{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b6fb168",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV, StratifiedKFold\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.manifold import MDS\n",
    "\n",
    "\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75f28a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('titanic.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34567fe6",
   "metadata": {},
   "source": [
    "# Знакомство с датасетом"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "26b735e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "546f361a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7136451b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f55a184",
   "metadata": {},
   "source": [
    "# Предварительная обработка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6991cc16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Удаляем ненужные столбцы\n",
    "df = df.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7826ffb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Кодируем поле Пол\n",
    "df.loc[df['Sex'] == 'male', 'Sex'] = 1\n",
    "df.loc[df['Sex'] == 'female', 'Sex'] = 0\n",
    "df.Sex = df.Sex.astype(bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f90a0ba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass    Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "0           0       3   True  22.0      1      0   7.2500         2\n",
       "1           1       1  False  38.0      1      0  71.2833         0\n",
       "2           1       3  False  26.0      0      0   7.9250         2\n",
       "3           1       1  False  35.0      1      0  53.1000         2\n",
       "4           0       3   True  35.0      0      0   8.0500         2\n",
       "..        ...     ...    ...   ...    ...    ...      ...       ...\n",
       "886         0       2   True  27.0      0      0  13.0000         2\n",
       "887         1       1  False  19.0      0      0  30.0000         2\n",
       "888         0       3  False   NaN      1      2  23.4500         2\n",
       "889         1       1   True  26.0      0      0  30.0000         0\n",
       "890         0       3   True  32.0      0      0   7.7500         1\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Кодируем поле Embarked\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(df['Embarked'])\n",
    "df['Embarked'] = le.transform(df['Embarked'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2aaacde7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Заполняем возраст медианой\n",
    "df.Age.fillna(df.Age.median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "17786cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <td>7.2500</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>887</th>\n",
       "      <td>1</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass    Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "0           0       3   True  22.0      1      0   7.2500         2\n",
       "1           1       1  False  38.0      1      0  71.2833         0\n",
       "2           1       3  False  26.0      0      0   7.9250         2\n",
       "3           1       1  False  35.0      1      0  53.1000         2\n",
       "4           0       3   True  35.0      0      0   8.0500         2\n",
       "..        ...     ...    ...   ...    ...    ...      ...       ...\n",
       "886         0       2   True  27.0      0      0  13.0000         2\n",
       "887         1       1  False  19.0      0      0  30.0000         2\n",
       "888         0       3  False  28.0      1      2  23.4500         2\n",
       "889         1       1   True  26.0      0      0  30.0000         0\n",
       "890         0       3   True  32.0      0      0   7.7500         1\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a170235",
   "metadata": {},
   "source": [
    "# Визуализация датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8abc136d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(n_components=2, \n",
    "             init=\"pca\", \n",
    "             random_state=0,\n",
    "             perplexity=50,\n",
    "             n_iter = 1000,\n",
    "             metric = 'cosine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b248c498",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = tsne.fit_transform(df.iloc[:,1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cccd1aa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a19d41f820>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ObMmfbnyHge8/6jjmml8RTdcoVakED792b45eC0VRlJuZ6jbKpa5PdmDh58uIOBWZ6YGoWzTK1yxLx4fbADDn/cX8PGGhx+eOOHWRE3tPUa1RZZdlUpJSWDtnM6tnbSAmMo4Ktctx98DONOnQoMh1OcnYcUAqAId3+3Jkj5/LsqYh+HdDIGdPeFG2ciq1myRy/+AL/PZjaKY1qK6mWzR+n7GWxu3ruywjhEAEDkP6PQBJi5HmOYQWCj69EJaMP6v7X+pJ8TLFmPHmXM4eCQccyfI69mvD0+/1J7hEwa6CriiKUhSo4CWX/IP8+Hj9eN7p5+iKcAQMEimhUfv6jP7pBbx9vUlOTOHnCQuyff7U5FSn26WURB6fxivdlnH6iCPlvTThxN4w1s3ZTOf+bRk57blc5Ta5kUj7MbDtSP/++AHPEvqd+M+HMhUds4Gad4hj5ifuB0IbdpNL56Lclkkj9DIQ8EyWa1h3euQOOj7chlMHz5KSmELZaqUJKJZ54K+iKIriGRW85IES5UP5eP1bHN11Ij2Aady+HpXrVUwvs2vNXhJjk7J1XouXhfI1y2babtoOwqVHefuhEpw77gc4AhcgvfXnz5nrqVyvAg9lkV+m0LCfzPCtl49nU569fUyEBlXrpnBkj3/66+SKpmuUKB+a01q6JISgUp3sz0ZSFEVRMlPBSx6q3rgK1RtXcbovMc79jKRrabpGx35tMs1EkfYwuNiHgzut7P/Hzad3CfM+Xsp9L/XEYi0CP2YtMMO3TdvGYbGa2G2uh235BRjUvzUhfW2jRVPLIYRwukp0GtMw6fp4+zyosKIoipJf1IDdAlKxdjmPy2q6RtlqpRk06dFM+2TU84CdnRsC0XT3rQ8xEbFFZ7FHaxPQSqZ/GxRi0PPxiwjh6jWQ3Df4Aj5+EiytEMHv88/asm4DF4DA4gHUv71OnlVbURRFyXsqeMkDpmlydNcJ9m85SExkrNMyNW6pSvUmVRxZdd0ILB7Ag6/cw+d/vZtpMKdpOwLGAQAMM9MEFud1M4rGbCUhLIiA4Rm2Pf36WTr1dYxP0S0STZfpAV2vJ2Lp98otUGI9WokZCN/eHr0WZauWyrOBzlLK3Gf0VRRFUTIpAv0J19eKqav58a15XDgZAThmq7S9rzXPfPgYoWVDMpQd+f1zvNj2DWwptgwzk3SLhsXLwsSVb1D/ttquH56JP6f/t17zhCwTofkFCirW8bzF50Yn/O4HGY+M+xBIxWKFlz87Rd9nI/hzfi2iY5pTokIF7ny8g9PxJQ3a1OWvpf+4zIis6RqN2rmeZeSpHav2MO+jJez4czfSlNS5tQZ9X+zBHX1bFbkZYIqiKNeDkEXso2FsbCzBwcHExMQQFJS/01B/nrCQqa/NyrTdMeizOJO3TSSkVMaU8yf3n2L6mDlsWrQNaUqEJmjTpyWPvflAhgG+zpiXnoNUR+I6KWFgu9qcOe6N6SSIEZrkvmejGDR5VS7u8MYkzRhIXo400qYod0foJbI8btfafYzsOM7lfk3XmPbfp5SrXibHdVv42TK+HD4NTdfSW3rS/t/nhe4M/uhxFcAoiqI4kZ3ntwpeciji9EUeqTLY5cKMmq5xz5BuPPfJE073x0cnEBMZS3CJII+nzZoX2oN5Nv37sMPevNy3OrGXLJim44EoNIk0BU1uj+OtH0/hU2Vf9m6siEsLOK9ek0q3aJim5OVpQ+jyaLtsne/c8fOEH79AUGggmq4xqPFL7tZn5O2lo2nZvWlubkFRFKVIUsFLAQQvM9+Zz4xxc92Oo/AN8GHBxWl5MtvHNFIhokGm7dGRFpbOCOXPeSEkxOiUrZLC3Y9epFPfKCxexdFK/5Xraxc1u9buY+Hny9i74QCartGi2y30GdadGrdU9fgcR3ed4Mvh0zJk4/UP9iMxLsltQNu0cyMmLH8t1/egKIpS1GTn+a3GvORQ+PELWTb/J8UnExeVkKnrKM2FsAj+WbmL1BQbtZpVo24rN2vhJHzudHOxEnb6jzhP/xHnM+/0u99t/W5WjdvXd5tB153UVBsrp67mqxE/YLdlTNWbEJPo9ljTMDn495EcXVdRFEW5QgUvORRUPOuVgDVdwy8wcybYpIRkPnnma9b8vAkp5eXcI5KqDSvx6qzhVKnvZOyLcSJ7FRSh4P9C9o5xIyYylpXT1nBo+1Gs3lZuvaspbe69FauXNc+ucSOz2+2M7/shf/223WXLiies3jfH66UoipKfVPCSQ+0fup25H/zqcr9u0bjtnlvx9vXOsF1Kybg+77Nzzb70abRp/57cf5oRbd/gq38nUapSyYwntNSClJUe1k6H4nPQtLz58a6ft4UJ/T/DsBuO6dlC8OeP6ylduSQTf3+DCk6yAHvi3LHzhJ+4QGDxAKo3rnJDDGQ1TZO/l//L1Nd+5uT+0xh2R+uKbtUwbLmbdq7pgtvvaZEX1VQURbmpqTwvOVSzaTXa3Hsrmpb5gatpAk3XeOT1vpn27Vyzlx1/7nE6VsY0TBLikpj30dLMF/R7zuO6SWkgLz7gcXnn55CYKZs5tvlZvnlpInabHWlKTFOm1z3i9EVe6fymy/WXXDm2+yQvdRjLgBpDeaXzeAY3fYUn6rzAhgVbc1Xn3LLb7Iy//0Ne7zmRY7tPpgcuQK4DFwDTkGxb/i9njpzL9bkURVFuZip4yYVRPw6j48N3ZMoWF1wqmIkr33C6VMCqnzagW1y/7Kbd5I8Z6zJt13Qd9Loe1UsIEFzCTFruttyl8Ch+emsez7X4H083HMGHT0/h0PajmPYzyIgOEPU4VaqtZsbWA3yx4iCt7ozJWFfDJOLURdbN3eJRvQCO7znJC7e/xt6N/2XYfubIOcbf9wF//Jj53gvKjHFz2bRwW75e4/zJCJ6sO5zwkxfy9TqKoihFmQpecuHcsQtsXLg109TYqPBo/lm50+kxMRdjXSZJSxMfneA8M6t5Ksv09mmkBDN2qsv9+zYf5PHaw/jxzbkc3n6Mk/tOsWLqaoa0GMXDlZ5jx5qMgUq1esm8Of0E3R6+mGG70ARblv7jWaWAr176gdRkW+aWp8v3Nfn570lJSvH4fHlh48KtPN3wRX6esLBArmcaJkNvHV0g11IURSmKVPCSQ3a7naG3jiI5wfmD9ucJC1kxbXWm7aUqZkymFhhip3qDREpXvHKe0HIhmcZ/mGYiyHg8HRYiBNiSz2baLmUSyZdmcW7Xkwx+8zAd+lzE6p0xkLgY7sXoftV5e1Cl9G2a7giIhrxzhsBi9ivnMyWpSZ51G104FemyyyxNYmwSmxb97dH5cstut/N0wxd5s+8HnNx3ukCumSYmIpZdazcX6DUVRVGKChW85NCciYtIyeKhPf312Zm2bZjvyLtSumIKr359gjm79vHl74eZsfU/Jq84RPMOsXR4+HYnZ8v+YNaU1IzLE8jUHcgLbbGmjKNtzwg69b3EK5+f4sdtB6je4NppvoINS0OY+8WVgcNCgMUq6Xh5PSFwzKiq0cSz/CgRpy5mWUa3aOlLLeQn0zQZ1GhkgQctV/vl3TcxY95AylQSYhOZ99ESnm7wIveWeIJBjV5i4WfLSErI3mrkiqIoNwMVvOTQnz9tyLLMxXNRmQazRp2PoXTFFD5bdpjb74pBv2pCULX6Sbz943Giw37mWprmiy1Vz1a3kV5s7JXvjXBk1JMg4xxBiAUsl2ftBoXYmTjnGMHF7deehV++zDjrybBD+apXtTZJSfeBnTyqU3CJwCzLGIZJUIn8XdZh8eTldPfpx6n/ru+K20f3+fD58E1888IzPNN4JN+8/CMnD5wm7lI8x/eFMeXF6Qxr9SqxF+Ouaz0VRVFuNCp4yaHUFM+6ShLjr3xy/nvFDgCeev0cAcEGlmtmMuu6Y+jHc2+dIDkhOtO5tq4q71G3kZQQGe5HYMkr03Jl4kyQyUDmLhvdAgFBBl0fvrZlRBAbZcF+VUwjBCTG6Y7VsQW8MGVQ5mndLlSoVY7qTaognMzQSmOxWmjT51aPzpcTy6euZvKwqVmOO8p/kshwK8tnhjBvchznT0Y4xjnJ9N1IKQn77wwfPu08QaGiKMrNSgUvOVS++pXcJv5BBq27xtC2ZzTlq11pldA0kSGZ3dlj5wkMsdPmrphMgcuVY8AvwOTwlq8z7Vu9pBOnj3kBZGiBSfu/aTr+f+mChcBq8zMenLwCZ4FLGqFBm+4xzvZgXhW8WKywfmkxmt3ZmEl/jqX7wM4uz+nM0xP7Ox7QLuKXfqP6EBSadQtNTsRFxfPZc9/my7ndLmjklAApMOwa7roETcNk8+J/ea7p/WxdPD03FVQURSkyVPCSQ0+/3x/dIhk45iyzd+5j3LQTvPb1SaZu/I/35h6ldIVUbuncCE278hK3f/B2SldIzdBV5IxhBzP1RKbtXZ/owqD2dfhlSkmS4q888GypEBcjOLTTlxVz6hBU80/8gqtnPFi6HzshBPj4XRvcSLy8TbyuShJsmj58sf1b3v3tVZp0yLzWUlaa39mYsfNHEny5ayitFcbqY2XA2Ad4dGz+LGkQFxXPs7e8jD312q6xvJK/CfaO7JK83uc3Fn/yVr5eR1EUpTBQGXZzqOYtVZm0KJG6TSLQrgkBG7SK55MlR/Cu8EmG7cGhQcRF61meW9PAlJmXH2h5d1OadmnK9+/s5Lu3ymL1khgmmHYNTdeo3qQKH68fnymrLwDWepASCRiZ9wF2OxzZ43vNVkGHey9d+VavilZyGpqes4y6aW7vfSst727KtuX/En7MkWG3da/mHq+unRMz35pHxKnIbB7lpoko29JaZnJ2Pikdx305cheteu+ndJV6eVQvRVGUwke1vOSUbTf1mx7JFLiAYzBsSGkDf69fMu2z+lfl8G5fDOcxRLoThzInpNM0jWFfDiS0XHFAYEvVMO2OCgSFBvC/Gc87D1wA4fcIrgKXtDovnZE2jdvxoC1bJYXhk86AdxdEieVoJVei6eXcV9xDFquF23q14N7hd9NlQLt8DVxsqTaWfb/K48HOuQ00nBN4+eTNOJsV33yfJ+dRFEUprFTwkkMyaSHguhVFYEJi5uAlNjKe6e+VQROOMSrXMk347adQjuxOyrQvOTGFV7u9zaXwqEz74i7F83qPCSTEuljZ2OsO8H3YcY2rYpi0IGr256XY//eVAKLzgzBtZ1f00hvRQr5AWK7phiokju46wZIpv5MU5+mUY0nXfpeYunE/z7x5BoRE03O+EOPVUpNz/+cmJZw8oLLzKopyc1PdRjllXsBdSwYAMgYpDYS4EuT4+Xvzz5ogJg6txLD3TuMXYGLYSW/B+e3HUKaMKc/TEzOvLL3qp/WcOpg58RyAYTc5fyKCldPWcO8Ld2faL4SAoLF88/ouOvU5SrV6jof50b2+zPuqJOsWF0svW7l+Rf7388fu7+0Gd/LAacbf/yFh+7OXx2XgmLPc96yje+neapG06BDHspnFObTLl71/BZC71hhB1q06WXdV+fg5/mztNjvhJyLQdY3SVUpmGF+lKIpSlKngJae0UjhaXtwEMCI4Q+AC0GlAO2a+NY+1i0LYsjKY2++KoVzVFBJidDb8FkzkOcdsol5DumU63R8/rkcI4XzpABxTa/+Ysc5p8AKOAEZ69WZI12VYvWyAICUp4wNPaIJOD7d1fU+FwMG/j/DC7a9lezr0/UPOpwcuaSrWSKHvoAgWfFOSfVsDstH15IqbwERAYDFBXJTrAEZKwd6tBj+O/4XFk5cTE+nIAVOqckkeGNmLXs91vSFW51YURclPQrp6EhZSsbGxBAcHExMTQ1BQ/iU7k6m7kJfczYzRwf8JtMBXMmxNTU7lbv9H3M6srd6kCl/tmJRp++O1h3HmsPsViUtWCGVW2Fcu9587fp6n64/AlmpDmhkroekavgE+TDv4GSGlgt1e50aVmmLjwbIDiY9OyNZxxUun8vO/BzJtP3XEmxG9axAfo2Ma+R8U1GhShqO7zl0OkrJ/vV7PdeX5yU/neb0URVHyW3ae3/nazlylShWEEJm+hgwZ4rT89OnTM5X18fFxWvZ6SkxM4q1+s+hRpRHdKjSiR9WGTHiuIqnpeet00Ioj/J7IdKzV28qXGxoz4OVzePtmbLXRNJOA4v58vMH5dNiy1Uo7ksO5oGmCMlVLua172aqleXvpKHz8vB2vsSbSz+kf7MfEla8X2sAFYO3sTdkOXAAunbeyZHooQIakfBOfq1RggQvAkZ3h6TOLcuLXL1eyb/PBPKyRoijKjSdfu43+/vtvjKum1ezdu5cuXbpw//2uWyyCgoI4ePDKm++N1gR+9thJnqg98qpBrwJbCqxdVJwNS4vxw9b9lKzUDBE8AaE7yTyb8DXVq82g+ovwwJAIdm4KIC5Kp0QZG7Wa2tFLzMPb33nA1n1gZ5erVQOYpvQoadwtHRvy86mv+GPGevZuOoAQgls6NqTDw23wdXHtG93xvWEs+/ZPVs3MetkG5wSTXy3P5pXB9HwskuoNkjm8O4Aje/3ytJ7ZqU9OvfvwJ7y58BVq3OLZmlOKoiiFTYF2Gw0fPpylS5dy+PBhp0HJ9OnTGT58ONHR0Tm+Rn53G/UKeoCkeBPnDxeJf7BgUVTmWUYA0riIjLgDcJUoTQfvO9FCPnW61zAMXu8xke1/7Mrc5aMJGtxRl/d+fwOL9eYZyiSlZPobs5n17gJ0i5Y3af8F9H3hbirWKc8nz36T+/NdBxarzttLR9OsS+PrXRVFURSP3DDdRldLTU3lp59+4sknn3TbmhIfH0/lypWpWLEi99xzD/v27XN73pSUFGJjYzN85Zedqze5CVwABAkxkv/+2uF8d/IS3M9QMiBlJdJ0vhCfruu8uegV7nuxBz7+V/K5CAEhZYrR7oHbMOxZzIAqApKSUpj93kK+HfUT08fOYda7CwDybr0iCbWaV8filbdBoCcLU+YVwzB59+FPsaXaCuyaiqIoBaXAgpdFixYRHR3N448/7rJM7dq1mTp1KosXL+ann37CNE1uu+02Tp92Pd11woQJBAcHp39VrJh5inFemfv+L2TdnC+Y8/5sp3ukEY673DAOJpiuM8F6eVt5bPyD1GruyLsiNOFYzyg8ms+HfMeQFqOIjnC2RlHhZxgGQ1uNppd/f74fPYu57y9m1tvzsz4wG4SAoNBA2vRtRbMujdwuIpkd975wNzPDplDvttpuxy3lFWlKYi/GsWnhtny/lqIoSkErsODl+++/56677qJcOdcZWlu3bs2AAQNo0qQJ7dq1Y8GCBZQsWZKvv868SGGa0aNHExMTk/516tSp/Kg+AOEnPWvV2bbiJGEHz2TaLrRQ3C2OeLkUaMXclvhu1Ez2bHDMjEnrPkr799TBs7w3oGiuQvxYjec5uO1Ivp1f0wVWbytj54/Ey9tKifKhdO7fFi0PAphWPZvh7ePNxBWvcddTHdEt+f+np1t1ju8Jy/frKIqiFLQCCV5OnjzJn3/+ydNPZ28Kp9Vq5ZZbbuHIEdcPLG9vb4KCgjJ85QfTNDlzJN6DkpLUZJOn6g7n5/cWZtzl2wP3qw/r4NUOoYW4LJEQm8iyb//MNOYlvZ6GyT8rd3HKSfBUmK37ZTPnT0bk2fnSei69fKxousAvyJe7nuzElB2TaNT2yrpBw74cSNPOjQDSA460lpPm3ZowafVYylQt5bKFRmiCUpVL0rh9fQB8A3wZ/tUzzDz5FX6Bvvm6nqM0JV6+Xvl3AUVRlOukQEZ2Tps2jVKlSnH33c6Tp7liGAZ79uyhe/fu+VQzzy39+g9Mw5OxzVeeRlNHz+K+ET2wWq2OPXpZpN/jkDjNyXEaoCMCX3B79kP/HCU1OetxDLvX7adi7fIe1Ldw+OblH3N9joBi/pSqVILQ8sWpUKsst99zK43a1XM7BsvHz5t3l7/G7nX7+WPGOqIvxFCqUgm6PtGB2i1qAPDqrOG81H4Mht3ENK60rAlNoGmCF79+JlP229CyIbyz7FVGdX0bW4otw3F5xTRMbrunRZ6fV1EU5XrL9+DFNE2mTZvGY489hsWS8XIDBgygfPnyTJgwAYDx48fTqlUratSoQXR0NJMmTeLkyZPZbrHJD4f+Ppqj4168YwyT/5qQ/r0I/B9S+EHCd0DKlYJ6BUTw+whrfbfn83RuWFFKPWgYicRG5n4cT3x0At/s/pCSFUKzdZwQgsbt66e3nlyrbsuafLzhbb55eQa71+1P3167eXWefq8/De+oy/Y/dnFy/2l8/H1o1aMpxcuE0OD2Ony7+0MWfraMRZOXu2xNywlN12jRrQlVG1TKs3MqiqLcKPI9ePnzzz8JCwvjySefzLQvLCwswyfSqKgoBg4cSHh4OCEhITRr1ozNmzdTr169TMcWtNQczto4suNYhu+F0BCBLyD9n4KU9SDjwVIVrM09ymlTq1k1rN4WbCmupls7NLyjTo7qeyNJTUliwftvsGjKUZIT8+ZX9btRPzH6J/etWzlRu3l1PlzzJudPRhB55iKlyp4gtPh69m76kAHVkjkfZnMMrjYlmkXj7oGdGfzx45StVprnPnkC3wAfZr+3KM9aYBq2rcvomXl/n4qiKDcCtTyAhyY9OZnfp6/L9nGarrHSNifP6gHw6eBvWPbtKkwny1JrukbdljVpeEdd/vhxPfHRCZSpUpIez97JXU91xNvX28kZbzy21FTGdH+C7WtScpwq3xlXSy/kFSlTkNHDIGUNR/f580KPathtAmlmrL8Qgk797+B/PzwPwKXwKAY1eom4qIRMAUzasg0JMS5WDM9wYuj+VGeGfz3ohkvwqCiK4s4NmeelsEtNdt/S4Urxcq4H3+bUoA8GULd1TYArA0WF46tE+eKcPHCauR/8ysWzl0hJTOHkgdN8+cJURnYYR1J8Up7XJy9J2wHMuEks++xJ/lmTcjlVft49hH388jd4k7FvQYojyJ35UQkMe+bABRzJ9f78cT0nDzjSABQvE8KH68ZToVZZwBGwpM1yatS2Ht/t+5gHXu7lQQWg65MdVOCiKEqRdvOkYs2lMlVKOZ6h2Wyn+nDNuDyvi6+/D5NWjWXt7M389u2fnD9xgWKlg7nzsfYsnryCxDOXMn56l45qH9p+jO9Hz2Lo50/leZ1yS8pUZMz/IPk3QOfX72og8M7uy52le56/K4/PeIU0IiFpPmCSnKixZWUwppPAJY1u0Vg9cwNPvN0PgMp1K/Dd3o/ZvX4/B/46jG7RadalEdUaVQZg4HuPolst/Hw5KZ+z81VrXIW6LWvm+b0piqLcSFTw4qFuT3Zg9sSFWRe8StPOjShXrUy+1MfqZaXLgHZ0GdAufduudfvcrjptGiYrpq7myXcfdkzTvYHI2PGQvPzydwanj3hfbnWRVKyRgrevSXiYF/ExOf+VDS4RRIcHb8+T+jqVuoW0DMoJsZrbwAUAIYiJjLtmk6Bxu/o0bud8cPBj4x7g9MEz7NuwkbrNkzANyYF//Im+ZKFEhVDGzR+pWl0URSnyVPDiofI1yvLQqD4eBzD3DOnK0M8LdpbUf1uPoOma20GfKUmpHN15goZ31C3AmrknjfOQNI+rE/h5+Zi0ujOWR0eGU6GaY7luW6pg3a/BfPdWOaIirNm6ho+/N1/tzL+xLgDIK4O6A0MMvHxMUpNd98yahkmZKk4W73RD0+J5/ZszyOT/EMLxehl2wZnTLSlV7y38grI3k0pRFKUwUmNesuHJd/ox9POn8A92v9KwpmsEFAsooFpdoVs0j+ZIj7v3fY7tPlkANfJQylquzTw87L3TjP4yjHJVUtO3Wb0k7XtH8+lvhwkOzd7sr7JVS1OiXPE8qKwb1iuz4ry8JZ3vv4Suu/55SFPS9oHWHp/eTFmHvNARUn5LD1wAdIukUpVt+NiGIqVay0hRlKJPBS/ZIITgniHdqNWsutsxpKZh8ufM9QVXscuadWmE6UGukLioBF7u9CZxUZ5kDC4AMolrfxU73hsNwDW53bBYoEQZG4+8eD5blyiI9Z6EtQ5Ym5C2ftUjw88TFGpHcxPA7Nt0MMvzSikxY9+DqIGA80U7wQTbdkj+I9v1VhRFKWxU8JIDkacvZjlwN9HNtNa4qDgWTV7OzLfnsXnx36Sm5M2n5aoNK3NL54ZZLvwnTUncpXh+n742T66ba5ZaXNvy4m7Yhm6Brg9dwurlWU4UoQlKVymViwp6TgR/AFoxpNQoUdbOp0sPc2unWITI/AujWzTC9rtedDRd8jJI/N6Dq2vIpF+yX2lFUZRCRgUv2bRp0TZOHTrrtozQBOVqls20PS4qnv/dOZ57Q5/ki2FTmT5mDmP7vE/fEk+w7LtVeVK/12YNT5+d4o6Uko0Lt+bJNXPNqxXoFcjOr6OPnyQwxPCorDQldw/qksPKZY+wVEKELibJfIiYSzoly9l44b3T9H02At+AjPWVEnwCfLI8p0yYimevjQmG6wHbiqIoRYUasJsNpmny9oMfZVlOmpKez96ZYVtCTALPNB5JxOmLmconJ6Tw8aCvEALueqpTruoYXCKIz/96l3tDnyApPtlt2ZTEVLf7C4phN4lOfI0Qr+EIYSdtxo47pglJ8Vk/0DVdo07LmnR8uE0e1NQzQi9FQPlxPNszgWO7TyBdNBCZhskdfVu5PZeUNrDv8fDKGuils1dZRVGUQki1vGTDgk9/w25z/2DVdcnny4/QqdvrmLETMU1HUri5H/zqNHC52tcvz8CWw2UIrmaxWqjbqqbb7iPdolGjadVcXys3UlNszBg3lwfLDaJf1a94pmNltq4qmeUUY8MO2/4MJClBd1vO6m2h+9OdmLjydby8szc7KS8MGPuAy8BFCEHrXs2pVCerxTOzM+3ZRPjel43yiqIohZMKXrJh86K/syxjGAJbikQjAhKnwoWWGKnHWTx5RZbHJkQnsv333XlRVe4ZepfbKdOG3aTX4K55cq2csKXaeO3ud/np7XnEXnQMQj150JcxA0rTs1oDtq8vheEkTjQNME3BrE8zdstpuobQBP+bMZQPVo9j0qqxzD33HS9MGYSvf9ZdM/nhtntaMPyrQVi8LAhNoFu09IzIUkq2/PoPQ24dxZrZm3C1SocQlsuDgLP6UxVgaQg+1+9nqiiKUlBU8JINUeejPSpn8br6QZRMypl+nq1LA0RfyJtZMa17Nqf7QEcXVPoSApCecv7x8Q9R45br1/KyYuoadq7Zm3klZQn2VI1xj5dm7aJiSBMMA+yXG6Rio3TGPVENU9TDN/BKUNK4fX0mrRpL5/7taNy+Pk06NCCgmH8B3pFzdw/qwpwz3/DMBwOoXL8i0pQZfh5Hdhzj3Yc/4btRM12eQ/g/xbUDmjPx6owoPh0hvPKo5oqiKDcuNeYlG8pWL8PpQ+4HRAYVt1O9fsb1g6xel9D1ShhG1tOYS1TImyRjQgiGf/UM9VrXZv7HSzm+JwyAOq1q8cDIXtze+9Y8uU5OLZmyEoFAupi2lZqs8f7zlZkxqQytu8bi628SdsibLb8HY9gFAcUuMD9iKjERsfgG+OAffP0DFVeCQgOp3rgKx3Y5cutcHbClTW2fO2kxLe9uSqO2mVdQFz5dkf6DIWEKjmnYaU1SAvCCYp+g+eRurJSiKEphooKXbGjSvj5/L//XTQnJ/YMvYLlmeIWuwx29rayd736AbFBoILd0apD7il4mhKDr4x3o+ngHUpJSEJp2XcZ+OHP68DmXXSVXCw/zZuG3mbPQxkcnoOkaJcoXjoyyv365Et2iYdidt6DoFo1fv1zpNHgB0AJfxPS6A+I/cQzglQZoJcCvP8L7tnysuaIoyo1HdRtlQ+dH2yKcvGJpWVR7DLjIfYMjnB7bb7gPutX9ANPhXw1C192XySlvX+8bJnAB8PXP3erOQaGB+fZa5ZWrg7PD24+5DFzAMQbpv62H+ePHdSz7bhUH/zkKgGkamPbzmMYlSJwGtm0gU4FUMM9B/PvIyHuQxoX8vh1FUZQbhmp5yYbiZUJ46t3+fDfqp/RtQpN0efASPQZcpGajJJfHVmnSgZZ3n3U76Dc5ISVP63sj6/BQG5Z8/Tummwe6K0IT3PVUx3yoVe5J+3FkwveQvARkElIrj/B7GG+/rAPH8ycjeP+xyenfV2+kMerz/6hU89rfq7Ruo8vBkXEKGf0CIvTnvLkJRVGUG5xqecmmB1+5h5e+f46SFR3dFdIUVKyRQvUGrgMX8OJi9F38tWS723P/MG4Oppn9h3lhdO/wu3PVEtRnWPc8rE3ekKk7kJG9IWn+5SUPAPMMMv4Dbu92KsvMx9c6vs9gRO9qXDid1etkgG070rY/R/VWFEUpbFTwkk2pyamcP3GBlMQrrSS/zWzEqnmlXKyJKKDYZLYu3ZFlYHL+RMSNtWBiPipXvQzv/f5Gth/o4BjwevZo9tY2ym9S2pHRzwMpZE6yJ+nR/yDevldme3nCNAQJsTrzvvJk5WkNUrdko8aKoiiFlwpessGRm2QCM9+ZT+zFK4sanjsWyQfDyzLjo9u40hOngfUWCF2E5tOepPhktGtXGXQiKc59VtyipF7r2tRuUSNHx8ZHJ+RxbXIpZQ2YEbia0hxaJpUJs0+mr0ielpcmK6Yh+H1OcU8WC1cURblpqDEv2bDi+9XsWrs304Mk7ftZHybQ/rEVVKlfEXHNyoKV6pRzmzQOHLODytUok5dVviHN/eBXZr0z3+PcN87caK+TtO3D8edkd1mmbtNL/HT0ZdbND2f3+v1s+fUfj16DpAQdu01g9XIXwZhgbZ7teiuKohRGquUlG36dstLtft2iseybPzMFLgDNuzaheNkQl5+2NV2jda/mhJYNyZO63oiklDzTZCTfvvJjjgMXTdeo26omletWyOPa5Y4QVrJcahzwDfDnrqc68cRbD3n8GgQUs2cRuOhgqQ/WRp5VVlEUpZBTwUs2nD50zm3zvWE3CfvvtNN9ukVn1I/Po2kiUwCj6RpBoYE898kTeVndG85nQ7/L1ZgeTdewelsZ9sXAPKxVHvFuj/sFJQXolUB3rPidkuTZophCSLo/csn9ebWSiGKfOQ2aFUVRiiIVvHgo8sxFrF7ue9k0TeAX5Od0n2mahB04Q7FSwRkyrGq6Ruf+d/DF3xMpXdmTgZmFk2EYLP/2z5yfQEDzro35fMs713VZA1eEtT5YW+HIgOuMRPg/kx5glKpUAt+ArNdcCihm0PfZa3O4CBChYKmJCHwZUWIJwlIxV/VXFEUpTNSYlyxcCItg8rCp/LVke5YZYU1T0u7+1pm2Syn5eNBXrJi6JuMiwQJMw0RoGiXzaFmAG9WxXSfdJmlzpXzNMrw+ewSh5UIIKV0s7yuWh0TIp8hLTzsy4Kan8b/8r/9zcNWKz96+3tz1VCcWTV7uYiyURLdIPvvtEMHFDaQER9zjA6Fz0ax1CuKWFEVRbkgqeHEj8uwlnm/9GjERMVkGLrpFo1yNstzeJ/OaQf/8vssRuEDGYRGX/79y2hra3d+aFt1uyaOa33giTl/M9jGarlG1YeUbsqXFGaGFQOhcSFmPTF4GMh70ygi/+xGW6pnKD3jzAXat3cvxPWHpaxxddTaKl7Jx+qgvQtMIKV0Kn9D7wG8gmqb+bBVFubmpd0E3fn53AdEXYtzOEkpbr6Zao8qM/3UUVq/MCcWWfvV7luvaLPnq9yIZvBh2g69HzmDxF8uzfaxpmPR4pks+1Cr/CKGDTweET4csy/oH+fHxhreY99FSlkxZSdT5GDRdI7hkEPZUO5qXL7t39qXK7d3wq1yqAGqvKIpSOKjgxQW7zc7K6WvdT28WULdVLZ5852EatKnjcsDksd3uu0wMu8nx3WG5rfIN6bMh37L8u9UeLcKYgYBOD99B085FewaNb4Avj465n/5v3EdKUipWb8sNv2aToijK9aaCFxfioxMyZNF1Rtd1ytcoS8M76rotl5aYzB3foKwHbxY2Z46cY9m3q7J9XEjpYPq+2JP7Xupx08ygEULg45e7xSoVRVFuFip4ccEvyA/dqmPY3E1/lQSXDMryXO0fuI1ju09mmGV0NaEJOj7UJoc1vXGtnb0ZTdeyTM4XEOLPwEmPUq9lLYSACrXKoVtU64OiKIrinJoq7YKXt5W297VCt7h+iQy7Sef+d2R5rrue7kRQ8QCn6/houkZQ8QDuerpTrup7vUmZjEz4CTPybszzTTAutKNC+bkUK5F1PpMv1lTlrv6BVK5Xgcr1KqrARVEURXFLBS9uPPJaXyxeVjQ9c9eF0AQdHrqdqg0rZ3me4BJBTFo9jhLliwOgW3V0q+MBHVouhEmrxxFcIusWnBvN+dMR3F/mabpo93Gn3p+7QhYyspfJyf8MhHmO2+78ly9/P0j5aq673yxWk2IBc5GX+iMv3o80IgvwDgo3acYiU/92rGYt3XdxKoqiFCVCZnsk5Y0tNjaW4OBgYmJiCArKfUDw37bDTOj/GWePhCOEQEqJZtHo/nQnnvvkCaezi1wx7AZ/Ld3O7nX7AWjYti6tezYvlC0Nx/efYlCDEZe/k1xJYCMRAt784TgtO8dht0PYIR8Gd65FxiQ3oOmSjvdG8fKnpy5v0R2J10IXOmbtKBlIaULqVqT9ICSvBtt2wObYKQLB7zFEwHMIoXqDFUUpfLLz/FbBiweklOxet5/je8Pw8fOm5d1Nb/iEafmtm/dDbsYDSSxWyZLje0hbSPvFe2qw/2//9BKaLgkMNvh8xSFKV7BlOFoU+9qjqcY3E5m6DRnzPzDOuCklwKcHIviDm2ags6IoRUd2nt+q28gDQggat69P76F30e3Jjjd94HJ8/6ksBjIL7DaNBd84ljswDKjbNOGq/ZIWHWL5bNnhTIEL6MgU9wtg3mxk6i7kpcfBOJdVSUheAra/C6JaiqIo140KXpRs+/md+R6Ukvy9KhBwpLW32660BNRqnMjOjYE82aYOL/SowZqFxa5a8NIEM2crThdVMv5jwLz8lRUdmfhLPtdIURTl+lKd40q2ZbVAZRpNl5w57sW8KaVYPrN4+vbDu/2Q0hHMHNzpx8Qhldm5KYDhk047ujucpNK/2t8r/mXeR0vYvf4AAI3a1qXviz259a6il6FYGpGQujkbRxhgnMq6mKIoSiGWry0v48aNQwiR4atOHfcLyv3yyy/UqVMHHx8fGjZsyLJly/KzikoODJr0qAelBLWbJvLcnbVYMat4erACZPy/6fj/ilmhrFtczHGk3/0uzzrznfm82v1ddq7Zhz3Vjj3Vzs41+3jt7neZ+bYnLUKFjBmVzQM00IpnXUxRFKUQy/duo/r163Pu3Ln0r40bN7osu3nzZvr168dTTz3Fv//+S+/evenduzd79+7N72oq2RBcIhgfPy83JSTevgar54WQkqRhmlkPHtU0yaLvSyACRyP0ck7LHNh6mOlvzAbIkPgu7f/Tx8xm/5aDnt9IYaCXIHt/pibC9578qo2iKMoNId+DF4vFQpkyZdK/SpQo4bLsp59+Srdu3Xj55ZepW7cub731Fk2bNmXy5Mn5XU3FA3/OWs/d/g/TRbuf5ERnyeckINF06Hz/Jc6f9k5vWcmKaQoO7wlE+D/mssziL5a7TRqoWzQWf7HCo+sVFkILAe+OgCdTx3WwNADvwp3wUFEUJSv5HrwcPnyYcuXKUa1aNR555BHCwlwvQLhlyxY6d+6cYVvXrl3ZsmWLy2NSUlKIjY3N8KXkvUGNX+K9/p+TmnTt7KA0ableBKYhWP9rCEJkbxa+xeo+Z86BLYezXOBy/5ZD2bpmYSACXwLhQ5YBjPcdiOLTVJ4XRVGKvHwNXlq2bMn06dNZsWIFU6ZM4fjx49xxxx3ExcU5LR8eHk7p0qUzbCtdujTh4eEurzFhwgSCg4PTvypWrJin96DA1NdncXxPVqteZ2xhiYvRyU4GId2i0bJHM7dlrN5ZP5St3p4nDSwshKU6ovgcsF4zIFmEgHd3ROAYRInf0UK+QWjB16eSiqIoBShfP6Lddddd6f9v1KgRLVu2pHLlysydO5ennnoqT64xevRoRowYkf59bGysCmDy2OyJi7J/kMxekjTTlNz3Yg83+01a9WjKqYNnXS70qOkat/Vqnq3rFhbCWgsROgtpPw7GSRABYG2iWlkURbkpFeg7X7FixahVqxZHjhxxur9MmTKcP38+w7bz589TpkwZl+f09vbG29s7T+upZORqNey8oGkaQoNXpg+lzq01M+3fsmAa8z5ezr6tjoClat1kdIvk0E6/DOWEEFi8LPQc3DXf6nojEJaqYKl6vauhKIpyXRVokrr4+HiOHj1K2bJlne5v3bo1q1atyrDtjz/+oHXr1gVRPSUf6bojANIuD7gtX7MMLbo14ZHX+/LT8S/p+HDm1blnvDaSMfctY+9fJoZdYNgFxw/4cGinH41ax6eXE5rA29eLt34dRenKJQvmhhRFUZTrJl9bXkaOHEnPnj2pXLkyZ8+eZezYsei6Tr9+/QAYMGAA5cuXZ8KECQC88MILtGvXjg8//JC7776b2bNn888///DNN9/kZzWVfDby0zD2/H0LcXHVKFutNHc93YnKdSu4PWbv2sX8OOEkAKZxpQsq7f+7twTQ8f7SJCVXpGHbenR9oj1BxQPz7R4URVGUG0e+Bi+nT5+mX79+XLx4kZIlS9KmTRv++usvSpZ0fDoOCwtD0640/tx2223MmjWL119/nVdffZWaNWuyaNEiGjRokJ/VVLJQqW55wg64WxDQOSEkxUsb3PncUrrq2WsRWfzFfDRNuswR42jJiWP84v9lu16KoihK4aZWlVayZLPZ6O7zsGM2dDbd3vtWxi14OVvHSCm5t/h9xMe479UsV8Xgh2MLsl8pRVEU5YajVpVW8oyUkrnv/5rj4zct2sb6ea7z9Diz4JPfsgxcACR2vhv1E4lxSTmtnqIoilIIqeBFcWvR58sdKflz0T737sOfeFw2NTmVn96al2U5ISQly9r45cMljGj7hgpgFEVRbiIqeFFcsqXa+HH8L7k+j2E3ObDVs8y3u9buIz46IctyulVy6qgXpmFyfO8p5ry3KJe1VBRFUQoLFbwoTkkp+eblH4m7FJ91YQ8Mu+01ht3+Gkd2HndbLjHWsxaUkJJ2oi44Foc0DZMlX/2OYRi5rqeiKIpy41PBi+LUd6Nmsujz5Xl3QgkHthziuWavsGXJ3y6LVajtfEXpa0Wcybiqddyl+DwLtBRFUZQbmwpelExO7DvF3EmL8+XcUsL4+z7ENJ2n+K/euAo1m1ZD07P5qynA209lWlYURbkZqOBFyWT5d6vQLfn3q2G3GSz45DeX+0d8+yxePlaPAxhN12jepTG+/j55VcUbQlJCEr998wffjZrJzxMWcnL/qetdJUVRlBuCWtVNyeTMkXMYdkfLiMXLRACGITJkus2tf37fxX0jejrdV+OWqnz+1wSmvf4zW379B7epiIRjfM7Dr/XNs7pdb1EXYvj02W/YtGhbhu1TX5vFbfe0YNSPz+Mb4HudaqcoinL9qZYXJZPA4gFoFg1Nl7w14zifrzhE6ztjEFpaEHH1v1cCi8oNKmD18Swezqplp0r9igz/+hkeeLkXpSqXJLB4AP7BjsUYNV3DYtUdXUW+Xrw2azgN76ibzbu8MV08F8UzTUZmClzSbFnyD2/e96H7gE5RFKWIUy0vSibtH7ydP39cT9ueMTRt6xgEO+b7k0SGW9i5MYDTR6z8/FlZIGNLzMm9pxGaQAiR5cO186Pt3O4/uf8UL7UfS1xUAqbhaAVK60YqXbkkzbo0olrjKnR8uA3+QX7uTlWofPXSdKLOR7vcL03J9t93cWDrYeq1qlVwFVMURbmBqOBFyaR518bUaVmD7v1XYtjBlPD+85XY+FuxLLuOpCkRmnCb1M4/2I8OD97ucr9pmoy5570MgQuQ/v/wExcILVecns/emb0bu8HFRMay/pe/skwIqGka6+ZsVsGLoig3LdVtpGSi6zrvLnuNSrUcz9Gn2tRh/a8hHo95kaakTLVSTvdZvS18svFtt8dv/30XZ4+ezxC4XHv+RZ8vw26ze1SfwuLM4XMu7/lqEklCTGIB1EhRFOXGpIIXJZOI0xeZPXERw7pXp1e1hpw/lf0pyKHlQnh+8lOUrVYa3wAfipUKoteQbsyLmEqV+hXdHrtv00F0i+62TExkHOeOnc92vW5kHk/1lpJyNcrkb2UURVFuYKrbSMngyL/HGdlxHEnxyZgGOOJbybXjW7ISdzGeXs91o9dz3bJdB6EJPFlMSWhFK/au2rASpSqV4EJYpPuCQnDn4+0LpE6Koig3oqL17q/kimEYjOn93uXA5erui+xPkW7erUmO69GkQ4P0qdqulChfnLIuuqYKK03TeHTM/VmWe2bSAEqUK14ANVIURbkxqeBFSbdt2b9EnLro0biLrDz7wWM5PrZRu3pUbVgJzc106vtG9ETX3XctFUbdnuzIk+887Gh9uiZm9A304ZXpQ+n7Yo/rUzlFUZQbhOo2UtId+OsQulXHsOVugcOuT3ZEiJwntBNCMH7x/xjZcRznT0YgcCwroFs0DLtJ1yc60OeF7rmq442s3+g+dBnQlpXT13L60Fk0XaNNn5a06tEsV6+roihKUSFkEct2FRsbS3BwMDExMQQFBV3v6hQaZ46e440eEzl18GyuznPP83cx9NMn86ROSfFJ/PnTBtb8vJH46AQq1SlP90FduKVjA/UQVxRFKWKy8/xWwYvCB099ycppa7J9nG7R6ftSD5Likrm1W2Na9WiRD7VTFEVRbgbZeX6rbqOb3I9vzctW4GKx6pSoEMrtvW+l/xt9CSgWkI+1U1yRtv1gPw6aP3i1Rgi1oraiKDcPFbzc5Oa+vyhb5e02gwHjHqBLFun9lfwhbfuQMa+Bff+VjSIQAgaD31OqO01RlJuCmm10Ezu+N4zkhJRsHSM0wcJPf8unGinuSNth5KWHwf7fNTvikHHvI+M/vT4VUxRFKWAqeLmJxUbGZfsYaUqO/Hsc08z9dGole2T8xyBTARevfcJXSONCgdZJURTlelDBy02sepPKOTpO0zXVPVHApBkLKauBLKaxJy8pkPooiqJcTyp4uUkZhsGZI+cpW710to7TLRrN7mysgpeCZl7CZYtLOl21vCiKclNQA3ZvQiumruaHcXOIPH0p28cadpP7X+qVD7VS3NJCcHzWcBfAGAi9aC2ZoCiK4owKXm4yv3zwK9+88mOOjhVC8PwXT9OkQ4MM25Pik1gzezNhB07jG+DDHX1bUa1RzrqkFOeEFoz07ggpa3DbdeSjlg5QFKXoU8HLTSTqQgzfvTozR8fWblGD0TOHUb5G2Qzb1/2yhQ+e/ILkxBQsFh0pJT+9NY+WdzfltZ+H4xvgmxdVVwARMByZsglIwWkLjP8ghJ69bkBFUZTCSI15uYmsnrkBM4vVmq8VGOLPwPf68/lf72YKXHat3cc7D31McmIKSEcOmLTVoP9esZO3H/o4z+qugLDWQoTOBEuta3b4IwJGIgJevD4VUxRFKWCq5eUmcubIOY/KdXm0He0eaI23nzf1bquNl7fVabmf3pqH0ATSyLzChGmYbFv2L4d3HKNm02q5qrdyhbA2gNDFjiR19uMg/MG7NUL4XO+qKYqiFBjV8nKTOHfsPPHRiR6V3frbduw2gyYdGrgMXOKi4tm5Zi+m4bolR7dorJ/3V47qq7gmhEBY6yN8eyB8OqjARVGUm44KXoq4fZsP8sLtrzGgxlDW/LzRo2Nio+IZd+8kFn62zGWZpPjkLM8jhCApLsnjuiqKoiiKJ1TwUoTtWruPl9qP5cBfh7J34OVeoK9GTOfCqUinRYqVCsY3wP0nfsNuUqF2uexdW1EURVGyoIKXIkpKySfPfo1hGMjMQ1I8IwQrpzpfcdrL20q3Jzui6a5/hSxeFjr3b5vDiyuKoiiKcyp4KaIO/HWI04fOpbei5IiUhB0843J3/zH3Ua566UwBjNAECBj+1SACivnnogKKoiiKkpkKXoqoc8dynyZeaBq+/q67hoKKB/Lp5nfo+eydePt5p2+vc2sN3ln6Knc+1j7XdVAURVGUa6mp0kVUUGhArs9h2A3uuK+V++sUD2To508x8P3+XDwbhW+ADyGli+X62oqiKIriSr62vEyYMIEWLVoQGBhIqVKl6N27NwcPHnR7zPTp0x1TQa/68vFRU0Gzq3GHBgQW9zyA8fEzaN0tho73RlGzUSIgqdW8Os26NPLoeG9fb8pVL6MCF0VRFCXf5WvLy7p16xgyZAgtWrTAbrfz6quvcuedd7J//378/V2PhQgKCsoQ5KgVjLPPy9vK4+Mf4vOh37ktJ4Tk4RfP88BzF/DxuzJA5tg+H0o3egVNUz2LiqIoyo0lX4OXFStWZPh++vTplCpViu3bt9O2retZKEIIypQpk59Vuyn0HHwnKYkpfDvqJ6RpAleCQKFJpCkYOOYs9w6K5Nr4sHLtZHRzINI+H2GpUbAVVxRFURQ3CvRjdUxMDADFixd3Wy4+Pp7KlStTsWJF7rnnHvbt2+eybEpKCrGxsRm+FAchBPe91JMFx2vw2CvhFCtpS99Xt1kCE+cccRq4AOgWQKYi4z4vuAoriqIoigeElDnOApItpmnSq1cvoqOj2bjRdabXLVu2cPjwYRo1akRMTAwffPAB69evZ9++fVSoUCFT+XHjxvHmm29m2h4TE0NQUFCe3kNhJFO3IS/1B8AwIC7KgtXLxD/I0wUaNUSpfxBa7gcAK4qiKIorsbGxBAcHe/T8LrDgZfDgwSxfvpyNGzc6DUJcsdls1K1bl379+vHWW29l2p+SkkJKSkr697GxsVSsWFEFL4A0IpCXBoBx1HUZidOWl6uJkmsQevk8rp2iKIqiXJGd4KVApkoPHTqUpUuXsn79+mwFLgBWq5VbbrmFI0eOON3v7e2Nt7e30303M2k/gbz0EJiX3JbLeiy0BUSxvKqWoiiKouRavo55kVIydOhQFi5cyOrVq6latWq2z2EYBnv27KFs2bL5UMOiSUqJjB4OZkwuz6SDz10ITWXJVRRFUW4c+dryMmTIEGbNmsXixYsJDAwkPDwcgODgYHx9fQEYMGAA5cuXZ8KECQCMHz+eVq1aUaNGDaKjo5k0aRInT57k6aefzs+qFi223WDfn/5tSpJg7eJi/DkvhNhLFspWTqF7/0s07xAHgPPZ0BoIX0TA8wVTZ0VRFEXxUL4GL1OmTAGgffv2GbZPmzaNxx9/HICwsLAMuUSioqIYOHAg4eHhhISE0KxZMzZv3ky9evXys6pFi20Pdjv8syaQ4/958+fcUE4f9UZoIE1B2GEftqwsxm3dohn23imWzyxBryciCQi+ahCvtREi6G2Epcp1uw1FURRFcabABuwWlOwM+CmqVn71DN+PO0/UBStXVmbMPLhFCElwqJ3oSCtWb5Pm7VMYPXMgPoH1ENaaBVpnRVEU5eaWnee3Sp9axKyc+jUfPX+R6Ii0RjWBs8AFQEpBdKQVAFuKxpaVvuxYX1YFLoqiKMoNTQUvRcziz38D6QhMciIlKTWPa6QoiqIoeUutKl2EHN/zL4d35W7aeNUGFfOoNkphdnzXRpZ/M4uzR6MJKOZFuwfa0aJXfywW6/WumqIoigpeipKIkydzfKyma9RuUZ2qDSvnYY2UwsY0Taa+PJw5H59D0yWmAZqeyqq5y6jV5Dfe/f1TgkuohIWKolxfqtuoCKncoBGanv3x17pFwz/Yj5FTh+RDrZTCZNmXE5nz8TkATMMxXsrxLxzZA+/cP+I61k5RFMVBBS9FSOkq1Wh0mz1bAYzVx0q3JzsxZfv7VKqjPlEXNZfCozh9+BxJCclZljUMOz+//w9XZqhlZBqCf9eZHNm+Jo9rqSiKkj2q26iIeWz8EN7o/SWJ8Xr6J2YHCQi8fExSUzSe++QJ2j94O0HFA9At+vWqrpJPtv+xix/GzeXAlkOAI0i989F2PPbWQ4SUCnZ6zOkDf3PhtPvfBU2XbFu6khrNOuR5nRVFUTylWl6KmFqNz/HpksM0axcH4son6DKVUvnf5JMsObaHitVTEEIQUipYBS5F0OqfNzKq29sc3Ho4fZst2cbyaat5vtVooi44XzbCnpridPvVpAnJiWpGmqIo15dqeSlqEmdSrloqb/90nIizVs6d9MIvwKRa/SQ0Dex26P7oJc4dPX+9a6rkg8S4JD4a+BVIMK/JP2naTSJOXeSHN2Yz/OtnMh1btmZjfPxMkhNdf6aRUrBzXSpSSkTWq3oqiqLkC9XyUsTo2qn0tYpKlrPRqHUCNRompW+zWKBijSQSYhOvXyWVfLN2zmZSkly3oJiGye8/rnM6BsYvMIRuA0ohhPsxUwe2nWfX2n25rquiKEpOqeCliDl5MJAlP4SydEYoYYcz53wxDEiK11j/yxYS45KuQw2V/HTm0FksWXQF2pJtXDxzyem+xyeMx5HKxXUAo1k0VkxdnYtaKoqi5I7qNioiIs9eYsLDn7J7faUrY12koGnbWP43+RTFStgB0HXYuCyYpIRkVs3cQM9n77yOtVbymm+gL6aZ9Wwz30Bfp9v9g0ui6d6A63Etpt3k/MmInFZRURQl11TLSxGQGJfES+3GsG/zf44NUji+gJ2bAnn5vmqkJAnsdjh11JtNy4LRNI29Gw9cx1or+eGOe0pjGqbL/ZomqNu6FqFlQ1yWCS6Z9YKmUeHRGIaRozoqiqLklgpeioAVU1dz7tgFDHvmh5ZpCMIO+bB2UQhhh3wY9UA17DbHj10NuCxaZMpmKpYazB09YhCai1wtUjJg7ANuz9PtiY5ouvu3hjNHwunu+zBh/53JcX0VRVFySnUbFQG//7AWKd13FXz9ZlkSYnXSVpg2DZPG7esXQO2UgiBlCjL6BcDGy5+eBCqxYWkxNF2iaRK7TeDtpzPim6E0v7Oxm/NIytUsg27V3bbggKP76NlbRrIs6efs1dW4ALbtICV4NUHo5bJ1fF6SUoKMBQSIQBXQK0ohoYKXIiA6IjaLEoKE2Cs/ak3T8C/mR4d+bfK3YkrBSV4J0pG/xdsXXv/mJCcPhbPxt2AS43TKV0+hfW8D/2otXZ7CMAzee/Rz1szelGXLSxpbip1fp6yk1+CuWZaVZjwydiwk/wakBUYC6d0ZEfw2QnPelRUfncD2P3aTnJBMlfoVqdW8eq6DDCklJM1HJnwPxlGkhP921iLyYieKVbyTBm3qoIkUx2uqFUMI52OEFEW5PlTwUgSUqVySS+eikB4M1BSawDfQh3eXvYqPX+5WoFZuHNK2H8efsz19W+VaKVSudSFjQSMcLJWcnmPeh0tZO2cTQJatLldb+NmyLIMXKW3IqCfBtpsrgQuAhJTVyEuPQugvGYIEwzCY/vps5n/yG7YUW/r2qg0r8coPQ6nRpKrHdcxYF4mMHQ9JMwHB9rUBfPFaec4c9wE2A5sJLasxaMwJ2t8TBViQ3t3BtwdC6KCFgqWuaqVRlOtIBS9FwN2DurBv80G3ZULLhxBSqhi3976VyvXKs2/TQQ5vP8at3ZtSunLJAqqpkm+EF+6mN18p5zxgNewG8z9ZSha9j07Zkm1ZF0peCbadLnYaYD8MSQtJ1e5j+huzWTVrAzERsU7HcZ3cf5oRbcfwxd8TqVg7++txyZTNlwMX2LHen9cfrYa85jIXzxlMGFwJ05B0vDcaUn6FlF+vvMJ6dQh6FeF9R7avryhK7qngpQhYO3eT2/3VGlXmy+3vcXTnCd556GPOHj2PpmlIaQKCDv1u58VvnlUtMYWY8O6ITPjKXQmw1AStVIatpmmy5MuVLPv+T6LCo3N07RpNs24BkUnzccwPcN2iE37wFwa1XUZygvtlCkzDJCU5lZlvz2fUj8M8qqOUEpKXIRN/SA+ipIQpb5RHSkfm4IwEIFm/pBgd+kSTqZHFOIaMGgjFpiB81DpPilLQ1GyjQm7fpv/Ytuxft2WO7TnJf1uPMLLDOMJPOPJzmKZ5+U1bsnb2Jt568KMsB/0qNzBrY7A2BVwlqJMI/8EZujqO7w3jnuABTB42lWO7wnJ86f/9MDTrQkY47gIXkLzQTWQZuKQx7SZr524mNTnrdZbSuolkzIuXu60cjuzxJeywD9J03f3z5KvnXNYXJDJ23OUPAYqiFCQVvBRy342emXUhCZOf/57U5FSnYxlMU7Lttx3sv7wCsVL4CCEQIV+Cpc7lLWkzyxz/ioCXEb53p5dPTbUxuNkrHgcLrtwztBu+AR4MZtXL4O7tZufGAKIisrdIqGEziI9OyLpgyqr0bqKrA6iL5903PAcWM4i9ZMnc6pJOgnkOUrdl3iMl0n4Umfo30n466zoqipItqtuokEtrScnKkX+Pu92vW3RWzdxA/dtq50W1lOtAaMUhdB6kbkAmrwAzASxVEb73IywVM5T9cthUDFvukszVbVWTZz98zLO6+fZFprru3lw+q3i2r2/1thAQEpBpe3JiCkd3nkCaJlUbVcbXPgNHEJfxfouXsmc69mrxMTov9alBryciee7tM66DGONshm9lykZk3HtgvzIOTVpbIIJeQ1jrIVPWIxN+BNsuEBbw7ojwG4Cw1vLkthVFQQUvhZ63r1eenEeaJnGX4vLkXMr1I4QO3u0R3u3dlls5fW2ur/Xf1iN8+eJ0hk1+OuvCPl0hsenl8SbXtv7pmGbWWX2vplk0Oj3SFi9va/o2W6qNGWPnsvjLFSTFORae9PKx0u3hSJ561cTHL+M5ajZKony1ZM4c8yYt/9HV0sbB/DqtBFXrJtG9v/P1oJZ+t50D/56lfI2y3PmQTqj//zIXsm1HXnwA6dMdkheRHkxJHFO2kxZAsU8QPmq5DkXxhOo2KuQ69W/rUTkvH6v7AkKoWUc3EXuq+1YHT0gpWTrld7b/vivLskJYESHfgU8vMo7L0cC7Cy37eBAApR2ha/gH+fHI633Tt5mmyfj7P2TOpMXpgQtAarKNpdODeLVfNWypGQMUIeDZNzO2mjivu+SXL0s5nYkVc0nn69GHWTVzAzPGzaF/7Z+Y/3UJMs/8MgHb5cAFMrYCGYCBjH7RkcBPUZQsqeClkOs3ujdWb/eBiaZrdH2ig9vEY6Zh0u3JjnldPaWIk1IyqtvbDLvttSwXaxRaAFqx9xElNyCKfY4o9hmi5Fq0kM+487HueHs4263hHXX5bPM7lKlyZebUtmX/8teS7U5zHZmmYN/fAaxeUCzTvls7xXH7XTFZ3KPg7AlvIs9l/jub+m5ZUpIkpt3ENCSmKfjmzXKsXZT5Wu6nskvAgKR5buuiKIqDCl4KOYvFwpQd72Pxct4DKDTB63NG8PhbD1G6ckk0i/Mf+QMv30OFWtcvTbtSsFz9HuTUwX+OMKLdGOKi4rMsK/QSCJ+uCJ9uCL1M+va3fv0fQnM+sMRi1Xnx60FMO/gZH6wel+l3ddl3f7oNzoUmWT4zNNN2w4DipT1rhZImmJd7vOJjNT59pQIrZmU+pxCSmZ+UzkHOHBOZuj27BynKTUmNeSkCKtetwG+JM/n+1Vks/34ViTFJWH2stLu/Ffe/1IvK9RyDNT/d/A5Thk9j/bwt6cm/QkoH89CoPvQZ1v163oKST6RtLzJh5uXBoV4In87g+yBt+rRk/S9b8uw6pt0k4vRFln27igdfuSdH57ilY0O+3fMRnz/3HXs2HsA0TCxeFlrd3YwXvh5EsRJXxsUkJ6aw44/dJMQkUr5mGc4ePe82K7A0BedOegOO/EbSBKFBfLTO36sDPaid5Is3ylOyrI2oCxb+XhtISqLz2VFSOhZDPXfSi3JVsp7KfeU4OHP4AsmWo9RqVt3j4xTlZiRkEUvuERsbS3BwMDExMQQFZW8Q4M0iOiKGU/+dxcvHSo1bqqJbsjdFVck5KU1I3YxMXgpmDOgVHbOBrDXz/lrxXyHjPyLjTBsNhA82/2+4t9QnpDrNjisBgaabmIbA2WBWVyrWKc/U/Z/ktuouSSmZO+lXZr4zL8PYFk0XmIb7t7Ky1Urw/T8N2b7sB1KTTbavDWT1gmIkuwhC3NF0efm1ce3r1QepUifZbZmrmSbMmFSGnz8tTaO29Rgz7yWCS6j3MOXmkZ3ntwpeFKWASDMOGTXIsaJyekBx+V+/pxCBr+TZejkyZZ0jA6xTGgh/kn1XMKjJWM5fNd1e0yR9B1+gWv0kzhzxISVJY8G3JTDsngUxwSUCmXdhap7cgzMzxs3lx/G/5OhYTdfoM6w78z9emse1yszL2+StH4/hG2BStW4yXt7u32bT3oXjonVG3ludU0f9qdawMpO3TUDX1YcL5eaQnee36jYqxGIvxfHhU1PYumxHes6OyvUqMOSzJ7mlY8PrXDvlWjJm5FXr+xgZ/038HvRy4P9o3lwr4Xuc5TZxMEHG46uv4KdjXxJzKY7fp61BmpJNv25j+9pUqtdPpt/w81gscGiPD7s2Zt21IjRJqUqup+5HnY8mPjqB4mVD8A/yc1nO5fEXYpj17oJsH5dGmiYLP1uGZtEwnayZ5AlNl9RslMi5k4HERRtOs/NquqT2LYn874EaAAQUs3PvwFj6vfEcupaCjB2X6Zi0mNU/0OCtH4/zeOu6HPn3OFt/28FtvVrkqK6KUpSpAbuFVNSFaB6pMpjNi//OkGzs5P7TvNJ5PMunrr6OtVOuJe1HIGUNzoOJy2USvkFK5/ulGY1MmIYZPRwz+iVk0kKkdJ4dV0oJqX+7vRaATN0KQHDxQO5/qReNOzRg/6ZDHNvnzcTnKtOzaiPuqdHgcuCSdauLNOHufjsxY17LsH3PhgO81GEsD5QdyJN1h3NfySd577HPuXAqMstzXm3tz5swjJwn1pPSMavOk9XXndF0ia+/ychPwhj1xWF0XaLpMlOZclVSeP2bE4SUdHTJxUdbmPFBcT4YfAbp8xBoZZydHgDdAqUr2GjVJRZN11g3d3OO6qooRZ0KXgqp13tMJDnedWr3jwd95dG6L0oBSVlHln9u5nnH6srXkClrkRfaIuMmQvIKSP4NGfM/ZERHpM3Vkg6ePKAzltm6dDv6VbOQTENcHg9ybeCS+dyaJqnTNJFO90VB0i+YyesA+GvpdkZ2HMfejf+ll7XbDFbP2siQFqMIP+FZXhO7zc7iL5d7dltuCCHcBi9CE1RtWInaLTIOmNV1SfveUUxecYhKNVNp1i6eT5Yc4bauMWia43wBwXbuezaCT5ceJjjUoGWX2CsnkLBq5gb2b9oIZrjbOtpt0LBVPKZhkhCd6LZsanIqf8xYx2t3v8uw217jgye/YP8W9yvMK0pRoLqNCqH46HgO/XPUbRlpSma8+QtPT3ikgGqluCVteDbwNWPAKe1HkFHP4WhFkWR4epsXkVGPQYk/ENqVNPlCCKT1FrD9i7vFEIVX8wzf21JsHo25adUllp2bAtIHulq9TO588BIDx5y7MrYj/kNs2m1MeuIL5OVFQK9mGo6Mzl+PnMHYeSOzvOaPb/7CmSPuH/qeEJogINifxNjE9Bl3V5Om5JHX+nLHfa2YMW4u8z5awh13hzPk3dP4+We8iZqNknjju5OkpghSkjT8Ag3ShqeYBvj6ZTy/btFYPWsj9cZ6VlfdolG+ZlmX+yPPXuKVTm9y6uBZhOYIyg79c4SV09fS67muDP38qTwbQ6UoNxoVvBRCezYc8KjcXg/LKQXAWp+sunHAC/RqGbbIhOlkClrSmWBegqRF4N8/wx7h/wQy+krOkNRkwZbfgwg77M2J/3w58Z8vCfEbKVfjGD2e6UKHh26nepMq2LNY78jqbWHER6fx9jU5vMcXaUK1ekkEBF8TCNiP89eS7cRedL3khGE32bz4b6IuxBBSKthlueSoP1j0+XyQuX8Qm4bJo2PvZ9m3f3Ji7yk0XcM0TMe/pslT7zxMuwduY/XPG5n59nwA7h98AR9f1601Xt4SL++Mr5umw8lDPhm2GXaTQ//Ggl4RjNO4akayWGHPXwEYdpPuAzs5LSOlZFyf9zl71BHQpbUmpQVkv365kop1ytN76F1ZvyiKUgip4KUQ8jQTqavEdcp14HU76OXBOIfz1hAdfPtkaEEBIPkPshy7krIKcU3wgncX8B8ECd+wekFxJr9ajoRYnSsPTAHEEh0Rx75N//HHjLWMmTeSoNBA4qLinXataLrGPUPuIjjU0aLTsKW7FZ0Fpw+dQ7doTls40piGSfjxCy6DF5k4l+Nb3yExLveLFmq6RrFSwdw9sDO9Bndl86//sGH+FpLik6lUpwLdB3aifI2y2FJtfDHsyoypclVS0bLRwW4YEHHGyr8bMv4sNU0QXCIY4f8kMvZN58faITLcyl9/BNFvdJ/0HE3XOvDXIQ7+7b71de6kxfR6ritadiqvKIWEeroVQo3a1cvyoQBw5+MdCqhGN4+khGTWzNrImtmbiIuKp1LdCtw9qDON2tZz20QvhAbFPkNeGgAymYwBiQaW6ojAl50cmdW4JXn5fNdeTyACR7JhWWneG/orGYOWq46+HKT8u3ovP7+7gDfmjuDV7u9gGmaG3y+hCao3rsKjY++H5JlguF6lXEoQXnXwDfTJMvcKgF+Qr/PzmJeQseOQMot1uVxI60oRwnH3QaGBvPf7G3j5OGZE3XFvS+64t2Wm4/5esTNDi1FCnI6Xj2dZeA07GHbB+8MqpS/smMY0JZ37twXf1pC6D5LnIdERGEjpGPAcF6MzcWh9QkoXY+X0NezbfJAez9xJ2/taZcjH9M/KXVm+B0ScusjZI+Eqc7ZSJBVISP7FF19QpUoVfHx8aNmyJdu2bXNb/pdffqFOnTr4+PjQsGFDli1bVhDVLDQsFgsdHmrjtoxfkC93DmhXQDW6OVwIi2BQo5f4+Nmv2bV2H0d3nmD9L5sZ2WEcnzz7NabpPpgU1oaI0MXg+yCIy1OFtTKIgBcQxWcjNCd5DSz1cP9nqoO1gdM9Ukq+e2Pb5XjFfZeLNCVLvvqduq1q8sW2ibR/6Pb0lrvQ8sV57M0H+XDdm/gF+kLAcNfnuRyrTH61JI071Hd7WSGgQu1yVKpT3nmBpIWAQZXayfj6ez7LKC2ILF42hPq316bZnU0YNvlpfjj8OVXqO2/JuNrFM5cy1Hv1/BCMa2IXKeHcSS+OH/AhIc7x85Fo/L2mBC/0rM2+bde0ulg0qjWqTJt7b0UIDRH8DiLkO4R3O9ArIPVanD3/EC/1bc7+vyUXz0Zx6Vw0ezcc4N2HP2FM7/ewpV5JKGga5pX51W4Y9pzPzlKUG1m+By9z5sxhxIgRjB07lh07dtC4cWO6du3KhQvOZxls3ryZfv368dRTT/Hvv//Su3dvevfuzd69e/O7qoXKy9OHULeV86ysXr5WPtvybgHXqGiTUjLmnveIOBXpaOyQGccYLPt2FYs+X57leYSlElrwOLTSOxGl/0MrtR4RMDhzd1Faef/+uBt0CybC7yGne478e5yzR8I9nqGTGJtE2IEzVG1YmVEzhrEsaRYzT06h7/AeRJ+PYd6HSzh9+Bya713g+4ijteCqc6f9/8cPSvPb1Eg+HvQ13Qd2ctkiJSU8Pv4hMCOQtoNI81LG/fZjgI6Pn6THgIsIzbMbSfvZRIVHE1KmGBOWv0bPwV0dgZcHipUuluE1W/h9CZIStfQAZuOyYJ5uW5vHW9fl2U61eaBBAz567RliWEullvOQmuPvUtO19LWaGrevz/t/jsHq5WhFEkIgvNuihXyFVnI1eskljH8snrNHUzOOyb7cMvb3ip38NP7Koo11W9XMkCLBmYBi/pSt7npatqIUZvmeYbdly5a0aNGCyZMnA46l6ytWrMjzzz/PqFGjMpV/8MEHSUhIYOnSK1kwW7VqRZMmTfjqq6+yvN7NlmF348Kt/Dj+FyLPXMLHz5sO/dowYOz96U3jSt7YvX4/L7V3P02kRIVQfjr+RZ5mRJVSImNeg+R5OJoD0v5cNcBEBL6G8H/M6bHb/9jFqK5vZ+t6U7a/T41bqgKOMRNTX5uFaUp0XcM0JaZhctfTnRj2xdPMfvt/NL/tTyrVTEZKOLrXl2/Gl+Pgv/7p53t32atsWLCV5d+vQtM0NE1g2E2s3hYGf9CZ7g+tgcv5ZkAD7w6IwJcQlhqYse9C4k+AndRkwdjHq7BjfRCa5li92VVXWAYCZhyeTNlqpT1+DVKSUnig7EASY5PSt1VvkMiY706wcVkw344vT9oSCldfp1TFEnzx90SCSwSxb9N/HPjrMLpF55bODanaoJLba+5at4+RHca5LeMf7Mfc8O/w8rZiGAaP1XieiNMXna7pJDTBg6/cw2NvPohu0dWsI6VQuGEy7KamprJ9+3ZGjx6dvk3TNDp37syWLc4XhduyZQsjRozIsK1r164sWrTIafmUlBRSUq7kO4mNjXVarqhq06clbfpk7rdX8ta/q/ZkOcYg8vRFzh27QAU301uzSwgBwe+Ad3PHzCP7f4AAr1sR/gMR3ne4PLZ05ZLZulZg8QAq168AwPLvV/Ht/35K32c3r3zKX/H9arx8rCz9+gw/2FwPpNUtOpsWbmPEN8/Sb1Qf1s7ZTNylOMpWK037e73wl0Mg9erX04SUtcjUv6D4bIRPN2TidAC8fCRv/3Sc9UuK8dtPoZw74YWXt+TsCfeD1wXwz8qd9Bzc1ePXwdvXm6cn9uez575N33Z0rx+Ptap7zZmvIuFCWCQ/jJ3DC18OokGbujRoUxdP7d34X/oYHVcSYhI59d8Zqjeugq7rjFv4MiM7jiMpLjk9gEk7R4nyxfn1y5XMnriIgGL+dHuyI/eP7EnxMiEe10lRbmT52m0UGRmJYRiULp3xU0/p0qUJD3eesyE8PDxb5SdMmEBwcHD6V8WKWfdpFxUJMQmc2HeKyDMXr3dVijzHw8GTLLM5SzvvjhAC4XsvWolfEaX3IUrvRys+w23gAlChVjnq3VYbTc/6z1wI6DOsO1Yvx6f6GePmuiwrpWTJlN+z7LaQpklCnKP1omy10vQb3YdBkwbQ49ku+Is3cXSHXft6GSCTHbNxrLeAtSWOZQ4c2Wc79Inmg/lHmbn9P1788EyW94UQ2FI9G2x7tZ7P3skLUwYRUMz/qq1Zr++0YuoaUlOcLXbpXlbJ89JcCLuSlbhGk6p8u/sj7hvRkxLli+MX6EulOuXxC/Tl4tmo9Jaj+OgEFnz6G4Ob/c/jpICKcqMr9HPoRo8eTUxMTPrXqVOnrneV8t2FU5FMHPAZ95V6ioENR9Cv4rMMu/01dvy5+3pXrciqd1vtLAc/BoUGZqt7IieEsCKE591SQz97EouXxWUAkzYmo+19rXn41XsBOLLjOJFnLjktn8Y0TPyDsxhDIoTzVqjUv8A8i+uxPAbY/gHjJCLkS/BqdXm7TnpjsfCl6q1jsVjdvxbSlNRqXt1tGVd6PNOFOee+5c1Fr/DiN88SFOp8XNLV7Kl2YiOz3/rb4PbaHpW7NntuyQqhDHyvPz+f+prFMTMILB5ASlJKpq4k0zCJvhDDRwOz7npXlMIgX7uNSpQoga7rnD9/PsP28+fPU6aM84FkZcqUyVZ5b29vvL09y3tSFFw4FcnQlqOJjYzN0IVxcOthRnV9m/YP3kZCbCKapnFLx4Z0eawdgSFZv+kq7jXv2pgyVUtxISzS5RiD3kPvwmK9sbIP1GxajU83vs2UEdPZvW5/+nZvP2+CSwRSs2k1ug/sTItuTdLHRSTFZ5567UzZamU4tutE+qDSa0kpueupjpl3GCc9q7xxEmGpgig+DWnbi0z+A2QSwlITfLoTrPnToV8Yq2ZucPozAfDysVKsZM7Hvnl5W9MXRvzufz96dIyvhwODr1bB1YyrqwhNEH3BdWB0cv+pDMswXMs0TP5dtYfTh8/lademolwP+fpO6+XlRbNmzVi1ahW9e/cGHAN2V61axdChQ50e07p1a1atWsXw4cPTt/3xxx+0bt06P6taaHz3v5+IiYzNtCpu2gNkzexN6du2/raDH8bN4Z2lo7PV/65kpus64xe9wksdxpEQk5j+sNQ0gWlKWnRtwkOje1/fSrpQ45aqfLjmTc4dP0/EqYsElwyiUp3yLgdxlq9ZNuPYYBeO7TpJlYaVOLH3FKZhIoSk0W3x1GyUjC0VqjUbQKlKTsbdiKxXqL62nLA2QDiZEv7sR49x6J+jnNx/2ukpUpNtPN1gBF/v+oDKdR3jeUzTZPvvu9j62w7sqXZqNa9Oh3634xvgPugoX6ss/2094raMf7BfjlbMDijmj8XLgt1NF5cQgtByrsesHN15wqNrHdt1QgUvSqGX7x8TR4wYwWOPPUbz5s259dZb+eSTT0hISOCJJ54AYMCAAZQvX54JEyYA8MILL9CuXTs+/PBD7r77bmbPns0///zDN998k99VveHFRcWzft6WTIGLK1JKkuOTebX7u0w/9JkarJdLVRtW5ru9H/HrlytZNXMDibFJVKhVlp6Du9LhodszJBHLLwkxCaSm2AkKDcj2rKayVUtTtmrW3VolK4TSrEsjtv/uqhvSMdPGNE3a9G7JLR0bcmDjYkZ+fIjy1VIxDEdQJ8REzKjtiOD3Mk4F924HeAOuFxZFKw3WxlnWNah4IO+vGsOD5Qa5DLYMu8GYe97jh0OOlaxf7f4uJ/edQr/c5fTbd3/y1cgZvDHnRVp0u8XltfoMu5sJj3zqtj49nnE/DskVL28rnR65gz9mrHPZimQaJk27NHJ5Dqu3Z8n81EzEG5s0EyB1I5gxoFdC6tURKb8h7SdAC0D43IWw1r/e1bzu8j14efDBB4mIiGDMmDGEh4fTpEkTVqxYkT4oNywsLEP66ttuu41Zs2bx+uuv8+qrr1KzZk0WLVpEgwbOE3HdTM6fjMgyq+61TFOSnJjCsm9X0f+N+/KpZjeP4mVCeHz8Q478JAVo62/bmfXuAvZvcawiHVI6mHuG3MX9I3vmy8Ooda8WToMXTZPoFskb3x3n7Alvjh1azcipr2JGfgLS0WrgiKkuRxIpq5BRz0LxGY4sw4DQApH+T0PCFy6vLwKGezy2Z/7Hv2XZSnT2SDhnDp/ltR4TOXfc0S199YDj5PhkxvR+ny+2TaRqw7KQ/DsyaSGYEaBXQPjezx19b2fx5Jrs35J55W+QlK2SSv8h85FmP4Tm76SMe4+83pdNi7aREJPodPCuEDCu9/t8sOZNqjWqnGl/k44NsHpbsKW4br3x9vWiYVvVCnsjkfZjYD+OxM+xmGrC10BSxjKAY4iqQCZ8g/TugAj+GKFlv5WvqMj3PC8FrSjneTl3/DwDqjvvbstK7RbVmbx1Yh7XSCkIi79YweTnv0/vokojNEHDO+ry0P96M+2N2YTtPw0CqjasxKBJA2iYi67CTYv/Zlyf9y9/52hpEUJya6dYHh15npqNkjDsYLMJfIJudQywdbMGkwiZhvC+Pf17KU1k/IfI+O85fdSLlCQLZSsn4R+kIwJfdpq7RhoXIWUtyESw1ACvlgih8XKnN9m5JusklvcM7cbiyStc7tctGh36teTlDzeCfS9puXQcA4UN8GpLktGdKS9M5o9fimMajm43ISRt74nihYln8A+SiMDXEf6PZlkfZ8L+O8Mrnd/k4tkop/s1XaNUpRL8cPhzp2sWffniNBZ9thxnb+tCCB54uRdPT+yfaZ9S8KTtMDJ2DNi2Z104Ew2szRDFpyFE0WlJu2HyvCh5q2zV0lRvUoVju096NK3yau4+jSk3rgunIvnyBccigdcOjJWmZPe6/RkG4gL8t/UII9qOod+oPjz57sM5um6Lro3Tc4a8+tVJajRMpFiogX/QlZY/3QKaLsG21c2ZAHRk8tIMwYsQGqsWt+THN49z9mhE+vm8fS2kJC2jZIVtdB/YmV5DuuIXaEXGToCk2YCd9AE5ekUI/gAff88G7B/bdTJTAHg1w26yYd4WXn4vbTX2tHt1BGUn9mxj0ffn2bYqCF9/g7KVU7m1Uyw9H79I8VJpf18CmfSLy+BFSskPY+fwywe/kppscyS3q1SS8YteoXrjKpSuXMLtgOm0hSy3/77LaRfXoPcfJSo8mrVzNqNb9PQVsw27QadH7uCJt/t58lIhpWTf5oMc3n4Mi5eFFt2aUKZKKY+OVbIm7ccxLz2INBKytejnFSbY/kaebwUBA8H/mfSWzZuFCl4KmcfHP8QbvbLXgqJbNOq1zv2qvErBW/H96str2GS3gVQy+/0FtOl+nJotOoJXq2xlWfXy8aJl96Yc3bGZO3rEuHyD9eyUBpjRGbbMnbTYkQTvquMNOyTGOYKA8BMXmPbGz/z50zq+XK1jlUu58hpc/tc4g7w0gHsGT+Cvpe4/vepWHd9AX5eBSxpbikRKI9N9bV4exNvPVAEJxuUWl2P7dY7s8SMlSWPgmHOXj5FgOM+lIqXkgbJPZ5wxJOHCyQieveVlHnmjL+3ua50hs6/Te7Ho/Lf1iNPgxWK18NrPL3LfiJ78/sNaLoVHE1o2hDsfb0+tZp5NGT+x7xTv9PuYE3tPOQLYy6047e5rzYjvBnu8zILimoz7BGmPR8v1MLl4ZPzHYJxBBGcvm3Zhd3OFakVAqx7NeOWHoXj7eYMAi1VPz9XhimE3s5VhVLlxnNh/KtutbA4CIWDVzD+QUY8hI+9E2lxPo3Vm7IKRdH7AlsNPhlfTQa+Q/l3E6Yt8N3qm4xs3tyZNiTCPY5VLXBQ0ATtNW/1OiQrF3dag2xMdqNqgotuEfUJA+eopmQKXSxcsvDu4MoZxJXAB0ruN5n9dis0rrmriFv7I5JVI274M3Tcj2o91O9V55lvziYrIOkeMlBLN4v6HUrtFDZ6f/DRj541k6OdPeRy4XDgVyYi2bxB2wJEAUJrS8dJL2LBgK2N7v5/lAqRKZqZpYhoXMc1ETCMWmbwiDwKXqyTNRdpurvX/VPBSiCTGJTF74kJmjJ2LLdWGb4APNZpW46FRfeh1OTi5+s057Q1u8MePOx3gp9zYkhNTSElMzTI4dcU0BDvWXZ7lY5xGXuqPNM56fLzFYuGxN1zPvvGcgfC9Mlj89+lrPW4FatfrIna3PZ4GpPzOF9veJNhFPpcWd93C8K+eofvAzm4fvFJK7nkiMtP2FT8Xx24XIJ3XWdMlC7+9akq4eQoZ/TzyYh/kxZ7I1B2YpsneDQecHn+1KS9OczsdGhxdR7d0auh0XEtuzf9oKQmxSU5nPJmGyc41e/l31Z48v25RZZqxmJeeggv1IKI1XGgCEbcjRF7/7HRk0rysixUhqtuokIi9GMeLbd/g1MGz6Z/Ek+KSOfTPUc4dDefjDW/R4q5bmP/xUvZt/g8hBLd0ash9I3rSpIOaqVWY2FJt/DBmDr9OWUlSnGcJ41wx7GkPXANkAjJhOiLoVY+PF0FDIcKDN0WtHJjncTpo1+9xhPVKBtkzR895stICAMHFDWSWH/RNihXbz9xD5YkMi+Lwzlg2/FaKS5G1eXriI9RqXoPUFBsz35nvtqXHx9/krkcyL7Vx4B9/t3UwDcGB7S5mfdgPIy89ysXkz7K6CQBOHTjLoPcfZcqI6U73C01g9bLwwm2voVt1buvVggde7kWdW52vMH/+ZARLpqzkn5U7MQyTxu3q0/O5ruk5b671+4y1Lqdqg+MD0apZG2jWJetp7Dc704yFiPYg46/Z4yZFQLbODxfDLaxZEMLt3WMoXyettSwKmfAjJP0C5kXQioPv/Qj/RxGa+xbKwkQFL4XElBHTOX3oXKYuBNMwiY9O4J2HPmbKjkm06tHsOtVQyamTB06z4OOlrJ//FymJqVi9LSTGJWV/mEsmEv+gq4MJA5IWQzaCF00vh6mVBfOcm1IBiBKLkLETIXkJcHltHy0U4T8I/B7PWDrYH+Fh9HLhjDVT8/rxAz4s/K4EW1YGYxowekoYzds/C+iUKGlQootO6y5h4OWNCHFkrv3o6Sms/nmj22slJ+js2+ZP/ZYJWK56Z9R0iRDgrqHD9VhJCdjwllPwaG0sKenzQndOHz7Lkim/X1kM9PKwJ2lKbJfXTjJsBpsXb2PTom28PmcEd9ybcYHWrb9tZ1zfDzANMz0gOfXfGX6dspIR3w6m2xMdMl07ITrBbf1Mu0lMRFyW96EA0S84CVzyjqZByXJ29m7z5/t3y9H+XhsvTQ/DK/HRyx8kLgeh5gVImIJMWgChsxF60UhQqLqNCoGYyFjW/LzJ5Sciw25ydNdJ/vhxHXs3HiDqQkwB11DJqR2r9jC46cusnL6G+KgEbCk2x4BNNw9KTddAgNCzfhieO3nNNEqZgwdP6ArAVd4SK5RcidCKoRWbiCi1GVF8NiJ0AaLkBoT/E5m6iNo/dHuW60SlWTU/JMMjf/PyIIZ0rcWfc4sTe8lCn4GRNG2bdk9Gxn9TtyBjxrJ5yd+smrnB9dihqy7w/rBKRJ61YhqOT7YATdq4fwDpuqRZO/eva4DvbvCgqyCkVBBCCIZ9MZBPN79D50fbUf+22lSqUz69nlcHUYbdRJqSiY9+RlzUlXpeCItgXN8PMGz2DO8baeU/enoKh3ccy3BtIQSh5d1/MtctWrZXK78ZmSl7IXVT1gVzyW6Htj0d7/frFiXwwaPPXv6gce2zwgTzPDJqYKaxWIWVCl4KgWO7T3r0Zj/p8S94se0YHio/iLcf/IjIs+4X11Our6SEZMbf9wF2m5Gt5IMBxbxo3MafUuWyGvEniLloJTXlqqezXi7b9dR0X7Qy/0LAcBDFAC9H6n7fJ6HkbjT9ysNMaMEIr6aOdP7CecNu3ZY1adGtiUerXV8M92L3dsf6SNGRGQfOevua9BkY4WZAsYmZuIhPB73j/iJXvY9HnvPiuTtr8/07ZTl1xJuYSzo1GiShW6TLmVWGIej7TESW91KtftZrjI2a+UL6/+u1qsXI75/j4w1vkZyQ4jKglVJiS7bx54/r07ct/foPTMN02Vqk6YKFny3LtL3HoDvR3IyxMuym8/WqlHSm7RBEFUxCUF0nvXVVmrBusT9hh12lDjDBfijDWKzCTAUvhYDVK3u9e6ZhsnHhVoa1fpWo89H5Uykl19b8vMllNlV3Yi8ms3dLPBdO28iqb8liNbFY08oIhJ9neT6uJqUk4vRFzoX3wR68Ea3MXrTS29GCR6Flc4kCcHzCf2PuCG67x7HgoaaJTD0q+uXB5p0eaUvj7pMRQWNZOadChoGzdZom4B/oPujTNLJsFblWQqzOvK9KMah9HR5o0ICR99bAbtMuBwKXp95wOceNkAydcJpGrTN3t6QkCaIv6hiXBxx//GdTNDetZQ3b1qVJ+8zj0xJjE7kQlnkg8dU0XXBk5/H073f8udvt2BXDbvLP77sybe/9fDfK1yzrMrC8e1Bnajat5rYuN73o4bheMT1vGQacPX4lWNF0yfolxbI+0H4Ieakf5qVnkKnbCmVLjBrzUgjUal4d/2A/EmISPT7GsJtcPBvFrHcWMOSzJ/OxdkpOHdy2B90iMOzZeeNwZLu9MhDXNV2XV+Vo0TC1Opw4eBtJCQcoW700Jcq57yJITbHx0/hf+POn9USccgxk9QvypfvTnXl07P25yvfhG+DL2HkjOX3oLJsX/01yYgqG3eDs0XBiIuIoXbkkdz3Vkbqtajm6nSyPcGDPKaR5JZ+L1Zr16yYlVwVvrgldpI8pyaIkILF6mXS+P4p7B0VQqWbGAZiHdvky65NSbP0jGNMU+AUa3PXwRR4aNoWF515ieMcDHN8bll5et+r0ffFuBk50ntjO4tGHF4HXVWsbeRQQO3lg+Qf788nGt/nyxWmsnb05vcU3sHgA97/Uiwf/d48Hdbl5SfspMNwv3pmXLBZYPuvK37GmSRLiPG2TkMiUNZC6Brtoi7XUFwjhWcLHG4EKXgoBLx8v+r7Ygxlvzs3WIE7TMFkxbTXPfvRYgSwaqHhOJi1Fty0BQvB46g14XFZoEk2XPDDkAuDN6qWd+OHdFMJPjHHsF4KWdzfluU+eoGy1jIs1SilZ+Okypr42i5Sk1Az7EmOTWPDpb+xcs5eP1r2Z5UrMWalQqxwPvOzZA1G3WBDiStK0Y/t9MQ3c5ssQAg7vdl9HzaLRuX9boiNi2fbbDjRdc9tqAQLDALtNpAcuUjqutX1tAGMeq4ppCkzT8bNKjNNZ+F1JtvwezCe/fs7XOzcgNM+XLvH29aZJhwbsXr/fzbg3I8Ng/cbt63Nk5wmX5XWLoFG72k73BYUGMmrGMAZ/9Dgn9p7C4mWhZrNqGYIjJTMpJTLh2wK5lmk6WhVnfVKKU0d80rfb7YJyVVLdHJlRWleoZl/PiS2DqXrb1Lyuar5R3UaFxMOv3cudj7UHrjSpe5IqIzkhJcNAPuX6k6m7kDEjad4hxqMWlKuOzLJE2krJAcUCeXvxE1Rv8yNL5r3Ne4POEH7iSteDlJJty/9laMvRhJ/ImBF21rsLmDJieqbAJY1pmBzddYIFn2QeM5GfmnVpjLzqNbh0wcqGZcHp3TLXstsdrSBH9rhevE63aISUCuaJt/vx1uL/8cbcEVSqWz7LJHCmIVizsBjxMY5yhh2SkwQTh1bGsIv0BHZXlw8P82LaxFBIXu7hHV/x0KjeLgMRzaJRqW55mndrkr6tx7N3un1/MOyS3o/OQ8Z/67LLILhEEI3b16f+bbVV4OKJxB8uL2FRAJeK0/h4ZAV+eL/MVVslVi9Jxz7O18VyR9OhfMWNbFqQ/d/N60UFL4WEruuM/P45PtvyLl0f70DTzg2pWKe808XZMhxn0VQ67xuMTPgeELToFEv5asmOsRN5pPvTbRj90zBmn/mGpl17EJ9Qna9fdv6Gahom8TEJTHv95/Rt0RExzBg3F5A0uDWeXk9E0u3hi5QomzGQkabk1ykrC7SvvNMjbQgqHphhPMaXr5Un/JQXxjXj2U1DkBCjM3GIm+SMAtre35rJWydQolxxLp6N4uuRMwjbfxrTgwHUdpvG0Ltq8kizunzySkUO/utH+95RlK7oKugT/DkvhITo0x7d79WadWnM8K8Goekamq4hhEh/HcpVK82EFa+jXzX+qFz1MvxvxjA0XUO3XIli9Mu/a8+OP0O9ZlHI+EmQ8FW266NcIc04zLjPkHETCuR6djusnF2cFbNCydgSKxjxUViG9ceyw2KFXX9MLTTjX9Sq0oXYkX+PM7jZKy73axaNO+5tyeuzRxRgrZSsmOENSUtUFR7mxSv3V+P8KW/SxrM448gz4thfp2kCT79+jrKVU7ClaGz4LZgfJpXBz18y5/wcLJYrn5J//XIlk5//3u0bkm7RmR/xPWcOhzOi/RjKV41h9JcnqVQzBdO8fG0T/pwXwuejK5CafCV4WJrwE96+BddPfnjHMf7X5S3ioxPS7yko1KTngAvcPzQZX99YEMUID2/Di92Ocum86xaD5z55gj7Duqd//0qX8exauy+LLqOMajdJZPSUk5StnIphd+R7EcD6JcF8OKIiKUmZ+7S+/qsD1W59zvObvkrkmYss/241x/eexMvXi9t7t6R1z2ZYrM5HAJzYd4qFk4bzzyo7poRGreK558mL1Gl69fg5L8cU92x0ZSkgzUuOGTuxb4EZTh4kZvLYzI9LMWPSlXwtAcF2Rn15ghYd3OfpycqkYRV5cOx0qtSvmNsq5ohaVbqISU1OZc3sTfz+w1ounouiVMUS3PVUJ+7o25K297Viw4KtmQboaZrAYtF55LW+16nWimtX+jnKVErlu3UHmfRCRbezBNJij9FTTtCuV8Y8Pg8MiaD305Gs/OW2DIELwPkTF9AtGnab66n2ht1g/1+Heb3HBEpXSOKDBUfw8XU8wNMa9oQOne6LIjDEYNzjVQCBbtWx5rA7YceqPSz89Df2bDgAApp1bsS9w3tQ/zbn4zDS1GxajR+OfM4fP6zjr9/+ITXJRp1ba9BpUBf8q5ZPL1empEm92z9i44IrK15rmsQ/2MCWaqHGLXXo8WyX9H2nDp7Jdtr78tVSeO+Xo3h5O14r/ap30zY9YvAPMnjtkWpcG5B6h3TK1nWuFlK6GNWbVCE1ORXdolOsZJDb8WyVaxu8MDGr+0qF5N/Br2Cm9xZ20riIjHvncvefZ/mK0o+Vni5m6v4cvZ+KYO4XJUlO1Li9ewyvfxOW6/MChB324d8//iA1uR01bqmaZcv+9aSClxtc7KU4XrzDsVBa2mDFc0fC+XfVHhq2rcu4+SPxCfDhjx/WAY704aZhUqJCKKNnvkDVhmpNoxuOpS7Y95M2ndLLRxIYYqBbpNsxMC07x9H+HucJCK1ekh79/8Y07Wia489aSklSYopHOWR+Gv8LpmHywJALePuaGR7EaXQdWt8ZS91miRzcGcAdfVvm6M1t1rsLmPb6zxkGxm5atI318/9i2BcD6fnsnW6PDwwJ4N7hd3Pv8Lud7o88e4l5Hy5h52rHQnVCQP1b4xk+6RQVa6QiJZh6IJEnVpFiq8/xvWF8+8qP2b6PB4acx+rt+rVq3iGe+rcmsG9bwOV6SCrW8qNcjZyt8H5o+1HG9plE5OmLjrFNEma+M5+aTavy5qL/UbJCaOaDTE9yPemONPJuSOkIcKRtO6AhvFqDd3uXuXyKKmlGIS89AMZZshu4QO4Dl7Rz+AVKuve/RLteMdRpmohp4NFYflcD3NOCqs+XH2bnxsl8++Jczp2uxpPvPEzHfm1yX+l8cHP95hUyh3cc45U7xxN/ydEUmNZMbl5uZdm38SDfv/ozL08dwmNvPsjWpdtJTkylSv0KNLuz8Q0dNRcFMZGxbFr0N/FR8ZStXoZWPZpi9XLfEnFo+1F+n1qPS6fiCClpp8sDl6jVOInAYobLhf/SPPj8eZef3BzbUiHxOwh4liM7j/PegM85sfeU23NqukbjdvXYtW4/Qkg69Y3KkBr/WnYbdOwbxeE9wTz0vz5uzy2lHZAIceU12bPhQPoYm2szvwJ8NuRbGt5R16Nma2mch5TVIBNBrwbebTl3LIIXbn+d2Itx6eeXEvb/48+oB6vzyZIjlCxng9SNlPDbyFuDqrBlRXCW10pTslwqfoEGEWesdOgTneVr1aF3dHrwIqWg/9hnPF6U8moXwiJ4udObjmR1OJYGSHNs90le6fwmX+/8AC+fazIq62XImgFuUsZL225k1LNgRpL2yJCJP4JeHkK+Q1g8W7G6KJAJ3+c4cMlLhgEPPn+BwGBHPTxZodowHAN9/QJNrk7PdO17SsNW8TS+LZ4PXrQx4ZFPSYxNosczXTKf8DpTY15uUEd2/r+98w6PomgD+G/2WnoPCb0XUUEBQZAqVakKVj4RRRRFbFgAFbtgRcQGomChiAVRQZSOUqSD9N4JJb3n7na+PzbtyN3lAkkozu957klud3b2nbu9nXffecsBHm/5PDm5dUw8Ybaa+e7YJEIig8tJMoWu63w5ajo/vP8bTqcTTTMsCCGRwTw56SFa39KiyDFOh5N37/+Ehd8ux2Q22msmw9LSvk8itz9yike6eF4yCQp18OOObcULZ67P8YSJPNLsObIzc7z6b+Q5fb639BWeaP0CVj+dX/d7X2JwOmHVH6EEVp3ssTifzFqMTJ8M9nW5Ml2BCLwP/Hrz+h3vs+LnNR6tQZpZo/vgzjz28QMeZZAyB5nyOmQajsXGI6cOWgWevKUpO9fEuR23ySS5tk0qb0w3krnpOmRlaNzZuKFb35TCNGufwoBn46h/TSYAc6ZE0vs+79YKpxP+nhvKm0NqGGMzaXx/cjIhESX/rU58+mt+Gj/X6/f57FdD6dT/OhB+CGFCOg5Czjrju3AexGPiNBGAqLAKIYo69ktnHPLMzYaCWOR4E2jhiKj5l7W/jJQS7BuQjsOQ8grge76tssLpwK3Fzxu6E6a+FYvNX3JT/3jCohwI4f5hSEojFUD/Jg3JygxgVtzkcgn8KMn8rR7NL1Leu/+TYhUXAEeOgx3/7CkHif67HNp+hJ8n/M5PH8xlxz97mPzct3z3zhwjgZcssCCkJKTyar/3WL+gaObSL5+fwaJpfwG59WVkQcXnZXPCWPhDFO1vtbk3KwuJ1c9HJ1JpVE/OyfKuuACExYTy2q8juLJVfTRNkJMlSE30PokLAS27V/OsuKRNRCYNAXuh1OOOXcjkZ5EpL7Ft5S6vy1i6Q2fbip1eZZDJL0LmdxiTqSRvUj24PZXtq457zoXiFKxbFkzcYcM6oWngH6jTvk+S1/N1uCWR1789QN1GBRW+01M0khO8f1ZSkn8uMCK08pZ33beXSPtWQ/mzb3Fxsl4842+P4woOc3DfyBO0aD4Ueepa9Lir0U+2Rp7pgkwZBc79eMv4KoJHuVVcAGTGNA+KC4DTWG7KnO2x70sdmf2P8Tkm3AUpz1Geios3s4LJjMcUAZ4QApb+HM7X78Ry97UNSTrtWfsRwlD2O9+eQHZmDsu/X1Wyk5UDatnoIuTv2f+wd9NB3w+4vIxnFw3JZ1J4s/94NizYYpj6RTGZSyWgGYpK4ck9PSWDnyfM8xjxI6Xg169iGfHNIyz9aZzbfnOyfHP207UGLJ25wquCIDRB61ua8/yMJ/OdPStUiyLu4GnmTYug35DTHp/qNA0IHeZ+HPbtyLT38iQpLJXxJ3MmJtMN3geQK58npGMfZLmfLPdu9XO73bUDwb5tfsRWM8KZHXaoUT/LY3O/ACePv30UBGiFCitWqmFn3jeR3D70lMfPymyG+TMK/FA0k+DAv4fdtpXZK5Apr+UqGrmYqkPwKIRfB6PKuBtCIxyM+2UPsdVy8uUQwoHuOGVEPnm7XrSKiOCnEf49C+SQmWDfCehgrg9Z8/Ce6l4is35HBN7rpc2liczZgEy8jwu1ROSxlpYDcrIFFqvv931dhyWzwzh51FCmA4J1ImK8az+6DtVzfxtLv1tJl4HtLypXhItHEkU+37zyvc9tTWYT9ZvXKUNp/pvkZNt5ttOrbFpiOH1KKX1KuS51ye51+zix/2T+ts1Lt5GT5d2KZs+28+bdH3rYK8hIs7BzQ/Fm2zTHo14jiwA0TSMwJCBfcZEyi5jqZkDyw2cVOH3cgsPTfc3cEM2vvdtdMmMm4M0aYaJ5pyyvygngtY6PzJzr8Ry+lAEAXG76QkB2pufbYNteSfgF6EUmklZdk5k3LYITh6xFnoDzdNSZEyq41J0BgdXfiu44gp74oGEhibsCPa4JMvF+cB5w7ch5GJn0EPqZXrzzw17uf/4EMVVdSxEMfum4i+KSh2by8kzjfw8i4jtE9JJ8xUVKO3rqe8hTrZAJdyAT7kKeagXO4gtOIi/PJJgy9S0KrHsXjrwcRrpupCxIjjfzy5Qon/xc8ti/3Y8JI6rkv8/JFkgfjLlZub+N9Qs2M2Ho5JKIXeYo5eUi49SRM+zfcsjn9uGxoYRGXb7rzReKv35Yzf4th3xKVuaOlPiCYoD2bN/su94qh+tOwci7ank3FQc+QmB4daz+Vi+NDEUsqkpkbjrzr3Ecv54AP2OpJiXBzBM967Lid9fMtcnxJr54oxJE/OS5Y/tWvD+lOuk98FCxSmBqQhr//r3Dg/BJeAqruLZ1KmaL9+/L5q9zVYuCXBhmC6z43b3DrmbSaNElwm1EjdVP8uDoEzzVuw6LZ4dhzymQKf6kiZkTotm+NoD612SQN/k5HU5a3hwIZzpD9lKMXD9OII3CBR8LDdb449hJvUbx9HvoFFNW7qTz7UYEUXCYg/a9k7xbyYogwLENYb0WIYwGUurIpMcgfRLIwnlCsnJf3tDAVBNdz0RPm4x+uid6XDP0052QaR8jfYp2Kn2klGxfvZvPnprKu/d/wsyxs4k/4Zp51ulwkngqmayMAoUw7uApZr0zhy9GfMb8rw6RcYH1MqnD9rWB7FgfwKYVQXz0fGUGtWnAD59VwGkX6D7enhx2QWa6oe1EVLAzbMzRYlUyswUat0zjh+1b+eqf7USFfc3uNSvPb0CliFo2ushIPOk+FNYTZ44msG3FTq5qfUUZSfTfZOG05QhNlLjiMxiOsBWqRQGGw2Pj65bxzIeHyUjVWPZLGFv/CaTwBFypZjZN26Vitkh2b/Zn2xrX/XlkpplZ8GMY3e5Ict2hVYTg4Wj+vdCAzve04/cvF3lUvHRdN0pNZExFpo7BZIKm7VNZ9YehBCeetvDmQzUIj7ZTrV4W9myNfdsCadatuXezsSh+2cbs59st57u3fuZqN9e0MFVGeljGCI10clP/BH77KhLpLnJLSPoMOk1AkHG80wFbVgWyZ0vR8gFCCIQQ1L/+WmCr2/O16ZFMQJCTr96OZeJLlalSO5vq9TLp80A8dw47DRhWi+MHrXzxemVOHG/Etde+TkkrDm9fF8DcbyI5uMMP/2AnbXsks3uLHwGBstilg7waOAXI3KihQmQvg+xFJZKp0Bkg+w849ScuypczBZk2ATKmQ8R0hLl8UjZIaScz+RCv3vEV6xfsLLAu6jrfv/cto7+uTf0mgp1rjjH9/TQ2LjOjaRotejRFM0lW/LweTRiWK4ejCp+8UJlHxxylyx0lT7nvXj5Y9ksYv0yJxGKRvPX9fq/thQbzZ0Sw8PuiRVTHPV2FZ8YfceO8qwF+FPbPaXBtJl3vjGfN4mDGz91LZKw9/7pwtxwtdePcVepkYzJBcJiT24eewm5/CGmfhbBc+PlGKS8XGZEVw0rU3mQ2sfCb5Up5KWWST6Wcm+KiCepcW5Mdq/dwdbMNBJrGEeIHHW4xlp163RfP5pWBvHxfTQCeGX+YVt1SjCcoadw0Tx+3cOKgFc0kObzHj3nfRuZPsCYhMJZNnGDtBqHvoZlcw7Pvfv5W/p79D6kJaW6dPG97qiexNQKRpwr8azr2TeTrd2JJSzbl1+VJPG0h8XRu3wL6PdWzSF8uY/frlJsHxNPnZuJMXE18cXpMT3JtI6Vk1S/r+Gn8fnatuRLNJLnuxhT6PnQ6PwII4MGXjpNwysyKeWGYTBKnM8+5UXDjrYkMeCYuf0LftjaQMY9Up3q9LHQdjh2wITQzTruDwLBARnzzGBXqRCLPfOVRzmvbpNG4vR8JWZ/hSF9FbPhznG2Pj62WwwufHyAt+wo0rXgn/IIxw6RXKvHTpOjcsQiEkPy7OoiAQCex1Yvvq6jfhAamSq7nyZxFwikbh3dbsNh06jXO9KAUGRW1PUjrZpsOegIyaRhEzjmnEPHikNIB9s1IPQmyV0Dmr4wdGM7GRSEY1dcNS2Cnfgk88c5RTJZ/0XMEDa+RjJ0Bp45Z+HtuKGsXp7Dxr0CQGrok16IhyM6C956sRlCIk1Y3pZyXrLoO7z1RlYU/RKBpEgQknDITUcGzOdWeI1izqMCy3m3QjRzZcYxtK3ex6McIzsRZuOfpeK5ukfvQq0UgAvojnamQOdWlr8ffOcqB7X5ExthdQqULfy35ikzutsLtTGbQtGxk4gMQvRwhLmyxXxUqfRHhdDj55Ikp/PLJHz4fIwS06tOcl398pgwl++/x+p1GdlZfErx5wmLT6TnwDINGnaBw4lunAzYsDyYw2En9azPcmv3zbiIOh+H4+eNn0Xz+eixfrsqkUp26iIDbwdrK44RwfF8c4x+exIaFBaHPQWGB3DniFm5/phdk/YpMftrlmD1b/Bl5Vy3SkkzGVCSNyUpoGk9OfIibBnnPDCv1JOTpLiBT8bR8dOqYiXuuu5LiMmp1ua8Dj38yGKvNgpSSz4Z/xU8fzHVJbGcySXQdnh5/hE79Cp6MjbwuASz8IZzEUxaiYu10viOB+tdkIiWsWVyVOZNNXHdjGp1uSzRy7AA52RZ03YyffyYSP4TfzYigB9jz1whq1dtSxMdA141RTB7bngfHT0TG9wTHXjxbVswUzq5cHPOnRzDuadd8Nyaz5KoWafgF6KxdHMI3a7YTGevw6Nzp7qlahL6P8O8BQPyJRD4ecj8r5hUorSERDm4feop+Q04XHCsCQISCfsJn+V3OGTETYW1yTsd6QmbMRKZ96GJJOrTbxoPtG7i0a9I2lTen7wc3YcEyd7VOaHB4j43RA2py4pBruQshJNXrZ/HZot0+J5mTEtYsDObXr6I4cchKaKSD2Ko5LPrRtYp8j3vPMGzMMbd96Dr8PDmKiS8XZI7+/uRkwqJDObr7OEd2HScg2J8rb6iPyZQNZIMIzQ2T32uEuJ8DPmUBtrREi/Ss1J8rJZm/lfJyEfHpU1OZPX6ui6Odzd9JbLUcMtI0hICoSnbOHLNw6pjxAzOZTfR8uAtDx99/gaS+PFm/YDMjur7utU14TKjrMp+bB1MhJG17JjHy0/NP3/3JizV59HPfFVuAE/tPcmj7UWwBVq68oUF+dWCZ/iV6ylsI4SpweorGwh/CWbMwBHuO4PhBG7ePGEKfR2/y6XzSvh2ZOCg3Y6uJwqHMedzTvAGnjlrxJSVovaa1aNTuSn54/1dPZ0QzwdRVO4ipUpwlQoBfT8j6GykTfPg+TEhMvHx/TVrfdIrOtyXidICuC8wWSWa6xgfPVGHZnHC+/HcIlSMfLnY8viIlDGpTn2MHbPnJC2/+Xzz3PnuCsChD2XpzSDVCIpw8+qb7yc/pBGThJQUNLE0REVMRwkLymRSGXjeC00dPGxlaz+LWh07x0Eu5yoqptvHZpY+n5A6sGiL4GUTgoBIe5xmZNgmZ9m6R7TM+rMDX78S6VPV+58e9XHlderF5URwOSDxl4cH29clIK2pV+OKvHVSp7b7oZmHsOfDqoJqsWRSCZpLouRazgvt64QtPcvcTp7hneJzxS3EaVkGTGeZ9G8GEkVVyjxdUu6Iyn//7vs8WLD1pFGT9SJk5HPvfjRb6cql2qWobXYIknkpmzke/uyguFatnk5GmcWiXX25hPsGZE1akDle1SGPrP0E4Hc5in4gVJadJp0a0u60ly39YXSTEWTNp1L+uNu8sfpnje+OY9fbPLJ6xwu0SjZSCZb+Ec8vgM1zRtGApRCJyb2G+3Vh0HR56NRspZYnM7xVrxVCxVkzRHVpMEcUFIDBEp/f98fS+30jA9kC7+lS7okqRdp4QloYQvQQy5yFzVhoOoGf5Uzw/8RBP9KybuyznfSx7Nx5g9/r9ude/2zMCgnnfRnLfiDjvwlmaQtYvxlE+fYROkDrD393L3U0aMu39mHw/l2MHbCz/NZTsTBNCExzetp7KbX3p0zcSTpk5tr/Ah6jvQ6d48CVXq8egF07w2M11iIixc/fjp3A4cvVnaThbJp2xEBmTp9BZwL8vImRkfsbjWe/8wumj8W4VF4CfJlbgprsTqFY3B+F/K9J5EEMhLWGCEaA0YkNOH41n46J/sWclUa/2Z9S+qmib7Ewt97o2vuCgUAeNWvpWrNBshshYO51uT+CXL6OL7E9P9W2Z5Ot3KrJ2sZGIME+JcuuDBYBg+gcxzJ8RQad+iURXziE53szin8JdItWklNzxbJ8S/fZF6KtILRiZ/g1CnMt3VgyZ09GDR6Fp3gMEygqlvFwkrJqzFmehyc9i08lM10hNMgMi/8ad92PY+k8Qta7MoGHr3tRqpOoXlTZCCEZ8+xhV6lfi5wm/k55sKB5WPwvd7r+RB8b2Jysti7W/b2TZ96u8JoQzmSV/zgp3UV5KorhAntPlcXAeA7PvyoRH/DoikwNBpnvMsCkEfL5kF9g+QOYMQViv86lrIfwg4FZEwK3ItM+Q2UsobH0JDNapVjeLQ7uKD/3OK4XhzT6sOyU7NlTCcJB1NxObwNSwIONvCRBCEhLh5Iabk1n6czizPq7gtl12ZqCPPZo8yOhKYctBUKiDgW4Us5gqdibM28sXb1bi/jYN6NwvgdhqOZhtkcTUv49613dCOLYCAixXI7Sw/GOllMz7fKHX61YzSf78LpIHRksIuN2IRjqnp3gdrK2M8+ppkPkzMnsRyGywXIUIuAOwgPMYUgTkZgi2gak6Qggy07MYP2QSi2f8ne+HZrHV5I6hJ7nlwTMEhRSMoUb9LJyOAkXJ5l9yedv3TiqivGgmSUzV4q0uWRmCX6Z6cBj3QsJJCz98FovFaiY7KydfpTeZNZwOnduf6U2ne0qmHQthRoSMRAY9zNfPDyPh2HF63neKmg2yvSrveb81n/SktI8h5MkSyVVaKOXlIiE9OSM/zTxA3UYZbF8b5LG9ZpIEhtgY5iWNuuL8MFvMDHz1Tu4aeQv7Nh1Ed+rUbFSdwJAAlv+wirH3TMCeYy/2fu50QELc2TWPzs2UK7PmQuCA/IyoUkrIWW1kQnVsB+GP8OsG/nciTEWfHvMQwg8t5Hlkyqgia9yFFQWhAfaVyIS/IfRthH/vksmrn8R46jau65NHLTzZuw6ZaRrXd0mmXa8kLBadrEyNtYtD+HtemNfilJ4w+9cD02lwHsX1sxVgqoYRjnxu2HMEdRtlsvTncLf7pS6p3LALmGaD8wgev1sRAqHvQtIQ3PrFiBgIGY2QCUT6HSAydjXxcWba9Ej2mMOmQhU7Iz85RFqyiTNZMwmOrOJaoNFkTHinj8bz++RZ7N+8i+s776FZ2/2kJXmuZwTGdXDqWBgi8mOEFgp+PZDpn3s9xi2W68FcEz31Q0j/FBflzb4emTG16LkBTFXR/Ycwus92tizbnq+4XH19GqO/OEhIuBOH3dVHo9VNSQSHVSYtxYTUBUlnzKQmmfL9mopD0yAoxLWtySRp2S2ZsMji+9i3zZ+s9HNzZNWdOkMn3I89y8HyH1aRkZJJrUbV6DGkCw2a1z2nPgGEFkbTHs/zdIeXOX7Qytjv9nn1a9GdJSg9YF97znKdL0p5uUioXK+iy1OQlAJNk+i6+ytMdwq2/aPS9JQHNn8bDVsW1B3atW4fb9z1Abqu+6SDmEwUm83SZ9LeQ2Z8DRFfgak2MvVNyPiKwk/0Mm0fpE+FiCkISyOPXYmAfiCsZJ54Df+AAt+doje13H6TR4G1NcLkpnqxp3NoUS6hzd99VIGcbMErXx2gWfu0/DBPpwM6357Ezg2nGXlXbTJ8NNEb8gqa39wCEfk0ZMxEZn5vOHFqMYiA28D/DuSpZj73V7R/iSPH/e/QZNaofU1N6l9XF5k1Apk01HM/wcMRfu3RoxZB6quQvRJwGI6wAXdC4KP5FcHNAXDr45OYPGoBURXt+b4QnggKdRJcy4KwFHw3hmL7D8e2fsqJvVuJiTRzw2MZVK+fja6D1RZDTrbnTo3zSfpEjiQzLYuKtWLoObANPe5eidXP16yzZgh9E5nwP7BvcrPfyw/IeYT1v41l0+Ja+Zuq1M7ijWn7MedGQ5nPeiaw2mDkp4d48Z5aSGHUDpv3rfes0YVx2OHQ7oLlOs0kCQpzctsjJ70c5dtwvKGZNMJjQunYvy1Wm4Vej3Q9t448cNUNDRgz/3nGPTiRCSOyGDbmmFEVzE2k0T8Lg2nVLdVjXy6IomkGygs1+10A4k8ksvaPTWxaspXsTCNBUvObriWsQmj+mqbDTrEJiHRdsPb39WUtrqIQuq4z+blvjF+6jzcqp1PQ6bZSTNalJyATBiIzf8hVXMB1KUIHmY5MGIyU3pOMCf9eBNRcTY7fl+TkBHtfntHtzP3wYeZ9vpDM9OKSl+Xi15O8Dyop3sSC7yIYNOoE17YxLCF5E0re37qNMnniHe+VsAujaRoBof50ubc9QgtBBD2IFr0ALWYjWvR8ROAghBaE0+F7Qq+zMVvgn4UhuecruNsLTRBZKYLR3w833vt1QoSNBy3KtQMRggh5FRFwl9GHuTJa+ES02H/RYnegxaxGC34iX3HJo+/wQbS+tQWJp81oJnKdhd3LKCXsWp/GslkrWf/nSnISP0aeaopMHEBsxVU0bZdKx76J1LwiG00z/DuGvHqMqEqel0KcDkGPew+TkZKJ1CXH951g0ovJPHd7LbIyfLOOSb8+OOJfRNqL1vvyhUU/hKOZCi7KWwefwWSRLiG8Z9O0XRrjftlDi07JaJpkxvgYDu/x8+n7N1tg7jeGAmgyS9r1SqJRy1RG3F6HYwe9V4wHqNkwC5sPdcjyskznhSWHRgXz1p8v5jvUlwXX3ng1U3d/SIf7P2Ll36PJyKjmcgvLyRYc3Wf1XXEB8O9b6nL6ioo2KkcSTybx0bAv+Ounf/JNoAEhAfR7sgd3v3Ar6//cwgu9xiB1idms47AXCrg/C6FJKtXMJv5EKNOPfEZwuOclJkXp8M+8DXw07AviDpwq0XFVauXwyYId2Hwuyuotn0YhtFjQT3ptK0LHIvxvLbYrqWcgT13jtY3TAcvmhPPWsGrE1qzAe0tepkI1z0tTYGRv3ff3/fz6+SE2rwwiPdVEi04p9BoYT52r3dfrOXHIwkM31i+20jOALcDGu4tf8mpWl1Ly4aC+DHvTfbI5b9jtMPerKD4dXQnNZMIv0CieGVU5iq4D23PTAx0JCnP1d5HSATkrwHkCtEiwtTV8OErIiQOnSDp9hj8+e5Ntq5M4uNMfTZM0aZfKbY+c4pobDEfUbWuC+OC5OhzeVXAdhEQ4uG/ECW7+n3elWXfC8l9DGf9sVZcIG6FJmndMYfTkg3Sv3ojC9yFNk/QdcpoHXjiJkQwtk7OvQYmRfl7Tzm96GXFHLTb+VVCF+4ftW70uAZ3tr5GdKcjO1AgMdbooPGe303Xj/z9mhPPBs1UwW+Ddn/bS4NpMHmhbn9PHLbzz4z7qNsos1hfk09GVmPNlFNKN1Vwza9S4siqN213J3o0HsPpbuaH3dXT8X9tyqdp8Nnr6dEh9hfzK7CVBhCAqrC3V/D0q2ugiJDUxjSdav0DcwdMuyc8yUjL4+tVZnDpymi73diAsKoTEk8leFRcAqQvCIhwc35/Dgq+WcesT3cthFP9NpJTM+eh3Pn5iSgnNwkbjo/ut9K7TiEHPn+C2R7zVitHA2g7MNSBjSjF9a6AXE12DCZmzziflBWGhOKVJSsjONq7J00fO8GLvt/hswzseb17SGceuJQN58W4/kuMjcycMwcJZEfwxI5IBz5yg/5OuiuCxA1ae6FGXnCzfjMLZGdmcOnzGq/KycdG//DbVRKPrQ2ndPdnrU3uBY7zx9+WBNVm3JCR3m05GiqFwRVTU6fi/NkUUFzAcJbG180n+oueXfPXyd/w47jey0zNp0TmF1X+GGk7QGNbWDcuDWbc0mMffOkqthtk8e1vNIhFDKQlmxj9blewsjVse8FwrSjNBm57JxFbL4elba2PP0RAadOqXyCOvH2XvVn/Ovg/pumDuN5EMeCbOsBSYKoDzIAXTiaNElklvxFTJyU/QB+AX4H2CPTsqzeYvsfkXVXaO7LERFOrMX849fdzCjxOj+eXLKKQU6A7J79MiaXDtUVKTTNw57BS1rypecQG4f+QJ9m/3Y8vKYIQmDSVGSAQQXSWS134ZQYWqUcX2U9ZIPRVSx+a+K6lZUkD4l2WSeNBXlPJSTsweP4+4g6fde/dLmP/lEuZ/uaTQRu8XReVaWWxbG4jQYPvqXdyKUl7Kgl3r9vH+A5+WqN5UAQXfoZQw+fWKRMTY6dg3yXN7UyQi+LlcJ0Zvd//SNZgKYUFa20LO33iKhjFbyC8h4HTo7N98iC3Lt9O43ZVFpZN2sk8M5OV7/EhJNLtEX+RNRF+/U5Fq9bJp073A3+bjUbnOlr5Gawijinebvtd7vJHOn7IEk1njvSerYfU7RMsuKThyI4hzy/uwf1sAm1cGEBjiICxSJyNNY8b4GI7sdW8xObbnBK/dMY5xy171Tc5i2LvpAN+/+wtLZq7If7jpfHsiC2YZSxiFJ+S8SKQPR1ShbiM7um7Kj8o6mylvVqTrnQn5JRHcYTJBgyaZvDzlIKePW7nuxhSiKhqT+s+T3VvWMlJNnDxipWqdZAgeB+RA1mJAknRiKcEhp70qiZ6QEjavDGT+9EgO77ERH2fJv14Ajuy1UaN+lseihA4HOO1g9fMeLTNzQgWW/RpGVKzdcEw+ai1yjS78PhynE9KSTfS4N97n8dj8JWNm7GfZL2HM+zbSSFIX4aTz7Snc9PhXBIb6GplWxmT9glFf61zuJRKyF4DVs09dWaOUl3Ji7ucLvIYllgghObbfBgiEIL9+h6J0OHMsnrmTFvL3z2s4tO1IkTwv54Zh1fj8tYpelBcnwtYGITSkuT44duH5xiKMJQk9Ac9PTU6E1XdHVRH0IDJhudt9Dgcc3WdjbaFU5SaziY0L/3WrvJC9mBW/JhB/0nMYv6ZJvv8kOl95OXnUwvrlwflJ2XxCGorE/i2HqN24htsmp4+cwenQcTo0Xh5YkzpXZ9CudxJBIQ6iKzlo2Cyduo0yqNvICGX/958AvnijKkf2eq7VpDt1tv61g70bD1Dn2pq+y+uGVb+u45W+76I7nS5KyoEd/gVP7h7YvcmKt8knO0vw99xQn2rzNOuQRnK8iYx0EznZxuS9ZHaYx/b5JQTSxoN+Kj/7bpj7oKxicTrgrWHVWDYnvJC1xXVsv0yJ4vG3jnrsw2w2XsWRnmLCkSOIO+x5Oc9h11jwXQSx1XJ8iFYqiKgDQ9Hv2Dep0G/dBNbWaBeL4gJI+27OPW8PhnN80ONuC5eWB0p5KQeklCScSCrFDgtuZrpTcm3HC6f9Xm6snb+Rl299B4fdWXrKZj6CxFMWks6Y8rOkumJGEmikXQu4F5ky0mM/YILAR4yoFbdoRhSLn+8WOWG9DkLfRSaPQEoHulPmJzw7us/G83fXco1+E7jkJiqMzFrAhr+CMZmlx9BnXRfs2hhIdqbA5p+rkJcwP0YeaUmeE5FFVAx3KSuw998A9v4bwKDnj3Pz/4pO6lc0zeD9OXt5tFtdTh5xndwqVs+m290JVK2TRWa6ibidM6l9zbPnXOclLSmdN+4aV0RxATi408+r4uJtXx4mE8Sf9M0J9OQRCwNaNDSOM0ucDqjeIIueA+KpXj+LjDSNv34NY9lvoVSoaCemag4piRrBYYYz7vmuIEwbF8OyX8IACllb8v4aief+/C6C1t2TaNImDaG5P6cv6e2r1M5GW4zHBH0FCOw5vixhCsCCYbV095twIgLv86GfcsSHQqpekcmgJ0MJog9LExVtVA4IIQgKLxuN2z/Ynw53tiqTvv9rnDpyhpdufQd7tuM8FJfirDSCM0VyvuThhKQHkdl/gf8tuZE64PozNQECEfo2IqA/BNxbaDsF7UUgImKyz46iUk8zasXYN0DA7Ui/O1jxezQLZkUwekBNHu5YnzMnXDNpOu1OrrqhvocOs9Cd3pPL5aHrAglelzaKo2JN1+RxDruD1MQ0nE4nne9pV+T7jKmSw20Pu/c/MpshINhJ/yddw2Nvf/QkU1bs5LaHT9Gqawod+iTSqsMUZPwtSGf8Ocm98Jvl5GTa3X5OnnK7FFD8h+t0QkQFY43M23fhdMDRQhl9nQ7DqiudgjY9E2nUMp3mN6byzIdHmLlxO+Pn7iHxtJmQcB0hzl9xyckSzJ4c7UV5FflyvXRvTb56O5b0FPfTly+ydLs7wSURoDfi48wc2OFXjKLjhODnQdgo+nsFETwCYbu47tPCrzPnbHUp6KRUZDkXykR5OXjwIIMGDaJmzZr4+/tTu3ZtXnrpJXJyvGcobN++fX4p+rzXkCFDykLEcqfrve3RTKX/cTe/6Rps/iWPZFAUZe7EBTjtzlJaJvKEpFINT78Do0qcTHkJABH6DiL0bTBfiXET9AO/bojIWQj/7sZvJHgUIvxrsHUCU1XSM+rxw+SuPHvnjQzv9D0zxvxESoL30EeZ9Qfy1A3IlNGQ8R1kfIeWPZMGzQL4ckwl/lkYUiTfkGbSiKkRTbNu1xTtT0pSkmNp2CzD6w1fCElstWz8Av0QIop613cmtkbJIgQ1k0aTTlfnRz0d3X2ctwd+RM+g/3Fr5H3cEj6QdX9somGrei6/v479Er2GzprNcOOtSVhsRqN2vRMZNCoOkVt3Ju8vAI49yKSHz+m62b1hX37Y7Nlc0zoVk8l7n2ZzbjlyD1j9JK1zl+W8TeomsxFpUxgpBUf325j1kVFeIs/PJDBEJzhMJ6KCwyfl1Bf2/Ovvc24fh13j5y+i0EzynM9ftU429ww3HN7dlclwRTDjwwoe/WzABJZrEAF3IaIWIIIeNX6z5rrg3w8R+TMi8CKsPWdpCpYmuD74lOT4Fgjtwi2DlYnysnPnTnRdZ+LEiWzbto1x48bx2WefMWrUqGKPHTx4MCdOnMh/vf3222UhYrnTb3hPgsICS1WB0UwaMcWEqip8Z+0fm8pgqagwkppXZBVjYZBGllj7eoTQEP590KJ+NPKBxG5BCxvnknhOCIGwXY8WPoFZXw6jb70APh99nM3LDvLvXzv48vkZ3BbzAH9+vdT92XI2IJMeB/LytjjIexqLrniU9342QrEL5zfRTBqBIf68+vNzmM7yYlz352YebvIMj7RZS7veiQQG6wgP4bJSQr8hiQgykHo8Ws4cJi9bR7e7fbNiaCYNv0Abj3xgmOP3bNjPI82eY/H0v3DYDa0pMy2LXz/7k7j9p2jZq1m+opDnqOkNi1Xm+jpI7nrslBdFzGkkYLNv9Elul3NYzB4djR12wyKFm8lVM0msNolT9x6VeO+zcQQGe7+mnU7YtiaAv+eFFdmnOwW/T4vEXkjfLizuuVpczv7sPSXjPOuo/P/a9U7CP1Ceh8XHj/8NP8nTHxyhUq3ivSeWzQnnyzdjc+XOu+Zz/5rrIsI+NX6LpgqIoEfRomajRc1FC33NqPd1ESKEQIR/Cl4SWbpDSuOVkn1hl8HKRHnp1q0bU6ZMoUuXLtSqVYtevXrx9NNP89NPPxV7bEBAALGxsfmviy1Xy7kSVTmSD/5+jcp1YkutT92p06pP81Lr77/CmWPxTB09kyFNnmHQVU/y/uBP2bvxALrj/BWXeo3z6hedPeEY1Y9HfuJj1JLzeInOO+/zhXwxcppLGH4eulPnnYEfs2HhliL7ZNpneAqRFjipVuc0Y37tQoMWdQmOCCKmRjR3jbiFz7eOK1JTa8XPaxh10xvs33KY08esTHmzIi9NOYDFKl0SjeX93+WOBG6+x1iayXv6NZszefLdo3S9MwmhCcwWU77CHxpdcC8QQtD8pmuZsHoM1RtWRUrJmP+NJzszB+dZ36Pu1Ek8mUTSqWSmHfyU574eRoNW12Mye7/9OexGle2IGAc1r/Ac4WJgyq3hVDKa39wEp8O9VrRuSSgd+iRisUiEZrzyPrvQSDv2HFGs38t1N6Z43e+ww8Lvwxl1dy2PvkkZaSYf/T6K5/RxC5PfiGXnhrzyFsarRv1MLNbifn8F8lWvm32OSQcF2LogYtZD6Md0vqcxX/y1i6/WHOSK60z50Wfu+P7TinzwfF8IvN+wdPr1QIR9hoicXaKs0xcTQguH8Cn4qgrkKZ1rFgYzddRrjOn/Ikd3l+xeVVqUm8NucnIyERERxbabNm0a3377LbGxsfTs2ZMXX3yRgADPKYizs7PJzs7Of5+S4v3HeiE5cyyBEwdOITThdpIpCZpJo2HLejRsWa+UpPtvsHnpNp7v/ib2nAK/lmO7j/P7F4u5qnUDNLPmsxJj83dy6+AzNLwuHV2HTX8FU7VOFrWvzOTPWRGFJgNJhSo5vPTlQarXz/baZz5a8b+VPBx2B5+P+LbYdt+8+j1NOhU8ZUmZDTnL8O47YaZJmxM0W/FGsTKMe2gikoL8Hr99HUXcYStPvneYXRsD+es3owpzbHV/bnvkMG17nCyS8j4vT8ejYxOp1fxR4g4mEBodwo13taZirRjiDp4iJT6V6CqRhMeE5R+39e+dHNnp+SYqJWxbsYt37vuY1355DqulGvLMXK/tE0+bufK6dBJP+3KbFCCLL9x3Ntf3aErluhU5ceCk2+tu0Y8RVKyRReubkzlxyIbFaiSPs9p0XhtcfJTTro0BVK9X9JpLOmNi/LNV2bY2gOT44h16bf4l1xTyFJO87/jkUQuPd69LcoKZ7z+OodaVmVzRJJ0mbVO44eZUPvpjNycO2vhnQQiLZ4e5JCk8u1RKVqZWTDIJVxJOmdm31R+EjbptBxBunQapbwImBE5iq0DfBw/w+uBqHvvQnTqtbumLFnzupSYuRoRMcSnh4Q1dN5zAm3VI5bqOqUj9O36fsQz6TKNK3aplLKkr5aK87N27lwkTJvDuu+96bXf33XdTvXp1KlWqxJYtW3juuefYtWuXV4vNmDFjeOWVV0pb5FLHYXcwpv94nA7neSsuAA2a1+GV2c9e0CRBlxr7Nh9gRNfXcdhdndTyntS3/r0T/2AnmT6svVerl8lH8/fkPhUbN+nmHVPJyhB0H5DAY28fY/WfwaQmmbmqeTqVa5VgYhPhYL3e5+Zb/95JWqLnaBuXdknpBYnVpC85HiToGcW0gTW/byT5dNEHh3VLQ1i3NASbvxOrTfLxuo+IqbgPkTLIY19CgNWaQp8hkWh+vV32xdaoQGyNopWdD/x72KfExJuWbOWDIZN47qthSP/+kDnNowzRlRyMmXkYzFeAM9SIrvCIA2FxEzJeDCaziTHzn+e5zq9xYv9JhBBFfGdOHPRj0Y8mJi3ZRUCQE5MZ1i4O9tDj2f0X/UCcDlj0Yzj/rg6k650JNO+UgsUq2bkhgLnfRHJ0X6HaPprkquvTSpSzZd3SYL7/JJotK4OQ0ij5cMvg0yz/NZSUBHO+o+z+bf7Ua5yRn46+Rv1sqtXN5vouKdzzTBwjbq/N4T1+aLkWpzzlpVoDQbV6FGMJM0iON/HxC5X567ew/PP6BYyl133x3PssmC0FVq9W3RJp2TWU1X+GFMkzJAS06Xs9Lbo38f2DuFTQwjBUAc/Ou2crovn+Xhrc3D+OdcuHUaXuz2Ur51mUyBY4YsSIIg61Z7927tzpcsyxY8fo1q0bt912G4MHD/ba/4MPPkjXrl25+uqr6d+/P19//TWzZ89m3759Ho8ZOXIkycnJ+a8jR3yvi1KerJm3kcSTyaWiuNz6RHc++Pt1QiJ9u4H915FSMu31Hxly7bNFFJfCaCZJ/cYZ+DKhj5+7O3c5xLixaZrxsvlL7DnG/626pdL1zsR8xcVX50IR/AxC+F7jJNUHxSWPrIxCT+EiyMgV4xWJMNcptt+4XIuiJ7IzTaQmmYk7GM/BrUWXr9xxYu8On9oB2AKsPuXakrpk0bd/cfpoPCLkBQgcAnhyeBdgikWEf2wUTvR4u8wLSz+3YnoVa8Yweds4Rn77GE26uPc/SDhp4dnbapNwyrgu6jVOx2o7+2lZUv+aDK7vkkz9a9OJrZbFtW1cnbV1p+FLs3VNIF+t3sEDL56gUct0GjbLoNf9Z/h82S6635ObkVcYDrH9n/CtHIaU8MNnUTx/dy22rAoyosikYM+//rz1aHVW/RHqknDu6uvTeOLto/m/ITB+N0JAWKSDt2bto0qdTOo3ycBh1zBZTDz7UTqTFm2iTfeT+ROqJ1KTNYbfWsdFcQHIyjDx/SfRjB1a3eV4kwlemHSQ/z11ipCIgu86NCqYe1+5k1HTn0DzVh3zIkTad6Mnv4R+ugv66a7oKa8iHa7zqRB+uSkVvGuDnqLKNA2atd1O8qldpSh58ZTI8jJ8+HAGDhzotU2tWgUVQI8fP06HDh1o1aoVkyZNKrFwLVq0AAzLTe3atd22sdls2GwXf7TNwW1HMJlNHte3S8LmpduUxaUE/D55EVNHzyy2ne4UbPo7iOKyG4NgwawIet9XtG6MKfdGnJQQgXSmEB5tKEv2HFg2J4zMTI2eAzzUmxFBiOBnjWrPJaBiraKWCHf4B/kR5uI3oiH974L0T/Cc6E4DH8oLBEcE+aSYS0cSJw5CTc/W+XzOxPlR+ari24FR2NTXJT+J5J+5G+jxUGdE8FPIwNzwdPsayNkCzngwRSD8+4D/rQgtCIIeQeb8A/bNuGpJJsCECPvwnOoX5WG1Wbjx7jbceHcbnu38KpuXbiviPL5/mz8DWlxBi04pXNk8nbqNM9i2JhAQtOiUwkOvHKNyzaIWPl2HnRv82bwyGJufztolQYz67AgBwbrLZJSX3O2xt45xaI+NPZsDeeq9I1zTOs2nMRw/aOXzVysb5yykLBT45bj+rm57+JSxDOFGHzCZjUrsbbqnMmO8cX2363mGjrcezm9zdkXkwggBsydFc3Sfza1fkJSCv34L49/VZ2jUskD5N1vgf8PjuONxwfGkbwCoWr8SZsullxJNZvyITBmFoXTnzjsZh5EZ0yH0XYR/j/y2ImgYMnsxyAzcZdgubrqRErIT50CFZ0tN/uIo0TcSHR1NdLRv0S3Hjh2jQ4cONG3alClTppyTxrpp0yYAKlasWOJjLzb8Am3o51rW9iz2bz7E7vX7qNfUvUKnKMDpdPLt6z+U4AjflMLkeM8/HWPZI4l+Da+iVsNMLDbJkT02UpOMY6wW6HpXngKjgbmOEV5pa59fw6Yk1G5cgxpXVeXgVu9Wx16PdC1yExaBDxiOpo4duCowRsZQEfKyT86IrXo1w+JnwZ5l99xISEbc9CGapvPFX1Ziq+W4Nf3rTog7YsUSeF2x580jPCaMmx/oyG8TFxRrgRFCkJNVMMkLLQjhfxP43+TlGH+I+BoypiEzphkRYdjArzsi8H6EpfR8zx56dwBPth1NZmpB4UpNk1zXMYXaV2ZhzxEs+C6CQ7uNa6Vlt2RemnzQ87AlNGyWycr5oUwdG0uv++IJCnF6nJB0Jzwz/gjBYc5iI5UKM/vzaGN5x6f8KZJmHVILlh/c4HRC9fqZ+e37PxWHrlPETwoKJtcDO2wc2WcjI6M6i3+yI72IbzJJ/pgR4aK85GGxZFPzKh807IsUad+Rq7hIXJUR43+Z/DRYrkSYDb8pYa4Gkd8hk58/p6g53Qn+gT7685USZWIDO3bsGO3bt6datWq8++67nD59mri4OOLi4lzaNGjQgDVr1gCwb98+XnvtNdavX8/Bgwf55ZdfGDBgAG3btqVRo0s/g2zLXs1KLX+IlJIx/ceXcT6SS4/EU8ms+3MT370zh+E3vsQtkQO5o+JgTh/xHHpr89dpfEMqzdqn5Cfz8oX6jd1XRAbjRmoyGZll92wJYPvawHzFBSRfvRODM/9+ohsJrPy6nZPiYpxP8NTnD2OyeDb7Vqodw93PFy1fL7QARMS3EDjYWPrIw9IYEf45IuB2n2QIDA3k7pGeLDRG/pq8BGS6rjFhVGWkLJrhNC+x3bTxV9GgRckUgkc+uI9WvYp3ppS6LBIp5QtC+CECB6FFL0bE7EDEbEELG1uqikv8iURe6DGG7ELLew2apPP1mu28+tVB+j8Zx30jTzBp6S5e/Xo/QaEOHn3jKBL3kzoU+IYMGhVHTNUcmrRN9brcopkgtqq9RIoLwO5N/j4oLsaJtcJ5cjwgBFhyE/VVqplDlVo5HscIxnVTrZ5G2x4pdLv939zMwp7lcToFJ49a3e/UzrHGwQVE2ncg078yXmkT8D69C8MCU3iLuQ5a5HeIqHkQOKxE5zZbICiifINHysQWtmDBAvbu3cvevXupUqWKy768Cddut7Nr1y4yMgxnQKvVysKFC/nggw9IT0+natWq9O3blxdeeKEsRCx3KtaMoVP/tiye/pfHImol4ejuE/z71w4atb04cwiUNRmpmSz8ehkrf1lL3MHTnDp8Gnu2L9kipVEWSIN7hsdxywNn8M/Nu6I74a95oXw8sgrJCZ5/GhabTtMOnhO/SWmY0N0jiI+zsnNDIFdelw5+/cB6gw9ye+eKFnX5cOUbjHtwIns3Hsjfrpk0bry7NcM+eoCAYH/3EmmBiODhyKDHQI8H4YfQwkosQ/8X+iJ1yfQxP+GwOzCZNJwOZ24EkesksmFZCKPursVDLx+nVsOs/O3Z2YIzJywMGBGGcO4C7Qqfz2+xWnhl9nOM6PY6GxZscavcayaN2JoVaNy+5M61hTnXcgDFMe21H0g4mZS/ZFSldhZvzdqHxZYbSl7IFapZ+1Tenb03v4hiceg63HR3AprpfPKjeMbql6ukerVe5imwgr1b/ah5RZZnZ2AJuzYb16zNr3hFynhoKHioCIlwcOa4p9+h4ePm8YHF7LlK+cWGdMYhk54E+3oKFJbiPi8n5Kx0u0eY64DlKp/LNRq+R9YSlSIpDYS8zB7fU1JSCA0NJTk5+aLLEZOdmc1b937EXz+sxmTW0HV5zg68QgiGfng/vYd2K2UpL36Wfb+Kt++dQI63JQo31L82nb4PnebtYVUZPu4o7fskFXmSczrgxCErj3WvR3qK+7tq9fqZTFqy2+N5pISX76vB6j9DPbZ5fUYyzXvcAwH9Ed6SS+Ry+mg8i6b9RfzxBCIqBnPjrVlEVzD8L4S1Cfj1RmhB5GTlEH88gZOHzxAQ5E/ta2qUe+HOlIRUVsxew/G9u5n59qJi6hUZGWCfeu8I/kGFU82bACciaDgi6KESnT8hLpHHWj3P6SPxLn4jJrOG1c/Ku0teviiXXHOycrg18j6yMwuWtJ549zCdb0/0qdhgceg6rJwfyt5//RnwdJzXaB1f6gOdzQ+fRTP5tYoeK4KbTJL6TdLZtTEQXdfoekciT77nPu+RroPTLujf7AqS4y34BTiZ9e82bP6+3y+/fieGGeNjvCbAe+Wr/VzfueiDiAgdh/Av38n4XJB6OjK+T+4yZgn9Kc110aLcpwuQjsPIM52K7SJvGU+EvO6zldYbJZm/Lz0vpEsYm7+N0bOGs2/zQZbOXEH88QQWTTs3S4yUEpu/56eKy5XNS7fx+h3vl/i4wBAnb07fj3+QTsNm6URXcv+0ajJDxRo59Bp4hhkfxrhtc2iXPzMnVODOYafcrsH/NTfEq+ICULnJVERgJa9t1szfwIEth9nxzx5WzlmLEAJNA113MuV56PfwGe4fFYfI/I0Fkyfy0+QrObDViBRp2LIetz/Tm3rNyn+SDg4+Trf+DtbN28jMYgot2vx1ho87gl+AftbnmLs2n/YemOsh/Dr4fP6I2HA+WfsWs96Zw9xJC0lLSsdis9CxfxvueK4PVepenD50SadTXBQXkHS4Jcmr4uJ0FL/8koeuQ0aqxvzpEfR/4iQI6XEZ5lwsM13uSGDmhxVITzEVURiEAGHSeGrSAEIqdmHxtFXEHTjJ/t1/UKveJgpXZZbShNSdjH20Wn4OmqwMEwtmRXBT/3ifx9vr/jPMnx5J4hlzkeUsTZNc0TSdpu3PVlw00GLAr3PJP4ALQdbP4DyMT6F2LpjA2tLzbudRzq6U7Q5hqo4IfQrh59lfrKxQlpcLzBejpjNz7OwSH6eZNGYc+YyI2EtvbfZ8eKbjK2xaurXEv9U+g07z0CvHva6Z5yElnD5m4Z7mnpfkhIBWN6Uy8rPTWMy5N0ARCQGDuafROk4fOePWqqaZNK5q3YD3lnjOTTT38wV89tRXZKUX7wB377MnOH3cwrxvoxCazI+s0DSBrksGvnon/V8o6utyLkg9CTJnI+1bQVgQ1rbg1xkhLGRnZvPX97M5tvUHAoOP0/rmZE4fs/BUH+/m9y53JDB8nDdHYw0sTdEi3edjKVZmKcnOzMHqZ7now1zTUzK4JWJg/nWjmSS/H/EeVp5noTBbfVsKGj2gJv8sDKFVt2Sen3QwP8T/7D4P77FSo37Jk+7t2RLM8/+7iuQzGWiayPetsfpbGP390zS/6VqX9lLqkPUbMuNrsO9AYuHUyUaMezybjctdJ86gUAfvz9lLldrZPuediTti4a2h1dm+rqAGjxCSlt1SGTbmEBEVBIainJskSItGRHzlU3qAiwE9/jawb6HkyouGiPo932G3MNJ5Anm6C+Dt/mOB0LcRfjeXauSrsrxcQtz3+p0IAd+/OxuHPe8C9H4xCE3Q9b4ORRSX3ev3Meej+WxYtAUhBE06NaLPsJuoc03xmTgvBdKS0tm0ZOs5Hdu0farPGTmFgIhYO3n+MUjhEkWhmTU0Ieg25A1sVZoWOf6ZL5szstvr6EK6LFvk1eIZ9tEDHs/9y6d/8NlTk7iqeTo2f52DO/2IO+w5BHfGhzHkZBmzT+GQ0Dxr3tTRM2nRvQl1rj2/a0BmLTTW1cnB+FAEMvMnSKvMsoVP8MHDP5CekoXJLNGdlZj4UiWq1s3CL8BJVobnmebaNqnFLFHoYF+LlA6EKPntSgiBX8DFn0oBIDAkgOtuupZ1840aW7pTcCbOTGSMw3NkkA4b/g6iRUfvn6PDAYd2+uUnt1s5P5ShXeoyevJBKtW05x/ndMLiH8OY9EoVPl++g7BIb0sRGvnO2AiwtqHejaP45kAllsxYwYaFm9GdOg1b1qfLwPaERBTNSyWEBv69EP69yMrI5vnub7Jl2XY0U9GBZKRZeKJnXV6akknjVvsReRO2iAJ5xq2EsVXtjPtlL/u3+7HwhzDMftG0veM+6rXogbTvQWbOAPsOEP5GlWW/nkZo/KWCnkDJFBcTIBGh77pVXABkxgyguCV5O2R8BVoI2NqU4Pylh7K8XCQkHvmJFbPe5pt3YnITUXmeatv0bcGIbx/Haivw3vtt4gLGPzIp10nSmDBNZg3dKXlq8sN0u893s/vFyumj8dxd7dyqjL85Yx9N2/mWrwKM1PB3XdOQJ987QmSsk5+nXMXujRpmq5lWvZvTZ9hNVGtQ2ePx21fvZuqLM9m46F/AUDhv6NOc+9+4i6r1jeOcDieLp//F7A9/J/54Av4hNtre9C/9hpwkKLRA6Vm/NIjxz1Xh5BH3k3Bhi8vZmMwaXQZ24KlJ516dXdq3I+P7YTyhut4u1i8N5fn+1ZEIN/dQ78q4EJIJv++hbiPPkVv5bWO2lShx36XKrrV7eaL1CzidOlKX3PX4Se55Os6rpeH+1g1o2Cydh145QXBowXKolAXp3HesD+ClgTXclAKQRFW0M+LLdjRqdyUpKXVY9dshMlIyuabVVmpU+8LjeUXQMxBwN8gkEGHnXWF4/COfM2/SAvfL6AIq1orhjd9GUrV+ZXTdYTiXa8EI4Y880w3pOOBReUtL1ri7yVXUuKouH/0z5rzkvJjQE+6DnFV4Xt4RoEWD8DP+t7VGBPT3alnSz/QEhy8J5wzlVYS+ZeREKgWU5eUi5eSh08z56Hf+nr2G7Mxs6jatzc2DO1G5Tiwyx4yfv85tj5xi9uRoTrkJ4atSO5uRUztTp8UtkPoYevJWQJCafgVfjY4DaXUpSpf3//sPfEqD5nWocWX51p4obcIqhOAf7Edmalbxjc9i18YArmntW5pzpxN2b/bno/l7qXN1Bliu47rbP0FonmtsnU3D6+vx9oLRJMQlknwmlciK4S4Zkc8cT+DxG57n1KGCJ8YhDx+jzwNnityAG7dOY/xvexnatR7xcUUncG/F+ZwOnT3r9/sstztk+hQKnrBdmfpWBWo2zKTXwHjDuiVg88pA5nwRze7NeZ9XQXis0IyQaadT0LFfIrWuLE5xEWBu8J9QXADqX1eH138bydh7JpB0Kplfp1agbc8kqtfLcvH1yPO1mv5BBRJOh3HXy58SUi8aslcgncfBeQQhHWQkpvDdmLX88GkEUhZeHzKsJUGhggGvPkbjrh0BCPWDbvfn+Ul1R6ZHIVPfx0gdbzhRgwkR9DAEPpC7ZHB+SgtAamIa879c7Nn/T8KJ/Sfznc81zWz4puQRMhIZ/yC6LLoMBvDFG5XIzhTsXr8PKeVlk+RTBNyBzFnhpYVEBI8smfOxzzW6cn2Ukl8EW0eEVr4Z35XyUk5sWb6dUTe/iT3bnr+UsGbuBv75bX1uCwlURwiJlILgMAex1bIxmSG2Wg73jzxOTFUH+DeFMx1d+g7yi2PGRnjvyaos/L5oQT/NJPjl4/k89on38gwXOxarhZsHdeSnD+eVIErL8AX467dQ7nq8+DTnutN4VatnJ7Z2E8OD3q/LOS1ZgOE8evbynq7rPNvpVRfFpUrtLG4Z7N70bTZDSLiDO4ed5OPnq7ht4w1bwHk6dmcvwF0kw8mjFqrWyeLpD46gy4IMre37JNGpXxIfjarMr1OjAIHQJHUbZ1DziizCohzceGsy1esVb3EBiQi87/zkv8Ro2rkxM458xj9zN3Bo+1H+mH2YKpW/o8sdCfnRNqeOWpj5YQy/T4/gua8foGqeFdCvg4udKzQUBo/bw60PP8nJg0exWCXHD1lJPGUlsuo1tOj3JjZ/z87lInAQ+PeFrN+RzpMILQr8b0aUoHCoL+xcsxdHTjEh3xK2LN9BpdqxReW0tef7L7pyY4/FRFUsWPJISTTx5ZsV+X2akWhRc5fO91LG1hmsbSDnb4o+XGhGjbSSlq2wXJvrBOxr9FIOZP0CAf1Ldp7zRCkv5UBGaiaje7+FPSvH5cnCdcVO5G4z/qYlm9i7NYA3p++nSds0wARaTch0TSwEBevcT487ws4NAS6F1cB4+j5XX5GLjf4v9mP1vA0c233Cp/b1mgg69jlOp9vi8z+nwr4Bef/n/d29xZ+3Hq1Bp3v/xz2jbyuTMWxY+C9Hdh5z2db5tkSvkSMms+Hc+unoygW+NyZJcJiD1KSi0RR5CCFofUuL8xPYw5NYdqbg6Q+OIDQwu0kzP/T1Y+zaGMDuzQFIXRB3yMaHcw8ATjBVA1MlyFmL15uk/51wVnHG/wJmi5kb+jTnhj7NAfjrp5Y80m0SNlscOVkaR/fZCI0OY8Q399Kxv3efA2GpS2TDX4msuwns26h7gxVsbRGmokqA2+O1MAi4q0RVnEuMr94LXtqFxPbmnutO0PiGNKIr55B8xsKG5UHYcwyFRTNrXNf1msvG6gK5+YbCP0WmjoPMGbnp/QHhD/53IYKfLPGDlwjsj8zyXAy5KCakY3/ZXh9uUMpLObDo2+Wkp2SUyK9KSoFAMvn1inw0fy+6DMfsxoktD5HrcjDkleO88L9aRfd7KZp3KREcHsSHK9/g65dnMXfSQpenNU3TECaBX6Af3e7rwKAxd2M2ZyOTn8u1Hhg3MSF0nE6N7AzBvm1+7Nniz+E9NvZuCWTPv/606NGUu0beUmZjKLC2FRAZay/28vALkAQEOUlLNqOZJFabzpPvHuHNh2tgl0WXjzSTRlBYIF3P19/JXA8cOzl7XT22Wg54KNYGxvJbr/vP8O7jRpr1jDSNv+dXpcnNQwmM6g76aWT87aCfoagCY4LgkYiAey6ryeZcaXNrC1r1bsbmpduJP5ZAeGwY13S40ueaO0IIsF5rvC5C6l9XB7PFhMPu/Wn/qtYNPO7rcHdrprwwg80rTUXqQgHoDp1+w3uet6wXG0JYESHPIYOGFfiqmOuXaJnbpT/L1RD8HDL1LXwJlzYCG85/6bCkKOWlHNi2cheaprn9QXlDSsG+rQF8NKoyf8+N4LstW10misx0jd++iuS3ryM5fdxKYIiTDn0Si/SjmTSadbnmPEdx8RASEcyjHw7iwbfv4cyxBCQQXiHUQwZZCyL8Y6TjIGQvM6wIloaYrS1JPZnM5k0LWDRtOekpmVSpV4kR33Sh/Z2tMPkai3kO2LOLevInnjEXq9zmZAsy0kxoJsEN3eGep/ZQvV42r0138sq9AWSk2fPDgXWnTmhUMGPmv0Bw+PlFT4iAe5ApI1226TpYiwniMVugcatcJ2khsedovPFgOPWbrWH8il4IU0WInI1MnwSZP4BMA2xG9EngQ0a9FUU+JpOJJh2vvtBilAkhkcF0GtCOP6cudXuf1EwaTTpdTZV6nnMj+Qf6MfbPF3mu86skx+emL5DGsVJKHvt4MI3bnV9m5YsZoQWUmnIqAgeB+Qpk+lTIWYb3m5MT4Vf+yVJVtFE5MHbAhyyZsaLEykthhIB5RzbnO6OlJWs83bcOB3f45VpSC5YSXJYQhHHTm7xt3EWbnOtSJC0pnTW/byQzNZMq9SvRqG3DIhYCqSdAxnfIzDkgU8BUCxFwJ79NNfHh0C9d2ta8IpPPFnnO2utwwOZVdal07WTCKoQSGBKAlA5ARwgrGamZLPp2uaEomzSuvfFq2t3eEqvf+ScylNJphElnzy/xsSePWhjgJl/OuOWvclXrgtT/UuqGyVv4l1nqfcXFgZTZkDUfad9plKLw64iwXEVGaiYju73O9lW7EZpw8WuLqhzBR2vGEFmxeF+b9JQMFn6znNW/rScnK4cG19Xh5gc7UbmOuv8Vh5TZkP036MlgqgzW6yDnH2TiwLwWZx2hga0tWvikUjl/SeZvpbyUA/OnLOG9QZ+cdz/fbdlKaKRRK+aDZ6rwx8wIr4XQhCbQNI3nZzxBm77Xn/f5FUaV6q9Gf8cP7//qUkupUu0Ynpn6KFfdYJi1pWM/MqE/6IkUmF0NE6xTa0PfuulkprqayJ/+4DAd+yUWTRrmFAhTAFrUnAtmjUiIi2fnkmG0aL/O5+yrDjv8+V0E4591jXIzmU3c8tjNPPTugDKQVHExI7P/MhRhmYJh+M+temy5HhE+AYcjgE+emMpvE//MnyfzFJnqV1Zl7PzniapcfJVzRcmQUkLGN8i08SALZR02VUaEvA56EjJ5FJCJy/dm64AIff+8w+TzKMn8fZm5Xl+ctL+jFaFRIeftd/LLlGiEMKwuC2aFF1vBtd1tLflyxwdKcSlFJg7/mhljZxcpAhl34BTPdnqFPRv2I6WOTHwY9CRc14tz8+/of/PR4hpFrodxT1dl9udR2HPO8l2x1UeLnFluikt2Zjarfl3Hgq+XsXXFTvZtPsgDVw6nctV/S+RXqWkw58uoojuFUcdH8d9C2rchEx8qNDk6yPd1sq9FJj7IrjV7mDtxgcsDfp4F5uiuY4zo+jpOp69RMAqfyfgSmfq6q+IC4DyOTHwATNGICisQIa9BwP8gcAgicg5a+MRSU1xKivJ5KQf8Amy8+fsonu38KulJGefcz8wJMXS928GZYxk47N71Tk0T1GtWx21YoeLcOHnoND9P+N3t8q+uS3DofP3yLF79vg04DxRtlI+kSpXFfPTP13z65DS2rtgJEpwOwbyZ11Gj+a00a58KMhssdcF8dbk4rUop+eH93/j2te/JSCkIYzaZNfwCHVStU3y5AqMf4/XO49U4uLOoH5LT4aRWo+qlJrfi0kCmTcRTviBwgn0j//z8mWFpcRZt43ToHNp+lDXzNtKyZ7OyFveCIvVkyJiFzJwNMhFMVRH+d4J/D4Qo3Zp2Uk9Dpo73tBeQyJS30aJ+gIA7yj2qyBNKeSkn6jWtzdRdH/JYy+c5sf/kOfXhdMDu/e9QIWoe4Nk/AozJw2JVX29psmTG3x5vrGA4yf4zdwPZKRas+Qm9PCCTqNvIxLjlryGlUUagvKs/n830N39i6oszi2x3OnR0R8lWl796O4bFPxWtuyUE2AJs3Hh363OWU3HpIaXdY76g/DaYiYpei+70nMvIZNZYOWftZa28SOcxZPzdoMeRr+jpCUj7Jkj/EhlwB8IUDda25xxR5EL2n4C3xJ86OLagJwwFUyTC1sEItb/Avmlq2agcCYsOZdhHg87pWCEEQeGBVKlXiTo3vOySrdUdUkqadW18TudSuCfpVDJaMUt/UkqyM3xcEsm1pgghLrjikhKfyrevfu9xf2a6ib3/+qEXY7HPq1w8+/PoIvtMZg1h0hj57eP4B7mLDFNctsgcik96phMQ5L2N1OVlv+Qokx4H/RSuFqrc/527IfU1ZNJjyNOtkOlfct5uq854jOzJxZCzCDJ/QCY9hDzTA+n0LddWWaGUl3Lmum7X8vC4gSU+TkpJenIGQ659ltF93qbXUM9ZEzWTRsuezbyGFSpKTnTVKJzFRIyZzBrWkDYUe6PWosBUo9RkO1+Wfb/KpbSEO77/tAKal3uclIaT7muDa5Cd6dpQ0wTX92jG+L9fp1Xv60pDZMWlhAgAzbujrQAS471HE0mg5lWXbwi9tP+bWyXaB78emYFMHQvpk8/vpKYY386HjuGnBDgPIBMG5kY8XhiU8lLOSCk95CPx4dhcx7UNC7aw7o/N3PSAUSbAZM7NIJmb+rpB8zo8+9WjpSDtfwMpJSt+XsPTHV+mZ8g93BI5kLEDPmTPBteaQDf2b+PV8qKZNdr2a4lf2A1gvgJvTzMiYOA5lxwoCxLjktDM3m8HS38OY+aHFQBDSYEC/xZdh7/nhjC0S302/lXIKihgys4PmJs5nZd/eoYGzeuW1RAUFzFCCETA3RQ35QRE9fca2KCZNLref2MpS3cRkbMBb0V53SHTPkTqvhedLYKtk6FclgjdUGDSSydE+ly4eO6e/wGklHzw8CTmTVp4Xv3oTp2d/+zhfy/2o/vgTvz+xWJO7I8jNDqEG+9qw3U3XVOmSdYuJ6SUfPLEFH6e8DuaqSCR4NKZK1g8/W9Gfvs4He68ATAS4d37yp18+XzREg2aScM/yI97X73DcK4N/wSZ8D9wHsO4GUnyC9v59YLAc1s+LCsiK4XjdBT39CWYMrYiK/8IoceAeGpflUlWusZfc8NYMCuctGTX24kQgu4PdaJKPc/VtxX/IQLuh6yFuVlgC1v5jN+HCB5B1wfuZNlP+9m8dJtLnhfNpKHrOsM/f5jwCp5rMV36nIs7bLbht+J/67mdUQuA4BHIlNElPzhtPNLWxsjKW86oPC/lyKJpfzH2ng+LbVe1ThYnj1jJyfb8lKKZNDrcdQMjvn6sNEX8z/HXT//war93Pe43WUx8s+9joqsYJm8pJb9NXMA3r8wi8WRyfrvG7a9k2McPUP2KAmdDqadD1hxk5q8gk/OT1GG94aJLeZ+WlM7tFR8oEgLuCZNZw+nQ6fFQZ+pdV4ePh31BdlYOZrMJXZfous7NgzsxbMIgn1PYKy5/pJ6GTPsIMr8DmW5sNDdABD2Sn6XVnmPn5w9/Z/aEeZw+YtQka9q5MXeOuIXG7S/fDLkA0r4bGd+jhEeZEMHDEYEPnN+5M39Cpr4H+umSHWhthxbx+XmdOw+VpO4iVV6GNh9h5AHxUhG5RoNMPvpjNz1qNALpfYJr0b0Jr/860msbhXeeajeabSt3ecx+rGmCu5/vy72v3OGy3elwsn3VbjJSM6lSr+Jlkb3z+/d+ZdIzXxfZrmkCk9VM70e6svXvnWSkZFLjqqr0GNKFazpchRCCjNRMln63khP7TxIUFki721sSW6PCBRiF4lJAymxwxoGweSwQKaUkMy0Ls9WM1WYpZwkvHHr8ALAXU7D0LETo+wj/kio9RZHSAfYNoCciU97IjXgq9uyICv8YBTzPk5LM3+qRqJzQdb1YxQUklWpkYzJBTOUcTh614smMaDJrVKwVUyay/pfYuXav17INui7Z+c+eIttNZhNXt7nCzRGXLrcN74lfoI2vXvqO5NMp+dtrNqrOE5896NVfJSDYn5tzfbAUiuIQwgZm77l+hBBF/AOzM7P5Y8pS5n6+gFOHzxASGUzXgR3oMaQzIRHeIzAvFUTY+8jEAeDYS8GSs7cDgsCvdH57QpjBalQyx3nSSFxXLNJIyFkKyktJUMpLOSKEQHq5EIUGJouRmbT7gHimjK2I9DCvOh26mixKAZNJo2iZxEIIY+nov0LPIV24adCNbFm+g/SkdCrWjqHONTUvtFgKBekpGTzb8ZV8R3opIS0xnamjZ/LbxD8Zt/w1YqoXDdG/1BCmKIj8CTLnIrN+BufxXN859wn+RPAohCi91AMy+29k+peQs9rHI0xG9GQ5o6KNygEpJZ8+ObXYwoxSh2btjPTMve+Pp+YVWWimsy9W433fJ3tQ82qVpfR8ua7btfnRWp5o1vWa8hHmIsFsMdOk49W06Xu9UlwUFw2fPfUVezcdzI9wy0PqkoQTibxx1wcXTLbSRgg/REBftIhv0KIXISK+A/NVro20isZyUUC/UjuvTP8CmXg/5KwiPyzaKybwuwmhnV/l+nNBKS/lwJKZK4y08l7QTJLQSAft+yQC4Beg8+6Pe+n+v3isfgVKT2SsztDx96uidqVEv6d6eMzdopkEweFBdL6nbTlLpVAoCpOSkMrCb5d7fAB0OnR2rN7N3k3eynJcughrY7SoHxGRvyHCJiIivkNELykVP5c8pH07MvWt3He++NuYQAQigp4oNRlKglo2Kgd++mAumiaM+jdnIyQCCAp1MmbGfvwCCtoEhug8OuYY9z9/gqP7bJgtguqNO2GJvKn8hL/MadiyPsMnP8K4wZ+CEPk3RyEEASEBjJn/AoGhF6bwmEKhMNi36SCOHO+WACFg+8rd520tlFJedNGAeQhLPbDUK5O+ZcY0KK6sSWGsLRAhoy9YpXulvJQxTqeT3ev2uq3GW6FyDpVqZtOqawqdbksgMMT9U0VAkE69xpmAQIQoi0tp0+2+DjRu35C5ExewY/UeLDYzLbo3pfOAdgSFKcVFobjQ5CXg9IaUvrVzR3pKBj9/+Du/TfqT+GMJBIYG0nlAO/o91YMK1S59P5rikI69kLWUYhUXEYoIex9M1S+Y0pKHUl7KGCGE8UjgRntp3T2ZB148jskE9hzB+qVBpCaZia2WQ/1rM/JK3+B0GE68IvRlhKVROY/gv0HFmjE8MPZ/F1oMhULhhnrNauMXaCMr3Xtl82s7XuV1vztS4lN5os2LHNt9PN86npaUzpyP57Pwm2W8t+zVy7YkgXQeQyY9A/Z1Ph6hI2xtylQmX1E+L2WMpmk0bnel2yeCv34LRQiY920EdzZuyKi7azPmkeo83qMutza4knnfRpCdKfh9WiRDOtUj09n7AoxAoVAoLiz+gX70HtrNa5vQqGAqVCt51MunT03l2J4TRZb1dadOekomr9/x/vkXP7wIkXoCMv5OsG8swVEXj8pw8UhyGdNveE+3jmanj1t5c0g1xj9btUhq9YxUE+Ofrcr9reszYWQVjuwNxOZvLS+RFQqF4qKi+4OdvO5PiU/jt88WlKjPlPhUlsxY4dERWHfqHN5xjK1/7yxRv5cCMv2b3Gy6xfu4SGmsAEx9O5zv3p5DQlxi2QtYDEp5KQda3NyErvd1cLvvr9/CPBxlrBmdOWFDmARt+l2v0qwrFIr/LAu+Xu7Vp0UimfOx96jOszm47UixNb2EJtizfr/XNhcjUkpk1mL0hPvRT92Afrozeur7SGdu1tzMH3GtMeVKfJyZWZ9E8+noSswYX4GRd9Xiuw9DmDzyW+6qOoRZ78wpn4F4QM2G5cDu9ftYNO0vD3uL92qXuuSuEedWdEuhUCguBw5sPew9Q7mEY3vicDqdPhemNVt9mAKlxGK7tKZKKXVk8ijI+gmXCKL0SciMbyFiKugJHo6Fr9+JYcaHBRncjWSpBXOV7tT5/LlvCasQSpd725fRKLyjLC/lwLev/VBsgjpvWG0WajVSCekUCsV/F1uAFaF5f9gzW0xomu/TWt0mNQmJ9F5WQALNul3jc58XBZnf5Sou4LospIPMQCY+CJr7KKofPotm+gexSF3kvzw9ZH/9yix0/dzntvNBKS9lTGZ6Fqt/W39eysu5hv8pFArF5cINfVp4vY+azBo33NK8RDlaLFYLtz3dy+N+zaTRtt/1VKx56dSRk1Ii07/As1VfN6wulkacrQLkZAlmjPd9rCcPnubQtiPnLOv5oGbFMiYjJbOYYozFc23Hq0tJGoVCobg0adWrGVXrV3JfzkMYFpLbni55RObtz/Six5AugFFw1fhrnKNCtSiE0Hh/8Gf8M3c9TqfvlZ4vGDIJnIfxXtDRDCIATNUwlpUMtqwOIj2lZLXcMtOyzkXK80YpL2VMSGQQtgDbefVx6+PdS0kahUKhuDQxmU2M/fNFqtSrlP/eZNYQQmDztzJ61nDqN6td4n41TePxTwbzybq36P5gJ1p0b0KD6+tiMmucPHSav35czZ9fLeGFnmMZcu0znDnu3lfEHScPn+ap9i9xe6XB/K/mI/ww7tcSy1dyfLE86YAVETEDbB3JUwUy00qmEgghqFQntsQSlgZCXmYB7CkpKYSGhpKcnExISMiFFgeAj4Z9wS+f/nFOFpjBb/2P259R+V0UCoUCQNd11v2xmdW/riMny06dJjXpfE/bUivjsXnpNp7u+LJbw4XJrFG1QWUmbnq3WN+al259m5U/ry2yXWiCL7Z/QNVcJcxXDu04ym+f/cn2Vbux2Mxc370p3QbdSFh0qEs7KSUyvgc49uLV+mJtjwifiBDCiECy7+DAtiQeum66zzJd0bIeH654o0Tj8EZJ5u8yU15q1KjBoUOHXLaNGTOGESNGeDwmKyuL4cOHM3PmTLKzs+natSuffPIJMTG+r8FdjMpL4skkHrjqKVLiU4ttq2mCsJgwrr3xKno/ehNXtKhbDhIqFAqFAmBE19fYuHirV/+aN+aOovlN13rc/8mTU5g9fp7X8yzQv/dZpl8//YMPH52MyaThdOTWX9MEfoE2xvz+Ale2qu/SXmb+hEz2PNfmIcK/KJIxd2jzEezdeACL1UGbHklUr5dFVobG3/NCObTLP7+dpgmm7plQqv5AF43yMmjQIAYPHpy/LTg4mMBAz9rxww8/zNy5c5k6dSqhoaE8+uijaJrGihUrfD7vxai8APz59TLeGfhRse1uvKs1I6c9Xg4SKRQKhaIwOdl2egTc7bYWXR4ms4lu99/IE5896LFNZ+22Ys91Y//WjPym+Hv9v3/t4Kl2o93uE5ogINifb/Z/THB4UP52w/rSGxzekuuZwNoGLWKSy9b9Ww4x4+VHeOytfQQE6TjsRnkakxlWzg/hrUeroet+vDFvFNe0L3k5Bm+UZP4u0+D14OBgYmN9Ww9LTk7miy++YPr06dx4440ATJkyhSuuuILVq1dz/fXXl6WoZU67267no0cnF+vcNPD1u8pJIoVCobj8kdIO2YuROatAgrA2Bb+uCFE0Y3lOVppXxcXoT5KTleNx//F9J3ySa/G0v6nftA7R1aJo0b0JVpvFbbsfx/2GyVxgcXGRRZdkpGSy4Ktl3PpEgW+kEAKnszinVifYtxTZWrPBaUZ8vA/QEQIshT6m6zun8OkSJ6F1JrkoSxeCMnXYHTt2LJGRkVx77bW88847OByeS5qvX78eu91Op04FKaAbNGhAtWrVWLVqlcfjsrOzSUlJcXldjNj8bfR/oZ/XNr0e6UbFmhXKSSKFQqG4vJGOvcjTnZFJwyBjFmTOQiYPR55uhzxr4pZS4u94lqiKOXjzFZFSUvNqz3m39m4+5HHf2Xz61FRe7fcud1YazJKZ7lcY1v252a3iUlie9Qs3579f+8cmnmo/mp1rDxeriOFGgZNpHyGEwF3EuWaCSlV3EBTk+xjLijJTXh577DFmzpzJkiVLeOihh3jzzTd59tlnPbaPi4vDarUSFhbmsj0mJoa4uDiPx40ZM4bQ0ND8V9WqVUtrCKXO7c/0ov8LfdFMGkaxaePqEJrgjuf68OiE+y+whAqFQnF5IPVkZMI9oJ/M3eLIfQF6IjJhYEGqfICc1Qj7MnrddwbhZWY0mU10Hdje4/76LUoe8ZSamM6bd3/AyjlFHXx9SQLnyDFCuOdNXsSom95g24pdrPozJDczrnucTgF+rvWipJ4KOSvxXu/IhMwqWRmGsqBEy0YjRozgrbfe8tpmx44dNGjQgKeeeip/W6NGjbBarTz00EOMGTMGm+38QocLM3LkSJdzpaSkXHAFRkqJlLKIN7oQgoGv3kmvR7qyePrfJJxIJLJSBB3uuoGI2PALJK1CoVBchmT+mJsC3535ITfTbMZ0RLAxf8jM2YCJWwafYd3SEP5dFZhruTAeMjWTROrwzJShXrPyxlR2n7nWF966dwIBwf5I4JoOV3LLsJu5smV9tizf7tWBeNPif3nltnfzo5t0p84f0yO549FT+AfqnF0tQXcar+TkHkSFgNTTIetXdvz1NQ0aFyelAJl2zmMsLUqkvAwfPpyBAwd6bVOrVi2321u0aIHD4eDgwYPUr1+/yP7Y2FhycnJISkpysb6cPHnSq9+MzWYrVWXofNjxzx5mvTOH1b+tx2l3UP3KqvR59Ca6DbrRpdZGRGw4/Z7qeQElVSgUissbmTUf74nadMiaB7nKi2GhcWK1wRvT9jPniyjmfBnF6eNWhJA065DKHUPjadS7dbHnbnB9XXau3lNimTNSMslIyQRg6cwVLJr2Fz0e7MymJVu9HqfrkhWz17ik40hOMDPqrlq8Me0AQSFOQxHLXQqy5whefaAGTXsco2LVnQRaPiH5TBbjn6nC12s0AoK8WXucCFPNEo+ttCm3PC/Tpk1jwIABnDlzhvDwolaG5ORkoqOjmTFjBn379gVg165dNGjQgFWrVvnssHuhoo2WzVrJG3d/gKaJglA2IZBScsMtzXlx1lM+FwtTKBQKxfmhn+kOjmIUCC0KrcJKo33Sc5D1C4WXTKSE7EwNs0XHbAG0WLQKy4s998o5a3nplrfPQ/pCCOgyoD1/frXUo+OuN/wDnXTsm8i1bdIQmmT72kD++C6C1EQzQpO5tYsgT9G7fegpBj4Xh8mNaUNKEMKCqPA3Qiv91YKSzN9l4vOyatUqPvjgAzZv3sz+/fuZNm0aTz75JP/73//yFZdjx47RoEED1qxZA0BoaCiDBg3iqaeeYsmSJaxfv5777ruPli1bXvSRRkmnkxk7YAJSSpcLK08vXPHzGuZNWnihxFMoFIr/HuYGFE59XxQTmAtWAYT/rZzt6yEE+AXkKi5oiIDbfTr1L5/+UWo16Uy5PpJj5r/AlTc0KPHxmekmfvs6itcG1+DVB2rQonMyPQeeATjLJ8YowDjr4wps/DsI51nxNU6n8XmIkJfKRHEpKWWivNhsNmbOnEm7du248soreeONN3jyySeZNKkgntxut7Nr1y4yMjLyt40bN44ePXrQt29f2rZtS2xsLD/99JO7U1xUzP9yCU6H07OFUsLkkdPYv+XCe2grFArFfwERcBfeHU+diID+BW+tzV1S5btiAq0iBPzPp3Mf/PfweRXjdZHSobNpyTaadWlMn0dvOud+NJPk+s4p1GucyezPozEmLHelBASjB9Rk3reRZGUW7M9z4ZQpL6MnDkXXM9wcW36USZ6XJk2asHr1aq9tatSowdkrVn5+fnz88cd8/PHHZSFWmbFv0wEE3ldXM1IyGXrdc7zy83NeMzMqFAqF4vwR1mbIgPsgYwq43KFz//frk6us5G4VAsLGI1PegszvgJyC9tYbEKFvILQwn87tF+RXWsMAIDszGyklfoHn6t9pOBvf8egp/p4XllvDyHMNJKdD46NRVdi50Z/h7x9Fl2DO1xYckL0ATrdHj16CppVOWYaSogozlgJmqxm3QfFn4XTovHb7e2SkZpaDVAqFQvHfRgSPQISMBVOh8GVTVWPpI3RsfrqK/PbCihb6IqLCCgj9CELHIaIWoEVMRph8T4Pf/vZWpbZsBJB0KoWb/e7i5KHT+Aefu2IUf9LCySMWt/4sZxMY4uCJt4+BKKy4FEImQfLT5yzL+aKUl1Lg+u5NfTIRSinJyshm0bS/ykEqhUKh+G8jhEAE3IqImouo8A+iwmpE1AJEQH+Eh2QuMmshMvFhSB4GyU8hk59FZv1RovP2eLgL/kF+parAOOxOxj/8OU67t6UwiihkuVsBeO+JqvgF6OjeuwCgTY8kTGaJ1/qT2Ut9ykNTFijlpRRocL3vSYlMJhO71+0rQ2kUCoVCURghBEILR2gRHiZ3Az31Q2TSI2DfgLHMJMG+CZk0DD31PZ/PF1UpgrcXjiY0yoiYMVlMmCylE22ak2X3uC8vwtUdUgoy03MTpHqd+Y3jr74+o/gMvThBJhbXqExQyksp8MQN7otmecJiLdOSUgqFQqEoITJnA6TnFc8tbE3I/T99IjKnaAZcT9RrWptphz5h1PQn6D64Ez0e7IzVz339otKiuMwnZovkzAkLfR88DcJdW2Obf1DhEOpiEKXr3+MrahYtBeJP+K55Oh1OWnRvUobSKBQKhaKkyIzpGKHVntZUNGTaZETEdT73abFa6HDnDXS48wYAFn673KvlpKzRdYF/oE7/p06imSQ/TozG4RBoGuhOQ1npcFdr7hl9G5UrzoSMid47FOHKYfdSJf5EYrHa7tnY7Z4LVCoUCoXiAmDfgvfQah1ylqCfuQWZ9ec5naL+dbURmo8WjXPEW+yI7hS07p6MpsH9o+KYvmE7j799lP89FYfZqgOCJTNWMP3FASQc/Lb4kwU+XGpylxSlvJwHqYlpPNH6Be8x0m545dZ32bmm5KmjFQqFQlFGCB/DkB07kEmPItO/KvEpWt/awiWFf1ng6Vla0yQtOidT84qs/G2hkU663ZmA2SJx5BjqQN+HTvHMh0cIj0r3fiL/29CCBpaS1CVHKS/nwc8TfufUodPndOwbd48vZWkUCoVCcc7YOuHblGj4wMjUMUjnCZ+7P3XkDF8+P+PcZCuEpkkqVs9m7Ky9XNm8oECiZtJo1ec6hn/xCBabGSEEZosJk9lwFG7WIYeRnxzJV27yMuhuWB7EN+8a9QMjKuTwwAvGmDw69WqxEPkLWugb5z2W80H5vJwHv09ehH6OWnTc/pPFN1IoFApFuSAC7kRmTAGZhavDrhcyf4CgYT41/e6tn8ksJsdXSFQwAsO1ICO5aFvNJLFYJCM+PkzdRhm888M+XrqvJusWh1K5bkWenPgQYdGhtOrdjMXT/ubIrmMEhATQtt/11Lkmipyk6Rz6dyKBwTkcP2Bl3rRIlv8ahu4UaJrkzRn7i4lEAvQkhNl9AebyRCkv50HCyaTzOl5K6TVsT6FQKBTlgzDFQPgXyMQHQab6dIx07POSp7YAXdf586ulXosqmswaHe9uwyMf3AfAgW2HmTB0Mrv+2UFONpjMkrY9k7jr8ZNUr5ed2y88++Exli58lK73dcI/yB+AkIhg+gy7yfDHdGw1XjmBzJ9RmY8eq+PW1aHrXQnUaJDtQ77VLNATwBTrw8jLDqW8nAehkcEkxCWd8/FKcVEoFIqLB2FtCtHLkJmzIfV1vDs0ChABPvWbk2UnKz3baxupSxILPRDXvLIa7y0ahuNEa9JTNPwCdaw2V3k0DULCc+g92A/h5+/an30rMnkkOHblb1v9Ux0EgW5H1WfQac/ljlwQSGn3SWkrS5TPy3nQ7f4bzzmDYmzNCqUsjUKhUCjOF6EFoQXeA7aueK9K7UT4dfOpT5u/tdi0/kITRFaKOOsUhzGZJSERziKKSwEmcBx02SLtu5Hx/cHhGhiSkyXdOvQKIQ2ri0/TmYQzndETHyuRz09po5SX86DPYzcTHhOEyVRyv5cXvnuyDCRSKBQKRWkggh7CMEO4szGYwHwVWFv71pcQ3HR/R68Pu06HTpd7258thA+966C5tpNpH2AUlnRdpqrTKAPNzXwlJdhzSmJL0SF7ATK+H9IZV4LjSg+lvJwH4RVC+WDZUBpeV0xI2dnHVQynfrM6ZSSVQqFQKM4XYbkSEf4piLwkbGbyLTGWxoiIyR7rI7nj9md7ExYdgsns5hgBNz3QkVqNqrtuN9cDUzW8r+VouZFSBlJPgezFuMtZ0/1/8bgvRSRYszAYR4lSkDlBT0CmfliSg0oNpbycJzG1ruHdnzOYtHQnj71zGF+SvlSu5Xt1UoVCoVBcGIStHaLCCkTIGAjoD4H3IyJmQsgryJR30E82Q49rhB5/OzLzV6T07JAbWTGc8SvfoFG7K122+wXa6D+qL49/Orjo+YVABD2J53lFgP9dCFN0wSY9EU/RUlVq5/DIa8cA0AqtiAlN8P2nFdA0z3li3OOErF+Q0nsUVVkgZEnTw17kpKSkEBoaSnJyMiEhIeVyTpn+DTL1NQAeaFufI3tteNOU31rwIk06NioX2RQKhUJResisJcikoRgKRZ51QwN08OuFCH27WIvMsb0nOPDvYax+Vq5uewX+gd79YWTGdGTKm4Adw/qjG+f3vwMRMhohzMicDciMGWD/F5z7vfa3YXkwP3x+HRuWJCB1Se3G1bnl8e50vj0NUkbkjitPNSheRRBRCxHmasW2K46SzN9KeSkFpJTIlNch8xu2rwviqd61crXXogpMnWtq8umGt8tFLoVCoVCUHlJPRp5qA2TjaVIXIa8jAm4vg3OnQNZcpPMIQoSB380IcxVj/kl9CzK+xHttpsJoiOhlSBGN1GV+IjsA6TwDmT8g7dvBeQAcuyku6kpU+AehhZ3P8ICSzd8qVLoUEEIgQl9E+veiYevvePO7vbz+gCQ9xbVdk06NeGPeyAsjpEKhUCjOj8zZeFNcQCDTvyoT5UVoIRBwV9FH4qyfcxUX8E1xAQIGI0wxRl9nGYmEKQqChiAA6diPPOMtokoDa6tSUVxKilJeShFhbYywNqZZP/i5H6xbsJlNi7cSEOJPj4c6ExIRfKFFVCgUCsU5Iu1bMCzqnpQXCc49SJmDENayl0dKZPrkYmRyQ+ZMpBYIgQ96XeIS5lpIv96Q9Yub/gWgIYIeK7ngpYBSXsqQZp0b06xz4wsthkKhUChKBRPFZ3EzJvXSRjoOGlFEMsuIQrK1B5leJJeLb50lI9PeB2ccIvRlr01F6BtIYYXMHzEUGA1wghaBCH0LYb2m5OcvBZTyolAoFAqFDwhbG2TWHC8tNLA2R4jSm1qlnoFMHgHZ843+ERjKQzSEvHp+nWdORwbcibA08NhECKuhwAQNg6wFhsJkrgW2DghhOb/znwcqVFqhUCgUCl/w6wZaDJ4z7+qIwKIhz+eKlBKZ9Dhk/5nff75fix4PSY8bVZ7POVm/CZn5o08thSkWEXgPImgIwq/LBVVcQCkvCoVCoVD4hBBWRMQU0PLS+OcpDcZykgh+AWFrU3ontG+GnGW4z9uSq8hokZTI3+XsPpzHz1m8C4laNlIoFAqFwkeEuQ5E/QlZvyKzFgFZYG6ICLgTYa5ZqueSWXPxHv7sBMdOsPWC7F/OauuLE69WSBG7tFDKi0KhUCgUJUBogRBwJyLgzrI9kZ7sQyMnhLyIsHdDZkwzqkgLf/DrDs6TuZFCnpUf4d+rFAUuP5TyolAoFArFRYgwV0MWZz0RwQgtCOHXCeHXyWWXdB5DZi8ynGyLKDAaWFsjpYC0SYAA2/UIy9WlOYQyQykvCoVCoVBcjPjfCmkTvDQwQcAdCOHegViYKkPEdGTyU7mZcvOWkjSwdQTHIUi8G5nn/pqmIy3XQOg7iJy1yKzfjFpJppqIgDvAej1CnKtzcOmiygMoFAqFQnGRItM+M3KyFMEEpiqIyO+LzXArpURm/wUZ3xq1j8g28sW41DDKQ8t9OShQdnJ9afz6IELHeFSWzhdVHkChUCgUissAETQEtArI9I/AeTR3qwX8eiJCnvUtNb/zCKSMAv0MnipOF6AXapOn2OQuOWX9DOY6EPRgyQZRBijlRaFQKBSKixgRcCv49zGqRcssMFUzah35gJEr5gkjL0yxiosP/WVMgcD7LnieF6W8KBQKhUJxkSOEZlg9Sop9Czi2lp4gejw49oOlfun1eQ6oJHUKhUKhUFymODLWoeul7WTrY/XqMkQpLwqFQqFQXIZIKfn9y6WcewZeN4ggMNcuvf7OEaW8KBQKhUJxGbJpyVbmTExCK/FM78lSo0HA3QhhO0/Jzh+lvCgUCoVCcRmy4JtlHN0fwPplQTgd7tvohX14RSgE9AcRjqt6kPu/tQUiaFgZSVsylPKiUCgUCsVlSMLxRHSnztih1Tm4yw8AZ667iiNXmVnxexhEfI+InI2osAIt5CVE1FxE0FAwVQMRBparEaFjEeGTLwqrC6hoI4VCoVAoLkuiKkdiMmukJJh5vHtdWndPpmPfREIjHZw4bGX+9EgO761Gu0GNXY4TpkgIGnbRWFncUSaWl6VLlyKEcPtau3atx+Pat29fpP2QIUPKQkSFQqFQKC5rugxsj9NhrAvZczSWzA7nhf/VYthN9XjzoRps+iuEmwd3vsBSnhtlYnlp1aoVJ06ccNn24osvsmjRIpo1a+b12MGDB/Pqq6/mvw8ICCgLERUKhUKhuKy5us0VtOnbgr9/WsPZlYBMZo2oKpH0frTbBZLu/CgT5cVqtRIbG5v/3m63M2fOHIYNG1ZsUaeAgACXYxUKhUKhUJQcIQSjpj/BFyOn88sn88nJsufugGbdruXJiQ8REhF8YYU8R8qlMOOPP/7I7bffzqFDh6hSpYrHdu3bt2fbtm1IKYmNjaVnz568+OKLXq0v2dnZZGdn579PSUmhatWqqjCjQqFQKBS5pCens/XvndhzHNRtUouY6tEXWqQilKQwY7koLzfffDMA8+bN89pu0qRJVK9enUqVKrFlyxaee+45mjdvzk8//eTxmJdffplXXnmlyHalvCgUCoVCcelQZsrLiBEjeOutt7y22bFjBw0aNMh/f/ToUapXr86sWbPo27evr6cCYPHixXTs2JG9e/dSu7b7jH7K8qJQKBQKxaVPSZSXEvm8DB8+nIEDB3ptU6tWLZf3U6ZMITIykl69epXkVAC0aNECwKvyYrPZsNkujrhzhUKhUCgUZU+JlJfo6Giio31fJ5NSMmXKFAYMGIDFUvLy2Zs2bQKgYsWKJT5WoVAoFArF5UmZZthdvHgxBw4c4IEHHiiy79ixYzRo0IA1a9YAsG/fPl577TXWr1/PwYMH+eWXXxgwYABt27alUaNGZSmmQqFQKBSKS4gyzbD7xRdf0KpVKxcfmDzsdju7du0iIyMDMMKrFy5cyAcffEB6ejpVq1alb9++vPDCC2UpokKhUCgUikuMcok2Kk9K4vCjUCgUCoXi4qAk87cqzKhQKBQKheKSQikvCoVCoVAoLimU8qJQKBQKheKSokwddi8EeS48KSkpF1gShUKhUCgUvpI3b/viinvZKS+pqakAVK1a9QJLolAoFAqFoqSkpqYSGhrqtc1lF22k6zrHjx8nODi42ArW5U1e6YIjR478JyOh1PjV+NX4/5vj/y+PHdT4fR2/lJLU1FQqVaqEpnn3arnsLC+apnmtXH0xEBIS8p+8gPNQ41fjV+P/b47/vzx2UOP3ZfzFWVzyUA67CoVCoVAoLimU8qJQKBQKheKSQikv5YjNZuOll176z1bBVuNX41fj/2+O/788dlDjL4vxX3YOuwqFQqFQKC5vlOVFoVAoFArFJYVSXhQKhUKhUFxSKOVFoVAoFArFJYVSXhQKhUKhUFxSKOWlnOjVqxfVqlXDz8+PihUrcs8993D8+HGXNlu2bKFNmzb4+flRtWpV3n777Qskbely8OBBBg0aRM2aNfH396d27dq89NJL5OTkuLS7XMcP8MYbb9CqVSsCAgIICwtz2+bw4cN0796dgIAAKlSowDPPPIPD4ShfQcuIjz/+mBo1auDn50eLFi1Ys2bNhRapTFi+fDk9e/akUqVKCCH4+eefXfZLKRk9ejQVK1bE39+fTp06sWfPngsjbBkwZswYrrvuOoKDg6lQoQJ9+vRh165dLm2ysrIYOnQokZGRBAUF0bdvX06ePHmBJC5dPv30Uxo1apSfjK1ly5b8/vvv+fsv57GfzdixYxFC8MQTT+RvK83xK+WlnOjQoQOzZs1i165d/Pjjj+zbt49+/frl709JSaFLly5Ur16d9evX88477/Dyyy8zadKkCyh16bBz5050XWfixIls27aNcePG8dlnnzFq1Kj8Npfz+AFycnK47bbbePjhh93udzqddO/enZycHFauXMlXX33F1KlTGT16dDlLWvp89913PPXUU7z00kts2LCBxo0b07VrV06dOnWhRSt10tPTady4MR9//LHb/W+//TYffvghn332Gf/88w+BgYF07dqVrKyscpa0bFi2bBlDhw5l9erVLFiwALvdTpcuXUhPT89v8+STT/Lrr7/y/fffs2zZMo4fP86tt956AaUuPapUqcLYsWNZv34969at48Ybb6R3795s27YNuLzHXpi1a9cyceJEGjVq5LK9VMcvFReEOXPmSCGEzMnJkVJK+cknn8jw8HCZnZ2d3+a5556T9evXv1Aililvv/22rFmzZv77/8r4p0yZIkNDQ4tsnzdvntQ0TcbFxeVv+/TTT2VISIjLZ3Ip0rx5czl06ND8906nU1aqVEmOGTPmAkpV9gBy9uzZ+e91XZexsbHynXfeyd+WlJQkbTabnDFjxgWQsOw5deqUBOSyZcuklMZ4LRaL/P777/Pb7NixQwJy1apVF0rMMiU8PFxOnjz5PzP21NRUWbduXblgwQLZrl07+fjjj0spS/+7V5aXC0BCQgLTpk2jVatWWCwWAFatWkXbtm2xWq357bp27cquXbtITEy8UKKWGcnJyUREROS//6+N/2xWrVrF1VdfTUxMTP62rl27kpKSkv/UdimSk5PD+vXr6dSpU/42TdPo1KkTq1atuoCSlT8HDhwgLi7O5bMIDQ2lRYsWl+1nkZycDJD/W1+/fj12u93lM2jQoAHVqlW77D4Dp9PJzJkzSU9Pp2XLlv+ZsQ8dOpTu3bu7jBNK/7tXyks58txzzxEYGEhkZCSHDx9mzpw5+fvi4uJcJi4g/31cXFy5ylnW7N27lwkTJvDQQw/lb/svjd8dl+v4z5w5g9PpdDu2S3lc50LeeP8rn4Wu6zzxxBPccMMNXHXVVYDxGVit1iJ+X5fTZ/Dvv/8SFBSEzWZjyJAhzJ49m4YNG/4nxj5z5kw2bNjAmDFjiuwr7fEr5eU8GDFiBEIIr6+dO3fmt3/mmWfYuHEjf/75JyaTiQEDBiAv4QTHJR0/wLFjx+jWrRu33XYbgwcPvkCSlw7nMn6F4r/C0KFD2bp1KzNnzrzQopQr9evXZ9OmTfzzzz88/PDD3HvvvWzfvv1Ci1XmHDlyhMcff5xp06bh5+dX5uczl/kZLmOGDx/OwIEDvbapVatW/v9RUVFERUVRr149rrjiCqpWrcrqf/ewuAAABGVJREFU1atp2bIlsbGxRbyu897HxsaWuuylQUnHf/z4cTp06ECrVq2KOOL+F8bvjdjY2CIROBf7+H0hKioKk8nk9ru9lMd1LuSN9+TJk1SsWDF/+8mTJ7nmmmsukFRlw6OPPspvv/3G8uXLqVKlSv722NhYcnJySEpKcnkCv5yuB6vVSp06dQBo2rQpa9euZfz48dxxxx2X9djXr1/PqVOnaNKkSf42p9PJ8uXL+eijj/jjjz9KdfxKeTkPoqOjiY6OPqdjdV0HIDs7G4CWLVvy/PPPY7fb8/1gFixYQP369QkPDy8dgUuZkoz/2LFjdOjQgaZNmzJlyhQ0zdXod7mPvzhatmzJG2+8walTp6hQoQJgjD8kJISGDRuWyjkuBFarlaZNm7Jo0SL69OkDGNf+okWLePTRRy+scOVMzZo1iY2NZdGiRfnKSkpKSv4T+uWAlJJhw4Yxe/Zsli5dSs2aNV32N23aFIvFwqJFi+jbty8Au3bt4vDhw7Rs2fJCiFzm6LpOdnb2ZT/2jh078u+//7psu++++2jQoAHPPfccVatWLd3xl45/scIbq1evlhMmTJAbN26UBw8elIsWLZKtWrWStWvXlllZWVJKwxM7JiZG3nPPPXLr1q1y5syZMiAgQE6cOPECS3/+HD16VNapU0d27NhRHj16VJ44cSL/lcflPH4ppTx06JDcuHGjfOWVV2RQUJDcuHGj3Lhxo0xNTZVSSulwOORVV10lu3TpIjdt2iTnz58vo6Oj5ciRIy+w5OfPzJkzpc1mk1OnTpXbt2+XDz74oAwLC3OJrLpcSE1Nzf9uAfn+++/LjRs3ykOHDkkppRw7dqwMCwuTc+bMkVu2bJG9e/eWNWvWlJmZmRdY8tLh4YcflqGhoXLp0qUuv/OMjIz8NkOGDJHVqlWTixcvluvWrZMtW7aULVu2vIBSlx4jRoyQy5YtkwcOHJBbtmyRI0aMkEII+eeff0opL++xu6NwtJGUpTt+pbyUA1u2bJEdOnSQERER0mazyRo1asghQ4bIo0ePurTbvHmzbN26tbTZbLJy5cpy7NixF0ji0mXKlCkScPsqzOU6fimlvPfee92Of8mSJfltDh48KG+66Sbp7+8vo6Ki5PDhw6Xdbr9wQpciEyZMkNWqVZNWq1U2b95crl69+kKLVCYsWbLE7fd87733SimNcOkXX3xRxsTESJvNJjt27Ch37dp1YYUuRTz9zqdMmZLfJjMzUz7yyCMyPDxcBgQEyFtuucXlQeZS5v7775fVq1eXVqtVRkdHy44dO+YrLlJe3mN3x9nKS2mOX0h5CXuMKhQKhUKh+M+hoo0UCoVCoVBcUijlRaFQKBQKxSWFUl4UCoVCoVBcUijlRaFQKBQKxSWFUl4UCoVCoVBcUijlRaFQKBQKxSWFUl4UCoVCoVBcUijlRaFQKBQKxSWFUl4UCoVCoVBcUijlRaFQKBQKxSWFUl4UCoVCoVBcUijlRaFQKBQKxSXF/wFIu+Doo0yczQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y[:,0], Y[:,1], c = df.iloc[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5401296d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mds = MDS(n_components=2, \n",
    "             random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "835c6abe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Андрей\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\manifold\\_mds.py:299: FutureWarning: The default value of `normalized_stress` will change to `'auto'` in version 1.4. To suppress this warning, manually set the value of `normalized_stress`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "Y_MDS = mds.fit_transform(df.iloc[:,1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c0306f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a19d64fdc0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_MDS[:,0], Y_MDS[:,1], c = df.iloc[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f272811a",
   "metadata": {},
   "source": [
    "\n",
    "## Графики по столбцам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8f42d003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = df.Survived)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "959232fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pclass', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = df.Pclass, hue = df.Survived)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "00bbd94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Fare', ylabel='Count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,5)})\n",
    "sns.histplot(x = df.loc[df.Fare < 100].Fare, hue  = df.Survived, kde=True,  )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1ae491dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,5)})\n",
    "sns.histplot(x = df.Age, hue  = df.Survived, kde=True )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ddb15368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='SibSp', ylabel='Count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,5)})\n",
    "sns.histplot(x = df.SibSp, hue  = df.Survived, kde=True )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e34f4ec",
   "metadata": {},
   "source": [
    "## Корреляционная матрица"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52722264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PtWppx2yhQqZUruzIpUtZD6tfunQSyclJzJ+/nUKFiqRb7u19FC0tHZo1Uz3XubouZfLk5enyC5GVXAuAChQogI+PD8+fP1dJHzx4MNu3bwegSZMmLF+uutO+nrZ7926aN2/ODz/8QM2aNRk2bBhNmzZlwYIFKmX27t1LtWrViIyMZPLkyfTu3ZuUlJS35gXYtWsXrVu3xt7entatW7Nx40aSk5OV+X19fXFxcaFatWo0b96cs2fP5kwD5SdBQWBrC+PHQ+qP2efq0SN/Che2okAB1XFWRYqU4vHjgEzLGRubY2GhuOMXHx+Nv/8/HD26hFKlaijTFcti2LPHjQYNhmBurn7j4jNSrBg8eQIpKarpjx4pengyU6KE4r+1asH06TB3LgwbBiVLqubT0oKvvlL0JIWF5WzdP5Z79/wpUcJK5QISwMqqFEFBme9X9+75U7KkTbr018vduXOTkiWt8fG5gItLR+rWrUyHDk3Yu3e7Shlf35tER0cxaFB3GjSoQps2dfnpp4UkJiZkfwNzSUCAP1ZW6dutZMlSBAZm3m4BAf4qz/G8UqpUWjkjIyOqVVM8gJaQEI+v7y2mT59M4cKFadFC8fxLXFwcgYEBhIbep1u39jg4/I/WrRuzceM6Ut7c4fNYcGgoCQkJ2Lwx1Mza0hKAgAy6Wf3v3QPA5o2DztrKSqVMz06dCAwJYd2WLbx4+ZIr16+zcccOGjo7Y5I6FLBHx44M7NFDZT2BISHcuXuXcqVLZ38DP5LgR49ISEzEJjV4ecU69WQWEBGRrkzJokX5rkcPylhYqKQfu3oVLU1NbIoVI+TxY17GxFDC1JSZW7fiNG4cVUaMYNiKFYQ/fZp7G/SRhYT4Y26e/pgtXrwU9+9nfswCjBkzn7lzt2JjUzHD5QEBNyle3Jr//rvAuHEd6dy5MoMGNeHQoe0Z5v+kFSiQs5/PUK4NgRs4cCBjx46lQYMG1KpVCwcHB2rXrk2VKlXea+x0UFAQDx48YO/evcTGxnLo0CF27drFhAkTlA8j7tu3j2bNmmFomPawuYaGBh07dswy7/bt21m8eDEzZszA3t6eGzdu8P333xMREcHEiRN5+fIlffv2pXr16uzYsYMHDx4wPXVowSfl6VPFRxAb+zLDSQu0tQ2Ii4t6a/mUlBTmzatNYmIc+vomtG2rur8cObIIbW196tcfwosX4TlW77ykp6d47udNcXGQwYgbpVcBkLY2/PorGBhA48YwdCh4eKQFO23aKNZ1/DgUynr4eL4VFfUSA4P0+5W+vgFRUZnvV1FRLylZ0jrLck+fPuHhwwi++24CAwaMwMamDEeOHGDePEUPY4cOX/HsmSJPUlISI0a4YmFRgosXz7J5889ERIQxa9aiHNrSnBUZmXG7GRgYEBmZebtFRr6kVKmM2y2jcqNGDePsWS8KFCjAt9/OVk6U4OfnS2JiIoGBAYwYMRZj40KcPHmMpUsX8OLFC0aOHJuNrctZL1Nv6hnq66ukG6T+HZnBfvYqzdDAIMsytWvWZECPHsxfsYL5K1YAYFe+PIu+/TbT+sTGxTFp9my0tbXp1aVLpvnym5epDy0avjHe1iD178iMTnYZOOLjw55z5+jVqBGFDAwIfPAAgIV79mBvY8PigQN5/PIli/fuxWXxYvZOm4Z+VidMNREd/RJ9/fTHrJ6eATExWf+G2thkfVPw+fMnPHkSwZIlE/jqqxFYWZXh9OkDrFypONfJEDjxPnItAGrVqhUWFhZs2rSJM2fOcCp1RjEbGxvmzJlDzZo133ld33zzDSVT71Dp6enh4eHBxYsXcXR05OHDh5w7d461a9emK9ehQ4cs865YsYJhw4bRtm1bAEqWLElkZCQzZ85k9OjR7N+/n5iYGObNm4eRkRHlypVjypQpDB8+PLvNI/KB5ORkUlKSVdKyuqub1SxKaetMpEePlSQmxnH69BrWrevJwIFbsbCoSECAN5cubWfw4B1oaqrnBIwaGukfE8uqWbK6SX7mDNy4oTp07s4dmDQJmjRRBEVlyih6iJYvh+TkzNeVnyQnJ6v0IivSMm+IAgUyb8B3KZeQkMCzZ0+ZO3c5jRsrnj9QPAMUyrp1HnTo8BW6uvq4u3tSsqQ1JUoo7u7XqOGElpY2q1cvpV+/byhduuw7b2Nu+Njt9rrBg4fRt+9ADhzYx7ffupGcnETHjl0pVcoGD4812NlVwdTUFIBatZyJjY1l06Z19O07ECMjo7dt2keR/JYeqQIZHKhvtne6MqkXWd8uXMju/fsZ1qcPzjVrcj88HA9PTwaOH88Gd3f03ggWIqOjGe7mxrUbN3D/4Qcs3+gZyc8+pB3fdNjHh/Hr1lGzbFlcOylm+IxPfTa5qLExHkOGKNvW2syMr+bP54/z5/mqfv1s1v7jyug3NKtj711+Q7OSmJjAixdPmTRpOc7OinOd4hmgULZv9/i8AiCRbbl6FVatWjWqVatGcnIyt27d4tSpU/zyyy8MGjSII0eOvPN6bGxslP+2srLCycmJP/74A0dHR/bv30+xYsWoXbt2unJZ5X3y5Anh4eEsXrwYd3d3ZZnk5GTi4uIICQnB19cXGxsblR+46tWrf1hjiHzn5MmfOHnSQyWtcuWWREY+Spc3Li4KHZ23X+hoampha1sXABsbRxYvbsLZsxtp02Yae/a4Ua/eIMzMbElKSlT+cKSkJJOUlKgWQVGzZtDijZmUr14Fwwxm+tbVzbhn6JWHD9PP9hYbC/fuKXqHtLUVQ99OnoSICNWeeg0Nxb/zY1C0bt1PrFunul81adKSJ0/S71dRUVEYGGS+XxkaGhIdnf6uaVRUpLKcgYEBGhoayskVXqlduz7nznnx+PEjihQpSq1addOtp27dRqxevRQ/v1t5HgCtXv0Tq1ertluzZpm3m6Hhh7VbRuWqV3cAFMFNaOh91q5dRceOXTEyMlJOvvC6+vUbsWfPDgIC/PPN5AhGqb04UW8MY45M/dswg4PUKDUtXZnXeoYiHj7kt337GNK7N2MGDVLmsa9Uiba9e7Nr/356de6sTA+LiGDIxIkEBAezZNYsmqnZRb1R6jNMUW+cvF71/Bi+9oxTRjYcPcqPu3bhVL48Pw0bhk7qc2WvepQaVK6sDH4AqpUpg5GeHjdSn7dSJ9u3/8T27arHbJ06LXn+PP0xGx0dhb5+9m4W6OkpznWvJld4pXr1+vj4ePHs2SNMTIpm6zvUhvQAZVuuXHGFh4ezevVqhgwZgoWFBQUKFMDOzg47OzuaNWvGF198wYULFzIsm5iYflpg3TfuLnXq1Ik5c+Ywbdo09u3bR/v27VVOKO+S99WdLzc3N+rUqZOuXPHixdHQ0Eh3h6xgwfx/kSrejYNDNypUaKSSdvPmUfz8vEhOTlbZp548CcTMLPMLxFu3jqOra4SNjaMyTVfXCFPTkrx8+YDQ0Os8e3Y/Nej6SaXs3r1T2bt3KrNm3c6ZDctF3t5w86ZqWuXKUKGCIih5/eZpkSKQOuojQ1WrKqbIfr0HCBTP/ERGKmZlNzVVTKndvLlqnm7dFB9X1+xtT27o0KEb9eo1Ukk7deoo586l369CQgKxscl8vypVqjS+vjfTpYeEBGFnp3jg2srKmpSUFBITE9B5bQjNq3Oprq4uQUH3uHTpHM2atcHIKG0Iclyc4qLOxMT0/Tc0h3Xu3I0GDRqppJ04cZSzZ9O3W1BQYJYBm41NaW7dSt9uwcFBVK6saLfr1//l/v0QWrZso5KnUqXKXL3qA8CtWzf4998rdOnSXeX7X7Vb4cJ5326vlLK0RFNTk8A3JhwICgkBoKx1+iGBpVOfFwoMCcGufHlleuCrMjY2hEZEkJKSQo0qVVTK2pYujUmhQtwJSHuu47a/PwPGjSMuLg7PxYtxfGMiAXVQyswMzQIFCHzj7kxQ6smsbCa9WSkpKcz+7Tc2nzjBF46OzO3TR+X9PyWLFkVDQ4P4DK5xkpKT0X3tfUrqomXLbjg6NlJJ8/Y+io9P+mM2LCwQK6vs3WQpXjztXKetnXauS0pStGlGM8d9siQAyrZcaUFtbW127NjBvn370i179fxP0aJF0dLSUk5GABAZGcnjx4/fuv6WLVuSmJjIjh07+O+//+jUKfOXSGaWt0iRIpiamhIcHIy1tbXy899//7F06VIAKlasyL1793jy5IlyfdevX3+nNhD5n7GxOZaWVVQ+trb1iIuLws/vtDJfVNQTAgMvUrZs+jvor5w9u4E//viO5OS0KdifPw/n4UN/zM0rUKJEZYYM2any6dFjJQCNGo1gyJCdubehOejFCwgJUf34+ip6e167fsLAQDF87c3g5nW1a0OnTqrv9TE2Bhsb8PeH+/fB3V314+mpyHf4sOLv/MjMzJxKlaqofGrVqkd0dBTnzqXtV0+fPuHKlYsZ9sy8UqtWPe7d8ycgIG3mqYAAP+7d81eWe9Xzc+TIfpWyp08fx9a2AgYGhjx+/JAff/yWY8f+Uslz9OgBDAwMqVixcra3O7uKFTOncuUqKh9n53pERUXxzz9p7fbkyRMuX76Is3Pm7ebsXI+AAH/8/dPazd/fj7t3/ZXlzpz5m6lTXQkPT5tZIykpifPnz1GunOJZhDt3fJkzZybe3qqT3xw6dIDixS2xtLTKkW3PCTo6OjhUrcqRU6dUhvIeOnkSI0ND7O3s0pWxtrLCqnhxDp08qZJ++NQpbFKXWacGVm/OInc3KIhnz59TMvVhvrCICPqNGYOGhgZbV65Uy+AHQEdLC4dy5Tji46Pajj4+GOnpYZ/JhA6L9+5l84kT9GvWjIX9+6sEP6B4hsjR1pbDPj7EJ6RNPHL21i2i4+JwsLV9c5X5nqmpOba2VVQ+1arVIyYmCh+ftGP2+fMn3LhxkWrVMj9m38Wrnp/Tp1XPdefPH8fGpkKGzx4JkZlc6c4wNTVl4MCBuLu7ExUVRatWrTA0NMTPz48VK1YoJ0WoVq0aBw4coGXLlhgbG7Ns2bJ0M4dkRE9Pj1atWrFo0SJq1KiBdQZ3tt6WV0NDg0GDBrFkyRJKlChBgwYNuH37Nt999x1NmzZFW1ubtm3bsnLlSsaPH8+kSZN48eIFs2fPzrF2EvmPjY0jNjZO7NrlSosWrujpmXDixHJ0dY1wcvpame/BAz+SkuIpXlxxUdGw4Tds2tSf334bi4NDN6KinnDq1Ap0dY2pW7c/OjqGWFqq3kF9+lRxl7VwYct0y9RJQAD4+Slehrp/v2IiwebNFb07r0+aWKyY4t0/r6a9PnoUBg+Gvn0VL0LV01OUi45WvBsoLk4RYL2ucGHFf58+Tb8sP6te3ZEaNZz47jtXhg93pVAhE9auXY6hoRGdOqXtVwEBfsTHx1OhgmK/atasDRs3rmLs2EF88814AFasWETZsuVp2lQxU1nNmrWoV68xS5fOJSYmhjJlynHw4F7+/fcy8+crHlivWrUmDg7OLFs2j7i4WEqXtuWff07y22+bGT16skqvUH5Ss6YjDg5OTJniypgxrpiYmLBq1XKMjIzo1i2t3fz9/UhIiKdiRUW7tWzZhnXrVjFixCBGjVK027JliyhXrrxyhrcuXbqzc+c2Ro0awpAhI9DS0mL79i34+fmycqXipafNmrVkw4a1TJ8+iREjxmBmVoyDB//k1KnjLFy4LNORB3llWJ8+9BszhtHTp9O5bVt8rl9n3datjB86FD1dXSKjovALCKCUpSWmqQfT8H79cJszBxNjY5rUq8cxLy8OHj/OkpkzATAtXJg+XbuybssWAOo4OhIaHo7H+vVYWljQrV07AH5YupTHT58yc8IEIqOiuPLazUJDAwNs1WgmuGGtW9PP3Z3RP/9M5zp18Ll7l3VHjjC+Qwf0tLWJjInBLyyMUmZmmBoZcTM4mJ8PH6aKtTWtatTg6mu9YgC2xYtjqKfHuI4d6b14MYM8POjfvDmPX7xg4Z49VC1dmiZVq+bR1uasypUd+d//nFiyxJU+fVwxMjJh27blGBgY0bp12jEbHKw4ZsuUSR+YZ6ZKlVo4OjbG03MucXExlCpVjhMn9nLr1mXc3FbkxubkX/ns3KOONFJycS7PvXv38ttvv+Hr60tsbCwlSpSgdevWDBkyBH19fSIiIpgxYwbnzp3DyMiI/v378/fff1OzZk1GjhzJ7t27cXNz4/bt9EODLl68SM+ePfnhhx/o2rWrMn3y5Mncv3+fzZs3vzUvwK+//srmzZsJCQmhaNGifPHFF4waNQrt1Hn6g4OD+f7777lw4QKFChVi1KhRuLm5sWnTJmrVqvVhDZOfXzbasKHioYtGjeBU1nP2f0zbt328KWdjYp7z11/zuHnzKCkpyZQqVYPWrd1UXmbq6dmbZ8/uM25c2ls+7949x/Hjy4iIuEWBAgWxta1PixYTKFSoeIbf8/RpCEuWNKVjx7lUr555L2Z2XLyYK6tNR08P2rVTDIfT0FA8x/PHH6rP+Awdqghg5s5NS7O1VQQ9xYsrhs/dvg0HDsCzZxl/T+HCMGUKbN+ee9s2ZUrurPfFi+e4u8/j77+PkpycjL19DcaMccPaOm2/GjasN2Fh99m7N22/iogIY/Hi2Vy4cIaCBbVwcqrLmDFuFC1aTJknLi6Odes8+OuvfTx79gQbG1sGDBhOw4bNlHmioiJZu9aDkycP8/jxQywtS9G9e1/at1c9J36o3HpJ7YsXz1m4cB4nTiiOx6pVa+Dq6oaNTVq7DRjQm9DQ+xw8mNZu4eFhzJ8/m3PnFO3m7FyXCRPclDO8AYSEBLN06QIuXbpATEw0//ufPcOHj1Y+EwTw6NFDPDyW8M8/Xjx79hRb23IMGvQNjRunte2H0ot8+PZM7+nIqVMs8/QkICgI86JF6dmpE/2/Vlx4el++jMuoUcydMoVObdKG/m3buxfPbdsIe/CAkiVKMLhXLzq0aqVcnpKSwsYdO9i2dy8hYWEUK1KEuo6OjB08GNPChYlPSKB6s2YkZvIScqdq1djs4ZHhsg/yEUZiHPHxYdmffxIQEYG5iQk9Gzakf+pYXO/bt3FZsoS5Li50qlMH9337WHHgQKbr2jR2LLUqKHoVL/v7s+T33/k3IABdbW2aVavGpM6dMX5j9r6cdtOica6u/3WRkc/x9JyHt7fimK1YsQYDBrhhaZl2zE6d2psHD+7z888Zvyl70KAm/O9/TowePU8lPT4+ju3bPTh5ch8vXjyhZElbunUbTu3a2T8eM5LVu+zyVIUcfo1GBtfZn7pcDYBEJvJzAJRPfcwA6FPysQKgT0luBUCfutwKgD5luREAfRZkKPp7+5gB0KdEAqBPlzzRL4QQQgghhLqQIXDZJgGQEEIIIYQQ6kICoGyTFhRCCCGEEEJ8NqQHSAghhBBCCHUhPUDZJgGQEEIIIYQQ6kICoGyTFhRCCCGEEEJ8NqQHSAghhBBCCHUhPUDZJgGQEEIIIYQQ6kICoGyTFhRCCCGEEEJ8NqQHSAghhBBCCHUhPUDZJgGQEEIIIYQQ6kICoGyTFhRCCCGEEEJ8NqQHSAghhBBCCHUhPUDZJgGQEEIIIYQQ6iIfBUDJycl4eHiwY8cOXr58iaOjIzNmzKBkyZLp8i5fvhwPD48M19OpUyfmzp0LQL9+/fjnn39Uljs5ObF58+Ycq7cEQEIIIYQQQoj3tmLFCrZs2cK8efOwsLBgwYIFDBw4kD/++ANtbW2VvP3796d79+4qaevXr2fr1q307dtXmXb79m2+++47mjVrpkzT0tLK0XpLACSEEEIIIYS6yCc9QPHx8Xh6ejJhwgQaNWoEwJIlS6hfvz6HDx/miy++UMlvYGCAgYGB8u8bN26wadMmvv/+eypUqADA48ePefz4MVWrVsXMzCzX6p4/WlAIIYQQQgjxdgUK5OznA926dYuoqCicnZ2VacbGxtjZ2XHhwoW3lp81axYODg507NhRmXb79m00NDQoXbr0B9frXUgPkBBCCCGEEJ+ppk2bZrn82LFjGaaHh4cDULx4cZX0YsWKKZdl5sSJE/j4+LB3716VdF9fX4yMjJg1axZnzpxBX1+fVq1a8c0336QbUpcdEgDlge3bUvK6Cmrnq+4aeV0FtfTV3bt5XQW1s/1w7t51+lR91eRhXldB/ZiY5HUN1NL4PxvndRXUzqKBN/O6CmqqUl5XIGP5ZAhcTEwMQLrAREdHh+fPn2dZdv369TRu3JhKlVTb2NfXl7i4OOzt7enXrx83b95k/vz5hIaGMn/+/ByruwRAQgghhBBCqIscDoAy6+F5G11dXUDxLNCrfwPExcWhp6eXabnQ0FC8vb1Zs2ZNumWzZs1i0qRJFCpUCIDy5cujpaXF2LFjmThxIkWLFv2gur4pf4SQQgghhBBCCLXxaujbgwcPVNIfPHiAubl5puWOHj2KqakpdevWTbesYMGCyuDnlXLlygG8dVjd+5AASAghhBBCCHWRTyZBqFixIoaGhnh7eyvTXrx4wY0bN3B0dMy03MWLF3FycqJgwfQD0Xr37o2bm5tK2rVr19DS0sLGxuaD6/omGQInhBBCCCGEusgnzwBpa2vTq1cvFi5ciKmpKZaWlixYsAALCwtatGhBUlIST548wcjISGWI3I0bN+jcuXOG62zZsiVz5szB3t6eevXqce3aNebPn8+AAQMwNDTMsbpLACSEEEIIIYR4b6NGjSIxMZFp06YRGxuLo6Mj69atQ0tLi5CQEJo2bcrcuXPp1KmTsszDhw8xyWQSmF69eqGhocHmzZuZM2cOZmZm9O3bl8GDB+dovSUAEkIIIYQQQl3kkx4gAE1NTVxdXXF1dU23zMrKitu3b6dLv3r1apbr7NmzJz179syxOmZEAiAhhBBCCCHURT4KgNSVtKAQQgghhBDisyE9QEIIIYQQQqgL6QHKNgmAhBBCCCGEUBcSAGWbtKAQQgghhBDisyE9QEIIIYQQQqgL6QHKNgmAhBBCCCGEUBcSAGWbtKAQQgghhBDisyE9QEIIIYQQQqgL6QHKNgmAhBBCCCGEUBcSAGWbBECfsLi4KI4cWciNG4eJj4/G2tqB1q3dKFq0TJblgoJ8OHp0MaGh19HWNqBy5VY0azYGHR3DDPOHhl5nzZqvaN/+e6pX75Qbm5K/WVrC9evQoQOcOpXXtfnovC5dYsmGDfgFBVHExISe7drRv3NnNDQ03lr2hp8fXUeP5tC6dVhZWKgs2334MJ67dhEUFkZxMzN6tmtH7/bt32m96kKO0cx5nT/PkjVr8AsIoIipKT07dqT/119n+f//zyNHWLlxI8GhoVgWL87gXr3o2Lq1Sp6jf//Nig0bCAgOpqipKe1btmRw795oa2kp8/hcv87iVau4fvs2Bvr6tGrcmDGDB2Oor59r25tTvM6cYcmyZfj5+1OkSBF6du9O/759s263AwdYuXo1wSEhWJYoweCBA+nYvn2GeSOjoviyUydGDBtGpw4dVJbd9vVlweLF/Pvvv2hra1O3Th1cx42jaNGiObmJH422NnzxBVSpAjo6cPcu/P47PHyYdbkePaBmzfTpGzfCv/8q/t2qFTRvnj7PH3/AyZPZrnqe8fLxYcmvv6b9HrRpQ/93PG/fuHuXrq6uHFqxAitzc5Vl/iEhLNi4kfPXr1NQUxPHypWZ3K8fJd/43RDiXeWrAKhJkybcv39f+beGhgb6+vrY2dkxevRoHB0dsyzv7e2Ni4sLx44dw8rKKrerm+/t3DmekJCrtGjhio6OISdOeLB+vQsjRuxHT69QhmXCw2+xcWNfSpd2pnv35bx8+YAjRxbx6FEAffqsS5c/MTGe3bsnk5ycmNubkz9ZWcGhQ2Biktc1yRNXbt5k6Lff0rpBA0b36cOl69dZsG4dSUlJDP7qqyzL+t67x+AZM0hMSkq3bMfBg0xzd2dg167Uq1GDq7dvM2/NGqJjYxnavXtubc5HJ8doxq5cv87QiRNp3bQpowcO5NK//7Jg5UrFftW7d4ZlDp08yYRZs3Dp2pX6tWpx9PRpJs+ejbaWFm2bNQPgzIULjJg6lTZNmjB+2DDuBASweNUqnj5/zvSxYwG45edH39Gjca5Zk+WzZ/Pg0SMWrVpFQFAQ6xYv/mht8CGuXL3K0OHDad2qFaNHjuTS5cssWLxY0W4DB2ZY5tCRI0yYNAmXXr2oX68eR48dY/LUqYp2a9NGJe/z58/5ZtQold/pVx49ekSfAQMobmHB3NmziYuNZeGSJQwaNozftmxB67UAU1306gWlSsH+/RAbCy1awLBhsGABxMRkXq5ECbh8GU6fVk1/PXCytAQ/P8W6X/f0ac7V/2O7cvs2Q2fPpnXduozu0YNLN2+yYONGxf7XuXOWZX0DAxn8/fcZ/h6EPXxIDzc3SpcoweLx44mJi2Ppr7/S/7vv+MPdHV0dndzapPxLeoCyLV8FQAD9+/enf//+AKSkpPDs2TMWL17MwIEDOXjwICVKlMjjGqqHoCAfbt8+Qa9eayhfviEA1tYOLFnSlPPnt9Cw4bAMy509uxE9vUJ0776MggW1lel79rjx6NHddHemjx1bSmzsy9zbkPxKQwNcXGDhQsW/P1PLN2+mUtmyLJg4EYAGDg4kJiWxavt2XDp0yPCHKT4hgV/27WPZpk3oaGunWw6wavt2Wtarh+uAAQA4V6/OvZAQfvn9908mAJJjNHPLPT2pVK4cC6ZPB6BB7dokJiayavNmXLp1y3C/Wrx6Na0aN2bKqFEA1K9Vi+cvXuC+dq0yANq9fz8lzM1ZMGMGmpqa1HV05PGTJ6zfvp3JI0eiVbAgG3/7jULGxixLDZ5ecZszh7tBQZQpVeojtMCHWf7TT1SqVIkF8+YB0KBePUW7/fwzLr16oaurm67MYnd3WrVowZRJkwCoX7cuz58/x93DQyUAOnbiBLPnziUqKirD7z524gRPnz7lt19/pVRqGxkZGzNwyBB8rlzB6S03MPMba2uoXBl+/hlu3VKk3b0LU6dCnTpw7FjG5QoWhGLF4O+/ISgo8/WXKAEXLmSdR90s37qVSqVLsyD1ZkKDGjUU+9/Onbh88UXmvwf797Ns69ZMfw+Wb9uGob4+62fNQi91HVbm5gybPZvrfn44VK6cexuVX0kAlG35rgX19fUxMzPDzMyMYsWKUb58eWbOnElsbCxHjhzJ6+qpDT8/L7S19bG1radMMzAwxdraEV/fzIdpNW06hl691qhcWGlqKi4CEhPjVfIGBV3G2/sXvvhiRg7XXg3Y28OqVbBpE2RyR/pTFx8fj/e1azSvU0clvWW9ekRFR3Ppv/8yLPf3hQt4/PorQ7p3Z0LqzY43rZk1i4lv3LHW0tIiLiEhZyqfD8gxmrH4+Hi8fXxo3qCBSnrLxo0V+9WrMUSvCQkL415wcPoyjRoRGBLCveBgAOLi49HT1UVTU1OZx8TYmISEBKKiowEYM2gQaxYsUAl+tAoq7hXGx8XlzEbmgvj4eLwvXKB506Yq6S2bNycqKopLly+nKxNy/z737t1LX6ZFCwKDgrgXGAjAixcvGDF6NI4ODqxdvTrD749LbRtDw7RhmCaFFL2Yz549++DtyisVKkBcHNy+nZYWFQX+/lCpUublLCxAUxNCQzPPY2CgGDSQQUea2opPSMD7+nWa166tkt6yTh2iYmK4dPNmhuX+vnQJj+3bGdKlCxNcXNItT0lJ4fDZs3Ru2lQZ/ABUsbXFa/36zzP4ETki3wVAGSmY+uOjra1NQkIC7u7uNG7cmKpVq9KpUyfOnDmTYbnnz58zbdo06tevT+XKlXF2dmbatGnEvNZ3vW7dOpo1a8b//vc/mjRpwk8//URKSgoAMTExTJ06lbp161KlShU6dOjA4cOHc3+Dc8CjR/4ULmxFgQKaKulFipTi8eOATMsZG5tjYVERgPj4aPz9/+Ho0SWUKlVDma5YFsOePW40aDAEc/MKubMR+VlQENjawvjxkHrh9LkJDg8nISEBG0tLlXTr1F7agJCQDMtVKV+e4xs3Muzrr1UuRF9XtlQprCwsFL3AL1+y4+BB9h49So8vvsjZjchDcoxmLDg0VLFfvdHTYp26nwVkcMvc/949AGxKllQtkzoU+lWZnp06ERgSwrotW3jx8iVXrl9n444dNHR2xsTYGABzMzMq2toCEB0Twz8XLrBkzRpqVKlCxXLlcm5Dc1hwcLCi3aytVdKtU9sxILWNXud/9y4ANjY2GZcJUOyHunp67P/9d36cM4fChQtn+P2tW7XCzMyMWbNn8+DhQ4JDQpi/aBFmZmbUcXbOzqblCXNzePwYUi8HlB49UvTwZObV6bBWLfj2W/jxRxg+XDGU7pVXA1ns7BQ9SvPnw7hxULFi+vWpi+DwcBISE7F5Y5SOdfHiAARkEu1VKVeO42vWMKxrVzQz6NUIefCAl9HRlDAzY+bq1Tj16kWVrl0ZNmcO4Y8e5fyGqIsCBXL28xnKd0Pg3hQREcGcOXPQ19enYcOGzJ49m0OHDvHtt99iZ2fHrl27GDp0KL///nu6spMnTyYiIgIPDw+KFCnC5cuXmTJlCra2tvTt25fjx4+zevVqlixZQunSpbly5QoTJ07EysqK9u3b4+7uzu3bt1mzZg3Gxsbs2LGDsWPHcujQoXz/jFFs7MsMH4jW1jYgLi7jIQyvS0lJYd682iQmxqGvb0LbttNVlh85sghtbX3q1x/CixfhOVZvtfH0qXoP1s4BL1OHwrz5YLhB6t+RmQSG5u/xQPSVmzfpPm4cAP8rV45+bxlHrk7kGM3Yy8hIIIv9KoMhWK/SDA0MsixTu2ZNBvTowfwVK5i/YgUAduXLs+jbb9OtMyUlhdpt2xIXH49JoULKZ4TyK2W7GaruUwapbRKZuvx1kS9fZl0mtd20tbQoU7p0lt9vVrQoM2fMYJyrKwcPHQKgkLExmzw9061fHejqKnqA3hQXp5gQITOvrv+1teGXX0BfH5o0UTw7tGwZhIWlBUlGRrBjh6LHqH59GDAA1q5V7XVSFy9Tz/fpjls9PSCL34MiRbJc79PnzwFYuGkT9uXKsXj8eB4/f87izZtxmT6dvUuWoJ/B0M5P3mcatOSkfBcArV69Gk9PTwASExOJj4+nbNmyLF26FGNjY3bu3Mn06dNp1aoVAGPHjiUlJSXDk3vdunVxdHSkQgXF3U8rKyt++eUXfH19AQgKCkJbWxtLS0tKlChBiRIlKFasmPI5o6CgIAwMDChZsiTGxsbKiRgKFcr44eS8kpycTEpKskpaypu3rV7zLrOxJCcn0qPHShIT4zh9eg3r1vVk4MCtWFhUJCDAm0uXtjN48A40NfPdLiQ+kuQs9jGAAjnwbFQJc3M2z59PSHg4SzdtovvYsezx8EBPzX7w5Bh9dx+yXyUnJ2eQ87UyqRcL3y5cyO79+xnWpw/ONWtyPzwcD09PBo4fzwZ3d5X9KjEpiZU//khcXBxrfvmFnsOHs3XFinzbC/SubaBSJgeP4T/272eimxutWrSgc8eOxMXH47lhA/0HD2bzhg2ULZP1zIZ5SUMj/aOcWW16Vs3m5QU3bqgGMXfugJsbNGsGmzfDlSsQEaF4tujVum7fVgwoaNlSPQOgt+5LH3jBHp+omLylqIkJHpMnK9djXbw4X02axB+nTvFVy5YftG7xect3v4zdu3end+ozFQUKFMDExAQjIyMArl27RkJCAlWrVlUpMy71DrG3t7dKeo8ePTh+/Dh79uzh3r17+Pn5ERISQpnUE/GXX37Jrl27aNmyJba2ttSpU4eWLVsqA6BBgwYxdOhQnJ2dsbe3p27durRr105Zn/zi5MmfOHnSQyWtcuWWREam7x6Oi4tCR+ft9dfU1MLWti4ANjaOLF7chLNnN9KmzTT27HGjXr1BmJnZkpSUqLywS0lJJikpUe0uuMSHMUq90xf1xnRIr+70vXk3/kOYFymCeZEiONnbU7J4cXq5unLIy4sOqQ+1qws5Rt+dUep+E/XGHWPlfpVBb4JRalq6Mq/1DEU8fMhv+/YxpHdvxgwapMxjX6kSbXv3Ztf+/fR6rYdRq2BB6qY+uO9YrRpNunRh444dzJ0yJbubmCte/S69OUmBsg2yarc3y7zqTXqP3zqPFSuoXq0aSxYuVKbVdXamzZdf4r58OcuWLHnndX1szZsrAo/XXb2q6KF5k66uYka4zDx8mH6a7NhYuHcvrXfo2TPF53XJyeDrC2o4WhB4h9+DD5xC3jC1B6lBjRoqQVS1ChUw0tfnRkDmw4U/adIDlG357lewUKFCWL8xhvmV95lGMzk5mSFDhnDnzh2++OIL2rRpQ+XKlZk+PW2YiKmpKb///js+Pj6cOXMGLy8vNm3axMiRIxkxYgTVq1fn1KlTnDlzhrNnz7J3715WrlzJ2rVrcc5HZykHh25UqNBIJe3mzaP4+XmRnJysctJ48iQQM7Oyma7r1q3j6OoaYWOTNmOPrq4RpqYlefnyAaGh13n27H7qBd1PKmX37p3K3r1TmTVLDW9fifdWqkQJNAsUIPCNp32DUv8u+8bzGO8qKiaG4+fOYV+hgvJ5IgC71OcyHjx+/IE1zjtyjL67UpaWaGpqEvjGMwNBqc+Ulc3g96F06gMWgSEh2JUvr0wPfFXGxobQiAhSUlKoUaWKSlnb0qUxKVSIO6kXUse9vDAyNMSxWjVlHiNDQ0paWvIgHz9zUKpkSUW7vfGMVFDq3xn1wJROHdYWGBSE3WtP9gdmUSYz90NDafbGZAq6urr8r3Jl7vj5vfN68sK5c4pem9f973+KiRA0NFR7fIoWVfTeZKZaNcVjoakDTZS0tODVQJWKFRV/X7uWPk8mk+zle6UsLBS/B2FhKulBqX+X/cDHBkpaWKChoUF8BhPgJCUno5vJzHGfPAmAsk2tWtDa2hotLS2uvXHW6NatGxs2bFBJu3nzJn///Tfu7u5MmDCBL7/8klKlShEUFKQcerJv3z62bt1KzZo1GTVqFL/99htdu3blwIEDACxbtoxLly7RtGlTpk2bxqFDhyhZsiSHUsc35xfGxuZYWlZR+dja1iMuLgo/v7QXEURFPSEw8CJly9bNdF1nz27gjz++Izk5bS7+58/DefjQH3PzCpQoUZkhQ3aqfHr0WAlAo0YjGDJkZ+5tqMhXdLS1cahShSNnzqgM5zrk5YWRgQH2FT7swfuCmppMW7qUdTt2qKSfuXQJgApveRYhP5Jj9N3p6OjgULUqR06dUt2vTp7EyNAQezu7dGWsraywKl6cQ2+8QfLwqVPYpC6zTg2s3pxF7m5QEM+eP6dkarC94bff+G7hQpJeex9J+IMH+N+7R4XUIDw/0tHRwaFmTY4cO6babkeOYGRkhP0bgR8oJjuwsrLi0BuT+xw+cgQba2us3pjgJCtlSpfmso+PynfHxcXx382blMznz8y+eAEhIaofX19Fb8/rpzEDAyhTJn1w87rataFLF8VzPa8YG4ONjeK9PwBVq0L37pDauQEonhmqVCktj7rR0dbGoXJljpw7p7r/nT2Lkb4+9q/dmHgfBnp6ONrZcfjcOZUg6OzVq0THxuKQwflAiHeR73qAsqKnp0evXr1wd3fH1NSUcuXKsXPnTnx9fZk3bx4PX+t3Llq0KAULFuTgwYOYmpry7NkzVq1axcOHD4mPV0wVGxcXx48//oiBgQEODg6Eh4dz4cIFHBwcAMWsOvv27eP777+nVKlSXL16ldDQUKpXr54n2/8+bGwcsbFxYtcuV1q0cEVPz4QTJ5ajq2uEk9PXynwPHviRlBRP8eKKk0jDht+waVN/fvttLA4O3YiKesKpUyvQ1TWmbt3+6OgYYmmp+kP69KniLmvhwpbplolP27Cvv6afmxujZ8+mc8uW+Ny4wbqdOxnfvz96urpERkXhFxREqeLFMX3Hl8XqaGszuFs3lv/yC6YmJtSqWpXbd+/i8euv1KlenQZq9j6RzMgxmrlhffrQb8wYRk+fTue2bfG5fp11W7cyfujQtP0qIIBSlpaYps5KNrxfP9zmzMHE2Jgm9epxzMuLg8ePs2TmTABMCxemT9eurNuyBYA6jo6EhofjsX49lhYWdGvXDoBv+val/9ixjP32W7p9+SVPnj1jxYYNGBsZ0T+fv4Nq2JAh9Bs4kNHjx9O5Y0d8rlxh3fr1jB87Fj09PSIjI/Hz96dUyZKYmpoCMHzoUNymTcPExIQmjRtz7PhxDh46xJIFC97ru0ePHMnwUaMYPX48XTp1Ij4+no2bNhEREcGiH3/Mjc3NVXfvKoKRnj3hzz8VPTMtWypegPrPP2n5zM0V7/551WF55AgMHQr9+ilehKqvr3iBalQUnEqd3f7ECUUQNGiQ4n1CBQpA48aKICif3V99L8O6dqXft98yesECOjdtis+tW6zbu5fxvXujp6NDZHQ0fsHBlLKwwPQ9nqUe17s3vadNY9CsWfTv0IHHz56xcNMmqpYvT5NP5PfgvUkPULZppGT1JO5H1qRJEzp27MjIkSMzzRMfH4+7uzu///47L1++pGLFiowfPx4nJye8vb1xcXHh2LFjWFlZ8ccff7B8+XLCwsIwMzOjUaNGaGlpcfz4ceU7hX7++Wd27NhBWFgYhQoVomXLlkyYMEH5Y/Hjjz9y4sQJnj17hqWlJT179sQlg7nq38f27dkq/s5iYp7z11/zuHnzKCkpyZQqVYPWrd1UXpTo6dmbZ8/uM27ccWXa3bvnOH58GRERtyhQoCC2tvVp0WIChQoVz/B7nj4NYcmSpnTsOJfq1TvlyrZ81T0fv2y0YUM4eRIaNUr7hcsvUqe5zU1Hzpxh2ebNBNy/j3mRIvRs147+qc9SeF+9isukScwdN45OLVqkK7v78GHcFi/m2IYNWFlYKNNTUlLYduAAv+7bR1BYGKaFCvFF48aM7NUr05fl5ZTt5z9eD9MndYw2efj2TO/hyKlTLPP0JCAoCPOiRenZqRP9v1YEht6XL+MyahRzp0yh02sv69y2dy+e27YR9uABJUuUYHCvXnRInTAHFPvVxh072LZ3LyFhYRQrUoS6jo6MHTxYGUgBnLt8mWVr13LLz4+CmprUr1WLCcOGUdzcPEe3kXe8KfA+jhw9yrKffiLg3j3Mzc3p2b07/fv2BcD7/Hlc+vdn7g8/0KlDB2WZbb/9hueGDYSFh1PSyorBAwfS4csvM1x/yP37NG3ZMt06AP728mLFqlXcuHkTAwMD7P/3P8aOHk3FD+wNzsz4ye8+HD479PTgyy8Vw+E0NBTP8fz+u+ozPsOGgakpzJ6dllaunCLoKV5cMXzu9m1FEPX6cz+WltCmDZQsqegtunsX9u+H8FyasHHRwIzfw5PTjpw7x7KtW9N+D1q3pn/qfuJ97Rou06czd+RIOr0xXBJg97FjuC1fzrHVq7F641i7fOsWS375hX99fdHV0aFZrVpM6tsX49yeYTCrlz7lpdeeY8wRP/+cs+tTA/kqAPpcfKwA6FOSrwOg/OwjBECfmo8ZAH1KcjoA+izkQgD0OfhYAdCn5GMFQJ+c/BoADRmSs+vL5AXHnzK1GgInhBBCCCHEZ02GwGWbtKAQQgghhBDisyE9QEIIIYQQQqgL6QHKNgmAhBBCCCGEUBcSAGWbtKAQQgghhBDisyE9QEIIIYQQQqgL6QHKNgmAhBBCCCGEUBcSAGWbtKAQQgghhBDisyE9QEIIIYQQQqgL6QHKNgmAhBBCCCGEUBcSAGWbtKAQQgghhBDisyE9QEIIIYQQQqgL6QHKNgmAhBBCCCGEUBcSAGWbtKAQQgghhBDivSUnJ7Ns2TLq169PtWrVGDRoEMHBwZnm37dvHxUqVEj3CQkJUeY5ePAgbdq0wd7eng4dOnD27Nkcr7cEQEIIIYQQQqiLAgVy9pMNK1asYMuWLXz//fds27aN5ORkBg4cSHx8fIb5b9++jZOTE15eXiqf4sWLA3Du3DlcXV3p3r07e/bswdnZmcGDB+Pv75+ter5JAiAhhBBCCCHURT4JgOLj4/H09GTUqFE0atSIihUrsmTJEsLDwzl8+HCGZXx9falQoQJmZmYqH01NTQB+/vlnmjVrhouLC2XLlmXSpElUrlyZjRs3fnA9MyIBkBBCCCGEEOK93Lp1i6ioKJydnZVpxsbG2NnZceHChQzL3L59m7Jly2a4LDk5mcuXL6usD6BWrVqZru9DySQIQgghhBBCqIscngShadOmWS4/duxYhunh4eEAyuFrrxQrVky57HXPnz8nIiKCixcvsmXLFp4+fYq9vT2urq6ULl2aFy9eEB0djYWFxTutLzskAMoDFy/mdQ3Uz1d37+Z1FdRTmTJ5XQO189WDB3ldBbUUHGuW11VQO3f/yesaqCcXl7yugRoyNs7rGoiclE9mgYuJiQFAW1tbJV1HR4fnz5+ny3/nzh0AUlJSmDt3LrGxsaxcuZIePXrwxx9/kJiYmOn64uLicrTuEgAJIYQQQgjxmcqsh+dtdHV1AcWzQK/+DRAXF4eenl66/A4ODpw9e5bChQujoaEBgIeHB40aNWL37t107dpVub7XZba+7MgfIaQQQgghhBDi7fLJJAivhr49eGPkxIMHDzA3N8+wjKmpqTL4AdDT08PKyoqIiAhMTEzQ19d/r/V9KAmAhBBCCCGEUBf5JACqWLEihoaGeHt7K9NevHjBjRs3cHR0TJd/+/bt1KpVi+joaGVaZGQk9+7dw9bWFg0NDWrUqMH58+dVynl7e+Pg4PDB9cyIBEBCCCGEEEKI96KtrU2vXr1YuHAhx44d49atW4wdOxYLCwtatGhBUlISDx8+JDY2FoAGDRqQnJzMxIkTuXPnDteuXWPkyJGYmprSqVMnAPr168f+/ftZv349/v7+zJ8/n5s3b9KnT58crbsEQEIIIYQQQqiLfNIDBDBq1Ci6dOnCtGnT+Prrr9HU1GTdunVoaWkRFhZGvXr1OHDgAKAYMrdhwwaio6P5+uuv6du3L0ZGRmzatAkdHR0A6tWrx5w5c9i6dSsdO3bk3LlzrFq1KtOpsz+URkpKSkqOrlG8latrXtdA/Sz4JiCvq6CeZBa49yezwH0QmQXu/cnklh/GxCSva6B+qha9n9dVUE+Wlnldg4z9+GPOrm/SpJxdnxqQHiAhhBBCCCHEZ0OmwRZCCCGEEEJd5JP3AKkzCYCEEEIIIYRQFxIAZZu0oBBCCCGEEOKzIT1AQgghhBBCqAvpAco2CYCEEEIIIYRQFxIAZZu0oBBCCCGEEOKzIT1AQgghhBBCqAvpAco2CYCEEEIIIYRQFxIAZZu0oBBCCCGEEOKzIT1AnzBtbWjbFqpUUfz77l344w94+DDrcl9/DTVqpE/ftAmuXUufbmkJI0fCzp1w8WLO1D2veF26xJING/ALCqKIiQk927Wjf+fOaGhovLXsDT8/uo4ezaF167CysFBZtvvwYTx37SIoLIziZmb0bNeO3u3bv9N6P1mWlnD9OnToAKdO5XVtcpXX+fMsWbMGv4AAipia0rNjR/p//XWW////PHKElRs3EhwaimXx4gzu1YuOrVur5Dn699+s2LCBgOBgipqa0r5lSwb37o22llaG6xw1bRr6enrMmzo1R7fvY4uJieLnnxdy+vRhYmKisbd3YNgwN0qWLPPO65g5cxR6evpMnDjvg5arm9jYKHbvXsjly4eJi4umXDkHunVzw8Ii6zYLDPyP339fyr1710hJSaFUqcp06jQea+vKyjynT+9g8+Zp6co2atSTHj1m5Pi2fCyxsVH88stCvL0PExsbjZ2dA336uFGixLvvZ4sWjUJXV5/hw1X3oxcvnrBly2J8fE4RFxdD2bL/o1eviZQubZfTm5GnvC5cYImnJ3737lGkcGF6tm9P/27d3u039c4dun7zDYc2b073m/rZkx6gbPvkA6B9+/bxyy+/4Ovri4aGBmXKlKFr16507949r6uW63r0AGtr2L8fYmOheXMYMgQWLYKYmMzLlSgBPj7g5aWanlHgpKkJ3bsr/qvurty8ydBvv6V1gwaM7tOHS9evs2DdOpKSkhj81VdZlvW9d4/BM2aQmJSUbtmOgweZ5u7OwK5dqVejBldv32bemjVEx8Yy9DPYDzNkZQWHDoGJSV7XJNdduX6doRMn0rppU0YPHMilf/9lwcqViv2qd+8Myxw6eZIJs2bh0rUr9WvV4ujp00yePRttLS3aNmsGwJkLFxgxdSptmjRh/LBh3AkIYPGqVTx9/pzpY8eqrC85OZm5y5dz6OTJdEGUOpozZzw3b15l0CBX9PUN2bzZgwkTXFi7dj9GRoWyLJucnMyqVXM5ffoQLVp0fO/l6mrt2vEEBFylc2dXdHUN+fNPDxYtcuG77/ZjYJBxmz14EMjChb2wtv4fLi6z0dDQ4PBhT+bP78H06XuUwVNw8E3MzUvTr5/qRb6xcdFc367c5O4+njt3rtKrlyt6eobs3OnBzJkuLFq0H0PDt+9nmzbNxdv7EA0bqu5HKSkpLFo0kpAQf3r2nICpaTF+/30t333XiwULfqdYsZK5uVkfzZUbNxg6dSqtGzVidL9+XLp2jQVr1ijOfT16ZFnWNyCAwVOmZPibKpAAKAd80gHQzp07mT17NlOnTqVmzZqkpKRw5swZfvjhBx49esSIESPyuoq5xtoaKleGtWvh9m1FWkAAuLmBszMcP55xuYIFwcwM/v4bgoLe/j2tWoGubs7VOy8t37yZSmXLsmDiRAAaODiQmJTEqu3bcenQAV0dnXRl4hMS+GXfPpZt2oSOtnaG6121fTst69XDdcAAAJyrV+deSAi//P775xcAaWiAiwssXKj492dguacnlcqVY8H06QA0qF2bxMREVm3ejEu3bhnuV4tXr6ZV48ZMGTUKgPq1avH8xQvc165VBkC79++nhLk5C2bMQFNTk7qOjjx+8oT127czeeRItAoqTu+3/Pz4YelSrt28meF3qZsbN3w4e/YEc+aswcmpIQBVqjjQq1dT9u3bQs+ewzIte/fuLTw8fuD27Wvo6KQ/cb1tubry9/fh339PMHLkGqpUUbRZuXIOTJnSlJMnt9C2bcZtdvz4ZrS19Rg5cjU6OvoAVKxYGze3Jhw//ouydyc4+CY2NlUoU6baR9mej8HX14dLl07g5raG6tUVbVapkgPDhzfl8OEtdOqU+X4WGHgLT88f8Pe/hrZ2+v0oLOweN29eZOjQH2jSpAsA5cvXYODA2pw69Ttdu34a1ybLN2ygkq0tC6ZMAaCBk5PiN3XLFlw6d878N3XPHpZt2JDpb6oQOeGTDiG3bNlC586d6dKlC6VLl6ZMmTL07t2bvn37smnTpryuXq4qXx7i4sDXNy0tKkoxDK5SpczLWVgoenNCQ9/+HdbWULcu7NmT/frmtfj4eLyvXaN5nToq6S3r1SMqOppL//2XYbm/L1zA49dfGdK9OxP6988wz5pZs5g4cKBKmpaWFnEJCTlTeXVibw+rVinGU2bS+/EpiY+Px9vHh+YNGqikt2zcWLFf/ftvujIhYWHcCw5OX6ZRIwJDQrgXHAxAXHw8erq6aL7W/WpibExCQgJR0dHKtEk//EBSUhLbV6+mSOHCObl5eeLCBS90dfWpWbOeMs3ExBR7e0fOn896KOWPP04iKSmJ5cu3Y2JS5L2Xq6v//vNCR0efypXT2szIyJRy5Ry5fj3zNrOwKEOLFv2VwQ+Ajo4+hQtb8PCh4g5ZSkoK9+/fpmTJLH5Y1NCVK4o2q1o1rc2MjU2xs3PExyfr/czDYxLJyUnMnr2dQoXS70cJCXEA6OkZKtN0dfXR0tLh5ctnObMBeSw+Ph7vq1dpXq+eSnrLBg0U576MxtMDf3t747FpE0N69GDCoEEfo6rqqUCBnP18hj7pHqACBQrg4+PD8+fPKVQorbt68ODBdO7cGVAcpO7u7uzbt4/IyEjKlSvHqFGjqJd60A4dOpQbN25w4MABDA0NefDgAe3ateOLL75geuod3fyoWDF48gRSUlTTHz3K+PmeV0qUUPy3Vi1FD5K+vqIn6M8/IfW6CwAtLfjqK0VPUlhYztf/YwsODychIQEbS0uVdOvUBgkICaFuBg1XpXx5jm/ciImREbsPH85w3WVLlQIUFwrPIyM54uXF3qNH6Z+6D35WgoLA1hbu34eGDfO6NrkuODRUsV+l7gOvWKfuZwFBQdR1dFRZ5n/vHgA2JVWHwVhbWSnL2JQsSc9OnRg0YQLrtmyha7t23A0MZOOOHTR0dsbE2FhZbv706VQoWzanNy3PBAX5U7y4lUrgB2BpWYpjx/7IsuykSfMpU6bCBy9XV+Hh/hQtakWBAqptVqxYKby9M2+zRo3SD1N68CCQ+/fvULGiMwAPHwYRGxvFvXvXmD69JQ8fhmBmZkWbNsNwdu6Qo9vxMd2/74+5efo2s7AohZdX1vvZyJHzKVUq8/3I2roi//tfbXbuXIGlZVlMTYuxc+cK4uNjqVu3TY7UP68Fh4Upzn2p561XlOe+4GDqOjikK1elQgWOb9mCibExu//666PUVS19pkFLTvqkW3DgwIHcuHGDBg0aMHjwYNasWcO///6LkZERpUuXBsDNzY0zZ86wcOFC9uzZQ+vWrRk6dCgnT54E4IcffiAhIYH58+eTkpKCm5sb5ubmTJo0KQ+37O309BTP/bwpLg6yGgXzKgDS1oZff4UtWxTBztChULx4Wr42bRTrymwonbp5GRUFgKG+vkq6Qerfka/dUX+dedGimBgZvdN3XLl5k1pduzLN3Z3yNjb0+xwDoKdPFcHPZ+JlZCSQxX6Vut+97lWaoYFBlmVq16zJgB49mL9iBY6tW/PV0KEUKVyYRd9+q1LuUwp+AKKiXqKvb5guXU/PgOjo9O35urcFN59i8AMQE/MSXd30baara0BsbNZt9rr4+FjWr5+ElpY2TZr0AhTD3wAePQqha9fJjBy5Cmvr/7F+/SROn/4tZzYgD0RHv1TpoXnlXfazrIKfVwYO/I64uGgmTGhH//61OHhwE0OG/ECFClncoVQjL992HsvsN9XMTOUGjhC55ZPuAWrVqhUWFhZs2rSJM2fOcCp1pikbGxvmzJlD0aJF+fPPP9m7dy+VUseF9evXj1u3brFu3ToaNWpE0aJF+f777xkxYgQJCQlcunSJXbt2oZ2PxqZqaKR/nCKrxyve7BV63ZkzcOOG6tC5O3dg0iRo0kQRFJUpo+ghWr4ckpOzV/f8IjmrRgEK5MDzKiXMzdk8fz4h4eEs3bSJ7mPHssfDA71P5SEqkc6H7FfJbzmoCqTe+ft24UJ279/PsD59cK5Zk/vh4Xh4ejJw/Hg2uLt/EvtVcnIyKSmq7ZGSRZt+1rMqpsqozZKTs99msbGRrFgxnICAawwd6k6RIoo7+eXKOTJixCoqVKilHCpXuXJ9Xr58wu+/L6Neva75/v/L++5nBQpkb3tCQvyZPv1rihWzZNy4ZejrG3H27EFWr56Gjo4uzs7qP1HJW89j+XyfyPekByjbPukACKBatWpUq1aN5ORkbt26xalTp/jll18YNGgQP/zwAwA93piNJCEhAePX7kA0a9aM9u3bs3v3bqZMmULZfHZHtVkzaNFCNe3qVTBMf/MKXd2Me4Zeefgw/WxvsbFw756id0hbWzH07eRJiIhQHT6qoaH4tzoGRUapd6Wi3pge79VdqjfvYn0I8yJFMC9SBCd7e0oWL04vV1cOeXnRIfWhdvHpMUrdb6LeuNup3K8yOEiNUtPSlXntjmrEw4f8tm8fQ3r3Zsxr4+TtK1Wibe/e7Nq/n16fQA/j5s0/sXmzh0pagwYtefr0Ubq80dFRGBi8W2/sp+zPP3/izz9V26xGjZa8fJm+zWJjo9DTe3ubPXkShofHEMLDAxg8eAnVqqWds4yNi2Bv3zhdmSpVGnLz5j+8ePGIQoXMPmBLPp6dO39i507VNqtduyXPn2e8n+nrZ28/279/A8nJSUyb5omRkeK5PHv7OkRFvWDdulnUrt0q3weNb5PpuS+TniHxniQAyrZPNgAKDw9n9erVDBkyBAsLCwoUKICdnR12dnY0a9aML774Qpn3119/xeCNg7HAaztXQkICt2/fpmDBgpw5c4Y+ffp8tO14F97ecPOmalrlylChgiIoef1GVpEi8OBB5uuqWlUxRfbrPUCgGAYXGamYvdjUVDGldvPmqnm6dVN8XF2ztz15oVSJEmgWKEDgG7M/BKX+Xbbkh01LGhUTw/Fz57CvUEH5PBGAna0tAA8eP/7AGgt1UMrSEk1NTQLfGPYXFBICQFlr63RlSqc+LxQYEoJd+fLK9MBXZWxsCI2IICUlhRpVqqiUtS1dGpNChbgTEJCj25FX2rbtRu3ajVTSzpw5ysWLXiQnJ6ucp0NDAylVKn/dnMoLDRp0w96+kUralStHuXEjfZs9eBBI8eJZt1lIyG3c3QeQkBDHmDGelC+v+szanTsXefgwmDp1VKd6TkiIo0ABzUyn2M5PmjXrRs2ajVTSLlw4ytWr6dssIiIQS8vs7WePHoViaVlGGfy8YmfnyLlzf/H8+WNMTNR7CvFSlpaK39Q3z32pf2d07hPiY/pkQ0htbW127NjBvn370i171btTtKjiBPPw4UOsra2Vn927d7N7925l/mXLlhEeHs769es5e/Ys27Zt+zgb8Y5evICQENWPr6+it+e16ycMDBTD194Mbl5XuzZ06qT6Xh9jY7CxAX9/xeMb7u6qH09PRb7DhxV/qyMdbW0cqlThyJkzKkMfDnl5YWRggH2FD3s2oKCmJtOWLmXdjh0q6WcuXQKgQuqzaOLTpKOjg0PVqhw5dUp1vzp5EiNDQ+zt0r/00NrKCqvixTmU+hziK4dPncImdZl1amD15ixyd4OCePb8OSVfC7bVWdGi5lSoUEXl4+BQj+joKC5ePK3M9+zZE/799yIODnXzsLb5g4mJOTY2VVQ+dnb1iI2N4r//0trs5csn3LlzETu7zNvsyZMwlizph4aGBhMnbk0X/ADcvn2ODRsmExGRFnQnJydz6dIhypatTsGC+We4eGZMTc0pW7aKysfevh4xMVFcvZrWZi9ePOHGjYvY22dvPytRojQhIX5ERj5TSb916zL6+kYYGZlka/35gY62Ng729hzx8lI9950+rfhNrVgxD2v3CZBZ4LLtk+0BMjU1ZeDAgbi7uxMVFUWrVq0wNDTEz8+PFStWUKtWLZycnGjcuDHffvstM2bMoFy5cvz111+sXr2auXPnAnDp0iXWrl3LwoULcXJy4ptvvuHHH3/E2dkZ63x8ByMgAPz8FC9D3b8foqMVPTYxMXD2bFq+YsUU7/551fFx9CgMHgx9+ypehKqnpygXHa14N1BcnCLAet2rmXWfPk2/TJ0M+/pr+rm5MXr2bDq3bInPjRus27mT8f37o6erS2RUFH5BQZQqXhzTd3yBp462NoO7dWP5L79gamJCrapVuX33Lh6//kqd6tVp4Jj+gkJ8Wob16UO/MWMYPX06ndu2xef6ddZt3cr4oUPT9quAAEpZWmKaejAN79cPtzlzMDE2pkm9ehzz8uLg8eMsmTkTANPChenTtSvrtmwBoI6jI6Hh4XisX4+lhQXd2rXLs+3Nbfb2jlSt6sTcua4MGuSKsbEJmzYtx9DQiHbtvlbmCwz0Iz4+nnLl0geZn5vy5R0pX96Jdetc6dzZFUNDE/74Yzl6ekY0bJjWZqGhfiQmxlOqlKLNtm37gZcvH9Oz50xiYyO5e/eKMq+uriElStjSoEF3Tp7chofHUL78cjQ6OnqcPLmF0FBfJkz49WNvao6xs3OkcmUnli1zpVcvV4yMTNixYzkGBka0aJHWZiEhfiQkxFO69LvvZ1980Y/Tp/9g1qy+dOw4BH19I7y9D/PPP/txcXFDU/PTuDQb1qsX/VxdGT1zJp1bt8bnv/9Yt3074wcNSjv3BQZSqkSJd/5NFak+06AlJ2mkZPWk3ydg7969/Pbbb/j6+hIbG0uJEiVo3bo1Q4YMQV9fn5iYGJYsWcKBAwd4/vw5pUqVon///nTu3JmoqCjat29P+fLlWbFiBQCJiYl06dIFHR0dtmzZkm4q1nfxsYaI6elBu3aK4XAaGorneP74Q/UZn6FDFQFMarwHKGYpbt5cMetbSoriRaoHDsCzZxl/T+HCMGUKbN8OFy/mzrYs+ObjDOk5cuYMyzZvJuD+fcyLFKFnu3bK6aq9r17FZdIk5o4bR6c3H7oCdh8+jNvixRzbsAErCwtlekpKCtsOHODXffsICgvDtFAhvmjcmJG9euX+i97KlMnd9WdHw4aKh8kaNYJTWb9X46PKaozoBzpy6hTLPD0JCArCvGhRenbqRP+vFRdR3pcv4zJqFHOnTKFTm7QpcLft3Yvntm2EPXhAyRIlGNyrFx1atVIuT0lJYeOOHWzbu5eQsDCKFSlCXUdHxg4erAyk3tSkSxecqldn3tSpOb6NwbEf7zmPly+fs2rVPM6cOUpycjL/+18Nhg1zo2TJtP193LjeRETc59dfM56qsmfPJlSt6sTEifM+aHlOuHs311adTlTUc3bsmMeVK4o2s7WtQbdublhYpLXZwoW9efz4PnPnHicxMZ4RI6qTnJyY4frKl3diwoTNgGIo3e7di7hz5yKxsVHY2FShQ4cxlCuXfprjnPCxrpUjI5+zadM8LlxQtFnFijXo08eNEiXS2uy773rz8OF9fvop4/1s+PAm2Nk5MXy46n4UGnqXLVsWc/36OVJSkrGysuXLLwdSq1b635acULVo3sy+eeT0aZZt3EhAcLDi3Ne+Pf27dQPA+8oVXMaNY+7EiXR67dz2yu6//sJt/nyObdmi8pv6Ub3xaox849ccvrnQs2fOrk8NfPIBUH6kjs/I5LWPFQB9cvJzAJRf5UIA9Dn4mAHQp+JjBkCfEukseH95FQCpvfwaAG3dmrPr+/rrt+f5xHwa/axCCCGEEEJ8DmQIXLZJCwohhBBCCCE+G9IDJIQQQgghhLqQHqBskwBICCGEEEIIdSEBULZJCwohhBBCCCE+G9IDJIQQQgghhLqQHqBskwBICCGEEEIIdSEBULZJCwohhBBCCCE+G9IDJIQQQgghhLqQHqBskwBICCGEEEIIdSEBULZJCwohhBBCCCE+G9IDJIQQQgghhLqQHqBskwBICCGEEEIIdSEBULZJCwohhBBCCCHeW3JyMsuWLaN+/fpUq1aNQYMGERwcnGn+O3fuMHjwYGrVqoWzszOjRo0iNDRUuTwpKQl7e3sqVKig8lm+fHmO1lt6gIQQQgghhFAX+agHaMWKFWzZsoV58+ZhYWHBggULGDhwIH/88Qfa2toqeZ8+fUq/fv2oUaMGmzdvJj4+nnnz5jFw4ED27NmDjo4O9+7dIy4ujt9//50iRYooy+rr6+dovSUAEkIIIYQQQl3kkwAoPj4eT09PJkyYQKNGjQBYsmQJ9evX5/Dhw3zxxRcq+Y8ePUp0dDTz589HV1cXgAULFtCoUSMuX76Ms7Mzt2/fxtDQkIoVK+Zq3fNHCwohhBBCCCHUxq1bt4iKisLZ2VmZZmxsjJ2dHRcuXEiX39nZmRUrViiDH4ACqcHcixcvALh9+zZly5bN5ZpLD5AQQgghhBDqI4d7gJo2bZrl8mPHjmWYHh4eDkDx4sVV0osVK6Zc9jorKyusrKxU0tasWYOuri6Ojo4A+Pr6kpiYyIABA7h16xbm5ub06dOH9u3bv/P2vAsJgPLAlCl5XQP1s/1w6byuglr66sGDvK6C+ilWLK9roJaCvFLyugpqx9Q0r2ugnqoUDcvrKqidpGKWeV0FtaSZ1xXITD4ZAhcTEwOQ7lkfHR0dnj9//tbymzdv5pdffmHatGmYpp4Q79y5Q3JyMqNGjcLCwoJTp07h5uZGQkICXbp0ybG6SwAkhBBCCCHEZyqzHp63eTWULT4+XmVYW1xcHHp6epmWS0lJwd3dnZUrVzJs2DB69+6tXPbnn3+SlJSEgYEBABUrViQ0NJR169blaACUP0JIIYQQQgghxNsVKJCznw/0aujbgzdGmzx48ABzc/MMyyQkJODq6sqqVatwc3NjzJgxKst1dXWVwc8r5cuXz3BIXXZIACSEEEIIIYS6yCcBUMWKFTE0NMTb21uZ9uLFC27cuKF8pudNEydO5K+//mLRokX07dtXZdmLFy9wcnJi9+7dKunXrl2jXLlyH1zPjMgQOCGEEEIIIcR70dbWplevXixcuBBTU1MsLS1ZsGABFhYWtGjRgqSkJJ48eYKRkRG6urrs3r2bAwcOMHHiRJycnHj48KFyXUZGRhgbG1O7dm2WLFlCkSJFsLa25vDhw+zbt4/Vq1fnaN01UlJS5MnVj+zp07yugfo5fDiva6Cevmry8O2ZhCqZBOGDnJFJEN6bsXFe10A9ySQI7y+pWPG3ZxLpaObXWRDOns3Z9b02jfX7SkpKYvHixezevZvY2FgcHR2ZMWMGVlZWhISE0LRpU+bOnUunTp3o378/Z86cyXA9r/JERkayfPlyDh06xOPHjylbtiwjRoygWbNmH1zHjEgAlAckAHp/EgB9GAmAPoAEQB9EAqD3JwHQh5EA6P1JAPRh8m0A9NqQsxxRq1bOrk8NyDNAQgghhBBCiM+GPAMkhBBCCCGEusgn7wFSZxIACSGEEEIIoS4kAMo2aUEhhBBCCCHEZ0N6gIQQQgghhFAX0gOUbRIACSGEEEIIoS4kAMo2aUEhhBBCCCHEZ0N6gIQQQgghhFAX0gOUbRIACSGEEEIIoS4kAMo2aUEhhBBCCCHEZ0N6gD4R0dFR/PTTQk6cOExMTDTVqjkwZowb1tZlsiz3+PEj3N3ncu6cF0lJidSp05DRoydTtGgxZZ6UlBS2bPFkz57tPHgQhoVFCbp1c6FLl57KPMHBgXTt2iLd+suUKceWLX/m3IZ+BHFxURw5spAbNw4THx+NtbUDrVu7UbRo1m0ZFOTD0aOLCQ29jra2AZUrt6JZszHo6BhmmD809Dpr1nxF+/bfU716p9zYlBzndf48S9aswS8ggCKmpvTs2JH+X3+NhoZGpmX+PHKElRs3EhwaimXx4gzu1YuOrVur5Dn699+s2LCBgOBgipqa0r5lSwb37o22llaG6xw1bRr6enrMmzo1R7cv37O0hOvXoUMHOHUqr2uTJ2Jjo9ixYyGXLh0mLi6a8uUd6N7djeLFsz4+AwP/Y/fupQQEXCMlJQUbm8p06TIea+vKyjxPnoTz22/zuXHjHxIT46lUyZlu3VwxN7fJ5a3KfTExUfzyy0K8vQ8TGxtNpUoO9O3rhqVl1u32uoULR6Grq8+IEfOUaR4ekzl5ck+mZVauPI6ZmWW26p7bvC5cYMnatfjdu0eRwoXp2aED/b/6Kuvz2rFjrNy8WXFes7BgcI8edGzVSiXP7oMH8dy+ncD79ylWtCgdW7ViaM+eFCyouPTqPXo0569ezfQ7bp88mSPbl5uioqJYvHghhw8fJjo6GgcHByZPdqN06az3q0ePHvHjj3Px8lJcezRo0JBJkyZjZlYsw/y//LKZDRvWc/To8UzXmZiYSM+e3dHV1WPjxs3Z2q58T3qAsu2TD4AiIyOpW7cuBgYGnDp1Cq1MLqjU3YwZ4/nvv6sMH+6KgYEh69Z5MHy4C1u27MfYuFCGZRITExk7dhDR0ZFMmvQdiYmJrFixiNGjB7Bx424KFlS0lYfHfLZv38zgwaOws6vCP//8zcKFsyhYsCAdOnwFwJ07N1PzbkBXV0/5HTo6urm85Tlv587xhIRcpUULV3R0DDlxwoP1610YMWI/enoZt2V4+C02buxL6dLOdO++nJcvH3DkyCIePQqgT5916fInJsaze/dkkpMTc3tzcsyV69cZOnEirZs2ZfTAgVz6918WrFxJUlISg3v3zrDMoZMnmTBrFi5du1K/Vi2Onj7N5Nmz0dbSom2zZgCcuXCBEVOn0qZJE8YPG8adgAAWr1rF0+fPmT52rMr6kpOTmbt8OYdOnkwXRH3yrKzg0CEwMcnrmuSpNWvG4+9/la5dXdHTM+T33z2YP9+FH37Yj4FBxsdnREQg8+b1wsbmf/TrNxsNDQ3++suTuXN78O23eyhevAxxcdEsXNgXDQ0NXFy+o2BBbf74YwXz5vXi++//xNDQ5ONuaA5zdx+Pr+9VevdWtNuOHR58950LS5bsx9Aw43Z7JTk5mY0b53Lu3CEaNeqosqxLl29o0aK7Slpk5HMWLRpN5cpOFC1aIse3JSdd+e8/hrq50bpxY0b378+la9dYsHq14rzWs2eGZQ6dOsWEH37ApXNn6js5cdTLi8nz5inOa02bArBx507meHjQsmFDXIcN4+mzZyzz9OS2vz/LZ80C4NuxY4mMilJZd1BoKJPmzqXbF1/k7obnkIkTx3P16lXGj3fF0NCQFSs86NvXhX379lOoUObXHkOGDCIyMpJvv1VceyxevIiBAwewc+fudNdpBw7sZ/78eRQrZp5lXdauXcO1a9dwdHTKse3LtyQAyrZPPgDav38/RYoU4eHDhxw5coQ2bdrkdZVy3LVrPnh5nWDx4jXUqdMQgGrVHOjUqSm7dm2hX79hGZY7fvwvfH1vsHXrfkqXtgWgfPlK9OjxBUePHqRVqy8JDQ1h69YNjB8/nc6dewDg4OBMREQY5855KQMgX9+bFCtmgYOD80fY4twTFOTD7dsn6NVrDeXLK9rS2tqBJUuacv78Fho2zLgtz57diJ5eIbp3X0bBgtrK9D173Hj06G663qNjx5YSG/sy9zYkFyz39KRSuXIsmD4dgAa1a5OYmMiqzZtx6dYNXR2ddGUWr15Nq8aNmTJqFAD1a9Xi+YsXuK9dqwyAdu/fTwlzcxbMmIGmpiZ1HR15/OQJ67dvZ/LIkWil3i295efHD0uXcu3mzQy/65OloQEuLrBwoeLfnzE/Px+uXDnBmDFrsLdXHJ/lyjkwaVJTjh/fQrt2GR+fR49uRkdHjzFjVqOjow9ApUq1cXVtwrFjv9Cr1wwuXjxEeHgA33//J5aW5QCwsirPxIlNuXjxLxo16p7hutXB7ds+XLx4gilT1lCjhqLdKlVyYPjwphw6tIXOnTNuN4B7927h6fkDfn7X0NZOf0PLwqIUFhalVNIWLBiJoWEhRo9emGUvSn6wfMMGxXkttTe5Qa1aJCYlserXX3Hp0iXj89ratbRq1IgpI0YAUN/JiecvX+Lu6Unbpk1JSkpixaZN1HVwYNnMmcpyduXK0a5/f85cvEhdBwdsbWxU1puUlMQPy5dTsWxZpo4cmXsbnUOuXPHhxIkTrFq1hgYNFPtVzZoONG/elK1btzB0aMb71aFDf3Hz5g327duPra3i2qNixUq0b/8Ff/11kHbtvgTg8ePHLFvmzo4d2ylUyCTLuty6dYs1a1ZTtKhZzm2g+KR98iHkrl27qF+/PrVr12bbtm15XZ1cce6cF3p6+tSqVU+ZVriwKdWrO/LPP5kPkzl3zgtr69LK4AegdGlbbGzKKsudOnUUbW0d2rXrolJ29uylzJu3XPn3nTu3KFeuUk5tUp7x8/NCW1sfW9u0tjQwMMXa2hFf38zbsmnTMfTqtUYl+NHUVNzFSkyMV8kbFHQZb+9f+OKLGTlc+9wTHx+Pt48PzRs0UElv2bgxUdHRXPr333RlQsLCuBccnL5Mo0YEhoRwLzgYgLj4ePR0ddHU1FTmMTE2JiEhgajoaGXapB9+ICkpie2rV1OkcOGc3Lz8zd4eVq2CTZsgk562z8X1617o6Ojzv/+lHZ/GxqZUqODItWuZH58lSpShZcv+yuAHQEdHH1NTCx48CAKgevVmTJmyVRn8QNoxnJAQl9Ob8lFdueKFrq4+VaumtVuhQqbY2Tly+XLWQyk9PCaRnJzEnDnbKVSoyFu/6/LlU3h7H6ZvXzcMDIyzXffcFB8fj/eVKzSvV08lvWXDhorz2rVr6cooz2sZlAm8f597ISE8evqUZy9e0MhZ9YZg+TJlKFyoECfPns2wPtv++IP/fH2ZOW5cpsN/8xMvL8W1R926aW1hamqKo6Mjp09nvl+dOeNF6dKllcEPgK2tLWXKlOXvv9PKrVmzijNnvHB3X07jxo0zXV98fDxubhPp1as3pUuXzuZWqYkCBXL28xn6pLfa39+fq1evUrduXVq0aIG3tzcBAQHK5TExMXz77bfUqlWLGjVqMHXqVMaPH8/kyZOVeS5fvkzPnj2xt7enUaNGzJw5k8jIyLzYnEzdu+dPiRJWKheQAFZWpQgKCsiklKJcyZI26dJfL3fnzk1KlrTGx+cCLi4dqVu3Mh06NGHv3u0qZXx9bxIdHcWgQd1p0KAKbdrU5aefFpKYmJD9DfyIHj3yp3BhKwoUUG3LIkVK8fhx5m1pbGyOhUVFAOLjo/H3/4ejR5dQqlQNZbpiWQx79rjRoMEQzM0r5M5G5ILg0FASEhKwKaV6p9faUjG2PyAoKF0Z/3v3ALApWVK1jJWVSpmenToRGBLCui1bePHyJVeuX2fjjh00dHbGxDjtAmr+9OlsXbmSiq/9aH4WgoLA1hbGj4fXAsLPUViYP2Zm6Y/PYsVKER6e+fHZuHEPWrceqJIWERHI/ft3lAGPvr4RtrY1AMVNi+DgW6xbNxlDw8I4Oqr3cMv79/0pViz9b4SFRSlCQzNvN4CRI+fzww9bsbGpmGU+UDwvumnTj9jZOeHs3Oqt+fNacFiY4rz25jkqq/NaalpWZYwNDSmoqUloRIRKnucvX/Li5UuCw8LSrTcqOpplnp60b94c+0rqcTPx7l1/SpZMv1+VKlVK5VrrTf7+/ti80fv1qty9e2nlvvqqOwcPHqJ58/TPF79u5cqfSEhIZMSIUe+3AepMAqBs+6S3eufOnejr69OgQQOaN2+OlpaWSi/QpEmTOHPmDEuWLGHbtm28fPmS/fv3K5ffunWLfv36Ub9+ffbt28fChQv577//6N+/PykpKXmxSRmKinqJgUH6B+319Q2IemN88fuWe/r0CQ8fRvDddxP48ssuuLuvpVatusybN0MZBD17psgTGHiXjh27s3TpOjp06Ma2bRuYNWtyuvXnZ7GxLzOctEBb24C4uMzb8pWUlBTmzavNxo39iI+Pom3b6SrLjxxZhLa2PvXrD8mxOn8ML1ODfkN9fZV0g9S/3xzH/nqaoYFBlmVq16zJgB49mL9iBY6tW/PV0KEUKVyYRd9+q1KuQtmyObAlaujpU7h/P69rkS/ExLxEVzf98amra0BMzNuPz1fi42NZt24SBQtq07Rpr3TL3d2H8e237bl58xzduk3ExCTjB7PVRXT0S/T107ebnt7b283a+t1v1Fy8eJyQEH+6dMl8SF1+kul5TU/xHGtkBjccIt+hjJ6uLq0bN+aXPXvYeeAAz1++5G5QEONmzUJTU5OY2Nh069118CAvIiMZ0iv9/phfRUa+xNAw/X5lYGBAZGTm+1VkZMbXHm+WK1Om7Fuf27527V/Wr/dkzpx5aGtrZ5lXiNd9ss8AJSYmsm/fPpo0aYKuri66urrUq1ePvXv3Mm7cOB48eMChQ4dYu3YtderUAWDBggVcvnxZuY5169ZRt25dhg4dCoCNjQ2LFi2iWbNmnD9/nlq1an307UpOTiY5OfmNtMyDsQIFMh9//S7lEhISePbsKXPnLqdxY8VdGMUzQKGsW+dBhw5foaurj7u7JyVLWlOihOLufo0aTmhpabN69VL69fuG0qXz38VrcnIyKSmqbZlVYPsuY9mTkxPp0WMliYlxnD69hnXrejJw4FYsLCoSEODNpUvbGTx4B5qa6nXoJb8l4C+QQdu8uZ+mK5N61+nbhQvZvX8/w/r0wblmTe6Hh+Ph6cnA8ePZ4O6Onq76TaQhsi83jk+AmJhIPDyGExBwjW++cado0fQzlLVrN4zWrQdy7tw+PD3dSE5OokGDru+3AXkko3bL6lyfk8/oHDz4KzY2lbC3r5Nj68xNH3Ree8cyM8eNQ1tbm2kLFjB1/nx0dXQY1KMHUTEx6GXwXNGve/fSpE4dSr/Rs5RffOxrj3cRFxeHm9tkXFz6YG9v/87lPgmfaa9NTlKvq7D3cOrUKR49ekTbtm2VaW3btuXEiRMcPHgQvdS7NdWrV1cu19HRUTmIbty4QWBgoEqeV/z9/fMkAFq37ifWrfNQSWvSpCVPnjxKlzcqKgoDA6NM12VoaEh0dPq7NFFRkcpyBgYGaGhoKCdXeKV27fqcO+fF48ePKFKkKLVq1U23nrp1G7F69VL8/G7lywDo5MmfOHlStS0rV25JZGT6toyLi0JHJ/O2fEVTUwtbW0Vb2Ng4snhxE86e3UibNtPYs8eNevUGYWZmS1JSovIiJSUlmaSkxHwdFBml9uJEvXFH9NUd0ozuAhqlpqUr81rPUMTDh/y2bx9DevdmzKBByjz2lSrRtndvdu3fT6/OnXNuQ4Ta2LfvJ/btUz0+HRxa8vx5+uMzJiYKff23H59PnoSxdOkQwsMDGDp0CdWrN8swX/nyDgDY2Tnz6NF9/vxzldoEQDt2/MSOHartVrt2xu0WHf1u7fYuXr58xn//edOjx7gcWd/HoDyvxcSopGd5XnvHMgb6+syZOJGpI0YQGhFBCXNzDPT12bl/v3K43Cu3/P25FxzM2AEDcmCrcseKFT+xYoXqftWiRUseP06/X0VGRmFklPl+ZWRkmOHolKioSAwN331/dHdfSkpKMkOHfkNiomJG1Vc3SRITE9HU1Mz3k3B8MAmAsi3/XnFl0+7duwEYkTpLy+u2bdvGwIGK8eBZ3aVOTk6mXbt2yh6g15mamuZQTd9Phw7dqFevkUraqVNHOXfOi+TkZOVddYCQkEBsbDIPPEqVKo2v78106SEhQdjZKQJBKytrUlJSSExMQOe1u1avTja6uroEBd3j0qVzNGvWBiOjtGc24uIU3fwmJnnTVm/j4NCNChUaqaTdvHkUP7/0bfnkSSBmZpm35a1bx9HVNcLGxlGZpqtrhKlpSV6+fEBo6HWePbufGnT9pFJ2796p7N07lVmzbufMhuWCUpaWaGpqEvjGUKygkBAAylpbpytTOvV5ocCQEOzKl1emB74qY2NDaEQEKSkp1KhSRaWsbenSmBQqxJ0sxpGLT1vDht2oWrWRSpqPz1GuX09/fD54EEjx4lnfZAkJuc2iRQNISIhj/HhPKlRwVFkeEPAvDx+G4OSkOlOotXVl/Px8srcxH1Hz5t2oWbORStqFC0e5ejV9u4WHB2JpmTM3p65cOZ36Lrn8/+zPK6VKlECzQIH057XUv8u+8cwjoOyhCbx/H7tyaRNmBL5R5sQ//2BsZETNKlUol/pg/uOnTwl/+FClHMDJs2fR09VNN2lCftKtWzcaNWqkknbs2FHOnEm/XwUFBVKmTOb7lY1NaW7eTH/tERgY9F49OYcPHyI09D4ODulvUtvbV2b27Ll07Kge79gTH98nGUI+fvyYU6dO0alTJ/bu3avy6dy5Mz4+PpQsWRINDQ2uXLmiLBcfH89///2n/LtcuXL4+flhbW2t/CQmJjJ37lzCMniI8WMwMzOnUqUqKp9ateoRHR3FuXOnlfmePn3ClSsXM+yZeaVWrXrcu+dPQICfMi0gwI979/yV5V71/Bw5sl+l7OnTx7G1rYCBgSGPHz/kxx+/5dixv1TyHD16AAMDQypWrEx+ZGxsjqVlFZWPrW094uKi8PNLa8uoqCcEBl6kbNnM2/Ls2Q388cd3JCcnKdOePw/n4UN/zM0rUKJEZYYM2any6dFjJQCNGo1gyJCdubehOUBHRweHqlU5cuqUyjCkQydPYmRoiL2dXboy1lZWWBUvzqE3XuZ3+NQpbFKXWacGVm/OInc3KIhnz59TskT+foeIyD2FC5tTunQVlU/lyvWIjY3i+vW04/PFiyf4+l6kcuXMj88nT8JYuLAfGhoauLltTRf8AFy79jc//+zKkydp5/bk5CRu3jxHyZLqM2GJqak5trZVVD5Vq9YjJiaKK1fS2u358yfcvHmRqlUzb7f34et7lSJFLPL9S09fpzyv/f236nnt778xMjDIcDIC5XntjRcRH/77b+V5DWDbvn3MX7lSJc/GnTvRLFCAxm8EOldu3MCuXLl8Pb1/sWLm/O9/VVQ+devWIyoqCi+vtP3qyZMnXLx4kTp1Mt+v6tatx927/vj5pV17+Pn5cfeuf5bl3rRixUp++22nysfOrjJ2dpX57bedWc4cp/ZkEoRs+yR7gPbt20diYiKDBg2iTBnV968MHTqUPXv2sH37dlq3bs3333/PrFmzMDMzY/Xq1YSHhyu7TPv370/Pnj2ZOXMmvXr14sWLF8ycOZPY2NgMZzDJK9WrO1KjhhPffefK8OGuFCpkwtq1yzE0NKJTp6+V+QIC/IiPj6dCBcXFarNmbdi4cRVjxw7im2/GA7BixSLKli1P06aKWY9q1qxFvXqNWbp0LjExMZQpU46DB/fy77+XmT9/BQBVq9bEwcGZZcvmERcXS+nStvzzz0l++20zo0dPVukVyu9sbByxsXFi1y5XWrRwRU/PhBMnlqOra4STU1pbPnjgR1JSPMWLK9qyYcNv2LSpP7/9NhYHh25ERT3h1KkV6OoaU7duf3R0DLG0VO3lePpU0RNSuLBlumX50bA+feg3Zgyjp0+nc9u2+Fy/zrqtWxk/dCh6urpERkXhFxBAKUtLTFOnqR7erx9uc+ZgYmxMk3r1OOblxcHjx1mS+m4M08KF6dO1K+u2bAGgjqMjoeHheKxfj6WFBd3atcuz7RX5T4UKjlSs6MTPP7vStasrBgYm/P77cvT1jWjcOO34vH/fj8TEeKytFcfnr7/+wIsXj3FxmUlsbCT+/leUeXV1DbG0tKVhw+6cOLGNpUuH0L79CDQ1tThxYgv37/syblz6lxmrEzs7RypXdmLZMld69XLFyMiE335TtFvLlmntFhzsR0JCPGXKpL+h8TZBQbexslK/GRqH9e5Nv/HjGf3dd3Ru00ZxXtu2jfGDB6ed1+7dU5zXUl9CPNzFBbcff1Sc1+rU4diZMxw8cYIlM9JebdC7c2cGuLoyx8ODJnXrcvbSJVb/+iuDvv6aUm8MgfO9e5d6jumD8vzOwcERJycnJk50ZcIEV0xMTPjpp+UYGxvRvXvafuXnp7j2sEu9Uda6dRvWrFnFkCGDGDdOce2xePEiypcvT6tW7z7jYvny6W9MGKQOUfzf//L/b2q2fKZBS07SSMlP05nlkHbt2mFmZoanp2eGy0eOHMnZs2c5ceIEs2fP5ujRo6SkpNCuXTtu3bpF+fLlmZX6puazZ8/i7u7OjRs30NfXx9nZmUmTJmFhYfHB9Xv69IOLZurFi+e4u8/j77+PkpycjL19DcaMccPaOi0AHDasN2Fh99m797gyLSIijMWLZ3PhwhkKFtTCyakuY8a4UbRo2qxHcXFxrFvnwV9/7ePZsyfY2NgyYMBwGjZMGz8fFRXJ2rUenDx5mMePH2JpWYru3fvSvn3OjJs/fDhHVvNOYmKe89df87h58ygpKcmUKlWD1q3dVF5m6unZm2fP7jNuXFpb3r17juPHlxERcYsCBQpia1ufFi0mUKhQ8Qy/5+nTEJYsaUrHjnOpXj13uum/avIwR9d35NQplnl6EhAUhHnRovTs1In+Xyt+6LwvX8Zl1CjmTplCp9deOLxt7148t20j7MEDSpYoweBevejQKm2YTEpKCht37GDb3r2EhIVRrEgR6jo6MnbwYGUg9aYmXbrgVL0681JfXpijiuXjGb8aNoSTJ6FRIziV9ftbPrYzXh/npyQq6jnbts3Dx0dxfNra1qB7dzeKF087Pn/8sTePHt1nwYLjJCbGM2xYdZKSEjNcX4UKTkyatBmABw+C2blzAbdvXyAuLpoyZezp0GG08pmgnGb8Ee8NRUY+Z+PGeZw/r2i3ChVq0LevG5aWae02Y0ZvHj68z8qVxzNcx7BhTahc2YkRI+alWzZmTBtKlarAuHFLcm0bXqlSNGdHYBw5fZpl69cTEBysOK916ED/rxQv+fb28cFl7FjmTppEp9ZpF+fb9u3Dc/v2tPNaz550aKE6XfOfx46xcvNmQsLCKGFuTo8OHejdKf25vmrLlvTu1IkJQ3JvdtCkYhn/DmXX8+fP+fHHeRw/rrj2qF69BpMnu1G6dNp+1adPb+7fv8/Ro2n7VVhYGHPnzuaffxTXHnXr1mXyZDfMzDI+/06ZMpnz58+rrCMjffoo3pW2cePmHNg6eGOG7/wjp2cGtVSfntuc8kkGQO8iLi6O06dPU7t2bZUHHVu2bMmXX37J8OHDc+27cyMA+tR9zADoU5LTAdBnIT8HQPnYxwqAPiUfMwD6lOR0APQ5yK0A6FOXbwOgnH4Mo/jnt398kkPg3oW2tjYzZ87EycmJb775Bk1NTXbu3EloaCitWqnPQ5xCCCGEEOIzIkPgsu2zbUENDQ3WrFnD06dP+eqrr+jYsSM+Pj54enpS9nN94aIQQgghhBCfuM+2BwigUqVKmT4nJIQQQgghRL4jPUDZ9lkHQEIIIYQQQqgVCYCyTVpQCCGEEEII8dmQHiAhhBBCCCHUhfQAZZsEQEIIIYQQQqgLCYCyTVpQCCGEEEII8dmQHiAhhBBCCCHUhfQAZZsEQEIIIYQQQqgLCYCyTVpQCCGEEEII8dmQHiAhhBBCCCHUhfQAZZsEQEIIIYQQQqgLCYCyTVpQCCGEEEII8d6Sk5NZtmwZ9evXp1q1agwaNIjg4OBM8z99+pTx48fj6OiIk5MTM2fOJCYmRiXPwYMHadOmDfb29nTo0IGzZ8/meL0lABJCCCGEEEJdFCiQs59sWLFiBVu2bOH7779n27ZtJCcnM3DgQOLj4zPMP2rUKAIDA9mwYQPu7u6cOnWK7777Trn83LlzuLq60r17d/bs2YOzszODBw/G398/W/V8kwRAQgghhBBCqIt8EgDFx8fj6enJqFGjaNSoERUrVmTJkiWEh4dz+PDhdPl9fHw4f/48P/74I5UrV8bZ2ZlZs2bx+++/ExERAcDPP/9Ms2bNcHFxoWzZskyaNInKlSuzcePGD65nRiQAEkIIIYQQQryXW7duERUVhbOzszLN2NgYOzs7Lly4kC7/xYsXMTMzo2zZsso0JycnNDQ0uHTpEsnJyVy+fFllfQC1atXKcH3ZIZMgCCGEEEIIoS5yeBKEpk2bZrn82LFjGaaHh4cDULx4cZX0YsWKKZe9LiIiIl1ebW1tTExMCAsL48WLF0RHR2NhYfFO68sOCYDygK5uXtdA/XzV5GFeV0EtBcea5XUV1E6QV0peV0Et1a2nkddVUD8eHnldA/XUpEle10DtaMqFx4cpXDiva5ChFPLH+fbV5AXa2toq6To6Ojx//jzD/G/mfZU/Li6O2NjYTNcXFxeXU9UGJAASQgghhBDis5VZD8/b6KYG1vHx8cp/A8TFxaGnp5dh/owmR4iLi0NfXx8dHR3l+t5cntH6skOeARJCCCGEEEJNJCfn7OdDvRrO9uDBA5X0Bw8eYG5uni6/hYVFurzx8fE8e/aMYsWKYWJigr6+/juvLzskABJCCCGEEEJN5JcAqGLFihgaGuLt7a1Me/HiBTdu3MDR0TFdfkdHR8LDwwkMDFSmnT9/HoCaNWuioaFBjRo1lGmveHt74+Dg8OEVzYAMgRNCCCGEEEK8F21tbXr16sXChQsxNTXF0tKSBQsWYGFhQYsWLUhKSuLJkycYGRmhq6tL1apVqVGjBmPHjuW7774jOjqaGTNm0KFDB2UPT79+/Rg8eDB2dnY0aNCAXbt2cfPmTWbPnp2jdZceICGEEEIIIdREfukBAsWLTbt06cK0adP4+uuv0dTUZN26dWhpaREWFka9evU4cOAAABoaGnh4eGBlZUWfPn0YM2YMDRo0UHkRar169ZgzZw5bt26lY8eOnDt3jlWrVqlMnZ0TNFJSUmTKo48sddIM8R70ImUWuA8hs8C9v6CgvK6BepJZ4D6AzAL3YWQWuPf3xrTC4h3l01ngcvo6MofnF1AL0gMkhBBCCCGE+GzIM0BCCCGEEEKoiewOWxMSAAkhhBBCCKE2JADKPhkCJ4QQQgghhPhsSA+QEEIIIYQQakJ6gLJPAiAhhBBCCCHUhARA2SdD4IQQQgghhBCfDekBEkIIIYQQQk1ID1D2SQAkhBBCCCGEmpAAKPtkCJwQQgghhBDisyE9QEIIIYQQQqgJ6QHKPrUJgPbt28cvv/yCr68vGhoalClThq5du9K9e3cAmjRpQseOHRk5ciTLly9nz549HD9+PFvrVDfR0VEsXbqQY8cOEx0dTY0aDri6umFjUybLco8fP2Lhwrn8848XSUmJ1KvXkPHjJ2NmVkyZJyIinCVL5uPt/Q/x8fE4OTkzZowr1tY2yjznzv3D0KH90q2/fv1GLF++Ose2M6d4nT/PkjVr8AsIoIipKT07dqT/11+joaGRaZk/jxxh5caNBIeGYlm8OIN79aJj69YqeY7+/TcrNmwgIDiYoqamtG/ZksG9e6OtpaXM43P9OotXreL67dsY6OvTqnFjxgwejKG+fq5tb26LiYni558Xcvr0YWJiorG3d2DYMDdKlsx6/3vdzJmj0NPTZ+LEeR+0XN3ExkaxY8dCLl06TFxcNOXLO9C9uxvFi2fdZoGB/7F791ICAq6RkpKCjU1lunQZj7V1ZWWeJ0/C+e23+dy48Q+JifFUquRMt26umJvb5PJW5UOWlnD9OnToAKdO5XVtPjqvwECWnD2L35MnFNHXp6e9Pf2rV8/yXPdKYnIy3XfsQK9gQTZ37pxhnsj4eL7csoURTk50srPL6ernCS8fH5b8+it+QUEUMTGhZ5s29G/f/p3a7Mbdu3R1deXQihVYmZurLPMPCWHBxo2cv36dgpqaOFauzOR+/ShpYZFbm5JrvLy9WbJqFX537yp+Q7t0oX+PHln/hh4+zMr16xW/oRYWDHZxoWPbtip5/O/dY4GHB+cvX6ZgwYI4Vq/O5FGjKGlpCUCTDh24Hx6e4fotixfn+J49ObeR+ZQEQNmnFgHQzp07mT17NlOnTqVmzZqkpKRw5swZfvjhBx49esSIESPYuXMnOjo6ObpOdePmNp5//73KmDGuGBoasnq1B4MGubBr136MjQtlWCYxMZHhwwcRFRXJ1KnfkZiYyLJlixg2bABbt+5GS0uLmJhohgzpi4aGBlOmfIe2tjZr1qxgwIBe7Nr1J4UKmQBw+/ZNDA0NWbFincp3GBsb5/amv7cr168z9P/s3XdYFMcbwPEvFppgQRERCzZQUEQFsaKiRjEmsURMLKhYMfZONFFjF7vYW2yxRk1ssWt+GiUWbLGCdBU7Sm/3+wM8PTiwUE/fz/PcE5ndmduZ3N7suzM7N2YMzs2bM7RPHy5evYrnsmUkJibSr3t3tXkOnTzJqF9+wbVTJxo7OHD0f/9j3LRpaBcsyJctWgBw5vx5Bo0fTxsnJ0a6u3PX3595y5fzPDycn4YPB+CWry89hw6lfp06LJ42jUdPnjB3+XL8g4JYM29ejrVBVps+fSQ3b16hb9/R6OsbsHGjF6NGubJ69X4MDdV//l5LSkpi+fIZ/O9/h/jii/YfvF1TrVw5Ej+/K3TqNBo9PQP++MOL2bNdmTp1P4UKqW+zsLBAZs7shrl5dXr1moaWlhZ//bWWGTO6MHHibkxNKxIbG8WcOcnnrKvrJAoU0Gbv3qXMnNmNKVP2YWBQNGcrmpvKlIFDh6Bo0dw+klxx+cEDBuzdi3OVKgytV4+LDx7gefo0iUlJ9LOze2f+lRcucC0sjLopF5+phcfEMHDfPkJfvszqQ881l2/fZsC0aTg3bMjQLl24ePMmnuvXJ/cP6QSBr90JDKTflCkkJCam2fbg8WO6eHhQoXRp5o0cSXRsLAs2b8Zt0iT2LlyI7gdcw+S2y9evM2DkSJxbtGBov35cvHIFTy+v5DZydVWb59Dx44yaOBHXzp1pXK8eR0+dYtyUKWhra/Nly5YAPAgLo0u/flQoX555U6YQHRPDghUrcBs6lL2bNqGrq4vXrFnExcerHs+1a8xYuJDv2n86/YPIXhoRAP3222907NiRb7/9VplWsWJFwsLC2LBhA4MGDcLIyCjLy9QkV674cOrUCby8VtKoURMAatWy48svm7Nt22/07euuNt+RI39x69YNfv99P5UqVQbA0rIa337blsOHD/Lll19z5MghAgL82blzH5UrVwGgcmULvvyyOUeO/MW33yaPmN2+fZMqVSyxsbHN/gpn0uK1a6lWpQqeP/0EgGO9eiQkJLB840ZcXVzUdkTzVqygdbNm/DhkCACNHRwIf/mShatXKwOgXfv3U9rEBM+ffyZ//vw0tLfn6bNnrNu2jXGDB1OwQAHWb99OkcKFWZQSPL3mMX0694KCqFiuXA60QNa6ccOHs2dPMH36SurWTf781ahhR7duzfnzz9/o2lX95w/g3r1beHlN5fbta+jo6H7wdk3l6+vD5csnGDZsJTY2yW1WpYodY8c25/jx3/jqK/VtdvToRnR09Bg2bAU6OskjhtWq1WP0aCeOHdtEt24/c+HCIR4+9GfKlH2YmSWfs2XKWDBmTHMuXPiLpk01c5T7g2hpgasrzJmT/O/P1GJvb6oZG+PZqhUAjubmJCQlsfz8eVxtbdEtkP5lwK3Hj1lx4QLG6YxMH7t3j2mnThGZ6mJU0y3esoVqFSrgmXLTyrF27eT+YedOXNu2Vds/xMXHs2n/fhZt2YKOtrb6crduxUBfn3W//IJeShllTExwnzaN676+2Flbq82XFy1etYpqFhZ4TpoEgGP9+slt9OuvyX2obtrv6nnLl9PayYkfhw0DoHG9esl96IoVygBo8erVGBgYsG7xYvRSyihTujTuo0dz/dYt7GxtsbK0VCk3IjKSET/9RNOGDdMNvj41MgKUeRqxCEK+fPnw8fEhPDxcJb1fv35s27YNSJ4Ct3jxYpXtS5YswcHBgdq1azNq1ChevHjxQWW+Lnfp0qX07t0bGxsbWrZsyY4dO7K4hpn3zz+n0dPTp379Rso0IyMj6tSx5/Tp9Kd8/PPPaczNKyiDH4BKlSpToUIlZb5mzVrw669blMEPQMGUC/fY2Fhl2u3bt7C0rJZldcoucXFxePv40NLRUSW9VbNmREZFcfHq1TR5Qh48ICA4OG2epk0JDAkhIDgYgNi4OPR0dcmfP79yn6KFCxMfH09kVBQAw/r2ZaWnp0rwUzDlIiTurfbUJOfPn0ZXV586dd58/ooWNcLGxp5//814ytGsWWNJTExk8eJtFC1a/IO3a6rr10+jo6NP9epv2qxwYSMsLe25di39NitduiKtWrkpgx8AHR19jIxK8ehREAC1arXgxx+3KIMfgPz5kz9v8fGa+Rn7YDY2sHw5bNgA6YzqfuriEhLwDgmhZaVKKumtKlcmMj6ei/fvp583MZExR47QvWZNKhQrlmb7y9hYBu3fj72ZGau/+SbLjz23xMXH4339Oi3r1VNJb9WgAZHR0Vy8eVNtvr8vXsRr2zb6f/sto9RchCsUCg6fPUvH5s2VwQ9AjcqVOb1unUYFP3FxcXhfukTLpk1V0ls5OSX3oVeupMkTcv8+AUFBavMEhoQQEBSU3EYnTtCxbVtl8ANQo1o1Tu/bh52trdrjWbp2Lc+eP+fnUaMyWzWNkZSUta/PkUYEQH369OHGjRs4OjrSr18/Vq5cydWrVzE0NKRChQpq84SGhnLu3DnWrVvH8uXLuXbtGh4eHh9V5tKlS6lVqxZ79uyha9eu/Pzzzxw4cCBb6/yh/P39KFOmjMqFN0DZsuUIDPTPMN/bz/G8Vq7cm3yGhobY2tYGID4+jjt3bvHTT+MoVqwYX3yR/PxLbGwsgYH+3L8fiovLN9jZVcfZuRnr169BoVBkUS2zRvD9+8THx2OeaqSlfMoUD/+goDR5/AICADAvW1Y1T5kyKnm6duhAYEgIa377jZevXnH5+nXW79hBk/r1KZoyFdDE2JiqlZMDzqjoaP45f575K1dSu0YNqlapgiYKCvLD1DTt58/MrBzBwel//gDGjp3NwoVbqFix6kdt11QPHvhhbFyGfPlU26xkyXI8fJh+mzVr1gVn5z4qaWFhgYSG3lUGPPr6hlSunHzOJiTEERx8izVrxmFgUAx7e+c0ZX6SgoKgcmUYORJSbj58boJfviQ+KQnzVNP/yhdJnl7p//x5unmX/PsvCYmJDHFwULtdt0AB9nfrxqwvvqCYnl6WHXNuC374kPiEBMxLl1ZJL29qCoB/aKjafDWqVOH4ypW4d+pE/nxpL61CHj3iVVQUpY2NmbxiBXW7daNGp064T5/OwydPsr4i2Sg4NDS5D31Hf/i29+lDQx484FVEBKVNTZns6UndL76ghqMj7qNH8/DRI7XHcv/hQzZs307vrl0xS/l/JMT70IgpcK1bt6ZUqVJs2LCBM2fOcCrlIVZzc3OmT59OnTp10uTR0dFh/vz5lChRAoCff/4ZNzc3AgMDKV++/AeV2ahRI+WUuIoVK3LlyhXWr19PmzZtsrvq7y0i4hWFChmkSS9UqBAREZEZ5itXrnyadH199fmGDHHn7NnT5MuXj4kTpykXSvD1vUNCQgKBgf4MGjScwoWLcPLkMRYs8OTly5cMHjw8E7XLWq8iIgDSLDhQKOXviMi09X6dZlCoUIZ56tWpQ+8uXZi9dCmzly4FwMrCgrkTJ6YpU6FQUO/LL4mNi6NokSLKZ4Q0UWTkK/T1037+9PQKERWV/ucPoGJFy0xt11TR0a/Q1U3bZrq6hYiOzrjN3hYXF8OaNWMpUECb5s27pdm+cKE7//13Gi2tfPTqNY2iRUuqKeUT9Px58usz9iplRNkg1ZSsQil/R8TFqc13NSyMtZcusbljR7TTmSKnnT8/FdWMDGm6VynBcpr+ISXIi0gnmDYpnvHo9POU2SZzNmzApkoV5o0cydPwcOZt3IjrTz+xZ/589NVMG8uLXr1nf/i29+lDn6ecr3OWLMHGyop5U6bw9Nkz5i1bhuvAgezZuBH9VMH2+q1b0S5YENfOnbOgZprjcx21yUoaEQAB2NraYmtrS1JSErdu3eLUqVNs2rSJvn37cuTIkTT7ly9fXhn8ANSsWROAu3fvUr58+fcqs3jKF5pDqjtgtWrV4uTJk9lU03dLSkoiKdWnPykp/VGWfPnSn//+ofn69XOnZ88+HDjwJxMnepCUlEj79p0oV84cL6+VWFnVUD6P5eBQn5iYGDZsWEPPnn0wNDR8V9VyRNI7RqTyqXleIHV7p8mTcsdv4pw57Nq/H/cePahfpw6hDx/itXYtfUaO5NeFC1WG9RMSE1k2axaxsbGs3LSJrj/8wJalS/P8KFBSUhIKhWp7ZDTK9z6rJn3qsqvNoqMj8PL6AX//awwcuJASJdI+qP7VV+44O/fh3Lk/Wbs2+Zx1dOz0YRUQGuld10jqvutiExIYd/gwPWxtsdHAlcky6539g5rRnfcRl5AAQImiRfEaN05ZTnlTUzqPHcveU6fonPKcVl73vv3hh+Z5vbBBCSMjvGbOfNNGZcvSuU8f9h46ROd27ZR5YmNj2bl3L99+/TVF8uBiS9lJAqDMy/MB0MOHD1mxYgX9+/enVKlS5MuXDysrK6ysrGjRogVt27bl/PnzafKlnoqTmLIiS8GCBd+7zNatWwNQINUdsKSkpI/+EswKK1YsYcUKL5W0Fi1a8exZ2mH0yMhIDAzSDzwMDAzU3qGPjIxQm69WreRVgxwc6nP/fiirVy+nfftOGBoaKhdfeFvjxk3ZvXsH/v5+eWZxBMOUO1CRqe7kvb6zZ2CQ9q68YUpamjxv3dUKe/yY7X/+Sf/u3RnWt69yH5tq1fiye3d+37+fbm+tIFSwQAEa2tsDYG9ri9O337J+xw5m/PhjZquYrTZuXMLGjaqfP0fHVjx/nvbzFxUVSaFCeSPwzU1//rmEP/9UbTM7u1aEh6dts+joSPT1391mz549YMGC/jx86M+AAfOpVauF2v0sLJLPWSur+jx5Esq+fcslAPpMGKaM9KRepOD1yE/qkSGABWfPkgQMrFuXhJSrrNfBekJSEvm1tD7pmxqGKSMSkdHRKukR6YwMvS+DlJELx9q1Va4fbC0tMdTX54Z/xlOF85L36Q8/Js/rfI7166u2UfXqGBoYcOP2bZW8p729iYiM5CsNCRxF3pLnAyBtbW127NiBqakp/fr1U9n2ennlt0d6XgsICCAiIkJ5MXvx4kW0tLSoXLnyB5d57do1lX0uXbqEVS7+1kHHji44OjZVSTtx4ihnz55OE5wFBQVSoUIl0mNuXoFbt9I+1BkcHIS1tQ0A169fJTQ0hFatVKf8VatmzZUrPgDcunWDq1cv8+2336m8f2xsDADFin3YKn3ZqZyZGfnz5ycw1VzuoJAQACqVTzslsELK80KBISFYWVgo0wNf5zE3535YGAqFgto1aqjkrVyhAkWLFOFuSgd3/PRpDA0MsH/rgU5DAwPKmpnxSAPmgn/5pQv16jVVSTtz5igXLqT9/N2/H0i5cul//j4XTZq4ULNmU5U0H5+jXL+ets0ePQrE1DTjNgsJuc3cub2Jj49l5Mi1WFraq2z397/K48ch1K2res6WL2+Nr69P5iojNEa5IkXIr6VF4FsLAAEEpfxdSc3qqYd8fQl99Ypay5al2Wbt5cWMFi0+md/6UadcqVLkz5ePwAcPVNKDUv6ulPLMyocqW6oUWlpaaZZvBkhMSkI3nZXj8iJlH5rS/70W9FZ/mJpKH/rWKm5v96HFihZNbiM1UzMTExPTrL538swZypQuTY1qeX/xpawmI0CZl+cXQTAyMqJPnz4sXLiQ+fPnc/PmTYKDgzlx4gSDBg3CwcEBOzW/ZRAbG8uwYcO4ceMGZ86cYcqUKbRr1w4zM7MPLnP//v1s3ryZgIAAVq9ezZEjR+jTp0+a98wpJUuaYG1dQ+VVv34jIiMj+eef/yn3e/bsGZcuXaB+/YbpllW/fiP8/f3w8/NVpvn5+XLvnp8y35kzfzN+/GgePnzTISQmJvLvv+eoUiX5i+zu3TtMnz4Zb++zKuUfOnQAU1MzzMw+rtPIDjo6OtjVrMmRU6dUpiEdOnkSQwMDbNR07uXLlKGMqSmHUk19PHzqFOYp28qndAqpV5G7FxTEi/BwyqY8VPvr9u1MmjNHOSoJ8PDRI/wCArCsXJm8rkQJEywta6i87OwaERUVyYULbz5/L1484+rVC9jZpf/5+1wUK2ZChQo1VF7W1o2IiYnk+vU3bfby5TPu3LmAtXX6bfbs2QPmzOmFlpYWHh5b0gQ/ANeu/c2qVaN59uzNOZuUlMjNm+coW/bTfKZKpKVToAB2ZmYc8fNT/a7z88NQWxubVD/SCbDsq6/Y2bmzysva2BhrY2N2du5Ms3QWHvpU6GhrY2dtzZFz51Tb7OxZDPX1sXnrBtiHKKSnh72VFYfPnVMJgs5euUJUTAx2GhRU6ujoYGdry5GTJ1Xb6MSJ5D5UzYp25cuWpUzp0hxK9QP1h0+cwDxlWyF9fextbTl88qRKEHT2/HmioqPTrAJ3+fp1atvYZG3lNISsApd5eX4ECGDYsGGYm5uzfft2Nm/eTExMDKVLl8bZ2Zn+/furzVO9enWqVauGq6srWlpatGnThnHjxn1Ume3bt+fIkSPMnDkTc3NzFixYQJMmaad75aY6deyxs6vLjz+OZtiw0RQtWpTlyxdjaGiIi8v3yv38/HyJj4+jatXkL9tWrdqwZs1yBg3qy5AhIwFYtGguVapYKFd4+/bb79i5cytDhvSnf/9BFCxYkG3bfsPX9w7LliX/6GmLFq349dfV/PTTWAYNGoaxcUkOHtzHqVPHmTNnUa5OGVTHvUcPeg0bxtCffqLjl1/ic/06a7ZsYeSAAejp6hIRGYmvvz/lzMwwSnnQ94devfCYPp2ihQvj1KgRx06f5uDx48yfPBkAo2LF6NGpE2t++w2ABvb23H/4EK916zArVQqXr74CYGDPnrgNH87wiRNx+fprnr14wdJff6WwoSFu32nm77PY2NhTs2ZdZswYTd++oylcuCgbNizGwMCQr7568/kLDPQlLi6OKlU0p7PPLpaW9lStWpdVq0bTqdNoChUqyh9/LEZf35Bmzd60WWioLwkJcZQvn9xmmzdP5eXLp7i6TiYmJgI/v8vKfXV1DTAzq0yTJt9x4sRWFizozzffDCJ//oKcOPEboaF3GDFiTepDEZ8wd3t7eu3ezdCDB+loZYXPgwesuXiRkQ0bolewIBGxsfg+e0a5IkUw0tfHUs2MiteLJtRQEzB9itw7daLXxIkM9fSkY/Pm+Ny6xZo9exjZvTt6OjpEREXhGxxMuVKlMCqS8Y88v21E9+50nzCBvr/8glu7djx98YI5GzZQ08ICJ/u0NzLyMvdeveg1eDBDx4+nY9u2+Fy7xprNmxk5cGD6faibGx5Tp1K0SBGcGjfm2N9/c/DYMeZPmaIsd8TAgXQfOJC+I0bg1qULT589Y86SJdS0tsapcWPlfomJidwLCKDtF1/keN3Fp0FLkdfWKM5jnJycaN++PYMHD86yMlNNLc4yL1+GM2fOTE6cOIpCkUTNmrUZPdoDc/OKyn169+7O/fuhHDz45i7Mw4cPmD17GufOnaFAgYLUr9+QUaM8lCu8AYSEBLNggScXL54nOjqK6tVt+OGHocpnggCePHmMl9d8/vnnNC9ePKdy5Sr07TuQZs3UP5vwIfQiHme6jNSOnDrForVr8Q8KwqRECbp26IDb98kXnt6XLuE6ZAgzfvyRDm+t9rd1zx7Wbt3Kg0ePKFu6NP26daNdyrNikDxXfv2OHWzds4eQBw8oWbw4De3tGd6vn7ITADh36RKLVq/mlq8vBfLnp7GDA6Pc3THN4guM4BjjLC0vI69ehbN8+UzOnDlKUlIS1avXxt3dg7Jl33z+RozoTlhYKJs3H1dbRteuTtSsWZcxY2Z+1PasoGYF12wTGRnO1q0z8fFJPmcrV67Nd995YGr6ps1mzerOkyeheHoeJyEhDnf3WiQmJqgtz9KyLmPHbgTg0aNgdu705Pbt88TGRlGxog3t2g1VPhOU1Ro2ysPPhTRpAidPQtOmcCrj36XKUV5e794nCxzx82PRuXP4P3+OiYEBXW1scKudvEy6d0gIrrt2ZTi1rfvvvwOw8a1nGN8W8vIlzX/9Neemxzk5ZftbHDl3jkVbtuAfGopJ8eJ0dXbGLeUBfO9r13D96SdmDB5Mh+bN0+TddewYHosXc2zFCsqk+k6/dOsW8zdt4uqdO+jq6NDCwYGxPXtSWM2zp1kqGxa0OHLyJItWrUruQ42N6dqxI25duwLgffEirj/8wIwJE+jQtq0yz9bdu1m7efObPrRHD9o5qy7Nf+nqVeYvX87V//5DV1eXFo6OjB0yhMJvLaT09NkzGrRpw6QxY/i+Q4csr5tSHl3pMNXjUJlm+RlODJAA6B00KQD6lGVHAPQ5yMkA6FORkwHQpyRPB0B5VQ4FQJ+cHAiAPjmf4Yp+WSKPBkDp/B7vR/sMH6PK+88ACSGEEEIIIURW0YhngHLT8ePqp+oIIYQQQgiR0z7XhQuykgRAQgghhBBCaAgJgDJPpsAJIYQQQgghPhsyAiSEEEIIIYSGkBGgzJMASAghhBBCCA0hAVDmyRQ4IYQQQgghxGdDRoCEEEIIIYTQEDIClHkSAAkhhBBCCKEhJADKPJkCJ4QQQgghhPhsyAiQEEIIIYQQGkJGgDJPAiAhhBBCCCE0hARAmSdT4IQQQgghhBCfDQmAhBBCCCGE0BBJSVn7yk6xsbFMnjyZ+vXrU6tWLUaOHMmzZ88yzHPp0iW6d+9OnTp1aNy4MePHj+fFixfK7WFhYVhaWqZ57dq1672PS6bACSGEEEIIoSE0aQrcpEmTuHDhAosXL0ZbW5uJEycyZMgQNm3apHZ/f39/evfuTceOHZk0aRLPnz9n8uTJDB06lPXr1wNw69YtdHR0OHr0KFpaWsq8hoaG731cEgAJIYQQQgghslRYWBh79uxh+fLl2NnZATBv3jxat26Nj48PtWrVSpNnz549lCxZkvHjxyuDm4kTJ9K1a1eCg4MpW7Ysd+7cwdzcnJIlS370sUkAJIQQQgghhIbI6hGg5s2bZ7j92LFjH1XuxYsXAahXr54yrUKFCpiYmHD+/Hm1AdDXX39Ns2bNVEZ2Xv87PDycsmXLcvv2bSpVqvRRx/SaBEC5QC/icW4fguYpWjS3j0Aj3fsnt49A8xgZ5fYRaCgvr9w+As0zaFBuH4FmOncut49A85ib5/YRiCykKVPgwsLCKFasGDo6OirpJUuW5OHDh2rzqAtsVq1ahbGxMZaWlgDcuXOHYsWK0bVrV/z9/Slfvjzu7u44Ojq+97FJACSEEEIIIcRn6mNHeEJCQjIcPRo6dCja2tpp0nV0dIiNjX2v95g1axYnT57Ey8uLggULkpCQwL1796hcuTLjxo3DwMCA/fv3069fP9atW0f9+vXfq1wJgIQQQgghhNAQeWUEyMTEhAMHDqS7/dSpU8TFxaVJj42NRU9PL8Oy4+Pj+fnnn9mzZw9TpkyhRYsWABQoUABvb2/y58+Prq4uANWrV+fu3busWbNGAiAhhBBCCCE+NXklACpYsGCGz+Lcvn2bFy9eEBcXpzIS9OjRI0xMTNLNFxERwaBBg7hw4QLz5s3D2dlZZXuhQoXS5KlSpQqnT59+72OX3wESQgghhBBCZKk6deqQlJSkXAwBkpe5DgsLw97eXm2euLg4+vfvz9WrV1mzZk2a4Ofu3bvUrl0bb29vlfTr169TuXLl9z42GQESQgghhBBCQ+SVEaB3MTEx4csvv2TChAlMnz4dPT09Jk6cSN26dbG1tQWSA57w8HCKFCmCtrY2K1as4OLFi8ydO5eKFSvy+PGbhcOKFClCpUqVqFixIr/88guTJ0+mWLFibN++ncuXL/P777+/97FJACSEEEIIIYSG0JQACGDKlClMnz6dQSmrXjo6OjJhwgTldh8fH1xdXdmwYQMODg7s27cPhULBiBEj0pT1ep/ly5czd+5chg0bxsuXL7GysmLdunVYWFi893FpKRQKRearJz7IY1kG+4PJMtgf5dQ/BXP7EDSOLIP9cWr8vSS3D0HzyDLYH0eWwf5wNja5fQSa6R0P6ueWgweztrxUs8w+CzICJIQQQgghhIbQpBGgvEoCICGEEEIIITSEBECZJ6vACSGEEEIIIT4bMgIkhBBCCCGEhpARoMyTAEgIIYQQQggNIQFQ5skUOCGEEEIIIcRnQ0aAhBBCCCGE0BAyApR5EgAJIYQQQgihISQAyjyZAieEEEIIIYT4bMgIkBBCCCGEEBpCRoAyTyMCICcnJ0JDQ5V/a2lpoa+vj5WVFUOHDsXe3j5b3nfcuHGEhoaycePGbCk/q5z+91/mr1yJr78/xY2M6Nq+PW7ff4+Wlla6efYdOcKy9esJvn8fM1NT+nXrRntnZ5V9jv79N0t//RX/4GBKGBnxTatW9OveHe2CBZX7/Hf7NgtWreLarVsokpKwtrRk5IABWFtaZlt9s8rpM2eYv2gRvn5+FC9enK7ffYdbz54Zt9uBAyxbsYLgkBDMSpemX58+tP/mG7X7RkRG8nWHDgxyd6dDu3Yq227fuYPnvHlcvXoVbW1tGjZowOgRIyhRokRWVjHHxMREsmvXHC5dOkxsbBRVqtjh4uJBqVIVM8wXGPgff/yxgICAaygUCsqVs6ZDh5GUL2+t3Od//9vBxo0T0uRt2rQrXbr8nOV1yUnR0ZFs2jQHb+/DxMREUa2aHT17emBmlnG7vW3OnCHo6uozaNBMZZqX1zhOntydbp5ly45jbGyWqWPPDacDA5l/9iy+z55RXF+frjY2uNWqleE5+1pCUhLf7diBXoECbOzYUe0+EXFxfP3bbwyqW5cOVlZZffiaxcwMrl+Hdu3g1KncPpocd/raNebv3IlvaCjFCxema4sWuDk7p/tZi4uPZ+3Bg+w5c4aHT59iYmTEVw0a0K9tW7QLvLnUOvjvv6zev597Dx5QWF+fBtbWjHRxoUSRIjlVtSxz+p9/mO/lhe+9e8nXHp074+bqmnEfevAgy1atIjg0NLkP7dWL9l9/rbLPrj/+YO2GDQSFhGBaqhRdO3eme6prmguXLjFv8WJu3blDYUNDWjRrxrBBgzAoVCjb6puXSACUeRoRAAG4ubnh5uYGgEKh4MWLF8ybN48+ffpw8OBBSpcunctHmDsuX7/OgDFjcG7enKF9+nDx6lU8ly0jMTGRft27q81z6ORJRv3yC66dOtHYwYGj//sf46ZNQ7tgQb5s0QKAM+fPM2j8eNo4OTHS3Z27/v7MW76c5+Hh/DR8OACBISF0GzSI6paWTBs3Di1g7datdBk4kN3r1lGxXLmcaoYPdvnKFQb88APOrVszdPBgLl66hOe8ecnt1qeP2jyHjhxh1NixuHbrRuNGjTh67Bjjxo9Pbrc2bVT2DQ8PZ+CQISqB+2tPnjyhR+/emJYqxYxp04iNiWHO/Pn0dXdn+2+/UfCtAFNTrF49En//K3TsOBpdXQP27fNi7lxXJk3aT6FC6jv2R48CmTOnG+XLV8fVdRpaWlocPryW2bO78NNPu5XBU3DwTUxMKtCr10yV/IULa2aw+LaFC0dy584VuncfjZ6eATt2eDFpkivz5+/HwCDjC6KkpCTWr5/BuXOHaNq0vcq2b78dyBdffKeSFhERzty5Q7G2rkuJEpr3fXn5wQMG7N2Lc5UqDK1Xj4sPHuB5+jSJSUn0s7N7Z/6VFy5wLSyMumbqA7/wmBgG7ttH6MuXWX3omqdMGTh0CIoWze0jyRWXfX0ZMG8ezg4ODO3YkYt37uC5bVty//DVV2rzTN20iT/PnGHgN99Qo2JFrvn7s2TPHu4/ecL0lD5l/7lzjFi6lM7NmjH82295Eh7Owt9/p8fMmeyaPBkdbe2crGamXL56lQFDhuDcqhVDf/iBiz4+eC5YkNxGKddqqR06epRRP/6Ia5cuNG7YkKMnTjDu55/R1tbmy9atAdixaxcTfvmFPj170qh+fa5cu8bMuXOJiopiQEo73vX1pdeAAdSpVYsFs2cT9ugRcxYsICQ0lOWLFuVYGwjNpjEBkL6+PsbGxsq/S5YsyeTJk3F0dOTIkSP06NEjF48u9yxeu5ZqVarg+dNPADjWq0dCQgLLN27E1cUFXR2dNHnmrVhB62bN+HHIEAAaOzgQ/vIlC1evVgZAu/bvp7SJCZ4//0z+/PlpaG/P02fPWLdtG+MGD6ZggQJs3LkTPV1dVnh6oq+nB0C9OnVw6tSJTTt38vOIETnUCh9u8ZIlVKtWDc+ZyRfVjo0aJbfbqlW4duuGrq5umjzzFi6k9Rdf8OPYsQA0btiQ8PBwFnp5qQRAx06cYNqMGURGRqp972MnTvD8+XO2b95MuZQg0bBwYfr074/P5cvUzaYRzezi5+fD1asnGDx4JTVqNAGgShU7fvyxOSdP/saXX7qrzXf8+Ea0tfUYPHgFOjr6AFStWg8PDyeOH9+kHN0JDr6JuXkNKla0zZH65JTbt324cOEEP/64ktq1k9utWjU7fvihOYcO/UbHjurbDSAg4BZr107F1/ca2tppP6ulSpWjVCnVGxCenoMxMCjC0KFz3mvEJK9Z7O1NNWNjPFu1AsDR3JyEpCSWnz+Pq60tugXS785uPX7MigsXMNbXV7v92L17TDt1isj4+Gw5do2hpQWurjBnTvK/P1OLd+2iWvnyeA4YAICjjQ0JiYks37sX11at0E0VqDx/9YrtJ08yysWFPl9+CUB96+RR7LnbtzPKxQWjwoVZvncvTWrW5JdevZR5K5ia4jJ5MicuX6Z13bo5VMPMW7xsGdWqVsVz2jQAHBs2TO5D16zBtUsX9X3o4sW0btmSH0ePBqBxgwbJfeiSJcoAaPmaNbRq0YLRw4YBUN/BgYDAQDZt3aoMgPYeOICWlhZL5s+nUMo5nZiYyMSpUwm9fx+zz+CGuIwAZZ5GL4JQIKXD09bW5v79+wwfPpz69etjbW2No6Mjnp6eJKV8Snbt2kXLli2ZOnUqderUYeDAgQAEBgbi7u5OnTp1cHBwYMSIETx9+lT5HvHx8cyaNYt69epha2vLwIEDefLkSc5XVo24uDi8fXxo6eiokt6qWTMio6K4ePVqmjwhDx4QEBycNk/TpgSGhBAQHAxAbFwcerq65M+fX7lP0cKFiY+PJzIqCoCK5cvj9v33yuAHQF9Pj1LGxgSpGfnIK+Li4vA+f56WzZurpLdq2ZLIyEguXrqUJk9IaCgBAQFp83zxBYFBQQQEBgLw8uVLBg0dir2dHatXrFD7/rGxsQAYGBgo04qmTH948eLFR9crt/z332l0dPSxtm6kTDM0NKJKFXuuX09/6kypUhX54gs3ZfADoKOjT7FipXj8OAhIHu0NDb1N2bLVsq8CueTy5dPo6upTs+abditSxAgrK3suXcp4ypGX11iSkhKZPn0bRYoUf+d7Xbp0Cm/vw/Ts6UGhQoUzfew5LS4hAe+QEFpWqqSS3qpyZSLj47l4/376eRMTGXPkCN1r1qRCsWJptr+MjWXQ/v3Ym5mxOp3prJ8NGxtYvhw2bIB0ZhB86uLi4/G+dYuWdeqopLeytycyJoaLd+6kyRMRE8N3zZrhVLu2SnpFU1MAgh8/JikpiYbW1rg0bap2n6BHj7KwFtkrLi4O7wsXaOnkpJLeqkWL5D7UxydNnpDQUAICA9XmCQwOVvahKxcvZkzKLJPXChYsqOw3Ifn6pECBAui9FWQp+9Dw8MxVTkMkJWXt63OksQFQWFgYv/zyC/r6+jRp0gR3d3devXrFunXr+Ouvv3Bzc2P16tUcP35cmScoKIhHjx6xZ88ehg8fzsuXL+natStxcXGsX7+edevWERQUxLCUOw8APj4+vHz5kt9++40VK1Zw+fJlZs+enQs1Tiv4/n3i4+MxTzXVrHzKFA//oKA0efwCAgAwL1tWNU+ZMip5unboQGBICGt++42Xr15x+fp11u/YQZP69SlaOPkCqkv79vTp0kWlnMCQEO7eu0eVChUyX8FsEhwcnNxu5curpJdPaUf/lDZ6m9+9ewCYm5urz+PvD4Cunh77//iDWdOnU0zNxRaAc+vWGBsb88u0aTx6/JjgkBBmz52LsbExDerXz0zVcsXDh36UKFGGfPnyq6SXLFmOhw/9083XtGkXWrVSnW746FEgoaF3KV26CgCPHwcRExNJQMA1fvqpFQMGWPPTT604e3ZPltcjp4WG+lGyZBmVmwyQPHpz/3767QYwePBspk7dgrl51Xe+j0KhYMOGWVhZ1aV+/daZOubcEvzyJfFJSZinmpJVPuWix//583TzLvn3XxISExni4KB2u26BAuzv1o1ZX3xBsbdu5nyWgoKgcmUYORJSbnR9boIfPSI+IQHzUqVU0submADg/+BBmjxljY2Z1LOnMph57dilSxTMnx/zUqXIly8f47p0oUWqwOroxYsAVElnamZeFBwSknEfmhLMvM0vpY98V55KFStSxsws+VGH8HB27NrFnn376OLioszTMeWZ2hlz5vD8xQvu+vqyZMUKLKpUoaqFRdZUUnzyNGYK3IoVK1i7di0ACQkJxMXFUalSJRYsWICRkRHffPMNzs7OmKZ8AfXs2ZNVq1Zx+/ZtWqRM6wIYOHAgZVMu/rdu3UpkZCTz5s2jSEpHOnXqVPbv309cXBwAxsbGTJkyhXz58lGxYkXatGnDP//8k5NVT9eriAgADFJN63g9JByhZgrW67TUDwqmzlOvTh16d+nC7KVLmb10KQBWFhbMnTgx3eOJiY1l7LRpaGtr0+3bbz+mSjlC2W5vjcAAFEppk4iU7W+LePUq4zwp7aZdsCAV3xH8GZcoweSff2bE6NEcPHQIgCKFC7Nh7do05WuC6OhX6OqmPW5d3ULExKifBqhOXFwM69aNpWBBbZycugHJ098AnjwJoVOnceTPX4CzZ/ewbt1YEhLiaNzYJaMi87SoqFfo66dtNz29QkRHZ9xu5cu//yIjFy4cJyTEj59/TruQhKZ49XrUNNXUo0Ipf0ekfF+ndjUsjLWXLrG5Y0eVB9Hfpp0/PxXTuVnx2Xn+PPn1GXsVHQ2AQapguFDKaENEyvZ3OXLhArtPn6ZbixYUSefB/KCwMGZt3Uq1cuVoUrNmJo46Zyn70PSuI9T1oR+Y5/LVq3yX8mhDdSsrerm6KrdZVK7M6GHD+GXGDDb89hsAZqambF63Ls0NpU/V5zpqk5U0JgD67rvv6J4yJJ8vXz6KFi2KoaGhcnu3bt3466+/uHr1KoGBgdy+fZsnT54op8C99vYd/Dt37mBubq4MfgCqVq1K1apv7qqWK1eOfPneDJQVKVKEmJiYrK7eR0lSKDLcnk/NHO7U7ZEmT0pdJ86Zw679+3Hv0YP6deoQ+vAhXmvX0mfkSH5duFBl6BkgIiqKHzw8uHbjBgunTsUs1d2zvOR920Alz0e0dXr27t/PGA8PWn/xBR3btyc2Lo61v/6KW79+bPz1VypVfP8VwHJaUlISCkVSqrT02+Z9nzWJiYlg6dIf8Pe/xoABCylePPluaJUq9gwatBxLSwflVDlr68a8evWMP/5YRKNGnTTieZbsarf3cfDgZszNq2Fj0yDLysxp7+rr1Z1/sQkJjDt8mB62ttjk4e8jkbdkxXf94fPnGbl8OXUsLBjdubPaffzu36f37NkUyJePRYMHq+138qqP6kM/ME9pU1M2rl5NSGgoC5Ys4bsePdi9ZQt6enqsXLuWuYsW0bVzZ1o2b87z589ZtmoVPfv1Y/O6dZQo/u5pwZpOAqDM05gAqEiRIpRPNXT6WlRUFN26dSMmJobWrVvTvn17bGxs6Nq1a5p9334wr0AGD82+lpfvJhim3EmJTDVVISLlb3WjCYYpaWnyvDUyFPb4Mdv//JP+3bszrG9f5T421arxZffu/L5/P93eWkb2QVgY/ceMwT84mPm//EKLxo2zoHbZ53XgnHqRAmUbZNRuqfO8vqv1VjD+Ll5Ll1LL1pb5c+Yo0xrWr0+br79m4eLFLJo//73Lymn79i1h3z4vlbTatVvx6lXa5+JiYiLR03t3uzx79gAvr/48fOhPv37zsbV9M2JbuHBxbGyapclTo0YTbt78h5cvn1CkiHGa7XnNjh1L2LFDtd3q1WtFeHjadouKikRf//0/Txl59eoF//3nTZcueXdBkvdhmDLSk3qRgtcjP6lHhgAWnD1LEjCwbl0SUq4WFCkXtwlJSeTX0tKI4FnkLMOUkZ/IVDc6X4/8pJ5xkdqvf/3FrC1bqFutGkuGDlW7spv3zZsMXrQIfR0d1nt4UC5lep2meOd1xMdce6TKY1KyJCYlS1LXzo6yZcrQrXdvDh09SltnZ5auXMlXbdrws4eHcn8He3tatG3LmvXrGZuHF2ASeYfGBEAZOX36NP/99x9nzpxR/o7KixcvePr0qbLDU6dy5crs2LGDV69eKS+K//vvP/r06cPu3en/hkZeUc7MjPz58xOYasGBoJAQACqpCRgrpMy3DQwJweqtubKBr/OYm3M/LAyFQkHtGjVU8lauUIGiRYpw1//N8wm3/fzoPWIEsbGxrJ03D3tb2yypW3YqV7ZscrulekYqKOVvdSMwFVKmtQUGBWFV7c0D+YEZ5ElP6P37tEi1mIKuri7Vra256+v73uXkBkdHF2xsmqqkXb58lBs3TpOUlKRyF+/Ro0BMTSuRkZCQ2yxc2Jv4+FiGDVuLhYXqCnh3717g8eNgGjRQXeY5Pj6WfPnyp7vEdl7TsqULdeo0VUk7f/4oV66kbbeHDwMxM8u43d7X5cv/IzExgQYNNPPZn9fKFSlCfi0tAlMtEhKU8nclI6M0eQ75+hL66hW1li1Ls83ay4sZLVrIb/2INMqVLEn+fPkIDAtTSQ9K+btSOiuMKRQKpm3axMYjR2hbrx4z+vVTO+1y39mzjFu5kgqlS7N65EhM1Hx287p39qFqpoFXSJl9ExgUhNVbs2wC38oTGRXF8ZMnsaleXflsEKDscx89fsyz58+JjomhdqprjeJGRlQwN+eun1+m66cJZAQo8zRnzDUDpVKmN/z555+EhoZy4cIFBg4cSHx8vPJZHnW++uorihQpwujRo7l16xbXr19n4sSJWFhYKMvMy3R0dLCrWZMjp06pBHqHTp7E0MAAGzWde/kyZShjasqhkydV0g+fOoV5yrbyKYFV6lXk7gUF8SI8nLIpHcCDsDB6DRuGlpYWW5Yt04jgB1LarU4djhw7ptpuR45gaGiITarAD5If1CxTpgyHDh9WST985Ajm5ctT5gMeYK1YoQKXfHxU3js2Npb/bt6kbMpiFHlV0aImmJvXUHlZWTUiJiaS//77n3K/V6+ecffuBaysGqZb1rNnD5g/vxdaWlqMGbMlTfADcPv2OX79dRxhYW+C7qSkJC5ePESlSrUoUEAzfjfDyMiEypVrqLxq1mxEdHQkly+/abfw8GfcvHmBmjXTb7cPcefOFYoXL6WRP3r6Np0CBbAzM+OIn5/qOevnh6G2NjZq7qAv++ordnburPKyNjbG2tiYnZ070ywPL9Qico+OtjZ2lpYcuXBB9bN24QKG+vrYpHOza96OHWw8coRerVszx91dbfBz6soVxqxYQa0qVdgyYYJGBj+Q0ofWrs2R48dV2+jYseRrj+rV0+QpX64cZczMOHT0qEr64WPHME/ZViB/fib88gtr1q9X2efM2bMAWFapQnEjI4oWKZJmtdZnz58TEBhIWQ1aTELkrk9iBMjGxgYPDw9+/fVXFixYgImJCW3atMHU1JRr166lm09PT481a9YwY8YMvvvuO3R1dWnatCljU37nRRO49+hBr2HDGPrTT3T88kt8rl9nzZYtjBwwAD1dXSIiI/H196ecmRlGKQ/6/tCrFx7Tp1O0cGGcGjXi2OnTHDx+nPmTJwNgVKwYPTp1Yk3Kw4UN7O25//AhXuvWYVaqFC4pPwQ3dcECnj5/zuRRo4iIjOTy9evK4zIoVIjKefgCw71/f3r16cPQkSPp2L49Ppcvs2bdOkYOH46enh4RERH4+vlRrmxZjFI6qR8GDMBjwgSKFi2KU7NmHDt+nIOHDjHf0/OD3nvo4MH8MGQIQ0eO5NsOHZJXIdywgbCwMObOmpUd1c1WFhb2WFjUZc2a0XTsOBoDg6Ls3bsYPT1DmjT5Xrnf/fu+JCTEUa5ccmC+detUXr16Steuk4mJieDevcvKfXV1DShdujKOjt9x8uRWvLwG8PXXQ9HR0ePkyd+4f/8Oo0ZtzumqZikrK3usreuyaNFounUbjaFhUbZvX4y+viGtWr1pt+BgX+Lj46hY8cNHK4KCblOmTOWsPOxc425vT6/duxl68CAdrazwefCANRcvMrJhQ/QKFiQiNhbfZ88oV6QIRvr6WJZI+0O5rxdNqKFhU45EznL/5ht6zZrFUC8vOjo64nP3LmsOHGCkiwt6OjpEREfjGxpKuZIlMSpcmJuBgazav58aFSvSum5drqQahahsZkbB/PkZv2YNhXR1GfD11/immrlRysiIUhoUELn37Uuv/v0ZOno0Hdu1w+fKFdasX8/IIUPe9KH37lGuTJk3fWi/fnhMnEjRIkVwatqUYydPcvDwYean9Hs6Ojr0c3Nj8bJlGBkZ4WBvz+3bt/FasYIGDg44NmqElpYWg93dmTJzJoUMDHBu2ZLnL16wYs0a8ufLh9tbiyV8ymQEKPO0FBnNERPZ4/HjLC3uyKlTLFq7Fv+gIExKlKBrhw64fZ98AeV96RKuQ4Yw48cf6fDWj3Vu3bOHtVu38uDRI8qWLk2/bt1o1/rNNBmFQsH6HTvYumcPIQ8eULJ4cRra2zO8Xz+MihUjLj6eWi1akJCYqPaY6trastHLS+22j5INv0h+5OhRFi1Zgn9AACYmJnT97jvcevYEwPvff3F1c2PG1Kl0SFlyE2Dr9u2s/fVXHjx8SNkyZejXpw/tvv5abfkhoaE0b9UqTRkAf58+zdLly7lx8yaFChXCpnp1hg8dSlXL91/d632c+qdglpaXnsjIcHbsmMnly0dJSkqicuXauLh4UKrUm7ulc+Z05+nTUGbMOE5CQhyDBtUiKSlBbXkWFnUZNWojkDyVbteuudy9e4GYmEjMzWvQrt0wqlSxy5a65OQ1SEREOOvXz+Tff4+iUCRhaVmbnj09MDN7024//9ydx49DWbbsuNoy3N2dsLauy6BBM9NsGzasDeXKWTJiRPY/V1bj7yXZ/h5H/PxYdO4c/s+fY2JgQFcbG9xSfnvFOyQE1127Mpza1v333wHY+NYzjG8LefmS5r/+mnPT4wYNyv73+FhNmsDJk9C0KZzK+Hepcty5c9n+FkcuXGDR7t34P3iASbFidG3RAjdnZyD5GR7XGTOY0bcvHRo3ZuHvv7P0jz/SLWuDhwdJCgU9Z6Y9R18b1K4dgzt0yPJ6KNnYZHmRR44fZ9GyZcl9aMmSdO3cWRmAeJ8/j2vfvsyYPJkOb/2+1tadO1m7fj0PwsKS+1A3N9q1bavcrlAo2LpzJ5u3biUoJASjYsVo6+zM4AED0Hnrh93/2L+fdRs24HvvHsWKFsWudm1GDBmS9SNAeXRp/HR+ZvCj9e+fteVpAgmAckMWB0CfhWwIgD4HORUAfUo06CZsnpITAdAnJy8HQHlZDgRAn5xsCIA+CxIAfbI+iSlwQgghhBBCfA5kClzmSQAkhBBCCCGEhpAAKPM+iVXghBBCCCGEEOJ9yAiQEEIIIYQQGkJGgDJPAiAhhBBCCCE0hARAmSdT4IQQQgghhBCfDRkBEkIIIYQQQkPICFDmSQAkhBBCCCGEhpAAKPNkCpwQQgghhBDisyEjQEIIIYQQQmgIGQHKPAmAhBBCCCGE0BASAGWeTIETQgghhBBCfDZkBEgIIYQQQggNISNAmScjQEIIIYQQQmiIpKSsfWWn2NhYJk+eTP369alVqxYjR47k2bNnGeZZtmwZlpaWaV5v27x5M82bN8fGxoYuXbpw48aNDzouCYCEEEIIIYQQWW7SpEmcPn2axYsXs379eu7du8eQIUMyzHP79m2++eYbTp8+rfJ6bffu3cyePZuhQ4eya9cuypQpQ69evd4ZWL1NAiAhhBBCCCE0hKaMAIWFhbFnzx4mTJiAnZ0dNjY2zJs3j/Pnz+Pj45Nuvjt37mBlZYWxsbHK67Xly5fTrVs3vv76aypXrsz06dPR09Njx44d731sEgAJIYQQQgihITQlALp48SIA9erVU6ZVqFABExMTzp8/rzZPXFwcAQEBVKxYUe32p0+fEhAQQP369ZVpBQoUwM7OLt0y1ZFFEIQQQgghhPhMNW/ePMPtx44d+6hyw8LCKFasGDo6OirpJUuW5OHDh2rz+Pr6kpiYyKFDh5g2bRqxsbHY29szevRolXympqZpyrx169Z7H5sEQLnh+vXcPgKNM3Jfs9w+BI3k6prbR6B5apR4kNuHoJmcnHL7CDTPuXO5fQSa6a27yeL9nP1HkduHoJHeGmTIU/LKKnAhISEZBk9Dhw5FW1s7TbqOjg6xsbFq89y5cwcAPT09Fi5cyNOnT5k3bx6urq7s2bOH6OhogDTlZlSmOhIACSGEEEIIoSGyOgD62BEeExMTDhw4kO72U6dOERcXlyY9NjYWPT09tXnatWuHo6MjRkZGyrQqVarg6OjI8ePHKVeuHECacjMqUx0JgIQQQgghhBAfpGDBglSqVCnd7bdv3+bFixfExcWpjNg8evQIExOTdPO9HfxA8vS2okWL8vDhQxwcHJRlvP3e7yozNVkEQQghhBBCCA2hKYsg1KlTh6SkJOViCAD+/v6EhYVhb2+vNs/8+fNp1aoVCsWbaZshISE8f/6cypUrU7x4cSpUqIC3t7dye0JCAhcuXEi3THUkABJCCCGEEEJDaEoAZGJiwpdffsmECRPw9vbm6tWrjBgxgrp162JrawskT2V7/Pixckpby5YtCQ0NZdKkSfj7+3P+/HkGDx5M7dq1ady4MQBubm6sW7eO3bt34+vry48//khMTAzffvvtex+bBEBCCCGEEEKILDdlyhTq16/PoEGD6N27NxUrVmTRokXK7T4+PjRq1Ej5u0DVq1dn1apV3L59mw4dOjBo0CCqVavG8uXL0dLSAsDFxYUhQ4awYMECOnbsSGhoKOvWrUszdS4jWoq3x5hEzjhxIrePQOPIKnAfR1aB+3A1S8oqcB/lxYvcPgLN8/Jlbh+BZpJV4D6YrAL3cfLqKnBjx2ZtebNmZW15mkAWQRBCCCGEEEJD5JVlsDWZTIETQgghhBBCfDZkBEgIIYQQQggNISNAmScBkBBCCCGEEBpCAqDMkylwQgghhBBCiM+GjAAJIYQQQgihIWQEKPMkABJCCCGEEEJDSACUeTIFTgghhBBCCPHZkBEgIYQQQgghNISMAGWeBEBCCCGEEEJoCAmAMu+TCIC6d+/Ov//+q3abm5sbY8eOzeEjyn2nb9xg/h9/4Hv/PsULF6Zrkya4tWyJlpaW2v3j4uNZe/Qoe86d4+Hz55gUK8ZXdevSr1UrtAu8+Zj4PXyI565d/HvnDgXy5cO+ShXGffstZY2Nc6pq2U5bG9q2hRo1QEcH7t2DP/6Ax48zztelC9SpkzZ9/Xq4ejX5361bQ8uWaffZuxdOnsz0oeeamJhINm2ag7f3YWJiorCysqNHDw9Kl6743mXMnTsEXV19fvhhpkr6y5fP+O23efj4nCI2NppKlarTrdsYKlSwyupqZJvT588zf/VqfAMCKF6sGF3btcOtc+d0z0eAfceOsWzjRoLv38esVCn6delC+9atVfbZdfAga7dtIzA0lJIlStC+dWsGdO1KgZRztvvQofx75Uq673Fbwz50p318mL95M75BQRQvWpSubdrg9s03Gbbjazfu3aPT6NEcWrqUMiYmKtv8QkLwXL+ef69fp0D+/NhbWzOuVy/KliqVXVXJUaevXWP+zp34hoYm9wctWuDm7Jxxf3DwIHvOnOHh06eYGBnxVYMG9GvbVqU/OPjvv6zev597Dx5QWF+fBtbWjHRxoUSRIjlVtbzHzAyuX4d27eDUqdw+mlwRExPJ9u1zuHDhMLGxUVha2vH99x6YmmbcHwQE/MeuXQvw979GUpICc3NrOnUaibm5tXKfFy8es2vXQv777wwRES8oVaoCbdr0wcGhTXZXS3xiPokACMDZ2Znx48enSdfT08uFo8ldl+/dY8CSJTjb2TH066+56OuL5+7dJCYl0S/VBdRrU7dv509vbwa2aUMNc3OuBQayZN8+7j99ynRXVwAePHtGF09PKpiYMK93b6Lj4ljw55+4LVrE3p9+QldbOyermW26dYNy5WD/foiJgS++AHd38PSE6Oj085UuDZcuwf/+p5r+duBkZga+vsllv+3586w7/tywcOFI7t69Qrduo9HTM2DnTi8mT3Zl7tz9GBhkfDGUlJTEhg0z8PY+RJMm7VW2KRQK5s4dTEiIH127jsLIqCR//LGaSZO64en5ByVLls3OamWJy//9xwAPD5ybNWOomxsXr13Dc8UKEhMT6de1q9o8h06dYtTUqbh27EjjunU5evo042bORLtgQb5s3hyA9Tt3Mt3Li1ZNmjDa3Z3nL16waO1abvv5sfiXXwCYOHw4EZGRKmUH3b/P2BkzcGnbNnsrnsUu377NgGnTcG7YkKFdunDx5k08169PbseOHTPMeycwkH5TppCQmJhm24PHj+ni4UGF0qWZN3Ik0bGxLNi8GbdJk9i7cCG6OjrZVaUccdnXlwHz5uHs4MDQjh25eOcOntu2JbfbV1+pzTN10yb+PHOGgd98Q42KFbnm78+SPXu4/+QJ0/v0AWD/uXOMWLqUzs2aMfzbb3kSHs7C33+nx8yZ7Jo8GZ1PpD/4IGXKwKFDULRobh9Jrlq+fCR+fldwcUnuD/74w4tZs1yZNm0/hQqp7w/CwgKZObMb5ctXx81tGqDFX3+tZfr0LkyevBtT04rEx8cxd24foqJe0b79EIoWLcmFC4dYtmw4CQlxNGzYLkfrmZtkBCjzPpkASFdXF+NPaBQiMxbv20e1smXx7NULAEdraxISE1n+11+4OjmlCVSeR0Sw/fRpRrVvT58vvgCgftWqAMzdvZtR7dtjZGjI4n37MNDTY92wYeillFGmRAncly7lemAgdlWq5GAts0f58mBtDatWwa1byWn37sH48dCgARw7pj5fgQJQsiT8/TcEBaVffunScP58xvtomjt3fLh48QQeHiupVasJANWq2fHDD805fPg3OnRwTzdvYOAt1q6dip/fNbS1ddNsf/AggJs3LzBgwFScnL4FwMKiNn361OPUqT/o1GlQ9lQqCy3+9VeqVamCZ8oNGkcHh+TzcfNmXL/9Vu0F9rzVq2ndtCk/DkquX+O6dQl/9YqFa9fyZfPmJCYmsnTDBhra2bFo8mRlPqsqVfjKzY0zFy7Q0M6OyubmKuUmJiYydfFiqlaqxPjBg7Ov0tlg8ZYtVKtQAc/hwwFwrF2bhIQElu/ciWvbtmrbMS4+nk3797Noy5Z0L8gXb92Kgb4+6375Bb2UMsqYmOA+bRrXfX2xs7ZWm09TLN61i2rly+M5YAAAjjY2yZ+/vXtxbdUqbX/w6hXbT55klIsLfb78EoD6KW0wd/t2Rrm4YFS4MMv37qVJzZr8ktLPAFQwNcVl8mROXL5M67p1c6iGeYCWFri6wpw5yf/+jPn6+nD58glGjFiJjU1yf2BhYcfo0c05duw3vv5afX9w5MhGtLX1GDFiBTo6+gBYWdVj1Cgnjh7dRPfuP3PlykmCg2/x8887qFjRBoDq1Rvy9Ol9DhxYLQGQ+CCfxSpw4eHhTJgwgcaNG2NtbU39+vWZMGEC0Sm38729vbGysmLlypU4ODjQoUMHkpKSCAsLY/jw4djZ2eHg4MCAAQMICAjI3cq8Q1x8PN537tDS1lYlvVXt2kTGxHDR1zdNnoiYGL5r3BgnGxuV9Iop00SCnzxBoVBw2MeHjg0aKIMfgBrly3N61qxPIvgBsLSE2Fi4fftNWmQk+PlBtWrp5ytVCvLnh/v309+nUKHkG4OhoVl2uHnC5cun0dHRp2bNRsq0woWNsLKyx8cn4ykgXl5jSUpKZNq0bRQpUjzN9vj4WAD09AyUabq6+hQsqMOrVy+ypgLZKC4uDu/Ll2nZqJFKeqsmTYiMiuLitWtp8oQ8eEBAcLDaPIGhoQSEhPDk+XNevHxJ0/r1VfaxqFiRYkWKcPLsWbXHs3XvXv67c4fJI0agXbBgJmuXc+Li4/G+fp2W9eqppLdq0IDI6Ggu3rypNt/fFy/itW0b/b/9llEpI9lvUygUHD57lo7NmyuDH4AalStzet06jQ9+4uLj8b51i5ap5ua2srdP7g/u3EmTJyImhu+aNcOpdm2V9IqmpgAEP35MUlISDa2tcWnaVO0+QY8eZWEtNICNDSxfDhs2QPfuuX00uerateT+oHp11f7A0tKeq1fT7w9Kl65I69ZuyuAHQEdHn2LFSvHoUfIdQz09A5o27UyFCjVU8pqaVlTuI8T7+mRGgDIybtw4wsLC8PLyonjx4ly6dIkff/yRypUr07NnTyD5zuipU6fYtm0b0dHRxMTE0L17d6ytrdm0aRP58uVj3bp1uLi4sHfvXkxSzSHPK4KfPCE+IQHzVMdXPmV0zD8sjIZWqs9OlC1RgklduqQp69iVKxTMnx/zkiUJefqUV9HRlDYyYvKWLew/f57ouDgaWVkx8fvvKVWsWPZVKgeZmMDTp6BQqKY/eaL++Z7XzMyS/+vgAL17g75+8ijP3r1vRntKl07+r5UVfP01FCkCDx/CgQNvRps0UWioHyYmZciXL79KeqlS5Th9em+GeQcPnk25cpbpbi9fvirVq9dj586lmJlVwsioJDt3LiUuLoaGDfP+nO/gBw+Ij4/HvKzqVL3yKR8Y/6AgGtrZqWzzS/nAZJSnXu3aFMifn/thYSr7hL96xctXrwh+8CDNsURGRbFo7Vq+adkSm4yi+Two+OHD5O+11ydRivIpF9z+oaE0THXTB6BGlSocX7mSooaG7FIzfBvy6BGvoqIobWzM5BUr2P+//xEdG0ujWrWY2K8fpUqUyJb65JTgR4+S2y3Vs0zlU/oH/wcPaFi9usq2ssbGTErpF9927NKl5P6gVCny5cvHODV9xtGLFwGo8voL8XMRFASVKyff3WrSJLePJlc9eOCHsXHa/sDEpBxnz6bfHzg5pf08hYUFEhp6Fyur5Bs91tYNsLZuoLJPQkI8V66cwsyschYcveaQEaDM+2RGgPbu3UutWrVUXn1S5io3bNiQGTNmULNmTcqUKcPXX3+NlZUVd1Ld/XJzc8Pc3Jxq1aqxf/9+Xr58iaenJ1WrVsXCwoJp06ZhYGDA9u3bc6OK7+VVyqiWga7qdKJCKX9HxMS8VzlHfHzYfe4c3zk6UqRQIZ6/egXAnN27CXvxgnl9+jC1e3duBAfjOm8eUbGxWViL3KOrmzwClFpsbPKCCOl5fV2mrQ2bNiW/ChRIfnYo5RpNGSQZGsKOHbBuHUREJAdMlunHAHleVNQrlRGa1/T0ChEVFakmxxsZBT+v9ekzidjYKEaN+go3NwcOHtxA//5TsbSs/c68ue1VRAQABvr6KumFUp5NjIiKSpMn4j3y6Onq4tysGZt272bngQOEv3rFvaAgRvzyC/nz5ydazXn++8GDvIyIoH+3bpmvWA57ldJOH9KOACbFi1PU0DDdcp+HhwMwZ8MGwp4+Zd7IkUz94Qdu+Pnh+tNPRL3n92VepewPUj0Lq+wPMnqo8S1HLlxg9+nTfOfkRJFChdTuExQWxqytW6lWrhxNatbMxFFroOfPP72h/Y+UXn+gq1uI6OiM+4O3xcXFsGrVWAoW1KZFi/S/s7Zv9yQsLIC2bQd81PFqqqSkrH19jj6ZESAnJydGjRqlkqab8iXfpUsXjh8/zu7duwkICMDX15eQkBAqVlRdkcT8rfnyN27cIDw8HHt7e5V9YmNj8fPzy55KZIGk1EMXqeR7j/nJh318GLlmDXUqVWJ0hw4AxKU8PFyicGG8+vcnX77k2Lm8sTGdZ89m77//0rlx40wefc7S0ko7XTuj5smoaU+fhhs3VKfO3b0LHh7QogVs3AiXL0NYWPJoz+uybt+GkSOhVSvVvHlVUlISCoXqt6Uig4bJly9z8+FDQvz46afvKVnSjBEjFqGvb8jZswdZsWICOjq61K/vnKnys9vHnI/vm2fyiBFoa2szwdOT8bNno6ujQ98uXYiMjlaZzvXa5j17cGrQgApl8/7CEam9s03yfdy9vLiEBABKFC2K17hxb77XTE3pPHYse0+donOrVh9Vdl6QJf3B+fOMXL6cOhYWjO7cWe0+fvfv03v2bArky8eiwYM/+v+H0Cwf2h+8z2qNANHRESxa9AP+/tf44YeFlCiRdkRRoVCwfbsnhw+vx9m5N3Z2X3zYwYvP3icTABUqVIjy5cunSU9KSqJ///7cvXuXtm3b0qZNG6ytrfnpp5/S7Kvz1kVDUlISFSpUYNmyZWn20091FzIvMUy50xeZ6s7l65Gf1HcCU/v16FFm/f47dS0sWOLujk7KcwKvR5Qcra1VOjfbihUx1NPjRnBwltUhp7RsmRx4vO3KleQRmtR0dZNXhEvP48dpl8mOiYGAgDejQy9eJL/elpQEd+5Aqkc58qydO5ewc6eXSlq9eq0ID3+SZt+oqEj09dO/+/4+9u//laSkRCZMWIuhYfI0SxubBkRGvmTNml+oV6/1e3equcEw5W55ZKo77a9HLAwM0t4pfd88hfT1mT5mDOMHDeJ+WBilTUwopK/Pzv37ldPlXrvl50dAcDDDe/fOglrlPMOU79x02+Qjv5Nffx861q6t+r1maYmhvj43/P0/qty8It3+4PXI0Dva7de//mLWli3UrVaNJUOHql1IwvvmTQYvWoS+jg7rPTwol0enh4us98cfS/jjD9X+wM6uFS9fpu0PoqPfrz94+vQBCxb058EDf9zd51O7dos0+8THx7F69Ti8vffj7Nybzp3HfHwlNNTnOmqTlT6ZACg9N2/e5O+//2b79u3UTBmWj4+PJygoiLIZ3Am1sLDgjz/+wNDQECMjI2W+kSNH0rp1a9q0yZvPH5QzNiZ/vnwEproaf/1QaqV0ftdCoVAwbft2Np44QVt7e2b06KHyew9lS5RAS0tLecf0bYlJSehq0APVr507lzxq87bq1ZOno2lpqY74lCiRPHqTHltbiIpKDmbeVrBg8jQ3gKpVk/9O/dx7wYLJCy1oghYtXKhTp6lK2vnzR7ly5TRJSUkqF5FhYYGYmVXK1Ps9eXIfM7OKyuDnNSsre86d+4vw8KcULZp3n9MoV7p08vmYanpMUMrflcqVS5Pn9QhNYGgoVm8tLhKYKs+Jf/6hsKEhdWrUoEqFCgA8ff6ch48fq+QDOHn2LHq6umkWTdAU5UqVSm7HVM82BaX8XalMmY8qt2ypUsnfa/HxabYlJiVp/NL+5UqWTG63VF9eQSl/V0r1TNVrCoWCaZs2sfHIEdrWq8eMfv1U+oPX9p09y7iVK6lQujSrR47EJKWvFJ+Hpk1dsLVtqpJ26dJRrl9P2x88ehSIqWnG/UFw8G3mzu1NXFwso0evxdLSPs0+UVGvmD+/H76+l+nS5Ue++KJHltRF00gAlHmf/Dh1iRIlKFCgAAcPHiQ4OJhr164xbNgwHj9+TFxcXLr5vv76a4oUKcKQIUO4cuUKfn5+jBs3jr///hvLPPzAhk7BgthVqcIRHx+VoehDPj4Y6ulhk3KhlNq8PXvYeOIEvVq0YI6bW5rOrpCuLvaVK3PYx0flYuHsrVtExcZiV1nzHkB8+RJCQlRfd+4kj/a8/b+4UCGoWDFtcPO2evXg22+TV4J7rXBhMDdP/t0fgJo14bvv4O1BOG3t5NXl1CzOlycZGZlQqVINlZeNTSOioyO5cuXNDyC9fPmMGzcuYGPTMFPvV7p0BUJCfImIeKGSfuvWJfT1DTE0LJqp8rObjo4OdjVrcuTvv1XPx7//xrBQIbWLEZQvU4YypqYcSvUjiof//hvzlG0AW//8k9mpRqjX79xJ/nz5aJYq0Ll84wZWVapo7G/a6GhrY2dtzZFz51Tb8exZDPX1sbGw+KhyC+npYW9lxeFz51S/165cISomBrtUC8ZoGh1tbewsLTly4YJqu124kNxuqaaBvzZvxw42HjlCr9atmePurjb4OXXlCmNWrKBWlSpsmTBBgp/PULFiJlSoUEPlVb16I2JiIrl+XbU/uH37AtWrp98fPH36AE/PXoAW48dvURv8JCYmsGDBAO7du8bAgfM/2+BHZI1PfgTIxMSEmTNnsnjxYjZv3oyxsTFNmzalZ8+eHD9+PN18hoaGbNq0idmzZ9O7d28SExOxtrZm7dq1VKqUubva2c3d2ZleCxcydNUqOjZogM+9e6w5coSR7dqhp61NRHQ0vg8eUM7YGCNDQ24GB7Pq8GFqlC9P69q1uZJq2kdlU1MM9PQY0b493efNo6+XF24tW/L05Uvm7N5NzQoVcPpEHnq9dy85GOnaFfbtSx6ZadUq+QdQ//nnzX4mJsmLHLy+sX/kCAwYAL16Jf8Qqr5+8g+oRka++THwEyeSg6C+fZN/TyhfPmjWLDkIOnQo5+uaVays7LG2rsuiRaPp1m00hoZF2bFjMYUKGfLFF98r9wsJ8SU+Po4KFd7/orJt21787397+eWXnrRv3x99fUO8vQ/zzz/7cXX1IH/+vP8V5t69O71GjmTopEl0bNMGn+vXWbN1KyP79UNPV5eIyEh8AwIoZ2aGUcoPKP7g6orHrFkULVwYpwYNOHbmDAdPnGD+zz8ry+3esSO9R49mupcXTg0bcvbiRVZs3kzf77+nXKopcHfu3aORfdoLCk3i3qkTvSZOZKinJx2bN8fn1i3W7NnDyO7d0dPRISIqCt/gYMqVKoVRkYx/fPdtI7p3p/uECfT95Rfc2rXj6YsXzNmwgZoWFjhpeJsBuH/zDb1mzWKolxcdHR3xuXuXNQcOMNLFJbndoqPxDQ2lXMmSGBUuzM3AQFbt30+NihVpXbcuV1I981rZzIyC+fMzfs0aCunqMuDrr/FNNcJZysiIUhIQfZYsLe2pWrUuK1aMxsVlNAYGRdmzZzH6+oY4Ob3pD0JDfUlIiKN8+eT+YPPmqbx8+ZQePSYTHR2Br+9l5b56egaYmVXm2LHN3LlzgaZNO1OsWCmVfQAqV7bNgRrmDTIClHlaioyeWBPZ48SJbH+LIz4+LNq3D/+wMEyKFqVrkya4tWwJgPft27jOn88MV1c6NGjAwj//ZOmBA+mWtWH4cBxShkQu+fkx/48/uOrvj662Ni1sbRnbsSOFs/m5qJH7mmVr+W/T00teprp69eSpcAEB8Mcfqs/4uLuDkRFMm/YmrUqV5KDH1DR5+tzt28lB1NvP/ZiZQZs2ULZs8mjRvXuwf3/yctjZQc1Pn2SLiIhwNmyYyfnzR0lKSqJq1dr06OFB6dJv7jBPmtSdx49DWbJE/Y2HH35wwsqqLj/8MFMl/f79e/z22zyuXz+HQpFEmTKV+frrPjg4ZM9DrzVLpl1COrOO/O9/LFq3Dv/gYExKlKBru3a4pTxQ7u3jg+vw4cwYO5YOzm8Wddj655+s3baNB48eUbZ0afp17Uq7L1TrvO/YMZZt3EjIgweUNjGhS7t2dE9ZuESlTq1a0b1DB0b175/ldVNK/YBbNjhy7hyLtmzBPzQUk+LF6ersjFu7dgB4X7uG608/MWPwYDo0b54m765jx/BYvJhjK1ZQJtVzKpdu3WL+pk1cvXMHXR0dWjg4MLZnTwqreUYrS718mb3lpzhy4QKLdu/G/8EDTIoVo2uLFrilfNa8b97EdcYMZvTtS4fGjVn4++8s/eOPdMva4OFBkkJBz5kz091nULt2DFbzOcwyqX4PKk9p0gROnoSmTd/c/coDzv6Tc5d6kZHhbNkyk0uXjqJQJFGlSm2+/94DU9M3/cGMGd158iSUuXOPk5AQR//+tUhMTDvFHsDSsi4eHhuZPr0rd+5cSPd9f/0161cSyquzhr///t37fIgtW7K2PE0gAVBuyIEA6FOTkwHQpySnAqBPSXYEQJ+FHAiAPjk5FAB9cvJyAJRH5WQA9CmRAOjTlffnjwghhBBCCCEAmQKXFSQAEkIIIYQQQkNIAJR5n/wqcEIIIYQQQgjxmowACSGEEEIIoSFkBCjzJAASQgghhBBCQ0gAlHkyBU4IIYQQQgjx2ZARICGEEEIIITSEjABlngRAQgghhBBCaAgJgDJPpsAJIYQQQgghPhsyAiSEEEIIIYSGkBGgzJMASAghhBBCCA0hAVDmyRQ4IYQQQgghxGdDRoCEEEIIIYTQEDIClHkSAAkhhBBCCKEhNCkAio2NZebMmfz111/ExMTg5OTE+PHjMTIyUrv/uHHj2L17t9ptgwcPZtCgQQB88cUXBAYGqmxv3749M2fOfK/jkgBICCGEEEIIkeUmTZrEhQsXWLx4Mdra2kycOJEhQ4awadMmtfuPHz+ekSNHqqTNmDGDf//9l06dOgEQFRVFcHAwK1aswNraWrmfrq7uex+XBEBCCCGEEEJoCE0ZAQoLC2PPnj0sX74cOzs7AObNm0fr1q3x8fGhVq1aafIYGhpiaGio/Pv48eMcOHCA9evXY2JiAoCvry9JSUnUqlWLIkWKfNSxySIIQgghhBBCaIikpKx9ZZeLFy8CUK9ePWVahQoVMDEx4fz58+/MHxsby7Rp0+jYsSMODg7K9Nu3b1OiRImPDn5ARoCEEEIIIYT4bDVv3jzD7ceOHfuocsPCwihWrBg6Ojoq6SVLluThw4fvzL9jxw6ePHnCsGHDVNJv376Nvr4+Q4YM4dKlSxQrVoyOHTvi6upKvnzvN7YjAVAuuFmqWW4fgsaZ2+dmbh+CZipcOLePQOMkljTL7UPQSPk/YO61SGFunttHoJHO/qPI7UPQOPUbaOX2IWgmRd78rGX1qM17xgxphISEZBg8DR06FG1t7TTpOjo6xMbGZlh2UlIS69evp1OnThgbG6tsu3v3Li9fvqRVq1b88MMPXLx4EU9PT8LDwxk6dOh7HbsEQEIIIYQQQmiIrA6APnaEx8TEhAMHDqS7/dSpU8TFxaVJj42NRU9PL8OyL126RFBQEN9//32abatWrSI2Nlb5rJClpSUREREsW7aMwYMHv9cokARAQgghhBBCiA9SsGBBKlWqlO7227dv8+LFC+Li4lRGgh49eqRc0CA9R44cwcrKSm352traaUaWLCwsiIqKIjw8nGLFir3z2GURBCGEEEIIITSEpiyCUKdOHZKSkpSLIQD4+/sTFhaGvb19hnnPnz9P/fr106QrFApatGiBl5eXSvq1a9cwNjZ+r+AHJAASQgghhBBCY2hKAGRiYsKXX37JhAkT8Pb25urVq4wYMYK6detia2sLQFxcHI8fP1aZKpeYmMidO3eoWrVqmjK1tLRo2bIla9as4cCBAwQFBbFt2zZWr17NkCFD3vvYZAqcEEIIIYQQIstNmTKF6dOnM2jQIAAcHR2ZMGGCcruPjw+urq5s2LBBudT1ixcviI+Pp2jRomrLHDlyJAYGBsybN4+HDx9SpkwZxo8fj4uLy3sfl5ZCkUeXuPiE3ZQFzT5YNaTRPoqsAvfBEkvJKnAfI//L57l9CJpHVs77KGcvZ/zwtEhLVoH7SHn0Evmtn8TJEt7eWVueJpARICGEEEIIITREdk5b+1zIM0BCCCGEEEKIz4aMAAkhhBBCCKEhZAQo8yQAEkIIIYQQQkNIAJR5MgVOCCGEEEII8dmQESAhhBBCCCE0hIwAZZ4EQEIIIYQQQmgICYAyT6bACSGEEEIIIT4bMgIkhBBCCCGEhpARoMyTAEgIIYQQQggNIQFQ5n1QANS9e3f+/fffdLefPXsWIyOjDzoAb29vXF1dOXbsGGXKlPmgvBkJCQmhefPmbNiwAQcHhywr19LSkhkzZtChQ4csKzO7REdHsmHDHM6ePUxMTBRWVnb07u2BmVnF9y5j1qwh6OrqM3ToTJV0hULBH3+s5dChbTx58gBj49K0betKmzZds7oaOeq0jw/zN2/GNyiI4kWL0rVNG9y++QYtLa135r1x7x6dRo/m0NKllDExUdnmFxKC5/r1/Hv9OgXy58fe2ppxvXpRtlSp7KpKrjp9/jzz167FNyCA4sWK0fWbb3BzcXm/drx7l04DB3Jo40bKfELtExkZybx5czh8+DBRUVHY2dkxbpwHFSpkfD4+efKEWbNmcPr0aRITE3B0bMLYseMwNi6pdv9Nmzby66/rOHr0eLplJiQk0LXrd+jq6rF+/cZM1Su7nPb2Zv7y5fjeu0dxIyO6fvstbl26ZPgZ2nf4MMvWrSP4/n3MSpWin6sr7b/8UmUfv4AAPL28+PfSJQoUKIB9rVqMGzKEsmZmADi1a0fow4dqyzczNeX47t1ZV8lscPqff5jv5fWm3Tp3xs3VNeN2O3iQZatWERwailnp0vTr1Yv2X3+tss+uP/5g7YYNBIWEYFqqFF07d6b799+rlHvh0iXmLV7MrTt3KGxoSItmzRg2aBAGhQplW32zU0xMJNu3z+HChcPExkZhaWnH9997YGqa8TkbEPAfu3YtwN//GklJCszNrenUaSTm5tbKfV68eMyuXQv5778zRES8oFSpCrRp0wcHhzbZXa28x8wMrl+Hdu3g1KncPhrxmfjgESBnZ2fGjx+vdluxYsUyfUAi68ybN5I7d67g6joafX0Dtm3zYsIEVxYv3o+BQZEM8yYlJbF27QzOnj1Es2bt02xfv342+/Zt5Pvvh1ClSg0uXvyblSt/IX/+ArRq1Tm7qpStLt++zYBp03Bu2JChXbpw8eZNPNevJzExkX4dO2aY905gIP2mTCEhMTHNtgePH9PFw4MKpUszb+RIomNjWbB5M26TJrF34UJ0dXSyq0q54vKNGwwYPx7npk0Z2qsXF69dw3PlyuR27NIlw7x3/P3p9+OPattR040ZM5IrV64wcuRoDAwMWLrUi549Xfnzz/0UKaL+fExISKB//75EREQwceIkEhISmDdvLn369Gbnzl0ULFhQZf8DB/Yze/ZMSpY0UVvea6tXr+TatWvY29fNsvplpcvXrzNg5EicW7RgaL9+XLxyBU8vr+TPkKur2jyHjh9n1MSJuHbuTON69Th66hTjpkxBW1ubL1u2BOBBWBhd+vWjQvnyzJsyheiYGBasWIHb0KHs3bQJXV1dvGbNIi4+XvV4rl1jxsKFfNc+7XdhXnL56lUGDBmCc6tWDP3hBy76+OC5YEFyu7m5qc1z6OhRRv34I65dutC4YUOOnjjBuJ9/Tm631q0B2LFrFxN++YU+PXvSqH59rly7xsy5c4mKimJAnz4A3PX1pdeAAdSpVYsFs2cT9ugRcxYsICQ0lOWLFuVYG2Sl5ctH4ud3BReX0ejpGfDHH17MmuXKtGn7KVRI/TkbFhbIzJndKF++Om5u0wAt/vprLdOnd2Hy5N2YmlYkPj6OuXP7EBX1ivbth1C0aEkuXDjEsmXDSUiIo2HDdjlaz1xVpgwcOgRFi+b2kWgUGQHKvA8OgHR1dTE2Ns6OYxFZ6NYtH86fP8FPP62kTp0mAFhZ2dG/f3MOHvyNTp3c080bEHCLVaumcvfuNbS1ddNsDwsL4c8/f6Vv359wdk6+oLWxqc+TJw/w8TmtsQHQ4i1bqFahAp7DhwPgWLs2CQkJLN+5E9e2bdUGKnHx8Wzav59FW7ago62tvtytWzHQ12fdL7+gl1JGGRMT3KdN47qvL3bW1mrzaarFv/5KtcqV8fzxRwAc69YlITGR5b/9hmvHjum34+7dLPr113TbUZNdvuzDiRMnWL58JY6OyedjnTp2tGzZnC1bfmPAAPXn46FDf3Hz5g3+/HM/lStXBqBq1Wp8801b/vrrIF99lXyX/unTpyxatJAdO7ZRpEjRDI/l1q1brFy5ghIl8u73+OJVq6hmYYHnpEkAONavn3wu/vorri4u6Oqm/V6at3w5rZ2c+HHYMAAa16tH+MuXLFyxQhkALV69GgMDA9YtXoxeShllSpfGffRort+6hZ2tLVaWlirlRkRGMuKnn2jasGG6wVdesXjZMqpVrYrntGkAODZsmNxua9bg2qWL+nZbvJjWLVvy4+jRADRu0IDw8HAWLlmiDICWr1lDqxYtGJ3StvUdHAgIDGTT1q3KAGjvgQNoaWmxZP58CunrA5CYmMjEqVMJvX8fs9Kls7v6WcrX14fLl08wYsRKbGySz1kLCztGj27OsWO/8fXX6s/ZI0c2oq2tx4gRK9DRSW4HK6t6jBrlxNGjm+je/WeuXDlJcPAtfv55BxUr2gBQvXpDnj69z4EDqz+PAEhLC1xdYc6c5H+LDyIBUOZl+SpwTk5OrFy5kn79+lGzZk2cnJw4evQoR48epVWrVtja2tK7d2+ePn2qku/48eO0aNGCGjVq0L17d27duqXcFh4ezoQJE2jcuDHW1tbUr1+fCRMmEB0dDSRPo7OysmLlypU4ODjQoUMHFAqFSvl+fn40bNiQMWPGkJhyd/nEiRN06NABGxsbWrZsyYIFC4iLi1PmefjwIe7u7tSqVQtHR0f27t2b1c2VbXx8TqOrq4+tbSNlWpEiRlhb23PxYsZDzAsWjCUpKZHZs7dRpEjxNNu9vY9SsKAOLVp8q5I+evQCxo1bnDUVyGFx8fF4X79Oy3r1VNJbNWhAZHQ0F2/eVJvv74sX8dq2jf7ffssoNRdHCoWCw2fP0rF5c2XwA1CjcmVOr1v3yQU/cXFxeF+5QstGjVTSWzk6EhkVxcVr19Tm+9vbG68NG+jfpQuj+vbNiUPNUadPn0ZPT5+GDd+0i5GREfb29vzvf+mfj2fOnKZChQrK4AegcuXKVKxYib//fpNv5crlnDlzmoULF9OsWbN0y4uLi8PDYwzdunWnQoUKmaxV9oiLi8P70iVaNm2qkt7KySn5M3TlSpo8IffvExAUpDZPYEgIAUFByefiiRN0bNtWGfwA1KhWjdP79mFna6v2eJauXcuz58/5edSozFYtW8XFxeF94QItnZxU0lu1aEFkZCQXfXzS5AkJDSUgMFBtnsDgYAICAwFYuXgxY1JuDL1WsGBBYmNjlX/HxsVRoEABlbYtmjKy+SI8PHOVywXXrp1GR0ef6tXfnLOFCxthaWnP1avpn7OlS1ekdWs3ZfADoKOjT7FipXj0KAgAPT0DmjbtTIUKNVTymppWVO7zybOxgeXLYcMG6N49t49GfIayZRnspUuX0qZNG/bu3UvVqlUZM2YMy5cvx9PTk+XLl3Pt2jVWrVqlkmft2rVMnDiR33//nUKFCtGnTx9lgDNu3Dhu3LiBl5cXhw4dwsPDgz179rBt2zZl/sTERE6dOsW2bduYNm2ayrzkwMBAevbsiaOjIzNnziR//vz8/fffDBs2DBcXF/bt28fEiRM5ePAgo1PugiUkJNCnTx+eP3/Opk2bWLhwIWvWrMmO5soWISF+mJiUIX/+/CrppqblCA31zzDvsGGzmTFjC+bmVdVu9/e/ialpef777zwjRrSnY0dr+vZ14tChbWr31wTBDx8Sn5CAeaq7lOVNTQHwDw1Vm69GlSocX7kS906dyJ8v7ekU8ugRr6KiKG1szOQVK6jbrRs1OnXCffp0Hj55kvUVyWXBDx4QHx+Pearn+cqnPF/hHxysNl8NS0uO//Yb7t26pfnMfgru3fOjbNm052O5cuXw90//fPTz88Pc3DxNerly5QgIeJOvc+fvOHjwEC1bfpHhcSxbtoT4+AQGDRryYRXIQcGhocmfobJlVdLLp3ym/IPSXiD6BQQAZJgn5MEDXkVEUNrUlMmentT94gtqODriPno0Dx89Unss9x8+ZMP27fTu2hWzlO+CvCo4JCS53cqXV0kvX64cAP4pwczb/FI+e+/KU6liRcqYmaFQKHgRHs6OXbvYs28fXVxclHk6tmsHwIw5c3j+4gV3fX1ZsmIFFlWqUNXCImsqmYMePPDD2LgM+fKpnrMmJuV4+DD9c9bJqQtt2vRRSQsLCyQ09C5mZlUAsLZuQM+ev6hcpyQkxHPlyinMzCrzWQgKgsqVYeRIiIrK7aPROElJWfv6HH3wFLi9e/dy6NChNOktWrTA09MTgKZNm9Iu5cvQxcWFY8eOMXz4cGxskod6GzRowN27d1Xy//TTTzRu3BiA2bNn06RJE/bt20enTp1o2LAh9vb2WKZMTShTpgybNm3izp07KmW4ubkpLxZCQkKU/x0zZgxNmjRhypQpyi+c5cuX4+LiwnfffQckX1BMnjyZHj16EBISgr+/P3fv3uXIkSOUS+kMZsyYoaxXXhcV9Qp9fYM06Xp6hYiOjswwr7m5ZYbbw8Of8exZGPPnj6Jz50GUKVOR//3vAMuW/QygkVPgXqV8ARvo66ukF9LTAyAinS9ok+JpR8je9jzlzuecDRuwqVKFeSNH8jQ8nHkbN+L600/smT8ffTXTUjTVq8jkz1bqh55fT4lJtx0/8Wm1ERGvMDBIez4WKlSIiIj0z8eIiFeUT3Vxqi5fxYqV3nkM165dZd26tWzYsBntPDzN8J2foci07RXxHnmeP38OwJwlS7CxsmLelCk8ffaMecuW4TpwIHs2bkQ/5Xx/bf3WrWgXLIhr57z/nfYqIgLIoA1Str8t4gPzXL56le969ACgupUVvd4a9baoXJnRw4bxy4wZbPjtNyB50YjN69Zp5E2NqKhX6OmlPWd1dd/dh74tLi6GVavGUrCgNi1adEt3v+3bPQkLC2DQIM2cRfHBnj9PfomP8rkGLVnpgwMgJycnRqmZCqD/1oXj2x22XkqH8jqIgOTniFJPgatTp47y34ULF8bc3FwZ4HTp0oXjx4+ze/duAgIC8PX1JSQkhIoVVVdiUXendNKkScTHx2Nqaqpyt+XGjRtcvXqVnTt3KtNeT5vz8/PD19eXIkWKqBx3tWrV1M6hzm1JSUkoFEmp0hTp7M17rcSVkYSEeF6+fM7YsYupXz/5jnPyM0D32bbNSyMDoCRF+u0FkE/N6M77iEtIAKBE0aJ4jRunLKe8qSmdx45l76lTdG7V6qPKzouS3vGtnO8zmOudlJSUph0yOh/z5Uu/TT42X2qxsbF4eIzD1bWH8kZUXvXOz5Cac/F98rxe2KCEkRFeM2e+ORfLlqVznz7sPXSIzm/d4IqNjWXn3r18+/XXFClc+ANrkfOyq93eVtrUlI2rVxMSGsqCJUv4rkcPdm/Zgp6eHivXrmXuokV07dyZls2b8/z5c5atWkXPfv3YvG4dJd5xsyg3qetDU0+jf9v79qHR0REsWvQD/v7X+OGHhZQoYZZmH4VCwfbtnhw+vB5n597Y2WU8iiuEyBofHAAVKlRI7R1JlUILpC32XV8Yqe8QJSYmoq2tTVJSEv379+fu3bu0bduWNm3aYG1tzU8//ZSmDB01D1e3b98eCwsLZs6cScuWLbFIGYpPSkqiT58+tFezqo+xsTF+fn5qOwd1dctt27YtYds2L5W0Bg1aER6edopVVFQk+vqGmXo/Pb1CaGlpKRdXeK1Wrcb4+JzmxYsnFC1aIlPvkdMMUwL4yJRpl69FpDMy9L4MUm4AONaurXIxYWtpiaG+PjcymP6kiQxT7iRHphrpSe8O/ado6dIlLF2qej5+8UUrnj5Nez5GRERiaJj++WhoaECkmhGPyMgIDAze/zxeuHABCkUSAwYMJCElKH99gZeQkED+/PkzfWMkqximjJR9yGfoffK8zudYv77quVi9OoYGBty4fVsl72lvbyIiI/lKQ25QvLMN1IxAfmgek5IlMSlZkrp2dpQtU4ZuvXtz6OhR2jo7s3TlSr5q04afPTyU+zvY29OibVvWrF/P2BEjMlnD7PPHH0v44w/Vc9bOrhUvX6Y9Z6Oj368Pffr0AQsW9OfBA3/c3edTu3aLNPvEx8exevU4vL334+zcm86dx3x8JcRnRUaAMi/PXM1fv36d+vXrA/Ds2TMCAgJwc3Pj5s2b/P3332zfvp2aNWsCEB8fT1BQEGVTzfdW58svv8Te3p59+/bh4eHB9u3byZ8/P1WqVMHf318lmPP29mbDhg1MmjSJatWq8erVK+7evUuVKsnzdgMCAtROI8htrVq5YG/fVCXN2/soPj6nSUpKUunsHzwIpEyZd0+XyYipaXkUCgUJCfFoa78JOhMTky+s1K0cl9eVK1WK/PnyEfjggUp6UMrflT7yN6rKliqFlpZWmmV1ARKTktDNw1ORPkY5M7Pkdkz1zFRQyt+V3nHz5FPg4uJC01QP4x87dpQzZ9Kej0FBgRlOXzM3r8BNNQtwBAYGfdBIzuHDh7h/PxQ7u1ppttnYWDNt2gzat88bv21WzsyM/PnzE5gyjfm1oJS/K6kZ6a+QMlIfGBKisopb4Ft5ihUtmnwuvrXQzWuJiYlpVic8eeYMZUqXpka1apmqT04pV7ZscrulekYqKOXvSmoWvaiQ0paBQUFYVX3zzGfgW3kio6I4fvIkNtWrK58NArBKaZdHjx/z7PlzomNiqJ1qIYniRkZUMDfnrp9fpuuXnZo2dcHWtqlK2qVLR7l+Pe05++hRIKamGfehwcG3mTu3N3FxsYwevRZLS/s0+0RFvWL+/H74+l6mS5cf+eKLHllSF/F5kAAo8z54Xk9MTAyPHz9W+1LXsbyvn3/+mbNnz3Lz5k2GDx+Oqakpbdq0oUSJEhQoUICDBw8SHBzMtWvXGDZs2Ae9X758+ZgyZQq3b99m9erVAPTt25dDhw7h5eWFv78/Z8+excPDg1evXmFsbIyDgwM1a9ZkzJgxXL58mWvXrjFmzJiPngqVnYyMTKhcuYbKy9a2EdHRkfj4/E+5X3j4M27cuICtbcNMvd/rkZ///W+/Svq//x7H3NxS7bNHeZ2OtjZ21tYcOXdOZerDobNnMdTXx+YjH+ItpKeHvZUVh8+dUwmCzl65QlRMDHZWVpk+9rxER1sbOxsbjpw+rdqO//sfhoUKYVNV/cIan5KSJU2oXr2Gyqthw0ZERkZy+vSb8/HZs2dcuHCBBg3SPx8bNmzEvXvJU3Jf8/X15d49vwzzpbZ06TK2b9+p8rKyssbKyprt23dmuHJcTtPR0cHO1pYjJ0+qfoZOnMDQwAAbNSsnli9bljKlS3PouOqPvx4+cQLzlG2F9PWxt7Xl8MmTKn3H2fPniYqOTrMK3OXr16mdx6cLvk1HRwe72rU5cvy4arsdO5bcbtWrp8lTvlw5ypiZcejoUZX0w8eOYZ6yrUD+/Ez45RfWrF+vss+Zs2cBsKxSheJGRhQtUoSLly6p7PPs+XMCAgOVPzKbVxUrZkKFCjVUXtWrNyImJpLr19+csy9fPuP27QtUr57+uff06QM8PXsBWowfv0Vt8JOYmMCCBQO4d+8aAwfOl+BHiFzwwSNABw8e5ODBg2q3LVy48KMPZODAgXh4ePDs2TMcHBxYvXo12tramJiYMHPmTBYvXszmzZsxNjamadOm9OzZk+OpOruMVKlShb59++Ll5UXz5s1p3bo18+fPZ8WKFSxfvpyiRYuqPN+UL18+VqxYwdSpU3Fzc0NXV5f+/fsTms5qYHmNtbU91avXZf780fToMRpDw6Js3bqYQoUMcXb+XrlfcLAv8fFxVKz4/hfiNWo4YG/fjLVrZxAbG025clU4cWIPt25dwsNjaXZUJ0e4d+pEr4kTGerpScfmzfG5dYs1e/Ywsnt39HR0iIiKwjc4mHKlSmGUzg9XqjOie3e6T5hA319+wa1dO56+eMGcDRuoaWGBk33azlHTuXfrRq/Roxk6eTIdnZ3x+e8/1mzbxsi+fdHT1SUiMhLfwEDKlS6N0Wfy43d2dvbUrVuXMWNGM2rUaIoWLcqSJYspXNiQ7757cz76+voSFxeHVUpg7OzchpUrl9O/f19GjBgJwLx5c7GwsKB1a+f3fn8Li7QLmxRKmRJWvXqNNNtym3uvXvQaPJih48fTsW1bfK5dY83mzYwcOPDNZ8jfn3JmZhil/AD3D25ueEydStEiRXBq3Jhjf//NwWPHmD9lirLcEQMH0n3gQPqOGIFbly48ffaMOUuWUNPaGqeURXggeUToXkAAbb/QrOcx3Pv2pVf//gwdPZqO7drhc+UKa9avZ+SQIejp6REREYHvvXuUK1MGIyMjAH7o1w+PiROT261pU46dPMnBw4eZP2sWkBxY9XNzY/GyZRgZGeFgb8/t27fxWrGCBg4OODZqhJaWFoPd3ZkycyaFDAxwbtmS5y9esGLNGvLny4dbHv/9JHUsLe2pWrUuK1aMxsVlNAYGRdmzZzH6+oY4Ob05Z0NDfUlIiKN8+eRzdvPmqbx8+ZQePSYTHR2Br+9l5b56egaYmVXm2LHN3LlzgaZNO1OsWCmVfQAqV7bNgRoKTSYjQJmnpcjoST+RLdL5SZksFxERztq1M/H2PopCkUTVqrXp3dsDM7M3i0eMH9+dR49CWbVKfTDZt68T1avXZejQmSrpcXGxbNvmxcmTf/Ly5TPKlq2Mi8sP1KuXdp5zVqhGzjTakXPnWLRlC/6hoZgUL05XZ2fcUh6M9r52DdeffmLG4MF0aN48Td5dx47hsXgxx1asoIyJicq2S7duMX/TJq7euYOujg4tHBwY27MnhdXMy89SufTw9pH//Y9F69fjHxyMSYkSdP3mG9xSlsz1vnwZ1xEjmDFmDB1Sfmjxbbv++guP2bM59ttvlClVKqcPncRS2XO3Ojw8nFmzZnL8+FGSkpKoVas248Z5UKHCm/OxR4/uhIaGcvTom/PxwYMHzJgxjX/+OUOBAgVp2LAh48Z5YGxcUu37/PjjOP7991+VMtTp0SP5tzfWr9+YBbWD/C+zdkWnIydPsmjVKvyDgjAxNqZrx464de0KgPfFi7j+8AMzJkygQ9u2yjxbd+9m7ebNPHj0iLKlS9OvRw/aOasGipeuXmX+8uVc/e8/dHV1aeHoyNghQyj81rNYT589o0GbNkwaM4bvO2Tj1MBsWFTnyPHjLFq2DP+AAExKlqRr587KAMT7/Hlc+/ZlxuTJdPjmG2WerTt3snb9eh6EhVG2TBn6ubnR7q12VSgUbN25k81btxIUEoJRsWK0dXZm8IABKs/e/rF/P+s2bMD33j2KFS2KXe3ajBgyJMtHgM5e1nv3TlkgMjKcLVtmculSch9apUptvv/eA1PTN+fsjBndefIklLlzj5OQEEf//rWU08FTs7Ssi4fHRqZP78qdOxfSfd9ff72d7raPVb9B3njGT60mTeDkSWjaFE5l/DuFOS6PXiJn9ar8qWb/fxYkAMoFORUAfUpyKgD65GjA6lV5TXYFQJ+6rA6APgt5cFVRTZBTAdCnJE8HQHlZHr1ElgAo8/LMIghCCCGEEEKIjMkUuMyTAEgIIYQQQggNIQFQ5uW9Jc2EQb77MgAAGZ1JREFUEEIIIYQQIpvICJAQQgghhBAaQkaAMk8CICGEEEIIITSEBECZJ1PghBBCCCGEEJ8NGQESQgghhBBCQ8gIUOZJACSEEEIIIYSGkAAo82QKnBBCCCGEEOKzISNAQgghhBBCaAgZAco8CYCEEEIIIYTQEBIAZZ5MgRNCCCGEEEJ8NmQESAghhBBCCA0hI0CZJyNAQgghhBBCaIikpKx95ZSff/6ZcePGvXO/kJAQ+vfvT+3atWnUqBELFiwgMTFRZZ/NmzfTvHlzbGxs6NKlCzdu3PigY5EASAghhBBCCJEtkpKSmDdvHtu2bXvnvvHx8fTu3RuArVu3MmnSJLZs2cKSJUuU++zevZvZs2czdOhQdu3aRZkyZejVqxfPnj1772OSAEgIIYQQQggNoUkjQH5+fnTp0oUdO3ZQunTpd+5/6NAh7t+/z+zZs7GwsKBFixaMGDGC9evXExcXB8Dy5cvp1q0bX3/9NZUrV2b69Ono6emxY8eO9z4uCYCEEEIIIYTQEJoUAJ07d45KlSqxb98+ypQp8879L1y4gLW1NUWKFFGm1atXj4iICG7evMnTp08JCAigfv36yu0FChTAzs6O8+fPv/dxySIIQgghhBBCfKaaN2+e4fZjx459dNldu3b9oP0fPnxIqVKlVNJKliwJwIMHDyhQIDl0MTU1TbPPrVu33vt9JADKBdWq5fYRaCJpNJEz8uf2AWiqYsVy+wjEZ+KtG7/ifSkUuX0EIgtl9f/Od8Q/6QoJCckweDp79ixGRkYfVGZMTAyFCxdWSdPR0QEgNjaW6OhoALS1tdPsExsb+97vIwGQEEIIIYQQn6mPHeExMTHhwIED6W5/exrb+9LV1VU+6/Pa68BGX18fXV1dALX76Onpvff7SAAkhBBCCCGE+CAFCxakUqVKWVpmqVKluHPnjkrao0ePgOSA6/XUt0ePHqm896NHjzAxMXnv95FFEIQQQgghhBC5zt7enhs3bhAREaFMO3fuHIUKFaJq1aoUL16cChUq4O3trdyekJDAhQsXsLe3f+/3kQBICCGEEEIIkePi4uJ4/PixckpbixYtMDY2ZtiwYdy6dYujR48yb9483NzclM/9uLm5sW7dOnbv3o2vry8//vgjMTExfPvtt+/9vhIACSGEEEIIIXKcj48PjRo1wsfHB0hezGD16tUkJSXh4uLC5MmT6dKlCwMHDlTmcXFxYciQISxYsICOHTsSGhrKunXrPmjBBS2FQpYGEUIIIYQQQnweZARICCGEEEII8dmQAEgIIYQQQgjx2ZAASAghhBBCCPHZkABICCGEEEII8dmQAEgIIYQQQgjx2ZAASAghhBBCCPHZkABICCGEEEII8dmQAEgD/Pnnn7i4uGBra0utWrXo2LEjW7duzdb3HDduHN27d8+28kNCQrC0tMTb2zvb3iM1JycnLC0tla+qVatSu3ZtunXrxvnz59+Z39vbG0tLS0JCQnLgaDVTbnxWPwURERHUrFmTBg0aEB8fn9uHk2e86/Pk5OTE4sWLAVi8eDFOTk6ZLlOTZfY77mNld3+R07p3767Sjm+/Zs2alduHl2UyqqelpSXPnj374DKzq5/MrmsGS0tLdu3alaVlCs1QILcPQGRs586dTJs2jfHjx1OnTh0UCgVnzpxh6tSpPHnyhEGDBmXL+44fP57ExMRsKTs3ubm54ebmBoBCoeDFixfMmzePPn36cPDgQUqXLp3LR6i5cuuz+inYv38/xYsX5/Hjxxw5coQ2bdrk9iHluvf5PO3cuRMdHZ0sLVPTyXdc1nB2dmb8+PFp0vX09HLhaLJPevUEKFasWA4fjRA5RwKgPO63336jY8eOfPvtt8q0ihUrEhYWxoYNG7KtwzY0NMyWcnObvr4+xsbGyr9LlizJ5MmTcXR05MiRI/To0SMXj06z5dZn9VPw+++/07hxY+7fv8/WrVslAOL9Pk9GRkZZXqamk++4rKGrq6vSjp+qz6WeQqQmU+DyuHz58uHj40N4eLhKer9+/di2bRugOg3ktbfTdu3aRcuWLZk6dSp16tTB3d2d5s2b4+npqZJnz5492NraEhERoZzSoFAo3rkvJF/AOTs7Y2Njg7OzM+vXrycpKUm5/507d3B1dcXW1paWLVty9uzZrGmgLFCgQPJ9AG1tbeLj41m4cCHNmjWjZs2adOjQgTNnzqjNFx4ezoQJE2jcuDHW1tbUr1+fCRMmEB0drdxnzZo1tGjRgurVq+Pk5MSSJUtQKBQAREdHM378eBo2bEiNGjVo164dhw8fzv4KZ5P3+azGxcXh6elJ48aNqVWrFi4uLpw+fVq574ABA3B0dFR+rh49eoSDgwNTpkzJuYrkMD8/P65cuULDhg354osv8Pb2xt/fX7k9OjqaiRMn4uDgQO3atRk/fjwjR45k3Lhxyn0uXbpE165dsbGxoWnTpkyePFnZhprqY7/7lixZomyrUaNG8eLFiw8q83W5S5cupXfv3tjY2NCyZUt27NiRxTXMOW9/x92/f5/hw4dTv359rK2tcXR0xNPTU/l9nbq/GDhwIACBgYG4u7tTp04dHBwcGDFiBE+fPlW+R3x8PLNmzaJevXrY2toycOBAnjx5kvOVzQHv+u739vbGysqKlStX4uDgQIcOHUhKSiIsLIzhw4djZ2eHg4MDAwYMICAgIHcr8w5OTk6sXLmSfv36UbNmTZycnDh69ChHjx6lVatW2Nra0rt3b5XPAsDx48dp0aIFNWrUoHv37ty6dUu57WPa73W/+Zqfnx8NGzZkzJgxytkqJ06coEOHDspzdsGCBcTFxSnzPHz4EHd3d2rVqoWjoyN79+7NrmYTGkACoDyuT58+3LhxA0dHR/r168fKlSu5evUqhoaGVKhQ4b3LCQoK4tGjR+zZs4cRI0bQvn17Dhw4oPKl8ueff9KiRQsMDAyUaVpaWu/cd9u2bcyePZtBgwaxf/9+hg0bxqpVq5gzZw4Ar169omfPnhgaGrJjxw4mTZrEsmXLsqB1Mi8sLIxffvkFfX19mjRpwrRp09i6dStjx45l7969NG7cmAEDBnDv3r00eceNG8eNGzfw8vLi0KFDeHh4sGfPHuWF1PHjx1mxYgWTJ0/m8OHDjBo1imXLlvHnn38CsHDhQm7fvs3KlSs5cOAAjo6ODB8+XGOfMXqfz6qHhwdnzpxhzpw57N69G2dnZwYMGMDJkycBmDp1KvHx8cyePRuFQoGHhwcmJiaMHTs2F2uWvXbu3Im+vj6Ojo60bNmSggULqjyTMnbsWM6cOcP8+fPZunUrr169Yv/+/crtt27dolevXjRu3Jg///yTOXPm8N9//+Hm5pbmokGTfMx3X2hoKOfOnWPdunUsX76ca9eu4eHh8VFlLl26lFq1arFnzx66du3Kzz//zIEDB7K1ztkh9Xecu7s7r169Yt26dfz111+4ubmxevVqjh8/rszzdn8xfPhwXr58SdeuXYmLi2P9+vWsW7eOoKAghg0bpszj4+PDy5cv+e2331ixYgWXL19m9uzZuVDj7Peu736AxMRETp06xbZt25g2bRoxMTHK56Q2bdrExo0bKVasGC4uLoSFheVWVd7L0qVLadOmDXv37qVq1aqMGTOG5cuX4+npqTzPVq1apZJn7dq1TJw4kd9//51ChQrRp08fZYDzMe2npaWl3BYYGEjPnj1xdHRk5syZ5M+fn7///pthw4bh4uLCvn37mDhxIgcPHmT06NEAJCQk0KdPH54/f86mTZtYuHAha9asyYHWE3mWQuR5Pj4+iuHDhyvq1q2rsLCwUFhYWCi++OILxYULFxQKhULRrFkzxaJFi1TyvJ32+++/KywsLBQ3b95Ubg8ODlZYWloq/v33X4VCoVA8evRIUa1aNcWZM2cUCoVCMXbsWEW3bt3ea19HR0fFunXrVN5/586diho1aihiYmIUW7ZsUdja2ipevnyp3H7kyBGFhYWF4ty5c1nVTO/UrFkzhbW1tcLW1lZha2urqF69usLCwkLh7OysOHnypOLVq1cKa2trxdatW1XyzZ07V3HlyhXFuXPnFBYWForg4GCFQqFQbNy4UXHr1i2VfTt16qTw8PBQKBQKxbp16xQNGzZU+Pv7K7efP39eERoaqlAoFAp3d3eFq6urIjw8XKFQKBQJCQmKv//+W6WdNE1Gn9WAgACFhYWF4saNGyp5xowZo/ysKRTJnw1LS0vFuHHjFDVr1lT4+vrmdDVyTHx8vKJBgwaKESNGKNP69++vqFu3riImJkYRFBSksLCwUPz999/K7TExMYqGDRsqxo4dq1AoFIpRo0Yp3N3dVcp9nS8nz6/s8CHffYsWLVLUqFFD8fjxY2X+06dPKywsLBQBAQHvXebrcvv3769yLMOGDVO4uLhkZ3Uz7V3fcdHR0Yo1a9Yo7t+/r5KvQYMGCi8vL4VCob6/eP0d/uLFC2XazZs3FXPmzFHExsYqxo4dq2jYsKEiMTFRuX3KlCkKZ2fnbK5x9ujWrZvCyspK2Y6vX71791YoFO/+7n/dVxw9elS5ffv27QoHBwdFfHy8Mi0xMVFt/51T0qunra2tYtSoUQqFIvkzNXToUGWeEydOKCwsLBSnT59Wpg0dOlTh5uamUCjU1z08PFxha2ur2L59u0Kh+Lj2Cw4OVlhYWCh27typcHR0VIwfP16RlJSk3P79998rpk6dqlLm2bNnlX3233//rbCwsFAEBgYqt9+4cUNhYWGh+P333z+q/YRmk2eANICtrS22trYkJSVx69YtTp06xaZNm+jbty9Hjhx573LMzc2V/y5Tpgx169Zl79692Nvbs3///v+3d+8xXdX/A8efgCBk+HFcUnQCGRCgQ8kSoa280EAQ6EPTzFgrgsFIGClimIoOSXBTwFyFUZlKFy9EbEWMpcNkbCViIiC3+eEqG0oY2SAIf3/w43z5ANoHVEB5PbbPxjm8z9l5v3mf1+F93pcPTzzxBEuXLh1y3N3StrW10dLSwv79+0lLS1OO6e3tpauri8bGRqqqqrC1tdWaV+Tq6jq6wrhH69atU97C6evrM2PGDOW6SktL6e7uZuHChVrHbNy4EWDI6jPr16/n9OnTfPfdd2g0GmpqamhsbGTevHkA+Pv7c+rUKby8vLCzs8PDwwMvLy9lEnJoaCjh4eG4u7vj4uLC888/j5+f30M9/+pudXX37t1AX7kN1N3dzfTp05VtT09PAgICyMrKYuvWrTz11FNjmoexVFBQwPXr1/H19VX2+fr6cubMGXJzc5UJ1wPvl6lTp+Li4qJsl5eXU1dXN+w9VVtbi5ub2wPMwYM10thnY2ODhYWFst1/L1dXV2NjY6PTOc3NzQGGlJurq6vSUzmR3S3GAQQFBfHTTz9x6dIl6urqqKys5Pr161pDlkH7edEfw1UqlbLP0dERR0dHZdva2hp9/f8NKlGpVHR2dt7v7I2ZFStWEBMTo7XP2NgY+O/Y329gGZaXl3Pz5k2ee+45rTRdXV3U1tY+mEzoYLh8Qt9csn799w78bxEIa2trZZ+xsfGQIXCLFy9Wfp4+fTq2trZUVVUBoyu/fjt37qS7uxsrKyutXqHy8nIuXbrEyZMnlX23/78HvLa2lpqaGlQqldZ1Ozk5KX9TMflIA2gCa2lpIT09nbCwMGbNmoW+vj7Ozs44Ozvj6enJ6tWr77i0aU9Pz5B9g2/0wMBAPvjgA7Zt20ZOTg4BAQFaDzBd0vY/NOPi4vDw8BhyXH+QGvxw7R+TPtZUKpVWMB/I0NBQ5/P09vYSFhZGdXU1q1evxsfHh/nz57N9+3YljZmZGd9//z0lJSUUFhZy7tw5jhw5QmRkJBs2bMDV1ZWCggIKCwspKioiOzubjz/+mIyMDNzd3e85r2NJl7raLzMzk2nTpmkdP7DedXd3U1lZyZQpUygsLHykJ233L7863OT7b775hpCQEIAh989Avb29+Pn5ER4ePuR3I10kYKIYbewzMDDQ2u6fG2BoaKjzOb29vYGhMaq3t/eO8XEiuVuM+/vvvwkKCqKzsxNvb2/UajUuLi68/vrrQ9IOfF7oEq8Hl/3Dbtq0acOWoy6xv9/AFQp7e3t58sknhx3+PbCxMdbulM+Bhvv7D2x8DGe4e9HIyGjU5ddPrVbj4OBAUlISL730Eg4ODkBf+YaEhKBWq4ccY2lpSW1t7bBxdLz+FxHjb+JH80nMyMiIEydOKHNGBup/Y25hYYGhoaHWhOe//vpryNuY4Xh5edHT08OJEycoKysjMDBwxGnNzc0xMzOjoaEBGxsb5VNWVkZqairQ96ZQo9FofafA5cuXdSqDsWRjY4OhoSGlpaVa+9euXcvhw4e19lVUVHD27FnS0tKIiYnB398fa2tr6uvrlbdOOTk5fP311yxevJioqCiOHz/OmjVrlHkEBw4coLi4mJUrV7Jt2zby8vKYO3cueXl5Y5Lf+0nXugrQ2tqqVVeysrK0vofhwIEDtLS08MUXX1BUVPTIfEfLYDdu3KCgoIDAwECys7O1Pq+88golJSXMnTsXPT09Ll68qBz3zz//UFZWpmzb29tTU1OjVaY9PT3s2bOHa9eujUPO7t1I6tNAGo1GKxYWFxejp6eHnZ3diM85OA5cuHABZ2fn0WVogjh37hxlZWUcOXKEqKgofHx8ePzxx7lx48Zd54vZ2dmh0Wjo6OhQ9pWVleHu7k5LS8tYXPqEoUvsH46DgwPNzc2Ympoq9+ns2bPZt2/fA/2OpvEy8Bnf1taGRqPB3t5+1OXXz9fXl/Xr17NgwQLi4uKUlxz29vZcvXpVKw62tLSwd+9ebt26hZOTEx0dHVRXVyvnGhwvxOQiDaAJzMzMjJCQENLS0khJSaGiooKGhgbOnDnDhg0bcHNz49lnn2XRokX8+OOPXLhwgZqaGrZu3arT2zgTExO8vb3Zt28fzzzzzF3fAt0prZ6eHqGhoRw9epRjx45RX19Pfn4+O3fuxNjYGCMjI3x9fTE3N2fTpk1cuXKFX3/9lcTExPtWTveLiYkJQUFBpKWl8fPPP1NfX8/+/fupqqrihRde0EprYWHBlClTyM3NpaGhgdLSUqKjo2ltbVVWnenq6iI5OZns7GwaGxs5f/48v/32mzJUqaGhgfj4eIqKimhqaiIvL4/m5uZxGx54L3Spq0uWLGH58uXEx8dz+vRpGhoa+PTTT0lPT1eGJRQXF5ORkcH27dtZsmQJERERJCcnU1dXN845vP9ycnLo6ekhNDQUBwcHrU94eDj6+vp8++23rFq1ioSEBIqKiqipqeH999+npaVFeQMbHBxMeXk5u3btora2lpKSEjZt2oRGoxl2CMnDQNfYN1hXVxfR0dGUl5dTWFhIQkICL7/8MnPmzBnxOX/44QcyMzPRaDRkZGSQn5+v9Mg9rGbNmgX01b2mpibOnz9PREQE3d3dWqtlDebn54dKpWLz5s1cuXKFy5cvEx8fj4ODg3LOyUKX2D8cf39/VCoVUVFR/P7779TW1vLee+9x9uxZnn766THMgbbOzk5aW1uH/dwtP/9lx44dFBUVUVFRwbvvvouVlRU+Pj6jLr+B9PX1SUhIoLKykoyMDKBvSHleXh4HDx7k6tWrFBUVERcXR0dHB5aWlri5ubFw4UJiY2O5ePEipaWlxMbGPhS9uuLBkL6/CS46OhpbW1uOHz9OZmYmnZ2dzJ49m1WrVhEWFgb0zVFpb2/nrbfewtTUlODgYP7880+dzh8YGMipU6fu2vvzX2mDg4OZOnUqR48eJSkpCQsLC9auXUtUVBTQ173/5ZdfkpCQwGuvvaY8BAauzjRRbNy4EQMDA+Lj4+no6MDR0ZFDhw4xb948WltblXQzZ84kKSmJDz/8kMzMTCwtLVm2bBlvvvmmsprSmjVraG9v56OPPuLatWuoVCq8vLyU8dbx8fEkJyezefNm2tvbmTNnDjExMQQEBIxL3u+VLnU1JSWFlJQUduzYwc2bN7G2tiYxMRG1Ws2tW7fYsmULy5cvV+bE9D/UYmNj+eqrrx6pYTZZWVl4eHgMGfcOfePrPT09ycnJ4cyZMyQmJhIZGcnt27fx8/PD1dVVGbK5aNEiMjIySEtLQ61W89hjj+Hu7s6WLVswMjIa62zdN7rUp8EWLFiAk5MTb7zxBnp6evj4+GgtFz6Sc6rVavLz80lKSsLW1pbU1FRefPHFB5rnB83FxYW4uDgOHz5MamoqM2fOxMfHBysrqyE9XgOZmJjw2WefsWfPHtatW4exsTHLli17pFdnvBNdYv9wTE1NOXbsGHv37uXtt9/m33//Zf78+Xz++efjOs8xNzeX3NzcYX83cF7vSEVERBAXF0dbWxtubm5kZGRgZGQ06vIbzN7entDQUA4ePMjKlSvx9vYmJSWF9PR0PvnkE2bMmKE1v0lfX5/09HR2795NcHAwxsbGhIWF0dTUNOo8ioeb3m1d+hyFEEKMua6uLn755ReWLl2qtTy9l5cX/v7+vPPOO+N4dY+uFStWoFariYyMHO9LEUII8QBID5AQQkxQRkZG7Nq1SxkOaGBgwMmTJ2lublYm6wshhBBiZGTwoxBCTFB6enocOnSIP/74g1dffRW1Wk1JScm4D5sRQgghHmYyBE4IIYQQQggxaUgPkBBCCCGEEGLSkAaQEEIIIYQQYtKQBpAQQgghhBBi0pAGkBBCCCGEEGLSkAaQEEIIIYQQYtKQBpAQQgghhBBi0pAGkBBCCCGEEGLSkAaQEEIIIYQQYtL4Pya3dZ0EY6FGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(numeric_only = True), annot = True,  vmin=-1, vmax=1, cmap = 'bwr')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "345f8d73",
   "metadata": {},
   "source": [
    "# Классификация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6ce53d2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,1:], df.iloc[:,0], test_size=0.33, random_state=42) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f868d885",
   "metadata": {},
   "outputs": [],
   "source": [
    "RF = RandomForestClassifier()\n",
    "RF.fit(X_train, y_train)\n",
    "rf_prediction = RF.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "378e01b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conf matrix\n",
      "[[143  33]\n",
      " [ 32  87]]\n",
      "Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.81      0.81       176\n",
      "           1       0.72      0.73      0.73       119\n",
      "\n",
      "    accuracy                           0.78       295\n",
      "   macro avg       0.77      0.77      0.77       295\n",
      "weighted avg       0.78      0.78      0.78       295\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Conf matrix')\n",
    "print(metrics.confusion_matrix(rf_prediction, y_test))\n",
    "print('Classification report')\n",
    "print(metrics.classification_report(rf_prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7087f545",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.66      , 0.34      ],\n",
       "       [0.965     , 0.035     ],\n",
       "       [0.83833333, 0.16166667],\n",
       "       [0.02      , 0.98      ],\n",
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       "       [0.06      , 0.94      ],\n",
       "       [0.13648918, 0.86351082],\n",
       "       [0.89      , 0.11      ],\n",
       "       [0.25266667, 0.74733333],\n",
       "       [0.03      , 0.97      ],\n",
       "       [0.56      , 0.44      ],\n",
       "       [0.935     , 0.065     ],\n",
       "       [0.94      , 0.06      ],\n",
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       "       [0.679     , 0.321     ],\n",
       "       [0.05      , 0.95      ],\n",
       "       [0.67      , 0.33      ],\n",
       "       [0.14376299, 0.85623701],\n",
       "       [0.81      , 0.19      ],\n",
       "       [0.96      , 0.04      ],\n",
       "       [0.99      , 0.01      ],\n",
       "       [0.43666667, 0.56333333],\n",
       "       [0.88      , 0.12      ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.99      , 0.01      ],\n",
       "       [0.91      , 0.09      ],\n",
       "       [0.68      , 0.32      ],\n",
       "       [0.901     , 0.099     ],\n",
       "       [0.78      , 0.22      ],\n",
       "       [0.38      , 0.62      ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.61166667, 0.38833333],\n",
       "       [0.36      , 0.64      ],\n",
       "       [0.39      , 0.61      ],\n",
       "       [0.70516667, 0.29483333],\n",
       "       [0.81      , 0.19      ],\n",
       "       [0.99      , 0.01      ],\n",
       "       [0.13648918, 0.86351082],\n",
       "       [0.2       , 0.8       ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.98      , 0.02      ],\n",
       "       [0.77      , 0.23      ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.82158261, 0.17841739],\n",
       "       [0.61      , 0.39      ],\n",
       "       [0.92      , 0.08      ],\n",
       "       [0.466     , 0.534     ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.53      , 0.47      ],\n",
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       "       [0.98666667, 0.01333333],\n",
       "       [0.04      , 0.96      ],\n",
       "       [0.87555556, 0.12444444],\n",
       "       [0.1       , 0.9       ],\n",
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       "       [0.74      , 0.26      ],\n",
       "       [0.65833333, 0.34166667],\n",
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       "       [0.7925    , 0.2075    ],\n",
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       "       [0.98666667, 0.01333333],\n",
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       "       [0.6       , 0.4       ],\n",
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       "       [0.97833333, 0.02166667],\n",
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       "       [0.78583333, 0.21416667],\n",
       "       [0.95      , 0.05      ],\n",
       "       [0.945     , 0.055     ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.8       , 0.2       ],\n",
       "       [0.89      , 0.11      ],\n",
       "       [0.98      , 0.02      ],\n",
       "       [0.03      , 0.97      ],\n",
       "       [0.95666667, 0.04333333],\n",
       "       [0.92      , 0.08      ],\n",
       "       [0.31083333, 0.68916667],\n",
       "       [0.09      , 0.91      ],\n",
       "       [0.95      , 0.05      ],\n",
       "       [0.99      , 0.01      ],\n",
       "       [0.5       , 0.5       ],\n",
       "       [0.1       , 0.9       ],\n",
       "       [0.95      , 0.05      ],\n",
       "       [0.06      , 0.94      ],\n",
       "       [0.14      , 0.86      ],\n",
       "       [0.66133333, 0.33866667],\n",
       "       [1.        , 0.        ],\n",
       "       [0.43      , 0.57      ],\n",
       "       [0.454     , 0.546     ],\n",
       "       [0.09      , 0.91      ],\n",
       "       [0.82      , 0.18      ],\n",
       "       [0.77      , 0.23      ],\n",
       "       [0.01      , 0.99      ],\n",
       "       [0.09      , 0.91      ],\n",
       "       [0.18      , 0.82      ],\n",
       "       [0.91      , 0.09      ],\n",
       "       [0.67      , 0.33      ],\n",
       "       [0.04      , 0.96      ],\n",
       "       [0.41      , 0.59      ],\n",
       "       [0.71      , 0.29      ],\n",
       "       [0.97433333, 0.02566667],\n",
       "       [0.17026299, 0.82973701],\n",
       "       [0.66      , 0.34      ],\n",
       "       [0.99      , 0.01      ],\n",
       "       [0.49      , 0.51      ],\n",
       "       [0.21      , 0.79      ],\n",
       "       [0.87      , 0.13      ],\n",
       "       [0.02      , 0.98      ],\n",
       "       [0.57504762, 0.42495238],\n",
       "       [0.98      , 0.02      ],\n",
       "       [0.74      , 0.26      ],\n",
       "       [0.99      , 0.01      ],\n",
       "       [0.07      , 0.93      ],\n",
       "       [0.26      , 0.74      ],\n",
       "       [0.9       , 0.1       ],\n",
       "       [0.8       , 0.2       ],\n",
       "       [0.07      , 0.93      ],\n",
       "       [0.93      , 0.07      ],\n",
       "       [0.32      , 0.68      ],\n",
       "       [0.12      , 0.88      ],\n",
       "       [0.955     , 0.045     ],\n",
       "       [0.91171429, 0.08828571],\n",
       "       [0.83      , 0.17      ],\n",
       "       [0.69      , 0.31      ],\n",
       "       [0.37      , 0.63      ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.91      , 0.09      ],\n",
       "       [0.65891667, 0.34108333],\n",
       "       [0.20331457, 0.79668543],\n",
       "       [0.05      , 0.95      ],\n",
       "       [0.23      , 0.77      ],\n",
       "       [0.45496429, 0.54503571],\n",
       "       [0.82      , 0.18      ],\n",
       "       [0.60490476, 0.39509524],\n",
       "       [0.09      , 0.91      ],\n",
       "       [0.92652742, 0.07347258],\n",
       "       [0.45      , 0.55      ],\n",
       "       [0.9575    , 0.0425    ],\n",
       "       [0.09      , 0.91      ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.99      , 0.01      ],\n",
       "       [0.43      , 0.57      ],\n",
       "       [0.07      , 0.93      ],\n",
       "       [0.14      , 0.86      ],\n",
       "       [0.34      , 0.66      ],\n",
       "       [0.77333333, 0.22666667],\n",
       "       [0.97833333, 0.02166667],\n",
       "       [0.95      , 0.05      ],\n",
       "       [0.09      , 0.91      ],\n",
       "       [0.3       , 0.7       ],\n",
       "       [0.31      , 0.69      ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.46621429, 0.53378571],\n",
       "       [0.52      , 0.48      ],\n",
       "       [0.6       , 0.4       ],\n",
       "       [0.56      , 0.44      ],\n",
       "       [0.13      , 0.87      ],\n",
       "       [0.8       , 0.2       ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.99      , 0.01      ],\n",
       "       [0.02      , 0.98      ],\n",
       "       [0.94      , 0.06      ],\n",
       "       [0.63738889, 0.36261111],\n",
       "       [0.86      , 0.14      ],\n",
       "       [0.97      , 0.03      ],\n",
       "       [0.03      , 0.97      ],\n",
       "       [0.        , 1.        ],\n",
       "       [0.01      , 0.99      ],\n",
       "       [0.58322222, 0.41677778],\n",
       "       [0.07      , 0.93      ],\n",
       "       [0.81      , 0.19      ],\n",
       "       [0.96      , 0.04      ],\n",
       "       [0.42      , 0.58      ],\n",
       "       [0.03      , 0.97      ],\n",
       "       [0.92      , 0.08      ],\n",
       "       [0.60954762, 0.39045238],\n",
       "       [0.24      , 0.76      ],\n",
       "       [0.97      , 0.03      ],\n",
       "       [0.58      , 0.42      ],\n",
       "       [0.71571429, 0.28428571],\n",
       "       [0.985     , 0.015     ],\n",
       "       [0.37      , 0.63      ],\n",
       "       [0.98      , 0.02      ],\n",
       "       [0.03      , 0.97      ],\n",
       "       [0.96369012, 0.03630988],\n",
       "       [0.94      , 0.06      ],\n",
       "       [0.64      , 0.36      ],\n",
       "       [0.14      , 0.86      ],\n",
       "       [0.53      , 0.47      ],\n",
       "       [0.07      , 0.93      ],\n",
       "       [0.97833333, 0.02166667],\n",
       "       [1.        , 0.        ],\n",
       "       [0.02      , 0.98      ],\n",
       "       [0.93      , 0.07      ],\n",
       "       [0.37      , 0.63      ],\n",
       "       [0.81      , 0.19      ],\n",
       "       [0.04      , 0.96      ],\n",
       "       [0.97      , 0.03      ],\n",
       "       [0.44      , 0.56      ],\n",
       "       [0.17      , 0.83      ],\n",
       "       [0.38      , 0.62      ],\n",
       "       [0.94      , 0.06      ],\n",
       "       [0.22      , 0.78      ],\n",
       "       [0.82158261, 0.17841739],\n",
       "       [0.30816667, 0.69183333],\n",
       "       [0.73      , 0.27      ],\n",
       "       [0.79      , 0.21      ],\n",
       "       [0.07      , 0.93      ],\n",
       "       [0.89      , 0.11      ],\n",
       "       [0.01      , 0.99      ],\n",
       "       [0.685     , 0.315     ],\n",
       "       [0.52      , 0.48      ],\n",
       "       [0.07      , 0.93      ],\n",
       "       [0.97833333, 0.02166667],\n",
       "       [0.82      , 0.18      ],\n",
       "       [0.99      , 0.01      ],\n",
       "       [0.97      , 0.03      ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.97      , 0.03      ],\n",
       "       [0.67      , 0.33      ],\n",
       "       [0.51      , 0.49      ],\n",
       "       [0.79      , 0.21      ],\n",
       "       [0.82158261, 0.17841739],\n",
       "       [0.47496429, 0.52503571],\n",
       "       [0.73      , 0.27      ],\n",
       "       [0.69      , 0.31      ],\n",
       "       [0.98333333, 0.01666667],\n",
       "       [0.05      , 0.95      ],\n",
       "       [0.99      , 0.01      ],\n",
       "       [0.03      , 0.97      ],\n",
       "       [0.08      , 0.92      ],\n",
       "       [0.36      , 0.64      ],\n",
       "       [0.02      , 0.98      ],\n",
       "       [0.79      , 0.21      ],\n",
       "       [0.99      , 0.01      ],\n",
       "       [0.95      , 0.05      ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.82      , 0.18      ],\n",
       "       [0.47      , 0.53      ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.57      , 0.43      ],\n",
       "       [0.8       , 0.2       ],\n",
       "       [0.84      , 0.16      ],\n",
       "       [0.67      , 0.33      ],\n",
       "       [0.08      , 0.92      ],\n",
       "       [0.98      , 0.02      ],\n",
       "       [0.72583333, 0.27416667],\n",
       "       [0.36571429, 0.63428571],\n",
       "       [0.05      , 0.95      ],\n",
       "       [0.60490476, 0.39509524],\n",
       "       [0.83      , 0.17      ],\n",
       "       [0.84      , 0.16      ],\n",
       "       [0.1       , 0.9       ],\n",
       "       [0.94      , 0.06      ],\n",
       "       [0.34      , 0.66      ],\n",
       "       [0.86666667, 0.13333333],\n",
       "       [0.17026299, 0.82973701],\n",
       "       [0.975     , 0.025     ],\n",
       "       [0.05      , 0.95      ],\n",
       "       [0.25      , 0.75      ],\n",
       "       [0.96      , 0.04      ],\n",
       "       [0.98380952, 0.01619048],\n",
       "       [0.17      , 0.83      ],\n",
       "       [0.23      , 0.77      ],\n",
       "       [0.89166667, 0.10833333]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Вероятности каждого класса\n",
    "rf_prediction_proba = RF.predict_proba(X_test)\n",
    "rf_prediction_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "34f7f23b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0,\n",
       "       0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 0, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7dba003",
   "metadata": {},
   "source": [
    "## Важность признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e5652417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0.087579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0.249358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.255040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0.051566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.040596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.275444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>0.040416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "Pclass    0.087579\n",
       "Sex       0.249358\n",
       "Age       0.255040\n",
       "SibSp     0.051566\n",
       "Parch     0.040596\n",
       "Fare      0.275444\n",
       "Embarked  0.040416"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi = pd.DataFrame(RF.feature_importances_, RF.feature_names_in_)\n",
    "fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6ca8c32a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " sns.barplot(fi.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e8fea763",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sef = X_train[['Sex', 'Age', 'Fare']]\n",
    "X_test_sef = X_test[['Sex', 'Age', 'Fare']]\n",
    "RF_sef = RandomForestClassifier()\n",
    "RF_sef.fit(X_train_sef, y_train)\n",
    "rf_sef_prediction =RF_sef.predict(X_test_sef )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7ba5e827",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conf matrix\n",
      "Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.80      0.82       184\n",
      "           1       0.69      0.75      0.72       111\n",
      "\n",
      "    accuracy                           0.78       295\n",
      "   macro avg       0.77      0.77      0.77       295\n",
      "weighted avg       0.78      0.78      0.78       295\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Conf matrix')\n",
    "metrics.confusion_matrix(rf_sef_prediction, y_test)\n",
    "print('Classification report')\n",
    "print(metrics.classification_report(rf_sef_prediction, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81cffad2",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4dae05fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "824c59ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conf matrix\n",
      "[[141  31]\n",
      " [ 34  89]]\n",
      "Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.82      0.81       172\n",
      "           1       0.74      0.72      0.73       123\n",
      "\n",
      "    accuracy                           0.78       295\n",
      "   macro avg       0.77      0.77      0.77       295\n",
      "weighted avg       0.78      0.78      0.78       295\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "XGB = XGBClassifier()\n",
    "XGB.fit(X_train, y_train)\n",
    "xgb_prediction =XGB.predict(X_test )\n",
    "print('Conf matrix')\n",
    "print(metrics.confusion_matrix(xgb_prediction, y_test))\n",
    "print('Classification report')\n",
    "print(metrics.classification_report(xgb_prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7b73d67b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conf matrix\n",
      "[[150  37]\n",
      " [ 25  83]]\n",
      "Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.80      0.83       187\n",
      "           1       0.69      0.77      0.73       108\n",
      "\n",
      "    accuracy                           0.79       295\n",
      "   macro avg       0.77      0.79      0.78       295\n",
      "weighted avg       0.80      0.79      0.79       295\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "XGB_sef = XGBClassifier()\n",
    "XGB_sef.fit(X_train_sef, y_train)\n",
    "xgb_sef_prediction =XGB_sef.predict(X_test_sef)\n",
    "print('Conf matrix')\n",
    "print(metrics.confusion_matrix(xgb_sef_prediction, y_test))\n",
    "print('Classification report')\n",
    "print(metrics.classification_report(xgb_sef_prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3964f444",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(metrics.confusion_matrix(xgb_sef_prediction, y_test), annot = True, cmap = 'RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "837596c9",
   "metadata": {},
   "source": [
    "## Попробуем настроить параметры для RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8ea4088c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sef = X_train[['Sex', 'Age', 'Fare']]\n",
    "X_test_sef = X_test[['Sex', 'Age', 'Fare']]\n",
    "\n",
    "params = {\n",
    "    'n_estimators': [1, 10, 50, 100],\n",
    "    'max_depth': [1, 5, 10],\n",
    "    'criterion':['gini', 'entropy']\n",
    "}\n",
    "\n",
    "\n",
    "RF_sef_gs = RandomForestClassifier()\n",
    "\n",
    "gs = GridSearchCV(param_grid=params, estimator=RF_sef_gs)\n",
    "gs.fit(X_train_sef, y_train)\n",
    "rf_sef_gs_prediction=gs.predict(X_test_sef )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1eaa5d33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy', 'max_depth': 5, 'n_estimators': 50}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bb7206ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conf matrix\n",
      "[[154  34]\n",
      " [ 21  86]]\n",
      "Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.82      0.85       188\n",
      "           1       0.72      0.80      0.76       107\n",
      "\n",
      "    accuracy                           0.81       295\n",
      "   macro avg       0.80      0.81      0.80       295\n",
      "weighted avg       0.82      0.81      0.82       295\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Conf matrix')\n",
    "print(metrics.confusion_matrix(rf_sef_gs_prediction, y_test))\n",
    "print('Classification report')\n",
    "print(metrics.classification_report(rf_sef_gs_prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08d86012",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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