TyeE1XBoMFHNkYHBAUKyhWUEijA2cdwgaCwQVi/gvxQXqA8nHvuuROKEOITjqImlIzxwY3nBOEYMTAaMM0FeXOej/N2goKUtS+Yb7/OsMFUUQjKpRjRiEIAof2tBIUTHiYoDFD4INV6E9apfOK0EowFZ555ZubJmiK0mbVGmD4HIwGKH5Q5rEmAQoZryXX9xje+kb1iyBclDFNskQ8cOI9BCWMIyjAUYSg8UGqx1gXKX8JQEJMnSjIUMBh7uD+4f1DcRbko5LjeKOHimqLgQVmGVw3nMUZxP6EEQuH1m9/8plWTu8K570rh+oeU+xHGtlV4qeQs49ftt8qDuORTni+Po4wIi+O6MgiLfMot++VfxIuwOGYbwrmyrOpxq3gR3mlLfkjTbRk3J+zwL/Ito3GP0ibuK37j/A55JmHYID7eXtyneFjF1C6RHgUgnl6zzz57Vnii2MNbC4nnCfv8jvAMQzmKEjN+u5xDMIZQJh5oPN/+8Y9/ZMUr9zWGVBSCGHxRKqOQZY0PlK4oIcvrQV7lcd1+hDXZlrwifhlGeUhd2H/PtD9XjRP5lNu6/WqZESfya7qNNnWKX+YfaSKM47p98izP9eS4Vb1QQOOJiPDM5XnX7vlXPttZR4ZnMu82pufjmckzGyMaz3TuQwz5vB+rwj3HOX4jvLeYQhFjBOl5rvPexSuJAQaDhr3jMJLzvuB5z3OYd9cJJ5yQDSnUmWPuZ94Pv/3tb1NZT+5t3ucYGzCyM0UkvzXecQyS4B3D+x1DQPV90en9wrsBIz6/Q367GBLi98SgBxT1tAPOtIl3F0ZJfrv85jvl3+k8yn6M/7zHonyuA+Xx/qPvQfvob4RhA0aE0T/BsNGu/0T/ij5ICO9X1vWpE541GEx5n2Iw5VnHoAX4Y0TpVFf6R9SLPgJ/lMP9AQPqjzEYzhhw6FPsu+++ddUwTAISkEAtgXFrQw2UgAQkIAEJSEACEpDAKCLAByxKHhQAKDVK4QMe4SMbIR5/jFREaY/yjo9gFgKtE0aRonRAAYkHA4oJRhtusskmWfnHPoYPJIwGKJZiwW8+3JlDnzxQZiIoOFBa8AGOIQbBeICyhHowKpTR2hwzWjS8NRilSLr4I28ERUu13fnE5/9QKCFRP/bL+nEcRoDIG160mXrHSFpGRTLNBAYk5uoP6VR+xGu15XpQR0bfsuYHI2lRkIQSLa4ZjFC4MIUPEuGRL8coafCoQCHM6E8MUbHOBR4YKE8YHUw5O++8c2055FPmj7J4jz32yOuhcE0Y9YpiJUbVogTjXqJMhOsb91sOaPEv6s8WI0ccl/txjrDqX8Svi9P0XBmvzCfCq3WJOOU24nZ3C5YyTfU4zrUKj/OjchvtL7exT7nsN/kr60h7yt9i7MfvGSPs+eefn5WrjGyvCs8Rfpc8V3j2YLzAeIlRJEaoo8DlGcIzi/sco2opPLfwxlhhhRWyUQMFJ0pV6kLelB9KQn7/GPkQPNGatDcY1cUNbu22wSvSx3GkifzL44hbbst05X6kr9uWYdV9juOvzK87+5G+07bMM+JGGMflfpPjiBN5tTom31YSZXK+yfOP+Dzbv/rVr+bnI0Y2jP28s3fdddf8TOV9wv2HQaFOuOcwZvBb4PnP2lbc91EX7mWMdBj3MHiztgOKeu557n9+G7y3OE96nv0YRHif8qxGyIv2YED45S9/mZXrPNcJj3ufLczwrsT4HeWzRdq9X1CuY1TAIxADIQMd8ILE+BhTN2HkwUC+00475TZgTEBZj8GjU/6dznMNKJ/3Ft5ZMIAjx6zjw3sToS20MaTaxjiu6z/hBcPUeCG0j3hVoay//OUv2bDDPQAL2oohievRpK5RD97HTD3KWmX0k2gP1533Me3E6IthVpGABCTQHQJ6bHSHlnElIAEJSEACEpCABEaaQIxmrcuID2CkGofFoVEioCxHAYgCvJ0wihmjBiNBWYgcBUrd/NbVPKL8CEeZwuhTlCgoF8LYEecZsYjCEmUNyh9GiTISkpGsjHCkHiH77bdfNubEcU+2UT9GUKJcCsHIwDoAIYyqZR5/DCBMp1WncI24PdmioMDIw5Y6MU0Fo1lLQQlLvTDKMMq2TlA4YbjgujKilmmn4toyHRmC0gtlLSND8UoJD5+6/AhjxO6KK66YlWkoofAmQUGFAYa84IbnB0oUlEIolSmzqkyu5h/sY8v5VvvVtBG3el9HPPKJc2Wecb7TtkwfcevC4lyrbZQd2zIeYWV43XEZP/ZpV5muGh7nY8v5YBFx2UYe7batzkU+cT6OR2ZLXihiMTDwO0ABitI3hGcCx/HcIT4jqFnjhqmjUCqGgRNlKGtxMC0fxg5GQCPcn+SDEhnjIQpA2Jx66qn5vsUIwu/hgAMOyL8h0mDYY+Q4wn2OwjLaHVw5ru6XcVqdjzQ588//lXHL8NiPfDku9+N8uY3zUU4cR5w4Zttkv5oujqvbKK8a3uQ40kZ9Ig3h1bA4x5ZzkTaO2bYLa5WGdJ0E77h2z7+4F7mXeSazZV0p7iU8Epi2kfIZkICxA4+BOeaYY4RiuecQpibkOUwavIziHcDvgXQoxhkYwDuVAQ08n3mXYpAgb96teHqwz2+AfMgbYwvCbwslOWlgxhRNlMPzHWU7Rn/C8eark3bvF8qM6Rt5T7Afhkx4UJeVVlopb4lbGsd59yLt8u90nt87AxPoRzCYg2uDFw3GDsLxHumO1PWfyDum0iMv1uSCZVXwGMFDg4ETeMKw5flFWgZJdKeueHtEX4Q88OJhQAN9BTw5WOMMnjz34rlZrY/HEpCABKoENGxUiXgsAQlIQAISkIAEJDBKCcQ0SSgKUAyicAnBOIBU188Ij4XYMqKylTDCkFGNjHBmKgxG74cnCOkZ5UycGPVIPii3kVJpjrfAYYcdlj/q8cSINSRyxM//odxAEckH+o9+9KM8EhWFP4p41vpAwRKCQqCJoDhCyvrFfkz9gWIIZU9IMOUYZRBGDZRSKKgYCVkyjjQjs0VhhOILQUGBVNcWoE5xvXKEmn8oLzBqIBE3rm0oksIDBi54d3SSyId8Y2F26oaCipHtGDmYyof8QokYZbbLG2VWSJP9iBtbyirTER7ls1891yqM8Kq0yps841xsSVuWG3lVy68et4pHeKu4ca5VeVGncltN0ynviB/xWm2JFxJx4ji2Uc/q+fj9ES/2+S1iOMUgx28bI1o8R4jH9Gc8N5hGDWGUOiOfyRsDBZ4WCPmhsMQIykh30sXaCCh5UWpi1GXUO9PkYWTFqwkF4aBhU/kw6hmJez1YEkZclIRluzrtky6Eukb8CKtuy/Kq5ziusqwe16WJsGrcOGZb3S+Pq+XGuci33Haqf8RtxaGad8Qrw+vKqAuLstptI98op13c8lzT5x/5xrM9nqXx3uFcGO9aPTO55xDWuUBIE894jtnHWw7jPAYDDM8Ylbn38QZAsR3CwAKE3wgGkPIdSj4ow0Mw6KGc57fDuw/vCd77GATqpN37hQEBtI+6s4/AnTrwzuI3hSH/4osvzscYfsITNLi0y5/82p0nb8qAexiceIfTP2EaJ7xZ4jqQVyeJ6xjbqGOndJzn3Unb6TcFf55p8VxrUtcop6xz1CXurTgO755I41YCEpBAJwIaNjoR8rwEJCABCUhAAhKQQK8SGDRMGYfSGwU8I/2ZFx4FAkaNGGkcSr/uFoyy/8QTT8zTTKD0+P73v981wpO8QhnCugr8MUKRemCA4MM9lPRMOcFimowq3WGHHXIdw+DARz4jC/kAZ9FS2oMCZfLJJ89tYFojDBucQzkREgr6OG61jXn3GaFNWbCJubDjHKNTGQ0bEnVjVCzrUjASFsUn65BEmojbG1vaBWsUFVG3Th4P3S2Xqb9QkjEiFCUKChamD0ERAttWEsYk6hijZ2GPAoZpLlBOkZ7rBaeqQaZVvlyLUuI4tnGuesz1Q8rwurBI32RLXpFHmTdhZTl1eXWKU6Yv9yOvuvR1YRG/p9tqG8kn6hPbVnnH+diWaVulqcahTSE8KxB+87EGDqOPCed+QjAwlApDjlEmI9xneHBxr6G8ZRR5qexFqYdHEspOnj0x1z0KTMoLwyyKRYzBxAulLiOqkTBsED+UyhFWcsiRe+lfed1LXmRfLbPTcZm+VdwIL7fsV4+jeREex3XbapyyHmX87sSr5hFpy/AIK8vgfF14GafdPs9/PB3qRruP7POvVbkYHTDoxTuIe477l3U4MCrQnvJ3geGC3wGeF3hp8N7H4IfRebVhU0vx3EdgwToReOkheClguGgl/J7WXnvt/Jw/YdgaHbwH8DIJxXk1Xbv3SywQzjuEqazm+NwzhXc7fRfqxpRyGMl/+tOf5rWl8NoILw3Kapc/75925ymTa8jzA29CBnoweILfPmXTTwkDEu9h6kn8MLpW29rkuLzvymvK9aRMDK8xEAUjFN6Vg4b1fcLY066uPNMUCUhAAqOKgIaNUUXWfCUgAQlIQAISkIAEagkw8pA1H5g2AuUAH+woRfCQQHGApwOeED0RlCNM04KCB6UK0zYwjQLCBzgGE+aHRsmIAQSlCiM8UQxwDmU3ihc8HhgtirKeD35Gl4bgNcB89ixUiuIRAwPKBxSMKFEYVY2EkSTSNd0yxQNKDxSjKHwYpcm0GhgRmGsciVGcZZ7U88gjj8x1ZR+msQYH8UiLIqKTYOjB2wWvhiivmgZlCmWh3MU4haBE6k3hOjHt2Mknn5z5ch2HDBmS5wFvZ9hgoVn4cV+heOZ+W2yxxfLIehRsKGhQ/nLNwqhRKt46taFUALXaL/OIOCiHQggrjyO81TbyKM9Xw8gvwmK/3bbMq6f7kT/py/0yP8J7Ik3TRZspo9wvy2wVXsap7pOGOqAwZKFjFJr83nlGYVTAEMFULHPNNVdXUubdZ8o6BKMqRkUUyvxWGEFOXJ5NLKSLoIRmeqBIkwOH/WMxckadM+c9U8mRL4JikecKRliebSiPGTWOoFhE4Yg3B8ZYZLVhiuJoe/Asj6v7xIl2l1vyKtNHvGo4x3US5XCu3C/jtgqPOHG+3LJfPa6WEecjn+o22hXhEb8aHuc7bduli7zJoy5eeT7KIV5deJyPLe8r1jni/VH3fOS51xvPvyiPLe/NP/zhD/n+xAsJgxr3HM9q3sOUyW+m9MLACIJHJe9K7lN+IxglCMdwSP35ffH8Zlopfmek53dE36CdYPDn3c37GW6tpqEij3bvF6Z/Wm655fL7k8EWGFdQ9jMVFgMGeCbwW0R4X/Kujqkh413SLn+uT7vzGHTincVABZjQP4InHqJwpnz6BITRl6GOTGfXHaEdGEsxkpx33nnZiMN7v7ymPG+WXHLJ7FlzxBFH5H4VzyY8bfBIo4/Qqa7dqdOYiMs1wyjF8xODGANeuM5hGKZO3IOs18X15t7lOjCNVkiTPLiGcKPfQd70mcopRCMvtxKQQPcIaNjoHi9jS0ACEpCABCQgAQn0AoGvfOUr+eMOpRzGDZT3fFzzkcyc8D01CvDhyfzfCIo/lIkhKBAxXjCPPSMIUVig+OaDlQ9zPtJRhjDK//rrr8/KJD5yQ3Ef+TDVFHExOqBIYF0NRlDjFcAi5WHYiPjd3fJRTf4nDBt1isKC0dwoL5jDv92IVZRfKCeoM4qi0qhBHfiAHtTAsAEPjAkYcFoZNlCIMAUP1w/jEPxQxvSmsIAtCjIUdozWRSGMEox7p53Qfu4p2gEHuNF2ri3XHwUZ669g8OCawRajFGtztJOqcrE8Lvdb5UEc6hBSPY7wnm7r8quGxXFsq2URXgr1jbCoe5k29qtxyjxiP9J32hI/4sR+mX9dmXE+yuqNbdQBwwJrXqCM5V7BwMFCvtw7/GHcCOGeC+FewwDB7yRGLKOsZY2NEO5nnhmlgoxzsYAu9y2/ebyWUGJiTOX+5xnEs+eggw7KxhEMJjyPeFYw/R5z6iM86/g9IMEtH1T+xblW20jPNrhEFpEmjqtbzoeU+xHWaVumif3YRtrqcafwON9qW21jxGsVzvl25yJ9ua2rc10e1XgRpxrOe4vnMefrDBsop9s9/+rWVyjrW7dPHTAk885lvQuEe453Iu9Xfg/8RqLObDmmHhipUZSjJEZJzz3NMxijNEYS3kHnnntuXnMDox3GDuJi/GglGNpRzKM8nmOYlwVTuLWSdu8XpuJiUfN4j4Y3Fr/n9ddfP2fJ4AyeCbxzUYrHYAPaTB3b5U8G7c7zO6Z81vfimsKSZwfvWcLhQB1Z84Ly6SegKCe8O8L1YLpKBjJgFKNcnjflNcWIsssuu2SDEWXRN6KNeKXSP2hS1+7UaUzExdCMsYYpxeCKIRtvXaYXDaMWA3EwanBPscVYTT+IexnplAf9GO4XvE9jIA/GMDx+SgPKmGi/ZUqgvxPQsNHfr6D1l4AEJCABCUhAAv2QAMoLFM6MekOpzGhIvCNQypXK+5///Od5VCjxERQXv/rVr/LHdF2zGWkZSr3q+ZgTGkXkd7/73ax8QaHCiEWMEYyKRFCg/OIXv6gm7zrmQxdFJN4dGE6YdoM8UAyheED50USiLdX4KBtYz4P8GB2OkoR9FAnlGiDVMki3zz77VIO7jmFbSqvyGWEbbSrjl/t8/LNwcRiOUAiEQYepxRiJyLUNgRnXDUH5Ulc2ShKUQ5xDuE4ssMzISYwa5ME1hAOCIoxyq+3i2qB8oB0YyDhGAUG+KMyYM53RwVEO8VCGVeuYC+kj/7i2VUVmu6oRlzQh1WPCI7+IF8eRptyW5dftt8sjzpFf7LfalnFald8unHzr2tEqvMyr3I/6EYbClt81ylvue+7LUGaVadjnmRD3OXER4kZYDij+xfQ9RVDexQjLczAUs0xFw/MJxSLPN+rEPcuzgbqhaET5Svkoh8MwPGiYITPWCKiWMTLHdfdTk/y6ex3qrmWU0+5cxGm3La9xxKsL41xdeF1Y5FNu6+K1qntdeDV9XRzK47mIkjSeh9XnGfdau+cf7xjuU5T5IUzthPEt3p2EM5UaBj7CeK5yzAh00iPcc1tttVW+TxnFzr2433775XMYk3n+h5Eegx3tQbHL/R73LVNUcc8/PWzgAQMeqDuGCvLmuFrPnPmwf9SHdzuGcPIqFf3V90Wn9wu/N/Lid8X7h/c0LGLxct7RMaVm1I9687vnmnXKv9N5nhuUiRcBAycoAwNmlE85PAd4z9KPoY/ENYBZvNua9J8YSIJ3K9eUvOuu6TLLLJO9ynjfw5/3NGm4HkinuvIO5prRtwph8Abv3vDCIc94TlKH0SUYjP76179mgxxGHgw13Nv0ATE6YxDE0+KSSy5Jv/71r/Nzl/sdozJeSbS9SR6nnXZavjcYMMK1pG+HVx8GD8pVJCCBnhMYZ5j1+/+GUPQ8H1NKQAISkIAEJCABCUigRwRQfvBXKiF6lFE3EzGNBqOZ+ZANJWQ3s8hKGfJAATEqPsYx+FDPdgaN7ta5U/yDDz44ezzgMVL1YkBJyohc6sOoWKakQjEFw1ElTa8TSigWc/7hD3+Ydhg2xQnXhXuqqhhEGcz9VjUodao/CqOQUrlY7pfnq+Vyrgwr9yNdu21dOdX4ZZ51+xEW22r68jjKa7eNc6Qr98t82C/Li/3qthqvmgfHUUa5ZT/+qnEiXhnOfjuJekUcjGfkw/2E0ndU3utRZt2WhcRRVK42bKofFG4osOMeRpHLSHgUZuWzjFH8KEWrbSL/MqxuP8LYNtmvq3OENb0OxKOsMn41jzhXbst94sdxdT/yivbEMdtqWPW4Lk6kr4sb59pty3q2i1d3riwzpkPj2VYaJurS9fT5V5cXYbSBv3j/cc8dcsgh2VCBwQJlOIpjFO9nn312vk8jL95vpG317qeuxOE+79SuyLO7207vF+qHxwgeU/xVJfoQ0f7q+U75dzrfiQH1g1ErhtX61B1TBu/yyIM8+au2ifcmgwKIVz1Hvp3qWlf2mA7DwMD9uvfee3cZXrjeTAP4+9//Pg+gwDsDozJTksY9gOcd67DhwYLRu1MeeB+xVlIYpmg305FhxN5jjz26jWHqSQ7vdhoTNCPw+tDvNotorDFKIJ5XVEKPjTF6KSxcAhKQgAQkIAEJSABFXKmMG11E+DBnVOvICMqlUWl0CMXlyNSxu2kZ9c2I2xgt3i59fOS3izOy53pyndpdFxRko0pJNrJtpd4olOqk3bmIT1riIeV+9XyUEXHjfGzjfBy32kb6urLq0kT8Tueq8aI+hMd+NY+6c2VYuV9NG8fEqRPCR+XvvK7MahijplGAXXDBBYl59xktzoc9ikZGlOOthddbVapt6nRcTd/bx5Rfdw0jLLZNym0Styyv2nbKqIZ1Oq5LU9a1mp5zreoZcVudL/Ot7telafJc6+3nH22IdkQdURYzbRqejKxlhDcf02NhfCul0/uNuo7sO7osr26/0/uFtrWrQ6lc60n+ncrvxID6dapDXb3KsOo9UXdNid+pr9aprmWZfWWfZyZTbeEhFIKXDgbs8H5iuk28Ycr+Dh43GI3x3miSBx5D8ME4hCGKgSEYUMJjJcp2KwEJdJ+Aho3uMzOFBCQgAQlIQAISkIAExloCzCGOAqNO6cTH/49//OM+aRhgigemhWDqiFEpdQrFsrw4z7aq8CvjVfcjbmw5H3lFXM5Vw+JcbMtyYz/SRPooI8IjbbttmbZVvp3Sx/kov7rlfIRF3Agr6xp1iTjV4wjvzrZVud3JY1TGRdG20UYb5fViWD+IKfxQjLEAOUZIvDWqwu8VJTOLjKOAQ9GMQo5Rw00MNUwVwyhlFHKsKdAf5Ywzzsij0ZnuxbnsR/0V5J5jeqq6e27Ul24JEugeAYxWMY0mKTESH3PMMdmbYo5h058h4ZWbDz7/F8YkzmGw65RHePrhEYuHB78PptmM/grTf4YQt50xLeK5lYAE/ktAw4Z3ggQkIAEJSEACEpCABCTQRQDFZythZCLeHH1RVlpppaz0Rwk7KmTQ5/OJj4q8zVMCTQig8MJ4h2HirbfeyiN/CcO4EYqzMp+Iz/oLPZnSpy//3st2tttnkWZkTHgFtqvX2HpuZO+5sZWL7er7BFg/hAXbMQLvtddew3loVGsfhvDYxvlOedCPwAiNp91zzz2XPZo4/u1vf5unHSUfntesF6dIQALNCGjYaMbJWBKQgAQkIAEJSEACEhjwBPiIL6dj6EtAVFz2pathXUYlAX6DrFnQVHo6RUxf/r03bfuYfl6hvAwpvY6a7JOujBf5tAqvxq0et0pX5ltV1FbzKI9jn22r/cg7zscx22pZ5XHst9qW6SNO5F09jvByW61PeVzut0tTLac8rtuPqY3KPN3vGwQwNPzlL39JDzzwQJ7yr/TAwHsVz4xS8JZDwnOD/XZ5cB5hTQ3WM8GTjukD8Yj73ve+l9f0iPvOQRT/ZeV/CTQloGGjKSnjSUACEpCABCQgAQlIoB8TYJFSPsaZjkbpPwQYAcq0QczFrrQmwALajLRlLvTqXP51qZjKiamWmJ6I9SqU0UeAuenrps4afTUYvSWFwjJKjePYEt5qv3qumkccx7bMJ8LKLQp34pSK9/J8dT/iV8OjnNhyvtyvxh/Z46hzbMv8qmHV4zJu7FfbVR6X+xG/uiVOE4l4sW2SZkzFGajvGvpGp556arrxxhvTzjvvnL3iymsw88wzp1tuuSWvjxEDKDBY8gxjTTKkXR7cj8cee2xaeeWV0wILLJC96+add948rSBTUiFMpalIYGwi8Pbbb6fnn38+zT///I3fNz1tv4aNnpIznQQkIAEJSEACEpCABPoRgeOOOy49/fTT6Vvf+laaa665xmjNX3vttXT//fenGWecMQ0ePHiM1qUvF/7YY4/lUaRcL66b0prAueeem+677748Vdraa6/dOuLnZ6688sp01VVXJeL21enVOjaiH0a4/PLL0xVXXJHnsF9zzTX7YQu6V2WUmiGd9svzpCmPy/26/CKs3FbThHI9tmXcdvvEj7zK/XZpeusc5VbrG2Gxpaxyv2nZ7dpSd65aj1blNIlHfVGO88xC4c10ciHvv/9+uu6669KTTz6ZFeZ4Z62wwgp5fZyI88gjj6TbbrstG2dRrvOOIE45JR1xbr311sT6DSjhF1lkkTyNXV39Buq7hutwzjnnpCFDhqQtttgiLb744tlADmc4wW255ZZLxx9/fH5u8czCIM5zbJlllskerJ3y4PpwHTC+M8UVU/w98cQT+S+myovrOrq2U80yadpko7nT7LNOMczT5JN0z30vp8svfCp98tGnLasw4WTjp/U3mSstNP80eaDFY0++mS44/7H03msfdqWZZ7Fp07prDkrTTjNJevPtD9N1Nz6f7rr+ha7zsTPehOOmBRafbtj6UNOms058MILzdpKpJkjrrT9XmnfuqYat7zROeubZd9I/Ln4ivfnC+8PF42CSqSZMO2y/cI53zpBH0wuPvZ3jkP9q6w5KSywyQ5p4kvHSy6+8n664+pn0xH2vjZDHqA4488wzsxFrxRVXHKGoe+65J/eF6YOMae9C6sm6MksvvfQI9WwSwHPrlFNOSaxnNcsss6SHH344r1fDt0fdM6dJnk3jaNhoSsp4EpCABCQgAQlIQAIS6CcETj755DxSasMNN8zKDKp90003pbvvvjt95StfGSOGDeb45yPu3nvvTShc+MjfbrvtxlrDRt016O7twyKjF154YVag9JZhozfq1d12jI74fETffPPNadVVV21UHCNliV9OOdIuISNyf//73+cou+2223CKyHbpPDc8gUcffTRdcMEFWYHSG4aN/nJdUH6GxH5sCW+yX41XzS+Oe7qtKp/KOnGu3XFPy+xOOsqvq2M1rDt5lu0q98mjetwu37o6RFhsSY+R4c4778wGjccffzy/BzE4hGHj448/TkcffXQ2bBAXQwXtxpvgf/7nf9Lss8+e36GHH354VozjQUAaPM94pu2www653g899FA65JBDEmWQB9MkoWz89re/nVZZZZURmjIq3jUjFNIHA/Aew2hB+zE+0EcKYZqp/fbbL484p9+E0vaGG27Ihg/6M7vuumuO2iQPFNcojllLA69Zri3eG+utt14UN9q2GAN+9b8rp9VXnXWYN+gEabxxx0nPv/BOmnLqSdIZxz/Qsh67/WDJtNXX508zTD/JMIPbZ+nttz9KCyw8Tfrl3telz4Ydz7nINGn/366SFl9k+vTpsHt2gvHHS2tvMEf66b7XpXtuejHnO+t8U6YVlp85Lb3kDGmeBaZJU0wx0XCGjXHHHyft/aNl0yYbz52mnHKiYeWwePvHaekVZkw//p+r09A3PhquflvvuFD6wV5LpXGHPZ9uf+jlLsPGrsPquvkWg9PMM00+7Pfxaf5NrLneoLTvPtemJ+8fvcYN+ryXXXZZWn755UfwvD3jjDPygvVbb731cO0aEwcMfIrnUE/K5zlF336NNdbIyV999dXcxyrfGz3Jt0kaDRtNKBlHAhKQgAQkIAEJSKBfEuCDn/mMmconJOZKLudGjnOx5aOVzni7OBEXxRpTPE066aQjKF0iDltGM6FgiKkMynPkwTQQ5NFq8evutIXR6Hyg8+GM0gTZaaed0ksvvZTmnHPOsug8InRk6z9chi0O+Gg69NBDs8GFhY9RtMQ81S2SdAXDB37wqZuSiWvF+U5TNlEm90NMx9UkHfcCwjzb3ZG6axDpm1xv4qJ0//nPf549WyJtuW13T9HOTz75ZIR7uF29mvw2yJN4wbCsD/vcx9znrUYfcp5zxKlKk+tRpuE3wTXlvmglTVmX6evqSN1QQiDbb7/9CAoAmFBWu7qUZRCf33orTmXcuv1O5QXLVteJPJvUodP17FSPat1ReqCEZWR0Vagz5bX6nfO84PlZMmtyXSin3bO83fO+07On0/1F/UqpHnOuDGuyH/mVcSOs1bZUrkecurA4xzbOtyuHOHG+3C/zKfOqho+u42hLlBd1jrrFccQrj2M/0la3/5+9O4HfrZr6AH4ykwYklHIbKMqYkEKZI/RmyKwMFQopQ4YGb6ZSCpVEmSrezFMZi8xjlDKmUBFShnoT3vf57tu69t2d8zzP/3//93aHtT6f59nz2vv89j77nLPW3mtHmeDV50YZOzHe8573lEOjrfzH2zwWZOcEATh64QtfWHYMfPCDHyy7yzbddNNu1113LfMQRcdDH/rQsuOMWT1KDDshH/OYxxRTfHYgyEOJ4VwHSlw7E9xbfYqNRXnWRNuXRddc8sxnPrO36THPeK/Yeeedu+985zsFY88uO2Tudre7lXLT8KBwt/vDgg5znLLez9ZZZ53euhdn5Dbbzuue8sQ7duef/5fuja85vbv52jfpDtx/q26vvTbvPv3JX3R/vXjh80S0Zd073qzb/fl3L4qIV+93enflX6/qXrHffbtn7XTn7qMf/nn3g69e2D3jWXfuHvmw9btPnnJu97ETftJt8aB1uueM0vfYc7PuWV//VLmkXZ939+4RD1+vW3ONm3Q3v9mNut+PdlLUtPbtV+t2efZdu5uufP3uZft8ufvfv13VHfDa+3VPfdKdug9+aLR44eRfL8hud8gLn3+Pbp21VylxN7rRfPH2hne9RfeCF27Wrb7aDbv9Dvhqd8lFl3e7jRQd2vbN71zUHXrmNxbwWBIeO3sOOOCAonisd0sT/JsPLPCp3/vNB55JQ89u72DSp33XaK9x6LlvnrCbqCXvkt6z6vbgYd6r34s9Y5/61KcWc6Atjzqs7Z6b9XdZnW5OjPf9em6t87T+VGy0iGQ4EUgEEoFEIBFIBBKBRGCZRoAQwOptgjNmHnxMEhAw63DKKacUwboLZAbJi/y8efNKnFV7t7zlLbu11lqrrKj08cBM0pOe9KReIax0JlWYg/Chqh5buAnuCN+s/lMfRQLb/z5ovcg/7GEPW2DD2cv9qaeeWoQOVv35cGD24MEPfnAR4M/mWg4++OByrT5GmOdh59aH049//OOOAISig2mLSe23MpEQ3Mc3RUSs/Nxqq62KQKUdJO9973vLhxvTPkxioCOPPLKshNxhhx0KJoQndpF86EMfKn3U8ugLa4ePP23wIadft9122wXCTQoc7fSR6MOK7erttttu9AG+alHkWP26+uqrl37+7ne/WxQb+nWbbbbpPvaxjxVM8LV9/p73vOdCZZzXwGQW2myzzYrQiGLl9NNPXyBoClMSbHTDSNu+/OXRqsDReGv7YFJ/t9fvmvTbn//852KuZJo+ufjii4tZDYok9elr40m/D42NcfeGNr32ta8twjfCGdduvLt/3BtrrrlmaTaB3Gc+85nuoosuKn3jA56QLT6U3YvuF+1zfxhX22+//YKP89NOO60I4twH+pFdZvfn0Me7++uzn/1sUY5ZxWyc1zRTrJUdaqP711jWn+iwww4rbWNCxvXagQBvH+TrrrtuuW7zSB+xOS0/MzTGEvvtxisBl3EDB4cMxwpOh8sat8affOPqizlHeWPnwgsv7Pbff/9rCPUpCE466aRyD+oL95SVlgQOL3vZy0r+Sf05rh191x1xcDrnnHPKuHAfxn1z73vfu4x148NcaoX0JptsUorJby6VRgAEY1ho+1C/jBvT+mCa+X7c3DOb8VULyGt/YFPH1f5ID3dcWuSp3VY41Ibl7YuLeqTxD7lRvi+/tIjn76O27jbcVybi+vLWcbU/ynDr+Lg28dHWSK/TpA9R5Jce/taNNIJOz6n99ttvIaWGdGN/jz32KO8CnpWE5u4ZuzzswkAUE5Qc5oSNN964vF8cddRR5VnmvnfGkHsGmaPw8Qyk2Ij4klj9zeZZUxVfZr3eyR7/+MdPbL952pxu7tQnofRQcBoeBMBW7JvnCInHKVonNmYRMzxiZObpxiMlwCdOPrc7/j1ndTde7QbdM562aXfHjW7e3eEua3Tf+8Jvr1HDve43uv7b3LQ7/RsXdMe9/Ufdv678d3fvB9y2e8aTN+ke8OB1i2LjsY+5/Wjsd9073vXD7ksf/mX3re9e1D3r6Zt2j3joet0NV7neSBnyz+6K0e6Lkz7809GOjq7bb58trlHPbW63ajFjdeHv/ta95x1ndv8cmcl62rPu0m11n7W6De9w8wWKjevf6Lrd3qOdHWusMTJ5ddmVRYkRzNZab9XugtEOlFO/8pvu2KN+2F35t392G951je7em9267CaJfEvK9c7MHJn31FqxYRc1BUHs2qHQ8A7leW+MGG/ene20Qt7DvDeZB7w7eNc++eSTRwqpvRa8J5knxNnd1Y4xC5t8E/gWUV5bPGfNDYhpTvy993r2ebf2XoKndx/vWe4V73HelZB5zG4kdbkWCtWnP/3pC86eKZmu/vMe+/GPf7zzbmFe9Q7pHTCUe94FPFftZrrgggu6l770pVOdl4Z9KjZqpNOfCCQCiUAikAgkAolAIrDMI+CF3As6hQMBlhdvq+2saPTBQBDgo8FHBoHB6173urIKzypJwnD5vaBTjvjwoJggHGjJBwjhGGWBF34fC/gTDhLIMrmCp4OJCX8JR88///wiQDz00ENLvDYQvBNABg8fE7aD+wCezbUQmGoDIqzQPkQwSPBOOE2gOKn9rl/7fVRpP8Gx8gT9hOQ+fmpiykF+dVNsyM/skZVXT3nKU0p+5pR8zBDeTkNnnXVWFyY39IOPHf1KYARjQn9CZh9680YKKtcLMx9wlFk+pLSJwoigXL/Ka/WqvrLClcLEOGHGg3CoLuPDz8cmoa+PO22neMIDX0L4UGxQvqibUHmoDyb1d4uJdlECGbNW6k7TJ0cffXQxewAPH6zuBTga533t0k/j7g0CGW1wz8CIS2jsA9THrHb5ENUPVrQaL8Y6BYgx4QObPXF16C/j3EplfUC5RunmI/mII44o+a1klc+9Zyz5SG7JmLb7x1jET3tcW00zxdrYHmojZaV7N8g4dM3633VbGQ1rwgLKCffzvvvuu5DwS1ljy73vIFrCR+NY/6j7Va96VQkbVz702XsnuBc2VgkcJtUXc457Ux9QQOHbEuHDcccdV8Y+ZaNyxhbhBRvw7pNx/Yn3TK67rv/73/9+GU+h5I37hrkOSiwKE+PGPXz44YeXNqpLXxsb6jZ/GItMgvX1y6Qx7b6H67j5ftLcM9PxFQLzGosl4Q/hetRVh2t/pNdupLfC/QjHNcnX+ofK1vz7/FEu0upw7Y/0cCMtXPFD/ijDjWup48LfpsU1RnrNX1wdDn/req77WURhFXdLBOg77rhjmV/Nm+5hYxGf2CFgp4a2eN55Frl/PafMGxTZ6M53vnOZj71PeN6bbxGFSB/N5lnTx2d5jtMHQ4r2aa/bM6IVOE9bdq7ybbDe/PNczvzxH0fjaLSb+NLRs/28y4pi4/Z3uFmvYmP9q8v89Bd/7v7x9/k7jM46Z75JpzuOFA7XG51pse7VOyfO+uHFpann/fTP3eVX/LNbbWRuao3b3rS74JxLu+NHpq4u/fP/drfb5Ga9io0LfnlZd/EfLx/t2LhBt+32G47m+5EJtZFC5fIrrurOGbU36FFPuEO3/XYbdMe8+8yROzJbNdqdEfTzH/6p2/flp4/e764oSg39drt1Vy3Jvzzv0si2xFw7FH1DWIjhHdgYQN7BLSqgqEeE/hY9mB8oOL1PUuabJyj0vYf5PrnrXe9a5o94N2PSMRYTWQjhfSrqKIyv/nPWhe8UilHvVs6JoajzHEUUIt7JLPrxPsD0mgUW6vTM/PCHP1ze5cwx5hRxJ554YlHAmJPw8ly1eGu90VkdLb3zne8scxUsXI9nqOf8gQceWLJ6FzDnUQR5h+m7hpZnhOcjGqF0E4FEIBFIBBKBRCARSAQSgWUcAR/8fl6crWTabbfdinDAB6mV6y94wQu6F73oReUqvVgTUiJlvOw/7nGPK+lW1/moCIFAyXT1H8EfpQaBAqUJYaAPFmEv7/hEO5gtIPx9yUteUj4GCBrs0iCIwMMKSsLdPffcs5i4IUQPkzfBYybXwoY2ATxyvVZkoeDFnWn7mbEioMZX+/pWfVo95gOS4oGA1McVpYEPoHkjIbsVawQvhLXTkh0r+NhNAeNddtmlCDKtNkdwIkynrJGujRQp0q0KQ3G9FBD6/R73uEcRnBI+s5GtX6y+JDwmVK3LWImGrxV1BKhRb41lKXB1PRHf1wfT9Hfwql08KRFQ8Dem+voE7j6MKQ4oBHywUgDZNUHY29cugrNp7g1125Hk/iF4I2D2YYyMZx+llDruOZhqix0xhPlWCttN4OMbnvIYGxRf6teHlBnGij5xzxLAaXMfUQa4j3xwq8u4ML6CZoP1uDYSRD3/+c8P9uU+hYW5gbCCkkI7/CgBCQj67hECeasdfbTLa7xSBMERhgSXypuTKF31Iz76x8f+pPpifMB0p5Gt/f33v+ZuDYopuBMoyGPesaJbvPJoUn9OascCoHo80caoK8LmBn1vfBCuUIwZI+47iqNQolFYUi4ifd7XL9OO6XHz/bi5ZzbjqweKa0SZP4Nqf8SFOy4t8nDrfPwR7vNHXOsGn3Fl6zx1+Tqef4jaMvJFXJSJ+iNtkhv5+/jUceHvyz+UFvHRhqFwxIcrv/vbT1wfifeM9Iw+5JBDFigYCTtjZbf3AXnMG/IQWAqb381FyEpvinn3EQUhZSaBpPeVIXIvTvusGeKR8Us/Aje5yfVLI51dEXTFlfP9q692o4hayP1PmX8tiL/if+f7V1/9Rt31Rjsorne9+eLlK6/m63Fy5ZXz86x68/mKB8qOSy++YgGP1nPRr/7SHfGOM4opqle88j7dviNzV+uus0p3wkk/6c769u9L9ttsuGq390vuNVLG/KU7+vAfdFf9c/77UfDC4/Qv/ro7+4w/lKhtH7th95Btbtf97uK/dycdP38nU+RdUu6jH/3osgDDNwLyrukdxvuZ+9kzy2IDig4Lgbx7+i5wNgdhP3J/eufxbuc7hd/CBHmQ5xllCEVHa+pTWe8l5gS8KSy0qaZ4FovjR+Yc73vqnDd6j/ceZyeu3bpMcppvzDFBNY+IC9d7qHarH08LRiy4cu1IWfxcv/ood6al6b8qpuWY+RKBRCARSAQSgUQgEUgEEoGlAAFCVC/QXvC9OPuAILwmeLeyyAc8YR7lhdXDiEuI7kPDymLCWukt+SixypqwzQu61dp2BFBUiLcqKojg0goo5EPGCkvu1ltvXfzitUe8FdmIoDc+LIRnci2EfiHktZrdh09LM2m/jxltRa6FQqAPE0Jpq7SspPYhRhiJKFdmoswoha7+Cx76jpkoq9YJeaO/9A/yoUURpU9ttbfaX//5SETq169cK+rhq70UInD2AWWVuBWw8TElL4EvVx6HeOu3achq2bYP7DLQx2iov4eEXW2dQ31ihZs+sLqd0MuHrzHg45Qw3Qr1tl2T7o36TBYfzHaPuD8I02Ic2IFAGE34vuWWWxa8KDbiDBCYIzhaCSivtlolbDwRalMu2XVBAMcEgzE3tLpY3yJ1hb14ZgzwQ7GSmX8Ia2k1jWsjBUOMJWWYSIKn3THmEGPErhGkD123VdZxvk1JGP0Zl4TicDL2kDEhL+EAYaR73RgmQIADBVKsYHzHO94xtr7CcPRnDLB1HWM54rnGuXvUvEi4QdhJ2OFQ26BJ/UmxMZPrDr7jXPe3/oTPPvvsU/jbFUMY4gdTK1qNR8pJSiBjua9fJo3pmD/Gzffj5p5pxteke1l6zPHT+uFXl6nDNbZt3REOV94hf82nz6/+umxfHnHT5ovyfTzbuDoc/mld9UTeujRRD4wAAEAASURBVM7AM9IjLG/427QoH/ERrvmHv3Uj7yTX/Gi3JwWHucXqaMrOWLSgvJ2d5iFzrXmUcpiJNu8lFPvmDnliHpGHopRSdloaetZMWz7zLX0IrNTTpPli7G50CHdP4ihqdDsM0ri0KLTSEOPIcLW78kgBcqeN1xi9y/1f9+vf/LWYrtr0TqNn1LzVuzVuvXJ3wd8uG53Zcc/OLpHdXvD57ncX/m1B265//et01x39/nXVfxQdWz70dt0rX71Ft8poB8g++32lO+cH85UdTbWLPeid0G4s72Xeoby7egZ7tiPfDJ7L7lnPOeRZ6P2FMsS7HPJO5D08yO4ISnjPR/e8Z5PdEy2Zh7wje3c69thjy3cBU3bxLGzzC0vznowoHObNm1feH3zrIDtRzC3qnIaYafW+Y5eHbx3XrN1+cX6H3SsWyMyUUrExU8QyfyKQCCQCiUAikAgkAonAMoEAoUCsWvJx4GPBR70PAwK/EDj4eAgiTCC0RVG2To98BG6E6AQIYZ+W0NiHgDSC0KDaFjPzE8jHi3x4awdhLyJI8QGjHbFyUvxsrkW5IZpJ+wMHvMLfh4lt8VuPhNGUGgS4fq6dMHy2FIqeWIWq36z4CrIbABHuILgFxvXHFowJn1Fcg75D0qKP6uuqywRP/TZbmqa/Y+xNqiOuQb7wa7vydqUwR+ADkvksQmEfvFbm99G090ZdV12n+LafYEeRFBR9QbljXCDKDAoOgjs25+0EITD34a8ffbwbU7ViJfjFCr/od/HyBk2DdeQNd1IbI1/tRhkf5nFdxpV7GAYtteNVunsbEVwiH/8UG1ZeEhogggr8pq3PnNSn1MBLW80z+jDM1sT4l46m7c9pr3s+1/H/Mabcp/ofGdPrjZQ0dvjYRULZQyAEM7s5CIhq4XPUMO2YVk/cc1F/zAEtBvXcY76WT58Mzd3BN9oU46Fur7gIt37lpLXlhsJRT7iRrw1HfLhteoS5bf1tmTpv65c3rq1NGwr38a/j+vwR1+dGnPpqf11/xEdbhfv843i0acGzdet6J/m9S9iBQWlhLqTUZBqGcDKIUJTy1NzhbBy79SjjCSwtsrAryzkeFBmEiM7ucBaWhQAUHtNQ3Bfyhj/ukWnKZ56lDwFnUqAbXn3YNv+Nbjj//YjpqD667NL5ZSLf/DLz35WVueoKz/J/jt6Jr7cQ3xvecH6eK/4+/3yqPt513Faj8zq2f+QG3fdG5qxeu9/Xuqv+8e+RsmWlYnZq+5H5qS+c8qvuqTvecTRvj94xHrpet80Wt+1udcubFBa773r37gb/Xqn7zIfm79a9x/3WGh0evmV3p9HZIW849Dvdicf9uPv3P0OFU9e6+P0W5HiOe7+xI9XuCfdvPIPdw+YdP4sPgtzP8f4rrn2u2CltgQVTjhZtUTpYzNJHdrZaQGFeOG+kJPXuZdFQrSipy8X8FXHqjjmgjvMeNw2Zn3wfaKNrivfp+lsnnv3T8KvzpGKjRiP9iUAikAgkAolAIpAIJALLJQJW2jMZQ+Bom7OVvj4w4sV6phftY8QLOAGaVetMS/gwYTrHxwBBOJNNyPbwoLAH76WewBkPL/VWnRPgIWZoCO7aj4rgMZNrCQFNlA13mvZH3pm4Vo/ZTk+w7lrHfWT18YUFgSVskHYSYtthQsCtv6xOIzzWj3CWRmhjlZfy/CiUESUwiz+8CJUIfaPfol0hCPZhGFT3c8Rxow+m6e/2+ms+0/gJvmFkRbtdGlboWp1Hqbf1SOkUO4fqds1kPA21IT7O9UUQW8uEw3YhSOd3/xkjyL3ien1Uu1es/rPLAKZ2Vp1wwglFCB/5KQUI49wz8aFfn6vhXgyaBuvIG+6kNkY+bvRpXLd2MZ3gniX0IzhgOqalGJMxnqRbpYmCl50I7n+CCsIHKxnjjJ/IM1Rf7GAqDAf+Ahvj1figPLH7oqaoZ1x/yj/UjprXovqtVqe0Mg4IZOHl3B0YxtiKOqJf5mpMD809dqHNZu7WTmMk2hlhrrh6zq/Dtb8vr7g+qvmFP1z5a38bbuvs4z+TOHXhOURtWyJfHd/njzhu7Ve+DddxwT/aFHnr66798kfeKBtl+sKR1rqRd8ilILV7zMplzzgUK6QpqLXh4IMPLkph6fNGq6gJMr3TuJ+9P5j/w3yNleKeneYebZGHQtK9m7RiIvDLX13aPXjrdbuNRudpILsc1p83fzycO0pD17neSt2qa44W6lx4eQkrg+6w4eplHBmH/OjcX11Wdkmc/9u/dhtteLNuw41u0V38i792t1p/lXJIOYXHH86fblHIBhvcbDTfX7f7/cWXd2d8a/75cOeOzsW4zmjHxx1HfL8+OrxcPX7rj/KiUJ64hjXWWrnE3emea3YHvGar7l6jA8MPfet3u2Pe+oPub3/6z7t4ybSE/yz2oFj0LWJ3FRO1QfFuSVFpR2hN3o2GyDzhzA0mnbwzeCbGIp66jPdq74fSfatYZML8pnPl7B71PFuc5P3NOx2TvZ7l3mu858FjLigVG3OBYvJIBBKBRCARSAQSgUQgEViqEfAhT+DIJQyljAilxmxWHxI8EkDa1k3IxhyKsyDw92ESK62BwjTVu9/97rLSicCCcIGg0scKs1EEGUwjWdFF2GjVFQH00IfGNNcSq7Wt+rZ6my3bmmbS/rrcJD/lAiGKrfNWcc3UDFUclOw8DCtTrXBj8siZAPqMsIapIitQCXUI7ylSfBxZpU+gTPhD8E2wPKRsmHQd0ik1jjzyyCJ4Z4YKhfmgWOFmtbYDHQntQ0BdMo7+2j6gvJrU384M8bEX1x+8pnUJqJ3xQvGz0047lV0RVtmJj1V1bbumGU+T6rcrx3kazo9gKoWSwUesVccOwfZB73BwO6YoN8Qzv2TMS3PNzruBkXsjPvJDWWGlvvNN3Gf6XR71OWvDzg/KkBDkaets7q1JbXTf+hgn7Fa3trqv+AkJCCrcVwTu5pg+cxDuB0o/uw+MIfc4zAgiIj8BpZWcDuo0rpimojhFM62vFGr+zAcxd731rW8tu2XCRFpkndSfc9GOqGuSSxBrDmAuy24t5jyQ+dvYhmHbL3MxpsfNPcwPTrqX47pCsF0LxSNOnohv4+pw8JqpW/Po89dxeEdYm8I/TZ3yRpm4nuDXhmt+UaaOq/1tGyI8zh2XVvMOf+QXbttTt71Ni/Lcmkf4W7fOP85P2es56BnmGWQnKEEostsUX3MnIaadocy3eTaag8z7TE/Kw4TMGWecUZ6dnoWei54B5udUaozrgeU/7ROf/EX37KffuXvso2/f/W50TsWat1y5W+92q3a//u1fujO/Nf8ci52fe9duq/vftjv2XWd2p59yfved0y7oLvr937u7bLJG96KX32s0//6je+gD53VX/uNf3WdPmW+G8cMf/3n3ir3u1e25x2bd+rdZpbvXlmuVnRWf/dJvur9fMp1S4cejA8L/OToz4x53WbN77p6bFVNUD33QvNIpZ/zo4u7cMy/p9n3ZlxfqpLcf+/BunbVW6Y5931ndl08+r7vFOit3Bxy4VbfN/dfpLhkdVO5+2PGJ880nKXjsu37UXfmX6XaQLFTRIgbWGy1eorTwjuaZTiERRDnp/ZkJRGYZ3acW6djlYMHHEHkGenfxHeI9qF68UpfxDkiJ4dlFsUAhoi7fHjFX1fnn2m9eo9zgesfhP2105hiazTdYKVj9pWKjAiO9iUAikAgkAolAIpAIJALLJwJMM/i4t5L37W9/exEWEBwQgBEIhCB12qu3HdvBzD4UbCn3cWAVFIGheAKGIII3wkwv8raYMzWjLYSZ8vowodygDPCCb3WyPEM06VqUI0AlLPbh4oOmVWzMpP1D7eiL13Z1MZthV8NMzVARxMCTaSKKDQJ+AmuCbR95MCTIjgPRCc31n5XqlBp2AcTKfx+KcVBjX1snxek3iivtIVzVt49//ONLMQJ2bdTvPlKNJQqDMBUkU18fTOrv9vontbFND4UO5Q9FgnEIE+YM4iO6bdfLX/7ysfdGW0df2Ie482x8hDOBYszpf/3nI3b77bcvO2soLyiipBHaifdRTUhMoafdMIcjAV0cdOvsDP0gL8WG/JRalALq0+dWJLsH0WzurUltVDcBglWOlCzuIQIHKxApNyjXKA0oPqyYjl09NV7GjJ0ddtDAAU/KN3woMILc/xQbBJGEEPIh9/5M6gt+tQsbJikOOuigssqTQjZ2aMinrkn9ORftqNs0zk8RRqnh3A/3oXEDZwefcvv6RT8t6nw/bu6ZzfiKPnSttcC8L76Nq8PjsGrT6nLT+KN8nTfiZuIqH9dY+1se4+pp0+owf4Rrd8iv3khr21CHo80RV4drf6RzW74RDrcvT12+9VsY4WBe8927RwsiKIEpMDxXdxopqxETVXbiEQxSqFKAmvvdE6HYcI97DtuZGkoQaeYaCuWkayJAQeQZPG+0C8a8U5NnkueN9wzPVf1UmwWr8/J7l/P+RaFE4MxsIqGzZ1VN5jMKfwsj7EhbEvStU3/bvePYH3VPesLG3V4vvVc5f+LC0VkVBx367e7PF83fobH5fdfunvDYjbsvfvW3RbFx0UgBIp3SYo8XbTZ6bv1zdMD3Dbq3HPX97odX76x4/zFnFsXH1vdbZ7Sb42bdrda8SfeNb1/Uven13xrNB9Nd2fe/ekF31Lt+2O342I26PffavPvHVf/q1rj5jbvjR4eHf+YjvygHj5/62fMXYnb55fPNZ/1gZL7q/J9f2t3tgWt3D3/wvJHSe6XR/XO97ilP+I9SQ8ETTzrnWlFseNd3Bg7zcHZr1Peh9ySm4pib82ymfKS8d//3vUvUAFhkccghhxSlSSj+63R+ClK7tyzE8T5NwWB3lwPA67mqLTdXYfV7b7dIyIIg86nnKPJdNNTuaetfabQKZsohNi3LzJcIJAKJQCKQCCQCiUAikAhcewj4mCQA8+EZB99pDaWGF2pC11hxT2BgFaTVU1Z/E8wTDiB5HcIsb98HLKGjj2AfrgQLBNsEcISaPmAIOt/whjeU3QUO6dUmHzKEggQMyMu91ZcE8IT26ie4x8OHwGyuhdkK10XYT9Cr/YSjVn0SchOg+oiY1P64fu0hSEbs81IkEOzHNZSE6s8HOqUKDHyoc1sKPq4TZkHKwUIbCReQj34ryuEDPztCCF7jowj+MHSthM3aa0eHD0W7OAh2tMEWfKRPXRs++gJRPFFy+UBUj7yEF1a0x6HQxhK+8RFoPGkbAQY8tYdgJLDp64NJ/d1eP4G+cyd85FpBPk2f+EikHGBGSH0EKVYJBs597Rp3bxhPhOw+hI0j4xLW7hd4hzDG+MbH+ICFcQcv+ZGdDPqJYAiGBD2ULXY34e1esrvJGLUCX3n9o8+VdS9Y0Ygn0u+u064OPJTRBgoo+SZhra3qk1cZNK6N0o0FChhjxdhVDp54ERIQUhEwOvshzMcoV5Mx4uwTOGmjlZnGTH1eCIEGwSbaeuutFzKrNq4+mMARzziUtK47/JQpBAzmLX1oLnB4OD9lHUHKpP4cagdBDKVNHxHOGg817n1zQYw3Y97Yp9w0dggXtdF8Df8w7dXXL+PG9LTz/bi5Z9L4iusP03gR7nPx6qOZxvfxiPlKWvjDreP6ykZc3Y7wT+v28Yi4ce5QGyOeO85fp6knwuPqlBbX1fonlZNe1xH+cKXX97hwjPOYV8UhcyRlhLnC88XCC4J28wp+4jzHjHvPOHMf3uYjyj5kLjIXnDcyZ2dO9Tw0T8acWjJVf7N51lTFl2kvLD1nmUSEKcUR5WiQ+fK1r31t6Rd9YN401+6+++5FgRr5ateORTt0vTd4Hpq/zMvOQvFMowCn6NfP+ojy4/jjj69ZjPXf/MZvG5s+KXGdkRmprbZYq1tz3ZuWcyzO/9ml3alfHh3mfOl8s4T33Xqd0bvxGt0XvnR+d+7Z88+MW/WWN+q2Hu2CWGfD1UbvVCt1F533t+70r/22+935f11Q3d3udevu7vdYs1ttjRt3fx+d5XHOj//Uff0rv73G2Ra3WOsm3WO223D0/P5nd+L7zl5QnmftDVbrttj8Nt0t1165mMS65MIruu+d8fvuZ2f2H1K9w44bdauvdsPu5M+dN2rTX7pb326V7hEPW28hnnXg+BPO7q78W/9ZIvJdcsXudfY59bunjQvvMfOufseNCtyz3gG9Q3uvcd/f6U53Kve9POYLz731rjZbW5ejNHO+Wn0GXaSH653Du4c2eCe2w1Q7vLMg5qy8d/km8q7pOU0RE2Q+8Qy2OCaIksQ3lV20xrDFLd6j8bZz1YIEbTJveb9SvznLe6l3L4pZZc1f3gU83y1KmIbqBWSp2JgGscyTCCQCiUAikAgkAolAIrBcIECARyhAgTBXhKcPXzxrIX4oNpgvsrPDSz+BYZ0n2kCY4kNG+qTVWVFm0rXgqU4v/7VwJcqHO9T+SF/Srva0GBF8uxYfPaHQqNsV+EmTZ1HIBx3FBgGtjzAfm/j38dVWv740bRjqg2hvX3/3Xf9srseYVE/98Rd8+tql3rm4N4xj/TeEiX5Efe2Kfm7vJfn7cJmmzeOwxrePxrVRncZE234f99Im3W9R37g6Is84d6b11byYqLNrhEKFUPS00cpvik/KEKu8a5rUn207CCqsSO0ju3rGCV76ykRcjFn3eN8c2dcv04yP4D/kxpg0nsfNPX33Mp4UG+Zf7Z+GJuWblN7WUc/9Q/4oU6f31RNxrav8uLjgH3kiPOTW7ZAnwrXb+ttwlKvj6/oivq9NbVwbrvmEP/hFvX3xFGozIfOxMTz0vqJd5hHpdhj20TR5+sqtSHFMTloQwDyP80soMWJXKBwoe5///Od3Bx54YBH6Ujy/6U1vKnP9W97ylmtARfFvrvMeQUFi7vAuse+++xazepRLhMZ2GxIqE2Rb0GFHyLS0qIoN9Yympe6Gq1y/KB3+cfWuh7p+Z2/866p/11HFf4ObXq/shvjfAXNOzue44crX7/4xUlr8a3T492zp+je+brfSSIHyjzFKiNnyHlducSo2xtUbae57z1XvEkP3deT1fDJuLKI68cQTi0Ih0oZccwa+Q+9oQ+XmIt585N20fX+aDe+aR5qimg2CWSYRSAQSgUQgEUgEEoFEYJlEgMC1FZov6oXgZwXeJKpfwtu8hCLT8KjLTboWPAnnJ9G07Z/EZ67S+/rHR9g4fGaD37Tt7ROiRtnZ9sG49vZdf9Q3E3dIGIZH39iYdC3T1j1pzI27D8b1cx8u07R5HNZD1zSujUN1zlRIMK6OoXbV8TOtry5LWEoBYRWx8W2FuF1p9WGmkX9Sf7btsGNqr732iuILudJmS31jtubV1y99cXWZafzjxqTyk8aX9MhXPFf/DQnLI3+dN/zKjEuPfLXb5p8UjrJ1vmiruL429MUFn5m6db3KRrjPFVfHR7iNq/m0fmEU1zg/dM1wXx5xUVeUq8ND/sg7yaVI61OmRTn8J92f0+QJfiuqa0cqBW8oNlocKDbshLEqPt4H+A877LAigG7nQIJjuxGZN7RLAzHLyeyjVfPIanzpTGYyD7QoZjMLw1n80bUOKSew61NqiJ+kaPj3P0cLdS6bv/ND/tnSVVf8a7ZFl+lyk+77uDg7H9785jeXsWPBwLSK00V994j6Z+OajxZH/anYmE1vZJlEIBFIBBKBRCARSAQSgURgAgK2eO+zzz6LbDt2QjWZPMcI+DjUb4SiSYnA8ogAodtLXvKSjvkZK0MJ7ZiWY4N7UYmpqT7TfYvKd3krT8DTCtMnXaMys6G2XBuexLNua58/+NUKjtavjsg3TX2RJ8rULn9fOOLbtLbuSI86wu3rj764yN+6Nd/aL18bbstm+NpDgHmfccQsH1M5odSQlxKESSq7N8KUT/CgtNhtt90WEjRTXFCAML+DmOKJ8zZybARy6c4EAQoQ52Mxj0ZxtiKPo1RszGTkZN5EIBFIBBKBRCARSAQSgURgSgTYViYsXJE/NqaEaqnKRijBjERSIrC8IhCrj5nUYvbCCsqcpxZPby/KLpXF06LkmggkAjNBIMx91WVi1bk052WYS5EdXpS79Rlkzu06+uijyzlZ6zXnI9Q8058IzAQBu4ye9rSnld1GtdJtJjyWl7yp2FheejKvIxFIBBKBRCARSAQSgURgqULAB65f0rKFAAHviv6RuGz1WLZ2tggY5znWZ4telksEEoEVFYFQBHPf//73lzMzYGFhxCte8YoFsFB6OGPNzo4Xv/jFOd8uQCY9i4qAsWeRQlLXpWIjR0EikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAIVAs6rsjOjpiuuuKIE7dxgCijMUdVngTHzd+yxx5azjPbYY4+FdnHUvNKfCCQCi4ZAKjYWDb8snQgkAolAIpAIJAKJQCKQCCQCixkB5nIICWqhwWKucpD9r3/969601hZ7G+4tdHVkrP4cl2cora9sX1xbvm5f7ZdvUrjmVeft8/fF1eX5o719bl9cXSZ4Rb4IhzvXpoD+/e9/d4RazEAsTfTHP/6xu/jii4sZlNve9raLvWmXXXZZWaXs3nRmR5hmWewV91Rw5ZVXllgCyCVJS+tYmAsMnB+wyiqrzAWriTx+9KMflXvq3ve+99i8J598crfJJpuU8w3GZszERGAOEVh77bW7b37zm8VsXxzm/pvf/KbcHze96U27BzzgAdeozdxw/PHHd1/72te6XXbZpZs0tq/BICMSgURgagRyb/zUUGXGRCARSAQSgUQgEUgEEoFEIBG4NhB4+9vf3h144IHdeeedd21UP1WdrWC9DY9jQvg/6TdUvlYcDOXpi6/bV/vbvEP8o72Rv84X/nDlqf1RJtxIa926XJsW4eDRuq5p3HW1+acNH3PMMWUs/vKXv5y2yBLJR/B2yCGHdJ/+9KcXe32Ue2984xu7ww47rHvLW97SsSF/bRFTL9py0EEHdZdffvkSbcbSOBYoJD71qU91RxxxRHfkkUd2H/3oR7s//elPM8Ll85//fBnjX/ziF2dUbjaZKTWMox//+McTi3/1q1/tDj300HLo/cTMmSERmCME7nOf+3SXXnpp94UvfKGcpfH73/++c4/c61736jUt5dn0kY98pPvEJz7RPeYxj+nufve7F1NUf/nLX8qB43PUrGSTCCQCVyOQOzZyKCQCiUAikAgkAolAIpAIJAKJwFKNAIHWOeec0z3+8Y/v5s2bt8htfd/73leEY4985CO7u9zlLjPiR1g+SaiO4TR5ZlJxy68W2kurwzPh2+at66n9kW9SXKSHq1z4ww1e4QamrSu9jov8k9y5wqKvnq9//evdD37wg+6//uu/ug022KAvy2KPsxqYMB8973nP61ZbbbUynr/xjW8sEZvbH/vYx7pPfvKTxbQK4d4NbnCDxX7NQxXYyXXKKaeU84xe8IIXDGVbLPFzORb6+nSmjabkOfzww7tvfetbRcnjfrOLxXh92cte1rvL6Ctf+UpZVX7nO9+522677UqVP/vZz0r/Ouj4QQ960EybMXV+5w5QvlCMbbHFFhPLbbvttt0rX/nK7p3vfGf3qle9qovV8xMLZoZEYBEQuOMd71jme2dpeBcxbu0Se+5zn9vLlXLxXe96V/eHP/yh3IvuvyA7217zmtdEMN1EIBGYAwRSsTEHICaLRCARSAQSgUQgEUgEEoFEIBFYGAGrpwk8h4RPbFZf97rXXbDikRBOmRvf+MbXOHR911137Zjaud3tbrdQJczg4MMsUJ8wWzoBRGs2yEpkwoY73elO11BsXHXVVd2//vWvsaZ1oq4hQf1CjawCM81fFb3G9QWvaEuddy78wT94tWHxbVyEw63z1HHBM1xprqN1o3xcY5se4eAzl26YGWL+LOrH/1nPelZnxe76669/jeqMmxiPbWLwa8dim68OR5m2Da77Ax/4QMn6jGc8oyg26nL8hNx995I0fKXj6x7sI/eOewGPlihQXOujHvWo7hGPeMQ16m/r1t6he7vmTUmhzsBomnLyvvSlLy191GeqbqZmqmCjD/t4aWu0Sb3jxkJ9XbU/ylM41HOj+HF9OmmuU8dpp51Wdmjc+ta37p7//OeXPqLooITST1aet2THhHr1Tyg2HvjAB3a3uMUtykrzNr926l8C2mj/pDlTfofUtwqwU089tTOW9t13347wuCbm3vCvD7e/733v2z34wQ8uO1KMvc0226wukv5EYJEROOCAA64x7s2BO++8c/ed73yn7H4yjim173a3u/XWZ8w+85nPHExrE7beeus5WbDR8s1wIrCiIJCKjRWlp/M6E4FEIBFIBBKBRCARSAQSgcWIgFXCVk4T+FrRyFwNgcDDHvawBfalrTInNLTKm+1pdqcJqCgZKBsoLwjM7nCHOxQh26qrrlpabAXxhRdeWOyrO6STEI65lbPOOqsoLgjhHv7why9QUhC0ST/jjDNKuhXthAdWBR988MHdueeeWwSozLQwD/G0pz2tnE/AdARzV4Sba6yxRhGijdvREcL4aWAlEFwUqsvXgvbgKb0vXnrE1zyi3DRuW64N4zFt3DT1RR4867aHP9L73HF5jMnjjjuuYxd9r732KsWtwDX2KLnsCCIIJwg+88wzi985A8YrgSreTOb87ne/K2PNGHnta19bbK+zs3766acXoe9tbnOb7klPelK35pprljp++MMfFhNRxpoyFHRnn312ETS7P1qiNGD25Nvf/nbhpw2bb755R+B8ySWXlFXuxjhixsc9FOTes6L9F7/4RbmXjHs/ZFyHMNmqYsJ5wm7Xdp3rXKf74Ac/2P3kJz8pgj0Cb/W+8IUvLGX9Ea673l/96lclzj2PzxOf+MSOubjVV1+9u9WtblXuu6c//ekF03H3NgVRlJs32on13e9+t9yXG220UbfNNtt0dobAmpKBYP6e97zngraER5vc5wiW8AmeBPz6ERGCMwvjOluq5yWYE6oTXO64447lzJKY225/+9uXOcpctP/++y80FowLigV5nvzkJ5cqmAVzTeqmNMDny1/+cmkj4eeGG27Ybb/99mWc2bnQ9un97ne/iXNdfS0XXHBBqV9/4qu/jWVtYDqtVWycdNJJpc3q/d73vtf993//d/fqV7+6zIF2yN3sZjfr9EWMC/ibi/WJe0gd7ilm0IxZffiUpzxlgWLKGDKOnf1CSeEeUyaURh/60IdK3npXiHo9R5ShdFt33XULdmuttVbpO3144oknFjM/qdioez/9c4GAZ0AfOWfDGDQ3uHdrhVub33vPEJ82r7BzY/ySEoFEYHYIpGJjdrhlqUQgEUgEEoFEIBFIBBKBRCARqBAg8Pqf//mfItgk0CVgPf/887uf/vSnxS46gSdFAkHo97///aJcIDilCCGcJeglGKPcIGwm9CRUJYj83Oc+VwSuW265ZVnZ++53v7sIPa3wxpewlkD29a9/fQkTKL7jHe8odTlImVCPkI2Cw6GfsYpbXYR06Oijjy71aAMB7Ze+9KWiOHnd617XEZAOEWF3CPVr/1D+uYivBf6T+NWC/vDPdXuDn7bU/klt60ufybX1lR8Xp7+NUcqFUGwYN+Ie+tCHFmEUhRjFAEEsBZuxR+hrrBIME7pSGlBkEPoSzhqHhP1cSjMCZQo6O42Y2bFq3pg3joxB9tl/+9vfFjNBfYqNz3zmM8WUibFJCUJ4TDht3N7jHvco91VcJ2E2++9BrofyUL3aQuBOaHbLW96yKHAI/Qnmgy/FgzZRMrrvjHvCfe1z/bViQx0Uf64TxWHl6oIhRQleF110UcFTX467t+tyVuxrt3nAKn7X62wIh5RrC3yPOuqogmup/Oo/mBDQI31a86ScoPgwB+lDCqdWuK9czEuw03dwgwXh5U477VSE+a5P/5nX7EBgCqkeC8aLPA6rf8ITnlDGj7DrINB3Xc4jgZ/D1s2NFGGUtI9+9KN7+xR+k+Y67Q+iwMJb35o3CWEpepB2taT/4ItckzYhY9W4DsV0jAtjxX2gP/SNPtFnlBo///nPi8KC8JcSxz3y5je/uYw/7dGO2OlDkaxeimntNd8ifWe8UEZZDQ9ryiL1UKi5JmOSIht2+hbuSYnAkkDA8zOUckuivqwjEUgEpkPgmssVpiuXuRKBRCARSAQSgUQgEUgEEoFEIBFYgAAhnB8zDQS6L3nJS4rglzCM4gFFHqsf2Uq3y4IZFCYeCIpf/OIXd3vvvXcR9hFWEpYhwkZlEcWEMgRlBJnKWNlsNbSV9wSdxx57bBEGs4G95557FiErYZn05zznOUXAiZeVzc5KsGLZymY8rTR3boFV8FYsW2VfE+FG/CI+FAYR5rZxbbjOOxf+4B9uXxuinjpPxC2KW/Mb8k/Lvy4/qUzkbV3lxEV8zcc4Mp6CYkzG+KLIoEgwNowfQth5I2VXKA/a/BG2o8IZD1b5E8hSXiAr9I1NCjbnHOy+++6dXUdRLtoRLkEx++wUhcyfGN/PfvazS5jChfKAmaEgpqjUHURIzAyK+4/Sj2KDwJ4QGF8r4l2T+0JZQv8wgRRtslLevbXbbrsF2+KKd43uC0SATfCPlCUcJ1zfb7/9uo033niqe1s51/zYxz62e9GLXlQUN2zTU77A33VQMFAQUJj0UbQ70oKnnSTwM88Q2ocCJPKFG+XtzNGGXXbZpcwFJ5xwQmlbpLs+17v/aLcGxVXEcwniKXQpmigvzCWwJgjdaqutyvihWKH00H/MWFEsUCDAs69PJ8110f5wCf0pqChgKBucCYCHw4v7dp7Vu2AolqIN9XXhHWFKC2PCGDae7NTQ38bSQx7ykHLNzuxADk827pmP0gfGE2WG843gSDlC8eIcjyB9ZCdfKAUp1UIRFcpoaXCE3UwPRY960k0EEoFEIBFYfhBI9fby05d5JYlAIpAIJAKJQCKQCCQCicC1jgABLjMyiFLDSmUuIWMQcyTMj4SgVDwTI0xFETpbQe1gXiuHrYqvySpfK++tDLerg59igvDNSnO7OuzQIABWD9dqdcI9Zk0cksuMCrKSmDkYQkACNsoPAmkCQmkEojPdrRHCdO3h5wbVaRE3Gzf4jCvb5ulrS93GSG9ddURc1NeG2zx1evi5QX2YBI8+t45r/cIt1XW1aZPCxoHV4Mas1eAUBVbUE9SPI+NZHmP6mGOOKYJX+SntXK9xGfcF8zzMU/UR4X2YkaIkUT+hM6WEeEJ/YznIbox6jFLCUBIi9yJ+fu4T9yIiUOa3ewpFG0tg9EcY7X5tz0SAq7oJl5EdEeqnOEEUEJQq2iNvCLmH7u24DjgTsnMpgrTHfe86YGeXgOt2DbUgvFQ68IfXIx/5yMITDztx9Ok4IqTXJnjbwUX4rt4gdT/1qU8t7Ym4cJnNg5u5i0CfEoCCi1IDHrBxPeYmuxBcCzKHSQssxEWf2rkj39BcFyavlKnJ9VKYMNlk7qOwiF0RdT7XE2OHsq1uQ50v/JQ3xrFxw2QV0m/GGaUE5TCFA9KPSFuMD3OsHRcwpbQKpUTdLgoeP7tYPv7xj5fdfHYoqVd/BsnjOaFvKO+SEoFEIBFIBFZcBP7zdFhxMcgrTwQSgUQgEUgEEoFEIBFIBBKBOUKAkC6IUA0R8NVE6EcAjJgfQSFgI/yKciH8Kxmu/qvjCBCR1byUEZQY6iJMczivMGLuqu+w55I4+tMWK7WZsCLwZbaG8MyOEauRaxoSmqsz0sIfbpQXRpEv4ms38tRx/G2ZCA+548pIi7ZN49a8on1RrzQUfFp/SRzzV/Np/RGu3dqPrXAbF/Fjqh1MYhudEJVSzRkPVtwTANs9EGO0r3AoAcK1oh1ZnY6M+aD6Hom4cO0MIbSlPAihL6E5s0DS7AQYN5ZrAbB7AFH8KatNsApFhD5z37jn6l0shMVxHdGuaVz3kdX0QdPe29oU7Y56Q3kiLfAKTIP/OLfmGfNJOw+15aMeuGsHAXtdRjwlyxBtu+22RbHBdFP0NxNn2kKY7wwf8xflKkE/grufPmhp0lzX5o8wBYNda/rXDhu7OOaCAp/oIzyjnyIu+ijazpwaJTViDst126ET4w02QXby2dlhh5+5nWLMWLTTo1YsBlYxF0X5dBOBRCARSARWPARSsbHi9XlecSKQCCQCiUAikAgkAolAIrDYEAiTISoIm+1WAw8RoaMV5cz/OHiXwIsfhUCyLut8BEQgRggdNq+VsdKZ/XVphJLMSBEKU1AQks0brWavD6oNwRghHAGm1cF2aRBCEgxaNbz1aBV3rLSPeksD5vgv2lIL+toq6rQ+v7i++JbP4g5rQ1yPuvrCfW2Ito9zx6VFXcE78kY4BLME/SFMrscroazzFaxAZ97ManrCaDssKDdq4WrwnOQaj4h5oqA4oyLCtWt8E9zaVWH1O/NGVrcby66nvSdqnGs+rZ+iLhQY97///cv1yMN0EuFxi1Vbfjbhmd7bs6ljqIz+1c/6POYhGIyjGAt2Apg/YBLKpXHlIs3OAljajaO/KVaNI3TyyScXM0tM3zlgm9LH7o4+ij6dNNf1lbXjzPlC5jNKWWe4hBK5L3/0e9TZl2c2cdpuZwfFWZwjYwzrF/dX3AO14si9Rxknv10+ztZ429veVvpvhx12WLA7I8wDzqRvZnMNWSYRSAQSgURg6UcgFRtLfx9lCxOBRCARSAQSgUQgEUgEEoFlBgHmct49OtzbCl7mSAjOQrjXdxEUCccdd1z33ve+t6wqJ0ymiKAM6VtpzFyKVeGEvXEwL8UEMzNWQhMkb7bZZmXXxRFHHFHMymiHXRjO10Cx6trKasIxAmvnF1h9vNPIhr6VxYSBhG+xsjraHoLACIcrvhUOtnFRts2HR6QFv0lu5J/kDvFRTjtat80f6REf9bXX0MYPhfGJspEneHMjbhp3KE/Np+ZN6EzQTWj9nve8pyjFmAUKIvi3S8Mqc2Z+KDPslNBeiobZEPNDH/zgB4tizRhXv3E7RNpIQG7HCKGu8e7ge212ToKdANpJaG43iPZSVEwi94XzCqyeZ+qJQpAgn/k2ijs855om3duhSJjrevGj1DjyyCOLEJ0ZKhQmukqg54/pJkoQeLv3N91003LQfE/W3igm7pie+vCHP1yE+kxTUUyhEMbbOUPpoX+DKNTMl22fTprrony45kDXTEHATBjliQPMEXNWTKq1FHOhXTwULc5NmQtiosr865wRyg33ETOCxp00ZgHNsUxTBVEoM+Om7Y973OM6OzgQ5UcoZyhGKONcT6vkCz7pJgKJQCKQCKw4CKRiY8Xp67zSRCARSAQSgUQgEUgEEoFEYLEjQEDKpBOhmpXPzLP0KSiiIQ7TJUi2o4JSgwCQEJYte+dctMQcEPMqDqEliKOcIHSm7KCYIDh2wO3hhx9ehL7OS6D4uOc977lAweJgaMJlB9USYL7pTW8qbbQ6nzAOH+1go/+ud73rQk2oBcChGJAh/OHWceHnohDIzw/9R9Af4XFuXTb8Qy4+kVbz1MZIi/b2xckT6eHnoiG+bXzwnV9q/n/EtXlbvpHe57ZxddlIq+vkpyxz7gplAMWGcRamdKTr2wc+8IHdu0eKOWdaWHXu8GWHc89W4GsXkFX67gkKPG0IZVlfOwm4HV5/1FFHlVX+lBCUeJQd4qO9hMMOaHYgdpgBcg1DxNST8gTElBsOJydQt0LePbo4aNK9rQ2Li/Ql5YGdEgTm8LPDaxzJR8FppwyhukPbzSczIVhSbOhjOw+ij/XXGWecUZRK6qHw0JfMMpn3mBdr+5Qidtxc17bL9ToMHhkzFDVBlLV9ig0Hpps7KUMoFZjOmgtyn9mJ98UvfrGMezhSNomHiXvP+UZM/8XOGgoQSg2HksNIPMWzdsfuDDvzmGOjLI97YS7amzwSgUQgEUgElk0EUrGxbPZbtjoRSAQSgUQgEUgEEoFEIBFYKhGgjLDanWDK6ly7J6yuRfvss08RVoX9eXHyU0RY6UwYR0hLCEeYHEoEq3QRoSyhmJXgBINWPlu1btWx1fXzRqamEGHdi1/84iKsk04o5kDgSLdCHS/1MYtCQPa0pz2tmLIijCN4J3hj2qpdFRyCSvWEv0/438ZFXuVqqvPV8eP8wStcecPfun18Ik/UXbvyC4cbeSNcEgb+olybPBTf5qvr6vNH3JCLX6S1fmHCfYdb2/lAcUVxQVFmHBkHiGBan1NoEDozZRaHZEt3ELOyYZbKIcrGZ5gNwueAAw5YoGywip9iw24ipnnsyCBwxh/vPqIIpHCwc0kZ41sbKEmCnvGMZ5QxbSeHePmNWfdOkDH9kIc8ZMGh0HYA7L777kWpQfGojPzB90lPelLZ1QCfcbTHHnssMDMkHwxdc6yqj7KT7u2+ctqrTfU5Ikwquc46Luowx6gb1YJufb3LLruUOUCaw8gJ0seRfqKQUJe223GBtthii1JHfX6I+HYsiINdtKfeqWY+e+lLX1p2hGgbs3vGGuF9zI9tnxrLk+Y6dQYZN1F3xIUbOzMiHC5lwstf/vKyc8J4MP9S6MJLG1E7LrQ/6jG+kbElLu4DiiGKGdhR3LgW/U1RzO/3qEc9qjv00EPLeRrmf8+FeBZQRmuLed74VCdyoLr7TV8lJQKJQCKQCCQCK41WN81/a00sEoFEIBFIBBKBRCARSAQSgUQgEZglAszsvOENbyiCUavNCYWt0m2FnUPsCb/tvCDAihXobKwzC4MvYTKzKpQNQYSQzNkQyPXVY0W6HRkEnqEkibLq00ZphGxB+EmrhaSRxiVwq6kW2g/55a/T6vKT0tq8dVv7/H1xLY+2zmjbkBvlIz3CQ+5QvqF4fOp21+E6PvytO5RffAha+YO0Qz8P9bF8xg6hczs+gse0rjFm/FI6EPZSVNgNYjxbIU/gPUSTxrf0aOMQj7541+9ec3/OdEdCH79JcVFffW9PKjPbdCaVnMfA9BHzc/BRf8wpfXwpBPSL+YUCdDaY9vHtizMfmYtCUN/mGerTSWOh5TPT8Ezn65nwxxu195udenYRUTwfdNBBC+5V/aUMjOrxaYePHSyUfO6pIWXNTNqWeROBZRGBm9/4bctis5eJNl9yxe7LRDtX9EbWz5PcsbGij4a8/kQgEUgEEoFEIBFIBBKBRGAxIFB/dEzDnrDa6uuaHMJL+EvouPXoEG+rqGuizGjL1OkEY0MCRPURfrZESTKO+hQkkZ9ALqj2j4uLNG5fmTo9BPoRV4fDH648tT/KhBt1cSNfHdfmi3DrRpk2PsKT0iNfuNEW4Un+SenBs3WVmzQ+x42dlt+4sPFi/DK1RhB72WWXFWHtzjvvvJCSro/HpPEtvU+h18erjnP94+6bOu9c+Jd0fXWba8F4HT/kny2mQ/za+HEKFnmH6he/OPts0v3QXsdMwkO87eCwg+jNb35z2Ynx9Kc/vbA1XvrmZuYHmXKzqyOVGjPpgcybCCQCicDyi0AqNpbfvs0rSwQSgUQgEUgEEoFEIBFIBJYYAlaeMzUVB77ORcVMzzDFwlQLk1Zh9mQueM+Wx7SKDfz7hPp9cbNpC+Ff0DT+yMvVhrpMtCncyBNl6viIG+fONH8fr7p9M/XjV5fp47+k4gjWmb+y2tzZAATbBLpM9yxOQfWSur6lrR6mi8xDM1H4MHVll0Zramppu7blsT0Ovmdaapr7lTk35qsc6p6UCCQCiUAikAhAIE1R5ThIBBKBRCARSAQSgUQgEUgEEoFFRoBwirkUAqqhXRIzrYSw0RkZBMCTVjrPlPds8zMhVFMrxK/Dtb8uE/5J6ZGvdVshYB2u/cq1YXFtvREOty/PUJz4SVTzHZe3r61tXB0e8td1xPkFddy14YdBn3mda6Mty3OdcGZGDE27W8M8g8xb9Zgqkfm32BGIe6Nvl0ZduWfByiuvXEelPxFYIRFIU1SLr9vTFNXiw3YuOdc7AXPHxlwim7wSgUQgEUgEEoFEIBFIBBKBFRQBOxna3QyLCgXBJPvrSxMNHfi8NLUx27L0IUBgPklwu/S1etlrEZynVWjE1c00f5RLd24QmPbeSKXG3OCdXBKBRCARWJ4QuM7ydDF5LYlAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAisHwjkIqN5bt/8+oSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKB5QqBVGwsV92ZF5MIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgs3wikYmP57t+8ukQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRGC5QiAVG8tVd+bFJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAgkAss3AqnYWL77N68uEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARWK4QuN5ydTV5MYlAIpAIJAKJQCKQCCQC1zoC17/+9a/1NmQDEoFEIBFIBBKBRCARSAQSgUQgEZgWgfyGmRappSdfKjaWnr7IliQCiUAikAgkAolAIrBcIHDd6153ubiOvIhEIBFIBBKBRCARSAQSgUQgEVgxEMhvmGWvn9MU1bLXZ9niRCARSAQSgUQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEYIVFIBUbK2zX54UnAolAIpAIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCyx4CqdhY9vosW5wIJAKJQCKQCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCiUAikAissAikYmOF7fq88EQgEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRGDZQyAPD1/2+ixbnAgkAolAIpAIJAKJQCKQCCQCSxCBv/71r4tU2//93/8tUvmhwiuttNJQUokfSm/bE+FwFQ5/69ZppZIp/+q21P4o3hcXaa0bbZo2vs03k/DNb37zmWTPvIlAIpAIJAKJQCKQCCQCSwiBVGwsIaCzmkQgEUgEEoFEIBFIBBKBRCARWDYRGBKkx9VMSo98c+3W9fYpBiK9Tou4aEsdDv8kV9nIE3zGueqP/LVfmWhbpI/j06YNlRmKb8tnOBFIBBKBRCARSAQSgURg2UUgFRvLbt9lyxOBRCARSAQSgUQgEUgEEoFEYAkg8O9//3tGtSwpwXooBTSurrOOb9PaC4ly49w2bRLPto66Pa0/eCtTp7U8IlznF9eGh+Ki/Irq/ulPfyqX3mLchmVq49pwX57CfMxfXz/1Za/r6itTx/X56zj823BfnRE3k7xRZsitr6P2yz8p3Jcn6mnLil9ttdUiOd1EIBFIBBKBRGCFQiAVGytUd+fFJgKJQCKQCCQCiUAikAgkAonATBEYEngOxc+Uf51/Wp4EnG3eEHrW8RFX18Ff5wl/n9sX15avw3311XHj/FEXfuOozVeHa3/LY1xam3d5C4dyrsY/rrGNGxeOtBrLiAt+3Dq9jp/kHypXx4c/3Lq+vri+Out8femLGldjUvvxrcP8bVuG4pTtyys+KRFIBBKBRCARWBERSMXGitjrec2JQCKQCCQCiUAikAgkAonAEkbgyiuvLDXe8IY3nLOanX2xyiqrzAm/f/3rX90555xTeN3pTnfqrnOd6yzgG0LhBRGVpxU0VkmL7O3jHULRNq0Vhg7laxtV8+GP8Dg/HpGv9df8ow3iwh9uHdf6hcdRXbd8dbj2t2njeC7vae0YrvvBtdfh2j8uLfK1mNdYjkur803jD17hKhP+IbfmG3nquNn4Wz6BQ8urjp/GH+XrvOKEh+ps44MHd3HMuTX/Zc0/l8+LZe3as72JQCKQCCyvCKRiY3nt2byuRCARSAQSgUQgEUgElnIE/v73v3enn35698tf/rL7xz/+0a255prdfe9732699dZbpJb/9re/7b7yla90v/vd77ob3/jG3frrr9894AEP6G50oxst4Pvzn/+85Pnzn/9cBOP3vve9u7vd7W4L0v/5z392X/va17of//jH3f/+7/92a6yxRrflllt2G2ywwYI8hODf/OY3OyZeCNdvf/vbd1tttVV3gxvcYEGecR51/PSnP+1+8YtfdI95zGN6s15++eXdmWeeWfC53/3u15unjZx0bZF/mvoj76K6f/vb37o3vvGNRUD38pe/vLvJTW6yqCy7z372s+W37bbbdg95yEMWmd8VV1xR2ojR0UcfvVAbW6FwXdk4wWKdb678Q/X1CUPrOtt0aTWv8HNbfxseKlvXx1/X2eePOG7U0ZaredZ5+vx1nHJtuOY1rd/9/fnPf7674IILyr1N6bX11lt3173uda/B4txzz+1OPfXUEr/zzjsvUI4RqH7pS1/qzjvvvILJWmut1W2zzTbdLW5xi2vw0OaPfvSjnbnprne9a3fPe97zGnlE/OY3v+n++Mc/lrlh9dVXL/PmuLmH4g4F5q2/Dfflq/up9is7ieaiL2oe4efW/mhHxAnX/kif1p1p2T7c1DVNfLSpztuWjfCkdi2OOTfatyy6c/28WBYxyDYnAolAIrA8IpCKjeWxV/OaEoFEIBFIBBKBRCARWMoRuPTSS7u3ve1t3Te+8Y3u97//fUfoRjlw2mmndc9+9rM7iobZEMHim970pqKQIPi56qqrupvf/Oad+N12260Il/gJ2SktrMon4KcIednLXtbd5S53KUKwd7/73d2nP/3pohy5/vWv3xFsU8Lss88+RVHygx/8oDvssMM6SgTpeFB+4P2sZz1rISFWex2//vWvu+9973vdD3/4w5KfAKpVbPzoRz8q6ZQahKGEqdMoNiZdm7ZMU3/b5kUNU1x95jOfKbjstddei8qulP/JT37SfexjHyv9MReKDWOFMgvpz5pmq9gwBlshZc13Lv11Pfwh+Iz4CPfVGWm1yz8pHLwiX4TDjbqFwx9tC7dNEx7i16ZFvnDr9DpOfNBQfKRzo6385hPzlNXv5qlb3/rW3WWXXdbtsMMOkheQMUMhZi5BT3va04oixP19yCGHdF//+tc7ylxEyWoOeNWrXtWtvPLKJS7+vvvd73ZHHnlkyfv0pz/9GooNc457iVKUIsMcpm2UJA984AOLErduf/Ctx3CbXof7/BEXLp61P+oQN4TvUHyUDbfl25aLMLf2Kz8UDt7hRr4Iz6Xbtr8Ohz/cqHdcWFrd3jYcPFp3ccy5bR3LUniunxfL0rUPtdWccOCBB5bkF7zgBR0FaVIikAgkAssaAqnYWNZ6LNubCCQCiUAikAgkAonAcoDABz7wge6DH/xgUTo85znP6W5605sW4SGBHUHgEUccUdLqSyVUtHuiFQTWed7//veXVfx2aGy//fZlVfOhhx7aXXLJJd2OO+5YeKrXqmpCwEc+8pHdt7/97U57jj322KKssIOE/y9/+Uv34he/uAgMKTqsuLar4/nPf353wgknFEWH3QIPf/jDuwsvvLA7+OCDywpqAk/KlCF63/veVxQ4VoJT8FCI1ETYcNBBB3Xnn39+SScMJUydhiZdGx6T6p+mnsgzydQJYS8Bm/595StfWYShdtG0hA/hXb2rJvLAAwb6vTYP9eAHP7hgd4973COyLnDxijKt0FAmuzPE99W3gEnlqQWLtV+WNlwVK95J6W3+2YRrYec4fx/vun38EQ5/uMqGv84TPCMuwtwa+/D3ucq27a758Nf8+/wRF25bpi8sbhxp08c//vFu7bXX7vbYY49yf1NSUGA86EEPWujQ5s997nPdySefXOYvPKMd5o2TTjqpu81tbtO96EUvKsoRPPB91KMeVXapRRvs7DD3UVAi4ZooPd7+9rcXZermm29e5oXrXe96JR9F7Tve8Y7ix7cmSrtJOzaiX5QL/zg3rq/OX8eJb8NDceN4tGWCJ7f21/naeGko4ueHFs9/YBYXHDNTAABAAElEQVTcI8yN+iOuzSNc54tw5OsL12m1f9ycO838Z942buJ5q+3mVLvt6nm4rrP2K2v+75vvI586jMuhPMprq2tpMQse2qT8pDYNPS+Gni/BP9Jdd98urUhvn09t+UVJh4H6hzCIusa5fe9P+vT4448vxSzImKTY0B+e1TEm2vpgMU1b7USFZZiljHJDfNt6MpwIJAKJQI1AKjZqNNKfCCQCiUAikAgkAolAIrDYEaC48DFNILH33nt3j3/844uAmckVOxS+853vFKWBXQz7779/EY5QQtjNoQwBoZXMTFe1ZPcH8y3PeMYzysplygmrn8Uz20LhcMoppxQB05Of/ORiDoay4n/+53+KsoPihJKCImGLLbbo5CH8tkraDgvCQ0Rxcuc737kjXLzjHe9YhJl2oFx00UXdH/7wh7GKDUKYRzziEUUQc/jhh7eXUMKUHfe///1LfZ/85Cd78/RFTro21zJN/X28xbk+glcCEOZ0YILgQKFDuKRvmenabLPNujPOOKPsxHne855XVqjLSxnEjA7hsD4kNNbnBC8bb7zxgpXu8lr9rt8pgAh28Nxuu+2KYPdXv/pVwedmN7tZ6YOol6Lj7LPPLsos/J/whCeUXR34UVp96lOfKvUTErkGCrD1Jpg/I3hpKYSU4mt/m29SuBZWzYaP8lGuzx/86zx1myJeHH+Ew1+7dZ46X82v9keeaFft1vmCb9vWNk/ki/jgP8mN/G35Or721+0wJ1BiGN8Ech/60IeK+Tgm5IxHxOzdUUcdVe4L81tNTONttNFG3UMf+tDusY99bNn9ZacRhSoezO8F4f2tb32rjEvzUE0XX3xxUby6f+1q22STTcp8aPeIcW6XGwWK+c6cts4665S+pCz+6le/2u2yyy6FXVybwCR/pE9yA/+6vbV/Urq8k/JEep9bx4W/5VnH122bSz+c6nrqcGCovtYfZer8kS/ShsLi+4gQ2q4gZM5F4+a/mNvNp+ZD45NywvPN+P/whz9cno2Ez+bgoV2V2htjTvkNN9ywXK85/ylPeUrhZywzt6Y95tZb3vKWZYGAZzdyn7lHvA8QpHseemYa13U7Pae///3vl/cBikKKEjszb3vb25Znsp2Z6CUveUnXPi/Ej3u+aNcXvvCFsouPgtF1u1cf9rCHleeca3OdMHbPez55DlIqhpJlHP9J9XseMp9lPsDfjlaY251ImWnhwzHHHFPi7TYNfhSsm266affEJz5x7PuT/N4/XAeyM80719YjM3stwfUTn/hEZ6eq+dDz3zsZ85xoUlvtVoOVeZDi42c/+1lRbHiPutWtblWeyd7VjAPvXOuuu27bhAwnAolAIjCIQCo2BqHJhEQgEUgEEoFEIBFIBBKBxYEAAQOhHRNOj370o4tAQD0+egkKCUB8CPvIpnAg2CAA8QFOEEjgQLhn50RLFBo+spmUQgSOwlZ8UmoQ9hCMIAIbdLvb3a7wI7yglCCIIQiRnyKAoCaEjPHBbZcGkp+yg5kL7cOr3YFRMlZ/hJurrbZaEbT0KTYIt1wbgf973/veblrFxjTXRtg5qf6qqdfwsvt/4oknFiGPM0Vcs2u3kpzCiaCDyS5nEhDIMHulX60GtSsGEcJQMuEDhxB2UIYQwMKfwIM5rje/+c2l752TQkCMJ3woI9RphwqBEqFX1Esgp7/1sz4lRLMDhrDMrhrpTHsZV9ppPIofR7VwccivfJ3Wx68WaEb6pDKRb8iN8rVQNPy1O6m89ODFbX+RXsdHHBdF+b7rnJ/jP//yTmpfpP+n1H/qiLomucpGnprPOH+bn8JNWwg3zQHmI7sqzFfyHnfccWVuoqh97WtfuxBrQuG73/3uZW4g8DQO7dZCMZ/wu4/sDKOYc1/EnCMNEfSql0KQUpWg8Ytf/GLhZ64iEGT+igCRQNYZH4S+FK7mTcqQoLp/wh+uPOGvXX7X2rp1/uAfbotjG458tRt1RlxdJvy1O+RXPtL6eEXckNu2YyhfHR/1RVnhafx4yBflJ4VrnnX9td/8Vs+5hNPj5r96bqc0I4D2bGOez7xrzqZE83w2Dt/1rneV52ZdJ3895sztFgWYi41587rnrjFJCU+BQpFivJ511lllnqaseOc731kUKdpM8C2dstpOp7qdBOzuE0pDzwuCd/caxQaliGeMe+wVr3jFNZ4Xk54vFATa6Wwd7aRIoMRQp0UPTFVSIhL0mx88u+LZ456fxH9Suue+e907jPcKz1L89avnn3cP10cZEIoNGInzfkKxMe79yfsVM5dBnq8WEPSRtlDcUj7AVv9btAALY2VSW5nN0y4LCfzUY06CAaWs57T6LVzxTvHqV7+6rxkZlwgkAolALwLX6Y3NyEQgEUgEEoFEIBFIBBKBRGAxIUBQQICz6qqrFqFGXY0PeCQPosSQ9z73uU8xC2XFp9WLVjL20b3uda8idCCAJISxu4BwhCCA8M9HMwUACjMIhERx6K4Pdx/eBBeEMojwm9CCAIEiBlkx6acdVoX66Be2WjQOBGaXn7A9fj7ekWskEBgi7aE0ILAfIgKM4MslFJjm2vCbVP9QnRGvPwhznvrUp3YvfelLi1KBcoCSAUn3I8R1UPjuu+++UHwJXJ0PHwIYiiQHr9tZA2tkB4YdIVYHq8euD/WEsC7q4aIIE2Ttueee5TyU6CNC41hVSwjGxBghNCLAIqwbR3gbizEeIxx1tmkR37rT5hsqF+XrdkTevrhIG+e67kiv/eJqijzhRt5w6/yRZ5wb5Vq35SMdBa/whxvxs3GnwUw9MVfUfnMFooT9yEc+UoSJBJotmUfMJ+YVStb3vOc9RUhJKWIFOjJHmUPMWZSa5qqapNuNYRcXpQYBp7lNu8yN6ifIpVQ1B8Y5H+5JQkXtirEDpz5/Gxf5wg2sWjfSufUv8kV6Ha7ztX756l+kR/naHfJHneEGv+A1jRtlJrmTeEUboy2Rv46PtDou6u3L36bVY6X2R1lx08x/8puT7XIzJ9tFSTFMeeAMBgoCz0o77FrFW9RbjzkC91133bWM+2iL5xQlHUXHM5/5zMLX89kz0T1FgM+kpHTlzf12EXiexnMfL0Jw49oZEXYo2EkhPsa+5yKcttlmm6KAifq5aNzzxTsCpYLnLAUhLCjnPUeUQ4TzlDR2Qr3whS8su2II6yk80Dj+k9L1gfrduxSSMICjMFN0lBvItbjGmuI6xUkT7nt/8p6i3UH6om8XjrrsDKEEYY5PW+wA8mz27JymrdEmz2N4OavMHOd6uJ7HTJJSpjHnl5QIJAKJwEwQyB0bM0Er8yYCiUAikAgkAolAIpAIzBkCPnanJcJoQgR2mQkACcDHkVXRBH9WevpYJ5wI8xDjyrVtIkx561vfWoQoPuitzKzJymorTAkeKWOsOrUS0k6POAA48r/hDW8oZiMivCiu1dqEm0FWascukohr3fba2vSZhAkomNzgIu0JsyfBh6KCIoogzCrbPqK8objQr8o7+yT6lmAKEXoR1hLoUljZrTOOrNh10DohMuESIRwBlfNU9I2wfjVG5NF36gyFVB/vwC5ceYb8feXnKq6uczY821XhLY+Wv3AbF2UiPlzxtV+4XlkedYcb6cpEXJ1feh9FHdzaL29fXMT38ZqUNlQmyoXy1H2w0047LVCQSjeu7EqLeUfbCH0Jba1SJlQMBaddFnZfMMtH2cFcGjI+CYKteCdYJACEEeWIeMo+98PrXve6cg8pQ7FnhT0KN0xgBc7S+vwRV7vyzoaib5St/bPhVfMIXtz4RXqdFnGzrW+oHGxqijojLrAbCkc8V9k+fm1clGnzt+HI1+d6Vo2b/+ycQPXcbj42hjz3KAjUxyQSZYcdHBtssME1qqrHnHlYGedcxTPAtdmBZ7U+ITbe8+bNK/OzZ6l5H2/ttVuB372Ezze/+c2ibFGp5wphuJ2CeDpDw44Oz0XCdgpy8Xbz9dG450vsxFTOc8LOFfc60gZtsfsEaR+FSyjHLTJA4/hPSvcuYaeLe9tOLH1jZ4QzdMTbPTIT6nt/wjtM6eHl+QrLlsw96vTsNY9wzV92llkkMZO22jEayl87cSivmPfyrkDx+/rXv77sRKGQiXmzbU+GE4FEIBFoEUjFRotIhhOBRCARSAQSgUQgEUgEFisCPmiRHQyEBQQYQbFTI/JEfOyoCDd2XUR67VphaFUjMwx2PhAGMiWBCBqtcpYnVj2KDz+FRBBTC0wh+ah/7nOf2/3Xf/3XNT62raAmiCTcUA+74QT+BPHshROwBBEIzBURKNUYwGsm17ao7SB0CKUGIQeKHSnBm5Aq+iviWhefOJA18sZ1Bb9QOBCoOQB+EgUffKMsXlaD6h9CKqbK8AvhSdQ5xJsgK2gaf+Ttcwnbah7yiENtfIkc+Gvz9vEdKLqgvjo9+IVbp/X5p6kPr7i2Ph5t3KT80bZwlQ9/uHXcOL+0cVS3O+YH+cNPMEhxyiSae5vCItLkO+CAA8q8EcJfq9QJJrXTXGEVNVLG6mwCVLbn999//2LOR5rV54SaTKdRcpjPrFRnHogg17xmFTmKsV73i1X6iFm7Gp8SOYd/dZ0127bONlznneSPsrXb+uswfhGexLtNr/u+TWt5tnkjPeKHwvj24dYX17ahDgf/Oq7PP+38V8/tMZca60jbPGfQ0JxZjzn5lIk5Xthzww4IynALAdw/Vu17zoqnVAiigEbuETufmBgMwpMwPIhCz84N5qgoZOyemDdSmLh3+mjc84VZRNen7fyIsF0b4MNPEc8Ek3ZQzgQe4Y7jj9+4dOa81OF9JBRO3pP0AwURhUD0A16TKPox3GjjpHLS9acxpi3xDmNOi3ltmrZGPXWboy3xzhVh89y0Yzr4ppsIJAIrNgKp2Fix+z+vPhFIBBKBRCARSAQSgSWOgA9ignir5Ak3mDQiQKDUiF0ITLfMhqxiZvv7hBNOKMI85oaY0wgiVCHkYx/fj/DdikQKCAqPENITZjhMk7kFJii0keks5KPbykLCAWd6EC7OGwlQCF1cgxWbFBsOOCecCArBY4QXxXWAqNWwQYT0015blFkU13VZRUsYYSU5as3nlMhF+GNWhxCXyRNjhiBIvxKEwH2IQpmkjbGKFvYEaHGAqb6BmZW2IWAa4ie+T9ASceFG+TYs3vgOkl6HxfeVify1W+fr4xFxfXW0fCKv+JpvnS/ytOniI6711+WH/MG3TR+Kb/MNhaNNkV6Ha3+kT+sygYYI3WJMEaSaQ8JkHQWD9CBmVigrkPME7OAiJCS8tYo8FIPSCfysyGaKxy9s3RNgMs1jfkHmGMpgYzaEula1o5hf5DfHRRxBqHY6uyYwaPtMfB2nbB3u80dcuFGGG/Xwo0nh+bkWvk8iri4ffLh9/r68dVzNk1/b+yh4D6X3lanj+srh2cZHPcpGWhs3KVzXe8kll5RdQKGwrdMWZf6r+bR+Sge/eD4ahzHmKBW0vxak2+1h/Hou20XnecnUkcPJHUgdZ1TBg5nCMMloXFNcDBEFg12LBO3O6PAcwC+UMm25cc+XOCDcM2TrkRksO0yQ57t3Bm2za4qSfL/99itnS4XJyqhnHH/Pr3HpdsfoQ/MHvLyXmAvc++pmFtOzEXkOa6f88eyLNszErcdZ3af6U53qi4Uo5rbTTjut4BLKnnFtDeXQTNqTeROBRCARmBaBVGxMi1TmSwQSgUQgEUgEEoFEIBGYEwQIQAiWmad49+iwXLsdxNkhQWhoNb2dELMhtuYduE3AQ1BoFaiVm8gHOIUJ4Qch47HHHlsEFbHiUBphtxWjTFpYLRqrJa3MDLJrgBkNK0PVQ8FASUNBQuhuVTUKJUmUm0s3TNi0PCddW5u/L+xa7HbRD2Fuo81HmMJEF9MSH//4x0syYcxcEuEvs2PGiDbpR/3LtNg4xQaTPgQ/+pFA2thiNoNgj4DNymXjjEA4lBq14G2aa6iFQHX+cfGEQ4tCLe8I13zF9YUjPlzt6CsvXvlIE64peEd65A23zhv+KCNc+9v0Nq0Oj6tPvjY9eM+FS2DIZA5lrPudIJZSwfxCuRErl9VF8McOPdprr71KupXd7hUH7lIu4OEgXWQVNlNrzNzVZMx/6UtfKrs6nvSkJy1QdOBvXqGEtbuDwiLMVhFqEjjazREHhTP74r4hhH3Na16zAP8Wr8CwxTzitS3SlA1/3Wb+4NvG1+FxecalBY86z0z9wSNc5YeuZShe2dmkjSuDZ30tkbeNGxf2vDKuKAw8X1sieJ6r+S94E6g7Y8q4NN7dF/WYIwwnkK93YVh8YIeSZ6VxypwRpYRnCsUg80j4eC9gNtB9xu8+sgtyHIU5KufNwNBZEEM07vnChCITSXEgOOUKYT8zW67PnOCakWcTpWaY4IpnyTj+nl/j0t3z8cyyaxQm3o/0oR2iDvBWv4UF4rzLaKM5YybkOuzC0E8US1uPlDie+3WfepZuvvnmpS/e8pa3lB0w6tE3TOF5R5jU1pm06drIq8+YDjOnej9gkstYCmWxNhnDzhTxnkdZph+23HLLBc2dhoc+9B7ivYNiyzuTvkxKBBKBRUMgFRuLhl+WTgQSgUQgEUgEEoFEIBGYBQKPe9zjyuplQjlCNx+KVjP7SHaI5WyVAlbgW+GICPlCqSFMgEh5wY59CCIIBglNfJgzN0UYQnD55S9/uQiaCAtDcI8HYmrKIeFWmfrAJ0SJD1V1hGJjfu4l+z/p2qZpDfyOO+64jnBlSLFBIOIDXf9ZxepcC/nnkhxgq/8czmxXiFXABMmucRwRTBhTBBFWxTtfhfBA3+p/ArK3ve1tReFBcEEwFKvex/GNtFq4OOSPvHPpan9dX/AWJ62laeP7eOJV1xf8h3i2dbfhKB98w434cCO+r3y0U94+f10m8oQbfKNcnbf1122Rxsa9nWWEsQSAFBzufzuU/Ahmg2oBrrFGAeE+iRXL5homq4KMZybu2JivyQ4PZNyyfW/Xkl0dBGPmzkc/+tFl7nFuD8UdhQkzVwSdFLh2dCH3ivkphK5D/RfxrVuYVH+RLir84VbZFotXPZNomjw1j7avpfXFzSZ+XBlp46i+jmhPxLXh4ENZ6+Bqc3OfYoNwetz813e+QvAecrWJItkzw3kXFBL1mDPe3SPRZq45VzuYBTz88MOLgsMuJWOasJggnak2z6CTTjqpPItD2EwZEoqDvja5nwjmCd3tshgyQ6XsuOeL54bD0ilcCLy9L6hX/XaZIOdamRMoBOSJRRDmAAqacfyVH5fuPlY/hag+tZMVX+8pFKfaoY077LBDUfbbpUpQLn4mpD+8z8D5Ax/4QJk/zDdtn+6+++5lB6trPW20U0O/2pXq/WCats6kTddGXgpB73LOcTPPUmTbrfuqV72qKIwotSzEocSSh0vJAz8KMDSJB8Wj8UIRbgEN/vp1//33X0iBcm1cf9aZCCzrCKRiY1nvwWx/IpAIJAKJQCKQCCQCyyAChBcEzgQZYQqKwINSwId1kFXGVoXKj+wQcFCuj+k+IoR0UGYfxcpqgkgH9zIjweSLFYt3uMMdFhykScDz3//9330sSpz8BJE+Tq3CDh7KEfBPK1yIaxmXn+DA7gwCm2lo0rXVPIbqt6LcNYUJkLpM+H38UwS5fuRjPxQ6zHZZiahvg/DSb8j19tUd1xorGF0L4Y4VspQaeOhDAhVEEKZefVeTVZQEWlYKU5AJE/apk8CMkJmgIjCVjzCsbWPNs/YTZoSQsY4f51dmHPWl99VR55Neh4f4R75wh/INxaujLtvnr+PG8ZEWbR5yo3yk412Xq8O1X55oR8RHuQi3PKWjiJ8f+s+/eAJbAkXCLMJE45Jgto/MCTHO5UXyRlxbRv4+csgzszvGN6JMsRKfYNH8tssuu5R50tyobYSehK/4mYNCMbzeyEye+1GZ9hrrcO3va8+4uJmWlT/6Yxzf2aQF73DxiPZFnRFu+bfxbTjyzzQ+ys3W1e66zqHrMC8SksZ82M5nFGDj5j/PL+M0xq32Mslknoxnpzim1CiDxZlXhSn8QjFSjzm7M4zFfffdV9GyQ8n8v9NOO5Xx7dnvejz7jfcYt3Yz8Ntxh7e2U1TgbQFE287CfPSnPS960YvK4drK18/W9nkx6flilwReFkh4/lBSeoZQECEHaTNLRSnuvoOHdsNPf03iPyndvBE7syh+1OEZGwd+q8fc5DkbO7m0B2bxDJ3m/YmpTbti4GyO6etT5wHZVeZ5r++9kyijP9CktnoG67MYI8p4f4hnrzDFUMyT2rCkSBssUqGQ22677Qrmxto+++xTlM76m0KZgslOFu8KxiBzpHYHU2xMw+N973tfGRsUWnbqUUTCwMINyqWkRCARmD0CK42035OXPsyef5ZMBBKBRCARSAQSgUQgEVjBEKiFCdNcupWQBCBDtrCn4TGbPISCdmRQktTCnJnwIpTBgwBiSX6MT2rjolybVeB2PDjPgpC0prPPPrt8/BOaOfiV+RA0pGiqy87WP+217Lrrrh075wQSTJxY5WtM1UJBbbCa1pjTZ9PS+eefv1DWEC6KHPJHgbb+Olz7I3+fW9fRl17H1TzDP+TW5Vp/1Fm7tV/+NtzyaMND7Wjj23IRrutr/W1YmYhr/cGvz422RBpBMT7GkxXcMxk3wWMuXAeJv/zlLy/nBtjlYWdGtMX4JBwm/KznMsKzgw8+uAiNw7xfe311OPy1O40/ri/yRngc/nVa5B/nRv7aHfIHn0iPcOu27W3D8i9KXFtfHZ7Utjpv+PvaIi3OgDC3GaPjaDbz3zh+rsMvnn/1mLMgwa47gmN+O5+M0yAKZmWH3hnM054xxvmk6wqeM3UnPV+0z71Pme/XUrxDxPW36ZP4T0rXX/GOUd/bUY/2wXEIw8g3zlUHnOMdDE+/9pqiP9TVpuE/qa3j2nBtpdmha4582cteVnYcaYf+tsDGLg0KMbszKJW9X8S7jp13ytkdZMfSJB4f+tCHiiIoFFPqsZiDEpvCcaZ0k5XeNNMimX9KBC7/v72nzJnZlhYEcsfG0tIT2Y5EIBFIBBKBRCARSARWUAR8rPd9sC9uOHyYE9AvChE0hTBgUfjMddlFuTYHFFvlHavFx7UtPvLH5VnUtNlci34Z6lsCssUlJBsSPAYGdXrtj/QhN/ISNs0F4RM8J/GTr84f/nD7yktDbR11OPytG/wiPngFvwhLD/9QWp2nj1/UFeXrcB2n7NB4asssrrBV01aQM4132GGHlV0c5h4CSbusXvnKVy4wh1O3geDOeUCEaDUFHhEX4daN9ElulKvziYs+ivQ2LH/E1WWn8bf88Wnjav7RhpZ3Gz8prHybZyiurqu+zihfx9V5+/yRN8q2eaaZ1+Z6/tOWtj0x5jxL7HSiHGQeq1ZqaHso5trriPCSeDeY9HxxbePu/UkKhUn8J6XrL7tEh0j7JrVhqGzEq6N+j+nrU3kn9cektkZ9S5PLVBpTW3bFBdml490mdj8xt2m3Sf2+Y8cNE6R+0/CgIIEP5RBFFIWI3S+Tzo2JNqWbCCQCwwikYmMYm0xJBBKBRCARSAQSgUQgEUgEVjgEmEWwMrVPWOLj/9WvfnXvytVrGygmHgh/w1TItd2etv5a+Ff7I19fXAgy6zxtXKSNc5XBP1x5g09fveN49fEJHkM8Ix3fPn9fXLQh0oJ38Ihwmy4caW3eCAfvcW7wHZdnSaYRqjnDhzmcn//858WEX5iFoYRkrqcl9+vee+9dzNMMXU8dX/trXnX8kL/OX/vlH9cf8gbPOl/NY6b+vjr7eES90mr/NOG+PH1x9TVFHXWcMn0UeaJMnSfS6rilxV+POUJfyg1C4Hql+tLS1mxHIkBpFWY0oUFJfMQRR3QPf/jDF+yEih0zNVrxfiSNwm4Sj1CK2BHLdCCzXg4oD9OazH/GfU3hN06ZVrcj/YlAIjBSuiYIiUAikAgkAolAIpAIJAKJQCKQCAQChFBDZGXiU57ylGsIAYfyL8l4K9KZ9bg2dv8synX2CS6Dn7QQdoyLi7TWVTb4hz/41fFtuToc+eu4ul2tv843zh/1j8uzKGl1u2bKZ3G3babtifyEY+zZExKzux9mcNi8D8FZ5OWKYwP+3ve+94LzcIaurY4Pf7g1zz7/pHzS63HUhvt41nF12Tp+nH9cHW1763Dtx3+m4bZNdfm4DnF9fmUjPvi04Yjn1rzr+GvTX485K9MXpxmpa/M6s+7lDwHnjLztbW8ruzCcHdM3p8ZVx70XbsRP4rHe6FwSSmg77X7961+XHU1MCB5wwAHl/QUf87Xz4pISgURgOgRSsTEdTpkrEUgEEoFEIBFIBBKBRCARWOER8BE/7mP/2gRoSSk0xgkaF8f1w7ytsy8u6o68IXCpw31+5SJv8Ag38kd4yB3XnrrMUD11nvRPRsA9uMYaa0zOeHUOJlBa7Nvw1MxmmXHaMTKO/aTxOKmOvmuu44b82lSnTROuryPajUf46/Twj0uLPLU70/x12cXtN+Zy1fniRjn5zxUCFA1HH31096Mf/ajbc889F5ihwp9yjhK5JrvlUOzc4B/HQzpyPpJ8zFuph2nBvfbaq+w2jfuZ8iMpEUgEpkcgFRvTY5U5E4FEIBFIBBKBRCARSAQSgURgBURg3C6WFRCOvORlEIHaVMoy2PxsciKQCCQCiwUBOz3f+973dl/96le75z73uWVXXF3R2muv3X39618v52PEAorf/OY3ZeeF3RZoHA8KiyOPPLLbeuutu0022aQsDtloo4262972tsVMoPI77rgjJykRSARmgcB1ZlEmiyQCiUAikAgkAolAIpAIJAKJwDKGgA9vZhKSli0E9Jm+SxqPwPnnn9+dddZZ3Z/+9KfxGa9Oveiii0p+btKSRcDZC0mJQCKwdCGwIj5rKB1OOumk7iMf+Ui3ww47FDN/f/vb34qpP+b+EHN+f/7zn7vPfvazZbfF7373u+JnMsp5ZNPw+MY3vtG95z3v6f74xz+W57kDyp2VtOmmmy5dgyBbkwjMEQLuH+9ksRNpjtj2slnpiiuu+L/elIxMBBKBRCARSAQSgUQgEUgEZoFAvTV/FsWzyGJCwIrBc889t9t1110XOuhyMVU3li3h8xlnnNFZCbnxxhuPzbsiJxJ+HHPMMd2GG27Y7bbbbisyFBOv/ZBDDilj6qlPfWr3sIc9bGJ+K3Q///nPl7zKJC0ZBAgH/bbddtvuIQ95yJKpdCmoxdwbAh7ukD+aGukRDrc1iRXxQ27NZ8g/VDbih+qs+clbh2t/8Olza97hb13l2rgI12nj/NKC6rLTtjPKTuvWdUSZ9ddfP7wFK2cseA46H8o5OX1k3Jx66qklaeedd+6uc535a5M9Q7/whS8Uk0ZMfm2++ea9B7RTIn7729/uzjvvvHKQ+9DOgBX1WXPZZZcV81AXX3xx6Yfa1KZ32de//vUds1MHHXRQd/bZZ3fMRFF8OACcCSnKjWl4nHLKKd3xxx/fOads5ZVXLooSuz/23nvvbjY7Qm+y0pv6hsvUcTe77U26R2+3Yff/7N0J/PfFtDjwSbZIiUQlnhZRaFUqRegfspTLRbai5FpCluKWULaE7EtERJZky77dlBBKZAmlRYuypVxdKv7f9zydp3mmz/b9Pb9fz/Y5r9f3O/uZmTPbmXNm5nPXdXzv49p0xk8vS1//wrnp2qvbD1Hc/NY3TQ971PrpXhvdPvfD3/z28nTC536T/vdP/1iQ7902Wz095MHz0uq3XyldfsU/0kmnXJhOP/niBeFhWfHmN0kbb36HCf+3ejruQz8P72yutNrN0kMftn7acIPV0oorrpDOv+DK9MUvn5Muv3j+819l5LtuvFra+j5rTtaU36YrLr3+ubCb3HSFtMP/u2vafNM10m1Wvnm67A9/T1/71nnp3DP/XCZvtP/93y9u9J+p58c+9rF8O2eHHXa4AYof//jH6Sc/+Un+dh0l2eKEY489Nq2//vq5T8+kHMYJ5d0jHvGItM4666Tvf//76Z3vfGf2i3lrJniHpBmfohpCpTHOSIGRAiMFRgqMFBgpMFJgpMBIgaWIAkcffXT+MOWuu+6aNt1001zyk08+OZ122mnpsY997GJRbPiQ7Omnn54FOb/85S+zoIWgZllVbDS1wbRdiPDEG9zbbLPNrCk2ZqNc09bjxohP6HTKKaekBz7wgYOyO+ecc3L8DTfccFB8t2Ze/epX57jPe97zWgWRg5Atx5HOOuus9NnPfjYR8s6GYmNpaZcQoIepCzTZm/wWpbt04SvDmvII4XxfvDq8djfh7vKTXt5hihv22izDunBGWNSpzQ3/TKDGW+OIcCf/f/SjH+V10Il9CofNNtuscT655ppr8ncfTjrppIzuKU95Sn7G6PLLL8+CdgqLq6++Oj+R9O1vfzuvEZQkAZQiRx11VDrzzDOzIH6TTTZpffJoLtaaKMeSbBIm77PPPo1FDEEzxdHee++dTj311Hzjwjc3HDbYcsstc7ohOKxLnq3S3gTAW2yxRb6tMROlRmNhp/BcabWbp1cdukN64APWSSutdLO04k1WSBdefGW67e1WSh8/6metmJ79gi3TE3bfKN1xjZUmt07+PbnVcnXa+N63T6940Unp3xP3epvcPr3mdfdPm2+yRvrXZBzd7KYrpv+3y7rpwJeelM747iUZ7zr3uG3abtu101Zb3jHdbePbT57zusVCig0KiRcdcN/0qEduMBkTt5jkkybfN7kmbbXdmullL/6fdNVfrk6rrrlS2u5+d07bbHmndPdNVk8brL9a+t6PL1lIsfHoJ26UnvPczdM6a98m/fOaayd1vEl64M53TQe85MR0/i/+0lrHuQigvPjyl7+c7ne/+y1QTEY+lF2UlEvC4Ypzzz23cR6KsvaZ1mJ82E477ZSjmlPwZDOdU/vyK8NHxUZJjdE+UmCkwEiBkQIjBUYKjBQYKbBMUYBgwIcay4+YTm4sZ6FJ180SQnjMeFecIBRmHk55hPAiwkrTZtZpwHijuQyDwzMQcPjoahNMU5evfe1rWYnhmYNQbNi8X3rppflEVol/Nspf4muz2zS9+c1vTt6mJpjRLmgyBII+Tjo2nfzSVnCttNJKjeGRhzwJguAB0gXdm/CKoy8AwoxpoKkNIn3Up6u9xfVdhEMOOSSttdZakXQhs6tPqeu1116baVIm6irXkLEBpw+pBg1L3OzoqZ+HUKgpXFh5MjbiDG3HiN80JiIszKG0jvhMdajLqGyEEGCvvfa6gQAATeRVzjU5csuf+PpcEx1akizk3Zdf0LKtnSDT3uajtrYSp689+8oBRwmEHj58TrBXgzI7Dd02zvV3ZS3L29cuQ/p013wf/aetTBHeN5bLuiqzH6jt4ZcDG/4iXRlUrjtN4aVfaS9xNNmniRvpZ5Im0k5ryquse1/6IXGHxOnLJ8KbcFFAUDZYiwk01cE81gTmagJRYwIEbb/0pS9lhbf14elPf3q+ielGprl52223zXOKZ2COOOKIfCvNU0oOORDGt8GirDVtOJcGf+P2CU94Qm9RfQ/DDVPzST0HDcGBfyDY1j7mG7zK4oIHPmxeetITNkrnn39FOuyQk9Pt1r5VevUrt5/cQNkqffGEs9OVl11/8yHKeJeNVkvPfc7mWRHx8lecnP5x5dXpv1+xXdprz3unzxz/m/Tj71yc9tjr3unhD1kvnfCV36bPHntW2vbB66RnTML33W/LtNd3v5BRPfPZm6ddHrpuWmP1W6XbrXbLdOnkJkUJa99t1bTP3pumlW99s3TAy76d/u9vV6dXvWaH9OTdN06f+NRZ6XtfviDt9PD10gv33TKtdaeV0xp3wHenyTo2/yYTXMbdfi+8T9piclvjTW//UTrz1EvTPvtunh45uQXy3VMvTm875NQyyzm3O5hy0EEHJYcpym9deZbMbaw999xzoX2B+cC6uvLKKzeWzTjXh4byGjWSNt7Dh+1XW221Onrm1fGTZXngQOeSL7ZfUpc111zzBjhKD2VXh7bym+fwHdbcpjm0xBX2UbERlBjNkQIjBUYKjBQYKTBSYKTASIFlggKEj24EOE3nmQen5Fz3t6n4whe+kAXrmGXC4t122y0/LUDY/t73vjetscYaefP6wx/+MDPebhPEKcmaOBhzT6o4xUfwIB/PEjiFTFjoY5MEEIQJnoPwDQCbWU+wEDQAQjHPSTjVJA5GXpindAg9Z1KX1772tenss8/OAvxPfepT+ZkENyO8dettaIqOO9zhDrl+XeV3SpRghXLEUwtOl9rQ3//+9891qOnxgQ98IAtYlD2u3L/1rW/NJ0Uf97jHZZrY1D3qUY9KH//4x3Mb1Tia3Mpx4oknZmWIjZB2ddU9hJtuoXhSyOkwGyttRoizyiqrJN9PcBXeZs0TEnHCdaONNkoPfvCD0/HHH58uvvjiTHc4tV+k8TSIPuKZAOCpD29waxfl0W73vve9F5yAdQUfjR75yEemb37zm41t0Nfedf1tfJ22/fOf/5yfyRjSJp7U+MxnPpP7u/y09UMf+tDc7m19o2tsKNMrX/nKLHx70IMelOtu02nz+tSnPjWPGXE8ZXLCCSdk+mkbbW2j7OkNgI6e41A+4RtvvHG+PRSb229961vJrSICOe3oI6vGZ4RnJMWf8QUfYfe8efNyuiK4d2yVccPeVkbjW1+mFANvfOMbcx/bcccdc/9xA4HizobcCdzHPOYxrcooc41bOOYDfcmTDcbEXe5ylyzk0HfcIkFb8PnPfz73W/1Pv9Zf2/KLOUd6feeiiy7Kt0xqIRqafeITn0g///nPc1sYU25SEFYQwKB/X3t2lSMXvOUPneRrTBqHMW4I/MxRBL7mUjfLjC8gvrlUGMUvGusb5tm2dunq00Pn+665Z9qxrB4hmGavIcLCFF7a6/ilwKctXu3f5+7Kow5rctf4m+JM6wdnWVfpm/y68Nbpu+J2hS0KnjItQaePR//3f/93q1LDWv3ud787K1BDsRFls24TNFqzzLHWB2uZk+H6vXFlfbIuug3y7Gc/O8/XXQLHmaw1UZ7lxdSGbevRUBqY8+v5eGja2Yq3y0TAv9Itb5o+/+Xfpo9+6GdppVVvnvZ4yr3SRne/XdpwcgPitG9ceIOstt5h7bTWmiunk793Ufrge36arv3Hv9J9H3DntMcT75kesNNdsmLjMbvebTJWUzryqJ+kbx1/Tjr1R5ekvZ56r7TLzuumW9zmphNlyDXpqsnti+OO/9XkRkdKr3jZtjfIZ827rpKfsbr4939LHzryzHTN5Jmsp+y1Sdp+m7XSBhveLis2qIRPPPl36azT/5je8Z7/N1nDrldqQLjizVdI95rcBgEfPubn6Tc//EPacPLs1Xb3XSttdu81sv+N+YdnphQwJkvFho/VU5Q9/OEPz8Wh0MBD4VMJ/ynT/vM//zPzCCLgw/Dj1kN9Ea/9xS9+Mb30pS9d0C/tXexz+NX9zJxiT4D3kF5ZrLOhzDBfwG+esPbhrfElcJpv8NCUgPYF9lZw4DXcNsEz4B/wnpStpRIkaG2e8i2bX//613kOl4/85QHwAuZ2B58803fggQem299+fjsGjjZzVGy0UWb0HykwUmCkwEiBkQIjBUYKjBRYKilAOIpBp3DAHBMguK1w+OGH5w0DgapNgziEbPxt6r2Du+qqq+b4NhuUIzYeFBPl8w5BFBsQyhCbBYI2ecWGhNCNYBDOO93pTumOd7zjAuUGAaJnWPjbILzjHe/IAkibBhsOmwlCdYK+mdSF0E79gHopHyAYtKEgjCX46Cu/K+XKT/Cq/KHcIPwlLKmFJATW4subYkP8oydPYhGi7rHHHjm+70RQFhDeom8f/PSnP82nTglr0OzCCy/M7WpDhcaE/oTMNnroR7lhQ6bO3r/27IcyURgRlGtXcSmS4lkJ5UQzeThJW6ax8SPM1maeD1Fnpy7hgJcyKt4sp+yQt/7W1gZ97V3TAx4CaO31nOc8J1/z72sT/YnAHz1sWG2mCYy9Ed5ULoqErrHhRN4nP/nJ3K5ohB4UZwS7Ns7KZSOqHdDUJhVdnEzWJ2ywxZe/9lIuJ5WNLTic8LNJJqAW30l+5RVuo2yTXAPlk9s/+se8iVJDuYydEqalNaVUWxndMjhv8oRIgH6ozuqp3todrQndKWgI/d20CeVbpNO35EEBQeGjH2sfc4X43NrXfPLEJz4xC+65zSsEDn35xZyjnxKG+hGG1EAx4tst+r4+rn8bj9p2//33z3Tvak94p6l3mb9xpE8TEO6yyy5ZUYZ++gA/tNMPjGGCXWWUl7bWN+Rt/vDMzvOf//zGdunr00Pm+765Z9r+VdIg7F2KgK4w6etwfafJv8mvTpsTNvxFvMDdEGUhL/EizUIBLY6heFuSd3oH7jAjcu0O/9ky+/Bb1/0coqBEbAI0/OAHP5jnTYcyXvOa1ywUzZgAbhAAc4kxb8yYp6wXlKeEkeYlwkzKaOu4eaX81kdGMPmbyVoTaUdz6aLA+uuumgt85s//OBmvKf398n+mc8/7a1Zs3G3D1RoVG+tdl+ZXZ/8l/fN/598w+tkv/5TxbDRRONz05jdJd5k8+wR+9pPLsnner/6S/n7VNWnVyXNTq9955XTRLy9PH508dXX5X/4v3fWeqzUqNi4656/psj/+fXJj4+bpYbttMJnv/5nuPFGo/P2qq9MvJ+UF3/na+emUr1+QLvvd39Lb3rVT9iv//nXtv9PpZ/whbbPVndLDJ0qcH9x2pbTFZvMVGj/68Xx+uIw/13Y3FO0h8OC+cUe5BfDgDhVY7wGhv7XZ/OBgDh6KMt+BFLwFPiz4b/MH/stauvPOO6ftt98+44CTgrPp5vd73vOezCfZA+CtKEjxxJ7WBBQiDmw59IP/p2igtMDT4tnkj5eTtwNE/By+ooBxeAYu/Ip1vWmOsV/Ca6CFQwnWUPU77LDDcv54aPiFm8+CTjmw528+RXsijcEjBUYKjBQYKTBSYKTASIGRAiMFlhYKEAr4YYoJVZ/73OdmYRihmQ3AC1/4wnyDQ30w1jb0QBrMvhNJL3nJS/JGAdNNwFqDeJh0gkRvLxMG2rBw2zwQ7kc5PDXjxKTTmTYIFBcEc05nwWED4UaFPD1xg/GPJ28CxzR1CeWBMquvE1GAEDnwDSm/NOITbFMMvexlL8uCFOVT5hpsbAh1fDCQgNQGjADGBogw2wkuQm8bmqGADvA4mYrG6EgZ5cYH8LFDwnQbNeHKSIAjXFsAdVBft0bQ+D73uU8WnBKg2tBpF23kVBq/Mo1NHbw2avK1iYzwoGX2mPyFm9nUBkPaO3CFWeIs/draBN1tjG04KQTUjwKIooywt6lcBMV9YyP6jpPGxs+TnvSkPKZsjAHhvE2p/n3AAQfkdiBUc4qYMN+G+Hvf+15WeKGncUkxQHinvbQhpYe+YrxSlngPXpmbQH6+1yI/bf6sZz0rn/KPuDOhdVcZnUYkRA9AW7d7zA2EFQQX+pGyEB4SLlB+1WDco5lNe8SnOOQnjOCe0IDiRH/TjvBoH5v9vvyiv2hT8xKBaH1qU7ugOyGoOMaEGyP8pQd97dlXjrrepTvKGHmF29yw77775v5DuELZoY+gAwWvemhnCsu48WZOaWqXIX1avl3zfdfcM5P+VdJgLuxBxz7cQfe+eDMNtwb0Cfeb4pRp2uxRpggfakonbhk/3LNtRhnLPCMPfuYHvyhLGT/sFJ+EnNZUQsYa5k2UucCcS8kbN534Gccg1mgKWzyO+dJ6SfGt/9YQ/afsH+xta02dfnQvPRS41a1ulgvr2xUBV/1jvv22q94yvBYyr09z7QL/q/5vvv22t71luuktV5zwdvPFy/+4Dq/l5B//mB9nldvNf8qTsuPyy65agKO2XHLuFemdR56Rn6L67wO3SQdPnru6yzq3Scced1b62Q8uzdEvu+Bv6dLzr8zf9ajTc//rmn+nNxx+avrr5OPlz3rGpumVh2yfdph818MNks99fD5f2JRuLv3c9iW0D74Ur2kvgD+jhMAHOWxA0eEgEB7LvsBTdNIBfBgeCf9hn4L/cTBBHGDfQRniJkf9xKWxTHHh4+Bwu4nutlcJMQeEHzfeW3x5Ulbg46TDG/OjODUPBdQ4wp9JAesgEHwUrG6345/UHUgrjrLBTbkzFIbvKoZiHOONFBgpMFJgpMBIgZECIwVGCowUWAIo4AQTBhqDj3H2jAvhNcbcLQLCM0IAyguCQ8DEyNto2HR4/1Z4DZ54ITAgbMOgY+7dCDjyyCOzf3l63Gmm+JgenASVhAye9Qnhp/Kwx0lMygFMfsA0dXGjIK6BE4DY+NQwTfkpJUK4oi6Erk00seGy8XGSGm0IJoFN1jTKjLKsBJrA5m/rrbfOT9O4fRHCWu0DPP2j3jZ+TqdK52S4TSKQvyeimNIQpCuvD3qis6dvnBJ3kpu9TsPtSSDtNwQ891W3AQFUX3t3CbvKfNvaRHpt4LS5za466gOeOnBDyI2kulx9Y6N8l52izGlg48Np+ugHbsAYT27q+KEpJYt+bfxFO/F3M0BcOAjc9EVCbbcb3HqimHGrR9vE92HKurMbx8ATD/Gx8A9/+MMLlJRu7vTROiMo/rrK6AmF6EuSeCKJcsIzZ+YQdXFrBGgD9dYGbjaVQCFAqIhGUW5CSXEJByghhenDhJtOZBPSq6f2Q+eu/CIvwgvCkejL4c9EG2NUuxAgUMqYIw499NAF0fras68cdb0XIO6weGpL3dGH8oKQhmKDoMP8ig6eudAfKScpgfTlpnbp69ND5vuuuWdI/yrHsnmpCcSJeb60i1u7m9KXfkPjD40XuMWfCQxNV8cr3U328JupGXWJ9OGeC3MmeeABzCvWqj333HMhAaWxb5603umf5ixzjf5ujgLGhP5m7ABzv/nDPEuZ6jAHBWETX5ATVH9ta00VbXQuRRRoGtHBbU6+I94IXdNAV1ggW6ENcUS4zrz1RAGy8T1Wn/Thf6cLfndlfrrqXhuvntafd9u0+p1unS68Yn6/rpLdwLnFFnecjJUVkyetzr/giuSWyu0mH0ffYHIj5aLfNh+YuAGSWfTAy8QhBjyU9d5YdmAB2DNYl93KpNQEeCi3IBwmosQAeA94Ahy6obA0D1CaWJvw3DWYi/DIeFj7FE9IWXODl67jc1sn8ZDAOjxvwkvi+dx6B5Se9gTyHALmIvzORz7ykbyeu11m7vLD1wO3V6KuQ3BGnFGxEZQYzZECIwVGCowUGCkwUmCkwEiBZYoCBIFxasnmgFCMsNNJcAK/uOZcnl7kR0gJIm0ZHgQifCA8cJKb0A0QGtskOKHupHVA4OF2GhvYvBA8wG3DEc8ywYmpV45SGDaTuuSMWv5mWv6oSxNN0IICxOkyQhfCTxs3wvCZAjqBeGdXu8XTT/xDmEPIDNDNNyUAJUUA/1CuRB1iQ4f+ygnKepVpynYLnNOaQ9o7+l4f7qiDeGFXdnV0Ap8SxgaScseGlKKJfxMMHRtlXmWe/Ot2QlOKpIDY+FLuhMCYMkOZ5R83QfQZtxekt3nXpwjpa7ARBsZFgE12wBBaR9ww+8oY8Uoz0hAqRL30K2NYHWqo+6vwqEP0V4rVUM7FUzOEF/ANzc8NnSalhvzQjjBdG1JqgOj/2TH5G9qeQ+sdeLvM6FP6sLEH9A+KOjdhPB1I4UsghGbmMAKWUAyUuIf2afnEmIv8Yw6oaVDOPeZr8bRJ29wdeJXLE4BoLH6Ud6i9rFefHc6AyIe7zCvczDIOd0CJJ/xKsy884tb4u9KVYaUdrnB3mU1h4VfiqO3cQ6HENzTNtPHcljJvE/QRAFLwBbzqVa/KSgkCSbfeKOiNZ8/USaM/U0QoJzsFMiGncUKwSmFiLXALaqhiI8aFMoQ9xkiUazSXLgpc/tf5feoWk+9sBNzyFvPtno5qgr9ePj9NxBPnlreYzytLc/VV105uwF0zWbNvmkq8t7guzlX/O//7VE24S7/tJ9/r2O3h66fTJs9ZveYVp6Sr//mvdJNJf97tEeun3R63YXrHq39QRm+0r72hD5BvMpmj/5UOfunJ6feX/T2d9es/pUMPul96zvO3SN/+yvmN6ebSk5LADSwHTtxIdXuCwjHWYOu/+dLPIZsAfBQeLqBcV/jBaVx7fsqhLWPdHNAEbjy70eG2pjnAOun2RakoKdPV8521OeaAiKc85pkh4NCK/YFDYOoUN3LLvU5dvyF4xbm+Jw9NMcYbKTBSYKTASIGRAiMFRgqMFBgpsJRRwKbflWcCRx/lJaQiQA3h1bTVsRkhOCDYswkh+KbMIGyzGSBcD4FXKZhwQglg6ilE4MDU7zh5SokADzhZSXBXbypy4ORvSF0ibS1YChxDyh9xpzF9GN11eps2GyebrHnz5g1GgRaE0rGRU06CG9f2Cbi1l9NpBN5OpKO7MHR1Ak36oHEoIwZnXkWEywlZG7nAGeWKzZ2nbALKduZXt8GQ9q7rH7iHmvoiGjnR7nSc2zXaw5NQbghRPNXlGtKf+vIPZVI85SW+t5ZtXH38UrhyGX/6CBCmvjbVTiRqa22KjgR2xxxzTB5fhPqAUoDA3pgRF7jhEWCTHjCE1hE3zL4yRjxmjKsoh3J5OgHY5BMceCe7huiT0Z+EO6UJgoabb755vg1CUCHMCWwCENCXX9w6yZFb/oI2+rb2cjum7rtRlrb27CtHS9Yz8nYThtJKP6A0QpMjjjgizy+el4uyQB7tMht9Gt62uYeweJq5Gx50j/IZg132GKNlnZqIV8ar45Zh8qrd4pd+TfjDb2i8iB9mW7oh/mWcsHeZZVhpV5baHeUrzYhT+s2mfSh+N5PMk8C8WAoNncjGc1j/3GJ0W8m8Gs8/OrThBLW8KI6NHWuAddhcG+Nc+hGWXwqcc+7laacd75LuPrm9AFa82U3SevPmf3fjt5MwcJObrpBWWWOldPnFf89uacCGG9w29y9zCjv47bl/Tdde/a90/oWTNX6D1dIGd799uuzsK9Md17tN/kg5hccfzv9bjtv3t/76q03m+xXTpRNlxBmn/j5H/+15l0/m2xUm3wAZ9iHp2615q7TG6rdKf/zz/6XvfefCdM0//pV+fPf59bvnRvN5h75yzEW4Qwsf+MAH8l7E05sUHAHBW7rt6FZGCeUaV/qzu+GAZ/ARcTwDfisO8ZRx8dX4Q+GUKA6ZHHfccentb397vj1qPZtLwL+5Veu2GB6Q4ta3wzyLORswKjZmg4ojjpECIwVGCowUGCkwUmCkwEiBJZoChAFOGRJ+ev6G4JFAAMzk9CHFg6dQCNAI2QgYfPxWPjYmhAsBTkj6KDVhuA8LEzpQZNiseCve8zQYfCe6QqhLAN220RhSl3ib1uksArV4CivKNE35I80Qk0CGAoIABl2nfYaK0gIt3MpwbZ4w05NHRx99dL4C77SXa/q+b0AITnhPcG9z5KaMjZvT0W54ECyXioch5S/jEPza9BG8+xAriOeD4oSbd8x9d4OQPp4iChx1G2jzvvZ2Qresf+Aaanq6wHdbCK58P8Gm1wk4dQkBWV2uIf2pL39vJVOeOFXvloFxZhNLEL377rvn2xs+Do5enjbg7/klm1snEikypEVfdIpNvrIBJxEpSihsKCY93+BWg9PN6qid4x1q8WcytpSjq4zGrfISUqurcrqNpD8SEhBUGFcUeuYYJylrMB7cpvE9DYJHOClYCSJC4UPp6kkm3/zQrzxD501+MG1+df7c5oOYu3yA3W2ZeLYr4ve152yUI/LqMyl63/Wud+UT5uaFuMVCAGyO9KvbZTb6dNfc44nDvrFc1sucGLd1tHmpaCjtZRr2rrA6LjfcNdQ4ancdv83dhLst7hD/Nnylf9jDhJc93KVZ+5dhkS7KFWHh7jOnjd+HryvcvKK/BDgs4dszwBNt5g2gP1FkSA0fAAAAQABJREFUmmvcFjOv+4ZSKN2dxH7jG9+4YG2k4MD3UGR6CmeEG1IA/+CpHodMQrlUxrK+mispV30zoQuMMzcQfSPMeoju5v+4fRppp8EZaRbV/PwJZ6e9n3rv9JhH3S39/rwr0hp3uHVa966rpAsuvCKdeer8p1ef9qxN0/b3v3P6wFFnppMnNxx+eOJF6ZJL/zdtcs/V0wteuvXkKaF/pp0fNC/945/Xpq9+Zb5y/vjP/Sb994u2Tvvtu2Vab83bpK3vt9aE/5iscd/6XfrfP19/86ir/D+ffCDcTYstNlkjPWu/LfNTVDs/eF5OcsZPr7/J0IXj0vOuTH/801WTA0w3T/u/fNt03uS7HY989Pwx9eMzhz2b1IV/pmH6FaWF7/DhFconG+dNDgA5nGMsu5FlnfM8Fd4Tz9MG+Du8y1ve8pbMB9W8fqTDA/q+jqdzrW34JnlRit4Y8xvFKh6d6bAGu/0QmMkeLOoV5qjYCEqM5kiBkQIjBUYKjBQYKTBSYKTAMksBGwWCbptWzD0hgA0mAZjNfghShxKA8MCHmQm+nZQijPZMDIEhIUR5IpLgjTCTIMLG2aktZSHMhMPGxAbD6UwMvrQE9m3QVxfpCB4Ji+G1ca43O9OUv60cTf5OVhOKEkTKg30acOIMPQlbKTacyCaw/u7kewNOudsMEew4dQZ8QNGp8rheTxBMqL3nnnsmG0U0nSloN4ort08IV52KI6QH2lkZ0dgmVV/Sp+I5IXGa2qCvvev6wzMNhELHs2uE9PoSmvg4dGyi63IddNBBnWNjSP6+f0LYaxNOsWIT7ekd7UdQ70147UR5QRGlb4jD36ZaW6MlJQGaG0ueSnnGM56Rsye80y+0iU0+obIxQikgP/V2GyLoP5Ox1VdG5SRAcMrRx+nVgXKNMJGig3LNzQ2KD4pO4TXoMz4Iqn3QAaADPGgQYI6g2KCMIoQIwQPFzjT5Bb7SRBunJl/72tfmk6MUsuWJUHn1tedslKMsU5edgFFfMAcYh+iFzgS3lDRN7aKdFnW+75p7pu1fymv+V9ZQLISp7mEPs/Tros2NHRb9cLbybcJX+tX2cJcme5dbWcvwKHv4hXuIOZM0Q/CWcQgc/QLi+TlufTqe3KOoeP/735/HA2XxPvvss+Bml7hudFgzfVMDj+PE9rqTJ2rcLIsbWeKNkLKS3QET/AJ+w5M9pWKDEtXaZX2muKZI71Ns4L08EaRtCHKlp9g/+OCD89owE5yz1Van/s+F6cgP/DTt/rh7pBftv3W6za1vPlmf/5be8OYfpL9cMv8wwVbbrZ0e95h7pG9ObjxQbFwyUYAIp7TY9wVbTg7hXDP5wPfN09vefXr6yXU3Kz7yvjOz4mPHHdaZ3OZYLd1xjVul7/3gkvTG1506meOGlf7071yU3n3UT9LjH3P3tN+Ltkr/vPratPrk2xgfnXw8/Eufnv8R7T5Mf7ro7+n1bzo17fP0TSdr3ebpT3++Kq291q3Tt07+XXrnm0/rSz5n4dYNvNiBBx6Y+xiFfIA+st9++6UPfehDeW22Zjh8JU4TLxHpmDtOlKGHHXZYfpKuPFRVxsGfutHlII611FqDr9prr70WzI9l/Nm2y983xPCIcZACPYB9UVu5h5ZjhYn2cGAXG4pyjDdSYKTASIGRAiMFRgqMFBgpsDxToBTqLw462FASgBHkxofvlINSw/NQbkXEqUfPR2H2CUWd/lZ2AgEgrtN2GG6npmsgdPQRcDcECI8JcAngCDUx7K6cv/rVr85CQsJYZbJJIRR0MgvYXDi57oYDoT2hhdsAlBc2AjOpi2cn4jSn+sHnNLinuOIEuDr1lT/qj1YEyeDEyfv2hCTq2PZGt9OMFD1ogJaxeckIrvsLPGhRfpxaOrRAb0IYYBPkhz5utTjNSvAaeMV3Wl5dbQCV142OOBUWJ+JtqoA2VTd40BlQPFFy2SCiHcGyWwVui8RNDKdc4Q3hlv5EUEFxhJ7KY4MWtGlqg772rutv4+nUp02uGwBD2kQZKAfcIpAfob832KO9msrVNTb0J0J2J+0I2UOBY7ygdwh49G949A/KOv2OIF98QMimrwunoHBikbLIyUS4jSW3m/RR4wRNtbM2l9ZYMG60AdDu8tNucGhvZYCTUKqP1tLKD06KKtBVRuH6ghOOlBf6rnToid7yJnRHbzTzawIf3xSfokcZneSFCz0CCL0o1IAnxOIJK+6u/NCE8M03Z+KjpNLUQDBHwCAfCij0P+SQQ3J7Gkv8+tqzrRzmUALXJqDo1B9KujfNBdHfzFu+XSS+chHymiPRA82CLk3t0tWnh873XXNPX/+q608gKg0YatY4ZuKOvCJt7Q7/oWbMf0Pjd8VrwlX61fZwM2t7m5/8y7hRnvAL9zTmTNNGf428op/HvBr+pWmsepseUOibW4HxSWlhbbDmmeND6ZEjTP70OfOBcWNczpso+82p1rYaZrLW1DiWVrcDKuZlCqXXve516Q1veEMWwkZ9zD+hQMffUIBYj7qAAkk/2X///fPapB18vP3oye1T68ZMcJb53WqFN5bOqe3rTJ6R2n7btdIad1k5f8fi/F9fnv7n2xekv1/+z4xrux3XmfSp1dM3vjX5qPUv5n+YfpU73DLteP910jobrJpvYlxy3t/SyadMbgKdP//2s4SbbT1Z17dYI626+krpfyff8vjlz/+UvnvShelf1ywsdr79WrdKuz5ig8ltlmvSx475xULlX3v9VdO2W62Z7rD2rfOTWH+++Kp02hmXpl+fef130yLBHnvdO5fluE//Ol35x+ufBlXW+2279oQPWSXdctWbpisu+7/0y19N1urvXZKundwI6YK///vFXcGLFIY/c1MBHxM8biDEP/zwhz/MN4LwNRT3bnjEDSvzBf6yvNElLR4Uj+5Gl8M+bYAnxHsEj4bnwKPFwS6HR/Bt9kR4TXubXXfddQE6PJq5xM2jALdp7anwzjFXscPtxolwNy2NBfyV+uHlHabAp1nX8Sp4FryAOYwCd1oYFRvTUmyMP1JgpMBIgZECIwVGCowUGCnQSYHFrdjoKhxhvlsRtQCgK01fGJwYejhD2C5NKDY8r+MpKnEIDEMwUeIlbPJcgXC/IdBXFzjlqT26BDFt5R9ShrmIozw1jQhe1YUgvaRx5B/0E9Z3ui3StJmUFRQbBOo+wG2zCZrw6kvK1hQmTVsbRHmb2rup/nBNCzab8mkaj03l6utPQ/PXjyku2saYcP2xqVzRztLWfaCJLvxs6ptwRXm7aB1xarOrjPLUJ2rhID/9gX/XeIu89Gdlq/FEeJ85bX4lvpNOOinfjiFgoxAiaKH49C2PoyeCtxL62rMuB0EFAV4TPOUpT8m3eJrC+vz62rGpXfgt6nwffbJv7mkay2WdSsUGf/XpMnPgLP1FXoGudof/NOaQPj4EXxOe8AsTHvZwh700yzjhX/qFnQkC13zX8P+ZppNDeTNqeI7NMfVLfbtt7YlUMW7a+m/EW55Nwl7K9HkTxY8PG9eKDTcBKSKEO/Huhl6XYoNQmuDayfwQNFuPKakdcnnmM5+ZbxdOg7Nun0VVbMC3wgop3eI2E8X2ROnwz79fU2eRv73h2xk13Hzlm07W+BXS/13R/EFw3+e4xa1vlv45UVpcO/n490zhZiutmFaYPGX1z7/dsGxDca5405ukm91qxfSPK6+ezLnDUs2lYmNICYxr6yq+Bi/VBeYBh3ccotIvywMSbenwHvD2zR1t6RfF33wk/5nyPW15j09RtVFm9F+qKeD00VCoGbuSWanDhuJsilfijfAmvwgLsyxDkz38alP68AtcfWZTeWq/2t2Fsyn/Jj842vy78AsLDXNfvDF8pMBIgZECIwVGCqAAYWktMF1UysBnU9wHXcJX6ytB+jTQVxc4h2wehpZ/mrItStym9rEJ66LPTOg3tIxdm78mJUuJt60NusrbVP8S51B7m2JB+qZy9fWnofn29bmu8K52bqLLkDJ30bqtTl1llGdTuH7S1VfqvLrmgzpuk3va/EocBP4UEG7QEMg7wekprPJjphG/qa4RxqzL4fT4AQccUEZZYC+f2FngOdDS145N7TKkf/Rl39Unpe0rV4lf3LY9T4Qxp4EyXRvuofjq9H1l6Qsfmm+Np3Szh7vJHn5NpvzDP+xRJv7TwkzSTJvH0PhDBZPK3LV2Ds1vWY7n9lcXUEiFUmpIH/BdFLf6yvnOekzo7AkxMC3OrvLNNIygv005AWeTUoN/n6KBouSqv84/ECL+TOHqq66dadIF6dzOuPaKmStXFiC6ES34yj7eUnE8L0cJ56amWxHxLa6+oi4q79GHvyvc+OnjKbrSt4WNio02yoz+SzUFaC6HwhAGro4zFHfEM4BrHE1+Eb/JjPTTmnBFmia8/OoFelp3G9463z53G57Rf6TASIGRAiMFRgosjRRwYu/lL3/5Ir8duzTWfWkus82hdht6c2ZprutY9uWTAp7retnLXpaFbE4Se/LCU2WbbrrpIhPE01FNT/ctMuJlDEHsBcNUvdJeV7fen0U4/7awMk69D4uwoWZfHn14hqQv44S9NNmH/pSnjBvuKGfgDXeXOU3cLjxj2LJJAYdq3RwElKlOpINagMxtvh1hpMBsUEBf870cz1565nF5nqdGxcZs9KgRxxJHgT7FxqIydjOpcD3R1G44a7+6nOEuzTZ7lDHCw91llvm32aUvw9rw1fmW7tIe6Zv8Imx5ML0LXkITjYf41XFqd5lHk72rHbpw1enCHaa8wh5m6VeXpYxTh3W5+9LVdehyd4VFGeo4/Jv8In5p2viPMFJgpMCyTQHv4hIWOlk5wtJDAbdBfQh76Hy+9NRsLOlIgfkU8C7/TjvtlIVxBHJOUI79fW57x7rXfTNobnMZsY8UGClwY1PAB599jwDgHzy5B+p96TjHZrKMf7NEAc9I7rnnnvk5yeX9IM6o2JilTrUsobnyyivzxy9nq06u4WGWZ2tT76q0j9X5MF/bdas2xUa9uMxWHYfiKRez0i49d1v5wr/JLP1KO5zhru3cJZRlGWKXtoxX4gp7W95t/pGOWcYp/dn78q3jLy1uTwIE1HWcxt0XN/JoM2dK+zpduNtM+UdYbW8r2zTxunAI66JTGVba+9JFnnWa8G8yzVXerh7yhE1T+mXNb7bXi2WNPmN9lj4KDH0qYumr2bJdYvP4NE8KLdvUGGu3LFOg77sQy3Ldx7qNFBgpMFJgNijgxDzZFPDsVzwDGTc3Ig97vrl4hifwj+byRQG8qtuWI6TUq9ggdPHlch/Oc8XKCVNXVDfbbLMbCIamIagPovhCuq+tu45Fs+nr5+utt94CNE4w+5DZxRdfnDcXTr3tsMMOC72L7G3Q0047LXnHbpVVVslfjfcRtCYh+nmTr7B/+9vfzvhpUZviLMh8YhmSv/jqQkN7zjnnpEc+8pElil67Dwv9/Oc/zx+H8a4p8O7eN7/5zda0rhmttdZareGLEvD1r389feMb30g777xzevCDH7woqHJaH0Ty4cz1118/7b333ouMDwK00S/QIT7GVCNuUmyUQtQ6/o3lrgWepbu0N5Unyl+atb10w1G7m/CGX5l/k730k6Z2B54wI2/usIdZ+tV27mnBHHLiiSfmj3rJw9u+Ps51m9vc5gaohH/+85/PbxLe+973TuYLMGROKpF9//vfz+8Zuv63yy67lEEL7IS0xrP5ydVT72quscYaC8JLC8VGTdNp3GXcNnuZX5u9bKM6Tom3DivThT1Mcdlrd/h34arDmtwl3qbw0q+uQ+keYoerjDfEXeZf29/3vvcla5P50Ty5PMNcrBfLMz3Huo8UWNYoEB9TXBLeCv/tb3/bSt5p1qQ2JPU60xavyX/atHV5S3dpL/Nq8i/9wh6mtG12YWWZ2cNd28u4Eaf0Yw8ow+fNmxfes2IuqYcS/vCHP6RLL70079fXWWedWalrFxLve1u7jU1PW9XPrnSlne2weN4lBIqzjb8N35LaF9rKO43/bB927Mr7Jz/5Sf4mwXbbbdcVLX3xi19M97rXvRb6dkFngjFwpMCNQIEHPvCBC+VCgYFXueCCC1L0afPkJZdckh7ykIcsFHd0jBQYKbDoFOhUbFiojznmmPS1r30t/e53v1vwZXYfwXnEIx6R/vM//3NGJcDYfuQjH0lf/vKXM/PlpIi8TjnllPSSl7wkrTu5pkkg+Na3vjWdeuqpWfFB6EcoSEC466675nwpKVz7IhTycRVfjsfEPe1pT8tCzbJwJpL3v//96Tvf+U723n333TtPYg3JH01+/OMf5w+v2eBIM1SxQVHkY22UGueff366xz3ukT/YpnAEoepVAxoBgti5UmxQ0Jxwwgm5DWZDsYHB/sIXvpC23nrrWVNsnHvuuVkptsEGG9QkWuAOWoVHuZkKv9k26zzKDVWZV/gzI01pL+PW9ojPrO21W9rSr8bV5I6yCQt7bUZY4G7Cw68MD3tt1vECV8QLd2lGeUo/85R+RhlJYelano3WC1/4whsoMClCjzzyyDxen/jEJ+bxJL++OanMT79+5zvfmedFJzRqxYYr/V/5ylfy/GVecOpTHuYh8R/96EcvONUReOs+W9ezdPfZ28Ijry6zi/bSlbhLPGW6sA814Ym4Jc6wd4VFnGnNsh7T2uVVpqnddVhX2b773e/mdUSfWN4VG3OxXnTRfmkJO+mkkzJ/RBGL9xphpMDySoF3vOMd6aKLLkrPfvazM5+6OOlQz/PlOhVhpd+0ZR2SNvIpcdd+tbuMG3lEnHCXcYQ1+ZdxhthLHKU90vKLcvAr3aU94pdmmY5/7S7jzpb9Pe95T7L3e+Yzn5mfmpstvIuKB0/xqU99Km2zzTaNHwFfVPxlevtXY5LgDg9r3z5X+9My3ya7w5eHHXZYbvuXvvSlN+qHkpfEvkAh4RAgGYnxoF0IXuNEeRMNa7+vfvWrye9hD3tYPkxYh8+mm1Lj8MMPT/e///0XCIHb8JP/fOlLX0ra+cZQ3rWVY/QfKdBFAbcyttpqqyzvfMADHpA/FE6mesUVV+T5uSvtGDZSYKTA9BToVGxgjo4++uh8U4MiwOJx9tlnp09+8pPJc0B3v/vd0yabbHKDXN1CIMhr+5I7RpDg3qL7vOc9L9/WIFS0ULkNgkmUNwH72muvnf7rv/4rPz2EYTrqqKPywuw0xnvf+950xhln5HfF7nnPeyabf2mEOa1dglP+JhOMD2hiqsv4ffm7HfKxj30sl9mzSBQuQ5kFzN+b3/zmzAhKR/BZPqlEcWTTVgLt7rvf/e4cD02mAXXVJrTGbcy+MpiA0U09nLqpAR7x4Gm67RJX7crTOpQPPr645ppr1uiyu6uvEA5TVs3kiZZaSNzU3k1+bfRpLHzlORRf5BEmNG32MovAX5q1nbv2gyP8SnxN9igHM9KUftKUYeEOXJGmdod/m1nHD3efqSw2VOYPY/sFL3hBPi1G6WBj5+ZROUeJY96gHAMxHwyZk6Is6iA/Sk39jMK1BOPAKXw3OuRNqacPU2q4feYGnPwoccurg/EUVdA7cJbuPnuEhwlHaQ+cXWa0UVMcuNrCS/+wl2Zph7t2R37hH+6hZle6JhqUftPalalMU7vrsK467LXXXlnBX95WFH/I/KfO5mRzbqy30ulL5Tzclr/+2/cMVsRpWz+Ex7rgA2ZNoO8rV9/V6671Qh74irb3Q407ZVHOGtDJWoM3CDrVcZrWrzLOkPzVv618Ja4ue9Oa6BDExz/+8VyHIYqNrrIGLbRF0zoeZbP2arNYf4emi/SjOVJgLihgn/CLX/wi39ZddxbeqrfHwUM4MLWoH0yOed9YCeBXusN/tky4I98mnF1h4kd4Xca+ctfx4Sr9wh6m8IAmvzIs8i7L1mSPNLUZcfmX9jreorpPPvnk/GLAYx/72MWm2LDmvfrVr85VsZ/2zQyH7hwULPnLRa1rW/rjjz8+ffazn831p0hZnE+3WbMIu7X5i170orYiz4n/bPaFpjadttD2Nm9605uyLAU/APCEDnYddNBBC9b1Eq9b7+Qo5sE4QHrWWWfl9sWfeiVhroA86G1ve1v6y1/+MuiD8/ig/fffP1EovepVr2rl6+aqvCPekQJDKfCMZzwjHXHEEXmedujyvImi8XGPe1zacMMNh6IY440UGCkwkAKdio1PfOITWeBigdtjjz2yAsL1VjcK/ud//id95jOfyYI7tyAoDpxEJqin/MDcbL/99lmwWJeFkJ4g/773vW8e3BZbzzjZvHuaCti8uFLqdMFuu+2WN/TyE8/vzne+c2Zettxyy1w2+DBxFBs2PSUoM0GjZ7RCkFmGN9n78o/rtg996EOzcIAgdRqgPPD0lPpixEqgBMAol6D8wNU1z3b1gbbDkHgyjIlZWH311TPeeZPr2GiCIcAEox0F0VOf+tR8e0SZ0JLi6qMf/WhuT23ldslll12Wn/dxupgyCVDsYGy1OyDoxXRghP74xz/mdPL3jJgbOE6yC9MWNpQELOolDyCu0/dujxCsKCOFCwHxEFBGjGEJTRup2g8zXPuVOGZir/GVecSGK/zCHWnCLd/wK+38yl+EhV/pZgclnvk+1/9HOfhE3uFXm2WcNpzhP9SEM+KyDwHlcspHm5sL3CIjuDz99NNznwzlQuD69Kc/nRULTi5RMgQMmZMiLsWEucg4dXK0hs997nP5qSsC6x133DErCuM5PSeRPKnnVBIFMSUukH/dZ6MNhPfZI7w2u9IKA200r/3hrv3mY7j+P8KbzCa/Ov+Icz3GYbaudG3lDlrJYVp7naZ2l/jqsLqs5lUHBSjBzNF981/M7fe5z33yHCktZtk6aT7V5yk25k3meR++DcG0cpRg3LjO7+SQfCnUrZ2EE+Zj66/11HOP7J51Mwf70Kj66a/4AE9K2pTKR1rhBOZRTmultV1665HNsw8puy0FlOFHP/pRHr/WBfSI9UK4m1fWSGOE0sCtFjxJCG34K6fNAtp6hk54nBq15ljPrVPSU56gVXy4vWv9GpI/fkj+hEnq7eCB9S9OER566KGZVs95znMyncV3QIMChnKzj3867rjjMs2shYQS8Dks0AR9tKKMxatZl/FdNlbK6qBGyRPoO9pEnvgAfJg1Xl9TbjfU9L8RRgoMoUCTsq5MR6lYKgWNY2malG/Pfe5zk5td+mgJlKeUtObCev4VT7h5rJ4PHXgyrjxrUis2CErNpW0K2TKfcl7nX7rLcs6FXV5lWSKP2q92l2WMMocZOJrMMl2EN/lFGLMtPPzrskVa4RHWZo+4zIhb28s409qtdfGkSIl/n332yfNm001L/Ua/rvubvANfU1hb2SJNfcAATezTAJ7TfqkGgu2msSQevMLh7TqYYCyIUwMFirpacx/1qEfdIP86b+VtG9sl7ibFepSzTSFv7B944IG5DzSNWeMfDH2mCm20YVO94Ym6aMeuviBuE0ivTtbi8sBFX5v2zXXyclMD72Cf4sCXNqLosD/xskQ8i1OWixxAX9I+2hPg5/CH8WRvGV857ePROsrfN2eqLz6sVoDhS/BChxxySJYllfkojzR+AeRLFC2eFcbPORU/wkiB2aTAa1/72rwvacPpVZN1BxyusG+idCa7NAeRdfkZ9zUMxVmnG90jBUYKzKdAq2LDwkmABywecRuB0Noml0DDrQZAcE5IR9ng/XiCEkJuAhHCmvpEM8HCfvvtlwUTBrbFMYSMIQwgoAE23hhJjIMwJ52dtuZvosCIxW2HGof0cH/4wx/OE4rF3TXVIdCXP2GNxZRAwFXcaRQb6kOgSQDi5Het2KjLR8hDIIu5esxjHlMHN7oxCBgbwgm0I5AgfLIhfM1rXpMFR9pMGAEP4ZDT7QTCTrpjVAi3nN6AixBM/uLBQQjiBg3wxBdFBGYRY4lB0f4EMIQ48HlqS50pK+SrzfQVTBH6/epXv8q3WPhT4lB+YNB9vwA+T3cpN+FbHziR//SnPz1H0/4BpT38SrMpvNzElHHD3pQmwmoTrogf9iYz0kXccIcZ/szyJ7x0l/EiLHA0meLX9Q2/2mxKX/pF3vzYw12aYY84belLf/ayjHAQKIJ5E0FHbFgIToH5IkAfM94w6ISsMV8IHzIniWcM6V+YeFez9f0SPIV19NFH56vU5gdjN25nSUNI6AYaxaB4T37yk/NYg9N3f8q6wVu6m+zh12WiUYSXNC/Lzd4VNiQ88AUeZpu9xBdxSr/ANRtmiT/woUf4T2uHo0xTu8swdhB5Zcd1f+FnrjNfuqasf/TNfzG3E1Lrx+ZY/c66aBNuA0vAbb4m3Ce4rkG/NH+b763P1gDfV4LLGDL3m9P1b/3WehsCQHlSYJiXKceNI/OyNVOZ4MK0Rzl/8IMfZLxwWDfN/9ZyJ5bg5rZOORhRrxfe7n7LW96SeRH8hfXCyUhr0Itf/OLsFk64Yr0g8KDEUKaDDz4408EpQEoPQktrjfQ2ys961rOyUKdr/dJGXfkrn/FNmeTAgfa27mpPpyIpXwgXtInxTUCgrdRZeSk2+vgnSlsHRgChgDo0QR+tKK3UhTlvMldSbmgja/nzn//8hXgCyld9SVyKK+2j3MqhjfQ1N0ibNmZNZRv9li8K2Bvga62r1kx9Fk9pzQyBGn6OsNhcYkzyJ0yjZDAXGeP6F96RP14bGC/Gt2fZ7EuMZUq3UMAaYxRvlKiAoI3wy9ykTxt3Dso4WERwYaxS4OFT9W9P2RpzcZCKcBMf6hBTrfjIGVz3V8/19ToQ832ZpsmvDO+yR35dccqwpvjh11eOMry0B/7ar3ZHvKGm9FG2oWnqeHX62q1PWmutZwcccEBObr9jnaPk8v0+Qih9i2Je3xHXtyDtleCzL7G30i/0kVe+8pVZgaZ/UeCbrwmWHRqLb6vphxTh+po05mJ49P/6SVOFsnZ4CoiC3vqnDNZX+3JKeeu4/gve+MY3LhBIcwe/at9lLClXPDGsX/ueorVTPHtBZbD2W0cJuB1yc2CIwFu+1twAMgL1DR7bGgiPwxT2w9Y+vAUew5hC066xbW8Z6QgL8Q3qZS1SZjdDjHvlxNOgQQ3KJA9grgmcxjxeyD4WEIL/x3/8R65n9ij+ynlJnbQ7xZV6mYNibsPPWEMdbnJjpuwL7PgjcbQ9MAepk7zNZ/DoI3AQ8tuz2N+br5radMfJIam+ua6oRl6n8ZP6KrzaW19WBnNezMORxo1QZUZz6z3+iZLBWu+wifbUFtEvCGvNxXGwxmFMfJZ6aQc0U3ftBdAer2tupaDA70kTSiMHYcgPlDdAvtYRPApZDz7Tvkpb6qPqpTzm6lGxEVQbzdmiQNs3XAO/Oc2vD+xzHOzF6xhfeKF6PQocQ3FG/NEcKTBSYGEKtCo2MFAUFKAWJofbgmaxxIT6WZydGLEAuRpIkIgxqhUb3KWfxdRiaEPy8Ic/POeJmQAhqCztwYSVjI2F2sJswYyToNJgTJxQcOqAQqZWbFiYMYcB6kbh0Je/+E6GgjZBA6YKUxCAubKYm9Bs+IaCTR6hkTo40ToEok0IkDBkhB6YFIys2zeYAnEw3pgsDJgNpHaItPIJewii4HH900YUYx4bQG3OH5Py9re/PQuXMHeBQz8JO5z6CkWHcoiP2acsI+yymSB8xrzauL7+9a/PzDFmK/peRtbwZ9Gwadhzzz0XCpVnQGkPvzDrxaYrbqQZasIV+MPeZnbhjDIxwy5+uMMs/cLOHAJwKKtf2EtzKA7x+sojPKC0h19tlnGUr2msxmkgcwWwWbCJNac5WYXBLmHInCQ+4YeNEyWpTWkNBIXmRXMQZobg18YOM24DaY4iULUpNRc5wYHJwbxTcpR1gzv6S2mPNqn9wr/NLOOzB9R58m/y41+Wh7uEMk3YmfETt7aHXxue0n+m9qYyK0f4d9nlGfSM/Ev3UByRtskMmjDNk33zX8S3uTO3629O8Fvr9EuHCWz0zIMENU2KDcJ/ghICBE826Kvvete7spIQfkDAY90xXqzL0hB0WwNsXN06sL47QR3CDxtyfdzaHOW0NsjDukrYSMBDGGATTBkAh3XDCTzCyaCDMlgT1EGdPM9oY//KiTDFBh2vYU0g9LEWy8N64lSi73e5vYWPIViKmyZ4EgoH49j6QwGEVm3rl/WtK380saZazykHAOGMNZvglPAk6JADr/sr/cLexj8RdlEmoBnBgnI3QR+ttIvDKvARCOB7CHEoXkKgpizmU+HmN/OXNARKceqTcAvd8Tf6zwgjBWoKGGeearWuOqxiHcYn47fxsPyNQeuxvmQdJDgVRjhLoKVvUW4Yf+a4GN/GtjmDoAA/TTFJ6GmNl5fxaJ4yD3CbBynh5EU5Ki+CWvy4QxHmW0DAKB/gmwF4BGUg0IMTP/uGN7whzyc50uQv5n9uYwfU60P458Dqr4xbBfU64S3zjwRD/SI+M8rRZ5Zxy/SLYp9JeafJrwm/9PqWPmqeC8WGfsOPEotQy97RHGh9tD/SJ8yH+irBML6N0mDHidDZ3ExhrT/pY/Yi9qXWdDyeG3vmXP1Sn7cuOQQAp35I8RDzcFk//dfBF33T/seaTDgtH7fmrKMB1gnrc4BxYj6XrzLpwxSC1mDre3yvRj83PvG2xgXljT2edQyfIE9C8lKxIQ/72xg/hM/q49AEGhJow2XtQE/9tWtsl+m8BkDpaR7ANyuD8WnPiVbqgv9A1xKUxToHtGmJk+JAm5iDgv7W6BpiXtIXtB3FDTkE2cLee++d5Rrqp/3wF354krIviCuO9oq9ALd2I7hULwcitB2eCO3xEvgbh574B0Sbol/fXBdpmJRBDpIoA74IbocTQMgusuO6P7xO9B3zdZQBrSgd8Gj6Z/QLfYWiR3toG3TS1yjijAnx8ajkC/gscyf+Tl9TDnsl9LVXl68w5TXfAm2nv1AqkgegM1mRfOBSJ2OSjADt8KTG6QgjBZZUCpgX/EYYKTBSYO4osEirQM2wz5ucPMHgARsIDI3FsgswLDYdBOw+9EvA3gd1vhZ+AhUMC8E4BQawkYnT1U5LlhMKBoc7nsGIPDGzBDBdUOffFtfmyW2RAMIZG7FpADOCUcOcKxsBzDTgJBHmDbMBD2YEsxvPSKGB2w02mm0bAPlhoOHBPLzsZS/Lm0jMCEYZsxlPkMCBucDsYGowNE2gf0Q7UWpgIpnazzVZzKzNMUYphNNNz/7UuDE7GKeyjdrsdVruiNtFi6Z0pV/gCL8Sl7DSHXHazDJuibe2c5d+8JV+ZVhpL/OVV4SV9jJO2MvwsozCA0eTWfqV9sAbfuFeVDPw2TyeODkdRdBoHNqEAIx1zAWRV9ucpI8ee+yx+XSejZpNCpAHoQrBJOGyjYQ+iCGXj82QzY3NoY0uetlMi2/ukj+mXVkCgqZR/qB3mOKFvYxb+klbuqUJP/aAyCPczCa/0j/yjDRl/LAzh9ibcITfXJlBlyb8ylzWr8sd9Qt8ka70b8qDX8Spw4fOfzbE5mTzYzxNZONpbrVRJhBpW38JZ+QvfczDhHdxolGZ9GGbWPOyOZ2QwlMTNpM2ssYDIIBgj3k6cOfAyR8BkLUrlI3clAnGmTVC36fUsAbZ3JZgPFm7hCurMhsv1kX43CYw9pQvTo5qA+HWJqf/3IQhCDnxutOR8BPCwNu3fjll2ZV/nE5UPvkAtIAX7cw3Q2FeC/+kfdAGUATZ/DdBH60IiAFBA8GV+UY7qAPla+BFS/2ISWCgPQm11A/9CfUIBfUBfWSEkQI1BfQTP2OUQhLfSpCH/yScopDV/8QxX+HP8cbWV/MCZezjH//4LJB9xStekXlXfoTHgZtpTXUL03h3aprAFp+Lr6fwhMOBBkI6eAg3KUUpQgh23Z4Uhtc0Vxgb5hdx7EncOnaox/gjjLOnINBsgnLuj/VAvLCX4WX68A8/9RoCZbrSHmnDL0z+pT3iRX7C2MOM8CFmnaZ0hz1M+Ep7jV9YCaW7zx7htQlf+JW41bfku4TxC5pQollL9WHrMuUxoTShK4g+HPHDbT/kBLl9jP2tvkixIb01iwKP4N0c62CXfhw4MuLr/ux/7GH1T4o9c7Q1kmCXwkVfp1CHE9jL4QvwlcCa7WCCulPoS2v/Zx2B11pFYa2s1nmCZCfgrQ9BB3sqH2iu98XGtD27/O31Hd6RDkhr/FCQWzuMbUqUrrEd6dTZPtBYjjTwU6pYi+VnTeLX9ARYTUduOB0cwndTXFjXCevxFDVEG6oLXgUf7+nYY445ZsHhIzjxLXh7OClYIp0wgnjl1w6UP+it3xD2W0fxKugpD/MO2YX+oEzasKlN++a6uBkS9UH3AHTzXVM4HELRR2owvwr3OgT5gAMrQH3iV7rN2/J0wEH/wEdoI7jNr3gLPJc5lcJPH3WLxl7I+JHmgx/8YD6AQl5gDl+34CfQDQ9l7o3btSFPocBCc8pAPCTa4keCT8oFH/9GCowUGCkwUmC5o0CrYsNpCz/MicW5ZCAwFMANC0xPQAguuMNOEN4GGAZMHaGDj+tYAAOfzTOI0yCl3WIWQEBBqWEDbjPuymtcWfckhA27emAACUACnFSUp4XURibACS8wNP9I12RieMqr6yUNm+I3+RFGUA44ZdPEjDSlKf2iHTCh8ZwYhiyAf9tGLeIwAw9GPNpI29roAacsYuPgtG7cpmlTbJRKJnQCUS4CNhsCDIuyYapAV1/KESZ/6oNRjzT82+yRpjbVo0wjPOpWxq3jlGGlfWi8Mg17Uzn4B74w+QXwa/Ivw8Nem9JFPUt7Ha92N+UXfqXZZocvwmp7nVfpDvr0jVVziNNdGGf90dindARODdn82aCArjnJHEK55vSWeSs2j/zcUIOXkMSm1RjB6Js/zUuYcMx8jMGgszyj39c0KOOIB8SZxl+aoFNt5w4o8+ZXuyNem1nGDztziH0m+bWVo402ET/Co1z8S/pEvJma8EYeXTjK/Ot4Q+e/mENjboYn1sbwa5sz9Utgjg0IfOGuv1djU77uZL20yQRwq6tNOVAnBxP0fZv8AGtqlIefza1Nr7EX+dvcN9EtlCUEA0Ac4ynAZhYY28YbQAPlEBevQoghnjXUBh8on1/f+tWXfwi4yg118BBxOjJnOOCvpFHY29qvCd20ZdVOsfYGHeFFN+s8iHJEvxIW/WSasmVk499yRwHCLzeNAaUGARSenGIjwHMkbjDi3WJuIRAldDRGCSIJuJwcptgogaBW33VS3Il2dny+uYiizzetrPn2AHAyCRwJHh1AwJ97/gTMmygWKT70a2s4XoBgmyJXmFtr5TiXxngooWn+L/1qe5k27DXO8G8zy/hhbzMDh3BlARE3yhamsLCHWfqxA2Eg8LB3xRfeB4GrNqULvzZ7hNdmX55N4fZs9pb6rjkRr+hEfd0P67TWM3H0aYoN+1RAsI82+mWMCwo2SoUmwFvqv/hHAmH7LELnI488Mvs7uEBQHWCNK/dz607W6zi4QEFnPfSjmDYWgbWT3QE1YNxEm3JT2huvsRbwA+gr71gb3FqUfxx4EN8eW3nEpdQBbWM76oHOlEhMadDMGFQP5cLvq7exPnRPXeJUBjyBNu0Cz3YpE3q7hUn4Hu0onby9fhD7jxIXGYQbZeYufAklBj5fu5tDrKHq46ATOYX2AOYwdAta8Is29TRo11xXKzakBWhGYUIBbO6jNNGPazDnRd+xTzFHdgElmzriOygpAOWI2yD2QXi8oFe0vbJQrOHDjA001cfVC8RtDXY8Hze6waV/OWCpXNozQFmtE2hYz88RZzRHCowUGCkwUmD5oMD1q0NVXwuvxZVgHXNPUI2BwQS5ngpCeF0lHeR0AsV7zzYme06uIu6+++4LMQiYMBp+CySwEFoEgc0SIFRwokN5MAmeaJAuwIIYN0BcjSw34txOmsZbu5EmmJQh+UeaNtMplbI85bNabWlKfwoXp2fUnZAJczstOKkCLPwhiA0h0bS4muJTbgGnNjAtGFibUPSN0ztN6UqFlRNRQLkwQphBm1PMEkYMs0f5NQQwSG2C4jK9stYQG6EIC7d44Venqd1d8QKfOOxDTPgjHXuNv3ZH/CZ/YUOhzHNomiHxynKFvTaH4BFHOuWMMaYfhV/0qQizWTAeMeHmnrhy7Yp+zCt9c5LxCAcQN4Si5hFuwE54AjDsyhfMtn4cY8/GzBgg2A1FYdAhJ56FP3mXOGt3UxZl/Kbw8GuKV/qxhzvstRuu8OvCG2HK3wclvqb4wmv/Nj95Rdwab5tb/DKsrbxtcRZ1/mvLj7/1kgBQfwsFW6yvwkvFv7XSqUJrrc2rchEGULoQllivQ4Fhs84P6PME+0G37Fn9UdCL43aI/I0XeTRBub5EuNN/xrHbEBGuXjb28lV25cWf2NBTkhNGEc6ggU1yQKRvW7/K8EhT5h+KgZhvxInnJyMtXsoaHmtemIFviBn0LPuNNZ0wKoQBkZ+6BNRlFeZUJgGu9Owg6hHpRnOkwGxQoBSG4uVAKOACP6EfIRewHoMQsLUp33KkyV8IBLnj1Lrx5bQyhQVe0Jixd4l1mUCySyBKaObbN+Y7N68oVKzbBMz8S4hxWfqxy7MOK/1Ke5020jf5l34l/rC3mdJFWOAo3VGe2oy4YUY4Nzso8XCXcbhnAoGzySz9Srt8uGu/8J9JOdw01B/0AafQKff1HafZo4824Y1+H2bsPWNvUt5+CEVxEx7zu3la/4153loXB2Uoz7v6cok79p/4WPyvMqEVQTOQj3ET63qUB/8a9Qi/IaYxHQcXxB86tuUfguvIN/a+yht1CpoOKUuJs20eqvFE3pQA7GQf0X7i8g95QZ2Wm5A/lLLR3g5lqIO9OD7EftUNcvwB0AZ+yltD31xXxw+322cUYcalGzZuE80GRDsEneCMdgq/ODQaZcd/kA8AChBtaM/UNJfgPb0OQb5D2Xbi5PaHvmhMxD4MnqAVuo0wUmCkwEiBkQLLNwVaFRvI4nq4k82UG66RW2hs4D3BQjDimvdMwGLuloWTGJg1i5qr4wGE+N6HxBQ4zUIIgikiNMFMWtSk8Sa4dOwYDCcj4tQmQQmBS8n0WRAt7MCpBULPuN0ReYfZl3/E6zIxwX4zBfSh+FHOHXfc8QZoMCuY3K52cFKCwAMDTIikvgRLcWL3Bkin9HB6Av548xSzp90IcZ2uaAMnVY6efDwZA+REFGZPm2HwMI+YYu3tdFEoNYYwsoRz3iEtv6USTJOylPa6bBGmLBE37HXc2h1pa//SHXHgZB9iSh/pmnCVfk32pjzEa8I5tK51PpGuCWcddxp3iS/yqNOL4+SQPkiZ9oEPfCAz1+YBjLc+pY/FuI/0rjTrd67gu/Y+ZE5yYq7c2JgHXec3J3kmABCehOAmNqI2xcpJiGjjKNx1b4y9U1I2WgQnUd+oa+mu7eLwq01lCL/SHn7MNog8Irx2h3+XGWlKM+zSDbG34Y+0XXVoS9vnD3cT3shT+giv/cItPOwRv3TXZWgKW9T5r84j3NYS31IwVgj4PYdA6G9tNxaMFf00wGbRhtg8rt+vO1Fc2LQqs42o9d8G2TjzLIR1jkDRmHDIIDabga80CRydAiUoMhY89xDChjIeu3VYOShUjDN5e65GWYxbBxOinE6d4hXwKJSVTs2G8pEgx/rntHiA9aRv/erL36lbPIh1Fn+k3spKMCN/wN96R8ng9KWbKtNCCFDcjiEQITRBP23mBKxDBH1l9Xyepx+0N3pQ/hDWmnsogWaicJm2HmP85YsCZZ8y/kAo97Oj+jMP4PnEdSiJsCrSNc0Rocwz9xJCB79trfWWvXlKGH4X/49fdgiBkMza66ZIPa87dGBewr+6pUEI6Tkr4818Y5wFRNpwh8m/aX6P8Nkwy7zDHib87LV7NvJdFBzKE3SJsoU7yhz4I7z05xf+tT3ileFNuMIvBLP2DCFMjoNg4lgf7D3sv6yX1kJzr2elrIelcDVw9plxoCCeTRS/PFBQp9e/rSmE6vqlMWAvpy+rZ62QLmlZ4yrdeFN41XvHyd7SWAD2XITHQcMyzaLapx3bi5pfmV490dleIOaTrnlI2ugL6I7+aBI8fYm7zY7XwrO4jaO98T1kEgDPQMDvtpoDGQ5c2Dc3QbRp31zXlNYTvOQk9i2UshQroTRqih/tHnk2xZmJn35qTrX/cWMX4M20i/EVChBxAow9yri48Y6GRxxxRD44gvfD6wFjATTdQskB499IgZECIwVGCiw3FOhUbBD6Pe95z8tCOEw9objFyJVAygcbh5mAjYJF3eKJeXDFsgSbCbdFLF7eqCeoxlhYyDwZgyGxIBJoSE8ITsDiF+CEhE1I+ZEswssAG/k4wRJ+pdmXfxl3LuzqR4CDGUOPJiZMOKagS7GBPq6gqjs6UVahDSHwbIATOd5wJSzxI3DCnCgzpRLhSRNgqjF3GDqnOTA70SaeJNPuNpP6XAh12nCV+NEJnoCSQSvtEd5nShPMXl/coeGBc6g5FK94yhr1DHttRrw2vFHfPrPGE/mEf1m/yKuME36LatqIufJ+9ERR5h1Zmxfzhb7uGrk869tDNqfAWBCHArdvTiKULEHfBTYsgZ8wxjxk3Jp/3HbzbJUNrQ2kU6XeAid4Vebo357QCwi6NbkjrM2UJsJqe5ObH5CmhNpdhrXZI01tih9+tb3JzW+mEH22KX1bWJt/jaOsQ6QJP+7SLm24azxdbpvXRZn/2nBbc/VF5aTYINB3i8EcTNht3oxTg+KYn/VfY8ozbsqlz+IJCNVtjuPtfP3dekIQZE2NjWtbWfiLg6eQ50Me8pDWOZZwkcDS90KUw7gikCLMN+6s03gCvID1x+aWEFP9xLMpNrbjTW7CFadfrYvWEwKqrvULLbry91FyggkfmUVH8QlzvedNYQOkd7MU/T1/Eac3c+DAP/kQktjcWxcpTdQLTmHmnz5a4acI5yiz1B0vZ15SVs/yzBZPMLBKY7TlgAIUZ8atNdmTK8aHdbAN8I36t7FkfSVU1C/101hjy7TGPx4Uf2uMW8vZjTPPTJm3zFnC3va2t2VhtHJwm7+ANRg46EBw6QkhBxbME+YWYwPvbO6JOTInmPypTwncbfN+GRbpuuKWeMt4kbYMD78+s0xT2qWTR22WcdgjPOzMsmylf4RFmSJeuOtwblCGh32I2RanxpkzmfzFTQS8ogMxlGL6RoA10BpF0WaOJ/wXRz0o2GcC1lQfkT5xoliTp3GhL7aBMlrLKLAJdfV3874yO6AWynQHaqx7yts1viIf48L6RMnuMEIcTHAAguJO3Wcb+sa2NXmuwNjFh9svW6uBAxhdYN9MCYLe0uP/0W0o2Lc6WOVAg7XWYY5aGG+Oc8OzfBYLH6Vf1G3aN9fV5YIznvoms3E4VN8DFLz4vxpiLnSLh6IFLzob4Gkxh1k9U+ZZMXICT/2po7CYY+P2vDwp2Shl3C4l4wi+iZIm+ic5Cd4W39ek+J6Nso84RgqMFBgpMFJg6aFAp2LD4mrxs/l3AsqCYrG2CGGqQvNv8bawYBoCCBWdhnJqoQYb9IMPPrj2XuDGZNlUxIfQCL7lRUkRQmtM7P77778gTW2xGNZAEOItfBBlr+OEWxm68o94TMwlvF2KkjJ+aW+inXD1wwhivpqujmJsbeDKGykl3rDbDGIkMDVOTXBjCjDMymzDVoI8naoI+kU7Yq4DfKgWQ4GRUE4flNWmGAxMGeZNfPR2qkk+GKkS9CEngW0a0BrTFnEIfLmdxMBI+sEdTGFdxhKvfqPMmPzYSAkv7U3u2BSVuKaxS1/nMSS9NJG2zYSnLF9bPpE+4rfhFt6Eo8wj7LUpbUCEhZvJrwl3U1gdt8ld4m6yS+OHAdenMcOED+wEfRjoJsBME4IYG2DInFTjsdHRt0ulo02hjZOTUk5Ie04tTsFRerh2buza4MaHC5WfcLc8rVTnFW1Z+g/1k6Ypbomrpn0ZNsTe1ualf5O99OvLRxmboM1f3LawNv8m/E1+yl3iKN1RpzK8xhFx+PuoqI2vvmj+6pv/PAPlBKm+BKxl+iEw5wLrLr/YiBsLBx544IJ5XTxruz6s35lbbXptLGNzS/lgzeBH6MDfemPcRB6e5CB4tEG3/uEBYq2oy5kTXfen7FFma2BAvV7Y+O65555ZSEkor67WW8JK9LV2yAftjDMn/4xHbnUUD5/gFoe01jX1Es96A0fX+qVcXfnjhwi90ET5tKu5BP1LOsqLkFZ8QgbjH38FmniAWHeDfzJ3mEusg+gsrdOe6EixAfpoZc31sWQCFqd+4dBeaNTGE+DhxIkTvfLZb7/98hxW+vEfYaRATQH9ypNO+E/9n1K0SUER6SgB8YSUDMaTedEcZgzOmzcvoi0wjTW3MSlC3EiTjtCZv7mUctMc5aPIhL4nTgTK+GZjxhwKKAkJdB1sMC8QwCmj0/ns8CiHecK+p4QQsPGLOd2cwl6aEc4v7MxwZ8/r/gJP6dcUT3jpH/Y2s44f+CM/6epyR1hpBv7wC3fgCzNwle6whxk4ws0s8TXZw680S3sXjjIfyjLPGbo9G8p7bR2gbd3IE0bRZS20D7YHnanA11xr3jYmHNizVtkzgahD5M80RztcSEDtEAIlhP2wNXbffffNfVM8/Kz+7ztwsa7wbwNrIbz2f/q9m4DKof72V3MBfWM7vtU1F3lrS8ohhyjxCdZVfEMXiKfdzUN4DmvnENqWOM13FBv2JPEMlXDt5faoecd8Vx64kB8+q25T3x/qmuvKfNnVN54ZM+dR1ASQpTQpNsgaKIrdlvBtmCGHVAJnl+kginp6VoriGh0pi/jr98Yi3sytW3OweTvkEA7OaAvxyZmUO27O4OvwMjtOlOVte72uco1hIwVGCowUGCmwbFFghckmYOGjui31I8h2EtGmumTmW6LPmjfGl1AFAzYtUzEbhVjc+bfVATPqmyKEHQQrNdjMOX3k+qlNIRpiWpuY5zrtTN0YDwxcF4PhVO3rX//6vKnEOCkXJqZWsKC7/lZuNKYpl5M25aaptAee8KtpUrpLe6TrMgNnVxxhJd6w12Ydrwln5Det2YQr/LrK0RQW6UqzLE9tr93ShV9tL3GW9igHPwxwgD5jrurqgxF3LkzK30MOOSRvFH1YkPICM27epKgj4KF0IZAtwebZCbqyXmV46V/bw12a7LU78IV/uEtz2naQtilN+JVmk71OX5altNdlrt0Rt8m/yS/iL6pZ467dXfjRo00xLWxR5r+mfM3NMc+ad83DNpROwlFu6IMEeDaeBDABBB42m23rh7ISJprH/eYK5NHFB8T601ROYfgWAp02iPRtc0df/mgK2tYs4eaBafpIXdZ6vSzbtIzbVdZoL7SYa56qjRZlWUf7skcBp9HdTKRAwJ8STFG8UbBS0gGKBvMKxUJ5OMBTUZ5gDeVbHGgKpS1hmziEjhRvxgQhnpPPDrRQKFK6ORxjPjJ/EQgT1jlUYD2miIwb59ZkT8nJjxIPfkoYOOPbO+ZJ8ePAT7QYIWQNxldA2MPkX9ojXphdYRGnNsv5JOxtZp22dEfefWakiXjhrs2m8Ca/Ol3pjnrwC/u0ZuCTLg5HhZ8+48YE3oxwGW/GjyCbAlh/0XcoNMypFNQU0xQL1hM3y62Z4kpDiI3/JMTWDwmUPU1onqVEAfqVm0xxoOCYY47JQu4XvOAFWdkQZQvTHE8I7gCBvCjH9UP9O9Yzfdt3CPR/Y8w6YwwZO3FzkFJPf5VOem3h1LyxVB5MsPYruz2mcUuZFweAokylSeGiLp5ZIhSHy41oZaPEKaFrbDel8x0uwnl8UvAklELahTBbe5WAZ6KQBOjtCUjKBWu6/SY6AbeyKKiiL2XP6/7c9FKf173udXnuQNN5E6WqAwjwqIPnpShPQzkqad0X+NkPxIsUDjPErQLl9O0vghVcNUsAAEAASURBVHk8jT2MQwvmQ8o0yty6TfW5vrlOngHaNj7kHn5h6kNNCiz5kxvo79Zuyih49FfztjLU/UL/pFQDu+22W05nDFHaqIf6gPMme3K49EFjR9tRMMVNDHynJ6TxoUFX+erHlHn6NPrp30FHt/D83OyIpz9zZuPfSIHliAK3WuGNy1Ftb9yq/v3fL75xMxxzW2QKDFZsLHJOI4JZpQAmFYPnNGrN3MmoVGyUz9zMaiFmgKxWbMwAxaAkGKsSptlQlcxuaYevdjfhbfIryxL2ElfYa7Mpz0hf5hP22hS3ya/EEXmGH7P0C3tt1vG4I6+wh5sZv6aw8GOCSDfftfB/lCN8S8VG+C1OEzP+0Y9+NG+qCEScLlJmmxynpo3NGghpgpkvw+q6Cgu/LlNYGV7aS/xN9pL2pb2O2xQWfqVZ2uHgDr9w17jDHeUe6havTtOWNvxrsyxbHdbkbsqvya9MG3m0KTbKuHNhJ8T3YUYbdJtcgh0bbKeS3RipFW9zUYYR57JPgVGxsey3cVMNQ7HhxjFBVa2Qa0pT+pkfCZJL5Ztn2DwLc+ihh2YBr+fnyuchKTDMawRgocAtcUa4PlkfzpKfMgor524CSGFt/ZgCpISY1/kNsZdp6zR1WJu7LG/Ya1Pa8GvCE2UNU5yw12akD/9wN5ltcdr8SxxleZvs4VebcDT58Q+BKHuAsvQdIoi+Q7AduCP9NKY+RuFHkUfpYN3F+1FYeEY1XiRowkmALL3+HQqNMp5w/b9NMV/GLe3qb6zN9cGEyDPyK8d2hM22SbBPsYGf+dnPfpbpI48uhX4oNty8doN0JjQdWg+4uw5ctLVp9Me2uW5o/m3x9DPt09TP2tIM9dfXjKF6TnWjw7Og9ku+6eqGFIj+UvdPCia3aCgaxcfHjjBSYHmkwKjYmLtWHxUbc0fbucLcfnxxrnIc8c4KBTxB4aRDk1JDBk7KYJKdzlmSwKkbArU4pbG4yoZZChi6UWmKx6/EBWeTX+TVZsIR6cKcKa62dPCCsrzhlwOu+yv9wh5mW7zwF6/G3+WOdLVZ4xHeVIY63ZLgdhrO29w2UoQxNq9O8VFqxNMydTmdnnKaKb4jg6l3qnTdyTOAQ4Ci05NBxvtMPmo5JI+5jONUnpOM5gUn20aYWwrYJHpq0SlQJ5Zt9OME3ajUmFvaj9hHCixvFKiFWH31t9bX8xAlrFPXTlNbI+q1sU8Q1xUuvyaBMMFhF5QKkuDhIn7J97TZI26YZbzw6zNLvijsYUrbZg+8Zbkj/za/SBNmxA93lzlN3BJPW/nDP0xpwh5m6VfiLO3i9vXPrr5T4uqzy4sSww0htyIuv/zyvPY6TFAq6ZrwUNa59dQGwpv6cFv88FemeqxF2FyYN3Z+ZR26FBplvLDPlKaRvs/sK09b/rPVH9vK1zce2tIN8W/ro277uLV0+OGH52+q4U9BW3/x/KCDY55kG5UaQyg/xhkpMFJgpMCyT4HxxsYy2sZOdNhIYIzKzdfiri4Br1MomBXM2VxBeWOj3lDVbmUpoXS32cv4Ya/x1u6IV5tNeZR+4tfuwFHnUbrb7NKWYYGrKY/wq01pwi/Shxm4S7O2125pwy/wDDWXtBsbZbltYl2b1+9tSuM7MmWc0u6kFJh2Y7Gkjveybl32G2te6CrD8hhmzMXpPMqOEUYKzCYFpp3HZjPvEdfio4C30j3h43kez0XNBniWxjMp1lBPXMVTUrOBe6Y4KIWb+JbSr7RHPk1+EcbsC4+4NQ8W7jDFK+1luqY8wi9M8dvsgSvMMl74NZlD49XlLt3T2qMcBKGLE9TdgRenzeOpNQJdB1Lie1iLs3zLWt5o7GUDvM2Tn/zkQdVz29pNCt+4GNtkEMlmLZJ9jDke9D0tdeyxx+YnB9ueFJu1Qo2IRgos4RQYb2zMXQONNzbmjrZzhXlUbMwVZUe8IwVGCowUGCkwUmCkwEiB5ZQCo2Jj+Wx4impCqq5nVqaljPffvXfvxG/fTYppcc80vu8C1FAL7mu3+E1+fXjqcO5SwF+7y7DS3oSHX1mmIfY2PKV/iaf0n9Zel790t9nlUYZxx0eH2RcnoEv91NriLM+ymjc6mzf0g6EHN8SXTvy6/yyrdFqS6hVjo+8WkbWg6wbTklSnsSwjBeaSAqNiY+6oOyo25o62c4V57o7Mz1WJlwO8rtqPVyuXrob2Ri9YUjbcSyr1nA73PJObRENuWrjd43Qb8AHDJen20ZJK49kq19inZ4uSI56RArNHgXFczh4tR0xzQwHrdN8zK9PmTNDow99LEtT8CKFcKQyt3cre5NdUJ/H6oMxL3NJd2uuwEm/kE/HL8pV2aSJumX6Ifabp4I5ylfnUfqW7tEeaJr8IWxym8vQJbhdHuZa1PNF52nloqAJkWaPVklKfoWNjVGosKS02lmOkwEiBkQJLDgVGxcaS0xa5JF//+tfTN77xjXwN88EPfvBiLZ0Td95gP+ecc/K13MVamCU4cydH3vSmN+UN2Ite9KJxw9LRVr///e8zrWzq3v72t3fEnB/kVBvagne84x0zekN4PqbxfxoKLK99+vzzz19IeBMCmTCDhrU7/Guzjle6wx6mtKW9xlUKZ9rsdZoSZ+BmNtnLuLW9zC/yqP1Kd2kXv3YHjiYzyhZhpbu0N4WHH7PMM+xhluHh9/3vfz8nf8pTnpIVqD4MfNJJJ6VLLrkkn970/ZcddthhgXDVqfRTTz01eXbHeCF03WyzzZLvXwVOCMXxjI65zNMWO+64Y+M3pqy14nk+wzd31llnnVye8m95HZclDUb7SIElhQJ9T0suKeUcyzFSYKTASIGRAiMFRgqMFBgpMFJgWabAqNhYjK173nnnpY997GP5zeBnP/vZuSSEGyeccEL+MOLiUmz4CLEPNZ555pn5dD1hivdGl0VoaoNp6+k91q985StZGPa85z1v2uSN8QnUTjnllPxBwUc84hGNcZZGz7/97W/pe9/73uAbSa6Fiw8o2obAMcccky688MIsHCRkHGF6CiyvfToE52GiXNjDLKnZ5FeGl+nDv0zTZo+4YZaC8vBj1v6lO3DzYw8z0tXhJd6IU/sNdUd+Q+O3xYsyThte0qG0B57wC5O/j1E63ekt7j/+8Y9ZoXrGGWcseMrCEzjWxRe/+MVZee3t7k9+8pPpoosuyt/EMVf5KKyPXt7//vfPWZ144onpfe97X7rgggvyN6Wc8D7ttNPSy1/+8gXPopjXPvOZzySHGijWfJPHNwSaFBtzMS6DJqM5UmCkwEiBkQIjBUYKjBQYKTBSYKTASIGRAiMFljYKLBGKDcILm/nyaiEhgU186VcTVzpCd6e/6yvhdVxuz+B4AqfrqqlTmOK1XRMWJk5b+DR1ufTSS9PHP/7x5ONxodh40IMelHzgbvPNN1+oCoGXcKWrrp6pEHdR3rambPn2t7+dfBjxr3/9ay7PQoXpcKAPaMt/SBtEu8IRHxjXHzxL1IY36KNdSmFVR1FzUFMblGn0L/2l6zqzPrr//vvnfJv6RVeb6EtO8tbl/ulPf5r7hnFRKzak4S9NW18IGrbF6WsnwjZx1K2JnvE+cNdYKumovG1tF/Hk1zW2Il6YbWX85je/mQWQG2+8cT49HfGZ8MecYS7oA/1OPsbdTKCtjCWuvjHR1/fhWtT2LMvDvih9uq28XX1amr753viPPlmXl1u7GqdtfXLa/gWncVSatT0HVn+RpvJegKsJR1uaGgd3jMcwS78yfoTDzR6mOE322q/EVdsDd+3f5i5xt8WZiX/UK9LW5SrdYW8z3dC43/3ul+dUCoovf/nLaYMNNkh77LFHVm64Mfa5z30ufwzZ+vz+978/K/7/67/+K8dz4+NTn/pU7n8UG8Y+pQZlCGXHhhtumMMpMLbaaqu0++6752K7oQmX/mmunzdvXrrLXe4SVVrIXJRxuRCi0TFSYKTASIGRAiMFRgqMFBgpMFJgpMBIgZECIwWWAQosNsWG5xmccr/b3e6WT0defPHF6ZWvfGUW8n7hC1/ITyARdnnegbDfCUbw0Y9+NJ199tnpvve9b34CgjDC9yge/ehHp3ve856NTeL0tlsQbiIQBHtSggAhTkQedthhWaggjx/84Ae5DOuvv356/OMfn1ZZZZWM03MUcDjhT0hD8LDrrrtmpcRM6iKfD33oQ1lg8oc//CG96lWvSk972tMy/l/+8pf5NGd8g4BghLDWKVLCYQIS5Vc2dTvqqKPSHe5wh1wWp0EJAKUlOKkF8hQWTpGi+xOf+MRcty9+8YvpRz/6Udpyyy0zXnk89KEPzbR65zvf2UjT2pMS5LOf/WxuG2F3utOdMq711lsvR+1rg0984hPprLPOSve5z31y23uyaOWVV0677bZbPu1KaKRehD5PetKTsuA10nj+Q1ofcvTUx2Mf+9gczylZp3Dh8UQU+M53vpNpSeh917vetbENtC0hLIGT/kW5Ir6yNCkuCLCc7AUPechDEmVJX5tQduhPbsWw68P630477ZSFX9pI/9eehx56aD7hy23M6A8hnN9iiy3yzQT9esjY6GsnCgjj72c/+1kuFyWbvhA3H/RNZUAXigG00hcp55rgiiuuyKeajVnKgaZ4XWOrCWdXGQ8//PAsbEQrp6Dl72kZCg3CSrc/fMOGgHCbbbbJ9G5SDomjfdRX+6KDG1QEmk5VH3300fl7KhRa0nuOhlBTvOc+97lZ8dRFx3LOcTPHfFbfijLPfPWrX03f/e53cxmMJUJZY+kJT3hCHuOL2p5N9OU3kz6tTzjFbo5Eb2NRf9Z3jjvuuDzv1H3amO2a71/zmtfksjzgAQ9IJ598cu73a665Zp7b1lhjjVz83/zmN+lLX/rSgieDzN3m5vhQ6LT9C91BbZZ+OUIRJ9xNZuCJsD53xAszBPHcYQ+z9Cvjl3mIy12a4kac0r/0K+MEbmaZd/jXfn3uSNdkRnmElfYmd/gxa4gy9JnSieNnzWcaawcccECe37bffvvcn/VT67ZvBFkrrZ/otcsuu+S+pr+bA8ydQL8zv5trKEes0fq/tdZ6bn2mzKDUwP8ceOCBGa+1s+0bWzMZl7kw499IgZECIwVGCowUGCkwUmCkwEiBkQIjBUYKjBRYBimw2BQbnlzyjINNvOdpCGoPOuigfMKR4JRCgyCAAICggICLINoTPYTT3qImZCY8oBggTH7rW9+ahYxlOxGcvfnNb04E47e73e2y0OJb3/pWFsDLj/CLAJQgk3CcQI7gQh5O/u6555457C1veUt+Gki5CDIpCAgjDj744CyIn7Yu8iO8BwT2hIEE3KeffnoWjhACE5ITmMr7F7/4RZo3EepTbiib+j7/+c/P73HLm5KDwoOgRD0IQwlnvAleAqGN+JQ6j3vc47LQnpuwhfAeEODDR4A7RLGBZoQzBD8+nk2RoN0Iswnlhfe1gTppF0J7Qh3CW2+Nn3vuublOaESAqc0JxwnTI42yEx6hJ7rqD/oLWqkbgVMoNtCG384775z7Q1MbKPcRRxyRaa/PER4TiCsDIXkN2o3gFshHueXR1SZohWaUJtrta1/7WlZiqDsBsZsywLjQDsC4eO9735v7CcWVNv7hD3+Y6bXj5N32vrEBR1c7KS+BPQWV0/N3vOMdszIAzV73utdlJZm+SFhHmWTcUsBoI/RuUhAQ9FHgwYeW2rcE7dU1tsq47ASJXWWkvNQeQPtH++qP73nPe/KYVQ5KL2XxRjalQgn6q5PWhOUEiTEPqTdhp/7kNLexRjgJH6WJNvc2fl8Z0TXmHOPdfKPda8WG8r373e/O4RSR+r9xr38Z1/wWpT2Vow1m0qf1Tf3YPGWeNJ7N3a997Wtb+zQ6d833+o++o48x0Ur7UL4+85nPTJdffnnuP8bBne9859wnKUDMAZ4M0ke7+ld9uyME+010qcNqd52mL7yO3+Q2XweEvTaFh18dl1s5yvDaHWlK/yh7mS7ilWZTeO1XuyN9+Ede4c8UFv51vHCLF3EiDTOgjMce7tqM+MYTZSegwNx0003z/PurX/0q9ye8hvXH3OvAgPUTmEusV/odiNuW4ps/KOLMISAOK+jHgOLU/GoeMs9TZJtH3ej4j//4jxvMqTMZlzmj8W+kwEiBkQIjBUYKjBQYKTBSYKTASIGRAiMFRgosgxS4yeKqE4GEH8Et5YHbGoRMNu5OoftWgZPPhL6Ef4TXINIRzD/rWc9KL3zhC7PAmVAhhMFlnQhenbynQHnJS16ShV0EYJ6DIIwscTox/4IXvCDts88+WZh97LHHZuElobaT0xQjcPg5LUy4STARZZqmLk4xu2UCCDXVlwAkcDGBp6oI7ZxYVlfCOifWCdIJOoG46OamgvJvu+22WZAY3ybIka77I5BWdu+CU6YQ2qsDgY2TqcApfILJoaAcBLUEjk6wK+O6666blT8Em9O0AaWFpz20PaEQhRSh/X777ZefAFFeAnwQtNIX9t1339wuFAXam+Is4ihXQKRhtrXBpz/96fzB1+222y7TnLJC3/LtBm3cBIE3wri72oQAmEDMiXb9mMJk3kQgTFDrBLCbK2CjjTZKz3nOc7JdGe5xj3ukvfbaKz9dRjlDQEYwDKIMbWOjr50oBfQ3NFZn/Y0A3Y0kigGCN7cLQqhMsRaCQHWtQT/1rBlB/DOe8YzchtHHIm7f2Ip4YfaVUT7GEUBbY0w/coNGP0dnfckJagJL9a2BYotA3W0PfRkd3BozDsUnpCS8JGhHG4Au6E8Z2VdG8aOt3B5zUtutmBqOP/74PMYf+MAH5vbYe++981wXaRe1Pev8anfkE/7cbX3aSXQ3XPSdpz71qbl/UtRQHKNjU582LuEbMt8T9JojnainrDB/A3O4+YBQWp81NysL5Ryl9rT9K+raZ6LFbEEI3UuBe/hFHmUYv9Jd2yNtbUa6vvhlvBpH7RY3IML63G3xwp8JutxD44hHUVDiarKLQ5FAKQ+4rSXmiDe96U3JM1T/n737gN+1pg4HHtygggxRmZelKMNNBRWotSpKEbUO1CruXeWvKFqV4SpaZ3FXXODeUEeriOLExVBcQFERBBdKBRf2/35zOdcQnvX+7u9Ocj6f902ePMlJcrJOzjnJw5Dhfve737KTod77ORVIce+aKryFcQooPgFlf0D4KXQBpUaMA4pTyhAnKCksnQ7pgnnGZVf6FtYo0CjQKNAo0CjQKNAo0CjQKNAo0CjQKNAosLZQYJWd2AgCEoD7WCfhvg0+YRghIYECwUAIAAjiSyD4dR82oeWzn/3sLGQkFI7rRyJuXONCoEo5AJx+IHCnDCHMCCBAJ7AkTGZ9zAqT5S/hGCEmIUZYWhKOENzCEzBPXQhH3d8NWH+GlWfgCjeE+K5WobBAI0oE9aLwiVMWTngQHroeSLh0LP9rYJVPYA8HoSxFAkEh+lD+LATQgNA9rvZBG7Qi4IWfkFE7TWkDihftqt19YBWoF4ERwTrhaV0vlrXSOFFB8MzqlRBbewxBXxs4jQMIkAie4CXo0h8ItwhRp8BQmyib/scqH60okvbbb7+suNAO0RaUadE30E+ZnESgaIgxoVwl9I2NsXbyHm7Ww04G8BMSy5OQDY2NL/2eEM+JIEJpbaYONcChjOhw//vfP+PV148++uhlUcfGFiVNCWNldD1MCCedpDDGnNow5oFvg/DHvEJZoX76bIDTYOYSdN9nn33ymFJu1ywRpB922GFZEaGfmKsoHigZnORAe2FDdIwr4OTndJQrrrq+9xGCTWMf7ZXzNa95TRbYS7u87VmWA74p0NenjQ99mnCW0oFCGv0pbPRl807dp+eZ79GIUg+dnPKIOSCuqDM2gkb6rD6qzcb6V1yxNqXu+og2AKW/L230qUgT8frCA2/Eq58jXZ9bx/dc921h80Bd9jJtlGMorI5TP5dph/zS1WUJXH11jPdTXUrJGvR3J5v0JXOck0MUtdajAONen8MrmDecsjQv1uWN+Nx4R/kGjA0nkOR3zDHH5JNP5ptQHOdIA39943IgSXvVKNAo0CjQKNAo0CjQKNAo0CjQKNAo0CjQKLDGU+DK0siVXCUCXUqNANb2BIME4nGCwTuC8RIIGgBhA6EaqOMIKwUHnkFcwUJAUUJcS6JM8BOOEfoTUgLPFAogvnURQhNhC62LtH1Ql19d41qLKJe0wkM4GrTpooe4hLUUG65yQmfA+r+sSw6c+MciGxB6Bw7X+8QVP3UdxB1rg6iDuGgNIqyuV4Srv28cAO22UAi6EohHexOwU3CEFe4U3ENtQtCv71JCUfw4MUMw7PRGCIDrPChBnBKC15UmygP66FGPjbF2iqtU4IyTCCzqCald9eV0Aat4wn3vKc+0Y5wkka4E4wVoP8oSEBbL+WH2F7QeG1t1fM9dZYx4pUvYiEb6JkUVIFhUL7QkYI+x4130V+MswqO/OpEgLeUgYb32M46Un/BdPaNOcI2V0fiLPMQvIU4HxRhV/jLu8rZnmddUf1+fVi6nxVibUxxRLpoPKHyckOmDeef7GOvR50NBRQEI0IgiNCDaYmr/kj6EzuEPN3CGKxxE/Aiv3YhXxy3D6zTxXMYJf5/blSbCFurKa6h+UZYSfxlW+sWpn8t0pT/ilXl3hXXhjHjluwjjlv4yzxjjZZjvtTgFZiyab411p+18DDzAXG6ejL7u6j3KSPMD8C4g/BQRwHgCS5YsWXZtJIWwPCjnp0LfuJyafkXFo/Bp0CjQKNAo0CjQKNAo0CjQKNAo0CjQKLCmUKDtYdaMlgr5vdKucsVGSTIb+rhT/ZBDDslXJhEun3XWWWW0ufyhBIjvFEjM6hKwtCwhhA4sLwnCCEAoKyIeoaVrVoQTrCkv4X2cqihxTalLCFhK4U2Jg1/5XU/h2iInBQhh+eNd9sz5x8KeEIdluuuoCK3rb3GMoST8RQ8ClaCPcqqLehGSuw7KKZN52mAs3673oWxAmzi9QNAZHd3E5J2yRhsHnq42UB8CU4LvsOIlRIYjLHW9hz+ULoFviqvvOPkDF7rrK6xzCXkpN1imd5WLJS9hl6uLWPI6tUEJOBXG2iney5uwjrIF6G877bRTLjPFBJoQ3BlHrmgxtpx80qdKuoSiyykJSkRKJ/4SIs++sVXG5Y/4fWUs48e4Ug5tr/323HPPTGPxnCZR5qB1pI3+6j1hJqF9zBnqAJc4PiB84uyqOx+xB3Gd1DxljDy7XHlRysjbKTL1CYG++JFP37iL91No1ZX/PGEUCJSJLNXRwckmJ3Oc7Nl7773zqZagc7TLlDlyrAxRRzQI8K0T/VCfjPdT+xccyhllDJwRVtch4ke8PjfwRfqueOJ0vY+wcCPP+rkLZxmnfC88yhThERZuhPe5XbjrsPK59Jc46/CyXOW7CC/DSjzhL9+Hf8yNtOE6+WfcOQkUa6P52hyCHzE3vOMd78jKdtcgUrjpY0DfpwhhmGHuoAy1flB0BB8S64hTTcBYiLkm5shaCZwjrmF/oXxfw4rditso0CjQKNAo0CjQKNAo0CjQKNAocBWlQNvDrBkNH/JepV2tFBs6EOEYYQDBAQFBKDVKgd48ZHbPPitiVuaECYSSBMis2Z1SKMH3AAgeTjvttHz11M4775wFY+7XZ1VPyeKaCcJQ8Vy1VOMIfFPqEidVWLz7ZoarVGogJHSHNyEKIScBCMtQgntKlbDartMNPbuqh8DGHf4EgKzPfXdjKrBCV15X9TzkIQ/JwkwCRG3lWwaUQRRU6OVqnnnaYGoZynjaVv6E5xQ1BEiUNwRDOjslFStagnqC6BK62oDFt4+mUyJQbrCude2P9N6plw8SS+tj0vOCPhj9yXVAlBnyILgLoVaUywkDp2t8LD3aWh+gNIrvzkwdG4TOQ+2kPQnj0DE+Jk9g7aTILrvskseja4BYMfueixMcIMZsTRd5Eciz4EdP44nFcwnzjq2xMsIdtJOXvkiZQRHk9I2rp7QjBZernlwjpT1K0GeVnWDTB8edoDGHAOMxhKQUPPqeOSEUHeJMKaN4Y0AhYEwde+yxuQ8TkMYJBGmXtz3H8p/nvav6fBeAou/A2TeTnHAyjwsPxiDaJfo0OkXfWeh875SMsWRO13eNH99GMp583Hne/hV11sbGY7R1+MeE6/E+8IQbeOK5y+2LU4aHv8/twru8YfKq6xX517jL8Cn+Or3nMl35vi5D+a70l+nDP+aW6flddUa5gRegNDPHuvYMmAspMnzbKPqvOdw8oIz6PiWHtYfy05zzute9Lq8lFH3AmgiMYXxJzDV4C2sn0HcbNAo0CjQKNAo0CjQKNAo0CjQKNAo0CjQKNAo0CvRTYLVSbBAmsgT/+Mc/nu9RJ6AOYRhh7kLAB8GdsiBQoCAg4CDU9F0PAv0SKFII5wg1CG19BDSEE/wEaBQMhKUsMgklSi1RiWtKXQhACPcIjuXruQbCOQJjp0LQgHCbJbfys/Z0p/1CwHVUFBuEjgS0IfiZgsupCFciSUOx4aocH1xHGz9CRQqTe93rXlkI7lsMU9tgSv51HCc2nGYg9CVM9YFo7ce61jcMtBvFBrrVJyy62kAawiwnIvQZbezUh3B1JszyMXn4FqrYILR629velhVB+golmQ8kU2AA/Zag1skOygQKNEoQ/UR9nNTQD4F6h/A4B/T8jbWTkzs+0Owj6XFVGUGxcqAbJQ+lBgGfsYImyuDbIFxKpZIuhH/GjY/vOpFCkaAvlEDwNzS26mOAlAxDZYSb0JBiBo0I1tH6iU98Yu4blBuUQgSVFF/GQQ36jvjoQKlgHqLs0QYHHHDAsuhxHZUx6WSSeGBKGZchGfA4cUBYSjCqjSkzY5xyl7c9B7Ke+1UoWrUzJZb+gi7mAN/AAXWfRt/lne8poR70oAflb+9Qqmpv49VHnilwx/pXXdGgLwF17S/DIl0tbI808X4x3MAZLpzhD7cMmydP6es6BK4yvMynC3/9vnwOf7iBvwvPlLCyXGX8En+ZR4TXbhmnxMNvHnZSzclDc661BN/g4/UUjuZ+8xYlhBNb+r8xaj3XH+O0m3XRemE+FcdpUMrRmHcoMp785Cfn000UcvosAwZzaqwFddnac6NAo0CjQKNAo0CjQKNAo0CjQKNAo0CjQKNAo8BSCqwzE1wu/RrqSqaIUwennHJKFpo6PRBAOOreeqcYCHz9CNIJAFheEzQQ9FMqELICAnqCB4LHuA4i8HEpKtz5Lh2hCEEtAUR8y8DJB4L4ww8/PAuIWfhTGhBcEtID1tIUECyLCVsJKVwZxCJ9oXWBl9CSBbNyEchSXpx55pn52imnDgD8TgwQrhIWE8YTghOCCDvhhBOykJYwBMBH4cESlMC1CwhbKCcAQU1cv1PG7cLtvdMqhMauxlIOQIipLtqK0Fh7aaP4PsBYG8BHWO5jvvH9iA996EMZtw/KE5TqG/LQJ1jaEwipO0WGttBG6lFff0XIrXzSaXN4StrUbaDM6ojm2oNATDoCWu/0FQJ8wisCKsJUAl1A+SGvsTbRx5SLQoNihlBcf9bmThDoYyfOTpegJwG8q6HkS6mgTBR+rIQpPpw+IiBGw7GxMdZOhHfGHxpR3kU+xguBsRME6EKIpy866aOd0b2mC7pRjFAmoKf+arzon3ChFRgaW8pT0la6sTLCRxGo/2pnijvjS7kpNVyjhqbGkbKHQiIX5vI/7aNfEG7Kz3h3HZe+WcLxxx+f63iLW9wiv493Y2WknNLGThzA3QXK7EPchKHGFBofccQRuX9RIBLqL097UgZq5xr0P1cBlnSf0qeVU9+Jq7EIf9Es5umuPm0+HZrvY27XhqFgMrb0PX0emDfMzfq+saDNzUvRrkP9q667fhqA/gGlX1j9HPFWhBsCebjDH24ZVvs9B5TlDX+XG2HSlf7A0+d2lWdqWOAs40fYPGWINCWe8NeuuBEW6fAFJViHnTbTf/Qr/dlpDf0LWEP1O/2PH++Bb7CGmV+APm8ecSLDXOhaPDgoiQPMFeIYi/zWKTjM7zV4P++4rHG050aBRoFGgUaBRoFGgUaBRoFGgUaBNZsCG6171JpdgdW49L+69Mmrcela0YICpdH6KlNsRGG6XMIMG/iyoF3x5g2LbzHUeEOx4W52glfCiDpO5BVXq3hfC0YiTulOqYv8xCOs6wPvCUYIWIbi9aVf7HAnBJyOqCHoEwqh+n1fG9TxpjyHYuPggw9OB86uv4G7q12Wpw36ykuRpv2n9IGhuhBYR3/rwiV/wvyS1sL0gfoKpaF86ndj7aR9jUECujJveNBTGfRFZSuhjy5Tyhxl6mrDMo/wTyljjSvGkXLXZQ+8patt/PTnrvYp43b5h8rYFb8Mo9R65StfmZUDhKqUWE4QEXyyFC8VIkG7vnHXVY5DDz00nwgp8+TX3vHdkPrdlGf9Bp3RvgvqPj1lfHbhqcPMj8reNz8Gjeo+UeKhyFKegNIvrH6OeCvSLfvdFP9QWcry88dzuNKGP9whfN51lSnC+ty+dGN5TX0f+Zb5dIUFPu/6rmPUn821ffNFnOYY6lfmef1PnL552zwj3hCeKG9zGwUaBRoFGgUaBRoFGgUaBRoFGgWuuhRoio0V1/ZNsbHiaLuYmO2bA1arq6iiUIQMZSEjfHndKTgJxmpBbplvn9CsjFP6p9SlT2BS44nrLcrwVeXvo9EYfaa0wULqhM59Qt3laYO+8vYJp+YtO+WAXx905d8V1pe+L3ysnbRvX38bonUfXaaUeaxMdV0WUkZl76tXjd/zVAVIV1phQ2XsS1OGO9Fw6qmnJteEOfW0/fbb5+vfSqWG+GO06yqHa+6cxFhsCGv1Prx1X5gyPvtwleF94z/ijNEo4ilPCPVLv/eeQbzPD5f/xbsybDH8Nd7yufTLq34u81fmeF/6++KU4X3+wOd97Y/nqW6ZR6SJsC56x7tw6zTl8xR/4Kndsf5szhvre2PzvDyXd66py92eGwUaBRoFGgUaBRoFGgUaBRoFGgUaBRoFGgXWdgr0S1TX9poX9TvooIOyVXZ9FUURpXlXQwr4gLXTNu7Rb9AosLZRgAW5uck1Nq66odzYaqut8vVZi1FX10Q1uCIF0LdBo0CjQKNAo0CjQKNAo0CjQKNAo0CjQKNAo0CjQKNAo8DqT4HV8iqqlU02V0AAVpWlZefKLkfLbz4KuLaDJS9r9L6TAvNhbLEbBVY/CnRdI7X6lbKVqFGgUaBRoFGgUaBRoFGgUaBRoFGgUaBRoFGgUWBFU6BdRbXiKNyuolpxtF1MzOVNIO3ExoyyU66CWswGaLgWhwJDVzgtTg4NS6PAqqdA1zVSq75UrQSNAo0CjQKNAo0C81PAd2vA2DVv82C++OKL0/Wvf/15kvTGZUzw/e9/P7/fcccdVxvDmRVBt14itBeNAms4BXz/yrfXnHaeAq5m9W07QpJtt912SpLeOL6p9b3vfS+ftr7pTW+av43XG3klvGhzxxWJvJjrxRUxt6dGgUaBRoFGgVVFgXZiY1VRvuXbKNAo0CjQKNAo0CjQKHAVp8Dvfve79KUvfSmdffbZ+SPrruFzzeSSJUuWizK+UfSFL3whXXDBBcuEVXe6052u8A23s846K8e56KKL0vWud710+9vfPu26667L8nUy9Mtf/nL67ne/mwiHfN9o9913v4LgixD85JNPTr/61a8yju222y7tscceo98+ikzk8YMf/CApyz/8wz9E8BXcSy65JH3nO9/J9LnjHe94hXd9D4tRtz7cCw3X1i9/+cvz6einP/3pc33zqi/P//7v/06f/vSn093udrf0d3/3d33RJoe7+vGpT31qjn/UUUdd4Rs62jig69s/XWERf2W49anz8rn0d5Ulyt7llmF9/i6cZViZf/jDFa/PX+Io/VGOCIvncIWX/og3FF7Gqf0E3ieccEI677zz8ti++c1vnu585zt3fpfxnHPOSZ/73Ocyin/6p39aphwjUBX+ox/9KNfXFch77rln2njjjevsctk/9rGPJXPTLrvs0nsNKWH8L37xi2Qe2WCDDfK8WRvs/exnP1uGv6SzwPK59A+9K+OV/mWZjHj62iWS9eEs04V/zIUz4gT+vrDy/WL5y7pM8cu3jBflqMPqZ/Hq7+8JWxFzLrxrKiz2erGm0qGVe+2gQDuxseLasZ3YWHG0XUzM7cTGYlKz4VpGAYyjDeFiWc0tQ9w8K50CP/7xj3Nb3vjGN86MMsENK0ZWTOUEstILNkeGmHkf9V3drik744wzci0IvxbTYrWLNKuT1di81nNd9Vlo2NpqnbUy+/j555+fvvGNb6R99913sBm+/vWv5/e3u93tBuO1l40CKPCb3/wmvf71r09f/epX04UXXpjMExQMn//859PDH/7wtNtuuy2IUL5N9KpXvSorJPAmrhwl9BH+6Ec/OguO+AnZzcnWCWscRcj/+3//LwsSpTvmmGPSJz7xiawcISxUvi9+8Yvp4IMPTttss0069dRTE+E3JYJTpISLhJRwH3jggZ0CqqjQT37yk/Stb30rnX766VmpYzzXio1vf/vb6bTTTstKDcJQJwimKDYWo25RzsV0rUmf/OQnM73/+Z//eVFQUwodd9xxuT0WQ7Ghr1BmAe1Zgj7SB13C0764Kyq8FnaWz6W/L/+oQ+nW/voZrggLvPFc51k+h7924YiwwNflRh7edfnLsDpOF76xsFe/+tV5nqLg1A/wx+ave9/73ldIqs/8x3/8R55LvDjggAOyIsT4DhwUlcqHBzQHPOtZz7rSyQLr7Zve9KYsmH7wgx98JcXGmWeemccS19zkdO/vf//7PP/svffeiRI36Bj9Np6jwOXzPP6+uCXemv7xri883odb5tGVJsJKt/TDE8+1P/KYxy1xjaUryy5u+TzFX6eZ8ixOF6yIObcrnzUlbLHXizWl3kPlxNcceeSROcoTn/jErCAdit/eNQo0CjQKrI4UaFdRrY6tsoaWyeafJdI973nPtNdee62htWjFRoEPf/jDWZjyoAc9KP393/99FtzYiD3vec9bbivalUHhH/7wh+noo49OlAeEWKsTELbZZL7whS9MrPVWFDiC/9rXvjYLxGx2H/vYx6a//du/XVHZjeJ985vfnFgxag/tsrJgdbXOIswkuCAYWX/99ZdZZE5VxK3MPv7zn/88vfKVr8zW4mOKDQLe448/Pj35yU/OVvcrq51bPmsmBd7//venD3zgA1np8MhHPjIL9yg5PvWpT2Xl+g477JA23HDDK1TO/GlOG7pi5D3veU+24mdRrc86vfGa17wmn6r4x3/8x4zzgx/8YOZZ8Cv77LNPopRTnne84x3pZS97WVY2vP3tb08Uo4TwFCMUHficW97ylulxj3tckg9Fh9MC1kqW0cbKL3/5yyzw7LKgjcq8+93vzricAjAP1FbbhA2veMUrEkMD7wlGb3SjG0XyQXcx6jaYQfVy7KoTwl4KA232zGc+Mwv61ltvvQpLyqdiCBC7DCjQAx8iXTlP3uUud8m0u/Wtb30lfHBFmlKgGBH1I9CVX8QpXfj6BJxd4XVYVxlK/IvhL/OY4pdnWc7wly5//CJ+/VyWPdIKGytDvC/dvvRleJQj8o13tVvHi/hD4REnyuSZ8mzzzTdPj3/84/M8Qknxlre8JfNV1vCAz3zmM+m//uu/8vwlLMpz4oknZt6aQuRJT3pS5gPhgNe+6Q53uEOgyGnf+MY3ZgWpQEZjJXzzm99MeCoKDUYE5gWKVXOV02XKxQ8vMGeWdSn93pfPXf4IC7dMU4ZFeNQ5XOEBXWHxrnRrvGW68A+58Q7O0t/1XOa7WP66/OXzFL9ylPGmPHeVfWjOnTL/UYzE3A0/WppTpxqOSav/Dc2xY3GsH7Hm1zSJOk819OlbL/rWl8DvvTysP5SINUxJH2tRuX4Fninp7evk30eDwDXkdvFP2hQvAxiUOPk1BNrDmt/Hg6nLlLKiB1qGkV+k68M7VKb2rlGgUaBRoCk2Wh9YNApgpjHoLAoXQ7FBCGrjb4FlQTAVCAHGYGUxuTXzUT6X/qnlLctd+qWvnwNnHR7P4ZZpI4yQ5x73uEdG4bQGIaXyYnBZu/BH+Uu39Evc91y+499iiy04iwYEsYSrrH1XpWLjne98Z76z9173utey601YgmIsMX0rEggH3/ve9+bNxN3vfvd0gxvcYEVmN4rbVTMsE+9zn/usVMXGYltnsSQnyHQ1xJiQv48oBKMEpuY4wgjjasstt0yPeMQjkk1XDZj92ppqZfVxcwJBr/Y77LDD6qJd6ZmQhWL0da97XW7nTTbZ5EpxWkCjAAoQEpijbG4POuigdN/73jdvcG9zm9tkhSzFn7FmnL3oRS/K1vN4i5NOOimnpRhmDe3qqhpcP2WMPvShD82Wy+5PZ/3sVIhrWyhLCB/17wc+8IGZZ3EFFYWAMU6I4pQSgaFrsR7wgAfkuZTizgkK/A5wjczOO++cbnvb22behwDSCRQKDvkMKTYIeqyzhByU0F1A2eGEhvw+/vGPd0XpDFuMunUivjwQfd/whjfkdYWglqIWoAMLdnXStu6Zp3BAM6d5H/OYx6RTTjklx7UuwUMIe8Mb3jBtttlmWdlrfbzZzW62zNJdZCdptAsFD8EOnPgUgl3zKPpoU+ki31vd6lY5/1//+tf5jnsKrSWXX2/m2jMnR1zjA/Qlp2XifQ7s+DMXBwS/1PcsvI4TPFGkWWwX/shzir8r/0jPrf0RVofDE2E1zjI86h9lG3IDT5k+wrgRPuaWccv0Q+ERL8rrmSCPccj++++f5yzrnPnAL5Rq+jOFg31LrYygXKWovetd75px6Ev2TBSqcJSKjQ996EPpa1/7Wh4Trr4qwdofV6ThGW5xi1vk+dDYMI5cpye9+c6chr8u+21Zp3n8EbfPVUbvoj3KMtdh9XMZdyoeOAJP6ZZ+uOK59ntekRB0kkdNl/pdlKMMj3Txrn6ucZbxSj8hdDnnmjOH5r9ybjcf6p8UD+ZW/f8jH/lIXt/MwxRnfadz0Z2BAt5RGZz4V2ZzLqM5+KzHrlszh+uj+EXjI66DxBvYyzm5aPxZD62Z3pfltE6r40Me8pAcH09trbGm4DOczAT4jHq9ED60vijXZz/72XyKj7KQ0N1YVU7rHNpYS+xvQvGBh7HvCwXGEP6x/K2Hrlp03SX81lFj3F7B/oHM461vfWs+6ep6x8BHwWpuuP/97z/IP4mPX1cPwPjOWsgopAZxtAdaaw/z3N6z02Gu5wRjZbUvRSvzoD2w02YUG/LCx+Fx0Fg/wJfZFzVoFGgUaBSYSoGm2JhKqbU0noUJhLa8riZmxELGMiPAs/AyzDvMBmsmG9saLMaYqWtd61r1q8zMdFkxYFpYEGBMasUGRmPIGqBmDkvGti7A0Ls67rzPgTvK4zn8Xbi8izRd7/vC+tLU4fUzfBFWu3Ve3pdlj+dwA1fUoYxb41rMZ/0Ac6d/BWy//fb5dEnfiQj9TV+UrgYCLYxWia+OUz6rP3xd1ksYS8wu5jIY9TKtcYQBt0HogqFxI37k3WXdwqJPene9s4SuFRvzjO2ybPKEl1Au6GdOwNAOWWQ96lGPyhuRro8yGs/o3keHMv+y3uL39TNl1CZ91lk1zvpZeUBdJwI685I2rxUb6iFf5eqy6ILPnMsS08bgwNlVNTvttFMW2BFwmIe7FBtoXltTTenj81hCqa/y121g40GAQ6BsA1NCV7s7jeNOcVdr/Od//me2/irTNH+jQFDAdUmEduZaQoDgKXxslaCQAOQrX/lKHmdOdZgrpeESzOivxqeTEzVQaJiTKB0AAYo5z1xJAM5PcQEIWMBWW22V8dlYEx4aYwQh4svHOAwhY2y4ndQQTonhSiuKVLi9H1PqEZCy9HbFVJdiw9ymbvgqJ0WmKjYWq26ZKD1/lAXve9/7Mj2NeXkygqCMsu4S/BDm+CYBgYy21K4Pe9jD8qkYaAlhnGyBBx28Nw9RhhCGWSsIPAi14rovYQTEcKIPAYy1Tv/QfyhLIl8COe1NyRRtSkFm7naqxnuGOPqTtZrA68UvfnFPjZcG63M1aP+A0h9hpdv3vm8d64tf4uSP9BHfc+0v44S/xuM50nFrf4TV4WW62l/mJV2UrXSlKSHilWGlP/IXFn5u6Y/4XWHxbsyNtBFPH1Vu/coYNw+Zk8xX4jICoKR42tOetswYIdJaP5302nrrrbPAE+8a32yJ+URc48h4JxwlxIw5J/DgFeTpBJr5zTpL8KpfU6QSCFL4EioLtx6X/bZsDzjL5/CHW74X5qeetRvxanoJL6HrfYSVeUZYmTb88a50S794nuuwOn08L6bbVYegVeRTPvf5xS3f1c/1u3jPrQHP6SQiMOea74bmv3Ju9x0Zc7K1jVGWOdOcTYlmTtcPKfJrPlle9j7eGSME2U4Z29dLb1637uKFKeGXzBQo9ijWC/O9eZiy4m1ve1tWpCizZ/3Z+vCSl7wkleUkYLeeUzZQsrsi0lizxvo2lTXG+m7PUq8XY+uLtYES3zg0dikS1E2ejB4I6tUDndXJ2kUpaewa82P4x95b9yn/rWORv3aQn/UP76F++I1QbKCRMPwJxcYQ/2QfgwcJsL76rk8XmGsoTK2nFKbmOnsitLDPHSsr/ki58Ah+8tE/0ED5KYPR2T4KT3HIIYd0FaOFNQo0CjQKdFLgylK9zmgtcG2hQGnhMI+VnQ8pWsgw1CzjLDgsJ9xnHJZKmBzvLHCYGGDBY2lAqIChsPBhNEJ41mfFgGlg0UxwRsBw+OGHZ2tmjI0yYFws6hgHVvmYGYweCDc/LPAvGOIFJs/JynLAVz6L0BU2Nb+yfKU/8HbhKeOFv3a70pVh4qtHlyte+c5zV7yaDuLV4KQDZoewK6yBwmL3CU94Qrbs0NcwlPoWAS6mFYNGEYbRw8xifDHQ+llYr2DICWspNtxBjPEDNpmYfwyhvqpf22DqZ4S0fYw7vPLz3qZXGQhp4ioTfZhgmMWwDWYAwYzNq3wxiDaiYd07NG5Kixf5YgIPO+ywK5QPE3ziiSfmrDCsLAiNYWNH/dRNHsbPlLEdFrDaAmOKwXYPvrGMySd8JES0OWEx1aVo0R7SUfBgYJ1AsGEyftECHQjIbMrLax2CXlGGPivccm4L6y1CNPUPa15tZKNEgeA6FNZUyoXpN585maTf6VfmM4D5Dkte/QNdtSkB3gte8IKsQCM0GLLoijpw9QNjgALYkW9lJTjVNvH9lTK+/t1lTaW8ZR8v+wVaTrWEshmUN6Ge8Wocsbg2joBNJ+GLtg5FljINWdkZc/oy63d3g09VEpb1bv61nwLmLn3OPFQrXm3ggU02EM+PpaKxQ8hiXnPVXJdiI9YNaY05ggnjltW+vmleN2eBMOwwLq0LBIT4HfwKPinAXE9oYf6yNoFQYCqHucEcZ5ywFo353NiMvKRxkkSdjTVQChVywOV/ykO5MgTm8xCOiodu8o38lqduQ/l6pz3M4axvWVta51hzmicpNqLN0IjAhYIm0mXP5X/iaQ9tg7Z4P6cztLG5xNxvvSKgYSFsXX7pS1+a8zE3Rz7cwM9PWGw9Ijw54ogjMi9qzjXf4kHxjU4J4WfN/dpXf/O+DyKPyKeMN/SujNfFA5Vpy7hT/ZE+cHuu/WXYEN7AVbr88Yu08VzGK9+Ff8hVxkg/FK9+V6YJP7f0SxPPtb/rWVgNQcMIj/HkOfzmCoAP+uhHP5r7Fcv2OGWZX87+8IcBxif+z3d28GNh+EKAa14zZ7k+FC9SgjnMaQwGK5QaBJyEquYUv3/7t3/LxhLwmQPxvkOKjZL+tV++EVa6pR99PYPSnwOKv7IdIm7xOnvrOIE34pXvw1+6ff4p+dV5RZ7zulGGEp+wvufyXZ126Ll+11fHKH/E9xzGN33zX+Ayt9/vfvfLp4Csn4T11mxKO/33Gc94Ru5/eMhtZt+bqgH/R5khH3w9obV5WFn89GW8p/I8//nPz/sgfdt6pv8bC2FEdOihh+Y1hlDcmDBXU/zBIz2e1fiyZsuP4ZC+j2/l4tGdrqT8jvy5YGh9cSrQfsoewhqB94DbFXLKRrFhT0lBbrwZd+rttgnlpNgYwm/9Gnrvekv523fZq9n/UAK4otJ3fGL/qi6l8rKuYzx38U/WRFfj4VOA55iPcsDlf9ZoVzzb8z3nOc/JvIl9gDlPezA6GCtrlAOPxOAOb/Av//IvuX7kRsKs7/bPFLNNsVG2QPM3CjQKjFGgKTbGKLSWvS8tHOaxsnvKU56SmW3aeJtAx50tZBQMhL6YCcoIAkKCAcwAwRqrEMI3m23MSlzDg9EeslKw6SQgABgoQjeLqk2zxdyiiIkhdCNgJCQoj3HXzYaptKBOganxxnDBUzKzY/EjbriLVQ75LiausXrE+3nrH+m4BEQ2icEsCguLkxCUOi6r/2H0MMzSsB6iACGUFl96Qq+wXqFAIIDBNOuf0mPgCK8waI7zYlIJkzCS0mG+fUy2Vmzo18pAGL1kJtCnZMBAE7BjQG1Y9VngXfTnHDD7kx9g8aLN9XnXCoyNm7B4UWb1xtQ/97nPzbjiz0ZDHQHX+DGOlK+2oJ0ytiOdMW6soS+rK/S2+YHbBkYdCMTrkwzKgclXN5b/rJoIwQgE0BhDTqEgH0LwA2cnGWqIMtjId1nhlnNbWG9h+kvrLAIE3wJSZook8xKFhM0SQSVaDVmyEejrHwDdQyBpLhyy6Crrgkbu68fQU2oAm0VQWm3mgNmf/hf5CAtrKsqLso9Hv5jHEgr99WHX/RAsU/S4Jkt5bDS1BbpTNDlZEjBkZadfik/IQjip7GPC2cDb3KsWBWKt66p1rFl1HMYU5nXjhwDQmBwCSgpj07xHiUrZGddDDKWL/COOTT/rU+PeelAKKcUx5+j7xqvxY30wD1o3WDgqRwDhjnl0MYAFpTkxgJIhhB0RVrvz1q1OXz7jxcydXHjxaNaJEghhlQtvaJ7uAgInSgvtas42d0Tbmp8AIRDhEX7TPBonfLrwCWOx6xov6xMhFF6RgMrpYrjQLZTq1iCCNHkuRLFR0rT0d5XN+7pfd8VblWFRB274ozwRVoaHP9yIyy3D1NtzuGW8CAvaRLwyTokr/F1uHVaXo+u5zIc/ylOH18/ywjtYF40D+5tSmW9O8BzzjvjWbsJPeylCxVDs4pPwshRueNM4paV/woO3s6biIZTv2GOPzf2WEsR4oPAzhgB+gjAaSF/SVVhZvy5/hJWudMpfhwkfgmiPiFM/R3jpDsWJd9wp/jG8gUM8dVseCNpMxSHvMs8oS4R1vS/fySeep+SJPxua/6Iv6svmZC6+UF/Cu+PhlckaxvDQ/mabDsWGuRVQOpiHpXHKKNYA40E6+zCGAdZUPLn52VqK94Tb2mpfxW8MwAM3xQYwtgjEpUEHp57lY37H6zNGEO40XxcMrS/yDIMjPDd/KDLRQ1kYyHHFtRcIY4wwjhrCrzxD7413+yZ8BOWQtnGKhgJBOCXDPNDFP8FtbxSA10fLGuxh7VftvyiMuOYvaRlpzFNWe/H45iMc9pOus8Ir2MNQbKCneSvmzbo87blRoFGgUaCmwNXqgPa89lPAAmyxt9G0QcdEYJTjqKr3fphjVnY+lGcBxYhj3llpSId5sLDHFSmRjgtYXhAGWKjEh4tQkDU+4UBYKWCcvMeYRzkw9O7iB5gnTLzFjiLDQs4ig+W+DcSSmVC5PDaJgSl/GcnsT1hAn9/78l3EXxFumU/pj7zKsPDXbsQdcyNdHS/Cw63fL89zF84ICxf+0h/5RV+KZ24Zpp9gBjFkFBcUCaHYCmWC+JiiMi3BiiuR9GH9iWUIQQeBhn6JeT5wJlR3/QhhjfDozxlR8aff6/+YfP0XTsIr44iQn8WuPID+Gv05ULAe0q9ZoRpXrFPA2LgJOhhDynpCIfVfAABAAElEQVRYdVoDDjjjChbMv3yMo0i70LFNaWE+YLlEwUn5xCIJvVgWUV4SSHVB5B30jGfCRhZgxr/073rXu/L8VOOI+DbrlDEHH3xw3nA5EUaoD8RBF2Vi4UUAGum4wYRr17BO0o+8s+mRlsBMe6GZcoHYrJirwhLc5oJAAh3Cost8hBYsnvStmBszksv/bAZYeQUec6t4NmeUdjU4mSGfALhZXQHlrvu4TahNHvqw3EYbgjr1oTizOYu+RtHML574fhR/lD/GBfw2MuiGLsDGkpWdtiIkdk2gTbI89OMAG1YCxWibCG9uo0BQIBR7BAVl3/E+lH36YwnmcBCu8dcH1gKGEOZU1oDmDYITIH1YXMeaIdzcAEqhudMC//7v/57HgvUjviGRI17+R7hhzrcWGbPGCOMNQMlHyB4/c8BigXEWeLlouth1Gyqr9ducA8wTgMK9hChTGVb7CS9CIFu3bQiS4gSMtczpjjg1U+OK58ADrzYBymYOZGVq7aCcN+8HHzLUn6Q3J8avfO7zR9zSLeNGuLAhiHgLceGVrnYjLL8o/vrCiygrxBv5hisT/vLXl3GkCTfShhvhU3BFHmXcSF/OFeHHh1KcUsgJs57Hnf5wMbZhXBJgbmD5DKd1GT8ApMVL4CPxkNJRxgFCXieZ7H+sq4wF9FtrOMHiktk+KIwuoq/r01FuLl6BG7/6OcKnusq1IuKWOOUByrAV5V+a0/S8In7tluWLd3XY0HP5LvxT2yryG3Knzn/l3B5zqb4OvAvlXd+cqX+COP0rTczxwvnx2JR4+GCKPt/vYPSlvgTbAfh1Cgp7fQqQcg2Fx14LfmCPQDhP6UIhw2jKuo9n74Kh9UV+6gc3Iyxl4CoD3kQ5KfLxzPZD4hufIOgyhF+8offwyQPdQ+FEMYU/0Tfw4fNAtGO4UcYpOKyd8sQ3Bf2d0rAW28fNU9boO/KNskTfimd0lF+DRoFGgUaBqRRoJzamUmoti2cjOo+VHWGtRQsD7TQGRsLigwFh8WzhrQFDASxMGAGLlM0rKwZCiyErBRvksPKVT1gT2MT7cChGQh0s9Pvtt18WYNb5x3MwO8pR+2PRFB5+6SJe4CjfRdg8buDrc/twleUKf58bOCKPusyRTrwuf4RFevFKHGV4+Gs3cHNLiHhlGH9feB2vfiZE9bMBJKDAXBHgO70RQpY6jecls80foTdgMaIfstDEABNw6GuEy67DcKLJPa59EAJ8Qi6WJsaAUwgYXxtcJwJc1wQw1dGfAx/FIObYmLDJZe0CxsZNpDcW3CNPyFMDqyp1AIRKMX4iXmlBO8/YRl+WVxjx5z3veRkdYT9aUjbZsIeVbeQ15moP5bPpYPFoftAeXacX4CK867LCjRMFtfVWmb/+RpGLxuYfFkMECGhF0aDvDFmyUZ6FJRMhm3KzYhqz6Orr5+rqSLv50ceJw4KpLDNmv2y/sKYibO2CeSyhCEUIctXb5g8oq3FFKEsYbDOjPwXor/pen5VdxDM+AWVhg0aBLgqYj81P+ghLf3Om/mdepjQGIfTrSj8URgFHEMiAgtCDwiEsPKULYQiBo59xrRyMPmzcQ0hPQRmnAw+cKZKVMeZc6yPLQryNK4/Ux1xv3lcHlpvmFe9KHikE9EPln/rOOmI+DFC2xahb4Btz1QutzbvWABBjfyzt1PfmZ0Iya6S13pykXc2NaNsH2hIoY1jPoj3+kULX+iu99mIxDO8UKPki8cvn0t+HK+KU60KE9aWZGh544OYfcuEs45fPtd9zDYFbePjDreOuzOeoU51nV3hXmHTq0QXmCmDMO5ULrLnC9SdAmV8KDD0TJgP9zAkufY0RBIMK4zUAD+pEEh7L3GPfBQgw5ReKWYJFymDxwoAmTgWHYkP8ECqrZ9029XOUocudEreMww+66FuHxXOkKfOPdxFWPvPH85C/rxyBM9zAFc9d5Yl3XPHH4pTxwx/5RNp4jvdd4cLqeBG/y3UqwkkH/FoNyzv/1fjimWIOPxnro36o/9pb4e+VvxwXFBfGgXUZX27NtBeyp7MviD2MujPWCuMayr3g9yPv0jWeGFqp59tmV89aBxigheC8jMs/tL4wckJDawhjNLwvsLbjXZTNlXIMgVypZI/AECrkG+IO4bf+DL2Xp/zNH/ZWlClONxj78sanhALJOqyc4ofSVf7zQtnPyjbVnvK0P5A/BYv9k73wkiVLlu1nh8pqTmvQKNAo0CiwoijQFBsrirKrOV6LUwiAYwNfb+pKK7s4OipuMOHeAwx3uRBG1cPaArNN2AsIK20IMBp9VgqRvsvF3Cg3wTHLDowORsPpjRA4qlsNyhfh4Q9X3NIfzyWOSBth4g9BGT/8fS488a7GWZYr/H2utGW5apyRbiiP8l0dv8YXz6Vb+uHy3BUW70qXfyq4q9sJIEo1ljwYK/2RxRor/T6waQwIP0Ybo6a+woKZLi1KIk3pxpiIfoeZjLEUfb+MX/tj/IXVMKYURNq+cRN4KPViAxFhU92Fju2gSdBOfrFZiLBy4zKlPIFTfeDQFvVcVOKJfMxDsYEv4wuPjX6ZLvyUOhhycwgrSPk5nm0TiPY2VTYpTrygb/TfvnrZuHknHgUF0JdCaWWjEXNmlIFLiEqpQclCOeQ0hXItLwQ94Qla1W1kDlbG6GtoEHO0uMquPuIAfTtAXVjZuQKHQodFKUGmDQ5BTUCkUf8GjQJdFNDfKZJdG0E4YMNuzFHaERw46eAkxELAOHZNi3maUIVFqNNHQP+mMHFlEyEjBQihiruz9VfvCLuND2PUGqOPGw/6fQBLRQpAHyrFB1EwED6Yu42jUGbHuhDpFtMNK84a5/LWDa9lPXXaxVzoWxddYN3yDSDCXcopQIi0mKAurh1zqhJ9tSOjGifXhhQb+APruX5F8Ky/Uc6b+2PdJ/yFM9aQvnk+6hNzYtfz0LuIH+uJZ/HL54gz1S3z68MTefS5kVeJK8LKNSDCuJFXpIl4pStevC/TlP7A0+eWcflBiXNpyNL/yNtT6S/jTPVHeer41jSnxvFPxrs5Cq9BEeEqFsYwAcaOk7yAUtXeh6DVWGFEIK65yTWYAP/BqCLS5MDZn4+R4xH0dcYPcSqDYNG8gn80txEesxoHBIvWY4YLB86UsQFBu6if5yF/vC/TRRicpT/yCDfSxHO4dXj5XPojflm+COOWccf85fsSx5A/8u2LM/ZeuilxuvCX5Q0cETb2DJ/1isESg5iu+RG/t9D5r6u8wqybvvugfzqFRKG290w5Ya62DsvTmAmeUxpKECcqrZX6qTGCvxSOR1V+48v87Vop40x64whvMAQU/tZu6zOa9V1DBcfQ+uL6J8oK/DFjCzIHwn5XYTFEMicYiwBPT4kZV0PGWjKEX/sMvbcOx5p11FFHZZrgj9DTzRboLH98vzC8jDKeeOKJuUxT/9SDspSShGyFQsW6X7ap+cY3RvD8r33tazNfZW7y7LQqHmGsrFPL0+I1CjQKNAoshAJNsbEQqq0FaTAh81jZxcYcI4/hwHxgpAHhYgixStLYUFJeECIEU2EBlrdN8JCVgsW+ZuAwCTYE0lpAMRE2t64T2Gam3AiBdqQry7KYfgzmlDzKOOEv3fArW+nvKmvkWbtdcbvCIl3XO3l7D2p/V/yIF++i7NzSX8Ybehd4SjeEs2Fxqa9GGcXTD2zo9CsnJvRFTB+LUcea5wVCK30Yw0rAhVnEXA+BMcGClPUQy3b9mh/EeIn0ZdkjrM8dGzeY+sWEKOs8Y3sx8w/LIqc0MOb6Sp+wTr7RJ9C7tMKdWib1xZxj/Fn9AtZiAG3HLHmjj0ebRt9Rnj6Lroy8+CPQ0F9Zb9ocuU6t74RKkSx7I986fCHP+hogVHGtlLqZZ82thLvGGOVIKKHFtbEkBOyzsotTJ7HZG2pL+BpctSngij79iVCOcsMVCzbX1njXrsX8NC+V8AXmFEDwFyeSPBMg6t/WChaEBBbnzATf+raNuU26sWB+cULUmDPvhOAeDuDUq7isTM0nvqthrDgV4CRpKDaWxl65/4tRN/SgTKDA6VNsWDcpCrSftRP9CGMWE3zAloCMwM4aa/40X9bXO9Z5ajN9Sj3wrK4KZBShbbW/+d73Vyg8tJn1h1LKqcDlhcWcp6eURX6xNolfP3fhmBJHOnijPrXf+8ATbhnGH1CWL/x9rjTxLtKXYZFXlCviSFOHDb3ryiPil654FAu+eUEYq69QcDz60Y/OfUf/odwI0OcCYi01TsJimbDW1TsB+rM5w+nfEuLaSP2WlTqeE39Mmar/m4PMPT4abh2nMDEfWbfxpYw2hiDoKE7pH0rT927e9H3tVOLvihNh4Zbx+fvC63h9z319oi888Iy9j3jcqXGjLhG/77nEad0yH0vTpdjA/w7Nf2GsVZZ3zK9cFMnWXFehAmuQNdH6ajwYI1EPrmflsI8nKKfgwDfq0+Zg/CkliTXIaXiGSJR2lB3ikkX0ATkBwTyh+zaFfKAr/tD6wgDNdavWD8qNOI2FN3Z7BWCcYU6gEHBSI3hedVbGIfzSD703juXv+17aFC3NHdZZ4eigjL55IX+KDTIZ4fOA9mBgxZCBUky+5puyTe2LXUVMYSQvvJE6OpWKP5hS1nnKtCri2v9oQ4ph/IOxYE8TRnTKpM74S/skdKZgKuftKTgop/RNvC/cjEHM8Q0aBRoFlo8CTbGxfPRbY1PPa2Vn4cJkECDbCJrsw3KRUDCYlZIgFknMCutnyg0CC9YTrAm8G7JSwIyFNToLEAuthcNiauOJkcCsxD2TmJ2ArrIIC4awjFeHLe+7Mn3tj3LVrngRFmlqN8pfu33xusKFlfUNXMJrfx3XMxAvoMsfYdzSX6atwwNf6YaQF1Nls0boVDKxBCgsOG3iCF2CIcAkYEDnBYxoWJq4S919oXG9UB8u/d7HxlnUsdrH5GCGglmXLvowhlwerIjGYGzcjKWf9/1Cxva8eQzFt7mnkMLoUSbZjITAvStdnxVuHMfuSlOHUYjBY9MVig5xCCswpdw+S95oU6czbLb0gzGLrjJ/Y5DVpvmTHz4bpjgxYYOwZHasu4Qua6ry/UL9+mPMqWhBUKMt1N38bCwZi/p2KMLHrOyiLKHkW5XC3ShLc1dfChjrBM42h9Z2ik7KQkK58roJV99RHsbcQHBx+OGH5810V+1YWvYJ9VxnBAgifS9I3ydQYbGov8bVbwQoz3/+87vQ5zC8DL7E6Q6KE+sAHPgj6wnhxxSIugzFNy9YQ2JtHMO7GHVjYRt16suPUMX3kUJx5Nq6GPOu7bJh17YBaKbdAKFAV92jrt4BdfGdIQIGSg04tKG1CxCEyVefKUHbMHhRD/O8Z3MpvARm5nDzWeQjnvWnLmOJczH9wQuVOLvCSr5tLG75PvzSw1u78X6qW+KQJvDV6fvCxSvrF/4+N/DGe3gDhMVz+MOt45ThpT/iDbniBxDYGteEt/q9fkkw2wXmhOjn4gJxI6xOE3xFHU4Jax4Mwy1X0ZifrNvmN2XSZ/HHykboRvgqf8LhWjFc1kde5XPpr8vhuWzXPn9Xur4w+UUb9sWZGj6EZ2o+XfXvClOmvvCxd1PrU8cr6e1d1LcsR4SZF12LFPNhPZ/pa0Pzn/VLPy33Uq52svbE2qkMTujilYWZVz3bg0kPlsz4WN/y1E/x1friEUcckd9RJpv/rf2hsFN+Ql7P0W/x1/q8PSAeX9m3me394fZclzMjn/0pj7UdHwpXKeiv14ux9cV4g8u4sv5Yp9EirrZkXId3xidH+ZTbuNc+Y/jH3ps35Gl/yXBCHva+kb98zAPWWXwMHkkboFmsbVP4J4YkTrdqU7i72tTe2Kky6z3640mk0R5grKzWYG2GtwpgHGLtjVM4cMY8qQwrE5ygcwoFP4GuTui5dvdZz3pW5gvwp04Ya2tzMtd+TjljLRjDwUCDIsy40Zb2vk75GLOlAmVl1rvl1SiwtlCgKTbWlpacsx4m4Xms7AiOWQewnHCsFFPCaoDwy+a1C1gQsC5y3yQBMCsjwjHhFvshKwX4MA4YClZ1FgHPBHFvm92Z6QN7Fm8COEe0991332VFKBfCYPS8lKfncOswz3V8YQFD7yJO7coL9Lnluxyx+usra1mPwB3li+cSVde7wBHxIk7fs/AadzyHW8YRFuF9bhmfP4AlDKsJV4PYROp/BEZhOU5RhrHzsTZKDv0KM8TijTsvYGAIZ1760pemT37yk5nRCAEaXFH+Ei/rPYI412BhbjCUmBLfvYh7kN3PimFRFwKUKYqNsXFTlmEx/AsZ24uRb+DQfsa360CUhQVknNiJOKXbZ4Vr0zEV4joqbYbRNp+BMUs2cTD2mHKMPeWaOXDMoku6AOOMUEI9wgoslBrioMGSyzcJkUb/q62p6jgRdx5XfdFbeSjojB2bFZuaaAPMuo+h2iTZKIfirs/KTv42sTYDNnsh5JynXC3uVYsC5toQfug7pRAiKIFfKMFcO3QyIE4OlWm6/IQoNubGI4tDa0GA/juUR8TDpxAqwkEAUfIgEWfIHauLtDbRIdwcwlW+W966uW5DfShu+8Dc5Aou86K5DQ0Dym9/RBh8JU276t5VV21E2NDVTujvVwPhjvVUGn2qXMfFJwjS35SphrKM9bspz/Iqeaoy7670Q+/jXYmvC8dCwuAM/F3pvYs4pStu37shfJEu8oq4tVvHK59LOtRlqOP1vS9xRFlKN8pThhESWp/1J2t32dfLePz6W92HKCD85gFrtF+AfK3ZBJUEbHhe+5/ow/LVrwnMyrmsrs/Qc/0u8i7Du/xdYdKWtBZn6Dnymtct8YY/XLhKf43buxLqZ++6wobCu96V9fZ+XpC+LkdXmP2HvhqKiXrOle+88x+hcwieo9wUBCUQ5itPrH9OgTtxZB0yzzutRGhuXg4FCaUDQyMCY2nrtV/Z8ejWF3E8R73wq/UYK8uDdw1hcxnetV4MrS9oru7GLiNKvHHwx/DiifHnsTapfzlmxRnCP/ZefeHDY9Q0kBZocwoS74OGZCMBU/gnbQKP/XTgqNvUnIKmymNPK160t7zGymoPEQq3KFttgGKvP9SukW6xXTItsionjbSn+R1NGLeYZ9WZEoKM4IUvfGHuDxR5TsuRi6HLFBzvete78tzMKNM8bZy4rpDCQ74NGgUaBRZOgb/u4BaOo6VcAykQgtypVnYWLhM7gR4LCIunxRzDss3MegKwpgTBTNsA0MRjSgh+MQcYGlZE/GNWCoSN7nJnVYfhER/DjhGi0MBEiEPArRwBJeMXfumBZ/4yvMsfuLhl2jK89Jc4y3D+wN/nlnHqtCXe8NeuNBHWlV5Y5F2+l6aE+rl81+cPvOGKF/6pbhduTOQzn/nM3M4YSG2sD2lz1jtOcTgS6/51FmoYEOEEsvqfu1hZfIgHwgKUciyApRArpBCIYEp854DyBD4WcI798semMdJyMYBRBsyMePBjJoPRI1zD6HkfljOHHnpobq9QnAhXVunB2LjpsnjJCau/sPSjBArosqCdMrbrdGgcFjUsAwE6Cot6RZ7hsrilSKiFc65TQGsbHjSldBgC9YejtsI1t8g/Nj2Bo7bOEq6Mz33uc/MmRV8J0BZDlmzioechhxySr6nRL7Sbug9ZdAV+rnGhb/dBzfRHvNqaSh3UN/p4V78Ys4Riyadt0RNzbUNjXvcc7YrR/sAHPpBPmJiPbWTGrOwcsabUFs8mpUGjwBQKmFeCf5gSf7HimAONheUB49oJ0tUNlqdu5gAWt/Wc3VXHUtDT9X4xwhZSl6F2sVbU68VilDNwBA8Uz6Vbviv9ZZzaX8ZbCL9W4uvjGcs4Q35lCRzhDsWv35V1CX/t1mniuSvvoEe8E7f018+RV+Accsu4/Kt6nBPyPuUpT0nHHXdcvtKSAta6TNDIohx/4dRbDWU94l0dVj6HP9xIE+7U8IjX1UZw1e8D/2K4cJf5DuGMckSc+ln41LDAUbpdaaNsZbwhv/g1nsBRhk+Z1xZ7/pN/WQb1wFe6Ng0fyHiJoYsbGfCZJXTtscr3yrq8a3SJr8s/tr6o21AZQhnQhVvYGP6x92M0UL6xMvSVLcLrPtHVpuKO8WpjZY38VieXfIF8ijFEnCihdLHfY8QIyAQo1Oztg+fhf9WrXpXn3yk49t5777zXjb2evat+Fd+AW51o0srSKLCmUWCdmfb5itLNNa0GrbxzUYCSgDYaY074RJCFKQqh6hRk0vjBYdFzVRSLY5puV01hqg8sPlgHp8kedC26FCLeK0OXUKOrjKzshMOnDCUQdJcQTF+Elc99/ojLLeOU4VP9Ub5wpQt/uIErnus84zlc8fv8gSvcMl6Ehdv1riss4ocb5ex6jnd9rjTxLvx9wnAbtZrRijy5yqrv6DfBZJTv5/G7LsoHZwmpMeEnzu4tZm1EeeIDs32gDKx4lKFrHEUZu/pqH84IHxo3EWcx3XpsLybuGhdFEiWSY7usmWI81/HimeLghBNOSAcffHCeX9BmITQNfEMuRUufJW+kk78+p38GRF8Qvrz9MXCWrnKN0amMP4/fWIO/piklNutQJ/zcrVsqkb1TZ2kCXF1jI+BKK0e0t7lc8R3vm9so0CiwZlCAEYt1zd3aNVAsu07P/Odq0NUJrN2MX1hYlorrxShjXLFX4jIHBpT+CKvdmv8p35fvyvAuvF1hkabGUz6HP9y+NMLrPOK5duu48T5wh1vm2eXvCou0fXnIK/ILf/ncl67E2+Uvy+L96nT60HrtNLvvBxG64UEJhwnkGCPUguM40VrXSb2GwuJdl9sV1odPOIh2Wfp05eex8BJH4BpzyzSBP9yoQzxz67Cx5640Jb7aH+Utw7vCyve1vy5TvF+d+K3oo66wZtluX8VAkQHaqlYOBr2a2ygQFGBc5wYBYyj2dZRyDB6dWmfUxiAOD+CEe4AbGRiRkSHYC43hCLmGMW8PRRbnGqoXvOAFidJjXtho3aPmTdLiT6TAry598sSYLdqqpEApg2gnNlZlS6wGeS9E+FYL7VwV5LsbBFkYf9bxNZSdrn43ZqXQVUabbb8ugK+EkmHkLxnC+l2ZrvSX8crwMX+ZV5e/DCtx1eGRf7ji9vlLPLW/TFO+6wsv4/T5y7J2+cfC+vAK71IUlPHhXiwGmVDXXeuUc/qcEyKs4H3YeQiUYciKZ3nKODRuhsq00Hf12F4onnnTDSmvunAtD0278NVhU8rT1TZjfaHOZ97nKeWaF2fE7xtrhCWY9he/+MX5CsBXvOIVkaTzJJMPolsLWJSuTpvsZYVunkaBRoFJFCCE6gMW4U5zrI7AGAFPE8KJFVnG5eGd6nJZP/rAuzqvrrBIL26Jr3wOf+CLePEcOGq3631Zhtpfpy+fI09hpb+M0+Uv8+h6H2FT40X82p2nTHXalfFsvWYcQkjMuIyATJgTnF1reVmf0l+XdehdHXchz/B39aOpuBaats63q55lWOlXtvq5L6wvvCx34CrDpJsHIm3gmiftyoobfdQ3P/RPvOTKmJNXVv1aPmsXBezjS+W1029vfOMb83XDsZdh0FafLoq9oHcUymM4Yn5++ctfnq/apvhz6oMhBmAcFiDukHwh4jW3UaBRYCkFuiXDjTprLQVc1/PsZz97UZkLx/Jc6WNydwdyLACrioi1pdKqKkfLd82igCOoTgOce+65+Uipfo1Bqe+VXbNqtfqW1ocGnT6II79jJXUfqY18MH9j8dv7xaOAI9Ou6DrjjDNGkboWQ9z63tzRhC1Co0CjwBpDAQK1LqOT1aECfUYvi1G2IWXPYuBvOBoF5qGAMdh34rnEE9eelGHN3yiwMihAmdGEsyuD0i2PxaKAbw2+/vWvzyeNfCx9iNcJ5WK4UYYxHEuWLMnf2HCCwwlTV7X55sZLXvKSZVe7Uwo+8IEPDJTNbRRoFBihQFNsjBBobXu9IqzsKBLcpW7ib8zL2tZjrjr1iXszCdvjA7Y1o3LVocaKr2l8zG6qEGplWuGu+NqveTm4oo3iegx8Y4ZFU31ybixde98o0CjQKNAo0CjQKNAo0CjQKNAo0CiwKihA0XD00Ufn2xucPC9PYNjbOJlRgmsAQZzc4B/C4T3wHUzyBt+hcYW7qz0ZhdlDxYksyo8GjQKNAtMp0BQb02m1VsRcEVZ2BFjxAdu1gkitEldpClDQDVlnXKWJs4iVn5fGUxUgi1jEhqqiwBTF9WJdDVdl3R4bBRoFFokCvmtmM+7O8wZrDgVYgJpfm9J4uM3cge5Ofx9lnXKC2zcqfDNm4403Tk4cNlh5FPCdNVbKVyVwVWcILvvckh7i9BlZdYV3hZX4xvxRpjJeGbY8/sBZ4oiwrnJHWLji8tfPER64up678izjryz/tttuu7KyWnA+V9W1Bm907LHHJt/NeOxjH5tvCSiJuPnmm6evfOUr2fgx9qROW5jDrne96+WoQzj0Qd/sdGW773+5asq3O+B1JRVwO0GDRoG1iQK//e1v820ovqdazt0roo5NsbEiqNpwNgo0CjQKNAo0CjQKNAo0CjQKrGYUePOb35zOOeec9OhHPzptt912q7R0v/zlL9Ppp5+eryScciJslRZ2FWb+wx/+MFuRai/t1qCfAh/+8IfTaaedlr8B45rcMfjMZz6TTjjhhHyl7ur63ZixOqyJ732P69Of/nS+w/6qdHVlCNjH3LJNI26EhXCoDO8Ki/jceF+GLY+/zLvP34d/SlmG4shv6L18I05Ztr7yrIjwsfLJ09rn49G+k7P++uvnq499NyeU12eddVb+KHVdPlci+i5DCer5sY99LH+jwRXK8ACC9q9+9av5G5IUFm4nuNWtbpV23XXXThpeVdca9PvQhz6UafiABzwgOaVOQQ60JeXFHe5wh/ytQfOWOYtC3Dy22267ZYPIMRyUH9qC8t0VV25R0cZ+cYtBznAl/m24xXppv323T1tvuf7spMll6ZTTLkz/ffz/pMv+9JfeUlzrutdI++y3Xdr55hvnvvrDsy9Kx330h+l3v/zDsjQ73GqTdPe/W5I22XjddNFv/5A+/8Vz0zdPOm/Z+/Bc/VpXS7e49Q3Tjjtukt7/9u9EcHbX3fCa6R77bJduuv2Gsyv010k/+vHF6T8/cVa66LxL8vurzcLu+Pdbpdvf5sbpete95qw9fp++fPJ56RtVPne621bpDrttlq633jXT+Rf8Lh0/w/HTH/zmCnmtjIf3ve99WYl1xzve8UrZnXLKKXk+wIPMa/h5JWTLGaCcPivg9NBC4JJLLknHHHNMuuc975m22GKL9L3vfS9/r8beY8q8uJA8I01TbAQlmtso0CjQKNAo0CjQKNAo0CjQKLCWUOCd73xntpS6173ulQUZqvWlL30pfetb30r3uc99Voliw4dkbeJOPfXU9P3vfz9v8l1nurYqNrraYN7u9fOf/zwdf/zxWYCyWIqNxSjXvPVYGfFtor/85S+nvfbaa1J2LGXFL68cGUpIUHjkkUfmKE984hPbifUhYg28+8EPfpCOO+64LEBZDMXGmtQuIWzvc5Et3pUkDKFQ+W4orEwbaSJ++W7IH+nEKf1Dacp38ps33bxlLPMr/fPmW6Yt/VPqMG+ZP/e5z6W3v/3t2ciA9b/ri7bccsv0iEc8It3lLnfJ2Vun3/rWt5ZFyX5X89aKDQoSpwEoLx784AcvU2xQdhBUuvLIdUmuP/rsZz+bHvnIR6Y999zzSrhXxFpzpUxWwwCnx97ylrfkD3pTPqB9ALodccQRicU5vonQ9gtf+EJWfOBnnvCEJ+SoU3AQXGsP39JwatbHwp3euMc97hHZrTR33Q2vlQ5/wZ3T3+615ew06DXT1a+2Tjr3vIvTDTZaN73nLd/uLccTn3bb9KADbp5utOm6M8XZ/6Xf/vZP6Ra7bJwOffrn0//NnrfddeP0opfsmW6966bpLzOF0TWvcfX09/fcJv3LIZ9Pp3zp/Ix3yx1vkPbYffN0+9veKO1wi41nir1rX0GxcbVrrJOe/qy/Sfv9w/YzZdy1Z/mk2TVgf0633+Mm6dnP+Gy69Nd/Sg88cKf06MfumpZstUG65NI/p2vPlCT7nL1desXLvpY+c9zZOZ+73We7dPAMz/bb3mB20uYv6bLL/i/tvtfm6WlP/swyBUlvRRf5BZ73v/7rv9Luu+++THkZWbznPe9JPlhv7K5qYPi0PDfxmHfx9jGP/eIXv8g81mLNx0P0aYqNIeq0d40CjQKNAo0CjQKNAo0CjQJrNAV8N8mGvrwqLe5KLu9Gritp04oZH4oTaQjWXPHkyrYhIQdrJlcQxFUGkZ4LB8EEHD662gXz1IU1ug26jTMLTfCoRz0qXXDBBam+EmMxyt9V3jrMpuk1r3lNVriwVCXQiXuq67j1szKiH/qEVWsZR1t5P3Zlkzz1h7iOa0o6fQG4Z3se6GqDSD+lvcUldH/e856XT7ZE2tId6lPqedlll12pDw+Va8rYgFO8oGFZHn79WD/vsz703jtxapjSHmUaY0KbDl2XOJXWJd6uMiobIQR4+MMffiUBAJrIa6gsZR7iG+t9dCrjdvnH8gta9rUTnFPKMNaeY+Woy07o4fovltE1KLP8+sa5+cL8WdJsSrvIZ2guH5rvx+aeKf0r5pCyvsoNwq39+eXlfxGnXF+Elc9l+jo83nWFl/ksjx/uKGfg6ctvarzAs7LcrvJ2hS20PPrBG9/4xqzgP/DAA9NOO+2UPv/5z2dFn/UlBILWSh+Vvve9771s/ZYnK+gSnCyAL64zipMGxuR//Md/pLPPPjs9/vGPz+uIq5Q+8IEP5LHTpdhYnrWmLNOa5jeXUPZ0Qcwz+AqKp6997WvJaVNrl1OUTsCAKTgo3J3+cGrDHCct/oxSa2XD3+6zJD3kQTdPP/rRb9ORR5yUNtp8vfTCw+6Unv7026f/PO7MdPGFV/yeiPJtdfMN05OfdOusiHjeoSelP1z8p/ScQ/dIjzpwl/ThD/4wfesL56WHP2qXdK+7b5uO++TZ6SPv+l7a/e+2TI+ZvX/KQbdNj/rS8bmaj3virdM977FN2nST9dJGG14nXfDzpacwggab77BBeuyjb5lPYjzr2Z9Lv//fP6XDX3Tn9NADbpHe+4HvpW9+4fz0jGfulm5+043SC478Svrhqb9Ie91jSXrUw3ZOf5ydNqHYuMZM0UGpcce/2Sy97NVfT9/7xoXpYY+/Zbrvfjukz510bnrrK/+qvIp8V6TrZM/hhx+ex2l5Wprg3/hn4FPy/fgac0Xf2o0H834qr1HXrW/d9/0Xp4lqwEvis8rywGFuLPlia+xDH/rQfB1ojaN8VnbrZrkvK99bH4Lfnzr/NsVGScHmbxRoFGgUaBRoFGgUaBRoFFjjKfDe9743H4EmOHM1jc3kU5/61LzJ/+QnP5kF6yp5k5vcJH/IccnsQ43nnntuttq74Q1vmDbbbLN8TYTNg9MEBxxwQKcQ1ntXE5x88sl5oyofR7gJJwjfWP/JjyKBwMGGFiN/97vffdkdzph7VpQsx1n92Ti49uCud71rFuAvpC4ve9nLcl1tRlzP455bG6fvfOc7yXcFKDo22WSTLPQeKj/LREJwm2+KiDPPPDPTgdXo3e52tyv1k3e84x154+YaoD322CO/f93rXpctIe973/tmmhCeOEVCwMLCfgooh82fMtjIadd99tlnmXCTAkc5bRJtrNxdve++++YrPihy3vCGN+RrOLTz17/+9azY0K4sXz/ykY9kmsDr+Pztbne7rPyJNL7X4NoQcNvb3jYLmihWTjrppHyN0c4777zsKgl3dKORsrHKJVSq22CsvWt6qJN2Y9Hnfu4pbUIgxlqXcEx+2lp/0u59fUNZ+8aGMr3oRS/K94sTzqg7wYzxY2xsuummudiuMvn4xz+ezj///Nw2NvAEc7FRNhb1N+UzPvSr/ffff9nm/MQTT8x3nBsH2pGVrI123+bd+PrUpz6VlWNbb7117ucl/ealtbR9ZTR+9WXtCV71qlflsmkT9XUCAb1tyF0Xo97mkS4w14jvjnZ9yT3r+isBl36DDjvssMMyC04fl9Vv9T/xhvKLOUd6fee8885Lhx122JWE+hQE73//+/MY1BbGFEtLAodnPetZOf5Yew6Vo6veEYZO3/3ud3O/MA5j3PzN3/xN7uv6h7mUhTTBLxBf//SOAAiN0ULZ+9plqE9Pne+H5p55+pc5OIT54QY9lseFa6rgZywfeJa3bFGWMTwRb6hMZZzwhzs1XcQbKs8UnIFnilvjK/PWD7w3lilHfdvH/Gg+OOOMM5aht04D66zra0LoWeN2hRJhu7nGWA8wr7AAl7d1TR7WAevut7/dbZG/kLUm8luTXTzZ/e9//9EqmKfR2dxJkRFKDwmn4CAAZrFvniMkHlK0jhZmOSPcc3bN07rXuUb62CfOTse+/dtp3Q2ulR7+Tzunm99so3TTXTdJ3/j0uVfKYbc7z+p/k+ulk7780/TWN5yWLvvDX9Lf7LVFeviDd0p73XWrrNi43713mPXvlN70llPTCR88K3316+dnhcM977ZNuvb1rzFThvw5XTo7ffH+D35/dqIjpUOfvfuV8rnJ1uvna6zO+9n/pre/6fT059k1Wf/0qF3Tne6wWdp+psz4+ok/TW967UwxMcvnfe/9fvr1zy5JP/3NpTmf299m6beyNttug7Tb7Jqq3178x/Tal389XfiT/03rrH+NtNcem6d7z06CrGzFBp7ZdWT41FKx4RQ1BUGc2qHQwENZ7/UR/Q3vHApNfBi+yVxhjOO1P/GJT8wUUk9fxic5wSXsGc94xpX6GH7YnsBeRHplsc66pg64mhN+fK+1D2+NL4ET74PPMlbwcXglgN92Gkl/Vhe858Me9rBl357JkS7/w8d+9KMfTXgLcxMeEg8Yyj28gHXVaSYnzZ75zGdO+l4a9E2xUVK6+RsFGgUaBRoFGgUaBRoFGgXWeApgyDHoFA4EWBhv1navfOUr84Zhxx13zJsGmwxCthe/+MXZCs81Ae67Fh+DTvBu40ExQYBZgw2IKwwIITD8Ngs2JISDBLKuXIGT8ILwl3DUB2QJEF/xilfkcGUgRCeUCBw2E46D2wAvpC4EpsoACCtCSEIwSPBOOE2gOFZ+9Vd+myrlJ5SRnqCfkNzmpwRXWYgvb4oN8V17xPLqIQ95SI7vOiWbGcLbKUAIc9RRR+W7qLWDzY52JeBBY0J/QmYbvSUzxYX6opkNHGWWjZQyURgRlGtXcSmStBXrSwoT/YTV6+tf//orpLHxs9nUZjZ3yk7xBAe8hPBxRzbli7wJlfvaYKy9a5ooF2GUPvu4xz0u98mxNmHB69oD9LBhNRbQUT/vKpd2GhobBDLKYKONRlxCYxtQm1nlshHVDoRs+ou+TgGiT9hgu0tcHtpLP3dHuTagHKR0s0l+7Wtfm+OzZBXP2NOXbJJr0Ked/tEX4VMedSthXlrr231lpKw0dgP0Q3XW/urto7NoTVhAOWE8P//5z7+C8Eta/dHYZz3tA+f6sfaR93Of+9z8rH1t9N33TnDvWV8lcBjLL+YcY1MbUEDBWwPhg6tu9H3KRumMd8ILd8AbJ0PtCfc89S7z/+Y3v5n7Uyh5Y9y4roMSi8JEvzGGX/3qV+cyyktb6xvyNn/oi64E62qXsT5t3KPr0Hw/NvfM07/US7nXNtB/S8F91K8WwEd47U5J24UrwsKFt/TX+Qy9W564ddqu5zJv/eCf//mfs6ICXwBCIRHCPWHGANDHzA/Wr1ve8pZZkGmOAdYkVyP5pgZFYODxzokC8wfAl1hHzMeg66SU8IWsNdJdlUBb9inap9JB+61KpYZybrfNBrm4p3/nF7Pxm9IlF83W9nN+kxUbO9x0w07FxraXp/n+mb9Of/zdn3P6b3/3l9l1esIpia02v/7S8FMvzO453/91vipqg9l1U5tscb300+9elI6dXXV10a9/n7beacNOxcZPz/pNuvAXl8xObFwr7bP/9rN5849pi5lC5ZJL/5S+Oyvvny69LL3lDacm592W7Lhh2nrbDdLd77ok5/fFryxV7m225Pqztfdq6cc/vTQrNbw87fSf5zg332Gj7K7MPycU7SEYYuCBYwzjwY1HinpA6E/Jifc3pvGTlPlOe1Bu4sPsT8wFjLCCN3OlYxgTMYTAT0UeZT1968J84MQW3sp3YijqrKOAQgRPxugHP2B+oSCVpzXzgx/8YOblKEntIYS9+93vzgoYSli4rKuMt3yrowanyPAaaKE+5jfr/Atf+MIcFS+AV6QIwsN01aHGGc9LZ8V4am6jQKNAo0CjQKNAo0CjQKNAo8AaTgHCEj+MM0smVzHE9SYs1wkWnva0p+VaYqwJKYE0mP1//Md/zO9Z19lUEMDWQPBHqYFJpzQhDLRh8Yx5hyfKQchA+HvwwQfnzQDFhVMaBOZwsEYm3D3ooIOyFSeBRVx5EzjmqctjHvOYLIBXZvVlkQUCF3fe8rvGioCaYF/5lLkG1mM2/hQPBKQ2VwSnNkBLZkJ2FmuEN4S1U8GJFXhYnaLxYx/72CzIZG0O0IkwnbLGe2WkSPGeVRiI+lJAaHeCIIJTgiB3ZGsX1peExyFQijQs0eBlUUeAGvmWtMyZXJ5PhHe1wZT2DlylCyclAgj8+lRXm6C7jTHFAYWADWtcL0DY21WuqWND3k4kGT8PfOADs6DWxhjozzallDrGHJoqixMxhPksi50msPlGT3H0DYov+WtDygx9RZsYs07DKHMXEPYZRzbc8tIv9K+AhdB6qIwEUU960pMCfR6naGFuIKygpFAOP0pAAoKuMUIgz9rRpl1c/ZUiCB3RkOBeenMSpat2hIcwzWZ/LL/oH2h64Oyqm8MOu/JpDYopdCdQEMe84xSVcOnBWHuOlWMZoTo8UcbIK57NDdpe/yBcIYjVR4w7iqNQolFYUi4Cbd7VLlP79NB8PzT3zNu/yis8QtBdu+oTYfwlCO96N09YV9wyD/4yTukv49Xhnuf51bjqtPX7eI58a9f7CIu4fWHxvs6zfI44gaN8tzx++PQDQksnAwGFpjXM+lPesa//y8sc4MfKmgGEeQ8Yq4SU5kdzX5yKyy8v/7PO+lFgUqYSmpprhr7VZCxOXWvKvJp/zaLAerOPaQPfrgi49A9L/TfY4DoRdAX3r2kuWxZ+6e+X+m9wg+uka1zn6rP+tlS8/IfL8VpO/vCHpXHW3+jaOR1lx0UXXroMR+05/39+m177plPyVVTP+Zc7pOfPrrvaasvrp3e9/3vp2ydfkKP/+Y+zb2bMfgc94/bphS/eMz3sITul/5ldq/XyI0/O79ftqN/vZ9/iAL7bsSpgv/32ywYY9ggAr4mHwZ/h761ZxipFB0MgvKd9ASMVwn5gfOJ58Hb2KfwME8QB1jPKEIoOPGIJ0uJLGOvAbb5RphJiLRbGD/C++D15Lpnx8fg4J1Kd1nXqDO8TSlPxSxyeSzBPKbf84WQwwuBK3YG08Km//Ch3psL0XcVUjC1eo0CjQKNAo0CjQKNAo0CjQKPAakABQlQMNAYf42wDQXhN8M6yyAaegIDygvUw4BKi22iwLCas9b4GmxJCCcI2DDprbScCKCqEs4oKIEyID37ayLD25+69997ZL57yCGeRDQh6Y2PheZ66EPqFkJc1u41PDfOU32ZGWYG6UAh00YRQmpUWS2obMcJIQLkyjzIjJ7r8L3BoO8IgVuuEvNFe2gfYaFFEaVNCINb+2i+sU+WvXbks6tFXeSlE0NkGirLDiY/YTIlL4MsVx0e8tdsU2GWXXa7UBk4ZaGPQ196EWVOgr01YuGkD1u2E5Ta++oDNKWE6C/W6b4yNjfKbLDbMTo8YHwRr0Q+cQCCMI3x3dQp6UWzEN0DQHAhnCSiusrIS1p8ItQn3nLpwmsAVDPoc5UYXaFsgr7gv3jUG8AHtOEbrHLH4GyojBUP0JUlckYSeTseYQ/QRp0aANlRvFv/xfZv8YvanXxKKo5O+B5RTXMIB104Y6/owAQI6sPQPC0YfCR7KLyOc/ekD7rqOvhzhXP3cGDUvEm6wviTs8FHbgLH2pNgYKkdd78A75Brf2hN9nv3sZ2f8TsUQhvihKeGs/kg5SQmkL3e1y1ifjvljaL4fmnum9K96LHvW/7ugfFenq+P3ve8K7wqr8dXP0kQ5wx94ynDp4rnGUT8HHuGBq47T91zGD3/t9uGNeFNw98UZCh/DH2m76GSuciLJfOh0RfAI0jCEsEaz5MZjUIa66vFd73pXvp6KMPDEmZLUfGGssfwG1j/zbnlNkvEtzLg3niiQjZ+p0LfWTE3f4q1+FOjiMmJmmn1HvBNm00IvDL2LROv0IY4Il7vXnSlAbrHjJrO+/H/pxz+5OF9dtfMtZmvUkhukTW583XTuby9aluL8C3yv62pps1n4RhtfZ9avN00nn3jubI5ZFuVKnqF3V4q8iAF4QteZ4cvwUHhXa7C1HdgzWJet99Y5YC3EK1KG4OUAnggfHuB0BCW89ZExj7Wp66pYcxUeGe909NFH532BK/FiLQx8pesdPhlQOCxZsiTPI/Y6wEkUPIo8p4BrWvE7TnnY66izcvuF8t+cx0BmXmiKjXkp1uI3CjQKNAo0CjQKNAo0CjQKrBEUcNVDWC3ZHNgssH62MSDwC6GEzUMAYSuhLYi05fuIR0BAiEDoEPfTEhrbCHhHEBpQChkcHwc2L+LBrRyEG4AAxAZGOeAPWEhdIm2XO0/5gw7whL+LJo7FE0ZTahDg+qk7YfhCIRQ9NlVAu7H4CnAaABAyA3QLGpebLTQmfAZRB20HvIs2KutVpgmcy3OdzJT2jr6XCzbwF3UQJfzKLr1TKa4jsIF0fRahsA0vy/wumDo2yrzKPIXX7YR2FEkB0RaUO/oFoMyg4HB6yMc1nQQhMLfx14427/pUqVgJfGHhF+0uXNyAKbSOuOGOlTHilW6ksTGPeulXxjAa1FD3V+/jShrXOgCbf4oNlpeEBoCgAr6p+ZmTupQacCmreUYbEnqC6P/5YfY3tT2n1jvwDrnRp4xT7Q/0aUoaJ3ycIqHsIRBCM6c5CIi6hMZT+7R8YsxF/jEH1DQo5x7ztXjapG/uDrxlncVX3toVRxjoqk+8yxEu/+sK86ovfOyd95E3HGP+KfjECRgqV8TpcyNt7YrfFdaHJ8IjTTx3uVPidKXrCqtxxTVrlKnmSac+8Q8BlBz6V6xZ+AqKDQpQfZsBhbFnXnfv/imnnJKTmjOM/fJUhjvxWXLHGvD2t789GwKU+UW+XW6MC+/CH2OkK34LW/0pcNFvll5Veu3ZdzYCrnPtpf5LLj/ZEOHh/uaipWkinvDrXHspryyNK6KcALnODGeJ99qXx7n0d0u/TxX4+tw7zb7Xsf+9tkvfmF1n9aJDv5j+NDuZcbXZfLT/vtul/R9w03TUC5eeypD+ba8/dcZzXzPdfOeN03ve/Q/pqU+7XTr2mDNma8OVyxpl6qtfX3kWK5xBjnUcf+NEqtMTlBqxBlv/zbl+lJABjGuC/xVWrytOSjOwcJUjoy1KB8YsXeB0FwMK888555yTeS9GQ6WipExXz1vyjjkg4gnDx00Bp4ntD5RRnYKfLvc6sfZPwVfG+WtPLkObv1GgUaBRoFGgUaBRoFGgUaBRYC2iAEt7V8YQODrmzNLXBiMY63mrajOCASdkYLXu+hgbE1dD2AwQhLuyCRAqBMR98Jh6Amc4MPWszgnwgGtoCO7qTUXgmKcuIZyKtOFOKX/EncdlPeY4PcG6ug5tsrrwogWBJdoA5STEZr1KwK29WKcRHmtHdPaO5RcrL+n5QSgj8sMC/uAK69dotyhXCIIJ5QPKdo4wbrTBlPau61/imeIn+EYjFrlOaTgNwTqPUm/vmdKptAqOcs3Tn/rKEJtzbRHgrmXCYVbF3vMbf/oIMFbU16baWGH955QBmjpZxTqZ8C7iUwr49owxExv98rsaxmLAFFpH3HDHyhjxuEG7qLdyuTrBmCX0Izhw5UwN0SejP3nPShMELicRjH+CCsIHlozxjZ+I05dfnGDKCHv+gjb6q/5BeeL0RQmRz1B7it9XjhLX8vqdhKG00g+czEIv391Bw+hbkUe0y2L16b65xym0hczdyqmPKGc9v0fZ6/CoW1d4V1jEj7zK5zF/4CvL1+cfwzXv+6h/pIuyeO7yR1jtRvo6XRne5Q88Xe8WM8w8pv/iP8zHTmdQ8gZ47/tBBIauqrPWxdqizykn4WJ8c8fcSZELGFSYE/EQFCHiPeUpT8musQqMef16qmIjJ2p/axUFzvqfi9Jd994q3Wz2PQ1w9WteLW27ZGn/OHv2DlztGuuk9TedGeqcd0l+lgbcdPsbLJvD+MHZ//ObdNmf/pJ+dO7F6Wbbb5i2v9nG6cIzL0432vb6+SPlFB4//9G0bwxtt92Gs/n+6umCCy9Jp3z1Z0vxn3PRbL5dZ/YNkI3T5jfdID3tSbedjYnL0uGHzRQfl/w5/fI31v510tZbrp/W2/Da6Sdn/2a2Dv8lbXrDddP1N71OuvjC38/4w7+WNSNdBX+UmMalvYiTU8Z+QPCWTmA5EVoC3qgPnHDwzQ2nuPAM1sRQiJZp8NX4Q+/tVRiZuH7Td+WcHjW3rEgwr+HpKF2t5fgafB56LAY0xcZiULHhaBRoFGgUaBRoFGgUaBRoFFitKUBASuDItfGnjAilxkKsDwkeCSAd6yakcB2Kb0HAb2MSltaI4mqqt73tbVm4wEKTYIKg0mbFtVEsvV2NxKKLAIPVFYFH30ZjSl3CWjssON1lW8I85S/TjfkpFyggHJ1nxTXvNVTxoWTfw3BsnoWbK498E0CbsfZyVZHvG1BsEN5TpNgcEe4QKLNMJfgmWA6B0Fi5u95Tarzuda/LgnfXUIG4Pigs3Fhr+6AjoX0IqANX3QaUV2Pt7b51m72of+Ca6hJQ+8YLYdiBs+8nEJgRkAkPq7q6XFP601j+TuX4noYrU3yHhZLBJpalsWtWbOh9HNyJKcoN4ayP9Xnv1Nn3btDI2IhNfigrWOr7volxpt3FkZ8759WRMkTfCFjI2Boro3FrM04oKG9lNa74CQkIKowrAndzTNd1EMYDpZ/TB/qQMY5mBBERn9KVJacPdepXrqaiOAXz5hf0KF0W3TF3/fu//3s+LRPXdkW8sfZcjHJEXmMuQaw5wHVZTmu5zgOYv/VtNKzbZTH69NDc4/rBsbFc1qtLORCCfP2qfF+mK/3i1dAVVsfxPDVeXaZIW4Z34V+MsKBD4CrLXPvjuXa70kZY7UbaOtzz0Luu+GNhQT+uNcUcwG8utv77AfMapaL5zhxifrSemhuBq9oo+XyHpgTrH97Cumi+pdAg6Iwxss3MaML8JE/zZSg5ShzNnzKP4fTLkiVL8jpV0wRPYA2z1lOyDgFaO4HoWkjrk9OF5v9SSG1uo4xmoa+tnSZ2enGx+19dzo8dd2Z69MN2Sffbb4f0s3N+O1MAXDdts/X66cfn/jad/tWl37F4xBNume605xbp6Lecnk765I/S1078aXL10647bZKedshus771x3S3uyxJf/jjZelTn1x6DeMHP/rD9Jyn75YOespt07Y3uX7a7Y6bzeboddKnTvhJ+t2v/mrgU5enfP7O7APhlBK32XXT9ISDbpuvorrb3y3JUU457cL0x9l3Pe77gJvNFHPXSn9Of5mdmPt1us3tGAKldNZMwXLxBZfmOF+YfUh8rztukZ7zvN3TGaf9Mh3wsKXXJ33k+DPL7Faq3ziktMCjWdMpJAKcssA/uwLRtYz4KEY6Tjkw+OgDayDexT5EPyuNV8o0eEBKDGsXxQKFiLzsPVZ0f1MORiyUG1w8Dv+Js+v0wEL2YDlh8dcUGwUxmrdRoFGgUaBRoFGgUaBRWifMMQAAQABJREFUoFFg7aSAu2QJulny+ggna0XCbwIwQskQpE6tPcGBDzPbKDhSbnPACorAUDjBcgDBG0EGRt4G1lUzykKYKa6NCcEGZQAGn+BCnD4Yq4t0NtCExTYuNjS1YmOe8veVoytc2eXlWwALuYaKUAE9be4pNgj4CawJemzy0JBgJj6IToij/QgHKDWcAiA4sBG0UYwPNXaVdSxMu1FcKQ8BhLZ1rQcgYFdG7W6Tqi8RUsVVQeJ0tcFYe9f1h2ceCIUO5Q9Fgn6IJq4ziE10Xa5DDjlkcGxMyd9G3PdsbMJdk6LPaX/tZxO7//7755M1BHQUUd4REgm3qSYkptBTbjRHR9+GcUULcB+9dhCXYkN8Si1KAflpc6chjEGwkLE1VkZ5EyCwcqRkMYb0MxaIlBuEi5QGFB9OzMSpnlygy//0GSc7nKBBBzgp3+ChwAgw/ik2KKMIIcQDxv48+QW+0kUbV1K89KUvzcJPCtk4oSGevMbaczHKUZZpyE8RRqnhux/GoX6Dzj58yu1qF+20vPP90Nwzb/8i5AxQXs/RpqU/4nS5Eb9+1xdex4vnOn5dNvHKMpX+wNGVJt4thluXMZ6nulGGiB/PtTv2vo4/9lzjCzpFuGdzBQFkrPuh1ICb0m7JTKhunXBllHkGb0Kpb3545CMfmfuNtbEEFuBAeoJTwkPzBKW7E47WBXOqdcs8TYje4K8UoHyghHD6xQmxxzzmMVdQbJjTrV1xik77jSk2tCte0zcKKF4p4RlCPOc5z8lrg9Nw5mDvCLE9U07BHQYUfy3h4vq++tlz05uOPi0d8IAd09OfuVu6/nWvNZtb/ze99BUnp1+fv/SExu332Dw94H47ps984dys2Dh/pgDxntLiKU9zYuLPsw98Xyu95vXfTKdefrLimDefnhUfe995y9nJjg3TjTZdL3355PPTv73kq7M5ZVodvvmFn6bXv+XU9MD73Swd9PTbpz/+6bK0yUbrpmNnHw//+IfOTL/86e/SkbNyPOrAXWbtdMt04c8vzfl86avnpze88VvpkouWnj488l+/kjZ40Z7p4f+0c/r5Ly5NN7nxeun9H/5++uCxS699nVaaxY1lzO+7777p0EMPzac1ynGIT3K66phjjsn9gvKRYpIRSBcvUZaMkcXLX/7yPPZD8V++57fn8Z0PcwJ+2hzhZJcPgMf8VKdZzGf549sZCRkH5kLrKLAv6iv31DKsM7OCmdjFpqJs8RoFGgUaBRoFGgUaBRoFGgUaBVYdBQjzCcCcnIgP3ykNpQaGmtA1LO5d3cBKjvWUjSfBPIEZENeGV9xakOA9oSNBtBMCLKsJtgngCDVtYAg6//Vf/zVbUfpIrzLZyBAKxge9Mfcs1wngCe3lT3APh43AQuri+if1Iuwn6FV+wlHCD0JuAhKbiLHyR/2VhyAZuJ+XIoGAJOqQXxR/rBkpVdDA5p9bQ+BRTzQLkA4tlJGAB7CQZFGOPujHgpXgNTZF6I+G6krYrLwsV20UneJw5YcyOIIPtKm6waMtAMUTJZcNonzEpRRg0R4fhdaX4I1NoP6kbJRR6Kk8BFBBm642GGvvuv4E+gQuNrksyKe0iU0i5QBBifwI/Qm7gs5d5RoaG/oTIbuNsH6kX6K18YLeIeDRv+HRP9BCv0Mv8YGTDNqJAgoNWbBStjjdBLex5HSTPkrYI7320ebSGgssGuEE2l09neqAQxploIASb4zWyio/caUBQ2X0Xl+ggNFX9F3p0BMuQgJCd0JE335Aty7QR3z7BJ2UkVBLnym/F0KgQZED9t577ytcqzaUH5qgI5zxUdKuMlCmEDCYt7ShucDHw/kp6whSxtqzrxwEMZQ2XUDpqT+UdO+aC6K/6fP6PuWmvkM4q4zma/SPq7262mWoT0+d74fmnrH+VdZfXBb4gD+g9EfYkBtzTx2nL7yON/Zcl2fsucS3vGWo8yrxhb90S79y1M9TyhZpyrgryu80lzoSLPaBddb6ZXya286ZXUVHqG4MUPDhabrKbD4xBsw7hJfAHGIMGMf8cFD2w4HPqGEha02NY019dorGusmK/WUve1n+bkkYT6iT+YoC3dyOv8GjUKoPge8oAN8HsjZRihBo+x6C+Z5BBAX34YcfntcvVwUdeeSRme9QnjHYaN2jxqIMvt9ydo3UnXbfLG261fXydyx+9IOL0mc/N/uY8+WKgT323nLGG2+SPn3Cj9LZZyz9Ztz6N7xO2nvPLdOW228w46nWSeef87/ppC+em372o4uX5XWr3W6cbn2bTdMGm6ybfjf71sV3v/PL9KXPn5v+8ue/znsib7zZeune+24/W7//nN79zjOWpefZfLsN0u63v0m64ebXzVdi/eq8S9M3Trkg/eD0pR+p9oHxPe6wWdp6dn3Weje45uyqqdk3ZM76dTr5a+enP/7v0m/mXeM6V0933muLdNObb5TWvd610q8vuGT2/mfpu6f89fsVV8i0ePjVpU8unhbXa/2P01XB40YO+Ac8oD6Gr6HUdOWccQ2si9Y9Jz9KkM5JDd9XK79BV8bhx3OYK5QBT2xOwk/p18ApL3yXPRFeU7+niAnAW1iDGccEUJLYUzltRinHuAUfDbc1j0GCMpm38Ffyx5fjS/FeTpJJi//BC1jfGSVMgdKArCk2plCsxWkUaBRoFGgUaBRoFGgUaBRYKyhAgBenIharQnASRBAWlEL8UGywvnOyA9NPYFjGiTIQeNjIeD9mnRVpxuoCpzwx/13CkBJPV/nj/cp21aumEcG3utj0hEKjLFfQzztxlgds6Cg2CGhtwmwa4e/Cq6z/n72zALeruNrw4O7uwd2tFGlwhxaXAsGCu1tJSgtFijX4j1PcneLu7u7uUiCE0P+8E77LymS2nHvPTXJz13qec2b2yJqZb2bP3nutmTX8cnHUoagPVN9cf+fa35720KeUYz/+xCdXL8ptxb3BOKb/ijChH6FcvdTP6b1E+hwudepchjV8c1RWR8pkTKT15+OeuKr7TeWVlaE0ZW6z5VlemKhDqIaADaHbXQ2TECg+UYaw28pSVX+m9UBQgQAvR6wWLxO85PIoTGOWezw3R+b6pc74EP8iV2OS8Vw29+TuZctTZ/8QRlsspdeKS+ft9FrprFsnjU2fK9uGWb/NV+RvRfnwtnzkt26RP82reiq9rovS2fhW+qWEa4Yn45d5nDmFHYTtIcaveOQwaA/PES0PSiTmQRQbKNM5kN0qNlDkI+RFMYRiil03ZYoN5kyEtfvtt1/bLk/mSXYZssiF3TQof+lTVsyLjjvuuLgQ48Ybb1RQodtRxQaMG7LmMMZ4o0Wlw0+NsypS4uwNzs5IafRxR23UfaTw4zf5A8E5n2OMcUYLPzWUFgMbh3+3l0Yba5QwUkOBImVFymfkRtzo44wa+n/LO1oaO+h61DFGDqM1DjTv/92A8MvAgkRJ1s5UbCRFZS95D2O81Lnvub8Ziyyiuuiii6JCIcvUBPLuwdgrekczSVvu5Xmi+aijzO07mJui6iiant8RcAQcAUfAEXAEHAFHoMsggMA1FZp3tPLws1vKi/jZl/A0DQKHOjxsvqq2wBPhfBXVrX8Vn1bF5/qHj7AyfNqDX9365oSoytvePiirb679Kq8ZN7cqV/lzY6OqLcpb5VaNubL7oKyfc7jUqXMZ1kVtKatjUZnNCgnKyiiqlw1vtjybF4EpCgh2QTC+2UXDrjR7mKnSV/VnWg9WnLNKOUfEtZdyY9byyvVLLszmqeMvG5Pkb8/4Ig8kpYGuY2Dyl8al10nypi/FT3WBAWG6VjzhCsMvsvEKwy0KJ87yKUunOOvW8efKVz7iRLkwxQ0PLuO37LlXp46M36p7uA6fETkNq+DLiNXl/KA6Y4bdF+zqQ1EiYp5kVTo7aKAVV1xxsJ0zCKhRnrBCf2gRyoAi5QR1yCk1CC9SNBAHsTvjh68HmYQaFNK+/wE/DCzNiKKirP5k/rn/L41fx+tSWpEWR6JAzynR02LY+YAyjB1ELBhgJ2Id6ui7R50yitJw/3RG+a7YKELcwx0BR8ARcAQcAUfAEXAEHIEOIMAW7wMOOKDDtmM7UAXP2g4E+Dik3xAqOTkCIyICmADbZ5994tklrAzlrApMy8mMTUfajKmpnOm+jvAcUfMi5LFCftvOVIBadW3z1vXbstO6pNfwTOuQllMVr/TNplN63NSfXtt6Ks6GqQ5FYWm8xcjGud8REAKYC2MXH4QiSbvx0sUFXLNaHcLkjyVMD2Jyj90iTo5AHQRQfnA+Fqaqll9++ba5sU7eES2NKzZGtB719jgCjoAj4Ag4Ao6AI+AIDBcIcCYDwkIrXBkuKuaVKEUAu8aYzHFyBEZUBFBkYJMbYRxmL1hB6fPUsOltcLfC81w/2DDr72iNc2WndbHXZeW1sl6UI37WTf1c25/y2XSqs8KKrhVuXfLUbb/N5/7uhQAHPrPbAuL9YeONN84CkI5BJeIcrXPOOSearULp7OQI1EEA82mbbbZZNKNWtrO4Dq+unsYVG129B73+joAj4Ag4Ao6AI+AIOALDJQKs3OPn1LUQQPiQfiRyiKuEEtjZdnIERgQEGOfpWB8R2jU8tsGapRke6+d1cgQcgfYhwIp5HcCM6TDt1NDODXHl7I3UDM+zzz4bz2DjwOR11123lgki8XO3eyPAOymLFJxCcMWGjwJHwBFwBBwBR8ARcAQcAUfAEXAEShDo0aNHSaxHOQKOgCPgCDgCjkB3ROAPf/jDYM1GocG5Ju+++2743e9+F+PYGffRRx+FFVZYoS3tG2+8Efr16xdmnXXWuPLehdRt0LjHEWgKAV9C1hRcntgRcAQcAUfAEXAEHAFHwBFwBIY2AggFOIzTqWsjgMDnhRdeCC+//HJsyBdffBGv33nnnS7TMA55xab68EafffZZxPK9994bKlXjgNzHH388PPzww2025YdKwZlCsFsv2/WZ6E4LGl7HQisa/O2337aCTS0ezzzzTBxHVYk5h6ArzRVV7fH4ERMBdmUsssgi4ZZbbgkff/xxNPl36623hm+++SYstthisdEffvhhOOGEE+LuDnZqoAghnh/nHjk5Ao5AfQR8x0Z9rDylI+AIOAKOgCPgCDgCjoAj4AgMAwROPfXU8MEHH4TevXuHYbV7okqgltpiT69bAZvMYaW80vD0Ok2va1vHOn7lw7Xp02vFybXxHHgJvfLKK9HlGoXHq6++Gq9Vd1zrJ7Lo2sZFJr/+kT49pNXGt8d/xhlnBEyTbbPNNmHmmWduD4tOyfPQQw+Fq666KgrOtttuu04pQ0y5F7gnWZGMcP+YY44JU001laKHqouS6Z///GccG3vttVfAFMzQouFxLKCQuPvuu8Pbb78dMaFflllmmTDJJJPUhgUh7G233RZWWmmleCht7YztSIhS4/jjjw9LLbVUwBxPGd13331RWLz33nu3/L4uK9fjHIFmEdhyyy3DiSeeGI444ogw3njjhTfffDOst956YbbZZous/vOf/4Q777wzcBYb52tYWnPNNcPSSy9tg9zvCDgCJQi4YqMEHI9yBBwBR8ARcAQcAUfAEXAEHIFhjwACrRdffDEertkKxcb5558fWNm++uqrh/nmm68lDUSIbgX5LWGaMLH8JeRPknT40pZh/WJcFaZ4ueQr8ounsEtd4m2Y0le5nYUN5T7wwAPhySefDH/605+GmWIDZcKRRx4ZYdhxxx3DBBNMEMczh9AODXMmV199dbjuuuvCLLPMEhUpo48+elWXdFo8q5tvvvnmeJ7Rrrvu2mnl5Bi3cizk+jRXZlkYSh5WgbOLhh1u3HfY+2e87rfffvGQ2TT/PffcE+6///4w77zzhjXWWCNGo3Skf2ecccZOVWx899134eSTTw5ffvllWGKJJdKqDXG96qqrhoMOOij83//9Xzj44IP9PIIhEPKAjiLQt2/fsOCCCxay6dmzZ6jzDsKOjZ122ilgboo5auGFF45zpc7fmHvuucMhhxySLWfKKafMhnugI+AI5BFwxUYeFw91BBwBR8ARcAQcAUfAEXAEHIEOIIBgDYGnVuinrFilP8ooo7QdXowQjjyYZEgPXWcFOqZ20oO7MVEFn3HGGadtNb8th3hM1BBv6fbbb4/CvrnmmmsIxcaAAQPCwIEDhzjkU/klNLfCesVVue3JA0+VafnDS+HWb9PIb9MpDJfwunXKpUvD0mvKUJhcwlIiTnWxLukUZ/02LOXVqmsEzRz2yg4A4QfvrbfeOpoXmWmmmYYoinGj8ZhGil86FtN09lp50jrQ/osvvjgm3WKLLaJiw+bDj5A7dy8RB1/i4cs9mCPuHe4FeKSEAoW2srJ4tdVWG6L8tGzqW3RvW94IAClTGNXJR9p999039lFut4ZMVEmgaMvL+cGGPszxIr3qRLllYyHH2+anPnZurOrTqrkO3nfddVfctYNgFKEqfYSiAyUF/SR7/7Zu7JhgLNE/Umwst9xycYdHTsBLPelfzO2o/lVzJuk5pD5VgLFinbH0l7/8Jcw555y2WvHeg7893P73v/99PKPg+uuvj2MPYbGTI9BKBNZff/1Sdigk+FURY3fJJZeMygzNo/Y5guKDn5Mj4Ah0HAFXbHQcQ+fgCDgCjoAj4Ag4Ao6AI+AIdHsEWCXMymkEvqzExVwNQtGVV165zcQIq8wRGmJnmlXCmB5BOMqKYpQNKC8QmGGuASHb+OOPH3FlBTGmqBAoTDzxxFEIh3Drueeei4oLzKysssoqbUoKBAnEP/XUUzGeFe2stGRV8NFHHx1XUZIG0z3YtN5ss83CJ598Eq699tpoZgjh5qSTThqFaNrRYYUSVEoC+Lodj0CwvZTmVV0Iz/mLyknrnPK1+Wyc9ZMmvU7DFC/X8i3yk1b1y7VJ8TZ/Lkx5bTr5GZNnn312GHfccQNmiyB2AzH2UHIh1EIQjiD42WefjX7MiDBeOfQV3s8//3w8BJZxwRj5+9//HhA6c4DsvffeG4W+mP/ZeOONw+STTx7LePrpp8MNN9wQxxp5UNBx1giCZu6PlBBIYwrokUceifyow6KLLhoQOHMuCavcGb8QZny4h0Tce6xof+211+K9xLjnBzGuJUzGZBHCeepA21AmXnLJJeGll16KK5YReFPubrvtFvPyRztpL2ZVIO55+Gy00UbRNBW7RaaYYop4322++eYR07J7G/vzmLQiH6ugH3vssdguzLMsu+yygZ0hHLiLkgHBfE4QSJ24zyGwBB/xRMBPP0IIwddee+0hlKbE2XkJzFFoYWZsww03jHOQ5jYO+WWOYi7q06fPYGOBcYFigTSbbLIJbGOf0ybKZj6DD2aiqCMCe3a8/PGPf4zjLNenmKNB4VA218WCfv17//33Y/n0J3zpb8YydXj99deHUGxcdtllsc6MJc5LOeyww+IqckytsUNuookmiqZyNC7An7mYPuEeogzuKcygMWbpw0033bRNMcUYYhwztyLo5R4jj5RGl19+eUy7/PLLtzWDcnmOkAel2/TTTx+xm3rqqWPf0YcXXXRRnKtdsdEGm3uGUwS4z61ybjitplfLEejSCLhio0t3n1feEXAEHAFHwBFwBBwBR8ARGD4QQOB16aWXRsEmAl0ErNh556DoY489NoajSEAQ+sQTT0TlAoJTFCEIZxH0IhhDcIiwGaEnQlUErtijRuDKCkhW9p7TsEmN0JMV3ghSEdYSjz1rrhEin3766bEszlhAqIeQDQUH5wJoFTdlIaSDTjvttFgOdUDQescdd0TFyeGHHx6KTENIED+0eyAn0C+qQyrot3W2/qL8aThlp2TDrD9NN6yv6W/GKMoFKTYYN4RxngCKDYTIKAYQxKJgY+wh9GWsogRA6IrSAEUGAniEs4xDhP24mB5BoIyCjp1GmNlh1TxjnnHEGOQMA0yhsWo/p9i48cYbw5lnnhnHJkoQhMcIpxm3Cy20ULyvhCXC7K+++kqX8T5AeUi51AWBOwrBySabLCpwdF6N+KJ4oE4oGbnvGPcI96kf7beKDQpB6E07IYTPKB8pCwxRlMCLg3HBk7FQdm/bfNzX1Jt5gFX8tPfzzz8PHFJOXcD3lFNOibjGwn/9AxME9BB9anminEDxwRxEH6Jwyu1a0LzEWKDvwA0sEEj26tUrCvNpH/3HvMYOBEwh2bHAeCHNdNNNFzbYYIM4frimHQj0aRc298Fv/vnnj32IIgzFxVprrZXtU/Crmut+hSE6KLDgTd8yb6KgQYkCUa+U6D/whWgT8zXEWGVcSzGtccFY4T6gP+gb+oQ+Q6nBGTkoLFBAoMThHjnuuOPi+KM+1EM7fVAkUy6Kaeor82n0HeMFZdQCCywQsUZZRDko1GgTYxJFNtjRt+Du5Ag4Ao6AI9B9ERi5+zbdW+4IOAKOgCPgCDgCjoAj4Ag4Aq1CACEcP8yNINDdZ599ouAXYRiKB0hppplmmmgrnV0WmEF59NFHo6B4zz33DBwMi7APYaUOlEbYSF4IxQR5EJQhyCQPK5tZDc3KewSdZ511VhQG77DDDmGPPfaIQlaEZcRvu+22bQcds7KZsxJYsczKZniy0pxzC1gFz4pldnSIUATol4ZxTZzI+tM4pWmlq/LklpVp0zRbh1xeG1bkrypH+eRWpbftUx65irPX4sc4YjyJNCY1vlBkoEhgbDB+EMKi7JLyIE2va3ZUcMYDq/wRfqO8gFihz9hEwcY5BzvvvHPcdaR8qodcBMUoNVAUcgAt45uDyrlG4YLyADNDIkxRUbYIIfFWW20V7z+UfCg2ENgjBIYvK+JpE/cFeRH6y6yV6sRKee6t7bffXmyjSzht5L6AEGAj+IfIi3Ac4fqhhx4a5phjjlr3Nvlo87rrrht23333qLj59NNP464I8GceQcGAggCFSY5Ub8WJJztJwI95BqG9FCBKJ1f52ZlDHXr37h3nggsvvDDWTfG0j/b2aezWQHGlcFwE8Sh0UTShvGAuAWuE/RyMzfhBsYLSg/7DjBWKBRQI4Jnr06q5TvWXi9AfBRUKGJQNF1xwQZwvMSmlnWdKi2t3waBYUh1su0ina5QWjAnGMOOJnRr0N2NpxRVXjG3mzA7oyiuvjOMe81H0AeMJZQbnG4EjyhEULzM2zvEQ0Ufs5JNSEKWaFFFSRhMHjmCHcsXJEXAEHAFHoHsj4Ort7t3/3npHwBFwBBwBR8ARcAQcAUegpQggwMWMDIRSg5XKuAgZRZgjwfyIBKWEY2IEU1EInVlBzcG8rBxmVbwlVvmy8p6V4ezqwI9iAuEbK83Z1cEODQTAlIPLanWEe5g14ZBczKhArCTGHAxCQARsKD8QSCMgJA6BqHZrpEJyXVMuxDV+G66wmODXNPIrn66bcVVGkQsvxaV+lau6VbnKr3y56zRMPG24rY942TClzbk2rMhveVk/6ZshxgGrwRmzrAZHUcCKegT1ZcR4Jg1j+owzzoiCV9KjtKO9jEvdF5jnwTxVjhDey4wUShLKR+iMUoJwhP727AN2Y2iMwg8lDLtJIO5F+PHjPuFehBAo42f3FKQ6xovGH8Jo7tf0TARwpWyEyxA7IigfxQmEAgKlCvUhrYTcRfe22gHOCNlxUQRRH+572gF27BKg3bSB/qlD8Fp99dUjT3iwE4c+LSOE9NQJvNnBhfCdckWU/ec//znWR2Fy2bkCbsxdKLJQAqDgQqkBHmBDe5ib2IVAWyDmMOKEBWHqU3bukK5orpPJK/JYor0oTDDZxNyHwkK7Imw62qOxg4k/WwebTn6UN4xjxo0OPqbfGGcoJVAOo3CA6EeIujA+mGPZcQGmmPKSUsLWCwUPPxTb11xzTdzNxw4lyqU/RaThOUHfoLxzcgQcAUfAEei+CPz2dOi+GHjLHQFHwBFwBBwBR8ARcAQcAUegRQggpBMhVIMQ8FlC6IcAGML8CCQBG8Iv5ZPwLyb49c+GIUCEWM2LMgIlBmUhTMPMD9cQ5q5yhz3HyMYfdWGlNiasEPhitgbhGTtGWI0MVQnLKVNp5JcbGTT+uBYpra7l2jQKw03T67rIzeWxYbm6wUvhcslj/bnrojDCq0j1J13q17V1i/y5/FVl5+IxR4UQFaUa5s5YcY8AmN0DGqO5fFICyGVFO8TqdIgxL7L3iMLksrIfoS3KAwl9EZpjFog4dgKUjWUrAOYegFD8kZc6gZ8UEfQr9w33nN3FgrBY7VC96rjcR6ymF9W9t6mT6q1ypTwhTngJU/Evcy1PzSfpPJTmVzngTj0QsNs8hKNkKaJVV101KjYw3aT+xiQXdUGYzxk+zF8oVxH0Q+DOjz5IqWquS9PrGgUDu9boX3bYsIujFSR81EfwVD8pTH2kumPqDyU1hDks2s0OHY03sBGxk4+dHezwY25HMcZYZKeHVSwKK9rn5Ag4Ao6AI9C9EXDFRvfuf2+9I+AIOAKOgCPgCDgCjoAj0FIEZDIEprLZzmrgIkLoyIpyzP9w8C4CL/yQBJI2L+cjQAjEEELrIFrysNIZ++vEIZTEjBRCYRQUCMl6NFaz24NqJRhDCIcAk9XB7NJACIlgkFXDPRuruFlpbwVwtj6t9FOfsnJsnPxFrjCqql9VmWl8ei3+1IO4HKVxqnOaVuFlbi4uDYOvwmwZEswi6Jcw2Y5XhLKcr8AKdMybsZoeYTQ7LFBuWOGq5VvmZzxCmCcS6YwKXVuX8Y3gll0VrH7HvBGr2xnLtCm9J4owtzzxo6iTAmOZZZZp2/mA6SSExzm8Uh7NXjd7bzfLvyw9/Us/0+eah8CgjDQW2AnA/AEmUi6V5VMcOwvAkt049DeKVcYRdNNNN0UzS5i+44BtlD7s7siR+rRqrsvlZccZ5wsxn6GU5QwXKZFz6dXvKjOXpj1h1J2dHSjOdI4MY5h+4f7SPWAVR9x7OneGXT6crdGvX7/Yf+uss07b7gyZB2ymb9rTBs/jCDgCjoAjMPwj4IqN4b+PvIaOgCPgCDgCjoAj4Ag4Ao5Al0EAcznnNA73ZgUv5kgQnEm4l2sEioSzzz47nHfeeXFVOcJkFBEoQ3IrjTGXwqpwhL06mBfFBGZmWAmNIHnhhReOuy5OOumkaFaGerALg/M1IK26ZmU1wjEE1pxfwOrjXg0b+qwsRhiI8E0rqyUATNtAOEJBucTLrzwSGqbXOV5pWO5afIiTv8jN5Vc+W2/yF12Tvk4bVAfxUdnptcJTV/kJlz/n5sJsntTPNYTQGUE3Qutzzz03KsUwCyRC8M8uDVaZY+YHZQY7Jag/iob2EOaHLrnkkqhYY4xTPuO2iKgjAnJ2jCDUZbxz8D115pwEdgJQT4Tm7Aahvigqqoj7gvMKWD2PqScUggjyMd+G4g6eraaqe1uKhFaXCz+UGieffHIUomOGCpKJrniR+cN0E0oQ8Oben2eeeeJB85mk2SBM3GF66oorrohCfUxToZiCJIxn5wxKD/pXhEKN+TLt06q5TvnlMgfSZhQEmAlDecIB5hDmrDCplpLmQnbxoGjh3JRWECaqmH85ZwTlBvcRZgQZd8RhFpA5FtNUIhTKmHGj7uutt15gBweE8kPKGRQjKONoT6rkEx93HQFHwBFwBLoPAq7Y6D597S11BBwBR8ARcAQcAUfAEXAEOh0BBKSYdEKoxspnzLPkFBSqCIfpIkhmRwVKDQSACGGxZc85FylhDgjzKhxCiyAO5QRCZ5QdKCYQHHPA7QknnBCFvpyXgOJjkUUWaVOwcDA0wmUOqkWAecwxx8Q6sjofYRx8qAc2+ueff/5YhZzg1wr7JbxPw8gsQbyNS9vFteJzcYSJj+J1XeTm8hCmcsiHv8pN+SgP4Smlcel1mj69LmpLs+EpX65RlnHuCsoAFBuMM5nSIZ4+Xm655cI5DcUcZ1qw6pwDnDmcu70CX3YBsUqfewIFHnUoU5Yh4Obw+lNOOSWu8kcJgRIPZQfhqi/CYQ5o5kBsmQGiDUWEqSfyIyBGucFh5AjUWSHPPdoZVHVvU4fOIvoS5QE7JRCYgx87vMqIdCg42SmDUJ1D25lPmiGwRLFBH7NTQeOW/nrqqaeiUolyUHjQl5hlYt7DvFjapyhiy+a6tF60l8PgIcYMihoRytqcYoMD05k7UYagVMB0ViuI+4ydeLfffnsc9+CIsolwMOHe43wjTP9pZw0KEJQaHEoORoSjeKbe2p3BzjzMsaEs173Qivo6D0fAEXAEHIGuiYArNrpmv3mtHQFHwBFwBBwBR8ARcAQcgeESAZQRrHZHMMXqXHZPsLoWOuCAA6KwSvbnCSM9ighWOiOMQ0iLEA5hspQJrNKFEMoiFGMlOIJBVj6zap1Vx6yu79EwNQUhrNtzzz2jsI54hGIcCKx4VqjDi/Iwi4KAbLPNNoumrBDGIYxH8IZpK60KplwJKYmH7LX1x8jGn9IVXafh4qHwnGvTyJ+65FOYeHCt+iiOa4XnXPIqj/goj65zbpqHNLmwNK/qRbj8qVsWp7Q2DX4Rwn0Ot2bnA4orFBcoyhhHjAMIwTR9jkIDoTOmzHRINvEcxExemaXiEGXGp8wGwadv375tygZW8aPYYDcRpnnYkYHAGf7wzhGKQBQO7FwiD+ObOqAkEW2xxRZxTLOTg3DSM2a5d0SM6RVXXLHtUGh2AOy8885RqYHikTykF9+NN9447moAnzLaZZdd2swMkQ4MabNW1Stv1b2dy0d9qZM9RwSTSrTThqkM5hjKhqygm77u3bt3nAOI4zByBOllRD+hkKAs6s6OC2iJJZaIZdjzQwhPxwJhYKf62J1qzGf77rtv3BFC3TC7x1hDeK/5Me1TxnPVXEeZIsaNylaYXO3M0LVclAn7779/3DnBeGD+RaELXtQRSscF9Vc5jG+IsUWY7gMUQyhmwA7FDW2hv1EU4+e35pprhmOPPTaep8H8z3NBzwKU0dSFeZ7xSZkQB6pzv9FXTo6AI+AIOAKOwEiN1U15Q6iOjSPgCDgCjoAj4Ag4Ao6AI+AIOAI1EcDMzj/+8Y8oGGW1OUJhVummws4idgi+2XmBAEsr0LGxjlkY+CJMxqwKygYRQkjM2SCQy5XDinR2ZCDwlJJEeSmPOhKHkE0EP+KskJQ4BG1FZIX2RX7y2rgcr6p4W0/y6zp1bVyuHIWpvNQlPhemfHKVRtepWxWfpte12sO1/KlbFSdeErTqGpd60c9pH9s0jB2Ezun4sGnq+BljjF+UDgh7UVSwG4TxzAp5BN5FVDW+iVcdi3jkwmk/9xr3Z7M7EnL8qsJUnr23q/K0Nx6TSpzHgOkjzM+BD+VrTsnxRSFAvzC/oABtD6Y5vrkw5iPmIgnq0zRFfVo1FlI+zV43O183wx/eUHq/sVOPXUQono866qg2pQj9RR4wsuOTHT7sYEHJxz1VpKxppm6e1hHoighMPFa/rljtLlHnL37YuUvUs7tX0j5PfMdGdx8N3n5HwBFwBBwBR8ARcAQcAUegExCwHx112CO4ZvW1JQ7hRfiL0LFn4xBvVlFbQpmR5rHxCMaKBIiUh/AzJZQkOUoVIzYNgjiR9ROWXheFKX9dV4J+0ssv14aV8VPd5JJWfrnKn14rPHXL0pXF2brD017n/LmwNF9aN12Tt2p8lo0d8anjMm4Yv5haQxD79ddfR2HtlltuOZiSLseranwTn1Po5XjZMNpfdt/YtK3wD+3ybJ2tYNyGF/nbi2kRvzS8TMFC2qLyCe/MPqu6H9J2NHNdxJsdHOwgOu644+JOjM033zyyZbzk5mbMD2LKjV0drtRopgc8rSPgCDgCIy4CrtgYcfvWW+YIOAKOgCPgCDgCjoAj4AgMNQRYeY6pKR342oqCMT2DKRZMtWDSSmZPWsG7WR51FRvwTQX46bUtuyzOprN+BH+iOn6lTV1btvxySWv9Nm9RuE1j/XXT27Yovw1r1i8ew9JFsI75K1abczYAgm0Eupju6UxB9bBs87AsG9NFzEPNKHwwdcUujdTU1LBsR3cpm4PvMS1l7+2itmPODfNVHOru5Ag4Ao6AI+AIgICbovJx4Ag4Ao6AI+AIOAKOgCPgCDgCHUYA4RTmUhBQFe2SaLYQhI2ckYEAuGqlc7O8Pb0jMDQRQLmTM68zNOvQHcoCZ8yIQXV3azDPQMxbdQTsMbH/tQwB3Ru5XRq2EJ4F44wzjg1yvyPQLRFwU1Sd1+1uiqrzsG0lZ7sT0HdstBJZ5+UIOAKOgCPgCDgCjoAj4Ah0UwTY0VC2q6E9sCCYxP66kyPQ1RFAYF4luO3qbRwe6g/OdRUaqm+z6ZXP3dYgUPfecKVGa/B2Lo6AI+AIjEgIjDwiNcbb4gg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCIzYCrtgYsfvXW+cIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4AiMUAi4YmOE6k5vjCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AIzBiI+CKjRG7f711joAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOwAiFgCs2Rqju9MY4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCIzYCrtgYsfvXW+cIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4AiMUAiMOkK1xhvjCDgCjoAj4Ag4Ao6AIzDMERhttNGGeR28Ao6AI+AIOAKOgCPgCDgCjoAj4AjURcC/YeoiNfykc8XG8NMXXhNHwBFwBBwBR8ARcARGCARGGWWUEaId3ghHwBFwBBwBR8ARcAQcAUfAEegeCPg3TNfrZzdF1fX6zGvsCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIdFsEXLHRbbveG+4IOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ah0PQRcsdH1+sxr7Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCHRbBFyx0W273hvuCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIdD0EXLHR9frMa+wIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ah0WwRcsdFtu94b7gg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCHQ9BFyx0fX6zGvsCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIdFsERu22LfeGOwKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAIjMAKffvrpEK0baaSRBgtLr4m0YUV+MbHxhP3vf/9TVMtcy1N+ubbMXFhaCZsmjWvVdYqJvS7yq2wbXxamuIkmmkhedx0BR8ARcAQcgW6FgCs2ulV3e2MdAUfAEXAEHAFHwBFwBByBYYNA//79Y8FjjDFGyyrw7bffhvHGG68l/AYOHBhefPHFyGuuueYKI488+Ob2zz//fIhyqgSkNj4nrByCoQmweU1wmzfHLw1Lr9syG48tB7+uy/xkt+kMu8G8tnz55ZKwyD8Yk8yFyiaqyK9sNl5hzbjTTDNNM8mHu7SMa8hi3ey1zZvzl2FcFhcrVvPP8pE/dWGVC7PhNYvLJhPvXKTFRfE2rI6ffKRLyykKUzlFbmfMuUVldYXwVj4vukJ7vY6OgCPgCHQHBFyx0R162dvoCDgCjoAj4Ag4Ao7AcIjAf//733DvvfeG119/Pfz0009h8sknD7///e/DjDPO2KHavvfee+Gee+4JH330URhrrLHCTDPNFP7whz+EMcccs43vq6++GtN8+eWXUTC++OKLhwUWWKAt/ueffw73339/eP7558OPP/4YJp100rDkkkuGmWeeuS0NQvCHHnooIPBGuD7rrLOGpZZaKow++uhtaco8lPHyyy+H1157Lay99trZpN9//3149tlnIz5LL710Nk0a+Mwzz4RHH300fPXVV2GCCSYI8803X1hkkUXaBPUIzR555JHwxBNPhB9++CFMP/30YYUVVggTTjhhyqpl199991048sgjo9Bu//33D2OPPXaHed9yyy2B36qrrhpWXHHFDvMDC+oInXbaaUPU8ZdffhmijFQAqQS5cCvYVLoqN8eHPDlBp8LFM5fG1iHlzbXCcn4bRxnptcq1ri0v51cYrviRX+GWl40nXNdybVjq59qSzWPD5bflc3/feuut4f3334/3Nkqvnj17hlFGGUXJ29w33ngj3HnnnfF6yy23bLvnEKjecccd4a233optm3rqqcOyyy4bJplkkra88lC3q666KjA3zT///PHeVZx133333fDZZ5/FuYF7l3kzN/ek49a2DX72uiN+W7fUX4V3mr7oWnzquvBR2iKeCq+bTumLXIshaey1/HLFw15bfy5ePKvq2xlzrurTFd1WPy+6IgZeZ0fAEXAERkQEXLExIvaqt8kRcAQcAUfAEXAEHIHhHAGE7v369QsPPvhg+PjjjwOrilEO3HXXXWGbbbYJKBraQwgWjznmmKiQQPAzYMCAMPHEEwfCt99++yhkwo8AG6UFq/JRMKAI2W+//aISgHznnHNOuOGGG6JyZLTRRgsIB1HCHHDAAVFR8uSTT4bjjz8+oCAhHh4oP+C99dZbDybMStvxzjvvhMcffzw8/fTTMT0CqFSxgXKCeJQaCEMRptZRbCBUPfPMM8Obb74ZRh111Cj0RHGx7bbbRuUFdbnxxhvDueeeG6gHwlkUPpTXKoVD2l6uUVxRLkK7vfbaK5ek6bCXXnopXH311bE/WqHYYKygzILoz5RSATHxOeFiLky8ckJLxeXcIl45PoTZ9DaN/Dbelqdw3NRfdg0PxVt+1q+yCZO/yFWaIp42POfPhakuNk5hdVzmE+YpVr8zT0055ZTh66+/Duuss85g2RkzKMSYS6DNNtssKhq4v//5z3+GBx54IKDMhbjnmAMOPvjgMM4448Qw/T322GPh5JNPjmk333zzIRQbzDncSyhFUWQwh1E3lCTLLbdcVOIKX3jW2bFh01f5FY/bDKbNpBUWqSseZa7iyGv9ueuUf6uuhRH8cn4bVpRGdUlxTq+VLnU7Y85Ny+hK161+XnSlthfVlWfa3/72txi96667durihqI6eLgj4Ag4Ah1FwBUbHUXQ8zsCjoAj4Ag4Ao6AI+AINI3AxRdfHC655JKodEDoPu6440bhIQI7BIEnnXRSjLOMEdCxeyIVBNo0F1xwQVzFzw6NP/7xj4FVzccee2z44osvwoYbbhh5Ui4KAISAq6++ety9QH3OOuusqKxgBwn+b775Juy5555RYIiigxXX7OrYaaedwoUXXhgVHewWWGWVVcIHH3wQjj766LiCGoEnypQiOv/886MCh5XgKHhQiFhC2HDUUUeFt99+O8YjDEWYWkUIN8GNnRgoV+add95YDsJ/zD+xK4M2kQah6M477xxmmGGGqAi54oorwu9+97uw2mqrVRUzWHyVqROEvQjY6N+DDjooCvnYRZMSfBBC2l01SgMeYEC/W/NQtAfsFlpoISVtc+GlPKkQkUTsziA8V14bk8QzLBQbSRXaLlOBLRG2nVb4af1pOstHftzUnwuDl03HdY5UL9WjyCWv4nJ8FKYyuZY/dW1c6ue6GbrmmmsC5qh22WWXeH+jpECBsfzyy8cdUeL1n//8J9x0001x/iJMdWLeuOyyy8JUU00Vdt9996hogAd811xzzbhLTTzY2cH9iYIU4toSSo9TTz01KlMXXXTROC+gwCQditrTTz89+uErYt5UHxBW5LdxSiO3Ks7G44fU/kFXQ14rPOeq3CIeCreu9cNT16k/V16rw1R/+Ob8aZitq43L5bdp03iuRWVzbp35j3kbZa+et5TLnMpuOzsPq7zUJS/zf26+V1rKYHwWpSE/daUtKS7iQZ3IX1WnoudF0fNF/BVPu3O7tBSfPp/S/B2JBwPKL8JAZZW5ufcn+vTf//53zMY7Q9WuTfqDZ7XGRFoeWNSpKztRwVJmKZWviG9ajl87Ao6AI2ARcMWGRcP9joAj4Ag4Ao6AI+AIOAKdjgCKCz6mEUjsvffeYf31148CZswlsUMBM0rsjmAXQ58+faJwBCXEXY3dHORBQMhKZkxXpcTuD8y3bLHFFnHlMoJ8Vj8TjtkWFA4333xzFHptsskm0RwMyopLL700KjtQnKCkQJGwxBJLBNIg/GaVNDsoEB5CKE5QHCBcnHPOOaMwkx0oH374YeCw3jLFBkIYFAgIYk444YS0CfEagf0yyywTy7vuuuuyadJAVpFD4IiQgjZQDxQbzz33XIx75ZVXolKDODDi0NlPPvkkKlJQKlUpNmgfglcEIJjTARMIHFDo0Cb6FjNdCy+8cHjqqafiTpwdd9wxrlAnLcogFE4Ih+lDhMb0OYKXOeaYo22lO2lZ/U6/owBCsAPPNdZYIwp22ZVCf9AG+kDlouh44YUXojIL/htssEHc1QE/lFbXX399LB8hEW1AATZjDfNnEijKhR9UdT0oVT2hvdI269IW1aPMD1+lS8tQOK5+Sq9ruQrPueIrIRx5VKcyV7yUT3ysCy+R/FVuml7XdV3mBJQYjG8Ecpdffnk0H4cJOcYjhNm7U045Jd4XzG+WMI03++yzh5VWWimsu+66cfcX9yTm4OCB+T0RvB9++OE4LpmHLHGfskuM+YNdbXPPPXecD7nvGefsckOBwnzHnDbddNPFfkRoCFlcm/ErbZWrfrB1rhumPCojzWev8evautYPv/RaZXS2SxtUdpmfetj4qmubFr9IZelaLkJodgVBzLlQ2fynuZ35lPmQ8YlygrmV8Y/ymzGJ8Jk5uGhXJfXhWXLffffF/LPMMktsJ3P+pptuGvkxljG3Rn0Yn5NNNllcIMCzG+I+4x7hfQBBOs9Dnk2Ma1tPnmMo8nkfQFGIooSdmdNOO218Jsu04D777BN3MdrnBeWUPV+o12233RZ38aE4pN3cqyuvvHJ8zoEN7QRj7nmeTzwHUSpKyVLGv6p8noeYz2I+gD87WsGc3YkoM1n4cMYZZ8RwdpuKHwrWeeaZJ2y00Ual70+k5/2DdkDsTOOdq2fDzF5K4HrttdcGdqoyH/L8550M85xQVV3ZrQZWzIMoPngPQbHBe9QUU0wRn8m8qzEOeOdil6mTI+AIOAJ1EXDFRl2kPJ0j4Ag4Ao6AI+AIOAKOQEsQQCCNgAQTTmuttVYUCMCYj14EhQhA+BDmIxuFA4INBCB8gCMIROCAcI+dEykhrOcjm3MlIASOXLPiEyE/wh4EIxACG4hdC/BDeIFSAkEMghDSo9RAUCMhoz642aUBkR5hCWYuqB+80h0YMaH5Q7jJ2RfgkFNsILSibQj8zzvvvFBXsYHgBTNPCAwQ+ECcCwBRL4g20h6EMFK+YOYKAtsqwu7/RRddFIU8nClCm2k7K8lROCHoQCnFmQQIZDB3Rb+iaGFXDIQQBiUTfMBBwg6UIQjDwB+BB+axjjvuuNj3nJOCgBie4IMygjLZfUNbEHqpXARy9Df9THuluEFYxq4a4mkz44p60g+EV5EExEpnBYrWr/jUTdNY4WSalus0fS4NYfBR2pxf5ZBG/hwv8cBN/QqTS37r17XlS7zKkz91bfo6fvJDzbrirXy6LnNVd9KgcOOae4w5gPmIXRXMV/A8++yz4/2Dovbvf//7YGwRCi+44ILxHkTgyTjUQfSaT8jAfcTOMBRz3Beac8QMQS/lohBEqYqg8fbbb4/8uJcRCGL+CgEiAlnO+EDoCy+RbVPOXxRGOO0sci3/HMa5MOWxblk6xVm3yA9PxaV+W14r/cJO5QorypDfplGY4nFtXl3bPEqrdLrGTYn5zc65CKfL5j87t6M0QwDNsw3zfMy7zNko0Xg+Mw4xechzMyXGHEp+nifM7SwKYC5mzDOv89wlngUGKFBQpDBeUb6zU5Fn1//93/9FRQp1RvBNPMpqdjrZeiJg5z5BacjzAsE79xqKDZQiPGO4xw488MAhnhdVzxcUBNSTZyj1RJGAEoMyWfSAqUqUiAj6mR94dunZwz1fxb8qnuc+9zrvMDy/eZbCn37l+ce7B+1DGSDFBhgRxvsJio2y9yferzBzKeL5ygKCHFEXFLcoH8CW/mfRAlgwVqrqyg5R6sVCAn6Uw/gAA5SyPKcpn4UrvFMccsghuWp4mCPgCDgCWQRGzoZ6oCPgCDgCjoAj4Ag4Ao6AI9BJCCAoQDAz/vjjR6GGLUYCeAnkESaTFjNJmIVixSerF1nJmKPFFlssCh0QQCKEYXcBwhEEAQj/+GhGuQHJDAKCIx26y4c7H94ILhDKQAi/EVogQEARA7Fikh/1YFUoH/1cs1pUBwJjlx9hu358vEO0EYFAEVEflAYI7IsIAYb44iIUoM3UGwwgVkVimgsFEitaIYQS1JMdLCiPEPojtIIQmNQh+gNhzp///Oew7777RqUCygGUDBDx/BDicm4HJq9seLz4NR18EMCgSOLgdeoF1hA7MNgRwupgymHXB+VIWKdycCFdI8jaY4894nko6iOExlpVixCMsYQQGkKAhbCuisQfV+My5ycu97P5lS+XTmFp+qJr0ivO+svCFFfkgkUuThgpriid4pt16/IrS5fDgHoUhRfVkfT8IM0V1s9cAXEfXXnllVGYiEAzJeYR7kvmFZSsOt8GpQgr0CHmKOYQ5iyUmlI6ihfx7MZgFxdKDQSczG3Ui7mR8hHkolTl/tc5H9yTaocw0LXcXLjC5JJW/pxLmA3XdS5M5eZcm8/6i8oXfxtfxDcXnobZMjvDr3qq3irDhlfFKY9c8haR0hBfZ/4jPXMyu9yYk9n9xzMC5QFnMKAg4FnJDrtU8aY6MOYQZHMvIHDfbrvt4rhXXXgGo6RD0bHVVltFvjyfeSZyTyHA57lFPPmZ+9lFwPNUz314IQSnDM6IYIcCOykI19jnuQg2yy67bFTAqHxcqOz5wjsCSgWesygIwQLlPM8R8kEI51HS9O7dO+y2225xVwzCehQeUBn/qnj6gPLBkd1ZYACOXGOKTs9q2pL2v9pJGRpXufcn3lOot4i+yO3CoSx2hqAEwRwfdWEHEM9mnp116qo68TwGL84qY46jPbg8jzFJijINc35OjoAj4Ag0g4Dv2GgGLU/rCDgCjoAj4Ag4Ao6AI9AyBCRgqMMQYTRCBHYjIABEAF5GrIpG8MdKTz7WEU7IPERZvrROCFP+9a9/RSEKH/SszLTEamhWmCJ4RBnDqlNWQrLTQwcAK/0//vGPaDZC1x1xWa2NcFPESm2UFiIE9axuZRfExhtvHAVAxGGeBixZ1c3ZI9SdNBC7HOoSAgpMbuBC1EdmT8QDRQWKKARhrLLNEcobFBf0K/k5+0R9i2AKQuiFsBaBLsqb3Cphy5sVuxy0jhAZ4RJCOARUnKdC33BNvzJGSEPfUaYUUpaX9WtsyCWuyG/zyU9arb5WWKtcy1t+uc2UQR5IbpqXcBsnv9xcesJoN2lSNxenMFxLKiPn5sLIm4Zbfh31w1vKU+6DXr16tSlI4c24QqmoeYf0CH0R2rJKGaGiFJzcj+y+wCwfyg7MpUGMTwTBrHhHsIgAEAxRjhCOso/74fDDD4/3EHlQ7ElZiYvAUrgTjx+iPmm4DVNcLr3CIqOKP3iKrF9h1q0bTzqlld9ewzO9tuUU+ZWnmfYV8RrewnlWlc1/7JyA7NzOfMwY4rmHggB8MImEsoMdHDPPPPMQzdTYY2ci8zB5eNboGQC27MBjtT5CbHj36NEjzs88j5j34U192a2An3sJPg899FBUtlAozxWE4ewUhCdnaPDM47mIsB0FOeFF5hXLni/aiUk5PCfYucK9DlEH6sLuE4j6oXCRcpxFBlAZ/4N95VQAAEAASURBVKp43iXY6cK9zU4s+oadEZyhQzi7R5qh3PsTvGVKD148X8EyJeYeyuTZS5/iMn+xs4xFGs3UlfcPKX/ZiYPyCvNevCug+D3iiCPiThQUMpo30/r4tSPgCDgCKQKu2EgR8WtHwBFwBBwBR8ARcAQcgU5FgA9aiB0MCAusqRTt1FAaVUQ7KuRq14XircsKQ1Y1YoaBnQ8IAzElASFoZJUzabTqkXD57WHSmFrAFBIf9TvssEP405/+NMTHNiuoEUQi3KAc7IYj8EcQj71wBCwiBAKtIgRKFgOLF0J6BDwIpagLwh92x0C0jxXhCBgQlCLcgBc7ITABVZcQOkipgZAD0o4U8UBIpf5SWOrCB6UGpLRql/hJ4YBAjQPgq0h84Ku88GI1KP2DkApTZfCT8ERllvFGmCWSXy7h1q90ddwiIWoz/OCh9PIXudQpV6byW1f+tB1F4aRTnC2DsFx9lN7GKQxX4fgh8U7dNK4sPjKq8Wfrr/mBbPJz76A4xSQa9zYKC8WRrm/fvnHekPCXVeoIJqkbcwWrqCHysDobASq7rPr06RNNSBHH6nOEmphOQ8nBfMZKdcwDIchlXmMVOaSxbjFjlT7l8VN7qvyKlwtv6+e6GSKvpfTaxlX5lRdXP/IU+RVXxdfGW/xseOoXngpX3RTOdR0/+dMy7bX1qyzrqgwblvPXnf/s3K65lLEOURbPUKhozmTMQZhShMijOZ5rnhvsgEAZzkIA7h9W7fOcJRylgggFNMQ9ws4nq3yHJ8JwEQo9dm5gjopnH7snejQUJjK1qHRyy54vKPtpH3WX4h9hO3UAH/wo4jHBRD1QzggPuWX8qUNZPOa8KIPntRROvCfRDyiIUAioH9SeMlf9KFd1LMujOM0h1EXvMMxpmtfq1FW8bJ1VF71z6Zp5TveS8rnrCDgCjkAZAq7YKEPH4xwBR8ARcAQcAUfAEXAEWo4AH8QI4hHAI9zApBECBJQa2oWA6Zb2EKuYsf194YUXRsEK5oYwpyFCqILABfv4/BC+syIRBQQKDwnpEWZwmCbmFjBBQR2lHOCjm5WFCAc40wPhYo+GAAWhC21gxSaKDcw/IZwQSfCo6464HCDKalgRQnroiy++iMoY7Jcj+GRFNwIfS+x8wawWAiT6gB0pCGsw5VGXaBeraBFGoCCBUvM5MbADf5jVQYiLyRPGDIIg+hVBCLgXkZRJ1FGraMEeAZoOMKVvwIyVthIwFfFTuBW21PErHy7jG1I+XduwmKDkT3nTJPAqikvTct1s+mZ5tIK/yqzTLpumjj/lbftCcbiWF4o/CKGbxhT3FXOITNahYCBehJkVlBUQ9yM7uBASIrxlFbkUg8Qj8GNFNqZ4+MnWPQJMTPMwv0DMMSiDGbMS6rKqHdL8QnoJlQmz7YgJC/7SfrPXRX7LKsUxLbfqOs1veSuvdVO/vSavrlO/5Ys/LdfmS+NsXtKVxdu0+G1660/j0mvVR2UVXZOP+Z9dQFLYEibq6PwnPqmL0oGfno+MOYTv3BsoFaivFaSz24Pxy3OZXXQ8LzF1xOHkHEitM6poL2YKZZKRcY3ioohQMHC2BIJ2zujgOQA/KWXSfGXPFx0QzjOkZ8MMFjtMIJ7vvDNQN3ZNoSQ/9NBD49lSMlmpcsr48/wqi2d3DH3I/AFevJcwF3DvUzbPb56NEM9h6kl6PftUh2ZcjSvy2D6lPymT8rQQhbntrrvuirhI2VNWVymHmqmPp3UEHAFHoC4Crtioi5SncwQcAUfAEXAEHAFHwBFoCQIIQBAsY57inMZhuex2IIwdEggNWU3PToj2ELbmOXAbAQ+CQlaBsnIT4gMchQnCD4SMZ511VhRUaMUhcQi7Efiz44HVolotycpMEbsGMKPBylDKQcGAggAFCUJ3VlVDUpIoXytdmbCxPBFMnHjiiXEVKX7awspufhCmM1DCQKw2RTiBYIZVs2AiZRJtYbcL/SBzGzGT+UOYgkKEnR/XXHNNjEEY00pC+IvZMcYIdaIf6V9Mi5UpNjDpI8UNAmnGFmYzEOwhYGPlMuMMgbCUGlbwVtUGKwAq8qc8lA4BEcS1/Gna3LXyF8XleKkMucqbXis858I3LduGyS9XPLi2pOvUtWlSv9ISrjoQJn+avug6l96GyW/Ls7wQGGIyB2Us9zuCWJQKzC8oN7RymTwI/rBDD+21114xnnuNe4UDd9kVBQ8O0oVYhY2pNczcWWLM33HHHXFXB6bkpOiAP/MKSlh2dyA8ltkqhJrc09zv2OWHZPYFP+1UG22b5ScNZDGu8osf+VI+hIlsnPUrHrcoXGkUL1d50muFW1c8ci75bTtyaXJhuTw2zPrJn17neDYTltab5xXjCoWBzlSy/BA8t2L+szwRqHPGFOOS8c59wZhjrkbwjzAcgbzdhcHiA3Yo8axknGLOCKUEzxQUg5hHgg/vBZgN5D7Dz33ELsgykjkqzpsBb86CKKKy5wsmFDGRpAPBUa4g7MfMFu1jTqDNEM8mlJoywaVnSRl/nl9l8dzzemaxaxRMeD+iD9khitlJymdhAWG8y1BH5oxmiHawC4N+QrHUs6HE4blv+5Rn6aKLLhr7gvcLlFWUQ9+wcIJ3hKq6NlOnYZGWPsN0GHMq7weY5GIsSVlMnRjDnCnCex7KMvphySWXbKtuHR70Ie8hvHeg2OKdyZoQbWPmHkfAEWgKAVdsNAWXJ3YEHAFHwBFwBBwBR8ARaAUC6623Xly9jFAOAQgfiqxm5iMZm/DtVQqwAp8VjhBCPik1uEaAiPAeO/YSRCAYRGjChznmphCGILi8++67o6ANYaEE9/CAMO/EIeGsMuUDHyGKPlQpQ4qNQamH3j+CLgT/fJizGhyhjA7iphZ8QEuxQZsQxKD8YFUsghSZBAO/s88+O4YVKTYQiPCBTv+xipVzLeDRSuIAW/qPw5nZFcIqYATJ9F8Z0X7GFIIIcOB8FdpO39L/CMj69esXFR4ILhAMadV7GV/ickLUNLyKh+JTwaTCU9eWmcbp2vKyfsXj2nDrt2nK/BLMkhfiWnzkEm7juRYpf11X+XBVlsLSa4V3houNe3aWIYxFAIiCg/ufHUr8EMyKrACXsYYCgvtEK5aZazBZJWI8Y+IOG/OW2OEBMW6xfc+uJXZ1IBhj7lxrrbXi3MO5PSjuUJhg5gpBJwpcdnRB3CvcD8Lc9hPx6TVhlmy89RelseHyk6+jJB7WlR/eRf6OlCvMUh658DSs7DqNg38uLC236hplLQdXMzfnFBsIp8vmv9z5ClVlgjuKZJ4ZmDxEIcGY45nI84Xxzj2i9uEy51IPzAKecMIJUcHBLiXGNMJiBOmYauMZdNlll8VnsYTNKEOkOMjVjfsJwTxCd3ZZFJmhIm/Z84XnBoelo3BB4M37AuVSPrtMIM61Yk5AIUAaLYJgDkBBU8af/GXx3MeUj0KUPmUnK3x5T0FxSj2o4zrrrBOf+exSRVBOeDNEf/A+A84XX3xxnD+Yb9I+3XnnneMOVtp6V2OnBv3KrlTeD+rUtZk6DYu0KAR5l2M3K/Msimx26x588MFRYYRSi4U4KLFIg4uSB/xQgEFVPFA8Ml5QhPPeBX/6tU+fPoMpUIZF+71MR6CrI+CKja7eg15/R8ARcAQcAUfAEXAEuiACCC8QOCPIkCkoBB4oBfiwFv31r3+NZhZID7FDgINy+ZjOEUJIDsrMkVZWI4jk4F52K7ASmhWLs802W9tBmgh4DjvssByLGEZ6BJF8nLIKWzzIh4C/rnBBbSlLj+CA3RkIbKqIj+wDDzywMJlVuLBDg50mCEMQ/sh0DZlZUU6bZAIkx5CPfxRBtB/iY1/8MdvFSkT6VgQv+g2ivbm2q61awUg/IdxhhSxKDXjQhwhUIARhlEvfWWIVJW1ipTAKMq4R9lEmAjOEzAgqhCnpEIaldbQ88VvhqY1Lw9Nr+kVEnL1WeJlL+pRnLr3lLX+RS37xtPVRWMrf1kHpc7zJp3jLQ2FVrs1T1w/PonrDoypeaXLlkReBLQJFhFkIExmXCGZzxJygcU5aiLQKS/OQPkcc8ozZHcY3hDKFlfgIFpnfevfuHedJVsxTN4SeCF/hxxwkxfCMjR1aUqrkyrFhuf5UfylO6dNrhbfaLevXtKw0bXqdpte12qhr3PaEpXmqrovKKQsnroiYFxGSaj5M5zMUYGXzH88vxqnGLeVgkol5Us9OwjClhjKYMOZVrlH4STHCmOMZwDhldwZj8S9/+QtZo/Kc+b9Xr15xfPPsp5949jPeNW7ZzYCfHXfwpu4oKuDNAoi0npF544/67L777vFwbfLbZ2v6vKh6vrBLAl4skOD5g5KSZwgKIoiDtDFLhVKc+w48qDf40fdV/KvimTe0MwvFD2XwjNWB35TD3MRzVju5qA+Y6Rla5/0JU5vsigFn5phcn3IeELvKeN7T97yTkIf+gKrqyjOYPtMYIQ/vD3r2co1iSPMkdRhaRB1YpIJCbo011oiYM9YOOOCAqHSmv1Eoo2BiJwvvCoxBzJGyOxjFRh0e559/fhwbKLRYRIIiEgxYuIFyyckRcATaj8BIDe13x5dQtL98z+kIOAKOgCPgCDgCjoAjMIIhYIUJdZrGSkgEIEW2sOvwaE8ahILsyEBJYoU5zfBCKAMPBBBD82O8mTrm0mL2A+VEKnwjLavA2fHAeRYISS298MIL8eMfoRkmrOADFSmabN72+uv203bbbRewc45AAhMnrPJlTKVtZDUtY44+a4Z0zgJ5rNC0yC/etvwiv9KWubacsnS5MhSWumV8VF7OTcPgo7AiniqbePy6lqvwovyEqwzc1J9ep+nL+ObiVC8OBoY344kV3M2Omxzv9oRxkPj+++8fzw1glwc7M1SXt99+OwqHEX7auQzhmcytUabapPLtdc5PmMLlT69zfMVfrvqGa+tXfFm40iifdYv8dfiRRm1RGVXXVXmq8qfxReUqPFdeLkxKWuY2xmgZtXf+K+JJH/DT848xd/TRR0dFBQsS2HWH4Bg/O59QYohQMJO36J2BeZpnDOO8ql3i2axb9Xyhftz7PC/5paR3CLU/ja/iXxVPf+kdw97bKof6gWMRhkpX5lIGOOsdDJ780japPygrjYN/VV3L6jCs4tihy3jdb7/94o4j6kF/s8CGXRooxNidgVKZ9wu967DzjnzsDmLHUhWPyy+/PCqCpJiiHBZzoMRG4dgsjT3SMc1m8fQ1Efj+f3vXTOnJhhcEfMfG8NITXg9HwBFwBBwBR8ARcAS6KQJ8rOc+2DsbDj7MEdB3hBBUSRjQET5DO68+znPlckAxK261WjyXRmFlfJSmo257+ol+KepbBGSdJSSjXARCoiJBpuJT16a3fEiX8k7z1rmGp/jYsurktWksD9VT/HSt9Arn2voVL9fGWT/x4kl4zk8aG8e1pbI4m07+tHyui8aT8nS2y6ppVpBjRu7444+PuziYexBIssvqoIMOajOHk9Yl1540ja6VVq7CO+LCq6jfOsK3LK8tM02Xtq2V15aX9asOuTDF4ZbFl8WRt8681ur5jzql9UJYzBlUPEvY6cQuEsxjWaUG9ZViDn+Ohsa7QdXzhbaV3ftVCoUq/lXx9Be7RIuI+lXVoSivwinDvsfk+pS0Vf1RVVeVNzy5mErD1Ba74kTs0uHdRrufMLfJbhP7vsOOG5TG/OrwQEECPiiHUEShEGH3S9W5MaqTu46AI1CMgCs2irHxGEfAEXAEHAFHwBFwBBwBR6DbIYBZBFam5oQlfPwfcsgh2ZWrwxooTDwg/JWpkM6qjwS08Lf+3HXdOqSCQfIRlvLPhaVlkEf85JdLWvlxRUqveIVbV2XnXJvP8krz61ppUpd4hSmtwtL62mubNpc+jS+7zpVfln5oxSFU4wwfzOG8+uqr0YSfzMKghMRcT0rcr5hNgYraVRSe8mrmGp5l/dMML9KmvNLrIn5pPXJttWHWD8+yaxtX5M/xKAojPEeWdxpfFpemHVrXjLm99947mkRC6ItyAyGwXak+tOri5TgCVQigtJIZTdKiJD7ppJMC5joxSQVpx0y8+PVP70fEyYyq4nM8pBRhRyymAzHrxQHlMq2J+U/Nayj8ypRpKsddR8ARGISAKzZ8JDgCjoAj4Ag4Ao6AI+AIOAKOQBsCCKGKiJWJm2666RACv6L0QzMcsw6Y9ejM3T8SPNRtF+k7Inwkb7NlUjdbrvziY3mqbopL22XDrV/p6vJSOcrXVdzhrd4Ix7Bnj5AYu/syg4PNewnOLLaE5dqQCyNfUbjlyTgoS6c4XDtm7LX1W945v+Vh/bm0hKW8uS4iG2f94qN8deJsmiK/+FnXprXhRf5m0xfx6axwxhznDiy++OJxZXpnmpHqrDY43+6JAOeM9OvXL+7C4OyY3JwqZHQfylV4FY8ZG+eSoIRmp90777wTdzRhQrBv377x/QU+3DucF+fkCDgC9RBwxUY9nDyVI+AIOAKOgCPgCDgCjoAj0O0R4CO+7GN/WALUmQoN2pUKVauuhQXpUuGH4jrTVf0oO+fvrLJteZQxLNve0TYKt47yaXV+7sFJJ520Nts6fVAnTVqgxrZc4q0/TV90XYSzDbd+lWNd8U7TKTx1bXutn3T2uhl/UdqUZ1qXsmvLsyzd8BaH2R1fdT689YrXpwgBFA2nnXZaeOaZZ8Iee+zRZoaK9CjnUCJbYrccpJ0b+Mt4EA9xPhLpMG9FOZgW3GuvveJuU81dKD+cHAFHoD4Crtioj5WndAQcAUfAEXAEHAFHwBFwBLosAuxm4FDMMnvdXbZxQ6niEjzY4nJhNt76SdtqQWVR+SqnLL4oztY59bcnT8pD1/CinnIJF3/V34bZfPLLVT5dW7cszqbDb+uSxnXFa4uj9benLRYb6095NYN3mpfrNL+9tv5c3o6GWYys3/K14fKnLukVprzptcLb67aaX3vr4flGDATYbcBZG5z70Z2Id6Pzzjsv3HfffWGHHXaIu+Js+6eZZprwwAMPxPMxtIDi3XffjTsv2G0BlfFgzjr55JNDz549w9xzzx0Xh8w+++xh2mmnjSbbyL/hhhviODkCIwwC7CplVxJjvrOfVa7YGGGGjTfEEXAEHAFHwBFwBBwBR8ARKEbg1FNPDW+88UbYbrvtBrMpXZyj82I+//zz8NRTTwUEBnPMMUfnFdRCzrK33UKWlaw4xPSMM84Is8wyS9h+++0r03fnBP/85z/jmOLg+5VXXrkSCgRZt956a0xLnhGVEKANT3TLLbcEfquuumpYccUVh6eqdUpd3nzzzcEUNVLMVLlURmlsxVIBEdevvPJKTGLjrN/mx5/y1bXcsjQpr/Ze27KKeJAm1440LL0u4peGF9WhKNzmt2XKn3Nt2Mwzz9zGgjIQ+vEcxIwi5uRyxDP7zjvvjFFbbrnlEEoH+Fx11VXhyy+/jAdRL7LIIm1sXnvttXDvvffGcyPY4UU5PXr0aIu3nu76rAG/yy67LFx55ZXR/BNm/nQuEfhgNgrTaqeffnqct5izOEODOQyTUZxHVsUD5ceDDz4Y3n777bDvvvvGw8Y5J4nfsFJoTDTt2GGtNWYJM0yHWayB4alnPgm3Xv9mGDjgFzssBvOPPs6oYdW1Zg7zzDlJHIevvvFVuO6aV8N/P+/flm7WBSYNKy/fI0w6yVjhq2/6h3vufy88ce8HbfGjjD5y6Llyj7DQfFOEMccaJXzy6ffhtjvfDq8/83lbmp6r9WiUMeSOxLvuezc89/DHYeRRRgpLrjh9WHShKcO444zW6I8fw4OPfBAeN+W0MWt4Zpx34rD6SoPOSzn5hMfDLz//dr6YTddZ/osuuigqsZZeeukhinjyySfD008/HU28MpaGJV144YWBOYpx3R5iF9O5554b1lhjjXgW2AsvvBDPqyFM82B7+NbJ44qNOih5GkfAEXAEHAFHwBFwBBwBR6ALIXDOOedEocnaa68dhR1UHQHH448/HtZbb71hotjo379/eOKJJ6Ig58UXXwxvvfVWQFDTVRQbzXZ/rg+a5cEho5iq4FD0Vik2WlGvZtsxNNLzEX3//feHZZddtlZxHN5K+tlmm61Welbk/u1vf4tpd91110JBZC1m3TjRSy+9FK6++up4MG8rFBtdoV8Q6iD8bIaK0hPeUSGR8qdlFNUzTddMO4rSqg7EF/G3aSyfFIP02qbtDL+tl/zWtX7K1zV+FBCPPfZYfA4i2OY5uMACC2Tnk59//jmaR7rnnnvIGjbbbLMhTEHCi90A7LbYfPPNgxQbzIfHHnts4FnLDgzuE4Tr+++/f1a50RnPmljp4fyPVeWYoPrkk08iPrwjiTAzdcQRR8QV5+uvv34U2tIXKD4wTbXTTjvFpHV4oDz/97//HQ477LC4a5ZxMM8880QFr8obWu5YE40e+h62dFj2D9M1duiMFkYZeaTw3gffhgknHitcfOZzhdXYcfeFw0YbzxmmmHysxnj6X+OcpwFhrnknCYfudU/4X+N6pvkmCX8/Ypmw4HyTh18a89Roo44SVlxtxnDQ/veEpx74MPLdocFjvQ1mD9NMPW5jB8wv8d5YfpUeYf997w5vPDtIubHeRnM2lCNDnvX2Rf8BUbGxYa+5wza95ws9pp8gfP/Dz2GMhrJk1TdmDsce/Wi4/bo3Bqv/qI24vfZZLCpbiDj95CfDTz8PHCxNZ1+gvLjpppvCkksuOYRikjHBQp/hYXEFSvgiBWsdjJhjmHdWWGGFmJw5hXesovm9Ds+6aVyxURcpT+cIOAKOgCPgCDgCjoAj0OUQQDCAPWNr6xtzTAgarG3ktGEI4XkZL0ujPLzMw5MyrABD8XJZzYRtfJkyUDguPBBMwAPb5Dlqpi3/+c9/ohKDD+f5558/suvdu3c8qNKuGiWiFfXP1TcN46MJQQsmHL766qvYL7JTnaZNr4UPZrRyZjLoK3hVmdFgLAwYMKDNHBf5hHuOL/VgLEDY2W6Gcn2g/GpPWX+TdtZZZw1//etfw9RTT62sg7llY4q2Dhw4MGJiM5XVq869AU+EOkUmzcCTcV60+pB44nJntdTtR7Und08oTm5drJUeN1dH6oYQAtp6662HEACACWXZuSYmLvgjPWMuh0NBlsGCq8oTlkX9BDP6m/moqK9IU9WfVfWAhyWEHqweX2ihhWxw9FNnhIZF9znjnbra+lb1S50xXTbfa/wU1UnxVffyEI3NBNCWVlD6HLLXtgzCuZbbirLFw5apMOum9SDOhtm0Ob/qrbj0WuFlblG7VXdbH4VZfgqzbpFf+e6+++5w5plnxmcxAk3KYB7LEXM1AlHtILD1If23334bV0Szq0PX0dP4owx2eqy++uph+eWXD/Bil8F0000XDjroICVrczvyrGlj0gU9zCW8G+VI8wz39jbbbBMefvjh8Nlnn8V3AXZRsrsDqsMDhTs7N1BkMY8x//F+NsMMQwrwc3VpZdiyq/YImzaUB2+//U048q/3homnGTv8rc9SjbM+Fg03XPda+PaTwc8Toezp55wo7LzTgo0dLGOEQw69N/T/dkA48NDfh617zRuuuuLV8OR9H4Qttp43rL7yTOG6m98IV1/4Ulhi+enCto34XfZYOGz9wPVhlvknCbvutnCYcIIxwqF97wtffPh92L6h6CDPQ49+GI599sHYzHnnnjQqPg75633h209/2w3y1AMfhDHGGy3sve9iYc7ZJg6HHflQePXpz8IfGoqRrTefJ/zU2G2SKjZWW3fWsN7aszXqPXrkrfszXgylPxamHHzwwYHFFNxnIsYS92ivXr0G+y5gPuC5Ou644yrpYC7vYDyz6r5rDJa5cVH07sH5LxNNNFGaPL6r8z5p6wMPsLTvxXwv0ZappppqCB42gLrThqL6M8/x3sEzt25/uWLDIux+R8ARcAQcAUfAEXAEHIEujwDCR1Yp8tGJmQc+Jvfee+/4UXH99ddHwTovywiL//jHPwYOakTYzqq9ySefPJpHevTRR+OLN7sJcqskAYkXcwQFfOwieKActnCzChlhITaZb7zxxmhGCAEEZggQvGOCBdMGEEKx2267La5qIg0v8sRhSgehZ3vacvjhhwdMUCDAv/zyy8PXX38dd0Y899xz4aOPPoqKjskmm6yy/qxMRBjCxzc8WF3KB/wyyyyTXWV41llnRVNX1F1b7k844YTAqq0NNtggYsJH3VprrRUuvvji2EcRhIo/6nHXXXdFZQgfQvQrW90ldGCFJSaFKIcPK/qMnSqYkPjwww+j4IePNfr5kUceibjMOeecUdhzxRVXhA8++CDiDk/6T3lYucYYwUwAtOiii4Z11lkn9gv1od/mnXfeNlMSbLcHozXXXDPcfvvt2T6o6u8UCj58n3322Wj6AjMidfqElaeYJuEjmvLo61VWWSX2e9HYIG3RvUGd+vTpE4Vvyy23XOwLPjr5eGWFMPcMhCmT6667LuJH39DXfChPPPHEMR4cb7755rgylvi55por7h7Sx+0dd9wRdxWx+pV+xC4z96fiIxPzx/0FP4REPRrmVchnqVmsyVtUR+5vxjL3FHTMMcfEMdazYTOd8cMOBJkcQlC17rrrFiqjmGvYhcN8wD2OoJF7Yvrpp49CDsYOu0jAFrr22mvjuGX8Ma7LytOcQ37GDgfUssuEeccSmF1yySXh+eefj/cR9xQ7KRBWIIAB/6r+LKuHLSv1gxPlck9yH+q+WWKJJQJz1McffxznUnaWcX9BpGcuJQ7FLxgzNphni/qlbEzXne/L5p5mxxfPHAQ2clNcOnqdEwCVhUlIrvoUudRLcVV1zJVXlMemTetSlKcqXPhWpbPxZW2zdUzzcK146xb5bX4EnZiIO/DAAwuVGjyrTznllKhAlWLD8sDP8533D55T3I8i7mPmEfDYaqut4o4Q5heUJMzR++2332CCVPK151mj8rqyy/Nlo402qmwC52FgOhNFKc8vvX+QsQ4PBMCs2GeeQ7CbzsmVFWhhgtVWnTmMNeao4dqb3gj/Pve5MNYEo4ctNpsnzDn7xGG2+SYNj9/23hClLbb0NGHqqcYN9z74fjj71GfCwP6/hMX/MG3YYpO5wx9WmD4qNtZde9bGPdHYFXHm0+GOK14PDz/2YVQ4rLbSjA2FxKhh6hnHD+83dobcec+74axTng79v/u5oeyYNCy+8JRxl4cKnW7acRvvLv8Lp/2rsbvivz83zGMNUvj+MvCXMOqYo4TTT3qycfOFcOklL4cvP/o+vP/1D7GcRReaQiyiO8WM44V99ls8fPHVj22KjcESDKUL3plRCvDOaBUbnOnCeEL5CKHQ4P7kPZUxwphjpxDvCBDvYbyP8zxknuFd+4Ybboi7sPSexLcL73LszErHGHMKcwbvHuSnLjxnpczgPRr+vNvx7OPdmnkDnig2eIfmXuG7gG8rePCuwW4T3hmYd3j3ZM6xSpBY+cYfJtww+YbpROYmyqF8yoB4FyCchU+Y6UMBO8kkk8S4qj9XbFQh5PGOgCPgCDgCjoAj4Ag4Al0KAUwu8YLOBz8vxwgQWJF39NFHxw8GBKp8NJAGIRvhfNRjB3eCCSaI6fnYQDnChwcr8xAqp8QHCMoQPhYQtFGWPkgQuiEYhOeUU04Zpphiiri6kg8KBIic10A4Hwj9+vWLAkg+GojnYwKhOh/A7WkLQjvaB9Eu6gchGOSDAmEsAsWq+rOlnPrzUUX9pdxA+IvpjHRVFgJ10lM2ig3Sn9MwiYUQdYsttojpMaeEEAahC/hW0TPPPBOOO+64KKQHs/feey/2Kx9UYIzQHyEzH3rgh3KDDzLavNdee0WzH9QJhRGCcvqVtGyP1+pL6glmCEJZ5YqJCOXhww9hNn2GyQ/ajHACHqRBGSUb2Sg7KJvxVtQHVf2d4gEfBND0F2Yv6vQJ4wmBP3jwwcrHNALjo446KlsvFAJl9wYCmUsvvTT2KxiBB4ozBLt8OFMvPkTpBzDlIxVcWJnMmOADm/SUT39RL1Yqc2/BgxV+fCQjoCY9K1mpL/F8KPORnBLKJ3b/MD56NJQa1It7x1KzWKOUKqojuwxYaStiHNJm2km76XewRuiOggYhIzttrPCLvIwtykABgcKHcUz/MFeQnmvGFfPJJptsEoWPXDOvIHCoKk9zDuMUYSg/hCEpoYjh7BbGPmOc8c39SN9iAx7cy/oTvs2025bPfcSYRhCz2mqrRUUZ+DEGCAM7xgH3MIJd6khZ9DVjg7KZP1ilvttuu2X7pWpM15nvq+aeZseXxQA/fY0Qx1IuzMbX8cMDkpv6VabiuVa5qRsZ/fqnOBsmv3gVXSs8dW1drJ90uk7zlF2n9ShLa+Oazaf0OdeGWb/K47nOD4UwSsQc0fazzz47zpssyvj73/8+RDLuWZ6v3BO8t1jFBvMqcwWEcBTi+QfxjOT+kMI5Bjb+2vOsUd7u4tKfEiC3t80os1OBc3t5tTffzDNOELM++/xnjfsshO+/+im8+dbXUbEx62wTZRUbM/2a5+XXvozKBhg89+Ig01HsnsDk0/TTjBf5Pvf0J9F96+Uvo6moCRq7PCZtKCteffrz8Jf97208/3+ISg3wnGH68WPa19/6KrrsyJikYRJr4MD/hX33Wbyxu2+s8PlnP4Trb3kzPHzHu2HADwPDmac+HZg5e8wxUZhhpgnCyiv0iHnvf+g35R68d959kTB3Q1lzYN/7w7FHDPkOHzMNhT+db8M7OGfcMQYg3sFZVMDzHkLoz7OZ+YGFObxDocxnQQrvFryH6f2b+YNxxLN0pZVWCksttVTkAU9MX+V2fnPOHu9JfAPwjGdhFu/EmNaEUIiwYIsdXrxromhAacE7Le9slM+7HGWzgIgwFl8xx7B4Bl68r/Bcz51Jx/cS8xbfUyxK4BlK+4488shYPu/Q8CeedxjhFCMr/gYhWpHIox0BR8ARcAQcAUfAEXAEHIGuggBCAX68FCNU3XnnnaMwjA9SPgD23HPPuIOD9vBizQc9RB5e9lmRtM8++8QPBV66EbCmRDpe0hEkYqIAYSAfLFzz8YBwX/XA1MyOO+4YV2fygYDiAsEcq7PgwQcEZ01QJiZuePHnYwESj2baIuUB+WkvK6IghB3iV6f+5CE9gm0UQwcccEBcsUj9qHNKfNjwMfnQQw9FASkfYAgl+QBCmM0KLoTefNDUJXCAD7spwBgcUUax4wPisEOE6XyoEU8d2U1APH0B0Qbay64RMMYGOYJTBEF80LFqlj5iVZqEQ8rDRx18+VCjXD4ixVNYxoBfy1FYrg/q9Ld4yRU/XBH+oj5BMM2HMR+cKARoHwogFGUIs3L1QlBcdW9o7LDSmPtn0003jfcUH8YQwnk+ShnfrAamH1jhx44YhPl8EGPfHYUXeHJfohhAeEd/0YcoPRgr3K8oS+abb74hdmEIA8rjvBbKo6wddtghrvJXfHuwLqsjqxERoovAlt09zA0IKxBcMI6oC0pAhAsov1LivgczPtqVHsUhYcQhpERogOKE8UY/wof+4WO/qjyNF/qUeQmBaCpEo1/AHcUBabgn2DFCuMZZVX9W1SNtt71WHVWWrpkbdtlllzh+EK6g7GCMgAMKXtpBP6Ow1I435pRcv9QZ05RbNt+XzT3NjC/mxDIqis+FE2bD5S9zFac6cK0fYYpPXRuXyyseyqf0urbxVf40b+5adcjFib/S6HpoujyjVV6Rn/oxP/AjbRGh+ETIyTMVIWNK3KtS+jFXpkoKng/MMRDzE4o87ieIcU/+lHQf4orwFz1rlMbdrofA2GOPFiv944+/mUD7of8g/4QTjJlt0G95fjuf4ocfB/knnHDMuJNi1FEHiZf7/8qXodS//6A04088RvjwzW/Cvbe/E1546tNYxqrrzhJWXHaG8NEn/w2X/XvQQpdxJx2j8U4woCFg/yXMu/AUYd6FJg+9d1wg/PWwpaIpKzL+/NMvYWDjt8fei4a/Hb5M2HzTucObDbNa/zzykba6L73y9GGzTeYKVzZMa91w/Wtt4cPKw25fhPZ6L+Vdk28B3s9QQvAexGIDFB0sBOIdi+8CdlmRD+I9jHck3j/4TuH9h4UJpIH47kAZwk6O1MQl9zKKC0zRwpud6OyotaQ5QGFc8+5NespEWcF7HPl4NyaMBR28+4lSHgrHZZ5iIRD8WLjB7nbmJ9oOkZc01A3eKHfqUv2virocPZ0j4Ag4Ao6AI+AIOAKOgCMwHCDACiZeoHnB58UZMy4Ir3kxZxcBwjM+8FFeaBUeLi/yfGjw0YH9W+JTwsQLK7wRtvGCzss9KyJPP/30GG5Xj7OaSYfpwRNBJUJZzPpI+El98KMIgFAO8JIvaqYt7CjQNvAePXoMtvVd/JqpP0oJCVdoC0LXHCZ8cPHhw0pqsJEghY+sZpQZqiMuAk2Ij7/FFlssmqZh94WEtfQPhOkf2s2HH1vtycfKcD4SIcrHRBQueRCkU1/sXoMzZsRQdiAAwp/m4RqTQPRfHeJck7QP2GVQ1d9lwi5bblGfkJ8+YLU5H7u0kTGAqQN2CLGyN61X1b3BjiURijJ2j3B/IFjTOGAHDPcTigt+YIqShXHN/ad+IpydAaSFB0pFxiJCbXY3sOsJxQy7eugbnQ+j8uVyH0OYeNBh4eedd16bkpJVyVVYi5fcsjpiQkFjifSYSEJweNJJJ8U5hLawawSiD2g3fcDOJksoBBCKg5Hqze4U0iIcQAlJHGMY4SamTxDS0076D5yZs4rKU1kILxCOaCwrHBdsuEfpFwQIKGUQjnCoraiqP6vqkbZbfMtcTG3RdvBBeYGQBsUGgg7mV3DAzAXjEeUkSiDGcq5fqsZ0nfm+bO6pM77svYyfsV/kgksap7AcZuJd5ZbxUH3EX9dyFS6X8BypDsRZf+7a5s+VY8NUXsrT8rBx1l9VtuXRjN+WIb91i/zN1Id3AOYVnlW9evUaTEDJvc88iTAQRShmarjXEFhCzKvMu6ThveTEE0+Mq66ZL3n+QNSRZ2hdmrHm878uP0837BHIqdR0dzfOEc9SY9gUUlmcMo2UMF5ypRnCQYcsEcYbZ/RwwKH3hBefHKTs+OHLn8IeO98Wxp1wjPDy85+HUUcbOey5/+JhhWWnD39qHDp+9NMPiGX48GPO6xo5TD3lOGHiScZsPBMmD4/c9V4Yb/Ixw34HLh5+HjAw9Dvx8cYz8zdlzKhjjxJ+aihkOOx8aBLvMlrEwDsUz3vuU+0G55uB5zI7rVBqQtzL7IJgMRFKDIh3D/iIWHTDAh/eOVCa8GzinTsl7nvekXmH5TsFE1I8c/Uunabnmuck75AQz+EejXdJ3vnY9Q6h9OSbgDLrEO+hvO9ccMEF8Xn+VmMXLM9qfpqT2L2ittbhqTSu2BAS7joCjoAj4Ag4Ao6AI+AIjFAIIAjUqiU+DhCKIexkJTgCP1ZVQgjSRIQhNISU18YrHcIHhOis5EboBiE05iOBFeqstBaJD9esxob4eEFoB28+OGSWCZ681FMP/KL2tEV5c25766+25DABCxQgrC5DKIjwkw83hOHtJZnTkJ1d+k2mn+CJwBnS6lRw40wJCCWFiHApV9QGfdCBP/WEbLtsHttv4tmsW6e/NfaqeKsNpJOfutNGVuCjhOEDEuUOH6QomgjPUd17w5ZlyyQ87ScwRZEk0ocvwjUJjFFmUGfK104Qxgy7F8jPxztjKj3sHp58CEPcFyI+skV1sFZauVV1VDrrKg9CBbWLccU9TBtSSscr8WqDxiuKVSnnUGxACC/gV7c8dujklBrwAjuExvQhSg1I4z9eNP7q9mfddotvmasxxRjm3oMYHyjq2AmD6UAUvgiEwIw5DAGLBOCWd90xTTm651S+5oAUAzv3MF+Tjj4pmrvF19YLP3kkwFfd0zCuq0hp6rpF/FQXG6+w1LVp5Ff5XFt/7lp5xJdr68/FKyzn2vKsn7TpdS5/3bCUl66tW+Rvti7slmLeRtCHABAFn6hv375xlxU7Pbn3MC3Zp0+fuFCCNCwm4H2EnXko+xmDPAcg7hUWVKA0lmIvRlT86b4gmfy6RyqyevRwisBXXw8aU2M0ztkQjTnGIP/3P/z2Pqw43K+/GpRH6Qgbc4xB78rkwUQUO0DGbPC0fMf4Nc0PjV0YooWWnrpxePiSYa6Gmah/HPtouOjs58MvPw9SNGAW65ILXoymrdiZAc256ORRsfG7RacWi+iec8rTjXfu0cKc80wSLr5ozbBbw/TUvy94Iaz6p1nCUktMG7797qewfe8FwhhjDaonmY47frnwj6MeDm88M+jdcTCGnXjBPccOLBacsMsKZSQLFvQM5vnPXMiPRTYi3qN4hxOlzxV4ogjF/BSLtrjPUUbmiB3P7OhgtyY7IXlOsvvCKkpsPs1pCuPZrDlAYdQHhWodYtEK3wcsAqNN7CKG7LdO2r46fEnz20ium8PTOQKOgCPgCDgCjoAj4Ag4Al0MAQQFrHJE4MihvAipEKBKeNVsc/gYQSiGcIGPEATfKDMQtvExgHBdAi8rmGCFEsRLPQIIePBS37NnzyjAI44V7Aju0o8K4qA6bVFePpJyVKf+uXxVYRyMznZ6Ptr4cOIjq0djlVddAgut0iYP9UQQy7Z9BNz0F6vTEHizIh3ciQNXVqCRXxhLGVG37DQdvFghy4eceOoDUx93mLIR2X4mLO2DOv2dtl+867qMRTBiRTur49hdQ39gEoodQiie0nrVGU9V5UuZJFNepMfWMh+urComnnpx/zFGIOJoLx/VrEikr+lTcGRHzfnnnx/vL4T6EEoBBPbcM6SF2OEh4iNdVAdrpZVbVUelw9V9pXpQL0wnQHzkIzjATnZKGpMaT8SzShMShgsuuGBU1CGoII5dCQhAoKrytOskJi74EzaMbfoLQWc6dlWXov6sqkdB0e0KZicMSivGAUojMOHcHeYXzMupLjBXv7RiTMO3aO5hRWyzczf3neqX8yssvT8taIojTP4q16YVL+qhfArDLQq3aVK/5WP9pEuv07z2upVlp+Wm17bcun7LQ37rFvnhr7g6ZbEziXkSYl60QkNWZPPOgSIS4SBzKD+emRDvIJoTMXfFjlXeJ0hz2WWXxTQsNGiv8DAy8L8uj8Drb34VVug5fZi9cZ4GNEpj18NMPQadu/FGIw4aedSRwviTjxW++uD7eE0eaLZZJozjmfsVP/TGm183Dvj+Jbz9XuMZP8tEYZbZJwmfvPZtmGKm8eIh5Sg8Pn37u5h2rkUmD33/ulRYrHFg+LH/eiyc0Tgg/LvPf1PeLdZz2vDnjeYMb73zTTj28IdjnrHH/HXhScM81TSzTRB232nhxjNrYOjb5/4w4Pufw+eNw8Mbd1mYYbrxw9gTjRHGn2CM8Ezj/BBo7vkmC6P9aiIrXs87WTwsPUYO5T8WLZx11lnxWwTTmyg4RHq3ZAcWuzIs2WecDcfPDgfeGThEnHcG3re0iMemZY7g/ZB4lCgsMmFO+Ne//hV3j/I860zi/Y1dtZiT5B0QxS1nh2EWsxXkio1WoOg8HAFHwBFwBBwBR8ARcASGawQQBrDKEOEn5m8QPEqp0Z7VhygeMIWCAA0hG+ZQOPyWcvgwYXu2iNXyHEqNMJyDhRFyoMjgYwVb8Zin4QWfFV0S6iKALvrQqNMW2aZldRaCTJnCUp2aqb/y1HERyKCAQAADrs2aoUJpARbsymDbPMJMTB6dc845cQs8q73Yps/5BgjBEd4juOfjiJ0yfLixipUdHgiWreKhTv1tGgS/fPQheL/mmmtilMwHaYUbZ41w7gaCI5kiEo+0D+jzqv5mha5tv3jVdTFdwGpeFD+cn8BHL0Is2iIBWVqvOuOpqnxsJaM8YVU9uwy4z/iIRRC98cYbx90bHA4OXpg2IBzzS3zcsiIRRQZ5wRec9JFP3SBWIqIoQWGDYhLzDexqYHUzbaSfZYea9O25t6hHWR25b6kvwm7aSj0REjIeERIgqOC+QuDOHMNKypS4H9hNgxkZdqzAEwUrgggpfFC6YpKJMz8YV5ihQ0gJNVteWj7XzAeauziAnd0yMtul9FX92Yp6qKwqF8HsySefHM1lMS9oFwsCYOZIfmm/tGJMl809CIyr7mW1iz5GCAhZv70m3FKRkN+mk7/KFV+ls7zll6u0uAqTa+OsX3wJs/7ctc3XUb8tS365ubJtXLNlp3l1bd0if64uZeUzr9hdaiyW4OwZCBNt7GDinCJLPB95t+BeZr4VoQR8oGHSjmcT8cy92OZ36t4IXNs4d2KbzecN6641a/jorW/C5JONE2acYfzwznvfhGcfHmR6dcsd5g9LLTNtOOvMZ8O9N78dHr3r/Wj6ab65Jw27779Yw5TQT2Gl5XqE/j8NDLfcPEg5f8U1r4YD91os7LHLwmGmqcYLiy05deP9o/GMaxz6/d8v+odJphsn9P3bUmHZZaYLX3z5Y5wvNmwoMURnnflM+H7Az2HjP88V/vv9gIYppl/irov1/zRrYz4K4ebb3oxmpNZpmKQaf/zRw8/hl4by78uw0CKcWRPC6w0Fy7cf/xBuu+aN8PrTv+3YHX+yMcNF5w/aQdr34PvCuy8NUtKo3KHlcu+itOAcPt4VrMnGHo0FQCzOYecnJqJ4zmGeindP3nmKiPc73l2OP/74+B6UvusrH++A/fr1iybqeLbx3kRZ7ILU3KW0neGyiIV3dFwWa+BnToLa8w2W1tEVGykifu0IOAKOgCPgCDgCjoAjMMIhwIcCgm7sy/Jyj3AX4TcCMJQcEqTWbThKCg5mRvDNSimE0ZiJQWCIEALBsgjBG8JMViwheGfVFnVBmAkPPkz4wGB1Ji/45EVgX0RVbSEfgkeExfBF2J1+7DRT/6J65MJZWY1QFEEkZeBvhlhxBp4IaFBssCIbgTXCGVa58zGEQJhVZxBCGlaVa3s9gmCE2r169Qp8KIJpe4l+Q3HF7hOEq6yKk9CIfqaOYMxHKmOJMSVzQpSZ64Oq/k7b32zdpdDB7BpCesYSmHA4tD6i03odfPDBpfdGnTpw/gnCXj7CUazwEc2qYvoPQT1mUegnlBcoohgbpCGcj2r6GixREoA59xLnPmy77baxeBQFjAv6hI98hMrcIygFKI92sxtC+Lfn3qqqI/VEgMAqRw6npw0o1zD7gqID5Ro7N1B8oOgkPiXGDAeC0j/gAIEDfMBAxByBYgNlFEIICR5Q7DRTnvhZF2xYNXn44YfHlaMoZO2KUMqq6s9W1MPWqcyPspSxwBzAfQhe4IwJDZQ0uX6hnzo635fNPc2OL/VfmZKgLE74iA/X8le5yitX6XWdunXqYfNYftZPmvTa5uuI3/KVv8hVOYrXdR03l0dhZW4aR1kKqyoXgSM/kczPcc2Y5hmLstPSzTffHC9ZUGFXe/Newgpx3jGYe3hu2jOTLA/3h/h+xjsiAmjtmrG48C6FEhiFEYdBlxHvcrx/sdACRT/mMnkP41kl4pmIkptzC5jL2FU5Y4EZIeVphfvwne+F0896Jmy8wRxhr30Xi+dcfPDBd+GoYx8JX344aDHBor+fJmyw7hzh9vvei4qNDxsKEOJRWuyyOzsmfg7jNs7HOPGUJ8LTD38Uq3XBGc8GFB89l56usZtjojDF5GOHBx/5MBxzxMNRMTHdrBOGVVbo0XiOj9RY3DBq2HSD35QaMLjoshfDK099Fo487tGw1ebzhN32XCSat5p4ojHDORc+H2687NXw+fv/DUc26rF1r3kb7wfzh08+/SGW88DDH4ZTT3syYMrqLX4vf9kG1cT/z95ZgN1VHH18cXf3BHcnOITiUrQUh2DB3QotWuRLsUKxUiy4uxV3d4I7xT3QBg397m+T/9vJZo/c+0ps5nnu3T0rs7v/3bPnnJnd2VkmaPPf3zi8/MfGLo9hQTw3eBf74x//GDALhUJehLB/n332iWfi8GzmGcfiK9Lk3iWUD7dnQxnap0+fMP/88w+xqMqm4f0UM7wsxOFZyjzPe9X2229fe26y/Jr1Uz5niPGOqIUU4AHxXWQXgzXLm/SjNW6yQcsHWsnteRwBR8ARcAQcAUfAEXAEHIEEASvUT6K65JKPSQRgCHJ18B0F88GKeSh2RSAchzDdwMs+QlFWf1N3DqqGSPv888/HF+5UkEA8QkdsVvPhivAYAS4COISavLAjUDj66KOjkBBhLHXiIwWhICuzID4uWLnODgeE9ggt2A2A8oIPgVbagvknlCiYpaF98GM1OKa4tAKcj4iq+qv9YIUgGbqvYd8eRRBtpK054qMfgQoYgKU+Xmxa8QELK2ghH1iAtz7w+QjiBz7samE1K4JX8SU9q+VpKx+A1JcdHVoVphXxfFRB9Cltgw84QyieUHLxgQh2CJbZVcBuEe3E4MBH+EpAxXhiBwLCDvCkPnygCZtcH1T1d9p+Pjw5d4KPXHYA1OkT6oBygF0ElIcghQ9e9VeuXmX3BuMJITsr7RCyS4HD/QLeEvAwvuHD+ECQxrhDmEZ6CMUUY514FBSsWERZxMpEeHMvsbuJMcp9Aqb0M31OXu4F7hv6AKLfKY9+gwf9TR3giVCqCmvyUh48UVRBZXUknrHACkeUF4xd8oEneFM2QnfwBjN+OUKIRXqEWtQRU1DwAg8RuxF0IDDCLpmwIr6sPDBBOYYQTYeSiqd12eGCgIFyUECB/1FHHRX7k3uJsKr+LKoHc+g555xji2vzo+hkPFjcc3OBxhvzFmcXkZ56IeRljgQPMBMuuX4pG9N15/uyuadqfKnRrLq1RD6R/HIJt36lk6u5h2v5q1zlTV2VU9dN8+ta5XNt/blrwlReXT/pLKmMui55ldbysf6q+JSHTS8/rvXn8lgFIvEa55pXCUuJexXb9BCKCebWlDgcnHuAZxSHFIuYZ3hmsHCD5x3zouqoNHJbedYo74ju8lxi5ywLIVhIgdCZdwAR8yRKeeZWduTRJzLtpTSpiylFVqTTJ8ynvHcw17OQgGca8ydnpqAAQZHFPME7GWHaoZfytNfjj3aCvWzaP1PDjNRyS08fpp55wvBz4yyL917/Jtx7//tRMQCzZXrO1HhvmDLcdU/jUOuXB51Hwc6Hno3dFjPNPkljHI4WPn733+HBhz8In7z3XVv5C/doPNcbh3hPMuV44T+NszxeaRwA/sgDH8QzNKadZaKw1urd29KmnksufTn8+O9fwqTTjh+WayhWZpp14nh4+BcNZcbjT34S3n5lUD0mmHycsMxS04dZGuazxp90rPDdZz+GN976Ojzx5Mfhp0b+lMaZsKFE2XzQ87jvef3CwF9+TZMMcT3gv/sPcd2RF7yfMS54j9E7rvjz/sC9zDs07zUou1BUMoYg5gveL+2OLsJ5B+UdnR1dZTuyeCdkTtA7Gu8cvKNpYReLR3hv45uIeYNvm/XWW48iIvGOxjPYHk7Oblq+qXh31lyFH96MaeLZacm8w/sV7eNdnrmQ+YjnOu8qvLPwLsDzHQVus+SKjWYR8/SOgCPgCDgCjoAj4Ag4AqUIDGvFRlnl+HDkQ5KX544iePJCD08J2+EtxQbmdTBFRRo+cHOCCYQ9rOojnl8dqmoLPCmT/igSZlBOUf3r1KEz0lCfFCMEr7QFQbrFWOULP+KqVrcpT5GLsgKhBsIHDl7lYxPK8WUsUbdcHHmK+kD1zfV3rv3wapb42KSc3P2Yq1fVeKpbPuMYxUXRPUY84zFXL/UzedMxkMOFMD7qc7xU3zKslSZ1y+pImYwJFF+WCGM8EF52vykP45m6pXwUX+U2W57lxyHD7I5BMYNCCEELik/O8rjgggts0jgvlfVnWg8EFYcccsgQPHSx1VZbxV08um7GrerHXL8Q1t75XmOyau7J3cu0jzMPqLsle13kt+nlt+NK/iKXPIpTfrm5MglTeOqm+VK+9tr6lY8w8VQYrg0r8it8gayoAABAAElEQVS95Su/da2fPLou84t3kWt5KI0Nk7/KVR0kQBSvznYZ+9QtnUs7u9wRjT87b1E4o2A47rjjwl/+8pe4ulztQLGqnYEInVGAoGgvIhZZsKMMRRTvEjzPEOgefPDBcUU+CypYOX/qqafGsro1dpdyHhbzJkLg3r17F7FuC2+vYgNGjaERxpmoodhuHNz9U2YXA2dvcHZGSmM3FAXsuvjh2/8dCG7TcD7HOBOMFX5qHCo+cPAB4Da+rn+s8cdojN3RG8qOnxtzxdC5Rm8oV8aeYMzw43f5+KFz1AvpTMVGnRpw3/Jc5b2GZ28Z8Xxi8Q6LqNg5ahdIFOXj3QO+Re+tRfk6Ipx5nvJbfe8pqoOboipCxsMdAUfAEXAEHAFHwBFwBEY6BPjA7+iPfPjZLeVFoJUJXxE+IEhvhqraAs86Hw91699M3dqTNtc/fISV4dMKfnXrWPbxl1OyWL5FfVBW31z7Lc+6/iLFAvlz9aoaT3XLrRpzZfFl/ZzDpU6dy7AualNZHSkzF884KRsraVll80GaNnfdbHmWBwJ/FBCsVEYgzwpOTGHZw0yVPtdWxeGm9WDH1EEHHWSTtPmtiZ22wJqeqn7M9Uud8VFVfNmYJG9VvZTGCu7Jo+vUX1Yf0orkT12Vp3Ryc+VQB4WLT1FYylfpLf+cPxdW1naVr3xyVZ51i/zksXEpD13bdDYs9YuXwu21/EVu3TLEuyPdqudTR5Y1IvNihTjvcN0aCgYUGymxch5ThcSjkKgycYlinJ2D5GGXBoQJSFbUs2oeYocIK9ZZac/YYdchfoTUdRQbkUk7/1AWFCknYJ1TahCe2xVBuAhFyff9By0IUVgr7s8DGgsXwsDCrL8ObAjJC5QrhZlGgAju2zr3LgeCo4RjpyYKsTo7fWh+e9892gMhY73qnaIV/q7YaAU1z+MIOAKOgCPgCDgCjoAj4AhUIMBW80MPPbTdtmMrivHoDkaAj0P6re7OmQ4u3tk5Ap2OAEI3Vg+zSpidPQjuMFVmzdi0WglMR+VM97XKb2TNh4AnJ+DPtVdCc+Lkl1sVZvkpT1ou4VVhlo/1i6fC0muFW1dprBLDlm/T4lf6nEuYDU+vbX7xVXpdp25ZvI2TX64ty4bZ8LQsvx72CGDWrowwmyMzYmm/5vKxM2f33XcfQtCM8BklsMyQYs6Q3XKWH3GYxXRyBOoggOJ+qqmmimYvMfNox1Kd/CNTGldsjEy96W1xBBwBR8ARcAQcAUfAERhuEMAuLsJCVvw6jTgIIJTgIOxR+SNxxOktr2krCHBmCwfZYsaLHysofby3gmR1Hp3nVJ3SUzgCjsCIiABncDCPQgibOUdIZ1oRxnkap59+ejwni4PJIZQY7M7gzAXO/OFMBc4wwEyPkyNQBwEUY7169YoKslF9IY4rNuqMGE/jCDgCjoAj4Ag4Ao6AI+AINIkACo1mTNM0yd6TdxICCHi93zoJXGc7XCFQdC7EcFVJr4wj4Ag4AsMxAn379o0HLVNFFkYcfvjhbbVlZ8Zpp50Wvvvuu3DAAQe0vVusu+664a677gr/93//F01SYQ6QA9wRVjs5AnUQ4F2V3ZZOIbhiw0eBI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCTSAw77zzhimmmCLmsGeBcQA0h45zltE+++zTZoaKhHPPPXc8i+ipp54K/fv3D+zs4tBxmWRronhP6giM8gi4YmOUHwIOgCPgCDgCjoAj4Ag4Ao6AIzB8I/DLL78EhARWaNDVNX733Xez5npygohcWN36tmISqChPUXhaP3tt/dQ5vVY70nB7LX/qpnlt/eS3rvWTt+hafBWva9yONgX066+/Bg6H5bDZ4Yk+//zz8OmnnwbOuJhpppk6vWocXPrGG28E7k3O7BiWB5JyTgg0zjjjdHq7bQHD61iwdWzVzwrziSaaqNXsTeV7/vnnAyvbOTy5jG655ZYw//zzh/YcQl/G3+McgVYQWGmllYbKxtxw4YUXxkPCd9lll8AB5ZbY0Ys5wAUWWCA+T5i7rr322sBZCU6OgCPQHAKu2GgOL0/tCDgCjoAj4Ag4Ao6AI+AIOAJdjACmHD788MOw6667hu7du3dx6YOKywnNiVG4hPgKs9eDONT7r5NPZYojedKw9FppcYmrU04uTVWY4lOXchWGH9K16mNdxadhMWPmj3RdQWeddVZ4++23w0477TSEHfWuKLusjEceeSRcffXVYamllgq77bZbWdJ2x7333nvRvMr7778fEOCdcsopYfrpp28331YYYN++T58+cUz/4Q9/6FLl5/A4FlBI3HPPPUGKWPoFwatWlNfBGNv//NZcc81OF7Si1Dj++OPDCiusUKnYuP/++8Ott94a6OeuUN7VwcrTOAIpAjzXrrrqqqio2HzzzcNiiy0WmKdEnMEBoRTmxzkbjGsIE1VOjoAj0BwCrthoDi9P7Qg4Ao6AI+AIOAKOgCPgCDgCXYwAAq2XX345bLrpph2i2LjgggsCQtn11lsvLLTQQl3cmiEF+q0UbhUCufx1hPxSGOTyE6YyFJ9el6Wpk1Z8xUf1kUu4/LhF6RQXEwz+y4XZ+Pb4H3zwwfD000+H3/3ud8NMsYEy4eijj47N2HPPPQOHgWOf/eGHH+4Sm9vXXHNNuP7662P7UaQMyzNp2MmFUJA+32+//drTtU3n7cixkOvTZiuE8PTEE08MKLnYAQGxk4bx+qc//Sm7y+i+++4LDzzwQJwHmQ+hV199NfYvBx135gpylDCnnnpq+Prrr8Nyyy0Xyy77W2eddcKBBx4YUCgdeeSRYcwxXZxVhpfHDRsEvv3222iCCoXFo48+Gu8/1YT78bjjjouXKB8Z/zqPDXNV3bp1U1J3HQFHoCYC/iSoCZQncwQcAUfAEXAEHAFHwBFwBByB+ggMGDAgCjyLhE8//PBDGGOMMQIHGEMIw8kz3njjxQ99W9Luu+8eMLWTfvSz2hGzQBy4mRNmE4+ZmtRs0B133BGFDZg1SRUbCEoHDhwY62HrgD8twwrwibPXad5cXC4szZde2zqQX9fWn+apuu6IuufaojC51MP6uU7rT3wapnwK57qzCUEzYwvzZ7bc3r17R5NPs80221BVYNwwrtPxRkLxy8UNxWhwgPKkdQCjSy65JKbafvvto2Ij5YFgO3cvkQ6+xMOXezBH3DtF5t9QoNBWBOGsMEaxYiktm/oW3ds2H+X9/PPPbfiRT/VE+Jcj7v0//vGPsY9ob0rNmqkCG/qwyOyd2kI/lo2FtB66VpsQcNq5kfCyPq2a6+DPTg1Wik833XRh7733jn2EouOGG24Iv/3tb7M7Ip577rlYLv0jxQYmcqaccsqw6KKLqtptLvVEgQLWqn/ZnElG+pB5PlWA3X333dFUz1FHHRU4p8AS9UkPt0f5gaLlxhtvDOuvv35YYoklbBb3OwLtRuDYY4+NOyyKGK288sqViysYt8wNOdL7DnGYDWSnEibfeLeZc845c1k8zBFwBCoQcMVGBUAe7Qg4Ao6AI+AIOAKOgCPgCDgC1QiwSpiV07PPPntgJS7mahB+Yc5EttOPOeaYKCxmlTernQlHmMaK4jvvvDMqLxD4cbAm4TLZwArijz76KNqjxqQKQi9WjL/44otRcYEQbq211goLL7xwrCiCNoRfzz77bBRSInj9zW9+E5ZddtmA4OLNN9+MAlRM93Bw57bbbhvNQVx33XXhrbfeioLfqaaaKqyxxhptig8r3BYahCHoq6I6aap4KF68cvVRmtS1aZXfpsmFKT6NS69JZ8PkT900neVv65cLh5fSWL/SWlfpbJj8jMl//OMfUZB00EEHxWBWq6PoQsnFjiAE4YwtDnxFwI3QCfvoq622WqxDv379wieffBLHBWPkiCOOiOZEGF+sfkeIi2B56623DlNPPXUsg3F40003xbFGHoRY8GH8M25TQmmAKaDHH388CpGpw5JLLhmFul999VU0/YQCADrhhBPaBNJcc+9xYO3rr78eV+tTL4RxEEL7u+66K+7sIB3Ceeqw+uqrR2UigvVXXnklCvYQeFPu/vvvH/Pyh3Cd9mKKC3rsscdieVtssUU4/fTT426RaaedNjzzzDPxngLTsnv7448/bsvXvWFi7oknnoj35TzzzBPrzM4Q7nvqyWp9MEiJOlEGxFwjntzzmGHC1BGEEHzDDTccSmlKnJ2XaBP9juKKdjEHaW5D8PjFF19Es3jsmLFjAT+CetLQ9xBzEG2ibOYz+DBG4IGQf4455ggbbbRRVCBhzivt0549e1bOdbGgwX8ffPBBmGuuueJYhS/9zVimDsx5moeV5/LLL491plwOMT7ssMMCSoZ33nknvPTSS7E/6QuNix49esTdHIx/FErsWmLVOe2iH8CMtkt5B/a33357nFsR6KK8II+URldccUXkw70lolyeI5wXg9KN8zRQYtCXKLdoF/VhrnbFhlBzt6MQ4BlQRsxp/MqI8V3Fh/w8HzbeeOMyVh7nCDgCNRBwxUYNkDyJI+AIOAKOgCPgCDgCjoAj4AiUI/Daa6+Fyy67LCDYnGaaadqUGyglMGlCOIoEzDQgREOBgOCUOISzCLQQbrIzA2EzwjNMyyDMuu2226LAdfnll4/CsXPOOScg9ESBQVkIazFVxepkrhEin3nmmbGsGWecMZaFoFYme7SKGwEj5UCc44EQjjpMNtlkkSfCyr/85S9RUF3e+vbFSglQxqVIYN8eQX9anuohtyhe4Wm69Frp6rrkL2pnXR5l6RhbjFGUC1JsMG4IQ4mFMIrV7Zi6YTU6CjbGxJNPPhnHKoJhhK4oDRA6I/S98sorozKE8YyAGAEyAmWUepx1gSKCccmYR+HBKl14YjoKJV5OscH4RTnB2ESwi9k0hNOM28UXXzwKk9VOhNkc5C1CMYFgnnKpE2OYA2oRoqHA0Xk1jHMUPSgFuC9Q3qBsRMGIQoUyEZJbxQZlIPTW/aPDyjElBIYItOGFMgI86c+ye9vmm2+++aLSk3mAHSHUgfsTxSNY0ZZzzz034qq24lIXBPQQfWp5ojigT5iDhD/KzZQ0LzEW6DsUNxzmizB+hx12CJrb6D92K/BDwWPHAmnBgP7Crj7jh2v6DUEo7TrppJNi36GABXtM7KGk3WCDDbJ9Cn5Vc51tCwosDnKnDsyb8P7yyy9jkplnntkmjX5M5WjsoOh6t6GkgMAKpQMCWsanxgVjBUUP/UHfgBNjDUUc9wTpUUCgxOFgeeZOlM+MNerx0EMPRXx79eoVyyWO+jLfQvQd4wWlIrtFwBllEeXAizZxT6LcBjuUKdo1Ehn4nyPgCDgCjsAoh0B+P+coB4M32BFwBBwBR8ARcAQcAUfAEXAE2oMAQjh+rETmkO9DDjkkCkYRhiGcghAakgZlA4LBtddeO1x66aVRiImgGPvpBx98cFz1jrASgSIk3rgIXC+++OIoKEOQSR7yYsuaVe4IOlmVj5Bujz32iPGs5Gb1MILdnXfeue2gY1YKs4IY4ScrmxG+bbfddoFzC1gljJAWRYwIoXsqeLfX1q88HeXSdkvptY2TP1cfGyZ/6ip/R7jiDS/rz/Guis/lKQvL8QM3xqElwoQnSjSEzuxiwAQaQliUbwhdIY1hpdc1u5D23XffuMofgSxjEbr33nvj2OSwY0wmYSIIwawtMyYc/IegGKUGYx+hOuObg8q5RuHCSvm99tqrLQvj1e5kYMxiBoX7CGUiwmOUHQiB4Yvwnh1KBxxwQMCMFUJ/mUBSnRAgc0Az7bfECnraOPnkk8dg7hF4QORltwrCdXYzsDq/7r1Nm3//+9/HOqG4QeCOcoT7kHmEOQXlEmE5Ur0VxzU8t9xyy4gfwnkUMgjrc6Q+RLkDLrvsskucCy666KLIR/zpV/qEHR4orpSPeATxKI9QNFEWcwnKDIT9K664YhTkzzDDDGGzzTaL/SczVtQJwX6uT6vmurQtKKJoA8oFlA19+/aN8yWHF2s3m81jd8GgWGJsQmovrr1m3mZMkI7xxE4NdgSBGYos2nxfY0cKhMKPORllNGOY8YQS5fzzz4/jBOUIiheUHiJwQ4kCtvQBim3tMpEyDWUg7UOphnLFyRFwBBwBR2DURsB3bIza/e+tdwQcAUfAEXAEHAFHwBFwBDoUAYRf2GiHUGog3MM0DWZdRCgTsJWOoBQBFoSJEVZTIyxkBTUCLlYOsyreEoJaBFqsfGdFO352biCEw4wMNqtZLczqcXjiInBE8MhKZs7UQDgMYRKIVd0I6RBes1IYwTargolD8YJwGLJCcuunXK4lBJRfrs2rNJGh+Uv5majotfGKU1iVq/TWVZ607kpDvOpa5Cet5ZNeF+VTHpUlV+Gpa/kW+dM8uhbvZlzM6bAanLHLanAUBayoT8dhyhMlGWkY0+wWQvAKsfMALBmXui9QsGGeKkcffvhhHL8Id1n5j9AbofPZZ58dwxH6I6gWsRuDnQQiBMUrrbRSvESZgjKAH7tVuBchBMr42Q0Bcd+ov7nmLAPu1/RMBHClbITLEGbnKB9lCUT6HXfcMdaHtCh1oKJ7W+0AZ86AwCUPmHEP0g7qxc4u2s29Tv/UIcuT9JiFok/LCCE9dQJvTGshfFc/ko+yt9lmm1iflA87GRDiM3ch8EeJgSKEfmcOYUcH7WFHD2dh0B8Qcxi4CQvC1Kc333xz6Vwnk1fksQRmKExQADP3oTRhHKfEnKexg7Itd6aGzcPuCtrI7g4UFRDKEXaDoEjGPJTwUt9TFxRrKFq4N8CUMS6lhHZrwAuFGdfgBi/GF4ou6kV/iqgrzwkw1PysOHcdAUfAEXAERi0E/vd0GLXa7a11BBwBR8ARcAQcAUfAEXAEHIFOQMAKQxGqQQjCLCH0Q8gFYTIHkoCN1eKYCoIk/IoXg/8kEOSSFcEQq3lZrYzCAqEYwjSEr7L1jkCyTCCK0IxVxwg/Md2DQgUhGwoSwqGcsJxy0nCFySUvfiiXNkaYvzSNiYpeGy9/lZvy4Fr1k2vTKMzW2/qVtihM9VG6Oq7NIz9uHT/8bTqVpzBd13ExR8V4YAxg7owV94wdVqprjOb4aNzLRVkGMR4hxrwIIXcRoWxAaMv4ldAXJR6KDnaNsCq+bCxb3phTgtiRxGp56gQmCJohyuG+4Z7DL0JYrHYorI7LPc1qelHde5vyJbhWubQXor5qkzAV/zLX8iyah9L8KhslAH4UQOo/0hKOkqWIEPJLKav+ZrcIbWAnB8J65rQFF1wwCvrhA+78qG9KVXNdml7X7D5DEcb9yQ4bdhN1BKkfhBM81U8KY6xBqjtnnqCkhlCA0Ifff/99dk5EKc5OI8yhoWy7r7H7g7HIPWEPFxdWdszGAvzPEXAEHAFHYJRDwBUbo1yXe4MdAUfAEXAEHAFHwBFwBByBzkNAJkMogVW8kEzXxIvkD6EjK8pJy8G7CKuUTwJJm4WDwiGEhQihETxDmG3Blj2reYnDLA4CMYTCmPFBSMYKZXaKEA9JMM9qcJQvrA5mlwZCSMxZIdjW4cvKEzMOzqsw+Miv+Cq3lTziqbKqXNIrjfLiquwi16Yt8itvUXwaTj3IU0S2nvLjWj9507BcfFEZEsyyw0fCZEwWiRC6cpYDOx0wH8RqeoTRmJXq3tgJYYWrylPlMh4hHbiNn/KLiPGN4BahOuOSewBlBmOZtkrpp/xlmCoNLkoS+NLung3TbdwLEKvnER4LxxjYQX/N3tsdVGxkQzvBGYG75pOyeYhMGgvgDv5gIuVSnbqxowHlF7tx6G8UrezYgG655ZYo4Ge3GjstUFKhBMmR+rRqrsvl5fyWM844IypkUMqiWJHSKJde/a4yc2laCWOcMqeiOMMUIIRZP/qF+0sKEKv05t5DGUedUZiA4cknnxxNe2GqjPOTIO4FKLcLJUb4nyPgCDgCjsAog4ArNkaZrvaGOgKOgCPgCDgCjoAj4Ag4Ap2PALscOOQXgSImVxCcIUgtIhQJKBGwvY6wD6EiB9EiEMZefEqYbGFVOCufsbuP2Rj8mJnBzBTCwCWWWCLGnXrqqVEYTT1Iy9kfEGZjIEwOIbjEhBDnD7BKHhv6mKxi9TmCUUyoQBIAxovB1xIGEid/Ll5h1s3lsfFlftWlyoWH0uT4dUQdytqtspVG17m6pHVVWutav9Lnwor4aycCQuvzzjsvKsUYGyIE/yizULQhfEb4j+KM+rPKvBVCMcYh0ijWKJP7grFYRNQR0zvsGEGoy3jn4HvqzDkJrGqnnuxGQnlHfcvuL5XDfcF5BRzgzIHWtAslJGafOBcDnh1NVfe2FAkdXS78uHf/9re/RSE6B8JDMtEVLzJ/mG5CCQLe5McklJQLmeRDBbGbA1NNnC+BEB+TXqkwnjmOsySsWSwUaoyLtE+r5rq0AvCkzSgIMOPEjhnGHoSCF5NqKWkuZBcPihYO/u4IwrTYKaecEs2UYVYMRQWm/mgjcZpjMU0lQsmGUgbzgJtssknbLid2zWh8ohhB8YxSI6f4Fi93HQFHwBFwBEYNBFyxMWr0s7fSEXAEHAFHwBFwBBwBR8AR6BIEEEBh0gmhGkoKzLPkFBSqDLsuECSjZGCFPAJBhIm9evUK3RrnXKTEimjMq6AIueqqq2I+hM6Eo5hgVT5mg0444YQo9EWgjOKjR48ecRU+/DgPAYEuJlJYOYwAjjqyOh8/fKjHuuuu23borgTotj6EWaE9/qIw8imteKQ803ilk2vT5/yE5cKVXy5pbFm6LnLJZ/mmecUXl7g0rb1W2jSdwpW2va74WRdl2YYbbhiuvfbaqHxjnNHXIsYu52CgmEPRxapzdgJxOHerAl+UbKzS554455xz4u4lmVRSG1U+LgJuDs1GQM0qf5QQmPVB2bHHHnu01RfhMOP/8ssvj3ksj5yfVfvwRWHHuOfQcOpB+1FAdAZV3dvUobOIvkQ5xDkVCMzZTcHB3WVEOvqdeQgF0s4771wLW8uT+Q7FBgpRmaEinv7irCHmHeY7hPJgj3KH8jAvlvYp5w+VzXW2XPy0V2bGmPNQ1IgwA5VTbGCmCkUxyhDOhtHuCuVr1eVcFdqJWSkU14xrlEWEM+65F1FgcNYLczDzNrs7COOMEvqC9CgwqLd2zrz33ntxB1PPhrJcZrBaraPncwQcAUfAERjxEXDFxojfh94CR8ARcAQcAUfAEXAEHAFHYLhBgJW4rHZHMMXqXMw7sVoYOuyww6IQS/bnCcPED4oITP9gcgcBGEI4hMl2lS5pEc4iFENgiGCQlc+sWmfVMavr4QUhrDvwwAOjsI6V7gjH2NmheITXCHgpD7MoCBhRpGDKCpvwCN0RvHGtVcGqSyyg8SfhPvVJ/RJYW+G99YsHrvLiVz78RWTTyJ+65FVY6ld5xKtORS55ld76LW/CLdn0Cs+FKS51xVsu8fLXdW0ey5/xw+HW7HzAnA19yw4JxhGCbAjBNOEoNFCYMXZ1SDbxHMSM0ktmqY466qhoXkcmohhPxx57bJtAHHNEKDbYTYTZHVbwX3TRRZG/VsvD1xJKNkzysHOJstgJgMDXHjC9/fbbxzHN+OceIz2KGu4dEWN6jTXWaMtH/r333juaZmPnAHmoL8oXaMstt4y7GsCnjPbZZ5/YFlbiQygVaXNq8oj7rezezuVbffXVYxvsOSKYVOI+tmGqHztPKBuySioE5SgGENhD7MoCwzLaeOONo7IHTLs1lKrsvoCWXXbZWAb1tZSOBeLATvVhThOhWD3kkEMCgnnmHoT4jDWE+pof0z5lvFfNdeKPy7hR2TYcf9HZINTj0EMPjeMR/Jh/UeiCF6YBoXRc0M8qR3wZW4Rphwv3E/iDHYob5k/uNRRMtIsfCos+ffpERQZY8VzYbbfdovkulHmMT+5FxrfG1k033RTvN+4pJ0fAEXAEHAFHYLTGy1qxoVPHxxFwBBwBR8ARcAQcAUfAEWgSAStcajKrJx+BEcDMztFHHx0Fo6x4ZyUywkWEeHUI4TeCZARYCNcgbKxjFubPf/5zFPByKC7mYUSsOGdVLwKwXDmKZ0zmFBPUkTgJzOGLeR7qko5jdp/kyArti/zks3GWT1G4TZP6bX3lT13yKCzNz7UtV/4iV/kVr+siN5cuF2bzp3W11/IXufBJ48Rbiild41IX+jntY5tGY4dV4eJt4+v6GWPcF5zvgNIBhQq7AlBY9O3bNyrwinix6p/8jG8Jdm1a4hn/za5cp/3ca9yf/DqbVJ69tzurzJdffjkqDFB49OvXL+JDWZpTcuVK6YTJKhRWrWCa45sLgzdzUa4/SV/UpxqPRXNdrqxmwhhn1KmoXs3wStMy1riH0vuNHR29e/eOuzFOOumktvNjNF7S8clZSeyiQdFIeilV0vL82hEY2REYf7QTRvYmDrP2Dfjv/sOsbC+4NQR8x0ZruHkuR8ARcAQcAUfAEXAEHAFHwBEoQSAVYpUkjVEIvhBGWuIQXsyjsNKdswpYAW6pShBXFk95OYEwgsMcpYoR0iCAs0JvrovCbbqYaPCf8tiwKr/lJb9c8hb5xdfWW365pFGd5CqfjbNhRf5cfqW15SlMblH9FS6X9PLLzfFQmFzSVo3PsrEjPnVcykKJce+998adRd98800UsrNzxCrpcrxQ1rHrqYiIz43hovQKp07pvaa4znC7ujzbhjKFhk0nf6uYKn+VW1WfovI7ajwW1a/qfijKVye8aIxiAosdRMcff3xgJ8Z2220X2RWNF8wPspMOk2yu1KiDvKdxBBwBR2DkR8AVGyN/H3sLHQFHwBFwBBwBR8ARcAQcgU5HANNRmDSRSZ+OKJCVudj/x1QLplww6zOsCGFbSjbMCupTgX56nfKpik/T23JzfhuW5uVa8UV1Vn3k5niUxaXpm0mruomHvZZfLmmK/Mo/rF0E2SgxVlhhhTZTawh0Md3TlcqFYY1DV5WPqS/moWZ2omC2jp0UqamprqrzqFwOZ2VwIHgdWnDBBcP6669fqRCsw8vTOAKOgCPgCIwcCLgpqpGjH70VjoAj4Ag4Ao6AI+AIDDcIdObKz+GmkV6RoRBAOIW5lDIzK0NlqgjA/jz27lnxW7STooJFh0VzJkERWcG99St9LkxxuFXxNi3+ImF+UXian2tbZh1/jocNszxseOrPpbP1tunTcHtdxw8vHTps+Q4LP+1OTa0Ni3qM7GWCM/MG46OucoP05CO9HVcjO1bDS/t0b1Qp+ngWlO1gGl7a4/VwBDobATdF1XkIuymqzsO2szj7jo3OQtb5OgKOgCPgCDgCjoAj4Ag4AqMQAig0qsysNAsHgkYO/h4eaHgRkA8PWHgdmkcAgXmV4LZ5rp4jRQCcm52H6ipA0rL8umMQqHtvuFKjY/B2Lo6AI+AIjEwIjD4yNcbb4gg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCIzcCrtgYufvXW+cIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4AiMVAi4YmOk6k5vjCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AIzByI+CKjZG7f711joAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOwEiFgCs2Rqru9MY4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCIzcCrtgYufvXW+cIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4AiMVAiMOVK1xhvjCDgCjoAj4Ag4Ao6AIzDMEfj555+HeR28Ao6AI+AIOAKOgCPgCDgCjoAj4AjURcC/YeoiNWzTjTXWWG0VcMVGGxTucQQcAUfAEXAEHAFHwBHoCAQGDhzYEWychyPgCDgCjoAj4Ag4Ao6AI+AIOAJdgoB/w3QJzO0uxCo23BRVu+F0Bo6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj0FUIuGKjq5D2chwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHoN0IuGKj3RA6A0fAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8AR6CoEXLHRVUh7OY6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj0G4EXLHRbgidgSPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIdBUCrtjoKqS9HEfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARaDcCrthoN4TOwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEugoBV2x0FdJejiPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AItBuBMdvNwRk4Ao6AI+AIOAKOgCPgCDgCjoAjUIHAjz/+GFOMM844FSnrR3/33Xdhookmqp+hJOXAgQPDa6+9FlPMPffcYfTRh481YJ2BWwkMHjUSIjBgwIDwn//8J4w22mhDtS4Ns9fWT0Z7bf1DMc0E/Pe//82EDh2U8k3z6VouHORPXXFXuK7ruK3kgW9af3td5K/Kp/ra/ArDnXTSSe1lm9/njjYooqcjnxdDcvYrR8ARcAQcgWGFgCs2hhXyXq4j4Ag4Ao6AI+AIOAKjOAII2h555JHw9ttvh59++ilMPfXUYckllwzdunVrFzIffvhheOihh8Knn34axh133DDrrLOG5ZZbLvrF+K233oppvvnmmzDhhBOGJZZYIiy44IKKDr/88kt49NFHwyuvvBIQDk0++eRh6aWXjryUCCH4E088Eb766qvIY7bZZgvLLLNMGHvssZWk1KWM119/PVCX3/72t0Okpcz77rsvvPHGG1FoN/PMM4ff/OY3tYT4L774Ynj66adD//79w8QTTxwWWGCBsOiii7YJ6uu0bYjKdMAFfX3iiSdGod9+++0Xxh9//HZzvfPOO8Ndd90VVltttbDyyiu3m9/3338f6wij0047LYw33nhD8Pzyyy+HuNZFqwJQ5a/rIpQrIyv0tH7ypNeWj+qfcwmz4dYPD12n/NLy7LX8cslb5Ld88afl2esiv3jYeIXVdW396uRpNn3KM1fX6aeffohkH3zwQfjiiy/iXDXJJJPEeXOsscYaIo0ufv3118DPUlrHsmvFyYWP9Vu+OX+uPbl0dcLEq8qFl9KIb3qt8GZd+FS138bX8VMHmy53XRRGeEqdMeemZYxI1x39vBiR2u51dQQcAUdgZEbAFRsjc+962xwBR8ARcAQcAUfAERhOEUDofuaZZ4bHH388fPbZZ1HohoLhgQceCNtss03o0aNHSzV/5513wl//+teokED49PPPP0elBOE77LBDFBzhR8j+8ssvR2E/K/VRhOy7775RCUC+iy++ONx2221ROYKwEKHgww8/HA444IDQvXv38Pzzz0fhN0qJMcccMwoXp5hiigDvXr16DSWgso3517/+FZ599tmAAgKlDgKoVLFx0UUXhZtvvjkgTGfnANig5KCOZTsJ7r///tC3b9/w7rvvxnqhMJppppnCtttuGxUjddpm69pRfupx++23x7rvueeeHcIWpdBNN90U+6MjFBuMFZRZEMqflFLBsOLBNKVcWCq0TPO099ryt374ptdpWaqvdfGXXcND8alf/G258ssljfxybZh4WLeovKJw8to4y6s9flvfKj510lbV0ca/+eab8V7CZW4aY4wxwg8//BCYf3r27BmVuGmZ5GeeE6Xx9rrKXxQv3jnX1j8XXzfM8sGv6yIXvoorKqMqvihfLtxiQ7y9ruNP89S5Jk2OOmPOzZUzooR19PNiRGl3WT15pvXp0ycm2XXXXQMKUidHwBFwBEY0BFyxMaL1mNfXEXAEHAFHwBFwBByBkQCBq666Klx99dVR6bDddtuFCSaYICo5/vnPf4Z///vfYY455giTTTbZEC1FMIcAj7RFdPnll8dV/Msvv3xYZ511Ars3Tj311Lir4ne/+13kec011wQUACuuuGJYc801w1NPPRWoz4UXXhiOP/74qGxAOcAKeYTw7NZA0UGehRZaKOy0006BclB0sFtg1VVXDZ988kk4+eSToyJivfXWi3mK6njZZZdFXuz0QMGDQNLS+++/H8sDh7333jvuNDn99NMjXpRnd5bYfOzy+Pvf/x6ee+65qFyZb775oqII4T/mn9jxgSKlqm2WZ5W/ytQJCgIUBvTZgQceGAV9ud0a8EHAyA6blBC+YEqHfFapQ3vAbpFFFkmzRF7KYwWKSsg4gnLlKU3q5hQbOaFoLky8cnVRXJkrnmX5bVzqL8qvcMqW37r49VOa9Fr1Vj5d46b1aCbM8rF+W478cklX5Lc82uu37ariVZXW1jfllcY988wz4R//+EdUaCy++OJhmmmmiQpM5ip2l5177rlx3lprrbWGYMV9KMWGrU8zfqWVSwHWP0SByUXajiQ6ewnvNJ+uy1zFwdT6c9fZgtsZmGKia7mwL/Ln4mwbyGev0/Rci8rm3DrzH4oRzd3wpFzmVHay2XlY5aUueRlzZXNsVRrGrZ75FjNbFgsD6tSp6HlR9HxRGcRTBs8flIgp1cmvZ1EOtzr52U1I+UUYpHXKXefen+hT3mUgFpRUKTboD57VRe9gtKVOXcEDLGWWUvmK+Oba42GOgCPgCAgBV2wICXcdAUfAEXAEHAFHwBFwBLoEAYQEV1xxRRSS7LPPPmHDDTeMH7iYS3rppZeiGSWUBigmjjnmmLh6HiXEgw8+GAUM0003Xdhss82i6aq0wpifwvTSlltuGVcuf/vtt+Hss8+Ou0Iw24Ky5I477ohCmk022SQqN1AUoOxgtwhClI8//jgKDDGL9fvf/z4KZtiZ8cILL0ThIWWusMIKYf755w+LLbZY4DwGlBDsQEHBQTkoQ4oIQc8aa6wRhUMoLFJiNwe7WOC98cYbR8ElAs3rr78+PPbYY4WKDdqK4IN8CCkQetJeFBvsToHqtC2tj70G37POOivadJ922mnjrhPiKROFDoIb+vbVV1+NCgcw4wyMHXfcMSpcSLv66qvHnTAIYaeaaqqAiR1MZyF4mWuuuWLfypwXO2noFxRACHZQYqCMYqU6u1IQ5tJG8qnchRdeOJb/9ddfhymnnDKg0Oo22LwZih12jmDGB2IssVtG8TGw4A8hUCpUJGkall6LXU4oqbi6bh3etpwif6488cZN/QpLw+GjsDKeaT1IqzDrEm75EZeGpddKn7oxY+NP4bpuj1tUn5Sn0hFep/yiNDb8888/bzORxg6seeedN86H3BvcR5jTu/baa+N8x5w244wzxrJRFiNsZH4V2fpV+RWfc239xLvIrZtW5Si9XPHVNW6Rn7SKS/3i01mu6g9//KpHGq7y03ClV36bLhdnw5QWFyE0Sm6IOZc5s2z+s3M78yEKfxQPzK0rrbRSfP7wfGMeRnGGYi1H1Icxh5lJ6oApSNrInLvppptGfjzfbrzxxjiHI9Rmnl5llVXanm0Ivtmx2K9fvyhIR4HNM5NxbevJM442brHFFjE9yhieNTxTeM9gZybEe0b6vCC87PlCve699964iw/FIUL3pZZaKtaT5xzY8CzheS3FB/fY2muv3ab4KeNfVT7PQ0wtYu4S/jxHucdR0LBLlAUQ559/ftzNiXlH8bv77rvj3MC7Q9n7E+nPOOOM2A787HTlWciikJRoK/0B1ig2UICwOwzznFBVXdmZC1YsWGEuYrcZig3KwgTprbfeGhWyjAPey9hl6uQIOAKOQF0EXLFRFylP5wg4Ao6AI+AIOAKOgCPQIQhgrumjjz6KghaEADrLYM4554yCawQgCPBRbLCrA2EFeXARzCBwQDnAzomUUGjwkY3SAUKAgnAFoQQCcPwI9yEENhDnV8AP4QXCw9lnnz0KQkhPOIIa6gvpg5udE4SjxEBpgJkLeBPPx3kZrb/++vHsi/feey/kFBuYqoIQLmlFIwIBiPYXEQIndpggMEDgA6X1rtO2Iv6Eoyy48sorI56cKUKbOWsExQRKAgQ/CHPuueeeKJChL+nXrbfeOu6KgQdCGExswYczQIhHoYQyBGEYgjAEHgi1OOsCpRJh7L5ByIOQDAEMyh7GB+MHwZ3KRSBHfyOEU58i4EFYxq4a4lFGMZ4QAiHwOvbYY6laKTHuRKkwMb1WOuvWSWPT1/WDh3jn/IRBpJE/x1s8cFO/wtJw8bVuylt1UtnwsGHptc2v8orCFJ+6ufoojeVV5ld9bZqURy5NrmzLo8qflqFrFJTMg+xAY3675ZZbouCVcY0iFYEgCl+Eyghkt9pqqyh0ReGKaT8UfpZs3eWXSzr5q1yb1vLHr7orPL1WOK7KKUqj8Jxrw+SHZ5GfuI4k1R2eKpOw1K90hFs/+dL0ZbxsHP4cIYBmJyLEnMt8Vzb/2bl9nnnmiXMyzzbM8zFnMmejRGNOZxwyrng+poSgnzieVTy3MKGIMoL8zOs8d9lZyAKDbo1nHAef87xgvmce5tl1wQUXREUKdeaa8czz4bjjjhviGYSAnec5ShFMO2Iikl2VPGNZJMEzhuc7ioD0eVH1fOHZgBKfZ+gss8wSFQm0jTJZ9ICgnnaAM23i2fXkk09GBQRC/yr+VfEI+1H+8xxT+fQD5fH8492D9vG+IcUGGBHG+wmKjbL3J96veAcR8XzlzLEcMdewQITnKQpTLfQACxSsVXXl/Yh68Y7Aj3IYH2BA/VkYAs4ocHin+MMf/pCrhoc5Ao6AI5BFYPRsqAc6Ao6AI+AIOAKOgCPgCDgCnYQAH7AIdliBiFDDEh/wEB/ZEOn4sVIRoT3COz6COQg0R6wiReiAYJsdDAgmWG247rrrRuEffhQfkJQGCJS0Q4APd3YiwAOBO4SAA6EFH+AoYiCUBwhLqAerQjEPwzWrRbVbg1WK5NMP3hCClrTdMWLwn8yEqH4E2/pxLSWAeIMXbabeWknLqkjMTLBSd/PNNydbrbbFhCV/9Ad1ZPUtZ36wkhYBiYRo6jMwQuCy8847R24KF2uuEdKwowKTW6z+RBGlcy7YgcGOD1YHU07v3r2z5cAHEn+US3vssUc8D4U+YdUrghWtqkUIxliiTIj+1XiLAQV/4o+CQ35ce42/7GfzdZQ/Lb9VvjRbea3fhtlw6ydNeq18cm09FdZZblpWel2n3Dp56qSpU5bSpPzsNbsxlllmmajUQMCJUJU5gnv+hhtuaBMScv+j5IPYiYZgGQEyvHI/yk7DFVbHVZqUB9fE2Xhd51yb38YrXHxSV+Wk6XLhlm9H+lVWFU+bzvrTfGmcvbZ+5Yudnfmz8XXmP9Izt2+00UZxfmQHAsp+ntm77LJLnFN5njD+tEAgLVZjjl0ezP+cbcUzV3XhGYySDlOMKLw52wFBPQsJeGaj2Oe5RTz5mftRiPA8Za6G4EV7UCAcfvjhUbjOvE64xj4uWLG7EuW3yseFyp4vCNdRKrAjEAUhOz7YBYkSX6abUPKgIN9+++1jG1AmIKxH4VHFvyqePqB8nlvszgIDcOT6nHPOic9NeNAW2ihK26jr3PsTu2B22203ZY3tI11KPKPPO++8qNhhDIAFbUWRRH/UqavqwfMY06OcVcZ7Eu2h33ke0052c6CYdXIEHAFHoBkEfMdGM2h5WkfAEXAEHAFHwBFwBByBdiOglao5RnwAQ2kaDodGiICwHCUCAvAyYhUzSg1WgrJaGQFKzr51ykPlKxxhCqtPEaIgXJCyQ/GsWEQRgrAG4Q+rRFkJyUpWVjhSD9FRRx0VlTm6bsVV/VhBiXBJhJJhhhlm0GVcVXvKKadEBQjmtBAypVTVtjS9vUZAgZIHlzphpoLVrJYQwlIvlDKsBs4RAicUF/QrK2oxO6W+xRwZhNALYS0rQ9mVoh0+OX6EsWJ32WWXjcI0hFDsJkFAhQIGXuDGzg+EKAiFWBVMmQhYykjYyyWt9eeuy/h1ZBz1SO+ZlD/xqm+aVuHKk14TTpgNt37FK791lU7lF7k2j/yqp3goXNepS3wuLM2n69RVeQoXL13LtenqpFG+1M3ltWHWj/IQASBlX3LJJXHcouzjfvjLX/4S7yH4o9hj5TjEOIdYUS4BqOoOb/wqQ/4yNzIzf+Jhgobyiv9QESUBRXkUbt0iP+wVZ4vKhQkTm64ZvzBTnvRa4R3l0oZm68zuuLL5T8p25nTmZFzOlWIsMX4wB0m5LEhA2cGOge7duw/VJI05TBMyD5OHXUZ6BvAcJh+CcRYG8ExlQQPzM89SFBLw5tnKTg/8PF/hA2+ZVEPBgpCcPGCBiSbKYX5H2I7Sn3B28+Wo7PlCmTLfyHMCvxYmgAd1WW655aJLWqscZ0cLVMa/Kh6lEQsTeI9gMQd9wy4alB2EY4aqGcq9P8Hbnk/FmVxgmRI7RtihwcIJdsLgYvqOvCzSaKau7PbQuwg82MXDggbeFdjJwRln4MlcVed9La2rXzsCjsCoiYArNkbNfvdWOwKOgCPgCDgCjoAjMMwQkJkkBAWs0ETgIkI5AGF32ZJ2LMhlRWURscKQVY2scMYUBqv3tROE/KxyJg0/EcJtyArN2S3wt7/9LX7UsxNDZ0goDy7CDex+84F+0EEHxZWoCPwRxHPWBwIWEQKBOoTgCLL1k1+mPxAMIewRCVOuEQah1EAohYCKlZAWY9LUaRvpigiBEYIvCAEFxE4aS9RJ/WXDrR/hBUoNSGnVtxIkaQcMuLC7o4rEB746mJ26IaBiZTtKDkz5wE/CQZVZxhthlqiOX2nlUpbNp/COcJvhXZa21fo1k4+0qoN1wUFxwiTla6/lT13xKeKh8JwrXsRRtyJSujppinik4eJJeM7PqndMyaFkZVcTAsJujZ0YrHqGNNaFKWGkhSaccMI2nvAuq3fMUPPPllWWxbanLJ2NUx1tXvmtW+QXL8XrOufWSaP6lOVXGvGz10V++BGnPK1c5+qUhtWd/6iL5nbNpXruEIdCASqaMzXmOOcCIo/meK7xs1sO5TwKAxTPKJXZWcRuAATbIpTvEM8/FCD2GQofhOEiFHoI5zkHgmcfuyd47qMQyFHZ84UdkbSPuuOH6B/qwDMLwTuK/Ntuuy1eo/jRTlDhUsYffmXx8KYMcJfCiWc47yeYcWI3i/oBXlWkfpSrOlblI55nJ23nvUn4YxqSH1SnrjFh48/WWXXR2NK1dvcoj7uOgCPgCFQh4IqNKoQ83hFwBBwBR8ARcAQcAUegQxHo1hDGIfRGAM9Kf+zCI0BAqaGVxphXaYUQ9vft2zeamUDosddee7Wt8ISfhCGcq8CPFYrUAwUEH+4S0mNygsM0WVXaq1evWEcpHPjIZ2UhH+AcWkp7EKAgPKQNmDVCsUGcVklTtgT0+MsIAQ3ECm3KAhvZwlYcq1NZDStS3VgVy7kUrIRF8Mk5JMqjtGVtU5oql3aBNYIK1a1qx0MVzzQe018IyVgRihAFAQvmQxCEgG0RSZlEHbV6FuwRwGDmAuEU+ekvcEoVMkV86YuUFCY3jbfXNg19WpdsvjSP+JAm9duwNF+r15SRq09ReN1ybF3FX+0RD4Vzbf2Kl1sWpzRVblp2Ln2dNLl8zYSlbUGwiDIYoaiEuqyohqTYwJSPhMqEIQhlXtD8k+urXJitZ1U8aVM80rqn15a//JZHml7X1rV+8ciFEadwpbOuLdeGW7/y10lr88lPfuW1fuJT3nWvySue+OlndjrkVru3d/6Df45QOrAwQM8gjTnO4UCpQFusIB3FBXMuOy/YpcEzE1NHKJ17NkxLMe9DtItzItilB7FLAcVFEfFsX3XVVeM8f0HjjA6eA+wykeA8zVf2fMGslRQYmLLqPnhnCs923l2o28UXXxyV5H/84x/j2VLs2tAuDcoq48/zpyyeMikfZRS7CVnoweIJ7n3K5j1FCiSewzzrSK8FEGlb61xrzJHW9in9SZnsPtVCFJRQ7K7s1nj3kbKnrK68czg5Ao6AI9BZCLhio7OQdb6OgCPgCDgCjoAj4Ag4AlkEWHnImQ+YjUA4wAc7QhF2ESA4YKcDOyFaIYQjmGlBwINQBbMNmFGA+ABHYYJ9aMxEoQBBqMIKTwQDxCHsRvDCjgdWiyKs54Of1aUidg1w6CUHlbJyEgUDwgcEjAhRWFUNSUmifHVdTDwg9GAlNgIfVmliVgMlArbGIa3itDyp5xlnnBHrih9MdQYH6chL+8rahiAJ2+HsdmFXg8qz5eBHmEJZmJZAOQUhROpIop8wO3bRRRdFfOnHG2+8MZ63UqbY4KBZ8GNcobxivHFwMqauELAhoEH4S59JqWEFb3XbYAVBaR4bh1AoJcXn4kir+DRfek068bB+m07hqWvTVPltGaS11/jFmzj8KSl9Gl73Osezbl7Vryx9nfrVSZOWUZYnbVOunmkYgkXmFZSwzG0Ij1k1DiFYRODIbg6UsRCCYh30ix9K+0plVNXVphMPueIbC0j+0nam17bcNC5h1XaZS5cLU4ayOKWpcm0907S5uDQsvaZOaVjKN02TXpNebeN5xTlHPD9y8yPzXkfOf5TNc/Okk06KzwzMpKFQ05jjOUyZPCPtLgyUIOyo5FnJOEXxjlKCcBYLUH+UdszfmJXCBCH5UczwblBGKPx5vvF8BtsiM1TwKHu+YP5pqaWWis9PFlugXEHYjyksFgygROBehFgYwbNapiH1LCnjT/+UxfMc1jOLhQpgwvsReLJDFJwpn3cCwniXoY733XdfrFPdP9qBshQlyfXXXx/PI+G5b/uU+WaxxRaLO2tOP/30+F7Fjhh22rCTlXeEqrrWrc+wSkefoZRi/kQhxoIX+lnKYurFGOS8LvqbsUs/YEZLVIcHfQhuvHfAm3cma0JUvNx1BByB5hBwxUZzeHlqR8ARcAQcAUfAEXAEHIEOQGCDDTaIH3cI5VBuILzn45qPZA7pbFUpwIcn9r8hBH+YoxBhXxzlxYYbbhgPAUVggeCbD1Y+zPlIRxjCKv8HH3wwCoz4yJXgXnwwNUValA4IEjhXgxXUrIjmkHIpNpS+WZePavhf0Fh1isACEw0ILzjEtGzFKgIuhBPUGUGRVWpQBz6gqWNV28ADZQIKnCLFBgIRFAX0H8oh8EMY05HEAbYIZRHYsVoXZRVCMMZOGdF+xhTtAAdwo+30Lf2PgIzzV1B4gAfYopTibI6OoiqBJeXUSdNR9anLhzpJeCqXvNafu26Gv9La9lu/4lt11Qabvxn+ddLWSWPLz/ktD/CFCBPWClNehJgoUxn/zEHMPSeccEJcoT/bbLPF+Yi5AvN72NSHmOuYnxDGiS/h1s+1JcXJtXE5f1k64iyl18QpzOJh81i/0tqwOv46vFM+ZXmK4nLhdcPS8ouuwSDHk+cW8zFxOcUGwumy+S93vkJRHRROXVAk88zlvAtIY47nK3Mwc6zqi8s19UBJjaAcITFCesY0czBKaZQkPIOuu+66eOYGSjuUHaSVySfVwboo2hHMIzxmlwXnchVR2fMFU1wcaq7nKIoLBNcoFNdcc83IksUZKGh45iIU12ID2kwdy/jDoCye+5jyOd+LPgVLFD08ZwkHB+rImReUz3sCgnLCmyH6A3OVLGRAKUa5vGPYPkWJsvPOO0eFEWXx/kAb2ZXK+0GdujZTp2GR9sorr4zmyzApBq533HFH3K2LeVEptViIwzzKmMJlYQfvQYxlqIoH7zGMF3afaiEPyjB2/FgFyrBov5fpCIzoCLhiY0TvQa+/I+AIOAKOgCPgCDgCIyACCC8QOLPqDaEyqyHZHYFQzgrvDz300LgqlPQQgosjjzyy7TyGtOmstJRQL42T7xjhdAAAQABJREFUTWgOvNx9992jwA+BCisWUUawKhJCgHLYYYel2duu+dBlpR67H1CcYOoFHgiGEDwg/KhDakuaHmED53nAjx0sCEnwI0iwZ4CkZZDvwAMPTIPbrsEWIURV21hhqza1ZU48fPxzcLEURwgEpNDBtBgrEelbEZjRbxDCl1zbEZIgHCIOop922223uHISpQY86ENwgBCEUS7tskTfIHygHSjIuEYAAV8EZthMZ3WwyiEdwrC0jpZnlR/sWxW6VvHu6ni1RS7lp/6iOhUJXdP08GsP2fqIjw2z/HP9YuOVX25ZnNLk3FbyqW7KW4Qf4cxPCBaZ3xAiM2aZG5hzEDQifGVOQjgsxXC3hqmYTTfdNArrVAZ1l1+ubY/C5Nq4Zvxqm/Kk1wqXW9R2xVe51Fc8rFsnX1maMhxycbkw8S+KKwpXvtRV+2w48yJCUs2H6XyGMLVs/uMZwxyNMF+EaSeUyXp2Es45GCiDCWNe5ZoV6OSHNOYYpygDGItHHXVUjEOZzPwvJT1mBmkLgl2e+xq3mKjC/BILD1jwQN1RVMCb67SekXnjj/rwbEcRDi8r6E+fF1XPF+43eHFf8fzhOQ0WOrycZ7RMaqp+1JtnI/1Zxb8qHoE5ZbKLgIUTlIECU+VTDvMAz1neY3hHog/ATM+2Ou9PLCRhdyt9Cu9cn/bo0SPsu+++8XkP/jynyUN/QFV15RlMn8l8HnlYvMGzV7tw4Kl3BOrQVYTC6Pzzz48KOZQ8vCMxtnlPYqEKCkF2Wtx+++3h6KOPjvMu4x2lMruSaHsdHpdeemkcGywYoS95t8NUKgoPynVyBByB1hEYraH9HnIZReu8PKcj4Ag4Ao6AI+AIOAKOgCPQNAIIP/hZIUTTTFrIgBkNVvfzIYuQoBVCKAMPBBCd8TGOwod6lik0Wql3WZ4TTzwx7nhgx0i6iwGlACtyqQ+rYjFJBQZg2FlUt58QQt1zzz3hgAMOCL0aJk7oF8ZUKjREGKzVt83UGWFZSrTdUnpt46w/rZONk78VXuJb11VZqauyy9w0Dh4KS/mVXauuaRobnuNrw4r8Kc/02pahuFyY4jrbVTtyLquEEVT27NkzCtwQYEspythkJTwCMzuXsYr/5JNPjmcZIEiF1D65dcOUPnUjU8OXa9VfcUVhNh6/eKfhurZ85ce1/lxahVW5ReU3G045RXlsHToiDTwQuEPMbVYxYcuSv9X5T/lTV/jr+acxh6IChQXKbwTHCN6vvvrqOE7Fg+cb+Yue/dSVNIzzqnaJZ7Nu1fOF+rFjhB1T/FLSO4Tan8ZX8a+Kr8KA+oFREYZpfXLXlMGzXDzSPlUenpssCiBdrr1VdRWf4clFwcAcud9++7UpXujvJZZYIvTp0ycuoGB3BkplTJJqDFx22WXxHDZ2sLBjqYoHu484i02KKTDAHBk7iffYY4+mIZl8vNOazuMZ6iHw1fe710voqYYpApqvqERrX3DDtPpeuCPgCDgCjoAj4Ag4Ao7AyIQAgjgrjOuqtvFhzqrW9hBCpc5UOkhw2Z46NpuXVd+s8i4z4yGe+sjXdWe4rfRTWb8gIGtFSCYhJEIfEWHpNXE2TGlxxcOGFflT3kXpOiNcZZe5dcotwkF5y/CoyiseqSue5Jc/TaProviicOXrDLeovgpn1TQCsJtuuilgd5/V4nzYI2hkRTm7tdj1lhKCuxdffDEK1RSn9sklXH65RWHikbpl/VUWZ/morTbM+qlbjpfC5ZJH7ShKb/nKrzy6tm5RXFF4nbw2Tc5fh7dtX515rdX5L1c/wqhjWk/GHGbT2MnIWUbs5sM8Fso3S1XPN+ra3me0LS/nr3q+0LayOljhWiv8q8qvwoD6VdUhVy8blo6JXJ+Svupdraqutszhxc+ciaktdgiJ2KXDYg3tfsLcJrth7PsOimJ20rB7ow4PdgyBD8ohFFEsDEGBoh0rKttdR8ARaB4BV2w0j5nncAQcAUfAEXAEHAFHwBFwBEZaBLAhjgAjJ3Ti4//ggw9uSTHQ2YBh4gGzEJiO6EySEE8CxfSashXWmfVoTznUvdU6kk/55VIX689dE9ZRRFmWVKc0zF7Ln7Y7vSZdLkz5y9y0Xkpbxk95lEY4yhUPBG3rrLNOPC+G84Mw4YdgbJJJJolKSHZrpMT9iqkgVpVDKkNuVZiNT/1cDw9EW4SVXOqFH7JtjQGD/zo73JbVjL+oXs3wGJZpNeY42B6hL8oNhMB2pfqwrJ+X7QhYBFBayYwm4SiJ//73v8fdFN0b5s8g7cqNF4P/pEwiDoVdFQ/tamVHLDs8uD84oFzvK5j/FJG2TJmmdO46Ao7AIARcseEjwRFwBBwBR8ARcAQcAUfAEXAE2hCQyZq2AONhZSK7OYZHWm655aIwk1WRXUESqKqs9FrhrboSzLaaP5fPCn5z8QrLlW3bJz8ulPLNXYt3e91c3ap4qp5KV3VNujSN8uIW1aEsj81v/TZPipvSqTwEXijvEBL3798/rvwlDOWGBGfKg6v0nMGBwA5Sealr42JCkzYXpzRVrupelU7xRRgoXi71T3nbMLVP6XNuUZqOCs+VObKHacxx5kdnm5Ea2bH09nUtApwfwoHtKOQ4U8Tu0EhrojlCruKreHRrnEuCEpqddv/617/ijiaujzvuuGh2FD7cO5wX5+QIOAL1EHDFRj2cPJUj4Ag4Ao6AI+AIOAKOgCMwyiPAR3zZx/6wBGhYmDNL2ws+qbA1TVPnui6PIiGwwlOXsm1YVV1IW4fSdkvYo/xpvOWpNDasGX9VftVFPO11kV9pi1ybjzRVdUj5FOVXOPzwy03zcw9yZkFdwtyNeJNH/tS1cVW8i+qmfDlMcmFKr7pwbdMVhSsfLmmUR+l1bdMpbathRflTfu25Lqp3e3gOi7wjolmiYYGTlzl8IICi4bzzzgv9+vWLJv/sDgx2r2rXm2rLbjlIOzfwl/EgHuJMDc4zwVwb5gM5t2PPPfeMZ3ro3u82+FD2QTn83xFwBKoQcMVGFUIe7wg4Ao6AI+AIOAKOgCPgCDgCjkAGAStQVXQuTHFlroQaZWlyceSzwtzUr3jxt/VT2pSv0trwXJiNt3xteJG/ip/N10xam6/Mb9tu/eRJr8WnqB51217EV+HiL35yVX57XZUjF37yy7Vl5MJsvOprw1J/nTTkUbq0TIU3yzflk+bPxefCyNdsuMoqqrvi5dZNp/TuOgKOQMchwAHul1xySXj44YdD79694644y32GGWYIjz32WDwfQwso2G3BTgvOJIPKeHB/n3322WH55ZcP8847b9xJN+ecc0azgpikgjCl6eQIOAKtIeCKjdZw81yOgCPgCDgCjoAj4Ag4Ao5ApyGACQ8od85FpxXqjEsRmGmmmUrjicQMBYfJs0LeqRiB999/P5r74EDW9EDhXC7Ok/jqq6/CFFNMEQ/NzqXxsHIEJJxPXXIpTBzSa4U34+aE9bkw8VSZaRqFKx1umsbG1fGnPNNreOTCysJz5cKjrK6txuXK6qqwDz74IBZl6y6/da1fdVOYruWmWDdzbdMW+SnHxnGd1kXXRW5ZHuJEyl/3WulSV/XtKBfhfEcTwnx2LnCOyohK9Ne1114bbrzxxvD73/8+LLLIIvHZRHvAHuXFUkstFc4999xw1113hZVXXjk+i+68887Qo0ePuIO1igfKj8cffzzw3MPEFSY933rrrfjbaKONRlTovN6OQCkC3377beB5Mc888ww1/5ZmbCHSFRstgOZZHAFHwBFwBBwBR8ARcAQcgc5CAOE4B0zyUb3ffvsN80Mkv/zyy/Diiy+G6aabLsw111yd1ewRnu8bb7wRTVnMNttsYYcddhjh29OZDbjuuuvCCy+8EM9rWXXVVSuLuvvuu8M999wTSDu8nvFS2YhhmADlUR0FUlpFhHcI81ZbbbUo0EvjR9ZrDg9+9913433M/Ty8EIIing2YItt///2HMIMzvNRR9eD5hcBXLuHWr3Q5V/kUV3adxilPR7uUYym9Ji4NK7tO48QbjMRLeJW5yjcs3H/84x/D5X3SDBbfffddVFpwoDfKh2effbYtO2amjjrqqCiY3WCDDcLFF18cHnrooaj4YPHJLrvsEtPW4cFz68orr4xnaaAI4rBwdm+sscYabeV1pWeyGccP664ze5hlpokbJrQGhude+CzcefM7YeDPvxZWY+wJxgxrrjtbmH+eKeLijTfe/ibcdMMb4T9fDlqIQ8Y5Fp4yrL5ytzDlFOOFb779MTzw8AfhmQc/GornGGOPHuZdZKow99xThqv6vjREPOWs8dtZG+VM2binQni+3+fhjhvfCr/8NKhuo48xWlh21ZnDEotOGyacYKyGoumH8OgTH4Wnk3KWW23msFSP6cOE448VPv70P+Hm294KH77ef4iyuuKCfkexuOyyyw5V3HPPPRffrxkfw9rMK/Xs3r17NIs2VEVrBAwYMCDeI2uttVaYccYZw6uvvhp4ljJPaF6rwaalJK7YaAk2z+QIOAKOgCPgCDgCjoAj4Ah0DAIXXXRRXNW09tprhwUXXDDaab799tvjhyO2l4cF8dHOB9fzzz8fXnvttbjScKutthppFRtpH7SCOYKRm2++Oa7i7CjFRkfUq5W2dHYePngfffTRsOKKK9YqCnMdpLd2z8syspK4T58+Mcmuu+4aD9cuS+9xeQRef/31cNNNN0VhByuV20sjSr888sgjUcCJMHN4UmwwL/NsYCffPvvs097uiPk7Yo6R4L2VCtUVeEkRoPRcp/5cmOpk4xTWqqu62PxpmL0u8tv88iutMM25SotLeuFgw7vCP7zeJ820HWHydtttl80iQTO7MLfddtvw5JNPBhZ6jD322HFeWHjhhWO+Ojx41rH7g10bLF4hL4qNOjtBs5VrR+B4k40djvzz8mGlFWdq7DAdK4wx+mjhg4++C5NOPl64/Nx+hZx33XuxsOlm84Rpph6vYXrrv+Hbb38O8y4wRTh8vwfCfxvXsy44RTjmuBXCIgtOHX5tjMuxxhwjrLpW9/DHPzwQnnvk48h3prknDcssPUNYYrFpwhzzThEmnnicIRQbo485WtjnoB5h/Q3nCFNNMT4DPHz6+YDQvdsk4fQTnoo8Nuk1X9ih94Kh28yThAHf/xLGaShJ1nx7tnDS8U+Gu296O6ZZbYPZwgEHLRlmn3XShgmxX8PAgf8NS684Q9h797vDNx8NKGxjZ0TwHn3HHXeEpZdeeqjdvJdffnn4+uuvw+abb94ZRTfFE2X+JJNM0lQem5i5iO+F3/zmNzH4iy++iO9tmtNs2o72u2KjoxF1fo6AI+AIOAKOgCPgCDgCLSHwyy+/xEMV+YgU6cBGe0Cj4uQi7OHFuSyN0sp0wvjjj18qDGDlER+vsqes/Ljw4MMUHhyQmqNm2sJqdFYJ8pGLYoPVfBwqibCCMix1RP0tvyI/HzinnnpqVLj0798/Klt0WGZRHoVTR/Cj7jmTTPQV8VUmmziIk0M2ZeaiTj7GAtSsCa+0D9QW3Dr9TTqE7oceemjc2cJ1SmVjinYOHDhwqDFcVq869wY8SScMbZ2q+om0jHOENtwLKdXpD5uHe4I+Tce0TVMXa5snV0fqhsAA2mabbYb6WAcTyiqriy2D9NzrEnTZuDr+qvKEZa6fxJ92Vt0zpKGviuqZw0r8cy4CCsx/YZ4lJeoMv6L7nPmC+dPWpaxfhAH3bm7eVfll833VmK47vrbffvvw6aefhllnnVXFtrll91RbIuNRu8CpSACd9q3ypP2NYPTggw+OYzGd46rGGPFQ+pwsm2NyfWia1pJXgnplpq05XAiH0jibPufPhdUtS+nkqg5yCbf+3HUaZtMX+ckjUntJK6ysS7o0zoaJT0e6Rc+nVu+TIn4dWee6vLjHNt5448rkrLiffvrpo+kt5jQ7r9Xhwf2KYHvJJZeMz8H0PqysQAcmWGnNbmGLTecJ7733behz1INh8hnGD0cfsVxjh/AS4Zab3gzffTZorrBFzjzPZGH33RaJiohDD38w/Pjdz+GQw5cJ2/daIFx3zRvh2Yc+Cttsv0BYe/VZw023vx2uv/TVsPTKM4UdG/F77LNY2P6RmyO7nXZdJKy1Rvcw9ZTjh8knGzcqLWw5Cy09bWMnzCJR2XLEEQ+H0Ub/bziyUbc9G0qV2+54J3z4zrdh/wN7hHnmnDz8uc9j4Y3nvwgrrtEtbL/1/OGnxm4TFBtjNhQdKDWWXXL6cPwpT4VXn/4sbL3zQmHDdecI9z/4QTj/5P/tyrFld5Yfk2VHHnlkYIGGVZQj+H/ggQcCi4bstwTvSjznit4HeAYRX/f9JW1X0fsMB9tjJi0l7lfe3Wx94MG8ZJ9DjOktt9wyYGK0jKg7z2Lumxwxv+kbQvNhLp0Nc8WGRcP9joAj4Ag4Ao6AI+AIOAJdisAVV1wRtysjOMM0DYKbvfbaK7z99ttxZarseWMGiZfubt26RWE79o6nmmqq+KH59NNPR6EwZpI222yzrBCWDwFMqjzxxBNRGEc5SyyxRFxZhBANEwSshEWQ9e9//zuuquOle/XVV287SJIX8XvvvTeuQML0AC/52F5eZZVVogC/lbYcf/zxsa18OGCeB1Mj2FxmtwRE+XxAV9Uf8wgIqFCOoIh48803Iw7LLbdcNCOTduqFF14YP7Iw7bPMMsvE6DPOOCOw62DDDTeMgkUE9ewiufrqq2MfpTxy19SDDzXqwEcX/brmmmu2CQFQ4FBPPuj4COIAzXXWWafxsTxxFCSeddZZYdJJJ439/NRTT0XFBv260korheuvvz5w1gJ82eq++OKLD5GHjylMZkGLLbZYWG+99WK/sCKZ8URbyAMdc8wx8SBQTEn07dt3qD7gQ7OqvyMj80ebXnrppbj6jkNC6/TJZ599Fm17o0iivCmnnDKOJxRcubFBvcruDds2Vog++OCDcbxz/3BvTD311LHGVf3Evcj9Qv24PxhX66+/ftuH9H333RcPWuU+oB+xocz9WfShzf31z3/+MwqFZpllljjODXRNY03eojpy/zKWuaegv/71r7Fu9MnHH38cdyCANx/PM888cxwnCKxyxPzDjgUOikVJh3CL8coq2/vvvz+AwxxzzNG22vKWW24JjFvGH+nKytOcQ37GzkcffRSOOOKIoQTP8OAe/PDDD+Ocwz3FnMd432mnnWK1MYN26623xvKYLxCeMP4lpCjCqqi/hAU4vfLKK5EP9yEH7DK3IJxjrDM+mEvZ2TDffPPFbKRnLiUOYQ0YgwXjqKhfwAI8OUeF+jP3MN5YPUof1Jnvy8Z0s/cybWOu4T7kntR8UXZPCTNcPQtYlc3uJFbkwocDgnmGoTTRXDfNNNPE+X7rrbeO91nZHAlvPRvYQQOmZWOM9GDK3Em/QYwb+oNnXdEcU9SH3CfcN0UkAXxRfCvhKs8KuGxY6lcdbHpbrtLbMPkVJ1fhctPwZq5tWusXb7mKs+0gTO2xfpunKD6XnnzsHDj//PPj4deYvITsMwthf9nzifTN3ifUhecAOz0Q3jIGqTf3OKZ4hndTl9S1as4ElzLiOTIslRrUba01ZwvjjTtmuPG2t8MlffuF8SYZO2yz1fxhnrkmD3MuOGV4+q4PhmpCj+Ubip3pJgwPPvphOP+sF8LAH38NS644Y9hm8/nCiqvMHBUbG603R6M/Qzj73OfDPde8FR5/6uOocFhrte5hnInGbChDfgnf//BLuOqa1xo7OkI4/OClhyqnZ8OM1dRTjhduuOWtcOE5LzQ0myGs0lCWrLvmrGHFVWcJF5/xfDj79IZiohF+5RWvha8/GRA+7P99LGeJRaeJ/KafbZLQo2Gm6tvvfgqnn/hU+Oxf/w6jTTxmWHGZGcJ6v529yxUbvIdzzgrvvlaxwX2AgkDmyLgneI7zDoEigfmW93HMOkE8h3gXe/nll+P7CO/vt9122xAma3k3ICxnqpDnDt8Z3PuMQ+rCs5t3bghzn/DnGcFcwLOIdx148j7FuxvzAu+GvFNAvMNz7zKmaQtn1fAso70p8Ry84YYbAu8rzAW8l/Kc164l3i94VmOmjfcdFnjVNaHpio0Ubb92BBwBR8ARcAQcAUfAEegyBHh55mUahQMft7wks+X/5JNPji/3c889d3zB54MAIduxxx4bTQFgCxZhOOl5mUZ4xEcCH8oIMFPiYwHhGMIqXs55sefjgZVDCGQxuQJPhEwIfxGOvvfee3Fb9UknnRTDqQPCKASQ4sGLP8I3BH2ttAWBKXWAEG5SP66vuuqqGIbAgQ+GqvrTfurPBxD1R0GCIAtBP8I5PlQsocAgPWWh2CA9CgBWSW2xxRYxPeaU+PBAeFuH+vXrF0477bR4ICb9wIcJ/YowAIwRgiBk5qOsW0O4R3vBjI8tlFl89FAnFEYIyulX0mKCiL7CBAQKE8YJK9/OPPPMIfLwkcaHIVvh+RCj7iieqP9jjz3WsOU8d5tiA0ExH45s/8/1Ae2t6u8UE+oFX8pB4FynT7A/jIkC8ODjknsBHBnnuXrRT2X3Bqvn1DYwoo0oQvhYZBxRr6p+YtxQBv3FOEcwSh+gXEOxwgft6aefHpVgCG5Jx73HWOKDNiXGNLt/GIvwoz60zVKzWJfVkV0G3LsixiEfyihhGH8PP/xwxJoPe4Tp3M+HHXZYm/JN+RiP3PuMHT6uGcf0D2X/6U9/iteMVz7KOXQWITPXjFWEA1Xlac7h3mS+YdU+fC3Rb4wRBBUI/FGsMOcwxjXOaBvtwkwK9z+8UGhxjyPc4JDasv605aX+Z555Jo4nKXlRWnLPYloDAR9CdcrhHj7llFPi/Uld6GvGBnXh/mMsYhIs1y/c44wP5veFFloopqH+zEUoHrnvwbVsvq8a082OLxQz9DOKDIStVfdUipueBYwF5jPuAcYDWCGcsnMdzw9w5BwThD1lcyTCJT0bmDO5J8vGNIqlc845J5rIY26QcI22/fnPf87OMdStqA+pO/eBiPrmiDQ2Lnedy1cWBj9bNmlzYWU8OjLOtq8j+Zbxsu21/rI8RXE8f7mvULhJsaFnFmMRAWbZ84l5q9n7hLmL5zbPABS6CDiZO7jHeW8b3hUbRViOaOGzdR9kbujFl75o3EMhDPjmp/DOu/2jYmOOOSfLKjZmHZzntTe/Dj/955fY5H6vfBlddk+wS2LmGSYaFP78Z9F997Wvo6moSRrmpqacccLw4SvfhEsapq6++fqHMMt8k2UVG5M2dnFAHzXOxPj5+4HR/3KjHBQb88w1RQw796znAzNPt7knC7PMOklYfZVuMd3Dj30U3em7TdR4Xo4e3v/w+6jUIPCFFz+PcfPMMXl0u/KPXY+Mb5R6vFfz7gHxXs9CBZT/EEJ/FlLwPcGCAZ4jKBfZ7cFcznOIbx6ekyzs0vseSm4tUGJxBe9oKiMyHvzHWRd8+6ywwgrxfY0ztNiVx7MZ4j2DZwoLiZgLOFOGd2TK5B695ppr4vsEcwb3KmGXXXZZVMAwZ8CLOYUFWZzVkRLPIt57wIL28Fzm/j/66KNjUt4veF9BEcT8kmtDylPXgxDVlbuOgCPgCDgCjoAj4Ag4Ao5AFyLAxzk/XnL5uN55553bzJuwE4IzJvbee+9YI16CEVJC5EGQygpY4tnizwcAAtiUEJCj1OCFGqXJvvvuGz8uuOZFGz6qB2ZcEP4ecMAB8cWdD3F2aSAwhwcrWRHuYt8cEzcIGGXyRjyaacuOO+7YZrqI9rJ6ChIv/M3WH/MQCDQR7FM/6pwSK70QEqF4QFjGhxBKAz5WujWE7AjAENgirK1LrFKGD7spwLh3795RSClBHDghfEVYSDx1RHhJPAIOiHbTXnat0K+LLrpoFAYifEbISb8g4EVgiDDQ5mHVGHxZ/YYAVeVaLGOGweUQDuX6oE5/x8zJHzwRRkMqlzGV6xNw5yMWxQEKAT4uUQCxyh4FRq5eMv1TdW9QNjuSuH822WSTKGDmIxaq6qdrr7027mDiQxk8uS8ZGyi+KJ8+RJnBWKFPuGfnn3/+oXZhxMIafygDuI/4OKb/GBd2NV8rWJfVEQXObrvtpuLjfQoWzA0IFlBSUA9+KAH5mM/dIwjkWZnIBzZpGa8oDsCROQHBPfmZkxDK04/wQeDPh3lVeRofYNqrV69wxBFD79bgIx/hBxjRDwiz2Rlhxxl1QSCAoJA01JWxxSp9hNRlWFF2GamOuJCumRvoe8YHghAUEYwR7jsUR1KiUV+UixB9nusXFDPMVSiHiOdeYS5HiGPvpbL5vmxMtzK+1M603UX3VGyg+VN+cNpjjz1inzCXMp547kCkAf+ePXuGww8/PCqq6s6RqlfVGGNeZYUuOLL6lTGMwAmFHsqg3BxT1of0gSWraJBfLumK/JZHV/qpj/2pbNVTrsLL3FzaNMxeF/lzZdi0Nt6G1/GT16azvPAzjnSP6Vpjt+r5lKa310X3CYJRxiSCU+YqhLzMHyoTHk6dj8D4jcO0oR8auydE3/84yD/pJIMUCwqX+788g5QNhH//wyD/pJOOG8Ycd4zG++Ig8fKPg/ny2Pjxx0FpJp58nMgKZcc3n30vtkO5b7z1VQxbavHp4kHkM8w5SVhkoali2DjjDDL9yiHiAxu/ffZfIhx97Aph6y3mC+80zGqd2OeJmG68TPt+aJzFAU066aB6xIsu/Ft33XWjQk/zP++vvBfxzsc3A88CdlCh6GBxEe+zfGuw8IX3AIj7hPco3hf59sHP7kbSQMzPKENQdKTmQ8nLuw4LgOCNwoI6WbL3IX6I92neISmTbwN2V7IblB3AfAPxPsXzX2R5KEwu77bUm/LhySIUlKN6DyEv/Gg/5aHcqUv1v1TqcvR0joAj4Ag4Ao6AI+AIOAKOQJMIIETlZZeXcV5yedlHeI3gnVVAfHyz+hyBl+yy4iJE56OAlcUI94hPiQ8IVqgibONlmpXx7AhAUUE4K5hECC756Ib46GA1MS7CJ/wQ9cHPClwIQa8+Arhupi0I/STkZTU7HykIIy01U38+PKgrRFtQCOQwQSiNgIuV1Hw0IYyEEJg3o8yImQb/iQd9h8knVq0j5FV/0T8QH0UoouhThG6s9qf/+KCDKJ9+xUUAB77UF4UIOPOxw8pnVpzqw4e0mJrCJQ2HeNNvdWiBBRYYqg9YYV3V32UCI1tuUZ+wGo0+YHU7wnI+UhkDfEgiTGeFejo2qu4NdsqI+LhlVT/3Byv1NA6q+gnMIXBk1R6CJ+rKjhTGE8JalEvsusDsAOYSGHMoN3JE30LLLrtsXCmIH5MD8IPoxyqsY0LzV1ZHFAwaS2RBEQCerD5mDmGMsGIYog9RriHkZWeTJcYlQnGUFIw9iHqSlg95TERwrzOG+dgHB3YoaLXh2WefXVqeymIMYJdaY1nhKg+eYIv5IHBnfkBAKGJHCX1EuWBMvyGUpF3Mp2VY0Z8oRJol7m/KAh/OfABXFBQILvhRNqtPGY8oJ1ECMZZz/UJ/gC/zEMokxgPESk7aJSqb78vGdJ3xVfdeLrqnVMfUZWUtONEOlBbsJEP5JbNd9CcHFzM+qUPVHMkYs4Rio2xMs6IXhZNMJlIGeHPvwouxl84xKDaK+pC8KQk7xh1+uWm6ovA0XTPXKjvNo3C5xFu/0hNGvVJSeJGr9OJpeaRh4kGe1E9YLi/hkHjJtWFlfuLaS1XPpzL+RfcJ73MQ7wCaq9itlb7zlPH2uPYjMFqGhe6CxjniWWrcKoVUFqdMoxUxVoLB7n23vBte7P1FmG/uKcJxfVYMPzUUI4suNMiEJqalLH3c2NXBzozpp50gTD7FuI1nzdThifs+aNw3NtWQ/rK4IVN27BXvmey45F2P9zLmep7PPLchvkN4H+d5z7MT4vnK9wbKEN4PId4FeLcXsTsCxT7PXJSGPO/YPZEScwjv3byPnXfeefFbA5OZej9P03NNHO/eEAqHbt26xXdvvp8gdqLwHNEzOwaW/GGWlncodnnw/USbqTc/nd/B7pVW3kmGfjKVVMSjHAFHwBFwBBwBR8ARcAQcgc5AABNQWmHEizwv9giAeIlH4KcPe170RXx4I7SFlNfGKx0CN4ToKDZkSxahMS/txCEIFSFoErHVG+JDg3Twph4IeyEEEnxsUA+76rGVtkSGBX/N1F84wEr+HCYIvBBGI0xEgMuPtiOQaJWk6OEDCKLfWJ0lkvACIR4EbsLYfhiBsQRoagN9BxGnPrLtsnnEk35rler0t8ZeVRlqA+nkp+7kZ1cKpgP42EPoiUCRj1N2BOWo7r1hy7JlEl7VT+oLlDuMCwhlBsJZdtNwECY7QRCq8pFOP/KhzZiyipWYsfGn1Xjqd8JJK6qDtdLKraqj0llXefiIVrsYV9zDjJ+U0vFKPPc2hAkGiA91FBuskpTQGaEC/OqWx5yUU2rAX2OYnSEa93KJh9L+pGwUgyLVo6g/la4ZV2OK+5T+hxjT3RuCclZis4sEZQ/CGzBDuI4wxwpxVR7KFRRk1BPhj5QZzKl2XqUc3XMqX3NAioGde5ivSQcuRXO3+KpORa7Klavyq9LDHyEQpD7FTzi7VUTpmCuaI5VefVs0ptn5BTGvaIxj5oRfESF8K+tD8bH56VfCq1zlyY0DxdVx0zroWi48qvyqA+mq6p3Gq46WR1GY8ireurm4XL0VZl3rF08bJj9x1q+0VS5js9nnk3jq/pCr+0TPAs2V1KvuvSfe7rYfgW/6D9p5NU7jnA3RuOMM8g8YvLNB4XL7fzMoj9IRPu7gHRTkwWwUO0DGbfC0fLXL4vv/DDrzSvyK3A/e6h/+dNADYc0NZm8ofCcI//rou/DpZwPCVI3Dxl99ddBzV3kvOPP5xnv8WGGe+acIl1/227DX3ouHSy5+ufG8GbquqlNR+8Szs1wW+fBuwDsTu1zZPYFSA7NOEO8UzAf8WLgjYsGO3qkJS+8Xdl+zaAPzkCgOUTqwQCZH7JZlUQaLBt599934PsdCJKsosfnSeYOydU8rHWF6ZiusyGWHMt8c1JE26VmYPueL8peF/28kl6XyOEfAEXAEHAFHwBFwBBwBR6CLEGClPSZjEDiyJZkVpXwM6CW42Wrw4YCACKEwq9YREvIRgdCHF3cE4ZhsgqypDVauQryAIxiCBy/g2KdFgAdhhgbBXfoBECMbf820hQ+aHNWpfy5fVRgrvdj6jmCdtpZ9EOV4gQUCS7CBqCeCC3aYIOCmv1hJhvCYfgRn4lilxYos8uOHpIyIFy38wYtV6gh91W+qlwTBCOUh0hVhrfA6/Z22v9lqI5QEI1a0s0sDAS8r6VDq9WwonbRzCL6qVzPjqag+Vf1EPIJi7j/GCMS9Qnv5AOZeQUjLLgMwZWfVpZdeGlfdKT0CWs4O4J7RR7k9V4N7UVQHa6WVW1VHpcMVduSBqBdmDrhnEbjxkZ8T9GpMajyRlxWVkHixE4H7H6ECggJWHeqMH6UpKk+r8yPDgj8Jw9ndovFt5yhbF+4tEXau6UN2lTSDlfK36rITBqUV44BV2eDFuTtgqLqIt/oFAQ87LjDDxzzB2EFZ1AzRxqK5h11orczdzZRflFZzDveOdijpfsjlaXaOrBpj7AaDGBvgzZhHsMUKYFbvsqtOpP6o6sOiZ5345FzyiD/x6XUuT1WYrYf8Va7lqbQ2rBm/8EzzwLduW6vqoPgy18ZZP/XSdVpHrvVcZF5hfHKP2LmlmedTjn8ujPkMRTbzAu8b4CSlRy69h3UOAm+9801YpefMYa7GeRrQGGONHmbtNujcjbcbcdDoY44WJp66sfjnowHxmjzQnLNP2nb/4ofefqd/GPjzr+G9D74Lc80+WZi9cRbGZ29+F6aZdaJ4SDkKj8/fq7/Q5K5b3w6vvvZVNBs15yJTho3WnSN81TiX48E73wuYptp7t8Wiiasjj3g4/Dzgl/Blf94nRguzzDRxGH+yccK/3u7fGFe/hqmnGi9MNPW44bvPfmi8c/6vrrHSw+CPBQcXXnhh/L7BnCdmb0V6X2XnqHb0KU7vAbq2Ljsc2BmISSfeQ3jOamGQTce7Ovc08Xz/sNABM5WcecOOSe7/ziSe67wnYn6O9wPelXh3BI+OIFdsdASKzsMRcAQcAUfAEXAEHAFHoMMQYPUpH7u4CENRRkip0cpHMIJHBJBswUbIhjkUzoKAPx8RWj1IAzBNdcEFF8RVSZhwQTCAoJIPC8x5sNIb00isvkIIwAopBNBFHwV12qLV2qz6ZvW2NddCnZqpP+nrEsoFFBAIuVhx1awZKoSnfJhwHgZCMlajYfKIsxjoM1ZmsRIbe9sILBHeo0jhQwbhBgJldicg6EOwbIUqddugdAhnzjjjjCh4xwwVJPNB7DaAWEWOkBkBdLrCLO0DlFdV/Y1pGdv+WEgTf5iQ4YwXFD+9evWKuyJY/Ua46pfWq854qqpCVT/x8c3h4OyYQrnBzibMLzHmiaPNnHcDRtwb+iCXsoKV+pxvwjim30nDeQ+ctUFfIOxlbIhaubeq6sh9y4czwm7Kpq7Yc8bPBz1CBe4rBO7MMWCSEvcDSj/GDQod7nHOSEBooPQoXVl1iWkoFAmYpkJxCjVbXlo+18xP3B/UEwEEq+lR+lpilxXtom6s/qcfECDQb5xbUYWV5dVeP8o55gDseLNbi/pCzN+MbTBM+0W7CpgTuDdluox8def7sjGN+cGqe5myOoMYOygfECTRNnYK8izSyvW0zKo5Mk1fNcZQmlI+9xumF3m+cHYJ9wDmxKB0jqGPyvoQIXgRcd9J4G9d0itOebkuI6scsOnSfPZa/iLX8mmvX2Xk6pnG2Wv5KT+Xl3CbRv4ytywOfjli/kO5wTOlb9++cX7HFJyozvNJaeu6PRsKe8Yipggpl92y2nVUl0dnpuO95bnnngvdunWLzz5bFvVkjuW9hWc2czPvPUXE3IXClnccnnnsWORd0QqpmS9RcLNCn+crO5TZEan+LOLd3vAbb3oz7LD1AlFh8Mm73zYUABOE7rNMHN7/4Nvw4uOfRvbb7rJQWG6FGcN55/5/e2cCdlVV7vGl1yGE9IqoSILgdFWckKGrqCCGWaIohKWpoIiZQTGIIgWGjyaIkhHKVDgAYpnJNSUR5xwuGohzitwg8IEcU0mTa967fwvf02K7h3POd87H8fv+7/N8395n7zX+19rT+1/v+z7n/nDPKvfUQ685XD8d3L6FGzqqS3RP3+CO69HWfbThn27hPRtdO97+X8vd6BFd3LAhHd2eu33RdenaKrrvb+EWPrDa/f3tjVYUxbU9sjo8YU/Xarem7vCoDS1aNHE/mbjYrXn1XbfTl5q6Pqf+R3Qv3cZ97D6JrDjecYd1YnGRcysiguX9v37oNkSxPx6NAol367q7Gz3mcPfis2+5087a6D5p/l2vFteEKqRqFy2IgrTgvY/3BAgJE6wseCdnznBv5lnOwh+sHFhEkiY8V3nG823DPAsXxIR5uJ55h+B5CLEAIUJdfM9Ue77RDp4bkBtseW9i3+43xT7nw/7E90VsxBHRbyEgBISAEBACQkAICIHNigB+X1F0szp92rRpXhmEco8PYRQypkgttpGYThOYmZd6VgfzIo+iCQUTx/lINUGpgzKTl24+NnE1Q1tQZpKWjwjIDcgAXsZZnUyaNMnrC/lQoKIs5iODj484sVFK+9PakXSctqMYIxYACo5S3VChAABPPsT5wIfgQGmBYpsPMjBEkW0B0VGyMn58yKMcQKHJRz4fbXzUgWm5wrihEKU9KAsY2379+vniGB+wpb2MKcpWlKvmvoZE8TEAl7zxjve/1LYboQP5A5HAPAQTXA/YB2+8XaNGjcq8NoppQ944nXzyyX6FN+QFRBRzA+KI43wAoySG0KPdYI7Ch9gwZ599tq+elemMA2khNkiPwhalEMpVxpyV6VyDSDnXVl4bqZuPfVYkQrJwDTHPWC0ICQC5hpIXBTPKX1u97Bv06T/mNZYdWNCAA2VCvlEOBIYJ8wtiAzIKhQHpEK79Uuqz8sItyg1wpX7ICnAzN15WD0oQ4hOhAAFf7iH0h3FGgZCHVVhfXfchwiA1iPvBdci8AWeClLJNGhfGiWuJ+zJ5UPhwLaCk4T5RjGTN6XLmVzF1FpOGPsyZM8dfIzw7CAbL/SftXpd3j4wTInlzDNIe9yPMd/4gu7jvcY9hnJD4PWb06NGZY5ikiLa5GJIZHLPf1BPu8xtJU+xzzspkP03CNLaftqUMO2flWf0ct/blbZPy2jHbhuWGx9LqtzS2DdMl7duxcBvuU479tn3rl9XB84d7A/dDiA3uy+G7UDHPJyur2C2rxSGVudaZR9Rh7bRtsWVVMh2WTbhWxBoRi6VBgwZtQmxw3ycWCPcjXOqxZSEF7x1J1n60jYUfLI4hRgH3IAI8U8/FF1/sSQysqK666ir/LsJ9nt+k555hizIq2cewrMUPrnEzZj3rTjt1Pzfioi7ui023ie69691Vk55076zdaKHR+YgvuVP77ufuf3SNJzbWRgQI5yEthgzFYuJj1yzKN3nqUvfM4nW++Dkzn/PER/ejWkeWHTu6XXfZzj3x5Fp39ZWLo+srbEH2/pYRGTL8ws4RNlu5Vavfd5eP/29304zn3Cf//D/31mt/dxOidgwccFA0Toe419/40Nfz+OK1btr0p90Hf9sYh2NClGeHK452/c880L3x5odut5bbudvueNndPnejK9nsFlTnLM8AYmVdeuml3loDMsuEZ/WQIUP884J5wSIcFgRwz056P7F8bFm4cc0113jSxBYThOfZh1TH1SPENu/oEAxYnBMAvD6uPernW4CFR7iE5H7EdYHYe7n/Uea/LaIVHSVMsTJrUTYhIASEgBAQAkJACAgBIZCAAApnlFmsfrMgdSSD1ODlFyWd+X9ldR8r2lB8sfobBR8KM4S0fDSSNmkVHUpHlGesnkOxwypVFDsoNfnYQOkzfvx4b11w+umn+zbx0YHiCKUtwos4K9dRSqG0p34U95TBS3s5fcEdA/1C2Y+il/Z3j1Y1ojRGUDzwUZPXfus/7UGRjOByBCIBosT64E8E/3AJAakCBriOYRsXK4d+mjKMNOQDC5S8baMVjgir/IkXAT7gh0UIilf7gAF/MKSvKJtpLxYdfNSxYhuXY7QBBQjCmNI3ymEsEIgnSC4+5qiHtChkfv7znxeCQjOXKJcPNuYQY8PKfLCkDygmUCCw0psPyPgYoKDOG+94/1HooxyhPMotZkz4oIMcMFcxKJdY0Wc4J7Ur69pgPqFk56OVPjAvwZrrBbwZYyRvnLAQYJzACQxZbQrZgqKUsrmWsG6CiIEkYt4yPow5ecGb1YeMAcK400+sCSiDPEZAkS4Pa/pMfaSFtEKy2sh5FFQQMMwV5i75wJOy+KBH6Y5iDUUVuCUJK3iJfcJ1RBtRQHE9hfFCUD5A5CBcu+bCit9Z9YEJOFKmBRAlT1yYVxAFuJJgbjEuKM0gXm6MrMsQsKRftJNrjfEAe8YfScOKawPSJEkg98gf4p50L7D5ZtcS5CZtRHHJnON+Df6GS3xccIfEdUMbaTtjbONDG5Bi7vdZczpvfsX7j1UMc5t7G4oi62PWNRWWMXjwYN9miAzmF/OA/vNsYpt0r7P8efdII9VQTvEcy5pjlEk/UCQzjyDjWanPeJilIvnD5w99zBpDrom4gK9J0n54LCmdHStnGyrlbD9tm1a+tS9tSz47F99PKjNMG55POh4es3aHecJjtl/qlvK4DuPCNcdzjDmCZQ/3tZWRZZG9R+U9n0q9Tugrz3jKZS5yLVx22WW+ft7BzIoo3s5q/4akoE2sYp84caK74oorCgsyqJvr53vf+567/PLL/Tso9/yrr77av3dAeMSFZy5kM+8mELfcB3mOjB071ltD8axk1T6k+bhx4/y1yP19woQJvkzakyfNm0zJS5J5vnXkRurIw1u5Xdo0c/+7IXIj9crf3IMPR8GcPyUGjujeOuprC3ffA6vc/7y4MQ7d9jt/wXU/urVrvfcO0XvaFm7tyvXuD4+tcetWvV+o69AuLV2Hw3ZxO0RWFn+PYl289MJb7vFH1rhPPv7XPYLEO7XazvXutXf0TvCxmzf7xUJ+dgg0fsHIju79N/7h1ry23i15ep17N3InZdK0+bbuiP9s5faI3Gdt9+9bR66mPnLLV7zjnnxqrduwfmMcvq2+8G/uqG67u333b+6aNNvGvfPXD6Lz69xLy/4Vv8LKi2/f/nBw/FDFfnP/hMDi+WzvzVY47yRcj7yX867EIi6ejzxDEJ5D3LvbfeoKN8yHpQYxccK4dnbetrwfMA9pA+/ZPAtoB+9BCO6suPb5zuL9lWc/RIwJ7ys81yHDTXgO8R6BZS7v1Cyw4N2csnnn5t2FNnHP4n5D/Tz/eNflWQ/RSV7uPbxfcK2kkYVWp21DIlbEhqGirRAQAkJACAgBISAEhEBNIYAy36wiKtUwymT1HaREqMQ3YoOVclh28IKOEjxMY23g45yPDs7zV4zk9YUyqZMXdVNaJJWb1v6ktPVxjPbEMULxTV/4QDFCI2yL4cc50tRF+PhCecCqNj6Y+DCk/KRy+fiirfH2Wv1pY2DtTRrvpP5beaVsmZPUE36oWf6kduXNJ8ubtc0bJ/IyjkhSuyx//FoifRIuxbQ5C2vKTZKsNlIncyLefuYC5/KuN6svqw5Lk7Uttb6wLKwwIL6whmCFMC7QIA+HDx/uLULCtNyXmN9J85908X6gVGD1aJKgmMtSkiTlsWM2Z7nGk+6RSeMCRvQv6Z5h5eZtbU7m3XuSruW8sks5b8TGyJEj3YABA4q6t4fl23UQ3iO5v0FeTZo0yRNcKIFCrPLmmJ3nXhkXG6/werBj8TFEARw+o0gXiv22rZ2L/+Z40jFLX+w2bIvtx7eUZcfi5YZtsP34ljx2LL4fLy/8Heax40nH7Fx8G7Y5ad+OxbeUY8ds3+KxxOugPTx/4vfIMF3W8ylMl7cPufbTn/7Uk/coNiE5IVZpGyvJTcGaV06lz0O6Q7ZDbEA6xIkNiAYsW1hUYfezefPmuWuvvbagjA3bBDFLP0eMGOGJa85xb0aJDHmBxRT95n7HinkT8kD8LFiwwA6lbutKbFBwpGt2235xa086bIhiVcSF2BvEzojLNs22itq+hfvHe8kBwYnPsW3Trd2GiLT4Z0SalCPUYSRFWn4sO7aJrDo+ep/3vuRUW227pds6Cmj+0foolkxk8VGMVJPYKKZ+vnu4X3NNMkeyhGce1rAszGJOQijkCe8BlJv2npCXvy7ni7nfFFt+eM+SK6piUVM6ISAEhIAQEAJCQAgIgXpFIEsJXW5DKDM0/04rJ3xhjqdBYVBMGWG+vL5QZpLCKSyD/WLbH89Xrd+0Jy58MGXhUw5+8TrSfpvSIel83kdc2hhktTep/0l15x2DHEiTpHblzae0ssLjeeNE2qzrICt/Ei7FtDkL67Dt4X5WG9PqzJsLYfnsZ9URT5v0u9T6wjJQchCrgZWPEAKs7MXyJskFXt49JN4PLKZQviUJ58qVpDkblpU0LnXByMrOmpOkKWd+WdnlbvOwSCo3qZ0oO5kHjCFu9kJSgzLy8Ms6n9TGpGPUA8ahkM4EpZX9jivw478tD9usc2G6pH2rj3O2b9vwWHyf3/H2Wj5rj20tLdtQwvPh8aT9tLThcas/nj88nrSfdyxeXvibvPH7Qnie/aznUzxt3m+U/gQ5hkhg5TjWpLgs3FykBu1lFXyWYBHHivLw/QISBOtfrDfibWel/fnnn7+JlQzWrVyDrFJHevbsuQmuKKhZIc8K/foSyIA0coI2JJEaHM8jHLDO+PDdjS6hSF+O5NVBmRAVWe0nzccffRL91a0tlFOfwr09fn9Pqh/LB8gw5haLEJKsspLy5V3vSXkqdayY+005dYnYKAc15RECQkAICAEhIASEgBBoUAiwku6SSy4pBLttUJ1rwJ3hQ45xS1KkN+Buq2uNCAFW90Iy4BYMwX0L7srSfGmXAg2uppJc95VShtJ+FgGUTLjTMPd5n01R+hGeUcQpQcHatWvX0guoUI44sREWGyrpOZ73O8wb7sfzhefC/SSFPufTjod54+nCOm3ftpYv/tuOx7d56fLOx9tm5af1y47bNi2/lbO5tjyvhw0b5l3tQAiYlUQesbC52mv1ssI9Tu6YcphzxL7BMhDh+uC+Grr/xGXm9OnTvYvKdp+6EcLlTyi4M8QFHNYiEiFQDAKQH1g74aqKGHnh9V9M/oaURsRGQxpN9UUICAEhIASEgBAQAkKgLASIycCHaGP+MCgLuM2ciZWRuMyRCIGGigAKMP7ibqQaan8bQr+ImYLyupKEa/cofgvxk4irUclyS8W7LsQGdRWj1C+1TaQPn91p+1nlhu1K2yd/eC6rvPBcOXnC/GF/OB7+TtuPpwvL21z7WCxA+BFLI8kl6OZqVzn1Gu5s58yZ460tKId3ktGjRxeKhPTAvSlEDu4DQ6sPS0SMoRujeElYYhHLSiIEikEAYpA4TmyT5lUxZTSUNCI2GspIqh9CQAgIASEgBISAEBACZSOAsiZLYVN2wcpYVQRQKjT2D7qqAqzCawYBWyFcMw1SQ1IRKMaNSGrmlBOMfy3MgaznZFyBH/8ddi3rXJiumH1TMlva8He4b+eTtmF70vbDfGGa8Hix+3n509odPx7+DvdpR/x3sW2rdjqIuSx3ldWuv9TysdYwYtnyEjMD4Zpkxby5owr7RZyEWbNmueeff94NGTJkEysOK+e5557zxAeET9++fYtyQWR5tW3cCHB9Y8UncU7EhmaBEBACQkAICAEhIASEgBAQAkJACAgBISAEhEAmAliMSIRAY0IAt3/EOCLekZGWq1ev9tZTzZo1c926dfsMHMTMmDt3rnvsscfceeed5y1V4okInj5lyhQfd4OV91JSxxHSbyFQHAKbRn4qLo9SCQEhIASEgBAQAkJACAgBISAEhEAjQWDVqlXuxRdfdPgKrw8hWOvixYvdsmXL6qO6zDpwJYKSqr4FVy38NTSpz34xdrfddlvB/30algRl//Wvf+3ee++9tCQ6LgSEQCNFABdwBGq+7777/L2EeEeLFi1yXbp0SbQYxRrnt7/9rbvzzjtd7969XYcOHbwrKu4vBBxH1q5d6372s5/52B1YajRp0sTff0iDpYdECAiB4hGQxUbxWCmlEBACQkAICAEhIASEgBAQAkKg0SHwm9/8xhMbrCrF13815fHHH3e33nqre+utt7y/cla0bi5Zvny5dyWy1157uXPPPbfemoFC/pprrvGuZEaMGFFzblsYG1yo7Lbbbo74RMVKffaLYL6/+MUv/Irpr371q4kKSGs3isQ77rjDBzXGZUzoTsbSaCsEhEDjRGD//fd3p5xyio+l8eijj3qSAoL2u9/9biIgkBe//OUv3RtvvOEJ+qeffrqQDtdVl112mbv33nvdgw8+6O+fxNcI5cQTT3RHHXVUeEj7QkAIZCAgYiMDHJ0SAkJACAgBISAEhIAQEAJCQAg0JgRWrlzp5s2b53bYYQd3wQUX+K6/8MIL3hXHcccdV1UoWOk6Y8YM9+STT3rFTilK82o0DMXUXXfd5Vfm1iexgaL9nnvu8XF/vv/979e5a0ljWmqhKPKwoHnmmWfcyy+/7P7yl7/4wKVZYzR79my3Zs0ad8IJJ7iDDz7Yr0SuZL+y+sDqaqwwBgwY4FdFZ6XFvczXv/51d91117k99tjDfetb38pKrnNCQAg0UATGjRvnLSzC7mFNcfbZZ7unnnrKE+4EQYfsPvTQQ8NkhX3ifp1zzjmF3+GOxQRr3769GzNmTHiqsN+yZcvCvnaEgBDIR0DERj5GSiEEhIAQEAJCQAgIASEgBISAEGhQCLCiHZ/hKG1Cwc0GFhOtWrUqEECbsVYAAA/hSURBVBvheciHDz74wK9qTwpOS5kEWm3atGlq8FrOE0DWlDxWPuUuXbrU/xw0aJBXMts5tlY3ZZvgJopArqyyT2qPpaNs6iQQLGL5wrIsrW333ntvr3zCMiFJKBMll/ldT0oTHjPXUtaG8Bz7YMe40KaLLrrI96cU6wH6ZGMTBnnOG1PGg7xZdUGOTJ482RMV7777ricpLIBuvB/2+/7773esVj7ggAM8sZHXLwgd6z9l2HgzR8P+WPm0GSsQ2s3YmtAfgvbuuuuu7hvf+EZhzDmfVCbzEJcxd999t7v55psdFh477rijFaetEBACjQSBfv36JfaUOBs8E7nncb+IP7vCTNyv0sqxdJ06dXL8SYSAEKg7AiI26o6hShACQkAICAEhIASEgBAQAkJACHwuEMBNxu9+9zv30ksveUX6Tjvt5I499li/ShVLiZtuuskrl7FWYPUqK1VNiLUxadIk72KDQKe457AV+yjUsW54/vnnfWwIyj3++OO9Qpv8EyZM8IQHfskJqPrlL3/Z4XLDZMWKFd59B8p9ZP78+a5z586O1aus8t9nn33cm2++6Yi/8eMf/9grlliVT5tRbhPUmPQ9evTwRAMxOiwfyqhXX33VK79x8bHLLru4BQsWeH/nLVq0cN/85jdd69atrSmFLfVhrUJsEfJZmXvuuad3R4LVAkosFOH0Jy4QCtOmTfNBYekH7puQjh07ekU6yvpf/epX7k9/+pPH/9lnn/X9gNSx+CKUjQXNn//8Z9ezZ093xBFH+DKuv/56Pw59+vRx++23n+8rJIIp+g877DBvKfHHP/4xcUzbtGnj/bwzFyAtUPhzDAU/Cry4QCJA9GB9gWsy2pwlEydOdATHhajAzRO+4/ElH/br7bffLuDTtm1bR1tJz5w65phj/BxYt26dJy6wqDBFIG3BjcsTTzzhxxDCBD/4X/nKVzwBQh207/zzz9+EoHjooYf83OMawCUMLmaYgxAj22+/vc/P/GZ+9urVK6t7OicEhEAjQwDiPIv8bWRwqLtCoGYQELFRM0OhhggBISAEhIAQEAJCQAgIASEgBKqHAArhmTNneqU+BMLOO+/sWFWPwv3iiy/2SmIUyQgBlVF4m5UBxx555BGvKMa9EMr+119/3ceC4NyNkZ9wyAhW3bNSHsUzyuUrr7zS/0a5jUIZiwwU3hARIbEB+QBRYLJ69WrXrl07rxDHpRDEwPr1673i/kc/+pEP3oofc9qL+yDyohinvZT7yiuveFdEWFvwR/BX6oV4gcygLEgSiACwGDVqlFVd2NJPlPgQB9/5zncKZdI/yBHKgOzBNRMKcY6HAka0HcU7rkuoh7RLlizxbUIZj8/2Bx54wBM01Lfvvvu6s846ywe9pixibEAyUQ59g9iAJMDNE2TSt7/9bU9qTJ8+3Z+HFCBOCW5TwJhxTBpTxuLaa6/1SnxIKkiWhx9+2GMyduzYz6xIhvjBHRdYQhDkCeNncweCiDbwm2DeCP0K8YFkwNKCIPUQFowlsTywDgEXiJ2pU6d6QoI5C2HE+NnYQ+rgPg2Cifz0+/DDDy80E9IIV1NgiQsZCCtwok3gjYAt40j/RGwUoNOOEBACQkAICIGaRWDLmm2ZGiYEhIAQEAJCQAgIASEgBISAEBACFUMAqwUU9SjGL7zwQjd8+HBv4YASHPdTxEHACgNBKU58h9ANE0pzAqaSj5X1KIApCyU2+VmBj8Ka82bhgBIawSKAP1x6/PCHP/TWHP7Ep/+wFhg4cGDhEKvtWYFv+SAgBkTxErDWQCA1IC+wKKE+lO78JmA0ymrLh5so/J2PHDnSNW/e3KfBzRB9Iy+EysKFC32ZSf8oB0IIsTJxPwXRQZlgglIdIidJyIPCnrgNtBMrFsgQU/BbmbhSAjv6jdhx9rHUYLUw1ingjpIehT91Y+nAPuQL+BEXhVgokDhYrKSNKcp/+g0m9IM/yJrf//733pqHekNp1qyZt2op1u0WVic2dxhHm1dhvyjf8MGaY+jQoQ5LE8gHSAvmGu3C7QtEx9q1az05xNhjcUQw+2HDhrn+/ft7wog5iBD0HQEbE+Y4ZIbNYXA+8MAD/fy1NJAk4Ay5IhECQkAICAEhIARqHwFZbNT+GKmFQkAICAEhIASEgBAQAkJACAiBOiNA4GcsFzp06OC+9rWveddMKOnvvPNOr5iHNMDdEMJx0oWC0plV7SjXISdQ2LPqHkUwq/JZMY/7JfZJg9IaZfzpp59eKObkk0/2rq/CmAicxBXQQQcdVEh3yCGHbOL2A+uNM844wxMurN6HpMGdEG6ksDhgxT8Kb46Hlh+77767d2tEwVioYAXASn76geIdl0m0F/IiKY5DoUHBTlgmpAb9Z5sWdBoyABdObMEEl12kD4X2kB/cwTQUlPH0n37jsgtXSQiEAWUeeeSRvlz6AZH02muv+fNYOtDnpDGF2MC6hvyQIAhKfaxAsGqBEKmLMJaQIQiEAW2I98vKpw24mmKL1QgkBH3u1q2b7xckG2QH/cP1lxEPkFLsY32CkA98ScdYMqdMsDiBIIGEY77jbqt79+6e3LA0uJkh/glzWCIEhIAQEAJCQAjUPgIiNmp/jNRCISAEhIAQEAJCQAgIASEgBIRAnREwxTLKbiMWzH0S1hYohbMExTOCgthIAAgMFMkmxKFAsJrArRJumEJBoWx1h8fz9iEvUHAjkDMQERAbHEdQYqP05hx9MQmDvEIaIORD7Ddui/L67jN8+i8sEywR3FKlCYSBYZeWnnGw9sTLQdmOEh5SA/dJ/NEGYqMgkCRYWjAmEAL0B7F4Jf5H7J+NGUQG5SHgwpjR3vqUEB/DwMaIc4Y3/WF82XLc4nwwdrSb/jMv+OOYzVH6QmwXrHQgdBYtWuTPQVCBLTFTEMq0MvwB/RMCQkAICAEhIARqGgERGzU9PGqcEBACQkAICAEhIASEgBAQAkKgMgiYUp0V/Si/IRiwAkAI9o1SF+UuUoqin5gVCHn79etXsLTAcqJ9+/b+XCX/UR9tZcU+cT5woYQlBm6xaAP9JJZFtQTSxgS3UggundIERTsEEAp6S487rFKEIOI33HCDu/vuu30ZWKhgCYHMmTPHu2bCioa4HVhtmFUH55PG1MYMKxtiTJAGwgBrhaRA6JRTrpQyl/LqADcjH44++mhvyUIe5jTkEP2A5KJOSBuzGmE+MMex+sHSCNdWt9xyi08Dtgjjyp9dJ/6g/gkBISAEhIAQEAI1i4BibNTs0KhhQkAICAEhIASEgBAQAkJACAiByiHQqVMnHzgbMoPgy7fffrtXllMDsR9QCptVBHEbiANBrIM8wWUVbp1QJKMwxhqA1fQo11FCV1pQYBMAGiX0lClTHIHJ2VI/LpSwCqmm4O7qxihYOopxXCeBGzFF0gRS4/rrr/d4z5071yfDzVIpss8++3jLAuKI4ELK3FBRBoQOAinBecYAMYuNpDHt0aOHt1YgqDexJ3D1tGzZMu86DLKjEmL1WoD0SpQJOQF5g+B6ij5DoGGJwZxlvrWL3HYhuOIyAZObb77Z9xHiwoglyDETI/zMdZcd11YICAEhIASEgBCoTQRksVGb46JWCQEhIASEgBAQAkJACAgBISAEKooAgbsJLj179myHgp2V7Vg6EGz6tNNO83URiwCiAvdGBOLmd560bNnSu/mhXEgGFNkojCE7zKVQXhmlnMddEf2YOnWqd8GEgpt+QHZwvBp1hu1DeY7lBK69IASIV5Jl5UD6pUuX+rZCFNFOLFtKEVwmQUbMmDFjEzdUlEEME8Zq/vz5nkwy91wQTFjmJI1px44dfcB18qDwJw8B2omjYq6fSmlfUlrIFwgFCC7ImHjMlqQ8ecdw6cUYY22ECy2IHAgc8GEcEMZi3rx5nsQgqDoCGUK8F+JrMBZgA4FBAHkTiB2ka9eudkhbISAEhIAQEAJCoIYRELFRw4OjpgkBISAEhIAQEAJCQAgIASEgBCqFAAr2k046yRMOrHLH4gEXSriLslXukB1Dhw71Fhe482nTpo3r37+/Vxqj9DYZM2aMj2WAGygsFrD4YH/lypVeQc5qfcps27atz3LJJZd4d0xZ1hS4DRo3bpxPb7EWCPTNMUiSUFBeo8xevny5Dx5NfXvttZdXzJMuKd+gQYN8/A0LUo4y3+oDm7gccMAB/nzcggEXUJAJkBQEnIYkiKcJy0IZf9555xWCdBMHw6wCIJSw3giDdVOmtSskaXr37u0gp1DqMy4mHMeKBesFw53xoF5zzRQfU+qgbhT/WCpgVcJc4Le5b7Ly49szzzzT9ezZs4B1/Lz9PuaYY3xbIZ2IZxHvF4QY/QxjrlAuRIzFvaCsYcOGeWscOwZBMnjwYE9qQC4xD8hj8xPLJHAi7kjfvn09UUOeH/zgBz64PBYuxH6hTYwdAgG0YMECb8kBgSQRAkJACAgBISAEah+BLaKVNNkR4mq/D2qhEBACQkAICAEhIASEgBAQAkJACJSAAIps/gi4jfI7LpyD2DCCIX4+7TcKYggTlM2hwjotfV2P12d9WDaMHz/eExFYixCrAeuGtH7ijqtPnz4e4yVLlni8y8G0WIxoD+OVRNJQRtqYYk0BjpAoSXOh2PqT0tFf2lWtsrEMYgziViaM1eTJk93YsWM9mWdtI94J7YnPz4ULFzpilEDiQZpUGgerX1shIASEQF0RaN5kSl2LUP4UBN7+cHDKGR2uJQTCRR+y2KilkVFbhIAQEAJCQAgIASEgBISAEBAC9YBAkjI4rDauKA7PZe2j5Gdlfn1JfdcX9iv8sA6Pp+2Xi2laefHjee1Jq79U8ipeb9ZvCALIs2oIZafNtV69erkVK1a4WbNm+fgnFlMD0ieeh9gsM2fOdMcee6w79dRTRWpUY7BUphAQAkJACAiBKiAgYqMKoKpIISAEhIAQEAJCQAgIASEgBISAEGhYCHTu3NnhUgs3R8UIrrlIn2bRUUwZSlMeArjVGjhwoFu0aFGu1RHjg+suXGfh0ksiBISAEBACQkAIfD4QkCuqz8c4qZVCQAgIASEgBISAEBACQkAICAEhsBkRwI0RLpuwFCB+RZ7ghonA1kiatUReGTpfNwSwxohbaCSVSOB04m5IhIAQEAK1joBcUVVvhOSKqnrYVrLk0EI1/22skjWrLCEgBISAEBACQkAICAEhIASEgBAQAp9DBHBjlBa/Iqk7ECAiNJKQqb9jxZAatEakRv2NiWoSAkJACAgBIVApBLasVEEqRwgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEALVRkDERrURVvlCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhEDFEBCxUTEoVZAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBIVBtBP4feh2Nmq576rwAAAAASUVORK5CYII="
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aabbd61af7df"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "673408ff9b26"
+ },
+ "source": [
+ "In this example, the search returned similar past action by the same service account on several occasions in the past, so the suspected action is deemed **not an anomaly**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NfLG816P4bxA"
+ },
+ "source": [
+ "In a timechart:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5FKlNvhU33or"
+ },
+ "source": [
+ "First, create helper function to plot the dataframe of past actions grouped by a given suspicious action:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "YHJmyNqGh958"
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import mpld3\n",
+ "from mpld3 import plugins\n",
+ "\n",
+ "\n",
+ "def plot_actions_over_time(df):\n",
+ " mpld3.enable_notebook()\n",
+ "\n",
+ " # Set CSS to style our custom labels\n",
+ " css = \"\"\"\n",
+ " p {\n",
+ " font-size: 14px;\n",
+ " color: blue;\n",
+ " background-color: white;\n",
+ " }\n",
+ " \"\"\"\n",
+ "\n",
+ " fig, ax = plt.subplots(figsize=(12, 8))\n",
+ "\n",
+ " df[\"day\"] = pd.to_datetime(df[\"day\"])\n",
+ " for i, ts in df.groupby(\"suspicious_action\"):\n",
+ " points = ax.plot(\n",
+ " ts.day, ts.distance, \"o\", label=str(i), mec=\"k\", ms=15, mew=1, alpha=0.6\n",
+ " )\n",
+ " labels = list(ts.past_similar_action.apply(lambda x: f\"
{x}
\"))\n",
+ " tooltip = plugins.PointHTMLTooltip(\n",
+ " points[0], labels, voffset=10, hoffset=10, css=css\n",
+ " )\n",
+ " plugins.connect(fig, tooltip)\n",
+ "\n",
+ " ax.legend(loc=\"upper left\", bbox_to_anchor=(-0.1, 1.1))\n",
+ " ax.set_xlabel(\"time\")\n",
+ " ax.set_ylabel(\"distance\")\n",
+ " plt.show()\n",
+ "\n",
+ " mpld3.disable_notebook()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "R7ABHBfO3OJG"
+ },
+ "outputs": [],
+ "source": [
+ "plot_actions_over_time(df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ea954aabc228"
+ },
+ "source": [
+ "The results will be rendered in a timechart as follows, where past closest matches of each suspected action are displayed in a separate line chart. The y-axis represents the distance of these nearest neighbors to the suspected action, where 0 is exact match."
+ ]
+ },
+ {
+ "attachments": {
+ "cbe85182-03c5-4aa0-936b-a94252b91d64.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "afee4a455a64"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "f293b5bcedec"
+ },
+ "source": [
+ "In this example, the 'orange' dots are not close enough matches and therefore the suspected action of 'DeleteRoutine' is likely an anomaly that needs to be further investigated."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3Egnvumyot88"
+ },
+ "source": [
+ "### Vector search of a new log entry"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WOWRvRf4iT2F"
+ },
+ "source": [
+ "Let's pick a seemingly suspicious destructive action from your recent admin activity log stream"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Iz9R9gUTnJ-c"
+ },
+ "outputs": [],
+ "source": [
+ "inner_sql = f\"\"\"\n",
+ "SELECT\n",
+ " EXTRACT(DATE FROM timestamp) AS day,\n",
+ " IFNULL(proto_payload.audit_log.authentication_info.principal_email, \"unknown\") as principal_email,\n",
+ " IFNULL(proto_payload.audit_log.method_name, \"unknown\") as action,\n",
+ " IFNULL(resource.type, \"unknown\") as resource_type,\n",
+ " {PROCESSED_DATASET}.getResourceId(resource.type, resource.labels) AS resource_id,\n",
+ " -- proto_payload.audit_log.resource_name as resource_name,\n",
+ " SPLIT(log_name, '/')[SAFE_OFFSET(0)] as container_type,\n",
+ " SPLIT(log_name, '/')[SAFE_OFFSET(1)] as container_id,\n",
+ " {PROCESSED_DATASET}.getChannelType(proto_payload.audit_log.request_metadata.caller_supplied_user_agent) AS channel,\n",
+ " IFNULL(proto_payload.audit_log.request_metadata.caller_ip, \"unknown\") as ip,\n",
+ " COUNT(*) counter,\n",
+ "FROM `{PROJECT_ID}.{SOURCE_DATASET}._AllLogs`\n",
+ "WHERE\n",
+ " log_id = \"cloudaudit.googleapis.com/activity\" AND\n",
+ " timestamp > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 DAY) AND\n",
+ " lower(proto_payload.audit_log.method_name) LIKE \"%delete%\"\n",
+ "GROUP BY\n",
+ " day, principal_email, action, resource_type, resource_id, container_type, container_id, channel, ip, log_name\n",
+ "ORDER BY\n",
+ " day DESC,\n",
+ " counter DESC\n",
+ "LIMIT 2\n",
+ "\"\"\"\n",
+ "\n",
+ "# Start the query and save results in an in-memory dataframe\n",
+ "query_job = bq.query(inner_sql)\n",
+ "results = query_job.result() # Wait for the job to complete\n",
+ "results.to_dataframe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "029c09ceb983"
+ },
+ "source": [
+ "In the following singular SQL command, 1) convert the suspected log into a text string summary, 2) generate the text embeddings 2) and do vector search across all past actions to find out similar activity (if any) has occurred in the past:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "CEqc2MSSpkv6"
+ },
+ "outputs": [],
+ "source": [
+ "sql = f\"\"\"\n",
+ "SELECT\n",
+ " query.content as suspicious_action,\n",
+ " base.content as past_similar_action,\n",
+ " distance,\n",
+ " base.day as day,\n",
+ " base.counter as counter\n",
+ "FROM VECTOR_SEARCH(\n",
+ " TABLE `{PROJECT_ID}.{PROCESSED_DATASET}.{TABLE_NAME}_embeddings`,\n",
+ " 'text_embedding',\n",
+ " (SELECT\n",
+ " text_embedding, content\n",
+ " FROM ML.GENERATE_TEXT_EMBEDDING(\n",
+ " MODEL `{PROJECT_ID}.{PROCESSED_DATASET}.embedding_model`,\n",
+ " (SELECT\n",
+ " {PROCESSED_DATASET}.stringifyAdminLogEntry(\n",
+ " day, principal_email, action, resource_type, resource_id, container_type,\n",
+ " container_id, channel, ip, counter) AS content\n",
+ " FROM ({inner_sql})),\n",
+ " STRUCT(TRUE AS flatten_json_output)\n",
+ " )),\n",
+ " top_k => 5\n",
+ ")\n",
+ "WHERE query.content != base.content -- remove potential dups\n",
+ "\"\"\"\n",
+ "\n",
+ "# Start the query and save results in an in-memory dataframe\n",
+ "query_job = bq.query(sql)\n",
+ "results = query_job.result() # Wait for the job to complete\n",
+ "df = results.to_dataframe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VrX4CSU57F-b"
+ },
+ "source": [
+ "#### Visualize results"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Q4mphXYM7JoF"
+ },
+ "source": [
+ "In a table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "8_jWjpjP7JBH"
+ },
+ "outputs": [],
+ "source": [
+ "reshape(df).style.pipe(highlight_similar_actions)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ouYmslmU8SFV"
+ },
+ "source": [
+ "In a timechart:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "0XT6wkTK8gs1"
+ },
+ "outputs": [],
+ "source": [
+ "plot_actions_over_time(df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "959b92998af7"
+ },
+ "source": [
+ "## Summary\n",
+ "\n",
+ "In this notebook, you were able to:\n",
+ "- Process your Google Cloud audit logs into a summary table for easier, faster and cost-effective log analysis\n",
+ "- Convert logs from semi-structured deeply nested JSON payload into a concise textual log summary \n",
+ "- Generate embeddings for each log summary using Vertex AI, and store them in a BigQuery table alongside the log content\n",
+ "- Create a vector index in BigQuery to semantically search all historical log summaries using embeddings column\n",
+ "- Search logs using BigQuery `VECTOR_SEARCH` SQL command to detect & investigate suspicious or anomalous activity\n",
+ "- Visualize findings in a table and a timechart to highlight past similar or equivalent actions, if any"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9633a0a9a970"
+ },
+ "source": [
+ "## Cleanup\n",
+ "\n",
+ "To clean up all Google Cloud resources used in this notebook, you can delete the Google Cloud project you used for the tutorial.\n",
+ "\n",
+ "Otherwise, you can delete the individual resources you created in this tutorial, namely the BigQuery dataset `PROCESSED_DATASET` with the processed data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "554bae6af364"
+ },
+ "outputs": [],
+ "source": [
+ "# Delete the created BigQuery dataset\n",
+ "!bq rm -r -f {PROJECT_ID}:{PROCESSED_DATASET}"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [
+ "WN2LVDh0-c_l"
+ ],
+ "name": "bq-vector-search-log-outlier-detection.ipynb",
+ "toc_visible": true
+ },
+ "environment": {
+ "kernel": "conda-root-py",
+ "name": "workbench-notebooks.m113",
+ "type": "gcloud",
+ "uri": "gcr.io/deeplearning-platform-release/workbench-notebooks:m113"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel) (Local)",
+ "language": "python",
+ "name": "conda-root-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb b/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb
new file mode 100644
index 00000000000..f539a0d787e
--- /dev/null
+++ b/embeddings/use-cases/outlier-detection/bq-vector-search-outlier-detection-infra-logs.ipynb
@@ -0,0 +1,5782 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Copyright 2024 Google LLC\n",
+ "#\n",
+ "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+ "# you may not use this file except in compliance with the License.\n",
+ "# You may obtain a copy of the License at\n",
+ "#\n",
+ "# https://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing, software\n",
+ "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+ "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+ "# See the License for the specific language governing permissions and\n",
+ "# limitations under the License."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JAPoU8Sm5E6e",
+ "tags": []
+ },
+ "source": [
+ "# Anomaly Detection of Infrastructure Logs using Gemini and BigQuery Vector Search\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "84f0f73a0f76"
+ },
+ "source": [
+ "| | |\n",
+ "|-|-|\n",
+ "| Author: | [Roy Arsan](https://github.com/rarsan) |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tvgnzT1CKxrO"
+ },
+ "source": [
+ "## Overview\n",
+ "\n",
+ "This notebook guides you through processing large volumes of logs using Gemini, vector embeddings and [BigQuery Vector Search](https://cloud.google.com/bigquery/docs/vector-search-intro) to perform anomaly detection. We evaluate the accuracy using publicly available labelled [HDFS](https://github.com/logpai/loghub) dataset, and contrast it with other common log anomaly detection techniques.\n",
+ "\n",
+ "\n",
+ "As part of this notebook you will learn how to:\n",
+ "1. Use SQL for preprocessing raw logs at scale, interact with LLMs and do vector search analysis\n",
+ "1. Use Gemini 1.5 Flash to translate a log sequence into simple natural language summary directly from BigQuery\n",
+ "1. Use a text embedding model to generate a vector embedding for each log summary directly from BigQuery, and store all embeddings in BigQuery vector index for fast lookup\n",
+ "1. Tune and run vector search-based anomaly detection using BigQuery vector search\n",
+ "1. Evaluate performance and compare against popular unsupervised and semi-supervised outlier detection ML algorithms\n",
+ "\n",
+ "\n",
+ "This notebook demonstrates how using off-the-shelf Gemini 1.5 Flash + Text Embeddings + BigQuery vector search yields comparable results to custom pre-trained language models including DeepLog, an [LSTM deep neural network](https://dl.acm.org/doi/10.1145/3133956.3134015).\n",
+ "\n",
+ "We also compare vector search-based outlier detection with common [scikit outlier detection](https://scikit-learn.org/stable/modules/outlier_detection.html#) algorithms (OneClassSVM, LocalOutlierFactor) using the same generated embeddings and we found that a custom SQL logic using BigQuery vector search to be more accurate and flexible for this use case and dataset.\n",
+ "\n",
+ "For performance results, see `Summary` section at the bottom.\n",
+ "\n",
+ " You can use and tweak this notebook to evaluate for your own outlier detection use case, with your own dataset and target sensitivity level."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DW8bwUaS2JQj"
+ },
+ "source": [
+ "### Data pipeline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Rt10T8iZrNSc"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sh4i937UcI2q"
+ },
+ "source": [
+ "### Task, metrics and dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YWviBrbU7cJ_"
+ },
+ "source": [
+ "Outlier detection, specifically novelty detection, is about detecting all anomalies including 'unknown' types of anomalies (e.g. system failures, new cyberattacks, etc.). Therefore supervised approaches where you train the model on both normal and abnormal logs are simply not applicable. Therefore, the approach described in this notebook is compared against leading and popular unsupervised and semi-supervised techniques. **Recall** is our primary objective for outlier detection (minimize false negatives) while also keeping reasonably high **precision** (minimize false positives).\n",
+ "\n",
+ "We use the open-source labelled HDFS dataset from [Loghub]((https://github.com/logpai/loghub) which is freely accessible and commonly used in log analysis evaluations, including anomaly detection. HDFS dataset provides 11M+ system log lines (\\~575k log sessions) collected from a real Hadoop cluster on 200 nodes, and labelled by Hadoop domain experts to identify the runtime anomalies. For the purpose of this notebook, we use a subset of the HDFS dataset (\\~60k log sessions or 10%) taking into consideration default BigQuery ML quota limits (e.g. 200 rpm and 72,000 rows per job with `gemini-1.5-flash` at the time of this writing). This way you can run this notebook as is in your own Google Cloud project in a timely fashion without necessarily requesting quota increases.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "61RBz8LLbxCR"
+ },
+ "source": [
+ "## Getting started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "No17Cw5hgx12"
+ },
+ "source": [
+ "### Install Vertex AI SDK and other required packages\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "tFy3H3aPgx12"
+ },
+ "outputs": [],
+ "source": [
+ "%pip install --upgrade --user --quiet \\\n",
+ " google-cloud-aiplatform \\\n",
+ " google-cloud-bigquery \\\n",
+ " scikit-learn==1.3.1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "R5Xep4W9lq-Z"
+ },
+ "source": [
+ "### Restart runtime\n",
+ "\n",
+ "To use the newly installed packages in this Jupyter runtime, you must restart the runtime. You can do this by running the cell below, which restarts the current kernel.\n",
+ "\n",
+ "The restart might take a minute or longer. After it's restarted, continue to the next step."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "XRvKdaPDTznN"
+ },
+ "outputs": [],
+ "source": [
+ "import IPython\n",
+ "\n",
+ "app = IPython.Application.instance()\n",
+ "app.kernel.do_shutdown(True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SbmM4z7FOBpM"
+ },
+ "source": [
+ "
\n",
+ "⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️\n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dmWOrTJ3gx13"
+ },
+ "source": [
+ "### Authenticate your notebook environment (Colab only)\n",
+ "\n",
+ "If you're running this notebook on Google Colab, run the cell below to authenticate your environment."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "NyKGtVQjgx13"
+ },
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "\n",
+ "if \"google.colab\" in sys.modules:\n",
+ " from google.colab import auth\n",
+ "\n",
+ " auth.authenticate_user()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DF4l8DTdWgPY"
+ },
+ "source": [
+ "### Set Google Cloud project information and initialize Vertex AI SDK\n",
+ "\n",
+ "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
+ "\n",
+ "Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Nqwi-5ufWp_B",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "PROJECT_ID = \"[project-id]\" # @param {type:\"string\"}\n",
+ "REGION = \"us-central1\" # @param {type:\"string\"}\n",
+ "LOCATION = \"US\" # @param {type:\"string\"}\n",
+ "\n",
+ "import vertexai\n",
+ "\n",
+ "vertexai.init(project=PROJECT_ID, location=REGION)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5303c05f7aa6"
+ },
+ "source": [
+ "### Import libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "01qn1OZeh_EP",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Import necessary libraries\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from datetime import datetime\n",
+ "from google.cloud import bigquery\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.manifold import TSNE\n",
+ "\n",
+ "import bigframes.pandas as bpd\n",
+ "import bigframes.ml as bml"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "jLc02A9ch_EP",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# initialize bigquery client\n",
+ "client = bigquery.Client()\n",
+ "\n",
+ "# initialize bigquery dataframes client\n",
+ "bpd.close_session()\n",
+ "bpd.options.bigquery.project = PROJECT_ID\n",
+ "bpd.options.bigquery.location = LOCATION"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "_68282UqlxrR"
+ },
+ "outputs": [],
+ "source": [
+ "DATASET = \"vs_logs_demo\" # @param {type:\"string\"}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aF5qTVGRh_EP"
+ },
+ "source": [
+ "### Create a new dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "referenced_widgets": [
+ "10cc4b44123d4135ad198a27aada1529"
+ ]
+ },
+ "id": "L6OQ55Axh_EP",
+ "outputId": "2793afe9-96da-420f-90ac-973fe7b3a220",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "%%bigquery\n",
+ "CREATE SCHEMA `vs_logs_demo` OPTIONS (LOCATION = 'US');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AxdBl98uUM09"
+ },
+ "source": [
+ "### Create Cloud resource connection for Vertex AI"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qsJ_dwvG-28r"
+ },
+ "source": [
+ "You need to create a Cloud resource connection to enable BigQuery to interact with Vertex AI services such as LLM remote models."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7EZlKHyyUMNY"
+ },
+ "outputs": [],
+ "source": [
+ "CONNECTION_NAME = 'bq-llm-connection' # @param {type:\"string\"}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "BVbxqd9C_Tki"
+ },
+ "outputs": [],
+ "source": [
+ "!bq mk --connection --location={LOCATION} --project_id={PROJECT_ID} \\\n",
+ " --connection_type=CLOUD_RESOURCE {CONNECTION_NAME}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pQphx8hz_cyj"
+ },
+ "source": [
+ "Retrieve the connection service account ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5hgJatLm_eFF"
+ },
+ "outputs": [],
+ "source": [
+ "!bq show --connection {PROJECT_ID}.{LOCATION}.{CLOUD_RESOURCE_CONN}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1YVxe9B3_hhe"
+ },
+ "source": [
+ "Copy the service account ID in the following parameter:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "XPqGhAt6_jUU"
+ },
+ "outputs": [],
+ "source": [
+ "# Copy the value of `serviceAccountId` field in last cell's output\n",
+ "CONNECTION_SA_ID = \"[bq-connection-service-account]\" # @param {type:\"string\"}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wlsjW4-X_vOw"
+ },
+ "source": [
+ "Give that service account access to Vertex AI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "oKYvvcZM_wMu"
+ },
+ "outputs": [],
+ "source": [
+ "!gcloud projects add-iam-policy-binding {PROJECT_ID} --member='serviceAccount:{CONNECTION_SA_ID}' --role='roles/aiplatform.user' --condition=None --no-user-output-enabled"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "E0qF8NR7_0o3"
+ },
+ "source": [
+ "If your notebook environment does not have `setIamPolicy` permission on the project, you (or your project's administrator) can alternatively grant permissions to that cloud connection's service account using Cloud Console following these steps: https://cloud.google.com/bigquery/docs/generate-text-tutorial-gemini#grant-permissions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HysosuDkh_Ec"
+ },
+ "source": [
+ "## Import logs dataset into BigQuery"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iVPIbKUzXx5l"
+ },
+ "source": [
+ "The HDFS dataset was originally developed by Wei Xu et al. in [Detecting Large-Scale System Problems by Mining Console Logs](https://people.eecs.berkeley.edu/~jordan/papers/xu-etal-sosp09.pdf) and made available in [Wei Xu's website](https://people.iiis.tsinghua.edu.cn/~weixu/sospdata.html). The raw logs are also available at [loghub](https://github.com/logpai/loghub) thanks to the work of Zhu et al. in [Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics](https://arxiv.org/abs/2008.06448). To facilitate log ingestion and analysis in this notebook, the logs are expected to be converted to structured format (csv) using a standard log parser such as [this one](https://github.com/logpai/logparser/blob/main/logparser/Drain/README.md) from [logparser](https://github.com/logpai/logparser).\n",
+ "\n",
+ "For the purpose of this notebook, the pre-parsed logs are available as .csv files in a GCS bucket:\n",
+ "\n",
+ "* Full dataset: `HDFS.log_structured.csv` (2.9 GB)\n",
+ "* Limited 2k dataset: `HDFS_2k.log_structured.csv` (< 1 MB)\n",
+ "\n",
+ "You will import the full dataset in this notebook, but subsequently process ~15% of the data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aRBDEawbh_Ef"
+ },
+ "source": [
+ "### Load logs from GCS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "2zn4ds3gh_Ef",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "gcs_bucket = \"gs://csa-datasets-public/hdfs\"\n",
+ "gcs_filename = \"HDFS.log_structured.csv\" # @param {type:\"string\"}\n",
+ "\n",
+ "gcs_uri = gcs_bucket + gcs_filename\n",
+ "\n",
+ "def camelCase(col):\n",
+ " return col[0].lower() + col[1:]\n",
+ "\n",
+ "df = bpd.read_csv(gcs_uri, engine=\"bigquery\")\n",
+ "# lowercase first letter of each column name\n",
+ "df.columns = [camelCase(col) for col in df.columns]\n",
+ "print(df.info())\n",
+ "\n",
+ "df.to_gbq(\"vs_logs_demo.hdfs_full_structured\", if_exists=\"replace\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CUseo2PAh_Ef"
+ },
+ "source": [
+ "### Group logs by sessions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mgfkFhITXVm-"
+ },
+ "source": [
+ "Grouping logs by session ID or other unique identifiers allows us to correlate events that belong to the same user or transaction, providing context and a clear chronological sequence of actions. This is essential for understanding user behavior, troubleshooting issues, and identifying patterns that might otherwise be lost in the noise of a massive, undifferentiated log stream.\n",
+ "\n",
+ "In our case, since HDFS logs are records of events related to file system operations at the block level, such as block replication and deletion, we need to group HDFS logs by unique `blockId`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "D-fsGL0Uh_Eg",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "%%bigquery\n",
+ "CREATE OR REPLACE TABLE `vs_logs_demo.hdfs_full_sessionized` AS (\n",
+ " SELECT\n",
+ " blockId,\n",
+ " -- Capture start and end time\n",
+ " MIN(timestamp) AS ts_start,\n",
+ " MAX(timestamp) AS ts_end,\n",
+ " COUNT(*) AS numEvents,\n",
+ " -- Concatenate sequence of events\n",
+ " STRING_AGG(CONCAT(\n",
+ " component, \" \", level, \" \", content\n",
+ " ), '\\n' ORDER BY timestamp ASC) AS eventSequence,\n",
+ " -- Concatenate sequence of event templates\n",
+ " STRING_AGG(CONCAT(\n",
+ " component, \" \", level, \" \", eventTemplate\n",
+ " ), '\\n' ORDER BY timestamp ASC) AS eventTemplateSequence,\n",
+ " -- For debug only\n",
+ " ARRAY_AGG(STRUCT(\n",
+ " timestamp, level, component, content, eventId, eventTemplate\n",
+ " ) ORDER BY timestamp ASC) AS sequence,\n",
+ " FROM (\n",
+ " SELECT\n",
+ " PARSE_TIMESTAMP('%y%m%d %H%M%S', CONCAT(LPAD(CAST(date AS STRING), 6, '0'), ' ', LPAD(CAST(time AS STRING), 6, '0'))) AS timestamp,\n",
+ " REGEXP_EXTRACT(content, r'(blk_-?[0-9]+)') AS blockId,\n",
+ " * EXCEPT(date, time)\n",
+ " FROM `vs_logs_demo.hdfs_full_structured`\n",
+ " )\n",
+ " GROUP BY\n",
+ " blockId\n",
+ " ORDER BY\n",
+ " ts_start ASC, blockId ASC\n",
+ ");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DhANs7B7h_Eg"
+ },
+ "source": [
+ "### Label log sequences"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xjRjHbkLawlP"
+ },
+ "source": [
+ "This is a labelled dataset where each `blockId` has a binary label: either 'Normal' or 'Anomaly'. Let's retrieve the labels CSV file and store it in separate BigQuery table `hdfs_labels`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "sbTbkLv5h_Eg",
+ "outputId": "173f7b98-5de9-4986-f5ba-1e70190f8f5b",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "gcs_uri = \"gs://csa-datasets-public/hdfs/anomaly_label.csv\"\n",
+ "df_labels = bpd.read_csv(gcs_uri, engine=\"bigquery\", header=0)\n",
+ "df_labels.columns = ['blockId', 'label']\n",
+ "print(df_labels.info())\n",
+ "\n",
+ "df_labels.to_gbq(\"vs_logs_demo.hdfs_labels\", if_exists=\"replace\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ADUU9kcJh_Eg"
+ },
+ "source": [
+ "Let's correlate the log sequences with the labels table, and create a new labelled table for log sequences by setting a new boolean column `abnormal`. We also capture the sequential order of rows in `sessionId` which will come in handy later on for splitting the dataset and batching calls to Vertex AI API given its rate limits."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 85,
+ "referenced_widgets": [
+ "c7d1dcab9f3b4cc8b9801fc80295025f",
+ "973989ab40164a19ac0d042fbe0c0843",
+ "da41deb1753b4ccfaf118b87ca5a38be",
+ "de7c984cd6694b84815f93f3b1af0c95",
+ "1a27e76e8ead4a76a4e412fb7fb1ee35",
+ "60a93efe312043c8bb9695daf89fa7f7",
+ "cea0f649281e432bbfc4244aa582e6ec",
+ "0bc0104d877f4765b1ca4476e01e9afc",
+ "7f03aba9badd4dc9990bb3f642354aa2",
+ "8686e6391261451a823ed1adf4e36a06",
+ "b57e7771490f4bc0a9da51743215debf"
+ ]
+ },
+ "id": "iFmql46fh_Eh",
+ "outputId": "3b871a59-a903-4641-edda-7e391df1a637",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "%%bigquery\n",
+ "CREATE OR REPLACE TABLE `vs_logs_demo.hdfs_full_labelled` AS (\n",
+ "SELECT\n",
+ " -- Capture sequential order of sessions\n",
+ " ROW_NUMBER() OVER(ORDER BY logs.ts_start ASC) AS sessionId,\n",
+ " logs.* EXCEPT(sequence),\n",
+ " CASE\n",
+ " WHEN labels.label = 'Anomaly' THEN TRUE\n",
+ " ELSE FALSE\n",
+ " END AS abnormal\n",
+ "FROM `vs_logs_demo.hdfs_full_sessionized` logs\n",
+ "LEFT JOIN `vs_logs_demo.hdfs_labels` labels USING (blockId)\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A52AUFlzh_Eh"
+ },
+ "source": [
+ "### Exploratory data analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "TgKum7IZh_Eh",
+ "outputId": "ca8741e9-695d-4e39-e706-9b6522b0818f",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "df = bpd.read_gbq(\"vs_logs_demo.hdfs_full_labelled\")\n",
+ "df.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "nUtFdr5lh_Eh",
+ "outputId": "5ac41146-ce27-4e31-9fb6-7bd3ccaeb32b",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "df.head(5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XwpnSSbjh_Eh"
+ },
+ "source": [
+ "Let's see the distribution of log sequence length, by displaying summary stats for `numEvents` numeric column"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "VZq7oaj9h_Ei",
+ "outputId": "0419d5cf-d44f-4b34-acb0-170c6520badd",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Query job ac2fb380-ec58-4e4d-8fb8-a0dbd6f06c9b is DONE. 4.6 MB processed. Open Job"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Query job 97653903-b3c1-42e5-9c7c-e0fd4959d188 is DONE. 0 Bytes processed. Open Job"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Query job caed98b2-96bf-49cb-a39c-256f42e28b3b is DONE. 235 Bytes processed. Open Job"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "I0000 00:00:1723737276.940233 20152 tcp_posix.cc:809] IOMGR endpoint shutdown\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
\n"
+ ],
+ "text/plain": [
+ " sessionId blockId abnormal numEvents \\\n",
+ "0 119899 blk_-9060541879596316494 True 2 \n",
+ "1 17072 blk_8929107803467731944 True 2 \n",
+ "\n",
+ " eventSequence \\\n",
+ "0 dfs.DataNode$DataXceiver INFO Receiving block ... \n",
+ "1 dfs.DataNode$DataXceiver INFO Receiving block ... \n",
+ "\n",
+ " eventTemplateSequence \\\n",
+ "0 dfs.DataNode$DataXceiver INFO Receiving block ... \n",
+ "1 dfs.DataNode$DataXceiver INFO Receiving block ... \n",
+ "\n",
+ " content status token_count \\\n",
+ "0 1. The NameNode allocates a new block for a fi... 30 \n",
+ "1 1. The NameNode allocates a new block for a fi... 30 \n",
+ "\n",
+ " truncated embeddings \n",
+ "0 False [0.02920735441148281, 0.04813070967793465, -0.... \n",
+ "1 False [0.02920735441148281, 0.04813070967793465, -0.... "
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "client = bigquery.Client()\n",
+ "\n",
+ "# sql = f\"\"\"\n",
+ "# SELECT *\n",
+ "# FROM `vs_logs_demo.hdfs_full_embeddings`\n",
+ "# TABLESAMPLE SYSTEM (10 PERCENT)\n",
+ "# \"\"\"\n",
+ "# df = client.query(sql).to_dataframe()\n",
+ "\n",
+ "table_id = 'vs_logs_demo.hdfs_full_embeddings'\n",
+ "df = client.list_rows(table_id).to_dataframe()\n",
+ "df.head(2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "JsVt6qXph_Eo",
+ "outputId": "5c5c3eea-2649-4390-b2f2-e6714f168766",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1. A block with ID blk_7780858808264270321 was allocated by the NameNode.\n",
+ "2. Three DataNodes received the block from different sources.\n",
+ "3. The NameNode recorded that the block was stored on three DataNodes.\n",
+ "4. The NameNode instructed the three DataNodes to delete the block.\n",
+ "5. The three DataNodes deleted the block from their local storage. \n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(df.iloc[70].content)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4PCddtSs8GPl",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Reduce dimensionality using t-SNE. Will take 5 minutes with 10% of the data\n",
+ "tsne = TSNE(n_components=2, random_state=0)\n",
+ "embeddings_array = np.array(df[\"embeddings\"].to_list(), dtype=np.float32)\n",
+ "embeddings_2d = tsne.fit_transform(embeddings_array)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 717
+ },
+ "id": "wEx5o4a07Dxd",
+ "outputId": "1c04cddd-9b00-4be0-c9f6-5cc81fd557e9",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Mapping boolean values to colors\n",
+ "color_map = {True: 'red', False: 'blue'}\n",
+ "colors = df['abnormal'].map(color_map)\n",
+ "\n",
+ "# Create a scatter plot of the embeddings\n",
+ "plt.figure(figsize=(10, 8))\n",
+ "plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], c=colors, s=25, alpha=0.7)\n",
+ "\n",
+ "plt.title('Log Embeddings Clusters')\n",
+ "plt.xlabel('TSNE-1')\n",
+ "plt.ylabel('TSNE-2')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "Results should be rendered in a scatter plot as follows:"
+ ]
+ },
+ {
+ "attachments": {
+ "830d2fb9-2eb8-481f-b3ff-47129a741321.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EWDzewJyiCHc"
+ },
+ "source": [
+ "We can visually see many blue clusters (30-50), depicting different normal types of log sequences. The red dots are the abnormal log sequences that ultimately should be identified as outliers. While many of the red dots are clustered together, some are scattered and at the periphery of blue clusters."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BVy4K8xnh_Ew"
+ },
+ "source": [
+ "## Detect outliers with BigQuery vector search"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dyCvM3ssh_E0"
+ },
+ "source": [
+ "Let's implement a simple outlier detection logic leveraging BQ built-in optimized vector search using simple SQL."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YTp-jko3ln7B"
+ },
+ "source": [
+ "### Split dataset into training and test"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7CBZnNDOmkHH"
+ },
+ "source": [
+ "First, let's split the dataset into training and test datasets in BigQuery.\n",
+ "\n",
+ "We use a session cutoff to split the first 50,000 sessions (ordered chronologically) to use for 'training' and then a subsequent 20,000 sessions for testing. Since this is semi-supervised learning on the negative class only (normal logs), we use only the normal logs sequences in the first 50,000 sessions.\n",
+ "\n",
+ "In the context of BigQuery vector search, the vector index uses a k-means algorithm to cluster these records as they are ingested. That process is managed by BigQuery during and after index creation. We still writing the training and test dataset in separate table to facilitate testing of other ML models in subsequent section."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "IWM6FBBDYadc"
+ },
+ "outputs": [],
+ "source": [
+ "session_id_cutoff = 50000 # Split train and eval datasets\n",
+ "num_sessions = 20000 # Number of sessions to use for testing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gVH6LbLBpRuw"
+ },
+ "source": [
+ "Retrieve and write test and training datasets in separate tables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 70
+ },
+ "id": "1D4PINTvTe26",
+ "outputId": "1d393a42-3945-4628-fab7-6187e4916f0e"
+ },
+ "outputs": [],
+ "source": [
+ "# Train dataset\n",
+ "sql = f\"\"\"\n",
+ "SELECT * FROM `vs_logs_demo.hdfs_full_embeddings`\n",
+ "WHERE\n",
+ " abnormal=FALSE\n",
+ " AND sessionId <= {session_id_cutoff}\n",
+ "\"\"\"\n",
+ "y_train = bpd.read_gbq(sql, index_col=\"sessionId\")\n",
+ "\n",
+ "# Persist the train dataset in a separate table to be used later for other models e.g. scikit-learn algor\n",
+ "y_train.to_gbq(\"vs_logs_demo.hdfs_full_embeddings_train\", if_exists=\"replace\")\n",
+ "\n",
+ "# Test dataset\n",
+ "sql = f\"\"\"\n",
+ "SELECT\n",
+ " sessionId, blockId, embeddings,\n",
+ " CAST(abnormal AS INT64) AS label\n",
+ "FROM `vs_logs_demo.hdfs_full_embeddings`\n",
+ "WHERE sessionId > {session_id_cutoff}\n",
+ "ORDER BY sessionId ASC\n",
+ "LIMIT {num_sessions}\n",
+ "\"\"\"\n",
+ "y_test = bpd.read_gbq(sql, index_col=\"sessionId\")\n",
+ "\n",
+ "# Persist the test dataset in a separate table to be used several times with different vector search configurations\n",
+ "y_test.to_gbq(\"vs_logs_demo.hdfs_full_embeddings_test\", if_exists=\"replace\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hRS72B88h_E3"
+ },
+ "source": [
+ "### Predict anomalies with vector search"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aY4PKVQy2MDJ"
+ },
+ "source": [
+ "The premise behind outlier detection is to identify log sequences that do not belong to any known 'normal' blue clusters. Given a log sequence test vector, if there is a sufficient number of 'normal' neighboring base vectors within a certain distance, then that log sequence is considered 'normal'. Otherwise, it is considered an outlier or an anomaly."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "d2iVXlbdWFSm"
+ },
+ "source": [
+ " To get the best performance in terms of outlier detection, the following procedure uses a brute force search to maximize recall of top-k closest 'normal' neighbors for every query vector."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "TEkbws0mh_E3",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def predict_anomalies(n_neighbors, distance_threshold):\n",
+ " sql = f\"\"\"\n",
+ " SELECT\n",
+ " query.blockId AS blockId,\n",
+ " ANY_VALUE(query.sessionId) AS sessionId,\n",
+ " COUNTIF(distance < {distance_threshold}) AS similar_normal_instances,\n",
+ " IF(COUNTIF(distance < {distance_threshold}) >= {n_neighbors}, 0, 1) as predicted\n",
+ " FROM VECTOR_SEARCH(\n",
+ " (SELECT * FROM `vs_logs_demo.hdfs_full_embeddings` WHERE abnormal=FALSE AND sessionId <= {session_id_cutoff}), # TreeAH index\n",
+ " 'embeddings',\n",
+ " TABLE `vs_logs_demo.hdfs_full_embeddings_test`,\n",
+ " --(SELECT blockId, sessionId, embeddings FROM `vs_logs_demo.hdfs_full_embeddings` WHERE sessionId > {session_id_cutoff} ORDER BY sessionId ASC LIMIT {num_sessions}),\n",
+ " top_k => {n_neighbors}, # Increase top_k value to 3x if using TreeAH with post-filtering\n",
+ " distance_type => 'COSINE',\n",
+ " options => '{{\"use_brute_force\":true}}'\n",
+ " --options => '{{\"fraction_lists_to_search\":0.1}}'\n",
+ " )\n",
+ " GROUP BY\n",
+ " query.blockId\n",
+ " \"\"\"\n",
+ " return bpd.read_gbq(sql, index_col=\"sessionId\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cZsxyyQnYq6s"
+ },
+ "source": [
+ "**Note**: While the above procedure runs a brute force job for best outlier detection performance, you can still experiment with using the vector index by setting `fraction_lists_to_search` to optimize speed and cost while trading off some recall. For example, you could set it to 0.1 to only search only 10% of the clusters, those with the closest centroids, thereby reducing total latency and slot usage. Since we're using a TreeAH index, the prefilters on the base table are treated as post-filters and may reduce the top-k result set. In that case, make sure to multiply top_k value by say 3x to have a sufficient result set post-filtering."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "T91-nLT25rpH"
+ },
+ "source": [
+ "Let's run the batch prediction: this will compare 20,000 test records against 47,770 'normal' base records, essentially doing 955 Millions distance calculations of 768-dimentional vectors. The following completes in **only 1 minute** given the small base table and sufficient parallel shards."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "id": "jv4n_N51h_E3",
+ "outputId": "93bf7acd-2b74-44f0-a397-f1b0dbaa9b9d",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "n_neighbors = 10 # Number of neighbors to compare with\n",
+ "dist_threshold = 0.004; # Neighbor within this distance is a match\n",
+ "\n",
+ "# Batch predict on the test set\n",
+ "y_pred = predict_anomalies(n_neighbors, dist_threshold)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "welzwxwC6gW6"
+ },
+ "source": [
+ "Let's see how much data was processed and the total time it took to predict anomalies across 20,000 test records:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7ZaKLkSHh_E4",
+ "outputId": "4cfcacee-85fe-4a54-b043-11a97ac811ad",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Job ID: 199dd3dc-10fe-4ca1-b81c-386b21283a55\n",
+ "Total Bytes Processed: 938.72 MB\n",
+ "Slot-hours Used: 1.58\n",
+ "Query Execution Time: 6.67 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "vs_job_id = '199dd3dc-10fe-4ca1-b81c-386b21283a55'\n",
+ "\n",
+ "client = bigquery.Client()\n",
+ "job = client.get_job(vs_job_id)\n",
+ "\n",
+ "print(f\"Job ID: {vs_job_id}\")\n",
+ "print(f\"Total Bytes Processed: {round(job.total_bytes_processed / (1024 * 1024) , 2) } MB\")\n",
+ "print(f\"Slot-hours Used: {round(job.slot_millis / (1000 * 60 * 60), 2)}\")\n",
+ "\n",
+ "start_time = job.started\n",
+ "end_time = job.ended\n",
+ "execution_time = (end_time - start_time).total_seconds()\n",
+ "print(f\"Query Execution Time: {execution_time:.2f} seconds\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2fhhcrCF6-gC"
+ },
+ "source": [
+ "Make sure the predicted result set `y_pred` is of the same size as the test dataset `y_test`, that is 20,000 records:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "_J7BqRzJh_E4",
+ "outputId": "6ef709c0-aa4c-4f55-857a-d0403e09d8a2",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(20000, 3)\n",
+ "(20000, 3)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(y_test.shape)\n",
+ "print(y_pred.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qmZ1waubh_E5"
+ },
+ "source": [
+ "### Evaluate performance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "kUxPZ0x-ASfL"
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, f1_score\n",
+ "from sklearn.metrics import classification_report, ConfusionMatrixDisplay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 86
+ },
+ "id": "AlrYy0Mch_E5",
+ "outputId": "5a131b25-5613-4694-9f5d-aeb443928037",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Query job 3bf95a36-e666-4fa2-a32c-74f4c0dc7731 is DONE. 0 Bytes processed. Open Job"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Query job 104da945-b627-47e7-b726-cef24d6b54f4 is DONE. 320.0 kB processed. Open Job"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[19088 81]\n",
+ " [ 66 765]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "cnf_matrix = confusion_matrix(y_test['label'].to_pandas(), y_pred['predicted'].to_pandas())\n",
+ "print(cnf_matrix)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 449
+ },
+ "id": "EcXfacuEAkf9",
+ "outputId": "0d034243-b0ce-41ec-e2dd-c351183457ef",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "disp = ConfusionMatrixDisplay(confusion_matrix=cnf_matrix, display_labels=['normal', 'abnormal'])\n",
+ "disp.plot()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ "3823d730-4998-48f5-86c8-874384d16f68.png": {
+ "image/png": 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ILim0wQqWIFd16tSxroK/a/v27ZRT3EWLTJeTqkgsShQBERCBEhAg0G04GgN1wSh57LHHzjzzTOtZb+gg6hrYrDodFtj++OMPHFmFaolOIAM4qdgszSCDFbqw0vksmE62QiXYfpVs2CJSBhEQARGwI+Bf3c/IecNT2ueLSkxMJPBgyYZd0UUcb9asGZYE1gPmAkVhcCAGaAM7u3btwrtF56ikpKRgt6stW7YgFTimyJCSkrJz506rULpsYZ0EVaSIKxWVJNkoiorSREAERKAkBHhjN3xpN8mGBhD8sIwDYhjEvekohXMJywCRIBYya9YsjhKcqF27NhFyOtSuWrUKCfn0009JIaTB0ebNmxPk2Lp1KxJCv6wePXqgNATPcYvNmDGjV69e7P/2229dunSxOuaa365kw5yVcoqACIhA0QQIV3gMetZyMr4sW+Ug3kCXWXQCwfj888/Xr1/fs2dPBmewYTFgQ3z11Vfnnnsuoy6wV/r27fvcc8+9/PLLyAbpt956KwYEkjBw4EDSx44di9lBp9srr7ySq+PUGjJkyKuvvkpQHWWit+5NN90UHx9f9F0VkyrZKAaMkkVABETAmIDf2jCbNSTQ28qmXMLXdIVCHm677Taysk8Kjim8UribiIVcccUVjPWzfFyYC4TKp0+fjgOKbruDBw/mFA4RCkEqsCc495prrjn++ONJZyRgnz59MC8wUJKTk+mei2lSokEbFCLZAEI4b5vXxM6ZnrZ9W1z1mvlH9EuvWc/f1S8nK/KPqWmrlifRgaJF231de+9OTPW/AHk9EfN/TVk0JyUvL7Jlu309jt4dn+hP35MRNf/X1DV/JubnR9ZrkNPz2IzqtT2k794VvWBGCukeT2TdBrm9+u9KrVayLhkUoq3iCcTG+nqdlNH64JzoaO/6FfEzv03Zvtk/TrjlQVmHHLW3buO87Zvivn6zemam0eLYFV9/B14RzfD/VzEIbzCTIfHz0LdAcOLGG2/cPw82x/6JaED3wFboEO4sOtqyFUpHLfoFtkLp5l+NYjjmxbkoJ+beU089tXLlShfVuRRV3bgmbuXi+O0bY374pBpPeauEr9+vNXlCzeTUvNTUvJ8npU35tJYn0G97zs+pH75cx5PnJX3S27WmfFKT/Bz6aVKN7z6qFh/vqVE7d/rk1Ikv1vd5I3JzIqd/W/2Hz6vFxXmqVc+b+mnaxOfql6KGOqXiCfQZsuvEkTv3ZkSlb4vtclRm/xHp1ar73wOatMlu0DinVr28Xv3TE1L8KdoMCXgiovzTUtGfyv4vyjQMYnjtCs9Wda0NuhD89NNPRx11VIUzr9ALtjskq2nb3A2rEsY/Uc+68O4d0T99UX3gyB1H9MsgpV6TVCTk4B57GjbPmTq5evP22SeftSMh0ZdSzTvlw5o9j94dG+ddNDO542FZxw/dEZfgq1Un7/VHGp5xWVR+XvTiP5Lad8secNqO6GhffILnf4/Uv+DmjVF6Q63QFi7NxXoes3vt8vjPX6lFr54B50S2PWRvtbr5GenRc6emLZmV3OmIffw8SlNuVT/HPmgBIb9RYmdtOByki62N/YdWOpx1pVQvuZq3dv28pNR/fEc52VFZmVH1m2QnJHv5q10/d+eWGHQFv8SmVXFd+2RWq+VJSPF2PiwzNzti3Yr4xBRvWk3PuuXxudkMHPIhIXUa5qWkeZOSPNVqejaujN23N5qnz5I5SS0PypZmVEorl/Sim9fGNWqZE5/sjYn11W2cu2dXzN4Mv9qn74reviEue5+Uv6RE/YFuw+F+2CX//G8s8XUccYIjZOP0008nYkOQh7GLTN/43XffwQZrgESGy9esWZMdOiOTOG3atKFDh955551HHHEEcSFCQPQruP/+++lbxjDI//73v4sWLTr77LPpZ3bCCScwVJJTdu/ezTxfzNXF+EliR5MnT3YE+MqrRK36efWa5E18ru7uXVEZO6MmTai5fV3M3j3Rmbuj8nIjq9XMjwz8KGLi/AbE7vTouHjfsP9szdoXdUGvgwa36fz7j6nXPbY2JjYiKc178sht+zJJbz+kTec/5ybd8uyayrstXbkEBCaMrbdzc+zbsxa+u2BB0zZZkyfU2LGl6joeSgCu+KyBsd/MnW7/hy/LMHhe/NUq+YgjZAMG7733HnF/BIOYz/jx45ki+MEHHyTwQH8y5mhk0hXm56JLGTkZqEKn44ceemjhwoV8pVcyXQKIUpCfQug5MGzYMGZZ4RApfNJ/mX7KzBzJiMrLL7/8rLPOWrFiBenFbdYQfKZzYaMaKFNxOV2ajkFw0a0b4hMjrhzQ7oZhbTLTo1se7LcSUAvidAVnGeDHzdxsOdmRX0+sERXpu++dlWMnLWt3yL7/jm6StScSwfjusxrRkb7731o1dtJS3l4fvLx5fq5NrM+l0MKs2ieevbNe49zbzmp147DWO7fEnnzujjoN/ppONczuVLdTHgSc8oqBuXDRRRdxh1gJGAdTpkxh1t977rmHdEYwjhkz5pZbbvnzzz8tBBgfzMBFDzOe6dgiw4cPp7cAc0aiMcz/xSyPDL/E5qAjM0qD5cE8X/RaozcbmkTXZka+oB/F0aSvGxrz/fffk4HTGS+D/7+4zC5Nb9A899bn1mTvjcz3RG5cHf/GI/WJdadV9xC62Lk1tmW7rMjoiOx9UTnZ0bihNqyIXzYv+dT/bO/cay+acPGdG68+qc3y+clJaZ5VCxOOHZJxSO9MOFx0+6b/9G6/Znl8q47+Gda0OZnAoPO3v/5I/Tk/pVDJ+ATfaZdtrdssf9umki264OQbrPi6EQnHSWXy0hQYt2GSseJvwvSKTpENaypgas2ARnRi48aNqAI+K6tDMZM4kmgNiEcSkAqrwzL56WRmzRPJDhvDJq0lRxjwwmAZxrOgFu+++y6dlymBjXE0SEgIPLjF6Ct98803k4exNoMGDYqI8Du7XLrRDyp7rz+Y4cmPzMqMZic+0btnVzRxuQT/TszM76vVqO9p3i4nrUZek7bZM79PbdaasIfn9x9SiYs0aZWVuTsmJta7aXV8q3b7cIWvXJSEhyoxNT82zhcZ7du8IZYYe1SMb+WSBCIlScnqfuOCX8rezKjGrXKr18r35kXWb5rrzY/Iy/E/yGJjfISyEhI9zMmdnObdm+HNzaaDkAvuqNKrCCTbbrVWJQNdrSq9vgdUAafIRlAGrLthWCPOJWswPSlWYMOaYoVOyjz9C950wbEqwX0rD2PxMRfwfWGRYKAw1IVB+Zazq2AJBfc50RIeK7FQxQrmdMX+lvVxX79Xe8fWmG3r4ia/V7NVp6xe/TNWLkxYMjc5Ji4iY1v0vj3RJ5y50xrPcfRJ6Z+8Vuuj12rzBrp6ccLxp++sUdeTlOI99KjMOT8lb10fGxPvWzk3vveJu5u2zkGHDj5s37zpyXjJCYT8OTd+4Hk76zYqPAW0KyhVtUp+NaH24cdlJKd5vJ7IOo3zFs1O2brWb2q06JR1/Bnp9Rvn1G2UO/Ty7X/OT5z+ZTWFPUx+HoiBYcTCMJvJRSsrj1Nko9D9t23blnjG+++/Txyb5/hbb73FpMFsrG9VKKftV2IVKBBD6tGbH3/8EWvD9pRwyhAb76vdKI+/dl2yuC+cTqwXyZvm3r30gIpq2CyXYX1N2/zlVurQZW/sZV6GAebnRXY9MrNDN7/3KT7J1/fEXY2b5W7dFOfxRrbukNWha2ZgGKDvqBPTKYF0/ie07bTvoJ6Z0f7FKLU5ncCUd2ps2xCHr5Il3RbPTl48Myl9h7/3VG5W1K5tcfwtnu2fV3VPRiwvB06/GWfUzxcRWHzJwDILjBJ3SlC5dPAcKhu4oS655BJWNLScRUyZcvXVV+OeKsVNsnpJp06dWASRMulthb+rFIW495Q6DfJOOXtbofpXrx3RqEVOoUS+EtJo0zmLv0KHUqt7D+391wrGBQ+l1fB0ObKI9IJ5tO9AAtk5UTO+KTzhNvVcvTSBPwdW2PlV4s3JH9v4tyOkyGoHJjUv8ohrEjGsDPSxnG+Hrk24j6wHOqEIOkcR1cAltWHDBgLUXBybg9V08RdxlMgEax8y9y/puJvocYsVwlf2WdaKHYITuKo4kflbsFrIhoVBd15sDuaGpATG1lOaVRT9d/la3P0R2xgwYMBLD61u07JkU30VV6DSnU/gvRcavHavf61mbVWBwF7fntg+WfTLJzJauvvlKTFy5MiMkyNrta9nMozPm+dd8MbMW/tcw/KuLvWBO8LaaNWqVbDB8CaxWV+ZuosteIidgkf5CnTmgLQysI9+BDOjNGzWV9ZEZAsesnYKFVXoqL6KgAiIgDkBXE+GIXGTqQzNr1spOR0hG5Vy57qoCIiACJQVgcBwP6M4EL4sjRIvK+wqRwREQASqCAEjgXEsC1kbjm0aVUwERMA1BKwJqUzUgOC5AwLKBwRWsnFA+HSyCIiACEDAH7EwWLaPnIFR4u5mJtlwd/up9iIgAk4ggAFh2Cc1kM3ELHHCbRVdB8lG0VyUKgIiIALmBPw9qfyLwtrrgdcJgx7Mb6yonJKNoqgoTQREQARKQgBrw98B12A5cQTG1DApSQUqMq9koyJp61oiIAJhS4DYhoGxQWwDgXH3Jtlwd/up9iIgAk4g4B+N4Y9a2DupAsP97LM54aaKq4NkozgyShcBERABUwKB4X5GExT6e1IZRs9NL17R+Yzus6IrpeuJgAiIgAg4lYCsDae2jOolAiLgHgIENgI9qexr7A+BeN39vi7ZsG9m5RABERABOwL+4X52efzH/QJjks/BeSQbDm4cVU0ERMAlBIhWGAYs/B1wDSLnTr5vyYaTW0d1EwERcAcB/5xUiIGBwcFwckOBceydu9vF5lisqpgIiECVIhBQAkMnlald4liAsjYc2zSqmAiIgHsI/NWt1kA5sEgMjBIn37lkw8mto7qJgAi4g4DPF+X1MkrcXjZ8ZHO5l0qy4Y4fpWopAiLgZAL0j0I5TGoY6Ellry4mRVVWHqP7rKzK6boiIAIiIAJOIyBrw2ktovqIgAi4j0BgyhAjGyIMJheRbLjvB6oai4AIOI0A0YrAVIb29TKJa2RkZKxatWrnzp1er/fQQw+tXbs2i3Rs3bp19erVe/bsiYqKqlmzZvv27RMSErje+vXrFy9enJ+fb127devWbdq0YT8vL2/dunVr1671eDwNGjRo165ddHQ06Tk5OZSzYcOGyMjIZs2aNW3aNCamZEJQstxWtfQpAiIgAiJQiICJHnCKyXC/FStWvPHGG4jHxx9//Nlnn/Xt25dH/48//vjtt9/Gx8dTCGJw9tlnn3HGGeyT/uijj3bu3LlGjRp8Pfnkk5EN9GbZsmUvvvjirl27YmNjs7Kyrr/++h49elDO3LlzSScDupKamjpq1Ki2bdsiRZxruEk2DEEpmwiIgAgUS+CvQLdJz9rA6n4YB9nZ2dbrP491aydYesOGDUeOHIlJgWxYiVgGmBctW7Zs0qQJBbz11luPPPLIiSeemJKSQgYskhtvvJGj7MfFxfGJUTJp0iQuceuttyYmJj722GNPP/30+PHjMzMzP/jgA1KuvfZaZOPee+/lEiiKpUbWtWw/JRu2iJRBBERABAwIoBkG0Q3LKHnllVewEqx3/NNOO+2UU04peIH6ga1gCrqCPRFM6dmz5zPPPIPpYKUsWbJk7NixeKK6detmFZWenr5w4cLevXt36NAByRk8ePAFF1ywd+/effv2zZ49+7LLLsOXxbnYMZ9//vnVV18t2Qiy1Y4IiIAIVAQB/+QivogoA90gJM5fnz59+vfvbxkZlpVgXkushOeff/7oo4+2TI0WLVqcfvrpaAbhCqyKTZs2XX755dgZGBy1atVCMyi5efPmaMaWLVuwbEhHlazLNWrUiJAJDivzq5NT1kaJcCmzCIiACBRBwG9DmE1ta81IhRHAm74lG9aTvYhCi0rCwrjrrruIW3z66afW6d27d8dJxT6Or2rVqt19990XXXQRjizEIBixQC3Yz83NJfpNCdaJFG99Leo6odIkG6Ho6JgIiIAIGBEwDIj/XRZSwXOc7e8Eo3954qMK33zzDXGLevXqWecQzLDiGTiaMEEwKehbhU7wFQvDykOnLM7lFJxUBDYwOKx0ou5ExUukW5wo2bDo6VMEREAESk/Av0K4STw8sN6G7RS42Ao85amNtYMZgcDQG4qw9rRp095++2265GJM8LgnAz4rcmI3kG3GjBnIAL4pnFTE1f/88090AtuCOErHjh3pakWxBNXpTIWXjHN///137B51wC19w+tMERABESglgUDEwiAi7l+mKYIgSMgNI+Cnn35CJxhjMXXqVB79dKOaMmXK66+/fumllzIagzAGFgZBcqSCXrm7d+9GJEinr9QNN9yQlpaWnJw8cOBAZIYoCF/pxXvllVdyTfaHDBlCOUlJSRQ7Z86cMWPGWMZKyBr966CsjX/h0BcREAERKAUBDAj0wMRT5Y0gJG5zBfpB4YliyAWxbiwGdvA4sdGBin5QbJyPABDQrlu3Lgrx22+/zZw5kwg5gzCGDh3KUSyMI444gmAG5WCOEO2gvxbpKASheCwV1Ig811133WGHHRYMddhU6+/Dko2/SehfERABESgtgRJ0RbLTDKpAxyf61+5fl/PPP3//RMb3se2fjj3BwA62QofQG8YJWkMFCx0y/CrZMASlbCIgAiJQLAGsDb8NYRDe8BslBspR7JUccMDGxeaAGqoKIiACIuB4Amaa4b8Nl2sGdyBrw/E/R1VQBETABQQCgQ0zayPCYFSgk+9YsuHk1lHdREAE3EHAb2ygGQZdqdxvbNj1A3NHi6mWIiACIiACFUVA1kZFkdZ1REAEwpeAlmkK37bVnYmACIhAeRDwe6nMyjXMZlZYpeSStVEp2HVRERCB8CJAYMMgHu6/Z/OcTiWkDrhObRnVSwREwFUESjDiz1X3tX9lZW3sz0QpIiACIlAyAoHJRRjxZ9+VyueNcLvASDZK9uNQbhEQAREogkDA9WSgGoG1xF0e3pBsFPEDUJIIiIAIlIyApQRGuoFFYm+UlOzqFZtbslGxvHU1ERCBsCTATFNeo+F+/g5XLrc2FBIPy5+wbkoEREAEyouArI3yIqtyRUAEqhABcxuCgLjLrY1QssGKUawaWFzDs9Z5cYeULgIiIAJVikCgJ5VhxMIwm3P5hZINVhNkcagi684atnfccUeRh5QoAiIgAiJQLIFiX8WLPcNpB0LJBivNhpANp92J6iMCIiAClUeAUeJGF3f7oA1uMpRs3HvvvQUxeDwejIyoKEXRC1LRvgiIgAj8vbSfiXJYfa7czCyUbATva8uWLZ9//vnChQtPOeWUo4466ocffmAd8759+wYzaEcEREAEqjoBNMP1YQujNrQ3HdLT01977TWkYunSpX/++Wd+fn5GRsZzzz1nVLwyiYAIiECVIGApBp8mf+4mYi8bO3funDdv3lVXXXXkkUfipOJ2O3Xq9Ouvv7r7vlV7ERABEShDApga/vlFAp1rTT7L8NIVXpS9bOTl5WFhtG3bNj4+3qpeTEzM3r17K7yquqAIiIAIiEDlE7CPbaAWRDLmz59v6UdWVtYnn3xy2GGHVX7dVQMREAERcAgBy8IwqQw5Xb7Zy0adOnWOPvpoghmENFJTU2fNmoWEPPHEEy6/cVVfBERABMqOgH8SQ7M+uH5flt/b797NXjaSk5NHjBjRsGHD77//nvB4zZo1n3zyyV69ern3nlVzERABEag0AlXB2gBuSkrKsccee/DBB+fk5GBwYH9o9Eal/eZ0YREQAecRYHIRLwP5DKyI4idsct5dFVMje2uDUX4rVqz47LPP6ICbnZ2NhHTp0uWMM86oXr16MWUqWQREQASqHgFDM8I8CuJUhPaysWPHDianIrCBwcH0hQz9+/DDD/nUnFRObVPVSwREoMIJWJrh8qCFITV72UAwli9fftddd3Xt2pWutxgcxDkYxiHZMESsbCIgAmFPINIXGWWmGSbrjTscl71sMMQPI6NevXpoBjeTkJDQpEkTuuQ6/MZUPREQARGoOALmrif3r7dhP9yPAHi3bt1efvllHFO0wbJly+67775CsxxWXNvoSiIgAiLgTAKGsQ1nVr4ktQplbXTs2DEzM5PSvF4vk6iPGzeODlREyKOjo2fMmDF8+PCSXEh5RUAERCB8CfitDYN+VAAgm11OHrOEA5ieg4Xy6IVkeXo4lZUsSOeBzEM4KSmJT395Ph/pdHMlPTY2lnRrFijSSWQjD/4hHEVWOtlItBbFYDQ3m5VONsMtlGw8/vjj1NuwIGUTAREQgapLwNxJZWCUMKSajkgLFixg3nF6sVrTjTOl09tvv/3++++z7iodWYku9+7dmyc+vZbefPNNJilnCg8iCA888ABzQaEZGzZsYBbab775BhGi++ttt93WuHFjNGPVqlUU/ssvv9BY/fv3v+yyy4hBlEg5QsnGwIEDC/4IqAeX5LNgovZFQAREQATKlkBiYmK/fv0uuOCCAQMGBEueMmXKp59+eskll9A76Z133rnuuuu++OKLWrVqff3118xQfsMNN7Rq1Yr5O6644oovv/ySZ/XkyZPnzp1LTCEtLe3+++8fO3bsY489ht9o4sSJ69atY9Q2MvPggw+iJeeddx5mSvBCtjuhZMM6GZ1AzebMmUNsA9OG2ljpF110kW3pyiACIiACVYJAiawNXwQWAG4iy8vEp7UTBNUusAW/skPmP/74gzHXxxxzTO3atW+88UbMjqlTpzLzEw9npidngATuqdGjRzNDOSPtGJf922+/kYilQnDhrLPOuvnmmxEPTJZp06adeeaZhx9+OMVS5qRJk5gHpIxlY8+ePe+++y5XWrt2LUXTq2rmzJnHHXecZKNgo2pfBESgqhMwdMQEsmE3rFmzxppug4d7nz59QtPDMbV58+ZDDz2U2Z7IiTnSrFmzlStXYnnwWk86mkF6mzZt2GFoNpFpxk7gfbIucdBBB5Ft69atOKNYC4Nzrcu1bNnygw8+QMNCX73QUXtrg2ugaSNHjvz9998xdvCF4Wvbvn17oYL0VQREQASqLgErym0X6w7yoYdqixYtrIgC7+LB9OJ2CDNjcBC+DtolKAdeJtJ56AcTEQlC35gUuIU4FLQhyMwh0hEV0oMjKMhsxcyLu26R6fayYRXK3IVYG9xk69atTzvttEGDBj399NNFlqhEERABEahqBKKMnVSMCoz0ReBW4l3c6iJliUdoYggGT3ye+0HLAD8QnigEgEJY1cI6naNWwBwhQTPodmWlk8ihGjVqkJNTiHBY6ZaQmFSgYPXsx21QJ2pM6UjikiVLMD4WLVoUrGXBsrQvAiIgAlWXQEmcVDypebTycGez/EihueHpIXbNsDkEgJwMjWDyjg4dOqAE9evXJ5hhpbOwBQ/nzp07022XaPn69eutaDQxDDpZERRBS/Bcsby3dTke5jisqEPoqxc6am9tUF06b3F5DA76eDH0D6W68847CxWkryIgAiJQdQkYWxv+hWPtNrxPq1evxivFRgCDBz3awOJ4jLMmFIGl8vDDDzPJE+FxvEw9e/akWxRPf1TklltuOf3001EIbAuOPvvss4gN0kKPW7pLIQ88z0844YQJEyY0atQI7SHEMmbMGAwDuxr967i9bOCDu/76662TXnrpJXp0UYlDDjnkX8XoiwiIgAiIQBkRwBqg7xNxAR7odLQlCsKb+uDBg3FM0Y+WEXVEwsePH0/EggsiA3iDWEmPzq50VkJCLFOGOPSuXbseffRRXFVEFphIkMycwj7pdKzC8rj00kvp6VtSayMyxDgM/FEhjmIBlREi5xaD5tNv+qWHVrdpWTI1du4tqWZ2BN57ocFr99a1y6XjYUJgr29PbJ8shjhYj+BS3BVPCaIUv7VsmtC0SUSkwUBxj2fr1988e+bZ55xzTkmf16WoXnmcEsraQNMwkYq7KkeLOxRm6Yt+T83cnBBmN6XbKY7AxjUJEdGh/l8Ud6LSXUnAUzK3fvH3iGAYaAbnm7uzir9Y5R4J9d+DGHhxslHSyHvl3uQBXv2xUY2TI1MPsBCd7hYCUbFxUUkyLt3SXAdaz8h83oz9M+8d6PaXGBgoh7+TrkG2A61QOZ4fSjZwfpXjlVW0CIiACIQTAYNYd3jcbijZCI871F2IgAiIQLkTsDTDRDnI89cMTeVeqXK6gGSjnMCqWBEQgapEwBfBID6TLQwmg7Uf7mcCQnlEQAREoKoTMJONMKAkayMMGlG3IAIiUNkE0AxD2TDPWdn3VNz17a0Nhm4w8yJjSVh+g4namaLq448//r//+7/iSlS6CIiACFQ5ApZmWJJg++lyOvaywXhCZi1EJ9CP9PR0ZjhhY2i7y29c1RcBERCBMiVgKUeZFunMwuydVIwVZ84slohiDSlruAbLgCxevNiZ96NaiYAIiEDFEyAeHmk4a7ovwj9drps3e9nAtuAGmYcqeJtMZWgyZWMwv3ZEQAREINwJRAZiGwbj+LxWThfzsJcNpmphVvcff/yRiXmZ+grLgym0mP3KxTetqouACIhA2RKwDAiXmxGGSOxlgxlwhw4d+tFHHzH3LdMxEuRASO644w7DCyibCIiACFQFAoauJ6/7pcVeNpjPncl4rXVrWQu2bt26bQNbVfgd6B5FQAREwIgAYmCoB+Y5jS5cCZnsZYNKYV4QBmcNEGtxWpdO9lsJdHVJERCBKkLAUDPCgoa9bKxZs+bKK6/cf8pbVvoLCwK6CREQAREoCwKGymGYrSxqVE5l2MsGTipWprUuz0KDrFA4Y8aMQYMGlVOFVKwIiIAIuI+AuevJPKdTKdjLBsvYPvjggwXr/7///e+XX34pmKJ9ERABEajqBNADg/63YUDJfpT4/jfZvXv3L774Yv90pYiACIhA1STg70Zl9mfY4crJGO2tDRY3nzlzZvAeWO+PPrhEyIMp2hEBERCBqk6AUeIgQDnsNiZON5xi3a6kSjtuLxtMLvLiiy8WrCCj/2699daCKdoXAREQARGoIgTsZSMpKemUU06pX78+sXGgMFCcoRtNmzatIoB0myIgAiJgRMDA1PCXY5jN6JKVk8leNvbs2TNv3jxmTS84LVXlVFZXFQEREAFHEghMZWhWMysEYpbXmbnsQ+J0ut20adOOHTuceQOqlQiIgAhUPgH32xDmDO1lgzmpunTpwjJN2BysvYHxYW3m11BOERABEQh/ApYZYfLpchY2TqoHHnjgxBNPvP3227nNl19+2ZovHfuD+UWYENfl967qi4AIiEDZETA0OAyzlV29yrwkG9kYO3bs+eefz6zpZX5hFSgCIiAC4UPAMjKqxnA/G9lgIVh6UjG+L3xaV3ciAiIgAuVAwHQ0BgLjX/rOxZuNbGRlZTEgPCUlZf9bHDJkyP6JShEBERCBqkjAcj2ZOKBM8jiboI1sMCb8yy+/tEZsFLoRyUYhIPoqAiJQZQlYQ8SjDJxUHvczspENPFQ33nhj9erV3X+nugMREAERKDcCvoi/pqWyu0IJRnjYFVVZx21kIy4urlWrVhroV1nNo+uKgAi4g4C568k8p1Pv3EY2CIk7teaqlwiIgAg4iYDhw5JshjmddHMF62IjGyzKVGQ8vGAR2hcBERCBKk7A0EPlp+RyzeAObEaJM9nt/svBVvHfh25fBERABAoTsGwI88/C5//z/e67765ZsyYdkdisqWNff/11FuFmAlm+WumjRo2yTsjMzOToIYcc0qRJkwsuuGDLli1WOjN6PPnkk+3atWvRogUTlm/duvWfCxzwno1sHHD5KkAEREAEqgaBkpgR+P+ZbsPaCsUCRo8evWzZsnWB7dVXX0UPjjzySAgefvjh3377rZXO/B2kcDpjscePH3/zzTd/8MEH6enpd9xxB6XRA/brr7/+7LPPHnvssVdeeeWPP/546623cnJyyqoZbJxUZXUZlSMCIiAC4UyAEXyGshHINn/+fDQgJsb/BG7dujU9j4JwkgMbX71eL+MfjjjiCDIsWbIkOzt7/fr1HKRrK+YIGdCJn3/+GVEZNmxYfHz81VdfzaQeJHJo8uTJAwYMGDRoEPtr1qx55513LrzwQvLw9cA3ycaBM1QJIiACVZ2Av1stDAyUgygIf7///jtzwlqz/A0ePLigbARRrlq1asaMGZgLRAqIF9Cj9bvvvmNxVewGNKBfv3779u3bvHlzz549LT3AVZWXl4fAtGzZcsOGDSeddJJVFK4qnFeYIMGSD3BHsnGAAHW6CIiACAQIoBkGw/0sWOeee+7IkSOZE5avls2xP8Q33nijWbNmvXr14tBBBx100003YWcgNpgO11577fTp03FSISFBGwJDBB3KyMiw/FREQawyExMT0QwUZf9LlC5FsY3ScdNZIiACIlCAQGCF8EhvhP1fwCJBKnjcW5slHgXK8u/ia0Iehg8fzphrvrKmaseOHRs1atS+ffuLL7540aJFK1asQCQoIWhG7N27F79WWloa1glD7nBqWWUyRxRfCadbXw/8U7Jx4AxVggiIgAgYE0A2DHxZRD5YnGLo0KH7l4tvikQ0A/OC5bo3btxohbuJl6ANuKQQiYYNG+Ljss4lwF6vXj0S9y+qdClyUpWOm84SAREQgQIEzMTAf4KBZuBlevfdd9EMjAzOwFygNxSfbdq0IZjx+OOPd+3atUOHDpgsBMyfeuqpTz75hLA5i+n179+faDn+qBNOOIFeWJ06dUJaJk6cSHg86LMqUOlS7ko2SglOp4mACIjAPwRKJBt2E6fPmTOHYDhjNYLD5nBJIQ/bt2/HB9WnT5+nn34a9xRXZx8hefjhh3Fq9e3b97777rM8VOgEVsgNN9yAhJx22mnnnHNOGcpGZKEuw/9Q0F5A5KGfNzUxOTJVPKoIgajYuIiEsumnWEWIufo2M/MzYrpvp7sqcePS3QhGAMHtuQnNkxs0MYmJM15j4/Svn7zyLB7lRUY1SleNijxL1kZF0ta1REAEwpaAf5km455UrqYg2XB186nyIiACjiDg1wyDoIW/robZHHFbRVdCslE0F6WKgAiIQAkIGA/3Qzb8GuPmTR1w3dx6qrsIiIBzCLhcDMxBytowZ6WcIiACIlA8AWTDRDlM8hR/EScckbXhhFZQHURABETANQRkbbimqVRRERABxxJQSNyxTaOKiYAIiIAjCRh6qKi7eU5H3iiVkpPKqS2jeomACIiAIwnISeXIZlGlREAEXEXAv4oGU4YYDPfzev3rbbh6k2y4uvlUeREQAWcQsJTA5XpgiFJOKkNQyiYCIiACIQmYaYbbTQ0QyNoI+TvQQREQAREwIEBPKsOx3z7UxUxgDC5bOVkkG5XDXVcVAREIKwKWGJjogUkeZ6ORk8rZ7aPaiYAIiIDDCMjacFiDqDoiIAJuJMAq4oZmBNnslmlyOADJhsMbSNUTARFwAwHEwFw23HBDIeooJ1UIODokAiIgAiJQmICsjcJE9F0EREAESk7AxwrbRmf5yGmU0bGZJBuObRpVTAREwD0E5KRyT1uppiIgAiJQ+QT8s4q43IYwhyhrw5yVcoqACIhAMQSMh/uFwQy4ko1ifgRKFgEREIGSEjAxOEzylPS6FZtfslGxvHU1ERCBcCRAlNtwsilmwHX7Jtlwewuq/iIgAg4gUJKQuNt7UmnchgN+cKqCCIiAywkYmhouv8u/qi/ZCI921F2IgAiIQAURkJOqgkDrMiIgAuFMACeVYdDC3J3lVF6SDae2jOolAiLgKgKGEYswcGfJSeWqH6YqKwIi4FgCpj1rXW9uyNpw7G9QFRMBEXAPASakYrIpg/r6Z64ydGcZlFYpWSQblYJdFxUBEQgrAgiG4fwihr4sJ9ORbDi5dVQ3ERABlxDAgDB0Uhlmc/B9SzYc3DiqmgiIgFsIMCeVoevJazOe/Mcff3z22Wc3btzIrUdHRx9zzDH33HOPx+OZOnXqW2+9RfpBBx10++2316xZkwzZ2dnffffd22+/nZGRcfjhh19zzTVpaWmkZ2Zmfvnll++//35eXt6JJ5545plnVqtWraxYSjbKiqTKEQERqLoEytD1tGvXrm3btj344IN16tQBqCUDM2bMePLJJxEGBGDs2LHnnXfe559/7vP5fv/99yeeeAJhaN269VNPPZWTk3PfffehMT///PMzzzxz9tlnp6amvvjiiwkJCSNHjoyNjS2TFqo42eAO8/PzY2JiIiNN4kZlcnehCqE+3sDsMOh5qHxV4FhCovfIQRmnnr+tYfOcnVtjn7298dxpKdx3YqK396CMwf/ZVr9J7o5Nsc/d2XjBz8le95vYVaBJi7jFi29bPXDYprh4/ysx/wUz98S8+UyzL9+uX79J9qBzNx3Zfzs/g1VLE8dc1DEvJ7pnv/Qb7luakppvrTz0/muN3vxvc8NViIq4dlVI4v+F4X+Nv3PyCCoODM/Jli1bNmjQgKdlVFQUFsOcOXMaNWqEWjRu3JhDiMQff/zBzpQpU7p163bFFVckJiZS4KhRo8aMGZOVlfXhhx8ee+yxl112GZfYsWPHe++9N3jw4LIyOCpONtavX9+0adMFCxZ07NixOFgVmU59HnrooS5dulx88cUVeV2nXSsu1nfM8F29+2V88GLdpbMTq9X0ZGz362hcjK/v0PRjBqe/+0zdlfMTq9XK37Yx1vQ/htNuUvWJiBj/VNP3XmhkvbJ1PXLXyCvWzf01Dc0YccX63Jyo+69pv3tnTJOW2T7PX291S+elfvxmw5WLkoGXnR1V/CNOcP8iYGhwWCQ3bNiwePFi6521bt26tWvXDnLEJuANe+jQoYgH/qgbbrihVq1ay5cvb9euXf369cnWqlUrBAM7gxN5jvEQQzNI79WrFz6r+fPnN2nSZN26dXi3rDIPOeSQV199lTKDlzjAnYqTjQOsaJGno64OsV2KrJ4rEus1y+3cfe+CGalb1sXXrOfdsSlm+/o4al6tnqdXv4yZ36du3xxfra53x4bYXVtiDZ23rrjxqlbJ7H1R/HHXmBqHHbtz6YLUDSsTjztte3yC55fva8XER6bW8i6ek5Kf/9dYrrg4T93GOdnZMRnbYzJ3u/tBUSFtTQdcrlOsAfFPHQK68eabb2IoWI+viy66aMSIEcEM7du3v/fee+vVq7d79+6XXnpp9OjRfMWASElJQUisbPivsCFQgn379lmaQTrGBBm2b9+ORUI6+a3M+KmQE7bgJQ5wp1x+DXjW1q5du3LlShxt+NSCIklduVVgkY7B1bZtW24YR9706dO7d++OIZKbm0s6+ePj48mMYC5duhRe3HaHDh3gSCLGGp/kxP0HHfYtcJs2bSJb586ducSyZctIb9OmDZrMDhzRai6EuYc+kydIn6OFNst5ZfmvsA1DGJKFTnTp19Ranhbts2Jivc3aZcXEebN2R3/ySp2VixJSauS3Ojhr397oVp2yYuO8vIp+8Uat1QsTpRwubehgtes1yerUbfdTY9rGxfsaNMpu2DS797Hb4hJ8MTHeNSuSJr7UOHtvdFZmdE5udI8jd3U/YlduTuS3n9afM63MAqrBmoTVjuV6MnDAW1luvPFGohTWg6hQyAFjgs2Cg5/qtNNOmzVrFnYJjyOeSzzEOMRTkUcrqmOZJlZmnqs8r6x0Sg6aFzwt+cpWVsDLrKCCFeL2UAKe79zVli1bsJiuuuoqKwORfe5zz549SB/eod69e69ateqss866/vrrAcETn08yH3bYYcgAkZ/NmzejFnwiG/jv6Dwwfvz4efPmHXzwwZTTs2dPIj8Ei44++miUmaL69etH4QgJ+9WrV3/ggQdq1KiBZkyePBmmXBQxo7PB8ccfX7DCBffpgUDPBE4hEdyrV69uHHFQwQxhth8bH1G3Ue6y+cmfvlKLX/5Z128ZePb2525rnJTiTU3z5OVGffxS7egY38jrt/Q5JX3D8oScXIP/GWHGKLxu57hTt+3cGjd/ZmpcvCcxyZNaLW/J/HqzplZv1DJ71L3LlsxNm/FdjTXLEsY/0zR9R0xqNc/AM7aMuGTt/F87efLV9MX/FIxX9/MbG74IHl9JSUm2gVWykYdP3FP0oeIpx2MtPT2d5xKvxbx247/iBRqFQBVWrFjBIxfJYR9zBP+VVV1e4slmvYsXfwMlOFIussFNEvHv27dvcnIyL/5YWFgS3CT14q4I2qCZBPfpH4ZhYVUWhURFMAgef/xxlKBHjx486BctWnTXXXdhlCC29CJAKk499VTyIwzs4LCjHDqlxcXF8RXL4+mnn3799dfvvPNOLo1sXHLJJXPnzkVRsDkIJaEfyAY928gWQjYQc/hadh8NxlYCnC7M6smP2LE1duHMpBUL/e7RGf+XduoF29jJyYnckx49d3ryqsUJfJ33S0qXI/bExvkkGy5s5H+qnJzm6XvStsnvNcjZFx2dEsFrwdpVSfNnpG1el8Df6mXJnXtkIBu7d8Tyx2nbN0Z8+2Gde17cXr127o7Nfh+AtlAEDHxUtkPEZ8+ezWMHO4NXWN6z0QAep/hdeGP+6aefcJZ89NFHvEDzbk0nq65du3788cc8M/HT0D2XN+mGDRvyvnvkkUfybDziiCN4mk2aNIkXdKfLBlh5QE+cOBF55MUfzeAdv3nz5qSffPLJzZo1w5Li9oj1o5NWG2CIgQmZoYfA1q1bsQxgR6gHRjzBQdOiRQvKsWSDc5EQVBc68EWQyMAO5xJZwt8FU7QEncDNZZU/bdo0bBSaAUFesmQJJ1rp+3/yCoDJYukKladJ8lfvnyt8UrJ2R21dHxfjD2f4t9h4b16e/6VyX3r09s0xMbF//T+IjfUhMNrcTqDLEbvoH/XDV7W4keysqB3b45q0jIz6++0xPs6LS6rQPcbGE0H0BUPlhY7qq0XAHw9ngT8DHGQMPcKDR98333zDQ5JXWN6zb7nlFh5xmAs8RXkt5go8Hnn9tbrn8lrMM423ajLz0CMzT0IUgi65GBl33303JSAkeHScLhtYA3QCw7lEKJ+HPnfF89qKFlg9wLgT7hAuQe+bNXTFunN8mHKRDwAAHVxJREFUXKQjPOgnKWDiE0UBltUoZA766TjEJSDFIRIp39rnEhg9FMWjf8KECTjNjjvuOArEdcYWQjY40SqBAimBr9ZFw/Vz2/rYFYsTe/TNWL3I/y55zOCM376pzs7ubTELZqT0HpCxdmkCRkbP43b/8nVaTnaY0wjXVrbui6f/sadum/ljrZ1b/G3t9UT+uSC56xE7ex27Iy8nskOXPc3b7Xv50RZxCd7WHTP53LgqoVb9vNP+s2H5wtSMXWXT5T+MCUeZxMO5f8tLVTyIQYMGYRzwDLSee7xPW06nCy+8kNdunmk88fD8WwVwFK8+799ElImB84gjnRN5Oyd8snPnTh6zvGET0yWx+GuW7MjfrxklO8smN/EMpOKOO+7AJuB9n5uk6tY5CxcuxLZCQghj8FDm1R5FKbI4tBQHFyw4imCsWbMGs8vKGeL+Cz3luS5AsTMwU4YNG8YVcXxx9WB9irx0lUrMTI/+5u2ap1y4/fon1+XnRv7yTdqXb/hfRfdlRn01vtaQS7bdOHYt6T99Vf2HD2vkybvt5h9Hi/Z7W3fIfPC69sGbWDon5av3GgwaufGEoVu2b4kde2erlUuSMS8atcg+ZcTG6rXy9u6JmTcz7bM3GyqwEYRW9M5fT7iiD5YoFZcJW6FTeLLx9GcrlM7DkNdotkLpvPsiFWyF0svka7nIBt4hXuctOwtXFW/6wVjCuHHjiO1w9I033jjjjDPwXOF6KvJOeNAT7XjuuedOOukkhs4jRf379y8yZ+hEulexURkiRWgYHdpC569qR+kZtX5V/Lg7/C8pBTfSN66OJzZeMFH7riawcnHKuUf3KHgLiMGMKTX4K5iYsy/y2w/q8lcwUfuhCfjNcLNehpY7K3RpDj9aZmZLwftEEunC9O2339I/CluJ13xS8ErhocNuev755x999FHiB0OGDCGRjX7Kll8I5SQnSssOAYyHH36YqA5eOZx6DLW34uf4+JBWy+BAgfkaVGYCRGhS0EmFvUYKmc8//3zsDELxn376KZemHE637D5r4H7BymtfBERABEpMAM3A4DD8K3HpzjqBUFfZGVfOurUyqA29gQcMGJA3NTE5MrUMilMRbiAQFRsXkaAuQ25oqrKoY2Z+Rkz37fTbxJdeuvJ4SjDd08ptTarVaBKYPN2mGK/Xs2rxtw/ccQ5bMIxqc47DDpeLk8ph96jqiIAIiED5Eojy+ngHjzBYqImchrHz8q3xAZQu2TgAeDpVBERABCwCeG3wU5nMS2U5stzMTbLh5tZT3UVABBxFwMTlb5LHUTe1X2UkG/shUYIIiIAIlJyAiaVR8lKdeIZkw4mtojqJgAi4i0Ak8QrD7kX+bO62OMqlA6672lu1FQEREIEDJeBuISjZ3cvaKBkv5RYBERCB/Qkw3M/UScVa4mYDA/e/ikNSJBsOaQhVQwREwM0EsDYMxcD9domcVG7+paruIiACDiLgfkEwgylrw4yTcomACIhACAIehvtx2F45/BNz2OcKcaXKPyTZqPw2UA1EQARcTwAlMBQDa/YqN9+wZMPNrae6i4AIOIeAz2BqESLnfnkxVBjn3Nu/aiLZ+BcOfREBERCBUhGwnFQGp7pbMvw3KNkwaGZlEQEREIHQBPwmhJkgGGYLfblKPaqeVJWKXxcXARGoggQMu+o6lYysDae2jOolAiLgHgJ0o/JPnG6w4IY/p3vuq8iaSjaKxKJEERABESgJgb9W0TDwU/lzGmQrycUrOK+cVBUMXJcTARGo0gTcbmrQeLI2qvQvWDcvAiJQJgQiWd3Pg/vJXhR8Wt2vTIirEBEQAREIBwIm3id3O6j8rSRrIxx+q7oHERCByifgfj0wZCjZMASlbCIgAiJQLIEo7AwTU8M/vzqDyd2tMAqJF/s70AEREAERKBcCGrdRLlhVqAiIgAi4iICxtWFolDj51uWkcnLrqG4iIAIuIWA+r63LTQ3aQ7Lhkh+lqikCIuBgAkQsouhZa7B5ze0Sg9IqJYtiG5WCXRcVAREIPwJGshEGty1rIwwaUbcgAiJQ2QSQDLOeVBEa7lfZbaXri4AIiIAzCPiNDRODwySPM+6omFrI2igGjJJFQAREwJyA39owy22YzaywSskl2agU7LqoCIhAeBHA9eQxWhSW247yhupNNXv27O+++27NmjVRUVEdOnQYNmxY7dq1OeuHH3746KOPMjMzLXADBgw444wz2M/Kypo6deqUKVOys7O7det29tlnR0dHk56RkUE5P/30E/vHHHMM+WNjY61zD/BTsnGAAHW6CIiACPgJ2M9iGODkH08ecvvtt9/S09N79+7N0/9///vfihUr7r///ri4uJUrV7I/fPjw+vXrU0CzZs349Pl8CMMLL7zQq1evGjVqsJObm3vxxRfziWZwOoKRn5//+OOPJyUlHXfccSGvbHpQsmFKSvlEQAREoFgCfjGw0YO/z/VnwzLYs2dPTIz/CYwksP19NAJzgf34+Hg+mzRpctlll1100UVt27bla506dY466qimTZuyHxmYbXf79u3ffvttjx49LrnkkoSEhLy8vAcffPCCCy7YunXrpEmT+vTpc+mll5J506ZNKEpZyYY64IJUmwiIgAgcGAFrNIbx5+WXX45xkBrYxo4dW/DaKYENhxKbpQ3ogZVh2rRphx12WOfOnceMGZOTk0MisrF58+Y2bdpUr16dbCeffPLq1auXLl2KJm3ZsqV9+/aJge3oo4+eMWNGwascyL6sjQOhp3NFQAREIEgAM8LQUxUxbty4M888M2htBIsouIOr6q677jrhhBMs86Jr167PPPNMo0aN1q5de+edd2JAvPrqq4gHFkZQVzBHKGHbtm01a9YkHQGyCqxVq9auXbuIgiAiBS9Run3JRum46SwREAERKEjAF0Gcm4XCbbdAbIMHPZaGFbsu8oyNGzfedNNNhDFuvvlmK8OhgY19rI169eoNGjQI/cAcYfP+HWNHKshgmSmkezwe61zSkShi7NbXA/yUbBwgQJ0uAiIgAhGRPJ/9HioDawN1CdWRyg+T6PfDDz9MeOORRx7Bl7U/34YNGxLopq8U2kOsmx0UAhEiZo48NG7cGCuE9J07d1rn0i8LM8WKl+xfWklTJBslJab8IiACIlAEgYBiGFgb/sh5qGxLlix56qmnuAABDB79eJYImCMS69evRxiIYSAS48ePb968OfLAIQIbxC26d++elpZGet++fUnHJUUUffr06bi2UJTJkycPHDiwiEqXKkmyUSpsOkkEREAEChLA1DCbytBONSLef//9CRMm9OvXj0gGVyAscfrpp+OtYtzGrFmzCE7QC4swOKJijedAD15++WW62OKbIv2ee+5BXbBRTjnllNdee+2BBx7AhYW60L2qYH0PZF+ycSD0dK4IiIAIWARMV/eznbqK3lCHHHJIECsGBxYGn/SmZawGUQrkgdgGIwGtPB07dhw1ahRuKKwK0ol8kI5yEAq57rrr1q1bR5CDoHrr1q2DZR7gjmTjAAHqdBEQAREIuJ1CeZ4KIPKHxENl7RLYCpzw1y7OKLb90wlmFHmIqDvSElSX/U8sdYpko9TodKIIiIAIBAl4bc2Iv7JqBtwgM+2IgAiIQNUl4A9shLIhCpAxz1ngJCftlk03XifdkeoiAiIgApVCwFA2KqVuZXlROanKkqbKEgERqKIEiFh4fEaDxP2xDXdvkg13t59qLwIi4BQCfueTiSQYd9V1yo0VrodkozARfRcBERCBEhOw6x/1T4FG0vJPdgfuSTYc2CiqkgiIgOsIMG7Dbs4Q6578PanMcjqVgWTDqS2jeomACLiIAGLg70xlsMnaMICkLCIgAiIQ7gQQAzPVKMnIQIdCk7Xh0IZRtURABFxGwNSMCEyx7rJ7+1d1JRv/wqEvIiACIlAaAn5Tw8zccHdcw89Gw/1K8wvROSIgAiLwbwJmmvHvc1z6TdaGSxtO1RYBEXASAeLhHm9EpMEyTXSjMnVnOekGC9RFslEAhnZFQAREoHQE/CHxwJ/96dgl7jZN5KSyb2TlEAEREAEDAmZiYNhP1+B6lZVF1kZlkdd1RUAEwojAX+M2TJTDvKuuQ/nI2nBow6haIiACbiJgHq4wURZn37msDWe3j2onAiLgFgIYHAYRceMQiHNvW7Lh3LZRzURABFxDwMe06XSRsq+vz8vADXeP3ZBs2DezcoiACIhAaAI+fwdcA9Hwl6LYRmiWOioCIiACIhBeBGRthFd76m5EQAQqi4Df+2QU3HD7uA3JRmX9xHRdERCBMCLAEPFonFQmfiqjEIiT0agDrpNbR3UTARFwEQETzbCUxSynU29d1oZTW0b1EgERcBEBn8/fl8rA2vBGeFx0W0VWVbJRJBYlioAIiECJCBhpRqBEel3J2igRW2UWAREQgTAk4PputeZtImvDnJVyioAIiEAxBPxDxA0H8ZnbJcVcq7KTFRKv7BbQ9UVABKoaAXf7qCJkbVS1H6zuVwREoBwI+BfbMLM2DLOVQx3LqkjJRlmRVDkiIAJVlwAzTXn9Tir74X50udKcVFX3h6I7FwEREIG/CWBtGAYtUBd3e6lkbfzd6PpXBERABEpLgIh4QDbsrY3SXsFB50k2HNQYqooIiIBbCWBqBKTDtv7+fH67xMWbZMO+8bwR+R6f6wd22t+ncgQIeL2eSDV3lfkx8L+7TO6Vsd8en1FRAWeWZKNMqDuykJycnPz8/LkRs6Ij4hxZQVWqHAjkR0ZlqmN6OYB1ZJF5EXld8zvyPz0xMbF0FYyJialWrdrSyO8ifHioTH453qjYiKSkpNJdzglnRbrdXCpXiB6PZ/HixXSRiIoy+TWUa10qtPD09PQRI0Z89NFHpf6/VKHV1cUOmEBWVtbQoUMnTJhQvXr1Ay7MTQVY/7s7dOgQHR1dunrzCN28efOuXbsoyrCE2NjYBg0apKWlGeZ3WjY5qUK1CL+kjh07hsoRpsd27tyJYBx00EGuficK08Ypl9vat28fLc7Ts2bNmuVyAWcXGhlZ+lA256IBbOav4AdyOSeAlGzYtILbG9jm9kIe5t6r8u2HZBNuB4MNHdwJtzss//upOuiqlu+l/H85YXIFnHJ169atOv8NwqTZDuA2aGtavKo5Yw8AWJU+Nfruu++u0gB080UR4PHRsGHD9u3b6zlSFJ4wTKOh69evT4sT4A3D29MtlSkBhcTLFKcKEwEREIFwJyAnVbi3sO5PBERABMqUgGSjTHGqMBEQAREIdwKSjXBv4Yq9Pzqw33LLLbNnz67Yy1b1q+Xm5n788cfjxo3bvXu3Q1j8+uuvF154oUMqo2qULQHJRtnyrOqlMWps5syZDPuo6iAq9v4Zl7p+/folS5bk5eVV7JWLvdqOHTu+//77Yg/rgJsJSDbc3HrlUHcmUzEftVQO11eRTiHAmGfUyCm1UT2cRECy4aTWKNO6XHfddf/5z39uvvnmtm3b9urV65133uEpsGXLljvvvJPBwM2aNTv99NPXrl3LNRctWnT22WdfddVVw4YNa9KkCdMk0B3z4YcfHjBgQOvWrUePHv3bb7/ddNNN7dq1O/zwwxcuXMgpWBUffPDB0UcfTYbu3bu//vrrzOpTptVXYcUSoL2uuOIK5i8APk22dOlSK+uGDRvGjBlDJ9qDDz4YnxXPfcZ+//e//x00aBDpnTp16tat2zfffEM6bwabNm26/vrr+W2w0cTr1q2zXhcYK37fffcdf/zxQ4YMeeihh04++eR77rmnT58+/DBuvPHG5cuXX3nllfwShg8fPn/+fOuUJ5544rDDDmvZsiW/h7fffpvfRrFV14GwICDZCItmLOYmvv3221atWk2ePPmss84aP378nDlz/ve//82dO/e555778ssvGeF19dVXZ2dnc/aePXt4+lx00UU//vgj87LxOMDDcMcddzz77LOchWYceuihn3/+OaXxrLGuxnPkgQce+L//+7/bbruNBwfFFlMLJZcxAea8QS0+/fRTGpE5LW644QZLs3/66acWLVp8+OGHl156KbqyatUq68I83xmTgcyfeOKJtCmZiYW89tprtPgrgY0d3irQGPJz9JdffrnrrruIlCA/+Bsph58BekAe3kKOOuoodqgDvwd+NpxCHV544YXvvvvuvPPOe++996ZPn17GN6ziHEZAsuGwBinT6vCCiRLwGtivX7+4uLgffvhh3rx5GBa8PHLoscce++OPP5irkWtiXvD+SDbePXkioCjYIr179+Yt8pBDDiHzsccey6GRI0cSusBqSUhI4GtycjIu9Vq1atWuXRuJKtO6q7BiCaANGIvbtm2jA0Lfvn1pwdWrV5Ob9jr11FOxQi6//HIanSe4VQQ2IjKDFcInRgZhc7QBs+PMM8/kl8B22mmn8bqQmZlJfn4nZEMbGO+ZkpLC6L/LLrsM/SBbly5datSowe8Eq+WII47AVLVO6d+/P75N7JXU1FR+PEHDxbq6PsOPgEaEhl+b/nNHTZs2tSYI4T9zfHz81q1bCZnylLdGAmMu4JEg0drhiVBwhDCJFMRUncxmyOODnHxlelSeFHghKIen0rRp01AOztq4cSPl/HNh7ZUnAZQb25Em4BHPJxszFnNBZgfBUmSH5m7evDk+K6sWtBob+5YMkJkfA4ZC48aNrQyYC7Qpj36+0ppokpXOJ7O00sTWr4hZDuvUqWPt896AycIpe/fuxQ+2cuVKLoHlSlgeB5eCIkGAYbkj2QjLZv3rpnh8WHvWf3Ue/TwUeNnkfzWHeF3lrdN6oGBtsBVkYZ1rnVjoEC4sjIxJkybh+B44cCDSwlNDT4qC9MpvH5HAN0WLXHPNNRgEy5Yt+/rrry346AEPcS5N9IIgFpEPqxo0otWO1g7Nx+n8GLZv325lwBOFkFgtziEaNFh/6xTrK4eCvyhSrMAGtg4uSmwXrE/LlLFiJ8EStBN+BCQb4demxd6R9RaJS5rXTGyO559/vk2bNvgugq+lxZ653wEeGbxs8kzhGYHvCycJXpH9cimh7AlYzBEPHvQ87gkzBK/x+++/0xY82fE9Iid0aggeKrSDMOCG+uyzz/gBcIjoV48ePUo3ST4/AH4J1u+B8TrEUQiMF7qcvoYZAclGmDVoqNvBR8HiS0QviYvy/xwXx/33349boxSygQvrhBNO4JmFCNGvBi8Hr6Khrq1jZUQAe5HINjHqUaNG4Vck5sTT3yqbDgsIBn2oMCiJkxOUQl2KvCySQ/j6qaeeIsRNBk4kzoEzqsjMoROJexF+p3seLyKNGjWydCj0KTrqdgKaytDtLVhs/XFBoA04o8nB44PhV1aIIiMjA782h3BTEMrm5ZS3RTrd8tQgg1UcnWfq1avH6yfvkpbfHHUhJx5wvB+ETMiGP4RDZOBEfNwc5VpcCN8XjzMrFlJs5XTgAAhY7UUgAanmYQ1wohr4G9mwRWgjPmlZq/mQEBqFrySygx+JXlW8MeDXotEtpxbtjq+SFqRSGI40vdV8FEUTE9JAZjhE0yNaXJF9fkVUgH1SyMNXEjmL3xWZ+bXQL4ufnPVT4ZC2cCIg2Qin1tS9iIAIiEC5E5BjodwR6wIiIAIiEE4EJBvh1Jq6FxEQAREodwKSjXJHrAuIgAiIQDgRkGyEU2vqXkRABESg3AlINsodsS4gAiIgAuFEQLIRTq2pexEBERCBcicg2Sh3xLpARRJgHMnjjz/OlE1clBlhma5xzZo1ZVUBBi7ce++9s2bN4ioFy2QaR4bOBefqKHjI2v/555+ZJ9gaAbP/0UIpjJZ4K7BZgyoKHdVXEah0ApKNSm8CVcBPgLm7mZq3c+fOrN7BOh9PPvkkY9NKgYbhZkzh/ueff3Iuy4oMHjyYoXBFloOcXHDBBQsWLOCUIjPsn8gDnQk8GMVW6BRm6KIcawr6/c8iBb1hzRKG6RV5tFAimrRixQpugUGUhQ7pqwg4gYAmF3FCK6gOETxzGeTM9HxM0M0TlgXJeXqyLlAQDaOaGedcoilMGBrNMGbOChZScIfL8XRGCQomal8ERMCWgKwNW0TKUEEEmKaCaS2YSPWcc85hCQeWG2LKChaYYvYkUlgnaurUqZgIl1xyCQtOMAMjyxeytAOVwy5htSGmYGJuJSZZCpopLEzEidaCIjiImMSJ6bOYz5EyWYRq7NixTPyOSjETBmtPoVJM+s16iEwcy/ISLGxnrYiOujCLFydiCT3zzDNWYggiuMUwmLgKJb/xxhucbmXGQGFlJGZwYs5HKmk5rNDCr776ijmmmISDs1599VVZGCHY6pBDCEg2HNIQqsY/BLAAeHpa8yAxtRFP85NOOumTTz7h2U2EgCmSmKb7/fffJxu+LD4JLbD4B49+ZnJlgizU5Z+yAnsICWsN4UdCXViEjn3mAL744ot79uzJVIzYHCyDysxOrBvBnIysmvfiiy8SF0G3eKwzOSACw2p3b775JoWx1mGhwgt9RQBefvllTucq1Ap9sjIwGRTeM5QMbaBMakLhLITHeotMI0h+hIpErl6oQH0VAacRkGw4rUWqbn14MWehch76xINZLhubABbMr8cU3yw1iCXBUawN1pVjVTve1lljDkUhBkC8AVMAaWH6VXSFqfoKQSQPisKyEBRFOXySk5n+mNEPU8Oa9Y+Vpoh1W8ug8kAnwxdffIHesBgqV2RZQ8ydoUOHEi8pVHihr0xJi+FCtAMBY40jamhlwL3GxMMYSazBRxRnxowZGC7WOld40lgpD5nE0mKR3UIF6qsIOI2AYhtOa5GqWx/m5WVpUh6grBx35ZVX8qRGSHiYEtO2Jme1VgHC7CADmHg680AndIEvC++WZZ3g6Sq4OJ1FkxMpgdVSi4NrPehxebHkUbBwpIhnPXLCjnWiFSwprhDSETOmNF++fDmCQeVZ9CJ4LvpkzR1LDXGyMTEtPa+IriOQTHWOgHE6SoYXK0T5OiQCTiAg2XBCK6gOfgJ4n+j4xHKBvKdbC8zx5EUVeM5agJjfm9WqcUaxroOVghigFuTneW2FNDAU9l+eFlFBABAkTA3rRD5JoXCrTxQ7lMxRPEvBFSPIQDV41gd71qJPaFuwhP13cGHh2ho3bhyFYEzcc889CJKVjcV0OZeakEINUQhrHnvWR7r22mstRSFnwZX19i9fKSLgBAJyUjmhFVQHPwEe09bCD8U9OgkaIy1ENYhD0JmVlSFYTo6nMG/o+K9++eUX1pti5SjkoRBQotDYJax2R3gDy4NPcqITPLsJmCMGlEYe1lhlpVue6QRU6BBMmRQ+aNAgenYREcHThQcJS6JQ4QW/EpJhQ+dQI1xe8+bNCx7lEoTEMWhY/w7zAisEZxp3RG1//fVXrsgoDSyPoFMreKJ2RMBpBGRtOK1FVJ9iCWBk3H777QSc6UOFxmBkHHPMMYS1jzvuOJ7pTz/9NJYH/p+gXyhYEMsHEXB+8MEHR48ejSbRV+rcc89FbLBs3n333YkTJ/br1w+3GN1/iXtjzVgCRiSDEugQRTZi5qw9RSA9aOgECy+4gwww6IRKsuoRqlDQ44SDCxGi5hhGVIDwBjVh/VS8VawNPmHCBCwnbJHhw4cXLFD7IuBAAlqmyYGNUhWrxLgNnqdYG2zB+8fjhGGBGPDIthJJIR7Aizmv87zUE0IgZmCFNzid5zKnk4cnMm4fnELk5FxKID9F8VLPDtELEvnkkW15tziLRzaGAoXTNcsqnDwoE9fFHCEbV0F+MBqC6cF6kiG41B2+KYqlBEwZMlBJqybkoTQ+OYShg9mEOJGBy7HKHqdTvnWnfJLCoeCKe8ELaUcEnEBAsuGEVlAdREAERMA1BBTbcE1TqaIiIAIi4AQCkg0ntILqIAIiIAKuISDZcE1TqaIiIAIi4AQCkg0ntILqIAIiIAKuISDZcE1TqaIiIAIi4AQCkg0ntILqIAIiIAKuISDZcE1TqaIiIAIi4AQCkg0ntILqIAIiIAKuISDZcE1TqaIiIAIi4AQCkg0ntILqIAIiIAKuISDZcE1TqaIiIAIi4AQCkg0ntILqIAIiIAKuIfD/Mkq0SC7hqpwAAAAASUVORK5CYII="
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You should see the following confusion matrix rendered:\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 225
+ },
+ "id": "88Nw7619Akf-",
+ "outputId": "d7be0554-9737-4b8a-9726-c33d1140d92f",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "target_names = ['normal', 'abnormal']\n",
+ "report = classification_report(y_test['label'].to_pandas(), y_pred['predicted'].to_pandas(), target_names=target_names)\n",
+ "print('Classification Report:\\n', report)"
+ ]
+ },
+ {
+ "attachments": {
+ "9f900d6f-09c7-479a-b93d-c441e1a97dec.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You should observe the following classification metrics:\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In particular, results for 'abnormal' show 92% recall and 90% precision:\n",
+ "\n",
+ "| | precision | recall | f1-score | support\n",
+ "|---|---|---|---|---\n",
+ "| normal | 1.00 | 1.00 | 1.00 | 19169\n",
+ "| abnormal | 0.90 | 0.92 | 0.91 | 831"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GZJ6u6Bih_E5"
+ },
+ "source": [
+ "#### Grid search to tune hyper-parameters\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 902
+ },
+ "id": "1aw_kVg0h_E6",
+ "outputId": "98638766-1be3-441c-a13a-e8c2674194be",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.metrics import f1_score, recall_score, precision_score\n",
+ "\n",
+ "# Define the parameter grid for BQ vector search (adjust values as needed)\n",
+ "param_grid = {\n",
+ " 'n_neighbors': [6, 10, 12, 15], # Number of neighbors to consider\n",
+ " 'dist_threshold': [0.004, 0.006, 0.008, 0.01], # Distance threshold for a match\n",
+ "}\n",
+ "\n",
+ "# Initialize variables to track the best score and parameters\n",
+ "best_f1 = 0\n",
+ "best_recall = 0\n",
+ "best_params = {}\n",
+ "\n",
+ "y_test_pd = y_test['label'].to_pandas()\n",
+ "\n",
+ "# Manually iterate through parameter combinations\n",
+ "for n_neighbors in param_grid['n_neighbors']:\n",
+ " for dist_threshold in param_grid['dist_threshold']:\n",
+ " # No need to train any model - simply do a vector search\n",
+ "\n",
+ " # Make predictions on the test set\n",
+ " y_pred_df = predict_anomalies(n_neighbors, dist_threshold)\n",
+ " y_pred_pd = y_pred_df['predicted'].to_pandas()\n",
+ "\n",
+ " # Calculate F1-score\n",
+ " f1 = f1_score(y_test_pd, y_pred_pd)\n",
+ " # Calculate Recall\n",
+ " recall = recall_score(y_test_pd, y_pred_pd)\n",
+ " # Calculate Precision\n",
+ " precision = precision_score(y_test_pd, y_pred_pd)\n",
+ "\n",
+ " # Update f1 score and parameters if current combination is better\n",
+ " if f1 > best_f1:\n",
+ " best_f1 = f1\n",
+ " best_params = {'n_neighbors': n_neighbors, 'dist_threshold': dist_threshold}\n",
+ "\n",
+ " print(f\"Parameters: n_neighbors={n_neighbors}, dist_threshold={dist_threshold}, Recall: {recall}, Precision: {precision}, F1-Score: {f1}\")\n",
+ "\n",
+ "# Print the best parameters and score\n",
+ "print(\"Best Parameters:\", best_params)\n",
+ "print(\"Best F1-Score:\", best_f1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "X4VepQhgh_Er"
+ },
+ "source": [
+ "## Detect outliers with scikit-learn algorithms"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KqZSOjAE8hL3"
+ },
+ "source": [
+ "As a baseline comparison, we'll evaluate popular [outlier detection algorithms](https://scikit-learn.org/stable/modules/outlier_detection.html#overview-of-outlier-detection-methods) implemented in scikit-learn, using the same logs embeddings that were generated and stored in BigQuery. [One-Class SVM](https://scikit-learn.org/stable/modules/outlier_detection.html#novelty-detection) and [Local Outlier Factor](https://scikit-learn.org/stable/modules/outlier_detection.html#novelty-detection-with-local-outlier-factor) are both semi-supervised learning methods suitable for novelty detection, and are available in the `svm` and `neighbors` modules respectively:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "iA2joe7Uh_Er",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn import svm, neighbors"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "25svuP1jovxf"
+ },
+ "source": [
+ "### Use binary classifiers (semi-supervised on negative class only)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oAUh7rFPovxg"
+ },
+ "source": [
+ "We will use the same training and test datasets persisted in BigQuery tables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "id": "g2djOrYJxqsa",
+ "outputId": "7b240b8d-d3cb-4b2f-b11e-ac8c25d7f9a9"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Query job 6423f097-470b-4944-a1a2-4afb4bea8bf9 is DONE. 926.3 MB processed. Open Job"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# read a BigQuery table to a BigQuery DataFrame\n",
+ "df_train = bpd.read_gbq(\"vs_logs_demo.hdfs_full_embeddings_train\", index_col=\"sessionId\")\n",
+ "\n",
+ "# X_train = list(df_train['embeddings'].to_list())\n",
+ "X_train = df_train[['embeddings']].to_list()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "id": "gtwOE_j5sdt_",
+ "outputId": "1da83a8e-49d1-4383-fe64-7d84b673a79e",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "df_test = bpd.read_gbq(\"vs_logs_demo.hdfs_full_embeddings_test\", index_col=\"sessionId\")\n",
+ "\n",
+ "X_test = df_test['embeddings'].to_list()\n",
+ "y_test = df_test['label'].to_list()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YxLPlWO5ovxg",
+ "outputId": "289d2409-22b7-4dbe-c862-a4f82a4ea24e",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(47766, 768)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(np.array(X_train).shape)\n",
+ "print(np.array(X_test).shape)\n",
+ "print(np.array(y_test).shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "O2GoQSEQovxg"
+ },
+ "source": [
+ "#### Use Novelty Detection with One-Class SVM"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 80
+ },
+ "id": "3FzknanWovxg",
+ "outputId": "8ba65f49-ed22-44c2-e05f-2d454d78d511",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
OneClassSVM(gamma='auto', nu=0.02)
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