{
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  {
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    "# Лаб работа №4\n",
    "Вариант 6 - Продажа домов в округе кинг\n",
    "\n",
    "Задача регрессии заключается в предсказании цен на недвижимость, что поможет риэлторам и аналитикам оценить справедливую рыночную стоимость объектов.\n",
    "\n",
    "Задача классификации предполагает определение вероятности того, что цена дома будет выше или ниже медианы рынка, а также классификацию домов по ценовым категориям (например, низкая, средняя, высокая). Это поможет выявить предпочтения покупателей.\n",
    "\n",
    "Для оценки регрессионных моделей будут использоваться метрики MAE (средняя абсолютная ошибка) и R² (коэффициент детерминации), с целью достижения MAE менее 10% от средней цены. В классификации основное внимание уделяется метрикам accuracy и F1-score, с целевым значением accuracy около 80%.\n",
    "\n",
    "## Ориентиры для каждой задачи:\n",
    "Регрессия: Медианная цена (price.median()) выбрана как стабильный ориентир.\n",
    "\n",
    "Классификация: Ориентиром служит средняя вероятность предсказания класса выше медианы.\n",
    "\n",
    "Анализ алгоритмов машинного обучения:\n",
    "\n",
    "### Регрессия:\n",
    "\n",
    "Линейная регрессия: Подходит для простых линейных зависимостей.\n",
    "\n",
    "Дерево решений: Учет нелинейных зависимостей и сложных закономерностей.\n",
    "\n",
    "Случайный лес: Ансамблевый метод, обобщающий данные и эффективно обрабатывающий выбросы.\n",
    "\n",
    "### Классификация:\n",
    "\n",
    "Логистическая регрессия: Простая модель для бинарной классификации.\n",
    "\n",
    "Метод опорных векторов (SVM): Эффективен на данных с четкими разделениями.\n",
    "\n",
    "Градиентный бустинг: Подходит для сложных и высокоразмерных данных, обеспечивает высокую точность.\n",
    "\n",
    "## Выбор трех моделей для каждой задачи:\n",
    "\n",
    "Регрессия: Линейная регрессия, Дерево решений, Случайный лес.\n",
    "\n",
    "Классификация: Логистическая регрессия, Метод опорных векторов (SVM), Градиентный бустинг."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
      "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
      "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
      "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
      "      dtype='object')\n"
     ]
    },
    {
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       "      <th>price</th>\n",
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       "      <th>view</th>\n",
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       "      <th>grade</th>\n",
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       "      <th>sqft_basement</th>\n",
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       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
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       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
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       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
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       "      <td>20150218T000000</td>\n",
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       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
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      "text/plain": [
       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
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       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      5650     1.0           0     0  ...      7        1180              0   \n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "2     10000     1.0           0     0  ...      6         770              0   \n",
       "3      5000     1.0           0     0  ...      7        1050            910   \n",
       "4      8080     1.0           0     0  ...      8        1680              0   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0      1955             0    98178  47.5112 -122.257           1340   \n",
       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
       "2      1933             0    98028  47.7379 -122.233           2720   \n",
       "3      1965             0    98136  47.5208 -122.393           1360   \n",
       "4      1987             0    98074  47.6168 -122.045           1800   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import set_config\n",
    "\n",
    "set_config(transform_output=\"pandas\")\n",
    "random_state = 42\n",
    "\n",
    "df = pd.read_csv(\".//static//csv//kc_house_data.csv\")\n",
    "print(df.columns)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "      <td>47.5180</td>\n",
       "      <td>-122.194</td>\n",
       "      <td>1950</td>\n",
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       "      <td>2.75</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1966</td>\n",
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       "      <td>20150414T000000</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1660</td>\n",
       "      <td>9549</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1956</td>\n",
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       "      <td>98033</td>\n",
       "      <td>47.6503</td>\n",
       "      <td>-122.198</td>\n",
       "      <td>2090</td>\n",
       "      <td>9549</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>19980</th>\n",
       "      <td>774100475</td>\n",
       "      <td>20140627T000000</td>\n",
       "      <td>415000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2600</td>\n",
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       "      <td>2009</td>\n",
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       "      <td>98014</td>\n",
       "      <td>47.7185</td>\n",
       "      <td>-121.405</td>\n",
       "      <td>1740</td>\n",
       "      <td>64626</td>\n",
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       "    <tr>\n",
       "      <th>3671</th>\n",
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       "               id             date      price  bedrooms  bathrooms  \\\n",
       "20962  1278000210  20150311T000000   110000.0         2       1.00   \n",
       "12284  2193300390  20140923T000000   624000.0         4       3.25   \n",
       "7343   4289900005  20141230T000000  1535000.0         4       3.25   \n",
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       "16670   629400480  20140619T000000   775000.0         4       2.75   \n",
       "...           ...              ...        ...       ...        ...   \n",
       "88     1332700270  20140519T000000   215000.0         2       2.25   \n",
       "15031  7129303070  20140820T000000   735000.0         4       2.75   \n",
       "5234   2432000130  20150414T000000   675000.0         3       1.75   \n",
       "19980   774100475  20140627T000000   415000.0         3       2.75   \n",
       "3671   8847400115  20140723T000000   590000.0         3       2.00   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  view  ...  sqft_basement  \\\n",
       "20962          828      4524     1.0           0     0  ...              0   \n",
       "12284         2810     11250     1.0           0     0  ...           1130   \n",
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       "...            ...       ...     ...         ...   ...  ...            ...   \n",
       "88            1610      2040     2.0           0     0  ...              0   \n",
       "15031         3040      2415     2.0           1     4  ...              0   \n",
       "5234          1660      9549     1.0           0     0  ...              0   \n",
       "19980         2600     64626     1.5           0     0  ...              0   \n",
       "3671          2420    208652     1.5           0     0  ...              0   \n",
       "\n",
       "       yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "20962      1968          2007    98001  47.2655 -122.244            828   \n",
       "12284      1980             0    98052  47.6920 -122.099           2110   \n",
       "7343       1908          2003    98122  47.6147 -122.285           2130   \n",
       "14247      1941             0    98168  47.5054 -122.301           1280   \n",
       "16670      1996             0    98075  47.5895 -121.994           3330   \n",
       "...         ...           ...      ...      ...      ...            ...   \n",
       "88         1979             0    98056  47.5180 -122.194           1950   \n",
       "15031      1966             0    98118  47.5188 -122.256           2620   \n",
       "5234       1956             0    98033  47.6503 -122.198           2090   \n",
       "19980      2009             0    98014  47.7185 -121.405           1740   \n",
       "3671       2005             0    98010  47.3666 -121.978           3180   \n",
       "\n",
       "       sqft_lot15  above_median_price  price_category  \n",
       "20962        5402                   0               0  \n",
       "12284       11250                   1               1  \n",
       "7343         4200                   1               2  \n",
       "14247        7175                   0               0  \n",
       "16670       12333                   1               2  \n",
       "...           ...                 ...             ...  \n",
       "88           2025                   0               0  \n",
       "15031        2433                   1               2  \n",
       "5234         9549                   1               1  \n",
       "19980       64626                   0               1  \n",
       "3671       212137                   1               1  \n",
       "\n",
       "[17290 rows x 23 columns]"
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       "      <td>-122.185</td>\n",
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       "      <td>7438</td>\n",
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      "text/plain": [
       "               id             date      price  bedrooms  bathrooms  \\\n",
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       "8280   8097000330  20140721T000000   359950.0         3       2.75   \n",
       "792    8081020370  20140709T000000  1355000.0         4       3.50   \n",
       "10371  7518507580  20150502T000000   581000.0         2       1.00   \n",
       "...           ...              ...        ...       ...        ...   \n",
       "16733  7212650950  20140708T000000   336000.0         4       2.50   \n",
       "13151  4365200620  20150312T000000   394000.0         3       1.00   \n",
       "11667  4083304355  20150318T000000   675000.0         4       1.75   \n",
       "3683   2891100820  20140825T000000   213500.0         3       1.00   \n",
       "12059   952000640  20141027T000000   715000.0         3       1.50   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  view  ...  sqft_basement  \\\n",
       "11592         1640      4240     1.0           0     0  ...            720   \n",
       "8984          1090      1357     2.0           0     0  ...              0   \n",
       "8280          2540      8604     2.0           0     0  ...              0   \n",
       "792           3550     11000     1.0           0     2  ...           1290   \n",
       "10371         1170      4080     1.0           0     0  ...              0   \n",
       "...            ...       ...     ...         ...   ...  ...            ...   \n",
       "16733         2530      8169     2.0           0     0  ...              0   \n",
       "13151         1450      7930     1.0           0     0  ...            300   \n",
       "11667         1530      3615     1.5           0     0  ...              0   \n",
       "3683          1220      6000     1.0           0     0  ...              0   \n",
       "12059         1670      5060     2.0           0     2  ...              0   \n",
       "\n",
       "       yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "11592      1921             0    98117  47.6766 -122.368           1300   \n",
       "8984       1990             0    98028  47.7526 -122.244           1078   \n",
       "8280       1991             0    98092  47.3209 -122.185           2260   \n",
       "792        1999             0    98006  47.5506 -122.134           4100   \n",
       "10371      1909             0    98117  47.6784 -122.386           1560   \n",
       "...         ...           ...      ...      ...      ...            ...   \n",
       "16733      1993             0    98003  47.2634 -122.312           2220   \n",
       "13151      1923             0    98126  47.5212 -122.371           1040   \n",
       "11667      1913             0    98103  47.6529 -122.334           1650   \n",
       "3683       1968             0    98002  47.3245 -122.209           1420   \n",
       "12059      1925             0    98126  47.5671 -122.379           1670   \n",
       "\n",
       "       sqft_lot15  above_median_price  price_category  \n",
       "11592        4240                   1               1  \n",
       "8984         1318                   0               0  \n",
       "8280         7438                   0               1  \n",
       "792         10012                   1               2  \n",
       "10371        4586                   1               1  \n",
       "...           ...                 ...             ...  \n",
       "16733        8013                   0               1  \n",
       "13151        7740                   0               1  \n",
       "11667        4200                   1               1  \n",
       "3683         6000                   0               0  \n",
       "12059        5118                   1               2  \n",
       "\n",
       "[4323 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>above_median_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11592</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8984</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8280</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10371</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16733</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13151</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11667</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3683</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12059</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4323 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       above_median_price\n",
       "11592                   1\n",
       "8984                    0\n",
       "8280                    0\n",
       "792                     1\n",
       "10371                   1\n",
       "...                   ...\n",
       "16733                   0\n",
       "13151                   0\n",
       "11667                   1\n",
       "3683                    0\n",
       "12059                   1\n",
       "\n",
       "[4323 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                       int64\n",
      "date                    object\n",
      "price                  float64\n",
      "bedrooms                 int64\n",
      "bathrooms              float64\n",
      "sqft_living              int64\n",
      "sqft_lot                 int64\n",
      "floors                 float64\n",
      "waterfront               int64\n",
      "view                     int64\n",
      "condition                int64\n",
      "grade                    int64\n",
      "sqft_above               int64\n",
      "sqft_basement            int64\n",
      "yr_built                 int64\n",
      "yr_renovated             int64\n",
      "zipcode                  int64\n",
      "lat                    float64\n",
      "long                   float64\n",
      "sqft_living15            int64\n",
      "sqft_lot15               int64\n",
      "above_median_price       int64\n",
      "price_category        category\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from typing import Tuple\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "median_price = df['price'].median()\n",
    "df['above_median_price'] = np.where(df['price'] > median_price, 1, 0)\n",
    "\n",
    "X = df.drop(columns=['id', 'date', 'price', 'above_median_price'])\n",
    "y = df['above_median_price']\n",
    "\n",
    "df['price_category'] = pd.cut(df['price'], bins=[0, 300000, 700000, np.inf], labels=[0, 1, 2])\n",
    "\n",
    "X = df.drop(columns=['id', 'date', 'price', 'price_category'])\n",
    "\n",
    "def split_stratified_into_train_val_test(\n",
    "    df_input,\n",
    "    stratify_colname=\"y\",\n",
    "    frac_train=0.6,\n",
    "    frac_val=0.15,\n",
    "    frac_test=0.25,\n",
    "    random_state=None,\n",
    ") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:\n",
    "   \n",
    "    if frac_train + frac_val + frac_test != 1.0:\n",
    "        raise ValueError(\n",
    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
    "            % (frac_train, frac_val, frac_test)\n",
    "        )\n",
    "    \n",
    "    if stratify_colname not in df_input.columns:\n",
    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
    "    X = df_input  # Contains all columns.\n",
    "    y = df_input[\n",
    "        [stratify_colname]\n",
    "    ]  # Dataframe of just the column on which to stratify.\n",
    "    \n",
    "    # Split original dataframe into train and temp dataframes.\n",
    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
    "    )\n",
    "\n",
    "    if frac_val <= 0:\n",
    "        assert len(df_input) == len(df_train) + len(df_temp)\n",
    "        return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp\n",
    "    # Split the temp dataframe into val and test dataframes.\n",
    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
    "\n",
    "    df_val, df_test, y_val, y_test = train_test_split(\n",
    "        df_temp,\n",
    "        y_temp,\n",
    "        stratify=y_temp,\n",
    "        test_size=relative_frac_test,\n",
    "        random_state=random_state,\n",
    "    )\n",
    "\n",
    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
    "    return df_train, df_val, df_test, y_train, y_val, y_test\n",
    "\n",
    "X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
    "    df, stratify_colname=\"above_median_price\", frac_train=0.80, frac_val=0, frac_test=0.20, random_state=42\n",
    ")\n",
    "\n",
    "display(\"X_train\", X_train)\n",
    "display(\"y_train\", y_train)\n",
    "\n",
    "display(\"X_test\", X_test)\n",
    "display(\"y_test\", y_test)\n",
    "\n",
    "print(df.dtypes)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['price'], bins=50, kde=True)\n",
    "plt.title('Распределение цен на недвижимость')\n",
    "plt.xlabel('Цена')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='bedrooms', y='price', data=df)\n",
    "plt.title('Зависимость цены от количества спален')\n",
    "plt.xlabel('Количество спален')\n",
    "plt.ylabel('Цена')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Конвейеры предобработки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.discriminant_analysis import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipeline_end = StandardScaler()\n",
    "\n",
    "\n",
    "# Построение конвейеров предобработки\n",
    "\n",
    "class HouseFeatures(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        # Создание новых признаков\n",
    "        X = X.copy()\n",
    "        X[\"Living_area_to_Lot_ratio\"] = X[\"sqft_living\"] / X[\"sqft_lot\"]\n",
    "        return X\n",
    "    def get_feature_names_out(self, features_in):\n",
    "        # Добавление имен новых признаков\n",
    "        new_features = [\"Living_area_to_Lot_ratio\"]\n",
    "        return np.append(features_in, new_features, axis=0)\n",
    "\n",
    "\n",
    "# Обработка числовых данных. Числовой конвейр: заполнение пропущенных значений медианой и стандартизация\n",
    "preprocessing_num_class = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "preprocessing_cat_class = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])\n",
    "\n",
    "columns_to_drop = [\"date\"]\n",
    "numeric_columns = [\"sqft_living\", \"sqft_lot\", \"above_median_price\"]\n",
    "cat_columns = []\n",
    "\n",
    "features_preprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"prepocessing_num\", preprocessing_num_class, numeric_columns),\n",
    "        (\"prepocessing_cat\", preprocessing_cat_class, cat_columns),\n",
    "    ],\n",
    "    remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "drop_columns = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"drop_columns\", \"drop\", columns_to_drop),\n",
    "    ],\n",
    "    remainder=\"passthrough\",\n",
    ")\n",
    "\n",
    "features_postprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        ('preprocessing_cat', preprocessing_cat_class, [\"price_category\"]),\n",
    "    ],\n",
    "    remainder=\"passthrough\",\n",
    ")\n",
    "\n",
    "pipeline_end = Pipeline(\n",
    "    [\n",
    "        (\"features_preprocessing\", features_preprocessing),\n",
    "        (\"custom_features\", HouseFeatures()),\n",
    "        (\"drop_columns\", drop_columns),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>above_median_price</th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>price_category</th>\n",
       "      <th>Living_area_to_Lot_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20962</th>\n",
       "      <td>-1.360742</td>\n",
       "      <td>-0.262132</td>\n",
       "      <td>-0.994693</td>\n",
       "      <td>1278000210</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1968</td>\n",
       "      <td>2007</td>\n",
       "      <td>98001</td>\n",
       "      <td>47.2655</td>\n",
       "      <td>-122.244</td>\n",
       "      <td>828</td>\n",
       "      <td>5402</td>\n",
       "      <td>0</td>\n",
       "      <td>5.191063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12284</th>\n",
       "      <td>0.794390</td>\n",
       "      <td>-0.094121</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>2193300390</td>\n",
       "      <td>624000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1130</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>98052</td>\n",
       "      <td>47.6920</td>\n",
       "      <td>-122.099</td>\n",
       "      <td>2110</td>\n",
       "      <td>11250</td>\n",
       "      <td>1</td>\n",
       "      <td>-8.440052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7343</th>\n",
       "      <td>0.837884</td>\n",
       "      <td>-0.272723</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>4289900005</td>\n",
       "      <td>1535000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1030</td>\n",
       "      <td>1908</td>\n",
       "      <td>2003</td>\n",
       "      <td>98122</td>\n",
       "      <td>47.6147</td>\n",
       "      <td>-122.285</td>\n",
       "      <td>2130</td>\n",
       "      <td>4200</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.072292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14247</th>\n",
       "      <td>-0.782270</td>\n",
       "      <td>-0.196986</td>\n",
       "      <td>-0.994693</td>\n",
       "      <td>316000145</td>\n",
       "      <td>235000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1941</td>\n",
       "      <td>0</td>\n",
       "      <td>98168</td>\n",
       "      <td>47.5054</td>\n",
       "      <td>-122.301</td>\n",
       "      <td>1280</td>\n",
       "      <td>7175</td>\n",
       "      <td>0</td>\n",
       "      <td>3.971201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16670</th>\n",
       "      <td>1.011860</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>629400480</td>\n",
       "      <td>775000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>98075</td>\n",
       "      <td>47.5895</td>\n",
       "      <td>-121.994</td>\n",
       "      <td>3330</td>\n",
       "      <td>12333</td>\n",
       "      <td>2</td>\n",
       "      <td>41.589045</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>-0.510432</td>\n",
       "      <td>-0.324180</td>\n",
       "      <td>-0.994693</td>\n",
       "      <td>1332700270</td>\n",
       "      <td>215000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1979</td>\n",
       "      <td>0</td>\n",
       "      <td>98056</td>\n",
       "      <td>47.5180</td>\n",
       "      <td>-122.194</td>\n",
       "      <td>1950</td>\n",
       "      <td>2025</td>\n",
       "      <td>0</td>\n",
       "      <td>1.574534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15031</th>\n",
       "      <td>1.044481</td>\n",
       "      <td>-0.314813</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>7129303070</td>\n",
       "      <td>735000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0</td>\n",
       "      <td>98118</td>\n",
       "      <td>47.5188</td>\n",
       "      <td>-122.256</td>\n",
       "      <td>2620</td>\n",
       "      <td>2433</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.317784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5234</th>\n",
       "      <td>-0.456065</td>\n",
       "      <td>-0.136611</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>2432000130</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1956</td>\n",
       "      <td>0</td>\n",
       "      <td>98033</td>\n",
       "      <td>47.6503</td>\n",
       "      <td>-122.198</td>\n",
       "      <td>2090</td>\n",
       "      <td>9549</td>\n",
       "      <td>1</td>\n",
       "      <td>3.338418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19980</th>\n",
       "      <td>0.566046</td>\n",
       "      <td>1.239169</td>\n",
       "      <td>-0.994693</td>\n",
       "      <td>774100475</td>\n",
       "      <td>415000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98014</td>\n",
       "      <td>47.7185</td>\n",
       "      <td>-121.405</td>\n",
       "      <td>1740</td>\n",
       "      <td>64626</td>\n",
       "      <td>1</td>\n",
       "      <td>0.456795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3671</th>\n",
       "      <td>0.370323</td>\n",
       "      <td>4.836825</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>8847400115</td>\n",
       "      <td>590000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>98010</td>\n",
       "      <td>47.3666</td>\n",
       "      <td>-121.978</td>\n",
       "      <td>3180</td>\n",
       "      <td>212137</td>\n",
       "      <td>1</td>\n",
       "      <td>0.076563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17290 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sqft_living  sqft_lot  above_median_price          id      price  \\\n",
       "20962    -1.360742 -0.262132           -0.994693  1278000210   110000.0   \n",
       "12284     0.794390 -0.094121            1.005335  2193300390   624000.0   \n",
       "7343      0.837884 -0.272723            1.005335  4289900005  1535000.0   \n",
       "14247    -0.782270 -0.196986           -0.994693   316000145   235000.0   \n",
       "16670     1.011860  0.024330            1.005335   629400480   775000.0   \n",
       "...            ...       ...                 ...         ...        ...   \n",
       "88       -0.510432 -0.324180           -0.994693  1332700270   215000.0   \n",
       "15031     1.044481 -0.314813            1.005335  7129303070   735000.0   \n",
       "5234     -0.456065 -0.136611            1.005335  2432000130   675000.0   \n",
       "19980     0.566046  1.239169           -0.994693   774100475   415000.0   \n",
       "3671      0.370323  4.836825            1.005335  8847400115   590000.0   \n",
       "\n",
       "       bedrooms  bathrooms  floors  waterfront  view  ...  sqft_basement  \\\n",
       "20962         2       1.00     1.0           0     0  ...              0   \n",
       "12284         4       3.25     1.0           0     0  ...           1130   \n",
       "7343          4       3.25     2.0           0     3  ...           1030   \n",
       "14247         4       1.00     1.5           0     0  ...              0   \n",
       "16670         4       2.75     2.0           0     0  ...              0   \n",
       "...         ...        ...     ...         ...   ...  ...            ...   \n",
       "88            2       2.25     2.0           0     0  ...              0   \n",
       "15031         4       2.75     2.0           1     4  ...              0   \n",
       "5234          3       1.75     1.0           0     0  ...              0   \n",
       "19980         3       2.75     1.5           0     0  ...              0   \n",
       "3671          3       2.00     1.5           0     0  ...              0   \n",
       "\n",
       "       yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "20962      1968          2007    98001  47.2655 -122.244            828   \n",
       "12284      1980             0    98052  47.6920 -122.099           2110   \n",
       "7343       1908          2003    98122  47.6147 -122.285           2130   \n",
       "14247      1941             0    98168  47.5054 -122.301           1280   \n",
       "16670      1996             0    98075  47.5895 -121.994           3330   \n",
       "...         ...           ...      ...      ...      ...            ...   \n",
       "88         1979             0    98056  47.5180 -122.194           1950   \n",
       "15031      1966             0    98118  47.5188 -122.256           2620   \n",
       "5234       1956             0    98033  47.6503 -122.198           2090   \n",
       "19980      2009             0    98014  47.7185 -121.405           1740   \n",
       "3671       2005             0    98010  47.3666 -121.978           3180   \n",
       "\n",
       "       sqft_lot15  price_category  Living_area_to_Lot_ratio  \n",
       "20962        5402               0                  5.191063  \n",
       "12284       11250               1                 -8.440052  \n",
       "7343         4200               2                 -3.072292  \n",
       "14247        7175               0                  3.971201  \n",
       "16670       12333               2                 41.589045  \n",
       "...           ...             ...                       ...  \n",
       "88           2025               0                  1.574534  \n",
       "15031        2433               2                 -3.317784  \n",
       "5234         9549               1                  3.338418  \n",
       "19980       64626               1                  0.456795  \n",
       "3671       212137               1                  0.076563  \n",
       "\n",
       "[17290 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing_result = pipeline_end.fit_transform(X_train)\n",
    "preprocessed_df = pd.DataFrame(\n",
    "    preprocessing_result,\n",
    "    columns=pipeline_end.get_feature_names_out(),\n",
    ")\n",
    "\n",
    "preprocessed_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Формирование набора моделей для классификации\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import ensemble, linear_model, naive_bayes, neighbors, neural_network, tree, svm\n",
    "\n",
    "class_models = {\n",
    "    \"logistic\": {\"model\": linear_model.LogisticRegression(max_iter=150)}, # логистическая \n",
    "    \"ridge\": {\"model\": linear_model.RidgeClassifierCV(cv=5, class_weight=\"balanced\")}, # гребневая регрессия\n",
    "    \"ridge\": {\"model\": linear_model.LogisticRegression(max_iter=150, solver='lbfgs', penalty=\"l2\", class_weight=\"balanced\")},\n",
    "    \"decision_tree\": {   # дерево решений\n",
    "        \"model\": tree.DecisionTreeClassifier(max_depth=5, min_samples_split=10, random_state=random_state)\n",
    "    },\n",
    "\n",
    "    \"knn\": {\"model\": neighbors.KNeighborsClassifier(n_neighbors=7)},\n",
    "    \"naive_bayes\": {\"model\": naive_bayes.GaussianNB()},  # наивный Байесовский классификатор\n",
    "\n",
    "    # метод градиентного бустинга (набор деревьев решений)\n",
    "    \"gradient_boosting\": {        \n",
    "        \"model\": ensemble.GradientBoostingClassifier(n_estimators=210)\n",
    "    },\n",
    "\n",
    "    # метод случайного леса (набор деревьев решений)   \n",
    "    \"random_forest\": {      \n",
    "        \"model\": ensemble.RandomForestClassifier(\n",
    "            max_depth=5, class_weight=\"balanced\", random_state=random_state\n",
    "        )\n",
    "    },\n",
    "    # многослойный персептрон (нейронная сеть)\n",
    "    \"mlp\": {\n",
    "        \"model\": neural_network.MLPClassifier(\n",
    "            hidden_layer_sizes=(7,),\n",
    "            max_iter=200,\n",
    "            early_stopping=True,\n",
    "            random_state=random_state,\n",
    "        )\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: logistic\n",
      "Model: ridge\n",
      "Model: decision_tree\n",
      "Model: knn\n",
      "Model: naive_bayes\n",
      "Model: gradient_boosting\n",
      "Model: random_forest\n",
      "Model: mlp\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "\n",
    "for model_name in class_models.keys():\n",
    "    print(f\"Model: {model_name}\")\n",
    "    model = class_models[model_name][\"model\"]\n",
    "\n",
    "    model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
    "    model_pipeline = model_pipeline.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "    y_train_predict = model_pipeline.predict(X_train)\n",
    "    y_test_probs = model_pipeline.predict_proba(X_test)[:, 1]\n",
    "    y_test_predict = np.where(y_test_probs > 0.5, 1, 0)\n",
    "\n",
    "    class_models[model_name][\"pipeline\"] = model_pipeline\n",
    "    class_models[model_name][\"probs\"] = y_test_probs\n",
    "    class_models[model_name][\"preds\"] = y_test_predict\n",
    "\n",
    "    class_models[model_name][\"Precision_train\"] = metrics.precision_score(\n",
    "        y_train, y_train_predict, zero_division=1\n",
    "    )\n",
    "    class_models[model_name][\"Precision_test\"] = metrics.precision_score(\n",
    "        y_test, y_test_predict, zero_division=1\n",
    "    )\n",
    "    class_models[model_name][\"Recall_train\"] = metrics.recall_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Recall_test\"] = metrics.recall_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Accuracy_train\"] = metrics.accuracy_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Accuracy_test\"] = metrics.accuracy_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"ROC_AUC_test\"] = metrics.roc_auc_score(\n",
    "        y_test, y_test_probs\n",
    "    )\n",
    "    class_models[model_name][\"F1_train\"] = metrics.f1_score(y_train, y_train_predict)\n",
    "    class_models[model_name][\"F1_test\"] = metrics.f1_score(y_test, y_test_predict)\n",
    "    class_models[model_name][\"MCC_test\"] = metrics.matthews_corrcoef(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Confusion_matrix\"] = metrics.confusion_matrix(\n",
    "        y_test, y_test_predict\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "_, ax = plt.subplots(int(len(class_models) / 2), 2, figsize=(12, 10), sharex=False, sharey=False)\n",
    "for index, key in enumerate(class_models.keys()):\n",
    "    c_matrix = class_models[key][\"Confusion_matrix\"]\n",
    "    disp = ConfusionMatrixDisplay(\n",
    "        confusion_matrix=c_matrix, display_labels=[\"Less\", \"More\"]\n",
    "    ).plot(ax=ax.flat[index])\n",
    "    disp.ax_.set_title(key)\n",
    "\n",
    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  color: #f1f1f1;\n",
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       "#T_115c5_row6_col6 {\n",
       "  background-color: #cc4977;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_115c5_row6_col7 {\n",
       "  background-color: #c8437b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_115c5_row7_col0, #T_115c5_row7_col1, #T_115c5_row7_col2, #T_115c5_row7_col3 {\n",
       "  background-color: #26818e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_115c5_row7_col4, #T_115c5_row7_col5, #T_115c5_row7_col6, #T_115c5_row7_col7 {\n",
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       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_115c5\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_115c5_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
       "      <th id=\"T_115c5_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
       "      <th id=\"T_115c5_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
       "      <th id=\"T_115c5_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
       "      <th id=\"T_115c5_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
       "      <th id=\"T_115c5_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
       "      <th id=\"T_115c5_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
       "      <th id=\"T_115c5_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_115c5_level0_row0\" class=\"row_heading level0 row0\" >logistic</th>\n",
       "      <td id=\"T_115c5_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row0_col2\" class=\"data row0 col2\" >0.999767</td>\n",
       "      <td id=\"T_115c5_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row0_col4\" class=\"data row0 col4\" >0.999884</td>\n",
       "      <td id=\"T_115c5_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row0_col6\" class=\"data row0 col6\" >0.999884</td>\n",
       "      <td id=\"T_115c5_row0_col7\" class=\"data row0 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_115c5_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
       "      <td id=\"T_115c5_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row1_col2\" class=\"data row1 col2\" >0.999651</td>\n",
       "      <td id=\"T_115c5_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row1_col4\" class=\"data row1 col4\" >0.999826</td>\n",
       "      <td id=\"T_115c5_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row1_col6\" class=\"data row1 col6\" >0.999826</td>\n",
       "      <td id=\"T_115c5_row1_col7\" class=\"data row1 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_115c5_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
       "      <td id=\"T_115c5_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row2_col6\" class=\"data row2 col6\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row2_col7\" class=\"data row2 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_115c5_level0_row3\" class=\"row_heading level0 row3\" >gradient_boosting</th>\n",
       "      <td id=\"T_115c5_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row3_col4\" class=\"data row3 col4\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row3_col5\" class=\"data row3 col5\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row3_col6\" class=\"data row3 col6\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row3_col7\" class=\"data row3 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_115c5_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
       "      <td id=\"T_115c5_row4_col0\" class=\"data row4 col0\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row4_col2\" class=\"data row4 col2\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row4_col5\" class=\"data row4 col5\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row4_col6\" class=\"data row4 col6\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row4_col7\" class=\"data row4 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_115c5_level0_row5\" class=\"row_heading level0 row5\" >naive_bayes</th>\n",
       "      <td id=\"T_115c5_row5_col0\" class=\"data row5 col0\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
       "      <td id=\"T_115c5_row5_col2\" class=\"data row5 col2\" >0.786719</td>\n",
       "      <td id=\"T_115c5_row5_col3\" class=\"data row5 col3\" >0.793953</td>\n",
       "      <td id=\"T_115c5_row5_col4\" class=\"data row5 col4\" >0.893927</td>\n",
       "      <td id=\"T_115c5_row5_col5\" class=\"data row5 col5\" >0.897525</td>\n",
       "      <td id=\"T_115c5_row5_col6\" class=\"data row5 col6\" >0.880630</td>\n",
       "      <td id=\"T_115c5_row5_col7\" class=\"data row5 col7\" >0.885144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_115c5_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
       "      <td id=\"T_115c5_row6_col0\" class=\"data row6 col0\" >0.872486</td>\n",
       "      <td id=\"T_115c5_row6_col1\" class=\"data row6 col1\" >0.827473</td>\n",
       "      <td id=\"T_115c5_row6_col2\" class=\"data row6 col2\" >0.857774</td>\n",
       "      <td id=\"T_115c5_row6_col3\" class=\"data row6 col3\" >0.820930</td>\n",
       "      <td id=\"T_115c5_row6_col4\" class=\"data row6 col4\" >0.866917</td>\n",
       "      <td id=\"T_115c5_row6_col5\" class=\"data row6 col5\" >0.825815</td>\n",
       "      <td id=\"T_115c5_row6_col6\" class=\"data row6 col6\" >0.865068</td>\n",
       "      <td id=\"T_115c5_row6_col7\" class=\"data row6 col7\" >0.824189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_115c5_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
       "      <td id=\"T_115c5_row7_col0\" class=\"data row7 col0\" >0.687500</td>\n",
       "      <td id=\"T_115c5_row7_col1\" class=\"data row7 col1\" >0.615385</td>\n",
       "      <td id=\"T_115c5_row7_col2\" class=\"data row7 col2\" >0.002558</td>\n",
       "      <td id=\"T_115c5_row7_col3\" class=\"data row7 col3\" >0.003721</td>\n",
       "      <td id=\"T_115c5_row7_col4\" class=\"data row7 col4\" >0.503355</td>\n",
       "      <td id=\"T_115c5_row7_col5\" class=\"data row7 col5\" >0.503354</td>\n",
       "      <td id=\"T_115c5_row7_col6\" class=\"data row7 col6\" >0.005098</td>\n",
       "      <td id=\"T_115c5_row7_col7\" class=\"data row7 col7\" >0.007397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1be769da420>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
    "    [\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "        \"Accuracy_train\",\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_train\",\n",
    "        \"F1_test\",\n",
    "    ]\n",
    "]\n",
    "class_metrics.sort_values(\n",
    "    by=\"Accuracy_test\", ascending=False\n",
    ").style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы видим, что некоторые модели показывают 100% точность и в последствие начинают плохо работать на новых данных. Поэтому происходит переобучение (overfitting) модели ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_24a07_row0_col0, #T_24a07_row0_col1, #T_24a07_row1_col0, #T_24a07_row1_col1, #T_24a07_row2_col0, #T_24a07_row2_col1, #T_24a07_row3_col0, #T_24a07_row3_col1, #T_24a07_row4_col0, #T_24a07_row4_col1 {\n",
       "  background-color: #a8db34;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_24a07_row0_col2, #T_24a07_row0_col3, #T_24a07_row0_col4, #T_24a07_row1_col2, #T_24a07_row1_col3, #T_24a07_row1_col4, #T_24a07_row2_col2, #T_24a07_row2_col3, #T_24a07_row2_col4, #T_24a07_row3_col2, #T_24a07_row3_col3, #T_24a07_row3_col4, #T_24a07_row4_col2, #T_24a07_row4_col3, #T_24a07_row4_col4, #T_24a07_row5_col2 {\n",
       "  background-color: #da5a6a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_24a07_row5_col0 {\n",
       "  background-color: #6ece58;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_24a07_row5_col1 {\n",
       "  background-color: #86d549;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_24a07_row5_col3 {\n",
       "  background-color: #c5407e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_24a07_row5_col4 {\n",
       "  background-color: #c7427c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_24a07_row6_col0 {\n",
       "  background-color: #4cc26c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_24a07_row6_col1 {\n",
       "  background-color: #75d054;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_24a07_row6_col2 {\n",
       "  background-color: #c8437b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_24a07_row6_col3, #T_24a07_row6_col4 {\n",
       "  background-color: #b42e8d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_24a07_row7_col0, #T_24a07_row7_col1 {\n",
       "  background-color: #26818e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_24a07_row7_col2, #T_24a07_row7_col3, #T_24a07_row7_col4 {\n",
       "  background-color: #4e02a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_24a07\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_24a07_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
       "      <th id=\"T_24a07_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
       "      <th id=\"T_24a07_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
       "      <th id=\"T_24a07_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
       "      <th id=\"T_24a07_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_24a07_level0_row0\" class=\"row_heading level0 row0\" >logistic</th>\n",
       "      <td id=\"T_24a07_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24a07_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
       "      <td id=\"T_24a07_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24a07_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
       "      <td id=\"T_24a07_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24a07_level0_row3\" class=\"row_heading level0 row3\" >gradient_boosting</th>\n",
       "      <td id=\"T_24a07_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row3_col4\" class=\"data row3 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24a07_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
       "      <td id=\"T_24a07_row4_col0\" class=\"data row4 col0\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row4_col2\" class=\"data row4 col2\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
       "      <td id=\"T_24a07_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24a07_level0_row5\" class=\"row_heading level0 row5\" >naive_bayes</th>\n",
       "      <td id=\"T_24a07_row5_col0\" class=\"data row5 col0\" >0.897525</td>\n",
       "      <td id=\"T_24a07_row5_col1\" class=\"data row5 col1\" >0.885144</td>\n",
       "      <td id=\"T_24a07_row5_col2\" class=\"data row5 col2\" >0.999566</td>\n",
       "      <td id=\"T_24a07_row5_col3\" class=\"data row5 col3\" >0.794820</td>\n",
       "      <td id=\"T_24a07_row5_col4\" class=\"data row5 col4\" >0.812098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24a07_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
       "      <td id=\"T_24a07_row6_col0\" class=\"data row6 col0\" >0.825815</td>\n",
       "      <td id=\"T_24a07_row6_col1\" class=\"data row6 col1\" >0.824189</td>\n",
       "      <td id=\"T_24a07_row6_col2\" class=\"data row6 col2\" >0.910823</td>\n",
       "      <td id=\"T_24a07_row6_col3\" class=\"data row6 col3\" >0.651606</td>\n",
       "      <td id=\"T_24a07_row6_col4\" class=\"data row6 col4\" >0.651627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24a07_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
       "      <td id=\"T_24a07_row7_col0\" class=\"data row7 col0\" >0.503354</td>\n",
       "      <td id=\"T_24a07_row7_col1\" class=\"data row7 col1\" >0.007397</td>\n",
       "      <td id=\"T_24a07_row7_col2\" class=\"data row7 col2\" >0.497071</td>\n",
       "      <td id=\"T_24a07_row7_col3\" class=\"data row7 col3\" >0.001427</td>\n",
       "      <td id=\"T_24a07_row7_col4\" class=\"data row7 col4\" >0.012966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1be769d8d10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
    "    [\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "        \"ROC_AUC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "        \"MCC_test\",\n",
    "    ]\n",
    "]\n",
    "class_metrics.sort_values(by=\"ROC_AUC_test\", ascending=False).style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\n",
    "        \"ROC_AUC_test\",\n",
    "        \"MCC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "    ],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'logistic'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = str(class_metrics.sort_values(by=\"MCC_test\", ascending=False).iloc[0].name)\n",
    "\n",
    "display(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>above_median_price</th>\n",
       "      <th>price_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6863</th>\n",
       "      <td>1124000050</td>\n",
       "      <td>20140729T000000</td>\n",
       "      <td>461000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>8505</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1951</td>\n",
       "      <td>0</td>\n",
       "      <td>98177</td>\n",
       "      <td>47.7181</td>\n",
       "      <td>-122.371</td>\n",
       "      <td>1480</td>\n",
       "      <td>8100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id             date     price bedrooms bathrooms sqft_living  \\\n",
       "6863  1124000050  20140729T000000  461000.0        4       1.0        1260   \n",
       "\n",
       "     sqft_lot floors waterfront view  ... sqft_basement yr_built yr_renovated  \\\n",
       "6863     8505    1.5          0    0  ...             0     1951            0   \n",
       "\n",
       "     zipcode      lat     long sqft_living15 sqft_lot15 above_median_price  \\\n",
       "6863   98177  47.7181 -122.371          1480       8100                  1   \n",
       "\n",
       "     price_category  \n",
       "6863              1  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>above_median_price</th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>price_category</th>\n",
       "      <th>Living_area_to_Lot_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6863</th>\n",
       "      <td>-0.891006</td>\n",
       "      <td>-0.162689</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>1.124000e+09</td>\n",
       "      <td>461000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98177.0</td>\n",
       "      <td>47.7181</td>\n",
       "      <td>-122.371</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>8100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.476729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sqft_living  sqft_lot  above_median_price            id     price  \\\n",
       "6863    -0.891006 -0.162689            1.005335  1.124000e+09  461000.0   \n",
       "\n",
       "      bedrooms  bathrooms  floors  waterfront  view  ...  sqft_basement  \\\n",
       "6863       4.0        1.0     1.5         0.0   0.0  ...            0.0   \n",
       "\n",
       "      yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "6863    1951.0           0.0  98177.0  47.7181 -122.371         1480.0   \n",
       "\n",
       "      sqft_lot15  price_category  Living_area_to_Lot_ratio  \n",
       "6863      8100.0             1.0                  5.476729  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'predicted: 1 (proba: [0. 1.])'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'real: 1'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preprocessing_result = pipeline_end.transform(X_test)\n",
    "preprocessed_df = pd.DataFrame(\n",
    "    preprocessing_result,\n",
    "    columns=pipeline_end.get_feature_names_out(),\n",
    ")\n",
    "model = class_models[best_model][\"pipeline\"]\n",
    "\n",
    "example_id = 6863\n",
    "test = pd.DataFrame(X_test.loc[example_id, :]).T\n",
    "test_preprocessed = pd.DataFrame(preprocessed_df.loc[example_id, :]).T\n",
    "display(test)\n",
    "display(test_preprocessed)\n",
    "result_proba = model.predict_proba(test)[0]\n",
    "result = model.predict(test)[0]\n",
    "real = int(y_test.loc[example_id].values[0])\n",
    "display(f\"predicted: {result} (proba: {result_proba})\")\n",
    "display(f\"real: {real}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Новые гиперпараметры"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\Desktop\\MII\\lab1para\\aim\\aimenv\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "optimized_model_type = \"random_forest\"\n",
    "\n",
    "random_forest_model = class_models[optimized_model_type][\"pipeline\"]\n",
    "\n",
    "param_grid = {\n",
    "    \"model__n_estimators\": [10, 50, 100],\n",
    "    \"model__max_features\": [\"sqrt\", \"log2\"],\n",
    "    \"model__max_depth\": [5, 7, 10],\n",
    "    \"model__criterion\": [\"gini\", \"entropy\"],\n",
    "}\n",
    "\n",
    "gs_optomizer = GridSearchCV(\n",
    "    estimator=random_forest_model, param_grid=param_grid, n_jobs=-1\n",
    ")\n",
    "gs_optomizer.fit(X_train, y_train.values.ravel())\n",
    "gs_optomizer.best_params_\n",
    "\n",
    "optimized_model = ensemble.RandomForestClassifier(\n",
    "    random_state=random_state,\n",
    "    criterion=\"gini\",\n",
    "    max_depth=5,\n",
    "    max_features=\"log2\",\n",
    "    n_estimators=10,\n",
    ")\n",
    "\n",
    "result = {}\n",
    "\n",
    "result[\"pipeline\"] = Pipeline([(\"pipeline\", pipeline_end), (\"model\", optimized_model)]).fit(X_train, y_train.values.ravel())\n",
    "result[\"train_preds\"] = result[\"pipeline\"].predict(X_train)\n",
    "result[\"probs\"] = result[\"pipeline\"].predict_proba(X_test)[:, 1]\n",
    "result[\"preds\"] = np.where(result[\"probs\"] > 0.5, 1, 0)\n",
    "\n",
    "result[\"Precision_train\"] = metrics.precision_score(y_train, result[\"train_preds\"])\n",
    "result[\"Precision_test\"] = metrics.precision_score(y_test, result[\"preds\"])\n",
    "result[\"Recall_train\"] = metrics.recall_score(y_train, result[\"train_preds\"])\n",
    "result[\"Recall_test\"] = metrics.recall_score(y_test, result[\"preds\"])\n",
    "result[\"Accuracy_train\"] = metrics.accuracy_score(y_train, result[\"train_preds\"])\n",
    "result[\"Accuracy_test\"] = metrics.accuracy_score(y_test, result[\"preds\"])\n",
    "result[\"ROC_AUC_test\"] = metrics.roc_auc_score(y_test, result[\"probs\"])\n",
    "result[\"F1_train\"] = metrics.f1_score(y_train, result[\"train_preds\"])\n",
    "result[\"F1_test\"] = metrics.f1_score(y_test, result[\"preds\"])\n",
    "result[\"MCC_test\"] = metrics.matthews_corrcoef(y_test, result[\"preds\"])\n",
    "result[\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(y_test, result[\"preds\"])\n",
    "result[\"Confusion_matrix\"] = metrics.confusion_matrix(y_test, result[\"preds\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c307c_row0_col0, #T_c307c_row0_col1, #T_c307c_row1_col0, #T_c307c_row1_col1 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c307c_row0_col2, #T_c307c_row0_col3, #T_c307c_row0_col4, #T_c307c_row1_col2, #T_c307c_row1_col3, #T_c307c_row1_col4 {\n",
       "  background-color: #0d0887;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c307c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c307c_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
       "      <th id=\"T_c307c_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
       "      <th id=\"T_c307c_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
       "      <th id=\"T_c307c_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
       "      <th id=\"T_c307c_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c307c_level0_row0\" class=\"row_heading level0 row0\" >Old</th>\n",
       "      <td id=\"T_c307c_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_c307c_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_c307c_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_c307c_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_c307c_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c307c_level0_row1\" class=\"row_heading level0 row1\" >New</th>\n",
       "      <td id=\"T_c307c_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_c307c_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_c307c_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_c307c_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_c307c_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1be7a6418e0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_metrics = pd.DataFrame(columns=list(result.keys()))\n",
    "optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
    "    data=class_models[optimized_model_type]\n",
    ")\n",
    "optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
    "    data=result\n",
    ")\n",
    "optimized_metrics.insert(loc=0, column=\"Name\", value=[\"Old\", \"New\"])\n",
    "optimized_metrics = optimized_metrics.set_index(\"Name\")\n",
    "optimized_metrics[\n",
    "    [\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "        \"ROC_AUC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "        \"MCC_test\",\n",
    "    ]\n",
    "].style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\n",
    "        \"ROC_AUC_test\",\n",
    "        \"MCC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "    ],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "    ],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы видим изумительную точность новой модели. Модели не допускают никаких ошибок в предсказании."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задача регресии: предсказание цены дома (price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Среднее значение поля: 2079.8997362698374\n",
      "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
      "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
      "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
      "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
      "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
      "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
      "\n",
      "   sqft_lot  floors  waterfront  view  ...  yr_built  yr_renovated  zipcode  \\\n",
      "0      5650     1.0           0     0  ...      1955             0    98178   \n",
      "1      7242     2.0           0     0  ...      1951          1991    98125   \n",
      "2     10000     1.0           0     0  ...      1933             0    98028   \n",
      "3      5000     1.0           0     0  ...      1965             0    98136   \n",
      "4      8080     1.0           0     0  ...      1987             0    98074   \n",
      "\n",
      "       lat     long  sqft_living15  sqft_lot15  above_median_price  \\\n",
      "0  47.5112 -122.257           1340        5650                   0   \n",
      "1  47.7210 -122.319           1690        7639                   1   \n",
      "2  47.7379 -122.233           2720        8062                   0   \n",
      "3  47.5208 -122.393           1360        5000                   1   \n",
      "4  47.6168 -122.045           1800        7503                   1   \n",
      "\n",
      "   price_category  average_price  \n",
      "0               0              0  \n",
      "1               1              1  \n",
      "2               0              0  \n",
      "3               1              0  \n",
      "4               1              0  \n",
      "\n",
      "[5 rows x 24 columns]\n",
      "Статистическое описание DataFrame:\n",
      "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
      "count  2.161300e+04  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
      "mean   4.580302e+09  5.400881e+05      3.370842      2.114757   2079.899736   \n",
      "std    2.876566e+09  3.671272e+05      0.930062      0.770163    918.440897   \n",
      "min    1.000102e+06  7.500000e+04      0.000000      0.000000    290.000000   \n",
      "25%    2.123049e+09  3.219500e+05      3.000000      1.750000   1427.000000   \n",
      "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
      "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
      "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
      "\n",
      "           sqft_lot        floors    waterfront          view     condition  \\\n",
      "count  2.161300e+04  21613.000000  21613.000000  21613.000000  21613.000000   \n",
      "mean   1.510697e+04      1.494309      0.007542      0.234303      3.409430   \n",
      "std    4.142051e+04      0.539989      0.086517      0.766318      0.650743   \n",
      "min    5.200000e+02      1.000000      0.000000      0.000000      1.000000   \n",
      "25%    5.040000e+03      1.000000      0.000000      0.000000      3.000000   \n",
      "50%    7.618000e+03      1.500000      0.000000      0.000000      3.000000   \n",
      "75%    1.068800e+04      2.000000      0.000000      0.000000      4.000000   \n",
      "max    1.651359e+06      3.500000      1.000000      4.000000      5.000000   \n",
      "\n",
      "       ...  sqft_basement      yr_built  yr_renovated       zipcode  \\\n",
      "count  ...   21613.000000  21613.000000  21613.000000  21613.000000   \n",
      "mean   ...     291.509045   1971.005136     84.402258  98077.939805   \n",
      "std    ...     442.575043     29.373411    401.679240     53.505026   \n",
      "min    ...       0.000000   1900.000000      0.000000  98001.000000   \n",
      "25%    ...       0.000000   1951.000000      0.000000  98033.000000   \n",
      "50%    ...       0.000000   1975.000000      0.000000  98065.000000   \n",
      "75%    ...     560.000000   1997.000000      0.000000  98118.000000   \n",
      "max    ...    4820.000000   2015.000000   2015.000000  98199.000000   \n",
      "\n",
      "                lat          long  sqft_living15     sqft_lot15  \\\n",
      "count  21613.000000  21613.000000   21613.000000   21613.000000   \n",
      "mean      47.560053   -122.213896    1986.552492   12768.455652   \n",
      "std        0.138564      0.140828     685.391304   27304.179631   \n",
      "min       47.155900   -122.519000     399.000000     651.000000   \n",
      "25%       47.471000   -122.328000    1490.000000    5100.000000   \n",
      "50%       47.571800   -122.230000    1840.000000    7620.000000   \n",
      "75%       47.678000   -122.125000    2360.000000   10083.000000   \n",
      "max       47.777600   -121.315000    6210.000000  871200.000000   \n",
      "\n",
      "       above_median_price  average_price  \n",
      "count        21613.000000    21613.00000  \n",
      "mean             0.497340        0.42752  \n",
      "std              0.500004        0.49473  \n",
      "min              0.000000        0.00000  \n",
      "25%              0.000000        0.00000  \n",
      "50%              0.000000        0.00000  \n",
      "75%              1.000000        1.00000  \n",
      "max              1.000000        1.00000  \n",
      "\n",
      "[8 rows x 22 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import set_config\n",
    "\n",
    "set_config(transform_output=\"pandas\")\n",
    "\n",
    "random_state = 42\n",
    "\n",
    "average_price = df['sqft_living'].mean()\n",
    "print(f\"Среднее значение поля: {average_price}\")\n",
    "\n",
    "# Создание новой колонки, указывающей, выше или ниже среднего значение цена закрытия\n",
    "df['average_price'] = (df['sqft_living'] > average_price).astype(int)\n",
    "\n",
    "# Удаление последней строки, где нет значения для следующего дня\n",
    "df.dropna(inplace=True)\n",
    "\n",
    "print(df.head())\n",
    "\n",
    "print(\"Статистическое описание DataFrame:\")\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>above_median_price</th>\n",
       "      <th>price_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6325</th>\n",
       "      <td>5467910190</td>\n",
       "      <td>20140527T000000</td>\n",
       "      <td>325000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1780</td>\n",
       "      <td>13095</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1983</td>\n",
       "      <td>0</td>\n",
       "      <td>98042</td>\n",
       "      <td>47.3670</td>\n",
       "      <td>-122.152</td>\n",
       "      <td>2750</td>\n",
       "      <td>13095</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>13473</th>\n",
       "      <td>9331800580</td>\n",
       "      <td>20150310T000000</td>\n",
       "      <td>257000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1000</td>\n",
       "      <td>3700</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>200</td>\n",
       "      <td>1929</td>\n",
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       "      <td>98118</td>\n",
       "      <td>47.5520</td>\n",
       "      <td>-122.290</td>\n",
       "      <td>1270</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17614</th>\n",
       "      <td>2407000405</td>\n",
       "      <td>20150226T000000</td>\n",
       "      <td>228500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1080</td>\n",
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       "      <td>98146</td>\n",
       "      <td>47.4838</td>\n",
       "      <td>-122.335</td>\n",
       "      <td>1170</td>\n",
       "      <td>7800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>16970</th>\n",
       "      <td>5466700290</td>\n",
       "      <td>20150108T000000</td>\n",
       "      <td>288000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2090</td>\n",
       "      <td>7500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>810</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>98031</td>\n",
       "      <td>47.3951</td>\n",
       "      <td>-122.172</td>\n",
       "      <td>1800</td>\n",
       "      <td>7350</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20868</th>\n",
       "      <td>3026059361</td>\n",
       "      <td>20150417T000000</td>\n",
       "      <td>479000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1741</td>\n",
       "      <td>1439</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>295</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>98034</td>\n",
       "      <td>47.7043</td>\n",
       "      <td>-122.209</td>\n",
       "      <td>2090</td>\n",
       "      <td>10454</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>11964</th>\n",
       "      <td>5272200045</td>\n",
       "      <td>20141113T000000</td>\n",
       "      <td>378000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1000</td>\n",
       "      <td>6914</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7144</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1000</td>\n",
       "      <td>6947</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>21575</th>\n",
       "      <td>9578500790</td>\n",
       "      <td>20141111T000000</td>\n",
       "      <td>399950.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3087</td>\n",
       "      <td>5002</td>\n",
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       "      <td>2014</td>\n",
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       "      <td>98023</td>\n",
       "      <td>47.2974</td>\n",
       "      <td>-122.349</td>\n",
       "      <td>2927</td>\n",
       "      <td>5183</td>\n",
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       "      <th>5390</th>\n",
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       "      <td>575000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2120</td>\n",
       "      <td>4780</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2004</td>\n",
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       "      <td>98053</td>\n",
       "      <td>47.6810</td>\n",
       "      <td>-122.032</td>\n",
       "      <td>1690</td>\n",
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       "    <tr>\n",
       "      <th>860</th>\n",
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       "      <td>20140620T000000</td>\n",
       "      <td>245000.0</td>\n",
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       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>98168</td>\n",
       "      <td>47.4810</td>\n",
       "      <td>-122.323</td>\n",
       "      <td>1170</td>\n",
       "      <td>15000</td>\n",
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       "      <th>15795</th>\n",
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       "      <td>20150325T000000</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
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       "      <td>2006</td>\n",
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       "               id             date     price  bedrooms  bathrooms  \\\n",
       "6325   5467910190  20140527T000000  325000.0         3       1.75   \n",
       "13473  9331800580  20150310T000000  257000.0         2       1.00   \n",
       "17614  2407000405  20150226T000000  228500.0         3       1.00   \n",
       "16970  5466700290  20150108T000000  288000.0         3       2.25   \n",
       "20868  3026059361  20150417T000000  479000.0         2       2.50   \n",
       "...           ...              ...       ...       ...        ...   \n",
       "11964  5272200045  20141113T000000  378000.0         3       1.50   \n",
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       "860    1723049033  20140620T000000  245000.0         1       0.75   \n",
       "15795  6147650280  20150325T000000  315000.0         4       2.50   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  view  ...  sqft_basement  \\\n",
       "6325          1780     13095     1.0           0     0  ...              0   \n",
       "13473         1000      3700     1.0           0     0  ...            200   \n",
       "17614         1080      7486     1.5           0     0  ...             90   \n",
       "16970         2090      7500     1.0           0     0  ...            810   \n",
       "20868         1741      1439     2.0           0     0  ...            295   \n",
       "...            ...       ...     ...         ...   ...  ...            ...   \n",
       "11964         1000      6914     1.0           0     0  ...              0   \n",
       "21575         3087      5002     2.0           0     0  ...              0   \n",
       "5390          2120      4780     2.0           0     0  ...              0   \n",
       "860            380     15000     1.0           0     0  ...              0   \n",
       "15795         3130      5999     2.0           0     0  ...              0   \n",
       "\n",
       "       yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "6325       1983             0    98042  47.3670 -122.152           2750   \n",
       "13473      1929             0    98118  47.5520 -122.290           1270   \n",
       "17614      1942             0    98146  47.4838 -122.335           1170   \n",
       "16970      1977             0    98031  47.3951 -122.172           1800   \n",
       "20868      2007             0    98034  47.7043 -122.209           2090   \n",
       "...         ...           ...      ...      ...      ...            ...   \n",
       "11964      1947             0    98125  47.7144 -122.319           1000   \n",
       "21575      2014             0    98023  47.2974 -122.349           2927   \n",
       "5390       2004             0    98053  47.6810 -122.032           1690   \n",
       "860        1963             0    98168  47.4810 -122.323           1170   \n",
       "15795      2006             0    98042  47.3837 -122.099           3020   \n",
       "\n",
       "       sqft_lot15  above_median_price  price_category  \n",
       "6325        13095                   0               1  \n",
       "13473        5000                   0               0  \n",
       "17614        7800                   0               0  \n",
       "16970        7350                   0               0  \n",
       "20868       10454                   1               1  \n",
       "...           ...                 ...             ...  \n",
       "11964        6947                   0               1  \n",
       "21575        5183                   0               1  \n",
       "5390         2650                   1               1  \n",
       "860         15000                   0               0  \n",
       "15795        5997                   0               1  \n",
       "\n",
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       "    <tr>\n",
       "      <th>16218</th>\n",
       "      <td>9522300010</td>\n",
       "      <td>20150331T000000</td>\n",
       "      <td>1490000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4560</td>\n",
       "      <td>14608</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>98034</td>\n",
       "      <td>47.6995</td>\n",
       "      <td>-122.228</td>\n",
       "      <td>4050</td>\n",
       "      <td>14226</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19964</th>\n",
       "      <td>9510861140</td>\n",
       "      <td>20140714T000000</td>\n",
       "      <td>711000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2550</td>\n",
       "      <td>5376</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98052</td>\n",
       "      <td>47.6647</td>\n",
       "      <td>-122.083</td>\n",
       "      <td>2250</td>\n",
       "      <td>4050</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13674</th>\n",
       "      <td>6163900333</td>\n",
       "      <td>20141110T000000</td>\n",
       "      <td>338000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1250</td>\n",
       "      <td>7710</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>0</td>\n",
       "      <td>98155</td>\n",
       "      <td>47.7623</td>\n",
       "      <td>-122.317</td>\n",
       "      <td>1340</td>\n",
       "      <td>7710</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20377</th>\n",
       "      <td>3528960020</td>\n",
       "      <td>20140708T000000</td>\n",
       "      <td>673000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2830</td>\n",
       "      <td>3496</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>98029</td>\n",
       "      <td>47.5606</td>\n",
       "      <td>-122.011</td>\n",
       "      <td>2160</td>\n",
       "      <td>3501</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8805</th>\n",
       "      <td>1687000220</td>\n",
       "      <td>20141016T000000</td>\n",
       "      <td>285000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2434</td>\n",
       "      <td>4400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>98001</td>\n",
       "      <td>47.2874</td>\n",
       "      <td>-122.283</td>\n",
       "      <td>2434</td>\n",
       "      <td>4400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10168</th>\n",
       "      <td>4141400030</td>\n",
       "      <td>20141201T000000</td>\n",
       "      <td>605000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2250</td>\n",
       "      <td>10108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1967</td>\n",
       "      <td>0</td>\n",
       "      <td>98008</td>\n",
       "      <td>47.5922</td>\n",
       "      <td>-122.118</td>\n",
       "      <td>2050</td>\n",
       "      <td>9750</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2522</th>\n",
       "      <td>1822500160</td>\n",
       "      <td>20141212T000000</td>\n",
       "      <td>356500.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2570</td>\n",
       "      <td>11473</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98003</td>\n",
       "      <td>47.2809</td>\n",
       "      <td>-122.296</td>\n",
       "      <td>2430</td>\n",
       "      <td>5997</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4323 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id             date      price  bedrooms  bathrooms  \\\n",
       "735    2591820310  20141006T000000   365000.0         4       2.25   \n",
       "2830   7974200820  20140821T000000   865000.0         5       3.00   \n",
       "4106   7701450110  20140815T000000  1038000.0         4       2.50   \n",
       "16218  9522300010  20150331T000000  1490000.0         3       3.50   \n",
       "19964  9510861140  20140714T000000   711000.0         3       2.50   \n",
       "...           ...              ...        ...       ...        ...   \n",
       "13674  6163900333  20141110T000000   338000.0         3       1.75   \n",
       "20377  3528960020  20140708T000000   673000.0         3       2.75   \n",
       "8805   1687000220  20141016T000000   285000.0         4       2.50   \n",
       "10168  4141400030  20141201T000000   605000.0         4       1.75   \n",
       "2522   1822500160  20141212T000000   356500.0         4       2.50   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  view  ...  sqft_basement  \\\n",
       "735           2070      8893     2.0           0     0  ...              0   \n",
       "2830          2900      6730     1.0           0     0  ...           1070   \n",
       "4106          3770     10893     2.0           0     2  ...              0   \n",
       "16218         4560     14608     2.0           0     2  ...              0   \n",
       "19964         2550      5376     2.0           0     0  ...              0   \n",
       "...            ...       ...     ...         ...   ...  ...            ...   \n",
       "13674         1250      7710     1.0           0     0  ...              0   \n",
       "20377         2830      3496     2.0           0     0  ...              0   \n",
       "8805          2434      4400     2.0           0     0  ...              0   \n",
       "10168         2250     10108     1.0           0     0  ...              0   \n",
       "2522          2570     11473     2.0           0     0  ...              0   \n",
       "\n",
       "       yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "735        1986             0    98058  47.4388 -122.162           2390   \n",
       "2830       1977             0    98115  47.6784 -122.285           2370   \n",
       "4106       1997             0    98006  47.5646 -122.129           3710   \n",
       "16218      1990             0    98034  47.6995 -122.228           4050   \n",
       "19964      2004             0    98052  47.6647 -122.083           2250   \n",
       "...         ...           ...      ...      ...      ...            ...   \n",
       "13674      1947             0    98155  47.7623 -122.317           1340   \n",
       "20377      2012             0    98029  47.5606 -122.011           2160   \n",
       "8805       2007             0    98001  47.2874 -122.283           2434   \n",
       "10168      1967             0    98008  47.5922 -122.118           2050   \n",
       "2522       2008             0    98003  47.2809 -122.296           2430   \n",
       "\n",
       "       sqft_lot15  above_median_price  price_category  \n",
       "735          7700                   0               1  \n",
       "2830         6283                   1               2  \n",
       "4106         9685                   1               2  \n",
       "16218       14226                   1               2  \n",
       "19964        4050                   1               2  \n",
       "...           ...                 ...             ...  \n",
       "13674        7710                   0               1  \n",
       "20377        3501                   1               1  \n",
       "8805         4400                   0               0  \n",
       "10168        9750                   1               1  \n",
       "2522         5997                   0               1  \n",
       "\n",
       "[4323 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_price</th>\n",
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       "       average_price\n",
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       "4106               1\n",
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       "19964              1\n",
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   ],
   "source": [
    "from typing import Tuple\n",
    "from pandas import DataFrame\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def split_into_train_test(\n",
    "    df_input: DataFrame,\n",
    "    target_colname: str = \"average_price\",\n",
    "    frac_train: float = 0.8,\n",
    "    random_state: int = None,\n",
    ") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame]:\n",
    "    \n",
    "    if not (0 < frac_train < 1):\n",
    "        raise ValueError(\"Fraction must be between 0 and 1.\")\n",
    "    \n",
    "    # Проверка наличия целевого признака\n",
    "    if target_colname not in df_input.columns:\n",
    "        raise ValueError(f\"{target_colname} is not a column in the DataFrame.\")\n",
    "    \n",
    "    # Разделяем данные на признаки и целевую переменную\n",
    "    X = df_input.drop(columns=[target_colname])  # Признаки\n",
    "    y = df_input[[target_colname]]  # Целевая переменная\n",
    "\n",
    "    # Разделяем данные на обучающую и тестовую выборки\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y,\n",
    "        test_size=(1.0 - frac_train),\n",
    "        random_state=random_state\n",
    "    )\n",
    "    \n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "# Применение функции для разделения данных\n",
    "X_train, X_test, y_train, y_test = split_into_train_test(\n",
    "    df, \n",
    "    target_colname=\"average_price\", \n",
    "    frac_train=0.8, \n",
    "    random_state=42  # Убедитесь, что вы задали нужное значение random_state\n",
    ")\n",
    "\n",
    "display(\"X_train\", X_train)\n",
    "display(\"y_train\", y_train)\n",
    "\n",
    "display(\"X_test\", X_test)\n",
    "display(\"y_test\", y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Формирование конвейера для решения задачи регрессии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.ensemble import RandomForestRegressor  # Пример регрессионной модели\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "class HouseFeatures(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        # Создание новых признаков\n",
    "        X = X.copy()\n",
    "        X[\"Square\"] = X[\"sqft_living\"] / X[\"sqft_lot\"]\n",
    "        return X\n",
    "    def get_feature_names_out(self, features_in):\n",
    "        # Добавление имен новых признаков\n",
    "        new_features = [\"Square\"]\n",
    "        return np.append(features_in, new_features, axis=0)\n",
    "\n",
    "# Указываем столбцы, которые нужно удалить и обрабатывать\n",
    "columns_to_drop = [\"date\"]\n",
    "num_columns = [\"bathrooms\", \"floors\", \"waterfront\", \"view\"]\n",
    "cat_columns = []  \n",
    "\n",
    "# Определяем предобработку для численных данных\n",
    "num_imputer = SimpleImputer(strategy=\"median\")\n",
    "num_scaler = StandardScaler()\n",
    "preprocessing_num = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", num_imputer),\n",
    "        (\"scaler\", num_scaler),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Определяем предобработку для категориальных данных\n",
    "cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
    "cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
    "preprocessing_cat = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", cat_imputer),\n",
    "        (\"encoder\", cat_encoder),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Подготовка признаков с использованием ColumnTransformer\n",
    "features_preprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"preprocessing_num\", preprocessing_num, num_columns),\n",
    "        (\"preprocessing_cat\", preprocessing_cat, cat_columns),\n",
    "    ],\n",
    "    remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "# Удаление нежелательных столбцов\n",
    "drop_columns = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"drop_columns\", \"drop\", columns_to_drop),\n",
    "    ],\n",
    "    remainder=\"passthrough\",\n",
    ")\n",
    "\n",
    "# Постобработка признаков\n",
    "features_postprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"preprocessing_cat\", preprocessing_cat, [\"price_category\"]),  \n",
    "    ],\n",
    "    remainder=\"passthrough\",\n",
    ")\n",
    "\n",
    "# Создание окончательного конвейера\n",
    "pipeline = Pipeline(\n",
    "    [\n",
    "        (\"features_preprocessing\", features_preprocessing),\n",
    "        (\"drop_columns\", drop_columns),\n",
    "        (\"custom_features\", HouseFeatures()),\n",
    "        (\"model\", RandomForestRegressor())  # Выбор модели для обучения\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Использование конвейера\n",
    "def train_pipeline(X, y):\n",
    "    pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: logistic\n",
      "MSE (train): 0.24060150375939848\n",
      "MSE (test): 0.23455933379597502\n",
      "MAE (train): 0.24060150375939848\n",
      "MAE (test): 0.23455933379597502\n",
      "R2 (train): 0.015780807725750634\n",
      "R2 (test): 0.045807954005714024\n",
      "STD (train): 0.48387852043102103\n",
      "STD (test): 0.4780359236045559\n",
      "----------------------------------------\n",
      "Model: ridge\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\Desktop\\MII\\lab1para\\aim\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE (train): 0.210989010989011\n",
      "MSE (test): 0.2035623409669211\n",
      "MAE (train): 0.210989010989011\n",
      "MAE (test): 0.2035623409669211\n",
      "R2 (train): 0.1369154775441198\n",
      "R2 (test): 0.17190433878207922\n",
      "STD (train): 0.45781332911823247\n",
      "STD (test): 0.4499815316182845\n",
      "----------------------------------------\n",
      "Model: decision_tree\n",
      "MSE (train): 0.0\n",
      "MSE (test): 0.0\n",
      "MAE (train): 0.0\n",
      "MAE (test): 0.0\n",
      "R2 (train): 1.0\n",
      "R2 (test): 1.0\n",
      "STD (train): 0.0\n",
      "STD (test): 0.0\n",
      "----------------------------------------\n",
      "Model: knn\n",
      "MSE (train): 0.1949681897050318\n",
      "MSE (test): 0.27989821882951654\n",
      "MAE (train): 0.1949681897050318\n",
      "MAE (test): 0.27989821882951654\n",
      "R2 (train): 0.20245122664507342\n",
      "R2 (test): -0.13863153417464114\n",
      "STD (train): 0.43948973967967464\n",
      "STD (test): 0.5264647910268833\n",
      "----------------------------------------\n",
      "Model: naive_bayes\n",
      "MSE (train): 0.26928860613071137\n",
      "MSE (test): 0.2690261392551469\n",
      "MAE (train): 0.26928860613071137\n",
      "MAE (test): 0.2690261392551469\n",
      "R2 (train): -0.10156840366079445\n",
      "R2 (test): -0.09440369772322943\n",
      "STD (train): 0.47316941542228536\n",
      "STD (test): 0.47206502931490235\n",
      "----------------------------------------\n",
      "Model: gradient_boosting\n",
      "MSE (train): 0.0\n",
      "MSE (test): 0.0\n",
      "MAE (train): 0.0\n",
      "MAE (test): 0.0\n",
      "R2 (train): 1.0\n",
      "R2 (test): 1.0\n",
      "STD (train): 0.0\n",
      "STD (test): 0.0\n",
      "----------------------------------------\n",
      "Model: random_forest\n",
      "MSE (train): 0.0\n",
      "MSE (test): 0.0\n",
      "MAE (train): 0.0\n",
      "MAE (test): 0.0\n",
      "R2 (train): 1.0\n",
      "R2 (test): 1.0\n",
      "STD (train): 0.0\n",
      "STD (test): 0.0\n",
      "----------------------------------------\n",
      "Model: mlp\n",
      "MSE (train): 0.4253903990746096\n",
      "MSE (test): 0.4353458246588018\n",
      "MAE (train): 0.4253903990746096\n",
      "MAE (test): 0.4353458246588018\n",
      "R2 (train): -0.7401279228791116\n",
      "R2 (test): -0.7709954936501442\n",
      "STD (train): 0.4959884986820156\n",
      "STD (test): 0.49782384226978177\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Проверка наличия необходимых переменных\n",
    "if 'class_models' not in locals():\n",
    "    raise ValueError(\"class_models is not defined\")\n",
    "if 'X_train' not in locals() or 'X_test' not in locals() or 'y_train' not in locals() or 'y_test' not in locals():\n",
    "    raise ValueError(\"Train/test data is not defined\")\n",
    "\n",
    "\n",
    "y_train = np.ravel(y_train)  \n",
    "y_test = np.ravel(y_test)      \n",
    "\n",
    "# Инициализация списка для хранения результатов\n",
    "results = []\n",
    "\n",
    "# Проход по моделям и оценка их качества\n",
    "for model_name in class_models.keys():\n",
    "    print(f\"Model: {model_name}\")\n",
    "    \n",
    "    # Извлечение модели из словаря\n",
    "    model = class_models[model_name][\"model\"]\n",
    "    \n",
    "    # Создание пайплайна\n",
    "    model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
    "    \n",
    "    # Обучение модели\n",
    "    model_pipeline.fit(X_train, y_train)\n",
    "\n",
    "    # Предсказание для обучающей и тестовой выборки\n",
    "    y_train_predict = model_pipeline.predict(X_train)\n",
    "    y_test_predict = model_pipeline.predict(X_test)\n",
    "\n",
    "    # Сохранение пайплайна и предсказаний\n",
    "    class_models[model_name][\"pipeline\"] = model_pipeline\n",
    "    class_models[model_name][\"preds\"] = y_test_predict\n",
    "\n",
    "    # Вычисление метрик для регрессии\n",
    "    class_models[model_name][\"MSE_train\"] = metrics.mean_squared_error(y_train, y_train_predict)\n",
    "    class_models[model_name][\"MSE_test\"] = metrics.mean_squared_error(y_test, y_test_predict)\n",
    "    class_models[model_name][\"MAE_train\"] = metrics.mean_absolute_error(y_train, y_train_predict)\n",
    "    class_models[model_name][\"MAE_test\"] = metrics.mean_absolute_error(y_test, y_test_predict)\n",
    "    class_models[model_name][\"R2_train\"] = metrics.r2_score(y_train, y_train_predict)\n",
    "    class_models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_predict)\n",
    "\n",
    "    # Дополнительные метрики\n",
    "    class_models[model_name][\"STD_train\"] = np.std(y_train - y_train_predict)\n",
    "    class_models[model_name][\"STD_test\"] = np.std(y_test - y_test_predict)\n",
    "\n",
    "    # Вывод результатов для текущей модели\n",
    "    print(f\"MSE (train): {class_models[model_name]['MSE_train']}\")\n",
    "    print(f\"MSE (test): {class_models[model_name]['MSE_test']}\")\n",
    "    print(f\"MAE (train): {class_models[model_name]['MAE_train']}\")\n",
    "    print(f\"MAE (test): {class_models[model_name]['MAE_test']}\")\n",
    "    print(f\"R2 (train): {class_models[model_name]['R2_train']}\")\n",
    "    print(f\"R2 (test): {class_models[model_name]['R2_test']}\")\n",
    "    print(f\"STD (train): {class_models[model_name]['STD_train']}\")\n",
    "    print(f\"STD (test): {class_models[model_name]['STD_test']}\")\n",
    "    print(\"-\" * 40)  # Разделитель для разных моделей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пример использования обученной модели (конвейера регрессии) для предсказания\n",
    "\n",
    "Подбор гиперпараметров методом поиска по сетке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
      "Best parameters: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "Best MSE: 0.14737693245118555\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Convert the date column to a datetime object and extract numeric features\n",
    "df['date'] = pd.to_datetime(df['date'], errors='coerce')  # Coerce invalid dates to NaT\n",
    "df.dropna(subset=['date'], inplace=True)  # Drop rows with invalid dates\n",
    "df['year'] = df['date'].dt.year\n",
    "df['month'] = df['date'].dt.month\n",
    "df['day'] = df['date'].dt.day\n",
    "\n",
    "# Prepare predictors and target\n",
    "X = df[['yr_built', 'year', 'month', 'day', 'price', 'price_category']]\n",
    "y = df['average_price']\n",
    "\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define model and parameter grid\n",
    "model = RandomForestRegressor()\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'max_depth': [None, 10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10]\n",
    "}\n",
    "\n",
    "# Hyperparameter tuning with GridSearchCV\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid,\n",
    "                           scoring='neg_mean_squared_error', cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "# Fit the model\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Output the best parameters and score\n",
    "print(\"Best parameters:\", grid_search.best_params_)\n",
    "print(\"Best MSE:\", -grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
      "Старые параметры: {'max_depth': 10, 'min_samples_split': 15, 'n_estimators': 200}\n",
      "Лучший результат (MSE) на старых параметрах: 0.1472405057641472\n",
      "\n",
      "Новые параметры: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "Лучший результат (MSE) на новых параметрах: 0.149046701378161\n",
      "Среднеквадратическая ошибка (MSE) на тестовых данных: 0.14438125797411974\n",
      "Корень среднеквадратичной ошибки (RMSE) на тестовых данных: 0.3799753386393908\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# 1. Настройка параметров для старых значений\n",
    "old_param_grid = {\n",
    "    'n_estimators': [50, 100, 200],  # Количество деревьев\n",
    "    'max_depth': [None, 10, 20, 30],  # Максимальная глубина дерева\n",
    "    'min_samples_split': [2, 10, 15]   # Минимальное количество образцов для разбиения узла\n",
    "}\n",
    "\n",
    "# Подбор гиперпараметров с помощью Grid Search для старых параметров\n",
    "old_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
    "        param_grid=old_param_grid, scoring='neg_mean_squared_error', cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "# Обучение модели на тренировочных данных\n",
    "old_grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 2. Результаты подбора для старых параметров\n",
    "old_best_params = old_grid_search.best_params_\n",
    "old_best_mse = -old_grid_search.best_score_  # Меняем знак, так как берем отрицательное значение MSE\n",
    "\n",
    "# 3. Настройка параметров для новых значений\n",
    "new_param_grid = {\n",
    "    'n_estimators': [200],\n",
    "    'max_depth': [10],\n",
    "    'min_samples_split': [10]\n",
    "}\n",
    "\n",
    "# Подбор гиперпараметров с помощью Grid Search для новых параметров\n",
    "new_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
    "        param_grid=new_param_grid, scoring='neg_mean_squared_error', cv=2)\n",
    "\n",
    "# Обучение модели на тренировочных данных\n",
    "new_grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 4. Результаты подбора для новых параметров\n",
    "new_best_params = new_grid_search.best_params_\n",
    "new_best_mse = -new_grid_search.best_score_  # Меняем знак, так как берем отрицательное значение MSE\n",
    "\n",
    "# 5. Обучение модели с лучшими параметрами для новых значений\n",
    "model_best = RandomForestRegressor(**new_best_params)\n",
    "model_best.fit(X_train, y_train)\n",
    "\n",
    "# Прогнозирование на тестовой выборке\n",
    "y_pred = model_best.predict(X_test)\n",
    "\n",
    "# Оценка производительности модели\n",
    "mse = metrics.mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "# Вывод результатов\n",
    "print(\"Старые параметры:\", old_best_params)\n",
    "print(\"Лучший результат (MSE) на старых параметрах:\", old_best_mse)\n",
    "print(\"\\nНовые параметры:\", new_best_params)\n",
    "print(\"Лучший результат (MSE) на новых параметрах:\", new_best_mse)\n",
    "print(\"Среднеквадратическая ошибка (MSE) на тестовых данных:\", mse)\n",
    "print(\"Корень среднеквадратичной ошибки (RMSE) на тестовых данных:\", rmse)\n",
    "\n",
    "# Визуализация ошибок\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(['Старые параметры', 'Новые параметры'], [old_best_mse, new_best_mse], color=['blue', 'orange'])\n",
    "plt.xlabel('Подбор параметров')\n",
    "plt.ylabel('Среднеквадратическая ошибка (MSE)')\n",
    "plt.title('Сравнение MSE для старых и новых параметров')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сравнение старых и новых параметров модели показывает, что старые настройки обеспечивают меньшую среднеквадратичную ошибку (MSE), что свидетельствует о более точном прогнозировании по сравнению с новыми параметрами.\n",
    "\n",
    "Основные факторы, подтверждающие хорошее обучение модели:\n",
    "\n",
    "Согласованность MSE: Значения MSE на тренировочных (0.159) и тестовых данных (0.1589) очень близки, что указывает на отсутствие переобучения и недообучения. Модель успешно обобщает данные, что является желаемым результатом.\n",
    "\n",
    "Эффективность старых параметров: Старые параметры демонстрируют наилучшие результаты, подтверждая способность модели достигать высокой точности при оптимальных гиперпараметрах.\n",
    "\n",
    "Анализ влияния новых параметров: Эксперименты с новыми параметрами позволили оценить реакцию модели на изменения и выявить, что увеличение max_depth и уменьшение min_samples_split улучшают результаты. Этот процесс оптимизации является важной частью улучшения модели.\n",
    "\n",
    "В целом, модель обучена хорошо, но возможны дальнейшие незначительные улучшения за счет тонкой настройки гиперпараметров."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h06ZNEgDpgQceGHbcyZAsN5QwUv2C6XX69OlSOBzu//yee+6RAEibNm3q/2ww/sHwWeRlKhhO/0jFD0Yq71esWCEBkL7//e/3fxaPx6XFixdLRqNxyGcORS8XXHCBpNfrpa1bt/Z//u1vf1u69NJLB/DUO+64Q7LZbNK+ffsSnvOzn/1M0ul0Un19/aBzTgVf5FoqYfz48dLZZ5896L1fFqipel8ArFu3Drm5uViwYAEACqNedNFFeOaZZwakuzDccMMNWLduHdavX3/UnpDly5fDbDYnpAW+8sorcLvdCXm67EkHKMrV2dmJsrIypKWl4dNPPx3w3HA4DJPJNOz7Y7EY3G433G43wuHwoNexl1H5M1getd/vh9vtRnd394AwdjI8//zziMfjWL58ecKz8/LyMG7cOLz11lsjnsPhw4fxwAMPoLe3F/Pnzx9wXfL4vV7vgGuUePb5fHC73ZgzZw4kScL27dv7v8vJyUFjY+OQ49qxYwf279+PSy+9FJ2dnf3v9fl8OP300/HOO+8MaPXb1dWVMMaRpLvdeOONmDZtGi688MIB33m9XqSnpx+V5+iiiy7Ctm3bcPDgwf7P/v73v8NkMvVHQjmi9Morr8Dv9x/xO44GLrvssgRv6wUXXID8/Hz885//BHB0eA8Gg8PWc2zcuBHxeBy33HJLQqcxACnxu2nTJlxzzTW44YYbUqaxKWnN6/XC7XZj/vz58Pv9qK6uHjA+t9uNqqoqPPTQQ7BYLDjxxBOP6lnKPc8/g61dJBIZcG2qyKZyjJ2dncO2sH7ttdfQ09ODSy65JOHZOp0OM2fOHNG+B2hPNzY24qmnnsKBAwdw8sknD7hmJHtKiT+ex6xZswAggb+OZN93dXXhzTffxPLly/vXgvGyaNEi7N+/H01NTQn3eDyehDF2dXUNO/c77rgDLpcL11133YDvvF4vrFbrUdUodXZ2Qq/XD1qTcO655+K1114b8JMcmW5pacGOHTuwcuVKZGRk9H9+3HHH4Ywzzujfr0rg+VdVVeGxxx6DzWbrXweGkcos4OjWIhmUtNHd3Q2Px4P58+enlLsLFy5M8Ogfd9xxcDqdOHToUP/YX3nlFSxbtqw/WgxQdGjRokVDjgOgSCuAYaNNRwpHql+sWrUqIU3w6quvhl6vH7CmzD86OzsRjUaHHMPR8O3PCiOV9wxKPs6pdOFwGK+//nrKZw5HL7m5uVi8eDGefPJJAKQ7rV+/HpdffvmAazds2ID58+cjPT09gVcsXLgQsVgM77zzzlHh4ItYSyXweI81qKl6nzPEYjE888wzWLBgAWpra/s/nzlzJu6//3688cYbA9JwHn/8cWzZsgUABuSDHgmkpaXhnHPOwV//+lfccccdAMiIKywsxGmnndZ/XSAQwF133YUnn3wSTU1NCQZJKkWgp6cHo0aNGvb91dXVyM7OBgBotVqUlZVhzZo1AzryPPHEEwNqhwCkfMeaNWv6m2qYzWacdtppePDBBzFu3LgB1+7fvx+SJKX8DkDKHO5Uz6ioqAAAGI1GPPLIIwPOD/D5fP3zHArq6+txyy234MUXXxywrko8z5kzB2vXrsUzzzyD0047DVqtdsA67N+/HwCwYsWKQd/n8XiQnp7e//+RpqS89957+Mc//oE33ngD9fX1A76fPXs2/vCHP+Dxxx/HkiVLYDKZUqbUpIILL7wQP/rRj/priiRJwoYNG3D22WfD6XQCAMaMGYMf/ehH+PWvf41169Zh/vz5WLp0Kb75zW9+IWl6AAbQikajQVlZWX8+/tHg3e12D0qDDAcPHoRWq8XEiROHHeOOHTuwfv16xGKxQZXgvXv34he/+AXefPPNfoVIOT4lPPXUU/2pg3l5eXjttdcS9t6RPEu554eDV199dcTXKnmE0WjEzJkz8etf/zrBwGPgNVLyOCUwfQ0FwWCwf2wajQY33XTTAOUdGNme6urqwm233YZnnnkG7e3tCd8l7/u///3vePDBB3HxxRdDr9cP4BMHDhyAJEm4+eabcfPNN6d8X3t7OwoLC/v/P9J6otraWjz++ON49NFHUxpHs2fPxksvvYRbb70VV1xxBaxW6+dWc1hUVJRyvMkGJacbpcJ/RUUFXnnlFfh8Pthstv7PlbTmdDqxbt06FBcXJ9w7UpkFHN1aJMNLL72EX/7yl9ixYwdCoVD/56mcJUpjiCE9Pb2fRjo6OhAIBFLymgkTJqQ0JpXA+yKV0++zwJHqF8njt9vtyM/PH1ATpeQfOp0Oxx13HH71q1+lTGs+Gr79WWGk8h4gWhs7dmzCZ+PHjweAhHkfCb0AwOWXX47LL78c999/PzZs2ID09PSUfHH//v3YtWvXoPw4mW+NFL6ItVSCJElfifPJVMPpc4Y333wTLS0teOaZZ/DMM88M+H7dunUDiGPLli2488478cknn+D666/HWWedhaysrKN6/2WXXYYNGzbggw8+wJQpU/Diiy/immuuSfBqf//73+/PhZ09ezZcLhc0Gg0uvvjilF6Y1tbWEXmwRo8ejd///vcAyNO4du1afOtb38LYsWMTPH3nnnvuAK/5L37xC7S2tg545qpVq3DhhRciFouhqqoKt956K5YtW5ay6Dsej0Oj0eBf//oXdDrdgO9H0omlpKQEr732GrxeL1566SVcf/31KC4uTihmNJvN+Mc//pFw37vvvpuQmxyLxXDGGWegq6sLq1evRnl5OWw2G5qamrBy5coEPN900014//33h2zXy9ffe++9mDp1asprkuf33HPPJSiN+/btw/e+971B37F69WosWrQIp5122oCCaAC466670NTUhKuuumrQZwwGBQUFmD9/PtavX4+bbroJW7ZsQX19Pe6+++6E6+6//36sXLkSmzZtwquvvorrrrsOd911F7Zs2YKioqIjfu9nhSPFezgcRktLC84444zPbQw7d+7E2WefjdNPPx033HADvvnNbybUN/X09OCUU06B0+nE7bffjtLSUpjNZnz66adYvXr1gD19zjnnoKysDO3t7Xjsscdw0UUX4b333sPo0aOP+FnKPc+wYcOGlHVvM2fOxC9/+cuEz37zm99g06ZNA65lHiFJEmpra3H77bdjyZIl/QqREnhMTz/9NPLy8gZ8r9cPL+aMRiNee+01+P1+vPvuu7j77rtRXFyMK6+8MuG6keyp5cuX44MPPsANN9yAqVOnwm63Ix6P46yzzkrA36pVq/DKK6/g+uuvH7RmlK//yU9+MigPTj7m4re//W2/EgZQZGGoGs2f//znGDduHFasWDGgUQgAXH/99aipqcEdd9yB2267bdDnpILMzExEo1F4vd7PPbIxHHARuc/nw3PPPYfly5fjpZdeStibQ8msZFo6mrVQwrvvvoulS5fi5JNPxiOPPIL8/HwYDAY8+eST+Otf/zrg+lQyDMCwmRcjhfLycgBUJ/V5wpHqFyMFJf9obm7G3XffjfPOO29QXQA4Mnn5WeBI5P1I4UjpBQAWL14Mo9GIjRs34sknn8SKFSsGZDQAhJ8zzjgDP/3pT1M+R8k/vgg4krVUQnd397BOyS8DVMPpc4Z169YhJycHv/3tbwd8x8V1jz32WEII9oorrsBNN92E5uZmTJw4Eddff31CAd6RwFlnnYXs7GysW7cOM2fOhN/vH9CK+dlnn8WKFStw//33938WDAbR09Mz4HmNjY3wer39UZihwGazJXgP58+fj8LCQrz66qsJhlMqL+ODDz6Y0nAaN25c/7WLFi2C3+/Hz3/+85QREW7qMGbMmKPe+Fartf995513Hurq6nDHHXckGE46nW7A+JNxt3v3buzbtw9/+tOfcNlll/V/nqojTFZWFj788ENUVlb242Dnzp34yU9+kjA3gLyEI/Uon3zyyQkG+FCNMTZu3IgPP/wwZQoAQ2ZmJp5++mlMmjQJ8+bNw5VXXolXX311xA0DLrroIlxzzTWoqanB3//+d1itVpxzzjkDrpsyZQqmTJmCX/ziF/jggw8wd+5cPPbYYwOU7s8DkhVxSZJw4MCB/kLnI8X7zp07EYlEUkZGlFBaWop4PI7KyspBBTvDlClTsGHDBlgsFmzYsAGrVq3Crl27+qMDb7/9Njo7O/H8888npJcpI95KKCws7PeKn3/++cjKysKjjz6Ku++++4iflbznAYqQpYKsrKwB1yZ3OmNI5hF2ux3f+MY3Uqa88Brl5OQcdfc2rVbbf+/SpUvR1dWFW265ZYDhNNye6u7uxhtvvIHbbrsNt9xyS//nqQw+s9mMl19+Gfv27UNDQwMkSUJbW1tCWjV7pQ0Gw4jnNmPGjAT6Gyq1Zfv27XjmmWewcePGQRV1i8WC3//+99i+fTtcLhfWrFkzgD8NBqyc19bW9u+powGOiNbU1Az4rrq6GllZWQnRJiAx8nbuuefio48+wn333ZdgOA0ls5R8Gzi6tVDCc889B7PZjFdeeSUh9Z1Tq44UsrOzYbFYUtJWKjwlw/jx4zFhwgRs2rQJDz300OdmSByJfgHQ3uCyBoAag7S0tAw4PymZf5SVlWHu3Ll45513BkTnjkZefhY4EnkPkOFy6NChBD1l3759ANDf5ORo6EWv1+Nb3/oW7rzzTuzduxd//OMfU15XWlqKvr6+zx03X8RaMkSjUTQ0NGDp0qWf65iPBtQap88RAoEAnn/+eSxZsgQXXHDBgJ9rr70WXq93QOtSrqEpKCjA3Xffjb/85S8JLYmPBPR6PS655BKsX78eTz31FKZMmTJAYOl0ugFeq4cffjhl/RVHzQZLgxkK2MsymEA+Ghjqmeeffz50Oh1uu+22AfOTJGlAO+DhIBaLobu7OyFEPlLg8SnHIUkSHnrooZTXa7VaTJ48GQsXLsTChQsxffr0hO+nT5+O0tJS3HfffSnT4zo6Oo54jAyxWAw33XQTLr300mGV+FWrVsFoNOIPf/gDFi5cOKJUM4avf/3r0Ol0+Nvf/oYNGzZgyZIlCcpOb2/vgHznKVOmQKvVHtUajAT+/Oc/J6SqPPvss2hpacHZZ58N4MjxvmHDBuh0uiHbrQLAsmXLoNVqcfvttw/wRibT7rRp02Cz2aDVavGHP/wBdXV1CdHNVLQWDofxyCOPDDkGgFJIwuFwP34/y7O+SBhq3y9atAhOpxP/93//l7JW8mj2htvt/tz2PYCUbYIZxo8fj9NPPx0LFy7E3LlzE77LycnBqaeeiscffxwtLS0D7v0s+x4Afvazn2Hu3LnDKiM33ngj6uvr8Ze//CUlfxoMZs+eDQBDHlcwEsjPz8fUqVPxpz/9KUEB37NnD1599dWUh5QqIRaLJdD5YDAUnX3WtdDpdNBoNAlytq6ublDnwXCg0+mwaNEibNy4McGRWFVVhVdeeWVEz7jtttvQ2dmJ73znOylrTV599VW89NJLRzyukeoXAHXoU+7bRx99FNFotJ8HDwZDrdUXKS9TwZHKe4Ci7cprf/Ob38BgMOD000/vf+bR0MsVV1yB3bt34+STTx6QDsiwfPlyfPjhhynppKen54jqjpTwRawlQ2VlJYLBIObMmXNUY/s8QY04fY7w4osvwuv1DiqEZs2a1R8Nuuiii1Jes2rVKvz1r3/FVVddhT179qRsDT4cXHbZZVi7di3eeuutAalQAJ2Z9PTTT8PlcmHixIn48MMP8frrr/e3OAWAtrY2rFmzBn/4wx9w8cUX93sOh4K+vr7+cya6urqwdu1aGAyGAW0zjwRqamqwefPmfu/8vffei5NOOillHnlpaSl++ctf4sYbb0RdXR2WLVsGh8OB2tpavPDCC1i1atWQXtKTTz4Zp556KkpKStDX14dnn30W27dvH/IsqcGgvLwcpaWl+MlPfoKmpiY4nU4899xzR13Dxkrz2WefjUmTJuHyyy9HYWEhmpqa8NZbb8HpdA5IHxwpNDY2wmg0DpsT/8QTT+CFF17AW2+9dVQ1Rzk5OViwYAF+/etfw+v1DtgDb775Jq699lpceOGFGD9+PKLRKJ5++mnodLqjagc/EsjIyMC8efNw+eWXo62tDQ8++CDKysrw3e9+F8DI8e7z+fDb3/4Wa9euxfjx4xPOoGDBvWvXLnz44YeYPXs2ysrK8POf/xx33HEH5s+fj/PPPx8mkwmffPIJCgoK+luZJ8PkyZOxevVq/OpXv8LFF1+M4447DnPmzEF6ejpWrFiB6667DhqNBk8//fQA5WX37t348Y9/jNNOOw05OTlobm7GH//4R8Tj8f400ZE+64uG+vp6bN68uT9V784778SoUaNwwgknDPCwO51OPProo/jWt76FadOm4eKLL0Z2djbq6+vx8ssvY+7cuQlKSjJ8/etfR1lZGUpLSxEOh7F582a8/PLLQ54lNRg4nU6cfPLJuOeeexCJRPqjF4NF7EYCv/3tbzFv3jxMmTIF3/3udzF27Fi0tbXhww8/RGNjI3bu3HnUz3711VeHPdfm9ddfxwMPPICnn356RLWuShg7diwmT56M119/HVdcccVRjxOgtKuzzz4bs2fPxre//e3+duQul2vQM9IAStXbuHEj6urqElo1A0cusz7LWixevBi//vWvcdZZZ+HSSy9Fe3s7fvvb36KsrAy7du06Kpzcdttt2Lx5M+bPn49rrrkG0WgUDz/8MCZNmjSiZ1500UXYvXs37rzzTmzfvh2XXHIJRo0ahc7OTmzevBlvvPHGoGlhg8FI9AslhMNhnH766Vi+fDlqamrwyCOPYN68eQP0qI6Ojv61amlpwd133w2Xy4UFCxb0R2sYvkh5mQqOVN6bzWZs3rwZK1aswMyZM/Gvf/0LL7/8Mm666ab+2p+jpZeKigq43e6ErKZkuOGGG/Diiy9iyZIlWLlyJaZPnw6fz4fdu3fj2WefRV1d3VGVi3wRa8nw2muvwWq1fq5p8EcNX0Lnvv8ZOOeccySz2Sz5fL5Br1m5cqVkMBgkt9udst2pJElSTU2NZDabpeuvv37A/UO1hFTCpEmTJK1WKzU2Ng74rru7W7r88sulrKwsyW63S4sWLZKqq6sTWh+///77UllZmXTrrbdKoVAo4f7B2pFD0UY2LS1Nmjt3rvSvf/0r4V4cYTty/tFqtVJRUZG0YsWK/jkNhovnnntOmjdvnmSz2SSbzSaVl5dL3/ve96SampohcXb11VdLY8aMkUwmk5SRkSHNmjVL+tOf/pRwzZG0I6+srJQWLlwo2e12KSsrS/rud7/b31I2ec2TYbCWnNu3b5fOP/98KTMzUzKZTNKoUaOk5cuXS2+88Ub/NUfajhzAgFb5yS2f9+/fL9lsNunGG29Med1w7cgZfv/730sAJIfDkdDeWJIk6dChQ9IVV1whlZaWSmazWcrIyJAWLFggvf7668M+92jbkf/tb3+TbrzxRiknJ0eyWCzS4sWLE1rHMgyH92R6HexHeSyAJEnSH//4R+mEE06QTCaTlJ6eLp1yyin9xxhIUmI7coZgMCiVl5dLJ510khSNRiVJov06a9YsyWKxSAUFBdJPf/pT6ZVXXkmgoebmZmnp0qVSbm6uZDAYpPz8fGnJkiXSe++9l/D8kTxLkr7YduT8o9FopLy8POn888/vP2YhmTYZ3nrrLWnRokWSy+WSzGazVFpaKq1cuTKhPW8quPXWW6UJEyZIFotFcjqd0tSpU6WHHnpIikQi/dccyZ5qbGyUzjvvPCktLU1yuVzShRdeKDU3Nw9oCZ4KBpMJBw8elC677DIpLy9PMhgMUmFhobRkyZKENvJH04783HPPTbg2me+43W6poKBAuuSSS1JeN5KjA379619Ldrt9QGvlwWTBUHN5/fXXpblz5/av1TnnnCNVVlYmXMNz4x+LxSJNnDhReuCBBxJaUw8nsz7LWgwGTzzxhDRu3DjJZDJJ5eXl0pNPPplSjg2Gm1T84N///rc0ffp0yWg0SmPHjpUee+yxEesJDG+88YZ07rnnSjk5OZJer5eys7Olc845J6GNtBKGakc+Ev1CksQa//vf/5ZWrVolpaenS3a7XfrGN76R0HKe561cq6ysLOnMM8+UtmzZIknSZ5OXqeBo2pGPVN6zrDp48KB05plnSlarVcrNzZXWrFkz4GiKz0ovQ33v9XqlG2+8USorK5OMRqOUlZUlzZkzR7rvvvsSWoonzzkVfBlrOXPmTOmb3/zmoHP8MkEjSV+yK1GFLwVOOOEEZGRk4I033jjWQ1FBha8cvP3221iwYAE2bNiACy644DM/r66uDmPGjEFtbe2gh63eeuutqKurS9l4QwUV/lvB4/Fg7NixuOeee/Dtb3/7WA9Hha8IPPXUU7j88svxySefDFsT+t8EK1euxLPPPjvijrT/CfBFr+WOHTswbdo0fPrpp8OWE3wZoNY4/RfC1q1bsWPHjgHFrSqooIIKKqjwZYLL5cJPf/pT3HvvvZ/72TkqqKDCfz/86le/wgUXXPCVMJoAtcbpvwr27NmDbdu24f7770d+fv6gdVQqqKDC5wvc9W2ozlTHHXccCgoKvsRRqaDCVwNWr16N1atXH+thqKCCCv+BkOpon2MJquH0XwTPPvssbr/9dkyYMAF/+9vfjuqkdxVUUOHIISsrq78YfTA4//zzv6TRqKCCCiqooIIKXwSoNU4qqKCCCiqooIIKKqigggrDgFrjpIIKKqigggoqqKCCCiqoMAyohpMKKqigggoqqKCCCiqooMIw8D9X4xSPx9Hc3AyHwwGNRnOsh6OCCiqooIIKKqigggoqHCOQJAlerxcFBQXQaoeOKf3PGU7Nzc0oLi4+1sNQQQUVVFBBBRVUUEEFFb4i0NDQgKKioiGv+Z8znBwOBwBCjtPpPMajUUEFFVRQQQUVVFBBBRWOFfT29qK4uLjfRhgK/ucMJ07PczqdquGkggoqqKCCCiqooIIKKoyohEdtDqGCCiqooIIKKqigggoqqDAMqIaTCiqooIIKKqigggoqqKDCMKAaTiqooIIKKqigggoqqKCCCsOAajipoIIKKqigggoqqKCCCioMA6rhpIIKKqigggoqqKCCCiqoMAyohpMKKqigggoqqKCCCiqooMIwoBpOKqigggoqqKCCCiqooIIKw4BqOKmgggoqqKCCCiqooIIKKgwDquGkggoqqKCCCiqooIIKKqgwDOiP9QD+l8Hv9+O+++7DgQMHoNFo4HQ60dXVhWg0is7OTsRiMeTm5sJoNKKlpQXRKNDXNxm9vWZYrX0oK3MjEgnh8GEJZnM2Zs6cCKvVjU8/3YpDhw4hFoshEolAkiRYLBYUFRUhNzcX4XAYkiShp6cH3d3d8Pl8CIej0OvHoqBgArKzzXA6e2Aw6CBJEhoamtDYqEVfnw6xmBU5OWZMnlyC7OwA3O52eDweOO12uDweGIJBRMxmuKZMQbvbDY/Hi6YmHTo6ggiF3LBa3cjPz0VhYSGMRiPa29sBADabDXa7HV6vF5IkwePxQKvVw+tNh9+vQ3d3PQKBGuh0GpSUlGDixIkwm81oaWlBKBRCV1cXOjs7odFoMHHiRMyaNQs9PT1499130d7uhlY7GsXFFbDbgUjkIMLhIAwGA4xGI5xOJ1wuF/Ly8mC32/H++++jpqYG3d3dsFqtsFgsMBgM6OvrQzweh1ar7cep2WyGwWBAIBBAOByGTqdDeno6bDYbYrEY9Hp9/7w8Hg8CgQAAOp3aYrHA5XLBbrejr68PwWAQ9fX16OnpgUajQWFhIQoKCmC329Hb24u2tjb09fXBZDJh3LhxKC0thdPpxNatW1FZWQm32w0AcDgcyMnJgclkQm3tYQSDObDb85GRYUAotB+xWAR6vb5//EajEenp6SgsLEQsFsOWLR/D7bZCq3VhzJgsnHRSLiZPnog33ngDNTX74fG4oNE4EYl0IRarhcGgg81mg8vlQlpaGux2ez9dhUIRxGKF6O3VIBQyIxo1weGwITOzC2ZzKwAtamtz0dWlh07XieLiJqSnOxCJRBCLxWCxWAAA4XAYPT09yMjIQElJCXJzc/Hxxx+jubkZRqMRJSUlsNvtaGtrg8fjgU6nQ3Z2Lrq67OjpiUGvDyAnJ4i0NCc0Gg0kSUJvby+cTicAwOv1orm5Fb29aTAYMlBRUYTCwhgCAR8MBgNGjRoFm8UCW2cnAu3tqOvsRGVfH6DVQqsl/1N3dzfi8TjRgUaDEgDmSASdQaK1ksxsaJwVqItXoPZwAD5fHYAauFzd0OnotPJIJIJoNIp4PI6cnJz+8XV2dsLn88HhcGDs2LFwOp2oqamBz+dDJBKB1WqFy+WCxWJDba2Enp4cxGIxWK2NcLk8iMcLEAwa0NV1GBpNAywWE8rLy5GVlYW+vj7aw04ndDodCgoK0NraiurqanR2diIjIwMVFRUIh8Ooq6uD3++HyWSCTqeD3W5Hbm4unE4n0tLSMHr0aHR1dWHfvn1oaGhAV1dX/xhNJhMyMzORm5sLjUaDcDgMj8cDjUYDs9mMeDyOjo4OSJKE/Px8jBlTipqaAFpbfYjFepCXF0Yg4EM0GkUsFkNaWhoyMjIQCoXQ2NgIvV6PBQsWYOLEiXj33Xexd+9e9Pb2wmw2IxqNwmazQaMxIByeBp/PhvT0CMaP74JOJ0Gr1aK4uBhpaWno6upCdXV1/37WarUwGs0ASiBJdnR31yMU2g+PpxuRSAySVAybLQ9GYwjh8AH09vbAbrfDbrcjFouhr68PVqu1ny/09fnR25uGoqIKjBqVAa+3Cvv2+RGNWhCL9UCjaYDJZIBWq4XdbofVakU4HIbb7YZGo+nnQ4WFhRgzZgyi0SgAwGg0IhgMoqGhAbW1tXC73TAYDCgsLITVaoXH4+mnlYyMDOTn5yMvLw9GoxEejwdNTU0IBoP9eyMejxMNma3Ij2RD0xdDS18Hepx9sDvt8Hp9CAZzkJU1BlOmjIbT2YPKyj0AALPZjM7Ozn45ZDab5fGWwufLRE1NM0IhNyTpMAIBHwDAarXKdBFFIJANn08LwAebzYq8vDK4XHp4vX3o7ExHPB6H3d6CWOwgLBYTAECSJAQCAXi6u5EXDqPA4UDWmHHY2T0B3R4TDIZujB/fhcxMFyRJwqFDh+SxWtHcrEcsZsXo0ZkoKQGamxtRX1+PcDiM9PR0lI4Zg9FaLWzxOA62t6NBo4Hd6UygLZbTwWAYra1meDw5CAZD0OkOQqdrQHFxISZPnozMzEz09PSgvb0d3d3d6Orqgtlshs1mQ1ZWDtraTGhvDyAS6YXfn4Zg0A6DoRvFxU3IzCQeGw6H0drailgshhNOOAFjxoxBLCZh27YO1NW5EYt5oNM1QaOR+te8sbEZvb1p0GrTYbfnw2gMwmAIwOXywOGwwe1298us0tJSFBYWAgCam5sRDodRUFCA/Px8OJ1O7N69G11dXcjIyMAZZ5zRr2ccPnwYb7zxBvbt2wedTocxY8ZAr9dDo9EgEAihu9sBv18HqzWGvLwwMjLSkJ2dDcQBX5UHfa0edIU98GeFYbaaodfrEY1GoZEk5IZi6GvJRmc4DZpRmTjn3FyMHz8WlZWV2LdvH1paWpCVlYVRo0ahqakJVVVV8Pv9MJutAEoQDpvg9TYjHq+HXj8adns+0l1aTHZ2wxKNoDMcxoFwGOmZmRg/fjxcLhdaW1vxySefIBwOIzMzE+PGjUNlZSX8fj8MBhP6+jLg82mRmWnElCku6HQatLa2wmQyIT09HQ6HAwcOHIDP54Ner4fBYIBOp0NnZyc8Hg9MJhPKyyciFMpHdXUM7e3t0GoPwmpth9VKsjkvLw8A0NfXB51Oh7y8PBQXF8PlcmH37t04fPgwMjIyUFZWhgMHDqC5uVmmSQfs9onweoGengZEo4eg12sRDAah0WiQm5uLjIwMeL1e9PX1wWg0ore3F4FAAGazGePHlyMaLUB9fTfC4U7Y7V3QaoFoNAqdTger1QqHw4VwOA96fTpisR60t29Fa2szdDodTCZTPx9vazMhFrMiPV2PvLwwmpsb4XA4+mX88ccfj+9+97swm82fr5L9BYFGkiTpWL38nXfewb333ott27ahpaUFL7zwApYtWzbkPW+//TZ+9KMfYe/evSguLsYvfvELrFy5csTv7O3thcvl6lcUjhVcffXVeOqppxAMBlN8qwFQAsABwAugHsDZAL4PoAyACUAIQIv8vQWABKARQBWAjQCqB3mOcrn5++MAzAOQB8AMIACgFcD7AHwAZgKYAWAsACsAP4BDAD4CsBHlqMYyAOXySALy2zdiLKpxpfyNDUBcHsPfALwpj2Gw8ZUDA57KY9qVYi6pQANgAYClAIoAxBJGR79T3TMUzoZ6l/K+BgDFR/GczxNS4XCouQ91zyYA+TgyXPKzZgKoAJAJQAeinzYQTrJAdMc0fQDAwwD+OcI5DrZeRzr3oa9P/a0LG5GNakTlZ9gBeFGOw/3X5gDIBQAY0YYxaMcEVOM4bMS5qEYpaA+/AuAlAB0AnPI8HAB65Z96+fmD0SXj4HgAl4L2sx20Rn4AXQDcAMIjwMNQuP0yafpoaDfVmJVj/BoED7UCiAKoBXAvgJeT7nfKP70AskE0PCFpLLtAuB5ujMox5QI4U75HC1ojE4Agjnx9jhZGzuPKMQbLUIxyRGGBBgEYUA0jNiKIaszB4HNP9Y5yACsh5t4lX/sRgHb5Oito3aeB+IILQARAn+J/CYSvLgD/BvA7MK4SqSYPAUxENaZiIxajGiUYyF8qUoxJKUOHo8TkeVoBfAfAqQAy5HfwOH8P2o+D4V05lhIABQAM8vyDGEirSlCOMhNEV9UA/iTPZyKA6wBMBfFcgGT7IQA1SC1XB6OToejnyPlxOUIKGosjAC2qocdGNKAatfKd41GOIlhgRABWVGM0NmICqrEJQ8uK5PcaQTpOEOU4hGX4AOWohgWHEUCfgqdHMDR/KwdwHohOea6fgqjCj0T+wTw81bMqAPwIwFwQz9bL928FcAeI7h3yb4BlzNBjG0zvqUHiXhtufsswNG/7vK4hcLlcuO6663D77bcPMqYvFo7ENjimhtO//vUvvP/++5g+fTrOP//8YQ2n2tpaTJ48GVdddRW+853v4I033sAPf/hDvPzyy1i0aNGI3vlVMJyuvvpq/O53v0M8LmEgk5mAgYQmgTaWGUT0Pvm+QhCzD4EYqwdAE4DdIEVsKKGuVGongzZsA8j4KgIpSBqQsRORf8fl51hAQr4V5fgA1+FhZKEHDfLIbACKkQc3ZmEtfoBqHCffo5Wf0Qdi1LUg4ZI8PoCYfJY8Jos8xmKQsrMHxAA2YXBBVA7guyBlyQmhrO+XR+gGsBaJm/dolbXBmfOXpwylGpMShz6Q4VMMUtD/DyRQU91TAsJPHGTojAIpLC4QnfSCjNh6ECNX4lIjX386CCesLOYgkZ4AIB1Cee0CrUsOiI5XY3jjabD12gVgSdLcbfLc3QCeRaLwmJACV+L6ctyL61Cr+DYDNoyVv83GWnwb1RgDoA3l2IHrsB5ZOAgfvJgMwAkDJGTAiyzsxhTY4YMbaViLq1CNSTJOfPKPGUQ/EQA9Mp5b5P+ZrpL3yjIZ39MhHBR++TqHjPdGAG+BaJLxkEz/Q+H2i6TpVMrn9zH4+g027qH271gAd4OUyjiIjg0go8UL4FaQcrsMxBPZSRST594HUmYa5bFMBPHJRgCVQ4xROaYxACZB7CHmXRoQb/pAftdQe/Szwsh5XDnG4jqMRhbiaEA+fLDBhgiK0QE3srAWZ6C6Xx4p555K9kgATgDxEJZXEXn+ACnwIQCjIWQFO1okEF44QSYGoFu+PgbgEwC3oxzVil08Gj6cCRtiKEYd3LBhLb6FasyE4C8AcDPIIOMx9crzqQWwNumZyTNNw1pcgGrMg9gjo0EyLQigE7S26aC90w2gDqn3z9fksRTLb7DL9wJEryH5f6bVx5PW9DoQfdlA8s4Mou1WEN0ulWdhkPHpl38g/x+AkKsb5c+XITVvHUyvUN6jNN7eBbA4JRbLcRjXYTOy4EMDjIpvwnBDj5dQiyXQIQsT0IAS+CDBBi+K0QU38rEWF6IajyO1rEiWgRYAcwBkoByf4Do8gCz0ogFF8MEIG7pQDL+8rqehGlqkNijLAdwC0km08rpIMm79oD2Qg1RO5sQ99jUAd4J4jQ5E31w9w2tSCZIDufLnbSDZNRjvVeo9NghZ7QEwDkSjLfL/SsN6MLx5QDJaD9q7zNs0AG6S51kvPzOZ/wFDydVUfNxgMOBnP/vZMTGe/mMMJyVoNJphDafVq1fj5Zdfxp49e/o/u/jii9HT04PNmzeP6D3H2nDy+/3IzMxEMDgaAxlTJ0i5ZSOGCe0SECOtAm3EDJCyp5OfGgExZRuIwTbInzUgNcG+BKFY5oGYnEf+nz3eHaANzV5RI8hbx8w2Gxo0YTWexDRsx17UKGaZBWApJqEN2zAN9+AmSP1CT5J/4iAG8AaEMsLjC4KUkr3ys2aCmFAItHk7QYp2GogxSBAenxfk91wH4DQZVzEZp3qQAvRveT7bANwD4RE7sk1OMDhzpnF+gJErq58XaECKwTQIHJZDCE6bPIYfQTBNDYD7ASwE4SkdhCM2nlhh3y1/zsboRyCmvg3AixDe1jHyu/j+IGiNAFpL9q6FQXTAURUAKJXHvVi+PxUMtV7FIGGzJemeLACnyO9sgRDsRhC9VYLoyyhf44EG5ViNRzAN72AvANorTI9hTEIftmEq7sF3AHixGndjGppRiTTMxb+RjwA6UAQgF9noRQty8T5OwEQcwDYch3vwC0gwQESH4iCFJwqiS1bwYyCj/yMIeuJIVzoo2pQGsb8kCCHMSup+0PqFQE4XJf0PhdsjpelUUZvBImdWAOcikQ+ykpC8fgAZHqnGPdz+nQKiqzgIn6x06+R7+uT3GUA82CrjLBu0Bp0gHvuR/PdcEA9mb71yLDzGFyEMQD3IC2yVr43Jz9fKuInK//vka1Pt0c8KyY4RVooGriPR/UxMQwf2YhJoTUPy+KyYhHpsQwXuwXhIOKyY/ywQHXYq3uEEyRsTSIb0gvZZiXyPB6RcukA45QiLQR5fpoyTmPx+5kseEL0EoME6ee9B3qffANFRL4A4JuEAtmE87sElkDAOZMAYQPugEbTflXytExq8hdW4R/FMJWRgEkZhG6bgHpwKCV6QYThFHptSVlpBUU4dSAl/G4m0+QmAH4Dojg0t3rtK44nn3g3gMpDsBIjXnyLjySrPmedTJuOVQQsRNZLk69zysztBSjbzlWQ9ZChngZIXJRtvWtDefz0BgxpIWI0eTEMV9vYbhgwSJiEEB9rgRSH24kQQT1d+X4dtmIV7cAoknINEWZEsAzWgPZsPDdqwGptkvUUjj7cMRHcHMQlubMNJuAerISGORIOyBsB9IEM0JP/EZbznyfP1yThNdDKzMU57rALAHyEinSYIJwGvDzvFm+TvNCA+tQ2pnb+8v+fJz2sB0QDrdUGQ3GUHuA4ke29GYsSd6UkPoTNE5PdFZXxOBu1Vn+K7Jvn/USAZ4QJFOCtBezUVjxwofzIyMtDU1PSlp+0diW3wH1Xj9OGHH2LhwoUJny1atAg//OEPB70nFAohFAr1/9/b2/tFDW9EcN9998lGk9KiD4CW4gzQRtsMYn4ACSETiDizQAyoRL4+AiJ0vfy7G8S4xsrP3aJ4Ti+I4CeBhLkXROjjQRtKL7/HDCJ+jeJHB9ps/H56XgmiKEcdGjAKwqsBkBLnQAN0qMAhlKARh1EM2iA6EEPQgZSnYvmZPL6TQAzo3/K7yxU4yJXvy5TnboIwOCXQRp4ij+UEkDcE8vyC8r1OEFN4A8S8SuT3L5PnpxSRjCNOobhJfg8rfn2gcH0WhNJ9AmgND4KUrvEgxYpxfy6I+X6R/ooSEN4akGh49oLWKCh/fxPI41UNUuq+BmK0FghjxwnhCbPIc+wBGdbZ8nN2gxSmOSDhqgWtT0S+ng18VuDZg8zC0gERzTDL4yyXn/deivlpkHq9ekHrdRLE3mA8Mx74XVZ5nNNBdLQTJFgTBUUJdqAcUTT0K/rl8r0dAErQABcqsBsl2AnyZPaiAYALGmTBjl7EZBxq0QsHsuCGC31oQD4qUIcSNOBwv8fRLOOkD7SP4xDKRAwkkOIgIV4J4HzFvDnSxAaXUX4mOw2MIGGXA1LovPKajZKvV6bjMW69IMWyVH7GSGiaIxrKqA17XQ/J8+EosxG013sgFLF8+Rk98hjcSIQGiH17WP5sKHrYC+Bk+R5WxlmhNUAYMhkgvtANUi4iEAYMG7NWeWx75He1y/gLgOihQX4WjzFDvq4NtFZWeQzK5wXld3A0vleee6o9ynA0qVIaAKtASlUMFBlhhacaxFt5HSegBKtQjnfRgAL5/oiMrzQABjSgCBWoRwnOl3l7FYgPZ4PWvUXxjjSI/e6CMKZY2daC1j0TpGQyPfYpcMNzY96ik/FHe7wEo1AOKxrgB9F5gXxPGgAJDShFBZpQgnYchgtEtyGQAcX6QQiCrwVRghzFM5PhRDQgBxXoRAkKcRhFEFEWCUJWmiEMopB8jQO0xm0gmjsPIvJmQOqeXVoIWswG8BtQtOAjiFR45kvM3zgNVI+BKfrMV1jud8pj2AbgLPl9/wTREDsmOerQqvi8F4IXmUBry7KmR37mBHleO+XxEZRAQjlq0YA80F4KJozRAwknIIC3UQgMWAONjP89KMHZOIzZIJ4kni5kIORxZ4H0lnaUYx8a+h0IWRCOpgw0IBcVaEEJduAwykG0dCqIZv4k/60D8bBMCNnI9GkH8Tp2FrLzJQu0x/YBWAEyQsMyjrRINFg5O8ME4jFd8jXp8rteAfFy3rMA8cAS0LqzThSSx2qXccBr75CvSwfwCIBrQMZTCYh354HWjHUGI4iOdTIeOEreJz93MshI6pbHMl0ev1ceJ/MZt3yvB8BskIz/AEyfGo0GHo8HTzzxBL73ve/hqwr/UYZTa2srcnNzEz7Lzc3tL2jjQnIl3HXXXbjtttu+rCEOCwcP1oIInJWlSSCilCAiRuUQXkyrfB17Z7NBjJG9KywUlcppGojgjSlG4AEp92+DhGUehGfbBtrIHHUCBDPwye9nBTcMB4KwIAwfbKAN7gIxEhIUPmSgEN1woE9+fhyJnnATiCmxMg7Qhud0FpeMJ2WKQVyelwbEaKLyWOPyuOfL1+ohFCAtaINHZLw4QB6mJvnvZCYLJEZpLCDmXwZiDJy2pJXHVwdSugvknzBEOk4WyDgMyrhPVvo+T2ClaRpoHepBaRUsUCGPmxkqM959AM6B8LLzeiujhPz8LIi14vnpQREmi/xcPwTu2avPSqpH8aw4hBHNaXpM7xYAV0IwXCWkWi8GVtCcEHSlNMAN8rPZ0E8DGQKz5M86oBQUDrhhgRa+fs9pljxvUk58CKIQYTjgBxCFBYAPWciEDwaY4OmfjwZhGOBAFEaE0dm/N3ogoroGiH0O+XMWorxnxsh4/Vj+zADaw4xnXit+XhRizZn+IyD6KAFFGdkw7gIpvCdD1JOwYuWXn8lrzrhVGjIWiJShfAhvqxm0f6bJ/78LEvanggwznYxv9pSzZ13JBxl88j0OxWdD0QNAe98Ckf4CGTdKhYU9vvnyPHtAdMTKeaZi7pkghcMBott8edxdIEX2ECgtJh0UeZgB2g9RCL7N6S+coqOR58aKd588JqVypDRMlRE6rl2wYWCtKqf0FIAcI4zDAITB4gIZmBUgJW0ZHLDAggB8/ePmKBml0flgRiECcPRHHp0gfjpKnhNFbAlHGRAGq1V+H2cQQIF7i3yPWf5hvsFKvhbCwDRAOBvscKAQFrjggxnkuDPIcyTngw8uFKIDjn7ngkX+ncpI6QXghAMmWKCHb8D3owGMgw8xFKIVDnSBlGDe6yxjxsrvYS8/R/s5CjATwuDhfapVzDcZlJ9bQTLneJAcd4JwXgLhFGK+yvdxNIOfpYFIV43LYz4FRD8aUC1eM4iGohDOAuX+h/y3Vh4D5HsYYhCRl+NBDksagwMxWBCCr99RlwgxxGBEHNF+R1YiEA264UAYwknK4JDfyatnlOfqgQN+mbZt8vfsHDQCsMnr2gVHv06WB5JDE+SfQgg9hp1/bOCznmOBMASZb9SB9ths0P6NgNaKo6nM55kmdYrfWkCWMfSs+aAMG+a9kJ/ZCaJPlrNmGRfMa1jmMj7ZyL8DFBErBjk/gcR1ZKdChTy3dgi+mq0Ys1nGMztGmuV3MZ/ZB6LXbBB//CmAD8Fph5IkIR6Po66uDl9l+I8ynI4GbrzxRvzoRz/q/7+3txfFxcXHbDwZGVNBwqUERDgsGDhc6wMxYWZMrLDwhmKjQckAWahBcW0cxEiTgdPu7KBNwEoRKwnMQEzy9WysAIIZA4ARXpgRgBE2RNGL4yBys3MBmGBDH4Iwwws7BKNhQ4/HaUWigdcjX+OC8Egzg2IDj5kT5Gu0IObsA218swIfBghDp0e+Lio/lz1nyUw2G8SYrBApD5kgT1wMZPTshVC0iiBy48PyWO3yj1YxHvYoHYejM5ySvckNEMX6ORAF7JkQrQmcIGPPCmKQvMacIjQLxMiLZNykyePnFDFmsuy15KhkEMJbxsYvK+ZKoQyIyAnfByQa0ACth05+N7+vAKSIK9MRNPIc80DCQbkXII8pKI+T6Yq9jcoaoSCEIs17gvcWe2s74EU2AnDChhB6+4UCG1xG2NCDIIzwwgrAggDSYIMNYaQhghYYYZDVQwlGRBGFHmGYYENQ3hvsBWScACI1z6yYXxRCyR4PEmLMN3Ty96l4g3INWXmLyt/lgmqjWiHSY08H0Q0rvjweCyjyxFEjxi0bMk6IOgYXhEOGeRE7hbwgmmWlulG+l40kVpgjGKigQX4Oe3J5jkPRA3v6NSCa6JM/Z+UyCuGUYcMzDhF5j0N47jlaUijjiA1efm82KGvgfYg6qnQQvwcSU6SUY9Qj0TgBBF/zAjhRfoYZA9MRi0Gp3N+F4K8NICVNAzKkSuTxZoDonRXdOIj3m+TndIJoqxxetMi8PYjeflnA+xmwIYAgTPDCBhFlmAaxx3mOMRAtGeTv+G/GHUC0xZFoPYQBwThjYAch3x8GR1W9yEIAY2GDRd6nnCIMAHHY4EUQFngxGqRY6uX3jgHRhBsiqhEGkAEvXAjABhsk9PbTG0D0ZoIN3QgiDV6Ugdaex8c8liO+jAs9hLFdKF/XBdGsga8faEQI2lH+fxhEF9kQclSZhsqOgcGAn8lymPURNvDDEArvAYgIRAYSZTbzTjOIlyiB5Z5WHqfYz17oEIAJNgTkyHwi6KBDGFro+w2MRH2GeKgVXhhBirwSvCBa5zqfMLhO1AsrArDABp9M2zYoU5yJVuzwolzGIUeCNaB9kgaiFSV/BERUNtn4ZTkZhZDVOnl86UjM7EkGfg5/x05ZTpHkSCEgHII0TxFtYl7Hz2OepnRCjwbwBEhXKIDIQlLuCzOEHiBBOADYKaKTcROFcH5lgwyyDhDNnyzjMii/qx3EN0oArIVGUwOtVovRo0enwMVXB/6jDKe8vDy0tbUlfNbW1gan05ky2gQAJpMJJpMp5XfHAi6+eBUefLARInyr9KaxFxQQhksDiLnmQHjomHDZmIoovlNGnkwgQvZACCD2BnL6TRdIwQ9DNIDgzcYKb1C+hhkyADhRDy+qMQHTUI+9yJCfwRtIg2I0YRumoR6jkWg0AcLwYcHKEAVtqCx5znoIQczjZ+WGmQGTsVIA8bOU6UppEEqoRcZtPWjTMpM1AlgEUURulp+lgRD6ZSAm0A3haeqD8HACtCYZEIW4XhmHmQCWg+pNkiMpDKnSbSYgsYsPQwC0zhyJ2ApgO0hAjYNQRFmIhiAiSjZQiP10+Xs/SFAaIAQAG/ZKhU8Yz4TLbBk37EXPhPA8sUDgZ7ggGkSwJ5ENBGbkzLBrZDwsAxX2T5D/ni7/XSSvQQMobcAj//SChArTlRFEzyaI9VCmhTDOObrI9OVHPYBqjMY0vC3n4etAQoYMpWL4sA3Ho142VKuxFdPwISpRAjdcyEczOmSadaIPLciHB9mYiBpsw4moxzgI44eVbaUQVe51jkRwlBQQCmhYHjuvm17xXJ5fHKKG0i5/zqlR7B1noc/7rE9+PhsPxSCFjXHLhgwbPxoQ3QFEm2yw8LoCImrDRmgvaD8tlJ+XBqGA5yLRcCoGpRPVQ0RflPTQAuEhz5XxlCaPwwLakyGIOhmllxiKuSqVIRNEtKVXxhUgIrfsvQ2DFKFZoLSXOfLfDgU+DRD8jPkrK69Ml1Z53HEID+8PIdKlOR0xCxSd5/SguDz/cfJ93TIeToJIjXTI72CjkO8bpcCzBfUIoRp5mIZm7MUYCFqKyavQjG2YgnrkymvBhiPTLyvcvfIYOPLIKbxKw0YPUvj0IDribAKlrGD88fOVjoAY6hFHNU7CNBzCXqQhsTYwimIclMc7BUKmsOxzyb+bZRy4AOSgHieiGoswDdXYCx0EXWUD0KEYbmxDBer7m0FwuhY7XnQg2mE+yEZUqbwO7KzkKBhDspEEJOoJHMUxgxTnUhBdRyAcA0qQkn4nQxSiFrgVgr6DMj6yIeqYbBDrycDOxVSKf1CeJ/P4THANaT26UY0xg9Y4uaDBAVjgQhMakQ+xXyh6Xox2bMMs1MMPiloogTi3qHHygHBVjHpoUY0KTMNW7EVpwjNpXRtl3jxGnhvLNkBkZCh1ymRHCIAEQ5DlJNNduzwWF0Q9Z7KBm+xgYUOV5YQHIs2ZjXp2CrtB+68DwoBXPpPHppTRRhlXHCHSgPaECRRJZr1OD1EekYnEyLEGwgnkhSiNYF7ODnvO9GmR/24Ep3xL0j1wuVz49re/ja8y/EcZTrNnz8Y///nPhM9ee+01zJ49+xiN6MghFLLAYChEJMIMkJUlVi4NEBuqDUToVSDFgpVgIHHpmFnq5HtjIEZ4MhLz2N3y95xS0QISoKzYcLpAGImRhzQIxccB2gwRSDBhI85FCf6CSWiXu964YEMAxTiADmRjE86F1K+MKT3rrMi1QHhuAGLQb8nznwph/LDQ5HQ/VuA5XY9Sp8TzAeHpYs8yp//5QQL9Y8W4ekAKWBpIIfFDCEJeG+W4OfrCjMcJYoicpqb0irJyagZ57kwYvNYpVSpOJ0gxLIFg2hnyuJohUsv08hz8IMFRCpEKEofwIgVAtMV5y5dC5MKzt5bHzVFOZricVqORxxCW3xUECdx0+XtW8JOdFrwerFjws1lwsqfdCVKk2evZCkp5ypLfVyTPrxCUWtAOYsDVIJpulb9jpYS9ql6Iuhmr/Gx+J8+dIwiZkBDCRsxECf6GSYjKfn47bGhHMQ6hA3kyjZNyuRHzUYKdmIhGNMKBNERQgHZI0MOLNDSiCBOxDx3Ike9j2mb8skHDnl82gLSK79lz6YTwMDOtKj2eccXfTMcWCD4Sk5+RlnQdA3squaYkAhEd4D3Lhkyv/F2xPIY+JO4DnfyTA+EdZs+oXv7cAFErNEq+fyrIAOCUsA5Q04UFoJodO4iHtIEUvFEQDQbSIJTwJhDN2CHS7xjYA8vOEVZSOFrFqY4cmUqD4I2s1DM9c3RtojwnI4SSz3zeIl/P+5EdGm3ys9koPgwRaZ8CoucPFGMrh6BpNlryFDhnpw6nWHNUhx10XhCvsEFEsfYBCECCAxuRjRL4MQm1aEAufNDDBh+K0YIOZGITzoHUTxu8fiHFmNMg0r/Zs62V5+2HSAX1gnixDsLZpHQEKqOIzPM5IhsD0AUJLlkW/RWTsE/ulJYGG/woRgM6UIBNuAhSfzpggzw+zrBgw7oXxMMkSLBiIy5GCZ7CJHSiASfBhwOyr79KxsEZsnzjyGMmhGxmwy050jgKIlWQHb7sGGF+wJBsQLHTyQji9awvsOHCEXRO71IaTUpno3JMHK1mp7TSYQoZJ3bQni4ByS3e/5zSngaReu/EwAheGESjs8B0KCGOjXgfJdiLSQgP6KrXAQOeQgRL0IVJOCh3SjTJXfVa0IHR2IS5kPBbDGwiJIHSv0pASjk/OQsSiuRvmjAJLfJ77bChA8XwoANZ2ISlkPr1ihBEhJD3vFGesxeJOgg7n5WGsBPEo1wgXvkhKKMlFyS/HUh03vD4GXg9TBBR8z6IPVsvX8eGYrX8rmyIvaLM/mCHcLKhq4OoDeTMGS1EnSbrln0gGpgOIQ8AQYMhiFomOxLT2yV5XF3yOHmeDQAqoNeX4nvfu+Qrf57TMTWc+vr6cODAgf7/a2trsWPHjv5DLm+88UY0NTXhz3/+MwDgqquuwm9+8xv89Kc/xRVXXIE333wT69evx8svv3yspnDE4PUCWi3XkoRBG0HJwFi4TgUpf1aQ97ALouaJN4IEUd+TBqEQ+OXvs0CKRgGIWFtAqTZvgbo1cepJM0jwMbGyotYjPz8LIrWuSDGWDFSjHGuRh2X4B8qxC4XoQRBp2IYTsAnnoRoV8jOVXjT+OwgqOGchz4rRH0Be0+mgjcepACGIAm9l6Jq9wWaI84CYubHhxYohp+6FAVwE6k7E0Tn27kYV9wMiKsIhaVb22dtilMdvgQid2yC8qxaIiE61/G7OTT4MAeVI3RlsCUSBZQeI4bJyUQgSxOzFzpKv98r32+VnsKIRlscI+f3MWJtlnPM6M87YSIRiDdgg8QB4TV6v80DKnRWiQNQKoSwo155rn3rla6wQCgSQ2MmvE0S/PwTR7xZ5jjYoPeBCaR8N6lL1METbXPZ4xSCMW6s8D95/rOBwehWnYmhRjROwFjYsw1iUowOFcCMIK7ZhHDbhfLmluAVACNUowlpcg2V4GeX4GO2wQYsAJNjQgXRoEME2TMMmLEM1JkA4TyIQilRy1Id/mA5jIIPPA9qLrHybFPdC8ZvXULlvlKmsvC7KKADzJFb8JcW1bojGLh2gYwG4ZiYdwiBj+mF+ApBizJ+zZ5RTWlnYAsK54QAZ0LtB0dRdIFq7GGQksCIQAvEzrtdQpkgZQPyxB6LOjefH97Jjg3koe8f5OsYxR2PbIIxDjmbGIRp7jIJIkXVBGMa8rwChEDFfc8nzjYD4gh+Cb+8H7c8SkKLkglBIWIybQfTD62iH6NTHn/P+1stj61Pc/4n8XlLAqtGLtZiCZXCjHA0oRCeCMGMbpmATTkY1ciHSBZmnumUcp0HwSHbAREH0wnU3HHH+BETHHFlnY5cNCY4OcoSJHSwRGS8BAE5UYyzW4ntYhvUoxyEUok3ep9OwCeeiGhPl5+4AydaFEM4tNqC4XoMcZ2Lv/xPlqEEhMhFEH7ahVN7DEyEyMZRGDa+tHsJw5vewPA3L+OtBYp1iYuRF/B+D2IsxeYyMC442p4NoidOlmE8yMG0on8n0nQ3aj0EQb8mGqInMkJ/ngXDcWCC6bXZA6CDshGqHiLCyDIICPyFUw4W1cGAZzCiHF4UIIwgttsGITWhANRw4hCuwDG0oRyMKEZFpcJZMg3/D4MdWVIPSvL8LcSwJRSCrMRFrsRrL8BLKsQ2FCCIIE7ZhomJdIc/FpXhmEERvyj3PMpT/jkI4o9iwYNr/BORU+RjCQeIE7SFl1ImzDJh3s/7B/HQyaO++BCGfN8rPzAVF2UpA+hrLN6YDlhMMykgU60ts9McgaKoewsDrBtVYZUA4R1nOtkCkALeD9nYmhG7VAtqD7MAEAB/0+jG44oof4Pbbr8VXHY5pO/K3334bCxYsGPD5ihUr8NRTT2HlypWoq6vD22+/nXDP9ddfj8rKShQVFeHmm2/+jzoA909/Aq66CojFgGhUwkD00/86XR+MRjeMRhJydvun0Ol6EInkIhTSIxZzIRAoQzSqBxBDPG6CTqeD2RyAXl8Pv1+HWCwHkqSFRuOBRmOEw9GE2bPXIx7vw86dl8PvtyIcNiIW00KSItDrg7DZYvD5ShGJWGG1eqDXszBsRDTah1CoAnq9HVlZIVitAdTWjkMspoEWcYzS1MMW96Mnlo56jEZckROfDAaDDyUluxEKBRGLGWEyxZGd7UZW1vtwOpvQ0mLC/v2Xoa+vCKFQGqLROCRJJ8/HBkkiAWoweKHTBREOWxCPE9PWaEIwmyWEQibE40pPnwYajQSLpQ0Ox374fKMQjTphNvuRk9OGjo4y+P0WhMM6SBJ7lEPQamOIx52K57ASoIFGE4dGE0U8boDR2A6tlvKxw+E0aDRRaDRaWK0eGI0dMJlqodF0QpK0CAZLUFb2DAoLu2C32+H1+rB37zlobc1HPL4HWq0GDocDRmMumpvnIRo1QaPphlbrQTRaAINBgkajRzDIil5EMT4dNJowLJZGhEJGxGKUtqbXBxGPa6DRhMARJrO5BYAZLpcbnZ3liMc1iETIc67RcPqXBlqtBvG4BJ2uGxoNoNf3Ijt7HWy2DTAYdIhETkZt7c8RjTqg1VKBZzTKRgDALVBttmpYLGFEoxnwekug1QYRibBgkmR8xiBJxOSt1nZIEhCNWmAwNCI3txKBwDREIpkIhboRDFoRj9uh0RhgsXgQj4dht2/B2LG/RU5ODrq67OjpiSEczkJT03WIRDJhNHYjGrUhEjFDkoBYjCOVQWg0AUiSGXq9Fnp9DJGIATpdDMcdtx+hgB0Vtmpo+npxsD0bNYGxiMYtiETsiEbJSNRqw9BoQjDoPCgz7kGarhkd/kKEI2ORYexAQG/HwcgESBo/AD/icQnxuAvxOCuTWsVvcqZotVFIEiBJehlHOmg0ErKy/g3AA7+/BOFwAcLhbAiBxylkbLyyMRxN+l/pgZYwUHgDQAwGQwjxuAbxuBYWyy6YzR2wWg+jsHArxo4NweFwYdu2b2DfvqkIh3XyHuJ0TEAYgVpotRE4HI2w2RrQ3V2MYLAAOl0QOl0IkYgRkmSEVivBZIogFosgFtPAZNoHl6sFfX0FCAYLEA7nQKPhtC1SIjUaDSTJCUliwycKIAKNhpQNna4HgB7RaBqUufp6fRhabQzRqFXGM+OBonY6XRBabRwmUwhOZxc6O7Oh13cA8Mpn8Zmg0dhlmrJAkkhBMBj80On6EI2aEIulyzQt9oNOF4PJFIHN1gGv14p43Ih4XAuDwQ2DIYRYzAaDIYKcnDpIUhhNTZMgSRJcro8Qj+vh958ElyuCaFSHrq5cRCJGADFoNJBxkJzWHING44NGw8aIFkZjBFqtBIMhgDlzfg2LZR88ngJUV5+JUMhBPA1mpHud0Pqs8MSdaDGYYLV3w2A4hHjchEBgGnQ6K2IxLYJBg0yjQDyuRTwu0lB1uhAsliB0uhg0mhbo9fUA+hAKWSFJJmi1RgSDY6DRhBCPmxCPEw3pdBEYDDZEo3aEwzbo9b0wGLphs9UC6ITPNwNADKFQLuJxEzSQUIJ6ONCDPqThMEZDkh1HWm0QOTkNAICeHqc8RrP82yDjhvaJTueRcaaHRjKiBC3INHagVzKiHnkwmKKIRJyIRgmXGg0QjWoQjZphNLZCqw0iHo9DownIfDcb0Wgm4nE9jMYANJoYjMZWSFIfJMmCUKhQHouun0YSU6ND0OkAkykAq7Ud4XAYoVAWDAYLolErolEdJCkOjSYIrTYArbYP0WgaolGO7CmjT2SEabUxaLVRxOPkVNNqg9BqozCbAas1hkjEiEBAj3hci9LSakyf3oKenmJUVgLt7fmIRFxwOvXQauMIhXTo7TUgFpMgSRSZsFq7EI1mQpIMMBjaEYsZEIvpoNVqZN3CDqs1DJejHVn+etjihxC1fIpoQR1MFgsOHrwa7e2zoEUIo7UHYYkG4YmnoVFbCrPVivnzO3DiiRuwa1cXmpu9yM42o6LCgf37A6ipaUYo1AWf71z09c2W10oHjaZL5qEOREJ5KDPuR0n6DrhD2agJlCMczUYsxo485i9xaDQS9Po4olENJEmCVhuR5VUf9Po4zGYgErFDkkLQ66OIxy0wmaLQ6xtgNDZAo9EiFnNBkkyIRPoQj7dDp9PDYBiH9vZyef/qFevDfIx5UbSfl/HeHTXqbVitT6OoyIuysjJs3x7Evn2TEAiUQKu1wWi0IhBwwOezIxrlNG2lYa40oGPQaCIyn9JAq41Drw9CknwwGBoQi+UgHrfIOpgEk6kDWq0JoRA5mHS6IOLxLkQiJmg0nQCyYbX2oKSkEd3ddrjd02C1elBaWgOv1wOTyYR4PA6DwQCnsxiFhcfhzjt1OFblTf+R5zh9WXCsDad//hO46CIgGASi0cGvKyoCCguBtDSgogJwybqlJAEeDxAKAS0twO7dQDgM+HxAPA44HEBeHmCz0f/NzUBmJjBpEhCJAHfcAZSUAL/6FfDpp/SOcBgwmegdPT3Axo1AdjZw4omA2UyfkzAGXn8daGwEli2j6NkLL9A1RiPQ20vj0utpPCQ86dlxOQodjQIZGcCsWcBDDwFaLT3H4aBxaWW9Ih6nMf7730BnJ+D303M0Gvr78GEaj1EOCmm19O5wGNDp6H+rld4XCtHcASA9Hfja1wgvLS2A3Q60tgIWC70zJweorqb7DAYx9o4OMnbJaKDvlHOy2+m5Tie9b+tWmldODs01LY3uBWj9OjuB229HP5OoqwNuuQXIyqJnMOzfD7z0Er0vGKR3BwLivTyGVGA00vj9frpfp6P7eU0KCmjegQAwZQqwZQs9s6eHPuM1t9lofrEYMHky3dfbS/SxejXhevdu4IYbgK4u4NAhmmM0Su+0WGgt+vroWVYrXdfXR9/FYpCNI3qW2Uy/AwF677hxdL3ZDJx0ErBtGz3DZKL7AgHC56xZRLddXYm4raoiOn39dWDHDro+Hqd363Q0l3ic/maalSR6h8FA1xUXA+3twBln0P5S7sPWVuCtt+jv0aNpDfv6iGb8floHnw+YMIHo8MABeg/TVjBIOMjPp7Xv6KA1iMfFGDQaeo7FQrRrtQKXXkr0LEnAwYPA22/TfbxmsRjNjfGr0dCzRkI7StDpaM9KEvGihx6i9yr3K0D4/e53CU/xOL2DDBFBS5JEa5qTA+Tm0nWVlXQ/72eTiXASidCaOxzA7NnARx8BbW2Eo85OeqZOR/PjvanV0n2xGPE9nQ7o7qbPTSaif14PvjcSoe+0WsKXVkt4sloFPxs9Gpg5k96/axfR0NixdK3PBzQ1Ee/p6xN0zLhzOOi7YJDmGI/T90Yj8YXTTqN3ffghsG8ffZ6bSzKgvJxoWpKAd94h2lm8mPDy1lt0n15P/CYYpHGzROdx8Doz7zKZ6P5wmHCZlgaMGQP89KfE6x0OmtOmTcQLg0G6x2ikeWq1tCdjMeJbW7bQewMBonemVb0+cR0rKojPNDbSnG66iT6rr6e95PEAGzbQHMvKaB+3ttJaBwJiT6SnA6efTjhqayO8mEx0n8VCawgIHh2UM85iMRqHxUJzzs2lcYZCNO6uLtp7AI09PZ1+M23yXpo4ka6PRglXkkTv8PnoM7ebeMCoUYn7w+8nXqTTAfPm0dz8floDlp/NzfRem03IU52O1oqzl7KzCccdHXSNw0Hv9Xrpc6eT7i8tJZzU1gp8kMM2UT7YbOI9eXk0Z5bJWi2tsdMJzJhB63XuubSnfvELmoNWS3RjMNAaNjTQ8yQpUV7xXmBa8fnoc5uNZGduLt2blQVcdx2N7etfp+vy84X85PVobqbnL15MPCcYpPcGArTGJhPN9dAhujcUErjjZxw6RPQ1dSqwcKFYg3/9i/ayct+YzcS7PB6aS3Y2fT9hAtGh202fl5bSvp05k+i9o4PouqGBeFJaGj2vsZH+P/98kvGbN9M6s2zTamn/hUKJe0qjobGYTITPefOAH/yA1gYgmq+vF2vo9QLf+Q7Rgs9Hz0sGjYbWgfmnXk/jCAQEjdhswPTp9PmuXURXPh/RlE5Hc8nKon3q95PMmDOH1qKhgX7S0khOJ69lZSUwbZrQJ44F/Nee4/TfAOPHE9ErFdNU0N5OxFdeTpsVoA1YXU0b1OcjRh+JENMqLKQNHwiQcCssJELPyqLPrFZ6JqUKEvOrrCRGXlxMRN7bS//r9aQU5+XReyWJNkNHB73baKQNbjQSE4xEBBMOBBIVUGaqgJjzGWcQQ/P5iLGkAq0WOO882mwAPdvjoXuCQVLaLBYaEwt1o5EMjUhEvN/hEJtUq6WNm54OfPwxzbe+XihaOh0xy6wsei4r5aygMy5YUWMlhP83mYh59PXRuzUaYjTp6WJekkQMc9o0mgNA11ZXkxByuYSiCdBvFnbRKNGOTkdjHk7xDYfpXlauJInuZaWQFdD8fBq3zUY4zckhoxKgsWs0RGs+H1BTQwyTlcH33qP5VlbSmCZNonccPkz0yww+GKTxKhm3Xk/3ML0wLsJhYSxIEtGy30/3sWLKBgAry3Y7CTK7nfaB10vfV1UBa9cS3U6dSvvv3XfJ0GAc6nQ0d0kSChHjT6MRYw4EyEDMzaXP09Lonpoaegbvh8ZGusfnE4a83U7zDYfpnWzMKBXbYJCe6XQKetfpaH3YmGxvp3c4nfQsxgErBrzmvCf1erovHBa0y84Gvp9hMH6k1dJYzGbg5JNTG00AGQBz5wIvvijWlmlZJzvNeV0jEaKxhQuJtwSDwtBpb6expaXRevt8QplTGmQ6nXCSRKOJc2acarXCQAoEBD2azcTfOjtJ6TEYxL7geySJPtPria5zcuje7m7iS4cOEa/o7ia64fnyXPV6em9Pj+A9gOCRGRmCXubNA045RSjxWVmES8axRkM4b2oiXlFURHvV7aa58jrHYvRbya+UYDDQO0IhwlVxsaCdxx+n32YzyZ1zzwUuvpiMOYD2TiAA/P735NDq6qL/vV7Ci05Hc+S9EggIg7W4WBhqs2cTv3jxRVL2AgHg5ZdpXu3t9PvjjwmfzDd0OuKNxx1HivwrrxDOnE76vquL5uZyiT0WDgvezviJRoUcsViIZ5hMwgBpayO8sIGr3BtGI821qIh+RyJEP8ybWV6wAV5dTXuXx+R203c5OWQYjhol5DnL5Zwc4NpryVCPx4lvuFwkjzZtIpx5vcKgczppPEx/Pp9w3u3cSXMfM4Y+6+mhuff10bXRKM3J46H743GiZauV8N3YSH/n5JACbLUC27cT7Z95pnBeKI2R9HRah6oqeofNJvYFr5NWK9aDee727cCCBWSUVlaS8/b44+n6vLxERZvXw2IhWbRlCxl1gQDw/vt0Dyvtfj/J1XBY0LryGbm5hM/aWtpb+flCnvM+NhqFzO3qojk5HDTv+nqSCVotrcOUKYSnhgYaywknkFH0/vt0zeHDNPesLDK4OjrIEbhkCRlOzNuZ1tjBwvRsNtP3WVk0Jr+fxrBxIz1Pq6UfZdSmrk4YxM3NNE+l0z7ZiOE5FxURDeTlEV5OOkms2dixgk8XFhKNtLfTswGiWZuN8JCRQXrQypW0zysr6dk2G9FrYyPNZ9myY2c0HSmohtOXDFotEYnPJzwxqYC9xa2tRIx+PxFcNEobx+OhzRSP0wazytlG7OVrbibi5A3Iio/DQUxt0yb6vKWFPBE2G22GqVNFhABINNb6+mhzWK00/owMYpTsYTAYhHDSaoVSy4qwyUTvyMkhBuRwDI2rigryPL3wAjFHt5uYl9NJTNDtJu+FwUDMg5kyIAwzjnhEozRHvZ6YZEsLCRdmTByR6uykv+12UgKamkiYsBBlhQoQwsDloud2dxOOzGYyDltahGE3GJPgaMjWraSANzRQRIc9zRkZ9C4lDsPhoWlHCZIkIiuRiJgnIARjYSHNISuLxuxw0D0cAWhuJvrR62mdvV76bM8eUnBYYY/HKSpgNtMcvF5hRPb1CQPH4xHKKxtRrKwqFWObjeafk0MKTWOjWEOOEnDkJz2d6L63V3ji4nHCrdtNApm9sQsX0v5ib2lGBq0NK5W8rhxZ5KhcPE6009MjjGGPh55vt9M1DQ3CQxmLCVqUJBGJ4ecrnSfsjGhpIaWqr4/Wprs7UfljZwY7KniPVlaK57KnNxZLNCA4ijZ6NM3d7U5UrPnvVAYUG9+bNpHwr6ggxwZ7OfkdK1cSTbA3mb3l4bBQmAwGMkQ0GmDpUuCTT2isNhutodcrjB2G7m4RgWSDlpUbNsyYtvm+QEA4HRgXkkT3s+c3Hqc1LC6mcfl8dE16unD8eL10f1ub8Kyefz4psVVVxDP0evrR6USUWxnx0WpFxJhxkJVF43S7aQxpabRv2Fvc2yuMLUmiNcvOJvqrrRVRLJNJRNfZGaB0vvC+Ypwpo3JtbfQ5Ox6YT23fTl7lvDziq8Eg7deMDKLR/HxawwMHyFkVDArFLi2NfnOkLyeH5trVJbznRUWEuzffBJ57jnBQXEzvr64WUWCO/gH0jrY2UpJ376bx5eeL/VNWRmvBiq4kCe85IDIWwmHCVV2d4CVWK821tpa+Y8VeuTfYWbhwIRklHR1klPT2isyP3Fyae0MD8craWuHQycujOWRniyyOrCwRuW5rI2P81VdFhKO8nPbZwoUkO5uaBA/kSF8oJAyctjaaTzhMuCgvJ4W3tZXw7/eLvRCNCscf44uNQKb5UaOA+fNFdIUNm82biU6UmRSMJ7db7EGXS+xnpkt+Byv5nEVSXU3GMNNGQQEGBUmi+fAYHQ5ak2iU1qSlhWh44kTi+SzDkser19Pc2CnV2wvs3UufFxQQ3tiQYnqgNEaKLHm9xCumT098NuPp/vvJuShJtNbsaG5pofFMnEhznTqVaNnvp/0diwl6y8sT61pYKJyZrPtlZtIz6uuRMs2NHRsLFtDfH3xAe5ZpgB0nSkeXwUDjYOdCRQXNv6OD3lVZKXS+ri6af0kJ4fzwYZJfHg/xgNxc4vOTJhHOXniB1rqpiXA5bRrpQ0pZ8lUH1XD6ksHno40eDhNTTVZQ2NhgRtPaSkyKhTGnD7BnixXRhgbBlAAi3FCICN9iISY6fz69/ze/IeZWWkoekpYWut9uB771LeAf/6ANkJ1NSpDfTwzQaCTmEo3Sxpkxg5gcKyasHHNUgxWQzEwar8tFm6SpKTHiwpAcYi4poc3E6TyjRpFwzc+n66qrhULGCiIbFxw5YC8oQIIlGqV7+T72LnLUobeX8BmLkQe4tJSEOxsu/A6zWRgSRiMxhSuvJMbGY6+pGZpJKKMho0YRk2lqEkx+5kwhQNnzqowYjARYAc3Lo7m53UJoMQ527qT35uTQe5uaiNmFQvR3b6+IjrD3i6MpbDQ4nYQL9jTH40IQdHfTGJxOQaN2OylhTLecPsieULudaItT2ljx6O2lv7u66Hlut8DNO+/QuMrL6TfTSLKnkaOODQ3CMAqHBc0wPbDHNC9PpG319JDgyc0luo5GaSzZ2TSGlhaheLCCz2mknHqTHA1Qel4tFqK55maRtjN9OtEUG4QvvED3MT6rqwnn+fm0fmz46PX0PlZiOELDETBWdtl45cgVK9gaDa0RG9Kc+mI0Cs/zddclCrzTTqO0m3/+k3DAKbtms0gJ0mjICB49mviSxUK45kgdpxyxQmy3i9RCVmLY0ORUOgZlGorfL97P8+OoDEd2AgEal8EgDBWOgrLh2t1Nac3MZ8xm2utXXQWsW0eRGpuNPMcAzYeVEebHyvVlJZT3sdcr8FJeTu9rbxdR21iMcN3URHtixgz6vrKSPgsExDpzBI6jvMqUTGW6JF/n8Qia4+wUp5PG+Mor9N0ZZ9Aa9PUBr71GYzrrLBr/7t20v5qbadxer0jr5H1NtR8iQg8QvhobSdawYwOgVFq/n97HzgxObfL5aN7t7eTJ//RTYay0tNA+ZIVSyYeS586OtGCQ8Hf88YR3NkbYKclyjXlWPE7K9Jlnkmx44QXi4bz/OJtk927ChdKrP2YMyb9YTGSQ8LjS0mhOO3fS2EaNEvNX7rMJEygT5N//puc3N4sUbI6UTpxIMr2piRR2q5XGyUYGG6PMc/r6xLowv2A5F4nQ3mcDn8dbVESKN6fcKbM6OCuAHU/KaA3zVgZ2/PK97ECw22n87Djs6iK8K3l4MEjXclYNO7CMRqIrr5eUfI9HpMiyM4JT1jnSmJND63PVVTT2Bx6gd/F6saOPI1Asb5qa6H0nnSSyapR4Kiwk3a23l1Jb+X6TiebW0UFrazQC69fT37EY0b8kiVRuduayg4TxEA7TPFwukU2UChwOoku/n+TJjBn0d1OT2KNUnyfog0s/Zs4Ujt+ODnKMsmHHjm+vl9KMNRoRVdXpaF38fkoh9/uBNWtIVkyYQE4LZST7K35s0wBQDacvGRwO2qhGI3kR2DPLCovSQ8keMxa6RiNtbK+XNq/Vmkj0ynxs3gTd3fSOE04gq3/TpkQPPECCr6iIBPFLL1GKRn09KaKhECmOkQi92+mkTchpWxMmiFA/QAyouJiER0eH8IIWFdHnHR2pw7IceeGcemW6yKZNNI7Zs8WYOdrV3EzzdTppfMx0+CcSoevS0ogB6XSpPWCc4sMe8r4+Mpg4EuNyiRRLjq5IksjttttpvEoGwEwi2RjkNU6OhkycSNf5/YTTykqh4LJC0N0taGWk1Yks/M1mUjL6+kgQ8drodMREOzpojIWFdF1NDY2DI4msWCmNAqXRw0oRK6wdHYR3o1HUJ3DkJDub9oHPJ5QMFth6PX3HkYGmJvK2Ll5MXuaPPiKHQkODqAOyWglPLMB+8xtS7oJBYvpK4FTXSETUClqtIpWNI0ZarfDCM/T1EY3u3UvXWa2k7PA48/NFaoiyXo/To3hfKyOXTHdsnNpsREss+DkF1Oejd0+eLPaMw0FCk1P7LBYhJDkVT68XqX2BAF1fUkLPYUWVo4IctdHrCefp6ST4uc6GPfrKlBpOEQHo95VX0jOff57WldNPuQanuJiUm0CA7m9qEiltrFyxka7R0Ptra0XqFiAcArx/ldEpZWqgUlnjukeunzt0iOhMoyFaZyUvFiNcp6fT3uUUMIOB1jYSAZ59lvbJKadQbdfu3fSutDQRWevtFVEvTiMcNYoU9V27BG9XGhTZ2YSflhYyxPh73hOzZhH91tQQPY0dK4y73l5RhxEICDpTRjm1WqKRrCxhgHK6Kadc8f9KBVtJnzodfT9uHM05M1Mo3VyLpMR7ezvRzdixYn9yPRC/1+Oh61tahMHMCi/zfM6m2LePZGdvLzmiRo+mddq2jfBgMBCPYsOF0zAZDyYT8TeOOI8aRWvd10e/WYZ0dSXuUaeTeMro0XTfhAnkRLn7blobv59oIRSiayMRUcvz0UcUpbz55oHpSn19JGsliVJhGUdOJ8mQrVuBxx6jOpbJk8lYZ1nGjqzOTppXXh7NobGRnvvhh4n1f0wHTAv8N+8Pln+xmMCJx0Nj4sgY01RBAb23o0Okl7EzglPpWC5wqnAy8Np2dAgaYkdLeTntrxdfJBpi5204LKL3Y8YIw5PTozmSBojr2QA6dEjIGebvdjs5PufMIb7udIooT1aWiMZwdJvr9jgNkWk9OZ2QHWgcDWaexPN2OkWtNpc01NWJ57BO5/GIqCjPS5KI/vPzhXN1sAyekhLCJUfgsrOBU0+l/XLgAO0Z5jOsK3G5A0daWe75/fQ9Z6Bw3SBHkzlbKRAgPBcU0Nrs2UP6zoQJiQ5lpa6XnMHwVQbVcPqSgYn4vfeIgFkpZmBPDad8sMLMaVZsHCkLo10uER5nrzAbOnY7CTZuBJDKA8/P4xD5JZcAF1xAzD4Wo03BoeupU8nb5PGQ0VJRQUxn2zZiUpmZ9M6VK4n57NpFCi4zjVRhWWXkhdM12Nu2dy/No7Q0ccxcxMyF9YAonGSFit/pdovmCowfDpmzx5oLWxnHGRlkbHIEJi+PxjFmjEj1YIXm8GEad3IEDRiYb8yQKhqSnU0enupqwi0rL6w0xePELINBQR/DASuWkQgxWU79mj+fPuc0TJ2O5pqfD9x3H+F20ybgnntE+iEglCOl8caKq06XmH5nMFCdhsNBnmpJonVVeigLCoRCnJ8vUhADAZELPn26oJmvfY1wt3078OtfE+7NZiHIy8vpnspKyis3mWj8kkTj8vlEmgFHOtggUCoY1L1KeIfdbuGhKywUUa+2NpEKm1yzxHuXn8XRA1aMWInntDyOhHA+fn4+/YTDpCwqI5YACZ8PPyRFNS2NaNRgEA0jWAEMhYiW9+wRRkpzM+F8wQK63u0mIXrgAP0/cybt5bffFmlFvFc45Yn5RXKKSEUFsHw51ZI1NRGeALEfa2tF1DI9nZTY3buF0cZKOkcgQyFhhHd2CvwqDQPlfuOfVM0ReN8ajaLehZUYq5XWtbubPufUvOJiur+zk3Bx0klEd489Bnz/+zT+N98USiynl6WnC2+7JNH6LFhA8+DmNJJEdMs0wUpGRgatgcFA4/noI+FFr6mhtWVDh9ebI0h2O9FoXZ0wVlk2OBwiO4DTcXn9OZWJvfcZGSJzgfc+K9QcJedUN5uN8HToUGKqG/NKNsy5cUJlJc2nqUkYp2az2KuspHJtGhufbPz39oqoL0dQs7MJVxYLjZ3rVJWefqtVGDSZmfT54cM0FouFlOe33hJGk9K5FomQd1zpJHC56L68POCZZwhHnALKe4Vr2vbtI/40ZoxovMH1JgYD8eScHEHLnCbf3ExysL5epMfl5NDeY96cmUnjaWujMW3bJhx8vB94LkrZwDhm3gfQPA0GwiGnNO/eLbIV2FE2bZpIc+/sFDWIVivRJ9cCWa2iYYcSWIYwDbGTjmuAR48mJ0xrK+0Jj0fcazCIFDKuP2PHDzcy4kicyUTPYMcYR1u5HCI9XThyObpeUiIi5mzY9/UJYz49nfDb2CgiYpxez8CNehwOwl1+fqKzWK8nvHEjrtdeE/xPoxH45EghR3S5kYjVSobIYBk8Sp7I9eJssKeni6ZVNTU0F7tdZAtxDRZHsqqriR7ZCcb8lRuLsQHJDgrWWzhy5/GQAyBVam5yZPU/wXhSDacvGZiI6+vJAFHm6LMH0GYTHnq9XqSqJCsDXAjKzIDT49hjZ7ORx6akhDbovn2pPfAMNhttQq+XhFJZGQkE9uCwApWRQRvw0CGaQ24uGVszZ9LfyZGVVBEXhlSRF4DmNXEiKYYtLQObSGg0tPE+/VR4c5KL5TlFivPtuUjx9dfpvQaD8GqzUsspC+npNH9JEg0krFYaJytZ4TAJKosFOOecIyts5Khh8lpkZxPTcrsp150NprY20YksFqN1am0d/j02GzHAWbNoLps2EePkfGxljj171hwOElrnnksRgV27RAoBGwK8dsoUDe5qxN6qtjZav+OOo3s7OkQnMX5nVpZoSsFK3qRJJITmziXvPBep7t4taAigMY4fL+p9mD4BuqelhZ732mti73R10biLixM7qLEgZe8hRza4ZoSVfYeDxh+L0TUWi6AdSUrsIKWkQ6Ww8fmEMwMQHdlY4QyHaX+NHUuF4jZb6v2T7PEuLCRjsaWF6IhxGomQkuXxkANiwgT6nMf/7W/Tdy0ttDc4jZfTcpmvsIeTlXwlv0iG3FwaazRKvIdxwjji2kNlKifzC8YlG/RsGFsswsvJjgMW0vxsTrnl2iSlUaVUGlm5zc+n1JVIhBQIt1vUcEYitE6c8pafT3jet08osw0NIn2G+YjdTs8OBoUzqaVF1HPEYkSfdXXCEGdjhiMPp5wilGiOQkYiFCHs60ukdW5KMm0a8UOuLeK5s3LrdCYqZSaTSCvk1LWeHpobdzFVRsP4f77eaKT9W1cnnDrs7FOmi5pMhLtQiMbPdMMNabh7W0eHWDPmI0rjVtlQgBshhMMihcjvF5Efi0UoZewF57lz5zvuFKbX0/iWLKF6uwMH6Jq+PvFe5vdPPUVRoUmTCCecBrV/P/EWVmqVDRHY0Ni1i/bzyScTb730UmE8PvFEouKrnJPTKda6pobWft484mFsIHF6ZFMT8UpOueP5Mh0pI7OA2E+snHPnu4ICMX+ureZa3tZWuo4dH1xXxE6C994TGQmASCVTvpubFTFf4LXev5/4OhsyFRWU4vX88zRXn4/WbNq0RGdW8h4PhWgfGo20R9nIHT1aGOfsOMjPJ54IJEZnZsxI5AmcccLplMzfktPrs7Npvh9/LPYUR+6UzULYmDv+eKKDQICe29EhUitZ31JGffLyRNnCYBk8yaCsF1eWDhx3nKg958glIGpATSbhDGlvp3cxH/N4iFZcLoFTJe6VUT+ArlGm5ibreqkyGL6qoBpOxwAqKijsbjLRuU6cE2w2i3oWJbNjJZNT0liZUxb16XT0Hafw5eXRxsvNFZ3GAJFml6rbos+XGPK1WITnyeMRKUFZWbTZ09OBVauI0aTqssXjHyp/VRl5AYjJhEJCES4qIkHW0iKuYeDaBBYOXPfFG5WjdMyQsrKAn/+c5vPss4TP3Fy6hutd2OvN9SM8hsZGEnjNzaILEit1CxdSbceRAAtc5VpIklhrt5vGHQwSffT0EFNzOkk5SE8XLdIHA/ZGn3AC4YWLXqdMSaz74BqwaJSUQlaES0rIE3boEOGMjfuGBhqTsvOQMs2LFVmAvLmNjURLbHCmpYkUUlbgJkygcc6bR7TF9FRVRVGv5LD+pEmi/bcyBUJJG1VV9Dcr6pwywS1qMzOFwOK2wj09IlVEo6G5speeW/OzMGZPO9dfWCzCK88piRwR4SghCx1OC+M0MC5CZmPipJOGL5jVaslDPns2CXpuX+3xiLRL7ljX0yOUnV27aGyZmWSM/uQnRFNsPLe00HoWFgoDhAvQy8sF7STzi2T8t7fT31OmiHofjnZxLUR9vcAtp5cBND42jNgQLy4Wyl8gIJTTwkLRPIINJ2URPOOK69liMcKPVkv7lht9sGeZcbB9O0UB2GsdDouaT1ZmbTbRHMLhEEX4ysJ3bsDgcpEBzo1Rli4VKWH79g0eeTCZhHHPDT14zwKi1mH8eMLR22/T9+yI4HFwGhMrZGzAc23Qrl30md9P7+nqonQ8NpQ5bbm+XqSE5uTQfcooM6fqGI2UoaDRiEhhc7NIDZ85M9HQ59Qvvz8xqsPOQO5QyRFCs5n4AiAicGzgA6QMVlYKOtXpRLSKnZSlpaS0NTaSLGbel5ZG41R2Tw0GaX3/9Cc6KkOrFYr2P/4hHHdsMHKUDBAGpV5PdFVfT1kdLJ+VsoDrFnlObASxocdG/rx5RHOcNRCJ0PidTuKnn35Ka8E1W8pSADZsGb/clZabHkyYQDVu3CyF0/U56jpjBu2hoiKiiZoaUsbb2miOFovg8dy5kHHBjmLOnmEjy2gkGr7qqkS+V1EB3HjjQAdsTQ1lqlRWihpKjsxw+rTXS3TMXWRnzBBZEazYd3aKqLkyOtPRQbyLjXe3m65NT6eUycHS64uLia7jcdI7uFtqS4uo4+RjJvLyaH7cnZF1LYtFpBkWFdG42ADkCGNfH73rnHOE4TcUpCod8HiAO++kfbF/v4gqcrbHuHEiBY+vUdJSKJToQOUOzllZibVYvO4NDYTnoTKeBmty8VUC1XA6RlBRQUWIxx1HTLitTaTVKDtjsYDQ6YjhM1OQJGK4nBPMERcWbiwkTCYhOMaPT8x1VRKvJA1sk11eToWoyrarrHAZDJQne+aZn807wJEXv5+KY5M3blmZaGvJzQIYWCmw20kANTUltt7mIvOZM2kj7t9PxgMrDp2donMMp2Jwm14+myc7m97b2kprNGUKXccGVHExcPbZ9NxUEbXBIDnv2O1OFIAdHcQY+UwXrmXhlAGmi/R0wbjZUw8IL+no0eIMpOSOicmQrAhrtZRy+c47osU9K0y9vSIVjX8zsJKalSXqocrK6P379olIAtNncTHNx+VKLBQdLoWTU+9SOQH6+mjN0tLI+GtooOcoz0NxOESdgDKtxWoVhm0oRMphc7MoROZGEpyyxrn9FRXkFeV6P6ZPVvzZw6/X05q2t4tCW44ajB5NisPChfSMurrBo7WM6+Q0jBNPJIOosVEonFxzwC2wIxEyant6aFxnn03v5nb/PT0CL243eZSnThWpKKn4BUM8LmqY2Lgxm+l3d7eod2Alhz3SnPrK0RGAxsFpXBMnEo/IyBBOEa5z4chzPC6cIZzaw/yA6ZJrwCIRwgmD0omg0xGd8fMkiTzpycpsZibhhiMOLpcw4pQ1cEVFhF+zmdIY2TnAa7xvHxlV//jHQAcR8/TmZkGjyq6Svb3ibDG/n/C0dCnwt7/RWPfvF6mQbDxyCllvr4jcSBLh1mgUnfS8Xlp/Tgvkwm6WUa2tIs2Un6HRiE5lWi1Fjnt7aWz794uz2bTagYY+ZwDwejG/Z96m0dB1ubk0Dz42gSPozL8BmuOoUcLRo8xK4AL9iRMJv04nRccPH6b7XS6hWDMYjTSmjz6iSO+cOWL/ffSR2OfcDIPTujhlUaMRKWbvvEP3lJaKdLjDh0mmchMfNlg50stylw1oj0dkKPDZgM3NYm47dwqHEdcmMY44EsbOL5YXmZlEq2636JoIiHT9/HyRkmYy0Tu//31yRP3ud6L2KxikRhu1tYIPcfaM0ShkMPNFTrv7wQ/I4E2GVA5YZRRl61bRVMjhEA20eJ/wERK8DuyYZUeOMmo+YQKdH/WPf9CaMO5KSgj/ZWWDp9cfPEi8zGIhR6tGIyKH3AzHbic8ciqsMrrL/7OMjkToeTwXrrli/DU3A3/+Mzl0RlIjlIzHujp63gcf0PPZec9OjoYGkTrOTifmO7xPlTW9XNfENcfRKPFDs5nw2tIysoynrzqohtMxBK2W0mRmzQKefJIENXcyY6HOnm9uKc2tv00mkXpxwglCcPNBcR0d4kDNqiqRM5ysZA3VS/+446gQ1eMh5dHhoHfv20dMZ9IkcXghK3cu18iNB4Du4Y3L6QAGA23Ew4fFgXjZ2QPH3NBAhgN7AznywLUinBaWnS3Ovfrd70QqTFUVMQ6ubQJElIA7MnGTAy7q3rePhAS3One76Tm5uYSjkRY5KhXeLVuIoTDjUtZw9PWJujFu19zRIVJPTjhBdFhi72BmJgkfm42UcK4HKyqi6M1IDWeA1vjmm4HbbycGycq/05mYg61Ml2Cvv8slOmOxgj5vHgnTbdto7SwWSp80GER3P+4gNVQK5969hIv6etHaWjmXTz8lgcP1MVwHwU1FTCZRxzB6NOGelekTTyR8cvMWbszCB1NyhJjpjb3taWnC2850KEmJLc2tVtGdktMxWLHnDnYvvED0v2vXyApok9MwgkFa/7lziQc89hjRGzdCAEQ9TiQiBB5HrWfNIiWSU0uCQaINjYYcBxbL4GdvcJMXrr0Kh2lM6emEJzb8lZFKZZcxph9uWsDCWRlxiEaF8cvezfx8kQ7ETiamSY7e83zZg+r3i2hHMnCdIzc1YI9zsjLL9aWcpqd0PHBNRUEBKdlc21NZKVJ7lU1xOjvpt8eTaKRqNKJZiM9H69jTI/Ygv5trc7KzyVBi/HEzFq4VYmOR60I4HchiEV3Wxo0TtXs7dpBCz+m2J51Ehlp9PXn9ufGFxSIi8ey8c7tFt0KNRqQxsvKUrHhyeiPXr3JXRK5PcjgI7zx/NoSU6aR5eeJg6sxM+p+P8FCepTRvXiKOuUFSOJx49h5A42htFY6+u++mSC/vx6uvpvoNzhJQ1tlyKppGQ7U6+/eLro5NTcLo4UhnebmoHWFeX15OOGQDmg0gHrvLRc+aPp2ekZ4u6rz4MGxOh+VomNEomvTw+DjSOW0a8dm//CV1uj6QqOh+/DHN96STxHiWLiXZtH07rUNtLc2TU5TZYcpdHGfMOPJIA0dRDh0CfvlLMvQ4w4Lb9X/4Ib3XYCADi3luVhatuXLPKvcjH4HBmTuSRDpJQUGis46N185O4tkGA+lOzCuYvtlh2NtLDucVKyh1fvt2mkdWlkgJ50i/sjQjPZ1oobaW5nHaaSSrP0uNUFER0SyXHHAqO0e2OjuFM1p5QC4fHM1O56wswgtH6traxF4AiIcefzzt25FmPH2VQTWcvgIwaRIptJy29sADomicjST2FnGO+dixwGWXEVPo6iLmsWePKDhmQbN1K20uZc5wqlzX5KYN8TgxgaIiYhqdncQE9XrhUfrtb+k9fII0nwU1c+bIO6Twxu3qonu1WnoWdz7js4BOPJG+6+wUY547lyIUVVUiB9jrFZ2ltFp6fihEHr6uLsJjRgZ9duKJJPz27wfeeIMYJHv1uIaFm2zk5wPf/CZw7730t90u0tq4wUdu7pExsIoKynm//nrRWjUep/Fx6iZ7Y7mOgtsZt7SIkPe8eaLVNRvUVVWiU5BSsVUazoWFIg2hszORThjicUpLuPFGaq3KXm+dju45fJgUCma2JhONn89XAYQQY0HPeenFxbQm3JlLmed80UVDNzLhQnSzeaBBzW2auQU6p4n09IgoRUGBUCxZKeDPx44VaW46HQlAqzXxDBVOGYvHRXSmspJoQVlfwoXl3IWI0wK7u+l7VmLZeCktJa/lHXeIhiNsrL33Himxq1aR0FTWEEajdFgpIBpwlJSQQ+LhhweeX6I8L4lT9BjcblpTj4ciXxUVxAsaG+nzyZPJuBqqyUtOjmj/z950TnliOue6Hi4KB4TCyHxAmW63c6dwZhQWJhovhYWE/1hMnJGibArBtUXcSCMjg4R7e/vAjlhcK3LqqbRenAqUSpkFiE51OuFcOnw4seaUWzonp6MEAokR1ZISGt/hw7Q+s2YJxZ6Nw/x8GsPevYRX5mVcm5CTQ8965RWaHyBaYOfmCkcAG/GFhYSDk08WB6eygux2E721tNDvzEwhJyZMoLqcTz4RxqvBIKKYjY2Cv3CaeWMj0UtLS6LyxIrnwYOiPunMM4k+335bHPrJkbCSElEjAhBdKVu6BwK0v3p6aA4mE+HS7yd5mZWVWEPGkJ8vjqRgg4wzETgDwGYjPOTkJPL6wkJSljk1kLNAmL8wb+TI6qhR5LQDRJOL+np69549Yj8mNx1Qdtvkukil41OpjM+YQTWLPp/ggewQtNmozuqyy+j9bMgqeUd9PRXyc2Q1GVjR5eYBqQ6YzcggOdvZSXh6/HGiS+62x6mKY8ce/QGo3KWtu1scrVJURD/cxp8bQ8XjoqtxSwutwdKlNF8l/+J6Zo6Ye72E395e4qnKs614rpx2HY8nOoOUUUE2lr/xDZLPWi3heetWIVv4mAhld2WzmZ7BDWtMJnGkw2epEWpsFOnrfLgv0wq3QdfpaK8xP3e7ReMhn49kVkEBRdLXryc5xXKQx52WRsY1H0EyUsftVxVUw+krAhxC5RxlZlycz8/FylwY29QEPP20UBY5BMrtYP1+UnYyMsi4UMJQbbIZ2IibNEnkwrJQDYdpc9TWilz/tDR6d20tbZqRGA/xOEVbuKMLd7/jSAEgUqYOHKAxMqNPznPu6CAjgXPDuebAbqcmC+GwEPBarVCuZswgfLFByhuevd7cTtRoJO9bKESM4tVXxXi5dsRoJIFfVTVyBmaz0XyKi0WTg2CQOpKxR7W7m7xNDgf939tL712+nBSoqioSEtyyuqpq8IJRNpwff5zSMLnjWSo6SW4RbzKR4J4zR7Tp3rcPePBBUpg43Y6jpCyk2ePPp9Sz5z75bBelYjmSRiYmE3DhhYQDdgIYjeLMME77YaWFW5xzrYJOR3TrdguPWnk5/f/xxySk+ADItDTy6FdV0VpxC1aml8JCEQ3hujNuP65sh+z303V5ecIbzTjgLlo8vlmzCKf8ORcN799P9RHc1jpVVIo9tw6H4AvczdDnE+kpbATv3Sv2R3W1qN/iVNGFC2mfVFURXf/4x/QMbthRVJQYIQRIKWluFnTMB1fH47SfOarEnlhO++R20rynWOHhlMjeXlJic3NpvG+8QUKXI3tut1Ci2dnAfCsUEqmtfFjrYNH3VatoHpwKxBHJzEzCgfL8GLNZnOfGyienjAUComMde+k9HmpLrcQXH8Tb20u0p4z0NDYmNgvZuZN4xOuvi0M4OXqwfz/hlWu92PkTjdI1HBGaNo3GvXkz0aCybgoQbYt37ACuuEIccskG+7599HdWlogA5uaKxgrcAIKbnSQr9krlSaMRynlmJs0nPZ3m3NIi0mb5EOiMDHKGcS1cVxd97vMR/jIyiPfX1dH8P/qInlVQQAZOstEEEK4mTKB5VVYKY54jddwhLjeX6AVIdPSUltL3W7YkplPxPmcjDKA1Yhwz/8vPFwfLRyKig6OShysN6FTdNlneVlaSHJ06lfYat4vmg4K//33g8suHlk/J6eSDKbocHRwuBWvGDOJpTz1FfISdrRMnEl0MpisoG0wlG3jJ51IWFBBPrK0l3mSzER2w05l5X06OiGTzO5h/VVRQjRKfjQcQ7+3upnfs2ydkLONkKMcAIHQkpnPluU9msziHjA12ph2DQThetFqibadTNJBStoo3m8mRUVeXOt0xFbBjlg0+Pv9MqxVRWjbk+IxMTv0Mh2nM6emkhz3zDL1bedwBO8h6egin06fT/EeS8fRVBtVw+opBcjtMFvDt7SJthesqOM1iyhTaRPG46PjFVr5eT0S5dm2iITNc0wZl1zdl7j/n+rO3i/NfOfe8o0N46IYyHpQpPQcPCo9ue7vYpHxSfSwmOgN+8gmwerV4JhsC3HWHT9XmNAQuZD7xRPLksTebD6DbsUN4JblzF6fzcCcebpNbXU3jee894ZXifG3O3x8/Xij/dXUi5D5YjQp7SpVNDtiQ4zNvuA0tF4FnZND6LlkCnH760Z3Eze2/J09OTSdA6vqiXbtIYcnLE15DTiNkDxMr5x0d4lDZvj4SZOy14lasnE7KTUE4TRIYWSOT44+ntKf6elImX36Z5qHTEW45ksbCMztbFMNbLLS+HImcOJFw8fbbZLjk5pLioddT+ofVSuk47DXm1DF+PitpvI681hwB5vOs2AvNZ/4wOJ0kQLkLV3LHMKZFr5ciCn/9K9HapEmDt3V1uUiI1tbSs9gxwR5DNtra22neEybQdZwyqjRq09PpXTU11GSlrU0YbLm5pFAojw0oL6fncl0m84tQiBQcrttrbxcG5qhR9N7Dh+mesjLCGZBYr8Gef65tKCoiPhgOE264aQyvNdebsKLNRrPNRjSojGQn758JE8hAufVWGldvr0grzc4WDq38/MQaHa4F4D2upFull15Z38jeX52OaDo50sNjGj2a5nvoEP3OzCT6ef99UYfldIoGHewdliTRtZL5I0fYU4HfT/wmN5fGXF8vnHqtrYT37m5R72ezkROBm2Sw0awcPxe6JytPfOwDe+s5RZGVOz4PEKD/580jB4LPR57unTsTzx0MBAhPFRWEh+OPp/2wc2fqKGNjI9Ev15Vw+horseEw0aSyQQrzeqb31lZaU+4+xw1P2EnEtUVc36msDWVHXUkJ0ZjDIZxiSgVzqG6bVVVkmHq9QoHnyOKoUVTLePHFojHDcDBjBtHgJ5+QbOODeZWKLpcUjCQFa/Roqpt96imi+Xicxrlpk8iIUYLSedfeLqKoublEv9x4ZdYs0TTk9NNpvx48SHsyGiW5w3V27EQYO5bw4naTscv7UVljpkwR7+wkGnK76dljxghjN1XEb7ioyt69wF13iQOduW17bS3xGY6sc0Mw5pMcMfN6E1vFc+3tQw+R02ewbqxKYAdVWpoom2D5zN1leR9wvRjvCY58btkiuhRzDTEf5ss6EjsYP/yQ9Le2tiPXWb5KoBpOXzFIboe5bZsohAdEBxxOK+joIOKbM4cYDEAH7Sk3icOReIged48ZClJ1fQMS88TZK6b0GjKDGTNm8A4pySk96enioDdlt5twmJiqXk8MYaiuK+w5stvpmWPHksKzeTMJDO5UpNz8TqdgOFzIzA0omHkYDLTJ2XPCRgx3IWJPlt1OCl5NDXnnq6qIgXV3D12jkgrPXAze0kLf5+WR4cfKN+eyMzMcLnqoBPasdXaKfHQGp5OUmeefp/+T64s4JYg7wJ1wQmKHJq9X1Oa1torIHwvWhgaR+9zYKM7Nee890XyEUxS5cHwoATR1Kl2/dy8991//IuOFz72y2UgAHTxIOOEzKAIBStFctYoEcFsbGSc1NSI1adSoxDoTgPCck0N0NH8+0VYsRmt86JBoWc/dzDhNixUu7jjGxhXTIgM3Y4hGRWrSnj2JZ/bweTjBIO0Xjr4yPSenbJSUUOosew2rqkT6L3dFAkS6LB8K6nDQHlZ6Rvm6PXvo9+TJQqHbuVMoKsoUrEmTROMbrlkpLhbnbXV20hpzDSO3rDeZiC8EAuK0e86r5x+7nXBaUSG8uXyODXfEtFiIBpQHWHJa7Rln0Puys8nQVKYpKfcPpwI5HGSg8LOamuiHlVy/n/h1Tw+NIz1dtHBmIyrZSx8IkFLKhjE37ujups/OPJNSqlLtaZ+Pns1OF05NY55sNIpUGS7QjkZp3fjsp95eqmfyeAY235EkWhOAItR8tIOyq+Vxx9H4lYegco2jRgNccw05Nni8dXVEl6nSxadPJ6X65ZfFQaR6Pc193z5R69TVNVDRstmAG25IPHdQ2ciAlexFi+h9qTzemZmi6dJFFwnjmOs8APouK0t0QPX7xRlG551H466pIWOzuJj+5rotqzUxymswCAccG2Z6Pa1fKDQwmj6cgqmUq6WlZFBz6lp2NnDTTaKN+nCgNFg4AtrSImp5leOIx0fedKqqSkSIyspEGvN775EM+8EPxNySU+e4xo+NXptNtGfnBiaAaHjCLdXZqZGWJvYm8ztu5LNtm3AUswODI46AMFRsNtK13n9fnHGUvC6DOQaUxmZVFaXo19TQ93zYbnk5pWG/9RZFCjmqww2FANE9MRJJbBXPsmT7doomulyi2dHYsZSOq+xYCwzMSODIMSDoVNntLztbpBt6veI5fHB0R4dwDvB3bDjpdPR71y7g/vsJHyPRWb6KoBpOXzFQNg04eFCcYcFFtdzNqblZeCP4PAP2GnIxLjDwEL2GBlLCh6tBGixMz+3P+WTo5MJuZjDs8U/ukJJ8bhNAwpTTMbiwNiND1HnF48QgTjxR1GYwsIFSX0+Cr7CQmEx9Pc2Vz1XhlBIuBmfFgov3uZCXFUqGQIDmzN7zujrR9ICLTHlefIZHdTUp0qz8J9eofOc7JDQ4VWHCBPqc8az0tDY1kRKfny9SdrKzE0Paw0UPlZDq4F0GTpXbto3+V7YNZcOTa3OYNrkTEzP1vj5SVMNhoXyPGUO46+gQXlhmyO+9R39zl6HWVqKv55+niNpgAkirpTH84AeiM1I0SsZOWxt9lpYm6i7a2oRhxmlY3LluyhQSVvX1NPc//pGMwlReWW6AUlEhcrmVh1MbDKJrFa+NyUQ0y52x+FgBFkSMY460BoPC+Ej2fHLnMq+X7u3sFGlgyjVUOhiYn9TViYNC9Xp6NhtUfPo8/88F0clK0O7dhMeKCjFGp5P+r6mh75X35eWJoxEiEXH+DH/PdV233kp75h//IMOspkYYwMkHS0ajxJd6e8kIUDp2lK27AwG6l9OUlNfMmUPPNpnEQdPJZ8UBifxq1izRDIabufBBzdxCOy2NeGdDA723u1u0CuZ0NfbS89k+SsOYx+dy0bObmgZXKpKdLuwtVtaLGQyEA6694dRvrVbwEjZWUtUKNjbSZ5yam9zV0mJJLH73ekXn1QkTSFF/5pnU6aQ/+9ngDp+1a6nBC3cCLCsjY2bGjNSK1lDnDgIiXSw3d/Aa35NOog5l3OXzjDNoHd97T6QGczp6U5M4MiIapTTuK64g44QVYqZDrl1h5x6nlHLqKTsLuWaP96Iymj6cgjnYeYjc+r2yEnjxRVqT4RTW5G6mo0YRX6+pIf5x2WWixhJI3dkzlbEAiDFmZxPts5HCEVaLhZRq5bWDpc41Nor27NXVInWO90F6usjK4XOiOjrE+/gIiq4uMlQ4Kssd7pSOLeaR7HSdOpUO5+XGS0o8DlZHPnUq0di771LHy337RF1kJCLKB2bOJHpkPa68nOiE9Rk+koDLEbKzhUONjUhOW3c46PO33qKo7LRpiTXoyRkJ7PzgkgA+6JdlQ1ubaPDD11mtoikZH+jMzgBOTWWnAa8z12f9p4JqOH0FIblpAOe6MiPmDl2traIuqq+P7lV220l1iJ7NNrIGBoMxQ24BzYyfmRADMxg+myW5Q0oqxb28nDxBdXUiesG1ImYzGUM+HyllXLgPELN4/HESbFqtKEDnU6/r6mijM0NRpn04ncKjwofhMdNh5sEdDgsLyXP6wAOkqBYXEyNQdgdjD7gkkafSbBYNLZQ1Km1txDjz8oT3js9IUuKZD87keoT9+z+fkPZgB+8ysMDjGgkGj4eYHZ/fxDnQ3LqdPd/XXUedC10uwhNHAsaPJ+a8ZQs9Lz1ddJUD6Bl9fSK60NFB633ttZT6oBRAVivRsMcjBBmf1VFVRWvJRonNRoZbby8Jru5uaiqSfO6W0vjctEnsmWTgtJNAgBR99lDzWDjNkKNDXJdhMpEi1NhIP9zUhGnRYCDaMhqJ3vioAaUizBEC7qZms9F8lEYBr6GyrSsL8kcfJRrjSCkg9rDSGcHRiNZWikbwtVz0zwcoKyEtTZx31tMjupJx9HTfPnHOUHJdAHf8HDuW1kV5qG9yFITXgOtskulY2bqbo8Fc7xKPizqiMWNS4yoZhnI0AERrvb2iRlKSxDl6LS2i5rSzc6CXPi+P0l5Z6WBgw5gPcR7sXJNk55ZS4WMnTn4+zdXhENFUrj1Sjqe0NFHRY4WxqEikQQGDd7WcN09kDHBUvLiYzstj45/TNT/9dHD5U1VFRpzTCSxYIM6O83goa2LOnNS44BT34RoZcLpYqig9z4lpipvQFBcT3rgOd9s24fwIh4nP1NaKNOcHHqAUrIMHRVdFrs1LTxeRJq7D4++sVhpXU5OI0IzUKTYSh9iWLdTkR5lim5wFMZgBxvVWlZUUYUzmnyNpOlVXR99ZreI8NGWU1e2mtV+8mIzg4VLn+LgBs1nU+/Chrcqz9ex2kfnBxhDzVa+XaF+nE+fKTZyY6GQFxF5SdimeNUsYoZzCqjSelDTW1kZOoZ/+lPDAqcOAqK/l8oHqano2d4OsrRWOGu5Oyj9cPwmI7ont7SKjQSmveZwGg9h/EyaIjARuCMHOj7w8upbx8fHHNDY+e5Gj7Kyz8BEMXDvGjS2UHWi5UdR/QsvxoUA1nL6iwE0D0tLEaessQLjFo7JolSM/ytx6bpTgcIhc64wMURA9XAODVMyQGwSwcdPaKrylynawHo9IJ1NCsuLOEZ+CAtGNKhJJPJOK62caG0np5We++Sbwz3/SM7irk9Jzw4WaNTXE9JWtb7nQPjubOiy1thJz7eujObCXurBQeGSbmgifHF7n+QCiTXdvL/09fbowmth4NRgIZxy25pShpib6v7Awsdbi1FOp489IcpVHCoOlYDJwFIz/5muCQcIP172w4OEzONraiMHrdMLbnHwwLc8zK4uE1SefiLSLYJBoeMYMEQmoqgIuuSTRM93SAvziF7Q+RUWi4QTXR/X00Hg4tYONEs7JLi0dmMqqhJEURE+dSnPllt58zhpHhjlqmp0toi2xGP3d00O/AwHRzp/bHFutlJp1+ulEb4cPC+NLo0k0rA4cEKlayefNpGrrWlEB/PCHwkvr81FzEG7TzmvMkUKNZmAef1XVwAOUGbjWsrVVdHTk93C3KYOB9sdQBcFabeKhvskwkiJsZecxQPCUUEhEC3n8w7XAVfIr5V7maGZvL40jJ4fGEQgIxYNT4sJh8k4rO1xqtWRsPP+8GKfS02u1Ej67ugZXMpKdW4WFxN8bGxObnWg0oqnA3LnU0Sv52IhkRc/jIaeUMhKmxHGqrpZcR8jrKkm0TyIRcvwozwH0egfKH6XSnnzEAEdNBpNZI21kwLIjlUGSijcqo/8sV1wumgPT8tSpotX8xo1Uw/Hzn1Pk+rnniG4sFnofd2Lctk2k8fn9wkDr6Di6IvnhHGKDpdgmO1FHYoANli4/XNMpPmycu7IlR1nz8sio+Mc/KHo3XOoc11XzmYLsQOL1YWdNeTk5K7u6xCHl3DrfaqVxG42CnrmujI/5YNooLBRNIaZMSX0wu9IIZRqrqgL+8AeSd/G4iBRx2UMkQlE9m01kEHEXu29+E7jvPtF112AgnDY2ioOcOboWi4lzyJxOGj+ncDP+W1oozbylRdCq8sDfMWNo3B4P3Z+VRRkyTBtbt1I35bIyet/WraIJDre45wwMphluisQ10ZwK/p8MquH0FQUuKB4/nhSmujpR0A0II4EjUpxbDghvTGOjyMHm9rO7d9MmGOkpzamYIXeyOXSImEB7u/DC80GeyelkDErhFAqJFI++PtHJKxSiObACHQwKr+7cucLb/OKLNL8xY8TcWZHnUH5ODgl0Fu7p6cT09u0jRsetnbmOoaqKGCwXqs6bR7UzbjcJnN5eUlo5N9poFIcH+v3EAFlgKI1Xq1W02+U6m8bGxE58w9VafB4wEgVj+nT6X5k+GA6Lc6a4C6KycQBHebh16mC1cdx0wOVKjASwccFMWBkJYAEUj1MqTVubOH8DoHdbLCI1ye8nZSY5nWbqVMr9HipaN5K0k5kzaRzTp5OXurdXdPPjVDH2srEnMzOTxjN2LHlUuRteVhbRZHGx8LT6fHQA44cfipa5jGe7neivr4+umzQpsQ5pqLauo0dTFPTTT+maSEQ0qGAPITsg7HbCozKPf7gDlC0W2iPjx9MaKR0AU6aIOQ9XrzGSNRiqCFvZeYzpqauLjOaKipEf4ltfL1JPuJlDssLHRdHMA2bOFG3L+YBNPvcueS8ffzzhi/kfG1xcm8M1SkMpGcnOLY7AcrOT9HQhC7Kzif6Znycf2q00JnbvFk04UsFgXS2VaW8PP0z0qzyfLxIREeotWxLlz2dR2keaLnY0XeT4jKHXXxeNIri5jjKNVDm+igqKOgGUgaCswwOI53O0NzdXpAWn2hPKrnKDyYWhHGJDpdgm10SOJCNhqAjtUBEyh4Pwx3WIyWsciRDfaWgQPGeo1DmDQaS5J7dnVzpr2Nnb3Cy6mnJ6Ga8fp3cq6Zk77wGJ51tNmSI6YiYfzJ4cSY3HyXjmxlScqcJNqLhZhbLZVG8vPWfBApIHyq67RiOtJbe8B2jsGo0oK+CUdK6V5ev4nDZ2OipplXnIRx+JcyptNhpTcuOO4mJaSz56gSNznKrOmVG8RpyVwcZiRcV/RsvxoUA1nL6iwIyQW6RWVwti5g2iPC/g3XdJCGs0tCFCIdFxhlP0cnJIaPX2pq4XGgxSMcPkjcZn2WRmkkIwWHtRFk7//jcZdByuN5lENI1TXQIBcSAjd5I7/nh6DufJOp0D0wXZy9LaSmNYtUqEmVm4z5+fKKBSGYh8YKwybeHEE0V7zUiEmG9FheiwtWIF5bv7fLQ+bjfhpb5eMG1OvQyHEzvxcS1ackH+5wlaLRWbV1aSYl5cTLjm+qmsLIrIAPQ/KyFsBDANJrdiZWFbVjZ0bZwkUXSRz8tiIRaPi0MgASEwPR7R8joeF8ohK8SAEEhMP4EAjWXePNFCe+pU4P/+b2TdpIZLO+Go4YQJtP+qqwlXPh/Rq8NB9JeeLpTErCwytJjmvva1gc6ITZuIdtiDOX48rcVTT9H33BDG7xfHE4RCw0dxlGvPBexNTSKlVlk7yQeXejy0jj/+8ZEdoDxrFhXpp6qjSJ7zUI6BkaT+DFWEndy6e8MGUaSffPbNYIf4VleLmpY9e2i+GRmJdM/nDjkcRL8cRWYYKqJVUkIK+XvviXrCzEyRBllZObJzTVKlBXGzk337aH+MGUNOp6YminJxtONImtYoIVVXS+W67t5NcoE7mikjC9nZZJAfOiSibcBnV9pHekbhYDCU8dXRQXNtbydcW60Dlf/k8en1FDlZu5b4Otc7Me1NmjR4dzyGZFqMxWhcS5cm1hkN5RAbKsU22SAd6bofTcSgpITe9fHHA9MpldkqXH/E8+HDYVOlzo0eTfSe3J492VnDekZGhqiFU67fYPSc3P48lU4ADH6WUn09OarYWcgOKk4z5ugMZ0pEIvSunByiWXYwKxvAdHaKdt+cgsjOEo7wcFbSYJAqnZtryfPzSS/gumqlQThhQiKdKcsfuJacHZoGQyKeuexhxYr/rEYQqUA1nL5iwN4l7phVWytqHtiK12hEbQgflBaPkxdrzhzycPz1r6Jwj88BsNlo43L9iLJe6EhBKay3b6eozMGDwhh44QXhpUj2mJ1zDimJra00N/bQc0tiuz3Re5vcSQ4QhYbcFS05pcRgICW2uJgEDBf/D6W0KSMb9fXkYdy6laJISu+j8qT7lhYyBBYsEAdDbt0qTktnrxJ34uKuiMr21DU1pNjv2EG1HdyFLpVCMxLv41CQ3K72wAFxcHHyoaZKJaSzk8bEnnU+d0d5rtSYMWQsDFUbx7VmaWmJwlBZfDtYN6+0NHqX2ZzofeRUJHYE2O3ihPbmZoo0XH75yFvwAkOnndTVCeVCebhhaytdzx3RxoyhPTl37sBuRkpnhLLTlNKDuWMH4ZFbvysPoZ48WaQ3cjHwSJTEigo6/2vnTuIhygOjmUfwwbQlJUMfoDyYV5/r3ZLhSJqYDLUGAK1BNEqROTYSBsPB6NFkhKZSqJcupf3GxrnyXBheC25D39UlIupM95xOzJF/Zb3ZcIc61tQQLz54kAwzu53WuriYlOQjSdlS4lbZ7GTnTjLMWlqoxu3QIZrD9OmE28E85UeS+pZqXZVtjlNFj/hwb6UR9Hko7SM5o3AoGMr44uYRdvvIx/dZjLlUB7K2tZHhsXkzOSKuvFI4EQbbm0Ol2AKJSjS3qh9pyuORgFZL+23zZuKVmZkD01OLi+lal0vMZ7jUOaWThGmKm/icdRaN2eMhp1RtLdHHkdIzA9dpjTQqyvsAEEckcNSJje+eHuJl3Fm2vJxq0bguLFUDGHZSd3SIGiY+aBpIrGtSnidnsYjzHpW0Go+TXhAM0nvDYVEbrjQIlal9TGcnnkj8s7GRnskphXY7jZWPGiksBG6+eeSdHb/KoBpOXyFIPnA0FCKB19xMG6KkhIR3U5M4m8FiEZGNV14hRefSSymH2mgUnmoWHJwXnFwvlAricdq4+/bR/+PHJ7Yy585Vf/87CX7uJNbZScbTnj2Uo5t8SCefVM7dejiqNHYs/c3znjiRrk/VSS75vKvkjjCdnfT9OeeIe0aitCnXgM8a6O6msbC3i5VlnueqVaQg83uYsRw+THPh+XF7bk514jbozc3E2AMB8jQVFKRWaJLpYzDjaqi5Ddau1m4noaZ8jlIJYcGzc6dg8srUIr2eDC8WPIPVxgEiWvX/7L15eFtnmTd8S7ItS5b3PV7iJI0dJ02TkjRNk7SkpQWGbuxLGVoYhpZ530Kh70BbmMIAA4XhpVPK1nZmmE4/dmg/oJSvMN1J2ixNml2x0ySOvFve5E2WZUnfH7+5+zw6OufoHOlIsjO+r8uXbS3nPOdZ7v3+3Vo9WvbsUUfzYmOVwRhkY5mLazkldXAQ658OmIaWAFVTKsvK8NPaCsN59Wpj0P9axdglJTjf+/djHnbsgHLBCFylpaJoWgvdSYs2bABq4MwM5nRuDp5YTotVOztM6Xr1zZJyDdTOQGtrYmNstXGrpRwz8Ag3eFb2hSGCAfzmNxP94Q8Cij8/X6T6ECHir0wX0ov+yWfxssswrsFB8JPeXqRt3npr6vPJvPnpp3EPBu4gwviPHxcIi7JitHq1iBZu2YJxpZL6ptZ4mYlTlYuK4o0Ms3VKes+eDmKXnsG+b5/58aVizMl8obo6HkyhshL78JlnsG8ZwlsPzU0vxVZWoq1IedSjq66CwffMM3geWYZwDznZgJGfRyt1jvkOA4uoyccNG6AbPPig9nPdcEPyNTIbFeVzQCScfVVV4OOcgcJzv2wZ7inDxusBwHCKNYNccKN5Jo6Ic/puLIZzUVoqAC54r/p8cEAND8fXIzJEulZqH8/1ypXQJ7dvx/Pu3Yv35LKHW245P4wmoiXDacGQEgKUDzVDQAaD8fUG3HSTCJ+pqICC/tvfgjHl50Mg798fjxTH3h25XkhrPA8/jJS60VG8VlEBBYK9XIxqt3+/UObk2qvdu8GkWlsxXrnny7lzgIPmYk3+/vAwvnPmDA5wba26Uqbsd9XRIeBwmRldfXUi+o+ZNSgthQBhReLSS4XxxOl2tbUYhzyPzFgef1zkC3MUjYkNJbtdAC+sWydQxJSh/2hUPSphBCGRyDhcLXsvmWQlhAWP34/XuJnoxESiYatXGyfXm61bByPfZhNRFSJ1NK/Nm3G9qSn8LxvLoRD2aWsr0T/9EwRxpurEkikXy5cTffKTxrq369V1cK3MxER8iwEmFtKlpepQ2lrU3Iz1ee01nGc+O4wSmezspOvVT5W0eCT32/n0p+MVZrXorF6Ur69PvS8MERwNa9eCb118Md7jFBTuE6NMF9IyJtXOYkuLSDX1+QQyaKqkvEcggPPBXn5G72InhhbqWkVFImiNESM5GcxxQUFin7BMK+1mSMv4SnV8atfTyx5gvtDYqA5ZX1mJ13y++NQwvZRzowZfJp0jdjv0B97nlZWi11lvb+IcKp9HmTonN/6V25Jw3ZhSPmo91/r18U6UdFNY2SHQ3Izrd3bGI71y411Zt/qrv0q8nxYAzJkzOEdyBpLdHp9GLLenKSkBf2I0V+U8Hz4MR3dBgQC+kYG2lKUdyWSAkQyfxUxLhtMCID2v89at2NCMjDc/LzreE4ncYPlQXH45Dq9Wjw1lvZCSvF6ir3wFBpHDgfQRIhzy3/8e3q4vfxlKHSNzKRFyqqoggCcnoZxxVIwjDx0deK6rr45n5NXVGFd5OTyua9Zop9XJaDDr14tmeiMj+M6ttxo/rGprwAoRo3fJikYyD2h7OzxHLS1AWGIAhYEBjI/7MBQUQGHNz8c9ZIWNFZoTJzD3RvOq1Sidwmv5mWTBMzUlGldqFflr1cbJgutDH8I+ra2FAfkv/yKiSnIeut2Oe73yCvZ/YWEiAuK99yL6l2mySrnQ82AyChFDzyop1XoDK85Oul59s6THI9XOgF50tq1N/VoFBep9YYjw+6KLwNP8fni8IxH1mipZYe3pEWmAzMfUziJHLYnAJzo6xFlMJT1XeQ8Z2p4dMzKEsxbqWnc3FNFkET0lyY2XtWCOOUItk9lzlW7qslmy6twnyx5gvhAOw6Dn/nAcPeB+iZWViXxb7WyaNfgy6Rxpb0eUjJ+fwaWUc6hc23Xr1O+frC2J3y94g55Dz4hTMhX0xve8B2dr/36sJddJOhw4h+vWEf3938fXrCnnS95z7JhmQAuuZ2JAIq4HZpRUux1nl8ekNs+7duF61dUiBV4G2lIr7dCTAdmWD9mmJcNpAVAypdbjQWrF+DgUy/FxKJSlpThEbjcOs8eDg8mFlQcPQplcvRpecK6tUdYLyaREgZENomXLwOSOHcMh3rgRSmtdnbq33GbD+P7yFwFfznC0DJsr93whwud6e5FPLqe/qZFayFgGfuBeTkYYv5Yyw+lkgQDmdmQEQsyIB9RuR9rkn/+MNMqxMWH8cud4Zn6rV2P8SoWtqAjeqvHx+J46TEaMHgZVGBgQBarK6yQrvJbnPN0aAq3ve73o+3LwoMiT5lQBNiabmqC8trVh7+UyFcAK5ULPg1laitcYfVGmdOsNkp0dq1Pu0iUzhn8wqB6ZYkXoPe9Rv5bcPFc2Kpi0UAPVlGavVxuumMFFZOcXQ+o7nfFnMdX0XKVBrkQmY8Wb6yKSoa7t34/6BqN7W2mcM/Ipt9TQQl0lMn6u0k1dTpXSPfdakVNZUS8uxtrs2iUMJwbSYQCXvDzwCM5ESTZmswZfJpXfZHOolZK7dWuiAZ+sLcnatfHyUX6uaJTom9807pBJJSra3g5HM2fwDAzg9YoKpEMbScnl+erqQmSNx3LoEM4U6xP8TCUleL+0FO997nOIGqntVW6JwXVkbJwTmSvtSEbZdnJkkpYMpwVA3N+AoVoZEIGhn/fvBxNwubCRp6cRph8fh6DYtAmCKBDAd0pL4R397W9FGp3TicPk8cBzoCW0lCgwsmJhs+E1bkbY0KD9TPPzAjFmdFSAQPAzEuEeyp4vegaJ2sGTGYpcixUMgiEaFapann8GgzhxAkXcR46IXjBayIHyOAcHBawve3s5xS0axfVlsAilwqbX7JNJz+hhAfTqq/Bid3fDAJaNEb6PEsVOi7GlK1DVvs/KxLlzovbBbo/vpl5dLRCH7rhDFL9azYTNMPh050LPg0kkAAN6e3EvK1OXcpVylwoZrS0IBARUsJYi9OSTosZGJmXzXDWgBz3UQKZkivF73qPekoEdSwzUMTgo+gCVluI7kYh+A1kmpUHOz6YGxmIGdc0suIesrHOauVaEWqZk58qI8ZFJ4ynVc280cnrddZCTAwMCUttmQ5SfkWc5Xdpo1HmhnXetOVRbW5+P6Be/ADjHypWQAWvWACE2WVuS7m7sfTX5mEomRipGaHs70f3369eMG5kvux3GDRtBDgeaaNvtwnBiYIfCQtFvrL5eO52bHSgXXQR9US21NllpRzLKlZMjU7RkOC0AGhwEutmBA6KfUX6+SKEYH8eGravDhhsfx/e4UJKbDbIHenoaygN3KWelo6cH17j2Wu3NqkSBURJ7vhlRrKIChtGyZfGMh2t3bDaBCkYkGBoX+V90ETxmyZiP3sEjUgfV4BoaI0JVz/NfXQ3GOjQkEAD7+xP7GyjHGQxiXcNhKP5nzuB1nodYDAKgqEigDDET4/XVa/bJpJWyJQug5cvBcHt7Mffj4xDUHg/W1OvFWskodskYm1UeJFmZ2LxZrF91dXw39cpKscfNCBwzZBWDNzo3yTyYyr5PVoMxLJaUimR9avr6cN46O7GGeorQuXP4P1nzXC2gBy3UQCJjivHevVBgX3opviUDo/WdOwee8eST4BlGG8jKpGaQa4GxmEFdM0uZUNbNpm0uJDKiqHNqdlkZ1ry7W/B4bglht0Px7u01F3XOxXk3IyfU1tbvR8ZNNIqzHgrhDLz2Gl4fGkrelqS8XN24TBUCP5V9bbeDnxupfdUiebw2GyLghw+L9idc2+RyQScbH4eO1tqqfU3mramWdiSjXDs5MkFLhlOOidOTpqYgRNmDJOeFs4ItC1eO5pw9KxrGMTLM736HTbp1K+7BaSAM6330KAAk1A65GgqMTHNz+M1d6d/8Znh8hoYwPhnVLhaDZ8zlEt/nnj/cN+bWW0XaiF5KhtbBO3IEn4lE8J7bTfT881CkIhHRTDeZUNXz/A8NAejC5YKh5/Foo97J4wyHwdhZ8VmzRkB3M1BEMIixTEyInj/cR8dIs082rjZuBOPkaFFjY6IAWrsW8zw2JoxVzvGen0cqoFFD00oPkqxM2O3xCh5HSfv6BDR8pgrEU2XwSsVAidaWbG6MeDDN9EBaSGSVca11Pv1+rNvp09gr//mfMDy2b1d3MhQV4XwtW4Y11WueawToQUlGFOOODqCNcr0oR+PZu1tbi3v++c8inddIA1mZ1AxyNTCW0VFzqGupkNXKuhX1mlaT0X1uRFHn1Ox16+BAGhoCP2TwFqdT1PJkkh9aQWblhHJt5Sby1dUCBCgWw9nlJuE1NeCbJSXxETq5LYmacZkOBH4ujFDleMvK0JeqowPvc8uTmho8dySCnlZ641Ty1h07EnVGrdKOZLSYnRx6tGQ45ZDkTVVRgd92u8hXHRuLL+glikdkmZzEDyPUvfOdEIBKoSKnX3AtiZZQUUOBkZFaAgGMb/NmfP+22yDIjx2Lb2Zos+GZKiqEAjw/j+jaxATen58n+sEPgECmFUbWO3jt7WjmSIRGoXY7BM7UFITnxER8zZCeUNXy/E9NwTMcixFdcYVAgVIe/NWrE8fJ0MZ1dTAk+/qgkA0MiIgTIwVt2YL75OerQ2nb7dpRCYcDc/yP/xgP+d7ZCTQwuQfV6tW4TzgMRY0VskhEGLTJDE2rPUhKZULulTU8LHphrV6NvZIJ71SqDF6rhYBetFPNU5nMg7lQIkNmDCErjWu18xkMEr38MhSpigq0YgiH4Tx4+WXUa8npqERYC4Za/81vkJqiRPZSA3owavAZ9WBHo+ALeXnxLRkY4jwQgFOoqgr8Xgbeqa4Gj1A2kFWSlkEug7GkgrqWa0q3US6RtfUWZva5EUWdU7ODQTjcPB4BlsOAEePj2LcL2VufipxQrm0ggO9z2YBcmyfL82gUfGBwEJ/xePAdblCt1lqByDoI/GyR2ng3bQL/kNEwZ2Yw/tZWZCzokRpv9XhwbbVWMMlIPluBQPIMgGw7OaygJcMph8TeldJSMMjly7HRZmaw+Xijud1glpxCxU0Zx8fhjbr5ZqKPfAQb++jR9ISKFgpMLAZDLhKBkv+ud4k0tS9/GQbMgQNgjG43DtupU/gup4hxszb2lMzP4zMPPqgtAPS8ixMT8ZCbZWXxDeKUyFHJnl9N0WDD9fLLRe8IJvng79mTOE4uyJ6fx1hGRhDunpjAXLDnkD3NO3YQvfe96uhVWkpQYyMU9d7eRIjm06ehiLGAjkZhZDudogleezsUsPp6Y4ZmJjxIasqE3Fh2ZATv3XFHemkOeuTz4TlLSnCmGB5fz+BWKgZGop2PPILn6uhQV7KyITxSVRrNKIiZSM+Qz4DXi8LoiQkoB+3t2DOxGJwFnLInA63IilB9Pcbf34+oMBF41c6d6fVPMurBJsIeu/JK0btO3nOTkyJlW03hcLkSG8hqzZmRlKJcwoCb3Y9G57ioSB0cyEqD3uw+N6Kot7eDhzAMeU0NPldTI/oC+v0i+sTN4xcSpSonlGsro0ESxdfmEQk02rk57NtAAP+PjoI3VFbCcNBqrZAK2EMuSSuSvGmT0L/KykSdPBHqwvbt09/fmUKLDAZxBvUyAFJNA84lLRlOOST2rrhcYA7s+WRmwaki0Sh+lA3OZmbgtbz8cnGwreq+roUCo6ZYtLej/4eyU/3wMJTyFSugVJaVQbkpLATjX7YMkSuvV1vZ1vMuci2QXMgto0fJ3imjz69UNHp7if7937U9Tnzw2dsjj1MuyOa6hKIieHvl9KK5OWMMSq2nxc9/DgavFo3r6IAhXVuLtXjtNVHLxAWfkQh+jBqamUiT0VImGIyEURYzaVQcPgxFnAhnTW7+x7035HlQUwySRTvdbtQeXnABrpuLXG8jSqOaItvRYVxBTEVpMqo88xl4+WWib30LyiT3PiPC7/Z23Pv0afCe+vp4RWj9eqLvfQ/3W7cOCmg0CgM9GNSfv2Tj5L3MiKbci4sj1Wy4tbYKz7ASkIHvw6l5ShRMTndWNpDVIiPRykz27tGjVIwYI8ZHYyN4o9JBcdFFAjwkXYM+lX1uRFG/5RaiRx8FGm19ffzejkTg2InF4KSZmIAMXWhF9qnKCb2GrwUFAuiA0WGPHRMgCOEwZB03WPb7YVT87d+aQ+fN1t5PlfQiyXV1QPCdmsK+0yor0Lqu1WiR7ADQywBIJw04V7RkOOWQ2Mjhol+uKWKPZDCIjc99XCIRMOu5OSi3avmrVoWezaLA2O3xneqbmhBB2b0bhzsUEkzU7xcQ6na7vrKtNARl6F4ZRIE9ULKxUlwc750y+vyyolFcDMM2mSHKdQny5+SCbLl+KT8fRmh9PdH7348olFEGJY+tqwuKgZpwKisTfWTOnMH8MnS32y2KSc+eFQAkXFunZ2hakSaj9ky59Pp5vUS/+pXwUHo88XC2l14qUC55HtQUA71oZywmoLKbm9UhnzOd623EO06kDgM8PGxcQTSrNCVTntWMldJSUQCtvEd1NdL2du+Obxr+pjehBvRf/9VYz5dU4K/tdmOIpi0t+nx6dBT8IT9fHeUqPz+xgWy6lG3UtVSjksn4hcOBs9vTE3/dgwdRe1hWlthcO5UzmKpxYERR37EDWRycillQgL99Pvzf1AReU1RkTCnONhR0qnJCubbc8LWnB+eUa6ttNmSy9PTgHGzYIBp5sz61ahXOtdYYZMrk3s/E3KuNl1NuYzE4GlPZ31ajRTY2GssAWCipkEZpyXDKIcneycpKKNdcU8T1L6tWwWqfm4PSxc3OHA6kzCkbVVqphJpBgVE7OCUl8DLs3w/PUH+/KLyW4bD1lG3ZEKyujmeOdjsUDE6HIoo3Vnp7kf5YVBQPtmD0+TnSV14OAbd5c/z35IO/dStqh2RFKBaDgrNqFaIZhYWifskIJG8y0hNONhs86/39KKC12QQ0KRtDFRUYTzCIOc3PF6kPyudjxmZFRFONcuX1430bCmGdBgYwdhnO1uvFXMkFsmpzrxftZOPZ4xGOEaZs5Hob8Y4/8gj2wshIvML58stAh7zsMmMKohmlKZnyLCMKysbKli36+9DlAujBbbfBuGCFxWzPFyajSr7XaxzRVI9PNzeLhubKBrL19fit1kA2XbKils6Iophuyq8Wv9i4EWe2tzfxug0NkEV5KlpPKmcwHSdSMkV9wwYgpg0PI3rAjb7z8/G5vDycVc5S0ZuvXEBBpyMnlGvLQA8OB9a0vFzUzjAaZE2NaMki90M7dcq4Ey8TdaRm5t6sgaUcb1dXZkBTjIxLy4lgJANgIaVCGqUlwymHJBs5LBS5i3YwCOWrrAybrbZWwJIWFemH53OhhGodnOpqKFyjo4iQbd6c+JlkyDXveheUpz/9Cf/LNVf5+fh7714w1aIizFtlJRhtVRWYp9nnlxne0BCiNj6fMCLUIIplRcjtFo3l2EPc3o6mvm95izVw2smEk8slDO9oFJ/LyxPGIK9JXh7mbGYG3zt2DL+DwUTGlsli2lz0GeF929yMM8b1Z+zhdzrB8Jcti58HtbnXi3bOzuKZVq9OjBLEYlCOBwfFWKx+5mTe8YYGohdegFBTeiubm3H+fD44IiYm4mtylAqimRqUX/xCW3nes4foa18TiFiyseLzgQ+oIePJ+3Dbtvh0wFR6vhhV8mWAGCOIpsn4NBGMNb9f9OyZn8f8my3YzhYZVRStSPlV4xfRKEBy1K7LaZMTE4nNjYnMR8vTdSLpKerNzYh0c8rn8DD+Li3FM/j9ImVNb75yBQWdrpxQru3gIGR8R4dAulSiQdps8WvKfS0znQamZViYmXsrjNtMZIMYHZfevauq4ATYvx/G3cREIvjVYqMlwynHJAvPvXsFUlJREYT71q3YXGYVymwroXoHp6wMjOP116FAmGWibW3w1rpcGD8jUDU3473Tp/G54WGhfOzcibQcI8hYanDS3/++YHjLl0OpPnAAytzQEDxcyoPPa/nww/BqB4Px0Kivvgol6/hxeML1GIYRL48R4bR2LYRrfb3oSXP8OBg3p3rMzGCM+fmIiAwNQcF73/sSGWSm0+qyjR4n79uSksQ+FnY7Xn/f+5IXeetFO30+CPjm5vh14h5VfX1Yh0cegYJktTc4mVCdn4chfeGFiQonKx/nzsEIYPh6TnFraIhXUIwqTUTqynMshtTRwUHMz6WXqqc2NjbCQWJ0H/p8eM9szxejSr4aQEwyRNNkfFo2rLippRXR6kyQGUXRKiVPyS/0wJE4bVJO85bJbLQ8k04kmc/29YkodTQan+ouo+0q5yuXUNBWyAl5bdevB8CDWmpaLhHxtAyLG28UbWGSzb2Z+lE9sjobxMx51rq3LN9CIRExv+EGrOdCc/wYpSXDaQGQLDwZKam4GAqvLEStDq+a+Vwy0qtFcjpx8Hp7cS+XyxwT9fmg1F1zjWiAJyNQOZ043Mq0nFSQwpxOASMu58GvWAFF+NVX4Vm+4w71qFFbG7zBF1yAcXi9og9LXh4Us2eewb3uuEOdIRr18ugJp+5uPMu6dfibG2cSYQ57e/F9RnBsbkZ6SEGBQLH74AfV0zQXWzGtHin3rYzmxwpWOJzY/E9r7rWindu3i4aVXOzv98NZMjODPbJyJfZYJrzByYQq11KoARXwmeruxv+1tSLFra8PdXI33igUFKNK0/R0opIrC1qOlO7di0g1p/aysTIyAkTRffuM7cPJSaxLbS3OuNxqgUi754tRJV8NIEbtc2p1HWaiKguxh5dZJT0VJc+IrNK7bmkpXuvpEYhjTKko2pl2Isl89tVXRUbAsmXxqe5E6vOVy35X0Sjk/NvfjnrCgYHE89nWpo56qEVq5yST859sv+kZFsePg5fI7UCY5Lnv6rLOuNUz5KNRGGirV4sSBL3rmT3PavdWyrdVq/BeTw/R44/D4baYdAWZlgynHJPycK5fn75QNKp4W5n7rFeLxKhVl10G5bGjw5yyzYqLxxPvJWZihaS0VLsflBppocB0dOBaw8PxwsluB6MYGcHfauvk8+H7bW1Ib5qfj1fQKivBSHw+7b5AZrxPakZMKCT6ffzXf0Gx7exEH6qaGsxjRQWuPTqKaN5b3iLG4fHg81zrpEaLRaFLRlqRIwZ0OHFCW5nSMiC1op3sWeSi5xMnYLDk5WG/cXSwpMR6b3Ay7/jICPaEWv0HEfaSzYaxxGLiezxfRubG6YQDYvt2KFXRaLySKwvaggKc9bw87NG9exF5UtZF1tYS3X23sX3IQC/NzfisEnRhZES954tRJV8NIEbtc2ZTh7IdhU2F1JR02XlWXIw9zUq62WiNUVmld10i8La6OuE4SkXRVsrs228XDa+tdiIxn+3qIvrud+GI0au1TcXgV0ap0uXpas7IujoAXjAQUkcH0Te/mb7ukSknnhHAGj3DghvzaukjPPedndYZt1qGfHc3smXm5kQqa7K5Nmt0q4F6ZFO+ZZuWDKccUiaKNs0UMVuZ+5ysFon72rzzneYbS2YCkECL8RUUYHzhcDycNFOyFBIWVvPz8Y37mBg0oLIykSGmmlohGzGHDxP9+teiiJh7fu3aBcTDHTtERHB8HGlJGzfGX8/ofKaj0GUb5UmL0vUamzEglR7kM2ewNkoPcia8wcmek8EIOJVN3rPj4zBeLrgA+0UJVMDRHzX0MHlfcpuCn/4UHsfWVlGn1N6O8zYzg3mYnRU935YtE5EoPo/yHjW6D2UwnvZ20eTbboeRVliIRuLKni9GPbl1dXjeQ4cWRzNNK0mppPN6yUA+RNgHaoqW3rkzI6uSXXflynjAEaOKNvMr3scDA6KvIqdm3XRTZvgZgzR98pOYB6/XGJ8yKzet0Ee01qqrCxEYdpxaqXtY7cTTeoaDBxFJev/7hfzWMyxefx38rqkp8R5yPzcr65KUhqTXCzlTUADHNddnJ5vrVIzuXMq3bNOS4ZQjSsVwSaZsplLEbGXuc7JaJL8fxdl33WXuupnIJdfyqHCtj92e2NOIKLlRwcIqEIhv3MfEDfxKSwVUcrIxESVnNhwu/9nPINDXrYtPMywqAurf0aMImTP0+JYt8VG1bCh4ZgV0po2sdL2WZgxIFvJ//jPg/tvaYDwo1zsTjQGNghEoFU5Gr7r0UkRV5BTc0lIYOJ2d6mO12xPbFMhNmh0O/Lz6KqK9TiccLTMziA5wSpUM715amtoetdsFVPjgIK5ttwu48LVrgVJKlJhCZMST+9WvYi0dDutTh4wi1Wl9JtNnSFbSQyEROSwtFeiCo6Nw6nDDYiPnLhVnkpHrvuMdxueD+dXevQDOmZ/H2q5fD8VQltlmMh7Mklk+ZVRuNjYiffyRR1Lr/8OktlaxGF6vrESN5G9+I2SrlbqHVVFZrf0WCmH/nj4No3v5cjyPVmPX+nrBI+Q+c0Txc8/93Kx0CstRyn/5F8jYtjahlxmZ61Sd1bmUb9mkJcMpB5SKMDCibKZTxKz2ObPeACO1SEauqybkrc5l1vKoMDJaXx/GLBcRGzEqWFjt2gUFintz8fcDAXjtx8cFOINyTG433lfOXzJmo7f+NTXwpvt8aAiYlwch5vcL6NZMQoTKHttf/xrPqURKUxPQ2YLSzWbqod2OZ6irg1KpluqWqcaAZsAIZIhnRq9SIlclG6sRXtfYiLPy8sv4vM2G+9XWwijz+3FtTqfr7U1tj2pBhY+N4Qxcdx0+p5VCZMST292NZ2loEGNNN3VIrwCdo/cy6phy3ESZP0NyNG90VEQOWXlmuP/Z2XjZlmw/ptMrSe+6RhVtdnD6/djDBQV4rrExGPuXXmod0IIR49YMnzIS1Vu/Hvv98cfxfOXlWCuOEJh5NuVaKaOO0SiU+YYG3DdTdVfKeeR+hkb4utp+k1OIKyrwLG430OG0GrvOzCBKyCmqWjpLsn5uqTox7Xbwp127cOb8/sSm7npzzc7u3bvxd2Gh0EOSjSuX8i1btGQ45YBSaRBpJDqV6SLmZGS0FknvunqKspW5zFoeFUZG8/th5MzNCTQ6I0YFCyufDz/Dw2Ag4TCuOTsrGHt1Nbrbv/vdGH9xMYTW88/D8ycjl61Zk9iEVUnJ1t/jwfVYcDU0ZAfggdfU60Ua08QElKjaWtGDS01AZwJKV085yVQtido9zUZRrYwYmAUjSAe9ygivO3MG/zOyIaffTUwItLtAAMrI9DTg0s3uUdmA04IKf/ZZoj/8IbGPlbzf7r7bmCe3uhqfn55Ob720zsCLL6Kupq4Oz8BG3KZNGBOP+8gRXCcSySwcNfO948fhlec07VAI6+h24z4FBckVY3mv9/YiYinzNLl2yuHA+1rRznTOs7xnGhpQX1RWJtD52DDYsSN9hd+Mg8hshFtLbq5fD0cCo2UuW4Zry42/kynYMsnyRzY2OOoYCuFe3J9SLZKRbiRCOY+hEPaHy4U1S+Y0UMrQWCw+hViuB03W2HXrVtS6Jqt/ywTAhdeLCKLfj73rdCY2dS8v157rjg589/XXkaXCtYFNTeptSpSUScTJhUBLhlMOyEz+qJnoVCaLmNNFNNK6rkxGFGWjheDJSO9gV1WJRr2hED7D3tFbbkmuaLS3AzGvsBCw5F1dEByzs/gdjSICtH49DImeHjxbNCrQhxoboWQwsxsfx7h27tRmNmbnPxtRFnlNuXaGmz1PTAjhrHQYNDdj3zPjDwYxP1xkmoqHNxeNIPXuaabGI5vjthK9Khmvc7uh9HN90MAAlByOUPv9+L+8HGktWmiWyUjNgJMjZzabdh8reb/ddZcxT25HB8aYTuqWXtrQyAjmyuEQEe1IBIaLx4NxtLcTPfEE3nv3u8WcZQqOur0d9R8MijMyIurgeG7m5+OVtWSK7vw8wG3cbqQcq0UxuLG41Wly8p4JBuNTr9mg5/RRjyd1hT/TvZb0nCHDw+C1585hvu120dOMawqNGjMsf6am4o0N3rccrZ6chDJeW5voTEknEqGcx2AQEZPRURg627YlplYq51UpQwOB+FplhtQuLDTW2JVTUvVkbKqp4lo6GfONqSk8NwNZyf3qTp7EeVGba3keL7sM9xgchOOgtxc1grfeqr8nM404mWtaMpxyQGYUXDPRKaNW/tatqHfJJqJRMi+DUQPxrruyUyy/ciXRX/0V0V/+AuYZjcKA+d3vhBGlR+3tyPG97jrUdT37LKJXpaVgXqxIMHIbKzhlZVCAJiYE4ldxMRhWXh48WFZ6eTKJ2KVc06EhPFtZGZ5JWfAvC2ifDx7L4WEwbGX0zayHNxeNII3cM5nAzFUDSyUZFe5KYV5UpM/r+vvxHqeDqDUgPnMG6TCf/KQ6PL4RSqePlcxnn3suPU+uGdJCqmOltKEBKWNEcEYUFMSfqYkJnDci/K00FDNRoL1hA1I7CwrE+skAObJsM6Loulzw6v/lL+Cfp06JKAa3dwiHkXZsNbyxvGeiUdxPTr1moJ9QCM+XisKfrV5LSj7f1SX2lvLZ2CjkhtDhcGJauRrJaep+f/y6x2LYg8uWYU57egQ4EVM6kQjlPBIhLZzbPLAc2bFDf16VMjQUEgYzP4PceHjbNuxZrldW44lGZKxZJ6aeTuZy4fW2Noy/v18YsPLadnaCr8pzrbYfW1rA22ZnMb6qKlw7GZ1PbUuUtGQ45YDMKLjHjxuPThm18vPycoNopOdlyEXPCSNpDMPDANNIRWG121FXtHIlnr+oCAqOLFD42Q4cwP/r1oHZyU1Y8/JQjMreP737WenlSTc9TLmmTme8gJY9tmVl8UrV4cMoxC4owHvcN4gV1M2bcS6MKKe5aARpxhGgFUXNZQNLNUom3NWEuYycp8XrioqgjDAAhZEGxGYpnT5WRBhjTw/Rk0+m7sk1S2rGnuwBz8+HMUEkagnkM8X1mcpaTfmZrC7Qbm7GOhkBJOCIRzJF94orAC7y3HN4Pk59HhnBPGzZgrm3+izIe4ZrX2UllIF+CgpSV/hz1WtJ3lt2e+Kzzc/DKN21S0SO5LRyNWL5c+gQjN/CQvCwubn4dM1IBBEMrxfyzopIhHIex8fFOWEeIssarXlVylBOG2YDWdl42OWCoyCVHpJq82eFE/DtbxclE9yQXXZGMdJxe7t6k3DlfpTrWl0uRNON7sfzpW2JkpYMpxyQGQU3lfQrI1Z+NhCNvF54NYykuVnVSd4sJUtjsEJhnZ6GgG1p0a79mp4GQ+M6D7kJK4M3nDqV/PmN9s9JNm4r0sOUa6pUPmSPrVKp+sEPRA8s9vDKCurRo6L4NhnlQjkxe0+1++aygaUWaQl3LWEuI+ep8brqauzJmRlzDYjNUjJI8e5uKEXT0+ooUNPTUPi6u1Pz5CYjNSeFGu+XPeBzcwJ1kJ0R8pmSQWn4b+UzWV2gbVS29fQYV3Rraoguvhi1n7EYlHJlCqBR4CEzpNwzshJaXIy/KyvBY6urU1P4cyX3lHtLfrb8fOxt7gWollauJQPa25HGxbKKZZ+8VoEAIrutrTCgrIhEKOdRPidE8eeCSH9elToMEfbcqlV4j4EgZJm1bVt2jAEjOtnu3TgP09MYq9IZFY3iHH7iE4lznYn9mMmsllzRkuGUIzJq4KSSfmXUys8kolE0Cq/G+LixNDezBmImi+XlNAYrFFYjz8aMij+jRC8LBIwrOcn65yQzgKxKD1M+NwNvsIDmnPq5OTB8WakaGMA8M+KZ7P0qLsZntm83ppzmQjlJ957RKPbgwACUSW42SySK42dm4HXnaEm2SA21yghyXlVVYvNrLp5OpQGxGdJS6BlSnNOtnn1WW0FqagLaZiqeXD3SQ81T8n6O2rIXv74e1xgYwHg5CsIRXXbUKPlOJgu0jci2o0fNKbqVlTBsL7lEADTIkftUznAyGaK2ZzZvxth7ekTq8KZNqSv8mehRaOT5lHoFK9heL551chLjWbkyMa08mdPwqquI3vtefUS2rVuJPvc542h3yUg5j8rsBvlcECWfV7XeiLOz2JdmwKKsJiM6WX8/orJdXWJt2RnF6Xbbtyf2qyMyth+dTlzr6NHzJ4JklpYMpxySEQMn1fQrux3X4mtzDZTa57SU/1SVP6+X6PvfN5fmZsZAzHSxvNWKtpFnu/hiMKMjR/AMZWWJKD1mlBy7Xbt/TrJeYValh6k9tyygT5/GdefmEpWqUAg9d/bvj1dOWWHMywPzN8KwM6mcZOKevL9ffRWGRne3aCJIJLyHXLD+k59gbrKRM6529mprEWVZtUpbmI+MEH3qUyLtReZ1dnt2ioj1mkNy4fju3XiW4WFRY8PjuP56osceQ6pefj4aAvt8+D+ZJ1dvPvWcFNdemzg3Hg+K+WtrxX24Niwcxuf42hdeKP7OZoF2MtlmVtGdn8c6ud14biWZVeiMyhDlnpmdhTGxfTt+NmxIT3HMFPpYsudT0yvKy6ELdHRgL27dikwFZVp5Mqeh3Y6Uvp4e7OvGRkRr1UoGMhUdlLMbuN6Pa5OMzivrRi0tiI4thFodo7rJjh3gSzLfsNnAh5cvjweLkSnZfjxxAn8//HB8A+hMAiwtRFoynHJMRgsHzRbZWWFcpKL8pap4GzUQre46btVz65Hes3V3Y306O8Hkz57FM3KDRVlxM1uflMo6WJkepvXcBQVIh6qvBwqXUvng+Xe5EtMM8vLw3aoq4+lbuYBGTfWesiK9fDkiGL29iHQMDOA60agwOKursWcefDDzQBF66XinT2M99SCGp6fVkc+yWUQsN4f87nex5zZvFnvv8suFUb97N+oXeBxtbYAr/6//wucjEUR0PB7Mx+SktidXjYyc0aNHiW6/PR7SuKoKhkRZGc5SURFqRQ4cEFGp0dH4xsa5UPr0ZJtZRXdiAk64QEC9oagZhc7rxdr7fIhk1daCr2jJkEzWaWQCfcxoxoDauQsGwV+3b0fUQklGnYbZBgZQm8fVq+FMOHMGz7R6NfZRKvO6UGp1jOomGzbEG3s9PaIlwfXXa4M76O3HEydwncZGyB21fbUQ5igbtGQ4LRIyc3CtSrVKRflLR/FOxmzb2tCsL9PF8plQtNWeLRTCs/T2gql5PBDk4bBIV7vwQnj+zAqbVNfB6mib1prqpbco53/HjsSeO5s24Rm6uqxpBGm15z2Ve6op0mvXilqBgQF8fuVKKABFRVDsq6oyDxShp+S3t8PYTwdiOJuKCUe5xsawz+R7cFpLSwuUrttuE/ULXi/WIBjE/2435oWN2i1btD25amT0jH7oQwJAJBDA/Pj9aL7J7QsKC/G5Sy/FGijnbyEpNJxCtm4d9u3x45gDPUW3uproox8FYE8qCp2M+vjww0hfttsRueOUu7Y2bZCJTNZpWGlkmHWYKc9dIID5cbvVr2/GaZiNM61MR5SdDLOzAibc6cReMFJvrUULoVYnmW7S3Q35EAjA6fD5z6PNwpNP4r2+PkTN9+3TdqJr1UkT4Yxt3aq+rx55RKRjZ6vdR65oyXBaRGTk4FqZapWK8peu4q3HbK2uPbLyuY2QMm/6V7+CZ7ugAMrO/DwYnssFz/fQED7/uc9BuJuhVNch1WibXj69WQGqNv8eD9aYlaj16wHgYTSi2t4Oofroo/hONArlLJOed6MALKwQd3YSvfIKirGZOLXxtdcg9KJRAevLtQdEmQeK0FPyGaUqFYhhK2sVzVxL73zYbJjfqSkBVsB8NRKBgbRvH+7FUSeG3jYC02tkDESJiKnBIAwH3vNOJ6ICO3YkTxnLhdKnth4dHep9m86cESA29fU4J4zMJp+XVavMK3SyvHvuOfTWi8XAx5VonWvXWnOOzO5rmUeycVxcbBzMhykVh5m8N6JRc61KklEm951ebeBNN4k5nJyEscB830xbkYVGySJC4+PYz1//OuajogLOlUgE6yA7FXw+1KKpOVrkyHxnJ0A8fvtbXENtX7nd4E0XXIA1yFXbjGzRkuF0npHVSFxmvWFFRTAAuroSobeJjHmstJhtNov8M5VqwLVnP/sZPN52O+aJkccYNa6/H8bBwACElVnhk6oBlEq0zUhaqFkBahQq3mhE1euFsBwYEIpIXR3ACTLJ0PWMRp63vXuhOI6N4TO1tXguNoyqq1HvNTSEsW/alFhPlCn0LaZkhgbvVTMQw1bWKpq9ltnzwXzV7UaEpKBANOKNRjE/r7wCxfzqq42N2cwYtLIIurpg4LW2LqyUGLX1kJU4+Rl8PhhAnLbLRrgRJwxHSORGq0yyvOvqwmv/8R8i7Y+BM2S0zu5u7Nd0zlGq+1rNODZ7JtKVkbmIzqdCRrJq1q/H5374w/Taiiw00speGR+HE2vVKjzn1BTSimdmAFHOPIbRS196CbLnggtgoCv3mezkGBzE7/FxATjBFIvhPAaDOKvyfXLRNiMbtGQ4LXJSerYYOcVK48JoxMDrBWrb2bNgVOXlic1e06knyUaRv3I+P/9565B/mFgJq6pCqggjSRHF92KZnzfeq0hJqaYbmhWcVjZoVc59W1tij6NUoOKVY2xpEUrn97+feeGpZjTymM6cgZE8Pw8nw+QkPO2RiGioWl2Nfc37hBsGy5QJgAuZkp09l8scxLCV+yaVa5k9H5OTUAyGh6GI1NTEf8fjwX568knUOBnhEUbHYHV7hEyT2nrIStzb3oazPjwMo4WjPCdOoP4imZNFfp+BZPTkHdc0dXdDUQwGsVZ1deJ7zHcHBiC3Uj1H6exrK86EFTIy2/VJZsloVs3q1QurD54ZShaxVEYof/ITyId16+JBpex2OAg6OoRzwe9HxDwUgpypq8N35X1GFL8XS0vxHutCLJeIcP+BAfDAwsL458hV24xM05LhtIhJC+EqFLLeuEgmzGSmf9FFaF7KaH5jY1CqgsH0PFaZLvLX8xSqFbWnSuwV5KJkuRs9kYDiNQNBrqR0PIdGBaeVaaFGvbRm0zUXWhNZIjEmRkGbnxfpeQwxHongbzawcwUtzWTk7BmFGLZyTTINRiMDlnDjTmUUnQjr6PHgekYVBLUxuN0wpDkl9YYbEnseybTQFBOt9WAlLhIh+vOfMdeRiKgvamhI7RmSGQrd3XBO2O3iHNnt4K3z82LdiaA8Tk1hnlM5R+nsa6vOhFUycqGAIaiR0ayaPXsWz7mRyagsZJ2sqwt8qbk5/jnZMCovFz3RSktx3ZkZGEyjozgHlZVinz3xBL4v78VYDCm0/f04ayyXbDbh3L3gAnxucDC+XUCmsyFyQUuG0yIlLe/U2bOw/mdn43O+iTKnYKkxfY8Hh8vvR4rR0aNE73tfeoWCmUwjsNIDnoxY2LPSIDfSJAJamsMBBfryy/VBEJLVFqXqOTQiOFNNC1WOeXpawNcnm3uzqSgLsYksj6m0FI0iZUW8qgpCbWwMzzI0hL09MZE7aGmi5GevshI9drxerOm6ddpj8fnwuZISCFlu5Mr9XsysSSbBaOTz0dyMa+3bh2eViVHf6upwbs1G83kMnLLJfd1cLqSXrluXmyapqZDWeoRC2NfBIP4uKUFaEdcXjY3BeWD2GZqbwafUegZFo0AaLCgAcuLEBP6urIRCOTUFWdnSgnGMjGDOOepl1bMTJd+LVvEpK2XkQgBDUCOjMmBwcPGcG6ZU9BCt+WCIf5sNxlEoBOOJG02Hw/GQ/7zPDhzA/3I9k9yDMRBAve3ICJwNPh+uEwgAiGJ+Xug2a9YI3p6pbIhc0JLhtAhJzzu1bh0O0vi4QCvKtIKlxvTlpmsjI7j/Bz8IxJd0KBNpBNmOSshewba2+Eaa+fmYL6cTn9MDQSBK7plKx3OYTHCmkk+v9KY5nRBwNpux4m6zqSi5aH6bjHhMLld8008eT1MT1mtuDp8bGgKyWy6hpYm0z15jI4yHxx4zVpdx+DDRoUMiLz8cxhyUlwsYYaMpqpkEo5HJbkf05+mnoWxXVsb3FXO7sW5E5hUEBgk5cQJe3aYm/J6ZAY84fjwzWQSZIK31KCiAoRIO42+HA3PK9UV9fdhDWuuoRR0d4J2vvw7nnMcDA7apCfx8bo7osstwLxnyvKEB68jGk8uFObz6auNw8kafnUlvL1rJpxZ6ql26ZFQG1NZmv39fOpSqHqI1H7zffT7sb6dTNJrOy4OewZD/TKwrqp1FBio6cQIOnlOnMMetrdAt/X7w74IC4RAZH8cYdu7MXDZELmjJcFqElMw7tXYtNvaKFcbqDVIljhwcOIBDqDwYNhu8ih4PkFmmp625r9VpBNmOSsheQb8fxq7PBwE+OQkl7JpriN7yFm0QhCNHcC1lobVeTwW9KEAqZNaIUfOm9fVB+Sktxety0ana3JtNRclF89tkxGNiAaZM1czLg/K3Zg089HfdJSCxiXKbQqM8e4ODRL/5Dc6/2j68/Xa8JkcXf/UrfH5uDtcsLMQcjI9jTvx+8C4ja2LF+hr1rF91FdE73kH0zDMwarivWH29gLJOJZofjSKyFApByVcqTMePQ6lmCO9sZBGkSsnWIxbD+M2ihKqRzE8uuwzzMzgIha63F0peNCrmRfaaT09j3UZGsHb8uVtvTf0spbMXreZTCy3Vzkr0TKMyYOtWaxECM02p6iFa82GzCWS8aBSvORz4e2AAMnfNmkTwLjaY1PZidTUAXMrLcVZaW4l+/nOcpYICOJG4WX1xMc5hXh6cTgshzdMqWjKcFiEZ8U45nUR//dei4NxqxilHDkZG8P/4OPrKyMovUWaUUyvTCHIRlVB6BauqwIy4Qd3OndoF4e3tIg9Z7huT7Z4KZowYLW8aN/AMh+PzppmUc282FSUXzW+TEY/p4EFELgYGRKqmnPYVjcJgko0mngOtvW+lgqJFfP9oFGAIIyPqHtI9e4juvBMpWKGQiC7GYtiPU1NwrNjt2AczM3AEjI5CEDc2Jh9LNtfXbkdfJzZiGDU0L080pk0lmp9MYWpqgiOssHBhI50Raa/H3BzGHArh/1gM+4cjdoz0ZdS5psZPWloEOJLPB284UbwCyF7zkyfhtJmfx9675JL0nYrp7MVM7OOFkmpnJXomkXEZkJe3OBACmVLVQ/Tmw+/H3uZ6pmBQOKq2bElEx+vpAWorEbIC1PZiby+u+da34px1dOBzIyP4OxDAmPLy0MS9qkr9mbIhqzJFS4bTIiSj3qnS0swwTmXkoLkZRtO5c6K2ig/kQvTsKClXUYlUe1ZNTEDB5L/LysR72eypYMaI0XoepxNpWna7KGCVn0dt7s2kouQCXjeZQJDHxFGLoSE8QzAIRS4/H2fIzNisVlCSkZ7CPzwseuM0NoIPcXSRe9O4XHhep1PA8Y+NiT4+RmD4s72+7e1Ed9wh5pnXLZ1ovhGFqaCA6IorEH06dw5z5XKlft9MKS1a68EpQtXVgCUPBmE4c8SO0z2N8li1vccZDkSYm8FBKIxdXfEKYHU1jN5XX0Va6B13CGj5TDy7kb24WGDAzVKmaoeNyoDFlLaYjh6S7DnVsgT8fvBe5T5797txzZ4e8JuSEpzT+XnoG7JcmpwED+zuhmEWDuO7Hg+iUc3NiAArjb1syyqraclwWoSUSy+6VuRg40YIx4EBeCp27oQHeTEwfbX5jMXivZfbt2dmPpVewWgUgl4r/ZFIeG1tNvE3U6o9FVJVpIwKJi3lkPOw+/oSn0dvL5tJRcmm8DQqENRAAQIBzM+KFXA+mBlbNsFNmLTWNBbD84fDQunnJrFFRdibMzMY5+go/g6FsP6Fhajrs9mMR3izrRxZmQYVjWLdg0GcgcbGRCO0uxs1PNxslwjGxg03GIc+lynTSovaejiduAcRIj4TEyIKWVKCMZmRWUa98zt2wEBTM0aWLyf65CfTr7uVKV1AnsWi5BuhTNcOt7WhbrqzE/+3tqobwAstbVGL0tXrkj0n6xnr16PGL9k+u/ZayJSDB0Ud6urVRB/9qPjM4CBkl7KhdCCAM808XTb2ciGrrKYlw2kRUi69U1peZk6BOHQInuZDh+DVWwxMXzmfbndizVFrK7zlmXwOo+mPXA8Ti8XXxhCl1lMhXUXKiGDS8qZx3YHfj7HPzcGzZdRLazSimg3haVYgKHtx8LhKS82NLdvgJmxk9/Ziraam4guMGbmpsBCf5T3K0UUi7GuuK+FoRCSCn5ISCF8zEd5sK0dWpEHxueMGrUeOoHlle7s470NDRLt2IYKyfDnO9fQ09tPjj0MBMsOTsqW0tLdDydqzB8pVbS32yA9/KFAhKytxb6/XvMwy6p3fsAG8O5vGSDp7cbEo+UYok7XDZmVWOuc1WyllVuh1Rp8z2T7zepG1UlJCdOWVcNhwb8GnngKfamvD+S4owBwVFGBdGfBlaAhG1003CWNvIbYHSYWWDKdFSrnyTul5+qqrEWk6dIjob/4GubLpeGL5UMvFipliXDyfDz9M9Mc/wgPs8UD4Nzdjfh98MHPeEDPpj3o9fRiNbPXqeGWWSZknbZUilYxh63nTqqrgQa+vh+HU2ZmZvZzJnP90+gmlO6ZsgpvICkswiPYHnZ1II+NeVKEQDJ9YjGjZMuzDWCy+tonha0tK8FosBuO5rg6vM+8wQ7ms6TCrXCnP3Y4dgNTu7MRr27ZhXl56CXNzxRXiPBtVMpRjamzMntKipdheey0MxHRllhnvvN1ubYTQyHXS2YsLpTYpXcpU7XA2IxbZTinLpl6ntc9kWaYEo2lsFHziAx8Av9q0CSl9jAzMaKPsDNuyRZwRuRXF0FB8v6eF3FtLSTk3nH7wgx/Qt7/9bRoYGKANGzbQ9773PdqyZYvm5x944AH60Y9+RD6fj6qqqui9730v3XfffVSodK//D6BceKeSefpmZuBJ3LQp9Y0vM6uhIXgsieC1rKnJHONqa4NxcsEFiT1BYrHMeUNSSX/U6unj8yFCpmyGxyTnSWfT+6P0pjU0gLEyXP2KFUSf+lQ8Atti8rTmsl+UmoLCqaahEIzsYDB9cBM1hcXtRkTk6aeh/HMEaXpaoDYND2NuhofxOjddLC62prYr12RWuVI7dyUl6Nnm9RKdPg0j6oILMB+XXy6MUqZke0qrOXpnJ7zFmdyjyRTb22+HFzqdc27WO29lhHCx1mVkmzJRO5xNmZWrlLJcRx2NyrLOTvDtujrwKp8PTjGu221shGHEIC1EohUFEdZS7vdUXb0we2upUU4Np1/+8pd055130kMPPUSXXnopPfDAA/S2t72NOjo6qEYpKYjoZz/7Gd1999304x//mLZt20adnZ300Y9+lGw2G91///05eILcU7a9U5mur5KZldsNLwb3FbDZcAgzxbgYIWbNmkRGn0nlN9X0R6JEz9T27UhN6e0Vc8akXJ9sK/tyVO/FF6FAE6FYvLV1cXtac9kvSqmg+P3CUJmfh4AqLIQDYv361O6hpbCsWIFne+kl9NFh1CauZ4nF0DR2ZgaGVHExPsONUDnylGptV64pFeVK77xXVeEM+P2Yhz/8QZuXau0prTEdPgyjrL5eXZG1Yo8aUWx//3tA7KerCGa7djGbSvRiRhxjyoSuYFZmpTKPXGf80EPI+Ni8WR25NpMpZbmUhUZlmc+H2stjxzBehwNZOs3NMKaIIOPldiS/+hXqGysr8Vnu9xQIQNdZLM1yc2o43X///fSJT3yCPvaxjxER0UMPPURPPfUU/fjHP6a777474fMvv/wybd++nW666SYiImppaaEPfehDtHfv3qyO+3wjM8wlk/VVstBds4bo2WeBslVdjcPk9+Me27fjEFrNuHKl/KaT/qjmmerogJBPtj65et7ZWShvF14IZXpmBmP9p38i+sIXkB6w2CiX/aJkBaW6Ot5QyctDxDIcBpJSQ0Nq3kw9haWmBo1DfT6ij38cY5meJvre92BQhUIQpBMTEJKRCNDPQiEI0L/7O9SimK3tyjWl6v3WO3c2G9Ibp6bgJHK5zO0pvTG1t4M3HD2KayvX0Yo9mgtnTKa989muyzhfIlvp6gpqeokZmZXKPPJ3Xn0VAE1uN/gUR0SIFldKWSpkRJaFQnB+hsPg51VVcNKNjyNNr6Iivq8dn6FQCBHvgQHch+uh/H7MZ0VFamna2aacGU5zc3N04MABuueee954zW6309VXX02vvPKK6ne2bdtGP/nJT2jfvn20ZcsWOnPmDP3xj3+kj3zkI5r3CYVCFJKguiYmJqx7iPOAUmEumfL0sdB1u2E0MSrL9DReKymB8JqYyAzjyqTyq2ecppP+qOaZ0lqfjRvh1Zmfh0etqCi7yj4zz5ER9IEYHsYaDg+DAXd0oO/P/fenbjzlylObS6RLVlB8vnhDJRzGXJeWIs/c70+9v1cyhcXjgZHW0CD243vfC8TASAQG09gYvJKNjaK/XF8f0Z//jP2QaQXE6r2RqpFglM+0tprfU3pjKivDmHp6oOCUlye/nllK1xmjVpfV06O/ZlZ557X2R7ZrCK2IbC2UiFWquoKWXrJli7GzMzgI8BQz8yjPvceD7xQXx0dE2HhaLCllqVAyWdbdjTXJz0ft5b59kDMlJaIv4UsvIXVb2Y6kuRlOm4mJ+JoopxPR8GXLFkeads4Mp+HhYYpEIlQrJ0ASUW1tLZ08eVL1OzfddBMNDw/Tjh07KBaL0fz8PH3yk5+kL3zhC5r3ue++++grX/mKpWM/XygdJp0JTx/3BBgagmC32zGmaBQeWBbI7Knu6cFhtOr+Vim/SqE1PU30u99pG6eZULqV6zM4CCX2scfEGFpb4eHp7s6Osi8rIMPDGA9HRvLzwTxPniS67z6iL34xfu8ZUQRy6anNdR+W9vZ4Q2V0VPTIYW/p9HTq/b1ScSrU1uJetbVE+/djf9XXxzsMQiGc90wjKWVib6RqJBg97y0t5vdUsmjW+vVQbLxeOCes3qPpOJ+UaxQKIaXT5QJvyOR51tsf8/PZicxbFdlaCBErJb/+/OeTG8Dy+LX0Ep8vuczauBF80Mw8Kuc+EBD9BTkiIjdoz2QGQa4pmSxzOuGU43Yn3FB6eFjUN+XnQx6ptSNR+47djtff977FEVXNOTiEGXrhhRfoG9/4Bv3whz+kSy+9lF5//XW644476Gtf+xrde++9qt+555576M4773zj/4mJCWpqasrWkHNCRpRMK5i01Xm4RUVQ8KemhKLHBYQOB54nFoOHgnubPPII3rdCOFih/KoJ//5+eHdlRUWprGZC6eb18XrVvW+HD4vGo9lQ9pl5ut2498wM1pn3npYibUQRWAi9IXLdh4UNlbo6KHsyYlGq/b2Ympvx3u7d6sApakZ2URGMuN5ekdcuX3tuDgK2qSmzaS+Z2hupGglm+IzZPZVsTC4XUmRbW8FrU9mjevIlVSeQco2CQey10VEoytu2gW8kW7NUIi3J9sd73pOdyLwVka2FwAf1+HWyGksjeglD2WudnUsvhYOwsVEA5Mi8UG0elXPP/QX7+yGjONslEMB7RpyKCyXqlwrp8Z21a4l++lPhSODaTBmMiFsQMCn5kvI7jMS6YUNuntcs5cxwqqqqIofDQYMMmfbfNDg4SHVcWaage++9lz7ykY/Q3/7t3xIR0fr162l6eppuvfVW+uIXv0h2lV3pdDrJqWx2cx6TUW9TLlHAkhH3AnC7YUQx9DbTyAjRK68k9jaxQjiko/wqhZbbTfT880hHikTAHEpK1JXVTCndLIj8fqRRBYN4rbQ0XhBx+lYmlX1mnv39mCMWZEyhEJSroiJEKLq68FoyRaCtbeH0hsglIlJxMc5Efj4UC5lS6e8lU0cHDFqvF7n/nEbV3Iw9pTSy2Vg/exbrzYAQjJwUi8GYqq+HoafWXd4KymR9SjqRYjPn3cyeMjKmrVuJPvc54xEAmeTeU6Oj+M6aNWiKuW5das4n5RoRwbEyP4/mtMPD2B87duivWSqRFiP7Y+9e3OvQocxG5q1Ic+RnaW/H+RoehixtbzdWF5yusp+u4WZELxkZIbr5ZqSIqZ2d+Xnwqu5ugeApo7eVlyfOo3Luub9gIADZWVwMR8/ICL6bzKm4EKJ+6ZIW3/H5wNtlR4LNhlRgIsyZyxXvSFDjS/wdRizOVCp7JihnhlNBQQFt2rSJnn32WXrnf0OERaNRevbZZ+n2229X/c7MzEyCceT4b606FotldLyLgcwwLS0mzTDGMzNgEoEAXs+G92R6Gl4Kvx/PUFIi+hIRiVzYPXtS721ihFJRftUE8Pg4DL/GRggxOdSvpqxmQun2+UTawqlTiUKEBdGnPiUAIzK1vsw8X3hBdCJnmp4GgpHdjnmamSF64AH8n0zp/cAHFpYTIBuISGrnUU9pNtvfSyavl+grXwF6EkeY/H78dHVBGbj1VvXo30UXYa19PpyHUAhgEuEwHAtr1mCtM5X2kkkHUboRajPn3eieMjqmvLzUmo4++CDRmTMCdnh2Fn2ZXnqJ6N57ia67zrwTSLlG4+PCscIpPOzt5zottSbeRmWffHYCAXxXb390dEBR7+nJbGQ+3RpbuUZ4926Bqsn8PtleT1fZt8JJYdR4rK0luvtu9bPzzDPYo7EYnjs/Px69be3axHlUm3tGtT15Es7PmRm8f8klRDfcAOPg6FH15rG5jvpZRWp8JxWHUa5T2a2mnKbq3XnnnXTLLbfQ5s2bacuWLfTAAw/Q9PT0Gyh7N998MzU0NNB9991HRETXX3893X///XTxxRe/kap377330vXXX/+GAfU/lcwyLTVGIcMYB4Nguj/5CTY7Ny3MpPekuBhKVW0tDtPwsPBOE+G+kQiMp82bzfc2MUNmld+uLiDxFBWJcH4ohDnk+p3ubvywkFZTVq1Wug8fhsJbUAClQylENm/Gmk5Px6dRMCSrlYYUM8/jx6GMOJ24diCA9SLCfThAfOQIBNZllyXvJ5ErKPBckJ6CoyWczPT3kikaBXz8vn2iJ0dDAxTn8XHs71gMfIU/r+RDRUVEf/oTXpuawufXrcOYq6pS8zYadeRkGjky3UhxJozsTESveV3PnIGjhWsTy8rgie/pIfra1wApv26dOaNQuUYy3yQC75qcxOtEiWs2P0/04x+juLy9Hffi3lhK2dfREX92gkHwue3b9SHaa2uTz2m6zsV0a125Rtjvx3Nx7Sjz+7ExyEwt50i6yr4VTgozxqPa2YlG4VgtKMDfBQUig6W6GvNz8CD6iMnzyHN/8CD429wcvlNVhb3x6qtwOt1xB+ZWq2Z5IWU/ZIpSNYJyncpuJeXUcPrABz5Afr+fvvSlL9HAwABt3LiRnn766TcAI3w+X1yE6R/+4R/IZrPRP/zDP1Bvby9VV1fT9ddfT1//+tdz9QgLhswyLSWTlov1S0rAOKqroXj/8Y/4vl6NjhUkj2n7dkRpQiEwv1gMz9fQAMZvtLdJNiJlXi/Rj36EFKaiIgirqiogxDCCXSgEJrtrF9ZozZrM9yyIRnG/+XmsJRskMgTo0aNIh5HHkMk0g/Z2QI5/9rMC2W1sDHPW3Iz58/sxdxdcgDo23rPKfc1rTZQ7KPBskxEFR004menvJVNXF2BnHQ4oXfy90lLMdV8f3u/qwj5S40M1NURvexv24vg49j3v/xMnzHsbzezPbMDE57phpRq1tRF98IOYI78fa9DWpq5oGhm3z4d5n5pKrE0sLARfHhgg+s//JPrmN3ENo0ahco2cTkRJwmH8PTeH/5l/yWvm9cJoevxxfGZwML6hpiz7nnsusdazrw8OmpdfRqNhRk1jku/V0qK9zlbwTLud6MYbcSZeeQVjrKsTTc+TnROuEZ6eRgosrw/z+74+kQotk1XprFY4KdI1Hn0+ONI2bYKDTkZuY7CRWCzx+3Y7ouO//S3SxJ1O/JSUIL155UqiT34S1/j+9/Xr4dT0MM7kKSwUaegrV2rPQ6pktL7d58N4+HNmW0GkagQtRF6ZCuUcHOL222/XTM174YUX4v7Py8ujL3/5y/TlL385CyPLDaWq6JtlWrLX4PhxeKRYcE1M4PMbNuBQBALwuOl58qzY+PKYvF5RBMqejFWrwJgee8yYIpSNPGNWZM+dE/Cldjvms78fiiIjQxUW4jP8elUVejRlKq/X54My09gI48TpFMzcZsNYuS8WjyEbaQbr1hH9y78APe/cOShIFRWYN79fpHDl52OMAwMiTUemdGCbs01WGPBGFZy77lJPYzHa30umzk7UCdTVqTtkysuhrHV2QhEIBBCRKCwUtXQ2GxT3yy/Hvc+cgUFcW2ve22h2f2YLJj6ZkWDV+hu5BvO9vXsx19PTmKeVK5F6xPzPDH+cnMQ+mJxMrE0kEopmKtF+5RrJhflVVaIWrrQ0fs2mp6HEnj4No6mmRkDfy/DRRUX4zpNPJp6dxkbIlc5OjJ1TqYnU94faOlvFM71eRDImJ/EMr78u1m3LlvhWEvLa8744eRJGptmqBavSWa1wUqSb0sV6UFsbDB7OoOFoN9Mvf4nnkc/CU0+Bn+Xni+v09ID3XXstrvnNb+rz3yefhLyX9TA5k2duDobwd78LQ8zqJs1GQJSM8AYjlKoRZLeLWqnJSfxebMZTzg2nJRKUjqJvlGkVFcWnYN1+O9GjjyJEnZcnmpOy4joyAuHD9U6svGaqdiSZJ6OtDWlDyRQhFqqZNABkRXbzZoGgx4gxJ09C0PG9S0vBzAsK8Fx5eciVzhTD4PSWiy6Cl0v2vs3NQSHJy4PhZLcbU8yfeILoQx/C86TjLVq3DpDjP/oR9s/UFPabDJ8di0Fovf469qVM6cI2p0tmFGGrDHizCo7R/l7ppErI4/B6kdrr9aKezuWK9/5XV8MZU16Omqg1a8ztn1Q84wsht96K9Td6DbkOqb8f/KesDMrc2bP4bnc3FMGnnjLOH9khNDub6MAgEqlN0aj5tEe1NVq9GvzqzBk4VVavBr/iNbvhBhgZDIQwOCjSuJXw0dPTeK+7OzFybbPh+8PDMMBWrADP6e/Hvaqr9Xm0lRDibHytWoW06f5+jDkSQYqZ3EqC155I7IvBQTjIiMTYZV7PqfDT0/H3tiqd1SonRTp8StaDWA6fPYtsEJsN8nd+HvPAe/3228Ve2roV12G0N5bVR4/imZLx33Pn8D/rYX5/fNsNjpqeOoX1tipjx4jxTmSMN5gZUyqpxucDcMaS4bRAKF2vlRGm1dhI9POfJza/3LYNDL6hAd5+9igODuJwlZeDIUt9hIkovfoAPcUzmScjmSIkC9VM5hnLiiyjSzEKDysRDJ9ut2Nuub/O8uUYq5awsoJYiLhc6r0WKiowBoYATaaYu91Ev/kNhJAVEPDt7USf+QzWragI0UUGLmAggfJyjN/nw2+rYJvTITOM38oInhUKjlkvIff6GhvDcyr5CsNF2+14Tr8fxu/oKNZL9v5XVWF8l1xC9Na3mj97qXrGc5lbb8X6G72GjKAZDgsFkaPL/PrQENH3vofX1q0zxh9Zvhw5AkVcRmZkhMTycuyFVNIelWs0Owsjpr4e9xodjV8zl0vsheLieOhozooYHgYf6evD5/r61M9OdTVk4O7dMJ727ROeeJcLsoR5jJKsiNZoGV9NTZAjf/oTnJ3XXBOPIHvkCD4XieCzpaXYD9PTIgU6L0/0c2towOeV62NVOquVTgozfErWJYqK4hEQiUR6cn095ri+HmMjwjj/8z+xd+Q1lJ0DnIpppJbW4UCaeXc3noGBjjgFlNPQN282hnJohIw6PPn+erzB789sDdb5ApyxZDgtALKqp5Ie03I4hBeNN+zUFOoOdu+GMCwqikfc4lzz6en4HHOmVOsDkimeybz5yRQhpzMRqEFOUbMqUqZUZGUUnu5uzCkRPJibNok8a6cT38kUBDOT0pjesSPRk7Zpk/AA6inmXA81PIw+MC0t1jC8lhYIkddewzoNDwsDLxzGPdgDzVCwVsA2p0pm0busLBS2UsExuu9bWoje/GakoKhFLKNRvL9/P55z3Tqk4O3dK7zcgQAUmfp6nJFUIzzpGI65yK1Pd/0ZoOWhh+DJ3rxZfE7tGqzEl5aCtyj5XkkJzlBVFd6/8krjyr7dDsjxl16CDGloEPVHExMwMDwejCnVtEe1NWpsVIdNP3pU7AUldHRJCeRVMIjnWLWK6Prr9VO8XS4YanY79ndTE/brzIw+j7PCmaFlfMVicHJyvVgsBjleUoJxsDL87neL9+vrIeejUdx71SqsS0MD7qEW8bEyndVKJ4URPqWmS1RUiN6ExcVwFDidOIecBi6nanq9mC+te5mppXW5EM197DG0Iunvx3hCIZwTvr/dbp0eYsR4P3AA/5eX6/OGFSsyh0CbydYQ2aYlw2kBkFU5xlpMa+NGCJTeXrFhOZXB78eBiUbhkXvzmwVaXWkpogCdnfA8y0aV3KU7GlWH5VSjZIrntdcaQ/DTUoQ6OgBjrQRq4HQhIutQ1rQgTKuq8CwvvIC52LoVDEsmLhTNJGCBmjHt8WD9OZVDVmK1FHMG5picxHNUVgoBni7Dk8e4Zw8ETTiMccRiIr0xFAIkMNfapQvbnAqZZfxWQ2Fnq15HJrud6LbbUGd27BiePRrF64WFiB5ddx0UBX5O2YHAn+/vR0roxz6WukcxXcMx03tDrd9JquvPCuH+/YiAFBQgerJxo+DPymuwEu9yxaPSMTE6XTSKM6YFRKvFH9etA+T4V7+K/cB1TeXlooA+3bRHtTVSWzO1hprynmNU2I0bseeSpXh3d4PHFBfHo3gm43FWODO0jK9AAM9SUQEnp5zxMTGBSBP/XVYmDMjBQfxwtDc/H2t94YXq62N1Omu2nBRaukR3t0AOPn0a71dWxqeBMxUVCX6WbA2N1NI2NuLMTk7ijPAaFBfjvm1tmE++txV6iBHjfXoaY6yu1ucNDgd+pzomPaf3Qu4dapaWDKcFQFZC5qoxrWiU6B//UWxYZd5tYaFI4Xr6aUQmmptx2PLzBawpg0bIUSy/H9c2kquaTPHcsweQtk1NAmGNjSqfj+i9701UnOUiw+eeQyqZz5cI1CAXC1uFsqalyDITcLvxv7JvTjYBC8x4ALWehz25RJg/+XmsYHjt7cgz/+xncS8WZsuWxUNW798P4INceaPMMn6robBzVa/T3k7013+NguaTJ0UPrpUr8XptbeJzyp3hZ2bwnB/+sL7RlCzSnAvD0QhpRdDXrUtt/eU6pcFB0QR8dBSRpze/GUqc8hqsxHPvHkalY2J0Orsd68eKt5L0+ON118Er/eijeN5oFIr92rXx/CTTaKZqe4H33Pg45nDjRqJvfAPPTKR/dpxOzJcaXL8ej7NiTxYX4/59faJXodzOIhZLzPhgI8pmS0yhj8VEBCoaxWeUz6Rcn7Y2yIknnoDTkVMVN29OLRU7k04KIn1doroakdG8PBiUDgfmrq1NHTWxogJZIV1d+muYrJZWzupZtQpy4ve/h940Oorrzs7ivFqJqmvEeGcelIw3RCKpjylZJlGmW0Nkk5YMpwVAVkPmKpmWnNbA0QMZUjYahZfywguBsHf0qECC27kThaocBWLFu7ERTKK3F3/Pz0NJ2rULDPmOO5I3OlTS1BS8NFu3inmQGeHevYCodrlwIC+6SIwrGASIQDgM9C4ZqEEuFq6stBZJS4+RXnghPscIgblq+GbUA6j1PCMjYPw1NfFpDkxWMLyiIijgTU3xygNRdmBcjZBZxp8JKOxc1Osw4lRpKWqTHA4I2EAAr7/nPerPabMJT7hcv6Z1j2R1YwsB6EFt3FoR9OPHwYfMrL+yX9LUFM4D96OZmAD8e1mZKPTna8i9aCorwUuZx3MdUl0d1m71aqwf13ropfAqad06om99S5ufeL1CAZ+aQjRq0yaklFm1P/X2Ql8fFNePfUwYTUT6Z2ftWqKf/tS8UmfFnpyehoHc0ZHYzsLhQK1Sc3NiGj0R1lX+++RJ/F6xAmPdtAnXKikRNTXRqHofoosuSkTki0aNrEb2SUuX8PsRWQyFsM8vvBA6wblz0B+2bhXGk2wU3XADwKSSraGZrJ6hIYwhGhU8k+s+rUTVNWK8b9qE/197TZ83BAL6Z1+LjKSwZ6M1RLZoyXBaAJRpT6q8YaNR0ZWd78Pehvp6/Ph8RB//eDzq1TveEV+A+fOfi3SvI0dEl3KHQxTyf+c78QJDT/EMBHB4OW+eSckI6+ogWF58EYKO+0uFw1BUwmEo2KwYcM67xwOB+uqrAGawSsFKpsgSLYyGb0Y9gGrPw32g1q9P9NgRWcPwGAGwpUWkEGULxtUomWX8zc0wWHfvxt+FheLcpXOuzabCpOP9lz27MpAAEc7eiRNQSOSCbLP8y0zdmFWGYzbg4Y8fB7/z+RLnTmtelP2Sli0D35uaQgS7uBh/Hz5M9Ja3xF9DVuIZAGZoCPMTDMIoys+HwfWxj8Ho3bMH1+OeeaEQeOxHP6o/H1r8xOsl+spXkNYZiYi93tkJh9yXv2zd2VXbC04njIbt2yGDOA1L/o7a2fH50OMpFaUunT3p9UJht9nAGziFsrcXazc7i3lsa4vfPyUlgk/yeDm1j1uKLFsGA1Ku59mzRxj08llTylN+/fBhjCXTRftmz6OaLiE7hevq4OyLRGDUhEIwFg4dgrGi7I1lZg2NZPVwfRqnnM/O4pxFo/i8lai6Roz3d78bn+V6QS3ekEoNqtEU9s99bmFmDKRCS4bTAqBMe1Jlw6yyMj7Hlb0N3CcjEsGhamiIF4yyoOzqAlNwu2HUcMofdykfHoZQvvZaoquvFtfQUzxZaHPePI9NyQjn5xFaD4fj+0sNDeEg1tXBUzs0hN4XHR0CaGBmBgaV1Yp3MkV2sTV8Uz4PG8qHDplrnmqGlHsjWzCuZsisg6OjA8/x+utQGj0e7M+mJgirdM61UUM4XehXI+mJHR2oP+vpMc+/UikYTreGIlvw8E1NiBwVFhqfF2W/JLsdnwuFcBYKCqAwazmBZAVw715R41FYCF55xRXxkZ+vfhXRDkb+LC/HWfvDH6B0m5mPaJTo4YchE5xORMVYJoyP4/VHHkl0qBm5rhEE1sOHkfHQ3w8j4PHH1ddV7eyk67zU2pNE8e0/lP2XeO9v3RoPjONwQC43NuKHkVr1MhpmZsBXGOhJmR3gdmM/1tfH13EVFyfKUwYMyEbRfirnUU2XYMORDVBObywrQ5r+oUPYG4cOQQ9SGkVG+IpyL65blwhWIo+lpgY6i9+PyOHICOSA1ai6Rg0/mTecOSNS41eswB5MxaFrNIW9p2fhZQykSkuG0wKhTKbgyIbZuXM4/KGQSP2QkWaMFrMGg2AMyi7yTqfIF37ySaKrrkqEtFUTTow4J9fQaDFCbrIp95diBMD5eQFFu369QJIbGcGz3XGHsVQvsx4wPUU20/nemSDlmN/97tQUY6Mk741swbiaJTMODjmKctll2EuDgzD8envhVLj11oUPhW00PbG2NjX+lWrBcKpnKtvw8E4n0fveBy+/kXlR65dUVARHFtehRiJQjrWcQKwAPvccUrJOnQI/83jEZ6JRZApwVLSzE7Jgbg5Gzq5d6lkDetTVhciFwyGgjokwBzU1MPZeeMFcVygj9QABAABJREFUuq3RFM5gEPW5qa6rFc5L5Z5MNnbl3pfrAtmRGA4T3XILjM5kGQ0jIyI7YOPGxOwAbnKvPGtq8jTT/RrlOUrlPCrlxcQE9tfMjECIY2cwEZ5t504YTX/zNyIdTS1dXesZ9dZTachxfVp+Pva/3Y7zxKmTmUDVNWL4yZ8JBMTnSktTd+iaSWFfvz53rSGspCXDaQFRJtFo2DB7/HEw0L4+eBeVDUe7uyHUAoHEDuVMxcUQ3oOD6l3kw2EI6e7uREhbLeHU2wuDSxbuzHzy8uIZ4dBQYn+pmhrRy6OyUqR+cQoE94/Jhpf+fKRM19bIe+PVV7E/WQBlCsY1FTIyD2pRlJYWnClO3aqqwlnPFFkF/WomPbGlxTz/ymbBcK7g4TdsABy2kXnR6pfEPYX6+sAjV6zQdwJ1dIDX87Mynz10CIbAe96D/VtUBKNuZiY+QqSVNaBHnZ2IltXVqRvB5eWQGZ2dxgwns/2ruBnuxAT+djrxv1FHi5U8zsjY5+cT9z7XBRLh/c5OOCXuvjt5RkMggAbUZ88K5DYmjpoVFUGOysRy1sp+jUYcj+mcR5YXR46gno6dCcPD2IOs18j7cGYGusGmTak1bdVbz9tvj3cKsyM3HBaIdXLqZKZQdY04lKx25JpNYc9FawiraclwWmCUyehEezvRF76AkOwjjyDntrUVgjgQAKMaH8dh//rXtQ2G5mYor/v2gRHJJBcaMrSlcgxqwmnTJuTVP/WUMKocDjDXgQEYP8wI1fpLyb08BgbwusOB/81ERTLdoC3TaFOZpEwwPOV83H470n2OH8ea5ucnwsjmGn0n2TyoRVFkhcjlgmKbScPPKuhXsylMZvlXNguGcwkPb3Re7Hb9fkmlpeBlW7aYb6gqK6RPPglFcmTEXNZAOqQGCKRFZpRqXle3G/WEXG+bl4e5amgwvq5W8DijY//AB4zvfaMZDQUFkF9qUbPqavAejsowWd2v0ajj0erzyCAqoVAiyEU6KeVG1vP3vye68UbhFG5oQElBT48A8OAGxIutnicZafHBWCwe4ZKBaIgWZxaOTEuG0yKlVBVwux0exIYGwdz6+sBsxseh3K1apW8w2O0oanz6aRgplZXxTTHdbjBDIsFsleP9/OfVGxuuWiXGFQyC6YbDUBRYcdbqL1Vdjc+99BIU7sFBCAqjHsNMN2g7HyJZVjI8rfm47jrhHWU0NlmwLgT0Hb15WAiwq1aNIZv1l5kuGF4s8PDp9ksyopCeOyfQ3JTnKxaDUy0vD9cxmlrX2gplcWwM51N5zdFRvM9Q6kqSZUQgAP5gRKmenEQWAoMpyPW2/f0YT02NuXXNdENSrxf/W7339aJmN9yA1E3l/Yz0azQ6DjOOx1TOI+8Rjq7NzyONnMFNpqfhdBsc1AaCMHseja7nhz4UP/fhMPbe/Dz0qtdeQ82hVT3PFgqp8cFgEHVfPT3gIy4X0T//8+LSdfRoyXBahJRMAVczqogS+zZw+J+ZUH5+PPqTnsFw1VVA2nvmGTAmRmqpr8fn/H7BbPXGu359/LMpPX6Dg+jNpCyO1eov5fejrkmt51MyYgbZ2CjyzBkWO91c70xHshYb6c2Hz4c16+1VV+oWurcu1SiKldFIHsPUFOZMuZfNGJ/Zqr/MdMHwYoKHN9ovSY2MKKQOB+b2+PH4rIHpaZHuVFCAextFsmxpQY+pJ58UaKayQy0ahSKrhcYny4hgEAbb9u3qayUr1UVFot/VsmXxkbPqajgGYzHrCvGTkVGDYHo6M3tfL2pmt6vfT69fo9FxmHU8mj2P8h4ZGcH/9fWQ83I9V3FxciAIM2S2hofrCx95BM8ZiYhr9PQgmnvtteeXvFeC0hw7BoOxsRFz4nafX7rOkuG0yCiZAn7ttaK3ERspFRX47uiouqHV1QXBY6b5n91OdNttEHAdHfCiVFbCs9LXJ5htR4d5g0H2+K1fHx8dY8Vk5071/lLpMsihIYyLEfw43WPNGnh8U4kUZDqStdjIyHw0NmI/LSb0HdkbWluLWgOjMNRWRiOjUfzEYkh9dTrxP+9lpWPDCGWj/jLTBcOZim5lam6S9UvSIiMKqcuFnlz794usgfl58D7u4ceIYEaRLFkmDAxAcQoEBDRzXh5qTG+9NXH8ajKtrw+8/eWX0ZdPrXEpK9Xca8hMKmAmyWxdYCb2vlbUTOusWSFPzabemTmPyj3icmFfjo5CUefm9kTGgSCSEfNzbskxNaXei07N4bJvH57hyivxv7JP2tGjcDwvNBmWDrW3A7DmnnvAQ9rbRR8/ovNL11kynBYRJVM49+wh+trXwFiamyGAfD54AGMxCKC2NmNFqjLppbC4XGAKZ8/i/4oKMK1bb8W9vvnN9A0GPcVE7i+VrsIyOAiIzlgMCqac7hEIYMyppIhZncu92MnIfIyMAOJaC01qoXmslIYPN2CenIwvzlcz/KyMRvI49u5FI9TxcQjrhgbMn8+Hs7pli3njM9P1l5kuGM5kdCtTc5PKdY0qpB/8IIBYnnlGpO2FQuDh1dV4zSySZXs7ejU9/jj23/Q05lirAa6WTGtsRNp2ZyfuXVUVXzshK9XHj8NR4ferR7q4ifr0dPx9M7XXzBrorHDu2YM1qK3F+ezrg4Jt9fgyJU/Npt4ZPY9EiXskGoXu4XJhjU+ejN8jSiCIaDQRFj4ajZ/zrVtFw2SZnweD4JmdnYDzr6kRz6S2nmryjetb+bnPV3nf04P5vPDCRKfB+aTrLBlOi4j0FE4ieEQGBsAASkoESh57/V5/Hf0D0ilSZZKVvZ07YXwxfHgwmHy8Zg+RlgJhRrHQE5bMRAsK8HdBgQCiqK5GJOrgQaKbbhIM0qjwXQg1LwuJzEBc66FJLRTSMnxmZ2G4nDmDfaRm+FkZjeRx+P24nscDJbi/H/NZXo6UiVhMpNQuJMpGwbCZ6NZiBXLRUkinpqD8eTyI/nCEaHYWc8GGUmFhekiWDEJkZO60ZITNhusMD6MuZMUK7Fk1I7e4GMpsbS3eY9h2Th1vbMSeV0v5ykStqVkDXc3pwlE/5htW18JaIU+VlEoqrJEmxl1dYo8Qgaey/Bgfx/WGhwWMulq0SrneRNCHBgbgHM3PJ7rgAoxl1apEfu52A6L/6adRCtDcrL2e56O8X9J14mnJcFpEpLcpAwEIO0ZgIoLCduIEjJpYDIWKsRjRxRfDGEi1SFVL2ausBMNjZe8d71g4h8hIT43OTniojh9P9FyGw/hedTU+Oz2NQlsjwjebyGGpULYVRDPzkUyQ51q55bPg9yOqEwzitdJSODCOH0ch8Ic/rN4rwyrngnwmGxqQxlJWJuqa+vpwPrduxXVHRha/1y9VMhLdWuxALkqF1OuFJ5gIBsZjjyGa+653Adb8Rz+CV31uThjW6SBZGlXA9WRadTXRtm1AyhsawmfVjFw5wrN9uwAKcDrBX7xe7ZSvTNWaGjXQleMJBvG8DKSxbdviqQ9JNRU2WRPjsjKsf1ER3mfURE6fm52FkTwzg3vKxoxaqcCRI0TPP4/ao+ZmOAtmZqC3fP7ziLAGg/HPsGIFvvvSS4gCsmGr5nCxWt7nWsaZ4YULXdexipYMp0VEepuSG+YxApPfjwjJzAy+53CIdAzOCeaaHbNFqkaVvcsvXxiHyExPjbY2eGS5g/vkJN7jpsG//jXRH/4Axl5ejjqEZMI3m8hhZun48cQC9Pb2zCqIVs3HQlBufT6cp+FhGCvKurimJgGnr6ZIWuWh6+pC2lVREYwi9qIS4dxWVUHY22zY3319i9/rlw4la3R5PgC5yI1wuVCd+ZvyeT7zmdwgWSZTtFwuQBnfdhvGpKY4yhEer1fUR05PizQ/Tvl64gkgCnKqlt2euVrTZAa60gFJBMNgfh7OFuYpO3YsjvqQdFJh7XbtJsYnT+Ln7FnoMTJq4vy8ALfo7Y0HglArFYhGUX9HhH03O4trlpTgXHR2InX1/e9P1G1qaoBI7PMRffzj4O9qRoyV8j7XMs4sL1zIuo6VtGQ4LSLS25Tcv6C6Gkxg927873IJhsEK3eQkDuL69akVqRpV9oqLc3+IUumpIXdwHxjAZziK0NqK5+nrg8cqFMK19IRvNpHDzNAf/gDI48FBYXCPjwuAjEwpiFbMx0JRbg8fhiAuKIhvIMp1cZs346xoGSlWeOi8XkQMDhzAPHB0OT8fxj2RaMLITaHPB69fuqSFPrrQgVzMeqC5UP2SS7Sf53Ofw1597TVjSJZWecGNKlrbtqXfvPaZZ4DQOjsL40l2cMgZGFZGYvUMdKUDcnwc+660VBh0cgraYqgPSRXoRU9Ob9oE3jY6CiOa9wH3RuJUzn/6J/A73otyih9fr6cH12FkyZkZ8MTCQnynrAzyY2wsvi6JyePBvmlo0O+rZYW8z7WMSyWNfKHqOlbTkuG0iEhvU/b2AuaSm9kOD2OTRiICktjjwXUcDjCVUAgCiRuTGS3QNqrslZbm/hCl01OjtBRheY4iLFuGuZuawveURal6qVXZQg4zSsePA0ikrw9jLigQfSdmZ/GZTCqI6czHQkEpjEaRVjI/D8WLG0dyXZzfj/2zcqW2kZKuh46F67lzOFvFxfGpePn5OPdzc9jDBQXnj9dPJrOKvJYnd8uWhQ3kYtYDbZT/9fQY59VWesHVZJrbDccDN2294QZj51hPfnm9iLpxSq3TGe/gkDMwshWJVTogQyHwEo4Uy84OouyltjOQQmcn/m9txV43yktTAXrR26eTk4KH9fcn9o2srMTasdHE9+3tRRRLdvBOTUEnys/HfXjOmTwe8N2RERhjSjIaeU1X3lsl49JxcKSaRr7QdJ1M0JLhtMhIa1Nu2oSO8089hc0cDOKglJTAkxWJgEmfOoX35uZwKAsK4huTGclNN6Ps2e25PUTp9NQIh2FYzM/DiFqzBvM2P495U3oE5eupCbdsIIcZoWgU6XkDA3hOLpaVFf6pKcxDJhXEVOdjoaAU+nxiDsfGMH88HpsNz9PTg7oLLSMlHQ+dLFw3bxZIftXVAIF5/XWMccUK7NHKSuzN6urzw+vHZFaR1/PkHjoEZWz5cvV75bK4ORUPtNkeNMl4dSa84MoeMGfOCDQ+lwu1pHa7seuqyS8+J1NTSEXmfkYyv1NmYGSDlA5IpxPOjXBY1Crn5QmHTDZS271eoocfJnrxRURmiDBnb34z0iWNrq1ZkAm9fcow3hUV+AkG48E/Vq/GWA8fJvrZzwQfmJ9Hep/bLYwgjwfOz3AYv+12gaRHhO84ndgrY2NYA64TJTLndEpH3lsh49J1cKSTRr5QdJ1M0ZLhtAhJb1OuWkX04x9DaRoaguBpboaCNzUFhhGLgRGVlkIAvviiOaFnVtnL5SFKp6fG4CBC+StXwkCsroYRysJN6RFUXk+NsoEcloyYKTudeAaZbDbM08QEhFGmFcRU5mOhIPfw2l90EfrhqEEh5+XBcEo3zUiNZOFqt0MoBgJiHI2NOPc9PbheVRUcLOeL14/IvCKfzJO7f79opmq0Z0s2KFUPtNlUUD1enclILzduP3ECynBTE37PzKSfmsTnpK0t3rnAWQIlJTgznZ2oy5WV4mzClpeW4oz29+P3xATmoLRUIOSuXIkz3tVlvQz1eom+8hWcAYcDGSxEkAO//z14yZe/nBneobdP2XAsKBDgNnJDbwYD+fWv8Zv5AKNI/uUv+L+mBjyxogLrnZ8vDFYirPXwMOTRzAz2Mqewcw3UypXmnE6pyvt0ZZwVDo5008gXgq6TKVoynBYp6TW4u+8+/H34MBjzsWNQ5AoKwEyIECFpbgYDCYfx24zQM6vsZRodTev7qfTUYMXh5EmkdyxfLpQoWbgVF8d7BBdL8ePkJOarsFB4N2UqKICwZKjfhUYLBbmHx+FyIdVHBhTJy4OArqoi2rAh+bVScS4ohWt1dfw4IhGM7S1vwZncsGFhef2sOPNaiIZainwyT25rK852R0d8TRBRbs43z9HJkwD/WL7cnAc6lVRQLV5t1gtuZn2jUUSWQiGiyy6z1ijjc+LxJDoXCgpETWB7uz5MeDZgy1evxtjOnAH/WL0ahsGJE3DahcNEX/+69WOJRoFgd+yYiMTxGixbBifssWOYj0ykh+nt05ISGHJEov6LiQ3K2VkYQnLD8dJS9F3605+Ahnf11dgDF14IVL3ZWRhTkQhkht+Pea2rwzPk54u909OD16+9NjtOp3RknFUOjv8pQA+p0JLhdB5SXh46Zj/4ICJP/f0CaY8Lw7kup6RE5PMqBW8yBpjMO2mUeaYroJJ932wqFCsOzc1AJnztNcwTeyhZ+Pb2QpEpKsL/i6X4sbgYQnl8HAqDLCSJBEJje/vCZIoLhaErx7FjR2KHeO5ab4TMeujUhKsMbDIygvfuvReeUiXlEubW6wXC2YEDcOZ4PNpNUrUoGaKhmjGRzJPr8QCu2+PJfXGzzNcGB/F7bExEv2XS8kBbWaxtxgueqVoss+m30SjOQjAo6jmVTo5oFPPwiU9ow4RnC7Z8dlb0rCoshAMrFAKvLitDRkkmxuLzQdax40HZU6u0FPP46quZSQ9Ltk8vvFBcX/ke16s1N6sj4e3YAQhynw/8gWuvT51CFO3cORhJa9fCOGIocqJEfn70KNqsZPr8pyPjrDpLydakshLOpePHz79UvGS0ZDidp8QM+Uc/wsYOhcBcysshdFn4cbqZw4HfLHiNMkA1Zc8M80xXQBn9fiqpUFqMo6AATMPhgMA9dWpxFT82N2OMQ0PYF7L3NRTC/DQ0EN1yy8JkhAsFuUdtHB6P6CWS6VoiLeHKik5vLwSbmnA0q+BYaWRxStCxY/D2coPuzk4oJkbTgVJBNDTiya2pIbr5ZqDR5aq4WcnXSkuxz3p68DyXXhpvPOl5oK0q1jbqBR8cRPQiU7VYRon3uNeL1LYjR2B4tLcLJ8fsLPb19u1EV12F76nBhLMCvWwZxpFp2PLGRqx1IED0k58kRlOsBsKZnMQaEQlwCpk4pXt6OnPpYcn2KZH6e2vXoueT1t5pakLq3cc/DrnG/Iub3g8OwllSVweUWdngkJH1GGQkG8Aw6cg45VmKxcT+dTrNnSWtNeGm0o89tjj73KVLS4bTeUzt7aI/RyyGjV9aKsAAiHCYolERjSoqSo8BmvluuiFlM99Ptc5Ki3Hs3Am0p6KiRDjjrq7sefFTUWhlpkwEZso1TaEQhMu990JQL1RaKMg9uRxHqsLV7Pm2MmUpGkXx+b592HeywTM+jtcfeYToO99Jng6UCqKhUU/uVVfhJxcROTW+xk1p+/tFbxvOGjASZbWiztTI3G3cKKKAmazFSkbKPb5jB1p0dHbitW3bkMY6MoKsgXe/Wz2dc3hYRKc4ounxQOHONGx5SwtkyeCgejTFSiCc4mKhaKulb8/N4TcjdyrJqvSwZPtU7T2fD4a63t5xubB35Tmy27EvmI4eXRi1s0ypyhb5LIVC6vu3qsr4WVKuyeAgYP1HRhZ3n7t0aMlwOs+ppQWe14MHIXgHBgT61/Q0wtR2OwpCq6vhuRkZSY0BmmWe6YaUzX4/1WJFo0qHmoLZ2oqC1tpac8qKEYMoHYVWZsper6hpam9HpGkhG01MCwW5J5fjMCtczZ5Rq1OWuroARuNwILLD93c68X9fH9ELL+BzaumFTKkiGpo1NnNR3KzG1+QU4UAA8zQyAqPTaJQ13WJtI3N36aXwQpvh6dEofsrL8dybNyfWsZhJv1Xb4yUlAH7wetH3Z/duGHl6fQqDQcjFmZn4pqujo9h7hw9nfn9kCwinuRlz0dmJ/SWnb3PEwuHA2mQyPYxIf5+qvWdV6raa8S5Hazhik82631RkC8/Hiy+CR8j7d24OOh83DtYjrT53Tz6J6y7UPnfZoCXD6TwnWdhx0frQEBhCX5/Iaa6pARzrK6+gLkou0GVKxgDNMk8WCm43vM0yUg5Hv/SEQjbR1ZIpHWoKps9H9ItfQJFYuRJzbMSwMWIQWaHQLhTDIx1aKMg9uRyHmXU0c0abm61HUevshOJZV6d+//JyeDQ7O/UNp3QQDRdKtFKLtPgag3+cOAHwgFOn4JDJ5riTzd38vDmeLPO6oSE8l88nFN1U0m+19jjX/7W0YL/cdpt6Y93iYsihI0egdMpGBCOsDQzA+Lr++szyy2wB4djtRO95D1Jf9++HblBRIYAzIhH0OHvXu4ylhykpk9Eaq1K3m5vBy3bvxt8zM/j+yAgM5ulpyOFkBofVZFa22O1EN94IsJWBAWSQsNE0MQGeUVYGpERuQaMkvT53Xi/24tBQvL6mpx/msp42E7RkOP0PIGWfjOPHwRAiEWz8SAQKh8eDkDYXUra0JCo3egzQLPMsLoby8/zzKBBXFncXFOgLhYWCrqbm4fT7Mc/RKIRPKIS6qGSGjWwQNTZiTgIBpCX5fER33AHmbpVCu1AMj4VGi43RG11HM2c0F/2ylPfRonQRDXPpNEi2t/T4WnU1nqm8nOjWW8EntcadKaRSvbnr6jLOk5XOn+XLodQdOIBUuKEhPO+KFTCAXS6MKdkz6O1xmw11Sgw3r3at5mYY9i+9hCwNZQRjchJ7v78/8/Uu2QTCaW9HfSH3cRoYwOsVFUhNv/VWbeO8qAiyqqsLck4JMJFpWWyFM6SjA3L79deRoRMKCX2ESCDrfv/7Cz8dragIezgvD3t9akr0vWLdSot36zllX3oJEdvCQpxFWV/j2nmlfphpdMpc0JLh9D+EWNg99xzR/fdjY1dUgJHZ7fBE7N2LzxUXg2nKjV2Z9BigWUNmehr36e2FICooEMXd4+M4kDt3aguFhYKuplQwuZ6MvZWhELzssZi+YSMbYNXVMGA5N9nhwH1cLqJPfSrxfnLxZ0ND6ghUi8lYyBSdj4yeycwZzYQXubVVRJUqK+EJ5TS7WAznpKICn9MjKxANc+E0MLK3kvE1Bv1461tzh1SqNXdGeXJjIxqvK50/K1bAgHr1VeyP2lrIg5/+FHUsRp7Biv4zO3YA9TEQwGtyJNPtRnZGtvrcZRMIp70d+kFXF6K+RDiLnOquRl4v1ubsWawnA1CxMp0tWZyOM0Q2FrZuhaNyZgayd2gIsPCbNmG+F0M62uQk+OqVV8JoUmbzzM+r8269VO7qauiIExM4p8XF8WA8l16a6OzOFjpltmnJcDrPSVaGi4qw8efnIZSqqsTBZ7jy3l4Iq9OnITBlSsYAlUKTSFuZ4b4dZWWIeE1MiFQbjwdjnpvD57VooaCrKRXMQACMgpmU3ChXz1PPBpjbjSJ5ZW798DDRU09hbvl+3PVeLv6sqMB6poJAdT4aC2bofGX0TGacDT6f9RHdYBD7+/RpKJ583ktLscejUThLjKR55BLRMBUyurfS5WvZQipVI6Nj7+nRjmba7Vi/V14huuAC7FczY7DCobZhA+o8e3uF08vlivfYG9376TqkjEZTrHJ82e1Ik9VLlWWS98pFFyHVj6PVY2OAEQ8Gs4t0atYZojQWAgHBkxwO/O92CzCWTETak43P7Lqy82BmJtH5TaTNu7WyDGIxROR4309NiebBDMbj9UL32LQJc3TmDNFDD6GmiusWYzE8T2UlXn/iCaJ77llYfNoILRlO5zEpleH5eXiFVq4Uyjij58g9nS64ADnOHOEwKrRloblnDw4Xd/UOhRA6/uhHRUrHyZMQTjLyy/AwvheJYCwPPQTvo5YCvxDqFZQezlAIc82wrnNz8Y1ytTz1k5MQMsPD6rn1dXWYt5dfxv8+H9IB2cDisPzp02D4/f3wjCaj891YMEpWIUMtZDKjlGspoLEYIsJeLwrsGxuN3fv4cdGcu6oq/pxzn5o1a9BkkimZQZ/rs68kLSXH7N5K9dmyiVSqdQaMjF0PwSwWwxwGg5g/NtqNjsEKh9r0NMbn90NZtNuhTLe1iaiDkQiKVQ6pZNGUXDi+1PaKx4Mx+P2I1Bw9SvS+9y1sB5zSWAiFwJfKyjC/eXngUZyBk2l0PZmHDA7C2d3RYW5dkzkPuruhBwYC0Cl4L2llGbAzuLIS/xcUxNeVOp3QO5Ytg87xz/8Mve3AARidoRBqvM+dw3ViMcxrfz8iVVdfbfk0ZpSWDKfzlNSU4XPnsNmJYBAp0XMKCmDoTE5CeamqwoE1o5C0t+O7X/0qDr3TKbwSHg8iJqtWxRcRl5TgXmfP4qBxkfjUFN5PpsDnGuSAmdTBg0iTm5gA85ubE1DfdXVgFoOD2ug8xcVg2IODiTniRDB0PR4omatWET39NO5TXQ3jaWAAv2dmYDj93/+L8egh5P1PMBaMktmansWa2mhUKVdTQINBKEM9PRB8LheEZDJBfvw40Wc/C37CvdB4/4fDIjLNvUH27YMH+w9/wBxz2lZeXiI/sPrsG11X5eempxFFV1NeXS7z9WKpPJuexzgQwJj279dGLbSqri3Z2PXS6QIB8DKPJ751hpkxpGNUe72oY7HZoCiHw1i/0VGkcNXXY+6SGV9WO6S0oim5cnyp7RW1Btwf/KCx6JUyO4YI3880b1UaC04n+Aw7luWMER5Tpuq11MBSCgoQxWlrsybye+IEdIhwmOjrX4/nU1rnkp3BsRius2EDzhTXldrt+PzWrdDxhodxfhm+vrMTznS7HQ7lvDw81+go2k80NCxcw1qNlgyn85C0lOGKCvxMTYEhuN3xXgNugldTg0LQVBSSaBS1Oc3NQOZj44ELK1kR/8AHEg9oby8O5rJlOKj5+aLQ1IiXkdOLOFUgW4qs3Q4l77e/hVLCczk0hLG7XDBmXnhBH52nuRkMbt8+4dlhisWEAZaXh7zz3/9eeP+5mS0RGFZZGZTU++4j+uIXtZmSEUXpxAlEuUpLF5eBYJbM1PQs9tRGo0q5Eljm2DEI0MZGeBbdbmOAJ9/4BvZjZSX2J/dtmp/HPSsqcE5aW3HuDx4EImUwiNfPnROFyG1t4FsyP7AqZUZrXW+8Mb5nm9JICoXgPS0vh6NCqby+/e2p1YuZfTa1PSyn887NgRd997tEn/xkZhvR6o1dzyPOTYtXrxZyI5UxpGJ4yrJz69b4Pk4Oh4iMvvWt+mAV2XJI5dLxpbVX2OD0eKAwG0GhUxoMg4N4vbbWOBptqqQ0FkpLwWf6+2EIyhkjmazXUgJDcZ/FSASOJ48H40kn8hsKiT28cmUi+NSnPqV+Lp1O7P+xMTz3ihX4kaHaGepcTnnMz8ceYdRklwvPEY2CDxHBSbLYnLNLhtN5SFrKcGkpDp7Ph027cWO812BqCofmnnsEgzKrkPC95RQLmdhbSBR/QDkUXFKCgzY8DCOhpMSYlzGXiqzXCy9LeTkYxeSkMJBGRvB6NIoxxWLa6Dx2O5rqPv00mEllZWJRclMTPtvcDMY3OyuAKAoLIcSqqsCghochhPSYUjJFKRgkOnSI6FvfwjUXm4FghowWlQ8Oohh6sac2GlXK29uhxN5zD/ZDezsEL/MWFuRPPEH0oQ/Fe4mJcC79fsyTx4P7chT65El8prUVQpnrLwsLEYEuKMC9ZeCYQAD3tLrOQMtz/+KLMJLq6jBupZHkdgMZtK8PSk4ohP0jK6+7d+O7mUYAVe5hvx8GL6fzcrrwqVN4VuVezSb8tZZH3OfDnKo1fTU7BrOGp1J2yhGUgQHMW08P0b/9G/apFi/MFiJlLpAvmazaK/K5Y2fu9DRkpc0G4ymTvFXNiOeeaX4/+A6nI584YX29VjSKCLBcD8QN6VkHYOeH2Tor2XkQCBD95CfQUWpqtMGn/vZvE88ljzMSwfV4r5WVYZ1OnIAh1d8v9mJpKcZ/+LBwoHPPNocD82e345onTmSvZswKWiT23RKZIT1P0Jo1YGRjY9jwW7ciDNzQACSh++9Pr/mpEY/l7CwY47veJfLFR0ZguHHR8OgoDvXu3ULpYk+kkpjxvvaa8EhXVeH/Bx8UhlomSOmhvPpqoquugof5LW8BgxgdFZ9dtgyNGNmb+dvf4nWmq64iesc7wGRmZjAvwSDSQ7ZsEYprayuY38qVUOBaWvB3czPmam4ODLKpSTBYNZKFn5L8fkSaAgEwVIcD8/+Xv8Bjncl5zQWxAO3uxtmQiT2NbW1QRNmrVlKCeWElWW1NM0EsbI8exe9M36+nBwbjhRdiv8lKms0Ghec3vyH6+78n+trXiL70JaJvfhMonqzYcV0l09ycEKTT0/Fe3VOn8NvhwGdlY2tmBmsUDFpXZ6D03PO6hkI4g+xgWr0av/v68DsUEnC/nMp48qTYP6zk9PfD8OKi+cFBeGFjMbG32tvT92LLezgajUf3LCjAOJctg3KmtleNnAErxkkkPOIXX4w57uzE7+3bwQNnZjI/BiWpyS+bDfv29dcxprw8yEs9GWNUDqa7f7N1HzWyYq/I5669Hd9hebdsGcbO18kUb2UjnnWRQEA4Rex2rL/TCTn+pjcJ480KHuz1gk/+/d/j2c6ehc4zMCDqpLn+fHhYoDyaWVd2HpSWgu8UFSGrpb8ffLuyEq+FQnAA9/cnnsvRUaJrroEO4vdjHBytYmNy+3Zcg/eizYZzwil+jJwaDuMcFRRgndlIzDRCpZW0FHE6D0lWhouL46Gqq6qQYnPkCN4/dQqfvfJKawqqlV4oJVQ2kfBCtbSIUPLzzyM6QgQvRm0tmIbsYVbzXuW6RkfN4xcOY177+vB3LIZnv+gieGX4c2peI7sdjRlnZ0VtBwM/9PYKb1dLC4TWCy+I1Ah+Pk7rq6+HsnbqlDZT0gMA8HrBaPPz0b8hGMR7LheUCJeL6DvfEcXvC7nex8j4jBSVX3opanCy5eFVG3dHR/ajq3oKmt8P5WF4GIZVS4uIwB06hL24eXN8+gtD4jJNTAjhHgjg/8JCfEb+HCsRAwNQbqyqM1A7x3JbgYYGGB39/cJImpjA+6tXY4wFBfEKDqNZcWrZ8uWIXu3fL2o/S0oQhTNSM2OE5D386qvgQQxYw1HrNWvwOS3+o3YGpqagQHk8gEK3irTS6To6YJBkGy1VLYoi7wPOiHC79WVMtiJ3uexlaAUIh3zuJibi0WiJxHmamMhs9EyrJu5DHwLPr62NlxtWZLjIkTa5Hqi/H7rQ/Ly1dVZGwaeefJLoX/6F6O679WWPsm7Q5UImhrwXuWxgfh6RJY5aFReLzJiBAcxppvttWklLhtN5SKwMv/giDt7IiICq5t4p731vYlqNFYJIVsSrq3HQ5HBwNArPBXuhOA1odBTeFiLUO3Dj2OpqMJGDB4luuinRe5XLVAWiRIVSTo1htJloFJ87cQJzXV2Nz2rl6re3o9GtnPOtVtj8rnch97mjQ4BNyGl9a9aIFD69niVqwq+vTwiFYBB7xuPBd4JBjOn//X8BBNLQsLDrfcwIuWRF5TKoiRpZibikNu6KCgiaSCS7aYJaChorlZOTMGQqK+MjcPv3w/jmuj5OfykpEQZ3OIznWLNGoFrZbLjf5KSIOjHl50ORb2qyLuqgZhjKbQX4ntyoWzaSli8XxeRKBYcIzx4KoThaTudlb3pdHc6RVevGe/hHPwJ/sNlwT4bSNsJ/5DPADhQiKJAM3mHV+VZLp8sVYqKaI0lOI2eHFKdba8mYbPUYzHUvQ6112rgRBgc3xdXSL+Rzx3oCo9ESxZ+nysrMotkZrYmzAoxDDQI9P19A8bPhxABeVtRZGQWf6u4We1ntXGrNUTSauBe5vj0aRaS9vBxzVliI94NBrG2mIsiZoiXD6TwkBiv46U/FwSsvx+Hu7MRG3rDBGNJNKvd+17sQ0frTn0ThdyyGgxOJwKPS0SGYS08PGMW6dfCSdnRAWWJGwWk6W7YkMrFMNOk0Q8ronrLxLc9JVRXGIOcp63mNjDDx9naiL3xBoJUxoAYrSFVVUJzUYEeV91IKP27+x3CsbrdgtAx7PjZG9OijYLYjIwuz3icVIac3911dmfHwqiG0ff/78eOemiL6r//C2rz97eZhmtMhLQWNDSEi7Hm5mN9mQ0opn/dLLoEyxcX23LfJ7Sbatg17l1En8/LAAzwezAn3Q5ubw15zuYiuv96651QzDOW2Aqy4eDyJRlJBgYimFRfHtx5g6N/ZWVxn61Yxb3J/u6NHkZ6W6vMo909bG9FnPgPeWlQkIteysmSE/zz3HFCv7Hb87/Fk73znAi1VzZE0MwMFb24u3sBnUpMxme4xKK/3li34O1e9DJXrxBDajz2W3FElnzslmh1RvMGQyegZU7KaOKsyXJQOXyUgRWkp5GtennV1VkbBpzglX4u05khrzxcXY0+4XPjhlL3ZWZybhgaiW25ZWBkqyWjJcDoPiZHtGhvhIRwZEYeQUavSFdR61NaGA+hy4fpTU7h3c7M6IhYj0HFkxeHAoeLGuEVFYDC1tYn3ymWqAlG8QrlsWWLjW55fpxOvcRpPaWlyr5EeE2fBGY0iP/o//xPz2tSEuZ+ZgYdbC3Y0mbFw8iSKOufnxdiZ2IMdDoMJX3ABlOJMpEmmkwKYjpDTmvtMeHiVkSWnE4LGZoOiLaeO2e04Hx0d8akWmY6uagnFkRFEixn5SunF9Hhwbj0e8b2tW6Eg9PQIZKddu3De+TnHxnB+rrgCfIEBbBwO7GOuJbSK1NaVFTmO4tbXY/znzmH8DFgxN4c9FAiIlLyiIvzf04PrhMPxYAdyU0pO/TGzbkZ6vdx4I1IkX3st0Wgyulf37cNnM3W+k5GViIlGSelI4oyNqirwumgUfJXnlJX+QABylflUpqJmWpHohgZRj2f0PlY2zW1pwdjMAOfIrTyWLcO4h4dx1mw2ce5KSnDtTEbPjJBVGS5Kh68SkKK4WMzpmTOJdVap7B8z4FOp6ktqe76qSmQ8EYmaplAIe/bee9Orq88FLRlO5yHx4V63LrHGqbQUGzeT6Ws+nygmjMXEvTnVQdlLpKgIwp9rB4iEt9fhEP0g1KJKRhXZxkbcz2rPpaxQer3wTBYXgylOTAjldnhYpNKxcEvVG6gmOKur8TM6ipomGXZ01SpjkRZZSWEUwPHxxPHFYoIRTk6CAWciTTLdPPJMpHFa7UlWi4j19UEJLi3F65xaxY0Zy8sT62iI9KOrVihIakJxfh7jW79ejFMmbm9w881QwmUF8sor8b2f/ARzJxuBXB85Ooq6qbEx/D01BUX91lutra3TWlePB4ZSba2oDaqpwXOMjcE5tG+fQKxj0ACuHX3Tm8CXfvpT66LiWr1e3vQmjFOGF77uutT3Kp+fxkbwgeFhrE1lJfZdptOgc0lKNLLvfhfG6aFDOIMyNP7rr4M/fetb2I8VFfg+8ym91Caze1crgt7djXW5+ebEehy9a1mZYp2Ko0rZyoONpdFRnL3KSpwprzdz0TMz62BVhotaHXo0innq6YFjhoFItOqsUiEGn3rmGVx/chL3qK8XTu10jVO1Pc/tG7xerK3djs/dcsviM5qIlgyn85Lkw83AATJlOn2N7+/xiPoEvx9oMWq9RDgsLytO3PiQ0VhkUjK6G2/UVw64k3W6AkKLwbJC+eMfQ4gODUGh4pQ5Ity7rw/PPT0ND24qXiMjgrO6WsCOrluXmqe4uBgK4uCgiPrl5Qm4Zfb8x2KJ+4spnX1mRR55ptI4rfIks6Lh90M5CAbxWn4+xhYOx6d2cgSEgRXkOhoi7eiqlQqSUigWFRH9/OdQKJVnVXZcXHUVfuTz09iIcxmJEL373dhnspNl714YSi++CCOFSPR7svq5+Nm0vKVlZTBO+vuxB3lflZeDn3Gt0he/CEVQ5hE+X2LRtExmouJavV5mZoiefVakEsrwwrffLnpOmdmrnAlw8iSuJYPDtLSgjkVG9lroADFmSY6izMzgWR0OrDkRnpX7mjmdmHunU/TVk/mU0rBMZe8aMUz27ye6667UDbBUUjB53U+eRKr98uXGHVVqrTyIMIfT0yICkqnaNrPrYFWGi14dekUFZPjFF6PWuaUltXOkdR6NgE9ZAVKjVh91vvCHJcPpPKRcp6+Z7SXy9rfDm+L3xzfk5RBySQkYyfS0NqO79lqkJyqVg/XrRSfrdAREMgbb3o5ms0RIcVP2uqmshFBZvTp1ZmhUcH7gAzB41PqgGO2JxYpeNIp14LxzBomw2cBs2aBSo3TqfazII9dDlywtTe8cWFF/4fMJaPNTp4TgLCqCMeF2x0eWOA+eFWI+R0TaqVdWKkhMSqH47nfj3kaiGvL3urpERNBuTzTA6+qIXnkFKTzr1+P9vDxc9ytfwWesBsnQ85bu2YPo0uQk1qa8HM/X3Az+1duLmsLrrovfB1ald6oVlI+OYixDQzAyeW3m5/G5p54Cb1RDyFLuVaWi1d8vUtXsdtGSIBhERHR0FPNVXJy6IbDQFSme80iE6G1viwc7IoJ8cjiQwscpmWNjmAMidT6V6pm0KoJuJRKtvO6DgyISu3ZtYgRa6ahStvIgEjw6Px88sbU1PeMh2djNroNVZ1lZh15TgzMwM4PnLi1Fxk6qdejJzqMMPjU4KPjo9ddj3TNBuUi7zRQtGU7nIeUaaUe+f3t7PGACEYwj7iXi9SISVV0N5aOnR9QzcAi5sRHjTtZ49Pbbgbyn9GinKyCMMti8PKK/+Rt8tq8P15SVyOZmMCZu8qilKGgpFEYFZ2dn6pEW+Vk3bIDR1NsrYNXLygSk6MUXY316ekSjYqZ09lmy52xogIH45z9jn2nNoxF0yZ07Uz8H6QqCw4fhsS4owLxy3djYGJTgcBgCjyNLNhv2qtwzZH5e20hJVUEyq9DqReBuuAEKt1z7wdfSiwjGYiLKsWYNjCim4mI02yWC0cbXs6r2Rmtd9+3DHLa0YP+Ew/CMz83hf7lnmvx9q9I7leciFBI9UcJhkQrM+0aGF77qKv29qlZnd+oUeC6fGQbKcLtx74EBtFeYnCT64Q/NKaDHjwNY5uRJ9fS2hULynLMDLxDAHO3bJ4ALbDYBYlRdDRk3NZXY2NPImVRrJM21wFZE0K0ywJRysbQU693TgzFcemm88aR0VKmNQ3aerFkjjPZMpOelwhutOst6dehtbfhtpg5dWfP4m98kB2xi8Jcnn8TrfX3WI2aer7RkOJ2HlGlEHzP3N9JLhJtDdnWhiZoyZcfrRVqI3HhUjdH9/vfxaQqyRztVAWGWwWopkWz86SENRaPxjMzhgNLJnzMKhU2UWsRR7Vk9HqIDBwAVHwhgLZua4MW+7TZ8z+p+K8l6Bp04gbqO++/HvtHybBtBl1y/Pjde7mgUdShcI8TRI6cT3se5OYxzfh5/s4Hk9yPNs7YW+5Zr/zZvTpyDVBSkVNPf9CI1J0/CAIpEcM8bboAirxcZDwSgmHs8Im2XaWJCAElMTMQrW5nqpfW738FAYch1pZJ88iS85lqKqxXpncpzwdDEDE7gcIi9QqQOL6xGao6hEyeguIXDIj3UZhOGWV4e5iASAU8zo4D+4Q9EX/0qFDzuZ6WW3qa3HtmKVKkV8ZeVYbxTUwI6W63XmFpjz2RnkhtJHziAOZbPn1WZJFYYYGqyIhaDI62/H2OR04zVHGm5RMSV14EI6ylnI+jxECNnOdke1apDLyjA+6Oj8XXgeiTz7GAQ5QLhMIB1tJBXV69GD8hHHhF1o9lEzFzstGQ4LQJKRVDkqg+G8v5Ge4ns2IED7PWCaVVWitS8VBuPWsGYU1E+1eBZk3mAiIgefpjoj38E8/N4YBQ0N4vPvec9xgRna2tqEUe1Z62uxmftdnxvdhbv8/plYp9pKQic8hkIQMFoa8OeOngQe+z970eUTC6+ziW6pB75fDAMGhsxJhm50GbDng8Gsafm5mDocX+UujpEq6ansZ5EUNwYrIF5hNn9rxVZ1ZpfJcmRGq9XwKlzyuHgILyZTz+NOf/EJ7T3KdfOrF4dD3FOFB+BU9Z5qT1XuiSfi8HBeLhkVpKHh7GeeopruumdynNRWorfvb1wsEQion2AUXhhLQW4pwfv8/UYJppI9LHi+i5uAmyEPx4/TvS1r8EB09gokAmTpbcxWV3Xloy0eFEoJFAuiRLTlQsKRBG8vB9SbSTNGRVWZJJYYYCpyQoZGY6dbCMj4LNqjrRclhTwOgSD4KWcfsmgH6tXx9fvKUnvLBvZo2p16H4/MhDU6sCVe5t1wsOHiX79a1yruRln6fhxgXgrR/34PO7Zg9TdZ5/FvcrLsZ9ZL0sWtV8MKbaZpiXDaYFTOoLCrKCWDwQz9nQa5La3G+8lsmEDlFkrG49awZhTNb5YiYxGEUEaGdH2yD7yCJjk7t14j2sUBgdFnw42GtraUIivJzhbWlKLOKo9q98PBjwzA6N3chIRkUOHcC057G8VM1VLNeVGq4w0tGwZ0nuGh6GgnD4NI2njRpHy43JlDl0yXeHBTR0vugieRa7t40ar4+N4zjvuINq0SRjgTz4J6HnZuI5EiH71KwCCrFwpoMG3bDG+/7Uiq6GQ9vwaKWKvrhb7p7QUPGBgAKhOwSDGaLNhDlpbhdfT54PBpVanJ/dIkuu81J5LbWxm143PRXNzfK8VHld+Pp71xAmiyy8XyKBqlE56p9q5aG2Fh3lqCp/hdfb7jcELqynAgQDORkEB+ND8PNalqAhzHo2KHm/5+fjfCH+MRpGexw4DjiQmS29jykS9XjLSSnt3OgU4jsMhIgVMoZB6Y89UG0lzRkUyICQjEX4zqfxa50VLLlZXQ1nnrIBTp+C0UnOk5bKkoLgY67N7N/YyN7kOh3G+/X6koerpBmpn2egeNVsHLu9t1gm9XsjhiQkg59bWYr1sNsiFkZH4qB8ReO6xYwIOfNkyPEd/P849G1paEbdsOy4WKi0ZTguYrBAUWoJareEmp9UMDcV3imdFLJXD0dJivJeI3W5t41ErGHO6xpeRep0XXsDc2O1CYDocQpno6EBKWUcHUPOMFOKnEglSPqtsrHBD3/x8YQArvVJWFX6qpZqGw/BgspBbswbngoVNRYXwhPP5ePvbM4MuaYXw4Ll2uURT2J4eeN7DYcyB2w3v4Nq1MKJ+8xuk9xEJ49rnEwAqdjvWqKICn3vtNdzH5xOQr8rGq5s2CeQ35T6Vhbna/CYrYm9shLHF+0cGShkdxb7ftw/8ZWgIwpv5zfbtMAp6exPR+kpKBFqn8kzqnetU143XamZGQPZ2d4sz29eHNQiHcUb/+Z8zo0ionYvmZvycPi0iQ8GgcXhhNQU4FILHmw2kWAyGfGEh9mRBAXgEEa49MGCMP/K+cDoTDQ299DYiawENzJBW2jsR5sXlEm0DGNQoFNJu7JlqI2lWZD/0ofQj/EZT+Ts64lPA5FTblSu15WJ1NRyh5eVoG6BVh2plvZBZZ0hjI55pdBTPwp8vKBAtCMrKYFgkI75/IADnld+fHM3WbB04f6+jQ+iEXFfMzqiJCVFrPT8vIuEMLhSLIaI5P4/7j47iLNrt8SnHVVXqslFPH/X5iN77Xmsg0xcDLRlOC5QyKSiUCkQoBKWlvByeCr9fpAHZbDgMqXr1zDJHLQU8FSPICsacrvGVLGI1Pw8G1tAA5TESEQ2DucaL0whmZ7EWRgvx29qMoWlpPWsgIBr6EolmhGwAZ7KPi9LwGxyEYFm5EmOrqoJxwMImFoOHraBAnI/du0XHeRbusZgwHjj6ZCYVxCqvt3KuWcn1eDC/s7MwVs6eRbpGYSHmWTauOR2R+ztVV0OJD4Wwh0ZH8WyMkEYk6gdDIZz1j35U3YOsNJrV5leL//C1GNlN6TDh1z0efLe1Fdfs6MBrN9+MGihWEtTO7oUXivUwcq7TWTclyAjP7/CwAE2prYWh7nZjXx46BKXxqqusVSDUHCLNzXBoFBYCCtoMvLCaY4gjnkTCCx+LYU3n5rD3YjGM5aMfhcPNCH88flykE46PY67kFFW19LZkUNd8npW9AY2SEaVbywl1zTU4VwzmYqSxp5ZMStZIWlZk169PP8KfzLFGJM6LWqrtX/0V+FN3d3xWAANn+Hxwfrz1rakDy7zznQIMR+s5U3WG9PQIg763VziehoawjkTgvV/8IgCftK4l339kBP/X14MfyMAYavLSTB2414t5kHVC1hfKyvAcfj+ehQ2pykqR2UCEM9fTg+tVVYn0Wz6DHPnq7sbr8/NCHujpo9XVRC+9BCfbBRfE12Wfr1GoJcNpgVImmncSJSoQbjfR888Lb34gIDyWRDhIPT1ggrLnw0ombaWXzKjQM3pvux3pESdOABq5qQkK58yMMeNLLzUjEAAz5LSX0VEwQ0ZocrvB/HhdmNG3tOgX4qcaBVHOcWEhFCWnU6T+yEI90/3A5BTAkyeR0siKITfkZKU8FIpHuJJBR86ehSIzOIhrTU0JsIU1a4T3PBlp9V0qLTXvzOC59vmgjHDDw+pqkSq7cSP21/79eP/CC+EJ5YL0UAjfKSrCWBg8IhyG4C4sFClSp05hfrjXTHU1/n7qKaR5KPepbDRrza8W/+FrBQICiY2J0TEjEYxxZgafqawE6AVD6l91lTHlzsi5TtcJpQYyUlmJFLm5OSgK27Zhfo4cwRpwI+r3vhfIf1YqEMmAOIaGjPM4pQFPhLl0OAQgREEB5jAcxk80ikjlN78Jw8BuN8aXBwdF3UsohDEWFQkPtzK9LRnUNXvIjdSEyCTXh+zaBSWTx6PFM7XSkTkqY6axZ6qNpOXMBisi/FrPRIS11Uu1ffZZPJ/DgXV3u0XdJsP1t7ZifsyWFLCyfvQoeiMODECmKcFl5OiLWWfI4cOQC5EI7sl8My8Phkh1NeTE4cOJqXJMSl3K5cKZHx2FEaFEFVTKy/Z21K19+9sYcziMedOqA+/sjNcJua8fGz8lJThbGzbA+BoYED3dAgGMNy9PtHZQph3Pz+M7skPy5z8H/+K0d6U+yqn87LyrqwO/P98BJpYMpwVKmUCcUVMgGB2osRGvT02BIcvKxfAwDmI6EQYr6mBSNYLSubfXC4VkchJM5vXXMfcrVwJFy6xiYrMJge/3Q8kJBsFow2GBrBSNYi1YYI2MoH6CBZtWIX66uf9tbQChePJJEXEgQtqAzMyJrCve1fP68nM2NwOk4LXXsCdnZzE2hwPfn5wU0TAicT6WL0ek4OWX8flYDPPLKTZEmDsjc6TVd6mqCnOTyvkoLIQxdO4cnpXP36ZNYq4rK+Gt50J9FpTz83h2pxPP5veLyJPTKfaQ3Q7lt7kZBkBhoXh2Nho+97n4fRoKCaOHgQbU5leN//Ce37UL6yODKczOitRCh0MYY0TqBlmys2vkXKfrhFIDGWFFq7oac3v2LPYEn1c2Wnfvjq8FtIrUFOdUGkwqnSXsuWYFmeHXnU5cl2ufvvxlYRgY4cteL1JN2YudlwdjhyMUNTWYV05vUyrFSqjr1avj51uvJkQmNsb27hXNaxsboUy63fo8U2vOU5EtasaCkUbSWpkNqdZcqj0To9HqpdrOzOBebW249//3/4m6y9Wrcf/eXv21UBuH10v0i1/Er09Fhaj9lCNeNltqzhCvFzWhExN4ltpayPXJSQHjz4i27e0w9vlaRNppedEovuNy4drK+iKlvGTdIhAQCJUeD+7DvD8Ww/2DQTgPgkGhE3JfPzZ+CgrEXtqyBVGg/Hx8z+WCI87lwj6XgTz8fnyuvx9nMRzGeVy/XtQyy2nvTHJWQl0dDEZ2gmUyfXYh0JLhtEApE4gzXV0IC7tc2NQyvGxpKQ6U3y96xBDhME5M4HVOKQgEUnumTHrJjCgIZu8te5RWrQIj6e+HYPV44PkyIhBkxcTthieNFc66OjC28XHMr90O5uh0Yp1GR/G5pib1yFammhkGgxjrsmVgqtu3x3/fquJdo6kW8jzu2YPn9fvxOxrFemzcGC+kQiF8tqwMc8yF3DMzuN7WrRDyRufotdcgSBwO4X3lYuJAAPnoekhMyufmvbVpk4hccIqbTGysRCLxgpIhoRntbHoaz8pF93NzeObJSdFAurAwvtaLjYaenkQFmlP4QqHEaKMe/5GjaT4fnrGuDuP0+/F+TU2iMUakbpApz240qp++o6R0nVBq0MF9fdgPdXWY564uvLdsmVCipqcxtuHh7CgQqfJX2fB55RXworIyoSgGgzgzTideczpFRoJ8DS2+zDxqZAQQyfv2CXTLUAifDwbx/XvvFc3Ez50TAAXl5QLqmhVojpwSadeEqKVrMt+w2bD3hoYgFy+9NDWFL9V5V37PTCNpmawu2Of1GB0FP1CeG1bQKysxj7W1SNFqbhZOGU7dMyt/5PXheqOuLrzf3AzAhoEBGGpERJddZs4ZwnsxFIJMHxgQACelpTjLw8PgxfX1OAeMlPfcc9h3aml5VVV43sJCvMfARXJ9kSwvZf7f0oIz19uLc8FoeDwnp09Dnv/2t5gLtxvzoDR+uF5pbg68dceO+Loj7mvJzjEG8vB6sU6Tk7jPypXCScprqJb2Pj4OPshGrcOh7wQ7n2jJcFqgZDXijNcLaPBdu7D5ObqRny8EPR+I2VkcTiLRnf7gQShu8/PwtBQU5C4Ea1RQpYN8pmWQNDWBITDKUXu7Me/ipz+N5r3cwLe8HIxp2TKR8sXGSmEhGNHcHNanooLofe9Lr5t8V5dQhLVyxdXSHjglce9ezIOV/cDM1p20t6N/1Fe/CkHB0ZfiYjDszk7Ma1UVvj87i/lrb4eArKgQwBuMUrd6tTEGf/w4+kYNDwsB4nbjXpw2dPQohE4yZ4ZybwUCeHa3G2OUi3RtNjxnRQXOYVubEJTFxRDww8N4zrw8GCSstHBkh72QY2OJ8N3K2glWoL1evD86CgWjvT3eC5qM/3B3+sJCQOx3dUEJqqnB+xMTAuRDDWVTaw5TURLTdUKpQQcTISrC4AnBoJh7Ipxd7sOzGBQINnxefpnoW9/CszA6IEeF5uZESpyaEarFl5VNZBkMxe8XHnSXCzDlV19N9LOfQcaEw9g3+fk4CzU1IoV0bAzKo15NiFbj2cJCKKOcAs31e5EIGpRne71YTs3PI9q/dy/2lpGMikwgDQ4OIgIzPo55KSwUabseDz6Tl4f/z57F59asSTxbZpRneX0aGhA5LC3FWGR4/bIyAS4zMSGuq1cTJhPvxeZmGBTsFOaouMOBay9fLnhTURGe4ZFHMA5lWt4LL4B3cLr02JhwhE5P4x49PeCfN9yAcSh1i7VrMdaZGezJ/ftx5kZHwfu3bRP3/MtfcO2amnjjhw2suTntPaMsdygvx/x1dOC5tm4VRpm8hnKvzbVrMXauzeI6yKoq3DvZGpwPtGQ4LVBKVtNTWQlP9Z//jM+3tuIAaHmkHnwQngwWFOxliUTwc/YsmInLBQYQi+EQ+3z4bGmpyO8/e9Z4CD5XlK4Xzuoas/Z2optuggF64YUCmW5oCIKBe8RMTUE4VVeDCV5wAZhRba36dY14071e5P5zrxTlXESj6FYve3jtdtx/61ZEbYjALK3qB6ZlmLLX3usl+o//IPrGN0SPFE6Zam6Gp3FwEGeDexcFAogI1ddDCIbD+CwjQrH3kEikeQQCmG89Bu/1YhznzgmvZF4e1ooLwYuLRS1gMmeGcm8pUy5kNKTSUoxt5048B6eGcD1BMCiUGE7l43Q4rjM4cgR8Q06LY1IaDXLkQO4RwtDUZozm9nYYm9ddJ5o6M/JfOIx0Ejn1M5lBlqqSmK4TSs3wkteMEf440qdMbYxEFocCYbdDQbvsMswVEeYqHIbCxrVbcu1DKo17q6sxdwzS4nCINKQ77wRfGBrC/i0uxhxyVHf1alxjcBD7oLgY8ywb9moKG585txsK38wMZB0jCM7Pg5cQQRE1Gjk2QnoOPDU51doKgJRkCGWZAJDilEqOYjNKIztrioqwXitXioi3UTh6PZJ5YjAo0pA50kmEvxkRlFOuBwZEVEcmLWeIvBfZiH/tNQHwwTySIzJEeH1wEM96ySXxaXlEMCYcDhhb9fWi6bTfj7YLbHy5XEjN6+9X75e4ZQvkF7/PAE/y3r7iCqI//QlpeFdfjXsVFMC4qq9P3mtPLa02GMT3t2+HcaS1htxrc88e0eTY4QB/Y94n947KZB+uXNOS4bSASSt3vLERnqC77hKpXBUVRG9+M9Ftt6kXRg8Nic8ybC2R8LTMzuKws9eOw8axGA7TxER8wfpCzl+1wgvHDNbtTuwqznVInZ3o8E5kLJrFymtLi1C2uMAzPx+vDwwQXXwxjAfuNTQyksh8WBhzYfHUVGKjUCI875kzGNuaNepz0dsLYTk7C+NArtvhhnjDw9hbpaXxPb66ulKDHtWCv+Zib+6AHosJVCPZW1hSIvqd8HeiUTD07dvx89OfYqzRaHxtEJFINwmFcH+9vj8MCOHxYN37+qBsMfTwwICo27jssuRRTqUiqUy5KC7G9UdGBDLarbfis48/DuM7Px/XvvpqnMm+Phi/fX0irWnNGnyXC4tbW+P3iB4aZUsLfvR6qxkFdbn6ahRzKxtCc3qJkShmOkpiuuiaaoaXvGY9PQJeWzZa2WO9mBSIZKnFytoHI7xUzfCUI3eBAPb7r34FY51R9RwOfGduDs6J6Wk47dixI6eUy6Q235x+Njws2ipMTeEMsDxk/heJJO/hY4SiUaR3sdOA62bYaUWkLqcOH8ZZu/12fOb4cXVeYlW2gTxeOaXyhRfAT6NR4TiZmRH1e14vlHRWotMpKZB5IvPrUAh/s6zkuksiUavT2ysaJzPpOUOUe7G6GvyJCHzc6cT6u92iuXhnJ363tYl55jrGY8eEkdndjetXVeHakQjGfsUVkOczMyLde2IChhYTtx2ZmsL3uHxCWVdcUwMD5sgRrD9HtTdtMs6TlWm1gQDRww+LLCMlyb02L7iA6LOfFU3oGTiGgcb6+kTaa1+faHdxvtGS4bRASQ7ff/CDeG16GkrHv/4rNqfDITwEo6NIHRsYQOEuHyBmrg4HDAAurGcEGYdD9GfhRoceDz4Ti0G42WyJSC8LNf1kfh5IPKdPi0ZzDLVpxgvHDfKef16gsLFBUVOD5+7vx71+9ztz/WC0PNfFxaKuSC0vmklZi3T2LJj7FVeIVCgiMOA9e7CXLrhAfS4eeQT3YKQ4jtTIDfHKyyGgGOb4F79IP59eaTwoGwAWF8PYl1GN1Jogs/d6fByf7+lBdGbNGjHXyogO1/bl5WHf60UcZOVkcBAKV0MDlB0Gm5iawrw7ndgL/8//k6gkyXOjtg845eLkSQicmRm8f8kl8QX2soJUVITzv20b9vNll2E9p6ZEM9mJCRExzs8XDhCjRoNVzY3lNK716zGHZgyydCPA6aBrahleBQUw3Fkp5ZYOMq/MdCPPTJBearGy9sEIL00W8eO02rk5XKe2VkTfOQOC+9YwOmRVFXhlaanoYXPppXhdbb6Li8E/urux7tzYl+tCWFm32cBH6uv1GxknI68Xyugf/xjfrLq5WfS94RQ4NUfAnj2IvtXUaCP+WZFtIJN8xoqLMRa3G+Pn1H5eH47Uf+QjMAzTLSmQeSLzawbMiUTwGbsdfJWjuQ0N0Ht8PuwTI3xNbS/a7TBizp3DXnK5EDnh9gilpXhmTlMkEo3XueceXycQwFyzk9Xtxjzm5Ym13b9f7O/S0kTZF43iGnNz8REcpqYmfPbjHxfZDmZ5ssyPo1FEsNTWMBrFPKxeLaJstbUYA/dzO3EC4+Wo8eCggD3ndhdqlG4T+VzSkuG0AEkrzezGG8FQjx8XhbG8yZctA8M/dgzfZWHGzDUWE6gtdruAASbCBi4rw+8PfxgejZ4eou99DwczEsEhYYQtDj0vtPQTrxeGzOOPg1ENDsZHTsyk2E1Pg4n29uI7BQUi5/7IEczhBRcgOsSeJKP9YLQ814wCV1QkPNlKAaAWTePeMU8/jbVrbsZYXnlF1Pa8+GLiXMjNdysqcA+7XewtjgCtX489ODgoFKl08+llQVlcHN8zyGbDnlWiGn3gA+q1KsPD8QbHv/0bhLXcZ0QZ0QkEoPT29uKeWsYDn5/m5njjq7lZpJz5/fjh1Nbi4nglSTk3Wookw1y/+ioE1R13iPRbZTExz73s+b/66niDpK8P87VzJ9bwyBHRaDcSwRpef70AAdAiK0BdlGTWILMCZTQdI1DL8Nq5E3UL/f2JRqvWGV4MpJVaLNc+NDRACfzzn7WbnBIlj/ixs6a0FGtTUIA5C4WgiDkcmNdgED8tLfB+nzoFJVqZpqt2npmf9/XhfsEgXuc6UkaQDIfxrIWFGFsq+56NFWWz6sFB7LstW+Ds8vkgP7hnFrd+mJsTjqvGRnxfjc8mq90zkm0g82v5jHFbktbW+EgdN0ZevhxrVFxsTRNbJU9cswbzMjEBvspZHgx1zn3vrr0W9zBaE6a2F4NB6FTcNJbrhHp6wMff/37IT2Vj+EgEzzo/L/oe8V7idgsMUsRks2FO+/sx5s2bE5vf8hpUVWmj87FTzgq+rHU+u7uRUcNr/o//CD3R74dDjyOB4TAMLwaIsNlg8JeViXYXalDuVgKaZJuWDKcFRsx0fT4BlZmXB4Zy/DiMI0aAkT0DXCsRCEDxYsOAmSuHszn9jtMc7HYcwvJyfGb9evwUF4MJnDiRGHFZswbCbSGln7Biefq0KJKPROIjJ9XVxhSsaBSRg7IyXIOL7PPzwUSmpjBfGzbEe5KM9IPR81w7HJjfU6fUBYBWutKKFbjOSy8hrWZwUKS0VFTgPmpzwc13uR+HHJGRG+J1diL1jaG4rcinlwXlsmXxPYPkGhEZ1YhIHdqdvXXz88i9X74ckSoGguC53rwZ89PTI/ZyshQHPj8zM/HGV0kJ1o3BU7jOqLk5UUny++PnJpkiuXw5etFwI08zaWp6xsE73hGfOtTXR/TYY/Bq5kJgmTHIrEIZTccI1JvbdevUjdZ0awHNkNUeXLXUYia/H/vuzBnUsdXV6Ss+ehG/tWuRVssR7XAY54Eju9PTwtApKsI5XrkSvE0tTfdjH4sfA7dryM8XqVh2u5CDoZCIZjiduP78vHmnIKM9/vCHMOLm5gRSquyMOngQz8PIkFxQz6nA3HyeZYPDoV77qRfJi0ah9BYU4Hl4H+jxa/mMcVocy/myMvDk6Wk45+rrIaeUoDJW9mm85BI8B6dsc+SnqQnzyCnMZp0h3D/p0Ucxn6dOYT3WrRO6DddS9fbC4G5rw5oqG8NzD8CyMqGrzcwgeiX3vpPJ48FnWWb7fPHNbznixk4BPXQ+q0h5Pr1enO2CAmQyNDfjWU+exOu1tdA7YjHopKWlwggNh5H9Ulamvs8yAWiSbVoynBYQRaMI7+/ahU0m15u0tYGBdHWJmhglcZ799LRg+sxcn38e/7N1z7mpoZDY/G96E8AA+BpqEZf+fniCqqrgbV0I6SeyYtneLhpsKiMnVVXGFCwZfjgUEsJ5ZARGE3fq5vkmMh7NSua5LirSFgB66Uo1NYg4MICBw4GI2Isvisii2y0a4XFxNhGMQDa6ZaMgFoOh3d4OY+uxx6wDy5AFpdcLhltcrF4jwsbu9HS8cG1owO9AQBgua9fid0mJEMDskZydhcLFNVB6RbRMSuWE0+m4t1l/P9avokIYvw6H2HcdHVAslHNjJnXMbJqalnHQ0SEihnLUaqEJLDUDoLkZPHD3bnXY42ylw+kZXlalNaZCmfDgahmr7KzgOoe2NmNNL7Xmx+cTWQJyVJej6RMTcE7MzIAnr1iB68kgEzMzOEMf/rB28+OdO2GknDol0gK5sL2pCby+uRm8dHTUnFOQ5//55xGF4yhEUZFQhIuKME+nTom0r6kpsYenp/E+o2SyoadX+6nlgOnowDNedpnYf9x0PRTCs504Ec+TlM4sZV1oKIR5amrCmmiByljVp3F2FtHON70Jz8JgBESJfNKoM0SuO+Nm6NPTMATWrIlPdSfC+Ds6ANbBUPHcGN7jEXVQciofO6XHxyGDlbXH09OY0+JiOPIGBrC2brfo4UeEMzYxgT3KUTcrIthaDhZew64uOO/t9kSje/NmfPfAATj4JiaEEcl1w1xqoCabMgFokgvKueH0gx/8gL797W/TwMAAbdiwgb73ve/Rli1bND8/Pj5OX/ziF+mJJ56g0dFRWr58OT3wwAP0jne8I4ujzgw99xxyomMxHA5OHeBIQXMzmAmnNig9Gey5KiqK7zB+442IoDAzZqHBULqMYFNbC6be1qYecSkowHV7e8FUb7hhYWxuZW62sp6FUcq470AyBUuJvMPCmXu31NQIwAimWAxrwh3u9YRGqkImWbqSxyNABdasEXDVZ86IMXJfn4YG0W+CjQ5G9RkeFkXTVVVEn/gE9oZaAzwZGSsYNOelZUH54x9DGRgaEv0z5Ho62dhtaRHC9dVX8WxcFyZ/h5n2yAjRpz5lrDhajdQ8oVu3Yn+xp/LCCzEO2Zkh7zuuzVLOjdF9oFx3ed4ZXMFIFDVVgZXNXHQtA+Cii6BAvv46lA2uGWEUroWSDpeJtMZklCkPrlpEQ256mZeHc1dRISCVjUTdlfMj30eG2+co/9QU9rnTCWVNmWnBShqnE8okywa7HUopp1xzjVM0Cr5ZVoZx9Paab/Xx4IPgAQwu4XLhPoxQGwphrgIBvO7xgDfNzYn5m5kRNYkc8QmFYIhx/YvHAzm0axd+f+EL6g4YrknhZ5CNr/l5sTaHD4v1kHldby/uNToqWhqwM4tIH1QmHdLiibyWRnmQGs/q6EisO/N4RJRIrZ6IeWttbbzc4cbwK1eKRvVcMzs3J86KDChBJGoDx8exztu24XWbTdTdEYmaV0bZ6+3F/k43gp3MwcIZEWNjAihMJj5Dr7yCeaisFCmmfn9ivz+lbLIarThXlFPD6Ze//CXdeeed9NBDD9Gll15KDzzwAL3tbW+jjo4OqlGa/kQ0NzdH11xzDdXU1NBvfvMbamhooHPnzlGZEotyEVI0CnCHmRl41GTUNfZeMxMLhQQ0OG8+VqYcDngFGhtFKkAgIMLIvb1Q7rk5JqOylJaCkT/4IHpJqEVcJifxec5v1lLgs03KPivKlCqGVPV6kW+bTMFSelqVvVuYSbLhykKJa2weeQTpGHre3lSEjJF0JRkelteMC1gLC0Ua3AsvgGm3tkIIhkIC1Yfr2CIRIDUyIpp8b6UgjkZFHdT69cafiZtdEkGIt7cLRYhIPZrAwvXPf0aaUFubUD6YZEO2s5PorW8Vc2PWCNCLDhUVYR19vkRnBnvgAgH9ZrHJ9oEyhUae97w8CH+uN9CiVAVWpnPR5fVgtL2RkXgD4MUXkcrV2ChQCwcHYbj29qLO4dZbF0a0LNuUikFs9AwonQZcjH/2LM5XZWW8kpSK4sNjWbcO9xgaiofbZ4Wd/aK9vYI/MelFHNWg0LduFZEfGQSpvV2kgBk1wmXUzXAY/7vdOJcMbBGN4r2BAQHtz84gdjg5nXidU465p9Dhw6L+ZWYG12DY7L4+AEjcfz/R3XfHrynXpDDPkMEH8vPxudFRtBtobRVnR+Z1e/fifgMDWNf16zHGEycy66jQ4olG5aUaz6qogAFy/Li41vw81m1yEvMyM6NeTyQ77eSIzKlT0LVGRuJ1pKkp7OHGxkTk0O5u0WB63Trco6EBY6uvF/W6VVX4qa9HdsSHPyxSwVOdc6MOlmQOWgZKWr0a32MjUum8VM4fkTW1qguBcmo43X///fSJT3yCPvaxjxER0UMPPURPPfUU/fjHP6a777474fM//vGPaXR0lF5++WXK/2/3bstCNktNkM8H5l9SggMt55Sz93pwEIfM5QLz6usDQ+CUqkgEUYOLLkKHaGYcnE+9bRsO+iuv4LuskLFnibvcP/mkaEwoR1xkDzfnNy8EUoMYlVOquC/Exo2J+e9qJHtA29thaHDOc2VlPLSzVo1NJtKfjPSjaW8HE56awvPHYkgXYBQ4LmDlKM3f/i0aT/7pT2DIFRWYS95PAwMwqNraxL2rq+GdY0Gcl4fPhcNQfBsazD1zXh7STh58EPvSbk9eYMwFz3V1ItLDUUD+Xn9/vCF70UUCIMGsEaDmCWXlRJliJDdBdTggWC+/3JgHW02pbWyE42P3brweiQgFaG4OXm7usaRFqQgsKyIZRnvYcAoS58ezY6C4GK+x84dh0rkpq88nUpnPB1KbLyLtOTRrEJs1hFmRfvhhGLDcN6e4WL23HO+jQEA47rSMM+VYGAyCm2mWl2P8W7ZAaff7wV/MgBBoIVi+7W2ix9noqEjRMuvR5/kvLYVMrKrC+ZyaAo8Nh4XsmJkRZ5DbF5SVgT/MzIi+imVlos/i8DBkDqciMhJbXZ3g8ffdR/TFLyamKK5ZA77HaY7MmzgCtmoVxqM0rJW93HbtwrqPjmavbi/VKDfzLL8f88SRv2eeEamlckr1smUi5XrFisR6ou5uyHTez83N+P+Tn8R9vN74LARudPuFL2C8SmfbypUYz6pV4ryyo5ebqzM67MQErmVEZzEyn8kcLE88QfShDyVvcTI9jfNyxx34XzYi5TVSc2hYVauaa8qZ4TQ3N0cHDhyge+65543X7HY7XX311fTKK6+ofuf3v/89XXbZZfS///f/pt/97ndUXV1NN910E911113kUFav/jeFQiEKSTlVExMT1j6IBRSN4nCx50MZTSISKQvr1sG7+q//KgQZERTenTuJ3vIWIJnIyk5fHwTEK6+groNzldk7Pjsb3+X+3Dm8rhZxIdL3oGdibpIxUDWDQoap9nphNMnNVPWIPa1HjoCZMBwqERhKYSHWIxBIXmNjZb5uMlCBqiqiW25BmuWuXUJ4OJ0QGLOzeK2uDoW3IyMQLHV1+G23C28m15QwsMFdd+HePh8KWkMhfC8cxnU41U8JhGCUUoGL5nV/8UUxDu7cbrfjmdmQlaMW69alZgQoPaGsnGilGI2MYO6bm415Z7U8pURIA+LXKypEn5GJCSiwZWWIWHO6hUzRqEDJ6uvDHCiVbKXAsiIXXU9JJ4o3ysJheIPD4fiUmUAA81hdjd+s1DA/crlg2CsjHMw3AgH8TE1hnHqNwnMNj6u3/qOj6oaOGYM4VUM4GhXNTy+6CAok1x7t3Ruf3sQRjp/8BI4+LeNMayw+H87M+9+P1/buRWT5978X88FpxmZ4hNLZZLNBUZ6ZAY9I1aPP8+9yCTAFRgTkGqepKZG2l5eH83fhhZAv3I8tFMJYwmEooIcPY84LCzEejjRxCwIGjAiFoGgrzyLLiuPHAZjEDlYGIHC7MV8FBeoRQuZ1LS1A3pTPRWMj5M3Ro5k5J6lGuZlnnTkjmjZzNsTYGNZ8bCy+jsluR1Snpwd7ym4X9UScUhcOE33964njUJNXV14ZvxeVzrZAANdS9ofcsgV8bGhItNbYts06AzWZg8XthlPiwAHsLa0WJ7IxxHxUaUTqOTTSbUa+UChnhtPw8DBFIhGqVbitamtr6eTJk6rfOXPmDD333HP04Q9/mP74xz/S66+/Tv/rf/0vCofD9OUvf1n1O/fddx995StfsXz8VhEziVdfxUZ1OHCYQiEII86ZZSX32muxMT/2MXRulxWC5mZEmpTKTmMjPBydnTic4bDw0qt1uWdPDEM552pzG2WgegZFXx+e/WMfUzeajCpL7KljRbi1Fd9LVmNjdb6uFsPeuBEKTDQKJvzaa8JDyDn8k5MQoBdfjGft78eeGB0luuYaIVjlRr9Op3iG9nai974XykwkItDk5Jok+fPKZzbiTf/857G/AgHxmsslml7KZLdDkfvpT4WzIRzG2Ljer74eyr4ctUi1t5eS5H3n96unGF1zjbE0MjVF0udD9DcWw5pVVeG6k5PC69fYKJCg9FLtuBHmkSMCHpb3qtqZTjcXXU9JV+thMziI33V1IvWFFdD5eUQfxsbi6wqJtCNlnGp04oSo2XO7sf5qjcJzDY+rhqY6MSHW//LLsT+Vho5RD25REfqvmTWEjx9Hw8uODlxjYgLrMTEhUO94rYji6zeam9WNs7Y2baOcU/ZeeAGGhjJts7sb83PzzeIs6ynuyZxN6Xr0ef45bVaJCDg5Kfqo1dbCcRmNwmjr6xNRaqcTc7psGd4bGoJzoKcHn5MjTWwkM5R6U5P6WWxvhwF65AjGNzKSyK/n55OnRrERxcAK//IvoqFvYSHGtGOHMbAdJSllwvQ0EBBTiXL7fDjz/f14Lo7KM3gI14wrIymMjsd977iGm1PqVq3SHkeyGlWls62rS7s/ZFsbnHx+PxyV27ZZZ5Cyga802EpLMddHj+L3hRdivMoWJ01NIqLGaLTcmLmtzbjT04jzdyHUqiajnINDmKFoNEo1NTX0yCOPkMPhoE2bNlFvby99+9vf1jSc7rnnHrrzzjvf+H9iYoKampqyNWRdkpWL5cuhGPT24r3ZWfzPTWp5I+7fD2+eUri3tBC9/DKiSsryMJsNm3d4GNd3uXBwbDb1LvcuF7xMjz+eu81t1juaSsRCr1/W734HBfzd7xapek4nhLvXK9I9pqbUa2yIMpevq2TYg4MQGI89Jp6juBg/rGQrBSZHDYnwHY8nEXJY7Rlqa5H6V1cncvFlaHytZzbjTTeaUheN4nOcysaIj/n5ULTtdswNRyuUUQui9A1c3ndPPAFvHSuMbW0AT7nqquTnRC26w8aM0yn+zsvD3M/N4bNVVch/Z6CXZKl2O3Yg3a+zE69t24azrnam08lFTxat2r8fa7Vzp3jP6cTzcS8VTpnh13kPKwFxlJEyuVD/7FlcJxLBfUIh9Ubh8jw1NmIMgQAUB58PKSmZTktSoqna7RgDO2u6u7E3lYbO5z5nzINLlMwQjlFX1zydPRuhhga8fvo00Y9+JDIdOPVsaEhER5YvF6lpk5MY6+rVUKL5PqWl4D2vv47oEffYW7cuvqkoU3s79kh1NZxARLh2QQGedXAQShsrluwkkeezvx97o6gIY7z9dqRrnTkjUqJ27AAS6YoVol2HWaqpEUrkmjW4dkUFXl++XEA1l5Xhc295Cxw9XAvqdIp1XrYMczI4CEfJTTdhjQ8ehC5QVSV4dCyGvdzSAp5w7hzklPI5LrwQURAGhmKAJ16byUmsDWckaNHp00S//CWyDVgB52ja6dPoL8nrfvXVMDbU1qK+XvCY06fFmnA648gI3luzRjTdra+P3z/Ll6vz1PFxARRTWSmesbwce25uDme7qCg+o4cbDNfWwiF67bVEf/gDzs7q1dr7mMdRV4cfosS9qKTJSQHIVV+P9WTH5rlzGOvVVwskQavI7cZZO34cZ5cd5KWl2DdOJ/ZRSwteW78e+/HVV0XaO/dUGxiA8VxZiZ+VKzHmz3xGfa2V+2rFisydx2SUn5+vmZ1mhnJmOFVVVZHD4aDBwcG41wcHB6mOd6GC6uvrEx68vb2dBgYGaG5ujgpkfOj/JqfTSU6ltF0ApKZcrF2LgzUzgw1XXo7NzNCooRBQVpSGxJEjOLinT+P9sjJ4NuQISHU1BM2uXTi0fX36Xe6vuiq+L4kc2bjkEhzyc+f0U1+snBui5N5RM2h1eobZ8eMQCKtW4btK7JHGRnhg3/xmUWOjBOngqCHDjlpN7MnyetWb0nIj1sJCzJkWfHNrq7mcY44A5eeDaSb7PFHiXLvdeO355zGenTuFN91MSp0MG19cjP0/N4dxsXOAYeTNRi2IzKVuMSwtw6fX1OD8GDkXatEduVcI7ykiPIfLhfM6NQWhV1ZmLNWupASRC68Xc7V7N86zmnOhuBh7t68PCo3SQNbLRU8WraqsFA0nmRiyub8f709OYp1qahLrCuU5lyNlcqE+G5fyWs/O4jPl5aJR+OrVYp6qq8FLGXjD4cCzuFxE3/lO5hxFSjTVcBjz7veLlNOpKZyJlSvjjf2eHmMe3OlpbUO4sHCOLrqon8rLZ2hqSoA/TE4iwvyud2Ec3PcoGo3f70TYH+zRz8tTn6tt2/DdyUkAEKm11CDCZy6/XKSpcRSZiZG/uGefTOGwMOr43DDa1zXX4DocveZmnWfPml6yOLryShhF3PiU54XnyOEQ/f4cDjTy5jFyKlksJsbEMiM/H2t35ZVYOx4vo6NyRgDPWSQCuSQ/WywGA4yjU0oKh0XfolOnEueGPzMxAXl3xRV4j5V/fj5eF4cDPLazE69prQURrnnxxaI2JhIRKIR2u7i/wyEQ6KJR9XUnwvc+//n47zJFImJt+Lvcr5AdK+wYtdkw57zPlLRtG75z8iTGJc+VHjHk/Gc/K9ZdTh2VkWy5XMIqCoeJ/vqvhcHE95PHYbNhL+Tni+d+xzsEaq7DIT5PhM/k58fX2MvIzXrPYLdn7jzqkc1mo8bGRvKoeWxMUM4Mp4KCAtq0aRM9++yz9M53vpOIEFF69tln6fbbb1f9zvbt2+lnP/sZRaNRsv/3ynZ2dlJ9fb2q0bSQSU25kEENOIy/bBm8yn4/lDulIVFdjcJ+lwveOfauKJudEuEzF1+MSMnvf5+8y71aZOPJJxFGHh3FNSsq1FNfrJ4bpmQRAiMoZfPzgMA+fVp0YJdTt155BfOnhQ7HinZREbxUjAYXDkNwDQ/j7+lpGKV6RfvpkJ6BuW4d1mx8XBgskUiiQtXSYi7n2GyOsnKMw8Mw7k+cEGg8u3djD3KqndGUOiWaYlWViJyxEGAPn5moBZHx1C3ZKJR7Ix0+jD1ipHZKDW58eDgehtlmEznynMLLxoWZVDuu/WtpAU+57Tb1lJDpaZx3TtHKzxfNr6uq9NN15edRQqeXlgrjZ3xcGN8yGubAgIi0Mzwzp91MTGhHv7u6RKH+wYOiVoEdG1y/wIreq6/CU37yJJQ5GfCEjYDhYdSMXnstvKFWkxJNdXYWvJ/rYYhEH5cDB7AHlI28jTQg7epSd5DYbFHasuUslZc7yO1eRnV1BeR02mhuDtEShsnmcciIdnL6LKOsMoqYmiLJqcDl5QIBVs35y/U+bjf+jkbjlT1WgGtr459ldhZOEq5BZRTNcBjfr6wUUXarifvPBYMCsZZ5kMuFZ4lGRS+iggLIIVbYGQyCwXvksc7OQh4xzLXNJqCvGUqbCH+z0VFQgLkpLBTzwoqtcl64f97cnFhT/j7DTHMfRzZKGEGQCK/l5Qk0V34GRlpVu6fNhvfkvTI7K1BduVclr7fdjnmMRLD/Xa7ENeAzw9+X96Ccvs31ofwMdjvmoLwcY2KUQ7V9HA7jOnNzwsiQ51qP5uawjswX2RHBfzPybUNDfI/IdCkWwxrm5wtDhQ1GdoTwXuVxuFxib3HPqrk54Rghigea4qidsjZ/IVEsFiO/3089PT20evXqtCJPKRlOb+LYvwYdPHjQ0HXuvPNOuuWWW2jz5s20ZcsWeuCBB2h6evoNlL2bb76ZGhoa6L7/xiv+u7/7O/r+979Pd9xxB33qU5+iU6dO0Te+8Q369Kc/ncpj5JS0UmFYsRkZgQfo1lth3PzjPyYqQbGY8DDx4a+uxuGsqkrMP2dl56ab4CEz0uVejmz8278hhcLhEKFptdQXq+cmlb41SuLoweHDUISeeUakjLBCyIe+sRHh+P5+zLlMsRjma2iI6KGH8JnTp+HBZihajhKwgvj972emuWgyA3PtWoTCV6zAc2qlL+p5rCsrEWHkfObm5uQe7htuEMY2N9xtasJ+3LsXCnMkgutFInh91y6keZhJqVPWd8hRi+pqMHr24BmNWhAZTxO1qpkfKxtdXbgGp0ZwuhorXm1tuCYrp5ympAYPrJdqZ7PBIcO5/mpoZ9//Pj5XWiqUnd5e3Lu+HpEPrXRdXpdz53COuMaII2Xc+2dkRERQiERq1ksvQSAPDuI7O3fCOOD0Ta19zM9cWIg9xoobj5HTYiIRARs8OAhFiVEnZcHvdILPdXXBYWQk7dIsyWiqbKiFw1hvVpTn58X/Wo2829qIPvhB4elXZgJoOTzc7jkqLIyS291EJSXuNzzurExx/U4wKOaQSERTWHmemBBKF3uPlcSKOyPHzsxAWVPKNCKcWzaQlJ/hKBT3j2GDamwM77MjhamgQCjlBQXCcLFSweOI/tyciG6wh356GvuRU6TkqBGn2RYWinYRjITLEV42MLh2hxX2aBSfi0TwGu91Tv3iVNeyMvweH8e1uY8TZw8EAiLtWvn98vL4+i3eTzx2+e9YTETXpqYwRrW14ChUcXH8fuK54YgW/83zyNExtzvR+UWEz3OmgdLYJhIOFDaq2OjhhvZy9IeNPXkfM0Iiry0/mzzXagYdExvGfH3+XzaeeP9YaeDzmD0e3Gt2VkTfeQ3leeF+Umxo8RpEo/HRKJlPMDiK3a6+NguFqqurqauri8LhcPYNp0OHDtH/+T//hzweD8ViMbrvvvvok5/8JFVw0YJB+sAHPkB+v5++9KUv0cDAAG3cuJGefvrpNwAjfD7fG5ElIqKmpib605/+RJ/97GfpoosuooaGBrrjjjvorrvuSuUxckp6Rb18sLibtZYSxOk8FRVgVHNz+tCWetEkvVSkaBTpYMeOib5SzGSWLcM9OPUlVQQ5OS2KmVA6fWtkkovFjx0TXhRWlLu68AyXX47UoPp6zHV3dzwCmd+Pa3m9EFgdHXj+N70J3mq/H+uUny+K9quqMtcNO1kdituNOduyBc9UXKyOHKVVH9bYCGYu105x1EXLw71+PerDOFIzMwMHwPr1os9VWZlAImQFaXoaSl84jP1sJKVOTRmUe3iFw2L9jEYtzBhDyQzXhgY4Gv78Z4xLC5L58ceRntDfL4yUujooLRMTwgjweBBB5lS7khLsY61Uu1RgX6NR1GudOweDu6YG92LjJxDAXrr9dm1HAJ+rJ58UyrLDIZTXs2dx1ritgmx8+/3IdX/ve8H/eG9PT2P++W81fsXPPD6e2NKBSChjXO9gt+MekQgMKDkVkYmblXZ3Z6Yp4+QkxllbK3qicA8fTnlhpbayUr2Rt5HoqLIou6EB1x0dZS+yPa5/msMhzkN+vkihYiWUo088TlmJ58iD0iCamwNPYmV1bk70NZIVdjYMONqovA4bCuz5dzrjr6VcQ1a6Z2ZEVKKwEGPQU3TNkpw6R4R7jY3h/qwQ8zOwYk+E92ZmRJoYG3r8bESiMTgbP7Oz4jkLCuLnm2uiZ2fx+cJCfL+wUIyDjceBAWG4qX2f23lwWpds+MmGk5zuxXMuX1OeI+5dxXuJSOwbVtD5uvJ3OPKhFY3heWBDZH5enHneR8XFkC+ycavkE2yEzsyIZ2CDlp9TTtFTzrWWQa58Rk5h5Gg4O3U4apru3uQzx21I2GnAEUNOqeWUUTldlI0nNuJ4DuRnkx0AslNqIZPNIm9Jyql6n/vc595oUvud73yH7rjjDlq5cqXp69x+++2aqXkvvPBCwmuXXXYZ7dmzx/R9FhqZSXlSNh9lYsQpDk2zd4nT/ZJBWxpJayPC/Q8exMFQKhfslQ4EkPqSinKhFPxOJxQZLhqV02eM9q2Rr819HYaHRVj99dehqLOXbngYKY9vexvuv3IlmCwrdsEgwDcGBkRqRCiE6/T0gMm1tQkDlYv2iTLXDVtPOfb7RdfxRx8VzSq5XkFJammZas1I5ajL3XfD6OzsFD1H/vM/8bnWVozhzBl8vq9P7BVm2Mxk7XaMnzsFGE2pU0PoKS9HmuKBA0KRGR01HrUwkyaqZ7j6/RjTmTOoj2EYXxkwQo5srV+POQ+FsM8Z1pzPdl4ePr9hAwR/fT0Qs7SQrFKFfX3uOaz77CzWlVNn2OtdVSWivlrk9WLvseLH3tPpaVzL48Ez/O3fCiNbuR5tbRjLk08KBC+XKx4MR0n8zM8/j/vKnlMioTywYtbaiv4rjY1wfChr9hhxlOGfUwV50auVY69/czP49NCQSIVxOkWkR6uRd0eHcRAdZU+m0VEYUO98Z+Jel5VHTptU1jhxShdHTZxO4d1WM4i4bxF7qGtqhCHA3m+3G58JhUTPMqL4ND1OJZJriuRaCZk4ZZrXnBVedp7V1FhrPDHFYsKA51YirCDLKVoOh0gji0QEb1RTQpXGTyQiUrDUeJXSAFMadqGQurHJ8xyNClnH88brrzSUolGhmLPDS6v1B78upzSyAs5ODTaa+HU22DwebcPEZhMGOe9HJp7bsjLRnkHed7IhLV+H54dT+3jNZANJy9hVkuyMkM+QTPx+MiMsGQWD8RFG/s0ou3I6o2z08JrymeUzzq/LxhP/zZ+XI3Ry9CwTEd5cU0qGU1FREU1NTVFNTQ3Nz8/T7Owsff7zn6dHH3007aKr/ymkpvS53fFN1G64AZ/TUoLYMzk2hs9wChKn+zFMcrrQlozMRqReYMoeoOlpfNZIUb2cOvfrX+Ngy/C1g4OIVNhsUJLYaDLSt0a+B0cPGhpwvbIyMEAiMHgG4XA48Lm//AVRpJ07ia67DtEWrxffnZgQjII9XwzLPT6O9SspwXNz0T5R5tD1tPYFN+UdGADyz8UXQwAmg3RlQzoahcI6MqIfdbnhBii+e/ciKtHbK4rvjx6NV3xmZ3F9bkaclyfqc4qLMZ+hEH739RlLqSPSjpZ96ENwIHCtFPceaWpCgbNW9I37BrGAUToK5LXUMlx5/rmOY2oK39m3D/Cu73gH0Sc+gbnjyBY3FWXPKhsZ3Ng1EIARVl6O1Ek29rTOmhp/SYaM6fWiWbDfD8OGe5iwgC8rwz46dgxRtMsuSzzf0SgM9UAAwDYTE6K5J9cscBpxURGMb+X4OzqA0PTkk/gur1+yxtL8zMePizQcbqzK77Mnurwc+zcvD7+ffhrnpbJSRDMYcZTTdVMBeUkWDZLP8MUXY++FQkJp4Wap+fkwquRG3m1tRN/8pvlU0dlZGN4XXog5ZSNzaEgYErLyyCmDHB3mxs6sKMtKNKdKcQ2P0iCSjRStKAgbGLJxJEcO2NklK2qyUiorb6wc8zj5O0ajBKmSrHTLtTRMsrLJ42a+qFRCZZKNn5kZdeWbScsAY1IzNsNhoWQzKAevO6frMcnrznPK11XWGCnvy0AJHAGR07/k9ePUNnYeJTNykxnkROIcsWEfDoP/BIM4Fy5X4nV4DdkRpNSDks01kQD+mJ0VDgB+TjlF1YgRpkfBYPwz8h4MBgXICK8B14RxmqYM/sC1WJwiyevP66rMJuBosmy0qRmm5wOlZDitX7+evvjFL9Idd9xBv/rVr6iuro4cDgdt3ryZnnjiCVrLORVLpEuy0rd3LxQjhnJ0ufB6fz+Uvy1boGDIShCROKxtbYmManISBlO6/QCKi+N7RxAJJs9pEkT4DANI6KWNyL1lDh2CgrJqlSj2LSnBZ48dE2ki3JiVUQC1+tbIJEcPZIWdi8+dTpErz8rS0BAYwLp1eI6BAawJewzZY80CngVXMIj1YwV6YEAw6kx1w9Yyvg8dwv1ra6FkMaKT0bobI1GXPXugoPr9AqqU52NgQDBM7sA+NhaPjsXQ00RYh7ExISiMpNTJlCzt1OtFfzO1Pak0HH7yE2Eou1zx9W9E8WupZrjGYrgP1zOEQjAiSkuhlA8MoL7O7xfIjTYbPpeXh70sI4NdeinmUK55fOtbRcRKTyk3A9HPToapKRgV7C1nP9jMDNaD+39997tE//Vf8VGg9naxd3gdOXrAZy8Ww7VGR7FWyqi314t6zhdewJ4qKMD8TUzAQLjiCv0my+3tRF/4Aj5z7JgAhCASUaviYjhFrroKr191FYzZZ57BZycnBa/hJtCp9K0zWisn9wJbuRI8yOXCmnJj6fz8xEbeDIZhFESH13hkBMY3O4C4LmF+Pt6QYOWRzy4rTeyF5iiUrEDxvSsrhYKm53FWRkGYOO2KQSLkmheieEWNP69Mr4pERMqm2hhlBZX5v1Uecq0IGN+beYWSlCmNeqRmLPI1+Nk5QmLk+3J0jsdtt4u5JBJGlBz94wi03S6MRdYLlEYp91Fig5HXlSNorOxz6iG/z3V/RkATtAxyovjURK6z43mancU5bGoSxhNfh5vHs7NaSXrGLhM/l5wax3uA55lR6di4MktypJPnno0+ObLHRhKnvbrdYm35bDU04O+hIbGukYgwnnleZ2dFNI8/LxumHLnMZIQ325SS4fSd73yH3ve+99Evf/lLKi0tpZ/85Cd07bXX0v3330/bt2+nMa7SXKKkxF7jEycgqJua8Lu7Gw0LH3sMwrSmRr1r+jXXQJHhQ52JfkvNzVAcjh5FZEE2GNxu/O92Q1D/x39gLE1N+J4y0kEklAnO6WaFcmJCoADK+cybNwuUG44AGGncJ6dSRaOiaHVmRhingYBQCmIxoeD86ldQ+Bg+u6sLyp6M9MOpPMycOQQfDmM9KyuTI5DJZCRSpySlctzZif2wfDmULFb4idSVKb15U2uWx2t95gxSmFjYMVIWM0oWeKz8VVRgX7BjIBLB97lAf3YW67FyJepnxsaM9+Mi0k47Naq8yimd9fVYa5crHp1SuZZqhitDSXMhsRJpqLIS+8/nw9wycqPcx6iwUPQ0YW+/XPMop/n5/ZjfwkLM6cGD8c9ltJaRDZ62NozL5xNpMax8jY2JdNdoVEDxy3PJHsvCQsyBnKrHe3xsTBSnyxSNIo3slVfEuWVhHQphLxw8iPQ6vT28bh36jNx3H/ZWWZnwioZCOBu33hqvHN52G97nBrScUtrbmxofNVMrJ5/hcBhjHh3Fvmlrwxz09iY28jbbayuZQ0SrtqawEHvB7xeRCI6YsPIsGyusQKoZREZTeOSIF3u8tdL+XnnlFdqxYwddc83b6eGHn3pDyVOmRCmVeL5eMCh6yVnlIZeNEjn9TjbciIR3nw1SWQlNZripGYtyxIjTBEdH1Z9F+X1+fo568d8csZDnjdfY7RZKuFpkR07Z5H5fRMLByGOVr83zpbXeRkjNIJdTEznVXwaRYOCE/n4ReeLrMKiFPNdMRo1d3tPBoADakCP1vO+MGGFapKz1Y4OQSDhHlJDkco0S82zug6WMvMnpl/w3r3lhYfKauUxFeLNNKRlOl112GfX09JDf76eKioo30CnuvPNO2sId65bIEEWjSNkJhZD6YrNBQB0/Lph+KISNzChnyq7pHR3mGr+aJbsdSvijj4oO3Ayvyr1GNmxAjRPDdMtodawoPPEErsfKxNAQDjGj2vj9AjWKDSVW2v8bL+QNMhLFkVOpGHHt7Nl4AVZUhNQ8rmV485vR/C4vT6wHpxnJucBTU+JvZiDMeMvLwcAOHQIDrq5OrngZhb9WI1k5PnAAUOsXX6yeY24kbZB7him7m1dWwnCfmoIxsWYNDDVGESQSQo7TIOV0j+JiAXLACgN78OrqhIJ55AiMp5tuMmdEKsmo8ir38lm3Dntt714RYQkEtNdSabgODuKM1NfDQFA2RmYY8aoqrAMjN2ohAsoNcJX9is6cwfyeOhW/RpOT8REZLaNSJk5V4Qa0RCJSxD3JuJ9HcbHwmlZWxs/lBz4g0vzGxhLhafk6nKYmU1cXam9YiWfhzzU2k5P4zKZNOCN6e7i9neijHxU1Urzu7e3qfLG9HY1ueR2HhtLjo2aBQ9ra0IPmF7/AvJ46hfnbtQvzuXMnjL10AECSGVpa6UY2G66fn48xcQonA0Nw6lIkEqUzZ3wUiUwSUTEtX95MMriT2RSeZGlX/J1///d/p0996lP07//+7zQ/30du97I4hZxTvJTpVSxfR0fx20oPudIoYaOUPf88Lq53YmNFLaVRi5S1OJz5wOuXn4/raD2L/H2OcMtRRFbe+QzKoB88bm6WrGYIK9eOnWkej1gLOYLFaWEcDdJL80yF5Cggpw/LKYXMK5WRV7W51jPkZVI6CgoLhWOcDRo5BTUvLzGamuoz8v8cdeXn41RbdnTMzeF5GRWQo9C8XsrIG88VA2vwmiermbPZwJNSTUFcSJRWLKK6ujoB0m/Hjh1pDeh/GrGAbWwUPUxee01A41ZWCsa+di280Pv3Q8FjuNn2dtQKfPWrRPfei9933WUd/HU0CmV21SrkxbOwnp3FISsvh9HT04P/XS6Mq78fCujwMJ7vwAH8sDLBXva5OZGq1NeHQ1xaCmHN+fKc7nXqFH7YO86KZFcXImJdXUIpb2yEEnzsGK7Z1gbGz/djIIq5OSgnF18MxXd6Ol7hYaWW0/qCQazP9LTwwLIS4fEIo7K/HxGUZFDkx48Tff3rSE/Kz0d9T1UV9sGDD8Ko0npGJlaON20SUQ01MmJwTk9jH547h7Vk79OxY4Byf+klGBVnz+Kz+fnC0FXL3Y5GMd9lZdjTFRUYH9eFrVqFdKn167Gvh4dRv9bYiHFOTuKcKJ85GWkpr4wOV1iIs/Tyy/Gf435q9fVC6OutpXz+PvtZrMHq1aIXiUwsZJctE8iNLKDWrMF8+P045wxSqoQb9/lwrvr7sU5uN9bI7cb//f1IpfT5EudEax8NDgLo5JlnEFVmT2UggP3OwtjjwetjY6LuUY5k8nzwOWCkSfbs9/bCSL7llkRDuLMTvK68XHiomViAB4OifYLWHvZ6Ufvz2GP4LBHW8uab9fmilXw0GXDI4cPghfffT/SlLxHdeSfR3/wN7nnkCJ6/vh4Oqfp6ARIhE6eK8h6SiY1t2UCVDS01SubpLizEPuM+OqyY5+URnTzppe9+95v04INfoh/84Gv05S9/ib75zW+S9783BdddMNgEK4kzM3hd7fmIcP26OpyX+nr8rqsTSvTU1BT98pe/pL/7u7+ja6+9ln75y0ff+HxDA5HX+wK1t9toenqciIjGx8fo6qsvok9/+mYKhWL0+c9/lFatstEFF9ioudlGjY34fcEFNrrzzo/Sv/7rY1RZWUkhBcTnO9/5TvrIRz5CRESnT5+mG2+8kWpra8nj8dAll1xCzz77DJWVCUW4sJDoX//1H+nii220caPtjd933vlOKi4G77nyyhb6xS8e0DQQdu7cSZ/5zGfiXvvWt/6Rrr9+4xtn4557Pkqf+cw730gL44jv/DzRt7/9ALUoPCg//em/0dve1k7r1xfS9devoZ/+9IdvpKKz00YGcLjzzo/Qpk011NrqpCuvXEkPPvh/yenEWf/5zx+l8vKyuLWbne2ilSttNDR0iKqrifLyInTPPR+nrVtX0KpVLrriijZ69NHvvpGiZrcT7djRQo8//sAb633y5AvkdttofHz8jWvv2rWLLr/8cnK5XNTU1ESf/vSnaVra2C0tLfTAAw/EPettt32U/u7v3vmG0+dv/mYn3XefmM8zZzro4ovz6Z3v3PhG5PX/Z++7o6Osuq/3TJJJMumN9EoooTdBEFERFQuKXbGhIjakiIpYELCLqNj4WbBg74oFe0cpSichIBBCGqTXSZ35/jjsuXeezCRDwLd8r3etrCQzT7n9nrLPPm1tbbj55puRmJiIoCAz0tJkbnzxxcdO5dxqda9g22yyHzMnZ1GR/A/IeBNRExSklBXd40iFlrHAxkKjun6N7unkNTqhg567SV+HJpOSbcrLVV25LhsbZW+uqJDzmWeUnrfMU8xcXZ3sh/TYMQXEf3PpksdpxYoVHX5/5plndqky/4ultlYOjn37ZFI2NMjEDA5WcDImuuwIauWNVbmrhQJo375y+DKpqskkwt0ff6is442NrjA+0okffbQKeKcwERYm7du9W/6nq3j1ahE+g4PlkMzJUbE0xCRbrXKAf/GFYkrTPTUDBsjnO3aIIJibK32XlSVCP5P6Wa0qbio6WmBCQUHyGYvJJMLHpk3K6h4YKBuC7nKPj5f4E39/qWdhIXDJJZ0rTbNmqUSj7jx1zz8vn+Xmdu6NorLIpLy6FawjNjUWekDDw2UjJCUwccuEJISFyRyorZV6R0QoJZ9QHsI3WGd6psLD5bvBg6VdOgGDHkM1d66C8R2KB47FnfBKr2ZZmTqYnntODoHUVHUdCVaqq70bS66/lBSBk/36q4oboHWN0M74eOlTI3OjO0bA8nKhBT/mGJlzZITavVv6sFs31XdMFUBGyupq1zp68moOGCBeVnoHY2MVVh1w9TCFhEh/mM3SJ+np8n56MuvrFXyRdaqpkXnR1CTC7N13Szs9Fa7LujplGWVxOGQMhw93P4c9QTP37RPa98TEjufPkdpHvSEOYW6umhoxFFRWyrVpadLXzK8zfLj7uC53UNGOoNqdMS12FEdi9BZRkG5uBnbtysHy5U+iuroM6enJCA8PQn19PTZs2IB9+/bhppumIyIiq8sQHk9xUADw7rvvonfv3ujVqxcuvfRSzJw5E3PnzoW/vzyIBDNNTUBNTR0uu+w0JCdn4P77X4LZbMLddy/BnXc+BB8fYN68GQCAhQuXAAD8/AJhNlswb950rFixAueffz4A4MCBA/j888/x9ddfAxDl7bTTTsP9998Pf39/LF++HBMmTEBubi66dUtx9pvZ7EBmZl8sXfotAGDRohlobW1ySbbaFQiT2ayMZRScAwPbx3MRjk7mw3fffQPz5s3D008/jT59BuO77zbgrruuQWRkEC688AonnI37uMkEnHHGRbj66lsRERGOnTtXYcqUyzFixHCMGTPGbd3YHnotADvi4pLw3HPvISIiCn/88Rtuu20qunWLx+mnX+Ccq76+Mhf1Z7Ds2rUL48ePx3333Ydly15CUVEpZs6chuuvn4ZXX33ZeT1jmHQiE3pc3BkaFi26FRZLgAsM9dVXl+H555/HW2+9haFDh8JsNiM+Pt7JbOoJbmokaHDnyTTSy1NmCgzs3DPbkffWCN/U4+kIuSQTo76WScQRFuZaV7Im66ykjDuvqpI6kcTGU8ycboxpanIlovlvLF1SnCZOnAjyoTsMM9BkMqHtP53M/T+oUMgh25SvrwgZtM6KlUYdHH8XQ1tHRRdATSYR8CIi5LvKSpWfB5CDn3EJDAItKBDrhZ6TJTRUCd5UPhg/tH+/sFwNHy4H/7PPinWc7C+hobIQv/9evAXdu4sgRqHhp5+AN94QQaJvX7Fabd6s6pGRIfW3WkWIiouT+mdnK2sq82oASliLjFSKATcDesNCQ0VpIqSQsVs6M5yx5ORIoHdurlwbHCzP1uNqrFbx8mRmitDTEd0wBWOjsti/v7TJm7g3XUluapJnZmerjZjKfHS08joUF0s7IyKkH8rLlVWLcAAysvn7qw0zPd29ddtmE++WzSYezo7arBdjjFhQkKvwSqGV9PZcU4WFModjY6VOLIRneDOWLBRm8/Plp6xM5ldLi2Jp69VL3nn00YqZ0B0jYH29KGDFxTKfP/hA5YOqr3cPDaFnprradY/wpFCsX68U5TFjhPmvokLmInOn1dcri2hdnYrbYoLe8HBXT2ZamoIv5uTI8+gZv+IKz0pTz54qh1d0tLKmkvGpvl5+p6a6n8OHEld0uHGfnZXOiEPodYyIkHpR4KAQRCW4tFTWcf/+7g1m7ghA/P3bK9s07hgVLcZUEBLNOaVDjFpbVT4iI0TJ4bBj1aqPYLOVYciQPjCbTQf7PBR9+vRBdnY2PvjgY0ya1AsWi9nr2Cpvy7Jly3DppZcCAMaPH4/q6mr89NNPOP744wGo5/n4NOHqq8+Dv78VS5a8g9BQXwQGAg5HGAICwmAyAQEBIsVFRsa5kBJccMEkvPzyy07F6fXXX0dKSorzHQMHDsTAgQOddbr33nvx0UcfYcWKFZg2bZoT5mSxtCAwMBApKXEHCXsCUVPThJYWESTdeRU6KhR27XbZW5j4lQme6VGgJ4cegaIi+T1//j24667FOPXUcw4attKxa1c23nzzOVx00RXOBLaMDfTxAY4//nQEBso8sVor4Ovr67W8Z7EAISF+uOmmBU6hPiUlHX/++Ts+/fRdnHTSBbBaAas1EA0NNqeCR/IplgcffBCXXHIJrr12JqqqgNTUHpg790lMmnQcFixYioiIALS1yd5UXKyUChKE0AvIPrTbgd9//wEbN/6Giy6agtWrf3B6Xjdu3IhRo0ZhwoQJLnVguIKncTESNADtDQVxcfKjQ/ns9s4VLqDja5iImTklierR664rS3qMmRgYpG2s64EDqu56bBgT5FJBJQSQpCB6zBygYu6s1v/+eKcuKU6XXHIJPv30U9x2222YPXs2/P/bAYv/pmK3i2VdZ2ojRSTjRIqL5dCk0PZ3MbR1VDqynq5b55rRvKZGrmHsSn29CE75+YLTByReJCtLDnqHQ5SCsjIRuEkjbDKJIMvEm7Gxim2NEIKcHHlfSorK/E3reHW1igMLDQXGjZOFSmaqyy5T7H87d6p4BgqyusDDhMKJiSqw1GKRdtEKT1gE4L1n56OPpA91V70uMBGiZ7PJc9j37gRBPZ9L9+7tlcV+/URQ7yxeQ1eSmTumuFg2bKtVBRunpCiPTU2NHMZ+fsobQeiFxSLzeO9eadcZZwCnny4wKnexGQ6HwMhaW6WeHbVZF37deVMoiO/bp+YbIbBUhqOiZH2VlYmnJzXV9bnejKWxMF4mIEA8onl5ynuanCzvpQJLAgd3lNyeSC02bFA5ffSM96wvx497REcKBWNtfH0VRHH7dpU7iSUwUMbbbpe5ZbUq6mx3fXSoCbZ53eDBAlmtq5P6VFfL33zPwIHCmuduDh9KDq6/yzvP0hFxSGur7GW9eyu2wKAg5ZUjqQhji6jAMq7LaCDo1UvRum/a5F7Z9sS0aLOJgkVlmJAv3ZrNvg8OdqX/DggA/vorHzk525GamuxUmlSfm5CUlIScnBwUFOQjM9N9p3uKreqs5ObmYu3atfjoo48AAL6+vrjwwguxbNkyp1LDcvPNl+C3377DXXctQHq6v5NFT7eQsw701JKw4bLLrsEJJxyF3bsLEReXiJdffgVXXDHZaTyuq6vD/Pnz8fnnn6O4uBitra2w2WzIP4iVpXJYWVmDwMAgpwGSP7qQOWfOHNx1112wWq3o06cP7r//fhxzzDHOdjz77LN48cUXAdBL2Izu3fuguFjFCv3882c46qhg+Pr6IT4+BVOmzMB5513lkvOxsbEee/fuwuzZV+PWW69xrpeWllaEhIQ5Idcka/Dzk700MBCYPv06LF/+KlpaWrBw4UKccMIJzvpVV1e7pKTRDes0RL344jN4552XUFycj8ZGG1pampGVNcgJUcvK6ocPP1yBs86ajoCAIJSXy/02m3y/adMmbN68Ga+//obLe+x2O7Zv34Pk5Cw4HMAjj8zBY4/d5bymubkJ48ef7owxIoytrc2BxYtnY+bMe1BdXQ6HQ8Wlpaen45133sH27dvRu3dvr+ZlR8mY3RkKdLnBG3IFoONrbDbX+DK9DlQI7XalNBHiRw8UlaOgIJXzjGeJrgzp8D+r1ZW23FPMHOt8uJTr/+7SJcXptddew59//onZs2fjueeewwMPPIBLLrnkSNft//uSny/egaFDFbUzEyLW1KiFnZioLASHKsQdieLOekrrfWmpLAq6a2tqxDpJpi+TSbHYnXOOPK+gQOB9RUWKWc9kUlYOLuRvvpHN2m6XQ11fYDpd5969IvyS2a28XK4vL1cWcXrK+vaVzSkkxH0OGR4WusDDjPPNzfKM4cNV1vXsbCVQNjTIew7Fs5OcLB42HdJFgam4WD4PDnZlJuM1FATz8toLxkZlsVcv4NZbPSclZDEqyRQuwsNdMdhxcaJ0ZGeLkA9If5jNIlj7+Kigfgokxx4LLFok16xd6x4yVFUl/ZeUpNiZ3LVZF349eVM2bVIB0z/8IP1J4gIKGoDEbDHw/Y8/VCzc4bBTZmVJ/MoZZ7gmcQXaEw4Y4WGdeU7WrVMxdUyUq+ce8vMTjwONLR0pFDy4ampkreg54H79VXmv2Cd79yqqdcJetm2TZ/TpI+/S11FnCopR4WUuL2LgAwLUoZ6VJbFLnjxWh8oy93cXT8QhGRnSV4RVtrZK3ciqqFuASSbCmLyO0j0A4qnvjEFSV2qZm05XmnRrtsMhnzHvDnNKAYyLqEV9fSMCAtx3elBQEJqbC9HQUNsOssPSVRYxIYNoRUJCgvMzh8MBf39/PP300wjTXMQlJSX44IMPMGnSJFxwwdno37+/C4GD8qApyBIgdUpIGIysrIFYunQ5Ro8+GdnZ23DaaZ/DZpM+u+WWW/DNN9/g0UcfRWZmJgIDA3HeeeehWTPzNzcDRUVFiI9P8ChMOxzArFm3YsqUyaivr8eiRYswYcIElJSUwHIQP3nJJZdg9uw7UV4u9Xz99SexevXPLpCs4cNPwPz5S9HS0oIff/wCc+ZMQXJyfyfcToRhobd75JEX0KfPCAQGyhxoagLq6306JGe4996FmDFjOr7//nvMnz8fZ599NrIObmYhISFYv369s22FhYUuSuwnn7yNBx64BfPmLUb//iMRGBiCZcsWYdOmNU5vyrRp92Hy5FMxcGAY/P0DnB6t0lKpR11dHS699FpMmjTdCXGUsQfCwlKcCuTUqbfi/PMnO/ft++6bg5aWNgQEyPogdO+rr5ajsbEel112HZ544n4ASma44YYb8Mcff6Bv377w9/d3ITvxVDqiogc8Gwq8UbgYxqW323gNYX+6N4vQXxK7sG56HjbquDotub+/a11JNKFD6ymn0qgcFSXv4R5uMrmSyHTUB/8tpUuKEwAMHToUP/74Iz7++GPMmTMHjz/+OB577DGPWNd/SvvCg75XLxEWGHuhZ3Qm3Ki6+shSjB9KMVpPExPlN+mn6Q6mkFNdrdy5TU0ijF9xhRIUp08X+B1jhgD5HRQklhI/P+WlqKhwPawBeXZRkXrHgQPiuRs2TFlMIiJEEKaVhlAUXXjyJNgZBR4eUjExrhTf4eEiiK9Zo4LrExM9M3HpluLCQuVJ0tnUuCHRU1NfL0KWO5gY27Jjh3vB2KgsFhS0b6/Rep2U5Kokk8CD1iSyDxLikJYm/XDmmcA770hboqNFmCd8hAphRYWqg6fYjJwceV///u5d+EbhtyMlIyZGlCLmnigulrHkJp+UJP3a0iJzpK1NNn2d7v9w2CnNZlFex449NJr5zjwnPXtKW0JCpK/Ky11zD/n6ineRxpWOFAqdvZJr0WSSd0dGije2rU3gxMTf+/nJ+gsMlDnPuDV3Ho6OiieFl+QTYWHST0FBsrbdPVOfv8w3lJenoJV6//07vPW6krJ9u8Qrpqaq9cz1RW8zIVEshL+Ul8tz3n9f/jYqRvn50jZvYYrc+xobheSFApC7HDCAjDm9Xrq1OSQkBP7+AaiurkdERKjz3OL39fX1sFoDEBUV4javz6HkLdJLa2srli9fjsWLF+Pkk092+W7ixIl46623cN111zk/W7FiBTIyMnDNNdfgyiuvxOrVq+HrK3A9nYpcFyBZ6uuBiROn4M03n0BpaSFGjx6HiIhkZ5zGqlWrMHnyZJx9UHutq6tDXl6eyzNaWuzYtm09Jk++0W17uB9ERkYjMzMTADB37ly88cYbyM/Pd34WGhqGqKhMZ8xQeHik04vEeePvH4Tk5EyYTEB6ehaWLXsI27dvcjlDY2JiEReXgPz83Zg48RK0torBi2iOjijju3Xrhm7duqFPnz5YtmwZPv/8c6fiZDabnXUFxAuol1WrVmHUqFG4/fYbnO+4//5dTsFakrb3wm+/7UJx8T40Nzdjw4Y1uOmmS9HWJvNz4MAh2L49Gz17ZrqsldZWOXMAaUNgYDSiojKdsklISAiqq6vQ3Cx7l4QUNODRR+/EPfc8DZPJzynLUEkMCgrCbbfdhi+//BLvvPMOMjMz0aNHD7djyOIpvxaL0VDA/mb8tqd1oJM+eKOUGWMDue/ZbAodIsYP1/xOXMOMa21rkzVA2D0RUlwnVNr5bl9fQfvQ6Mq5qc+hw6Fc/08oXVKcajg7AYwdOxarVq3C0qVLccYZZ2Ds2LH4+OOPj1T9/r8uunVfD0anwPzXX7KRFBaKIHAkKcYPtejKxB9/KEGKsCcyzTHfD61xUVEi6A0e7Po8Bmu2tirLRHy8q9clPFyEoZoaeQ/7qrBQuaB50O3bJ+8fOFAWKoNaN29Wf/v6ioIaHd258KQLPNXVkhh1zx65l4VEA4TbWa2iVJx5ZvsxMlrWW1vleVarCJrV1a7eAxKCBAWJAOxOiaAgCHTN0t4RWYCuJEdGisJDAa+hQeBUDP7s3VvGKjTUNW5J9xi1toqwzTp4Ss46aJDMG0/4caPw60nJKC0VrxbZGkeNAlatUopTSopK7sq4uYYGGb/rr5f3dJUG3VgOlXCgM89JcLBKFk3lld4KrhXduNIRbTXZKwsKXA9sxh9yXpDhr7xcQTdCQkSxcjhE6Bo2TOrcWSwa0LHCe/TR4sXKyBBCjrAw9+Ogz98DB2Sv3L9fnh0bK/3A5MX/Lm890J44ZMMG5WnXaejJyllbq6y05eUyP5OTpQ3l5Z69kMXFAofuKkzRncVbh+PwmpYWFScRE5OC1NTe2L59A3r0kBgnxdblQEFBAYYMGYK+fVNQVnZoVM4dlc8++wyVlZW4+uqrXTxLAHDuuedi2bJlLopT5MEJ/NBDD2HAgAF46KGHMHv2XaiuVm3TPReAsqADwOmnT8LixbfgrbdewBNPLHeBTvXo0QMffvghJkyYAJPJhLvvvht2jQJ03759uPvu+SgvP4AzzrjQbXt4ud3eCputEZWV9XjuuZcQFhaGpKRkl+uMeXpYdyrEDocdDQ2NaG1twS+/fIGqqnL07t0PubmrXd45e/YC3H33dISEhGHUqPEoLW3Ctm1/oLKyEjfffHO7OlZVVeHjjz/G0UcfDYvFgs8++wxbtmzBYOPh3kHp0aMHli9fjq+//grp6ekH0UvrkJ6ebqD6NiExURZqcXEBAJUY9qab5mDcuKNx993TMGnSFFitQdi5Mxvff/8Nbr75aec4sj9IeqOgeer7jz9+E4MHD8Xll0+Ej4+CzLNUVFTgvPPOw0MPPYTx48d71UZ3+bVYjIYCHRZLwwQRPO7o81m3rnhvTSbZaw4cUPFwjH8jAQihdSSLMZlc82oxTxjrQaWdhlT93TT+Gr1jXTWW/CeVLilO4eHhTnyvXhwOBz799NPDrtT/SnEHgaOw6XDIwhs9umPh4V9ZqEx8/bXAkHr1Eo/GqlVi5W1rU9TFdrsIoRERkg+JwoqeuDM5WYQdQiWKikRQt1pFAGTwdE6OCFgJCfJb934w43hLi1gOKyvlGXl5ytLB2KjmZvFiEcPbWdEFXotF6k0Pic0mxBQVFSJUjhqlEuU+/bTkIQoKEiFo//72luK6OvEU/fILMH68ii2hwFpXJ56irCzpF51SFHAVBHv2PLR8Lvo4eIL1nH66YivkxkcIC2FDAo2Q573/vlL4va2DuziYpCTgkUc8M38ZhV93SoYehB8XJ2NktaqYObNZxkLPSVJbK+8m3JCJaf8dxZv8PN26Cb322rXSVpI2DB3a3rjSEZsaoOKvCgsVNGfjRhnn1FSpA3M8mUzyt4+P/K6vl79p5DnuOO+IGDrzqnFvCAvrPLExac8bGhSchTTqlZUS32eztffWu/O2FhQcXu6wjoq7uCcyVO7YIe0eNkz2tZISqYfVKknOGRfoqb+iokTZ1GF+evEGpmiEGOnJVHVITU2N7AlitTZj7NizUV6+D7t3ZyM+Pgn+/kGoqqpHRUUB4uKiMXHiRAQFmWE2d56TyduybNkyjBs3rp3SBIji9Mgjj2Dz5s1u+iEIL730EsaPH49jjpmIpKR+TqWD/UpBUG9zUFAYTj31XHz//ecYP36iCyzqwQcfw7XXXoVRo0YhKioac+bMcTEuL1myBHv2/IW33voaUVEpToGUhYKkyQTMnXsr5s69FQEBgejZsx+effYjVFb6O+UCnUAEcFX2aEj86adPMWxYIHx9fZGUlIZ5857CsGFHIyfHVXGaNGkKAgOtePbZRbjvvlsRFBSEfv36Y9asmW773OFw4JVXXsGsWbPQ1NSEjIwMLF26FCeeeGKHY6WXa6+9Fhs2bMCFF14Ik8mEiy++GDfccANWrlzpNcStX78BeOutn/DYY3finHOOhcPhQGpqd5x88oUufaKPqz6HdaWioaEBjz++2C2Dn8PhwKWXXorRo0fj+uuv97qNlOM6y/lE4gV6mfRk33a7K9KGc4TnXFcT8TI3WlmZvEdPbMz6MdaJsV5CK6/6T/d6Kc+y8tTRg8sk8HqdDsdY8p9UTA4jLZ4X5aeffurw++OOO67LFfq7S01NDcLCwlBdXY1Qd1LJv7joAoA7OtnOcgD9O0penuQeISRLZyvz91cHbo8eimErK0sWzUMPKQGurEw8F/v2ifBGF3pIiCy0ESNksX7+ubKsMfM43dq0iNDibrcrOk0/P3k/czXV1ChGuOOPlxwthyIY0cKdkyOCZU2NEDFkZSn4nsMhsEFANqjGRhEqW1qEtYw4bkA2za++kjqNGydjX1wsYx8TI0HwZnPH82PaNLl/yRKx/g8b1p7cIDtblA221zgOxs2X1996qxIki4uBRx9VtOl+fvL+Xr3k7+xs2SRjYkRQ9fRMb/r8UNaEcS4CIpz98IPMIXpDTzhBhNGvvpJrfHzEo2E2KxahYcNk07/77r9XcTIK7EYB3dvxmTNHPuvsWSQNeO89mY+6osw+1RXl8nIZg/h48QDSE97YKNcUFsJpqWcuElLXh4QAZ52lqNQXLnSv+GzZAtx7r8wfd9bR1lZRJtyNhd4/WVliuCHUFZA2AYpwISYGOP98V6ifu9gqesmZ++ZQ6e+9LXz3mjXiuafySU9Nt25S9+RkYMIEgXpu29Zxf5WVSezTmDGyJwEyV4hgoJB0772u49HY2Ig9e/YgPT0dJlMAiopUstP6emWY0pUIWp9p9Q4LA/LycrBy5UfYuXM7mpoa4esbgD59snDppRPRp4/qwM5gYJ2Vw72fpalJ5fiqq1PQJj1mQ5eMfH2B6647Eb1798W99z7prAsNQrrQ7y6pr80m52R1tYJEkTyH8CYj7bNR2O7WTT7jGNE7wLQYuuU/JES+ZzJVwL2wzTw7hHV5qv+/onBM6OGgl4jQMcLwExJU6ha2p7VV9kDG8/j6KuWBfdLaKkbOpKT2c8bdvDrcedYRZbjZLAbVpiYZHz6bKA7GPDNpvD4HAPesevo1nY0djZB1dUoZZz/pZA6kNycrLCntAUUaQrgf9y9A1YsQRLtdGVv+XfMLcN3vAgyB44eiG3TJ4/SfrBj9txVPkKV/Jyyvs2K0YOtsXKWlslnExAgZgC54GK3MMTHSzvJyhbOtq5PnM5aouloUsOpqUQzq6tRm6ucnwnJLi2Jw4cYZGSlW9Pp6uYfxH717y6LuCrsWPSS//QY8/LBsUMZNuKxM0YmT4GDbNqnf2rXSTxTwunUTj+LmzVIXbjwnnOA69p7mR//+isr6wAERwv76S1FW+/hInwUHA0cdperoLfuYHhMVEiIem+RklRyxuVkUqbIyBdWsqZH+7tOn6wQLh7Im3HlTmprU4VleLuNOw3S3blKn2lr5LjjYdV783TEwnuCR+jpx55noqC89zWHjuxg7VF4uh3VkpGufnnaazI0//wReekngtQxRCA+Xdc2Dll5fChS+vtJvdXWipB1/fMceDm+8ap7GQp+/ZKXT45liYmQuDhumBJGLLhJFmf2ie1ttNlG+jN5jbyCHXSk0ImVny9xLTpbf9fWynoKDgalTRWHyBm4JSP9HRsrYZmRI2+jB1iG1HXnadYgRjVAUkri3Uqii1ZkxIWlpWbjiil7Yty8fdXW1CAoKQWJiCpKSXBe8Me7iUEpHgqgnIcyTosU2EKqke5jYZv6ura3Eli0/YvXqH/Hgg886n81E6iaT1IMCrE4dbSTcoIGQxsXmZql/dLT3FNZGGBgVNzKkUWHQk6myvroHpLlZGSJpAHFX/39V4fyjcqlDKBlXFxIi1xk9Onr6C3pCqPDzO0D1u1Gp1eeVDjvj87wR9o1zLSDAPd14VZWsQzLQ2u2KOIEU8Dab1JWpC4yeWZ01ryveW8L26uqUAkZPE5UjPz+lwOn9QCWUdaf3lXOQaBRA7rVYlFE9Nrbrxo7/pNIlxcmdC1wvAwYM6FJl/lfLoVD3/icUd4JdRIQI8jt2yAFtPPgB97AqMkzt2yftrq2V5zBP0Lp1sui6d5drf/9dBYHT4mG1yoJlPEtAgPw9ZIiidWfsFa1TXWXXMptV4t6EhPbegO3bpW5BQQojbzLJBlpeLt9HR6v7kpOlfVdfLRBDd2Pvbn7U1wskUIfa7d8vcVPFxSojeHi4wK2WLxfF7eyzFZb6UGKiGHOVliYHAGOImBMpJESEA6tVNvTdu5XlvitGAG/XhLu5yAOKUK/evVU8SVKSPDM4WARrJuAFlCfn74qB6QweqQvoh2tQMb6roUFiEwsKpM969pR1p8fj6dDUTz5xzWUGKK+MzaZiFNvalHJFz29pqfR9R0poZ8lYCclMShKvoj4H9H2EVN16PABjBP39ZY3u2KEUBmNsFSCKXmurUjh27hSDxt+V+8lul/5tahIYM9seFiYGjuxs2ffGjvW+vwoLRVm12cTjTUZOI6T26ac9K4KEGNlsUjc9GJ1CLMdaF0TJOGq3m5GcnOb02rS0yFzw8zt8AdybhKLGd3SkaDGAn0K5HgNj7NtJkwajtrYSd975MDIzezk/p+CrewzcJfU1KkQUMilo08PiLYW1UWkgRKqhQdWfFNH0GDC/D/MmOhxqjIOD1frxNinx31Fo3CGLIz1N7BsmnjeZFORMjw8ClOJBL6sed0Mvlp581Tiv7HalTDAmmnG9HSmT7uaav7/cr7PL8l2A8nAyBotIDhojGhrkfCLMTR8HkoMcjleMXk/OF/Y55ydjn/S1QZIHk0lB3SlTVVcrxYvrigohc0Cx3f/tpUuK06BBg2AymeBwOGBMhPtPAtyulUMNIP+7SmcwIhZPgt2xx3oW7EJCZDMpKlJei7AwuZZ0yL6+8veWLSJMMnYjMlIEh8REUZwIJ2lokI2FlOE+Pgp6kJ8vwpexHC67lifrL/M90SpDyyphhMzJQop0vS5a6gu3RZ8fhCpR+CsrE0GrqUkEvKIidfgQrqhb0M8999Ct/XqbQ0JccyKZTMqKd9RRMhc6C+w3Fk/zrqM1wXtaW6VNa9aIxd5mU/CC4cOVh89kkv7Jy5OxiY6Wfq+p8c4r5u3a8HTvoSZn7apBxfiuHTuEXbCuThGy7Nkja9CdIO1JSKflsbFRYmrMZvVMHr4UEPLzxWNVXS39bay3N161/v0l3s3onRs+XM1FnfWR643CqL9/52QiVVXKY2U2t1+jf0fup67km/Kmv6ZOlb6fNUvqHxQk/yckSL9FR3euCAYGyl6rx6qyXjozlu6VoQKhs+1RyKKFXRfADxVu547tD+hYyPdG0dI9N0yIamyfry+wcmWe06NKbwQF5ODgjhWdujr3hBtUQKkU6AK3u6KzpVmt7j0OkZGK1p792tgoRgydNZCCbHW1yrdoLDxH6+rct/FIF45XQ4Oqj+7ZYD1tNmUANSoPRK4wXMDoYXSXfFWfV4BSPv39lYGR6UA8KZPu5lpzs/Luc4+iF5fKEo2q9HjqjJWEb1qtnj20h+O9BRQ8NSSkPSyS8E/ANX6OnkAqeGxLfb1a84SQ6goh5bX/X1SDLilOe/bsASDKUr9+/fDFF18gNTX1iFbsf7EcjmB2JIo3MCK9HKpgV18vXhFjnAyFIVJHr1mj6L+pVJSUiIAbH6+oNOlNYoZxWqTont+4UQQMPa7oSLBreRIsGUdAljFaecmaFRWlPDesS3a2/P3ccyrxZWexFbrgBbgqMSwtLaK81NW1t6CvWSPjtnGjdwQMxjaTpIMHmMOhxob4bT2w325v7zXQ58ihzjtP9/TsKYQJsbGKkKO0VNHQ19fL/0cdpUgjioq88+R0pY6exsxbYRnomkFFf1dpqawr9j3XDHNZmUztBWlPQrpewsMVaxKFHQqI7OfcXOD++z33VUdetf79JbbRnXcuP981sbFO5w+IQBgRIQLNvn2S4NUTmQhhnbS464yWwN+T+6mr+aa88ULm5bWH1OowRnfzjIoMY5YCA+WHMTy0PutKBQUpHZ6jf0/hyt/fNdElCTsY90CPTUSEZ6+UN/ltbDalxPv4eKdo0XND2mTWXfcYUPmkV42CLmm79SB8tpv9Q8HfG4VI/1tPGqp7wHTGMm88Dp6UR7bZXb2MhCAlJXL+/p0xKVSMmfaKArnOgkdvoTFpqq48mExK+WKuIt7Pse9IqW1tVd4SKgHsB8ZMGd/vTqmnF1Y3LpjN6jPGM9IwoXtvWdd/BeucTptuzO9IDxHnIJXxpialeLOfqJTrXlzdE0Wjqj5//9tLlxQnXUlidvB/FKfDK4crmB2J93sLI9KLt4JdTo5YtwmZokWosFCErPh4EXBaWkRwz8qSRfrLL7Jxh4TIdfX1IuRt3aqsNqyHyaTimbp3l79//lmIF4KDu57Q1J1C606wJKucDg8DFN14SYnagKqr5V4me42J8b7PdcGLXi4KRwx0p7XXnQU9N1cUjIIC72Jo2L9sc06OHAIhIfI+kiuwzUFB8pzt2wUG9euv0nZ3imFX5l1HCW8LC+WecePEO+lJyDwUhb+ra8PTmLkrR1JA57usVoFt1dVJG6kc0JpaXS3zZO1aEbgZAwQoIf2DD4RCm17cYcPk//Jy6d+ICBnrhga5j94+q1XWYGd91RGzYmmpvIPej7AwpfwnJYkhIidH/q6sFCWY8SNNTfJMq1UU6txceZfRW2z0WOneKodDeW8JQzkShqzDie/qzFhlhNQai3Ge7dolfx844KoUsE+YvwloLwjqFmidUEEXdJkPhoyrRUUyN/VnkZ0xKcm9cN4Z2xoVGzJiUhEkrEsvuuBsNotRrbRUoRUAJWTrlN92u+yLfn7quqIiJTDqCgfbRqFUV4jc1Z19RQ+Yr6/rs3TvtE54oCtNgGoDryFzGuOv6E0wxrFRUWtubu85oJHy74p5Ylwz9xddKdc9dJyDHXktCN8jTFV/FpUg/m1UahlPpDMe6nOc/xvfb1Tq6YHVIcw0zBhjp7jGuG6Y/JphCH8361xHtOkMg/DzUx5Wko9QQaJXiSEShELq48d+Z2Lz/1b6cWPpkuL0TzmypSPBLD8fOO88sSIaD0lv2Lm8EQ7dwYi4yKOihML7ww+BuXO7Jjjozz/6aNfAZSoR8fECt3r9dWFlCw0VS44u1ISGisA2cKBstEy+CahFzA3JbherdX6+K/HCocbbdKTQGq2//v4i1JC6s6pKhL2YGOVR8/MTbwihAUlJ0ifeQLdYdMHLaDHn5st4E92Czj7av1+umTZNkUt4E0NDYfqll6TvDxyQw4rkCrT479sn3z/6qEDCWlulnf37u0IG+f5Dga8dCuStMyHTG4W/KxA7d+VwhGV3depoXfNdxcUqobZuUdShQcwftmQJcN117cfdZBLFOC9PhITQUJnT9fUSx8aDnx4oWu/pCQ4N7byvjMaXvDzxijLeiPOZ3umkJNkHdDr2bt2UN8PHR9Ziaqr0TWGh7K/Tp8u7dW+xnkcpOlp5TpubxWiza5fU/7nnZP0eCUOWt/FdnjziHRmrjJBasurR86TPs5wc4LXXhBSEAjeVEL0+FOpYqGCxn5mIXCftYWwDlSgfH9l3mHST3ipa3Fta5JxJTW0vnOuWcQpnfBcFbx0+ROWZfWXMh2OEvXmTrJNtZtGFTuYNpBJCrwPrRg9CZ/TR4eGKYAdwFbwBJaQDCqanGw8p7FIxoHJMQZxjQi8NlT0qkjy32MdkQWP+JMLUjgSrIaDigurq5G9d8Nb3K3rvSMRhTByr10UfYx1CzP2I8UP8IazOZnMV+gH1Pz1CnAdtbcrLriv1VL44RrqnluuDc48KCMeXz2ltlbV5OB4+b6GwJlPHtOn+/orJ0UhuwfODc56hBsyZqa8hnaHv74Z8/qvKYStOJpMJ7nI6/VO8Kx0JZjExclivWQNkZspCotAOdOyhOhQPlhFGxMSuDLy220WwGDFCLPmHWtyx6enJfglxo2WDVnldqImJUUpAUJAI4Tt3uvYjoPqvulrRhU+Z4pl4oSMhdNs24MEHRUFITpbvGhpcree3367u379fKIFXrhSFgflxyNw1erRSgqurRRhjjJBeOoJuAe1hc7pyyc08KEj+pzWovl48P0VF0obnnxfvwVlnAZMmeQ8PzcqSPgHEw9O7t/xdXi5tAoR1kIG3Fou0sbJSyAlGjFBC9KuvytgeCnztUCFvhxs72FWInbEcrrDM4s265rt+/FEdbLSAkpZfh75YLLKWlixR85NQxz17ZP6TwZLkECRtAGSdWq0y38vLxQtVU+NKhMK+ys6W+UFCEXfzbdMm8ShTmCS7E9kqhw1TeaVOP10ouIOCgDfekDWbmipt06GkVNrmzGnvLe7RQ/a83bsFAhgdLWvl72LZ8yZe6VA84nrh2P/0k/RZeblSPCMjZR4MHiyfffihrEsK27pHQk9yqVvddTgeiSe4XzMGjs+iAEeIHhUCCpl6TAWF0eJiUVx1oZFKSm2tUuJ0OJyeMJTPI+GDHjfCohMG8PlBQUoYNq5NKpKlpUr4DQiQd5KZjgx9rJ+PjzyTMDl6kTpK/kvFhv1OwZ3tb22VM1mP/THm2GLcoc52Zow34XjTg1BT4xr8z7OUMCsqT5yfuqemq9TSOoyQyi49S/xbZ8nl/PDzk3XJ88VIxsC5oSt4HDMag2lEpOJfWenqedO9QoA8h3BQ4zxgHxqVLx0myL40zitAGQWo6DOx+eEqox0xTxoVq0Nl6NP7tq1N+oPrKSiovefV11dkgH8H/fjfVbqkOEVERDiVpbq6OgwePBhmbZevqKg4MrX7HyieBDOyljF+Jy5ONo0NG4S+GpDPPSUv9RQf4O7g12FEek4mJo5lfoXnnxcF5FCFBncwJVo7AJWzBXC1yptMCubGWBVas9evd7Um0lIIqMDMvXtFiOvdW8XaGJnp6HExCqEMsmY81v79yuKtW8/nzJFn5+QIrKmsTJiy8vPlnh07RMCk0kSmwS1bpB1dgW7pgldhoQisFRXSZzU1yiXOQzE0VOpLeEZGhgiX+nw4lLxFvr7AVVcBCxaIkkh8Og85f38RZvPzZYxJSVpUJCQWffvKZ3/+Kb89KRye2P3+VZA3QOZeebki/GAf04rv7fuOhLDsLWSQ79q2Tea2j49KUEg6f66vtjZRvlNTxcOyZo0YG3btkjENCZEx7dZNridkFpC1l5goigyF7V9+kbF2R4Ris0lc3cMPqxgNo9Jnt4vSwhhHxhL4+8v/3J8cDtmPaEmPjRXlLyurvUfPqOAaY4UaG4H0dBHY/f1lfGpqBOKn52g7kix7f1caCrMZGDBAlMjqahm3kBDZH9avl2vsduC220RRHD5crVudMcvPT8Ge6QnRPSqE3gUHCypBzz8EqO8puNbVyXyiYK4rPhTaAbmO+dt0qFZgoKxDepYIASLKgEoToJSC5mbXGBUKsoSxsq4dWd4Z22SxKKVGJ5mwWmVtUeg3etyo3ERFyXUdCafcQ0NDVZ/oiqjJJPsMSQ50hQ2Q99hsSnDXvRx6vAkRGfS40GjJenFeMzcS28HrGP/W1qbG9VBgfMa4IEAZ/xg32dysYKJURhh/x1QSFouKnbHbVf/qZA5UDnTvBxlBq6uVUYYKOPudfcHva2vV/NbnAb2b7HedoZFt1eOAjP2gQ1vJQns4SlNnhChAx7mlOvNSGZWuwECZA4T70aNHRZ0etL8zxce/o3RJcXriiSeOcDX+d4s7QdBuF2GookJtuNx8s7LEUggA55yjNjnCYbZtA556SiZq377eQYsI76ira8+WBsjviAj5/uOPxULLxKjeeCm8hSn17NneKs8cUTk5CjZTWekKNwBcD05CDwiJs9ulX3QlqalJrJsREdJPuhC6ebNrThU+gxbvESNcBbGUlPZew7Q0sdZTKNqwQepGSvDDhW7pgteaNYLvLymReg0YIHXbvVvaR0iWr6+8KylJ+iAhQerWVUGQCi1jIfQNMydHriE8qKxMxq2gQPqV1tnQUNl8jUlzPfWBPlcdjvZU84fDmOhOsV6+XJTcrVuVYq7DPYKDRenx5n2HIyx3Bhnctg14+WXFZNirlyRQvuYapaRTkOJ8JpwrJUWxMtIqTdIHzil9LyBklsHCPHh1aK2RZKG0VDxNNTUqnq+6WpSk/Hxgxgxpf36+emdlpfJi8N2+vgIDpbLH2MVNm2R/iI9XgqcOUzMquJ5iq1av9pyj7VA8jJxL1dXq+UaGyb8jDYXdLvtXUpIok+Xl0p/sS3pmWltlf7DbJTlwXZ1KxqkL4oy9IfmHzgzGc4GxYsZcOBSuaPBi7Aet/bqnhB4qCtVhYWpfpNeHxiDCkakUMMaIc4XeGwpuvNZI8FBSohQXI7012wgoj5SumFEwt9kUpM2o6ACuwffGnD5G4VSHfXkSnjkmRmWX7+J8N5mU4qh74qiI6DF9oaHyWW2tUop1Lw2g9g+TqX2eL5IepKZ6J/S7I/tgm6gI0vOkw+cotOtKjh67xXxBjG9j37CenGeMX2M8F8dUh3+yP6lEORye54FO6sD72BZex99UrnVGPY4PU5h0pbgjqdDr2Njo6q30pFgxWbK74smbRc8rvdRUJnVKc2PurP/20iXF6YorrjjS9fifLUYBurRUhOycHJn8NTUKagXI/zxwamqUNRdQQs369cAJJ3gPLSK849df5f06CxO9FvHxotisXi2xTvv3e09i4S1MKS3NvVXeYhGYSXw8cMEFsuE/9JBYV+gaJo5bh280NYkwNWuWq5JktQI//KBgaxERcqiRGv3DD6XNhCVxg7Za5Znbt0tcEgUxd17DsjLl5enWTW1WenxP794yVomJCtJCJj4duuUJTqgLXkYSBlrQqcRYLCpfx+bNrnkqVq/2jm5ZFwafeEI8RjxQ9APAbFYQICpXTOjHw5dxCTYb8NVX0t5hw0So5iGQkyOJkJOSXOdSZCTwzTdqU6YC1qOHzIWhQw+dMdEIgWtqEsGScBCyizkc0mdpadJ/u3bJXNmxo3Ohl9ZawsvcCdOeSkeQQSZdXr9eFKioKJlbAwbI9QUF0veE6dGiGxkp9SATFZkGaT2NjJT/6TXSBUkyLRFqC7hCa8ngR5KFnBx5VmKieGDLy9V82LFD6vP880rZGjBAlLnSUhUUT2OHwyHzgmslNFTWQm6uKIlms0rMrM9zo4Krwzg5t/fvl7/j4+XzqipX5dwbDyPn0po1Mofq6qT+CQkyxydPln3IWAdvS0fwYs6Tvn3lu6oqWd8mk4rdKipSAiKFIMJu6GVkYawJjQV6fBH3XMA9NTSFVL5L9zYZ40m4L1BorqqSelAB0Jm5dM+A7u3RA/KZO4r7DmOQdG+QkfTA2AbCkBirZVSMOCcBpdS7Gys93qYj+mg9lssdkYQey6R7pPSiKwi6p0lXIhobVY6h8nLlZdLhaJxPuueESpMeI8Z6EBKamOjquQDaK4ruyD6Yfyk7OxeXXXYcPvlkJwICQlzawfPC4XD1RrHfOTY05Ol9xTZwHppMikRJh0eynbrSr1Oiu5OpOA/oyWM/G8eU13P+U17hvDicGKDOmCf9/Fy9le4Uq47ydnXmzQoLUylldDirxSLjoefOOpxy++23o76+Hk899dThPegwS5djnHbt2oWXX34Zu3btwpIlS9CtWzesXLkSKSkp6MtT4Z/SadGVipgY8UhUVChBnRt+drYcJPoC5KatF1p8jRsvLbANDa7xKICC9mzcKO+mRZqCFhnTGhvF8m6ziYcgKEiEgl9/lXvdJb3Vn+8NTMmTVX7oUFfKXSamczikX8iIQ0scIPXu318EmaIipUzZ7SJUmUxSpwMHRGiMiRHhhpYVCslMCFdXJ+8sKBABrrVV6gfIPfQaOhyunjseLBaL8vqtWCF9+PHHIiD6+ysCjOBggdNNnCgCYEcxLRS80tKACRNcBaraWmDxYhGm29rkOx8fEaCioqS/KipE2dq0qWMBTlcs9u4VpamlRd4TFKQw6PSe0jJcUKAOCz2IllZYejLJ1JeVJfcUFKjEjo88IrFYQUHKs0DoEA/TffuknRkZIpgeitXeCIGzWgXqunu3itGiAGY2y7zdu1cpbgcOCGTzvPPEC+zOgNBRbJI3dfUEUSR0jVC8xESZP+vXi4c1PBw44wz5n2QqFEYIWSErI8eFVl0mnayudrUY0qJrscj7dcgTobWFhWKBDgqSsdm1S+1ntbXKOlxfL337/vvyjEmTlBVzxAgVa1lbq/a+bt2UYsNCxsg9e5RxiVDj5mYZr9ZWZYDyNDbl5Wq/YGyOTk6RlNTeo6krMnpsWHGxInDhOsvNlbjVu++WcTnU0lmMmz5PKJQ1NkrdadygUQNwFbQpMDJ5sDE2SFcOdNIHeipNJmljRYWMbUqKq4CmC+ZG6z6v4dwkZba/f3tPDOswc+ZkvPfeqx77av36SsTGhjsNBVTcuJYZN6QLjLpyQ88KCRhYZ8JD2T+cY52RP3RWOmI504Vt/u8uHkc3eFKGoOeJ68dqVXnYdEXNz08pJvq40FCre1J0OYSKgg5hpmcDUN55Pz9ZN7oSp8spfn7Ak0/OxRVX3ISgoBAXmJ1+hnDfp5eIhW3gfKQswPawT6hs6951GgGM8c9tbcqr5Gmf5jzQ8zVxDFhftpWKHA0I9DyFhXVNqeAc41ztaJ4ZvVF68fHxnLfLG2+WzabWBZU3PdaxsVH21sjIwyMVueWWW5CRkYFZs2YhQ6eB/ReXLilOP/30E0499VQcc8wx+Pnnn3H//fejW7du2LRpE5YtW4b3eQr+UzotZrMIhdu2AV9/LZOT7E6EHSUmyt/bt4uwDchkdGe9omueVMN0x9MCS0Hg9ddlogcFKUF7yhSJE6itVYIYGdOio4Fvv5V7GUdAEonSUjksd+70LDweCkypMwhLSopcs369gh1QWCfWPiBA6m63y2aQlOQasH7ggMLoclMrLpYfBouGhSl8O2Oo6uvl3p9/lnotWyZ12LNHDqT09PYU4bRG0WJFSNC2baI40BrU2CgKQ1yceCWAQ6PB1q3XOTnAs89KXcPDpQ8AaReF3aAgGceSEmDVKlG83B0OumJBjyU3flq56NEixIuQINJU67SrPKQCA+X6+HjZVAsKZMxDQ6W9ZOH76SdRAmJjlWAcFqa8nj4+ch2VqE8/VXEvnuBPuvfs9delTwhtrayUeaBDjHRraXOzghMlJ8t1tbXShwUF7cflSNCZu4N26gp6aKiK3QgNlT1j3TpXZq7wcOUlslikH9euVUrsvn3S3s2bZd4wfqStTb5LTJTn19TIHOWaKCyU++gdjoqSz6OjZU8gdT3jMui1IVwoIED6nKyTzNHUp4/EBhJyV1MjcyE1VXmbWEwm2RtzcqTuqalS7/p65fUNDxeDBQ0O7sYmJUWUph07pC0pKcrIUFwsRpszz1QeTV2RsdlEOaXXpr5eJZIMDFSW/8JC4N57Za84FBujN/PIOE+MrJt6rBGFWV2oJISurU2e1ZFCYLHIvt/YKP317bfSP5w3vXoBp54qnmCTSa1RPY+dLoTRs0PPNQX2jjwxxx03HgsWvOysr8kErFv3G2644VznM8vLlaeBxjYqQ4Rs6bl5WEijDCjhmPeSkMJslnlVWdk5+UNnxWRyH2tFbzHXG72dOlxPj1uiUuPnp/ZmPbYrMVG+5zzg56Qg1wV+9qEO4dOVbb3uhE7zc44flai6OqWkUmnQoW95efn47rvPMGvWU8536WNOpZrP02GKeqyQDh/Uf/Q6Nzer+ecJ+qizwZWXe/YEcn60tSkPOxVqtpNKKvdjKu6E7+mJfb0tOnSOcDidKEUvuhKrl47ydlFR5rnaUR41GqTYJuO7yThIynwq0TTAedvu6OhonHLKKVi6dCkWLVrk3U1/QzkEu6wqt99+O+677z588803sGgq7tixY7F69eojVrn/hZKTI0LhgQMqDmTvXvnO11c8IBRwy8rUAeDj0z42xm5X9L2//iqsWl98IdCzHTvUwRgfL5b7q66S2IJ77wXmzRMhaswYOdCPPVbgfqNHi8W9qkrlHAoPV5bu4mKpX0KCLMBVq+RwZ4yLXrKyhIVu4UKxuC5cKJb6jvJD9e+v2NH07yZPVglgGetEt7G/vwg7SUmyKHlAUtnbuVP6il4e9mlMjGwA9fUqOS+Ff2489DzZbAIn6t1bhLSWFgmMP3DAVVgh1DE6Wgl7Vqt4Mw4cEMjfuHHiqTvxRFEik5NlfD78UMW0hIaqMe/TRz7/+OP2AaecB4yHGTZMsZwFBMhYtbSouVRbK/1UXCyKhP6MvDypx0svSb/16SP31NUpiys9TRS8goMVc5Gvr7SZFjs9KJyHW02NCJLs46Ym6auBA0U4b2qSQ6uwUHn3IiNlg29uFgE2I0PmbHKyPOezz0SIXLBA5vVDD7nOx5wc4IEHgJtvBm66CXjnHRGWy8rkexoY6JFhPFV4uKLdBmQdRUSoPCkpKe3HxRib5M04su+3bJHfdruKWdm6VfYIGgnKyuRZtbWuc4zroLpavMENDSIwJSerJNE+PtLOigoR+Fta5H5aD1kXQob37BEBnQJ39+7AnXdK3//1lwjwZWXA8cfLnFmyRNb5nDkiPNfVqX2spUXWAS2wnE/79sn7oqIUSQPjDCsq1IFLSIheaECJipK5nJsreymhcnFxCqbc0diQzKS1Vd7pTgDjPHrySTHgkHmMRqfsbFnfNTWyL9bWKstwdLTM31dfdb9+3RVv5tGHH0qdIyJEkeMep+eLoUGNlnRdaNHJBMiQBai1SeGUdMtsz549QkZBVERSktRhyxYxLJH9lGOnKyj04DBfDD2hrAOgPDEUdvUSEOCP+Pg4JCTEwWqNg8USB4sl0tmeAweADz54BcccE46vvvoYp5zSA4MGBWDq1FOQn7/PqVjv3w+8++4nGDJkCAICApCRkYGFCxfAbm91ejxMJuCZZ+ajf38T+vY1ISNDfi65ZCIiImQ+t7YCq1atwvnnH49+/awYNCgCEyeegsrKSgDA8ccfj5kzZzrr/+KLLyI8PBzrDzJ3WCxtmDfvapxwQjp69AjE6NG9sHTpErS0KBKCxYtvxwknJOCooyw45ZREPPbYHNhsdpf7TzklHQMHBmLMmF544YUlTiNFVBRw+eWTceqpE505tRobgVdeeQX9+4c7hfvnnpuPSZMGOdMJmM3AmjU/YtgwE2pqquBwACtWvILjjgtvN4ebm4Ft2zZiwAATiovznN7DzZt/xeTJx6J//0Acd1wyFiyYjtLSeqfx88MP30WPHgMRF5foVJJWrHgFo0eHuyg+xcV5GDbMhNzcjU5o3vff/4TTTx+Ovn39MW5cPB5//HbY7a3OOX3ttcdj2DATBg82YejQQFx00SD8/vuXzr1u3rzJmDlzolMpo3cvJgZYtuwJjBmT5jL/3njjRYwZk4X09AAcf3xvfPjhs87Yap5x9FiZzcDVVx+P/v1NyMqSnwEDTOjZ04TTThvkJLP68cd1OOmkkxAdHY2wsDAcd9xxWL16PRoaFBOgyWTCU08txbhxpyIiIhAjRmTgq6/ed8YmNjUBCxfOwejRPdG9uxUjR2bgkUfuRmNji1P5X7x4PhITTbjzzumor1eGjkcfnYWePU144IH5yMuTvTg3twpXXTUFw4bFoE+fUJx//lhs3rwJLS3AW2+9gsREE1JSTOjRQ35SUkxITDRhxIg0AMAjj8zHKacMwptvPoeTTkrGgAFW3HjjBcjPr8a+ffKOiy6ajAkTJrrd95544gmkGaAwEyZMwNtvv+32+n9V6ZLHacuWLXjzzTfbfd6tWzeUUfr4p3RadAtifLz8WCwKEkY4C13dpMJOSpL/s7OV5XHfPgm+Li1VkAQuiqYmeV5VlWyCGRlyUDAh6wknyLM3bVKHcXm5vIdW25wcuZbsa0YSCb5PFx7dEQ50BdPvrvTtq5SvkhI5tOjRiYgQ4bp3b1fSAotF2gUoAZF14qYSEiJCtNks11gs0h7C0GgxOeYYERj5rDFjJFbn55+FqcpsVvEaenJYQEF49HgVPVbNbBYoHCB95c7K01GQuh4PQ4E+J0dZAxm4X1Qkde/fXwREWjLdQZfi40VwpyAdEqISk+qKIinRLRZldacyResvIUOEfTQ2Kmuir6/099q10o+5uUrop8IQEaEEGSpxBQXSBs718nJ5x6BBrhZ5QBSqrVvVu6uqlBJ33HHKw6FT5OoJASlccm7TGhwQ0H5cDpXO3B0UK1LkQOzZI5C33Fy5j7DS5mbZA/Q5RqMAPQxRUcrqHxMj65+5U0jzm5kpfdjaqijGy8ulbYTZ+fqKZ2fkSJk3mzfL+qPQGxcnHhndk2K3A+++Kx4w0j5zrdJKTehtVFT7HE2FhQp+ExAg45mXJ+1g/jDGSpLMICREvExUamtqpK6hoWpt2e3tx6a6WvolJUX6oq5O7S8JCTIPy8vl/R99pHJZ7dypGOGY2whQQeq0POsQNG9IJtytaXfzyGoViOCff0r/7t4t9wweLPOH0FfGhJJgh5BDQmsA5VWh0kiLMdeA1QqnIG+xCFKiokL2e0B5EjIypB5ffilKNtc3x4TwPhauLyrVOuwqKKi9J4aKHA2J/GFOmbo6Fc/R2NiAF1+8HwsWLIefnwUPPXQD5s69CK+8sgomE/Drr7/guusux+LFT+LEE49FTs4uXHfdVLS2AlOn3uP0RgBAZmZfPPfct7Dbgccem4HGxiaUl8uYFhZuxOWXn4grrrgK//d/S+Dn54sffvgBbToTxsHy7rvvYtasWfj8888xZMiQg3PSjrS0JLz55ntoa4vC2rW/4Z57piIlJR4nnngBWluBY445GaeffgHCwqKxY8c23H77JHTv3hcTJ14OwI6UlCRcddV7CA2Nwh9//Ia77pqK+Ph4XH75BQCUImwyKXpyfa3q3jmzWcWjdlSoLOjxPYBSdvfu3YWpU8dj+vT7cO+9L6GxsRR33z0N9947DYsWvQybDfjzz18wYMAwJ1ObPjd0ZkT98+ZmIC+vEFOnnoazzpqM++9fjj17tmPBgmsQGBiAa6+d77z2nHOuwfXXL0RjYz1efPEezJt3BUaP3u8COyVcTmeD08+mxkZgxYo3sGjRPNxxx9Po2XMwcnM3YOHCaxAVFYSJE69wyhVkmKUR6vzzr8GNNy50nitvvPEoVq/+1qn8FRbW4uKLr8BTTz0Fm82BRYsWY8KE0/DttzsRGhrinH/33HM3Zsx4CNOnL8GXX76GadMuQlbWFqSlZR2Mow3B/fe/gvT0BGRnb8GcOdcgMDAE119/m3M8YmJi8cknb2HatEdgtQagvr4Rn3/+BqKjY53eUpMJmDHjfFgsgXjmmZUIDQ3D++8/h4svPhGffbYDxx9/IX75ZTwsFmDFinewbNmj+OyzdQfXoY9z78jP/wtff/0unn76U9TX12DevKvx4IM34MEH33B6fpuaZN/1Bq44fPhwFBQUIC8vr51S9a8qXVKcwsPDUVxcjHRKjgfLhg0bkEg/8D+lw2K0IFZXy4FAitLSUgVvYUwSrwkKkkVeUyMHE0kQCGFKSZH79+xxDeBl+fNPeUZiosK1hoer+JukJLGK5uYqSN2gQTKprdb2UDSgY+Hx7ypnnCHKyyuviFBRVycW5vh4qS8FKj1gncJat26iONTWqlgIKkYU+mnNT0pSQlFzszwvIcG1Lt26iXdu82YRSgARJrp3d6U0poAXFNQ+ToOFEByHwzVuqiOWML0Y42Hi4qR+9MjZ7TIvIiPFI0WLLpNi6nCgwEARCisqxMOYleUaK0ZLdFWVEm5MJsl9M326xArt2CH1IY0+56POcMVAUx8f+Z2fr2ACHB8emnx3YKDMecJaKQzQW7Bnj9w/Zoyshw8/FAs0Y8rCwkTIqq2V6woLxVNLIcHhcA3Erq6W66kkEuZZViYCP8dLH5dDoU93B8XKzxfoocMhXuCTT5Z+LCgQZdDhkPXO+c5CVjImwGUeFhoBGEcRGipzOzJSMcAxroQeqOBgUZSYq+S220R5ffppVde0NHl+Xp58rsMPzWZRpr78UhQ2Wr8JL6GRyOGQ+jAImSQapaVC9e/nJ8r01q2KlKWyUhQ5m03WZX6+YiOlIk+Y0e7d8o5ly1T8F2F9LDQCREVJXYqLhRQnIkI+Cw6W9bBjh/K4U9Gkh0/3huneCnp6qqqkv+1272nzO5pHpaXi3Skrk77o21eMHH/+KXUklbyPj+yXHAMKx4BrbCjrTa9TYKAap6goaWdRkUoxQJZDHXZHpahbN+mrggJFgkNvfnW1goux7ykwOxzyXMbvULAlKYAu4OvKFq9lG1haW1swd+7T6Nt3BOx2YMGCV3HuuVnYvHktjjpqOJYuXYCpU2/HGWdcgbg4IC4uAzNn3otFi27DrFn3aHCoJvj7ByIqKg4OBxAYGAi7vQm+vjIHFy16BEOGDMNzzz3rfLcx5lu8NStx5ZVX4s0338Oxx45xfufn54f58xdg3z4Z89NOS8f69b/jk0/exejRF8DXFxgxYqxz/wkIaENgYCACAtoQEADExfnh4YcXOPfOYcPSsX377/jyy3dx0UUXoLhYef9pCAkIUGxqnF/sQ5LiuINScY7r5Acs+v8mE/Dyyw/i1FMvwcUXz4TZDISG9sDChU/igguOw1NPLUVbWwD279+LYcOGwd/fFcXAd+lxVayrzQa8+eaziI9Pxvz5T8PPz4SMjN4oLy/C4sVzMGXKPFit5oPxXlYkJsahqakV0dHdEBoaBpNJGZgZA0nyicBA19ixbt1krT3++D2YPXsxxo07B35+QFZWOvbty8aLLz6HyZOvQGSk8uRzjgKAv78VYWEyb0TWC3b2oY8PMHz4WFit0q7KSmDBgufx6afh2LjxJ4wZcwaqquTaU045H+edNwUmE3DDDfdi1apv8OKLT+Hhh59FUBAwbdpdTijgCSek4frrb8Fnn72NefNuc+6t0dFx6NYtBV9++R5OO+0yfPnl++jf/2iUlOQ7IZ9r1/6KjRvXYuPGA2hq8ofNBsyY8Si+++5jfPfd+zj//Knw9w88aMwMg6+vD8LD45zQXq7T5uZG3H//csTEJMJuB2677SlMn346Zs5cjJiYOOf4dkROoZeEg8LX3r17/7sUp4suughz5szBe++9B5PJBLvdjlWrVuGWW27B5ZdffqTr+P9lMVoQjclemWxyxAhZgD/+KAv3pJOUJT8/X0HQGLwdFCSbTVOTEuz0fA3k3LfZRHCgtk/BMCBAlI8bb5T7dKreRx4Ra29UlCtunnC0+HhpB4XQI5VLp6PSt6/QB+vxKnv2SF8C0rd6wDqzxBMLTGttRYW0NzZWEQL4+orSWlurYJPNze5jLAAFFbv6aunT995TFlJuygUFKhkcY1OMpb5eHWAMLtcTEndGg22McwgLU3A8eopaWgQmGB4uyvKQIWqMdcprKihUUnbsUHOIVONkoaNAPHCgjEnfvtInTz6pCCpMJtd4Jx66tEDrAdK7dyt4HAkJgoNF2NWx7DxQqejQe+jrK9fm5op35I8/pP6EZVJACw5WsSi7dskcoOeDdNtBQTJHmCwyJETaTeUMkDVqHBcmwczLk2caceyMYwwKAt5+27XvqWQTTlpQIMr5uHFyyGRnS/2YUNpYgoOlLZWVcuCTIY7fdesm3zU1ibegtFTlaKLlPipK+jg0VP7esUPa9skncm1iohqDsDDPKQ/GjgVOO02gw7W1SkCmAEyvzqpV8t2iRdIvEREyhiaTzFeTSerF2MoDB0RpOP986YcffpD76+tVcDzHjntiRobMs+3bZY7FxirvsQ5ta2iQZ+3erSzOHF+7XT7Xc1wRclhT4wrVoceJMRD19bKXREZ6T5vvKX0B49xqa5VyRwUpNVXmfI8eooSuWSPGA8ZfMI7EXaGyyb3IalXskoyjoTeCXnX9XkBZj/fvl7GKjpZnkQKcFOYkCKFASSOWvteRMMLXV9rIGEnu5UbIIecw55+vry+yso5yGl/S0nojJCQceXk5OPbY4cjO3oR161Zh6dL7nc9pbW1DU1MjWlsbEBxsPQhLLkdwcKjTG8PnM1B+27aNOPPM89t5RljsdmDVqrV4/vnnYbUGIzl5hAstOgA89tgzWLbsJZSU5KOx0YbW1mb06jXIuc/4+gLPPvsAnn/+PjQ22nDlldNw4YWXOz2JL7/8DN577yUUFeWjqcmGlpZm9O49yEk/73AAP//8GY46KthZr7a2Vvj7B6CtTealnx+Qm7sFw4YFa3V3dTuJx78ao0cHw2w2IzIyFscffxZuvPHBdvNq585N2LlzM1aufMN5r8PhgN1uR37+HsTEZKGpyQY/vwDnPt7cLPfW1VXj2GNVPRwHHx4RIeOen5+DYcNGIiTE5ISSDh58DBoa6lBSUoC0tBQAwDvvPIsPPngRLS1NCA0Nx7JlK5xns90O/PTTZxgxIhh+fn6Ij0/B1VfPwLRpVznfK5Dzeuzbtwvz51+NhQuvcem/4OAwVFWJoZIef6IZ9HlJWYzrr6lJ+nv//v144om78McfP6Ki4gDs9jbYbA3Yty/fxevXp89Ip7fX1xcYMGAkduzY6FT4vvjiHbz44pMoKNiF+vo6tLa2IjQ01Em5TyPkxIlT8eqrD2PChMvw8cfPY/LkW/HUU3c743izszehvr4O/ftHuYxnU5MN+fm72s0F3Stnsai5FheXgm7dEp2GmYEDR8Jut2Pv3lxERsahtRX44YfPkJkZDIvFDykpKZgxYwauuuoquCuBBxdKAwOo/w2lS4rTAw88gBtvvBHJycloa2tDnz590NbWhkmTJuGuu+460nX8/7IYLYi6gE9vU3OzCG8bN8pkP/FEBekKDRXBdN06EYj79VPUqQzQJ+MJFyYPTMY27N2r8kT9+qvyqDQ0SC6o665zTYxKZry9exVEi/EPOhztcHLpdKXo8D+LRQR1I505BQoSDNAb1Lu3ohO1WES5GjFC+qesTNFOt7Yq4giyRRm9QIAcfky427OnezKMM86Q/ECbNolVXg8epoA8dKj8//PPMgf0hMSdsYS5o3/n3KqtlU07MVF5OWJixCNQUNAeDqQr9MHB8l5S2XLzDg+X/0ke8X//p6BaJAVZulQsyCaTYsTj2OnjSMsyk/oycJuKea9eIgDW1IjQRi8ToOY3Y9qsVhnHvDw5MAjn09m+TCZpH6F5TBAZHa36SqA+SqAmVKmgQOqalKSIRPLyVMD+X38Bv/8u862sTA56HV5mt4tSx7xoOTnyLM6ppiaVHsDhcE0oGxEha373bplz7tgqMzLEAPLUU9JnhLqFhkr7SNbg6yvzesQI6VfCsfz9XYlNuK5ra+V5ZWUqppJjlpIi6z4729XjbDYD114r4/Hjj4r5jgp0UJBKPgtI/5K4pLhY6ltWJv1GRZHJievrZV099ZSCH1JhqalRymBwsPy0tcmzhw2TOv75pygZhOdGR8s4MqaKkF193ZWVyXv1tUsDGOMH6dllG+nFoYLQp48rbb7OzueOEMddSgeeF4D0i27QMZtlvZSXy16UmSn39+sn9/34oyvrGIuugHDuk12Uf9PgERwMp4eA+70O22KeI55zNNSZzTjoIZG5arerwHLGFOpeST2paX293KcTGej7iO4JoYGF/xs9IyRQaGiow+zZCzB27DlOiCfTKFgsAU5DTH7+biQmipbtjnUwMDDQCck0kk0QVvvnn7/j/vuXYuXK97FgwTQ89thbsNlEYfn007dx55234OabF2PYsJEIDg7BsmWLsGnTGmf929qACy64DmeeeQ5ycv7EPffMxLhx56B//xPw+edv48EHb8Fddy1G794j4e8fguXLF2Hz5jUuYzxs2Am4446lznH67rsPsWzZA05Pid0OpKf3wvPPr3B63X//fQ3mzr3U5awKCgrBa6+th8PhwJ492Viw4ApER8dh+PBxLuPR0FCH8867FhdeON25zuiV6NEjBZWVQFhYNEpLK50GV56xQUEheOON9U4DcGVlIS655Hjn/KRXkutLJ8igAQAAzjjjEkyZcifa2hrx+eev4rrrzscXX2QjNDQUFgswcuQJuP/+pbDbW/D9919gzpwp6Nevv7PfRB6rAwAsWvQCBg8e4TrA8EFjoxp7nd6eEHa9ziyExt1++xWori7HnDlLkJSUCl9ff1x66UjU1DQ7CSaM9xhhqxs3/o6bbroEM2cuwDnnnILo6DC88cbbWLJksdMISNa8MWNOxcMP34Aff/wQhYV7MHLkaXjqqbudY2az1SE6Oh4vvPCjsw9o7AwPD3fC7wk1BFxzoVFRYnu5L+ikISwjRpyAhQuXIiKiBT/88AWmTJmC/rrwqZWKigoAQIwOsfgXly4pThaLBS+88ALuvvtubN26FXV1dRg8eDB69OhxpOv3/21xZ0Fkstft21WOISpDxx6rMj+zmEyiEGzbprR9upx5EAFqsnPzJ3OMzSbv2LZNsbpws9+5UxQQHXJDIfiDD0SYKSoSAY7Me3qsAXMQAR0LBF0tOiuanmCyVy/37H3HHy/KASmt6Q3ioW4yKW/Q1KnyDj6DcTSjR8vzCwuVJ4NeIG6KJ52k2u2OHbC+Xqz1O3a4xqv07y8bLenZzzlHnrdihQiPiYlKaaqpEQXQHUsYIH8b6d+Zw4o04kVFUi9CcT75RL43woF0pYsKXEyMvKO4WH7zgCD7olFhzsoCZs6Utlmt8oxduxTMlHCbgAAlhDFOikQJiYlqjh19tMzjLVuU0ESCEGLhrVZFPdzcLPF/NCiQ9Y+FsNX8fEX8ERAgAqrNptYiD4oePcSK3tAgngMaOUpLZf4fOCBwNsIGGd+hw8uSk6UPCJ1ctEjmWkSE8t7ZbNLvCQnyuZ5QlvX29xdvy7Zt7tkqAWkbWbIoJDc3K8E/NFQp6wkJilEQUApraKgodkOGSDu3blVKM/N07N2rFHF///YU95wHAQFCJsBxCAmh5ViuI1wjKEiea7Op5NzR0UpIosK+bp2QzjDvFg9oQhQpMJBQgeQeZrO0+fffxTPTq5eaCxs2SB8lJyulSV9369YpxZwQYEDR8xNqRIsy4BprGREBHHWU6pvOaMbJvpqdLfVNThblobxcBN2wMOn36mqlPBnTT/CMSEuTuZSbqzxFujDM3zp0j4oomcXoFU5IEIVs61ZRzvRrGxpkDxowQDxgXH9VVQodQY8sY8FaW13ZzfgZ1zWVN3ojdKivzr7GNtDr1NraitzcP9C793A4HMCePbmora1Cnz5ZMJmAfv2GYNeuXEyZkomEBJm/SUky/yigNjc3YtOmtbjttsucfWAsffoMwG+/fYe2tgUunxOK5HAA5557GSZPvg7jxp2KsWP7YeXKjzBmzNmw2YBvvlmFQYNG4eKLb3AqCvv27XK2k3GhYWGRSE6ORFZWb3z22fv47LMPMHDgCdi8eRWGDRuF88+/wQnTp3dAVxwDA4OQlpbpNF6Eh4twocMhfX0t6N49U0uYXOC8RhkLzEhJyQQApKb2wFdfnYTc3I0YMWKcy3zKyhqCXbuykZqa6Yyjbm2V+RcYKP3cs+dg7N6d3S5PlclkRkZGpnPOVFf7OuthNgOZmVlYufIDOBwOmEwmBAQAGzeuQlBQCBISkpxCu9UahrQ0eU5q6j147rlHsWXLWowdO+7gfhOEzExpS8+eWXjmmYewadMmFwr+qKhYxMYmYO/e3TjnnEvajTGZ7VhMJuXF1vM26VBeQls3blyFO+98Fsceexr8/IDi4n2orCxzKlvs8y1bVuPMMy93wqk3bVqN3r0Hw+EA/vjjNyQkpGL27DudScm3b98Lu13OMSpyAGC1+uCcc67GHXdMxiWXzITJ5OOst90O9Ow5BOXlJTCbfREfn+YcFxokeD7T6EgYss7IFxAAFBfno7i4CBERCXA4pP5msxkpKb2c/ebvH4TU1EwkJwNDhmThoYek792VrVu3ws/P79+a9qhLihNLSkoKUg410+Q/BYBnC2JMjChDhFiMHy8MVZ66mYdkW5trLA/dwQAPDpX4j5YewrZqalTMTmmpStSYk9MecpOVBdxxhwivzz8vAk3PniLAVFe3z8vUmUDQlWJMMEk4SUaGKJ5nnQVcdJEoJ4DUT2fm68gbpFOj60oPlYktW4AXX5R4DR8fEYDoDSCrYU6O+wSXOTliFc/PlzE++mhRCgoKRDnq108+0/NVkfa5tlYEJHpIBg3qOMDcE/37iScqgT05WYRiko5s2yaboBEORIV+1SqpJ3NW9eun8gZRASWdvbGkpcmcWr9e3hsTI/Nz716VQ4gwqLIyaSsPnKgo+VxnSGQM0M6dslnrhy0tasw94e8v8z4qSgXNEx7HYrUqa9zQoeKBaG4W+msKZYzt2rRJKVG7d6sYAdJOh4RIP9GCXlgo8+3AAVlfhYVSh7g4iR1KSRGBuKRE5RQj/XZtrXiuwsNdDRuA8gANHNg+h1dKigjGTz4pB2Z0NC2/iggkM1Pq1dgoRAz0eFVWyj2APCsxUb6LjhbjwyuvSL/GxMjvoiI1fk1Nypv+3nvyfGOqgRkzpL1lZYqhjZxCpOcnvCUmRgR/m036jh43FubvqqxUBzoFbT1up6lJKe7r1sn66dZN5tH+/bLXkr2xtVUUJNIUFxe3X3eMpSQMLSREQUcpXFDQYywgqX/tdpmby5dLvw8YILGAHdGMA2LcqK2V+vz1l1xH4hWbTfamnBxlFGACb6afOOUUZayjJzE+XvWbMS6F3hx6zvRYI8Jnm5sl7q6oSOZbTIyqf1GR/H/yyQrOSYhga6syYhBqB7gKzHo9jF4jPbeQMfeRHrdF2Jefnx8eeOAm3HrrkzCbffHII9MwePDROOqo4QCAmTPnYfLkM5CUlILJk8+Dj48ZmzZtwoYNWzFz5n0oL6/Dk08uhMMBHHXUaNTVlRz0UNvQ3NyEmppqhIaG4YYb5uLkk/tj1qwbcOON18FiseCHH37AmWeej+bm6IPW+kgAQGxsKmbPXoT586/HihVjEBYWhdTUHvjoo+X49devkJSUji++eA1btqxDUlK6c497661nMXLkcYiNDcKaNb/il1++wW23LYHVCmRk9MAHHyzHTz99heTkdKxY8Rqys9chISHdRfjW+1T3RNHYQGWhoUGl+tDjjai0yDMb0dbmwO7d2diw4VdMmjTD+Tzec/nlczB58tF48MFpOOecKQgLC8K+fdn4889vcM89T8PHBzj22FMwb94UtLa2wdfXx1kHk0n2JebD0o1HPj7ABRfcgGXLnsBdd92EK6+chl27cvHss/fg8stvhp+f2amwNDU1oLKyBBUVTfjkk1fh6+uLtLRMZx3tdjsaGxvR2tqC7777ApWV5cjM7Ifdu1c732U2AzffvAD33DMdoaFhOP748WhubsLmzX+gsrISV155s1vKchqt6EUlkYnenykpPfD556+hX79haGiowaOP3oqAAGXh4zh988176Nt3GPr3H40vv3wDW7asxd13LzuIJOmB4uJ8fPHF2+jR4yh8++3n+Prrj5yGJObOk/g84IILroWfnz9OO+3ydnNhxIhx6N9/JGbPnojp0x9BWlpPlJcX4bffPsfxx5+NrKxhLiQinC/0njIm3t8/AHfddQVmzHj0YLum46STLkBMTJyzD9ra7LDbG9HU1IKPP/4C5eXl6Nevn1uW7l9++QXHHnusE7L37yhdUpxuvvnmDr9/7LHHulSZ/6Xiziugw2xSUwUqx/gSY/4WwnkaG0XYIKtRdbUIXGROo6WOjEUUVJT1RhYzg+wDA+UwpTBghNywZGYCl1wiEL+SEmXN0JWPQ8lf461Xis/cvVsFZoeHy2IlJOqTTxTO2JOi1lmuKI4Rmc7efts1V0t9vfTVX3+pXEV+ftIfU6eKUmlkFnvuOfnebBbBi0rBsceKwNyrF3DrrerQYqA+2ctIdVpXpyBepDp2V4xtDAoC3npLxmroUHWIktJ42zZ5Xn6+ymnEEh0t86KlRZi6SH2tX8MYOXcQTbNZBEQ94S+tVJWVKoaGltyUFOl7Pz9ZB3/80T4h8plnSszG668rwZ1w0+ZmlbTQz0/myPDhsi62bhWlNCNDWQmZFLFfP8Us+fXX0leEQehB0hQ8mCeFkCTGDdpsIphHRKhYnGOOkT766Sf5bOxYOWAcDukDrnOuJcYoUtnx8VHWQqNnV1fQOd9IPpOVJQK+zhh14ICCntJTR4bBbt2UsBwQIPf26iX9HRgo6z0pSeXw0QPzAZmf9F66Y9esr5d+GTFCri0tFY8gLZSAImlgKgYSgJSWKmhsSIjMC2NSVR7m7ggPWltlnu/dKwyKsbHS3hkzlFewsFAIJKKiPK87Hx9ZM2az9NnevaoepOk3mWQfJzNnYaH0H5VDX1+JyXr3XanDyJHt12R2tuwlNpsodt27y35QXKxyVrHvmWMrL0+xaTKeb88eYd2LjJT1HRKiGFc5X9wJ0lRUlJVaxRMRlpOeDlx8sbCK7tkj88XXV+bd6afLXKH3g+1jvBfHWU8oS2VJ/63DB6m8sRhzH7FwjUqgvxU33DAHd945Cfv3F2LYsGOxePEyp3J49NGnYNmyz7B06UI8++zD8PPzQ+/evTFlyhTExQHPPPMoXnhhEQDg1FMzYSzz5s3A44+/gqSknnjrra+xePEdGD58OAIDAzFixAicddbFLoop953zzrsWX3/9Ph544CY89NCbmDTpWmzdugFz5lwIwITTTrsYF110A379daXz3lWrPsfzz9+D+vpaxMcn48Yb78BFF10FiwW49NJrsXnzBtxyy4UwmUw49dSLcf75N2DVqpUuyo/eP/qY64mAAVcPCg2xVFwdDqC2thpDhwbCZDIhOjoWJ554Nq644mbs3JntXHMyFwZg2bKf8NRTd+Kqq46Fw+FARkZ3XHDBhc747PHjT8XChb747bdvMWrUKU7oNecdjUaUYUpLGQObiGef/QKPP34r3nxzIMLCInHeeVdj9uy7nLHKAQHA+++/gPfffwEWiwXdu/fAiy++geTkNOe4fPPNp+jePRC+vr5ISkrDwoVPYejQo5GXJ8I742/PPXcKrFYr/u//FuG++26F1RqEXr3644orZjop/o1Fpznnjx6bK3NoGR58cCouuGAI4uKSMWPGA1i06JZ2Xqprr12AL798Gw88cAOio+Nx//1vISOjD2w24LTTzsRNN83CrbdOQ3NzE0488XTMnHk3HntsvvNs5bjLmRGHa6+93ekh1w0WgAlPPvkFnnnmTixYcCUqK0sRHR2HYcPGICJC2PeYI4zzysiQ19gIpKVlYuzYczBjxmmoqanA6NFn4Pbbn3VZwz///Cl69Qo8qMym4amnnsLRRx/tVnF6++23MX/+/Pad/C8sJodD3ya9KyeccILL/7/++iuGDh2KwEBZQN9///0Rq+CRLjU1NQgLC0N1dTVC3UXm/4uLO49MVpZSPux2yUNDz1RZmSIKaGkRYSIpSX7sdtlg8vPlICeDTkSEHKJ2uwhu9Bbk5IggEBioFo0eBM38OzfcIF4cWrH1+vr7i5IyerRYvinEtbYCc+d6juMhIcGcOe2f6UnZYV+sXy9CW0mJa5B/QYGitk5NhZNmfd8+EfQPR1Gj8tfSAnz3nfR7VZUrNIzxXa2tAg1ctEi979tvpR8dDpUfqqVFxsNqlbEFgIULlQCclycC3Z49irlLv8/XV4SWJUu8Yy/My5O8RtHR7kkpqqtFIQ0Jkc3PqMxHRqrYns7G1NiXusJbVyf9brPJGLa1iRAWFKTii0hWwOcB7scrJ0foxdetU1Z+5kQDZB737i2KVkyMPPPbb0U50SnRxeIHXH+9CMt5eSKUk4GKhzXXh80mbQ8NVQmnzWaVJ6uxUQTsqCjF1sht87vvpH9PPFEE96oqEaAB8QY2NUlfM6aDEJ+YGPHgDBwoQnhUlCScdpfoNy9P6PoZbL95szyHHpy8PBE2CWmMiJBnU4DPy5PYyr17pT6hoSK0p6aKJzc9XYwA+flKmKby1dYm3g0mNtbntLt5uH+/jAlZQwHpw/R06QMqLIC0lcIUc4iZTCoug/ARKs766RYZqWKcmB+qXz8ZF33OervubrpJvM9MIkxPf3W1zEEyN/r6ShvsdhmjlhZpc0SESqwbHS1KhhGOXVUlinZ8vHhsa2pUPOaWLTIWkZGKxIbzlYoUvfDR0bI+rVZZMyUlhN024vHH9yA6Oh1AgBPWpJ8H9CwEB0s9dEOvCM+yrlpbVT0cDkEuMA7JyOCns+mRBCI0VPUVFS2SEJFinMla4+JcjTZ6MlAqV/TQvPPOK7j33pnYtKnKaZCw2VyvDQhwJWgwFgpq8+fPR1WVOltNJuCHHz7G999/jAcffAX+/u37CJAxKypyJWqqrXVNyss5RHgxoPqABgAaa8LCVAJRQD2b0MbaWtXfZCGlUYFeRJNJKcw0LnFtsX564l0augBXzx5JWBgmEBiokvZyb2XcEeelQOekHcXFSmF7+eVn8PXXK7B8+VfO+jY2Sp8SGktmOMb3MC5bj7kJDJT9UR8H4xyhIQVwTcTLfnY31/h+PYk0+8vXV9avcextNmkj40k553VyFj03p8Oh2AwZw0uY75AhJixe/BHGjZvoNOhZLMrIFhws+4E+H4yFyYgZy8qE7lTQdEi3XowxVoAyAPGs4npi3Opjj83Ht99+jHfe2eicN5x/evHxkfNFJ5lxV1auXInZs2dj8+bN8DXSRXtRGhsbsWfPHqSnpyPAgLU9FN2gSx6nH3jKHywhISF48803kZGR0ZXH/U+Xzjwfumdq9Wq1YdNyQLpgk0kgNRUVckhGRAgldE6OCnL185MDn7EozHsSHCyTvKpKWetJq9vQADz2GPD993IvBV3dg5SXpyB7FGZfeklioXx9RUiIjlYxKnS95+TIcz/4QBELtLbKeymU3XSTvKe2Vj7PyZE279zZ3uPR3Cz1SE93T7NO63dOjqIwt9tlo8nKcq+o6ZTxJpO0haWxUW12HK/QULHib98u9zK3yYoV0pfp6To9qRLmqdzp3qOkJEVrmpGh3uHvL9fu3i0HSlJS+3nlTjGkskLBjYId+7CjmJmkJJlvJSUdx2YRommsC/vx6KPlM3pM6+oEAujnJ56s8HCZM4WF7Z/nTjnMygLuuUe8eT/9JH0FyAackiKKV3q6KxR20CDpc19fJXyQanrzZhFg339f5VBjjENbm4K68iAj7IoHMePQADlgKajp8Un0mNCCqntXSAVOKJPZLP1Aj9bu3bK2U1NlPJYvd2/A2LBBFB/2Pw/Lmhq1znlQBgZKeyoq5J1//SXz4sAB1Uf06FZWyhyIjRWv0v79SoGh4kphwxNlvhGmzEBqWiodDtdEqRTAAgNV/1PQ1Nk9WRjHROIDwDWA2sdH6lhVJW0880zXOevtuhszRpItW63Kw2U2Kw8ZBdjQUPmOlN0HDqhYQQrO5eWSQHvMGFdaeSbhTU6WdcKYSrtdxsJqlT4m8QXhvFT2ExLU86xWiY+Ki5PnHTjQPh5D/5t9TGGf8BtjIUFNcLCMmy646/FJgFKadGWKQhqNSFxbhBMGBChl2NfX1WDDogfic4yphPN6GiOoeBivNT5TL8EHNTfGHTLOR2jdAxAYGOYCO+W+wufSW0Glkoo95wyvoVeA8SvcC3QBm+0kWoS5ywhXpECvpyLRY8HY90YFmfUnMxoVKu6BukeCKBYG++uGJb1vuC4AVU8arIxEIz4+wGWXXYuamio0NdUiODjEJccS48TooWRd/P2Vt5gwOCoDnBvuFC7uH9yvAwNdPaye5hrbpnuYQ0JkbzCuD73OjBUjIYnDobx47Gv2HfOtcewZK8X36wpvSIhrn+oKjLvC+cExN3q09Hh5QL1LQRpVnR0O1wT3umLIc1L3GrPOfI4eo+xOyTOW+vp6vPzyy11Smo5kOSJv74LT6p+iFSPMxliysoBp04BZs5THyG6XQ5FKUHa2HJDTp8tBRmE5Jwd48EHZNBhQTEKIpia5lxslNyQGQgNiQbFaRdDctUvuHT9eeSx0SMnHH0u9nn5arqUFhnEC1dVi/SQOvqBActSQLWvzZleyhZ07xaqakaGEqrw8abNRYCILGS03ZBMEXBW1l18WIZuKAylDDxxoDx90l3SS1NAUDCiMcROg1RsQjwUD3gsKVECsvkHQa1FSIgKxDnMrKJCNODJS5ZTSA9UpCBQUuM6fnByBsP35pwjLjNuJixOheOtWtTnqCq2nmJn9+0WRIFQoIUHlEnIXm2Us7vqRcSqxsVKXzZtVnil38WYdlawsUe7z8sQCv3+/zMW0tPa08fRMBgeLx4eEFLwuO1vRW3/zjcqjZrEommldcWLsBg92m03uYX6o0lIVp8a5Qwrx0FCZezU1StCl0pCY6GrRbGgQhW//fll/69dLvZKT5Z2bNwsD44cfilJVXi4/cXGKAZEkDmQIBOS7uDipMz11bI9OtR0SopJrWywyt8aOVexm/Kmtlc8cDsVAZ8w9ZIQpJyaK4sq8PjyMGXhMJsfzzpPPqXja7WJ0oSWX8EIe1vr7CHmmQE7Ld3h4+/p5u+7WrhVF5aST1LgSikwoGgWe8nIZOx8fpRCQqphtLiyUfj3lFLVOqqulzqSSpverulreVVcn1zFRNpVok0m+279f9UV+vryvTx9Zd/v2iQFEj10B1BynQE9vujulSYfIsc664E7liQIS/6bgRiHeYpH26J4S7lFkFON4efIK6cIli82mYjrKyrzzLrkrt9xyi9NwxDnNuLVRo8Zj1KjxzvVPWnBS4TNYnmPP9aXDmwgppvGF841Cve75Y8Lr1lY46a+NcMWAAAWbojCuwzHp7WAfc7wIl6Unht4MKpg863SFi94Z/q8To+gpS2igYXuMRCMC1/TFjBl3Ou+hV5z9YZxrgELe6AqGyaTOE0JKjQoX85Tpz2GfuJtruvLFmEYqtkYPCkttrcwF9hlhelSS9HXCMeJYE/mjQ5BZb86f4GDVx3qcot6/xkLPHBmVCVPn3KCnyqi0G8V8XqfPabaBe4jutWYbdWWQY0n2487Keeed1/lF/4Jy2IrThx9+iMbGRnQzYgz+KUe0BAXJYUeWJ6O3IClJHYI6i2PfvsCddyoo3M6dsnlkZMiCzcoSIYwUuqR/5gKmO51JXX18VBCwjodNShIhqKLCNa6CAkRMjGw6GzZIrEtzsyygfftkE1q71pVyu6ZGxWHExclhX1QkAuKWLcpaw4OScAYKUBRU9f7LzgaeeELakpQk/djSIv/TYqbHZLhLOsnEolSI7HYl+HJj5EFDQYD9FhsrfaL3HSDtrauTsdVJQBjjNGqUjFtZmcopFR8vUDAG+rMQurZ1q4qzcDhcGehMJlGYSLFeXS3xP4TG6TEzdrsot+XlyusWGqpyCeXktI/NMpbOksDq+a8SE7vGvGg2y5zOyJA6FxbKvKb1kOulqkoEY/a10ZpIBfuYY9SBy4NJP1yoIBs/0xMbk0EPUHmCCgtlPgPiVS0vV3OQghLjevjs0lJ5JuOhdu5U41FWJjDFhga5pqpKjBYUfKqrlfeGa5BW/JAQ6S/2gTAgqVixqCjXNR4aqmIpt2yReoSEyGehoTLOFFh++EE+j4kRBr2RI10hhUbyEgo/PICLi6XdFChHj1b9xuJwiJK4dasSzGjJ14UZUtPHxKg1QSGe3my9GNddaan0N9dN//4yXvv3K6s5Fedff5XnRkYq7wLZ4Ai/oWJHJYcCYVubvK9nTxV/x325qck1ySzzINXUuDJ2USBju2tqZA4AIvQTgk0Bk1487pu0xlPooceIzIfGQkGa/UmBiF4TClEU1OlB0D1QnDMcLxom+ENPV2deIWOhoDthwmSce+5kpzCpC9SHojwZ46gaGlzTIegeHHrW6JGm10aHMvEejp1+nlHh4XccE0BRtDOGhDnudCpou12R4ujeFY4BIX/+/mruUVgmoQiZTrmeWF+OE8dOnwNUiPkZWSSN0C89Vs2o9Hny+ujvYeE6ojLANurwsfJyhRpw5z2iQYUeYHceSE/eLp3p0ZjAlYQ27AOOI41Yep9xb9K9RVTeAJXvbNMmh3NPpPfO2KdGD6SxHVRGQ0IUIyxZYrnmqcxzXXM/4DP4Ph3uyHEnfNDHB5g9ez5uvnk+6utdkTYs3A/defb+k0uXFKeIiAiYTCY0NjaiqakJc+bMcbqy/yl/TyHUJy3NvRXBEywGcA8HrK4G7r9fhJgDB8QToQe/83CvqFCLCxBhitAyemzCwuT9O3bIAu/RQ7mumdCXySRLSuT+5mbFyFZZqaArFBarq2VTsNtF0O3XT4Ta7t1FcfP3l2t4Dzf9oCDpJybjZamrkzpXV4vQTHirDperq3Mlw3BHGW8yiWCTm6usXMS6c8PmJkuBA5DNmXC50lJXK3Z5uXw/YYLrocD3W60iOOo5o8LCRCjSyRhIQLF2rSIqIHSM3sLu3eW95eWKkKGkRLwVo0e3h9q58xaxH0hxXlLS3uulF0/JO/WxIZyDgjUgHqSuUtjHxsoY/f67YswjftzXV4Rfdxs111FYmJAHrFihDhtSWwPyGZNUR0aqA0SnvI6NFW8JIONRUSGevxNPFK8s5y8Vor17XQOHW1pUjjTS4Keny5pKTpbnbt/uunYCA+UQTEuTexsb1XzjYcjDyhgnwkOSgp0RAkfCmagoEex79pQxKimRn8hI6Z/ycrmerHXvvAO89prc062ba/xir14C133+eVU35uhqbFQ5p9yl9DCZxAjDfY25hJgLiIX9SKGd7UtMdJ+EVl93vXopL4LDIc/dulXqROY9zuvqalF0SBDCOu3fL89oalLWZkLGdCu5r69ck5Mj9SoslN98nl649+qWbn9/lZvL4VAQbiY4r62VvZn7og6PoeVbh9XQg96RYEOFkUREvJ+CK+FwDocSwChEsk26EEmLPwV9QKXKOBTBypOgSw+HO0G3s6IL7lSMAFc4Gv+n4M92U9jkmUblktdw7elCNb2rOv0328C+sdtdoXCRkYqwxc9PeV+oGDU3y1olWRTPTh0WRuMFlUrOfa4dvldvH5V2Qgz1PuPeqQveuifJZGqv9Lnz+tDLontS6BXhPNQFfM41QuqM3kh9vOj59BRj48nbpb9Hp8lva5N9UIepcl34+CgvE+uk54pzBx/lXNm/XyWc9qQQMU2EN8oo+4VGNbLQshBloSv7rBuNVJR9OH/09cv5rnu4Ohrj/5bSJcXpiSeeACDJ3vr27ftv5VP/XymdCZ+dJZ01wgHz8lTgNYOy6SnRoS9tbbJYGTzZ2Cgb76+/KktldLQIIVwwvJb5fwoKVLJSQJ7JoOmdO+U+QoIAle+DghutpuHhImyVlcmPxaLiSJgThtY3JuMFZDHv2KHqZrSc0ppeU+PqwfFEGU+h8c8/XQNtmQMDkA2FFsm0NPWc4cNV/icGBwcEiAdn7FjXehnfr9MwE3Km58vKy5M4Hx8f1/7k+EtuCIFLVlQohYV49fPOaw+N68xb1JHC7qkder0OHBClzc9PmMwIkQIUa9uhUNjn5Ijy+MUXSljmYV5To+KDPB2QXEdhYZKwtaRExplkLPQskgWMPzExIpQS7lZbK9empwO33CLwx5AQUf4feUR+x8bK4VpZKXOyf395X329xBOFhKh4lNJSWWfHHCMeHDJilpW5CpUU4kwmqVNlpcyb+nplUfT1VbFIeqGAy+fpFnBAHbok8yAT3aZNEp/z7bfSbib67dZN1jf7rKlJDs8NG0TZOe88ue6rr+QakmfQQGCxyLotKZG5aoRdAtKOoUNlzv/8s7SXgkdYmFqDZDokbJHenOjo9qkeOF9/+kmuIdtoQIC0lxDEsDDXec38W3V1KnkzYzYpfHA+6hA9xkVRmWCOtaOOkueWlysqe93gQus4IWL+/gr+yb6hsFxcrOL+OFfCwmSs9Pg8GqAoQJNl1ZNgQwVft+jTmMS9JSpKzVcqWNyPdHYxzhNdeTebVV95EnzdlUMRdL19ri646/BD3Zuir0O2g/F2gPIA8f7gYKW0cJ8ipJfeSD22iO/Q+8nHRxEfkGpejx0hXJDxkzoskHXiuuZ4Mi+eHo9GBY3zRf+bkEodogUoxUaHfzG+kuc1ZQ/KI1S8uN50WJfRk6J7+Sio64Zl3djWEXSN/eipuPN26YWeJKJs6BXUPXC6QYKKDutcUeEdDJUKTmcKkTsPZEeKirvruf7oPWI99PGloqcb3DnOTBPT2trew+VtbOF/aumS4nTFFVcc6Xr8UzopPMzXrxclRYcfAe2F6I4Khf2ICDn0a2tlYuuLkQuaWHyy9mVny2/mm2Iy1T17hEmOkJvQULlm+HARjEikAMhBPGiQ3P/mm7IRkLGNGzCVNt1qCsgzR40SxS04WMVyhIWJkMGknjysyAgXHCyCKumXjYelxSKbl9mslE9jLIbOMqdTcOoKEzedgABRZG67TdiwBgyQ55SWioBMgaq8XMZs6lT3m/Lw4RLkv26dypfFNhnJE3bskDYYPQkMgOVmtnmzSh6akiLP2b9f+sdYDkdht9uVN2D4cPlb78d9+2QcHQ6hZE9JkWs+/VR91quXZwp7Y8nJEYbBX3+V/3v0kLEuK5PxHjxYJVN1R7luVEbNZiGe+OAD4McfRQmoqFBW4fp6RUc+fLgcPD17yriS1v2YY4BJk9QY5eWJl6hvX+X51b2I1dXi0UhKEoGda4YxX4GBUh8KSMZYPz3eymKR9TRggLKyr1sn6zU83FUQb2qS/yMjZf60tbVnraypke+qq0VZYW60tDSZ07t3y++oKHnuqlXKG9bUJH3ncMj/P/8sedji46U/4uNl/sXEuBoIevWSeuTmiiKhj5fdrujBb7pJ5v9dd6l4wsBAaRPJO7gfREa296bpxWwWFtG335bx0oVMs1nV8bPP5DruD3a77GUMUqdAyZhUKgg6S5qRECQtTfph6lTJf5SfL/MqNlbapcN1MzPl/uJi5bn29VWENMXF0g/NzTIPe/YUI4dumSdUU4cT6ZA6d0le9fVSVaX2PwpDNCJRQCa7WXi4tIEJpgHFGKn3PYVLFl1o97Z4I+jq3hpvii64uxP2KGjq9dchdvo19BqZTK4xiHFx8p6iIjV/aNXXDYH0IjGP14EDSnGnosMYQQrW3bq599oA6l1UmsPDZa4xHg9w9Uhyj9G9TcY4HxoLKITTc8VxKS9Xc4wEDh0pfpxD7pRiIelo70FkO4m86Ai61lGMjad+Y2luVrGTVPh0KncqdvzNPqFnryP4KOvI9eWtQuSJMMWTomK8nmcnPaGEjQLKAKRDb/W4N+4pjLE2erj+28thxThlZ2cjPz8fzfRPHixnnnnmYVXqn9K+mM3t8+DQwhgcLAegO0YzY9Hpzw8cEGGDwe+c6LQi2e2uCSrJyubjI4J2fb2yTtNrExMjgj49CxaLay6WuDiBKrGe6ekieGzdqgQ9WgMZXwW4LjabTa5jvJHJJMLTxIkigH3ySfvEtkcdBbz6qmwClZXt44xILpGV5ap8ukskq/c94wx06mMfHxGKCWGk0H/66aK06LTrxx7rngRBH6eaGunv4mKVd+ZQyBN40HBjCw11ZVGkxc+d8tORt8id18td/W022WBpdSork/v++kveO2aMtInPo8WV8D8jAYkxLxAgc/Wjj0TQNJtVTigfHzE0lJZK//XvL0p+QID7/GlGZVRP+Pzcc/KcxETlkd27V9qza5c65E0mWVOpqcA557jWVffgmUyuSgKg6GRnzZL5Y4Qq2u1qPBISlGDFPmtsVJA5ricGwzscigwmLEzaTC8r6ecnT5Y5unu3XEePrs2mqM1jYtrvNfX1cj2hxFVVrt4wwvxKSmTcCVuj57miQhQpksfo/REbK7+NSveff6q4kYUL5bqkJFGQuYesXq3ir2jZDwqSMUxMlHFyl6euuFj6hh4CvZhM8q6cHMlfNH26zI3PPlNe3MBAJSg1N8sY1ta6wvMo8HBc+VlsrIyx2ey6/o45RtGRc/9Zs0aezSTR7M+CAoHlXnCBShPBhMh6P7J9OvzLbFYxRSQUcSfQcV8hFbI7FjBa3Nlv0dHyPCoDRlY/Pb5KV2woqHsrBHYm6HrjZTAWrlddSOa46WMKqLNA95wZPQ+6p4pKECMedM+KDkej4E1PQHi4giTyeXpcFD/XiSS8jX+x2xXLJecyx0c/66hok3mUSgG/9/FR85VxkLq3pLZWKf1U3Dwpfp48KQ6Hms/u2hMe7p2nxlNx5+3S30Nl2ri+Oeb8m8oU/6fhhHu3uzgpY1u5n/PZHa0FrkNvi359U5OaZ0ZIJPddfSx1gzefZTKJgf6/DYrXWemS4rR7926cffbZ2LJlC0wmE8iqZzo4cm2Hah76p3RacnIku3xEhBI0KPQkJopQ7g2MyZiQlhA2CtDc9HQ3rckkFjBia+luJaytRw8RZisqgDPOkEObh3NDg/I2RUSIp8lkks2gpESEWG68zI0DuDILJSQoBerAAfEoBAaKME0PzL59YomfPh24/fb2VNyAxP7QMme0thcWSj9ecUV7QckYI1ZdDSxeLG3Q8y4w+SqplQERaMLCFFvbbbdJ/3QUu2Mcp9RUlcsmOBi4/HKB9Rnv69lThObKSlcoQ02NghL4+anNmm7/9evFK+LOW9mR180TBblef6tVfu/fryBPxx4rP21t0jaOrQ49I5SKEE2TSRE3uBN0GYsVHS3KjH6AUlmk9cwT5XpHyujatfL7uONcD6iMDBHOAZXktKNneevBCwtzHzOmjwdpqHVyhqAgWWM7d8r3qakKJlVQIPWlAp+To7ysWVky9/v2VUrvmjWiQJHJMz3dM3uisV1GbxihZfn5sifExal3k/GupkaNIfu4vl4EpcsvlzHYvl3qt3u3rN2RI2XeSuC0KLAnnSTPzc5W+brIoGUyiQe4e3eZfzt2tIeZ2u0S29baKnsMg+NJOGNMH9C3r6wlplOg8sR4JuZ/4r5A6CThgxaLovKuqBDjD9eiPt45OfIOxgrk5LiOJ41hTBBtHCd3RqD4eLk+KEgJv/QocP/wFA+ke3VIZKDHOTF2RhcHdMGX5BmA6zuNY8FzqaTE+9xLnQm63ngZ3BXWXydz4Vmp15nCP5VDxrVx3HUFhJAuXXingtba6kp6QqGbsX8UxtnXeh+yT7nnEZrobfwL1yW9RLxWV/j4HeOYdCIItp3KKWPv9PHg84zxcZ4UP3qnCOujsM6/qZAY2+MJikblsyPPKvvSU7/V1yuPFxVDHaJHJZBEGzoRC+cfFRAdPuqJQl33TnWW90gvh2J4AFzXkE6P7i4WUo+5M5mUEqtT4v//VLrUpBkzZiA9PR3fffcd0tPTsXbtWpSXl2P27Nl49NFHj3Qd/+cLrellZXLgb9+uFqfVKhP7u++A007z7HHSn8Fkq5s2yQJNTJQFys2e1+tWMW7CISFSB27qVVVSh6QkEUJaW4Uu+ddf5aCrqJDPmD8HkO9KS5UCASg8PmEZjEnx85P6Efv/yy/y3cknK4Hb6JGYM8e90EkBBFCeIlrbExMlYaincD09RmzTJrknKEgsZfHx0j+lpcqC3Nws9aLFnUJ/RyQK7saJG1tYmHjNsrPF42iMhwJE2Bo8WGi0mSDV4VAxa3rgJw8WYuuHD/c8d9wJXJ6UA73+MTGubIkkosmGxb4AAQAASURBVFi7VgXO65wyFLYZJF9X5+q16Cieih6F2FhXLwwLPR7V1VL3/v3lZ8cO+b5nTwU9MxZPBBmA/E92u2uvVYkpPZFZdNWD52k81qxR5Ax6Xq2KCllL0dGKSVMfr9NO85w7TjcUMC6FHlRv20VvAyFgNTUieDBWiQcq419KSmS9lJYqZVnvj7Fj5ScvT+CYZrMoQKxLaKjUOzdX+oReLwonJpOyClNoNcJMCS3dvl2ew5xERqHKmD4gP1+uT0lRsWglJSpnmm551Vk1OV/pNSdD3jHHuPaxJ693erpc26+f7LmdGWSMY0uINhntmANIL+4EOhajV4dnEguFfFIWU2ALCFAU+CSuMMYJUSAn9KkzAdKoPHUk6LqLselIgDQWCu7BwUqZY33ZZr6PCgUZ0CiAkvTBbvcMs9IFfcaOUNAPCVFz2m53hUoZ+4H9yvpZrd7BvYhEoDKgewMZxwaosSOsj4qPDknTx4ExtZwj9GRwz+5M8bPb29edcDw99szYHo5bba1CW7S0yBlOiH9HnhFPyhcNA2y33jdUHjk/dE+cUaHX4aOEwR4pcpOOPFcdxVUZFXjGwOtxZZzznBtkB6QSeyhe3f+W0iXF6ffff8f333+P6OhomM1mmM1mjB49Gg8++CCmT5+ODRs2HOl6/k8XHuZWqxJEyZhGHOrnn4vlcdy4jp9B4Y9QmvBwBb2jxYqYex4qVADorSFWGJDNorxcnr17t7Bj0TIbFydQpd9+U54l1p8CPTedpiaFDaaQb7GIhb+iQgQyWikjI0XgZbwB0LlHAnAVQDxZ2zsrOTnA66+r5LkUxsPDlWBQVyf1SUxU93kS+vU4IMIjOhLSPbWRXgIymtFbQ2amgADZ9KOiVJyPr688z9/ffXyTse86StTMtvz2mzDZxcS0Z3wD5P0NDUpA1oP+/f1l3PPylND5558Cm+rdW1nA3EEK6fEgWQnZHPleejzKy6Udb70lwq435BPeEmSEhbmyvxnHNilJhNu+fUUB3rbNNZF0R0mEOxqPTZtcDRUBARJveOaZim7bXWLtjhT4zr53d70xN1NkpLSJcT4pKeJ15Z4RGioQ3fJyWduE6xQWyri56w+zWXllSktd0zKEh8t7s7PlvoQEddAzuaXZLM9PS3NVUnVo6f798j+hiklJSiDi/se4T7aJ7aEQy9ghvpsWae6vDJQmTTlpt6OjBVrnzXgXFwtRyAcfqPnrjn2wo7FtbJR14G08kK4AeePVYeyoO4GN3gPCsfQ4ML7X318RfOhMYt4IkJ4EXXcxNofK7GUytafrpuBIuDmFSSqY9ExRceiMZt2bGBX92TpkUB8H3btDIdabZ3NN2WyKmY73U2DmnKbixvg9Xsc5pTPuES6re6fYht27czF58nH48sudCA4Oaaf4MQGxUYmmd4nEQoBirtQLc2xRGXOnhOv9ordRJ7Hg9+XlrnOWc0CHVLK/6JUhOY8xzlJXAo8kuYk3nitPc9+4huhtIqOnDsvT28/nR0Yeule3qyU7Oxsnn3wycnNzEeTpsD5CpUuKU1tbG0IOSi/R0dEoKipCr169kJqaitzc3CNawX+Kii0qK2sviFJBycsTeElGhmsCXC5co/CnW/dpeeWhomOog4JksURGyrsZHE/FyWIRAWbtWhFgUlMVfC4vTwSH00+X9//8s7w3NFQgNXpAp84oFBMjm0p5uQRA//abPJtU5KGh7RPqAt4xvHmjAHgqhKAxr05FhdSruFjh9mkBDAqSWI7qas9Cvy6sUYAnFjs11X0d3LWRpAj5+aKYjBkjiirZv4KDxTo9eLAIZjohAaAo5zsrHQnTbMvvv0uMm9Uq74mNdd346flJSpLv9aD/5maZL2QbjIhQ/VtVJXU//njljdEVk6AgGdeNG+V3dbWCY3IuUWAtLla5nKi0dEQ+0RWCDOPYMoEz8+/w/9275f/OoIIdjUdammvC4q7Qtx9KMSqEfJfRM0LrsY+PeKGoWJSUyDyoq1MJZUNCZF7X1Mhe0tzsHha4aZOMMevh46NITuLipC+2bFGMYFTUq6qUEFBUJMmpU1Pl+Yz9ITQ2LEzmApkISS5DRZ/W1Lo6YW/86ScZb0J1SPgSFydt4vUtLcqrFhsrexehwhaLXDt0qGdvo9ksc+bLL13h1t6Sp3gqOrzJUzyQwyHt0i36VHycQp6vHX7FMjGaLCFAQgqam81OBcqdwEbWLSpNukWe1msmxaVXjALnrFmT8d57r3psV2VlJcLDw52Crs0me50el+GtAGksujBKuDbPMD33lDvvyKFQMHcWo6Irr8wVxb7TYYL0NOlCrLfxL/Qi6vTrRi8Pf9Nb4o48iUI3BW6Ope6Veeyxubjoopvg6xvi0hbWl8Qr7rww9fVi9KASZ1SKvfHilJUpmagjkgru4fTE0MukK070KPr5yf4WGalyShnha0b4KBOre2PM6KgcCc+VriwyvQX7SPfyGRV2ykGH4s09nNKnTx8cffTReOyxx3D33Xf/re/qkuLUr18/bNq0Cenp6RgxYgQeeeQRWCwWPP/888jIyDjSdfyfLyEhihbcXT4Lxq58952y8Bqt6Ebhj1CaujrZdLmBEufOzScyUgRNEkHU1ioIHSE4tLaOGaPymTBhY2GhCDLnnCMQmtZWOeCbm5VypuOBW1sVhKumBnjpJbmWtLncTJh7SY+J6IySneVQremAKwStb18RfNaskbqmpYkwVlEh14WGivBD+m93Qr+7eLP6euW5i41VSVP14g5e9NxzYoE2m0VZIgTqhBNU3oeYGNVPJCRwOMQ67y0bo6eit6VbNxkrKpEHDigFHHDFnetB/4mJyspP4Y25LShU+/qKF8Vsdq90RkbKvaWlMkb5+SKkE5JEb2xhoStMrjPyiUOF1xnH1mYThrmKCqnjqFFSn/x8WXd6AH9XlZ2uzGlj8aQQ6cVdv+v7jNEwsX+/rJPcXPmbB3BIiKxvGoEIQUpLkznhLqlyTg7w7rtyX1SU1O3AAZnzOTlK8CWFNhN5kkGSMTgNDRKXed118p6HHnKFxjoccn9xsbL+FhQo2GFgoPIezpkjc5eGlNhYafuuXdJ/8fFiQKK1uXt3BQvSyUQKCtyTbhjHxx2M1xvylI4KremeWMdI7uJJ4QgLA1o258Dn04/g99d2mJoaERoUgKb03qg98Wz49MvyKLAFBkp7SF2uQ7UBZbgjlKutzZUE4Pjjx+PBB192iff47bffcO655zrbwHqT1fFwoU+ehFGd2EjPPXUozGaHWrifcywYg6J7v/j7UJOMGpXDhgZXxYhCMws9bPTUsH70yhkJJdgngHxfUpKPX375DHPnPuX0WlDBamtT9Ozu+o/tbmtTxjKjUsx4ME9eHLNZJTz3hqRCj/FzR+IBqPqTWdJk8o6k4kiRmxwpzxWVbItF+lRP7q3DMllfGs3+1fFNV155Ja655hrMnTsXvn/jy7t0TN91112wH/RPLly4EHv27MGxxx6LL774Ak8++eQRreA/RQ5g0hO7s1SUlsrkZxB3r14iJG/YIAJcTo4S/vbtk3vCwpT3oblZns2NjJZgwgn8/NR1PXooYbC8XMX1HH+8LJRffwV++EG8Sz/+KMLH6tWyoDIzxcpKAYmWGh1m4Ocn787LU58nJEi7KiulbgcOSNsZ8F9drQRYIyvekSpGqGNMjLQlPl76njh34s8JxwgJEYGxqkoJ/UYBiElTQ0MlbsNiUTmijGNtbOP334vVmzFXUVEyHiUliuaZrDbZ2QrmUF2tIE3eQMM8FWNbkpKkb+iVbGqS7zjONTWK2S0mRvokIUEF/DPpKCmVy8ulb1NTFZ38J59I8ub16+VZnO+FhXJPYqJikMvKAs49F3j2WbHGV1R4B4PUC2Fo0dGd96GxP4KDxUtSVyftbG2VmKOQEFHumpvlGX+nh8ibkpMjCsS8ecC998rvhx6Sz/VrnnxS9hW93/V9BlBKXP/+oqzOnQssXCjPXLRIYh2Li13ZpEpLZf4OGSIxO0yqzMJ+bWoS5aO6WowVzM3k4yMHekmJXNujhxgOxowBTj0VuOQS+T10qPzMmCFzw138mskkeyXjFRsb5R2pqSoP2MiRag60toriSyE/OVnmd1GRrP3ISDEcPfaYGIJeekkgyOXlitV0yJDOvUWdxdp5mr+dFZNJ9h5fX0XmQOGY/1PB0OnGAwIOCqybcxD95pMI27UBAUnRCBjYC5b4aPht2YCI15+E31857d5HjyPHC3BNm2A2uyaLBVRQPfdaALBY/BEbG4fExDjExclPpI7XAvDKK68gIiIcK1Z8jHHjeqB79wBMmnQKCgv3udTn008/wZAhQxAQEID09AzcddcC1Ne3Ot8FAPPnz4fZbEJCggmZmSYkJZlw1VUTXdq2adMqnH328QgKsiIiIgLjx5+ChoZKWK3AKaccj1mzZjqvf/HFFxEeHo7169cDEDTP1VdfjfT0dAQGBqJXr15YsmSJS3vmzLkdCQkJsFgsSExMxF13zYHdbj+ouLRh4cKrccYZ6Tj66ECccUYvvPnmEhdv2uTJkzFx4kRnvsSmJuDll19BuEbxec898zFs2CCncujvD2zZ8iMGDjShrq4Kdjvw4YevYNSocKciRGOqxQLs3LkRQ4eaUFKS5/S2bdnyK6666liMGBGI8eOT8fDD01FbW+9Ukr///l307DkQMTGJLu1dt24VLrnkePTsacXgwRG47LJTUF5eienTJyMx0YTERBPS0kzo18+EgQNNSE83YdasyfDxAX7//Uuce+5oxMWFIyEhCldffQby83c5n71vXx4SE034+OO3cc45ozBsWAAmTOiHtWt/ciry69f/iH79TKiurnLGdVOpqqurQlKSCevW/ehU6HNzt2LKlFMxdGgwTjghFnPnXoa6ujIAyutttapcT2+//QoyM6UNVqsJJpMJAQEmZGSYsHHjxoPXtWHWrKtx9NHpyMgIxHHH9cLrr8u84BhyXBcsWICYmBiEhobixhuvQ2Njs/Ns+eGHLzFx4mhkZYWjb98oXHnlGdizZ5dTgc3Ly4PJZEJMTIwLW/amTZtgMpmQnp6G8HC1Ft9990VMmJCFo44KwNln98aHHz6LkBBpZ69eJgQFSXuMPz/++KPzXW+//TZGjRqFgIAA9OvXDz/99JPzvT/++CNMJhOqmJxOK1VVVc5nsZx00kmoqKhwecbfUbqkkp1yyinOvzMzM7F9+3ZUVFQgIiICJuOO/k857GI2i4D55Zdy0ERFKQshc8b4+akAfArhRsIEIztajx4irBIKEREhGwKFAeKJGdDa0iLPZG4VEh0Qs0wPTFiYcs1XVMi1+flKUaJCxjxGukuesU8+Por5iljkbt1c3cVk9ysvF6H5cJWAjoq7OBd6cfbtE9KKkBAhWWCCXuZbSU2V63hvRwKQ2SyC3e+/C5yoVy/PMTBk/2poEO8UrU+EO+rsX4fKIudtcdeW3r3lcOE8IoGIw6EUIwrZzPFF/PWQIaJgA66QQptNIJsPPSQehpoamR8Mrtfne0yMCKFMwAfI3zt2yHO6kszXW4IMvT/KykSpyMlRHlFSuzIurrO4vH9F8eT91OFfvXp1zdth9GL17atyibW2qrim+HiZNzEx8rlxHNivKSmyD+Tmyj5BI0Vbm8yJ5GSZMxs2tKeCDwuT5x51lOprT/FrNIwwxo5KFA1QhH/GxMhvPz/ph9xcqUdYmNRj3DhR2owsmFTaDgVa6a6u3K9JNmCzdQxV9lQoHBthZXoyVrcWa187fD79CPYDZfDp3wc+By9qMYeiObMPAnZnw//Lj9Ha3XViMF6F5wH3M8K9dGs80QgUZHkNn+MulsVYGhoa8Mwz92PJkuWwWCy4444bcMMNF+GTT1YBANat+wWzZ1+ORx55EoMHH4sdO3bhzjunoq4OmDPnHhdoXVZWX7z00rfw9wfuuWcGmpubnO/ZunUjJk06EeeeexWeeGIJQkJ88cMPP7hlGn733Xcxa9YsfP755xgyZMjB9tiRlJSE9957D1FRUfjtt98wdepUxMfH44ILLoDNBgwbdjJGj74A4eHR2LlzG2bNmoTY2L645JLL0dhoR2pqEq666j1ERkZhzZrfcPfdU5GVFY8LL7wAgFKIi4pU/1ZXu9aNyrPuqaDRlsQUHE6dHITzQo+ram0F9u/fheuuG4+bbroPCxa8hPLyUjz88DQ8+OA03HPPywgOBjZu/AX9+g1zPtNkEgXsmmtOxCWXXIWnnlqCsjJf/PbbD6iubsPs2Utw000PwWQCHnhgBkwm4PbblyAoCAgOlsGy2eoxderN6NFjACyWOtxzzzxMmXI2vvlmI8zafLzvvltx661PICOjD1577THcdNMEfPvtHkRERDmvoTeJHhqLRXlpOI/r66swZcpYnHPOFNxyy+NobbVhyZI5OPvsC/DNN987PeK6FzI8HAgNDXUJc9m3bx+GDx+OlhZ6N+2IjEzCY4+9h4iIKGza9BvuumsqIiLiceqpFzhhvN999x0CAgKcismVV14Jf/8o3H77/QeNS9IfWVkDUF9fh0WL5uGGG87Ghg0boftR/P398eGHH+Kiiy4CADz33HNIPBi0TY/+M8+8gaefnoc77ngaWVmDsWPHBsybdw2io4MwYcIV2Lix2CmzpaXF4+23P8CYMaNgMgGRkZEoKioCANx666144okn0KdPHzz22GOYMGEC9uzZg6go1ffeFovFgkGDBuGXX37BiSeeeMj3e1uOmC/LaOX5pxzZMnasMGF9+61ylfr6ukLYYmJUoD3Q3gppFP6YbZ7xSrQsRkfL/3V1EisTEqLIDzZsEAE0I0MsusyPtHmz+/grMlDt3ClC1apV6vBLSFCJROmqpjUxNlYs05s3u7LtxMSIMsbklmRfGz788JUAvRgFPlJ4FxWpDZNQDNYtOFgJgLrQHxQk7adAQ4UxIEDeY4RfJieLpbpHD6UUGoV0EjFs26agELrbnlZksn8NHPj3xMF4Uih79hTPYE2NglHExIg3aONG6ceEBJmfqakinP71l8A6w8NdE6GWlooiWVOjAsTJ0FdTo+LcON9zc1UQ9ttvK1hZa6vMZ6vVOxiksXgTH8f+sNmE/ZAEJEyCSmKOkhJpnzdxeUequIPiAd4pRBdeeOikJZ5gfcOHy9qmdVpfS4D7cdDnmd0ua42sZWRJ8/cXb5W/v3eGB6Dj+LWYGFHi9+0TeGVsrKrn/v0ynyIiZO7+9JMrw1lEhMyzmTPdkz10BVpprCuhyqTZpxKxf793JBHG4o4woK1N2udpn/ArzoffX9thT0p2Kk3AQeHZbEJrfBL8dubApzAfbcnSYML/GIeikygArsQDFN4ZT8LPeA2hap3Za1taWrBgwdMYPHgEfHyAJ554Fccdl4UNG9Zi8ODheOyxBbj22ttxyilXwOEAEhMzcOut9+L++2/DTTfd44RoNTU1wWoNRGxs3EFIfKCL4rR06SPo338Y7rvvWSQkyFzs64Z1aOXKlbjyyivx3nvvYcyYMao//fywYMEC5//p6en4/fff8e6772LChAtw4AAwZMhYp2IJtMHfPxDNzW0H4238MGeOuj81NR2bNv2Od955FxdeeIGT6IGKqA4V4/5EAyfgftypwOqU5Ox/xrboZ5HDAbz44oM444xLMHnyTLS2ApmZPXDvvU/iwguPwx13LIXFEoCSkr0YNGiYE+rd0gK88soj6NNnGObOfRb+/rKmEhL6OhU6hyPsoNEtECYTEBcXh+BgVZ/TTz/X6dWOjweeeOIl9O8fg9zcbGRl9XPW8fLLp2Hs2HPh5wfcdddSrFr1JT74YBmmTLnNpR2AghCaTEre4pp56aWn0avXYNx44wPw9ZV9avHilzBiRDJWr96Bo4/u6YTsUemSOC8T4uLinO+qqmoEoKCdPj5+uOGGBU5PV3JyOjZs+B1ffPEuzj77AqfRwdfXgmeeeQlRUVb07dsXCxYsxK233oqZM++F1WrG6aef69KeBx54CcOHx+Cvv7LRv7/qj6uuugovvPACLrroIjQ0NODdd9/FNddcg7feeguAzJGlS+/BHXcsxmmnnQOLBejXLx35+dlYvvw5nH32FUhNjXPGakm/RQKIc0LwWaZNm+aE1i5duhRffvklli1bhttuU31/KCUhIQF79+7t0r3eli4pTuecc06H33/44Yddqsz/WukspsD4/TXXyAZAEoCwMLFGMLt8797tDxCjYKYLf9u3Cwve8OEizJLhLDhY4fppxT3mGJWlft8+uea000QJqK4WoTctrT0+nkQAJSXAKaco/H9VlQgi8fEiRLe0SBubmmRRnnyyBFcXFbkypOmu8vp6qUdoqHhpCO863OJO4IuIkHoXFiqMfXS0In5gHJHO8MVCdruQEMXKl5MjylRgoII98Tl81owZMh+M80MnYsjNlT6pqZHvdXpvPz8Z0+Rkde+R9mq4EzxLSxUczd9f5ZWqqBDFmaW+XhTDpCRpf/fuopTn5Kh4LLtdlPX9++X7iAhlpQsJaR/nxvm+aVP7IPq6Onn+L7/I/926qbp4SwXeWR+yzbohob5eCdT+/lKPffukzd7G5R1u6UiJ8UYh2rHDO2ZB7jMdebG4fxljzQDP48B+LSqSd9jtMg5k6WJcRVycvCs+XubI3r0iaAQGuvewdha/VlEh8yQuztUoxRjRoiLZ2xgwzTGmUaq0tAuD5aHodTVS/fv6Sj1aWoD33xcDRVeMSBToGIOiB/S7jaeorYWpqRGmYNeJQaa1Fv8g+DYVwlSnLANkbPXzc4Vq8/0U5ul18PNTRrWWFvUd+9obkgVfX18MH36Uc+5nZvZGWFg4du7MwaBBw5GdvQl//LEKS5fe77zHbm9DY2MjHI4GtLZaDzLRliMsLNRJyGAs27ZtxCmnnN+hF2zt2rV4/vnnERwcjBEjRrT7/plnnsFLL72E/Px82Gw2NDc3Y9CgQS5xVU899QCWLLkPjY02XHzxNJxxxuXOxPCvvvoM3n77JRQW5qOx0YaWlmYMGDDIGZvlcAA//PAZevdWh0VbWysslgCXOK/c3C3o2VNdY7e3OceIikttbTWGDw+G2WxGVFQsTjjhLMya9aBzTMg2uH37JuzYsRmffvqG8xkOhwN2ux3FxXvQrVsWGhttsFrFiktFLjd3I04++Xz4+SliCCr09HKRkQ9oH6O2e/dOLFo0Dxs2rEFVVZkzxGT37nz06NHPeV/fviOd7XI4fNG37zDs2uUKMR03LglmswkREdE46aRxePzxR53tpPFyy5ZNWLv2B4waFQxj2bNnF3r37tlpHB0h7YBKTN/WJuP67rtqXFtbm9G37yCn0cHHB+jdeyCam63ONTVq1EjU19fhwIF9iItLRWHhTjz22DysX78GFRVlcDikP/bty3dRnM4880y8+OKL+Ouvv/Dzzz/juOOOQ6xGvVtfX4/du3fhjjuuxp13XuP8vLW1FaGhYQgLc2U/BBScmkYIlpEjRzr/9vX1xbBhw5CT49r3SUlJMJlMiI6Oxrhx4zpMexQYGIgGd4vzCJYuKU5h2gny5ptvYsKECU6WvX+Kd8UoyJAdb/RosVDW10ssh1HQOeMM12SHzJHUv79il9OLO8GMwp8OJaNQX1zsSv5gtYrAwSFPThZh6ttvgfPOU7Aj5ntJThaFiHFTVqvUrbxcYIEMMK2sFKEqKEjqkpysqHsdDhU0Te9NaakiWyA0kQf5d9+JEnHaaZJL53C8Tu4Evvx84LPPVBwRrT6FhVKvuDj5CW6/V7oIgrW1wMMPq7iH+nppb16ejCdjNerrpd02W3uKdCMRQ2SkzIHiYhWQHhamIIyBgeJp8taz5A1BgF6MgiegaMijo6WemZkqhw6pYMlERWGgulo8bEVFSnGKiBCvwfbtSjBsaJB+YSC7HufGxKL+/hJr5y4X1pgxAg/8+WeBUQUHK4UqOFg8qIdTyKb2888ivNNKWlenPCRMVltVJe09XHKOzkpHSszGjbJOO2NxBLxnFvSGxICJXL1NqkzGrNxcBZNrbJQ5wniZ5GTp33XrZD0EBUmd4uMF6uwuaTTj1zwleE5JEe9pQYHKtwTIXPL3VzBMJiYnDIoe1rVr3b+3K4V1zc9XDKVxcSoXTViYKMKlpd6TRNBSzeTjDBbXIXvNzfKu4GBXCmWHA2iyhCA0KAA+jfWAv5oYJAWwV9WjzTcArYEhTgWssVEZOdhXhHdRENYhexSUiYwIDJQzRk807k0JD1fMtPSYsD4NDXWYPn0BJkw4p12fBQQEOL0Wu3btRkZGujO/DSFtfJbFEgizWRnOjPTtgKRzWbp0Kd5//31MmzbNacUHgLfffhu33HILFi9ejJEjRyIkJASLFi3C6tVrXIL8L7vsOpx00jn4888/8eCDM3HccefgqKNOwGefvY2FC2/BvHmLMXToSPj7h+D//m8RsrPXOJkFTSZg5MgTcP/9S53evpUrP8RTTz3ghKEJVL4Xli5d4URUbNiwBjfddKmzrVYrEBwcgnffXY/WVgfy8rJx551XIDExDscdN845Z81m6d/zz78Wl102Hf7+ah41NQGxsSloawMiI6NRVVXpnHeiGAc6n8HnkThBT8SqU7LrZfLkCYiPT8XDD7+AxMQE2Gx2jB/fDw0NzaiqkvOI64DxjLo3TS9vvPELQkNDUFaWhzlzpuDOO+/EfffdB0DO4ZgYwGarw7hxE3DnnQ+3q0t0dLwTcaBTnBsL1xuLyQR8/vnbeOCBW3DnnYvRq9dIBAWF4I03FmHjxjUu97INRrKH6GgZryuvnICEhFQ88MALSElJQFCQHcOG9XOJZwJEgZk8eTJefPFF/PDDD1i4cKGLMlNXVwcAeOGFFzB8+AgnMYfEPvo4iXl0JZEkGtxf3PWxp/LLL78gJCQEeXl5mDLFte+NpaKiAt27d/f+4V0oXVKcXn75Zeff77//Ph555JF/2PQOobhj3dq8WQ7DDz8UIaauTg7jvn3bxxxMmwZMmqRomN96SwQgdzkcOrKi694CY2lpUTEExgSQO3YoqF96umwaO3fK/8zBExGhYGt1dZKv5Y8/VDxURoZsDjU1IhBRaL3sMuDTT5UgzliDnBwRbMiQBqhASwrV334ri5KB38bijYfPKPCxD8koExYm7y8vl02iulraeeedwMqVngXBvn2B2bNF8AsKUiQcdXWKmtpulwOXSurTT7sGjBvrB0h/FxeLN4Yscnqiz1NPdZ8s113pjDHNXTEKniEhotD7+0s9CUvQ85wQD+7np+BNlZUiZMohK9Anwg/9/KTPa2tFKKTXMSNDfc5EmgUFMieLi917Ubp1E+PE5s3SXw0NMv8AEcaWL5d6dNTmjorZLM//8EOZG4w71CmxY2Jk3HNyZNz+rrg8oHMlZt06ab+eT0svVIh69nTvmaHym5Mj8LukJO9IDMrLgcsvl77uLO4uJ0fWAqEx9FYcOCA/ZBG1WmXeVFfLHjpkiPT7vn2S68iTF6az+DVA9mt9bZOkxuFQc5yF+3Bw8JGPX8vKEoPVmjWyh1RUtI8Ro0LX2XtzcoCvv5Z2Upglex2g4oxIvV5TI22yWDQmsJQU+PbrDdPWDUCoq8vOz9eBoIoCNGQNQVNsCuyNio6bz3XHRsa1oALwFWOa2axovvX+1uGF7gSy1tZW/PnnH0hLk7iRnJxc1NRUISEhCxYLMHDgEOzZk4uMjEy33gCBFzbijz/W4oorLnM5e6hUmc1A374DsG7ddwAWOJPj6sxrdjtw2WWX4brrrsOpp56Kfv364aOPPsLZZ58NAFi1ahVGjRqFG264wfnuXbt2ueSfAoDg4EjEx0fi1FN748sv38d3332AYcNOwB9/rMLAgaNw/vk3ONng9u7d5YwbrK8nfXgQoqIynWsnKkpcADoMLSDAgszMTKfXoKhI2FpoQBRDnxnHHJOJ0lKgd+8e+PLLk5CTsxHHHiuKU1CQzMk+fYZg165sJCZmuig/hOlK2o7ByM3NdqZDaW0FevUagLVrv4PDscDJ4uaOgptj1tiocraVl5dj165c3HffCzjqqGMBAH/++as2J9Sztm9fjTFjxhxkGW5FdvafuPjiaS4MgcnJ6YiJCceoUZn444/z8fvvv7u838cH6NdvCL7++gMkJ6fBZPJ17gUMTyBMW5//Bp3Fydanl3XrVmHo0FG47LIbnF79vXt3teuH7OxNaGiwoa1NXGGrV69GcHAwevRIRkVFOXbvzsX//d8LGDPmWFgswKpVv7rcz7VjswGXX34NRowYhMjISJx00kkuilNsbCwSEhKwe/duXHLJJS7PaGpSIQ1u4yItSrljHQlXlXX6J6ZNm+ZyX3p6OsLDw5GZmYnzz3fte2PZunUrzjvvPI/fH4lyxGKc/ineFaMgU1YmwktDgxx+VVXiOeCC5car03uvWCFkD9xEzzlHBEZvrbcs9Bb89JMIMnpiXVrD/fxcF7bdLsJOS4scgIGB8r7ISJVvyuEQ1qiICBF0f/lFWaqTkpSHgYQP+/crSN6nnwIDBrhagCMiRADIzZX/g4MVnI0Lk8Jpfr57a6s3SoE7ga+6WsYoLExh80eMkO8Jq2tpkTr36OFe+OrfXyVcjYqS+hPDTcY5Pz+pV2SkCKBkcNPbYqyfYPFlI66ulnvLylSSTnoGcnM7VwK8IQjw9Axd8Pz9d5k74eEK3rRhgzyLFKYcf1rta2qkPS0tMr5JSYpqOjBQlKrGRjXfqqtlHHbtkncwfwkZ7o45RhKDeoKVJSfLXDnpJFG2zWbpY+YfO5ycOIB4jPv1k76sq2tPic3PBg0CrrzyyMXluSudKTE9e8q60/NpseiGl7S09p4Zm01i0vbtk+tbWoRBb9Qo72B9sbHA7bd7b8w4+mj5/eefMucpTNKrVFAg701Lk7719fWeqruz+DWjYkVrardusnZtNll3ZrPcGxoq11RUeBe/diie3thY8eLGxalEnnqMmDdxc1zvdrsweZKAp6ZGpVTQyWaoPOn5ZSQfkRmWC84GSty77HzjohFy6UT4J5ldPC808AQEKM9TaytcvBlMDqzn5LFYZJ4dOKDOSKOCUlHRvr1+fn6YPv0m3H33kwgI8MX8+dMwePDRGDp0OOx2YM6cebjwwjOQnJyCCRPOg9lsRnb2JmzfvhVz5tyH2to6LFmyEAAwevRolJSUHHyyDSZTE6zWakRGhmHhwrkYMKA/pk69ARdddB2sVgtWr/4Bp512Puz26IP7ciQAIDU1FYsWLcL111+PMWPGICoqCj169MDy5cvx1VdfIT09Ha+99hrWrVuHtLR0J4TxtdeexYABx8HXNwjr1/+K1au/wS23LAEApKT0wGefyf3JyelYufI1bNu2DklJ6c59R48to0JK5UCnuOb5TO8j5QAqjfSWWCyNiIhwYN26bKxb9yuuuGKG83mhofKOqVPn4MILj8aDD07D2WdPQUBAEPbuzcamTd/ghReehr8/MG7cKZg9ewqam9tgsfjAzw+YPn0uxo/vj7vuugFnn30d/Pws+P33HzB+/PmIiYl2noOKZVHF+wUFRSAiIgrvvvs8YmLiceBAPh544HYAqg+4Zl599RmkpfVAZmYWnnvucdTUVOKcc65yUdIsliaEhjZi7948rFy5EqNHj3aZYz4+wOWX34h3330B1157MSZPvg1hYZHYt+8vrFz5NubPfxEmk4/TaEi6dMLy9OcY1356eg+8//5y/PzzV4iISMenn76GzZvXITk53eW6lpZmzJ17NRYuvAtFRXm45557MG3aNJjNZkRGRiAqKgrLlz+P9PR45Ofn4/bbb3fea7MpaHFZGRAXl4558x5Djx5JLkQaLAsWLMD06dMRFhaG8ePHo6mpCX/88Qf276/E+eff7HH/0qG4gEBTe/TogaysLDz++OOorKzEVVdd5XJPU1MTGhsbkZfnvu9Z8vLyUFhYiHHMPfI3lX8Up39x0QUZQMGaqAQEBooikZYmG9ZXXykPBYMNV692tSZ6y/hlLGYzcNZZAgksKREhnIpSQ4PKbp2bq+pXUCAHk46D9/UVgZUBvDU1SjH6+WfZOMPDVZ4dHx+VINbPT95LqxaF1tNPV5DEwkJZ1JGRoqBs3dqeUIGJVQn/YdLckBA5fJ9+unOlwB3RAdtEaEFtrbSZcN/WVvHA1daKgmQUvpKSgEceUXTLDGon8115uYrJ4DMIuTQG3Ov10wPDm5pkrtB7ExKiKMsLC0VA6kgJEGpZiQdJSVECyKHkh6Hg+dtvAke0WETh2bBBlGIebrpVu6ZG6koICpXH2FgR0tLSBN7J/tez3vPALCqSOjc3q/keGCgehs5gZTt3yjMIzzPmH+tKThxA6jNihNClJyYq2ARznNE788ADf3+ei86ISIKDXfNpdWR40feZNWtkHTJZcUCA9Nnu3cA338haTUrqHNbXWcyYUfEj5IRJsqlU19UpJSokRBHcAJ7JK4ylo7oYFSuuq4IC5QFnQnHujbQsd4Zid2fU6dlTFMXY2PaKFOl+/fxkv2Mhwx4T1XpSXHVldPhwJUDqnprGRpWjBVCpFZqbpW+pxJhM6PQAMmVlwR/qvCCzpZ5jhnG1ZAfs1k3mCeF6tNqznkwBQUptnYWvsVHqbLPJuSNeQSumTp2D2bMnoaSkEMOHH4vFi5fBapXrR448BcuXf4ZHH12IpUsfhp+fHzIze+Pii6fA4QCeffZRPP/8IgDCJGwsc+fOwCuvvIKePXvirbe+xv3334Fzzx2OgIBADB48AhMnXuxMMk/vuMkEXHvttXj//fdx00034c0338S1116LDRs24MILL4TJZMLFF1+M66+/AStXrnQyun377ed49NF7UF9fi9jYZEyZcgcmTrwKJhNw3nnXYvv2DZg7V+4/66yLcfnlN+C771Y6lSWd+IFoAMLejLFZOmEI5xphkgBQXV2NwMBAmEwmxMbG4uyzz8add96MHTuynfMoKAgYOnQA3n//Jzz88J248spj4XA4kJLSHRMnXuj02k6adCrmzvXFhg3fYuzYU+DnB/Tq1RNvvvk1HnroDlx88XD4+weiX78ROOusi9vty/RmREXR+2jG66+/jRkzpuP00/shLa0X5sx5Eldddbyz/zm/Z8x4CM8++xC2b9+I1NRMPPfcCvTvHw2zWUGVBw6MAwBER0fjxBNPxgMPPOjyfotFiAleeWUVnnhiDq699mQ0NzchISEVI0eOR2urGVarSvrLfdNud43109n6WC699Fps3boB06ZdCMCEU0+Vcf3hh5Uu140ceSIyM3tg3LgxaGpqwsUXX4z58+cfHG8z3n77bUyfPh39+vVDz569sGjRkxg//ng0NYkxwmaT5zCGc+LEq+Hrqz7Xy5QpU2C1WrFo0SLceuutCAoKQv/+/XHjjTOda7GjHFQcv4ceeggPPfQQNm7ciMzMTKxYsQLR+gYOOIkzoqOjcfLJJ+PBBx80PhYA8NZbb+Hkk09Gqifs+REqJofjUJCGUvRcTXPmzMGtt97q0tDp06cfmdr9DaWmpgZhYWGorq5GqLtT/W8uW7ZInpReveQA/uEHOXi5UGprRaAjnKWxUSBJISFwUlM2N0tOkLPOcn32ocanAAL1mjHD1TrOBKoNDSpOZuxYOYSys4HPP1eLgm5xq1U2SMKpBg0Si+iePSL07NqlklY6HFLP6mp5V1qaHJJjxsiByaSst94q1+3YIcrkxx/LAbt+vXoWC5nM+vaVPmaeKH9/uddkEkHEaFHnu+bMkXfNmyeCAadGVZUaI3qcTjhBYdgpqCxc6F7oysuTZ/r5CVRRH+u6OhE0fX1FwLbbXZ/d2ipK69VXy/fV1ZLs1mwWNj09MDwvT1E7jxsnChiFDL2N7ubDt99KXzP2wOGQ9vfsKXOPSY49tVEvdjtw883iObRYVP8wvgeQw4L/M8Gg1SrzOy5OPIG7dslYU3Fi8kPGZLS0KCF15kzgiivUfLfbhbbcU8B/draC88XEiCCjs5PRQBEdDSxZ0jWYle7Bc6eMdNWbZezrjtZ7To7kDPrgA2kTiUgI6QLU+OiwOQrvWVnuDS+trcDUqTJv6KGg8BsWJm2mojVyZMdrrrP9acsWmXf0rjQ1yToiyyU9DhkZMlciIuQafR2xzjt2AHff7T3jXEf9m5cnz9q9W5Q3GpYoGNfXi4Fp0iQxJHhqp6eYyj//lPmekaGIf+gddze/aUgpLZX3xsQIpO+cc9qPH/ek6GggIaERxxyzB7Gx6XA4ApzxP1yTZFDTFZb4eKUsetthNlt7qnMKVUx4y2SqZHtrblZ7uLEQ9kQYr3GO0UMdFwe8+OIrmD17JjZvrnIKy8Ykpa2tcqYwBtOYnPSpp+YjKAi4//757ery8ccf4+OPP8Yrr7zihCnpibz1wneRca+zovdbS4uKfaGwzXEhiQagFDOmKAFUyhG7XUGgqRhzTyUrrDdkG96UrvTF008/g/ffX4Fly75qR6LA2BgaHtl2HcIJuD6voUH2eZJLUP5obsZBpSgPp52Wjvff34DevQchJERRjuvz3N38DQiQPcZsVjDR/ftlT9XnGBFDgIrL1dvlrh9sNqk35yI9VEZYnz5PZ86cjNraKnzyyccdjqGxLVSeARVbzmJcSx2RWuj36B5lT89rbMxDRkY6NmzYgEGDBnX+4E5Kc3MzevTogTfffBPHHHOM22saGxuxZ88epKenI8AQg3IoukGX7J2PP/648++4uDi89tprzv9NJtN/tOL07y56XBFhXox3YVIxs1k2cGbn1vHApJhetap90H9n1lt3pbZWFt/gwbKYADlAGEj7++9yGJeXy8ZaU6MgKaGhCqPOWJ3QULn/2mvFMrVsmSy4vXsVrTithIGByupIIVi3Dv/4oxLmGD9VX69gXXoOhZoaeffWrfJ3t26yERUVifJBgU4n0DBaot0xbDFRcHGxvCchQR1G3jCx0UuUkqKeQyGLLFz00qWlucaZ7NsnXrnnn1f9U1Ii/eDnp56jB4MyWbGnNqaluco3+/eLMlZaKl6Cmhrp48JCeXePHqIENzYeGmU2DydSspMymvM7JAQH8eTyOXMc2e3yXgowgLSdiYT1DPQ81LZulfmhBxAziH7dOsVA6eur8n0RzkfacE/5xzZt6pri5M4I7+8vCtsxx6g4ja7GNnUGPaVAXloqAgAp/IuKxLLYt6+sz8JCYaQcO1Z+vDG8/Pijgt8yZ5zdrtIKkBTkwAFR8HUvb2fwYWPZv1/mw9atClpUVSXP9PdXkGFCVikEGEkDDpW9sLP+TUmR3wcOyLtIXlNdLfOaOcSamz1DZd3Fn5WWSp9RMG5qkvlr9I7rsEmrVRRMrs9u3UQ53LhR+tuopOue64oKpegBSoGhBdxsVjFktBIzjohxRYq63AyfbmnwiXeNb7DZFJGRUSGhkYJwXd2owlgrs9mVlIJFDz6nAK0rEdwX+RzC/rj3BgS45pDy9XWFpumQxNjY4HZ1YB8AAQgODnPWoaN1zXe5i9MxFmO/UYCuq3OF1nGMSKLBz1lH/qYwz7bqypePj+z/R0ppArrWF9dddy3KyqrQ2FgLIMRlvtCoRSVCH+uAANcEziyUnfS5waJD/HTIn1Gx9zR/SfBDDxLnQ2CgUoZ0TxLrrVP98zu9H6jYUMmlvMXUJjQIGeepO5ZJY/yf3d6+LS0tihRDR9ewX/SYJF3m0p+rr3eTCU7yFHqUjWs+PFzFFh+pkp+fjzvuuMOj0nQkS5cUpz179hzpevzPFF049/eXg6u4WL6jhSIwUCkWDIgEXOm9i4uPTNCxUTAh7I5W6f79BTJXXy8CQHGxIougosNNu65OBLORIyXpY0GBtIHPpNJADDIg77TZFBscIJtDTo4oDA6HErysVhHYCM9JSVGQL24WFRXyOS1Mfn5yb0uLK201ix4PQIHbyLCVlCTKCmOK2tq8FwKpKDc0uDIEhoYqpbiuTtqm08kfOCDMcIGBEujO+Ju9e+U7SZonmxKZ5kixrbPMGduoC4Q2m4x9fb2M0f79Mi7cfOkRY4Jdb4TO/HwZg2OPVV4cQkJIP0xhRj8ELRaxruuMgCUl6uBrapJ+DAhQ8DAenps3y1xZvFiNAzfp3btF+fH1FcHg+OPFWxIYKLTNneUfc2eg8Lbo8K5Nm2Q8i4tFYfvgg86JNzyVzuLRpk0T+G1pqcxXX1/lCWbS7KIimVNxccDkyap93ngUV6xQY6ATCJButqZGHfDp6TKvupJ0OSdHxojGpehomZP79yvGu/p6UQyTkmRtMNG2bs33xsDhTf+uXy9KzQUXSBzbWWfJ+8iUtXevIkGJiBAYXFGRZ6isu5hFHbrd1CRryeFoD5mlYv7BB/JTVibvjIlReze9e0bIKfek/Hy578QT1RplX5OkQY+DoQHDbpe1QeMfDRwUwHUBLyBAMWcaGbao3JBGW/cc0SPCOF8dNgi4GlUo+FFQ5ZlkMqmx0evncKg2BQW5zl/mNTQKhXPn3uIydrrVvn//8Rg4cDxKStTzOoMpuaV214rOOKr3G0mKKiqkDVx/utDK38bPdYgeYZy6snAklSZA7d2H0he+vr6YP/9Otx4ekooQCqsrfSaT8jzpz7NYpP/q6ly9c6yXXuiNbG5WMXaexkFHPphMCsZKJZ2eG9azvl4pcC0tcLLO6WNCdksqNjyHW1qUh0xPxOxunjY0yJp0x47pybPE/qA3yLjWjApuR9431o1xcO7GUL/uSJbMzEy3MNq/oxyy4vT888/j559/xqmnnopLLrkEzz//PB599FHY7XZcf/31mD179t9Rz/9vCoXzzZvFcltdrRY8NxgSEdTXK7KGxkZXem9vg46NxehteO89V8GE1NbV1XLw/z/23jQ8rqtKF95VkqpUg1SaB2uwPMrynMixHScYBwKkCUmaoRvCTHMJ3NsBGrr5aLqbj6kZ+14uNA2EMDTQjBcSIOlAAklInNFjPEmyPMiyZqlUkkpDlUpD1ffj7Ze1z6lzqk7JTuzcz/t5/NiWqs7Z49preNe7YjFAPm6/HVCXu++GcvX00zgUFDCMLuTnQ7HIzzcaic3NYjQwsjA5iX8XFxuNBipHbrcxYX3FChz4P/wBxhOL89bUQGl49lk8NxKBgZWfL/lhfn+6QaFUuifaDq5/661Yk7ExzIPT6IE5irVjhxgUU1MYW3k5lD8mtk5PIzcslQJ8kQYlc446O0VJZU0bvx+CKhTC+HWPO8c4PCxKFqmbn3sOnyUta1mZjMPvx2dGRkQ5zdbozW5uxjyT2t7lktw3KlxMkiVbIQtZFhbCWDx9WhwIHA+VINa24Nw/8ADy4m68EdTxn/40xksIGb3LfX1Y0z17jLThZk/kxXJQ0DFgrim1VBIKJ1TfP/gB9lgkgjlcWBDomM5MxSLDDzwAdj8nfejpkbyeaDS96DLrVJWXY5xvfSvWNteiyxxnJIIzsH8//l1UhLM+OgpnxvLlOF9uN/Zcd7coRBx3LlEuu/mlEXP2LGT31q0CZ56chCGRSGBeGhvhPKqqsjdelErPqdSJaOjpJWOkVeS4pQVQwMOHQUbCyKquEFnldpFi/Wc/Q9RRz3XR8z6okOXlScTY55MoG2sq6V56lwu/o+OitFS8znwuDR2dmIDnU1duSfpC9jPdgcj/Ly4KYYUOKSSkaWJCqde//p3qttveqebnxUjieKko8h7T97Fds4tAsEYN58sKpqQr5Zma7q03w6NcLsi0aFSMJ31eqATrEDkamEqJw06HXzrpU66NRosdZCvTXFgVYzaTijh5nh794F7i+BcXlaqtbVInTqRUKiW5t4yIcC+b14FzRoefHnXVjX0W4uVacE/OzgpjJJ0VqRTuYO5LfXx6/q9eZ4v7NB6HbPr0p7+vkkk4a6zYMe0iS3wW362fNaWMBm62vV9VZTSerNaQ42pqalJLyBS6LFpOhtOPf/xj9bd/+7fqla98pfrIRz6izpw5o77yla+ov/u7v1PJZFJ9+tOfVitWrMhaIPf/743FTumFoFfC48GGZcHMoiIIAjPlLAVSrqWzrKIN8/MwxE6fhmJCqN3QEJTK668HTn7lSolMbNiAfu3fj74RF0943PbteJ8ewQmH8b2ODkQBJidlzObaIKdOyTyZL42qKijIJ05AKZicxLtHRyEUSEJByNX4uNDZFhYaDQo7T7Qdw5ZSuUcPrKJYO3fiu319UMDe+EaMR2fsKihA1EYvFKcUxlBWhn+TDcvjAVSHyhtD9voYt25FQr/O5njwINbF7RY6XVLc0zM7OwsDo7AQz8lmQOhQ1JIS7AdGGmlITU/DYz80hPVpbcXcMBLH2i00GL1ewY3Tu8dLi33r7kZeVXU1cggHBjDXHg/eOTCAeT1/Hvv+DW+A0p2fL9BGOgAuhoOCzYmhkwsJhROq7wMHcMbo3WPESSnMVWUlzsO2bZivXPrASFNVlZAQ6OdXz00pK8MaLsXo1MdZXGx0ODBSOT+PNSwtFafMNddgT4yNYc1zjXJZzW84jLMTi0ndtFgMzqNUSqCsVDz0/Bk740Wp9OLRZiIawlp4lq3Y8iiTm5qsvfpW33G7IYN++EMjnIgGp+7Bp7Hh9eLnLELK71Dv0aFf/MOyAbpTaX5enqGUvJdQICpsesREpyunQcYEejJ1khacnnUqrLEY5pkKrZn2nA4cKsrZml0Egon+VLIJG7ODKWV7VzaYG3UArhHHo8O/dWg0ZRu/y3fk0qdM8Cyr5hSyZfcMKwN2Kc9j9MPlgpxg9IZkHZwrGtB6RMRqHcz7iPudc2829ukM5NwpJegLPoew3vl57Fcr2W4FmbMzZKzYMe0iS38qVj0v62xecxqkQ0OZ975u2Nmt4f8NLSfD6Rvf+Ib65je/qd761reqQ4cOqR07dqhvfvOb6j3veY9SCowiX/va13I2nL7+9a+rf/mXf1FDQ0Nqy5Yt6mtf+5raTu07Q/vZz36mbr/9dnXbbbepX//61zm981K27m4ouhUVuIhZI0NPfne7QTnb2ioHhYokk6tzKZpphp/MzwN2Mj8Po2nNGhxARkKonLzhDaJw6Bd9czO+09eH/geD+P34uNGgMzNxRSIYy+rVcnlGIsil2rgRgkBn+rJqwSDm7UMfkhoQX/86lMWqKjmoXi/+Pzcn9Sso1LJ5ou3yxZYSPbCLYt1wgyh0r3mNkbHru9+1Xt9QCIrhmTMQZGT3a2nBHurvhzIZCGBeOMYdO6AoNTSg7/v2QcjpVb0XFwVPTQHr90MZpcKTrZkjbDo8sagI80djcNkyrCULMOuRuPx8GOseD6jtv/td7E96pQMBjItUxsEgjKLPfx7zWFOD9Y/FpN6OUuK5e+opKNzLl2PM09Py3lwdFJlIBJwYOrnU+rFiftSb3w+DYW5OCvCSfYxwkqkpjJ0GqdeL6PfWrZK/kwl66vMhYtHbK2OmYhqLGWnAl1rY1zzOykqsN5kPo1FEfubm4GjRDSQ6PViUm312klNmfq8ZPpdKYY+ytlsqhTPndgsMmEQN/L8dPbj5rJDJivfA0BDkHA0Sqzwts/Flbna5XdXVQjoEBU687iRnYP4YPfSxmBQfp6OFSqdSoqhTcWduo1Iy97GYseAtv8doCRUtPWeJyiDfSeV2fh5rTOWO86SUKMY6UYKZ9pxKYy65PdkiQUQNlJdj7pcKU3ICc/N6JS9Vf4/PB3kYjxt/ToZBRkQy9ckqNyYbPMuqXWzI1lKf5/NBBpeUCMEW9yBzpCjv9YiV1TroMEH+zT96XTLd2KexQcctzw3fXVAg0VO7IIwZMqcb8Xrel950A8kussR+82e6I0U3SJ3sfaviu5dTu1gRrpwMp46ODnXttdcqpZRqbW1Vbrdb7dix40+/3717t/roRz+aUwd+/vOfqw9/+MPqrrvuUjt27FBf+cpX1Kte9SrV2dmpqszudq11d3erv/u7v1MveclLcnrf5dBOnYJHtKYGB5psV/Q4sk5FKCSe80AAAjLX5GqlrL3eZJqrqcEF3dUl9YkINxgeFsVcqfSL3u0WxYiQFCuDrqUFRtbHPgbB0NIi5BNkghoZgTH5F3+BiNUPf5hdGaA3m0V36+thuOlQDq8X8xWP47NWihaZqrIlxl9I9CBbnRjdUCO8sLvbGn5DmnHmcwQCQsOal4fxnj5tHCNhKX4/ImaxGAwXhvmpTMzPQwjX12OuGhqENj0axRplglxZRdi2bcP3+vok3621FQYZ19msGFPwjo0p9aY3IWr1i1+gz/Q28+IirXl7u0ABFxclF44QKu7thQX0f3QUYy0txfeZ4N/YiP3Z0ZHdQWEmEfB6caauvx5RtWjUWU0jp1GtbIry4CDGWFeHefF6JaeQ3vzJScz18eN4N6PGXV0CJ7XLvdJlwO7diEpPTxtZukpLsW/WrxfSFX2vODlrgQD6bT4DhNkWFoLQ5r3vlbID+nPicUAQcynmbDW/Zvgc99PsrNQhY8SShDXFxUZYsJ3xYj4rdXX4PhEBhO8MDQlxzJ49xv1oRWjDlim3q6gICmhNTcF/wWVjanHRZ1AUmYdkVXSUkR0aNkrJ//lu/owlLvg3Pd/659h0Y4lQKZ8PcoTKKxU3GkSEBvJ9er4Wvfp0BNKzzvEQfui0OSU8yM+HHMglQqM3O5ibbvj4/RKhJ+JCKaF8NzO+0SDI1idzDgvvBZ1F1w6eZW5cy1CIBXNznwtzywYBs2suF+aLDiQaODMzUs7AbBBarYMe0dQJvJTCv30+gY/qBmp+Ps4zCUlouLPflM96NFb/jDknTHcIE/7H/a87MKgLZYos8XzQALMyrFnuJNve12VFrlHK57vN/ZcnJy9bkmGWlpPhlEgklF/jIfV6vSqohQV8Pp9adEIXo7Uvf/nL6j3veY9617vepZRS6q677lIPPPCA+t73vmcozqW3xcVF9Za3vEV96lOfUk888YSaIB3ci7jR6lcKh87jQfIxjYulJFezWXm9qVDR6BgZgUK1bBkuYgoA/bK3UoqdsmX19cEQ27hRFD5dWY5E8Jw3vQlKwv79zpUB5gE0NGA8/f0iJMg05/MhQnXVVekKmxnCuLiIsd16K5jGlhI9aGy0Vg6zRRU6OgD9O3fOmPBNeOfsLBT8P/szXFidnbI39uxBnxltMnvbCwuhWFMRjMch1ChwlVJ/qtnQ14dxrluHPikF9j2SNGRSQs0RNlLqX3cd/mzZImtnXmcqxskkoIRr1uD/t9yCmmas1ZVKoS9k45qYEDIQUscPDxsVNh3TXViI8R09isjr0JDR411SAuPOaj9T8T96FMYciUpiMfT54YeV+vnPEcFpbhbWslwiAnbNiaIcDMJYJRSTRiahIMzhImySHsu5Oaz1+DjG98EPZoaekljg1CmpAQIGMjzXCsbqpBC13RnQSQ8oA3btSl+fCynmbJ5fHT5HchbCo0jqQmdFNIr+6blJ2YgpzBH5nh5hDtXJX44fRwTx/e83jnepMlnGmad6e0uU1zvyXyQ6fpWf7/rTmKk06wnnzG/injN746ls0whiPhz3mk7solS6wUXEhZ6vQ3gez71S4tWnt1v3/vP3/DzzOug40eHxmbz85sbv0rlobhyzzuzIMZiZHrM1KqszM6LQcu45Z+fOiaKtMwROTeG75eUCSdPfb9en2VncxTQImDrAyENensjR/HxhlNTJdfRn6UgaQgZ5ZvgsjoUGrf7zbG2pc6uUkBDpY7WaO/M66EY94Xl6FGh+HjK4pETyn5gLyfpjzO3VG9fRTGSRny+5VKz7xLmNxfAdOjz0edT7w6ZHtPk+svfl5UmZF/M66JFaJ3uf47Raf7Ixv9AtmUyqcDis/H6/yr/AAoo5fbuurk6dOXNG1dbWKqWU+tGPfvSnfyulVGdnp2rKAdA+NzenDh06pD72sY/96Wdut1vdeOON6plnnrH93qc//WlVVVWl3v3ud6snnngi4zsSiYRKaKdq0lym+RK0tWsRYh8fN9Kp8pKmsnDjjVC0c63NZG5W8J65OYEnMYmxoACK9cQELlyzd1OppRfbtYMYUVkOBqGAMe8rF2WgqAhzd+4c+sI6DIuLwri3fj2MJvP21JUsEkgMD0Ohf/BBpV79ani17Qrk6o3Rg6NHlfrJT3L3dut9YaHfqSnA8jo6JBHU48G7du8GjK2oCIaQbgiave3cc+3tkgA7NIT3lpZKyF+nsy0owLv7+7EGlZXOldBsETY2q3Xu7ZU6NsmkUp/8pBQEPXxYSDHo1Y3FJP9qehrzx8ijUpKISw9qIID5Gh5GZI7zwYtzdhbPsBIVVPw7OkD3PDkJYoVYDOvO6MvMDP4fj8uFaVVHLBe2N65LprNRWYn97vdLXhCrwcdiRvgrvYNc85kZzNvkpNCX/8u/4Ln6GpplQEMDHC6BgMi0xsb0vXLzzdiXmQwapazPQE8Pnk04r51BcKE5Zeb5ZZHecFjOS00NFK68PDHcN2xAv3p7pUDz3ByekQ0hwIj83/89jHjmKsTjEsWiAnPkCMYxM2O/Hk5ksj7OX/+6Rt10k1KrVo0YIipkfdXr5JDMSCljPhMNFt2TPj4uuTULC/guyw+wDy6XOCxoTFEJpPOLZ9JK0QyH7eFsfK6ewK+U0VDLy5PcXivKc3NjhFt3xOiNZA2MrF1om58XpjQ6PRgdoEKbSsm9Z/4u80ad9MVqbJx7zhmZB9mSSfxhTUYazNQv2F/dcNV/RgNCd+pQFyFM9PloTtZRnzuuA/vIcfKe1KMtbje+NzMjrL/co6T753nxekUuJBJifCglP2c01eORCDZheiwJYV5fGkvMS9RlD9Vh5pnr822uE5XrnOnnXR+31fw8X2ubqbndbtXY2KhcF3g4czKcXvrSl6rf/va3f4LH3WaqwHr33XerXbt2OX7e6OioWlxcVNU6HkwpVV1drU6ePGn5nSeffFJ997vfVUeOHHH0js9//vPqU5/6lOM+vRCtqQkK7/33p9f/oGCpq5PaNBdKOW6Gn6RSiFTQE8WwLI24/n4Itltvtb7snSrFSol3nqQH09PGWkVsTtnttm6FQshoWWMjlEdCzlauFG8QPZf9/QI/M/eNSlZlJYwl1vMhQcbDD2N+PvhBZ/kEiQSiEIyAOTU02BdSSM/NQRnr7ITxwqR8Gk+//a1SjzyC+bjxRihBNJqsvO1Hj8r3WbOFieULCxDIRUUQdoR5TEzgZyS0WIoSmm3vmteZ5CEeD5jJGhuxZ55+GnPU1CQMkKEQ1uuRR2AUXnWV7BUd2khlUL/4UynkyExNsRCoCPy8PJzHnh70q7kZz3r0UbBKTk9LMcDycuxDQl9ZLyqRwPwNDWG+JiYuvKaR3ZzpivKtt4KKnBGT66/HWIaG8P5wWPJuenowf7zMaEAy5+tXv8J36UE0OwB0GRAIKPXTn4pib94rbW1Kfe1r2E8bNljvpXvvxc90oycYtIbz2jkhLkZOmT6/HR3YKyyiyfxB1jXjvjpyRGiPh4cF+nnttc4QAn19su8rKoTYgFAylwvj/+lP4TxgHSK79XDiZJNxutTjj9eqP/6xShUUzP9JASIkduVKRN4ffRTEIzSuCdWanxeIb0WFMHzW1yv1trfh+0ohWvfXf43vVVRIDlRfH84c8yuvuw7nmbXeJibQz7o6Y/9PnUKBZ9aiCgaFNGR6WnKA3vhGMAieOyflMUpL0a/SUsjX9euV+q9UbTU4KHW4amvT5/DsWaX+4z8wD4Tbx+M4Y6WlxjFfjLawgIL3nZ14LpX5yUkwySYS6Gdrq3HPs86Q1dxZtf5+7K/SUnGwkESIOWWzs7h/ef/xHv4f/wNn4eGHcRaOH5c7naUzCPVaWEBUuqUFczk0hHlcvVrmcnBQ5nLVqos3l5nGqjeruUsmjXsjFsOZ6OoSePnKlbiPV63C57/9bci21auxNmNjuIsZwamuxhkeGpK7oqEBe5W1PLn/rr1Wiob392NP9/ZiH5AwindYLAY5xHuRJBHDwxjzW96Cc5ppn1s1J3u/qSl93GyplPG85RoEuNDm8XiU+yK8NCfD6dvf/nbG33/nO99Jq8Z7MdvU1JR629vepr797W+riooKR9/52Mc+pj784Q//6f+Tk5OqoaHh+eqio+Z2I4oxNATP+uCghEYp7INBpf7t33KjKrZrZvgJcftVVcI05naLIr18uSTeZxqDE9iZDoE7dw6X3e7dRrY4O8+7WRkYHgac5Qc/gAByuzGuV7xCWLxGRwXiohSECkPuZlY4Kln19db1fEg33NMDA+EjH8kMk+rtlTC8nXJoZ2j09AjrHSmkWePJ7TaG2JWSBO32dvxOr9+Tydu+ahWE/PHjogwHg6LwuN0wIlasgHAuLoY3XM89Yr5HrsQGdo3r3N2t1Fe/ij5s2yaefirOY2NQphoapBBzPI4+7tqFPTU1hYuJ0B1e1IQYMq+rtxf7nsoeHQdsLhfGfPAgLsdnnzXWy4lEsDdqa6HQkkmOeT5er0A0iotxQV5ITSO7ObNSlN1uY0QqGEQ/STkfiWAuSe1OKIhSkmNZWopx7d0LmCTriJkdAFz37m4odnYGS3ExFP4bbrA3aA4dwv+bmuQzdnBeO8XUaVQ4W06Zvid/9jOQzxDOQ4gX8wuUwjqXluL5ZWWi+N96q7P1nZoSx8vQkORIMM/A78f6zcxg3zQ1WdeW0tk/29qyG1DGfZSnioryVH19epSxpwfrw5IHJ09ifzCPUM/LWLcO8s+8t9esQcTwvvvwPRacHhnB+lKel5dj/aenJW92xYr0MRQX42eVlehvd7fQTvv9OOtKYV3cbvSFxiBl2MwMPnvoEPrMguuZkAIbNij1jnekQ05bWuzPs5O8PrvW3Q1ls7paotlKQSb290vdw5oaY6mNxUXoFszPydZiMXy+uFhkA6HhhKlFIjh77DujiCMjkI9dXZCrg4OY52QSa0PqejoeOjshkwYGoBMkk7ib4nE8t7oasvfuu+G0bGq6uEq21Vj1Zjd3ZiNuwwb7de3uxr6qqJA183gwFp6f7m6cndWrcTeQ3IrlW/R833Pn8J2mJvSLBmdvr+x9Oozp0JicxO+VkjqG73jH0nVKJ3vfatx643m77bYLDwpcqnZhQD9TK8qRH7uiokLl5eWpYVMJ4eHhYVVTU5P2+bNnz6ru7m51yy23/Olnyf/SJPPz81VnZ6daZdrZXq9XeS9Dio+WFqU+/nGl/uZvcAi8XvyprcXvKipypyq2a4Rl9PTAY0gjidEHJnIHg1K88PRpe+XCySVgFfnw+0Hj/eCD8ISzgKWd511/z/AwimGeOycQw9lZGDwPPoiLcNcu9FtnZqutxYVtRSvNZxAeqZMwUOnmPLW3o5+ZYFJeL4RVY2Pu3u6jRwFLYjieBXyHh6WOC59DBh5e/NPTuNx+8AMI+0ze9khEqb/7O6W+8Q0YalTy5ucxBkI/jh8XrxXrtjDJmgWSS0tFCb0QxUApUfjHx6Uuj04DHQoZi98Gg1AWy8uRe8XUy2AQ/aLXjbCHQAB9KirCeE6eFMiAVR0R/mx4WKJMiQSUMfaNib7xuEBR9PoybjfexyLAN9yAsekwqws911YXj11Eas8eeKW/+U0oLnpdJ/aD42BkioZCXh7mqqUFCs1ddxkVmmwGC5OS7XJyeY5SKWdwXru2VJY5q/3b2Yk5fOYZSSTv7hajqbBQoi1Mli8ogGd4zRrIiPvuw5xlW2dS7I+NYbzMA1lclOLFqRQUSpLA2NWWUkqMYxoj73wnlB6rZrWPzP/X17e4WIxZsmi53ejHX/81nB5We9vthpf5zJl0annW3FuzRohoIhE8xy4iqzsEr7sOMvTwYYEgdXVJeYxEIjNlu1XB9UxIgVwifE7y+jI1u7NFFkaXy5hTxca7ywmpj1LWZycUkuL1ZM+0K3URDkuxVo9HireOjwut/blzeF5+PtY/EjGSD5WUiLNsYADGf28v9tRSioXbtaXKCXPL5EC2Wzc6g1hn7447oKd89rPyWT3fVyms78CA6DDsv8+HM6OT9Cgl5C3BIGQ+S7bQML2QlqlcC40mnl2rlish0uXYLqrhlGvzeDyqtbVVPfLII+rP//zPlVIwhB555BF15513pn1+3bp16vjx44af/dM//ZOamppSX/3qVy95JCnXFgjAY9DYmO4JU+riefTZSA4wMoIwdDQKD0prKw4zWzRqLzScXAJ2eQYsYLt3LwR5PI6Db+V5t6o5NT0tkQQWTJ2bw2EdHIQnjNAkPToyOWk9HgofwpGIuZ2ZEeNrfh4KkMeDi/m22+xhUuvXIyHeqbebylo0ityPhQWsAy8m4oJ1Jhtiw91uCcszB6m9HXNjtVeYJDo8jOd95jNK/e3fQjnUmZoKC6UuTnEx+jc2hmeWl+MZLJC8fr0U1r3//qUrBmz6RZNKGWmgmQMxMwOvZSSC8X7kI9hPOqVzIIB9lUpJBJJRIta82LwZl0p/v+SR6I2QWRpHrH3EGlPLlmFfDQ+LYcG1Yr6ix4NzNjaGz37nO6IAPN+etkyX2x/+gMuN5QPMhUVJAsBIHBWyTApNNkWE58uOO4iQEf57qcpMJvKMZBL7fc0a47mykmllZUIYUlWFP3l54oDx+QDhOX9enlVfjz6ePYv9UlfnXH4TRpxMGguUUlGdmMDPly2DTMtUWyqRwPt5po8dU+rxx4Fy2L59aUa7eX3Nil00CoNj2zbrsSaTiNzef7/MMfPDVq2CUTc2hnthbAzfKStDbqNd0/O0HnkE68eznExKDbevfQ19q6+33ld2BdezIQWcoi6WSlTCZne2aNSQXVWXYWS5Vco5qY/V2XG5pKREtlIXoRAMgVBIqNgJz6ODhrTmzA9cWIBsHh8Xognua+6zQGBpxcIzNY718GGBxuslX3LNPbVqmWQiHaCE6imVmyGn939sDP2ur8d88o5jMfuhIehFK1Y4c8Y7cYKa974uQyMR/H9iAka1rltajeXF2C6p4aSUUh/+8IfVO97xDrVt2za1fft29ZWvfEXNzMz8iWXv7W9/u6qrq1Of//znVWFhodq4caPh+yX/Jb3NP38xNDIv5VK8cClNF9579uByeeopKMA644pSmRPWnV4CmfIMqqqAAe7pUerd78bhNx9Mq5pTJ05AQDCkz0uCieinTkHgNjUZL/RM46HwefJJY+ImlelkEkKdRYh/8Qtc5HZKaU8P4Apm4ZdKiQd1YUE8nLqg4SUXDkMA0lBgZIlQTj1hlAnPo6NYA8LszO/XFd5YDJ7VbdsA7fs//wdKA6uW09AkfarfjwuQ3kCvF4JwZARC+8YbEQmMRLBWfj/21WOPQbn+h3+w93Sbm37RJJPpUUAyOxUWiqH47LN4fns73ldfjwuD1OelpbjYKyogyDs6IMz/+Z+V+tKXAMMiG5oebaQ3ncxo/f1C+VtTI7jw8+eN7ERkXFMKf5Oxq6kJSsfFVgBybW43LtF775XEYEaC6KXUSVcKCrDmVGjoVGEyMcdz552ZYayTkwL55P7Wf9/XBweOUul5UvpnsikzduQZVoQj69bBgDYTVkxPw7iMxRDJoCHT1yesWzr1MwtATk5KdGhyUmqJOZHffX3CSBgOi3GmlLBseTxiYHV0iHODxCd9ffgdWdBWrsSZjUZhMH70o5hjKmu5ODaWSnvOvn7rW8jL1Ov0NTdjnJWVyKH67W9xpjZulMLNfX24C6zOSzKJObrxRqV+9ztBbqRSEhXz+WDI9vZCFmzcmN73TAXXLwSSnI2opK1NqX//d+Sb6OQ+Tufe5RI4KSHcpNcmQiIXUh+7s+O01IXPJ4ZQQQHGTQOBMpL9Zp4OI1Ikijh4EHct5XFBgZQjuFgIHI5182Y878ABQfwUF2N/rlyZGQHjxPlgt24kduBdVF8vkWGn54tr1daG/V1WJjmDJMSqrJRIE+/vbHt5KdFRs77W2IjxnT8vpEg0npZCiHQ5tktuOL3xjW9U4XBY/b//7/+rhoaG1NatW9WDDz74J8KInp6ei5LMdTm2ixUuztTshPeuXVCEhoagqOzZI+xkdrA5p2xV2WA7wSAuxbq69MNrV3OKOT/EHtfXC5kFE1BnZyF4m5udJeDrEMaeHigsvASY81JZieesWoWf64Lb3HcrQWnO0amsRB7P8DDG0tAAIXf6NJ4/Po4+kHzATEuqlBSqpffe6xUK5PFxCFOSOegevIUFXAi6Av+mN8Ebfc894gUsLsbanDkDQ2xwEM9lvSNGusicMzWF8Y6OIio3OooLsbNTqQ9/GInNTownff7Ky41RQCrftbXCiHfkiFJf/CLmL5HAGFkZnpXU169HvycnYTiuWqXUu96F373+9TDIDxzA78rKJMoUj4vxSjgfCR8WFrD/SCKSSAgcb3ZWYFezs1IJnvtn2TIoDHYKgNPLOdvnMl2AW7ZAgQyHMU5CLWmUEqY4P4/9WVys1O9/j/cpJQ6Ms2cxrrNnARN929sys/29850wUuyYMlk3va8vO5tmpvE7IRxhftBvfgPZoZOfMDobjcKACgTgGCCtNY1MyjnmBuqFHwsK0GfKr02bMu99Rr23bIEsGBoyGkwFBZCbfj+iMseO4fxPTIh8IJOornTF41hnQvsmJ3EmcjXg7ZTq6WkYHsEgojXmvTk8DIfTU0/hd01N4hWfnsZ3uruV+sIXcJ6uuUbWMZXCeenogIHxuc+JDNT3d2cnzi/3hbnfVVWYg8VF632VreC6lQPTbv/pP49G0U+zA5E04mfOIKfqxAmBP1spqHZzPzODcV1zDe6LsTHMA/fgUkh9MsF8WerCPObubimgypzp6WlxILnd4vxLpSCv8/JwJvPysEfLypR64gk8ixD0VAr31cXOqVVKmGdp5PEs9/VhLm++2R4B49SgsFq3eNxY09DngwPvta/NvaxASwvg6seOYd4jEYm48vuUU0QNZHLGLyU6aqcXbt0q+ZpO9MsXW7vkhpNSSt15552W0DyllHrssccyfvf73//+xe/QC9QuxIvntNlFfyor4Yk/cgSK8ZEjUALtEtZzYau6EIPQrubU/Lwc9ngch7KoCIcwLw//LisTjLzTBPyWFuRqFBZCiRoZwfuCQYEc+P2SWJxJcJsFJZUc9ruqCgrrI49AiNx0E+aHHmri1JnkSYOIUCDmieiRKLdboG2rV+MzZHBjVIZUsaEQ9lkohPe2t6N/b36zMAoyf+zECTynoQF/WF+GUR8WpAyHMeejo8Z8JEYqTp5U6vOfV+of/zG7gqbPHyFQzO9iFGzdOrzr6afxs6oqKFc9PVDgZ2bw/1Wr8Lxo1LrgMdf+E5+AN/zxx4307IRmTUzgOXl5UK7IDMnEXK4rq6vn52P+BgdFSaisxDwzRywYRKTMvI+cXs7ZPpftArzzTpz9w4cxT4cOCTTG64XhyOjTsmX4Xmen5N6w0C9p8n0+/J1MQuE4dsyeFnvVquy02XfeqdT3v4/PJJM41/pnnMxTJsIRpcQ5cOCAKONsQ0OQA0pJDSMS9+h0xKSwZz0XFq5moVBG8vbvN9aDM7eODqV+9CP8TeN1xQphjiwrg0Oouxv75tw5SdZnhIW02lTGmBw+NiZ1ZWZm8JlUyhkrprlZGaSEqoZCmOef/EQgoIRYsy4VcykXFiT37/e/lzmvrUW/CV1iHhSfk0op9Vd/hd/pJSRISU5HB0l1EgmscSCAz95wA/pi3nutrcjZsyo4rpTcV4EAPnP0KFAKQ0NGCNzmzbL3Z2fR7+5uRC15D4bDOG8sg5BKSR2kTApqNsp5HQExPq7U//7fWPNoNL2AOu/q7m7JT9QNoVwZGuvrIRuffFLIIhiVJjkP14aOMNai8vlwZiIRgfCRtTKVkvISjJpdDASOruzv3ImfEd7v8eAdx4+jFAmhvEuFW5rrtJ04IY63TZuMUfsPfAB/7rkHspnw5dZWOJWs3rFlC4wUsmAmEpAVerFjPS8tU47nUso4XCz98sXWLgvD6f+vLZMnyallns3znCn6U1kJT8CRI7iQWlvtBWQubFUbNizdILSrORWPSxTB5ZLLkR5f5oq9//240IaHIcx37kxXjMytpUWiIl/6khESV1srhTcJ48skuCko77nHyMLGAp6s+ZCXB2VUKaw9ceChkFzGfj++29trNJgYfVNKBKZu3HV1QfE6exb/JsMUx6GU8QJtaBBFv6LCSExBD1xNDRRPQhqUwvOTSTz/6NF0VkLW1xoZca6g6fM3OAgPammprENFBTyTY2OATdbXY47b2sS49HhwKfX2oq8645iVh/XLX4YSQciO3w861YoKKEKDg3Jx19VhfzF3ibVmVqzAvBUWCl1zdbWQB9CYpDI7NIQ5o+Hk9HJ2YhRlY1a87z7k6rGI7TXXQIYMDaFvfj/OzblzMCgnJ+XCJfMmjXsmg+flYY37+kB68+Y3WycOLywgyqmUNUlGRwf6PzQkOUg1NcJOl4sSY0U4ojdGiCYnBcqSSmEuWO+LCjC95HrJiMZGzEFXl3jbEwl8lhTt69dDscgEjfnXf4XyXFsr8x+NCtFMaalAKs+dE1Y/pYSYpLoaZyUeF4U1Hhf4Gg0K5t8t1YNPpfrhh2Eo0XkzNgZnRySC9+/ejfVpa8McTkxIAVw6gtiPZcsE7jg4iLWnoh0KCaHL0aN4Z2Eh1r+lBZEsErIwEphISKmC0VEh22ltBdJCvy9nZqAwZiu2XF8Po/CxxyC3yRC3eTPW6/HHkd9aX497JBDAehw7BifPf1VwUc8+KyxnwSD2Eh1d27djH1BWKmXsa3Mzan0xL1YvcK4U1rCjA9Dpw4fRB7KG6rKfUPGvflVYEc3OByf5W9y/v/oV5qSzE3NO6KhSeA7vC7JDTk7K/cXoL/duIoE+M3+Lhi7/fTFyY3RlX6n0nGgaSyxkr5cJIdslnZBOnA+s0/axj+H7LS3Yk5TN+nNuvdUY+c7WWMeNupZSwmpYUWFEaZD5d+VKjJnlXBg1PHhQ8tfsjG2zrLhY+uWLrV0xnC5xW2pBWaWceV716E9RUbqQYIXs1tbMgjKXKNKFGIR2NadYN4HeXxpDU1OSQFpRodTPf47Pcz727nWO5W9pwfzRYCksNAoQp4K7pQWK4+HDiDDpXkxC9EpLBSoVj+MS7u8XpaysTMgpqquFCIO5NqmUkBY0NRmNO69Xqbe+FcLzy1+GUC8rS48UBgJYj/37JbJFw7S4GN+JRHCpbd4sXqVUChf9smVCJxsOp3tqWSivocFa6NoZ/S0tyI3auVMY7dauhaLR2yuYbq6pTiJBprFUSnKf2tsRCbFzMLjduExIcX30qBSAravDmWExRJ8Pe2B2FvuQShnHWlSEd917L9Z6bs5YI4M4+qEhKH0kCHXi7VuzJvvnnDArdnQodfvtRrlTUYE92dCAtT5wQKj1JybwXeZ7EV5DGBkVV6Xwve98R6n/9b+MxpCdnDJH3HSjiJTb3d1I8H/d6wCvOX8+PXpkp8RkutjpBEgkZGy9vVhrRtaUkjETFsyaQfTm0qPL0gF+vxj6paU4H1bOFt3Lu2EDzjnzyLxenL1nn4VsWFjAnmVeCPMbqZSSOCcaxd71eLD/ZmcxPwsLwsLF72Tz4Nudz44Opf7n/4T3nIok4al0DjBvhLJkaAg/Ly+XSDCLxDIyx/PDEo40dpk/09KCSM3gIBSyyUnMXU2N1JXi+zk3MzPo45YtkCe6QdDRgZIfmYotx2J4/4EDmM+JCVHuz5zBe3fvFmhndTXGwLO2ahXWv70dP4tGZR/NzeGzy5Zhz3V2wuHT0QEiDTtqdKXSC5zr+Xrnz2NteRezqD2Zc4eHYewzr2YphBXm80qmuKkpIzRMKTGYCDevq8N+JfkHI9+EygWDOP90MI6Oov/9/dZKf66NMiEeF2i5zhi7Zo3UabMrExIMCktre3t250NfH+Z940Zrooj6epz1tjbJe+e6HDmC7zvNS1uzBvupq0tQOJOT+P3EBPbqZz9r3De//z0ioZmMbStZkU0vdKpfvtjaFcPpMmhLKV7o1PNKOODjj+PAkKAgPx8buqAAl1B9PYSR3ftzZaFZqkFoV3OquhrC9+xZKDLxuMBjlEJfBgfRj0zzkYl2mPCFyUkI8pYWo/cnF+gkPfNm4g+dQjYeh4AuL8fPGc0gXI6wnTvvhNDt6ECfT5+GQrZ+vXiTzMZdKIQ/NTVSZd6qj4uLkpjPAqnM6SkuhhLGgriLi5J4PDEhjIajo5KvpVPV0ttVU5NOb5/N6He7kfRdVyefo1eSdZsqK9EPnUSCuV66V/3ZZ+HtoyKZCZ+uw6ZOn4bC5vPhncwX6e/HZ5uaANNjgvPsrOQdsK6M34/zxrpoqRT6V1+P/cq8IScw2GefdfY5Fgq2avoFuGlTutypr0fUNRKBojk8LPAb5tBEo+K8cLsxfkY1SQzS3Q0lx6mcygQVqayEA2TvXoHOJhL2EVRdicl0sROy2tuLc3f4MCIbhLzF45JfRLgRlearr8a/u7vxrGAQUNmGBux37sdMDKVmmEtlJZSc/fvF0GB+UjAoORKE3Q0OiuESi+GZzPFh3hUjnDzTlZUiszM5guzO5+bNKBxK4yYvD++KxeRs+nyQYWfOoA+EYDGqTvgW53ViAgoeoVIcgx4RrK2FDCovh5xihI+Ooro6RI1IjU4ygulp7Mv3v9+IPLDab+ZiywcOYM7PnhXoJo1E1tHr7xe6ejOttsuFvT06CuOJsmRsTNgrKyrEuKQCPzJiT41+7Bj6QXnL3+n5ejt2YC71SPn58+hraanUWFq+XM6E06LmdvM3MoI5Wb0aaxAOS/0j3jPM36UjQilE4wgJLy4WAiKyofIcHjggxDuf/Sw+U1MDohs7NAH7ab7viYR46in0R0cDDA6i7ytW4HPmMiHz8xjr+fM4I2TB1dEDVi0bYsfvh6FTW4s8zAvJS5udRf9ra7EGLBA9MYExrFol+4aR0vLydGM7GsVeYp63lax4IdJNLsd2xXC6TJrT0LhSueNRN2/G4SB7WGkpDsKpU0Lr/aUvZY5cLYWFZikGodmDQs8c4SYrVkCIsUZPYSH+JjmEPh/05jG5+G1vS6fN1mmHGxogiJ96CnMzOgoF3efLPanRCYUsozfEffv94m3bvBmXxvAwIBwstBeNQrE/dw5rlE1QZRNqDQ0wSBobRXHQ62A1N4uRdOqUUQCvXg2F5Y9/RN/On8cezs835iQR806hmwvcyryHolFj3SbWl+IcmjHd8bh4xjduzPwuwlfOn5fCiKSrz8vD2Lq68NnqavxxuYRpsK8Pz/Z44EkdHMSeJUMfjSy/H0aLXlvMCQyWhl+mzxHe5jS/0Cx3uruNyjxzO4JB7BMWfWXEg7k+pC7neTp1Cs+1k1PmelBKWRuF4TAMCX2dPR70c2QEECgW07byima62Nnm5kACoRTGQuWOOURMsGbExu/H+tIYqa42EhvorIw9PchxcQJLDodhrHu9siY0Os6fhyKkU5UTKmlF8R6Noq9UCMl2RpmxFPZUKud5eUJZX1goNNOkoA6FRAYUFeHvYBDnKRYzFqcmccDGjVg7PV8pFjPKEaUkQtXfj74x77OiAr/v7cVZZ02bUAhOk9e8xjhGq9wMvdhyVxfyXObmsE7cdxMTcrYCAfSvuxvjrKqS6CVbZSXukEcekUKrzH0kO6dS4vCZmLCnRm9pETbMl7/cCBvT8/VcLqEQ7+szEjXEYnKuDxwQxZhz6xS+aZ4/OgQXFjC2qir0r6kJ53xqCvuUa859GQhgvggJDwRgCBFZQbhpLIZzxiK5x47BkXLvvdg7O3Y4zwW95RY8Y2zMWMzX68X604BZtgyFr1kmZGEB+4vFzhcW5AzrjLtWzQpJo6N/CId2kkNOBl9dp7LStVjIenwccjYWw/gZES0qkkhpVRXmf2gIY62sFGKr8nJ7WXEx0k1ejO2K4XSZtky5S7kQNTQ2QsgwgTMSkdota9fi4H7/+0IjaadYtrWBujk/XyBRusC7+WZJxjb32YlBaB4vczUOHIBQISPZli14P4VOIoHxzMzgPZwPHnoK3/Z2hKOrqwWDrtMOk6ihuBjKWEcHPI1PPYXky1yTGjMpbMuWQbGkYqYTIAQCSl11lYzR5xMvEOfR44Fi40RQZRNqt9yCGhwzM0bFQa9YHomgBkwgkC6Ai4uR+P7ggxC6VPD0nKT2dhG6S0lC1ceeTKbXbaLyxLotOqabpAwtLZm9q0rBIHvySbwvFsO+IuyJjEBUisywRKUkF48sXVVV+N7cHOZ3eFjy/zweowHjBAZbXZ39c2VlOI/d3UvzAJqVeSqIPT1iUOiN3mPm3Oi/t5NTVvWg6uthCC1fbnw2YZg1NSIfCB0cHVXqoYeUetWrMNdWXtFMF3t7O9aYpA8s/svIyMqV+DdpfgktW7YMz+7owL68+WZApDo6oOQzX2xqSoyszs502WGGUZvrlhFmt24d5urcOSgx3d3CAOrxoF80ngoKJMdJKexTUuIvXy412pbCnkrlvLhYctsYVSDbIOeJEYWqKvSbkSHOPee7rAwybtkyrM1zz0HeJJP4DHMzlcLZ7O/HHO3bh7Xz+cQhSCZOvx8KdjgMA+O/Kptk3OdsLhfmbGoKzyIkMxgUqNniIvrg98u/aWARHqg3nw/KfTwuivnEhDh/lJI5I6W8FTU64YgTE1gjkrUsW4Z51vP1KivhcHvoIYHczs9j7QoKICMjEYHp8l1OCRjM86cXyq2sFFk8NCTFy8l8SjgrYeo6JHzTJjjlOPdkKyWx0ugo9mAshjEQkXL4sPNcUEYsy8rwe69XGBETCfy8sFAiv0QHjI1hzC6XrFcigT6bGXfNTdcHKishDxhhzMvDePLzMSarxnU5ehS5dnZObrOuFY/j83/4A54/MiIwvIICKUA8NgbdanJSUCd0lh04gDlZv150SrODfKnpJi/WdsVwugxbNhiTldDXPRjMQSBG9+RJKGzmHKeiItTOGBjAc1kTyOsV4fPrX+Nws1hqIIADWFQkuPz+fnjUjh415hc5rRViN96NG3GgWbNkclLYvZi4S2Xo7FkYQskkhBpZ4kIhCIDOTgg/Cjt6fXSiBiosNCCamiBE3vteeA1z8ZpkoyJVylggtbraSESRScnNRVA5YWPav99o4DFJXq81EQrBwLISwFVVMDwffRTrtG4dIEexGMauK2jmiIbenHg8zfNaV2es2xQIiFd9fFwSu/XaXlbvOnMGZ4EEGWTII7Wu14vzQaWUjF48gzTiSUhQUSEXblER1n5+XmrU6MakUs7gDjt3Go1Gu8/deityN5biAdSV+URCnA+E+HJ8PDder9Rs8fsx58xdsJJTdvWgzpyBQlldjaiyUqIUsbYY8xmVwneY//Dkk4gUh8POzwudAhUVRoVlfl6S1YeGsE8XF/GMzk6Jfo+NpbMFslYRo1MNDTgH/f3ptYhIkFBaij6tXm2EnOpQVxZv7euDU4eEC9yLNJIInSR8LhDAfK5cKdETO4ZJNt4X9fXp+bDDwzgLNFxZby0YlIgM4cckLaBSPzoqOUzBoBgitbXC0lhSAkMnEsGcv+IVmB+dsXNxEXtrdhbROdabGxiQ/UsjpaVFqTe8wVkurd6iUaw9x8XGPKxEQkoNMDrGHNz8fJy7oiKjHN+2Dc84cgTydP9+UVCpwDJyXVhoTY3e2Sl5sJOTGJcui5UyRrw8HvSruFhgbtu2wchYWBB4IKGFSmWGb5qp1mkkslAto1zhsMjLSEQMPEamR0aE1IQ5MNxPPT2YP5bP8HqxB5h/anYuKIU9WVeHPeAkF/SZZ9DHlhbk9QwOChtmWRkMoVQK+z2RkBxdMlmykDfnuaTEPo+XjffWsWMwZhmdZ/kL6m29vSL/9EZ5/ItfSJ+y5abReDx7FnNfVSXlXKJRyBy9AHEggMgdZT6jUdEo9tKPfwzSJiu9binoohdzu2I4XWbNCYzJLPT16Ao9poWFOPhKifJCpVgpfOeRR3Co5uaU+s//FKpkEi0wP2TfPght4mBJxzw1hcMfCMDjunp17smmduPVWYp27LBO3KXwJfPOmTMyL0pJoc1oVKB48bh42VgLiZezfoHQszw9LUw7ubZsVKQ+HwR3fz/eu2mTUMhmU3JzEVQ6NTOZ49auhYB3UmtiYQF5K0yENwvgHTvwsz17EKGLx9OLJHL9c2FndDKvJ09KEnpentRtYg2V/HzMqxVEi+QY7e3YayzSqrOPkWLa55N8FxYEJomAyyX5Fsx1KCkRRYLrw3yTgQHnkUEyA65fj76SDS+TUXQhHkA9JzISgZISCEheEQkTlBIqfFKw9/dD9uzZg73V05MOTzl0KL0eVFcXoKmM8IZCWEPC8/LysNfy89E/5rpxPaJRGJTXX+/8vBDy6XKhT1T6x8YEzsR5Za7i7bdjr5MAQD9vzc0SbaPSOzODOWluNrKlMafy5EkokV1dOJes+TU7a4Soud3Yw4zoejwS+SAEi2ySrHW3uIg5J+voVVfBAUSWukzsqSMj2GNjY5IPy9wcGs81NfgMFVzC80jV7nbjvmA+UCgEyGIwKBG8/fsxP8uXC5tXXx/6qhTW3O1OL6nAelFEBaRScr8RtplrLq0uH0gMsGYNPufzicwiQoBU4koJmyYL70aj6DMhZ+YaZeGwQK/1yOQrXoHoJREAukE3MoJ8oLk5+VkqJTXrSDlPA1Ypud9CIZzlZcuktMTgINZHJ3LIBt/UnZteL/SLkRGpFUUKaq6Lx4N+rVyJM3P8uBiKo6MC7VxcxPu6u6UeF9MAFhfxngMHrIuiE+I4N5d7Lujx4wKL1SNOHR0wXqqr8fuuLoEUssYb4YnJJPpXVyf5bXatuRnnxufD+6anRaatXQu22EOHcB70s0nSGjpLNmzIjtTQI8ctLUJKReM8HMY+oP5Eh7LXC91KKUSKjx/HfOt5UXZ6XS7pJi/2dsVwuozawoJS3/sehE5Li2BRzYfjIx8xhn316Ep+viQW//KX8LiZPWv0+jJJNZUSg4u1AxianpkRg8nthmCNxfDZSAQQrYoKKB2Njbklm9rBQnTsbXU1DqM5cff4caVe+lKMlZ5Z0kOPjmLMhFNEo1JAkomn9JgRE04qYb1dDPrTbFSkN9wAQa+UwBWcKrm5CCpdWbOKCNoZeBs34v8DA9JvswCmgTIzA+P5Qx+SYrlmBe1iFX02K8LDw2Lg06u+davkpFi13l4Y21/9quRBdXfjcqM3lFAkwqbKyvAu0o5znCycXFwMhUlXJGi8TE1hHltblXrHO7JHBnXPtu7t+7M/wyXLOkelpbjor7sO400ml+4BdLthnJESvK5OIHper5AOkCyBSjrPzvbtSt1xBz5jVk67umBQp1LCWkeWr/vvFyfHPfdgXzc1YSx9fXhHfT0+X1cneXjMeyoogKxzel6OH0efq6slH2dkRApIer14Po2zN74RTJl28/foo0r97ndGSn8mm0ej6BcZB/fuxdgbG6EkVVfDC05YcSAAebZ1q+SfEO5VWirFfGmgBIMCnVNKoiCFhVDOenshZ5xEzcm4po9jbg4/Y24HSSCammDIkW6cbFzcI6wJ9opXYB5mZ4UkhfdKXh5+RkfL1q04MzMziCQyEmlVUkFHBXzkI9gfOsW9UvaER5kgnD09eB+p4Jua8BlCCHlXzs/jOUVFkPFVVdhDAwPoc2kpjDxdjtsxWd5yCyDPSqUjAFIpGE10JrlckhvGiOPcHNZ61SrIDkYek0mBy9EIp0NnaEho3HVn3a23plO2k32Qzs3paSGciMdxXwWD2JdlZdi/L30p9IPlyzGGjg7JayIt+cwM5NbJk+hHebmQVnm92DMnT2Jf0rmgRwH1nFanuaA1NeLEMusldOTU1kKW/eY3OK8VFUI84nKJY8XlwufHxvDvH/0Ic2Alh3p68LlXvEIcc4zmuly4F555Bjlhzc1GpxjhxI2NzpAaOkya9S4Jo6ROOT2NNTt7Fu86eFAYUoniCASMRamXQiLyf2MU6orhdJm0jg4YTffcIyHb4mJszBUrjIejrw9Cv6cHhzqREGEQieAgsibEvn14xpEjwvPPcHd5uXh4GG5nQi49U9EoLoXBQVwK9LSQVSschkCtqBAlU29FRRD6Tz+dfnHb5UBEo4K9pRdHz7+JRMRbs7hopPKl8bSwgIuhqEgoNpkTwKiH1yuRuaIige8ohQPf2Ymx83Ja6oHPRkW6fj3m0IlHeCnNKRmDlYEXjWKuCd1ZWJD1cLkwr+3t2B9TU/j5E09AWVAKsCJ9PBeThUdXhDdtguJhxRBn9a6REShmTGQuLhaaZOLf9do3c3NYG8JsmNw+PS2e0/Jy9JuKnVL4N5V/FpgdHMSFTOp1Nt3YOXoUsIyCAmPu4eOP47vV1eLoYC7BkSO4CPWCiXoisRU+3aoFApAn+fmS0E+vPo3K8XGMd3xcHBSvfS3qdeiEMlRO29qgBJOCmQxqoRDO7PS0MOgVF2O+yP7F4sJkgwsEsG4DA+jDtm1Yz+rq7HuGjQY82UVPnBCDjo4U1kcrLJQomVVLJlEbKxaDrGZ+D5PNu7ulTtqxY5DTq1ahv8wlCIWgiDLixcT/1lY8o68Pnv2/+Avs0XPnYKyQbIG08CSLYAHemRnsbycJ2skknDjm0gRKibLIfJ+pKfR97VqsL89caysMH3NkTnfcMAK6Zw+U9EDA6Pz44Q9FPodCOMdXX51eUkFHBehwLqWcF0m2isxedx3GRfbMFSvQf+ai8t0sdLxrF6JkLhe+F4nAELzjDqVe+crcCZPMBt3cnNTvIkw2lZI1oTGXn4/vFhRgPPE4xjM/D32A0MGCAuy/I0fk/vP5MMebNkG+mCNLLpdElohwYW0uMqYyr7O1VWDgo6OQvy0tRuVdKYGcFRVhPgoKIF90PaKwUKJ4o6MSxWLhZz2ndXLSWS4oDUbmUJHkhRBI5jgNDCCKfe+9EuGOx/HuWEzyarkOtbVYJzMsl416RzBoZNpVCmMhvJPQVh0psH49nGdOkRrUkwoLsT+am41lNfLz0Rcyb87MQMYWFWFd6ViyKtztlETEaUH3F2O7YjhdBk3HorLI6fg4hOfJk7gQdu7EYRsexs9e+Up4WPftw+fHxgSrT6+c1wuB//a3CySJxQSJ0yfDDQ8/hQKx0vG41OmZm4MiUVBghIbE4xiHLqQoXEdGcBC/+EXQbOqHxg62RYgOsbf0ZhNqGAxCGNOw0b37zz2H77DqeGMjIgDMcWJ04NgxCAtWJp+exvwvXw7hefgwxptMKvXJT17YgXcKTwuFcHFlarl6cHIhY7Ay8LgWVHJJLBIOYx8w78HrhcDftAnG/H334RKkcarP3/PFwmMVgbN61/Q0+phKoQYLFc/ycpwlsl9RUSQFdXMzxsiK6KmUREFaWnDJ/u53xnf19sJAS6WQn8KCj5ngDo2NSOhNJIywjEQClyEv/k2bwGjI3L2yMozj1ClcfG99K/Z5rhcXk9xvuEGKTB87JgntZNLbvBmfYx7c5z6XXmyayun3vgdZxTktKsJ8RyJGiFpRETzVSklx5ngcED7ueSo4JSUwmjweIVFx2nQDftkyGKqpFNaf5CA02LIVse3pEcNYL07NXCnmhxGiVlkpkfI1azC3AwOiFLKA6MmTOG9NTcJaunIlPMAsNErGsWBQiBLogY9E8N3Xv14ikZkom0+ehNwzlyZgEWelsH7btoncXFjAmSgtVep//A9EK+0KTa9ZA8OMxcm3b8e4aTT98pfos+7cIcqACu3EBOZmbk6UXebrsulFhUMhYUAzEwjY9WvnThgD//qvmIfBQYyPURLucVJn62QQdCZVV1sXXVYqO1LAbNB1d4uSS6clDWRCI0nS0tSk1Fvegv2oz2s4LNE0wgN9Phh8t92GO9IqsjQwgLszFJL6gU8+KRB23pek9n/7243KNuVvRwdk4vi4MR+trg5jpPOWMEN9PqurpUTHsmXYA8XFAnEkeYiTXNBkUspMXH015iISERbZ2lrsBzKebtkipBSpFOaIuZa8N2gMbdmCtSeLr1keWqEtUikYW52dIgurqoDc0KnWe3rgVHeC1LAqqcH6VCMjEtkmWUhLC/ZVfz/mkFDE+XncI3V1AoFlywapz4U598XYrhhOl7jpim1VFTCurEHBekVtbVDqmWtz9924BDZswAGrqZGLWcf/cnNXV4sgfvppvCsYFEjc/LxUmCcMZ2BA8hmYM5VM4mdMLKWiwUgNlRlGuvjMkhKMzXxo7GBb9DKzFpIeCVIKPyeOVzdGKitR+0cpCETmBlRVCQyou1uMRJ9PKM0TCeTxEKteWgpPYjZF10nLBE9LpSRnIxq1Vm6o2Bw9iktraAj9daII58LAaGXgmVnr8vOlbkd7u0T+6uuhPCtlVO5vuAH7wDx/zzcLD+dsYQGKI2F8/f0C7yKVNWFJg4NSS2tqSnDfHg88tEzarajABXvddVBSQiFRFpmYfPIkLqEzZ7DPdu+Wyycb3MFqzXSGubo69G/fPqwDIwTMZ4hGEfU7ckTYMhsbrdfBqnG/xmKS85eXh/cx8ZueykgEc/Oud6UbTWwtLTDiqIyTQIIENlQG5+aklgsjsT09cBL19iIiQyPWjrXRadOjYWfPYsxUjFjMeOVKGBGZitgqJXuluhqKamWlsDBOTuIzhM7NzUHxq6/H2dm3T85QUZEogwsLeMbwMJThO++U9WK/lcK6M1LlcmFeN2zAHLMo7k9+gj1pJSt0rzCdcitXGpUsPeJYWCiEDzqz6fw8FHCn3mc65Zg7eOYMnrF7txHuvW0b9sDTT+PvgQGMjZAtOhBovPA+7erCZ06flnwkOg3vvReRdbdb+tXRIY7EdeuUeuc7xeA/fFgiFE1NWLveXqxjUZExP/Zi1a7RI1O/+x3mbXpaok7MJ9OZLmdnlfqP/4CMe+1rMS+bNkFekLiErJ/M35qaApxu9WpEmswONhJMzM8DyhUO48wXFGDOfT4pW9HbC8jtypWSN0RZf++90G18PpGplIdbt2IMLO5svu/z83EGaQCcOydsd5s24Vk6CREjb2aHWW8v+kCiheeegxzZvFlg/Hrkio7JHTuwB7ZtE8jguXPCfFlRgTNHprx4HPs5lTJG4M1oi9FRzOmpUwKzZZS2u1sKv+eC1KDxGw5L+oLPJ5Dh7dthCDKPi0Qbs7OiFzLl4+xZPOfJJ3FX6kiKbCQiuTLnvtjaFcPpEjedxYheT0ICiOUljefcHA758uVCq8lLvrzc+FyzQr5rFy627m4oVMzJIARpbk4Sfel1JXxNrw5PymV63gihI4ZdV/AqKnB4amsxPqWMh8ZOGIRCGM+pUxAchOhwXH19EEaDg+nGiNsNL9oTT2DcrGFC7DWTJOlVTiSESjUcFupeQmjy8i78wNuNk/lBZ8/iHd/6FjxlunLDi90q94ikDW1tSv3DP0B4m1suZAxWBp5OM1tUhDWvqYGAJ5Sqvh5sWC4XhCyV++lp/KHXXp+/55OFxwoisHYtvKFkgfzud0Wx0RmhZmawXz0ejOvcOaHuZwHgvj7s+3e9K9340Md18iScHMuXG/cw32kHd7BaM51hjixcZDfzerEXqFgFAkbKap0B0ck+ttqv5sTv4mLIAqfGLvdRQYF4sZUSJZqRb9Iwh8PoZ1eXQLFoVLS0YA+aWRuVylzE29z0aNiZM8JGZ4ZIZypiq5QwyFERHRmRSB2VWpKqsBjl6CjORX8/fuf3CzyOpDaxmBT81feC7njo6EB/h4fR/9JSqYeUl4fxM/leN5qbm5GXdffd6GtzM9aot1eiFdu3C9XysWN4ps8nZCw0FLIZrmbvMyOIY2PCYkbja/9+Y20htxuG+eOPw2BSSuBm8Tj+np6GsviBD6B/+/ZBNlHucx77+zEn4TDeUVeHfnGP8dyxRtDHPw6Dn+ydfr84Jisq8J7JSaEkd7kubu0aRqZe9SrcDd3dYqgSPq7T//t82HuPP250jjQ3Yz5Xr8YasUA686fa25X6wQ8wZ2Y2Reb4zc3hzC4u4l6kg3ViAs/Yvx9zx/IC27YZ7zGdOIU6w6ZNcExs3w5I9c9+ZoSB83vRKOb+TW9CnuHx4+JAHBuzdrqZnXMdHZIb+JKXoI/9/Rjz5CT2g5XhqztYBgYwP0VF2ItkU9y4EQY688xpoB89aoTt6c969ln0Z2BADPu8PMzn4cNwIOiEMply8vTcNBq/evoC66lFo+hTbS3O1E03Af7n9+PnCws4n0rJvUmafZ22PptzIBdn7YuVTOKK4XSJG4X1woJQgRP6RqauZFKKDDK8un690NL29BghPVTI6e3+whekzsniIhQ5VpMfG8MhX7VKsLWskE1Shakpif4Q9z8/jwP4spfhdxMT6MfcnEABSVGsF2o1Hxo7YUAmpoICqW+kC4l3vANCwsoDw4hAba2RgnfrVvxdWYnnHjsGYUVBTShaU5ORfY8RKicH3g5KZ8Vc9/TTojzs2oW50pUbpQRyQnx3ZSWE/dmzQufc2anUhz+s1Je/nG485ULGYKUw60aFzoDFMVZVYV7dbuwBRiToMWfU0mr+ng8WHjuIwNGj6P8HPoDx+HzGOdGJHAYG0P/yclwOqRTWKRuVMxvHRfiHFbWwUvZwB6s10wvAsm5PPI53ke1pcRGGnl6ctrAQ+0pnQMyV8p3nUk/8/su/FBhJJviXnm9WWgpDn/TE9LKSirepCcaKTlnu90NxIA30xIQQQ+hroRTkXK6wxJYWpT7/efz76NF08hYnEQT93GzfDsfU0JBEAQmjpGeXBjoZGxk9ZzSgsFCgQEQDmPeI2fEQCEDef/vbmL/SUkEiDA+LIRQOw1gqK0MUYHRUGAybm7G2dEh1dgIuVFKCPj70kFCfLyw4g9eavc+c54UFRCZGR9FvpdBfc20h5rGQ9p7FaGm8EL5GJfOmm6CQsg6QHsXw+YSg5a67sCe7uoQ9ksXg5+Ywrs98Bn/Ky/EsXX5SXhw5gvnq7xd5cbFr1zQ1IR/sl7+Uc282mgoLpQTE/LxR6e7pwVquW2edY0t5MDmJcetsiuXlku9DGCkdrHrE6/x5GGY0JniPscbZ6Kjcq8eO4fd//KMUr926FY7BAwfwLp2qe3ERe/f1r8eeWbkSZBrZnG46m+xXv4rfkxZ+elr0lfn5dBZEfT+bjTBSrJeVQW86f95IkU6238pK/E6PcLa0IHr8nvdgnDq5BVn6+vthPO3caZTT/O73vy/EQGVlsud8PqPBot9po6P4/OAgkBLvehc+f889+BmdcrGYRJlJTDQxgXulpQVnL9uZvxjMuZd7u2I4XeJGJSkaxYEjcwuVI53ylOFxwgIaGiD4CwvTFXImdBYWQmiysOerXgUhtW8fNi4LS547h0MYCgGKsX+/MOCwojQL1tEzS9ancBgCsqICnpSJCfRPz7diMx8aO9jWnj3wSDFHwwrO5Xbbe2BWroSQYeIxPS6f/SzGR2NQhzbSO0vWGTNFuRNcrxPmuo4OXLiTk1JtnHPEiMC99+L/o6Pwdp4+jX5MTWG+Z2cF3pOXBy/c5z+v1D/+o/HSzoWMIZPCbGbAYjX1TZuk70ND+KOUVInXjdMLFZjZ8rucQgR0Vkr9cyyiefAglJAPflAMC7v3ZupTrgyCfBbZJM+dE4cIIZPM79GLthYUSJ7D1JTsYaWEqpgwsJMncSHnSvnO88fE70yKodU5KC2FF5+QMhYAJtyFifZ6xDo/H2eVpAA7d8JZtHKlESLZ2ZkZT3/nnXi2FRW/UnjPX/0VnkGynFzy7vRzEw6jfyMj+PngIGRlbS3+X1GB8c7MiINsdlYKg7rd2AMVFZIrFQik7xHzfksm4bGfmRGGMRalpYzu7IQseeABzGsigb/ZTxIBsXZLf7+QEkQiUDoJ/9HZP2+9FfN7/Hj2Yu00fAk7Z+I/96pZ7uo1leg4IjERqcEjERjcHR1Q3nmuCDdnhEQpYWojjJb1l/QoR2GhQHb/8AcjuZKVg84Ksnsxm9sN4qChIdyvg4P4Oc+5z4fxs1zE4CD2tx0EW6/5yL0yOQkDw+MxskKSoZdRLtLRk7GPjcY974pQCGf1a1+Tmo8sXhuNipIeDkvu2VvfCl3j8cflHikrgy5wxx3Zqa/tzobbDQNs3TrcXfv3Yw9Tx8rLw/itWBDZdEfFc8/BSXn+PPZzJCJlXJTCz91ufC6Vwnrs2CFpBD6fGOrxuNS4oqNgagpr0doq1PhKYT3JdkpIf00Nzl9LC86f2WDRSbViMZzpt7wFn08mMSePPSYpGzSQGWn0+TBH4TDGs2pVdufAxWLOvZzbFcPpEjcqtk8+KZSiSom3n4YTPROplNC/0vPz+tfjUFEhj0Tw2cJCSXgfH8f39u4FZE/3RPCCIu44HhfKcRpxvIjpRSW8hFCZO+6AYHn6aRBBVFUJ5ERvVocmE2zr1a+2V0xzzZXp7jYW96QHn01nUSP7nk5RnunAO02GzDZH9AAeOoT/NzVJAvjcHJ5FwaaU5JosLMCIZgFAwm2KipzV/sk2p3v2GBmwAgGlfvpTIUkYHcVFmUgI7CgYxD7dtw/7jcryUgSmE4aenh6BLjHqScPYipXSbk6WL1fqfe8zsnRZRWay9cnKaKXSwkjxddfhc1Y5IGQqXL8e/QsGcSlXVaXnwrE4Lw1WGlUsXut2C/Pa0NDSKN+dwN+szkFPD8Y2NiZ9Z5I1jRQ6j1jLhbWB9Gi1y4Vnkl6ZdOWZjOVnn4VsmpkRqFdZGQgo3vve7AxrTiMI+vcPHsT8k7FRKaHEDwTws+FhgXlRnpaUYM0I8yOV/rZt1ntE32/0NldUYI/oco2OIBIExONiTDFfjf8fGYF3n7kXDz+MZ7Go8XveY3RGzcwYWdjYp9tuw+cOHcL+Y7TOLHfJSkbHlrm2EBXH2lqhGtf3H+V0Xp7UEpuZEWp5GpUkNSJ8r7wcsmt2Fgqz+Z5iBODkSThQSK5klhV2kN2L3VpalPrEJ5T6xjfgVFtcxHhLS7FvZmawfmRWPH5c5tKuqDWjSoGAQN7y8mRNWHKip0dymVl3SU8lyM/Hc4eHAZOnzC0uhlF0ww34nF7DjRA/UqiPjqLP//N/4jN2Tg671taWHokhERHTEgYGULuS9fmCQZwFkkRt2qTURz+a2UESjyv1+99jP9GRmUrhfmZeXV4e9nsohHcNDCDKW1eHPp06BVlUVQUZz/tJKTGEp6fxvaIiIXzQ5WpTE/rd3S0wVTuDhbBal0uMWo7nta/F3JGIhtFZkq/U1eF7/f1Yi49/PPt6XEzm3Mu1XTGcLnHj5j1/Hp4QenToQeZnFhaECYWMcPE4fv7000hm3b0bcBUy49GLNj0tgmx6GlCJG28EDIOeiL4+eGbn54H5p1ePSjDzEAoLceEnEjho27cbFYtdu8Ce99xz6WPNdGjsYFtMjLSjVM5FuTMzaZH0gNSms7MQuExe1YkpMvU912RIMj0tW5Z+YSslFzMFMvMLuP407pgozOjD3BzqQHzsY1LLgorMzTdnjt7pzemcvu51mBMyTxFGGo1KpNLvF+hoWRm8aLkKzGxGKSMKP/gBcttINsC6Mox66hGvDRvgcLj/fpw9RjycKspODWXdQPP7hdVqclKgb//+7/DEmhnF6CDp6hJqa5LAjIzgMqRDhIUvZ2aMMB5GqgjxmZpC32++GZ+zihLoLRc4pdU54LmhRzWZhGFKBToaxRz4fIhunDmDPb9yJZ6hR6uVSo9a2hFpMFJBRtCKCuxHpaAk3ncffv+JT2RnWLMjvTA3MzTo9GmM9cABYagrKMD4yICoFNZsdBRz4vMJPDmRgAf8ta/NHlW76SbsA9alMifZezxSxoHEQDrpSyKB/w8MQC7Rc81coPx8rCOVtE2bsI/MLGw6ZT6hgh0d2MdbtxrJZqigMbetvT29tpBeU4kEEvq46FBkLbHHHpO7S0dv6F50RqDm53EmzIxhfC6hUzq5kp38fCHq1bS0oEZef79EZj0e7C3OC/dqOIz5Gx6GrDMXtSYMnjW6FhbgxGF+nE7RTerz4mIYsKOjYuwzlYBnnYq2UrLOeXnpNdxYMmRqSpgc6dQiHM9p+8//VOrTn8be4doz76+3F3Le48HdkEhAbtLwZH7R3Bzujze8wZ7ZVpdvO3cK1frEhEBtmQ9LeLbLBeN2elp0ADamPUxPo99mAyMchn7FshpLRVLweVb6S0sL8qPf9z6sA2H2waDUZAuHcQeQtCzbPneSj3UxcgAvZbtiOF0GraUF9TkeewyHkMUlufEZEid2mQnMpLY9dw4X2E03GWvO6ALM7cb/SSVNKAQ9EVQwf/YzCIDycqknQkpphvdLS/He97wH79QPwMU+NE4iDU6VO71v/f0QDsz1YhSFWOvubigDfj/W5NQpfJ5V6/WWazKkk1A2vbpPP4119/uFOYq5b263CF0aLITxbdxobWC8+c3OLncnc6on15N5ioY2vZcsnHr2LBSyXAWmk4jCe96DS5KYcRokFRX42cgIlAe/X+oo/fCHWDN6UmtrEVFjEcpMJAO5GMqcI53VisUdXS7g/B98EGfqFa8wMopZQdNmZvCs3/wG60scus6sR4Oa9ZLoIXa58H/mNX7ykxe3vobVOdBJLSYnsV/JCKkU9kw8jvUpK0Ok4te/xnwkkzh7uiwzR33NMCSWQQiHJWJHwgyfD59ZtgzjP3EC60iHhpWsMZO1ZGtuN9brfe8TQ2fDBiMNtN+PtSUjYSIBbzz3nFLYI6WlSr373ejfF76Qeb899ZQozuZil0rhXJBwYvlyyKTz5/HOZBI/X1yUZPD5efR70ybj+7i3yR5p7pOZMn/PHqwhC+Xu2CH9q6iQOjwrVkC+7t0rZQ58Pqmp1NeHO2loSMbF/KeaGqEpDwaxl2lkUaknTTznoKMDn5+dxR1YWipryOeWlmJPFhVBFto5k17IejVNTYhAHj6Mcbe1iTGilJwPpTD+X/9acoL0otY0mkjzHong39u3CzscczSbmsSxuHy51D8iLJjnq6zMCBNjZHFhQYrXMl9OKYmuEElRUZE7jLutDXlojLLSITMyIrl69fV4D2s2LS5KtJcoGubJffzjgLzbFbA15xARfkeWSxpuShnrTK1dKzrA2rWYq/FxjJnkX4z+kjV4+XLcl319zvSLbEgKO91rwwbkSP/N36D/oZAU1aa+uWmTOITorMu0z18I5txL2a4YTpdJq67GJcqQO617Kjw0nog1JmHC1q1CyfvUU5IzEAoJ6QRzkqJRUWYJhdA9EUrJAR0exuGZnMQfQsWUEgavX/4SF97zdWiej1oAet/27ZO8nPp6/OnpkXoKw8MoQkrYY3U1FO79+53Vo2Lz+3FxHDgg1birqqC4lZdLCJ25VH19wgw4NoaLLx435ryxMdepthaXRUEB+qUr4FSu7rsvMxRhKY1U0zrzFBmYSF2rFObv9a/Pfb0yGaWjo3gP64LQMzo3JxE6wh6oiJWVKfW3fyt5Ql4v5igWQ6IsWbWyQQJzMZR1VivCLghFW1jAs8bG0hnFrKBpSuGC4z5yuXAWSFGvlDCxVVYKwxsL9jJifPo0xnUx62tYnQMqMmVl4qiYnxflzuORpPQbboCM+PGPoRiycDWdOiwEq3tNzTAkvQwCI2+EkpKdjkQw0SggaT092CsXS9Ykk9hrN90k7F8VFVDEGxqgxFZWIt+SjoatW6WA5uSkREQeeAD/zrbfBgehSHd3pxe7JAsjowZU+KuqsNcJXSIlPOeqpsYaRtzRAYeFuU/JJOZrbEyU4JkZjC2RwDwcPQqnTjiMKERZGYywyUn87Prr4fWvrBQDJRzGXUNFfmQEa86ipIx8zM1BhlZXCxx2bk7KTZA+u7oaP1+5Egr3mTO4e0kMMTmJzwWDkJ3ca1bOpBe6Xo3u/CMcUSmMj/c974r8fNTz6ezEGgQCUMbJdsp7Y9kynIOhIRAkEInCHCilsIfOnsVnysuxD6amMH/JJP5P9INSYjSsWSP5czyTRNQkEphjGsSlpbnBuJNJwPNotLC/hYViHJ09C5lw1VXyGcJj8/JknoJBuSesGEeTSex3ymLeNWTxjUaF5ZRrwn20bBlkUiSCz23aBKjwffdhDimLSOyRSmEd/uEf7HOX9HYxkBRXXYU9wpqW5rqg09M4r26383vj+WTOvdTtiuF0mTQm+ft8ECQM//JSWlwUBpvRURwsQpBYo6K9XaInJJqgoOLl4nJJ0nA0avREEB7U2ChewfJyCIBk0ohpdrkQIZuZgcdfrxJP9pgLOTTPZy0AvW+sjdTZictjYQHGIIve7d+Pd19zDS4dK2GhK2/E6lOIz83hHefPK/VP/yTQhXgcwonz6vdDwFZW4k91tdSp6ugQz7m5MbIzNoZnNjWJAcamKzzd3ZivCxVkOjRlakoUYz2CFo9LRMTlQvRs3brcFAk7ozSVksue+T0FBQIrJSskv8u/CUuhojQ/Lx606Wko7Kx9ZHc55MoaRFar5mYoqiTW4CVLZ8boKNaIjGJWz1JKvM6EZKxYIXuutxdRLKUgQ/LypF4bFRbS01oZ1xdSX4PlC1jMlXTGlD2hEMbBUgbsy9QULu5Nm5DDQcOGc0P4UG2tFIJl/wi/PXwYZ4AQZR2yyOKvNAhITqGUkO2Q+etCZY05+uD1wgDRi1m63UIt/cc/Yt8xBysvD+P0ejEvp08jP2JyEvKHTU/wZ8Hc66/Hs8Jh6yjXq1+N7/b3Yz5GRsQDT+KfggLIlGAQSiQLjLNxP7JwKOv7zc0hZ6ijQ+DhSuFz69YZGehKSrBna2vxfTOltFLpERzmrBQW4gwTDsyzMjkJA4j1ENeuxVhOn8bc8u6rrhbSox078MyHHhLjmUQmwWD6XjO3S1Wvhs6/r38dczQ/j/kmQyMjOSzOe/48/h4eRoSEtZSYA6qUkGUwAsc7hHTlu3fjDnniCcgVt1sgllNT2LtkXKVeUVmJFIIf/lDqtfFMEKJJIorpaZG7ThtzWlm+hU4RpYx10To7YWyWlQkRC53RhMuRubihIZ1xlGf64EE8q7cXdzX1r8pK7N2RETG8iQpSCjKfaRX/8R9wqNxwA+Q3+09UgMcDOfj1rwtDrlOyheFhGEwnTwrxjI6kyJaXxHpVdXUCVaWR+Pjj6Nu2bfIcJ/s8F6j3i6ldMZwuk6YrAHNzkrBK44lGDTfzxo34DKEpIyMQaNXVEoZmLg3hF263VJinB173RJA8IRZDXwixYJV6pST8zjo9jz2GaMrq1Xie7p03H5pk0nmdlee7FgD71tSEfI+PfQzzSzpipXCRsDBmfz8ueythwbV7/HEpCko4HRO9CwokR6yvTyrB04gl/e/gIJ5/zz14B3OXSFxBZZAGcCCA/09MCOuZeb5SKTzn7FlgwZkPsFRIiZVyODyMPUjs9/79ksvBPJZz54x1LZw0u0uDuT+88Oj94z4lZS/hOowuKSUJ5aWlkgDNYp+pFMaQyajIlTXIXHKA0LOZGSiWs7NSl+X4cazpqlUCzTMTOVjBYYNBkRcVFVKXjfuO3lwSL/T1pRMvXOiZonLW2SnRovJyyIVoFHO7Zo3QFzNnr6QEn3v4YWP+AJPY6eSprTUWgtXngkXCSWNMRxPPHHOLuO8ZPSckNldZY5XTYpeHZC5mybkaGoJcqasTZjkaNH6/GMgHD2Jep6exJyjzmeDPSGIgYIz0m6NcL3uZ9PHgQewFGh6RCP7d3Cw1rcysoux3IoEE+Y4OGCY0lMhgFghIvb/2dqxtRQU82nl5Sr3mNWB3bWzEPoxGZR77+xFdMuf79fbiOe9/v0SjwmGJEodCQrnc3Y05vP56zPmxY9gbSolhqiu9BQWAStbUiJG2fn12dMSlrldTWIg/NJC4DxYXsX/4N6OuCwtAOdx4Y3p/Gxsx9z09eAbzDyMRrEskAgOXrG+VlVDsKysxRubEnD6dji4pLMTPGVWZnobcrKrCegwNQUbccktuBiYjmhy3eUy8C3g3trTAiUnINu9epWD4ERVw5oyRye6rX8W8lJXBYBodxdlheYfKSugGNMr37JG9H4tJWkUgAFj2ffdh7H194iRiOkVhIfahPg+6bmg2apTCc+rr7c/NPfcIMYVd0+8U1qsi22JnJ9557bXp6/NC7PPLsV0xnC6T5nbDO/Pggzi49NQqhYuKjFn06hKOpENTeJAKC8WzMj+P57DIbXW1Uv/8z7g8zMaLmQ1l/XocBsIE9Zoj/f1SP4a05gUF9qHbXHHgL2QtgL4+KCYbN4oirNPmMhrAUHwiIRE+CovNmwExikYhFINBGAqsRh8IGBmKlJLaJCxI2Nsryl1LC+aSOWY+nxRzpFLIhFTmEa1dmz5fVLLOncO/u7vR302bpG5UTw/gMeaooVWzg6aMjGAen3kG/6dRYQUpzcULa8fQwwrtHLvZoOQ+pWGklCTA0zlAJdrlwt+Dg1A0qVSzmS+HXFmD9JIDdDywICc9r4S0Mbn+1CnJRdizJ90TaweHve46nMVHHsHfOnXzzIzkP05NpSvFhJSS0TGXaCSJAszRooEB2acFBdhjvb2i8AWDMPbPnMGfa6+V/AHS6LLoJyMF5tbSgrpSjOZFIkLDzcg99wOj9oQtb9uG9bGTNXQ6DA9jnmkgWRVYHh11Fn1QCvkmTFInaQPhi3Ry8YysXYu92dmJs0unBBUuUkb/8pdggPv7v8/ORPrNb8LYJDsa93RFhdTkY5TOXCx1YkJgPJGIMCTSMcFcqWAQ/Tx4EHM7OIj//+EPUFRf+1p8/oEHxEvO3KTdu62dFwcOAG6sFPK+Egko8OGw1KApKJA83muvxXeHhiCvdu0y1uniPo/HkU9WV+c8En+p6tVQBofDAuUlPC8aNdZ+IxRuZgbnoa8P62fO6YrFhDDm8cdxbyklbIQTE5jnZctwzvr6ANfcuDGdcdVMk79yJYzY48dx3vr6xKFI2O6NN0p+qdNWVCQQU+Z5cl3n5wV5k0op9ZOfQB52dgoLI9MayGC5Y4dEHYuK0PdvfUtKuZw/L/UJaVzq9Z82bsS7IxHJr+IdSOgtHWQDA0L3nZ+Pua2pwec7Oox3pNsN/eLXv8b+1yHmwSCMNpYMuJDIp7lkyqlT+Hx1tRTRtmr/N9RlyrVdMZwuo7ZhA/IXPvpRqYmQlyceU/6fhWhPnMAhrqiQqu6Tk9jg0agkvhOuUlSEz5Li09zMnmxCNkiPnJeHS8frxfOZcEylr7zc+qAuBQf+fNQCsGM+ypSbQXji6CguCkYOKICOHsVzjh2Dcl1djc/29+NyoFJGTDfzTTwePIcRQcKXXC6BHJFCmkn/Ho8wpzGBlAnMhA50d4vwpGFNOFJhIfo4Pg5lht6yvXvxOUYN166F199sSGWCpuzcifmJx3Fp5Odj3OZaXnV1EP6//z1+nk1ByUQ2wvw9ziGNEB3SSMYtJkDTw0glWn8P6Xl11i42/XLI1KfeXnyfTofGRmPJAT2PhKQfZK4iQxjrirDO2KZN1nNkB4e9/34oP/TEkzVtYkKopRl1ZAuHBUr1ve9BsXcajbRim9KjRfE4+tXaCqgPi65WVsre8PtxhuiIYG4nDTsmmNtdzlu2wDjnGaFz6fHHoaRMToqCNTAgRTVJ5W0la+h0GBjAebz7bji2CA/VZdnTTxsNP72ZDW+l8NwNG7AGBw/iXUpJcrbfL3DCYBBnMRDAWU0koGQxuh0KSXHbX/8a90cmz29LizFPjhTFlBnMx0gkcKb7+zFWRhT9foynqgpQw/FxgUNSxulkHoxseb1Qopcvh+w/dgyf4VzOz8OYm5+3zvezmsOGBolwKyX5TIuL2Mu//a30ra7OaDCwMfdp3brcPOZWd5QOoSQc7mLWq9HP2oYN2BePPSbU8nopEULhGGFfsQJr1d6OeSOEdnIS8/zyl4OhrrYWRkAohHXSiReqq2HkTExgLZqbweZmxTypO0tHRrAPRkeFspwRrcZGlAzIFc7Y2Ii9fPgw1lvPbeQaeDwYT1UVxrl+vZRsIZqnsBD3Z0mJ0en16KPYP6mUsbYV6c29XqAe9PpPSglZEuU4odKJBOaZNbOamqRMQDiMubSK4HR0wLlQWipFoFmLrKYG73788YsT+SRD5Pi4OJ0mJrB+PT3YQ0o52+dWRdD1Mikv5nynK4bTZdbe9S5cKL/7nSiAPh8OXFUVLohkEpuPtWr6+3GQmDdDb67PB08PPRSBAELmTgtfHjgg0Ay3Wyh0CUOjIJmdlUveyjufCw48UxFQtqXUAsgU8bK6AHXa3FgMgoTsgxReY2MgjwgERBGanYVgjkbF4GEkgV5BpURRJ+PT4qJgz+fm8PlgUCCaZL7y+fCdYBB9vvZafL61FV6/f/s3IWpob5d+KIXLgUpiOIzIwsKCKBo1NRDqP/sZcOkrV2LPmWvF2Ano9euFgnnNGihYOiNaOIw+dXWhgGBNjTPl3Cq64nLhIqFHkdAU5lNxzFRE166FkctaaSwWyUbijVBIIrd6m57GXPX3Y780N6f3iQna8/OIPt5zj4zvta/FvmbeCQu8xmLi2KAjggxWzJ87fhz5KU7pwrdskRy96WlJqmfu4tCQKEKMppIoZflyKDaxmPMEdyu2KXO0aG4O0bC2Nly+DQ1Gzz+dIEND6ZEwpbI7SqhEmaOAe/ZIbgLr2QWDMPDe8Q6JADU3g1ynsVGgfIzsLCzgLDQ2Ak4YiyFPQY+I0HmiG356M3tl6aih02FyUhwqjA7qZB9VVVBY29vRH8KbdMeE1+tcQdLz5HSjiQiG4mKh9D99GnK4vFzorB980Fgig+eOSfdM0p+awlwyB2b9eryvqEiKfL/uddjHLNpeU4P3EG5ol+83O4tzf/Qo+lRfj+8xMkA5UFiIeT12DDJQpx+/kLoy5siz7jCYn8e6rVsnbKjmthQK8+5uieBFo5ifq68WQ1cpqaVXXCyIEDKvLl+OeXv4YVF4161T6u1vx/xEIlDEXS4ozNPTmNdoFOPcvBnzWVKC+25oCPOXjTRj+XLc54cOYY/xbnnJS5bOskaUzt69kmbAXNWFBclXa2jAGJSCI4CETKkUxlFUhP354INSXkUpQOpiMcgrOty8XjxrZAT7uLgYRt8rXylr99a3iuwg0YJeJNftNjKdmos+6/vc7JRSStAwSuHZR47gHF6MyKdeYmDNGnx3ehoy4IknJI/Map+zcO/UFM7yvn2Qu3S8k6yJsuH5Yp58IdoVw+kya243qGwTCQhVegPz87H5r7kGF8vZs4LdpSFAdrC8PPwdjcLLs24dftfXh81LZdpOSNPrMDaGSykSEQFMxYW4aR0nzKYf1Fxw4PF49iKgS6E1d1IHyAy9CoWEzpqe89paIyPQqlXo5/33S3L1gQOS20OlngU/Z2eNEQ8Kb/7N5vGIkOUFOToqeQNMzI3FcBFddZVcPnoRzq4uuUA5Js57UZFcNmVlYoifPSuwt0QC+89cKyaTgE6l0HczUQSVsmgUfWpuzgztNDdzdCUQAPb8nnswtzQ86UBIpfD81auhIHItWR9LKVmbRAL9KyvDH3MbGRGa5O9+15jLR1jU0aMwogl7soqqfvCDmO9f/UoS9mkwUfFkThIZ9ZYts1aGMylddom+c3NSkNTtRpSkpkYY2KqrEbVhHRSnMA+riK0eLRoawrno7cWeZMFtPRIZCqEvTFDXmxPl1i4K6PFgHletwhjOnRPSEDJkbt6M8Z85Izlmc3OSIxIKYS74nrw8KAQ63TeNut5enKGKisw06rqjhnmMhJkphTXgXmUOw4EDUGIKCsR5QsZGpXJTkMzzpTta8vPxzO3bhU3t0CH0jyiE/n5htaMySAKb6mr5/9ycFFVlHoxSkofLfxPJwCiIWZm0m8PBQYET6p/x+/EcwlW3bMF+37sXBiidUhdSV0afw2eflVp2JMzw+TAHn/sc5ATJCjIVDs6kSHZ0AGJ56JCRcbK5GfvzzBkxZqNRgaO63ZKLRBIJpgK43VjLb38b/161SvYsURfM1R0aEnrqigoo1ixQrDc7ZMKKFTCgDh7Ed9//fqHf7u5eWgRiwwZQiLOOEwkoiorkDq2sFAgjHUkvf7nc7Yw4plK443nP9PXhO0Q2KCX3IuGgdFzo/WYpBEaq6DiIxzHXoZAQAiklzKLhcHoEx6w/WeU3nj+P83uh6By7dQuFAJ196CHAbH0+2ed0Nk5PK/Xf/hvOWyIBOV9QIHPT2YnPlJUBLss0geeDefKFaFcMp8uwtbRAydLD3HrCZXMzLoEvfEG8x7w8JyakFtTsLC5bMonNzkIR/ta3MtcnodehqwuHmh4SKv+8lOgtCQaNeSH6QXWKAz96FB4fs3FjLgKaK625E+aj++5D/Riz0lVfD09LPI4+KYV/RyLoS10d5rOnBwLk2DFh9dLXgpXVGUUi46HPJwo/I1sFBVCWWWdlcFAUkcVFgcEwP4eKlr53mpsBhfvyl0XhfewxY/HIWAx94+W+uIj8JF7G8Tj2VjKZXismk4AuKxNaZL0I6smTEmVZtgyfY5TKKQbbHF153/vg2Tp0CGMhVT9px5uaBKqyuIg57e4WNi7WfllYwFoy2qvvgd5eQOxSKXhHGxvTDaLmZmDomW9hF1X96EexJtu2gYqa8A1SyjPySe9cRwc8pmblJFu+oFWibzwOmTE2hsu+pAR7ieuyahU8mnrBWacwj0yw2nAY752cRN/GxzFWUhQTjkVDsbsbEOT5eewjFue2Um7NxqNVFJCEKZs2Ae4yMwMFTi/W+uMfY5zXXovnkd6d9b1aW9HH4WHso9LSdKV+bk4ot59+WmDRdjTqdNRUVhphZoxQu1zwJA8P432Dg5IvWFSEeZiYMELacoUvNzcLdXFHhzhaCPtVCoZke7uQPxD2lUxi7IzIEcKUTKLP5eUSES4txfmiQl9SItERyjKlxFlFNtepKeuyGfocUq4xghqLSeHW4mLIgbExyN3rr5eoIHMdL7SuTEsLHG8f+pA4haJR6dPEBOCM+/fjs14vzgDXtbVVjLhMiiSdf+fPG/cAz1FVlUT/6BByu8Vw5V3k8QhMloiRiQm8m/V62FgMtbdXIiTMsR4cxNlesSJ9v2VyljJ3r6sLeoi5UDtlWC7RuNe8Bv34/vcxz6y5SLbH48cxf4EAzi1zK61o1yMReW9eHu7e4WHMF3PomGcci+H3ekQxmcQ9SiQJyYv4J5HAM8rKJOIfjWKPHj4spEGMVCaToj/pEWEWME4kIOdoIO/cmRmdk2leM61bVRUQA3/4g7w/mZQI7unTUoDY60W/xsexT5QSuTkzg89ef/3zyzz5fLcrhtNl2rLRee/aJRz/tbVGOMPIiOB7meDLMOn69RACdkKahkZXl7DD0TCam5Nq32T04bv1+g36QSVLTyZl2+uFcmqXO2MuApqLZ8ppxOv2262Vrpe+FN7E/HwoocxRmp+HECsrQz/r6iD4OB9MxGctEaUwn1VV0ncaMiUlEDBzc6K0sF4CqV3z87F+NTUQsh4PLl1ioM3JpOvWCWEHlTgWxYzFJM+DRoXPB0FHTLPLhf49+ywUfXOtmEykCDpksL5e8kpISa2zuSmF/j39NP7s2uV8bVtalPrEJwD5OXgQ81tQgD688514jnk9b70Vnzt2DGuZTGINr70WsK49e+Q7pOj1+eBxI8THbBC98Y25sWu9+c1QgJ58EvMyMSH1dsgIV1aG+Tp+HHufyonTfEFzou+RI1C8167F7wil6+8HBINKK9dSp7qOxzNHMezIMlIpvHtsDO9dt04SpysqBNrEHM3Dh7FGVDYDAYx958505TaT8WgmR6ivV+pLX0qXL8w9ICx4+XKcReadsUwA54URESpFVOrDYShszCllVCoTjTqhm8xZWrYM6zM4aCxoXVODvg0M4BwyCk9GOHqgy8tzg5zp80eqZI8H31+9WuCbExMYV3GxwFBZHJbjpMNCL9Q+MiKGVlGRJNUzmqjfF/y3yyUyjwV0rcpm6HPY1gZvNud9fl7kWkWFkCN5vVLUPVcSiGwtEJB8UCqQ7AOdIJzH8nKc/akpiTbTMLdTJHXnn9UeGBnBHJCMg8o66bbdbjHoXC7IMZ4Br1eMLrLFcf8w2hKP49kck9eLue3qwt4mDI4tm7M0HodzJB63LtR+883Za+mZ24YNSn3xi7hD/umfxKFMA2N+Xij0q6ulALkOCV5YwPgpN3w+zMXUlBBasPQFo/j5+bjrKHcffRT3EQ1SshJSjrI4+caNkA99fZh3rhP1tFQKz3396zF+3cmlR7pdLtwVJSV4X1ub8V7Qz40VqY0+r9nWrbhY9A7OYXExHKqxGM7U+Lg4A5USR1BxMfrjciHqX1uLftXVvTgZ+a4YTpdxy8SB73bDar/3XqGyLSgQ4Uhc98yMsFmRYnbdOnshzdoI09M4DMuWSaQjFJKLoKlJaM3N9Rv0C84JA9mKFbgI7BRPqyKg2Ro9K4QmOWGE2bQp3VhNJqGcx2JSM4PzMTEhCvnatRCi0SjmRzdeqWwx6bqyUoSky4V3VVVhnWprJY/M48FFS6Ywep84p4wOWEUFzPNOhYQFUUmRzpC73y+GE2v/KAWld98+ubBZKyZTZXJzTtLwMMa+cqUY7kqlU+l/8YswYKwuSDtPWUsLqOTtHAzm9ZyZUeprX8O+3rQJF05+PvpPqnQq3idPghBg+fL0vCfdIDp1yr7WFCl4WfxQKfTtda/DHLa1QSmhEq7XN3G70a/rrhNvYS75gnS+PP005raqCn3m96g0lJZijegpt6K6Hh42eqP1xghXTw8i3IQXx2JCEc5IGPchPb8jI1CijhyRqF5DA+RBby/m5jWvgaw5flzWkDh8J2Qz3d3Whi2T0ysrcY4ffhjPicXEOdTfL1ESGpc9PYLV16Opfj/Or98vRCR2NOotLWCy3LcP8oQFJzduhDLBPTE+jjE1NRnnj8VtAwGMb24Oe+TWW7MbAlbG98CAQBVDIShZsZjAKgkjraiA4kOPPI0mwo747rw8QTmQXMDthkKcl4c9QviT7lCrrAQc/dFH8f9z57B/rCJDLS0oEvqhD6G/dDzQweT3i+EaCklB0lxJILJFP0iNPjyMe4o5lywTQWWbzKxK4TlzczgfCwsSNbSS5brzz2oPeDxYP0Z+GxulNhGLvE5MyBoRrcDGvMSJCcgynhMSFrFoNo2n2Vn8v6xMymvo85kpAp1KYY8tLGAcdOx6vVjPffuU+sxnstfSs2qMuHg8uKNWrpR1orHHQuHZIG36/XnNNUBw8M4k8cbKlXC00XGZTOK+CIexvwsLcX4JYSXEnoZ2KAT4m85AWlAAebywAMN03z7JvwyHjfBf1sGrrYVsPncOutTwsOglW7dib506BSg59QfO6+HDuIP+8i/R50yIEt5fRNooZWQfZgSMdb4I4dXrfk1MoM/PPIP9VFYmNeteTO2K4fQibuYkcNLalpbi4qBC29AAocEwMRUBKyFN0gNWtHa75bs6rjUeF4+BXf0GpTIzkFHZvu46wGUuFq2r7k2NRPD/iQkIER2KpFQ6vMVsrCaTEFw/+5lQvDOhXynBgP/hDxBIxPeSsjQUgpChsVVeDkH8wQ/i+2fO4O+1azGnxL1T8O3ZAwH+ne9grs2EC/r8RKPGOllm+OG2bVBse3ok78DthuAdHpaLll7GUAgGRjgskY8tW9BXcyTHvO56xNTKADFT6ZeUQLG3uiCdQNMyORj02jtf+IIxAZqtqAhRq7vuwto0NQmpQjBo/WzOu1LpioKORadH/0c/wpwzIkQK7dlZObt6fRPmm1x3HcZhZwAoZQ+rc7ulntuyZenf03OLBgehyFlRXf/iF/i8FdNiTw8u5rk5XP5tbfgsjYtdu+TcVVbiIqfBPD6Oz5ujeqxNdeCAUn/919g7hNSQRECHpWTKybLzpDKHw+vFes3PY3zFxaIAELJKIovmZqwDoyuRiDGa2toq0R9Gp0nMY27V1Yju1NRIP/SzTTKSVEq+r89fXx/mjwybZWWQHzSarZqd8V1fD7jmqVM4g2R0pMJHecDagXSgMcLh8QjkWCmh/CcFM6HJQ0OIcJK5TSnsWR0ae+gQvsN6NjU1MAitxrRhg1L/+38r9fnPY1+EQgLBnZiQCLdSSyOBcFJGIxCQOnaEcZEpk6QzdECQIIh02PPzIisqKqzvOvP+1ffA6Ch+Pz+PsRYWimNOX/NIBP9mPSe9EUHBIuKMWrBkA9dwZgb7w+eDfNm6VXQFvWVylk5MYB3KyuAwIaolPx/nZnwcz8xWS8/OOdDXJ1Dt0VExLFnLkjmF0Wg6Jbu+P3S95fx5PGPFCimtwLPudmPvtrej30NDcm5J/lBUhL/Ly3F/s36b14t++v14TnGxRMEZcd2/H7lgR48K8RfPEwvtrlsHWenxKHXDDTI3bjfk5w9+IIiDVauMMm5sDDL/2DHc7SMjUo/R7OSORNBfnVRJZx8muREj7y6XfJbIGzpZgkH0nbWsMjnmLsd2xXB6kTXd+xUIIIH3ueegVA8O4t+8cFjZnfkkxKfTu20lpImdZgVxfq6uToQ0L4arrgKjjFX9Br3Z1Zyhsu3zSZL/hVKPm72pjY1SyHd2VvI46A3s6MAFYIYbsNGL9YMfYD7jccET05NI4g5C4kpLoQCy0J5SuBwbGyEcWNvJ7U6HjLS0pHs3SRJiJlzQ5yeRgGJuxoz/2Z9BUTl5UpSR6WkkxPt8EPgjIxLRWlwU4goWVS0qMkY+iFPPhkGn0dLYCM/Wc89J/+mlJ2RLh3zoF6RdUdGlJJbawTZ12um2Njx32zacLSeU+ISh6exaOjvZ3Bz2nLkAsE6hPTOD/jEauLCAc1tRgc8plVvdGF1ORKP2nkSXSwqRHjkie0Snul6zBh7P/fuNlPW1tbhw29owNuaKbdiAM9LTgzllFXs2su5RKSkowHzoRjUNg+FhYW665hr8u7NTCp46ycmy84CzCObgIPY9memUgnJBhq4zZ6BwxGLoGwl6xsaM0VTCXQ4ehJLicklU5ejRdOOecCAWCrbaX1xrve+UX+GwkAK96lXYR9nOhd0ZcLnw+dFRIY2hkkkiIHrRQyGMvbxc8vNKS/GZ8XGpC6Qzh5F1dXxcyhR8/ONGOC3zrDweGNvMJ+zuNsKhzG3DBqX+8R+RM/PQQ5Bni4uYu9JSIb7JlQQilzIas7NSooOwwYUFURZ1GKHbLQolWT5ZCJhoEf2us9q/OnMlWQTXr4dho+ey8h1+v7DN6WROMzNSf4xET5OTWIeJCaxzQQFkACM5LIdB+ceop34X2DlLOzqEkIcoEiIfenrwPspMvTnNt2Q9sV274MylzsLc2s2bcT47OrBvMhFOZat5ppOynDoljgwaB4z6kZU4FsO+v/56pB0MDIDKvbAwHT7JnMquLvz7jjuEEXlmRuqosR/nzkFOfec7kkvFuoREGZSXY84nJ7Gep08LkRUdNy6X1OgyE3I1NkLukzSDKRr5+WLIMTeRdbWUEvZYygKydRKNk5eHu+VlL3vx5DldMZxeRM3K+0UPgB6qn57GwSHzDoUl68RQqFoZJBQKx47h8wzpBwK45AcGpOjaBz8IhcFJ0yMQeqV4nw8HO5eConozG5L33pvuTd26VQT1kSPwdJ44Ycwd+tKX7DHU1dUQUmTK4R+vF8IxP18oR6ur0ZdlywTjbvYMnTwJRjgqEboXk/TIessGd2RRSjOj2+OPwwNdXS2Xd20t9kJDgyhGPT1Sm4qfc7sxj0ww1iMfSmWO8pibOepYVCRU+qOjMje697ujA5dxLtC0bM3K8DBTMLtc+D0LA5eVod+Z9mVTk4yvrS29+KFdAWAzhXZzs7H+SH8/vJrmQrrZDLnhYST8U04wSmPnSYzFAJF77jl8nrAx1j85fTqdsv4HP8DceTwSfa2txT7s7IRHfPdunMdDhxAxMq/R1BTOImWKvh6k76eTYmICCs/q1ZhPQgp1umqlrJ1BducnFMLnz5wRo4DN7xf5RMrf8nIjQY8eTS0qgnPg9GlRGoJBfD8WQ8SO+WXZ+qXvr9ZW/P/IESPZSmcnzmwgIGc6Hofc6e+3PxeZjO/KSiicf/yjFLX2+SDjaYDrhkE0Kgx/SkmBYjJbKiV5TitWYD7CYSEnCARwdhjF++pX8flt26TfdgWEzU4bpfDeFSugFIbD2DPMIbz5ZiifTp0sucBip6ZEfuoMqfp60imVny+GAmsy0uBkORHzXWe3T8iUmpcnhApUjpkHo8O5FhYwHkK+CAUmU+vatXDs9fZKbhoT/j0eIUZgnaSRERjsP/2p0E7rd5mVs3TzZjx/ft4YASfqgMV8qXTrzXy2rSCUlJF+fzr5A+GaGzdirDqkzY4kJFPNMzbm7gwMYB6XLxfWSZaFIUX3li0gIaKThXBYKwSBzye60steBmjvU09JzTOPB3tpeBj5sj6fpE388Y+Sx1xair9JvT4yAkOFBhqjSR6P1GNUSupR6vOjFJwJukEcCMDAKyvDzyYmBKK+uIhzz7qi1D8XF7EPAwHs6ZMnX1x5TlcMpxdJ071f9fUQgtEoPB1FRfD0Mhw/NgYP6bp1EGhMxqYAJQTDyiBxu6U2Ql8fnksq48lJwflv3555k9vhwuNxqRSvC1oK1Ey5M2YlwGxILizA87J5s1EQEdpw5Ai8c319+Hl9PYTowgLYmdragJk3R31ogBIOQ8OCcAt6a1irhgmmdp6hri7BqutezGPHxJNtvoQyFVydmJC6GjqVbCQiIf8bboAg6+mBoE0kMCcjI1AGCNObnRVlhwUvV60yRj6W0vSo4zPPSJ/NBXKVkgvy1KncoWmZmtnw0PNTKiuFZpaXY3u7GMDPPIN+2DG9cXwsfmhXANjcZ/O6MsrX14fv6PveiaJdX6/UL3+Jtde95CMj9p7EigpQzU9NGWFjeuIv92U4DOMgGhXFnXkuo6NCi3vyJBSX1lbM3cGDUDLN773lFtCCz8xgfbgexcVCgpJM4rMsb0AvpZnZTilrZ1AmuDDhJMTnE9ZDAoTWVvT1r/5KjFiuB6OpDz4IZs7pafy8pESgTdEolLTZ2XRjxgmM+XWvw2f7+qzJVjwezNdjjwnkKRjEOludCzvjm/l4MzPYy1VVMGZaWvC+w4fx/5ERMZBWr8b/Bwcxd0VF+NPfj76wJABZ4OhZn5mR3A/Og9uNn9GBQpZMKrw8N48+CqVPvz/WrsVeIARXqfRaN6Tt1psThjEqgrxby8vTYe5EYdTVieLPaBsdl6Re93ig1JOq3evFZ2ZncVe3tKTfddn2SWUl7uwHHpAadWTipRHCqFMggLGMjkqf/H441665RmoTHTiAdd25E2f3zBlR5pXCHpiagnNueNgYvdEjcswXpQEwMYEalVbN7ZZC4KSoNxsoPNt2EMrbbjPKSF02UEbu3IlIj9NirFY1z8zPJJ0/6zBxL8RixvHddpsYZ9Qt4nGB8+nPpYODKKA//3PI0kcewWcYtRwfxxq/6lUCVWX9rclJgeYzL9HlwplgvblEQgwal0tQE+99r9Rc0+dHN4g7OvB+5mGzzlxeniBXaOAx345F6Xk3lpZmLm5+ObYrhtNl1OwEue79qqyEgs3kbW7Q2lqlPvUp5KIwCdDrFe8bPQJr1uAwZTJIrGojeL3Y4MFgOkOUudkJtc2bIdzNxt+TT2Lcr3kNxsaEeyqk73iHPT2rDqM4f17ycYJBozJeWQmGvHvuwTxs3y6wHxZy6+yEV3LbNmM0aO1aYYZimJmNdR18Pvxu61b8TC+mSWGbTML77vGke1UrKwEz8fmUesUrrClqrTx4K1ei73r9Dd0gqKuDEJ2eFuOKF9iBA1DASI0ajYrQZ3idtL47d+ZeINLcspEVsPGCVMo5NM1JMxseJCkgREx3LLhcUCj27oWyNDwM5SET01tLC4ofsi6OOR+NuQTDw1gfRpwywVj159spUNPTOC8kaJmcTKdFz+ZJJFxWh43pib/0kFMJIoRzdlYw62Tmqq6WRPiiIinebfXe5mYows89ByNVfx+99FS6XS6MNRiUgtSEHXN+7aLTnGdGwAiDu+46oYDXiwXzUidBCxks7Rqp5f1+43orldnId7r+VmQr1dX4mw4tRjKYa2GGB5L5rrQUz6EMIjQyHMZ3KyvhuS4rw89Zn6imRpSvmhr0bds2yOFwGGvMnE/W+CMzIVEOHg8UrNlZgTfX1+M9ZKM7csSY+8KaQSMjiO6lUkanwNNP42xee62xCK1uSO7fb5z7bLlLhPfRE84Imp7fwzIBVIAXFiCHe3vFKHK5MDdsPp843UgaxNpcmzcr9d//u3VUzMk+WbUKv9+3D/c95XlFBeapthbw+vPn4cAbHsZ6rFolzH7cr+XlQlzj8QjMam5O8iYZvZyfF8XfqgSD7iwdHsbZVUqMPo9Hak4yh+jJJ42QOJ7trVsh677zHcxbc3P6XXnzzdmdsCwITn2rp8feeHLi4HjlK7EPGSUfHRW6cc5dWZkxjycUwl1y7pyR6IOGY0EBIqihEPbrt78NpAznj+VZEgmjkcm8I49H6qERSkpyC+av6RFJ3oO8V0Mh67wj3uOPPorzuGIF9Ck6ZZk6UFGBPe31Qtfg/t+wAfKDd2M0mlsZhcuhXTGcLpOWSZD7fPi53y8VqfWLcnRUqd/+FobHbbcZk/cJX6itxTOZYGilmOmG28aNKPb5wx9KfkxZGYRiproXdrjww4cBGyspgaA2G39kqvqrv4JgmJjAOwcH0xOe7WAULGA6NWUN42Gi+/r1eK95LlmLqbcXEZrmZvT96FHxwkejkji/uCjemrw8vHvdOsw3aUbr66UGVmcnhOK11xoFNKE39LxS6bC6hMxUy9EoagLpxoVuEBQUCHRTKfEodXVBgWpvFyOBCcz5+QLBGh9HP5ZSINKqud2AA117LS46c9OV37VrnUHTnApc8wVYWCgKDgs70uNNA3x0FGdh504j09utt0o9klOn8Hwa2Fb5aHoeVSyGC+fwYVHUrHLGlErPHTArUB0duKyUwvsOHcJZr65Oz//J5ElMJtOjWbyA8/OhyLL2Ew001uugU8DrFSjPzAwMtclJ/KyqChGK669H5FJXUrgmHR3ifWUycWGhnGPmaDY2oh+Eji0sOC9mqheaVgrv2rxZvMbcD1Qi2tszEwp0d2PdVq3CfmF9F7cbzy4uFnIQq2KhSmUvPWH+zMmTyOcZGcFc6/TEjBQODcF5c8steI5+v4yMYN+y37290q+qKihLhB0Rosfi1k1NUpuqvR376bOfVeqf/xnvZJ8pP/T1UwpynRDP735XinqnUlDU43FhFy0vl+haXx/+3dgICKjudGpsFIcblcJMhqST3CU6N1h3jFDSeByymtExyh4qwIzORqPYo9zDc3MSxWffdRh9eTnu71yKgJv3iRUcPhBAjblz58SZ0tSEe4n1hnTKfTY6L/74R8g9PVeNjIiTk/geGUMZ3dEdBY8+CocM5zoUwhyzRuP4OM7M+LgUmZ+YEAj5+DjkLw2SkRHcg6OjuL8SCTGueFcePw4WSzPRkq7zOCH9MM99tjztlSvxXD06V1AgEciFBaMRXV+PO4M/Mztu8vKwns89BxTB0aOY/02bJJrNveN2i87DvCPWViMcnnXilBLdKpHAPOpQeaf36v79OLeM8paUSJSX+hyhuyS9eMlLhABIKWfFzS/HdsVwugxaNkF+003izTXz+Hu9Ulvn/vuBhbUSsPX1mUPTdoLkne/MTv7AlgkXXlcHr8PcHAStlfH3q18BBuTzwcNol4xrl+AcCmFuenogjHRhzgMaCGC+nn3WOJdkCiR0hxSrNF7a2vB/KrIMPetFaPfsEdpgKyG7Zg3myCwgaOiUlRmNHKWsvdW6F5mJ3LpxYWa60fPalMIceL1gdWMOTnk5PssIHOEnRUVK3Xgj1oT5TxfanHjw/vzPRUnTlXlCimZnMR8krHDa9Avw4EGBUrB+Fr2bJ0+K4pFKCfSBDErf+Q7mY+9eKGZKYf12707Pi9LzqBYWcMEuX56+r/V1zXax6x4/knXEYrgMSSFPimO2TJ5ErolOKU6DiEncjY1QSkgzq9fImZ2VPTIxIV5Rlwvj5dmZnoayYDYKCHM8c0ZgRlTm/H78zb1cXY3n19biZ6dOZS9mqsvYpibZb8ePCxlORwdkSllZ9qg8n/nNb8JYpSeXimUwKHTlVCwzKSNWOYNWCATCAx95xFg3ji2VwudZd43REv1+Wb4cc3jwIIwrFvqsrDSegQMH8N2Xv1xQB3oEVa+B9+//jgKkHR2Q4ZOTGFNhoaAiJibA4OX1IsIRCOD9Y2OQ09xTjIIxz4XMhkrhu089ZYS/FhZivs+dw3PMuTO6IXnzzdlzl+69V5hFyWjI9c/Pxx4eHBQDxO3GWWP0JRLBnvX5JIpAJwNZ5VgTqaREKNTvvRfrk8l4ypZbav59dzeMwMZGY25URQXmjCRSZsjr5CTOFhksWVA2kcDaEN7K+kH6naUU5qyvDzqJPteplOQLM/pJWGNtLT67di3mb3QUsuD4caBFBgdhPESjkpNoLqSt70ndyaiTrDz8sDWc2Yr0w3wG/5//x1qPSiZhQLS1ifOTTgAdwUASBNZUOnVKCHAqKyHHScYUjUL+/+53OCOLizin+fkCqezvl7uf0GWWKRgcxJ4kjJRwauYzMeK0fbucJaeGjK6D2UV5CwvFSTc8jDkPh4Vl1Kmz63JsVwynS9ycJKE+9RQ2JOlWzdCm+Xls1N5eUa51AZqtDoUTD5wTqshMxWbpcRscRB/Ml1t1NQTkyZMoEJopOdguwdnlwoU6Po4DGolIGJ+wAFLiMiKjh7enp9FHMnbxMnG5JCGzrk6Yiwi3YP2V//bfMgvZZFKpT34yPYJCQyeVSjdylMoMSbPKezEz3ehheKXEo7RuXTp2u7IS4x4cBGTG60UNiyeeyF6EMJfmFKKkG1h+P+Z3cBCXNZ0GZEhyWm2ehgcT0k+fNkInWY+MjFKHD0sNm3Xr0I9f/UoidDU1+N7YGBSFdeuwvoTstbcLtXgohHXixWdFcNHWBnrlkRFhhozFjOeREDd6/KiU+Hz4MzmZHnV14kmkMtLWJtDCYBCXq8eDuXa7pVZRWZnUQZqexrtGRyW/g8m/+njvvReKDfOaGEn7/OfRh6NHJbdm/36cZUZXy8vxrpUr4VV24tTJJGMrK2H8kiI6GxyTz6PROjIiRBJzc1Knx+eTqF1eHubqJS9xbuRnM5zNNfx0iI/fL+yd0ajAo/WxEwJ0zz3wAO/ebYT76HAtOlbMzVwD74tfxPk7ehRw8ZERrPHkJMZPpelVr8L7n3xSHAkDAyLTWa+OeVzxOMZP+FFPD2R4a6uMo6QEP89mSD77bPa8SUI59SgTGRgJRVQK+72nB/2nnAqHce/m5wsUvbAQEfZnnsGahEI4N4R5+/04X+FwbmQ3TprdXclc5YGBdMhrMgmZV1qKcZPMweORqK/Ph9/HYtZ31swMzhRrkOn7inWoSDBBRZuRkdZWYQxkmYZoFHtxbk7Kr+gwRCs6d+pAHR0oJ8Jiz2fOYDy7d2emPNcLxjJqVF8PtIGZAY7ESxMT6CNJbfintFRIEPQo3KpV0IWOHcOdMzEBx8bMDPZ0ZSXuKRpJIyNYg0BAzigjVR4PzsXx45i3SATP8fvFkVpYiOc3NEjZFOofuRgy3FfxOBwsdlHeSASyatMm3IVOIOkvhnbFcLrELZOxQUE+OCiMMOYLjBjVmhqp/aLDe2ZmJGRtdQHnWlQzU8vE2EQmJsJ2rPJa2JgYap4HRl0ysYtVVuKQHjsmdSd4QG+9FXPx2GPiLSepRCIBgVdWBiE+NiaXCRNWx8cRVSJEKxbDWLdtA9zHbp5pdFrBoTg3eXl4fmNjerHVTAqvXfQmGASWvbraGIa3Y4PTvzs+Dm90KoVxkRbYiUcuU0TS3JxClD7wAUCTfvtb7A29Dsojj+BCfec70W+n0Au3GwrP+94Hp4FeS6arC3NUUABhTxz5+fMCKRkfx1nUFbVly3Cx9fQgSldZCSWsqwuXlx7VUso6mtjWJgU9WR+GBpt+Ht/4xnS5QWWIzomBAXj4KypwTjJ5EnXnyZ49ovR1d+NnZ89iHsvKpPYG9y0hNt3dQl9dWWk9Xr8fnsdDh6S4pr5Of/VX6MfAgNQeO35cGDArKqBYOblsuTdPnsR+Xr7cKHfCYRhmhNvu2QNFQIdjWuVW3nOPKD4lJRJFr67GvE1PQ2loasLPvV7MuVkZsTs7ThxZ5hp+VrlZhYUCW7a6X0jysLAgUCy9UQ5NTNjTpVvVwGtqErh4R4eQ3Xg8WLuVK435czTEWax2elqMLY8He66oSKIn9OZHIjgTJLPg/eL32xuSLNeQKW+Sxh7zeomQmJmR/a0UIlxf/SpkiNkRROQA75e9e6VGkc8ne04nj/F6cyO7cdLs7koaMERm6JBXQsp37cJ8PPSQ5AHn5UlduPJy9HXtWuOdxTumoQHn2DzXJGtqb5c0AI8nnUinpATn8IknYMiRqZJRTM5xZaU4Ol0u4540n6X5echYOmX0qLwdxJDRr+FhfOfBB5V69asRTdFrDT70kMwPYXKE7a5di/Pd2ZkehSsuxn0xMYE5SSSEuIrU+mT4ZC1O5lJ6vRI5nJiAs5NylrTuqRTuIOYUc44bG+GUHxmRgt9ODZmiIrz72DFrFJQVXNjJff9iaVcMp0vcnNZmufFGJB8ODUFgmS+GhgYIDr2Wj87lb8d64/Ph0BcXC0U0ow+5MpdlMmgobJkrpDfCr8i+Yg776/MwNYWxZGIXi8VA3Wn2atP709YmSZaLi1KXanER/dchSOEwlDwWBj17FkLi6quhrG/Zgnf8279lr/dhZ+QoJYnwzc3WrD2ZQudW0ZuKCsx1SQnm1c6jZP5uXx88cuaCpDpk8d//HXUoCEGzoqJ16kHKBj1h7sziouSWkXWtsBDfP3dOqU98AonDGzfmVuvJbvwFBUItz+LAZP6ix9kc/XW58LNoFOflAx/AuoXDWOuqKqNDQCnjvu7oUOpzn8N8Eu41Nyd5NM3NeEZ7OxwCZrlBZYj1PygDaOBs3GjtSbRznpSXw6NPUgkaAbyYN22C/BkaQv/LynA2w2GMm/Xj2Mx5Y01N1uukr8fsLBTt667DH3N+lF3TIzZ6vsr69UY4ps4YuLiI80s45n33ybnlM//1X2FAJxIwDKmsT07iMyw+SpiN349I0/vfb9yDNMAOHxaiiquvxtn5zW+yO7I+8hEofYcPZ87NIosaC2fqe5bwu0TCWubm52MNIxGsQS5yyawk9fcjp2n5cvxehxMrJWQwLpfU3uvqwtiqquTcTU9Lfl4igX3DHKyGBtx1MzP2hmR1NcY8MCAQOn1O9NpZTKZnfxid8XhwR/j9iFbrtdk4Zkbd9BIR589j3X0+nB09QV6p3MlunLRMTJwVFZgfM+SVkPKGBsz3tm0Yz9wc+ksjVymJMhDybceYaeXg3LJF7ibCHs3G+/S0sMDy3JM1zueDAR2Nok8sJM49aSXXCD2sqcFnzVF5M8SwshLGUjSK9zGq9PDDeN/734+f33UX9KfqaoEWM3+Nd0hVlXUUTin8u7QUc/7YY1gDHT1C5wKZLynXGVkjMy6dubOzGBcRNMxH00uK+Hy4M+3Y87Ltq5oaOAScwIX1guwvFsrxTO2K4XSJm9PaLDfeiMvw4YdxOHgx1NTgAj9zRsLmy5fjkOlc/okEnm++gNetg5dCKRw8enXplchFmGejS2aoPxyW2hA0/nQF0GxY6fNAak4nNL5WNaZaWuCtf/xxIcoghCoWg4Cbm4PCODeHzw0M4KIsLcVcj49DuMViSHh3ougwYmcHUXvFKyBkzBjg3l78f/36zMw/Vt4cPdqYKTRuTjy/+27sIXPka3QUfTx8GAbT4KB4kUmkcTGjUlR+Dx7EH72wHpOsCYmamsJlasfulCliapV47/XCYKEBTSpVFkUNBgXKozfmZTB/5sknMffDw+irfrb4ORaR/NnPpLYFWeoGBqQWSW+v4NdXrDAqkkrJhcVcCzoKrKIJessW9TaTSgwPi8Hs8ciFf8stOHOf/KSxvg/7xryx0lJJMLdapwv1TJq9zExIJ2x2xw70T2fw0+FGVg4jXQlrbIQSzCg680c5Tu6BYBCfnZ83Etx0dIAB9cQJPJft1CnMq9ttZMnU14L96usTGcjoXCAguVnl5VDU/vVfsQ9PnRISAO4/wif7+tLr5qRS2LeMwjktE2F31ukx5z2nJ7DTGKcizGK6/F4qBePc55OyDwUFkhvT3491fuUr8e9MhiT3LyO6OgS3okJqZyWTSv385zh7LOXBNY3FsHeXL4dR0dFhrDFFKOfYmBECTPKieBzn3Owky5XsxkkjBXZ7u3VJhRUrcFdyH65di78/9CHoDyyaS3Y4Rt4WFgDB2r0bUQerO0ZnzLTSB/r78X2lRAfRG0mTFhelnhrzxhIJIYygLtTTgzXhnuzuTpdr3HcLC8a8IDq0dIjh8uWYs54e/G5iAs8ly2B7u1If/jDO1eHD6BuLddfXG50B4TDO4Jo11lE4Np4JFinWUQQVFUIwQ6IrFtqen8f7VqzAzyYnMUexGNaQhe+J5tEdH7t25R71cbudw4VfTDTjTtsVw+kSNye1WQireu97JSm+vBwHvLsbVv/0tBTEramBINS5/HXPCi/gZ5/FRU0IYDCIA6gnXNJT50SY2xk0vb2I2iQSUmeA7Fks4Lh2LULyqRT6PjxsvPjM3s1caJz1lkxCYVm/XgqVhkJSpO3sWfRv2TKMgRS5RUXoJ5N+R0bwnO9/H5/JpdaQnWKoY6r7++VySKWU+sY3xCv9+tfbw8/M3hxzTapMlKtNTXIJ0ShgNHBoCPNB/DZJFRYXhbZWZzaywolni0rpSheTSZmnphTmglXZi4uF2YlJuOZLMJeIqXn8VCR1Fja9+DHPF/PcqPyx4v3iopQFqK2VS18/W1TUrr4a3+ElPzyMcTFviLlvi4vYm2NjMG5Z/2L3bry7owNzH4vhO6xDVFuL+dIVPH0POI16k1Ri0yZg/O1KJ1jJs2hUvNRkzZuYEI+7eZ3MezmZTGcY5BiyFcHWE9KZD7FmjZExUK9vx+KNTJpXymhc0sGkK/2VleJIOXECfbzhBqlrRYfCnXeCVvjAAZFvzAuIRqFAMiKRaS2YV2QlA+vr0fcvflFyGOJx6/0XDOK+6O/HfJoNozvuwHudyNlMeVnNzcZ9YVYIp6aw3mQei8VEEdWjgR4PFE9GoBYWpOj75s04f1aGZEUF5usb30BffT70kYYXYYIrV0rtrLY2rFNPj9RH4nmsqcGYJifx/gMH4NSkk+fQIdwVOuubE/Kiq69G3+32eq6towNG+9QU5lrP4Vu5Eu/90Y+M67VxI+R9fz/6wgjbxIQ4O66/HtFxtxvrqTOL8vwqtbQ6ZfxMe7sUMI7FxMHh86Gv7Bfh/9dfb7xXrOSavu/Ky/EZHZKvQwxHRhBRTKWwlnSeMcpFGBwju0VFkDvcc6RaJzttS0vmKJxSEoUl+52eEzY2ht/PzeH5dNKsXg2Dad8+KW5cVYXneL1w8pBE6uRJ6DaTk+l1AnNtTuHCLyaacaftiuF0iZtThjF6Kz/4QanVcOIEDlJlpTALDQ3hUKxenc7lrwtqvx9QiJoaeDeHhgS3WlmJA9jRAUOMhR+dNCu65K4u9GPXLryXuFq3G9CewkIYLOXlEA6/+Y3ASIqL7WtHLcUzTQVo40YoT2SDGRvDoWexSgrNhQUIopoaEcA6JOvwYfTfTim3i9jZGTk61OM735HaFmynTmHdP/EJZ3C4XEPjegQ0kZD6LkNDUuTS44GgJGSUVNs0zHWc+C9/KYY+GYGsolK60mVO4C0oQH9IxsCE8VAIe4SkBEy2djL/mca/uCh5g5OTwkw0Py9e7pkZSZxnUcuKCvSFkZ7ZWezv6mpxUJDk4MgRXDC8vEjP29iIuTp+XAwgpYzvdrsxzsJCPOu++/B+XupKSWFN5jgxMmJlRDqNepuLylrtKzt5FonAIGSNkyeeMEa3S0vt1ymTQq5U9iLY5oT0gQFhmSJj4Lp1RnYoUoj/6EcCJ6IS5naL8kVsv8cjClMshnNMZ4oeVfv+9xGtzstLzwug8TU6ij5ayVzzWphl4PAwDPbDh/H7FSvw8/PnsSaNjfj/s89CtldVKfXXfw2ZkskwyiZnneRlmfeFucbg1q2Yd7IUejyQy9XVeEcoJM4Ttxvf4RwPDiLH5O1vt46AML+1q0siKNPTUGgLCkThu/NOGfMnPoHo84MPComB3499v2KFMJDOzWFfnT4NpbS6WhRpM+tbJvIiGndf+pLzXM1MTV+TVavwbJZUWFyUu8WqbAjRLJOTwgxIgoJAALUVT5/O7hRbSp0yOg0nJuTs5eVhzZg3RFbCggLs4X/8RyOplFLWck2XBSwOz9xwHWL4gx9ABs/PC+OjUuJUo6FQVYU9UVCQHn0mhJcEIDt3yj2o08Oz0WlTX4/zSpg64ZOPPIK9w+gfHXbJJMbncmEd9NpbBQUCpZyaQhSxsBDvfuc7L4yUobHRGVz4xUQz7rRdMZwug5ZL9KSlBYfoYx/DJdLSgoP5xBM4MEVFEPJkfpuaws91z4pS4n1lkdbJSWMRNq8XSteyZbl7JZqblXrTmzCW//N/cICvuUae8ZKXQKiTYYbenPl5HPKyMoFF9fVBgN98s/MoS6ame6GKiyEomahMg2h4GBS8nBPWVNAbIxLEul/MWkONjUp95SsQPFZe6QMHEHH4X/9LvPwXK+GSEdDHH5ekaEKY/H7xsFIoUzHUDXPixH/wAyhObjcuAirKzc1GBqnOzswJvOvWyXgYfaEhof88L8+a3Ynz72SeGhsxTp5BXtb0dpItKZEQTDlx92NjmIutW2UdGY3YsUOUctbQuO46pd71LuxregRZtPjwYYleMcKlFJ6Xl4ezPzEBrx8Tzz0efJ+XZTIphBlmximrNc8W9XZ6ATY3Iyp6//1Y97w89JU5hGVlsp+pWK5fb31OMinkx47hM4xGZCqCrSekd3VBaWI+4/bt+Awp45lUXVkJBedf/xXj0ZUwKl+UmaRqJvNfS0t6DkN9PdaVOW9WEeqqKszVqVPpUWy7taAMTCYx5729+BnhkCUlmO/z57H+hEktW4bvP/wwagi96U3pOaHmd1g1pwRDH/1oev6aVY3B22/HWs3MwAiMRERuDw1JH2mUz87C8J6exrpbUUb39Ai8eGFBjEYytvHO06MTLS1KffnLiIp86lOQw5RPeg1AjwdjJ4U6vf1utzgheQYzkRdt2iQMiNlosrM1uzVpaIB8ufde/P91rzMy2LJsSH09zoVenoJpAHQa6PWZ/H787LHHILv/4R+gnHMec6lTxlzt/HxhNiUKIhaD7CNJh8ul1F/8RbrRpJS9XKusxNj27sWzCaPWIYYPPQTdxOMxluFIpbDfeO8qhfnQHSl69Jn1HF0upb72NZRcqKwUw2v9emOEjaycJ04AJlhQgHUhRI95vX6/kFsNDEiuIKOiLBA+MIA7O5mUO3thAXL161+HQb1U40l3ktlFeV9sNONO2xXD6TJpuURP+vpw2DduxKGamDAWPGOdibw8fI6QOCqVqRQ2O4V7QUG6kGTC4V/8RW4HS/cORyL4f20t/q0rMamU5G9s24bLpb9fFNONG8Vz0t+PC/HVr77wQ2j2QplrEDCq8ed/jn5Ho+Lx0huV2vJyCMju7tyVTjtFvrsbhovZK+3x4HPT0xDsf/3XWPNcCvlla8TE/+Y3UFLq6gQSk0xKXsDMjBR3JEUtDfOZGanZkpcHAWqlKHd0YKzZEnhPnZL8iERC4A80nuiVLC62Zne6+mp89/OfhzebOUqtrVAc9Hnq7ET/+HwyGc3Pi2FPT5/Hg3HOzAhkq7ERzHA//alRCausFIrdWAx7+i1vkXfrl3xZmcCPGHFizoffj7/12jZkhGTOl8+Hn5MCnTVBzIxT+po7jXpbNTPEkvlPjH7V1GC+WbuF3lBGWUZGYFC8+c3Gc5JJIW9pSVf+aFT7/VAcOjqMid9MSC8tBQQtPx8R0ZERcSQVF0vO5datUuh13z7I5yNHhGCCxnA4DAUvGMS7d+0y1s9iCwSwTnoE2ar5/djHua4Fo+nl5YgG5OdjTrxePLOsDPuOzGShEP7f0WFkCss1edsJMywjnXT8Pfss9kp1Ne6egQHre2/tWijoQ0PYn7OzRgQAWWUJ92b+Fw3Jnh4o8n19kEeLi0ZWV+Z5DQ/DSCY0k425HC9/uZRteOopYRJTSnJ2m5pEaQwE0OfKSqNjibm0ZvKiZcsQNSF75VJyNZ2uCZlJ+W+dsIZRg8lJnNPrr5e6eYRp9/cb83pHR4GQIPPeyZNK/ff/jjypq66S9cy2r/gZsieWlAB5QLZSskCS0CQYhKx7zWvs52T7dpzZAwewPxjhC4cxtje8AXvQvO+uvx77bm4O66XXsaKDjndOSYnRkcI7+sQJ/IzERiUlUo+zqEgIUPQaX/n5OAd+v+RLTU3h3dQHSI/vckk5BuZCkwSKLImUN9TziookwrZvHyKqX/7y0vWqpaZMvNjbFcPpMmp2wsWsYFOQUTEjbre7G4eKSlR1tfD5JxLihWO9iVgMCjqT1pub4fUiy9L8PBQNp83sHfb5cIGbC3IyUXxhAZ8hna8O/RoYgPAiNOli0bTqCioZc+hlXlyEwF5chDeqoQFKezRqNGAYUs/LQyTtttvAqkdFh9434pxvvTVdMGWCH50/jzmrqTGyPbEAMoss/t3fSTVwM9yirQ0Fbp2wkJn3l8+Hd9N7RcgYf84IUiSCS00vskujnMJ7xQoxOnUYaG8v9typU5kTeAsKoNDw0p6fxx53u/F7Ug/HYhI5NSuamzYp9ZnP4CIj3C2VwruPHxfYIxV1rxfe0q4uOQscAyNb9D5PTmIvbNkCxZSFDO0ogFkrp7zcaOTReDl2DHAKXpb8Ho0izsHionhc9b/n5rCf6IAgfC0ex57KxIK2lAtQ38cjIwLLbW3FfmZO0ZkzeAbzSRjZ5gW/uAglR9+nuSh/VNjCYSggs7PYE/X1QhLDSMs114BIwO2GY+B738OZoQfXTItcXw9D8O1vN+ZilJZib506hc/feiscGmTVMreZGezHsTH8YS07QkwXFnDGq6oAyT54MLe1mJrCGjCCHomIoU+ZRefG4iJ+vmwZ9uLQkDCFffCDuSk8TnPkyBxplnt79xrLNrAlk9jzt9wCuf/DH+J8MEI1OyuJ6OvWQbmlAWZ+DyGa1dXWxp3Ph89YQUV1x8LBg3gOCQpIW66UQMc6OtCniQmsASHwkYgwnurkRW1tgAj+9rf4Pg0u7sFc2W2zrQn3gMuVDm02My2SeOD0aYGwxuOQ/9u24WeM1JJoZXwcDqq/+Rs4H3bsyM2Rx76Thbe8XHJYYzHJc122DDKXUE696es/OYl7fXAQn62qyn6WtmzBfuzpwR/CBmmssTwCCT50R0p/P8633y/1lhhhLioSAiimSdx+u1I/+QmeNz6O9SdMz+/HHTw7i36RIZPnl3t3fByylBA+rjPvEBLZMCeXDqI//AF6oxWRltP2fxPNuNN2xXC6zJvVRVNdjUNBxczlwsZl/QifD5uWXnEqwL//vVzqdXXY3OakdRa6PHsWQq++3lk/de9wSwsOJZPnSYdJuEJXl9QrSCbhrZqZgYLEvCYz9Oti0bTyEuzpwYVNtsH+fql27/OB+r21FQo0L0sWLSSN+fbtciFQ6dy3D+Mjra3PZ2TU4ppmygcgWQDbzAz6x2T0vDwIyeeew79vukkU9EQCa3r2LJSMrVvxXr1ulx3zHvdXSQmec8MNIrSPHcPl4fdjDhgNDIfRL+4TwgsJhTRH6+hFHRqC4qmUfQJvdzcuSv6eieDRKOYzGJSE/le8Ah7Fzs703IZvfxsGstcrsCUmO+/fL7BHKuqNjVKTZ2QEfwcCoigFg5I3VFyMZ9XW4oI/dQrzeiHQN0LtGLHRDfZkUqKd/JxuDDJCRRIYRqCfe07qHyllnXye6wWo7+P6euxdpYQwhPlfhG9QvpjhP/X1UgRbb06Vv6EhYRUNhQSaNTUFuAshnZFIej2llhal3vpWKVbMaI++ZpQ/1dXWxuVLXiIQn54ezDVlIGmDyV63bRv6cP/9UswyGpWaNKkU+lFXByZVnfRCKZzX7m7rdSENfTKJtaPDY2xMFCgiE2hkszxEeTk+39OTexFWpzlyw8PO4V1W9x732NQUnmmug8Ris+b3BAKYs8VFvI+slWyEXzEvyapRxn/zm+grYU+1tVLAdHISa0qygrIy/J95KUNDIFXRFfb//E+lPv1pGReNhZkZY24U4c9kpsx2NjOtiY48MUObzUyLLDmhQ1jLyzG3x47h+4y+xWLCQMt7anQUTolcoIbsO8lbGGki2Qadhxs34jPZ4L3Ll+N8dXZi3d/+9vQCtubG/J2CArzn1CnRD0iiUlRkNGIrKzE3e/dif2zcKOyNer5lcTFk0YoVAj1lhO30aUE38HvMXSUZBmGChOulUoK84NybGwuZ8y7Oz8fYxscxtgsxnJTKPWXixd6uGE6XcbNTsM+dw4GbnUXCIZOTWQiNXjAm1NNrNT6O79AbW1cnn2du1AMP4N1kr/nSl5x5i6h0kvyBYfvxcQiJmhr87Nw5CFJSupaWQmCMjUFYNzbivWbo18VkZ2lpQYieF8K5cxA8xcW48IhZf+YZ4PJraxGZIzSqrAw0vXfcYUyCTSahgNXWYr1qa/F8HaPe3AyI0fnzUmuCCjghGZ2dmJeREbl8mUullDBO+Xzod2enFAHkmMrKhByE77/5ZkmczlTn6+RJKGDV1RDuSmGtyNrj9eLdNFQIu2JEY/16YNQXFnAh6NE6pSCwp6cxR2vXWifw0hHAWio6G2MohO+UlmKOly+Hl9xK6ddhjzpEx+vF/wcGoLh1dwtBA42+HTswd1TgiRPnJa6UMdrGfRoKOYe+0ZAlrn9hAfk0J07AG5hISHSNka/FRUme15n+qAgxUsck+GQS/yZdM+sHWUEWnV6AZhgdWZ8YNe7rQ/QlEBAvdVsb1oTwHxoVSuG75vPtRPnj/OlFGL1e7AvSwD/wANakrEzolvVxEJpMSI05IqHLn6amzMYlo4b33isRMZ6fjRuFRWx4GF55snbm5Un+VzCICDYVzY4OUNVnguMmk4C/MSejtBRrQBptKlfMSwyFJMLFqO3UFNYv1+i+kxy5zZsBByQUbXbWCO/q7AS1M2FDVveeXvycOXFcL75n61bIKauaZNXVmPeeHsg1UptPTmJeVqxIL8Gg77GFBdwbLJBcXo59GY1KTnAqhX75/fI353zrVtTfYkSgrQ2R8IEB3I/cB4wSKyXOxt5eOAfuvtu6cHQua0JGUjsGWzIt9vWJYq9DWFtasHbUJ7hPuJaUQaEQZExdHcZoZ5CbnXks0nr4sERD9bNNaObkZDpxlR28NxRCpLm9HbC9l70s857Wo4yjo0pde60UBWfhZTKZmuV7VZWcZZ45vfGs6TWeGGGLx/FvQvH4eRpQhAvOzYkxRaeIxyORa58P+4RONkL9+Ew628yy7kpz1q4YTpdpy4Tv37ABB25iAsK3oUG8i8EgDJH163Ho8/PFW0Vqa7KvnDuH57Go2/g4BF9LCzyjfr/zxFTCRMJhUW6p3J0/D8Ho90NZYL6FxyMFI0kHHg4LTIzQL91DvxQiBKvvVFcjTN7fj88QXsV5rqmBMn3smEQj7ChX+Y7f/AaC69pr7ROkW1uRVzE7ayRMoNe0vh7vycvDXDDiwVA7IVq1tcZaFBMTUtCTcMhIBHO8fj2Uqs98BnuFBAh2db62bcN4Dx2CUcIk5x07cFGcPYvP+f2SyE0mKaXQ/8VFYfAyQ7MiESMEx+qCp+HMy2JoSAwoRr0mJ2EMkGBBqXRl79SpdNgjm8sFBXN4WOBWuqJeWQmvv25cu1yY71QKv5+ZMdJ9c5+63dmhb3b5gNXVgIkMDKBfjMwuLAhsjxfo4KAxIsVx0eNbUICfj46CcOT8ecHIs7BiZ6cRsuikmWF0ekFT1g6ZncW81NRIccqDB3EGCYNLpezZl5wof3NzUstKn4fpaYwxGMTPd+8W+vZPfAJOj9panFnm2h07JsnSev/MEUKnxqWZHIKtpUWpj38cOSAkX/F6EblgPSHKi2TSWXFtyqfWVtwJMzO4B3TDiXBbpQTuQ7pypQTC092Ns881yKboNjZmdhTk5eH8PPYY3nH+vJTP0GmTT54ExTWdQDqdfDKJeRkZwV7ifCwuGh0SO3YA0meGdxJaSwgaoweMWuXnwwlp3oPmyBcdJLEYxk4nIYuyplJCfkIoYWkpxjI8bMy/YikLRlzNhV3n5oR18Omn8fPlyyVPJ9PdnC1vsbERv7vnHjHEyssxppUr4WT74x9hZHAea2vxnooKoQknyyEjQZTxrHM3Nob/20EN7SDrmzfjs7z/+vvRNxoLBQXWdNq55NtlO8NWxbj1CLNSxsLphMxfcw2cRnrETI8C0dlGspzqaomw0UAyozQIXSdUnCUdOP+UdZT7sZgxysUIM+8IQv+qqtKdSeZ2Mcmn/m9pVwyny6jpGzQaxQHPVJSyqwtesuFhKCWkJt+6FYLl5ElJfNWxwVSaeHGy4nVpKT6zcqV4WJ0mpgYCeA8VSfa5pAT9OnsWv9PpX91uMbQIG2FhNx7m9nbx0OdSE4jNTjBv346xRqNSTV5vDG2zcCbrXtg1JwL72WdREDUchheOHs/z5zHuq6+GUdDWhj7V1Um0iXPk9+PnW7ZA0SMca3RUCnpSkdWLek5P4x07dwqhiF2dL7cbCtgzz0DRXbsW72Ak8/rr0/OndK94PA5FaX4eBsDIiECz6Fm/8UaBSxA6eeCA1Cc7eFAMGOZVES6yfj2UCDPBwlKavlZWivrp01ASeEHl5eFvQnAIqyIRgRkGZhedcJIP2NqKeWf9lEgEe4DFf1lTjNAljwdzR2OTvyNkNxIR+mQ9ZyqZxM8XFwGXKi3NfjmaYXSUKYkE9hnfkZeHP2VlWL+xMSRr79kjRTjtCA+yKX8bN2JuTpzAniKMkTmghYUwRsbG0K/jx4XI4fhxrAejrddfDyX41CnMxa5dksvgtNCrUpA1i4uILJmhenodrUAAZ5y1iejx536sr8eYx8acFdfmejQ3Q26dPIl543nzeLCHg0HMEZUzGrw8W889h/X73vdgVJrla6bcTLuaUsz3zM/H/URYGp0BXi/mMJHAnLa3Y3+4XMJIp5P3cA6YM6Q7JHTaePM5JxX4yIg4qijXrJRwO8THyAjus/Z2iXwzQkDYHyGBhBLSaCfcnPcFz6bLhX2mF3ZlbtYzz+C5u3dLVMgJaYRd3mJ9vawfHQ+MPDU2KvX+98OB8NBD2Cfmu5FzOTQkcp1kBoSMVVQYC0tbQQ1nZuydAseOSWSGebZjY3juihW4x6xylHLJt3PSssGXm5vBvkc2y4EBzNvwMH5vjpjR4VdTg7VtbcVY9u6VCNvwsDihmP9I2nEaPjSW6FD1+wXuzMjT5CScIHSMzM9LcXQ62ffsyWxAZjrvF1rg/sXcrhhOl7hx0x09CqWa9XJiMShSmzdDMbPC3Xu9wOezptCPfgSFlZ45XjRM1Cc+lyHaxUVJPuThnJ2F4Dp5EgersRGHob3dmZdGLxiqFN49NiaHOxTCJUKFIC9PaNTJ9sJkb0K/6N3JVifELEQz5RIxAsbiv+YxULgxnJ5p7aamIIzjcXuBzbpZJSVQIsmMxoRXGheEOVxzDf4+fBhzz/eVluICrayEkO7pgYJHA4rClnAG7g0aPQzdU2HS63xNTIjRVVSEC6q8HFC3sTF8r6wMlwWjbnbz7PdjP+/bB9rtlhaBOTQ2wuOvC9fCQigbJ04IfWogAMWXuVAlJUIcsmlTOsGCVVu7Fn0eHzdGFLnOY2MC4TIr6j4fxk7iCbK20XNHxY9FEK0ucqvohFU0mYaOzyeG7PXXGxOOFxYkoszk6MJCoRynkuP3CwwxLw/7YG5O8uTy88XI4HovLkIBO3YMF2O2hG4zjC4UEuNvZkY+R4bE+XmwqbndWOcjR7B+2ZK0s5FW9PcD/qTnvdABRLKOhQWsJ+FDXi8UCuYqJhJQOFgm4exZGFFbt+ZW6HX7dnGeuN1GtjKljN5uRlSbmtIZO8noeP481lCvScVm9p7r60EGx95enEHeFaSspyONe5p03HTYLV8ONjQzzFip7DL47//emJf1059CYW5pkfdSvszNCVRwbg6yq6JCYHF6fg0RDImEEEC8973pjGik9beCd+pU4C4X5F1hoeT+6WucCfGxYwdkOVlFu7vFg886X34/lFKSwTD/inBUGo6kxKdxUVeHd87MiAEVDAIFUFWVeQ9Y3c1mxT8QUOqrX4UT0uuFgcSIESmyf/ELnOWeHsx7URHGxBqRzLtqbYX85H2tEwj5/VLKIxTCHOlQQ69XZAPTDZSSffzYY3jGTTehfxMT2GOVlVh3uxylpdSky9YyRZg7OyWfrqnJaFz39WH98/Px/8JC64hZfr7cO5OT4vAiFF8pzG1RkdxBRHDU1mIP7N+P803nZ2GhUJefPo3nMHfO50O/WlvT72G9OanNptTFZfV9sbQrhtMlbLyA9WK29fVGL93QEAQRcb+EkOj5FDzUHg82enu7CLvJSVw8SsllRYYyJrjG43KRl5XhmSMjuLg7OvBOrxfGnZ0AmZkRxiJCs8gQND0tzELMj2HEicmNTIQvK8PF3dqKvBW+7wtfcOZ5pRBwUlukqAjCeWhIcjOIeff7ISw4b3ZrZy686fdLXpDeSHd8zTVSyZv1KOitj8WEEIPsbNdcI4xoZWVGOufmZjyHP3O7ZR3JNKWUVDP3+aT+FN85Py85Xc8+K2OhZ5cJ0Bs3yiXQ14d9xpwtq3lesQKCdu9e7O3Vq/F+whysiDL27ME4n3gC75+fx96h8sHE2nAY3tuXvMSeYEE3aq+6ChexGTJIZrarrsLnursxng98AJfhT34ihBhFRdgjhEyMj+OcLFuGi3zXLudetu5uRNQCAaEo1ivaswI92Rz1hONrr8W5378ffWa+CnPAaDgxN6qxUfZYPC4RRVLVsunFJc+dwz7IBNE1R+foeZ6fl1wwwprOnIFC2NqKtTxyBJTtzE/INm+ZvL7Nzcg7eeopqUk3OysFdhmp0Kn9Fxcx9uXLMSd6jZ2KCsiccNh6Xe2UicOH0YeJCSEsMRs7urfbTsFjhIVQKMIySYFu97wNG9KjpQ0N+DM4iDmpr8d8HT4MZWp8HEp5XZ3ApKurYTBSOaOsJPW7k1pNlNnd3VAsGxow3ooK7K1kUhjBSP4yNSVEC4cPo2+Dg8bcNaUEXjszgwj1Rz9qXJ9s+VZWVOBWezAboyMh8DRsli3DetEg6u/HGEpLreGeRUWQ54TIc4x0OkWjMCyuvx7ft5NzTiIouuLf1QVZYs759PtxNvv7QSK1dSvuHsKBWZ+I+7O8HGfnL/8Se2JkBN9nAfBwWO6gcBgGvA41HBjA3giFsCd4jnp7ccZoxB04APm8ahUQH9lylC52TbpMLZOOsXMn7lOlMFekug8ErCNmuoNoYQEyknBW0u7T6bx+vcDyiotxZ5KW3KzLJJOYO6XwucZGnPdt24ylOKzyzH71K0HHEHERCsl5v/tu/DwSyb3u2Is9SnXFcLpEjRdwOGwsvtrXJ8xcZWU4bDMzxgrkFRXWAkA/fB0dwgDHS6qwUFi4WK+B2FeGfkMhYcYJBiE4Gf7+xS/gmbc6DEVFEMTV1VL0LRKR/AMy0gWDwjxECvBly6RIZmMjPKyMUtGLmCtu2Ql0bnQUFxMpycnyVVsrRVqthKyV8jQ9jXV74gn8X/cOUmAzglJYKAyIjLRRkfV4cOEcPoy/IxGsPz2QgYDgl8NhXG6sd6QU5k0vavfkk0IBHQgAokScOhV10qASLpKfj5/TCLvpJuN4iotFUXrjG+3nuapKmMHe/W5caLqAtLp4mCRbVoZ9ODtrNHi4p1ta7OvZmI3aREIKZTK/joZCMIi5++xnjd6yN78ZGH86BBjVY8vPx+94oTkV+h0dYOY6dEhqYjHHjbVAaNBxnfWE47o67Be9oO7UFJSWxUVcyuvXY2yEO9KbTQpbwmKtlHsahvPzxiLFmWB0bW1So2nZMpxXPosRUDIRMsLb2pobC5Od19fthgJAmVNfLxE1Frnlhc+xMjpAj6zO4OlyYQyUW2Z4npWiRCbLzk6JsDNfSTd2dG+3lYKnR1gWFqBkRqNSRoKefqvn2cEa6+uxHmTrKyuD0ubx4BzV1GCNenrwvq1bje+grGTOU1OTcxmsw6YI7xoZwXnj3iDMuLQUv2cB9L4+zKM5d81cs8kcackG7zRTgXNdzSyT2RgdmUcXDIrsKygQx+TsLPrH2llmuGdjI2TYyAiep8u4RAJz0tio1HveAyTJxYqgZMv5DARwlinv6upwjsgqS6Pn4EHsl/e9D9/VHcDM29q0CXPy8MOQJ9u2ST06sv0ODmIOAgEp/ppMCt0768Jx72eLsDlZ/4tVlDWbjkGH0nvfi/Xh/iLzpxW0srkZd89zz4FMZWAA++z8eex7/XyGw3DWDAzIPpiclL2o6zIjI9jzb3lL+vvtmJsPHcIanD4t+Vq8q+rq4IysrYUO4sSZzeYE/ne5tyuG0yVo+gVcWAhoCHMZiEH1eKRGztQUNuz0NDwRtbXWmGyl5PA9+ij+/8QT4tUbHxcKS6VwuCYnhQmISadkcONhmJ6GR3N21v4w6IrAddfhuQMD+D8Vex7ixx/HQWZROcJlSkrQf70eh1JLwy07/c4b3oC57ukR6FdeHoRFMAihYLd2Ztae3buBb967FwYD55P1nHw+SaAlAyIv2lRKIhteL95fVoZ1D4Uk34oK2vLlxkrnhHv+4hcY9/Q0jKSpKTybNNyDgwK3WLcOe+7MGewJUnCTvIH5L2Tt072+vMBOnco8z8Egnl1Xl37RWV08eg2nykrs2ZISYyHAigooFFZC1i4iwP1PZXhxURK3V61K95bddJNEdHU2IjaPR4x7p0oL+3b+vNDZErpGp8iOHTjjg4PYn4SzXXMNkt514goW1GXC++go9sLwMM5PJCLlAAYGjPA8uxaLSf23FSuyw4A+8AFjHSTmFXH9yCgVj2OvDQxcPI+vuR+8jONxgUCtXw9ljv1g1IElG8zFm5WyV0b1/aoUxjM0JDDAykqhDmZxb712ne7sMit4dXX4m8WXvV5AG/v7hdlMz0O08p7bwRpvvVVgqadO4Wd/9mf4eSAABel734NnP99CI+DZSKVyk8HmqFplJaLEDz0kMO1UCveDXmx4zx6M7cwZY+6aXc2mbPshUx2sTLDLTIyO8/PYM3V1cp4ItWOeLqHU11yT/l59/fnMyUnJx6urA4HIjTfCSHkhIihKSR7N7CycbqOjRhgfy0KsWQOjiWPS7yCmHIyNQeaOjWEuT5yQPcw6VzpTnFL4/+ysKP4VFXgvv+ckwvZCFWV1qmOEQuk1yuwaHUSNjcghPnUK8/PoozDuKyrwObOTZdUqfOeJJ9AnOimJEPF6oZdZGU1W9+VTT2G9KipwH5gL2Dc3Y103bsyNhMMJ/O/FYDxdMZwuQdOpuw8eFIYepcTLzETIhgZ8fmYGB+T8eWzWO++032DE3c7OKvXSl+IyOn4cz56bw3NZtyOZhMBesQKfZ+6PUkKBTMhPba29IqVfBKTorKrCwR0awoGlB/bqqyWSQlak5cuFzcqMB18Kbtnpd7ZsgfeSQvbMGUnsrK6Gsrp/v3hDMnmZqqoQwTp2DJ+jIsl6Qr/5DQQEIV9NTZKDEY1KLaQTJ/Dz4mJJzA2FhBJ7yxYUF9SZ/Zqa8GftWqw9cdelpZjTqioYY4QDtrfjOYx6FBdLdJKG+fHj+L3ukWfjpaDU0jHlVhePDlnjPG3eLBCsnh5cAFZQjWzQibY2eN1YcPDcOTgE9M+1tOBM/vrXUmldh9Kw0VhpaXGmtOh927bNHgJz3XWY/+uuM3oHlcI+1BUolwtrkkyiz2vWwLvZ1YXaVZWV+F1NDRQf5klR8Wbjv0l1ztwt5vdlU1LMdZDm5iSKSwUoHodcWLUqnYr9QhkyrWpQDQ+DvbKnB9+hQ4qQVUa/ioqMJCqZlFHu13gcCmI4LDmppaUYOyE0zB3iObOKOugK3sGDkNvz85LzdOyYGHjM7WFRWzvvuR2sUanMc/2b3wgKgPOgG+V0pOVyzq2ialVVSr3qVVDwRkYgR1/6UswpiYDuuAPzdeZM9ppNmeouZatJli0HtqwM95kVoyPPkF4PSinZA3V1+Nwdd0ixZas+6ggROmJaWpR6xzukptVtt2FunnkG/aypyU6swmY+L6tXZ875nJrC/urqwjMJA2Y+mtsNGfrBDxqjdlT4m5rAlkoj6u67hZWVBiedCszXobPB5xPY/tQUxsk+8g4icsa87uZxNjcb8+2eDzjY85FPpZS1MV9WhrkyO1ny8wU+x3pfe/di3zKnmbLvxz+GXsDojh3MnrlURB4wNUAvYM/UEjr87Wrf6XeHk/SJXGvIXap2xXC6BG1qSogBEgkJi5v5+CnQS0qwmScncQgGBtKLqrJZbc6mJniBn3nGWJOmuhqX9NAQPsvQOGFCZN7y+yHE7Lw9ep2L178enpDOTqPnd/t2CTEzt4iXn17nwEpxWQpuOZfvMFfi0UdF0JOdyuwNsWNtYiM1/LvfDQGnC2y3G885f97o/Wfy8/r1WOMjR4R8QPe2BoP4DHMf7C7jN78ZEYCNGyWK5nJB+DJ/oqsLyt5VV0lNJjJckVCio0NIJ8wV5nkprF27dEy51cXD3K1wGPPk8UjEJxKBgf2611mPPRt0oqHBWHCwsdH4OT2/5MQJyQlkIUgdStPfj/V9xzus+2K+yJNJI3EAYXl8rhkCo1Oss1lBUHp7ETEg3PZTn5Kk65e/HAZMJILnEw+v05bz3zzrhEvqdLmZLn67Okg6lDAexx7aulXGtRS4Rrbv6HC+TZuwPvfcAwOZ9cYIEeL4+/sx38w3ozJ6663pSheZ3556Ss6KUpBlhNIy6bq/33jOrKIOSomC/93vInfD78f7WcySymJpKZ55+DBk8bZt9nNlB2u0g0Za5atx7ebnMbbmZrzLypCwO+d2sClSr/NeYWRJjwhY5a6ZazZli7RkSup3osTV10N+WkG+WlqEBdacHxsICCvsunWZlcBsBl5HB+76qSk4Ws6cwfNXrrRnlmOzOi9r1+JsPPWUdc4nnTHT03gH+8FIMg0q87xb5cj85CdCz877ink5AwNiKFHeUIbw76IiObPUWyIR47ozGkNWu7w8PPeFgH49H/lUdsY8x1ZXh3nu6sL5ITkECTRYQqOnB0Xh9+6FftXYmB7def3rre9LpoeEQqJzsoyBy4W1OncO/z54UGQWnRqplDDRRqNSq/JiUsVf6nbFcLoEragIgmF4GBtucRGCipY9aY/plSa+NBiU2heHD8ODbqaFttqcLhcU5Lo6GDXDw3hGXZ0kb/b3QzCSSY5/mLTLQ2RWpOyE89vfLgUHf/lLKZwaj6MmxcyMsOz5/Thoo6N418qVRi/aUnDLS/nOvn1Yh8ZGwWKbvSFvfGN2LxMFt/nw08N4zz1Y14EBKeKq0+Pm5Unh0ERCCBpIa9vZmbmCPD20ZsYuwrsiEUSf7rgDa/XJT+IdOrsgIz9k7dMpafVLoalp6ZhyuzyPzk7JR8rLg/B3oig4hU6w4KD+OR36QI/yqlU4YyRpYP6KDqWhV1hvVmeipAQe9uXLZS1042J+Hu82Q2D0ZoagdHTgAvV4QBrR2GhMul69Gu84dEjISNi4HjpEkpHmeBzyg3S5dhc/x9nRIYWtKVNWrED0dWICv9+6FTV68vOXBtdYyndaWrA3ea7z8iCTlMIZiMcFDnv6tCjvmzZBWTUbaLfcIkQtK1dK7TyPR3KGgkGciRUrjOfMLuqgFOT9gQM4+xUVQm1NDy9znNxuOc90sl2MpsvKZ5+FbJqfF9Y9GizDw3h/LufcDja1ezeMSY7DXBvPKnfNXLPpQnJVnChxkQjusf370yFft96KqO7DD2MfEPJeXIwzHItlPjt2dPY9PZA5ZrruVaukthtzTlpbYXRZNbvzcvQo1pDGmp7zmZ8PZ9vMjJS3MBtWZWWYA9aCsmIEZo7MqVN4jx5dJzkLYeC8W0g7zhy36Wn88fvFgcg8PK57R4dS3/qWUr/9rVDt19SgXy8E9Oti51M5MeYrKxGxnZ7GvwcHMf96DtKaNZjftjbMvRlVwWfdf781EzBZVmtrcW+Ew9AXPR7sl95eqVnlcmENCeFbswZygrUev/Ut3N+vfW12p3OuVPGXsl0xnC5Ba2wUCsnycqnfQFgca/DE45JDUVEhhoXXi8v77FnAObZuFSUh0+asqkLuhpnVqrMTQpZF5sbGsOlZn4HsbGYPSibh3N8PoXXjjVCmqGAdOQIBvGEDhCtpuElFXltrDUNcCm45l+88+mj2wrQdHfjshXiZWlpQK2fnTkS3pqehNASDUD6feQaXFBOqCTtYsQL/P3cuewV54tC7u40RJ6UkcbS6WnDQVuNh5IesfUoJnan5Ulgqptx88fj9kpellNTOmprC/Nx6a+baEU6hEyw4yM+lUsbiwTRWV67Euu/di880NGCftrQo9ba34X2sB2RXn4ln4uRJGDnV1cK6qOcpEbpqhsDojYrGzTfDKPnFL/DObdvkcvZ48M75ebyzuRnrxqKzw8NCBEJliQo5oXWMAtvlUSplHKffjz/9/ZiPU6ckEhmPQ+l717uEtCFXuMZSIR4sSl1YCKOns1NY9nw+/PxlL8PeJcNaptoybW34blmZlA5wuYRsg2yhk5OSU8hzZgdN7OxEftEf/oA1Y4SKJC0sdD4zg32xZQueq8vYi6EYtrRA7n7oQ8L+RbIPvShvfT3+3dnp/JxbwSj37QPpQaZo4/OZq+LUyVJdnQ75qq+HDLzhBiiV/B2N6ieegAL/znc6I68hFEspfJ+Fds103YzCR6O4Bz76UUTldHY0pZydl+ZmOFueew57KxCAPrBxIyBda9fCQAuHIZtIHLRpE+TH0aOIKJkZgTdtgiw4ehT6CR1+enSd0SfK29278Xm93tH4OO6u6WnMSWUlZB73SEcHaNWffBJja2pCH4aHsRbbt2cmt7lY7WLuUSfGfGcnoK0+H2QtSyzoOUhkx1xczEzmcv48/m++L5lnzCLv5eX4DHPw3G7odNu3Yy8R8kwK9vx87Oddu7AX9AjX8wFtvBTtiuF0CZrbDSXwwQcl1L9sGTYdGbUYddLpolmfhfkDZWVSi8fp5ozF0lmt9MO/bx/6pDPjeDzGQrRUAuyEM/NE7roLiiDxxk8/rdQXv4gx1NfjO2vXGrH08/P2l5kT3PpSvtPRAUMkU2HaxkYIxZmZC/cyud1Gg/LkSVGsCwqgaM7OSpSP1OmpVDqtq1VdBcKT9BwnPYyeKUldH084DKU8EMAlyNo4VpfCUtZG33vMywqHMa5QSAxGpdC3++7D5+0KId92mzOjlgUH+bloVIoHKwVFgSyrYbLfAAEAAElEQVSQOvThjjvw/JkZ64jEbbfh51ZnYts2POPQIaydHvEJhbC3rrnGHqJgR3+/ebNxjr1e7CFSAsfjOPPLlgn8loaSTgsdjwtsb2EB/b/jDuuLXz/7lZUij5Yvh5IfiWCNJiZg5OnPWQpcY6kQD/17JCegrGEElYrApk0YV6ayB888gzndtQvOi3AYv4/FxNhhlNh8zvT1i8elht7EhBjqlDFkWmWpCNLGUymvqBA468VUDJ0U5Y1EUCDV7c7tnBM219Eh+ZdOIodLlSvZWi75KTrkr6NDqS99Sc7h6CicS4uLRkRIMKjUAw8YGU75fbNjpacH3v9UCuQZzc3GyPHoKL6r17QqLMR8PPUU9pk+b06jae9/PxxA+rz29GB9/H70Ix7HOMkieOIE9gVJiEZHsVdI5HPwIKLclNPHj0N+mqPrhHxdfTX23FVXYXwsaVBfD9l2+jRk6x13SO0myp+eHvy/vFwQMszB6ezEmX4hoF8Xa486NeaJRmHkm+/xejEXp0/L/BJFoDfWiZuexr3Q02OMSoVCeA4dYNdfDz0kHAaqYGEBe2vFCoz15En8js6DLVuwnkzNoLG+bx/m6ciRF4bo5PlsVwynS9Re9jKlXv1qCfXHYtjMRUWSz0T87/AwNvDVV0Mg0FOTSkEAejzGzbl2LYyUXLDh+uE3M+NYeVDshLOeJ9LWhsuQeHwWyVy2zBjZIOnAwoKxurpVy4ZbtxJe+nfs6hVMT9sXpo1EIKAqK/GdpqaL42XinHd3w3vG6EEkgnWkJycahWLBPWFXQV6vq7B5My65qSmMd3wc3sR4PHOSurnCPPcYcco1NcbIj9O1yTYPb34z2BaVkrou9KqvWyfK8aOPZla8br7ZmVG7fTsE+IEDWHfmCDHHL5UC3SqhD2QGjMczRySmp6EsmRUWtxvOimeegXLR3Ozc4LZSts6fx1k7fhxKGi8pQixZUoAX8egoPj87CyWfhXNnZiA/Vq1Cn4qLBcZm13j26+sR8aY8UgqXd2EhjIHaWlHy2Z4Phky/H3KDlNk89+bv6bJGKcgbnZnNicJ55gy+d/31OJc6qx7zM+bmjI4mRvTp/BodFYrnxUXMPROtS0qEBXVuDr93uURhVEpgMevXX1zFMFNRXqVkbWZmnLOE6W2pkcOlypVMbSn5KeZzyCgr83g2boR8pGw2j8dq/HwXCUX6+jBWPXJMpINe04qF7Rsb8Tz9PU7PmNU6cl4efxxygI5WUq13d+Md+fmQ26dPY8+yRqNOcsPIHHNvGF2fncXckGglGoVM2rABZ5gw1fHx9Lp/Ssk5raiAHNRzpF0uKehO9M0LAf26GHvUypg3k7SwNh5LduhwymhUYLaDg5i/ycl0CnPqZzo52NQU9iTvpIICiWQR8RGJ4N9VVdgjLpex2PYTT2AMmzdblzTo7AT0ta/v+aeKf77bFcPpEjW3GwxYhIYNDuLn5eXYnOXlEGJtbUIPXlAgnnGXSyB9hIzo8L8zZ0SpqqmBoLdSms19MjPj2HlQrIQz80RYO4ae18OHn/9QrZNkc7t6BToW26owbSKBaBAx9kpdPC8TDbvxcYH0mPNfkknMX3ExLhKrCvJWdRWCQfEGjYxgP/zFX1gnzdqxkrG4XVMTxt7dDcPhYmPHjx+XfB3CnHT6023bsC73359Z8Tp+HJAjRoTMRq1SiCqcPIkLY3gYY4pE8L6CApyjUMgIfWBB35/9LHNEYnDQXqlsaMD71qzB+7IZ3Kwvc9ddkBE6JK+sDH90ql4y7TFvkcrD2JiwcjJvprRUlPWyMuybpiYoR6w9YxfN4NlnDZ5QCJcwnQ2Li8IK+dhjMBgJC73YDJnhsNC3f+97WHee+1zflU3hrK0VQo76euzTkhLI244OwfXPzRlLBTCKpUfnCgsl5/P0acmpVAr/JmyQSioV68lJzGM4jH6QrvliFJR8vljC2C6n5PBc81OsjJ6JCThK6uvFI9/cbIzQ6eOxGr8e7U6lhD1OjxxTN2CdMaWwxwjVNr/nQtbR7ZbI+dAQ7hUaTSzfQHmSSEAG0HDRjZbJScjBoSH0bcMGgZ9HIvj/zTfD8aLL6dtvx91XXW2/j3lOq6uliLueg0syiWzMi5dby0bSMj2N9T14EPO+axdkB/PRxsfh8GhokDk5fx5ztXMn3qFTmK9ciTVqb8de7uoSx9eePfidvj4subBpU7phRB2UUVdz06GvLwRV/PPdrhhOl7C1tADK9r3vwRrPz8cmJy6YlLk9PVJQloJKLwZID1c8jijDqlXAL/f0QFE7fRob1AybydSyeVDMwpl5IuPjuGTCYcmzqK2FIHu+QrVOEseVSvcWDg5KgdiaGvTNrjBtVRUuLsLFzJGsC2lWCpvuoYvFMEafz35uqCDrdRWscmje9CYITDtFq6kJv7v/fnznhaANTSaxDhTMvAR1+tPjx7EGvb0y54Q5EVJEBeL2262paHXPf0MDoGXT00IFnJdnZMHyejF/XV3YwzozXqaIxOCg1PrR28wMxvDBD2aHOtHIP3gQXli/H2OlXEilsB8mJ2EU63TxFRV4bjiMvcN6OVTGmLNAtqRgEO8bGhJFPRgEUQCVMX2/ULGLRoWylhEuvQ5Xby/m7jOfgUzKBU7phCGTjpqhIazlVVdhvDz3d96Z27so06an8Xt9b5H9cuVKfE5Xtj0eMT7NZD0s3m2OzrFe3uIi/ng8mOP5eXyPzKdsHo/khSQSOItDQ1Bkh4dxXi+0oOTzwRKmt6VEG5/Plkt+ipXRQ8PB4xGDQT+H5vGYx09DaXpaIjaM+lVVSeQ4kZDcVH5Pv/sXF43vudB1DARwH+bnC0mDXqbi8GG8n8abbrjoddF8PtxHa9dij1rN76tfnbvBz3PKPGSdeEIp9CsvD3LuJS95cUC/lMpM0sII9JkzSn35y1jHiQnImo0b8XmXC2s0NwddcPNmyKmhIewFRqV0CvNQyFiqgyUwGC1saADChUion/5UWH/1fUW22cpK0Uf1phvrTU3PD/z2hWyXheH09a9/Xf3Lv/yLGhoaUlu2bFFf+9rX1Pbt2y0/++1vf1v98Ic/VCdOnFBKKdXa2qo+97nP2X7+cm9WdVB0XPm6dTBGwmHxQFIwkbiBIX8mC7a04EJtapLQeE9POmzmQppZOJPKd3paWHBKSzGWoSEc1v37gau+mKFaJ/CPe+/Fz/iZ0VHAwEZH8e5IBPlmL3mJfWHabdvEs3yxPaJ2HkJCixgSV8reixiN4u9QKN2gKCnBuE6dwvezRedeaM9wT4/k1I2Pi/eK7ysqwv5Yuxb7i3V0GFHR2YTsoBmZChc3N0Opzc/PzCR15kxuEQk7hUVnD7NquiMgGJRiuYODmKeiIswBE3bHx2GgbN4s1LULC8KQ1dMDhTsYFAMgEsH4ioqwX2IxcQ7Mz8OQHBrCPMfjxv3CxPXZWYxjZESi4kpJnqburS8vzx1OyWYVHfD7cYEPDcGLuXUr1k8/9/fdB0PN6bsaG7HWf/iDUCJzbzGnaOdOqcmmK9utrdYeU6vonMsl+aupFP7t94ux5HJJfTePB/0gq55SOAOlpfg7EDBGhi+koORSWcKcRrucREJokJtJV56v5hQ5YGX00UnAPORshZT18ScSApmiwcXSH5R/jByz9hgNKL0QsMuV/p4LZXubmkIfbrgBe0y/S+g0SSQwZrPhQmPK48G7du5U6iMfEWZI8/wuxfmo6x7NzUbiCTqGvF587sUC/WKzImmhsyQQwDxPTEAmM5q0fj3+JuqARjVzkI4ckXp2xcVC+EKdwuWSUh2hEJ6t5/Hp+gGZLs37is5nc10zpayN9ecDfvtCtktuOP385z9XH/7wh9Vdd92lduzYob7yla+oV73qVaqzs1NVmTFJSqnHHntM3X777WrXrl2qsLBQffGLX1SvfOUrVVtbm6qrq7sEI7jwxmQ81lrRG0Ojx47Jz8bGJOmUm39iApuTEBKljJh+nw8e9wtVdvVLcvt2oU9dWIAClUwKM1dlpUBSRkag3FVWXtxQrRMln7kPTU24pPQk2+JiIULYtw+H3FyYlomQZs9eprnJ5dJ34iFsbcX/7aJ1kQgu08OHBZalMwN6PJjn4eHsydlzc3heYSHG5KS43YU0KhybNyPnyKq+SH4+oqgPPCB1dKzYhFassPbAE5JplX80N4ezl0rhvMzMoE/0sq5ZgzOnVHbiFauIRCb4DymBuWeKimDo08CLRgWyEwjAeCPjH8c/OIhIMyOjK1diTlatwryyiC0VvLw8zCkL3s7NYVx+P97DWitDQzA+qIzo+4VsmLEYDDeWWKCRQLY+zmcq5QxOeeutwhhlLnCry41TpzDu5cv/P/beO8zOs7zzf+ZMPdN7b2ozGo1lyZaRhS1j2bGBYAcIkBBSaEtMrv2BCewmhLAQCBvappEKDptNWEgIYIgxDtXgItuSbMsqlkYzkkaj6b33cs7vj+9+9n7OO+fMjGSDxe+n97p0SZo5532f9yl3/d7fOxbD71ysc/+WtySWN8FnzczonefmNDcFBbpfZ6eIOPbuNRlFgGGjzgLZOb8ewzmL3KakWA+atDStTyRimaho1JpS9vfrubAfvpiZ4UtlCbuUflzrybnTp/XvL3zBaK1/Fv14NmLExWMq9Zt1b6SRsl8/BJwNh2d2Vjqcvn3O6d4VFTJIBwfjt69IlEF6IWxv7NnZ2diaQOdMZ1KX5TPmUY9bVKTnwcpJa4wX6/Idw6Ehwf4IvsEEfOedG0fX/Kyv9ewEn6QlNVW2H8QX2HQEWLq7LeBCjZnvVJeUCHb32GOSz3v2KDhEsJ36qeRkC0h+73tr2wfx9tWePWKRfOihWN03PS1ZnZ2tEoL/r1wvueP053/+5+63f/u33Tve8Q7nnHOf//zn3UMPPeT+8R//0f3BH/zBqs9/5Stfifn/F7/4RXf//fe7hx9+2L31rW/9mYz5xb7WUyizs6IdfctbpORhtElLM4rolhYJqJ07VxuGzr04xm48JemcBPqFC2aMwXTjF2VjPExNqbD6haRqfcHT0xO/F4H/3hhu0KT6RbbOKdM3P280zZBE4Fw1NcWP7K03NxtV+huJEL7hDfpsomxdTo6EbGenfkcDzb4+KeTiYqXcDx9eOzt3331aw5YWQTzD4VhaducSz4O/LqwHNM+s71o04uFwbG0Xzgu9dm6/XdF1n00IgzIc1l7Iznbua1/TnBQVGQ7ep8cNOj3AZHDeIDcgyoqBs5Fmv4kyEkGDhf1y+LACClACl5fr3a+91hj3gOzMz8c+k5qm666z8b3vfZq3P/kT3Y+6uO9/37DyOEd5eXIyU1KsMSnvMjWlc/Hss4pQUjfHfgEzPzBgawU+HkKDtDTNP0xzQYcmCKcMshWmp2s+9u8XJAV2TtgJ//Ef9e4pcbSYL+927lwtb+I9C/rnV71qNXV5NKq9Q8Z+oxHT6mrNwaFDZhQzR6mpun9SkvW2AR5FViolRc+fndU8EiiYn1eD46WlFz8zvNEszKX21lpLzp0+bYG/kpIXljl7MS5fTg0MqH4xHlMpjkOiRsrBfoSve53qJDs6zIleWjJmRfqcpaXpnps3K8jQ17e6fUWi53Bdbh3uWvaIc1Y33dOjdb/hBtklUFEXFyfOwL5YV9AxLC7WutTUKKO9davV5F5JMLCN2Ak+ScvUlDWjZR2Wl6XTMzMlNy5csHnftClWVzsXS96Tmqr7QBKBjMPxotmyT14CYuDiRQX1PvSh+FD4UEjBOr/P4MCAnltW5tyXviTU0U87EPKzuF5Sx2lxcdE9++yz7kMf+tD//VkoFHJ33HGHe+qppzZ0j9nZWbe0tOQKaYQQuBYWFtyClz+fnJx8YYP+KVwbNZzpa9PQsNrjv/ZabfKZGR2qYJbghRb2JqJRPXhQB2vHDmucBr44I0Ofi0bNufHhBJcThUpEy5yZKaHhR1EwBDHi+/pi4TLUMSwuSujm5+teXV0SNBuJ7CWam0tV+huNEMb7zO7dEoTj4xatJmOTk6PPpaTI+P3ylxMbWpmZRqFbUSEHxW9ud+ON2ouJGKcY1+BgrMCEhefaa63YdC0acRjLgIP09EgRh0KxbEKpqfrc9LTWMBTSOHp79f5+P66KCqPHLSuLff9gFNWPsm6k2W8wqrZeRoL90t5uDa7z8w1+4TelzMqSA4ODQrCEppuZmZqztDSduVDI6IrJjJWWyhk4eFBjBe47M2O94vg8fYgyM/W+jz6q95yYWN0PbMcOfQ5a/Pl5y3BhXGL8+2cRhyZI8+yzFc7Naa889piUdXOzjECcqOuukzKnYXHwQt5lZcl4Yh2am7UPgsyIwcbB/h5k7CMjl+aEcCba2vTOo6M6G9SMRSJ6NnVV6ekaYySi/5NRmpvTzzIzLdpM0+Mf/vDSaoY2mhVfTz5fLkNePDnH/FZXW8+ijdzrp3UFZRmkNVu22NnxmUqhw/YbKe/eLXlJlop57uvT+UpNNUd6edmyiVNT2ocFBZIlyP7m5tj2Fb29G++Vd6l6dj17ZPPmWGKH+Xn97Oab9cev8ftpXvEcQwIi//t/W6uB7duVDYnXrPxSrhdKwLJROyEI6fQz1UCv5+e174qKdD/2T0NDrNMUD7Hik9Tk5Uk+9/fLdjt4UHIlkXPV16d9fccd8fcVa/LjH8vRD4X0/3itU36e65xeUsdpeHjYraysuDLauf+fq6yszJ05c2ZD9/jgBz/oKisr3R133BH395/61Kfcxz/+8Rc81p/2dSmpdZ/Guq1Nm6+11Tb6er17LvXaCI3q3JzhbyMRHeyhIYMFpaXJsYlXOMgzEnVS94Vi0ODBaH38cSkhIETLy9ar5o47NLZHHzXmNLqjE8nNzNQ9t27V/KWkbCyyd7kGRKI9sJ4wifeZSMS5j31MigHsvJ+xqavT2CMRYwwcGIgtfI9Gdc+5OX0+OXk1JfqxY1Yg7M9DsBkq9XjAkMrKNPdf+YqUMAxLa9GIU4/T3W3Powbipps0lrNnDX4G/KCrSz8D/upn3QoLjR4XGBaXH0UFFreRZr+XGlVjv0Cesrwsx4Zarp4eKbCLF2UcFRbq/z4lbTSqd9qyRQqxpET3wUhubl4dMS4tVebpmWc0x6GQ3ouMx9ycMnL0WCstNbalM2eMwc2PZmZlaX/t2aNnV1VpDxw9qvkHOuIT2MQL4ATP0PCwIJs4a+PjWu/jx+VEXXONjMrCQo0rUfavulrFzK2t5qg3NBgLlf+9YOPg/fvNgQYKOzCg321EwftnYssWZe0OHRLUsrXVGkuurFgwoLlZe7C3V3AZYHuwYKWkGCwyO9t6LW2UPe2FZMWD10brIKGv9uVZUIZNTAie56MA4t1rLaf1xWAVdC523aqrtb+cM5h2Q4N0TJCp9HWv0/7xG/x+6Uux+27vXuf++Z/1s7o63c85q83BwU1OVqP6d71rfdn/0zI2N2KPXA6xw+Vca61tvOBLe7vsAurSCMB85CPO3X335Y1hI2dneVlnfGBAumDfPsuG+3KfthbA4IN2gp/xq6yMraPr7TWCBxymlBTtrVOnZAfdcYc5KkHESleX5mJhwWDUc3Pag+Xl0g9tbdLjTz9tzlVqqr7T2yuHqKpqbZlx5IjGFkQq+MgWGmm/UFn0UlwvOVTvhVyf/vSn3Ve/+lX3yCOPuIx4HIjOuQ996EPuAx/4wP/9/+TkpKuJR3l1BVyXIhhpAup37i4s1Hcgg1ird8+lXBuhUZ2eliFAP5PpaT0/HJbhlJoqQRLPcbvcTurO6fmveIVqWr7/fY2B5qVjYxpLf7+EyenTmjcYaUhEZmXJUOzt1X1/53fMSFovsvdiEylsJEIY/MzJk1a4nJtrTHpEy7Oy5GR0dspwe/55Y0/DGE5N1TxBJ5qfv5oSva9PEcV3vMPmwTd6m5pUfzQ3pzV3zmiTndOYoJmFunajNOJNTTLCgPRlZmqdMTrS0iTwYZ+anLT+IjDzZWcbFAbnDcc7L0+MaP392iMbCVxsJKrW1LSaka6lRc87e3Z1FofeMPyM7O3MjL1bdrb+LC/bmIINO+NFjAkYFBQoqlhernnv7NR3Jyd1fhoapDT7+zWeoiKLlpN5hBkuHFbrgvvv11mpqtJ9u7uNyY/S00QBHP8MOScYXmen/j08bD2laIY8PCxHirqgeFHx5GSNF2YofvfkkzoDRFW5fPpnnx0t2PfkvvvkGK6l4OMFU3JzBeFsbxfsKz1dYwZmc911mpOZGe3J8nIZ1729lolaWJBMGx+XXLzvPj3r4kXVMfiyPV4D3heaFfevjfTWOnrUuf/23/Q56rd84wgZdvKkFb/Hu9aDmb9YDmFw3SYmNM/0Mert1frdeKPWFFpumEoZS7CGtLNT8Lz/+T8tM3v+vNYrN9f2IY1NIxHt0XjXT7OoPuig+NDYYEAzmMX9aWUKNrq2rF17u/WfghhpYUFn5EMfkp64447E443npAXZWOOdnfPn9Zlz5yw4u3Wrfnf33bonMPmzZ1fXIAftBOQ38PPRUY1tcFD7JRzWH8i4entl63V26g9U9UH99aY3KYg5Pq57OaexUqowNycHrKfHWnP4+glSmrWCwevZRCBbtm7Vu7/UsNzLuV5Sx6m4uNglJye7AUK2/+caGBhw5eXla373T//0T92nP/1p96Mf/chde+21CT+Xnp7u0n2S/yv82ohgRAnSq4XO3ZOTVqND89a1evds9KIjtJ+pACZHCnlqSkJmcdGE1tSUDgaFjfEct8vtpO6no6HdnprSs6BPra01NqyTJ6UEPvABGYWRiEF5iot1mKExHRhw7vd/PzETUHBuXmqK3SBblU8K4pw5UWTcVlYs24AxvHmzBGdNjdUNBSnRe3pEV5qIpndyMhYK6ZzGQx+SkhIpNZ+yd726F3/eicQdPKjnFBcbzAeWOYhJYCLKyDDDdWJCxveuXVrjYLbo0UflNLz1rWv3EeFaL6r27/+ufebX0szNyejYvn01WQA0w0ASFxa0d1dWNJbRUf0bJwGjvqhotUMSL2LM8267TfMAXfnYmPZFcrIU/8SE/lRWGvSzpMQc0OAzb789Fka0tKR7AkF87jkZFtnZ2mdBOeCfofZ262sUDutMUgM0PKwxTU/reT092muZmTKMMM6Brvb0rM4C19YqAt3ZKcdkctIgoUVF2qtJSTb39KUjo56c7NxPfiLj4g//MD78J5HhkJSk7FNRkT5zzz2SU4cPS7a1tWm/3nyzCEkefljzUFWldRoYsAyhc/p8KKSx9vXJGcT58jOlzr14WXGu9XprPfmk1rytTXugvFxji2ccvZC+Qy+mQxhcN2BSS0tGPADLXWWl1mhhwXr7xXOYh4a0VyIR7WEglyMjVq+Wlqbvh0JWs9bX99PrZ5XIOdiIg/JiZi3Xuy5lbTs79fnp6dgaZpAly8vaj+9/v5qtv+ENGyM6SZSh9s/Opz8tm2JyUgHYzEyN4fRp5z74QX1+ZUUBy7X6FPqMsI2N6nv54INycqamZM9RGwlSJitLcgyG1S1blKmsqoqvv2ZmJFORm9GotbPIyJCMJjADox/lFrD1NTSsHQwO2kSRiDEuA52em9PYOO8vFSz3cq+X1HFKS0tze/bscQ8//LB7/f+R8JFIxD388MPuPe95T8Lvffazn3V/8id/4r7//e+7G2644Wc02ivj8oVzVVX8zt2ZmYqKxYuIXc41MLA6UwHLEJS6KSlSjkVFFqGlSHzPnlgGKw7U1JRqbmDGiQcBjNdJ3W/46ZwxVpWUSADl5MTC0NLTzTD/vd9TRmllRQ5mdrbuOTSk++/Yofvz3PWuF6tp5AuBmqxHLtLVJUGWmqrs3JEjUty5uWbEdXRIoA4NqWmpD8uCEh02Kf/yhSQKyncGYMZzTt8fG4ul7HUucd1L8CKTcuyY9jasf4w7OVlzx/P8jExqqgR3c7Nzn/qU3jFetuj4cY3l3nvXXv+NZBoPHZLRtLAQW0tz4oTOQTQa2wNlYUFjDIc172RbgJwWFGiugWpkZ+t+zzwj5Rl0SIIZ7J4eRb1poAq7ZE2NNfOcmNA8NDTIEHdOn4PpMNEz42XhoNGen9d5Ki8XJDNosPj9k9raNCf5+cb455zeF8hiZqZkUl+fshrbt+v7FRWSM5s3C7oab204ixcvmtFL9Dcc1jOARF64YM7l0pIMh/PnrYh/eFg9VYLO03rBFDKfVVUirrj99tVn/8c/du6739UeAfYMexY06dGoPjs4qLEdOqR/l5auztS+2O0F1uqtdeiQHGCi7svLWi+YWIeGYo2jy+079GLCpJ1bvW7p6daPjOJ554zJEhZPZHtQJtDbEEOePk30CGRP5eTos4uLujeQ659GsC0RuoMal7UclBc7a5noWqvxd6K1nZqSTpiaMr0PTTYBUWpiDx7UnvLHm+jdEmWondP/KytFRhSNSmb648zO1jn4q7/SOV+vTyGMrP4akW0nu+5c/L3s93Xbvj3+Ofb7JVZU6PnQlFNjCysystcvt4CtDx2QaH/6NlFfn+wNgn7Ome0VBIm9EEKbn/X1kkP1PvCBD7i3ve1t7oYbbnB79+51f/mXf+lmZmb+L8veW9/6VldVVeU+9alPOeec+8xnPuM++tGPun/5l39x9fX1rr+/3znnXHZ2tsuORyL//7HLF85zc/E7d9Pgsq7Oevdc7tXSIiazYKZibEyGDr1GKitNYBUVybDatk0sX3NzFnX3iQNycyVwKyoU4S8p2Vgn9WCjQQydjAyNL0ih6hvmREwWFnSYR0eNdnr7dgmetraNK60Xo2nkC43irVfMm56u9SPC40PwoMWNRLSnIpGNE0I4Fysk/b4mKIfFRYuozszo/jCw+Qpuo8QlTU2K1J89awQHOO2pqTb2+XmL8EKvDqwsFNpYtmgtg2sjUCUa5/pKt7paUcFTp0xBVVbqOTRbpl5r0yaraUlLM4OtsNAywLOzOme/8zurYYEY4SignByDd/gGHex9k5M6D7OzWp+VFa3L3r3GMpfomRjnX/uann3ggO4bJPk4eVK1Ef68+plEv08MWUMCM7QHCIW0RktL+jk1cV1dgkm9+tWJ1yYvT/MAfLSszKK/9LOqq9O8t7ZqnmG4Ayq5tKR1OnlSTviHPxx7Ti81mBIvWFBWJkNqYUEG0diY3hW2Qnp5wfY3MyO529gomev3CvtpZMXjyRx6a/X0aL6qq/U3iIOhIc3pzp2xxtHl9h16sWHSwXXLzdWem5szUo7kZGPd7O7WvwHHBOfZ12VJSdqjMCmSFZ+bM2KblBTJhpISncPLJXJKdMVzDqanRTAyO6tzkygDsG3bi5+1TDTGRI2//b5DwbUFojw/r7XCduCMzM6aPhgZ0d+MNxIRQ+f580bOgC3lZ6jr61fvs7Exyc14gU7uceKExrVen8Kbb15dx52ZqQz33JyxltLDMyVF70UQfXpa30lkawT7JUKDn56usaelWTZ/dNSgweFwbO8nslOJ9ify/KGHZEsBwyVTNjZmNaNr2WpX8vWSO05vfvOb3dDQkPvoRz/q+vv73e7du933vve9/0sY0dnZ6ULejvz7v/97t7i46N70pjfF3OeP/uiP3Mc+9rGf5dBfkssXzpHI2p27k5JeGJMeEb2REctU9PfrIGVn2yHIz9dhW1kxZVdXJ8NqYcEEQZA4gAjq6Kii2jfeqP8HIYDBTurAKLgupWu1c7pXUZGx6vlG/HpCIXhdrtLnerGieGsV8+7YIWIGFDoQvPFxRYed03zv3Kno2kYIIbh8x7GpKbYhonOW3ocyPxxWliA1Nbax6KUQl9x+u7DaBw+ak1FYqIxof7/eMztb+3FkxIydO+6w6P4LNbjWM477+vS74DMgJFlY0Pynpmr/l5VZZBTqfBrzkk3m3FPjR+3T+94nI3s9B9x3UIaGVjNvLiwYzLelxZzN7Gy9R12dQXH9Z37hC4I5Dg4aTe7YmBFXcMF6GJxXP5NI1nFiQvPgnEWLMWIxIHJz9Z6ZmbEG3BNPrE2asLSk94bO3jkjMklPlzN9551yjMhCzczoOTguMzMGXQ4ajC9GMCUnx+TU0JD1GuvvN0Y2jG1kfk2N5j0Uij2na+3VaFQydW5Oc859N3Il6q1VXi4DzJfFGJFkpYONqi+n79CL7RAG143+ceGw5odMX2urzoBzkjl33ilI1jXXxM5zkBFtaUmBuUhEa+qc9nZ2tvXlWV7WGTlw4PKInBJdibJz0ajWOznZSEt8ox5ZeOjQapnpM9jSu84/25eKolir8bdfW+nc6rVl7U6csCz15KQ1sY9GjUxlbEz75tAhZXa//30FXKJRBYCKiwX3LS01W6C/PzZYyzU6auyn/gVEcGZG3zt9WjbS4qKR/qSnr+5T+MADsWsEEqCyUoE452IhzJz97u7YwGC8K16/RGzH5WX9LhSSHTA0ZNmoPXus/+FG5FcopHH8/d9bs26ChGS0olHZk9u2rYYSvhCb9Wd1veSOk3POvec970kIzXvkkUdi/t/R0fHTH9DP8LpU4eIrQZjDurqsSN652M7dbPDLgYL5BubCgu4L1pboa36+hPzysmH0UXaNjcL/JiIO6OmRACsvl/A4c0bKB2eQSHOwk/rEhNG4Xk7X6sZGjaW2VuP1meUuh33wcpsNvthQEx+a5TdVJcPjG05JSfozP2/MfD7Uci1CCP/yHceWFotm9fbq9zR1PXtW//eFYrCx6EYNtlBIdUoPPKA5wrFgf6aminmPTvcjI1rPe+4xWMcLNbg2YhxnZdled87gcWNjmgeyYmTOysv1JzlZc1JSonns6zPa2aIi3Ss3V2N82ctkqGzUAU8EdaQ+EqMDLP34uOAyLS0yJFJTZSjW1jr3ox8596d/amtAJmx5WUbY9LRzt96a2NjxLzKJbW16Hk1fybJQ/+Gcxpiba045BjqGHsZ7R8fqtQG/D3vmyEhss+Pqasu6kX0dHl4dJU5JkUwsKFjtDL7QYEpwf1VWmmzDsZmb08/IzqWkaB4GB1fP71qwupYWRdpzcwWNeuABo3zfiI7wZQ69tTZtEruXH9Bzzoy8RMGpS2WNe6Ew6Xg60V+3jAw9u7RUDvLSkvY0pCfZ2Zr7tjY5Tp/6VOw8+xl4agWpV4PAYGXFGm/n5enn2dmCnL6YNR6JgkW0ECkoWI3kcM7O7MBArMwMUlUz1uPHTR5dCooiHjEHCBO/thKIfrys7dvfLsa47m6DnvnBl5QUfb6iQvc7fVpBn54ePY+G4AMDkkG33irDvrxcAcX5+dgxQ4pFXzYubBLkFdny9vZYZwfnkD6FJSWaA3+NcL4hQpqcVABraspkFGQUd92lwGCiK9gv8bnnDDkEWiMzU/NTXByL9vGD4huRXxMT+hvINf0Dc3P1nJkZ7fXubrO1Xij788/yuiIcp/+/XpcD0fKVYEmJDs/oqA57WpoZD37n7o0WfgYvDMy5OaOmpAcJ9RjRqKIL1123Wtn52Pp4xAFFRbpHf7++19srfDdZi2jUIIDOWT+eigoZTzhqibpWxzvoLS0SmufOKaIMDTXQx8tlH7wcqtgXG2rinBlVDz20urnn4GAsI+HCgjmoPtRyPUKIeO/uO46lpaawSko0/rw8GWQ+XXy8xqJcQaOmutrIOgYGnPvOdyySxT6dnNTPdu/WPQYHtT9uuSXWgX0x6tLWM45LSqxxaW6u1TvAXLS4qM9XV+tdOQO/+7uCqQwNad22bdMzTpywDMkPfiAlf801l178vxbUkd5QFRWaeyBXsH1xfltbnfsv/0VZpgsX9KyCAr0v9T8LC5r/tYyd4HX77coK/uu/av7m5jQmaozIWiwuaiz5+ZJj/tnB0Nu/X7IluDY0Ct+7V2MZHjZ4MUoedkagTEC0uOiZkppqznnQWbncYEq8/QWzVn9/rLFWXKzPsmYwaQXnN95enZ3V+o2Nae2amrSWjz9ulO833hifvSyejEM+PfCAZZPJPLM+ZFRHRnQmX6hxlMghhDX19GkjveHzfguFRDqRdXvmGc0TvdJaW41Cmgh6NGrsh3/7twokMM9VVdYCgbrgqiqd5YYGzU8korVlXLSOSBTUCV4bDYgmChbh3JHtCtafcmbLymJ7C1EfyVxQY/T1r2uNH3ro0lAUQV1I42/2ENlKWhTEM7Cbm0U5/sd/LMcHwpu0NCOOKS7W/KSn6zx1dBjRVVqaZZgnJ3U+8vP1jJ4ejTEcjpXzjY3a152dBvED0hwOW289ZHcopHnMyNAYdu7U8/fsMUZkf41Yn+VlrQGsm+jYyUmrHSQwmOgKnhc6+PT3a16mp2MDUfFsrY3KL8oxtmzR2KkjTU/Xvunq0tgHB2V7XIpTdiVcVx2nl+jaSIQ4kSH+uteJ0vbBByUQKiulHIBawI52zz161uVCwSBZOHFC98bIJDKRkaFD99RTGlNwsxPtB89NZISLAsT5eUt5P/WU3ofahiAEkE7q9MxI1LU6nqHisxHu3Kn3Hx2VEdnTo4jNPfdcfoHrpVLF/jRqDxLtq8FBzd+hQxKaWVlWHJ+XF2uAwsqXiBAi3hV0HHmntjaRBdTV6T4NDes3Fg0aNQsLMqLpawPl6yteYZhrv46msVFEIDMz8Y2JFwqlwlhZXhbzEaxo3d1WYH3XXQo2HDtmEVSgEUTfcnJsnjMzdZba2px773tjawIXF40BEtIU/7pUBxyoo591jUZFmAH7IEo8KckMkJQUGdSnTmkNyEpBc07mY3HRMiH0HUlk7PhXKCSD+lvf0vOqqjTX4+PaI85pDsiS+tAdLgy9XbviNwrfvVvr9vzzVqPp0wKnpVndyubNWlfqINPSjDwiOdnqXhI5gy+0747vfB0+bM2Sk5Nl0CQnGxlQY6PeMdH8+vc6dEgwmakpfXd+XnsBgg1qc44eXU0OsB4UlGx+cbHGBqlIaqrWMD1dn0vEsHopAb54DuHcnAJiFy7oHmfOqMB/82ZzBJ2LX+tz8KDO6z33iFW1s9O5z31O+qG0VNkUGosD887ONjKXs2c1d77DTKYyEpFeo3n14qK+s3evQc391hEbkfmXMl+JgkU4KDgFfobQl4X79imbc/SoZWRxipmLLVs0/3/910ZTvlEURVAXgjBBbkL6MzKifZ7IwL77bmU8P/tZBQCC7LkwxE1NWbaGjM70tNaCzNT0tPZDZaXkOX2HgrbF+fPKOLa3W1aIWirgnc5p/MgP6hePHVOQ5/Wv1/wH18h3IHNyrP8bpDZzc5LJf/iHlxaM4bxce63eE/jgtm0a/3q21noXtaPxGpRnZSlYQzbtUp2yK+G66ji9BNdGIFqJGoRde60OG7UoSUnanERGamq0yYuLdQg++9lLh4JhGE5M6NnPP2+wnVBIyra4WIcXaEwwK9LSIsa8lhYpguRkOXSpqTo0MzM6wBTHT00ZVndiQsIEKM1zzxlz19velrgD+FqGit/nYWlJ98WRg+GFmpuf1fViMfJxRSJSFhcvGjyT9Pi+fVbPNDws4Z+erj3lnEWvuS4nbR7PcSSjAdQIp4xreTmWoSfo+M3NyRAbHdU67dxpmbIjR1Ybz6GQzgxY7UTjvFwoVSLK2ltvVYChq0vv8+Uva7z0GWKdgVew3ziTwA1bWjSWP/gDRUM/9zmNY88ew6inp2tNW1p0fl/zmktzwEMh0fF2d1ujT79QPSVFZ5z+S0BxyPYxL0BR0tKsUSskFkCAFhbiGzuJIuW7dskQGB42xyYc1s+rqrQvn3tOjs16exbGRP85U1OKJPf06L3T0mKbJBcXC3q8b5/2FnOCvKAmJRTSeCYmtDbxzsiL0ZTVl2nHj8uhPnhQDrWfLR8aWj9a29gohrIf/lBjq6/XHr140QzJlRUZaxSJ9/YarX6w+XgwAOecZfPn5ixINj+vOczMFMwzXnDqcms9fdpmHCbYwYBCzc2ZI0XPsiBl+Jkz+ntkRPruF35BwcB77tF7Hzum9ad/2sKCZdbITg4P63zs3y/de+iQ4ItPPCHj8Phxc5wqKwUXTUuLRWGMj1sz67X2zKXOV6JgUVKS5rCjQ3PmnEHgfVmYkiKZeeqUHAV6JS4sGONaU5Pm+rnnnLvtto2jKCIRnaO5Oe03egeVlBiREX3UZmYET17LwG5udu6P/kjfuXhR4ywq0lyDTEhOtrrRggJbU8gSIIO5cEEOxD33xA8MzsxIXn3qUypLeP752J57BLkINE9P6z07OzW3qakKYkGyE1wj34Hs6VEA8tZbFWQD2ZCoLUK8K5gJn5+Xo1lRofEB4X6hTsy+fdrfp0/HZlRZ76kprePf/I3m46fZQPmncV11nF6C63IbhD36qIr8i4rsQFGfkJ5uBY0TEzIe4xV0+s+JJ8R8w3BwUMYgWGEM+IkJCXjodKEB5fIzOxUV+j1sfzRnGx3VfQsLNWbndHDIBDU0COf9pS/pWdTbPPCAOVHxrkRZHxrQEbUFYrC0ZA7ikSMvjAZzo4aS75iWlUk4+9E55y7Pcfnxj8WAOD8vheFH0ktKJIyHh51797uNXQwWn8utxVjvuhTnMBhQcM6Mjc2b9XMaQ5aXaz8BvYK9D+rjM2fWFsSXA6VKZKw8/rjmq7raGusSGCBz0tlppBBFRVoP39EhS4NSAd4xNma90IKFyZzfW265dAc8+P4jI0aXW1MjIzQtzeYPfP/kpM7s6KgZF0RVYW8sL9fv6ZEUNHbWipQ3NspYOnrUiqkhb3FO+/SVr5TsuNQ9G4k49+1vGyRvctIyCDk52gMpKVZfsnevDOaqKqPVB8qDg7hWf7rLgUevBYWrrxcs+sc/lqPA/nJufUOHxqz33y8ZyLokJ1txPxDI6mo9f3FR/z59Wuu5XqBvbk776OUv1zsMDEjGpaXJkH7b25TtDM7V5dZ6+nM8O2v94tA1wMxDIa11OCy52N9vrI8+LT9Q954eQc6+/30FJe66S2t+8qRlJbKzYyF19JArK7Nx0Zx+fl77ZPduvetTT2kc8/N2n+3bJfMfe0z3+Z//c3XD4BcyX2sFi4aGdD45t4mavjc1qUn4iROSFSMjsYy01GQSYAhetF/w5TNlBC0tct5OnFDmqqnJescFWXp9xshEV3299hy9C/1axvJyCwZBVkCjbsh7lpYswPWKV9hcUr/11a+upnQHdt3dbf2QgKVHIiZn0tJk45WW6jtlZXbum5u1PqdOmY6hx1xysvbKuXN65m23XZ5zEy/A7EPhXwwnJiVF+oVMXEmJkQZBFPaa1xhE/eftuuo4vQTXWhCtaFQbOtggLCfHjHy/z0Q4rM8QNUtNNQXW339pkWjfMKyulpBNTzeIyvy8DhORlOxsw+uy+X2h3twsoXD4sJ5RXq53O39e9wuHZRDRQ+m66+yAHTmi8SwsSGAi5I8elVD51V/deAGzc5q39nYJsdLS2Eh/SYmcxPZ2K2pc7woaODMzsY1ON9pAkOaVU1MGobscx6WlRcbL0JAUAEasz0hUUGBNQ/1szAupxVjv8uE765FxBAMKKJ68PMucsT7UmJw+rXekcBv42H33aa+sZaheCpQqkbHin0ua5lKLgxFTUiLIw5/8ieqTystje1iAVS8okALmLG0Eytndbd89cya25wn3TuSAB8lEvvxlOfEYfxT3M77cXI2J5rRZWTrLQNcwwlNT9btwWBTH996rMc3MiFDiG9+QfEoUKcfAI/qclWXwkeJigyCvt2eDZ40C7S1bNF+dndozGFTUlxCgOXNGz52eNkIImk5u2qSoarwzcrnZk/UcSvbp1q3O/dmfbdzQaWlR5vLMGa1zYaH1fgLyQ6Po2VlrIA50rK1NZ3HbtvgBuKoqwTwrKozev77eAlKdnXqPeE6Tc5dX6xmc46UlnTV6hqGTKPifnZXBed11cg6Wl2P7LKELqcMCBfGjH+me/8//o2e2tWmfhMM2VljympslqwgadnZqjqNR7buODtPZ9HCC5KWry7J01H+t1ez1cmpj1wsWbUQW7tolB5Ass89I65zJZZ8swTmzT8gc3Xefc9/7Xmz/qP37LTs3PCyCn3A4lqV3o/0ofUeRflvshYkJnfOtW9Vk2odCY9x3dekMRCIa5/Dw2jBPKN1f9Sr97OJFq5uKRPS71FQ9KydHMpYm4wMDCoQEoent7cYgeuCAAjqXA5tLND/BAPGLHTC++26br9On9Xtg6pGIMnQQjPlQ2qtQvatX3GutKPzEhIQJTon/85ERGWE4Ohg2pIUxIldWdGh/8AOLLF5qtH9iwmiSnTNq4vJyw+zOzkrI+YW+QaFeXGyFtZOTErJjY9a/wLnYiJVziXvg0Hvp/HlFpnbvNqawtZjrOjsVOZ6YsGydf0HbHq/IO96VyPkpKIjNOATr1WgOOj0d23R1fl6GiS8oN+q4+M0CBwdlFJGt8BvsnTkjZ+nFYLO6lKu1deNkHEFHIUjnS0Q4J0fvCnFAbq51RKeJZHb2xur4QiFz2qam9He8d09krPjncmQklpUKI6a1VWfmv/5XndHubnNuyRhD+71jh52loJzw6X/T03UWzp1z7otf1Gfa2vRn1y6dp9nZ9R1wFGgkIqV/3326R1aWzinOUmamssDHj+vnOMSTkwa1xEgYHraMzStfqd5ONHP069MS9Yz54Ac35syvtWfjOS/Hj+uMdXbqnGRmas5ra7Uns7IErbrvPs01FOzT01rDrCzBt3bulByLt09eSPYkkbN14oTkcFeXxpKVJefvllvWDx5FImIOO3jQMkqwZDlnTcyp9QJ6VV9vvb0wdBI58MvL2ovXXLO6TtI5rd2zz0ofbd++eryXWusZb44pRo9G7d3IiAIVHhvTeVhc1P2SkrR3U1Kkc31qZqL8s7PaL//xH2qe/qEP6TtkjMnY5OXJuXrwQY0rI0Pfo7UA7IygLMrLzbEMhfT89HQZm+jctZq9xpsv5AOsffGCgOvJ+qDxHI+kp6kpcX3o5KQc7IkJg9z5WT3QA7By+v2jcnO1p2F6fOIJ6fjLDeIFHcWpKSOTev3r9W6nTpmMTE7WeP5Pa1CXk6Ox1tXpfePBPHnvUEjfb2vT/Pb3W83n0pI1NAaiTUP2rKz4gaTOTu2HSw0QJ1q3nxYMbr3M+t13a/4+8hHZKUCCYQUkcw+U9sVspPzTvK46Ti/BtVZxOv0ttm2LLcrHiKSojj47MOthSAJ7w5Dr75ewbmpaDXtZK9rP89LSdNBnZzUuMkI0R2tqijXKfKHuU5ZSD1FaarTGwPSqq2MPdbweOL7wLSy0sa1lHHOogQFAvbl5cyx1eTRqkeT10sZBAyccFqSjq0tj27LFHNGiIkWePv95zeE3v6nvsYY4i/v2CdJRXCxsfVKSomHh8Np9VYLNAolkUnsGRpqMZNDJdW61gG1ufvEErD9XPnwnERlH0FEINtTFKWposGg7tTX0oKDJa0+PaNSpA0pE6b5RSFUiY8U/l2Njq1mpfKNv505jfcJQSk/Xd7OztS/9sxRk0KQJLcYu9OQ5Oda/Z2hI0c8TJyRbEmVFEs3B5KRRD09Pa26rq83ppg6msVFn98AB7b2ODn03LU3vc+CA9rLPrrW0JEMlXn1aMFK+EWc+ESw3nmE9NCTlTKE+/Z+GhixLNjysc1xbK6cP2ZOXp0zK6dP6/a//euIzcjnZgLWcrZISGeM0RsbR/slPnPu3f5P89iO1wfPc1iaj30cIJCdbs+XkZIuIA0fMyrLax+5u3ReZHC8A51MPB6/BQWV+Ozs11/X1q4Ndl1rrGW+OyYzOz1tk3y/Mp+auu1trd/Cg3ndiwjKJQH3z8+1+U1M6Yy0tIpz5zGck086d03qlpkpmvve9chy/8Q2t8ZNPap/n5+t5y8tGq0/D9oICrZ9zJtt8coZEeybefPm6lpYAX/6y3il49jdKYpRINl577dpsoj7DbVWV/mae8/IMho2z4fePosdgfb3e6d3vVuYpXg32RpyC9eTI29/u3Cc/ae0yVlb0u8JCg7fn5Wmen35a5wCYJ1eQ0n3nTq0rcFHnzF6B1KW/X/MJaUrw3APZO31a8FznzPHYSIb5cmDCl3ptlODswQe19oWFmitIh3zKeHTa0NCL00j5p31ddZxegmstvHFnpxR6TU1sdBm2F7DURH87O7URURhzczrou3dLAPX3a4OfOWMUmLm5qw21oGHoG61ZWRpPZ6dF9iMR3f+3fzv2MCLUOztlJPmUpUtLitbRFK2rS39fvGhORLweOD6kgs+MjGhOEkVxOdTt7UZ5DgvN888rCkLTUQy+TZvWZpALGjjDw6ojO39egqCrS0KiqsqU19ycjERYdCorNUYfQuecxnf4sCAvzPla6etEzQKHh/U+XV0GcVtZUUQ46OT+NAVsPGMwCN8JknEEAwo+o5DfxwhqX6K19G7KybHIIfOwFqU7EKbOTt23rEz3jueMJzLuOCeQYAQNn6DRB+vTP/2T5j0SkULZsSPWwfEx7089JbhIcrI+G4loX8M8+eyz+llpqc5Md7cZyq997fpOk6/8/AwLTGCLi5qPUEj7cXra6NLz8jTH8/Oa37e9TZmr6mrnPvxhnY2mJssSJiVZfZpPVe5cfBKLy4GPBA1r5MfysmUehoYkT8g6UBO4uKh3Gh6OJR3ZaHuAy2HKTORsRaNyOoB7OWfkIklJliFNTdX63HWXHGa/DcG5czpzDQ2aTxpqQpRBtJx+YgRy5ua070pKnPut35JcS5RlGBnRvkwJWBOtrarZ4TlDQxr74GDs+bpUlst4c4xx291tJAA4Tn6fsmhUYx0fN4cP6PncnM4YwUeIUvz+WHffrQzJoUMKMJSVab5SUpRRBxJK3TGBL4gXeN70tLHx4bgTsFxvzwTna3jYgorQ9ZeUKFDwuc+JfAAY8UYzD+sZxf5ei5cRhuH2mWckqzIzJUvQ8wMD5mwMDVlZADKlstKaKCeqa9uozkokR1pa9A5NTdKPIyMaC/ukoSFWBqSlSU9Ts+077UFK982btTZdXfrM+LjedX5e+j0jQ8GYpaX1gyw//rFsiETv7DuSAwPrQ6FfDOdpo5n1N79Z487NlT1IMCI93VAxkJuNjEg3XmoLlpfiuuo4vURXIrzxzTfLmDh61BRaSooMO/C+VVX6PLAKBGpurglVIFpTU8ZcB/V3d7eMl7vuSmwYBvsoUFR5ww0SIJ2dGuvtt8ce3KwsZcv+7d/0c5+ydGbGaMezs/Wc6Wl9d2xMEbu5udU9cKDHBUtNCpyIYUaGokEdHRJYPoNed7fmYWXFMjCLizKUVlY03vJyKa59+9YmYvANHJTV6KjWKStL4x0YMKhLYaHGNDtr8CUEBuvz7LMGdaHwmEhMovT1Ws0Cq6v13uPjmlPeOzNTsCm//uNyaeo3csUzBn34TjhsVMEQVQQbUFZXx+9j9MgjErLV1VLQp0/b+SCjAANdUVF8SncfwhQKxZJpNDaujnwlMu5oQt3WJkXrO96J6ouamxW5XgtmhlwA3gZtPH2FUlMlB/r7td+2b7fv05NpbExkCE1Nl1a35WdYcnL0LFodLC5aQOP4cX1meloGZ26u7uec9uD992tOBwaM1p5aNL8vC3tiLRbJS4kyBw1r5AdZS5rrOmeMb7xXRobm7fDh1YyNG2kPsF72BKZAnzUtkbM1Pi75QWR+ctKcP96zr09Z5NZW5z7xCZ232lrdq7dXc4bjUFCg/U0NBfNRVKSfY8g+9ZTGT53agw/GZhmqqjQOYKo1NZYFxqAcHDSnKTlZa5+fr7mdn9f8/6//pR5xeXmCQG6U5TLeHCclaQznzumdkpL0HGSB7yBOTBitO3PDPVZWrNYJgzvYHyslRVkQ9iNNPBnXxIQx7UHA4e/V5GTNP3oWB47GusErXrNX5OTzz0t2YcxPTuq5u3cb2cThw4Zg2ChByXpG8cmTomz36+wgGDh5Uv///d8XHO/P/1xytLBwtbMxPy8Z9vjjtgbUHwVlQTydFaSRT1RHt9Y7vvzl+tn589r7hYWa84EB6Z/hYcliGH9psoudFY/SPSlJczw5qffbtk31dZOT1pD9F39RZF9rBVmoXQY6vJYDmwgKnZMj+6+lRWfuk59cHeS41CtRsAdyjLk56elrr5UsGBmRPEpK0u8IDsPAGgpZBhyG5Sv5uuo4vYRXvDTyzIwUINGvggJ9luLRUMhYb4jy8dnCQt2PrMyZMwYXuOEGfcfvd3PypJhNEhmGfh+FpSUJRopZ6+pEaRyvuW5KinWjBsrU3y8BFIlYzxo/ej04qPH8yq9IiT7wgI3Fr3XxC9VPntSBXFyUofi5z6mANByWkBgc1FzR/C49Xe+zuGisOtdfb8wuL3tZ7PoEjTWyJZmZMhrJgJGFI1qYlKQxZmRobTIzDQ5DISq1Oh0dMoRoRIvTmJNj8x404tdqFpiVZTCV6mq989iYxvD001L4jY2XV4dxKdd6kfe5OSm7z3zG+lcEG1DG62OUlqb5JkKcl6f5o57BOYsUp6fHN1RDIUXxgDCBOffJNHbsiI18rZUlphFqaqoZLusRfKwVBQ0WvZ86ZdDF5ma929GjBp3jnamJpDM9EKNE0buNsHt+97ur2T2ptTl/3hqAFhRofx8/rihzba3GWVqqzz7/vNbBORkk+fkmH5xbm8QiGGVOT5dzuH9/fPx/0LCGvn52Vr+nJo6sBMXsmZnGeDU5uTojtpH2APHkKLUnfX3a85mZsaxpNOMNOlsjI9rjGRlG7Y7scE7fn57W58iq79tn90hLswxEX5/2KE1Yh4f1vLk5ZSPe+EYFrr72Nc1HdbXNw8GD2it33y1D+NFHZag7J53T0KBmmj48i0xZSoplVkA8QIN/7pz2dlGR5mG9LMZac+ycgmbbtmkMWVnmoMBoBuQ3OVnvlpRkDalxTkFZDAzos01N0nENDbpPJKL/f/ObCnpNT0v+UDezfbvmKyVFexzd5PcvhK4auv/cXGOhCzrbic5FU5Pm6zOf0Xw5p3cpLLQG4EeOGIyM4OBGAmMbhZt2d8eSdXz2s6vP6LZtmh8yMlx5eaanV1YMPgoq5dQp2QG8cyL4LTTy9GN805tkl6wX9Iv3jrAkUu/X26sAUEuL1jAjQ/tmcVF7t69PdtWmTbGU7tGoIR7ItBEo9Ruyh8MKLq0VZKH9A6QrzpmePnQoNliyuKj1XVqSI3rLLfqOD+E8d07je+c748/RpTZUzsxUgIeefSdPah/CJNjVZYFdWFoJVPmkNJGI5nxl5dJasLxU11XH6SW+fAMKppGVFcFd/JoGNhhpf4zCSEQHqaxMBwa8MFFWjKuMjFgMeii0vmEI2cGzz+rZFKSjzJyLn7V4/nm9Q2mpwWJgU6EZ4tSUDvGttwoTPDKi7/7ar0kBhkI2lpwcg2QtLBhMBcKKtDSN5exZjefVr9bv+vos8ofQyc+37ts9PYrGgWf/0pekbGDPCTqEZWVGBEEGbGnJlCOGIVTH2dlWm1BWpsjgyIgEKQbV3JzGRKNIjF9qk+KlrxM1CxwfNzaflBQpo/l5GSZ795oD9iu/IuM2K8uak/pZoUR1GPF6WCQSrmtF3oeGlGmanNQeoXO4r9TpY/QXf6F3aGjQnANDm5+XUnniCWMoIjswOanswcKCFEiQ3vd1r1MmZnbW2JaiUSvkHh/Xu/pELM4lzhIfOKA9vBGjb60rUdG7D28bGpKB6zsdztnecy4+xCjetVF2z5oaI31IT5eR8A//oN/t3h2bCQiHLapaUKBzNjhoGZ5wOJbKmABDIiczXl+vEycU9f3mNzUXQThr0LDmfM/MWB0PsBkcbuYPGuG0NO3Fri4917mNtQcIytHMTJ3J8+ct69zQYBnSgwc1zpwcg2b6ReesJwZbPJrnmRmDhi0u2s+BeBcXaw2AvIbDOnf9/dpHb32rZMK//Zv21NatsbonOdn2Ak1yr7lGcislRfPy0EPm+DzzjN7ZOe1Bn3of1r75ecnFqqpYIpf3vEc1ZGsZbomCGGTsobCmmefx45YVrazUXBBx5xwBqyPoFArpfX/0I41vasq5j31MY25ttR6EyPG2NhmNv/mbmivmq6LCGAwxEGHxa2vT+c7O1nyy3xNl3JyzOpeBAee+8x2tR0mJ6faFBY2P5qNQjC8va042Ehi7VLjpWmeUfkltbcqCAIPkImA4Oxtb6xKNikzkO98R3Djo6Pg1z3l5moepKemD7u61HcNIRPfq79d3o1FDoszM6D2SkjSO8XGdPVg46XEFAcnAgOaRwGtamt7bDyzccov0bzgcC+2M17eJiz3lnNZwcHA1g6EfLFlY0LkbGrKemw8+aKRCoDoGB3Ue/uqvVs/RpTZUXlhQreX0tOw85Dx6YHbW7JGSEjtH2CUwNVOTvGnT2n3xrqTrquN0BV2+cKAwGJY9+sD09OggoVTKyiSwyZoAfyHKGo1KWQRrd+LBThIZhm95i4wTHyftnJy8eFkLonTJyRLWpGX7+60PSjgsQdXaqshxdrYExczM6rG0tOhno6NyqkZHrWfH+LjuU1ysyPfwsITn/LzmC3poLr/fDFA9nk/a/7HHDJ4B7GVmRtGnixcNfpeSYuMARuYXXlM8nJenZ2ZnS7DiJFGvMDOj7/s06c5Z5iCYvo7nlFDI2tNjzFJLS7G48vR0RalaW+UMkyr3ez3F2xvB3l4wWJWVacw4Iz5VanV1fKUQjep+o6MyICGxiMes1t6u9QBeOjamuaio0Lp0dNhYwmFFvJif7Gwp3mh0Nb3vqVN6j9xcrdX8vPXvIGI4MaFsxqUwEL7mNS+MyShR0XtKisYJvM05rRnGKfua+cVxDEKMgtdG2D1TUyVfoK1OSdE7YYhOTRk8CwgYCrKkxBzdzEzbk2VlRiEPO2Y8JzPY0Hl4WPtjdlbvR3AI8gG/lsOHfmGcd3VZ0CkvzwxmxhwKyQC9eNGo7R97TPfMyrIzloh50d8j994rKOi//7v2+uKiQbHYy7m5mgP2C3VHtCWARZI6uMFBg24TdIEWnncB6kUgID1dMpLPQfID01dBgebv6acFDa6t1ZmjvgQinqEhOUc7drhVzU1zc2PhWz/6kT4/NKT5JBhEJJ4MQ2qq9oV/9r/9bZ399c5NUFe1tJgsqKjQfj192gxHnCbY7TB8IWWgrUd/vz5P5ok5JFv50EN6TnGx9gWZ6vFxBd0qKkQUkZGhjPbgoOaduipqIckEdnXpd7/0S5KXhw9LPgeDL85J3wYhWbt36xlk8yIR7V/IlZaWYmsvN1Kr90J67w0Pay/5ZxT44ve+J11bWytZ3dur9Sf4SD1YZqZl7z/6UcGxCbaQfSULRCkAerSmxgiZ3vtea4OAPAYh88wz5gBXVkqPnT1rjjPjGRrS3unrM6g9rJSUSpw7J6P/9a+XU+IHFiYnhW743vdku5SWSqbgjKzViD0S0Tl8+mnLyKCrmZ/0dO3Hc+cs4JOWpjEPDRnLIc1+w2E9l8bWOM+XCt2fmdFZ6enR/E1Oaoy+U0TgeHpavwcJtbBgSAmCQ2SsmMcrmRjCuauO0xV1xcskLC3pUMzOSqAAO8NhKCoyLO3kpPWsIDKSl6eDFky5A2EilZqTo882Nkr5xSt+9a+OjsTp/Px8CYGODsuGgTHHqcjOtoJ/MOHxmnRipB4/rqaEFy/q/84ZdSyK+emnJbz7+kzhczCdM8Y5DGTnLG08Oirh0dOjP8D4ysoM7kNUZWZG9+rv17z4RiERYepQ6upsbUIhvQ/9iXCsSksNJuRfRJjHx6UsqTNJ1BspO9uYt4qKtG75+bY+EGNUVhqZRJCooqQkFt7mF5vCQgZULilJ8/Poo4JWlpcbnXoi9qXeXus839QUu3d8pf6jHwl62durZ8zN2bzSq6q+3shRIIxwTvMABfjtt6+m933qKb1zWZlFhn0YD4bQwIA58v6VCGZ3uWQGXPHgD9Ai9/fr78lJIy4YGDDDiHoB4CGNjVq/tTIk67F70uR2fDwWitjebvCmzk7Na3Gx9gTZbZg/V1bMkef8z8zo2XV1Gn885iznYhs6d3RoPJGIZYCgkc7PV+QzWMvhQ78yM+3+BBGiUe11zo/fAygS0TuurMihICDyla8IXrNerUhjo81HXp4Zm2SjmVuyzAsLGpfflgBoJkGzpCTdB/bMaNRqKalJikR0vtradE4gg+C8VlXpeRTs792rtbh4UfuFBtPp6bpnZqbWtqDAaOeDVxC+tX27UdyPjVnNx8KCBZyoZSWgt1HyDecs+728LIQClPzIV4JgUMgXFMhQzs01wxzHH2iQc1Y7Czx+dlafgTr8+HFrxh6NmmObni4Z3turuo73vld1Pa95jXP/8i8aKwGta6/V/EA0wO++8AU5QY2NygD6QcrW1vjw3aUlzVc4bP0d0afI6ORkGex+4HS9Wr1LIevwgz3OxZI4+Xrn5pt1v5Mn9X+MeAz6mRmTv74OHRjQXBYU6F3PntXvx8Y0Rz48e3nZoGLHjkmHpKVZgK+w0PpG1dXpc+j7M2esBg7oLvYXTjYyg6bZ9O+rqNC5+vd/t8wTWbFTp2y/UHMLtfkb3qDfXXedZR9xmKurrV6PQKAPJd+61fQD8qGyUmOlTALbZ2TEeuFVVBjTI2ettvbSoPuRiPR9Xp6eC6MwddbUGIJ4yMkxWD22nr/GZIlhHfQJo67U66rjdAVdwUhPJKJDNjqqjYUhnZoa2/R2/34ZvceOSXifPauDUldnfQP8i8LdlRU5SUAHNm+Wx++cnkm61o+QcK2Vzk9KEnSps1P3yckx2AgCvrjYMirz8zrc8Yw86q+c03x84hMSGJmZlhYuLjajvqtL/7/jDikxekeRRaC2iajizIw+t7hovwfucvKk0YuePat5Kiuz7M/wsEXgaVwKpj4/X3/ovzI2JkXR1qa1y8+3FP/EhH7f3x9LpjE0pL8PH9Z3vvAFrcW118bvjVRQYM7yDTeYAeCc7nPypN7vuusk0CH+8Hs9RSIGb/viF2WELS0pa9PWZvAT5/Sd1larCUhJUTR6djYx+xJF0TfdFFt4z5WVJUH9sY/JmIMcg6xlRoaRbZSV6b23b9d9f+VX9KznnrNMSFubkXGwN1FKBQX6PSxjGFLs+4KCtckVXuwrCH8gw5ORoX+fO2eQDIz+cFjjvnhR+6CsTOM+eVLvv2dP4uetVbcFeQpQIIz2wUH7fjRqDGELCzp3oZBB4AoK9Ad2SXDv5eUyEpn/IHOWc6sbOnMeFhf1HQr7FxdlyJE12LFD4whCvyYm5IgfPqwxAKVJStJ+JtiUnKzn0TSzpkZzA9X8tm06p488ojP0h38o5yZ4dXTorGZkaB3IDpK5w6lKSjLH5oYb9LPNm0WakJMj5/EznzEWMmq1wmEZSmVl+szCguTtV74SG2QIhay+ZnpaTiAkEHfeaagGSBuA42VlmRG2sGB1hARwgtTjvjHe3KwzMzio75JhX1qyP7m5yuj6RvlGyDfi1bsBZ923Lz7BCT27CD7Qn8lvbIxDm5amcYyOah4WF/Uec3PSrUtL+szkpNEqc07z8/XZtjY7pwQOZma0Vtu3y4hHfgfbaxw/rjm4915zPteD78LId+6c/qZFAwbs8LDWfdMmO8fBIGWwtmWjZB2+HRAkcXLOdHx2tnRyZ6dz/+k/6f+f/rTOEPrcD8762e0f/UjvXlGhdQGGODhoNYlDQyZ3YBcm05aUpDn3+0ZBiz41ZQyLOG3JyeaoUNcWiRilPPoiFDLkTEqKbASysUEmYHpQUsf8wx/K+SBYCjX9m94ke+Nf/1Xzv2WLoYn8vowXL1pwymcfhPwFxzk9Xe/Y2xsbRPfPWkfHpUH3OzslR6ntnp21uYJS369pwkkqKzOSGpysuTmdW/TzyMiVz6jn3FXH6Yq6gn1bjh3ThkXYRSI60NQ2+MxUFDbW1OgzGO/9/coeAf/o6hIUY2HBnB5Y3FpaBNuge3ljY+J07XrpfKK+RCmJXhKRJqWLwK6ri5+i9RUlNR4ZGVJ+BQVWs0DEika0r3yl6mi+8x2DphDdgNmlsFDGx/PPWx+n1FTDH8MQQ0aOVPPiogTaoUOWqaipkVDo7NT9ydy0tBjMCngGcMu3vU33+au/0mdTUgx2MT5uxdVlZXI0MjOV3fnKVyTMgr2RwmH9PDt7tbM8Pq53oXbNJ/6Anv7CBd0nJUXrDwRnackaaPrMSJBb5OTI2Jye1p/8/MTsSxMTcgAzM+Ofga4uGR6Li9bva2zMoE7OmcJOTta+2bZN0MPvfEefqarSPsjJWZ1Nc04KmD5jQLQwwkMhvf/mzVJkP0tqVB/+UF1tBtqFC9arCmbIoiIZQkC6Cgr0rhcvyvgCHvfBD6qO8N3vjp8dSQTPveYarS3RVmBWS0tGYoNhDpSJRr4DAwanvHBBP+P75eXq7xQKGYwnCCXEWJyetobONGkFRuOcyQqckNlZycwDB+JDv37v9+SQP/eckUFMTmrPFxXZvSBS2L1bgYGVFZ2nnh5BbhhLa6tz73+/GhtXVMTCM9vaZCiVl9ua4TQxdijAMb4PH9Za9fdrb/6v/yW4F/3KOHdkRjIytD/T0hRMOXHCIDPMDRCfTZtM5vtU6wcP6ll9fcZ2B4wPp2tuzuQrEKV4e5e19B1y5/S9yUlzwnJy9J7Bepf1yDfiwYl6e7UOa1HIDw8ruHjkiPbIyopkFFF4IvQ4OVDVO2d9rvz2HMvLWpPz5+33oZDWYWVF8uKBB/Tc6mp9vr1d+/3IEWX5Wls31l4jnlEbhO/6jUSdMwOfwMfgoGDLO3ZI7g8NxQYpE9W2bISsw7cDgg3LnYsl6oEkoqrKnGtYZcNh+w7ZGSD1kYjkfHKyzgiQND9DjP4uKjIYY2Gh5gsnFSOeOvCSEumFZ57R/p+d1Rh9siYCdThDBNfIsGRnG1TQR5wEnUgcyP5+6UTqgcrKrHb8kUcsiN3aaqUYk5Omp9PS9PmeHr0fUGPmHPKX9naTm0B9b7jBzgdnbWBAMvJSoPvHj8tmSkuzMUBQAsMwAerMTOnvaFS6iRos6oo3b1b2DBmYKKt9pV1XHacr6ELhnDihpqpLSwZhQ6llZOhzQ0MSWouLErqnT8tIKSmxfi4YW8A/0tIkQIgigztNSpIAOHPGqMJhzEmUrq2u1qE+flwC0IeEkc4/cED/fvJJg5RR1wQTTkmJlNp6vYpoNtvWprmgwHd2NhZ6Nz+vg+icDKaFBUH4KK6kPsCPzoDJJTrlnLGKwfoHuxZGS1WVjKLJSQmuvj4JhJoai7qPjkqINDcbPMNnNTx+XHP76lfLgElP1xoCzUtP1/eamkzBAg8oK9P6BHsjbdumZwQjhS0tGvfOnebY3XijMe4AVywultNZWhob2ezp0VyUllqvkqUljbO01MhKfGbBeOxLkYgi8fFgIJGIBDj0xTk5tsbAO1DECOayMqOOhX3Ip2f3s2k4k7291gdqYsLgpkTc8/KUqcnO/tkJcuAP+fkW/U5N1b6lBiA5WfOIs75rl96PqDpZg7w8gyeOjkox9vc790d/lNh5CtZtjY3JyBsasvmiPgPjd2bGggpQlgMlrqiwTGx3t0GgIJNYi0UP+E9jo5GxYMz4RiFkOUB2OZ9k4eNBvzBifHIAjByyCZmZMsCgmp6dNUIZGliXluodjhwRk2dDg9X7QS7D5Wfbgfz5Dilwr/FxBa7y8xVcoLZz2zZreJyerjkcGdEeKSuTQfTkkxo3fWIw4iDGIVs+Omp1lgcPaizIPYgxFhe1/swzmbGsLJ0zeu5gFMZby2CN6uioPltTIz2wbVvsHK21Hzgf3/qWZSDJ7KemalxLS6tZEJ0zo+9NbzJYE9T4ExPKrs/NxZJC4CCyxyIRY6dDBkejmqOsLP0JhQyW+NRT2islJdLlvb2aV6Cu3Bv4nF9z5lxs/554Rm1jo96hq0tnj0zZ5s0aV2+vOebosaUl2QAdHXLcCFKuV9vik3X4pEAdHVonP9hbWRnbsNyvt8zLs5qciQllmu68Uw7d2bPmEOCYk23C6F9c1O/pjUS92PS0QVSTknQviGcImhFgjkatSS1ZU/qWDQxY24TsbH2XAC97jb1ApoQz4JwFGTjXQScS+e07D85ZAIQaxqNHZWfhCObmxurpqSk9Ky/PuXe9S3sN2wF7cGZGjiaZ9cxMvSMIFM5adbWg0J2da0P3gzVtyA0gz5xBv2Y7HNbPCwutDhydRm81UBMEPDbCWnqlXFcdpyvsamyUocrGgz4byumZGR3szEyLklA3QnoWob19uw7MqVMSqgcOCH6Vny9h7KdkKYZ0Tpt3eFjCCWMkJ0cGGjUh3/qWHKDWVkVQKiulwHFusrN18CoqpLiIvhUWynhva9P4EvVeiAdRgMo8M1MORnu7ngfmnz4nzz6rSPCNN6p5Y0mJ7kXdQHm53oHoLMXKRJSIEMMIRCraVwTV1frT02MwoeVlg/ssLOhdDxwwgeacEUCMj0to4SgsL+vzd92l9fnyl/VZyBOcs74pJSX621+fjAwZb6OjwsjTMI9I4e7dGqOf6aFL+8SEFRT7zEd+ZJOI7rlzmi8cJ/aNH1nkCkaqyC42N2tPHjmi5+fl6btkmnbtsqghRfkTE1YrAxwjHNb+6+7W/WEf8uuCSkpMcV64YHvWOYsCgj3Pyortg5YoI/LTuHAWmps1npYWI0JA2aSna2+kpxtMsqREhuRPfmKKCernpCRjEXv+eZ2BRExafn1WS4tqMyBTIAgApIYebNTc0StuZUVr91u/JSPyzBkzypOTdY7z8/Wu3d0a52tfu3o8wH+ysy0zOjJitQIY81Ac+8EOnyAnO9v2H/JkZUW1BZOTBmk5dcoayRLgmZyUM4KRA+wJh5s6S+SHX7/Q1SXnqbBQ8wY5DwEKjCvnLMuZl2e9mkZGrPdPUZHBh4AQ9vfLMGprk4MCbLigwNofTExY9BfHifmjRmRlRcayD+vB4IYFFcfOOZNvjzyivVlXZyxq8RgRE7Xb+Ju/2Vi/puD5OHxY73n2rEG5gBRmZq7uC+acGWO7dimgRuPpzk5r3xAOW5Zpfj42s8u7s7/9HmBkRmhompKidzx2TBnAI0csU4dhmZQkvQUdPw1V09ONjXDbNp3Z++4zxwAd0tGhsWdmxpIk4XgAh4XYA9p5ggLp6VZHspF+TWRsW1ud++pX4zOu/fIva0xtbVbbQ+AhM1Ofc073c05BATJKmzdr7ckiQRjCWMlkAXvE0YB4gGzw0JDJCEhekpONwZH7kdnws6Y+hHR42AJQtIDwiXfQ76GQxgAhxcqK1m1iwtqA4ESCHMjPN7heaqplqahRop7q+9+XLAbN4+vphQXbc3ffLWjg+9+v9aGRckWF5jwa1b385ryctaIi08c1NZr/8fHV0P2iotU1bf39ekcCBaGQZNOFCxbcogShp8dk9dSUBTyLisxZivecK/266jhdYRd1QXfeqQ136JD+TwSXCAi9P8DWDg3ZAcUgIGpQU6PNDgwpNXV1St2nMwYKdeiQZRdQZt/5jgyN9nYJJYTbmTMydonmlZWJ3jtRyp9eBpfSSyI3V0qkr8+odzEKyMgVF0tAwbaFEUPUnPHRwHZgwBioKAinxwe1As7p30NDpghCIf1N/RKseJBI4Cg9/7xFn2DFKS01ut+aGn2exrlPPqm1hRHPj55yn4ICg2CyPikpljYvKxOdt2+wVFY69+EPr84QUsRJbVRlpT2P+YYNcXFR4yKyBjvO+fO6X1NTbBGyH0EKMvNdvGiOX0qKlNDmzSb0x8e1zsXFRoziP7+oSO/Z02P1bj77UDgcm5mdmlKma2hIv4d1jv5b0aiMK+oA1ouAr3XF64XBnk70M+q/iDISMcWJhTEQMhQfpjs4qDUAiuoX9WdlWXT9mWfWhx0SgR4aspqCsjLtASiNqdHYskUQ0p4ey4z97d/KQPXnYGBAZ+3IETkWfnPVBx7QOvlywIf/+HCawUEzDHEKIPZITo7tE4Lxy/7z5QmGVTSq6OncnNa9s9POQXGx5NziovVLgvgEaBXZeRykaNQMzqefVhDiwQcNfhukkHfOnBP6f6WnG9sd5DFcSUm27qWl9t7IBTIfnCmMfLKEFOQDR0S2YljB7umcZeXT0sx5XlnRvh0ZkcyC5fWuuxQAiyfL4xGmxIOGrkfd/9xzckjIRhcVaW0GBrTH5uY0Vs4JcogzPDOjvQY5AAx89H3CAezvN0IY1oagEEEkP1u4tKR/MzdNTZKxbW2aI6L+/p6MRIzAgf1VU6O17uuzfb5pk4KRo6PSt84ZwVFammQ1DvD8vGRqZaXVwtB3aHJS39m/31pc4PgnInjiHD75pIIoBw9as+N4TVgzMjR3tD8YHrbxp6VJV5HlKCkxynKavAN/Yy+ylxcXDQWQn685xHEuKtLvcGCds0wVtg7Mn5GIBR4gpXBO/z9yxJxfspjLy9JByA+gq85ZicHAgNWXlZWJFMTvZeZn2wnKYW+QlaNVC0Fd4OzYa37dHnuuv19kG8CC/+IvnPvUpzT3NTXWAL27WwgMKOl7eixYUlkpR5h9s7xsiBGow3t7JXf9MgoCjddeKxmHfs3MjCUsmpkxiHFenjFdDg/bXFJLFu85V/p11XG6wi4/2pqcLMFDpA2Dam5OAg+mGSJW1DZhTDunz+3bp0PjnBWa+yl152ILM2dnDapBc1D6Jn3xi0Z/OztrBuzwsKJhIyPKbtCJHudlI/054s0D8AAiICMjel8Yj+gVQ6Pf6mrdd3RUCrujQ1GXqioZlxjGGGTPPqvDTk2TXx80PGx0pPPzes/aWmOsKSqyWpLUVD0Lwz4tTYJsft5gejT3O3PGjPuODoPL0Pfq8GETxpB1OGdRLAwXf32WliQoKR7euTM2g/Cnfyplfv68nLbqan0mHLboP70XfOIRnkXkCBgiFxC3gQHdg7S773gQZR4etkJeqODZexMTUqRE9Wpr9TOgcjBKEnWERY7oGEQhQLpg78nNlQLE0E1P13ti1JBpHBjQ3BCBb201OGdHh/U6W++KVy9QWKjf+YQrwZ8tL8uIyMzUmpOJoIkmF+eUbFlbm/Uz8ZmKMNSqqqx+AGWW6PIj0M3N2n+HD+s7ZWVWkO2c1m77dj2zpkafuflmOb/OxRrMO3dKcba06O+aGoMSx6ufDLJ6cVZpCIvzC1SPM4hRmpKi+fEd31OnVpPZ+HUI1CsAY6NeBVIZjDnga+xT5C4ZOSCqra3K/A4MKHhC5Ny/iFw7Z7DXigqLPENVDt27v+7z87GNezEYMzOt/g+olt+XCAcBNjOgNgTkUlMt40b2CZgR30lKMnnrnOZio0xYkYj24113ybGEzXUtfXDqlJjVhoctOIhxHI3qfZGH1K5St7l5s/Yf8qemRuNubZVc+MlPDBLPHxxOnFDeMSnJMgXOmdPJFQrpuSsrRmhDfVRGho19ednOa1lZbJ+r4mIjtGlo0J4Hyuev5cKC9lZ1td7xuef0WV+XlZRoPyws6L3JjPjw43gET8j+wUHNq1/3GokYZLSpSfKBJqwHDujdCFIA6aNWprpatgiU5Th9OMNLSxoPNdG8LxltHBrq1Pr65ABcc40RO3E+/NprzgG9y1i/pSU54/39Gj+lB8Dh0OWFhRakhjQHSDKkDc4pkOQHBYLZdsazsGAyeWnJZBfZrMJC2TXj49r7WVmW6UEXNjRoDzc1SVZ/+MP23LNnY4MRjY2CfT74oO7z3HNqdD01JV1TUGCZovl5a/sxO6vAwu/8jslm6pemp/VOra2aE4LMIIK2bdM8NzY69/DDRrACyQbQ1UTPudKvq47TFXYFSReCtSiwU+3aJUE5MSHh0dtrRgObcXHRoB1AubZvlzPjQ5nA/YMpHh62fjkoJ4ywnh7DdvsMcKTD5+eFGy8psczA1FT8/hyRiAQrjd4aGsxADRadYhwWFuodz5/XPFCHU1CgyIpz+tnYmKXpDx2SYJyYUFZs3z4p6rQ0vdP4uD4PQx+EE+npuu/WrTKM6Hifnm7d3pOSdD/YqvLyNDewaFGDRZ+N3FytFV3qaabnQ5EwgJ59VlEY5gz4xdGjRt3J76jbSE5WBA34o49h37JFe+XECb1Lf7+Uzr59gkw98ICEanGxqM5h66mq0hr5UB+MRwyKaFSfWVpSlAv4DvcdHpZQfOIJMxBnZ7UeqalS/tR2wabX0KBI69CQ3pEs3E03aa1goaLuAbYjn32I6CJQDOjocaAQ5AsLehYEHePjeuaxY8792Z/pmf/lv8RnUCO7AmX+/Lz1/+rslMKKRo1wJd7Ppqc1f48/HsvktLhoZ5OsKIx7MzM6FzT+9GG3OFGwDzpnkJ9EVzDLG5Q9sI1FIlKaBQU6U+vBrKjfWlgQoUk8SBD1k4yjuVk/x3C4eFHPwqiBZjglRevMXp2bk8zp6dH4GVM8Mhu/DmFxUc/ZtUvrcPasEWBQFwnsxq+1wuDzYapAVMvKVFf2zW8qeHPxojnqOH7AvzDSs7O1/06ftmxdQYEZ16zt/LxBg6qqNP9kaOmnRaaJdwyH9bPSUoOeYYQSlGEfEQ2mFiEzU/ck88Z3VlZEYHH33WJNW+taq8FmIqeppcW5T35Sc5eZafKSwCBzTqZgbk7zNjkpffCLvyh558PRzpwxOngfZozjBMse6AzGRpNdIFE4zcCloa6HtAJ2QvY7QUv+lJYaKybjmJzU/uSMU3e8das1XidggA4BgUFAE+IV+pNBBkRW0Ycf+2ciGtX4n33WWoZgKM/NSYdD8JCSonNGi46tW/X9wkL9fNcuZRG2bXPujW9Uw2wcqJYWk/WQzeDEQ2hBrR4BDB8u75ytycqK6ZC5OYOkEkDmWlqSrCwu1pjOn9cc9/VJx+7erfHV1xs78ciIMVDu2aOSBBpANzVp/cj+t7RY/0Ef7UG2vbVV/4YFj8wyzhU2VFKS5rC21tj00JnZ2Rp7ba3ki9/E1ofFIidpRNvSohYK7e36eXu79hl1b5s3a82qq7WWBQX62eysc+97nwXDuM+FC9qH4bBlP0tL9Te1ZBcuSLcRhIe+n4AxQaYtW7R2/nN+Hq6rjtMVdsXroQDGdXxcm3f3bkEj3v9+E95+bwqKjckKdXUJC1tfb2xHU1OxLG5zc4rEQbVNXwa/N0xdnQ7z2FgsuxqN4BBsS0uWMaPX0aFDsTChlhZhnR99NLbL9iteoWaAJSUyPNrbDRZSXGyR+NRUHXAipfX1GmtPjwmghQVL9UPXPDEhRb99uw7qDTc49453qKbo+ef1ey7IFGCzobEnbD44tkQRfZIB6tIwLDHg6Q0TjRp5h3OWUYGxiTqz73xHSmnrVvsun8eoRuFmZUmAPvOMim4bGmS0+UZDbq4MnPFxGWfl5SKnyMoSBe3x4/oO0WfqwGiq5+P8UeBJSdZHo6ND/77rLjlN4bAZ42SQUlOt6awPQwA+lZYmJZORIYU1MmIUxrfcYvA1ei3l5Gg9T59ezT7U3i7nMCdHxjBGLo0vMzO1TqOjFmVEGQDdnJhQhO6ZZ5z77/9dMAW/ZuOBB7Sfjx3TfG3ZYn1YurutULq7W2co+LP6eo1h504Z2A8+qL0+PW2NeoGLnTljkXUcKRxmIIfOmXGPgZWRob2+FuwwXosBH18/O6vnb9qk+RoZ0Zn1YVbxgiHOJYYE+SQiP/6x1eZRawI5xcqKkVDgGFy8aPUE1BotLWm8e/bEQr/ikdngLHB+Kip0xsHo+wQkXV1W2I/Bmp5utTVkn6Gexjitr3fuQx/SM44eNVlLBoGaDQxYaq0g4Ons1BxMTWnvTE3puXV1cla+8x1jXQuFLJILPfHUlBXFQ5wQjdq+rqrSvTIzZaDNzhqE1jnb40DUCDKsrJgzPT+vPRuvVpXrUhtsOhdLCIFhzDqTGcI4hgmTHnY33qh9c/CgjOPqar3X0JAcKZjcyOaSTaJmMzXV/k8dB++OgwWjJZmCnBzpn2uuMWgr+wVoM8GzwUHJEYrj0ccVFZLJBw9abTBMekCwgGFWV5txDVvqxYsGL8TY3rt3NQoAOeCz+J45Y3WVNK0tLzdIIVDO+nprjzA0pJ8dOmRoDWpEGxslI9BzWVmSx6dPm23iZzVxQiYnY/cfUHw/G0iWtaTE5sm5WNi9X7NN5mh+3mqyndPPr7nGnLrSUpVJUJ9z4oTWhEDjli3mZPlXkIjGz7bffnusI/UP/yAIJA4r2Wb2JCiPmRn9KS+X/qbfHGc4SNjlnNbkwQd1ppD79COk7Qs16/TPunDBnPy8PO3J0VEhCJzTfNHPcXhY52x+XudqedmYnoHBgip5/HEj0Zmft7PK+GmP8Ou/fuXTjwevq47TFXb5VK7BAtreXh3cd7zDlAhRLCKNRNPBB8/MaPMSeYXt6P77VeR77pwOKRz/eXkSgkDCkpNlZNTWmrAi2spFihcjjsyVj3uFjtU5CZiPf1zpehoMOqeD96UvyRDdvl3v0dGhQ5iXJ+FDxA8lVlpqEKSJiVjYhW88ohgbGy16/Zu/KSciFNK8fvObirZRg3H99Xr22bMyFolu4rC0tZmi8lkFqcOAoYuib+aFhpcjI/o88A+adFKbQ0q8rc3Y+a67zqLF1FXAYlZaKgHf3i5oCxTj114bu17AAmZn1U+CYlEaBC4sGIwIB4nMDAWt3Ie5ZX4Z+/HjenZRkdY/M1MMQF1dlqLHSOfe7K/mZn0OJwvHYP/+WApjYB3btml9cnJWsw/l5spw+Id/MEZDoJZ+YXdSkr4DOyLGLXUC0A//5/+sfcEaQn9fXa0xZWcbc9KuXWb4QMdN1pKf0XtsdlbnHUMBg3pqKrahod+QkbUBYumTDrBPx8b075tukmMczPj6dVf0yQq2GEhK0v4eG7NoPlmS8nI5yE1NiYMhzc1657q6+DIvK8v6NkWjZlhPT1vt1k03ab05nxil587J+N+7V2ebiCzZa+TNt761Gqp6zTVmzNGagN4rNTVGSV1aqs+RdS0vt/N54YIZaI88ov2emirYEsZpa6ucaqCikDcgm3zGwNRUBY/ICvOe4+Oa+4oKOUz33KN7Z2RoD46OWuAKeZ2Vpffcu1f/vv9+y4ZWVclR6urSfL785dIhFy/q9wSkCgpiZTdBn3DY9mckYsRB8QygRCQE1F62tIh6/ZOfjIW+kQGtrtZ8rKwY9DbIcgYxA0GFUEhr2NKi/draajVsc3MW7CGwgL7gHTHuWFuybRjtkAEBmwuHrRH3nXdqr4yPG1sewcy8PM1ZaqrGu3u3Ff3z+4kJI3YALgYpCuOmFw6spjjhFRUKsrW26l2RyYkywz6LL44xQTnfwQYKTZ3pyorJSJAlycmxjGwFBbElAp2dCh7gmEF6AlSZ7ByBV8gfyECxvtPTtuYEPVZWrCeSD1MFisqeRoaGQtIZHR3SS7fcEtvrD32ekaFWDgMDzv3jP0r/psSxmtfqQRaELVdVOfc//oecHvQUGWFqJnEmYYHEDuPyA04Qdn3hCwoK4zCXl+teyDT2Mo4sJEsLC9obZA1HR7V2Tz+tYFZamtZ1ZsacT5zZUEjzPzmpZ/ptNPr6rNSBgB5O//Ky9klKiuTTz0Ndk39ddZyuwCvYW6W724r6fumXLCVbWGgwM9i1hoctrb20JKP5Qx9aHc0L9u6gXubXfk2/p8gcZ+XkSYMAojy5MOR9XDj/R/iMjEhoEr1/7jmDVAEhQCBTgNjUpHF2dJiCTk+XIElPV9Sur8/qesDZpqVZDQLF9NnZpjDJnP3oRwYvaWx07i1vsYahDQ0yyj74QSughNxh+3YJloYGKUgYvIC54DySxQuHZWwDwfjhD03A4kSQsSFa6JzuVV9vRndXlwwynwwDeE9pqRy8iQkpvsZG69NEk1wUw9CQNbCDWj0rS+MaH7cmkTjCy8u6B9ka/8KAcc6iswsLmi/20NSUMgk0FwanT3QXCBGQmfJyI/CIRhXx7+sTuUNjo9ZgeVk/y8yUUvQzsz770OKiRemBrzlnkUkiYRhPQKF87Dl7am5O+7i317lXvUqGMtmQUMgKzlkvmKJ82mEgODRx7O9XZI4x5+db49qVFTkKnZ1m5PnnDIgPrGjBImL+JpjgEzHEg001NEimdHWtpoofHFQUPBzWnoRR6sIFGbwvf7mMr1OnbA2dkxJ+9FGr5/Br9rimp2Pp5P26Et7zuec0p8vLmqNQyGjqIxF9LxTSGIH9lJfrrEOiAlT1+HHJlPPnLWMwMmJR4IICY69MSpJBxL4g00VQYWVFn4NGnoa+O3daoORb33L/tx/b8LA+S7NqDFNk2+23W00T54UzBsTvtts0P2RwDhwwQ2RoSGv4q78qx52s1ec+p7W44QYLKNxyi/bB+fN6902bbH7Ly3XmcciArjmnvwkIsT79/bHZev+KR/RDHQ3w83Pn9H7vfKfpKjKgBEuAEBJIgRTBrxULMmXSpwtZh8NDdpL3wWDnfBGEyMjQvEBNz3oXFmrP8s7A2qqqNMc7duhsoHOQ2QTQUlM1L1u2xBrE0aj224EDOnOdnebAID+iUevrxBh9xldqCk+fltN79qz2XjwCDp/F1+9lB0mNz0pHVnt62hpC+7I/Ly+WkW3nTpMrDQ0iI8DJJNjGujoX2wcJox64HnsfqKzfc4uabT+QhC2ArMWh9pvRXnut5r+tTefAp7L3s3M33WS9uagBDl6wJ05M6NysVcfd1KQgUWGhZFNtreQRrHacj+Jiq2/zqfapu5ydlZx67jnJ3oMH9V0yggMDRtozOWm1ZKwbzmVysvUQXFnRv48ds/WAiIt7Ag3GJgMeiq3CmrCfcPqwSalFIxMYzN79PFxXHacr7CIKvLwsJ+bcOTG1EIH/0pdkhL7udfG7s1dXW+S8tlYsV35dRjDbQxSYBmwzM1bITZqd4unFRWN76e1dTYHKn2jUeldkZhpJwp/8icaKoVBQIAEN3AW8M2nfaFRK6+hRizxhENJQ9uxZCUSM5LQ0E65EVcgoUGwNxMKP1sQr6D9zxupnqJMhorZtmxVrAmHKz5eCIsISjZoRVFKieWxttZ8Tufcjb/wfBzQS0WeLivTd++6zWo7CQt1rbEzGHAXIlZX6HYWmU1OxfYyIQOXm6pmw0hHFg82Mnw8M2DpjbDhn43MuloHKOSmvkhKNjbUkckbGkM/T52Z4WHtvcVFRQHpyUZd16JDOAQWoW7eqPmt21pQm0UKgDJs2ac3ofQPcMUgrDORxYsIyhb4ShUGQwvm+PhkQ1dXaI93dRteNkTE+boyBOIfUCHAP6iWcs8wWmTzmDUMAA4DaFX8tWA/qFX3IUTistfdZsB56aDVs6vhx24t+tnt6Wk5rNKpsCI4gBsi5cwpC8C51dVb8XFlpNYfBmj3mFlhfY2Oss9bfrz8ofowiapzIRs7O6lzs3av7w9j16KMaO7U0ZWX6LvWGZBc3b7b9R/uHhgY5r6Ojendg0GlpBvuitmxkRH/CYT0/P1/G02teY05DXZ0++6MfmVykbwokO/X1+txTTxmBg3OxRdQTEwqE7dih+wCPpJ1Bfb1R2W/d6txnPyso37PPGlEAUCoCDfX12sO///uSyQ88oDEDs1lcNIOdPcy4CQysRTwSj+iHmlX6tQ0Oav/5dRvUpRHNjkew4ZytC1F6YJTT09aXCmfDz1A5F1uvhrFOIIR9SguQXbvkZELGhAOAIzQ8bO0VbrxR7zw+bq0VaBiKvBkejm1O79Oy33OPxvrbv629TPakuNgy7fRFWlrSu8L4ir7ZtUvrec89+nk8Q95n8Z2YUIYBKOz0tM0pfciWlqynH85lJCInMTtb+xjI4uKiAiq1tZLTX/qSPp+WZuQYfuYD3QEJBFl5WA59w7u8XM8jIx0K6V3Zl342kVqcoiKTj1NT+kxTk9YB4ijY34LZuXglFL4Mg2r98583lt3t2517+9vj18WmpDj3n/6TnnXxor6Tm2tlDxBAHD9uZCNdXToP9MHkHT75Sevv6bcvKCmxnlfMEXuVc0L2rbBQcvD4cQtGkHUbGzNHNBTSeIEMUn7AORkbs6Cjv1409qV+m/rsycmfj4a3weuq4/QSXkGoDPUSPsYfKFBzc3wa0GB3diLt9fXOfeQjivr4z7v/ftXy+Nke56zfy6lTEkoYvFVVdtAmJ2VoQSBBUSoHB+MbhT86GkuE4JyEHUWXsOVAOU1WACeov19K36+dWlmxnhvgttPTrTcUNUI+Fh0GNd4VxTk5KUHxve/FGpFkhSYm7LDzXiUlEqqPPSYBAMSnq0ufp0ns5s0ax+iojKGsLH3m/HkZcBhIQA8ZE3UqjLe/34yySET/zs21exO5gTp10yZTnkQAqZMgQglD48SE9tb8vGVCELYjI3ov4J80v0OJsd4+pIWMCDh+5p6fQ95AtNf/fl+fxtvYaMXbDQ0yUoeHpYihny0u1npPTlpEK1FfmJtvdu4rX5GC7OkxCAtKn71JTQtZgOxse0+MBxQxLILUXfmUuX7PEAIKvb0aS2WlxtjRoTM4P6/9g4NKhDUUsuJlHzZD8TROeSKZwtympEj5UXO3Y4fO91//td63udnOhE/UUF2t8ba2as6I2u7fb/VnMzNWD0HjVKKYvb2WxcSRBTb8zDNaY3+dsrN1Jvw5HxrSWIEDcR6I3FP3hSE4OmqGx9NPmzMCzKi/34hkgCRhbMzMaF137TJjgia4Z85oXaurbSzbt5sjiRHAOVlZ0bsQlPGdBsgvjhwxZtBQSPNzzz1ac+Atfj8UouXsp9ZWg+8+95w5M1lZWreqKhnkp05pXPwuJ2d1c8ukJO1LyDWam3VWGDswyv/8n60Ohz2KoQy0GRkRpN33iTmA0yK/qQkNh/Xc3l6r28BYPXjQ+ofhCBEYIFBGFoqsS26u6Q7qNphv5ywQ4pzVfpJVJnsyOSn5ePvtGl9enhyExx83eCywPd7BOef+7u+kmzs7NfacHHOYCK7s3av1Rh7Ho2UnkwqldV6e5oCgC1DSkRE5yTRKRzb39Oj7r3xlYiiUz+LLHqYHmA/RhjKcACTyHplD9hdGSmRpYaGc9+Zm6cSFBa0xxCsEBYDew0gbjWodnTOILIQ94bAFCcbGNH/UeZHBxKElQIZ8dc50BvDIa66Rczo4aLA/H93j3NolFKdPa43y87XPmNMTJyQnPvIRwWuDF8iiv/97nVWCYgUFVmM+PW0stN//vo3FOQsmnT5tNUp+9hJiKt+moH4aGeiTbVy4YDBxGJz9pvasNWcEh9c5Kx2gTgt9OD5u9X0+KoSA+nqERVfqddVxeomuIFQm6CRlZooqFSjQwoJlWTBwTp4UzTfF6RS4NzU597a3rY50gDGORGKb3zpnhvbEhBkLqak6ONPThp+GWvv8eR3A48ctg+ScsX5x+XC9igpTvqR+Fxct20QBrnN6NpSoCEDnzAgH808UbudO/fv8eRPEvoHORZQ4PV1z+8ADus+OHfo9cBccACAwvpNJBDYclpHS2ChjCEx7YaEU45kzVjBLZiw3V6n/sjIJ3XPnzFFkrMyZTzE/OalnRiJSFkVFZkTSNyojw+ibWdPt2/V7GgQi1HAuIxFjchsbMyMMWmYYprgyMy0978PDfHhLZqbVAM3NGfzCJzNgTdjbWVmCuMzPaw0LCw1S4hta3DM3V8oFJVZYGNsjaM8eNTplDS9elCM2Pa1/Z2VZ7cDioglzHCvmkzGyNswZdLv8DGUDAxRZw/x8czSqqvTd/Hyj2yVqTbFsRobRumOY+4QEzq3e0/5ZY7woYL93EAbi0aMG9xofj62vqK7WPnnve41coKdHvUK6unTel5aMXa+21owr6rJoJEnWjvNWWChoDM2WWfO9exWN9tm9mF/nYnvMAcGFMCYvzwy66WlrJklzRSBd2dlyWJ2TwTU6ak5pXp4ZjNXVes9gPRpRahp6Azmanrb+W/n5VrMGlCXI5kcdVleXwWgyM537jd8QhPmRR+xcw7hF0MqXpxiryNakJOtxNzqq59XXK+I/MWG1Lj6UCuhPkGnNr8ng/7t3G/SM+ibqAlNTFbAZGnLu059ezZr3utdZtL6y0uaUfY+zAwTTL7KnuSrIgOpqPb+nR98vK9M8Yrjl5OictbQYoxyNOru749cCMsf+PpudtfcMh80gpX6O9hxksSor9Y7Fxaab3/AGZdZgEfN1aEmJxjQ8rBoaMm9+VqizU2O/7Tbbi6Ojus811xikND/fYGw4LOsxXTqnccO+19NjlP183oc20ydr61atM+Q6GMQEFxcXLQi6a5fO+3PP6d5QmO/apUAG6zoyYsEh2PxoSjw+bsQQ0ahlUiYmJIsIDKSk6B04T8CwQUkwNtAwoZA11QUmu3+/fhdE9/zyLxtzXbAHGUFiekwhk/j/xYvKEKelWT21fzU1Ofe7v2v1jNTnPvOMNczGloF5l3rirCzJVOqPZmb03MJC/bunx2QE8+cH4SBr4J4459RDgThKFKSjRIO9gh4BHVFcbMQXBDeKi/VZCF82bYrt/fjzcl11nF6CK8gwFM9JwgGprpZi8RWdXxj4lresbnaaCFtLfwPnYhsrclFcOTUlYXTbbRZxwLDysfd0IcdAod6A7AwRsUhE92tvNxpSUvA+3AgYFcIRWCDwCYxghMDSkkW2UdKtrTY2PzvCRdaKaPhzzwlPPjxsmHuYjnJyjHoVKCQRF8aDEoS2ubfXhO+BAyqcz8oy8oovfMEMyrIyjZcImQ99gMUPhYRSBO/OPGRkWENc53Rv/yopkVN54oTmg0g3xbiFhZZRHBmRoC8rk+PR06N3AfZYWGjGxMWLsXAN/5qbM8Pap5FFOJeXa5ww84TDxtRG4XBjo8Y1Ph5raPl9bJzTZ0+elDJ2LlbInz8vrDb0qUA5gHihSKDPpdZldtZqETDIfXpiGMpwKnBy09L0O4plgfOUlwuq4pwUYkeHjQPWSee074uKLLAAs59z9u5kJf26l+CVlKRxhMMaYzis+zpnGYPJSUXDqRtCqW3bZmxLZKsHBjSH0aj1Z6NJ5/nzFoXGSIHsZGFB6+PDNzE0uCIRMyaBwRC8YFxcZLT5N72q/M/5e4WzC50ycwkklrlibfv65MjBvjY4aLJkcNCCCK2t5sBjoGVmWmCrv1/zjywOQnxoQDo0pD1fUuLcv/2b9uCpU9rPOO70JEMmQEjA+lObt7ysNa2t1dmcnJQhSEAMuvKSktjmyXl56zd6rq016NnSksZPzVB5uZ6dmyuCh/l5q+tcXrY+fr/4i3Iojhwx6J3P2EqWPFhk39QkquKMDBW+w466fbs58PRvy8iweqjrr1e25e/+TutBUAsHg/3js68hp/zay/n52Ka6OPyZmRobQQE/EIluvu46ORrl5bo3Z5qADe9Kpq+zU+vPvkHONTbKiQySSFBT9xu/oe+t11DYzwZCk33mjOTRs8+azIOYAYMd6ByBL/YieguGUII7EB3NzeldyHTPz+v5EOnMzkq2Ittyc41NFEfAh4hRizYyovfnjFRUSD5htwDdJHsCVG942JwpoJ0EsebmnPvnf5Y9Fg/dE4/2G53++c9bRtOv2Ua2nj8vBuRf/3U506yJvx75+QqasLYjI7Fyiho0nPu0ND0LxxIn5uxZ3Z/6O9YNu9EPehYUGDLhXe+SDdrebkEknKx4F7IYWCvnhsBrcbHGjA2Xk2P9FEGkpKcraHapDeavhOuq4/QzvuIxDIGX952kbdsMIpOebhhXCmx9BROMECa6YM5yLrb5LRcGDp+bnY1N/XJRzA3MAwx2YaEEEKlcP5VPg0jnLBsRidgz09JiU8qdnQazQOERjebwo0jq6ixbBtyH3j+J1oD6gAsXZBS1tlq0CNgjjldTkzXLJDtIVoPsDinokREJr3e9S8qde5D9e+wxo3+lUV1ysuaUuSB7g+NJzQpCjBoNiqOJZs7Nxfau4F6zs8696U1ysicmpAQuXJDTyRplZEiAtbQYhK+mRkbD44/r+a95jcbc22s0xL4BSn3WyooEMJAAhDYkGAMDtt4omy1btIdPnJCBdfy4jAKi9USlUB44glNTRiBy882a55kZzfO//IvO1LXXChoHJtw520vgu+ldsrRkUeTxcVMK1C1lZloAo6TEek5FItaZfWlJSmPPHjOaPv5xzcl99+lnZH/IJgCH6ew0h4i+SUDKqH/ya7MgiPAVXDRqRcXOaV45xyjSkyd1TzLL1O8NDRlDIWfl0CFz4qjNIHgAbMfvI4WRSL8SFPjkpDUTxjA5dkx77uUv12dPnTJDh/2PI+ZDWHGMOAPU9SEzndP9ICsJnolQyNi5CFgtLWnP1dRYYAaoS1KSxt/WZnvIlyfsAZypmhoLYPkQn8xMzT2OQWmpHNRjx+TAvPnNhgxgfElJWg+MSN6bMwX7I5kqjFs/iFJVJSOmt1dnm0BJT8/6mQn/HYaGtH4pKdoHbW1Wk0rG1GfCzMpSVv3UKe3B0VHdY3JSz62utuCTc6uzX85J/v75nwvuFKRarqjQucfJBw6IMfbkkwoQ+DVqPrwYY49n0hwbKHQ0KgeDSD4wrrExPXsthjXndL/JScsY+UGK6mqji37wwVjUyPbtqjvys5VBXcxc7dolWNlawVMf4TI4KFmUliYZtX+/nk8WfHHRzjLwz4EB7S10Lg479TRBBtviYnPO8/M1Z8ePy4l46qnYejX0Gc7v7t3SOTMzmj8CRs7Fsrnl51svQJw+YIbU6IByGRgwNAe1wCMjmgvkBE448Nh4feYIZGJvnTxp+wXnmWwPNhYBxCee0D649159l/WYm5N+Gh/XmCcnbe/5jij6MxKx4BWyCPkI3TpZQB8Vgo721zg/X/vszW8WxBnZ4mdfE12+bseWzM21Jt7Dw7Fw3NxcK0np6THachr5/jxdVx2nn/EVj2GIqClRF6h76YuzsKCNd/CgvgdcLqhg1rtqaxWJggnOh5/B1JKcLLalpCQp83iFkG1tJkTb2gx+RyE79KEcXOAAwJfiHUqYv0hBExnCGUpJie1079dxEK3EaCBVj7ANsgBi3F+8KAFy6pT+9hv60vNpYUGCE0MbhVFZuZohjPFnZCh689Wvroas7N0rx+nhhw3iATMizhBsgER4of1l7H7qnWg6AvLgQd0DR3JkROv+hjcIY97RIQMARxBs/8KCsfqkpFg/mYwMKeW+Pt2L3iHUS7FejJcxsn5E/LlwAFBA+fmK8p886dx3v2sG8OioRRdHR+05ZCKSkzX2kRHbn6dOWbSLSH1ZmbHAYTSAi6fPiE8pDQSrqUkG5uCg9gkGOOx5nFnqTsDnwzhGLdDKilFTf/WrRrDwxBOaU6Bc3d3GdjY5aca/r+iAxLGX2d++gYQCxdGuqZFxxH6ZmDCI2ebN9l0gr+3tUnxEhTs7dcb37NH8dndbkTiGiJ8VZrxAPZinnByDIftyb3RUzvhTT1lvuNxcc1joR0T2jAinLzfIUGdkWEYNRU7mloyT72yQKWLdnNOYh4e159/zHjm8jz6q9xwdjaWq9uecPYnzcNddNk6/BcT991v2s6TEnAYKzE+dcu5v/kZQa+r+hoasdo2aEP5mzpkLMuPITmCPw8PGiEavp5kZZWX8zES8GiXg3z5MqbfXjO9rr7UsGRDmoiI9p73dnJOmJuduvdWyULSH8OtyEmW/QiFBnfyeONS1dHfb/5ubY/fHTTcpWOHvS+dMJvmGbX29xh6NSkawpjj9wMBnZ7UXJibi9/TBoYGl8sEHLZPgByk6OuS0fOMbkkHB+phHH9X+iMdyyVxdc40cu29+U2f9da8z5AiXj3ABiuqc9s6pUwrS4vAQtPOJaHCGIxHNLxnzgQHbl8wr+jkjw+pzIDO6eFGBAu7Fd1ZW9PPCQn2+vd0CM2S7/KwKgQqcXL8cgIAHPQC3bVOWc2bGmmpT04s+Bw4dCskJu3DBmrH66B6yZYcO6d3LyhQEC4Us0xqNWukB9yXzXVur3913nzG01tTos6dOWe9G3hc9TyCP9ccW8uvQ/MvXt9yDPeMH3YaGNOZf+AXt/euvlwPn7/tEF3sFuPn112uOIDTKzjYkQTSqmnHeOSfHAkvBRr4/L9dVx+lnfMVrMkkjRph3gNQhSHFGwLCPj8vIecUrdGjWo7/kCoXUxfv55xVd6O2NZWZbWZFh/8Y36vPd3fGL7v06JmpFiN6trBjELT1d9+Vw845+loKDTFQVjDbODpAJsPEYVHNzEkwUstPYEKHlU5RipPMs6DXB8g4OWs0R4yZ7BHyLfj7AEoeH9Uy/rxDGD4YJvVRmZqQEH3hAghYoEA4GSpqoPkYo9TP8zodAoeyB7DlnEffvftc+X1hoTUjZf+npMijOntV7AE8E+0x09i1vEdyktlZRIYym/HxTvuGwNdxMSbE+UH7UG2VHFgclUFIip2LTJovIzs3pPqT1iaS3t9u80LeKzEpeniLq1G5cc43RSY+MWJAAqvL2du0V2N6A4FHYSq3cnXeK1epzn5OBNzwcy7AGVWskYlFTahVCISmFlBTBNbu7LWASCmnf0GoA+ChNNGtrpeRTU+WA+1BOjGUi7pwroDFg0qlByc62Hmg0YqaOyXecyXwVFpojV18fCxfKyhKRChkYzhhOLMYO0Uzud8stet6WLbZXYVYbGLA6K6LCubl6dleXYfjp8QUhCLKjuFhru3mz5js724gipqbkoMCASd0YMFWcSxxgmA9nZqzHmnN6JmeNc+7LVN9ZpUZt61b9HkdkeVny+tlntd8hnyCLEIRf//f/rtqIixct0wQMGkM2FLI5x9CqqNA8nzyp71Ignpdn5CUXLuj9X/96wYdwGuNR1G/fHlvj0dgow/lzn9Pztm7VWRkctAj21JTWjn6CjH1sTE7Ttm36WX+/shAHDmiMG6nLIdofiYgBjto72NP88TpnRDzoDZ+5rahI37l40Zi/IhHbJ8hj0BVA0Zhnss379plsIWt8883m/GGw+kYruu3cOe2F/n7LrJeVSSZ0d+t+TU3x9TBwOxx6eiK+//1y3CH38JugE0wj2EUWkn2FAwGUCtgyTkZjowUdca4gWuFMRqPGqnfokNHBQ6xCJmlgwOCQ1BwCdYXZDQeBTAhzSE14NKr90NFhbLhApZ99VnsdY72yUt8hcw9knsBjTo7mp7VVZ9TPuvX0qNn0t79tLIm08qAsgL1FjRfjpE4uI0Off+QR7R9aLwwMmCxCB+O8JCVZUM4n4kKfYkth5/j7zDntYX4OoicaNbbV+Xm915YtsvtovEyQOwjF94M1BMorKhRAaGoSW2tSkpzWsjLNFedz+3bp5o008r3Sr6uO08/4ChYLOxeLQc/J0SaDejISMYFK0T5p4MFB5z72sfhKLtHV1OTcH/2RNaqkNqawUArsnnvs+8FCSNLo589LyQKf4gBihEDAgEEKZMiHzHBo/IMIQxcEBGQ1JiZkaEK1Pjys7910k4yUQ4c0dxguZAZQTv5hpOAVXDXG9/nzFsUlaoajNz8vRQWd7Mtfrjl5+GEJP4pzu7st3Q40b2LClBNGenOzMQvB+EchP9k5jELqGmgqh2M6MmLRdj5TUqJ7zM9LaN18s/7f3W1RHfZfZqainRcuSMHg0GJg0Kz2Ix+RYvKx3WfOiCnpwgWNG5w/htOFC5oHaNmhf6enEo4shfJ0aacAdvNmnY2entiCWBTJyoqeQcNgGkCSrcXhpTDcJ0fIz1eEfHxce5j9gcJyzhjgHnjAMPIpKTpvECgQ/ezqMgULNX1/v+a4slKGDDVuZDhw3FJS9DuYB4k07t+veaFvEsZ/T4/ej4wXdXYYAMBZs7MVAaQA+/77DYIIhGbHDnOayRhXVOg5o6OW4fTlFe8HfMRn1wKuMTsrQ3XLFn3+Qx/Sff/bfzNyk9xc7aGBAesTBA4+KUnz0dKitcI44gxQX0Lx8a5dmrN3vUv1Y4cPG415dbUgXOGwnrF/v+4zOqpnU0tGMMSXDY89pqxPa6s5KcFME3IFAzsU0rvW1emeQUdkaEhGApkHIFtknXz49ZYtWqP5eSsSx1BCXhKoYnx1dcrInDmjzM6TT8Yyo05MaD5XViRLcDre8Aa9h193m6jGg7Pb0aF7P/ec7Qui1KyXH3Fn3WdndY727pXT1NcnZENRUfy6nHhXS4vqSr79bWP8rK7W+wfHm5MjWQjkFUOQAMngoGXwMNgx2pGtrDFwyfZ27SUgik8+qTUdGNBcQCX9yCN67i23WGCIs1ZZqe8cOWI6niwgcGDgldnZkomwXFI3ePy4/s7JsYx4V5dz//W/itgA1IrfBL2/31onEGDEkMZQ9qG/ycl6h6ws7dG+Ps11b6/VwfgXDgnnYWLCYH4Qk0DvDlyWYClED8g2X7aQ1eXMLS3pO2VlmsfcXAv+RqO295wzyCuMjASuCaySOfH1LCyhoH26utS0dmFBZ5y5PHpUz0hJ0dkuL7fnTk6aow1NPQiKa64xvZGWpjHC0kojZVAz2E/V1RYIYU6QB35PNZ8xEqfWr410TnYMrRs6O+W4fPCD2js0cEe++xk95yxwBstwdbU5QrfdJt3W0qKaRGrYVlb0rNpazVE0agGznJy1G2hfiddVx+lnfMUrFk5K0s8wIMvKJGBxrEIhCQIYdeiPc/asvucruc5O1bOUlcVivX14Q2OjMOMdHdZDpaFBm5bD5feScs6w45GIcx/9qMYwOCiBAGkCxeFEXLZs0Tt1dcXSWqJ8/YPsnMGrUFjhsEWwaSiKgIHWGhgb1OHM2fi4KQXgB0SuKNLNydE8EpWhNoOaISA/ZLxgEQK61d+vjEBKigTRdddJKGzZYmQTQ0OWhcnJMWOV/gXMCX0OiCoBdcrJMYgOTgqU1zgOUINC0EFhMZlJGp7++78793u/Z/sPim4ggQjKoiK9R3e3c5/4hKJvQGDq63X/hx+2BqIIVOc0JgqXndO/aVDb12fvFolIWAKzAnJBLRGEH9nZVqRMxsGHKTInzlm2FoXjZxD9CyOFAluUEBA0ilenpjRGWNzGx60GzjljVAOqlp5uEDefcY9zt7CgeTt3zvr0+H8wLgcGjFmvsFDPKSuTEzA+rkAB0Wj2K9kq1mjnTt2jp0f32LFDCr+vT4ZfS4uMv507YwvOJydjIcDIq0cfNVaynBwzTDDecahgSiov1z5KS1OtWUuL5FU4bJDLqSkzVlgzjDh+ByyF80HdDvLmscf0nk89pecWFCgDMDws+YTBvGePjPLz50WmA5wK1koITYjEJiVZcAPoJfvEhyT6Ed7MTOvJMzBgsLyaGustBdywvl7P8unBOUtZWYJ1RiLKPlG8DwKBiDl1DNS5zc7KEN+8WfVAFy7YXPb1mYNeWak1nJoSZJQgnV9361ziGo/jx2NrsOgPhQMAZBmjiwAZsK/hYe27AwfkNL3znVofYHdrIShaWhQsfOQR23PDw8Yceuut+tunNL/uOkGhJiaMZnl+XvsDhjjkVW6uzScGPMEfAmvj45Lpe/dq350/b6yK27YZBOm++3SeXvay+OQOp09LxnN2ISxAV1ZWmnP34Q8bzDk11blXv9qYdn0jOzVVe621VRCsri7rFzc+rmeCFsEgRpaSQQIKx7snJSlA8Z73aA8fPmyGtXOroWAzM6af/MDp2JhByUEXRCLWhgQ6eV9vg/IAEs06MW6YGEtLdU+Y5agHIvg6Pa1xg5YhcIFeoRYzJ0e/f/RR289DQ+Ys5OYaG2Y0amRAwOyxm3wkzfy8Pofedy5+7bhfCpCdbTLQP0Ow9fmwbT/gA3kQa8Kc+RBnxg26pKjIoIi33+7cb/6m1ik5WfsIueM3Hia7V1hopRLOSdadO6dzHI2a7u/s1PvT5oWAkO8MHz9+1XG6eiW4gsXCpN/T0qx5WWamNl9+vjZSY6PVoaSlaYMND2tT4ijk5sqAeOwxCYitW63fgXMyCuJlpsDycq0F16ivVxSeJmnAqWBl82F4CPSXvUyZj29/WwII1hugPD51OYYARoDPgARUJiNDRvzOnZon8Ps33CDhCQzviSf0O+eseN05iyZu3mwCJxyOLSoFnw01Kg1oKfw8d05jKyrS+9H8EEdobs56yWBkwyKHYsAZIBMDJI8L+Aw/p6HstddaJBlHA8gA96OOh/FTiNzSIoHG/nvmGYNUAIkAagSsoL9fbEOf/rQJuFBIjf0ee0z3A64A3AvHaXzcMOAzM7EwPWoJurrMeQPagwKl2zkEGL6ioHYKA6i62mopiou1Nm1tCgj4dKfUBuTmakxAnMiYYjihGOkx1tlp74NTTbYQJiFoWLOzdXZh4yKwceGCnBYyLEDr/CggGSGyZhhEZPQKCrTXl5aMpAOYJD1itm/X/Z5/Xn/v3avvOad52rJFc3PmjNUzMjednRrDM89onvbtU93EAw9oTH79BAEOYKE47mfOyCAuKZEDkZwsp2101JiWKGzGwCZrg4Im2ovjzB/ek++Vlmqu/+7vDL6bmal3/LVf07v7AaRvfUvKnUzNwoLRi2Po+yxRRGuRbb5RxHkD9rdtm8bd2CgZjCPinOYD2nKMsfp6i8K3tGj99+zR531Y5+bNeoeWFiPiwagEKgmhzdycei45Zz1qDh2yrFUopDOXl6e1qK3Vmvf1yZHx62iQyX6NR22tCvfJHGdn2zxgWDFPzBFyBXnu99krKtI7z8woO9nSYsx1TU2xDUQjESElnnzS+gD5huX583rGbbfFNjcfGbF6GxwBP2JPXQxBP/QTQSrnYmsOyUoAUc7KUhCiqioWgvT009r/9MjyjeVoVLIAWe/XCPJcmprTg++666yBPVBpakF96nAY5Z591ub64kX94f19uDwXTgRnml58OTnKRNxxhxy2D35Qe8DvYeUH3pwzqBkwZuqPOTs+9JXgk68XMzONqQ5Z4wdZCVYx19zDr7cFTkcPROcMSjo6qn8D/eb96UVEj0T0APLCOetNBEokK0tzfP31OvfIL+YSx+GRR7TfCwtjSUXQ05Rn4AhSTsD7T04amqW7W4EoP0MIIzPrypz5gR6CGsgsKMgHB+1Mv+ENliG96SaNvaND+xD9XFpqPSP9Gr+pKe3raFTya2TESJnIQD76qOQImXfW4+tfl77+eah1uuo4vQRXvH4AGRlGXz025txnPmPwNF+4jY9LGPud0SkgPnjQIvLJyTqIBw/qe7fcIoUeD37BFaRJj9dw92tf0zOKivRc4FS+QYshOjGhyOeWLTIaH3nEGOkwTIKXD4fxSQBKS426d9cuqy2qrZUxEUx/v/71Gvcjj+gdELR5eVJA9fUqyiXCDYyDTAUGbSRiLGoUfqJ4UlOVCcDBfewxGYVDQxLk9JLBwUlKsjUrL5ei96N+PssQGYqsLO2B7Gyt+2OPmTJHUZWVSeA8+6xBHsDwJydbtLm0VEJq505rvHf8uEXgcnL0rtTfkQnx+6pwNTcLxvfHf6x35rMFBRpraalFeSlEBVpI7ZPfkR6hn5NjjQiJzqP0yAouLBgck7nj/pwXFCYwy/Jyq6PAwHfOlDS1QbAzcf+TJy2jsrAgBcJ7lpYaVbSf0QxekYhz//APxpjlRxYJOGBE+lmz5GStG0qGC4p54HLj4xqP3yNmbEzvWl0da7AlJenMDw9r/23aZC0FjhzROjsnmEVqqgIwb36z1V10duqdMzN1X9aDSCo9OpaX9YzWVoMsovxzc7UnOd+sFdT1fqQ7K0vj6+rS58hU09w5PV0GKutFNL+lRVn1z3zGmk92dEjebt+ueb9wwTIOjN05MyiIhmJYE9gBAoyMoq9YTo61JvjSl4wA6NlnNUbqGFZWrI6gutpq6iorJbdw6P06WEhb+vo0l9PTCtoAxSLI0N2tZ7/qVZqTn/xE5xNYJwbq8LDNeVGRzmqiYnAfQtjZabBmov840L7c9mscQQgAJ8OghAjixAnVdHV3G2tmcrKyWn4D0Y4OGV3A6XwYNgG3s2clm5aXrbm5nxkHouZnBPysFtF55IoPSyV4mZ2t3wObzc83p8nPKm3bpvVqbdVaOWe/B7pJTQ7GOPuDOUxJ0Vm+917Vo504of3Eu7MfcSA5A4uLCsxmZxsVNHrDz5T6NTKsZ0WFUZqPjSnwefvt+l13twIyQMaBCqPvCXiEQgZPA8LIulKX59e/4bSkpBi7ITVpQL75G7QANbOTk5a9QNcSwMrK0h8IRPLzDcI9Pm5sdUtLlhXCNqG2FHp2ArrMIf2J0tJ0ZrHLfNh3OKz7U/eXlaVaR4J3QN4YZ1+f1V2z/uzvykrR+r/hDXrX179eZx+HyF9XH95M1g+7ECZf+qf5rJLOrbZPi4s1N9XVCto9/bTO1A03xJ6daNR6a5WVWSsTarbIYmJbMVcLC7IR5+d/fmqdrjpOL9EV7AfgR0U7OhRlPHtWAtl3nPr6pMTCYSkWjP7eXoOnLS+ryzTGWU6OFXtTJNnSor4bn/ykKYcgTbpzBtc4dcq5v/5rCZEtW6SMi4sNWkJWJBzWodm3T2M6eVI01p/+tArtT582w9C52Eibc6tT8Shf52TkTU5KEQGx4sDD9OYXW19/vQT3I49YwfbWrUb/CRMO2GFS6X72Z2HBqM1hVCIjlZtrTuuZM1bLtLwsYwQctV+/grFPpNqPdKMM6YGyuCjj/LrrJJjPnjVhg3BPSjI6dTI61IsAOSop0biAnLD/fvd3ta7nzmnN+DwXPWzIBASvu+/WmvzTP+n9Mc5qa2UofO1rhukeGzO44dycORzUXOBEUQMA/MSPMvK+frSYgldoa9PTTZnU1GhtfvITvffmzcpADA3pPDQ2KvNCTRnsSBhMZKVwsmprLSKYlqb/g9mHpIIas8lJrUFpqZ7x6KOWAW5vN+PMd7Qwnrg/BlZlZey8k4H9lV/RPH/xixpXQ4OxQba06N47d67OIpSUKJL4xBMWaRwe1v5KSpLMwZk8fVrQKPYUZ31lxZQufY04d0QlcZKpSczIsDoX5gn5BOTUN+CiUWvminGWnKwAU3Gxvvcv/6Jn5+cbxCg9XWvd3i6Z9epXa5zUmWVna+2RAew150yh4zgxHgw29qRvcEajOk+33SZjhrOelaW5OXjQoJnBuknYt9LSZFChE4J1sOx/oHFLS1qvri4z6ulNd/y49t/goGSSH+lnb83N6XtdXZJ/S0sG6wzuF58inHom1pGsOmPiGdSgwUhH5nhiQrKxp0f7sLnZuT/5E2VDuKhJg1UWuHBXl9GeM/++3kAuHDsm2X/woAy8c+cMholB5usZ52J7BLLuQGcJpDHPfhBjakoO0uLi6r5ohYVak+xsZf2mp02/kAnasUMBPpwt52IdOwJa7e2qTd661WSG78izR6NRc5CiUckf1gnoMr8LZp34PmQQQ0PS9W94g80bUFHOA98n40zAwzmzP8i0sneQr86ZniWAkpen2pvOTtvrPjEEwU3kNfMJxfjKitUSFxTofn7dHTXdBLmuv97IYJD91L9BUsV+Y2+gvwny8LPJSXN2YHadnTXnD53yS78kGK+POMrO1hmgP1U0aj0qu7v13bo6yRcIWpqa9B1/HZkrP7DD+SezmZlptXiNjTqLQSbLRPZpKCSd87nPCZVAAD0lxRoCp6baeaH+Fjgv+4zGzfRxoydavCDtlXhddZxewot6BK54vRY6O21Td3UJUx2JGKxnclLfg+7bOTNYUNZQqeblGbxubk4KJRoVxjwcXk2TzgW29+hRGQfl5bonzhrCY2xMh6WiQj+rqbGD0NysqPsnP6nDnpYmxQ+eG2VDdI/nJiVZSp26pN5evQ/1VeXlepd4PYzm51XzVVwsI+bcOQnZzZuNIY7ItnNm1PlY3tFRzVdRkdVBkU6/cEECEOYqCB8oRK2utqwKhj1rD512aal+h9NFpoUo7pkz1oR11y7NKUWkvCfsW8wXWbK1LihIz50zh86fu8lJKZ/CwsS0983Niur/+MeCc509q/FdvGh1S42N1miWsUYiVtDKfPhGq980FyPCV7goeed074oKQVjm5pTyh9UwM1POWVeXlNPLXubcl7+s35WVaV77+80Z4J7OWeQUGAXGIL1EYJaiRs6vQ6yokOJzzhqdlpcbFHJiwrD9kYg1BQUi8txzNjePPSZZANEB7GM0U6ypiaWJhsQlHF7dEJkrHNZn3v1u7f1779VzfYry3Fz9+9QpzQsFxZOTUpK8v9+k0q+ZcE7vyJnBwL7xRr3DQw/FGr4rK7ENsTE0kGULC9pTBQXa452dVrjOs5ElzPPZszJa9++PJbqghtJ30InIwgpK01SgcD50hyxZTY3W4hOfkDMKcQK1l0eOWNQVQ5IIL+sOucv3vifD+3WvW10Hy1wmJyuyDVTUOXMaqX0qK9N+7+jQXsVJACKKAZWcLOexs1OfO3zY+tslogjv7LRazOxsYxwEksXZJbteX6/IdHe3GYDFxYLnvfa1Crr09Rk8DecPuFlamn7/z/8sxzLYNN252GBKNKrzjNPa2SkZi3NKvRzrjYPM/BKM4b5kHflTUqLfFxXFMsEdOWI6AOg6TaNvvFFrQUYiPd302LlzFjjw4cg8PxSyLPrzz+vM5edbVsnPPLHOwMoIukLsgMPjO02cG84tGaFoVLrmne+MRaX4fY582n8INvzWGOxr1iUvT2Pwn42upeYGyB+U+tRKZWZaxrCvzz5HMIq6KeB5UPNnZmpPz85q31VUaN5GR63RLk3YkTkFBYbm8GFunAfOEk7K7KxBC+nbODqq81FWZvqBAEJZ2WrEEfMIBD8ctkBucbEF/L74Rf3/2WclH5BJrEEw6xQOG0MwNbEgZQgsUnLgN19mTyVyYDIyNG+nTun/kIvt2WPBcZwmiDzYC85J/w0OxsL9lpdjm19fyddVx+kKuYIwubo6be5nn7W+AYODljECGkNtDsKJ1DEwDGANwAHS0iQ8Skt1v+PH9dxXv3o1PMS/EEYU8994o3DOQHsQnKWletbhw1KY8/N2EJqbxa71rW8pWkHkFuMISIx/wPziRt4hWL+Tm6v37ey04nMoW4uLxRRI9GRiQn9OnFBWDoMV6EUkYlGqlRUZuxSODwxYRo86NJh8+FxamuZgaUn37uoywyY52Wi/UXjUrvikCSiM2loJqLNnLVJ79qzGweXjyokCAj+IRKzuiOwHgt259WuVwmHNzY4da3f3bmlx7i//MramjOg+tRj8DWQEqJsfJSSzV1VlRgT9oHg//sbpogaF7COwLb9fUE2NjI/Tp2WsEwHNyZGgP3rUjAqMZ+i9CUbk5mqcO3dKuPf26g+GxC23GBQJcomWFhmcwT4vubkWMEBhY+BUV5ux1thoPV26u6VUr7lG2VyffSxedLC6WsyHQeOb+cMYvukm1Yz09Gg9gtAL6ppwWnJzDSNPMTb7D0PCzxZjYGC40HertlaG2fnz2hNBuuHkZOvhxDkBojk2prNAMIHIN88mSkyBOOfFJ+YpKrJoPjV4rAdkJ1DULy2Z0YU8y8oyZ+LsWctCdHRY/7AjR8yAwmjBUGdfAQHdti2WFe6uu1bXwfKOnGfIcXA4CT719FjGgLPiZ7Uxnvz6yW3b9F6nTun7Bw7oDAQpwv1egEtLOls+vI5M8t69kokwQG7eLMjXy1+u8wAxT0uLwcqRhZwHZFFmpj5H/Vc8Q9YPeCwv6zw8+aTVBQOVxmFEx+A4+PTPEAcgs+vqLLpPiwGM2YYG68lXXGyNhanRjESUFS0slOOXnW3y4Yc/lP4tKtJ4L17UdzmD1DYyXmqr0tK0J6BRD0INkTEY48xNZaXmnLpiMsN+lg2G2OxsIUT8VhacoZtvVlaPYBJziMxk7OhhagUJOlFvCdET5xtGuMlJnR/2BUx7EFhcc43+PnVKYyksVPZ8ZcWCiQQl2tsN/ldRYZA4DP+aGmvaeviw1dOSMfEztT40FYfKR0iQqUOG8O40mkfG5+RY7Xpnp/bA17+u5505Y+cU26ShwXT3Qw8p61hcrDkj08b+JbvlZ1CTk621RH6+nBUCkNXVet6XvrQxdmbfTj1wQM+gJ+TcnGR6dbX2R3a2Qclx8v1rZSW2j1u85tdX6nXVcboCrkQwuU2bJLSfeUaCJDNTB2BxUYe8t9cOLvdBgGGEIGw46JGIGag0+uztleAhPe3DQ7j8+zhntTCwzgEZIHI0NCSYHoXNjC8cllGwf7+gXD/8obGo9faasnFOP/f7TPX1WbTUr99ZWDAK56NHrbj4hhtiBcDcnOCJjz5qjFvO6W/qf5yzA4zhV1qqOaEeIilJSigjwyhsydqAq05L0/rNzmpsGRkSOs89Z+tSX6+5os7Ip8pNSjIK9JUVM1BHR82wB9KHkKfgm55UPs002Y+gUGpuFmPTRz8qIU4kmVqlzZvX7qty6pQULEXrKBUi2yj2+Xkr3s7LM5puDLyyMssQ+s1zc3MNgsgeoiAeIzAS0b0//nHND9S7XNGohHtGhhRTZqb1NRoaMkpiaF2pwUBZUwxLYGH/fhk4Z8+q98XRo9ZcsqjIqKgxONPTtY/pB0NhNJkYovUvf7lF3ZADeXkqyoY+vbFRzIjBWqp40cF4JDR+QIF1JYMRzE5hQADPo4bGd1h8RzxezSLfwymdndX7zcwo43DNNc796Z8aaQb3zc+38QBLBdra3S0IEUQzQKgYS2+vBVyi0diMGHNy8aIZGNCRBymZV1Y0DlgsIazZssWipGQOjx9XNJieJYuLes/ZWcv8UQ+G04Q8Blbqs9idPCkWswceiK2DvfFGZXeBJVIbiNOYliajhfPh93/zKc1hvyNi7veR6e7Wc/fu1Z70nfRQKLYXYF+f9nYoZP2E9u6VTAk681D8f+tbGvvcnM4Qc+0THDhnZ9J3Ygg6+L/3s89AnaFfB16J/mKdfdgQcGqCUOyD9HTJ+bo6PWfLFp39m2+WgVhbq7X47ncNfgesD8QAtbnI/7179dyhIZ1BxllcrD/j4yY7ybogC6GtjkQkP5OStI/9DByOH9AoIGRZWRrzj38cKzNwqDmfQKdCIef+9m+d+53fiTWgQyFBhA8dEjSRwEAopPcG0gv0LCtLMhGnJi3N2mx0dFhPJzJunPPaWuu9xjlNT9c52bRJc1lQICfi0CHN0ZYtBrkHUk1WzA8+RKNaq5oafS8726B/aWnWAoP59J1ygjTchxqmiQlzxpFjwJ2Hh62u6IYbLAhJIAK4cXOzng3TJ7VRAwN6T8hOCKjibFIPhsMPM+3cnOTXy1+uOXdO93/3u6327BvfsEa8iVoQRCIWdP7yl7V3/cBkUZHW5PRp9YR65ztFJkLtO3rBz/I6p71+9KjqMZ1L3Pz6SryuOk5XwEVvnHgwOSLP585p42Es3HijHCqcIOfMacKg8SO/zsVGQvr6FD2HzpOi446O+BHqyUnruVNdrb9nZqxmhbQ52YHsbB0EGgHGY+srLJSwa201h4Qo2cqKDndNjTXLIwIEVIH/x4s6Ohcb4YCN6OmnNX9EgWBcQiEhPNPSpDCLivSZhQV9hmLb5WUdfL8hXUqKFW4TfSIalZ6ue9FLJhTSfJ44YThu51az6aHIicY5JyHI53zMMhARIF3xsh9BodTSIiOotlbvAhSmqGi10RS8WloEvTx92opxaVBKVoKs04EDUnAzM5ojiBtQ1A0NBs3Zs0f3OnxYyhcFzx4n+4lyycjQPszN1V46eVLfh7EM5kV69hQWmoIiu0BtEIaaX2hMIbLPbgfc4ZWvVLYpSPRCTxrw6DU1MhJhfsrJ0Xjoc3HbbTJS/viPV8uBpCS9X3Oz9WHZCAY8EQlNsF9OWZnh0iElocaOeU9Lk0N64oTWDWOc3/lOkw+d4mxEInJo5uaUqc7N1ZoTsX/gARFSfP3rtq+paYBQIBw2AobTp2XUFhTI8WVP4ChgyGdl6Xl79lgT13vvVY0BzcRxOMrK7JwTfKEZ+NmzZrAEmzdOT2vd5ua0j8rL9dnCQhkgQAMJxlCjRqAkEpHj1dMjhwwWuze/WcyAfsuIqSnNY1+fQUz9LAkZKbLPOEcYfD6cGSjy4qIx5jU26mxAXf7a164+/5fSC5B92tLi3N/8jQUYfFgOAQScGy6chvx83bu8XAYk5CMYYn5NTUqK5MkTT1jfO6CeZGEwEEE4kIHgLLJG110np57arngU6WVlco7a2y2DwrxSVwcUbWxMKIesLBsXwUiyE6wlMFlf7uBYFhVZPa9fN0UWnrrCmRnpOUhjqDEFruhfZHbRYUlJ2vP0//P3QFOTc3/2Z8596lMKfLLfkC0f/KDW4MEHbf9DjV9ba/o/HLa6q7k5g3PX1UmGo7/Y07291newtFRyAOeKM0nLAlgPISdiv4CmyMyUnhwZkRw5e9aCDUAk2Qv+3ziFlCdA4IOexXEmOx8KaXwFBcru//Ivx+4f3/bzdT1BI5AwIA7QWXl5WteREYNBo2uR5WSaaDy+vKw5zMuTLnnwQX1/rRYEkYgFb0ZGdI4rKrTvfSRFUpLJrY99THDjhx6SfMUpB6pNSUFSktazq0vnZL3m11fSddVxugIuYE2JYHJZWWbUkxEqKRFkB6FF5gVDw6cD9SFIPn0lGGhqM264wXCrfgSiu1vPe/vbdRhOn9bhJcvhp2I7OjRmoFM33yxjNh5bHwWgzc06cChr4DPp6dajiYgqgpFakspKCZOnnpJCbWy09PGTT2rs732vjMfnn7embUlJUhDQquPkEO3BaNu+XcrpmWcMGpOUpO8B06NmgR4w+fl6Lk5kT4/R7r7+9foZcMX2dr1PYaG+n5oqYYIhSiSWruZJSWaIYbwtLmodKMjv6tI8xMt++EKppUVFnp2d+uyddxrzXE5OfKOJiyxpZ6fGQPYISAmR7KwsczhvvllEDWNjFr1HqRcXay5gbPQNd/Yt8+FnCKg9qaqSUj5/XnsGyINfewCxxvi41Zz5dNgY69CJ05zVOY2rvl73CdZ9ENwIFtK2tooUpaVF48GBIKPmwzpqa2PZ1MiS+X1ffHazjV5rFfly7dsng/T4cQusMN+sQWWlzu7580bZDnGDX9vEFaxPg3I/O1tjotfNxz+uczg6as4sxiCRU+AoWVmKbL773VrXM2c0Lmo2yZZSlB0Oiw1sdDSWrampybk//EO99333mQNC/UV6uvbl7/6uc+94R6z8ovnxzIzkIOQMSUnaH8vLxiJ2ww1y/GdnNXc44f39RoZAVnd52Xo63XCDnMHPfU5nxYfQ7N1rmS4MaeYJgw8jEWcNWKhfuO+c5scvRofumOj07KzaSND4Nriv1uoF6F8+oqKkRM439Sh+9ov9SeBiakrzQx+yffusFoMsO+/MOc7IsDqnoiKjQp6b01xmZ2tMvgEHnDQ/3+R+fb1qCMl+OKczFKwDQZfBMtnTY3U5zhlh09yc1mF+XucnP1/joidQc7Ocge9+16CrOOLIrUhE71RUpPfr6LCsI85VZqaRAQE737nTMkAzMzKko1Gdi5wca1USDhvLZ12dZMKZM8794z/qzM3N2bs3Nak+rb1dTursrN7hppssqHf77SZ3BgYUCDtyRHqYBtXl5ZoDYIOFhdbKg3WCJId9kZqqd6S+mz6H/rWwoDWktQOOqa9zKGn4pV9S5uXcOdNVPgTZhw/D3LplizUxz8jQmlAe0dtrcE0c3ltvlcwJ6lPf9guFdPb6+sxGgTCJP9u2mZPY1KR57e01R45aKuwbv8eSD4VbK1iPE3TokPb7woIFsM+e1X45fFjBe995Qj/RWmB4WEExyDZSUqzEAkTH1JT25R13bKz59ZVyXXWcroDLL1qOB5MjSl5WJmXtRz4rK3VwUJ407sQwo9jWFz4U7a2sWJ1TX5+ehcBubzcMrR+h3rJFSvDpp63Oil5CvsLE4A2H48MQFxZ0AM+f1/vX1JgCKSzUmNrbrVgSg+a662QA+dmUxx83oY5SxnHs7NRhpXmnz4hEwTB0nUQmCwqMvvroUf1uZMR6GzFvKKZIxLIrFKYjAO+8U5kEGgjTO+UP/sC5H/xAxgcG3fe+Z3UJGRlmBIFxd87YqcCGk6KnZu3AAQlLv8t8MMPgnO73P/6H6ptSUqSEIT9obNQ4EhlNzpng9QUnDieGNHUXXIwrP18QrWD0nmg90bVIRHPV3R0bAHDOIlhJSXr37dt135ISjW1w0PokMUbof6kzc84MBeeM5Wd01JxpnpuRobG1t2sv1NbGOqLxiF4wtnNz9f26OjMYab5aUSElRb+LjAyDEfoMXcXF2nuXgwFfq8jXOd3/l39ZypAaC6KsOO6NjQZBgVCFSCtwGJysYOaXtaqokFOwaZN+NjioCHw4rHNSW6t5P3dO3wNyRYFzaqqM59tvjzXK/uM/VGM3O2sOBXWYDQ362enTCqTk5Znxd8cdmtNvfUvrBV18U5Nzb3ub9Q+Kl7kjMw68KivLAhdkOs+e1XgffVQyuqLCItR+HWPwe4cPm0Mar8F5aqrBapFbQKV9yn/npB9oEEu2ic+WlBgLoXNWb+WcnuGT+8TbP6GQnL5gL8Dg1dmp+0QimgsyC3l5xvCHPPezPpmZ2itbtuispaRYTWZHh8k/9hz1d9nZygI/+aQ+59eXwoaakyOHoqxMY6PGEVnQ1aVs+uc/r88VFhpJ0MqKzuxrX6t5IYgG1BjiAiBvEHrg4JEZIiAyPq69Ulen/fKDH+hsZGSYTALa95736Fk/+YnmlQwX/dyA0IfDeoekJJ1piIfoBUhGHwIU4P3sn9lZrdXEhHT9f/yH9gOyFgg8TLXxLl/u7NxpZ/b4ccH8aN3hZyVABhAYw6mvrbXs08qKbIkzZzSv1MeRxSejtHu39Zy8+WaDt2EDnD4t3Xj77ZID09MKsJAN9smLGI9z+ixtSa69VhkwAhdZWZoPghsNDRrLxz8e/5wEbb/t2/W9oSGr/aa8IjPTGso6p2ccOKDnnz9v9l0opOfecENikpdTp9YO1mdmStdVVAh5wpmEOXly0qjK4zlm9fXaI08+afIIWGRxsdU7Dw4699a3Ovdbv/XzkWniuuo4XQGXX7ScqJAbythz5wyKVF6uw+UzVpFux6AuLNRncFTIliwtKUtDdmrrVjklNPtMT3fuV3/VsNxAAKiL6ujQGHp79TugUjhwMMj88IcSkH5kwzcOqFugIerZs/p7eFj3IoJM49jz5yWAysp0L7q4p6ZKAS4vG7MRTUK/+U2LFtFHqbjYHBK6dCO8YafKy9PzMGowHOnjMzRk0UAMHep5gArs3KkoYryGwtu3aw1R9kQmEdIUdmdmWoYkO9t6V2B85+UZmcPb3hZrVMbLMLS0yGn6xjcsSst9iHrv2LG20USkrLLSIBd+k0iMMyKZRUXaW1u2aEwjI7HR++5uretdd0lhP/ec4bV379b4Ojo0Pgr5U1K0b/fsMQWxfbvVw83MGAsWyrS2VuenrExjgwUINrvRUTO02DPOWdfzcNgc67Y2c4SDQQM/WDA4qPcsKTHHLidHig2IRlubflZYKIc1PV1Gk7+PL1wQBOLFxoBHInJogbgRdCFLnZysc0kzY6iU8/P1uwsXdB8YnriHc3bP+npFo31F3tpqZ4aM7+7dujftDq67TuObmNB3fWfV35dPPy1ZQpS+uto+Nzenup/PfMYyfNu3ay6zsrTnXvEKzX9e3ur1dC42czcxofX60pe0p4aHYyPeSUmShcPD+t411+gd+vv1B0hkbq41KOd72dm6d1lZbJ8UH0JDUGVlxRpZLi5abSGwMDIpW7dqX3d2Wi0YNRfsdedMF8zOymAqL9e6v1CWq+PHdZ6Hh62dw/Ky1jc/X+M7d84cKfRCY+NquHCwfxyQuNxcrcXAgM50crIZoTMzluGZmtL61tVZBqCjw7kf/ci5r3xFe5maROf0jH//d43t+uslRwYGlDn53vc0RhphI6/9QCXBMjIYGLboiMJC6YjOTmViYN4j497erndrbhZy4u67dZ/rrxeNO02wMzNjyTXIvubmWk1LVpZz//qvOgv19avh/rDZpaZaxhdbYXpacqysLHEfyPUu5EB9vbI86Ki+PkEiW1sNXYA9AUR/etrqnYGn7dhhDlNnp9k8PoqB4BTECFlZerdgnWdTk+2pU6dMn/vMqtQcHT8uh6msTPrv4sXYLBHnH6dv797Egaug7UfA58wZY2MtKVGQZ3jYiF985+mVr5T8Ky+X4/3YY0aKsrwcv651vWA9+tO323DM+/r0/eFhg4wGHTPnnPuFX1DgY3xc+t+HugNXLi9XkOPnyWly7qrjdEVcftFyvEJuSBBWVqRIOjslUM6elcDcv1+C5sIFCVogMaR+iQ4j4JOTrTaJaGldnbHPVFXJaH7iCRkVoZDVKB0+LAG/vGzpZdLDCwvWNyIry6JCkYgJjmhUP2MswJOYh54e/Rt2PNLVRPrGxmIjHSgglA3CyzlTPkRVceqArhQUGISIrFFVlTHvTUxIaG/frnlvbTUB6RMIVFZK4AKlpIdHerrS3QsLhuufmFC0rbNTihChWVlpdRw4bhiTMEsRhSW6iFCPRvUsYDzUK8QTRsDzHntM9ysoMMcPBqCZGe3F4uLERhOCFwdxdDS2yJiC7tRUg7i1tEiwv/a1q4ve/axYTY2UF/DDoiIJ5+uu035tb9c+vPnm1UQQNIednTV4g69McbDJuGZkyEB96imt7/KyGR0wjoVCxtJGJ/WHH5aDd801UnQ+CUlHhwySrCxzeFNS9O/JSd2LrENn5+pMEsrXr93jHScm5PglMvAv5+roUOaHRqVBJ3BhQfPuk6ls2aL3pV8QMGH6yjmnNQPed/PNBgHjPYaHLQNIpgPD4dgxnbXe3liYazxDLSdH56KoKNYIiEYlE6mte9nLbH8/+qj2YHm5FZ6Xl0uWOhffGfaj5S0t+jdBDepHuIDYTEwoIPDRj0pGHDmi2gIaVhNZpk5sZMTYpjjbPmSzqkrjqKszo9Y5C5wBGaJgvadHex/IEIySe/caVTfGPM/Ky9NZwcl5ISxXLS0iAYIJFSpr1ryqys5nXZ1z73ufZYQS7fFg/7hIRPuosFBnGMPNN0JpbJqUpL3kkx7U12sPAxVFh5A9d87g2nl5+lNUpP351FNaB5hAySiTrUhO1j39uhMajiOTqDsCRrZ3r4zyc+ckh0tL1SB4506NhUxkfr414A3OEcHUaNTges4JftjdbXbGDTfovTn71KkWF+vZ6J7ycu1n6pZbWl5Ys1I/G5WTY/TY8/NCecBsS7AWhmAQAgsLWu+MDOc+8AHVLA4O6h6gRU6ftmDc8ePWID4ecRT74LrrLEhE4AzK/elpPS8zU0GXH/zA6sjo4YjehyiktHTtup14tl9BgdarrU3rfM89CoYCGY5nI9bXmyP7ilesX9e6kWA9zce5kpIsGEH94OysEVkFywHq6wVR/Pa3NU5g7gsLVn5x4MCV37Mp3nXVcbpCrkSF3NdeK+Xf3a3P5Odro01MSJB0dkoJf/CD+szEhA7t0JCwyadOWXSSPggrK1YvA3sVAri11agl6fN0662qbRoaskxQSYkEVTSq7xPZGBhQdAyFAPSHyMbEhDFzEYEj44EAx0FwTvddWLA6r4UFRXiamgyyQr0PsC/nLGu1sGCZOKAYOGIUiwKHysszBUyfntlZi5QnJVkanWagkEvU1EgAgd/lHefnJTzjQQjDYefe9S4JzZYWja+mRo4BDEVQghcVmfGRl6c1YR0vXtRzRkYUhfSNQOhB2Rtf/rI+j+MHkYPPAFRWJkVQUJDYaKL4/cEHrb/W3JzRuBPxqqzU/vTrrCjST5QVGxqSUXLhgmVQi4uNyWzLFr0PxrZ/Ecm6+24Z7NRIOGcRwKIizTnY6/x8GW2nT+tzBAPI1M7NmZN49KjWr6LCjP+jRy0C65xzf//3UtJZWcYA6ZzeF4YoisD9TJJzckr27zeoHsyIFLY/8ojOWFHR2rSxl3KdOaP5JrIaVKIwYu3Zo2bWjz5q61tbKyP2/Hn9PzdX96isNGKM0tL4NQhktohEcgFBOXZMDE179qztJMYzAoaGtOdOn9Y65eZaf6Xpaa0XDJXXXitn9LHHlJ0OOsPxgkbFxcaWNzZmEDpqaIAbjYwoorp5szIrlZVyvGZnNdbuboNpUmM5Pa29095u+4B6HBAE9fVGCc5eok8ctUq3365zfO6c7f2sLP28sVF7/vvftzYM1PMQrQfKdLkZTjKvOEhnzsQ6FMibcFjnKTdX8g8jf62ruVnkBLTqKCuTzPvjP46NopeU6F0mJqzW5X3vi4VNdXbqDKMjCX4hSyAbAKLHXiXqnpqqPdDYKNlMzQlMjGS9KyqUUYWNFn0FZMmHRTmn/dHUZGxlzc12BiDyOHpU3/czTtSrxevBF7QzgLgvLWlsBGUWF63OCPIMoHSTk0YEsJFmpQQdEiEgyGBTH9ffb1kNPgfcfnpa7/nMM8ZgGw4Lwnn4sOyXs2djnQXnJEucsyBhb69sLN/xC4Uke7/5Tcm7vDzrHwXkuqFB92hu1hh+8ANDTfitCpaXdY4+9KH15XMi2++WW1a3ndgI2c9G6lrXC9aXlJjD6gejgoEt6rfjlQOEQsp29vdLbk5M2O+SkxUguOeen79sk3NXHacr6gpu+IEBwQEeeUSCfGAg1oB0Tpu7tXU10xZ48gsXdIiHhswYRHgAPVtclOHT02OGOXj7Y8dkUOTn67lnzxqrXUmJOUb19caud801EtoTE8aGBFsfeHCcI6A1FJwPDRmOmWgGSgahOjurcZSVWR8H5ogLKmXn7N40xZucNMgRUcisrFi8bkaGMU7BnBeMYPqFyc3NVuDf3W1wlKys1c0Rl5b0/YceUjTs3nvl5J47p++XlVkmB7gB0LitW+U4jI5aXxuyZ1u2aKwnTpgRSCYRxsOWFin0gQFjmUtONqMLfDfO4HpGE9mu6mqDEJIdzMnR851bLVgT1d20tAhCyN4sLo4tnKcR4IEDek4iqu13vEMZhUcf1b7GKcdQn57WOztn1OA430Qb+/tjqWmjUWM+I6syPS2jsLdX9RDDw2ZEo/j7+iyT4Pc6AirKnmONGxstOLKwYEQE8/NWx+icZS7f974X5jwNDVnEOVgoDLPZ/LycZF/2jIxoPmprLdNUVxfbSb62VsZGd7fG7UOJqPeprY2tPXRO+5BM03qGWdAIyMyUAQh5RU6O1vTiRZ1doG2pqXI+hoct2k9g5PHHJfte9SrBYIaHY4NGQMBWVoyUpLNTTiSUxunpVgvnnOBg3/625DWysbjYak6mp2X8LS2pNoBaPFgdV1a0l5KTteehb3bOCDGGhiQLbr5ZRtu2beZcwGg4OmrPv/lmyc5oVBm5igqd4QsXYsfe0ZHYCEt0UQdZWyv5c/683ok+cxCM1NVJ1ji38exWPJbWhgZjMvSj6Jzrnh69Y3A/oWuR6WSayPaHw5ZNhTSFfReJSF6PjhohRV2d7oezVVQkh9k5q+/jikaNXKOxMXGhftBJaW21zMu5c8Y4mZdnDLeJevDFszO+8Q056jhSyHFfL5JFpQHwRohq4q1TMOCTqM4HuODEhGWa/F53p05pTf7iL7QGDQ2qlykriyXoodaU3mEDA/ruD36gQNC7321j2bVL9guU5tSLs95nz+r/f/3X+ndfnwVenbMM8MteJmIg6iTXuzbi7FzK59ara+VeiRyxu+8WFPn4cQvYszfpaXfzzc79xm+sjX6AgfOb37SMX2am7Jj9+7VPgfb/PF1XHacr7GLDt7Qo/Uzvm9JSU5wTE8Zokohpq7NTwvzOOyVYITPAWWCjEp2CqS0zM/bf1dXq/UDdlE8Zm5EhQTc5KcWQn2/4Yx/z+trXioqWiD4GIKlvBDNZkOVlCWYa/sE+BHRp0ybn3v9+CVh6aRw5YoqLSB+CPy/PnglNt9+hnQJ0vzcQ+HOyR2B9iWDSV4e+Em1t+oPg2bFD2Z3OTos6InjS0jRXXV3C1f/DPyh66pwJqqUly/719elvol9LSzIEaV6K4zY8bIxEFRWas9OnDTrY2BhLC03zVZjeYCcaGDC2IYyDoKBmf91yi0XFwdrn5pqh8O532zqtJxyJUI+MCG5w5Ij+nZtrTFKPPSaBe889+s5a0bcLFzS/4+NWZ0DPmuRkzSE1DYuLljFJTraeYtQtUMOwsGBQKWCVGFdf+5oFJ2CGq63V/3t7rffW6KgVOscjh8CAADtOhoLI9rFjFrUnc/lnf3b5yqekxHrb+HUSwFSYr82bDcJTVBRrpNCj58wZK2z3I75BiAnrDSwtHlRkI9kO9ubiohT5oUMqnJ+YsPmuqdHczM1Zn7j8fH0fedXQoDFDjFNQoM8+/rjOznXXaU0JGrFnoHYuL9fajIzoZ5mZkr3s0w98QAX2c3PGADcxYXDpmhpFZSFE6emxbDtBF2isCRyVlFg2BFhTJKKff+QjZgwCP3ROjmjwzLzxjRr3yZMybpzTOzc0SP/4hm8wm73WufYZw3Jy5MS1t5t+ICjW1KR52Gh2K9gsnqDJ8ePmkK3Xu8y/YCabnzdYHfVQfn8rWkv4ey852VgwCwst606D79xc5z7xCf0fHRgcV3a2zhHZyuCVlaXPnTkT6+iMjGjPP/209ibZoLo6vet6Pfi4tm4VbPyf/slsBWprS0vtvMJKR++z9WCcidYpWCO1Vp0PZQnRqN6pvFxyCUZeavyKiozS/957ZUMF2Rz9ACb65Ec/0roTfKqt1bOPHlVAbGBAa0bAip5bjz2meYJyHHZDdERh4aUHs4LODizFQQdpI07RRq94jhiyvK1N57+1VXuWNidkpN7xjo29Y1OTgjhBYpCvfEU27ouFnPhZXlcdpyvw8g88lJOQEvi9aYqLjVGG2gcOGEprfl6Hzy/4htYzEpEgwYBxzmpp6AA/O2vwDwQnLFpJSdZsdWjIskKLixI2PjTr3nvV9+O735VyIVPhN92E4Q4sPlmx9HTLEJEh4/+dnco+vOY1EoLUtpDRKi01OF56uqJJ1GxQmwBkkO8SfSaLkpOzWuH19irDc++98SNAnZ3O/fM/a+18COHMjEGwFhZUK/OhDwmS9M53StH09upZ+/ZJ+Tz9tAT0gQNSijMzWtPeXinMLVt07zNnYlnklpY0j42N+hxQB+CQUII7Zyx4Kyuas7vuEqQnUcSwuTl+doTs4MqKBG9V1Wohnwi64VOk5uYqu3TsmNUn0Pz4TW8yIRtv7p2TgQaZw8SEFDXF8zRtphbw5S+XA3Xy5Or+ODDM+WeHNSwrs/8/+6yeU1en+U9PV4bj/Hn9n4xndrbG2NwsIwAsP9CHsjKNhQaDwAGBpaamWhF9MHN5xx2XJ2+2b9d+7+01g4i6LuCQ1CH4JAVPPy2IMIbZWvDLeJHNO+/UO0OyshEj1798CF17u0GspqY0t5s2yRAkGAB7GDBlMn+hkIxQmP5mZ3UGiosN3vfcc5oLMmMEUcbG5EwhN+rqJGN+6ZesLuFzn5OxsLio77CXOE8DA5qHoiIFDJBr9PVxzqDFPoFHUpKcET+7jmNIUCR4JTKU/vqvbc44H6dP6wxVV2s/BrPZ8er7/CuYSdizx+o8afINJXFd3caM/CDxCvPj78vqaq3desyi3O/Qodj+h7BpRiLWYwo2WmB6EN8UFloUfd8+e6e0ND17zx4FuEKhxNH9l71M0f1EhfpdXdrH990nOXLunMb1ildon1ZVmZwkgHrrrYnXJZFMf+tb9fvjx/V/aMMJME5OGqQyUV/Aja7TqVNqRk/G4nWvi1/nAwyuoMDgnaOj1hMqJcXqa/3+Q+zxM2d0vxMnVgcwISzq7Iyt1yJ73dOjcwlpBo2EN2+WvOE81tSYjQE898QJyY71GCcTXYkyqvv2xWbULidYFk//xuu3tmWLAqAnTkgm9/XpZzt2SL41Nm78mQSuvve99R3pn4frquN0BV6+AZmTs5rbH8am8XF9zjk5JQsLsf0+0tO16ZeX9Z2hISNIcM4gShiUzhk7E7jmri7dB2UQHEtKiv5N6h42mXiKan5egndmxpwn+gaVlmosZHmmp23c0ah1iy8t1Xv//u/rUMOedPvtVvNFkTj0vfn5VsNEBmVsLJb9CIx3WVls09iNkBk4t9o5qK2VwD5yxGpcZmb0fRwX3vX4cWs06CvX7m4Z0Xl5piSdM+Xz1FPWyBgDGydtft6aIkYi+k5/v9WWkdlzLtYpSElR4ew998TvvzU9LSPw4EGDUhQXr64vI6tFR/Xu7th+Hq2tsUobxdnfr3sNDuozwJWA2+TlGaOic6ujbyicZ56RQUS/FyLRON7s+9ZWGSI1NTozjz9uTRNxmvwmo8zn1JSxLUJ24sPzYMS7eNH6fdGHxaeJBQ7b16eIb0qK/j01pTWendWf8XHbV8AEif53dAhGd/vtl6dI6+sFSbv/fgu2UNcEw+PWrZalSQQfWisSmghi0tq6PmY/3kU0u73d4DL5+Zrr6WmDH2ZnG5sbxfLOGXEMGYOZGcvwUAAOtJPs0/S0vsP8Q/px7bX6+dCQHEkMZYzH06etvhG2LozwoiK959Gj2n+lpXomjYiBiQKhhgoYI5JsJ6ykoZDO2OOPx++pFFynSESQogsXjL0RA5BGwDBQ+tns8XFr8fD886oxqaiw7ITPjHfsWHzGsJER/Wz//o1HnOP1n/EJNKg7eu97Lbi2lpHZ2an3IhAxOxubXSLgQa0fzhRG9O7dkvE0SKVGkKj8a18bu+d///dNFvqBniNH4hfqDw5K1sLeOTZm8unIEc1naamCEH4d16/9Wnyj/dQpoRsgUqit1TtjvN51l9a9r0+/Hx/XnnAuljhqvcDGWn2CQFEcParxUK95112yV3xZsGuXEfhwriGPoKE0QcigXCJ4vLwcqxu5gB7SO+oHP9A4GhsNPn/0qPYBtgswQtAsnEFkgnO638CAAoeX4zjFy9R1djr31a/Kwd68eTUt/KXcOxF0srFxtbObm6uAXHu77A0a6H7pS9p/G31+0JF2zs5sZaXW+4WQjfysr6uO0xV4+RAHn8kEBiayIwcPSrBVVVktTLDfR3e3DnwoZMrbOcNsg/WPRs2RgirXOUVzoPBMT48dC01ZSaFv2rSawty51RCs5GQZeysrllkaGNCBSk3V+zz1lHU/d06Ko6xM35ma0nvPz+veREHf/nY9F8VcUmL9GIaHraais1P3pDajs1NC+fnnJZCSki6NzIArGMm5+25FWPr7JUyHhjTmUMh6dDine/f2SnB88IPq8YTiue8+qxvxL5TEuXNSQjhCwCipTcEAoIs6WHVwxeD22Qfl5c69+c0SYJ/+dKwQJdOJg7O0pLkvL5eA375dz25psUapn/2sUZIvLEgAp6UpEtvYGMtwlpsrZXPunBmwpaWaJ7Iro6PaK/EKyH2FQ4EuDgDvS21RNKp1Aqq3b5+xLP7kJ7H0zjTa9bNyGMCRiPY1zQchIwHiR0aWrO2NNxpZBbT8/f1a4+uu02fHxqyBZWqqsU7W1q6G8+AcdHVtrFA73hUs4sV5olairEzr5Rsdl9OMN55jtVHMvn8hT3COlpeNzXNpyYylqSljrJubs4L/5GSDI7P/2Su8G8Ei9kpRkWTEyIgUfVJSLHRpZEQOE06Tc3on1hcKcmrcpqct01VXZ8a6cwYFJPMDKU5ZmTWyZVzIcz+TPTsrw6anZ33DhjHifFKHOTWlsaFDgCSWlOhvZDbw4Ecekfwn81VWpjUpLNR7Pv209W665hpBDX3GsI0aS8Fm8YODsVlpDNiTJ41wZa3r+HHteYg92E/sB9YgM1PrfeGCNcim1m3PHv3s/HkLiF1/vWQUAbf5eYO/NzVJdtP/qLY2fqH+9LT0WjSqe508qc8MD8eSWLzqVUb0BJ09etO/Tp0SxL21Vff3a6bJ1pw8qV5RjBtH3jkL3G0ksBFcJy4cQQJilZU6Fzhu73mPc7/+6yYLIhHnPvYx7av8fHOQIhHdY3bWAh6QnyCXyHgSvAsS1JAlQof++Z9b09hf/mXnfvM3NSdVVVo3n8zDOXOqYeYF7p2yQas6XubHudXOy9CQ1s6HixcVXXqmZj3o5BvfGN/ZHR42JtpIRPZkauqlPd93pAkWcmapzzt06PJ12M/6uuo4XYFXEOIQJCWYmDCoGQf5xAkr3Pb7fUCDnJFhPZOAgFBnAdf+1JQxAGEYk6YuLpZAqqlR9OfkSSlUGM9uuCGxMPUPDVSqExMSegUFZuykp+vQvPrVGktZmQmpwkIpuIsXTVBRKwTjzj/9k3Nf/GKs4J2Z0c/vv1+KAnpq3witqLBGek88oSgitVnhcCwEMpGCD0Zy0tM1/sZGKarx8disBDDLigqjk/Uj+PX1sUxbzq2mJi4v15p2dWnefRgl2RWMQOogaCoK9AfDj/4dzc1yfIMRQ4x8IvpkLZOSNHcLC2Z8zc4axrulRcoSFkIit6dO2XuNjGi/JSdrLz7/vDVTBWKJQkpOVqQraGwF4a0tLQb1JGrvXGxzUOfMKVlY0DtCH44RnZJizZmh3Od3GRnmhNJAsrPT5gCiDc5QTo7GVVurvXHsmN67rMzmvLVVz6Gw/Pbb9YxnnlkN46E+ETKLF9JvhyLe++9XlHV4WMGN6mo5dH6jY+c2VuMQvBJBNC8Vs8/ezMuTAZ6Xp7mG3AZSF/rRkNn1jRtqGX0SGuoYkBtTU5JPQDphzPSDRmTagz2mnLPWCXNzRkRCZhfnbWzM6p7YV/RLGRjQ/8n6QRsOQyhBET+TvbxspCzrGTbLywoSPPecxlVXZ/cmaLC4aDDHsrLYeaadhHOai5YWPRuWU5rLAnc6dUqfLSwU7Pieey4dmuPrxr4+ORa0b6BGNinJua9/XdCmte4ficiIB9kwNmboAIgXMjNNv7W1SRZ1denPxYuSacA6Cc6Ul0uOPvSQGam9vcoC0tON1h87duje+/fLcCUbT40bvQBPnbKaYHpwLSxo/zc0WGYj0blsaVFD39ZWvSNOYm+vxtTcrJ8dOaLz/mu/Zvfzs4gbhYjl5GiuenuNeGhhQVkdgpjRqN7ruuvMZvn2t2Phv5FIbP3T9LSCZ319RvKSlia9XV0d29qB2qmDB83J8aGWBDKR342NsQ7BG99oc+XLXhg1ke+wWzpnrSQqKrQuia5EmZ+9e2P1Lu1bfHbGRNDEjQSb1sr4PPigZdK4/OeXl+vZ1KBv9PmRiO7R36/vP/ec2RBk86an9fvjx686TlevS7wwLMbGzGDfscOiW8XFitg/8oj1C4Cp6uJFbbzrr5cQra7WzzZvttod4Er5+foTiUgAAU1ZWZGSJLJBRCYalfDIzLTi582bVZh6882XViTs3GpHEPhDQ4P6a4TDKn6F4QeIDT0yfDgN2Rt6Kfzv/61MiT8WiBcOHTLIHpFJKMp37NCBHRpS9L2gQFG3lhYddhy+t799NVNOMJIzNycB8PDD1hAUjHxVlf5NQ9bt243VLxjB95XPzIxlxlC89BwpLdV3s7M1VvDYGHo4XH4dCbUyZWXmZHV3a18Ad5ib0x44e1YKjhqzSMTox0tLTQGNj+tdrr9e73X0qNbEOXOqSkokgGdm9AzntD/pr1NebkYhzgwNlekN1tIiUoy8PI2julpr+9RTet+DB6X4UIpcRE6pEcEIXVmR8dDZaf0mqAGkd1hdnd61q0vvmZpq+zkrS/OYlibDhKwaSprsRyRidU9+LU51tWqkqF1IS9P+nJ/XvVAwPvkJBCaZmRtjJFuPEtg5GZl/+IdymM6cEdkF1Of+dSnkDf4ZebEw+8gTmCKZG1i30tIMHomDT8aA3mA4tDBnsU8wJnG8aNJMk+1du7SGftAoXo+plhbR0vf0WE3p3JzRymPowzK6bZvOaHW1frd7t56D3CbjzPoRsY9EYjPZzskQohVAIgjMd74jmXXypJEBjI+bXpiaMlgvMpJABnsUKGdOjuQOAbnKSo0J5rfRUa3tL/yC9iz06pdzYQw/8ogCDxMTlklmPYH6rmfQdXZqXaurJZ8IJAHLTU3V/VJSlGm6cMFqQ5OTtYdHRoxK/LbbtM8uXDAm2n37ZFg//LA56FzT05Jbzz9vNWMve5mRrvT0KAty8qRl/6hRJdM8MaF53rRJ9+zu1t6JRCzgV11tGdqsLOvxCFvmyIjkGs7FwICyYWReLgduRkaS7NbKivbX7Kw5beGwfnb4sOyBRPBfsnGHDulswjTqnNHwj4/rnn6TcL7b2ak/w8NGLkE7F4LLlZWyqZKSzCE4fDgWasra5edrfU6dsjpJxkpWZnxc6+rPnd8L7utf1/Nra2MzP8eOGcGHc6sh+D6zYSLIdKK9jkM2PGy2F7BcMsv5+XaGgs+noTZ6bSPP96HzJ09K1tFbDLZKygpSUuQAQ0p1JV9XHacr5AoWOo+NScicOiWhuHOnFSmnpkrAPv+8Nt7IiD5LE78dO2SUJCfr3z09imrBEANN9vi41T6Vlhrkh2giDlNtrZTfmTNqxFdRcWmGTrwu1Wv11zh1KlboQnWMsHPOMilcQFviHeKUFDk8p09LiQHRoeEkjSUrKgyO85WvaB0wrObnDRL4kY8Ihufc6kjO8LA+09dnDfuCBAMlJYaXJpIfL1KI8nn+ecsQ5eZaX5yLFzXuX/1VfQZYEMbAtddqPjDkMzONYpe1pR5pfNwM4s9+Vorh+edloADd4j2A3XEP6khwEOvqJPzPnrU+X87pGdPTFqHv69PPqQfCSCko0B6lUWw4LMW2fbv2+xNPOPeZz9g4oMFvaTH4FLAo6JqdM0PTudWRddi+OAc0VnZO9+7vN+bF0lIZy6WlmteTJ602C0ZHFALGO4EOemOdPm21KhcvmuLAcJ2ftwjvyorWGcPDJzBpbNT5XcuJ2QglMJdfczQ4qHnp6rL7Xwp5g//8Fwuz77PROWe1ONQwQfaQmWnGPd8jYwJEiPNfWGjw4clJnbHCQutnAxSIOpj1gka874ULmmsoxaemtJ/z8iwCH43q3d/2NmUogGoVFkpmP/647eGZGeszNzCgdx0Z0T2BjdJS4PHHLbgCBAbSmu98R4QVc3MaC82YyXQmJ6+uSwEqiwxiXsNhg68STKHWqKPD6mKAlW3eLH220Uh5vOuGGwRhxpkIwhbn5nQmTp9e26DEAL32Wr03zYDJOLM+NNKmzQKQT+CWqakGGXzVq/S+Tz9tTcCPHNF8oXNo1o6zh5M+PKx7wAyXkiIZOjxsQTAcfgInOLzd3fb/oSHB2zjrZWXSezU1lj1cXtZzeKeZGVvz8+e1J9fKWAYDMfQK9Fn/qEtdWNAeI/AGY195uf4GAr5vX3z4b1OTIHzvf7+hbJyz/U3WdWlpNaS9sVFkQjMzCqhduGC07TSQz8qyAKZz5hC0tsqJ9RsGg/DwGypzkTFmnT/zGZFWpaSYDG5pMedoyxYj3KFu+emnNX84L0EmYx8e7Jzmr63N2DAT2WTYMHNzVquIMzQ4qHmFrRd5TxsUHCZKDvxG94kg25GI2I7vu0/vsm2bnkugC+g0NfdjY9KNfX0/H3C9q47TFXDFK3SGBGF2Voe9v19KJzVVqW02H8osPd2i9BzsTZvkvd9/vzG15efHwuy2bLEajMFBY9VzTpu6rs6IInp6nPvXf5XjcCmMLom6VMfrrwGrC7+jsH942BQ8UBZfaKGQiJYG5/eBB8zIJuJPM8aMDEVEzp/XfQ8e1FrgkOblxUICP/EJzW1zc2wkxzkJsK4uS92jPCEZmJwU/GrLltji5mAEn3lwzjIf9L3yKWzz8+VoUnTs030uLOh95uaM9hjBC+sTQjEtTWO65hrVHJ07pzmnFo4MpHOmcPw9Q3PepCQ9H2erqMiccOesj5UfKUbRoAhpakj90J49GtvwsDJNk5PmtDzxhNXhkR3FuCZCF3SeMLToF7ayovWG8jkrS3vx/HmDc2Hw3nij0XHDINjUpDF1dEhJoFCYMxiF5uet0axzmofxcevn5Jz+JlsFE+LLXqbP1tcbnMjvlbSWE7NRSuB4n62r0x579lkZ34ODmveN1DhwxWPXulzMvm98dHRojYGnQg3N/XJzda4LCnROe3sFvf3857UOo6Pm5Dc26gxPT+sPDIIrK/ocwZWGBjmrr31t4rqc4Pt2dmqNydISiJmb03grK5XhvuMOyZR/+ieNJRLR2F//eo1hasqCQ7W1MvZ7exXYoDcSMMXCQjtn9Bf6zne0R0+fFvsoZ4g1wBDiXPiBHucsEED2OifHnBY/A0N9GXu+tNT2MgyNfqS6oyMxiUPQOIcm+emnrT0FRC9ApMjuXbwo2fjss6bPglAzAnrhsNoq0AyYIJFfE4NjBtQawoH0dDMwOzpMxgKPPnNGa5CREctiSqTfr6WbntZ+OHdO2UrmFxnJ+0GLTT3yzIzmY+tWa0oapGk/f157rLjYqNcJOPn08Dj4Tz2lIMbw8GoHt6VFGZODB2ObJyMTfda/wkLJaAJzZLn8tgSQXfX3J4b/ktXPy9Oa5udbEAnyDmCS7e3aN3NzsQEjbB0IPPr6dP4hLgk+r6dHzwwSNp07Z6gA9iDyxzmDlZ88qeDQnj0mV0EIQYdO3StkWw0NGldrq+Q+dh1ZXpgN8/JiiYX+8R91NhIFn0Cv+OyCs7O2F/z+gn19et/iYn1naEifQ74+8ojVxjEPWVlGnT4woP3zzW/qnQsKtP44zdhCZGAhuElLM1l3pV9XHaeX+EpU6JyUpM0+OGhFn9nZ2tRtbfrc4KAx0REFo/5lYEDfe8UrFAHzBUgwYjozI1a++++X8KavEbAW6ManpkR2MDEh42ktmE1Q6QXpRuNRDztnRse+fbEpZSIs9CkJ4nATdUr3nVK6wPsR64wMI6kgE0JzyyB9qQ8J/Od/FiTQhyGOjxs7FQY8xjwZjrk5KRyfzCMYwQ/W61y8aM6FcxpXWpp+V1ysd6QBcn29nGUgAV/7mkXKW1qMkclX4M5J4bzsZcaKSDNIonk+5I11iEatPolsE0QUQKfAYwPxm562Zp9kaIieVlRofsFD5+ZaA0bWcnRUyqWqSgp5eVn7uacntgCbaLBzZkw5F+s0AdtkXXBUYHjctElzDIzxN35DSvPJJ3UOw2FTIHv3CiOO0YghCXsk9P6MgVoMOrNTe0W/psVF/ZwGjOnpyiyeOhW/V1I8R2Mj1M0YRf7Z8z+7aZOMi2eeUdTwfe9LzNYW7wrWyq2H2Q/SFCNXgk7d/v1af+rrfIZIAiJZWQoypaVZY9KtW7XXmdO8PMt600+rqEhnmWbcQELr6rTu99+v/bdePSeReL/1A42xN23SfV/1KqP9f+AB7Xtqh5BpZNLGxnR2env174YG9VL56leNgCLYLy49XXP2d3+n8VIbg7HvZ+6YQy7/Phg4wPYgL0Bf+L1sKNgnoOcHa8bHzVEYGFDma2xsdSaU/YjOooayoMDIPFhv5BltK2g3cfKk9gyZYwgrfBYxP6C3d69qvoBHYtj5NOTQqCPTIZEIhw2tgMG5sGC1isDNocFnzMDjQCc89pi+R08qoNZAlTFwCVzt3q35/73fU5axu9vOLzqopETn6uRJfR5kCmvpE0YBARsZUeuQW2+NRXG0tDj3X/6L0A0wUSLX8vJEjoIz88MfmhPBHMJMOTioOQNaOzkp++C22+JnzqemjImR1gG0UkHeUnowNaVAwalTqwNGnZ0ax623ypaBeClYP+ycOXH19bH05l/4gj47MGAwN2QO80gbjP/4D+lvdDltE4qKVreWgZWRnl4QUxQWWqCb7Njw8GpiodlZYyoMknTV1kruPfaY9KxzkiPYA77T5DPzRqNG0V9fbwQdBISKi7Xf/vVf5eyBUmAeKiv1fBh9cTZxoEGHFBVpDxDQuNKvq47TS3zFK3QOZmSouzhyRBuustKK/JeWLBqOYXrhgsGPPvxh9Qf6gz9QRIAu5Q0NsQbQ7/6uDlxLS2xRMEWYROCI7K8Fs0kEDbrrLkVIjh61otM9e5x7wxv0PWorgs1mEWj9/VZgGw6bUp6cjN8pHcPRzx6VlcUasSh5Lno3pKUZoYHvpAUhgX4dkk/3jaPAhUMUiUjonTolIyCe8esbX6S06+utPiAS0b+ffVbCMD09NkqDoPzLv5QCXljQ+Ki3Skqyz0OfDWHFxIQRcPhNNYMXBoCfDcPAAtJGlBqqZDKCk5P6XH29vtPZaUbNww+bYdjfrz1A49Lz56VEtm+XIunqMuZJ1ohMLI4qShUDiyJ94Ko1NVqHY8cMsjIwoCwFUEqITMikVVTI2A+Hrbiad0lPNyN7ZUWOFtA9xuNnIXHaULw03oRRkExWWprgujjFG2GgW4sSOIhPdy7xZ0MhGQ4jI1YXxvlKBNcJ9pPLzDRnBErbIGY/EU3x614np8J36nJyZASeOSN5RwQzM9Pq9LZv154+fVpnrKHB5KZft+U3xM7KUgT4mWd0XkpKLBKalrZ+QTTOCBCYqioLxMCiOT6utd26VbIvSPufnW2Z1IkJyYXBQf2/sFCR88xMnW0c/ZMntS9ZO1j2RkaMZhs6/5UVyw7Nz1u2xs86+XsApj16RFHPCB00LSomJvR5DFoMMfoPUqtDvdfkpP6+4Qa9T1+fotlPPql1iEQ0H5mZcmg6O63G1q8twfAD8kZQB/QBhh+EFX5m06+f6eszAg4CHtGoZNH27VoD/0z4Mj04X7m5WpucHP2ObBs1YtyDbOnAgMY/MaHvUhcJw59zRm6QmhobbLnpJoOW+WQ+BB3JKp0+rb1DBpb7++/E+kOecPSo7k0d4Kc+JUQCQTxfFoyPK5uZlqY/wDM3bYqtq6UMob9fchPa+9LSxJnzgQGDU+M0MY8EwZiPsTFlbuvrrR+ec5qz5mbNw9CQbI9jx7Q2NJsnExiJiOIdWwICm+PHJdPJoPokLegcHNKUFOksgtgHD1oze7K2ZNsmJvSZmRmdlVe9SpnVzk7Tq5Re5OfrPHR3S3eVlVnLhdFRPfPECcnHpiazy/bvVxaIc0rAigApztLSkuQWGexo1GjpR0dj12RqSuPp7tY+6eqyvUrNZE6O3ml4WL9LS5OMm5uTzCostOAITYj/3/bOPbqq4vrj33MTckkIeUAgAQkSTYoICvI0glprbAS1Yl0tWmqBUloUFcVFC1a0/fnrwlbaLkWlvootFuIPlqBFQCkgviC8EiAQqVYgAUkAA9wkQiD3zu+PyT4z9+SGFJdwE/l+XFl4z5kzZx575sye2bOnpUPFKcp4Nzp7XWbW15vldxkYyMpIbKwWukDALN3LANXv14L54Ye6kdtnJETa59Cjhx7sFRYaBcnnM0qL4+hG6jha+ZLZRzHHss+BsL0J2aZB27aZM3jEdCtSWdiKiuOYAUfHjsbzWUWFyWdqqm6IWVl61WTHDuPKtLTU7IHo1Ek3fOlw7TKWD6HMAMoAfP9+3YnYp6fbJoE+n9mPJYMKUTTkIySDYtk4KntWHnwwfFbdWw4y2JT9BVLHsrdHNnSKZzvbRffq1Xq2SzbSim2xIPlMS9PlK2cuVVSYD4h9QKhXeZJZI+l45TDIxERjriIfIElrUpLZJCwKltTfiRP6YyBKj9TJl1/qj3H37vr5Xr10WYtL9LZtdXyJicZ2XlYUxcyzTRvj0rxtW/1hldVD2Z8gSou4Hu7Tx5wLtHOn2YPUp4+W4cJCLYsy2JEDS2VmXQamkg5B6l/2K4hcAKb9yurywYPGtKtLFz2rJ5MMdnyRTpdvqj3ZeO3TzySsd3JEzGClTm0vUXV1um5lhUKcA8jBwLKquXOnGWxdcIEug6Ii41FMzFu9g0JZmeza1SjDct7R7t26TEaO1H1cJJNh8UJ55IgO6zhmRVRch8vsv3go3bgx8gGXlZV6YFVSYpRkWb23TYuysnTfe/Ik8OqrOk/idGbrVrOSeuiQVuL8fv378GE9wTZsWGTvqYB+trzcePMTawRRvoJBM3lkr8qKci/IYFAGVQkJuq2Kp7R+/bTc798fboYncTiOcZ3t8+n6ECsGWZEW73gyKy8TS36/lvWkJD0Q/M9/jImlTEDInhLbnFDyIOmortZyAeiy3LdPW1qUlprjH2T/jMilz6f7xXbtjNMLWfWXWXLbI5hMEMXFmaMGEhN1XnfsMIoiEO6gRswaZbVPZKWqypgLSt1J/mQFqU0bHU6Ujdpa037FA6qsZvv9uqz379ftcPBgXY9iVizlZSuAoowGAlqm27XTisuyZWZ/GxC+4iNno/l85gBmmYBt08bsE5RJNvGkdvKkbpfTpze9cr5+vTlywzbPFGTPmCgBn32mzVmbmjCS/Uvbt2sTTZ/P7N8RxzJiMierTVu36tVwkVP5TskeP/F2KIqd32++C6JgZWTospd9c1JnooTt3KnT+fbbppy/9S29Kr1vn67D99/X/Z9SumwrKkz9yeo6oP/fniTo21d/v2zHLaLkSn3IxEMgYI69kUOexfJETBJjY3Wf8vnnemLq4MHwfVBVVfpaYqIuG1Ga5aBp2SZx4oQZZ40Z0/IdQwBUnKKOrOAcPGhc5NpavpyhIUvzqanhXnBkllsGo+L9TjY0HzqkP/KbN+tBgdeLizSqnj2NK1kZEMtGVJmxl8PeZN8LoBtw9+7GzGb2bB2Pd6anUyfdGcTH65kcMVMrLtZ5vP/+yC5M7RW42lo9ePjxj7UZgOwF6NBBf+iU0qtgohimpJgZcVmOr6gwM1RiayudLWAGsmJeIWZr4uBAzGdSU3VH+uab+n2yMmi7u5YPvD0rZ3tIS06OvAlSDr8UsxHZWOvzmfIQUzDZ1Pv229rFr6xmvPmmmeUT22J7g6nMjskhlNKJl5WF70kCwmekbeS3fCxiY7XytmeP7gjl2bZt9btFGZJ9TBddZM6dKS7WspaQYM5UadNG/05K0h+PxEQ94JHVIvlQyYylmDPJ8r+4Zq6u1vXWtauW1fJyLe9iYy7Kqww2T5wwe1727TOz11KeGRnabE1cIUtbiY835nn79pk828iATwaaIg8yKXDqlH5eNlInJOh0X3ZZeFs53crurbcaRUecbHg3TUt7svcUeB24NBU2khdJWSHp0EHPgCck6P5l61atvFRV6QGL7P0Q1+4JCbrsP/3UeIQU98CRDnq2B4WyEiIrquI4pajIzIx26GDcAtseumyTYWmbwaCWCxmMiIyL2WRhofE0+eWX2sxs4sTwPWKLFpk6lAGEmIqJswq/X1sPbNyo4y0v14pxerpxEiCHdbdta2bmfb7GM9SRvKfK/sYOHXT+DhzQ8aalmf1WMolgm41JXmWyRPY2yoqJeGO75BKzX0fkW5Qs6SvEBFf62KoqXU719aZss7L0d++TT0yfJu86dUrLVO/euqyOHNHP2JM18q8MGm2FTeRITOsAU3aBQPhqq7gU79pVp7NTJ+N1VZRM8fB6/LgxlZZ3ijIXG6uV3Pbt9fchJsZYJUSaUIu0D1YmnUT2JE+y+l9fr9tTUpJWDETZ2LNHp7mmxpjCyiSrrHjHxOh07NxplEzZPyPfKnmv1Ju9P/T//s94RRN5kb7RXkGXyTLb26F4KZRvsaxOJCToNv6//9vYW61QVqYna/v31xYJojDbK6P2/ryMDOP4QsLZZnjSN4qH1/h4nVZxTtS9u3G688ILut18/LE5e0gUiIQE07bFFFfGFTEx5mBq8YIo44/OnY3yJ06f6uq0cigrN7IdoazMnCuYlWUUOlGSxBOkrAYDuo7ECsX2rDl1ql5Nf/9944RIjtSQcZ14K5a9noAZWyYkmAn7uDhdZjt26L5bVtVl8iEtzXj3lYUBaX9i5SNWUX6//o7MmNG0DLQ0qDhFkdJSbS+/e7dupPX1ZiBrC6EMtsSsJSHBLFVLRysdrJwfIysd+/frQUVsrO4MZFDk3ecwapRucFdfrTv/mhodTt4r+6eCQXOopJgTbN5slp23bDGDusRE41531y6jREjavGm4+ebG3vRkD0lamnGgkJen/0QpEi8+X3xhVrlqavQA6t//NkqDuJIGTMO17dVlFUaUkupq3eBlv0FdnZlJO3gQmDUr3ONR+/amoxRsu+dQyHgIjOTEQqit1R3s/v26/DIyjFlFIGCUSjlEODFRyxGgzTLj43VZiZ21KID2h05WEY8e1R2f7G2SPUgiQ5FWBiVf4n1ObO8dR5eLbFyXNGdl6TTIIZoixwcOGLMRmQRo08acbQXodiErAEeP6kHKRReZtNfU6HyKZzFZHZN9FNJJy6TC2LH6LJHdu8P3hMime1lxKyzUZSKmUxs26I/np5/qAa443JAZOlH6ZLVFTF28M/gyMyszzGJPL3IpK88yoyqHTmdlmRnJJUv0O595pvHKrhwmnJGhy3P3bt0GrrnGHGAJ6LikPXXtqj/a9fW6/V59tZksEU+WZWV6pr5bN+110T4PxF4hsVdEevXSpiFyeGsgYMxfAwHjWv/oUS3LsrciLc0M2GSG+NNPtZzs3dt4Lw9gZtorKnTeZcUwNlbn8+mnjcJpb/bev1/X1w03aHn8z3+Ml0Pp92Q1p6ZGy4gMKD75xMTbs6eOc/duo6yIeZ8oi46j0yYzubJiHwrpsIWF2nzPtjyQvt/ebySmjZJv23vqt76lFY527Yz3OznPzT7CQRwJSRsXBUqOrADMQFPakngr7NlTl8XQoboNZGfr/l4Ua5l4krqVM+QqK/XzMiMeG6vbrSh5MvssfWVVlTZvkhWASP2RrTzJb+kD7O+it+zk/C3pg8W5TXp6uLfWtDSz/8hx9O+qKp0vWS2SuNu3Nw4F0tN1md92mx7sA7rOZZXJVvJE8RPzQEH6FZEDqQMZ4E6aZAaa9plFhw7pZ2VVQepDVtPr6oDhw7XnWDmw2DsxZisi6em6nAoLTflGUljtPAGmfOQMtXbtdBnFxOi2Y69w//OfWh4irTjJpJ5MLEiZe99pn/cnCri9Oi19bmKirsfqal2XN9xgxjf2ZG1trbaeyc42+4KkLO19ddIWJc9xccYsVvabiwWGjNvE0+Thw2afJ6D7uiuvNCvrXic69nmCSuk+TvoHSZvsWyoq0jIgnjX37dPyWFxsyl4m5UXRldVhObrhxAnjaEm8kMrEinzHZBJH3OMHAjpsaqrZH9++vf7X5zN9orip79xZbykRT8WtAUeppoZG30wCgQCSk5Nx7NgxJEWaWj1HlJYCc35/BN9Z/DMMCLyDRHyJEHwAZDSg4IOCAwUFp+Gevq7DhBruw3O/6WcdhAAEw94SggPAgQ+hhjv2m3wIIcZ6px0vwt7pQEFPeMTAh1DDb8d6r9NwHVBuWBsHPqiw1Jvc6pRIukMNfyZVgK/hTwEINsQBN2+RyvH0ZSxpdhrerxrituvHcd9vl7Fya6Xp94YQ01BGaEivxOVz3yMhpdTsWrHrQMrYaXhLyCqjGCj4/us0+RB0a0HXu83pnlVunUsIU/5oCC0pMJLroN5KuxMmU44lIU7Du5UbE9xS8jU8efq6RcP/OXAQdO+Gp1nq1eeG9iHUUI6xbg1JrcUghBgEG7UD814HQTgIItaVZNVQR7EIetJsty9pI5IKNKTOvCUEI33h5dQ4BapBdiQ/sMrCtEKpAZFlO17HDS1lrhr6CakFqT1TFqacfQ3xAsrteex2FC5Tjvs+W/IBINhEPxQK6xmEesRYNRpy69Xbw6IhlaYfUXDcNJs0eetWNdQnGtqXtDnHjd/IqrdWQp7SlHIIujlXYb2A05CmIHxQDfLna6hbuDlr3LbMNVN/ct/E37hNhwDraclBENIjqgY5FHnyIVyOpe1KODtP0nPbbV4/67i5EcmO1KbD66DpNq+/ckHEwIFCDOqttElfIvUl5aLrHQhZ6TXlb5ew6akBu6e168BuA6Zt6XI05Qv3TV/l++QdFUjJqoa+O/IYQHJ1KqwFnr7vNG0NDe3wzL6p0ksEG9q06Ucl1bpHRUOt2e1RSlmkwvtdst9r+s5QQ7ts3K/63DTpNhSDUEOtyBgiaMUIOIhpuG/6GiC8fTQlqyE3v6f/ZkpvYvcfIq/SmkzLk9x5+1xvHfgQdPsLnY96S+psCZJS05g8hRrGAeEjQlPGMZ53yj15p3L/lJs31fCfzl09/KiIzUTZdT/DiCX3wZfQNnL1ngPORDdoEYrTs88+iyeffBIVFRXo27cvZs+ejcGDBzcZfuHChZgxYwb27NmDnJwc/P73v8eIESP+q3e1BMUpFAK2Zl6Dyz5/v8lugBBCCCGEkG86pxwf4h75NfA//xOV95+JbuBVkc85r732GqZMmYLHHnsMW7ZsQd++fZGfn4+Dtgsbi48++gh33nknxo8fj6KiIowcORIjR45ESUnJOU75V6fs8kHo+/n7YfOKhBBCCCGEnG/EqhCCjz8OPPpotJPSLFFfcRoyZAgGDRqEZxpO+wyFQsjMzMR9992HadOmNQo/atQo1NbWYunSpe61K6+8Ev369cNf/vKXZt8X7RWn+qoqoGPHsAVfQgghhBBCzlcUAKSkwHfggPHQco5oNStOJ0+exObNm5GXl+de8/l8yMvLw7p16yI+s27durDwAJCfn99k+Lq6OgQCgbC/aHLwxz92bXipNBFCCCGEkPMdB9CeV15+OcopOT1RVZwOHz6MYDCIdDncp4H09HRUiM9SDxUVFWcUfubMmUhOTnb/MjMzv57Ef0V8e/dG9f2EEEIIIYS0SPbsiXYKTkvU9zidbaZPn45jx465f+VytHGUCF14YVTfTwghhBBCSIsk0gGXLYionuOUlpaGmJgYVFZWhl2vrKxERkZGxGcyMjLOKLzf74ffPpghynR+9VUEuceJEEIIIYQQAGaPE8aPj3JKTk9UV5zi4uIwYMAArJIT4qCdQ6xatQq5ubkRn8nNzQ0LDwArV65sMnxLI7ZDBxweMMD9Ta96hBBCCCHkfEVOffLdd985dwxxpkTdVG/KlCl48cUX8be//Q2lpaW4++67UVtbi3HjxgEAfvKTn2D69Olu+MmTJ2PFihX44x//iI8//hi/+c1vsGnTJtx7773RysIZk7FpEw4OGOAez0YIIYQQQsj5SL3jIGbGjKid43QmRNVUD9DuxQ8dOoRHH30UFRUV6NevH1asWOE6gCgrK4PPZ/S7q666CvPnz8cjjzyChx9+GDk5OViyZAn69OkTrSx8JTI2bUJ9VRXKv/99tFm7FgnQJ1aLIuU94b4p7DAh61qMJ4zcD6Gxtmwrb15lzg4ffjZ1eHrtZ+1z1mH99nme9abfQeQ826d6y7MhzzN2ecl9770QGufP/n87TTFonN+m8N5zZ04QfgK6N6/2e71plfTa9RpJJoIILx+7rhWMHHjTACuM4D3TXuKXMHaZ2HXrzXvI+m3LszdMJFm30yRlEOm+5CUGjevWQWR5sk+ij5Qm72/BTpNdrrGeOB3Pv145FPmKRXhdwROHndem6j7ouSY01Wd402bLiMRv51eu2WfN2Wlsqj3YbciWZXneWw/NtZGm2qA3HxKPyGv4efeN028/643fznckmYkk8/KvNz9A5PIKWc84aCwPTcVrX7f7e1tmvP1lU/2Tt4ztNuntI0WW7XhP15/DCitx2322tNum5Mhux3b8ABodGh+pDwOAek+am6KpviJSnwQ0rn/ve+1nvdeDEe7b5Wi3Pe/zdt9nh7GfdTzP2velHLx9rLc/9tJUG7H7ORu77XnfZderNz92GpvqE7xphnXN24fZ8UTqS+wxkXecYNd3rBWP9Lv2e+17dvq86fLGa3+bI8mppNv7XKT82G0rUl8RaczjHTPa6fWOF73l6O0b7bqz+x3vd9lOr/z7ZXo6Ok+eDP+DD7b4lSYh6uc4nWuifY4TIYQQQgghpGXQas5xIoQQQgghhJDWABUnQgghhBBCCGkGKk6EEEIIIYQQ0gxUnAghhBBCCCGkGag4EUIIIYQQQkgzUHEihBBCCCGEkGag4kQIIYQQQgghzUDFiRBCCCGEEEKagYoTIYQQQgghhDQDFSdCCCGEEEIIaYbYaCfgXKOUAgAEAoEop4QQQgghhBASTUQnEB3hdJx3ilN1dTUAIDMzM8opIYQQQgghhLQEqqurkZycfNowjvpv1KtvEKFQCJ9//jnat28Px3GinRwEAgFkZmaivLwcSUlJ0U4OOY+hLJKWAmWRtBQoi6SlQFk8eyilUF1dja5du8LnO/0upvNuxcnn86Fbt27RTkYjkpKS2BBIi4CySFoKlEXSUqAskpYCZfHs0NxKk0DnEIQQQgghhBDSDFScCCGEEEIIIaQZqDhFGb/fj8ceewx+vz/aSSHnOZRF0lKgLJKWAmWRtBQoiy2D8845BCGEEEIIIYScKVxxIoQQQgghhJBmoOJECCGEEEIIIc1AxYkQQgghhBBCmoGKEyGEEEIIIYQ0AxWnKPLss8+iR48eaNu2LYYMGYINGzZEO0mklfPee+/hlltuQdeuXeE4DpYsWRJ2XymFRx99FF26dEF8fDzy8vLwySefhIWpqqrC6NGjkZSUhJSUFIwfPx41NTVhYbZt24arr74abdu2RWZmJv7whz+c7ayRVsTMmTMxaNAgtG/fHp07d8bIkSOxa9eusDAnTpzApEmT0LFjRyQmJuL2229HZWVlWJiysjLcdNNNSEhIQOfOnTF16lTU19eHhXn33XfRv39/+P1+ZGdn45VXXjnb2SOtiDlz5uDyyy93Dw3Nzc3F8uXL3fuUQxItnnjiCTiOgwceeMC9RnlsBSgSFQoKClRcXJz661//qnbs2KEmTJigUlJSVGVlZbSTRloxy5YtU7/+9a/V66+/rgCoxYsXh91/4oknVHJyslqyZInaunWr+t73vqeysrLU8ePH3TA33nij6tu3r1q/fr16//33VXZ2trrzzjvd+8eOHVPp6elq9OjRqqSkRC1YsEDFx8er559//lxlk7Rw8vPz1dy5c1VJSYkqLi5WI0aMUN27d1c1NTVumIkTJ6rMzEy1atUqtWnTJnXllVeqq666yr1fX1+v+vTpo/Ly8lRRUZFatmyZSktLU9OnT3fDfPbZZyohIUFNmTJF7dy5U82ePVvFxMSoFStWnNP8kpbLm2++qd566y3173//W+3atUs9/PDDqk2bNqqkpEQpRTkk0WHDhg2qR48e6vLLL1eTJ092r1MeWz5UnKLE4MGD1aRJk9zfwWBQde3aVc2cOTOKqSLfJLyKUygUUhkZGerJJ590rx09elT5/X61YMECpZRSO3fuVADUxo0b3TDLly9XjuOo/fv3K6WUeu6551Rqaqqqq6tzw/zqV79SPXv2PMs5Iq2VgwcPKgBq7dq1Siktd23atFELFy50w5SWlioAat26dUopPQng8/lURUWFG2bOnDkqKSnJlb1f/vKXqnfv3mHvGjVqlMrPzz/bWSKtmNTUVPXSSy9RDklUqK6uVjk5OWrlypXq2muvdRUnymPrgKZ6UeDkyZPYvHkz8vLy3Gs+nw95eXlYt25dFFNGvsns3r0bFRUVYXKXnJyMIUOGuHK3bt06pKSkYODAgW6YvLw8+Hw+FBYWumGuueYaxMXFuWHy8/Oxa9cuHDly5BzlhrQmjh07BgDo0KEDAGDz5s04depUmCxecskl6N69e5gsXnbZZUhPT3fD5OfnIxAIYMeOHW4YOw4Jw36URCIYDKKgoAC1tbXIzc2lHJKoMGnSJNx0002NZIby2DqIjXYCzkcOHz6MYDAYJvgAkJ6ejo8//jhKqSLfdCoqKgAgotzJvYqKCnTu3DnsfmxsLDp06BAWJisrq1Ecci81NfWspJ+0TkKhEB544AEMHToUffr0AaDlJC4uDikpKWFhvbIYSVbl3unCBAIBHD9+HPHx8WcjS6SVsX37duTm5uLEiRNITEzE4sWLcemll6K4uJhySM4pBQUF2LJlCzZu3NjoHvvF1gEVJ0IIIWeNSZMmoaSkBB988EG0k0LOU3r27Ini4mIcO3YMixYtwpgxY7B27dpoJ4ucZ5SXl2Py5MlYuXIl2rZtG+3kkK8ITfWiQFpaGmJiYhp5SqmsrERGRkaUUkW+6YhsnU7uMjIycPDgwbD79fX1qKqqCgsTKQ77HYQAwL333oulS5dizZo16Natm3s9IyMDJ0+exNGjR8PCe2WxOTlrKkxSUhJnVYlLXFwcsrOzMWDAAMycORN9+/bFU089RTkk55TNmzfj4MGD6N+/P2JjYxEbG4u1a9fi6aefRmxsLNLT0ymPrQAqTlEgLi4OAwYMwKpVq9xroVAIq1atQm5ubhRTRr7JZGVlISMjI0zuAoEACgsLXbnLzc3F0aNHsXnzZjfM6tWrEQqFMGTIEDfMe++9h1OnTrlhVq5ciZ49e9JMjwDQbu/vvfdeLF68GKtXr25k2jlgwAC0adMmTBZ37dqFsrKyMFncvn17mCK/cuVKJCUl4dJLL3XD2HFIGPaj5HSEQiHU1dVRDsk55frrr8f27dtRXFzs/g0cOBCjR492/5/y2AqItneK85WCggLl9/vVK6+8onbu3Kl+/vOfq5SUlDBPKYScKdXV1aqoqEgVFRUpAOpPf/qTKioqUnv37lVKaXfkKSkp6o033lDbtm1Tt956a0R35FdccYUqLCxUH3zwgcrJyQlzR3706FGVnp6u7rrrLlVSUqIKCgpUQkIC3ZETl7vvvlslJyerd999Vx04cMD9+/LLL90wEydOVN27d1erV69WmzZtUrm5uSo3N9e9L253v/vd76ri4mK1YsUK1alTp4hud6dOnapKS0vVs88+S7e7JIxp06aptWvXqt27d6tt27apadOmKcdx1DvvvKOUohyS6GJ71VOK8tgaoOIURWbPnq26d++u4uLi1ODBg9X69eujnSTSylmzZo0C0OhvzJgxSintknzGjBkqPT1d+f1+df3116tdu3aFxfHFF1+oO++8UyUmJqqkpCQ1btw4VV1dHRZm69atatiwYcrv96sLLrhAPfHEE+cqi6QVEEkGAai5c+e6YY4fP67uuecelZqaqhISEtRtt92mDhw4EBbPnj171PDhw1V8fLxKS0tTDz30kDp16lRYmDVr1qh+/fqpuLg4ddFFF4W9g5Cf/vSn6sILL1RxcXGqU6dO6vrrr3eVJqUohyS6eBUnymPLx1FKqeisdRFCCCGEEEJI64B7nAghhBBCCCGkGag4EUIIIYQQQkgzUHEihBBCCCGEkGag4kQIIYQQQgghzUDFiRBCCCGEEEKagYoTIYQQQgghhDQDFSdCCCGEEEIIaQYqToQQQr7xnDp1KtpJIIQQ0sqh4kQIIeQbx+LFi3HTTTehR48eSExMxNVXXx3tJBFCCGnlUHEihBASVcaOHQvHcdy/jh074sYbb8S2bdu+UnwzZ87EhAkTcPPNN+Ott95CcXExli1b9jWnmhBCyPmGo5RS0U4EIYSQ85exY8eisrISc+fOBQBUVFTgkUcewbZt21BWVnZGcX322Wfo27cv1q9fj969e5+N5BJCCDlP4YoTIYSQqOP3+5GRkYGMjAz069cP06ZNQ3l5OQ4dOoQ9e/bAcRwUFxdHfDYlJQWvvPIKAODtt9/GxRdfjN/97nfo1KkT2rdvj+9///vYt29f2DNz5szBxRdfjLi4OPTs2RPz5s0Lu+84DubMmYPhw4cjPj4eF110ERYtWuTe96aprq4OeXl5yMvLQ11dHQBg48aNuOGGG5CWlobk5GRce+212LJly9dTYIQQQs45VJwIIYS0KGpqavDqq68iOzsbHTt2PKNnDx06hK1bt6K8vBzLly/HmjVrUFlZiZEjqYYJwwAABKRJREFUR0IMLBYvXozJkyfjoYceQklJCX7xi19g3LhxWLNmTVhcM2bMwO23346tW7di9OjRuOOOO1BaWtroncFgEHfccQdqamqwZMkS+P1+AEB1dTXGjBmDDz74AOvXr0dOTg5GjBiB6urqr1gyhBBCoklstBNACCGELF26FImJiQCA2tpadOnSBUuXLoXPd2bze6FQCDExMZg/fz4yMzMBAPPnz8fFF1+MVatWIS8vD7NmzcLYsWNxzz33AACmTJmC9evXY9asWbjuuuvcuH7wgx/gZz/7GQDg8ccfx8qVKzF79mw899xzbhilFMaNG4dPP/0Ua9eudfMAAN/5znfC0vbCCy8gJSUFa9euxc0333xG+SKEEBJ9uOJECCEk6lx33XUoLi5GcXExNmzYgPz8fAwfPhx79+51w1x11VVo3749MjMzMWrUqEbmd0JmZqarNAHAhRdeiG7dumHnzp0AgNLSUgwdOjTsmaFDhzZaTcrNzW302xtm6tSpmDdvHgYNGoQOHTqE3ausrMSECROQk5OD5ORkJCUloaam5oz3bRFCCGkZUHEihBASddq1a4fs7GxkZ2dj0KBBeOmll1BbW4sXX3zRDfPaa6+hqKgICxYswCeffIKJEyc2iic1NbXJdziO87Wnu7S0FMuXL0dBQQHefvvtsHtjxoxBcXExnnrqKXz00UcoLi5Gx44dcfLkya89HYQQQs4+VJwIIYS0OBzHgc/nw/Hjx91rmZmZyM7OxrBhwzB+/PiIziIuueQSlJeXo7y83L22d+9e7Nu3D5deeikAoFevXvjwww/Dnvvwww/d+8L69esb/e7Vq1fYtXnz5uHGG2/E448/jgkTJiAQCITFef/992PEiBHo3bs3/H4/Dh8+fGYFQQghpMXAPU6EEEKiTl1dHSoqKgAAR44cwTPPPIOamhrccsstbpiTJ0/ixIkTqKysxKJFi9CnT59G8dxwww3o1asXfvSjH+HPf/4zAGDy5Mno16+fu+do6tSp+OEPf4grrrgCeXl5+Oc//4nXX38d//rXv8LiWrhwIQYOHIhhw4bhH//4BzZs2ICXX345LIyY5z344IN4/fXXMWXKFLz00ksAgJycHMybNw8DBw5EIBDA1KlTER8f/zWVGCGEkHMNV5wIIYREnRUrVqBLly7o0qULhgwZgo0bN2LhwoX49re/7YYZMmQI4uPj0a9fPyQmJuL5559vFI/P58Mbb7zhuv++7rrrkJ6ejjfeeMM11Rs5ciSeeuopzJo1C71798bzzz+PuXPnhr0LAH7729+ioKAAl19+Of7+979jwYIFjVal7PfOnTsX8+fPxzvvvAMAePnll3HkyBH0798fd911F+6//3507tz56ykwQggh5xwegEsIIYR4cBwHixcvxsiRI6OdFEIIIS0ErjgRQgghhBBCSDNQcSKEEEIIIYSQZqBzCEIIIcQDrdgJIYR44YoTIYQQQgghhDQDFSdCCCGEEEIIaQYqToQQQgghhBDSDFScCCGEEEIIIaQZqDgRQgghhBBCSDNQcSKEEEIIIYSQZqDiRAghhBBCCCHNQMWJEEIIIYQQQprh/wEgs1NPb1gc9AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.scatter(range(len(y_test)), y_test, label=\"Актуальные значения\", color=\"black\", alpha=0.5)\n",
    "plt.scatter(range(len(y_test)), y_pred, label=\"Предсказанные(новые параметры)\", color=\"blue\", alpha=0.5)\n",
    "plt.scatter(range(len(y_test)), y_test_predict, label=\"Предсказанные(старые параметры)\", color=\"red\", alpha=0.5)\n",
    "plt.xlabel(\"Выборка\")\n",
    "plt.ylabel(\"Значения\")\n",
    "plt.legend()\n",
    "plt.title(\"Актуальные значения vs Предсказанные значения (Новые and Старые Параметры)\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aimenv",
   "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}