RealEstatePricePredictionMoscow02.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6c895a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# импорт используемых библиотек\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score as r2\n",
    "from sklearn.model_selection import KFold, GridSearchCV\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# отключаем предупреждения\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "# устанавливаемый единый размер шрифта\n",
    "matplotlib.rcParams.update({'font.size': 14})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e639b2ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# функция для визуальной валидации результатов предсказания\n",
    "\n",
    "def evaluate_preds(train_true_values, train_pred_values, test_true_values, test_pred_values):\n",
    "    print(\"Train R2:\\t\" + str(round(r2(train_true_values, train_pred_values), 3)))\n",
    "    print(\"Test R2:\\t\" + str(round(r2(test_true_values, test_pred_values), 3)))\n",
    "    \n",
    "    plt.figure(figsize=(18,10))\n",
    "    \n",
    "    plt.subplot(121)\n",
    "    sns.scatterplot(x=train_pred_values, y=train_true_values)\n",
    "    plt.xlabel('Predicted values')\n",
    "    plt.ylabel('True values')\n",
    "    plt.title('Train sample prediction')\n",
    "    \n",
    "    plt.subplot(122)\n",
    "    sns.scatterplot(x=test_pred_values, y=test_true_values)\n",
    "    plt.xlabel('Predicted values')\n",
    "    plt.ylabel('True values')\n",
    "    plt.title('Test sample prediction')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45009ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_DATASET_PATH = 'C:/ARTEM/GeekBrains/Python4DS/RealEstatePricePredictionMoscow/train.csv'\n",
    "TEST_DATASET_PATH = 'C:/ARTEM/GeekBrains/Python4DS/RealEstatePricePredictionMoscow/test.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14b0f3fc",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>DistrictId</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "      <th>Floor</th>\n",
       "      <th>HouseFloor</th>\n",
       "      <th>HouseYear</th>\n",
       "      <th>Ecology_1</th>\n",
       "      <th>Ecology_2</th>\n",
       "      <th>Ecology_3</th>\n",
       "      <th>Social_1</th>\n",
       "      <th>Social_2</th>\n",
       "      <th>Social_3</th>\n",
       "      <th>Healthcare_1</th>\n",
       "      <th>Helthcare_2</th>\n",
       "      <th>Shops_1</th>\n",
       "      <th>Shops_2</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>1260</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>49.090728</td>\n",
       "      <td>33.272626</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1981</td>\n",
       "      <td>0.300323</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>52</td>\n",
       "      <td>10311</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>B</td>\n",
       "      <td>119367.455796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>16265</td>\n",
       "      <td>27</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.307684</td>\n",
       "      <td>37.038420</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1977</td>\n",
       "      <td>0.072158</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>629</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>199715.148807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>2795</td>\n",
       "      <td>178</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.648057</td>\n",
       "      <td>16.555363</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1958</td>\n",
       "      <td>0.460556</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>4386</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>165953.912580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>14561</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.330292</td>\n",
       "      <td>22.326870</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1969</td>\n",
       "      <td>0.194489</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>47</td>\n",
       "      <td>8004</td>\n",
       "      <td>3</td>\n",
       "      <td>125.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>171842.411855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>7202</td>\n",
       "      <td>94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.815476</td>\n",
       "      <td>22.301367</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1975</td>\n",
       "      <td>0.127376</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>43</td>\n",
       "      <td>8429</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>B</td>\n",
       "      <td>177685.627486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  DistrictId  Rooms     Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "9995   1260          61    2.0  49.090728   33.272626            6.0      3   \n",
       "9996  16265          27    2.0  64.307684   37.038420            9.0     13   \n",
       "9997   2795         178    1.0  29.648057   16.555363            5.0      3   \n",
       "9998  14561          21    1.0  32.330292   22.326870            5.0      3   \n",
       "9999   7202          94    1.0  35.815476   22.301367            6.0      9   \n",
       "\n",
       "      HouseFloor  HouseYear  Ecology_1 Ecology_2 Ecology_3  Social_1  \\\n",
       "9995        12.0       1981   0.300323         B         B        52   \n",
       "9996         0.0       1977   0.072158         B         B         2   \n",
       "9997         5.0       1958   0.460556         B         B        20   \n",
       "9998         9.0       1969   0.194489         B         B        47   \n",
       "9999         9.0       1975   0.127376         B         B        43   \n",
       "\n",
       "      Social_2  Social_3  Healthcare_1  Helthcare_2  Shops_1 Shops_2  \\\n",
       "9995     10311         6           NaN            1        9       B   \n",
       "9996       629         1           NaN            0        0       A   \n",
       "9997      4386        14           NaN            1        5       B   \n",
       "9998      8004         3         125.0            3        5       B   \n",
       "9999      8429         3           NaN            3        9       B   \n",
       "\n",
       "              Price  \n",
       "9995  119367.455796  \n",
       "9996  199715.148807  \n",
       "9997  165953.912580  \n",
       "9998  171842.411855  \n",
       "9999  177685.627486  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# загружаем обучающую выборку, проверяем данные\n",
    "train_df = pd.read_csv(TRAIN_DATASET_PATH)\n",
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "18438e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 int64\n",
       "DistrictId         int64\n",
       "Rooms            float64\n",
       "Square           float64\n",
       "LifeSquare       float64\n",
       "KitchenSquare    float64\n",
       "Floor              int64\n",
       "HouseFloor       float64\n",
       "HouseYear          int64\n",
       "Ecology_1        float64\n",
       "Ecology_2         object\n",
       "Ecology_3         object\n",
       "Social_1           int64\n",
       "Social_2           int64\n",
       "Social_3           int64\n",
       "Healthcare_1     float64\n",
       "Helthcare_2        int64\n",
       "Shops_1            int64\n",
       "Shops_2           object\n",
       "Price            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# смотрим типы данных, цель уменьшить размер датасета\n",
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e790eca4",
   "metadata": {},
   "outputs": [
    {
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       "      <th>DistrictId</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "      <th>Floor</th>\n",
       "      <th>HouseFloor</th>\n",
       "      <th>HouseYear</th>\n",
       "      <th>Ecology_1</th>\n",
       "      <th>Ecology_2</th>\n",
       "      <th>Ecology_3</th>\n",
       "      <th>Social_1</th>\n",
       "      <th>Social_2</th>\n",
       "      <th>Social_3</th>\n",
       "      <th>Healthcare_1</th>\n",
       "      <th>Helthcare_2</th>\n",
       "      <th>Shops_1</th>\n",
       "      <th>Shops_2</th>\n",
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       "      <th>4995</th>\n",
       "      <td>10379</td>\n",
       "      <td>29</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>30.339945</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1962</td>\n",
       "      <td>0.069660</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>31</td>\n",
       "      <td>6119</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
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       "      <th>4996</th>\n",
       "      <td>16138</td>\n",
       "      <td>38</td>\n",
       "      <td>3.0</td>\n",
       "      <td>93.698122</td>\n",
       "      <td>94.521465</td>\n",
       "      <td>10.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>15</td>\n",
       "      <td>2787</td>\n",
       "      <td>2</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
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       "      <th>4997</th>\n",
       "      <td>3912</td>\n",
       "      <td>101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.656723</td>\n",
       "      <td>19.003259</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.038693</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>28</td>\n",
       "      <td>6533</td>\n",
       "      <td>1</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>5722</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.635155</td>\n",
       "      <td>20.976257</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.089040</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>33</td>\n",
       "      <td>7976</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>11004</td>\n",
       "      <td>21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>67.122742</td>\n",
       "      <td>33.944344</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.194489</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>47</td>\n",
       "      <td>8004</td>\n",
       "      <td>3</td>\n",
       "      <td>125.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "         Id  DistrictId  Rooms     Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "4995  10379          29    2.0  43.177521   30.339945            5.0      6   \n",
       "4996  16138          38    3.0  93.698122   94.521465           10.0     21   \n",
       "4997   3912         101    1.0  33.656723   19.003259            5.0      2   \n",
       "4998   5722          10    1.0  38.635155   20.976257            9.0      8   \n",
       "4999  11004          21    2.0  67.122742   33.944344           13.0      9   \n",
       "\n",
       "      HouseFloor  HouseYear  Ecology_1 Ecology_2 Ecology_3  Social_1  \\\n",
       "4995         5.0       1962   0.069660         B         B        31   \n",
       "4996        27.0       2018   0.060753         B         B        15   \n",
       "4997         5.0       1966   0.038693         B         B        28   \n",
       "4998        14.0       1970   0.089040         B         B        33   \n",
       "4999        17.0       2009   0.194489         B         B        47   \n",
       "\n",
       "      Social_2  Social_3  Healthcare_1  Helthcare_2  Shops_1 Shops_2  \n",
       "4995      6119         4           NaN            1        2       B  \n",
       "4996      2787         2         520.0            0        7       B  \n",
       "4997      6533         1        1015.0            2        5       B  \n",
       "4998      7976         5           NaN            0       11       B  \n",
       "4999      8004         3         125.0            3        5       B  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# загружаем проверочную выборку, проверяем данные\n",
    "\n",
    "test_df = pd.read_csv(TEST_DATASET_PATH)\n",
    "test_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae193efd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Строк в трейне: 10000\n",
      "Строк в тесте 5000\n"
     ]
    }
   ],
   "source": [
    "# размеры датасетов\n",
    "\n",
    "print('Строк в трейне:', train_df.shape[0])\n",
    "print('Строк в тесте', test_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec2ebfcf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# проверяем размерности датасетов - обучающий нимеет на столбец больше проверочного\n",
    "train_df.shape[1] - 1 == test_df.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "17395fc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                object\n",
       "DistrictId        object\n",
       "Rooms            float64\n",
       "Square           float64\n",
       "LifeSquare       float64\n",
       "KitchenSquare    float64\n",
       "Floor              int16\n",
       "HouseFloor         int16\n",
       "HouseYear          int16\n",
       "Ecology_1        float64\n",
       "Ecology_2         object\n",
       "Ecology_3         object\n",
       "Social_1           int64\n",
       "Social_2           int64\n",
       "Social_3           int64\n",
       "Healthcare_1     float64\n",
       "Helthcare_2        int64\n",
       "Shops_1            int64\n",
       "Shops_2           object\n",
       "Price            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# приведение типов к строкам, чтобы модель не применяла <> для этих данных\n",
    "train_df['Id'] = train_df['Id'].astype(str)\n",
    "train_df['DistrictId'] = train_df['DistrictId'].astype(str)\n",
    "\n",
    "# уменьшаем размерности этажей,этажностей, возраста\n",
    "train_df['Floor'] = train_df['Floor'].astype(np.int16)\n",
    "train_df['HouseFloor'] = train_df['HouseFloor'].astype(np.int16)\n",
    "train_df['HouseYear'] = train_df['HouseYear'].astype(np.int16)\n",
    "\n",
    "\n",
    "# проверяем преобразование\n",
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c6491ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Исследуем данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5ff7bb43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# гистограма распределения целевых значений\n",
    "plt.figure(figsize = (16, 8))\n",
    "\n",
    "train_df['Price'].hist(bins=30)\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Price')\n",
    "\n",
    "plt.title('Target distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07ea1861",
   "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>Rooms</th>\n",
       "      <th>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "      <th>Floor</th>\n",
       "      <th>HouseFloor</th>\n",
       "      <th>HouseYear</th>\n",
       "      <th>Ecology_1</th>\n",
       "      <th>Social_1</th>\n",
       "      <th>Social_2</th>\n",
       "      <th>Social_3</th>\n",
       "      <th>Healthcare_1</th>\n",
       "      <th>Helthcare_2</th>\n",
       "      <th>Shops_1</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>7887.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>5202.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.890500</td>\n",
       "      <td>56.315775</td>\n",
       "      <td>37.199645</td>\n",
       "      <td>6.273300</td>\n",
       "      <td>8.526700</td>\n",
       "      <td>12.609400</td>\n",
       "      <td>1984.764700</td>\n",
       "      <td>0.118858</td>\n",
       "      <td>24.687000</td>\n",
       "      <td>5352.157400</td>\n",
       "      <td>8.039200</td>\n",
       "      <td>1142.904460</td>\n",
       "      <td>1.319500</td>\n",
       "      <td>4.231300</td>\n",
       "      <td>214138.857399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.839512</td>\n",
       "      <td>21.058732</td>\n",
       "      <td>86.241209</td>\n",
       "      <td>28.560917</td>\n",
       "      <td>5.241148</td>\n",
       "      <td>6.775974</td>\n",
       "      <td>53.112756</td>\n",
       "      <td>0.119025</td>\n",
       "      <td>17.532614</td>\n",
       "      <td>4006.799803</td>\n",
       "      <td>23.831875</td>\n",
       "      <td>1021.517264</td>\n",
       "      <td>1.493601</td>\n",
       "      <td>4.806341</td>\n",
       "      <td>92872.293865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.136859</td>\n",
       "      <td>0.370619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2005.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59174.778028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.774881</td>\n",
       "      <td>22.769832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1974.000000</td>\n",
       "      <td>0.017647</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1564.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153872.633942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>52.513310</td>\n",
       "      <td>32.781260</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "      <td>0.075424</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5285.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>192269.644879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>65.900625</td>\n",
       "      <td>45.128803</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>0.195781</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>7227.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1548.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>249135.462171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>641.065193</td>\n",
       "      <td>7480.592129</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>4968.000000</td>\n",
       "      <td>0.521867</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>19083.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>4849.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>633233.466570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rooms        Square   LifeSquare  KitchenSquare         Floor  \\\n",
       "count  10000.000000  10000.000000  7887.000000   10000.000000  10000.000000   \n",
       "mean       1.890500     56.315775    37.199645       6.273300      8.526700   \n",
       "std        0.839512     21.058732    86.241209      28.560917      5.241148   \n",
       "min        0.000000      1.136859     0.370619       0.000000      1.000000   \n",
       "25%        1.000000     41.774881    22.769832       1.000000      4.000000   \n",
       "50%        2.000000     52.513310    32.781260       6.000000      7.000000   \n",
       "75%        2.000000     65.900625    45.128803       9.000000     12.000000   \n",
       "max       19.000000    641.065193  7480.592129    2014.000000     42.000000   \n",
       "\n",
       "         HouseFloor     HouseYear     Ecology_1      Social_1      Social_2  \\\n",
       "count  10000.000000  10000.000000  10000.000000  10000.000000  10000.000000   \n",
       "mean      12.609400   1984.764700      0.118858     24.687000   5352.157400   \n",
       "std        6.775974     53.112756      0.119025     17.532614   4006.799803   \n",
       "min        0.000000  -2005.000000      0.000000      0.000000    168.000000   \n",
       "25%        9.000000   1974.000000      0.017647      6.000000   1564.000000   \n",
       "50%       13.000000   1977.000000      0.075424     25.000000   5285.000000   \n",
       "75%       17.000000   2001.000000      0.195781     36.000000   7227.000000   \n",
       "max      117.000000   4968.000000      0.521867     74.000000  19083.000000   \n",
       "\n",
       "           Social_3  Healthcare_1   Helthcare_2       Shops_1          Price  \n",
       "count  10000.000000   5202.000000  10000.000000  10000.000000   10000.000000  \n",
       "mean       8.039200   1142.904460      1.319500      4.231300  214138.857399  \n",
       "std       23.831875   1021.517264      1.493601      4.806341   92872.293865  \n",
       "min        0.000000      0.000000      0.000000      0.000000   59174.778028  \n",
       "25%        0.000000    350.000000      0.000000      1.000000  153872.633942  \n",
       "50%        2.000000    900.000000      1.000000      3.000000  192269.644879  \n",
       "75%        5.000000   1548.000000      2.000000      6.000000  249135.462171  \n",
       "max      141.000000   4849.000000      6.000000     23.000000  633233.466570  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# смотрим на параметры для данных выборки\n",
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "983e0cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# количество комнат \n",
    "# 0 - допустимое значение для студии\n",
    "# площадь"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8923291f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Id', 'DistrictId', 'Ecology_2', 'Ecology_3', 'Shops_2']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.select_dtypes(include='object').columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "30f9ec22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27     851\n",
       "1      652\n",
       "23     565\n",
       "6      511\n",
       "9      294\n",
       "      ... \n",
       "199      1\n",
       "117      1\n",
       "207      1\n",
       "209      1\n",
       "174      1\n",
       "Name: DistrictId, Length: 205, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['DistrictId'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81c3d0f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    9903\n",
       "A      97\n",
       "Name: Ecology_2, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Ecology_2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "80b59c3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    9725\n",
       "A     275\n",
       "Name: Ecology_3, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Ecology_3'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bb41a69d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    9175\n",
       "A     825\n",
       "Name: Shops_2, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Shops_2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7761c352",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Обработка выбросов\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "20aabf1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0     3880\n",
       "1.0     3705\n",
       "3.0     2235\n",
       "4.0      150\n",
       "5.0       18\n",
       "0.0        8\n",
       "10.0       2\n",
       "19.0       1\n",
       "6.0        1\n",
       "Name: Rooms, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Rooms'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5acd823b",
   "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>Id</th>\n",
       "      <th>DistrictId</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "      <th>Floor</th>\n",
       "      <th>HouseFloor</th>\n",
       "      <th>HouseYear</th>\n",
       "      <th>Ecology_1</th>\n",
       "      <th>Ecology_2</th>\n",
       "      <th>Ecology_3</th>\n",
       "      <th>Social_1</th>\n",
       "      <th>Social_2</th>\n",
       "      <th>Social_3</th>\n",
       "      <th>Healthcare_1</th>\n",
       "      <th>Helthcare_2</th>\n",
       "      <th>Shops_1</th>\n",
       "      <th>Shops_2</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11809</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>115.027311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.075424</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>11</td>\n",
       "      <td>3097</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>305018.871089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3013</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.832524</td>\n",
       "      <td>23.169223</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.118537</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>30</td>\n",
       "      <td>6207</td>\n",
       "      <td>1</td>\n",
       "      <td>1183.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>177734.553407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8215</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>78.342215</td>\n",
       "      <td>47.671972</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1988</td>\n",
       "      <td>0.025609</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>33</td>\n",
       "      <td>5261</td>\n",
       "      <td>0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>282078.720850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2352</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.409907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>1977</td>\n",
       "      <td>0.007122</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>168106.007630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13866</td>\n",
       "      <td>94</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.285067</td>\n",
       "      <td>38.562517</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>1972</td>\n",
       "      <td>0.282798</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>33</td>\n",
       "      <td>8667</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>B</td>\n",
       "      <td>343995.102962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id DistrictId  Rooms      Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "0  11809         27    3.0  115.027311         NaN           10.0      4   \n",
       "1   3013         22    1.0   39.832524   23.169223            8.0      7   \n",
       "2   8215          1    3.0   78.342215   47.671972           10.0      2   \n",
       "3   2352          1    1.0   40.409907         NaN            1.0     10   \n",
       "4  13866         94    2.0   64.285067   38.562517            9.0     16   \n",
       "\n",
       "   HouseFloor  HouseYear  Ecology_1 Ecology_2 Ecology_3  Social_1  Social_2  \\\n",
       "0          10       2014   0.075424         B         B        11      3097   \n",
       "1           8       1966   0.118537         B         B        30      6207   \n",
       "2          17       1988   0.025609         B         B        33      5261   \n",
       "3          22       1977   0.007122         B         B         1       264   \n",
       "4          16       1972   0.282798         B         B        33      8667   \n",
       "\n",
       "   Social_3  Healthcare_1  Helthcare_2  Shops_1 Shops_2          Price  \n",
       "0         0           NaN            0        0       B  305018.871089  \n",
       "1         1        1183.0            1        0       B  177734.553407  \n",
       "2         0         240.0            3        1       B  282078.720850  \n",
       "3         0           NaN            0        1       B  168106.007630  \n",
       "4         2           NaN            0        6       B  343995.102962  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# пример, где считаем выбросами количество комнат равное 0 или >=6: создаем столбец Rooms_outlier, \n",
    "# где указываем недоверие к числу комнат\n",
    "\n",
    "#train_df['Rooms_outlier'] = 0\n",
    "#train_df.loc[(train_df['Rooms'] == 0) | (train_df['Rooms'] >= 6), 'Rooms_outlier'] = 1\n",
    "#train_df.loc[train_df['Rooms'] == 0, 'Rooms'] = 1\n",
    "#train_df.loc[train_df['Rooms'] >= 6, 'Rooms'] = train_df['Rooms'].median()\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8c897557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0     3880\n",
       "1.0     3705\n",
       "3.0     2235\n",
       "4.0      150\n",
       "5.0       18\n",
       "0.0        8\n",
       "10.0       2\n",
       "19.0       1\n",
       "6.0        1\n",
       "Name: Rooms, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Rooms'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "070297c7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0       2460\n",
       "8.0       1306\n",
       "5.0       1169\n",
       "10.0      1075\n",
       "6.0       1038\n",
       "9.0        843\n",
       "0.0        697\n",
       "7.0        609\n",
       "12.0       249\n",
       "11.0       233\n",
       "13.0        67\n",
       "14.0        51\n",
       "4.0         39\n",
       "15.0        31\n",
       "3.0         22\n",
       "16.0        16\n",
       "20.0        14\n",
       "17.0        12\n",
       "19.0        11\n",
       "18.0         6\n",
       "2.0          4\n",
       "22.0         3\n",
       "30.0         2\n",
       "43.0         2\n",
       "41.0         2\n",
       "112.0        2\n",
       "25.0         2\n",
       "51.0         2\n",
       "37.0         2\n",
       "58.0         2\n",
       "32.0         2\n",
       "21.0         1\n",
       "73.0         1\n",
       "75.0         1\n",
       "36.0         1\n",
       "27.0         1\n",
       "63.0         1\n",
       "1970.0       1\n",
       "54.0         1\n",
       "53.0         1\n",
       "60.0         1\n",
       "26.0         1\n",
       "66.0         1\n",
       "39.0         1\n",
       "29.0         1\n",
       "78.0         1\n",
       "31.0         1\n",
       "84.0         1\n",
       "48.0         1\n",
       "96.0         1\n",
       "42.0         1\n",
       "40.0         1\n",
       "23.0         1\n",
       "72.0         1\n",
       "35.0         1\n",
       "62.0         1\n",
       "123.0        1\n",
       "2014.0       1\n",
       "Name: KitchenSquare, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['KitchenSquare'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d0c61fac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13.0, 0.0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['KitchenSquare'].quantile(.975), train_df['KitchenSquare'].quantile(.025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e64fb693",
   "metadata": {},
   "outputs": [],
   "source": [
    "# обрабатываем выбросы в площадях кухни\n",
    "condition = (train_df['KitchenSquare'].isna()) \\\n",
    "             | (train_df['KitchenSquare'] > train_df['KitchenSquare'].quantile(.975))\n",
    "        \n",
    "train_df.loc[condition, 'KitchenSquare'] = train_df['KitchenSquare'].median()\n",
    "\n",
    "train_df.loc[train_df['KitchenSquare'] < 3, 'KitchenSquare'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4e632236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0     3183\n",
       "8.0     1306\n",
       "6.0     1227\n",
       "5.0     1169\n",
       "10.0    1075\n",
       "9.0      843\n",
       "7.0      609\n",
       "12.0     249\n",
       "11.0     233\n",
       "13.0      67\n",
       "4.0       39\n",
       "Name: KitchenSquare, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['KitchenSquare'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "38abe743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "        26,  27,  28,  29,  30,  31,  32,  36,  37,  38,  39,  40,  44,\n",
       "        45,  47,  48,  99, 117], dtype=int16)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# исследуем этажность зданий\n",
    "\n",
    "train_df['HouseFloor'].sort_values().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "03135765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 37, 42],\n",
       "      dtype=int16)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# исследуем этаж\n",
    "\n",
    "train_df['Floor'].sort_values().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d2b2bb71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1825"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# находим случаи, когда этаж больше этажности здания\n",
    "(train_df['Floor'] > train_df['HouseFloor']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b5f769f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# добавляем признак достоверности этажа\n",
    "train_df['HouseFloor_outlier'] = 0\n",
    "train_df.loc[train_df['HouseFloor'] == 0, 'HouseFloor_outlier'] = 1\n",
    "train_df.loc[train_df['Floor'] > train_df['HouseFloor'], 'HouseFloor_outlier'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "863234a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# некорректную этажность заменяем медианой\n",
    "train_df.loc[train_df['HouseFloor'] == 0, 'HouseFloor'] = train_df['HouseFloor'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "35b81479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  17,   19,   21,   25,   26,   33,   34,   37,   39,   44,\n",
       "            ...\n",
       "            9913, 9921, 9930, 9931, 9938, 9953, 9960, 9968, 9970, 9975],\n",
       "           dtype='int64', length=1610)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# смотрим на id проблемных записей по этажам\n",
    "floor_outliers = train_df.loc[train_df['Floor'] > train_df['HouseFloor']].index\n",
    "floor_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d8c40d18",
   "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>Price</th>\n",
       "      <th>Square</th>\n",
       "      <th>Meter_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>214138.857399</td>\n",
       "      <td>56.315775</td>\n",
       "      <td>3988.383777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>92872.293865</td>\n",
       "      <td>21.058732</td>\n",
       "      <td>3755.437224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>59174.778028</td>\n",
       "      <td>1.136859</td>\n",
       "      <td>208.293451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>153872.633942</td>\n",
       "      <td>41.774881</td>\n",
       "      <td>2918.525328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>192269.644879</td>\n",
       "      <td>52.513310</td>\n",
       "      <td>3910.052336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>249135.462171</td>\n",
       "      <td>65.900625</td>\n",
       "      <td>4692.375993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>633233.466570</td>\n",
       "      <td>641.065193</td>\n",
       "      <td>230463.537966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price        Square    Meter_price\n",
       "count   10000.000000  10000.000000   10000.000000\n",
       "mean   214138.857399     56.315775    3988.383777\n",
       "std     92872.293865     21.058732    3755.437224\n",
       "min     59174.778028      1.136859     208.293451\n",
       "25%    153872.633942     41.774881    2918.525328\n",
       "50%    192269.644879     52.513310    3910.052336\n",
       "75%    249135.462171     65.900625    4692.375993\n",
       "max    633233.466570    641.065193  230463.537966"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Добавляем цену квадратного метра AV\n",
    "train_df['Meter_price'] = 0\n",
    "train_df.loc[train_df['Square'] > 0, 'Meter_price'] = train_df['Price'] / train_df['Square']\n",
    "#train_df['Meter_price'].sort_values(ascending=False)\n",
    "train_df[['Price', 'Square', 'Meter_price']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9f6e136e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# подстановка для значений этажа\n",
    "train_df.loc[floor_outliers, 'Floor'] = train_df.loc[floor_outliers, 'HouseFloor']\\\n",
    "                                                .apply(lambda x: random.randint(1, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "37b4218f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_df['Floor'] > train_df['HouseFloor']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a83f6692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8828    4968\n",
       "6073    2020\n",
       "5641    2020\n",
       "2350    2020\n",
       "9091    2020\n",
       "        ... \n",
       "6250    1914\n",
       "2066    1912\n",
       "2795    1912\n",
       "6794    1910\n",
       "9163   -2005\n",
       "Name: HouseYear, Length: 10000, dtype: int16"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# выводим года постройки\n",
    "train_df['HouseYear'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b5f4456f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# если год постройки еще не наступал, то меняем на текущий, можно менять на медиану\n",
    "train_df.loc[train_df['HouseYear'] > 2021, 'HouseYear'] = 2021\n",
    "# если дом слишком древний, до 1910 - это первый разумный минимум. Заменяем медианой\n",
    "train_df.loc[train_df['HouseYear'] < 1910, 'HouseYear'] = train_df['HouseYear'].median()\n",
    "# год можно также предсказывать по этажности, по району и т.д.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9c753033",
   "metadata": {},
   "outputs": [],
   "source": [
    "# обработка пропусков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f310a637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                       0\n",
       "DistrictId               0\n",
       "Rooms                    0\n",
       "Square                   0\n",
       "LifeSquare            2113\n",
       "KitchenSquare            0\n",
       "Floor                    0\n",
       "HouseFloor               0\n",
       "HouseYear                0\n",
       "Ecology_1                0\n",
       "Ecology_2                0\n",
       "Ecology_3                0\n",
       "Social_1                 0\n",
       "Social_2                 0\n",
       "Social_3                 0\n",
       "Healthcare_1          4798\n",
       "Helthcare_2              0\n",
       "Shops_1                  0\n",
       "Shops_2                  0\n",
       "Price                    0\n",
       "HouseFloor_outlier       0\n",
       "Meter_price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d914ad63",
   "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>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>115.027311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.832524</td>\n",
       "      <td>23.169223</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78.342215</td>\n",
       "      <td>47.671972</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.409907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64.285067</td>\n",
       "      <td>38.562517</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>62.528465</td>\n",
       "      <td>47.103833</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33.938750</td>\n",
       "      <td>21.772930</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50.391814</td>\n",
       "      <td>32.893256</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>46.887892</td>\n",
       "      <td>44.628132</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>79.867477</td>\n",
       "      <td>75.085125</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Square  LifeSquare  KitchenSquare\n",
       "0  115.027311         NaN           10.0\n",
       "1   39.832524   23.169223            8.0\n",
       "2   78.342215   47.671972           10.0\n",
       "3   40.409907         NaN            3.0\n",
       "4   64.285067   38.562517            9.0\n",
       "5   62.528465   47.103833            6.0\n",
       "6   33.938750   21.772930            9.0\n",
       "7   50.391814   32.893256            8.0\n",
       "8   46.887892   44.628132            3.0\n",
       "9   79.867477   75.085125            3.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Square', 'LifeSquare', 'KitchenSquare']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b68c9268",
   "metadata": {},
   "outputs": [],
   "source": [
    "# если нет данных по жилой площади - добавляем признак и пытаемся расчитать\n",
    "train_df['LifeSquare_nan'] = train_df['LifeSquare'].isna() * 1\n",
    "\n",
    "condition = (train_df['LifeSquare'].isna()) \\\n",
    "             & (~train_df['Square'].isna()) \\\n",
    "             & (~train_df['KitchenSquare'].isna())\n",
    "        \n",
    "train_df.loc[condition, 'LifeSquare'] = train_df.loc[condition, 'Square'] \\\n",
    "                                            - train_df.loc[condition, 'KitchenSquare'] - 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e1c3b780",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_df[['Healthcare_1','Healthcare_2']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "383b8664",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop('Healthcare_1', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a113fc25",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataPreprocessing:\n",
    "    \"\"\"Подготовка исходных данных\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        \"\"\"Параметры класса\"\"\"\n",
    "        self.medians = None\n",
    "        self.kitchen_square_quantile = None\n",
    "        \n",
    "    def fit(self, X):\n",
    "        \"\"\"Сохранение статистик\"\"\"       \n",
    "        # Расчет медиан\n",
    "        self.medians = X.median()\n",
    "        self.kitchen_square_quantile = X['KitchenSquare'].quantile(.975)\n",
    "    \n",
    "    def transform(self, X):\n",
    "        \"\"\"Трансформация данных\"\"\"\n",
    "\n",
    "        # Rooms\n",
    "        X['Rooms_outlier'] = 0\n",
    "        X.loc[(X['Rooms'] == 0) | (X['Rooms'] >= 6), 'Rooms_outlier'] = 1\n",
    "        \n",
    "        X.loc[X['Rooms'] == 0, 'Rooms'] = 1\n",
    "        X.loc[X['Rooms'] >= 6, 'Rooms'] = self.medians['Rooms']\n",
    "        \n",
    "        # KitchenSquare\n",
    "        condition = (X['KitchenSquare'].isna()) \\\n",
    "                    | (X['KitchenSquare'] > self.kitchen_square_quantile)\n",
    "        \n",
    "        X.loc[condition, 'KitchenSquare'] = self.medians['KitchenSquare']\n",
    "\n",
    "        X.loc[X['KitchenSquare'] < 3, 'KitchenSquare'] = 3\n",
    "        \n",
    "        # HouseFloor, Floor\n",
    "        X['HouseFloor_outlier'] = 0\n",
    "        X.loc[X['HouseFloor'] == 0, 'HouseFloor_outlier'] = 1\n",
    "        X.loc[X['Floor'] > X['HouseFloor'], 'HouseFloor_outlier'] = 1\n",
    "        \n",
    "        X.loc[X['HouseFloor'] == 0, 'HouseFloor'] = self.medians['HouseFloor']\n",
    "        \n",
    "        floor_outliers = X.loc[X['Floor'] > X['HouseFloor']].index\n",
    "        X.loc[floor_outliers, 'Floor'] = X.loc[floor_outliers, 'HouseFloor']\\\n",
    "                                            .apply(lambda x: random.randint(1, x))\n",
    "        \n",
    "        # HouseYear\n",
    "        current_year = datetime.now().year\n",
    "        \n",
    "        X['HouseYear_outlier'] = 0\n",
    "        \n",
    "        X.loc[X['HouseYear'] > current_year, 'HouseYear_outlier'] = 1        \n",
    "        X.loc[X['HouseYear'] > current_year, 'HouseYear'] = current_year\n",
    "        \n",
    "        # AV\n",
    "        #==========================================================================\n",
    "        X.loc[X['HouseYear'] < 1910, 'HouseYear_outlier'] = 1 \n",
    "        X.loc[X['HouseYear'] < 1910, 'HouseYear'] = self.medians['HouseYear']\n",
    "        #==========================================================================\n",
    "        X.loc[X['Square'].isna(), 'Square'] = self.medians['Square']    \n",
    "        #==========================================================================\n",
    "    \n",
    "        \n",
    "        # Healthcare_1\n",
    "        if 'Healthcare_1' in X.columns:\n",
    "            X.drop('Healthcare_1', axis=1, inplace=True)\n",
    "            \n",
    "        # LifeSquare\n",
    "        X['LifeSquare_nan'] = X['LifeSquare'].isna() * 1\n",
    "        condition = (X['LifeSquare'].isna()) & \\\n",
    "                      (~X['Square'].isna()) & \\\n",
    "                      (~X['KitchenSquare'].isna())\n",
    "        \n",
    "        X.loc[condition, 'LifeSquare'] = X.loc[condition, 'Square'] - X.loc[condition, 'KitchenSquare'] - 3\n",
    "        \n",
    "        \n",
    "        X.fillna(self.medians, inplace=True)\n",
    "        \n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ff95fb13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# построение новых признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "11e1d608",
   "metadata": {},
   "outputs": [],
   "source": [
    "# заменяем A/B на 0/1\n",
    "binary_to_numbers = {'A': 0, 'B': 1}\n",
    "\n",
    "train_df['Ecology_2'] = train_df['Ecology_2'].replace(binary_to_numbers)\n",
    "train_df['Ecology_3'] = train_df['Ecology_3'].replace(binary_to_numbers)\n",
    "train_df['Shops_2'] = train_df['Shops_2'].replace(binary_to_numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8a00e237",
   "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>DistrictId</th>\n",
       "      <th>DistrictSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DistrictId  DistrictSize\n",
       "0         27           851\n",
       "1          1           652\n",
       "2         23           565\n",
       "3          6           511\n",
       "4          9           294"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# добавляем размер района\n",
    "district_size = train_df['DistrictId'].value_counts().reset_index()\\\n",
    "                    .rename(columns={'index':'DistrictId', 'DistrictId':'DistrictSize'})\n",
    "\n",
    "district_size.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a479ebce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Id</th>\n",
       "      <th>DistrictId</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>652</td>\n",
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       "      <td>0</td>\n",
       "      <td>89</td>\n",
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      ],
      "text/plain": [
       "      Id DistrictId  Rooms      Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "0  11809         27    3.0  115.027311  102.027311           10.0      4   \n",
       "1   3013         22    1.0   39.832524   23.169223            8.0      7   \n",
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       "\n",
       "   HouseFloor  HouseYear  Ecology_1  ...  Social_2  Social_3  Helthcare_2  \\\n",
       "0          10       2014   0.075424  ...      3097         0            0   \n",
       "1           8       1966   0.118537  ...      6207         1            1   \n",
       "2          17       1988   0.025609  ...      5261         0            3   \n",
       "3          22       1977   0.007122  ...       264         0            0   \n",
       "4          16       1972   0.282798  ...      8667         2            0   \n",
       "\n",
       "   Shops_1  Shops_2          Price  HouseFloor_outlier  Meter_price  \\\n",
       "0        0        1  305018.871089                   0  2651.708272   \n",
       "1        0        1  177734.553407                   0  4462.045990   \n",
       "2        1        1  282078.720850                   0  3600.596698   \n",
       "3        1        1  168106.007630                   0  4160.019670   \n",
       "4        6        1  343995.102962                   0  5351.088794   \n",
       "\n",
       "   LifeSquare_nan  DistrictSize  \n",
       "0               1           851  \n",
       "1               0            78  \n",
       "2               0           652  \n",
       "3               1           652  \n",
       "4               0            89  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# добавляем призак в датасет\n",
    "train_df = train_df.merge(district_size, on='DistrictId', how='left')\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "aebec7b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     5069\n",
       "False    4931\n",
       "Name: DistrictSize, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# большие районы\n",
    "(train_df['DistrictSize'] > 100).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bdc9ecf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['IsDistrictLarge'] = (train_df['DistrictSize'] > 100).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9bc15614",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  DistrictId  Rooms  MedPriceByDistrict\n",
       "0          0    1.0       142799.434052\n",
       "1          0    2.0       176210.812334\n",
       "2          0    3.0       300610.359425\n",
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     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# медианная стоимость по району\n",
    "med_price_by_district = train_df.groupby(['DistrictId', 'Rooms'], as_index=False).agg({'Price':'median'})\\\n",
    "                            .rename(columns={'Price':'MedPriceByDistrict'})\n",
    "\n",
    "med_price_by_district.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b6129f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(675, 3)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_price_by_district.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0d3d34ef",
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 25 columns</p>\n",
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      ],
      "text/plain": [
       "      Id DistrictId  Rooms      Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "0  11809         27    3.0  115.027311  102.027311           10.0      4   \n",
       "1   3013         22    1.0   39.832524   23.169223            8.0      7   \n",
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       "4  13866         94    2.0   64.285067   38.562517            9.0     16   \n",
       "\n",
       "   HouseFloor  HouseYear  Ecology_1  ...  Helthcare_2  Shops_1  Shops_2  \\\n",
       "0          10       2014   0.075424  ...            0        0        1   \n",
       "1           8       1966   0.118537  ...            1        0        1   \n",
       "2          17       1988   0.025609  ...            3        1        1   \n",
       "3          22       1977   0.007122  ...            0        1        1   \n",
       "4          16       1972   0.282798  ...            0        6        1   \n",
       "\n",
       "           Price  HouseFloor_outlier  Meter_price  LifeSquare_nan  \\\n",
       "0  305018.871089                   0  2651.708272               1   \n",
       "1  177734.553407                   0  4462.045990               0   \n",
       "2  282078.720850                   0  3600.596698               0   \n",
       "3  168106.007630                   0  4160.019670               1   \n",
       "4  343995.102962                   0  5351.088794               0   \n",
       "\n",
       "   DistrictSize  IsDistrictLarge  MedPriceByDistrict  \n",
       "0           851                1       213530.788443  \n",
       "1            78                0       146032.829834  \n",
       "2           652                1       244005.272380  \n",
       "3           652                1       146735.671740  \n",
       "4            89                0       201584.441255  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.merge(med_price_by_district, on=['DistrictId', 'Rooms'], how='left')\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "16892db7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# категоризация\n",
    "def floor_to_cat(X):\n",
    "\n",
    "    X['floor_cat'] = 0\n",
    "\n",
    "    X.loc[X['Floor'] <= 3, 'floor_cat'] = 1  \n",
    "    X.loc[(X['Floor'] > 3) & (X['Floor'] <= 5), 'floor_cat'] = 2\n",
    "    X.loc[(X['Floor'] > 5) & (X['Floor'] <= 9), 'floor_cat'] = 3\n",
    "    X.loc[(X['Floor'] > 9) & (X['Floor'] <= 15), 'floor_cat'] = 4\n",
    "    X.loc[X['Floor'] > 15, 'floor_cat'] = 5\n",
    "\n",
    "    return X\n",
    "\n",
    "\n",
    "def floor_to_cat_pandas(X):\n",
    "    bins = [0, 3, 5, 9, 15, X['Floor'].max()]\n",
    "    X['floor_cat'] = pd.cut(X['Floor'], bins=bins, labels=False)\n",
    "    \n",
    "    X['floor_cat'].fillna(-1, inplace=True)\n",
    "    return X\n",
    "\n",
    "\n",
    "def year_to_cat(X):\n",
    "\n",
    "    X['year_cat'] = 0\n",
    "\n",
    "    X.loc[X['HouseYear'] <= 1941, 'year_cat'] = 1\n",
    "    X.loc[(X['HouseYear'] > 1941) & (X['HouseYear'] <= 1945), 'year_cat'] = 2\n",
    "    X.loc[(X['HouseYear'] > 1945) & (X['HouseYear'] <= 1980), 'year_cat'] = 3\n",
    "    X.loc[(X['HouseYear'] > 1980) & (X['HouseYear'] <= 2000), 'year_cat'] = 4\n",
    "    X.loc[(X['HouseYear'] > 2000) & (X['HouseYear'] <= 2010), 'year_cat'] = 5\n",
    "    X.loc[(X['HouseYear'] > 2010), 'year_cat'] = 6\n",
    "\n",
    "    return X\n",
    "\n",
    "\n",
    "def year_to_cat_pandas(X):\n",
    "    bins = [0, 1941, 1945, 1980, 2000, 2010, X['HouseYear'].max()]\n",
    "    X['year_cat'] = pd.cut(X['HouseYear'], bins=bins, labels=False)\n",
    "    \n",
    "    X['year_cat'].fillna(-1, inplace=True)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "95906706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       2\n",
       "2       0\n",
       "3       3\n",
       "4       4\n",
       "       ..\n",
       "9995    0\n",
       "9996    3\n",
       "9997    0\n",
       "9998    0\n",
       "9999    2\n",
       "Name: Floor, Length: 10000, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 3, 5, 9, 15, train_df['Floor'].max()]\n",
    "pd.cut(train_df['Floor'], bins=bins, labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e47e6ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         (3, 5]\n",
       "1         (5, 9]\n",
       "2         (0, 3]\n",
       "3        (9, 15]\n",
       "4       (15, 42]\n",
       "          ...   \n",
       "9995      (0, 3]\n",
       "9996     (9, 15]\n",
       "9997      (0, 3]\n",
       "9998      (0, 3]\n",
       "9999      (5, 9]\n",
       "Name: Floor, Length: 10000, dtype: category\n",
       "Categories (5, interval[int64, right]): [(0, 3] < (3, 5] < (5, 9] < (9, 15] < (15, 42]]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 3, 5, 9, 15, train_df['Floor'].max()]\n",
    "pd.cut(train_df['Floor'], bins=bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "20ca77d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Id</th>\n",
       "      <th>DistrictId</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Square</th>\n",
       "      <th>LifeSquare</th>\n",
       "      <th>KitchenSquare</th>\n",
       "      <th>Floor</th>\n",
       "      <th>HouseFloor</th>\n",
       "      <th>HouseYear</th>\n",
       "      <th>Ecology_1</th>\n",
       "      <th>...</th>\n",
       "      <th>Shops_2</th>\n",
       "      <th>Price</th>\n",
       "      <th>HouseFloor_outlier</th>\n",
       "      <th>Meter_price</th>\n",
       "      <th>LifeSquare_nan</th>\n",
       "      <th>DistrictSize</th>\n",
       "      <th>IsDistrictLarge</th>\n",
       "      <th>MedPriceByDistrict</th>\n",
       "      <th>year_cat</th>\n",
       "      <th>floor_cat</th>\n",
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       "      <td>10</td>\n",
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       "      <td>...</td>\n",
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       "      <td>305018.871089</td>\n",
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       "      <td>39.832524</td>\n",
       "      <td>23.169223</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1966</td>\n",
       "      <td>0.118537</td>\n",
       "      <td>...</td>\n",
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       "      <td>4462.045990</td>\n",
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       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>146032.829834</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8215</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>78.342215</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1988</td>\n",
       "      <td>0.025609</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>282078.720850</td>\n",
       "      <td>0</td>\n",
       "      <td>3600.596698</td>\n",
       "      <td>0</td>\n",
       "      <td>652</td>\n",
       "      <td>1</td>\n",
       "      <td>244005.272380</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2352</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>40.409907</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>1977</td>\n",
       "      <td>0.007122</td>\n",
       "      <td>...</td>\n",
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       "      <td>168106.007630</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
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       "      <th>4</th>\n",
       "      <td>13866</td>\n",
       "      <td>94</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.285067</td>\n",
       "      <td>38.562517</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>1972</td>\n",
       "      <td>0.282798</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>343995.102962</td>\n",
       "      <td>0</td>\n",
       "      <td>5351.088794</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>201584.441255</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id DistrictId  Rooms      Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "0  11809         27    3.0  115.027311  102.027311           10.0      4   \n",
       "1   3013         22    1.0   39.832524   23.169223            8.0      7   \n",
       "2   8215          1    3.0   78.342215   47.671972           10.0      2   \n",
       "3   2352          1    1.0   40.409907   34.409907            3.0     10   \n",
       "4  13866         94    2.0   64.285067   38.562517            9.0     16   \n",
       "\n",
       "   HouseFloor  HouseYear  Ecology_1  ...  Shops_2          Price  \\\n",
       "0          10       2014   0.075424  ...        1  305018.871089   \n",
       "1           8       1966   0.118537  ...        1  177734.553407   \n",
       "2          17       1988   0.025609  ...        1  282078.720850   \n",
       "3          22       1977   0.007122  ...        1  168106.007630   \n",
       "4          16       1972   0.282798  ...        1  343995.102962   \n",
       "\n",
       "   HouseFloor_outlier  Meter_price  LifeSquare_nan  DistrictSize  \\\n",
       "0                   0  2651.708272               1           851   \n",
       "1                   0  4462.045990               0            78   \n",
       "2                   0  3600.596698               0           652   \n",
       "3                   0  4160.019670               1           652   \n",
       "4                   0  5351.088794               0            89   \n",
       "\n",
       "   IsDistrictLarge  MedPriceByDistrict  year_cat  floor_cat  \n",
       "0                1       213530.788443         6          2  \n",
       "1                0       146032.829834         3          3  \n",
       "2                1       244005.272380         4          1  \n",
       "3                1       146735.671740         3          4  \n",
       "4                0       201584.441255         3          5  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = year_to_cat(train_df)\n",
    "train_df = floor_to_cat(train_df)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a3fb003c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_cat</th>\n",
       "      <th>floor_cat</th>\n",
       "      <th>MedPriceByFloorYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>357700.724533</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>324303.812481</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>429825.896082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>274992.472366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>467230.539057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_cat  floor_cat  MedPriceByFloorYear\n",
       "0         1          1        357700.724533\n",
       "1         1          2        324303.812481\n",
       "2         1          3        429825.896082\n",
       "3         1          4        274992.472366\n",
       "4         2          1        467230.539057"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!!! медианная цена по году и этажу\n",
    "med_price_by_floor_year = train_df.groupby(['year_cat', 'floor_cat'], as_index=False).agg({'Price':'median'}).\\\n",
    "                                            rename(columns={'Price':'MedPriceByFloorYear'})\n",
    "med_price_by_floor_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "16adca39",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>652</td>\n",
       "      <td>1</td>\n",
       "      <td>244005.272380</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>207856.713420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2352</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.409907</td>\n",
       "      <td>34.409907</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>1977</td>\n",
       "      <td>0.007122</td>\n",
       "      <td>...</td>\n",
       "      <td>168106.007630</td>\n",
       "      <td>0</td>\n",
       "      <td>4160.019670</td>\n",
       "      <td>1</td>\n",
       "      <td>652</td>\n",
       "      <td>1</td>\n",
       "      <td>146735.671740</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>183469.921382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13866</td>\n",
       "      <td>94</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.285067</td>\n",
       "      <td>38.562517</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>1972</td>\n",
       "      <td>0.282798</td>\n",
       "      <td>...</td>\n",
       "      <td>343995.102962</td>\n",
       "      <td>0</td>\n",
       "      <td>5351.088794</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>201584.441255</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>173955.556579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id DistrictId  Rooms      Square  LifeSquare  KitchenSquare  Floor  \\\n",
       "0  11809         27    3.0  115.027311  102.027311           10.0      4   \n",
       "1   3013         22    1.0   39.832524   23.169223            8.0      7   \n",
       "2   8215          1    3.0   78.342215   47.671972           10.0      2   \n",
       "3   2352          1    1.0   40.409907   34.409907            3.0     10   \n",
       "4  13866         94    2.0   64.285067   38.562517            9.0     16   \n",
       "\n",
       "   HouseFloor  HouseYear  Ecology_1  ...          Price  HouseFloor_outlier  \\\n",
       "0          10       2014   0.075424  ...  305018.871089                   0   \n",
       "1           8       1966   0.118537  ...  177734.553407                   0   \n",
       "2          17       1988   0.025609  ...  282078.720850                   0   \n",
       "3          22       1977   0.007122  ...  168106.007630                   0   \n",
       "4          16       1972   0.282798  ...  343995.102962                   0   \n",
       "\n",
       "   Meter_price  LifeSquare_nan  DistrictSize  IsDistrictLarge  \\\n",
       "0  2651.708272               1           851                1   \n",
       "1  4462.045990               0            78                0   \n",
       "2  3600.596698               0           652                1   \n",
       "3  4160.019670               1           652                1   \n",
       "4  5351.088794               0            89                0   \n",
       "\n",
       "   MedPriceByDistrict  year_cat  floor_cat  MedPriceByFloorYear  \n",
       "0       213530.788443         6          2        164803.070010  \n",
       "1       146032.829834         3          3        192448.500518  \n",
       "2       244005.272380         4          1        207856.713420  \n",
       "3       146735.671740         3          4        183469.921382  \n",
       "4       201584.441255         3          5        173955.556579  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.merge(med_price_by_floor_year, on=['year_cat', 'floor_cat'], how='left')\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1fa55c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FeatureGenetator():\n",
    "    \"\"\"Генерация новых фич\"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.DistrictId_counts = None\n",
    "        self.binary_to_numbers = None\n",
    "        self.med_price_by_district = None\n",
    "        self.med_price_by_floor_year = None\n",
    "        self.house_year_max = None\n",
    "        self.floor_max = None\n",
    "        self.district_size = None\n",
    "        self.meter_price = None      #AV\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        \n",
    "        X = X.copy()\n",
    "        \n",
    "        # Binary features\n",
    "        self.binary_to_numbers = {'A': 0, 'B': 1}\n",
    "        \n",
    "        # DistrictID\n",
    "        self.district_size = X['DistrictId'].value_counts().reset_index() \\\n",
    "                               .rename(columns={'index':'DistrictId', 'DistrictId':'DistrictSize'})\n",
    "                \n",
    "        # Target encoding\n",
    "        ## District, Rooms\n",
    "        df = X.copy()\n",
    "        \n",
    "        if y is not None:\n",
    "            df['Price'] = y.values\n",
    "            \n",
    "            self.med_price_by_district = df.groupby(['DistrictId', 'Rooms'], as_index=False).agg({'Price':'median'})\\\n",
    "                                            .rename(columns={'Price':'MedPriceByDistrict'})\n",
    "            \n",
    "            self.med_price_by_district_median = self.med_price_by_district['MedPriceByDistrict'].median()\n",
    "            \n",
    "        ## floor, year\n",
    "        if y is not None:\n",
    "            self.floor_max = df['Floor'].max()\n",
    "            self.house_year_max = df['HouseYear'].max()\n",
    "            df['Price'] = y.values\n",
    "            df = self.floor_to_cat(df)\n",
    "            df = self.year_to_cat(df)\n",
    "            self.med_price_by_floor_year = df.groupby(['year_cat', 'floor_cat'], as_index=False).agg({'Price':'median'}).\\\n",
    "                                            rename(columns={'Price':'MedPriceByFloorYear'})\n",
    "            self.med_price_by_floor_year_median = self.med_price_by_floor_year['MedPriceByFloorYear'].median()\n",
    "            \n",
    "        if y is not None:\n",
    "            df['Price'] = y.values\n",
    "            self.meter_price = df['Price']/df['Square']      #AV    \n",
    "        #    self.meter_price = \n",
    "            \n",
    "\n",
    "        \n",
    "    def transform(self, X):\n",
    "        \n",
    "        # Binary features\n",
    "        X['Ecology_2'] = X['Ecology_2'].map(self.binary_to_numbers)  # self.binary_to_numbers = {'A': 0, 'B': 1}\n",
    "        X['Ecology_3'] = X['Ecology_3'].map(self.binary_to_numbers)\n",
    "        X['Shops_2'] = X['Shops_2'].map(self.binary_to_numbers)\n",
    "        \n",
    "        # DistrictId, IsDistrictLarge\n",
    "        X = X.merge(self.district_size, on='DistrictId', how='left')\n",
    "        \n",
    "        X['new_district'] = 0\n",
    "        X.loc[X['DistrictSize'].isna(), 'new_district'] = 1\n",
    "        \n",
    "        X['DistrictSize'].fillna(5, inplace=True)\n",
    "        \n",
    "        X['IsDistrictLarge'] = (X['DistrictSize'] > 100).astype(int)\n",
    "        \n",
    "        # More categorical features\n",
    "        X = self.floor_to_cat(X)  # + столбец floor_cat\n",
    "        X = self.year_to_cat(X)   # + столбец year_cat\n",
    "        \n",
    "        # Target encoding\n",
    "        if self.med_price_by_district is not None:\n",
    "            X = X.merge(self.med_price_by_district, on=['DistrictId', 'Rooms'], how='left')\n",
    "            X['MedPriceByDistrict'].fillna(self.med_price_by_district_median, inplace=True)\n",
    "            \n",
    "        if self.med_price_by_floor_year is not None:\n",
    "            X = X.merge(self.med_price_by_floor_year, on=['year_cat', 'floor_cat'], how='left')\n",
    "            X['MedPriceByFloorYear'].fillna(self.med_price_by_floor_year_median, inplace=True)\n",
    "          \n",
    "        # AV\n",
    "        X['MeterPrice'] = self.meter_price\n",
    "        X['MeterPrice'].fillna(3910,inplace = True)\n",
    "        #X.loc[(X['Square'] > 0), 'MeterPrice'] = X['Price'] / X['Square']   \n",
    "                \n",
    "        return X\n",
    "    \n",
    "    def floor_to_cat(self, X):\n",
    "        bins = [0, 3, 5, 9, 15, self.floor_max]\n",
    "        X['floor_cat'] = pd.cut(X['Floor'], bins=bins, labels=False)\n",
    "\n",
    "        X['floor_cat'].fillna(-1, inplace=True)\n",
    "        return X\n",
    "     \n",
    "    def year_to_cat(self, X):\n",
    "        bins = [0, 1941, 1945, 1980, 2000, 2010, self.house_year_max]\n",
    "        X['year_cat'] = pd.cut(X['HouseYear'], bins=bins, labels=False)\n",
    "\n",
    "        X['year_cat'].fillna(-1, inplace=True)\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6e21b269",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Id',\n",
       " 'DistrictId',\n",
       " 'Rooms',\n",
       " 'Square',\n",
       " 'LifeSquare',\n",
       " 'KitchenSquare',\n",
       " 'Floor',\n",
       " 'HouseFloor',\n",
       " 'HouseYear',\n",
       " 'Ecology_1',\n",
       " 'Ecology_2',\n",
       " 'Ecology_3',\n",
       " 'Social_1',\n",
       " 'Social_2',\n",
       " 'Social_3',\n",
       " 'Helthcare_2',\n",
       " 'Shops_1',\n",
       " 'Shops_2',\n",
       " 'Price',\n",
       " 'HouseFloor_outlier',\n",
       " 'Meter_price',\n",
       " 'LifeSquare_nan',\n",
       " 'DistrictSize',\n",
       " 'IsDistrictLarge',\n",
       " 'MedPriceByDistrict',\n",
       " 'year_cat',\n",
       " 'floor_cat',\n",
       " 'MedPriceByFloorYear']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# отбор призаков\n",
    "train_df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b70cf62f",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = ['Rooms', 'Square', 'LifeSquare', 'KitchenSquare', 'Floor', 'HouseFloor', 'HouseYear',\n",
    "                 'Ecology_1', 'Ecology_2', 'Ecology_3', 'Social_1', 'Social_2', 'Social_3',\n",
    "                 'Helthcare_2', 'Shops_1', 'Shops_2']\n",
    "\n",
    "new_feature_names = ['Rooms_outlier', 'HouseFloor_outlier', 'HouseYear_outlier', 'LifeSquare_nan', 'DistrictSize',\n",
    "                     'new_district', 'IsDistrictLarge',  'MedPriceByDistrict', 'MedPriceByFloorYear', 'MeterPrice']\n",
    "\n",
    "target_name = 'Price'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6cdc1577",
   "metadata": {},
   "outputs": [],
   "source": [
    "# разбиение на train и test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "31fa43e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(TRAIN_DATASET_PATH)\n",
    "test_df = pd.read_csv(TEST_DATASET_PATH)\n",
    "\n",
    "X = train_df.drop(columns=target_name)\n",
    "y = train_df[target_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0fac92ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.33, shuffle=True, random_state=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3209cde6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6700, 22), (3300, 22), (5000, 22))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor = DataPreprocessing()\n",
    "preprocessor.fit(X_train)\n",
    "\n",
    "X_train = preprocessor.transform(X_train)\n",
    "X_valid = preprocessor.transform(X_valid)\n",
    "test_df = preprocessor.transform(test_df)\n",
    "\n",
    "X_train.shape, X_valid.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f9474705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6700, 30), (3300, 30), (5000, 30))"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_gen = FeatureGenetator()\n",
    "features_gen.fit(X_train, y_train)\n",
    "\n",
    "X_train = features_gen.transform(X_train)\n",
    "X_valid = features_gen.transform(X_valid)\n",
    "test_df = features_gen.transform(test_df)\n",
    "\n",
    "X_train.shape, X_valid.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6fba65c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train[feature_names + new_feature_names]\n",
    "X_valid = X_valid[feature_names + new_feature_names]\n",
    "test_df = test_df[feature_names + new_feature_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a67cd879",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 0)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.isna().sum().sum(), X_valid.isna().sum().sum(), test_df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4c9c6e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# построение модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "48adceb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=21)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor(random_state=21, criterion='mse')\n",
    "rf_model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "984202de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2:\t0.966\n",
      "Test R2:\t0.708\n"
     ]
    },
    {
     "data": {
      "image/png": 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KmxDi21CSPx/bS7o/t7/Of7L/Ac7POK0SQvwVStn161LKbp3TPgVlx4ZnhBAzoCRzboCyZeQPh7Aen4iIopiUskEoWz//AkCpEOJVKE3tL4Ky9fBaKLv1+RIvAMq95xohxL0AjgM4JaXcbHD5B4QQX4RyPzsK5cP5t6F8UF9vP+bPUKpc/iyE+DOUe/T/s49h+hBeupGTUCYzSqD0RrsBSnPb+1wa7Lr6BYAvA3hNCPEHKBU/aVCqUG8GMAXeKzQCvbbqB/bjtwkhnoJSgZQL5X26Ecq22YByz18K4A9CiEsAnADwdSgxk0dSysNCiIeg/Ey8J4T4E5TKlJlQlpiryaI6AP9PCPFjKNXCXVLK1w1Oez+UZWnv2eM4devnYdAkhIiiHZMtRNHrNgCPA/g3AMlQuuzPQegrCj6BUmUxD8qNzQrl5nyDlPI1AJBSHhRC3AClQe7PoWzF9xyUkt83QzSuW6Fsl6huzfg0gO96e5KUcqkQ4kMowdpPoSQYjkHpiWMU7AXl2vbr24QQCwDcDWVG5nooM1INUMqid2kO/1co5bNfsx+3GcquQK5N/PSu85Y9mfNjKM0KzfZr/FFz2J+g7CZ0q/2PgBI8uyVbpJRWIcTVUNaafw1KgHMIynbZrrtcERFRHJFSPiqEqIdyP7kfSoXFcSj3pZfsh3mNF+y+A+B3UD6MZ0DZjc/o/vsalK2nb4dSXdkKYBuAh9Rly1LKPwgh8qFUjF4D5d70HSgTDNOH9sp1tUNJIq2CstXwaQDLpZQ/9/Qke3+ZLwL4IZTJlcVQlvXUA1gBJZESkmtrxnBKCHE5gAegJGr+HcAZKBVI39Uc1yOEqALwBJSkSw+UJVt/heflyerzHxRCHIFS+fsQzv8saMf5EIALofxMZUOZSNRNtkgp99k3YvgZgOVQfv7qAHxLSrnFl9dOFA1E4LuCEhGFnhDiJ1ASCIVSSl8Ck7i4NhEREUWWvcp4lJRyvLdj4+naRBQc7NlCRERERERERBRETLYQEREREREREQURky1EREREREREREHEni1EREREREREREHEyhYiIiIiIiIioiDi1s8hMGLECDl69OhID4OIiCiq7Ny587SUcmSkx5EoGI8QERG5C1c8wmRLCIwePRp1dXWRHgYREVFUEUI0RnoMiYTxCBERkbtwxSNcRkREREREREREFERMthARERERERERBRGTLUREREREREREQcRkCxERERERERFREDHZQkREREREREQUREy2EBEREREREREFEZMtRERERERERERBxGQLEREREREREVEQMdlCRERERERERBRETLYQEREREREREQURky1EREREREREREHEZAsRERERERERURAx2UJEREREREREFERMthARERERERERBRGTLUREREREREREQcRkCxERERERERFREDHZQkREREREREQUREy2EBEREREREREFEZMtRERERERERERBxGQLEREREREREVEQJUV6AEREROQ/m03iaGs3mjusKMi2YHReBkwmEelhERFRCPF3P1HsYLKFiIgoxthsEhv3nsQ9L34Ma78NlmQTHrtlOuZOGhVQ0M3gnYgo+gX7dz8RhRaXEREREUWAzSbR0NKFbYdPo6GlCzab9Pm5R1u7HcE2AFj7bbjnxY9xtLXb7/Oqwfu82q249akdmFe7FRv3nvRrPEREFHqefvcTUfRhZQsREVGYDXV2srnD6gi2VdZ+G5o7rNh/stOv8+oF7ys37kPRMAt6+gZZ6UJEFCWMfvef6rSibGRmhEZFREZY2UJERBRmQ52dLMi2wJLsfAu3JJuQnmL2eF69qhfX4L0wx4JFFSVYtHo7K12IiKKI0e/+/CxLhEZERJ4w2UJERBRmnmYnfTE6LwOP3TLdEXSrFSx9gzbD8xotF8rPcg7eF8wsRu3mepapExFFGaPf/aPzMiI8MiLSw2VEREREYabOTmoTI/7MTppMAtdNKMDa6lloareiMMeCSYU5ONbWY3heo2qajcsq8dgt0x1fM5vAMnUioihkMgnMnTQK42sqcarTivwsLvMkimasbCEiIgqzoc5O2mwSb+5rxqLV2/HtP36IRau34819zSjJTTc8r1E1zckOK66bUIDViytQUzUG5flZLFMnIopSJpNA2chMzCobgbKRmUy0RNBQGt1TYmBlCxERUZgNdXbSqEplQ02l4Xk9VdMca+tB9Zo6WPttKMyxoGZOuWMpEcvUiYiInIVrG26bTeJoazeaO6xsWB+DmGwhIiKKAHV2Urs0x9egytuOFK7nBc5X07gGhqPzMrDjSKvjfE3tVqzZ3ogls8swtSgb5QVZDO6IiIg0jCY9xtdUBm3JbbgSOhQ6TLYQERFFAX+CqkB6vniqpnE9X1O7Fc+814ANQQwaiYiI4kU4tuEOR0KHQos9W4iIiKKAP9tBB9rzxWitP3e4ICIi8l04tuEe6s6FFHmsbCEiIooC/sySBXtHCu5wQURE5DtPS3ODZag7F1LkMdlCREQUBfwNqvR6vgxFsM9HREQUr3yZpBhqc9twJHQotJhsISIiigLBCqq4cwEREVHoeZqkCEZzW1adxj4mW4iIiKJAMIIq7lxAREQUecFqbsuq09jGBrlERERRwqiBra/8abILKMmZhpYubDt8Gg0tXbDZ5JBfAxERUaJjc1sCWNlCREQUN/xpsssqGCIiotBgc1sCWNlCREQUN/zZitLfKhgiIiLyjdqHTb0ns7ltYmJlCxERUZzwp8muP1UwREQUG+K5SXosvTY2tyWAyRYiIqK44U9wxxJnIqL4Es/LQ2PxtbG5LXEZERERURzxtckuS5yJiOJLPC8PjefXRvErrMkWIUShEOIPQogWIYRVCPGpEOILmq8LIcRPhBCfCyF6hRDvCiEmuZwjVwixRgjRbv+zRggxzOWYKUKIv9nPcUII8SMhhHA5ZqH9+ufs/73R5etex0JERKQnFnb5UatgNtRU4oXqy7GhpjKqZwiJiMizeN4BJ55fG8WvsC0jsidE/g7gPQBfAtACoAzAKc1h3wfwXQDfBHAAwI8AvCWEGCel7LQf8zyAEgBz7f9+GsAaAF+2XycbwFsAtgC4FMB4AL8H0A3gl/ZjrgCwFsCPAfwJwAIALwkh/klKucOPsRARETmJpVJnljgTEcWPWFgeGmjflVh4bUSuwlnZ8n0ATVLKb0gpP5BSHpFSbpJS7gOUShIAdwP4LynlOinlHgC3A8gCcJv9mAlQkizVUsptUsptAP4NwHwhxDj7db4GIB3A7VLKPVLKlwGsBHCPprrlbgDvSCl/KqXcJ6X8KYB37Y/7NBYiIiI9LHUmIqJIiPbloepkxLzarbj1qR2YV7sVG/ee9Kn6M9pfG5GecDbIvQHARiHEWgBXA/gcSlXKk1JKCeAiAKMAvKk+QUrZK4TYAuBKAL8DcAWALgDva877dyhVK1dCqUC5AsBWKWWv5pg3AKwAMBrAEfsxT7iM7w0AS+1/92UsREREboxKnZs7rI6vR/suCkREFHuifQcco8mI8TWVXisso/21EekJZ7KlDMD/A/ArAP8FYDrOJzxWQUluAECzy/OaARTZ/z4KQIs9OQMAkFJKIcQpzfNHATiucw71a0fs/9W7jvYc3sZCRETkxqjUuX9QYl7t1pAuLYqlbTGJiCj4onl5qKe+K76MN5pfG5GecC4jMgH4UEr5QynlR1LK3wOoBfAfYRxDyAghqoUQdUKIupaWlkgPh4iIIkSv1Hnlwql44LXdIV1aNJTy7ETDhv1EROGnTkZose8KxbNwJluaAHzq8tg+KM1uAeCk/b8FLscUaL52EsBIbaBi/3u+yzF654APx5x0Oc7TMU6klKullBVSyoqRI0fqHUJERDHKn92F9Hb5uWCYBY2tvU7HBWsXBXVs7x48hQMnO5CbnuI4P3vFuNM07BdQGvZPAHAX9Bv23wWl2f4pKE3yszTHPA9gJpRecnPtf1+juY7asL/Zfo5lAL4H4B7NMWrD/v+FUvH7v1Aa9l/u51iIiKIe+674LxZ2NyRj4VxG9HcA41weGwug0f73I1ASGdcC+AcACCEsACqhBCcAsA1AJpSeK2rflisAZGj+vQ3ASiGERUqpRrHXQukRc1RzzLUAfqEZy7Wac/gyFiIiSgCB7C6kV+ocil0U9MZWM6cca7Y3oqldKdc+030OAHvFaDga9mseO6L+xbVJvv2x26EkOW4D8DtNw/7Z9mb9EEL8G4Ct9l0LD8C5YX8vgD1CiPFQGvY/Zl8SfTfsDfvtl/+pEOJq++O3+jKWoL4zREQhxL4r/oml3Q1JXzgrW34FYJYQ4j+FEGOEEDcDqAHwJKD0XgHwawDLhRALhBCTATwLpSHu8/Zj9gHYCCXQucI+I/Q7AOvtgQ3sx/YAeFYIMVkIsQDADwA8pun18jiAOUKIHwghxgshfgilae+vfR0LERElhmDsLhSq2Ty9sdVurseCmcUAgNK8NHx2ppdLi5zdAGCHEGKtEOKUEOJjIcRSTdWsbpN8AGqTfMB7w371GL2G/RdAadivHvMmnL2hOYcvYyEiihnqZMSsshEoG5nJpIEH3N0w9oWtskVK+Q8hxA0AHgHwAIBj9v/+t+awnwNIg5KAyQWwA8B1UspOzTG3QWms+4b933/G+V2EIKVsF0Jcaz9HHYA2AL8E8JjmmPeFEF8F8DCAhwAcBrBISrnDz7EQEVGcG2pDPyB0s3lGYxNCSej85MuT8O//+2FAOz/EMTbsJyKiqBeM+IMiK5zLiCCl/AuAv3j4ugTwE/sfo2PaAHzdy3V2A7jKyzEvA3h5KGMhIqL4Z7S7kL9LgEKxi4LR2CrHjMC1E/Kxtf40AzV3JgB1Usof2v/9kRCiHErD/lWRG1ZwCCGqAVQDQElJiZejiYjISKR3+AtW/EGRE85lRERERDEnmhv6GY3t0tHD0dM3COuAjTs/uGPDfiKiIfLWuDXWG7tGww5/0Rx/kG/CWtlCREQUa6K5oZ+nsRVkW/D6JydQM6cctZvrHc31Vi6cmuiBGhv2ExENgbfGrfHQ2NWoX0o4l+FGc/xBvmGyhYiIyItQLAEKFqOxjc7LwPK5E7By4z4smV0GswmoKB2OK8vyEj1Q+xWA94UQ/wll2+UZUBr23wc4eq/8GsB9Qoj9AA4CuB8uDfuFEGrD/mr7efUa9v8YSsP+h6EkdH4A4EGXhv1bhBA/APAqgBuhNOyf7etYiIjCzVsiIhoSFUMVLf1Sojn+IO+YbCEiIkLk12YHm2NGbFQWZ8Q02LCfiGhovCUioiVRMRTsl0LBwGQLERElvFgoeR5KMkjG1lL5kGPDfiKiwHlLRMRDokLtl+IaFyT4MlzyE5MtRESU8KK95DmQZFAsJJCIiCj2eEtExEOiIhj9UuKtYpb8x2QLERElvGgveQ4kGRTtCSQiIopN3hIR8dLYNZB+KdoEy8CgxP2v7UZjay8nPBIUky1ERBRzgj1bFO0lz4Ekg6I9gURERLHLWyIiERu76lWU1swpx5rtjWhqt3LCIwGZIj0AIiIif6jBzLzarbj1qR2YV7sVG/eehM0WeGMSteTZkqzcFqOt5FlNBml5SwYF8hwiIiIKjF5Fae3meiyYWez496lOaySHSGHGZAsREcUUo+UxR1u7Az6nWvK8oaYSL1Rfjg01lY5SX5tNoqGlC9sOn0ZDS9eQkjqBCiQZFO0JJCIionhiVFEq7IW3nPBIPFxGREREMSVUy2P0Sp6jpclsIOvf42XNPBERUSwwWpIsJSc8EhWTLUREFFPC2V/FW5PZcO40EMj690RcM09ERBQJerswrVw4FUXDLFg4s4gTHgmIyRYiIoop4dxS0lMVzei8jLBVvXD7SCIioujGilJyxWQLERHFlHAGM56qaMK1tXK0LGUiIiIiz1hRSlpskEtERDFHDWZmlY1A2chMn5MO/ja79dRk1lPVSzCFoiEwEVGiCGeT82hoqB4L+D5RomBlCxERJYRAKkQ8VdGEq3eMt6VMXF5ERKQvnJWBrEL0Dd8nSiSsbCEiooQQaIWIURVNuLZWVpM6WpZkE0ZmWrBx70nMq92KW5/agXm1W7Fx70nOEBIR2YWzMpBViL7h+0SJhMkWIiKKC97KkoO97EetetlQU4kXqi/HhprKkMzMGSV1zCYwYCUi8iBcyz3Dda14WH4Tzu8JUaRxGREREcU8X8qSQ7HsJxyN8IyWMu040moYsLIxHxFRaH7vh+Javuw4Fy/Lb8L5PaHokog7K7KyhYiIYp4vZcnhWvYTCnpLmYyWFzFgJSJShPP3fqDXUpMo3paExsvym1i+F1PgfP05jzesbCEiopjnqSxZrfIIx5bR/szaDHWGRw1YXWc5GbASESnC8Xt/qNcySqKMr6l0qlL05T4XC8L5PSF3kaou8fXnPN4w2UJERDHP17LkUC778afEOxjl4AxYiYi8C8dyz6Fcy9ckSjwtvwnn94TOi+RStHhJFvqLy4iIiCjmRUNZsj8l3r4e660ZotFOSUREFBt8XRIaDfc5im2RWopms0mkpyQl5NJnVrYQEVHMi4YqD39mbXw5Nl6aIRIRkTFfl4RGw32OYlskqkvUWGblxn2omVOO2s31CbX0mckWIiKKC5EuS1ZnJ3PTU7BgZjGEAMwCGJXtPmvjSzl4oq5vJiJKJP4kUSJ9n6PYFomlaNpYZs32RiyZXQazCagan48pRcPiPlnIZAsREUWlWNsicHReBlbdNgP1zV14fNP5mZtxo7JRMtx57L7MZCbq+mYiokTDJEr8icYYJhKN9bWxTFO7FU++cwgAcOXFeRF/P8KByRYiIoo6sbiExmQSuCgvE0uf/8hrNYrJJHDdhAKsrZ6FpnYrCnMsmFSYA5NJOAI0kxBx0wyRiIgoUURrDBOJpWjx1Ng5EGyQS0REUSdSTdxU3hrTGjnVaVyN4nr+N/c1Y9Hq7fj2Hz/EotXb8ea+ZgwM2LBx70nMq92Ku9d+jGVV5WyGSEREFEMiHcN4Eu7G+one2JmVLUREFHWCsYQm0BLeocxI+TqDYxSIra2e5Xi8qd2K57Y1ovqqMsy4cBhK8zKiogyZiIiiWzQuYUkkXAZ8XqI3dmZlCxERhUSg1SGA71therq2WiFy61M7MK92KzbuPekYg6exDWVGytcZHKNArKnd+fGmditqNx1CWoqZWzsTEZFX3u5/FHpDjWHiTbiraaIJky1ERBR0Qw32hlp26ilh4m1snmakvFFncDbUVOKF6suxoaZStyLGKBArzEljgEZERAGL5iUsiSLRl87QeVxGREREQTfUbYuHWnbqLWHiaWxDbebmy64SRjsCTCrMDvtOAUREFD+4hCXyEn3pDJ3HZAsREQVdMII9T0kLb+vRPSVMvI0tHFsjegrEjHYpIiKi2BKJ3imJvvtLtOB23gQw2UJERCEQymDPlwa23hImnsYWrhkpvUBM3aUo2raLJCIi/0Rq+99wTBgQkW+ElGyWFGwVFRWyrq4u0sMgIoqYUAaZDS1dmFe71S1ZssFliZI6o+iaMIlUAOwLX19brBJC7JRSVkR6HImC8QhR5ETy97nR/S+SuEMSRZNwxSOsbCEioqALZXWIr0uUjEp4o3ktNdfaExHFh0j+Po+2JSzRPMlBFEpMthARUcA8zVSFKtgLxhKlaAtEVVxrT0QUH/j7/LyhNs0nilXc+pmIiAIy1O2dAxWMLRVtNomGli5sO3waDS1dGBiw4fCpLmze14z3D7dg59EzOHq6K+SvxRW3iyQiig/8fX6etx0CifS4xmrhjsmCgZUtREQUkEjNVA11GZBrOXNpXhrumlOO+1/d4yhvXlZVjowUMy7I7cKccQVhK3OO5iVORETkO/4+P49VPuSveFl6xsoWIiIKSCRnqtRlQLPKRjgSO77OfrgmieZPLXIkWtTX8Pimepzu7sOu4+042tod8tej5fraYimoICKi8/j7XMEqH/KX0YReuGOyoWJlCxERBSRaZqr8nf1wTRIJAd2kkZqvYXNaIiKiwLHKh/wVLxsGsLKFiIgCEi0zVf7OfqhJIgAozLFgXEGW498qS7IJJgGYBHxKHmnXFX/y2Vn842hrzK4vJiIiCjZW+ZA/tLGaKhaXnrGyhYiIAhItM1Wt3eewZHYZhP2y63YeR1O71W32Q7tz0lOLK/D4pgOYM34UHn1zP75zzVj86u2DOj1b0rwmj/Qqa2rmlGNt3TEsnzsh5tYXExEREUWSOqHnWrUca0vPmGwhIqKARXoLZZtN4vOzVjzzXoNbokM7+6GXEHnkxik43WnFl6cV4S+7PsfSq8cgPysVFwxLQ1qyGXmZKSgZ7j15pFdZU7u5Hktml3FrSyIiIiI/RcuE3lBxGREREcWso63dWL5ul1uiY8X1U5xmP/QSIve9shvt1kE8vbUBcycX4n93HMP31+1GklngktHDMXqEb2XORuuK1V4w3NqSiIiIjMTDFsehEA9Lz1jZQkREYaVdzlOQPbSZCqNER7JZOJ3TW0JErUR55r0Gv9cDGzUKljI21xcTERFReMTLFsekj5UtREQUNmpQMa92K259agfm1W7Fxr0nA57FMWqgVpBt8ek4ab+std8GswkBrQfWaxRcM6cc63ediMn1xURERBQe8bLFMeljZQsREQWNt6oVo6Ai0L4mvjZQ0zuuZk451mxvBKAkSKrG52NK0TC/Z5K064qbO6xITzGjf9CGuZNHxeT6YiIiIgqPeNnimPQx2UJEREHhSylssIMKXxuoqccVVc/C1vrTKBuZiZUb96Gp3QpLsgkrF04NKNGiPX8kGwUTERHpCebSXQo+o6XIXIIcH5hsISKioPClaiUUQYWviQ6TSaCnbxCPvnkQhTkWLJhZDCEAKYGiYZagB58McImIKJKG2g+E97HQi5ctjkkfky1ERBQUvlStRDqoUJM9Te1WPPnOIQBKsmfhzKKgXocN74iIKNKGsnSXiRrfDeW1xssWx6SPyRYiIgoKX6pWIh1UhCvZE+zeNEREFLsilXgYytLdSCZqYkkwXiuXIscvJluIiCgofE1khCqo8CWYDVeyhw3viIgIiGziYShLdyOVqIk1ifRayX9MthARkU+8JTP8SWQEe5bPn2A2HDNIbHhHRERAZD+MD6WaM1KJmliTSK+V/MdkCxEReeVrMsOXREYoZvkCCWaNEj7BSARFujcNERFFh0h+GB9KNWekEjWxJpFeK/mPyRYiIvIqmDNzoZjla+6wIjc9xbHDEACs23ncMJg1SvhcN6EAb+5r1n38WFsPmjusSE9JQt/gIPIyUg2D1kj3piEiougQ6Q/jepMgoV52m0gTDon0Wsl/TLYQEZFXwZyZG8q5jALEwhwLvnFFKR7fVO8IdpZVlWNUtn4wa5TwWVs9y+3xlRv3oX/QhuXrdjnOXTOnHGvrjmH53AmGFTlseEdERNH2YTwcy24TacIhkV4r+Y/JFiIi8iqYM3OBnstTgDhogyPRAihJksc31eO6iaN0EzRGCZ+mdvfH508tciRa1ONqN9djyewyNsEjIiKPou3DeLh6yMTLhIOvVUDx8Fop+EyRHgAREUU/dWbOkqzcNoYyMxfouYwCxKOt3TjVaVwts3HvScyr3Ypbn9qBebVbsXHvSRTmWBzXV1mSTSjMSXN73GyC7rmFOH8NIiIiI+qH8VllI1A2MjOiVQ+eqkvJmTrJ4xpD2Gwy0kOjGMHKFiIi8sqXmTlfG8t6O5fReTwFiEbVMukpZtzx7D/cEjR/uatSt6x7UmG22+OXlg7XPbeUbIJHRES+CfYufIGKdA+ZcAnG+x3N2zpHy88TecZkCxER+cRTmay/OwwZncvTeTwFiEZr4vsGbboJmpYuq2HCx/Xxktx0t3OrPVvYBI+IKDaF88NqKHbhC1S09ZAJhWC939G6rXM0/TyRZ0JKlkEFW0VFhayrq4v0MIiIwqahpQvzare6JUI2eJn9cQ12TQKY+7j+eUpy0/HqJydw/6t7HMHFwzdMxg3TipCUZHKcS5s8OdraHdC4PI0zPcWM/kEbhnvYjYj0CSF2SikrIj2ORMF4hEhfuD+sBnqPDBW9+6UvrztWqimC9X5Hy/fN9X2XEvjSE5EfVywLVzzCyhYiIhqyQGZ/9ILdR26cgtz0FDS1W93OAwBP2BvTCgFIqfx7ZkmuI7GiBiIluek42tqN1u5zWLlwqtNOQoHM4LH5HRFR/Aj38pBoq5AI5J4WS9UUwXq/o6EKSO99f/SmaVH180TGmGwhIqIhC2QNuF6we98ru1F9VRlqNx1yO09zhxWNrb148p1DTudp7rBi/8lOx7lK89Jw15xyRwVMaV4aVi+uQLJZRPVMHBERhUe4kx/x0CclmvuXAK4VqEkozUtDY2uv4+uBvN/RsJOU3vtef6oz5n+eEgV3IyIiSjA2m0RDSxe2HT6NhpauoHTVD2SHIaNgtyQ3Xfc8arCqpTbB1QYi86cWORItANDY2ovqNXUoyLZEfBcIIiKKPKP7Sag+rAZzR79IieZdjFx3DVq0ehvumlOO0rw0AEN7vyO9k5Te+/5i3XE8cuOUmP55ShSsbCEiSiChKgMOZPbHaKbvZIcVS2aXwWwCqsbnY0rRMJhMwucmuOqWzFpqQOi63IhVLkREicfb8pBg9yaJhgqJoYrm6hy96o/7X92DtdWz0Ns/GJPvt0rvfW/r6cPMkmHYEMM/T4mCyRYiogQSyjJgf9eA6wW7NXPKsWZ7o6Nny8TCbGRZug13ClKTJ3oBoOu/R2Vb3BJNKxdOxQXDLMhjs1siooThKfkRykmJWO79FQ39S4wYVd309g9iVtmICI0qOIze95LhGY6fKYpeTLYQESWQQNeph2IHAm2w29jajY8+O+uUaLEkm7D7RAfuXvuxU6DrGqy6BiKvf3ICD98w2WnXosdumY5BG9wSTcvX7cKS2WV45r2GqG30R0REwWeU/Ij23iSREs3VOdFcdTNU0fy+k3dMthARJZBAApJQ7kCgBruj8zLQ229DW0+fY0xqlYu3QFcvECnJTcfMklynwGTHkVbdRJO67IjBNBERRdvOQdEkWqtzornqJhii9X0n75hsISJKIIEEJOGY5dMmTA42d2L3iQ6nKpfc9BS0dJ4zrKzRC0TUJM7R1m7sONKK9JQkWJJNyE1PwYKZxRACMAvAkmx2vC4G00REiS2eqyTiVTirP0JR6Uvxi8kWIqIEEkhAEq5ZPu3a47vXKsmdwhwLvnZ5CUblWPD+4dN4se442nr6HJU1AAyDHteKnNK8NPzipqk43taLxzfVO5JND98wGQ9dPxHPvHeEwTQRUYKL9yqJeBWO6o9QVvoGMhYmfaIfky1ERHHK6Ebsb0DiaZYv2Dd7m01CSuDRm6bheFsP0pLN+NnG/W4NdO958WNMXFaJT5s6DYMe14qcxtZep0QLcH7Hgl/dMh33XjcOJbnpAY+diIhiH3tkkJFo6ecTTUkf8szk/RAiIoo16o14Xu1W3PrUDsyr3YqNe0/CZpN+n0ud5bMkK7cMRyf83PSAr2GzSTS0dGHb4dNoaOmCzSYdY/7SE1ux9P8+wq831aOnfxC56SkAlKCmdnM9FswshrXfhuaOc7pBz9HWbgD6FTndfYO6VTr7Tnag/lQXjrX1+P3+EBFRfFEnJWaVjUDZyEx+gCUAnit9w8ko6aPGPxQ9WNlCRBSHAp19MapUMdpyOdBruM7IrLptBkZmpLqd7/FN9VgyuwxPvnPI8ZgQQGleGgZsNtxZWQYAWLfzOJrarU7Lm/QqcrJTzaipGgM1H7Rup7IsadAGCAH2bCEiIiJd0dLPh02cYweTLUREcSiQG7G3slTXpUeB3uxdkzS56Smob+7Crv52w92CVJZkE7JTzbhrTjmW/KHO0YvlgfkTcehUFwZtNozKVoIe13X3pXlpGJ6Zisc0W0IvqypHerIZ//P+EVw/vYg9W4iIiEhXtPTziZakD3nHZAsRURwK5EasTYIU5liwYGYx9p/sQNGwNEwpynEro3a9RmGOBTdXFKOnbxANLV2Ga9xdkzQLZhbj8U31uLOyTHfM6iksySY8MH8ixuZnYvH/fOAY56KKEqfAZ9yobJQMd6/ISUs2Y9Hq7W6VM8uqyvHVS0tQXpDJBohERBRWbHQaO0LRzyeQ73+0JH3Iu7D1bBFC/EQIIV3+nNR8XdiP+VwI0SuEeFcIMcnlHLlCiDVCiHb7nzVCiGEux0wRQvzNfo4TQogfCSGEyzELhRCfCiHO2f97o8vXvY6FiCic9HqceGLUZ8XTjVhNghTmWLB4Vimeea8BtZsOYdHqbbq9WLTXKMyx4BtXlGL1lgb867N1Hvu3qEkalRBK4mPdzuOomVPuNOZ7rh2LtGQzls4Zg2VV5ejo7UdL1zncWVnmSAjVbnZueKtdt6xdd99j0K+lbEQm/mXyKMwZV8AAl4iIwiaY/dUoPILZzyfQ77+a9NlQU4kXqi/HhppKNseNUuGubDkA4Iuafw9q/v59AN8F8E37cT8C8JYQYpyUstN+zPMASgDMtf/7aQBrAHwZAIQQ2QDeArAFwKUAxgP4PYBuAL+0H3MFgLUAfgzgTwAWAHhJCPFPUsodfoyFiCgsAuk6H8jsi5oEMUpguPZi0V6jpfMcbv/9Bz71b3GdkTELJbHS1G7Fmu2NWDK7DGYTMGdcPk52WHH32o+Rm56Cb1xR6rRlc82ccphMMFzKpPaVUWeL8rP0q33KCzIxegTXOBMRUfB5qlyIlt1tKDKG8v0Px1bXNHTh3o1oQEp5UvOnBVAqSQDcDeC/pJTrpJR7ANwOIAvAbfZjJkBJslRLKbdJKbcB+DcA84UQ4+zn/xqAdAC3Syn3SClfBrASwD2a6pa7AbwjpfyplHKflPKnAN61P+7TWIiIwinQrvP+zr6oSRCzhwSG0TVsUuo+p7nD+Tlq0Jmbnoy11Vfg5W/Pwo0zihwVMk3tVjzzXgPGj8rG6e5z+Nlf92HJ7DL857wJbls2126uR9GwdKcqGUBJoBRkWfDeodN49eMT+PvhVtzx7Ac40trld7UPERFRoLxVLkTL7jaR5G/lbjzh9z/+hbuypUwI8TmAcwB2ALhPStkA4CIAowC8qR4opewVQmwBcCWA3wG4AkAXgPc15/s7lKqVK6FUoFwBYKuUsldzzBsAVgAYDeCI/ZgnXMb1BoCl9r/7MhYiorAJV9d5tVKlaFgaVm9p8KvfS3pKkm7VSHqK2fFvowqdmSXDUTI8w6kKxySAuY9vhbXfhiffOYSlc8Y49YZZMLMYQgC56cn49aLpuHut885Gu0+04/vrdjlVwaxY/yn+cMdl2BDEtdZERERGvFUuJHqj00Aqd+NJon//E0E4K1t2QFmWMxfAt6AkNN4XQuTZ/w4AzS7PadZ8bRSAFimlI91p//spl2P0zgEfjhnlcpynY4iIwsa1xwkQupuxySQwpSjHsALEaAaqb3AQ37lmrNNzvnPNWPQPng8gPFXomEwCo/MykJ9lQXOHFc0d55CbnuL2mrX9ZFZtPoQ7n6uDTQIbl51ft3xRXqYj0aJep3ZzPeZPLcLJDmvQ1lpTbGIPOSIKF2+VC4H0V4sngVbuxotE//4ngrBVtkgp/6r9txBiO4AGKEt0todrHKEihKgGUA0AJSUlER4NEcWTcHedN+r3AsBwBmpERirSkk2ovqoMNgmYBJCWbEJeRqrjvJ6CztF5GW7nXlZVjue2NaKp3Yp1O49jWVU5evsHdfvJrK2ehctG5wEA3j14Svc6ZhOQlmyGzSaZZCH2kKMh4y4y5I23yoVQ7G4TS4ziguaO4FbuRqtE//4ngoht/Syl7BJC7AVQDuBV+8MFAI5pDisAoM42nQQwUggh1OoW+wxRvssxBS6XKtB8zdMxJ12O8zQWvdezGsBqAKioqEicxYZEFHKRuBnrNV5raOnSnYGauKwSZ7r7cLq7DzYJrNt5HE3tVliSTXjxwmGO5+sFnaV5aUhLNuPdg6dw4GQHctNT0NRudWzLXH1VGWo3HUJbTx/KCzJhgtANzDbtP4UTZ61ISRLYfbxdN7gdPyobNS98hOVzJyRMiTIZGpBSut3TXfu22R+7HUoV7W0AfqfpITfb3j8OQoh/A7DVngQ5AOcecr0A9gghxkPpIfeYPY65G/YecvbL/1QIcbX98Vt9GUuQ3xPyQ6IvfyDf+DJZksiNTo2SUf2DMmEmRhL5+58Iwt0g10EIYYEy09MEpZfKSQDXuny9Eud7tGwDkAml54rqCgAZLsdU2p+ruhbA5wCOao65Fs6u1ZzDl7EQEYVVMLcaDFRr9zksmV2GpXPGYOmcMSjMsSA3PQUfHjuLrz2zA7WbDuHprQ1YPKsUhTkWWPttaGo/3+TNtVy2NC8Nd80px6LV2/Gvz9bhd1vOPxdQkigzLhzmWB40Z1wBRo/I0F1SNWgD7nnxY+w63o4X69y3kH5g/kT89t1DaGztTagSZTJUZl+ac0QI8YIQosz+uG7fNigVKlfaH/LWQ049Rq+H3AVQesipx7wJZ29ozuHLWChCEn35A/mGW/R6NjovAysXTnW6X9fMKccDr+2O2f+XErnhL7kLW2WLEOJRAK9DqRbJB/AAlETJH6SUUgjxawD3CSH2AzgI4H4owczzACCl3CeE2AhlVqnaftrfAVhvn0WC/dgfA3hWCPEwgLEAfgDgQU2vl8cBbBFC/ABKRc2NAK4GMNt+Ha9jISJKNDabxOdnlZ2CtE1nhQDue2W3W3+UJbPL8Mx7DRiVcz737Vqhk5ZsxqLV292e+/ObpuFgcyfMArhoRIbTtsyus4SleWlYPncCDp3qwp2VZUhPMTttIS0EMK4gC49s2OdI/ISiuTDFFLWH3H4o8cj9UHrITYLnvm1F9r/r9pATQrj2kDuucw71a0cwtB5yRaCIClfjcop9rFwwZjIJXDDM4rhfSwms2a4sH47F/5d8rXjjEsTEEc5lRMUA/g/ACAAtUPq0zJJSNtq//nMAaQCeBJALJRi6zmVN8m1QdhJ6w/7vP+P8LkKQUrYLIa61n6MOQBuUtdGPaY55XwjxVQAPA3gIwGEAizTro30dCxHFGd78jB1t7cZynaazv7plumF/lIdvmIxJhTlOX9MGndsOn9Z97qFTnVi1+RAsySaMG5WNkuHnvw+OHZOqZ2FHQysyLMlOQc0D8yeiMMeCpnYrnnxHOUf1VWVOFTbs9J/Y2EOOgoG7iBAFR15GqmMiRxWr/y95230K4BLERBO2ZURSyq9KKS+QUqZIKYuklAullJ9qvi6llD+RUhZKKS1Syi9IKfe4nKNNSvl1KWW2/c/XpZRnXY7ZLaW8yn6OQimltqpFPeZlKeV4+1gmSCn/5PJ1r2Mhovii3vzm1W7FrU/twLzardi49yTLP+2MZnGHZ6ToLuu5qnwkbphWhKQk49uMul2063MHbcr2zktml2H/yQ7sPtHu9H1QdkwahvGFOVix/lOnoGbF+k9xc0Wx41yP3TIdU4tz3Dr9l+Sms8yXACg95ACoPeS0fdu0dHvIqV8MQw85o2P0Xs9qKWWFlLJi5MiRRofREHEXEQqHYCxJifZlLfH0/5K33aeAxFiCGO0/c+EUsQa5RETRxJfZiERmNItbkJ2q2/xvZkmu1xmavsFB1Mwpd+wupC5N2rinCYtnlToeX72lwW3Wx2QSSDbrN8tV+7xod1HaoGkuXJKbjjf3NXNWiQA49ZB7B8592/6h+XolgO/Zn6LtIaf2bdHrIbdSCGGRUqpRtlEPuV9ohmPUQ85oLBQh3EWEQs1mk9h8oBm7jrfDJgGzAKYU52DOuAKff870qihWLpyKL00u9DgZEk7x9P+SLxVv8b4EkZU7zphsISJC/N/8/KG3nMpoR4WS4RkoGZ4RUJCUl5GKtXXHnHqrPPrmfsyfWqS7vbNr4ssoqCnNy3D7nmnXyxvtqsTEWmJgDzkKFvbioFA6dqYb9c1dWL3lfK+0ZVXlGDMy06mXmSdHTrtPJC1ftwu56SmYPWZE1Hz4jZf/l3zZfSrelyBy8tIZky1ERIj/m5+vPM1IeJp5CiRIGp2XgeVzJzg1u/32F8agteucT4kvX4IaPUysJTz2kCOiqNfccQ6Pb3KeeHh8Uz1mluT6lGyx2ST2NXXo3u/qGs+gODeN97wg86VKJ9DYJVYwxnLGZAsREeL/5ucroxmJvDsuw8isVIzWqRoJlMkkMLEwC9VXlcEmlV0I3tzThK9fMdow8eVadXPdhAKnJUK+VNUwsZbYpJRf9fJ1CeAn9j9Gx7QB+LqX8+wGcJWXY14G8PJQxkJE8am7b0D3Q2tP34BPzz/a2o36U52697tBGxL2w2+oeavSiadlU3oYYzmLjsV6REQRpt78NtRU4oXqy7GhpjIh15cazUhsPXQ6JE2Dm9qtqN10CKs2H8KT7xzCxKJheGj9XtTMKXdqlrdy4VSU5KY7mhjf8+InePXjE9i49ySkBC4bnYeykZk+fb/iqRkfERHFp9LhGbpN5EuG+3avau6w4sW643hg/kSn+13NnHKs33UiYT/8RgM1ITOrbITPsUusYIzljJUtRER22tmIRN0G2mhGQsrQrLt1vV5qkgmNrb1Ys73R0ctFSuCCYRYca+vBPS9+jNz0FKcGuv42X4v3WSUiIop9F43Qr7i9aIRvH1oLsi1o6+nD2g+O4bFbpmP/yQ4M2oC1dcewfO6EhP3wS6HFGMsZky1ERC4SuZO63nKqmjnlWLNdaWfhad1tIAkq1+uVjXSfyUsyAckmE9p7+7BkdhnGj8rC917+ZEjN1+KlGR8REcWnoX5o1d5fV6z/FDdXFGNCYRYWzLgMF41I3A+/FHqMsc5jsoWIyEUid1LXBneNrd346LOzWLO9EU3tyu61RutuA01QuQaTA4M2/HDuePT0DzoaA1qSTchITcKoHAvW7zoBIYrYfI2IiOLeUD60ssIgshK1QpqcsWcLEZELT53UE4Ea3H1hbD7Gj8pGW08fAM/rbo0SVEdbu32+XkXJcKQmm5GfbXHbgeGxtw7i0KkuzJ9a5BiLViI3XyMiItITz71Bopk6ATWvditufWpHSHreUWxgZQsRkYt47aTuOstSkpuOY209hrMurrNiIzMtMJuAHUda3Y73d6s/vbG8vb8Zx870oOuc/g4MNgkIAazbeRw1c8rderZw/TkREVH4ae/phTkWx25HiVrRkcgV0uSMyRYiIhfxuA206zKf0rw03DWnHPe/usfjsh91VqwkNx1/2dOE5et26R7vT4JKb8nRz26cApMJ6D43gNQkk+65TAIYtCk7GK3Z3ojqq8ow48JhKM3LSMhgjoiI4p+n5SjRsFRFe0/PTU/BN64odVoGnCg977T8nYCi+MVlRERELuJxG2jXWZb5U4sciRbA87KfgQEb/tF4BodbunBnZRkKcyxuxxtt9VeSm46Gli5sO3waDS1djsBw5cZ9WDK7DEvnjMGdlWX49aaDONjcjd9taUBmShK+c81Yp3MtqypHeX4m1u86AQBo6+nD+FHZ+MLYfJZGExElKJtNut1jYo2n1+BpOYqvS1VC/R5p44sFM4vdlgH7uqQ4nqgTUFrxUCFN/mNlCxGRjnjrpO46yyIEfJp1sdmkW0WLujtRU7vVcbxeI76S3HS8ua/ZrUJoZFYKFlWUOC0DqplTDpNJGcPPNu7HsqpyLJldBrMJmH3xCORnp+KC7DSUDE9HU7sVhTlpmFSYzSQLEVGC8rUxezCrP4JdSeLtNXhajgJA92vj7qrExfmZfr1HQ6GNL3yNLeKV9ufjqcUVuP+13Whs7Y2LCmkKDJMtREQJwGiZj96yn4EBG/Y2taOp3YqRmalYs+2IUzBXu7keS2aX4Zn3GpxmaVwTVA0tXbqB4B+XXO5ItGjP+atbpjv+PTw9Bb39vZhZkouCnFQU5egnbmK94oiIiALjS18Mm01i84Fm7DreDpsEzAKYUpyDOeMK/L53hCJx4e01eFqOIqV+YmPfyQ7H1s7h6B3iGl+Eu+ddNCylUsfh+vOxcuFUFA2zYHhGKpc7JyguIyIiSgCuy3xe/+QEHr5hstuyn+KcNLz6yQksWr0d3/7jh/jaMzuwcGYJphZlO85l7bfBbAJWLpzqNEvjWqrc2n1ONxA8Y/B4d98ACnOUoO1Eey9qNx3Ct/+4Ext2n8T2o60B73ZERETxx5edA4+d6UZ9cxdWb2nAqs2H8LstDahv7sKxM/7fO4ay616gr8HTchSjrx1s7nSMKVS7K2rv91ICq26bAUuyCet2HseyqnK32GJ0XkZIljNF064/ej8fy9ftwvCMVC53TmCsbCEiSgBGy3xmluQ6/j06LwO7T5x16+Xy4Pq9+PlN01Dzfx8BUIKnK8rycGnpcKcmfXozOqV5aWhs7XWMw5JsQkF2mu7M17EzPbi5ohhpyWY8t63Rcf3HN9Xjwa9MSujSZCIicuZLY/bmjnNuPUQe31SPmSW5GD3Cv3tHKJqeensN3hr2P3LjFNz3ym63Zb5XXpyHspGZIdld0ajCZ+OySpzssGJUtgXXTRyFlq7zsQWAkCxniqZdf9gUl/SwsoWIKE54mzVSl/nMKhuBspGZSEoyOf3bZBJoatcPFqx9AwDgSKJcWjocSUnnbyFGMzorrp/iNsM1qTAbD9/g/HjNnHK8VHcc5flZeG6b0g9Ge/30lCQ2myMiIgejxuzaisvuvgHde1qP/Z7mj1A0PfX2Gjw17DeZBGaWDEP1VUqz+SWzy7BmeyPaevrckjWe3iN/GSU4bBKYVTYCo0dk4uL887EFAOw+cRb7T3YYNtkPVKgqdwKRn8WmuOSOlS1ERHEgWGvJC3P0q07KRmbiherLHbNUruds7rAiNz0FC2YWQ9i/tG7ncSSbBd64uxLN7edwuvscioalAQAusQeINglICUeACMDxX+31m872YOXCqW5bT7PZHBFRYtKr2HS9P5UOz9C9p5UM9//e4a3KJFSvwVPD/pLhGRg/KttwTL6c31/+VHC4xialeWl4YP5EHDrVhb5BZVnxUKo+QlG5o47bnz4wNpvEkdYuLKsqd9v2mnFKYmOyhYgoDgSrlFapOpnsWEpkSTbh4RsmY3rxMKdKFleFORZ844pSpyDjnmvHIjc9CR8cacMDrzmf77KLcjE6L8Op/PmB+RPxp52foWZOudNORcuqylGWn4kvludjSlFO0AJGIiKKbd52DrxohH6C5KIR/n8ADkXiwpfXMNQxBXt3RX8SHNrYpDDHgkUVJU7fi/L8TMy0yYDfw1AkwAKZvDra2o2lz3+E3PQULJldBiEAkwAmFmYxTklwQsrY248+2lVUVMi6urpID4OIEsi2w6dx61M73B5/ofpyzCob4de51N2ITrZbMSrHgkmFOR4TLQBw+FQXvvTEVrfg69GbpuHelz/RfTzJBNggsP9kBwZtwPpdJ7CoogQb9zThi+PzMeWCHGSnJaMgOxUlw5lYiQdCiJ1SyopIjyNRMB4hOl+lwER9cPiTjNDGJv9x9Rg8816DWzywYYj9VYL9/W1o6cK8Wvd4ZkNNJUbnZehWvAQzBqPwCFc8wsoWIqI4EMxS2qQkE6ZdmItpF/r+nFOd+mXFRuvlu/sGcMGwNHzruTqnr9durkf1VWUYPyobVRP835qTiIhIK9iVHYnOnwofbWwihP5W1UNtIBvs76/RMqkz3eew/2SnbpIpVMuZKPYx2UJEFKV8XTNss0mYhPuuBOFcK6w2hnMNNNTGtq6PZ6UmobWrTzegmVqUgznjmWghIiKKRr4mOLTLfAAMOSHhby+VQBglTpLNJsPl2qPzMrDqthnYdbwdNgmYBTClOIf9WojJFiKiaORrma72uNz0FFRfVYaxBVmYMCobF40Ibam0NujJsiS5NYZbVlWOp7YcduvB8uBXJiEt1YwDx87qBjQXjchkooWIiCjGaatgznSfQ3l+ZsCN7oO1EYCn86sxzVOLK3D/a7vR2NrruE5P36BhZc7ovAz0DUis3tLgNDYiJluIiKKMzSax+8RZnxreapvPNbVbUbvpkGNtcagTLdqgp6ZqDF6qO+5oDGdJMiE/OxUHT3WhZXsjqq8qw0UjMpCWZEZOehK2NZzBS3XH3RIxj9w4JaDGhURERBQ8waoi0VbBzLTJgBvdB2sjAD16iZyVC6eiaJgFwzNSHb1ajCpzQjk2im1MthARRRH1hr//ZIdPa5v92YIxmNTAQt3uuTw/C209fXjynUOOYypKc7DqtpnYdfwsBm3Ar98+iOqrLsbPNu7Dl6cVoa2nD2u2Nzp17p9ZMizgJFE4youJiCgy+Ds+fEJVRTKU/iqhjHf0kiXL1+1yat7raeejHUdaIxKLUfRjsoWIKIqoN/w7K8t8WtsciqZs2oA2PSUJfYODyLPP7KhBVnOHFbnpKVg8qxS1m+uRm57itoxo8RUXYURGMqrG56OnbxBXlY/AR5+1obG1F+t2nq9qefKdQ46gpWR4YFUtoS4vJiKiyOHv+NAwSmBFY6VGKJvQ+pLI8dQYmA1yyQiTLUREUUS94WuTEZ7WNnuaaQmEXkBbM6cca+uOYfncCY7AtiDbgpsrih2JlgUziyEE8OhN05CRakZpXobbrGNDSxdMAGqqxsAmAQmJZVXl6O0fRNX4fEwpCryqJRoDQyIiCg7+jh8avaQKAMMEVqSqZj0JdryjstmkYTN/12SJUWVOqMZGsY/JFiKiKKLOjjS1Wx1LbMwmGCYj/NmC0RujXjG1m+uxZHaZU2A7Oi8DY/OznKpb1ADjlzdPg5TAjiOtyM+ywGwCmtqtGJVtQV6WBY9pdkx6YP5EzLhwGCYV5gxpdjIaA0MiIgoO/o4PnFFV0MTCLMMEVjRWagQz3lGp783Kjft8muAK59iCjcvwIoPJFiKiKKKdHWlqt+KZ9xrw2C3TPVZ9DGUNtEoNOBpaupyCq8IcCxbMLEZJbhrurCzDme5zKBup7BY0oTDbUd2iPic3PQXHzvTguy994pRQ6bT242MJxzIjQAnsVqz/FNVXleFMT/+QysGjMTAkIqLgCNbv+ET8wGlUFfSHOy4zTGBdNjovKis1ghHvaGnfG18muMI5tmDiMrzIYbKFiCiKhHp2RBtoaqtO0lPMWLlxH34wd4IjoC3MsbhVrZTnK7sJmEwCF41Qqlu0wdqCmcWGCZULc9N1AzubxJDLwVnCS0QUv4LxOz7RPnCq9/uDzZ26996evgHDBFYsVGoEg7Ziqqnd6mjyf+XFeXH1WrkML3KYbCEiijKhmh3RCzTv+5fx6LAOYGRWKu69bjyeee+wo5R2wUznqhW1O/+Uohyn6hZtsCYEdIO6JJMJaSlm3cBOyqGXgydKYEhEFCmRrAoJxu/4RPrAqb3fGzXcLxnuOYEVzZUawZIoVbFchhc5TLYQESUI10AzNz0F3X2DWPXOIaclP2/vPYkls8tQmqdfiaK9OV80wjlYMwvoBi5j8jN110TXzCnHmu2NsCSbMDLTgoaWroAD+UQIDImIIiEaqkKG+js+kT5wau/3Rg33LxqRgYtGZCT0JEWiVMUmSlIpGjHZQkQUZQKdPTR6ns0mceR0Nw40d+DOyjKs23kcTe1WwyU/j90yHSvWf4qFlxR7vTmbTALXTSjA2upZaGq3ojg3DWMLsvHdlz52SuCs3LgPja29WLO9EUuvHoNRORYcO9ODNdsb0dbTh8dumY4jrV1Y+vxHCVHeTUQUS+KhKiQeP3Aa3fddl8eo/UimFmWjvCDLKa5I5EmKRKmKTZSkUjRisoWIKIoEOnto9LzrJhTgzX3Nbls5r9neaLjkZ//JDiyYWYzXPzmBFddPxgOv7TG8Off1DeL9I6348FgbbBL42V/34YH5E7F+6Wzsb+7EweZOdFr70djaC0AJ+h598yAKcyz49aLpuPLiPGV9uADmPr41pgN5IqJ4FQ9VIbH4gdPT5IuneME1saQ23N/gck9NxIbBrhKhKjZRkkrRiMkWIqIoEujsodHz1lbPMtzK2WjJz6ANKB2ehvlTi7D2H41YvbgCyWbhFogNDNjw+p4m/KdmK+cfzh2P+uYumITAhFHZGJ2XjqOtPW7Xaevpw8isVMdr2nb4dMwH8kRE8SoeqkKC9YEzXAkKb5MvnuIFXxJL0bA0LNiYPDKWCEmlaGSK9ACIiOg8T7OHgTyvqV3/8bEFmchIMePHX54ES7JyK1CrXtbvOoHGM7148p1DqGtsR/WaOhRkWxxNcVV7m9odiRZA6QHT0z+IxzfV41+frcOXntiKg81dWP03pemu9jqP3DjFKehTA3mtWAvkiYjilfrhXft7PNqrQvSoHzhnlY1wu6f5Qk1QzKvdiluf2oF5tVuxce9J2Gwy6GM1SqYcbe0G4DleUBNLG2oq8UL15dhQU+mWRPF2/lgTzu8Nka9Y2UJEFEZ6sy4AHI+lpyShNC/NsewGcE46GM3apKck6c46jsxM1X38cEsXajcdwtSibDx2y3TsP9mBQRuwtu4YFlWUYM32RsfxRhUmrd3nsGR2GYQ9dstIMbv1gLnvld2ovqrMsV5cCMAkgJklw5yCvlgs7yYiShRchqAIZ+8ab0u3PFUb+VLhEQ9Lw7RC8b1hpQwNFZMtRERholeyu+q2GegbkE6PPXzDZDyxuR6Nrb1OSQdPJb99g4O6O/2YTXBLYjx0/WT86q2DAIBdJzqwYv2nWDCzGFOLsnHdxHzUvPARmtrPV9LoVZjYbBLtPYN45r0Gp0a4uekpTs9Vqmiy0NbThyffOeQYc8lw5yQKA3kioujGZQjhTVB4W7plNElRkpvu0/Igb+ePtURDsL838bjMisJPSMnSqmCrqKiQdXV1kR4GEUWZhpYuzKvd6hQM1FSNweotDW7BztrqWejtH3RKOug935Jswvqls3G2tx8nzvZgWHoK9jV1otM6gPW7TuD337wMo/MycOR0N46d6UZ6ShIyLWYs/M02t/NssK/z9iW4MBpL9VVlqN10yOmxv9xVCSHAJApBCLFTSlkR6XEkCsYjRMFldO9zbTw7FGqSo7X7HD4/a8XydbsM78fqsdr769HWbo9j9OX8AGIu0RDs7004vtcUOeGKR1jZQkQUJnqzLjapvyNQb/8gZpWN8Pp8a78Ne5s6nIKlH8+fhLc/bcLyuRNQkpvuCMRKhmfAbFJ2JXhqcQXuf223W/WMrxUmRmMpz890zJSp571oRIZjRpSIiCjaGVV16FWTPHLjFJiE8pxAEhHaaxXmWPBpU6fj/KV5aYZN6gH9aiNPFR6uEypG529o6QrbcqlgVdAEezlyLC2zirUqpETCZAsRUZjolewa7Qik1xjWqOT3cEuXU0D04Pq9+OOSyzG9eJjbts/Lqsrx3LZGtPX0YeXCqSgaZsHwjFSnG7MvpeJGY0k2mfD07RUwC/3AkIiIKJp5Wz4yd9IojLurEvtOduBgcyd+8cYBtPX0BVT54Xot12rXxtZeVK+p86uawtPyINe+JkbnD1eiIZhLdbSTRUoPPDP6Bm042todUCwSKztwcblTdONuREREYaK3m8OU4hyfd3jQe/7DN0zGS3XHUZhjwX9cPQZL54zBnZVlONPTh8YzPW4zU49vqseCmcWw9tuwfN0uJJtNaO6w4mhrt18d+/XGUjOnHD/buA+7j7ejMMd99yIiIqJo522XHpNJQAjg3pc+Qe2mQ45d/wLZycf1WkbVrt52JNTytHOUrzsehmuHwGDviKRWH7X19GPR6u24+bfbA96VKFZ24Iq3XaXiDStbiIjCxGiJDgDDZTuupaHXTSjABvuxIzMt6LL2IyVJYFFFiVNz3J/eOAXHznTrBlXq7kHWfhs27T+F2k2H/J4JUV/LiDsuw3uHT2PQBqzZ3oimdise31SPmSW5KBmewbJWIiKKKb5UdQSr8kPvPEOtpvC0HNjXao1w7RAYigqaYO1KFCuN+yOx3InLlnzHZAsRURgZLdHRe8xTaai67vp/3juMH8+fhP/3/IdOgcV/vrIbf7jjMt2gSu2Lbkk2YdD+JW/BiNGNtatvwKkhrnqunr4BlrUSEVHM8SUhEawlJq7nWbfzOJZVlePxTfU+JTmM7s1GsYavSZRwJRpCsVQnmMmHWNiBK9zLnbhsyT9cRkREFKU8lYYeO9ON/Sc7MOvikbAODOoGFm3dfbjn2rFOJbDLqsrxpw+PO5b9/OnD407P0StVVm+s82q34tandjiV5JYOz9AtNc7LSGFZKxERhZzNJtHQ0oVth0+joaXL7+Uirkpy0/HwDZPdluyW5KY7jgnWEhPX87T19KG8IBN/uasSL1Rfjg01lYYfYj3dm42oSZQNNd7PryYaZpWNCGhZsC/fl1As1QnXEqhoEe7lTly25B9WthARRRHtLFVvv34S5Uz3ORw70+tooresaozurEZKkkDZyAw8dvM0DEogLdmEzNQkjBmZifzsVNS88BGa2s8nV0rz0pCWbMa2w6edZsi0N9bCHAsWzCzG/pMdKBqWhkmF2bqzZOcGbTHTxZ+IiGKDayVHSW66WyP4oc6yH2vrwROb67FkdhmEAKQEntisLI9V71/BqvzwdJ6L8zMdr7mhpcuteiXQ5TLhqNbwtfrB1/fRn2Ur4VoCFS3CvdwplnZpigZMthARhYFeoADAY9BolERJNptw3yu7HY+/WOdedvzgVybhbG8/7ntlj+OxmjnlWFt3DMv/eTwyUpLwvevGo/5UJ16sO46UJIG75pSj5oWPMH9qEcwm4NLS4biiLM9xYy3MsWDxrFJHb5jVWxrw2C3TnfrIqDf5o63dMdHFn4iIYoPeB/jViyuCvkVxc4cVja29ePId5yWyrh8mg5W08HQeT0mLaP7Q608iyNv76O+ylVjptRJM4VzuFCu7NEULJluIiEJML1BYddsMnOuX+O5L5x/7+cKp+OVbBzwmUR67ZTp6+pwrXprarXhuWyN+/81L0T9oQ/e5QaQkCTy0/lOnQKd2cz2WVZUruxS99InjnI/cOAVjCzLxH89/6NZo92c3TsX0C3NgSTZhwcxix9fUc97z4seOLSO1N/lEm1kiIqLQ0vsAX9d4JugJh1B8mAy0oainpEU0f+gNZiIokAqeSPZaiffmsYzv/MNkCxFRiOkFCruOtzuWAamPfX/dLiy9egweffMggPNJlD/ccRkkpMeqkZQkgaOtPXjw9b1OlSzqDkHqNYpz0/G9lz9xuu599ma686cWOSVTctNTcKS1C5YUE55aXIF9Te0+B0+hmlmK9yCGiCgRBPK7XO8DvE36vnuPr9cM9ofJoTQU9ZS0uGx0XtR+6A1mIshb4iaa4oJAvtfRNH5fJGLl0FAw2UJEFGJGAaJe8JCfler0WFtPHwA43cz0AsEfzJ2A77gkdGrta87VUmhLsgk95wZ0r9vTNwCz6fyYXJcMqVUupXlpaGztdTzXU/AU7JkldsAnIop9gf4u1/sA//onJ7By4VQsX7fLY8LBn2sG+8PkULYi9pS0iOYPvf4krLwlGzy9B75+X8OV0PD3ex2rcU0s7NIULbgbERFRiOl1xjcL6HbLH5Vz/lh196C7137stMuA624Cf7jjMtSf6tJNophN589dM6ccbT19+rsHZabgkpJcx9f0lgz98JVdWHH9lLB1vHfFDvhERLEv0N/leruuLJ87AV+aXOh1dx1/rznUnXi0PFVmeONtp5lgjjOYfN31yJcdlTy9B758XwPZtSlQ/n6vGdfEP1a2EBGFmN4Mz4TCbNxz7Vg89tZBx2PLqspxYW46NtRUorG1Gx99dhbPbTu/DEg7O6KdVWho6cKgzaY781NRmovffn0mCnMs6B+0wQShe90O6wCe2HwQK66fjAde2wMh9Ctvks3CrRluuIK7aG4GSEREvgn0d7mnSg5vs+yRvH8MZUlNNFeveOPL98WXShBP74Ev39ehVBb5y9/vNeOa+MdkCxFRiOkFCiW56Xi3/hSqryqDTQImAZQXZKJUE0DUbnLeCcHoBjw6LwNTinPcmukuqyrH8nW70dbTh5/dOBVfmjwKfzvUgt///ajTlpbPbWtEdeVFuLxsJFq7+vDU4gp09w3oBgwF2ZaIlY5GczNAIiLyzVCTD4HcgyJ5/xhqD5h4XrLha7LB6D3w5fsazoSGv99rxjXxj8kWIqIwUHutAMqNHwC+WJ6PshGZurNVejfg0rw0pCWbse3waac1xyaTwJxxBRgzMhOTi3Jwtqcf9ac6napifvjKLgxLT8KRli609fQ5bWlZUZqD7PQUrHzjgCM4+OmNk/HwDZNx/6vnt45++IbJMAk4ljKFGzvgExHFvkj8Lte75sqFU1GSmx6yawLne4WMzErB2upZ6OkbdOsZEmsNUoNpqMkGX36WwpnQ8LcSiXFN/BNSBn+9WqKrqKiQdXV1kR4GEUWIXuAEAJsPNGPX8XbYpNKzZUpxDuaMK9C9Cbs2TSvNS8Ndc8qdkh96TdQOn+rCa5+ccKuKAYCaqjFIMZuQm5GCFfZtoUvz0vCj+ZPwH89/6BaIPHN7Bdq6+9FwuhsDNhvy0lPwP+8fwT3XjsOXJhciKSn8bb/U9zbWyqlJIYTYKaWsiPQ4EgXjkfgXqx/UI/G7fGDAhvcbWlHXeAaDNmD9rhNYPneC0300mO+nL81PY7VBarAE4/V7+1mK5Hvsy88T45rICFc8wmRLCDC4IUpcRjf1iaMy8feGM44kh7rM57LRw5GbkeLxBnym+xwAga8/s8MtIbJBs+bYZpPYfKAZ/YMS31n7sduxS2aXYXReOlLMAiaTCSfO9mJCYRZ2NrbpJmeWzhmDp7c2OLaQbuvpw5LZZXjmvQasXlyB2WNGMCAgvzDZEl6MR+Jbon9QB/xLjjS0dGFe7VbD+2iw309v1/P1mGCI5qScdmzpKWb0DdqQl5Ea1DFGIqHB/z+jW7jiEe5GRERkZ7NJNLR0Ydvh02ho6QqoU71eI7b/ee8wGlp7HIkW9fHHN9Xj83arYWd8denRqc4+bKlv8drh/mhrN5Y+/xF+884hrLh+slPn/po55Vi/6wQ+a+vBsrWf4NE39yPHkoydjW2wSf2dkaQ8v4X0gpnFsPbbHI1z6xrPsFs+EVEEJfpOJnq7zLy+63P842ir7j3c204xwX4/W7vPYcnsMiydMwZL54xBYY7F7b49lJ2KfOVtN55gxD5DocY6bT39WLR6O27+7fag7xgUiV2bEv3/T1Iw2UJEhOBtDagXOH3jyjJ8/NlZ3YCq59yA4wZ87Ey3W8Cj3qxTk0y6CZGRme5N4Had6MCabUex6raZqKkagyWzy7C27hi+emkJXqo7DgCYP7UID67fi9QkE7YfbsFjt0xHTZUSEJbmpaFmTjn+9OFxxziFOJ+AsSSbMGhDUINBIiLyTzg+qEczvQ+zy9ftwrsHTuvew9XeHVra3h3BfD9tNonPz1rxzHsNWLX5EJ7e2oDFs0pRmpfm1CvE25i05ws0IeLpQ38otkUOZKzxmJhI9P8/ScFkCxERArvR6wUUeoFTb9+AYfVIS9c5AMDY/Ewcae3Bqx+fwN8Pt+KOZz/Axr0n0dp9DtZ+G0xCYFlVuVO1yrKqcphN58fR2z+I+/5lHO69biyuGpePgUEb0pLNEAK497rxTg1zhQBy01MwMiMFCy8pwT0vfozaTUpAWH3Vxdi4p8lxrCXZBJOAozpG/W9+lgU2m8ThU13YvL8ZOxpacfR0+GfFiIgSka8f1OOV0YdZtQLT9R6uNiPV3ke1zUiD+X4ebe3G8nW7nGKK2s31WHH9FKfmp97GBAx9MsjTh/5gJzkCHWs8JiYS/f9PUnA3IiIi+L81oNFa3OsmFLh1li8Zno7/fvcQauaUo3bz+a2Zf3rDFLR0WTG1KBuLLivBv/9xp+NrNXPKsXLjPjxpr04Znp6C093nsKyqHN19g44tmy8dnYtPmzpxz4sfIzc9BXf802iseuf8Ne65dizW7TyOhZcUo62nz+k13FxRjOPtVqze0uAUaK1Y/ymqryrDrhMdKM1Lw0++Mhn9A4OwSeCrl5bghX8cw/K5E1CSm+72HiyrKkd5QaZh418iIgqORN/JxGiXGbUdpes93NtOMcF8P41iimSzcLo3+rJ7jVFCZLyPfV087cYT7G2RAx1rPG6BHKkdsCi6MNlCRAnJtVlcYY5/N3qjgGJDTaVb4FSco+wk9MTmeiyZXQazCZgwKhu/efcQDp7qwgPzJ2L1lsNuM2DLqspxsLnLkQxRkzBbDpxC5dh83FxRDEA4xvG1y0vw2FsHnc7z2FsHsayqHM9ta8SyqnI8vklJxLz+yQl855pxONTSpRtozbhwGF7+9iycOGt1SgI9cuMU/OGOy1AyPEP3PXh8Uz2qrypD2YjMoDb3IyIiZ/5uMxtv9D7Mqg3dAf17uNq7Q+/+FMz30yh5kJ5ihs0m3RIuRmMC/J8McqV9n3LTU3BzRTHG5mdBSvgd+3gT6FjjMXFoMglcN6EAqxdXOHbAeuytA0g2m9gkN4Ew2UJECceoKmXVbTOw9PmPfLrRewsoXAOnG6YVoTw/Eyfbz2FvUzseWv+pY5nOivWfYsnsMjz5ziGnc12Ym457X/7ELQmjDUjUxwFgZGaq7phK89Jx37wJaOs+h+//8ziMzEpF/akutHadg9nei8U10Cq1v+6vP/OB03Xue2U3NtRUwmQShu+BTSLgWTEiIvKdtw/q8UybHGnusKJ/UOKB13ajqd0a8If1YL2favJg5cZ9mD+1yDHJsmL9Xvzr7Iv9+rA91KoP9X2adHcldjaexX2v7A449gnWWPV2R/KW6IrmHZWMHGvrQfWaOqf3w5+qJIp9TLYQUcIxqkr5y12V2ODjjJa/wU9SkgnTLsxFT99pt22Wrf02mF06aFmSTTCbhG4yY//JDsfjai8Ya78N6alJumMatAHfe/kTxxKfL5bnY9IFPTjTfQ6nu/qcKl7U6hWTAJraPSeUjN4Dk0BMl/4SEVFs0CZHbDaJ33/zsqio8lGrGs71D+KHmuSGukR4/Kgsnz9sB6vqo7G115FoAQKLfYIxVk9bIhsluqJhG+VAkj3BXqZFsYfJFiJKOEY3v5Yuq2NbQG8CDX6MEhTjR2WjNC/NMQN2SUkuRmalGCZPVOt2Hnf0gjlxtsctcbKsqhwpSSbkpqfg8U31WL90NpKSTE7B6bEz3ZhZkosz3X042tqNX7xxAG09fXhqcYXHhJLee6AmdGK59JeIiGJPtFX5HGvrcSRagPPVqUtml/n1YTsYy5uOtnajrvHMkGOfYIw1kL4uQ+1bM1SBJnvisRcN+YfJFiJKOMG4+QUa/LgmKErz0rB87gQ0ne3Fj+ZPwkPr96KxtdfR3PaHc8fjZxv3OzVXe+ytA47zNbVbsbbuGB69aRqOn+1BfnYqqq8qg00CJgGkJ5vxyIZPsXhWKdZsb8T+5k6Ujcx0G6cA8B1NIAMA97+2GysXTnXsqOCaUFLfg3F3VeLYmW6kpyShIDsVJcOjv7SXiIgolIwmdswm/6s/h5pIau6wIjXJFNQP/kaVHqHoQRPpCpFAkz3x2IuG/MNkCxElnGDd/AIJfrQJisOnu9DSeU63uV9TuxWPvXUQv/n6JVi9+BKc7elDaV4GJhXmYNAmndZcf/XSEvx0wz40tVtRmGPBj+dPRIe1H2kpSThxtgd9AxK1m5XGtQebOzGxMNtR1bL5QDM+b+tFarLZLZBpbO1F0TCLx/Jik0ng4vxMXJwfHTOJRERE4eBtWYnRxE5F6fCAP2wH2rekMMeCrNQkt+rXny+cGtBYhrKsJ5AJr0hXiASa7En0JtbEZAsRJaBI3/zUBAUA1PzfR7olxk++cwjWfht2Nrbh6a0NWFZVDgiBSYU5mFkyzFG9Mq4gC4/YEy2q7v4BHD/bC5sEzAL49lVl+O2WBpTkpuOXbx3ElRfnoWxkJo62dqPhVBcGJdDa3acbyAzPSI2qsmwiIqJI8yXZYLT175VleT7HG9rkSn6WBUdau9ya2fqS4Bi0AY/8dT9y01OwZHYZhFCqX6cU5QQU+wxlWU8gE16RrhAZSrIn2pa3UXgx2UJEccvTDJC3m184ut6f6tSfKRH2y1iSTchIMWPJ7DL09g/CJAT2N3dgYmEOxo/Kxj0vfoy75oxBW0+f4/nfuKIUpzrOOW0XvayqHHdcWYqzvX1o6+lzBAfNHVYUDkvH917+BLnpKY7eLyx1JSIiMuZLsmGoEzt6CZ1lVeXITU9xNLD3NcGhxhtN7VannQ+vvDgPFwWQBBjKsh7X92VUtgWDNmDHkVbDeCvSk2SRTvZQ7GKyhYji0lBKXEPV9d41gZOfpT9TIu07DP1w7nhYB2xYv+sE5k8twsHmTmSkmGErkJhYmIV7rilHXlaqoyw4Nz0F0y/MwbaGM7izsgyA0kD38U31+M3XZqK+udMRHNhsEukpZgBwBGBrtjc6ZryuKBuOK8pGsNSViCjKxOIWuPHG12TDUKoa9BI6j286X/1qdE09wV6GU5hjQU3VGNik8u91O487TeZ4o74vo/MyfI63IlkhEulkD8UuJluIKC4NpcQ12F3vbTaJI6e7sa+pA5+f7UHfoETfoA1XluW5NaB9YP5EZKWaUX1VGTrPDeClnZ9hUUWJU8XJyoVTccEwC9qtg3js7T3ITU/Bff8yHqUjMvDZmV6nqha1B8yZ7j6UF2ThC2PzAcAR3Nw1Z4wjAFNnvCzJJtw4vYhBBBFRlImGLXApPD1EmjusyE1PwYKZxY6K13U7jzv+7s81jZY0leSm+z0um03i06ZOtwraQHYijPQuQ/7gciAKhCnSAyAiCgVPs06BPrextRsDAzY0tHRh2+HTaGjpgk2d1jGgBsZfemIrfrphHwYlsOqdQ6jddAjffPYfsCSZ8PTtFaipGoMls8uwesthmEwChTlp6Bu0Yf7UIkeiRR3H8nW7kGI2wWyC43Gz2YSdjW340Z/3uvWAubmiGBkpSSi1z8J81tYNm03iwS9PQtmIDKy4fhIsycrtQA3cLxrB0lgiokiw2aThfcbow+nR1u5IDTchqckL13unr8kGT99jVWGOBd+4ohTPvNeAVZsP4emtDfjGFaXITjX7dE3tNY62duOacflYvfh8vPHYWwfw5r5mr3GMK6OKm5EZqX6dBxharEYUC1jZQkRxaSizTkbP/eizszjd1Ye27nP4/fuNaOvp8zqjeOT0+aDk379QhuEZqXjwy5OQnpqEp7YcxrK1H+Mvd1XihulFONVpxYIZRTjb24dfvPEx7r1uPOpPdeoGIj19g7i0dDgsySYsmFmMFes/xZ2VZbrHlgxPh8ksMDovAwMDNuw40oYfvbbHMSP14y9Pwn3/Mh4F2RaUF2SxNJaIKEK8Va5EegtcUgxlWYmv1UmDNjh2DgLOJzVerJ6FF6ov93hNvWusXDgVj711AI2tvSjMsWDBzGLsP9mBomFpfjXKNfoZ3HTgFE60W/2qsor0LkNEocbKFiKKS0OZddJ7bs2ccrxUdxwPvLYHXX2DWDyrFLnpKV5nFBvPdMPab8PUomykJptx78ufYPmfduN7L3+CWy8rxdj8TLR0KUHyrLIRuGhEBgYGJb48rQjHznRjWnGOYxwqS7IJ/YMSl48ejpULpzpVuOgd29J5DhePyITJJLC3qd2RaAGU5z34+l4Mz0hFeUEWykZmMtFCRBQh3ipX1A+nWvxw6h9fqkp8oS4rmVU2wq97p9H3ePeJs05jMWqi39M/6PWaetdYvm4X5k8tQmGOBYtnKRUztZsOYdHqbdi496TP74Par2XpHOVPYY7yMzlog99VVkOtECKKdqxsIaK4NJRZJ/W5eXdchq2HTkNKYM32Rsf2yjYJpy2aPc0oZqQkwZJswp1XXYzvv/yJc5Jj/V48etM0R5CsNxN173Xj8PObpuL7L5/v61IzpxwPvLYb/3P7ZZhWnIPCHAtWb2nAup3H3XYUuufasSgbmeFYFqTuYKBl7bdBAgxuiIgizFvlCndFGZpo6HljWBmy/xROnD1fGeJv1Ye2cbJJCMeuRdprmE3AgpnFbsuTfe2TYtSvJT3ZjN/aH/OnyoqNZyneMdlCRHFrKM3MTCaBkVmpeHprg+5uQeoWzd5mFAuyld2CevsGDJIcEiZxPkhynYl69M0D+PUt0x27BGkTP/tOduDelz7BFRcNx5O3zcQnx89CQuKea8pRkJOGbEsSLMlmXFo63BG4FOak6QZvJblpDG6IiCLM2wdsfjgdmmhoyGr0PVYrQ9Sx+JNYM9om+rlt5yeKLMkmVJQOR13jmYCXohn1a1l69Rg0tVsDqrJi41mKZ1xGRERkwGg50Z8+PA5LsgkmAd3AR1uiPGgDJl6Q5Siz1bIkmzAsLQW3//4DbNx70nC2q98mHQ3ynnznkCOgOdjcidz0FFwzaRT+4/kPUbvpEJ7YfAgZlmSs2XYEdY1nccez/8Cxth7H+SYVZuPhGyY7vaYV10/GhFHZoXgLiYjID74sqwh0+QpFR0NWT7GFdixqYm1DTSVeqL4cG2oqDStwjJIgN1cUO67x2C3TcWVZHqrGFwS8FM3o/bMOsMqKSE/EKluEED8E8AiAJ6WUS+2PCQA/BlANIBfADgD/IaXcq3leLoBaAF+xP/RnAHdJKc9qjpkCYBWAywCcAfA7ACuklFJzzEIAKwBcDOAwgP+UUr6i+brXsRBRbNKW2hZkG88KqoHOuLsqse9kBw42d2LNdqUx7iM3TsHMkmEoGe78XKMS5SnFWXjoK5Pxoz9rGtPOn4THNx3AbZeV4viZbgxLz9Gd7crNSMYD8ydixfpPHc995MYp+MUbBxzNcbUB1or1nzoqYVxnq5KSTPjKlAtQNCwNn53pgSUlCc+934CM1CRuHUpEFGGsXAmtaGnIOq4gC0/cOgPnBmz47EyPowLFdSy+Vn0YJUFmXDjMrZnulKKcgJeiGb1/lWNGYMGMIv6sErmISLJFCDELShJjl8uXvg/guwC+CeAAgB8BeEsIMU5K2Wk/5nkAJQDm2v/9NIA1AL5sP3c2gLcAbAFwKYDxAH4PoBvAL+3HXAFgLZRkyp8ALADwkhDin6SUO/wYCxHFAG1ypTDHgk+bOn1er20yCVycn4mLRmRgYmE2rrw4z2Pwq51d0nb7L8hKxdv7PsfvFl+C5nYrLClJeHrLYew60YE9n3ei+qoyfPfFXbjn2rF47K2DTjsF/ecru9E3IB0JFJMAxhZkoq2nD6lJJt0Aa3ReOizJZpTmpbkFkcfbe3HHs/9wet6ez8NbRk1ERPq4rCJ0wtXzxmhSx2i5D+C5Oay3SSKjJEhpXobbz5HJJHDdhAKsrZ6FpnYlLppU6NtuREbv36Wjzy9X9nVCiygRhD3ZIoTIAfC/AP4VSrJDfVwAuBvAf0kp19kfux3AKQC3AfidEGIClCTLbCnlNvsx/wZgqz0JcgDA1wCkA7hdStkLYI8QYjyAe4QQj9mrW+4G8I6U8qf2y/9UCHG1/fFbfRlLKN4bIgo+18CmpmqMo7Eb4Pt6bZNJOAKg5g6lxFcvgFBnl9Ru/2oTutVbGvDA/InoG7DhV2/XuzWtUzcBSDWbUH1VGWxSSaqkJAn0DUg0tVvx5DuHHM+5oizPvhzIrBtgHW3twTPvNeDhGyajJDddd4xa3DqUiIjiXTAqhzwlUo62dqO1+xw+P2vF8nW73CZ1jJb7/OGOyzAyK1V3LL409fW3v8ub+5oDahLs7f2LZANiJnkoGkWiZ8tqAC9LKd9xefwiAKMAvKk+YE+WbAFwpf2hKwB0AXhf87y/Q6la0R6z1f5c1RsALgAwWnPMm3D2huYcvoyFiGKAa2BjkzBMNHiiBhDzarfi1qd2YF7tVt2tEtXZJb1u/yvWf4rdJ9rxjStKUZhzvtpEbbq7YGYxfrZxP2o3HcKqzYdQu+kQ/vOVPY4119rjk80mvPBBIwYHbaiZU2649vv+V/dgb1O70zi5dSjReUKIHwohpBBileYxIYT4iRDicyFErxDiXSHEJJfn5Qoh1ggh2u1/1gghhrkcM0UI8Tf7OU4IIX5kn9DRHrNQCPGpEOKc/b83unzd61iIyHdD6XljFAsMDNgcj7974LQj0QI4b99tNNkhIQ3H4m07cPU1DaW/iz9bNnt6/4zOfeR0d1C22zai933ZfKAZh0+F7ppEvghrskUI8S0AYwDcr/PlUfb/Nrs83qz52igALdreK/a/n3I5Ru8c8OGYUS7HeTqGiKKQtjltQ0sXWrvPuQU2gSQafAlObDYJKYFHb5qGCYVZyE1PcTqHtd+G9BQzevsHcfc15Vg6ZwxK89LwwPyJ+NOHxx09VlyfU56f6ZZM+e5LH2PO+FE4frYXa+uOYcnsMqxcOAVLZpc5bVOtbiepTQz50oCRKBH4sKz5LihLkk9BWUqcpTnmeQAzoVTczrX/fY3m3Oqy5mb7OZYB+B6AezTHqMua/xfAdPt/XxJCXO7nWIgoxGw2id0nzurGAnub2h2PG93LT3VaA5rsMErQnOk+p5vAkF5yCqFsEmx07n0nO7xOVg2Fa4yWm56C+uYufOmJ0F2TyBdhW0YkhBgHpSHubCllf7iuGy5CiGooARtKSkoiPBqi+OStRFSvfHXlwqkozUtDY6tS7LZu53EsqyrH45vq/Vqv7W3pjS/bLpbmpSHLkoxfv33+2g/Mn4jS4RakJCmvQ10SpPZ7MZuAkuEZePnbV+DNT5sxaDu/9XPt5nosqyrHoooS1G6ux52VZXjmPfetql23k2QDRiIuayYi36n3+P0nO3RjgaZ2q9u9V68JbyA9Y/T6sZTmpeHEWSu+/swHsPbbUJqXhrvmlOP+V/c4nVevuiWUTYKNzn2wuTOk2227xmgLZhY74rxQXZPIF+GsbLkCwAgAe4UQA0KIAQBfAPD/7H9vtR9X4PK8AgAn7X8/CWCktgTX/vd8l2P0zgEfjjnpcpynY5xIKVdLKSuklBUjR47UO4SIhsCXZTx61SfL1+3CiuunOGaS2nr6kJ5sxtKrx6CmagzWVs/yaS2xt9koT9suFuZYUFM1Bj/+8iTdnYNsUuCea8fhmvEjHcmhxbNK8cx7DajddAiLVm/DweYuvFR33LH1s/r83v5BrNneiOqryjC1OAcrF071up0kwK1DicBlzURh5Vp5GktVBuo93ib1q2MLc87HCOt2Hndb3qsmVIyW+wAwfG/0qlFXXD/FaanS/KlFjkQL4HlpUCirW/XO/ciNU/BS3XGn44K93bZrjOapuogonMLZIPdVAHUuj/0eQD2UipeDUBIZ1wL4BwAIISwAKqGU3QLANgCZUIITNcC5AkCG5t/bAKwUQliklOr/UdcC+BzAUc0x1wL4hWYs12rOccSHsRBRGBkt49HOUhhVnySbBf5wx2XYeug0pAR+u6XBkbC48uI8nzvwr7ptBnYdb4dNAmYBTCnOcWqaq3ftacU5uDA3HQ+8tgd3VpbpHtPTN4jm9l5MK87B1OJcjM5Lx6LV251e632v7Eb1VWWo3XS+Sa4l2YSq8flOOyQpY03Hpv2nnKpg2JOF6DzNsuav63zZ01LiIs0xbsuahRCuy5qdP2E4L2s+gqEtay4CUYyIZOPUYFDv8et2Hsd3rhmLX7190Ol1TCo8v51yU7sVa+uOYfXiCiSbhVslrutuU97eG71qVNeYw1NyQW83Ir3qVkBJ+AylwazeuU1CmejSCnZM4loxZBbG1UVE4RS2ZIuU8iyAs9rHhBDdAM5IKffY//1rAPcJIfZDSb7cD2Xm6Hn7OfYJITZCKeGttp/mdwDW20t2YT/2xwCeFUI8DGAsgB8AeFATFD0OYIsQ4gdQkkA3ArgawGz7daS3sRBRePmyg45R+WpBtnJzfXqr+xIbf268fQPSsZORGgypjK7d3WfDA6+dn23SO+bTpg6kJZux9/MOlAzPQE/foO5rHVuQ5Xi+ev2ctGT09A06jjOZBKYUDcOJs9aQb21JFIu4rJko/HyZMIlm2soJS5L7roGA8l/t432Dg5g9psBrwkLvvVm5cR+KhlnQ0zfoSHy4bgeuF0/4GuO47rBoEsCnTZ1BSYbpJZOCud220ZJybZJnVLYF40ZlMw6iiAv71s9e/BxAGoAnAeQC2AHgOillp+aY2wA8AaXMFgD+DGCp+kUpZbsQ4lr7OeoAtAH4JYDHNMe8L4T4KoCHATwE4DCARVLKHX6OhYjCxCiZkZZsxrbDp1GQbUFJbrrHG/pQbvbegqHCHAtWLpzqtNVjzZxyHD3d7XiOWlqs7lKk9mxZtfkQ2nr6UH1VGSYUZhu+1vEFWdhgDyRGZlpwpLULcx/fqhsYsScLkSHtsmb1MTOAq4QQ3wag7vRTAOCY5nm6y5rViZwwLGs2GosTKeVqKEukUFFRETvrNCiuqRMmaj8y9X+9M93nYiLZolZO7D/ZgZ9t3O92f15bPQtLn//I7fENPiSTmjusyE1PcbwvackmZKYkOSpcLckmrLptBi7Ky3Q02XWNd17/5AQevmGyW88Wo62fj5zuxr6mDtSf6sSLdcdxc0WxYzIJCG4yzFNM4u92zd6qgLRJnpLhGSGJg7jFNPkjoskWKeUXXf4tAfzE/sfoOW3QL/vVHrMbwFVejnkZwMsevu51LEQUPnpN5R6+YTJqXvgIja29jhvudRMKHAkJ15vrUMpmXStrCnMsWFRR4hQM1X51BpbMLkPJ8DQcO9OLNdsbsfCSYkfipKnd6uivcmFuOj5r60FHb79jSZNNAqc6rbhsdJ7ba11WVY4DzZ2YdEE2Lhudh6Ot3U6BnWtg5Bp0EJHDq+CyZqKwKsi2oDQvzdHQXb23lednYqZNRv2HVTVh4DoRAug3yFUf11vG46owx4JvXFHq1Lh/WVU5ctNT0NRudeyso97zjeKdktx0zCzJ9Zhc0EtW1Mwpx4BNBjx+X+jFJIEsLfOnQioUcVCsL4ej8Iu2yhYiIgfX2QNtYJGWbHYkWoDzN1x1Fknv5urLOumVC6figmEW5GWkOgUqarWJOvtUkpuGz9t7HcGQtd+GvZ+345n3Gpx2BXKtZmnr6YMlyYxV79RjUUUJntvWCECZATMJKOubTQITRmU5ypGlBJ7b1uiofhk/Khsjs1KcZsIApXImWIERUbzismai8Budl4EV109B9Zo6tyb2U4pyouq+ZVS5oC690as8LcxJC7hHyKANbjvnPL6pHktml+HJdw4Z7qyjF+94Sy7oJStqN9fjFzdNC3uPk0CWlvmypDyUYn05HIUfky1EFJU8zR6UjczEtsOnHYkWlacbrl7wZLR70ZLZSrJEO1uhNsitb+5ymn2qmVPuaEL7Yt1xPDB/IlZvOexIsKiN8n51y3QAEoU5aeg6148JF0zGT/68x9G8dllVOcoLMh3VNkdau52a4Tpeh1S2cX6xepbuTNiobDZ/IwoCLmsmCiKTSSDZLCL6QdkX3ioXjLZunlSYHfBS5VOd+gkEdSLFn+a33hglK06c7cGyqnKnmCLUPU4CSZyEcttqX0Q62UOxh8kWIopK3mYP/LnhGgVPI7NSDAMc7fXUxExWarLb7FLt5nosvXoMevttMJuA4Rkp+OqlJRACePSmaRACSDKbIG0SqclmZKclY/IFOXi3/hS+emkJhqenICM1CXmZKbi0dLijkiYjJUn39UmpXLe1u093Juy6iaNARP7hsmai0Iv0B2VfeIs9PPUf8dQrzVOfD6P3RV2VEsyddYyu1dM3iKnFOfjLXZVo6Qp9rzebTSLdIM7x9LqMkl3hanwbCz/DFF1M3g8hIgo/T7MHwPkbrmN3AJcbrs0m0dDShW2HT2P3iXas3LjPLXhKMZscz1epCQ31uI7ePry+63PMq92KrYdO645p9IgMmE1K1cl//XUf8jJSYEky4acb9uGzth4cPtWFu1/8GHc8W4d5tVvxlz1N+O27StVKemoSus8N4JPPzuL42R7H2NPsPWmWVY1BYY7FUUXzpw+Pw5JsQnpKku5YWrqsICIiijbe7tuRpMYMB5s7PcYewPklybPKRjgSMEaP22wSh091YcPuJrz28Qnc8+InmFe7FRv3noTNpgQbo/MysHLhVKf35TvXjEVmihm//2YFbpxRFLT3Te978MiNU7BgRhHmjCvAxfnuryvY1Amwmhc+RM2ccr9el5rU2lBTiReqL8eGmsqw9kuJ5p9hik4+VbYIISYCGFTXIdvLYm8HsBfAz6WUg56eT0TkL2+zB9662+s1gFOX+wBK8NTTN+g2Q6IeBwCleWk42zvg2GFIHYPrmA42d6J20yHH85989xCun16EO/5pNC7MTcd3dJYqPfHVGTjU0oVH39yP+VOLYDYBR1t7cEF2Gt4+cMppTA99ZTLO9pzD799X+rY8dst0FGSncnaFEg7jEaLYFQ075dlsEsfOdKO54xy6+wZQOjwDFw5Lw/ajZ1DXeAbl+VkozUtzWqYc6L3VUyziWi1zwTALlswugxBKn7Zn3z+KpnYrXqi+HKNHZHrdWcfXHXL0vgcluek41taDHUdaPT43WLvwaKuHNu5pws9vmgZr3wDKRmZievEwn15XpDYAiIafYYotvi4j+h8AvwZwQAhxIYDXALwL4D8AZAP4YSgGR0SJy5dSUaMbrlEDOLXZXGGOBTdXFKO3fxATRmXhxepZ+Lzdipy0ZPzyzf0AgJqqMagozUVdY5vHrZuXVZU7mtxqlxWV5mXgdKcVmalJuLOyzPF8tZluW08fXvjHMbedGR65cQoe33TQaew/+vMe/HHJ5ZhSPMwRbABD28qaKEYxHiGKYUb37XBsp2uzSWw+0OzUe600Lw1Lry7HA6+d3zL5x/Mn4bdbDjntdBjIvdVbLKLt85GXkeporK9ynWAySjD4u0OO9ly+PjeYu/BotwGfO7kQ33/5E91zDgzY8Jc9TY4Jr2jZ+Ye7PZI/fE22jAfwof3vNwHYIaWcJ4S4Gsp2iQxuiCiohjJ7YLQEyWxy3mIxNz3FrcnszxdOhdkk8N2XPoHNniTR27q5KCcN6alJeGTDPke1jHqdUTkWPPrmfiyqKMG37DsvaGe02nr6kJaShPlTixyJFvW5972y2ykppO42NCglLr8oz+n1c3aFEhDjEaI4E+rtdNVEztHWblj7bE79zuZPLXIkWgDlPvzg+r34+U3TkGoWKC/ICvjeahSLCOFeLTOUXiRD2SHH1+cGcxcetXJ5wcxitxhI2yvv/YZWp8pi7vxDscjXZIsZQJ/971UANtj/fhhAQbAHRUSRE47ZJV8FOntgtASpanw+/uniEbj99x/A2m/T3U7x++t2ofqqMlj7bUhLNuHFus90t27+9aZ63FJRjLaePqdrW5JNOHamRzeRUru5HtVXlaEg24Kmsz0wm/R3GEhLNmH53HHITkvGivWfwtpvw9NbG9yCT9fZqWj5vhGFEOMRojgTyu10XRM5NVVjnO67Rjv9HDrViRumF3m8vrf7bn6WfiySkWLGIzdOQUluOhpauhzPv25CATYEcYLJlx1yfH1usHbhsdkkpFQ2EIAActNT3Cas1P44dY1nuPMPxTxfky17APy7EGI9lOBGnTkqAnA6FAMjovAL9exSuBjNEE0pGoYdR1odN2+jICvJZEJhjgWZKUn46qUleOEfx7D06jEYlWPBsTM9WLO9ESlJAlOKc/Cj+RPxkD0hYkk2YcX1k7Fm21FcNS5f99zl+Vn4087PMH/6BbjQoBP/hMJsfHL8rFsiSBt8aoO8/CwLjrR2YenzH8X0943IB4xHiOJMKLfTdU3k2KTSj23+1CIIAYwryNK9D1eUDvdYVeItXrLZJLrP9ePhGybjszM9eLHuONp6+rCsqhwXj8zEuFGZeHNfs+7zgzXB5EufGV+fG4xdePTeM3UptppwUc/Z3GGFTer3yUtLNmPb4dOcWKKY4OtuRMsBfAvKuuj/s29lCABfAfBBCMZFRBFgNLt0tLU7wiPzj2u3+r/cVYmJhVnYeewM+gZsTjsQ6e1GNLYgEz/5yiT8bON+PLetEV+9tASleRlINgt8oXwkHr5hEpZeXY6lz3+EFz44hl/dMh01VWOwZHYZVr1Tj3+ZUoi0ZP2djoalJWPe1AvQ0nEOWRYzfnrjFKeu9g/Mn4iH1u+FTeongk51Wh0By7zarbj1qR340hNbUd/chdz0FMdxsfh9I/IB4xEKiHaHuoaWLsdOMBR5agWIliXZhJGZQ2/47prI2XLgFL591Rg8814DVm0+hEff3I+Hrp/sdB9euXAqrizL8/gh3lO8pN6jb1m9Hfe+tAu/29KApXPGOBILfYM2DAwiaPHWUHbI8fW5wdiFR+89e3xTPW6uKHY7Z0G2Ba9/csJtt6IV109GzQsf4dandrjt6hRv+DsrPvhU2SKl3CKEGAkgW0rZpvnS7wD0hGRkRBR2oZxdCjd1ic3ovAzHTMqS2WVYv+sEHpg/ESvWf4p1O487/q7tq/Kzv+7D964b73gvpAS+p2ngtnpxBR54TakiqRyb77TbEAA8vqke91xT7tZM96GvTMbjmw6grrEdpXlpeKBgEnrPDeCpxRX45PhZ9Pbb0G3td+yCYDSLZBSwqL1e1Mdi8ftG5AnjEQpEvFRtxiuzCVhWVe7UP21ZVTnMvk4Je5DuUkFaOTYfD67f6/h3Y2svnnynHmurZ6G3f9Dn5Tue4iXAPZGyYv2nWDK7DG09fZgwKhunOvWff7C5EwC8jsF1CVOgS5B87Y8XjF14jN6zGRcOwwvVlzudc3ReBpbPnYCVG/dhyewymE3AFWV5+MGfdjlipHju4cLfWfHD12VEkFIOCiHMQojLAXwspTwnpTwauqERUbgFo0w0mthsErtPnMX+kx24s7IMKWYTGlt70WntR/VVZbBJIDPF7LTdoro9dHffgG4DN2V9ca/bUiRtM1tAeS+bO6x47o7L8FlbL0ZmpaB200HUNbZjalE2Fl1WgqXPf+i4iT4wfyJSrP2YUjwMlmST7s5H6oyPdimUSm26p4rl7xuRJ4xHyF+h7AlCQ9fUbsVz2xqd7sXPbWvEjJJhGD3Cv++PaxLCJm1O91K9XmmNrb3o7R/ErLIRPl/HU7zkqUn/Y7dMx0UjMhxNcl2fv/tEB+5e+7HHD9aePogH8vPsa3+8oe7CY/SeleZluJ3TkdwZleVI7jR3WJ225AaCN7EUib53nq7J31nxw6ecsRAiSwjxEoBTAN6HsjYaQojfCiF+ErrhEVE4BaNMNFqowcii1dtRu+kQnt7agLEFmbAkm/CH9xuRl54Ckz1RkmMxw5JkghDAwkuKUZqXhpbOc/jh3PGYMCoLd1aWYemcMZhalI3Fs0rx+dlep5Ln0rw0LJ5V6ihLfnprA7r6BjEyy4Kfv7EPjWe60dxxDnWN7SjMseDbXxzjqKYBzs96XVI6HElmgZULp6Ktp8+x89Gq22bgL3dVOgIvNWDRsiSboMYFsfx9I/KE8QgFwlsVAkVWYY4FN1ecn6z404dKfxN/Jwxcl9jOq92KY2d6sXn/SSyZXYYf/Ms4XFKai5qqMVg6ZwwKc5TzBzI54Sleys9M1b1Hf6F8JK6bUOCo3HB9fs2ccvzpw+O6S4q0S0p2nzg7pCVIkVqe4m+MqSZ3ZpWNQNnITMPYZ6gTS3o/N6FenuTtmvydFT98rWxZCeACADMBvKd5fD2AnwL4SXCHRUSREIwy0WihNyvws7/uwwPzJ2L1lsOwDtiwekuDY/vnVe8ccswQ/fjLk/DmnibMungE7tUsH1Kfm2NJxo/nT8KD6/di3c7j+NH8iU5LidTkyb3XjUVdYztuvqQErV3nHJUy+0926N5E/1bfgtpNh1Cal4bViyuQbBa6MyxGDYAnFmbhyovzYvr7RuQF4xHyW7xVbcYTm03i06ZOrN7S4LSEqLwg0+8JA737/vJ1u+xLf3djUUUJ/m3NTqdlw2vrjmH53Al+X8soXgKAU13ndJdF7Ww8g7bePlyUl4lTnVaMK8jCxmWV2H+yE7tPdDgqa9WxqxUb3nZUcj3ek0guT9G+Z80dVqSnmNE3aMPR1m6fYpahbI/tSSSqSLxdk7+z4oevyZavALhRSvmxEEKb5tsHoCz4wyKiSBlqmWgweSvr9PR1vVkBdQnRd68bj+/bkygLZhbjhX8cc5QvA8Bv/3YIK66fjGp7UAY4r7kGgN9uOeR4TnffgG7gc7ZXWYp0or0Xeekp+M41Y9HdN2DYYX/Qdn6c1WvqsMHgRu8pKeZvyTVRjGE8Qn4L1Yc0GjqjHmR/uavS7w//RtUAyWaB2q/OwKLV252uU7tZ6dUypWhYQIkGvXipoaUL2xpa8VLdcbdlUbdfWYr6ZvedAycWZuHutR+7xQSjsi1oaOnC0dZuHDjZ4dgm2SiG8PRBXI2XWjrPRXR5ilrVs/9kJ+549h9+JXxCNSEYiX6F3q7J31nxw9dkSy6AVp3HswAMBm84REQKX7ZV9PR1o1mBnr5BHGzudDyeZTFjUUWJU2+UmjnlaO3q070RpiWbYB2w2ZvqKc1ol84Zo3stkwBWXD8Zbd3n8NstDUhJEnj4+im4/7Xdbv1YHpg/Eas2H3K6lqcbfTQlxYjCiPEI+S2eqjbjjdGHzpYuKy7OD84WyGoPNb3r9PYP+tWI1tvPjbplcVtPnyNGUMdxwbB0x0SPev17XvwYf7mr0u2D9arbZuDTpk6nx2rmlGPN9kbDnm4lueloaOlyG6s2XrqzsiziGyEMpZIkFLFPJKpIvF2Tv7Pih699vv8BZTZJpc4m/RuUNdNEREHlbRtqb183Whs8tTgHZnF+y+eiYelODXDV2a7CYWm6a4NnlOQ6PR8A1u08jnuuHet0rZ98eRIyU8y47KJcXDNxFH61aBpqvzoDj286gDvts10PfnkSVt02A6sXX4JXPvzMUT6snoPlokRuGI9QQFz7P/BDS3Tw1ofDU38R16+V5KYb9gQxus7ITIvH8/vay0MdS2//IDJTzPjh3PFO43hg/kQcPd1tmFiaO2kUNtRU4oXqy7GhphIX5WW6xTi1m+uxYGYxmtqtWFt3DGurZzmOv25CAd7c16w7Vtd4KRR9T/wRbf1IItGv0Jdr8ndWfPC1suU+AG8IISbZn3OP/e+XAbgqVIMjovjlbbbIW4mlt697Wk89ZmQmSvMycN8ru3HEIPjpsPbjO9eMxa/ePujUyyUlCZhekotf3jwNn53pwR+2NSIlSWBkZqpjhyOTAPoGBjGuMAsDg8CpTuU1luSm49tfHIOGlm6ntdz3XjcOX55ahD2fd7JclMgzxiNEcUCNAVq7z2HlwqlYvm6X2/3PUwUrAN2vGW2BbLQs40ir+7IetULW1woMvXHec+1YLKsqR2//ICaMysaL/ziGhRUXGlYzuFZsbDt8Wjc2UXcxWj53gtPyp4aWLsOxauMlTzsdhku09SOJRBUJK1cSh0/JFinl+0KIKwHcC+AwgCoAHwK4Qkq5O4TjI6I45EuDNm83Y19u1kblpqNHZKJkeAamXzgMLV3nUJqXhvlTixw9W17/5AT2nGjHiIwU3HvdWAxLS0FL1zmYIVF39KxTomTF9ZMxKseCbz1X5zaW1Ysr8KUntjq9xrH5mY7ADlACokffPIClV49xrO+uHDMCl44ezpsukQvGI0SxzzUGMGoK7ymBAED3a2qvM8OthDUfbk0CmPv4VrdzFNn7uPjay0MvKfPYWwfxhzsuQ15GCtp6+3DNpFH4xRv7DRMdrhNQhTn6MU7lmBFYMKPIrwkqbbzU1G517HQ448JhKM3LCPuH/GjsRxKJpdlcDp4YfK1sgT2IuT2EYyGiBOHLbJG3m/FQb9bqTa44Jw3/cXU5fvTanvMVLPMn4f8+aMTBU1149KZpuPflT7Bkdhl6+wcdOyao437gtT149KZpukFOXeMZt9f431+bqXtsktmEJ988qOxYNKPIEfj4u16cKN4xHiGKba4xgFFTeE8JBCnhd+8RX6tHNu0/hRNnrRiXn+lTBYbROCUkTCaBrfWnHbHDmu2NWDK7DGYTUDU+H1OKhgHQr9JZddsMt6qbS0cPd7yH2rggP0s/OTMy0+IWL7X19GH8qGx8YWx+ROIJVnVQIvEp2SKEGO7p61LKM8EZDhElAl9mi9Sb8cRllWjuOIfuvgGUDndey+p6sy7JTfdp96LW7nNIMSvNcpPNJkeiRR3Hg+v3YsnsMrR09SE7LQl3VpbhwmFp+Oxsr+64syxJukHOmPwsLJ0zBoBSutvUbkVGqv6x5fmZqCjNwb/OvtiRMPJ3i0YmZijeMR4hin2uMUBhjgULZhbjYHMnADjuXd4qWIe6FMXo/IM2pWrm99+81G0LZ71JHU/jVBvmql9rarc6GudeeXGexwqev9xV6VgSNSrbgkEbsPPYGXx+1uq27GrSBVm6202bTdGZ3GBVByUKXytbTuN8Ezo95iCMhYgShD/rdV278WuTDdqbtV5iYuXCqfjS5EIkJZkcX1+5cZ/T7kM1VWN0EyipSSZ844pSx/bPy6rGOBrjuo77wMkOPHzDFNz/6m7HtR/6ymQ8934D6hrbHcuNRmalQEobHrlxCu575fyxNXPK8dD6vfjlzdMxsyTXEQD507Hf38QMUYxiPEI0RJFOzGtjgMIcCxbPKnVbWjN30ijdCtZHbpwCkwCKh6UPeSmK3vnVHX+s/Ta3LZxNAphYmOX2XnmrtFVjh9z0FCyYWQwhlMcKc9Stnbvwi5um4cjpbvQN2hyTMy1dVlw2Og8mAXx47Czue2U3lswuwzPvNbjFBX+44zI8t63RbbvpGSXDMHpEZsImNyL9s07ka7Llapd/JwOYAeDfAdwf1BERUdzzdQmQP8kG12Nz01NwuKULb+1rxtiCLJhNykzVktllTrsP2aR+AqU8PxPf0Zzvxbrj+PcvlLnNHH3nmrH4/ftHcceVpU5BzpPv1mP+1CLUNbY7lhv95msz0dk7iNQkk9Oxa7Y3oqnditauc341CQ70vSKKYYxHiIYgGhLz2hhgwcxitx0BtfeuuZNGYdxdldh3sgMHmzvxizcOoK2nz2MzXF+pFR9F1bOwaf8pDNrO34/VChctmwROd53D6BHe+8FoG/NOKc7Bff8yHt19g07xw4XD0/HCB42YM36UU7KpZk451tYdw8hMCzbuPYn9Jzscy5CE0F8+1dM3oLvddKiazvqSxPB0zFCTIL5eP9I/60S+Nsj9m87DbwshGgDcCeD5oI6KiOKaryWt/iQbtMfqzZQ9cuMU5KanuAUqep35l1WVux3X1G7Fb/7WgF/eMhWP3jQN3ecG0NJ1Ds++fxRtPX1otw46BTkAHA131XHvPHYWT29twG++NtNpZgpQgqJROc5BkVEFkIBSdqx9z/x5r4hiFeMRoqGJhsS8NgY42Nzp8d5lMgkIAdz70idOx3lqhuvvWKYUDcOJs1a3ytg12464xRLl+ZmYaZNu8YqaWAGU+zFwfjnUnHEF2HPiLG5Zvd3pfb//1T34+U3T8P2XP3F6vHZzPVYvrnBMEt1ZWeYWB7j+Oz0lCU8trsD9r+1GY2tvSJvO+pLE0GuCvOL6KUg2C+RnWTzuAhWM6wPR8bNO5HODXAMfg1stEpEOb7MOvpS0+rrcyGaTSE853wtFb6bsvld2O7Zm1p6zqd2KtXXH8OhN07C/uRNSAn/d3YTqL1zsdu22nj582HgWZpNwmp16YP5ErN5y2GlMlmQTpHT/t7Xfhp+8vhcP3zAZ9796vinvwzdMxqTCHKdz6FUALasqx91rP3bM7KnBRbRtpUgUZh+D8QiRV9GSmFdjAMB775VwjDklSThiBJMAhqUnoaZqHKrX1DnFEsvX7cKUohy/l/J29w3qvobecwO6jyebBZraz79u9T0ymiD66V8+xRfH5+O7141DYbYFI7NSUTI8NEtmfEliaI8pzLFgUUWJ471Ux5ybnuJ4jf4kQXxNokTLz/pQcBlU7As42SKEyARwN4DPgjYaIooLwSrdNFpuZBLKLgIF2UpT3Df3NeN/3juMn944Bf/5ym7DMtvy/Ew8+uYBt0DlP64ux0837ENTu9VRFaO3ReMD8yei09oPE4Cnv1GBD46ewaANWPvBMac+MGqPllXv1AOA0zpwQNl5obO3H39ccjlau85hVI4FkwpzkJRkchqzdvavsbUbH312Fs9tU0qcATgFF9G4lSJRODAeIfJdtCXmfbl3hXrMR1u7HVUW2vP/xmD3wECW8hq9hnSDpvkF2eebAGsTLOoE0arbZsLaP4iDzZ346+4mzJ1c6Nb3pmR4aO7/viQxtMfoTYA9vqkeS2aXOSqC/UmC+JpEibafdX9xGVR88HU3ok44N6QTANIBdAP4WgjGRUQxLFilm67LjUZmKqWncx/f6rjx/HrRdJzutOLWy0uRYhZYMrsM4wqynG6whTkW3FxRjNQkE773z+Nxpvscfn7TNBw93Y0Bmw0XjUhHW08fAOegQLtF44RR2Xho/aeOREdpXhqWXl2OB+w7GbVb+/HYLdPR0NKFqcXDcFlJLgqyLTjZ3ouzPX3oG5RYeEkxAOD1T07g4vwsp2a4nt6DspGZaO6wonaT8zIl11LraNttgCjYGI8QDU20JeZ9uXeFeszqh3d1VyR1CXB2WrLbh/XSvDSkJZsdEz7qWL0lAPRew8M3TMYf3m9wm9h5+IbJKMlNh8kkHM/ZuKcJj940DYNSIi8jBb9++wCumTgKgzbga5eX4vP2XrdKkXF3VeLi/KFVcOhVVviSxNAeYzQBpl1q7WsSxLWS2dPzo+1n3V9cBhUffK1suQvOwY0NQAuAHVLKtqCPiohiht6NOJilm9p10Edbu7H7eLsjoMhNT8GR090wC9hLdAfxzHsNyE1PcQQvuekp+MYVpW7Lfj4/24P/++AYmtqtGJGZige/Mgk//vNe3aDAJoFUl6qTvgGJkVmp+MVN0yAE8NmZHqywJ2MsySZsqKnE7DEjcOxMNz442ubYXlqtepk1erhfiRBfgpto322A5bAUBIxHiIYgVIn5ofx+93bvCvVkQkG2BaV5aW4VqmPzM7HqthmOqpfSvDTcNacci+y9V7SVBt7u0eprGHdXJY6d6UZ6ShJG5aRibH4mtje0OiaAzg3Y8MTmelSU5sImgdz0ZLz4b1egsbUH99p7u1iSTfjh3PFISTLh1287N9ZVG/xa+23Y+3k7zCYEvJzIqLLiugkFXpMY2kSH+l64vjfqkHxNgmh3lXRNUOk9P9YnoeJhGRQBQkpPOyhSICoqKmRdXV2kh0EUckY34nEFWfjSE1vdbqwbAsjG611DDSgWzCzGM+814Bc3TcP3Xv4EuekpWDpnDFas/xS56Sm4uaIYMy4chn//3w/dxlJ9VRksSWasrTuG+VOLsPXgKdx51cUYlpaE6jU7kZue4tYYT7sUyDWBow1yAODlb8/C8IxUtHSew+2//2DI70Wsl5PG+vgpOIQQO6WUFZEeR6JgPELhEOu/3202ifcOnXbqzwIo9+q/3FUJIYBTnVakJZsdiRbtMRtqKjE6L8PvprFq8/5fvHHAETsASjXu9/55HO57ZbdTjxPtMuKaqjGOHYq0Y1GX5qhxDgCMH5Ud0PeioaUL82r1Y7nReRk42trtMYmhJuDOdJ/DibNWLF+3y+m9mViYhZMdvidBtONRq5DMJqBqfD6mFA2LiZ81f3h6/5lsGbpwxSOGlS1CiOG+nkRKeSY4wyGiWGJU4rhxWWVQSjdtNondJ866XaN2c71j62Rrvw099gZzTe1WdFr7sayqHMW56eg5N4DTXX26MwM2CdRursdjt0x3VKR8/+VPUHvrDCyrKkdv/6DbGmP1umYTHIkW7dd+cdM0HGjuxPbDLThx1oqvP/OB2y4C6vH+zkzE+gwNy2EpUIxHiKJbrP1+16vCSTYL3Xt1S5cVs8pGoGxkJrYdPm14Px+dl4GMVJOyW2HfADJSkpCR6lwRq/c+qc37tcuEb64odiRa1ONce5zYpPHSHO0E0MJLigP+XnirrPBWSautWJppk5hSlOO+NfYI38ekHU9Tu9XxXlx5cV7MxEL+iPVlUKTwtIzoNJxLdfUI+zHmoI2IiGKG0Y34ZId1yIkBdQZo/8kOw4DCbA8qTnefcytR/Z693HZZ1Rjd8lVLkjID1NM3gK/PKkGySWB4Zio+b+tBZmoShmek6F63dHiaY1ch168daO50bO2sraYJVoO2aF8m5AnLYWkIGI8QRbFg/H4P1zJToyqciYVZXu/V6lKh3PQUR28XswBGZVtw7Ew3Pv2806nidVlVOUqHZzgSCkbv01hNnzll+VKW1x4navzjOt5xBVlYMrsMa7Y3oq2nzxGvBHKvNVoaJSDQ0NIV1KViQxlPrDS89VesT7KRwlOy5eqwjYKIYpKnG99Qb6zqDNCdlWW616gcMwKjclJxSelw7Dp+Fr9eNB1Pbz2Mktx03PPSJ45gKC3FjB/Nn4iH1n/qCGS+c81YWJJMWPXOfucS3feP4ovj83HxyExcMMyC5XPHobtvEACwbudxtPX0ofFMr2PmyHVMalDz0WdnHV/T26YxEWcmEi1IoqBiPEIUxYb6+z2cy5CMqnD+cpf3itzReRlYddsM1Dd3OSVVxo3KRkFWqlvF6+Ob6jGzJNeRbPn/7J17fBT1uf/fs5vdbDb3hNxMSCAkAQJJACOiP6CVqMU23gDRarX1YHM8FaFaW6yKVlFb1EJFtBa1Wu2xgnelFi+gRU+9ReR+SwgkJuZG7slms9md+f2xmclOdjYJEDDA9/169XWS3dnZ70w4fp95ns/zeQLdp/GJEbzj80CtKAN7nKTHhfLrH4zl4Xf36fzgHnlvL+UNnTp1y9HutUbKisUFmfxy7VaaHK4T3ip2Oio9TuYim8CL8Gw5DogeacHpwvEMkD49cJgfP/W5Noq5b7LiwvEJvLenVvvutNgQbvp+JpXNDl4urtR9Ji02hN9dMoGGdhcVjQ5CLGZdUAS9Bm23rttqaKq7uCCT5CgbNS1O1m/3H7Po69mycFYGT3/c20utTkOaPDKKtNjQ07IycbL39AuGBuHZcmIR8YjgRHCs/30frDfFUKhf1NiiLy8Vnc3UUbED+pAcqGs39KR7+rp8fvLMF37nffZn+Zw3LkFbv5Fny5TUKM3EVpYVKho72FLRrPNsWTF/EgnhwXy4v56shHCe/08ZF+cm0+BwIStgkmD0iFDaOruJCLFSdridl4srB0yKDHRP1ffLGzr4+ptmXi6u1Hxjvgv/EHU9QukhOFa+c8+WQEiSlAhYfV9TFKViyFYkEAhOGo6nxFGtAFW3OHUjmFUjtL7VqcLcZO5+ayc3zEjnivwUnd9KeUMn//P3LSwuyMQjQ4zduEVIbVmaMyXFsEK18LwMVn9YyuKCTP61o5qimemMT4xgT02rzhz37W1VLC3MZlmPmqbJ4WJcYgTfy4rXGeWdTpN5hBxWMNSIeEQgGB4c63/fB9OGdDQJHaN9NinSxqKCDIJMJkaPCKWq2UGny0NihF6RG2iPrmsL3D5tpEZJjelVXfhOJNpT08r+2jYefneflhDxLSJF260UzUwnIy6MUbF2spMiqWhy8PTHZdwwI52z0+P4/Ya9mopXBg4d7sBmMfPM/+1j1ZWTOXdMbL9/i773NC02hGWX5mAxS7prTo8Lo7bVqfOVMfobnQiE0kNwsjGoZIskSZHAKmA+fQKbHkSPtEBwmnK8Nj5fuWh1i5NnPiljxfxJTEiK5FBDB/tr23RBjWqW++pXldx6QZZOVaL2Vo+JD2N/TRuSBIsLMljXp0LjkfXn8sXZLRMXFtxjeOth4axMokMtRIVY8cgK8/NTWFdciTVIYsns8VQ0OHjkijyiQoI4I8quC3ZOV5WHCJIEx4qIRwSC4cmx/Pd9MG1IR2rCa7TPrr56Ml3dijbFR1WlvrG1irGJETp1SaA9OtBaa1qc3HJ+Fis/2K995v7LJpIWY/e7T5IEt728TXeOW9dtZW3RNO07q1u8yQ11qlBls1MbubyvphVFwnBq4tLCbFxuhU63h2npI/q97773NCnSxpX5qdpEpr5xiWgFFgiOjsEqWx4B8oDLgNeA/wKSgcXAr47LygQCwWmNUaUsNdquVX36ermEBZtZVJCBrEC4LYi02BCuOiuViBCLNgo61Grm5a++oTA3GbMJ7r44mz9/WMr+unbuLszmL5sPaN9vFFSEBgdp3i33XzaRbo/Mtc98oQUmD1w+EZdb8ZMInzsmTpdEOdkmNwgEwwgRjwgEpxiD8eI4UhNeo312e2WLblyys7t3yqDvHtzfHm20VrWNGNAmJY5NCOeR9/Z6PVt6xiSrKpmGji7Da6luMb5GWUHzlbEGSYRazYyJDwfwm5q4bP1uimamDyoJ4ntP50xJ8TuX7z05Hf1SBIKhYLDJlouAHyuK8rEkSR7gK0VR1kqSVA38N/DKcVuhQCA4belbKSurb9c2+s376rRWnWi7FZMkaUFUflokN30/g8rmTq0daM6UFF76soIr81N1VaBll04kIsTC45tKtPde/aqSxQWZOs+WRbMyefi9vVw7LY0XPivnrjd2UjQzXReYlDc4/AK5O17fwaSRUbpgUEzmEQiOGhGPCASnGINpQzpSZYXRPtvfuGTfPXigPdp3rSEWM4te+lpTyT7+oVeNsmB6OuUNnTR2dLG3pk2XpPj95Tncf2k2NW0uXv3Kq7C1WUwkRRpfo2q+X9HYwcIXv8bZLZObHMH100cbrjMrIXzAJIgsK9itvUUqq9nU7zUH+huBNzY7XVqiBYIjxTTwIQBEAeU9P7cAsT0/fwqcO8RrEggEAkPUACg3OYIbv59BU4eLh+flce8lE1jx/n4tUDg7PY6739qlC6yCg0wU5ib7VW6WvrkTl1tmf1275g0z98wUzBI8/19TWVSQoY1RLG/oZNWmEuZMSdGqTb4ECuTKGzqQfQ5Wg0ZfhBxXIBgUUYh4RCA45VCLK9PSR2gP976oygp17/RVVsiyQll9O58eOExZfTuyrBjus+q4ZF/UZIbvHjzQHu271pzkKJbMHq9b16JZmby2pRKbxYTFbPJTyfz29R3EhNl4e1sV105LIy02RGuT7nuNvueyW4O082yvauXb5k7DdaZGh/Sb8FDbpK5c8xmrNpby9MdlZMaHDRiX9P0bAWzYVcMPV33Mj5/6nB+u+pgNu2p08Y5AcLozWGXLASAdqAD2AFdJkvQFMAdoPE5rEwgEpziBDOgCvZ4QYSMtNoQrp6bqqkRLC7OJtlu1ypKv54paJRo9IpSSujbNTE7qiUNe/aqSLrdHU8moValbzs9ia0WzoSFccJBJN4ZRRQ3k+lalvv6mmc5uWet9FnJcgeCoEfGI4LTidDNTD0R/ygojfxXV38T39ZyUSMMWoLXFFbo9+Ej2aCPT2xc+K9dMbx0uT0BDfrUA9Mi8PLKTwgkKMjF7QiLJRdMoqWunotGhnWtxQSahwWZdjPH3zyoMVbguj+y3Tl+M2qT+sGEPD1yWw51v9E5Auv+yiaRG24/oPKIlWiDQM9hky3NALvAR8AdgPbAQrzJm8fFYmEAgOLUJZEDXd6SzzWJi+dxcfjQxidRoO3+Yk8v1z31p2KP8cnEl15ydSnZSBDaLiVe/qmTRrExWbSqhqtlBXkqkbuyzOtK5vq2Ll76s4PGrp1BS147D5cYWZCIuItgweZI+IpRbL8giNsyqvd9fIKcGS2oAIibzCARHzXOIeERwmnA8zNRP5uSNkQmvb3sx9D7wv7NoRsDkzLhFM6htdWK3mun2yMyemKi7D4PZo/vex9EjQhk9IpTspAjdFKBDDR2GcYRH7i0M7a1tY0S4lVEjvPFBq7ObmhYnyZEh3PHD8VQ2OXj+03JykiN0MUaTw0Wo1cySH4xlRLgNR5ebJoeLEWHB/d5Hozap8oZOGtqdmueMosBjm0qYkhodMHEiWqIFgoEZVLJFUZSVPj9vkiRpHJAPlCiKsuN4LU4gEJyayLLCjqpmwwDJ141ffX3Jq9uJtluQkNhW2Wy4uY9LCGdRQSb3vr2LaLuVxQWZvPRlBbKicO8lExgZE8LB+o6AI53LGzq56cUtLJierqlbls/N0ZI1vskTWZEJNpv4x+flrJg/idK6dtyyTEZcGKkxocReP5WPSw+jKOhGQvsGIGIyj0Bw5Ih4RHA6MdTKgeE4Ce9Ykz8DPfAb7bN9Xwu0hkB7dH/3se9nRsWGsnxuLkte3e6npinMTfaa71vNWruOLCvUt7lY/WGpdvwt52dx/blptHd5GJsQzobFM6hpdRIXZqOyuYPd37bx61e2acenxoZqk5WMCOR/0+L08PiHejVvf4mT+HDj88SFiZZogUBlsKOfJymKslX9XVGUCrwSXoFAIBgQ30AmKdJGaX07TpfMDTPSATSDOGe3TFVzp2Hg9MWhJp7+uIzHr56i29yTIm1ckZ+CZJKoaekk2u6dBhsbamFRQRZ3vt4riV0+N9fw3E53byCrthc5u2UibBZWFO/XVXrWFlfw8Nw8vm1xcnZ6HMvW79aSKeeOiWXUiDDiwoN5+uMyvwBEeLIIBMeGiEcEpxNDrRwYbm0fQ5H8OdaRxIHWkJ0UTnWLcQLo4GHj+zj25hmMidffR5NJ4kcTk4i2Wykub8Qje+OIK/NTWVvsbQNKiQ7R2nUONXRwR0/cop575Qf7KZqZzo1/32J4j/77hS1H9Dc1apNaPjeXFe/vA7xx1ZwpKZhNEGIJQpYVw7+H2YRfG9PigkzMARxBT2ZVlUBwtAy2jWiLJEm7gReAFxVF+eY4rkkgEJxC9A1k7rhoLB4Fvx5jtdVGVox9T1Q3/j9/VMKySyey9M2dZMWH8T/fz2BPTSu7vm3l7W1V3Dwrg6SoYKxmM58fbOSGGelaMqesvj3guY1+PnS4g6vOStWt9f7LcrAHm/nHFxWUN3TqzqMGd8KTRSA4boh4RHDacKyJhL4Mp7YPVeG6t6ZVt08v37CH5CgbDpdnUA/kx7rfBkqcFM1MZ9XGUsPkRnlDh6H/W9nhdura/BMJQUEmpmeMICU6hPKGDsYnhlPZ7KAwN5nnP/XGPjnJkcgK7K9tM/wbqZ6zfZMpdW1H/jc1apNKjbZjMZtYvmGPbmrjms1lARNg1S1Onv+0XFeQev7TcianRjFqhP67h4OqajDJHpEQEgw1g022jAOuARYAD0iS9AneQOcVRVFajtfiBALByU/fSlpSlF2Tu4I3KFi1qYSimenE2q3Utzq575IJ3P3WLr9kTG5yBP89M4MWZzd/+cmZtHW5uaWPP8prW77hyrPSWPrm136fX1dcqRnh+lZhnv+03O/npYXZhAebqWp28rvC8SRG2altdVLb2sljm/Zz86xMHttUQnlDp19wJ0YkCgTHDRGPCE4bhjpxf7TJm6F+ADV68F40K5MNO6uZPTGJK9d8NugH8mPxQJNlhT3VrUeU3JBlhRCrievOSfNTdCgK/Pipzw3XrbYl1bY6uf65Yt33JUXa2PVtK/tq20iODGFxQQbriis11axvIUhdk5pMOdq/qVGb1OwJiSRH2bT7b3T9viRE2GhyuHStR4G++7tWVQ0m2TMcEkKCU4/BerbsB+4B7pEk6Wy8gc4DwGOSJP1TUZQrjuMaBQLBMGUwAVjfSpqjy20Y2OSlRFLb2sV963cTbbdSNDOd9BGhWuUE4Jppadz80te64EadQqQmbR6al8dvDJI5C6an88wnZbR2drNgejppMSHUtXURHmzmwcsn0ujoJjEimKIZo0mMDKHb46aisZMgs4TZbObGv3+lfe/Swmwa27tYOX8SDpebpEg7o0for71vICM2cYHg2BHxiOB0YqjN1I8meXM89i6jB+9A+/dgHsjV/VY1pP38YMOgkkKHGjooqWvrV/GqrkNNbhxq6KDF4Tb0f1s5f9KA6zZKjlx/bhoVjQ7WbC7zKwSpk4jUOEhdX2KEjbL6dho6uvw8YY42IWcySQGnJxkpZY7k39N3raoaTLLnu04ICU5NBqts0VAU5XPgc0mS/hd4Eu+4RYFAcJox2ACsb2BxuKPLMLDpdMnc16M4qW5xavLdP//kTJocLhael8E9PWoX6A1uVENb9bVOl3EyJ9Jm1sxsg0wwJi6MZocLk8nE//zvFu0a7rl4As98coA5U0ay+sNSFkxP508f7NR9rzr9aE9NGx6P1/Nl9Ij+AxuxiQsEQ4uIRwSnA0Nppt43eRMXZsNsot/kxFDsXX0LM4EevJ0B9u/BPJAfTVKottXJuuJKPyP8pYXZrN5krNaobXWyv67dcJ0lde0DrtsoQTEuKYKiF77yi2+eui6fMyJDONjQTpPDpa1l9dWT2V3dpp0jLTaENdfmYzFLx6w8OhKlzJEkA4e6Je5IGUyy57tOCAlOTY4o2SJJ0mi8VaRrgAxgM3DDcViXQCAY5vQXgKnVpdpW7+a7+urJLHzRq0hZV/wN9182kbve2Em03coV+SmkxtiRJDSVioqzW6bbLbO2aJqmXvHF2d1raAvejXtkTIjfhp4WG0J0aLAuuBkZ4+1PVhM86vnufXsXK67I49aXt2nnDyQxVpMuhztcpI/oPxgWm7hAMHSIeEQgODp8VSCDSU4c695llAR56tp8wwfv9Liwo34gV2MSXy+VfTWtZCeF+/mHqOtyexSaHC5e+KzXdyQi2ExGXBhX5KcgK/D2tiqWzB6vqTXs1iA8smy4zi63PKC5rFGC4tDhDsN77PbIjIkPY/SIUN7xOV5R4EePfax9pryhk6IXinnHIAF2pC1gR6p+Gmwy8Lv2shtMsue7TggJTk0GO43oJrwBzdnATuCveI3pqo7j2gQCwTAmUADW2NHF3po23Ya6fG4u7/5yBtUtvUZs+WnRbKlo1lz3fWWz1S1ObcpQU4eLXd+2cNaoGMNNcFxCOAtnZfD2tiqKZo7hqX+X+bnj3104gZte1Lv13/XGTp64eorhNXTLit/3GEmMffu6Bwo8xSYuEBw7Ih4RCIaGwSpWjmTvMnqwN/qeu97cYdj6Mikl6qgfyGtbnUTbrVw7LU2nUknrMwZZXWN9Wxd3vblDU7U8/mEpabEhLDwvk58++4UufrlwfIL2eZfHQ4zd6hdn3HfpRD7Y/S0LZ2VovnCBzGX7JiiUAIMBUmNCDY//9MDhQSXAjkbtM9Sta8f7vINlMMmeI0kICSNdwWAZrLJlCfAP4L8VRdlxHNcjEAhOEgIFYBazyS+wWvLqdtZcm8/0jBEAWqDTd7zhoxu9RrkvF1f6mc9lJYTxu4sn8Lu3e41zFxdk8sA7e2hyuFhamM3aLyqob3cxNT2WR+bloQBBJimgAZ7LY1ydigsL1l5/9St/ibFquGuzmFD31oGSJt91VUcgOEUQ8YhAMAQMVrEy2L1LfbBfvmEPhbnJmE1wVloMNqvJ73vKGzpJjrLp1Brqw+rRPpAnRHgLNOperV7PHa/vYNLIKM3cVk0+3DAjnfKGTp2qJSsh3M8zZsmr20mMsBEXHsyo2FBiQ4N5e3sVP5+ZwRNXT6HD5aG+zcm09Gjiw4M1fzf184NpuRo9wvgeB2pPHmwCzCjRNZhpT0PZunYizjvY7x7o39Zg//0JDz7BkTDYZEuaovhaRQkEgtOdQAFYIHO14vJGkqNC2FfbpgU6RseNT4zg7sJs9ta0am1F0XYrFY2dvPRlhWZuW9ncqalgwNvSs/C8DEySpEvS3HJ+FmMTwg0Dk4OHO7jn4gnc2yeBE2I18eDlOdzx+g6qW5ysLa5gzbX5tHV1s6+mTRtTvbggk1CrmTOiQwZMmnzXVR2B4BRBxCMCwRAw2Af2we5dhxo6/MYG2ywmfn95LmmxIZQ3dOq+JyY0WHvwNlIJHOkD+ajYULLiw/tNIFU0dmijptW4oLrFqfm+LSrIMPz8x6WHefpjr0plVmYcN52XydffNGttRjfPysTVrbCloumoWq6OND5IjbazfE4uS17rVQb98Qr/BFjfhFpSpI0r81MNpz0Bp7xSYzDJnsEcIzz4BEfCYKcRicBGIBDoCBQcHGroMAzgPDKUHW7XbVBGx+2padXMcVUFyZwpKTy6sYRouxXwSm5XbSzVrcfZLRMXFsw9b+tNdFd+sJ8lPxjrN/L5nsIJ/OOLclqc3TwyL4+9tW2YJEiICCYm1EpeSjSTRkb5jW4elxDB5JFRWINM2ILMxIZZdRLlge7Zd1XVEQhOBUQ8IhAMDUeithzM3lXb6qQwN9lPWfLb173K1qIXig2/x0glsPrqyYyODaOubfAP/iaTxPikiIAJJFlW2FLRrE38SYsN4Z7CCdy7vrfYMj7R+PNq2/DyDXvo6vbwW5/250WzMnlsUwn3XDwBOUA7UFzYwO3Cg52oJMsK7+6uZcUH+1gwPR2zCcYnRmAy4ecP0zehNmeKv/Ln1nVbGXvzDK0QJpQaAyM8+ARHwhFPIxIIBAIVowBsVGyoXy/2olmZrC2uYHxib9XJqD3Hd7yhs7t3ZLNqnqv2Yt8wI90woAkNDjLcAJs6uxlhklh4XgZOt4yiwJObSynMTfZWtCQwSeCR4dGNJSyZPZ6R0aGGweWY+DDGxIvNVCAQCAQnL0OttkyI8E42MtqDLWbJsGUI/FUC0XYrJbXtmqn+kTz4B2rHURMYvq3L5Q2dPLm5lEfm5dHR5aaiqZNnPjnA/ZflcNcb+mTKC59545LC3GQt0aJemxqnhAYH8fa2KsPJRgcb2hk9YuB7O5j2lEMNHfzqZe/7r22pZM6UFPbUtJKVEM7e2lYmJkdp5+ubUIu0mbWYCrxxWHWLk4pGodQ4EoQHn+BIEMkWgUAw5OQmR7Lm2jOpaXFS19bF2uIKrjk7jcpmh7ZBVbc42bCzmid/cia1LU7iwoO5842dftOIJAnCrGZdL7ZRouaeiycQbjMbboBnj47ltpe36c4NIEne979pdOiUMiLIEAgEAsGpSKCWnWM1/BwVG+r1aDHYgxMibAGVMX1VAqqS9Wgf/LOTwvnb9VNxuNykxoRqSQ4jNUJ5QydOt4c/bSyhyeFixfxJjE8Mp2hmOslRIVQ1ez1d1NghUDLJbIIgSeLWC8ay4v1excm4xAie/KiU/XXthpOC+jKY9hT1OpIibX5mwPdfNpHspEjt7+abUGvs6OKbRicrPvAvhNmtxoUqodQwRnjwCY4EkWwRCE5jhtpN3agqs7Qwm2umphJsNvHqlt4kSbTdyrwzU/i2uZP71u/mhhnpNDlcuvPZLCbyUiJxdst0uXu9YKpbnJqpXU5yBJEhFnZUtuAKtXLHReN48F97te+/9YIsOlxuw3ObJFhckOl3HSLIEAgEAsGpRiDlxIXjE3hvT+0xtZGYTBLnpMcaThnq7yG0r0pAkowTGgPtyYGuTTWZDaRGyIoPY+WVefp24cQIzX9GjR1sFhPZAdqUxidGsPAfX2MNkvjdJRPZUdlCl1tm2frdWqJGXX9/cZeRx8qcKSnsr20DvA/56nUYtQTd9cZOpqRGa/dJlhUqGr0DCdyyt6WrrypnzbX5JEQEC6XGESA8+ARHwhElWyRJGgGMAbYqitJ1fJYkEAhOBMfDTd2oKrNs/W4WTE/nmU+8Uls1STI+MZz9dW1a/3SgtqJWZzerNpYwP3+kLhiobnGyfnsVZ0TZ+OXa3mu45fwsFhdk0uHy9LQGKZTVt3PL+Vms/GC/dtwDl0/EbgmipK6d9LhQkiJtWlAkggyBYHgj4hGB4MgJpJxYWzRtSNpIgoJMXJx7BjnJkYYPoUaJhr4qAbNk7Hui+q4ESlQMpAoJpEaYmBzlF/PMnpDIuMRwGju6eP76qXx2sJEut0xdq9Nv5PPSwmz+/FGpFj/8z9+/omhmuma623f9/cVdvgkhI+WKmhj74xWT2FdrPGVRTcykRtv5qKSOktp2Ht1YEnAogcUskRqjvzdpsSEsuzSH2lbvNYlEgj/Cg08wWAaVbJEkKRx4BpgHKEAmUCZJ0pNAjaIovztuKxQIBMeF4+GmHsg0TK1UmU3eJMkzn5TxyLw8ZAVd8kRNxKTGhFDR6J021ORwsWB6On//rMIvGXP77PHc0ucaVn6wnwXT03XTBTwyhAWbeWReHvvr2ogOseByK9z5+lZdYkf9vr6VuKFWAAkEgqNDxCMCwdETaI+ubhk6w89AD6H9JRouHJ/A2qJpVLc4SYkOISshQvMlUY9Ljbb3m6gYyLT0SNQIvma1O6paeOKjUpzdMgtnZfDqV5Wa78nYhHAefGePX/tzls8ERN/176hqHnRCKJCZ7TuLZnDRxERSokO0YpWKzWJiR1Urv1y7lTXX5rO9skV3TKAWr77tRlXNTj9DY2GWKxAcHYNVtiwHkoEpwCc+r68HHgB+N7TLEggEx5vj4aYeSKar9Dj0Tx8zgqmjogkPttDQ4WJcYrjfSEizCVC8UuK4MCtzpqSQGhPC3DNT+PJgAw/Ny6Pb7eGMqBAOt7sCJnfU7548Mord1a1IEjzwzh5unJlOfITNL0nz6MYS/nb9VOLCg/0qcUOtABIIBEeNiEcEgqMk0B6dFBly3NtIAhV4shfPYHe1fhLO6qsn88+bZ1Dfrp90aPT5sTfPQJLAJEkDXsNg1Ai+xRW3R+GFz7wjn/fWtJIZH441SNKKOQtnZRi2KI9PjNAZAqdG23lvTy17a4zVKEYJof21bf0em5Mc6afUUc18nd0yxeWNuoKWkXrYt7Ck3huAnzzzxbAxyxXFLsHJzmCTLZcAlyuKslWSJN+xi3uA9KFflkAgON4cDzd1I5muasC2uCCT+/+5m4tyknQS3HsunsCT/y7F5Va47pw07b202BBunJnBvet3EW23ckV+CpdOTqGh3YkJieuf+zLgVKKxCeEsLsggLjyYvTVtdLll8lKiSI4Kpq3LTUOAgEdB8QsmjocCSCAQHDUiHhEIjpJArTQTkiKOyfBzMA/EgQo8ta1dfnvswhe/5p1FM5g6KlYbg2ySJKLtVj8VyZ6aVm57eRvRdqtfi8+RmpYaFVfuKZzA8g17KG/oxGYxsezSiaz+sITyhk7e3lbFQ/NyKa1rR1bALEFOSqRmyqvGCGX17dy6bmvAmMWoRSozPrzfGK1vYmZHVavOzFddj+9Qghc+K6doZjqTR0aR1tPCNdi/03fhYyeKXYJTgcEmW6KBBoPXwwHP0C1HIBCcKI6Xm7rvJIAYu5W2rm5SY8ZR2ezg5zPH8OtXvEHRnCkpSBLUtHTycM/oxf/53y3ae6nRIXzb0klWfBizJyb5eblE2628+lUlSwuzWbZ+t+69B9/ZQ5PDxX2XTuSlL8u0IOn+y3KoaelEVgL3hPdlOAUeAoFAxCMCwdHSXyvN0Rp+DvaBOFCBp8PlNtxjGzu62FujV7yo7b6+/mqqAqS6xcm/dlTzyLw8TCZIjrIzISniiB7KjYor967fxcPz8thX2+aNOd7cyfPXT6Whw0V9m5O61i6tVUe99r6ocUQgdYlRi9TqqycPGKP5JnRU7zqVt7dVseQH43QJqCaHi3GJEXwvKz7gfRlOY41FsUtwKjDYZMuXeKtJf+r5Xa0m/TfwnyFek0AgOAEcaXDlW3WJD7dp/itqFQswDBasZhOHGjpIiw2ly+0h2m71M31LibYTF241fG9pYTZrNh/wa/lRfVnanN3eMY2RIVS1dOoCsbvf3Kkd5+yWueuNHbzwX1O57ZVtfgHP8rm5homm4RR4CAQCEY8IBMdCoFaaozX8HOwDcaACT1pMqOEeazGb/M776MYSimams2pjKTaLiQcvz+Hhd/cB3sk9sycmcdsr245aBRGouLKvto2nPy7T2nQ2lx7m6Y/LdNfT37WrcYSvN53ZBOekx3JWWgwVTQ5Ddc+GxTN07UiBYjSje3vzrEz++n9lFIxP5JF5eZhNEpnxYVq7UiCG01hjUewSnAoMNtlyB/CuJEkTej5za8/PU4GZx2txAoHg+DLY4MqoctXXUHZsQrguWIi2WzUXfPUzK+dP4op8f9O3pW/u5K8/zTd8T51mpPZIq6MQU6NDWDgrg3e2V3NRThJVLZ2s2liqW7evf4v6e2OHi6WF2ZTUtnPvxRMItQURG2rlrLSYQQcx31XgIRAIRDwiEAwnBvtAHKjAAxjusQ6Xx/C8k0dG8VLR2cSH2zBJaJ4pgQxlj0QF0Z/vnLNbZm1xBXf+cDwdXW5umJFOZWNHgNYop3ZvEiK8ni3qSGx1SMCiWZnc/tp2nv3ZVN09VGMcSYLati7OSovRrT9Qy5bvvQ2xmFn00teUN3RSXN6iXcc7i2YMmHgaTmONRbFLcCowqGSLoij/kSTpXOA24ABQAGwBzlEUZcdxXJ9AIBgGGFWufNUlt67byhPXTNFtiHOmpGiJFvUzf9iwh1svGBuwb3tKajQ3zPDaLrz6VaU2IcFs8h5nNApxcUEm6XGhAVuDFB9XB5vFRHlDB4mRIX593YECib6BR2KEDY8Mnx9sEGZtAsEJRsQjAsHx42jMSI/kgThQgcfo4f7g4Q7D86bGhDImPkxbr5qoUace+nKkKohAvnMvfFZOUqSNK/NTdcqZpYXZfib/NouJbo/CD1d9rG8ViglhwfR0wm1mzoiyc+hwBxfnJdPY0aXdw77qXlU9o6pzBmrZUu/tpwcO69Z0pPdiuIw1FsUuwanAYJUt9AQxPz2OaxEIBMOU/kY6qz+HBgdpgVFSpI3U6BCdNwvA5n11RIVYDAOo8BALN/79K78Ap8nhIjclCpvFZFi5enRjCXdeNB5ZUfzM8e4pnMCTm0u171hamE1Th4vfvLr9iKpfvmMghVmbQPDdIuIRgWDoOVoz0qF4IO77cC/LCgcb2v329FsvyOJgQ7tmQOtbDGlo7+Lpj/WjkNNiQwixmPn0wOFBJY98z1fe0MHX3zSzYWc115ydSnZSBL94cYuf8nb11VNY2PO62pK89M0dfjHG2p9PY/32Kq7MT+U3PgmbzPgwJqVEaxOPjNQ58QvOptsjY7eaj6ht6WRXhAwnlY1AcLQMKtkiSVJMf+8ritI4NMsRCATDEd+NW5W4mk2QGR9OUqSNJoeLhPBgVsyfxPINe7gyP5WWTpduupDNYuKhebkcqG9n2aUTqWxysK64kiaHi2WXTmRZz9QhNTnT5fZw/blphNos/O+nh3hoXh6yrHDDjHRN9QI9qpj2Ll79qpLrzknjkXl5SBKEBgexetN+CnOTMZtgXGIET35UyvfHxfdb/eqvsifM2gSC7xYRjwgEx4ej3d+OxwPxoYYOFr74NdF2Kwump/eMdgaPrGiTitQ1mUwSo2JDKTusT86kxYZw86xMrlzz2RElj3zPNyLUSmHeGax4fz83zEg3jB0UWWFt0TQcLg8JETYaOroMVSWNDhe3zx7PLX3u8ZJXt5OTHMnsCYl+CRL1mM0l9azaWMqigoxBqXdOJUXIcFHZCARHy2CVLYfpNaEzwjwEaxEIBMMUdeNWEyl923gyE8JIjQklNSaU+PBgfvLM5yw8L4PVPca04PVwqWrq1CVflhZm09rZTbgtCJdb8WsRWnbpRIItMDU9VlcJUlUv1S1ObBYTJslr1vvoxhLtvcd+PInlcyexp6aVbxodlDd08P1x8ZyTHqtNDlCxWUyEWMy43TLv7akNWNkTZm0CwXeOiEcEguPAsexvRsqUsvr2I2pHMlpLdYtT82sDWDgrw3BNRsmZcQnhWsuPei23rttKctE0cpKj+l2Per6+cYyRWmRMvH8iwOg4uzWIHVWN/d7jUbHGZsGenl8HO0lRKEIEguGDaZDHnQfM8vnfD4DbgXLg2uOzNIFAMFxQN+5VV002bOMZFRPKoYYOPj/YwOH2LpzdMk63PKCHy7L1u+ns9hBptwQ0zo222/w+t2pTCXOmpPSMc55ITnIkT1wzmUevmqy1HsWEBjMmPozZ2YmcEeX1aFm1sZTbX9vO/ZdNxGbx/udPTd4seulr/rmzmuUb9vgFZ4caOoBehY8vJ6M0VyA4iRHxiEBwHBjM/qYmUT49cJiy+nZk2T/vqbYj/XDVx/z4qc/54aqP2bCrxvDYI12L0pNsiAuz6dbRNzmzelMpe3tGQvvi7JbZuLduwPWo5/ONYzbvq+Oewgm62OH+yyYige5+qMUp3+NWzJ9EQkQwHlnu9x4bfXbRrExe21IJoI2O7ntuI8WKmgCblj5iwAlEAoHg+DFYg9x/G7z8gSRJZcANwItDuiqBQDDsMJmkgNMB9tS08utXvD4oiwsydIGAenxwkLE8dnRsKCU1bYweEWr4frPDZfh6+ohQVl89hWXrd1He0InNYuKW87OwBkm64KOiycESH4+W8oZOHttUwt8XnM3mkno8MppKZsmr23WTj9Tv8q06nSrSXIHgZETEIwLB8WGg/W2wni4VjR3srWnVmd33145k1LobyKh2bXEFK+ZP4mBDOwtf/Fp776lr8zWDWbUVeVxieECVyEDtUb7JHvUcM7LieXJzqaacsQWZcHS5+dlzX2jtymelxXBOemzAqUs5KZF+PjS+91gtbCUXTWPj3joy48N55L29Wtt0dYuTtcUVrC2aRme3RyhWBIKTgEEb5AZgK2LUokBw2hAfbmy6VlLXrr22rriSh+bl0upwce8lE7jnrV3e5EicsTw2MdLGQ+/u456Lsw3fjwxgqBtpt2iJFvAmRVZ+sJ+1P59GTkoUAGX17ew3qG6VN3RyuL3LcFS0uY/ez7fqJKS5AsGwZSsiHhEIjpqB9rfBeLrIssKWimatVde37deoHam/BI66ltpWJ+G2IDpdHsYlhTMiLJib/6E3qr3rzR386cpJHDzcofNsuf+yidz1xk6/tQzUHuXbOn3L+Vms/GA/kuSNHdRizE3nZfDyVwf9WquXz83l4twztLYq32TS6NgwMuPDmJIajcPlJjUmVDP79f075CRHUdXsNGzdXjJ7/IBtUAKBYPhw1MkWSZLCgF8C3wzZagQCwbDGbMKvKrO0MJu1X1Rw03kZSBKEWEzIskJdu4s3t1bx6FWTcbllzJLEPYXZ3Lt+t87vpb69C2uQRKvDxe8unsDv3t6lvX/vJRN49pODLC3MZpnP5xbNymTZ+l0U5ib7qVAaHS7+70A99W0u7nh9BzfMSDdM1iRFGieO8tNitNeNlCvCrE0gGF6IeEQgGBr6298G4+lyqKGDO17XT+JZtamEopnphu22AyVwfKcALt+wR1OQ3D57PH/+qJTtVa2ANwkSGWLRtRyrKta/XT+V/ztwWKdiHaj9V008ZSeFU9/WxWM/nky4LYi3t1VpBR5JgsLcZNYWV2hqF4AV7+8jJzlSS7QMdsJTX4XPheMTGJcYTmNHl86AVxR4joyjGWcuEAwlg51G1IbekE4C7EAHcM1xWJdAIBiGVLc4ef7Tci2wUBTwyDIX5STpEjCLCzKxW8243AqH27u0RElabAgr50+ipK6dLrfM8596/VUempfH79/Zw/98L52V8yfhcstEhVqQFYVPDzZy9phY3Xe+8Fk5AOMTw1k4KwPwSpWbHC6+/qYZjwzPfOKtrKk9zr6VoRXzJzEhKdJQMn1ueizvCOWKQDAsEfGIQPDdMJhxwoESMlkJ4Ybttv0lcEbFer3g6tu6DBUeSwuzqd9UqiVPKhodhirWLw81Ygsy+6lPBtP+u7u6TRcj3H/ZRB7bVEJ5QydmCUKDzX7rWjQrk8aOLtLjwvpNJqnXV9vqJCnS5vddalJGFHaOnqMdZy4QDCWSogxsWCVJ0k/7vCQD9cDniqI0HY+Fnczk5+crxcXF3/UyBIIhp6y+nR+u+lgX0CyZPVZXTQLVtC2PvTVthpN/+vqirLoqj4rGThIjbYRag/jDhj2UN3RyYfYIrjprFI5uD7e93DtVICnS5jdWenFBJomRNp795CAzx8azelPv+dVx1bnJEWT2BH0mk6RVPERiRSA4MUiS9JWiKPnH8HkRjxwBIh4RDBWDeXA1ihFsFhP/vHkGY+L9kwaHDrfz2tdVqF61atHknzfPYF+tN/lww4x0bEEm3VQg9bwLpqfzzCdlLJqViYLCY5uMj3ltS6Xm5aIocN7YEeSPitWpHpIibXhkqGvzKiAUBX70mP+1/O+Cs6loclDf6mT8GZH8/Pliv2Ne+vk0IkIslNa1UfTCFr/r/sfPz6bJ0a3dy0UFGYax0jtH4HMjYhd/Av17DHRfBacXxxqPDJYBlS2SJAUBocAbiqJ8e7wXJBAIhi9GpnVnRIUYVqYcLg+pMXbD9ySfmCAtNoQut6IFUmplaMPOar43NpFfvLiFaLtV1750Rb7/ZKNHN5awuCCTGVnxgN6ct7rFyTOflPltsKIlSCA4eRDxiEBwbBzLQ/pAni6yrGCS4MHLc7RWIjUhM3qEv4pElhV2V7fp/F0WF2SSmRCG2YQWZ4QFmxkRFmwYS6TGhLBgerqmdu2rYl0+N5cV7+/TjZC2WUzMnZKsSx5F261+BZxVV03WtQe9+lUl1S1O6tq6+O1r3uu7ffa4AMqcLq566jMenpdnqAaymE06xYusMGCLlu99E2qNwXEs48yPFJEAEwRiwGSLoihuSZIeBv55AtYjEAiGAf1Vey4cn6Brs2l3ug2DCVuQmdpWp+F76v5js5i4ffZ4bukjs121qYSH5uXxm1e2aeMcn/+0nKKZ6YxPjMAtK4YbaIfLgyQRsHVITA0SCE5eRDwiEBw9Q/GQHqhA0TdxUTQzndRoO/XtXYQGmzh4uEOLIdSHULXFxneCkLPbQ1KEjb01vcb2bo/CN40Ow1iiqrlTp5LtO6knNdquS2z4xgK+LT5zpugLONF2K4fbu7R2ZN9pSB6f+KPdZRz/mEwS0XYrVc0Ov1hk0axMWp3+UxYHatFSGYxR8bHiGwParUG4PB5iQ4NPugTCYFrfhgKRABP0x2ANcj8DzgTKj+NaBALBMGCgak/fPmK3W/Zz/L/3kgmE2czYrXY/Q91bL8hiVGwoq6+ezP7aNt0kIxVnt0yny617vbrFyaqNpSwqyEBWjAMTdU9TxyOunD8JGYVxCRF+jv8CgeCkRMQjAsFRcLQP6YOp2PueW92rVaXK7m/b+O8XtvjFELWtTrLiw7jx+xnsrWlFVuCNrVXER9hoc3Zre7zTbey99uDlOUSEBOkM7ftO6pFlhbEJ4TxxzRRCg4NICA8mNca7fl/VgzfR0xtPzJmSwn09XnPqvVq1qYQ/X3Mm+2pbte80Wpdq4D9nSgrtXR7e3lal85xbW1zByismabEMwOZ9df2OhPbleKs1jBIHaqJpyezxJ1UCYaBx5kPFiUiACU5eBptseQp4RJKkVOArvEZ0Goqi+DckCgSCk5L+qj3qBpJSNI3ObpmOLjdx4cGMSwjnuevPorHDRUyolUfe3culk1IIsZr9DHWf/b9DzD0zBZMEqzaWsnBWhmHixG4NMnzdI8NrW/wDnFsvyCI9LpQxI8KYPDIKuzWIhIjewEogEJwSiHhEIDgKjuYhfbAV+0DnTom28+tXthk+hCZF2rhyaqrfQ/2azQe44syR2h4P0ORw8cJnvbGESYIpqVGkxoQGNLQPtPbUGO+Ddl/Vg+/PfZMv6tq73B7+8UWFtja1sLP66ins/raVLresTTxSVbbXTktjbXGFNklp6Y+y2Vvb6tc+lX1GOP+8eQb17f17yB1vtYZR4mDVphIWTE8/6RIIA7W+DRUnsl1JcPLRb7JFkqS/4h2n+GLPSysMDlMA89AuSyAQfFf0V+0B7++VzZ2UNzjocHkwSxAbauXP/y6jyeFixfxJnJ0eR317FxLeIMlX5uttMTKRFhvKooIM7FYzt5yfxcoP9muBxwOX59Dk6GLZpRNZ/WGJFqSMT4zgzx95pw+ogVdaTAijRoQS71OxGi02N4HglELEIwLBsXGkD+myrLCjqnlQFftA53Z0uQM+hMaF2bRJherr6kO9qmZZMD2dM1MjyZyby5JXt/P4h6V+SRPwFnLUNfu2vyzfsCfg2n1VD69+ValTlpglY/XswcMdFM0cw5rNB1gwPR2zCfJSovjzRyUUl7fojjVJXpXtpr01LCrI4k4fH5vFBZlE261Utzg1zznVRNjISNiX463WCJQ4UOPBwSQQhpN/yYnw5jtR7UqCk5OBlC0/BW4HRp+AtQgEgu8YWVawW82avDXEYjLcQCQkv0lA152TxvIN+6ho6MBs8hq+bd5Xx9LCbNZsPqAlTPJSomhoc/LIe3spzE3G4fKQc0Ykd1w0jvAQC3ZLENFhFqqb4YwoGzd+L4N7396lq3zVt7s009sF09MJtQWRnxYjFCwCwamLiEcEgmPgSB7SVVXI3prWgMkS39HF8WHBPPuzs/i0rAFZgbe3VXFlfipNDlfAh9BAD/VmE3jkXmP7uVO8Y5JzkiN16gTAT7niO5pZjRdUpYnv2tPjwjTVQ9bC6eyqbqW2pZOF52Xg8sicNSqaUbE5/NYnQbJoVibPf1rO7y7O5tJJyVoLUEO7kwuyk9j5bZt27C3nZyErCosKMvheVhzXPP25Lunz6MYS3VRGZ7dMfbtzwEQLHH+1RqDEgaIMLoFwOvqXnKh2JcHJyUDJFglAUZRj7o2WJOkm4L+BUT0v7QLuVxTlnz3vS8A9QBEQDXwO3KQoyi6fc0QDq4BLel56C7hZUZRmn2NygNXAVKAR+AuwTPGZcS1J0lxgGTAGOADcqSjK6z7vD7gWgeBUw2iD/O3scdx6QRYr3u9VnSwtzOYPfapFj24s4Ymrp3D7RWNJ66k2mc0SEbYgXt/yDUUzx2gVLJvFxB0XjePqqWk6NcuDl+fwrx1VvLf7sBbYfNvs9GtjUitf6rjHtcUVTDxjPBWNHYwaIRQtAsEpypDFIwLB6ciRPKSrrSQ3zEg3fPCOC7P5xQuLCzJ5udg7uvn3l+cweoSdKLuV1NjQgA+hRucenxjBfet36441UiccqGv3U93c9cZOLYnhGy+oSY202BBMSGzaW0taTKjm5bbi/X0U5iZr7c5/+mA/d/1oAo/My6PD5aa+rYsXPiunyeGipK4dgHGJ4aTGhPDZgQYUYOX8SXS43FQ0Ovjn9m/5/rh4Jp4RSVVzZ0CliO91D0YF0VcxMnVU7JAnMIwSB2qsNZgEwunoX3Ki2pUEJyeD8WxRBj5kUFQCS4ASwIS3SvWGJElnKoqyHfgN8CvgZ8A+4G7gfUmSxiqK0tZzjheBVGB2z+9PAy8AFwNIkhQBvA9sBs4CxgHP4u3p/mPPMecAa/EmU14D5gAvS5L0/xRF+bznvINZi0Bw0mIk8axo7GBvTSs3zEgHvL3Gv9+wl1duPIfclKl8UnoYRYE2ZzflDZ268zm7ZbplhQibRZsstKgggzWbvcqTvlLhwx0urV9Zfe2O13ewYv4kdlS1ew32NpXw8Lw8wyAlrWfc49riCopmjuEPG/bw0Nw8kWwRCE5thiQeEcUfwemKUdLCKB5QVSeBJvv5jmYGf7XGb1/fwTuLZjBqRBipMaF+D6GA4Zjo+y+diCQpLJ+bw8hoO2kBHlhlWWFPtVd1kxRp06YZAYQF93YSqkoZ8CZabvxeBtc9+4XuWuLCrZQ39E41Soq08bNzR3HVU58RbbdyRX4KGfFh3F2YTWe3h+qWTi2p9Lfrp/Lsf8qZMyWFkrp2MuJDyYz3+sb97u1deGQIMvVv6D9YFcSJUoz4Jg687Vhmuj0ysycmDiqBcCr4lxxNG9SJaFcSnJwMJtlSI0n9/wNTFGXAHmlFUd7s89KdkiT9D3COJEk78PZi/0FRlFcBJEn6KVAHXA38RZKk8XiTLNMVRfm055j/Bj7uSYLsA64B7MBPFUXpBHZKkjQOuFWSpBU9Ac4vgQ8VRXmgZx0PSJJ0Xs/rP+4JbPpdy0DXKhAMZ4w27NVXT6a1060zbFPlt+1dbkIsQTz9sfe9QIa2ElDT6tSSNUEmk67PV7cGxdgLZm9NK3OmpGhVKfXcfb/LZg3CbIIls8fz5EellDd04nC5j9MdEwgEw4QhiUcQxR+BAAj8AD82IRybxaTzRzOboGBcPDnJUXx+sKFftUbfdh3fh1CjMdHjE8MJsQZxz1s7tRagFfMnkRYgAXGooYOSujbSYkO4Mj9VlwxaWphNUqSN6hYnNouJC7MTOHt0DF3dMr94cYuf4mJt0TRdnHHN2ams/GA/0XarZm7b9zvU+Ahk7i7MZk9NKy6PzPINe7kyP1X7jFtWdIa66ufvLswmOymCc9JjDR/mjR72jRQjyzfsITnKhsPlGVJvlGNJHJzs/iWnYxuU4PgymGRLEdA8lF8qSZIZuAIIA/6Dtwc7EXhPPUZRlE5JkjYD5+JNcJwDtPccr/J/eAOXc/EGIecAH/ckWlTexVs1GgUc7DnmsT5LehdY2PPzYNYiEJy0GG3Y2ytb/JQmqzaVsLggkxCLmbpWJ49fPYX71u/yM5JT24IOt3fpkjUr50/CZvGWlPpuvIHM5zwyWrBms5iobHIYjnusaXbgkWHZ+t1aQBVtt56gOygQCL4jhiQeEcUfgcBLoJaPDYtnaK0kqnfKivmTtLHK/fl6qD8Herg2GhOtKmH7rmNsj2lsX2pbnXy4t46lhRNY2CeBsmz9bs17ZeIZkRyo72DJq9u5YUa6YYKoscPF6qsns/DFr3F2y8SFB+Ps9k5jVNuQ1BhE/YwaH33b3OXn66JOHlLVuUaTlFo6u3G6PZwzZoTftQV62I8Lt+rWnxRp48r8VK5c89mwSgqc7P4lp2MblOD4Mphky9uKotQNxZf1SGo/BWx4EyeXK4qyQ5Kkc3sOqe3zkVoguefnRKDeV36rKIoiSVJdz3vqMZUG51DfO9jzf42+x/ccA63F6NqK8AaCpKamBjpMIPjOMZJ4BlKanBEVotvIlxZm0+HsJj0+jLsLxxMbZtMSKv/9wle6zekPG/awtDCb17d8w9LCbJ1nS0JEMPdfNpG73thpGKT4mtEBLJieTlZCGFF2CzVNDtwyPPOJXoXT3tU96HswnJzyBQLBoBmyeERFFH8EpzOBWj5qWp39elAYPVAvLvDu2QM9XPcXg6gtQcFBJkaPCKW03uuPonqrqCRF2rgoJ4m91cYGvqkxdn7z6nbN360/pezX3zQzJi6Mp647E2uQGUlSvGpdST+Fp+93jIoN1dqm1dfU5Iz6marm3oKROklJjaNCLEHIsuIXewR62O+rwFGTQdF2q9ZGta+mleyk8O+0pfpk9y85FdqgBMOLgZItQ+XXorIPmAREAvOAv0mS9P0h/o7vBEVR1gBrAPLz84f6vgkEwNAkCYwqUoGUJgfq2/0qRndcNA6PB2LDgomwBbG3upW4cJvf5lTe0ElSZDBzz0zlyX+XajLk8YkR1Lc5eemLQ6yYP4m9Na14ZFhbXMGigizCbUEUzUzXTRB45pMynvzJmTy2cT8Lpo/hiR4vGNXMbm1xBWem5Q36HgqJqEBw0jGk++qpXPwRCAZLfy0f/bWS9H2gjguzYTbB5NSoAR+ujb4zzGo2bAlaNCuTB9/ZzZLZ43V7tEeGRzeWBDTwLe2JXXwTJUb+M2o7UJPDpSVmHpqbyx0XjaOhwxVQnWuzmIgJtRo+lKvTlGwWE21OD+u3V/Hna6ZQUtdOSrSd5Rv29LRKlRjGHoEe9h0ujy7BZTahtTr5XlNabCipMYOPDY9H8elk9i852dugBMOPQU0jGioURXEBpT2/fiVJ0lnALYAqoU0AKnw+kgDU9PxcA8RJkiSpAU6PxDa+zzEJfb42wee9/o6p6XNcf2sRCE44Q5UkMKpIjU+K8DOqe/DyHJ77v4MAWrUpPDiIlGg7T3xUwqxxidoGv7ggg7TYEK44cyRxYcHYg4OobnYQHGTWxjar5nM2i4knrp7CzLHxVDZ2MCU1mob2LlZdNRkUqG3rIiHCRpPDpR2/uCATWZZZMH0Mr3/tTcrc6bPWxQWZJEQED+r6hURUIDgpGepM6Clb/BFKW8FgOZaWD6MH6sEoKvp+Z1psCMnRIdw+e3xApYjvHi3LCofbu7hhRjohPVMTf79hry52efjdfdr3qQ/Oqv9M0cx0kiNDqGjq1BV11MTMb17dztM/zcdZ1cLSwmzWbD5gaBKcEBFs+FA+LjGC5Rv2sHxuLslRNuZOSSY12k5MqFVTCqvXZxR7BHrYT4iwcfboWC3BZTF5E0F9W5zueH0Hk0ZGDSqeEcUnf072NijB8KPfZIuiKKbj/P0mIBhvhacGuAD4EkCSJBswA/h1z7Gf4pX5nkOvdPccINTn90+B5ZIk2RRFcfa8dgHwLXDI55gLgId91nGBzzkGsxaB4IRztEkCo6qFr9N8t0dh6Zs7iLRZWDl/Ent6lCaPbtzPVWd5A/XZE5P8DOjWbD6greXDvXX84vsZ3PPWLl0CpLXTbVihOdzexepNXkntLedn8dx/DrH6x3kcbOjkrjd2aqZ5qdF26tu7CLcFcecbu2hyuFhamE1MqFf9Iive/ufMBO/Eg8HQ0NGlqWLAW+2qbnEKiahAMIwZ6njkVC7+CKWtYLCc6JYPNR6JtltYW3QO3R4PFrOJK9d8xi++nxHQdNfZLVPe0EFDRxffNjtZ8up2Lda45fwsFhdk0tntoWBsPGazpBVr+qpZmhwuEiNsPPHvUt1URV+/GWe3TLdbpmB8Ik0ObyHI5ZFZWzRNZ0QL+D2UL7t0Io3tTv4wJ5e4sGDq27tQFPimyUF1i5OH5+VR1ezgb//xJnmM2lP6e9j3TXB9eaiBkdH2Y2p5OZHFp5Olfftkb4MSDD8G49kyJEiS9Afgn8A3QDhec7fvAz/qkd/+CbhDkqS9wH7gLrzS3hcBFEXZI0nSBrzmdEU9p/0LsL7HjI6eY+8BnpMk6X4gC7gduNdH7vsosFmSpNuBN4DLgfOA6T3fM+BaBILvgqPpI+2vaqF+5oerPsbZLXPTecnc0jMdYM6UFC7OS8bZ7eH2i8bxX38r9msnUkc8AszIitcSLeoxj24s4ZF5eYYVmrq2Lu24lR/s5zc/GIvTjebhoprmqUa79/UY4YLXFPdv10/l0rxk6tv9N8L+NnRZVvi22enn97K2uEJIRAWC0xtR/BGclhyPlg+jfRgIaPy6YHo6o0eEBjTdVb1VPD5+bdAbQxTNTGdcYgT1HV0sW79bS7BUtzhZW1zByvmT6HC5OXjYwUtfVHDVWak6k//e6UK939XZLQ+o8MhOCueReXnsr2vDI8OK9/cDcN05abrzq342TQ4XiwsyuXFmOk9uLqPJ4fKLPQb7sB8bGsxXh5qOqeXlRPmTnGwKmpO5DUow/DhhyRa8vcd/7/m/LcB24CJFUd7tef8hIAR4HIgGPgcu7DPa8Gq8ZnLqZ96i10gORVFaJEm6oOccxUAT3hGLK3yO+Y8kSVcB9wP3AQeAK33GLA52LQLBCeVo+kgHqlr4brTBQSa//t+02BBGjwgL2JesEshAruxwB3cXZnOfjznuLedn8c/t33LbhVlay1FsqIUtFU2G59hV3aolWtTXPB6ZMfFhflMKBtrQDzV0aBUx9VyrNpWw5tp8IREVCE4TRPFHIDDmSNUHvsfbrUG4PB5i7MFUNnfwVXkzsuL1hMtJiWR0bJjh6OJfFozlmU/KiLZbdZMO02JDWDJ7PGX17Tx+9RT+/ukhJqREGcYJk0dGMXpEKLMf9RaPfMdVZ8SHc9/63cw9M0UrENW3u/jb9VORFUVT96qTDdXEizVIGnCscnWLk721bazeVKq9dtN5Gdo1qOt7dGOJVqB6dGMJRTPTuSI/hXGJEYaxx2Ae9kfFhpKREOY3HfJIWl5OlD+JaN8WnM6csGSLoig/G+B9Bfhdz/8CHdME/GSA8+wAZg5wzCvAK8eyFoHgRHM0faQDVS3iw23a2OTspHCuyE/R9f8W5iZz8HB7wL5k9fVABrtuWSY5OoSV8ydxoL6ds0bF8PC7e/hR7hms/GC/ruozPinc8Bx94zybxYRb4Yhc/I2SS773w2KWhmV1RSAQHBdE8Ucg6MORqg+Mjl80K5Ot3zRw1VmjtOPe2FqFzWImOMjkt/9edVYqBxvauWFGOgD/2lFN0cx0suLD6OzZv33bl5sdLsM4IS02VGvLAW8SRE2sLJyVQZPDhaL0+s+ZTWCzmMlJjgTgobl5fFx6GEVBU7gMZqxyQoTNL/4JVHxSW5ed3TKyAmemRvG9rHi/ezvYhJfJJDFrbAIZcWFMSY3G4XKTGhPqN7mpP06UP4lR7BVtt1Lf1jXs24oEgmPlRCpbBALBMdCftDTQ5txf1UKWFQ42tLO4IJPYUAuS5B2v6HusJMG6Yn8H/wcuz8EWJPHc9WdR3+aiutnBbReO5ZH39ukSKPERwfzx3X3sr2vnvksn0tTeyS/Oy2DrNy388vxMkqPsHDzcgbPbQ1VTp9+I6Acuz8FqlrRrUIO5pW/u4NmfTfWriAyUXOrPeE4gEJweiOKPQODPkaoPjI5fW1zBwvMy+cWLW3R79ktfVvBAcg6rr55MZZOD9i4Pm/fVERFiMWznueOH41n65k6/9uUlPxjrFyfcf9lEUqPtgHHRxyTBsksnsvbLcp1yd83mMi2BEhcezNMf97Yn3XRehp/xrNG9GBUbSk5KpE5dEqj4pOrZ1DWZTf42VEea8DKZJEaNCDvqUc8nyp+kb+yVFGnjunPS+OmzX5wUbUUCwbEgki0CwUmEkbS0v805UNUiNdrOjqpmtle2EGO3YDabuenFr7l5VoZfkNDkcGmSXEnyGtK2Olz8at02kiJt/PoHmYyMCaXbI/Pc9WfhcLkxSSa+bXYQHxbMDTPTsZhMpMaE0O5y801jJ29ureLK/FR+/co2XdXKI8ua9Pec9Fjq2zqpbnbqxjyr0wPUBEpfGXNabIif8Z0qiRUu8wKBQCAQ+HOk/h1GxxfmJvslSdSJQp8dbOTpj8tYWpiNCfj1D8Zx15s7/I4tmpmOw2Vsrh8VauVPH+zX4oRxiRH89ZMDTEmNJjXazrJLJ2rfr8YVrZ3dZMaHsrRwQsBpQH1jA7PJX50SSImhqkumjorB6fbgcss8dW0+d725o2fEc69ni/qz3WI2LBp9F+02J8KfpO/9vSI/xa/VSrQVCU5VRLJFIDjJGWhz9q1axIXZMElek7rKJgchFjMjwmzc1pP0+PtnFToVy2cH6nn86ilsq2zGbjWjKJAcFYLdauZ3F49nZIydQw0O7ni3dwrRrRdkMSLMymtbKvnVheMwSxJRdgsHDndgkiQe21RCYW6yX9Vo2frdPHH1FBraXcSFB9Pe5SZvZDRJkV1c+9cv/KpEcWE2w0TT/ZdN5LFNJVqQ45tMES7zAoFAIBD4kxRpY8nsscTYrdiDg6hqdvCPL4zN42VZwW4N8ivOGCUp1OSFR9Yb7P/8hWJNyaL6sjm7ZbISwokNtRiqQw4e7qC8oVNrEbJZTCyYnk5dm/fznS63NqlQUWD1plKaHC5+MGFGwGSSOuUoOdLGn6+Zgj04iBCLmTWbe5UuRkqMh+bmkpMcSU2rk6RIG7VtXbpYRB39HG0PpqXTpd3XyiYHT24uM5yCeKIMa080fWMvh8szqOs8WSYYCQT9IZItAsFJzkCbs1q1GBUbqktMpMWGcPvs8bR1ublhRro2AnnDzmpWzJ+EW5Zpc7q56cUtRNutXP//RrHCx2fl1z8YizXIzMPv7tMlTVa8750udNXUNG5/bTtX5qfyq5d7FSyLZmXilhXDNW/5ppmnPy7jdxdP4C//LuWheZOICw/2M4Dzjnl08+WhRr9E011v7GRt0TQ6uz2GyRThMi8QCAQCQS+yrLDr2za/ffa2C8dqLTq+x27YVcPyDXv8WozzUqICerwtW78b6PUvibZbcbo9/LIgk2+aO3n1q0qsQRJpMXaaO7v5/ZwcDh3uYF1xJU0OF3cXZvOYjxGtei6zCeLDbTR0eMcsh1jMuut48PIcUqPttDndhmvbW9OGoqC7jtVXTx5QifGbV7dTNDOdVRtLWVSQoUvOOLtllry6nbVF0xg9IpRDDXDP27v8vrtvIutEGdZ+F/jGXmX1xl6Avtd5sk0wEggCIZItAsFJTlKkjUUFGcg9/cCb99Xx/XHxOFweyurbtWSDrwImKdLGlfmp3OKTeFlamE1pXTujR4SyfMMebrtwnNYXfc3Zqax4f78ukHj43X386cpJhkmTuHAbv35lGwump/spWFZtKuHhACOhFcV7zO/e3sVD8/Koa3OiKPD8p+W6VqLnPy2ns9uDrBhX0Tq7PUxLH3Ec77pAIBAIBKcGhxo6+NXL+sKFOjUnO8mBJHkLO/HhNjq6urVYQm0xjgwJIishnNWb9vslYJYWZvPkR6WaesVmMWELMun8U1RVbFqsnU9KD+uSJXcXZtPu7CY+Ipgmh0u3bpvFRH5aDKNiQ2lzdvP7DXuJtlt1bc/jk8J4b0+tYXLo1guy8MiKX5yy8MWv2bB4Bu8MoMRQ465AscjGvXUcanBwUXYiq6+ezPbKFt2Epr4tzP21Op9KKo/BtHSLCUaCUwWRbBEITmJkWWF3dZtWUbFZTNxz8QSe/HcpqzaW6ioBvgqYOVN6pw6piZe+EwWqmjq14+PCgg0DCavZZJg0CbWateqV0eeqmx38dvY4GhwuLfCIsVt5cnOZdozT5SY+PALw+saosmH1Ozyy18D3VK0CCQQCgUBwIgikkJUV2FPTym0+6tT7L5uoHatO/bnpvAxu/PtXOLtlqpq7NE+ViUmRNDu6+P64eGaOjdf2eke3x08psuL9/TwyL4+XvqzQkiUAf9l8gMLcZNxumRXzJ7G3phVZgbe3VfHbi8ZzRqSNzw82YJIkou1W3SQigP83ZoRfcshsgqz4cBrau2jpNvaHqW7xJjUUBWJDg/s1vVV/7/u+R4Ylr24nPjyYrm5FF6utmD/J7+8QqNUZGFYqj2NN/AympftUbakSnH6IZItAcBJjlPm/9+1dLJiezuMflmqVgOSiaZhNvVN9fJMgRTNG09TZrY1efPWrSj/1SZjNvzfbZjHhURTDFh97sBmbxaQd1/dzOSmRVDU7+f2Gvdrnbjk/S3fMyBi7FmT0rYCofd6AX6VKGN4KBAKBQDB4ArWvmCTYX9umizG+aXRoxyZF2rjm7FRSou3cMCOdzfvqmJEVjyR51R41bZ10umRdkuHWC7ICFmI8ssyV+am6PX3RrEwibGY6umXufH1HryfKnFzMJonC1Z/oVDRtzm7+9h+vD4zNYqKjqzeZ0nck9NMflwVU2nZ7FH646mNN/dvXfFc1vQVv3NQ3FlLjFGe3TEO7S/PGU6/VV6VhlLzwTSiU1bcPG5XHULX3DNTSfSq3VAlOL0SyRSA4iQmU+Zck/e+fH2wk2GzSggHwblpZ8WGEBltY/u4+XbCy9osKqpsdWiIjyOezvoGG3NPS88i8PPbWtmGSYGSMHYfLzeIC77jHvsmQRbMy2fpNi19Va2XPhIFnPilj2aUTOXNktLZx+1ZAQixmFr30tSZJfuGzcopmpjN5ZBRpsaEntbRWIBAIBIITjVFbx+KCTFJj7Nz/zz26Y9cVV7K0MJs1mw9w9dQ0Vvp4ud1TOIEnN5dqBvWrr57Cwp4x0NCrYHn+v6Zis5iItluZMyUFSepRuIYFs+Q1/wlFz11/Fj979ku9J8pr2/nND8bqVDBrNh/g0knJLJyVwetbvqFgfCLBFmMFrtq27BvraImcubks9ZmUVN7QyeoPS3j2+rOoauqkvq0Lu8WstTU1OVxkJoSxrmgaH+ytwyP3Tk7sm/BRUVUaff30jJIXw0nlcaLae8T0SMGpgki2CAQnMYEy/6q0NSnSxhX5KaTG2HHLCu4WJw/Py8MaJHHfJROwW4P8qi3L1u9m5fxJmE0SZYfbeWheHi63B7vFrLn8mySwW8wcPNxOk8NFRIgFk+SdNvDwu3v5+Yx0bEEmfn3hOGxWE09fl8/Wb5rp7JHxzj0zxTBwGBVr57nrz8Jskqhs6dQSJ74VEFlWWDJ7vLYBNzlcjEuM4HtZ8SLJIhAIBALBEaK2dYy9eQYVjR3YrUEkRAQD+PmkNDlctHZ286sLx/GbPvHDvev1ytrtlc0BFCwKq6+eTEltu66IkxEfZnh8Q7vL8PX4CBsP+RSLFs3KxGSCZet3s/rqKaz6YD8vfVnB8rm5LHl1u5/qxGYxcXZ6LN0embVF03C4vMb6B+rbKW/o1H1feUMnTpeHu97Yqal61JakgnHx5CRHAXCwweH3XU0OV0CVxmCSF8NJ5XGiEj9ieqTgVEEkWwSCkxijzP+9l0zgiY9KtVGFfWWtj7y3l198P4PUmBBqW40DmD01reSlRPKnD7yfvX32WP7xZQWFuclIPUmVv/7nIJdOSua+Syaw9M0dusDk/n/uYeF5Gdz7j6+xWUysnD+JR97br70fEqDSFBVi0apXgaSpYgMWCAQCgWBoMZkkxsSHMSZeP3rXSPHy/KflXJFvXDTxVdbKinErseqFsvDFr3VJhtI64yk1MaFWw9dR0Kljutwess+I1BI9P585hgff2UNylI1/3jyDPTWt7K9t44XPymlyuFgxfxI5yVG6+OFAXTu7vm0x/D67NciwJencMbHaOX40MYlou5Xi8kY8MqwtrmBpYXZAlcbnBxsGTF4MJ5XHiUz8iOmRglMBkWwRCIYhgzEfU49JigzmyZ+cyZaKJjwyvFxcwZLZ41EURRu5DL1y3AXT07nnrV0suzSbkTGhhpum1Wyi2dHN4oJMYuxWRsaGoJCqS9wsLcxmzIhQOt0evwqQs1smLixY+9mjKKTFhmjJmvS4MO64aBwP/muvLoA7eLh9UNJUsQELBAKBQHD8UGOMuHCrTvVhNsHk1ChCLEG6ccfgbxr79rYq7r9soqYGGSjJoLYoqZMQ1SKR3Wo2bGVu7Ojym2p0d2E2abEheGQoqWvjunPSGBEWzKgRYYweEUp2UgTnjok1LNTIssKe6lbWFVf6tRY9eHkOCRHGRrm+iYagIBPTM0aQEh1CXZuTuVOStaSIUZFoMMmLvkWmuDDv3+Hzgw0nfDLRcEr8CAQnAyLZIhAMMwZjPuZ7jOpz4rtR7/x2K/dePCFg1cnZLWMxB/HIu3v9AqHFBZmEWs0kRtqoa+vCHhxEXavLb/zy6k2lXJGfwrljYo0rQMFB2s+N7V3c+L0M7n17l84Qd3FBJh0ujzbOee6ZKX7rFc7zAoFAIBCcOALFIWeP9io4Ro0IM1S9qMpa8O79V+an8tIX5aycP4nWzm7OiA5hZHQIAPHh/kmGJoeLxIhg7/HObuzWIP72nzJyUyL8WpnjI4Kpa+3yG9t83/rdPHHNFPbXtKEAj24s4cLsRGDgQs2hhg5K6tpocri0yUXqCOkpqVGkxgwu0RDoe4xeG2zyQj3nYDxejidCXSwQHBki2SIQDDMG07/re0wgV/+RsSGGSRBbkLeFp7qlk6rmLuLCrFoAoyY9mhwu1lx7JgC/fmUbN8xI141fVr1gkiNDcHsUQ5VKU0cXiwoyGBltJzTYzK3r9Cob1RBXPac6+cAX4TwvEAgEAsGJxSgOWb7B247jcHk0NUVfnxeXx60pWBUFbWpgo8OlU6usmD+J0GB/4/1FszK5b/1urjorVYtFHrg8h6TIEHZ/20pmfDgOl5v6ti7+8Xk515072jD+2VLRzNMfl3HbhWOJtltpcnRRVs+Ao4prW518uLfOb8T04oIsUmNCj0ui4UjPeaIMagdas1AXCwSDQyRbBIJhRiDzsXKftqK+xxglVaLtFj857uKCTMwSLC7IJCw4iBtnpvNVRTOrNpb6raOl001nt0cb53jPxRO49+1dRNutfl4wyy6dyKofT6asvh2Hy8PImBDanB5NYryoIMPwmsym3vWumD8Ja1DveOrjIU0dTHuWQCAQCASnA4H2xL4xRlKkjSvzU7lyzWd+aorRI0KRJG/sYrdYWL+9StdavKggQ4tDoDc58Mi8PCQJVlzhnWboO8Hn0Y0lWjFm1cb9LCrIYsUH+haiG7+fwegArdDqpKFH3tvHyvmT+LbFycclh1lXXKl5tRgpQZIibVyUk6RTjSwtzObMtF5fl+ORaDiScw6nyUQCgWBgRLJFIBhmBOrf/fobb1LEZjHx1LX52jGvfuXfW7z66snsqW5nzeYDmlv+uMQInvyolGvOTuNPG0tocrgomplOcJCxWW1lk0P7vlvOzyLIBEUz08mMD+fXfSYQLH1zJ0Uz00mMsDG6R15879vbtWMCmeRlxIfzl59MITMhXEuqvHOcpKmDac8SCAQCgeB0oL89sW8cMmdKil+7zq3rtjL25hnsq23TnWPZpRNp6uiitcuDWYLxSRGGyYG9tW08/XGZt1izsVc1e9N5GUgSjE0IJynSRmFuMne+vkNnhOvs9hAREoTJJPHg5Tnc8foOv0lD6vfsqWnVYhn1vUBKEI+MVkhSP79s/W7+efOMY77XRkmtoykADafJRAKBYGBEskUgGGYY9e+q7v/g3fzvenOHNsqwusXJ2uIK1lybj8XsNVuTZYXC1Z/g7JZ1bTpFM9OpaOqkusUJeJMg4cFBhgoYFbXlp2hmOqs2lrJwlrFKRVbgvvW7tZYk32OMEkKLZmXyx/f28uzPpuoCnuMlTR0O0luBQCAQCI6VoVBpHjwceE/sG4dE2oIM9/1yg31VLb6s3uRNcCyfm0tabIhO7WKzmBibEE603Uplk6NHjWv1M7u975IJJEXaSIgYR6fLG4uo76XGhvL05gPsqGrnkXl5OFxuKps7NXWM+j0euXe9qzaV8NC8PPbXtlHf3uV33+rajFUj9e1O3ZSmIyFQUuvC8Qm8t6f2iAtAwqBWIDi5EMkWgWCY0bd/V0Lil2u3asEDQHlDJ8lRNkMViCwrvLOj2jBgSI2288f3vSOYbRYTEcFmzGaToQJm5th43WdlnwkD/cl21eN8j1ETQs/+7Cw+LWvQxiEumT3+hAUIQnorEAgEgpOdoVBput0yVc0ObpiRDngLItUtTt2eOHtCItmLZ1Db2oWCYrjvW4JMAYsv6s9LXt3O0z/N54a/FeuKLY+8t5drp6WxYWc1SwuzqW11+qln7n5rF0Uz00mIsPHiFwd17935+g7+fM0Uzq7voLyxg7gwKyEWM00Ol7Y+X5WL+rnSujZWbyrl6Y/LdPdNlhXs1iDD64wLs1FW335Uya1AhZ61RdOOqgAkDGoFgpMLkWwRCIYhvv27ZfXtWvCgYrOYiAkNNlSBHGro0CpFfQOG+vYuqlucWmvQ6BGh3PzS14YKGI+s/z51HzdSqdxyfhayorCoIIPM+HCe2nyAW87P0lWhfnXBWM5KiyEhwqYbhxhopPVQ+6oI6a1AIBAITnaOVaUpywr/3FnNkle3+7XeNDlcuj1xd7W3ReiX52caqlMdLk/A4ouKs1umw+nmuevPoqpHWauqT1ZtKqFoZjpxYcFEhVgCJm6Wrd+tM9RX3/v6m2Y8MjzzSRm3nJ/F5JGR/PGKPNqdbsJDgli+Ya+uUGWzmLD2mMX1VfJs2FXD8g17/K5zxfxJHGxoZ+GLXx9VcitQoUdNbvV9fTAFIGFQKxCcPIhki0AwzBmsZFSWFcrq2znY0EGIxezn8n/L+VlIKDxyRS6JETYaOrpocXbj7JZJirRpvdAAeSmR7K9tZ+GsDMwSpMeF4paVnilGXpXKn66chMstU9XciS3IxO837NV9V3RIkG5Moz3YPGCAcDx9VYT0ViAQCAQnO/2pNEfFhg5YrDjU0KElWtTPqkmPUbGh2p5Y0djB3ppWbpiRTnKUnb/9p0wbhWwLMhFkAqfLbdiGrLY9Q0+CI8jEkle3c9VZqYQHB+nWnRpt5563dvHTc9L6Vc2qhvq+73lktImMKz/Yz5prz6ShvYs//7sMa5DEzbMyeWxTCYW5yZhNXv+YNoeLpEibTskDaLGBOvLZbIKCcfFEhliY/ejHR53cClToSYoUBSCB4HRAJFsEgmHOYCSjsqzwr501/OrlrayYn8fvN+wl2m5lwXSvAW76iFAsQSZcbpnXvvqGOWeOpLyhkzNTo8hPi2TWuERdJefeSybw0pcVlDd0aoZ3CjKLCzJJibbj6HJzoL6dSFsQk1Oj+NmzX+oCEV+PFxWbxcQ7AwQnx9NXRUhvBQKBQHCyE+jhPTHCNqhiRaBkTUpUCFNSowA4dLid4vImbaKgzWLinsIJPLm5FJdb4bpz0rTpQGmxIay5Np/i8kbGJ0ZQ3+bUtfIsLsikpLaNwtxkHt3oTerMmZLC4x96PV0ibBbmnpnC6Lgwbp89jj/4FG5UxY3NYiIvJUo3rXDRrEzWFldQmJusXUNdWxcAq388mehQKyYJimaO8UsGXXdOGss37NOSG773pLrFqSlozh0Ti8PlOaYW5ECFnglJkaIAJBCcBohki0BwEuCrCJFlhYrGDuraumjp7CbCZiHEYuKhd/ew5AdjkZB0rv0uj8wD7+xh7pkpvL2tihu/l6FNE7JZTDx+9RRuenGLLsFxz1u7NMmuanj39HX5HG5r0X32gctzaHa4+u3Z9n2tv+BElhXq27r67SEfyvsoEAgEAsHJRqCHd4+MYbEie/EMZAVN7RIfHiBZExlCSpSdDbtq2FvTqiVa1HPdu34XT12bT0tnN7f5TCQsb+ikuLyRVRtLSYq0cdsPsvSqVouZJzeXMffMFC02kCTvd95z8QQe+Nduyhs6SYsN4bYLx7K4IJO4sGAqmhxaa9Pigkw6XN08fvUUtlU2a75vV+an8sJn5SRF2rgiPwWPrBAXYSOoZ6//vKzBb+z0oxtLeHhenl9yoz+VydEoUHxboscmhLNh8QxqWvWFHlEAEghOfUSyRSA4iZBlhU37aimr72DF+71+KI9eNYm7fphNi7ObYIvE9f9vlO59dbpQYW4y9769Sxd4bKtsNkyWSJL+91an28+87s7Xd/Dsz84yDET6xgv9BSdG7UOBesgFAoFAIDhdCfSQ/vnBBr+9PNpuZUtFs2408uqrJ/sla5bPzeXc9Fgqmhzcum4rN8xIN4wLvixv9Js2CN7JhmqbcX2PugS8o5Sf3FxGk8OFovTGBpNHRjH56snsr23n4jyvMsUWZOLXr2zXtTZfkZ/CmWnRBJkkVr6/D5dboWjmGBQUpqRO4L71uwC47pw0Xdv0A5fnkJMSRYfLbXgdVrNXaasmN/prM5ZlRVPuyAq8va1qQHP/wbZEiwKQQHDqI5ItAsEwRa2KNHR0YTWbcLg82K1BlNW1a/Jd8AZTrZ1ulr65E2e3zKKCDL+K1KMbS1h4XgbBBpMD1CCpP4M7m8VEiNVsGLS0dHb79Wzfcn4WtiCTTvLbnzzWqH1I7SEflxghZLUCgUAgEPRg9JBu1F50RX6KlmgB79668MWv2bB4huE0Q992GqO4QPVI6fve29uqWHbpRFZ/WILbo5AaY+ebRgfrvqqkyeHSWn6WFmYTbbfwTaMDe3CQLkFy/2UTDVt5FhVkAPDzGWNwdHvYX9fGy8WVWIMk7r1kIm1Ot6a4Va/xztd3MHlkFGkxoYbXER1q8bufRgkswG888/K5uVw4PqFfBcrxbIkWCAQnFyLZIhAMQ9SqyPINe7gyP1Xnp+INVqyaw/6cKSlaogUwrDqp0t3M+DDDIOmeiydoihebxcR9l0zk8Y9KADQPF7vFZBi0JEbYGJcYzgv/NZWaViclde08959DWIMknvzJmQQHmQacKhSoh3zyyCi+lxUvZLUCgUAgOC0Z7IQ+I3XG2IRww721ptXJtPQRfg/+asLGaOrg0sJs1mw+gMut+L13ZX4qErKfP8qySycSE2rB4fKwtHACy9bvoryhk0UFGbz5fwc1w12Aw21d3kSITxu0WYKclEiWvrEL+cwUIoLNWM0mflmQiT04CLdHpqSuLaCnSn5qDMvn5uomL916QRaLX9pKk8OlU5sYJbDK6tv9kiZLXt1OTnJkv0mT/kyM1c8dr8mLAoFgeCGSLQLBMEStiiw8L8OvdWfZ+t2a+WxucgTjk3qDqaRIG2MTwg2TIpnxYRxq6GDZpROpbHKwrqcytLRwAh5Z5qnr8qlrdTIiLJiWzi7uuGg8TrdMfHgwB+raeOCfe/wmHC2alUl7VzeTUqMZNSJMCx7OHRN7RP3HgQz/0kTwIRAIBILTlCOZ0KeqM8bePIOKxg4ibBbau9xH5Dfim7B54bNyimamMzo2lOAgE40dXdw8K5O73tipvZcabaem1Tuh8A9zcrn+Ob1Z/tI3d/L3BWfT6XKy0Mcbzm41+xWSHrh8Ig/Ny6WqqVMXZ/zu4gkkRwVjkmBKWjT1bS721LQiK/DZgXp+8f1Mw2u0W8y8t6eWFe/v06YLjU+M4M8flWrFKl+1ieqHV9vaRYfLTVpMKHVtRzeeOVBMo9734zl5USAQDC9EskUgGIbUtjqJtltJjLQZbvRZCeHcXTieEWHBRIZYSIsNIdJm4cbvZ1BW387K+ZP4w4Y92jSh+y/LwS3LugDmnosnEG4zs72yGVnxVpAy4sN47v8Osre2nWunpekCoUWzMvnXjmqKZqaTHBlCRVMna4sryB+Vh9stExRkOur+YzGWWSAQCAQCPUfTjrKvto1b121lwfR01m+v8lOhLJ+bG3Bv7dtOExdmw2yCvTVttHV56PYorJw/Cafbw8HDHfzx/f00OVz88Yo8zCZ4eF4eji43hzu6+PtnFVS3OKltdRJlt/SY3HoAGD0ijMUvfa1rBcbj+AAA9kZJREFUhy5vcDB5ZJQWp6jX+7u3d/HENVPITAjlQJ1DS7S8va2KK/NTeenLQ/z+8hx+6+NLs2hWJp+VNWgt12pLks1iYsH0dLZXtWrnr211khpt57NDDdS0OL3tT8Xe9qenrs0/KnPcgWIa0WYkEJw+iGSLQDAMSYjwOut/0+gw3OhDg4N46N192ib+8LxcHC6PbmNfWphNh7ObtNhQgswSN734tW5jf/LfpRTNHKMb7bi4IJOrzk6jucPFty2dWruS6qGyYLpXUbNwVgbPfFLGolmZ/PqVbdw8K5PL8pIJCjIBRy6PFa78AoFAIBDo6duOohrH7q9tA/DbJ30f4iXJOynohc/KtXYdRYHkKNuA+7FR0eTg4Q6W98QdabEh/Pai8dz5o/F82+SgpdNFRaNDV9C55fwsXvyinN3VbTzzSRmLCzJ5tcfD5b5LJmrxRVKkTSvuBDLm3f1tKy2Obl1CRY1xJqfGMjImhHVF09hb00aINYiqZgcyxi3Vvub/abEh2IJMvLHtW+56Q5+seeGzcu56cwfL5+ay4v19FOYmYzbBWWkxpEbb+/27DRTTDKbN6GRFtEcJBHpM3/UCBAKBP6NiQ8mID2Ndsbdv2mbx/r+qqlL53Vs7dYmTkrp27nlLP2Vo2frdtDg9uGWFbZUtfht7YW6y4UjEbrfMktd28JfNZVw7LY2kSJv2vmqMNy4hnAXT03nhs3LKGzq5642d7KpuAXrlsT9c9TE/fupzfrjqYzbsqkHuOwu6D+pEgPhwG7WtTg41dAz4GYFAIBAITlZkWaGsvp1PDxymrL7db89T21EALSnxzCdl3Pj3LYZ7a9+HeHVC0OMflrJ6UynPfFJGTGjwEa/TI6NTnJQ3dPLLtVvZX9tGi9NDTWuXnyJl5Qf7WTJ7PK9tqdTiizlTvOOf735rJ1fkpwBe3znfdmn1en2vITU2VEu0qOdftn43AJkJ4bQ53TQ6unn8o1Ju/sfX/OmDEsJtXtVv33Opz/1psSHcODODD/fXa4kW9dyrNnnXWt7QSUxoEL8syOKZT8pYtbGUn79QzHt7agcV06THhWneOCaTpP29TZJkeJ0n++TFo43/BIJTGZFsEQiGKYk96haTCW67MItH5uVSNDOdw+1Oyhs6dccGMsU1myDKbtEmDvliNhl/pqPLTVKkjQXT03G6Pdzxw/EkRdq0IGVpYTYPvLOHxz/s7Xt2dsvU9PwcSB57qKGj3+sVm7RAIBAIThcGs+ep7Sg2i8kvKWG0t/omZ1STW99iTX/tuf0lfgJ5l8iKdzpRoBiktK5dFyeoqhJnt0xqjB2bxYQk9X7WaM1LC7NpMPj+aLuVUJuFhS9u4frnirnx719xZX4qST3t18vW7+Z3l0zQneuW87OYeEYkiwoy+NWF47h3/a6Aa1eLS00Oj1+iZzAxjdH9Vf/ev1y7lcUFg//bnCwcbfwnEJzKiDYigWAY4Cu7TIq0sbu6za8l6LEPSyhv6GThrAy/1iKzwShGm8XEuMQIHN0ePjtQ79e3nZ0UYfiZtq5uP7+Wey6eQLTdggLUtzlpcrh067dZTCT2KGCOVh4repgFAoFAcKrSt73CJDHgnufbjrK/NvDUHfV4X6+Q6havce2aa/OxmKV+WzoGMmwNZPhq6km0BIpButz63xWl9+dQaxCLCzLJSY7kaZ8pRCYTPDIvjy63h7LDDtZsPsDdhRMMx1r3Veeq7c6Pf1iKs1umtdOtecUoCjz3n0PMPTOF1Zu87dC+ahqja7vl/CwOHe4YkpYf3xinusXJ8596TYYnj4wiLTZ02LbbHElb0KncHiUQHC1C2SIQfMf0rW699nWVXwC2bP1uCnOTAW/lZ2lhtq4iEmO3cusFWX7VoL9+coDS2nYWTB/D2uIKFkxP549X5LJy/iSe/vgAt5yv/8ytF2TR7VH8qmf3vr2LHVWt3LJ2K50umd/OHtentWkiE5IiAX1lTWUw8thAm3Rtq/Oo761AIBAITm8GatU5UWvoq2LZUtFMtN2qO059MPVFbUfJ6pk06EvfvVVNzryzaAYvFZ3Nsz+byvSMEZwzpreVxWhtO6qa2VvTyg0z0jVliK8iwVdho37vw/NyyTkjkohgM6Ni7X5KjXsvmcD67VXa74sLMnltS6XmifKHDXtQFLCYYfXVk7nuHG+L1B/+tY/bXtnG4XYXr22ppLyhk/vW7/KLe0ZG2/v1ZLFZTByob6ez28PqTaWaGldNDKnHGKlp7r9sItEhFp77zyFcHnlIWn76xjjVLU5WbSwlxGoO+Lf5rjlSxfHRxn8CwamMULYIBCeIQNWBvoqO/lqCwLtBr/2igpXzJ7GnphWPDH/9z0FumJ7O6h9PZntVCx4Z1mw+wNVT03juP4f41YVZ/OycUYwIt+GRFcoOt1MwPhEFhYfm5XHocAdZ8WF0dLspb3AEDGDUPuyF52VQNDOdjLgwxsSFEhxk5svyRhIibKREhnD/ZRO5642dWoXs/ssmDmgoZ7cGGY9vtJqH4O4LBAKB4HRjuIzYNVJu3vH6Dopmek3nVQYay7z66slsr2zR1CQ5KZGMig01jC8GoyQwuj/3XDyBbo+H1k63zoj3wvEJrC2aRnWLk7iwYL6qaGLF+/s1w9wHLpvIiivyaHO6qW/v4n8/K+fSScmMT4zAHmwmymZhZLSdjp5pRS63t7Dzwn9NxWbB0PNFVamUN3TS4ezmL9eeSX1bFxWNDuranIYxg9LTNq2Z3BaO146zWUzkpETyxysm8dC7ezTFrzbmekQoI6NDGBEWzOxHP8bZLWvJGF+179G0/Aw0Dvp4czTGtUeqOBaTJQUCf0SyRSA4AfQX8BkpOow25PGJvW0/++va6fbI5CZH0uHycOmkZJ746ADWIInbZ4+npK6dwtxknvvPIZocLurbulAU+PUr23Ru+89/Wg545bgtnd1EhVrJT4sOGMCAd7PNjA8jMyGc1Gg77+2p1V3XmmvzeaxHyqtOP3hsUwlTUqP7Df5cHo9fQLNoVibdHjngZwQCgUAgCMRwaU8NpNxU1SqDfTB1uRXdBMEV8ychy4rfPjzYhNLBw/735963d/HE1VPY2t7Mzm9b2VPdSnZSBG5Z4Vcve2OI1T+erCVawGuYe8PzX2nJEZXtVa08NDeH8sYOYuxW7u4x8rdZTPx29jjautwcanBgs5i16US+98dXpTI5NZp9Na089clBfnXhOFBklhZma61EqqI3zGrWDPybHC7Cgy1sWDyDmlbvVKDUaDsfldTx6wvHUd/mZM21+WyvbKazW+ZPH+zn1gvGMiklelDtWEeSwPguExFHm3Q80rYgMVlSIPBHJFsEgiGiv023v4AvIcJGWmwIhbnJSBJkJ4Zy3yUTufstX2VIDpIET12bT5uzmyCzCY+s0Njh4oyoEBrbLcw9M4VYu4Ugk4Rb9gYp1iCJxQWZWMwSj7y3X/f9qzaVsPC8DMwmSTeu8fGrJ7O4IFP3mlohgl5/ltpWJ23Obr/rKi5vpLyhUxdwAQP27MaGBmutTmqSZm1xBbMnJg7530ogEAgEpz7DxUMikKohNTqEf948g/r2gR9MA8URa4umHXVCqbzR2I+kxdmtS+osLsgktCeJERxkwmyS+lXg+l6j3RpEXVuXlmgBr7mto9vD6h5vFfU7nv+0XEu49FWp3PbKNq6emsZ/nTua0ro2Xi6u5MaZ6RTNTEdWwCRBuC2IP763j/KGTu1zS9/cwbM/m8q09BEAlNW3s/DFr4m2W1lamE3RC8W6a1ny6nZykiMHTBocaQLju0xEHG3S8WjUOIFGhwsEpysi2SIQDAEDbbq+AV9SpI3rzkkjJdrO/ro2xowI47YLx/LrV7ZrFaN1xWU8NC8Pl9tDlN3Kkx+VcHZ6HGYTTEmNprLJwf3/3KNr09m8v55Z4xPY8W2vxPi2C8diMUnYrEGGVaO02FBN7aK+dt/63fz6B+MomplOkMlEZnwYf9iwh+oWp/Zdi176mvKGThYVZPgFXOrkoyOVyo6KDWXJ7PFCfioQCASCIeG7bt1QMVI1LJqVyc0vfc2S2eMHpUIJlDiqbjF+vbyhY8CH+dAA7bsHfUxhnd3esc1P/uRMHvzXXpzdMosL/I36bRYTk0ZG6ZQ6i2Zl8vB7e1l4Xqbu2DlTUvzahh7dWKK1ValFJqsZFp6XwQufeZMwahtzZnw4TQ4XT24uY86UFE0BkxZt1wpXioL2Od/kmnofq1ucVDd3agUe8Hri+R7fX9LgaBIY31Ui4miTjqItSCA4dkSyRSAYAgbadNWAL9pu5caZ6Ti6PbqWnsUFmVoyJMgE/zV9DHtrWsmMD2fZ+l1cmZ+qa6/xPd7ZLXPXGzv52/Vnsa+mTbeupg4XHS4Pj24sMawaRYYE+W3A5Q2doCh8LysOl1smKdLGX386lfp2JyEWs5ZoAePEytvbqlg+N5clr24/os15uMlPj6a/WSAQCATDh+HysKjub8lF09i4tw6P3JsIGKwKJVDiKCnS+PWvv/G2xvSXyEmMDNb834KDTFhMEnERNr5t7iQp0qYb21zn88C+rrjSr4XnnsIJPPGht4XYbIJxiRE8+ZHXb6WyyaFbo++4ZxW1rWrVVZMorW/nj+/to8nhYtGsTN0xLo/Mg+/s0RS4j39Yqv1dI+0WnvmkzO9ehFjMfHrgMAkRNu1+Rdut2Kxmnnlvny45tLa4YlDJuOGimhoMR5t0HG5xmUBwMiKSLQLBEDDQpqsGfHtrWmlwuDR5rnrcoxtLeGheHt82O3D0GOc5u2UWFWRQmJvsNx3o0Y0lrJw/Cbes4Ogxm/MoCh0uj5/0Nz0uzLBqtOzSieytbtVtwEmRNq7IT0GSJLZXNjMuMYLqFm+yYeqoWD4/2KAlWgBD47gls8dz4fgEcpIjj3hzHi7y0+FiqigQCASCo2c4PSyaTBIOl0dniAuDf0APlDiakBRpqJpRPUuyF88AoLa1iw6Xm7SYUEaP8Cabdn3bpvvc4oJMnv3kIN8fF8+tF2RR1+bk759V0ORwUdfWpa2lusXbRqy28GQlhPPH9/ZS3tBJcXkL4H2YXzA9ne1VrXy4t45ll05k9YclFOYmMy4x3PDhXzXk9b1HvuOcbRYT54+L5/+NGYHL42Htz6fh6PaQEOH1YilvdPDIvDxK6tpYV1xJk8OlU+Oq90w1GjYaHb3m2nzDZFzfAkygJNdwnLxzLEnH4RKXCQQnKyLZIhAMAQNVDdSAL9xm5tsm48RMaV0bHhn+9MEO7X1ZAbPJuALU4XLrJv6sufZMQ1nuE1dP0X5Pjgxh4awMrQ/a5VG4/7KJfNPo4MO9dVyUk6TzalHVME0OFyvmTyI7SR8gqcZxa4um0dnt0QWyJ/PmPFxMFQUCgUBwbAyn/ehY2pr6SxzNnpBI7PVT+bj0sK59JinSxq5vW6lodOj29hXzJzE2Idxvn3t0Y4nfQ/ndhdlE2S0s37BXW0tSpA27xUxOciRd3TJtXW4uzkvW2nDU80mS99iLcpJY+2U5/z1zDPet30203RrQG+7HU1N1162ex2YxsXxuLo0OF+1OD2WHO/DIMjkpkYZm/X+8Io/ESBufHmjQre3WdVv5580zMEnGvjMWs+SXjAtUgFl99WQWvvj1sG+xGU5JR4HgdEMkWwSCIaC/qoFvNUSWocnhMgy2MuLD6exy615/9atK7i7MNjy+otGhC5Lq2roMA4eGDpf2mfr2LlZv8laHVs6fpDOne+LqKfzixS1+gZdaUVIDlL7XuWT2eHKSo06pTftkkgcLBAKB4OTgWNuaAiWOTCaJuPBgnv64VzWbFGnjzh+Op73LjbPbo2s9vnXdVh778WTDfW5vTaufj9ujV07mpu+NobLFSZDJRHZSOI0dXWyvbDFMmKgeb4rinXaoxhL39ShJqlucPP+pd9xycmQIFU2dmhInMz5M18Jks5jITgznyZ+cyepN+5k1LtGvrToxwqZLHEXbrVQ0OrTpSX3X1uToIjbUyqKCDGSl16vFa+jb23KkJiQCFWD+efMM3hkGCYzBtD0Pp6SjQHA6IZItAsEQEKhqAPhVQ347exy3nJ/Fyg/263qd//jeXi7OS/ZTjjzzyQEevDxHay1Sxxuu3qSXIte2Og2TMurrd1w0jqTIEG67MIvxSRH8+SO9CmZrZbNh4JUaE6IFPvXtztOiOjJcTBUFAoFAcOpwPBQGvg/aT12bz6Mb9/H9sQkkRtp07TS+UwXnTEnBLEmG+5xHHwbg7JYpqWtjRFiwrk155fxJ3P2Wvg1nbXEFv/3heErr2shLicItyzQ7unF2ywQH6b+rusXJqo2lLCrI0FqEFs3K5A8b9nBFforW8rxoViblPcqcBdPTDduqJyZHDmjAq7Yjrd9eRVWzU+crp3q13Dwr06/laPaExIAFmPp2J9PSR3ynCQzR9iwQDG9EskUgGCKMqgYH6tr9qiG/37CXxQWZmgP+/xsTy+2vbae8odPQA+W6c0YBaBWg0OAgvm120ORw6b5/XfE33HvJBO7pGa9os5i45+IJtHS6WFyQSWe3zM0vfa0LLqqau7TqUaApQlXNnVw7LU0zjTsdqiPDxVRRIBAIBKcWQ7mH9n3QTosNYeF5mSx9c6dur3/hs3LWFlfw0LxcDrd3cehwB3/6YL9fK8/dhdn8ZfMB3XfYLCZSY0P5TZ/JhQfq23F2e03050xJIdxmJtxm0Y5TVSdqC9DoEaGGMUZ+WjRrrj2T4CAT1iCJLw+GMmZEKI/9eDIAFY0dBPW0/AQy1nV2ewZlwGs2wbJLc3TjntVEzN8XnM2vXt6q+dL5tg8PlwKMkYJFtD0LBMMbkWwRCI4Tsqywp7rVcMOPCbXy/KclWIMkMuPDiLRZeOLqcbg8CjGhFv76szORkGh2uAkLNrOnupWXiyuZe2YKr35VyY0z0/2CpIXnZSLLHn7zg7HEhdsItZq5842dVLc4uem8DJ1Dv2+V5/EPvQqZt7dV+U0X8DXZC2Qadyoi+psFAoFAMFQM5XQ7WVY4eLiD8sYObBYTlY0dZMWHMSMrnnGJ4dqkQ+jd6xcXZKIo8PPni3X7+792VGuFnIgQCyFWEzd9P4O7fYo2i2Zl0tCmV3bkJkeQlxLFHReNJdRmYdn63SyYns6fPvBXnSw8L4NFszKpbnb4FZMWzcqko8vDHzbs0dQky+fm0NUtc/dbvWreBy7PIS02BDAuCo3uUyAxS8bHFYyLx+HyGMZlh9u7dAMA1Nfr2pxMHRXrV4B58PIcTJL373EiYoNACpa4cKtoexYIhjEi2SIQHCcONXRQUtcWUC1y3Tlp5KVEUtnk4Mdnp3GrT2/xvZdM4OXiCorLW7Tq0I0z02l3uWlyuHhycxnXnZPGyvmTcLo9HDzcwYr399PkcLHs0on87T9lTBsTp6lf+qvyqGu6Mj+V17d8w5prz+SLQ006kz3A0DTuVOZ0UPAIBAKB4PgylG0eRue69YIsrv9/o/nt6zu4YUa64V6fEm03TMIsmJ6utfJUNHUiSbB5X502PdFqNhFkgsSoEBYXZLCuuBKAK6em8vMXilkwPZ0VPQmWgKoTt8yrX1Xy0NxcHt24j4fm5dHpcmO3BvG3/5Txw5wzdGqSA/UdfhMb73x9B2uuzWfpmzv8EjYr5k/S9mq1QJIYYWNsYoTfPc9JjuJQQ4dhXDYiLJi02BBdwkVVr6gFmLE3z2BPTSv7a9t4+N192vCAE9GyE0jBsrZo2rBQ3QgEAmNEskUgOE7UtjpZV+zfFuSrFvnb9WcxMiaU65/7UreB3vPWLh6al0dx+ddadahoZjohFrOmPlm+YR+LCjJ0QQnA0jd38uRPzkQCsuJz+M2rOwDjKs/UUTEsKsjAI8Pa4gquzE9ld3WrzmRPPTYhQmzcAoFAIBAcCUPZ5mF0rhXv76doZm+SxWiv73IbqznUFp+cMyK5+61dzM9PYX9dO09+VMpNszJp6ezm8Z5xzWYT3H1xNlVNDk0B2zfBYvTdoVYz1S1OPIrM3DNTdW1G91w8gW63R7cuWTFO2rjcHh6amwfI/H3B2Rxu7yIpMoQJSRFaoiM9LkxrrYkLt7K2aBoOl0enJjJqE140K5NfvbyVm2dl0tjeRWuXB7MEOSmRmqLXZJKQJLjt5W269R3Plh1fRVRnt/Hf0OHyiLZngWAYI5ItAsFxIiHCRpPDxQuflfPwvDz21bbp1CK5yRF0uDy0droNN9BOl1v3u6xAh8sbABTNTCc5KgQCBCXF5U08/XEZ9182kVvPz0QGvxahBy7LIdgikZ0Ywf66dgpzk3nhs3KsQRLLLp2o6/lePjeXho4ugNOunWYo5d8CgUAgOL0Yyul2gc4lK96fjXzfFhdkkhpjN0yEmCS446JxSBJcc3Yqk1OjmJIaxe7qNsrq23npS28Rxvd8y+fmGiZYAn13SrSdx348CZMkce/bu3SJonvf3sUj8/IANO+XcYnhhmt1dsssfXMr152T5jfGWlWWBFIRnT06Vtu3VZVKctE0Nu6twyP3xmV3vbGTopnp2tTGFfMnHbe/5UD0vZbFBRmG9yUhwsbZo2NF27NAMEwRyRaB4DjhWz3ZV9vmN5Lxyqmp/OJ/t/DwvDzDDTTEGqT73STB+MQIQqwm6lpdVDU7tPf6flbpScLc9cZOnrhmCr/43y1E260UzUwnNdpOTat3stCdb+zg/stysAVJuDxgDZK46qxUJiaH87frp+JwuTGbTCx9c4efO//psJELl3+BQCAQHAtDaa4a6FyhVjM3nZeBJIHJBIsLMhkZY+fQ4Q6e/7QcW5DJz0D/vksmosge2lwyC//Ra57/hzm5vPRlBb/4XgaFucl+03/K6tsNEyzVLU7WFlfwpysnsb+2nS63zPOfelW8RTPTGZsQbpioQIK02BAtqRNtt/p50i0tzKa+zWk4ZchXWTJYFZHJJOFweVi1sdRvPWriKtpuZW9NKzaLiVGxoYyKDT2hRrl9r2VdcaXffVEVLKLtWSAYvohki0BwnPA1WW3s6GLMiDB++7p31OAV+SmayuSpzQf43cUT+N3bvUHQvZdM4Pn/lAFo1aGRMXbcssz+mg48CoyMsZMYEUxocBYr3t+vC0rWflEBqNJbmaKZ6cgKeGT4Y4+3y4Lp6T0JmR0smJ7OM594lTBx4VbGJkRiMkmU1bfzw1UfD4n8+WREuPwLBAKB4FgYyul2Ruf69Q/GEhcezG9e6R1lfE/hBJ77vzLOTvd6t01Ji6bV6eKheXkoioIkSTy9+QAXTkhk9Yeluj3u9te2s/C8DOzBQZhN3qTDnCkpSD31hQ/31mnqVzXBsnL+JErq2nHLMrUtTv7xRYXm9waQHBWC2WQ8ajo21MJDc/P46bNf4OyWqW5x8vyn5RTNTCczPpwou4W2zi7q3f7jo9U117U5GRUbSn1bFzfMSAe8Kp/qFmdA5UmgxImieAti105L8/OGuXB8wlH/LY9UJdtXRaPel79dPxUFRShYBIKTBJFsEQiOI77VhkkpMskxwTi6ZJod3domur2qlXluDyvnT8LllhkRbqWqycGleSn89Nx0woLN2K1mOlxuzJIJKVrSeobTYkP4/ZwcnrhmClu/acYjw5rNB7gyP5X6dhdNDhcmJBIibIZThsAbqITbzCyYnk5Fo4PM+HBt/SdSMjscOd2vXyAQCATHxlBOt/M1ai2pa6Pbo2ANMrH4pa/17Tnrd7FgejpmE9x/2UQkCewWCz9/ZQs3zEjXlLbXnJ1muMfFhwdT1ewgPy2aEItZp6ZYXJDJyGgbC6anMyrWzjdNDu5bv1tLrqjHeGSFuLBgQm1BhNvMdLsVls/Npay+nXXFlTQ5XDxweQ71bV2YTSai7VbtHNUtTlZtLOXWC7J44qNSbjk/ixe/KOf2i8Ybm9uGBvupUH398YyUJ0aJq8UFmTz/aTlzpqT4KXpuXbeVdxbN0O5/RWMHdmsQCRHBA/7djkYla5QManK4iAsPFvGHQHASIZItAsEJ5JtGJ3f2TAzw3UTr2l3c/dZuABbOyuDtbVVcmZ/KA//aowsc1hZXcNVZqUTbrQBcmZ/KZ2WNrNlcplWfLs5Lpsvt4fpz00CSiAwNYs3HpTw0Lw+rWWJ3datuylBabAgRNos2snHN5jItCBisZPZU9TU5kZJhgUAgEJyaDGWbh8kkMSbee54fPfYxv75wLAump2vKE1XRYTZBVnw4jR1dNHW4uPutXfx29ji63B7Nw80eHGS4x8WFB7OuuILJI6P82nYe3VjCU9fl88wnZdwwI51Xv6rUKV8276sjym7V/FnSYkO48XsZ2u82i4lll04kJtTCfet3ay3KaqLDN2mTGR9GtN3Kyg/2s2B6uqbe9S0eLS7IpMvt8VOhrtrkHSwwLjHCUHnSNwkWF2bjYEM7TQ5XwMlKqoJmX23bESVOjkYlO5SKKIFA8N1h+q4XIBCcLuyqbmHVRm/AYDWbWDl/EmmxIQCYeyYCgDdQWjJ7vF9VZdUm70SARzeWMGdKilZ5kRWvzPfaaWk880kZqzeV8pfNZUTZrYxLDKOt080F2Un88b29JEeFEGIxayOhbRYTt88ez309gYv6Xbeu28qhhg5ts1fXZrTZqxWbH676mB8/9Tk/XPUxG3bVIKuNz4NAlhXK6tv59MBhyurbj+izx5PBXL9AIBAIBINhKPe60SNCWX31ZCJCLNre//THZVw7LY202BDGJUbwwDt7ePBfe7EGmVl26QRCgoNY8UEJqzeV8si8PK8qpiBTt8fdekEWEnDNtFGUNziMkw6tTu65eAIRwWauOydN9/3/8/0MnRFuYW6ynzHu0jd3sq2yRTfy+dGNJVyRn6KtY9GsTP6wYQ9zpqTg7JaJtJlpaHexZvMBFkxPZ1FBBivmT+JfO6o5UN+hFaFUou1Wzh4dQ7TdwqGGDsN7rSbBpqWPYEx8GLPGJvDOohnMyByh3RMVtdASKHFyqKEj4N+qP5VsINRk0DuLZvBS0dmaquZUKGQJBKcTQtkiEJwgGjq6uDI/lbXFFRTmJrOnppW7CycgSQqKAncXZmtS3LL6dsONWW33SY0OwR4cpAUXV+T7S17vfmsXf7wiD1kBCYXFBVmUNzp4/tNyrQqmKFBSZ/xdaqvMQPLnY/U1Gc4mtEMp/xYIBALBiWE4qi2Px14XFxrMwhe/9ivMrL56Cqs+2A/Agunp1LV2MSLcyge7v9V8UcobO1hX/A03TE/XfN1CrWYSI23c+L9b+p2AkxwdQl2bk7SYSP7rb8U6XxePouhaggKpROw+pr7gLTSlxthZOCtDN7kxNTqERQUZ5I2M1nxdHv+wVFtL0cx0SuvbvXFQj+FtUqSN685JY8Hfio/oXqvJl/5UJZ8fbDC8nsaeiY1G/+aOViXbnyJqOP4bHwpO1esSnL6IZItAMEgCbQCyrHDwcAfljR2E9vTvpsboNwdZVggPtrC22H+M4gOX57Bq435+PDWVopnp2K1mxidFsKggA7vVjNuj4HTLhFnNxIUH86cPekcyLy7I5F87qrn2nFGGm39Hl5u6ti7iw4N7KisemhwuLVABAgZTcWE2yurbteudOirWcMM7Vl+T4W5CK1z+BQKB4ORhuCbwh3KvU69xb02r4f67+9tW6ttdfiav914ygZoWF9urWjFJEgv+32gUpVfxMTLazm2vbOt3As6vfzAWRVEIkkxUNnVqytq+I5/7tgT5rjMtNoRwn/Zl9TMRNgurN/XGJzaLiUaHS0uiGF1rarSdP76/nzt/1OvnckV+/1OLBqK/QotR4iQtNoSqZic/eeYLw39zQ90SNFz/jR8rp+p1CU5vRBuRQDAIZFlh075a3thaxf8daODNrVVs2leL2y2zYVcNP3rsY+58fSf/d+AwxeVNfFp2GLdb1n22ptVpOEbxztd3cNuF4wCICwtGUeC/X/iKl4srURRY/WEpqzeV8qeNJdS1dmlqFlV2+/1x8dS1OQ0lr4mRNqxmiYYOFx0uD+uKvWMafSXDWQnhhq0yBxvaB9UapAYefb97sL4mRyOvFQgEAoHAiKNp8zgRHMte17f9qKLRe42yguH+65ZlQ5PXe97axQ0zxwDQ4fLQ6nTz4L/2smqjN84oO9xhOAHn4Xl5LCrI4Onr8okLC+b654r5xYtfU9Xcaais9W0JentbFfdcPEEXY9xz8QTNd8X3MyEWs+64xQWZvfcgwLXWtDppcrgYnxihtdxMGhl1zHGFWmiZOioWgM8PNlBW305qtN0vZlp2aQ5LXt0e8N/cULcEDdd/48fKqXpdgtMboWwRCAZBeUMHJbXtrNlcpqvCJIbbuHXdVsPKzkNzcynMPYNDDR2U1bWTGhuK2WRcmdlX28ZnB+q57Qfj+NmzX+Ls9gZKRsZ0C6ana8oUZ7fM2IRwr6v/ZTnc+cYOnQFdu9PFig9KuOX8LMKCvV4tL3zW20ZkkmDCGRGkxoTqKjgmCWY/OriRz8dasYkPN5bXxoUJE1qBQCAQHBnDdYrckRrON3R0ISHR3uWiqcPDb1/vHe384OU5RNutvPqVt4DiG3ssu3QinS43kSEWY+NcCf7603wc3R6c3R7dekaPCDWcgFNS10ZKlJ0dVS26uGRdcSW/ujDL8H6PjLazfE4OSVEhVDS0s/C8DJxuGUWB3d8aK3JanN26NufnPy1n7pkp2vr7Xqs6OGDF/EmMHhGqJUjK6tuHxNze7Zb5585qLZHiOwL6HZ+YaTD/5oZSJTtc/40fK6fqdQlOb0SyRSAYBN+2dBomPiacEaElRvpWdn7z6nZGjbDT2ukmJdpOqNVMXkqUYQAQFRLE3DNT+c+B3l7gQH3Okk8hxGYxYTGZGBlj5+43d+qClNUflnDfpRNxdsus/GA/C8/L0AKVxz8s1YIGteXJNwj49MDhQW94x+prYjbhJ1NeXJCJWejuBAKBQHCEDNcpcoMpTKhtFMs37NFajhdMT+eZT8p08cUdr++gaGY6qzaWagUUswky4sP543t7ueqsVMxmk/Y538SEPdjM1xXNyAqMSwwnLTZEM6qtanb4TftZWpjNiLBgHnxnNxfnJfvFBqEBJhodanDwzCdlLLt0Ine9uVv3mYWzjNuXgyRJ1+Zss5hQu5yqW5ysLa7goXl52IJMJEQE0+2RmT0x0S/mONoikG+7eHy4jbo2p6Fi5Z2ewpNvPHQi/80N13/jx8qpel2C0xuRbBEIBkGb022YfFA38UCJkQP1Hfz2tV61yX2XZGtGuOprdxdmk5kQxm0vb+PivGTdRmO06ajxhNp/3ebqprNboryhUxekANS1dvWu1S3z9rYqnvzJmQQHmfo1HjvSDe9YKjaqTFlNFIVazXhkhb01bcgKwhxNIBAIBINmuI7MHagwIcsKO6qauXXdVhZMT9cKOIHii6yEcGwWE9UtTp75pIxFszL5/Tt7qG5x+nmvOLu9xrl/vmYK3zQ4dCrdey6ewJP/LqW8oZNOlwcTaIa5igKrN5VyRX6KlpBRY4OkSBvXTkvjD//aE1Bxcv9lEznc1uUXT7y9rYoHLs/hztd746P7L5uIZOo9v81i4p7CCTy52dcMdwx/fG8vf/3pVG389dHcayOM/EKWFmbrzH7Ve9m38HSi/80N13/jx8qpel2C0xuRbBEIBsEZUSGGyYcRYVb+eMUk9te2Gr5/0Kf/2dktc/dbu1lckMnC8zJIjLRR0ejgsU2lNDlc/Hb2OCxBEssuncjSN3eyeV+dX4XplvOzkBWFhbMyMElwRqSNn7/wFU9dl2/4/cEWs/bz+MRwFCWZsGAz+T09yIE4kRteQoRNM+1Vg7fVH5YKczSBQCAQHDFDMUVuKCeiGJ0rPS7MT0VxsKGd7ZUthgkWo/19XEI4f19wNptL6vHIvdN7VC8R4wKRt3Xo4Xl5OLrcHO7o4sl/l7J8bi7dHoWOLjc2i5mKhg4e/NdebR/OTorAZjHpWnl8Fb2+6pr8tGj2Vrdy++zxnBEVTHCQN6Gjjn+2WUwsPC+Tf3x+SPvMpJFRjIwO5obnt3gnLsaEUNHYyT++KKcwN1lT7CqyzO2zx7O3phVAax0y4kiLQAcP+/uFLFu/W1MQ+d77voWnEz258FSdlHiqXpfg9EYkWwSCQZCdGMH9l03krjd26qowL31xiBlZCeSmRPolRu67dCIr39+vO4+zW6bbo9DllrVzgXdMYZdH5vcb9hNtt3Lr+ZlEhQbz+IclWjAyLjGCJz8qZXtVq3a+x348GWe3TLPD5deKc+8lE3h68wHSYkO4ffZ4SuraCTLBiLDgAa/3RG54vokdo3as4TSZSCAQCATDn2NRWw7lRBSjcy2fm8tF2Yl8sK9O9/rigkzsVr1BrLNbNvQqWVyQidPtQUEh1BrEyg/2a+/dduFYIkKMW3vCbBZCbRZ+3aN6UYs4Dpebrd+0ICtgliAjPozVP57snYQYHMSK9/Zpa3jhs3KKZqaTER+mnb+6xakpa5fPyeHBf+3DZjGx9ufTGBUbyqGGDk0tY5Kg0+WmqrlLN8L52Z+dpSl0F87K0Fqg1JgnLTaEhedlcstxmFQjywp7qo19ZFKj7Tq1zfK5uYaFpxM9ufBUnZQ4mOsS46EFJxMi2SIQDIKgIBOX5JzBiLBgtlQ04ZHhsU0lLJk9nlvXbWVxQSahVrMWTIxLCOfbZgdNDhfgTaZcc3YqceHB3sSKW9akqUmRNn77w/H8pif4qW5x0uL0sKJnxLNvMLJgeroWeNgsJoJMEjaLiZK6dl4urtQZ345NCOfWC7OoanbqgpOxiRF+o6mNONKN/Gg3P9/Ezv7aNmGOJhAIBILvjKEc0Wx0riWvbic+PNjv9Uc3lrBy/iSWFmazZvMBLbmhepX8+ZoplNV30Ojo5vlPy+ns9nBueiwhFhO3np9JUpQdh8tNbKiVCJu34HP3m70Fogcuz8Hl9vhNAVr5wX7+9l9nEWIx0+HyANDS2U1CpI2Fz3zBw/Py2F/XTr2PuT5AbKjVMKFjD/Y+WmTFh7Gvrp3Kpt62Jd/j+pr9N7S7WFyQwbriSsMEkxpvHY9izKGGDkrq2gyvp769y0e1E8O56bHiwf47RIyHFpxsiGSLQDBIKls6ufHvX+k24r013kpIh8vD85+WM2dKCpIEXW4Pz/6nXAuarp6apqs8LS7I5H++l84rX1Uye2ISpXX6JEOgHm3VNFZV1jzzyQGWFmazelOprrKkMnV0jF9gdTyUIse6+amJHfXahDmaQCAQCL4LhnIiSqBzVTQ6DF/fU9PKy8WVXJGfQvYZYfz1p2fxTZMDuzWIxz8sYda4RF7bUok1SGJ8YgQNHS6yEsLo7Jb5+huv6e0TH5Xyu4snaMpYtQUnPDiIbT1tSr5E262U1nX4mdQrioKzW+apzQe4p3AC967fpZnr33vJBLo9sp+i9tYLsqhscgBQNHMMt72yjRtmpBtea1+zfxn4y+YyFhdk8vyn5dqUIYfLTUWjg9K69uNSjJFlhfq2LswmEyvnT+IPG/ZQ3tCpTX2akhpFTatoaRkuDGUyVCA4EYhki0AwSNSgKSnSpiVVMuPDNcmv6jsC8MQ1k2lyuGhzdvOrC8dpqhXorWAVzUynaOYYHn5vL7ddOM4wydD394z4cBYVZDBpZBSSpPDTc9Op8lHQ+B5rNZuoaTkxY/SGavMT5mgCgUAg+C450hHN/ak5A53LbjVu8/H0/BpiMVPf1s3SN78m2m7livwUrjwrjYb2Lm76fjpBZjO3rNtKtN3KwlkZuhbmRbMyKalt8zPNXz4nB1nxjy2uyE/xK8o8urGEp6/L546LxpIcbUeS4IUFU3G5ZdqcXl+XysYOLGaJey+egD04iOpmB2PiQnn0gxIWFWSgAL88P5O0mFAWFWQgK73jp/ua/f/+8hzqWjt7EjMeVl6Zx38ONLBsvXeK0bXT0uhye4a8GBPIFLfN2Y3D5WFKahSjRoQxaoR4iB8uiPHQgpMNkWwRCAaBLCvYrWZuuzCLMXFhWuUjLTaE+y6dyOMflmiS12i7lRCrmXsvmcC3zZ1+qhXwbgzJUSGEWE3817mjeeS9vdxyfpamfnl7W5WfodzSwmy+bXbgkeHet3dppnFvb6vyk9suLcymtbObbxodhsFJXJiNsvr2Iet3HarNT5ijCQQCgeC75EhGNA+k5hwVG8ryubna+GA1GfK3/5QZvr5hZzXXTkujs9vD0jd3Em23cu20NN3+/sDlOaza6I0V5kzxT5Ss2lTCw/Py/PZ+R7fbMF4YExdmuH+3OruxWcz86uVtRNut3DBjNA+/uw9nt0xabAg3fi+DR97Zq1t/iMXEz/7faO54fQfRdivXnZOma2NWpxRdd84ospPCyYyfjDXIxDeNDh55r1f9mxYbSm5KpNZ+tLa4gkeuyCM52q5rjbr/somkRtuP+m9tVChSTXHH9bRcC4YXYjy04GRDUtQB9oIhIz8/XykuLv6ulyEYIoyCqkWzMjXn//y0SG6alcWh+nbGJoVT2eQkNtTC/tp2EiJsJEYE89vXd+ByK5oixtwz4jg2PJj4sGC6ZQWHy0O03UJjh4uIEAsSCvtr24kMsRIWHKSTtqqVFwAJiRd7HPt9jXRnjo3n1a8q/QK1By/PISIkiIUvfj1k/a5l9e38cNXHfpvfO0LWKRAIfJAk6StFUfK/63WcLoh45P+zd+7xTdX3/3+epEmT9N5CL7a0GNty6QUoVdEBE6qMuSLKTadD5w/X7zaRTqdzU5EhToc6nIibMt2m7OtEZV5gylDQgV/xUlTuBUqhtaUX6L1N0qQ55/dHek6TJilFbgU/z8fDByT5nJOTFPt5nffl9f5mqFUrwYL+J7LndXXJfFzeQElFI24Z1u2oZmHhSIbGhlHZZCMsNIQwo57bX/6CwtxkXvionNsmWFmxqYzbJ/UYxXq/z2OzRrG/ro0h0Wbu/ddOv+u/e0omF0Sbuc9rtPJzP8rjSItn+pC3XqhrtbN0/T6/9yguyMDucrN8Yxl3T8lkxQdlxFiMzMhLYXhihGay633MS//vEm7+62c4XHLQa3/q+jHUtth5dnM5c/JTUCCgp8u/75iAXgcN7U4cXW5sTjdVTTatdVtRPN/l3358yTfWGVsPHuOHf/nU7/m//Tif72bGi0TPAER4tghOFWdKj4jKFoGgD2RZYWd1M6W1rdw2waqVwC7fdIDiggzaO91kxofhcLqJshhp7HDx/p4jXDXyAp8+5qUzc6hr7WTZe74TA9xumV1HWn3W/nZaFmFGPfe/uZsmm5OVc8diDPE44B9pdhBpCmHJv/dogZf7vj+cxddkU1rbRmqshaXdQZlJw+Npsjm1kYyqcW5mQjiznt3q1/Iz7I4JSBLfqNpFtP8IBAKB4HwhkEG8d9uQ3eXudzVnSIiO8emDSIkxU9/mYPbYZPbUtFG44iOf/XJh4Uht9DN4Ag6ShBbgiDDpSY62cOhYBzrJ05IzJz8lYJY/KzmKyFA9xQUZxFqMWEJDaHO4abE7tapYt4zWptN7muKdV2by948PM3NsCgBDYswUF2QQZTbw0Lo9QX1YjrZ1as8H855rtjl5dnM5NS0OXi2p4tffHx70u+xwdnGgrt1HI915ZaamxYCTah8JViWRJipqByyiAlpwriGCLQJBEPqqaBkcbiQjIYLSmlaMIXp+v34vzi6F2fkp3HjphT5Gug6XzMGjHT6ZG4dL5okN+3hi1igefLunBDjGYuRIi51oi4H7fzCCaHMI/9pWRUpcGMs3lmnZpiiTgdsneQRTQ4eTYUkR/PH9/Vpvd2ZCBFlJkQxLjOSuV7/STO2WzRkdVCTurW3l7te2f6NMgdj8BAKBQHC+0lsPFBekn1Arg3fwpvxoe0CPs3/fMYEYs5GVm8u1aTwhOrj5sjRe+bySGy9J8xnZvGhaFht21fiZ1C4sHMnRVjvlDk/1x6LuduQVN47B7nQHrDaxDrJQXJChVYz8/ePDNNmc6CTPNEWnW8HucvPURk+r9LCEiMAtyhGhPs8HWlPZZNcCJU02JxdEmUiLM2tBIPC0R1uMeraWN/hppyff369NMvqm7SPegbO/zM3ngbd2agmsgZAoEqON++Z8HXstOD8RwRaBIAiBennVipZIs4H5L3+BwyWTnxbF72fk8nWjnQff3sUvrszwGY+4ZlsVITpdwABHh7NLez4pyuTX8lNckMGEYfG02V3aMRv31nLz5UN54M2evuUhsRb+84sJ1LT4BjrS4sL8AiCHGzoCCiDvscvfxOBWbH4CgUAgOJ9Qb3qPtnX66IFXS6r8ghz9vUkP5nF2tN1Bfmosv7suh/vf2MmqTyr43bVZfFXVwq+njtC8T9T1i9fu5ukbxvD0pgP8+aY89tW1c0G0mSPNNuxON/lDY/nJSyXaMVVNNuLCjH7X/btrc2h1dJGZEMFD63ZrfnR/ujGPNoeL+38wgsf/U8q0Ucmah8wTG0r9vF8emp6N3dWlPb9mm/93tPiaLF4rqQTQAkOSpDB/UgYLe3mxyIqCrASujpEkgnrpHC9IESiRtnRmLsnRJmLDQs96YEO0yQgE5xci2CL41tHfjEFDR6df0AQg64JIilZ5KldykyOZmZeqZV9iLEYiTAb++L5vyetFg8MCBjjCvCYSzMhL0YQL+E4tyk+LYf7kdMwGHdkXRPI///jCZ90Db+7i1aJxjLMO8vkMgQIggVp+Hrkuh8f/s8/nWOHuLhAIBIJvK943vb3bZmpaHLy0tYIXb70EBeWEqjmPZ/BpDtFRNNHKoHAj4Dlfl6wEDDq0OFxcMTye3Uda0UmSNvnQM73QY3yrTlBEkUiNs3CszcFzc8fSbHMxOCKUZRtKKalo0YIfbllGr9Px8+6EklrVq9N5JhepOkVtUdbrID0+gj9sKOWxmaNYXVLpo53UVqbKJjt/+rCMe6YMZ2JmBxnxnmEDqk9Nb02zumgceilwdcxl1lhmjEn2+c77G6QIlEi7d82OAeMxJ0YbCwTnFyLYIvhW0d/NWJYVjjQ7NAGgBk3MBh0lFU3aJnjbxIv41evbNSEWaDLAk+/v564rM/yyQI9cl4Oxu3rlqY0HgvY3ywqUVDSxYpOnZPaha7KIsRi1Mlx1XU1LJ7lDjv8dBGr50UkEHB8t3N0FAoFA8G2k901v75v+JpuTwRGhJ3wDHMzjLDXGwsflDdzVPf1n/uR0LeCx4sYxAYMOcWFGwowhfN3YwbL3fZM1kiSRFmfm+vxUH+2x+JosHnxrl9Y2s2ByBtXNndS0OFiybg+PzRqlBW3Ucy3fdID5k9IZEmPRnq9pcWijpedPTqeiwc6B+jZuvyKDB9/e5ROo+ePGA5pmCQ3RkREfTmltK84uJaj2sTndZCZE+FXH3HllJqEhOr/vvb9BioE+OvibXJ9oOxIIBi4i2CL4VtGfzVg1xVVHMqrrnnx/P0UTrchKj+hSZJn5k9JJiwujuCAdfZB2odZON2u2VWnZHkWBFpuTulYHUeYQVs4dCwTO4OgkMOp12rkefHs3RROtLN9Y5rMu3KTv9/fQu+JFlhVhcCsQCAQCQTfeN72qh4p30KK/e2SgG+FAHmeHGzooqWgMmLiparL5BR2KCzI4UNdGTko0ZmMkt02watcKYNDrWFiYpbU8g0dDLHp7t+Z5ogZSvB/bO7sC6hin23PDH0inKN26KNJsxN7p5IlZo1CAA/Vt2uRGde3OIy2aB92CyRkoKAHPmRBpIinKRHWTjcdnjcLW2cXR9k5e/qyCq0Ym9Pnz8r7u3kGKgTA6uK/gyIlen2g7EggGNiLYIvhWcbzNWN20Smtbg1aZbN5Xz8LCkazcfBCXrLCiW6CYDDqevH500ICJdxbIe3Tj0vX7KC5I582vqv3E3MLCkbjdMori8XSpafFcf2qsRXsfVXRdEGX+xt+LMLgVCAQCwbeRYDe+3je9NS0OVn1SQdFEK2OGRHPhoDDcMnx6qKHPSgJZVti0r44dVS3ICuglyEmJYvKwBKyDw7Ugy6eHGrC73Foyp3e1x4sfV3D39zK1hI9OAotBz5ovqhiWFMkXlU2Ax1j2pxOtOLpkil/5kp9fkR7U8yTQY5NBhyU0JKCOufyiOPYeaWXJ9Gwff5UFkzNYXVLJnVdmgiLTZO+irVNm1SeHuT4/VauaVbXKS1srtPdVffD6CmSlxIb1KxHU3yDF2Z6eGMwz5oJoE3FhoaTGWE7o+kTbkUAwsBHBFsG3iuNtxuqmddsEa8B1sRYDP70incb2Th6fNYqb//qZzwb3+3f3+gmRO6/MxBSi8wmOLJicQW2zTcsGxVqM/HTiRTy7+aDWA52bEs2RJht//m85TTanj/t+lMngI7rS48NJO0mhIAxuBQKBQPBtoq+qgN435U02J8MTI5mQPpgNe+v6VUlQ2djBgbp2baKOGnBIHxxOamyY34Sjtds9SZfOLrePBqlpcdDQ1qmd1y3Dmi+q+H5OEv/T7SGnaovOLjdPvu+ZGjQ8MTxoJUrvx2qCx2L0GNR6m/AvLBxJZUMHdpfMv3dW8NisUTi73AyJsdBsd3LP94Zj63RR3ezgqY0H+M3U4dxwcSoXxYez+ifjsLnc6CSJX6z+yq8FusPpZu32ap790VhCQ3R+wav+JoL6G0Q528mlYJ4x88ZbeeGjcpbNGc2UEQm808/rG+htUQLBtx1J8f6NKzgl5OfnKyUlJWf7MgQBOF655daDx/jhXz4NOBno7inDSIgM5Z7XPe1FS2fmcO+anX7v8evvDyM52oLN2UVlo51/feEp6Z2Rl4IkwbCECJ7YUMrCwixszi6Meh2PvruXKJOBn12RTpvDRUX3cd6iZP7kdJ7fUs493xtGTkok7XY3TrfMRYPDuWhwuKhCEQgEAx5JkrYpipJ/tq/j24LQI31TfrSdq5dv8QtGqGapatVL73afvo7x5tPyBm7522d+a1+89RIGR4T6nCc3OZIfXprGs/8t44aLU4kyG3iou5XIZNDx2Mwcalo6efL9/ThcMgsK0n3GIqvnfvZHYymtbSVEp9OqS7y1zKJpWTz73zKcXQqz81OwDgpnULgRm9PNwaPtvPJ5JQsKMqhpdjA4IpTqZjuDw0P504cHGRxu5PpLUrUWJ+/KlhsuTsUUomPxur2YDDpWF40jKymKyiYbDR2dSEhsPnAUWfG0OtW0eFqSnpg1ikizgcutcYSE6E7q5xno5zXQtJGqM3szf3K65s13Ima9x/s3LBAIAnOm9IiobBF8qzheRiM+wkRanJnC3GR0Onh81ih0Euyra8Ni1HHP6zuIsRiZkZfC4PDQgBkjm9NNaW0bESY9eh3MHJsCeMRFk81J0UQrP/tuOs9+eIDL0wcTZzFyw8WpxEeEUttiJzHazAsf7fY777CECOZPSidEkrj5hc99jHYNeonU2IEnKgQCgUAgGKgcryogUMXniVQSdDgD+5/YnF3UtfpOGPp+ThLNNic//2460WFGdMAfZo/CYtTzdaONsNAQXv5sv+b9lhxlDnjukoomdBKs3Ow/NWh4QgTP/vcgj87IobrJ4VOF6xnDDNNGJbN84wFuuDiVC6LMRJoMyIrCLZen0eVW/IYAqJ4vT208wOOzRnl9Rjcb9taxdP1ev4CPGqApLsgkOzmyX/qlPyaw50KFbrAKazX3faJVKWe7LUogEPSNCLYIvnUE24y7umQabZ38/Ip0Fr29W9u0Hr42m5RoExajgRiLUat4+ZfFyD3fG8bj/9mnrX2wcCSxYQb+9cXXjE0b5Fc6nBJjJjbMSIfTzS+uzKRLVpAVha++buHFjw8zITOeTrfMg4UjfTJaCwtHUt1sQ6+TeHR9qY/Que+NnRRNtDI8MbLfhmjCuV4gEAgE33a+iVlqf4+RZYVBQZIyqbFhSF5jjZOiTESaDSxZt4cYi5GbL0vzMcNdMDmDf2ytoGjiRVqwo7ggPehNu0yP54u3X9wTs3JpcbhobHdpgRa61y5Zt0drZfnN1OFY48P5/HAjsuLxgrnp0jQuiDYF9YBxuGRsnV3adViMem79++fMG2/VAi3q+uWbDvDErFFkJ0cydNDxgwrnkwlsoODIgskZrPrE42Vzoma9Z7stSiAQ9M3J1esJBOcJsqzw7101bDlwTAu0gEcUPPDmLoYOCic+ysj9V49g+aYDWnVLcpSJZ27MY0FBOvPGW3lu80EqG+3c+h2rVuqrnuepjQeoarJz92s72FXdQm2rp6z2WHsnr5VUMTU7iRc+Kuf37+7juc0HWTl3LC/+v4tZfE0WzTYn//yskkizJ+DjjWrce9erX3G4oeO4n7WrS+ajsmO8+VU1/3ewgVv//hnrd9ciy6KlUCAQCATfHtQbX5PBI4f7UxXQn2PU4MAd//zC49vWa+2Fg8J8zuM9fWhGXooWaIGe4MR3MgYxKDyUP9+Ux7M/yiPMqPc794LJGVrrsvq8ismgo7LJxr1TR1B+rIMYi5HbJ6Uzf7LnvxiLEUmCGIsRm8vNA2/uxC17fOF+PXUEG3bXYDbqA55X9Xw52t6pfUZZUZg33kpqTOAKnNK6NmpbHfSHYCawhxs6kGWF8qPtbD14jPKj7QNey6jBkXcWTOCfP7mUlXPzWV1SqbVVfZOqFDWJOM46SKvIEggEAwNR2SIQ4NnI712zg9smWAOKgob2Tr5ustPe2aVVt6wuqWRY4nB+4TVWMSnKBIqCyw2Lp2VxrKOTf3xSqU0RGhQeyoOFI/n9+r1UNNgxGXQsmZ7NrZensez9HnHl7FLYfaTVL7O1cvNBZuen+I19VhSPQDra1tlntYoaVFLHWqvnXbp+L8MTI06o9FZUxwgEAoHgXOabVAUc7xhZVthZ3UxpbSvTRiXz7x1HtDaeguHx5CRHa2unjEhg5dx8WuxOrT1oaJzFT4fEWIykx0fwRWUTsgKfHDzKTZcO5WibQ2vd8R6zvGZbld+oaLV64q6rMok06fnZd60c63BqU5Lu/l4m4aEGwox6Xvm80q/1Z2HhSK11+b43dvq1BD1yXQ5DYs1cnTOB1BgL/95VwwsflQcdOKCT6HcFR7DWrcaOTkpr2865ihfvCmtZVvjbjy8RVSkCwXnKGatskSTpN5IkfS5JUqskSUclSVorSVJ2rzWSJEm/lSTpiCRJdkmSPpQkKavXmhhJklZJktTS/d8qSZKie63JkSTpv93nqJYk6UFJkqRea2ZKkrRHkqTO7j+vO9FrEZz7qBmR/XVt3DbBSnho4KyNW4Zl7+1HVmB2fgqrSyr5f5dfiA64bYKV+ZPTyU2O5KcTrbgVKFpVwr3/2snTm8r48eVDSYrylB0fbrBx56tfcX1+KklRnnLchW/tIjMx0kdIBMtsFeYmk5kQ4ZfJ2rK/npsvS+OWv33GD//yKVcv3xKwWkUNKgU6b31b/zJM6ve2fnctVy/f0uf7CQQCwUBD6BGBN9+kKiDYMereeP3KT1i+sYznt5QzNTuJf31RxfKNZdhdbp/zVzbZWPiWJ3DxwkflrNlWRVj36GWVpCgTN1+Wxk//sU0751Ujk+hwdrHs/QPc8c8veWJDKUlRZm3McpPNSXKMmeKCDOZP9lTervqkgiabk6+bbFw4KIwOp5uVm8tZsamM5zaXU9/aScWxdiLNBmaPHeLX+rNk3R70kkRoiI5Xi8bx8m2X8o95lzIiKZzlN4zhmtwLuHhoHNbB4Z4Kmm6tsWZbFQsmZ/joluKCDHJTovpdwaG2bnljMugw6HVBK17OFURVikBwfnMm24iuAP4EXA5MBrqA9yVJivVa8yvgl8AdwMVAPfCeJEkRXmteBvKAqd3/5QGr1BclSYoE3gPqus9RDNwD3OW15jJgNfC/wOjuP1+TJOnSE7wWwTmIGmD5/HADa3cc4erlW/jpP77g+S3lSEj8ZupwH1Hw0DVZVDXZNdEwNC6MGy5OpdMtc/fr21mxySN+rr8klc4ut1+Q5Mn39zM7P4U7r8zkX19UaQGOGXkp2prGDqePkFD7n71xuGT0OkiNMfPirZfw55vyKC7wZKquGB7v976BBId3digpysTtk9K5bYKVEUkRJEb2v0e4r5JegUAgGOBcgdAjgtNAoL1R3e9NBh1mg96n3aWu1UFhbrLmnzIjL4Xfv7vXJzgxO98/+fLk+/upb+vUnqtosPPsf8t4bNYoLbhi1ElEW4w8v6WcZz4oo8nmZMHkDF4rqaLN4a9Vntp4gKRoC0vW7WFwRGgQE+BOfvnadt4vrafZ7iIvNYZJwxMZNSTGZ5KQt9aoaXGwflcNj80axYobx/DS/7uEq3MSmTwsod+BhWCtWzanO6hZsUAgEAwEzlgbkaIo3/N+LEnSXKAF+A6wtjvT8wvg94qirOlecwseUXEj8JwkSSPwCJrxiqJs7V7zP8AWSZKGKYqyD7gJsAC3KIpiB3ZJkjQcuEuSpGWKZ9b1L4APFEX5Xffl/E6SpEndz/+wP9dyqr8fwZnB22Rt/qR0VnxQ5idg7p6Sycq5Y2l1dBFm1OOSFaLDjCwoSEdWID4ilGhzCD9/+Uu/rM/js0YF3PhTos00dDi1Uc6qoRx4RMPgiFAWFo7Uerb1XsZ5KiaDjjFDornjlS+1FqSlM3N5+oejaXUEnnjQ2NEJoLX6qBU23ka/PWMlc0mJtvRr9OKJTGMQCASCgYTQI4LTRbC9Ua+Dh6/NZoHX/r1szmiGJUSg16H5wKXGmKlosGsThPqaOtS7kLSiwY7d2eUz+TA/LYanrh/DziMtKAqaCatC4ISOrdOjJRKDmADXdrdEqz5xI4snICv4tRN7mwgnRZmYmp3Er17frumNpTNzSY6y9DvYEqx163BDxwkbHAsEAsGZ5Gwa5EZ0v39T9+MLgURgg7qgW5xsxpN9ArgMaAc+9jrP/wEdvdZs6T5W5T/ABcBQrzUb8OU/Xufoz7UIzkHUrFOMxUhCZGBX/SGxlm4j2+3c+vcS3t99hDZHFys3e0p8XbIbR5cS8Fi7sytgqWtFo50Op1t7Li3OTGZCBAsK0nn2prG4ZZmVmw8yb7yVBQXp5KZE8eteFTaPXpfDMx8coKLBrr3fvWt2EBsWytC4ML/3TYszU93s8Gn12VPTxoobx3h8X3qVCP9qzQ4+r2jsl8lcsJJeIXAEAsE5iNAjglNCsL1xYsZgnt7ku3/f9epX6HXwnfQ4br4sjRc+KufrZk8gRp0gtGJTGUda7AHP6R2nSIoysaAgHYAHp43k4WuzyEmOIkQvYTbqtOoWgLnj0qhusgU8p2pwW9HQwcPTs300yJ1XZvK/n1b6+MR9UdnMrX//jI/KGjym+2XH6OqS/cx/e+uNe9fs4OPyYxys77+hbaB2m29icCwYeJxrJscCwYlwNg1ynwK+ArZ2P07s/rOu17o6INlrzdHubBAAiqIokiTVex2fCFQFOIf62qHuPwO9j/c5jnctPkiSVAQUAaSmpgZaIhgAqFmnGXkpVHWLjd4ZEbNBz0Pd4xdvujSVYYkRdLpkfn5FOunxYUSGGtBLbq3SZc22Ks1FPiHK5GdKd+eVmbz8WQXTR3v+6aTFmbl9UoZPlmfRtCzmfedC6ts9ZnV//rCMWy6/kKKJVmTFMw3AGKKjurnT5/Oo1SSXDI3zGyW4ZHoORatK/Fp9/n3HBHSSFDBYtLW8geUby45rMhdodKEQOAKB4BzlvNIjglPDNzGBD7Y3KihaoEXF4ZI51t6Jy61omkH1N/GuOo21GLnrqkyWvbdfe+7ha7OxGHQsKEjHYtQTaTLwUHdlrGpie6zNwR3/3EuMxajpEjXwEWMxcueVmdrURG+tsmByBn/+bzm/+f4w7p6SSbTZSGWTjb9/fBhjiMSyOaMpq2/n/h+M4O//V+5npLt0Zi7Tci/QKlH217UF1BufHW7i+S3bfLTGiX7nJzv2WJYVKhs7qGvtpMPZRVpsGBcOEga1Z5Lzaay3QBCIsxJskSRpGTAeT/mt+3jrzwUURVkJrATIz88XIdkBipp1kiR4taTKT2zcPWUYEnDf1cOJthipbbZzrN3J4rWecdBpcWZuvyKDB9/e5efEf8PFqdQ22RiZFMGfb8rjaFsn9W2dvPxZBXPHDUWWZZ66YTSJkaH838EGbptgBTzBmsVrd/PErFE8+PYezfX/iQ37fMSZyaCjaKLVbxJRfIQpoOAIVs58tN2hVcL0DjS55Z51d736FcMXTAjYFnSyAkcgEAgGAuejHhHJn5OnrxtAIGBAQA0UDI4wsrpoHDanW3s9WLuLyaCntqXTRw+s+qSC5+aOZVtFE24Znt1cDkDRRCvJUWaquytd2jo9Brfzxlv54/u+lSP3vbGToome6Yo1LQ4+K2/gL3PzaehwctsEK2u2VfH3jw8zb7yV0BAdo4dEsaemlcLcZM1It+xoB2aDnpe2HuaK4fEsmjaS9s4un+9k0bQsnv2vbzv2vWt2kH1BFBfFh2v6IdBnVxRfrTE0LuyEv3Pwnexzoj/jTfvqOFDX7pMgO903+ufjJMeT+UzBPACD6U+B4FzjjAdbJEl6ErgBmKQoSrnXS7XdfyYAlV7PJ3i9VgsMliRJUrNJ3f3M8b3WJPR62wSv1/paU9trXV/XIhig9PVLX8067attxRgiEWMO4YlZo+jo7CLSYkCRFR54axcLC0dyoK6d0BCdFmgBKMxN1gIt0GN+98SsURgNOg7Wt2MwuBgSYyak+z1njx3CCx8doqbFQVqcmTsmZ7Byc7nfOMYOZ5d2ziXr9jBvvFUr+02KMjEjL4WM+AiKC9J5tcTTkx2omkTNs8ZHBO65VgMjS2fm+o2AVvu51evoy4PlmwocgUAgGAicr3pEJH9OnmA3gMPumMC+Ov9Rw1NGJLBhb53f85deGOfT7qK+nhZn5pHrcthb06aZ46qJljaHi6NtnVpiRd3/ZQWiLAYabU5QJBa9vVvzf/Pe59X1yVFm7vleJhGhIZiNIfyku9LVe79/5gNPJeuTc0ZrARvv15tsTp7oHi1d1WTjiQ37fb6TxWt3M39SOk9s2K+9v8Mls7e2VasQUfXGsvf2UZibjF4HIxIj+fOHZdp61dD2RL7zkw2IHG7oYEdVi6bHvN/zm97oHy/ocD5WcZzsZxIegILznTMabJEk6SngejzCprTXy4fwCIergM+715uACXjc+8FT4huOp8dZ7ZO+DAjzerwVWCpJkklRFNWO/CrgCHDYa81VwONe73+V1zn6cy2CAcjxfumrFRlZF0SQPzSGXdWtPLi2p/S2uCCDGy5OxeGUeeXzSn5xZabPJhB8SpBEm73LJzuysHAkKzZ5xMSMvBQkCYYlRHBPd/uQeuzyTQcommjFbAzpdU7P35OiTH5mto9cl0NeajSpsT0Ztd6fe8WNY4K2+uh0Ej/ITiLGYqSkopGM+Aie2FCqGfjC6fNgOR+zOgKB4NxC6BFBXwS7AaxsDByEWV00rs/svHc1aENHJ3WtnTR1uLRAi3rMknV7KJpoJT4iNKiZfXFBBuXHOvwSKaoZbe/1CwtHsuKDA366Y954Ky98VM6iaVkYQySemDUKBU9Q5aWtFZoecMkyj63fxy8KMgJ+J/ERoT7PmQw69te1MTIpUrtZHpkUyV1XDaP8aLuWLFpYOBI+q2R/fXuf1bjBvvOTrXyoa3UgK4E13Te50e9P0OF8rOI42c+UEMSMWXgACs4XzliwRZKkZ4C5wLVAkyRJah9yu6Io7d29zn8E7pMkqRTYDzyAx4DuZQBFUfZKkrQezySAou7jnwPWdTv/0712EfB3SZIeBjKBXwOLvXqrnwI2S5L0a+BN4DpgEp5SYvpzLYKByfF+6as3+kfbOn2CI+rapzYe4A+zR9Hu6GL22CEcPha49Lf34/DQEO7s9b5L1u2huCADRUETPgsK0gNu7NZB4Ty/+aDPOfPTYoOay933xk7eWTAh6AYeYzGyo6qF7Aui+N95lyKjENdtpKseExKiY3z6IFJizDR2dHLXVcN8Kl1OhwfL+ZjVEQgE5xZCjwiOR7AbQIsxJOAeXtNy/Oy8WuXRanfxdaMNtxzYaD9Ep+PwsQ6KCzLQS9DudPu0Gb3yeSUPTc/WfOM276vXfF4C6YUl6/Ywf1I6dlfPFMQ126oYGmfhyTmjqW91cHv3dEU1OHPL5Wm8+LGnssUUoqemxYElNCTwTbHXd+VdFXP5RXEBW4PU15es28OTc0ZjCJE0rXEi3/nJVj4kRJqCTn78Jjf6/Qk6BAso9Z4ceS4loU62MkV4AArOd85kZcvPu//c2Ov5xcBvu//+GGAGngFigE+BKYqitHmtvxF4Go9bP8DbwHz1RUVRWiRJuqr7HCV4pgv8AVjmteZjSZJuAB4GHgIOAtcrivKp1/v051oEA4y+ful7b/q3TbAyJDr4OMUmm5PEKBN/2LDfx6xu7fZqHrom28ezZdG0LFodroDnGhJr4e7XeipZZCXwxh5pCmF/fbv2eNmc0VxujeOdPszlvDcy788dKLP18LXZjEmJ8du8vVuB8mSFnOSo0+rBcj5mdQQCwTmH0COCPgl2A6hWnPTew5OizMe9aVeTDbLsMcR9fNaogMcMHRTGr17fTmZ8OD8al8ay93dr13DnlZmYQnT8z6ptPsGLTaW1PHNjHnaXO6BeSIwy8cCbPbqluCCD1DgzblnhzldLA1bX3HxZGmFGPV83dgBQ3WzzGwBQXJCBW5Y1M391vHSTzYmExM7qZr89X62qeeaDMvbWtjJ9VHLAViv1O0+IDPydn2zlw9C4MHJSovw+0ze90e9P0CFQEE+dHPmjFz47Z5JQ3hXKFmOQIFw/fz7CA1BwvnPGgi2Kohz3/5ruTM9v6RE7gdY0AT86znl2AhOPs+Z14PWTuRbBwKOvckTvG/1BYQYiLYaAaweFGzHqJSLMBppsTlZ9UsG88VYkyTMVaFhCGH+/9WKOtnUyKDyU6qYOQkN0QYMo3s8FmjSwYHIGf/rwgGaoZzHqcbplKptsfWZ7vDcy788dKLP1wJu7yIgPZ9SQmKDf3ZnwYBG9uQKB4Gwj9IjgeAS6AUyNsfDhgfqAN+dZSZHHzc6rGuTR63JwuGSqm21+Jv0LJmdwpNmGwyUzITOeB9/e7bOXP/n+fs34Vn1u+aYDPHX9aKoabdhd7oB6obLR5lfF+5eb83F1ycRYjD4txGrS6amNB3jmxjHatb34cQU/+67VZ0qixaDnqfcPMDU7ya/V6Rerv2J2fkrAPV+Sekz5j7Y7uCg+POhNN3BaKh90OonJwxJIHxxOXmoMNmcXqScxjag/7TCBAkrBJkcO1CRU7wrltDgzD1+b7RPMO9Gfj/AAFJzPnM3RzwLBKaevcsTtVU08NmsUOsDplvn9u3v9Ah8PX5tDaU0rgyJMdLq6WFg4kiXr9mgmcndemUlFk51fvd7TbvO763J4Z8cRFhVmsXhdTwZq8TVZlB9t99l8a1ocrC6p5IVb8vmyshm7S2Z1SSX3Th1BVlIUG/bWcevfP/e59ikjEo4rNLw/dzBfmdoWB6OGnMmfhj+iN1cgEAgE5wK9bwDLj7Yz/+UvibEYtQRMZKielGgTn1c0MiwhgvXFE6ht9c/Oy7LC0bZOfn5FOhdEm0mLM2Mx6gkLDaG4IIPB4aFUNtlY9UkFc/JTtKmJMRYjN12ayuDwUCyhIVQ32/yu0+GSaba5+OvHh/h/l1/oFwx6+Nps/uBlYKse8+mhRp7fUk5xQYaPR4v3pKC9NW0U5ibxTncAJCHCRKPNyX/3H9UmJdW0OGhxuFhdNI7GDidfft2snS9YNa9OQpvkODOvZ4J5sJvu01X5oNNJDB0UztBBJ3+T3592mBOZHDlQk1C9K5QrGuw8vcmTMLS73KIyRSDohQi2CM4rgmVGurpkyo/ZuP+Nncwbb2XdjmoKc5PR6eDxWaOoabGTkRDBb9/eRUWD3ac0d9mc0ZTVt9PZJSOhaIEW8GyI97+xkydmjeLxDaWaAFMU+NOHZTzwg5FawEbdfO+6chgXp8aSFGWmvs3BzLxkbTRkoBabdxZMOK7Q8P7cta12nt/iL24So85+QEP05goEAoHgXES9Ka5pcfDMB2UkRZn48eVDmbPykz7bPwJ5lS2+JovBEUYWr91DYW4yLreDrAuiGFkYSZeisOKHY6htsXPrd4ay7L2eypfiggziI0NJ6t7PZ+SloNdBUrSJGy5O5dH1pT7BoDCjnviIUJpsTp/P4h1QeWqjx6R/+cYyH08Vk0FHbkqPEb96458mK9S1dvp8nnunjiAnOZpPDzVoU5QgcDXvwsKRtNpdvPK5J9HUn/3/XKh86G87TKDPci4loQIFhyoa7NhdbsZZB52lqxIIBi4i2CI4Lwg04UbdyGRZ4dOKRu5/YycOl0yESc/1+ak+m/+yOaP52T+2BewrvuvVrzTX/oevzQ7s84JCRYNdG9WssrO6hcHhocyflI6jS0YnwaghURiNer/N9njZjeMJDXUDT42x+JV0PnxtNllJUSf1HZ8KRG+uQCAQCM5Feldm3nRpqtYCBL6jii+K79mrAyVSFr29m+U3jNG0SIzFyM2XpflVpPzlI38T/6KJVm69PI0uGb8ghtoSpGqR2yels/CtXQEDHuq0RIdLJjM+ghU3juHrRs8koiabk6Uzcxk3NDbg9MBg+3jv70it5lWrHgaHm9DroLbVwfeyEk9q/x+Ikw2/SVDoXEtCiQplgeDEEMEWwTlPsAk3U0YkUNlk4/CxDjqcPaZxydEWv/HLpbWtQfuKHS6Z1Fgz88ZbqWt1kBZnpjA3WXP1X7u9mhCdFHDzyYyP4OsmG094lfBeflFcwJLVU7WBhYTouHZUMhnx4TR2dBJhMuLs6vGAORfFiEAgEAgEZ4JgN/HqTfHS9XspzE0mJcYSUDdUNnb4BFuCJVJCdJIWAJmRl+IzHTHGYqSy0cbPv5vO18121myr0iYeyQqkx0fw85e/CGhs611Zotd5qg68vecUBdocLp+2ob21bbzwUTkLC0dy6+Vp5KXFMjolmg1763y01dKZufwgO4mQEF3AfTxQ4ECtevHWHifbtnM+TTY8mSTU2Qg4nWvBIYHgbCOCLYJznkBZo6Xr99LpcvOb7mqW4oJ0LZBx6FiHn/AJZnCrdPcbH23r5JkPyshPi+LnV6Sz6O0eb5ZF07J47fOv/fqkFxaO5Hfv7GXm2BSfcwYLnpzKDSwkREdOcjTrd9fys//99JwXIwKBQCAQnG6OdxM/ZUQCLrfMvWt2cNsEa9BRxeVH22no6CRUr8MtKwHXNdqc2nPeXmuBJgqqrT1NNic6CWzOwFOHMuIjtJHQeglyU6IwGXQ+1S5qO9Ltk9LR62BEYiR//rBMC9g8c2MekgQVjTY/bXXvmh3EWIyMTx8UUEecqerVMznZ8EwENL5JEupsBZxEhbJAcGKIYIvgnCdQ1qgwN1kLtCRFmTDoPSW5Xzfa0Ot8+2OTokxEdJvUvfJ5JbPHDmFwRCjxEaF83Wjjvu8Pp8PpJi3OzJ1XDWPei76u8YvX7qZoopX4yFDunpJJq6OL4YmRPPthGU02J2FGPcBxgyenegM7WTEyEEt0BQKBQCA4XRxv36xssnHvGo9vWyA/kruuykRWFH71+ldclzeElZsPcvt3L2LRtCwWr91NjMXI7PwUrIPCGRxu9NEifU0UXL7J0z5kNuiJNocQExZ4mmK4KYSVm8u167nne8P4zdThPLq+VHvu7inDiAkz8NQbu3yCOUfbndS0OGh3uHho3W5+OWVYwIBOSUUjKTHmoDriTE42TIoyMSMvRas0buzoPKXvO5AraM5kwKk3okJZIOg/ItgiOOcJ1H6j16FtxGqGSBU5Fw4K43fX5WgeLrPzU3jh/w4x7zsXcsfkDB+vk+KCDMJDQ7goPpw7Jmfw6aHGgOJjRGIkLlnmosHhHKhrY8m6PTTZnBQXZGAdHM593x/GqCExuGWFQ8c60Os8vcy9gxincgM7GYf7gSwwBAKBQCA4HQTbNyu6Ew92V09FSU2LQ2vPSY01U91sJzHKxN/+7yD/b/xFmt9bVYuDt76qprggg0izQTPMT4szs/iaLP70YRlhRr1mph8aogt4DcMTI0iICKW6xcF9b+wMOE3xt2/v8rn5fvw/+3j2R2N5bu5YGtudRIcZOFDbxv1v7PIL5qjedJFmA7/+/ggqjnUEDOi4Zc76pJyESBNpcWY//72M+HDyZOWU6ZSzGdA4HgNxipFI0gkE/ohgi+CcJ1D7Tc4FntJZNUMUYzH6lOWmxZl5cs5o7C43YaEhmEL0HOtwsvLd0oBmdMMSIthV3UJGfERA8bG3tlVz8l9YOJL7rh5ORYPHaO7Wy9OIMBm55W+f+QRxVBO60xXEOBkPmIEsMAQCgUAgOBH6exMYbN/88utmlm8s82lJBk/A5YWPypk33sq/vqhidn4K14wagqwoxFiMSBLIisc3pb3T7ePLUtFgZ+PeGn57TTZfVjYhKwp3XZlBdnfrT+9riDYbqWnp1CYiqoEevc7j4VLbbKOiwe7zeRwumS8qmzR9smhaFrHhoQFv0vU6PPrljV002Zz8ZupwP7P9QOOazwZD48JYMj2HolW+lcb3rtlBTnLUKdMpAzGgoTLQjGpFkk4gCIzubF+AQHCyqH3Uq4vG8eyP8lj9k3HUtTkoLsggyqSnuCCD312bzaBwI3dMTicpykRFg507X/2K5GgT0WYDji43yVHmgJuqrEBD99jE6mYbd12Vicng+V9H82wpqdLWL1m3h4oGjyluk81J7pBoFq/b7RfEmZGXogUxDjd0nPLvJTXGwtKZuT7X2l8PmL4ExskgywrlR9vZevAY5UfbkWXlpM4nEAgEAkFfqDeBVy/fwg//8ilXL9/C+t21AfcfNXnjvW8WF2Roe/wHpfUsmpbl8/qCyRls2V/P3HFprNxczvx/fsndr21n/uR0si+IRC951vWuWEmKMvHdYYn87B/bWL6xjKc3ldElQ3l9OwsmZ/i9x96aVjqcXT6Bnmc+KGP5xjL217XR2unWjlExGXQY9Z7n1Lbn0BB9wHXp8RGaca7DJfPo+lLiwo0smzOaBQXpzBtvZXVJz7jms7mf63QSBr10WnSKN2pAw5uBMnkn0L/Vs2lUGyxJdzr0rUBwLiEqWwTnPLKs+DjmP3/zWB7+914y48O5baKV6ia75tpvMuh4sHAkrQ4XFqOe6mZPOW5vE10Vk0GHToK9NT2VK7+ZOpziggziwoykxll45N97NVd/8Gwwjq4ek9xj7c6AgkDtMT4dWRL1O1n23j4t85WfFsvl1rh+ZRhOR8ZEZD0EAoFAcKY5XqVm76qXKSMSeKfbO01C4herv9L2+AmZ8Tz73zKf1qFVn1QE9FlZsm4PxQUZJEebWDQtiwhTiM++OiMvRWspUo9ZvukAj88axV8+KvWZHqQGOfbVtgY181+3o1prRfI26u/q8rRU90wzknmwcCQPea1bMDmDP2wopTC3p2LF4ZLZWdXKPz+rZHZ+Cvlp0czMS9Zu5vu7n5+u1pIzUdkxkCfvDDSj2oFcBSQQnE1EsEVwztNbSLkVPEIoxoxOknjl80ofMfNQtwAaHG7iTq/jXi2p8pso9GDhSEJ08OT7Zdrxj673iKDB4RLtnV3sr2/3uR6TQcfwhAienDMaBYWaZkdQcaT+/VRnSby/E+8JBO/0sw3odAgM0ZokEAgEgtNFsJv6vm4Ch8aFBQ0aWAeHc/hYO7PzU1CLNsJD9VQ02HnmgzLNE67J5kSSPOOavc1aN++rJz0+nC63goKC2aj38VlRveV6X1dFQwdFEy/yCZo8fG02f/3oINXNnX5eLWprz0+/m86abZVagiX7gihWbDrA/vp25o238swHnoSRJOlodzh44ZZ86ls7qW62s7qkkhsuTuWlrRXatZgMOjq7ZJpsToYnRvLdzHjtRr78aHu/9vPTmWT5JjrlRAM/Ay2g0ZuBZFQ70NqaBIKBggi2CM55vIVUbnIk7Z1dvPBRuY8QWfVJhZaZcrhkUmIs7K1t9dkUalocvLuzhmduzGN7VTNuGZ7bfJAbL0nzeT9VJB1t72RQRCj3fG8Yj/9nn/Z+D12TRVy4AQkJe5ebQeFGv75n1bPldGVJTjbDcDoEhsh6CAQCgeB00NdNfV83gX0lAYbGhbGnps1nus+DhSNJizNT0WDXDHKLJlq5OC2GSFMIy97br/nC/fyKdO7455c+x24qrdUqVoJ5wFkHh/Psh2XaumEJETyxoZQbL0nj7x8f1rxaJAkuGRpDU4eT6aOT+eenFUzIjNeCPQePtrOjuhXwjJY2GXQsKsziDxtKqWiwa5UvqXEWHpuZS11bJ03dLdMmg45Hr8thSKxZq2bx3v/7u5+fziTLieqUbxr4GUgBjdPBqao8GshVQALB2UQEWwTnPKqQirEYWXBlBjuqWrhtghWANduqNJd9NRM1Oz8Ft6wEFDpXDI/n9u6WI5Un39+vHQ8eEaJOH1r09i4WFo6kaKIVWQGdBA6Xm7te3aEFd0wGHXddmaGJI1OIDr0Ej1yXTVpc2GnJkpyKDMOpFhgi6yEQCASC08HxgibBbgI/PdTQp+9H73M+tG4PK+fma8asTTYnCZEmXLKiBVoACnOTWfT2br9jn5wzWquozU+LYsn0bBa+1ZOIWVg4kmc/LGNHdSs7qj0tQ/PGW6losPtoEbVKJc4yjEERJmQFJg6LZ822Kk17zJ+cDnj22bwh0Tw2a5QWaFGvacm6PRRNtFLf6iAl2sKyOaMwhegJM+kZOySWkJDA1o597efeN+86SSLGYvRrtT5VSZYT0SmiutafU1l5NNCrgASCs4UItgjOWbw39L/MzafJ5qCxw+WThVKrWqJMeu6ekklilImvG20sXV+KMURiUWGWZl5rMui4cFBYUJd+8IiJJdOzOdrmIMpiwNml0OmSSY4yYwkN4YIoE012J8YQidsnpaPXwcVpscjI/M+qL3w2M++S3FPNQMwwDMRrEggEAsG5z/EqLYLdBPYVNAh2ToNe4p0FE6hrdWDQ6zjW3kmXW6G4IIOUGAu2zi7CQkO0BAugBUEO1Lfzh9mjsLvcRJoMHD7Wzp9uyuOLymZMITq6umStNdlbw6jv7a1FFhWOoNXh5p7Xt/tpniabE0Xp0Sx6HZTVtwWcViQrMCjcxC+89uaHr81m7JDg33ew/Tw1xqLdvMdYjMzOT+GXUzKpa3Xwj08qqWlxnLUki6iu9edUB6DO9yoggeCbIIItgnOS3tH4tDgzj1yXw7wXfccALt90gLuuzCAmLNQne6QKkmc3l/HYrFGU1bcxIjESi1EXUHiNSIxk/uR0FAWWveeZMvTknNHcfFkav3ytR+g8NjOX3JQo7picyQNv7tSe/+P1o3n1f8ZxpNlBUpSZrKTI0xrtH4gZhoF4TQKBQCA49zle5WSwm8DjJQECnVNttSitbeOuV78iMz6cn0y8CL1O4p7XtxNj8Uw+7N3OvLqkki7ZE9ywObuQFVj2/gFum2Dl+S2etUlRJuaNt5IWa6bZ5sTpVpg5NgWAtdurSY+PYP7kdHQSpMSG8ZOX/DVP0UQrCZEmwkP1FE20Ynd2ERpiYmRSZNAhAOXH2n3O88Cbu8iID2fUkJiA33ew/Vy9eY+xGJk7Li2gt4w6zehMI6pr/REBKIHg9CNGPwvOSQ4d64nGJ0WZuD4/lU8PNQbcNDITI7VAi/rc8k2e0csVDXYURcGo1/HnD8vo7FJYWDjSb+yjJVTP81vKeeaDMppsTn47LYujbQ6e2niAGIuR2yelc9sEKwePtrO/rl0LtIDHNO/QsQ7mPPcJP/3HF1y/cisb9tad9jGJqrgcZx2EdXD4gAhqDMRrEggEAsG5zTcdg6sGDd5ZMIFXii7lnQUTtBaKYOdMjbGws7qZ0tpWbptgZf6kDKqabFob0Yy8FG3SD/Rojl9PHcGgMCOPvLOXY+1OHuzWJWu2VWmjnmtaHLzwUTmR5hAizEZWfFDGik1lPL+lnJ9+N53nNx9kxSbPuOfGjsCTDpOjzKzcfJD9dR0s31jGI++WcqzdxQVRJh65LsdP36QPDtNGW3ufp7al7xHKgfZz9eY90HSm5ZsOsPyGMSdtjhto5HR/xlAPtFHJA4GBPNpaIDhfEJUtgnOSioYObRO/+bI07C43uclRAbMWx9o7/QRJZnw4l1wYQ2psDjEWA50uI1Ozk1jwypfEWIwUTbSSGmOhttXBS1sreOAHI3j2R2Oxu9zEhRnZX9tKaIg+YPbmoWuyfHqUZ+SlaBOOQPQJCwQCgUBwKjmZyslgVS+BzpkaY2HD3jqfSpilM3KJtRi1PV6SAk8Zsjm7+PN/y6lpcRCi69EqqtHuvPFWhieGE2kycPBoO491G++rxy9eu5t5462al0tsmDGg5qlusXN9fqpP+5Hd5aay0U6YUc8/5l1CQ7uTsNAQzAYdSJJmjOt9nsQo0wmbpyZEmkiLMzM8McLHO08dO213ufs8/njvF8hjZMWNY3B2Kcf1HRHVtf6I9m6B4PQjgi2Ccw5ZVgjRS5opbpTZwCufVxJnMbKwcKTPuMQFkzOoa/UdvZybHMkPL03jf1Zt8wqQZLOptBaHS6amxcHyjWWaMV2TzYlOkvjNv3YyOz+FlZvLibEYue/qEczO98/ePPj2boomWlm+0WOoG0x49S7TPFWO8AKBQCAQfNs4VX4RgfZi9Zy9Rx7HWIzodRKDI0J9dEagIEhNi0MbDT16iG9ySK1oef7mfG57qYTbJlgD6obQEB0LCtKxDgrnSFMHi6ZlsXhtj+/cQ9Ozae7o5G8fV/iY9EebDdzRnUyanZ9CaqyFQ8c6SIg0sfVgPYuvydIMfVXPlhEJkX7+K5nxEYxIiuTCQYH1SWqMhTsmZwT1kemrYqI/Zq2BPEZ2VLVoXn3qc8ESWsJTxBcRgBIITj8i2CI45zjc0MHhYx0UF2QA8NC6Pcwbb+XR9aX84soMbTKQosCqTyowhkj87roc7n/D09pTNPEi7u4WAqAGSHbx2KxRlFR8qb2PakZ355WZPLRuDzUtDmQFrZrliQ2lzJ+UEVAQpcZYNCGllwILL2/RcSod4QUCgUAgEAQmWGJDlhUqGzv4orKZ+97YGXAv9va48PirXMjd3T4txQUZPLXxAGu2VWl/V8+xaFoWri43Kz4o1Xzmehv0L5icQUNHZ58Bm7y0aD471EjZ0XbWbq9mQUEG8yel4+iS0UmAIhNuMviMcH742mwefmdPwErc4oIMCkZewF82l/Hc3LHUtXiSQKNToqlssmmeND+9Ip3S2lZK69p4fEMp904dEVCfVDbZeOBN/7btoolWhidG9lkx0R+z1kAeI7LSv4SWIDAiACUQnF5EsEVwTuAtjvQ6idQ4Mwa9nlZ7FzEWI6kxZhwumRc/rmD+5HSf6pbiggxe/L9DFE20khEfgULgjdnh7PJ5TjXGVQMtAHoJn2qW+jZHQEFU2+rwtCLFWmizu7jrqkytnztQmaYYSSgQCAQCweklWGJjyogENuyto7S2VauSSIoycdOlqbQ5XHxy6BgXRJlJiuoxWZ03fihPbNinVcS+tLWC4oIMEqNMHG11MH9SOk63TM4FURj0El983cxtE6ys2VZFRYNdM+jfX9eGosDqkkrunjIck0Gn+bh4B0YWX5PF/W/spKLBjsmg484rM2lo62RweChfN9v5sLSe6y9JZeXmg8wbb0WvgxGJkYSbdFQ02Ll9UrpfJe5TGw/wpxvzKKlo4fPDTazYVMYrRZcSEqKjrtVBjMXI9Zek+nxfCyZnsHT9XoYnRvjpk2CGq2OGRB93AmN/zFoDmdz2J6ElEAgEZwsRbBEMeAKJo0evy+FoewcoCjdflsaRFrtWpmvQw8q5Y2nscBEXZqDJ1slPv3sRSFDZYGN4UkTAjTk5xqw9rwZpwkL1PhmiwRGhhIcatGP/8UmlnyBaOjOX5GgTMZZQ9DqobXWQGGmiYHgCR9sDl2kKR3iBQCAQCE4vwRIbr/xkHKW1rSRHmbltgpXN++r5Qe4FvPxZBYW5yVQ32xmRFEmUWc/zt+Tz2aFGMhMifPzZaloctHe6+c2/egzyk6JMmC/T+1S5qG01FQ12yurbWLGpTKt++de2r7WpPZIET8wahV4v4XYrPL6hVBvd7HDJPPn+foomWnnk3Z3a8c/+t4yKBjvPfOBpYzYZdPztxxeTFmfWklLeOFwyHU43JoNOGxWtBikSIk3Mzk/Rklfq+uWbDjBvvJW6Vn99khRlYkFBOqo/7ZptVTTZnKT1ozWlP9OCAnmM5KRECd8RgUAwYBHBFsGAJ5A4OtTQwVtfVXPv94ZTWtdGaIiO30wdztod1bhliaJuP5a0ODM/vyKdRW/39A8/dE0Wj87I0QSRyaDjt9OyePHjcv7644spOdxEZ5fMS1sruOnSVC3zpJMgPsLE1402TRCoxnZFE62kRJtJjDJzuTWOkJAed/ehg3rEyEXxgQMnYiShQCAQCASnl0CJjRiLkf11bVpFi8mgY2HhSN744muuz0/Vkik9emK3T1Lmpa09/ih6nW/lbCCDfDVY8cJH5dooZ0WBZ/9bxvTRyazfVUPRxIu0IEdanJk7rxxGRYOdpCiT5vsCYDHqtfMuXrub+ZPSeWLDfu391alCP52YriWleuuMo20OLcDjHaQYGhdGZnxEwACNXtfz3iqyrLCnxvd7LC7IICMhXDtnX950/TFrDeYxAgR8rvxou/DBEwgEZxURbBEMeAKJI4tRz/X5qdz1Wk8Q5c4rMykuGMZPVpVo6wtzkzVhBD0GtndPyeSxWaOwO7sIM4YQZtKzYc8xCoYnsew9j1AxGXTYXTL76zyZJ4A/3TSG0anR/H5GLr/+1w4cLpkmmxNTiJ4n3z9Ak83JO9+g9Uc4wgsEAoFAcOrxvsG3GEP8Ag6z81N4sJdOWLJuD4/NGsWvvPzdAumJpzYe0AzxTQYdI5Iifc4fzCBfr4MFkzN49J29WqAGIDMhgmEJEfzytZ73rWiwU9tiJy3O7BP8UYNCSVEmbdpPfESoz3uZDDpPMuqNncRYjH6VuA9PzyYrOYJOl8x30uOwOd0cbujQAhO9P496zuGJkbjcvp8rUGLsqY0H+PcdEzRPnL686fpr1hrMY8T7OeGDJxAIBgoi2CIY8KijBAtzk7VsTmZ8OD9/+UufTf3J9/ezdGaujygIJnSa7V08/O8eM9ylM3MwGXREmQ3Mn5yOXoKhg8L44/v7KcxNBjwCwy3DXa9+xa++N5w/zB7F3to2zYhXFUzfpPVHOMILBAKBQHBq6X3TnRZn5uFrszUTV5NBx9C4sKA+bsfTEzEWI6NTolk6I4cwUwguV5ePR1swP5H8tBgWvrVL0w1JUZ6WHWeXjEGv83ufF7dWsOTabOa//IVfUGjeeCvPfOAJ9sR7VcmqSajKJptPJe688VYkCYYnRNDY0YnFGMKhY20BAxMXDgpj6cxc7l2zwyfA89ePDvLYrNE+1xisHfpou4OL4sP75U13qsxahQ+eQCAYKIhgi2BA09Ul43R3cfukDB58q0ccLZme7dMrrZbWJkf79guHh+oDCh1FwedxmDGEJdOzeXT9XpxdCrPzU1AUWPiDkSzfeEDrs37x43LunTqC8qPt5KZEsXZ7tdZDrZ5Lbf050VHOp9MRXoyVFggEAsG3jd433c4uhaNtnSy/YQyyomDQS+gk6bg+bt7Pe/ux3HxZGj/vDoCowY1/7zjCstmj6HTLdDhcPFg4koe8TPsXFo7kQF07v546gt93a46bL0vjqY0HiLEYuf/qEZqOWbOtipoWB002J64uOWAwQ+oO6BQXZKAoCsUFGbjcCmPTornvjZ1MG5Xs0/qsBmbUVqbhiZF9BiZ+kJ1EjMVISUUjbhlWbj7IvVNH+FXeBmuHHhzu0URn0ptO+OAJBIKBggi2CAYsXV0yb+04QkVDh9YDDJ4Nc+Fbu7TS3aQoE3PHpXH4WCuVjWE+/cJ3XZXJYzNz+NWaHn+Wh67J5pkPDwBojxOiQrn7te04uxS/0YgPFo6kxe7i3Z01TM1O8sn+eBvSebf+DKQS1oF0LQKBQCAQnCl6j2r+8eVDefL9nsmAxQUZvLuzxm9U88LCkTy/uZxF07JYvHY3MRYjYUY9S2fkUn6snVdLqrQASe8K26KJVroU+OP7+7k+P5XVJYeYN96K2aBjZFIki9ft1jTDwsKRxEeEcsc/v9RGMz++oZTC3GT0OniwcCQvfHSQycMTqWjo8KvyXbu9mmEJERRNtGIx6Pnt2t3MHjsE66Awdle3MH9SBis+OODXPqSa9DpcMse8xk2reAcmQkJ0jE8fREqMmfo2BzPzkgMmbAK1QxcXZHCooZ0LB4WdUW864YMnEAgGCiLYIhiw7Klp4f43dnLbBGtAIZAaY8Fk0DEjL4XVJZU8ND2b/+k2xlXXLHtvPy/fdinzxlsJDdGRER/OG19W8sspw3E4u7ggxsyRJhtbDzYGHY34UHeZ7oTMeL/XFq/dzUu3XkKHs4vU2DAuHOQRIOVH2wdMCasopxUIBALB+U7vCs7UGAsWo16rEok2h/DEhv1+niLzxlt5aavH6D45ykxlk502h4sP9h+jocPJcz/Ko661U/N1MRl0/O66bPSSf7uPwyWTmRBBc0cnhbnJmmZ45oMybp+UrlXBqGuXrNvDn380FodL1rRMb1+W312Xw4v/dwiA26/I4MG3e6p8F1+TRZOtE7cMz24up6bFwZBYC3d3e82kxZl5sDCLyoYOVs4dy46qFuwuWWt9Nhl0JEcFruDxDkz0p/JWp5MYmeQJ/MgKKAq8tLVC87I7k950wgdPIBAMFESwRTCgUMVSi91Jq72L2yZYyfIa1ay2C+l1YAnVc9/3h5MSY2F4YgS1LYHLRqua7PzrC8/4wUeuyyY3JZbDxzpIjw/n/jd2UtFgp7ggHZNBF9TjRc0iBXpNRmHS8ASf5wdSCetAuhaBQCAQCE41gSo4H742m6c3HdCqSJ6YNSro/l7T4mD5xjLmT07nhY/KmTfeCsD3c5KwOWUfA90Yi5FQvR6bs4vignReLanSWppNBh3769pIjbVgNvR4viRFmRieGMFtEzwVLjpJosPpBsDlljUjW+8AjXp997+xk3njrZ5Kl+5Ai/raorc9E4gAZo5NQS9BXYvdx1z39pe/4LFZoyitaSUlxqKZ+6sBiKwLfEcnp8WZWTI9h7pWB4rimbBU0xK4Bbl3gKuu1fM99kbVG2fKm0744AkEgoGCCLYIBgyqWFq6fq/f2ENVNPXO+Cy+JovF63YzbVQywxIiAmZnyo628+iMHNo7u2hs79TGIqbFmbn3e8PZU9tGmFHPomlZ1AYZjRgZqicp2uLXR61mf3oLjqSogVPCKsppBQKBQHA+E6iC84E3d2nmsQ6XDEHMalUPN5NBh05CG+cMkBprYW9tq0/QZO64NK1yxHv8c5PNyUPXZNFoc/L0pgM8PN1jvK+2B93T65g12zxJoEXTsvjdddmEhujZX9cWMCCk18GQGEvA1xKjTD6Gv3demalNKFLXlNW3MTQujMKcJHJTomjs6MSg12FzuqlssjFlRALvLJhAY0cn1c0OirqnOvb+fN4tyIECXH+Zm9+n3jid3nS9OZPvJRAIBMHQne0LEAjAE2jZWd1MaW0rd08ZzsrNB4mxGLl7SiY//246CRGhLCrM8sv4LHp7tzYtqKbZxsLCkZgMnn/Wal/yayVV1LU4uPu17bhkRQuE3HRpGkv+vZfnt5Sj1+sYlhDOJRfG8vC12T7n+P2MHCLNRu55fTvLN5bx/JZy5o5LIy3OzLI5o0mNsbB+dy1XL9/CD//yKVcv38KemjZW3DjG5zxnq4RVLacdCNciEAgEAsGpJlgFp+RVyFDVZKO4IMNnLywuyOBfX1R1+7dlkZMcRfYFkeQkh/OnG8cgK5ARH6EdMyMvxU+HPLXxAI9cl03RRCtPvn+AP75/gKKJF4Ek8/C12czOD3zMjLwUrR3ZYgjB7ZYZkRipvZeKyaAjMz6C+jZHwNcqG21+vjEz8lJ81lxmjWNsWjQhIZ7pS/VtTq5f+YmmWTbsrWNoXBixYaHa5KFA13rXq19xuKEDCBLgemsnS2fmCr0hEAgE3YjKFsFZJ1B25M4rM7EY9fzunb3EWIzMzk9hWGJEUDG1ZlsVP51oJTEq1KdfeNUnnmyMJTQEh0vm8f/s49kfjUWWFSoaOpidn8KIxEhsri6cXR6nfltnF0/OGU2bw0VilJmKYx387t29PoJi+aYDrC4aR05ydFBPlH/fMYF3BkAJqyinFQgEAsH5TLAKTu/Jg//8rJIFBRmaRshOiqSysYMfXpJKenw4S9fvpaLBTlqcmduvSGfpfzxGtWaDjmduzOOhdbuDthp/+XWzT/vMknV7KC7IINocErQixbs9uc3horLJzicHj7KwcKRW2Wsy6HjkuhzMRh0GneT32sLCkazYVOZ3br2u5ztYMDmDdmcXQ2I8AY++fNyOF7TybkEOtLaiwU5ytGlAaB+BQCAYCIhgi+CsE2jjVx391fLbTaW1XGaNCyqmalocPLu5nNuvsJIaG8YDb/ZMHyouyKCqyaade1d1C09s2E9anJmFhVk025wkRJp4YkMp1c2dzM5PwWwM4Wh7J898WMbPv5seUHzYXW50OimoODna7mCcddCAKGEV5bQCgUAgOF8JZIiqth+DWsWSyd//7xBTshIZHB6KpJOIMBtpb7H7aJDC3GSe+bDMr235ketyiDSFBNQhbl8J0N0CbcHmdAdtK/ZuX4qxGIkOM5IWa2HFBwc0j5YxQ6LpcHZx+8tfam3Vf/5RHo3tLioaO2hzuGiyOX3eW62EefqHo9FLEuXHOogP72kbDuZvp7ZBH+9a1ZagYGtjw0KF3hAIBIJuRLBFcNYJFqyQFU/J7qbSWmbmpbKzqtlvfOHia7L404eerE6TzUmrw82rJVXaVIHqFjsWg55nN5cDHiFw0eBwfv39YUSYDMzvngygVtOYQnQ8ur5Ue27B5AyiLIaAgsJs0LP14DEsxhDS4sxUNNh9XheeKAKBQCAQnH4CVXCmRJnJiA+npsVBUpSZaEsIT210oZMkFq3drQUv7p06wmd/lyQCGtXe98ZOVtw4hjuvzPQZH72wcCQrNx/0uR6TQUdpbRvLN5aRFmdm8TVZLPKaZqT6oJgMOn47LYuqJhs6SeKvHx/Sxj4PT4wECe5+raetp6LBzm/f3s1vpo4gIz4CY4hOG0+tnnvJ9GyabZ10OGXtOld8UMayOaOZMiIBKYh3jcWoDzq+Wb1W75agUzXxp7fn3be5EkZ8FwLB+YekeNdYCk4J+fn5SklJydm+jHMCj1dLC9ev3Oq38RcXZBBrMWIODeFXr2/ntglWPjl4lHnjL2JvbStuGT4tP8r8yZmUVDShKPCvL3qmAjw+K5fw0BB+310arI5QNOglyurbWbm53O89iyZafUqBTQYdL8+7lKpmO79as8MvY6aet/fj3iZyYvMUCAQCkCRpm6Io+Wf7Or4tfFv1SKD25GVzRhNjMXDr3z8nxmJkRl4KkgTDEyO4+7Xtmh6YPzkdnUTAqToLCtIxG/S0d7qRJMi+IJI91U0MiYtg4Vu7/AIUqh5JizNzz5ThlNa1YQrRoddJ2F1uRiRG8tC6Pd0BIZPWMl3TbEeWFYbEhtElKxw61oHTLbN5Xz1Ts5N6jYbOpqnDSauji/T4CF76uJwFBcM0k1sVk0HH6qJxfFreQJeMzzkWTM7gkgtjyB8ap2mW+jYHg8NN6HVQ2xq4Jch77TdpGQr2c1L107cJ8V0IBGeWM6VHRGWL4KzhPX3o7inDeGLDPm2Due/7wxk6KIwWu4toi4HF07IIN4Uw/qI47v3XDp8qki8rm3h+i3/gxGLUo9dJLJmerZ3XJcv8+YMyfvwda9Bqmt7PdbplLKF6rc97eEIEj28o1a5BnXqwumgcdpfbR3CIzVMgEAgEgjNLMF+SF27J19qT1WBDWpyZh6Zn82B3sGTt9moWFWYFbRfqcLp55oMyTAYdz80dy8RhCew50uqjEX73zl4t0ALg7FIIN4Wgk8DR5XmPmy5NwyXLWhtQk82JKUTP3z4qp7hgGNurmgF4oltveFfReH+u+9/YRdFEK6YQPY92v29JRWNAjVPT4qC1083a7dXMG29Fkjz+dqtLKpmanQgEbjseOihwS1BfLcr9STT15R9jHRz+rUpWHe+7EAgE5yYi2CI4a6gbS2Z8OHHhRk2ohBn1RFuMLHxrFzdeksY9r+/wyRb9v8sv5NnN5ZqQebWkys80bsn0bBo6OqlrdfKvL6o80wA2lmntQlWNtoBCqvcergZtbv375zhcMklRJn5RkOET7IEeD5dx1kGAR2SUH23ncEMH+2pbibEYqenukxabp0AgEAgEp49g7cnVTXa/6UDOLoWG9k4enzUKgGNtDlxdXfzuuhzuf6PH/23B5AxWl1RSmJusPd5X00pmYiSPvFvqUxnj7aOSFGXi5svS+Ok/tmnnerBwJK98Vsm88Rf6BD3W76phanYSP/EavbxgcgarPvFUySxZt0cbZ50UZdKqc0anRPPH9/drukhWArcKxVqMrN1e7edHs3Rm7imdGNTfRFOwn1N9m4OhcWHfqmRVX9+F0IsCwbmLCLYIzgqyrHC0rZPbJli5eGgM/7Nqm7bJ3D4pnV+t2cH8SelavzH0jCAsmmhlRl4Kz3zg5dVid/H4rFHsq2tDJ0FsmIH9dZ1a9kk1r1PNd4sLMvwCNA9Nz8bh7NIEirqxO90yDpdMbnIk11+SypEWu7ZGFTt6HZgNIcjdpTG9BYK3WBKbp0AgEAgEp49g5q31bZ0+04GSokzMHZfGUxt7Ag93TxlGSIieOKPEn28ay5dfN+GWPdUfRRMvos3hYt54K6s+qWDm2BTsTrfP+6zZVuXjLzc7P0U7P3h0yEPr9lA00Yper+OFj8p99E9vr5jlmw5oARZ10pB63b1bgY62O6lpcbB2e7WfxikuyODhf+/hhotTeeXzSs2ENz8tlsutcac0gNHfKo1gP6f4CFPQcwy7YwIXxZ9/+qmv7+JU8m2qFhIIBgIi2CI4o8iyQmVjB19UNnNfd8bo7imZfuZ0MRYjCZGmoK0+ZoNnrqEqMF753JNteuGjchYWjuTPH5Zxy+VWn0CH9znsLjcRoXoemzUKu7OLxCgTT2/cT3Vzp5Zl0kkwMikCWfH0W//0inTuevUrYixGLcPlnR1aubmcpTNzyUqK9BMI3mJJmOcKBAKBQHD6CGTeqmqBH41L1W5qZ+Sl+AU3ntiwT/NvS4sz89tpWTjdMt/NGMQ9a7b7VLbqJWjs6PS5Sa5pcbC6pJLnb87nSLOdCLMhoJZJjbVg0Es+5rl6XeDR0uroZZNBx4jESL/qHFVnPD5rFE9sKOWGi1NZ/ZknoJIa4xkWoHrIHG13Mjs/hTFDokmLCzstN9v9rdLoy2T300MNAc+xt7aVCwedfwGCU2U43Beitf3biwiynT1EsEVwxujqkvm4vIFWu0sLtCRFmUiPD/eL5s/OT6GqKXirT9YFUdz3/WG0ONysLqnkjskZNLZ7AiUrNx/k+vxUzEadJjy8e6dNBh0XD42hvs3Jr17f7iPE1u+q0dbJChxt72RsaixLpudoPdA1LQ5WfVLBb64eoR0PHhFw75odPDlndFCxdDo2T4FAIBAIBL6MTIrgxVsvwebsIi7MyB2vfElNi4N/fFKpVZ5IUk9ww7stJyM+gqQoExUNdn72v1/w5JzRPPafvfx0YjqL1+0mxmJkdn4KFw0OJ9Icwn3fH661EuWnRVF85TBtClJlY0dALZMYacLmdPOnD8u0JE9+WkzQ0cuqTvnzh2XcNC4toM44UN/G/EkZrNp6mB3VreyobmX+5HRWbOox+61pcbB8YxmvFF162ips+1ulEWiKlHoTGOwc++vaGJkUed5VB/f1XZwqhC/MtxMRZDu76M72BQi+Hciywr931VC0qoTSujbtF/2MvBQefXcvCyZnkBZn5vZJ6ZhCdIxNjeGD0nrPOGavKpbiggziLEZ+u3YXIy+IIi3WzKMzcnl60wEeeXcfz3xQRkWDneWbDqBD4okNpfz0u+k+51h8TRYut6z1YoNnw1ldUsn1l6TywkflrNhUxvNbyvm60TMRwKCXtB5o8IiV/V6fQ8XhknErirZOxWTQMSF9EO8smCB+uQkEAoFAcIpQPdK2HjxG+dF2urpk1u+uZepTW7h+5Sf87H+/4OsmB7/5/ghMBp1WebJybj7j0wdhMui0thx1/7/n9e3MHZdGUpSnwrbN4WJmXgoXDraw/IdjuOd7w1i5uZwFr3zFT//xBdFhRl689WJevu1i5lycxk9eKuGe13fwk1UlWIwh3PO9YT46ZMn0bMJC9Rj0EtNGJSNJnvajPTWtFBdk+K0dnhiutS7tqG7l6yZ7QJ3hlmHhW7u4Yni832u9H5+uCltZVlAUeGLWKIoL0kmKMvWZaFJNdsdZB2EdHK7po6FxYTxyXY7Pd7FgcgavlVRR3+bwO8/5QLDv4lTRV8XRqaT3/5Ny7+kTgjNKsCDb4YaOs3xl3w5EZYvgjHDoWAf3do9Ohh7jNkmCigY7nx9qYEFBpo8Z3X3fH45bVnj2R2Npc3Shk6Cy0aaZ49a1Onjw7d38/Ir0gIa1Hc4ufj8jB5db5vFZo7B1dnG0vZPGDifVzXa/DacwN1nrb1bPcd8bOxkUHkpytJm126t9+rD1UmADuq8bbTx8bQ4PvLnTJ4J88dBYEWQRCAQCgeAUEShju3RmLsve2+ezl//yta8oLsjw8SkZNzSWzQePUlyQgd3lDuqV8sJH5cSGGTna7uTHf/tce8577a9e30HRRCuXXhirTTVSX/vNGzt56oYxrJw7lvbOLioabKzaepjv5yT5eMUsmJzBOztqmDU2RRsYoJNAp4MnNuzz0Tlrt1fzx+tHs6emFVnxtDPFWow8u9lzXZkJEZo+Wbu9moevzeaBN3f5aJLTUWEb6OfxyHU55KVGkxp7YlUaOp1EXmq09l0oCqz6pIImm/OcacXuT+vGmWzvOBO+MKKKYuAhzJfPLiLYIjjtyLLC3ppW7X90b/M48Pih3DQujfn//FJbE2Mx0uF0+wkR1WRWNbpzuGScbjng5nGgvp202Hg2ltbz3OYeYTR/crq2xvuYYL3SJRWNpMYmc+/UESxdv5d5461EmfTkpkQzJNbiI2BU/5hls0fzzmkoBRU9lwKBQCAQeAiUsb13zQ4enzWKR7zGL3sSMG7NWD8tzsyTc0aj10laW3Gg/V+vgwWTM5AVePa/nnaftDhLwLWyAlVN/okch0umxeYkJs5C0SpP6/Htk9L9THOXb/IMAFAUmJA+iC1lx3DLsO6rI9w7dQTlR9tJiwujptlGbko0DR0uVnZrG3XSIvT4unhrkNQYC3mpMaetPaWvn8d9b+zknQUTvtH7pcaGMTwx8rT6mJwu+hN0ONOBiWC+MDoJth48dkp0pWhVGnicKfNlQWBEsEVw2jnc0MGB+jZMBh0xFiMz8lIIDdGxat4lOFwyo4dE0eVWfPqmA/mheGeZigsytPP3dv5XRcffPz7MsMQILrXGkJuSx/aqZmQFIkP1/O9nlX7H5KUG7pV2y1Db6mBqViLDEiKoabFR3+bkx3//nBiLZ2R1aqyF2u7S5BsuTiUu3MjQQeGndGMR2QKBQCAQCHqoa3VoukI1kV2zrcrjXTI5nTaHixc/9lRDKIpHX9x0aSqJUSb2HGlBr9fzs7VfcNsEa8D9Pz0+gkff2cuvpw7TDPG913pPJMyIj8CglwKeJznGTLuzS3ve2ytGxeGSyYiP4IkNpSyalsXyjWVae1Nvo98dVc0se983WPPk+/spmmhleGKkZiDrrUGsg0+tJgnE8UY5n2iy6Ez4mJwu+hN0ONOBid7f5+BwE4ca2pn61JZTpitPVxWFSDZ+c86E+bIgOCLYIjjt1LU6+KC0nt9dl019aycb99Yyb/xFVDXZ6XC4GBJrISLUwN1TMrEODqNLVtB1TyTyNrZ1uGRSY83MG2/lpa0VzM5PAdAMa4smWkmO8rjuy4pCk82JQafjaJtLa2EyGXQsmpbFTyZY+cuWcp+S4ktSY3j0ulx+88YOH1GzuqSSmXnJ6HQSF8WHI0lw20vbNLPc5Rs9E4YenzWK6aOTyUgIJzX21P8CE9kCgUAgEAh6SIw0cfNlvqOb1WTMku7xyvMnp5MYFco/Pq7wGZe84odjuLs7qRMoabOwcCTPbz5ITYuD6DAj9/5rp8/a3hMJTQYdD/xgBA9Nz9ZaiUwGHQ9Nz6a6yUZ1s8MnEBMoKFPVZOPeqSNIiw3DZNAFnJakTh0KdEM7Zkg0382MP2s3ocEy6ImRpm+cLFKDRueazulP0OFstHd4f5/lR9uZ//KXp1RXno4qCpFsPDnO5aDl+YAwyBWcdpKiTHw/J4kORxehITp+NO5CSmtb+dtHhwgzGdhT08Ytf/uMJzbs565Xt1PVaOfx/5Ry82UeczoVk0FHZaOdZz4oo8nmZMyQGM04rcnmxBSi548bD7B8Yxl2l5sFkzNwy4qPV4zDJbN47W7iwkKZPXYIeh0UDI/3mOSZQpiWm8TKufksKEhn3ngrq0squXfqCJ/ob7DN0aiXuHZ0MpOHJZyWX2BnythMIBAIBIKBjiwrVLfY/dpxntp4ALesaK09S9btYfvXLVw3NoUocwiLp2Xx9A/H4FZ6KmprWhys31XDsjmjtf1/5eaDTM1OIinKxN4jvob4sqJwz5ThdHa5ibEYtfd++N97kd1uHps1isdm5vCXm/OJCtVR3+ZEr9Px5PWjSYszs2ZblZ8R7sPXZjMhYxBTRiRw4SBPJjpYe7PN2RXQ9DatHzdQp9O8VM2ge3+uZXNG45b51hl0qkEHb3oHHfqz5nRyOnRlsH8DJ1NFIQxeT57Tbb4sCI6obBGcdtwyvPJ5JUUTL+KRd/doUekn54xmb22r1nMMvu1CT2309C+rlSPFBRm8tLUCk0HH767Lwa3IrJw7lpKKJtwyPn4uFw+NZVd1C51dMrdNsLJmW5VP7/bumlae31LOsjmjyUmO1n7phIToGJ8+iJQYM/VtDmbmJftFf4NF7TMSIk5r5kX0XAoEAoFA4OFwQwefHWoMeLPo6JK1kckOl4zFqKe6ye5TAfOXm/N99tQJmfE+N3SApkfsri6tFdq7Oqa3n5zDJSNJOsrq28hPi6FLljnc6GDFB2Xa+oevzcHh6iIjIZwVPxxDW2cXh4518IcN+2myObWM/dSsRJKjzT4aCTz7/tG2TooLMnw+T39uaE93hUCwDPqnhxrOeAXH2aY/rRtnu73jdOjK01FFIQxeBecyItgiOK3IssKx9k7unjKcJzaUcu/3hpEWF0aH001YqJ4QnS7gL1C1nzk5ysyCgnQuGRqLJMGt3xmKdXA4tc02QkMkJMAUovfza6lvc/RprjshfRAzxvgHUuD4JaupMRY/Z/+Hr80mNcZyur5G4OxvygKBQCAQDBTqWh3ISuB2nMhQPcvmjKasvp3ignSGJUbwZWUzt02wAh5fl1Vby30mBw4KMzBvvNXH+6WmxUFmQjgtNidPzhlNq8PFkWa71ubsnSB65gNPYqjD2UVCpIlWRxdfN9r8Km8eeHMnj80axS1/9Z9sBPi0ceQkR/nt+2oL08LCkfz7jgkcbe//De2ZaEcOpKG+jcmi/gQdznZ7x+nSlae69evb+O9HcP4ggi2CU45qYtXQ0UlDu5M9Na0MS4hg/qR0wkMNfNVtVLt2ezW/nZYV8Beo0i2gqlvsJESauP/NnSyZnk1OchSHj7WTGG1hybrdTBuVzNrt1ZpAUhSQULj/Dd/Ri97muic7hrmyycbT3edT3/PpTQfIS40JuLGcKlOvs70pCwQCgUAwUEiINLF2e7Wf18pjs3Lp6HT73EAuviaLt76qpqLBjsmg48HCkSREhvLnDz0ThoYlhOGSYel/9vkFNS6INnGk2c4jvQIevtUsaBW4o1KiOXi0jbe/+pqrc1ICJpTs3Wa5wYxyKxo6tP1d3ffrWh1YjHpcbpmp2Yna6xfF9/+G9mxVCAS6qV86M/e0J6nONv0JOpxNT5pzRVeKZKPgXEYEWwSnFO8SVdWk7q2vqnlsVi5l9S7u9PpFeeeVmdhdbr9S2DuvzOTlzypY2C2Glr9/gIoGOzXNDhat3c2S6dk8+6HnuTXbqvxKepdMzw4oJnKTPaMQg20k/Q2K1LU6qGiwayMkVQKJlVNdsnuuGsUJBAKBQHAqGRoXxr1TR7B0/V7mjbdiNujIuiCSQ8c6eKw7aAKe/X/R27u16hOHS+ahbvPcq0Ym8fePD3Pf1SO4/w3/CYh/vikPWVYCjmn2rmbJT4sh2pzJCx8dZlB4KC4ZalucNNk6AyaULMYQH0+L3q9/+XUzdpesaYVTte/3t0LgVE9+0ekkpoxIYOXcfEoqGnHLsOy9fRj0OmFyepY5F3TluRIUEggCIYItglOKd4lqaqyFX762nXnjrbR0dLFk3R4fsfLk+/t5YtYo3t1Zo1WJ6CQYNSSK5OjhfN1kY8WmMq31xxIagsMls/CtXSybM5rq5j3aJKLHZ41iX10biuIJegQSEzEWI4cbOlAUtLGIKicSFDmRckYxQUggEAgEglOPdgOWGEF9m4Pw0BA27KkjOcoctD3Z+7GswJPv72f+pHQOHesIeMy2ymZ0QapP1GqWBZMzWPT2Ln49dQRNNifxkaH8z6ptLJszmmc/LOOR63K4742dPlUxL35czqJpWTz73zK/yhy1aqbJ5vTRCqciANKfCoHT5etS2WSjaFVJ0JYpwZnnXBqnfC4EhQSCQIhgi+CUopao5iZHopMkHC6ZCJPex/VfxeGS2V/fxtTsJK0cF2BIjIWjbZ0+4qO4IIOqJpt2XGltKzPyUnjmA08wpqy+jee3ePqek6JMfuJl0bQs7lmzXSsh7i0cTiQociLljMLUSyAQCASCU4ssK1Q2dlDX2kmHs4u0GAuldW2s3FzOPVOGBW1P9n6cmRDBbROsjEyKZG9ta8BjTCE6zWy392vDEiKYN96q6RenW2bZ7FFUNHRoOuWK4fEkRZkommglRKfDOigMo0HHlJGjMIRIWAeNwuV288It+Wwtb0RR8NFDqlY4VQGQ/lQInK4kkdBDAwsxTlkgODOIYIvglJIQaSItzsz1l6RSWttKWpyZEUmR2J3ugGLFLeNXjmsy6FldUskTs0ZRWteGTgKLQc+zm8t9jtPres4TazFy55WZPPn+fmpaHKwuqeTPN40FFDo63Ty+oZSKBjsQWDicqAgYmRTBi7degs3ZRWpsmF+lDHg2MotRz4KCdGSlx2xPmHoJBAKBQHDiqEGW3Udaqew2n42xGJmdn4J1kGe6T1ionruuymTZe/t7Ei6FWTy72dP6azLoeHxWrpbA2V7VzJBYs5/xfXFBBhaDng27q/wSOMUFGTzyzl4tKGIy6Dh4tJ2spCg6Oj16x6jXkRZn4df/2qHpD3XtOwsmkBYXTlqcR18crG/XEkbe6waHe7TC4YYO/vrRQR6bNQp7ZxeW0BD++tFBhidGnHDly/EqBE5XUESYnA4sROW1QHBmEMEWwSllaFwYS6bnsPCtncwdl8adVw7D7nTT6nD6CRm1VLa3uVxNs40bL0nj6yYbphAdIy+IZPHa3VqgQjWt+/XUEay4cQzxEaHc8/p2nF2KTztSpCkEGYX61k4foQP+wuFE+pgDZQIuHBR23HXqdd87dYQw9RIIBAKBoJ/IssKhYx3srWkFCW3KT6BRzMUFGcRZDMyflE5CpInDDTb++VkFhbnJSBKEGfW0d7p9vOKKCzIYlhBO0UQrsuIxvn9pq6eVZ954K6tLKnls1igqGzoYPSSadkcXc/JTeLWkiiabU9Mz5IPZoKe4IAO95NEW3vojKcrEjLwUDje00+ZwYXO6SYg0EaLHz7/uvu8Pp6PTxdaDxzDoJW66dCi/en27TwCp1e7Uvp9TVaVwunxdhMnpwEJUGgkEZwYRbBGcUnQ6CZNBx/X5qTyxoSer9NtpWTjdXbxwSz6fHvKYo3mPYh6WEEHRRCvxkaHUt3by0tbDWsboubl5PFiYxfaqZtwyrC6p5IaLUznc0MFTGw/w+k8v44aLU3lq4wGtOmZh4UhcspuaZidHWuzHFQ79FQH9zQQEWrd80wFWF40jJzlalGgKBAKBQNAPAgUSVCP8GXkpWqAFPHvtUxsPUDTRilsGk0GvjVbeUd1KbnIkd39vGDUtDv54/Whqmu002Fw4XG5sLpnlG8v83t9s0LGwMIvy+nZGJEVy/5s7tZbkhYUjabW7tMCMW4ZB4aEse28/9/9gBPERoZr+SIoyMXdcGptKa8m6IJKNpfXaZMbigkwf/7owox67S2bOyk98AkLeI6cXr9vNP+ZdCpzaKoXT5esiTE4HFqLSSCA4M4hgi+CUoGY4alscGPSSn/j57drdPD5rFB1ON2aD3id7s7BwJBajjjGpMTyzaT8lFS3aeU0GHdFmI6s/q2B6Xgp1LQ4emp7NH/6zj4nD4nG4ZNocXWR0Z6RCdDoy4sP5/fq9FOYm88JH5cRYjH4lwL2FQ39FQH8zAcHW2V1uISwEAoFAIOgngQIJVU02TAZd0NHJsgKSBNXNNm3/z4wP54eXplG0aptPAGPNNk91yp9uzAt48zk8KZL5L3/hV5Vb0+Jgybo9zBtv1apbVpdUcveU4RhDJEYkRpIa2xO4mJGXwuqSSoomXuRX9frUxv1MH52sBXtun5SuBYnUz/TUxgPMn5TOExv2a88da+8ETm2Vwun0dREmpwMHUWkkEJwZRLBFcNLIssKmfXUcqGsn1mIkJsyobcBqyWxoiI4ocwidXTIZ8eHcdWUGrZ1udBIoikJdq4OH1u1lxY15fqLm8f/s5WdXZLCzqgVHl8wzH5Zxw8WpgEcIJUSaSI2xMDg8lFZHFz95qURrTXK4ZG1ikZoxmpA+iIuHxvoFPfojAvqbCRAZA4FAIBAITp5AgYRXS6pYWDiS+tbA0wd1ErhlsDvdvPlVNfPGW7nkwhj+pzvQAj0BDNUzbvG63SyalsXitbt9qnKXrNvtV6XqPUY6LdastRp5qnpLuWNyBmmxFp/Axf66NgC/yYzq+TITIrTPotcFDiIlRplIijJpVcFJUR5Ncao1x9nydRGcOUSlkUBwZhDBFsFJIcsK++pa6HIr6CWIDjMSaQ7xOP3Hh3P9JamasPD2LSmaeBEyYHO6iTCF8MKWQzhcMhXHOlg5N5+SCk+r0fpdNUzNTuL2XgGYVz73tBItmzOa1BgLG/bWcderX3HbBKuf2FADLmqL0VUj4r/x5+1vJuBMZAzOpZF9AoFAIBB8EwIFEppsTpIiQ0mPDyMlxsLCt3r84B4sHMmgiFD0EkhAtMXI0vWlpMVlBwwQqCOhKxrsRJlDNN8WnQTRFkNAzzf1GJNBR2pcGFXNdgpzk7WKlwfe3EVeagzWweFa4AJgT01rwGvQ62BItJn1xRM4dKyDjiBDBSobbdx0aSorPijj4WuzyUqKAs58lYJIKJ0fiEojgeD0I4ItghNCvcFv6OgkVK+j0eZEQeJYm4OMxAiaOlxEmvQ88IMRxIWFcmcA35J5461a6a0aAJk33sr++nYSIk2UVDT6lNL2bklSzzE8MYLvZsb7lbOqAmDNtiotuFOYm4xeByMSI1mybjf/b/xF38g4rr+ZgNOdMRAj+wQCgUDwbSBQIOGR63KwuWR+/vLnZMaH88frR7OnphW3DM9tPshPv5tOm91JYpSZiFAdT84ZTUyYoc+R0CaDjtLaNh/fluKC9KDHmAw6Fk3LwuFy+Xm99K7y6OqSabO7yIyPYMWNY1j534PsqG7Vzjc8MZIFq7/k3qkjGBxh5L43dvHQNVk8+PZun0TTqk8qeHDaSFYXjSMrKYqQEM9YxjNdpSBaUAQCgaB/iGCLoN+oN/hL1+/l+vxUNpXWcv3Faaz44AA//W46P/tHT/XJih+OYUd1S9AskndmSM3qPFg4Erfs6bVWxU2wfmy9DtK6hYR3OeuabVU+I6A3ldayYHIm97+500ewLF2/12dk4onQ30zA6cwYiJF9AoFAIPg2ECiQoJNg6lNbcLhkJmTG84vVX/lohcVrd1M00Up1s528tGhu+evnPHPjGBYVZrF4XU8Ao7ggg5e2Vmj+cSs2+QZNXi2p8gt6LCwcSZvDRdFEK64uN7Fhgas8zAY9sqwgywpvbq/2mca4aFoWfFrB/vp2FhaO5NkPy6hosHPXq1/xatE4mmxOoixGn+lIqz7xmPDGWoyMGhIT8Hs6U1UKogVFIBAI+ocItgj6jXqDr/YmLywcSXl9Ow9Pz+Enq0p8bvx3VLf4BE1UvDNC3tmky6xxPPrOXiYNj+eTg0dZNmc0pbWtZMRHBDxHXmoMqTEWwL+c1RSi0wTK8IQI7u4elahem1oZcy73Fot+aYFAIBB8W+gdSNh68Ji2B/ZlkvvUxgM8N3csmfHhGEN0uGU3z80dS7PNRXionhCdxOz8FNwydDhcNNmcPudpsjlJjDJx95RMIkINVLfYWbGpTJuWaDLoeHfBBL8qjwWTM1jwiqdSJTnKpAVa1GtbvHY3f/vxxXxU1uBzPodLpqHDybI5o6k41u43UKC4IIOEyNDT+l33F9GCIhAIBMdHBFsE/Ua9wQ8N0fHjy4bS5ujCrcDnFY1+QkcdZ9h7CpDa1uOdTfrddTms/O9B9te3c/vkdC6INmuiJS3OzJLp2T792IumZbFi035sTjdTsxJ9ylln5KXw6PpS7XrmT04PWhlzrvQWB/JmEf3SAoFAIPi24pdkCZLYcbhkOl0y8ydnsLemjWXv7fcJXKTFWTAb9HQ43QD8dloWv/UyyH342mz+9tEhPth/jAUF6Vq7kGr+L0lQ39bJlBEJrC4ax8bSetwymnfLXa9+xbI5owLqkGPtTp+JQ+p1W4whfDczlsrGDnYfafXxkMlICCc19sy06ghfOIFAIDh5RLBF0G8SIk2kxZnJuiCCEL2OfTWefuOL02JYUJCO3F2psmZbFWu3V/PT76bz7H/LmDfeitmgY1RKNK0OF7dcNpTkGAszx6agKBBlDmFMWiw5Q6KJsRgpfqWnHNjZpWB3dvmIjTCjjp9MTKfiWDuVjR2kxoYxLCGCP9+Uh8mgJ8Kkp73TzZptVUBgEXbJhbF+vcUnKizOhBAJ5s0yZUSC6JcWCAQCwbeS1BgLv7suh/vf2MnmffU8OWc0e2tbtUTP9fmprPrEk9CJCzfS5ujSAi0AMRYjdpcbu0umw+nRCzUtDtLizDwxaxSyohAXbmR3dQulde2YDDrGDInGZNARYzEyd1yalkh6fks5y+aMZnCEMaB3y6Dw0IA6JNpsoLggI2Dlik4nMXSQJ7AyIinyjLfqCF84gUAgODWIYIug3wyNC2PJ9BwOH2snIyGc6LBQnvngADdeksbKzeU+YsFi0PPPTyv49dQRtNpdWEJD2HWkhZe2erI98yens2JTjznuCx+V8/C12bTYXD6CZEZeCo+8W+onUoomWjEb9Byob+eLymbue8PXk2Xt9mp+fPlQzN391d4TkYoLMnC43D6f7USFhTruekeVp11KL0FOShSThyX4rD/ZgEwwb5Z3FkwQ/dICgUAg+NbR1SXz7101LN+4n6duGENjh1Mz41erX//5qcffpLgggyPNdnSSpO2jSVEmn2CJt/lsRYMdu8tNU0cnC175SqtuSYuzUF7XxoLJGTi63H7G/Xe9+hWri8YFDKokRIby8LXZvp4thVn89aNyrhgW32flSrBWnRPRFt9EhwTTHsPumMBF8WevbUhU25y7iJ+d4NuKCLYITogwox5LaAhbyxtZubmceeOtPPn+fp8N+amNB5g/KZ0d1a3srmnl+S3+ZbKqb8vD1+YQGiJRNNHKHzbs58FpI33EyvF6sZ+YNUoLtKivqZ4sT76/n6KJVl4rqaJoopXUGAvNdicut8KOqhYGh5vISY5Cp5NO2HC2srGDA3XtfkGm9MHhDB3kWX8qMkPH82YR/dICgUAgOBf5JjdfsqzwcXkD967Z4dECssLi7rYf6PFDeXLOaHYdaeWlrZ6gy1/mjtUqcIclRPDEhtKAuuGFj8oxG/Qcdri11x54cxfr5o8nPSGSP732FXdMzgi4L9ucbp+KU08bdA7VTQ6GJUTw8rxLKTvajskYwvObPdOIyhs6WH7DGOwud7+TJieiLb6pDgmmPfbWtnLhoLNzkyyqbc5dxM9O8G1Gd7YvQHBuoP6i3F7VzANv7kJWfCcLeeNwyTjdnufWbq9m8TVZmAyef2qqk/+whDD+OGc0Q2JNHKhv11qQnC43CyZnaOv1EtrfVbx7sTs6u/qceJQcbQZg+cYynv7gAGZjCE9tPMDyjWVcv3Ir63fXIstKn0GNQNS1dmqlv+rapzYeoL61k60Hj1F+tJ1DxwIHcA43dPT7e1f70nt/fuHNIhAIBIJzFVVTXL18Cz/8y6dcvXyLth8HWlt+tJ2tB4+xs7qFg/VtOFwySVEm3IoScO9utbt45gOP8WyMxUhdWycrN5ezYlMZ97y+nevzU0mKMvkco9fBwsKRhOglLEad9rrDJfN1k41RKdHcMTkDk0EfcF+2GPVMzUrknQUTeP2n47jrqmEUrSrhxuc/ZfZzW/m8ogmHy82vXt/OjupWTAYd904dQU5yNOOsg7AODu/XjWew5FAgbVHZ2EFpbSu3TbAyf3I6MRZjv3RIMO2xv67thDTMqeREPrdgYCF+doJvM6KyRXBcZFlhZ1UzpbWtjEiM1H5ZpsWZGZYQoWWL1J5nk0FHerzn+fT4CJ7ffJA/Xu/JMukkiDCFoJOgye7iF73c+03GEFaXVDJvvBVJApNBz11XZbLsvf3EWIzMzk9hSIyF+jZPb3WYKaTPiUfVzXbmjktj1ScVFOYma+1E4Fu9cqKGsx3OwEGejw4eY/lGT3vUE7MCm+KdyMQgb/Nf4c0iEAgEgvOBvqpJU2Ms7K5poabFwQVRZmpa7BSv7tkDl0zPJj8tiumjU7B0Bz56792DI0KZPzmd8FA9abFhWpuR+l5qJcszH5Rpx+SnxbJ84z4utQ7mhY/KtdaiJpsTizGEkBAd145K5v8OHg1o/u9yy1rbD8CPXvjM5z2ffH8/8yelUzTRypgh0aTFhX2jVor+TiOUZYUvKpt9KnDVz3Q8HTI0LoxHrsvxa9Fe9UkFl18UF/TY09kqIqYwnruoPztvY2mAxo5O8bMTnPeIYIugT2RZ4d1dtfzyNY9QefZHeaTFmYmzGLj9igzu6R6r7D1p6Pr8VB59Z6/mzbK/vh2zUa+dMynKxKfljaz4oMxP/Dw5ZzQ3XJzqYxh33/eHs7poHPvr2v2mEsluNw8WjuQhL08W9ToWFo6kzeHC7nRz82Vp2F3uoBv1JUPjTiiokRYbFlDgdRf04HDJHKhvO+mJQTqdJLxZBAKBQHBeEezGuaPTxVs7jnC/101+cUEGMRYjNS2eY1Z8cIDfz8ylusmO0y1z3/eHa95u6vqy+jbWbKvi5svS2FvbGvC99N2FG2oAZ/nGfVyXN4QVm8o0TVI00UpCpEkbtxwSomNIbBgPvr1bSwopCqwuqWRqduJxP5+jS2bFpjJeKbr0hG4yvYMYFmPgJFNvbXG4oSNgm3XRROtxdYhOJ5GXGq35ySgKWuAp2LGnu1VETGE8d1EHbFyfn+oTpMyIDydPVoSmFZzXiGCLoE/Kj7ZrgZakKBNR5hB+cWUmBp2Ou7sDLdCziT82a5QWaDEZdOgkWDA5g73d3i0PX5vNjq9bcHTJAYXIwaPtJEebfQzjosOMdDi7tECLunbx2t0UF2QgSVA00UqITseoIVGU1bVz79QRLF2/l4oGu9a6lHVBZNCN+kSDGhcO8q84UbM+Kq+WVPllhr5JVUowgzyBQCAQCM5FAt04p8WZaehwaYEW6GnRVatQkqJMXJ+fyq1/+1zbVxdfk8VdV2bQ2ukmMlTPhYPCOXi0g9/PzOHpjfu55XJrwArci4fGctdVmXTJMpFmA5enD6bV7qKmxaG9d0q0mQtizJpprSwr6CQoLsjsc28PFhhQq25PJEDQO4iRFmf2M9wNpC2CBXwyEyL6pUNSY8MYnhjZ7yTUiXrfnSii0vfcRR2wUbSqxOffx71rdpCTHCX0reC8RgRbBEGRZYWyo+3aL8YZeSlUHOvA6VaIDQvltglWoEe8OFwyZfVtmpB56JosGjqcvPJ5JUumZzN/UjptdheNNld3i5C/EEmNC+P5zQeZkBmPJMGYIdEkRoXy7q66gKJheGIEEaYQqpsd3LtmB7dNsKKT4PFX9/n8Ql+ybg93T8n0K/195LocbaM+kaBG7+CM2aBnwStfaiINoMnmJC81mndEVYpAIBAIBBqBbpyXTM+hpKIxqA8beHRI70lAi97ezbLZo6hsshEXbuKOV77UghI/nZgesAL3hotTKa9vZ80XX3N9fiqPvLOHGy5O5aWtPQkTk0HHoIhQ4sIMgG/QI8ZipGiilcyECEYkRvqZxgb6fOp7BwoQ9NV+0zuIUdFg5+lNB1hdNK5PY91gAZ8RiZH90iEnmoQ63W0+otL33EWnkzDopdP670MgGKiIYIsgKJWNHZgMeqaMHMTccVZsLjfhoXp2VLXwyLu+4kUtL02Pj2D+5HR0EjR0OHlq4wGKCzKoaLDx2rav+fXUEazccgjAL/BRXJChVcWo5nHjbsmnvdONrAQOzqg9zzGWDv50Ux5RZgNHmu2BDfMcXbxWUqWV/uokyEuN/sYbtXdwRpYV7p06wi/jkhob5tPDLRAIBALBt51AN851rY6ge726Tet1gU35bS43Nqeb+9/sqYopzE1m8TrfSUXeFbhNNicr546lxe5i7rg0kqLMNNmc2nsuLBzJH9/bz/76dpbNGc2whAhtj69pcWj+bO8smOCnI7w/n6f1R4/LLTM1O9EvQNA7iDM7P4XM+AhGJHmCOIGCGOqI6nHWQdo5yo+2+wRrglWCXDio/5UgJ5KEOhNtPqLS99xFtIEJvq2IYIsgKHWtnYToZKZkJfOTVSXMG29lbGq03xQetQfYbNBrAmbJ9GwaOjp5fNYoHul+7tkfjaWzy02TzYnDJbO6pJIVN+aBohAbZqTJ5mR2fgqy4plidMPFqSRHmVHwPO4dnFk6M5fUGItfj/ALt+STFmemMDdZy4at3V5NflosKzeX88wHZT7BkFOByLgIBAKBQNB/eics2hxdAff6P8weRWyYkeRoM0lRHu+Higa7dh6TQUe0xYgxxPdGLti0xP11bVoVak2zg//9tILC3CSiLQae/uEYwkP17KxqYcWmMm3dXa9+xZ9uzDuhzHx/AwNq5UqMxcjccWk+n33ZnNGMTIro8ya1L68Ub12SGGnCLcOnhxpOuXktiDYfQd+Ifx+Cbysi2CIISmgI2Jw6nt60n3njraTGmLE5A5vMDkuIoLLRxsyxKegkaHe4+OP7nj5rVaw0tHdqEwBUU7kl63bz4q2X8EVls0//88LCkaz+rJIxqdFcMjRO82CZN96KXgf5abFcbo2jssnm1yP85Hv7mD8pw8dM9+Frsxk3NPa0tvSIjItAIBAIBCeGGixYun4v1+enahMJ9TrITYmiod3JL1/rqaZ96JpsnvnwgObJtmByBkvW7eb3M3MDBiV6PzaF6Lh9Ujp6HUSYQ/jRuFQaOlzMe7FEe487r8z0uUaHSyZELwU8X2Kkya+q5ES0hVq5EqhF6q5Xv+Lfd0zo8yb1eF4p1sHhDI0LO63mtSCSToK+Ef8+BN9WRLBF4EdXl8zBY610dkGrw0nRxIu0kcm/njosoNiobLSxdP0+LbDx9KYDPoaxJoMOk0HP4muyWfT2Lk0kLZszGreMn2P+knV7NMd87Rd0YoTfL+hA5bWXWgf7mek+8OYu8lJjRDBEIBAIBIIBhHewYNUnFfzm6hGU1bfhlqG0ps1vcuGDb+9i5dx8vvq6mc4uzzE1LQ5a7S4euiaLB9/2tA6t3V7NosIsrZXIZNBx11WZhOp1rPig1Ce588rnlb5Jm/f3+42GLq1pZenMXO5ds0M7dsWNY9hT03ZSQQy1vSJYJc7RdkefN6n98Uo53ea1KiLpJOgL8e9D8G1EBFsEPnR1yWzcV0ezzcWi7tGGL3xUrm3QL26t4K6rMln23n6fqhFnl5sFBelcnBaDW1G4Z8pwqppsAJofy8P/9rQTLZ2ZS3K0idiwUIbGhfHpoYY+HfP7Mo4L1AMarKdbmHAJBAKBQDCw8A4W1LQ42F/XxopNniDH/MnpAffzkopG3DI+wRC3DM4ut8+44n9+VkHRRCvDEiKoabGTGhvGgm4DXfVcS9bt8QmsqM+nxZr59feHoSgwKDyU+jYHabFmnwpZRYEfPL3lpIIYanvFvtrWgMmsweGmPm9S++OFcbrNawUCwamhr3sewbmJCLYIfNhd0wKKxKLuzFDvTEtNi4O//d9hVs4dS3tnF4PDQwnRw4G6DrIuiGJfXTtPbNinBWIem5WLUadjf307M8emsGZbFfeu2cE7XkKkL8d8oM/S10A9oBenxQYVLCdT6isQCAQCgeDUEkwDqI8DveaWPYkV9fGCyRksXb+X2WOHYDboNW85k0HHj79zIQ0dnTTbu7DVtgUMOqjn8n6P+rZO9DrJ51wpMRam5cZo+mXrwWMBz1dxAjdLavXuyKQI0uLCfFqqiwsyONTQ7jftyJv+eGGcCXNScZMoEJwcffkvif+Xzl10x18iOJ9QHeu3HjxG+dF2ZFnxeb2ho5MuWfbbkL1psjlpsXexr7aNjs4uXG6PWNh9pEULtADEWIxUN9n5xatfsey9/Ty/pZy549KIsRipb+sZkawKBfV9vB3zvUtfk6JMzBtvpbS2lZ3VLciyoomUdxZM4JWiS3lnwQQus8YFPN+hhnauXr6FH/7lU65evoX1u2v9Pr9AIBAIzn8kSZooSdLbkiRVS5KkSJL0416vS5Ik/VaSpCOSJNklSfpQkqSsXmtiJElaJUlSS/d/qyRJiu61JkeSpP92n6NakqQHJUmSeq2ZKUnSHkmSOrv/vO5Er+VcprcGWLu9moemZ2My6FizrYoHC0f67OcLJmewbke1Nv1w3ngrqz6poKLBjqNL5qWtFTw+axTzJ6dTNNFKlDmEP39YzvNbysmID/fTNGpyx/s97p4yjIyECL+BAPeu2cHhhg7tWDWI0ft8X37dfEJaQ6eTGDoonLzUaIomWrXP9dLWCua//KXPewY6trcO6n1zFkxnnSpzUvUmUWgsgeCbE6zdr6///wUDnzNa2SJJ0kTgbmAscAFwq6Iof/d6XQIWAUVADPApcLuiKLu91sQAy4Frup96G7hDUZRmrzU5wArgEqAReA5YoiiK4rVmJrAEuAg4CNyvKMobJ3It5xrHi5jKskJEqIEwo6JlQDbvq2dh4UjNs0XNtKgThh6+NpvUUB1x4UZCdL5Zkxl5KQEnFz0xaxQGnY6uLpmQEF2fpllq6WtSlMnHpX/l5nKfa+9dXtv7fDoJpj51cqW+AoFAIDhvCAd2AS91/9ebXwG/BH4M7AMeBN6TJGmYoiht3WteBlKBqd2PnwdWAdMAJEmKBN4DNgMXA8OBvwEdwB+611wGrMajN/4FzABekyTpO4qifHoC13LOomqAYXdMYG9tK/vr2hgcYdTagUJ08MyNeWyvasYtw+qSSoomXsQfNpT6TSVSFE9CqLS2TWsLWlCQztxxaaz6pILfr9/rp2nuvDKTo20O/nRjHh1ON3WtdgzdCaRAVSt1rZ5kUV2rR1+suHEM81/+0kcjvbuzhtsnpSNJsK+2lZFJEQwddHytoY6UTooyMSMvhZljUwBo7OjsU6sczwvjdJuTnilPGIHgfEa0+52fnOnKFlXcFAP2AK+rguIOPMKkHo+giPBa8zKQh0fcTO3++yr1RS9xU9d9jmLgHuAurzWquPlfYHT3n69JknTpCV7LOUVfEVM1EHOkqYP6tk6KCzJIizMzNTuJlZsPMm+8lQUF6Tw5ZzTv7qyhpsWhGc92dCr87B9fUHa03SfDE8zsrbSujZte+JQ3t1fT1eX7utIrCaJmjYK59AeL9qrCY5x1EEPjwig/1hH0F5hAIBAIvl0oivKOoij3KYryOuCzOXQnW34B/F5RlDWKouwCbgEigBu714zAo0GKFEXZqijKVuB/gEJJkoZ1n+omwALcoijKru73Wgrc5VXd8gvgA0VRfqcoyl5FUX4HfNj9fL+u5XxAp5OQJLj7te0s31jGvto2TCF61m6v5li7i4fW7dZah+6dOoL3d9dyfX6qT6XGwsKRrNvhGR39ry+qtOfdMizfdIAZeSlUNNiJNIXwxzmjteqRv398mEfeLWVbZTP76tpodXTx6PpSZMW/stdk0OFyK1oFxw+e3oKzS2F9saeq5MVbL+HdnTVMzU7ihY/KWbGpjOc2l/NFZXO/qjwSIj2jreeOS9OOf35LOdXNjpOuEvHWRdbB4X6BluNVPvdFXzeJAoGgfwSrlDuV7X6CM88ZDbYIcXN2CbYZVjR08GVlE3/96CARllDueX0HL22t4JdThrN8k2e84jMflLF8Yxl3vvoVEzLjfY7/8usmHC6ZNduqWDA5Q/tFoZcCCxVF6ZkQtLumpc/yU7X0NZjprZphCoZ67u1fN4tfYAKBQCDoDxcCicAG9QlFUex4KlQu737qMqAd+NjruP/DU7XivWZL97Eq/8FT2TvUa80GfPmP1zn6cy3nBd6VrMnRFmRF4aHp2awuqfTRIXe9+hUjk6NZ9YnH/Hb5DaN44ZaLCTPqeXh6DptKawFPRcuS6dlYjDpiLEakbk0SE2YkJETi+S3lPPNBGU02pxagWbOtiiExloCaxmTQsXRmLgvf2umX+JEVGGcdxOCIUK4YHu+XHLrvjZ39agUYGhfGkuk5fsf3bl861XR1yazdceQbtwGJm0SB4OQ53e1+grPDQDLIDSgoJElSBcVzHF/c7CO4uFmCR9wc6l7zdK/3/w8w/wSu5ZwjmEFal1vhaHsnv7l6BEeaO3G4ZG0igCp8ZuSloIaqwkP1Pse7u09X0+Jg1ScVzBtvJS3WTNogC3FhRh55t2fEovc4aE+wpBNo7rP8dGpWIsnRZlZuLve7dpdb0bxbAqFW88RYjCyYnKEJGPELTCAQCARBSOz+s67X83VAsteao97tyYqiKJIk1XsdnwhUBTiH+tqh7j8DvY/3OY53LecFalXH9fmp3PP6dj/dUNPiSa6o5v1NNidpcRY6nDK/evFzH2N+u9OtGf2rI59RFJbOzGXc0Dh0OkmbKmQ26Fnwypfa+evbHJgMOh9No9dBwfB4nG7Zp3VJ1Uf76zzdXKkxFjLjI75xK4BOJ2HQS2e0lUCWFT4ub9BGWqvvdyJtQP0x6RUIBH1zutv9BGeHgWSQ25eg8BYdfuIGT4uP95pA56Afa/ojbhIJgCRJRZIklUiSVHL06NFAS84qsqygk+CR63K0iGlanJk/3ZSHgkJydCgt9i7szi6KC9JJijJpa3qXs0aYDCRFeQI3D12TzaflPZ+3psXBCx+VMzjCxOPrS+lwesYwLp2ZQ9FEq49gMhl0GEN0bCyt77P8VKeTyEmOYunMXD+TvIVv9Z0tUjNl3qJp/uR0Xrz1EuHuLRAIBILzjoGuR6B7cs2xdj4tb2BTaR0H69tJiTLz+xm5flUdaguQSlqcmUuGxrB4WhYWQwiL1+72WV9W364FWtTnlr23n3EXxTEt9wLNK05tqclJjubeqSM0ffFqydc8fG22FnB54aNyhidGkpMcTVxYqLZO9ZJ74aNyfvqPL7h6+RY27K0j64LIPqs8jteuc7wqkWDHf9M2oMMNHZRUNPa7DSjQ+/THpFfQNyfTxiU4fzheu5/g3GMgVbac0yiKshJYCZCfnz+gfkN6G+PGWIz86nvDGDrIQkO7i5//7xdcnZXA+Mx4v3GD7+6s4ddTR3Bnr6qTJev28PisUR4Dug8PcP/VI9n1So853MLCkYDMrd+xsq+uTTN7mzsujSabE/AIh99dl8Oit3cxbVTycUcS6nQSF0R7phFJksfbRQ3c1LUGz/aombLC3GStMmft9mpmjEkWv8AEAoFAEIja7j8TgEqv5xO8XqsFBkuSJKkJoO4W5PheaxJ6nTvB67W+1tT2WtfXtfgwkPUIeFpW3i+t49CxDs1EPy3OzB2TM6hstPU5mjktzszPr0inaNU2HC6ZBQXpfutlJXDbcUO7M6BPyeGGDmIsBlYXXYbL7SY2LJTUGAt5qTF+2WXvCo5gXnL/vmNC0CqP/ox27atKJNjxU0YksGFv3TcaGVvX6tD8aY43Gvp419+XSa8gOGLkr0Bw/jKQgi3ntLgZyFQ2dlBa28ptE6wAXDjYs2F/3WTjoekjGRxu5mf/u81HMDy18QCPzxoFQUxu99X1OP3LikJxQQYdTjemEB2tdhd1rQ4KhieQFmdh5eZyv3LciRmDUVCoaLCzeV89y+aMprS2FVnxBEPunTrCr/w0LiyUFz46sVai1BgLd0zO4IE3d2kb2MPXZpMaYzml37FAIBAIzhsO4dnrrwI+B5AkyQRMwGO4D7AVj+n/ZfS0Nl8GhHk93goslSTJpCiKWiJwFXAEOOy15irgca/3v8rrHP25lnMGtWVlT02rT2twYW4yD7y5i9smWAPe9KfHR7CgIJ38tBgt0AIEDBKofnG9zxGi11F+tF0LnAS7wc1LjQ0aOPAu81dbrb1xuGSOtjuCtgKUH20/7tSevloJgh2/umjcN54GlBBpYu32ar9W66Uzc/10mJg6dHoQ36tAcP4ykNqIvAUF4CMovIWLKm5UAombCd3HqgQTN/RaE0jcBLuWAY8sKxysb2dHVQsSsGZbFc9vKSdEkqht7eStr6qJDTNpBrfeOFwyB+rbcMtKUJNb9e97alpRFNi8rx6dJPHUxgMs31jG9Su3UtloZ8WNY3zKcXNTooixGGlzdHHf94fxg9wLuOvVr1i+0dOm9IuCTKaMSPALngyNCzvhVqLKJpsWaFE/1wNv7qKyyXYKvmGBQCAQnItIkhQuSdJoSZJG49FCqd2PU7uTOX8E7pUkaYYkSdnA3/F4xr0MoCjKXmA98JwkSZd1Tzl8DlinKMq+7rd5GbABf5ckKVuSpBnAr4FlXu3QTwGTJUn6tSRJwyVJ+g0wqfv96c+1nEuoLSsWo15r650/OZ0Ikz6oKe3CwpEcabbxWkkVNc2+Rv+B1qfHh3PXVZk+zxUXZLCrusXH+LWvCY19oQZiMhMigrb7BGsF6O/UnhM9Xp0QebzzBmJoXBj3Th3B6pJKbfLkyrn5/CA7yU+HialDpwfxvQoE5y9ntLJFkqRwIL37oSZugEZFUSolSfojcJ8kSaXAfuABeokbSZJUcVPUfZ5A4mYRHnHzMJCJR9ws7iVuNkuS9GvgTeA6POJmfPf7KMe7loFOoIzNb6YOR1Y8FSB1rQ7unjKc8vq2oOWjwxMjefbDMooLMrRSX1X4rNhU5mNc12Rz8tisUfzq9e3EWIzcdGkqg8NDkRWFhEgz64snUNvqICnKxFdft/CDp7doJcDe2S2HS+Y3b+xk1JBon2i+LCtUNnaQEBnKn27Mo6HDSV2rQ2slUo3jVAFV1+ogIdJEQ0dn0AlM6hpv86nex/fXmOqbHicQCASCs0I+8IHX48Xd/70I/Bh4DDADzwAxwKfAFEVR2ryOuRGP2f5/uh+/TY/RPoqitEiSdFX3OUqAJvj/7d15fFT1vf/x13ey74QAIQYSiAmyg5hSbYW2YL1YsaKidLO3Xlu6iFB7vbWt1VqxWqvVSvHWWv3Vpde6VlupO0jVirVBZQcJkSAhhBBCtsk+5/fHmTnMGhIyZIH38/E4DzMzZ875zjcj308+341fA3f5nfO2MeZLwC3AzcAuYJFlWf/yu093yjKg+drIisNNnF00jI9rmvnNa5sC4or8rCTKa5p59J1ylnyukJEZiew55GblGnvHoGVzi8jNTAqJV4zBHokLVB5209zWwSlDklg8uwCPBS4DI9ITqG5oJTM53hkxUNPU6kxNBjtx4x9PHO2z1DS1cvslU52FZbuzKGxORiJL5xbiW47jmfV7qXW3dXvXnuCNDnIyErm0eBTpSbHdmgYUjjOSZmTaURfljLTRQl/tOnSixlr9Xa8icvz09TQiBTd9JLjHxrftYUu7hysfLgkIcF7Zuj9k+OjyCydz39pSNlbUU93Yxh0Lp7HzQAPTRw3B5YJLzhgVsG4K2EN3l80tYkhyvLNgna9HaUxWCsPS4tm+v4H4WBffP6eIxtZOYl2uiMkQ/6G+a3ZUsbOqMSDpc8054wC7QUpNiOWj6kbe//hwwNoztyyYwk/OO40/vh24MO/7Hx9mxerSgHmxwDHNmdVcWxGRwcWyrLVAxH+gvZ0zN3mPSOfUAl87yn02AbOPcs7TwNO9KctA5msjn3t/DxdMG4Whk+ufC9w+efmqrdz7lRlc9dh7VNa10NbpcUal5mTY67U1t3fS3NbJT84bz60vbiczOZ6vn5UfEBcsnVPEvWt38YsFk4mPcTF6aDK7qhu58+UPnS2eH32nnENNrew73OJMTfa994mSPV3+gRvc3udnJXH/5cXExRjnj3+AsurGkISAx2OxtbLB6WDyxUdF2and3rXHfz2XcSNSWTQzj+WrtvJUyd6QjrGe7AbU3fVW8jKTuf/yYkrKD3U57ft4CBdr3XrRFGbkDSFvaPSSLv2R0NFuTiInLuO3sY9ESXFxsVVSUtKvZVi36yBf/sO/nG0Jx49MY+eBhrDbJ9912XRuf2kb86fmEuOCqaOGsOK1D9lYUe+cc9/XziDWZYiLgYS4OL50/7qA6+RnJbFs7jh21zSFvccfvl5MrAv21rZQ09hKUlwM/+/tjwIW4PWVNcaFMzx3zmnZ7K5p4rkPKsJed/HsArKS42nt9NDY2hH2nLsvm06nx8IYqKh1kxgXw+/+UQbA18/KZ3RmMglxLrLTE7n6z+8FbOuYGOfihaPMmS2rbuQLK94Mue/fr56FMZxwPTAiIsfKGLPesqzi/i7HyWIgxCNl1Y1c8dC73HzhZLbuq6e5vZMVq0tDzvvReeM5ffQQYlxwqKmdxY+udxbX9+8MuuaccRgsxuekB6zfAnbbe+XZBV3GPItnFzB3/AgW3f9OyGsPXzGTWncbORlJTMpJJzY2cJpQpPbeFyd01fmyu6YpYqxw6ojur8vhG+n7UY2b7/7pyOf3jXI5ffQQ8rNSyMtMZk+tOyAGAY45kRDus91+yVTOn5wTUk/HQ6S6Xzy7gPEj06PSwXUsnWddJWd8v6uq+laa2jrIH5pC/tDQ34svGbe7pklb/or0kb6KRwbSArkSRTkZiVw37zSGpSawt9ZNxWF3yCgSX3KjqbWDa88dT+VhN4Uj0jjY0MKHBxqBI/Ol9xxq4rC7g/9dW8rdl03n1oumBIwguXH+JK567D2+99nQnQFa2j38e/chkuJieGSdPeXoB58fx5c+kceDb+3i51+cxP+uLWVRcV5AQHXD/In8c1c18TExJMfHhL1ubkYS1Y2trHy9lG/OKgh7zrb99c4olmVzi4iNMQxPjeeSGaNwt3dy7dMbAoK4h97e7YyCyUyOp7qhtcvAJNJc223767n2qQ3dbrBFREQGg+72/ns8FtUNrVx77niaWzu5Z/XOiIvgjhqSxMiMBLbsa2DH/noS41xhd/y5+7UPufLsAg6728O2vTEuqG5oYdyItLCvj8tOw93WGTYe2l3TxMe1zdz24jaunlPEgmm5AYmErtbWKBieykcHIy902tV7u9Mx41/nyfExvL8ncL29yroWVqwu5fHFn2RMVkpI0mDlV06nrcM65lG44da4ue6ZjUzJzeiTRVwj1Z/HImqLyfZ0odqukjNAyKhs365b/ps2aDcnkRObki0nIN9QVf/hpD+7YBLDU+OdACdcb9EN8yfS3tnJk+s/5u7LptPeaQcBTS3tZGckUjQije/PHcf2/fUMT413dhaaNmoIdc3tZCbHUzQiNWwQ1emBe1bv5MqzC7j39VLuevVD7lg4jU8WDOd/15by3+eO54fepAccGVZ85dkFPPhWGcsvnMxPzjuN+tZO3thxgFnjRhDjgsyUOGJijPO+SPf2XfOe1TtZPLuAb84+ldKgXi//IO7e1+3tqr9+Vj7/+cd3uwxMIs219d+pQCvLi4jIiaC7vf/+533/nCKy0xMDFsH1jz+Wzinijle284PPn8Zdr+6grcNi6ZwiWjo6w/6BnT80ifhYV9i2d0ZeJmOykun0hI8JJoxMd36OFA8tnVPEb9fspGhEKlNyh/glOWKdtWX8rzkiLZGODg9bKusiJlQixQrtnZYzYsNXlxNz0qhubCU+xoW7rZMRaYl8VNPI8lVbmT81l7yhSRHX2xuZnsimisOUVTdyx8JpVBx209jaGfBHv69cPYlLIiU7PqyyZ9f3duRMOIEJpvDr0lhWYMKrN4I/oy8J5/8Z/T9PV8kZgI1768LuuqXYUOTkMZB2I5IoCfeP/8+f30JZdSPXnDMuYm/R8lVb2VLZwNVzx/HxoSZqGlvxWPB/7+6horaFKx76N798aTu/f6MM43Lxl/f28lTJXjbsPewMX/3lS9tCdgZYOqeIv7y3l5Z2j7MQne++GYkxlNc0R9xC0bfOzA1/3UxdSycPvFnGl2fms2pjBStWl7L08Q9ISbAb4Ei7GKQlxrBkTiE5GYlOL0hzWwceK/y21jHe/ysuLR4VNjAJ3qnAN9fW/763XjSFp0r2hlxbK8uLiMhg1t1dfPzPyx2SzO6DTc7OhI++U+7sfPOrhdN49J1yymuaue6ZjcyfmuucUzQi/I4/ew83U93Q6sQ0vudvnD+R1IQYKutaiHER0jbfddl08ocm81FNI8vmFkWMh1as2cn8qbkcamrlpS37+cKKN/nyH/7FovvXcfWcIvKzkgKumZeZzNtlNew60Bi2vMNTE51YIT8rias+V8jSuYX8/mtnsGL1joB73/7SNrZVNrD7oJs12w/wgyc3cP5v32RfbTNfmZnPg2+V8XFts7Nds//n+82i6WytbGDR/e9w5ysf8j9Pb8DjsddWGZaaEDER5PFYlFU3sm7XQXYdaGT3QfvnsupGPN7VfH3JouDPtqmi3tnl6c3SAzz3QQX/3FXDXz+oYM2OKuf9PeVL1vnqfunj73HLgslhY7z8rKSoLCbr/xl9SbgH3yrjO396jy+seJMXN+8PqJuaJnvR5as+V+jsrJWZHM+Bhhaq6ltC4kxfTOsvUmzo/zvx/z1Ew/G8togE0siWE1Ck3of61k5S4mO4c+E0mto6Ig7HXF9ey8j0RMZkJfNfD5dw5dkFYRMzSz5XiMsYnijZQ256AqcOS3V2EvAtqNvpObKIbvCW0TsPNDAuO43r5p1G3tDkiD0Wvnv6Gqmfr9rijD5paffwyxe3ccP8iSxftZVH3yl3dixIiY/l5lVbKK9pDlj8zmVgZEYi+w43h73n3PEj+NSpWSHDjH3lCO49cVbyXzrLmWvrMlDrbgt4r1aWFxGRwe5oU2nCnffRwSaeLDkyoqWyzl6cdumcIm57YZszdde/w6OyroVbX9gWsvCr/y6Iy+YWOTsKTRiZRk1jK1978Mho1JVfOZ2/Xz2L6sYj62DsrmliyWPvk5lsj9DNy0yK2PGSlhjPd//vXwHxz0+f28ydC6exvaoBl4H4WEP5ITcl5Yd4yu8zZibHc2nxKMYOS6GprQOAcydk097pCdjB6Ib5Ezln4kgefrscgEXFec5advlZSSxfMJnSqkaGpiZwx8vbndFBl5+Z72zXHOOC4vyhnJKRyPyVb4Ukjq48u4C9te6wMY/LGJ7fuC+gTMvmFjnTvn2jlsIt4ur7XfiSRItnnxqyAHDh8FTGDOv5qI3gKVnlNc38ds1OHv/WmZQfcrOrutHZqeqWBZPJy0zu8T2C+X/GcEm4/37qA5bNLeL2l3Y4ya0rPj2Gu179MOAzj0xPxGPZG0eEq/Pgx8Gx4fHceEGbOoj0LY1sOQFF6n0Yn53GqMxk9h5283Ftc9hzXAYKR6Tx+zd2caipPSDJ4a+l3UN2eiJPlOxhUXEeFfWtVNY1O71Wt76wjcTYGB58q8xJtCyba49w8a2Nsnb7AT4+5Oae1Tu55e/bnF4mX1muOWccf3lvr/M4OPHiU17TTEtbJ1eeXcAlZ4xidGYydU3tXPXYkcVufQHHj+ZNYHhqAr9+eQdDk+ND7nnXZdOZkjuEMwuGMSYrJWwdhUuY+ObanlkwjILhqeQNDR3topXlRURkMPN4LGc6h79wbaN/LNLW6aHW3eaMaFkyp5A7F07jiZI9TqLFd52po4Y476t1t5ESH8OvL53GkjmFXHl2gdOB09Luoamtk3tfL+WBN8uwLJxdiq76XCHfnFXApr11xLhw2vTdNU3OSNrKuhbufb2Ujw+Hj4dOHz2Etg5P2Phne1UDK9eUsmJ1KUsee5/yQ014LJzPuGxuEUvmFHL/G2Vc88QGLvv9Ol7asp+9h91OUsN3reWrtuJu6+TyM/P5+llHpjPlZCSyqDiPJY+9xy9f2s61T21gUXEeORmJzsif+VNzmZqbzoLpuZxdOIzqxtaIo4SfLNnLrRdNCYhLls0tYn15bUiZ7lm9k4tnjAoYteTrWHph6Szu+9qMgN8F2FNklq/aGnKdqvrWHn7LbOWHmkI+S3lNM9WNrVz3zEZWrC51vgc/fW4ze2rdx3Qff/6fcdIp4df8GZoc7/y8tbLeSbT4nrtn9U46PXbiZsqojIA48/kNFSGjc8LFht0dPXYsjue1RSSURracgML1PiybW8QvXthGrbuNG+dP5LUw2z3fMH8iiXEu9h128+N5E0iMjwloEIIz8Vmp8cyfmsuKNTv5/jlFWBbc/MVJ3Pi3LVTWtfBEyR7uumw6ew65mTYqAwuLH6SOo6q+hYfe3s3FM45M06msa+GRdeUsnl1AbkYSFXXNGCwnUePrPfHd238TrcQ4FzlDknjgrY+odbd12VMV6zI88FaZt8Eu4+tn5XPnwmkkxLkoGJbK2GFH5uP2Ziu+cKNdtLK8iIgMVr4e8du904X944dwbaN/G/rM+iNbE9/7ur1g/a8WTuX7c8fxY7/F9n82fxL3rd3pjNbwrb+y55CbB94M3VnI8q5bcsuCyaQlxpCZHB+y/kp+VgqjhiTzyrYqfvDkByEL9PqXzfee2y6awqcLhrG3LvwIWP8YpKXdQ0pCrDOtZ8WanTR6FwMO/oP24StmRhxVvGLNTu5cOM15PdL0Jt/IXt/oIP8dEyOtC2N5E0Ez8obwwtJZlNc08f7Hh3lkXTmXnDEqYoLG93NVvT1qydexBPD9Jz4IeF+MK3zHnNs7qqenUiKs0ZIYF37DhGis2QJHOs+qG1rD3j854cifTpGmo1c3tnDqiFTmnJZN4fBUZuRl4m7rIM+7G9GMvMwuY8Pujh47Fsfz2iISSsmWE0TwzgDnTsjmyW+fyd7aZrbvb+CRdUd6H25etZW7L5vOL1/a5gQ0p48eQlKciw/21vHnd/fw4/Mm0Gl5WDa3iNXb9jvTdPyHjpZWNdiL1CbHk5YYx/JVW/n+OUUsnl2AxwLLguWrtlLrbnOe8w+WgkfM+FbSXzKnkJVrSvnVJVO4Y+FUUhJiuf2lbU7iZfmFk1n5+k7gyJowt7+0jUuLR3Hq8FTuenUH1547PmwjmZ4Ux3XzJvCDJz+gsq6Fe1bv5K7LpjN3fHZIY9fbhIlWlhcRkROFf4+4b4RKjAvGjUgjPja0XQxuQ7PTEpldNIxD7nZa2jspyEohPyuFaaOH8GFVA22dFr9+ZTvlNc2UlNcBR7ZV/lTBMG6/ZGrAVJfbL5lK7pBELpmR64xaubQ4NEHxk2c3cerwFKfswQv01rrbKMpODZluBOAyhOy+6Jti45MY5yI7LYHr5k3gdm9clTc0fIePu62jy0VeE+KOLPobaVSxb5pVuCRXpKk+vs6vvKF2DFNV3xKw/XZXCaXEOBfJ8TEB5Qh3n0/kDw17nbyhxzaiNzs9ISQJtmxuEZnJcd2aitNbke6/128ETaRpQr6yuFyGMcNCp1EdLTaMlDSL5ro0x7v+RMSmZMsJINz8y1svmkL+0GS2728IaFDhSIP/8y9OpqW9k6Q4Fzf+bQttHRaXFo/i2nNPIyU+lntW7+CyM0Zz0YzR3P/GLiewmjAynd+tLaW60Z4nCziJmIffLg/pVbrmnHEYLPKHpXLj/Inc7D03UiPl66nKSk3g5lV2uS6eMQpj7MBnzLBk5k/NxRg7oeMbxnr66CHMKhxOXIyLvYeawjaS2ekJfGLM0G4nUJQwERERCewR903BAVgyp5AH3gwcYeHja0PHZKWw51ATO6oaAxIX/tvkPvdBRcAuPxDY437B1FOYkpsRse0ek5UScbtn33QTX9l9yaKpuekUZac5CQvfFswuA1srG/jBkx+QmRzP4tkFjMtOY3x2GrsPNTlrsjkL5A5NIW9oCuNHplFV30J8TPidknxTjMOte5LoHWHrSyr53hN8Dd+6cuHqwD/B5dsiur3Tw7zJIwPO9f+DO9zuUL6Ekq987Z2BdRquMyovMznks/360unEuGDdroM93p0ob2gKRdmpTmedy0BRdirjs9OPedRxT4S7/6jMJO58ZQdg/y6mjMo4LmXpzcjq/ry2iIQylqUVqKOtuLjYKikp6bP7lVU3OtsG+iTGubhj4TR2VDWEHXp7/+VnsGVfPTEG7nptZ9ihtzfMn8iItASu/vP7Ie/3bcn88H99gn2HW7jmiQ3O676t8vKHJlF+qJlVGyu4/MwxZKXG85vXPmT+1FyS4lxMG5XBtv0NAQuL+XpgvvOZQlLiDBV1rQEJE9+WiPPuCf28vkDP47HYc6iJLfvq2VHV4DSSU0dlMOe00BEsIiLSN4wx6y3LKu7vcpwsohWPeDwW/yw9yLceLQkbD9z7eimPL/4kZxYMC/vel7bsp7ymKWBqje/9f796FmOHpUS8/hOLz8Td1kl2uv0H/Z5ad8SthXdVNXC+3wKxvms8ufhMLrv/nS7jBv9Oq6VzCwO27PU/3zeKpqsOm64WIQV78ddt++v5sKqBp0r2BixE6/FYvF1Ww/bKOlK8o4aDrxHtRVLzs5JYfuEUOj0etu+3tzluauvEsmDVxgr++I2Z3ep08o2yPtDQwvBUe7vqJY+9f8zl97+ef11Hej7agu/j+/753xc4LmU5np+xr+pPZCDrq3hEyZbjoK+TLet2HeTLf/hXyPN//tZMPvi4jmGpCeytdfOkt0G/ZcFkGprbSY6PBQPXPbOJqz5XyINvhQYWdyycxtV/fj/k2kvnFjIlN4OxWSl8VNPE5oo6nizZ60xV8g+QfD0rw9MSONzUTmV9C8NTE0iIddHU3kFHp0Vbh73oWENrO+/urnW2Tf76WfmMzkwOWFMF6NZK6mpMREQGFiVb+la04pGy6kaueOhdFhXnhXTK+HaECTeyxeOx2LT3MKt3HKBoRFrYeOL3XzuDouxURmUk8eLW/QFThW5ZMJnfrtlJeU2zd8vkIm786+aIbf/ug428uHl/yKjWeZNGsm1/Q8S4IbjTyjedOVikhFI4R4tBunrd99qhplbiYlxOsul4/8EN3YuvuiNSR2C474mISF/rq3hE04hOAOHmXxbnZ1BxOHBUyA3zJzIqM4lfv7yDjRX1LJlTCNiNX6S5wZHmF59VMJSKwy3OFoP+w059PTRTcoeENM75WTAlQo9PRnIclfUteLz5v8q6Fm5/yR6ued/XZjiLtXV3LRVNARIREem9qvoWymuanek3vmm8nR4PlxaPYtyINCzL/gPeP2Hg39Yvm1sYNp5o6/TwhRVvctdl0zl/co4zVSgpLoalj7/vTC2aPzXXSbTAkUVnx/v98e5bbN+/jI+sK+f0vCFdxg3hFg3t7boWR4tBunq9L+KXSPeI1uL+WohVRERbP58QfPMvE+Nc5GQkct2807h67jh++tymgKBk+aqtJMfF8NnxI1gyp5DTstN4Z1c1S+cUOeun+EuMc1Hd0MrSOaFbMn/wcR0/fS4w6Lln9U6u/8IE7r+8mHMnRJ6uE2nbub+8X8F3/vQeD7xZxuVn5pOTkejcc1NFPV9Y8SYvbdnvBHP+Wy1rxIqIiMjx4evU8a3VsnJNKas2VpCaEMf9b5Sx5M/vc/5vj7TRENrWP1myN2AbXP8FR31xwJ5at9O2u9s6A9ZwidQpdKDhyNbR2emJ1LrbnDLe+7o96mZEWmKXcYP/NtVwZIeio23ReyKKVnwVXKcQ3YVYPR6LsupG1u06SFl1o/O9ExEZSDSyZZDyeCw+OthE+aEmUuJjmTAyjWe/dxY7DzRReqCR/YfD9yhUHG525iEnxrn42QWTeH17JRednsdtF09h98EmZ7qR/3bL/lsyJ8a6qGvpCHv9prYO7nhluzO/N3iXpDFZKRF7O3ztZEv7ke0NH3yrzClHuF4sEREROb7CLap50xcn890/rY840iRcW9/psbhj4TQAKg+7SYiN4b43ypz3+496iLRrSlejTY518c/g90Xaoeh4deyEi5UGeyfS8VyItas1cQZ7vYnIiUXJlkGoo8PDC5sr+fWrO5g/NdfeISgnnRFp8VTU2smUb84qCBuUlB1sCgiMfv78Fn73tTOcgMm3k1FORiI/+ou9Gv6lxaMYnZnMgYYWZyG3OxZOC7h+TkYilxaPAuBH8yZQ39wWsTGcmJPW5TaDvrJNyEnjyrMLeGlzpbMbEcChplYlW0RERPqIy2U4d0I2j3/rTKq8U3wONLTyzVkFPLP+yHpt/gkT/2TJ1Nx0Fs3MC1js9eYLJ5OVGsd/fiqfxtZOnt9Q0WXi5PkNFfxs/iR+vmpLxD/euzvNOFxyI9L7Th1xfOONEzVx0N3fxbGINEJanXEiMtAo2TLIeDwW7+yu4dev7ghZqO4XF03h8X/vcbbyu+accdz92pGdfpZfOJm7Xv0w4Hot7R7e31Mb0GD95NlNPLn4TL46M48hyfHc+LcjgY1vlEnFYbeztXJmcjxfPys/YH2Y2y6awpDk+LCN4d+vnhXS2+Fb78UnMc7F6Mwk7nh5e8jnLBqRyvQOD+WH3M7Inuz0BPKGDv6eIBERkYHG47F4ZVsVt7+0LaRN9sUFlXUtASNN8jKTWX7hZFa+vpPvfLYwJB648a+bWTy7gMTYGJ7fUMHVc4rIy0x27un7Y/20q2c5O/f8+d1yZxvmCSPTGTus5+u1dZXc6I913k7kxMHxWntG68GIyGChZMsgs7umif11LcyfmusEO2A3Mtc/u8nZghEgMdbF4tkFztbHCXEu4mMDg5LEOBedge0VLe0e9tW1UNfSyV2vbQm4x4o1O1k8uwB3Wydn5A/h4Stm0tTawfceey/gvB8/u4mHr5gZtjEsO9hITkaCs1vRiDR7e8Bad5tTprsum86knAyWXziFxY+WkJkc74xuqapr5u+bKwN2LFg2t4ii7NReb+18Ig7lFRER6Y09h5rYvr+ea88dz/88vSEkLvBN+/UfabKn1k1tUyv/c+54tu+vjzh9eMWanSz5XCE/fW4zM/IyA/5Y9o0sGTsshYk56Xzq1Kxej5AYaMkNJQ56LtIUs2itByMiEi1KtgwyNU2tnDIkiT2H3GEb5xjvWmQXzxjFbS9tD2mIgkeU3DB/Ive/sSvgOr6FdrdWhg+OCoenMm10Blv2NfDtR9/lm7MKerST0aaKOhJjY3iiZA/XzZvAJ8dmMXZYCi/4DTXNy0xmT60bd1sHV88pJDU+1vk8S+cWcpc30eK71z2r7SRQwbBj70E5UYfyioiIHCuPx+K9PYedKcrh2vtJp6TxwtJZTqKlrLqRD6saaPdYtHXaSZVI04db2j2MTE8kMzk+YoIhmiMkBlpyQ4mDnjue68GIiESTki2DiMdjse9wC4lxMZyRlxm2cT599JAut3IuPdDI4tkFjB2WQkVtM0+8uydkSLBvVEl9c/hkyaRTMuj0ENAzFO68vKGhjaFvuHGtu40rzy4I6E3yHeGSHsvmFpGZHE9lnb01dKQest4ESwOtt0tERKS/7a5p4ifP2rsbJsW5wrb3w1MT2F3ThAG27W8IaL9/s2g6z2+oYOmcooBY44b5E1m5ppTEOBd7at1cWjyqTxIMR0tu9PUIVyUOeu54rgcjIhJNSrYMInsONZEQ42JoSiyVda3Omin+a7Lc+7o9nHf8yPCL0BYOTyHG5aLO3crK10tpafdQ19LO3d6to/OzUpwG65NjhrL8wsnc8NfNzj1uWTCZ/KHJ/Lv8kHPtZ9bvDQmibr9kKmOHpTB2WApZV8zkzdKDWBbOvG44so1jcIIkXNLjntU7A6dIhflsLkOvArWB1tslIiLS3/zbRpcxIbHHsrlFtHZ00tTaybbKen7wVOA0o9te3Mbi2ady/xu7uPLsAntR/5Hp/G5tacDOh9efP6FPEgxdJTf6Y4TrQEkcDLZp1MdrPRgRkWhSsmWQaGnp4OPaZjotC4/H8MOnN5KZHM+VZxdgjL0mS21TKyXldZSU15GTkRg2IGru6OThf+6iurHNCXoKR6Txu7WlXH/+RKrq7UTImKwU9tY1s9KbvDEGLAt+u2YnozOTSY6PdRIelXUtPPqOvWjdqCFJDE9L4NMFw5xGenhaAg+8WRZ2+HC4obKRkh6+3YieWb837Gcryk7tVaCmobwiIiKB/NvGprZOnlm/NyAueGRdOT86bzx3vbqDC6blhrTf5TXNnJKRwIovnY67rZOU+BjeKath9mkjmDVuhDPadcLI9D75476r5EZZdWO/jHDt78RBd5JMgy0ZIyIyECjZMgi0tXXyTvkh3ttTa897jm1ykhy+kR4AS+YUOj9X1rXwyLpy/vcrM3jv48NOQFTrbmPx7AJWrC7lwbfKWDqniAfe2MV5U3L4zz++S0u7h/ysJJZfOAV3WwcXTMsN2NYR4M3Sgzy/oYJbFkzmp8/Zo15q3W1kpydS09TGvWtL+eM3ZjpBQ7hepKVziniiZE/YobKRkh6+Nr3W3UZRdiqrlpzNx7VukqO0G5GG8oqIiATybxvBboP9Y4/EOBe7qhuZPzXXeRzcfudnHUkkeDwWew+3hLS1Y4el0NHhYUtlHZV1LeRkJDEpJ53YWFfUP1Ok5MbJOsL1aNOotaadiMixUbJlgOvo8PD85kqu986XToxzOVN+goOZlPiYgPfWuttobOtk5ZrSgOdzM5JYOreQcSPS+MUL27i0eJQzSiQnI5FFxXksfrQk4raOlmX3VP12zU4euWImb3inCK1cU+okZfwDE/9epKr6FuJiXNS3tLHiSzOYlBPakxUp6TExJy1kJ4LC7LSo1fVAGcorIiIyUPhvwfxxbRN5mZO48W9bQmKES84YFXZacXCnRaS21uOxeG5DhdOJ45u6vGBa7nFJuIRzso5wPVqSSWvaiYgcGyVbBrgtlXVOogXsBu6XL23jxvkTuXnV1oBpNLlDksjPSqK8ppnEOBc//+IkHnm7LOB6iXEuKuqaGT8ynYk5ady9aBrutk7n+hfPGBWypbT/to6+oArshEtTW0fYKULBgYnLZRiTlcL2oIXzwvWMdJX0GDPs+Dbq/T2UV0REZCDaUdXA7d74Y/HsAjwWzlpste42LIuAacWnjx4SsA6cv3Bt7aaKw06iBez446fPbaZoRCrTRmf2yWfsrxGu/T1F52hJppN1xI+ISG8p2TLA7Tsc2sCV1zSTmhATEOz4pgj9/vIz2F/XQlJ8LM+u/5g540eyeV+DEzTcetEUxmWn0tDSgceCmWOy2F3T5DSykXYxmnRKGotnFwQscBtpxyH/heb8gwfLots9I0p6iIiIDAz+IxvueW0ni2bmsdyvw+dn8ydx3xv2KNpadxvjR6bzmXEjepQwqKwL/wf9/roWpo2O6seJqD9GuA6EKTpHSzKdrCN+RER6S8mWAczjschKjQ/bwKUmxrFidWnIe9aX1wY8X1bTxBOLz6S5vZPhqYl8VNPIwvvWBTSm507IDpiPHe5+p2WnA4Zad5vznG+O9dhhKSGBCRASPNy5cJp6RkRERAYZ/5ENGyvq4d09/GrhNCzLYmR6IrEuizsumUZTWwd5Q+24oKeJgpyMpLDxx8iMvv2Dvq87ewbCFJ2jJZm0pp2IyLFRsmUA232wkeb2Dm7+YuD86FsWTKaqrjlsUHJ6XqbzfGKci+vmTWBK7hBnlf0lj70f0qC/sHSW08geampl3IhUfvjMxpCF68IlVXwNcXBgEm5F/50HGtQzIiIiMsj4j2zIyUhk3uQcfvj0hqiOxJiUkx6w8L4v3pmUkxHFTzLwDJQpOl0lmXzJmInLZlFV30pTWwf5Q5VoERE5GiVbBrB9dS00NHdy79rSkO2Xv/SJvJBF6JbNLaJgWDIvREiIHK1BLxhub518uLnKmaLkMhAfa7+/J7094e71ZMlebr1oCj/xW+xXPSMiIiIDm//IhnBru0VjJEZsrIsF03IpGpHK/roWRmYkMikno88Wx+0vg2mKztbKo6+7JyIiRyjZMoA1tHRQdrCJ8prmgG0WAZraOnlm/V4Wzy4gNyOJirpmirJTGZ2Z4iRFgnWnQd9d0xQw+sV3zgs9DKLC3avW3caMvCERk0EiIiIy8PhPM/mwquG4jcSIjXUxbXRmn63RMhAMlik6A2G6k4jIYKNkywCWnZ7A9v31YRMklmUnL04dnkrukEQ+MXboURMX3WnQozWcNdK98oZGTgaJiIjIwOTfdg+WkRiDQX8synssBsp0JxGRwUTJlgFsck4Gew83s2xuEfesPjJd6PaLp5KbmcglM3J71CB3p0GP1nDWwRI8iIiISPcNlpEYg8lg2IFxME13EhEZKIxlWf1dhhNOcXGxVVJSEpVrtbV1snV/PfUt7bS0exg7LIVTh6cet6TFQNiCUERETkzGmPWWZRX3dzlOFtGMR/x5PBa7a5rUmXISUXwoIieSvopHNLJlgIuPj2F6Xmaf3U8jUkRERKQrg2EkhkSX4kMRkZ5TskVCKIgSEREREX+KD0VEeubE3k9PRERERERERKSPKdkiIiIiIiIiIhJFSraIiIiIiIiIiESRki0iIiIiIiIiIlGkZIuIiIiIiIiISBQp2SIiIiIiIiIiEkVKtoiIiIiIiIiIRJGSLSIiIiIiIiIiUaRki4iIiIiIiIhIFCnZIiIiIiIiIiISRUq2iIiIiIiIiIhEkZItIiIiIiIiIiJRpGSLiIiIiIiIiEgUKdkiIiIiIiIiIhJFSraIiIiIiIiIiESRki0iIiIiIiIiIlGkZIuIiIiIiIiISBQp2SIiIiIiIiIiEkVKtoiIiIiIiIiIRJGSLSIiIiIiIiIiUWQsy+rvMpxwjDHVQHl/l6OXhgEH+7sQg4zqrGdUXz2nOus51VnPHc86y7csa/hxurYEUTwiqP56S/XXO6q/3lH99U5X9dcn8YiSLRKWMabEsqzi/i7HYKI66xnVV8+pznpOddZzqjMZSPR97B3VX++o/npH9dc7qr/eGQj1p2lEIiIiIiIiIiJRpGSLiIiIiIiIiEgUKdkikdzf3wUYhFRnPaP66jnVWc+pznpOdSYDib6PvaP66x3VX++o/npH9dc7/V5/WrNFRERERERERCSKNLJFRERERERERCSKlGwREREREREREYkiJVsGIWPMbGPM34wxFcYYyxjzjaDXjTHmJmPMPmNMszFmrTFmUtA5mcaYR40xdd7jUWPMkKBzphhj/uG9RoUx5kZjjAk65xJjzFZjTKv3vxf1tCzHmzHmx8aYfxtj6o0x1caY540xk3tazpOszq4yxmz01lm9MWadMeb8npTxZKqvYN7vnGWMWdmTcp5sdeYtgxV07O9JOU/COssxxjxs7H/LWrxl/UxPynmy1ZkcP0bxyDEzik16zShWiRqjuKXHjGKYXjMnQ0xjWZaOQXYAXwBuBRYCbuAbQa9fBzQAlwCTgSeBfUCa3zkvAluAs7zHFuB5v9fTgf3e90723qsB+G+/c84COoDrgQne/3YAn+xJWfqgvl4GrvDefwrwrPezDVWdRayzC4HzgEJgHPALoB2Yqvo6at2dCXwEbABW6jvWZV3dBGwHRvodw1VnEetrCFAGPALMBMYCc4EJqjMd/XGgeKQ3dafYpPd1qFglOvWouOXY6u0mFMP0pv6GcBLENP36P7eOqHxRG/ELbgADVALX+z2X5P1yfNv7eAJgAZ/2O+ds73OneR9/F6gHkvzO+SlQwZGFlZ8AXg0qz2vAn7tbln6qs1SgE7hAddajejsEfFv11WUdZQC7gM8Ba/EGLaqziPV1E7A5wmuqs9A6uRX4Zxevq8509NuB4pHe1p9ik+jUo2KVntWX4pZjr7ubUAzTm/o7KWIaTSM68YzFzqy+4nvCsqxm4A3gU96nzsIOit72e98/gaagc970vtfnZeAUYIzfOa8Q6GW/a3SnLP0hDXsKXa33seqsC8aYGGPMl7ADwbe7WcaTtb7uB562LOv1oOdVZ5EVeIdkfmSMedwYU9CDcp5sdbYA+Jcx5gljzAFjzAfGmCV+Q2FVZzKQ6PvYM4pNekGxyjFT3NI7imGO3QJOgphGyZYTz0jvf6uCnq/ye20kUG1503IA3p8PBJ0T7hp045yRQed1dU5/uAf4AFjnfaw6C8M7v7ERaAXuAy6yLGsTqq+wjDHfwh7K/NMwL6vOwvsX8A1gHvAt7/3fNsZkoToLpwD4Hvaw2//A/rfsl8BV3tdVZzKQ6PvYM4pNjoFilWOnuKXXFMP0zkkR08R29aLIicYYcxf28LKzLcvq7O/yDHA7gOnYQ0wXAg8bYz7bj+UZsIwxp2EPhzzbsqz2/i7PYGFZ1ov+j40x72A3uv8JvNMvhRrYXECJZVk/9j5+3xhThB2YrIz8NhEZyBSb9IpilWOguKX3FMP02kkR02hky4lnv/e/2UHPZ/u9th8Y7r8Ks/fnEUHnhLsG3Thnf9B5XZ3TZ4wxdwNfBuZYllXm95LqLAzLstosyyq1LGu99x/CD4BrUH2FcxYwDNhijOkwxnQAnwG+5/25xq9c/k7mOgthWVYj9sJmReh7Fk4lsDXouW1Anvdn1ZkMJPo+doNik95RrHLMFLdEmWKYHjspYholW048H2H/0j/ve8IYkwjM4sh8tnXYc1rP8nvfWUBK0DmzvO/1+Tz2qsu7/c75PIE+73eN7pSlTxhj7uFIMLM96GXVWfe4gARUX+E8h72bxHS/owR43Pvzh90o58lWZyG85RiP3QDrexbqn8BpQc+NA8q9P6vOZCDR9/EoFJscF4pVuuc5FLdElWKYHjs5YpquVs/VMTAP75dquvdwAzd6f87zvn4dUAdcjL011eOE3yZrE0e2ydpE4DZZGd4v1ePea1yMvZKz/zZZn8LeFutH2P+4/Bh7y73gbbK6LEsf1Ne93rLPwZ5X5ztSe1LOk6zOfun9B2QMdmN8G+ABzlN9dbsO1xK6haLqLLCO7sTuSRsLfBJY5f08+aqzsPX1CW+5rseeZ3+pt0xX6Xumoz8OFI/0pu4Um/S+DhWrRLc+16K4pSf1pRimd/V3UsQ0/f4/to5j+nJ+FntLq+DjIe/rBns7skqgBfgHMDnoGpnAn7xftnrvz0OCzpmCvcpyi/daP8O7RZbfOQux95hvwx76dXHQ60ctSx/UV7i6soCbelLOk6zOHsLOLLdiLzL1GvAfqq8e1eFaAoMW1VloHfkaqjbsLfieASaqzrqss/OBDd4yfAgs9f8sqjMdfXmgeKQ3dafYpPd1+BCKVaJZn2tR3NKT+lIM0/s6POFjGt/e0iIiIiIiIiIiEgVas0VEREREREREJIqUbBERERERERERiSIlW0REREREREREokjJFhERERERERGRKFKyRUREREREREQkipRsERERERERERGJIiVbRKRPGGMWGmMsv8ffMMY09lNZVhljHjrO9+i3zyciIiLhKR4Rkb6iZIvIScwY85AxxvIe7caYMmPMncaYlD64/RNAQXdPNsbsNsZcexzLIyIiIv1A8YiInIhi+7sAItLvXgMuB+KAWcADQArw3eATjTGxQKdlWVbwaz1lWVYz0Nzb64iIiMgJQfGIiJxQNLJFRFoty9pvWdbHlmU9BvwfsADAGHOTMWazdwjqLqAVSDHGZBhj7jfGHDDGNBhj/mGMKfa/qDHm68aYcmOM2xizCsgOej1kWKsx5gvGmH8ZY5qNMTXGmOeNMYnGmLVAPnCHr+fL7z2f8t7fbYypMMb8zhiT7vd6srfHrNEYU2WM+UlXlWGMSffe/4Kg58/19raN8D7+pTFmh/fc3caYXxljEru47k3GmM3dqIMLjDHrjTEtxpiPjDG/MMbE+71+sTFmo/e+h7yfPaBuRUREBiHFI4FlUDwiMsgp2SIiwZqxe5V8xgJfAS4FpmEHOH8HcoH5wOnAG8AaY0wOgDHmk8BDwP3AdOB54OaubmqMmQf8DXgVOAP4HPAP7H+nLgb2eq+R4z0wxkwBXvG+b5r3vOnA//O79J3A54FLgLne8s6OVA7Lsuq95f1q0EtfBV61LOuA93ET8F/ABOB7wJeA67v6jEdjjPkP7OByJTDJe/2FwK3e10cCjwMPe+87G3i0N/cUEREZoBSPKB4RGdwsy9KhQ8dJemAHIKv8Hs8EDgJPeB/fBLQD2X7nzAEagaSga30A/ND782PYgYD/6w/Y/+Q4j78BNPo9/ifweBdl3Q1cG/TcI8CDQc9NByxgBJCKHYx91e/1VOAw8FAX9/oi4AbSvI+TgHrgK1285ztAaRef7yZgc9B7gs95A7gh6JwF3vo2wAzvZ8vv7++ODh06dOjQEa1D8UjEeyke0aFjEB9as0VE5nmHjsZi9yD9Fbja7/W9lmVV+T0+A0gGqo0x/tdJBE71/jwBuzfG3zrgyi7KcTp2sNUTZwCFxphFfs/5CnUqdoAS7703AJZlNRpjNh3lui9633sRdgD1Re91n3NuYsxC4PtAIXbAFOM9euMMYKYx5jq/51zYwdVIYAP2nPbNxphXvD8/bVlWdS/vKyIi0t8Uj4RSPCIyiCnZIiJvAIuxe4z2WZbVHvR6U9BjF1CFvXhdsProF69LLuweqrvDvFYBjDuWi1qW1W6MeRJ7qO4j3v8+a1mWG8AYcyb28NmfA9dg90x9EXuIcCQejgRePnFBj13eaz4V5v3VlmV1GmPOBc4EzsUOFm8zxnzGsqwN3f+EIiIiA47ikSCKR0QGNyVbRMRtWVZpD85/D3txOY9lWWURztmG3QD7C34c7H3sOcx/iPB6G6E9Ne8BkyKV37uIXrv33mXe51KAycCuo5TnT8AbxpiJwDzs+eA+nwYqLMta7nev/KNcrxrINsYYy7J8C+pND/N5xnf1+/C+dx2wzhhzM7AFWITdyyQiIjJYKR4JT/GIyCClZIuI9NRr2POZ/2qM+SGwHXtI6TzgNcuy3gRWAG8bY34MPA18FnsIbFd+ATxvjCnFnmNtsHtLfu/twdkNzDLG/Al7x4KDwO3AO8aY+4DfAw3AeOACy7K+7R2i+yBwuzGmGtgH3Eg3htdalvW2MabcW5aDwGq/lz8Eco0xX8UONP4D+PJRLrkWGAr8xBjzOHadLAw652Zglfe+TwId2IHYTMuyfujtwToHeBm7N+90YDSw9WifR0RE5ASjeETxiMiApt2IRKRHvD0ZXwDWYPf67MBuiE/DDh6wLOsd7CGl3wU2Yq/Kf9NRrvsCdgB0Hnav0j+wdwDweE+5Ebsh34XdK4NlWRuxV8Af4z1/A3AbdsPvcy3wOvCs97+bsYcqd8f/Ye8q8LhlWZ1+ZX0euAP4jffzfd5bvq4+3zbs+ljs955bg855GTjf+7nf9R4/AvZ4T6nD7sVaBewEfg0styzrT938PCIiIicExSOKR0QGOnNk9JiIiIiIiIiIiPSWRraIiIiIiIiIiESRki0iIiIiIiIiIlGkZIuIiIiIiIiISBQp2SIiIiIiIiIiEkVKtoiIiIiIiIiIRJGSLSIiIiIiIiIiUaRki4iIiIiIiIhIFCnZIiIiIiIiIiISRUq2iIiIiIiIiIhE0f8HaByxSfNM6TkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " # оценка модели\n",
    "y_train_preds = rf_model.predict(X_train)\n",
    "y_test_preds = rf_model.predict(X_valid)\n",
    "\n",
    "evaluate_preds(y_train, y_train_preds, y_valid, y_test_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f880df39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# кросс валидация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0290cf24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77280206, 0.72851638, 0.75368855])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_score = cross_val_score(rf_model, X_train, y_train, scoring='r2', cv=KFold(n_splits=3, shuffle=True, random_state=21))\n",
    "cv_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5d373cf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7516689994037202"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b5be4638",
   "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>feature_name</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MedPriceByDistrict</td>\n",
       "      <td>6.486684e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Square</td>\n",
       "      <td>1.067982e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LifeSquare</td>\n",
       "      <td>3.114880e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HouseFloor</td>\n",
       "      <td>2.124666e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HouseYear</td>\n",
       "      <td>2.101132e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Social_3</td>\n",
       "      <td>1.984151e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DistrictSize</td>\n",
       "      <td>1.976566e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MeterPrice</td>\n",
       "      <td>1.767325e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MedPriceByFloorYear</td>\n",
       "      <td>1.759345e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Floor</td>\n",
       "      <td>1.538490e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ecology_1</td>\n",
       "      <td>1.497777e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KitchenSquare</td>\n",
       "      <td>1.478966e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Social_2</td>\n",
       "      <td>1.436299e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Social_1</td>\n",
       "      <td>1.299079e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Shops_1</td>\n",
       "      <td>7.863885e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Helthcare_2</td>\n",
       "      <td>5.410690e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rooms</td>\n",
       "      <td>5.144853e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HouseFloor_outlier</td>\n",
       "      <td>1.434990e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ecology_3</td>\n",
       "      <td>9.522110e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Shops_2</td>\n",
       "      <td>9.272947e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LifeSquare_nan</td>\n",
       "      <td>8.568115e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>IsDistrictLarge</td>\n",
       "      <td>7.734220e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ecology_2</td>\n",
       "      <td>3.324003e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rooms_outlier</td>\n",
       "      <td>4.981087e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HouseYear_outlier</td>\n",
       "      <td>2.952486e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>new_district</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           feature_name    importance\n",
       "23   MedPriceByDistrict  6.486684e-01\n",
       "1                Square  1.067982e-01\n",
       "2            LifeSquare  3.114880e-02\n",
       "5            HouseFloor  2.124666e-02\n",
       "6             HouseYear  2.101132e-02\n",
       "12             Social_3  1.984151e-02\n",
       "20         DistrictSize  1.976566e-02\n",
       "25           MeterPrice  1.767325e-02\n",
       "24  MedPriceByFloorYear  1.759345e-02\n",
       "4                 Floor  1.538490e-02\n",
       "7             Ecology_1  1.497777e-02\n",
       "3         KitchenSquare  1.478966e-02\n",
       "11             Social_2  1.436299e-02\n",
       "10             Social_1  1.299079e-02\n",
       "14              Shops_1  7.863885e-03\n",
       "13          Helthcare_2  5.410690e-03\n",
       "0                 Rooms  5.144853e-03\n",
       "17   HouseFloor_outlier  1.434990e-03\n",
       "9             Ecology_3  9.522110e-04\n",
       "15              Shops_2  9.272947e-04\n",
       "19       LifeSquare_nan  8.568115e-04\n",
       "22      IsDistrictLarge  7.734220e-04\n",
       "8             Ecology_2  3.324003e-04\n",
       "16        Rooms_outlier  4.981087e-05\n",
       "18    HouseYear_outlier  2.952486e-07\n",
       "21         new_district  0.000000e+00"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# веса признаков\n",
    "feature_importances = pd.DataFrame(zip(X_train.columns, rf_model.feature_importances_), \n",
    "                                   columns=['feature_name', 'importance'])\n",
    "\n",
    "feature_importances.sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7d95cb34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 26)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# выгрузка результатов\n",
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "90e14b5d",
   "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>Id</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4567</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5925</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>960</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3848</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>746</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id     Price\n",
       "0  4567  200000.0\n",
       "1  5925  200000.0\n",
       "2   960  200000.0\n",
       "3  3848  200000.0\n",
       "4   746  200000.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit = pd.read_csv('C:/ARTEM/GeekBrains/Python4DS/RealEstatePricePredictionMoscow/sample_submission.csv')\n",
    "submit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2c01f0a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([166108.90509395, 127232.78534428, 141508.34570682, ...,\n",
       "       148166.23205735, 189297.95637356, 250788.19751388])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = rf_model.predict(test_df)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6c9f1523",
   "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>Id</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4567</td>\n",
       "      <td>166108.905094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5925</td>\n",
       "      <td>127232.785344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>960</td>\n",
       "      <td>141508.345707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3848</td>\n",
       "      <td>170928.860105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>746</td>\n",
       "      <td>175241.143352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id          Price\n",
       "0  4567  166108.905094\n",
       "1  5925  127232.785344\n",
       "2   960  141508.345707\n",
       "3  3848  170928.860105\n",
       "4   746  175241.143352"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit['Price'] = predictions\n",
    "submit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e6052d38",
   "metadata": {},
   "outputs": [],
   "source": [
    "submit.to_csv('C:/ARTEM/GeekBrains/Python4DS/RealEstatePricePredictionMoscow/rf_submit.csv', index=False)"
   ]
  }
 ],
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