13_loading_and_preprocessing_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**13장 – 텐서플로에서 데이터 적재와 전처리하기**\n",
    "\n",
    "_이 노트북은 13장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/handson-ml2/blob/master/13_loading_and_preprocessing_data.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 몇 개의 모듈을 임포트합니다. 맷플롯립 그래프를 인라인으로 출력하도록 만들고 그림을 저장하는 함수를 준비합니다. 또한 파이썬 버전이 3.5 이상인지 확인합니다(파이썬 2.x에서도 동작하지만 곧 지원이 중단되므로 파이썬 3을 사용하는 것이 좋습니다). 사이킷런 버전이 0.20 이상인지와 텐서플로 버전이 2.0 이상인지 확인합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 파이썬 ≥3.5 필수\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# 사이킷런 ≥0.20 필수\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "try:\n",
    "    # %tensorflow_version은 코랩 명령입니다.\n",
    "    %tensorflow_version 2.x\n",
    "    !pip install -q -U tfx\n",
    "    print(\"패키지 호환 에러는 무시해도 괜찮습니다.\")\n",
    "except Exception:\n",
    "    pass\n",
    "\n",
    "# 텐서플로 ≥2.0 필수\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "assert tf.__version__ >= \"2.0\"\n",
    "\n",
    "# 공통 모듈 임포트\u001f\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 노트북 실행 결과를 동일하게 유지하기 위해\n",
    "np.random.seed(42)\n",
    "\n",
    "# 깔끔한 그래프 출력을 위해\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# 그림을 저장할 위치\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"data\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"그림 저장:\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터셋"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: (), types: tf.int32>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = tf.range(10)\n",
    "dataset = tf.data.Dataset.from_tensor_slices(X)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음과 동일합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.Dataset.range(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int64)\n",
      "tf.Tensor(1, shape=(), dtype=int64)\n",
      "tf.Tensor(2, shape=(), dtype=int64)\n",
      "tf.Tensor(3, shape=(), dtype=int64)\n",
      "tf.Tensor(4, shape=(), dtype=int64)\n",
      "tf.Tensor(5, shape=(), dtype=int64)\n",
      "tf.Tensor(6, shape=(), dtype=int64)\n",
      "tf.Tensor(7, shape=(), dtype=int64)\n",
      "tf.Tensor(8, shape=(), dtype=int64)\n",
      "tf.Tensor(9, shape=(), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([0 1 2 3 4 5 6], shape=(7,), dtype=int64)\n",
      "tf.Tensor([7 8 9 0 1 2 3], shape=(7,), dtype=int64)\n",
      "tf.Tensor([4 5 6 7 8 9 0], shape=(7,), dtype=int64)\n",
      "tf.Tensor([1 2 3 4 5 6 7], shape=(7,), dtype=int64)\n",
      "tf.Tensor([8 9], shape=(2,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset.repeat(3).batch(7)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.map(lambda x: x * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([ 0  2  4  6  8 10 12], shape=(7,), dtype=int64)\n",
      "tf.Tensor([14 16 18  0  2  4  6], shape=(7,), dtype=int64)\n",
      "tf.Tensor([ 8 10 12 14 16 18  0], shape=(7,), dtype=int64)\n",
      "tf.Tensor([ 2  4  6  8 10 12 14], shape=(7,), dtype=int64)\n",
      "tf.Tensor([16 18], shape=(2,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataset = dataset.apply(tf.data.experimental.unbatch()) # Now deprecated\n",
    "dataset = dataset.unbatch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.filter(lambda x: x < 10)  # keep only items < 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int64)\n",
      "tf.Tensor(2, shape=(), dtype=int64)\n",
      "tf.Tensor(4, shape=(), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for item in dataset.take(3):\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([1 3 0 4 2 5 6], shape=(7,), dtype=int64)\n",
      "tf.Tensor([8 7 1 0 3 2 5], shape=(7,), dtype=int64)\n",
      "tf.Tensor([4 6 9 8 9 7 0], shape=(7,), dtype=int64)\n",
      "tf.Tensor([3 1 4 5 2 8 7], shape=(7,), dtype=int64)\n",
      "tf.Tensor([6 9], shape=(2,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "dataset = tf.data.Dataset.range(10).repeat(3)\n",
    "dataset = dataset.shuffle(buffer_size=3, seed=42).batch(7)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 캘리포니아 주택 데이터셋을 여러 개의 CSV로 나누기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "캘리포니아 주택 데이터셋을 로드하고 준비해 보죠. 먼저 로드한 다음 훈련 세트, 검증 세트, 테스트 세트로 나눕니다. 마지막으로 스케일을 조정합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "X_train_full, X_test, y_train_full, y_test = train_test_split(\n",
    "    housing.data, housing.target.reshape(-1, 1), random_state=42)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X_train_full, y_train_full, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_mean = scaler.mean_\n",
    "X_std = scaler.scale_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "메모리에 맞지 않는 매우 큰 데이터셋인 경우 일반적으로 먼저 여러 개의 파일로 나누고 텐서플로에서 이 파일들을 병렬로 읽게합니다. 데모를 위해 주택 데이터셋을 20개의 CSV 파일로 나누어 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_to_multiple_csv_files(data, name_prefix, header=None, n_parts=10):\n",
    "    housing_dir = os.path.join(\"datasets\", \"housing\")\n",
    "    os.makedirs(housing_dir, exist_ok=True)\n",
    "    path_format = os.path.join(housing_dir, \"my_{}_{:02d}.csv\")\n",
    "\n",
    "    filepaths = []\n",
    "    m = len(data)\n",
    "    for file_idx, row_indices in enumerate(np.array_split(np.arange(m), n_parts)):\n",
    "        part_csv = path_format.format(name_prefix, file_idx)\n",
    "        filepaths.append(part_csv)\n",
    "        with open(part_csv, \"wt\", encoding=\"utf-8\") as f:\n",
    "            if header is not None:\n",
    "                f.write(header)\n",
    "                f.write(\"\\n\")\n",
    "            for row_idx in row_indices:\n",
    "                f.write(\",\".join([repr(col) for col in data[row_idx]]))\n",
    "                f.write(\"\\n\")\n",
    "    return filepaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = np.c_[X_train, y_train]\n",
    "valid_data = np.c_[X_valid, y_valid]\n",
    "test_data = np.c_[X_test, y_test]\n",
    "header_cols = housing.feature_names + [\"MedianHouseValue\"]\n",
    "header = \",\".join(header_cols)\n",
    "\n",
    "train_filepaths = save_to_multiple_csv_files(train_data, \"train\", header, n_parts=20)\n",
    "valid_filepaths = save_to_multiple_csv_files(valid_data, \"valid\", header, n_parts=10)\n",
    "test_filepaths = save_to_multiple_csv_files(test_data, \"test\", header, n_parts=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "좋습니다. 이 CSV 파일 중 하나에서 몇 줄을 출력해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedianHouseValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.5214</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.049945</td>\n",
       "      <td>1.106548</td>\n",
       "      <td>1447.0</td>\n",
       "      <td>1.605993</td>\n",
       "      <td>37.63</td>\n",
       "      <td>-122.43</td>\n",
       "      <td>1.442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.3275</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.490060</td>\n",
       "      <td>0.991054</td>\n",
       "      <td>3464.0</td>\n",
       "      <td>3.443340</td>\n",
       "      <td>33.69</td>\n",
       "      <td>-117.39</td>\n",
       "      <td>1.687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.1000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.542373</td>\n",
       "      <td>1.591525</td>\n",
       "      <td>1328.0</td>\n",
       "      <td>2.250847</td>\n",
       "      <td>38.44</td>\n",
       "      <td>-122.98</td>\n",
       "      <td>1.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.1736</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.289003</td>\n",
       "      <td>0.997442</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>2.695652</td>\n",
       "      <td>33.55</td>\n",
       "      <td>-117.70</td>\n",
       "      <td>2.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0549</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.312457</td>\n",
       "      <td>1.085092</td>\n",
       "      <td>3297.0</td>\n",
       "      <td>2.244384</td>\n",
       "      <td>33.93</td>\n",
       "      <td>-116.93</td>\n",
       "      <td>0.956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  3.5214      15.0  3.049945   1.106548      1447.0  1.605993     37.63   \n",
       "1  5.3275       5.0  6.490060   0.991054      3464.0  3.443340     33.69   \n",
       "2  3.1000      29.0  7.542373   1.591525      1328.0  2.250847     38.44   \n",
       "3  7.1736      12.0  6.289003   0.997442      1054.0  2.695652     33.55   \n",
       "4  2.0549      13.0  5.312457   1.085092      3297.0  2.244384     33.93   \n",
       "\n",
       "   Longitude  MedianHouseValue  \n",
       "0    -122.43             1.442  \n",
       "1    -117.39             1.687  \n",
       "2    -122.98             1.621  \n",
       "3    -117.70             2.621  \n",
       "4    -116.93             0.956  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.read_csv(train_filepaths[0]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "텍스트 파일로 읽으면 다음과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude,MedianHouseValue\n",
      "3.5214,15.0,3.0499445061043287,1.106548279689234,1447.0,1.6059933407325193,37.63,-122.43,1.442\n",
      "5.3275,5.0,6.490059642147117,0.9910536779324056,3464.0,3.4433399602385686,33.69,-117.39,1.687\n",
      "3.1,29.0,7.5423728813559325,1.5915254237288134,1328.0,2.2508474576271187,38.44,-122.98,1.621\n",
      "7.1736,12.0,6.289002557544757,0.9974424552429667,1054.0,2.6956521739130435,33.55,-117.7,2.621\n"
     ]
    }
   ],
   "source": [
    "with open(train_filepaths[0]) as f:\n",
    "    for i in range(5):\n",
    "        print(f.readline(), end=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['datasets/housing/my_train_00.csv',\n",
       " 'datasets/housing/my_train_01.csv',\n",
       " 'datasets/housing/my_train_02.csv',\n",
       " 'datasets/housing/my_train_03.csv',\n",
       " 'datasets/housing/my_train_04.csv',\n",
       " 'datasets/housing/my_train_05.csv',\n",
       " 'datasets/housing/my_train_06.csv',\n",
       " 'datasets/housing/my_train_07.csv',\n",
       " 'datasets/housing/my_train_08.csv',\n",
       " 'datasets/housing/my_train_09.csv',\n",
       " 'datasets/housing/my_train_10.csv',\n",
       " 'datasets/housing/my_train_11.csv',\n",
       " 'datasets/housing/my_train_12.csv',\n",
       " 'datasets/housing/my_train_13.csv',\n",
       " 'datasets/housing/my_train_14.csv',\n",
       " 'datasets/housing/my_train_15.csv',\n",
       " 'datasets/housing/my_train_16.csv',\n",
       " 'datasets/housing/my_train_17.csv',\n",
       " 'datasets/housing/my_train_18.csv',\n",
       " 'datasets/housing/my_train_19.csv']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_filepaths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 입력 파이프라인 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath_dataset = tf.data.Dataset.list_files(train_filepaths, seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'datasets/housing/my_train_15.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_08.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_03.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_01.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_10.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_05.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_19.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_16.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_02.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_09.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_00.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_07.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_12.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_04.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_17.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_11.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_14.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_18.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_06.csv', shape=(), dtype=string)\n",
      "tf.Tensor(b'datasets/housing/my_train_13.csv', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "for filepath in filepath_dataset:\n",
    "    print(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_readers = 5\n",
    "dataset = filepath_dataset.interleave(\n",
    "    lambda filepath: tf.data.TextLineDataset(filepath).skip(1),\n",
    "    cycle_length=n_readers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'4.6477,38.0,5.03728813559322,0.911864406779661,745.0,2.5254237288135593,32.64,-117.07,1.504'\n",
      "b'8.72,44.0,6.163179916317992,1.0460251046025104,668.0,2.794979079497908,34.2,-118.18,4.159'\n",
      "b'3.8456,35.0,5.461346633416459,0.9576059850374065,1154.0,2.8778054862842892,37.96,-122.05,1.598'\n",
      "b'3.3456,37.0,4.514084507042254,0.9084507042253521,458.0,3.2253521126760565,36.67,-121.7,2.526'\n",
      "b'3.6875,44.0,4.524475524475524,0.993006993006993,457.0,3.195804195804196,34.04,-118.15,1.625'\n"
     ]
    }
   ],
   "source": [
    "for line in dataset.take(5):\n",
    "    print(line.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "네 번째 필드의 4는 문자열로 해석됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(), dtype=int32, numpy=1>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=2.0>,\n",
       " <tf.Tensor: shape=(), dtype=float64, numpy=3.0>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'4'>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=5.0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record_defaults=[0, np.nan, tf.constant(np.nan, dtype=tf.float64), \"Hello\", tf.constant([])]\n",
    "parsed_fields = tf.io.decode_csv('1,2,3,4,5', record_defaults)\n",
    "parsed_fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "누락된 값은 제공된 기본값으로 대체됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(), dtype=int32, numpy=0>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=nan>,\n",
       " <tf.Tensor: shape=(), dtype=float64, numpy=nan>,\n",
       " <tf.Tensor: shape=(), dtype=string, numpy=b'Hello'>,\n",
       " <tf.Tensor: shape=(), dtype=float32, numpy=5.0>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_fields = tf.io.decode_csv(',,,,5', record_defaults)\n",
    "parsed_fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다섯 번째 필드는 필수입니니다(기본값을 `tf.constant([])`로 지정했기 때문에). 따라서 값을 전달하지 않으면 예외가 발생합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Field 4 is required but missing in record 0! [Op:DecodeCSV]\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    parsed_fields = tf.io.decode_csv(',,,,', record_defaults)\n",
    "except tf.errors.InvalidArgumentError as ex:\n",
    "    print(ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "필드 개수는 `record_defaults`에 있는 필드 개수와 정확히 맞아야 합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expect 5 fields but have 7 in record 0 [Op:DecodeCSV]\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    parsed_fields = tf.io.decode_csv('1,2,3,4,5,6,7', record_defaults)\n",
    "except tf.errors.InvalidArgumentError as ex:\n",
    "    print(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "n_inputs = 8 # X_train.shape[-1]\n",
    "\n",
    "@tf.function\n",
    "def preprocess(line):\n",
    "    defs = [0.] * n_inputs + [tf.constant([], dtype=tf.float32)]\n",
    "    fields = tf.io.decode_csv(line, record_defaults=defs)\n",
    "    x = tf.stack(fields[:-1])\n",
    "    y = tf.stack(fields[-1:])\n",
    "    return (x - X_mean) / X_std, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(8,), dtype=float32, numpy=\n",
       " array([ 0.16579157,  1.216324  , -0.05204565, -0.39215982, -0.5277444 ,\n",
       "        -0.2633488 ,  0.8543046 , -1.3072058 ], dtype=float32)>,\n",
       " <tf.Tensor: shape=(1,), dtype=float32, numpy=array([2.782], dtype=float32)>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess(b'4.2083,44.0,5.3232,0.9171,846.0,2.3370,37.47,-122.2,2.782')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def csv_reader_dataset(filepaths, repeat=1, n_readers=5,\n",
    "                       n_read_threads=None, shuffle_buffer_size=10000,\n",
    "                       n_parse_threads=5, batch_size=32):\n",
    "    dataset = tf.data.Dataset.list_files(filepaths).repeat(repeat)\n",
    "    dataset = dataset.interleave(\n",
    "        lambda filepath: tf.data.TextLineDataset(filepath).skip(1),\n",
    "        cycle_length=n_readers, num_parallel_calls=n_read_threads)\n",
    "    dataset = dataset.shuffle(shuffle_buffer_size)\n",
    "    dataset = dataset.map(preprocess, num_parallel_calls=n_parse_threads)\n",
    "    dataset = dataset.batch(batch_size)\n",
    "    return dataset.prefetch(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X = tf.Tensor(\n",
      "[[ 0.5804519  -0.20762321  0.05616303 -0.15191229  0.01343246  0.00604472\n",
      "   1.2525111  -1.3671792 ]\n",
      " [ 5.818099    1.8491895   1.1784915   0.28173092 -1.2496178  -0.3571987\n",
      "   0.7231292  -1.0023477 ]\n",
      " [-0.9253566   0.5834586  -0.7807257  -0.28213993 -0.36530012  0.27389365\n",
      "  -0.76194876  0.72684526]], shape=(3, 8), dtype=float32)\n",
      "y = tf.Tensor(\n",
      "[[1.752]\n",
      " [1.313]\n",
      " [1.535]], shape=(3, 1), dtype=float32)\n",
      "\n",
      "X = tf.Tensor(\n",
      "[[-0.8324941   0.6625668  -0.20741376 -0.18699841 -0.14536144  0.09635526\n",
      "   0.9807942  -0.67250353]\n",
      " [-0.62183803  0.5834586  -0.19862501 -0.3500319  -1.1437552  -0.3363751\n",
      "   1.107282   -0.8674123 ]\n",
      " [ 0.8683102   0.02970133  0.3427381  -0.29872298  0.7124906   0.28026953\n",
      "  -0.72915536  0.86178064]], shape=(3, 8), dtype=float32)\n",
      "y = tf.Tensor(\n",
      "[[0.919]\n",
      " [1.028]\n",
      " [2.182]], shape=(3, 1), dtype=float32)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tf.random.set_seed(42)\n",
    "\n",
    "train_set = csv_reader_dataset(train_filepaths, batch_size=3)\n",
    "for X_batch, y_batch in train_set.take(2):\n",
    "    print(\"X =\", X_batch)\n",
    "    print(\"y =\", y_batch)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = csv_reader_dataset(train_filepaths, repeat=None)\n",
    "valid_set = csv_reader_dataset(valid_filepaths)\n",
    "test_set = csv_reader_dataset(test_filepaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation=\"relu\", input_shape=X_train.shape[1:]),\n",
    "    keras.layers.Dense(1),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1e-3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 1.4679 - val_loss: 21.5124\n",
      "Epoch 2/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.8735 - val_loss: 0.6648\n",
      "Epoch 3/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.6317 - val_loss: 0.6196\n",
      "Epoch 4/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.5933 - val_loss: 0.5669\n",
      "Epoch 5/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.5629 - val_loss: 0.5402\n",
      "Epoch 6/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.5693 - val_loss: 0.5209\n",
      "Epoch 7/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.5231 - val_loss: 0.6130\n",
      "Epoch 8/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.5074 - val_loss: 0.4818\n",
      "Epoch 9/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.4963 - val_loss: 0.4904\n",
      "Epoch 10/10\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 0.5023 - val_loss: 0.4585\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fcb280e3898>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 32\n",
    "model.fit(train_set, steps_per_epoch=len(X_train) // batch_size, epochs=10,\n",
    "          validation_data=valid_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1/161 [..............................] - ETA: 0s - loss: 0.9555WARNING:tensorflow:Callbacks method `on_test_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_test_batch_end` time: 0.0036s). Check your callbacks.\n",
      "161/161 [==============================] - 0s 2ms/step - loss: 0.4788\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4787752032279968"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_set, steps=len(X_test) // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.3576403],\n",
       "       [2.255291 ],\n",
       "       [1.4437604],\n",
       "       ...,\n",
       "       [0.5654392],\n",
       "       [3.9442453],\n",
       "       [1.0232248]], dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_set = test_set.map(lambda X, y: X) # we could instead just pass test_set, Keras would ignore the labels\n",
    "X_new = X_test\n",
    "model.predict(new_set, steps=len(X_new) // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global step 1810/1810"
     ]
    }
   ],
   "source": [
    "optimizer = keras.optimizers.Nadam(lr=0.01)\n",
    "loss_fn = keras.losses.mean_squared_error\n",
    "\n",
    "n_epochs = 5\n",
    "batch_size = 32\n",
    "n_steps_per_epoch = len(X_train) // batch_size\n",
    "total_steps = n_epochs * n_steps_per_epoch\n",
    "global_step = 0\n",
    "for X_batch, y_batch in train_set.take(total_steps):\n",
    "    global_step += 1\n",
    "    print(\"\\rGlobal step {}/{}\".format(global_step, total_steps), end=\"\")\n",
    "    with tf.GradientTape() as tape:\n",
    "        y_pred = model(X_batch)\n",
    "        main_loss = tf.reduce_mean(loss_fn(y_batch, y_pred))\n",
    "        loss = tf.add_n([main_loss] + model.losses)\n",
    "    gradients = tape.gradient(loss, model.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(gradients, model.trainable_variables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.Nadam(lr=0.01)\n",
    "loss_fn = keras.losses.mean_squared_error\n",
    "\n",
    "@tf.function\n",
    "def train(model, n_epochs, batch_size=32,\n",
    "          n_readers=5, n_read_threads=5, shuffle_buffer_size=10000, n_parse_threads=5):\n",
    "    train_set = csv_reader_dataset(train_filepaths, repeat=n_epochs, n_readers=n_readers,\n",
    "                       n_read_threads=n_read_threads, shuffle_buffer_size=shuffle_buffer_size,\n",
    "                       n_parse_threads=n_parse_threads, batch_size=batch_size)\n",
    "    for X_batch, y_batch in train_set:\n",
    "        with tf.GradientTape() as tape:\n",
    "            y_pred = model(X_batch)\n",
    "            main_loss = tf.reduce_mean(loss_fn(y_batch, y_pred))\n",
    "            loss = tf.add_n([main_loss] + model.losses)\n",
    "        gradients = tape.gradient(loss, model.trainable_variables)\n",
    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "\n",
    "train(model, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global step 100 / 1810\n",
      "Global step 200 / 1810\n",
      "Global step 300 / 1810\n",
      "Global step 400 / 1810\n",
      "Global step 500 / 1810\n",
      "Global step 600 / 1810\n",
      "Global step 700 / 1810\n",
      "Global step 800 / 1810\n",
      "Global step 900 / 1810\n",
      "Global step 1000 / 1810\n",
      "Global step 1100 / 1810\n",
      "Global step 1200 / 1810\n",
      "Global step 1300 / 1810\n",
      "Global step 1400 / 1810\n",
      "Global step 1500 / 1810\n",
      "Global step 1600 / 1810\n",
      "Global step 1700 / 1810\n",
      "Global step 1800 / 1810\n"
     ]
    }
   ],
   "source": [
    "optimizer = keras.optimizers.Nadam(lr=0.01)\n",
    "loss_fn = keras.losses.mean_squared_error\n",
    "\n",
    "@tf.function\n",
    "def train(model, n_epochs, batch_size=32,\n",
    "          n_readers=5, n_read_threads=5, shuffle_buffer_size=10000, n_parse_threads=5):\n",
    "    train_set = csv_reader_dataset(train_filepaths, repeat=n_epochs, n_readers=n_readers,\n",
    "                       n_read_threads=n_read_threads, shuffle_buffer_size=shuffle_buffer_size,\n",
    "                       n_parse_threads=n_parse_threads, batch_size=batch_size)\n",
    "    n_steps_per_epoch = len(X_train) // batch_size\n",
    "    total_steps = n_epochs * n_steps_per_epoch\n",
    "    global_step = 0\n",
    "    for X_batch, y_batch in train_set.take(total_steps):\n",
    "        global_step += 1\n",
    "        if tf.equal(global_step % 100, 0):\n",
    "            tf.print(\"\\rGlobal step\", global_step, \"/\", total_steps)\n",
    "        with tf.GradientTape() as tape:\n",
    "            y_pred = model(X_batch)\n",
    "            main_loss = tf.reduce_mean(loss_fn(y_batch, y_pred))\n",
    "            loss = tf.add_n([main_loss] + model.losses)\n",
    "        gradients = tape.gradient(loss, model.trainable_variables)\n",
    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "\n",
    "train(model, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Dataset` 클래스에 있는 메서드의 간략한 설명입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "● apply()              Applies a transformation function to this dataset.\n",
      "● as_numpy_iterator()  Returns an iterator which converts all elements of the dataset to numpy.\n",
      "● batch()              Combines consecutive elements of this dataset into batches.\n",
      "● cache()              Caches the elements in this dataset.\n",
      "● cardinality()        Returns the cardinality of the dataset, if known.\n",
      "● concatenate()        Creates a `Dataset` by concatenating the given dataset with this dataset.\n",
      "● element_spec()       The type specification of an element of this dataset.\n",
      "● enumerate()          Enumerates the elements of this dataset.\n",
      "● filter()             Filters this dataset according to `predicate`.\n",
      "● flat_map()           Maps `map_func` across this dataset and flattens the result.\n",
      "● from_generator()     Creates a `Dataset` whose elements are generated by `generator`.\n",
      "● from_tensor_slices() Creates a `Dataset` whose elements are slices of the given tensors.\n",
      "● from_tensors()       Creates a `Dataset` with a single element, comprising the given tensors.\n",
      "● interleave()         Maps `map_func` across this dataset, and interleaves the results.\n",
      "● list_files()         A dataset of all files matching one or more glob patterns.\n",
      "● map()                Maps `map_func` across the elements of this dataset.\n",
      "● options()            Returns the options for this dataset and its inputs.\n",
      "● padded_batch()       Combines consecutive elements of this dataset into padded batches.\n",
      "● prefetch()           Creates a `Dataset` that prefetches elements from this dataset.\n",
      "● range()              Creates a `Dataset` of a step-separated range of values.\n",
      "● reduce()             Reduces the input dataset to a single element.\n",
      "● repeat()             Repeats this dataset so each original value is seen `count` times.\n",
      "● shard()              Creates a `Dataset` that includes only 1/`num_shards` of this dataset.\n",
      "● shuffle()            Randomly shuffles the elements of this dataset.\n",
      "● skip()               Creates a `Dataset` that skips `count` elements from this dataset.\n",
      "● take()               Creates a `Dataset` with at most `count` elements from this dataset.\n",
      "● unbatch()            Splits elements of a dataset into multiple elements.\n",
      "● window()             Combines (nests of) input elements into a dataset of (nests of) windows.\n",
      "● with_options()       Returns a new `tf.data.Dataset` with the given options set.\n",
      "● zip()                Creates a `Dataset` by zipping together the given datasets.\n"
     ]
    }
   ],
   "source": [
    "for m in dir(tf.data.Dataset):\n",
    "    if not (m.startswith(\"_\") or m.endswith(\"_\")):\n",
    "        func = getattr(tf.data.Dataset, m)\n",
    "        if hasattr(func, \"__doc__\"):\n",
    "            print(\"● {:21s}{}\".format(m + \"()\", func.__doc__.split(\"\\n\")[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `TFRecord` 이진 포맷"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TFRecord 파일은 단순히 이진 레코드의 리스트입니다. `tf.io.TFRecordWriter`를 사용해 만들 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.io.TFRecordWriter(\"my_data.tfrecord\") as f:\n",
    "    f.write(b\"This is the first record\")\n",
    "    f.write(b\"And this is the second record\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그리고 `tf.data.TFRecordDataset` 사용해 읽을 수 있습니다.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'This is the first record', shape=(), dtype=string)\n",
      "tf.Tensor(b'And this is the second record', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "filepaths = [\"my_data.tfrecord\"]\n",
    "dataset = tf.data.TFRecordDataset(filepaths)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "하나의 `TFRecordDataset`로 여러 개의 TFRecord 파일을 읽을 수 있습니다. 기본적으로 한 번에 하나의 파일만 읽지만 `num_parallel_reads=3`와 같이 지정하면 동시에 3개를 읽고 레코드를 번갈아 반환합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'File 0 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 1 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 2 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 0 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 1 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 2 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 0 record 2', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 1 record 2', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 2 record 2', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 3 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 4 record 0', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 3 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 4 record 1', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 3 record 2', shape=(), dtype=string)\n",
      "tf.Tensor(b'File 4 record 2', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "filepaths = [\"my_test_{}.tfrecord\".format(i) for i in range(5)]\n",
    "for i, filepath in enumerate(filepaths):\n",
    "    with tf.io.TFRecordWriter(filepath) as f:\n",
    "        for j in range(3):\n",
    "            f.write(\"File {} record {}\".format(i, j).encode(\"utf-8\"))\n",
    "\n",
    "dataset = tf.data.TFRecordDataset(filepaths, num_parallel_reads=3)\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = tf.io.TFRecordOptions(compression_type=\"GZIP\")\n",
    "with tf.io.TFRecordWriter(\"my_compressed.tfrecord\", options) as f:\n",
    "    f.write(b\"This is the first record\")\n",
    "    f.write(b\"And this is the second record\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'This is the first record', shape=(), dtype=string)\n",
      "tf.Tensor(b'And this is the second record', shape=(), dtype=string)\n"
     ]
    }
   ],
   "source": [
    "dataset = tf.data.TFRecordDataset([\"my_compressed.tfrecord\"],\n",
    "                                  compression_type=\"GZIP\")\n",
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 프로토콜 버퍼 개요"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 절을 위해서는 [프로토콜 버퍼를 설치](https://developers.google.com/protocol-buffers/docs/downloads)해야 합니다. 일반적으로 텐서플로를 사용할 때 프로토콜 버퍼를 설치할 필요는 없습니다. 텐서플로는 `tf.train.Example` 타입의 프로토콜 버퍼를 만들고 파싱할 수 있는 함수를 제공하며 보통의 경우 충분합니다. 하지만 이 절에서는 자체적인 프로토콜 버퍼를 간단히 만들어 보겠습니다. 따라서 프로토콜 버퍼 컴파일러(`protoc`)가 필요합니다. 이를 사용해 프로토콜 버퍼 정의를 컴파일하여 코드에서 사용할 수 있는 파이썬 모듈을 만들겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 간단한 프로토콜 버퍼 정의를 작성해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing person.proto\n"
     ]
    }
   ],
   "source": [
    "%%writefile person.proto\n",
    "syntax = \"proto3\";\n",
    "message Person {\n",
    "  string name = 1;\n",
    "  int32 id = 2;\n",
    "  repeated string email = 3;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 정의를 컴파일합니다(`--descriptor_set_out`와 `--include_imports` 옵션은 아래 `tf.io.decode_proto()` 예제를 위해서 필요합니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "!protoc person.proto --python_out=. --descriptor_set_out=person.desc --include_imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "person.desc  person.proto  person_pb2.py\r\n"
     ]
    }
   ],
   "source": [
    "!ls person*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: \"Al\"\n",
      "id: 123\n",
      "email: \"a@b.com\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from person_pb2 import Person\n",
    "\n",
    "person = Person(name=\"Al\", id=123, email=[\"a@b.com\"])  # Person 생성\n",
    "print(person)  # Person 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Al'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person.name  # 필드 읽기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "person.name = \"Alice\"  # 필드 수정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a@b.com'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person.email[0]  # 배열처럼 사용할 수 있는 반복 필드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "person.email.append(\"c@d.com\")  # 이메일 추가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\n\\x05Alice\\x10{\\x1a\\x07a@b.com\\x1a\\x07c@d.com'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = person.SerializeToString()  # 바이트 문자열로 직렬화\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person2 = Person()  # 새로운 Person 생성\n",
    "person2.ParseFromString(s)  # 바이트 문자열 파싱 (27 바이트)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person == person2  # 동일"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 사용자 정의 protobuf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "드문 경우에 텐서플로에서 (앞서 우리가 만든 것처럼) 사용자 정의 프로토콜 버퍼를 파싱해야 합니다. 이를 위해 `tf.io.decode_proto()` 함수를 사용할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Alice'], dtype=object)>,\n",
       " <tf.Tensor: shape=(1,), dtype=int32, numpy=array([123], dtype=int32)>,\n",
       " <tf.Tensor: shape=(2,), dtype=string, numpy=array([b'a@b.com', b'c@d.com'], dtype=object)>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person_tf = tf.io.decode_proto(\n",
    "    bytes=s,\n",
    "    message_type=\"Person\",\n",
    "    field_names=[\"name\", \"id\", \"email\"],\n",
    "    output_types=[tf.string, tf.int32, tf.string],\n",
    "    descriptor_source=\"person.desc\")\n",
    "\n",
    "person_tf.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "더 자세한 내용은 [`tf.io.decode_proto()`](https://www.tensorflow.org/api_docs/python/tf/io/decode_proto) 문서를 참고하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 텐서플로 프로토콜 버퍼"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음이 `tf.train.Example` 프로토콜 버퍼의 정의입니다.:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```proto\n",
    "syntax = \"proto3\";\n",
    "\n",
    "message BytesList { repeated bytes value = 1; }\n",
    "message FloatList { repeated float value = 1 [packed = true]; }\n",
    "message Int64List { repeated int64 value = 1 [packed = true]; }\n",
    "message Feature {\n",
    "    oneof kind {\n",
    "        BytesList bytes_list = 1;\n",
    "        FloatList float_list = 2;\n",
    "        Int64List int64_list = 3;\n",
    "    }\n",
    "};\n",
    "message Features { map<string, Feature> feature = 1; };\n",
    "message Example { Features features = 1; };\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.train import BytesList, FloatList, Int64List\n",
    "from tensorflow.train import Feature, Features, Example\n",
    "\n",
    "person_example = Example(\n",
    "    features=Features(\n",
    "        feature={\n",
    "            \"name\": Feature(bytes_list=BytesList(value=[b\"Alice\"])),\n",
    "            \"id\": Feature(int64_list=Int64List(value=[123])),\n",
    "            \"emails\": Feature(bytes_list=BytesList(value=[b\"a@b.com\", b\"c@d.com\"]))\n",
    "        }))\n",
    "\n",
    "with tf.io.TFRecordWriter(\"my_contacts.tfrecord\") as f:\n",
    "    f.write(person_example.SerializeToString())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_description = {\n",
    "    \"name\": tf.io.FixedLenFeature([], tf.string, default_value=\"\"),\n",
    "    \"id\": tf.io.FixedLenFeature([], tf.int64, default_value=0),\n",
    "    \"emails\": tf.io.VarLenFeature(tf.string),\n",
    "}\n",
    "for serialized_example in tf.data.TFRecordDataset([\"my_contacts.tfrecord\"]):\n",
    "    parsed_example = tf.io.parse_single_example(serialized_example,\n",
    "                                                feature_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7fcb1a578208>,\n",
       " 'id': <tf.Tensor: shape=(), dtype=int64, numpy=123>,\n",
       " 'name': <tf.Tensor: shape=(), dtype=string, numpy=b'Alice'>}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'emails': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7fcb1a578208>,\n",
       " 'id': <tf.Tensor: shape=(), dtype=int64, numpy=123>,\n",
       " 'name': <tf.Tensor: shape=(), dtype=string, numpy=b'Alice'>}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=string, numpy=b'a@b.com'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example[\"emails\"].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'a@b.com', b'c@d.com'], dtype=object)>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.sparse.to_dense(parsed_example[\"emails\"], default_value=b\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'a@b.com', b'c@d.com'], dtype=object)>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_example[\"emails\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFRecord에 이미지 넣기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LKSrwKIpmucdtfXI/2wWFV2w9gxDEBtyFdlG0y2pZhrUQ/PkuEKOoeqHlaoh1u3saxy8PpAYNMrTZWsVUabysOuZUJU9Ba3+s1djnYOU4oZb87q4N2TiqIAaPxwJVQ0HVeqtYYz6rhkCR+r76Phuc68N0Q39dGyoT8r8CnSWlR+sy4p/1Nut3GJvRQXRYhKG2ajPi/RKA2E6MQrvhLBP7HdVkEXLJ0w1slH5uAuQEMTj5XKSh1GJDB3OCyRlaMrFKxpvfG+yr01xHaZRNGPVMztBzhrUUyM2wtnsCaG2RqoyQD1gCpjSRgtkUoG0RhAPb2uZh35uMtuh3w3RbSr9zqCrxTC9p7DFfcojW3YbmkaiX0oWT3knWwn0CMtjeIuDv25gvimi6toa3kfg3D6Llz7SVEZhtyLCtxWmoYoc9myLo/iK1EGMIA/NHNUou9f75AdbSit6SmYp4X2hUNvwcN1BhKN281mdqBC1UQY1zxXihTCbUSi313sFoDdWnh/g1HLgVNnVMXyKcMBEmXdQwtZVoGDAbLSGjVa3rWCAaa13ZIddPVUMSoeOrQTJ7lT5IQ4dwn/Zh88STBgP1XQgkESKgOTQ1rKW4GppSrElYo0zuPNiVNAlaVIXKBQij3zA8aCMOwOVaWmuRkBSfDQ/go5mKbfNZxnZagt9QdThTcQHGTLNslFeHMQzMokVLb8VJLlQGNCb3n4RMgMwaaxuDUfU0303v+NVeP8I8S3SRXaNGQvapErXEcNe1YbAcEwOpEp+LSLcW1TI3NrwH7WMkahHrLaQGkzOe4Yzql7Gerox67bEdYqogCdcFzkOZQiPVUOmop3Oj4P3lSFhpllZNhQUFVoMPG8RY1A5artamWoO2XVUmmz9BM21zJw7HyZJ8qNF01jou5wu+DBtqjh1O7zTLGARLaapI8XxTM2ozOdvaGRlGptUObwPRVBlmijfbtRQpNkyzG9mG1vtpgiTogw/KBMIcVW7EW2hHg4G2a+NOWxtmzofvJiu3OYELLRivTbaU11Z+2/1BcEygORpBGOX9GHgzyKDwTsgtXaTGw4FyElPkFEueHC4WbVXdk9vM+eiDX1pxdNFGr+XvvB3SWKghni97b0TRvlGWRKk4eNeZN4kDc4vPmMw6HFZozlgDE3eJulQIRtwyYioBcf02mqej4RWDph/ckJR0Tc955SvvR8Bd6Q/WIhd5KfN+ZB5EbjJDuWZYRLAN/LRNhLyD+cc912UgsTQrfxQDGmhjVkfqXUMrHYGQdpOxncmOYqpNBhp+Nweq7RP7YhTEIibDTc9HLY1406fCotRV15vBNV3G6EgH0dqlfVhFq3YctLlGQhucv6g0N5uMN5bTopEPwyU4bVo0TOEGXtXfHzHm4i2S/PXm86oM0GazjMEqypVhCSeLMfW29uVt0YF5pBDp3qFi2GKRKruW1vmEhRfN+WRj1kxznqU5UprrLhskaIS1d3elctpptRhvwdGaBDdDg/4tbjCsRO00mulqEWvdX08X4j0Dlc8VdfnEFq0seMbq9DbrXUol3UJKXQsMMm8PEANW7m9YVupcVCoSc2NqFfoW+tbSq2tqVfqLZIksWUNFJPo6jVXsEOd9aLj4gTIihFUlwSdkrUWt+iQBM9S/OfjXJVc3zerWuhfpz1KeaWOGeRpVk7k6CV0uLhgWUBqmsatq9FFHqC09U9MRoaoNhjEwFtU+dW+BSlZgOESPPoYOLi1eyGIsRT/ahEToV9CAXISzGaRqtwoW71fjnvUMsVCn3KWQQJ8CbP72CNEf2mcyAdqRoKm2BQbbLdmcdw70VRRbt+MvgIuCZ4GaVFrqR7QWTEH1JbOylIfR5kHBdjdT/OuZSqlZ+VJ0UGAEKy7kLqeMmvK5JiTmFPDskNrMl5UCpyn3XHzdCR++fsEzUq+JQjGuOS5S2ySOaQqWDVq9LoibVsJROYtErMd/Y46nlCmJdSympI72oWYe3lBOPkFjukoD51G6+wYnfcS/7BNpBeJmBOG9IFVyv1NT+2wSUVvdQ/u0yP1Qfv58U8LnkOc1NoMGh8O0kzkWgnhVkryVMGbHWejVLFQ1dC2rxitM3WWiD/XCIayOO6ZjvMfbqgsqZO6UYcKmPTfTDmr8DAqsMJHa7pWgfkIl9XAU8031Lj5euVltCxNv6dpl8XRm+5oYPxBys3HgfR0MGuXvDG87Pl84py/HVDXzaTbN27B6p3jPr/LLe5U3tRoildSX2bbctG38nXbn2mbrModTAuoCc6qTkAjuqbBIw1RJQYpclhrBbUxiTK6sJIEV+Y62uG6ikhPaGxip8SsfSQn7Pm6igZkDKqZZYFb3YqO5CEM1dZnILSLUqFviB6jfcszHoNJECASerVl3kb1GYnh42WsR6rc283lU/hGXY2HVEVAh1ZbAHO8NtDHONrM3QJ572tz4pJkMn64HqTpo5uX1AazswWwHJo9awcc+8wHmrvkicnCKI9et5+SjN/PAU05g8ifOZootHGAl6vGk4jIp3M5crvw81Sr1JZj3zthx6Seelfj2ulhIxjhJczesVhPEB8va0qpKDSg9M4hP0RpjStJMGhON62UOpA382gaXlBa5lCPBBgbohX2cmILWLdkQxY9S6AatLeLPpDa6+WAxXmER0ngUVo31Od+eARivvFiBio5PL5OYUhamfNDs8sCwqlIXml7SRDshnUmctuYkXxJSJd5SeU36Fc8wTZb+VYU6kzKJZL6PZupeJzLLFGBVTSvcICwBbWr1fpy8plkINmMcZde2QS9pzOLudUMs+kR/o1eLjWaoGaNI+2SagU0bciZS5Eb6TospTYH0ontITJjcg6p9NC28OVGsVW5oBfcFgx2mZaWJMZjon0xZiokkIjHimLevWVaugYcARGI4lulV0JkwPPPhD+KM49azaiWsUcvMXbuZnDvE5u4C07vvodc9kvs99KncPH4CXSpUFvyEHJ6NEEx8dz12Y6Qvs8kw1Q72Me9H/l7+frwnNjINVacmDYypJw9Vr7k0cB/MyUDvVtzuU3XM48jb39otQrDHpRu5PM/wvIv2JvwZSe0NbbDW0ulUXpvJ+5jyPwHEaDRNS79tWJXm2tEU7KpuBoRA/6DVESw6UPE7aOVjVvYWyPZNUklZM1YhZIYM2VipaJdHUm2VykBtStoJq9fCzFF1OnmVCbrAuMOGJLnQD6lPpbYc8Jpy3gnY1NQ/CDm8zh/ruqdeEw3tCcy1kcqVRehyrXcULJqH2vzuNFBnukUmmRNaY9egtklZTEbVqPbH+tRpt02jb5ipNYyh5oxqqcx2sfa2PT+sXYMTot23M6yNYTL26z5HrJzg+PWTrNi9h87m9YgoZqLDzp33cEtnlmM3TrBh1Xbu/s4c5rGvxOgMVY3Lx2mpfxROloKrUa6WpqugOd4D0PBzNnejci9njysFcQ8KY880NbHSsn3DmWobjJpITXzmzzbrjPdUfJRfQf2GLDFf2zPO+GrJjONVdULPMXbr94f1ykdwSKfavcBZWqfL7V9Hc5GmwFSSqZ7PZxOyUnwAqxKlg8QAZSinjqZ64jft2qi739wDrA2sdyfU/knHs9NYBSE0gIc0YAM0OgyWFJQKBG5tMiHFD74NklIozP7FmEnpb0rRSfesN5tCb2M7qoxwm1rHaEY4jLkPeye8F4TEMGYwoJWT46CFMQxpq+tf6YMLE2ztgrARw/TBfaxYXbN6/gDr16/GdBUjfaoVY9x9wELvENXkAvSd2V43Flq0uSya19o0yzYcLbY/bNsOXYv1f6n32+sYvO9MfhtN6sOFoQJgkfbk5v3wspNLoa7BVLkW5H2qDAoPIC5UEYJWLaXLjUF3g4gU8Y8AQwV5DDovgjufLx7nl4LUfu8D3GpIt8FTmvMS+pbZm+qd3/VAVkUQDpJ9LyE51RQb/RaN/kDMk1eGM9EmJCuqqR23w5IYak5UxpjC5DC4dAug3NJRKKP3njpyrTE02PlIbDZJ3c4+Lu1fUtkk4tKGWt9EQFNjaNPMAsPK+zgYlc+1jlJAtMOgiZm/M2imlffbrs2biumDyvx8TdXto+OGPfum2XLMJsbHK8bn+kxMTtAbW8lRx53GFH36OsZCZRlvlNkmkHJ8HA4DGSYwm/WNYq65lnA4ELTSwXoHtdW8ojDplwqlRTHcEnHPhGXIw5mQyfZAUetcFFVM1rceD4E5hTaT0Wmad87np1GhSTtbMeAKU9UYjCk7QEP715L5DlEiYn8aR9xUcTs8fz+u6wz9d/PJWi82oo81PbcUi3JASCs+5cutmApVqmYMNyuyytedt5af2pL6XOyq2wqLMtRBBjVo/lZOa88a3S4NbWNj5VyrzQk3StZ84BBq2/OT02ZmhcRVJqX5Eb759kbtNlx1qeYxpcskwyFnrgboxwULeUS/HAwVP5E03VftD6SRZNiI1wai7MYN9rjWzHeUsX6XXXPKqiOPYf3ELPTm2HMIdMc0qzatYHrLaqoZWHviBuSYh7GyXmC+6jqiMYr08atolE6Vgl6tew1YG0VCk6ibvtfQ/oSLNKGtrbN9XVP/A2MK7zdXTMFgahq4LduAeL6PUlNTkfzwQs+mUGZEJUI/S7XzOxJSNdbzx41Aow+zigIm7TU70NSIF/cXRjF5CRM00LoJ4+BzH63xx270gao1yyZ8sRBXukXPWy7gaus3BCLK+GH8NCRwqU1pUKHQwMyHMbn8sAA3tBKWzYPf4jD4g5Wwism1Wa0ZiFP7kjJXzTBNP7lCAr2q19zrnPm09Nn4Z4OAqYV4dlR6RtzKMb/HSFqhNhqWbAuN8pkFrLtOecTnn5b3gVamO6y+woebJTenbgx+nOugwqUgSdbd0d1ual4lmKysrDwdPKNqKT7KtmfdEQ2WeRGquouKZWxmP8/6uWewcfVapmdqVpgu3Vroz9Xs2TlFv7+fifFTmVizhf3jFePmEB3riDaU37rT0pD2jdIQlmqSL1bHUgi0rU2H4xNPdfnvw2gujp8b16W6iQ7HnQTt7g+nsYX8VXX7rlpxwaH84yGOomjMZGhrV0i6X2r7XcZQCmyNMouDsMmtmmg9Wv/J5lx6bnRi/GA9eVuD9u3Kq8Ny8dBOsQP73OJbIOo2VAlZBCKOi4ooHVwwrYP/LtAxGjfeMdXSLaglzbAC6Q3iaTMdc1DVmIc6wEisFkwoTy4fKKdFCou0q965Wd3GEO89tCWJD2qguVvD7fydPk4Dzrw+bSaVX3anpgIMtgsdxvj1l76S7/5gF/1xZYXdT5dZJtaMMV7XrFw/ydGP+nn29oU1vTHmqwXnsFdTtGspsJjpv5jA+FFhqe6QH62O4tdhvTss4JbKXry8JjMCb4ZS0lEx52zY+cK61DLVEcYnIC6DJv+bf8SU/DY31b3ON9DXZpv9IiiXTinQ98LBWnXuVX9Ua678WOtNfjtYnmr5sZLXS3QXiAhV5bfkE40nDgc1p/LfwyGiHeNSCaNgsrVboSUad+8KDLuD+1tVJvmfRRAxiyolS+IuTWnURMIoRpsTh7EuJ7BC4p4AxhikMnEZWD5Zm59m/uQwn2P4xPKXoCk2y2iW13wvr2MYHtygV1RVFfuXtyeYkwO4M4Ko2ylBmaevlnp8jOnacvMdt7Fp935+5REPZcXOPfSuu4PTxseY7q9mX2XozwuVraGeRI2C1ENxNQonh8Msl+J7DQKzbeyaDHqxcftR2hXkX5j0i6Hl3mieTdpIfwfLdhPZbaEXNFFVnzubOI/LSQ1zSg2GCq3Fma3WvWusN1WzuWUyN1vgpfEZHYIjxVuAzoqwXttsm+eDNO/oV41gRegr9Gy+jNNlOltKQR+UVzWNj2fYEusLX9xS1FJDdvirxK0UrMQ4huoZZyVKt3KLBrpVRbcydExgvPjnHc4qA0YUwWLEUhlLp7J0qtEEs+QNpnMERkSSuU4zbTC/3ny3uOelTnSMa7s53Pz+HwWqIZI66O4YxkBEpIiS35uzlQAqKmpbo8aVMd9bYH5qhrN/+rG86OzTOftxD+bXf/fX2H7HHdxz85WsWn8/+nMzdCYm6feVrh2nb+Zamf5izO9wmSlZf0YJr/tiDO9bOsjxQkG0MsR310YL7eU2V/sNPpVbKiZOAk1F+xgAACAASURBVPfLHRg4vJ8JD1ngVstTA/J8zLz0gfZHt27y9Q72pl0oh5z0qN3acN2Xplk9oe1e6ywX6dDq5w1zsB3KDXxEiYuJcjpJgXT3O/ntNb0HhNTU4P8Nbqkg9FL2QHdoixZlqIX2kGmduXO9qa3GX3FjYlDCoVcN01gyx3Zjsg/4voy4oMkILSpH4ihw3p0k+nKNuqktom71RRyGhjZisnWBTQ2sElt4jdKm75XHY5hUSWIbq1i/3KTGsNJ2ONQ5wEcu+F1+9tFb+O62a3jC0Q9GTZ/NRz+AFfcf58yTzmb39AHu3rPAnnXHsK4H2rdotxsZXrPtTWgzXxd7FtwkaBM8TeZXSTpw0NUT/npm29hZq42pjWybJCbWpnkZP3E0am3BJaUuP7LI4khtcP7Ncoeitv75N6PGldpSlpn6kp+EID4mphivqil98uWlkbbEEkLY6nxpTiPN6gqMUjMmM9Qiw9nWgSmq576RuQRmpwlvzbEJ5rgfhpga1cYNw9LUfK+JnJ+EZ3JmqhGBnnFnfc3BWuuOgC5OZi0zMZLw8DgJsc2wu5c47uDeq4lxE7/KYVQm2eiVUvkAhEYZp1K6hrkoubU12JCD586cqppRcFW3BKxxrpJ4gg7M1VGwIraOeaiOSTs/icmQ4cp1Iyu+jQUswhcs6WysVF4ZYTbqIsyaQpfxvnvBG1DiPfGkieoqkUI6CsGvYyPFibgjHdxyO6VvDB3cJjSVwqGFOT7/5Y/wkGMq9u0/REWXamwV9cFZqlWGNUdsojcxz0bdxpo1R3HFrg3M112qsQlq6rK9I2BJWmQ447zpfmE00xY//lX8HSaJW84ctC03hn4vXK8yONoptb4UkQ8TxmUUGL/ix9rMuvCT060dd757a8pMFNEQ5Q9sJJTt6hEjUX3KhWbIZHAMJWQTeEYmpbbn5kypVLSNgVUXEAnH9jSPLo8MIQoihp7oCwyJpMenkLYtEE2Luhj6nO1gZhpl28hJw/MUgaJiC0Or1GHcK69c2VRP6INR3I0YOHR8I54oHPHk8BuYWuUzFnKVJtFOaSHk9xIe801plgaHbfK7yoBMdlRiCradAjWDg5t/t+p2nApEH8sHqExJ8CQzJp/4Q6XuEkxDzeoM0NRsQ46tQQl7MRZaCoExBuSn9Cz1jiGnXCeGGlK2XAEGd+KlTzAxjiAs+K3EhBXHrebrX76Mk9nLjoUx9uzazfdf+QFMZZncvJqjJg1HrFvD/3zaTzO2fobjzSx3mvUs9BcYkxqVzqKJ+EuBYjxGamktmv6wcTLlCh7Nxrd4bgluiqZ1ENqg6gImtSTtKRyTkrdKo/8pwOAy48HvgxZV8+TU1K9BTazVx6tZ+/yzdZ02Z48ara9facF3m6XXTHHQRJNlP4lKzMDY2lJINBcQLcZ6mk1wOPHapmq0qnMOYiSspnQD5kx4Z92JCWl7oV2pzVbdRiv5/hgOLYM++/bgcuUFVem2q6p7mYfaDBDlfhu1LrUHSOeTk6S224KtJPIQMQu/K3UKj/HahFWX2qD4VMyCcREP28s73WxnjpTcn9nKgI2JidF5H3OQysRAAdSE46ajaSfq8BB2o9EFGgVgJO0d63ZB72cajVvHLiJ0okbh15JXHebm+rzm/JcwffcdXHPgEHs7FUcuwM033MX8ujVw1zRj/YrJ6gfYe3rYrnL6w59I57RfpJYxuhh62RgsJmSWej8/3iRfOjxKyOUQYiki4TyIeIewwiwfj1Hlhr1hgXiwXJPhd/FCO6iP2bPuR4WYOtaZ73zfhhvj+x00phya3qbAQ0tFJJXZNi6O8aS9NKoqaM7hL36bxgrU0mSgrS6vIYnszfoLt0HrC2U3GjJioBwXlc8CxRoNWgx9JxI0jHl+4oB/xgjGu8jC2VuV10bdYZtl/6MgCsIoMOnsKHKn1XpfdcbLQr9FxJ0iYqpW18IwGOlobDJSZ6KHm0P2Jc38pG1aRv58LaQTAEQGdhg6nMnf9mxuLhG3jU2fAYHtiVcloz07yChyIeH3vY11hGcGovmaJm/YrT3sOTrQbnH9WdGp2HzUGnZ86yo2yQRabeTIaoxDKww/+Ygzud/6dRzf3cKRE1v46Qc+nC9eu40dM2N87rLPsX58JWN1PzLTNhyVGs/hQXqnfR+CpWjDYZsdwWsf+fs4AWs1aWCNBpCv4o6uCE10W9DnEPqINC5hO7xwdHajH1qOv0jSFINpnxSGpEEGWhLxae5NgR1zpNugDExZKvI8a5M9U2pb984SCfSfj0Ub2htyaeB+83s8RzIIY68YWbHU6ua9iIvKd4ylW0G3go5xH0Mw58EZhNbThmDFH83X2IxbVaitobZhjgW/NvE7CGiF+r/ho9Zg67QsOLyX3h8Oh33qqb+TERSQb1wbGUmTcAbNEbcKISeC2jHq0dl1sQw3+Emdb2uze27xI5Bzv4n4lBFHOH2CSA5BhIADINtztDQ3U8fTcrU8YCINH3MkNpxJYa3l0Pw82/bcw4mbj2Qjyu6Z7WxZuw7pz/OoB92fM6Y3cdf2aSbWjHHcih4nPuXp9MeFPXv2sGvfFNX4hFsBZm3LRG4P3BQOfE1R3FGwVIZsAz7UMQO3W55zpTizKkoxwvr7oQwij5ArMdBUuI5arJj4jq2d8PeMblQPBo4GV22Y9V5jbJj60V/oNbphiwpyLb9R80AdTXB02uaKGA2Fm6tNsPo2l0dHD2qxTUghEh/gaeyolsqwLslefS6siAsmiRA8umF1qHP1eYHn55s2+uDfcOSk4t9bKk6aczFDg7dAijKq4dSyiA81D7LUSWUP0jeddZs554uYdtZIIQQbVDKNz6Q6JGodidE2IWidiWjr0g9mByX1cHPRLRdNGmfAlQ+BSUoi1uDUohNXarjreepLpvWFTkVirzNTJttspmGaVSL0egt0TIeKLivHJhnbeAx33HQNG9Z22NEz3G/TJq68cx/bD+1hZcfQ33+IK7bu4jlbNnLk5mNZc8RD6GHojnXRfi/iwpISnXN8tmr6frPi0Dp3EGCghZJAm0GT0jLw9QhJuIilJ44RVrXjOPkO/W66NKId9SDDKfahDcEM8rFpF7KI+CAU4EKKaEPTTgxAvR8uw5ExDby5cW4yf9EUZEn0sAhkDKzOy/J1NLeDFK8qtrkMBhhJy/lq0d8ffI+ZOR/cdDmEEouijScX76sTEYKPK7iGKvxKLFVvDfj8T5PqAucL0KKusMdHSVtu3NusIu/7jT5Vh73kxuCwIB9jb0iPhHuVh1qYSpn5m0dem1FY967XPIPfKRPZzl9hcInowpL73XJ4UGjXUpZaDvdfOd8nVb5BGF5ghL4N+m2bR6AkLT4ro4EX1w//DoowBiL0pGbllvXs2LeLqd4CZsaiE8qxxz6Q+WM3suPaXeyemaZ7sOL4zVu4ZcVavn/bXRzRPcgpZ9RglL7psEIt/YaGExhRc7vFwk+u5cQbhj+AZtCridfKClplZnnAWWWovYbi/PK185m1uGMCjgH6at2u7i0+9Xw84vfWe8PdOeEZjYfIO5p1WR9hkucbobQIf6FgDsOoMTI1LTXlqkFbuQ5fMMTQbuvUPeea04HZn6M09lP8c4oz9TMlINXhLuenBsQ22iDEfP80lOXfDea+OiZXGUW9QuTeDXs++OfpR9wBCJ2UGaQGMWBrUpDT9zkEnPI2twXmco17GJRui/QjvNsZoaEuynGa/plmJfnvoK25jiSmEbc1U++/FPfJJ4mpID8XPbgO8o+hdLYPC7KI1bisNfgu3bl3Gj9GiWt7wxK1qL0Z8e2rMw0oYwZW4vOVJ17j2xPSV5rasYS1wV6Ci0InnG9j08bSVVXRr/owNsb4ZMXzX/DbbNx8JJ3uStZu3sjC9AyPeNITuO3Ou/jh1C72Tx9k5RFHcPVt23nXJy7l6DMfxm337GbzplXMzM8yXrngVmWdX6qu+64/6rVOvwonrHc2jnIKrbPVJxkIKNvasY1mAtQZPdiM0eBTw8QvqwzjNDCmUrooOmIg24F9MYZPQ/gHn9vg6QVZIndRjv9rchpMkWVfRaHRhYwCYEBAlOCEdEh0N2riqqi8zWbgnaSTiAhSmUhbVpyVFT6Qe1/dTvqVcfRfoVSEVUf5vMQvb1VMZgWEeyKJ8ZlQsGe84b4x0DHi/KLi6K6yzuWiOLeLrZOAVzpYrUA7uDQpcFtDGxfIVLd+0Fqlb5Va3Zr+sIzVWktd1405mLd7kJ4H6TtppY4xL92VsmhQKnZ0EcJdrBxIymQgguYqoqj2e3muTaLyUjyQiYrb6aqDUCnxY0SojHFmBhQfQ0UlnZijmPcvr6vwt4UyETqmmynW+WqYtFwy/90Oqb6wTLBv+0jl1jl3GOMA+3jr370DHZ9motdn3659zO7ex/iheTav6vDYk0/n1O4afvGUx3Bgx53Us/N07EZe/pqLOf4xD+czn/kot99+LbMLU6jtY63re6eSuJdtc3lnU1MLqTr5+Ifn2nyyo6BwMwyMeflcm2932PPN8YO0/rtZ97A2Nt1Iie2470Ewj2rb4cyLEBYNezu0KwUmLhMNS0ax6q8Fgeg2A2ks0XfvNzZ4EWPLiRDrsbH/HTF0jft0jD95tCPZh3jd4BisEfwSzfRxyoo2NpGWdF6VEZAKpEIq99uqobZCrdC3Sq+Ghb6w0Hffe7WlV1sW+jW92vp9UwUVU3wwBpXKbw1ovNtkqXzL8R63sUtOW9YLiMUzWBa3iWFg0gGxovA9D14Mmk2DmmRgJINlh99ZxFw8Y/REnSsPeSQ4fJCUqBzuW1X6KJYa67NL65b1yYWZmt2L5UmvNcViMUTn4BKpTcShqmVFp0utyoGFBfqTfT7yjx9Au4fYtG4V133v26xa2M/em7/PT575IPYu9JiX/Tx08wbe8w8Xs3PH7ayuYe/N9zC/b4p3vvv9XPX973LRn7+OnVM7mJ3fD123T6Rd0GzpXykQmmM2NjZWLKNtc2U0BVF6n8YnWQuFDiBCchPZ7HsJbe1sewYGtcGc6aZ2urpSX2x2rcbafromQt+nd4W387BpKYiGtS19j8qc45KRpiuRbP/QfIGJZs+Wlk/4PgBZnMJ9wnHJjXaZ0nUXNzaxtuVTWl7uMIhs3gft1KS6A35rVccs0cgce7VlvrYsWFiwSl+FXm09c3XaaBo3H8cwbve4gNMcr6rhaOlEn22uqJxRDsZ8XL+qyuW3ur/h2OnBlLgmLGk/1ICYuu5nSa2amXt+YhrvUDbi8zcbR5s0/EyV3z+0jQEr/WI/zcozgSo4u31RdUOdN15KgsaVNkDYppEQ7BAfaTZUbjlky0wQkWgOgYsGV1hsMNVjoCo9n4O2BJ0chACeYnBL2xbmDkG3w8T6ST7yl6/jwKmGTfM19YYNnDQxwdFPexRzd+xi86NPYWZ6hoevUm7/3mX8/BrLWY84lU/dupc9c8KmrrCyv4p77pmiv2qc17ziXN781rdDV1jZWcdYt8N8BVVfHWP3KGqOQT4ZcmbW9JnGCWYSu1EYyGJwvfb0EVxibn5QW0UyBtJ0meR/gzbVFNLOF1d594XEyH1pkmd98rRpoumqcUyDdaHqVwWJoxPy4VY/eWLSh1Jn/vzSzCS2XURiXrM7Ktov6NDkFxRxLi+t+8RTH3xwzNFVGic3FmVdYSjz77ZOGRv5Nn1KOKDOKY5W/I5N4TApTeWoXxIby/U+jThGUOw06C4H/44fV8HvQ9pcOupmbacy7gQDCfvEDs6ffLUVJB9v+l0K/nzFZxBeoW0SNLUCvKB1ah+FMxxG7S89WkNtNqyqOq33nJdDCmxqbQcmxzAY1BJzPyngMhXdhPQ72YRng+M+GWlNIz/lnLlyq4iREOHPJ21zVygRQ43S91Rss3ebJnCbyd/UjlSVWvtUHWEMg+2MYeuKg6ZmfGIN/+dtf8DKk4/mmOke3YVZnrulQ2dmOzd97Qquuv67bDlmC2a1cHRnlh/efCcv/s1n86xHrGXv967nAatmWDu+joXeQfbfuZ0nPfRRrJsc59xXvJyrrrmCgzKLGa+gZ1EROp0qmmPNscjHeZgGG743j5wYBsFN45L6vWBULfKPm+PRBgOuIMq0uLZ8z2b/mu4OtcRUreCLcz8CN1GXIa6aIugikfAUoSPQkeAHDos4/ITPotkiwd/pcemZftQg/QSvss3A3bmBDnlRkfH9rK1ia/fp96z7bm3BsMSAqSQeteLK9Yy2dv3vOw8E1rqgX1MzVSvxd9ieL+C5rmvqvpJ78YJlUluHup763aZU3DmAwoB1GQ5aVhWXA1qT+Er2STRAxNuAFVykcGrEMV6LdnESh9cwNx3eSlo3lRP8Vcd9RsGSTj0NBecwSOzhuZhUVUTUAuMNkpE8P2NI3UsF03xeU52liyIFhByjLVf4uDY3N18p3QKj8NHm32vjCeOmQvqWWaN06opZDiIr1/G2d7+SY44/iT13fpdebThtzw/43v4tbDn9aNZ2xjip3gAnHsfKai1fveQD7D3qFM549PFc+Jr3cdqxmznjgcdy8Wev4YiHncbUHfvY172es5/+s3zh8q/x0ff+Lbt2383zn/MC5hYOoWYMxFCJ+pSm0C9nOoW188YkghSJOlJCMhCCKqMguUxC8n0pkJcKbS6kNvM3p10TmVmKzof+hDb4kS7Ksv56aF7of8LDsDamvwVTy2hENZ1IYTxNRs3Sp/7YLKolEjaWMZRqfsaUxTNer331m6lmecJ+UD8awxZeFzVYsZHZx9eFgqbV+pH3OaLqr4Uc0tBIK8RofG5VaiNi3rGN8n2TDbZYvuxFXzyK2yMJIn8he7ZxQYNdECwrnDUb6wzWQ4mZpPkPp/XDivI3rwfBHaLdBvz+ghq1gFiRGdToij62aBRtvtfWNirJjMqIPy8j7AHJkAhyaGPb/qRNRjocH4szBkf8wqzts2J8jKmFae6Z2cmXv/Nhdu3bT7V/irG5mkdPTvPy8/6ITXv2ouOTbP23WznuSY9k5fQMD7/ne3z32is5/3d+juu+/WXu3jXNG8//Tc465VTe+ye/zuOO3cyaIya4Y8ed3H7VdTzlCU/k1J94AH/zF2/gwj8/j81Hr2GudwAx0Ol0os9rFCRLo0Ub1cWz70LpA9FuGe43HdWW8kKisyZzbROCbX+b46dqE8PTMkskrmVs6WP+aXqSbKjL6qCV2QK2Tpsx17XG45A1MNYQ+Q6x/AbzrqpEy8YIaRY0coRpUW+cluL25RWDwf81xjFGT8fkWn3oc9Das2oqhI5C17p0KVO7aH/Hf7oKXU0rosLHmHROVZFZI0Rhnz/fSsZZxE4bfui2oGCyrGuC77ocxeGwdDWQdqbXvBefEUtt5xFTYyqL0mudtE3fXdv9ptugYG659jCCqeUa6lL7N4xxhmfbzj5qa2/zXWudH7bWPt+69nL2HLqLG2/6Pg8+63TuvvUW3vYLT+OXHvlwvvy+j3DTjq1c969XcuwpD6B79ASPXLGWfTd/h+lOj/tvtnz841fzpKc/ignZy3ve/9c85H6T7P7iV1i1Stk3XnPg1rv4wdeuZHzLeo475kgu//IX+cu3vIla55me2RcF0Uhh0Nh2MQVw+qjWhBS4YowaH0gKUun/iuZE/CwuQkc0NdJG7mYpNcM2YT68/748msx68bYMneCxxGYb0l9bQ22sM4vBbR/ZoL3QN2LbDNb7jy3+PCcJpuxoDi7glI3glrLOVA/uQ+8NYFCm+sB0xtBUldq7I2wl5So2sdF8Nj7YJpKoJOBr2FwMFm7TTdjEeclgyyCyEz4lU81TJLVxL9UrDDslJJY/aiJ98ZodmvsHw8qekHPZtoNRZBrkyxbdtSpTTyJx2EHtLw+G5AGItohzXlaAPLewLW+1yfzyMvID/2wMXHQcsdq6ta2hjwaN9qVblWQQXQDtAgarh7CygqnZKcaOXMWXLvskqyfhnju3Q6fPsWu6/CrC9T+4imkWWDvW4f0f+AprT3oApz3+pzjirMfwlBXC21/8XB70s0/kARsrPvGxS3nzW1/P+974Dm6zqzhu4R6u7K/jV5/4ZM75+rVUew8wM9HhhONPYeOGLtNbZ5gZg5Ur1/DEp/88T3vqM5mf7WNqYazTcYRlnDZUVVVpojbcMAlHJQ5VlY6WhOd2Xm+X7pGOjLMgOgyOPfR9feWYp/G0xfXQjsoYZzqHuhraad4XU2dJ8p1qaO5oGy7Sdb/8MZjqOCYUVtcNtjHUT1Rv1B9O3zHNRSWBfkPD3FaZxUo1m8Yrx49IKrcAI4XZH5P3bXJZhKh5lbU34a3p1siZIiClNp5btgHByTVjqLQm8IvSyiznnIuBSumSCMy2iduqVPjKsS+zKRrIGcSXe3aomFw0KNXUPPMXbNsAZe+GPDqnppcBmqxxGFNhTMUo30TODPNPHkTKj1HJJ2Sz3vx7OJ5kmDYayojMNXMLuDYZwl6c1i9YEK2pVKHfp9Nbw7h2oAszsz3GN8E1N1/FJ//1I1x31b9S3TLDwf3bOasyPHjPLdy98waO3bCZk9Z0OPGBT+bYR57BySeczBlP/hnuuGs7d+/by9TkZn7zfzyOSz52LU999tO54vPf4ms/3MEr/+DZfOOGef7qredx8JYr2Lj1Fo4/4SjWj6+jd+ut3HX93Xxv1zae8JCHcky3y4fe+pe86z1vZ3L9OFNzuzHjQq92K7WCybMYfURaaPGDhgBUTP7OTOfmggujBlOHFVIphclpmmmT3zT2uQ807fk6oH36v0ayaG9LkAN85kiLWhmCMyXdDsfFsOk2TKMKf4NlGtJ12t53fwfzZHOtNTyb02m+z0Axf0L96nNPvCqatyv0L0dtmlMNOrBKXat3USjad+lOITDVt7ULoqlmebXilQ+AMp/bmHLOQTD18dkK+GdCJpL15UP06Wbz3bkUUhqaK5sGPYl3tdjsb/qMgpEMNfdZhNVEAZlAETHMQVWL90TVJyGbQeYlLkCQG4fDfFz5hGktK3Qq99eGkwsbkXsI+aVuxYYYf65PVi/4xQdiCaZMeD49l9qt/vkw4W2lzI732Lawj/1ze7htYSdf/OI/cccPv4P93i1sPv5EtL6Vlz30JO7X3cXpnYqj121gxaZdHL3lAUwt7GDD2Eo2PvXRTNtZTt60iZe88Hd4zu/8Jld98gt0V3V47Gk/yQc/9ile84cv5p49d/N7F/4et17+Od7zuWv4p3e/jkes6bD7thuZtgdZqDqspcsnP/NpfuN3n8fkMUdw2Wf/iQ++991s23YzPZ2Dqoe1NVVV+UlRngPVNOtFHBPsqD8zzK98ajvKwjE1d5S1EY1n9jQ/kTQa4x8S4UNaS2C4ErjREIjmbKAPpZi0QbMrNNI6TaCcOcXb9SCTjeb6Es6qamDH+0fTu815WyoGg4tiAp7yv/n1NpeUqsYAkrvX0jJ1DEasFMylzbprU3pq43JPrYD6I8ClI+4jXrBi46eqnHJi/I77ro7AuLM2keWG2yQw0sF9wbNcZ+1pEfphI6TIRIG4e1WwskzLtXYYyVCdJWGLTzi90zUuJD8PQps2GVmP1wIiGxXrPqZPXJbqmVz4PeyjzU/YsSq8W7aKYBBE5jiC8gcYf7ZlYWCsmrcH4j6uapSZ/kEmxmeZXznHZ6/+Erdf9TW+dMM3eOD69czoFI/SHi86biNrpm5l8/wssmacGfpMjJ/I1t038tVLP8vq405n/eRG9n/nu6y/4zae86tn84CO8r7Lvs45v/kM3v3293PKGWcyua7L+ed/kAdt7nLRhZ/iTRf8L7502We4+mtf5Btvew0P27IeO7UH2X+IOVvzmre+mYc++ifZvPFIrvnWN/j0P/w9F//1Raw5YjX7D+x2fR5yYqrD3eBYk39gYNKp1n7VVj/eS/RRR+bYRvhNt0ITVL1618b88zSYYAbWNgqA8CnM5LxfeR2UzLhs42GFJCIE5uFL8d9L2m2rL1w/XCgECQ1/d2ZRBFdFCBSByyUWdR9amKwTQOF9pQN0xX3GBTrW0lVLpZYxU7kVWFXltfJydWTb3HQacMKNc0n4/vhDKZN26xL0y3LCYiTnY0k+donXsIrW1mvrfo8JKzHFbBSMpgCreYAsrVSKmmIjWigSHeh1CzIk+6Ba/BbwEcX2lVVFOdk948RYKqPtPQkrYkDVpYJUkqfv2PjXZrscmew4lKKfqs6/6OvvaIXtGLftnsChmYMcsXYtV3//Bq686utc/m+Xcu23vsHKFRXr+j36t9zOL3ZX8ILHbKY7tp/5fo/Vm9ayYA8yv3qMuSlYd8SJzFdHcMyDT2Xz7CHOeNj9ecebXs8f/NKvcfFFb+dRv/BMxjvbuWPfDH/06j/krRddzKt++5f505deyLPPfw2d1Wt5/4e/wN/9/Vv59qWXsPO623jOmcfR3zLJWN+yd8d+vvnZL7Fj517O+pmfYmr3Vr79tX/jbW99Ezff/j26q8eZWzjkNDVq1Ap1XRebN9d10BgZ0FpKbbJ22qcIpuUEWPepIjMNkyFpoOXS54Ec0nyzGWcSxXFz67/9mFqNK5G08akFv2m0JtOfUqPLx7/5N1grBe1qaf4PI+lyqgwKjrZ4QbpvW4N9o+ZP0X5R8n/lngwkzTl0KTBcAxihqrzvszL+2OWQjuWWrYbE/ygsfZqTYdBN1K7cBJ9rwkvV2IRaQnsIGqdm35u/q3it1NQzxhlWMpJcRrl1MApGBqW+cvWO7KZnLh4hYSMPyJAdURBMweRUd26ApUnwYU7/5r0QyMiJODD9tvdi2wUqa1CxcaMOVWXMVNQyj9qu8+lJD6WT1HzrpJ+1ls5Yl/n5eRgfR/oWqSpmZg4gx63l+hu/yT3bvs/O6b2YbT/kqAc9lK3X7WGCLv9zzQJHrJnmmPUbmO9N0ekYZOV67P47mequ4Z3f2cOzjjuebTt/SOdhz6Brejxqss8f/8NnSoEh3wAAIABJREFUePuTH8oVl1/NtTdt41Uvej7nnfuHPOcFL+Kaq67mnoMr+V8vOourvrmNh50wwTmveg+veP053HXLbXzi41/lz99+Dp/70CX88013s/bkB3PD3QvUHcuuqVlOuv+D2HD6WmZu3cXK2YNsPTDF6T/1BF58zh9TTy2woTPOQt3FTnTZP7WbtasnsT03kWzdY6wrhPN+RFwUtdNtHwP3JXcdJSHqkbyoJpYL3YGE+VzY+pV7am1BezktxGu4rQRzhSE3ZZvtKNoQGH28F+pJQZ7AFEQcExo27Rx9O+ZsfH5vk+bzdhkDEmIZRkbOnQKfAW9AE9sxBuEyc4PPxLlEqnIxTKWFUVL0X8Ql6ou41KdguudztbRgbFrvj/drZnRlRAibf8UVuur6kq/OEhK+gZRlsERYTKHrdoYnoixq8ufOWxFpdVXFnZ2sYKzzt5g6ExtZztxSPy7XbQnPNZGxhGedWZ7p7qp0xWD7NdIbo9IOUlXU0vFCoY/1O7rXCsZ06R3qs2Jsgqq/wO5dd7JyTZedO2/jS5e8k/q66zjljoM8+8iTWa+b6X3jBl735GP5g023csz9eqzrTjA3dpDuSoOdt3BgF8gERx1xEtu2LnDN6hVsPOOxrNlwJOv0IN/f1eU5p5/Cqn17+cRn/o1zX/ki/vLNb+ThP/dLbFmrXHnDrbzs3CfzgQs+xKNPPY4L3/0BnvrCX0btCt73oU/wrk++nv/zvy/hmtv28+n3vIFVN/2AVdPbmNUOqyfXcNfWbey85nauvfMOfuqXf5ENm9dx51e/wlv+7Dw++tVPIkeuYqa3H+b2c9sNVzKzbxfSn2VlpYxbZUU1zlin4886rxivOthaqfu2+Nha3G7ollb/Y+6rbH7aoCmfB9wSLVplfNZbXMEKaysjv5a3o62eXOvLfXqaT3T/Xr4mvq0u/21km1zZdeszw8puatbxugX8aqlQViVk6/LV7ZxfQQelK+mv0zxLPOQCJYBXx3wGnmeu4hSiMNcxIcAb7M2qmLcKLr9VkpCyxAVsqLrdp/rq07xEsCL00eITUrpUs/SubAUYNANRWozjKDjsKP/QgjSgzZt5WW5iMv/ayx5W930JjuCyFAmjMY2nEyOMYKoxerZPv55zW8SB97XWzlVQOUlajXc41J9l5tAMq44/gm9eezlbZ+9g394DXP+NKznm1Adw6VWX88iNC7zgMSdidtzIcUdOslLH6VfQ7x2iMzPL1MH93LptP3ftPsCchX/82MepxlZTr1zJFbtuZ2zNBF/4zr9z1vGb+OerbuO0U47H3rmT26cWeOJDTuc737yJIzbfj+3XX8etO/dw3be/zq7+ap75jCfztx98Kw9+wuPZ/sPtXP3Nm3j+C57N1z//b+yd3srrXvY8ju4vMG7mmKmmmN6zjy3rN/DBz13GTz7uKfzEWY/j61/4Kjd/62r+99+9iy3328jKdSt41Z+cz9ojJtmxcxtUlr7UzNeWXl3Tt5aFfp/5Xs+big3fVDYWwxz8beMe/aNZcnabZG/TJEeV3cbQGUHzzetLYb7u+XAvKCltCwlKBpxX7e4Nzh9XDhEXzfKac8z5ubXAnWNd7lqImku2YUi+qq2J8rwNThiGfhVP+WeFWp2GWWtiiE3TexAc0lSGK1ixJnFhzdoz1vCh8SmYf6twk8Yn73NLEzNY8hEoIuJsZc+YktkhiLhVJXld+bJOCLmhpX/GHYfRIXhU8rpCzusw9bt5PTcjanGrMHp++zB330mjSntYDf476NU9FgQ6fWcq7T24n/VreqxftZY92+eZnDAcMJYxO06lBjrz7J49yNp1K5mfUK694gb23ngP2+7eyv7rbuG4x5zOxM6NfOOm7/KYuR08eeNa9k9/j1UrNzMnHepqL2vWwVgNM6bP+ESHQ2aCy3dOc9XHPsyJG9dwTF/pzPdYOWe5aUfN4zfu5rarxvja//0G77jkYt59zrn83DOfyc1Xf4mPf+Ma/u7v38tLX/Bizn37ebziZa/hIx9+P5e8/vUcueYEfue5v8iLf/uV/OnbX8L0tgNc/I4P8YHPvIu3vOINnH3siXTXncgHfnATU3tn2DA/yf67budL+2fZuf8efvslf8z2G/6dT//t/+Ga717N77/o5azYeASXXnopDzr9dA7OHWCeii49pCNov8ZIhYowZiSu7HGMKkxMdaqOPxjNWks3pve0H1WTj28bEwqWkzTcP1pbZ9qrpKWGXgtu7t/qzMlCVU1fG9fa6K6NJtUSmZS7HyZk6ZsvXFbSnLQhC8bEZaFu/gWfr2+hKvgoes5lckZVmcg+CYdTAhipCQd8u+Wlxu2xICFAGIKOg0KmthB3xoqZMwlUvEke0rZ86psFdwxN2BBbAsNObQ+Hdtaqbi8P1dg3MZ7HaDLpVfEnGpgCz2iJYwNFGwetoHRSwWLmfxNG+lC/es3OeNNNijprfMjzFEw4xiRrgKrGtJqcQYZ3E4RD0QYl8PDk6ZTIHP09xbNuHXLHQm0tdSV0cWexV1T0+32oLGO1oVNVTOkCVVWx/+AMKzet5hN/+w5OXHsUjz/7GbB6NZUusDA7z8yqMTauW83WW67je3ffwe67tvJ/r/82Dzv+ONaPj1Or5eRDhqc/eDNHzt3N7NwMC/PK2iPXIf0ZZqb3srozQW9sDK07rKTmwFrDW/7hOua2PII9269h48ZTeOKZp7CwUmFe2X/PLTze9LnoLR/g4n+8iIve9E7WbD6SX3nmMzj/lX/GO952IRe9/p2ceNaj2VLBY5/2FN779rexff4AL/ujV3He//pDXva689l6xaV88gvXcNF7/4aPvPktHOyM88SnPppz/ujdvOjFz+Xzt81w2803cWV/P6dvPJFj9+5ka7dL9/gtrDIT3P/Y07l65z3s3HErG8c73Hjd9ZzzklfwrF/6DXR8DO0pplNRVT6H0jg8F+Plhay1fmsUcZqrIZxamvtPbTHmVaf0ITZ9pk0aiL5BEZdXWSz5llYzX2zyg4b3lTRh4yGLQwR8ZUzBEHM6zxWOfB+DuKuVN3ttDfidljp5qpcR+iQ9X7WmMoI2llMX+2+YRntbzocSwTEq94vAakK6YR2CtHEdaTknQ7J/6HdYG9/kK0GB0drvHYF1braaGLBsFu/KEL/3aQnJ4syWtzfM8rCgw/l5MyHTwh/zIc3708ZQx7rDfaijGeq1dxU3Jfo0bIOh5o1p1xwHfFjxmTwoUU6i5i5Err6mFmOKyWXF+3SNYOb70KmoK2FMnPQKeXGrejA7VsGhOVaMj3NgQ8WbX3UOZ06uQR+6mc0HxvmZpzyXXfvmWHv8em666wbmr7qe78zNcMXNV/KsQ2tZ9cjTOLB/P7t23MbqtWt4yI4b+YUnP4S5uVl6IozV0GWBfXv3smH9asbHK3bNTjM5tgXmLKyyHKhP4JkX/z0veNrTOeHRZ6L7ttM7MMWOhd2csGsFa+QO3v83/8gFf/XHfO5Dn2Bu7ZH82uNP48K3fZrff/Wr+edL3s3Rpz+G0x9U8Sfnvp+nPPk0Zu5Wnv/8J3PBK9/GC8/7Q+668htc+t2t/OUbX8XLfv+PePzZP8PPPetnOf8lf8ob/u7tvPfCv+CAHMVxJ23kgku/x9j61RxhVjLe7TG3v88jTj2Ja6fu4Ts77uSYiY0cMpYX/tZzOfbM0zCdVWwcW8UDjjkRqorxsXH27Z6m2wHT6cZofl27PUZjpkjVIWhfQUMcIM4laAZNhlqYuOonciGr3XN1FswKWl0eWLLqTGCD27zcaBl8bTX3h1xX1SK40xbQqijniTGClYbGq+r3UQ05uYPCIVp1DQESgkzud9k+49sS+6dKFQJE2lBqGi+75qYVYum5wXzi8ne4H4Rtqju1q8wucNptek68G8BdU79LQY6zPEWqnQ+VkNwuEWfGDGQsTYwNJ8wlnykVTbK0/Y07tbQWt3rY92yYOdQ6OVQRsd5UcCq8VRlASOisu94sJ5hE7p5Rlx1bWQsTHb8aR5m3MN5TDnVrFMNCz1LZQ9h6Ad1vsb0es7vu4tRjhEMzx9Cr1/Avd11OrzPDoU/t4Nobrmd6epoHn/xQ3nrm4/jK7FamZn7II8cMv/DY+4NsZ+f9jqY322Nsbp6qa6j6Qm9MWL1mLXNGGLerGLddVq9cxTbdzrrxMVb1lLl9+9i2fS9zN1zHiskxNtR9fvW4M7l26xf4yOe/zhv/+tV88C0fYtUDH8wzn/jTvOh553Lx59/BZRd9kH1Ynn204e1/+jEu+ey7+fOXvo7znv80XnjuhbznfW/h45d8getu3M2b3vBSnvfsl/KGv38nP/jnL/PC37mAv/noG3n5C17FL//ZH7Py1m9x8Tsu5Y7Pv5Nf/63XoWYfP1x9IlV9D/9yx408csMpzK2smZ2cYG52iu985ctc/o8f5wlPeirf2n+AyVWrmT44y8ajj6U7sYLn/covM99T5ns1HS8sY7qTdb+tuE0zNKgEBWmUmmgbPebPxusKYeVani1rxGub4VIIwCCIig9UulQeEUflVbZiKD+6LFChjYWFmyPMQ2kz51sey9xkbe6EdCVkrCgdoV3tGii7/boVp5kKYdcqwfQr+mZwtgVtOpTnPCflctuifX5p7KCrpuFnTiiM163x5rkGa6NFYVPHP+IAkxbXJMboC14cQ/55izFCXQ+Ow2JQXXDBBUNv3rZ95gLBDJoOBKJK+XfuNEO/5hb1xzIFDcQ/m31Q684wEnHfQ5/FEFdPiSKNYEaB0PwMKs/gqWtMBVIbpjp9qn5Nb2qatZNrmJtQOn2LdDuso8senWXv3DQnrjmKqcrw6884m03Td2DuvpUjzSz/+vFP8rA7LHfVNT9xxsOpuivoTm/lyMk5/sfmYzj9wDYefGqX/q67mFuxCV3ZReemWDHRoerUzM3PUHXX0F01iRycZXyyQ9WdZ3dvmtn9K7h5ZhNnv+eTnHrcsbzk95/HBz79WR520ql85aYb+OsPv48zzn4iv/Zbz+TNL/pznvD7v8SpC8LfvvdveOs7XsG3/voSbl9d86LHPoE3f+iDvPaiN/LmV76KP/yj3+PC176Nv/rwRbzn/DdzwrEn8Ku/81T+4Pmv5p2XvZ+PvepNdNeN8wfn/h6//ZyX8Od/ch7f/KfPMy+T/O5vPZ1zXno+F73hley98RYedeqxHDHf5dbOXqamlZmFuzkoHTZ0O8zPH2LLkVv4wbY76KvFdMfYs28vN373Gq746tf4yUc+nFWr18DYOOM1mNoyK7Bu7Sr2T00zPj6O9BYw3S5Ivzit1n/xQtulPoVNiZM8d4xZSILcMRwpaAUjcdu5nI7C4s18EaeIT2ON18q14YCPhGtUKJzGmG/wEaLcStgRXiQtl8z7mLssTBZ0812PR4ug1i0RDUdxqMu7NhJOIBaanXRllJkJo9iCBu0vFDUo40LJsfzBMnLfo/9dlQG4ZjBugFllDDWqVXH1oXcVm6RYORdSGBeJvt9mu3K8NJn5MMjbkX86lbx22Dujl576xb3hXJu8EtdQiRMBoxiLy4kLm8Bm6rlqWrcbjj9pS40yajGWVKf2XBv8MsXwF+05h5P2iEsWbU2lXmddaVg/1+eDH3sfK9d2ueySD2Amhe7cQSY3dvnaVz/Ld//vZZx59PH8y798lBWdaa7ddQvn/vWXueqBD+fyyZWcdNZZ3P4Tq9lf38aT5u/hhB9+lj89bZyzNxxCO1dw0gkdDu5YYGxyI516lhXzMGlWMaYTjLOW7uotrOjMUc3tozuxggPzE9x1YC376gfxvI9fx8u/9C0OfHsrW++a4qWvfSP/+pnPgu0wbTu879Nf4z1XbeNZb/x7Hvj6/4+0946X5Kjuvr/VcfLMzWn3btJmabXKWQgRRRRJyGAw+TGvsQwYXrABy2CMMRgesMnGgBAIk4VFRoDiKu9qc857c5w806neP7p7pmduwPCWPvPR3Znu6qrqU6fO+Z30ce7aOcOnjpzheR/7JO+94wccWnMxl73kNfzNV+7kjR/8NJ/66Ae47PXv5/ZPfYuPfetbfOw9n+PCF76YTKfC7X/3Se741pf5lz/7K65+88uJ5XJ847Nf4gcP/ITvfOarDK9djeqp/PuXv8FX//NO/u4dH+ZFt92KfvAwAwbcOrCZxKl9vGHzpXSMHccQUBMav9uznxOjE+x+8gkOPv0EZ48dpzA7Ry6T4tOf+ggDvTnKxXnq9Qq6oaC7NS66YAOpjEmhMo80dWqWiy5iTSKW+IwykipPBFmQkF6Lm5MQAuFJVC+QJkO1UTSdxv+QkQtoYrrR38F/XnuAghIEgUgbKYPaZhEcv/15mqa15JQImZyqhgafMGfBIm5ingBPNCKTFFQa9ZrDZCuh1wOBLKKwCBNrSsdRCTn8/v9PC/sID43oM1oOjMD5PzQKLhU2vlhr5tMItRb/e39Nm8/yvwuNWOAhkEJp5OtQAresEMdv9zAI3adacgmw8LPseiyHKTy4Z0K6rt3IOiREM/sQwo/L9k82l2gNnBBjkrItFdsf8fYURVuAd7RYdVv6avUS8JDoQmVKVDi892EyU/NoHVkcBdxSlXnX9Ss5FmbQjApb4lm0jizl+RIT9RkePnmSZ2XX4vVC7cgRXnnFRoyqxbxSw/Rm6MwNIeoeRV0Sj6eYms8TkxZ6tgMZy5LPV8krGnktxqxjYlkWZ12b7Hlb2H1ilHu//2PW967iwBd+wOZbtvHAzx7lmbe9navNFD/9+T2U6xYDa1ZTs+H8F9zAow89yOqBdVx40VY+9x+f5V1/8Q72VU6w/6cP8++f+zxvecOf8673vo9f3/sjXvCaP+fTH/owt73n3Rx4Yh+7d+zkxre+irs/9Ene8rXPcM/HP8vmDWvo2byaB798J3/5pY/z/U9/gU5Vo3fbFh7+0Q955+0f5CvvuZ0X/t27+fE995DUDJSV69i36zG2XXU9v79/N9dv2cKK4RV846tf59qbb+bRhx4mFouxdtUwv/31r/jK5z7N/Y8+Tc1Vee1b38Lq3g4sL8arb3kFua5O/vHDf8+Kjh5UqePgYSiG/yYDycYTETxQiBaNNsRGiSZQlhKh+NUVGhk/PN9QJpyob2TA+NrorbEhaZVIm1rRYjaASIQWSostIaTLVrywvU/RuF8VcsH2EJGMXc1r3UYJk+j3YVsMImlIx22R2CLytb+mIVYJip+PZkELVfvltnITCVmqBNAyzWsd/3Lwx8Lnthqow0sUWqsXAMuOq/HM4OwKgw3C/rQ/1Sh1/9NnJdBMMCKVhZOVCgin2WEUFmh76YuJ+i0gdGSSntfE2ACkUBGqQrRUtZR+oo0W4hQSV9EwbYGaMXjxMy7lxlc+m2ESnIt5dKtJhOphWRanRyapIBnT5lk9X+Ljr3s9+x75Ps+5+AJQi4hCmbSTYiJt0q12UXAcqskOzuZLFLJdqEqcGVHBqunUOjtw6xb5qRm6unuYrFk4jsfQUB/lcplYLIFwPT77/g9TGp3HLJxk4+YL+M2DT5PoSZLMpRkfn+Ci4c1USjNsWbua1735Dfzzd/6Lgb4N9A72cv8jT/GO297NL3/zfeqOwi2vfBV/+48f4F/+/iN88fOf5mXPvIXv3vV1PveFz3D7Jz7O9m3bSSR1fnnvb/iHf/1nbnv7X/H2V70RLxvjrs9/mQ998qO8+y/fzqc/8wX+5wc/oq7Alq3r+cVPfsPt//A+3v73/8D7P/hefvGDn2CaJl1rhjn2xF4GN65h5ORRTh4/wY0vfCFPPbCTWnme7nQKJ1+mr7OPzk0r2XThdv7nG3fT1TFIz8pO8sk0xw48wXmD/Zw5dY7C3Dw7fv0QlUq1xajkeR5CUxvfeZ7XxFoD2pGKaJSbjtJZO6NC9Y1SCxnqIjkoohE1Xnt0Uvgs3yNFLLKp5TI0vZSa25Dg/IcueU1TQJELogHbU1w2PAdEq8SoqIvv9QZuHDAPBfG/YqhSti5ZsGw0DVJ+MEw4dt+gHJYQCt6n12qcW9AhbXi6XPieoy0cV+ScDaK0FvYZXVsgovk0D5bF7tGXMUotL6E+fVouPO2CqKeWCTWrRy52YkYZZTuGstR17ZiLF5wS7TkqvbZwOxsPbIltu3R2ZvjAB29j3izzjhfezIRd56cP/o7TEyO841kvIpaOU+nL0OkYnJmfYDhhUkoniTsO9lyezngMPdfJuVoV1UwQy8QYGRnjGWvWsW9+DgeV0tQsg919zIzPcv0FF6F2p7j/7l9iruhDeJLM0BD/etv7eOEtN3PX7R8nZbv0DfRyrDjL3HwRU4+RyGUoYzHQO0BhepaUZpJLx9n71E5WrVrFbe/8az76b//Gq978Fzx1YC/FqSr5SomO7m6EEOS6e6BSQenJsXXTZr7zf7/A897waqxKnbMnT/HsFzyfr3zrDj7wlnfwn3f/kMsvvIihDau580tf4f23384//tV7eOt738neHY9zdn6a5z3nmfz4jrv42/e/m3/6p9t541veyq4H7qdv7SoOHziG4WqUFRMSScZ276MvBl0JkwN793DtC2/ilw/uIJYZZtOKFMdnzpJdsQV19ig1sxev4lLX4yS9Co/dfQ9H5+vocZXZ6TyqUNB1HVM18BSBLh0sVHRp4YkYQgT5BBbJy9rewkJ9/rURV76QVtqKAvpFwhcybgC/AKDSwoIVFkpKctENvvjek1KiKX6OJb+/hVJsGOrc2FdeiLG2HiDtgshi/tv+dc2/Ww4dTUG6XkOd1QJf1bBczcI5hc+SgW9r4yl+36oSwT2XFqbC0NOWa5YIEw2ZKQRajOLXpQrtOD4D9FBFWHSw+Tw1sg7NZy3M0yEaybqXeH4gparLhJ7+UQy1cTqI5kndviDNlw3Q+sLbF7Xd8qcoSlstGn9jyKhzcVsLa7BrQb5VRwmwt5jO8fFT3PW9O/jFV77EF770Hzzy83tZ8awrmaqXuHLtVp6uTLAuPcDI6ClefOHFzI+O0n3xlTx0YA8dJegZ6OLY6aNU+3rYMryBPUd2I6bnuHr7RRwcGycXi7Pj3t9idGZ4++vfwnUXbGfT8DAf+bdP8NJn3UTH+etI5yWVUoGsGePEyWNsvnALp0bHqDqSTCJJqVCkZpV5wStuJh6L8buf/4a5fJmOXC8z42e45KKNeFJl9PRZzhtezaEDB3nRX7+Vnb+6j+3PvAZdNdj/9B5e9Kqb+e7Xv8naDRt56sxRnrP9Cn72yIPcdP2N7Hr0cYa2byZXg/lajXnhUJqaYf369UxOTuJYNjKmY82V6Fk3zLm9xxGGgkgmyIgYI5V5MobB5OwMat0ilUnQqat4hQKr1q5i7eBqvvq5L7B6/To6V67i7Mws6tQEuaE073vjn+MceIRP7i0ST2SoOTY9AwNcdtG13Pjqm9mQy/KlL36Dm170HAb6Bnn8ySfYuH4DqqpRtiTCruMoGqbqY2HRpNcthNxGG1FVjTZJNtqizEdZIo9A9N6G217EiBb+5kXSOi6mqi7ss6nSK+1W6CDZdpR5hnHrCyzdi6jDi0MBS+z1yLPCMt9/CJ4LfxZEMVPZYIqtvGAJidJb+N3ybk0+E1QlIFRcZMBc/XuixsUocLPYVBYbSzijqDazUKAERf0TGepDu07L8OHRF7XgQWLhSxSB3NwirkcEi8Wd/JduflKEReYRSqjBHP2SC6DpCje9+LkM9vUyZKjc99QOLly/jn07dvKt73ybL//427zoz15BNaUzeug0Q1dfQmlultL8NFvVHp6oTpDTY9zynGfz9298FxPVKu/64Pv53sf+nZ889gAfvv2f+cH3vsv8iVNoSYPZsxOkVQ0zZaJn09Tmy9RrNebtEsODQ0yOjrBuw2aeOrCfeDpHQmg4tSqubYGmY7sWPT1pVHTm58oYmk6hVqNm1wL3RUl/Xw+pdJqJyXNccel2pienODs5zabV6yjaNUYmx1m5ciUz49PY5SorzltLcXSSfL7Alsu2c+yRnay4dDunn9zLS193Kw8/8hA5YZAa6Gb0+Gmuf/Hz+fqnP8eb3vvX3Hf3PWy66hKKo7PM2VU2Da3lvt/8imdcczW//Mkv2HL5xdz3yCM875qrOHDsFF1b1zNXLnN612E29A1TosT3vvQ57vzZL/g/N20mffgx8lNlpjqG+eSPdnDSzLLp/CswdYeEq5LQTW56wXVcd8ONfOazX+Idf/OXHNy7n5179vHSF7+IcqFOPB5vpYmlsDSBn1RDNJOiLNgYtEmowsc6ldB1KMiGH1WvffuA1zjxF0qArQEni7Ulsc+2foUQC6RRPNnSd/S3aDULWBiZuJQG6N8fwiINaw9iEYwxVPXDhCcQeCsF+Guj34aU6SdoXhKvbMdLBSiLJa1XBdD0wRWubCnuFzX4tekujXFH57zc+2n+3jpv/34A+aer/A/tapVQG0ywJfR0CSxjMQAmckK2Zr1v9tUitRIhLm+JhWhT+RvEJUFLa/zXN/+Luz7xKdLZDHFshlavYMehPQykuqgKjVv+/DXMHzvGj//nR1x28wt41TNfwB3f+BrHxk7TbaQpjExj6ZJaDDpqKslkGqtaQUsbFMoFKvkKfZkOzJ4ukrpJxa1RmC8yUSxglCwszU+mnOpIIG2HwlwF0zTJpuPUXJtMJsOpk+cYGlhBIqnhSZ2p6WnylXn6u3soTpfBUMhms1QqFWp2DcNzWNHTSbFcoli1KNYcUq5CyRQIKckKjVlZJ2vEg2VXEJ0J4pYEW6L15oijMlstkUCQ7uumWq0i6nUc1WX9wABj9SruyRnS64YRhSojpTyrVw0yfvwc5yolFAmma5HMJlBiObZs3MRl11zDD7793xw4fIh1PV1ctLGfHb9/jLd++vPUO+pc1GmwcvwIhfFRMn0reKpg8vXfHuT0ySNcd9311OsGRlzFsyxSyTTxmMn4xCSf/MQnUOyQFpoYXIOsllGrFYUG3rk45ha1W1DjAAAgAElEQVQ41qsqeIvnx21RoSPeKy1Gs0U0tcVKBEX7bE8SHU3YLoRoKcXePt/FbQ9BdirP72s5hhqFTpTAeV8Ri3CRxv00smR5ASYtFImQYgmGGqZ1/CMYqvSTsLS3cK7hYad4BAy11eovWqccYaDtTHIhz4r+8w/JeH+yUerh3a0SqqIoLZhKO5grxeIndXidI5dWqaJ9Korix4W3vQhb8fGTaHggEclVSokjPGKepFRymMtJBntyPG9oDRXpoqguiVSczp5OHBtW9HVx4tRxSvNF8CCXzuAaKrqu+9mTdJ2EYfpjB6Zn8lRrFrZlYdUd6vW6f7oLFaGCIjw8x8VMxANczgM15h+wXg1VFdQdl7otySTidGWSSBSKlTKqYVLKz6IbCsUqdPetwLHm0TGwPBuvZpFNJxmfnaYj3UGhPEdKT1Cq1VGNOIlYHMPUyOfnKBcrJJNxEDaJRALpacyU58klUvR05JgtFClVqvRm08zNzaIoCSxZJ54ySZgGKVWjVBdYEsrlMtmObsp2ma5UinPnzrFq5WqmZyfIpmNcdtE2Ht1zHMVzGT83iqIodHZ3+aG8xVne/KqXc++Pfshzb345p02T7Kphhi5Yi1t3KZ+eZKpcJJVJc+zUSTr0OI///hGUeIIe3aR3dT9PPvw4u379e87NFUmlMn4OdtdGVfUFdLiUKtxuxPE3fZtRQvoSopC+kSZMTQm0hE03O2+NzPKl14UtZNaLumYtwSQbRqVIjyGs5bKQkUal0+iYwty10QioxZ6vBf6boQFvMYYDvrynBAYfry1HbVR7lbJ56DSe0QyrXzh/4UUYWjuja0I2Sxn2FptTtC28r3XsS/UR8jL//81+/2QMNWSoYachThl92GKDb1ctwn9HGeqSuEo4MG9h/24A2DQl5SaQHI7LxiXuwEgM/uNzn+af3/SXPPOay7AdBxHzk3YMrlxBvWZz5sRxBof6yc/NY2omrutnTpLSxbIsBB7pdBpVVShVKggthqnFqVQqZDIpHMdBERLXdSkUCgwM9FGtW7iOxHEc0qkElbqHbdsYqodhKtRrNg6Cro4OOpJxFEUhX61SsWw8y8a2qwgzg64baLKMqcaoeRZDnT3Mz80xOTdPLJbAjBv0dXSxe+9+cp1dWJZFNpumI5vjxOmj5Dp6GD13lrUr1zE1O4WrquhSkIzFSXVkGR+bJJdK4kiHjkwHY5MTxJOxgCigUKiQSMVJpjJMzU5x3ob1TI2MMbxyNWdHRrCsOoqwWdHXQ//weRw+dIDZySlymSyZXBbLk+imwsSp4zz32mtI6yY7n9zBi255NTOGzsniPKsvvZhNazcxcugEo9OTKJkMdVMnMV0hLyvoBmzpW8XNVz0TaeZwHL9Qn7aIX+dCtTfCZAJX/XZDaos6LSSRJEsLw1jbIK4FNCwWGkwXtGUMLu2GW3+IrZhsY0wB5rkYQ21XoVv7a849/D4UUNwgv/FSTFdK2chHICITjRqWWjTNtqlqUjSiy/z1arW/iDbcs6UtIsn+bzSUpZoQy0ON7Qd0yFDDW5aTUJcNPW0kG/B8f1KJXLB40baY+tPSXxuDVNoMUI17FwGrIQDA2/7dJO5gQlLBiqkM5pLs/uW9vOHAbnr6e5idmcGI6ai6zuTUHMX8PKlMllg8iWN7KIrG1PQsmUwGXdepVirU61WkpxJLJvxQVS1GvW6RTqepVivYnotpmtSsOtmuTqZn8jjSwdT9GPa65aChk0onmS/M4rgupmFgWXWm5/PUqxWSiRiqYaKqHqoOnueQ7ehkYnSUoa4kyXSO8clRKuUCdqVKwtBBUzEMAxybNUP9EItx6twIq7KD1CoFsokUuq7S1dNJJqNhu3FsFPL5Itl4inqtgGEqlOpletMZpG6j6lAt10jEDGKpOKqqE4sZzMxNs/38rRw/eYJLr7yCo0ePU6nmWT28EqTL4OAAZ84dB2ljmir9A90cPnQUoRgkskm6Vqzi8eMnMYRg04WXMTMzz4k9+6lYVToGBjiTz/PY40+y+dqrWDs0SPX0FA+5EwwYaU498ASbn9PNrX/7l5hVjW996y7q1Tqe5yKkQFUUpGy48ONLVmFseHv2s9Bi3lRLvQB7l1KGp3OEQUeIrR0zFRB4bzYZAs1ggnaabziLRzS5sL+oVNcYyxLbVZF+nk/fRrswQYw/z+Ye1IJDxM8JoCLaii6KyJxUpan2t0hvsnmtIpvwW0OwUlqfHcIO0eCLhtRKyIybkni0LSdgNca8AIpY/p723/83z2hKpM2xt+OxS7VlQ09Hxwv/GAbz+Z+m037Y2ge3VAREqAL7Bfv8Gj5+yKlsfIf0nUiaNXb8xZfB9x4eqicb9yiexGtz0takgoPEPjfG3KZONq9bxW9+9BOynVnMeJx8voxtuawZHsbQFGby8+iagabrVC0LMxajVCkjgHrdQlENVF0lFk8wN1ugsyNLpVzFNGJ4niSmmRiGSbVSIRFL4jkWuqrguQ6eVElISblcIJ3LYNk2qhB0plOoRoJyYQ5h2ZRLRerVCr25HNX5PHXLQ5Mw1NHF+HQRz7PJJuNcetGFzM3OMzdfpliYZbinB1MVlCol+gf66Umk0F2XuBljbHKKzZs3oto1Brs6yBcKdPf3omMzlMtSsyxi2QRxBRSnjKbp6LEYXd1dJE2PqckZhKLS1dnJ+MQEW7dsZt+BPcxMjpJNJkjFNSqlAvPFAudvuQBFUymWS1SrDplcFxI/09fMzBzlqkW+YnP87Ax7Dh7Gzer0rxmmzzPZ+ZNfcVH/KnqNGKfPncRMaJzbc4gezWT8yClOHDjG8SPHec2tr2VoaAWGqqMprXJAWAZHVRQfAJNNNT9aNjqqnoeJkYUSck4R/L1Qe/KkF/QTMANJ8JwwBBQQEtWTKLL5UQlCW2VgUEHi+dwIGRhZZEDfqtfcAw36DvaeQiD9hnODZlrT6HWBeqyItmxaAYMTuH4+Z8WvdupPyWvMS4lIbmoQqagqCigR63+k+evohTmi8UPOBYpfeapl/CLY19LzxyMDDyHpiUakV3vfTeaxvCq/HIa+GAP1Md/WyLT2T4hvN+8VjUTTqrZ06OmyEmo4mBDAFkJEYqoXnsZe8FOI3bTjFC0nRZCMgUV+jyai8KVkBSn8khBSRBioEqgOkTE4QlKrOxT7TZS7jqI8+0KyrkTWatScIEO363D0yBFSqRidXTkQKvW6ja6rSENiuFCpOqSyKeoVh3K5iIeBmUwzPjHFQFcXc8UqubRGsTJLMpmitzvN1FSZuJkglYghhCRfrpGOq3R39VC0qrhxnZSRQHUEWiyBke6gmJ9Bi5l4dZUrL9pArXY+Dz+xn7XrV9GRMihXi+ixKulEjeOHTlKu5Nl83hYShsu+gwcQVYtkT4xaQWNkbJZScRIpY6xYtxqrZpE/lycxpNOR6Ga+UOTSzesZ6kxTrBxmslZj49Yt9CRMfn3/g5TjSSYOHuHmG66hXPSYzddIJj2s2jSHjxzk3MhZzl+3hUy6G1eW6O/vZtV5K3nisV3M5gukUinqNZdSqUQyrlMvFBkaWEGlVGFmYoKBoX5mi2W8os39D+zll/ZOuvs6EVaR3oOnKOfnGDWPkcyluf/H9/Dxz36e87Zto0vrYLo072ORDW4ZkcbC+PkF0Uat14mAmXhSgus1IqYEopGdLhqTL0MGRSDhqkrTvUYJnOjD59POsFuhqHCDKsIPSpBIXBHxTonsmzAiMZSmm/2qIN0WiOB/I3FFVfyF97Qzs6aFRwqQOE3XxYhGGWU00fn5v3mwSJYqGWGMIgjZkUjwwix2oSQokSIi1f8heNA/T4NDxvcAsG0HRdH8VJJBlj/X9Y3VihYpLy/Ac2001UCoAun4FrdwfV23md3uf4MsLIuhPrLrjATQNQ3HsVFUz4/dj5zk7Wm3lFBKWOxhi+BSQjTDCv3Q5pCyFgYL+PloW2fltU3SRTBbKnD/r+/mO//9TRzHI1apUKlVsTMp5qfmiOsa0raQrkM8YZLNdFCz6uSLZbK5NK5TIJ9X6O9JMz09gUhmIV8gnVSw7CR5O09vsoeYMJl2psgZKmhxyiWLkuNgShfVUUkme3CKIySyCVw1SS5j0pHrpuoIijMzVEsxugeyOLqkOjPN+952NcdO1ym6DuMT51g12Mux0Tk2D/czMnmKkakush0GZW8IM10la8RJVMc4MzLJ+i0XYTsOPRmJqcFP793LpvXdxJQUIyPHWT3QD7Ek+dmTpFb1knMznDh1kK6efuK2wuHxs+hGEsutUp4ok+ruoKomqdVKjJ+aI5WNMXBeN2OnJ5idGSNp5KjbCiQk0/NFzlszwJnT5+js7qJcL2BXYbC/j9HRcbLZDoQWx7ZnMGwoVhz6Vw4wWyjTlcuiI9l/dD/pdJoLt22jODPHd390D/NVFy2RwPQEtuu1+DcqEUYZ3eQtRqKoUQHRCi81yNCLMLHWkiJhtEzYl4poZsiKSDBhV+22g3YGEB1fiEm2tNBQhs9Q1Uh/UUf50N8zCicEHbT218CJo14Ai48HaNmHGgsNSz5za6uy0AYjNPqRCzXVKMQggWYJ+cg1jXDbpdP/tTTPDySIvlvbczBVxR+aFAhdNJipGqSS1DQVXIkTjMYfsljgs7wYxPAnu03teOq4VBTNL0qnaX6Z50itqJaFBlCWx1BpZ5BhVEfLRb5yL5RmqJoIcgZ4bjNeeqlnSENFR8NxK8iqhSYsXvHSl3L87CnOG17LVH6WWrWMLqAjl6Ji2bieR7YjR6lSo1Jw6Rs0mc8bVKuzqNJDz3XjTUxhomDFdFLpDkr1ETpjSeJdMQb0LJmeIayyi2Fo1CtzlJwSthEj5XnMVycgvgGtfAIz1UlBWckrr1vHo48/yIqhtQz0djJx7hiXX78ByypieCb37XmaXLIDz4OtG9cynT/HfKGLgW6Ds0+Oce0rL+WBJ+e4eoWFTK7j1KlRCuhsXdPDQE83v991DK18mNlymbXDK5mdPI0r48w5W3neBQl2HT1LhT5k4TSq4dK/9mK+9+0fctOLbmT/kTm64vNkTQvNNNjx+FEu3NhDvryWTMago2eKmekiI+eqjEzOMzJ6ms1bzuPU2WlWr9vCzqefYNOm1Zw8eZLunj7y+TyKoiFQSWazuNU6hqlSLBewcdA8jzWDKzh54izbLr6Er975NaZnayhmGqG4SMdBKGokLVto2Fic3loMKYrSMOC0Mp9FQk/b7o1uqvDfXhD6qYtWum23dEfvg2aEYbhffEYchFEvGEPIUBezS7SuwWJz/6OxyEXiR7WI9hheq7TNbal1lCgLros+IZyHbDAO0bjTZ6jNfMt/TFMDCbUuXXTDfxe24/kFNwNPC3/E/jtUhMBRQEVBkRJPNg+qBvShKo2kLGH7k41SbmWWWLoDV9Nx/bgE36/PJThNg9NW0BIxEV2isC3uniB9Jtz61MjvanBvkPlFjdSE8n9p/DtshispeFViKGAmqKFy/6M7iXem2PXI4zz7xuvIdOTQpUupVkXVDBRgZi5PtebQKWKs7uyhHNNYt/UKfvS9u3nWtduIXxwHu8zhw4dJdPZQKfWSU11ETGFVR4Kj5TxDnWtRVUE2m2VDOsb8XJ7u1DSY3YxVuhhQClhKhsenJEqlgOM4eLIXV3RT1+t8/fvHWb3SxfNKbNl4OXufPM4Vl21jqGeICy/YzsMPn6S3L8XD1gjHRscYzK1icmISrWOEnkwWYdmUCvOU4zmkYXLFxg2MT88wNjnChZs3Ee/o5p6nLaqnztE32MfkvIXq6Wy8NMfYsSIiMcMV56uMjAsS/X3kjCm6e7v42b6TnB09ypXXVljTt4qO3k4OHl3JmrWDlMtF+hNxdh54DFm3eWrHr9h2yYVMjY6hyxhz00VWrl7JyMhZerp7OH32FN09OZyKxY1XXsGBk8fZvXM3b3vr/8O7bvtbTp0bJV+UYJh40kFxXFQEblhHKsD+/FcegZDamIhCEO8fqOT+4Ry9tpUuFzOQRP9uSMaKEqjrvqS6pIW9bXP693sN48xiPlZN67kILmi1/Pv9L2Ty0bEuJSUvNrZ2gajRl/AtJ54i/AJQi/TXZJjts1hsn4uAP4RGKx9djtauCufdNB62P2fpAyIcl4OHEEFZbaFRr9c4ffwI512wDdfyGgKdr0W72K6DZpoIwK3570bTtBb4IoQpoprFokkOwhksJ6F+59t3y/Wbt+AKFTQFxfaQitqyir6kEDA62bRqhnjTYlY2CK9rOgSHv/lZptwmRhVYUkM3qgU+dYu5ooTPVQNpVwhcBUrlKtVaife/512MnTpOpVRAM1N49SpSVcCxGcxkePkLt3B6rMrk3ASKYzA0sIGaJujvyFGaO8uRsxNcvHmQhFqg4HSRUiVnqhaburKkzQ52HTnB6lXrOT5TYmufgdbtcexsionjD1GxHMzB8ylX5nCK0xw9MIFRKqOoLpaWJZO2edOrX8FPf/xbFFcBw6FAjZhjU7EdKo6GPTnOZ+/4Ip/6x09Q0gZIyilKNYGWyeFWZpkaL9C9fh2UKtgyhZbUSHiQSHYgMzpmaY5CPM5aQ0d2ZijYo2zatJ29h/bx+pdfyy9+v4c1wwNs6o4j47BjX53dD97Ni67dyGDfCubLFh2pzaSSSVZtVbHrHgf3HGJ60kMqHdSqHpOzo+zdd4R8qUrRqROTGienjtPR28kNV1/DnieeYu3adVx39Q28/W/fw+mRMZREHMcDXVH9OHGhgAiYoWwGgrTjh741uimxhjXgF9t07VJnlJZC9dj/zfUPe6k0E/R4XiOCKOwrxNhCFihl04dVShloWuGY2xnI4lFcjb0lWxmqP4fmWNuZeTsTamZJCiRMWplv2FojKf1xth80MvDRlbiN+TXWKbrOgbq/YC7QonGGkWgLrmvMoZnreNnMUAjUAALxULA8i/pskY6+LlQVPvju9/GeT/0LpusgbQfDFniawsTEFLtP7cGTkquufAYpNY4mJAi15V2Gh1v0HS2n8i8rofamu3yQ1jBQXBWwfFenkEOLSGJomi9SxT/ZwoQmjcm3qwFBjH7zVFKQWIBASNXP/6j4J7qn+M7ECxKvee0vRUGowVVhcglPoqCQS6To7u7mwx/5BMKz+cDf/b/YtTJC9ZgdG8OWkppdIdndSU6xGJ+f4uxsnUu39rN/ZA6rJ8fpc7upuQnIZJgqdOGkSxSmLQ4eGePAgGRk4gyyXMF7bA+OVeGXKnSYaTTqGNlukiKBVdhPTVHoNLIMmzUwVRwRozQ9hpns44tfvJMbNl9EqTDPselRHKHj2R4rBvuZrkuUwSSf/5cvMJhJMDKxl1rJwtQT2M44MSFZn4hh1qfx3BJ1o0xcU0hqKrFUHbVkEcsI1JJF3ClRkDEypNj3q/s4e3CcFbfcwrnf/hC3Z4wTpsSq15ibq3Hu5AQ/KAk2bo9z9tAxLtzaxw03buXs5EG6kp0YCZOhFYK1azczOz3DmQmFnoEYuAapRB8PPfokZ2fO8PxLnk0qHuej//SvbLpoO3VHcGYmD4kU0pNoEnCkn/dWhFbtVhco/526/iYnMH4rNGgJxIJ8qCI0RoWMQihNAVdEbQEuQigRtc+3z/tk7jXu95mJaPwuG88KmUVTg2vS//Luhsu1KKNZ/FoFj6YAAkHMeyAOSyl9oWGRFrVoS+ksYM4+Zi0D3iIWvbdZN6w5a/9epbEOQqh+DangK0Vdai2a8w2L+y3ZXBcvqHktPQ/Fs7G8PCOVGr2ZPt72hjfx81/9hItyQ8xlNEbGRrnusmux5vMc3rmbuiHYdvElpFIGntTRFYG7IPfD8u8m2paVUJ/YMyIVzwLp4oS6vhuo4n+gKqAQrclYW5lp86SOYkv+Zggx1KCfaMhpW/x/u7rT7LRJUOGTQsODbddRNJ10Os13//vbTM6MMHv8LM944U28+93vpD47y9v/+jXMFPNMnhqhWBQcOXCczp4uJsfP8YKXvpD9h/Zx+uAhVg1sRCplekwDz9SIqaBUJcVajbOVEq7l0plIMzk2Tq4vhzS7GBzIUp8+g/A66Ojoopg/R0WobLv8KuanJzl+8DCmJ9m4cgXlqRlSqwbZePUN6K7k7m9/HS3Rh1ecZHi4F80SCFMydPkzqNdcUl0pert6KUxPc+e3vs7rXvEK5stV+lefx/CqQSb3HcDMpkitGKRcqrJvz36qisdj3/0eAyt6mB0vsGntCgpVG0PUmVZNRMUhXp7haNFlZUcXF25ZBykVp1zmqcPHKFHgvf/0MR5+4EHWrR1m64XXoesap3btQNbG6O1PMlOXpFLbuOvH3+OC4SQf+MQ3GJtwKFplpKqgKzqeJ9EUFU+6hEaXFmwwCicJ0TBQhbSj0JSWVKKGmPDdy0XppuUZSuvvYQm48BpXikZp8VAzW0wDa/xbKg0JdSmMt30M0eeHeQja8dIonqsoSiPJuxL4m4aq9YLnNuxJEYwzcEn0hcZocuulwjMXqvzt0n+YdSt0+m+V8JqZ94WyCP7qLa/uh7CHlF6w/i6KEcOzPRTpR9Hd+a2vc2ByJ7qtceMlLyc3lOaJu3/BOb3EufHTvO2v/wr59BRidT8OKuvWraMzZqJnc4iaiyl9VzEnGHc7d9P+VAnVrddBC4BzR/Gt/IFPXOPkkLLhvxdCQKH7VHtruqREiToaDy0IHbOV8IQXonHqtTNPn+gWRslILyS4kPBlUCLYJW6aVKtVaiW46bnPJ9OT5ad3fR9iMbKdOboGuimUJQd2HSZpKbiq4PxNq7BqNdS+DDvv/w2d2R5WdvVSqZyjs38Dmb4cas0lubKLyy6+iicffIjLtmxmXc8Kvva1L/KmW1+F5cQolPNU8gV2j86iKzXidgFsFzOZpF5xUGMZPMdFd3w12nEcCrUKs3aF/HyRqqiQkCXwNBTPZrps0R2LIVMJBtZ1oBk5Tp46TdJQqXuCGUcweMF2cD0eOHqcS4dWEzPgxJnTFAo2F196GXuOHqCMg1Wv4igKQtqorkPHcA8bt16PGTd47Hv/iVVx8WwHq16gjEFaSFxH4ZbLnsfTX/xvLt64kcLZMe7Z/R0OHN+JMdjPLTc9h2xvjmM7j4Cs483XqXRUqVShKl0MzfRpxXLQdI0wz3qIW4XveFH13QtoT4RGGq9Jg7560ibVRazOshn1A2FUlACcQELVEEIQtWcLIfzaWGGuz0WwSf+ZESlQtDr/L9TQWjHMxm3+XQsYf4h9+nvGa/ijNhNdQ7i/1FCijErJjaGHjEw24buGVChbxhyOu1naZJG0d6ItaVIgtYZSe3TbNlX5Vgij2bQFDDocQ7sAFUJBhoDxqREeePReVl18PuO79nHpM7eR6OsiJXVmR0a5/MbLWVudZccjRb74f/8vL37GSzhw307e8No3Upka57/uvINb3/M21vZspma5qHg+zMQf15ZlqAlFpepZOFJDR0dKFzUowxCGOksioHZwX1jwTLapXUs3X20KS0I0UYzwxPP/2bDOtblftTNaJbRcyMBBOjjNBeB4EIvFEBJMI87cbJ6bb3klI/OTXHLRVgrTo/z2t/eSMmL0prvwKjXOjZzAyHaSSWbATOK4NXBUFE/l1te8FEPoPL7nIJ7tsOeJfazfuJWT1QqP7XiUeDpDtVpnYGAFZa+KU45Trlt06i6yLrClytTUKNuuuoYjx4/gaSqO4uLYFXRsVg8OoLm+CppUVaRjo0qXerVGwtHRTAPV8fBqNXTd/7/vuValoztLtVygvyPLkOjgycd3IAxBYuUaNm4+j4MHT6KlM8SFgeJKhKsgUPGcErG0iScleatIuVbHsz1EykTVklxwyRWMH3oa8JgrT9C7ZohDp48gTYf1/Vu4/MqrODZv8aWPfYbzL13P9stvoK64PPPG69j11P9Qc1wkDsLz93jMMKnbti8hKYqfpMSHTwOeIGnfdn7e0nBrEhSaW1o5a2FmokkfsvEBRaiNZOotiUuk4h/sAf1FPZ8bjETiq52y9bdoa2WK0cEtP+bw79DbpUHnkkYUY+MZgJ/gAj9QoOX5DReZZr+iuZ/8zP1/OBIy+u+l0ik2r4k+P9pv8A6WYVmLSfXhvQ0owlOYm55menyUp/c+zm+PPMrN11/J+PQ0R08+TXWLQm1qjgfv34U0oFQuUMvX+N3jO+jKJSmUpnj0V/dTqxS44647+Js3foh03AQBqiMRcmH12eXasn4Jv7n3O5SKp5nLn0LVaxhSInAQmteiHgnFB99FUG/XL17mNT5Cui0JdJtKPuA5KDgIaTeuRyzEiYTwy/mqws+opAqxROJY2UjSojb2TdSi2cwoJD0HxTAoFCw6eno5cuYgazdtQPEkcZnAth3c2jwrevu58qrLWLlmHUgdRcbQNZdEOsPunfs5MTKDKxSk6xDTNcamZkkbcbr7+qnWK2h4nBg7Qy7XgXTz4FZB6qgxFcWzUeImVqlEzNBQpIXmgeEKLE9ldnweU2pk1BRCJtBsnwuZKEyVSwxkeujOdqGRJGkmEZ6KW9PQLBXdUlDrDhk9gai4dHRkSWdzZHUDqRjkcjmSmomux6gHqrbtSWypksv2ULNtFEdBuAqKVUd1XRzX4qmdT2KoYLrg5cHYuo3kBRfw8te+iVVXXsLZc+PMnjzFy657LjO7pzj2u50YEyN8+8t3kooNseOhR/GE5weMILGlD/EI3Gb8eruKH/lbiGbCj4ba7clIXfZFUknSqupHcUPP80A4EEg80SzyCqJZx6xBv6IZqRTUuJJSNv6OfsLv8aSfPSpyT7S+WnTMTVp2EdJFDYJawJc8VeF/F+47RZV+rSvFA2EHWaBASD8QRwljlxrjEpEPSNcFz/PLZaOA5+F5Lp7nBhJlZK8H+zP8RGszhfsvOp9WPDR6fWtNp8baRmCH1uuD66Qfbabi4CgKqiewZ8s89eBDVPPjnDu5m49+81OcLIwxfnaMXAxWXYwAACAASURBVC7H2v4hEpkk5dlJ4vE4Z8+e5fpLL+FZ1z+Tn3/tq+we3c/FF2zlmssvY/TMQUxdJeYG6xaBNt227GCLtWUZavewyh1f+yTHdz9GUnGbmW48iee4IIOPFxJpuKj+S1iwWMEn/F5tEFGAsywjdYYvdIHo78mWj0+wXvAJQlyjBB15WSEcYADzxRKXXXsje/YfY8vQahK4OJ6Doeh4LujpHFWp4jnBnISPsOWLdVIdWWr1OqtXrCSTjaGZEolDOpfCKhaozM2AZaMLlbim0ZnLoqgC13GoOzYaKulcB/0dvTiWi6eouKqKLSQyY9I12MXQUAdeTFDWFFxPQeCSS2gUZiaYmjmF7eY5OXaETdvWUhYlqnEYL0/Te94AT+zbi+hJ0bdmNauGhunpyHD8yAFWrehjdmoCrDqgIG0bRSoYjoUzXyeXTJNKZpmvlIjFVXRF4pVr6KqGa1sYKGS6k5y/eiu1eZeD+08x5Qgq8SxX/MUtPHDwCYo1h3Q6zS9++H0Gslm2rNnGho1bG9mXhCfBcdGQqJ7S+CieRJV+JiRV+hJp9OOHQnqBFOsXbyQwbrZv8GhrSJ6ilTG0HMiyyRgJxqgEDLtRPBBapMVWJhLSdWtMuxDBuD2Biur3GfmIyDMUCWqgYYWMTcg2Wg/oXLpehPb9e/wcB62fxf4LVXiEB9IhxDtDjDj8SFyEIoMk2hHG0raOy31EELDT+CzSwvBOX2NtdQELFtL/DRWp+PPUVcGTDz3CwNAQqbrLJUMDPDlykP9z++2cnxnmgV/fx+tufSMjh0cYGlzJe9/3DtatHuDUsXGKJYux6QkefXQH61ds5JLNl0DFT4ykqD4tua6LUJRmVq5l2rIq/70PPkx3Zz+qo6I7Gq5wfakP1Z9Xg4C9wFjlR0mFalq7NL9A03E9UFqlSyISpZRNCdNfy1YsavHWqnJFjYRRfEfBx3p14aArCoonuPfeRxmUHvOVKqoHtl3HcVySMQ0hIaZLDK+OpivULBsUDy0eo5yvMLximJrnoelJ1p43zJ5jR5nMz6AlM3h6DCOXYbIwx+qNm3nioR0kFRWrUgbXpTxf5cC+p3FKVRRVR7gOtlVDSA9zrsiT3/om8UyCC7r7OXZ6hroOntRwnBLdfd3s3/EE2USMkl1nIpVGEzbbOrMYUxM88aO7ETUVo1im5rjM1ssMbtyKVanzs5/9jFxfD1BDMTJIpdqQ2PLz8xhOnljXIKaqYTkSCw81pvGMC7cxOXISy6n5dBB3MGMKli3Jz81SmJgm5iXoVzuYU2bpy3Sxt+qSSEJ1roCm+i4umhS4qo+HerLJwRqGBxZXM9sluej3nnRoj9KJ0k/UYNQg0AWqdStW59ti2qTHAC8NVfGmYBAxhLdZqIUIoq88H9pqT08lwrGGh0JoAAt9T4PLGyXogy0WYpWNsYZzCKcXef6C9aApgUpFtEji7VCEP1eVhluUbMJy0Wta1qqNcS6Gjbfu54UHYfRwlLgglYaEqyQ0nn7qMdZs34w0JUrVZO3aQV5w4/N56sl9vOS5N/Hu2z+EEIJLt17Nb+99gJpZ4mXPeykXb38e+nSeC7dcy/ypSRLpTsrFAl5CEFMSOJ6DlBZCM0BKHOmhqcuyzOUZ6trUGtZu3YCjpFCkiqr4qoEIAH+HQHIQAmhKpb7jrEB4rQKwpzaZWRi650XCfsUCn9LmC2tuouaiu67bkrcy6KXBFMK0A+GJ2qzSGrrJSDRXo6RJkhYc3/UUazJZenq6yAgd0/WoSJtu08Su2RiJJLbtomkGoFCqFLj6yq14epz7HnqEjQN9rFmxivt//TOS2RyyWiLpuIweOsjckf3UajWsfIEO08SpO2iaiW3b9GfSTJw6guaAUDU8VyMtY8iUYGpqCs2qMzknwdFRY904lVPo2mpM02PXnqN46Hh1G5nQGRuZxlVAaCoHHnkSLcjvOnnyoG8JNwQje3fhKh4ZTad8UmXzhjUcH5tBVwPYItdJzJMUD+zk7JOPcPnll/OLBx4CKVCsOlmnzmgV9EQMe16Sr1YZXtXHeSs2MWIIJn/3e2KpNI4tUU0bV7GxbBfdqlArlrEcG13XUVBwhOvb5IWIMKd2SpRtuKUPDjWoSyq+BBXQifST5Pp4bMtmlL73iAg0mIBW/KQdPiNYzBDkCd9A1ZB8hee7XXm+ixHSp2dFURtU6//hQhBGqqhKwAB9yVx6NjJC20JEnMM8AjzWD4wM/VE9EXozNJlomHYvnGNLNBPtDCv8PmSmgFR9KdjfIXii6QbljzPi5N6owxXucdkYY+PdRLNP4Qtf7Z4P7ZBeC7wXYcCL4rgIvwyJ7SA8jVN79vG7h3/HqChQfvocz3jx8zj66AMc3PEQ77z53eTnphjqGeS/v/k9UJM4sRjnr16HfnyWC/5iE7d//ENcam2md6iX73/k4zw8fZTb/vXjnDepcmJ8jBOzZ/mzP3sNux57lA3bLyRlK6h6csG4wrYsQ73n13fzHOs6YrlBqmsvxUvG0T0XR/V8dcRHvoOr2wB5xceAWgP1A8LwV6v54tuPUogcvf5zGqc3TZ83RVHxXC9yQ3ND+Xw03Bj+S5SyaWxwATWIilBw0RWTRw+dJBVPIKSHo3goro/DFmtFOuanEAJcXCzXQnEcrtq6hXu++g2E8Ot+7zy0lx21GvV6HU/6hQINKSlK3yhmmiaqJykJia6oVC0b1UjjoOBqOoNr13D42HHyBqxWFUYLeXKDK4jPzZOwLdRMDJsKk7aF7TkUy3nMRBxVEVRVDd3TEKpGJpGiXC6jGDqqoVLHIx7PomsauuZi2w6qroH01yFlu2wf3squXbtwLZeZeoGOTA5hKejoVIoO11x9I0/tuJ93vOT5HB0bI+kJlEKdvmyGo//zS7oHujgzvZP0hrUk4wlUu4JQHFxVpTSfx1QNbOlSKeQxVQUcL6AjFSmiZX4VopbkBo1EsCwfS3OaUSHCa6Gd8D7XbeYTDTMzhX2JNhe89r8b0iS+H3PoUtTCHCJh0xJww3GHtKgEDpdeU3JGeH5aPVr7a5FMpYcIXGUaRjSa+JwnmxFYUQOaL6E3xxcGHSxWmiWU/kFGmGAzCbYXYNlKdI3xBZLo2oaHyFKtNWmKL7GGuOhCYai1LWbAE0HgkK0JqNXpGxhCujanDx6lZ2WWb3zh61x59YWsS3bhukWkoZMvlTEcl9e8+c9I/kRjqjbHpAdf+94dbL/4It7zrvfijhXpedsK3vPaN3LyzFl6iknsmGTi1DlGzpylb2gFddsiS2LZMS/LUG984UUcevT3vOBlb6TmFBGe4WMLBKdUAKyHeGQ0zK5BKESITLTlYwzpUtI0NEil8VtDZZC+4ashbUSKoTXeZ4Nxe40Il1BiJkK4fsVLEZyuAjzwPBs0jZ7+QSrlIqm4geXamJqBrNhYdoFDu5/CcQUaGrbloeS6ePrcKLorkYqLJ0DzAvXVM3AcB02Y2KrvtRAXus/8dQPXLhNXBbqq4qgKdddDMRJMd6fZuOZZnKkX2fnoPvQ1w3SvWof1yD46HZ1a3ZfV7JKLV3VxyjYxPYVNBdUDFJeaVcXAQdgWmqYgSx5xXUfaZSyh4hlJDD0GlsCzHTRNo2oaoIJrqHgCEmqMqgJVKcGxMTSdSr3ILf/yXnj0DOffcAnHRmZ42YVvJXP0DOfScUYO7md9Xy/TT5xj3XA/R598guuvvor7H3mEVDKLqejUpYWpKAjXl0I9zwmymEVppjUOvIF5B/h3Q6Ulcpb7RNGAmfzrIhIQIqLdh7lHQ7r0nfzbazL5ym8QSCJamW90o7cblKKtYaAJfLlkoMV50g0kzJBGQ8NL2F+4NxaHuBa1JUSe10LrLHS+Xwoya5fOm/cGc1zE4hJl5kvBLO3Gp+jYFmuLzTf8Xgb5GCzHxlA19EyGowePsyLXxanJKeK5XjZe8SzWpvs48vCT7Jw9wdToOSZHJtn+2ju5bdtHeOq+hxl2dH5nn+DqK66mnK8z59p0CI0L1p2Pe2qG04kqsyOzbNy4EdWVTExNcEFPP7btstwxsCxDNTMOl19yMUcPnea6K9OUlTrCTyWACNSs9sVtvtAQXA4XRDZUHIgIoJFFVIUSOHaHIFFwEa5/YodWWPAlBqKbyotcrzQMCyi+uhgC3GGCDEVVKVSrpJMxDEfFtX3VpSYFliLQ4ylmZgpo6H7ShLqD0HQ8zSOOoFotkBIS6SmgKtiOh4fEFX51TiHAEQ7g4AAuFppp4ql10BPMeA7dHd0kNINaqYRmmly1dSuWFmfq4H461p3HhFrh1Pwc9U39eHEdyjZ23WLLNVdQ9Bzs8hoKhkHdlgx09zI6Og4ISqpKNp3GkpJarUbM9EuyxONx5isVTC2Q2DwXu14noXp4QufaV9/Kif276RgeZvLcGMm4Sd0C3Yhz43Nu4L6jB9A7k+QPHmDF9ovYd2wfN6xawWc++kke+s+vsHZ0hBOjR5mWNkqpjF4XXDK8koRn8aqX3MS/3/FVTEULYrhDBhZalMPyx2LReksNuolurjaJMvy+vbVAlTKAFJoOqwG9tquYTa+BBhOOVopYhnnQ8nuTCfvza0Ud2xlxO1NcbO7R6xYbR1Sgae97qb7aW/t3vlbQClG0G/6WY9TtfS02vj/Uwusdx/H3WJCZ7rVveBNTk6exdu+gZJfZunETxd2n2FcZ49brb+Ijf/9Onvn6V/P2d9/GK579Ct72klfygX+4nTffcgv1dIq+bAfp3i48PJ7/qpexa/4U93zhK7zh797F2UOnOLx3P2pfF3Hd4Gc//hEvvvWVS45xWYa6c+cT3LDmWbz+dX+DK5O4ohIsiO9iI7wwttrHdaKSaigdhpum3aGgZekDlSq6aEsZH1B9t6cwGkMJptC8xvPHhp/EomH51FRs2yamG1iuQ7VW47wtG+mJmRw9fBgzlWGsWoFsik3XXMWOJ58E2yajCBRXoCoaqhscA2YSJ5OlKgW6rqOmkriqilO1iKXSFEpFjFiMqu1guCqqaVCSFnXPQfEE6YSJoWicKMzS2Z3ByWSZLU7xn//+H7zrC5+jf3aOifgcXrXEcPdK4kqcdE83TrGGqceYqszTYZoolRKW55HUVGxF0LMugxCChBmjVquhGjrV+XnchJ/pqVB3SHVlKBbLdKZSSNsiY2iMTk6TTHexp1Age8FFHJ4pklyzjo3nb2Tz+rUc2n+Az/7y5/RnenlwRZzpvWc5/ruH6IzrvPSD/x9r7x1nV1Uufn93O73NmTO9ZSZtUgnpEAKE3jsighWuF0EF0auiyAWuV/QqWLCAgIggIEUREEnoJQnpPZNMMpne6+nn7Pr+caacOTkzoL93fT575uy11157rbWf9eynP3cjhAo443tf5xufWse5RYWUJJzUlLgIGE4SdjudQ4ME6qp4/LHHePnpv+P1uokktYxCUgTBFEmlUrhcLsjyqsmLFLLw1FSILN99k/KQZVqOt8+l9MZbWGMurDmIbJpUJrm/s2F4HPFkmK5JsuKpxpGPAs0352xKcKxM3o8fT+lmhwjMavWx4/pXEeO/UnLfpSCKyIKArqcwTItn3ngJJZoiPhQhFotw/UWn8dEbrxDpjfDCjk3MrKqgO6FRU13Hf9oKMQSVB3/9M370v/eQSiSRbXbiA4P093ayf7Cbmf5ykjuPcP+zf+Kmi6/kyttuobakCntxAe2NR6ioq5l2vNMi1AtPPJ+aGWeR1hUEewTFdIwiqQyQiWPuf5Y5Hgk8I8g2sMRssn4UCLSJYK3jcVNHWf1J8SWzvniThdfSOGs3RpkmbBBKi0TsIBkWNkFBMEyiNnAlDTTZQpUEFLuAljTRRJ3SBXX89ZVXWfmp86kNFJD0SKQGB6iqnYvg9PD3De/h9xVQOnMOyWgcy24jSSbAsE1WGFQNbIqAgozo92PoGnaHQsoRJ6LrCD4XcV3DtEuZYLaCiRpJkEikcLg9tHf2kDLUjBw1pnHfXd9gb+MOHNeeR/OhvRzp6MBhgd1KEkvG2NrfSEl3GTGnSLy1nZhuwyFZSIVFxHqHkD02BMkBbhWP5qYvOoJogu4VKfX5GWiNY+pp7LqCwxpCt/nRjTQCMi4jTthUEYUuNE2luMBFfzJOoSWy6eB+/HqauCpTYFMpXrCEjkSMw30DRAeG6JBkbrnjPtyym4F4nM/c8N+837SNxaFiLENHTKdREhpt7/cQGx6hMBpBUnQu/+K13PDpq1i+8ExCZQUIdpFv/+AO7vj29wl5fESiYVwuF+ZoQBxBEJBHTWQkQcrk8hql9jKhSzLKmUxdJsexyEQc+Ix8kwm4gwkTulHFpZAxL5kUD9McU9WIY4hQH6fUxmB9DE5Hbzqe8jPzEAcZ28HxfZGPfc/uN5f6zqs4y0WGY3E2sDAtfVxmOXGvxcTfzDpk9vPkgO0T8tfjxzUVUs3HRWSP7V9BwBZgGTqIUkYAI5jo6TTvvPZPWpqbqJ5Rzd/2b8SyLOQRk+0HjlFXFEJXdM4/fQ2NBzZy/qcvpO9oN0G3jy0ffoTTstHX0YGvuBBDgE2vvsZwwCLc0cWI9zCN7Q0Ed75NWrGYPaueDb9+DFulzt79WwksWcXSlSumHO+0CHV3wyZOP+N6DEtBw0QU0limhJAVZDazOJO/mqO6pOM8pY7PHCmMf/GnWvDslzIpne9oUVSTtCjgNCVUwSRt6mDqOOIWI5aBKMlISUj0DBMKFWNUBLns6quIDo8wo7gCU+3hj4/9gb8++wKaZCMQ8JJQk7i9LlRRRHJ7iKXimCIYmkZci6Fj4XS7SKViGMMRRNHC5/cQHclkL3Xa7Fi6jiJLyF4/kiAiayZBtweHw4bb60JRTUTJQNM0Nr3zT1wLA8gHmlhxwnI+OtTJLEvi3Z1NrDzDz76OYWpmudh4oJPlddUMjmhUl9rY19nLwhIXYT2Jy+fBnYyTVMJUBMErFqBZKeLJYZaXFjNoRikx3PQj4LL8GEaamJym2qrgmB7BZjeIplI4VYu1q84g3tlFaW0FYixCdKSbQEENkf52fIko5TaFZGElQ2aYkUgvQsDF+cvO5tEP3qWirIyfPvESXYND+Ap9rDv9ZCrOP5N+VSPpsXHCSSdy9JV36I/0EHWnKXKAQ7GxY9sOHn7uKW676SY8khtTM7ApSsbemQyHYVlmxudfVkZFAqOBcyx9EmIUGDOazyqj0cvGRAlSFtU2Sdk5Sn1mK37G2mVzX2NtspVGuYgmHzWZXT85TFyuqIBJ92bX54pD8rHgkxxvhMmRscbnmmevTcee5xOrTLVns+vGzo9TUOUpecUcY2tgWZgpFUnVmFs/i1ffeonWdD+uZJKwaHL+dddz8vwFvPjU4zz6/musrK2nLaQwZ6YXQ04xJzQDSxX4/I1f5J8v/RHn4jn0Nw9y7qKFPPHSw8xauISGhiMsP/MsfDGFm757K/U1NTRWz+C3z/0Kf22I61ecmXfcY2XanFLr3//B3W9veJMzTr0USfSgYkx8yceF6cf79o6fW9aEsmCalzAuLs3HdglTC/0BnJZEwtIz8i6bzEBkhGLZSaLEQ4EuY1oWcUtDdpi0hruxognOOe004r299HX3knK5ONLdieGwk7BBub+Mru4+PP4QfZEYODwZ90hZxq24cTk9ODxeAl43hX4/LoeCxyYh6Bp2p0Sw0I2aHEYiRWmxj/DhRorsMmp/D8meNsqdMtpgF+mRbgI2EWMoTOUJ9Xxm3XkodRV09LYTsts49PqrXLiwlMPbd7CkuAa1bTdzAlWYvYcpFlX8qWF6W5o4d34FrTs3URpSSOzewpIyOLp1gBPmpenacoxlxTKt2/dxYpWDxOEGTC1NUWqIhoN7OKfYyaY9O1krpRncs4u5Lg/XzpvJ+qd/x8NfvYCn7voJN583n6F97QxuXc/XLz+Dp37xJxZVVXPgaCM1Djeq3YulixxraGDPzl3sfesdzl13KiODIwwNjLBzxwFm1S7g8d8/R6/q4LUDDYwoLor9Rdxx38/ZtGMXuw4cRpck+oZHePiBX/Dpy6/EMHQUKYNEDV0b934zRxMj6qNpxvNFXxKEURvWDJSOf9jHxAmCwDgMTyiBRqmzcXnpWHyAbCRzvLPKVPCa+zt3j4zJVKdqOz6Pj9kT+WTJGSSUR5acJ99bLtLLViDlk9NOVaZal9z55SLmj+t7dFQIQuYTqSUTbNm8mfJgIQebG4iYCSocXpoaGjntsktYNWsRu/ZspXzBTPTOAaIujTPPPJv5VTNxlhewqLSMXz/5CEk9xtxAKQNqnF27NhFNDjFn7nIKvMXMnzeL53/9c/QSgYH2CFv/8S7emXWsXH4q23ft5czTz75nqvFO6ylld8qUVXrZ+tEGBENFEG2ZxRk1r/i4r4woCJOO3C+zZY0pjI6nSLNf/nQvK4YOloW9tpR9u3ZztKUZb2mQX97/AMkKP/vf38TG1gbaWtq58967CaoCWirNa++8STqpY6Z14kMx1ME4IUOi9dA+qgMelMgA8nA/UrSX2YVO3MkhtN4mCs04vuQgWtNeXD1HCYZ7ELqaKIj2Ua9GKe1upy4ywjxNo7C7k2pZxRvtZYbDZEHAiT8+xFybwLLSAuzJKIah8dJzz5E+Noy9eYAls4ppeeYZrlt3BmcuqKUjYvDgV05if+cgN12wmLYjh/nUGas4um87ly1fRt+BrcwLVHKFXWcgbtKw4ygramMEGgewJ1uwwn0Ycg/BgWF2dxxmXUCna/9+rlg9k43vb+TLZ8yjqkjn5EV13LY2xENPP8UHv7yJb954P7+670t89NTfWVgS48rTlnP3D+7m2R/fQNvBnVy9tIhYez/LQy469u+iqMBBQDeIGjovPf4XDmz+kI5DDQwN9HPf/ffT0d/O6bUlLK8o4Kwli1hYVkCVS6FvoJUPtrxDXyRMx5FjHN61DW+Rn/VvvUlcSyPaFeyuTLptj8dDQUEBw8PDWIaBpqkIwoRSaQwhjnsSGdaEBxH5P/wT8GVO2vAZd9QJOJ0AuslmelOx6XktXnJY4Fz5aL4j3x7I7WeqPTId4s9tk4vwchH2VGMdX5YcRJmvTb7n5faZWwQYd+E1TRNHwM2BnbvZd/gQ0f5hTqqqo2OgF73YQ//hY9x3//9xqLedr11xHSU1lfhQ2PnuB3y4dyvb/vEPPtz7AZGhTlpFATEBL/3hKT7cvJUPdu0jocKKlavZvXs3R3obGWlqoPbEE/jU12/kc9d9HvojNB7eMe16TotQfZ55BApnkIwlcShyJmq/TIYtEi0QdARp6i+plXWYowsytph5Bfh53n9uuzEbtvGXIApE0mn+9v567r/vJ1x6xeWsLp1N+Qn1tL2/g/v/8QwbHn2KO375S8Ipk4tuvYXPfflm+gbCxKw0emQQvb+HUDJMcSpBmc3CnYpRLKjMcFlUijGszkMUGWHmBR0UxfopjQ6ysNBDsZUkkI4zz+9gtkuixC7ikXQcTgNNSaNaKoKVxkxEcAgaXqcNVU9jc4ikh1OMqCqmKPClC85nuCDNgWQXO576K1f+xyXU23QefPoFDj52Lz+/40Fe+L9v8syDv+Lcs87FvnsDVfNO5Kpig4bGXh647TKe/OuHvP/LW1lVVcp3T1nOpl37eP7HN9Gz5QBv/uIWtmzYwobffYfWnQ1cevYS7GGNWTPnsCwU4IN3tvK9q07nrvue55+//i7f+uqvuOHqJbz955doNgzWVM7klw+9xDMP3cdd9z7CTTeciZySePFH/0HDpp1s/9l3ESMD/OSWWwgPhBlKpWkcGUKxudF6hpAGBjhxRjEVsSauUsOsbjuA9cEGbl6+kLmmjjcWp8gOl5y7jq/cdBN33f0Drr7uep577kV62jsZjEVQHAqyAN/4xjfo6u3B5nVgGMaoPeSEq+ikkJFCZktmGKSPV64c5y45WmdhgGBMrrNy4DDPkQ9ms5871bXsPZJ7Pd8zcsUBx/m/57T5pDLM6eY13T2fpN/ceU01Jt0wMj4YppXBP6LA6Recz9I1J3Px8nUcbGrlzjvvZPXcxWzbtp0v3PZ1Zixaxs2f/jKDHoWz5p6CzZBo3nuQyvpZFLp9lNdVsf+DfbS1t5K0mwwM9uF1+ekc6uVwx1H+74H7GHa62bX7KH985Dfo5R7kuE6kvQ+Pe3o71GlZ/o5DTXf3JhM4XHHm155MDAFLMJAseyacrZmx6bTGffmnlpEIgCCOAuXoZ2dC/DSOdjN1Y8yaNXE+foydZ7RfONIGRnGAa665hhkz63jokUcpXXcSTY2tvPbq68iWScym09XaQY3XhxgfQRFSzHe7CMgW5nCYMo8bRU2iSAYoMnZBwEwmUNDxO12kIsOgGximgShLaKaKTdSxGSYSEnFBwykJ6NE4BbJO0GZDTFlIuoDfW4ikKPhl6B7pIiQ4me/wUnjiCprTdtIk2fDGu1xzxXUcOtjMnt5+pLmz2HDoAHde/QUeefRnBGtPZE6xk42HjnHX5y/nW79/iSduvZyfPvRnHr3/Nn74hydYc9F5dG58na9+6Xy+8J3HeeQP3+D+u57g0q9cwt+ff5dFp69ieO8+2nQ7Zy+o4dX17/PL//oCt9/zIH/43Q/56td/yI8f/iFPPvh7yubWUFRl49VXW7j3ziu44fZH+dMTd/C5G+/hP6+/hGP7umhua+Wfb23nf2+7ll8/8RQ3f+E6Hnzo13zna1+lqfkwC05YQXt3H6Yicca6M9jVcJj6upmErGE8fpGgR6TAaVInp1gxq4hLVi1h82t/o3n/PoYGIrzx3ib6dJXdB/fy6j9e5aW3NrDvSCOmU0EOFvDOO2+ydNGSTG4nKxNAnFG5/RjVOgFDwmTRU85GntjYwqjkNfM/04UA1lgIuqx6JkQFE2y2OUoVWFiY4+nQhVFrBrAmwX32gWCN26nmuz52bfy6CNl++tnXxiiT6ajDqUo+Vj+bWs1Xl+++7du6swAAIABJREFUfKKCXOo8V6cyFWGmyCK6muYPTzzBrIXz8Hu8FBQXE6oup9jlJ6rD7r2H+NzV17NpzxbOXHwKPd1H6VA7cakutmx9n9oVdQRtHp574Slwm7RsO4x7di2OuICeUmltb6c/3kd52s6aqy6l1llJdCRGMhVl3ikrqXCVoIg2TrroLCpLZ0zJ8k+fpO/FZy3PvEVsPvACPYf6uPq8m0haJorNhW6oCIwGms4bjPb4xRTEsUhP4rQLnl3yGQBnvxi3YKddi3PIGODxr/6APiNN2qaQjMXwB9wMdPRSpVnUl7uwBIlofz8yAraAl+buPrx2J6amI9kUTJuEJWs4dLCl0ricCqJDRhRtmAY4bU7CsSgyBl6HjKGaWKIN0ePDbbcxkuhFVg1sbj9H2geZM3s2sb4m0ATmLFxIvR0u+cxF/O5AI/c88DjFuKgRTWZefjY7du/B7BvknCuuYDhl0SsbHHvlQ847+zTWzgjw2BN/5Dtf/Tbf/6/buOe+n/HWQz+jdFEldaaPv6Q7+NxJZ/Lk719gTqWdRaes4NDWfnq1KNWhEvY3HOS681bx8Esb+Pwl5/LQy2/z2ztv4yv3PMS3rz+DV7YexFcTouVgO0NxjXVrl/LPP7zA756+jy/dcA+//tE3+cE9P6XuxOU43X5aD+1j3UUXsK+pg+0fbaRs6Ukk25oxpRDv9Ozny1/5Ho8//DCLViykdfd+dF3Hcjq46JqrOV0YYFVQIiUZiGoC0fThMA3CdpGm1k6KfQUookST7uDP69+FUBX7u8Mk+gaYs3A+hqbjcTk4ec0Krr/8GuyKDZNMcOopgXwKNj8f1ZaLdPIpW8bg8ri2WcGk8yGPTyJ7zWXzs69l101lGD8Vsvwk1GB2v7ljzceij7XPJwvNd3/u73wIP9d7KkPJGox097Bx40ZmzZtL10AfnYeP0KGGOSlQQ7TMzzmXXExk8yGc9SW8+drrOIwo9uQIRyNhDja1ko71EkHF45RAMTl/xkWI65Yz8tcPeav5AOefv477H/wJIdHPf9x9N0LvIIWKg8PhVvosnR9++S6MNDz6p9/yla/dNuUCTkuhPvnCf9+tar1EoyozqwsoK1hASlewOUR0LWM6Mm5yMsWi5S7quL+zNeaFcnybqUouoJqmiZZII9YU85v7H6CvrydjIqNZCKEAUcvAldBYNH82gwMjyFikY0n8gQCKpuHy2PB6bciKgN3vJmHp9A1G0JMahW4ffm8AzTKJahIxQ0K1BGJRFUtUGE6rCCVVxCUvPYk0h7t6WTB3JW2RCDvbuqhZtIg1S+ZRt7CehacsJzzcQem1l/Lf6/fy/nv7kZq6qC4sZm+sk86mRk48aQXXrTuL3/z0Ia6+8ALWv/gqK09di/eEen73m4f59M038fSm93FWLWR/7w52mWUsvGApD7/ew8lf+yw/ffCPXPqtW9h1OEqkdDFNvf1oq07lzQ/f5IJv385jj/2JU26+g9+tf5tLbriZ/3t5PcsvuoS/t7QRCtZwULPobh9m2Ve/xJsvfch137+TB574G0svPJvnXnmPmtPXEAsVcbBtmPoLzuMvL76LUVTEoCEwf81aXnlzC85T5qA2RNh7ZB/1pSH6D+/DSERYvXgpA5Ewbf191M+pw+GzkdLL8WsGbhekTA3FdFBVXoqmSBzqH8SejrOorACXmmZWdTUFtVXYCn3MrJ9FT8shAgUhVq9YhYiALIoYGBPazTywlE/mN+kamdQpeZUwo26XmRCD4qgG3ZgQ8I1zWNOXXCSbSwXmlo/zJsorZhurt8QsSn1izlPty4+7lk2JftIy3dymek5ukUUJzdRwyxLJcJjIwADPvvo3XJE0SrGP3s5u2kf62Hv0MLNnz0XvH6Gmfib+UCmpvgSP/u3PnHfZ2XQfaqEn2kdRqJjBpk66Bjt57vlnOGXtWjbt2AR6iusvvIaaC9Zy5gmncvLCFUTCCV55+2VuvO1WCu0hbKKNSpcHb1nxlBTqtAj1wUdvu3ug4yjLZ17O668/RftgA3YxhCdYgixYpE0duySjmwaSkLEnHVuSychPAgREccKNzbKs8ca5Cz+ddnSsjAFk0qVw+pnriCZjpNt6MU2ddDKJX7TjcrsZjqUYSiVIuH2kZQdKQRDV7WMgGgfBwXAsjWAP0tw1gOwtRPAUEldsyCVlDDvdNHSH6dUEzECIHhUsTwDd7cNSfBhuP0KggJZ0Ak99Le83NnDOJWdz+aUXc84VF3MgNkDUHSBdVsSKaz7H5adey0hLN4HOBoorq9nT1YERSbNg8VL2HNzLpl27QbbR1tGJo2+AMz93IX9/9BFWnXYJe1v38c67+/jUf36R5554nut+cAc//NFvuPqWm3nuoZdYcuoZrH/rfT51yw388dGn+cJ/3c6fHn+B/7jjLn73iwf59E3/xY9/9mPOufjz/OXpZwjNOoGUpvHW1gbqV6zkjbfXc8XXv8cDP/shX/jBT/nNQ79h3mln8N6OHRRWV9NpyjQcbKPy1FU8/eRL3HLf93jvg20su/xyHv7JD/ns7bez++33KKkoJ+gwibR389kvXUtPZw9HW1tIR1Se/Mn/8vTb6wmsWkNjymSTYqPJU0+vvZwmU6IjJWIvqqVTkxCFEIJUgDFzPlvTGkUuheLqega7Orhs3clsfu8jYpEIixYtRlEULPPjN25+KgkyGuT81GGm7QS8jbtSCxmb18kiKyGn7/xa7HwILJc6zR7DdMrfqSjtfLfkEiTHlVF37an23lRIL/cZ051PtSZT9mVZ2JCISxY1FVVs+eADCoJBfBVBuoZ6uOyqq7jq7AtZd8IKPnz3XQIzy9nbeJBT15yJJ22js+kwaU2jvH4O+sgIlf5iYoJMPBZDU2wwEiatxTg22M3Xb/4mdkvB6ykgpgj8+YFfcvON/8nOo0eoq56FU1QQC13YbPZ/D6FueeOxu9WYSJxBRLGEM1ZeSXXFPHRMDMPCVCwUAwRRHg1wMpGKAjIsUEYmNSrfESfcUSG/GUfugk4FnGO/k0MRlp26mldf+TunLFuBqiWINB1DsQvovYMkjRSiWyGg2Djc14fs9iOWBOmS7HQLAsqMWbTrBunCAMOKE6c3iBYoIK64aRqJI4SKcYUKMZ12BpMqZlGQpMdGVzqBf2YNDUeOEjLhqV/9nGN7d7DyxMXMmzuXyy68khuv/SJ33v5tjny0m+d//TSlXouVhV7aZBd98UGWn7ya4YTKwYMNnLNsGUMRk57hfprbOlm7bg0P/eZRagUFw+Nk5OAxauctY7hpM6rTQ8v2PQhDOnJhhANHWyieEWCgZ4itO7fg9jv5qHEf1sgQus1N484P6YoniQ8NU11fw9FjR1m6dCkNe/dwwVln0dnaxEWrz6Kr6yglUjG25DAHWrtx+dIMN2uUVNexf+8errr8U7z3xhvULZ1Lx4cN1C1exHvr/0F11TwOvfsuS0ormDe3hu1vf8SAMERZ9SyGYkmamgaRMWhvO0jDlp0smnsKDY27Ucrm0pfoBdFBt2Sn0xWgJW4h+ktpLPCzhxS+ghAlpo8RxaSrv5/KUIBiv5dnn/0b137megqDwbyUXL6NPTV1NQ0rnhtibxJVmdWDZR0XKCSXEp1KFpndbz4qOp8oYLr+PinCmnjO6Mcgn1Y4z1z+X8p0CDbfh0YyTURZZPuunRSGgtQvmk9icITOXXs5Gu5i//ubOdxxlJauNiyHjSd/+TPm185kR3sHwVkzuHjdmRxq7qKqaga9Ha0khoc4MtDL2evO5/CRVvyKDU1PUzq3BkW3GDLSzCgIYaWSFJaV05wY4foLr6Svvx/J6yQtpnHJzn9PhnrT10utvqMyZZWFfPmq36B6nMg2B5ZdxBIFEmqUSFMHxVU12EQ3SCaWkAlZllms7BzbY2ljjxdEf5KXNJWcRkSgXYsSqC7mxrMv5tD+fViWxemnnMShI40YloISCiKFIwzYvRTYFZw2gV5Noswm0hcexiaLeJ12kgkdxeNAEARccZ1hNYFqE6gOBOls78LvDuCyGfT19eF1e8AQMbUUCY+APpAk6PMSVxMMRQcoLi5F11V0y8SOSHnQj+jyEo7rpIwULUePEQgGKHMpXPOpFfz1w4P4XAWUKSLv7T+K2z6D6EAjhixy+uL5NDY3oYoWdYEKhtNheo/2U1lfyUhawuuwaOk7xuLixRw6vIcTzz2ZnmOtDEYF7IlB5py4mv0fbMJZXYaSVEmKBiSSlNdV0RWNQkSlrqqMWG8/3YKMmEoyY+UCunbtQ1NBckiUVRbR1RwmLEaoqpqLU41zqKmVVYtqSSR1mnrChKrm0LDpLT5707l0JXyYQz28tbERSdTx+WciJDr5zPkreGfjLi68+FLkkgrs3gCpdJqYIOJye+lsPoaoWFhJnaRgIsoCXq+f4Y4WfDYbxS4PX/j8f2B3BYhGo0iKPO7bPSarzIiD9Bx5Y65sMDsFszQJpvJRjrm/88FvrsxzKoozW+74SfqbmAPT3jf5PB/yOz7wTOb8eOubTDr3yXEFjs//NLWs9ZOIBj4JtSsiIWKyf+sWvnvnN4lXl/DP3z9D6zub+WCwiUIJ9jcdpPVgC3f97iGuPn0d605dR+nqeZy/9GQGu/tpaDhALB1h5+b3GIoOYivwUBqo4q23P+BL117FgQN7WL1iKR8e24lXsCF63JTNXc7t134LZzLFhg3r6dbj1M2ay8raegqqKv5NGepzD9+tGDrza+qoX3UFoqVgWRopIcU7m9YTTnYQkHTKq4rQNQsLBdPSMcyM73jGgEwiEzbPQpIzVGwuEs02IZlu0fMhVEuGux/9JQ9973+xKzYMRcFSdVrbWlkwdxY93S04JYkyp0QknMAjq+jRLgKGTGqgGbsaxydaSGoch2DC8AiSpSGODKHHB9C1KGZ/H7KWoNSrgGFSGfLjkCyMZAxTi6MNDSGIMv6Ak9rqEirKi5AAwQD3iEaZv4S+SAvRqIVhmASkBO7QHGaWeklJTopKCnH2qriDPtrjMa488yJw+Fl7UhlCTCfusBDTCo6Ccjo7OgiVlNHnkBjWkgwPRnArMt0dEAh6sNx2Brr6kWw+YiSxAgKtXT1IRUFUPYzT52cobiIX+1GGVXoFi4Ss4dBitOkxSgUnnTYFo32Ypu5+5lRVs/9YEy7Fi1DgZI7HQUiy0ApClBWXU1BSS1o2OP/0k4gnhlDsIgWhBTjiUVxWmvols6n0BSlQeulNeWjpbqcsVIRoCIy076V37y5i3f3EW/cSPbAHrauD4Z4ukm1HKLTZcJsCjliMWl+AjpYuvv29e4glUyS0FKIiYRmZTAyiqGQ08CZMMEnCxGGNxugVJnyoMmz7hBlPNseUbSudC4+51OHYMXZ/tnwz13QpF0Hno1Bz+8vtMzsyVj7qLtN+sulUBike31/m/HhqOnvamXNj0nzyjTF3nLljyj2mW9fxdbJMVMFCUU2khE5pbTmPPPx7Tli4mOLyMioKKzi8dTt7BzrwKX4WLpxPUSjA4O79vPTyn+mx+tnwyt8o9Nk52niEefMWIpkKDXv3U1lRSjIxRIHPwYHDjXhLQyypq6f7aCORVIyG9/YhuL3YK10UigaWLBHpGqFqdt2/x/I/98J9d5+8fB3hHoEFC5ejGjKCo5DBVAeWo4+DG9/GZQxgF2Zgc5dgl5xoqIiiiSIqGTMjMQO+siRiGGPZJcmYowiTBfPZAPmJvnKCgN2Ctaeso7W1Fd1l59i+fcgOBVMQCI+MMLOujJJQEZ2RYfRkFJ/XRlEwhEtKUlpeTFmgkISRQnC4EHSd+EAPfZE4BUUu7AE/TpsNxeNC8jiJxxOE0wl6YzHCyQiaaENHxyx0YzdtRDWRru4R0mmNmspaurv70IKF4FVwOAoQRRsubyGmw80sj45ZGKQqYOIXHKQlldNWnUytXWDXEMheO7GwyPwiG8ci5fjLa5A9FVxQK9KdNAlV1VNmd3PJsnJKQk7WLSuk3QxBqJiQ28bIoMQZswKMmNUE3CEcXrho3lJiaR+67KGkLMiFc+ag+Z0oMT9qTRkXly8i5i3ELrioDVVy+tIaCJosr5vBoKSiDRvYnKXMmTOTI+EYWBbJpMCZ8wp5cW87/kSA6rk+eppaqCkqxAgtgkgv7TE/K5csorOziUsvvpS+dpWSSoGuiJ0hJEYEjb7hFD0JnaM9PQSKCphVWU5xZQlb9m/HVJNc99nPceYFF5HQQBBtKFgIZiaCmSiNeu5nJXM6HoYmzJkspvCusibbSucr01Gr+RBldn32sz6JqCsX+WZfn6oua2vk63mKMeTbZ7nPzhHn/T+y/rkle+2z18sSQRdkSv0hNr31Fppu0KFFOf20sxhs6yUY9PPoHx9lW8N+vnXrNwl3dmMvK8QeMeiMxKj1VzPU309STSPIJvPn1iKrGn3JFJYoYLdbaJZBxJC4cOUl7Gxoor2tnfryek6+7CJe/+PDPPuPJ/HoaX7+zOPc+NnPUlBY9u8h1M07nr179eplzAydxrvb3qJu7UL2HNzK08/+hHhbA3X+KiJqivkzVyPY3KhYYIrYBIOUpqIoEoZhYqCPsiAGgmCMAn2OgiqnTAcs4/WAqRo4sXHimtVc8+mreOzRRyj3O4inLQzdIBpJEY4NMnvOYoxEDzOqZxFHwWdptEQEdC2OaZlInmJkUWNWRS1dI32EdTdub4CAJDMSSVFgc2CpAoJgo8BRhFNSqPQWY1pgCBYrZ56IqpsU+SRmlQZwiSqFfj+GQ8DuMvFINgokL4HCKvyFQUqddiKuIkqdAcIxgWBFgAVzKxgcLKNT72d++TwEp4hLTjMwAGUVJXhcboJqDDXoo6RoHo6QC5uhsGTFXFK6i/5hB8GKKopkN/H+FIU1HuYsWIuv2kfP0DCVhTZ0JYinupJw3yDlRQUMuwqZUVGBw6lSKLpJBkxmuF0IcgXD9gHWLl7ER9v7WL16HsVzqsAK4/UXU1U7l4qqRUgpiyKnm6oTaymvLkJNelD0KEuW1VA1az7KcASX08Xs+hkM9Q1z8rJ5bN21n1B1AbLoZaCrn1ll1QTcNvSUyor58+gZibFsdg0htwdLLuQbt96OEizDEERESRhNMS5BHspqbEPmQzpj51NRS7mI7P8vpPGvsMG55d9nnfMjySmekqefHK8xJiPp7DX7pCZZHzfu3HcBIJgCppZGVESq59UTbe/ja9//DqJsR48m0YZGaFYHWbh6OSfOPZHly5YiSk5OO+98li1YxfLFaziyax/NQ73Y/Q58dheH9u5lKJ4gnYwze/4MAoUFSG43MwLlaLJOS/NhvLqNl3duJjbcg9PlodgXIG3qvLZxPZ+5/Iv/ngz1hpsrLVMY5pwV38el+NmrHma44QBDyUMsWbCcSnkZh9NDtGzewS3fvAuHUkxENTENAafsIqmGsSsmAg5EwY4xni5BHNeJfhxCPf4FTRYLmKKAYHdiRGPIaQOlppC//e63fPnrt7Nl9x4uv+ZqyhWBktlzWOhy0hZLoWIyv66I/UeGcQn9KIodX6gGQwvj84R5c2MXot3LklmzMFMJ2mJ9VM+qQ9Jt9PcPEigoQkjHEW1OElaKopCToe4wbpcNTI2+gTDBqho2f7SHtfMWIElOXEVJVFXF65VwKRZtES8L/CHe6W6mwOWnxhVm5twQ9z7Uxa++MZ9jvQ4qymN09PtZ4ksyHPHiK7TTHAGfcoy4aqK5FB59soGbrj6Bnz/8Jrd/4TxSKREl5SLhSSJZNuoXlHOo4SiaXou/qIFixUvLgEyRx01ETNKWcqENK5zkCrI/0U6hbifNEHE1zuFIkrPPKuanP3qdO792CeFwGr/Xh6uwDsFMEu3X8BSJHOmVWVHUz/vt/RQKJbR2NbDu1PnImkJgxmrm+WPsbhshmhRRo80sP6GCnjYbCbkVM1FLONlOdWEAXRR58om/svycc7An07hcDtoGElx44YW4HYVoegrFnpHFW6bCWCqNbPYzL5Dn2eRTUaj/St2UH/op661xDu2TIJ58Y8w3lon/GRvvTMlHhedL+Ty53WR7WXHKdpD/w/VJZMNj7fLNKVecISJkTNokEUmwoegGGz54m4LyChbWzuTx7/6Aok+fRFVlDV17uliyZgVz3MUMaWmcLjtqZx+PP/wLnt2ynqqqCupr6mjYthNdTTBj2SI69xygqLKY9vAQJR4HI3096DaNsopyZs1fzcubt3Pvf32fjc+9QUd3J9sPbWfbluYpX9600aZEOYme0hmI/oNwLIq9pIyu5iMYoSQNDQeYfc5SpO4w8xdWkRI1Nrz+JOedezkuTynDmsqr6/+OGOvl81feTMQkK+K3OapUlMYXbipWKluJcNz4yETQEWNRFLuCbHeQiMS48au309nWTllBKc+//jrfuuJyQjVlqD0JSuqqSJOmN9pHKGRHkyuQNRDtCnpBAUcauogmJc5ftxSb3opuVbLKWchAOIEmmlRXV6IldApKi5DtPrqHunE4bTjcNlxBF07RIKHq2N0eikNBLGQcdhsyDkyHicvpRCFNKqUi+m3McTlICwbplAMrVsz8gsOoehDDmyboKWZPyzCNgRlYficRy0bCoWJPDJIUnfjVQWZXFWKlJU6cF0K0x9FUJ4I3wWxfOa3JQT7YFOZASzfxSBTZFsamdTKkSyiWhaarON1BRvo72a7rXP+ZddgQUCwXkuxgYW2aPz+zkdsvvpBoeJBUWOTBR1+ksLgC0WbhlXyk5RiDXVBz66VUuEuo9cu8/8phnj1wkK/f+y2e+ctfkESFU+pmk/YIyJKfvhETd8iJ4VyGN6Lg84RIH9tPcE4V5TOq8UsSy09dS89gL55QApddxO1xEI9rqKkUoiwBOliTbTVzo+5nw9DHfpjzOKdks73ZdRNwOjla1ITi6LghZN07GalMhv3Je2BsTGPjOn6Mwvh/QRjbK5ODvk8uuRkzxtxyJwY8WY8xITedisrOlhVnI9XJz5jahvU4xCtlMsWOs/+AblqYlonD0lEVgcJgkLSpEQ6HkWQbkaERNnV2szhUj13VefnDlzn50ktp3XuQ5iOHKSouYf78+QTsdjZtfB81lsKlOGg61kmRK8iWY+1U23wc6e5myYLZHBloxKYbbDq4nd79h/nF/T9iZvF8jja3o+jHTWFSmRaheuUaEkKc5sYRamtn0rilF9QAdTNrad3WzI5d79Ef7uPEFSvZsf1thoUO/vL4vVx80bdRZ7iJpwyWlM1AG+wj6S/FYbiQZBnDVBElHRN9NNWamFFiAWOAPmkTWCKZeI2TX6AlgIKYSSdrmGioKIZI98AQoiiTVg0MG3iCPirLS4hbXejxIRbPXURjl0pZoZ+EmiY5HCPo9ZEQ7IQDEWZVdxNywHAkgOCUiZkaR451Itg8oKfRdRga6kOUJdIRDc3s58qLr+HIgb04BT/lM7z4bDYigo6/MEVLyxHEYS879x1haHAEuyBhd4J/1Spaeto5ccFM3n5rI2pBNT/4yqU4S30c/OAAHR0BHnvk9xQWFpKMZRLbdfd08ZcH74WeBP2N+ymKpvngL6186nMX4/PYueHO/+GW277CtpZtrD5jDY/+/iE8HnB6fUQH0thEAQGJwWiUgaFBHA4HqmRSZnNRO6OKuCXgle28v/5DbN4oxzbtILhmDcWlywicUMMfn3+Zjo4uykMeIrLKsfYOil0Bfv/Ar7jtm9+lpMCg2uOk3LJwRlQ2v/s6SVs5ZYbOvLogHR0dbH9viKN9Kd7dup+AlSIsGTx53/+y4dGnef/Vd5h95x0caeli49sb+NTZ5/KbO75P7cpTOPfSy7FECdMSxqLsHYdEczXTY5v63ym5VFS2wiW3TMfST0ZGx1u6jFGXubfnE2FkPysfcpturh8nPpiOAh07n446/yTy5emeB4wjU0HIhEoUTQO3JTHolPEnLQxJoLOjm9MuOAspZZIOJ3n9b69x5pWX8ebz/8B5WpiXd7xOY6Kbc+auRAp56NzXQ0dzM4FZs1mweBnF9gB9ZppSh4u//PUZKmbWknQqCMMKaV1iXvUShnq6iJtRbrjxelp6mrnyqk9zztqLeeBnd0+7htPKUB/93U13r1lzGnv2NdJ0tInVJ8xl+5a9ROJRFtdXow6H8btltn2wBTHZRaCskoqys0i69nH0/Ze45NxLOTi4D8FnEjJKsBwpRCk1ahwtY1nCqJdVJsr+WM6pzDpne6CYTJhsTC0bG311WJaBLFgkDYuO1BBVNi+//sn9nLywlvbWw8TiUWKGwXMvr2fXrv00Hz3GSSuXcuzwbhJmlHjbEVxCkB0HWlm0sJbGjj72NbQT6elD1eJE43FiySSptI7LbcepODm09wDFxdWcsmY5UirCh/98i9pCD76aWopDpXy4cRuReBKX042sgM1pp72pBRxOFs2fS+vuJsqKCll+0lIaBtq468ePcKS5n9mzArglN6RU7BZ4FBvPv/ZPXnx7E9d+7grmzF9N49ZNrDz5BNqHwgT8Id5660O6mgdoa9xCXUkBQY9MwCZQ4pFwOUw8Do1Cv0zADXYxiSypxMNhnn/pFdb//R2e+Ot6kuERLrvmWqIftTL/5NU88dLTbNu2g4DXh98p4bOLNB89wshghPRwhHNOXUNzczsIBm+9sZmLZs6hORFj79EORMMg2tPEReddQHFPnLuf+SOtrYOsnFVPuddN2jI51HAU2RQok2xUz5vP4tVns2LFakaGujl5bg2LTjsfFHki/cgnQJhTsfqZzf3JNnj2PfmQ2sch62zkN4Egc8eSX4n0SfQIudenohKn6zu3/dj1j/tIZN/3r6zddGPJsPijxJLNRkxNImo6R5saefftDTR3t7N580bKK8ooKSnjSGqEz5xzEcZIkjPPuoD9W7bw7pa3UBWDysIQ//Oje1l7ztksrV/MG69soPDE+SRbBrnn3h/y0rMvUO4O8lrLTs6ZuZiFq5b2ymSCAAAgAElEQVTS0duOEFNJqSqSzcFA9wAjw2HWv/kmc+fM4sILz2bGzAX/nlKqZ3DD3Z1dwxQXlRIb7qduVgmlM8pxK06i2iC97X24TT99kUG6YhIF4RTDYpxDjQdpb9nOroZdjLRtp9BjY/bC00glJEzDiWA5sCkipqkzBtiZF5NfQZC19HlMQY4XjouCAKaBJdu4/vLLadiynTneAtasqGfZ2gu59Tt3ITgc/PWlv1NRUkw8lmDj1h0UhcqRrBTJHpO1Z55NQ9cxdu7Yx4EDDTgFAcnU8DgUJMsC3USNx1DjUZIpEUtK0t/ZzztbNnLK6vPYf+AQhttFc0c/b73xHslYClG0kCQLh6wwGAsjGgJ90QThcAIppeIt8PHB/jYadsaIJYb49EWL2Xx0hJFInHAijeRyk7AsKqpCDPaN8N5H24ildIKDFqbbzfPvNdLa3E9UiuAuLqClNYLdMxvVLaGqfvbsO0Rl3QIimoN4ykZTe5iColm4dDum6aQnkqAi4EBLqujRJPvbW6gwdF7bu5mE4Ofo0WM4bG5sSLgdHvxuDwN9YQIuBwsXncDRo628/PLrOCU3JxYH+efBQ7y9fT/XrFtDT1eE/3n8T9z1xc/wbuNBIt0j1JQEQVcp8xUwFE5x/4O/ZtuG17nx67eRsnlxO32cfcnZ3HDu+cSdAZweP+l0EsVmm5zeOAsesjfomOY4P0zlZz9zA/zkmkDlg82pKNd8iq8xOM5uk/k/eRy5MJ1vrrnsdO48J3FzU1Cb+RDiWH12FoB8CDgfEs4t0yH23PFlzKRAM3VEEQTL4uCmLfQqabb/9WVeeO9VJLfM3AVz+Ml/30PVKctZGCglbql0JSOcd/rZdDQ1YwwO8PKmN2ne30BfdwcF3iB/+fOzrDp3Hfs+3MTslYv50+8fQ3OIVBaVYAoCm/fspLuzg1Q8zWBXO6UzKqivmc2+bfvRDI3B5CApc4Qtuzdx5SVfmBKhThu+r3UojGhzEQsnWbJsKZu27iMlO9i3o5sjLQmEtB895cNdWsvA4W7mrpjLUOc22hp3oBo63oCBEoCm9gP8+MFLaet7FbvnGLrZiqrFYSyalDABUJmEbRPHWJDfqezb8oUBM8nYFMoIbNq1nYpAENHl5aNtezntjPPp7k2x5oxLQYeg6II0iDY7jU2H6d3bx7Ag8fMnHqXzWAuSJFHgcOEUZATBpLtnEKfdQcDlwWUXqZ9bR3lRMX6nk/p5M6mqqeLFF5/Bb3PR0djB0Eg//oAHj8OOYoBXtBNQnFhpKCkpQ1EU1LiOKMoE9SR93S143RY+WwFOeSZVNpgTClLukihySswoDlJi8+CRJYqtMrAkHNYQHl8KgNoKL2tXr8Iul2OlU7gcUYL+DAKyiSKKoVJkt+HUU4QkkQqvj1Cpk8IyD2WhUtw+Bw7Zg+RJM6+oBiwnlUXzKXeFEFNpCvzl2H0hDOz43X68kkQwIOOz27DpaRyKCzUaptBrp37WTBSHh4qQi2VL5lHuCOBxKDg1Bw4NFi2ch68sBIKG0yZg0y2iMR2Hw4Guqyhple6ufuwpOwUFBaRSqfE0wtnve4w9nJA1muPIIDvYxhiSyD7GQgBmt5+QYZrHwVtuP2PXx+7LPrIRU/aRK2/MbpMtrxwb19g4c3NE5bbP7iu7Pl9/U61Z9pzGFFRTjX+qNck+JsuA81+bvGYCsixjaDpaNEpZYRGvvvwSf/7HcyTiQ2zbvpE9u3fg8zjZ8dY7DBpxvIrEPLeXBx75Of76GvxFZdgSEmlNZeVZq3ju+Rfx1JUz3NWGPyDRenQ/smLgDPqYteIEUgcH+Oyt32HdmvP4v5/9hnNOu4C+aITtu7YhOkUi0SRW0sIjBhhuS02HMqdHqC2bGxns6qdpqInO/mYioge1O0Hl7CB9Q8MsqK+mV+1jhmcRi05ZgSrNQAxWUl4zh5H+OEZEpbszQmdfGzVV5Tz32t38/re3sm/737GZEgI2TEkgKWioVgZ5ZhZfQBTsCKICophRPMlgSiAYGoZlYIoCkjWBjDN5xC0swUI2TTTRQJYEWjvaCRtxLJuJGYewrmLYRUSnHa9sx+tzI4gWHmx4nW5ES8NpmSTjKtUzZuIQZGREXC4HNlHC7nSimpAWTNwuL+HhGL5CB/5AOYmkjoyCZgpgGNjsMmpUBUMA08QjK5n89KKMJEFaBkW3iKWjSJKEjhPFcCDZ4gQkgXiqEZt/FprXi2wZ2AJBLEcAWRJxeJzozhR+TUV3BEkORXDoCVJOO8NH27EJg5S5HFg2O0M9IzgLZBw2B9F4mpiWxldchIGFHushhoeQt5BUXMUrO9FJUKD7cVgpTEnHQkXw+rFZHpDTuHwF6MYgipxGM3TclhMxHEFR7DhVjbBbJh1Lsf3DzRQLJmZCQ9Z03F6wj6gIyCiSwuDgIB7dYtkJq5ENCVkdIRWLo6HiMkU0UaM0UIquR5BFDUFKgyKBKWRk8KZKJmuqPsrtjKY3kSSwRCzDxNBUDMMYRzZj+eTH7rMsA0nKiJ0sy0I3Mpp4QZDGswQYlomOhY41niLFskaz9OrG6HN0TBMyeeuF0a0lkvHCmkg3MuaVlTGU17MOxhMQZsZqkAk2nHWMejpNzGHMKyx/zNSxeY89TxDtmJaMINoxTAlNF44bb+YZ2fVgmaPPFkwECQxLxxJMdFMb738sKhSIyJaEgoxkigg6YApk/AIy/YtICJaIYYqYJph6RgeCaWX61jUs00ZalfD4PPzz+RdxeLx86oorGejqIRj0cfrac5m76jROX3E6D//Pz7n1K7fw2OO/IjHQxt/ee4cFs+cSj8b4aEcbv33kSS48+1JOmLOEmqp6Du9uQ3J6KfMpuAJ+ggUVLF9ST3FlCeGBToyURl9URRdh9qITOHPNqcyumYmzsJD66rn/PkL97GXXYpomC4pno6YCxBs1EmYpJcGlVJasZs+OHlYuOJ/rPncHp57xFaoqFrB4wam8+e4OsFdiD4boaRcRUwXEB6LM9V9MwkgxFD5GX+8HaP8fae8dZdd5XXn+vu+Gl9+rnBGJUAQBkAApQWBQsLJkjSVbcpIlt2xJlsOypj0O3Q695O722C2PPW27x7Ikq21LsrKsaIoURUrMAQwgiEBkVEDlqlf10g1fmj/uK5BanuYfGmDVQqVX79XFvfues8/e+9g2NlKUdI7QCITRoA2eM1gbY22MM4bAl5hUIbRFSh8hBNokGJP+m7ZMCIHtZmQ6YykWi9dOrNR01886i3AaT/jgZXfiQqGAEFmIbRD4hJ7Pxlodz5eEYYjfPVK5wKOYy1MuljDGghCsra2xtLzA+lqdtbU1kiQhSRKEEAwODpLP5/E8D9/3KReKWGsJpKSYCwkDj56enuwi8SSlXB7SlJz0GekboJrPUyz0IF1IX22YvtowJk2QwuEJkZ2EOsUmiuH+PsZHhikUCoyNjYGxjA4P09tTY2RoGKylp1qkVMpRLOUIPZ9cWKJUDCgVPQr57PmdcxQrRTpxlJ3oaRvpUkLPoJMONo2QGFTUIfB8fN+jUMhhnEUBvb29mFTxipe/DKcNl6ZmOD81T6VUpdFp03AxxXyAbHbQOmV26goyjolabepRCyUEGo+OstkwrdnMrJBWoGxWUSolETKPsRJcxsdrI9AIYmVRWAzdcwEw1l7jX51zWOODC3E2QCuZPdY4rAWlLUpbtLJYI7FGkiQGYwSJciACjPBJkMRO0DGO2AmMBWPJQBmJ7QK0dQLrPIz1MFagnUQ5SWKyt9R6pFZgXPavxsMR/tuWmOznG+GjnOw+n+z+7M3fIXu8ctnnlab7ee9a16e1wpiMbktSTZIalHbZcbOCRDs6iSbRjlhBrDWpssSRQ6Xy2pvRPkniEUWCNPWJY0k7NmwkKetxQlNpWtrQjFPaqaaTaGJliZWmowztJCGKFalxpIbs+YxH6nyMs1QqFaZmp7jjjluptzf4yte/xsHr9/G5L3yOV77jtZx/6iE6nTpb9g0xvq+f1958Ex//6lfYOjLCyOQWXBCh/Tp//4VPcPiGPXzvgbvwZEplqMr48DBptMCxh7/Ew888zB98+D/yrbu+zQMPPIDRCYv1Ra4b2cYtB19B39adpPU2Y4duYN/WfS8JqC855f/aI4+xdWiUU6fPcmjvdpLD27jxhgGOnXiEt7/ubdywby9P3PcY333k6zSTU3j2LdRXBAcPvJU3HL2Jqfp32bNjnMGhHay0LuI3ztJ2IQvL06wsX8bvvwPPWDyRed6lX8D3shPfWQ8rQKkkAyOZw1ibcaNBhm7Se2ETgOxWsp6UYEH6PpudoS8k0ji0J5Ceh8Ei0UgPdJIS+h45T4JOrl1wnoRiIUcxDIlaHZyxGbgI6KlVcJ7P1cuX2btnNyv1OuVcAR0nFEplcgjCZowvJX4uRxiGpL5PIAU4jbAGZy0qTQml6LrIMtAPkKAVxTAgl8sR5hTGhaiOopwrkhjJWpxQ9H1CJDZN8aWHlJJKucjC3AxHJieZiTps1NexqcKXlrWVJdK4w0BfjdX2OgtLc1SrZXqrfShbJ7WanlqJwVovoT+NcJnLTRhNrVykPFjmrOpQqxRwEmzgM1ip4MvLBEJSzReYvjKFXy6jTBaEMzrQS6VQJLYeulJjIgcOD1tvUYxTDl1/PWfmLuPJAsYYWrGimRjiyJBoQ0JMudZDGsckqSGKNSaUOKXwcgGpVgiX/X/5vo8yFqP1NQCSMtOQWKsJvIyyyQDqhRtzJkUCawVe4GOMwhhDGIYZMBqFF/hYPLSyeEJgjEUphfA9XuBiXTfHIvujjL1WcWY27BdRV0LgBOj0RVyoAGsjJNmNV0qJ6G4rtdbid89nbS2ymyuc0Qxd6aF94TqwL3KKSSnBdL/mXlTBs2lPzVLiMCCExCp1rTARNlv/vhn0Lp2P1dmxEt1Qee1MN3TbspnaZTdfExaBl1FwzmUDaCPxPIGxBuM8JA6vK0Vy0uGsQJBd8yfPnuLLX/sy9dYqSkcsrkXML83S3zPMh3/r/+D/+stP8LUvfZPPfOVO1pMG56Y3eO2Nd3DD7/4CX/3jPyPK5RHLaxx5z8v53D/8E6VqhasLVymO9fHciXMYf52iSxkZGeIvPvVJ7vqnz3B2/irnHniSUm+BJDY8+b3vI3cNse/AJId37Oaeb3/9RwfUOHJ88duPceTAdhbaU8hchSdPrbCwOMvVy1ME5QGunJ+mlFsnZ0MuXXyAm29+G/XWCvff8w1uuf1GbnzjLTx03ymuO3g7547/M7cfPszp4+eo9b0KbVo4WWI9UTgMMrV4nkMpBS4kNiqjAIwlnwvIB2G20TKUSCFw2hClUcaX+j6e52HJKIBWs03RK137XSSSVBtW1lsI3+HHugtwKhsyKYN1ipCsq/MEOGPxcQQik+n4ErQQRFFEnGTtTmujkV240sMpTbm3RnNpldBadJrSbjayC9T38Z1BCoeLY3LSY2x4hNlmhNaagi+xZAOrtNXCJ0cp55PGK8QOfBmhkxWE8CkVcug4IfQEKsp0mX3Dg6h6k76eGs2NVXLVKqVCnr5qCe0UjTi7qeSDEJtGhIFkLW5ly+gaDTpCsL7RJhgZBGeQxqFaHQoya1vTpI0QDp2mlHv7aK9p0jjJwFwY1q4uEPoBLSAQHhsbG+zesYW03aBaraKjkPGtW1jUMfktQ2x0Imgn2FYHP++hog7tdpNmu4XudIh0ER1aisU8utNGK0uiTWbJTx0pGq01QS6PNhqTZtxWPp9HK4WUMqNeAN/Lo17EizoHqPRFvCc4bUlM2uUnfYzLziUrLNa47tbUDAyFACe9bibAC172bJiScbzWZG14tjPthy2VQmZbAbrIda1t97x8BsbaIoRFSO/aTMCY7sBJejghQGT6zGvaVStwwkOZF2iz7KbyIt+93xX2m3+7bfXFmlMpZXcZJ92uLaPXpDAgfYxzXYqNF46f21zgKTcZjO6tJgN/TXYDsNbiO9mlR7LjaUUWiyjxqPghTz3zOMMTY2g0iRQ889TTFMshvcP9LC7MEbVWGcpLfuuPfoXP/te/5n0f+Hnuuedubnvbm1l+5iJPPfwDrr/+CMWoxY+9/SD33f09lmeXMUQEJYEodDhwyyv5k9/5KH/4n/8Dl5/6No8+8TBPn53lg7/0Ps5N3s6t+w9w373fINAtnnruDP0DZTaeeYaLi8d+dEDVacJPvO1NPPLA3bx53y3Mrs1RzE8gdMSA6ye3dYSBgX6uLM5yaHg3QSnm0cfu5vz5b3P4yMs4dNt7mX/uMr4o8tTDD/Oqt7+bhcce5sANLyPtXWXm3Dm2br0Z7RXICY8ojSh4gp6giCka+rwiqTRUTI7EatbbG9Rqg0zVl/GdYLDWSy4P7SQlbSfkS0Xqaw36qr0Zv2Ut09NXwRkC6bDSw/hFlHYsbMT4AqzSBEGA8CSeALQBY5FAvpAjCAK0TvFcxiNJJ8jn8+SLBeZmsjvx1oktXLpwkXYrYtfICK7RQjTbFKRkeGIL09MzaOnQSYqf+njGIsKASrGIMAYRCKQDXwYYFLYdI7Sg0K3Qt43tYDF6Cqsjij1DpGGOqB1TKXh0Oh0K+SqBcThr8LWmZ7DK9GKdtNFmvV5nYrDI5ekrKCxJKyIf5ihqS6VSwvMdociRBo6R4XHyuYDQaEQUEccdKs6jEHpcXFyl4IcMjQyy1ja0Oh0GyjWMtKStDiZtMOo7nqzX2bVrCz19NfprZaxJkL4j31uiBmw0lnBOoGLFzOIUcSfGC0u4uEO8EaFMii8sqU4o1IpUS2VU1CKOUzQ+0XqLvsEBnLXEkUK7FCEk4GG1wZjshuwRkGAIpEcap9led5dROr6QWJNSLpezrseB50mM0Sjt8D0PlMk4VATOmqz6kgJtsgxUbQzOvTC08TwP0a0QM8S2IDxshr5Yp/FFNxlLOJzTCE9mCwW7+uo0zUDeGYtEIIIMUDfTtIzRXQVLJisS16a5WZUpX6SU8WX3eLwIOLV21yrbTTB88W4sIbJbhH7RYGpzgOTLTCvuSH4IeCUWKTeHbA6szl7bpmbcbQ7EHFY7tFXoNCTI+d3jKEBFlPt62dhYJ1QbqLTD0088TFApcuLkGY4evY0nnnqKogt49ctfRv9IP5dmzjIcVPjge97FfLTK4PgoNSe4e/ksQ1GF21/zUxwd3c7Bg/t49oH7ODY3zcEbryf1mkQrKyy4iKsrbV77np/k4mNP8a2vfo3f/w9/TLMRcd31N7Jz8iAbjWW23XyIxn//DH/+Vx/lPe+4HWv/fwylZp5fZP7SBarlHuJIU2+2mF6a4uTZK5xvPsHz99zP05eeYNiH84vP89CFeyjV5tg2OEjsr3D3PX9L37YS4wclr3/Hbfzzp/+K/uF+cvk+Hnro87QWnme8mEf6AdqDkYF+hNT4oaJgU5yJ8GOfmcYM2CIYiW4uM9wXUqiWcUmT9bWI3lIF40IGigU8z6OdWjqpRRnwcyFCSiKVktgU1dFYPKwDLSVKGWyqUGlMqrLhg1IqO2nhWitujCXuRIShh5SCIAjwAw+HwjlLmAtwLkGojNfFD9AY0jjFE5JABjgDaazQwuELyeLSMgofFytSk+IJTc53eMKS2pR6I6IQVlhpKnK5Anm/QIcczuruSWzxMGiTIBKP5eUpwpzg+KlFlF2lVBgkYY0T568wMDREsRSwsNakaTX9lV4aKmalvkKtr5+hcoX6+hJeKURKHyUFOQ0t3SbQgkpPDSECrLJ4eR8bJdTyRYTwkM2YVx+4AefnyYmAogSjQx5+6GkGa0NEs0v0VMsoX7LS2qCTxHithNdcv5uXb9uNH0haKqG9sUFTKWhK2tYw31gjVyqjWwLVjmhohfB9mp0O9fV1hJO4bjubKIPWlridog1ESuGs6A6LLBKHcJYAiecEvgyIooQ01URxSjtOiLUl1paOMmxEHVpJQqQUkTG04oR2ktJJFR2dkliNthqLRVuN6lIFSmkcgiQ1pKkhTTRJkpAmmk6aEGtDy6RsdN9XQtBJNZEytJUl1o7ECdrG0kkiUqOQgYd2Bm0FTgZoKbAua92vxRUaME5inEDjSJ3BBhLjC7QHCeba8M5a3aU21DUFgdaWJFGk1qGUQeusAzDGoK0hTlOUTki1JdUWZQTKiGs8bZJaktQSGUdHW1qJoaMcbaXoaE2KJXIW5cBKS6wVShkKokPUmuFjf/d/8qV7vshyzuPRR44TVvuoHz/DbW+4lStT02wZG+HiwlXyfpmv3fkNZL7KW9/0Dv7wz/87b3zDu9i36yDf+MrnOXjDYUynxLa9N/Dg577Ih37tvSzUFwhCj9Q22H9oHxVdpWpzHD9xH4898BC/85u/w9TlBT712b/n/PRFiqWAj//9/8PjJ59j/vIS+3bv5Xv338ve/Yc58uafeklAfckKddethzh7/jxvfvUOJif2sLYc4YoxP/HjRzn21AK/9ptv45/+8Qw7tlepFBxR1XHizHNUnaCyeoj77rsTEwUMjFRIWy1uvWEMGVt6S8PcWH01vvGYm3+UYOIQUTskl5bASDAeQhoi65GXCiUHUX4dIfMINFGiWF3z8PJgtESnHTQOmbTI+YK19Talkod0ID2PRBtKQZbv2F/Isxp1CJUEobt3YJ1Na4XEeR5Bd3DlhwGi27qhDZ7M2h6VxmxsNLLqNldidm6OXJDDWcHc9Cw60ThtCHAsLcxfk59k1IBEOZdRCL5PrlSlp1qm2YyzXfOAM1Aq5KmvrzI42EtLFGh2VumpFljOBySdCM9mXJ5vBGnokKmlUCqyc8cOrLlKeWiCq/FxqsV+hEsYHhgm6dQJiyGh9GgrRd5J1HqLdm+JvLMILFGng3AS7Sx56+j4jo3lOmFfjRTJ2vISZqgfrMALAqwX0F8rUPEcIskuwrBcRJo2ng5JVcRthw5xVfpUKGEXZikKyQ3Dg1wnYq40FhlUmlqqSFZaqDQliReQpT0QS6rlPpq2wRYvQEkHqSbpZNxiYhTOKfCyG5aPR6oM1ssqOIzGk36mCgFSrQn9AInDKNXldrJORgofbTP5mtZdfbTZ9Bn+MDeJznJ+zeZ0fpOvtFn1F6caYxxekKkHrmlhLeBJdGoBj8RYhHWoRCGEd+2xm9Ilz/PoJApj4gw8HWjX6b4kixQGKfxrdFfGp3a5W/vD0XpSeliZtdzGbnKs2XMYMmsnAmxXQSDdpnpG46RA4NAIhNDXKlvjwJj/b1NAdlzIKtNuNq0XBHhCEOsEoyw5Cd+56ws88ti97Ni7m22j2xgYytE/2MfhVxzlq8efptNO2fvmd1J55hJ799/EwuI02/fs4MKzz+E6mYNx+8BWFuenOXr0EKtOcvSOozx5/3e53LpCRTWoxymeB//uF3+Zj/zZR5koFnniO/+Ts498n77Jgzz9qX+hVwtGlWHvDTt54s4vMzoYIL0cf/SXf8ChygQn7pnnTTcewRsY+dEBtWQW+OBP7+XBR88xUspRHfRYmm5hE5i7cp5P/u2n2L1vnEb9Es6v0Uk8FubqjG/bx749E/zMuz/L//jErzM/75Pke9i+fQ/NmTonnjzOa9/6U1gds2V4nDnjoVxCI1lH+zk8bamLq3i5XhKvTJiAqYR4LiXGEebKFCxYsvbC4KNUE0MepRShX0SZGO0XMomNy+yW1oFJ2wRSc2D7KJ63GQTRDfw1BikyHZxzrmuDIwMPT+LhyOfz5HIFPL+ANQK0RxiGOKOyFlH6DA72sLLWwAmo1SqkqcbolCQyeATEaUK+XMomrqpJq9XdOW4ETkhazhJqw0hPPxenG7SSDp4Mia3Gi9ukcUJOeEgjsipMhniiRa1nO+cvzhEWHO12Qls1KBf6KBU6XL44xc+99+dpAevnZ2kHgoHeAcYKJVo4isUiQ8OjDNT6rwFE6Pm0I0clX6QRx4T5HEUP1tstOlGMJaNHTC6lklryOUPvYJEKBXSQMFYusmfHOD21ArOpwWhJm4SN1VUqtRxes83NN2zFlHv5zrGnWDTrHDmwn44yWA1KaIrlHNKkLC5eJRgdQlgP6UviOMXPhVkrb7IqXVkAiY/EKIUGsgZNYpIIIQSdNAGXdQiGF1pgYdMuaKof0jgDeN21IMaYawBE9+ducqZZwLXB6ex7rAOVptAFIOnA4BAmk3QB6Dj7Wna+iR+SOkkp0dZcCxzEWbwwBOXw5eZry/hMhMM4g7P6Bc2nATDXPn4hePuFQBkpJcZttvdeF6d/2Kye5a1kISVCWDbXcV8T6Qv9bwD1xR97CJwUWAsm7R4LNCiHai9z+swJFClve+Pr+ea3vsHj378LM2/5xkPf5gMf+nWO5odo+nA51iypZZIopXd8lGhmg5XlOkolTM1cplor8vjTJ1hNfVo7JrkwP01n4Sqi7OjUDdv3buEf//FrXDdxExPjOexFTZx4/OGHPsw/xx9j7okf8OBTT9Fop1ycv8zb3vxGzj/0CDsLPmfXztKT68NWyzx493f5rXd/4H+JmS/Z8g9ssUwtNin6fTx/psOQHETHAYWelPf/6pv42XcepS9f55ajtyMLPp0Nwatvv4PpuTmuzl7kZ3/urUytzLLjuq1cmpni5PSDnFw5yeSBQZKNJj3lbbT1KO2mxZgE37NEXpPnzj7DxanjYK9y5szDBHqV1Ph4SpL4KcYoSj0lUtVEI9BIlBAoEZAGBVKtcH4e6xSuq3cSRqNxJE6RCotLFJ4M0V7Gd0kpMWnS5Zo0npCUSwVU2sFqk23VdDA22At+liEAoKIOnnSUAoH2LFbAWqeJM1D0Q5RSWJNgkk63vVIEaPKeIDAZT5dGTXK5HLvGBxgIS0hlQbZxzrE0swDEBC7ArsbkVAffghIOi4+TjsAUibUiSCJGB0eY2DLAzoFRRCColisM5Qvs2DlBfW6VvkJIby1PoFKS9TqNjXUuz82zdPkqql3HBl2QxCF8jzRWhMJy9sQzFKQa1HQAACAASURBVIVH/eIcJx58krJwVGyOIDGouE3UavF77/hJ3rj/OjamZmkpza7de1ldXKKcF/j1NriI/bVxRvIFagaWWoqVuQVWG2sE8TqLZ6c4UOpl/cwJePRfqT39Pd60ZYDVtWWGB2okcYehviF0kjI6NIxJO6AMojt118LRiiM2Nja6m1Ad0kEadRBWYZOIUDisVVgpcMJ2dY8GK7IBlNZp9vunqsuTGlKjs31pvpe9f03cbknTCDAoFSMEGKNIVEyqE1IVE8cxaZoSd/WwUdTGWIU2MdqkICxB6BEnLTpRA6UStE5ROsLYDkq3EDLFuohOvIE1EUpHGRdpDbo7nEvTGG3MC2+22847m32f1iRKo4xFGYu2jkRprHYYk/G3m1pa05WPaW1R2qGV61ajjlQr4jRBW0OqFaky12RmSluMdpncrCsj0xaMFmjrUEajrUElGong4tlzWG3w/Dy///v/CRErJifGuDR9lk5zhRPPPEl9o8V3P/7X/M3X/5oTxx5gdm6GtatrNCOfQmUEvDJt1aAjEtqpoNnq8Mwzz7DWavLyGw5zcWaZnuEBfC9kaWmZ/kqJQu84wt/K3335G/z+r/4B0ViVsd4x8oPjXDp7icNv/1luf80v8vyZdQYLeQp5nz/71MfZeG6KJ587/lKQ+dLW01PHvviRlbVVvLBNxW/TISLNJbQ7sDi7xMbsAnu3jbDmlmkkKzSbJXpqCUdecRPPPvEoO7bXuHKmzdrqJca29GOSEGvXyMs+esf2s0KFhfgKWiVgCmwYzXy0QByfYub0OVYWFlmOnqMsHKkdZMPPsRaltCNFvRmjpMKogFanTaQl66sN1rUG6dFWEUmaMp80OP6d71AQECnDq97xMygTsp5E/MtnP09BCuJWB2sdYeBRlD5BmKelFKkTFGRAY6ONj0PblMm9O1leb7LRaZNGEb1DA/jO4eclSbuDXwyJGx18l+A7aImEThqRwyNJNSE+rTilXCgwe/UqQmmSVkLJeNyxrY+JrTt56tJxvHwPh7dv5bP330+8EdEvPZ6vz7FUX2cwb5lpRQRSMV6osasvYEdY4ZsPP4Qs+9j5hJZp4GYVsb/IeGULpUqZr3/2C8wdv8Qr3vAa9vaOceXiZdZWl/nQL/47xntznJxbZnstz7OXZsgh6SmXCUxMT+hzx0+8hQfv+h4FrXn1W17HmeePM7FtjIXpdeqNNX5ychKKZWbSddoLht1bBtk1OMTgSC9DZY/J63ZzXf8ITz/5MB/45Q9y+PYjFPdch9g6StH5/OxrX89MSfKWnYfZ+rI9NPfuZWagxpbhUZaeeIja7gOs+BJhwNiIucUVCkDOZu4fFRuiRJM6SxBKBms1mvUNfOFTLRYJjKGcyzM2OkrgBay3WgghMcYipMCqtJtqJElMJuOz2qB1pt1USqG1RWuV6ThtijM6C5fTCilApQmepCtSNzgpQGTttHUOYw2e72GsRhuFEAalE+Io6gb/GKS3Kfq3GG2RQmK0yVQD1iKcxSP7nlSpTKZkDM6BVfYanSE2hQTOdmViDid8pPeiCL9N+ZbLaAIrBK6r17XWIj0vOzbdwdWLA6A3q2ljLNY6NjciWJfN2ZwFa7LqOvsLVkpipSjkCwQGTp5+mp6BHs48e5r3vfeDfPxTH2Ny6y6uzFxFeJbVKzPoIEcQKq6cPcFyfQmvWGOtscpgWGXrwBYC2Wbfni3sHB/lyuVpKkNVXnnkNuYuXuZVb3wt1w2NsbC2ys0HX0kiNMO9ZeZSOLTjRp6/cI57/vGznL8yw7kLF0Fb/u/Pfpa1+RWCKKUcFDl9cYob77iZ+lyLW268nUrR8cY3vOVH8/LPPfedj4yN9eIFBWRfD8dmLtNJ8tCwvOq2A0we2MpD919ixaTMXYrYOZpjdW6ZhfkltIqoDuyiNxfSWGrTsgtsxB5KNSnUoKfndjbMEpc3/hxbB+ks62aO50+fY31pnZ6+cSp5wamL53nwqbt4zZFfwrcRlpTUgwiDT4CQFuE7CMjaK3ycSQicRGhJy8Q88a/fInCWxbjN1cU5pusz3HvXXZw/f4K8kCgV4wWSVMdUwwrCk7Q7LQ4f2s/s4lVUmmlKY52yffdO5pdXGSz3MBM3GHc58sM1mqpN0orp7x0j0pZaM0FL8Hpr5GRAK4mxqUa5TFht0ezYt5+rc3MUA4+SL7hty3ZKBXjwxGmM51EeH2Xp2Rkavma9uc54sRe/oUA65jcaJJ5kxMuxs1RiYniQSzLFdQSRNtSnTrP38H760xA3tUi9UecV+2/k1gP7mT72JGE74sBNe3nNnr2oC9OMDg1woH+IwtVlXn3jLWzrH2S92SRVDW7aOcaB2gi7d1/HnsldFAjYlitSrvaxdds2hvqKTO3fyU6ZZ7ZUo/7w8+zYO8reyT3s+Lm3cqbd5rlmihUx+vwMXz//LOM7Jgl7R3jovu8z+9xpVhsxTy0vcP22cez0Ap35JUbW1+mdXWZtep7itl00yhWipiZXyeFiSUtHpNJlQ0YpSawBIZDW0Og0sVKQKk07jWh0OiQ6YWlphY1GA+sUuC4ApSlWZiHoUgA6wXc241glmaRHGjzf4otMX+kQ2bZf30cZnXVFTpIqc81EoOM0Ax+b6WMFApWmCGtxxoAFqy3mRfZSrbMhkFIW1zUxbDqjhBDZoBSdQZSwCDKJn3UOJy3GGoT00AZAoJ3pAqRhuFqhGHhU8iHFwKNayFHOB1SKOaqFHPnAQ2IIPAlWZ1W61Thr2IwOzMDesWkJ90TGr26uSMG5bL28c9CVRW3KtDwncCbFUxHzawvceOvLeObhB7hh626WAsE73/A6nj35JMXeGsPbdvKK219La3aFemsdvd6mYw3hcI6KJ6gEAY2NDkG1RO+WGmcunUeKAmuNNiVPsX9yD48ce4SR/iKzU/NIpViYv8C6tLzr/b/Epe8+yIWVC/z6f/mP7Kjt4yP/9Q/5u499gtxNe/jF17+ZlakZjh49xONnHyBBsGd0iEcvHGPE7+OVP/ZjPxqgfvGrX/rII088TkEMUsuXeddP/hz//Om72L97C6OjBZ5+/kkGdxQQQYlbJg8xP3uKJx5fYd+27ew6eBPf/+53ObxlKyq5SmHHbtqX1xksFLlhzw4euO9zlMqnyCNQbpFz03fj6ya7hvr44j99muFty5w/f4oh4XH0le9A+LupF4rojofQOUTSIZcoUidRKkUah1GCWIORhiiOM75Ot3jqzrsZsBKnFdeN7SAlQS81QSUkzYThgX60MlTyFcrFjDLIlSu04gRjwMejr1TDIdGRptWMWVxfZbI8yHK7QXthhWh5nabn45ZWWUlXqdgiqdFI7ZhZWmCsNoKSkqFSL0E+z9j4BDkXMjTQS3+tl7jT5PrBAXqNYv/hW5i8/hC1ZofBw9v4sb4Jbrp1P/sndzLqPLbfcCPXFcoc3Lqb1UBQlrCnmCeKW7zy/e/hB5/+ClqE3Pqh9/CdT3ya06tzvOUjv8Y/feIzTJ1bZtsHfp4LnZjFu09wvCCQ73gdX/jbz/GDS1fY9u/fx2c+/23uX7jCnvEd9Hc6vGLXJOb66/iTj/0Nq40Ox5YWWLhwhaevzjK9tsKFk2f5i1/9bY596UtUe0Y4deZpJndvYXCwwuLcLKsXF1i5MMWt27ewceYC737DG9AuIj+zwoSUTBSrDAnB3laA36ozWg2Ymr6M2lhjdfkyyosZvG4v8/k8UGChU0c7i3IOKzyUNrSjiCTpgFUkaZJNqk23FbUm2z8lQXgOQokxAm0sVlv8fAiAtWmmnBASbaDVaIH0gYySiqIIozJ7pe3qKtM0fZFH3mGMJk3VNfBLtULpTO6UwXA3X+1FXvbNqtB1J/fGGqQIMmG8yBQlxpgX8lZtJoB3LstIdd0W3pJ9TphN37zKwFFYqpUS/ZUCvgdGx+RzHtakhIHAtxlxlvMF+ZxHMe9TKebpqRbp6alQqxaplotUynl6K0Wq5QI9lSK9tTLVYo5auUC1nKdaKlCr5Cjnc9RqJWqV7DG1coFKKWQgX6RWzvPwt75IM4j42je+wcyFK7zvVz7IlY11zn/+TvbccSvLa216TZlbjhzBz0m+d/ed/NFf/SXHvnUvykKz08LPCVqtFklsqSvLH/3unzJ3YYXrJvqZmrrE1fUW1ivj/IDrrptgavoCfuhx421HCV2O8cEJVtdmaJgGXgMGtkke/cEjHL5pN//wN3/F6NgIv/sHv0Nvb55doxN8/Z6vcevOHRyfOsO73vHuHw1Q7//OVz8yvnUrq6urSL3G8tzzDI6XuPHGQ5w8cYVcaRd3fe8J6nOK+alZXn7bDRibsG18kpnpJUphHmublHYOoaSiE8Xs3HuYdW+WjSRiy/AY66tFLtdPIoIcZlnx3OMPUBncwRte+UYefvpBggb07xAsNTrUFx5mpFrDJAmEFdq+ot22rKeaRqSItcXFlrSdsL0QEGDZtnWCR7/wVZRQ1KVg/5GXs3339YS1Gvc9+gjvfPdPc/+Tx3jd295GoX+Q2VOnyBcK5Gs1ZpZXydd66duzk6uXpnjdr72bE888y+jWbdSu38b54yc5+t530mg2WE463HTHqzk8XGP/TTs4d2GVcs4xvHUHh173Kk4/8yxH3vhmHnj0GG/91fdzz7e+w1t/4Wf4xjfv5A1veT3Tly7ystFBdhdqTNx6hF/6bx/lsdPP8/bf+DCf+ocv8uxGk28++hhTS3V+4kO/wl9/8mPcN3WZVx46zFijwY2VKu3RXkoTu/jSPT/g6toa7/up9/LNO79P0S9zcNctPPbIk8gen/bsPGuXLqPGeoiWlzhQKdMfJRwcGKA/VoyW8owllrKQNHXEgVKejQuLHNm9m92D49xUK3F4YitHBkfZO9DPwe07uHz/A+wa66U9v8hl0+HQwDDFuM2F82fJzywzIQOipUWieJ1Sp01jYRFRX2NtdY6NTh2d81ivCqqVMj6C9UaDEB+NRhVDVLlGvVxGeEWUSjCdhESBM4a8J/BCHy/wsM5RLJZpNqIMTJXFaUmUJF2QgVRlOlptBJ1Y04k7eMJDpx06SYqWPmsbDXK5AnHSzuyiqcHzQlKVTcmFtGidVaNyc0gjHEKC7YrrhXQIKTLbrATVdXbhDMK90EpDpnfVOuNnw1xIFLXxPQ+lFEqpa4MrnZWeGGeRfh7h+UjfEsjMCRX42evI+ZJCIYfAUMoHWRZEIDFWkw8CpIB2q0k+F6JVSqlQQpkstNtY3aUKVKbBdRZpHWLzfbIBLsZkmcZdaYpzCrD40oFSmZXcWKSwGKkpGNBxkwfuvZO7H7mHD7zlnUy+7DC9W3fxlpsO83v/6Q8Y3r+fm249ylB1lOnnzxNt6eN/e/u7ePquR3njz76LM4+fQGtwUrB77w5caJieXqLVsJhY8+xzj1Oo9jG73ODXP/y7PPjte1lvrNB0mXWgmCshEp+NOEGtLrPRnqOxuMon/+enyAUepx99EGc1X73rW/SFRarjo9zzL9/ExpptIxOcmDvBB37hwz8aoD55z6MfcWGOd7/nN/n2PV9HVtuMjI4g0hqXZuZZbM+zvX8XTqxQKuToHShQKAzw9JNPMj60jcXVOaJqh8JQyNxzs2zpHyCpgwtiAuvYVTjM+dUTjEwc4tiDMxw5dITV5gJnzq7jm3VG+sZZWVxmqDrC/Y8/wuHJ6/FdyHhhiM7SEkEYUkhTKn5AVQsGczkKRlOr5emsL2NizWrS5sFvfouiFJA6Tpw6QRgEnHziGEGa0p5fRrcilmfmWJmepeR7CEBZR7vTIVEaOimDyuPi5UuUUkvabBO0Eop46CtXyVnIGcHS/AKmvszJy5cRUQ7fU1lAzNoyhbamkXaoaI+006KQaNYWF6lIn9VLV6gZODwxQk+nw2Iccf3kQX769pdhLz/P5N6tvKk6wGtv2MPNO0fh2cu86uaDvP36A1w+dQ5Bi221fuYaK5z81r0c2jLEjbUyJ+/5DuO9gj09Zezp44x4in4NW2NJcb1OXwB9yxt0rswSSM3SxQuUDZw89gjKE9CJMXqFfT1l1lsN1kyDhm6Ts4YkbdJorVDxQ67MzDI51ktTpAxWiszNx4wPDbN15wS5sIII8mgsneYGkU0Z6emlGUdMbawikAyMbGFiYoweWcLFinythzkVc7HVoHjDdpZKjvGdu5ncu4e+fB7XbqI2HIkX4EwKvkElCqMhTjRxnGbTcSOQIsQYl21CtYIo1sSJI000RvgZp5mmuDRGyhLOlVGpI+9LnB9TKYRYA76fQ3oByphM4K8zx5W2FusAJ7rmAEOxWMoqR+vwpIdAYJRCJSoLwVGZTMp0W3rP89AmG/JIz8vi1qW4pgDYnM4HuSBTHFhH0A1fFhjCAMrlIuViASkltZ4quXyOWrVAtVigmAszRUtmfUJ4EmU0QS4HAgLPZ2lpkSROKBYKeH4ALkCQyxJbRMBm0Mu1ABgy9b5vA0Q370ASIPCQXROM7RphkjTFBgFogecEZ04+y7ab9vO9r3yZi/EGI4PjXHjoEX77L/4L9zz2PZprK7zjzT/OD+59jDf96i8RL6xx2/U38fTUBT76p3/C4/96H6YYcuHiBbSzaBOhzBq7do6w0fa5fnI/5y8+S33uIs2lFSZ2bmN1uU6rHWGtZerKLNaPuXLpPGHR8fzxOfYcOsrl8+dIjSDWig/+9u/xigOv4AffvJOgnGd9A0y5F9No8f5f/o3/JaC+pGyqE5RpmYh/uecHNKMOr9o5ydMnp2i3l4niJv39E/iNBjtG93L20jQrdUsQlKn172J6/gzFhuXQzbcytLOH68tXeeKpk5w7vcLtQ29ifm6a9dFVTh2fY2SqRDzb5q7vP8HMpRY37xplZDzP9KlF3va6V3F85gw/fvsRevBYrk9hm5rJ7QeQuTLtHs3MwhzSGbYPTRBoy/DoMBeerqNkkfvOnSLUgICwkKegEwqeoLFwFRMnzK3XCfI54vYaAgiCEKMVUdyiFuSzMOqkA3gkqwYnfZpJSr7ZIPYtSUuzoWJkmuKqAbNrOZoFmFANktCnpRpES+sIZfCnIozSpKfrBM6jsapJk5jAaHK+pJ3ELFYdM5eeQ47vYGp+hdZ8k40gZSa12MTQ8mBsoEa5WcSmBiM98spgWnUmSmWuDKSIRpu0mkcbQW+uhPVjXFggMglhuYRODOXhIeJORLG/SO/YVqauXKJn9zaOL84QVgbI9ddQS+ukIqSkArycxW9H5MIiaEdUEujYspCs01MbJkkiqrLGOh08awmFZmZqmqZWFMICNx09QhTCc5/+F9g6wfylWTqJZaa+yuxYynXjw9TGRxm5YTf9/f1syfXhdIRbW0A2lmm3lliaepK1xgYVE7LKADI/jG8zqY8ygjS1WDLXW6Z9FDinsSjKhRJKpdmQROYI83mMtXSilJHefnrzeZI0op6kxFGHUqmEZ7pedWfpaIOvPLzghZZbG4GUAVoZjDDoriyoqTrgZCYPCixSeBRyATk/zNQkXrbK2hOSOI4JcjmW5hYzqZTWdKyjVMiD9Mjn87RaTfL5AutrdSqlIlu3ThCrFGENcZySJhF9pUFmpi4Ra83VeJ6xsTGeO3GWoy9/GSpN8T1BIALqjQ16e3sJvBBjMgcYQjAyOo7B0elEGbd8jV6wONMVAwmb5Ttci9oDZc2LJFQCozPn6abNVXgBzmVmmZIM8cs56msrjA7kSf0NSmmbUhIRTm7l5978Lv733/5l/v7rX2Pb72xn79EbWD9znukTz9IaHeT8fffzp9PnSfKC0eowvlVMX51ldGiAnmKBM6cfYHzrAR5/8LtMbhth5uoFwmovl66cIkRy8NBeHj92DEWOoapP3FolShscPLyT/Tfu4/Sjj3A1neOOyZu4cOIJBvxeyj05bv+Fn+b6g6+mON/kf/zn334pyHxpQO0fvZFKrcOV6VOEPQM8d2qNRx+6SqWvRLOhqc6dY/t4D2XbT9/gKBcvGDyzzvzsAq88coAjr+4ndAmXVs4zfWWZjSDlnb/xfr775XtQXo4//m/38gvvnUR0DNXcdh586gLtRcXpdodaMaQ8mmd9pcHOiZu4dPESx+rfZ31D8IF3vp9qLUDqhB4voG/nGHOqxXp9DVSHh6fPMxEOM99e41/vvpvVbgZo4hTkAu79/r3UcgUSLcgVCiirSLRGWodCYJwlVyjQUQrPkwggsgl5PyBJEkI/JMUglINSgOk0CYt5TJySSk2ofBJfYWKF8XOoOCUMQ4zRGGnp8fIk1tFJOiinCYISwlkqhSKeXKW6bytzV+vUjEfPjmF604SZ5Tn27N5J5CzPnzvHWHGQRj7ENzFeLocoBsRxTHtphbA6Qmt+iWpPjavrS/T01whVgg09YmMxJMiNhEK5h7ZqUWwvUS0ElAOfoapPsbdIJxV0KjXCjZRipUKv09hyBSGqGN2mryCojE8QxQ6vWOHxk08zf/kqrzp6hGdWH2XwYp5bXv8qHl48x5Ytw0T5lMkDt3DbP76F6vAW9oQeVraQ8SrL5y/SjiNWr0zz9BeepBx4hLKJa3VgpI9VPyb0CwSuQLGd4rRE9gyTppkDpyQlnhDk8yA9Ac4jVdmSP+ccifYxNqZYDOg0NUrF4IcImzmnmq0OrVaLku8RSEExlydqR+RyOYx0+FbjGYsTjlQ5fOETRRFDQ0OUiwXiTgvnLFGaksuFbN++g6mpKeLYEAiI0wSEj++HODSFvEQQUKpUCcOQ5049n+1SkhKVGmo9FdqtNtILaEcd0jTNUplSTavVYnV5CSklQVigHUfkS5ky5cLUIvlyBYvgyswSftjD6bNX2LNnOw5DaqDWN4C2mdRK2MzrPz2/yFqjyZ7JvdBVAQhrENZliQOOTOnvQLowmzd1McJuhp50w1LiTkSxXEKnKgsUCsBzHsZtSq5Sbn/NG3nq0bupjG/hkbt+QDsK+Yk7buPd7/4Zzp58lnRlht/44I8zODnJ+ECVXddtpS4dg7VelrViOFfmy/fcybaBAbCG/t5+egsVZpeXOP/8KWKbojvr7Lvler5/1wmOHNhDag3HHn+SJJ9nS98oF8+fZuvWIYYqVZrLEValzC8vMb59C+dOn6M2P8frPvTvefK5R0lX5zkwXuIzn/84k68YeElAfcmtp3/60U86HUi8giJtHefYY98hXjbYgmSjscqOLb14QxWmnlhhsF+yZXA32677fzl78yDJzrPM9/d939lzray9qqu6qlq9qVftrdWybMuWFxnvgMHA+LJcz4BhYIDAA4gB32HwXLiXC8EwA8R4bMYLNrbxInnRLkuW1Ju61a1eq6uruvbKPfPk2c/941R3i7mBb4QzoiK6qrOyMjMy33y/932e3zPDdx97krEtOe6ZnqYa1WnE8+SSCp1Yw9ZT8uYE3zt1nDfd9wBf+Oo/cGDbdpotFy/UUIU8eT2mPx/g5lOm051UF2N23n8X33zuK9w1vZu37L2XdW2Q+994FydffJEdB/Zyoltnq2sRBC5LVsqTX/wu6z2XM6+9yvyrJxl1QzpxQCwgl8vhbQqw/dgnlZuEqSjFJkWKzEkkdI1UaPihh5SZ1z9OISLFVBqh76GbFoHXQ1cafhhhaWJz3pRgKA1faJt6xjSzv3k9nLxDzWtg2hY7d97AmVdPU041PrTvAEMD0JvWWV9pMu6M4QYR9c46A8Mamp5ilAzUlQrNnk5OL/D04ecZqDV588wga1Jyyfe4HGmsBi0KZh+R6xFGGnbQpNptECWC2OsgDAdvfJhyopicnsK1LRwJB3ZtY7A8QCokuYE+tu2api/0KIwNk+oKszxKHEpCrYlhGPQ6AsMuY1kSz+0hUjCNlNCt4TV6BI06Z0+8wsTWGayyRn1ljrDbo76+Qq1Wo3+giKUXIUkJem0CTRDnc0SRR6/RoqDn0ZGMknCm6+PkFJHuMG/somtNYaQ+aaqj4ky4n5mFUpQusS0ze95Ng1arQV+5iKnpGLpDEKV0Oh16PX+TN5pgAI5tEIQRYUpWAEVCf6W8uXzSsg5OZXQlkog0jrAMjTgKieLrYOarX0kSYuUcpNLQdRNIyFsW6IqXjxwnjBIKxT4SkVGwrpoE4jhGIZAKLEMnDDNJ167RfoTXJogDLKEIpCI0TUJfsrbRxFeSIAgQm64uU1dIAUnsZws5IJ/P47aaFPIOhlCkaAShi9ANoiDGsHTGp8YwEx02ASypyJ4PNjtSkWaGhlRlOqmrsiqJolqvMTQ0lH2vCdJYkiYQJwGOoQi7Pf7jIx+nl7O48x3vQdSrnL10kVvyEzRVl/WNOV585jFuvOUQeXuMx595il/9xL/nc3/9af70b/6Sj3/w5xD9io3qKp1uA4OUkdEBmu0WMslzpbrIr/z0Q5xcO8PSeZ/lSw1uu/cQh48dJiKlP9EZ2buVoNvE0E1mz1dxuzGdXg0hCwjVY/f2Gbr1kFtu3s/nvvIl3vnQQxz9wTPs2LeFr3z5hPiRCuoHfuLH01tufYil1hOcP3eEobzNzHSRbz35Au980w0IYXNxY5Uzx9Y4OLOTmeEDtL0aC0sXKTgl7txzB69cfokXnj9FRZPsPjjJLftvpN4JePnKKn49x7nFi/zqR/435pY2OHl0nsrNo5z8wXfYWZR4lSlum76XAcPnsimYO3aY++57L5VyP1euNDi0b4ojr8xy07138NWTx7iha7O0dpmhfdO8/LUnKIwNYypFu7mO77lYxRwihSDIAu9Cz6fpdkDJTGfY8xFRgKEMau02IIkSiOLsBWrpBqGXWR3jOCaIA2zDJgo2tZBxkL2ZpSQJA+IkIlSZLbDVaW5+pCeYlk5fsUDghcxsm6S+usZU/yBOrY0mY8buN/F6KVVvG8MmjAw4kOpoRo6BiSGKI9sYtgq4KmGgkEfaFna3RmqXEfkShApiQdBdRUsl6UABFbYJfR89lATtJmutNrKbsB7UwKbW2wAAIABJREFUKAUdTl+YxUgEqS5oVjfoRCbuWo3zr56iLC2qnQAZx3hunUAEOCJAArGRI86bdOsNpie2ENQ3KIuYlfYq/WNbKJiKMA548KP/O2v1JrpISaMUw0wh8sk5klqji2FVsPpLuLV1Sug0OjHVXhPD0HA0g9S26CYK6fUIlIM/eRf1roGZJiih0fG6mw6lCEvXiNIETWaLnjSRCGJUmlCpVKjXm6RpmjFwpSJOM4eajAJ0TRElMWYuR7vdIlUqO9ornTiKMHQT0zIoFHIEbg/X7TA5Mc78pTlmpnewsLDA+Pg48/PzDI8Nc/nyAuW+fjaqa9i2jd9zkVIjkQI/jPDjGKFbm3CcTDWg63oGJNEhjSJ0TUOkgthLiNOMJ1AwCrixi61rpEmMnkgSTRI7WkY30018P0QpnSSK0Q2BoYnNY36KY5tEQUgssiJuaipD5ylFGAZMTA5TsguYmsq0tnFyDQ8oUGibsJmrkJarx/5Op0MYxuTz+Yy8n/iQSkzTZnVtkaGBQeKWy5Fnv0498pmY2o5XX+GM0SJ6fpHX1pYQfh2DDqvdHm95+AN8/5tPYW8d42fe9wH+9E8+xejYAGtJyKGd+3ju8UcZHBzk8tIC5cE+zp86z5bJQd540zQvz51D8wxm59tEscAplrB0g7hRY3hPtmy3rRxnXrnM5OQU6xtXCBLJ8GgBEaWErYiZyUE6acrZU2coOUUiEfLamY0fraB+8j9/Mr1yZoHh3Q1evfgS8ycXObB/C6nmIFsL3DhzgJPnV9AGNWqzS5T1m9m+v4SutdDiMb556iWqZ5b5+Z/7PY5d/DZ6fplwZZEde3YyV+vh1C20vq1szHmM7d6PaQ6i92t8/xv/kxtyAb1+nfZyjzfe9TbOih2o2gsszZ5lvN9gy753IN0BuriEFrTjlFIX3vzA3azQ5I8++su0gi45IaGTef3zysBPQizHJogjgjgmZ9rZESWKIEkp2Cah7xPJrKOorW8wPj6OkpAzzYy/KiBWmXRG0zRsq4Ab+CQypNNskcQxuq7I2SbKtLIXcQphGBKnCW4UocKEscFh3F6LlbVVHvnN3+BCYxkr1NDp0j05T5TkSQdiAr9HWF1BDzLIRre7Cp0It5tSXe8wMJLi5HXSnkfPUKxcrjJWsVlqB/QPzHD05AWGBnOIoQpurU3OlmjSpGkq9jo2wrZxJehEFDTwcPCVjtFZIS0MU0194nYHM9ah2MdGo05ZKTTTItR1et0Gyo7JFfJ0NqpM9RVZT1MazQ47Bga4dPECH/zZf0UahqRRG82RiMAg55joluDS/Bq2M4CpS5QlMfMVzi2t0uqts6NSYVxVqIWSahoT6HmkXeTSqsfgth10oh5aLDFshRQaruth27lNEXpWANJIoWsaahMmrhkafi/Etm3cXg/PyyhPgd+jVCrgOA5e4NPt9FBKYds2URiSIgn9AM93M1LZJqfU6/WwbQtNy2ailUqF1dVVhodHWVndYHBwmHq9iqkb9PX1Mzs7x5apKTbqDZpdFz9OkHGKprIl1FU4uSQhjSM0LXstxgnoKmFkbJSu65IGPkXHpFlvMDk5xaUrc7jE+GH2OwqNMIyRSiNOsoRhKSWaEIRhmPGEAd2S6CLFNh2EkuTzDsOVIrbSM9E+GXdCJJIg9pFk91MTcpNZHBJFEaZtce7cOaantl2ztkK2xIrjEMs2iL2I/lyeT/y7n2e1scbu229lQCpemH2FN4zfxEapxMvfe5RWY5GeSunGkpJv0LYEInJ58E338drJo9xyz1u4ZedBHn/0S+y8cT+f/8cv45MgkpByTiE6ATccuoMTTx7nvR/9ab79pW/gJiFTE+M88+zj3Lj/RtYaLgjF7Qdu4fSJk3ieR6PTZWRLCb+3ylvueoDVuWVOzl9ieLCP5moNzXB46vvn/8WC+kNnqJcun2JteY7EsSgUHLZt28HE4BCvnn2FosohY8UNM9tYdBcZGa0wMTTG4ZNPcdOOUUgU3Z6LUANsrKzQN7WFVrOGYVuMjI+x4S8wkxvmicOv0N+/F6/Xxk0UjeOz7J+pMFx2udyO8d2EUl+JwUjxxS8+xs6JCqvzdd78wV9m9ngvs9GFMQU/xYsjvv/0E2zkU5qdNu1endS2iXsunkgIDAvT0HB9lzRN6bku7XYLURX4PQ/TyFihURThJQlxAgPlfhqdOnEQQhQSC0kSp6S6ni09pMjeAFKSy5u0anV0ocg7Npahoxct2s1WhhNEEEYRSulYlsXy8iJB7GLogi9//n8QVdcwkGxIF2PdZ82PcXuKiq1hJVUqJRC6wXDOJHJiJvIOuRGLWEDadsn1FQijiFtvuwXPXcKaKrPRjXnne++m0a2xHHqMTIxnNspcDsv1WO/WEYkgsm1a1TpjVpGwELBSXWNItPDWOxgDW/FbDRzTodmKIGpg2WW6fgvD6me0z6RTXUYjpM+A2GtQETZ5TSNqVhkqGthhg0AKEk0QuWDndRpBB11JAk1gCklqWrTiNnrQo2IWmcwVWF5Y4rsvnyCUAWO3H2T84C5Sw6E/lyIJ6cs5OIZFIlI0pTM6MoLb9QjjYJO/mUVsZAg6DV3XcYMeSZrS6Wb2Xk0H09So9A1mVuFN1mdfXx9pEqEJiZ3PkaQpvu9jmRU6nc4mRAWKhTwAYdTDRCcIPfIFh14vY8h6Xoco9CCNuXTpIkJIVtfWEErLvPJhjFSCKPBRwoQkQtN1DM3ENLJlGkLiBwEjpTLLF2bRbJ2+QpHF+VkqA8NcvHgewzLQhY5umiRJkvFipcILNgu0rmWAlSBEaRnDV6QRgRdiOxqx72I6NnnbIm9YBJ6PrhtILYOqXIWu2DmHyA/wIz8ba2zyAJIkYXx8nCTKTmzo2rVxgFTZh40hdNbX17n19js4dupl3v2+9zB38iy3SXjqO8/y7n/zcb40v4RpRdn4x0voJinVepPJraMESczM1DZmT5zk6JPPcOjQQQ4fO0y32+XA7Ydo1FZZX55l69Agx068RhL5nL5wgQ+994N89bvf5NixI0zv3kGjVsfrpdi5PJZhcPcdd7C4vMZLx4+zurrKWx64iV63w1NPPsfQ9imazQae26S7Uf9hJfOHy6Y+/omfeSSoNvjIh3+Ttc4GLzx+Am95jgfetJMLF2t0WGaxfZb19R6D/UW8eJX8lhL+UpPxbSZDcoyDb/xx6pe/y5nGAq8+scqEOcxLRy4T6X3MvjTHbbcdwhntIw18fNMkWTzCjimDp197leePL/JL7/xx6uoc1fMr7N3roOe3EtkVCoVh7NQnyveRhD3eNLYda7ofvB4X/B5nj53GC3p4cYBAYNkOyjCIohjNsqi7XWTBwU1jEk2hOSax3KTtmBpJzgLLILYNPJkS6gKVM1E5hTAViaGIBEg9RXcUwpZIxyDUQDoGRs5Cs3WqcQyWhRuDmySoYonQUEQipdtuI6OQEI2hwQH0AUnP6uEHCpn6DM5An+gyeeMwypaYwwXigTzLboBh5lgXEXZlgBfX1sj3VWja0Cs5rNTXaBKhLI31tSopMc1WlW7oY7oxbrWOEUm8Wpdpq0i3V2d5YYEdY8Okbg1lm+gbUOwGFAoWpy8tcsPYOErZuElEGKdoQUq5MMh6N8Iw86BsWpFGWze43GhxpdXFExZXZMi6hH37DlFPIzxp0QlsamFALFIsw6BsVWg0Xfx2nSvzCxw+fImFhstir05UGeIHF5tU/XXcVHHnofcRNhKUEuQtg0RIbBRaYlOw89TXVzAMhUozPmyapCgR4ehg6xqWlc0BlS5xTEXOVpRsA0NEJIFLwbGysUToIZIA3++itEwcL2KfYt5m+coVBvv7CLyso7VMHdPQMIwcfX39aJpBPl8kl3Mo95XI5WzKpT5ytsHQyDADAxWkruG5LoaSFOw8SiYYuo5tWZiGgaFrECXoUmQe+DTGyOWotxpo+RKpsGnUWli5HH4oCNzMEutHCrcVE/oBUhhoykCisg/0MFsUTY6N4Xa6GVfCNDCExq4tI4wUHZqtBrpUNNZWGSz3E0U+QRQgNMXpM6cYGhii1+tdO/JLBIaug8yahWx2LDK0ZRKjtAweHadRphBAp9dqcuLoCxB5eKFgaPd+osUWzbzO4tk5/G6HVAmSVBDWfEoTQ8TdLrpyuOOe+3n1yGlW52fpdVaJLZOFxSv0ggjPUzSabWLPo+wMEBg6/nqHph4QLjZY3FgnikPq0mVrqcKW8W2cO32ON953N//05c+ztLRErbWGk9fJK5uiPchGtYcwHfrKDrcdOIgITH7i537xR9Ohfucb336k0egyv3aGMDrN3TffRH7YJLzkcoUu75razYXEodRJWey2mKjcgEoMTrw2z9axbUS9CmJoF2cuHMGdW+XA/ht4bX4NN4nZ8+D7EadXKe27mcef+zrD5TzPPPdd3nbLTbzYVIwrxdDWcTqyQOJsY+HyUS7NnWDf9oNoxd3UoyK1eo0k0WhXW8x36jRqDRZqNUr5AifOHUfWaxRMGz8MCTwfRRa9kB13RLahFNkxCAGB28OxHSKlIaWFIx3cXpeyZaEJC1MIwrSNJhyiKMaUEqEpLNMijSI818029egEQYBEYEqNbqNJ3syRRBEGCYaQEMfkDANLU5hSMZjPMy49motL7Jycxs5BJfLI6ybnr1xgamqSPk2nz4rYqMWUdA9HFrnYWGH3RD9VkaecCjaudBFGQo8cNorZlRC9lBKWxzC7TTytwFLgEph5klTgJTGGWUAfGcTtJLiaTZToOIbF5UKRer3Nlj3bSIKU9aBHXVWwtBwqN0hcHqLZ65JoJp6uo5uC7nqVyb4BAs8HYTFTGsDqrLB/5wwDJY311QL15hpzpxfYqC5SXfPZqEYUt+6g7mgUR0bY8GHr/kMUKrfjtUZIel2WW8vc/Ia3sFJdZte2behScvIHL3Dm5CtEusbgSIVW2CNvmshNXqihWygpSUmoVPrxA58rVxboH6xgkJkCNCnRlMWJE6dp1luMjo6gpELT9Yx5a+gEvS6OZWKYBmEUksvliIjJFfNsRu6hlERXcPbMKSbGR9BkimkoDCHQBOiawHEsNJHiWBqWCZHvIUVClAYoEiQJIo0hDRBpRJL4KAVSJFi6xCImTAOGynnKjkIRAjFCJpiOjmbqCMC2dEaGKpi6zET4MjPXa4ZGmkS0ahvYmqRcdHBMwXjFIXRduq6PNAyMvM1wfz+GZVOv17BME1PTMGRW8NOMaZ1liPkBURxl8CBUZlwQ6SY/IKNZqVQgNQ2BhiCllM9xZWmeo4sXOTi2jTPLF/nHz/wdY4ODiMowd913Nwsnz/CBn/950obPz/7Cx7j/3Q+zNDuL23M59uxz3P9jb2NjaZleq0PRztFtdui2u0SRz+/8hz8gFSXGCsOEos38hVk2ui3qtSogKDgWQgtpbaQ88MA9LKw9j1kSTA7OcOnsZbZNTzJ75Wxm+NAdpma20e56XJi7iO3k+MmP/IgF9Xc/+fFH3v7Wh9k2s5s4cmmsbzBgObRaHi+dXuDuPTfzxe88x203bsfu0+nUPIQ5yfjWW1hcP8v5S8cpK8HYniGk0eW5J1/jpnvuI1eQNNfq3H7oPpoqYduO3SRJiRv3b+XS7Gn0/DgnnjnBbQf2sNbcYD2ZpFV9ljNnl2g1I4andhHKFCVj2u0uwtIJ0oTl6gbf/uZj7Nu1n6V2g43LC6RBQC/0SZVBlCr8OMWPASURpsrkKRjIWMOUOkRt/DggiSOiyMewcoSk9BRESQBRjK8kaaoRquzY5AUhYRCjDIs41Wi2XQqlfBa7okmsQpFuktKLEpSdw3N7aJqBXXRoRQFR4LHjhhkS6VFXgpWgzQYuseGw2lXkt2zj3NwCbmqyFOjUdUgDHU86mKND9BpdtEjQ0BVGqYxPl/7+Edb8K0jLYsYZIPY9zrcVtrLI5cukymRleYmhyTH0oEsrzLB3/XkDoesYScoYXexYMDawg061QV+3hhA2dtwlqi/TqtdpdRt4vZiVuSWay1VWzs/SW+9gWAVeOX2F1VaM3jdOjVHOtBImRndRGe0RO1vIDW7F3jpN/8wuerk8ric4fuwc9a5NO0pQ5RzDexQHHtjGm265n/GBfp45f4SJoX4uLs5x/uKrnJ+bZWN9henpbTiauRkXEmNqOtqmMsMxTdIwgjSlVCqiGQZ+GGDYJqhM0qOZOpPbtpIKUFomgtdUtvDJ2c6mPTTryNI0wTI05FVXv5ZBx6UQDAwMoOsaSmWxPBAj5dWw1BRdV4goo2ClaYKmICVG0xKUStB10LTsy7QUmibQdYFUKZquYxoSiEniCM1QmKaGoSukUui6Qukyi0WRIYgAw5BouiBnmxi2wslb5GwD2zEROghd0It8hKZnCzfHIZWQd6zN4myj6TpxlGIYBrZt4/lZ5lMURUhdy/YK8urzJpDXElo3ISkpZFaAlBhJEiWMTGzl5m0zfO0zX6KpQqStMXfsLJ3+Er/+q7/KC489yWrYY2J8Cn92hUe//wR9KbTSHqvzCywtLfDGu+9jZXEFkSoszURoAjfscvjYES6dvUBfPsep107i5C3q9SpbR8dJeiFuL6TZ7OL2arjtNuuNBcYmcqwtrPPR9/8Yih4bbogvTfyeS6Ppc9PBA+zfczNpKHj4vR/60YT9k9u6zM0/TanvIV568RyH9hU48fJrbH3gHu42Nb7z2iVKOqhyQl+nRP6GGdJCjiMvuSws++w2yuy69RaOv3aSgzvfypbxBMvUOHZkkdumh1lPdHqJpLEYECQWRqfN4M7bmT3+CnOdHp/6k8/xUx+9By15ldlTG3zwvR8jiPNEkUEUhaRCR7MUSRihVXIc//Z3mTt1Fqsb8eA73sUfffFRdk6P0amv0Ou0sJxiBjhNUnRps1qt0T84SOB52SZVgZ1qRHFIuX+QZttF6gIzUqRB5pm2yBMlKU1NECQS07IJei00ZRLFKVJIdNOi3fEwrjpqRI8oBk1qWOi4iY5IJG7bw498RCqZX9xgaFAh/RwrVxYY6SthqAAtCujN1tldKaErQdMNKSYB22NBrFLc1RrFXMqAFtFRDlLz6dZSrN4iObuf2WIfS1eusNDy0AOHpVixHvQIkhQRpZy7/Aq+kydqNAiFwLJzSM1HlzalLTO8euksD9af4zunF3j7G94MVkpFWVTzk9RaCUOahzUyDP0Wo1sDqktw7sL3ufXmXdy4YwZNC7AtuPltt7K0WKVQGef80gI1c4i1+hpXzpwnya9w+013MD22j3smZnBjl06nRaWYoyKH6Jzr8EJrgzWvwfTALn7jVx9hfGgUu1elUDCJpcHcuXMMTE4wWhmgv78fXddZX6/ieR7apkBeKYVSGkkYZaGMcYQUiiQOGegrZuMeTRAmWQROEgdYmnGdiyqyYqU2Y05WV68wNDJMHIdEaaZPvbrckjJzD4lNGpZmGpv6zsyeKqSGYSp0w0IpCKMsu+mfZ9Sn12j8SZJkWtU4IiZGKR3IoEJSCLTNmb4XBvi+j2kqlDI2u2ftGgs2SRLMnI6hZSqXkJhEKkKRopsauqHj6NomojDDTSbRdWZwu9NEN3SSJMK0dI4dfYUDNx0kCEKCKMi2XOnrqKBJCiqL5kZmcTJuECCVQYkch189QZ+aYd+N25EdxU//4i+RzFYZ2DLJWrPJh3/rdzjx3acZevIyzz7/Ap7X4/bbb6XaqvHoE8/gdlpYlgVIYiXw4i5mz+bAnhv5/vNPECHoJjGhkqw2a/Q8nyjSSNGYmiliWxr5/AC7pyYYTJv8j3/4J0LLBBscenhhj3y5n5W1ZS5enOXWW+7+YSXz/4fYP7WX1Nf5wlf/HwYHRjl2osru6RkuvXIR07aZOHgL/VYRkQ8ZToo0ooSV5Tl27LqHnTdNkks8Ts2t4vQiXC/AGhzlq3/3N+zYdwvL1S6qYNLpddDkGDgBIkhpxiaH9u3m6HIN2UoJpURLfO65/x14DOJjIROdMIlouCFa3MYa7OM//MIv42wZQjoG33n6uzz2iW/zsd/4TQ6/9CwFPCoipdFoYxhFlARD0+jrK9Fr9bBzFq0442k2lpv0jw6ztrKeibLbPUSqIS2DqNfGiiw85WfJAsB6mEGn/cC7DiHWjSzm2kvRDJm9yTYL+eraMoYmiYMEfTPCwjFhtb9Lda1Bq9YlTjvUl7scrQc0UwlOjiQNMriHMJHSQPV1iIhAJCxfXOeue6Z5/Omj7DiwHdsfYHjcIYggtYbZNVbBnDbY8CTT5hWGYgvbKaInGokwOOcNcUNvhahQYrGtMyw6TG8RzC5JZjst7jg4yg8QbNl3C35vFktU6cYTmHbKUH6FvhGDZm0IszBHaWSYtRM1XnzpPCNbpsgVbG49dDd/+VdfYi2y2LflPHfvHmb/9hFKt2yn6tZZqQWYCpxSSGIUcZc8Tp1KWNh4nl23jjC5dSe7ykPcO7KD6tEjDBk+YdqmVMyhOQ4PPvxO7r73Ph75/T/iwz/7EY4cfomXDr/M+9/3QW688UZWVpYybaaCKMpA0ipMNoPrQMnMtZQocQ06TZISxyFCy+DMhlJoQuCFIYVCAc/zGB4e3USdZHbOq57+q+F/ulSkm9bOON7MfMood1m21aatNEVixGoT4Zdegz2HKdf0nddA0UmMsiR+EKEphUwTkk1wShCGGEpi5XObiaygaQqxqeQxTQNNyxIJdKUItSxhNY1TNCMj/2uahhIZE0aROcGQ6abVNoNUh5vRKGmasmv3DuIw+z7LIhRsLvdJRYoSklTKzeczglgiLIkKBc16h3e8+2HMiQpPP/8UhThH2XQ49eIJXluc44777uHL/+Vv0St5Xn7se/hl6NMcnnzqWW69904Sq03JcOj2erieR6lcJG13ubJYJXRfRLcMLs6uYGk6xcEcbuBz9733EKdQtIb4x698mqnpMcxAcux4g3DjCpUte3h1/iJ37t9PY/YMfTeM0qg2uTDXYvvOG+gbHf+hBfWHyqZ+8qf2pMvLTcp9NuXCFK+dOkN7fZ033b2fdULC0OSe/TexkVthv3BoalB3bHrLOV69uMjQrpuJe03cM9/glvvuYrZpsmNoF+cXWvT8Nl40hBFEGAWNmAgzcFlqzfHhu/p5em6I1+ae5oahSUw1QbcXIUUXpzACeoJKfEhNlpaXabpt3vfeh/noT/wEff1lur0OcStk8uBNiD6dxrnTVPpKhF6KQR5T0zFzGprcwDRNglCnG1gkmMyefIHde6dpd1yKOYck7CCkDSom6IYU7TJB6mNISeoLIpESJx6J0okiF8fS8MOsewh7LoZVJEpCwiTGMCS6FtJNElSSRyaKsFllz03bmF9dZqSUY3l2ncJkHrsyxJb8OPl4livBFKWcjYq7zM4u0T+0B7v0Mp3uBKGZ5wdf/iYf+PB+/vbLx3nwrW9gSBtheqbIlXOrqP4ZRoqreJFiKeljq1MlNhzWag0srUCUGiTaAHY75NTlyxzYs4v1K4ssyzol3eBItcVDUxEnrnhsL2znbK+JbL1GI7+VVk+Q9DJMntYRGDkP0RYEvR6X1s4xPjjIx3/h5yhVbBZqAb/3m5/gn/7hmxgDLdY7EqeQI+nEHJ6dpzq7SGFoGK2U49DUAElvhWGrwPmXTxN7LXLBMm/Yt4fVU6+StHvUDMm3FmPm6z3I6UQ5i+H+EQZzRUglgUhw2z43HNjDc88/zcd/8VfQyVxvySbLU0p5TbsKbC5uMvvo8vIyIyMjWUjj5lskDEMMw+DI0aPcfOttxGGEKTWUyhIeEnkds/f6i+I6KT8T8Gd80DAMSdMsTTV53fvwWoSI5BrMOgvfy9KopdSIBQiZImOBZZhouqTVbmcYQK4H8Wkie4yk12OtpZToSsvA6SKrfonM+KdKboJXpMzytzZvJ46v378s2SD7t75J3ko3f5Cq9JqLSsD1FIJEIBKIVcYpSKKYuRPH+fO/+ku275xmQwYc+6fn+PunHuWz/9dfs/Pm/bz47NPcsvtGvvatr7F77w6+/cz3GFMF7nnf+/nrT/0ZelGjbJUy400S0/FcRibGuTi/xEDJxpCKv/r05/nUJx4hxEPpgttuuosTc2d546H7WV9a5ytf+m9YFY1uw2DvrVt4/KtHKA4WePO7HuLFJ59g245BHLvESn2V6akdTO29i0/+yu/9i7KpHzpD/b1PfOIRXc8Rey7T5TK7x4eYueMWjr56mAOTu0jCgPL0EJcXq9Tmqjxw3xSffvz7JPMXGRnej26N4HY7lMaKJM3LlEyLjmfhJwXaocIUAX7kkTdMdAnIlImxArMrXVLPZqx/L7qpkfYCSrtH2bhU5dUTRylNDbFn2zRf+sxnMPpNxgYH+Ls//ws028KsVFBpwBZTR1UGaa23aTVdel4KsY6XCrpRStOTrDR0NjYSup5BFEAQ+RiWpK8yQCcI6fUC4liCXgTLRphljGI/Zm4Lej5PkBsksAxKAyY4JYg1Cn0Wlj1Gs9ui1N+HbY1Q6htByDzl0hC5XJH+UgVpVKilGht2wvaRES7bERXlcKrhkiBoR33s3rUHo6Sh9e2iODhIosPQTXuJjElmbEFqjOGZJRqNHnv37eHyUsjk2E5mV+ucuTiH202ZbTR4/kKVU7MeK+uC7740x5E5lxfPbHD47AqnFtY4dvY1zvUizlXrnKnOca6+TqOWcKHTpLNYZ25eYyUJOLJRo5H4rHd1LjZ6aIHESzoUpEVHRMjYoqmlKKfInfffz97t0zz90lEs6XDjbTt57AvfZHVlgwO33cm3Hn8c3S6zsdphZrTInbtvYHq4SHu9xtJTj9IXthhvLDMVb6Cqpxk18sggQDgaVsUiJ0PuGjQ51Bdxz+QY6brH4R8cZn7hEsfnl+kpi6ULr1FbXCFJdO65595MCxz4mY0y2Uz1jGMy9KkiTROUrhElMX39lWwhddV+LMAwdKRMGRkbxdB0DJlxBKSNlDbQAAAgAElEQVRISeIwS0a9yuFLU9IkIr4aY5Imm3SpDOJ3tXBmtTNBKK5tyIUUyE3Ls5JZ56ukyBZsUpDKNOsihUBXmc023ExGRVcITctiy4VE0xVKUwgpyRrIbNYrU4G6OmIgya6rVLaQ03QM3UCSUaolMpvTXv1dAYpsLqoAJQRSCVSGBUBtfsnNIaomJTIFXSqQEqFJVJrQb9psPbCT9SjgoTvupxp22XnHbTx0532cPXWC9uISz596iWePHuXPfueTPPfqETprazz+gyP85s/8El4cU11ZZ73ToaDnSaVOJwopSAOtXCav2zz3xDO86/3vYu/UNJcbVUYrwzz07vdgaAY5YTBY7ufo2Vfxuz4zO3bQbXcIEmh1a2gypFLawuFnX2Dn7hG2bB2nkZq86963/Ysz1B/aoe7ZY6b9I/1MT4xRNjVq6xFdOU7BClibe5K9t+4nsG/EMCWFyOXgrgKf+c459pc1RvfdyHJ1GM8JKFspq+e+ze4db+TMZRenNEokQvr6ykRhSm21SRhHjA4OQ9giKeWordfw1yWV8Ty6FrEmQr71p/+dkbtvJmy5nL10ERGEhK5PqjTyfYMUhwZx6xvcECegxXSLFU6dOE3qaORMm5yVy2KG481PTamRamCZBnHQI8En6EjKhSL1VmatlMSEceYIMQwT13OxjRJhbw3T6SPs9dBog1EmDlNyjqDZTCmWTDy/jRAaSSrQ9AKhl5AmISpJEFpMGZ1V2eA9WyYZfPMbePKFk5gypu01kTmLpJs5rmRuDL+1gaWLLNpFemh6SNtNsQyTWr0FaUIkdAbzJez+fpaWF7hxZJSLtRVsp0BfeQRhBpy9coF+s0TQ9ag1u4xPTtHtVpka30qtVWXr9BY0aTIwMEZuyGLacfC1Mrmki54vMV6Gy1Uf0fJZb3dI61WagY/falEVLvW1FkvdDaKuzWBJQ+Z0Ln37MK24jlXMYzl59DTkb//rX1ObvUjdq2eaw/lZbhYmre48o+O7OXrsOAdv3IGVeqSBixeVWN6YZe/kKJpmsVJfITALBBisryzTl0REJiQba6xufTOfS3SKC1dY8iKMhs8jf/z7dLrdrEBt7uYXFhYolUrYto2maa87cmfCd6UUuq6TRtcz669aS0+deo3de/aRy+VoddqbXWD8z7vTVCJk1n3+8/fZZiyPuE6+T4W8dp3Xz1CBaw4l0qzDFQJkKkhEBni+et/SNM00yWmKsSlrujqCSNOUcBNUrW2OOCTiWhT01cd1NUtNsVk8N8/vCXFWDF/3+K5OSq9qXrPrXf9ZdiVxLXdLkwqETqzFOEHMmR+8RGPPCHcP7qYnBIKYMIViIsgXLH73Y7/MKxdOsPNDD/P2iQOcOnGE//zFv+GjH/8t3rf7Di5dmeOTf/D7RFOD3GD2sRF0MR2bf/ehn+OPP/e3/MUff4q//uM/o9ra4MypE4zetJtckrK0vMG+fft48aln2XHwJhxb8f3DP+De++/k+ceexsiVMmBSr0qpUCR0XQrFNgMH9/ALH/ld3nHo4R9N2H/3G+/g4sVFTp9apL8vYHCywLAWsaV0H/fvu41muAHFCfKlAf7+H34Lw7gZM5dno3OS6coe6IU4KmJjPSLQR1lqRuiWjjQFfXmHJHTpNZoMjfWTSEHcbZGSEjSqmDlFReap1Zp4QczhhVPUDA3/6WOsqxDh9igFETPjg5C3Wfa7+CshPjE93abqwUbSQhomRqFAe61BM+ogTAMhtcyKalkYlpHBeRMQep7CiElto0kgEuyiQ9D1SURmzVNSUio6+EGPrdu3sbzWRkNDqhymntGLmr2AoakBrixfplQu0602yBdz+EEbu5yj3XaRuiRxIxYtjaDlUXzP2zlaXWR+fZacU6bS308Q9giwGBgbodqqMTBuI0wTvx5RHt7CBbfHtq0T6L7P5B6N/lSSqoCBXAnRV0SYd9MvTGbqdVQxh+/W2bt3N88/+jzu0iUcrUD/zC5OXTxPKALOzZ5Fddv0zs9i6hZnk4SOphjeOsB65DBpSUaH+7n9zvt5+fhR8pUKDS+gL58yUsjjjA0xjqK1PeAtZpF2fYnID3j061+jKyUFPc/46Cgnz87yhjv2UXvhGYbdKpMFhdMUdHNDGKJHsNyjYy2w78AOLs5vsG/fdhbnz2PkJONyhGq9gbQMVH4cz29QiGOMYgWfgN5ijdro3Xx6cRGVjrC21OMX/s17ePSL38Bt1XGjCMtyMJNsVjoxMXGtWCVJQooiFTFKSgylkQiI/ABN6pkEiCwzTKSCbTPbUSLm+CuHmZqZJo2zIMhrtTRNs5lqKjJmqsoKbIa/izab2BQ2AwATrgOkr/7+1YtSikqlwsbGRtb5kXWputSyOa8QqE2gtRQZuCQrkGTpp6SkZN1sVgg3AwEliM0NfZLEJFH2IeI4Dr1ejzQFmUp0ITM5VJwlpaZpikIQXV2aJem1Dykprhb9rHgrFGEcolQ2oogIIY2J3B5//9n/Tun+W3nTvz1E+8o6DgpLhJx66TA79uziG89+j0/917/kT/6P/5PnvUf5sbc9yE++54P0DQ0xMTPF9IFd1M7OcsPb7+HC49/n+bOnsEcH+c3f/h3EzDAVu0BntUlpuA+/lOe14+fouW0eOHQ7Dz5wH63lBU7MnqWSmEjTpOe1CHyfRmcJ0zRRyqDbqLF16wRuz6WglWjOr8Ghf7lm/tAO9d43j6RekMNSJju2CrZMS9yLWxkY2s7Mtu1cXjvLYusiw317Ob70GDtL27ncWmf/tCR0bqfZ0PBDl77iKDmrR7PbIYwllmniul0KTj9hLyImJVdwCHseZV2xb9deXjx+jsBN6Rsy8H2Xx149zqtHj2MFJlV8xr2AbY5FYER0E1gPEkSuTKoU+Xye2bkNjLwkWlklckz6nT7cNMi2jakkigPUJiVHKInX65IqjbEtwyxcnieJIvLlUgZ2CCKC0EUpnV6nnb2gcgW8KAIhsMwcnluHKERoJdKkCcIA3aSU5ug0Wui2TmJKBgcGCHoewmuiD4/T7zZ4/2//CmvVK2y9YRc/eOYZZOAzunUbRx97jU6ynm2RE5tG0kEVDbaUt3Ds7BH6E0WunMNOdAQ6nSQmtW1yFQfHtpmwcqS6IvYgVypy5xvu5bFnn6KQyyFjj0LZxBA6w3aZVKUUCjn8VINOm9SLqVarNENFzV0g39Vo1C/SiXVEr42SUIrbPH12gVvGRzl8Yo6J4RFGhyy+d/Qc3/7jj/PGj/8pP/nOm/ncc7McGNI534Rb9uzEKgk2Ls7xH//1z7J9Sz+XT52haVvk6nV2Djs4KsfF+fMUc2UCKdgIA7ZPjOCu1VApVLsummOiS4PJokZz9RIdiiyOHuSz1YTFuTPcPLiTv3vqG/zSWx/g61/9Jolm8Bd/8RdZ0Uo2t87/y0VKrnWGr08HTV+XbipFShSnKGVc45RenTMK4v9PAijieobT1eTRrHN8fdd6PX30alf5+oRSz/NotVqMjo5eg5Ncvd7V24VMfrX5lzanxdcvme9+s8NM0mu3n3nzk3+2TFtaWmJwZPja9eSmqiGLsU6u2UqFEJlq4SoQhmwRh7i+RLv6GMIwpL+/n1qrja3DkHB48MMPgWHy8Ac+zKGDB6mdOYfsy7N48jwNlfK1L32FQFPsnZ7h5fPn+PV/+ytsdGo4KseWsa1sqfTx+b/8L4ztnuGJb3ydd7/97fzR5z9L0U347BPf5Gfe9h5iN+TWe2/n1MoCv/3f/pz2p7/NJ/7x75CtGgcPbOOZZ05z8NZbaa8us+HWMSxF/coGpVIJdBNDwEa1y44b+/G9kNqGweXLl340L/+b3nkgXVm6Qi4H06M5bNPhY2/9NfRhB2dilLmVReYvHOPYC98jtYrs3D6DUxqjQMjlZkCYrFMubCHwBeWCjWaUuLJeJW/adFttAtdkpG+c3Tt2srq2QKu5wc7xrfSX+9ByBmZOsr7W5OjZi3zhW9/j4uwl4nKRcpTQqi6yd2oLaatNvZuQH51ktdmiiUfi+RSkQRx0mMwZzFZXGRscZqPbYWF+ha0TU1y8eD6TruRsEk0jCAKUACF0TJXJMFIhCKSPFHr2f0phGCZpkNDzPXJFh9SPUInEKlgEIsIPDUJ/g1QUkJqG5qS0Om3iMCJn2DiVARorNVRe4V5Z4V0Pv52lC6/wpn/1Efr7R/nCZ75AIgJ+7V9/jKeOnaXTXmXn9l206y5+IKkYAeNaHs2SkOi0vBYFCZ3VecLCIDJKWOl18NeuUNvwWHY84jChvRGw7jXoDzzmNmpMbtnO4tlTbN+1h7mjx3nLO+/hW8+9wL5te9llRzyzeJ4/euiNfOj//grdr3+Kwlt/Df8rf8i29/8BT332EX7+1/4Tf/LrH+An/vALPPeH7+Oh3/gML37rz3jwwd/gf3723/M7v/QI4/fdxdmVBZa8Mh3pcnphjcHyEMrK0/RaFDXFL//U2/nQ297ET/7Wf2LEFPz2W24mFiuMl0sk3QBN5jhx4SJbDEVSHqC/UCJ1JGsbKyRxTGFomle6JV4zbS73ugyoAtVmjQ2vyg1BzFBllDsf/hAa0G02MnmclKRXj6Sbxebqcf/1BS2rTdkHcHKtVG1qTzcLZRpfjzLJCtv12xWQKVdfd0zO/n294FyNMU+SZPN2r//tKIqu3a6mZd2oEtdv7zqH9HXk/9cVWtJ//revjxquH8MhuVZ6X1+oNV3PZPhpioxTUpktYGET2fe6+5mm2VRYIVAiew6ujiKCKEKTkv+Xs/MOjqsw1/7v1O1Fq1Vvtorl3jsYbAM2YHrvCSEQUoCQkF5uwk3ITS+Q0EnonQC2waa694pt2bJsq9dV3b5nT/n+OCvZZOYy892dOaOVpVnvrs4+5y1PwRTYs2cPxZXllBfkEe2I8L1ff4fW7m5Ul5/vffd+sp2dxIJOPJpEXJQ5seUALYlhdmzZyJSFsynTJNSZ1UwvrmHpgrOpDIW55EvXIxtZIskhzESSoYEEl335Ona++wnBcAHzly7hzddfR3MquDQJr9/PNUuXcTzVSdORHXilArpjKQqcTgbiQwylRrjrhi+x+p13CZYVUexRGIpF8YdVjEGD4UyQzVu3/N+WUj/65Xd+UeEvZlLtBPwlISx9hI59RzH8GX778IMc2LueEsHL0osu5nhjH5JTJ8+fx8lDLUyqzWPTxk9wOf1omoimqZjaIKqiIksyommh+jw4ZRPMITLpQabUVjMYH2D3kRMcOt5Gd2SAIx3d7GprZ9uufZiWhJoy6IoM4C8soU8UUEJlxP1+WvsjZHQNdzrBlKCTIn2ESRUFtDa34BBMJhWGiXd2k+/zEu/ppK6skCK/GzmbpdDjYFxBEL+eQrU06suLCCgmgx1tLJ83lYHONszoCBOrytCyKframgl5HeT7Vfpam5ElE90wiPYOoJpxivwBuluaKPCpyMkEiqHjkx3Ig3FMUyM/m2G4d4hbr7+UEw0nWHbLxVxd6MdVUsKnjz3N1QtKePnx18mc3MTA9s20H25iaP9W9n2wFu1wO5v3fITedJz1hzdQ0dnBm5vWMNMv8fj7q9m2bS9//foq/vCnf/KVW1aQ2N7ExJp8gsf2c97CyUxLm0iKm2vLZZJpg4fmBknJLn66OMjWLU288Msv88JDL7L91Qf5nx/9jad+8VVefPh5br5qOce3vEdNfRGVmSgNwz3s2dTAs9+/lqf//jzXX38BH69ZR8mkEmodXbyxp53f3n4O72w8zoVL6mhtOMaKhWcTygxwtK0Zwe2nuDDMnmMtPPHSW2Qsk86hAaZecR36qS6CooxqxEmoFsXlJYSrQuw43MrwgIlZVE7HuElsTuazI+2jWwliuANIsklrew+V02fhiKa49NwLKRg/EUE0yWo6YNrpoZaFhWGHzudUPzb73k75tATG1ECmaYJlJ0GJo6BjWoiSbV49Kq0URQvDBEM3+dwYlTOBc/SrkDMdsU2aRylInPF0sLBdpGQZp0PF43aRSCZzHE8bqu3xgR2WByAKsk1tQkIQJHvWesbcUxRMBMFCwK60RcGyY1sASbIXYpZlIsuSLZDIZsE0EYUcMd+yxr4apoGhZ+24F9Meb1iGOfaq7ZTX0fcaZFWhoKgYv8+LJAuEgmG6W5to6ekhUFDAkiVn8/Yrr5JXUcaRzxrIKDJLp8zn4df/xfaX3uXRZx/FNDT6Bvq46LJLOLltL9d/+TZuuf/rnDzaiDqSJBF2YqU1ZAS6+wcwBIFsKolDkJk4fxbv/Hst6ZO9MKeMlXVzSCt+Yv199PT3EUvHSaYSuGWBzogd5FhVUYZbFRhfWYKuJWg41YjDqXPHHff/r0sp8X/7AcC8GRcwODKMpxCisXZkIYx3nEQi1oXhyFA/rg5fGJ564g9MqywiX3Wy5rX3OXzyGHsPnSRcVElt/QSKiwtxemxTW0n20D+UREMiY6aJJBKcHBiiX9fYe/IIHfFeimuCHOw5wTHg7fc3E4sk8SIyedZ0pOnjmXn+Mnz+PHTFTfNQP8PtJyk3YgR6W1hUXsxIxwnqa6poOtpOScl4JtVN5dDBw5SXVFCQn0dJSQm6pRP0e5lbU8m00jBifIg81cWKxYuRUjGID3P7Tedx4shn+FSJ8+fNpEg2cA9FWFpXybSgB1dXD+dNm0CVaBAcibC4qpBKWcY51Ma158wnbAwxy+clnExQLImMK/QhJvsYV+ni5stX8dGna1hyzgJ0wWDPwCCTBvZy3ew5/GSWiifWyis/vAbXkMVH31mKvr+NDd8+C/PUITb9+mpOHNzHpkdu4OTuQzz3m+9w6NV1HH3oMq4t0sju30IpJjf6OujetpmfLczj+KkufrpqKU9+uIVHv3shL7yxi9/+1108+synfOPK2Tz15Hq+e9sy1j3xAnd94xK2Pf4c5XWTmepvYX1zH7ddOonn3mnhB9/6Lv94ax2P/+xmAgGDgLOLAVHi4rn57NnbygNfvZ6H/r6Xr9y+nA/XbcTJCMumVVOgSXx/loPGI83ctGAaxVqaZMtxTjS2YApuvGoeTjXA6qceZt6UML3pBB3OQk51q/R2iTR2OQgsuZ6102bwZJ9JUyREzBdEKSthYGiQaCKOL1SKU9SJtrdw0XkXgEPBki3MrImupe3MJux23jTIHaKdG68ZmLoBpgCmHZw32hLbbTFjIIEooBn6WHa9aY4FeyIp8ucqyDOB1MpJnXMkAJsulTtGfz56mKaJaYmYlsiOnXtYs3bd2Md1tKqVcjxme18vYVg6pqnnsrP0sWXRKGAbY3HRdutu5hgGo485Or7Qc+mxkq17xchdWETTQrIxGNkSUHPWlEg2jUqQcgss08Sw7Iob0TZsT2sZBFVEy6ZJWjq6lqVQcJNXWIhTdvHxJ5vw+wqInuhm//Y9HDjRyLrdW/jjn/7EZ8eO89Nf/w+PPvI4j/zkd9TUT2Dzjk3c/dsfEjt+Cq/PSao6SKmpkBcM0NnWRnltFalklMaTR5HcEgFT4PKl51Fz7nzWvbee3sgpuo/sJZRfhtOlIBhZnLKAP8+LbGUQlAyRjl4+azrGoeOnUOQCRMPDSCz1RZD5xRXqay/+/Rd5Bfn0tLYxuXY8yGksQ8HhdVJVWYfPH8Lns5BdLrq7W+nsSVAxeRayz8dA/yBTps4gnTboHxjJ6aIF4mmBdNrE5/PTPdCLIMrEYnFSiaSds2NZZFMxvrnqOu774S8oqh9PS/txgqE8RoZj5KUzDLU2Ig20cnaeh56Go6ysKUfqbWblWYvYsXc7E2prOHbkEA6vg/xgHrFIH0G/D9XpIB7pI1yQh2oKmJZOXUkRHScb8bpUptRPZiTaQXygj+qKMk4e70PLylSPG4+eipFJZ3C6FeLxKC09/YybXENjawtyMI/K2koGe4dQFJXquvEMtHVQWVdN/3Cc/ECQmGjQEx1meWklzSfaiET7uerGZXQNjzD34iV4utpZ85s3uOvmC/l403PMH19DoLMfygup15rYHvNzWS3siHm5UE2yunmYq4oFDh0+xbVLF/LI+9u5bYqL8dXFPP3uUb556yre/WAfNbMmo5tJQlU1FKLRORTjulCKLScHWbVwFs+8t5U7r1jBw2/s52f33sxvH3mNn90zk788vYVv3T2Ll57YztzzqhnZvo9sqJjKzGe816VxeVUdC85ZwSvPvceyW67ls0+a6HD7OGdeIWs37eWH91/Fgz9/nz/86ib+/Ng/uerq5WzbuosJ1QUscMc5MGQhl+UjWC4iaQ1nQQGnjhzkT3/5PfG2bkakIF2+Kj4rKuQtdA5lVVKuItxxFy6Hk5HUMCkNXC4XljbI4JED1EoyxsggK5avRBQUTFHEMu35n03tsWeEjFaZoohpGuTlBUnm8sPKKyrI6hqZjO3wZNOd7Cpr9AAQLBMQPtdSWyI23xO7gjMNu0IdzZcyzyDqjwLuaYs76z/A1+Z+mqZFYWERFRXlCLmKFk4Dr+1TmnN3smx6l3CGR+mZz08S7RhrKbfNN7SsnTGVq6JtPqmZQ/scN1YUcm78gi2CMs2xKtqybPqUaUu57IVV7rWPsQNyr9USbdGLIks4LDvR9aUXn2fP8WNMPn8pf/zF/7B8+fms/2AdzuICps2bz4UXXUR9TR29kV6mzZhO76Emtm/czJ8feZiW/laOHj2ANhiltbcNyzCIJpK274HbhWCYqC4n/UPDFBaW0d7XxyUXr8RKDpFvAGaGTfv2MNLXRVdkACurU1s/DkmSKS0uIj44jNvnYO78RYwMpOjuP8nkqZPwBHVuvO6e/1uFWpTvYOXS5RQGq/ELIYaHY5TVOjja0EJ7YxNtkQbWfPwpekyksCxMSe0cDEclLm85kyefg5ZxkU7LBIJFSIobQ7fw+d24PQpZM0WeN8jQYBJJCqBbLmIxA0PLYKpZ3t7wHj987I9EDx7DFU2ixU3CxUUcPtXMQCbF/HMXcuroPi46bx67D+xkylkLeX/nFmpmzWBoaISiwlLmzV7A4HAnPq/InFlzyeoxvHkefB4H0eEhpk6YwGBshKhpUlBcyslTjXQcj1JZPYf2njgDiU7OW1BEeqSFkYEILqdCUPGiWya3rDiP+MkuJhVUMreyiv5DTVSFPcycMQFRU5lQX42YNUnEBwmHnQx3tXHl3JnUVgV47tH/ptjnJm0ILL14JYHuPqZqGtXVFkWhHna0qKy8+QE+3tPJHcvHs/rTIe67cxkfbujjW1fPZ+ORw/zgzvm8tGYf133lAt56ZwNXXngWaz9tpKA0zPFTLUwfJ/D6p41cetFCHn90C3deMJcf3/8EP/zeNfxj7U5uuG46z738OqtuX8Wnm1Yze/E4Xl27Gc+4CbR2pOge0vGXV3CgK86Xb17F+h0Rvv+TVfz5mT08eu8D/OW5J3E6G/j4SDfTqtz8efVOfvnA5Tz6u+e59vrz2PjwGubdsYi0UMhwp8Ily/zsa+nn+p//mrt3Brnr5z+ntUVj6ZeuQ1NSTKuuZf6sBVxx69f464jBq4KXRlcBUiafokwBWlKmLd2Ld7wXb10RTUNNmFonJdEBCrszCEMGvUMDnHP+cvRoDNEQMS0BRBNZOA1YBlZusSKMBc61trZjWQKypPD4k09xqqUNKzcjNU3bG+B0VLQ5Rra3LGPsME0dw7Qjq20METFz7bw5FhNtL7RGq+OsZpBJZzH003SpM2+KnFsYYY4BmSjYcDZa2drmPvKY3+uZc83TogDGXvsojUnXdRRFQbDOoD1ljdMXh9yc1DAMOwhPsOwDCz33uPa01EDJVaqGCHpOHTVq2ycJMrIoIVsCDkRk7IvaW2ve5ZavfpXrbrmVb9x3Hzs+2cLfHn+EC6+9hrnnnsudN9zGlOIqxklOtq5fz47tW3FPq+W8y1cR8nrRJRl33KQj3ofkdeCSFNK6QSKeYTgWp7m1jfb2bvzeAMf6WvErTtasfZvnX3uGAxu28M833yCR0RgYGMIfzOOcJcvIpAycqptjx5rQEenr76epYT+JkWGC/mI2bttES3vXF0HmFwOqYHl5c+0bjC8P0dvfwYy6OXT1OHAoMo2tbWTa86msuZSKglq8nhLc3ho6ujUEM4+sJpJIC8RTEOmPMTAYQ0tBciSBrqXQ0mlESSWvqJS4YZE2JZRAHkNZnYFohpTi4+O1HzEkWxj+IEejEQ4ePEh53TQyVoBPdrURL57Jtn2NTJpxFnu3HGVaxSQ8/XHk1AiLzppFU8txEpqAx19Ac3MzXR3d+P1emk+eoKZ2PK0nmmhq6WbK3CW098YRdQfT61wkEqcIB/2cu3gJg4095Hs8TJk4HsHQkLwCyxedzfaG/YQqAlRUhdi9ezsLF07D5RNJ95zAL/eRjfUTTAwya1ol/pEIJ955kXNn5pOIpHjqT39n0uJJ/OCme+keSFMbDLL6kSepXzSJti1byAuU4Dr5ElvNBEqhnw3d/UycOJl3TrYyMz/D+p2DLKhzcGBvL3XjAxz8ZCNfvmU+u48lMVIDuH0q0f1HmDurjHJnJ2a+yuBQP8ZED8GjO/jo4BCXzK9g95YG7r18MS+taeS2W6/mwCe7+NLtK3nm+d189Uc3sPG5rSy7to4Tb21iqDSNMZChKyDj0g5ydMDHmjc+4IKLZ9O04zD1F0zCaDhE40mF8y69nF9tOMot8y/n4aeeZ+b99zDjkUE6ZlzGA4/9m8r6PO7/y+8ITBzH1pdXs6B2ApHGvRzYf4RZ8xdT6yqjqqCQTDpGStVwhCUKvA5SB4+idZ4ktXc30wZMMg1tHDlxEqm8lItuuJplU2bw3ouvg8OFJNp8SlGwKy3TtCs+wRTsgD3dsGlBoojT6bTBw9RZuvRcQqGQTfGxTAwELEnEFCW7ysq1zKdB9QyFE7ZmQBwlzIuMcUB1I5sDV30MpEerN8PMjun1zwTB0dRTm1plg7Nt5mwbOmuahq7bo7SMlvvezOZa/xaLsyoAACAASURBVM8vvgRBQrEEXKKCbAk4nU5cHvfY8xtt3UfHA5ZljcVam9jjAtM07VEBOWDFnp2a5C4sho4CmLqGZWQRTYOsnrLHDAhIkgCm/R5cevll9PQNsei85RSMZAmoIucuWcI5cxZx86VXkUjESFtpVBP27ttFrK+PdQe28vPv/Qjd4+TyC6+krz8GgkJSM+iJDCFmDJSg27bqlBz2+6cqFIeC/Oq3D9F9vJkhyeSIMcBIJIU3rwDJ48USLd595wO6u4fIDxUzMJQgMjBCKD9IR1sz7Z19bN16hOKCelatuO7/DqiO4nycqkzl3IlUlJdQXFEHUpSaWdUkLB1BtggYFpWzlpFJl1M7robSkgJETUGxBHTBQTxlYgpORMlHakDELQapLqwhTy4gT3cQzMrU5hUwa3wt0nCKUKAQn7OImOqhpqiK4f4BOvfsYEVVMeFQmOMth3CpBlk9g1aUR6Mrnx2GQqSggrZAkGOJKPWLL2R/Qy8DHRHm1U6m9/hJBjtPMHtyPWJSY/7EKaTiAwhSgovOnkw62kIoLLBwbgVayk1KF6iePo7BplbSaoaa6dPpHoiRSqUYMjM0HT/FhPpp+Hxetm7ZzSWrrmZoOMJwX5LKukqiHf2M8wl8+6YFXDpxFnfeehV/f+ZxdmzaQ7HVw19eW88L727nmhvuoDoAqz/cSe/S2Zw/sYJnthzhpkvms+toK9NnT+HI1gMUz5xI9PBhPNWlIAuYngBKwqQzEMDdl6ZJL8DZ08r27hF2rm7hvm9dypHPhrhgSS371h1k1WVTWLtmJ5dcNI8DjWlqV17CqU83UDAtn86DTQQLQ1RFGziajLI0pPJZRxfzffm8+fFhbrrqMv76wlYe+Ob3+d3T27jhh9/nsVeaqL5kEY9tj3LuzXfxnfcOccm37ufKl4+z9E8PcvbPnsVYci7fa27mXS0f3/SZ9Ozay/m338TaTz7mxt/+FPpSaKeaEawehtub8ZoJNKeHtpMd7P7328zJ6owc3M9QawsVukU5MdSRFB8+9Rxu1UVLpIvZs+czuXYK0+uq2Lp5HXlukXNnzSVhmBh6EtG056cZU7eXQJaAoZtgWIiSvXjRTcOe/YkWsiziVGUUSQDDNpaWEBCM0Vx6C9G0t96qJCPlZquiYDfNMllEwQBBwzLTCOhYmEiyaFeDsogk22R/SRZOH5JNNTJMmxVgIWKYoGdBy5hkNdCzNtvANniRkGUpp9yyl2hGrs1PpVI2h9TU7ZQB08QywMgapHWDtGEwnEzScOoUB5uOoxk2Id8wDBA/Lyg4c2wwSvaXLfu+aNoMBwzsxFd0ZBFES6evv5VorJV4so2PN7wBSgZBFkhldbKCRRYdSxK55JIrmFVQhyhJFFeUUlhYxItvvsKTTz1O0OMi8tlRVlx6IWX14zG0JG/88RFKasppOnmMebWTKa+pIW0IlHnCYFjE0ynisSFGosO4XAr+oAdf0E2B6GDX7h3kl5QQj8SREybukMK02goUSaW3Z4grrl7BwiWz2LJ1OwWBAF6Pi77+Yeqnz8XtEykd5yCrwT8ff/kLAfULZ6gvPPfkL2RBxekowqsIaGKcgc4+PF4f0ZiHdOwYhWV1GCMBnP48RNlE1Q1cpo437Mchqgz1DFJeWEpvSyfjwiHcikJeIMikCRMwtCzhYB6SYDv0xFJxrJRGKqsj5od4/oWX0BIpygrzOX64ESkbxe1wkkhmKZk0hcMnThIOFxAbSmBV1/FZzwhmSSVbj55ia98geu1E9mY1jlsqZsUUWrM++vByTHIjFNQTmjiHfY1txCJZptbOZ2tvlFh6mHPPvoCmpl4Ciot8d5jtO/YwobyQr991E01rP+WspeOpq6zho7UbuP+e2+hvbsQcHuDJlx/myf/6G6+sf5GasELPQJoFZ9XxzuaNCC3NXPr71bRFOrjwhz/lohnz+NVff82/nn2awwND3HLrj/jyn18j76yZFATG8/Ebq3ng7ptY98lGLr78Cho3vEv5xMVImzbTW1+D92QDQu10xFPbUEuqccWP4pswh7UfHeLr37yJXz/8Gl/+9h089o9XueW+7/OXR5/irp/8koceeYNf//or/PixD7ntxz/jwceeY9Y93+Fbz7zCyju/wb3vrqVq4fm8c6qHI6VBDvV62e/28GY4wP4jneSVTebdfQ1cfPM3aPEG2bH/FL2OPBJKEe/vOcjSK67hiadf5pbv/4Dn//hXFtx4HRv++g/OXbacvY2NpE71sn/tekqLw6yYMg3NEsHjwlRERKeAkk4xb0I+H3z8EZHGZoRIgr3bd1DmCGI5ApyzYi6Kz0vPiQ7Kq8uYNv8cErrJ8gtX8NmufZi9ETyTpqGINqnclCVkQQIBREm0t9mSHZ4nikJuuz1KWD9zKz4KLKNbbpslMEqYH1Wsf257b8lYuQp4dBtvmSaWaUdPi9iR0vaM096qZ7NabiRwujocrVQFrFxss73Nt39uji217PmphWwKKKaAqZuE8opwqG6yWtbe1hujFSoIor2+Ek0Lj9dDMBhEMEatBUE40+3KzIXvmebY/dHfEUXRzk0zDETLRLJk0HQclsmWbR9hKB6SMReJGJy/7GKG++Iosoyup1BEEUlUkRFIixoJM4NgmER6I/h8XmrLK5AMk97GZtZv3siUsxdy/EADR3u6mF4zkWUXraRm3iyuXnI+D/359zz+jyfZsP4DsqkUKRXCoTAO2cHIwAiyKuPy+rlw1UXkaTJNnW0sWbiI5o4uovE4vV3dOB0+SgsKaTrRSEdrN4GAl6yRRJItnE6F1pYeysoqmTN7Lrt37cHlU7n/vh/9rzPUL1RKObwOqmpm0N6bIGKqVPkKOd74AX0dIeSAxfwZt5BOVxMZjpOM9WL1iZB00D+SwOw1yBfdLFlwLuXVRdSUF+N2SGR0ndauDtr6B4lEIliiBG43liQSlU3cDhXVADGZ5b6vfRNJMMHSeOR//oAVHWD4xCnGj68hsmcb1X4f/e3NuEvCtH62m5LKSrTBKEm3h4rx9aTTSfo6R5g5Zy79Q/30DPUwfnI9A9sOMTKzgOPN/ZhSEVJhHlu723GmVahYwIadrWTUDLcuXsprH73H2rdfpuvIQfZ0tvOnDZ/y2uN/QwoXs27fJr7/9du4+6v3YOoZ3n7+RV7du5q1T/6LCfV11C2o5XhzL0uu+hqDZZXc/qf/RmvpRxmEzwba+OfjzxJpSzFtzkwe3rMVye0lNvlKfrjuFeatupsfdfs4ptXjEl38q0Hib/dfxRWXvs2PP3iFOSuv5b9e/TkX3HAZ/3zrp1x3+Zd49MXbWHtM52v7MmyfOZcHWkw+qlmEcayP6Mpb+c7qQ/RWT+UPe0bYJ1cxIRpin17AjZVT6OlwUX/O5Zx4/DXuvfMa7rn3y3zrkWd45YEfcun3vs7mvz3LBXfewB+ee5rrb7yJB/70J675xf08evM9PPTmv/jeZbfym/ee4zc/+SVLL1pG27GjuLxFRONJ9qYGYc8m5KJKyvO99Pf2UDe+kP2xJgJSgoKCagSXm0mSlz3bD7GnpYeaqvEU15QiFQRZVFRFIKLg97o5uHctxcWlBFWZWTNm0hMbQhDcdPXF+P5PHmTbM09xXJDQdQMMHU0FycqR9PXTPNNRwxIrJxc1Ld028BCk/+B5nuaNjmbR20F3NmiOLonIqZc4YyFk8zNzev3cYss0dWzeqf45ldZo/hWcAeqm9Tku6mmeKKfVXZY9mjBNA8Xp5KMtm9E0nSUL59gOW0inH08wsUwLRRKQRBlNz9oXGxOMHAfWsixkwY60Fiw4U/+Q82FHMA0sBGRZJKulkJGRRAVBzhAqDDBz8VSSgyaR7m6OHt1MZ3cjIh6qq6dyqKODZcsuoLu1FUQBbyAfybRIRoaIDQ3yX7/5JX/43e/5+0N/pENPcOP5i5nfOYem1AC1NfV8fGA3X7/tK/zPH37PrEnT+dXPf8mJtjZMycLvc5FJacRGRgjmeSirrGDnjsNcd831fPD6Ok52tONUnGSTWVZecDGJ2BB5/kKuufpKvva1O1AUB8PDw1RWFbPiouVs27ibfK/GcDxJf88IlqEjODxfBJlfXKE2HG76RWHFJNxeAVH2sX3/bq68fBFqXpi0VIiIRJQYkmRhqE5MS6U0v4Kzly+mOzqClkghqhKaw2DfwT2cau8mms3SH09iCCpDgzE0UUSWFYyMiRsFpyDjcrvxeUI4ZScIFiYyC1ZcyPJrruW6m29BDvr45P21JJMxXP4S/IKKqKr0DgyQzSaZXViKNjxIarCXUL4PAZ3evj58Xh8jbd1k/A7UpIZpZjGtLJoDYm0dFNWXER7spzio8PiPfkbD0e389r572L17O/tb+rjpjvv447P/ZEQJMmXpJfz1r3/n9m/+nI0nOtl8qIUv/ehX3P61H2PUz8Y3ZxmrNx7l8c9O4Z4znT/+7XkGNh7A6u7GSkd4+LF/0WhG2fjm61x61jI6Nmxn1vmz2fXxS8QG0tz4pa/zyYv/Rp04FW9xPXvbeymYcw4f7zzOsnMXsOlwE1++/DLWvPsSM2YtY92mjThr8ik563w+evvfTF6yjF1r3+OCS65m9bNvMPuqFax9czXjrl7Om08/z4JbL+bNF15m8a2XsOXVd/GXlbG/rRlBi5MxTbS0Tr4TWvu6Sagm/a3ddMeTpBp6MCWR3uOttK/fzvKrr2Tbc29RMXEiZkMHpzraOH/mYj5c+xoBtws276C6spwSU8ZTGCKcP46UlkRMD1Hic1Iz8SyaDnUxkMhy1oxKeqI6K1ZeRdfQIEsmTaZc8VI3cSaeikKkYJi9n2ygr7mLOdMmUzd5JjFDQzJBUAXeefEVblmyhHaHA1QJSRfQJTsKxeYICQhCzvBDyGUq5UDSBs/Pk+ZNc9Q+VwQkTEu0FVamkFtqnZ43WpaFaQlj1aOuG/aYQchp8HM0KSPHr9I0jVmzZhGNRu25qHFatz/2/+eAexQ8jZwW36ZZgWVYWIJg82UV2/C5qLSEkrJinIqCkXPOEsQc31QU7IA9RPsSIIooooSAgCrbkdGyKGGZo+xZxu7bFwZbLSVJEpZhg7MpmLhcdtx2ZKgfU1FQJS9iWiYdi+L3KpxsPEJN1Ti279xLPJmi6VgjgwMRJtTX0trRQUleAScON9DT3cF7Gz5EFWXe+mg93/jRA8imyKKzzsYcipExdSZMnMDxjTsxPSpuh5u+wSG0VIJUNk0mkcClOigoyMflELEMk5lzp9DW2sGnm7fiFCXa+rtxqzK93a20tLbS3tnKyy+9CqJFUXEJiVSMiqpyXn/pPaIDQ3gUL0XjKrnxusvYsXU/eBzc9/Xv/K8V6hfTpv61+hez5ixgePAzMukMimqiD5eSkEQGMjqW5kSTBRQURJcDRwby0hpSrJ10nm2GmzEN2kdiCIITUZJwegIoqgen4mXS5Jl4CvMZSCeRHU5kQUXxOpEDHhw4UXQJEwsVC8VQkJJZzLiOr6SMq267ndvuuocP3nqMkbYmjO4+QqaFnBrhRNsxzHQCJZtmqLkZfSCC19CxBvpwpeJ4LZOgqRPr7SAdjeIyBUrD+Qxt3saHzz6BocfYsXU9915zO29u34BlyaxaeQEbP1hPSVUZS6dN49nXn2XywskcOLaPju52fvyD+/nG7V/mrJuuYNlZ5/DCq09w/fkraD7QRKL5JFvf/RBvtJ2i8iB5XjdP/Otpll9zJV37jrNz7zZUh0jegEbHiS7cwWLeffZFBl0+pi6dwVvPPMMNP36Ax3/zO2rPOptdW7bjqazk2AdrmbjyKk7t2k5p9SK6+w6xcuZi/vnnJ7nzu/fz5lOvUX/2OBo27sNppug7cgrHcIZUt4Y4EqOlo4P6onIOHdnI4hW3sun9F7n1K7ew+6MtXHj2uaz+5yvcctMqTvxrDTPmzaT1/U9ZuHI26b2HEYkxMc/H8SP76DtxlAIzizMg449G2b9tI5VI1E4MM1JdSupEOx5/FTHZR9bKMjFrcLyjk3HjJ1NYWUdr5yBzV6ykIChSN2c+FV6FnZs+pSDsp6m7kyXnrmJ4qB9R9HKioYHezg5Wr36Xq268nYxgoVgClqSx7rW3uWHxQgYLChFVEVkHXRZz/P3RdUHOBMSwqUz2csjKgacNeIahYxjZ3PbezP27keNXWpimboM0p1t+u20Xxtp1IOdcL2CaAroFWcPENCz0nPHKzp27UBT1DH7q6dmlTYM6bX93phTWMOxRQM6pGsmy3agECwRDwymJkDUQJDtJwEIHLJsWZVmg232+JQpIudmoqRsIuc29kKuCzzR1sXKyWsECKxcpbRgGRizNQE87nR0dlJSU43C6iHT1UZRXRCg/n/7IIInhLEcOH6KouJTFi89jzpyFBHwBjh4/zpTZM5FFmdjwML/940PEZIOdGzbzlR98B6/gYGr1BBxlYeocQfZv28H27Vvoa21HkRRe+PdbrLr0Uvbs2kFhwE/CJ+NyOvF5VNLJOJ0dPQSDTk41tSH4HIiaBopE0OPGSCXQLUhpmj0Dl0CQJQwxQ1G4gGuvXEVfxwAd7T10Dnayb88+pk6aTk90gPvu/t+J/V/Y8pdMn0JzVyuKZwIeNYnhdZNJmRg4CHkkFEHEpaiIgoTD4wW3QiQTJSMKWEnA7SNjGsimHd8gu9yIqjNHsXDw6b7D+L0KBfl5diKiz42oWKimExOJrGKhCkqOgGzYUyxBQNJMNMukK5MmKJgES+pIRNtxKwq6kEQ0HQiJYSSfm1pRYNDMIo9ECARCqGaGzq5GonETWRHxKArpvnZ0VSGY52b2pRfiUTyIiszmrZ+RTaWJGCbvv7uOWOcwbfFe7v7aV9n25qfs3bKb3/36t9z/3e+QHdT4yX//nsf++QSP/fVp4q2DvPb4W5SG8okPDlOuOiiYUEdbNAZqAQvPWshfv/ML8gIqA/0JIiNtNBxrYfrE8YixZoZcWfSBU4RO9THcG6Xho60Mdw4wYfIAzceOMWXZKtpjB8nvHWH/wRaWXTeBDW8L/L37dcpmT2bNky9Ts2gmJ1//gPr5c+jfvYPli+azb/925k+u48Th3VSXlND1yQdMTZm8/d/fZvnCKWx8+I9Eh0Y4Em1mYmU+7z79ApXjwvT2nqK8OEBTwwF8IpSH8mjoGyQSi1NfU0xeKI+9e5sRPSYBxzjK6lz0JRyEfGFa5JNUBjP4Zkwkk7FIW1lS3V6Kq4opqA5yd90tCI4sj/z9XULZJJddfxkuK83ihWdx9RU3cNuXvoduehCFIQSPD2IpXLqMKthO+Qgyhq6DpZNMxcmoAgHdAtVCxo5BsVtq+3Q3DRMDc2xmmEMxbD2QTZq3QQQM07BBEQPDEpCx7eiwhDENv616kjD1HAd01LVKAi2jjenkdT2LKMjIqhMdC09+ProsI+mmPZaQxBxVyUJAwrS00xWqZWGadhy5KMrouoko2tZ5hmDaRs6ChSDJZHPKJdHuzxEkA8FUbP8ALDTJwjR0JEMiBfbYjVEVmAGWrYYSRdGWkFq5ShsJQ7AQBBNBt1BVJ/3JXgJFXjLiCJlEFxs3bGNC3SQ2HV9NcUUJr/37Na679AriRg+9A0fobrX4+ONGRHeQpWetQE4MYwku8sL5LDv/Ap5+8xWKyqt49eln+e6DP2PDmg8oKCvi4otWomkae48cwCE66d+1jXBZCb0dbVSUlBJNxUj2jxAqVEinNdKpLFMm1xPp7icxFCVriuhmFlODrMdB0oS0aeBV/WS1NMhZnF4RKeqksaGVieXVeJwZEqKBQxOpqh7PrkO7kNTTBjj/34DqCniQRAFNA0n24nKrGHIKKx7DaYqobhcOjxtJUe1ZjaSSVey4AcmyUKQMkmmiqioAiuREECwcDhcWIpMnVKMZaXRdQ5JVnKqa23GeJjwbWGNsavsEt41wNV0nL5SPHM7j+JEI9bXjcTlDJNqPYgaDKHoG09QwJIN0Nk2+ZdKdiKNm41SILoZ8OeWIpuNxWgRCTlIpi/pQGHST3kiEVHQIK50hnJePFuki3+3A7wyz9rkXiPQOENbS3H7dLRT6g7z3/npWv/o2wVAehkNGTKfwWwLy8AgzqqvpjA+xo6cZISvicSTp64swb5afzq404+tD+ASd4x0WDe19+J1psoMS4QqFxnUfUa0Nsv/RJ6mbWEly3TrkjmE6BwcorHKx+/knGF8TZmDzBxT1NjGtpAIldYqja3cwc8kcRLebnZ9+zNklefQc+QCPp5xsOoZHcTE+mmVvf4aewBClteNxJhOkkl7yi8tJhUIUJ4cpq59Oh6MAr2Qhpk5Q5UiiFbtQsCgaV4Qm1qJmmlEdEmePm0/SoeNzmUixNPmkQcxj/IqLqSwYZJgTyO6phFZOJN7Qjy8gM622gsiRfh76ze/p7Y9y9R23cbivl6XnLudbd9/PtIoSZMlhnwuGgcvhtN2TshlkRrOZLATDxKWoKMKoF+dpr9HRm2Fkx8Dyc4T8MbL9aUOSM7fdWCKiYNrBjiYYloUzV1me+ViyLKJnsxhZnQm1dYyMjNCTitiPx6jfaY7QLwgEAoGcbJMz9PiSDXxW9nOt/qi1nm36pNu+q6M+UlLOvNnIIghSLnQyi2naDvwCMpZgYhhy7vnaMSwGBjm1AeTemVGDFNPMzW5znFJjzCXLfq8cqgsBic6eTgYzKkMjg0QjMWorxxGL9bFj5zaKOytYuGQZhxpO8vY7H6MqIjNqllJaEKCyfjoup8j+wwfQsganGpu56upraUsmqA2WcKy/nd37DpI62MqXFs3jm/ffy8SycaSyOkPxfi46/xx27dlNRXkhRz/L4HIq5Ase0NLEjQxpXedkczuYAoYloWlZvH43WjaN4pCJR9KkTIubv3oFH65fg6DKJIdTFIYKOdV6grXrPiY/oCAJEoZpsPfwEYLBAAVh7xcC6hfSpgA0w8yZMyhIoozD6WXKlGlcfskVFBeXojpdADgkGUkWkUQFS7CH+w6HC0VxIIoykqRgmCaqw2WfJFkdDA1Fsv/odi4MuYVBbraVew6j6hZBEHA4HJ9TmKT1FE5FJj8QxKO6KR5fS2F1LcHxZQRKQqiVpYSqKlG8XvKcHlz+AJZDprJ+HAkDRkwT0eshntHp6O1ncKifTCZNMmnnuquqQjoaJRYbYSQWRRYlsokkil/BK+lMmzSOgkI/tXUVTCjKR0kOE21vJagY5ClZZFWiZ6CPnoEIgm5S7M/DpQYgI1IZqsBhuckms/hVD+UFFYTyC4jhpKDUT0tPN33CMHK4EL/XQVXAz8zps5H8XvyFPkrCYbwFRWQEATIqSG6ONbSjqn4qS0ppO9bG/pY28gpLGO6JkV9YRO9Qln2NJ0kFXXhDIvlTg5SNn0U4VEw25KNs8TTC1dVkjBSmw09n9zBWNkoqNsQRwU3GLXIoGaNwyvl48svwlk4iNLGC4HgHLrdIsUel3K9QUFTO5NrpTKypsk2Ys04SA14Mo4nkQAvz8gU+fXstw5EogeIKHFgYusbgyCDnLDqX/fsb6Y9EsCyJtJ5Ct7IgWDhy2UySCIJpmziPzjAVSSadTOX05/9pSHIaPP/z+zOPz304xmz97PY+m8ngUNSxgDpyWvjRQzN0BFEkPy+fHVu3E43Gcj6r4ulzVhAwcu2zOKrjzy2ZRlkFYC/Lzrx93l0KEC0s4bRgYNQBamyei4FhChg6trTWEMZGEWO+MJZdvozezDPEAGe6bp15G622tWyatJagbuZkUv1RdqzdwORps9i0fy8ffvgRHW2dfLJhA709gyQzMZ54/DEWzJ9Jc/sRHnniQUR3L93d3YRCpfgDYbKGyNtvrsEbDNB1/BTFE2u49YpruP6mGykrLOCOO+5g6tQpOGWF2gk1HD16FEMATc8ybcY0SkpLUUSJgnAYVVKRZJWMbnu4YokEg3nouo7T6SQWi2GJEsGQn/7+PiZNn0g0HkcwJFpaWvD5nRiigdfvQZVlMpkMRUUl1E+ahMvl+kK8/MIKVZYkBNPC1O2/ggAgiOzcsY8GRwNTZ04mGunBKajIloRlCSiqw9b1mrZkT5UducG4iGgaiBYIogLYG0wME0VQcieBhSxIdkyECfbch7Er6KhywzQMTMHiH//4B/GOAfLkAOZIBFmBcDjMOcsvZ0gfZstrL5HndVMoOGgeMagJ+JECEr36MNFUEvQ0s6fVI6mQNXQqxlfTHunFRKXQH7RPdN1EjyVIOy3SlkqkJ4LsUyl0BkjHojS1thJ0+WhpGSCoS4wvL2bY7Aavl9jQCLjcmGaWcEEJjkSK7o5ewrXjkU2Rg1t3kl8TZN7sRTR8uAePbzx5JTqFyQlohz9h3rgZbG5rweHwUlsUJNsX5fVjEXzhAqykC19XgrLpU4j3xzAcWcr9AYaNEBMm5RFxJRj0llKQsYhiISk+Ut4y5ky0SBUrhLUseXKKKsNJnt9BuV9mKKERcoQZ7BKYUV9AxAqhFPVRmZ+kLWIiyt3ogpvl9ePxCp0kM31YsXa6h/pJZIY4cbSN/qEWEqkEPpcDn+ojlY1jSQZCMsvPH7oTIyMzkrTIqFFcvRKlqsI9934bh0NH0gwcSZ0J1XXs2L4ffziI1+kg67BVPYYl4nN7kAQLWbTt8kTBhgQdi4KCgrGli2nqWIaBIcqQi/SwLftyiqkzcOI/7fDOpC7JsgyGgdMhE/R5GYkmkVU7njpXJo6BmJRzNentj5DIZmhoPEZ5eTkOh2NsHimKIoZhYgmG3YlJtquUaYkIooA+alItCbnnPQqo5ujHAEGQsESb/zo6f4WccCH3GZEFGYdsf34FLOxQ82zOvEVAM8iJBk4vwmwpvpgz185V66I9BxYl0X7vDJBFONncQCDoZmCon5SoU1ZXzVtv/ZvJdRNZ3XiK6vophIoK2LH9E65cdRn/ePRBDh/+jHkL53H9tdew4f11TKqfimU5KCytYNq0KZSHyfz5mwAAIABJREFU83ns+ZcZjA6SSQ0SMgWKl0yna9M+Wvpa2bNhE5XFBXQORvA7nIgOhX++8iJnz1tAS1sbv3rw1/zlv3+N3xckK8RJp7Kk02mQZDvU0zSJx5KECgMERDBki+3bt6KRxunycNakWfjDTj7a9AkZ0tRNqWfT9nUUlIQ4ebyJk6eO8bOffvv/DqiCLmKSAclAtiSclkRCMKkYV4soZElGwSdJGJYD02HZcRJIWHqu7cLeBII9zBYEw+Zh5Dh6sqrkToScj6MggJizRzvDtEEQbDd0CcjqOqqq4pAk7rrrLn68ayMOU8fjlCmsn8oFl1xMXDPIc5bwRMNJkqQJpuHp9R9w97fvpGh4iLLycvqdHko9QRQTsrqEiEAqk8SVdTJuXLUtW8TEIZqYmoFGlG6GKdDz0c0YWT1IUypNVeU4orEk4/3FqIpBJjpMXkkFmmni8oUxsgK6UyWlqCguGdEfw51fihTP4gwZRAZlooNOFs2fzqe9xcguE4cvn86jJt3ZbpZc9yX82SQtjduRy6pZaAQwEej3OEmykQkejdbSCsKGQv/UIja+sIXShIvXj+zhW9+6HsfxXXTKCsVhJ1bNPEoEi96kisNh4NQHkRMxYnGV45kU8fZW+tobaI0c5CMk7rzyHBojPRyITkXhGO6YyN6G/Zx99TUMGFk0KczMyWG6uht57PGdzKqrRZAVtF43+S4nHpdOVXgCR461Egw6+P2DbyAKMpdcu5CSiirOrzNp3raPkM9NXzKLpmcZ6ernrTXvMpKNUldZgZyI48iYRDUNpBROhwMpbaJoBlnRQEFCQ0cwDdxBP5nkSK4aNMGSbCmkZeZs9E5b5fE5M2gY5Z2OghIIuUA7k2wmyeBgFFURCATDWEYWndM+pGOgbJh2mqwqEy4rsau5nHP+aNtuGMaYa6n1HwAOtusTgg2kgmEiCvLpxxfsNt0Ov1ORZIFMJoNpKAgkESwJWXVjmUlEy42hZbDMfnRLR1DyMI0siZEEoVAIC5Ns1g53RjAQkbAMAYS0HS5ojWVfo5sGipTLnbIsgn4306ZUsvrtlykfV82Ms+bi8/tJ90VZ8+YbCKEwRWUVpNJx8vICvPfvd6kaX0TN1EkUhQrZsW0X997/PQ7u2IwrXMyWN/exaN4c9m3by3BHB3tPHeaCueW8vv5Nbv/SzTQf+YzDiS76Yn2IioEv7MUa1EiPpHE53fhcHirLyqgfN4Fz5y/jqVdeJr88TDDgR4uliCVTqLIDh9OLqSUQJJnqunI6OtooKiimub2deXPm4xF9fLjm39TMnER3f5JPPvwMWYCRwRilVWVUVYeIRQf+74AqkSElChRkvfQpcRRLwq0aCGiIkkIsO4yAC0kCM2siCU77jyeMti3/oVG2FARBzp28tspi1JjWyv2egJDjvtmGuPY9A0m0aSmSlUXLGphZif7eiJ0A6nUyrMlcvPBchjMZLGRkUUaTRKy0jiGr9A4N8V8P/oYHv3E342Q/A/sPEnI68fhkZG+A6Eiczw4eIZoyOHjyJKqqomUyueed87mUYemMKWQyGm5VY8a4MiKajk82CHsd6IJBsLCW7kSK8opxqNkMemKAlCWgOzyM9MeYUDMFDYi74kT0SirNCP6wk988tpl77i0jJUwjI5xkzk3LOWWIlIUqiXU3Mf28lURjKTz+IhS3SMtwFH9iMmpcx61Z6Pogef3tLByngTPI188/C6V3O91Z/h9n7xlm2XWV675zzpV2rhy7OuegbnUrWrJkZdmSLSeQA8g2YOBcc0iXeA7wmMzFNsaEywVjG4QtJyHZclJsZanV6tzqHCrntGvnFeaa98fa1S2de49+UL+qdtVTYdfaY405xve9H8bpYD6c4gf//DdU52cJdEQqm0niVaw0ntDEyoAIqVfBcwVRaPjGIz+mra2VHVe0sGvrZi5MzCMrqxgdH+L693+Eiz98ks99//NUKgEpkSLrFAjrU3TkBCnbIY4btLUWsL2YbguivEvJS3H2pcMcuzjG1e+8HplewLVc7KWQXGgoF2fZFhsqlQqFXBuZMObFpx6nJwrZcv0VjMo6sa0gbeGKpi8/trC0wJIWUa2BJEq25sQo6TZXTW8qflIhxVuvz8SbLlACYtFEzwkLZUssK0Mmk0JK8KPkxk6cdM3wprgP5KWEUYukmDrKuqQ3XS6eQl7mpybd7ZvYrEmYdHIsF5LI6ObIIBlx2JZN3JR3xWGEjG1sp5p0uBjiqE6EhUrVMeESpfkRZuem2bTlGmqzCyzMjmC7q8lkVyNjn1h5CGGhVIilBSZ2iY3GsW2kiQm1ahK0Yhwgqpf46re+wbXXXsU1V97B0OQQgwdPMb7vNB/67d/gj7/wOdY7htLSNH29Kzl67A1eee0gv3HrrzL6/Eu8WDnJQGcfo6ePgp1iemKQD7zvbn70w6e5ZudVbN5W5bWzbzB4YYg1t65m/Mw4V99+M1/7g1+ls6+b6Yuz7Nyzi3bZwv7DB1lYXOIHP3gSY+DmO+7CAVItHovFhSQnS1oUOlpYuWIVhw8cpKW9haWFMlHtNDffchXHjl1ExIpjhw5TnF4inVWMTs9z8x330N6/mr/47/+D1nSaerlKR2oNP3noUf7iD/79v1ZQTZTCzsX8P//5WX7j/k8xFHrYegipS7TnByj6vURRnViBMh7CBNiXkhollxeoy9o+1dTpNeMR1OXcm7eIoU2TfNPsUiWgNShhoTHIOKA3o7h61Wr+w7YYHx8hFKtIZ7PUZIwOYlQQo4XAMqCtJlHc9pibLfH6oWP0pzJMxz4bWtfwvZ/8mDgIiUwzxiEGHUYoqYhjjbLjpJ5GMDY5z5YtawnLDcLFGcoLhoyIKAeafH8fCw2fV/btI3zhVSxAOg7CQGQktjH81B13I6x6AvQPNfm2NiQB2zf2YOqzlPUoi8VFwrDCT148R7nyBH6YRB/HQTJ7tlIphDSIWo0/+4PPoCsOZw6ep7cvS29OMPzkq7zjvbeDiehftYFz5wdpK9iooEwoIgrpNEYahOdRLS0ROxI3sIk9SRQ28LwMM7GPU7exF31+/PhPeOZxj1tvvIH5uSopu5VP/9pvomJBIe9CQyNSFq6SpJ08i5UFrLREhBbTo6MIGZBJdSIJ8YVg48atHBueoKejB+VI7LQilhpPCjKZDPue3kvBtelv6aI+NMx7duzkwMIhvvr5v+HQ6ARxLSTre9ihJoWgGjdoOD5h1qYWNYijhIhPs2DFJiYmesu1KFGXZvSYRJNJbJLnheXlDFjSaqL0DI0woTMlBc4mjpdF/E3YdEyTndoM0cMQhuGluaPWmqYMNpElNaVXyWz1MtXq0jw1Fs1o6uXC/6YkARGjXB8tQxzRjZZVKo0MDj6WXUH4HuXFEBGsZPPqK2iUSpTm52hvSaFSBml7xJGPrRyMjpHKYKIY24vxtQaZQE90DWQcIaiwsLDI3MRZMvmQZ17+MddceR3tbpqzQ6fo2D7Ar370AQrtbfjVmGdeeo333N3K+oF1nOs9z6FXD9O3bhNxqULPujW0tHUTCM2WndfypS9+id/79d/h1Zde5t++8iD3/tSHaV+dJZg/z7TyGVOthCWfwXCYaqnGKy+9Sn+uk6WlIoiY3t4OpJQs1Mo0dAM75YEv8Ws+oYyQRnPu4hnaulqplKsobHq7e3jhmRdphHm0genyJA3fcPd972do9AJeLLjrtlsZe999PPHSXto62jg3MUSmu/ftSubbF9RAVkmlFRt680zPvYaVzZLXNYqzo1SCi4TpPkR0Ezg5IupIEaBjhYybMoz/hYK+PL/6/0OWLX98KU5CJNtUIUSSaSOSDkpbaSxcisU5ZGMRZTvkXYEJEreIsSS+1EitKfR10hisoU1Mh5emkcqQLeSZrBXpUWmq9YhvPvJ9LFvhCoFNTIAiwuA6duI0MQobgzaGSFoMTU8zXSriVwJ+5Rc/wsL3nmDlhg043d187wePJTcKKVHSEMSGlOsmjhQFnoFj5w+zbcsqZs9PglPDn8swcWKQe6+8gnKjwMxkxJZdObzGWpZ+9DK2I6EOUkjqYUAhk0YrQxQqYuPxP/78n5DSokUa/vT//lMuHBohfOMM61aupbBtBx/60P2s7Gwnl7GoBT5KOTiejSFCChtfediOwpEOuIawYZC2wPVtdFBHprJIFI5lszQ6QhT5xFLQ09XDQnEBzzhIEyAxuBpsAQgHz3GJowA7TnLpc+kMi+U5bNslrgfYjiL0K+TtXvIZhwoBDTtmdmKaez/xcb769LNkczla2lv5y9/7bbJSsfGqrXhr1nHssRc44fgEzxzE3rmR1jUrKU2P0JLN06jWEGED7HQCKpFJb7psr1zuBk1TWnT59GTQUiJFM1AuNggkRofNE1MyTzVGJ1Iks3yUfyvRSUpJrJvXdfP6dlwbrZMcqYQZqpuFebnRSJxW6k2/y2WyFIlGVAiM1EgMSgo0FiYqkE8pXnruS2jdxdU3XE/a6SYs21ipNwga87RkBpiZG+PiyGk6swWq5SV6shks2UgKZqNMeWmGQv86IiKimo/jWiwtzDIzM8H2TTtQWnP86H48p5Wjhy7Q2tfJe+69l4tnB/nJD79BIw3h1Ctsuut2NsyX+dcHH+Qjn/hZXj9yAOFL6sUa3d2rkMDC4hwX9p7j5Otd9PV2cf78GbraWtn36ivkCyn2XL+T/fteZJtZz65Na+ju7ub/+O0/QpMi5bWQSklCfGYW5tDK4qbr38GxY8cYHR3HmMRSK4xFEDSI4kRv7NcDbAW+7+M4DkLC9PwkG9ZvYWyqRKnSoBFYtLS1cHj/IXp7OvjGtx9kVd9KdqzZxPELpxicHqS9L01L28r/ekGNUhle3vvv3LR1CyNqhJn9+7h4ZJI9V7dj008hfSWx26AcKTzLRqMSfKJyUPgs07qXu88Ykyy6ml7hyFx2hSwX3Uu0HUtdvsC0wWgfKUKqDR8cG9vJ4C/bBrViabGIqywa9Tpee5Yek2JlVx/DoxNUIp9PfeoTvOuue6g26pggJrIlVAI+ev8HeeR738cVNkQBxJIQkagQYgOWIDBxQsrRMZFONqbSEnzxKw9xdX8HB46fZOyVfSgLPKEQoSaWFhATVcooS+L7MYHlcu7CFKeGJim4LtffuIWl10ZpT6Up9K9ioH8d333iX3l67wRxpYHIeaRwaNBA2hZWJEDF2NIiNFVCpVECHKmpa8Mv/MLvkQM+sHUjf/65LzKTyiCMg53N4QcBmVwXtZkJnNZWBBodG2wLPOWirYi8k8FXNSxhY0UV0paFo2tYvsF1Q6LI4IrkpGAjSVtZXAe0FeCQTvw3QYRjgy0Mtptl/fqNDO8bJwqWKHg5FvwGQ9NTpLUk1iEtyiZju2zq7uPUwgkyra18f/8hpIFsJgMS1nX1MBRXePXp4xydGaIn3YYlI9bdcSOP1xc5+dJLZMZnSFd92rs78W2XWEYgXWQMNgq9fMNuai4TkfvlgipM8vs4jkOo42RuT8L4lCZxJsVNjWYcByCt5vWdHOF930dayXFZNZPy4ub1GQbLjiWS5a5Uza642VTEAt1cBL0FTh3HIBNFg61U82dqkAbbgFQRUbFK1llDz4o8b7x2jLSbxcs41JaqdLe3c+zs6zheByu6rmH43DMcPX2AD6xdQxwZGvUinu1QCedpZQWubUjZLmEgkIHPqv5+Hnn4m1x79ZWMT41x4tSTxI0Gq7bdwhPP/oCPvP+nkNVb8dIZTk/O4BcbBEpy5a7tdHd3Mz87zuzECJ/7wp9gre3k8Lce5coP3MbrrzzLAw/8EkvFKb710Ne48847eexHLzFXrjI3N0KlXOWFwyc4efQ8F/do3EyBpWpEY34e41g4GmrS0GpZPPfCc0ip8NIugR8lqcaBRkcQhhGWY1GrNsilHLSJkSpGaUVLVxcnT07i5h1KxXkkFq2FLIODg/hRGd2I+LM/+zPCekTDD+npa6Uzm+PUkZP/9YIqRZl8S47J6TGKQZ2Dh4a4651305Erk04vMDb5NK2Fe0m1dmO0wIpbMbFCiypI601SkORNCfnWjtW8aX7VHNgvd6h6GW0WRijLZuXKtQwMDDAxN8OpM6exbZsgMoTNof1SZYHQL/H6Sy8xUl3EKYWMzIzgGIOV9vidX/lV4r4OvvedryEtge0ohNYUyw1QDmGosZSNIBGDx82LWtI8dsUJpFgIjTIRUaRxnURCoYwhl83RCOrEQUTWsgliCA3EdgLgsGSMEBFGaGIjKPqa4ycH6VMGH4unDpzk0Je/TSaTIvYNjrCII4VlWzi2wnUFkR+Tlm4STSwthNYIZSMB20SsctOkLYcoFvS2duFrjU+E0QHp1nbMwix1meSwL3dW0rJRCDKpLEpKXCeF6zjNPC2JcF2sRoCFIm3bBAZk0LjEwIQgecGLGEcY/KhOW1cLnu3RmF0kk/KwpEXacQkbESnbwdIG25EUPAdHGWYnx7hu+06OHH4DN5fh0z/3cZ569FFy0mE6irjzjls4PFnkDfkGu9Z0MTw8RGpWEB87z4GXnuCqe26nd+sqbrlrNy/95ZdYKz5IpDWxiVBxkmK6HL8slt1CJlmcLneRliVoNHxWrOjDGMHI0DCW7b7lZr88MwV5adG0fJRPpVKXHEQJdlQg7eWR1vIRv9mNymUH1rJ8SyPtxKGkddTUqyqUksRE2K6VbN5jebk5CSqkVUhFD5JNKSpTEZaeJWpUsXKrcTNphO2BUEzPDjE1P8HWvgKhXsPs7DRL4iztrkQUMnheL40ln/LcWSr1MktLEbt3bqVWWmDH9iuoLBSpVxqs3baL6aFzhGGdtrTLY9/9Nn3ZDFZGcWb4NGMnZ3FcwQc/8j5Gz09QHZvjzPGL/OOXH+L6W67j+muu5fOf+1Nuv+9Ofu8P/yc97d1s2LSB9Ru38dF0L19/6D/I9aziHR+4hZ+8tJeZxXmee/FJJiYq9Kzvwp8tYjyPYLFMS1srQblG4BtyLS7Vep1cLofRMLO4yI7tOzh79hxxDI7rYjkWJmpgdEwqlWV8fBJCi3ApwFYuRJo4iFizeh03veudHDlyhLVrN3LyyBFOnB0ikrUkhXfPLW9bUN9Whzpy+hHy+c0MzjXQxTlaUu3sO3SIqelZhkYMF89OcPbcwyxOH03abKXR7iJKaoTRb2ErGmMQ8eWNarysmW5+LgxDgEuRDVEUEYYhOjYsViqcGxnl0R8/wfjwCKrJWtRI0hkP23OxLZtvfutb5Das4C9+9w+466672NLTj5VyCOoNTp0+wdzkRCIxiQ1+rY7jOExOjCWeZ8DCYNEkDL05j1wohGUTixgjBUYqpGURxAFEkpRjUyuXMSbBAUsFlkzmcTq20bGNEhmkcEE62CTw44WJRQJdx5KCodOnsBoNMkJiRIQxEbaIiY2Psiyk54FyE3OE7RE1IpQSGCtGxREoQ8aSZF0LaXnk0hnaWlpxbUHazRIUF7BTKaSVwH7TmQKFbI64uRiM6iFRHKEsj5Qw2MJFKIktIFaJ0iLWAWEUY4c+luNiWykc10qeMwUi1LjKIq76KNvCARZnpxHYhA0fz3EJiFi9ZSNuZwf1EHJIdrf2cHFphv/20Y9g1Sts29pDHUgXMjgNn4d//B32PvR1cp0lfvr+d5M2HtfvuJbXx95AuyVud1vRZcOTew+QES5aLGKFiTgdEdIQERERQRzghw2iOEQmgFTQGhEndtN8Lsujj/wnr7z0IqmUiyEkNgGWMggiYhOAiBAyBBGhLINUMbYjEFIjbYNyQDiSWBkik/yPLmlL8RM7a5gsZJcB1yARsUAqjWXFWFYMBMQyQgoPTxVwlUtsQmrVMtLEpB3FuROjjA/P0N7WixE1RgbHqZbnGDxzgp72PIWMx9mT53Gx2bCih5lykXSmhU67lRVteRbLJY4fP0c+10JtaZ6lmWn62ztJu5JXn3+e4uwsfe05Xn31GYaHzrC6q5OsZ1NZWODqXVdjYpt0ZysnLhzlj37tl7jvg7fxa5/5FfY+/Sr/8a2H8Vb1snrdFeze0cZH7rqBmdoUn/rlB+jKpPijP/mf3HTdO7jjlnfz0Nd/wPxCmYmZWXKFNrbdcTWr2zqYW1xgsdLge19/kIXxOebmS7R1FVCupF4rkymkyWQdIhORa8kRGSiVqrS1tqOUxc3vvDFRJwQVwtC/FL09O1OkXguIBVRKNWzbobO9g7nFSaZmpnnk4W8zNz3HqZPnmRibAh3R2tKNZ2W4OHz2bQvq23aoC0uTrF+VQq9ymD+ZZsP6FuZMjUW/wc6tGxGFYQg9xgbP091xPUHg4iuNEzmJ7EOGb/l+sXiTkJrLPujLEv43FdlE0IFt2xgrmR86KYtKtYrnpghNDEJjey51EkdVd1cH3/vOwzjViAe/8jX6WrOJ1jUWpNJZ6nOLgMSyHBzHwfZ9xoZHcCwb4wdYalnK0iQSNTfFifP1sozr8t+gUcrBEGF7HkaACMImQEJgtMFzE4eJarq/fBOgdPISUwoCFEQhjtFkXRelbESTexlHBivjYOuIlJejLKtYyuDk8pQXFhAkhHQbiRaAJXE9C+KAsF7Hd1P4kcaTEpXKstioIy0P17GRXpasiljIhNjK4FgpYivETTsoWcUySQidjJMuzDIKVyW58srEaGnjpR2ioIJEJFSkIMB2HOpBhETiGEOlvISUFlnbQemInnyB4vgkYcmnGhpGF6bIeZLc1AzIKhO6yCPffwSpIKUMGdfitz7/Vzx+8SJfffAxDv/1vzNfnGBtPsOKTD9XXrmCbxw7RTqt+URfH3uVJPANKRkTxT4iNmjpXM6GUsuLqujSCWq5C63XI1KpFIWWPA2/fkmUD+bSNaqj5JpOfPpcGmnJZskUQmJijWimky6PsJK8JvtSx2qMuHRiE8QJxkk7ydxUJ8d8K9bMLBxmsDzB6jXXslSElIioLY5wdvwM61atYGq8ymuHHmblymu4875P8MoLL/KOG3dx9Mh+Ll68QL41w/HTRzh29jC5QoqdO3Zy8ewZBkefQ1gZHvjEJ5ifG+LiyZM06hUiGixWlthyxRWsWbeJH3znP+nr7sF0hizOjbNqoIcDrx/i6MEzrNu0hTMXLrJ9625+/OMn2LBpB0GtwfCFk3zyF97L4MI0H3zvdXzxr7+Krz3mxk6zZcMaOvv72fvwY9Q8ydxzs0zOjvHav50ixGJiscaf/O4fcf9738/Bc2eYWazyV3/9WVb19bFh61aOnzuMm/VYXCwTVwydHS3MziySSqVI5x2kjNEmoKU1RazrdOSzRCJDQ8RIKyYohyhl42YdSuUqN9y4i+NHTjC5WOZDH7+T/a8e4dbrr2Rxrsi2a27hrz97BK01p08O0duR5/4H7nvbgvq2HWpbzqHGi/z4kR8TapeqU2FufpJsazeV+VlqVUPazTE7M4qhjqCGZWxCu0Fgyv+f6IhkMJ+kNEqRbFLl/xLbsGyvs5ruEh1HKCGwbImb8tASQiEIDQSNOrlsC1IIQpIX9H/72Me5eO4c7Z0duCkPAzhxxOuHXuM73/52QmwPI/w4AB0hZZLIKFTiaRZCYBl5qWAujyOkTGDDkMAkgtigvCTat9ljEDXHFI6ywBgsIwiCiAQJp5qMS1AkHDRLSISQWMJCC0lG2qSljSZEmRhXJPM6SwnCUKOUwBKSej15sVsyCYQTyiLWAt1krcVhQHs+TT6bQwqLbEse13VJpTLoIETGNpYAx3axZNR0wsnkeN6M+4xpCs8NWDE08LG1BZFBCgcnnUXZDgqdxGgLhWUlFs2WQhbPdgjjkK3r1mEaEZPz09y55wp62zOsbusgqJTwRUR/dx9OUAMiaipmZZTngY//DGUt6FERi9owc+wM7fU5/uhnP8g9t13Fw//wF/z+L36Mai2ko8vlzkzIupOnOHN4H3Ec0+N6zcWXxMgUWeGiTAJFFjrGhEmYndYhURQkNkujsSzJ5s2byeZyuO5lu2scJYYUJRKJX1OglNxsJUQ6TD6ONVHkJwOjOCKKguaLLLlyEuK+bo5WLCxlgAZSBjgi96abtcaSMX6tztHXL9CW3kFclbRnIjLCYnZ4jkbNcPTIIAsjDdKew779z3Hq5Ak8O8O//ss/UyvXOH9hjDC22LXzaqpLDe646142b9mBk3GJoyoD/Xme3/soR/Y/RaU4x7atKzn02jOsXdnK6PQw3/vRw5w8c5jp8TEsNKdO7+fIkUO4XoFde3Zw4exxMnaBn/zwZYaH5jl+5CinTx9h1fr15Ow2PnrT/fzwR0/w87/zbrxMg75V65lb8tl/+BwthTZKiwtE4Tw/85Gf5Zc/8/Ps3nkdd99xI/e/58Psff4QZ04MUsgqTk2O093fydM/fhw7tNAhuK6LJQVuysH365QWiywtLaFSAuloxkeHGLxwkfniIrOzSzRKPilL0d6axdBAipjOVoeu7lZWr+rm3nvfQU/XBrat3kVK9LLv5dN8/WsPc99778UWKfKtaW68+V28uPfgf72g1mrn+ebf/IC7b19JX6dPHMd0tRe4+R1XM3xqmtiPGBycp6/7CuLAApNIMEwYIEj8+2+2+SVk9CQFUes4udvHl0XNl9BlbxoH0HRYOQgsaSOFQocaq5lV7tk2rrSQ0uLC2BgvPvciAhgZGmb4/EXCILG3Dg8PY1kWnd199PStTPiNzbmY53mEOmq+sC4X0JZcCwApxyUKks5ExSCiGEcqUsombuYBWVLiOU4zqK3ZDTW/l+M4l2UzTfOCEJIYiS0Mru2QspKuXimFHQtkrLFcCxFEKANthXzyPYSkraX1UlY7TfivLQyepXCkjYw0YagIavWkK4okCwtFXNfFTbuEUY0gNtRqNVBpTBzieWn8oE6uUEi6LR0mQGRjsKXCVhIlQpQAVxpMVEcKjevZCGWBElha05FN09/RhiVMYsDIpqgHVbb091KdGuKGd14JuoSOprtyAAAgAElEQVSIQ6QrsdAYbZHBQcRQbHV47flXMBgyRqBVQMYEbCusIjs3zp6br2GwYwVlK0VWjJJ9bT+ViVlcRzHQ0UPZidm1oZc733UFW3pXsDA5TknX39Ipvvl/YtvJdRBFcSJxMjEi1gSNesK/NIkNNDIRcRSghEkSpzHEkUaHEbJJa1pOArWkQomE/m/iqHmSUQhiLKWJtY/fqGBhGLl4jtmJEeJonoZ/jJTdYGHxDJEo4UvDhz76AaQqU5w/zfD513j8ia9RaQwjgpigVmf/8bPknZ2sat/CxbOvUa2MkU+1cOTQORpLgqAsOHdqnNC3+Lu//Qo/eOxJJscmWblyNblUnnq5wuD5U7xx+ijPv7wPN+VxYv8RShcukinX6Cq0sHXPLganZ1i7djOr119JrBWvvrQf13W5cH6IdKGdYqXGuXMX6e5s44EHPsmZC2/QMZDmwKEz7N83w4bVuxno6eOaq27mp++/n+f276cv287V113LoQOvoucX2b57MzNnhnnixf2sGOjmH7/5t2wYWM2977mPU2cuYIRkYnQEHfgIk4xaKqUq6YxHLGJs2yYsB9SXQqbmilQbGoRDa1sLhZZc8r9RgrbODtrbO5HS5vixk0gVUp4rEs0NceXaVh75/neoOorNO/L8X3/9l8i0RuPy6ksvc9vtN/7XC+pisU7/gIcIFZ600PUyG1f089zTPyCdt8h1rIBsB52ruzg78jyOU8LoBmmVuSxPaR6pkuOVBmKUACHemslzGeZ7+U0phSQh8cg4xIT+pQLdqFWxMThxDJFGKsXo2AQHDhzi+af3kvY8Muk0tkw6Q8fxSKcyOI6HsmyUsvFsp4lpF5g3gYWVlRwBoyhCCkGj0cC17KRTkRJNAlYJwhCrKd7WOmwGk4FlSyxxWYIDYOSbUyjjy/BeHWO7Dl5za2yRRHQooVAYTGTIpjMU54t4jtMEzTSjNywFUYyJIwQxQkdYQmCEQFkeCo3QDTKeYkV3O66SZFIujguu4ySz6sAQhT61ahnPdqgWl5LTgJVkDymTLJziKEbHzblupOlsaSNjO+AHmFCjDBD4ZIRg08p+XAFxGOGJpCuMg5CWbAblKoZmJ+nvHCCFhQwhzOaZzxaYbc2x/a6b6Fm7lvfs3oU9v8R7H7ifyM7gaov8u99PwcvRc+oY0csHyUxWyQaSlJWm2N3J6NZe7vw/f5kgltRnJxlwIt63Zz2WiNAmeV6SaihBK0RsEYcCE0mkUSiTMClcZeFIhSOTx5a7W+KkiBqdDEBtZbCVQQmNrRI2KMTEJkQqQxSEWLYAWUGoGsLS6DgCobBsGz+osXHjSiwZknE9lhanmBydoKethXqxxNLwBE899p9cvHCC8alpTg2OYuwUR44f48TJV6k2NG57N/mONqSK+ZmP/TxfefAhDr9xlolywFIYcOcH7uPMyAXqUYN16/vp6W2ltS3N6dOHOH/+OBvXb+Lqq+/g1tvuJFaateu2MDa9iG8rJuqLTMxM89zjL/Chez/GE0+8DkJy5wfu5Nobr8NVrcxXfRbKS/T1dvGuG/awanWeyQtPs2tFF3/+x19gz81Xs2NrP2MjI/zbw4/x7Yf+gy995cvcuudO2lat5ktf+Hu6+1r56n8+hCUC9txxMx+84Q6W7DKrsg73XH8Le7/3OJX5MinXo7OzMzE4SEUmk6FSqeB6Hsq2CBshKTuNY1nExqdSL5LPZ+juaaWjLUdbax7LNvj1CtlUmpTrUCqVGBtZpFCQ5LKS/QePEKegv99Clxb5my9+kd6eNhAhUzNz/O3f/+vbFtS3naGOH1bc/4vX88IPD5Jyz9K3cSvT08Ps2bKdc6dnWdV/DydHvoE1kqKzBR595E+4/e7P4Pu9ifDcagqem0QgaS5LQqRI3Cqxji+JBJfzcRIpSXxpy27MsjY1IeuEBg4dOoQTR5SmZzBGo6OA+ekphGORdbPElgNx2IT3xuRyOapRhIUgijV+GGAZRb1eR6TzCGVhTEyMwDSjHUISernRMTpuvh8nHYplWYRx2Jy/aYRIEIW4KrmDNr/GEBOHUVKESf4uKQSeSJZWsbQoB4ndD6OTJRAQS4GKI3Ti+yGTb6dcnEWGdWKTI5VOJ1/P8uY30SuGcQMjM2SkxsrksLwcfgjFUh0vnyWfzVLIOqAkdqaLUC9SXKrR2t6CUoqUFUAYg5IYk8R+KAsCrQh1sjTMpwyivoD0fTLZDKEo4ugYLMnVV+5iZn6GcqlKqqNALpdh3jJ4KCw7x+TIHDLdQi6ShNIi1iG9vd1cbCnw8Z97P7Ffwa5XaNiazquvwO9Q7Hv2BW7/8AcwP/oRrReHKJVrCBvmLY90excnswEbbr8F1XDoHlhNY3qEzLpenv+7f2BQWbRf/+5mhhTQhEJHb6JQvdlCqprPfzLnbM42m4qU5PNv7UEEAprXrBIJ2i9uEpxSrocOQ2xsdGChLZ+U6xJFEZkURFVJuTjB4vwJejot2jMxM9MH+IfvPsPPf/o3Ue4sO7ZdwdRknWJ5gc2bujl5+BS220EYhuw79Ap333sT+4+8RFBK8Ru/9wU+9qlPMzd5gnfe8nEyrSn+5m8/zw3XXkUh7XF66iS9K/uozBZZPbCe+bkJZhcWcb02hkfP0tvVy6M/fIL33vk+GpUy5147yGC5zK3vew9/+s9f4q5778Z1BX/4u39B/6rV7L7uGi7s28/6DTu44+YbOLz/BY4cPsj4YsDuDQO888Zt9LWtZGJ8mEJLF5t7VzA4Ps4f/vavM39hkMnaEn2r13B68DQ7d+6kVSq++bWvs1Aq8Zt/+Bme/vbjvHZqhFApbrntXTz11FMoRxPqiGw6RXGpTLlRIa01sR+Ty+aYn5siMoarrtvFkSNHmJ2fI9LJiCzSAQMDvXS0Q+CXaevIYJdjtqzfyOsHThPTyr7T55BSUp6JKAqHr/z7g+QKWa7ctZKenjyNcvVtC+rbAqbRT3722VPnac3YbLvmet64cA5lXBarE2zccDWzUyeolS5gpyqYqI27b/0MNa0RCpT0ks6veewVIjkWLRfU5PibuFFMnHRs2iSMSoNByUSAa+ImGFcIiEO0AWXbzE/OYKoBxdkZ6sUFFoIGaSuFVoJ0OgOWiyVCgko1mXZJCylsdBSg4xAnjDCBT+BaIJLNu/YbSCEJY500Mc0jomNf7k6VUsQmQscSN23TIRMFwHzDxw8DUo5CmAjbSAKp8I1OWLC2TcZNUa/VsUhy4V1LkZKSrkIOoQ1BPSKbtnljcYmUZfAcG6NjQhPj5HJ4UtOZdynWdJILLw31WgNXKKS0SVmQThlsXLLZLLq1wNDwKKtW9BGagIX5GaJGhaARMT42wtzsBEG9jB0HtOUyEFapLC0SBQHlcg0jDQXLxbgOfj2mw0t886u9DIvG4Ho58rk8I1PzpKSiK1dgemGOrvYupIR2NDnL4uD5QdZ0p9i4YTuDR8cpB4KulWvpC6u0dLVyXi8SF1o4tu85qidH6SXi/tveTXFolHETsGepjczsHPOnTlGTIaWaT/vAJkbmxvDeuQNr3Vo2bNpOW64d1Sl5/YffZOzAccb8GSqZNJnOtQTCEIoYI8C2LcIwQEgByyi7OLpEW7rkRiJZpGodI5pMUmNMIsp/kwTw0jUdJhNyW7hJuJ+AqZmjjI4/S29/B7ZpQ6JIF2KeffaHrOzv5sDhfawe2IRr1ZgcO0tXaweTIxOEZolaVZPJ5rEtl0OHDxP6IQ9+46usWDFAW1sru3dsY3Z8GGnnSSuLO+66Fi019YUKYyNn8OwM27ZuY/+RA8w3SrRmWvnKP38tARkJjR8aCq1dvPzSK8SmQT6XwXFTfPexx1nUMSvWrmP3ldfh1mvs6OqkND/H0TemWLGplQ+/7y6+++X/4IMffj+ZlMvDj36Pp19+hY7+AVZ29/Cjp56mWmqwecsuxifGGR4Z4pX9R9m6cye1Wg3PNHjimWfYsn0r05NTrB9YybPPPkssLEYvXuTaPe/gnx58kGy+jRjB0VMHyGULSCkIgKVigNYhufYcxfkKnu0yP7uI49nEWEzNTlDIZFEmWSxX63U8L025UmJqskhfTxdhmNwcL16YRhIzNTVFR3sPtXqdGM1iuUjoS9o7OqnU5unr6eTQgTf4tf/+B3/8vyuZb3vkv3h6lLSzSCMao1xMIUyaK3dfx9ZNv8r0os3k1BIthd2sXXcfmfZeRufOgM5j2+3NnJv4kl5Pa510PVIkgn4liUyU0HWsJI9necZl2zZh6CcLAZpHcqMT2ZJOYCuxSWQrRgY0wgbGJPg0LWP8sIGWy1vXJm4sCgmabpXl38uyrKZ0xSSuKHHZY738vm3blxxcutndQOKGcm0PiSIMNZlUmrbWVmxpN7OJxCU1g1QK3/eJouBSzLBqbpuTUYK55IoxxuCI5PtjkqgNV0F7PoUwAZlMioGeVmw0rrKRWiURFyyHtoEUFlEUYOo1sg6cOXqIla15tqzoo7ZYYWp2ATdTIOXlKGTydHW2EUchjrJYs2o1Xd09GKMTkjuCWEqIQ1wEph5y7bU7OX/+LJWFIjMzM6zs6SaKAjK2i6jW8SemyFYX8VKKSb9Gynh4pZh8FOGv7qL1ik34aSCIKM7McfXajbxj+1Z+Zs+72Hn7PUxt2sXg7Awd83N0HTtMaeYo588cwkQN0qkC5Wyeid0DqHtvZM09d3Hlqm2YcomlmRO89vm/Y2ppkjE7xjYueSuNMREmDpAmyZ8PA/2Wuf0yuCRZ8CVKAN0MxVvG62mTFONloEkU62Y8srn0eeUYjNQE2ieKfUKt6e/ZweJkB4Pn5wkbk0yOHGLw5BnCSohtMlx1xXbGhl/m8NHHOfDyOC+/9Do9HVfw4jOvcfHsKR797nc4e/Y0MYaXXnmdz/zKr2FZFi3ZHBfPnmOgZz2b166lr7+TbLodFUm6enu44Z6bmF4cZ+36ddx6w600JhfJujnuuuM9rFmzjrn5RXr6V/DEEy+SchWdHd3sP3iawfEiH/7wR+i0XVa29fOd73yHvjVreHLfQR589EnuuWsXt+64CVFVXH3TJg4dO87Djz5G38oB2lq7efG5V5lfqvDJX/g0rd3d/PFf/BnDkzMcPXWWT/3cJ9i+fSs9fQOkUt1MjJX44l9+g59+7yfJFzTvuO4qcsbF6ejml37lt0iRI9OS55+//DmkNmhdpBGFBH4Sye3YKepln7SbxnPTKFviZBx6+jspFHKUa1Uafkgjigh1TNVv0IhC1m3oY3ZuiWNHBrl4boZPfvIB6o0KUjoMjQ7jZdJIV1GvhaxZ08m504Ns376Zfa8dZsWqnrcrmW9fUFtzGVamdxAVM7z26j464gEO/+QwJ0++QkMramEaofqYm+sAsYHWztWIOCAIGglc4k2WvASZFl96PIqiJIvcQBgZwuhyDETQ8BFoMCFah8RNqYoxiXOlWi3j6wpG1qnXyqQcO9lg6+ToHzXqRLVaEnNLcrHbtt2MtUiKrGVZCVat+fssL42Wt7lGJ12zErK5jU+6kkvurthQWlwkiiIcS1JcXGRmaoZqudIkw8smDg0cx6GQz5PP5S49H0kB1Bgd4ykb17KTzoc3OcQAHRmCSo3y9AzzU3McOXwCx9QQQRXbhEB4ucgLmfxsP8QNaoQLU9x9zS5ueccuOlKG7rRi57btrOrpoDOXRkUBlflpFheLzM8vMTuzwOjQKOWFEusGBhL0m7KYm18CWxESE1sWBWkh/IjK9DzVxRm8sIqLRhmftB3hRGVs7ZFv2GSqAk8KpqWG1jSj1gJ3fvhmlqIqFVexqrOLXC5H1nU5ZVU5Pn2altcPoV4/wXh1hg7lcrw6TW7dCkYKLuPrutC717F595XcdMuHiCoG4ZR4/Mtf4PTeH1LpcbF8QSZymFpqUFEp3jhxlmefehqLJPHTlgrHcxPnjKXeWjDNZXu0tBTSUm+5yS4X4eWBwWWFisbxNJat0aLOxOwwmhKIOu+6cxfpbI6zF8ZYnCkyO3qOKzZ2sO/FH2HriLMnJtDhKnrXr+XUxSXeuHCe3XtuY2DFOvr7BwijpLkotLUjlYfv+5w/dYZcpoVn977CS889i7BS/Ou/PETKbsGPbEaPnqW/rZO9P3maV19+jVtuvoWTx05x5MBxZmfLrFm/jbNnh/i5T/8cx4+PsHHdtQys2sq5kyMUZ4vsvnE3/QNdxNpw5OhZutt7uPXm6/jDP/0K//iNf2GyMsSBg8O4eZdGVOeWd91EZ0saV3o8s/d5vva1rzA5OQ5CcMfd72F6Zp7Z2VniGEZHR0m1tvHOd93Ey68/ye9/9n8yVyoxXVnkujtvxIsirtyzh9OjQ+Q9h9/69d9nzbpVuNk2LMcmCjRxFFAr19DVkHKxlowu0i6pjEOtXsZ1bTraWhDSoENNynWIfY2IJBiX4aE5Orpa2bRpE3//9//Ihg3rcL0M3b3doCRxGPO+997G+OAIGzcPMD55ka7uVkqV2tsW1Lc98j+990ufLVaLjF6osWKLi1kKuH79lVj5OlNTQ3Sn8mzdsoeFxixj8zP4FUlpcp50wUY5Ljq63Am8xbOfPJDM/ZoYFCUE0iTW1LBRxVYBOgxwbRuMIIkAj4hiQbG+wMHXn+Pg688SlXxUIwIvTVuuFcdTtDhpIiMQQYNGvYbGoGwHx01hKQXC4MUx+VSG+aCO5aSIfB8ZJxvbiGZIsLmcn3PJp41ASTDakMrYdCgLo6DYaJDJZfAbIZ7tJLpFIQkjTb1SRUcRpaUiylYokiWYVNDmpehKpxHAQqlMxrW4sFTCsxW2UugIslmLKGrQ3tZCT28vUVCjq7WNar3OYqlC2nWQ0sLSIRlPUUh73HrVbh47cYqxhTpLtTLTi2WmFyr45RJBpUQY+MSRn/B3I0Pkh+jAR/sBkR8SBw3q1QYtuRyzlRLCUvTkWmj4dXa1ZMiu38jps0NkPIVrp5mpVWhPZxmbHkM2ArAsyHukrt7KVVt3cuPuzXRmOuiJBMPPvMzZ06Pkqw3y3VkOzo2hUwVaikVyL75BI5oDK0nVLKztZTgukelfTbB+Nat3XcU119+EKzQiKxl77LsM7XuKalhkRrjkVCuLhZh6vUGXU0Dme9CpVqbnF1i9bn1yUhKGuu9j2TZBGCas3mVATzPaMzaGSOtLwXUqeRBLyEtzVd2EpsRohJScv3iORj0gm8qyWJyno7UNKQ0zc2O09/QR+R5tbRnOXjxI0LBJuf08u3c/oW5QL+W5OHic7Vt34WYr7HvhJKOjM5SrAdPTM9Tqde6++07Onxuis6OdydFh0vkWliqatlwf26/dxtjMCTZuWk1r5wD9XauYHJ1hcGyUq667iu7eXlau6md6do6+/o1Mzszy0Y98hNnZKbZs2cnk9CLFpQoPfOKjHD9+nOOvnWTTjm0ENNixaxPX7bmKybEFVu9YSc3X3HDNHSgZEJaW0DpiYnKW22+7jVjA7qt386EP3MPAigHeef1NlIsldu64gtJSjYcfeZibb30n3//eo1y5ZyenTx9n7do+hgfn0TJmzeZtZL0MpXKJf3vo3znw4uu8ceYokYyZm5ulstTAQWE7Ah1FeK5DoxFhhCGdTpHPOlQqDdpbsoT1gNg3FFpy1Ko1MIp0Os3QxSl6eztZXJon0kv4DY3n2PhBg1KlRCZtYxuf0twsGMPsXIPSUgMj0oxNTfL7v/nZ/+2R/20L6qvHv/XZdL6FgwdPsyLbzpreNubHRjn7+gXWtmfp6Ojhc9//IdnuHh579HnyBYfV3WvJeh6h0cSWC8LCxIqUo3CtAN1wiI2LlY5RdoP6wgRWUCasF/EyNillIUSNxbnjlEaH6OxdSVVnSVkWxnGJjWB6ZpLx4dMsTs+gAklaK8qRj9Xewcd++mP8YO9T7L72KkrlOi2FDIWUR2TZeNLBtW0sHZHSyda6Hmks1wVi4nqdlHDQQCCSo7lRBuLkCG7FYBmDjAU1qUlJK1m2SJeSXye2BH4YYRtFylIEShCGfpIokM3hBz5+GGMJhed5OK5Luzbs7umlkA95Y7ZIX6bAiVqRVGyRchUZbSjFEIQhuhZQjH1kXRE0DEJ4kC8gikWMF5LCpk1k6WvNs6tvgEdffR1VqREFDUzZYEoxQUNTlZJMTRJI0J5InDk2pKWF66aIpMKVLkYpgkaAbySLIqZd2cT1kHu2bGHSFtRLDVb0dvDbn/gwP3PHrdy2fTu9uzYzEKcJtmyh2N3G7NlROu+7lX/7zb/iip+/jxePnia/eT3n3xihv8OhLxdz7ZqNNI4P0hlVyRvJ0uIscbaLUkWwL30Ef7Wk4uR438c+SaquUI5F8ch+nv/GPxH3pPFL5zElQTHbSX+2zszYAl7XBvylMo9OL1EsFtm6eSuWlLjKRqCbsSoRlkpE+gILE4cJSAWJbdxkZu4I4sjDD4tYyuKFZ15lw+ZuwkgSBS7K9gmDBSxp0de7jpZMA9ucZGbwJLXSGwyNTrFh4zWEvqaQTTF89iytqU46+/p5/sm9ZDyLDevXse/Efno6W5iZnuHsyQnWbRhg8xXXcPyNU5y+cIGO7m7Onx/lm9/6Dms3bmFhsUa2kGVyYpTrb72e/QeOs379Fi6M/L+cvfeXZWdhrvnseHKuU1Wncu7qnNW5pVZqSSiACBZYXEw0BgyYYDNjbLj22DjC4IvvGLgEAQKEAkJIopVDS+qcQ+Vcp6rOqZPjPjvOD6WZNWvWsn9g/xHv+r79vc/zztIcFanoWW698QCCRyE5OsfxU2d46tmnKVSqZFMrLKRTTF0aYy6fZubKCCcuX+DQwZv413/4JzQTbn///Yxdvk5PWxfXLl2jJdbKv3z/h3S39rFjew/LxQU27L2NycsTaHoZTa/w/Atv8fKLZ9i9bx3f+eZ/cOX8GG0tYZ5/7jgvv/4G3YODXL46RiLazrvffTP7dt7CZ77wZxw8vB9vSKY16JBOpfBYPto3duJqNHj5jVNoJYvFZBLZ7X1buL3m1lBdb7tjBQGz8faAoGNRqtbBpeDoDhLQMG1MbHTdRsTgQw8eplKpUsw1uOfe24mGHS6fXsBW1zSh733PbVw6ew2fN4RhQaVqoHhFamYdl9vPl/70L36/QP3VT//h631dbSiiztZ1mzhz6jWC7iBN7S1svXEreXOCrJVkw7oQrf5mpEYV0ZmktVmmvLJA2O3gcQlIisjla2+Rmh0n6JPw+wRKhUXOvPUixdwSyaVpMtkkyYUpqrUiqZUUlVyWqM+hkMvgd0l4FVhYXKK/r5fjx5+nWFyiUiwgNECtm8g+L/lKlVKuxPJKFq8/yPLKCsFgGGQJt9/PpvUbmFqYJdHZxtLMJH6fB0GWcUSZcDSGI0o0B/yIsrAmtjAMNMtBVuQ1Jt4tYasCjbfJKFEziHm9yKpKtlzGtCxEG4KKC7dgYVgWZdNCkSUcG3Tz7Z4rDrppUdY0IqrCwaFB1rW28tKVcTo8fq42igR1EUFRqJngKC4ajoBtglQ2SVs5DKuOXCqBbCDVNTTZiyi68aguFEtnU3ucBdGFWavhdXkQ3FBUayiOiabVSYkGRQecooHoCtDd1UdHTyd7N27i6J4dvOfu23nXnbcyvH6Adx89wi2dA0S3DzB6ZZTdnVEWcyV8sVYu2g201jZeWJjj5IkRBj75R7w1k6ThEdne3c+mnduZeuEtbNFhazzGcGsPZyZG8RRrxKwGQ5EW6tUSLtPC0uusVCCwqY+sqnOyMI6rN8qn/ttP2TS4Fyoyoprh+a99HENKUW/ouMMD5LMmBbuGJ9TF1akw9HRSV4I8nptHVLeSX5ylVs7S0hTH6/ZimGskkyhIWNbaPpIkrj2UWraFKIk0rCqWbWMaOqAzPXGdaDjGsZeeZvPmbej1OvnSdSShSHJhkbaWGKXkHGdPP8X84hjhwDAGfvr6e1nNLLGazlDJJzEaVRK9/Vy4cJ1tN2zj9MVz5CqrSC6JRlEitZJDMyVa29ppb+ng/LmLyLKC1+Nl376dJBJxhgZ66OgIoygi/b2d4NSol4o8/MNHuOnAPYycGyG1Mo0qe6BhIzgyP3/iMTbt3odZbNA/tIGYK8RH/o+v0rluHVFvkHff/x6Ov/4aRw7vRxbgnXffzxsv/I4fPfwLAtEwS8vLyG6Z5FwSyZbYu2GY7//dt+jZ0ITH60ZUdN7zng9w800bCKouPvunn2F+aYp0tcZSvkiis42dW3q5bf8GmqI+xudW+fZ//2c2b9iBqFWYm1qgulqna6CX9HIeW1QQrRqHDx/mV796lIF1/VQqlf/3duBWXGvY7v8z8W3bmKa9RiV6VHTLANNG0wxk1cHv8+JSXWi1Orpeob29GdWj0NbZzuVLE/gDKnXDJBRRqGsNUqks+YKGZpiYjoDkWpsNVxSVL/6+gfrsz7/39dRimljIi+M47LppC56Aj7OT1xDaY2RX55FLOf7wzrsYH7lAazRCyBWl0lgkn51Et7LMLc0yMT1LPreEqZUplBdZzS4yvzBJwO2lVC1RN0t4/AqmXaXRMEglV0mlVmmOulHcFbKZEUyhQbmo8d+//nW2bx9mZWVmTWyhO/gVF5qhYwG5hSSJSBO1Up6wR0E2TLS6juNIVPIlLEzKhTySYSDbNqLLhY5IpdHgwKGDvHnpLAdvv42J2VkiwQhdzZ3E3H5USSYWjODzuBFFCcsRkS2LgCqhaTolvbEmBGbNDKTKArYsUtDXyvymYb1NLDhr1RsbFBzaVQ+DIS+JpijPTE+i+hTy+TqqKON3LCqYhKw6AdvAUQxkt4XHdOGT3YiqjONREBwLn+Fg1CqIjkkrFusVD0teP0JHJxu2DnNk92bedeN+7r/zNj50+CZu7urmaLyZHS0++rduIBEMcOqVNzgzO8NNH/9TPv7pz/PC5XO89yMf5qs/+J+8+dIrbBscZHbsOvcNriM5MUdsoI+5UhY7U2dzop32mCzpCVIAACAASURBVI/Q1AQxq0B4egqjlESfmsRILeIvFolKKs0DXWza1Mczjz9Ll6rSG2tCVDUyWoX2vUNcyKQZvP0GhN71/LfP/gXDLQNIlRxS0MXor7/FzItPUvKF0csWeFuxPC34xSR2cA8vrjhUhzpob4tw8vwlPnHL/Vycn6UxO8r1y9e5fPU66zZvRlS9GPUGoiQhCRKGoa/9CnBkTFug0TCwBQFRcKHKEoXMKJGAB8vJkius0qhV6WiLsbIyidUQaW3q4dFHHyaQiCAgYOkGs1OTLM5MgxSjf2Ad+eUV0sk8suLnuTePY+o2giyTzxcwLcimsnQlBvD43GSKDVrbo6SW0tSqFXK5LLreoLuzm5ZomEw6jdcDiq2TSiUZ6O9m++ZN3H7rEX7w8++CkyedydLV1UNjpUi+pqO6A2xq7kGM+jnz5pu8+3Mfp9kXpDi9zE9/+BALC7MsL8xx/7vuYqi3n49++FPs2rmZD3/iI9x5xx18+1vf4o6jtzA5v8CVqRHWbetn/ZZWLp8d4YMPPsil81NcvHSRwS3D6I6P+XSaW2+7maXFZXrb20jNzdHVM8QvH32NlaUipXKJD3zqw/zLt/+NgwcO8NLxc9RNi+2DPchNYcJKmCujV3jlpaf5/vd/wLPPvUw2u4oDGA0D24J6vYGAjGnbiNKauF5WJAzHxOv14FJc6IaOqor4/QFkWaG1pYXMaoaL55OkVkvML07j8bhYzdQRBBflsobqEmhubqZYLmOKFrbl0KjpRIIeGtUaf/7Fv/r9AvXfv/2XX090NJPo6aJo5FlaKODoGv3rBnj+rdcZbu0iJraRzk0gRxs0HIuqtowSKFGsGQRiXkr1MrlsAdGQsGWZulmjblgkk3kEBVweN4qsYlo2Xo8HENm8bQuZYppQU4K5+Snau5t46fhpnn3yNaKRIIJooJtVSsXq2r664NCwNBRVIRYJ06gUsPUqChaWoVEtlqmXKjiGTqNRwSWJKIaFLIno5lr3VJYkssvLaPUGbo+f5Moqdd0m3tNJqVxj6/59LKZW8YZjDG/dzmo2x46NGyhm08iKi0CkiUg0TpO/ie54ArQaPleAmiIgiAK2ZVG3LBDW+ra6beOSZdyiw+FNg/iKZRZqNZpxMa6XMVWZimpRrpnkVZWqx4viiyDLAbZu6mTH+nXs3rqVuzfu4pad2/jg3ffR3JbAsRvIxQLdfd00Nypsvu0gj37np4znkhy9+338yZ98gadfP8Hdf/91PvTP/8L1os7FcpE3xibA70dS3ezbvJ5IxM/Ojh5CxQq3Jnq49eBe0hOLLMwtc6AnykC0lUK2gF+QCeVrWOk5hhMdJPOr5CeTxBLtpJMpljIl1g8N09nfR+uhrfRu28C1V99kcSmHR4GEV6Bz71YulVM07x4mtnU7Ww4cxY+D0EjhDncx+rN/JPn096iIElWtjKk6lFxhVDlPLdrLxXQzb8wuc+j9D7An5CNtNIiu286/fucfSV2boLS8RCgaQzd0Eu0d9HR1sjA/TzAQWrOWNTRkwaRUruCgIsleTEtGxCC9PE0gUKMz0caxZ39Nb0+AVPoyPq9DrZxjZS5NoTCN251FbKTIpmvUdT9qxKJBGY/qMDJ6lkAsxMYN2/jhT39JoqWJdUNDvPT882xcP4Rb8bKUyiPYEsVqFn8kyKsvvUlLopWKVscXCPC5L3yev/6bv2NxYZqJySkO33wEC5Hx0XmWJpa5dOU61yYm6Onu4xe/eYV3vu8+vvc/H0KwFeSAj2ePPcvY4jSyS+See+7AqhQ489IriKbDP/7LP5DLrnLffXdTLuSZmpzBH4/RtWGIY888TXZhieRCCluS8XhE1nV2c9P2vYxfOMfw1q08+sRjtLY3MTubZmV5hKOH9nL29HX+/X98h+6uYR5/4rc88MH7GRm9zlJyhBtu2Mi6zTuo10vced8tdMd9WE6Z7Rv7kKItWGmNn/36V+zYvpltW9bzjb/9ex74wB9hCzbTU/MYugO2iK6vGcXWHpXltYBdAxEJh3xUCwVUZQ37VkQXy0srrGbzGKZEXRNJJFpQVYO6VkJRVUTp7aaMKZDP1VHdEqalIAoe6pUGisvB0OErf/57BuobI498fXp1gng8yosvnSRfrKGICpNzM2zb08/M9Uu0D4aZXVjBDBqYuou25iZSi6sEhSZaIkECcZVCQUNrmGvCXkUgmypTq8no2irlehnF40MSvdQbGrncHLZQJqBGmZ5dxmN7mZtfoaCLxDtaAYWOzlb0mk69WkdUFTS9js8fwHRM6nUdxeehUK6g+FyYtoLsVvH6XLhVB1sQME0N2zBQBIWGZSAhYlV1rFqJsCWTW83g8chEPH7KdQ1TLFEv5yjpdcyMhWCYNHSNiNfN1MwMflUm0rOOi0uTHLjvnTz72nPc/p57OTF+jT3btjPc2095NctATx/+cAiP6qUpEiEsSfjdMl0BNxFbIrZtgK6+IT507x9w4+ZNfPjgIY5u7eNQZwcHBBfv3LqRniODnP3lq9z6pU/wzLMv8dbcLP1f/Ch/9md/zS33vZvRhRnUpRW29bZSMwW+MzmBUJXJeSw2bT5IQ1IY2L2OcE5j5+Agt+7cyhG3wjsSHdygShwJ+CmOXKRy/Rr5xRXyhQIXr1xicj7P+evXwbC5qTuKI1jops6mnYPs/sBtdO3YwHD/EBNXRrjnwfcjt4QZ2nMD3U1xFhp1hje1U08u8POXHmdv3xCvnr5GSyCAP6aQ2LaDwPAmTk1c5dZb78Bu1PF3tzH2xHNcfOx7GF6LvBVE0ZbJ+pow5DAhS2ZGCzI6Z+FOxNj03lsIVupodY2Tl1YY7u/mxsAqJcGDR5SIt7ezactGfIqCUDMZ3LSOF198AZ87QHJxmrn5cZxGnWh3J0Z5jeZzhBq51ARWPcDXv/ZX3HbnMKIlMT0yw3JqhdZYK7qWYvuuYebHl+ndMYjq82CUDeIRG68SYmFhmU2bt/DMb5+n2Kiwe/c+Hn3sMWanZ/H4QoyNzXD14hjL6QyNRoPZxRSZ1Qw3HtzH8uIyieZmpsbHcRoG+XyOL/7xH9EajfHQT37Bkf0Hmb7+Fm0RkQ2trdSKU1RIcfT2g1w5MUlrrJX5So4Xn3mBL/zNV5hNrrCup4/2zjivPfMSGw7vo1rSGB+7xBNPPMlqLUNbazfZpQzbt23nty8/Szad59KFS9z//g9w+tRZak6dfKHMRDpF2/YhnvrpccKxZiSxlbGxUQYGB5k4cZbO9X0cvfV2vLKGkV9A9Ih41QJ7dq1n+5Z1XD4/z/ZdB3juiV+TL2fYt2M/v3v8BX7182O8+8F7SPQ0UVnM8vqFswwPbODhnz1MpaFTr2ggSRi6g8utYLM22CmrCqZpYZsOHpeCKksoioiqyJSKNbzuGC5ZwLR0dEPGGxLIZAp84H33kkkXyKSyDAx1U8gXsK01a5UgObgiaw+P2/ckiHWaCHKDz3z8a7/nlf+1f/v64soSql1HRcGvNDG9UCXaGqAhZOho66QhpmhtirKUyrNusJ/l1ASFaoZEewJJ0JlPLjMxpxJUQwz1DnD+2iqqJRMJO9ywOcr9t+3DsIssroyxc7CHoC/AzFSS1986xQc/dC91U+HxR14hEmonnSzR2dxOYbVAsVJHs8AriWtLjrKMy+ujalvIqoKsSjiKiCZqGDg4oo3sV9fWJhURnAY4GoYu4/L4iMSi4OjYch3d7cHXqJNr1GmJqaxP9DAzNUU5XaM9HmGuOMYNG7rZdrSNA9t62X/LXZx/8yyq4kdo6KwuLrC+t4/RCyM4spf2rj5WCkW2793DtdExbr79NpbyObbs2c7C1Dg9qouIKlCbWyL+sft57xe/xJf//Et85p//lenFFbbd805+8tZJfnP6ImMljYWGTFO8h5dPXyAgBTnU1EOkxUfp2nXUbIZqPs+WaBgfNoORGN2iTisGuZOnaaSWEFYX0RcXqU1OY6+sULcsKlqOFUWnEgjQ29yOZQt09nZQr6zQ09NOxGoQ7ewFq0pnWxhvIEC1KciU12H1+gJjZy+wMjuPXSlRvnSd9NUR9MICJy68RrZY5ZdvXMDd68PfblPx++g/dBsDh7YR3b4Vs7WN4c03sKV3O34lSC1T4Mo3v0Lak8Ow/LjtNCW3Tc1pxatJRHydvJj3M0mMG+6/j54mD1YyjSS6eCOd4o6NcXjzSbp0h2GPzu+uL6DIZZLLGvGBVi6dGWH0ynUWC8tI+RS6LWLqMr/49eMcuWMPZblKRPTic3tJr47iDpTweVqYnc2zXJgnW0vTldiIrYkkWjo5ceoc7V2DjF44RT6ziK7rRFt6efO1M8iiisvloqm5iY5oDyoeXn31NUzLhSDYXLp4hcGBdey8YS9nzp6jVtexLYempggziwtk8nlExU25qtHakiCzmuZnjzzFXffcztXxqyTaeik2dJL5MvsO7eenDx1D9bazklqiraOfyUvX2H3jIXJLy8SGOzl0262UlzOcuThGR3MnXW3tfOOfvsGeXbtQ7TqXRyb53kO/5PU3T/AH73s/LeEwfp+HkxfOc9e99/LGsVcIej0kojG29fTxrj96gJWpK8ysLHP/XfcxMzFDz3A3nb2DpLLzfPu7D3PboV08+fwFmtraieFlcbTIw28eo6ezlYGeNnRD5oXn3iTQ1Mrw5l6WkmlSqSqO47B9+3Z+/ovH2HvwCPPLSRr1OvVGA0laQ7zBRq+vrSuAgCQK2IKN2y1hWxYet5vllTqlUhHVLWI6Ig1NIxQJY5smVy5fJZcr09bexeLyDLYt0zAkREGlvaMZ3TKIxZU1Z0DIwiO389EPfv4/DVTh/79F/v/9PvtnA46rxUd2Ko/f04Rc9TIyMc+uu4ZoZJK8fHyGe/a00LG9m7nxWaLRdq6OjjK8tZuJiWU2dN2AJmVZKItY+QKdAx6OvWVhLIMqwkCvG8lyEONtzBVLOMsOHfF2fvbIY9y0bzc5fZGJ2QyVqg+fz0G14N6jt5FoasXARLMNlvMZFLcbxe0HUWJhYY6ZuVmqqxmcchXbEXC73QiOhuGsTZrIkgetWMMnrcmwBRnWb9jC9GQSO1uhoJfwOCpf/swn+eXJHxPrCTG8vp9g3UM9V0eVWqmVZ3l1epobB9bx5NMXkSsmpi+MpVUBG0WScckuRMWPJToIbgm/34teqxJraWUhlaKpyUv56hj3bhxkm+JiQqvwljdEJlPjww+8iwsXziHJcOe2G1gwK5gLqzheGXl5juLq6hr5VK5QrZTQ1SAzuQbucAC7UeQLO3ajSTJXLQOX6aYaFKBRJxbwY1fqLGdT+MJ+cMs4mk0YiXpEITrQTGl6BQyJer3KYF8vjVoVl6Qy6isxNznLnZ199LpiXL4+QinWxNVro8S7ewh1d+Bri9Ozfgh/Wwur5RV2d/TS4gpjhKJMT42jlae54dDt2BkBq76KXbdw6ibLpVWWV5PYqQlMo0ih7tCmNSj6TWw1jEtzKBk2FXc/Zxs2Hd2tJDq68SU6KGk1rl24RCjWTGD6Kjcl3Jx97mm6hzayUssSL5ZpW7+J0VKRiymDK+evs6rI1N0BYq1daCbIjkCzHKY9XmTvul7qxSxZktSFFSbHVtiz5yhPPP4MN952C5ZZp7xYRlaiXLl8ksOHd5JezdC3cQezk5NEwxHeOHWBd993NzMz52iKhwAvb7xxgXLVYTHZQJMc6tkKQ+v6uTY5ScgfZGp0nJtvvpVsJk1HexOLi0u0t3fi2CKCIHH23DkGh1tILWXZuWsbFaNCk9/H4OAgZ86cIZ2epTXSw2unL3LrLTfx9JPPMbBhCMtysFSDr372yyiCyPLkLMnKAi+/+SrLRZPbd+6nraUdX0Dk2sgIe/Yc4a//6u+oNWrcfdedZHNLKD4vlmHSEokTikeYnp1hZXaBL/zl50lXUiyeGuc3J84ze22al048xh9/4DMcOTpMqxKhhMX89CUWF0yGtu9kan6CO/btopyqcXFyFDXazOUrV/nkRx7k2MsvMnJhnE/96Wd55vknmRyZIBCOce877kWUwLEF/tcPf0I8FqJYymNYArYl4JJdGEYDQbSR3DI+n0TA60ERFAb7t3H61Cu4XQqVhkWhqLFv317GR8fIpPNIioDX76Vha3S1RxFlLxMjc7jdCtsOdmGZS9RzPnShTKKlh5d+Oyr8Z5n5X55Qn/jZN76e1wWsmkmlDFLFi+y2KZhTbNvQRaVRY0d/D6Yqk55PEwx7qZbrdCaaCASjyIIH04b0aoGQ6kN0ydSMIKIJtiUT8Im0xGNkKzlylRILk6sEQzHqpovk0jLp4hK1sojiqJiFNK2+EGa5gqRbSIaJo+moqoeWSDMRXwixYdPe3E57Vw99A8M0JTpQg0HcQR+ioGM6NhXdwUJC8EgYtk5DU2nrDjK4IUTv+m4uXZojFg/gFzyotQrhniba+2J0JeLUdTh/4ir9bQrXR67Qs62F/vVDvPD0OYJKEMGtIjomoihg6jqm3kBVJAy9jtfrZjW1hGOZlEtlRFlC10r4RIU+v48ul4TiFtn6jjs5MryJ1OUTbA21oTQy6K+dZ3bsEosjl7g4cpps1iZt2tQ1cHd3Y8QCxJpCOC4XtltCrFc5lOigVtPRfQE0O48ckAi4vBT1KorPTblWxufxoErgEUEUvbgTPuSgiSB7iLW24ImpTCSnKRSKTC7MEYqrSAEPdcPG7EpQ72+m2NfMkQfexdY7D3PzXUfpWj/IQG83iUCM9f0DxGIxGoqF3+8jFojS6gqSujyKnCuwdOUK1y9e582zFzl79gqZa2O014qUvBYBy8awZExDoSYbCHoYmtbz5moOX5PAnltuwGW4Secm0LKwavvZFdTpWxrBW1pBjocRlDAtooda0GDk7CQD/gDN7e24FJ0Hbr2J5MQSVyZnMV0ilenrvH7+VZ77P/+KcH6JobiMR23h0qUkt9y4k6unr3LjkZt56qmnWd+7HsMQOX9+jIF1g9SrBfx+L/OrKeZmFnC5VHRBQ7B1xkdmeOvliwz2DbG8kmdxKUdztJOW1la8Xg9el4JpmZQbdQb6+rl86SKFQp5cIUN+NYtlWLQ0t5BcWGBsYprNm7ewuryC4RSZmVth5/odrKwmOXNyhEgkzvTsIu2dnQiOitcfxHYcOrrbkESD7OIST//6Oc5dvUijWGb33u1s37kZ2dZ56je/IZdbwRZFXj55mlyxQsDvpVTOklxaYiWVx27YRAJennnuWR74wAd45ZXXObBnG3PLSfKZNNu3bObd77uVf/vm99lxQz8+t5+m5jbOnr1CQ7M4cM8R+jb1MTjQxcEbDvHy8VO4XG5uvuU2jr9xkqeef4kbD27G54mwvJJBUky2bNrGtbFrCJZNb18fS8klRscmEEWLhrYmoJeRcLncaFod1aXgOGujg4qkUKnUmJqcIhj0IApgmDZgsbiwTK1aI94Up1rVQLGR3M5a8V80CPpCWJZN//pmAm43LtwsLxVIZzN88bP/+ZX/vzyh/u1fbHWyXhknKzI/O0+TFaJpcwhNr4NgMzOySMWw2NIVY7WUJxSLMnl1lTvv6kcOuakVLbIllc72MFrJZCKTYWnFRzHdoCkeZuduA2NZQPTHUBU/84uLXBxdxLEjnLgyRlR0E/N7iMgGbo9I1ZZQXG4WFuZomAa6buI311yiiijT0dpBS1sz/W2d+EJB3LEQBdMCv8TxV49xw4EbifqbsCwBwWNSr+aolK5Sys7QP9CGLqjUfe386GtPsGFHOzdvGCahQno+h7s1DGHQ3A0aQoXxyVlahDb6Y12cPX2Nq4sGqfQyzeEgjm4jiSJut4KuW7gUlUatjmE7SLKMY605VT1CA9V2cWNnE/sVD5Woym/HU/QQQO3ygO6i0mKQWKxhtQVx2w62rlMoVRlPTnP7xt0s2HWsUg3REqg4EsXVVWLtQY54PQQScX6dmiEuhwlG3AT1GGVFw5I1dKOBEXRzcXyUpVQJs+qhs19m16HNuOgku7zKuq09uKNxmrs6KYoGXf42Qq1BmsJdiI4MgrYGRKwsYZmAZZNemGd6bprmphAjJ84S8seZvzjJ1ZGz5JBJpwy6+v2k6zUCrVEGuvtZXK2iqjJhucK2QT9ROcCCZeJtduNddaOFdjJSniRda+GOPzyEUTmJv9HKshJh5mqFzt4owvnfsK3ZC9U5TEOiVLaQvD7cgspKZhmfEqB7uJup09PMyBa3DPXw5w8/Q3jPTXgDXmanL/H+B95DaG6S3PQs0Y4I88k0t+6/DafW4IVTr5MvQ9dwO7994hV23XkzTz7zPF3DHTRKq+iajqzK+P1eWuPNdHa3sbg8y+pyDquqs3F9H0Iwwpsnpxns7ePYc6ex7QK3HDrIUr7Calnj8L6D/PiHPwDbolHX2bZzM5PjUywlU2zcvBFR9OP1mezatJ7JhcsIHgsKPhazhTWtoyvMW+ev0xSTKBWKHNh/I5ZR59LIBX772I/53Jf/jkBHNzOj49x/02762mK8dfkCVa2O4HhpCgWYW0ySyRlcuDjKpm0bqJUrzC/MsnXHFpqCbi5cvMahAwfo6u/kY5/4Yz7zx3/CqZPneee9R9i1YT2jo+PMNQz+8tOfoS3kY9dd99MS62VjbzfX5i8haho3bdzJr0+e59v/8GX+7ts/wh206els466b7+QHP/kOhYKJ7PKzNJckGm8mVUjhkd0srBTo62jDF4wyMz2CY0uYxtuUo2nS0dXBam4VCYFYSxCP18Xk6AxuVUWWBBqVBh29HRTyZQLeEIKoMzq2gupS6NvYQaXWIOQR2LhuM+cunGdpOc36nZ2ItTJNbh/R+HZmSs/z5nON//SE+l8G6i8eOuK8cHKc4qKGXhZwqlXW37KXySuvcvimI4iOxqPHTtLXEqdQgVvvGuL88RQdCXC5OnFkA8wWpqYusXFnG0WrSGeojUsLSeqrAtXyBC2xvaRW8iQzaZp7B+lu6uPJp48hmEEUqYJbM3DsCoqiIDZkTFVitZhHRcQxHepOA5fbh2XryArYprEGEihu9IqJxwf5gkwwKtMSijLUO4hlGTR3yxBS0C0JQcnTP9jK/Nw4bjFIUO0mbaWxLSinMpSyM9zxnlt57YUXkKUAWknF7a/QIW8kHm7m0sXn0bxt9Hft5KGHHiHoD2FpNo5gY8jW235VGU3TkWUR1VERbQdVdGhub6GjnuP2phgNW1urOpVFYhvbGRsZo+9dN5H/xSvUFRl/S4zqapn2liC6rOOLBNEVF37HQ2a5zpXMEsMbunn+pRf5fM8gywGdVCKCqMJkOU0mI9Pd30JHZwtyw2LL8AaC0QjNff3Em9rQqkVUVcUjeUHyYNeK1KeWweuDqkk6OcfE2FXKuTJYDQqZVfKZIrOpRQp1B61Rw6larB+KIwsGLr+CoahI/ggjq2kkTBrZGv6mBK4GNPQK3f3tLK8sUizX6PSI3LlvHzNViVBERtI9XLO8LOEQDAZ5363byOkakzWJSlVgpS5y90CC3AvfpafJTWqhQN+6ZorpIpboxarpePwRiraOV2+g59JkV4o0RRIIiQH+8qk3uOETH+P4Y4/RFW+msyXB6def5eiGIe7Y3MeTZ39Lf6CDhqeVjFahc9N6Ll47R3NzKy+98BZuj4pDjS3btvDWW1eYnc3S1R7HLblo7Q6wfdsWTp8+y66d+8inpwm1BDh/ZoV8ocz1sXk+9dE/4qEffRd/tJnz1yYJxaP4fS5kWaVYLPK++/Zz6sw4O/cNc/bkdW7cdRDRF0arZjh9/hiC7EOqi9QcnUg0zu5dB/jx937EAw88wBNP/ZZgKMqGjf3s3T5MczjKuYmrvP+BP+TF373Is4//kgN7txIIJ/gf//FT/uBDDyKL8MtfPUZX6wBnLl2lrSuBbdh43Co4JpGAl+nFRbZsGEIWLE5emOIzn/0YUrmEjk5ycYWBRJxjp69TqdYpppY5et9Rjt69H5/q5uHvPkLJUvCHVMxGhWJumW1bt7N18zDZQpnv/6/H2Ll1AFEM89yLr6B4Fay35UC2adKo2ciyiOyTCfl8pJIZBFHB5VIpl0u4vSp13UCVQVUVNEtfuy1oa+rIcrGCaK85hRVZRNMaDPYNMT41zpbt27hw7hLdnZ309CmUDIN0pkY0pLFpwyCp2SymFWUhfZGxK87vF6gf+0jAkcLN7Inv58rIAjGfj4wxheSPY00vsKSn2LLnIOPz1zh8+DBjExepFoIE3HN4Pa1EegReeDKNYNjoYoGBLetw6wIVG6i72bEvwcXTSzgo+H1hjp+/zvS8hmD5aCgSMcvB7xSJ+r2E8LCklZGCHkZnFta2ixwdxZSRJAXb0QAw35ZZRMIhtKpOtW5jYFIzNSLeIJZToruji3vftYG5uQVWlqus5ktEom6GBkK0RPycSq4QyCi0tIQoKF6aE83MTc/QHLLoao0iek1Gc/MUz4hs3reO9vYQ+cUMP//dFJOjefSGjUuVUVQBn6isKYvqOi5nzZdoBRQE28Ku17n93e+gvVGld24WW/Az5/EQioUhrtCYStN+tIfiS5OcmZhHDvgxag2KrRKHd29BC1gkZxfoindhhVrY2jtE3awQH9pEZ6CFdKpIzl3lUCKC4HehWEEQTTAMHB2q2TRWw8RJZsgYGQpzaeaSeUqpeZLLq4zmswzEw1w4OUlTyEPeA6193XhkL/6Yj1SxhGNJxMoF8lYFKRykupQErwef7EVWqngBu+FmMW/QEYzj8yqMpBbxRHw4qgtfME4hu4IhCOzet4OEaqNYJon+HTwxs0Q4GuDoga148DFTqaIJYRbnl9FCMXZWz+NfWqW8uIwrESCiRKisThMbGEBtVHH0CuWKTb4Cgxt7WZkaw1YVFoIdjMzneHalyG0Hb+Whf/8G//Yf/xfHfv0sR7ftYm5uDJ+Ro8c2Kbsb6IZFS+dmjp04R3I1RTTqZiWTp6c1zo4df0GsgwAAIABJREFUw8wvV3n+1bOgmjiORSaZYvuODeTyGXZs3cujTzzFrXfsIDWT4syJSVoGtjLY1YKpG1y9PEGkJcD0/CKr6Tx//89/wyOPP0Z7VydtNEjWlijVTcSagVsPM1deIZcts33rVm66cw/HHnucmibS1NrCletT3HzzEZ741SO0JXr5gwffy7lrp+nvauHyW6fYf9d+vKILs6Bz8B23c23yKlOnRrh6ZZm2wR7GxifQNQOtYVOoFohHI6yurKLXDCSXiGHW2LFrDzNj14iFgixmcmxbN8yGfbt49pFfM7eSY/3Ojdxy5DCN4gJqcxwxs8Qzjz7PRz/zIS5evoQpijSFFSbTNex8AcXlQxdMtuw7wLqBzYy+cYKf/uI3lDULyS1SKlQINYUorpZQRJnm1hCmYyDYDoYm8eAffoiHfvK/6OxKkFxeolioEm8KoTeqGIKAI4jYlkBrcxyXajEzlcW019zB/X095PNZsqkS+27cQzjiI7O6yK4dh3n0dz/A7XOzqa+PbDFJuQSmVScWaeK15xd/v0D91JcijlteR7tPJaOVqGVlzs6eJznnJtEisqe3A0X0QcyL16giKTqZWpWw10/VyNMx2EY5FcXnLiK6RLINi6WkQzw4wJmTb/LOe2/it88cZ3CwH38wwMj4DGMZk9RigYYt41NhMxKCy1qbbhYcdFSuTk3idqu4PTIrmSqxDgePJ04kGGN2fImmJj/d3S6mRpfZvqePqekZ/P5OZq6lqGslRARUnwtVUjHsAvfdvw3dLKA7EuXlLLmqi5VkDY/PJtHdQjZXYN+Wds5fS/KOO7YyOzdHrDlKdcZg06Fh5pJzZM7Psn7/QTRLwxvuwFKiNMwIUxNLVMp15pdS1PUi2UKeUrFOo1oh4nbT1Rvj9qEQ4twSXWqUs2IVbyJCU0sTLjFIR1zBH2ihOZpASISwNI2mqB9bk/G4JVraozhmC4LqBlnEKFVQShqWXV8jf0o5Hn74p/jcAvlchtpqkWoySzJrUtfzaILNogTDzRGEkMpQKMqCU0LI1QlIQWoNjWSjStDlxzbqSE1h7GwFRJuaY+LSDGJRL0K1wUhZZ1vITdXVQqa0hOgRKWR0AsEwJhqyqSDLCoJXRhNUcFw4Qg1ZbmBkGvQ397Bl7x6q7gCnU0nedfshrk6dY9PmDSwuGXS39/PK9Vk29rZSOPZ9bt/cztzoLLG4H63qRnYLrJbTDHi7GaHA3ngn1fwshVINWYljCzqarHE91M+5WoSxN06xYeMwV+ZGODTciSSH8MkS7f1dXDl+EdXlEIkIXLp6gajoYmk1hy0reMUEsTaViZmLxBKdvPnKCP/blz/F0soU56/O4/U4PP3kce66aw9LCyUExc2ew/1cPX2Zvq7NjMzNEvP4mZ2YwpZcNFTQKgKOaFGu5SnlCnzuwx/mzQtvkctV6OxoJaAGOHN9itYmlc27tvGT7z/OC8/9mKXVLF/962/R29wKgsOly+f46p//FZ/7y7/lj7/0aY4fe5rc0iIf+fyHePOV84hYfPITH+D61WsM966n5OTp7t/M8uU5/vVb/05NFUmmVviTj3+Wcn6GiSuTXB2ZoO44xJuilKslbjtyIxfOj/Kpz3yIb/z9d2mNuPB3hnjXHffwi0ce5eOf/CCPPPQQ0UgzqttAjUi0heJMzeTQsnk2bOllNZdlYHgXJ8+ewe112L3zJn7z8ye45949VGpwZWyZF185zp49N/Dm2TO4ZBdet4fevg7mp+ZQXC6Siymi4Qg+v4e+vh7Onb+M3rBwSQYuVaSsmSAK1KoGLa1RTMFkJVXGq7jANAiGXeSzDT79p5/mRz/7MdVKHVly2L1jN2dPnmXHvgHmlkY4evMBXn/+Cq4mgTvfvZd/+OKx3y9QP/2pdU69aZZ2pwvbbqaWnWWxWGJqFjZ3dTDUFiPkD/HUlVMMREP09kVYKM7T39XBudNVsrkcPp/E1nV7WC1MEIz5aG7ewcWRVVyyxeT0G0TCm4m7Q7QNtOAPaJwbT/PLH51EaunC72uwtS7SEKsoSgijpmGKIo4qojWqvPeB++kbWubRx48TDneiKAqVepHubptayU2+nCcaF2nt7uTrX3mVRgW+/IXbyOazRDoVktMml06NcNs9g0iim6XFPLJbwO+Pk8+7WUmNk8sXqVgqG+MmjZqHtk4Pzev9pCZMWnoUNFPl9ZfP8pGb3sFI9io7Nq9nMVcAQcJXj3DL4XcSjbfhDkQItwdQPG5EbOrlBg1d58qJ53C5XGzdsJOppWlkx01XxI07HKUqepArFcRihRoOlXSKar6IoGvMLyyjuEVGJsZpmDr1dIaxK3VS4ts2nPwSfZ0JdMOivylAvlKgu7ONZb2BE1eZyypIZh1fvY4tqDiSRkvQRV3x0piYQ05EsUoF5qoaLZ0JQnWHXN1moVaiO9aKNxBkYWEWMQBi2YUnqFNMtNK2UqDoEXAqIp1+G8msUze9TGcaNDd7CQc8ZJYVnGIBX0SiZpqEgg7Cxg30H/0Dzr42QSzm4radnVQ9OsaKBJF+JpLXkENtJLRlNsyPUliexlQNtq8fZv7qOK5oANt2U82maAgC8XCCor9OHBd6rYY3YTAz02Ch+wjnMwZeQaasLbL9wAGKp8b4xXf/g8HeZspGjcWVVdq6+ynpVdq8Il2bBjlx4gRb9+xgKZnFq3vp7Pfw3LG3UOQg+WKZnTv2o7pNdAEkW8eu27x2/HWGNw4RDndw5vQb3Hx4D5oGq3mdlZVJtm7bxfjYGAPD25lOzmKVajiNBqIoI/i8NHdE8Zoyr774Oh0bO3B7JMp5jfZoJ6cuXeaTn/0YLz/1BI6uMbh1K5aiMLSln0amTDwQ5/Hf/JIHP/xBHv7RIwi2xHsevJ9f/+YJDuzbT2ZhhURnN1dfPc7gUAdNO3by84d/Re9QH3sHdyNLLr7yta/x/ve/j4vXr1KuVvGrbmrVHMgishhAps7c+CruVh/f+tsv89b5E4QIEOro4Jv/9M8YhsKD738nWnmB7FydK1NX6V0/gMsXYOzKFJ/+/Ic5ffo0pUKOqiHQ0BRinhVaOno4e2GWQslG00oU6hqIMgGfi1KhgldxU6nqWJbNX331K3zzm99EFCHR1kK5UsEyG1h6DVv0oLgVFFEhk80iSwq6bgIOsZgXU5fI5suEIiEMu4Gua9x5+41cOH2Vrr4YY+NTuKQYmzf5uXZ9GksQuPu+/Xz322/8foH6hf990BloCVPVXFhCliY9TnnW4PHnx+lY58ajehB9Cg2pAXqBo7fcwOjcVXyxALMLFaJeL47lZjm9jGg3k2iSaO3tY6FYI19KkVuqoucD3LR/P6XiMldGLnLjXbuQgq187i9+SGesk0HbwVDr+HSBsqXi9YqYNChWs/Sva2fTjgQDg92ksimmZudYWV5l40A7zR3tzC7MkF+tMNTRjuZVSU0tYehlvF4Pgl8lOTfPDXsPcPnMKImOFqpalYG2fh567BgbhjZQXK1TLhVQg2Gq2SytA814ymU++oEPceL6b/jp8VHev+dmZP8ShYKfcDRANZnGLQq0D23i8WdfpCW+iReeeZ1WbzOtPonWiJ9AIERHSxNDA1to6Q6TGOqlupDFlopomovpy+cZnZymNJ9krqFRzBuY9SKDPc0U5xZpTrRSFwVMCZpicSqVNGXHIhyPUctXCEgqhqghYeAL+VnNpPDGm2noVdrNGBUjQ93qBEnHsTRWciX27BiioKdx234ujc7gj4YpVU1afWGy1QqCx4suqjQrNjVHxlbdVIUa6cws2yMdhOpliMTwFQw8FCl4QyyUipS0GnndTzWcoFhJETdMcr4IZEu883AP2UodwavgdrVy0wc/jCVoiGWNvuYoMzmT2ZpBvlyjO9FCfPkk6+tLrFyaYFKH5qBC84ZuatMVOuIhzo5fxtPZRbNt/9+c3WeXZWdhp/3rnLPPPjnHyjlXV+es7larlRMIBZDAwgKMbTADWIhkwDL24xkbsI0TMDCAQRYICSEJSa2cOlRXh6rqyjnXyTmHvfd5XjRvZ17oO9zrv9be676vH0aTlaikpd4qkgwuY7O6eKnmJGz0ka9W0cpV1OoKwauLbF2cJRULo5gk6uq8OJwWBncO8Mrp52jwezEa9ZRqKkpKEbvFRSaUJZGKk46L1Lc5GB+bpKern3QiSbqSpSZp2Tc0SDwRRm8ygGIkk0wRCW/hq/NjcXl4693zfPlLn+WF556mwdvCdaeOsTy1yM6BXv7p337AajDF4IGdKOUiJquLdDHI1z/+MFeGh6lZXTz5wls8dO8pPCYTZpcRj8/L3377B3T0eGmy13PlwmV27+7A4nczfmWJ++9+gF8++QQDO/o5dedBnn/hPMPDw5y8+xhHD+3F5/SQnFnn+//yc4LaCjbJRKaYIp6q0t3VjFQK07W/j8HePgx6Ez/519+RV4rsP9LFgcETlEpblDIFZmfXmZxZRmuqcf3JYwS2EuS2EuSUEqFYnL7WFkLJKDlNDblaZX9fL53tXbx8+nnuf+hjPP/c01Qrahqa6jl3aRwV15rEUkXm05/8FD//2S+pVCpUKhI6UUCtVlGtVnG5bbidDpKJbfbu2cXs7DwWq4OZuXUErQYVGspliWpFweW2Ua5kKJVqyLIKk01EaxQppiuY9Coy6TLHju1nfukqpYIam9VEjRh9A00cvK6Jb37p3Pu7NvXK6f/5+MZyhHOTS7SLdlIbeU50e5lZnGH3/n50oonV+W0EtYTNpMdvt+F1u0kU8yiIyJk0ekWPwVtPoSCRT2aIpzIIlK4FXysm0uk0iUiYRDxJg7eTi+ffxeqTueuug8TiecS4DmoqihoVVApEkkE+9emPMbSnH5vLRngtiEmrp5rWYDOoqEpZ1IKVbE6LXm+nRg1JyaJX6ymV9dQ0VtYW1wmFS9hc9WxuLdDd5UUrqklnimjlJK46B2fOzyFJCoK2hNFipZjUIDo0bM7JRBcXKdXpuf3AAIvxBYzaZmootHW7WFheoVCRWUtG8XnsjA6vUyqV0AA5qcpKIsJ0OM6V1QhnJqZZHTnH+ItPcvHy27x3dpoL754lnUigVJMoegM5bY3WZjeuJhuSQyRRK+Pz2lkNbeJtbScW2MIoigTiGSRZi1LVIBdLqLBiNMkY9TW8bj+bqzGMooVsrUBOyZGs6SmY1MQKOVSN9VSVHAVVjZysEN7O4bAb0KoryOUqyapMu9WKUs2BWUdtexO9kiK9sk2HroWkVkRKJZksq9loHWBZKJA0iUhGA+ZGD9oGF2qzC6fcSqunDVVTI2sbq6h8Pqo6M6mUivs+9SnOnHmFw72trMQjmDzNvH1lirp2N+XJl+nNLNKXjRAqSczmM3Taa9j0dZhFgWgoyPrKCv19O9BKJQxWM5HlOfrdal66NEfP0AGerfpY03nwyjly2wEG9+4mPrnCb/7Pb+ge6CWYSHDkjk7ySo7tWADQsbQSJJ82cPnyMmVFz4fvvItf/fQVtmM5zFobvoZ6lpcX8Lpb0GpURGJFqoIK1AUG+4ZAMFGt6VEhUc4VkWs1ovEU28EIH7r3Bl5/8wwrK3H27t7Bb37xDBubK6ysrHPylpuo6TQsT80zNNSL3Wemzmbm9d+9QlNvB6Nj4xh1Bj7y4L2cffctLpwf4crZEb785x/hmWffYDWU4dZ7bidbqaKWoJTIEZFKZMpFDDaBjbVtluc32b27C6dGjctk43uPfZuOPd3c94mPc+bFt+g62ElXbw/5dJpYOExXZzOxWAiv18vZ08O09Hj48lc+yRuvv8LVi5PUuz385tnXaGir54ZbDrM4GyO4naWjsY1gNsjxW49TCJVo7mriyqV5Pv2Zh2hq9BMLhghGQ1SUCr29Ayythbn7AzczOjpBU1MroViYalHhQ3fdy1O/+Q2yLKPVavF43JSrZTSqGiarQKVSQaMqYxK1hIJhIpEc2VwBnU68RtQoqj9E5TW43DZyuRwej5tKuUpFvhafl5QaNpMBjVBjfWMbp8uNjIbAdoybb72O4PYyDX4XN97w6ff3UuqpN//+8b0d+4jkI4S3wFGWaNErJFVuWvd088avh/nILSfAqCKfK9DW4GNlcQut1Y5apTB+Osh1xwZIl9RsRecwOcroakVOHT+BSiuQTuZobLdisjiREQiHl2hoG+DsmyECS7B30ElXr4bJ+RnUZTPdB5soFLOIBg1VciytziMbYHl1lkIljYSKnr4BLk1PEFrdIpdI0tziJ0cJRVFRVWAlvEHHYBeZYB7RUgEs5NM6ZiYXaGxxodOpMHscNHX52d6M4HC0geBkdnySHb095IphUuocuVSSqlpALqlwOnRcvRAiay/Q6mxEUxDwWxyo9HU0djexa6ifqal51CoJjUqDYNKi1daoUCMtFWnr60E0GyhpFPImkZJcJJHNIAgysiRjExRMlQLRYJRmsxFZkrD6/Wxuhmmob0KpVChUFUSDFvRqihWFIDmaenzoNGZySh69xXNNJ9CZiOUVaoqAqiziFb3UFco4DHZKq1HEVJlur4NaKo9otBIQdMQNMpJRS7IksFx2YFCMuI91M7yexOw/gtLQicfVQlaxYm3YR6fNg1JrJ1jsZytlJZ1SUyrpKZJjs5RGqObwyhrivjYysQy33XOSvEWgnJN57PN/y9c+9XmujF/k8K4hpJeeYqgYxKjVUytV2BidoMHjRC5K2NsbUIoZcqkcNreRitt67aVVKY9iqrExlaLt8CFeMNoZnV6gyetiaWudG26+lbEXn6TD7GIxFKZmVdHVpUMs6SiEjbgddq6Mn+fE9SfQCHpMNgsboU3y6QxOp5Pm9gEmJsfJ5CKoMTM3t0FjcyNrW1vsP7gLs9XC5OQ0kXgMu9XNwuwMVpOZYk3mjrs+wMTYOG6nlYHBJg4d6ufl08MoogB6O6lCnMDWGkNN3ThseoLBELua6skGY4QNMm67n5nReU4cPcjXv/FP/OmnP05/Ty/7d/cxtbDMrkMHCGXUtHW3srCwxbvnR1DrtVhdVlxqO5fOzrHr4H6efvYl3A0OmprbWd5YwjvUydzEDJGNDK+/M4JcUbMwM02pLGN11WH31rExv01wM8P0yiJ/8rG72VydwaBx0NLWyuuvvcbNd58klsgzObZEW7ObtlYfK9tLmG0e9tU3cmVukmxBQm/UcWr3Ln74Lz+nrrmLTEbG7fFz9sw56up1/Opnr3Ps2ADDZ69gN9kRBZEro2NYbUZkWcZkNpFOJqFWo77BQy6bw+FwEwolEbQi8WwRjU5HWZFQiyrKsgR/qIspsky5WsJiMxOLJlFkNVIVBJ1AjSqSXEZSQNDoCQcTNLTYySbLjF1Z5ba7DjI3E+SPPvrF9zeov/zxtx5/+q1lugYc5BIVJrfjjKRlSroaS4EERUXB291AtraNRpDxO5qpKXkytTzvvDrDvffsI5LIk0omcVj9bEXS7O/qYjud5srSLBQ1nNy3AzkVocnjpKW9i5pUwiCkOXhyiO3pNWrVEh0dLXi6/bi8RtR6FRarjqZ2O/OrkwTSJSavhuho68DutoBFJB7ZpNXfhMHnIbsdYdfRY4yMTLG+GMDr6GYrUqISWaJvoB9PnYdkbI7urlZ0ggWzSSSTTiFVZYb6e6hu5RDtFcpVmaX1da471knDQAuDfb08/+x7HDi2k8mZAKV0Dr/DjVGUmByfxa/bQVQV4F//+Q2WriyjdZtQiteM9rJSw2Y0oxTKGEoZ2ussOEQFh85MORVDtPoxWw0kKnFKkkAaFXqdBdnuIptLUjCLxCTobGgjFEmR0dspqvXoVVrcVifZVByvoMNbk/HnJfzGa4emXCigV6lxlGWytQI4Wwisz1EVVCyqrShWNxg8hLz9ZPVeku01Ksa9+O0ijR4PgUQKffsQkYpMb4+N2HqUmrUebbXARiRHAR1SJkwoWSZZySJJWwiaJEadQFXKka5UcVnsRPM6okoRVb7A4J4hJhamqFM7aGrv5s3nfofZ60WzPccuaRFvNcH4VIjOrh62ttfRddVT0Bjob+0ilY0TCG5iMRhRLF4atBY2V9dJLWxxoLOFGU8PbypOAmvLdLT3sR0JcKC/lWogxt9964e8ffYc2UiC4HYCUWgkl60SLSwjGh0U8kVmprcIheJUSnk+dPddTM8tsbEVY/+Bg6xtbSIgkkrlsDmN1DQijV4nqXiElfkwgzv6sbqMbG9sItQEVleX6O3dwbkLF9CbzczMbDJzZZHgZor2ljq6eoaIbq7z0P13YjAZ+PUzr3Py5l14/c1Igo5LY1M0ev3IShl3kwdnXQtWu4356VmmRkexWG24nA62V7aQyzFmRy/S2+uhu6uHSCDN4K4DyF41eVWagc5O7rzlJp575jUMXgdtvX5u2rGTFnsdolPLzfed4K4bbmBueY3FxU2S8QQYBSxtDTg1enRNDlKhEq1aH68MX+Li2DKf/PTDTI9dYaivn862BraDq7S2thCPpJm4OstGrgRKmesPD5BJlnj74kUe++pf8NIrpzFbbMhyhb37dlCulvjuP3+df/xfP0VWC5SlPNVaBUHUYDGYSWey6MVr0KFGo6G+sZmN1QjpZBa9wUpeyl/j3FFQpBo6QY/LYSWVyCHLChpBoFyu4vE70AoiqWQRj8+CWq1QqUiotQJlRUYpS2i1YNLZyWbSmCwi3d2DVAjx4P3vc1BfeOY7jzc2COzpu5Mzr49w4tBNyJoMge0ayWiJnfuaUcnX2pEOh59Cqcjy2gq7dx9ArxfIlxREaz1FNdTUSeq8dqLhBC3NDVhsFjoamhkbXSIazJDOVTh/6TIagx6rw0Axk8JkNBOKRPHXedHpoJhKsmtwiHwpQzxVxuNvRZtSc93hvdhsWoKLQeosXlQ2LaFIAZ9oJVQoIGUzjI6t0t/loamhnvYuC+VKlWg0hqCOo5Y8+OvqiCRSZBIyGrUeg0nk0sgYZrOLickNugf7qKu3U6sJlFMSo5c3iG6mqfPaWFsKg0pLbi3DzPIGRw7cxk9/9SqzY+tYjAp6zbVsMYpIhQoqVZWyAjmVGpPHgUpbYjESRGUy4ezdx/r2BsVKEZ3eiVQS0ZssCNoiqqIJiipaRBM+jYzP5aScTdCtTmFKbqN2WTFuZQkJNRySwqrVy1OzKmZrVfIGBzGDilW6EG0SstdOznICnUmD3tuCpPPibm0n63CQN7tIZqLUqmmqcR2ZkkIsokYRbJRzeVKFFPNLUQyeTmazeYKZBHm9nogBUmYtOaFGtJZDdNhRdAJhg4DB56ej1YrH7+HEHUe47+a7GZ4fJVeTEMtlNpZX+cWPfkS9y8bs8BXu+cgDONUypXyEnKxQN2Tm6oUgqkqJQ4N+ClKScFZFNZFAUjTYTU4qySAGY4WC6OZixwBTSpV4JEu9x0ckGGB/ewfVWJZvfvXr7NjVhc9pxu72EsskUNQV1OogjfX1oOhpbx+kXMxh0RmwWKycHx5Goxbo7GhkYuIMyXgZk9GIRgPFfAlZltizZyeRrTAP3P9hnnjyWZw2L5WKgkpV4Y7bbubNt96mvrmJdDZHPp3mIx/5IB6PldWFLTbW1rjrAx/g+//yY06duoGltQXaGpp54YXXCAbDdHfvoKnOh7++EaWQR0rnmR2fQyvosdV5KRZkXvr9afYfPkG6BOurEf74gT9CVld55+1hBBK0OUxUUhnOvTfBlavnEdU2gvPzPHzTPTz52xfJS1UqlQSVbB63R8fZN97h2ad/jqXBQK/PxydOHWb3zSf5+b/9AklSiFgVIpvrfOyPP8n25jwtDU386qlnWd1K0DfQg1ql5rW3Rujpb2Nnby82nYFQKsfK9AqlUpUrExOEgnE0gkihVKZSKhEMbqJRYHZmGkHQUMzJKIAGyOYK6PV6crk8KsBoNFEsFCgWc3T3thGPx2msc5HJZqlKErJUQy0KlMtl1BpQ/wEH1ekEcpki2XQel9tEpVqhXJGRq9dYIlEngATFQo1KNY/eeE1vnp1dRJZrfPZPv/r+BvXpJ77/uKLomB5fwG4HT4OO4TObWO0aGpr1iBoDxWoas0HL+kaYMjXamjuYmZpjK7BKnb+Hjs6drKxs097YSDyZYaB3L0vzq+h0JubmlmlsrmegvxtZKZHMlYil4jS4m+jt2YkGLV29fWwHokyMBohFchQredQaAZ3KAIUakrpCQVNA1quZX1hmZnoak1NLYDNKT1c7ztY6Lp27gNUr0l7nRJJUzE7MMTK9xcBOH6W8nnyxxsJqgMWtVSIbKrSChNWmRWfU4W/pIxTOEtiOUS5nWV/dZmUpTi6fYWioE622wmogyvU3HcVqA4NPoJhIEknmqNMa0AkCGrUOUZExGvTIlSzl0jXRVFHVUNJpmtw2HIoJQQCbr4Wtq9MMaMyIFYloPks6VWA7lyeUy1FRh1klQ1QNF7J6Nqod6AwdhLUNzPn6kCo2QjY7BoeCu7GBSNxAU6OR5sZ2ilkFsa4JlcFAraBQTOgIVTMEkgmq2SKRRJL1bJyt4BoSFhK5CklZJlMuktSbmc5XKRUlsqKARnSi1mipOCQMvgbsHpGdfU20+Iwc3d3JDUeOcOOxfTS7/fhkBV0owOsXF0lvLHJ+dp5oMM6OW24hNjXH7Moya5Mr2EwOdFVAFHjlqd/x8IO3kQ6ESGaz1KvrqOk2cOmM6NxtJEZWsfjU5OIKO/e0kZdyTJydp25gD284W9iSrai2JErqBGa9lXqzBLEQp599nltuvp1UOolcqtLWA7d9cDfJaBarQSCZKCKIZi6OXAEkYrEQWtGMopLJZjLYTA6CgSz+eg+BUACH04lep6GtpY3TL7/JqRsP8Nyzz9PXs5PR8Sv4ffV4fW70RoGdu/eysLCESqWmo72FQjJHMpnH4XQyPjFNqZDCYNQzPbNMNJ5mMxDE7nRjthq5dOUyiysbuJwujA4NPX0taE0mtrI5zlwcxmJyTgVvAAAgAElEQVQ10VTvpaqoeeOdN/izz32cf//Jz7j07gQOvx271YnH52Dfvt1oKjKHdu/j7Ln3+OI3vsXZqVHslnoQqgx2drO2vcKFsRlu3XMjn/nat3DZ6/n9cy9xZiHIlbMX+eJffpbV1UmK4Sh33H0HTz7xGwa6OqmVVZwfmeGLX/4cG5srTExOcnD/IWSlRiAYZnxinuWVNRoaGvA31LO6uo7X56dalXA5XWxtbuPz17OyOMvOwU6kmgZBMJHN5VGrVKjV13wvVQ00f9AwWptbqVTytLU1kcuUUGv+cMVSrUFGQhA0VCsVpKqCw27FZDJgtdrQoMfrt4JKJpPPU6mCItcQBS06rY5quYKg1aDWCmiNNSSpRj5dw6g384XPP/b+BvXff/APj69s1djeTFGplrHU1ZMLJbn7thOkMiHKmQLHD+2mVsvS1DpEPF4in00RSARxelxMjS1iMhlwOpwYNU7i6RTnh6fobhsgHEpgNFnRCHqUagW7xUp9g4u9QzuIR9cw2gWee2GYUqWGoItz74dO8vqbo5SqJVZWVtlc3aSrtYuN5SUUGRKRFIVMmqGBHqqVIjv7jzM2eZVYdJu2zla2AzFKlQrhVJ7xy1uY7U5cTjdORxtLKxuMjwW4/e7been3o4h6EadTwepopqqyMjq2RbEax2pxkIhKtHd0U1/vQiNWcDWpaWh1kV4pIJnSFFIqWlrbSWdTVLMqHnj4GP37erAZNfhafEjVCj6HiE3QkK9KGEQBcw2KmTT5RJZEOkre6SVotKE2KkxXbFgGevE507R7zazpLHT6+nFbW3G1DWDy+cmrZXKJKF6vh0I5h8apRlWr4vU2EMloCSt5wjGZkKQQi4eJZU1cjm8RTCVJFAvUzB4UvY5AvoDGYidXUZBNAjWjBZvXSFWjo6HeRFd3PTv29jE0WM/Nh/dw/OAgu6023IUylkSeZDDEG29dJLgeY3RslJQMkUwcvceD2O3lUNdO+ncewBpbJx1JkTj7NGtX5xBzJRLVLA5FJK6t4pBq/Pf//keSi1fIhFbIFioolRqJpWm6OjpBZSVthvnZbToP9QMi2+E8KUHDmNWH1bFCPA6KqMXmdpJYX+NAnwG1dhulYOC3L77Hbbcex+NRGL28AjUPdX4XaqFGupCjUpUQtBoGBnagUkNbRzNb2wH0ehMPPHA324EVVpeTON02WttbsTnM6HVgtZo4fHgHE1cXcDrt1FBIpGLEE0kqlSrj41MYTSbcHiepRIz5hRXS+SIWu43eoRZMZi2pdJrV9QA1lYLZ4SCRSXH7rTdT53TiM6l489wwJw/t5qUXXiQYDFPOw+f+7OMEA0H0BiOh+DZ/9bVHiYVjrC3P8a2/fpQ3Xh1hZHyO0HqCxqYufvrkb3j08U+SyVbo7K7jzdfeIF3OoNaYWFgcZ2F2ia9+/lHQKWyuzyFz7Sy5G23s7mhnYmMDEwZ6unYzcvYSzc1tvPXmGbJ5FVqDnqnpEZRKBZvNz/nzIxgMZuZW5mnsaOTkoSNspLeJx1OYjBZqGg16o5lUKkM2m0VncVAsxThydIjhi7Ok03k0goBW0P5BO1VQZBmPx0W5XEaWK/g8Tvbv28XZ9y6i0arI5YroDNfQy3K1glalQqc1Uq2WSafzJOI5dHoIhxNUyjIen5diqYwiyzTU+cgkU4haLZWqhFbUYjCoMep0VAoSUrXCV7/6PnuoL575yuNDh/bROaDm+uO7iETKaCQdZ94b465TN9LgNxBfS2CwG9jYTKAqm2jr8OJudrAaCDHU10cyuYWiLrKwuonH52BufhVVDfbs3MHSxiJNTU1ENkJYTWaW5jegKiNqbAQTVcpqHSaLlad/8RbX77+etZUpHnjoJN2DPQSjGfJlFYJORzGrYtfO/ezcPYDT10Ato8JssoJJTSVWJFGJM3k1Skldw9PSQjyQJZ+sUcyloCaRT5dpa2rm8sg0iA5MVjNmvZVSSeHs+RkqchaH24HJYCa4WSCRClxTP2M50ukYNx69npHfjmIfaMBQ1DOxtoqjoY6LCyF6ujXo/AJ+u4EMRY6eGMRi0HNoaA9jK2u09HayEAgjeszkTXY6OxtQN+wgndPTZnWRU9Xh1fWTkJpZD7SSFBrYjKpYjiiEw1sU0utUKjJbaonNWJKCViSaqRLO6jmbipIz6kiXIVTWUxHLBKpajE4rqoKKWp0flQF8PhdivYHegR56mxs5cWgHB4/00d+zD5eugq4skpidJDy9zsjYOEJKxeTGHKPJKGXFgc7vpmv/Dhp7munq7eZATweufI7M+hJTr70DsSC/+u5PefZ7H+WBu/+Eq888xAt//wy/fXQH58cD7BtwEw3lSdr1VENpVFaRJ373Bg898hGGL86jE1TUu1SUi3qadzaT0uTRpiCjqeA3mUnLKlJVhReFNoLVZgxkKScjDO7qITn+Hrv6zKSzIa6+G2J2JUooF+bWDw5QLQqUpSJGp5nZ6Q1kpUxHTxsms5VoJMn42DzNrW1YbDp27T5MV08X4WCIilQgFiuSzMRBzJNO5tjYiHHn3Tfy3DNv4/b4yGQLZLJpHvzo/czPrVMsgtlkRFYkXB47t958C2OjC9x5xz0sLk0TjqdJ54sksxlUaHC5LJgtfvKZNNVMmqG2RlTVDJViFafXzfHjNyNqRTyNZuKhBMVcmXMXZjh14wmkXA4kGQolhvYMsLQ6R19nJ9fdfh0Lq/N87x8eY3zkAjsGW7g8PMY9d5xgfSPCxJXLHD91A2VRhbvZzaVL47gbmwimyvQ0uYlFI7jr7Bzf1c35N85SUsnkq1lO3noIh9PElatX+ebffoXu1mZcNgfz80v4/A1YLFbMRhFRbWBxYZWaVERTU7O9FcThdLO0uITdasbr9iBa9HS1dnNpeIaiXCZXLKMW1JQKJaiBQa+nXJLw+33kshm8Pjv5TIZLFy9jMZvwef0US3mqchVUapSaCqNegyypkOUqRqMRtUZGK+ipVmqoVRI1ARS1hKhV4XQaScSzqFUaakoNq8WAUa8nHklhsxioFEt87a/eJ9K3uf7M4yNnJ7nrzn28cWYbVTiHoleolgVS6VVSmTz2+k6SmSiZskJVBqkQQm+pIld0iOoEhYqa5ViA7o5+3n1jnMNH+mhub+W5l89hMPh57aURuns7SGXytHa2c3F8jrfOTjIzvcn6wiaFtMTB63bz9qURQEtmO0g0XWA5mCK4HGB9M8zttx5HayhSKsqMjo6gM5lo8NfR2tJAPB9nT28Lk8ub6HBQyeQplNMcObafcCSKVDFgcznYjCSx2RtoqVMQ1RVSCYnZ2Q327/fT2eFlaiyM3qRCEI00NPkZ2D9ArJChvt5JUQ6x+/odhMPLDAx24/JJhKIFmur1iIKISpYxOMoISo1CWuBff/wGr41EqGk02O0+orE0rT270TtbSZctbGxtkS/nWUqX8XltbITPYLQFGejSMBccweh0IOk1pNVqkmoBxWKjpFEjG0UyWpmSxUBDrw+H3YLdoKOnp42dOxo5dstxjtT5GDx+jMOH9tHj1tBqNFOqQmlug8jyImdWg6gja6zNhYhJEQTdLrobNXgP9dO6t4e9jYMIxiImDJQWgyTnLnPx18/S6bHw42/8fzz+yEGe/ccfcOqhfZjOzPH3f32EqSdf4c2f/im/+cY/8NUvPcj5p1/nyMnrWJtZZCKcRTLAXX++C5WqyF986R5eeG6MB//4Di5uPIW53c7VyDoGmxGbwY2hUGMjsEk8tkmLx4TT1suwSuK1tQB1LV462+2ocwuc2NvI+soFqvEcr7xyAbvNw/zyJPd86AgOu8Tm+DaJlEI0pENQl7B5FZDVvPzCZUp5cHtc1FR5AhsJjFqB0PYy7XV2xkev0NbZh6vOS2B5GbtPxmQ0cerEPi6evUhSkjBWJTSyhM5k48LwJdBAKpNGZxF56MH7WZma5emnf8eBIweYXxpHqVX4i8/8OZcuXMHvbUCv19Pa2sHeGw9TiUUwaLQsxxLY/A5C8QIT8xNMj88zFdhGqMmYRchWJPbu38fmyhI6bYl8NsaZ5WVuvOkIk+NL7GxpRc7kWJxZ5Y3L4/Tt6CO4uMWvnnyTdKVEcHaDuvpGZiam6Wry0uDyE1kPkUjI7O8ZwKTRsry0QSmXJZKM0dvRQfvAAPFwiIpUZGZ8mlKmysLMHKgLzC9v09k9QLycR6/VsLK8ztLyKk6LjppGJBCO0dDQTCafQampsVut6AWFwQ4/p3//Orl8iVK5ikavQa5U8bgcZNJFjCaB5uZmYvEQPT3tBLZD5PJFNFo9uWKFVCZNVVJRLktoBA06UUBTqaHT6ymXZcrVKnq9HqlWoaapIdW4VqhSajhtNqqVCn297cTjUQwmI0dOHGX08iR1TVpkWcRqUfP5L/zV+xvUJ376+OM2uwezIGGyOTl3bgyNRSCdlrF5tEgaBatfZOTsPEO763E1CJw/u0Bbl5fV+VVMagcaiwVXVxNvvDWGpHJTLBoZuzBDLlLAqpe57sABrFYtje31jIxfxdfspfdIMx/46B8T2ogQWJtHqBjp7bMyu7KC2eHkvfcu87k//yPG55a594691Lu9DF8aJ5JJ0tG6g83ECudffAtPp586t4sz781x6pYTnH5uBLfHiKDRE08kWV6MUqNEsRLgwP5+tjanKOcrbK6VaOvsJBYPsTafxOmVqamTpNM5bjx5G3PzU6hVBbKJJG0t7QQDKRpbmimUYWl5mRp+JiejGHV2LHUJrCYYfnMRR/uNPPn063z8kSEGe0scu/4Ew1Pz1Ew2gpkkGm0Mh9eIv67M0aN2XP4SZTnEwf0d1LmgHNnE7rKhqfPh9rnx+R24nSbqGs0M9Pdwd88OjhwY5EhrNzucVm440ItJr+biwhxrb4wwd+YSlxZmCQbWSUZXiFiGEIzQ0tJHy6CL7oZGmjyN+CpV5EKSueFLVNcu8dovnqNbSPLs93/Dp27rJXz6Ha6/uZczv36T89/oJy6b+LMegWxNz8MtUU5fDvHV6x386PlRPn3HdfzwqTN89MM38HfffYevPHY9//i93/PZz36Yv/3Oc3zw23fj8dexNruCHxO55RD5tEQ5ncbv0ZEU3LQ6VKSMXtq6O/ibJ9/k3vs/BbkautYGRmpaVmQL2VwUbw2S8XlKyQjVNORiVTbnNjh85CBGgwWv14xOMGA3dVIxmsjV1ujZr6Kpu52pqTn29p7kyIFudg21odPVUZU1HDrSTqWQpa9rHzWxxsTVMbbmV8kkMhw4sAdVIUVwM8iBI4c4+/YIN950G3pBYG51EYvLQzZXJJ255sQ/8fNf8dijj3Ho2PWsrAfYDm+zHdymrqEJjaTm8oWLFLI5EtEEK4vLqLZj5OUMnYf7MIsGhFieycU5bj98J1uJddx6J9FEjN6+XtYX1ojHw5RiMQY7ehiZWubOY7dw6dyrHN27D22zjXAhj85kQ5VSuHL5CofvOMSpIwcwO8w0dvtZCCzxoVvuQEpG6GhvZSO+ysceupfhsUvkVRJNjY3YG2y41Do0FpFsvsjs/Brx5Q1MgoE/+dxn0Ks0DL85zGYsTO/gAEo+T5PPxwdvu5vw5iYHjxxh7OoURw4fJR4L0+Cr54tf+AuqUompyWkWgiH6hnayvrVNc1MLlXyeYr5KOl2kvs5FPleiXK5QrZbI5bOUKxJaUbwmdWh15HJl5D/AmGq1imq5glxT0OpFKrKEWgV6vZFSsYTCNXpaoYJWFCkVy5QLZcLhBOWShFyTWd9awudyUqnocddZSaQyPPaX7/OTfzn8/ONXFhYQqmZWZmbp7W7h6LHjnHt7DptdRqnk0YigkvR4PF7C0W0yxQINjW7imTSZpIrdx+sxO7ToZBWHb+tifGyOfDbPI5+4icsTYwztOszS4hzZVBG7ycqOng50pTxashRRsbwdRRQqWNQKTqeb1WiE/d17OLp7DwuBJS5f2sTh9pJO55ifmqK50YPeoGf3vp1YzSZefeUdroyvYrV4ySSjGMx21Go1kWCOxiYPJqOevs4epifmMGgMOJxu9AY9Z85M8Zkv3EMqv836UpYTx3aya+A6nnr6vwmEszS2mhF0akYuLHHkZB9zy1dobekjE4NsKYRG0NPUbCETTZMOl+hqstPZ4yFTy6A3upmYiyAaZY5d18jhoQY629SUVUu4xBxWUxaHXkU5E6O1Tk86HkEvarFrrfg7BmkWZVp8UIhGWB6dZ2l4lbWJKa6uzHFhcwNdNovVoqE+Z0LjttHocjNw00kcLpED3S20+euREgkCZ09TmbrCS7/6JX90oo+/+vJ3ee/7H+Khz/wzM//+IP/7h8/x4jceZuTMJX792BHWl3J8+ViG//N2iM/fuoc7j+3jxf9+nkc/updfP/8un3j4Pp5+9QL3feRDLL23im1nPyuj8wiN7VhyKYJaNYdsAj88O8dD9x3i3GoUUxmePn+ZBw8OcHFikX/59CP883eeoX2wix8/M0y72s1iIMfYWws8+eolMjk7754ZRtk9RM5Yz7uxBJvrJTr37GZhcgKt7GP66jz1jQIms4FcPsvC5ioXL22QSoPd40OlyzNxeQafpRsJhaWVdRS1lVAyTK6oZnVzg3A4SmOzlUxSRTFboFStsBzcwmC0MjQ0iFRVMTY7zo7+kxTlJFojZOLXuq9jo+MkCgVyhSKVioTL5aJYyrO0MMXG+hIev5fl5RX6B3bQ6KujVlEYmRjH5HXw+cf+khdfeZmGtmZ2nTqBWy3TblbT1tnP+YlJ+rt38NzZ1xjs6yWWy7J7/2F6unZy+vW3WQ6G6B/axaVLo0TTWRbDGTzI3HDoOPpkitPD45x+5T06B3vxOq0897MXmVhdQFQbqHc6EfM6RhcWWc8muHR1mmgyxOj4Fa4/dis+m5ef/PLX7OzrI7IW4craFpXtGNlcngZvJxWlyu9ffZeRi+OgAavFydE9B9nR0c/uxk5+/F+/4PLiAmsbW3zzm9/CoBI4uHM3O/sH+e53v0ckEqO3f4Ct5TWS8QRGo0gmn6ZUrCKIIqJeS7lcRBT15HM5TEYjbo+HUCDB0SMHEXVGBEGLwSACNcxmM6l0Hq/XTbVWQW8WMZmNFEtF8tkyRqMRFTI+r4dy+drLK6PBRCFfoqaoEQQNGq2Kzt42UrE8wWCSZDaNx2Pgf3z26++vh/r3f7e7FtEoZDYDZIIx7EYPW4kkjfW9LM6FOLC/jbVAkI6WNi68u8qe6y2ce3eRJq8bT6PI9nqUjz5yFwvLCaqpPNlqAKu5hbXVMCpEGtvamV8IEdxcoVQocN3+I2QyGXLxHDZnCWN7I1PzSSjk2LiYwNWmRyNa2F4LMNC7A7NLy+tnxilmCnT3eLCKAkMdHTz9+3e55zO3sfjmOSqywKHbjjI3u00guElRktjYSOJ2Gunv7SaXjrO6EsBf50JTE9i938ulkVViqTSiqMfnbKeQi7B3dysXzs2xa1cvNWOOzvYOwtEQ8XwFl0uDCpnhd5fZOdBFXYseQXSxubnAwuUtdg020Ns/yFpyCbUkoBdd5Irb6HVmkEU2t5ZoavGCSqSSr+G0WikVy5y/OEZbcytmu42F9UVuP7qP7XKZREbC0bSDzZiM0eJBJUGTtZ54IECqlEZbKKPPbHF6YoXd9U5++9ooL37vj7nzSz8j/9zf0PLIt5n61h08+vo6XzlgZ9W1C+Wdf6Ll4W8Teu4Jeg7fxNzzz1MdOoJzfox1Zx27Oo3UdFbmX3sb7/5bKEoBbrnxIe776Kd45iff5faHv87Lv/ga++/7Gy6d/Wc+fMdj/PLHX+NLX/xb/uof/4wf/92vefjzH+cnP3mGHfffwGBzK++Mv8vGZo6OOw5Ql01xbjJFbyzMO5tZ6juNTIXCGLV7uDA5RjRZxaEyUTJbcNkNHL/hEPZDJ4gurpDXhuhztNDf18vyW8+QTSZw+uzEI+u0NHfzxvDPuP7kTYxe2SSVKNPS5Of8lRXkrIZSPsOeQTsqpYHR+Ti3PzSA3+PnxSd/j8fdzfD5i3R2tCIazQiaEsF4ArVoILC+TnvzDgKJTUQxRqP1MBcvv82+/QP4XI3MbwRZX19Hr9Oh0+vRCGrsFj1dbW0899KrGAQTze0+vF4v42MzaHU6VJoaHpcbARWJWJLtWBSHRcsDJw7ga+3hOz/4MemCxKvP/BcPfvLTHN5ziOdff5f6pib279mBQSsyNneVfXt2sXd3P6+89g4nb91JYDHK2MQYgbUcVqudbCFNTRE5dvwQV4fPc+dHHqBUTfLwBx/gzz7zKP17GmhpdPLfv3yHE8e6mJpfRTAbOH7wOP/1H//Fjj1HyBYTrC+FyWTSlFVqSmXoGWhnY2ONT33uE1x49Tx+m5Nzw+fJU+XI4aOMXb5C/+AAa0tLeCxW+gcHmJyaIVMokcrlaWpq4NSxE/zHf/4nZocBtV4gmctiNhgoFYuIWhGVokGvM2A0iEiSxI7BQUZGRigVK0iKgsfnwmgSiUbiVJUqGkGNzqBHrYVkLIdJFNGLOiwWG9FIklwhj1arQWsUKZfLiIIOo1ZHMp3C7XOSLiYgr6NYKmNxGKirqzF7tfh/fXqq/r+uKWBTbKxOLhFNSnjbO9A3N9A/2EZTm52ybGI9tIlU0LG+FeZzn/8ioyPLNNa14jZp6GrtZaBxH+vzWTaXc+QzWvLJHhZnYpCV2FrNE5rbosHnpGeonwcevo+RmSmuLG3i39NDStPEyMubJJfWafUNcsOH+wnFCyQTBerbWkCrZnRqnC/8+f3cfMtBoqkSa5ECq5ES93/yQWbOz6K2mrn5zj2k0+tMTI3T09/Go9/+U/7i87ewb38rs3PjhIJJVGoRj6sZh0vH5maZXFrAZW/BbHSwvL5Kc5uHxcVpPvTBUyxOzTA7uskLv36JbChBV7OBYqTKysw6p67fi6pYIhFeR6uoqAartHd7CCUj5OUiT/zXNGqdh7XgFGjURONp8lKR3fsPs7QWIxnNs7i5RrQYYTO+ytHb92Fr1XB27BInThxkbHSUOpOJRo0FQpepzJ6m9O7vuPiDnxIdf5bTP/8XvrNX5tzrT/Corcjf3NfOwfQYwTf/msiLv2DjpS/xk+/9KxPfOcpPn/g937ijlSd//RZ3NNd44yzsVgd5+/ISWl+eiyNRTh4U+N3IInffcYT//I+36d2zl2fPxTm2r4k3T5+hHJnG1dJNePslThzuInLud3zgjuPIo5OkJDPayBXikhVfDUa3izS3aLm6EuKDfV4+8z++j1DXzzsXN3GtSFx5fot7bxjkvN2Eut7NSjDGH93xAL969W3ybhtum4OUQSCX2aKYKKJYdAhChKoUQ1F7KNocLP78Ca6+Pc2ewZuJrmbZXI6xtriBV9fB8mSMYjbJIw/fz+pCmrtO3YzTKXHHXdfx7sUljH4f9z3Uz5nnR/jR//oxfr+ORDpBuaRQZ/OztbhEW0c/WpWG+Facwb5eBFOClekNUttlFhanePBj96LTC0wvLVAoqhANeoqlCh6Ph0Q8zckbbiYYTdLa2kF9azPLa9tcnZqls7eTqqqGLMHS8gKB8BaSWsHq0rOjtwuVr5H/fOpX/NM3P8tDJ/r4wZP/zY7mTm67/UZa2tuJhsPUt9Vj0eSx1HL85acfIRZdp7G1HakoE4lECAXKJOMhNGoFdU3N0EA7L/32BfYM7OCH3/0PfvSjp/jpU78gkNgiq+QRrQ4e+ey9NDf1cN3RXtKxEBtLE9xz43GKyQR5KcO999yOzemkUCrRs6udxdkZfvg/v8K+bJjPPnIv7a0GPnD7dXzzww8RXpvkCw9/iuDcFF999HPcdtdxTp3Yzb49gxgNIs1tfsKxbV5+++VrgRe5iCRJWEUjmlqVrs4OVDUZjbqK1+dCpVGTSCSYmpqgXKpSkWR0Bi3ZQppgLExJLmMwGRH0OorFIoKgps5vQ5ZqpFN5wqEoFosFl9NNMS8jFxTMJjvFYpFi+drvA9QqiiXQ6FXU1FApFSnmhf/XZP6/B/Wlly+yv2cnTtFKMhVmdHiSZDBDLB+iZ4+eZEREL5hoa2nji196FI+lnj3d3RzY28/zp1/D2mAjH6shxSq4/Q2YHSksNjVGn5uDhwdIpSXKFShmigQ2ogSWI7TXW5i+eIZP3PtBGvtdGE0eRq6+x/xCisZGK/ksqHQlZiYvE9sq8MxTbxGPZVCpK+RyaaRKmcXtJbbms1htfmLbUTw2H0ODQyzMhvi3b/4H4ekVWrqseN0e9uwcwqBRMX95hXQoh9tpw+oS0Agg/f+cvfd/3HeZr33NfKf3GY00o967reJuyzWuIc2pEAg1wMJyKLtLAgfYQ/Ys+8BzYAMLyy6wJEBIIJBG4iSOHfcuy5YsWcWqo5FGI03v/fudeX7Iz/uc14t/4vrc9/257/cl5dm7fwNXL7qwlpnIFlMkBBW6aguaqiqsDdXcvDlFY5Odge0HOX9ulHg+Qy6tZnr8FqqSHKmYRdBq8aX87Npfw4rLS2tdLxO3F7EpTDj0dtYCMTqb16EzWWnt7SafU5FJCIyPjxOPFujuqCOXLGK22Lk1Mk5a7adWaaTNoOD5rwxgD/r5yV6BnUo9P/v27zj5rbt5/fwVHuu2cc1VwBmbYWhMpNas5MpcjMrWfsbXBNori0RLBvKeEQrqSvKuJVZFM7XuVZadTgzBNMtmM/LAIDG1AeXkCSrba5Dmj2MwO/mvH/yYp/52L++/NMEDn36As6c9fOSeGp5/6R2+/vSDnH7vGo8/fYT33xin/8gWht84S0NHJ4HBa1gNdobfPYWjoZZug5ppl59jfzjB8MXrHDr8CDeG/Pz2/bf5yxufZ++WnWx78B6UYoknv/wVpFKeHXW1RM6dYbe1Au/Ns9TP+wjFwxSLafoaVOzeso7XXxslPj3HwIajdDXs4P7DjzN+awq1Lsng8HlM+ipSQS2NzhauXjzFyy9cI+xDNzsAACAASURBVJ5QYTR3srSgYfDqbUpyGVfHbxMWSwxevkHYG0cSZOQLoJU76OhowVZWxZYdnfg8C6TiAouLHpTqAMFgAIu9jPGpO8QicZ790U+4Mz3HssfLzPw09goHwXAM75qfgM9POhrHYrFQ31hPsZjmY499jKnbLsrVJqoMenoMeu5q38DEzSWUGjm/+q+fs+fgAPZyI8VcipOXh6htrGXb7of49Y9f5/jLr/GnX77FnVuzLCwss3XzdsrLDMSjCS5cGSVcyPPS8QukZHKe/PTHqbSasajKeP25y0wM3aG+Rs3g6FneO7XG6pqKTFBFLJJlfi3MPTs+hOu2C1GZY93ebva3O/naEw/h9nkx9A5woNfC/3pgF89+9jG84XnURSvTgQn6NnaQjSSYmk5w6doi0/Neouki065VCoUCXs8qHo8Hjc6AWCyRTqepr6lFkEpIhSJOZyUTt2eJxyKUV5RRX1+PTAC1VoUoKyHKZCgUCsw2CxkxR0ES0Rk1KESBkC9ONlugotqBRi8nGvNRlPLU19uQxByJcAqryUghL5JJ58mmCwhFMFmMlAogKyrR6cr+eqCKZhnxdI5IPER3ezuPPLIDOUbmJ1wY9Xn0xmqsZQ5uj02zsb+efbvXMzU+wvDUOAfvPsiFS4OoNGa2bN/J+yfPkAsokMkqsZmrWJxfYOeeLcxOz1Bb10I6kuWuXTvYtWU3n/ibT/H828e5dnacTFGiqb2HxZk5dBYrRx5pIRLNIwgCuayaXCnP4vwdHji8n67Oapp7LGzucPLxrzyC3qTmN2+NYdDaMdsyNNdZ+Mi+g8hkGaw6GwNbdkKuQNiXxl6mxmRRcOXSGEtLPkqyJOFwlnSyhAw1FquD4Vtz2O0VCKU0TquGCp2F4LyIazHKsddOIBQFFDobje2d5It5dOut1FT1IOXArBKodmopGZT88a0z1NetQ9ToePPyJfK5EtqiQDaSxD1zh0I0TZleS02ZkxpjPU5jBcGAG2N5GVX1lRj1agLFOJpqK5fOD3LoYxtwLyX45GfuYjSao7a2kotxFUWfm4ruPvIji8g7rMSHPTRu34D3xASbP9RJ9I7E/kfvY2U+yEee2sPolTE+9slDvHtylMc/uo1rZ4f42qc/yvXTgxzZ1cmd4UWOPHmQd04tsm1jA9cXlLTIpjk76qdDp+T3Uwla1q/jwrSPA+0qfv5emHvXbeTfjl3iy08+zsmTE/y/X32Mn702wbf/4bOkUlp2bOvnlXfepntXP+OLAVQaB79+93v0PrKLAzt6efb7L9PYIKKqE8lb45z45W+xVWuwWquRpQQ0FRpqMyLHfvszVDKRRDTB3371uwh5BzatGnNISaVGDmIOt2uJs6euolXVEPJBKJbiwuBlYpki9rIm7v7QAaanZ1hwzeByudm2bRtmu476rmYa21vRmvU0dDupbXYwszSFziynKMvT1dvBsXcucvrkBFeu3GBg6xbEhAy7ycqqexmzXodcDiqtBoPehFQQ+dqXv4hRq6V/fQ8Wo5aezlbq6qtxOBwgSjTayvn1f/wWTzzKrQU3OVQ8+P3fcdm3QiDiQ1nuYHo+gDwrUGarIpMCm70Si97Brj272XTXLgqKEqG0xJx7FZNRhTcU4sboHGa7AbEY4+4jG6lwlsinsrz4wh/55QvvUNTFOXDfJkYmJnnuuVdIxqMghXnwsS46NziZz83wiSM7iQQWeWSgA3VOS43Ghr2nF2Wrk7mpUX752mt84Ue/5hfnBlEEA5x+/xIfOtjKwf4uxpZ9vHrxFOnAFBMTQ0STKQSlCoVCQaYgUhAlCgWJWCyJmC8gUykoycDn84FMRjQepbOrAZVCIBIKMjMzg8FgopAXKYkyVKjJ5wrE4lEUggxBDkrhgw69KIGlzIgvuIqlwoSjykZJliUeS6DT6TAajYQCCSodVaiVGjqa29ErtBRzJTQaAZVKxaJr6a8HalaUMzk1xroNW1jzrmI26Onb1sj2nk3EAylUZXP84eXzKJVG8vESw1en0ensLIcinDt3E53RxJWhKyx6PNTVtiPKYOLGKJFYCJNdx6ovxIaBftbCi/hSPsYXJtDpVSyMTWAnimVdLTu3d2BXydn12FHiK0nSaVhY8NLa2sGeo1UUhCC7tm9i8MIQuaRIIa1BDBQIhqdZ19HOoSPr+a/fvULErSIUSpNI5ZmJSrz16i3OHBskEQize08nbu8qXes6aWtvQm/SkkxmUagK3BgepSDKWfJ4cLlDjN2eoaW5noIsxanL16lsqmVhwk9OUhHPpEimQgTXMtjKzBQiOd6/eoNQrsSVwQUykSINZVVs6l+Ha9HP0G0XvZ2bCfhWWXIvkMlkSKVyLM0nqaqpIxhJICsqCAXjOOxlpLJRpifd3Lk9T16nQy6YWYhlMVUWOBOH45dG+Icf/IjrL/2JulYrU24/n/74Aa6Mu7j3vseZuDPJpx8Z4NyFi3zx0QFe/MsQ+/bv5fevT7Kr3srz7y+zuauVP48F2bK5lzdHogz0FHj1ZJrH/mYXPz+5yv6Oen43IXBoczv3PXkv/gkv9bu7yFweZdtD21k9fZH1ffu5ds2Nsb2b5LXXcDY6kQWu8344j8YscdWVpGpdOVPXb7PtsXt57noc2wOPcGMlQE9/NwM991InU/L+lUts296PzOOhzz/Ps3//RSxldlYn3Hzxo0/Saqvg2c8/RUFnhXCQxcsT6JRK5BoV529dx2RSsCLP4o54GBy5zPSsh8q6StZWk6QyKSzWMuQqDau+AG7vAufOXmRjby/N9fXU1zdwe/wWmUyGhZlZNrQ3Y1XKcOgr0Wey3L/7CLeu30TMppFJMR68v5uvPv15fvjjf+Tk+xcJRpKEY3Gy+RzJfAZdhYmSRklWBqlshj/96Y/ML7hY8a6xuraG17NEPpekqqaSZDZDIvGBRnnv/oMEIh7KbFUYq/WcH3XTXGumpNXQ01lNKu/B75nj9JkTjNya461Tl7n/yDaGLl0klysQyyRAo0WrMbHoWiIcibPg9mIx1zE2OsXefQNIMgWxWA6tMsuz3/8+AV+SeEbCt1Ygla4hHoFouMArf36No/cdYmJ8hkQ8z3MnhlgNrnLt+iS/+PaPWZi4gcYiQyjmeOsvIyy6chwbHWH8+Husa+zm2shVlNk0mkKO+WgaTyTGrgN7qKuvRqOQIeZFVHotRRmYzSZ2bN2BTFZiZXmJfDpNud1OWVkZ8/OLyEpF2ltbyWTzhEJh2traoFAil8qg0+jRqdVoFDKUMigUChRKOWTyEoKyiFqtpJAvks6KyBAoySCXLXD/0f309LaysLBIQcwQDgUQswUS0QgAuXwBsfDfjk//70Bt7mgkmhW5ee0GkjLM7dkxFhbdLEwskAwU0VkVdG9vIJ9W4w3CtDfLUkSO2VTHhnWdhCMZ7j76OO+eOsnYrWm6mvtI5DOsrrgwmNXYbSpUKYmRm1cQCiH27WhjbPgCOquBrEaiUyZnbMzD8MwY8rkxjj7UjLNSS09fCw11AhplkmJOjXfVQ14usOD2MntnnqXAGm01tURiKzitFXR3NNLc6kRlK2d4aZmNre0M7GkjLslJ5Yrcvj2PWm/lxKnzLPuTOMurcdZVoVYJdLR2YNBJCEolgjJLVYWNSCjIjk17SQRgbcWP0Wqmqa6c5roK7PZyvB4XC54o+VCG5noVatI4LeV4F7MoiWCSqSjT62izNzE7M4NCpUNhqkBtNiGmZDTUWZEVBExaE/O+eSTyjI/OEPQmaW5tIJeR41+NIgpyJopFBK2R29EsbVvaMZZHGVkSeOpz93FjWUYTd/jpuQDr7HJePjlCWwO8e0dAvebm8kwIy9xrLKatKPzzrJUbMEfCBI1aNAUZebmG9Pw0YqMTtcfFvAjy6WEqWhuITo5wzyYN75xc5MmP9vLCu3/hw/tr+M9fneAzX32YNwcv8NmvfoyfXIrx0NN/z9/8apTHvvMNPvKrQQ5++VMc+c/TOLfv4WcvvU+hu4GbF1+moXuAscUIb/78TSorG2mt7Ka2pZ5YIMTtwSlO/uJF9h9q5jfH/o0vf/0hipk0IZmOwOQo2x8+hGlbF75wkbLqOjKyAO2bqsibVZweuone3owk1zM45MFk0xMPSGzcvAmXdw5Bp6C8vB7PSoDO9esIRMJojBryUp6uTZ10b+rknkO7iYh53jj+LpG0iud//xp6rQm90UIsEGbiVoSzp45x/fo5jtw9QH9vzwdBJs5KWprrEfJacokM8XCAJz52FNe8H4NeTz6TRq1SIRaL6JQGJkfG8fsiBNJ5LBUVXDl7lsaGLmZm7uA06mlsquHjH30QfUlAUCiQZWOY7XrWd3Sh15doaaziBz/6McmcjERcpL7KSW21hc71NdgqVBw4vI1tWzdSZrdSKKj580unPkhBk4PFVMbnvvgVlGqRe+/ewUOP7cViS+DyeElHDXzjkx9Grlyjvr6MWxN3cDTUsGPfDmwNZTR0NNNZ1kXkhp/qKg333b2T2WiEcUMtn/s/z3DlxPvUljcgyWA5A3MrixgMcvLZFLdGBxElifLaKg7cdRePHH2AXCLO/NwkTbX15LIgk6tJpOLMTS9itej4h69/h5vDM+iNBtpaWllZWqZ3XSeCvERBypMvSmQzIul4DvICxbwcjUZNLiuRSBcoiFky2RRFSsQTBWSCnHfePsHMlAuDRo0gV7PkcVOUFT/QcEsSyEqYzea/Hqhu9zRtjTakfAF7WT0Wk5JVj4+dOwewlcuIJQuYTSIWm4i+xkJaoeDG7VEOHzxCLJKg3ObgzJkz1Dd2suqLcuHaOT7z8Q/T09NHeC3HibMTNDQ0cHDnHuzlDpbckxgddi6fPU9lbRtvX5lheWSGLr2VolDHrQs+3FOT2Cx5Mskc/ZUbufeBzTgay+nqq+TgkXYeuX8ztZ3VnDp+gmwRzp4bZHY+hSil2NmpZ2t/OYd2deB2yVDIU4QyGcyVBhz1dvYf2E0hlUKmilKmk2FzWAmn3LS0lNPc0kCFoRqtVktjTR3n37uITZ+j1mbCpMoS9gfYuHE9Brkcmc2CvbyGWElGg6aOh/fcT2jJT19/NbF4numpKaocdVgqBJSSius35zj1zjB6jZJ1XV0kA1HkeZFSKoVMLVFWWU0iJdDRvp6ZmXEaqyuR5dJkskFyejXeCJSrDZhZBs8NJvXlVGpSXBpyYxeSKB1qTKl3UGgbSFx5k/qmFnxTE2x/+ABhT5RtD29iZTnC/U9sZ2ZkiHv33svNt5/jw1/Yxa23Vzn6aC9v/H6cTz39RU6+e5vPfvFRfn9ukoJSyZmkDUXzPp6LafDWbee9kpFfBYr86moWT007/3J5GW/rOs7MLZKrryREnns+/RiFWzc49NjDKFwXuffux8n5cnhWJyEyzSe//zDp3DjD/iEMBS2BQIa/nF+lc/NeKmsSBIZ/jsGupraugsYqLV0bu3DPjfONPY/StK6bcyeukfV4MBtFys0GwsE8v/r1q7iXVinkRKLBEI5qE797/iX+xxc/y4P330c4EKSzo43XX38NpVqBvaKM8ooyPv7xJ3CUqbh2aZCgew2zpQJBq8PZUMvcqo9opITbqySRLhFYTDN66jqqnMDk5Dh3H9lPc30D/e0d3HvXep788MNk4nlefOEvfOazjxGLxcjmc2QLJURJYHphllgqjdFmor69i5DXzUNHH+C3L7yExurk1o0VTp+5zjee/jHz7jXqmtpYDYg0t65j5NYM9xw+glQQWd+9ldYuG/sOtnHoYD+xYJibNyZxWB30dLUxNnYLrRq62tvI5YvIhSIKhYrR8VnMZTZKJTVXLt3k98+dZ2a+SHWTky1be7k+HWbNE8LiFPj4Rx9meOoSIyszOCUFKq2GH/32t2htlbQWKuiRR7hvdz1//I/nWAznuGlQ8+2f/om66hrW3DO88sPv0N9cxfmT72HS6FEq9CApOX7qfUqyImqFhtBqCpfLhV5vJJXJY7VakQkKUqkCX3/6W+j0Wgo5keXlJeRyGevXr6ckShQKBQRBiVqtRS6XEwqniccypFM5clkRu9VEPJlCrVYjSRIGg4ZSSaK9pZnuznZyucIHFWm2REEsIUoglwsUxSKxaPSvB2q5qYZSWk9bcwtGU5H4SoLGOgexVAqFUYnVpIK0H6U6z/qN5Sy6F/jXH36Wk6eOIxb0REJr9Kxrp1RSksoUCIYl/vzCm8xPzKFXlhjY3cufX3uVwWuXaKhrpL9jgKA3wP67DhLze9lxdxfbd7VQMBWJxhaotNroatrKuuYmljJRgsplguEAJoMGu7WEa3aamYVx1lbm2Hf3AOHwAlMTae5+8AB6pZWbN+bIBmQce/06TU0VqGVaDEYlC/MRmhrbOfbKTWqqtKzvrkBvzDGwvovFpSDTM0vUV9VQYRZIprPMTMaosDdQWdVIPicRiWVw1lRzaXAIg1yLVaFBEHPoTCpkxRxGrYblpSiuJTde7xotXW2klSKTkRCOplpaG6vYtL4apxm8K2tY7GXIBIG+hkashjJcC0sEQ1GWvCtoDEoi4TRKlYmiTEOloZaSUc/kbRd+s4YFUcJzewVFaBmFoCY7t4pcX0ZiOk9lTztxT4RN9w0QjoKl1kA8ImNbfy0+T5pNm5o4c+YO6/e2sjzpxdHkZHghwN6OOoamIvRvbOP1lQjFqmp+OZnh53dSRJsa+d3QNJb1u1iejyFz1KB2B3B0NjM6Pc7ejdsIeddw1NYjZnIMdNbyyulx3NMeSnoZ9967jsMbnVw6eZMnv/BR2ne0E5uexVlh5ol7H0elc2CzNeCs1lPeLufW1Umi+XIc1UouzNxk455NvHfsOgF3nvzaAh++q5u+PX2MTwUQJTsKtYkKZz0KQUlNTQ0anUQoFmV5Ocrc7CoKuZKwb4XtWzdR5ahg6+aNFHIiWo2RXRv78U9OkvWH0KphecmHSi6jv6OZDqeTH/7zJ6iuzrGlq41MOoG52kZFUwtvn7lAKg819eVoyuRkciHu3bsH1/wCclkBhULOK38+hsFkQFAKZFIpkvE0NbXraGnqRSlAMr7MT773DHcW3dzzwBEUctBq1WxZX8fuD+3g5rVhhoeH6axqxlKQU64TmJuZ52Mfe4Jg0E9dfRk6tZnVpTCFQpHtOzahUlo59u5pyitsKOU5JsdGaGysRyaDjf19IBNYcsVR6zOEI2n8viy7DzVS1yJy/OQfKVXIGb+VRWt3EIi7WfHkya9muDI6SefWDdites7cuMxbd4boaVpHwRNGbzKiKOmRqRR0Ndi4fHOMf/ne1/mv373BhdFF8sUSqZIcfXU1tqpaGlq7uDU+hcVmp7auAqVSSbEoolIpSafTyAQBmVKFyaxHoVKSSqQoFksoFEp+8/uXQKFEoVAgSRJ5sUC+WKQoh7wkIcgUIEpIeQm1Qk0ykUYhV1AqilRXOZmauI0/5EcsFdHpdNy1bz+iWMJoNKJQKBBFCUn679dM4f+y2P/OOz94prtDTyQWIpnKkI8LZBAIJZZR6xSsuGVMXM3Q0KJhYsrH5z6/kZFLE1gsTuQygUKxyPTSGLcn5unf1IN3yUNH90bePz6IQmOlrdVBLl1EDMjZ1NNHWiFiU+qJZUqMDI2Ri6Q5sPcw8XSKgV0bGF28QGtvC/M3F6itszN9PknP+m7ePHkbf2iFvo6NzC2FEeNpbs2Msm/HNj79mYMsulaZmlmmIKgYHRlEoSlSWd/JyMgI2/bVk0onGL4xxaMP7MVUZWfq1gxt3V28euI03es7CfslQssuFIJILKHAZFDidq0wOrGAtaqR2WUvK0E/zqpytHoN5dEQW1uqKCQyUMoxPb2As6mb5YAPa7mNVCqNUm2isbqMC5cGuXuTg1y0hBUF8SyE80ViyRS1zkpmVgNoFTqUKg16o4re/g5Wc3GQROSKIotDw6xrc6BS2Uh5g5Tlwmj27qJxahnt/gHid1xse/IJ5t87ywOPrOfPL9/g8Yca+PFPL/H1rx7hW//yBp/49Id4+tm3+dhj2/jX30zypc9s5e9/dp0Hjz7IV/94Gt9HDvPrFy6S7WjiheODVPfs5NTINDcWwvzj33+Nl597ng2H9zNx9hI7H3uMP732Bn33P8LL//kc3Ye3c/YXL9K+fRO3R2fwelOY5RAMTXLq7ZNIaNFo48hZpq1ayejILZDK6WzdzDpDJWvhcUL6ZQburUETEDGVVSJK5aQDKzgdDn7xk1fpb+/lU9/cgsLgZLXgYXuNE3XHB8leSSnFe2evo9fr8XhWSCaTbNk6QGdnGx7vFDN3ZjDoLPj9PmKxKNVV1Wh0akxmM+PDN+lu7eAPv3+Nnft2cnjvXh545MN87/v/zLaBXobG5ti3aYAXj7/FQw/t5J1j1wikC/zNFx7nsx+5lx//9Dfs7urn44/u4dz5y7z+9lkEhZmC+MFZpVqlRCEIyOUKlIIcj3eFgN9DOJigrrKJRCzA2eExDh3YyfLiMulMjrk5F9Mzq9x3/15u3lpgYnKKqXkX4WSceCLBqVPn8Xp9FEsFopEE7iUPglKLx7OKQa9CKgp0drWRjQaxWowEQgmSuQKe5SUa6+sY2N3CxXNT6PQaqmpriIbXkMvTBPwSG7vLuOvwEW6N3OLcpTu01bfjWVujrNJMKBJn1eVl28ABivE4iaDICzevYauwcPvOEgZJxqaBfYhRL9u3b+DU2Dh967dSV1nBms/Fxl39KGQWYiEfTruZaHCFRDyJucyMVMyhVMvQGZSEwkly6QI7d23Dajayturj8OHDzC3Ms2v3LhYXl0FWpEQJeVFGIS+h0ShRyRUU8gUkCVRaNUWZjGwmj0pQgbyEPxhBUEAsnUaQC6SSaebnXWi0SnLZLIWCiCDIUSoFvv3t//XXXUq989aLz6gVMDnlQa5UUNOjx2JUoVPUYne28vaf57nn7v3cHpvg7546xNJcitr6OsqMVdwausCTn3scbyhJeaWTqdFJNFqR+eUIGE3MznkZPnuL6sZ6hia9TLhXqCzZWE6s0lTZSkkp0dvbTziXZvbOHDabkanBFRLFEFrBit5iRm1Wg5AnH/PR1FGPYBHZvWkb63p7WZj3k87G8LkiaPUqvMkEpVKYvQf3s3nnNn7xHy8Ri+UZHVlFIRiodnRwfWiYPVu2M7LqQRUrsOe+PchKeu4Me/i3Z3/M8dMXSORKeFfXUCmVhBM5JB00NVvpbWpBa1RSWWagQqnCnowxsGs/Y3NrBGNxZvxBciUlKnWJnt5GTEoVpXSIg1u24JmLIaGiXGtjdS2CVwxTTMg5NTmJIisST4nkk0VMejViJkOd3YlZr6FYKmCtd5AuZrFpLTgdBipDIQ5v28k//foYf393D6+NBPlQew3f/M0VPvX5j/F3L5zhnrsf5NlLw1RsPcwLFy+xXLcb19IqgxV1DK+FuZmVE7QYubC4SM/GfmJeHV0tlcRW4xze2s/Zdy/Tt6Ofa++dxbGtlvffucRjD36M//2rn/LUs9/jX5/6Pk995yucfektGqrqWJiaoquigRNvHuPgwF3Mzw9xpKcZU50NOzp+85tTKLUFSmkjh+7ejcfvpaW8klnXVUSDg8C4hK5Qxukz1zE47LSWC6irW/CkfQg1VnLRGFqlnNmZKTCZOH/qBlv795KPFLgxPMWm3ZuZXVjAaDChVOgYGZ5ibmaRgwcPoNUYGBufIJXJEIoGCUYCTN25w83h2yRTEifev8BX/v5hmtusvPTSW/zwJ8/x4P5DDM/cZO1OCrtW5Ohde/k/P3uV3199DUV8gY8cfYzrY6PUOLU42ip59/Isz730FiW5QDaXoUgJEMhmsuTzEslEhkcffAC1VqCYjdJYU83Rxx7mJ//xW+pbGxm9OIQ/5CNPlqJaiUarZnF2mWw6TUFVosxhx2D6IOM3L4nojEZisTzpVJZNW/tY9nhApoCSAY0hTTqlJC+mqHJU4gsmiIcTaPVK7r3vAFPzLj752Qe5cv4yKq0enz9EX08/zS3leEN+JsYHmZ3NsOyOsvfQegr5EjNTi9itetZt2srJ909x9P77eGPwIhZLJYFInnQ8wq7d+5gbn2TOE2DvQB8FCfLFPM0NZczM+IlGQtyZHuOpv/0crtkpilIJr8+PXCh+oBASSqx60zQ0OZAp5ES8a1RXVrO84sOzskg2n2bR7QZJjsGgRpLyKOUChZyEVqMkn81jMGspqUqUSiWSmRxqpYJcvoBSpUWmKFGQlTBYTRQkiVJB4slPf5LxyQnEQpFiqYQklTAYdXzz6f/+Uur/F6jH3/z3Z3T2EpFAgcamZhobW5gdlTh+8jYXT06we6ARjWoFvdFIYM3P+JiL19+9SaNDYOe2PoLhVRoanPSut9C7vpLbY1F0ZonaFivJXBqNqZqJ2Um6WhpRqkpYmxoZGZ1CbrMTz3oZX3RRY1Zz9cooAY+S2mYVU/NJiCVIp3VM3ZhjZmGFHXvuolxQIY+KZEUDC5E53j8+Q12lhkpnJSWFFkejHO/cCja7k5dfOc72Ldvp6a/n6CP7P3jNo2t8+KPbyZGhQmkmGMtS69DR4KgjnoXXX3+TBx55iOHJa3z6sx8hL0ocOXoXszNutFIGeVzOoW4LK6KMF6+MsRrNkI2FGfO6URrM6MssrPniyBUK6iuMTCwuIxWSWNBibCvn2unbJAQBnVVGLFFCq1ZjtllY9gbJpSXkahG9QUUmK7ES8FDMSzgqK0hKWSpEDQptjrBdgdsfJB4LI69vYLpM4srIIsstEv5SkVGDgqaGMlw+E46Oav68Oo+zaz+5FTemHRsYO3OWL33lW1x85RUGHvsoN964wP7PHOXVn/2G/d94lBf+9y945rvf5D+ff4VP/o8nmZqYI3jLxabtO3n72Nu0tLTy0jefxdlWQXJ+hpRMQl/Io6g0oinkcNZU4JoaYmFkCWufFv+tBOfvzOI0mhDzEpGwinwsiFpIhQjD3gAAIABJREFU4E5n+cOZmxztu4sfPvsag1eH6a/dwKb1nczHczQ3bCEw66bS4Gds0s30eIa9bX2ki0mUcgvGXJHzrmlCyJmfWUSpVJJOiGTSIqUS6HVa1BoFy8vLJDMZ6uoakYpFlpdWKBRKaLVaBLmG9s5WRkcCPPf8W2j1VswGDclchrb2LjYc2MCjd9/Hj372R77wjacoX53DorBg0gooPMuQEXjn2FnOXRihKMppaW4hFIqiUMoQBDVSSaRUlBAEmJ6bI5mMcfDgYSRZEYNBwerKCrlUkcM7B9i0bSPZXJrejRtRKTQE1wIUJJGiWCSTzmE2mQkGk4CcoiQnmy2gEATWVoPk80WSiRStjVUo1XIsVh0KRR4pByNj86xb380//dN3+c3zv6a+oYzrg4PYrAaUSiWOigqSEZGZ6WVW/Qk0KiuJmAyfP8KiZx673Uy53cTmDX288vLbOCvNnDw7yKbtO1hZXsRu0rGvdwtLES+za15MJiXziws88YmHefW1twgFRA7ddYAzJ8/w5b/7Mrdv3KSy2sHFS5fRGYxk0hkMBiMloLa2Ae+yB5VahyRKuJc8pLI5NmzqIZ2JYzToKBZFUukMgkIORRmiKCEWiqhVSjLpHAqlArkgoFGoEQsFpFIJSiLFUhFR+qCyLWRyVFdWcfHiJT7zmU9yfXAYtUqJRqNAUJT45tPf+euA+ovvffmZgBijsbOdcNBFJB6mwbIeizPFnfEMHdVWJmcWUMpUVBjbqKy1YrKUoSwKyBRl3BqZZ37az43rQxjtdqKZLD3rO7h5bopvfPlxutcLIKkQtCrcs27u39lPXbcdRSaF6/IE9/ZtxrquAWMqjodVKuRVbN/Sy+Wri9wIeFFpVHz8U0/w/L+/gKQpceW2h+ZaB2ueMEpBZOvmPuorqzBYlBRzMjpqm/EGA9jLLdy5OY5VqcY1vYRrcoWt/W00VTeQFWRYDAL+ZIhL78+Ri2QZnZlFpRTxrSxjL9fgdq2QSeZ45+0zVDi0xFNpWto7ub4yjU5TgSVvoGDQMrwcQVFu5vbcMt09HYwMTaDRyIgXkpi0tUTiSeKAd82FoLXi9SaYd6dJrUZo3dLJyrKP6qpmEvEUzmodFdUmkukUjpKNOnsla+EAdV1N3JwcQyzqqE6AWVtONJLnumsGmcFId3snQkZGMh8jF5hla88AczEXstZqVo+d5bGHPsK//fN/8pVv/S2/+F9/4CM/+Dg/efq/+KdffZ8ff+MZDhy6D390mslXr7KUzROK+bl9c4ZD7WWUtFoKS2Po9SoyCy4cTfX0lwsY8iZmrkzh0MiYGZulfLXEjrsOE8hUc/H6FWqdNsrkIj07anjyo58iThT3YgKVIk+ltp2UpKKYS7Cvfyc///05bGYLnzz6MO39JjY7N1NZU0tvgwGVVsHw/DKbm7by4ptn+cru7bw2N4lkVCBqVMyNTbG0sIKjtoFQIIZc0FAoZGlpq6dQyCIoFAQCQVQqDdPT8yTiabZs3Y5KpSaXy2EwqdGbVMSybtb19uLx+2hqa+Dq8CjauMT9ewZ4+PPfpLy1kg9vMaKqbECtNPF33/o+UUs1WZuepr4u/GEXX/vbT3B18DoqrYIySxmimKbGacagllFXbcPhqGZhycvY2B3imQitdSZWQ0lCoRQBn5uro0M8cNchXvrjK2SyadRqJbl8GqWgoK2tnVXfMu0dTpwOE+FQgIIokc8XEAtFJKlITU0V4WCEgd1bOH36MtGon6984Wsce+c0ZpuJv7z6Cl1d7YRCflaWslC0Ek/GiIVjTN9ZoK6hnsqqNi5fuU4kFEehUCPKQCFXU19Xi88fwmIwMje7Qo3VSEmVpyAKbO3vo3tbF8fePkHUH2Lb3rso5rK8/epV/OEg/pif3nUm+tq6GLx6g2QyzemzZ5CrVUSiKcrtZSQzERQKDZlMklxGIpvJIJMryIsFFFoBlUpApVHjWw0giTIKlCgJkMuLyJUCcpmSQiGPSqVAjpxisUQ+lUOlUpEuiOhVSmSlEgq5gEatQqNSkk1naWxq4MbQDXK5AkqVgEajRhIl/uc3/0qgvnnqp8/kMhXMzy1R5bQSdPno6HNwe26ElvW1nBucRmsWiHqLBPNBMpKCijIN01MuqqqraOys5cS7g3z2E58gk5vAXAkymZJ0chWfd5FKmwPf2hz+2ThPfekJ5vy3SS8vkLWBwVSHTxRZGnPT172RdMRPWUMr84srrG+uR5HOkTfJWZoZRSpCf9s6BgZ6effaeUpFK/NTbvbt6iYYCBCJrhEORllZXaaxoQ2r2UIkliYUyLPmW+XhD+8il40zMzfD0OVb6E0ZVmb99Hb0cWnoEk67nSMHd+P330GrsFAigcu1yCc//QRXL42z50P7GZoYo9nZhkrIMeqZJ54vEIpE0GiUbNu2lWDQQ0W5hmQ0QFtLK7lkgApnGf5YDEXcgSSUsFfosVXZCcQzLPkDNNY1UMhk8XiDlAoCN4ZmaOtqR25QIyklkohEfXHa9WWo8ypSBoG0VsHinI+OvnYCsSSzngThfJzaGjOJghxJEhmaXaCptpx6vQyXawGlUM0rf/wddY5yXvnhz1g3sJ7X/59fUtfWzPydq1Spob7GwsbuWuxrIXqbnVx7+wSttUbO3ZxCEy/izoYpM9kxJbO8N38HmdVCTZme5RUfGouJ3voOcnIVg0OXSAVC1PZ0kbi9yr/++k8oJYlAyo9JpUSmU2KVa2lZt53rY/O0OFs5ffJ93HdcjF+dpP1ID+GVKXy+OKcvLlDZYuHE8G2+8MTjzEQnEWQqxoamGZtfwFZhx1xhZ2p6jqP3P8Ti4iIlmcicy01RKRGJxtDrdMQiCXRqHXq9gcnJOwBIxQJlNhMqmZwqYx2VRhuIOTKJLEVRga22ltfOnOad4z/EpoDluRAapZYvfutfyJdKODeWYzfJSC3NUFNZwfHTQ7hdPkwmJbFohO9+5++4dO4ikpilvsZKjdNOV0M91Q1VTE96uDE2T6koJ56I0thQSygaw+XxsHFbPzqNihW3l4ryCqCETCVDq1EQ9IVIJ2JYzHqqax2UUFAoFJFKIrFYFL1JQ7nTxo2hGQ7vO8xrf/gToWSWe44cIB0NE0vGmZ3zYTbZCEaXkAsF+nq3EIlkcLvdeNwezHoTjz76KJFImIAvSHNjO6OjY6z4w/j9QbZ1tjLjXUMlt1BhMmIu0/Lyi39GX2HG1GjFoYO1SJAH799KKpbkyD0H+eXP3qCxuZ6xiWm6e/sxmAy4F5cpFopAicpKByurIeSyEkWUZLMF5CqBkkyGTP5BtxEIBlAIAtl8ERmg1CihKCEIcoolUGo+ALBcrkBWlCPIlRRLReSCnFIJsmkRhSD/wEVVggfuvYdcJs+8axGFQqBYLFIoFBDzIv/4j9/964B64doLz6zMetDpixiMFmYnEswszaDHQOO6ElqDgyP7HyStzPLwvl68vgnyahG7tkBLWwXBrIttm+qwmdVMji1y977NiMkY7c3tqDR6vKFFNrRWYquvYyFcQhS0vHPmCk3lLZR0enTGMk785TjNm/qYuDZFIhHnwo1ZxianOTiwl5HLN/in732bTD7K1Ws3aWuqZcWzQJmjjK7+WhTKMGKhhAzI5ZKs29zFwuwEPl+QHXf1kM6EWfOnMJrKiUSWkQtw932HmZ9ZJREpkcwHWbduOxfODBGOrrCuv4l0LkdZjZUSauZc09hrLay43djMRqamZqhwqtHpFNjKFei0ZQTXgigVImM3Z9mwsRW1qkRFuZ3qxmqWXLOkQ1lOnJrGUq4hk0mQzqvp29LL7KQLo16PTBBpbGnGt7rM3r1buT0zRm9vG6VsDINORdDnxWLSUtTrGXZNYzJr2bphF8urISRFns2bDzN24QZKWZFCHnR6DTatloXRK3R0dKHSqMjKQtRp9GwZ2Il8NU+5xUTv+nLSmVWMhjzWMpGNW+uZGQ9zZeo25xdctPaXU1KKVHfUosmL/Pv//A7f/bfn8S/FMZUgmkrTVFlGwBNEazHR1NREUmFi5NZF1EjcmvCwUV9FKBdjYSVKncPO0vQaBrMDk74asaglGk2jN5sYujmCoijnS4/spaGlCoe1HL19E2+eOk1dhZkOSc9LZ09RrVbiqC1nzh2kpa2DO9N3KCJHo9QwePUStVUOIqEw67vXE00mqKuqIRwI09bazorXSyqdob6hklwuST6b4/4je0jEwuicDkZGhplYWGbg4cPcGrzGktvDj555iuTMbYaHbvLLN65w6cIo4WSaD+3rZZ1FxqaaNm7NuLDW1DN7x4VKr0erVtHTXc3g1SvkRA09feuw2ExEA0mCYorWylpKagU6sxF/IECZ1UpRzLD/rr10re/jxtA1ipKMbRt3MDc7h0whRy6Tk8umaGyo/8BMkS3h80VRCArEQo6qmnJ6NrWhU+tYWfXQ1FhJJJhCrVezuhYmL4LXt4Beb0RnNOPzrXLkyH14FteYn5+nuspBPJYkm86hUKi5evUa2VwCs1mPTm9hZWWJRKbAD771dUZvXaFnQxeiIGKvtPL+qfMYjEYyUpJsPI4k5gl5s4zemkChkbjnyGHiqQxvvX0WBCWpfJq1tRW0Si2FQp6s9IGKJJfJIhaKIAdBo0CQQSadR6NUEQhFsNisZDJp1EoZyIuoEJDyEpIkQ6lWkCvmUaoE0ukCspKESq1EphAwmozkMgXkUgmZTIZICa1Bx/jYGCseH5Iko4hESYR8vohcqeAfv/NXfkq98R8/eEZZY0CtzlGuUSMz6fGsJrjvgU3MXF1GY4a1tAdVRk9RVWLL1n7UBS015Q5Wl93UlLdw5cIlBg6sJ6ePkUqWcM/GkRdMlJfrsFbJcadCWBRFsjk/TeUCzp5qqqqruTY2xfmr17Ga1NxY9LK0GsflXaWjvp2WOidvnrxMe38lnoCby6PjbN+zGQoFSkkjO3f24ayRUd9kJBmRyMZEssU8RRGaG5uwmE34fUEqqsw47HrqHFZUMoGBfTuZuDOFe9nD+r5Wbo5MYLJZ2XOoDpk2zvJqENBx/doMgqBBJpMRiYaJBVPo9XL0BjnVdQ4sFgPxWIKNGzqJhmMoFXLisTDldgN2WxWhWJREIkAhIbJz+x7i2SBKpQVkeQqSSFNDO7lcgtqqKu5Mj4C6QKkoks9JLC6lCfs8kJeodTRQ5awnWSiRECNU1zchZjToTRZOXDmNpMiTCMbZtKWfW2PXsNhVuN3LFFJBDGUGbi1MUUwV6OvQk3anae6sxuP2c+rcGMWMgmIpw8CePhLhCCF/Glshw/4dA9hMCXo7N3Pj0iSGkglLi45Tp35L+84eNq7fxtzkDBprGXa9Gt9aCLPJgrO6hoK2nOEb5xEoIpZKtKiMrESjNNZWokCGkCvSs2Uz49NzSKISpaBDUMHI2Ag725vY3GjmxeFZvIJE2p6jb2MHmaCC3194my/dv5f3b45S5WxgbGSSBbcPo8GCPxzH5w9Rbq9Aq9ZRXV3L7duT2C0WgmsBGmrqGZ+YwGqzI+YlwqEQjY1ODh7aSTabI51McX1wFK3OwtNfegK9L8H9H9qH2aLhxVePMbIcJJpT8NWvfp6J5XFsNg3ORid//PNlLM0b0GkdvPbHVzFbnATWfDz80L10ddVhs2ogm0ahKuPc+etMra5xcMcAx8+cRYznsVnNyPQyxFyOKkcVpy9do71jHeOTt0knk3g9y5RZbUTDEbq7u9BoVMhlGlZXAyjUWjRqHfsObEQkxcL8KlqDgHt2jWxKJOCLsuheIRbPIROU+P1e7DY7Rx89yplT5ynkweNdIptPoRDUBAJBMuk8pRIYTTpaWpqQ/j/O3vNN0ru88/08oXIOXV1VnapzT/fknBVGloSQEBiMFzhe26zXxgbs9THHYW2zOntwWK8vrnU2ThyzYAwIJJBQHo0maHKe6ZyqQ1V1dXXl/MTzornOH6C3z6vn1f27v+H+fnWdSrVCaj2Nx+vHkEyeP7Ofpck7nDp6kItvXMLmMamVBAw7iKKCTfSQXmvg9VvIbdbo6+/k299+hVQmi8uvU6/rSBYdh81CvVqlpbYQRIl2u4Hdut0nhWAiywJejwtNaWGxSNitNhq1BqpuYLNb0TQDQzfRVRNNMREkE6fdiiQKOO12LLIEkokgQbVaxSpKNOsqdpcdzTRw2OzU6w1On97Pwsw6kiRiCgKiJDIyMsLnf+3zH2yg/tk//NkLB7sdTOwb4MHWAvlMkf17Y2wstSnUoam0cTttLC9usSPhJ97t4y//4QfUa+AK2pme3WIgsZ9SaouxRIQtzURRTZYW01jtTjRVwmsTiXt2kKs3KG62ePz4UZYXH6AWSowfOgBCiJnrDwhFwhTyFbp6PCQrWY4e7KWYT3Nq5yi5Won1tQ1OHjrC+ct32TXaz40LU0zP3MXSDrO+nMLlj6GrMqvrW1x49x7Z5TyjgwOotHB6bfgCOt/81zfY2FAZ2RkhmXrIiROPk1pdZmA4SLOl01RVvH4vFouJN6hQrqiE/AHsFivdiRDVahndUOnt6WdtuYbNKjC/sIjP42NifD/thsHi4jQOh4aMA9m00ii4GBrop6mmEQwrQVcH07dTNEp5uuId9PeHmZ5J0hWJEA51Ua7nSAzGsbgEmq0Wuc0ct+5N4cOCWNOhKbAwn8QTcGG1+LEYNr7/g9dRJJ2gP8KOnTsY2TWK3eLG5QxSzFWwEaZczCK4CwS7O7h7fx6rq8LY6AhrqTl6ozHapoK9K8D5uZtsFWqEB5xIMTdlbY1gh5sDu08R7LAgbbZ56+YMmqYxGAuTTmUJ+AK4fSF6+kc49/5ZMEUUU2fEI1HQLdgtBtVinZDFhSXgp65BKBDERCfcYeORx/dz7UGSQwEHPQcSfOrQft77wbf48dk7/NZn/iO3p29RzKzySNchHqyv4ursYWCwn0Rfgkw6Ty5XZt+BA6wk12g0FRRVo16tYbFYWV9NoagaksWGqhjs3TlBoi9BIb+FVbDzxoWbHDl5go8+/yH+/h++zsc/9yn+9I/+gvH9Y/jcTnaNJNi5O8bVe1cpb9Y5ffQUJ4dGCCUSSIqDydv3OXryGHduT6JrCnfvTbK4mGF1IcPiepFEfy9uvxVJ0bE5nUz09REbjGDW2uQLBTo6whTLNRwOJ9du3OLkiZP0dPVRKBQpFrdQ2ior62t0dkZIZzao1mq0tTaapqCZRXoTUdptG8FAmFg0QK1a5X/++Ve4NXWD3FaVHePdbG1UsUoClWqNXeNjhPxONjI5At5ONEOlUW8hiiI2m429+3bz8OFDBEGkWGwwtnMHleomYW+I9ladw6M9XDx/B8lhkM41tv9FNLGLXhpVDZvdSqNZJ9oVYP/+HSwtZRBtEh6fFZclQLmcwyJacDjsGJqBZLEQ7exANAQC/gD1WpO2ptGqtfB6PJimiaLoCEi0WxqqphGLdKK0VEzTRBRELBaZVltBVXV0TQPTRDUUJFlCREBTVCRJotlUEGWJdlNBkAw20ps/oR1ENEPHEEwa9Rr/9fc+IIf6zptff2H8YBClbVKqNihV6jTaTZIrW1j9MqZq5+juI9y8skHc66G7q4NDjxxiZCiIbLQ49cxOXCEH1y9M4sZPR6yB2yXg9AXpSYxx7s2HCA0fS+uL2AUdXdYxtto0NZWJ7g7ShSZCrU7LZsO+2eQjT+/jqcf2MDHYiYLOWHwHbodEvKsDn92gXcgyNN7B9dvXmZ0tcGDnPgotCckp4bZZ8QdDbJXzPPrIaUb6YjisIh7fMBtbVRaSi+zeM0ijkaOvp5d4vB+rVaCUT6G02wwMD1Mqt1lP5dBaTRo1nWPHd4NZoXcgCLJJf18IQyvz4N62cry0lEJTHWQyeW7fWsDhshPssKNrEO/pJZVN0VBUkotLDA4G6O8dY215BTSRUCDE669c4MCBMfKbNbz2IHPTU/j8Thx2EW/UhdFu0y41OLR3P52hPpIzSQ4dPML04iZemwUKEk6pydhojI99+qcpF9bIbWzy9oX3SYS7iOoRPCEPq8IafcM9rGbThKN+nnziUc5eus39yTSRDj9rSxlyuSxjPQmWlzYYDkeJRYM0Wy0S9hj3ZpZp1xUK+RYPzy0gBt3kS2WGYh3ks0UcFhtBq5u3v/stMmoVq2xD1U12hfzMFQrEujuxynbMWpuGJBDq7MYQdax2k3opz/2rtzi+7wxMXid8oJfvvX+Rx554hoP9Yf74b7/CcFVkx8ED/D/f+y79iQHmF9eZmZ+hVCiRTK4SCYfoiUcp5LcYGx1heCzB7XtTOGwObDY7DocDj8dHMOinpVUpFHM8/+yHWZhbxSnLxASZF7//Cr/z+V9k6foUy1tlpmaSlKp5Bno6SS8l2b97nLA/yOLtafB4mX3/Dobfz3J+nTsLd4l2ddPREaalNyjVivR0+QjbnVy7co/9u3YT6I2zcGeGhgzn37qOUq/i8LgIR8IM9A+xNDuHbLNRKuRYmF+gf6iPtc11vvDF3+D+/VvEunpBkpAlgd/+rd/k4dRdnvmpT/Ldf3+DJ585jmq0WVxfwuO2Mjl1H9ljQXYaPPbICW7ffYjNamNtfZOVtTUMo83o+DCZjXWaTYWenl4azRrDI4Pcvn0bj8dL5SctBG6/B4ek8NQTT5PZKPDqWxcwvS6KNgsVDXRTIRYKkV7L4nLbaTQq2Ox2tgoN8rkt2m0VVbPS1xWlJxpE1RoYqolp6MhWmXy+is0iks+VqZSbGDrbgpRqYJjbJXymBIqugAE2QULXDQB0U0ewiOiKiiiAxSqz7YIAQzEREREtMu22iimISKKJRTZpNnQk2YIgy5iCiGpoOGxWDE1H03T+25c/IIfq2bj7wlazRi2T5kDvCEKHid3vIjEUI51O4TIHcTu6cXhc6IUKfeOjXPjOu9xfWmLf4HO0Kzmsgog7EKZlE6jVyqTuQlffKL5Ii8l7U9uxeBWN7vgB5hZm6O524I8EuDI1i7Iu82DxPp/65HO88v4VEASmZ+fJbWXpisWZX8pika047Xb6o71Eujxk6hUymyV2DfSRqZaYX9jEp5r8/Cc/zouv/YiTxx7j5vQUi2s1tjaa3F1eQdEUTu4/TbldptAUyaytcmjXEFv5dYL+GIl+H5MPrxIKBvGEAhzeN4rLqbO88IC9+3sIRV1k1vLE/UH8UQc1tYFe1Gi0bUjYGBoeJJsrY3Mo+H1WotEAqmqSSafZOdbN+mqD5fQmhmFgFX3k8002KsucfGqUdm2TgGOEzFaeptEgHPCSnN+gpzsKhonb60e0OpgvbDKfXePu9Bb3Hj6kr8sPxSq3ZsocOTrCyz86x427KbwWmYldOzBp8e0fnMPX5WH6ZpZ2u0zA7SXo8/P9b59lbOxxcvkkmVWTWDSKKDmRjCLdsQR4rSxlipSLTVLZKtlqlfjoEGupFY6eGeXU4Sh2mx9Z09lKN7D5rOj1MhZDYVUBU5IwzRYD3QmETg97h3ZAW0FrKvh7+3CLXuw2CdFQQbZwb3Gemq+CZyrF+OPHuDG5gleBZklBaJgs6lUmp1KEBvpYSW4wOj5GMptiZX2DWE8PqtrC6/Yjy1YOHTjE+2ff48PPPcfayhrF/CZHjxwm6uukb7gbh83AY5VYXV7iiWc/zNTCA9o2HQSDuZUsbk+IoNeDUq5z9MlDNJttwt4O3nv3Ap849TxWj48f/PANNtoKvWMD7I0Oovsd1LY2cIZcjEZC/MwnTjD/cJVazeCZZ08xOTVNaj3F/p0TOHSD8YkeMvlNFJeHZrXJ5Nw8TdqgCBQbVRKJBBsr8/zJ736ZlflFstk8xXKRjo4AtUqdH/7wdQzTYGt9lueeforX3zrHY6dO02y2+dmPf4rrV+5hKhJCU+bBtXsEvAGy+SK6JvDomZNoEpTyDerlIjv3jGEYBu22Sjab5tSp06RTq2iqwsjIEJn1NM2GwfzsIjv37OHm9BS6XSK3VaLdbmE2DZq1OrppYndasdltVCttRFHDNEwMFWxWiXZDYzmZJpNq4fU7MQyZ3EaBQNBJuVTftpnpJqIkYmg6dpuNltrGYhFxWOyIbMf2Kcr2Zmq327HIVtS2gmQREex2dAEUXUUQDExTRNUFTHREwWBbuRIIhP10hLwUc1XQty1VoiGhawYCAhbZwh/8wQesQLnx8t+9oAVzzBQ2SGVr+J026o0KWjtP1NtL10iVZrPMyuImsuzkH8++Tv/YGKdGElx6cI2HU5tYHZ14fX3Uiw2C0Z1YTRWHrvPD61cpbxqMjnRSNJpsNWr0dEWZur9I7/AuOvxBkpubfOTMKb7y1W/zC59+jo5gANNoM9rTh8WmI/msLM3dorevi+JWGl2UcLtjLM8r5Eo1Qq4+Hj1ygLLa4MLdh0hmgPmH14jFXfSMRLBLGseOjeB0KLQqJSanbvLw7hrjo6M8vH8fWZLRMZhfXCTa2YMv6GF6fh7ZFMll8zz+U49SLhexWC14nT7y6Q0UVUQVTDCDbBXSjO5I4PV6sDlN3B4Nf9BFYSuHpIPLGqDeKKG2Xew9EiTR201iyGRivw13yIY3GGVpZRG314IpCMR6vMxPZUkMdlMqlhFNAYvhQa/pHN57lP5EgJOP7GfX+Aizi1m8ti7CiSAzNzPYfC3ausbgcC9r621WlrP0DdoJhBPML68wONJHZ1eUew9n0NudGMg4HQ1mpzdQdQGXCwrNBo6Qi0pTwSKJzN1N8sjpEyyup1idnOfUvh20ZJ25dJapdIP+kIebGyVs9TI10wRZYK1W4fTBvUQsDvw9cVyBALVWC6XcxCXLiB1uSjYdVXJisXqot0ukVjNoFZ0DfhsTpw9y7OBpgoFOvvvqWdbVNvVinZbkxNRlwuEYU3ceoogiTtlOyO2mXChSLJY4fvw4b77xJkpbIZfZIpVKMzQyzOjOCa7eucxobwJNaTO3tEgoFOP69et84XNf4O2XXqN/aJj5lWVu4+QMAAAgAElEQVQ6IhECfQl+7Xe+wMXbN3E6/dCE7qF+Tj7xJP/rr/6KHbt3YbNYKJXrXCus0I0NXWswPBCnZ3SAdLrCrgO7Wd9Y5tLlO5iWMJulMrKosZzPoTVN0pk8vrAXTWkR6XASdAZpKCohnwuvy0u51WJpc4N3r19jbNcEVovMzPQCXV1xGo0KGjpWf5jlVJpqocjNW1NEO92o7TbrC0vs3rkLiyCyWakw2N/HZjqDIRg89eQZ3nnjLdxuD5LLydT9BYqlCqZp4HK7mZycod1W6entZXFxmUZdpd1WsDptJNdWaTUU6s0GBiIW2YIhqni8HhxOBxbJRiQaILW+hc/nwTRM2q0WPr+fjcwmVqsVU1Tx+0KspzLIFnC5vFSrDUzTQDcMVHWbHxUlE9myvY0qbQVdNxAQMXQd0zSRJIlKpYYggM1jQ1FVdE3DJkv4vC7U1nZUIIBpmgiIuD0eaqUyqq6jGTqSLG97Uw0DXTMRhG1HwJe//AFFqe/8779+oeGqUhJVbl7PML6jj0qzSCXjp7mpsn/Xbu7O1WjVTaLRQaymj599/ineefscd64u8ZGPPktnQsFwz9E36Ce5NklyqkouW2PvqRHCMYnOLj+baYHeSD9XL73Nk88+yfU330VRRHoS/RjuOk8/s4u1ZAZDE7l46SE9oSC7d3ZjUmbPxDHKjQJWwUNnvJtcLk+rXSOciBN2OxC1Esu5dSYmYtT0FoM7RrE7A2Sn8qwvJhEsIucvXMbtjtDT08OJw3FaSpU2EsWqTv9gL1OLczhsAoreQNHaFHINBod7sFlkBFFnY72BLNjYSqfBZeXa9UVK9QY+d4CpyfmfpLXvxes3mZ8s47EHKayruO0RJIdKrrAJYoO56Rm64t0sLiXp6ummqRg8mF3EasnT2xtndmaDeNxHrdHg4ME9zE/PoDQ1HLYwk7dTHNh1gNnJGWq1BpF4Ly53J+mVVe4vTILbSmd4gK1ChVq9Tizip7tzhHJzi45IBI9PZWlpAYvspKNzkMXZOXq6O7FJYarlLZ46fZDe0Q4WkwsE7TZ6e/wEO6wkF1Ok54uEBr0M799H/eE6oyPj+Ozr9A/7OBrv4z8/+xFefu1dXJLMkWOH2UgvcuaRx5lMb9ButrDYbCzNLaBXS+ydGKXabuLxG7j0KmHdQnJ+GZfVRahUZz1q4b999S/YssC5G5MEZCey182t61M89viT7Dx+hNtLc9RKFZRKnZDPQ75Y4tSp07z++hvs2r2b1ZUVUltZTpw4htdqJTk1xVA0xuXrt7G57bQ0jYVUmicefZS//9rXUFweNgslbA47v/bzv0guleLq2Zd5ZGwveyf2c2LHbgSzzde//zKFWgVF1RgeHKDZqGF1OCisp/jUJ3+Gm+9fJNwTYmrmPu9cfEBmJc/Y8B6SK4s8fvoQE4lhlqsFJu8vMjwxQSFbIxT2caArQWdHkMXlFJqhsLy0TiQQIb+eIRaJktvMUcxvEYuH2MpnSfT30t3VQ3plgVPHD+CNdRDp9rB7aC83r9+iUCjz2ONn+LfvvUy8M8Ld+zOM7tlBVyhILNLJnQcP2MqXsTucIIkEOxx0dQWxOTzY7HZkq0wmncXr89NUWkzsHie1tk692kAUZUTRJNHbR61SxpREKvUmSrOFrrXZzBZwOd0gaMiyQKutU6zXCISCGAZYbE6arTKGZuBwuLFa7dSadUxAACQBvF4XbWW7Xhq2A0lsNjvttoKmgiAAmJimgdVqRbRv16VgGFhlK0qjiSDIGAg4nBZ8Xie6YnDi2GmymSyKruPxuFBVFV03kAQR0zQxDBOr3cIf/P4H5FD/6KtfeaFc3sKryQz1DzO1sERHZ4xOT4S1pSTBwBCZkkby7h1ylSwTgX6m5h4yOr6bR44e5ObUa5w+/TgLs02u37qH06NhD1jwxk2czSp4VB6+O8ORR7u4/uAtzhz+NAv3Ugzs2sO7l67z0TNHKBTznH3nFnOpTUYCndh8XtxhF1N3k4RlD5FIAKsrwEYqx/vv3cXn82Ki0t0bJ5mcI+yMU1ZNfvyDCzy1fz+iLuCz2clpGTrGEpQLm4zs6mcuOY0g17h0YY6WZjI03s/I7hD+QBtd0jHVBt6AnXhPD91dIcpFHcmUUFsSilajUW9TL+nsG9vN7p295GplqkU3sXgM06wiyzWyqS2OHh0nnUkS8FuJ9gQpVAuIoki000dHuJv11CqJgTGK+U3azRKlisF4Ik7Q4yebqiMCqXSaUiXHiZNHWUst0Dtio1L3srg0i9UuYLcUuX/nPt4hmc2VHCfODKPZyhzYF2V5NcvqSprOcA+NZppg3MX68ja35vf4sMl+cpt1JA06OrrRzRaGWeTh3Rl274uCqNLWVVSxQb5aRnba2L9vD5VKkTvnL9MZDFM1VGzhDuplO/lqnssXU+QVlS5/gGomxzMfe57vX76AR3KjOUQEU6KcSbHb62L/RD8paxWLT+Y/ffbn+Mgnn2VdrNIz7GemXKQz0cOZXUd58/vnOHZwP+/fu8Mf/Ppv06iUGRkZ5Fvf/AZhnx+bAJV6A0UCiyngcDipVqtkshme+tAT1OsKmxsZfu/3f59vv/wDTjx9nO7eAc5fvUS+WkIULJTKVfYc2MOTjz7JzINJFmaTzCaXqKoVnjn1CDWxhtFSCLm9GNkCXR39FEo1khspHi7OI3vtNNdy7Ih24SpUePvBA+ZTSZ46cIzBvh5OHjuE3WFQa7UI2h3sHBrF3RWgVipRb7Vp1SusrWT4yCc/youvvE6if4S52WWefeYZ5ubnyVcreP0uYtE4GxubBIMRTEOgfyBBOrXCJ3/uE7zyozf52NMnKW9W6Oob4JUfn2XHRD8zc1M4Al6G9+4hvbxEcSNHS7Bw8eIV7LKMzWHF5vGAqRONBhga7OPmtXsEwz4sFhnZYsHEoCfRxdbm1jZ81w1sNjuq2gZDQ1Pb2yjPAEMFUzdw2O2cOfNTpNMpdENHlG3Y7DYa9RaNWoNWW9lW3G0WNEOjpWropoHVYtnmPEUBTdMQRBFJltB1HUkUkSSRZkNDFAETwECSRDBFVFUDEzTdwON2obZ0mk0NBANRNNCUNqYB2ewmjUYVm2yhWqljsdrAMFAUA1mSMEwTwSbwh//1A26oX/ubL73gEsOM9++gWFjGZfUzNzNPKC5Sa1Rw2GVmVpMEXDEKJYFHjvSzsL7Ge1fmeffWFC57B5ffXiHm66C310lc7GEhmWT3oSPkMwKVkok70Mfk9VVSyTKukMxGepVP/No+sskSbYtJJVfC5bPwzKMHEZwCghNUY4vhvjHevHKfmMdNMdfGMBSOHNjDuUuThMJemoUae04muDh9B5vqI5tJcujYIf75pe+R3cixb6KPSNxFvM/KanKa6EAUj2RluDdKs1ZhRzyBXtLxObtIF1awuJw4bTJ3b83gjdhYms8SCbop5NPodQ3NaNGViNLKlqhXKkT7XeTaNeyChd6BMOsrqxw6MIGmKXi8boZH+1hJb7CaaqDrDVAlrAEfydkyqZV1RCqEww5M3WAsOkEuU8AXBl2FvsEYTreDO3encLjcxDpHyZVW8Xk7QDJYy65z9/4Wly+vc6Knn7cvLPKxx8+QnNfYKhU5cuhRBKGEP2jlzsNVJnb3omo2iiWd3fsOce69y8zfT/LLn3ueUq3I8aO78QTclKoZ5tcKDI/1Uc02MDYNdjh6GAx3840XL/DM8z/NW3dvYndLzM8t4rTqdPV3UZedpBYn2QJG9u3j/XvT2INBZNXA7g/hdrlorW4w4pHwdzq5Xk1jT8Mjj+zj3Tfe4NL5OV78/ps00hWGrC1OhncxFRcxKzJLC/MQ8HHlyhUGu+N4DZmBzjhOrKh6E7OtU6gWkUXIF4rYbQ7yuRzrmS3KW0XOvvEme4ZGyKVLvPr2WQ4d2EdqZYu/+POv8srrr7GV2uCl198gMdbD8x9+jnq1gWLqhOM9SLkM3b1xmpEwa4rG33/n3+kZHSUSj1IuNBgbGmRhcZp0KceSvkl/pA9PW8aOncX0Groo4usMcvPOAwKhTr714vept0qEwlGi/jCL88tEBrv50dvvbJfH2W1ITph/sMChJ/bzG1/6z1w8f4W1pVXKlQbZzCZOt5sOXyc37jxkYWGVtq6xmEwhGSavvvIuh49M0NMRo5hNE+nsZn5qjmg8gsVtJ53O4I8GcHs9VItVKtUyvYkYGys58rkaTocDrzdAPl9EVQ183iDF3BbhUBgMgbbWxJDg8N5xJBmefvpRRFPAYbFRLlcRBBAkg9XVVWRZpl5vI+gm5WoDySJhEyzINhtet5d6vYpshbamYLXaUVQV6SehJ16HDc0Eq8OB1mphsVvQDANZEtFUA5/fhaKoiKKIaRoYqonVZkGUodFsIgsyggkWq4TTY8Vls1MstFDUNpIsUquqyIJEu6lhtckYho4oiVjdNkxJ5w9/5wMO1IfvvfiCwyVjig0yyzqxHi/5UoV6TcPq8TO5uk5nyM7ont3EOvopzKaJ9vVw7FSCgb4+bt3KkqmtEtANov2D/MubN1h7UMWndTBbSqG1HNhsFUKRCI+ePM3K6iazuQWundvk8eN72VyeIVXTibqj5LLraMgM+iOE3HVcYRF/pJd33rvJ7Qcp7J1VFItEutzk0dPjJDo9TF5/wEjXMJnlBfafHGR1IcXOHaMYusHa6hJ+D8QSQfqdfeianbs354j2DpPJbtDdFaCpN1nLphkY7sXQZKbvJxkadhNyRxEDNkbGY9y9OYPHZ8Pp8BALBIlGvDSLCsO9O7h16x6uUJD3L96nq9eGwxHh+rV1ZGuRarPJRjZHX3eURIcfn9/K3aszdHR76OuNoehuipst/N4AaxtpTIvEUnoDT8hPqVKmWm3QVAzSmxlM0Y0hFbFboVFsc+/iCp/5xcdoVOxEQla6O2Xurs9w48I6Tzx+lFdeusDy6hp2h0h3vBu/y2RtQUFphsjmViluiYRDbiRZYTOf5cqtG4g2F+lUAZvQicdt5e6DKrlqG0uHTLKa4dTpvczPzhKP9hDxu2iVtxiNjVKpeLh76wqSEUF2eplrZgiFOtizuxdLs41LllCtLRgSiXu89Pg9fOGLv8fEoTE6mgKTq0vML2YYHImybyzCZw59lK+8+hLD+yeoCTXe+uZ3efGll5hJpjn+qacJuK3c2Nigf/8ezr97HlOQCHWHKdQLdPV0Eg468DndOG1WfuZnnuf8lZs4g17UVoV/+uu/5rv/9iIWw+TVs+eRNIXf/+3f4Gc//iHkaot/f/l7PPXch/gPH/kI3/n2d4h2dNEfiaGsbcJmlUbVJF9M4u+QUNQ6pVad5556gl0Dw8zenaWqtdlz8hiTyRUkt40rF64w2D/GwwczVBpNTj5ymo3lFdyuAHdu3KJ3Rx8u2c5mKofLbudjTz7C9OQiiUSMzg4PpBXmF5ZQrSZBp51dE7totZoc3rcHWTZxuKzs2zNBRzDEnTsPGB7uo1gscOToBJeuXCeTLVGs1kAQOLB7H52hMOFQiLnZWWRRRjMNiqUyNruDUrFEq9WiXKshyQKyRaZY3EIzDJrNOlhFnnviDP/nR89w9tYMLaXB4Ggv585doK0oaKqKLIlIokyt0qJRb6FpBk1Fw+dz0G61EdhuDy4Vt48GEARMU8DAQJQEHA4HVllClmWqlSYOuw1FaYMJVosFXTMxgXZTwWqxYhqgKDo2p4xqqljsFlRdRzTB4/ZSqzWQZJFyqYkkCdhtNjBBFE00fftKS5IEVM1AFAXamorFauUPfucDQv7v/PArLziCEXJbZY4cGEV0GUzNrNI2RTp6XTjdVlqtFpVCgZ5QnPWNPHVJI+zzkZyZ4fQnd7HycI6RHbtZWl2jUU+jSz4uTl0lYBEZiHai1tq0SjUEEyIeJ0cOHMeBxLnr77PvwHEq5Tr5lRJHzpymLKnkymu88+4kq+s6+cIGHb4B0sl1fvbjT/HOa2/jl4JsZQ3OX3pARzzKxL5x8vV1hrp2sX/vCO/fucpn/sMzyFaNzWKd5nqb+ZLK5bNzHDl8koaWZWxkgEaljCi5CHbEMGsq3mgXR44f5OyPrlIq1fHjxCXKrKe2GBrZwbnzN+nodVI0GrxxYY533rlNriAiCQo93SEiESfL83XGd4fJrBm4A1bSqU1sFgvJZIZ2yUWwI8r6WpVsdYvR/v3UlAZeAW7eWmRwJM5mIUtvTwTZYaIjkBhKcPdWnpOP9bO1WaG/34auVPjQhw/yr//6Fn2JTmbemSIw3INg82OoIu6gQLkODbHCxL49lAs5Mrk6z3z0Md5+8xZXL61js4nYnTK5XIpYLIjF4uPW7WlaukwxV6V/zItgLbJnTz+JhIRkd5HeWKS/f5hGvUAgJOFyBEjs2UG13MBiVzl86BgNPcexYT9dCRlXXWG5UqCJnRF3NzMvv0FhKs3nPvMRVhtN6moF0xQ4NLSDN1/6PrGBbvSlDH3DE3zr7ns85R3my7/yy1x+6yIbm1sMjSQ4GurnwK6dPOKP8X//9z9lYGSYTCbD/r07sCIy1NNDebOAqauM7+lnbnGOHYOD3L1xD7vXx3e/932sLg8Vo8FvfuG/8ODefZLpFV7+5rfJVnV+/7e/xI9f/AG371zjE5/8BG9dvMAbV97jpfPnyVosuAZ7iQ/0c+PCu4QliZk789y5f5f4QB9LySwSJpVigdmpFP4OOz2JHfzoh68TD3fgD4Yp1LaoFyocPHSQ0dEheoeHWFdrBFwBCo0s9VodydHJn37tj3nzB2+wsLRAUa0Tcns4dOgAv/Ebn+Ps6z/m3J1rROJxHDYXD+/fRxIEOmMxwkE3hw7u4d///T1sDh/VaoXOSAflYoniVoHNtRSra2t0RmOUKhUEWUBEptFs4fF60doahmBiCBpen5NGo8r+nTspbRUpNVvoDY0fvfQ6DdHg8eNHuHzlBrppoCgqTruTZrONYJpYbVZM08BmdyDJIobeQlNAkCREU8DQdWSrQFtXsEnW7TxUmwWl3UK2WGi329uCl74N2UVJptVqo2gGpmH+/1DdME0sNguCTUBnOyTFVMAqy1RqdZxuB41GG6vNhtvtpF5rYOrbp6eyLCLJIkpLRxAFdExEgW1R6oNC/ncu/e0Lb7++QKOZZ7O8hQpUVQkdC7VyE7ctjNG0szRdxOt2cHMmz0Zxjddfvc/I4DD3r9zmscfPkKksEPSBPzbA0swsP/8LP89Mukxpbo6BY6fZrNXwxgJUHA7+6l/+X6KdfXzo6SfZWFvFarNTqJjkVsuY6TWssoN8UeLw/kPkc3mWMgV++v84xf3JZepqE1WxcfxYAsNQeDA5jYCN29OLFDNwe+Eyv/TJn+be5C18PX2sr22Q6N9FYmIQS7uFZi2DYaNebhOLdZNez3DlwnUku51sOsVaNkkk3k25Vuf2lWU0Q+D5J55Fqxvs6h8l4LNi9du5cHOOkyePEYtHQcszMthPtVTD4+ols5GkrVV5cCfHUx86jNvjZjNXp9rQkG0GSytZ1KYFm6/E5bPL1Os6A/29bG5miXdFaFYqGIrCoycPk5xfx+drM3e/zO7xbhaX79Pb28fK6gqnzjzHt75xjnAXOAyF7vFRJuemWV0poUoZ9h8cI58pEosF6IyF+cY/n+UzP/cxJEuL0R1hZuan6evuZWFuBa87QmGrRa1dJ7eh4bTZyCxXUaqgt3MIkoTPv13JC22KtQp372VxuOpYLXZ64x7+7E+/z9986RNcujDHSqbE7NU5Dh3YiSEGESJ+3rp9jVA0xqcP7aYcVkmnc2TnF3n9X75D11gXqt2GIxhCtcmMHj3Fq2++xIkjp/mtv/lfHDl9kgNjuzn/xo/oCHfyf/3b1/jl3/oiZ19/DbfDjaoppJJp3DYXtVqb8T27eXhlhkYTqrUa5WaVQqGIrusIkonSUrl+9Qr5rTybhQq1mkZnNMr/fvG7DO/YyakjJ3nrtdcJuwIkEv2M7hwjs5qm323n9p3rBMMeipUKGAbHTjzGtTv3iMZ7Wd/YoFSr4fCIJKIjJDpjeDxOhveOcuf+bRrlKsFIJx63k4uXr+ALOLj26iXiLitem4vhXQPIjhLecoMRf5BvvHGVkN3H+laeoUQ3r3z3m0wc3EM0kaBYqLEwM0u5UkfXTexWC9cv3yMe76FSL9PU6oR8HXRGwtSqVfbt28/80iKSxUKj3UKQraiKgmiCZAq0G00kyUKz3cLptlOuFHG5XUgWiURXL5l0loMnj2APOdi/a4jX3rmIxQ66LlKvNgGQZQlN1/B6fCiKAqZBd7wbQTBoNRUkwcTpdGwPJouJaRi02xqSZKXeaGO1y1gtdhrtJnZ5m6uVLRKqomG12NBUHUmSthP7HRImBgGfD4d7uzHVbrGitTVkm4wu6IiShKbqWCwy9WoDiyzhcv9EjNJM3G4XzaaCaWwLXbIoY2omX/6gt/xzy3//Qnuryd4jO+meCDI9P09NaWAXffQlulm6v4LVGeexx06xsLjCrv37WF3doDfaz9UHk4SiEdYW81gMidkHS4SsPiSXl61MlXouiRGJ8PW/fYXKaga/xUMlvYldsBKwWnj/vdeIDifYyizh9Pfy1vsXccbDKIKTuaV5OjwSFrPKkT290PbhdLdwOjvR1Cbp/Aa59SynjhzF44rQF+9HRWdpJkl8oJOHk7PQsuKwdiL6o7z4F9/ikz/3MZKL8wwMjGAoIrMPpjAFiPgDuBI+RqJ9hO0ehsZ8zK5n+akdx5EtVmZWkxQqOZY3VggIFnoGerCbFbpjPjRljYEdNt55e4lTJye4ff82ourD4pUprLZx+Bq4XU664i4aShUVC48cH6FctqDpVSTTh91pYX55iWhXEJfDRrOh45d7UGo1NHOJRO8eGtUmYzu9CLoNm9WL3WdlfmmWYCSMw+dm/zPDrKfmaJk29u/pZ+b+IkeOjTA22s3W+hJOS5MTJ44wdf8eE+M+/IFOaiWZ9OYmmUyNarFKqVZFa7f5uU+fodraJNYbRrSJdMXjzE0nMdBQ1Tb9A72sJKd57PgxRF1ka6OMYVRxub1UXE2IOji8dxilq4dKRsXdkjB3uFm/OMPAiJ/b8jpKS6S3q4vrZ69g9Qd5uJ5kdS7Nw8lJ5hdWOXvpKv/4d3/N7/7pH2H4nTgNg//xl18j2OfjzmKSP/jVL/DiN77NRz/0NAcOn6TeqnD8yEF0U2d+aY5cvsznf/3z/OjlFwl2RvB1hNk5tpOeRITf+9VfYaCvh13DQ/zh736J//S5X+XKzWuM7xvn9o37tNpNbkw9wBRkMvkCk9P36Qh7sEhw4eY1yrUaVrXK4R17EBQLM6tp8vkiVo+ddCbDkT0HqFabWASV8+eucuTEPtR8jfX0GlpbIF8oUKs2Ea1WvKEgj378Gc5feo/hkR4+/txHODJygtlrt8i0mvh6QzgcKmZLpdWu0dcX49rV+5x57AgzD1bo6YmRSW7QbFeplxoMje3i1p3bOHxN/EGRU8dP8tqPz2GzWcgXizidboqlMrppIkkiLpcLTVHRte1BpWsqskXC4pJw2O0oTYXURhHdNAmHPKyurTI60skbr16kIxqgVGzSbm1HDCKKmIaOxeGkXK5u92GJ0GhUaDY0DEMn3OGjVKvjdQQxDZW6bmARRQQEdNPAYgoohomuadvbqL496ewOK5quo+sGLpcTfyiEpqv4AwHUdpNao4XFtDM2PkqusPUTuwBgmkiiFXQdDABzOx9V3g6V8fk81Gvbj4HdZsU0NUwDvvxBjf1/9fUvv6BVHXisXt574xY9tjiBnjYNVefEzg+xumbw+KnDfOdH58lstPjos6f44etvk0pV6epy0Cgp9PT5uHZvkVwFSvU6778/x2BfhFh3mIink1/+7PNU8wWqRYVdeyfwuqx0j4SJd42xsVpFqTQ48OQObl2+i8clMZgYZHS4g+WNNKcef56VpVUiHS7e+PFturx2BE1GsFhwWjso1KtoosyLb/8YrZLhUz/9ETAb9PZ2UG1VWNnI4neGWdvY4s69uyzOFhGaKm6HnbVslr1799AR81ArmSzOznNw3zjrW1lqG22SyQWm5xcZ3eWjWFY4cGSY9fkSxWKBQq3B2vIi0Z4gkteLKyiSXFlhLWnhzJmdXLs4zeAuF7m1Gh1+H/dv32NoYDfhSJB//acL/Mx/PMn195aJ9AUwtQadkV4kUUU3q3idXayuZ1HteXSjm3/6h8s8+4kB7lyf4/CjJ7h35wHRoB2P383M0gI9gzHyy9N4nRHW1rdYXSyya18X60kTxVjF57EiGn2k17N0RCVuTc4wOVXgxvV5ZMnEVK2oFoGhHT0E/E5Si2nGxmO01TbZ1Ca+gB0kmYnxcRbm1slms5SKTYoNK8tLWW7cXsIR9VI3TGolL11BkeRMkqA7hr1vN8ZwB380fpJvnnuHEx86QThXZyVd59Kr55itFVlLVdk7fpzhzm4W1zLE4/1YRAeWZpE9R09w9/3LSKaVgd1dLKRKDOxI8Lff+AaqAJPTMyRXMsS7Y9y4cZvFxTRf/Px/4Xd+/Yv80md/ka/88X/n/atXya6lECT49DNPcfXBbSb2TRDwuMkXsyxsbnDr6jWGu7qJ9MVZnF/ELUq44l66e3vJlbfwBUMEI3G2snm6YhGqzTKhrm7evnaPumliaipeh5t2q40qQsjrY2Fqll/51EeZenCbzY0yTV1gdGiYVrOJqmlUqnVMtxV7rs4zx0+zf3CIP/mbf6RWyTH29An+x9f/FZui8+D+En/3z1/l/t3zuHt7GQo4Sd6b4je/9EX64908/eFnefPsu9g7QtjtVhwuB4cPHKJVU0jOLLHn0F6mphap1pqUqxVMzcTQDFx2O7VyBVPY9rwjCIiyiClu39LrqoqpgddlQ1HqeF1+0GB5eQXdhGqlgixbkKwiTocDVVMRLTK6oqHrOg6HA01TEUQDTddxOG202mHAuJsAABC7SURBVAbtuo4ug2Zp4LWHqdab+N0etHYb2dg272uqhgnoAj/JMNWRBIl2y0AwDarVBoPDw7SabUwdXEEvjUaT1Foam11E1U0kSUbXVAR9m2c1je1vmrrtdRVFaDVVBEECTAxDR0BEFEX+8A8/IOTPFl5+QW20yKWyhAI+2nqb4YkgVqeff/7H96hUGkTjnZx45AC3Ll9CsaoUsnl8Tj+aoJOc3yTYGQabwNp6DqckceDgLtZS69y6NUlys8hrb1wiX9iiqbeYXlyjZqiYxRoPc3N0doa5t5hkV9coU9dmSIxE2Zno5M7UbYY6RqhtNunt7+HSgym6436ylTJ+j53OiQE6g3HevXuedqbKU2dOsWvnHm69/5BKOc/8zCyqLhINhtDL6yR2DpBKFSnXFB5OTlOrN4kN9pPbyDDY182LP3qXroCHW7NTnBk5zPvLD/jwkz+FLmqc2HuA1fV55u8usJQtUq5scXTXAdx+P9V2jbBpo9sTYmish+m5EoV0G4Ia0UCUoN+J7A2zMtlEb4sMj8fYyFa4fn6OY6f3ce39B8T6o7z96iwuVxubVcJmidLfMUwqneTW5RV+8ZeOM/lwk76hIAsPF4jHYiyurpKvVBge2cHyQoZ0HkTTTizq4eHCIvktCdG5ypHDB6jXYP1BmrbUpto2sKhhPv7pcY6eHOP8+QVUTWQk0c3jj+3kvXML1FWN+ck0XpebidFxtHadWklmdTVLwB9E18o8eeZR/v5/vk1XVz+FVInezgjZcg7dYWNxa5Xhk08weWOemY0CRqrASxffo9AoopQy/OzjJ5h4ZJxL03d55rGnMUolPEfG2HAJ3FlK8guf/SyvXTzH7Owqc/Nz3H+4ylf+8s/56p/8FR6bjZvn7rBzYASf30kgHGZ6eoF6pU6zUCObz9Fqb5FdybGxVeTcxSt85uc/y61rd8ht5NioFijnK7z5+uscHz9EPBDg/2vvTn/jus47jn/vNvtCcjhch0NSFHdRkrVGtC1bsSxX3uU4jpugifui9VIEDdCmTbdERtCgaFCgRRs0bZE2cF64aeOmteultiPLUmVLskSKFBdR5jpcZ4azb/fO3fqCbt+1KAIDBYr5/AXn1e+ec+5znufm5C2G+3sJNrewy9fMW+9dRHPCM2eeYOL6RxRzeeo8fhJrG0guka5oM2srm1R0kV19Q3R2tBIK+Bj78AaNoXoq2TzD3V14An7u2DfMSy+/SUtrC4orwPTMBLKsoPidNLW2kJxfZUvLcmViAqkxzIE9w8zMfMzRlhGWVm6RW8vR0eHnuUdOcW1sgg+nZ0hmbSa3kiAUWL+9wYkDh3hnaZY6xYVLkSjlC7jddRTKBi3tLaiFFPceH2VifBZRlBDlnZEs/1mD6fU6MS0DW7QxbQuPx42uqridnk/a2YkgSaRTSXK5IpoJLo+CIjnQVB2f1892PI1tgoCILMnYWEgSVKs6elXGF/SiC2VaO0I88Aun0JUEhVgZXZbwNwRxKxIZtYjiEqkKOorLTWNrmKqpIigKXp+PxnAz+Wwel9uF0+NkPbaG1+dhO7HN8PAw61ubuLxeDLFKtWwg2AK2+Uk5lGHhUGT0qoEkiwiijWWB06lgWRa2beN1e7EsE9M0+da3zv6c/VD/4Q/PupxugvWtNLU0o4k6Lqefoprn2edf4O133uT2xBIbyRTHDgxz7uo4qeU8nZFmiqrF3n39jN5zkLHxOdRCib17B7n64QT9gz14g27mbyUYHAgxevg4sgJP/9JTrK1vkIltUCmV8YsKbn+AKx/ewNfQRDDopbC8zH2nn0AzHYiUmZmaY+jQIGtLa1gOAYcZ5Py7F9guVnAZBpVMmVRaw1enEIuv0dBcjyfoJdTSwcriEr2tvUzFFxFKNoPDbVRsCYdQRyy1SrQjzI2PLrCV1OiMRLm1vEJ1u4IQDDI7NUdZ1QlZLuJmgY2NMkPD+6kUVKZuTtMcaWFwpJf0gkVAbOKtC5coVC3q3Crtgw5CniAhv8T02jz7e/q4Mj6B1/aRlQrUeQXSqSzt3S3E19Oc+OwIc7MLHDxwjJdeOk9dtERTWwv3P/oZ3j73Pj39bcRTGfSyTbmSJehvILaaIZ5M0Bppxe1QmJy+xb5DuwjVtxOLbfD4meNcv3aZtQWTE/fuYisXJ5XN0t/TgFpOs7GZ4K67jnLtg1Viy2vs6qrj1OlTpLNJ8vk0suBl7NrHZPMqiUIZExGHV6Qp3MT1j65y5319RDoi5JMZxsbmaGkOc6Cjn77metqCLrRL4/z22d/i3Gs/47F7T1HWC1xaXKFxZo7WhQKFzgAzG2s4VAkzV+F45wipUolTJ+7hX199m021wIHROxGKOQzJpqUpzDPPfJFQi4fYyjyzN1fwBIMkEqs8+NA9tHZ0UTEKDAzu4/biCk1tUXLZFE9+4QwXLr5PrqSyv7+ParWCLYrYVZMf/eQnpPQS0Y4w7d27efHsWdxhD3t2dfLuxXMc2LuXmbk5JJdAJpdkeSmD3+1BMG0kZJJbCQrZNC6nQia1TU/PbkZG9jI1P8PcrSUcDpnu3h6uT99kdXsdwbB5+KFHcQYVFFliO1EkHJT42q8/x+TlceqDXv70xW/z9s9eZW5tATkU4GBvD3/83e9hqA4M0abRF6KY0wjgZGo+RmskSHJ1lUhfmGpCJZVMsBrfQrNMcsltvA4n8bU1Ojo72NjaxjSM/2oo4vG5KZbKNDY0UqpU8Af9OCWFSkmlWKwiyjKSS8KqGoiCA1/Ah8vrxuURKWU0DFNHNS2qqoEogKwoFAsVFIeCaVVRJAGjaiM7FJrbw1QNC2+jky89+ACPf+FJzr/xGjnTQDZMDKtKJNJGqVhCEGVK5QJNTU0Y1Z270WQ8+cnucqdjmzfgwTQMZFFgO5HcedCxuoxoKyiihKmbSJJMVd2Z4GCaFpIk7ASfAJK08wFwOBR03cDQdWzRQnSIfPP3fs4j/9/8xTfO1gUDLK/lSGczqEYRU8sxetdJvv78Dwj5HTz2ZAt5U+PdfxnjzjuP0NgY4fr1m6hVg6aQH1HRERWJumA9s3PLeAIeFhYW6WyP0NvVzAdXP6Yp4GRheZXljQ0StxegoQ637eTOo0e5Y+QQznoPt5ancNR7GWjbw9xGmtXEBhOTN+nsa0XPGkgumJ9eYXk9y747DnDt2mUEu0h/Vy9VOU/Q76CtO0JrZ4SSZjB982OK5TLh3g6626IoDoFcJkkqm0F2+TDLGSSHzoH9eyioFgvzq/zaV59jObHGnv4eBkcGaI1GyMSrXB6fZ6C/h5XFBZobw4weO4oHmdX1FT4Ym6K914U/3MCrP17l6c+f4sr4MiN9jWhajv5oB2Nzq9iaj3JJJdrRynYmDhIYqkVb1Ed8c4P9e0/yxhvvce/J3bR0BUjEbxFpbqNcKpJIp2hsaEB26iDZtLR1cmVsjqHBboq5OMN7OunujNLZ3cD8/DwPnD7M1I2r7O4eJhINEUsm6R/sJ9oRYX5hAQkLd70DI2Pw4OlRFmJbpFMq41fPc/99xwiFfRTLKbx1PorVMsfv2UdDyIXLpbC+sU337n0Uimu0NNQRamxkYzXF8K5B6nvdvH3+I1ZXbmGVclTXVR45coJXx69waWGa8mqS37n7Hn44McuhPQeoVAwq0Vb+bfoGb09cJVzfwMzMTYb2DbO3d4hQOIhfkjl1/zH2DPXxvT/7Pj6vm+5IG5YFo8cPM3XtFslUijt62hAKZfb39bMSW2Jh8Tan7zvG5888yNXxOerr/Vy6dIUTd59mIbbKrZUYZx56mH1Hj1DYjvOZw8f5wY/+nmN3HwVbopLJ0N4aJZUrc+TIKEZZw+sPkk9vk0puYJgSwUA9IhKTkzOcffHbvPHmO8zMTZKpaHR2RklrKsuxNWRbRJQdKIKL6zeus7kZ48mHniSd2SK+nWVmcorWpjAtQR+vvPoGN/IrnDo0ir61zHd+8cuYvlY+3rxBLicQkGUeeOw0sclJmnZHad7lY7Crgxce+RwLsRWSySwb8QSlXJ6eXXuolAs0hev56KNZVEMHUUDARlJEZFlGEAQKxRIelw/bNCkUCkQ6IqiqiiAJuGUFTdVRtSpOp4yo2FiqhaqWERUFQxAwDQO3y4mmqZgWCFiIooLX60EzTLA0NEMEFJ5+/gj//Cev8/I7F/irP/8m594bRzIdFMslKtk8suSkUlJxOT1kshm8Lg/pTBLJlgkGvJQrGrZg09TeRjqeRJGcqGqZzc1t3E4H1WoFS7cR2OniL4kiikNAcUogiSBYYO/8QNMNA8u2QRAYGOijPuQnV8jwB7/7c+5Qr5z/4dmSpiLKfmLrqxwbPczkpRK2bGGaOh3NdXjcJabnynSG+3nvwjXa25vwBSVSqQqipWPaFqpmsR5LcfjwXrLZFHV1ATbW19ANnVC4ma3VGG2dHcQ2Y4xEOtgsFNA1HaNa4pXXzuGRbQZaPBRzKlJV4cc/fR3DUumKNuJy+bh88Tq5UpKRkQG2ciUefeQU6a3bPProUcrlDLt3jVDaLhJocLEwv0gpX+DxBx8hFGrg0pUPqBQL+NxekolNDh0cQRR0JLFKONrPjatTPPbwIwQbPZRyabw+N/lcCkTYjCeodzeyGN9AtHUEYHZpnkKxSD6dQvY70CyVUMgglTPZd8zNymyczXyagMtBc2MDCwtJPP42bowtcv/JgywszNHdGwUE6oKNZLPbKB6b27eS9Az5aI4ahIItGKUSHilAwNNAYjNFpZhld18bNhrpzAaHjx7C6dBwuUwMS6Vc0iiqCSqVCvX1InUBD0tL6ywuLxNu6+DNty4Q8NcT6WjCIUjMzS8hWzaS6OLfr06TSxpkUkW8AR3JHWd4b5T29kaCdR5ciknA6yBU30AqXiSXLhOs87G+skU+W0KvCrhEif6+Jhobbbp3RTm59yhqxz6e/aPvcKyunXRqm6+/+DUSM2OEu7sYOnyY9c0k5arAtZuzDDVHeOHpL/PCl77C1dffxVRceBvcrMzO0d7dyfe+/9f88le+wsTYNaanZhjYt5+fXXyPoaE94BBQfE7aol2UjCrdQ72MHjzKyy/9LU88/BAT47e5ef0GXfsHefY3vsrswhxmQeXiuQu8f/kyv3LmKb747At43TJPfO4xLr5/BU0zuTE5QypbYn5+AcmWKBfTDOxuZ6Cnk6XFdZZX1ymUypRVlfPvn6dYKhFuqOOpxz/H2IUPyZWLlAsl1EoFzdLxuvyYmJw8/Vn+6R9/iiYIFCoG+XyRUqXExbFJpLLByQeOsL29yaN3DuIPNPH7P3qF4YFdmLKHoZFBNgrbHBwaQGtw8KtfeJwLL7+JJTtoag8DMnedOM709DRLi8skE2kEq4qmGlRNExt2Xj3ZFpZlEY10UK6UMC1zJ4wsKKhFbBsMTcewDLweH7pe5fjxUbbiW2i6hWgLOFxOVF1HkQVsw0IUJQzTQpZFNMPEMnV8PieSLKC43FiywVNnHua1v3sL2py89JevcuTuQyQ3trAEMDUbbAtJllGrVdweF6pWoTFUj6bpaKUK/oAPj9+HYUGpUKQr2km5kker6iDYmNZOYFoWwE6ZlSQLmKYJgoAiy5iGhWXtHPtFSQQLtreTNLc3U6kW+cZv/vd1qIJt/89zpmtqampq/nfE/+sF1NTU1Px/UQvUmpqamk9JLVBrampqPiW1QK2pqan5lNQCtaampuZTUgvUmpqamk/JfwAh5KZn4HHN6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_sample_images\n",
    "\n",
    "img = load_sample_images()[\"images\"][0]\n",
    "plt.imshow(img)\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Original Image\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = tf.io.encode_jpeg(img)\n",
    "example_with_image = Example(features=Features(feature={\n",
    "    \"image\": Feature(bytes_list=BytesList(value=[data.numpy()]))}))\n",
    "serialized_example = example_with_image.SerializeToString()\n",
    "# then save to TFRecord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_description = { \"image\": tf.io.VarLenFeature(tf.string) }\n",
    "example_with_image = tf.io.parse_single_example(serialized_example, feature_description)\n",
    "decoded_img = tf.io.decode_jpeg(example_with_image[\"image\"].values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또는 `decode_image()`를 사용합니다. 이 함수는 BMP, GIF, JPEG, PNG 포맷을 지원합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "decoded_img = tf.io.decode_image(example_with_image[\"image\"].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(decoded_img)\n",
    "plt.title(\"Decoded Image\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFRecord에 텐서와 희소 텐서 넣기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`tf.io.serialize_tensor()`와 `tf.io.parse_tensor()`를 사용해 텐서를 쉽게 직렬화하고 파싱할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=string, numpy=b'\\x08\\x01\\x12\\x08\\x12\\x02\\x08\\x03\\x12\\x02\\x08\\x02\"\\x18\\x00\\x00\\x00\\x00\\x00\\x00\\x80?\\x00\\x00\\x00@\\x00\\x00@@\\x00\\x00\\x80@\\x00\\x00\\xa0@'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = tf.constant([[0., 1.], [2., 3.], [4., 5.]])\n",
    "s = tf.io.serialize_tensor(t)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 2), dtype=float32, numpy=\n",
       "array([[0., 1.],\n",
       "       [2., 3.],\n",
       "       [4., 5.]], dtype=float32)>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.io.parse_tensor(s, out_type=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=string, numpy=\n",
       "array([b'\\x08\\t\\x12\\x08\\x12\\x02\\x08\\x02\\x12\\x02\\x08\\x01\"\\x10\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n",
       "       b'\\x08\\x07\\x12\\x04\\x12\\x02\\x08\\x02\"\\x10\\x07\\x07a@b.comc@d.com',\n",
       "       b'\\x08\\t\\x12\\x04\\x12\\x02\\x08\\x01\"\\x08\\x02\\x00\\x00\\x00\\x00\\x00\\x00\\x00'],\n",
       "      dtype=object)>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serialized_sparse = tf.io.serialize_sparse(parsed_example[\"emails\"])\n",
    "serialized_sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value: \"\\010\\t\\022\\010\\022\\002\\010\\002\\022\\002\\010\\001\\\"\\020\\000\\000\\000\\000\\000\\000\\000\\000\\001\\000\\000\\000\\000\\000\\000\\000\"\n",
       "value: \"\\010\\007\\022\\004\\022\\002\\010\\002\\\"\\020\\007\\007a@b.comc@d.com\"\n",
       "value: \"\\010\\t\\022\\004\\022\\002\\010\\001\\\"\\010\\002\\000\\000\\000\\000\\000\\000\\000\""
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BytesList(value=serialized_sparse.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.TFRecordDataset([\"my_contacts.tfrecord\"]).batch(10)\n",
    "for serialized_examples in dataset:\n",
    "    parsed_examples = tf.io.parse_example(serialized_examples,\n",
    "                                          feature_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7fc85261dbe0>}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `SequenceExample`를 사용해 순차 데이터 다루기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```proto\n",
    "syntax = \"proto3\";\n",
    "\n",
    "message FeatureList { repeated Feature feature = 1; };\n",
    "message FeatureLists { map<string, FeatureList> feature_list = 1; };\n",
    "message SequenceExample {\n",
    "  Features context = 1;\n",
    "  FeatureLists feature_lists = 2;\n",
    "};\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.train import FeatureList, FeatureLists, SequenceExample\n",
    "\n",
    "context = Features(feature={\n",
    "    \"author_id\": Feature(int64_list=Int64List(value=[123])),\n",
    "    \"title\": Feature(bytes_list=BytesList(value=[b\"A\", b\"desert\", b\"place\", b\".\"])),\n",
    "    \"pub_date\": Feature(int64_list=Int64List(value=[1623, 12, 25]))\n",
    "})\n",
    "\n",
    "content = [[\"When\", \"shall\", \"we\", \"three\", \"meet\", \"again\", \"?\"],\n",
    "           [\"In\", \"thunder\", \",\", \"lightning\", \",\", \"or\", \"in\", \"rain\", \"?\"]]\n",
    "comments = [[\"When\", \"the\", \"hurlyburly\", \"'s\", \"done\", \".\"],\n",
    "            [\"When\", \"the\", \"battle\", \"'s\", \"lost\", \"and\", \"won\", \".\"]]\n",
    "\n",
    "def words_to_feature(words):\n",
    "    return Feature(bytes_list=BytesList(value=[word.encode(\"utf-8\")\n",
    "                                               for word in words]))\n",
    "\n",
    "content_features = [words_to_feature(sentence) for sentence in content]\n",
    "comments_features = [words_to_feature(comment) for comment in comments]\n",
    "            \n",
    "sequence_example = SequenceExample(\n",
    "    context=context,\n",
    "    feature_lists=FeatureLists(feature_list={\n",
    "        \"content\": FeatureList(feature=content_features),\n",
    "        \"comments\": FeatureList(feature=comments_features)\n",
    "    }))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "context {\n",
       "  feature {\n",
       "    key: \"author_id\"\n",
       "    value {\n",
       "      int64_list {\n",
       "        value: 123\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  feature {\n",
       "    key: \"pub_date\"\n",
       "    value {\n",
       "      int64_list {\n",
       "        value: 1623\n",
       "        value: 12\n",
       "        value: 25\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  feature {\n",
       "    key: \"title\"\n",
       "    value {\n",
       "      bytes_list {\n",
       "        value: \"A\"\n",
       "        value: \"desert\"\n",
       "        value: \"place\"\n",
       "        value: \".\"\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "}\n",
       "feature_lists {\n",
       "  feature_list {\n",
       "    key: \"comments\"\n",
       "    value {\n",
       "      feature {\n",
       "        bytes_list {\n",
       "          value: \"When\"\n",
       "          value: \"the\"\n",
       "          value: \"hurlyburly\"\n",
       "          value: \"\\'s\"\n",
       "          value: \"done\"\n",
       "          value: \".\"\n",
       "        }\n",
       "      }\n",
       "      feature {\n",
       "        bytes_list {\n",
       "          value: \"When\"\n",
       "          value: \"the\"\n",
       "          value: \"battle\"\n",
       "          value: \"\\'s\"\n",
       "          value: \"lost\"\n",
       "          value: \"and\"\n",
       "          value: \"won\"\n",
       "          value: \".\"\n",
       "        }\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  feature_list {\n",
       "    key: \"content\"\n",
       "    value {\n",
       "      feature {\n",
       "        bytes_list {\n",
       "          value: \"When\"\n",
       "          value: \"shall\"\n",
       "          value: \"we\"\n",
       "          value: \"three\"\n",
       "          value: \"meet\"\n",
       "          value: \"again\"\n",
       "          value: \"?\"\n",
       "        }\n",
       "      }\n",
       "      feature {\n",
       "        bytes_list {\n",
       "          value: \"In\"\n",
       "          value: \"thunder\"\n",
       "          value: \",\"\n",
       "          value: \"lightning\"\n",
       "          value: \",\"\n",
       "          value: \"or\"\n",
       "          value: \"in\"\n",
       "          value: \"rain\"\n",
       "          value: \"?\"\n",
       "        }\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "serialized_sequence_example = sequence_example.SerializeToString()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "context_feature_descriptions = {\n",
    "    \"author_id\": tf.io.FixedLenFeature([], tf.int64, default_value=0),\n",
    "    \"title\": tf.io.VarLenFeature(tf.string),\n",
    "    \"pub_date\": tf.io.FixedLenFeature([3], tf.int64, default_value=[0, 0, 0]),\n",
    "}\n",
    "sequence_feature_descriptions = {\n",
    "    \"content\": tf.io.VarLenFeature(tf.string),\n",
    "    \"comments\": tf.io.VarLenFeature(tf.string),\n",
    "}\n",
    "parsed_context, parsed_feature_lists = tf.io.parse_single_sequence_example(\n",
    "    serialized_sequence_example, context_feature_descriptions,\n",
    "    sequence_feature_descriptions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'title': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7fcb1a6cb358>,\n",
       " 'author_id': <tf.Tensor: shape=(), dtype=int64, numpy=123>,\n",
       " 'pub_date': <tf.Tensor: shape=(3,), dtype=int64, numpy=array([1623,   12,   25])>}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4,), dtype=string, numpy=array([b'A', b'desert', b'place', b'.'], dtype=object)>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_context[\"title\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'comments': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7fcb1a6cb390>,\n",
       " 'content': <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7fcb1a6cb320>}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_feature_lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.RaggedTensor [[b'When', b'shall', b'we', b'three', b'meet', b'again', b'?'], [b'In', b'thunder', b',', b'lightning', b',', b'or', b'in', b'rain', b'?']]>\n"
     ]
    }
   ],
   "source": [
    "print(tf.RaggedTensor.from_sparse(parsed_feature_lists[\"content\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 특성 API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2장에서 사용했던 캘리포니아 주택 데이터셋에 범주형 특성과 누락된 값이 있으므로 이 데이터를 다시 사용하겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "import urllib.request\n",
    "\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/rickiepark/handson-ml2/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
    "\n",
    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
    "    os.makedirs(housing_path, exist_ok=True)\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "fetch_housing_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def load_housing_data(housing_path=HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_mean, age_std = X_mean[1], X_std[1]  # The median age is column in 1\n",
    "housing_median_age = tf.feature_column.numeric_column(\n",
    "    \"housing_median_age\", normalizer_fn=lambda x: (x - age_mean) / age_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_income = tf.feature_column.numeric_column(\"median_income\")\n",
    "bucketized_income = tf.feature_column.bucketized_column(\n",
    "    median_income, boundaries=[1.5, 3., 4.5, 6.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BucketizedColumn(source_column=NumericColumn(key='median_income', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), boundaries=(1.5, 3.0, 4.5, 6.0))"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucketized_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "ocean_prox_vocab = ['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN']\n",
    "ocean_proximity = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    \"ocean_proximity\", ocean_prox_vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VocabularyListCategoricalColumn(key='ocean_proximity', vocabulary_list=('<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'), dtype=tf.string, default_value=-1, num_oov_buckets=0)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocean_proximity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HashedCategoricalColumn(key='city', hash_bucket_size=1000, dtype=tf.string)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just an example, it's not used later on\n",
    "city_hash = tf.feature_column.categorical_column_with_hash_bucket(\n",
    "    \"city\", hash_bucket_size=1000)\n",
    "city_hash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "bucketized_age = tf.feature_column.bucketized_column(\n",
    "    housing_median_age, boundaries=[-1., -0.5, 0., 0.5, 1.]) # age was scaled\n",
    "age_and_ocean_proximity = tf.feature_column.crossed_column(\n",
    "    [bucketized_age, ocean_proximity], hash_bucket_size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "latitude = tf.feature_column.numeric_column(\"latitude\")\n",
    "longitude = tf.feature_column.numeric_column(\"longitude\")\n",
    "bucketized_latitude = tf.feature_column.bucketized_column(\n",
    "    latitude, boundaries=list(np.linspace(32., 42., 20 - 1)))\n",
    "bucketized_longitude = tf.feature_column.bucketized_column(\n",
    "    longitude, boundaries=list(np.linspace(-125., -114., 20 - 1)))\n",
    "location = tf.feature_column.crossed_column(\n",
    "    [bucketized_latitude, bucketized_longitude], hash_bucket_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "ocean_proximity_one_hot = tf.feature_column.indicator_column(ocean_proximity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "ocean_proximity_embed = tf.feature_column.embedding_column(ocean_proximity,\n",
    "                                                           dimension=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `feature_column`을 사용해 파싱하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_house_value = tf.feature_column.numeric_column(\"median_house_value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'housing_median_age': FixedLenFeature(shape=(1,), dtype=tf.float32, default_value=None),\n",
       " 'median_house_value': FixedLenFeature(shape=(1,), dtype=tf.float32, default_value=None)}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [housing_median_age, median_house_value]\n",
    "feature_descriptions = tf.feature_column.make_parse_example_spec(columns)\n",
    "feature_descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.io.TFRecordWriter(\"my_data_with_features.tfrecords\") as f:\n",
    "    for x, y in zip(X_train[:, 1:2], y_train):\n",
    "        example = Example(features=Features(feature={\n",
    "            \"housing_median_age\": Feature(float_list=FloatList(value=[x])),\n",
    "            \"median_house_value\": Feature(float_list=FloatList(value=[y]))\n",
    "        }))\n",
    "        f.write(example.SerializeToString())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_examples(serialized_examples):\n",
    "    examples = tf.io.parse_example(serialized_examples, feature_descriptions)\n",
    "    targets = examples.pop(\"median_house_value\") # separate the targets\n",
    "    return examples, targets\n",
    "\n",
    "batch_size = 32\n",
    "dataset = tf.data.TFRecordDataset([\"my_data_with_features.tfrecords\"])\n",
    "dataset = dataset.repeat().shuffle(10000).batch(batch_size).map(parse_examples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: the `DenseFeatures` layer currently does not work with the Functional API, see [TF issue #27416](https://github.com/tensorflow/tensorflow/issues/27416). Hopefully this will be resolved before the final release of TF 2.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'housing_median_age': <tf.Tensor 'IteratorGetNext:0' shape=(None, 1) dtype=float32>}\n",
      "Consider rewriting this model with the Functional API.\n",
      "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'housing_median_age': <tf.Tensor 'IteratorGetNext:0' shape=(None, 1) dtype=float32>}\n",
      "Consider rewriting this model with the Functional API.\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 3.7619 - accuracy: 0.0016\n",
      "Epoch 2/5\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 1.9311 - accuracy: 0.0027\n",
      "Epoch 3/5\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 1.4434 - accuracy: 0.0026\n",
      "Epoch 4/5\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 1.3579 - accuracy: 0.0030\n",
      "Epoch 5/5\n",
      "362/362 [==============================] - 1s 3ms/step - loss: 1.3473 - accuracy: 0.0038\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fcb1a0ff4a8>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_without_target = columns[:-1]\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.DenseFeatures(feature_columns=columns_without_target),\n",
    "    keras.layers.Dense(1)\n",
    "])\n",
    "model.compile(loss=\"mse\",\n",
    "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
    "              metrics=[\"accuracy\"])\n",
    "model.fit(dataset, steps_per_epoch=len(X_train) // batch_size, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 7), dtype=float32, numpy=\n",
       "array([[ 0.        ,  0.        ,  1.        ,  0.        ,  0.        ,\n",
       "        -0.14504611,  0.7563394 ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  1.        ,\n",
       "        -1.1119912 ,  0.56957847],\n",
       "       [ 1.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        -1.1119912 ,  0.56957847]], dtype=float32)>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_columns = [ocean_proximity_embed, bucketized_income]\n",
    "dense_features = keras.layers.DenseFeatures(some_columns)\n",
    "dense_features({\n",
    "    \"ocean_proximity\": [[\"NEAR OCEAN\"], [\"INLAND\"], [\"INLAND\"]],\n",
    "    \"median_income\": [[3.], [7.2], [1.]]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TF 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import tensorflow_transform as tft\n",
    "\n",
    "    def preprocess(inputs):  # inputs is a batch of input features\n",
    "        median_age = inputs[\"housing_median_age\"]\n",
    "        ocean_proximity = inputs[\"ocean_proximity\"]\n",
    "        standardized_age = tft.scale_to_z_score(median_age - tft.mean(median_age))\n",
    "        ocean_proximity_id = tft.compute_and_apply_vocabulary(ocean_proximity)\n",
    "        return {\n",
    "            \"standardized_median_age\": standardized_age,\n",
    "            \"ocean_proximity_id\": ocean_proximity_id\n",
    "        }\n",
    "except ImportError:\n",
    "    print(\"TF Transform is not installed. Try running: pip3 install -U tensorflow-transform\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 텐서플로 데이터셋"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDownloading and preparing dataset mnist/3.0.1 (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /home/work/tensorflow_datasets/mnist/3.0.1...\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your\n",
      "local data directory. If you'd instead prefer to read directly from our public\n",
      "GCS bucket (recommended if you're running on GCP), you can instead pass\n",
      "`try_gcs=True` to `tfds.load` or set `data_dir=gs://tfds-data/datasets`.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7cedea767ed43689e364c75aacc212c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Dl Completed...', max=4.0, style=ProgressStyle(descriptio…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1mDataset mnist downloaded and prepared to /home/work/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "datasets = tfds.load(name=\"mnist\")\n",
    "mnist_train, mnist_test = datasets[\"train\"], datasets[\"test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['abstract_reasoning', 'aeslc', 'aflw2k3d', 'amazon_us_reviews', 'anli', 'arc', 'bair_robot_pushing_small', 'beans', 'big_patent', 'bigearthnet', 'billsum', 'binarized_mnist', 'binary_alpha_digits', 'blimp', 'c4', 'caltech101', 'caltech_birds2010', 'caltech_birds2011', 'cars196', 'cassava', 'cats_vs_dogs', 'celeb_a', 'celeb_a_hq', 'cfq', 'chexpert', 'cifar10', 'cifar100', 'cifar10_1', 'cifar10_corrupted', 'citrus_leaves', 'cityscapes', 'civil_comments', 'clevr', 'clinc_oos', 'cmaterdb', 'cnn_dailymail', 'coco', 'coil100', 'colorectal_histology', 'colorectal_histology_large', 'common_voice', 'cos_e', 'cosmos_qa', 'covid19sum', 'crema_d', 'curated_breast_imaging_ddsm', 'cycle_gan', 'deep_weeds', 'definite_pronoun_resolution', 'dementiabank', 'diabetic_retinopathy_detection', 'div2k', 'dmlab', 'downsampled_imagenet', 'dsprites', 'dtd', 'duke_ultrasound', 'emnist', 'eraser_multi_rc', 'esnli', 'eurosat', 'fashion_mnist', 'flic', 'flores', 'food101', 'forest_fires', 'fuss', 'gap', 'geirhos_conflict_stimuli', 'german_credit_numeric', 'gigaword', 'glue', 'groove', 'higgs', 'horses_or_humans', 'i_naturalist2017', 'imagenet2012', 'imagenet2012_corrupted', 'imagenet2012_real', 'imagenet2012_subset', 'imagenet_resized', 'imagenette', 'imagewang', 'imdb_reviews', 'irc_disentanglement', 'iris', 'kitti', 'kmnist', 'lfw', 'librispeech', 'librispeech_lm', 'libritts', 'ljspeech', 'lm1b', 'lost_and_found', 'lsun', 'malaria', 'math_dataset', 'mctaco', 'mnist', 'mnist_corrupted', 'movie_lens', 'movie_rationales', 'moving_mnist', 'multi_news', 'multi_nli', 'multi_nli_mismatch', 'natural_questions', 'newsroom', 'nsynth', 'nyu_depth_v2', 'omniglot', 'open_images_challenge2019_detection', 'open_images_v4', 'openbookqa', 'opinion_abstracts', 'opinosis', 'opus', 'oxford_flowers102', 'oxford_iiit_pet', 'para_crawl', 'patch_camelyon', 'pet_finder', 'pg19', 'places365_small', 'plant_leaves', 'plant_village', 'plantae_k', 'qa4mre', 'quickdraw_bitmap', 'reddit', 'reddit_disentanglement', 'reddit_tifu', 'resisc45', 'robonet', 'rock_paper_scissors', 'rock_you', 'samsum', 'savee', 'scan', 'scene_parse150', 'scicite', 'scientific_papers', 'shapes3d', 'smallnorb', 'snli', 'so2sat', 'speech_commands', 'squad', 'stanford_dogs', 'stanford_online_products', 'starcraft_video', 'stl10', 'sun397', 'super_glue', 'svhn_cropped', 'ted_hrlr_translate', 'ted_multi_translate', 'tedlium', 'tf_flowers', 'the300w_lp', 'tiny_shakespeare', 'titanic', 'trivia_qa', 'uc_merced', 'ucf101', 'vctk', 'vgg_face2', 'visual_domain_decathlon', 'voc', 'voxceleb', 'voxforge', 'waymo_open_dataset', 'web_questions', 'wider_face', 'wiki40b', 'wikihow', 'wikipedia', 'wikipedia_toxicity_subtypes', 'winogrande', 'wmt14_translate', 'wmt15_translate', 'wmt16_translate', 'wmt17_translate', 'wmt18_translate', 'wmt19_translate', 'wmt_t2t_translate', 'wmt_translate', 'wordnet', 'xnli', 'xsum', 'yelp_polarity_reviews']\n"
     ]
    }
   ],
   "source": [
    "print(tfds.list_builders())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3))\n",
    "mnist_train = mnist_train.repeat(5).batch(32).prefetch(1)\n",
    "for item in mnist_train:\n",
    "    images = item[\"image\"]\n",
    "    labels = item[\"label\"]\n",
    "    for index in range(5):\n",
    "        plt.subplot(1, 5, index + 1)\n",
    "        image = images[index, ..., 0]\n",
    "        label = labels[index].numpy()\n",
    "        plt.imshow(image, cmap=\"binary\")\n",
    "        plt.title(label)\n",
    "        plt.axis(\"off\")\n",
    "    break # just showing part of the first batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 28, 28, 1)\n",
      "[4 1 0 7 8 1 2 7 1 6 6 4 7 7 3 3 7 9 9 1 0 6 6 9 9 4 8 9 4 7 3 3]\n"
     ]
    }
   ],
   "source": [
    "datasets = tfds.load(name=\"mnist\")\n",
    "mnist_train, mnist_test = datasets[\"train\"], datasets[\"test\"]\n",
    "mnist_train = mnist_train.repeat(5).batch(32)\n",
    "mnist_train = mnist_train.map(lambda items: (items[\"image\"], items[\"label\"]))\n",
    "mnist_train = mnist_train.prefetch(1)\n",
    "for images, labels in mnist_train.take(1):\n",
    "    print(images.shape)\n",
    "    print(labels.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 31.9656 - accuracy: 0.8425\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 25.7180 - accuracy: 0.8684\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 25.1185 - accuracy: 0.8725\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 24.3676 - accuracy: 0.8766\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 23.8195 - accuracy: 0.8782\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fc852c73ba8>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets = tfds.load(name=\"mnist\", batch_size=32, as_supervised=True)\n",
    "mnist_train = datasets[\"train\"].repeat().prefetch(1)\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28, 28, 1]),\n",
    "    keras.layers.Lambda(lambda images: tf.cast(images, tf.float32)),\n",
    "    keras.layers.Dense(10, activation=\"softmax\")])\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
    "              metrics=[\"accuracy\"])\n",
    "model.fit(mnist_train, steps_per_epoch=60000 // 32, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 텐서플로 허브"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "keras_layer (KerasLayer)     (None, 50)                48190600  \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 16)                816       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 48,191,433\n",
      "Trainable params: 833\n",
      "Non-trainable params: 48,190,600\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_hub as hub\n",
    "\n",
    "hub_layer = hub.KerasLayer(\"https://tfhub.dev/google/tf2-preview/nnlm-en-dim50/1\",\n",
    "                           output_shape=[50], input_shape=[], dtype=tf.string)\n",
    "\n",
    "model = keras.Sequential()\n",
    "model.add(hub_layer)\n",
    "model.add(keras.layers.Dense(16, activation='relu'))\n",
    "model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = tf.constant([\"It was a great movie\", \"The actors were amazing\"])\n",
    "embeddings = hub_layer(sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 50), dtype=float32, numpy=\n",
       "array([[ 7.45939985e-02,  2.76720114e-02,  9.38646123e-02,\n",
       "         1.25124469e-01,  5.40293928e-04, -1.09435350e-01,\n",
       "         1.34755149e-01, -9.57818255e-02, -1.85177118e-01,\n",
       "        -1.69703495e-02,  1.75612606e-02, -9.06603858e-02,\n",
       "         1.12110220e-01,  1.04646273e-01,  3.87700424e-02,\n",
       "        -7.71859884e-02, -3.12189370e-01,  6.99466765e-02,\n",
       "        -4.88970093e-02, -2.99049795e-01,  1.31183028e-01,\n",
       "        -2.12630898e-01,  6.96169436e-02,  1.63592950e-01,\n",
       "         1.05169769e-02,  7.79720694e-02, -2.55230188e-01,\n",
       "        -1.80790052e-01,  2.93739915e-01,  1.62875261e-02,\n",
       "        -2.80566931e-01,  1.60284728e-01,  9.87277832e-03,\n",
       "         8.44555616e-04,  8.39456245e-02,  3.24002892e-01,\n",
       "         1.53253034e-01, -3.01048346e-02,  8.94618109e-02,\n",
       "        -2.39153411e-02, -1.50188789e-01, -1.81733668e-02,\n",
       "        -1.20483577e-01,  1.32937476e-01, -3.35325629e-01,\n",
       "        -1.46504581e-01, -1.25251599e-02, -1.64428815e-01,\n",
       "        -7.00765476e-02,  3.60923223e-02],\n",
       "       [-1.56998575e-01,  4.24599349e-02, -5.57703003e-02,\n",
       "        -8.08446854e-03,  1.23733155e-01,  3.89427543e-02,\n",
       "        -4.37901802e-02, -1.86987907e-01, -2.29341656e-01,\n",
       "        -1.27766818e-01,  3.83025259e-02, -1.07057482e-01,\n",
       "        -6.11584112e-02,  2.49654502e-01, -1.39712945e-01,\n",
       "        -3.91289443e-02, -1.35873526e-01, -3.58613044e-01,\n",
       "         2.53462754e-02, -1.58370987e-01, -1.38350084e-01,\n",
       "        -3.90771806e-01, -6.63642734e-02, -3.24838236e-02,\n",
       "        -2.20453963e-02, -1.68282315e-01, -7.40613639e-02,\n",
       "        -2.49074101e-02,  2.46460736e-01,  9.87201929e-05,\n",
       "        -1.85390845e-01, -4.92824614e-02,  1.09015472e-01,\n",
       "        -9.54203904e-02, -1.60352528e-01, -2.59811729e-02,\n",
       "         1.13778859e-01, -2.09578887e-01,  2.18261331e-01,\n",
       "        -3.11211571e-02, -6.12562597e-02, -8.66057724e-02,\n",
       "        -1.10762455e-01, -5.73977083e-03, -1.08923554e-01,\n",
       "        -1.72919363e-01,  1.00515485e-01, -5.64153939e-02,\n",
       "        -4.97694984e-02, -1.07776590e-01]], dtype=float32)>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 연습문제\n",
    "\n",
    "## 1. to 8.\n",
    "부록 A 참고\n",
    "\n",
    "## 9.\n",
    "### a.\n",
    "_문제: (10장에서 소개한) 패션 MNIST 데이터셋을 적재하고 훈련 세트, 검증 세트, 테스트\n",
    "세트로 나눕니다. 훈련 세트를 섞은 다음 각 데이터셋을 TFRecord 파일로 저장합니\n",
    "다. 각 레코드는 두 개의 특성을 가진 `Example` 프로토콜 버퍼, 즉 직렬화된 이미지(`tf.io.serialize_tensor()`를 사용해 이미지를 직렬화하세요)와 레이블입니다. 참고: 용량이 큰 이미지일 경우 `tf.io.encode_jpeg()`를 사용할 수 있습니다. 많은 공간을 절약할 수 있지만 이미지 품질이 손해를 봅니다._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()\n",
    "X_valid, X_train = X_train_full[:5000], X_train_full[5000:]\n",
    "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(len(X_train))\n",
    "valid_set = tf.data.Dataset.from_tensor_slices((X_valid, y_valid))\n",
    "test_set = tf.data.Dataset.from_tensor_slices((X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_example(image, label):\n",
    "    image_data = tf.io.serialize_tensor(image)\n",
    "    #image_data = tf.io.encode_jpeg(image[..., np.newaxis])\n",
    "    return Example(\n",
    "        features=Features(\n",
    "            feature={\n",
    "                \"image\": Feature(bytes_list=BytesList(value=[image_data.numpy()])),\n",
    "                \"label\": Feature(int64_list=Int64List(value=[label])),\n",
    "            }))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features {\n",
      "  feature {\n",
      "    key: \"image\"\n",
      "    value {\n",
      "      bytes_list {\n",
      "        value: \"\\010\\004\\022\\010\\022\\002\\010\\034\\022\\002\\010\\034\\\"\\220\\006\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\001\\000\\000\\rI\\000\\000\\001\\004\\000\\000\\000\\000\\001\\001\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\003\\000$\\210\\177>6\\000\\000\\000\\001\\003\\004\\000\\000\\003\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\006\\000f\\314\\260\\206\\220{\\027\\000\\000\\000\\000\\014\\n\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\233\\354\\317\\262k\\234\\241m@\\027M\\202H\\017\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\001\\000E\\317\\337\\332\\330\\330\\243\\177yz\\222\\215X\\254B\\000\\000\\000\\000\\000\\000\\000\\000\\000\\001\\001\\001\\000\\310\\350\\350\\351\\345\\337\\337\\327\\325\\244\\177{\\304\\345\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\267\\341\\330\\337\\344\\353\\343\\340\\336\\340\\335\\337\\365\\255\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\301\\344\\332\\325\\306\\264\\324\\322\\323\\325\\337\\334\\363\\312\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\001\\003\\000\\014\\333\\334\\324\\332\\300\\251\\343\\320\\332\\340\\324\\342\\305\\3214\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\006\\000c\\364\\336\\334\\332\\313\\306\\335\\327\\325\\336\\334\\365w\\2478\\000\\000\\000\\000\\000\\000\\000\\000\\000\\004\\000\\0007\\354\\344\\346\\344\\360\\350\\325\\332\\337\\352\\331\\331\\321\\\\\\000\\000\\000\\001\\004\\006\\007\\002\\000\\000\\000\\000\\000\\355\\342\\331\\337\\336\\333\\336\\335\\330\\337\\345\\327\\332\\377M\\000\\000\\003\\000\\000\\000\\000\\000\\000\\000>\\221\\314\\344\\317\\325\\335\\332\\320\\323\\332\\340\\337\\333\\327\\340\\364\\237\\000\\000\\000\\000\\000\\022,Rk\\275\\344\\334\\336\\331\\342\\310\\315\\323\\346\\340\\352\\260\\274\\372\\370\\351\\356\\327\\000\\0009\\273\\320\\340\\335\\340\\320\\314\\326\\320\\321\\310\\237\\365\\301\\316\\337\\377\\377\\335\\352\\335\\323\\334\\350\\366\\000\\003\\312\\344\\340\\335\\323\\323\\326\\315\\315\\315\\334\\360P\\226\\377\\345\\335\\274\\232\\277\\322\\314\\321\\336\\344\\341\\000b\\351\\306\\322\\336\\345\\345\\352\\371\\334\\302\\327\\331\\361AIju\\250\\333\\335\\327\\331\\337\\337\\340\\345\\035K\\314\\324\\314\\301\\315\\323\\341\\330\\271\\305\\316\\306\\325\\360\\303\\343\\365\\357\\337\\332\\324\\321\\336\\334\\335\\346C0\\313\\267\\302\\325\\305\\271\\276\\302\\300\\312\\326\\333\\335\\334\\354\\341\\330\\307\\316\\272\\265\\261\\254\\265\\315\\316s\\000z\\333\\301\\263\\253\\267\\304\\314\\322\\325\\317\\323\\322\\310\\304\\302\\277\\303\\277\\306\\300\\260\\234\\247\\261\\322\\\\\\000\\000J\\275\\324\\277\\257\\254\\257\\265\\271\\274\\275\\274\\301\\306\\314\\321\\322\\322\\323\\274\\274\\302\\300\\330\\252\\000\\002\\000\\000\\000B\\310\\336\\355\\357\\362\\366\\363\\364\\335\\334\\301\\277\\263\\266\\266\\265\\260\\246\\250c:\\000\\000\\000\\000\\000\\000\\000\\000\\000(=,H)#\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\\000\"\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "  feature {\n",
      "    key: \"label\"\n",
      "    value {\n",
      "      int64_list {\n",
      "        value: 9\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for image, label in valid_set.take(1):\n",
    "    print(create_example(image, label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음 함수는 주어진 데이터셋을 일련의 TFRecord 파일로 저장합니다. 이 예제는 라운드-로빈 방식으로 파일에 저장합니다. 이를 위해 `dataset.enumerate()` 메서드로 모든 샘플을 순회하고 저장할 파일을 겨정하기 위해 `index % n_shards`를 계산합니다. 표준 `contextlib.ExitStack` 클래스를 사용해 쓰는 동안 I/O 에러의 발생 여부에 상관없이 모든 `writer`가 적절히 종료되었는지 확인합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "from contextlib import ExitStack\n",
    "\n",
    "def write_tfrecords(name, dataset, n_shards=10):\n",
    "    paths = [\"{}.tfrecord-{:05d}-of-{:05d}\".format(name, index, n_shards)\n",
    "             for index in range(n_shards)]\n",
    "    with ExitStack() as stack:\n",
    "        writers = [stack.enter_context(tf.io.TFRecordWriter(path))\n",
    "                   for path in paths]\n",
    "        for index, (image, label) in dataset.enumerate():\n",
    "            shard = index % n_shards\n",
    "            example = create_example(image, label)\n",
    "            writers[shard].write(example.SerializeToString())\n",
    "    return paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_filepaths = write_tfrecords(\"my_fashion_mnist.train\", train_set)\n",
    "valid_filepaths = write_tfrecords(\"my_fashion_mnist.valid\", valid_set)\n",
    "test_filepaths = write_tfrecords(\"my_fashion_mnist.test\", test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b.\n",
    "_문제: tf.data로 각 세트를 위한 효율적인 데이터셋을 만듭니다. 마지막으로 이 데이터셋으로\n",
    "입력 특성을 표준화하는 전처리 층을 포함한 케라스 모델을 훈련합니다. 텐서보드로 프\n",
    "로파일 데이터를 시각화하여 가능한 한 입력 파이프라인을 효율적으로 만들어보세요._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(tfrecord):\n",
    "    feature_descriptions = {\n",
    "        \"image\": tf.io.FixedLenFeature([], tf.string, default_value=\"\"),\n",
    "        \"label\": tf.io.FixedLenFeature([], tf.int64, default_value=-1)\n",
    "    }\n",
    "    example = tf.io.parse_single_example(tfrecord, feature_descriptions)\n",
    "    image = tf.io.parse_tensor(example[\"image\"], out_type=tf.uint8)\n",
    "    #image = tf.io.decode_jpeg(example[\"image\"])\n",
    "    image = tf.reshape(image, shape=[28, 28])\n",
    "    return image, example[\"label\"]\n",
    "\n",
    "def mnist_dataset(filepaths, n_read_threads=5, shuffle_buffer_size=None,\n",
    "                  n_parse_threads=5, batch_size=32, cache=True):\n",
    "    dataset = tf.data.TFRecordDataset(filepaths,\n",
    "                                      num_parallel_reads=n_read_threads)\n",
    "    if cache:\n",
    "        dataset = dataset.cache()\n",
    "    if shuffle_buffer_size:\n",
    "        dataset = dataset.shuffle(shuffle_buffer_size)\n",
    "    dataset = dataset.map(preprocess, num_parallel_calls=n_parse_threads)\n",
    "    dataset = dataset.batch(batch_size)\n",
    "    return dataset.prefetch(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = mnist_dataset(train_filepaths, shuffle_buffer_size=60000)\n",
    "valid_set = mnist_dataset(train_filepaths)\n",
    "test_set = mnist_dataset(train_filepaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for X, y in train_set.take(1):\n",
    "    for i in range(5):\n",
    "        plt.subplot(1, 5, i + 1)\n",
    "        plt.imshow(X[i].numpy(), cmap=\"binary\")\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(str(y[i].numpy()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "class Standardization(keras.layers.Layer):\n",
    "    def adapt(self, data_sample):\n",
    "        self.means_ = np.mean(data_sample, axis=0, keepdims=True)\n",
    "        self.stds_ = np.std(data_sample, axis=0, keepdims=True)\n",
    "    def call(self, inputs):\n",
    "        return (inputs - self.means_) / (self.stds_ + keras.backend.epsilon())\n",
    "\n",
    "standardization = Standardization(input_shape=[28, 28])\n",
    "# or perhaps soon:\n",
    "#standardization = keras.layers.Normalization()\n",
    "\n",
    "sample_image_batches = train_set.take(100).map(lambda image, label: image)\n",
    "sample_images = np.concatenate(list(sample_image_batches.as_numpy_iterator()),\n",
    "                               axis=0).astype(np.float32)\n",
    "standardization.adapt(sample_images)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    standardization,\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Dense(100, activation=\"relu\"),\n",
    "    keras.layers.Dense(10, activation=\"softmax\")\n",
    "])\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=\"nadam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "      1/Unknown - 0s 454us/step - loss: 3.2469 - accuracy: 0.0312WARNING:tensorflow:From /home/work/.local/lib/python3.6/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
      "Instructions for updating:\n",
      "use `tf.profiler.experimental.stop` instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/work/.local/lib/python3.6/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
      "Instructions for updating:\n",
      "use `tf.profiler.experimental.stop` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1719/1719 [==============================] - 11s 6ms/step - loss: 570.1452 - accuracy: 0.8415 - val_loss: 82.8086 - val_accuracy: 0.8806\n",
      "Epoch 2/5\n",
      "1719/1719 [==============================] - 10s 6ms/step - loss: 639.2133 - accuracy: 0.8783 - val_loss: 147.8436 - val_accuracy: 0.8906\n",
      "Epoch 3/5\n",
      "1719/1719 [==============================] - 10s 6ms/step - loss: 98.1982 - accuracy: 0.8908 - val_loss: 361.5944 - val_accuracy: 0.9063\n",
      "Epoch 4/5\n",
      "1719/1719 [==============================] - 10s 6ms/step - loss: 437.1281 - accuracy: 0.9008 - val_loss: 150.4529 - val_accuracy: 0.9139\n",
      "Epoch 5/5\n",
      "1719/1719 [==============================] - 10s 6ms/step - loss: 198.3818 - accuracy: 0.9075 - val_loss: 42.5060 - val_accuracy: 0.9149\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fc85189f908>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "logs = os.path.join(os.curdir, \"my_logs\",\n",
    "                    \"run_\" + datetime.now().strftime(\"%Y%m%d_%H%M%S\"))\n",
    "\n",
    "tensorboard_cb = tf.keras.callbacks.TensorBoard(\n",
    "    log_dir=logs, histogram_freq=1, profile_batch=10)\n",
    "\n",
    "model.fit(train_set, epochs=5, validation_data=valid_set,\n",
    "          callbacks=[tensorboard_cb])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning:** The profiling tab in TensorBoard works if you use TensorFlow 2.2+. You also need to make sure `tensorboard_plugin_profile` is installed (and restart Jupyter if necessary)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 1815), started 3:53:48 ago. (Use '!kill 1815' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-72c883de66667b1\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-72c883de66667b1\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir=./my_logs --port=6006"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10.\n",
    "_문제: 이 연습문제에서 데이터셋을 다운로드 및 분할하고 `tf.data.Dataset` 객체를 만들어 데이터를 적재하고 효율적으로 전처리하겠습니다. 그다음 `Embedding` 층을 포함한 이진 분류 모델을 만들고 훈련시킵니다._\n",
    "\n",
    "### a.\n",
    "_문제: [인터넷 영화 데이터베이스](https://imdb.com/)의 영화 리뷰 50,000개를 담은 [영화 리뷰\n",
    "데이터셋](https://homl.info/imdb)을 다운로드합니다. 이 데이터는\n",
    "`train`과 `test`라는 두 개의 디렉터리로 구성되어 있습니다. 각 디렉터리에는 12,500개의 긍정 리뷰를 담은 `pos` 서브디렉터리와 12,500개의 부정 리뷰를 담은 `neg` 서브디렉터리가 있습니다. 리뷰는 각각 별도의 텍스트 파일에 저장되어 있습니다. (전처리된 BOW를 포함해) 다른 파일과 디렉터리가 있지만 이 연습문제에서는 무시합니다._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
      "84131840/84125825 [==============================] - 12s 0us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/work/.keras/datasets/aclImdb')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "DOWNLOAD_ROOT = \"http://ai.stanford.edu/~amaas/data/sentiment/\"\n",
    "FILENAME = \"aclImdb_v1.tar.gz\"\n",
    "filepath = keras.utils.get_file(FILENAME, DOWNLOAD_ROOT + FILENAME, extract=True)\n",
    "path = Path(filepath).parent / \"aclImdb\"\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aclImdb/\n",
      "    README\n",
      "    imdb.vocab\n",
      "    imdbEr.txt\n",
      "    test/\n",
      "        labeledBow.feat\n",
      "        urls_neg.txt\n",
      "        urls_pos.txt\n",
      "        pos/\n",
      "            0_10.txt\n",
      "            10000_7.txt\n",
      "            10001_9.txt\n",
      "            ...\n",
      "        neg/\n",
      "            0_2.txt\n",
      "            10000_4.txt\n",
      "            10001_1.txt\n",
      "            ...\n",
      "    train/\n",
      "        labeledBow.feat\n",
      "        unsupBow.feat\n",
      "        urls_neg.txt\n",
      "        ...\n",
      "        pos/\n",
      "            0_9.txt\n",
      "            10000_8.txt\n",
      "            10001_10.txt\n",
      "            ...\n",
      "        neg/\n",
      "            0_3.txt\n",
      "            10000_4.txt\n",
      "            10001_4.txt\n",
      "            ...\n",
      "        unsup/\n",
      "            0_0.txt\n",
      "            10000_0.txt\n",
      "            10001_0.txt\n",
      "            ...\n"
     ]
    }
   ],
   "source": [
    "for name, subdirs, files in os.walk(path):\n",
    "    indent = len(Path(name).parts) - len(path.parts)\n",
    "    print(\"    \" * indent + Path(name).parts[-1] + os.sep)\n",
    "    for index, filename in enumerate(sorted(files)):\n",
    "        if index == 3:\n",
    "            print(\"    \" * (indent + 1) + \"...\")\n",
    "            break\n",
    "        print(\"    \" * (indent + 1) + filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12500, 12500, 12500, 12500)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def review_paths(dirpath):\n",
    "    return [str(path) for path in dirpath.glob(\"*.txt\")]\n",
    "\n",
    "train_pos = review_paths(path / \"train\" / \"pos\")\n",
    "train_neg = review_paths(path / \"train\" / \"neg\")\n",
    "test_valid_pos = review_paths(path / \"test\" / \"pos\")\n",
    "test_valid_neg = review_paths(path / \"test\" / \"neg\")\n",
    "\n",
    "len(train_pos), len(train_neg), len(test_valid_pos), len(test_valid_neg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b.\n",
    "_문제: 테스트 세트를 검증 세트(15,000개)와 테스트 세트(10,000개)로 나눕니다._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.shuffle(test_valid_pos)\n",
    "\n",
    "test_pos = test_valid_pos[:5000]\n",
    "test_neg = test_valid_neg[:5000]\n",
    "valid_pos = test_valid_pos[5000:]\n",
    "valid_neg = test_valid_neg[5000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### c.\n",
    "_문제: tf.data를 사용해 각 세트에 대한 효율적인 데이터셋을 만듭니다._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 데이터셋을 메모리에 적재할 수 있으므로 파이썬 코드와 `tf.data.Dataset.from_tensor_slices()`를 사용해 모든 데이터를 적재합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imdb_dataset(filepaths_positive, filepaths_negative):\n",
    "    reviews = []\n",
    "    labels = []\n",
    "    for filepaths, label in ((filepaths_negative, 0), (filepaths_positive, 1)):\n",
    "        for filepath in filepaths:\n",
    "            with open(filepath) as review_file:\n",
    "                reviews.append(review_file.read())\n",
    "            labels.append(label)\n",
    "    return tf.data.Dataset.from_tensor_slices(\n",
    "        (tf.constant(reviews), tf.constant(labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(b'I saw the trailer for this film a few months prior to its release, and MAN, did it look scary, Especially how this film was based on a real life phenomenon. I was incredibly interested, and thought that this could finally be a decent horror/thriller film after years if crap. Well you know how movie trailers make a film look better than it is: maybe by showing all of the creepy parts or by overdramaticizing certain elements. The advertisements for the movie did both, which lead to my ultimate disappointment.<br /><br />By no means is this \"the most disturbing movie in years\". Hell, I doubt it was the most disturbing movie of that week\\'s release. This movie takes the whole \"based off of fact\" thing too far.<br /><br />This movie wasn\\'t complete crap. I must admit, it held my interest and Micheal Keaton was believable as a man searching for answers by supernatural means. Other than that, though, this film is one big clich\\xc3\\xa9. After John\\'s wife dies, he learns about EVP, which transmits the voices of the dead into everyday electronic appliances. He all of the sudden receives messages from his wife! My God! It\\'s not just his wife reaching him, it\\'s other dead people. Gee, imagine that. A movie about helping dead people. Come on, give me a break! The clich\\xc3\\xa9s don\\'t stop there. There\\'s also the obligatory clock-stopping-at-the-same-exact-time-every-night trick and three evil spirits that menace our hero. Not only was the movie clich\\xc3\\xa9, it WASN\\'T SCARY. The film literally had two jump scenes, and those two scenes are almost identical. The ending is horrible, as it leaves the door WIDE OPEN for a sequel. There\\'s also an ending message with a message saying how only 1 out of X voices heard through EVP are threatening, with a nice happy tune playing. Way to break the mood, you guys. Jeez! In the end, if you want another forgettable horror film, see White Noise. The only reasons I could possibly think anyone would watch this film for is that either that the person is a Keaton fan or that they are interested in EVP. Sure, the EVP aspects may be slightly interesting, but I don\\'t like my movie concepts to be shoved down my throat and blown up in my face. This film tries to be scary, original, and disturbing, but it\\'s just the opposite. You know you have a lame movie when the commercials use the ghosts to talk about there film. \"I WILL SEE THIS FILM NO MORE.\"', shape=(), dtype=string)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "\n",
      "tf.Tensor(b'This Is Pretty Funny. \"Saturday The 12th\", a?... Great Work... I Laughed Every Minute of the movie... This Is Like \"Scary Movie\" for the 1980\\'s. great STUDENT BODIES-styled gags...<br /><br />Too Bad This Isn\\'t On Video... But You Can Still Watch It on FLIX...', shape=(), dtype=string)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "\n",
      "tf.Tensor(b'This is a worthless sequel to a great action movie. Cheap looking, and worst of all, BORING ACTION SCENES! The only decent thing about the movie is the last fight sequence. Only 82 minutes, but it feels like it goes on forever! Even die-hard Van Damme fans(like myself) should avoid this one!', shape=(), dtype=string)\n",
      "tf.Tensor(0, shape=(), dtype=int32)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for X, y in imdb_dataset(train_pos, train_neg).take(3):\n",
    "    print(X)\n",
    "    print(y)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.1 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r1 for X, y in imdb_dataset(train_pos, train_neg).repeat(10): pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 데이터셋을 적재하고 10회 반복하는데 약 20초가 걸립니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "하지만 이 데이터셋이 메모리에 맞지 않는다고 가정하고 좀 더 재미있는 것을 만들어 보죠. 다행히 각 리뷰는 한 줄로 되어 있기 때문에(`<br />`로 줄바꿈됩니다) `TextLineDataset`를 사용해 리뷰를 읽을 수 있습니다. 그렇지 않으면 입력 파일을 전처리해야 합니다(예를 들어, TFRecord로 바꿉니다). 매우 큰 데이터셋의 경우 아파치 빔(Apache Beam) 같은 도구를 사용하는 것이 합리적입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imdb_dataset(filepaths_positive, filepaths_negative, n_read_threads=5):\n",
    "    dataset_neg = tf.data.TextLineDataset(filepaths_negative,\n",
    "                                          num_parallel_reads=n_read_threads)\n",
    "    dataset_neg = dataset_neg.map(lambda review: (review, 0))\n",
    "    dataset_pos = tf.data.TextLineDataset(filepaths_positive,\n",
    "                                          num_parallel_reads=n_read_threads)\n",
    "    dataset_pos = dataset_pos.map(lambda review: (review, 1))\n",
    "    return tf.data.Dataset.concatenate(dataset_pos, dataset_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1min 10s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r1 for X, y in imdb_dataset(train_pos, train_neg).repeat(10): pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 데이터셋을 10회 반복하는데 34초 걸립니다. 데이터셋이 RAM에 캐싱되지 않고 에포크마다 다시 로드되기 때문에 매우 느립니다. `.repeat(10)` 전에 `.cache()`를 추가하면 이전만큼 빨라지는 것을 확인할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.4 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r1 for X, y in imdb_dataset(train_pos, train_neg).cache().repeat(10): pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "\n",
    "train_set = imdb_dataset(train_pos, train_neg).shuffle(25000).batch(batch_size).prefetch(1)\n",
    "valid_set = imdb_dataset(valid_pos, valid_neg).batch(batch_size).prefetch(1)\n",
    "test_set = imdb_dataset(test_pos, test_neg).batch(batch_size).prefetch(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### d.\n",
    "_문제: 리뷰를 전처리하기 위해 `TextVectorization` 층을 사용한 이진 분류 모델을 만드세요.\n",
    "`TextVectorization` 층을 아직 사용할 수 없다면 (또는 도전을 좋아한다면) 사용자 전처리 층을 만들어보세요. `tf.strings` 패키지에 있는 함수를 사용할 수 있습니다. 예를 들어 `lower()`로 소문자로 만들거나 `regex_replace()`로 구두점을 공백으로 바꾸고 `split()`로 공백을 기준으로 단어를 나눌 수 있습니다. 룩업 테이블을 사용해 단어 인덱스를 출력하세요. `adapt()` 메서드로 미리 층을 적응시켜야 합니다._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 리뷰를 전처리하는 함수를 만듭니다. 이 함수는 리뷰를 300자로 자르고 소문자로 변환합니다. 그다음 `<br />`와 글자가 아닌 모든 문자를 공백으로 바꾸고 리뷰를 단어로 분할해 마지막으로 각 리뷰가 `n_words` 개수의 토큰이 되도록 패딩하거나 잘라냅니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 50), dtype=string, numpy=\n",
       "array([[b'it', b's', b'a', b'great', b'great', b'movie', b'i', b'loved',\n",
       "        b'it', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>'],\n",
       "       [b'it', b'was', b'terrible', b'run', b'away', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>', b'<pad>',\n",
       "        b'<pad>']], dtype=object)>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def preprocess(X_batch, n_words=50):\n",
    "    shape = tf.shape(X_batch) * tf.constant([1, 0]) + tf.constant([0, n_words])\n",
    "    Z = tf.strings.substr(X_batch, 0, 300)\n",
    "    Z = tf.strings.lower(Z)\n",
    "    Z = tf.strings.regex_replace(Z, b\"<br\\\\s*/?>\", b\" \")\n",
    "    Z = tf.strings.regex_replace(Z, b\"[^a-z]\", b\" \")\n",
    "    Z = tf.strings.split(Z)\n",
    "    return Z.to_tensor(shape=shape, default_value=b\"<pad>\")\n",
    "\n",
    "X_example = tf.constant([\"It's a great, great movie! I loved it.\", \"It was terrible, run away!!!\"])\n",
    "preprocess(X_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 `preprocess()` 함수의 출력과 동일한 포맷의 데이터 샘플을 입력받는 두 번째 유틸리티 함수를 만듭니다. 이 함수는 가장 빈번한 `max_size` 개수의 단어로 된 리스트를 출력합니다. 가장 흔한 단어는 패딩 토큰입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[b'<pad>',\n",
       " b'it',\n",
       " b'great',\n",
       " b's',\n",
       " b'a',\n",
       " b'movie',\n",
       " b'i',\n",
       " b'loved',\n",
       " b'was',\n",
       " b'terrible',\n",
       " b'run',\n",
       " b'away']"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def get_vocabulary(data_sample, max_size=1000):\n",
    "    preprocessed_reviews = preprocess(data_sample).numpy()\n",
    "    counter = Counter()\n",
    "    for words in preprocessed_reviews:\n",
    "        for word in words:\n",
    "            if word != b\"<pad>\":\n",
    "                counter[word] += 1\n",
    "    return [b\"<pad>\"] + [word for word, count in counter.most_common(max_size)]\n",
    "\n",
    "get_vocabulary(X_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 `TextVectorization` 층을 만들 준비가 되었습니다. 이 층의 생성자는 단순하게 하이퍼파라미터(`max_vocabulary_size`와 `n_oov_buckets`)를 저장하는 역할만 수행합니다. `adapt()` 메서드는 `get_vocabulary()` 함수를 사용해 어휘 사전을 계산합니다. 그다음 `StaticVocabularyTable`를 만듭니다(16장에서 자세히 설명합니다). `call()` 메서드는 각 리뷰의 단어 리스트를 패딩합니다. 그다음 `StaticVocabularyTable`를 사용해 어휘 사전에 있는 단어의 인덱스를 조회합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TextVectorization(keras.layers.Layer):\n",
    "    def __init__(self, max_vocabulary_size=1000, n_oov_buckets=100, dtype=tf.string, **kwargs):\n",
    "        super().__init__(dtype=dtype, **kwargs)\n",
    "        self.max_vocabulary_size = max_vocabulary_size\n",
    "        self.n_oov_buckets = n_oov_buckets\n",
    "\n",
    "    def adapt(self, data_sample):\n",
    "        self.vocab = get_vocabulary(data_sample, self.max_vocabulary_size)\n",
    "        words = tf.constant(self.vocab)\n",
    "        word_ids = tf.range(len(self.vocab), dtype=tf.int64)\n",
    "        vocab_init = tf.lookup.KeyValueTensorInitializer(words, word_ids)\n",
    "        self.table = tf.lookup.StaticVocabularyTable(vocab_init, self.n_oov_buckets)\n",
    "        \n",
    "    def call(self, inputs):\n",
    "        preprocessed_inputs = preprocess(inputs)\n",
    "        return self.table.lookup(preprocessed_inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "앞서 정의한 `X_example`로 테스트해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 50), dtype=int64, numpy=\n",
       "array([[ 1,  3,  4,  2,  2,  5,  6,  7,  1,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0],\n",
       "       [ 1,  8,  9, 10, 11,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0]])>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_vectorization = TextVectorization()\n",
    "\n",
    "text_vectorization.adapt(X_example)\n",
    "text_vectorization(X_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "좋습니다! 여기에서 볼 수 있듯이 각 리뷰는 정제되고 토큰화되었습니다. 각 단어는 어휘 사전의 인덱스로 인코딩됩니다(0은 `<pad>` 토큰입니다).\n",
    "\n",
    "이제 또 다른 `TextVectorization` 층을 만들고 전체 IMDB 훈련 세트에 적용해 보겠습니다(훈련 세트가 메모리에 맞지 않으면 `train_set.take(500)`처럼 일부 데이터만 사용할 수 있습니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_vocabulary_size = 1000\n",
    "n_oov_buckets = 100\n",
    "\n",
    "sample_review_batches = train_set.map(lambda review, label: review)\n",
    "sample_reviews = np.concatenate(list(sample_review_batches.as_numpy_iterator()),\n",
    "                                axis=0)\n",
    "\n",
    "text_vectorization = TextVectorization(max_vocabulary_size, n_oov_buckets,\n",
    "                                       input_shape=[])\n",
    "text_vectorization.adapt(sample_reviews)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "동일하게 `X_example`로 실행해 보죠. 어휘 사전이 크기 때문에 단어의 ID가 큽니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 50), dtype=int64, numpy=\n",
       "array([[  9,  14,   2,  64,  64,  12,   5, 256,   9,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0],\n",
       "       [  9,  13, 269, 530, 335,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]])>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_vectorization(X_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "좋습니다. 그럼 어휘 사전에서 처음 10개 단어를 확인해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[b'<pad>', b'the', b'a', b'of', b'and', b'i', b'to', b'is', b'this', b'it']"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_vectorization.vocab[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 단어가 리뷰에서 가장 많이 등장하는 단어입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 모델을 만들기 위해 모든 단어 ID를 어떤 식으로 인코딩해야 합니다. 한가지 방법은 BoW(bag of words)입니다. 어휘 사전에 있는 각 단어에 대해 리뷰에 단어가 등장하는 횟수를 카운트합니다. 예를 들면 다음과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 4), dtype=float32, numpy=\n",
       "array([[2., 2., 0., 1.],\n",
       "       [3., 0., 2., 0.]], dtype=float32)>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_example = tf.constant([[1, 3, 1, 0, 0], [2, 2, 0, 0, 0]])\n",
    "tf.reduce_sum(tf.one_hot(simple_example, 4), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "첫 번째 리뷰에는 단어 0이 두 번 등장하고, 단어 1도 두 번, 단어 2는 0번, 단어 3은 한 번 등장합니다. 따라서 BoW 표현은 `[2, 2, 0, 1]`입니다. 비슷하게 두 번째 리뷰에는 단어 0이 세 번, 단어 1이 0번 등장하는 식입니다. 이 로직을 간단한 사용자 정의 층으로 구현해서 테스트해 보겠습니다. 단어 0은 `<pad>` 토큰에 해당하므로 카운트하지 않겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BagOfWords(keras.layers.Layer):\n",
    "    def __init__(self, n_tokens, dtype=tf.int32, **kwargs):\n",
    "        super().__init__(dtype=tf.int32, **kwargs)\n",
    "        self.n_tokens = n_tokens\n",
    "    def call(self, inputs):\n",
    "        one_hot = tf.one_hot(inputs, self.n_tokens)\n",
    "        return tf.reduce_sum(one_hot, axis=1)[:, 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "테스트해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[2., 0., 1.],\n",
       "       [0., 2., 0.]], dtype=float32)>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_of_words = BagOfWords(n_tokens=4)\n",
    "bag_of_words(simple_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "잘 동작하네요! 이제 훈련 세트의 어휘 사전 크기를 지정한 `BagOfWord` 객체를 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_tokens = max_vocabulary_size + n_oov_buckets + 1 # add 1 for <pad>\n",
    "bag_of_words = BagOfWords(n_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 모델을 훈련할 차례입니다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.5429 - accuracy: 0.7158 - val_loss: 0.5160 - val_accuracy: 0.7367\n",
      "Epoch 2/5\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4714 - accuracy: 0.7673 - val_loss: 0.5050 - val_accuracy: 0.7425\n",
      "Epoch 3/5\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.4246 - accuracy: 0.8006 - val_loss: 0.5135 - val_accuracy: 0.7441\n",
      "Epoch 4/5\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.3585 - accuracy: 0.8443 - val_loss: 0.5365 - val_accuracy: 0.7439\n",
      "Epoch 5/5\n",
      "782/782 [==============================] - 6s 8ms/step - loss: 0.2801 - accuracy: 0.8924 - val_loss: 0.5654 - val_accuracy: 0.7357\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fc851872cc0>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.models.Sequential([\n",
    "    text_vectorization,\n",
    "    bag_of_words,\n",
    "    keras.layers.Dense(100, activation=\"relu\"),\n",
    "    keras.layers.Dense(1, activation=\"sigmoid\"),\n",
    "])\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\",\n",
    "              metrics=[\"accuracy\"])\n",
    "model.fit(train_set, epochs=5, validation_data=valid_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "첫 번째 에포크에서 검증 세트에 대해 75% 정확도를 얻었습니다. 하지만 더 진전이 없습니다. 16장에서 이를 더 개선해 보겠습니다. 지금은 `tf.data`와 케라스 전처리 층으로 효율적인 전처리를 수행하는 것에만 초점을 맞추었습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### e.\n",
    "_문제: `Embedding` 층을 추가하고 단어 개수의 제곱근을 곱하여 리뷰마다 평균 임베딩을 계산하세요(16장 참조). 이제 스케일이 조정된 이 평균 임베딩을 모델의 다음 부분으로 전달할 수 있습니다._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "각 리뷰의 평균 임베딩을 계산하고 리뷰의 단어 개수의 제곱근을 곱하기 위해 간단한 함수를 정의합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[2.3570225, 3.2998314, 1.4142135],\n",
       "       [2.       , 0.       , 0.       ]], dtype=float32)>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compute_mean_embedding(inputs):\n",
    "    not_pad = tf.math.count_nonzero(inputs, axis=-1)\n",
    "    n_words = tf.math.count_nonzero(not_pad, axis=-1, keepdims=True)    \n",
    "    sqrt_n_words = tf.math.sqrt(tf.cast(n_words, tf.float32))\n",
    "    return tf.reduce_mean(inputs, axis=1) * sqrt_n_words\n",
    "\n",
    "another_example = tf.constant([[[1., 2., 3.], [4., 5., 0.], [0., 0., 0.]],\n",
    "                               [[6., 0., 0.], [0., 0., 0.], [0., 0., 0.]]])\n",
    "compute_mean_embedding(another_example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "결과가 올바른지 확인해 보죠. 첫 번째 리뷰에는 2개의 단어가 있습니다(마지막 토큰은 `<pad>` 토큰을 나타내는 0벡터입니다). 두 번째 리뷰에는 1개의 단어가 있습니다. 따라서 각 리뷰의 평균 임베딩을 계산하고 2와 1의 제곱근을 첫 번째 리뷰와 두 번째 리뷰에 각각 곱해야 합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[2.3570225, 3.2998314, 1.4142135],\n",
       "       [2.       , 0.       , 0.       ]], dtype=float32)>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.reduce_mean(another_example, axis=1) * tf.sqrt([[2.], [1.]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "완벽하군요. 이제 최종 모델을 훈련할 차례입니다. 이전과 동이하지만 `BagOfWords` 층을 `Embedding` 층과 `compute_mean_embedding`을 호출하는 `Lambda` 층으로 바꿉니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_size = 20\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    text_vectorization,\n",
    "    keras.layers.Embedding(input_dim=n_tokens,\n",
    "                           output_dim=embedding_size,\n",
    "                           mask_zero=True), # <pad> tokens => zero vectors\n",
    "    keras.layers.Lambda(compute_mean_embedding),\n",
    "    keras.layers.Dense(100, activation=\"relu\"),\n",
    "    keras.layers.Dense(1, activation=\"sigmoid\"),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### f.\n",
    "_문제: 모델을 훈련하고 얼마의 정확도가 나오는지 확인해보세요. 가능한 한 훈련 속도를 빠르게 하기 위해 파이프라인을 최적화해보세요._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.5535 - accuracy: 0.7114 - val_loss: 0.5226 - val_accuracy: 0.7343\n",
      "Epoch 2/5\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.4963 - accuracy: 0.7528 - val_loss: 0.5087 - val_accuracy: 0.7428\n",
      "Epoch 3/5\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.4886 - accuracy: 0.7585 - val_loss: 0.5057 - val_accuracy: 0.7417\n",
      "Epoch 4/5\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.4832 - accuracy: 0.7608 - val_loss: 0.5062 - val_accuracy: 0.7409\n",
      "Epoch 5/5\n",
      "782/782 [==============================] - 7s 9ms/step - loss: 0.4768 - accuracy: 0.7643 - val_loss: 0.5075 - val_accuracy: 0.7438\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fc82c5d49e8>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n",
    "model.fit(train_set, epochs=5, validation_data=valid_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "임베딩을 사용해서 더 나아지지 않았습니다(16장에서 이를 개선해 보겠습니다). 파이프라인은 충분히 빨라 보입니다(앞서 최적화했습니다)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### g.\n",
    "_문제: `tfds.load(\"imdb_reviews\")`와 같이 TFDS를 사용해 동일한 데이터셋을 간단하게 적재해보세요._"
   ]
  },
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDownloading and preparing dataset imdb_reviews/plain_text/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /home/work/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n"
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     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
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    {
     "name": "stdout",
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     "text": [
      "\r",
      "Shuffling and writing examples to /home/work/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteNAG432/imdb_reviews-train.tfrecord\n"
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     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Shuffling and writing examples to /home/work/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteNAG432/imdb_reviews-test.tfrecord\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Shuffling and writing examples to /home/work/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteNAG432/imdb_reviews-unsupervised.tfrecord\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDataset imdb_reviews downloaded and prepared to /home/work/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "datasets = tfds.load(name=\"imdb_reviews\")\n",
    "train_set, test_set = datasets[\"train\"], datasets[\"test\"]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
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     "name": "stdout",
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      "tf.Tensor(b\"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it.\", shape=(), dtype=string)\n",
      "tf.Tensor(0, shape=(), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "for example in train_set.take(1):\n",
    "    print(example[\"text\"])\n",
    "    print(example[\"label\"])"
   ]
  }
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