DL - Car Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7ac350d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder,OneHotEncoder,StandardScaler\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation,Dropout\n",
    "from keras.optimizers import SGD,Adam\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.wrappers.scikit_learn import KerasRegressor,KerasClassifier\n",
    "from keras.regularizers import L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae305a2e",
   "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",
       "      <th>Levy</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Prod. year</th>\n",
       "      <th>Category</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Fuel type</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Drive wheels</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Airbags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45654403</td>\n",
       "      <td>13328</td>\n",
       "      <td>1399</td>\n",
       "      <td>LEXUS</td>\n",
       "      <td>RX 450</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>3.5</td>\n",
       "      <td>186005 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44731507</td>\n",
       "      <td>16621</td>\n",
       "      <td>1018</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Equinox</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>3</td>\n",
       "      <td>192000 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45774419</td>\n",
       "      <td>8467</td>\n",
       "      <td>-</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>200000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Variator</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45769185</td>\n",
       "      <td>3607</td>\n",
       "      <td>862</td>\n",
       "      <td>FORD</td>\n",
       "      <td>Escape</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.5</td>\n",
       "      <td>168966 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45809263</td>\n",
       "      <td>11726</td>\n",
       "      <td>446</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>91901 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19232</th>\n",
       "      <td>45798355</td>\n",
       "      <td>8467</td>\n",
       "      <td>-</td>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>CLK 200</td>\n",
       "      <td>1999</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>CNG</td>\n",
       "      <td>2.0 Turbo</td>\n",
       "      <td>300000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Rear</td>\n",
       "      <td>02-Mar</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19233</th>\n",
       "      <td>45778856</td>\n",
       "      <td>15681</td>\n",
       "      <td>831</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>2011</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>2.4</td>\n",
       "      <td>161600 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Red</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>45804997</td>\n",
       "      <td>26108</td>\n",
       "      <td>836</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>116365 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Grey</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19235</th>\n",
       "      <td>45793526</td>\n",
       "      <td>5331</td>\n",
       "      <td>1288</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Captiva</td>\n",
       "      <td>2007</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>51258 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19236</th>\n",
       "      <td>45813273</td>\n",
       "      <td>470</td>\n",
       "      <td>753</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>2012</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.4</td>\n",
       "      <td>186923 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19237 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  Price  Levy   Manufacturer    Model  Prod. year   Category   \n",
       "0      45654403  13328  1399          LEXUS   RX 450        2010       Jeep  \\\n",
       "1      44731507  16621  1018      CHEVROLET  Equinox        2011       Jeep   \n",
       "2      45774419   8467     -          HONDA      FIT        2006  Hatchback   \n",
       "3      45769185   3607   862           FORD   Escape        2011       Jeep   \n",
       "4      45809263  11726   446          HONDA      FIT        2014  Hatchback   \n",
       "...         ...    ...   ...            ...      ...         ...        ...   \n",
       "19232  45798355   8467     -  MERCEDES-BENZ  CLK 200        1999      Coupe   \n",
       "19233  45778856  15681   831        HYUNDAI   Sonata        2011      Sedan   \n",
       "19234  45804997  26108   836        HYUNDAI   Tucson        2010       Jeep   \n",
       "19235  45793526   5331  1288      CHEVROLET  Captiva        2007       Jeep   \n",
       "19236  45813273    470   753        HYUNDAI   Sonata        2012      Sedan   \n",
       "\n",
       "      Leather interior Fuel type Engine volume    Mileage  Cylinders   \n",
       "0                  Yes    Hybrid           3.5  186005 km        6.0  \\\n",
       "1                   No    Petrol             3  192000 km        6.0   \n",
       "2                   No    Petrol           1.3  200000 km        4.0   \n",
       "3                  Yes    Hybrid           2.5  168966 km        4.0   \n",
       "4                  Yes    Petrol           1.3   91901 km        4.0   \n",
       "...                ...       ...           ...        ...        ...   \n",
       "19232              Yes       CNG     2.0 Turbo  300000 km        4.0   \n",
       "19233              Yes    Petrol           2.4  161600 km        4.0   \n",
       "19234              Yes    Diesel             2  116365 km        4.0   \n",
       "19235              Yes    Diesel             2   51258 km        4.0   \n",
       "19236              Yes    Hybrid           2.4  186923 km        4.0   \n",
       "\n",
       "      Gear box type Drive wheels   Doors             Wheel   Color  Airbags  \n",
       "0         Automatic          4x4  04-May        Left wheel  Silver       12  \n",
       "1         Tiptronic          4x4  04-May        Left wheel   Black        8  \n",
       "2          Variator        Front  04-May  Right-hand drive   Black        2  \n",
       "3         Automatic          4x4  04-May        Left wheel   White        0  \n",
       "4         Automatic        Front  04-May        Left wheel  Silver        4  \n",
       "...             ...          ...     ...               ...     ...      ...  \n",
       "19232        Manual         Rear  02-Mar        Left wheel  Silver        5  \n",
       "19233     Tiptronic        Front  04-May        Left wheel     Red        8  \n",
       "19234     Automatic        Front  04-May        Left wheel    Grey        4  \n",
       "19235     Automatic        Front  04-May        Left wheel   Black        4  \n",
       "19236     Automatic        Front  04-May        Left wheel   White       12  \n",
       "\n",
       "[19237 rows x 18 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri = pd.read_csv(\"car_price_prediction.csv\")\n",
    "veri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d9e761da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                  0\n",
       "Price               0\n",
       "Levy                0\n",
       "Manufacturer        0\n",
       "Model               0\n",
       "Prod. year          0\n",
       "Category            0\n",
       "Leather interior    0\n",
       "Fuel type           0\n",
       "Engine volume       0\n",
       "Mileage             0\n",
       "Cylinders           0\n",
       "Gear box type       0\n",
       "Drive wheels        0\n",
       "Doors               0\n",
       "Wheel               0\n",
       "Color               0\n",
       "Airbags             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07247d1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19237 entries, 0 to 19236\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   ID                19237 non-null  int64  \n",
      " 1   Price             19237 non-null  int64  \n",
      " 2   Levy              19237 non-null  object \n",
      " 3   Manufacturer      19237 non-null  object \n",
      " 4   Model             19237 non-null  object \n",
      " 5   Prod. year        19237 non-null  int64  \n",
      " 6   Category          19237 non-null  object \n",
      " 7   Leather interior  19237 non-null  object \n",
      " 8   Fuel type         19237 non-null  object \n",
      " 9   Engine volume     19237 non-null  object \n",
      " 10  Mileage           19237 non-null  object \n",
      " 11  Cylinders         19237 non-null  float64\n",
      " 12  Gear box type     19237 non-null  object \n",
      " 13  Drive wheels      19237 non-null  object \n",
      " 14  Doors             19237 non-null  object \n",
      " 15  Wheel             19237 non-null  object \n",
      " 16  Color             19237 non-null  object \n",
      " 17  Airbags           19237 non-null  int64  \n",
      "dtypes: float64(1), int64(4), object(13)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "veri.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c991c9b1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "       '1110', '399', '1470', '1174', '5877', '1474', '1688', '526',\n",
       "       '686', '5908', '1107', '2070', '1468', '1246', '1685', '556',\n",
       "       '1533', '1917', '1346', '732', '692', '579', '421', '362', '3505',\n",
       "       '1855', '2711', '1586', '3739', '681', '1708', '2278', '1701',\n",
       "       '722', '1482', '928', '827', '832', '527', '604', '173', '1341',\n",
       "       '3329', '1553', '859', '167', '916', '828', '2082', '1176', '1108',\n",
       "       '975', '3008', '1516', '2269', '1699', '2073', '1031', '1503',\n",
       "       '2364', '1030', '1442', '5666', '2715', '1437', '2067', '1426',\n",
       "       '2908', '1279', '866', '4283', '279', '2658', '3015', '2004',\n",
       "       '1391', '4736', '748', '1466', '644', '683', '2705', '1297', '731',\n",
       "       '1252', '2216', '3141', '3273', '1518', '1723', '1588', '972',\n",
       "       '682', '1094', '668', '175', '967', '402', '3894', '1960', '1599',\n",
       "       '2000', '2084', '1621', '714', '1109', '3989', '873', '1572',\n",
       "       '1163', '1991', '1716', '1673', '2562', '2874', '965', '462',\n",
       "       '605', '1948', '1736', '3518', '2054', '2467', '1681', '1272',\n",
       "       '1205', '750', '2156', '2566', '115', '524', '3184', '676', '1678',\n",
       "       '612', '328', '955', '1441', '1675', '3965', '2909', '623', '822',\n",
       "       '867', '3025', '1993', '792', '636', '4057', '3743', '2337',\n",
       "       '2570', '2418', '2472', '3910', '1662', '2123', '2628', '3208',\n",
       "       '2080', '3699', '2913', '864', '2505', '870', '7536', '1924',\n",
       "       '1671', '1064', '1836', '1866', '4741', '841', '1369', '5681',\n",
       "       '3112', '1366', '2223', '1198', '1039', '3811', '3571', '1387',\n",
       "       '1171', '1365', '1531', '1590', '11706', '2308', '4860', '1641',\n",
       "       '1045', '1901'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.Levy.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7461d608",
   "metadata": {},
   "outputs": [],
   "source": [
    "veri.Levy = veri.Levy.replace(\"-\",np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5bd12dc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                     0\n",
       "Price                  0\n",
       "Levy                5819\n",
       "Manufacturer           0\n",
       "Model                  0\n",
       "Prod. year             0\n",
       "Category               0\n",
       "Leather interior       0\n",
       "Fuel type              0\n",
       "Engine volume          0\n",
       "Mileage                0\n",
       "Cylinders              0\n",
       "Gear box type          0\n",
       "Drive wheels           0\n",
       "Doors                  0\n",
       "Wheel                  0\n",
       "Color                  0\n",
       "Airbags                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e333d00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19237 entries, 0 to 19236\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   ID                19237 non-null  int64  \n",
      " 1   Price             19237 non-null  int64  \n",
      " 2   Levy              19237 non-null  int32  \n",
      " 3   Manufacturer      19237 non-null  object \n",
      " 4   Model             19237 non-null  object \n",
      " 5   Prod. year        19237 non-null  int64  \n",
      " 6   Category          19237 non-null  object \n",
      " 7   Leather interior  19237 non-null  object \n",
      " 8   Fuel type         19237 non-null  object \n",
      " 9   Engine volume     19237 non-null  object \n",
      " 10  Mileage           19237 non-null  object \n",
      " 11  Cylinders         19237 non-null  float64\n",
      " 12  Gear box type     19237 non-null  object \n",
      " 13  Drive wheels      19237 non-null  object \n",
      " 14  Doors             19237 non-null  object \n",
      " 15  Wheel             19237 non-null  object \n",
      " 16  Color             19237 non-null  object \n",
      " 17  Airbags           19237 non-null  int64  \n",
      "dtypes: float64(1), int32(1), int64(4), object(12)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "veri.Levy = veri.Levy.fillna(0)\n",
    "veri.Levy = veri.Levy.astype(int)\n",
    "veri.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dc765925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['3.5', '3', '1.3', '2.5', '2', '1.8', '2.4', '4', '1.6', '3.3',\n",
       "       '2.0 Turbo', '2.2 Turbo', '4.7', '1.5', '4.4', '3.0 Turbo',\n",
       "       '1.4 Turbo', '3.6', '2.3', '1.5 Turbo', '1.6 Turbo', '2.2',\n",
       "       '2.3 Turbo', '1.4', '5.5', '2.8 Turbo', '3.2', '3.8', '4.6', '1.2',\n",
       "       '5', '1.7', '2.9', '0.5', '1.8 Turbo', '2.4 Turbo', '3.5 Turbo',\n",
       "       '1.9', '2.7', '4.8', '5.3', '0.4', '2.8', '3.2 Turbo', '1.1',\n",
       "       '2.1', '0.7', '5.4', '1.3 Turbo', '3.7', '1', '2.5 Turbo', '2.6',\n",
       "       '1.9 Turbo', '4.4 Turbo', '4.7 Turbo', '0.8', '0.2 Turbo', '5.7',\n",
       "       '4.8 Turbo', '4.6 Turbo', '6.7', '6.2', '1.2 Turbo', '3.4',\n",
       "       '1.7 Turbo', '6.3 Turbo', '2.7 Turbo', '4.3', '4.2', '2.9 Turbo',\n",
       "       '0', '4.0 Turbo', '20', '3.6 Turbo', '0.3', '3.7 Turbo', '5.9',\n",
       "       '5.5 Turbo', '0.2', '2.1 Turbo', '5.6', '6', '0.7 Turbo',\n",
       "       '0.6 Turbo', '6.8', '4.5', '0.6', '7.3', '0.1', '1.0 Turbo', '6.3',\n",
       "       '4.5 Turbo', '0.8 Turbo', '4.2 Turbo', '3.1', '5.0 Turbo', '6.4',\n",
       "       '3.9', '5.7 Turbo', '0.9', '0.4 Turbo', '5.4 Turbo', '0.3 Turbo',\n",
       "       '5.2', '5.8', '1.1 Turbo'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri[\"Engine volume\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1135b803",
   "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",
       "      <th>Levy</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Prod. year</th>\n",
       "      <th>Category</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Fuel type</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Drive wheels</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Airbags</th>\n",
       "      <th>Turbo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45654403</td>\n",
       "      <td>13328</td>\n",
       "      <td>1399</td>\n",
       "      <td>LEXUS</td>\n",
       "      <td>RX 450</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>3.5</td>\n",
       "      <td>186005 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>12</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44731507</td>\n",
       "      <td>16621</td>\n",
       "      <td>1018</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Equinox</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>3</td>\n",
       "      <td>192000 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45774419</td>\n",
       "      <td>8467</td>\n",
       "      <td>0</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>200000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Variator</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45769185</td>\n",
       "      <td>3607</td>\n",
       "      <td>862</td>\n",
       "      <td>FORD</td>\n",
       "      <td>Escape</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.5</td>\n",
       "      <td>168966 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45809263</td>\n",
       "      <td>11726</td>\n",
       "      <td>446</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>91901 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19232</th>\n",
       "      <td>45798355</td>\n",
       "      <td>8467</td>\n",
       "      <td>0</td>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>CLK 200</td>\n",
       "      <td>1999</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>CNG</td>\n",
       "      <td>2.0 Turbo</td>\n",
       "      <td>300000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Rear</td>\n",
       "      <td>02-Mar</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>5</td>\n",
       "      <td>Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19233</th>\n",
       "      <td>45778856</td>\n",
       "      <td>15681</td>\n",
       "      <td>831</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>2011</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>2.4</td>\n",
       "      <td>161600 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Red</td>\n",
       "      <td>8</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>45804997</td>\n",
       "      <td>26108</td>\n",
       "      <td>836</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>116365 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Grey</td>\n",
       "      <td>4</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19235</th>\n",
       "      <td>45793526</td>\n",
       "      <td>5331</td>\n",
       "      <td>1288</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Captiva</td>\n",
       "      <td>2007</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>51258 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>4</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19236</th>\n",
       "      <td>45813273</td>\n",
       "      <td>470</td>\n",
       "      <td>753</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>2012</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.4</td>\n",
       "      <td>186923 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>12</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19237 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  Price  Levy   Manufacturer    Model  Prod. year   Category   \n",
       "0      45654403  13328  1399          LEXUS   RX 450        2010       Jeep  \\\n",
       "1      44731507  16621  1018      CHEVROLET  Equinox        2011       Jeep   \n",
       "2      45774419   8467     0          HONDA      FIT        2006  Hatchback   \n",
       "3      45769185   3607   862           FORD   Escape        2011       Jeep   \n",
       "4      45809263  11726   446          HONDA      FIT        2014  Hatchback   \n",
       "...         ...    ...   ...            ...      ...         ...        ...   \n",
       "19232  45798355   8467     0  MERCEDES-BENZ  CLK 200        1999      Coupe   \n",
       "19233  45778856  15681   831        HYUNDAI   Sonata        2011      Sedan   \n",
       "19234  45804997  26108   836        HYUNDAI   Tucson        2010       Jeep   \n",
       "19235  45793526   5331  1288      CHEVROLET  Captiva        2007       Jeep   \n",
       "19236  45813273    470   753        HYUNDAI   Sonata        2012      Sedan   \n",
       "\n",
       "      Leather interior Fuel type Engine volume    Mileage  Cylinders   \n",
       "0                  Yes    Hybrid           3.5  186005 km        6.0  \\\n",
       "1                   No    Petrol             3  192000 km        6.0   \n",
       "2                   No    Petrol           1.3  200000 km        4.0   \n",
       "3                  Yes    Hybrid           2.5  168966 km        4.0   \n",
       "4                  Yes    Petrol           1.3   91901 km        4.0   \n",
       "...                ...       ...           ...        ...        ...   \n",
       "19232              Yes       CNG     2.0 Turbo  300000 km        4.0   \n",
       "19233              Yes    Petrol           2.4  161600 km        4.0   \n",
       "19234              Yes    Diesel             2  116365 km        4.0   \n",
       "19235              Yes    Diesel             2   51258 km        4.0   \n",
       "19236              Yes    Hybrid           2.4  186923 km        4.0   \n",
       "\n",
       "      Gear box type Drive wheels   Doors             Wheel   Color  Airbags   \n",
       "0         Automatic          4x4  04-May        Left wheel  Silver       12  \\\n",
       "1         Tiptronic          4x4  04-May        Left wheel   Black        8   \n",
       "2          Variator        Front  04-May  Right-hand drive   Black        2   \n",
       "3         Automatic          4x4  04-May        Left wheel   White        0   \n",
       "4         Automatic        Front  04-May        Left wheel  Silver        4   \n",
       "...             ...          ...     ...               ...     ...      ...   \n",
       "19232        Manual         Rear  02-Mar        Left wheel  Silver        5   \n",
       "19233     Tiptronic        Front  04-May        Left wheel     Red        8   \n",
       "19234     Automatic        Front  04-May        Left wheel    Grey        4   \n",
       "19235     Automatic        Front  04-May        Left wheel   Black        4   \n",
       "19236     Automatic        Front  04-May        Left wheel   White       12   \n",
       "\n",
       "           Turbo  \n",
       "0      Non Turbo  \n",
       "1      Non Turbo  \n",
       "2      Non Turbo  \n",
       "3      Non Turbo  \n",
       "4      Non Turbo  \n",
       "...          ...  \n",
       "19232      Turbo  \n",
       "19233  Non Turbo  \n",
       "19234  Non Turbo  \n",
       "19235  Non Turbo  \n",
       "19236  Non Turbo  \n",
       "\n",
       "[19237 rows x 19 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri[\"Turbo\"] = veri[\"Engine volume\"].apply(lambda x:\"Turbo\" if \"Turbo\" in str(x) else \"Non Turbo\")\n",
    "veri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f5cfff6",
   "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>Levy</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Prod. year</th>\n",
       "      <th>Category</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Fuel type</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Drive wheels</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Airbags</th>\n",
       "      <th>Turbo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13328</td>\n",
       "      <td>1399</td>\n",
       "      <td>LEXUS</td>\n",
       "      <td>RX 450</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>3.5</td>\n",
       "      <td>186005 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>12</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16621</td>\n",
       "      <td>1018</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Equinox</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>3.0</td>\n",
       "      <td>192000 km</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8467</td>\n",
       "      <td>0</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>200000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Variator</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3607</td>\n",
       "      <td>862</td>\n",
       "      <td>FORD</td>\n",
       "      <td>Escape</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.5</td>\n",
       "      <td>168966 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11726</td>\n",
       "      <td>446</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>91901 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19232</th>\n",
       "      <td>8467</td>\n",
       "      <td>0</td>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>CLK 200</td>\n",
       "      <td>1999</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>CNG</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300000 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Rear</td>\n",
       "      <td>02-Mar</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>5</td>\n",
       "      <td>Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19233</th>\n",
       "      <td>15681</td>\n",
       "      <td>831</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>2011</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>2.4</td>\n",
       "      <td>161600 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Red</td>\n",
       "      <td>8</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>26108</td>\n",
       "      <td>836</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2.0</td>\n",
       "      <td>116365 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Grey</td>\n",
       "      <td>4</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19235</th>\n",
       "      <td>5331</td>\n",
       "      <td>1288</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Captiva</td>\n",
       "      <td>2007</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51258 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>4</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19236</th>\n",
       "      <td>470</td>\n",
       "      <td>753</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>2012</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.4</td>\n",
       "      <td>186923 km</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>04-May</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>12</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19237 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Price  Levy   Manufacturer    Model  Prod. year   Category   \n",
       "0      13328  1399          LEXUS   RX 450        2010       Jeep  \\\n",
       "1      16621  1018      CHEVROLET  Equinox        2011       Jeep   \n",
       "2       8467     0          HONDA      FIT        2006  Hatchback   \n",
       "3       3607   862           FORD   Escape        2011       Jeep   \n",
       "4      11726   446          HONDA      FIT        2014  Hatchback   \n",
       "...      ...   ...            ...      ...         ...        ...   \n",
       "19232   8467     0  MERCEDES-BENZ  CLK 200        1999      Coupe   \n",
       "19233  15681   831        HYUNDAI   Sonata        2011      Sedan   \n",
       "19234  26108   836        HYUNDAI   Tucson        2010       Jeep   \n",
       "19235   5331  1288      CHEVROLET  Captiva        2007       Jeep   \n",
       "19236    470   753        HYUNDAI   Sonata        2012      Sedan   \n",
       "\n",
       "      Leather interior Fuel type  Engine volume    Mileage  Cylinders   \n",
       "0                  Yes    Hybrid            3.5  186005 km        6.0  \\\n",
       "1                   No    Petrol            3.0  192000 km        6.0   \n",
       "2                   No    Petrol            1.3  200000 km        4.0   \n",
       "3                  Yes    Hybrid            2.5  168966 km        4.0   \n",
       "4                  Yes    Petrol            1.3   91901 km        4.0   \n",
       "...                ...       ...            ...        ...        ...   \n",
       "19232              Yes       CNG            2.0  300000 km        4.0   \n",
       "19233              Yes    Petrol            2.4  161600 km        4.0   \n",
       "19234              Yes    Diesel            2.0  116365 km        4.0   \n",
       "19235              Yes    Diesel            2.0   51258 km        4.0   \n",
       "19236              Yes    Hybrid            2.4  186923 km        4.0   \n",
       "\n",
       "      Gear box type Drive wheels   Doors             Wheel   Color  Airbags   \n",
       "0         Automatic          4x4  04-May        Left wheel  Silver       12  \\\n",
       "1         Tiptronic          4x4  04-May        Left wheel   Black        8   \n",
       "2          Variator        Front  04-May  Right-hand drive   Black        2   \n",
       "3         Automatic          4x4  04-May        Left wheel   White        0   \n",
       "4         Automatic        Front  04-May        Left wheel  Silver        4   \n",
       "...             ...          ...     ...               ...     ...      ...   \n",
       "19232        Manual         Rear  02-Mar        Left wheel  Silver        5   \n",
       "19233     Tiptronic        Front  04-May        Left wheel     Red        8   \n",
       "19234     Automatic        Front  04-May        Left wheel    Grey        4   \n",
       "19235     Automatic        Front  04-May        Left wheel   Black        4   \n",
       "19236     Automatic        Front  04-May        Left wheel   White       12   \n",
       "\n",
       "           Turbo  \n",
       "0      Non Turbo  \n",
       "1      Non Turbo  \n",
       "2      Non Turbo  \n",
       "3      Non Turbo  \n",
       "4      Non Turbo  \n",
       "...          ...  \n",
       "19232      Turbo  \n",
       "19233  Non Turbo  \n",
       "19234  Non Turbo  \n",
       "19235  Non Turbo  \n",
       "19236  Non Turbo  \n",
       "\n",
       "[19237 rows x 18 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri[\"Engine volume\"] = veri[\"Engine volume\"].apply(lambda x: str(x).replace(\" Turbo\",\"\")).astype(float)\n",
    "veri.drop(\"ID\",axis=1,inplace=True)\n",
    "veri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "146cb78c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.5,  3. ,  1.3,  2.5,  2. ,  1.8,  2.4,  4. ,  1.6,  3.3,  2.2,\n",
       "        4.7,  1.5,  4.4,  1.4,  3.6,  2.3,  5.5,  2.8,  3.2,  3.8,  4.6,\n",
       "        1.2,  5. ,  1.7,  2.9,  0.5,  1.9,  2.7,  4.8,  5.3,  0.4,  1.1,\n",
       "        2.1,  0.7,  5.4,  3.7,  1. ,  2.6,  0.8,  0.2,  5.7,  6.7,  6.2,\n",
       "        3.4,  6.3,  4.3,  4.2,  0. , 20. ,  0.3,  5.9,  5.6,  6. ,  0.6,\n",
       "        6.8,  4.5,  7.3,  0.1,  3.1,  6.4,  3.9,  0.9,  5.2,  5.8])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri[\"Engine volume\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82b4e4b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19237 entries, 0 to 19236\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Price             19237 non-null  int64  \n",
      " 1   Levy              19237 non-null  int32  \n",
      " 2   Manufacturer      19237 non-null  object \n",
      " 3   Model             19237 non-null  object \n",
      " 4   Prod. year        19237 non-null  int64  \n",
      " 5   Category          19237 non-null  object \n",
      " 6   Leather interior  19237 non-null  object \n",
      " 7   Fuel type         19237 non-null  object \n",
      " 8   Engine volume     19237 non-null  float64\n",
      " 9   Mileage           19237 non-null  object \n",
      " 10  Cylinders         19237 non-null  float64\n",
      " 11  Gear box type     19237 non-null  object \n",
      " 12  Drive wheels      19237 non-null  object \n",
      " 13  Doors             19237 non-null  object \n",
      " 14  Wheel             19237 non-null  object \n",
      " 15  Color             19237 non-null  object \n",
      " 16  Airbags           19237 non-null  int64  \n",
      " 17  Turbo             19237 non-null  object \n",
      "dtypes: float64(2), int32(1), int64(3), object(12)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "veri.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "936d42f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19237 entries, 0 to 19236\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Price             19237 non-null  int64  \n",
      " 1   Levy              19237 non-null  int32  \n",
      " 2   Manufacturer      19237 non-null  object \n",
      " 3   Model             19237 non-null  object \n",
      " 4   Prod. year        19237 non-null  int64  \n",
      " 5   Category          19237 non-null  object \n",
      " 6   Leather interior  19237 non-null  object \n",
      " 7   Fuel type         19237 non-null  object \n",
      " 8   Engine volume     19237 non-null  float64\n",
      " 9   Mileage           19237 non-null  int32  \n",
      " 10  Cylinders         19237 non-null  float64\n",
      " 11  Gear box type     19237 non-null  object \n",
      " 12  Drive wheels      19237 non-null  object \n",
      " 13  Doors             19237 non-null  object \n",
      " 14  Wheel             19237 non-null  object \n",
      " 15  Color             19237 non-null  object \n",
      " 16  Airbags           19237 non-null  int64  \n",
      " 17  Turbo             19237 non-null  object \n",
      "dtypes: float64(2), int32(2), int64(3), object(11)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "veri.Mileage = veri.Mileage.apply(lambda x: str(x).replace(\" km\",\"\")).astype(int)\n",
    "veri.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "06c91248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([186005, 192000, 200000, ..., 140607, 307325, 186923])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.Mileage.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a9bc7a77",
   "metadata": {},
   "outputs": [],
   "source": [
    "veri.Cylinders = veri.Cylinders.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "149cc1d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['04-May', '02-Mar', '>5'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.Doors.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "195a54f8",
   "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>Levy</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>Prod. year</th>\n",
       "      <th>Category</th>\n",
       "      <th>Leather interior</th>\n",
       "      <th>Fuel type</th>\n",
       "      <th>Engine volume</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Gear box type</th>\n",
       "      <th>Drive wheels</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Wheel</th>\n",
       "      <th>Color</th>\n",
       "      <th>Airbags</th>\n",
       "      <th>Turbo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13328</td>\n",
       "      <td>1399</td>\n",
       "      <td>LEXUS</td>\n",
       "      <td>RX 450</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>3.5</td>\n",
       "      <td>186005</td>\n",
       "      <td>6</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>4</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>12</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16621</td>\n",
       "      <td>1018</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Equinox</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>3.0</td>\n",
       "      <td>192000</td>\n",
       "      <td>6</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>4</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>8</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8467</td>\n",
       "      <td>0</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>No</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>200000</td>\n",
       "      <td>4</td>\n",
       "      <td>Variator</td>\n",
       "      <td>Front</td>\n",
       "      <td>4</td>\n",
       "      <td>Right-hand drive</td>\n",
       "      <td>Black</td>\n",
       "      <td>2</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3607</td>\n",
       "      <td>862</td>\n",
       "      <td>FORD</td>\n",
       "      <td>Escape</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.5</td>\n",
       "      <td>168966</td>\n",
       "      <td>4</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4x4</td>\n",
       "      <td>4</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11726</td>\n",
       "      <td>446</td>\n",
       "      <td>HONDA</td>\n",
       "      <td>FIT</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.3</td>\n",
       "      <td>91901</td>\n",
       "      <td>4</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>4</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>4</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19232</th>\n",
       "      <td>8467</td>\n",
       "      <td>0</td>\n",
       "      <td>MERCEDES-BENZ</td>\n",
       "      <td>CLK 200</td>\n",
       "      <td>1999</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>CNG</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300000</td>\n",
       "      <td>4</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Rear</td>\n",
       "      <td>2</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Silver</td>\n",
       "      <td>5</td>\n",
       "      <td>Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19233</th>\n",
       "      <td>15681</td>\n",
       "      <td>831</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>2011</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>2.4</td>\n",
       "      <td>161600</td>\n",
       "      <td>4</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>Front</td>\n",
       "      <td>4</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Red</td>\n",
       "      <td>8</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>26108</td>\n",
       "      <td>836</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2.0</td>\n",
       "      <td>116365</td>\n",
       "      <td>4</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>4</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Grey</td>\n",
       "      <td>4</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19235</th>\n",
       "      <td>5331</td>\n",
       "      <td>1288</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>Captiva</td>\n",
       "      <td>2007</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51258</td>\n",
       "      <td>4</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>4</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>Black</td>\n",
       "      <td>4</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19236</th>\n",
       "      <td>470</td>\n",
       "      <td>753</td>\n",
       "      <td>HYUNDAI</td>\n",
       "      <td>Sonata</td>\n",
       "      <td>2012</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>2.4</td>\n",
       "      <td>186923</td>\n",
       "      <td>4</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Front</td>\n",
       "      <td>4</td>\n",
       "      <td>Left wheel</td>\n",
       "      <td>White</td>\n",
       "      <td>12</td>\n",
       "      <td>Non Turbo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19237 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Price  Levy   Manufacturer    Model  Prod. year   Category   \n",
       "0      13328  1399          LEXUS   RX 450        2010       Jeep  \\\n",
       "1      16621  1018      CHEVROLET  Equinox        2011       Jeep   \n",
       "2       8467     0          HONDA      FIT        2006  Hatchback   \n",
       "3       3607   862           FORD   Escape        2011       Jeep   \n",
       "4      11726   446          HONDA      FIT        2014  Hatchback   \n",
       "...      ...   ...            ...      ...         ...        ...   \n",
       "19232   8467     0  MERCEDES-BENZ  CLK 200        1999      Coupe   \n",
       "19233  15681   831        HYUNDAI   Sonata        2011      Sedan   \n",
       "19234  26108   836        HYUNDAI   Tucson        2010       Jeep   \n",
       "19235   5331  1288      CHEVROLET  Captiva        2007       Jeep   \n",
       "19236    470   753        HYUNDAI   Sonata        2012      Sedan   \n",
       "\n",
       "      Leather interior Fuel type  Engine volume  Mileage  Cylinders   \n",
       "0                  Yes    Hybrid            3.5   186005          6  \\\n",
       "1                   No    Petrol            3.0   192000          6   \n",
       "2                   No    Petrol            1.3   200000          4   \n",
       "3                  Yes    Hybrid            2.5   168966          4   \n",
       "4                  Yes    Petrol            1.3    91901          4   \n",
       "...                ...       ...            ...      ...        ...   \n",
       "19232              Yes       CNG            2.0   300000          4   \n",
       "19233              Yes    Petrol            2.4   161600          4   \n",
       "19234              Yes    Diesel            2.0   116365          4   \n",
       "19235              Yes    Diesel            2.0    51258          4   \n",
       "19236              Yes    Hybrid            2.4   186923          4   \n",
       "\n",
       "      Gear box type Drive wheels  Doors             Wheel   Color  Airbags   \n",
       "0         Automatic          4x4      4        Left wheel  Silver       12  \\\n",
       "1         Tiptronic          4x4      4        Left wheel   Black        8   \n",
       "2          Variator        Front      4  Right-hand drive   Black        2   \n",
       "3         Automatic          4x4      4        Left wheel   White        0   \n",
       "4         Automatic        Front      4        Left wheel  Silver        4   \n",
       "...             ...          ...    ...               ...     ...      ...   \n",
       "19232        Manual         Rear      2        Left wheel  Silver        5   \n",
       "19233     Tiptronic        Front      4        Left wheel     Red        8   \n",
       "19234     Automatic        Front      4        Left wheel    Grey        4   \n",
       "19235     Automatic        Front      4        Left wheel   Black        4   \n",
       "19236     Automatic        Front      4        Left wheel   White       12   \n",
       "\n",
       "           Turbo  \n",
       "0      Non Turbo  \n",
       "1      Non Turbo  \n",
       "2      Non Turbo  \n",
       "3      Non Turbo  \n",
       "4      Non Turbo  \n",
       "...          ...  \n",
       "19232      Turbo  \n",
       "19233  Non Turbo  \n",
       "19234  Non Turbo  \n",
       "19235  Non Turbo  \n",
       "19236  Non Turbo  \n",
       "\n",
       "[19237 rows x 18 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.Doors = np.where(veri.Doors == \"04-May\",4,\n",
    "                     np.where(veri.Doors == \"02-Mar\",2,5))\n",
    "\n",
    "veri.Doors = veri.Doors.astype(int)\n",
    "veri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ad7f06c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Manufacturer',\n",
       " 'Model',\n",
       " 'Category',\n",
       " 'Leather interior',\n",
       " 'Fuel type',\n",
       " 'Gear box type',\n",
       " 'Drive wheels',\n",
       " 'Wheel',\n",
       " 'Color',\n",
       " 'Turbo']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat = []\n",
    "for i in veri.columns:\n",
    "    if veri[i].dtypes == \"object\":\n",
    "        cat.append(i)\n",
    "cat      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a313d0c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "for i in cat:\n",
    "    veri[i] = le.fit_transform(veri[i])\n",
    "    \n",
    "y = veri.Price\n",
    "x = veri.drop(columns=\"Price\")\n",
    "\n",
    "sc = StandardScaler()\n",
    "x = sc.fit_transform(x)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2, random_state=0)\n",
    "x_train, x_val, y_train, y_val = train_test_split(x_train,y_train,test_size=0.1, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5251b1aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(64,input_dim=x_train.shape[1],activation=\"linear\"))\n",
    "model.add(Dense(32,activation=\"linear\"))\n",
    "model.add(Dense(1,activation=\"linear\"))\n",
    "\n",
    "model.compile(loss=\"mae\",optimizer=\"adam\",metrics=[\"mae\"])\n",
    "m1 = model.fit(x_train,y_train,validation_data=(x_val,y_val),epochs=50,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6f3c6b91",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(m1.history[\"loss\"])\n",
    "plt.plot(m1.history[\"val_loss\"])\n",
    "plt.title(\"Loss Graph\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.legend([\"Train\",\"Validation\"],loc=\"upper right\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f01cf646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10697, 42337,   314,  2800,   251,   157, 21395,   706,   470,\n",
       "        1700,   282,  1098, 18817,  2274,  7370,    25, 13476,  3293,\n",
       "       21953, 43134, 48534,  1568, 29793, 15681, 19757,  7370, 12083,\n",
       "       28532, 40954,  5488,  6586,   157,  2901, 26343, 16621, 24123,\n",
       "       15367, 15681,  3607, 28078,  7524, 18189, 81539,  3293,   549,\n",
       "       15053, 12544, 35752, 17249,  1725], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:50].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1ff0a577",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121/121 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tahmin = model.predict(x_test)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(range(len(y_test[:50])), y_test[:50].values , label=\"Gerçek\")\n",
    "plt.plot(range(len(y_test[:50])), tahmin[:50] , label=\"Tahmin\")\n",
    "plt.title(\"Fiyat Tahmini\")\n",
    "plt.ylabel(\"Fiyat\")\n",
    "plt.xlabel(\"Gözlem No\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a8ecbd39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Burak\\AppData\\Local\\Temp\\ipykernel_5516\\3481215030.py:21: DeprecationWarning: KerasRegressor is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.\n",
      "  regres = KerasRegressor(build_fn=modelkur,verbose=0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121/121 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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vHwmC7aI32KAVAmiGqXZsU0USDgTbs8jCTFKS1DvIbs0AAAAAAIx25xM7tS9b0JvWrpC2/EZafKyU6hz3/tGkH2x7OYJtIMy8Qml6E9slV68loQEItmeRVYv8/+E+tWOgySsBAAAAAGD2+c6DW9WVSeilaxb6wfbK8ae1pf3BdiHL79lAmHnT7NimiqS1EWzPIos7UuruSOqRbfuavRQAAAAAAGaVfdmCfvrYTr3+pOWK73layu2dcONISYolM5KkQpaJbSDMvGJZiShVJHMNwfYsc8LyTm3Y2tfsZQAAAAAAMKvc8dgO5YtlnX/ycmnLvf7BQyYOthPpINimigQINa9YmtLEdjxqkgi2Wx3B9ixzwop52tgzoFyh1OylAAAAAAAwazzbOyTJ/71Zz98rpeZJXUdOeE483SGJzSOBMCuXnQolN6WO7crENlUkrY1ge5Y5fvk8lcpOj7/Q3+ylAAAAAAAwa/QMeFqYSfibvm35jbTyNCkycayRSLVLkkreUCOWCKAJ8sHUdTI2+SqS/ZtHEmy3MoLtWeaEFf5uzo9spWcbAAAAAICKXf2eFrUnpFyftPOxg/ZrS1I67W8eWc4zsQ2ElVeoBNtT3zyyUHR1WRMag2B7llkxP6156bg2sIEkAAAAEAr7hgrNXgIQCj0Dnro7ktLW+yQ56ZDTDnpOKhHTkEuqnGdiGwgrr+jX+U6lYzsSMcUipnyJKuBWRrA9y5iZTljRqUfYQBIAAABoeVv2DOmU/3O77n1md7OXArS8XQOeFrUn/RoSmbTi1IOe05aIKquERLANhJZXnHoVieTXkRRKTGy3MoLtWeiE5fP0xAv97MwKAACAujGzP5vMMczM1j1ZlcpOW/YQqgEz4ZzTrv68H2w/f6/UfYy/eeRB+MF2UipmG7BKAM1QmdhOTKGKRJLiUWPzyBY3qb9xM8uYWSS4fpSZvcHM4vVd2tx1/Ip5ypfKemrHQLOXAgAAgPC6dIxjlzV6EWHXlytKkgbzvNUZmInBfEnZQknd7XF/YnsSNSSSlIpHlXMJqUCwDYTV/ontqQXbiViUzSNbXGyS9/uFpLPNbIGkOyStl/Q2SW+v18LmsuOXBxtIbtun44LrAAAAQC2Y2cWSfl/SajO7teqmDkm9zVlVePXn/H7tIa/Y5JUArW1XvydJWq3tUm7vpDaOlPygK6uE2pjYBkJr2sF21FRgYrulTTbYNufckJldLukzzrl/MbMH6rmwuWx1V0aZRFQbtu6T1h3S7OUAAAAgXH4tabukRZI+WXW8X9Jvm7KiEOvLBsE2E9vAjPQM+MH2YblH/QOHTC7YNjPlLaV2gu3G2fy/0tM/l37nb5u9EswRXmGaHduxCDXALW7SwbaZvVj+hPblUzwXUxSJmI5b3qlHtrGBJAAAAGrLOfespGclvbjZa5kLKlUkQ3kmtoGZqExsL977kN+t3XXkpM/NR5KKlnL1WhpGe/gm6YGvE2yjYSod28n4VCe2I1SRtLjJ/o3/uaSrJd3inNtgZodL+nndVgUdv3yeHtvep1KZ3VkBAABQe2Z2ppn9xswGzCxvZiUzY7KixipVJHRsAzNTmdju3PWAtPI0KTL5AKsQSSlWYmK7YQZ6pHJBKnrNXgnmiOlWkcSjEeWL5G6tbFJ/4865/3HOvcE597FgE8ldzrn31nltc9oJK+ZpKF/SM7sGm70UAAAAhNNnJV0s6SlJaUnvkvSZpq4ohPqywcQ2HdvAjOzq99RhQ4ruenzS/doVhUhaMSa2G2dwp3+ZJ89AY0w72I4xsd3qJvU3bmbfMLNOM8tIelTSE2b2/voubW6rbCC5Ydu+Jq8EAAAAYeWc2ygp6pwrOee+LOkVzV5T2PQxsQ3URM+Ap7PTz8nkpJXrpnRuKZpS3BFsN0p2zwv+lfxAcxeCOSNfnF7HdjIaYfPIFjfZlzKOc871SXqjpB9KOlTSH9RrUZDWLG5XIhbRI1sJtgEAAFAXQ2aWkPSgmf2Lmf2FpEyzFxU2lWCbjm1gZnr68zot8Yz/yYpTp3RuKZpSokwtRkM4p+iQP7HtvP4mLwZzxXDH9pQnto3NI1vcZP/G42YWlx9sf9c5V5BECU0dxaMRHbu0Q49speYQAAAAdfEH8n8f+BNJg5IOkfSWpq4ohPqHN49kYhuYiV0Dnk6wZ6SFR0jp+VM6txxLK+EIthsiPzD8Z+0NEWyjMbzC9Ca242we2fJik7zf5yRtlvSQpF+Y2WGSSFzr7PgV8/T9h7bJOScza/ZyAAAAEC4nSnreOZeT9NFmLyas+rLBxLZHsA3MRE+/p8NLG6VlL5nyueVYWnEVpVJBisbrsDpUuIGdqqQX2cF9SjV1NZgrhju241Ob2E5EI8M1JmhNk9088tPOuRXOud9zvmdF/17dnbB8nvpyRW3Zw+7NAAAAqLmLJD0V1JAcO5UTzSxqZg+Y2feDzxea2e1m9lRwuaDqvleb2UYze8LMXlN1/FQzezi47dMWTHKYWdLMvhUcv8fMVtXm6TZHXzCxPUgVCTBtzjkVB3apq/CCtPzkqT9ALO1fFvjdut56d2wZvu4NMg+JxqhUkSSiU988kiqS1jbZzSPnmdm/mtn64OOTon+v7iobSNKzDQAAgFpzzr1D0lpJmyR92czuMrMrzKxjEqf/maTHqj7/oKQ7nHNHSroj+Fxmdpz8AP14SedJutbMKu8Tvk7SFZKODD7OC45fLmmPc26NpE9J+tj0n2VzOefUP9yxzcQ2MF39XlFryk/7nyw7acrnuwTBdqP0bN8fbOepIkGDeMWy4lFTJDK1toMEVSQtb7IvZfynpH5Jbw0++iR9uV6Lgu/opR2KRkyPbCPYBgAAQO0FG8R/W9KNkpZJepOk+83sT8c7x8xWSnqtpC9WHT5f0g3B9Rvk781TOX6jc85zzj0jaaOk081smaRO59xdzjkn6Sujzqk81s2Szq1Mc7eaXKGsQsnJTBr0mNgGpmtXv6cTLdg4chrBtsXb/CuFoRquCmPZt2vr8PVClmAbjZEvlqfcry35wXahyBaCrWyywfYRzrkPO+eeDj4+Kunwei4MUioe1ZGL29lAEgAAADVnZq83s1sk/UxSXNLpzrnflXSSpL+a4NR/k/QBSdUjTkucc9slKbhcHBxfIen5qvttCY6tCK6PPj7iHOdcUdI+SV1jrP+KyjtKe3p6Dvp8m6EvmNbubk/KK5ZVKvPLMzAdPf2eTohsVrb9ECm94OAnjBJJ+MF2KU+wXW/ZPduHrxezZBloDK9YUjI2tRoSSYrHjCqSFjfZv/Wsmb208omZnSWJ9/A0wAkr5mnDtn3yB1kAAACAmrlQ0qeccy9yzn3cObdTkpxzQ5LeOdYJZvY6STudc/dN8muMNWntJjg+0TkjDzj3eefcOufcuu7u7kkup7EqNSTL5vnbpw3Rsw1My66BvE6wZ1RY/KJpnV8Jtj2qMequ1L9De9ShsjOVcwPNXg7mCK9Qnl6wzeaRLS82yfu9W9JXzGxe8PkeSZfWZ0modvzyTt183xbt7Pe0pJP9hAEAAFAbzrlLJrjtjnFuOkvSG8zs9ySlJHWa2dck7TCzZc657UHNyM7g/lskHVJ1/kpJ24LjK8c4Xn3OFjOLSZonafeUntwssS/rB9n+z/H7NJQvqSMVb+6igBa0b/dOrYrs0OCKk6d1fiTVLknysgNqq+G6cKDYUI8G412K5Ysq5wm20RhesaxkfBpVJDE6tlvdpF7OcM495Jw7SdKLJL3IObdW0u/UdWWQ5E9sS2wgCQAAgNows18Fl/1m1lf10W9mE75v3Dl3tXNupXNulfxNIX8WbEJ5q/YPvlwq6bvB9VslXWRmSTNbLX+TyHuDupJ+Mzsz6M++ZNQ5lce6IPgaLfn2xUoVydJgYpuebWB6ojsfkSSlDj11WufHkn6cXchSRVJPA15RHaU9Krct0pCSUn6w2UvCHDHdKpJENEIVSYub0t+6c64v2GBGkt5Xh/VglGOXdcpM9GwDAACgVt4uSc65DudcZ9VHh3Ouc5qP+c+SXmVmT0l6VfC5nHMbJN0k6VFJP5J0lXOuFJxzpfwNKDdK2iTptuD4lyR1mdlG+b9zfHCaa2q6/pwfZC8driIpTXR3AOPI7N4gSYouP3la58dTGUlSwWOCuJ6e7hnQIu1TvHOphpSSMbGNBvGK06siSUQjKjupSLjdsiZbRTKWltyZvNW0J2NavSijR7YxsQ0AAICauEXSKZJkZt92zr1lOg/inLtT0p3B9V5J545zv2skXTPG8fWSThjjeE5+/3fL68uO7NhmYhuYnkX9j2pHpFtLMgfsIzsp8aQfbBdzTBDX06aeAb3a9qmwYKn2Pt+mSIE/bzSGVygrMa3NI/1zCiWn2NSbTDALTP1vfb+WfDtgKzph+Tw9uo2JbQAAANRE9YDK4U1bxRwwXEXSmZYkDRWY2AamY2XuKT2fPGra5yfSQbDtUUVST89t36WMeepYtEJeJK1okT9vNEa+VFZyGsl0PBoZPh+tacJge4zeveH+PUnLG7TGOe+EFZ3aujernz++8+B3BgAAACbmxrmOGuvLFhWPmhZmEpKkIY9gG5gyr1/LS1u1s/2YaT9EMu1vHlnymCCup107npckRTuWKB9JK06wjQaZdsd2cE6+SLDdqib8Wx+jd6+6f28mNSaYgjefslLHLuvUO2/4ja67c5NadO8cAAAAzA4nVQ2rvGgqm0diavpzBXWm4mpL+FNkg3mqSICpctt/q4icBhYeP+3HSAYT2+U8QWs99e3a5l9pX6xCLKN4iT9vNIZXKCsZn07Htv8mNjaQbF0zqSJBgyxqT+rbV75Yv3fiMn3sR4/rvTc+qCwbzwAAAGAanHPR6mGVGm0eiTH05YrqTMeVSfozQUN0bANTlnvufklSoftF036MdCopz8XkCLbrplgqq7Bvh/9JplulWJuS5WxzF4U5w988cvpVJATbrYup6xbRlojpsxev1fHLO/XxHz+hTTsH9PlLTtXKBW3NXhoAAACAMfRlC+pIxaomthlOAaaqsOUB7XMLlOmafhtqOh5VTgm5AkFrvWzZk9UCt8f/pH2xyvE2pRx/3mgMqkjmLia2W4iZ6T3nrNF/Xnqant8zpDd89n9199O9zV4WAAAAgDFUqkiSsYiiEdMQVSTAlMV2/FYPl1eruyM57cdoS8SUVVLGxHbdbOoZ0CLt8z/JdMvFM0orJ1GligbwiuXhkHoq2Dyy9RFst6BXHLNY37nqLC1oi+vy63+jfUOFZi8JAAAAwCh+FUlMZqa2RFRDTGwDU5MfVHrfJm1wq7SoffrBdioe0ZBLyopMENfLpp4BLbJ9KqcWSNG4lGxXVGWpmGv20jAH5Ivl6U1sR5nYbnUE2y3qiO52febiUzSYL+mbv3mu2csBAAAAMEpftqCOZFyS/GDbI9gGpuSFR2Qqz3hi28zkGcF2PW3aOagV8X5F2hdLkiLJdkmSN7SvmcvCHDHdju3KlHehxDsLWhXBdgs7bnmnzlrTpev/dzOvLgEAAACzTH8wsS1JmURMg1SRAFOz/SFJ0mM6XPPT8Rk9VN6SihBs182mngEtj/VLo4Lt7EBfM5eFOaBYKqtUdtOa2GbzyNZHsN3i3nX24XqhL6cfPry92UsBAAAAEMgXy8oWSupMBRPbSapIgCnb/qD6owtUyixRJGIzeqh8JKVoiVqMevGrSPqGg+1oulMSwTbqzwsGPZPx6QTb/r8rDIu2LoLtFvfyI7u1ZnG7vvDLp+XYlAEAAACYFfpz/j44nelKFUlMgx4T28CUbH9Iz8TXaFFHasYPVYykFCszsV0Puwfz2jNUUGdpj5Txg+14qkOSlB3c28SVYS4YDrZnUEXC5pGti2C7xUUipne9dLU2bOvTXU/3Nns5AAAAAORvHClJHalKFUlU2QIT28CkFbLSzsf0mB0+o37t4YeLpBQveTVYGEbb1DOglDwlSoNSe7ckKZnxg+38YH8zl4Y5wCv6/29NzGDzSKpIWhfBdgi8ce0KdWUS+tIvn2n2UgAAAACoamI7xcQ2MC07HpVcSfcXDtOi9pkH26VoSvEyVST1sGnngBZZsElkMLGdbJsnScpnCbZRX/nhie3pd2xTRdK6CLZDIBWP6g9efJjueHynNu4caPZyAAAAgDmvL+uH2PurSOjYBqZk+wOSpLuzK2sysV2OpZRwTGzXw6aeAS2PBgF20LGdavc7totZOrZRX7WoImFiu3URbNdbuSTlB+v+Zf7gzMOUjEX0pV8xtQ0AAAA0W18wsT1cRZJkYhuYku0PqZxeqGdLXTWZ2C7H0ko4JrbrYVPPoI6fF7xokPGrSNoyQbCdY/gO9eUVajCxXWLPulZFsF1vn14r/eCv6v5lutqTevMpK/Xf929R7wCvQgMAAADNdODmkf7ENhu+A5O07UFlu06QZFrUnpjxw5WjaaWUl8pMZtbapp4BHZUZ8j8JJrYznfMlSWWPYBv1VenYTsan37FNFUnrItiut0y31L+9IV/q8peullcs62t3P9eQrwcAAABgbMNVJFUT28WyU563OwMHV/SknY9pz7zjJKkmVSQu0RY8dnbGj4X9coWSnt89pENTwTvVg4ntdKpNRReRPDq2UV9UkcxtBNv11rG0YcH2msXt+p1jFuurd29Wjh3XAQAAgKbpyxVkJmUSfrDdlvB/4c7Ssw0c3M5HpXJB29uOkiR116CKxOJBsF0g2K6lZ3uHVHbSsmi/lJovxfy/K4tENGSphlSzYm4bntieVhWJSZIKTGy3LILteutc3rBgW5LedfZq7RrI67sPbm3Y1wQAAAAwUn+uqI5kTJGI/0tzJdgeJNgGDm77Q5Kkp+NHSqrNxLbiaUlS2SNoraVNPX7VSJf2DteQVGSVlhX480Z9VTq2E9MItmPRiCKmUL2bauve7JyqViHYrreOpVJun5QfasiXe/HhXTp+eaf+9fYntbOfjTEAAACAZujLFob7tSWpLZjcHmIDSeDgtj0opebpmWK34lHTvKrvpemKBFUkHpsZ1tSmnf6fZ3txj5QZGWx7kbSiBNuos0ooPZ2JbcnfQDIswXauUNKr/vV/9PV7nm32UhqGYLveOpb5lwMvNOTLmZk+fsFJ2pct6D1fu39OvUoDAAAAzBZ9uYI6U/vDuEySiW1g0rY/JC07SbsG81rUnpSZzfgho8kg2B4iaK2lTT0DWjE/rejgTqm9e8Rt+UhasVJjhvwwd1UmtpPxqXdsS/4GkoViODZ27h3Mayhf0tM9c+ffOYLteutY6l/2NybYlqTjlnfq4xecpPXP7tFHv7ehYV8XAAAAgK8vW1RHsHGkxMQ2MGmlgrRjg7TsJPX0e1pUg35tSYokM5IkL8vEdi1t6hnU4d0ZabDngIntfDSjeJFgG/U1k45tya8wyZfC8aLz7oG8JOmFvrnT4ECwXW+Vie0G9mxL0utPWq53v/wIff2e5/SNe55r6NcGAAAA5rq+3MgqksomkkNMbAMT63lcKnnSspO1a8CrTb+2pFgwsV3IzZ1JxnpzzmlTz4CO7opLXt8BE9vFWJsSZYJt1JdXnHkVSXgmtj1J0g6C7foys78wsw1m9oiZfdPMUma20MxuN7OngssFVfe/2sw2mtkTZvaaquOnmtnDwW2ftlq8P6nWKsF2X2ODbUl6/2uO1suO6taHb31E9z27u+FfHwAAAJir+nPFEVUkbcNVJExsAxPa9qB/uezkYGI7UZOHjQcT2wTbtfNCX05D+ZKOm+eHaaMntsuxNiXL2SasDHPJ/mB7elUk8ZipEJKO7d7KxPY+gu26MbMVkt4raZ1z7gRJUUkXSfqgpDucc0dKuiP4XGZ2XHD78ZLOk3StmVX+a71O0hWSjgw+zmvgU5mc1Dwplm74xLYkRSOmz1y0Vivmp/Xur90/p/7DBgAAAJqpL1sYVUXi/wrDxDZwENsfkhIdKi9Yrd7BfM0mtuNpP9guEmzXzKad/p/lmrZgKrt9yYjby/GM0o4cAvXlFfz/r8aj05t1TUQj8kISbO8e9IPtXQOeiiF5TgfTrCqSmKS0mcUktUnaJul8STcEt98g6Y3B9fMl3eic85xzz0jaKOl0M1smqdM5d5dzzkn6StU5s4eZ37PdwI7tavPa4vr8Jes05BX1x1+7T7kCP0gDAAAA9VQuOw3kiyOqSCod24N0bAMT2/6gtOxF2pMtqlR2NevYTqTaJUklj2C7Vjb1+H3lhyaC3vJRVSQukVGbsiqVw1HzgNnJK5WVjEWmvcmsX0USjhB4V1BFUnbSrmB6O+waHmw757ZK+oSk5yRtl7TPOfcTSUucc9uD+2yXVHkPywpJz1c9xJbg2Irg+ujjBzCzK8xsvZmt7+npqeXTmZyOZU0LtiXpqCUd+uRbT9ZDz+/Vn934wHCxPgAAAIDa6/eKck7qZGIbmJpSUXrhkaBf2w9lahVsJ9s6/C+Rp/O5Vjb1DKgjGdO88l7/wKgqEku2K2OeBnJzI2BDc3iF8rT7tSV/88iwVJHsrgqz58oGks2oIlkgfwp7taTlkjJm9o6JThnjmJvg+IEHnfu8c26dc25dd3f3WHepr46lTakiqXbeCUv1kdcfpx9v2KHLr1/PpAgAAABQJ33ZgiSN6NiORyNKxCJ0bAMT2fWkVMxKy05ST78/eVirKpJUmz+xXWZiu2Y29Qzo8MXtssGd/oHMyLwlkvRfTBgc6G/00jCHeMWykvHp9WtLfhVJPizB9mB+uJJlrtQRN6OK5JWSnnHO9TjnCpL+W9JLJO0I6kUUXAb/MmqLpEOqzl8pv7pkS3B99PHZp2OZH2y75r795rKzVusTF56ku57u1du/eI/2DvGqKQAAAFBr/Tk/vO5Mx0YczySiyjKxDYxv+0P+5fKTtWvAD7ZrNbGdSqZUdBGpwGaGtbJp56CO6M5IAzul5DwpnhpxezTlB9tD/XubsDrMFV6xNKOJbb+KJBx1ObsG81qz2P++28HEdt08J+lMM2szvwDnXEmPSbpV0qXBfS6V9N3g+q2SLjKzpJmtlr9J5L1BXUm/mZ0ZPM4lVefMLp3LpMKQ5PU1eyW64NSVuu7tp+jR7X166+fumjP/oQMAAACN0pc7cGJb8nu2Bz2CbWBc2x+U4hmpa81wsF2rie22ZExZJeWoIqmJAa+oF/pyOqK73Q+22w98d3ws7U/J5wb3NXp5mEO84syqSOKxMG0e6emoJe2KR40qknpxzt0j6WZJ90t6OFjD5yX9s6RXmdlTkl4VfC7n3AZJN0l6VNKPJF3lnKv8NHilpC/K31Byk6TbGvdMpqBjmX/ZxJ7taq8+fqmu/8PTtHVPVm+57td6tpe3YgEAAAC1MlxFkh4dbEc1RBUJML7tD0lLT5QiUfX0e0pEIyO66mciFY8qp4RfdYIZezrYOPKI7nZpsOeAfm1Jiqc7JUm5weYP+SG8vEJZidjMqkjCsnlk70Bei9qTWtyR0o45UkVSm/9DTJFz7sOSPjzqsCd/enus+18j6Zoxjq+XdELNF1hrHUv9y/7tUvfRzV1L4CVHLNI3rzhTl/7nvbrgP+7Sm09ZoWWdKS2bn9ayeSktnZfSokxSkcj0dpUFAAAA5qq+oIqkY1Qg15aMaZAqEmBs5ZK0/bfSWn8Lrp4BT90dSflv0J65aMSUU1JGFUlNbAqC7TWLgyqSJccfcJ9kxg+281mCbdRPvjTTzSMtFJtH5golDeVLWphJaElnUjv6CbZRK7NsYrviRSvn67/e/WL92Y0P6su/2nxAWf68dFzX/+FpWnvogiatEAAAAGg9/eNUkWQSUQ2xiTswtt5NUmFQWn6yJKmn39Oi9kRNv4RnSUVKBNu1sGnnoKIR06ELM9LgTqn9FQfcJ9XmB9uFITaPRP14hZl3bIdh88jeQX8fva5MQkvnpfTEC3Pj+45guxGqJ7ZnmTWLO/SD954t55x6B/N6YV9O2/Zm9UJfTv9x5yb95U0P6QfvPVvpxPTf1gEAAADMJX3ZcSa2EzHtGSJUA8a0/UH/ctlJkqRdA3mtmJ8a//7TkLekIlSR1MSmngEdtrBNCRWk3L4xq0jSHfMkScXcQKOXhznEK5YPqP6airBUkfQG+xJ0BVUkv3hyV5NX1BjN2Dxy7klk/B2C+2ZfsF1hZlrUntQJK+bp1ccv1SUvXqVPvPUkPb1rUB/70ePNXh4AAADQMvpyBbUloopFR/66lUlGlaVjGxjb9oekWEpa5Nd3+hPbtdk4siIfSSlamhtvz6+3TT0DOrzSry2NuXlkOqgiKeXmxuQomqMWm0fmS66GK2qOysT2wmBie8AramAOvEuMYLtROpbOyontibzkiEW67CWrdP2vN+t/N86NV3oAAACAmerPFQ6oIZH8zSPp2AbGse1BackJUjSmUtlp96DfsV1LxUhScapIZqxYKmvzriEdUenXlsac2I4m2yVJzmNiG/XjFWdWRZKIRpQvtv7/m3sH/GB7UXtCSzv9d7u8MAc2kCTYbpSOpbOuY3sy/vq8Y3T4ooze/18PqS/oCgQAAAAwvr5sUZ3pA1sf2xIxOraBsZTL0gu/He7X3j2YV9mp5hPbhUhasXL4g55627Inq3yprCO62/cH2+0HBttKVILtwQauDnONVygrGZt+fW4iFlEhBBPbuwf9KhJ/80g/2N7RF/5/7wi2G6VjWUsG2+lEVJ9860l6oS+nf/jeo81eDgAAADDr9eUK6hhjYjuTiGqoUFK53Pq/QAM1tecZyeur6tf2A5paT2yXYmklyl5NH3Mu2tTjT2Af0d3ubxwpSZkDq0gUSyivmKxAsI368YplJWa0eaSpEJLNIxOxiNqTMS2dR7CNWqtUkbjW+yF27aEL9J5z1ujm+7boJxtaL5wHAAAAGqk/V1RnaoyJ7WRMzkm5ELzlGaip4Y0jT5a0P9iu9cR2KZpSwoU/6Km3/cF2ZuKJbUk5SytSoIoE9ZOfYRVJPBpRsexa/kXn3oG8ujIJmdn+KhKCbdRM53KpXJCGepu9kml577lH6rhlnfrQLQ8P77QKAAAA4EB9uYI602NPbEvSED3bwEjbHpSiCan7GEn+xpGS3xVbSy6WVtLx++xMbdo5qEXtCc1vS/ibRyY7pXh6zPt6kTZFi0MNXiHmEq9YVjI+g47tIBTPt/jU9u7BvBZm/H8z04moOlMx7aBjGzXTsdS/bLENJCsSsYj+9W0nqS9b1IduebjlX8kCAAAA6qUvO/bmkemEP8U95BFsAyNsf0hacrwU80OZelWRlGNpJZVvyXdSzyabegZ0eLffn62BnWPXkATykbTiBNuoE+ecH2zPpGM76kejrV5H0jvgqavqXS5LOlNMbKOGOpb5ly3Ys11xzNJOvf81R+vHG3boT755v3IFfiAHAAAAqjnn1JcrqmOMKpLKxPZgng0kgWHO+cF20K8t+RPbyaArtqbiaUVVlkr52j7uHLOpZ8Dv15b8ie1xakgkqRhrU6JEsI36qExZz6SKZHhiu9jiwfagX0VSsXReSi/0hf8dKgTbjdLiE9sV7zp7tf72tcfqtkde0MVfuJtaEgAAAKBKtlBSqezGrCJpC0K6IYJtYL+9z0q5vcP92pK0ayCv7o6kzKy2XyveJklyeYLW6do9mNeeoYLfry0ddGK7GMso4bINWh3mGq8482A7Pjyx3drv5Ng9Kthe0pmiigQ11L7Ev2zhiW1JMjO96+zDdd3bT9Gj2/r0pmt/PbxxBAAAADDX9WX90HqsKpLhiW2qSID9tj3oX46a2K71xpGSZAm/B9rL8jvsdA1vHLm4MrG9c8KJ7XK8TalyVo76F9SBV6hlsN26E9vZfElD+ZIWVu1LsLQzpZ4BT6WQVwkTbDdKLCm1dbX8xHbFeScs041XnKlBr6g3X/tr3fN0a26KCQAAANRSX64gSWNWkbQlmNgGDrD9ISkS8zu2A7sGvJr3a0tSJOFPbDcl2H7mF9KnTpT2bW38166hTTv9P7s13e1SMS9l90iZ8YNtF88ooyyb5qIu9leRzKBjOwjFvRauIukd9NsURkxsz0upVHahb1og2G6kjuVSXziCbUlae+gC3fKes7SoPaF3fOkeff2eZ1v6FS4AAABgpvqDYHusKpJM0v/Fm4AHqLL9QWnxsf4wWKBeE9vRpF+f4Q01Idi+/6vSvuekez/X+K9dQ5t6BpSMRbR8ftrv15ak9vGrSJTIqM08DXi8oIfa84K935LxGXRsR/3Ko1bOs3oH/H0DujL7/91c2pmSpNBvIEmw3UgdS0MzsV1xaFeb/vvKs7TusIX6m1se0Us/9jN95o6nhnexBgAAAOaS/VUkB05sp4c3jyTYBiRVbRx58vChYqms3UP5ukxsR5N+fUa+0RPbxbz05I/96+uvl7zWrULZ1DOo1YsyikbMryGRJpzYtmSHMsqpP0ewjdqrbcd26wbbuwf9YLu6imRJp/9v6Ash79km2G6kjqUt37E9lnltcX39XWfoy5edpqOXduqTtz+pl/zTz/S+mx7Ub7fsbfbyAAAAgIbpm2hiu1JFwuQi4OvbKg31jujX3j2Yl3NSd1VAUyuxpF9Fks8N1vyxJ7T5l5K3T3rp+/zLB7/e2K9fQ5t6Bvb3aw9UJraXjHv/aKpdactrIBvucA3NsT/YnnkVSb6lq0j8YHvRGBPbO0I+sX3gGAHqp2OZ/4pmqShFw/VHH4mYXnHMYr3imMXa1DOgr/x6s26+b4v++/6tOumQ+Xr76YfqdSctG+4VBAAAAMKoL5hKHKtjOx1nYhsYYe/z/uXC1cOHeoJ3/9ZjYjue8qtICo0Oth//vhTPSC//gLT5V9Ld10qnvUuKTD+Ma4ZcoaTndw/p/JNX+AcqE9sTVJHEUh2SpOxAv6RFdV4h5prhKpIaTGznW3hiu9KjXT2x3dWeVDRiVJGghjqWSq68v4cqpI7obtdHzz9Bd33oXH349cdp0CvqA9/+rc645g793Xce0WPb+5q9RAAAAKAu+rLBxHbqwIntSMTUlogysQ1UZPf4l+mFw4d2BV2x9ejYjgXBdslrYLBdLkuP/1Bac64UT0svvkras1l64rbGraFGnu0dUtlJR3T7f44aOHgVSSzdKUnKDe2r9/IwBw1PbM+gY3t/FYmryZqaYfdgXolYRJnE/hfLohHT4o6kXtgX7qpgxmcbqXO5f9m/Tepc1ty1NEBnKq4/PGu1LnvJKv1m8x59897n9K31z+urdz+rtYfO1+tftFy/c8xirVqUafZSAQAAgJroyxWUiEWUio89idmWiDGxDVRkd/uX6QXDh3r6/RCmHsF2Mu1XaJS8oZo/9ri2rpcGXpCOfb3/+TGvk+YfKt3179Kxr2vcOmpgU4/fDX5Ed1BFMtgjJdqlRNu45yTb/Iltb5BgG7VXqQ9JRKf/7odkCKpIdg3ktSiTkJmNOL6kMxX6KhImthupY6l/GcKe7YmYmU5fvVCfetvJuufqc/V3r/OnuP/h+4/qnE/cqd/5xJ36x+8/qv/duKul/yEBAAAIOzNLmdm9ZvaQmW0ws48Gxxea2e1m9lRwuaDqnKvNbKOZPWFmr6k6fqqZPRzc9mkLfhszs6SZfSs4fo+ZrWr4E52BvmxxzI0jKzLJqLJ5JrYBSfsnttuqJ7brV0WSTAcT2/kGTmw/9j0pEpOOfLX/eTQmnXGl9Nyvpa33NW4dNbBppx9sH149sZ0Zv4ZEkpJt/sR2fqi/rmvD3FTbie3WzaN2D3ojakgqlhJso6Y6gint/u3NXUcTLcgkdPlLV+snf/Fy/c/7z9GHX3+cVixI66t3Pau3f/EenfqPt+tjP3pce4fyzV4qAAAADuRJ+h3n3EmSTpZ0npmdKemDku5wzh0p6Y7gc5nZcZIuknS8pPMkXWtmlbGq6yRdIenI4OO84PjlkvY459ZI+pSkjzXgedVMf64wZg1JRToeZWIbqMjukSwqJTuHD/X0e0rHo8oka/8G81SbP2lcbtTEtnN+v/bql0np+fuPr32H/5zvurYx66iRTT0DWjE/vX/vrIEdUvv4NSSSlMzMkyQVswTbqD2vWIuObX/KuZUHLXcP5tWVOfDFwKXzUnRso4Yy3ZJF5tzE9ngO68roD89ara9efoYe+PtX6fN/cKpefnS3/uN/Nunsj/1cn7njKQ3QPwgAADBrON9A8Gk8+HCSzpd0Q3D8BklvDK6fL+lG55znnHtG0kZJp5vZMkmdzrm7nHNO0ldGnVN5rJslnVuZ5m4FfbmiOtLjB9uZZExDTGwDvqHdfuBb9S2+a8Cry7S2JKXb/Eljl8/W5fEP0PO4tPtp6ZjXjjye6pROuUTacIu0b0tj1lIDm3oG909rS34VyUEmtuOV+pccwTZqb3hiOzb9KpJErPU3j9w1kFdX5sCJ7cWdSfXniqH+uYNgu5EiUal9yZye2B5PJhnTq49fqs/+/im67c/O1plHdOmTtz+pl//Lz/WlXz2jXIGpFgAAgNnAzKJm9qCknZJud87dI2mJc267JAWXlRG+FZKerzp9S3BsRXB99PER5zjnipL2SeoaYx1XmNl6M1vf0zN7NmfvyxYmrCJpS0Q16PGzLSDJn9iu2jhS8ie2F43xlvpaSCfiyrqEVKzNxPZBf0997Pv+5dGvPfC2M/5YkpPu+VxN1lJvzjlt6hnY368t+VUkB5nYVqLSaz4w8f2AafAKM5/YToSiiiSvhWME20s7U5KkF/aFd2qbYLvROpYysX0Qxyzt1BcuWadb3vMSHbusU//4/Uf1ik/cqf++f4vK5dbdpRYAACAMnHMl59zJklbKn74+YYK7jzVp7SY4PtE5o9fxeefcOufcuu7uiScGG+lgVSSZBBPbwLDsnhEbR0r1ndiORSPKKSnlZx5s7x7M6+R/+InufGLn+Hd6/HvSytOkzmUH3jb/UOm486X7bpC82T/N/EJfTkP5ko5YHATbpYK/+Wf7kolPTPr3d14De80xZ9SyY7tVq0iG8kVlCyV1jbHh7nCwHeI6EoLtRutYLvUxsT0Zaw9doK+96wx944/OUHdHUu+76SG98dr/1W82757wPK9YUokAHAAAoK6cc3sl3Sm/G3tHUC+i4LKS9GyRdEjVaSslbQuOrxzj+IhzzCwmaZ6kiX8AnEX6ckV1pieY2E5GNUTHNuDL7j4g2PYntusTbEtS1pKKFGdeRbJtb1a5QlkPPr937DvsfU7a/pB0zOvGf5AX/4nk7ZMe+PqM11Nvm3b6wfQRlSqSwV3+5UGqSJTw7295JrZRe5UwujJ1PR2VKpJWndjuHfD3qBurimTJPD/YDvMGkgTbjdaxlCqSKXrJEYv0nfecpX9960na2efpwv+4S1d9/X49v9t/lb1Udnp4yz5dd+cmvf2Ld+vEj/xEL/3Yz/TbLXubu3AAAICQMbNuM5sfXE9LeqWkxyXdKunS4G6XSvpucP1WSReZWdLMVsvfJPLeoK6k38zODPqzLxl1TuWxLpD0s6CHuyX4VSTjT2y3JQi2gWHZvVLb/iqSQqmsPUOFuk1sS1JetQm29w4VJEnP9o4z/f34D/zLY18//oOsXCcdcoZ097VSeXb/u7Cpxw+m11SqSAaD1y8PVkUSD4LtAhPbqD2vWFY0YorNINiOD1eRtMyPGiPsHgyC7TEqnPZXkXgNXVMj1X6bYUysY5n/qnTRk2L1+5912EQipjefslLnnbBUn//F0/rc/zyt2x/doRcf0aWHtuwd/qHimKUdevsZh+onG3bowv+4S//8lhP1prUrD/LoAAAAmKRlkm4ws6j8IZmbnHPfN7O7JN1kZpdLek7ShZLknNtgZjdJelRSUdJVzrlKenOlpOslpSXdFnxI0pckfdXMNsqf1L6oIc+sBrxiSV6xrI4JOrYziZgG2SAd8I2qIqkENPWc2M5HUoqUZj69uC9bCbbHCWwf+77UfazUdcTED/Tiq6SbLvGD8OPeMON11cumngF1JGP7X3QYCPY2yBwk2I7G5FlSEYJt1IFXLM2oX1uS4lELHqtFJ7YH/dB6rI7tTDKmjmQs1BPbBNuN1rHUv+x/QVpwWHPX0oLaEjH9+SuP0kWnHaqP//gJ3f/cHr3y2CU6+8hFevERXVrc4b8a9SevWKP3fP1+/cW3HtKj2/r01+cdM6NX8AAAACA5534rae0Yx3slnTvOOddIumaM4+slHdDP7ZzLKQjGW01/zg+sO9MTTWzH5BXLKpbK/HyKua2Yl/IDI4Ltnn4/oKnrxHYkqVgNgu29WT+Ef273GBPbg7uk534tnf2XB3+gY14nzT9MuuvfZ32wffjidvlvslHVxPbB9zjIR9KKlwi2UXtesTzjYNvMlIhGQlBFMva/m4s7kwTbqKGOYNMIgu0ZWTovpU++9aRxb+9qT+pr7zpD/+f7j+oLv3xGj7/Qr89efIrmtY3/SwYAAAAwE33BBOeEm0cmo5KkoUJJnQTbmMuye/zL6mB7wA+26zmxXYyklCzNvIqkMrG9ayCvAa+o9mRVvPLEbZIrT9yvXRGJSmdeKf3og9KW+6SVp854bfWwaeegXrKma/+BgR3+5cEmtiUVom2K1WDDTmA0r1BWMhad8ePEo6ZCy05sj19FIvn5GZtHonYquyHTs1138WhEHz3/BH3sLSfq7qd79YZ//5W+fd8W3fnETj30/F491zukvlxBLVTZCAAAgFmsMrE9URVJW8K/bcib3X26QN2NFWwHE9uL6zixXYymFHM1qCIJ6jAl6bnRPduPf1+ad6i0bPxhrBHWvkNKdkp3//uM11UPA15RL/TldESlX1vyq0jibVKyffwTA8VYmxLlLL97o+a8YknJ+MyjzUQsonyLTmzvHswrGYuoLTF2wL+kM6Ud+8IbbDOx3WgdBNuN9rbTDtWaxR268mv36S//66EDbk/EIvq71x2nPziTCXoAAABMX18umNieoIpkeGI7T8825rgxgu1dDZjYLkXTSng1qCKpCraf7R3Uccs7/U+8fmnTz6V175QqtR0Hk+yQTr1Uuuta6ZUfleYfMuP11dLTwcaRI4LtwZ0H3zgyUIpl1Oay8oplpeIzn64FKrxiWYkavPsp3uJVJIvak/trgkZZ2pnSzn5P5bJTJDLJf5NaCMF2o6UXSNEEwXaDnXrYAv3iA6/Qtr1Z7RkqaO9QXrsH89o7VNDPHt+pj9y6QYcvyuisNYuavVQAAAC0qL5s0LE9QRVJOl4JtpnYxhyX3e1fti0cPtTT7ymTiCo9zuRhLZSiKSWcN+PH2ZctaNm8lLbvy+nZ6p7tjT+VSp507CRqSKqd/sd+sH3v56RX/58Zr6+Wntnl92OvXpTZf3Bg56RqSCTJxduUsR4NeEWCbdRUvliuycR2PBpRvtia7yjoHfTG3DiyYum8lIplp12D3vC+dGFCFUmjmfkbSPa/0OyVzDmpeFSHd7fr1MMW6Nxjl+jCdYfoj152uL5w6Tod0Z3RVd+4/8C3kAEAAACTVJnYnqiKJBP08A56TGxjjhtzYjtf140jJcnF0krWINjem83rkAVtWtAW17PVv0c+/gOprUs65MypPeD8Q6Tjzpfuu8Gf+p5Ftu31J9xXLEjvPzjYM+mJbZdoV5tyGsjx7x5qy988cuYvliRbvIpkomB7SacfZu/sm/m/e7MRwXYzdCxjYnsWaU/G9IVL1sk56YqvrueXDAAAAExL/ySqSCodmExsY84bs2M7V9caEkly8bTStejYzhbVmY7r0K6MntvtTzSrmJee/Il01O9K0Wm8Qf7FfyJ5fdIDX5vx+mpp296s5qXjIzfIHNgpZbon9wCJdmUspwF+154z8sWyPnTLw9q2d+YbtU7EK5aUjNWoiqRVN48cyI+7caS0P9h+IaQ92wTbzdCxjIntWeawrow++/tr9eSOfr3/5ofY1AIAAABT1pctKmJSZoIaheGJbTq2MdcN7ZYs6m+aGGjIxHa8TXEryRXzM3qcfUN5zW+La1VX2/6J7c2/kLx9U68hqVh5qj/pffe1Unn2vPi1bW9Wy+dXTWuXitJQ76QntiOpdmWUG95gF+G3ceeAvnHPc/rZ4zvr+nX8ie0aBNsxa92O7UFPXRNVkVSC7T6CbdRKxzKpj4nt2ebsI7t19e8eqx8+/IL+/ecbm70cAAAAtJi+XEGd6fi4GzhJTGwDw7J7/Gntqu+Xnn6v7hPbFm+TJOW9wRk9zt5sQfPTcR22sE3b9maVL5alx74vxTPS4a+Y/gO/+Cpp73PS49+f0fpqaeverFbMr+rmHeqV5CY9sR1NdfhVJExszxl7hvwXjnr661t/4RVqU0WSiLZmFclQvqhcoayuCf7dXNSeUMSkHQTbqJmOpVK+f9b1ZkF619mr9aa1K/TJ25/UHY/tGD6ezZf0/O4h3f/cHj28ZV8TVwgAAIDZqj9XnLBfW5LaEv7tQwQ8mOsqwXYgXyxrX7ZQ94ltS/jBtjc4/WA7XyxrKF/SvKCKpOykLbsHpCd+KB35Sik+gw3ajnmtNP8w6a5/n/5j1NjW0RPbA8HvypOc2I6lO5S0ogazM3sxAa1j92AQbA/UOdgulmq4eWTrBdu9A/6f80Qd27FoRN0dydBWkUyj9Akz1rHMv+zfISU7mrsWjGBm+qc3n6iNOwf0J994QIs7k9rV72lw1ETNxacfqg+//jh2dAYAAMCwvmxBnanx+7Wl/RPbo3++BOac7G6pbeHwp72DfgBW74ntSBBs57ID6jzIfcezL+v36c9vi+uwLv/x9jz5az/wPeb1M1xgVDrzPdKP/lrasl5auW5mjzdDfbmC+nNFragOtgeDeonM5ILtRNrPPbwBhvvmikZNbOeLZSWiMw+2E7FIS+631hu8gDBRFYnk15GEtYqEYLsZOpb6l/3bpUVrmrsWHCAVj+pzf3Cq/um2xyX5b9tY1J5Ud3tSizoSuueZ3frc/zytR7bu07VvP0WHLGxr8ooBAAAwG/Tlxgi2y2XpnuukUy6Vku1KxiKKRkxDdGxjrsvukTpXDH9aCcDqPbEdTQYT29mBaT9GJdjuDKpIJCn51A+lSFw66tUzX+Tat0s//7/SXZ+VLrx+5o83A9v3+mHYyIntHv9ykhPbyTb/JQQv21fTtWH2qkwS76x3FUmxXJOJ7UQ0oj0tWEXSG0zET1RFIvkbSG7uDec7Jgi2m2F4YpsNJGer5fPT+szFa8e87XeOWaJTD12gv/yvh/S6z/xK/3bRyXrF0ZP7HzoAAADCqz9X1KGjhx62Pyj9+ENS+xLpxAtkZmpLRDXoMbGNOS67V1pywvCnuwYqE9sTTx7OVDSZkSTlZ1CLsS/rh3bz2xLq7kgqHY9o+Qs/lVa/TErNm/kikx3ScW+QHvvezB9rhrbtzUoaFWxXJranUEUiSYUhJrbnisrE9q5GBNs16NiORyMqFF0NVtRYk57YnpfSPc/sbsSSGo6O7WboDILtvq3NXQem7dXHL9X3/uSlWjYvpXde/xv960+eUKncev8IAgAAoHb6sv7mkSMM9QaX+3+hzCRiTGwDQ7ul9P4qkkZNbMdT/otPhdz0g+29Q0EVSbBZ7Mvn92iht1U69nU1WaMkad4hUm6vVMzX7jGnYUsQbI+oIhnYKcXSUqJ9Uo9hwf1KOYLtuWK4Y7vfk3P1y0q8YknJWA06tmMRFVpwYrvy59x1kBcEl3SmtC9bUK4QvhfVCbabIdnhb5Kx97lmrwQzsGpRRre85yy9ee1KffpnG3XBf/xaX7372dAW8gMAAGBifbnigVUklUC7EnBLaktGNUTHNuayoicVBkdsHrkrqC6od8d2PJjYnkmwXakimRe8kPW7sfUqy6SjXzvzBVa0d/uXgz21e8xp2LY3q1jERr7gMNjjr89scg+SrATb069/QWupBK75Ull92fq8kFsqOxVKriYT24loRF5Lbh7pKRWPDG9MPZ4lnf6GtmHMqwi2m2XBKmnP5mavAjOUTkT1iQtfpH95y4u0d6igv/vOIzrzn+7Q+Z/9lf795xv11I7+Ea9OOudUKjsVW/CVQAAAAIyvVHYa8IrqSI365XJ4Yrsq2E4QbGOOy+71L9Pzhw/19HvqSMaUis88pJpIPO2HrEWvBhPbbX6wfYb3az3gjlR5kpspTkrlsSq1H02ybW9Wy+anFI1UhdgDOye9caQkKeG/mFDyCLbnit2D+eHXPXoG6hOm5oMguiYd2zFryYnt3sG8ujIHfzFwaSXYDuEGknRsN8uCVdL23zZ7FagBM9NbTztEF65bqU09A/rxhh36yaM79PEfP6GP//gJxSImJx1QVXLm4Qv1t689TiesqEEHGwAAAJpqIOdPpB1QRZIdY2I7EdOgRxUJ5rDK90VbVRXJgFf3GhJJSqT8kLXsDU37MSoT2x2puLRns5YOPaUvFX9fy/tzWjYvfZCzJ6nSXz3Q/Int5aOf02CPNP/QyT9IUEViIQy2B7yiys4d+G6dOW7PUF6HLmzTs71D2tnvac3ijpp/jUqwnYjWoIokGlG+FYPtgfxBa0gkaek8/9/WHQTbqJkFq6THvi+VS1Kkvq9IozHMTGsWd2jN4g5d9Yo12tGX008f26Ete7KKmili/n0iZsoVS/rWb57X6z/7K7157Uq9/zVHa+m8VLOfAgAAAKapL+cHXZ2TmNjOJKLDtQvAnJTd419WVZH09Ht1ryGRpFSbH7CVZjCxvS9bUGcq5k8xP/4DSdJPyut0bu9Q7YLtTKWKpNkT2zmdsXrhyIMDO6QVp07+QSpd3IXp/5nPVh+4+SHtHSroG390ZrOXMms457R7MK9zjl6sZ3uHhvvza80r+u98qsnEdjSiQgtWkewenFywHeYqEoLtZlmwWioX/A0kp/JKJ1rGks6U3n7GYePefuU5R+jff75RX/7VZv3g4W264mVH6I9fdrgyyZgGvKKe7hnQ0z2DerpnQP1eUVeec4QWdxB+AwAAzEaVCc7JbB7ZloxpcPf0p0WBljdGsL1rwNOxSzvr/qWTQRWJK0z/e3DvUF7z24Iw6fEfKN91jJ7dulTP9g7qzMO7arHMqont5gXbxVJZL/TltLx648hyyf93rX3qVSSREAbbz+wa0qaeARVKZcVrMDkcBgNeUYWS0zFLO3T7ozvqGGwHVSQ16Nj2N4+s3yaX9bJ7MK+jlhx8Gr4jFVcmEaWKBDW0YJV/uWczwfYc1ZmK6+rfPVbvOOMwfexHj+vTdzylr9/9rGJR046+/f/wR0yKmOnnj+/U1951hlYuaGviqgEAADCW/qCK5MCO7QOrSDKJqIY8OrYxh1W+L9L7J4F39XtatObgk4czlc4EwXZ+ZlUk89JxaXCX9Nxdir30LxXbbnq2t4YvWCUyUjzT1M0jd/R7KpXdyGB7qFdyZal9yeQfKO7/DhsNYbC9e9BTvljWUzsGdNzy+r8w0wr2DPov9B66sE3JWKT+E9ux2lWROOdkk90Utcmcc9o14E1qYlvyhy939tXn76KZCLabpTrYXv2yZq4ETXbIwjZ99vdP0R+etUdf/OXTakvEdHh3Rkd0Z3REd7sO7WrTI1v7dNmX79Vb/+Muff2PztTqRZlmLxsAAABV9leRjJ7Yrgq2nZPM1JaIaShPxzbmsFET27lCSX25YmM6thMJ5V1MmsnEdrbgbxz5xA8lV1bkuNdrxf279Wyt34nR3t3Uie1te7OSpBULqoLtynoqVSmTEYnIi7QpXgzXO1UqlRuS9MjWfQTbgd5BPzztak+ouyNZt2A7V6hMbM882K48RqHklIi1RrA9lC/JK5bVlZl8sB3GiW3eJ9EsnSukSEza/UyzV4JZ4tTDFui6d5yqT771JF31ijU674RlOnJJh5KxqE49bIFuvOJMecWyLvyPu/T4C33NXi4AAACq9AVVJPPG2zyy5El5f1qxLRHVUL4k51rvbc9ATWT3+L8PJ/230PcG4WAjOrYlKaekrJid9vn7sgW/duix70vzDpWWvkiHLmzTc7Wc2JakzOKmdmwPB9vzqyoxK+uZShWJpGI0rWQ5q0ILbtA3nv6gckOSHt66r8mrmT32DPnfzwvagmB7oM5VJPEaVJFE/TC7lf777A326lg4yWB76bxUKDu2CbabJRqT5h3iT2wDk3D88nn61h+/WLGI6W2fu1sPPr+32UsCAABAYMwqEuf8Se3KZGNQR5JJxlQsO+Vb6BdooKayu/1p7eAt/5WJzkZMbEtSzpKywgyC7aGCFicK0tN3Sse+TjLTqq6MNvcO1vYFq/bF0kAdqkjKZenHfyP1PDnh3bYGwfaIDTEr68lMMdiOtSljOQ164Xm3yu6qTYAJtvfbHVSRdGWS6m6v38R2pYokUYNu88pj5FtoA8nqyfjJWNKZ0s7+nMrlcL2oTrDdTAtXE2xjStYsbtd/vfvF6kzH9PYv3K3/ebJH2Tz9jAAAAM1WqSJpT1YF2/lBqZSXuo70Pw+C7baEP11GzzbmrOyekRtHBsFXoya285ZUpDS9yUXnnPZmCzo00uO/E+OQ0yVJh3W1qT9X1N6hQu0Wmumuz8T2vueluz4r/fITE95t656s5rfFlan+d214YnsKVSSSyvGM2pQbfhEwDCrB4prF7Xpse5+KvFgpSdoTvANjQSau7o6kdtYp2M4PT2zXoGN7uIqkdf4OKzU4XZnJ/bu5tDOpQslp91D+4HduIXRsN9OCVdK27zR7FWgxhyxs03/98Uv0ji/do0v/815JUjoe1cJMQgsycS3MJLX2kPm66hVrlKhB1xQAAAAOri9bVHsyplj15Fhlw8hFR0rP/Xq4bzuT8H8NG8wXtWCSbyEGQmVUsF2pKmjUxHbekopOs4pkMF9Sqey0KDrgH2hbJMnfKE+Snt09VLvv6/bF/r8bpaL/ru9aGdjhXz72PckbkJLtY95t296slldPa0t+x3Y0KSWn1iftEu3KaEgDIZrYrlRBnHNUt774q2f01M4BHbuMnu3ewbziUVN7MqbujqR2D+ZVKJUVr8FkdbXhKpIabR4pqaXeSTWdKhJJemFfrmEvIjYCqVczLVjlvwUrx1tWMDVL56X07Xe/RP/ylhfpA+cdrXeceajOOHyhutuT2jOY1/+74yld+Lm7tGVPuDbnAAAAmK36coWRNSTSyGC76vO2pD+xzTvvMGcN7ZHSC4c/rUxsT/Yt9TNViKQUnebE9t5g2rHLKsF2lyTpsK6MJOnZ3sGZL7Ai0y3JSUO7aveYktT/gn9ZGPLD7XFs25sbuXGk5Afb7YuHa2QmLZFRxnKhCrYrE7MvP9qfXqeOxLdnMK+FmYTMTIs7/DC1d6D2U8L7g+2Zd2xXwvHWqiIJ/i2aQhWJJO0I2QaSTGw304JV/uWezdKyk5q5ErSgeW1xvfW0Q8a87baHt+sDN/9Wr/30r/Spt52k3zlmSYNXBwAAMLf05wrqTI2zceSio/zLUVUkgwTbmKuye6SlJw5/2jPgqTMVq0lANRmFaEqx8vTCnX2VjWJdn3+gzQ/oKxPbNd1AsrJB48BOqWNp7R63MrHd1iU99E3p5IvHvNu2vVm9+IiukQcHd+7fN2AKLNGujHLaHaoqEj9YPPWwBcokonpk6z69dd3Yv6PPJbuH8lrQ5oetlXdh9PR7wxPDteIV/P+H1nJiu7IZaCvoHfCUjkfVlphctLs/2K5PNUyzMLHdTNXBNlBDv3viMn3vT1+qFfPTeuf16/WxHz1O3xcAAEAd9WWL6kyPntgOgu0FqyWLVgXb/v2GQjS5CEzJ6I7tgf/P3nuHt3Ud5v+fi70IgHuKokRtyZIl2/KeimNn2hlOnMRJmqR19my+Wf21adqmdZNmp3GTNM0edhwnzrTjveQt2ZatYYkalDhBDGLv+/vjXHCCJACCJECdz/PogXhx78UBCGK85z3vm1i0GBKAtN6KqVRhW8vQdqohsUFznltNepqdZk74yihs5woay52zHRoUr0lnvweOPQSjfdN2CcZThBJp2txTxMiwBxzFG6f0lhpsSpzQMnrd80WS2ExCWNzc7pKObQ2f5tiGCcJ2uPwu4UQ5M7b11ZmxXWgMCYjfhaLA4DJzbEtheymRwrZkAelqsHP7By7gred2cvMDPbz1+09wcDBIZpk14EokEolEIpFUAsF8ju2csG1vEK5OTdgez9iWjm3JaUg6AanIlPLI5KJmvmb0lpKF7YDm2LZnRkXOtGFcWFpZZy9vFMmYY9tTvnMChAfFuc98C6DCvlun7dLnFxnkbe4pUSSR4aKLIwEMVgcO4oSXkWPbF0mOxUCc0e5if78skAQRRVI7VdhegALJckaR5PrJElUURTISSdJQRHyTUa+jwWFmaHR5CdsyimQpsbjE7K4UtiULhMWo599fdwY7u+r43G/3cfXXH8Zi1LGhxcnmNieb2pxsanVyRrtrctGRRCKRSCQSiaQoQvE0a5vyZGwrOvG531Y/LWM7mlw+Ao9EUjAxv7i0TS6P3Ny2eKV7Wb0Vk1qa0JaLIrGmAmMxJDk662089FIZRehc5MdCOLYdzVC3GlacB8/9Ci782KTc7P5AHmE7m4XIyLiTvAgM1hpMxAnHy5+1vFSMhBPU2YVwe0a7i0Q6KwskEVEk9ZqwnRNehxcg/iJZ1vJI8dyvLsd2gsYiJwRbnBbp2JaUmdouKWxLFpxrt7dzzycu5b+u28Zbd67EbNDx++f6+YffvsDrvrObV33zER45XOZCEolEIpFIJJLTiGA8hdOaJ2Pb4gadXhO2hYN7zLGdkI5tyWlITtie5NhOLKpjWzVaMVOa0BbQokhMSf9YcWSOlXU2hkOJ8hXDmmvAYBEZ2+UkNDSe2b3tzeA5CAPPTdolJ2x3TBS2Yz5QM+NO8iIwWp0YlCyxWBmjWpYYX2RcwN3S7gLKWyAZjKf42K/2MlhFDtt0JksgmhrL2DYb9LisRjzhhXBsi78zUxlMeqZqjCIJJ6kv8nWz2WlZduWRUtheamq7wHdsqUchOQ1oc1t541kd/NNrNnHLe8/n+c+/nIc/dTlfuW4b0VSaG37wBH/746c5PlLGpXMSiUQikUgkpwGqqhKKp/NEkXjHha8JUSTSsS05rZkibMdTGUKJ9KJmbKsGK5Z5OLZNeh26mG+asN1ZrxVIlitnW1GEOzqyEFEkWk725teB3iRc2xPoC8Qx6pXJEw45gb3E8kiAZDRY0pArkYkZx6sb7GMFkuXi/oPD/O7Zfv68b6Bs51xoclE9E7Ofm2rMCxZFYtLr0OmUuXeeg1wUSbJKokhUVWVkwsRKobS4zNKxLSkztV0wehIy8kOtZHFRFIUVdTbecFYHd3/8Uv7fVet5rGeEK7/2IP/x5wOE4qmlHqJEIpFIJBJJVRBNZshkVWoseaJIxoTtCVEkRv3YcRLJaUcue14rXcwJXsUuqZ8XRitWJUkqXfz38NFYEpfNiBKdLmx31dsBypyz3Vhex3YmLeJEco5tay2sfwXs+zVkxr8D9gditLqsk0XDXCRKCY5tTOKxScVCpY68olBVFe8EYVGnU9jcVt4CyUePiFXVe3r9ZTvnQuOPiKiZicJ240IJ26lsWWJIoPrKIyPJDMl0tqjySIDmGguBaIp4avl8/lj0jG1FUdYDt0zYtBr4J+An2vYu4DjwJlVV/doxnwXeA2SAj6iqepe2/SzgR4AV+DPwUVVVq6sZr24VZNMQ7IPalUs9GslpisWo54OXr+G6szr40l2H+O5DR7nl6ZM0OswkM1mS6SyJtLi0GHW8/bwu3n1RFzVTXUkSiUQikUgkpyFBzRAwLYok6gf3CvH/nLCtqhj0OkwGHRHp2JacjkxxbOciChbTsa2YhLM6FotgrHEVdWwgmsJtNU6euNJYqTm2T3jLGLdhbxJmuHIRGQbUccc2wNbrYf8d0HMfrLsKgL5AjDa3ZfKxY47tEoRts3Bsp2PhEgZdeeQTFre0u/jFkydIZ7Jl6bDa3SMmQ/f2BuZ9rsXCN4Ow/ezJQNlvK5HOYDaWV9hOZqpDUvRqr5tFR5G4xN/0cDAxtsKk2ll0x7aqqodUVT1TVdUzgbOAKPBb4DPAvaqqrgXu1X5GUZRNwPXAZuBq4DuKouQqT28GbgTWav+uXsS7Uh5qu8SlzNmWVABNTgv/dd02fv+hC7l0XSPdjQ7OXOHmojUNvGJLC9ed3cG2Djdfu+clLvnS/Xz3wZ7y5cdJJBKJRCKRVCnBmBCo80eRaOVytnqRTRsXbj67SU9UZmxLTkemCNsjmpNzMTO2c8J2PFq8yDoaS9FoyUIqMiknHMBtM+G0GDjhq2DHdmhQXOYc2wBrXiZeo5775dim/kBscnEkjI9jHo5tNbE8HNv5hMUzOpzEU1mOeOYv3p/0RTnlj7GqwU5fIMZwlcRH5ITtXMY2iNUYCxVFYjbo596xAMxVFkXi1R7noqNInELYXk5xJIvu2J7CLqBHVdUTiqJcA1ymbf8x8ADwaeAa4FeqqiaAY4qiHAF2KopyHHCqqvoYgKIoPwGuBf6ymHdg3kwSti9dwoFIJONs7XDzjeu3z3j9cycDfOXul/iPvxzkfx85xocuX8P1O1eU7U1FIpFIJBKJpJoYd2xP+HqlqqJozTpB2AYhdlvd2EwG6diWnJ7EfKAziGJElsaxrdOE7UQJwnYgmmKzQxQrTnVsA6yst5ffsR0dgWxGFNHOl/CQuHRMELYNJtjyRnjmRxALkDI5GQrGJxdHgnB7601gKc7lDoCWsZ1NLo9Op3zC4hm5AslTo2xocc7r/Lt7RAzJ+y/t5lO/eZ49vX6u3tI6r3MuBr6o9rg4Jju2o8kM4UQah7l8MmQynR3Lxp4v1RZF4gtPf5wLocW1/ITtpc7Yvh7ITQk2q6o6AKBd5qYA24GJ625Oadvatf9P3T4NRVFuVBTlaUVRnvZ4yly6MF+c7eJN3S8LJCXVw7YVbn7y7p3ccuN5rKq38/nfv8jLvvogjxweWeqhSSQSiUQikSw6uW6SSTFtqSik45MztmEsX9hulo5tyWlKzC+czorIbh4JlSbQzAe9WRO2Y8WLrKOxFM1GTbjOI2x31tvKVx4Jwh2tZsezyedLPsc2wLY3QyYB++9gKBgnq5LHse0RQrtSQlmfJmyTWB5RJDlhcWLkxqoGR9kKJB894qWxxsxrz2zDpNdVTRxJLmPbbRt/P8xNWpXbtZ1IZ8qYsS2e09Xj2BaPZdEZ25pje2hUCtvzRlEUE/Ba4Ndz7ZpnmzrL9ukbVfV7qqqerarq2Y2Nxbf3Lig6Pbg7ZRSJpCo5d3U9t7z3PH7y7p0YdTpu+METfPb258dcSxKJRCKRSCSnA+NRJBOcaFpR5HgUSd2k7VaTgegyKm+SSAomJ2xreMJxam3GMcfkYqA354oMS4siadZrgngeYbur3kafP1Y+56dd0zAiZYojCQ8ByvQ4kbYd0LAOnvsV/QEhek0TtiPDIhqlFLQoEl1qmQjbebKk9WUqkFRVld09Xi7orsdi1LO53Vk1BZLeSBKH2TBpNXdTjRBTyy9sl688Muf8rhbH9viKgeJWujgtBqxGvXRsl4lXAHtUVdXWwTCkKEorgHaZe9U+BayYcFwH0K9t78izvfqo7ZLCtqRqURSFS9Y18uePXsyNl6zmlqdOctXXHuL+Q9M/eCXTWZ454eP7Dx3lqeNlchxIJBKJRCKRLDF5yyNz7sppjm0hbIuMbRlFIjkNiU6I6EE4thczXxvAaBHu4VS8OMd2KpMlnEjToNfE2XxRJHV20lmV/kBs3uMExgXocuVshwbFuPVTOgEUBba+GXp34+87BORzbA+XVhwJY45tfaqMbvYlZExYnLLSYEu7i/0DQdLzEEiPDIcZCSe4sLsBgB2dtTx/arQq3MT+SJJa++Tn1oI5tlPly9geL4+s/McYwBtOYjPpsZqKu/+KotDiskhhu0y8hfEYEoDfA+/U/v9O4I4J269XFMWsKMoqREnkk1pcSUhRlPMURVGAd0w4prqoXSWFbUnVYzHq+dwrN/Kb91+Aw2zgXT98ik/c+iyPHB7h6/e8xFu//zhbv3AXb7j5Mb745wO86buP8c17D5PNztw6nMmqfP+ho5z37/dyw/8+wf882MOL/aOzHiORSCQSiUSy2ITiQqCuyevYzi9si4xt6diWnIbEApMc2yPhxBII25pjO16cezgYE5NYtWgFiDNEkQDly9nOCcmRMsWqhoemx5Dk2PpmANyHfwtAm9sy+fqIp3THtlkI24ZMlMwy+D7nDSewGvXYTJMzo3MFkj2e0rPEd/eI94nzu8Xza3unm0Q6y4GBYOkDXiR80RR1U1zE48J2ecXURDqD2Vgmx7a+usojfZFk0TEkOZpqzFVTRloIS1IeqSiKDbgSeO+EzTcBtyqK8h6gF7gOQFXVFxVFuRXYD6SBD6qqmvsE+H7gR4AVURpZXcWROWq7xHKsWACs7iUejEQyP7Z31vLHj1zEt+49ws0P9nD7nj4UBTa1OnnLzk7OXVXHlnYX/3XXIb5690s8fcLP19985rQX5SPDYf7fbc+xtzfAuavq8IQS3PSXg9z0F1HQceGaBi5d18jLNzdPzrOUSCQSiUQiWWSCsRRmg26ycyymLRvPOVNNDlG6lnNsm/VEZXmk5HQk5oeWM8Z+9IQTbOtwL+oQcsJ2JlGc+BzQhG03msA4QaDPsTInbJcrZzsnJJfTse1ozn+dewV0XUx3/5+os71ssmibzQphu1THtsFCFh12JU4kmcZZ5d/hZhIWcwWSz58KsL6lpqRz7+4ZYUWdlRV14rm0o1M8z/b2+tm2wl3agBcJXyRB45SJKrfViEGnMLwAUSR19vII2zqdgkGnVFUUSX2JE4ItLkvVRNsUwpII26qqRoH6Kdu8wK4Z9v8i8MU8258GtizEGBeV2i5x6T8O1jOXcCASSXkwG/R88qr1XLu9jZP+GDs6a3FZJ39w+dqbz+ScVXV84ff7edU3H+bbb93BWStryWRVfvDIUf7rry9hM+n5xvVn8tptbSiKwlAwziOHR3jkyAgPHx7h98/1Y/6tjpdvbuH1O9q5eE0DhkXM5pNIJBKJRCIBEUXinPJZZ5pjW1HE/yc6tmV5pOR0JOYbz5wHRkKL79g2W4V7OJMozlU7qgnbjkwQLG7QT5dUmmssmA06er2lO3YnYXGLSbFyZWyHBqFp48zXb7uehuMf5Arn8cnbY37IpqdncxeKopA22LCn44Tj1S9sC2FxurC9qsGBTSuQvO7sFXmOnJ1MVuWxHi+v2NI6tq3NbaXFaWFPb4C/uXBew15w/JEU65onC/o6nUKDw1z2KJJkOjuWjV0OjHodqUx1rCbwhhNjRZDF0uK0MBRMoKoqSilFsBXGkgjbkilMFLbbzlzCgUgk5WVNUw1rmvLPUiuKwtvOXcm2Djfv//kzvPm7j/HhK9bywEvD7O0N8PJNzfzb67aMFU2AaPB9w1kdvOGsDlRVZe/JAL/d08cfnu/nD8/10+Aw89ptbeza2ES9w4TLasRtNWEx6pbFC7ZEIpFIJJLKJBhPT44hAS1jW5m8ItNWP5a9bTfpiUnHtuR0IxWHVHTs7yKaTBNJZsaiChaLnLCdTRbnqh6NCmHbng5MEucnotMpdNbZyhdFoiiiQDJchiiSbFYrgJzBsQ2w8bXE7/g4r1YfAm4c354T1ksVtoGMwY6NOOFl0C/gm0HYFgWSzpILJPf3BwnG01ywZnLMzY6V7qpw2foiSerzONkba8x4wgtRHlmejG0Ao16pqiiSja3Oko5tdlpIprP4o6mS40wqCSlsVwIThW2J5DRjS7uLP374Yv7fr5/ja/e8hNtmnOTSnglFUdjRWcuOzlr+8dWbuP/QML/d08fPHj/B/z16bNK+JoMOt9XIeavr+cdXb1r0D84SiUQikUiWN8FYarr7MOoV4p1uwpduW90Ex7aeaCpDNqui08kJeMlpQjwgLrUIj5GQKOBryCMQLiQWW2nCdiAmxmtJB/Lma+dYWV9GYRuEsF0Ox3bUK1zXM2VsA6q5hnvUc9gVfQDSCTBo351yUSilRpEAqtGOQ4mP9RJUM75IkrXNjrzXbWl38csne0lnskWvKN7dMwKM52vn2L6ilj/vG2Q4FJ9k/qokYskMsVSG2jxiaVONueyFhYl0BnMZHdsmg74qyiNVVcUbzj+xUggtLvH8GRyNS2FbUiYsTvGmKIVtyWmKy2rku28/i/sODrO1w1208Gwy6LhqcwtXbW4hEE2yvz/IaCxFIJYiEE0RiCUZCSX5w3P9PHzYwxeu2cJrtrZKF7dEIpFIJJKyEIynp8WuCWF7iqPTVg+DL4j/mg2oKsTTmWnlYxLJskVbsZD72xjWyuQW37EtMrbVVKyo43KObWMiAO72GffrrLPz6BFv+Zb6O5pEhMh8CWvnmMWxHYyl+XXqIl6tPAov3QWbXiuuyJVXzsOxrZqWh2NbVdVZS0/PaHfxQ61Astic7d09XtY2OaaJ1ztWugHY2xvgqs0zT0wsJb6omPips+V3bJfqYp8J4dguo7CtV0hVgWM7nEiTzGTzOuMLIRdhMhSMs6mtNNd3JSE/QVUKtV3gPzbnbhLJckVRFHZtnGVJXIG4bSYuWNOQ97r3XbqaT972PB/55V7+9Hw//3rtloqd7ZZIJBKJRFI9hGIpVtRaJ2+M+aY7OidkbNtNwskdSUhhW3IaMVaqKhzbOQdnzkG4WCgGMxlVQUmVVh6pj/vAtnXG/VbW24ilMnhCCZpKzMGdhL0JBvfN/zyhIXE5i2O7LxDjkewW4pZGLM/9alzYHnNsN5Z884rZgV3xM1Llju1oMqMVF+YXFnMFkvv6RosStpPpLE8e8/GmszumXbe5zYVRr7Cn11+xwrY/ognbM0SReCNJMlkVfZlWKSVSWczG8kWRmAy6qnBs+7THud5e2oRgs1McV24H/VIhW9Yqhdou6diWSBaYtc01/OZ95/PZV2zg/kMeXv61h7jj2T5UtToKIiQSiUQikVQmImM7j2M7n7Ad80N2XMyOLqOcbVVVGV4mX5TLzu8/DH/5zFKPYumZImwPBUXmbks5xN9iUBTiihmKdWzHUtSYDShR34wZ2yCEbYATvjLFkTgahWM6O0/RrQDHdn8gRgY9wTXXwuG/QkQrwo0Mg8449rsrBcXswE6ccCJV8jkqAd8sAi7A6sbxAslieO5UgFgqw/nd041aFqOezW0u9p4IFD3exWK2x6Wxxkwmq47tM19UVS17FIkoj6x8YXskrD3OJUaR5Mx9Q8vk/VpaAyqF2i548XeQSedtVpZIJOXBoNfx3ku72bWxiU/++nk++qtn+fRvnsdpMeK0GqmxGHBajLhtRnZ01nL5+iY6tQ+mEolEIpFIJPkIxlM4rVPLI/3QMsXRaasHVIgFsJvHHdvLhXsPDPO+nz3Do5+5Ymyps0Tj+KNgLi6SYFkS06JINFF4KBjHZNBNj/JZBOJY0KeLjyJpsmYhHpsjY1tEnZzwRjmna2YBvGDsTSIbOx6YVVCfk1ycySyO7f5R8ZgYtr8FXvg+vHg77Pw7UV5pbxRlliWitziwUf0Z294xx2x+YbHUAsndR7woCpy3Ov/veHunm18+2Usqk8VYZHb3YpATrfNlbDdqsS2eUKIs0UPprEpWpezCdjJd+aY33xzPv7kwGXQ0OExS2JaUmdouUDMQPDVeJimRSBaMNU01/Ob9F3DbMyc5MhwmGEsTSqQIxtIEokkOD4W449l+Ps+LrG60c/n6Ji5f38Q5q2rL2rwskUgkEomkuomnMiTT2fzlkVMFqJwQFvViMwnXYyxV3QLPRI6OhElnVU76olLYnkp4qGh38LJkahTJaJwWp2VJum+SihmlSGE7EEvRaYlCnFmF7Xa3FZ0CJ7yReY5SI5drHR6en7AdHgKLC4zWGXfp88cw6XW4V+2A5i3w3C+FsB0ZFs7xeWCwOrEr1Z+x7YuIlQazFe9taXfxqydPFhW9sbtnhC1tLtx5MqoBdnTW8sNHj3NwIMQZHa7iB77AjDm2Z8jYBvCEE2W5raSWhW0qa3lkdUSReLXHsH6GjPdCaHZaGByVwraknNSuEpf+41LYlkgWCb1O4c3ndM54/bGRCA8cGub+Qx5++vgJfvDIMRxmA2/ZuYK/vXi1/MImkUgkEolkzHnotEz4apWMQjqWpzxS+znqxWYSS82Xk2M7tzzaEyqPcLFsSIQhGYZ0XERJ6CrPabloxPygM4DJAQjH9qLHkGgkdRb0meKEndFYig0mLV5kFmHbZNDR5rZywlumKJJcrnVkGNhQ+nlCg+CYPZ+5LxCj1W1Bp1Ng65vh7n+EkcNCFLeXXhwJoDM5cBAnXO2Obe21bqbySMgVSB6nxxNmXfPcqzViyQx7ewO868KuGffZsVJMCO3p9VeksO2PJtEp5F2BkYu/KNf7Q0ITtstpOjPpdVVRHjnXioFCaHFa6F8mwvZp/I5aYeTEbJ8skJRIKoVVDXbedeEqfvLunTz7T1fyg3eezRUbmvjBI8e4+D/v57O3P8/xkTK5MCQSiUQikVQlwbjIinVO/CI/FreQJ2MbNGF7+WVs5wSLcjnylg1hrbAvFyVxOhP1iQkfzaE9FIzT5Jx/LEEppHRmDJkiHdvRJC2GuYVtEDnb5cvYnuDYng/hIaiZOV8bRMZ2m0tzdJ9xHSg6eO5XIopklmzugjDZsSoJIvHy5CwvFd45MrZhQoHkqcLiSJ4+4SOZyXJ+98zPqzaXhWanmT29/iJGu3h4I0lqbSYxKTKFhhrxWJVP2BaTwmWNIjEoVZGx7YsksZv0WOZRnNnssiybKBIpbFcKzjZRxCALJCWSisRmMrBrYzPffMt2Hvjk5Vx3dge/2dPHFV95gA/+Yg9PHfcRiCZlEaVEIpFIJKcZwZgmbE+MIolqZWuzRJEsx4ztEU3Qlo7tKUwUI3Mi9+lKzD8WQ6KqKkPBxJI5ttM6C4ZssY7tNI36sPhh6oqMKayst9NbriiSnFM64pnfeQpwbPcH4rTXasK2sxVWXw7P3yJue55RJJgd6FCJx6vbHOSLJDEbdNhMMwuLuQLJQnO2d/d4MeiUWTPZFUVh+4ramYXtmB+OPVzQ7S0E/kgyb742iO/TDrOhfMJ2SnNsG8sYRaKvniiSUosjc7Q4LfgiybEJgmpGRpFUCjo9uDulsC2RVAGd9Ta++Loz+Oiutfzg0WP8/PFe/vT8AAB2k572WivtbisdtTY2tjq5ekvLrLP5EolEIpFIqpdcFEnNxCiS6AyObevEKJJl7NiWwvZkJorZ4WFo2rh0Y1lqJgjbwXiaWCpDi2tphO2M3oop5St4f1VVGY0lqddrouxcju06G/5oitFYav7lmNZaUPTzc2yr6pyO7VQmy1AoTpt7Qgb3tuvh9r8T/59nFAkmUaqZiYXmd54lxhtOUm83zZoNr9cpbGotvEByd4+X7Z1u7ObZZbodK93c+eLg9BLGRBh+ci0MPAsf3gP13QXdbjnxRZKzfu9trDEzHCqPS3ghokhEeWQVCNuRJPX2+a10adZWygwHE6yos5VjWEuGdGxXErVdUtiWSKqIJqeFz75iI49+5gr+54Yd/H+v2sh1Z69gZb2doWCC3z/Xz+d+u4+dX7yHd/3wSX63t49IlRelSCQSieT0RlGUFYqi3K8oygFFUV5UFOWj2vY6RVHuVhTlsHZZO+GYzyqKckRRlEOKolw1YftZiqLs0677pqIpBIqimBVFuUXb/oSiKF2LfkeLIG8UyZhje4rwZbKB0TbJsR1NVr9bKod0bM/AVGH7dCbmH1vJkFsG37REju2MwYJRLVxkiyYzpDIqdQQBBazuWfdfWS/Eot5y5GzrdCJnOzKP5088IHLeZ3FsD47GUVVod0/4nWx41Vgm+lgkSqlo58nEw/M7zxLjixTmmN3S7mJ/f5BMdvZVvcF4in2nApzf3TDnOXd0irfXvRNd25k03PYuGHhO/Hzk3jnPsxD4o8m8xZE5Gh3mCo8i0VVFFEluYmU+5PrClkMciXRsVxJ1q6Dv6aUehUQiKRKX1cjVW1qnbVdVlYODIe54tp8/PNfPx255FotRx8s2NvOGszq4ZG1jwQ3ZEolEIpFUCGng71VV3aMoSg3wjKIodwN/A9yrqupNiqJ8BvgM8GlFUTYB1wObgTbgHkVR1qmqmgFuBm4EHgf+DFwN/AV4D+BXVXWNoijXA/8JvHlR72URBGO58siJGdua4JAvqsBWD1EfFs1lFlkmwnYmq+LTcmdlxvYUJgrb8xEmlwMxP7RuA8YFlaWKIsnqrZjVwp+ro7nYoWxQOKh1sztFO+uEO/mEL1Keoj9Ho8i5LpWQ9jysmVnY7guIzPFJjm2THTZdA8/+fLzEslQ0YTubqHZhO0ldAY7ZM9pd/Gj33AWSTxz1kVXhglnytXNsaXdh1Cvs6Q3w8s0twon/57+Hw3+FV38NHv0G9NwL595Y1H0qB75IkrNWziJsO80cGAiW5bZyzmpTGYVtc5VEkfgiSTa3Oed1jtxKmUEpbEvKSm0XxEcnLc+SSCTVi6IobGx1srHVyaeuWs8zvX5+/2w/f9o3wB+fH6Cj1spbdnbyprNXTF5GJpFIJBJJhaKq6gAwoP0/pCjKAaAduAa4TNvtx8ADwKe17b9SVTUBHFMU5QiwU1GU44BTVdXHABRF+QlwLULYvgb4Z+1ctwHfVhRFUSu0yGLcsT0xikRzbOf7TG+rg6gXnU7BZtITXSarubyRBFlVdAJKx/YUQkNQ0yqeFzJje+zvYnBUCCrNS1QemTUWJ2wHouJv3ZENzhlDAiK+EOBEORzbIGJA5jMxEh4Ul7MUQPbnE7YBdv4dnHwSmjaVfvswFkVCsrqF7ZFwku5Gx5z75SY09p0anVXY3t0zgtmgY3une85zWox6NrU6x3O2H/4KPPMjuOgTcPa7YehFePaXkE6CYfHiMLNZFX80NauTuNFh5qGyObYXJookla7IjxpjqKqYRK53zO91MzehmHsdrmZkFEklUdslLmUciUSy7NBpRSD/eu0WHv/sLr791u2sqLXx5bsOccFN9/KhX+zh8aNeWT4pkUgkkqpBiwjZDjwBNGuid078zq1XbwdOTjjslLatXfv/1O2TjlFVNQ2MAtNUJEVRblQU5WlFUZ72eOZZqDYPQvEUep2C1Tjhy3XUCxYX6PP4iGz1Y8K3zWRYNo7tkZBwa6+qt+MJJcjOsfT+tCI8JMREe9P8HLfVTioOqeiYsJ1zbDcvkWMbgxULyTljInLkHNvWdKAgYdthNtDgMJccRRKKpzjpm3CsY57PnwIc2zlhu32qsN22HT789PzLIzXHtlLlwvZcWdI5uhsdWI1zF0g+1uPlnK66gkXa7Z21PH8qQGbvL+G+f4Uz3gS7/km70V2QisDJxws6V7kIxdNksuqM5ZEgMrZD8TTx1Pzf9xYmikSp+CiSUCJNMpOddxSJy2rEbNAtiygSKWxXEnWrxeXIkaUdh0QiWVBMBh2v3trGL288j3s+cSlvP6+Lh17ycP33Hucd//ckA6OxOc+hqirhZeLwkkgkEkn1oSiKA/gN8DFVVWdbV5wvc0udZftsx0zeoKrfU1X1bFVVz25snKfYMg+CsTROi2FyiVjUN7PwNUHYtpv1y6Y8Mhc/srHVSTqrEtBEQAnCKVvTokVJnMaO7bGInpywncBtM2Ixls9xWRQmGzbixAr8GxyNickbSzJQkLANImf7hC9S0vA+9qtnefN3HxvfkMvYLtUIU4Bjuy8Qp95uWrjfiVkTtlORqjX0xJIZYqlMQRnbep3C5jYnL8wibI+EExwcDHHBmsKeUwA7VtayPbMP5Q8fhq6L4Zr/FstlAFZdDDrDoudseyPiPaDOPnNRam6VcjlW9SRSmmPbWD5Z06TXV3x5pC8sXofqC3j+zYaiKLS4LAwGq3+FlRS2K4n6NaLp2HNgqUcikUgWiTVNDv7pNZt44nMv4/Ov2cTTx/28/GsP8bu9fTN+2NvdM8Lrb97Nls/fxft/9gwvDVV3q7hEIpFIqgtFUYwIUfvnqqrerm0eUhSlVbu+Fcitlz8FrJhweAfQr23vyLN90jGKohgAF+Ar/z0pD8F4anJxJAjhOl++NoxlbIPm2E4sF8d2TtgWy+1lHMkEwsPCbetoPr0ztqcI24PBOM01S+TWBhSjDb2iEo0V5qjORZEYE+MFmHOxss5WUhTJi/2j3HtwmP7ROIGoELJwNEEmKeJLSyE0BEY7mGeOxOgLxKbHkJQTLYrERpxYGVy7S0FOwC3UMbul3cWLsxRIPtYjJjovKKA4MsdO2yDfNX6VUVsnvPlnkyNHzDWw4jyRs72I+LXnae1s5ZGasD1cDmF7IaJIDErFZ2yPTyDMP2amucbCkIwikZQVg1mI28MHl3okEolkkbGa9LzrwlX85aMXs665ho/d8iwf+PmesRImgGdPBrjhf5/grd9/gsHRODec18nDh0e46usP8eFf7qXHU91L+iQSiURS+SjClvwD4ICqql+dcNXvgXdq/38ncMeE7dcrimJWFGUVsBZ4UosrCSmKcp52zndMOSZ3rjcC91VqvjaI5dc1limRI7E5HNuJUciksJv0xFLLw7E9ojm2N2mFVlLY1shmIOIBR4tw3IalsJ0ThYeDcZpdSyhsm0QGdiJamKNaRJGo6OK+goXtznobg8F40dELNz/QM/b/Ho82PruW8BQpMY4kPAg1zePO3jz0B2K0uRfwd6JFkdiJE45X52tf7vtZIeWRIAokY6kMR2f4rra7x0uN2cCWQssAg/00/+EG4oqVbzT/B1jd0/dZcwUM7lvU1xtfREz81M/yuDQ6yujYXoAoEpNWHlnBHznw5hzbBT7/ZqPZZVkW5ZFS2K40mjbC8P6lHoVEIlkiuhrs3Pre8/n01Ru458AQL//aQ9zyVC/v/enTXPvfj7J/IMg/vnoT93/yMv7t2jN4+FOX8/5Lu7n3wBBXfvVBPnHrszN+aJJIJBKJpAxcCLwduEJRlGe1f68EbgKuVBTlMHCl9jOqqr4I3ArsB+4EPqiqak7heT/wv8ARoAdRHAlCOK/XiiY/AXxmUe5ZiQRjKZyWqY7t2YTturF9rCb9snFse0IJLEYdK+uFI9MTrv4vy2UhMgJqdoJje0SI3acjMW3hxQTHdssSFUcC6M2asB0rbPVjIJbCrU+gZJJFRZGoKpzyF+7aPuoJ86d9A7xii8jCHjOv5PKtSxUrQ0NigmUGVFXVhO2FdGwLYdtGnFCVxip6i4yCGCuQnCGO5LGeEc5dXYdBX4A8Fw/Cz69DiY/y3Y7/4P7BGcbQvUtc9txX0BjLgU9zEtfOEkXSpP2956Kr5kNyzLFdXmFbVSk4d38pyE2szDeKBKDFaWYoGK9oIb8Q8rSZSJaUpk2w/w5IRsYbgyUSyWmFXqfw/su6uWx9Ix+/5Vk+/Zt91JgNfOLKdbz7olU4zOMv3bV2E5+6egPvvmgV332wh588doLb9/RxRruLq7e08IotLazO09idyaocGwlzaDDMthUuOmpti3kXJRKJRFKlqKr6CPkzsAF2zXDMF4Ev5tn+NLAlz/Y4cN08hrmoBOMpVjdMea+NzuLozAliUS92k2FZFDeBcGy3OAy0hF8EpGN7jFymtqNZCNxqRjw/5lvCV41MiCJJZ7J4QomlK44E9JpjOxkr3LG9whKHDEUI2+I7/QlvlDVNM0eATOS7Dx7FpNfxz6/dzL0HhseF7THHdonCdngQWs6Y8erRWIpoMjO9OLKcGExkdUbsSvU6tr05YbHAKIiJBZKv39Ex6bq+QIzj3ihvP79r7hNlUnDrO8BzEN56K019XZz4y0FGwgkaHFMmiFq2gq1B5Gxvu76gcc6XnGN7toiMersZnVIux7YQtk1lLY8U50plVMqYcFJWvGMrBsoQReK0kEhnGY2lcM8SIVPpSGG70mjaCKjgOQTtO5Z6NBKJZAnZ2Orkjg9dyN37h7iwu2HWhukGh5l/eNUm/u6S1fxubx9/3jfIl+86xJfvOsT65hqu3tJCi8vCi/2jvNgf5OBAaCzXrtZm5Ifv2smZK9yLdM8kEolEIlk+TIsiScUgFSlI2LaZa5aNY3sknOQ1xqew//g/WWP8Bp7QqqUeUmWgCdt7/WZsCRvrQQiTp7WwXYc3kiSrsrTCtkWIzsl4gcJ2NEWHOQpRChe264R4XmjO9sBojNv3nuItOztpdlpY1WCnZ1gbn0MTtsMlRpGEhmDNlTNe3RcQBfYLKmwDWYMNezJOuEod274iM471OoVNbU72nZru2M7la184V3GkqsLvPwJH74drvgNrdrFdL1ZA7O0NcOWmKYWgOh10XyEc29ms+HmB8UeTWIw6bKaZZUa9TqHObi6rsF3WjG3NNZ9MZ7GaKlPZ9oaTOMyGshS8tmhRUIPBeFUL2zKKpNJo2iQuh2WBpEQiEW/Ur97aNquoPZGmGgs3XtLN7z54Ibs/cwWff80mXDYj37zvMJ+9fR937O3HpNfxlp2dfOW6bfzi786lxmLkrd9/nEcOj8x67ngqw6NHRogll8cXcIlEIpFIykEwNqU8MpqLWyjMsR1NVqe4MxVPKMFqvRDczrD5pGM7hyZsf/3xUX62Lzpp22lH1Ac6I5jsDGqFZUspbBvNQthOFejYDsSStJmE+FuosF1nN+EwGzjhLew2vv/QMVQVbrxkNQDdTfbxmEFbPSi60hzbyQgkQyJjewb6/OK+LWgUCZA12rETJ1TFjm2TXjdpFe1cnDFDgeTuIyPU202sm8vN/8BN8Nwv4LLPwva3AbC1w4VBp7Cn15//mDW7IDoCg88XPM754IskqStAHG2sKZOwncqgKGDUz5wZXyw593clF0h6I4myuLUBWrTX38EqL5CUju1Ko24V6M0yZ1sikcybNreVd124indduIqRcIJoIkNHrRWdbvKb/23vO593/N+TvOtHT/KN67fzyjNaJ12vqip/2jfAf/z5IH2BGA0OE++9pJu3ndc564y8RCKRSCTLnXQmSySZmZyxncsRnq08EjTH9noiy2TCeCScoNUt7nu3aZTHypChuiwIDQJwMGwlZtAek1Idt9VOzC/ytRVlrLCsZSmFbc2xnU4UHkXSbND2LVDYVhSFzjobJ3xzO7a94QS/fLKXa85sH4sJ7G50cNeLQyTTWUwGvbjdUjK2tefhbBnb/YHFEbYxO7ApVezYDieps5tQZinhnMqWdhc/2n2co54wa5uFiK2qKrt7vJzXXT/t+9kk9vwUHrwJtt8Al356bLPFqGdTm5O9Mwnb3VeIy557oe3MgsdaKr5IsiAzlhC25y+kJtJZzAZdUb+HuTBpInmqgoVtXyRZNmG72WmhBS8b73ortP9ifFVIlSEd25WGTg+N66VjWyKRlJUGh5nOelveD01NTgu33Hg+2zrcfPAXe/jFE71j1z1/KsB1//MYH/rFXmosBr70xq1sbHXyxT8f4OL/vJ/vPdSzbJxmEolEIpEUS85x6LROmOiNiqXlM0aRWMfLI+0mA8l0lnQFf4kuhHQmiy+apEEVwvZKg186tnOEh1HNToZiOg6FNRH3dHVsx/xjfxfDmrDd7Fq68kiTNSdsFxYTEoimaNLn3NMz/H3nYWW9jd4Cokh+tPs48XSG91+2emxbd6ODTFYdd3zbmyBSwsRITtiexbHdPxrHZNAVnB1dKjqzAwdxwvHUgt7OQuGLJIsu7tuap0Dy2EiEwWCcC7pnmSQ59TT84aOiDPLVX4cpIu6OzlqeOzma/z3E0SQy1Y8sToFkoYJro6N8USTljCGBCY7tdOW+J3vDSRrKUBwJoszzYv0+mn1PwamnynLOpUAK25VI00ZRCCCRSCSLhMtm5KfvOZfL1jXyud/u4yt/PcQnbn2W1377UY57I9z0+jP400cu5k1nr+Cn7zmX2953PpvanPz7nw9y8X/ez3ceOFJU27tEIpFIJMuBnLBdY8kTRTKTo9NgArNTOLa1DM9oqrpd275IElWF2rSINWtVvFLYzhEeImUTLrhR1Yqqt5Re/lft5BzbiExXvU6h3r50wrbZKkpfs8nCHdt1SggUPZhdBd/Oyno7J/3RaTEUEwnFU/xo93Gu2tQyqWSyWyuBHyuQdDSW5tgOz+3Y7gvEaHdPX91ZbnRV7tgeKcExO7FAMsduLV/7gu6G/AelE3DHB6GmFa77IeiN03bZ3ukmlspwcDA0ww3vgpOPQ2KG68uIPzrlcVFV2PtzSIQn7dfkNOMJJ1DVmf8eCiGRzpS1OBLGM7Yr2bFdzigSs0HPGaYB8UOwvyznXAqksF2JNG2EYB/EAks9EolEchphNen53jvO5nXb2/nWfUf443MDvO/Sbu7/5GVcv7MT/YQPuWd31fHT95zLb94vBO4v3XmIi/7zfl79rYf59n2HOTK88B+eJBKJRCJZaoKa49BpyefYnsWFZ6vThG1xXLTKCyQ9WuyIPSmcpI3ZEfzRVEW73haN8BAxU+65oJC0lBglsRyYIGwPBRM01Zgnfb5cbCw2JwDZAqJI0pksoXgaNyFxH4oo41tZbyOVURkYjc24z88e7yUUT/OBy7snbV/dKFzlPZ6Jju1Soki0VQI1s0eRtLkXPhpGZ67BoSQIVamw7Yskina15wokX5gkbI/Q5rLQVW/Lf9DDXxGGx1d/DSz5J1J2dIq/pxnjSNbsgmwajj1c1HhLwRdOUjsxY3t4P9zxAXj255P2a3SYSWVURmPzc+yf8sdoqinvxNhYeWSFCtuqqmorBsp3vzfqNUE72Fe2cy42Mhy1EskVSHoOQud5SzsWiURyWmHU6/jKddu4bH0j21fU0jnTBy2Ns1YKgfv4SIS7XhzkzhcH+a+/vsR//fUlVjfa2dbhJhRPMRob/xeMpVnb7OBjL1vL5eubZs1Fy2ZVXhoOsbapZkm/+EgkEolEko+g9sV8UnlkTBMYNAEvL7Z6UR5pFo7tSJXHenlCCQykMceFY9udEiKaN5Kg1bXAeb2VTniIoHX92I8RUz3m01nYbj0TgKFgfEmLI2E8ikRNzSw45wjmYoeywYLztXOsrBOfp094o2PZ2ROJpzL84JGjXLy2ga0d7knX2c0GWl0WeoZzju0mkdGuqtNiKWYlPAh606yvS33+GJeuayz8nKVislOjxAlXaXmkyNguXlg8o93FrU+fJJNVUYDHerxcsaE5/3ehwReEsL31zbDu5TOes6PWSoPDzN7eAG8/P88OK84Do13kbG94ZdFjLpRkOksokZ4s+PuPi8u+Zybt26iJ0Z5QAncBZZMzcWAgxGXry/t8rfQokmA8TSqjljUuqFvVYkhHpbAtKSdNG8Xl8H4pbEskkkVHp1O45sz2oo7parDz3ku7ee+l3QyOxrl7vxC5nzzmw2k14rIaWNVgx2U14jAbuefAEO/+0dNs73Tz91eu58I19ZM+1PUHYtz2zCl+/cxJTvpivOqMVr5+/Zljs+gSiUQikVQCwbEokimObbMr77LxMbQCuOXi2B4JJ2lkFAUVTA7siSFAxROSwjahIbzWccUpoKul7nQVtqM+sLoBGByNj7mRlwqdSYjManLuOL2cu9SeHS1a2M4ZRU54o1y4Zvr1v376JCPhJB+8PM+ViBiLsSgSeyOkY5AMg7km7/55CQ2JGJIZxPBEOsNwKLHwxZEAJjs2qjOKJJ7KEElmis7YhvECyWMjYZJpFX80lT9fO5OG338ILG64+qZZz6koCjs63eyZybFtMMGqi+HIvUWPtxgC0STA5PLIgCaY9u2ZtO9EYTtXpFksnlCCkXCCja3Oko6fCdNYFMn8YlIWCl9EPM7liiIhPkpdRsvsr+IoEilsVyKuFWByyAJJiURSlbS4LLz9/C7efn7XjPt89pUbuO2ZU3zr3sPc8IMn2Lmqjo+9bC2BaIpbnjrJQ4c9qCpcuKaeXRua+dHu46SzWb71lh1lz1KTSCQSiaRUxqNIJmZse8E2i1sbhDA2fBB7LmN7GTi2WxQtW7x9B4ZjD+EkInO2E2FIRRjKunDbjOgUhRHVxerI/qUe2eKTiglBdiyKZI7SvMXAoDnGU3ML2znhzpoaBdu6om6m1WXFqFc44ZseeZLKZPmfB49y1spazl2Vv5ByTZOD2545haqqKA6R1054uDhhOzw4a3Hk0Kj4W21fDGHb7BDCdhU6tnPCYimO2TPaxwskvWFxnvPz/Q08/h3o3wtv/GFBJaXbO2v56/4hvOFE/oiK7l3w0p3gOwp1q6dfXwa8+QRX/wntysMQHx2LU8kJ28PzeH84OBgEYGNLacL4TFR6xrYvIh6zskWReA4BEFRt1AT7qNb10VLYrkQURbi2pbAtkUiWKUa9jrfs7OT1O9q55amTfPu+I7z1+08A0Oqy8OHL13Dd2StYoS3d7Kq38c9/2M8Hfv4M//22HWVvwJZIJBKJpBTyRpFEfXM7OrUoEuuYsF3tju0EKwxadmzHOXDsIdoUnxS2wyKS5WTKSYvTgkGvMJB2QmREuDL1p9HX8Vx/lLWWWDJDMJ6maYmjSNDpiGNCSc8dRZJzbJuT/qId23qdwoo6G73e6QL675/tpy8Q41+v3TxjPF93o51wIs1wKEGzXRO2Ix6o7867f15CQ7Pu3xcQj0F77WI4th1YSBCOV9/rQ06QLsUx291ox2LUse9UkOPeCKsa7NMd8t4euP+LsP5VsPl1BZ13R6cbgGdPBti1Mc/kxZpd4vLIvbBzYYRtvyZsT8rYzjm2AfqfhdWXApMd26VycED0OW0ot2O7wqNIRsKlT6zkZVhMsj6S3cIrgs8VH3FUIUjbW6XStBGGXhRPLIlEIlmmmA163nF+Fw996nK+/Mat/Ohd5/DIp6/gEy9fPyZqA/zNhav4t2u3cM+BYW78yTPEU/kFgNFoipeGQmRnaZ2XSCQSiaRchDTHocM8JYpkTmG7DlIRHHohllV7xvZIOEG3RTjo6DgHgFbFK4VtTdg+lrDT6rLQ5rLSm3AA6njJ6OlCTHP02+oYDMYBaFlqYRtIKGZ0BQvbKoZE8cI2iJztE1OE7WxW5TsPHGFDSw2Xr2+a8djuRgeAyNl2aJnCxcbZhAfnLI4EFi2KBCAdn7u0s9LwjjlmixcWDXodm1qd7D3p54mj3ukrFrJZ+P1HQG+GV32lYIFxa4cbg06ZOY6kbjW4V0LPfUWPuVB82oqGSY9LoBc6dor/94/HkdSYDViMurHS4VI4MBCk2WkuXySHhlEvHvNKLY8cWzFQwvMvL8MHyeitPJ1dj5JJiIn5KuQ0miKuMho3wp6fiJlYx8xvchKJRLIcsBj1XHf2iln3ueG8lRh0Cp/97T7+9sdP8/13nI3FqOPgYIj7Dw1z/8Fh9vQGyGRVGmvMXLG+iSs2NnHRmgbsZvl2J5FIJJLyE4ynqDEbJhccx3zQuGH2AzVhzJERYnC1Z2x7QgkuMwYga4KWMwDoNgfmJVwsCzRh+0jExupVVswGHT09dlC062aJhlh2TChVHcoJ266lF7aTigVdOj7nfoFoihpiKNl0acJ2vZ0nj/lEnIgmWP51/yA9ngjfesv2WcvUu5s0YdsT5oKmnGO7CGE7nRCPv2NmYTvn2G5djN+JSdyfbDy88LdVZsYzjkuLgjij3cWPHxMRHRd0N0y+8pkfwolH4LXfAmdrwee0mvRsbHWy50Qg/w6KIlzbz98K6aTI3S4z0xzbqgqBE7DtevFcnZCzrSgKjTXmeU187h8Ilj1fGyZmbFemsO3V3lPLJuh7DpCqW0dfn/aaFuwD+xJHRJWA/KZfqYwVSB6QwrZEIpFoXL+zE4Nex/+77Tmu/e9HCcZTDIyKLyOb25y8/9JuOmqtPHx4hD/vG+CWp09i0us4d3UdrzyjlWvPbB9b9i2RSCQSyXwJxtKTY0ig8CgSwJ4W8R3LwbHdpvMLR2hNKyh6VhsDPHy6O7ZDQth+KWrnQpcFq1HPvqQDzBQnTC4HJgrbQ+KzW7OzTDmx8yCpmNFn5ha2R2MpahURf1CKsN1ZZyOSzOCNJGlwmFFVle880ENXvY1XnjG7iNlUY8ZhNnBkOAw7tYL3sKfwG9cmWGabSOkPxGhwmLAYF+FzsiZskwwt/G2VmfmW923RcrYBzls9IT979BTc/XlYdSlsf3vR593e6ea2Z06RzmQx6PMEM3Tvgqf/D049CV0XlTL0WcllbLtt2vthPACJoHCKt+2AU09N2r/RUbqwnUxn6fGEuXxD+XWySo8i8UaSOMyG8sVyDh9Av+pyBk9pz8VgP7RuLc+5FxEpbFcqTZvE5fCBsSwiiUQikcAbz+rAqFf42t0vsa3Dzcde1shl65tonrCc9fqdnaQyWZ467uP+g8Pce2CYz96+jy/deZAbzlvJ289fSVPN0ruEJBKJRFLdhOIpaiwTvlKlE5AMF1YeCVjSAaD6M7Y9oQSNVh/UtIFODzWtrEjJjG3CQ6g6AwEctLgs2E0GPLi1604zYTu3xN1aN+bYbq6AKJKU3oIhO3cUSSCaot2kRYkUUOg3la4GEbF3whulwWHmkSMjPH9qlJtef8bkFR95UBSF7kY7PZ4I6I1grStuYkSbYJnLsb0oMSQwFkVCohqjSJIY9QpOS2lS2tYONwAbWmrGCwBVFf74cVAz8JpvlJRxvKOzlp88doKXhsJsasvjZF51CegMImd7AYRtfySJy2ocK18cK450dwIqvHi7mIzRonQaa8wcH5m7tDUfPZ4wqYzKhjIXR0I1lEcmyxdDEvVBeAhjy2Zi1mbIAsFT5Tn3IiOF7UrF0STesIZPw8ZsiUQimYNrzmznmjPbZ93HqNdxQXcDF3Q38LlXbuSp437+9+GjfPv+I3z3waNcc2Yb77l4FRtayr+MTSKRSCSnB8F4CqdlSnEkFOzYNib8GHQ2olXs2E5lsvijKWpNXnCeJTa6OmjxemUUSXiIpKURNaqj1WXBYTYwomqOzdNN2J7g2B4c7cVu0lNjMc5+zCKQ1lkwpuZ+no7GNGE7RYmObSHm9voinLWylv++/wgtTguv2zH759kc3Y0OHjuq5bI7mop7/oQGxOUcju11zeUXCvNiFo5tUzZGIp2pqlJ4bzhBnd00a3TMbHQ32qm1GSe7jff9Gg7/Fa76D6hbVdJ5d3SKydQ9vf78wrbFKfKue+6Fl32+pNuYDV80NdnFniuOrF05/vfSvwfWXQUIYfvJY6XlOR8cFBFemxYgiiQnbCczldnX5A0nyxhDclBcNm3EVmsi7dVjCPaX59yLjCyPrFQURbi2hw8s9UgkEomk6lEUhZ2r6vjeO87m/r+/jOt3ruCPzw9w9dcf5o037+b/HjnG4Ojcy1AlEolEIpmIiCKZUhwJwqAyG9oXfSXqw2rSE6nijG2xNF+lJjUsHNsArnYaMh7p2A4PETWJ33Wry0K720oUCym99fQUtnVGMNkZCsYrwq0NkNZbMRXg2B6NJWkxlu7YXlFnRVGEY/uZEz4eP+rj7y5ZXbCo293kYGA0TjiRBnuj6OIqlPDsjm1VVekPxBfdsW1XYoTj1TWp54skS87XBlEgedfHL+FjL1srNoQ98JdPi9Ldc99b8nlX1FlpcJhmLpAEWHMFDDxXXIxNgfgiCWptEyaqAhMc263bQNFNytludFjwR1MlRX4cGAhhMuhY1WCf77CnUQ1RJPXzeP5NImeibdxAW50Dr1InokiqEClsVzJNG4WwrVbmbJFEIpFUI10Ndv7lmi089tkr+MwrNhBOpPmXP+7nvP+4lzfevJsfPipFbolEIpEUxjTHdqxAx7bFDSgQ9WI3Garase0JJXASxZCJjxeeOdtxJoeJJVNEEtV73+ZNaIhRvRBBW1xWGhxmjHqFsKHIKInlQMwnBGFFqShhO2uwYFLnnoAJRFO0GLTojBIc22aDnlanhRPeKN+5v4dam5G37Jy9OH0i3Y1CxDvmiZTg2B4UwqK9Ie/V/miKWCqziMK2cGzb0YT6KkIIi/NzzDbVWMYnNP7yKRFf9dpvixinElEUhTNX1LK3NzDzTt27xOXR+0u+nZnwRVKTBf9AL5hdYK0VDv3GDcKxrdGk5et7I8VPfh4YCLKu2ZE/S3yeVEN55Hyff2MMHwRTDbg66Ki1cSpbizraV55zLzIyiqSSadooChVGT4G78Dc9iUQikcyN22bifZd2875Lu+nxhPnz8wP8ad8AX/jDfr7wh/3YTXqsJgNWkw6b0YDVpMdhNrC60c76lho2tNSwrrlm0jLaeCpDry/KsZEIx0cipLMql65rZHObs+QlixKJRCKpXELx9OSM7Zxjey7hS28AqxuiXmxmPZEqztj2hBM0K5pLsEYTtl0d6NUU9YTwhBLYzafp187wEF7LKmrMBhzaY9DqsuLP1lKbc9GeLsT8QuQCBoNxzl45Rw79IpE1WAsStkdjKRr0YZFTbC4tAqGz3sajR0YYDiX4+yvXYTMV/nexpkmIwT2eMGfYm4p0bA+CvWlG4bQ/IBzr7e5FmmzQhG2bkiBUhY7tFbW28pzs4J9E9vTl/x80bZj36XasdHPPgSH8kSS1+cTP1jPFe9ORe2Hrm+Z9exPxR5Kc0T7h78J/QsvX1mjbAS/dKUybikKjli/uCSVodRU3oXJgIMRl6xvLMexpGPXi+1qqAh3bqqrijyapK1fGtuegeN4pCh21VgaydWRG+6pSJK7GMZ8+NG0Ul8MHpLAtkUgkC0h3o4MP71rLh3et5chwmLv3DzESThBNZoinMkSTaaLJDMF4mt/u6SM0wV3SUWulzW2lzx+jfzQ2bZHNl+86RIvTwhUbm9i1oYkL1zQsTuO8RCKRSBaUbFYlFE/htObL2C4gqsBWP+7YrjLX4kQ8oQQtina/nVoUiVPkBrcqIme7awGWjFc82QxERxgwOWlxjQuGbW4LHq+T1QsQB1DRxAJgrUVVVYaDCZpdleHYVg1WLCTIZlV0s5Q4BmIp6mpC4u+2RLNCV72dx4/6cJgNvOP8rqKO7ayzo9cp9HjCooAvGYZkFEwFiKyhIaiZvTgSWPQoEgexqnNs+8qVcRwLwB8/Ac1b4MKPzv98jOds7z3p54oNefLUdTpYfTn03AfZrPi5DKiqim+qmB7ohfru8Z/bt8OzPxPba1fSWDMubBeDJ5RgJJxg4wLka4OIitEpkKxAx3YwniaVUcvo2N4P618JiO+zPWodSui5scmHakIK25VMozZr5zkA616+tGORSCSS04Q1TY4xV0w+VFWlLxDj0GCIg9q/gUCMc7pq6WroYFWDna568S+ZyXL/oWHuOzDMHXv7+MUTvViMOnZtaOadF3RxTletdHJLJBJJlRJJpsmq5C+PnCtjG8aEbZupuh3bI+EJwvaYY1sI222K9/TN2Y54QM1yMumkpW6isG2lb7AGwgeXcHBLQNQHtV0iVzeTpaVCokgw2rCSIJ7OzOigVlWV0VgKlyNUUgxJjs56IULfcN5KXLbiijNNBh0r62xC2N6oFQ9GhsHUNffB4cGxyaZ8jDu2FzeKxEa8qjK2E+kMoUSahnI4Zu/+R/H7e8svwVAeoXJrhwu9TmHPiUB+YRtgzS544TaO73+SF7MrOTwcYkOLk6u3zDzxMReRZIZkJkudTbsfqioytrsvH9+pbYe47N8zSdgeLvL9IVccubF14YpOjXpdRQrbXq2Mub4cz7+wR6wwa9oEQEetjUfVOvTpGMQDY6trqgUpbFcytjrx4VAWSEokEknFoCgKHbU2Ompt7No4c7t8jjedvYI3nb2CRDrDk8d83LN/iN8928+f9g2wuc3J31zQxWu2tUkXt0QikVQZuSX006JITDWFCRW2egicxGbRMxJOLtAoF56RUJIV+oD4ISdsOzsAzbF9ugrbWtTIsbiD1gnu5Ha3ld6kA7I+yKRAX5zAWbXE/NC2fazHpFIytjFasZAklJxZ2I6nsiTTWWqyo/MSti/obmBL+wDvuWhVScevbnTQMxyBczRhO+yB2q65DwwNjQuLeegPxDAbdOVxIheC3kBWb8GeTlSVY1sU5TKv8kgAjj4Ae34inNrtM/9eisVmMrChpWasQDKaTNMzHOGIJ8ThoTBHhsP4Bi3cBvzqlz/kfzKvBaDZaZ6XsO0fe1y050/UC6kouFeO79S8BfQmUSC5+XVj4myx7w8HBjRhu2VhHNsgcrYrsTwy9/wrS3mkR9MYtQicdreVAVWbkB/tk8K2pMw0bRxvK5VIJBJJ1WI26Ll4bSMXr23k06/YwO/29vOj3cf4f7c9z01/Ocjbzu3khvNX0lQz+xc9VVX5w/MD/P7Zfi5d18Drd3ScvtmlEolEsoQE4ymAyVEkuYK8QrDVQf+z2FwGIr7oAoxwcfCEE2wzBcFYB0btPczegKo3057xnb7CdkgI24djdi6ekCHb5rayT3WLHyKe8fiW5U7MD1Y3Q8HKErYVow2zkmY4ngBHfsEoEBOCkj0zCtbSI0LPXOHmjx++uOTju5vsPPSSh4ytGT0UVkCaSYvn2RxRJO1u6+KuIjTZsSdik+L9Kh1veIqAWyp/+iTUdcNlny3DqCazo7OWW546yYU33TcWMQNg0Cl0NdhZ07qS4YE1vMd2jFe/7iLu3j/EN+49PL0IuQh8U4Vt/wlxOTFj22AS4nb/XkB8L3LbjEW/PxwcCNHitOTPEC8TJoOuIssjR8r1/INx82yjiD+2mw1ELc2QBYL90LJl/rexiMhvwpVO0yZ46n9FRts8WnIlEolEUjnYTAbeem4nb9m5gt09Xn746DG+df8R/ueho7zp7A7ee0k3K+qmZyY+c8LPv/1pP3t7A9TajNxzYIgv3XmI685ewTvOX1lUhqmqqjxzwo/dbFiwnDqJRCJZzgRjQpCZHEXiLULY1jK2jTqiiSqOIgklaNP5Jwu0ioLibKNr1M89p6uwrTm2Pap7kmO7zW3lflV73w0Pnx7CdioG6RjY6saE7ZYKydjWmcXnrXgsAuT/PDQaE5NYllRgXo7t+dLd6CCZydKfcrACxPNnLiIeQAXHzKsM+wLxxcvXzmGyY1PiDFdRFMmYY3Y+URDxUfAehpd9AYzlf8yv3d7Gi/2jdNTauP6cFaxpcrC22cHKejtGvZap/ddXwuM309igp79NPOd7hsNs7yzNpZt7XMbE5oAmbNeunLxj+w547paxfO+mGnPRwvb+gSAbFjCGBEQUSSqtzr3jIlOW51+O4QNgcU2a8FJcHeAHgn3zP/8iI4XtSqdpI6Tj4D8+OXxfIpFIJFWPoihcuKaBC9c0cGwkwvce6uGWp07yyydPcs22Nt5/WTdrm2s46Yty050H+dPzAzTVmPnSG7fyhh0dPHcqwI93H+enjx/nh7uPcdm6Rt527krO667HMYOLO5pMc/uePn7y2HFeGgoDsGtDEx992Vq2drgX8d5LJBJJdRMac2xPjCLxFS582eohk8BtSBFJVo+4M5WRcIImfFDTNfkKVwftoRE84dNV2B4EwKO6Jom47W4LnpxjuxBhcjkQE9EIWGsZDAhhu3EGd/Rio9PKF5Ox8Iz7BKIpFLKYkvOLIpkv3Y0im/pwxCqE7UgBBaTa83A2x3Z/IMbl6xvnP8AiUMwOHEqCo4nUot7ufJjmTC6FnJu5rrQ4mrk4a2Udt3/gwtl3WrMLdn8Tjj/CmqaLADhcBmF7rNQw0CsuXVNWN7TtEKZN72FoXE9jjbmo94dkOkuPJ8zlG5pKGmehGA3KgmdsP/iSh7teHOQLr908PuEwB76IeKzK4tj2HBQm2gmrNOz1bWT8OvTB/vmff5GRwnal0ySWBjC8XwrbEolEsoxZ1WDnP16/lY/uWsf3Hz7KL57o5fa9fZy7qo69vQH0OoWP7lrLjZesHose2dFZy47OWv7hlRv5xZO9/PyJXv72J0+j1ylsbnNyTled9q+WcCLNTx47wa1PnyQUT7O5zcmX3rCV4VCc7z98jNd++1EpcEskEkkR5KJIaqY6thvWFnYCTSCr14WIJjOoqlqVhcKecII6nRec50++wtVBy8mDp28USXiYpNFJIm6a5NhudVnx4BI/FBIlsRyYIGwP9SZocJgwGQoTcxYavebYTkQjM+4zGktRQxRFzSyxsC1W5h3xJbnC4ipsYkSLxMGRX9hOpDN4Qgna3dNXCi4kismBUxerqvJIrybgNswn49h/XFy6V86624LSeT4YbdBzL51rXo5Jr6NneOaJnbnwR/M4tq21YJmyAiKXJ963RwjbDjN7egMF306PJ0wqo7KhZWEd26ZFKI+8Y28ft+/tw2E28LlXbizomJFwkhqzAbNhnkkOqir0xc2vn7S5ra4Gj+qmOdhHtX0SkcJ2pdOwXlwOH4CNr1nasUgkEolkwWlxWfjHV2/ig5ev4Ue7j3P7nlO89sw2Pvny9TMu221yWvjYy9bxgcvW8PhRL08d9/HkMR8/e/wEP3jk2Nh+Bp3CK85o5W8uWMmOztoxAeWdF3Txk8dO8P2Hj/Labz/KFRua+LuLV3Pe6rqqFFkkEolkMRiPIpnwlSrmB2sRUSRArRImk9WTzGTn/4V1kUmms4SjMRwWP9RMidRwtuPOePEFqzc/fF6Eh4gaxe+41TkeOWA3G0hbGkBlLK5k2TMmbIsokrn6RBYTg1m4oNPxmYW90WiKOiUkflhCYdttM9HgMIkCSXtTYRMjY47t/FEkuTLPNvci/05MdmqUQJVlbCcw6JTJq3SKZaaYjsXEYIaui+DIvRheqWNVg50j8xC2vZEkRr1CTW61aKA3v3DfsA6MdujfA2e+hcYaM8OheMGTurniyE25CMXggPgbaN1W8tjzIaJIFlbY7vVFURT43kNH2dHp5uotrXMe44skyxNDEhoUkThNkwX1jlpRIFnnP8Ui1ciWDSlsVzpmh3hRyIW7SyQSieS0oM5u4hNXruMTV64r+BiTQccl6xq5ZJ1YTppIZ3ihb5Qnj/nJqipvPKsjb1lTjcXIBy9fwzvOXzkmcL/l+4+zqsHOm89ZwRt2dNBYUxlLhiUSiaRSCMamOLbTSUgEi4siAdxqEKglmshUnbDtjSRoIoCCCs4pX8xd7ejIoosMkc2q6HSn2URpaAi/vg6rUT9NCKtzu4kFbFjDBURJLAeiPnFprWVwdLRi8rUBjBbhgk7GZ3ZsB2JJ6lh6YRtEHEmPJwyOJijk+RPShG17/viGPr8oGGxf7IxtswO7kqgqx7YvkqTWbpqf6cN/QmQbW0uL/Sgb3bvg8F/Bf5w1TQ5e6B8t+VT+SJJa24THxX9immgKiM64tjOFYxtorDETT2UJJ9KTVz7NwMHBECaDEOIB+OPHhUj+yZdKHns+zIaFd2z3+qJcs62NY94on/z186xrrmG1FjU0E95IokwxJJq2OIOwvSFQfRnblbH+RzI7TZuksC2RSCSSojEb9Jy1so73X9bNBy9fk1fUnkhO4H7sM7v4ynXbaHSYuekvBzn/P+7lfT99hnsPDPHEUS/37B/i9j2n+NGjx/jWvYf51r2HOeU/TR15EonktCWUSGMx6sYjFWKaeGcrULDQBDJnVrjQqjFneySUpEXR7vc0x3YHAI3qCIFY9eTolo3wECOqi1aXZZoQ1ua24lXcp6Fju5bhUHzOzyOLidFSgGM7lqJep11faDnsAtHdpAnb9sbCHNuhQfFaY8gviPUFhLC9+OWRDuzECFeTYzuSHM+RLhX/8aWNIcmxZpe4PHIv3U0OTvqixFOllRj7IslxwVVVYfTkzI70tu0wuA/SyTHTTKFxVQcGgqxrdmDQ64TjuOde8Rqae30pE0a9jtQCCtuxZIbhUII1TQ6+87YdGPUK7//ZHqIzfQZQVbjrH7jIexv15egmGD4oLhunCts2BtU6jJF+cZtVhHRsVwNNG+HI3ULczjfzNR9CQ/A/F8JrvgkbXlnec0skEomkKrGa9LzhrA7ecFYHR4bD3PJUL7/Z08edLw7OeMw37j3M63e08/7L1ow7KSQSiWQZE4ylcE7K184J24U6toVAVqMKp1w0WZqosJSMhBM0K5qokMexDdCmePGEyuQ0qxZUFcJDDBjOpKVuuojb7rYwdMxJx2lWHpk0uRkJJ2l2Vs4qMJNVfGbJJGeeoA9EU7SboiI+pgIc2/5oiri5Hkshju3w0Iz52gD9WpnnorvoTXasxKtK2J4k4JZK4AQ0ri/PgOZD/RpwdULPfazZ+AqyKhwbibCx1Tn3sVPwaY5tQDzf0vGZxfv2HZBJwPB+mmrE5KcnlJjTrQxwYCA0XnJ66E7IiGxvvEeh46yixz0TIopk4YTdk5oZqLPeTrvbyjeu3847f/gk//DbF/jqm7ZNXxGw96fw2Ld5HQ2csL19/gMY3i9exxyTC2Pb3cKxbUhHxeozi2v+t7VISGG7Glh3tWit/c550LIVtl0PW94wa7NxwTzzI9GmvP8OKWxLJBKJZBprmhz8w6s28cmr1vPkMR86RaHGYsBpMeK0GqmxGPCEEnzvoaP88slebnvmFK/Z1sYHL1/DuuaFLXeRSCSSpSQYT+G0TimOhMIzts0uUPTY00LYjlSRwJPDE0rM4tgWwnarJmyvX+DCr4oiGYZUlN5sTV7BsM1tZTDjJBMeorrCZ0ok5gO9ieG4WN3QUkGObbNNCGqZxMzC9mgsxRmmCCSoAGFbCPEjuOhIjEIqDsZZHs/Q4Ky6QX8gRmONGYtxkZ+JJgcWtbrKI32RJFva5yH2ZbMipmPdVeUbVKkoCqy5Avb9hjUXiYmmI8Ph0oTtaJKNLdpxgV5xOZOw3aYVSPbvoXFFNyAKiOfCE0owEk6wITe+/b8Dg0WI6N4jZRW2TQbdgq4y6vVqwnadKGy9ZF0jH3/ZOr5690vsWFnL28+b8NgNH4Q/fwrV7KQlMUK3vgyrfDwHRSrEFOxmAyFTk5jAC/ZXlbAto0iqgc5z4RMH4Or/FLlEd30OvroRfvo6OHJP6efNpODp/xP/P/5w1S03kEgkEsniYTbouXhtIxeuaWBrh5uuBjt1dhNGvY42t5V/fu1mHv705fzdxau5e/8QL//aQ7z7R0/xyyd7x5a5loKqqqjy/UkikVQgoXh6SnFkkY5tnQ5sdVjTAaA6HdsezbGt6s3TIxosLrJGu3Bsh+NLM8ClIiTEh2MJB60zCNsjqgtCVeDYjo/Cnz8Fj3y99HPE/GCtZSgkHJbNFZSxbbYKYVudxbE9GkvRqI+A3gSmpV2V1q05W/tT2kRRZA7XdnhodmF7NLb4MSQAJgdGNUU0Xj2vDSPhxPyiSMJDwq1cCVEkIHK2kyG6EwdQFEoukPRPdLL7tXJMd2f+nWu7xORv3x4aHYVHkeSKIze21kA8CEfuhTPfBijg6ylp3DOx0OWRvb7JwjbAhy5fw2XrG/mXP7zIsycDYmMqBre9G0x2Im/4BQCb43vmd+OqKsTyxg35r9YmpAlWV862dGxXC44mOO994p/nJdh3Kzz3K/jF9fD3B8HeUPw5D/xBtCSvuxpeuhN8R6G+u/xjl0gkEslpQVONhc++ciPvu7SbHz56jF8/c4r7Doov7d2Ndi5Z18il6xrZ2OpEQRgCcpq1ispQMEHPcJgeT+5fhBPeCOmsilGvw6QXWbZGvYLVqGdjq5PtnW62d9ZyRrtr8d1GEonktCYYS+G2TRA5co7tYhydtnosqYA4vBqF7VCCs/UBlJoW4QCciKKAs53WuI/eAjNUlw1advZg1sV213TRsM1t5ZDqRp8chXQCDJUTzTGJnvvgjg9D8JTIdL7wo9N/z4UQ84O1jqGgEDGbaypH2NabhLikpmYXthuUsPjbnk9xYBlod1sxG3Qci9vZCSJn270i/87ZrBZF0jzj+fr8MTa0LsFqCm2CQE2UJqYuNsl0llA8Pb8okoAm+tauKs+g5svqS0HRYz5+P511F5UkbGeyKoFYitrc4xKYQ9hWFBFH0r8Xl9WIUa8wXMD7w8FBTdhuccJLd4gJgq1vEpG93iNFj3s2TAZlQTO2e31RHGYDtbbxFV86ncLX33wmr/rmI3zgZ8/wx49cTN39/wDDL8LbfsOQ60wCagNdwafnd+OjpyAZgqb8wraxth1GEY7tKkIK29VI4zq44v+Dza+Hm8+H52+B8z9Y/Hme/L6YMXvZF4SwffxhKWxLJBKJZN7U2k184uXr+fiV6zgyHObBlzw8dHiEXzzRyw8fPT7n8Qadwsp6G92NDnZtbMKs15HMqCTTWVKZLMm0aFDf1zfKX14YHDtmU5uTs1bWctn6Js5bXYfZMLPQnUhneP7UKE01ZlbWy0xwiURSPMF4ms6Jrx9jwnYR5XK2ekyJgDi8Gssjwwk69H5wtuW9XnF30D5ynGdOU2Hbo7rzOrbb3VZG0JZ5h2cRJpeKRBju/id4+gdQv5bU1rdhfP7nItZiapZ6IUQ1x3ZwifKcZ8OoTTykZl5dFoimqNWFljyGBIQAtrrRwUth7TGcLWc75oNsekbHtqqq9AViXLGhaQFGOgdm4TzXpaKkM1lRCFjB+KNitcG8hG3/cXE5U7HiYmNxQcc5cPR+1jReXZKwHYgmUVWoy4m0gRNiEsxkm/mgth3w8FfQpWM0OMwFOrZDtDgtQkDf/zuoaYWOnSIr3Ftex7ZJryO5wMJ2Z51tWpa222bif244izfcvJuf/t+3+Kj3B3DBh2Hty/Ad99GT2cIbvE9ANiOSHErBoxVH5okiAXA0rCB7TIHRvqqK95DCdjXTvAnaz4I9P4XzPlDc7PHgPujdDS//oigvcLTAsYfhrL9ZsOFKJBKJ5PRCURTWNtewtrmGv714NfFUhieO+Tjpi469ZSkoY/+vs5tY0+Sgs86GscAvOJ5QgmdPBtjT62dvr59fPinEc7tJRKdcsbGJKzY0UWsz8ULfKLt7vOzuGeGp4z7iqSxGvcKnrtrAey5ahU63tC4siURSXYTiqclRJFE/mBzFuW9tdRiGXwIgkqg+x/ZYeWRN/jI0xdVBm7KnIOFiWaEJ28OqO6+I21Rjxq+4xQ+zOW6XguOPwh0fEJEC538Irvj/+OaPb+Xv+TkMPFeasB3zQ20Xg8E4Jr1uklNxydGE7dkc24FoEpc9VNyk1QLS3WhnX6/2OhOZJc4mpJV+z+DY9kWSJNLZJYoiEZOCNiVOJJHBZatsGc0bFsL2vKJIcjEdrgr6e19xDjzxPdbusPDw4ZGiJxnGBH8tVoRA79xRK+07QM3A4PM01hQqbAdFDEkiBIfvFrqVTgd13XDyKbEEtEyrKRYjimTNDGWZZ3S4+PLLa7nsvv9isGYTLVf8EyCef49mt3B98gEYeFbogKUwfEBczhBF0lbvZAQXNd6TLMGrQslIYbva2f52+OPHoG9PcYH5T34fDFbY/jbxArDqYjj2UFlfECQSiUQimYjFqOfSdY1z71gEjTVmrtzUzJWbxJe2eCrD7p4R7jkwzH0HhrnzxUEUBWxGPRFtmf/65hquP6eT81bX8du9fXzxzwd48CUPX3nTNporqNBKIpFULqqqEoylqbFMKY8stDgyh60efdwvDq9Cx7YnGKc+653RsY2zg3oC+EOhxR3YUhMeIqMYCOCgNU8UiU6nkLU3iTLCcIXkbCejcN+/wuM3C0fpu/4MKy8A4K/eRj6uKqj9z6Jff3Xx5475oX07Q6NxmpzmaU7FJUVvJIUB3QyO7UxWJZRI47COgq0yVjd3Nzq4Z58ZzMz+/AlrwvYMju3+gHDQt9cuTcY2gIMYoUQKVyVNduTBFymDYztwQpTszlb2udg0b4FMgm02L8lMlpP+GKsaCl/J6IuIksU624SM7bbtsx+UK5Dse4ammp1jz8OZSKaz9HjCXL6hCV66S8SQbL5WXFm/RkRrRDwivrcMGA1ipehCkM2qnPRFZ14lkUnz2iOfJ6GH13j/ji8cCXD5hia8kQS7s5vFPkcfnJ+w7WiecZKuo9bKgFqH2S+FbclisuX1cOdnYe9PCxe2Y354/laRSWStFdu6LoZ9v4aRl4SDWyKRSCSSKsRi1HPFhmau2NCMeq3Ki/1B7j0wjCccZ+eqes5fXU9jzbib8qrNLdzy1Em+8If9XPX1h7jp9Vu5esvMJUsSiUQCkEhnSWayOK1TyiOLdXTa6lGiXkCtSsd2IuzHREIsC8+HSyuiGq2uvM55ExoibKzHlNLP6E42ulpgmMoQtk8+Bb97n8iqPedvRVSlFhWRTGc5HFA5amyl5cQe8vsM5yDmE1EkQwlaKnACOaGY0aXzC9uheApVBVt6tCKiSADWNDmIqyYyRgf62cojtRLTmRzbuXLv9iUqjwSwKQnCicqf1PNGhKu43jGPPHz/8cqJIcnRLMTSDUovUM+R4XCRwrZ4XGrtRhGRMXpqXHSeiZpmcLaLAsmai3nu1Oisux8ZDpPKqGxsdcL+O8TzecW54sr6NeLSe6RswrZJryOZXpj34+FQgkQ6y4q6GaJaHrwJ5eTj6K75PvaHWvnYLc/yxw9fhC+cxIuLbNNmdEcfgIs/UdoAPAegaeOMV3fU2jim1rFKZmxLFhWLS7xwvPAbuOrfZ88yyrH355COwc6/G9+26mJxeewhKWxLJBKJZFmgKApb2l1saXfNus/1Ozs5Z1UdH/vVs7zvZ8/wlp0rePXWNkbCCXyRJN5wEm8kQSCaos1tZUNLDRtbnaxpcsjCSonkNCUYEy4151THdinCtpqhwRCvOsd2Ip3BlhgWrtGZ4imcQtg2RgYWb2CVQHiIgK6WVpdlRneyvbZ16YXtdALu/3fY/U3xu3rHHbD6skm7nPRHyarwotpF29Bzxd9GKgbp+FjG9sZWZ3nGXkaSigVdJr+wPRpLoSOLOVU5wna3FmMQN9Vjn4djOydsL2UUiZ044Xjlv/aVLYqk66IyjahMNKwDnYG2xFFywnZuFWQh5Bzb9XaziL7JpmYujpxI23bo30PjBjPecIJMVkU/QyRgrjhyU71exJBsv2E8Y7p+tbj09oytMJkvJoOO1AI5tnt9IvJoZT5h++iD8NB/wZk3YNr+Jm5eEeE1336ED/x8D1vaXdRYDOi6LxfpC6nYeD9AoWSz4DkEO9454y7tbiu71TrM0YPFnXuJkcL2cmD7DfDcL+HA72Hb9bPvm83CU9+Hzgug5Yzx7bWrwNkhCiQnCt4SiUQikZwGdDc6+M37L+Br97zE/zzYwy+fPDl2nV6nUGsz4bQauO/gMAktd0+vU+hutLOlzcXfXryaTW2V92VdIpEsDEFNiKmZlLHtg7rVxZ1IE8rajBGiyepybHvDSVoUn/ihZoYoEleHuDoxRDKdxWSo7BzdshEewqO6ZnUnN9e5GFXt1ISHlqakq38v/Pb9wsG3/e3CJGWZ/j52fCQCwAvZLq6J7YaIF+xFCLwxEbWjWusYDMa5bP0SFBXOQVJnxpDJH4cQiKZwEUZBrRhhe1WDHUWBUb0b+1yObbNrRgGsPxDDYlyizHPNsW0nRqgKHNu+SBK9TsFlLfGxSich2Ae1XWUd17wxmKFhHWbvAZpqLiq6QDKXse22GaFfyxCfK2MbRM72wT/SbkmQVYUjvqkm/+vlgYEgJoOOVf5HhUFz0zXjV7o6QWcUju0yYdQrpBaoPDInbHdOFbYjI3D7jcKB/sovAdDVYOcr123jxp8+w/6BoDhm1aXw2Leh93Hovry4Gw+cgFQUmvLnawPYzQZGjU2YM2GRZ26uKe42lggpbC8HVl4oPkTv+encwvaRe8QSmF2fn7w9l7N9+K9C/NadJh86JRKJRCLRMBl0fPrqDbxuezv+SJJ6h4l6uxmX1ThWLJnJqhz3RjgwEOTgQIgDA0HuOTDEb5/t4407OvjkVetLyunOZFUUkAWWEkmVEIxrju2JIkfUV7zwpe3fYowSqTLH9lhxJMzp2G5VvHgjibx508uS8BB9me205imOzNHmtuJRXZgCA4ufZXr4HvjFm8TS/bf+Gta9fMZdj2nCtsexQWSCDz4H3VcUfltRMfkRNziJJjM0O+cR5bBApHWWmYXtWIo6RcuILzZDf4GwmvS0u614si7atKLSvIQGROzDDPQHYrS5rUuTea5F3diVRHU4tiNJam3G0j+njZ4E1MqLIgERR9L7OGubHRwZLq4PwRtOYjfpxQrGQK/YWIiwreVsr069BFjxhGYWtg8OhljX7EB/4Fdgb5zszNYbxGSBr6eocc+GSa8nnVXJZtWyfy7v9UXRKVNWSWSz8Lv3i0nAG24bW80A8PLNLbz/sm5ufqBH5LuvPBt0Bjj2YPHCtkdzYTfOHEUCkLK3QhgIDkCjFLYli4WiwJlvE2Uf3h6on6XU4snvgaMFNr5m+nWrLhHOb8+BsawliUQikUhON9Y1z/whTri0HXQ3Onj1VrFtNJri2/cf5se7T/DH5we48ZLVvPfS1dhMkz9m+SJJjgyHOe6NcMofo88f45Q/Sl8gxsBoHJNex+pG+9j51zQ56G6ys7apZsblmRKJZGmYFkWSSUFitITySLF/syGCp8oytj2hBC1ojm3HDN0EJhtJk5u2tBdP6DQRtjNp1MgIJzM1tMxyf9vcFkZw0RycRZhcKA7+UQiLH3hsvHNpBo57IzgtBmq7z4b9oPY/h1KMsK05tn1ZO5ChZRaxf6lI6y0Y0vmF7dFYilo0sa/YqKEFpLvRwakhB9sy+2beKTw0Y742CGF7SfK1YUy8sxGvioxtXyQxv+JI/3FxWYjou9g0b4Z9v2ZLZ5afPx9BVdWCJzv80SS19gnFkTC2UmdWtILJtshBYDueUGLGXQ8MBHn5WqcwYW67fjyGJEf9GqGDlQmjQdz3ZCaLZeptzZNeb4RWl3Xy6qUnbhb37RVfnpyqoPH3V67j+EiEtU0O8brdsROOPlD8jQ8fEJezOLYBdO42Tdjug8Z1xd/OErAkwraiKG7gf4EtgAq8GzgE3AJ0AceBN6mq6tf2/yzwHiADfERV1bu07WcBPwKswJ+Bj6qqujBhOJXOmW+F+78Iz/4Cdv1j/n28PXDkbrjsc6DPs4SmK5ez/bAUtiUSiUQiKRCXzcg/vGoTbz+vi/+86yDfuPcwv3yyl7fs7MQTTnBkOEzPcBhvJDl2jKJAi9NCR62Vs1fW0l5rJZ7KcmQ4zJ5eP79/bry0pcVp4ZrtbbxhR8eMovuxkQgPHBrm+EiEG85bydpZxPkc0WSaZDqL2zaPL2oSyWlKSHMYunLlkZp4V0rGNkCjPszxKnRstyh+MpZa9MaZxcqMo43WmG9W4WJZEfGgoDKQdbFuFhG33W3loOpCCS9B/rjnEDRtmlPUBjg+EmVVg53uzg56X2ikoXcPBbQ6jaP9bQxn7ECwpFVNC01Gb8WUjeS9bjSapFbR4hkqJIoEhLB99LgNdH4xsZbv+31ocLxkLw99gSXMPDeKZ5FDiVWFY9sXSYoc6VLJCduVFkUC0CzE1B2WAb6bsDAYjBc8CSkeF+1zZKBXFAnP8n4whtUNdd3UBvYxm7DtCSUYCSfZZXhexGhsunb6TvXdcPT+siUPmPTiHKlMtuxdOr2+KCvrJ7yC9u+Fuz8P6181YySwQa/j5hvOGt+w+lJ44CZtlVgRnzmGD4hVVJaZu4cALHWdcAqyo31LE5NVAkvl2P4GcKeqqm9UFMUE2IDPAfeqqnqToiifAT4DfFpRlE3A9cBmoA24R1GUdaqqZoCbgRuBxxHC9tXAXxb/7lQAzjZY8zIhbF/+uemzWABP/UDkD531N/nP4V4hXmiPPQTnvW8hRyuRSCQSybKjs97Gf791B+++0M+//Wk/37j3MG6bkTWNDq7c1Kw5sB2sbrBPd2tMIZbMcHQkzMGBEH95YYAfPHyM7z54lM1tTl6/o4OrNjdzeDjMg4c83H9omBNekdln1Cv87Ile3n7eSj72srV5RevRaIr/e/QYP3z0GKFEmu0r3Oza2MyujU2sb65ZmiXJEkmVkYsiqck5tqNecVmisF2vC1VdxvZIOMl6xYfinCFfW0NxtdM2cojnThdhW4uG8KguLplF2G51W3lEdWGMv7BYIxOoqlihOzGndhaOjUQ4u6uWzW1OXlBXcelAkQWSMeHqH0hYqFxh24JJ9eW9bjSWojYXRVJJwnaTnf0ZJ+iAiEfoARNRVfFcnCGKJJbMMBJOLE1xJIBOj2q0YUsnqiJj2xtOsnE+XSqBE6A3CeG30tBMjWs5AaznyHC4YGHbH02OO9kDJworjszRvgPr8UcA8ITzvz8cGBDFkVtDD4i/v5UXTt+pvlsU1Ab7hKY1T3Kfz5Pp8uds9/pivGyj1jOQCMFt7xaRUNd8W7heCmH1ZfDAf8DxR2DTawu/cc8BaJzdrQ3gbBKPYXSkF0fhZ19SFl3YVhTFCVwC/A2AqqpJIKkoyjXAZdpuPwYeAD4NXAP8SlXVBHBMUZQjwE5FUY4DTlVVH9PO+xPgWk5XYRtE6cetb4ee+2DtlePbMyl4+Cvw5Hdh8+tnzdmi62JRQpnN5BfHJRKJRCKRzMpZK2u5/f0XEEqkqTEbShKKrSY9m9tcbG5z8YazOhgJJ/jDc/3cvqePf/3jfv71j/sBsBh1XNDdwHsuWsVl65qwm/V89e6X+Mljx/nds3184sp1vHVnJwa9Dn8kyf89eowfPXqcUCLNyzc1s7HVyf2HhvnyXYf48l2HaHdb2bWxiV0bmzl3VV3ZnSoSyXIhGBNCzFgUiZYjXLTwZXKA3kQd4aoTtj2hBJfrAuica2bdz1jXSevR3dxzmgnbw2rtrBnbDrOBoKFelHSlYjMW/JWdyIhwURcgcMRTGfpHY3TVd7Chxcm9ahevDD8J8dE5XX9jaI7tk3HxWMxWqLlUqAYrZjWRN4IhEE3RrNfc3JUkbDc6eEjVfgfh4enCdnxUiH0zxAT9df8gAGeucC/gKGdHMTlwJxMMVoFj2zvRmVwKfk30rcQus5oWsNbRGu8hJ2xfvLaxoEO94SRrGjX5M3ACVpxX+O227UDZ92tWmUMzOrYPDAQxk6Sh7z7Y+iaRqT2VOi2K13ukLMK2ccyxXd4wiGgyzUg4wYo6m5h4+uMnhJP/b/5U3KR4+1nis8PRBwoXtrMZ8LwkyifnoK3BzYjqRJXC9qysBjzADxVF2QY8A3wUaFZVdQBAVdUBRVFydcntCEd2jlPatpT2/6nbp6Eoyo0IZzednUXMIFUb664GWwPs+cm4sD18AH77Xhh4Dra+GV7xpdnPseoS2PtTGNwHbWcu+JAlEolEIlmOKIoyLniVgQaHmXdduIp3XbiKw0MhHjjkYV1LTV7x+YuvO4MbzlvJv/5xP/90x4v87PETXLimgVufOkkkmeGVZ7TwocvXsklzHn38ynUMBePcf3CYew4Mc+vTJ/nJYyewmfRctKaBXRubuHx9E00VKEZIJEtFKJ7CqFewGDWRIufYLjZjW1HAVo+bINEqiyLxhBO06HwzF0dq6N0duJUIo6P+RRrZEjPBsT1XnnTW1gBRhDC5WKVyYwVi6+fc9aQviqrCqgY7VpMer3OjGO/gPui6qLDbi/lBb6Y/DE6LAaup8iZMs0YrdiVJIj09eiAQS7HKGAHFCqaiQlgWlO5GB56csB3xTN8hVypZk1/Y/tnjJ1hZb+OiNQ0LNMICMNlx6ROEE6mlG0MBpDJZRmOp+WdsV2K+Noj3oebNWHwHcVpey5HhcMGHjjm2M2kY7YMzinNsA1xoPcFwKH+W88HBENc4DqCkojOvMqnXJld9PcUXKuYhJ2yX27Hd6xOrKzvrbLDv17DvVhETPLEMsxD0RvH6W0zOtu8YZBLQNHtxJEBHrY1BtY6G0b7ixrWELIWwbQB2AB9WVfUJRVG+gYgdmYl8Nid1lu3TN6rq94DvAZx99tnLN4PbYBJh+k98V3w4eu5XcN+/gbkG3vTTwmZzcjnbxx+WwrZEIpFIJBXI2uaaOTO0N7Y6+fnfnstf9w/xxT8d4Ee7j/PqrW186PI1rG+Zfmyz08L1Ozu5fmcn8VSGx456ue/AMPceGOKv+8WX460dLl67TWR9187ny51EsgwIxlPUWIzj7s5YiY5t7RhnLESkysojfcEwdeoo1MweRZIrEssETgE7F35gS01IvGb6dW4a5sjk1dU0L6GwPbdj+9iIcCp3NYiiP33bNjiCME0VKmxHfWCtZTCUqMgYEgAMVqwkiCUz04Tt0ViKRn0ELJXj1gZocJhImLUxhYen7xASjux85ZEHB4M8ddzP5165Ad1SllObHNToEhVfHumPio6UeTm2AyfGhNyKpHkLyp4fs7bRVrCwHU9liCYz4jNhsA/UTHGvYy1bQdGz3XCMW2ZxbH/e9BQY68a1qqnUtIrM9jIVSI5FkWTKLGxrsYGratLwm89BxzlwySdLO9mqS+GlOyFwsjCXukcrjmycW9hud1vZrdbTshT9DyWyFML2KeCUqqpPaD/fhhC2hxRFadXc2q3A8IT9J/6mOoB+bXtHnu2nN9tvgMe+DTdfIGZuN7waXv11cBS2lARnK9SvFQWSF3x4QYcqkUgkEolk4VAUhas2t3D5+iYiiXTBYrTFqOfy9cKl/S/XbObQUIh7Dwzz1xcH+bc/HeBLdx7iqi0tvOWcFZy3un5pvxRLJEtEMJbGaZnwVarUjG3tGGckUHWO7THhbA7HNk6xqFYXrB7317wIDxHR1VDrdM75+mhyt8AQEMkjTC4UnoNgdhWU9Zvrb1hVL4Ttzs4uBg7XUXdyL+bzC7y9mF8I28HEnA72JcNkx0YCXyrD1DrN0WiKeiVU2t/2AqIoCq6GNhgh//NnFsf2zx4/gcmg47qz5h/bMC/MDhxKdKyMt1LxaeXf9Y4SyyPjo+LvoBKLI3M0b4ZUlHPdIW45WtiKw9zjUmc3QeC42FhMxrbJBk2b2Bg6wkgeYTuZztI77GOH9XHY+ob8Bakg4l3qVpdP2NaL1+1UuYVtzbG9Zv93RCTU235devzv6svE5bEHhQY4F8OFr9Sxmw34DQ3Y4odLG9sSsOgBP6qqDgInFUXJPaK7gP3A74F3atveCdyh/f/3wPWKopgVRVkFrAWe1GJLQoqinKcIq8Q7Jhxz+tK0ETovgHQSXvc9ePPPChe1c6y6GE7sFstJJBKJRCKRVDUmg65kh7WiKGxocfLBy9dwx4cu4s6PXcxbz+3koZc8vPV/n+DyrzzAzQ/04J2h9EciWa4E4ymc1glfsqM+4RgrJSfZVo89M0o0mSGbrZ7FpYaIJmzP6dgWwrYpWj3ur3kRHsKvmz1fO4ejXjw28cAiPjaeQ0LcKKD/4Zg3Qq3NiMsmnuub21y8mF1Juu/Zwm8vFgBbHcPBOE01lSlsK0YrViVJLJGcdt1oLEUtlSdsA7Q3NxLDDOE8USQzOLbDiTS/3dPHq7e2Lv3qK5MduxKveMe2NzxBwC0F/wlxWalRJDBWILndfApvJIk/Mv1vYSqThe1esbHY+9i+na7EITyh+LSrjgyHuYDnMWeisPna2c9T3y0ytsvAQpVHnvRF2WoZwrzn+0KMbtte+smaNoK9qfA4kuH9YtLBXFhqdtzagi0ThGS09DEuIkuVXP9h4OeKojwPnAn8O3ATcKWiKIeBK7WfUVX1ReBWhPh9J/BBVVVz6/TeD/wvYkFUD6dzceRE3noLfHwfbHtz4c2qE+m6GJIhGHi27EOTSCQSiURSvWxocfLPr93ME5/bxdfevI1mp4X/vPMg5990H39/63PsOzW61EOUSBaFUDxNzSTHtq/0YjlbPda0+NuJpaojjiSeylCT1MS0uRzbNW2oKDgSQws/sEogPMRQdu58bQB3o5gUiHgXceGx52BBrj2A4yORsRgSgE2tTl5UV2Ed7Slc8Ij5UC1uhkMJWlwlOl4XGJ1Z3Md4LDbtukAsiVMNVlRxZI7uJgeerJNUcHD6leEhMdlmnhw/9tu9fUSSGW44rwJEVpMdmxonXOGObW9knlEkAU3YXqy4oVJo2giKjjWqGOsRz9xxJLmIljFhW9GNRU8VTNsObJkg7mQ/sSkFygcGgrxC/wQZs2vu0sO6bvE4Z+af1z5eHlleYfuEN8I/GX+GYrTBrs/P72SKAqsvhaMPiiLKufAcLCiGJEc2t6InVB0T0ksibKuq+qyqqmerqrpVVdVrVVX1q6rqVVV1l6qqa7VL34T9v6iqareqqutVVf3LhO1Pq6q6RbvuQ6payG/0NMDiLLylOh+57KJjD5VnPBKJRCKRSJYVFqOe123v4Nb3ns89n7iEN5+9gr+8MMBrvv0Ib7h5N79/rr/sXwgkkkoiGEtNLoiNesE6NcSgQGz1WFKj6MgSTVaHsO2NJGlRtDLIuRzbBhNRUz0NmREiFe7MLAdqeIj+tLMgx3ZbXQ0+1UHcv0jiQcQr4ioLyNcGIWznYkgAau0m+i3r0JGFoRcLu82Yn7jRSSar0lKhGdt6syiFTERD064LRFPYMxUqbDc6GMFFPJBH2A4NCrf2BKObqqr8/PETbG5zsn2Fe/EGOhOmGizEKt6x7dNWpZXu2D4uLis5isRohfo1NMeE67mQnO2cY7vWZhKudGf7zHEhM6Hljm9TjjIyZfXf4f4RXq57BmXDq+c+b/0ayKbHnePzYKw8ssyfY9s8D3F26hm47DPFpyrkY/VlIoZo+MDs+2VSMHK4oOLIHMZaEVOUDZyaxwAXj6VybEsqGUcjNG0SBZISiUQikUgks7CmqYZ/vXYLj39uF//46k2MhBN85Jd7ufCm+ziuFY9JJMuNYHyKsB2bn2NbQcVFuGpytj2hBE2Kn4zOVFBEQ8LWQqvixTNDQdiyQVUhNMRg1kWLa+5Ymja3lRHVRSa0SBnbI4fEZQHCdjyVoX80PsmxDZBt3Sb+U+jq3pifsOIEoKlShW2TuI/J+OT3rHgqQzqdwlqxwradEdVFNt/zJzw0LV/7mRN+Dg6GuOG8lePFt0uJyY4lG6t4x7YvkkRRwG2bRxSJ2VX65Odi0bwZq/8gFqOuKGF7zLFdTL52jqZNZPRmtuqOMjwljsRw/CFqlBi6uWJIQESRQFlythciiiSbjPPe6PcZsayEnTeW56Q5F/tccSTeHsimihK2bQ1C2A57TpQ4uMVFCtuS/HRdDL2Pi6xuiUQikUgkkjlwWoy856JV3P/3l/HDvzmHS9Y1sqLOttTDkkgWhFA8jdM6pTxyHsI2QJ0SIpKoDsf2SChBi+IjbW8uKPowW9NOm+JleLkL24kQSjrGsOouyLHdVGNhBDf66CIJ257CC8RyxZFThe22Fd141ZrCcraTUUjHCSByXSvVsW20iPeqVGyysD0aS+FGE/gqUNheUWfDhxtjLF/G9sC0fO2fPn6CGrOBa86cY5XFYmGyY8pGCSdTFd0v4I0kqbOZ0Jdalh04AbUliL6LTfNmFP9xNtcXJmz7I0l0CrisRnEfS8kQ1xtJ1G9mm65n2sTnet+9xHSO8aLE2ahfIy7LkLNtGosiKd9zMvzQN1mpDPH8ls8U72qfCfcKEcEyl7Dt0RzdBa7UAahtEb/L0PD8HfCLgRS2JfnpvgJSUfjOuXD/v8NIeYL4JRKJRCKRLG90OoXLNzTxX9dtK/1LoERS4dz1sUu48ZLu8Q1Rb+nlctpxtYSqxrE9Ek6IKJK5Ykg0dO4O4dgOTi8IW1aERY64R3UXlLGt1ymEDXWY496FHplg+CCYHAXl4B7TVtx01U+eoNzc7uLFbBfJU3vnvr2YiKvxZoU4XshjshQYLGJ86cR0YbtW0eJJKrA80qjXkbI2YEmPQmbKa0doCGrG8+9Hwgn+sm+QN5zVgc1koCIwO9CrGYxqmmgF9wt4w8nSY0hARJFUcgxJjuYtAFzo9BTm2I4mcdtM6LMpCPaX5tgGaN/BFuUYI8Hx3P7hQJBLsk9yqvkyMBTw2NvqhSu+DMJ22TO2Q4PYH/86d2fOwrj+yvKcM8fqy+DEo7Nniw8fBJSCuxUA2hrr8asOkv6T8x7iYiCFbUl+1l0Fr/uu+NDz4Jfg22fB9y6Hx2+G8CI5CiQSiUQikUgkFYWiKP+nKMqwoigvTNhWpyjK3YqiHNYuaydc91lFUY4oinJIUZSrJmw/S1GUfdp131S0dfGKopgVRblF2/6Eoihdi3oHC2RFnY3GGq0IL5OG+Gh5HNtVkrHtCSVoxofBXZiwba7vxK4kGPUv8+8RmrA9TGGObYCkpQFHepGE7VxxZAEu++NeTdie4tje1ObkBXUVZt8hSM/hwNeE7eGUFZ0yj/K9BcakCdupKcJ2IJqijpywXXmObQCDswkdKkRHxjcmI5AMQc24Y/vWp0+SzGR527kV5Bw2CSe/ncqOI/FF5iFsq6oW01HBxZE5mjcDcKbpFH2B2JydCL5IklqbEUZPAmrJ5ZiWlWdjVxJkhg6NbRt69q+4lCjpDdcUdhJFEXEkvgqMIrnnnyGb5F/TN7Cyzj7n7kWx+jJIhqHvmZn38RyAulUiR71A2t1WBtR6MWFRBUhhW5IfRYFt18M7/wCf2A9X/qvI5bnzM/Df5459SJFIJBKJRCKRnFb8CLh6yrbPAPeqqroWuFf7GUVRNgHXA5u1Y76jKIpeO+Zm4EZgrfYvd873AH5VVdcAXwP+c8HuSbnIfS62lurYFoJZrRImVi2O7VCcFp0fvau9oP2tDULwSPqqY1lzyWjCthc3jQ5zQYeo9kYsahwSczsk543nUFHFkfV20+QseYTgcdSwBr2anru0LOYDoD9ppbHGjEFfmfKDySoE1kw8Oml7IJqc4NiuTGHbVidc2ang0PjGkFYm6RAZ25msyi+e6OW81XWsba5Z7CHOjJZtblcShBOzOE6XGG8kQb2jRGE7PATpeHU4tl0rwOyiO3scgKOe2XtSfJEk9XbzeGFjiY5tXftZANi9z41tMx76PUHVSsuZryj8RPVrypKxbdSLib+ylEeefAqe+yVPt76VPqWFVneZV610XQQos8eRDB+AxsLztQHsZgNeXT2mSJ5i2gqkMt9ZJJWFsw0u/Ai87xG44TfiA8rBPy/1qCQSiUQikUgki4yqqg8BvimbrwF+rP3/x8C1E7b/SlXVhKqqx4AjwE5FUVoBp6qqj6mqqgI/mXJM7ly3AbuUimg5mwVNvCs5qkATxOuonoztcNCLleSkqIPZ0GnRF2qgbyGHtfSEhLio2psLFnF1Tk18XOgCyZgfwoMFL0c/NhKZ5tYGUBSFdPMZ4oeB56ZdP+02gZNxC80Vmq8NYNaE7WxysrA9GktRlxO2S524WmDcjeJvyzM4ITJAm2DJObYfesnDKX+MG86rMNew5ti2ESe0XB3b/uPishqEbUWB5s00RUWcxxFPaNbd/ZEUtXYtXxtKjyKpX0MEG/WjL4qfMylWDN3Po/qd1LqKmIip74bRU5CKlTYODVO5okiyWfjLp8DRwm9sb6bNbRmLOSkbtjpoOxOOPpj/+nRCiP1Nhedr5whbmqlJDs29YwUghW1JcXTvEi9Y+3+31CORSCQSiUQikVQGzaqqDgBol03a9nZgYkDjKW1bu/b/qdsnHaOqahoYBfJaJRVFuVFRlKcVRXna48lTnrZYRLUYiVKFbZMN1WCjVqmejG1GB8SlszBhG83ZrQ8v4rLmVAy+fwUcuXfxbjM8RBoDdndDwYdY3ELYDngWWPT3vCQuC3VseyN01edfNt+4Yh0h1Uq2/9nZT6IJ2ycipooWtg2WnLCdJ2Obys3YBmhqXQGAd3DCS+oUx/ZPHz9Bg8PMyze1LPbwZkcTth3ECM8Re7FUZLIqgViKgRiexQAAc39JREFUOnthKzCm4c+JvhU2qTATzZux+A6i1zFnzrY3J/gHekFnKLhzYRo6HSfM6+iIaeW2xx7Cng1yuP6K4s5TvwZQwXestHFolC2K5LlfQP8euPJfeCkAnQtVqL76Mjj1ZP5VP94joGagaVPRp03ZW3BmRyFV+d0YUtiWFIeiwKZroee++cWRxIOQrQ5HikQikUgkEomkJPI5rdVZts92zPSNqvo9VVXPVlX17MbGxhKHWAaiOcd26VEFqq2uqjK29RFN2C5UyHA0k0aPJTawcIOaysDzInd0322Ld5vhIbyKm1Z34VmmzgYh+geGT82x5zzxaLEhBTi2o8k0Q8EEqxryCzGb2t28qHaROPXs7CfSvi8eCZtoqWBheyx7NpXHsa0LoxrtReXTLiYdHUIwDXsnTBqNObZbOOmLcv+hYd6yc8WYYFcxaFEkNiVRsRnb/mgSVYWGUqNI5utmXmyaN6MkQ+ysDc8qbKuqij+qCdv+E+BsB33ppaRDjo10pY9BOkHmhd8SVi2kVl1e3EnqVovLeeZsl6U8Mh6Ee74AHTth65s46YsurLCdTcOJ3dOvy8VFFTihORHFKd6bsqOVn7NdYa9skqpg8+vEH87BP5V2/NEH4asb4eYL4aW/ikIFiUQikUgkEkm1MqTFi6Bd5jIVTgErJuzXAfRr2zvybJ90jKIoBsDF9OiTymLMsV26sK3Y66klRLRCXYtTMcc04axQx7ZOT8jYQE1iEZc19+8RlyceXbSbVMNDDGXdtDgLF0Frm8SfQsS3wOKB5xAYbeCaW2A7PiIE3nxRJACb21y8kO3COLJflKfORNSHqjczGFNodpboeF0MNNFaTU7N2E7RrI+gVGi+NkCNq44ERhKjE7JwQ4OgN4G1ll8+2YsCvGVnBQqr5lx5ZJxQhb72ecNJgPlFkdS0grGCJ3Ym0rwFgIscQ7MK28F4mkxWpdamObZLLI7MMVp3BkbSZPueRT3wR+7N7mBte5ET1vXd4tJ7ZF5jGRe256FTPfQliHjgFf9JOJnBG0nSWe7iyBwrzgW9OX/O9vABUPTQsLbo01rqxce30aHj8xvfIiCFbUnxtG0XS2le/G3xxx78M/z8OjGjl0nAL66Dn7x27nw2iUQikUgkEkml8nvgndr/3wncMWH79YqimBVFWYUoiXxSiysJKYpynpaf/Y4px+TO9UbgPi2Hu3LJCdvzyOBVbPU06KrDsR1PZXCnR8QPBWZsA0QszdSmPWSzi/Tr7NOE7cAJkbu6CGSDgwxlXbS6ChexmlvbyaoKqdEFdrN7DkLDOtDNLQEc94pIjpmiSFY32DmkrMaQiYP38MwnivnJWNyAUtFRJBiFk1JJT15yH4ilaNSHKzaGBABFIaivhfCEjPbwEDiaSWSy3PLUSXZtbKatiFUEi0auPJJYxTq2vZEEMB9h+0T1xJAANImSwa3GU5zwRmeM4/BFJgj+gRPzdqQnm88EIP3INzAk/Pw5cy6bWossOrW4wN447wLJXHlkotQokpHD8Pj/wPa3QfsOTvrEhNmCObaNVug8D47lydn2HBSCv6H4icWaJvE7DQydmO8IFxwpbEuKR1GEa/voA+NLLwvh+VvhlhugZQu8+074wBPwii/B4Avw3Uvh9vcu2odOiUQikUgkEknxKIryS+AxYL2iKKcURXkPcBNwpaIoh4ErtZ9RVfVF4FZgP3An8EFVVXPK7fuB/0UUSvYAf9G2/wCoVxTlCPAJ4DOLcsfmQ8wHBiuY5vGl1VZPnRImWgXCtieUoEXxEzfVFvVlOWlroxUv/mhyAUc3gf6942JLviXaC4AaGmJYddNShLBdY7PiV2rILnR5pOdQwcvRj41owvYMjm2DXkesoYACyZifpNENUNRjsujoTWTQoaRnKI+sYMc2QMJcjynhZWwOMDQIjmbufGEQbyRZeaWROXLlkUqiYjO2cwJufckZ28erozgyh9kBtatYlT1OOqtywhvJu9vY42LOiokUd9e8btbW2MWI6sR0+E8kdVZ267bPOLE2K/Vr5i1sK4qCSa8rPYrkzs8KsXnX5wE44V1gYRtEHMnQC5MnuACG95cUQwLQ0CaiXaIjvfMc3MIjhW1JaWy+trg4kqf+F26/EVZeAO+4Q8x6G0xw7nvhI3vhwo8IB/i3zoLexwsfR4UbeCQSiUQikUiWE6qqvkVV1VZVVY2qqnaoqvoDVVW9qqruUlV1rXbpm7D/F1VV7VZVdb2qqn+ZsP1pVVW3aNd9KOfKVlU1rqrqdaqqrlFVdaeqqkeX4n4WRdQ3f0enrR43waoojxwJJ2hWfKRszUUdpzrbaVF8eEKxBRrZBOKjwkl85tvA7FqcOJJMCn3ch4fiHNsAQV0dhtgCFqDGgxDsKyhfG+CEN0JjjRmHeebMXNeKjcQwoc5WIBnzE9UL12VFO7YVhaRiRpee/NwcjSZxU/nCtmprpDYbwBMW7mLCQ1DTws8eP8HKehsXrym8zHRR0YRtl77yhe2SHNvppPi7m2dMx6LTvJnGiFiJMVMciV97XFpU7XVrno7txhoLz2VFlMgz5p10Nddj0JcgV9Z1zztjG4RrO1WKY/ulu+DI3XDpp8EherQX3LENsPpScXnsofFtqZgo0tRc+MXS1tTAqGojE1jgYuMyIIVtSWm0nilmHguJI3n4q/Cnv4d1V8PbbgPzlCUlVjdc+S/w4afBWgv3/VthY3jsv+FbOyDiLXLwEolEIpFIJBJJmSiTsF1DlHg8UZ4xLSAj4SQtih+1iBgSAH3tCsxKmoBnEQokc2Jrx9liifbxRRC2Ix4UVDxFOrYBIqZ6LIkF/E4z8pK4LNC5d3wkyqo53JIb22rZn11J4uTemXeK+QkrVSBsA0nFjD4zOYpkNJaiJhuseGHb6GqmQRmlZ1hz14YG8Otqeeq4n7ed24lOl6+TtwIwWgGFWn2SUKVGkYSTKArU2ozFHzx6ElCrK4oEoOUMTKPHsJCYUdj2aStv6pLa6/k8xfvGGjPPZ4VD+PbEOWxoKTKGJEd9t5jYiQfnNR6TQUeyWMd2Oinc2vVrYeeNY5t7fVFcViOuUp5DhdJ6pohimZizPfISoJYsbNtMBjxKA/qwLI+ULFcKjSO5/z/g3i/AGdfBm386e2mCuxMu+DAcf3hu13bEC/f/O/iOwt3/VNJdkEgkEolEIpFI5k3UO698bWBMGNfFK7snE0QUSbPiQ+9qL+o4a4Nw9MU8xxdgVFPo18TW1u1ixaj38PQl2uUmLIoxPbhpqilOxE1bG3BmFvB3P3xAXBbo2D7mjdDVMLu7cHObkxeyXRiG90F2BgEo5sePA4tRh9Mys/u7EkjpLOinZGyHo1Gs2UhlZ2wD9vp26ghydHgU0gmI+XnGZ8Fk0HHdWSvmPsFSoShgcuAyVK5j2xtJ4LYaS3MP+4+Ly2qKIgFo3oyCyoVOD0c8MwjbmmPbmdCE7Xk7ts3ckb2A5xpexe+jW9jY6iztRPVrtAHON2e7hCiSJ24Wt3v1TSKdQKPXF11YtzaATg+rLhH6XC7VYOx1vzRhG2DU2Ig1toilzyUihW1J6Wx+HagZOPjH/NcfuhMevEksAXzd90BfwAzVWX8jZsQf+q/Z93vkq5CKijE8+zM4/kjRw5dIJBKJRCKRSOZNzDd/R6d2vCHhL8OAFhZfMEyjEsRcV5ywXdPcBUDKf3IBRjWF/j3CJWmvh66LxLaFztkOiS//SUsjJkNxX7NVRxN16iiReGohRiYKxAyWggS2cCKNJ5SYMV87x4YWJ/vVVRjSEfAfm76DqkLUx0jGRovTguiJrVxSOgvG7Liwnc2q6OLa32OFC9uOulb0ikr/QN/YBMuD/TpevbWV2lJLDxcLswOnLkl4oZ7788QXSZZeHBnQSveqMIoE4HzH4KxRJGaDDlPoJOhN4GiZ1006zAaGDB18KvU+EpjmIWyLOJP5F0jqiiuPDA3Bg18WKQVrXzbpqkURtgFWXSpWCfi0BLfhA6Azjj8mJRC3tuBOL/CkcBmQwrakdFq2Qu2q/HEkwX743fuh+Qx41VcLat8GRDPy+R8UuUT9MyxrCw6IzO6tb4ZrviM+tP7x42J2WiKRSCQSiUQiWUyi3rIJ26YqELYTAbEs2VCsY7teOPrU0UXI6+zbC+07xP9bt4HRtvA525qgqHMWL/AYnc1YlSSDIwuUs+05BA1rhatvDo5rxZFzRZFYTXpG3ZvEDwPPTt8hFYNMguGUreJjSAAyeguGCcJ2KJGmlpD4ocKjSBQty9c/3D82wXIy7azc0siJmOzUKPHKdWyHk/MojjwhhMUiY5uWHHcXGO2cYThFjydMNju91ywn+CujveBaUbjeMwOKotBYY+bQkPib29haYhRJnYgzma+wbTboSGWK6HN74TZIhkTE7gQyWZVT/igrFkPYXn25uDz2oLj0HBSv+4UYTGcgW9NKnRogm6psrU0K25LSGYsjeXByznU2I4oi0wm47oezx4/k45y/E/lAM7m2H/qyuI3LPiPa51/1FZEf9Og3Sr8vEolEIpFIJBJJsWQzEAuUJWMbwJIKzHtIC002VyTlbCvqOMXeQAITxsgC53VGRmC0F9q2i5/1Rlixc+Ed25qwbXEXL2zb6sRj6R1cINHfc6jwfG2vELbncmwD2Ds2k8IAA89NvzImJmn6ktaqELbTegvmCcL2aDRFraK5VStc2M6V1EW8/aghEQ1R09DB9hXuJRxUgZjsOHTxis3Ynpdj239cRHQUMKFUUeh00LyJrvQx4qksfYHphb++SJJam0mI92VypDfWiAmEVpcFt63Ex9xoBWdHeaJIinFsH7kHGtZNi3saDMZJZVRW1i+CsF3fLe57Lmd7eH/Br/szYXCLCWzf4Il5Dm5hkcK2ZH6MxZH8YXzbw18ROdmv/LKYISoWixN2vldEnORygXL4jsGeH8OOd4wvpVt7pRjHQ/8175k5iUQikUgkEomkYGIBQC2bY9uWDsx3RAuOLjIo/lOsC1FR8OkbsMYGyz+oieRWfbbtGN+28iIYenH2bqD5Eh4igINGd/FL6J0NQjwIjvz/7d11eJxl1sfx7z2TTNytTd2p0ZYKpS3a4ixui+2yyGILrLPyLmvswiIL7OK+uLu3QClWVyq0pd60cfeZ+/3jzDRpGxmLTHo+15Vrkklm5kmeJDPPec79O9vDvVVQVymFfj/ztX0d2/4UYkb0yWCtpy8N21sqbMvPemtNTMDDNLuCJyoOl63DevNpy2oaIqZjmwQpbFOVz8ZNcjx8zOSDu338CwCuJOLpvhnbxVX1ZCSGEEUSaTEkPjmjyahaD9gW40iKq70/l9KtIedr+2QlSmE76MGRPhlDoGhDSHcRHWX8Hx5ZXy0Dioceu9+nthZVA3ROFIkxMPgo2PS5DM8s3Rr04EifuEz5/S3M2xz69nUgLWyr0PQaK8s9vn1DPt7yNXz2TxkWOf6C4O936tUQnSBF8ubm3gaOKDji13tff8KtEBUD7/6iKSxfKaWUUkqpjlTtXbUY6vBI7+3jG8tC3KCOF1PtLUwH2LENUBadTXJ9B+d17lgCGIkg8RkwDbDtD6gPQWP5LvI9KfRKiQv4tqlZUtiuLs4L92bJylbwu3NvU2E1OckxxLvaH/Y4OjeFVZ5BsGv5/sdg3o7tIndkRJF4ouKIpW5PMau0pp50EyGF7cQsADJNGQtWrMFtDcdPHt3FG+UnVwLxtqZbFrbdHisF3KA7trdE3uBIn5wxRNWV0oviFgvbJVX15MQ2QnWhRMOGga9jO+h8bR9fYTuEupArkOGRm78Ad91+2doAW4vlRGGnFLYBBh8p/3tXviwfh1jYTus1EIDKfO3YVj2ZL45k0+dQuAFevVz+sZ18l3wuWPHpMPkyWPVqUxd2/lpY8SJMuQKS9+kQSeoFM/8kyy58f8RKKaWUUkp1JG9XashRJFEu6pwJpNhy/w+mu0hCXQGNxgVxaQHftiauFxnuDsqR9tm5VFaNxjYrjvSZCM6YDs3ZbizLo8Cm0juI7uQoby53Y3kHdLMXrJXLAKJIBraTr+0zOjeZb+1AoutKoGyfbnNvYbvEJpGTHGRGcSeyUfHEUUdtvbewXd2sYzvUE1cdLTYVj8NFlinHUbWbalcG8bHd/2cOgCuBWFtDZW3jnm757qK0uh5rCS6KpLZcnh/CVPTtdN4BklPi81osbBdV1TMoyvv8F66ObW9h+6CQC9tDobYspBU60U4H9f5GkWz4WOY49J+236e2FlcT5TBBPS8EZdCRcjn/IbnMCq2wndV3EAB1xZ0w9DkEWthWofPFkTxxomTLnf343i8kg3XYdTJs4Yu75ONPb5Eu7uk/b/nrJ/1EXrR++PuOXWYYqTZ+2vpATqWUUkopFThfx3aohW2gzpVGmqmgut4d8n11lJp6N+meIqpisoJqYmlIzCXLFlNX30GDqKyFnUv2jiEBmfnTd1LHDpCszCef1OBiN+LTcePYk9MdjIKKVn6mBWvB6YK0QX7dz+bCKgb5ka8NkBrvYneCt2C+b86293is1CbQKwI6tomOI87UU90gncNlNQ2kmwo8riSICrJjt7MYg0nIJNOUkW1KiU6JoGGFMYm4PDU0eix1gWQad4LiqnoA0hODOElQ6u1wjeAoEoBDE/LYULB3YbvB7aGitpF+xnuSMkzF+z6pstplTG6ItaT0IXIZQs62K8rhfxTJhtkw8PAWZ8ttLa6hT1ocUc5OKr0m5UD2KChcJydz0/37v9+a+KR0KomD8g6ejREiLWyr0OWMkbNiVfkw6+amCeShSsqBiT+C5S/AmndgzVtw2DWQ0MpSMIcTTrlbXkS9epkMtfR03wODTtVQCy/9CN7/bVdviVJKKaVUz7GnsB16VEG9K410KqjpxoXtwso6epli6uMDH5AIQEofnMZSsruDur/Kd0pxuKXjkQHTpPhaVxH+x7WW6Or8oDu2cTipdKYSXVMU1MN/tbGQybfMZtHmFpp7CtZBxjBwth8tUl7bQFFVvV+DI32ieo+Vovy+hW1vx3YpiRERRWJc8cRSv+fvr6ymgTRTgQnDSavOYBKz6e+qZFBsJbFpgccEdRlXIi635BB3twGSRd7CdlBRJCWb5TJSo0hiUyClP2Oc29iQX7lXN31JtfxcellvrFSYivenjOvNi1dOZXBWYmh3lDFULkPI2fY7iqRoIxR/D0P3jyEB6djutBgSH1/XdtbwsAwuLXFm4qrqgJisMNLCtgqdMXDkTTLwceq14b3vadcDBl7+EcSmwmHt3H/vg+HYv8DW+fC/U+HOg+DdX0qY/4Fc5F7/EdSVSeZgfXVXb41SSimlVM/gWyUYhsJ2Y6x0bFfVd6/iTnMFlXXkUIInMbjCdnSaLFmv2L0pnJvVZM/gyAn7f27AdLAe2DY//I9bV47TU0e+TQ26iFsbk0lCQxEeT+BxDM/N3wrAnLUt5JcXrA14cKS/USQAw/tmsd7TB/fOZXt/oqaERkcMtcSQHQFRJMYVRxx1VNc1dWxnOiox3T1f2ycxm/Hp9QxwlUuDWKRwJRDtrgZst8vZ9nVsBzU8ssTbsR2pUSQAOaPp37CJspoGCivr91xdUtUAQGZDHkTFQUJWWB4uJsrJoYPD8PeWNgCMsynSNgjRTgcNjX78L94wRy5byNcG2FpURb/OLmwPPkouQ4wh8amMySGxLvjVRJ1BC9sqPA4+B076FzjC/CuV2g/GnQ+eRpjxczlz2J5pP4Nfb4BznoIBh8HSZ+HJk+CuUTDvLnA3hHcbO5rHI4NuQskcW/EiYMDTANsXhG3TlFJKKaUOaNVFstw3OvQDV09sOummguq67tuMUVBeSy9TjCOlT1C3j8+UIk9t4dZwblaTnUtk0Hyvsft/rt8U+dzmDogjqZCD/hpXBrHRwXXINcZlkk4phZWBxbSUVtfz0bfy+F9uKNz7k/XVUmDze3CkFLb9jSKBppxt945le3+ippgqRxLpCS5iokLvGuxoDlc8UcZDbV0tID/XTEdl9x8c6ZOQjas6H0d1IQR54qlLuBIxWGKpp7K7dWx7/xaDytgu3QIxyUHNIug2ckaTWr0ZFw175WwXVcnPJaU+T/K1Q5mt1hGc0VLcDqVj298okg0fQ/pgedtHeW0DJdUNnd+xPXA6xKTIc14Y1Mf3It1dGNRJ186ihW3V/R39B5h+I0y50v/buOJh9Olw7v+kyH3WY/ICd85f4OGjIytreunT8PjxMkgzGDUl0rE94UIwjo55Ma+U6vmqi+H+aZH1/1MppTpaTbEUvsJxYB+fQRrdu2O7vKSAWNNATHpwhe3kXgMBaCzd3vYXBmvHEsgeCdFx+3/OlQC9x8OWr8L/uN5sbE9C8J2yjqQcskwZO0prArrdW8t3Uu/2cNyoHFbuKKOsulkTT+F3gA2gY1tWdg7I8L8QM7pPCqs8A3HV5ENFs+GXNaVUmESyk7p/tzaA0yXF/LoaKe6XVksUScQUthOzJBrUeiApkgrb8nNPoJaKuu7VgOaLIkmLDzKKJG1A9yv6BiJnNMa6GWp27JWz7evYTqjeGbbBkWGXMTTkju12h0c21MKmeTD02BY/va3Y+/+0swvbMUlw4wqZQRcGJjmXLEopLNt/iGh3oYVt1f0l95Z4EVeQ/xBiEmHs2XDRK3Des1BVAI/MhI//BA2BvXBs1c5lsPqt8NxXcx4PfH2fvP/ZP8EdxIHO6jfBXQ+TLoPe4zp2aI5SqufaNh/yv4W173b1liilVPdRXRyWwZEAJGSQYOqore6+B491xTsAiM/oF9Tt0zMyqbBxmPId4dwsYa2cfG0phsRn4HTYsTh8xwA+3sK2Mzn4gqIrtReZlLGzJLBte2nRNkbnJnPFEYOxFr7+vlnXdsE6ufSzY3tzURW5KbEBdZ3npsSy2TVMPshb0fSJmhJKbGJwwzS7QFSsHGvW18jfX1lNAyk2ggrbCdlN70dUYVvylBNMrd8d2w1uDx+syqOkqr79Lw5BcVU9KXHRRAcz+K9kS2THkIDMUgPGu7azYXfTbIJib8a2q3Jb9x2OmT5EhkcGuerdFWXaz9je8iU01rSar+0rbHd6FAlAXGpY8rUBXBn9cRjLrp0dtNIqDLSwrQ4sI0+Ba+dL9/KX98AD0+QsW7CshfkPwaOz4KWLYeOn4dtWkAm7hetg1OmylGbly4Hfx4qXZWBM7gTJFty+SM4uKqVUIHwHq9sXde12KKVUdxLGwrbTOyC9oaKwna/sOu4y6bSOSgluOF1MlJN8k9Exg6hKNkFtKeS2Mch+wHRvNN/C8D62t7Adm9Y76LtISM8lxjRSWNhCTnYrVu8sZ9WOcs6Z2Jfx/VJJcDn5onkcScFaiV/JGOLX/W0uqgpocCSAMQZHLymA7TVAsrqYQncCvSJgcCRAVIx83421Utiurq4iztaE78RVR0tsVtiOpCiSGG9hm9p2M7attXy6Np8T7v6cq55Zwj1z1nfophVV1Qc3ONJaiSKJ1MGRPumDISqWKXF5e3VsF1fWk0Q1jtrSbtyxPQQaqqEiuOcal9OPKJINsyWKbOCMFj+9pUgK2/0DWAHTHSVlyT4u3b25azekDVrYVgeeuFQ49T9wyVuyVOupU+DzOwK/n7oKeOVSeP83cpYuYxi8eS3UlIZvW7/+LyTlwpmPSJTK3NsCywgv3QZbvoCDz5VlUANngLsOdmhhSikVoF3ewvbOJbKaRCmllGRsx4WpsJ2YCYCtKgrL/XUE44uaSA6+gFvszCKhdlf7XxioHUvksk8bhe1+hwIm7HEkjeW7qLNRpKYHP0QtNlV+phVF/nezv7x4Gy6ng9PG9yHa6WDKoHS+2tDs96dgnSzJd0b7dX+bCwMvbAMM7tubTbYXnmaFbVtTQn5jHNkRUtiOjksCoKFWilGmJnyDYTtF8wF+ETY8EiC+ncL2ul0VXPL4Ai59ciEeCyN7JzN7zW5sKHOo2lFcWR9cvnblbmisjfzCtjMKskcy0rl1r4ztkup6RsR6/z66a1e672RekHEkMjzSj8L2wOmtJgtsLa4mNT6a5Fj//v92V+m5gwCo7qjZGGGghW114Bp8JFz9NYw9Bz75G3x+u/+33b1asrpXvwmz/gLnPwdnPCS5ch/cFJ7ty1sBm+bCoT+FKJdkjZdsguXP+38fq16Ry7HnyGX/wwCjOdtKqcDlLZeuhNoyWdqnlFJKCtthKnzFJEthylZ3345tV7W3IJ0UfGG7IiaHlAb/u5L9tnOpPE9lj2r9a+JSpVkkzNF8tcU7KSCVXqkhdOZ5O25rS/zrMKxv9PDG0h0cOyqHNG/xbfrQTL4vrGrK6S5Y63e+dlm1DDoblBF4YXt0ruRsN/oGSFrrjSJJipiObVesfN/uOsnYdkRaYbt5x3bzWJLuzhtFkmhqqWghiqSwso7fv76SE+/5nOXbSvm/U0bx4Y1HcMlhA9heUsN3uzsuuqm4qp6MxGDytbfIZXct+gYiZzT96jexu7yO8lppsCuuqmdETIl8vtt2bA+VyyAHSEZHOWhwt3HSpGSLzDBoJV8bpLDd6fnaHSDOGz3mLumg2RhhoIVtdWBzxUtB+uDz4JO/+1fcXv4CPHKMFHd+9DbMuBEcDug7EQ7/pRSe17wd+rZ9cz9EJ8DEH8nHw0+QpZVzb4dGP/PEVrwMfadAupxlkxfzY6SLu6dqrJeBmUqp8KkuhrJtMOZM+XjH4q7dHqWU6g48bom+CFdhO0UK23s6Rbuh+Lp8KpwpEBX8QMDa+N6kekqhsS58GwZS2O41tv3u5AHTYdtC/19P+6GxfBcFNpXeoeRJewuT7ordfn35nDW7Kalu4JxJffdcN2OYdP1/uaFQogdLNvmdr72pSAq6wXRsj85NZpVnEK6KbfKaoaEa466j1CaQkxwZwyNdcU2F7bpGN/HuMvlEpBS2fcXs+AxpiooU3sJ2sqNur47tukY3D87dyNG3f8aLC7dxyWEDmfvro7lsxiBcUQ5mHiTf7+w1/v29BKOoqo70hCB+f0u9he1I79gGyBlDfEMxmZSx0du1XVxVz+Bo78qQ7vo9JveVE51BNuP4okhaXRGwYbZctpKvDZKx3SX52uEWm0otMTgqdnb1lrRKC9tKOZxw+gPtF7e3LYSnz4TXfwp9JsJV8/bPUzri1zKg8e0bobKg5fsp3ymRJatebX2byvNg5StwyMUQlybXGSNd22VbYenT7X9fu1bJoLeDz937+gEzwv5ivlt581q4/7DwHywpdSDzxZCMPVsOQDRnWymlpMnBesKWwRudKIVtRzcubCc3FFLlCq0b1J0o+dy2LIwDJD1uGebeVgyJz4BpMvBr59KwPbyp3E2BTQmxsC3xEY6qVo4h9vHy4u30So7l8GFNERQjcpLITHTx1YZCKFovv59+dmxvLpTC9qDMwAsxg7MS+c4xWD7YtWJPk0kpieRESMe2L2PbXV9NWU0D6XiH5UVKxnZcGhhnZOVrw54okrToeiprG7HW8t7KPGbdNZdb31/L5EHpfHjjEfz51NF7ViYAZCfHcnDflA4rbHs8lpLqhuAytks2y2V37WYORM5oAA5yNMWRFFfV098UyjGBr1bR3TgckhEeZBSJK0pKpa12bW+YLfs3c1iLn250e9heUkP/nlDYNoay6GziajvuJFKotLCtFLRd3N62EJ45Cx6bBXnL4Ni/wSVvtjxtOsolHeB1FfDOjXtP4fV4YNHjcN+hsPQZePUKWPdBy9uz4GHwNMKhV+19/dCZ0oE97872B0CufEmGxYw+c+/rB073vphf0vbtI9HOpfJ9V+TB6re6emuU6jl8gyN7T5BBtNqxrZRS0pkKYevoNHFpeDBE1XbPlWfV9Y1k2SLq4kIrbJsU6TCuLQpjXmfhemiokueo9gyYJpdhXMHoqi2gwKbSK5TCdmwqbuMkvqGImnp3m1+6u7yWz9blc9bEPjgdZs/1xhimDcnky41F2Py1cqW/HduFVRgDfdMCL8Q4HYaG7LHyQd7yPYXtEpsY2s+kExlvTq6tr6asuoE04ytsR0jHtsMhOduRlK8Nezq206PqWZ1XznkPfcM1zy4hPjqKpy+bwuM/nszQ7MQWbzprZA7LtpVSUBH+hqaymgbcHhtcxnbJFjnBEB0Zv/ttypbC9hjn1j0DJEuq68llt0StGNPWrbtWxpAQMrbl+2poaYBkYz18P1e6tVv5/vPKamn02J5R2AZq4nJIbsjH4+m4TPtQaGFbKZ99i9sf/K6poL1zqWRp37ACpl8vgxRakz0SjvkjrH1HYktAXmw/dQq883PIHQ9XfSFLJV/+MWxbsPft66ukAD7ylKYIER9j4Jg/QPkOWPJU69vg8UjH95CZkLDPi7H+3hfzm3tgHMnsv8gAp9QBsOixrt4apXqOXStkSV9ChqxY2bWy/ZNrSinV01V7l2KHaXgkzigqSMBVF2DHtrXgbn3oWrgUVtSTY0pwJwafrw3gypAuxsr8zWHYKi9fw0auHx3bCZlS7A3XAEl3A3ENpZRFZRDvauMYoT0OB/UxmWRRxs6ymja/9LUlO/BYOHtiv/0+N2NoJgUVdRRvWSkdvL6s2XZsLqoiNyWO2GhnUJvfv29fdpCFzVu+56RPpSOJ9PgIicWIjgO8he2aBtLwZjd3147Uloy/AEaf0dVbERhvx3aKs57FW0r4vrCSf5wxlnevn7HXaoSWzByZjbXw6drwZ/YXVcnq5uAytjd334iOQCVkQFJvJsXuZMPuSqy1FFfVk9m4u/t3pGcMkTgmT9snClvickqptL6lAZJbv5YTqW3ka28rliG0/TN6RmHbnZhLDsUUVnbPVfFa2FaquebF7W/u37ugPeNGiGn5bPF+DrtWCsjv/wbm/BUemA67V8Gp/4VL3pKi9oWvyET5586VieU+y56TvMbDftbyfQ86UrIB590JDa286N3ypRS/940hAXlyyh4V9qE5XW7jp/D9p3DEr2DKFfKEs/vbrt6qveWvgY//BHeNhnd+0dVbo5T/8lZA74Pl/T4TwdMg/9OUUupAtme4XPiiCspNMjENpf7fwFp49TJ4/DhpbOhAhWVlZJpyTHJuSPeTkCXFkLB2bO9YIrNpWlkWvp8B02Hr/PCcEKiUolpjXGbId+WJzyLTlLGztPXCtrWWlxdtY/LANAa1kIc9bag0tVRsXSVL8f3MQ99cWNXi/flrdG4yK90Dady+bE/HtjM+HYejG3d0NhftLUA11lDq7dhudCW3n9nency6GQ65pKu3IjBRMeCIYmrfWG6YOYxPf3UUFxzanyhn+6WqUb2TyU2J7ZA4kmJfYTvYjO20HjA40idnNAcZ6diurndT1+gmrT4vAgrbQ8FdL3OCAhS9J4qkhefVDbPBEQ2DDm/19lt9he0e0rHtTO1DDiVsK+q4Ya2h0MK2UvvyFbcvfDXwgvZe93G/nB2cdyeMOAGuXSiZ2b7lKolZcNFr8k/x6TOhbId8/Tf3Q59J0G9Ky/dtDBz9e6jcDQtb6Upe8aIs6xpxUsuf3/NiviGw76u78nhg9p8hpR9MugzGXwhRsa3/fDpTVRHMfwgeOhLunwpf/VdewC16vPsV3pVqSX2VTP3u5S1s950kl5qzrZQ60Pk6tsNY2K5wphDXWOr/DVa9Km87FsO698K2HS2pKJBMbFdan5DuJyMtlWKbiKd0ezg2S+xcKqsiHX52Gw+YBvUVTTMkQlEpRTVPYugREI7kHLJMaZuF7SVbS/i+sIpzJu3frQ0SJTIwIx5X6Xq/87WttWwqrGJgEPnaPqNzU1jlGUh06UYolZMWrqQIifGAPR3bNNRQWtNAuqnAxkXQ9kcqY8CVwMh0w8+PHU5SrP8nEowxHDMym3nrC6ltCLwrty1F3s7UgKNIGuulwSy1JxW2x9C7YQt5xeXkldWSQhUud1X3L96nD5HLog0B3zTa17HdWmF7wGEQk9Tq7bcUVxPlMPROiQv4sbuj+Mz+RBkPhbsCP0nQGbSwrVRLHE4YNivwgnZz6YPgolfl7dz/tZx3lj4ILnpFhg89c5ZElxR/Lx3fbeVVDZwhnduf3SrxJuvel+ITSDzA6rdg5A/A1cqL04HTZflM3vLgv7/uZPUbkn9+9B8kyyw+XbLFV7wIteVdt13fPAh3jpDOfeuG4/8Jv1wHl8+GmGSJvFGqu9v9LWCbOraTcyGpt+ZsK6XUnsJ2+IpfVc4U4hvL/PziQnmNkTtBlr1/8e+957uEWW2xFKLjM1suqPorKzGGPJuBoyJMwyMb6yUiy598bZ8B0+UyHHEk3sJ2dEroQ/tiUnqRacrZUdp63NdLC7cT73Jy8tjWI2GOGJxCdv123Jn+FbZLqhsor21kYEbwHdsjcpJYw0D5YPM8AOJT2o6S6FaipADlaKzxRpFUYPaNdFQdw5XYdCwboFkjc6hpcPP190Vh3aSgo0jKtsnQ1p4SRQKQMwanbWQgeSzZUkI/441+iYSObYCi7wO+aUxUK1EkZTsgf7Xka7dha3E1fdPi9pqBEMmSc+QkRkU4I8TCSAvbSnWkAYe1+0+P3uPg/GeheCO8eQ2k9IeRp7Z/36f8GwYfCctfhOfPh9sGwtNnwPu/hroyGHtOG9vlfTHfE3K23Q3wyd8kXqV59Mrky6G+UorbXaGhBj77p3TeX/Wl5Kofdo106senS1b7uvekc16p7sx3AszXsQ0SR7JDO7aVUge46mJwuvYMPwuH2uhUktx+FrY/uElO4J92H0z7mfxf7sCoucZSKUQnZoVW2E6Ld5FHBrHVu8KxWVJkcNcFVthO7i0xHWEobDeWyfcRG2InO4AjKYcsU0ZeSctFvur6Rt5ZsZOTx/YmIab1PO9ZORVEGQ9bHP7tq02F8nihRJHEuZxUpsmgOTZ/Sa2NJj01Jej763QOB/W4cDbWUFZdT7qpwKmF7c7hSpTjtiBMHZxBvMvJ7NXhjSPxRZGkBZoRX7pFLrt7N3MgcuTv+iCzlQWbi+lrCuX67t6Vnpgtv1shdGw3uPc5Wbxhtly2ka8NkrHdP4QThd1NbLo8l/hOcHc3WthWqjsYfCSc8RBg2h9O6ZMxRAriv90El7wJU66Esu2w5H+QlCsd3a1JzIbM4W0f/NRXB/xtdIkl/5Mu95k37738tM8hctJg0eMd2r3UqlWvSVb60b+HXmP2//zUqyEhWzLYu2L7lPLXrhUyOCmlb9N1fSbK3111gAPOlFKqJ6kuksGRba2yC1BtdCpJtrz91wbrPoCVL8Phv5Siw/gLISELvrg7bNuyn4o8AKJT+7bzhW1zOAylUdkk1IapsL1zqVz28WNwZHMDpsHWr0LOJq8qkoJ/SkZo2eMAJGYThZuy4oIWP/3eyl1U1btbjSHxOSROinxfl/vXMb3ZW9geGEJhG6B334EUkgoNVZSQRE5ybEj319nqHLE43NKxneGowCSEnpuu/OBKCLqwHRvt5PBhmXyyNh8bxmOq4qp6kmKjcEUFWDIr8Ra2u3vRNxCZw7COaEY6trJgUzF9jff/U3fv2DZGaiahRJHs27G9YTYk94HskW3efmtxNf3Te0YMCSDfM2DLwrTSKsy0sK1UdzHmTPj1Ruk0DkRUDAw+Co6/Ba5bCDcsh8s/br84PmA6bP2m5SnBq16FW/vBG9cGvSysU9RXwdzboP9hMPz4vT9njPws81fLIMnOtugxyDqoqTt+X64EOOLXsOUL2DgnfI+7aZ5EnGixXIVL3go5SdS8cOPL2d6xpGu2SSmluoOakrDGkADUu9Jw0dh2kae2TKLoskZKYRskH/jQq2DDxxLL0QGiq3ZRT7Sc7AxRZWwO8Z5KqAvDIKqdSyA2FdIGBXa7AdNlHxasCenh60rzKLGJZKcnh3Q/gDSfAPVleS1++uVF2xiYEc/kgW3vg8Tyjbhx8N4u/1YTbC6qwmGgX1pog85G56awwj0QgFKbQK+UIAbvdaEGRyzR7lpKvVEk4fhdV35wJYR0zDlzZA55ZbV8uzN8EZRFVfVkJgbx+1uyWWZohThkt1txRmOyDuKQmB1sLa6mn8nHxqRAXGpXb1n70ofIyvgA+U5o7JWx7W6A7z+DoTPbPKFdVtNAaXVDjxkcCUB8OvXGhauq5eemrqaFbaW6k4SM0Lt+0gbu3VnZmoEzoK58/6E562fDa1fKIMZlz8Ijx0B+aC/4O8w3D0iu4ay/tPxzG3MWxKR0/hDJnUslf3jST9renxN/LGe65/w15G4hAMp3wosXwee3w7LnQr8/pdwNcnKoeQwJQO/xgNGcbaXUga26KKyDIwEaYtOa7rs1H98MlbskgiSq2TL5yZfJsusv7wnrNvnE1eZT4swMS4d6XZw3H7o8DN1fO5ZKDEmg2xWmnG13+S7ybSq9U8LQnewdQOmpzMfj2btJYUtRFfM3FXPOpH6Y9r7XgrWUxeSyYFs1VXWN7T7spsIq+qbFB96duo/RucmssgMBKCMx4jq2Gx2xRHlqqa6sIJb6sJ+4Uq2ISQq6YxvgmIOyMQbmrMkP2yYVVdYFPjgSJIoktZ//g2wjRc5ohiPd6P0chZDWzbu1fTKGyjDbxvqAbhbtlP+xDc0L29sXSv3EjxgSoGcVto2h0pVNQt3+z03dgRa2lTpQ7cnZbhZHsvUbKYxmj4SfzoWLX5MDq4ePhqXPds12tqY8Tw7cRpwE/Q9t+WtcCTD+Alj9JlSG74VOuxY+BtHxMO78tr8uygVH/V4yjNe8Gdpjejzw+lXgrodeY+GjP0JVeIeodKjdq+G+qVKU7wgf/RFeuLBj7rsnK1grv1O9x+19fWyyrEjQnG2l1IGsAwrbnli5P9vac/imebD4CZh6DfSduPfn4tJg0qWy8q5kc1i3CyC5oYAKV3iGAXp83YxlIeZ1NtTICdhAY0hAmguS+4Y8c8ZRtZsCm0KvcBS2E6RjO9Vdsmd4nc8ri7fjMHDmIX5keResw2aNoMFtWbC5/diwzUVVIceQAIzKTeZbj3TOl9jIK2y7nbFEe2qxHTAYVrXBlRDS6o3MxBgm9Etl9prw5WwXV9UHV9gu2dKzBkf65IwmzV1EGuUMcBRgIiVqJWOoDPMM8DmxxeGR6z8GR5TEyLZh657Cds/J2Aaoj+9FNkUUVtZ19absRwvbSh2o9gzN8Ra2d62EZ8+FlD5w0esQmwJDjpGhh30nyWDL16/y70VHY51kW98zHv47GbYtaP82330I9x8mAzAXPirdx/tyN8rXvXgx3D1WDmZm/qnt+530E/A0wNKn29+GcKgphZWvwNiz5WfYnoPPlaXEn9wi31+wvrkfNs2FE26FMx6Ws8kf/1/w99eZNsyBx4+Hwu8kRsU3lCNcakpgwSOw9p2m3Dvlnzzvio59O7bBO0ByscbeKKUOXNXFYS98eeLk/hoqWshYrq+Gt34mRZOj/9DyHUy9BowTvvpvWLcLIM1dRG1cTljuy+nNZvWUhljY3rUSrBtygyhsGwMDp0vHdgjPZa7aQkoc6STFRgd9H3t4o0iyTBk7S2v2XO32WF5ZvJ3Dh2XRO6Wd3FZ3AxRtILnfWFxOB19tKGzzy621bCmsZlBG6N2FqfEuCpIOAqDUJtIr0grbUbFEe+owNSVyhRa2O0eIUSQgcSQrd5Sxu7w2LJtUVFVPRlCF7c09K1/bxzsz6iDHNvpQEDnfY8YQuQwwZ7tpeGSzwvaG2dDv0HaP8X2F7X49KWMbILkPvSlmW0lN+1/bybSwrdSBbID3xXzhBnj6TIhJhItfh8Rm3ThJvWQ45VG/g+UvwIMzYM7fYNPn0LDPC4f6Kvj6PrhnnGQ/xqVBY60ULT/5u7zQ3ldtmWR5P3cueBrlxcC7v4S7RkoMyrw7pTvpoz/Kdc+dK8X4KVfAVfPaHdxA1nAYdAQseqLlPPFwW/4CNNbApMv8+3qHE475IxSth+VBxofsWglz/gIHnQKHXAI5o+Cw6yRKJsQupJBU5sPW+W0fLC5+Ep49R6Jvrl0A2aPgtZ9KR364LH9Bfg9BuveV/3atkNUHvheFzfWdKN2KHdAVqJRS3Z7HAzXFMjwyjIy3A7y+ooVi5Gf/gJJNcOp/wNVKETI5F8adJyf0K1seQBiMqtoGciimMb5XWO4vNqMvHmuoLQrxhLNv1kPuhOBuP2AaVOUHNVwMAGtJbCikJiZMQwZjU/A4XPsVtr/cUEheWS3ntjM0EpDhzp4GonuNZOKANL7Y0PYKvqKqeirqGsPSsQ2QkTuEbz0D2OAcQkJMOzN/uhmPM45Y6jA12rHdqVyJIRe2Z42Uk27hiCOx1lISTMd2bbk8L6RFSNE3EDlS2J7uWEUsdZHzPaYPlssAc7b3K2xX7JbjoqEz273tlqJq0hNc4TnZ2Y240vuSY4rZXhyG2RhhFlnPNEqp8Bo4Qw58HjtWulYufrfl6cYOJxx1k7z4n/M3+OLfMO8OiIqVs5aDjpAlPvMflELXwMPh9AdkqGVdBXzwO4mYWP8xnPkwZI2Q+934Kbx5HVTshBm/kMdwuqBgnXTXrn1X8qdBlv0MOx4mXCi5VlEBvNCYdBm8/CNY85YU89310lXubgB3nXSh15bt/VZfCf2mwJCZrR887sta6VTvMwlyx/u/fQedLLf57FYYey5EB9Dd0lADr14hJxF+cG9TvuSRv4VvX5MTDFd9IUNGO1NNCTxxohwoZgyFiZdKLIxvybbHI8X4L++GobPg7Cck3uKcJ+Hho+DVy+WESntDUNvTfJ94GmD1GzD9+tDu80CSt0JeyLaUE9jHuwR+x2JID3Bgl+o53A2w6jUYfUZg/5eVinR1ZfLaJ8yFL0eiFEj369jesViaByb+WF53tWXaDRIht+AhOXkeBsWFu+hnGjDJvcNyf5kpiRSQQkzxNkLqE965VHKpgx3Utidn+0vIHBb47WvLiLYNNMaFJ6IFYyAxm8ySMnY0K2y/tGgbqfHRzBqV3f59FKyVy6wRzBiWxO0frqOwsq7VQXibC6WgGK7C9ug+KZy85p8MyUogQtYO7uGJiiOOemLrS8GFFrY7iysRGqrk+MARXO/l8JxE+qXHMWfNbi44NLT85/KaRho9loxAh0eWek/U9cQoksRsPPFZzKr0nkxsqWbQHcWnywnoAE9e+uYN1PmiSDbOkct28rVBMrb79aR8ba/ErP64jJvi/B2AHydZO5EWtpU6kPlezLsb4MdvS3dzWwYdAZd/LGejt3wlXdub5sInf5PPDzsODv/V3pnXsclw+n0w/Hh4+wZ46AiY9WcoXA+LHoOMYXDZxxJ34pN9kLwd8Sso2yE5vv2n7d1JHoiDTobEXvDyj/2/jSMKvrpXulWHHQujTpPvLyap9dts/gIK10lRPxDGwKyb4akfSLf6KXf5X4ie/WcoWAMXvSrDR31c8XDSnfDcOfDlvXDkrwPbplC4G+ClH0nsxzH/B+s/go/+ICcpRp8Bh1ws0SCr35ComBNvbypgZ42Ak++CN66CubfBMa0stfbXlq8k4uS0+2XQ6Jy/eLPvIqTLIBjuxtBPCIAcXOxa2XpWfPYoiIqTYsvYs0N/PBWZFj0B7/9aThgecnFXb41Snafam1sc5ozt6IRUGq0DT/OM7cZ6aQRIzIFj/9r+nWQNl9c+Cx6B6TfKirwQledvBSAqzY98Zz9kJcWQZzMYUBbi8MidSySGJNiBlhlDJdd6y1dy0iBQlZLpaxPDE9ECYBKz6VVWxrfewnZpdT0frd7NBVP6ExPlx0C6gnVymTmcad6BaV9vLOIH41ou/m/yFrYHZYSpsJ0ry/TDkjneyWy0dGynmQq5QgvbncPl/d1rqGr7WKsNxhhmHpTD8wu2UlPvJs4V/PDGoirJEA44isQXeRgpMR0BcvQazUHffyYfRNL3mDEUigLr2Hbt6dj2rjxe/7E8B/ca2+5ttxZXM65faqBb2e250qWYXVWwDZjatRuzDy1sK3UgS+0HJ9wmReVAlnDGJsOIE+QNoKpQOrPb6tocdap0d791HXxwE2AkLuOYP0J0G/lTKX3kLRTOaDj/Wdg2XzrCo2Lk0vcWkyhZWbEpEJsKMcmAlUL1mrdgzTsSYeGMkSL3rL9A5tD9H2fRY3L70WcEvo2DjoDDfynRK/mr4dz/yf5py4bZ0iV/6FXS9byv4cdJQf7z22HMmS3HSXSED26SEx6n3S8d9kf8CnatkmFXy1+EFS8ABo77u/wO7HswOv6HsHmebPeAaTDk6OC3ZdHjsl9HnwGVu6SwvfrNru3abqyXDuiOmJa+8DH49BY5WRTq/i7ZBPUV0LuFfG2Qv6ve42C7DpA8YDXWyaoLkBNVWthWB5I9he3wFr4SYlyUkIipahZF8sVd8trghy/4N78DYMbPZfXbkqfgsGtD3q6aIsnCjsvoG/J9AWQlxrDaZjCksoWZKv6qLZdGiTEhnFw1Rl5rbP5SVnkFWCB3l+/CCbhSwxPRAmASc+jlXLsniuSt5Tupb/Rw9kQ/f/YFa6Xo5EpgbJ84kmKj+HJDYauF7c1FVUQ5DH3TwpMHOyo3GYCcpMgrbJvoeGJNPemmAovBxKV29SYdGHwn3+qDL2yDxJE8+dVmvthQyLGjgj/Z5BvcGnAUSU/u2AZZxbmnsB0hHdsgx0Tfzw3oJntFkbgbYeMncsK4neeIRreHHaU1nNrK/9uI5l0Z1ViyrYs3ZH9a2FbqQDf1qtDvIyFT3tqTlAMXvCQRGSn9JOqjs/SdtHdXuD+GHC1vJ90hRfHVb8Ly5yVn/Ni/wuTLm5bLVeyGNW9LkbmtQn1bZv5JTjC8cY10tp/1SMsFa2slU/KNa2Tw5Kw/t36fJ9wGGz6RTvCLX/f/gC3YpYALHpHhn9Oul6K2T68xcPKdclLg29dleGlL35vPSbdLJ/BrV0qUSlIQL06rCmWfTb5MOtjTB0shtrPiSFa/CUuehtrSZjE35ZLBHpcOw0+Ag06SIa2uMHRJNdRInE11kayO+NHbwXewAeQtl8uWBkf69J0k+9zdIIXunszjgfLt8r8rlJ9rT7LsWSjfAX0ny4FOdXHYu1eV6raqvR3VYc7Yjnc5KbFJpPsK57tXw+d3SPF2xIn+31HfSRIN99V/YfIVIUcFNZRKZ3VyVniKGb6O7djqFUEVlAHv85SFPkEMjmxuwHR5bVC6NeAVXeWFO0gD4tLDU/AHIDGLTOazs1Tmg7y8aDujeiczpo+fJzUK1kGWDHCMcjqYOjiDLze2PkByc2E1fdPiiHKGZ/xWbkosY/ukcMiAtLDcX2cyrjjiqCONChpcKbg6oglB7c/lLWzXVULwdW2mDEonKSaKOWt2h1bYrgyysF2yWRqk4iLvd98vOaPlMj4jLCuBOk3GEDmGr6/y+5grJS6aeJeTt5fv5LycPBy1pX7la+8srcXtsfTvgVEkJEuzoaMijLOwwkSHRyqlOpcxMOaszi1qh8rhlG6eE2+Da+bDwOmy9P7p06FMOphY+j8ZfjnpJ6E91sgfwJWfydDOZ86Gz26Tgpq10vU8569w73h49Bior5bid1uF9OTeUjD//lNY+bJ/27B+NvxrEHxxd2DbvmEOvP9bGH5i68X2mETp6myrqA3youOcJ2UlwGuXBzf4c9mzkqs98dKm60afIQVz31LBjmCt7LeXLpE8N1eixHYMPx6mXA5H/1Fibda9By9eBP8aDM+d7y2Clwf/uEv+JwOwxnk73pc+0/5tdixpWrK8r10rJJKnrQGtfQ6RnPrdq4Lb5q72yk/g9qHy8//8dinO+vaBxyN/c/Mfkv10x1C4eyx8/d8u3eRuw90A8/4t+fUn3S7//9a+29VbpVTn8RW2w3wyJyEmihKScNQUy3PfW9fJSrkTbwv8zqbfKHNM/H3+b4P1DnVOzQlPYTshJopCRybRnlqZyxGMnSEOjvQZ6MvZ/irgm1YVScE/JSs8ES0AJOaQ5CljV0kla/LKWbmjjHMm+Vk4dzdKBJtvng0wY2gm24pr2FpU3eJNNhVWhS1fGyQS4u2fzeCiqREUVeATnUAcdaSbCtyxPbQ42R35io31oQ2lc0U5OGJEFnPW5uPxtDG8vh3F3o7tjMQgokhSB/TcBghfYTuSurUB0r2rWIu/9/smcS4nfzplFF9tLGLZpy+DccDg9lcRby2W/7M9MWOb+EwaTTSxtbtC+vvqCNqxrZRSgUjuDRe+IrEaH/4R7p8GJ94Ki5+SJ7twxH1kDIHLZ8vgx8/+IUXp6mLJ7zZOGcp5xG9kOZQ/SyQnXybxH29eC3XlMkyztRdcS5+Ft34mcS2z/yxFzeHHt/8YBd/By5dKh9BZj4QnZiN7JJz0L9meR46GY/4kZ8r9ebHo8Uj274DpktfuM+p0+b46Ko6koVa2d+VLMO4C+MHdreeluxvkIHrde1IQ/O596bg+44H2B4Ptq7EOvrxHsuhPu1+6zj76gxTQW+t23/Q5PHOWnBi5bPb+Gft5K2RFQFt57328qyB2LA69sNDZdiyGVa9KRFLxRvn5A2Agc7icJPAVW1L7y/DawnUSF3TIJf7HAYTi+7nw9vWyAmLyZf7dpr7a/4G3oVj+ApRtlZUYvcfLsluNI1EHkpqOiSKJdznZZpOIqiuGbx6Q/1VnPdbmyrh1uyoorKxj+tB9vmboTMgZK88P434Y9FA2gKiqXRSTTLorfPES1XG9oA5Z+RHMCYIdSyClv3+rBtuSNVKi5LZ8IXFoAagrzaPORpOZ6cdQR38lZOPEjae6mKe/2YLL6eD08X4Wzks2y8yDrKbXPr7fiy82FHJBxt4FKWstm4uqmDJIV9sAOGPiiTGNZJoyiNN87U7jahZFEqJZI7N5d0UeK3aUMT7InOOVO8pwmCCjSDJaiKvsKbIOkmPRSCts+/ZJ0Qa/MrJ9zpvcj0/W5hO14ROqe08g3o/nKV9hu39GDyxsOxzUxGaTVVlEYWUd2cndJ25KO7aVUipQxkhn9tVfQM4oeONqKNvmf+HJH64EOOMhGaSYv0YO2k6+C371HVz8msR8+Jv753BKMX7QkRJJ8tqV+79wtFY6Vt+8RoqqNyyX+JBXr2h/2EZVETx/nixzvuCFkLLx9jPhYjjzESkwPnsWPHkybJ3f/u02zZWM6Obd2iA58L44knCrKoT/nSpF7Zl/gtPvb7so7IyGwUdKF96NK+HS9yE6Fp46FT78gxSr/bX8eSkMHPErKVz84B4psr//m5a/Pm85PH8BpA2SnPlnz5bt97FWOrZby9f2Se0P8ZmwfbH/29pdzL1dlope9CpctxB+uxkueg2O+p3E1ow4GU5/UPbNjSvlhMPJd8rv4tf3d+y2edzw6T/hf6dJ989nt0rUTHtWvQq3DYT8tR27fe5GmHeHFLSHHSv/E0ed3hRHotSBoLpIVrWE8zkPb8e2TSKueid88neJrRpzVqtfv72kmvMf/ppLHl/Agk37/P0ZAzNulJNye07eBSe2djfFjhALyPuoj+8t7wQ7QHLnUugThpOqDoeszAuiY9uW76KAFHqnhiefGoBEKZJnmjJeXrSNWaOySfO3wFbg/f/frLA9JCuBnOSYFuNICirqqK53MyiMHduRzOntHO5NESZRC9udZk9hO7SObYCjhmfjMDBnze6gbv/F+kKeX7CVCw71c1irj7XeAfUDg3rciBAVI8ca4y9s/2u7k/TBchngAEljDLedmMsYx/e8VDqC2ob2VxBvLa7G5XTQqxsVfcPJndibXFPEthI/jks6kRa2lVIqWOmD4cfvwrF/g9FnSgRHOBkjxfKbtsCl78n7wXYlxadLvvnRf5QlyY8cI13WIEW0d38hB9AHnydfl5gN5z0rRfEXLpBIkJbsWind1GU75OvDfQbfGDj4XLhusWSdF66Hx4+DZ8+Vx27Noseli27Uqft/riPiSPLXys80bzmc85QMAg1kGaJveNVPP5eTJl//Fx4+GnZ/2/5t3Q0w7y7IPUTyugEyh8GRv5EC/r7xEMXfS8xNbIrkrv/wBajcLfu5QfI8qdgFVQVyEqC97e47SX6ekSRvuRR5pl7bVJSKS5PuxqN+KydoTr9POvea/07nTpC4oK/v67gCbsUuKWjPvRXGnS/7pyoflj3X9u3cjfI37K6D+Q90zLb5rHpFugKP/E3T7/noMzSORB1YqovleSbMS87jXE6KSSLaXSMnQE++q9XHqG1wc82zS2h0W3JTY/nZ80sorNznpOio02Vp/Bf/lsJLkBLrC6hwZQV9+5bYFG+8RlkQg6iqiqQ7MjfEfG2fAdPl+bE8sOxQR3U+haSSEhfGORPewnaWKaPBbTlnUjvDxJvbU9huWoVljGH60Ey+2lC43/LxTYXS6BDOKJJI5oyVLsvepoioxPCeyFFtCFMUCUBagotJA9OZvSY/4NuWVNXzy5eXMSQrgT+cNCqwG1fmyxyd1AiM4AnE0b/3bzVvdxKTCEm9Ay5sA6TlfYEDy2vlI7n1/fYbR7YWV9E3LQ6no2fG0ThT+tCLYraXtBxt1VW0sK2UUqFwOCXS4pwnwNnN050cDjjy11LMrCqQgvTyF+DFi6UQPOPn0iXuGzCVNkByrgvXw+tXSbxHc8tfhEePlcLqj9+F/od23LZHuWDKFXDDMumG3vYNPHg4vH3j/gXGil1SXBt/Qcsd06NOl8vVb4a+XUUbpZv2sWOhsVZOQIw+Pfj7cyXAKXfBBS/LPnr4KPjqP/v/7Jtb+Yoc3DcvMgJMvwGyR0uXfm2ZXFeZD0+fKdnjF78GKX2kMH3GQzIg9c1r5LH8GRzp02ei5HkGm5Hqs+4DiT/pDHP/BTEpcOiVgd/26D/IgdcX/w7/dm38VIbTbl8kkTJnPCgHD30mwlf3SvG6NStfkqJM5gj52/Sn8P7Fv+HRWfDq5fDpP+T/wdb5UFnQegHM45bVHTljYcRJTdf3HiddSt++HtC3rFTEqi4K++BIgPhoJ0U2WT449q/yf7oVf3l7NSu2l3HnueN46KJJlFY3cMMLS3E3L146o2Daz2D7wqA6kn3SGgupiQ1j3AYQk9qLBpyy4ihQO5fKZbhisAZMk8stXwZ0s9jaAiqiMjDhPMGRKBFimZSRkxzDEcMCOKFQsE6GHO+zkmDG0ExKqhtYnbf3LI/NRVLYHpShhW2A6Fj5ObiMWwvbnSkmfFEkIHEka/LKAyq+WWv5/esrKa6q557zJxDnCjBWsdTbMNOTO7YjWcZQiR4M1PqPIT6DiYcdzZNfbeazdW2fMNlaXN0z87W9YjP708sUs71YC9tKKaW60pCj4afzZKDh6z+VjOcTb5eBj/semA0+Eo77G6x9R7KFQQrZ7/0GXr9SCm4/nQv9JnfOtrsSpBv6huUw9WoZmPifQ2Dxk03F36VPg3XvH0Pikz5IIhSCjSOpLJCBgo/MlMf+7Fb5OVw+Ry7DYfhxcM3XMPRY+OiP8PIlLUdReNyyX3LGynL15pzRcOp/pBt79p9lKOIzZ8nHF7y812ApRp8u+3/Vq5LrvmsFYCSOpj1DZwFWuoWDlb8GXrwQ3rgmpI5Cti2Qkx01pa1/za5V8vs89ergcrKzR8oqggWPyEmUcLBWiu1PnyEdoFd+KnFD4I0S+Ll0SK9p5WSMu1Fu33scnP24dAwteartxyzZAp/cIhE02+ZLsfr1n8qKiDuGwuPHw85l+9/u29clo/CIX+39/8IXR7JprsaRqANDTUnY87UBopwO3jeH886gP8LEH7f6dS8t3MbzC7ZyzVFDOG50L0blJvPX00bz5YYi7pmzfu8vnnCRxEZ9eXdQ22QbakmjnIb4VmY2BCkzKZ7dNg136fbAb7ynsD0+PBvT62BwJQVc/E9sKKY2JswF0AQpZPd1lXP+5P6Bdf4VrN37+d3Ll7P91T5xJJsKq4l2GnJTe+ay+UD5CttAh/x9q1b4OrbrQu/YBpg5Uv5XfbLW/67tlxdv5/1Vu/jlcSMY0yeI14clm+UyrYd3bEeq9MHy+jUQHg9snANDZvLbE0cxIieJX728gqJ9V0Y1s7Womv49uLAdndqXGNNISWFgq5s6mha2lVLqQJTSR7qsZ/4JLnix7c7VqdfA2HPh01tgydPw1A9gwUMS43DJG3uWzHaquDQ44Z9w1TwZ+vT2DfDYLMl6XvyUDNhsa5Dn6NP9jyNpqIWt38AXd0th+M4Rkl3dWCfddD//Vn4OqQEsFfZHQiac/ywc/w9Y84783Kv2ycdc/SYUrYcjWok+6TsRDr1aOvKfOEmiTc79X8snIqbfKJnmn98uX58+2L/s2D6HwGHXwcJHpashUNZKV7mnEXavlJ91MNbPlnzyxU/Aixe1nlH++e1SvJh6VXCPA3DUTdL1/vkdwd+Hj8cj3/+nt0jB/IpPpHje3IiTIWOY/A62VPhf8aJkyh/1OzkZMfBwWPBo2x3ec2+TCe+XvicZ4n/YBdctkiiiWX+W7u+Hj5K/raqipm39/A7Jbh3ZSsyPp1FOHCjV01UXQXxah9x1fUw636Sc0GoEyaodZfzxzVXMGJrJL49rKmKeO6kfZx7Sh/98sp7PvytoukF0HBx6Faz/SE7uBai6eCcANik34Nu2JSsphp02g8aSIKJIdi6R/4vhGuTrjJKVZ4F0bDfWk2zLcSeE+XVQTBJExXHNpGSunznM/9t53LKCqlm+tk9OcixDsxP5YkPRXtdvLqyiX3o8UU4tCwC4YhObPtDCducJ4/BIgCFZiQzKTPA7jmRLURV/eetbpg5O54rDBwf3oL5jikgbrHigyBgqz9uBrDDNWyq3GXYssdFO7j5/POU1Dfz21ZXYFl6Pl1U3UF7byICeODjSJ1leB9QWBfG83YH0GUwppQ5UUS7pfm4vJ80YGUbYayy8dZ10cZ71GJzwD+kK7ko5o6Uwd8bDULoNHj1Gsjpb69b2aSuOxOOB7z6SAY6PHgu39pPu1dk3Q/EmiZ65+msZHjr9hjaXiYfMGDjsWilG71opsRGFG5q28/M7IHN4y0VGn2P+IC+yd6+UgZbDjm39sU75twwZrchrf3DkXo/xfxJ78ua1TUVQf614SQoJx/9T4kEWPhLY7UG6iJ8/HzKHwgm3weZ5MtR13wiX/DWyzw+9Uk6OBCt9sHRALn4ytKx2d4OsfFj0GEy7XiJhXAn7f53DIb9ru1bAxk/2v4/P/yWrEHxd+1OvhvLtsPbtlh+3YJ0MHJ1yxZ4XqETFSDb78OOlQ/xni+Wk1pKn4T8TYP7DsPp1KFgDR/xatmlfe+JI3gjyB6JUBKku6rDCV7zLSXVdy0OqSqvrueqZxWQmuLjn/PF7dfMaY/j76WMYlp3IjS8uI6+s2UqfKZdL8ejLewLenrJ8+T8XlRr+wnaezcBRtB7KdwZ24x1LwhdD4jNgmnQ8L3+h9dkizbgrZDidSQpvJzvGQGIW8fVFgXVrl26RWLQWOrZB4kgWbCqirrHpd2tzUZXGkDQTHdusINUBUUOqFc5ocMaEJWPbZ9bIbL7ZWERlXRsn+YFGt4cbX1yG02G469zxwWcjl2yGxF5yIlF1P76Gp6Lv/b/NhjmA2TPDaGTvZH5zwghmr9nN8wv2L+xuKZYTMz05isR33GuDHfrcQbSwrZRSqn2ueDj/ORh/EVw+G8ae3dVb1MQYGHce/GyRFOKGHAMHndz2bVqKI3E3SpH1gcPguXNgwcNy34deJd/7rzbA9UukmzUnwIEyoRp1KvzoHagrl870LV/L8MP8b+XkhKONHEBXAlz0Glz4qgwjbIszWoroQ2bKQFR/RcfCmQ9LF8Tb1/sfJ1JTKlErfSbKz3nCRVJ4DiTiY/FT8MpPJCv8R+9IJ/asv0isysf/t/fXfn4HRMfLaoNQHfEb6Xiee9v+n6urgC/vhVcug+8+bDkjvaFGOstXvgwzb5bIn7YyWg8+F5Jy98/2Xv6CHEwd9bum2w8/QYYXffNgy/f16S3yc5jxi9YfLzZFTl5d/ZX8rbz/a/l+MoZKZ3ZLjJHPff+ZxpGoni9zhLx1gHiXk+r6/QvbHo/lhheWkV9ex/0XTSQjcf85EvGuKO6/cCJ1DW6ue24pDW7v/5+4NIk2WfVqwCfkqgvlAD42I7wrk7KSYnjBfTSmsQYeOgI2fe7fDcvzoHKXrBhqw5KtJVzy+AKeX7C1xe66/Yw5C1L6SyzTv4bACxfKHItWitxlBRKh4gpzwR+QnO3KAIffFayTyxY6tgGmDcmgtsHD0q2lgGQKby6q0sGRzRiXRpF0GVdCWAvbM0fmUO/2MK/56pUW/OeTDSzdWsotZ4wlNzWEonTpFo0h6c4yhsplIDnb6z+WE6gJTXFTP5k+iBlDM/nbO6v5vmDv39et3tzpnhxFQrIUtqOq8vYbRtyVtLCtlFLKP6n94PT7/Mtd7gqxKRJPcvHr/nWS++JIijZKcfS/E+G1K6RYedZj8LvtcNlHUnA86GRIDGB4U0foN1lOKsSlw/9Ohfdvku7YMX6cZMgcBsNm+fc4cakyWHJUG13gLek1Rjq3174DS5/x7zaf/kOGZJ58p3QAT75MoiwWt5MP7fPlvVJIH3KMFO/jUuX66TfAlJ/C1/+Fr++T6wq+k4LOlMshIQwHqyl9YPLl0vlc8J1cV10s39O/x0hRff3H8Ny58N9J0vHsK47UlsMzZ0vR++Q74fA2Csw+UTFw2DXSjb59sVznbpBoldwJe6+8cDjh0J/KkFVfDq3PzmVy8uCwa/37OWQfBJe8KSc8eh8Mx/297RMpo06XjHuNI1E93aXvyt9kB4h3RVFVv3+X4T1z1jP3uwJuPnUU4/ultnr7odmJ/POsg1m8pYR/fbC26RNTr5HnON//RT81lEhnVmJm+AvbX3tG8+Fhz0nh/X+nycm79orQO5fIZW7Lhe3KukZufnMVZz3wFfO/L+J3r63kuueWUlbT0Pb9pg2UGR4/+RAmXSqvEV69DG4f2qzI3VTIKMuXwnZCegcUthOygyhse/d15vAWPz11SAYOA19ukFiz3eV11DZ4tLDdXPNuWy1sdy5XYtiiSAAmDUgjJS66zTiSxVtK+M8n6zlzQh9+MC7Ev+OSLdJUoLqntIHy/Pf9XIl4XPK0rGCa/Wd463ppNnnyFLh/Gtw5Ev6eA9sXeGcJNXE4DHeeO46YaAc3vris6eQxTYXtHt2xnZCFx0RxUn8P9e4WGne6SFRXb4BSSinVJUadLi9mHpgmS3dzJ0ie9fATW45Z6A7SB0tx+/kfStHyB/dILmh3cdh1kuH6wU0wcIZ0xrcmb7nEjky+rGk5ecYQeQG56HEp9rZ2gsJ6h1XOu0M6hM94WKJ1fIyRkxwVefDh7yGpl8TLRMXCYT8L3/c74+cSR/LRH6UTZPET0FANB50i3dC9D5Yi8jf3S8fzJ3+THPMtX8LuVXDWo4Gtfpj4Yylkf/lvOO8ZKaqXboGTbt+/23vCRVJk/+ZBOPOhpus/+TvEpkph21/GwKjT5K09vcdB2iCJhznkEv8fQym1R0LM/h3bn67N55456zl7Yl8umNJ+huup43JZuKmYR+ZtYtLAdI4f3UtOyB18ngxePvI3e3WhtcVTvpNaG016ZnizpDMSpON8g+0r8wXe+pk8L29bKNFZvpOV+9qxBIxTItL28fHq3fzpzVXsKq/lkqkD+OXxI3hu/lbu+HAdy7aVcu8PJzBxQBtRVA4H9J8qb8f/U4brrn5DIpbWviPPI8OOhVGnU5cv0WApWX1D+jm0KDFbiiqBKFgnK3ta+bklx0ZzcN9UvthQyC+PG8GmQikiahRJM9HNClLxGkXSqWISZQVawTpIHxLy69sop4OjRmTx6bp83B67X8RIZU0df3vhU45KLuLvo4HFy2TFoO/N4ZTXbMOOa/+4wN0gEXBpA0PaZtWBomLk92rZM/Lm44iWE6vx6dI8lDYQ+kyQ6xKy5HdgHznJsdx65liuemYJd8/+jl8fL6tkthVXk5noIjGmGx2bhZvDiSO5N4dm1EJ0G40unawH/8SVUkqpNqQPkmzq6mI4/OcSv9FWFER3EZ8uHbSb58k2dycOB5z+ADwwXZZy//i9lg9MfAMT49LhmD/u/bnJV8Dz50kBobXIiwUPS1H7kB9JLnhLHcQOJ5z5CDxdAK9fJZ3gU68Jb+d9YpbkWc+7AzbMlriQ6TdKl7PP2LPlbdtCmP8AzH9QCvbnP9d+vv2+YpLk5zPvTti9WorcfSbKQde+YlNg/AWw6AkZcpqUIxE2Gz6WqJZwDVzblzGyGuLLe+VvSwsDSgUs3hVFUWX1no+3FlVzwwtLGdU7mb+fPgbj53PVH08ZyfLtpfzq5eWM7JVM/4x4mROx7Bl4+ccw5Ur5/xEd2+b9OCt3sYt0+iW2/XWBckU5SIuPpqCyVv6/nf0E9DtUThY+fBSc93SLxWt2LpUhu66mImR+eS03v/Ut76/axYicJO678BAO6S8F7KuOHMKhg9K5/oWlnPvQ1/zi2OFcfeQQHO1l6TocMOAwefMVub99XU5Yrnkb33/6zF4dVNiuKpSYNH8LfAVrW83X9pkxNJMH5m6kvLaBzUVS2B6Y2YO7CwPlK2wbh5wEVp0nbRCsexfumyJ521kjIGeMxP/ljJb3/RlYb61E95Xn8cP0DbhqF7Dr7S/p4yiFyt3S9FCxi7iKfN7AewLxlWa3j8+EpN5QXSivRdMHyyrA8RdAbHLLj1m2DaxHo0i6uwtelIaQuHRvITtNVgoEcfx3wpjenDupL/d/tpEjhmVx6OAMthRV9+xubZ/kXCjvXhnbWthWSil14Drv6a7eguBEx7Y+BLKrpfaTeI3XLofP/iGDBvcdpLPsGdi+UIrg+w5xHHasLOVc8EjLhe1tC2Ww5/AT4ZS72+6iiY6VAvLjJ8gL2Wlh7Nb2OfwX0tEx4sS2D2j6TZa34/4u+dq+ITaBOvQqiVh57lw5kDr5rtZfkE/5qZwEWPQ4HHUTzPmr5LZOuTK4x/bX6DMkTmDN2zDxRx37WEr1QAnNMrZrG9xc9YzEDz140URiA+iQiolyct8Fh3DyvfO4+tnFvHr1NGKzRsj/oS/vhZcuhphkOck79mwYdESLJwpjanZTZDIYGOxQtTZkJcVQUFEnHxgjJwtzJ0jh/dFZcvJy/AVNN7BWokgOOgWQ3PHnF27l1vfXUtfo4dfHj+DKIwYT7dz7uWFC/zTevf5w/vD6Km7/cB1fbSzk3+eOJzvZz2J9syJ33rSbmTv7bfj2Deqtg4uTE8Pwk9hHYjZgZUipP8MpPR7pdD2k7f+504dm8t9PNzD/+2I2F1bhinKQm6LD7vbwvV6JS+++q/d6qnOfkiHfu7+VGTK7v5WB2cufa/qa+ExvkXu0ZMm762XwbEWeXPre92Z1TwWmRgNLkWiZpN6Q1Itt0YN5o8TNyGHDmTVlnKzsS+olEUC+FYDuBljzlqx8++C3suJtwkUygDx98N7b7ptboFEk3VvGkOBff7fg5h+MZv6mYn7x0nLeu+FwthZXt70iqKdIzoW8FV29FXvpksK2MWYzUAG4gUZr7SRjTDrwIjAQ2Ayca60t8X7974DLvF9/vbX2Q+/1E4EngTjgPeAG69dkEKWUUkp1mIPPge8+kM7ir/4jRYr+h8GAaZL9+fHN8vG4H+5/W4dTsqs//j85qMkZ3fS5qiIpdiT3hjMe8O+gMz5dstIr8+WgJdxcCTKw0l/JIWY4JnqXRS58BPpM2i/7by+ZQ6Ubc9FjEhGy9Ss46Y69uhw7RK+DpfNq9Rta2FYqCPExUVTXN2Kt5Q+vr2J1XjlP/HiydFwHqF96PHedO57L/7eIv76zmn+cMVZO8h16taz8WfmKFG+WPSNFnTFnwthzZDWI96RZQl0+m6OHhfvbBPYpbPv0nwo//VwGA79xtXRKn3CbnKws2SyDinMnsCG/gt+9tpKFm0uYNiSDW84Yy6A28qKTY6O59/zxHD40k5vf+pYT75nHHeeO4+gR7XeBWmv55vtinv5mMx9+uxuPTeKYEb/kmqOH+t1BH5AE7zZV7m6/sF1XCZvmShRWOx3bhwxIJTbawZcbCtlZWsOA9Pj2O9cPJL7CtuZrdz5ntES49T547+uripoK3btXyYq1RU9AY4183jilYJ2cK93dQ2fJ+963G9/bzXfVibz3S2kIySur4ZS75zGwdwJXXXQYOFt5LemMloGyY86SvP1vHoSFj8rKu+HHS6PB4KPk/2TJZrmNRpEcUBJiorj7vPGc/eDX/P71lewsreGMCX26erM6XnIfWPeBnGjuJqudu7Jj+2hrbWGzj28C5lhrbzXG3OT9+LfGmFHA+cBoIBeYbYwZbq11Aw8AVwLfIIXtE4D3O/ObUEoppVQLznhQOgC3fAVbv5Yu4y/vls8Zp3R1t/ZiaMJF8Okt0rX9A+9tPB4Z7lmVL4XqfTu92xKX2npWaySa9jNvpMif239BeehV8MyZ8rNL7d9uN19YGCNd21/eIwek4RjWqdQBJD5aOrafW7CVV5ds54aZwzj6oODzrWeNyuGnRw7mobnfM2VgOqdP6CPxFkOOlreT74T1H8LKl6VgNP9BKdCMPQfGnE1qYyHVSdPD9w02k5UYw0erd/PzF5fRNy2OPqlx9EmLo09qPLk/fJXYef+UFSA7l8kQW+9A3Ge3Z/LnN+aREBPF7WcfzNkT+/pVYDbGcO7kfhwyIJXrnlvKpU8s5PIZg/jNCQfhitq/wFVV18hrS3fw9Neb+W53Janx0Vw+YxAXTR3QsUvOE73F7Kp9Bt953BI5sn0R7Fgkw4QL1kgMgnFC30lt3m1MlJPJA9P5ckMhxsAAzdfemzNaMne1sN19JGTIapJBRzRd53FD6VY5EZGQ1eZQ67FjN/HGO6vZWlRN37Q4fvnSchrcHu4+b/x+Kzta1WcinPWIDJRf9Li8PX06ZI2UYd0F6+T3JtTmBRVxJvRP44aZw7jrYxkk3/9AiCLpO1lOujbWtRtl1lm6UxTJacBR3vefAj4Dfuu9/gVrbR2wyRizAZji7fpOttZ+DWCM+R9wOlrYVkoppbqeM1riOUacKB/XV8tB+JavpJu3eSf2vuLTpSi+4kUp3salSvf3xjmyLN03bPJAlTYAblju39cOOQYyR0DhOjjxX3sP2exIB58nWZS6lFupgEnHtpu/vLWao0ZkccPM0Lulf33cCJZuKeV3r61kdG4yw3KSmj4ZHds0ILamVHJlV74s/3c/vx0X0BDnRxxGEM44pC87SmuY/30Rb5bX4tln7W1W0gzOSE7kxt13Yf47g5LkEWQSxZ/nw0nje/N/p4wiMzEm4Mcdmp3EG9dO55/vreHRLzYxf1Mx9/5wwp6O740FlTz99RZeXbydirpGxvRJ5l9nH8yp43IDioMJmm8exK5VTc+f2xdLYb9BsrGJTZWC28hT5LLPJL9OJM4Ymsk/319LlMNw5PAwzp3oKaLjdT5Ed+dwtj2gvJlZI7P52zurmb1mN40eD19tLOLWM9te3dGqpF5w9O9lQPi3r8E3D8A7N8rn0ge3WWBXPdc1Rw1h7ncFLN5ScmAUtkefLm/dSFcVti3wkTHGAg9Zax8Gcqy1eQDW2jxjjK8toQ/Ske2z3Xtdg/f9fa/fjzHmSqSzm/79258irpRSSqkwc8Xv33HTlslXwNJnYPnzkqP46S1SLJ14acduZ09jjJwcWPmy/Pw6S/ZBew/RVCpAxpgTgHsAJ/CotfbWLt6kTpPgkuJIdnIMd583PixREVFOB/f+cII3b3sJb147nYSYFg4F41Jl1cyEi6BiF3bVa3z6/ivszjk85G1oyZHDs/YUVxvcHnaV1bKjtIYdJTV7LleXHs4VDbn8X/U/OahkIavNUB659DCO8iNCpC2x0U7+ctoYpg/N5DevruCUe+dx9VFDmL+pmHnrC4l2Gk4e25tLpg1kQr/UjokcaY2vY3v2zXLpiJZBmhMulAJ230lSSAtim6YPzQSg0WMZGExxr6dL6qWREj3IgIwEhmUn8sz8LWwrrub40TmcN7lfaHcaHSvZ/+N+KKsSFzzS8qBbdUDwPb8+Nm8T4/qldvXmHJC6qrA93Vq701u8/tgYs7aNr23p2dq2cf3+V0rh/GGASZMmaQa3Ukop1d3ljoe+U+Cb+6VbLesg6dbuJlluEeWgk+RNqQhhjHEC9wHHIs0rC40xb1lrV3ftlnWOARkJJMZE8eBFE0mND98qi14psdxz/gQufnw+o2/+kHiXk3hXFIkxvsso4mOcJLiiSPBeFxt9DA/WD+D3WcPDth2tiXY66Jce30rEx6F46k6javY/GNZ/EqNCLGo3d9zoXozpk8KNLy7jjo++o3dKLL86bjjnTe5PVlLg3eBh4UqQ4cDuBili9xoLUeHZllG9k0mNj6a0uoFBGkWyvx+/2/GzKFSnmjkyhwfnbiQ7KYZbzzw4fCepjJH5MQOmhef+VMTqkxrHn34wqqs344DVJYVta+1O72W+MeZ1YAqw2xjT29ut3RvwBYptB5qfUusL7PRe37eF65VSSinVE0y5QrKhoxPg3KfkQF8pdSCYAmyw1n4PYIx5AYknPCAK2yeM6cXMkdn+578GYMawTJ68dApLtpRQXd9IZZ2b6vpGquoaqapzU1xVz7biaqrr3VTWyfVOh2Fk7+Swb0ugHDEJJJx8S4fcd25qHM9fMZX1+RUMzUokqgN+9gGbfFmH3K3DYZg2JIP3Vu7Sju2WJGo8S0/zg3G9efabLdx57jjSEjopkk0p1Wk6vbBtjEkAHNbaCu/7xwF/Bd4CfgTc6r1803uTt4DnjDF3IcMjhwELrLVuY0yFMWYqMB+4BPhP5343SimllOowo06Dte9KhEbWiK7eGqVU5+kDbGv28Xbg0H2/qCfHDXZEUdunefxHe6y1NHpsh25Pd+F0GA7q1fUF/M5w4aEDMMbQK7l7DP5SqiONzk1hxZ+P69w4IaVUp+mKju0c4HXvP5Uo4Dlr7QfGmIXAS8aYy4CtwDkA1tpvjTEvIR0ajcC11lq3976uBp4E4pChkTo4UimllOopomKkU1spdaDxK3JQ4wY7njGGaKcWg3qa6UMz92RtK3Ug0KK2Uj1Xpxe2vUsKx7VwfREws5Xb3ALst+7MWrsIGBPubVRKKaWUUkp1mdaiCJVSSimllNqj568pU0oppZRSSkWShcAwY8wgY4wLOB+JJ1RKKaWUUmqPLhkeqZRSSimllFItsdY2GmOuAz4EnMDj1tpvu3izlFJKKaVUN6OFbaWUUkoppVS3Yq19D3ivq7dDKaWUUkp1XxpFopRSSimllFJKKaWUUiqiaGFbKaWUUkoppZRSSimlVETRwrZSSimllFJKKaWUUkqpiKKFbaWUUkoppZRSSimllFIRRQvbSimllFJKKaWUUkoppSKKFraVUkoppZRSSimllFJKRRQtbCullFJKKaWUUkoppZSKKFrYVkoppZRSSimllFJKKRVRtLCtlFJKKaWUUkoppZRSKqJoYVsppZRSSimllFJKKaVURNHCtlJKKaWUUkoppZRSSqmIooVtpZRSSimllFJKKaWUUhFFC9tKKaWUUkoppZRSSimlIooWtpVSSimllFJKKaWUUkpFFC1sK6WUUkoppZRSSimllIooWthWSimllFJKKaWUUkopFVG0sK2UUkoppZRSSimllFIqomhhWymllFJKKaWUUkoppVRE0cK2UkoppZRSSimllFJKqYhirLVdvQ2dyhhTAGzp5IfNBAo7+TFVx9H92fPoPu1ZdH/2PLpPe5aO3J8DrLVZHXTfqhvT1/gqTHSf9ky6X3sm3a89j+7Tnikc+7XV1/gHXGG7KxhjFllrJ3X1dqjw0P3Z8+g+7Vl0f/Y8uk97Ft2fqqfQ3+WeR/dpz6T7tWfS/drz6D7tmTp6v2oUiVJKKaWUUkoppZRSSqmIooVtpZRSSimllFJKKaWUUhFFC9ud4+Gu3gAVVro/ex7dpz2L7s+eR/dpz6L7U/UU+rvc8+g+7Zl0v/ZMul97Ht2nPVOH7lfN2FZKKaWUUkoppZRSSikVUbRjWymllFJKKaWUUkoppVRE0cK2UkoppZRSSimllFJKqYiihe0OZIw5wRizzhizwRhzU1dvjwqcMaafMeZTY8waY8y3xpgbvNenG2M+Nsas916mdfW2Kv8ZY5zGmKXGmHe8H+v+jGDGmFRjzCvGmLXev9XDdJ9GLmPMz73/b1cZY543xsTq/owsxpjHjTH5xphVza5rdR8aY37nfa20zhhzfNdstVL+09f4PUOg/6tU96fHbj2T97XgAmPMcu9+/Yv3et2vEU6Py3seY8xmY8xKY8wyY8wi73Udul+1sN1BjDFO4D7gRGAU8ENjzKiu3SoVhEbgl9bakcBU4FrvfrwJmGOtHQbM8X6sIscNwJpmH+v+jGz3AB9Yaw8CxiH7VvdpBDLG9AGuByZZa8cATuB8dH9GmieBE/a5rsV96H1OPR8Y7b3N/d7XUEp1S/oav0d5Ej//V6mIocduPVMdcIy1dhwwHjjBGDMV3a89gR6X90xHW2vHW2sneT/u0P2qhe2OMwXYYK393lpbD7wAnNbF26QCZK3Ns9Yu8b5fgfzT7YPsy6e8X/YUcHqXbKAKmDGmL3Ay8Gizq3V/RihjTDJwBPAYgLW23lpbiu7TSBYFxBljooB4YCe6PyOKtfZzoHifq1vbh6cBL1hr66y1m4ANyGsopborfY3fQwT4v0pFAD1265msqPR+GO19s+h+jWh6XH5A6dD9qoXtjtMH2Nbs4+3e61SEMsYMBCYA84Eca20eyAsoILsLN00F5m7gN4Cn2XW6PyPXYKAAeMK7jO1RY0wCuk8jkrV2B3AHsBXIA8qstR+h+7MnaG0f6uslFWn0d7Zn0+ebHkKP3XoWb2TFMiAf+Nhaq/s18t2NHpf3RBb4yBiz2Bhzpfe6Dt2vWtjuOKaF62ynb4UKC2NMIvAqcKO1tryrt0cFxxhzCpBvrV3c1duiwiYKOAR4wFo7AahCl6xFLG/e2mnAICAXSDDGXNS1W6U6mL5eUpFGf2eV6ub02K3nsda6rbXjgb7AFGPMmC7eJBUCPS7v0aZbaw9BItuuNcYc0dEPqIXtjrMd6Nfs477IcmoVYYwx0cgLo2etta95r95tjOnt/Xxv5Myx6v6mA6caYzYjS4ePMcY8g+7PSLYd2O7t2gB4BSl06z6NTLOATdbaAmttA/AaMA3dnz1Ba/tQXy+pSKO/sz2bPt9EOD1269m8kYOfIfn4ul8jlx6X91DW2p3ey3zgdSTCrUP3qxa2O85CYJgxZpAxxoUMRnqri7dJBcgYY5Ds3jXW2ruafeot4Efe938EvNnZ26YCZ639nbW2r7V2IPI3+Ym19iJ0f0Ysa+0uYJsxZoT3qpnAanSfRqqtwFRjTLz3/+9MJB9T92fka20fvgWcb4yJMcYMAoYBC7pg+5Tyl77G79n0+SaC6bFbz2SMyTLGpHrfj0MaIdai+zVi6XF5z2SMSTDGJPneB44DVtHB+9VYqyvnOoox5iQkN8gJPG6tvaVrt0gFyhgzA5gHrKQp++n3SFbbS0B/pBBzjrV23+EzqhszxhwF/Mpae4oxJgPdnxHLGDMeGTriAr4HLkVO3Oo+jUDGmL8A5wGNwFLgciAR3Z8RwxjzPHAUkAnsBm4G3qCVfWiM+QPwE2Sf32itfb/zt1op/+lr/J4h0P9VqvvTY7eeyRhzMDJwzon3Nb619q96DNcz6HF5z2GMGYx0aYNEhj5nrb2lo/erFraVUkoppZRSSimllFJKRRSNIlFKKaWUUkoppZRSSikVUbSwrZRSSimllFJKKaWUUiqiaGFbKaWUUkoppZRSSimlVETRwrZSSimllFJKKaWUUkqpiKKFbaWUUkoppZRSSimllFIRRQvbSinVTRlj3MaYZc3ebgrjfQ80xqwK1/0ppZRSSil1oDPG5BhjnjPGfG+MWWyM+doYc0azz99rjJlkjHnUGDOqjfvpkNfqxpgfG2M8xpiDm123yhgzMNyPpZRSnSGqqzdAKaVUq2qsteO7eiOUUkoppZRSbTPGGOAN4Clr7QXe6wYAp/q+xlp7vffdyzt9A5tsB/4AnNeF26CUUmGhHdtKKRVhjDGbjTG3GWMWeN+Geq8fYIyZY4xZ4b3s770+xxjzujFmufdtmveunMaYR4wx3xpjPjLGxHm//npjzGrv/bzQRd+mUkoppZRSkeQYoN5a+6DvCmvtFmvtfwCMMbHGmCeMMSuNMUuNMUd7r3+02QrNAmPMzc3v1BjjNMbcboxZ6H19/lPv9UcZY+YaY14yxnxnjLnVGHOh9/hgpTFmSCvb+Q4w2hgzYt9PGGN+6L3tKmPMbWH6uSilVIfRwrZSSnVfcftEkTTvqii31k4B/gvc7b3uv8D/rLUHA88C93qvvxeYa60dBxwCfOu9fhhwn7V2NFAKnOW9/iZggvd+ruqYb00ppZRSSqkeZTSwpI3PXwtgrR0L/BB4yhgTa6293LtK8zSgCHhyn9tdBpRZaycDk4ErjDGDvJ8bB9wAjAUuBoZ7jxEeBX7WynZ4gH8Bv29+pTEmF7gNKdCPByYbY05v8ztWSqkupoVtpZTqvmqsteObvb3Y7HPPN7s8zPv+YcBz3vefBmZ43z8GeADAWuu21pZ5r99krV3mfX8xMND7/grgWWPMRUBjGL8fpZRSSimlDgjGmPu8qyUXeq+agbxGx1q7FtgCDPd+bSzwMnCdtXbLPnd1HHCJMWYZMB/IQBpUABZaa/OstXXARuAj7/UraXpt35LngKnNCuQgRfPPrLUF1tpGpFHmiMC+a6WU6lxa2FZKqchkW3m/ta9pSV2z9900zV04GbgPmAgsNsboPAallFJKKaXa9i2yOhIAa+21wEwgy3uVaeO2DwKvWWtnt/A5A/ysWbPLIGutr4Dd/PW8p9nHHtqYqeYtXN8J/Hafx1FKqYiihW2llIpM5zW7/Nr7/lfA+d73LwS+8L4/B7ga9mT0Jbd2p8YYB9DPWvsp8BsgFUgM65YrpZRSSinV83wCxBpjrm52XXyz9z9HXqNjjBkO9AfWGWOuBZKstbe2cr8fAlcbY6J9tzXGJIRhe58EZtFUeJ8PHGmMyTTGOJG4lLlheByllOow2oWnlFLdV5x3yaHPB9bam7zvxxhj5iMnKH/ove564HFjzK+BAuBS7/U3AA8bYy5DOrOvBvJaeUwn8IwxJgXp2vi3tbY0TN+PUkoppZRSPZK11nozqf9tjPkN8nq8iqau6PuBB40xK5G4vx9ba+uMMb8CGpq97n8Q+KDZXT+KxIosMcYY7/2eHobtrTfG3Avc4/04zxjzO+BT5DjgPWvtm6E+jlJKdSRjbXsr1ZVSSnUnxpjNwCRrbWFXb4tSSimllFJKKaVUV9AoEqWUUkoppZRSSimllFIRRTu2lVJKKaWUUkoppZRSSkUU7dhWSimllFJKKaWUUkopFVG0sK2UUkoppZRSSimllFIqomhhWymllFJKKaWUUkoppVRE0cK2UkoppZRSSimllFJKqYiihW2llFJKKaWUUkoppZRSEeX/ARRSIj8hafPTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def modelkur(units=64, activation=\"linear\", learning_rate=0.01, hidden_layers=2, dropout_rate=0.2):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(units=units,input_dim=x_train.shape[1],activation=activation))\n",
    "    \n",
    "    for i in range(hidden_layers):\n",
    "        model.add(Dense(units=units,activation=activation))\n",
    "        model.add(Dropout(dropout_rate))\n",
    "        \n",
    "    model.add(Dense(1,activation=activation))\n",
    "\n",
    "    model.compile(loss=\"mae\",optimizer=Adam(learning_rate),metrics=[\"mae\"])\n",
    "    return model\n",
    "\n",
    "parametreler = {\"units\":        [32,64],\n",
    "                \"activation\":   [\"linear\",\"relu\"],\n",
    "                \"learning_rate\":[0.001,0.01],\n",
    "                \"hidden_layers\":[2,3],\n",
    "                \"dropout_rate\": [0.0, 0.2],\n",
    "                \"epochs\":       [50,100]}\n",
    "\n",
    "regres = KerasRegressor(build_fn=modelkur,verbose=0)\n",
    "grid = GridSearchCV(estimator=regres, param_grid=parametreler,cv=3)\n",
    "gridsonuc = grid.fit(x_train, y_train)\n",
    "bestparam = gridsonuc.best_params_\n",
    "\n",
    "bestmodel = modelkur(units=bestparam[\"units\"],activation=bestparam[\"activation\"], \n",
    "                     learning_rate=bestparam[\"learning_rate\"], hidden_layers=bestparam[\"hidden_layers\"],\n",
    "                     dropout_rate=bestparam[\"dropout_rate\"])\n",
    "\n",
    "m1 = bestmodel.fit(x_train,y_train,validation_data=(x_val,y_val),epochs=bestparam[\"epochs\"],verbose=0, batch_size=64)\n",
    "tahmin = bestmodel.predict(x_test)\n",
    "\n",
    "\n",
    "fig,ax = plt.subplots(1,2,figsize=(25,10))\n",
    "\n",
    "ax[0].plot(m1.history[\"loss\"])\n",
    "ax[0].plot(m1.history[\"val_loss\"])\n",
    "ax[0].set_title(\"Loss Graph\")\n",
    "ax[0].set_ylabel(\"Loss\")\n",
    "ax[0].set_xlabel(\"Epochs\")\n",
    "ax[0].legend([\"Train\",\"Validation\"],loc=\"upper right\");\n",
    "\n",
    "\n",
    "ax[1].plot(range(len(y_test[:50])), y_test[:50].values , label=\"Gerçek\")\n",
    "ax[1].plot(range(len(y_test[:50])), tahmin[:50] , label=\"Tahmin\")\n",
    "ax[1].set_title(\"Fiyat Tahmini\")\n",
    "ax[1].set_ylabel(\"Fiyat\")\n",
    "ax[1].set_xlabel(\"Gözlem No\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "743efa84",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}