email_classifer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "758b7384",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91a16a00",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>spam</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        v1                                                 v2 Unnamed: 2  \\\n",
       "0      ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
       "1      ham                      Ok lar... Joking wif u oni...        NaN   \n",
       "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
       "3      ham  U dun say so early hor... U c already then say...        NaN   \n",
       "4      ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
       "...    ...                                                ...        ...   \n",
       "5567  spam  This is the 2nd time we have tried 2 contact u...        NaN   \n",
       "5568   ham              Will Ì_ b going to esplanade fr home?        NaN   \n",
       "5569   ham  Pity, * was in mood for that. So...any other s...        NaN   \n",
       "5570   ham  The guy did some bitching but I acted like i'd...        NaN   \n",
       "5571   ham                         Rofl. Its true to its name        NaN   \n",
       "\n",
       "     Unnamed: 3 Unnamed: 4  \n",
       "0           NaN        NaN  \n",
       "1           NaN        NaN  \n",
       "2           NaN        NaN  \n",
       "3           NaN        NaN  \n",
       "4           NaN        NaN  \n",
       "...         ...        ...  \n",
       "5567        NaN        NaN  \n",
       "5568        NaN        NaN  \n",
       "5569        NaN        NaN  \n",
       "5570        NaN        NaN  \n",
       "5571        NaN        NaN  \n",
       "\n",
       "[5572 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"spam.csv\", encoding = \"ISO-8859-1\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d926a36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "577b0d55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5572 entries, 0 to 5571\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   v1          5572 non-null   object\n",
      " 1   v2          5572 non-null   object\n",
      " 2   Unnamed: 2  50 non-null     object\n",
      " 3   Unnamed: 3  12 non-null     object\n",
      " 4   Unnamed: 4  6 non-null      object\n",
      "dtypes: object(5)\n",
      "memory usage: 217.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f8ccdf01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>spam</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        v1                                                 v2\n",
       "0      ham  Go until jurong point, crazy.. Available only ...\n",
       "1      ham                      Ok lar... Joking wif u oni...\n",
       "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3      ham  U dun say so early hor... U c already then say...\n",
       "4      ham  Nah I don't think he goes to usf, he lives aro...\n",
       "...    ...                                                ...\n",
       "5567  spam  This is the 2nd time we have tried 2 contact u...\n",
       "5568   ham              Will Ì_ b going to esplanade fr home?\n",
       "5569   ham  Pity, * was in mood for that. So...any other s...\n",
       "5570   ham  The guy did some bitching but I acted like i'd...\n",
       "5571   ham                         Rofl. Its true to its name\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis='columns')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "580dccad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
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       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>spam</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Target                                               Text\n",
       "0       ham  Go until jurong point, crazy.. Available only ...\n",
       "1       ham                      Ok lar... Joking wif u oni...\n",
       "2      spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       ham  U dun say so early hor... U c already then say...\n",
       "4       ham  Nah I don't think he goes to usf, he lives aro...\n",
       "...     ...                                                ...\n",
       "5567   spam  This is the 2nd time we have tried 2 contact u...\n",
       "5568    ham              Will Ì_ b going to esplanade fr home?\n",
       "5569    ham  Pity, * was in mood for that. So...any other s...\n",
       "5570    ham  The guy did some bitching but I acted like i'd...\n",
       "5571    ham                         Rofl. Its true to its name\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(columns={'v1':'Target', 'v2':'Text'}, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6aa9fcad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "data['Target'] = encoder.fit_transform(data['Target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9cb98f2c",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Ok lar... Joking wif u oni...</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>1</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>0</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>0</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>0</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>0</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Target                                               Text\n",
       "0          0  Go until jurong point, crazy.. Available only ...\n",
       "1          0                      Ok lar... Joking wif u oni...\n",
       "2          1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3          0  U dun say so early hor... U c already then say...\n",
       "4          0  Nah I don't think he goes to usf, he lives aro...\n",
       "...      ...                                                ...\n",
       "5567       1  This is the 2nd time we have tried 2 contact u...\n",
       "5568       0              Will Ì_ b going to esplanade fr home?\n",
       "5569       0  Pity, * was in mood for that. So...any other s...\n",
       "5570       0  The guy did some bitching but I acted like i'd...\n",
       "5571       0                         Rofl. Its true to its name\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba8c3f9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Target    0\n",
       "Text      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a60fad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "403"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4188ffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop_duplicates(keep='first')\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4da9e89e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b93165b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4516\n",
       "1     653\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "57cd6ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(data['Target'].value_counts(), labels=['ham', 'spam'], autopct=\"%0.2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb32dd5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\user\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "88388fce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp/ipykernel_25228/1555652371.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['num_char'] = data['Text'].apply(len)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "      <th>Text</th>\n",
       "      <th>num_char</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>1</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>0</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>0</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>0</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>0</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5169 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Target                                               Text  num_char\n",
       "0          0  Go until jurong point, crazy.. Available only ...       111\n",
       "1          0                      Ok lar... Joking wif u oni...        29\n",
       "2          1  Free entry in 2 a wkly comp to win FA Cup fina...       155\n",
       "3          0  U dun say so early hor... U c already then say...        49\n",
       "4          0  Nah I don't think he goes to usf, he lives aro...        61\n",
       "...      ...                                                ...       ...\n",
       "5567       1  This is the 2nd time we have tried 2 contact u...       161\n",
       "5568       0              Will Ì_ b going to esplanade fr home?        37\n",
       "5569       0  Pity, * was in mood for that. So...any other s...        57\n",
       "5570       0  The guy did some bitching but I acted like i'd...       125\n",
       "5571       0                         Rofl. Its true to its name        26\n",
       "\n",
       "[5169 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['num_char'] = data['Text'].apply(len)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "34feda36",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp/ipykernel_25228/654638185.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['num_words'] = data['Text'].apply(lambda x:len(nltk.word_tokenize(x)))\n"
     ]
    },
    {
     "data": {
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>5568</th>\n",
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       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>0</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "      <td>57</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>0</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "      <td>125</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>0</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5169 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Target                                               Text  num_char  \\\n",
       "0          0  Go until jurong point, crazy.. Available only ...       111   \n",
       "1          0                      Ok lar... Joking wif u oni...        29   \n",
       "2          1  Free entry in 2 a wkly comp to win FA Cup fina...       155   \n",
       "3          0  U dun say so early hor... U c already then say...        49   \n",
       "4          0  Nah I don't think he goes to usf, he lives aro...        61   \n",
       "...      ...                                                ...       ...   \n",
       "5567       1  This is the 2nd time we have tried 2 contact u...       161   \n",
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       "5569       0  Pity, * was in mood for that. So...any other s...        57   \n",
       "5570       0  The guy did some bitching but I acted like i'd...       125   \n",
       "5571       0                         Rofl. Its true to its name        26   \n",
       "\n",
       "      num_words  \n",
       "0            24  \n",
       "1             8  \n",
       "2            37  \n",
       "3            13  \n",
       "4            15  \n",
       "...         ...  \n",
       "5567         35  \n",
       "5568          9  \n",
       "5569         15  \n",
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       "5571          7  \n",
       "\n",
       "[5169 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['num_words'] = data['Text'].apply(lambda x:len(nltk.word_tokenize(x)))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b2e9f32b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp/ipykernel_25228/2504098751.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['num_sent'] = data['Text'].apply(lambda x:len(nltk.sent_tokenize(x)))\n"
     ]
    },
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       "      <th>5568</th>\n",
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       "      <th>5569</th>\n",
       "      <td>0</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
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       "      <th>5571</th>\n",
       "      <td>0</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
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       "<p>5169 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Target                                               Text  num_char  \\\n",
       "0          0  Go until jurong point, crazy.. Available only ...       111   \n",
       "1          0                      Ok lar... Joking wif u oni...        29   \n",
       "2          1  Free entry in 2 a wkly comp to win FA Cup fina...       155   \n",
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       "4          0  Nah I don't think he goes to usf, he lives aro...        61   \n",
       "...      ...                                                ...       ...   \n",
       "5567       1  This is the 2nd time we have tried 2 contact u...       161   \n",
       "5568       0              Will Ì_ b going to esplanade fr home?        37   \n",
       "5569       0  Pity, * was in mood for that. So...any other s...        57   \n",
       "5570       0  The guy did some bitching but I acted like i'd...       125   \n",
       "5571       0                         Rofl. Its true to its name        26   \n",
       "\n",
       "      num_words  num_sent  \n",
       "0            24         2  \n",
       "1             8         2  \n",
       "2            37         2  \n",
       "3            13         1  \n",
       "4            15         1  \n",
       "...         ...       ...  \n",
       "5567         35         4  \n",
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       "5570         27         1  \n",
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       "\n",
       "[5169 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['num_sent'] = data['Text'].apply(lambda x:len(nltk.sent_tokenize(x)))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f78e1074",
   "metadata": {},
   "outputs": [
    {
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       "      <td>18.455407</td>\n",
       "      <td>1.961308</td>\n",
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      "text/plain": [
       "          num_char    num_words     num_sent\n",
       "count  5169.000000  5169.000000  5169.000000\n",
       "mean     78.977945    18.455407     1.961308\n",
       "std      58.236293    13.322448     1.432583\n",
       "min       2.000000     1.000000     1.000000\n",
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       "50%      60.000000    15.000000     1.000000\n",
       "75%     117.000000    26.000000     2.000000\n",
       "max     910.000000   220.000000    38.000000"
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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    "data[['num_char', 'num_words', 'num_sent']].describe()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "18e39ccb",
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "text/plain": [
       "          num_char    num_words     num_sent\n",
       "count  4516.000000  4516.000000  4516.000000\n",
       "mean     70.459256    17.123339     1.815545\n",
       "std      56.358207    13.491315     1.364098\n",
       "min       2.000000     1.000000     1.000000\n",
       "25%      34.000000     8.000000     1.000000\n",
       "50%      52.000000    13.000000     1.000000\n",
       "75%      90.000000    22.000000     2.000000\n",
       "max     910.000000   220.000000    38.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Target']==0][['num_char', 'num_words', 'num_sent']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "75c6d6c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_char</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>653.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>137.891271</td>\n",
       "      <td>27.667688</td>\n",
       "      <td>2.969372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.137753</td>\n",
       "      <td>7.008418</td>\n",
       "      <td>1.488910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>157.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>224.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_char   num_words    num_sent\n",
       "count  653.000000  653.000000  653.000000\n",
       "mean   137.891271   27.667688    2.969372\n",
       "std     30.137753    7.008418    1.488910\n",
       "min     13.000000    2.000000    1.000000\n",
       "25%    132.000000   25.000000    2.000000\n",
       "50%    149.000000   29.000000    3.000000\n",
       "75%    157.000000   32.000000    4.000000\n",
       "max    224.000000   46.000000    9.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Target']==1][['num_char', 'num_words', 'num_sent']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7b781062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_char', ylabel='Count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nefPgCb70yAseaZjZBcvtnEJE/MY4L10/zv6bgE151TPXFapqqKqupe7SReUuxcxmsZlyotnGcbFXKJmZnQ+HwgwzctG8ia5QMjObag6FGaa4aN4Bamp2selTVwEw/9LiFUqnjx1iY1s7tbW7efg2rwRiZlPPoTAD1TU0kpzqZWNbO0nfCeqbWqkvec0L6plZXhwKZVA6RTT2n+gV1TU0ktQ4AMxs+jgUymB4imjkKMDMrNwcCmVyMaOA4ZHG4cOHqZvVC32Y2UzjUJiFTvcc4q6vv07f0W7+IJwKZjZ1HAqz0MDJ49z99EMcfW+A8DkHM5tCDoVZalGhmqjySiBmNrX8p7FmZpap+JHC8DISXV1dE14eOhNEJPQfP8bAyd6ZXqqZzVIVHwrDy0j09R6hvqm13OWMKZKE0z2HOdHdyd3PfoNjPpdgZjmp+FCA4o1uZvKv3gMne/n8I7/PoVMnWFhTC1VV5S7JzOYoh8Is8XNVNQw6DMwsZz7RbGZmGYeCmZllHApmZpZxKJiZWcahYGZmGYeCmZllHApzSBJBX+9Rdu/ezdtvv02SJOUuycxmmYoNhSRJ2Ldv36xY3mKyjg4Ncnvbf+X4b/0ON3/1cTo7O8tdkpnNMhX7x2uzZXmL/hM9DCYJk02uhYUqqqrnUXfponyLM7M5qWJDAWb28hYRCSe793HXEw/SQ5DM8007zSx/FTt9NNMMr4Daf6KHSBIGTh7nxr98gMsElxUqOrvNbBr526bMRq6AmgwN8sefvoPk9AkaChe/1tHw0uAAzc3NFAr+PcDMxudQKJPsfEHvUT7/nYezFVCPStz5xIP0JEMkEXCBo4RIErq6uujq6uL3ntwNgodvW01LS8sU/yRmNpc4FKbI8PTP0KkeCoODRN9J6uoWjLv/wMle7vjWn9ATwcJ59WetgLqoqgoJjgwOXnA9/cePsbGtnaTvBPVNrdRWVxevtIJRl6oWCgWPIswMcChckOEAOD2/nojiF+zAyeN84cnNHH1vgIZC8Ut982e/MuH7LKqqRkl+Z7rrGhpJ0pvx9PUeYWPbARoW7+fovr+jULeApO8EhboF1NRUs+lTV7Fs2bJxw8HTUGaVYcaFgqQ1wANAFfBQRNxX5pIyw2HQd6g4/19dU8v9N/w28y+5DIBFhWqiaoiFhSoCGDjZw6mjB+k7fpTC4CDJ6ePZ41AAkU8gJBEMnOxFvUfO+syq+gbmX7aY08cOUai/lKSmtvh4qpeNbe3U1u7OppiGQ2B4VHHgwAF+78ndBEkWIJMZcYx8n7H2nUzgOJTMpseMCgVJVcCDwCeBDuAlSdsi4tU8P3d4fr8wlFBdOHPyd+SXef/h/dntMBfW1FJbVZMdP/K+yceSIe584kFO1NSwYKCPXhW4NJKzHhMJNPU3zjmWDPGl7zw86jML1bV8464HxjymrqHxrCmm4XMRfcePZKOK+qbWLEDGGnHU1taOOm9R+vcg441OJjrvUXoP7ZGhdD7hMDKcPGVmNrYZFQrANcDeiHgTQNJjwFogl1A43XOIvuNHGDx2kM89/sdIVfz5dTdRmDeff//cNznSf5JLC1X0JkPZY1JdSyRDHH4vqFaB4wc7ONl7mDXbtnCotq54grhQRU8yREOhimRQxNAQCUGQnPV4hIQeCkQkHH2vPztu+PFoMsSRmHif0sfjScLAewP0UKBhxGcVHwc51rGXvt7DFObVk/SfGvX4+Z/8iAULF3P83beZ39hM0n8qOzfS13Mo+1Ifz3CojPd8WP/xY3zxa8/yvp9roqfrTeqXXE5tbe2Yx3/5kefpP3GM+iWXk/Sd4Itfe5aamhr+8Dc/wbJlyyb1v3Xp+xTmXXLex5vNNHldNKLIaQrjQkj6DLAmIn47fX4z8I8j4o6SfTYAG9KnHwJev8CPawQOXUS5c4n74gz3xRnuizPmWl/8vYhYPNYLM22koDHazkqtiNgCbLnoD5J2RsSqi32fucB9cYb74gz3xRmV1BczbUK1AygdEy0Hxp5/MDOzKTfTQuElYKWkFZJqgXXAtjLXZGZWMWbU9FFEDEq6A3iG4iWpD0fEnpw+7qKnoOYQ98UZ7osz3BdnVExfzKgTzWZmVl4zbfrIzMzKyKFgZmaZigsFSWskvS5pr6R7yl1P3iS1SHpe0muS9ki6M21fJOk5SW+kjwtLjrk37Z/XJa0uX/X5kFQl6ceSvp0+r8i+kHSZpMcl/TT9/8cvVnBffCn99/ETSd+UVFepfVFRoVCyjMa/BK4AfkPSFeWtKneDwJcj4sPAx4Db05/5HmB7RKwEtqfPSV9bB1wJrAE2p/02l9wJvFbyvFL74gHguxHxD4CrKPZJxfWFpGbgi8CqiPgIxYtc1lGBfQEVFgqULKMREQPA8DIac1ZE7I+Il9Pt4xT/4TdT/Lm3prttBW5Mt9cCj0VEf0S8Beyl2G9zgqTlwC8DD5U0V1xfSLoU+KfAXwBExEBEHKMC+yJVDcyXVA3UU/z7qIrsi0oLhWZgX8nzjrStIkhqBa4GXgSaImI/FIMDWJLuNtf76H5gI1C6bGsl9sUHgIPA19OptIckXUIF9kVEdAJfBd4B9gM9EfEsFdgXUHmhcM5lNOYqSQuAbwF3RUTvRLuO0TYn+kjSDUB3RLRP9pAx2uZEX1D8zfgfAn8aEVcDJ0mnR8YxZ/siPVewFlgBLAMukfTZiQ4Zo21O9AVUXihU5DIakmooBsKjEfFE2vyupKXp60uB7rR9LvfRtcCvSvoZxanD6yR9g8rsiw6gIyJeTJ8/TjEkKrEvfgl4KyIORsR7wBPAx6nMvqi4UKi4ZTQkieK88WsR8UclL20D1qfb64GnStrXSZonaQWwEtgxXfXmKSLujYjlEdFK8X/770fEZ6nMvjgA7JP0obTpeopL1FdcX1CcNvqYpPr038v1FM+9VWJfzKxlLvI2zctozBTXAjcDuyW9krZ9BbgPaJN0K8V/FDcBRMQeSW0UvyAGgdsjYmjaq55eldoXXwAeTX9BehP4LYq/KFZUX0TEi5IeB16m+LP9mOKyFguosL4AL3NhZmYlKm36yMzMJuBQMDOzjEPBzMwyDgUzM8s4FMzMLONQMDOzjEPBrEwk/fPh5bvNZgqHgtksla7oaTalHApWsSS1pjeX+Vp6g5VnJc2X9IKkVek+jelaSUj6TUl/JelpSW9JukPS3ekqoz+StGiCz/qgpO9J2iXpZUk/n760oORGN4+myywg6T9Jeim96cuWkvYXJP0XST+geF8IsynlULBKtxJ4MCKuBI4Bv3aO/T8C/BuK6+dvAk6lq4z+EPjcBMc9mn7OVRQXW9uftl8N3EXxpk8foLgsCcB/i4iPpjd9mQ/cUPJel0XEP4uIP5zUT2h2HhwKVuneiohX0u12oPUc+z8fEccj4iDQAzydtu8e71hJ7wOaI+JJgIjoi4hT6cs7IqIjIhLglZL3+ISkFyXtBq6jeJevYf9rcj+a2fnznKRVuv6S7SGKv5UPcuYXproJ9k9KnieM/+9prPX3x/v8akl1wGaKt4fcJ+k/j6jj5ATvZ3ZRPFIwG+1nwD9Ktz9zsW+W3tSoQ9KNAOmSy/UTHDIcAIfSmyNddA1mk+VQMBvtq8DvSPoboHGK3vNm4IuS/hb4G+D94+2Y3iv5axSnpP6K4n1AzKaFl842M7OMRwpmZpbxiWazKSTpQc5cVjrsgYj4ejnqMTtfnj4yM7OMp4/MzCzjUDAzs4xDwczMMg4FMzPL/H82zWRBPhG21gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data[data['Target']==0]['num_char'])\n",
    "sns.histplot(data[data['Target']==1]['num_char'], color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3a5e5eed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_words', ylabel='Count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data[data['Target']==0]['num_words'])\n",
    "sns.histplot(data[data['Target']==1]['num_words'], color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "50c83eda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x21fda0abe50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 582.375x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data, hue='Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4e0c8d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e671d9d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(string.punctuation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3027cf6a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stopwords.words('english')\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f3dfe99a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_text(text):\n",
    "    text = text.lower()\n",
    "    text = nltk.word_tokenize(text)\n",
    "    list1 = []\n",
    "    for i in text:\n",
    "        if i.isalnum():\n",
    "            list1.append(i)\n",
    "    text = list1[:]\n",
    "    list1.clear()\n",
    "    for i in text:\n",
    "        if i not in stopwords.words('english'):\n",
    "            list1.append(i)\n",
    "    text = list1[:]\n",
    "    list1.clear()\n",
    "    for i in text:\n",
    "        list1.append(ps.stem(i))\n",
    "    return \" \".join(list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "06f1779b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'love yt lectur machin learn'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform_text(\"I loved the YT lectures on Machine Learning. How about you?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7a8b4373",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp/ipykernel_25228/3500509508.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['Transformed'] = data['Text'].apply(transform_text)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "      <th>Text</th>\n",
       "      <th>num_char</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sent</th>\n",
       "      <th>Transformed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>111</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>go jurong point crazi avail bugi n great world...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>ok lar joke wif u oni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>155</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>free entri 2 wkli comp win fa cup final tkt 21...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>49</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>u dun say earli hor u c alreadi say</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>61</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>nah think goe usf live around though</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>1</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "      <td>161</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>2nd time tri 2 contact u pound prize 2 claim e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>0</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>b go esplanad fr home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>0</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "      <td>57</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>piti mood suggest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>0</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "      <td>125</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>guy bitch act like interest buy someth els nex...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>0</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>rofl true name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5169 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Target                                               Text  num_char  \\\n",
       "0          0  Go until jurong point, crazy.. Available only ...       111   \n",
       "1          0                      Ok lar... Joking wif u oni...        29   \n",
       "2          1  Free entry in 2 a wkly comp to win FA Cup fina...       155   \n",
       "3          0  U dun say so early hor... U c already then say...        49   \n",
       "4          0  Nah I don't think he goes to usf, he lives aro...        61   \n",
       "...      ...                                                ...       ...   \n",
       "5567       1  This is the 2nd time we have tried 2 contact u...       161   \n",
       "5568       0              Will Ì_ b going to esplanade fr home?        37   \n",
       "5569       0  Pity, * was in mood for that. So...any other s...        57   \n",
       "5570       0  The guy did some bitching but I acted like i'd...       125   \n",
       "5571       0                         Rofl. Its true to its name        26   \n",
       "\n",
       "      num_words  num_sent                                        Transformed  \n",
       "0            24         2  go jurong point crazi avail bugi n great world...  \n",
       "1             8         2                              ok lar joke wif u oni  \n",
       "2            37         2  free entri 2 wkli comp win fa cup final tkt 21...  \n",
       "3            13         1                u dun say earli hor u c alreadi say  \n",
       "4            15         1               nah think goe usf live around though  \n",
       "...         ...       ...                                                ...  \n",
       "5567         35         4  2nd time tri 2 contact u pound prize 2 claim e...  \n",
       "5568          9         1                              b go esplanad fr home  \n",
       "5569         15         2                                  piti mood suggest  \n",
       "5570         27         1  guy bitch act like interest buy someth els nex...  \n",
       "5571          7         2                                     rofl true name  \n",
       "\n",
       "[5169 rows x 6 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Transformed'] = data['Text'].apply(transform_text)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a8a4987b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "wc = WordCloud(width=1500, height=1000, min_font_size=10, background_color='white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ee59d3de",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_wc = wc.generate(data[data['Target']==1]['Transformed'].str.cat(sep=\" \"))\n",
    "# spam_wc = wc.generate(df[df['target'] == 1]['transformed_text'].str.cat(sep=\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aa511e5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x21fdc322580>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/bz4IIa4aKiilpC86we/99Gcc6bucf0hTbWuqSs4wyOg6Eohlsnzn2EnODo/wufvupjoYmFcQO1SV3735Rs4OjXBhdIycYfD5F/fSVlbKsqLQvOfkTJMvvLg3L7Ttqspv37ydFSXFc47PGSbhlDU+zpnjM9nPpYyPoiiztPhFIXxztVSlCJQg0ujFctBdY8y40Q9mBJk7jhn+1Vm7pBwHJNIcslSkKXuxjCCz+5G5k2CMgExNRl5MOSHznUPxfAxDP4cZ+TTCtgHhuh/sN4My//jP6pph8sreDiLjcTbcsIyCSWEHIHPnAQVhX3/VdixhnYXcSWT2kDVWMgFmFGTyDVP/0skMI5cjVDeVTvbLoKLET0VJAMthqgCWUDalxDRB1yWZbJZ0Nk0qJYkns7x85BL/+eODi17LNCWGKUFAOqvTPxKlKOChLGTZsJcF7udS7CkOj/wjNd4dlLs3kDRGCGcvogo7le5tqMIyHxTYG1CFI+8vqPbcSF9iL0FHPSXO1ZZZRVjBBtOQJPURGnx3WKaYyV01FYVUl8/1aUz1V9cNMlmdTFYnkcqSSGZ4bt8FHn786Kse/ykY0qA/1UWlqw5VLPxuXgveVIK71Ovhm+99CGPGxM4aBr/72M9oHx0DYG1FGX9+522LximX+xaO9ZRSEk2n+aMnnubwZIihTVXZ0VDH21YspylUiEPTSOVynBsZ5fsnTnOwtw9TSo5dHuR/Pvks//Tgffidc5OAhBCU+bz88a07+cyPHyOWyTAYi/M3e17kHx64F4/dPqcvPz17nkfPnMtve/uqFTywsnVe4VAd9PPt97171koklcvxPx55nN5oFIBttdX88a4dCwoXAVT4ffPumwsF5sRha4ANZJbriQWWMgWYIFyguGftE7hBrbLMCEw5A89iRP8AjEGEfS2oDZZzEpB6++zzhUDa1qIWfhOZ+hFm+jFk9H+CWoHi+QS43oEQC0/5XFYnlcySy+jEJ1KzBDcyMTkeS7DrywTmxJ8iM0+D1ojQVoLWaI1ZduF8hdcaKzc3WvNgcr4ks8dJ587hd93BWPybBN1vI5x4GMNMYlOLUYSLkPf9xHP/gdTG8QZD1Fd/nKHRCaaC2hbCaDTB2a4hXjpxCZ/byVA4ljeVSCnJmQnqvLsIOZo5H/0xlZ6tBOx1FNgbKXK2kjMT2JWp91bMMu0UTp6T0AdZF/oEYNm6dTODKXMYMoMq7ATtDfQm9mJTvBgyi0crRRHzx1KrqkBVwW5Tcbtmv5ddfWOvatyvRM7M8rOB7/Ghuv+BqrqufsIS8KYS3DZVpSFUOGtbWtdxzLARu212mkKFS07AuRIS+NrhVzjQYzn0HJrK7++4ifeubcOuzn7ILcVF3NbUyN8+/yLffuUEEtjf08v3T57iIxvn17yEEGytqeITWzby+Rf3YkrJi53dfPPIMT6xdVP+gyOlpGNsnM+9sJfspE18VVkpv3nTNuwLaMwOTaPxivGJZ7LYtenjvXY7TUWh1ygBJzspoGciYznnlBKuhzFBCB+gWtEpvj9a6KhJs7CBGf8HMHpQgv9kmVeYvNfkf1qCcU77wsrY9PwaqvsDyOwBzMS/YU78GYpShHDewkLmHbvDRn1zKT0XhwlckeghlAASA2lGFjUOSSmRmaeQ6UcRno9atm7hsj4qejdG8ltXGaHXBkIIVHXa5g7gtrcRS+1hIPJXhLy/hEAFVOxaNU5bM7H084BCgeedGOYEQ9G/xzCXFmZaUuClta6UkkIvpQU+BsYmcDmmcyPGM+0MpY6hCI3W4DtRsLGm8CO0T/yUgdQhCuxNeG2VeGwl6GZmVtuqcFDl2U48N4Bbs0xel5MHGUwewZAZzkW+T3PgQZYF3kZH9DFOjv8HXls5y4PvJD9f3mBIKUkYMXqTl/BovvxHL2Ok6U91kTKSlDmrKLQXM5Tpx6m4CdgKkEh6kh1UuGoXbf9NJbjfCPRFovzX8ZP5gXz7yhW8b91qbPMISyEEHruN/3HjNg709NExNo4pJf91/BQPrVpJwDV/Np2qKHxow1qOXR7g2Y5LmFLypYOHWV9VwaaqSoQQJHM5/vq5FxiIxQAocDn5/27dSZHbPW+bPxeYMdC7kErpjDC3bjDHwXkr1zV9tGpQS5G5I5OhfYtkwskMUu+0tHD7xry2LKWJ1M+zmA4ohLDSwB23oaoVGOO/hMweAMctC5rls5kcF071U7esFJv9ivlgawMEMvMC0r590YxQmbsAKCiO2xHClTf5SKMbZGrh+33doWHXKklk9mNXqzFlGlXxogg3quJHIEjrHYQT38NlX40pk1ix6kuDpd1bf5eH/LO2V3tuotpz06xtLrWI1ZOOxSmUONdwJYQQ1HlvnbWtyr2dKvf2Oce2Bt+95P6+nsiYaX7Q91Vq3E0kjThx3VoRD2cu05m4gFN18uLoz3hfzacYzwxzKXGee8vfy0Quwu7hn/D+mk8v2v6bIx/5KnitfO9SSp5uv8howuJ58NjtCwrt/LWFoMDl4vbmpvy27nCE00OLO5jcNht/dMvN1AStZX00neF/736B8WQKU0q+efQ4L3Za0TKaovCZ7VtZV1me1+IzRoqEHrlqWN/rixxm4stgDiBlFswRzMQXQSgI5135o6SUVvy0TE8KJhMwLFu0zCDlDEeqCKK43g25s5jxv0cal5EyiTTjSL0bM/0U0kxMHmu3bOzmKOi9SGkgZRqZeRqZeWpOb6VMYaafQeq9Vhq5zIJMWLZxmUNchTtF1VQrxbp7lEwqN2ufsK0H2xpk6nvI1MNIc9y6hhlF5s5OxoxPHquWAQYydxqJjpQ6GJcw4/9ijcusPsvJ+8oAKfKJTjJt3avMzZoDUppWbLtMg5x0qMsMkvTkdmPeOSOlJJO7QDp3gbLA7zIa/waSLLPfLoFujGA5Bb1Wht81IKfrHDjTPa8JX0xGZE39m2/blfsXO3+p/35eGEr3owkbO4rv4eaiu7Erlmm1zFlFk7cVn1aAKU1ieoQG73JGMgPE9Qna46do8LTkj18IvxAa92slugzTzAtLgIbCAhoK5zouroQQgray0nxYkW6anBwcYltt9cK2ZCGoLQjyh7fczO889gSpnM6pwSH+ae8B7mhu5EsHDuVt1fcsb+Y9a9oAScpIoKBgSoOO2CusDu4gocfyD3IqS0xdxFb7mkEpBSEwxt4LaimYw2CMITwfRdg2Tt+7OYQZ/SzSHLeSUsxhZDaMMfYQKG6EWo3i/0sQXusc94cR5igy9TBG+iegFFgmGTMCSgFq4X8BHkBD8XwQM/pZjPDHEFoTUkbBjKC43o+Z/PLs/sqsFfNtjlvx2sIN5gSY/Qj7FoTzvkVD8DRNYee9czU+AIQX1f9nGBOfxZz4M4j/Myg+y9loRlB8f4zQaqz7c94Bqe9jxv8OkX4ChILUexDOXUgZu6JhiRn7W2TusGVHNwZAJjEin7Scw8KD6vtdsG+wjs48Z308ZdK6TwzMxL9B6gcg3CiOWxGeT85/C8JBsf9X0ZQQiuJBU0IEXHcjhIaieCn0vge7VouqBBAIygv+J4riB0bmbe9KqIrCwFiMrz9+EKfDxr3bWgl4XxubLkwqXt0dbCitJOS6vpVpNJNm3+Ue7qhb9rry+eRkFptiRyDQFBuaYkMieXLwB2iKjQZPi7VNShyKizpPM+dix7gQO8Xtpe+46kfnF0Jwv1aIZbNcGh/P/67w+0jpOhn96pqFXVVRhYIuraVj36QzcDEIIdjV1MAH16/lSwcOI4GHj5/kyQvtRNOWHa+5KMTv7bgRh6aSMmKcjr6EqthY6b8RRajkZI5L8WMYUseuOLEpTspdDXi14HWNwbVBRfH/KTJ7BJl7BbAhHDcg7FtnO/mEB+G6DyHnz940cdM1EAMy1JYXIoQLxfeH6La7GRt9lmJ/AoQToTUg7BsxRZChkShlRX6E4w7UglLMzHNgRlHULQjHrWTMUlSlDrRGpJTohkkma8fp/zsU/Qij4+dxOyQuVwhhW4OwbwexOEHRYi+LEAKpNaMWfMXK5MydRBoxzh8dYagvxJrbNhKakiVKOWrBF5HpnyH1blA8KJ5PIOxbkNkjCDHbxCYc2xBaw5xryqn/zEgaElodiuuhhfupWbZR0zTnaLcOW13+OJd9JQCaOh01pClBADyO9Qu2vxg0TeWDd20klcnhsKl45nHgvxpI4PFLF6j1F1y34I5kUjx28Ry31za9rnH0IXsJ4ewow5nLjGdHSOgxpDQZSvezq/R+nIqbhG75D4QQtAU28oO+rxGwFRByXD109b+V4I6k0kykpx0fL3R2cd9X/3NJ5+ZMA92ctvfFs1kkVzfjaIrCJ7du4vjAIAd6+sgYBsNxyxTgc9j541t3UOazNNHJvC9UCSkjRtKIMpbpJ20kLO+6VkZ/qp1G79prvPPrhbRiuV33g+v+BY8Sig/hesfCrZgmg+M9/HjPYf70E3fhdNgAGylzFYe7XNx74+w490w6y1cfPcDvf/hWy/FqX49qt4SJlJJzXcN8/bE9fPjeLaxoKCESS/HNxw+R0w0KAx4+ePeH+dmJI6xrqaRtkvNlKTBNkwun+knE0jSvrMQXvCLqRVhx4sJ5D9JxN2MDER771n9x78duweYsnm2iUMoR7o/MmR+KY/tkhh+T0R4CxbFj7phJSd/5y8TCCVZsrZhuW21AuOYK+ZkwcgZ7vn+AXe/ZZgXCvEEmA9M0eXzvGeLpLAJ41y1r8biuMVz0KhDAqdFB9vReYkWohO2VNSDh5cvdnB4dxmd3cHd9M4VOF4aUHBjo5fjwAG6bnQeaZifFXIqMc3J0iHsbWtCuM9hhIQRtIW4qvouXRp+k1FHJ2uBWNMXGjpJ7eCW8F5+tgE0FN+NSrTlWaC/GrjhZ7l+LsgQL9n8rwZ3M5vIRHACpnE4qF7+utkxz6QYcv8PBH95yM7/8Xz/Ia9oAH1y/lq01s80tHi1AvWc1ilBp8m7AqwVxa35swsFIppdKV/MV8as/P8RTGc53DbN+eRU53eSV831saK3mQs8wF/tGKSnwsaG1Gk1VWNdSxVP7z+XNXqlMjn0nuijwWxNXSkkynWXfyS5smkpWNxa0kcVTGRori8hNrpS8bju/fP8W7JrKX33taRLpLKYp6bw8Ts9gmOaaEpqqixgYnSAaT9M7FKa4wMva5spZY5/N6FzuHqV5VRV2x+KvxmDXCI99eTeDncOc3tdOTUsF44MRjjx7ionxOKqqcP/HbyU6Hmf3d14mm8lx8zs2U7WsnO6z/bz86GHsTju3v/9GAkW+OcK1v32Q7/7dY+hZnbMHO7j3V3YhTcmz391LeCjKhltX0bq5iece3kfrliZUTeX4C2e56e2beOo/XuDZ7+5loHOYbfeuo2lt3XU932tFTjfRVIV371rLyyc6GY3GcdiDaOprJxSzpsGF8Bi31Tby5ROH8drstBWXkdENtpRXs/9yD187dYTf2XgjT3a182RnO+9Z3kbG0PP5C0IIOqNh/vnYfj65evPS8xquAUIIWn1rafWtnbW90dNKo2f2B8SQBrFcFJAs885PFXsl/lsJbkOas2Kg3TYbHvvSChVcCd81LgM7x8Okc7NNCZfGw2QNA+dkIQen6qHRuxaXasUPu1Rrae9Q3Uhp4reF8NuunkjyRkFTFZ7Ye5ZlNcX0D0c5dqGfTSuqSWd06stDPPriKfweJ631pXPOtWsqlSUBHn3hFNva6hACHn7mGAU+N163g8hEcp4rWi/EphU1tPdM2101VcXrUugZDONzO/Isjp39Y9y0rpGvP3aQz37kNjp6R3n64Hke3NmGxzlXE7TZVHwBNz0Xh/EF3DgW0RaLq0LsfNdWcukc9338VlweB/0Xh3jxR4f4tb99P063A0VV+NE//ow1N7fiCbh5+HOP86v/5/18/wuP8+Cn7qC/Y5DHvvQs7/+jB+e0X1pXzMpty3C47Gy/fwMOl4PHv/ociqKw451b+Pb/foSa5ZU0r6/nh//0JIoQ3P2RnThcDrbcvY72V7p48NN34HK/tuaKxaAqgmgizXeePko8maF/JMLWlXW01pUSS2cYjMQIed2MxpLYNZVEJkuh100incHncmBTVbxO+6IUEZqicG9DC21FpbRXj3FkqJ/VxWUEHA6ODQ8wmkoSyaSRwHM9l/jwynWsL63IvzPhTIpIJs2f7dvNb2+8kZbCotftfZqKxBoLJ0gkM6iKwuDIBGtWVGGzTQdEnI4c4Xj0AJsKb8atLo1v/L+V4LapKqoi0CctHu9YtYJPbd+y+EkLwKmpS9J7p+K1/3bPS2SM2bb0p9s7+PYr5Xxk0/q8w3FKaF8JIRQK7HMF4OsGEbSyJBdZtjlsGisayjh+oZ/23hFuXFuPYUpGwnGGwzEisRSx5FwqAQBVVSgt9OXj8U1T0jMY5oEdbdg1lReOLp0zWkpJOJbie88c4923r0NTFRQhuGldI2uaK/jZvrOks9ZHc82yCta3VM37shqGyVB/mGQiQ92yUgKFCyfbaDYVl8eBZtdw+5z59qqWlVFeX4IQAj2r03P+MnpOx+awESz2kYgm6b84xPM/OIChmxRVFjCfzc1m17A77dhddtw+F1JKes71k05mGOoewVfowdANKprKQEImk6VmeYVFU+txoNlU3D4X6muo7V4NNk3lw3dvmnffcDSOYZqc7htiMBLDYdPyikxpwIuICOLpLLevapr3/ClYBFTTBHQCwfGRQb5+6ii/ueEGemNRHu88v2gbyVyWreU1PN/byaqiErTXKJtxPpim5Indp3A5bUQnUlSUBTl3cZC25ZX5Y1YGNtDqX4cmls7P/d9KcPsdDlyaLe+MTOZyFHvcr6sGG89m+ctn9+TjtYs8bhpDhRzo6cMwJf+y7yCry8vYWFXxptGkQUH4/hcXT3WjDsfIpMPYHbY8b3JDawUdp/tZubGeratq+cZjh1AUaKoqpn8kyv5TXXzqnTcyPG6ZoaS0UovNyRRjadeQEnK6gWGa6IaBpqr43A76hiN4XXYSqaXT+CZSWb7yyH5u2dBEod+dN2PNt0TX1IWrkgghaGytoOfiMKrt+l5mMYO7W7WptG5poriykLqV1UgpCRb7aVxdw9odK/AVePAE3As6Stw+J91n+unvGKSsrpjWLU2M9I6x4bbVZFIZfAUeju05TUGJH9WmcviZk2y+cw2aTSUZT9N9po+S6hDe4LWzOF4vFhrbYr8HRQjKgj6KfB58TgeGaXGCpLI5Kgv9jMWSVzWrSCQ/uXiORC7HC72dfHr9VlK5HEJYRUj2Xe5BSokAbqlp4BunjxLPZckYOutLLX9HucfHZ9Zt5f8efolHOs7y9mUrX78IE2HxuaczORCCVDo7J3lQFeo1p8L/t4rjDrqclHinJ/HFsfFZNu/XGoZp8uUDh9k7SbtqUxR+88bt/M3dd+TDEKPpNH/x7B5GEsmfc8z2NIQQ6LqT4QGDS+cGiY7HqV1WyoUTvYRHYgx0j3G5a5SJcILSkI/yYj+bV9Zit6mUFvqoLSvkkRdOUV0apMDnJjyR5Pu7jyOE4Pu7jzMSjtMzFOaxl06jGybff/Y46WyOd966lhePXuTAqR7WLa9iMX9RRXEA/yRndt9whGg8xXNHOvjij/YyNB6jqjSI3+NAIGiqKsKuqRQG3JSGFk73F0IwPhIjk86haVd/kfwhHxtvmybsCoS8s34DPPhrd2Bz2Dj18nkM3cDm0HjfHz7I6OUw549cmiXor8S6W1ZRVFXI2QMdGLrJjQ9son5lNSdePEtyIm3FZiezPPBrt3P/J25DzxmYhonH7+bej+3i5MvniY5dnw8HIJnJ0Tk0fvUDlwC/y4nX6SDodtFaWUJVKEBtcQF1xQUsryjG53RQV2y9E1Zsu/VvPJ5kZCKRF8YPNa/krvplnBsf4aNtG2grKmN9aSX3NbRwfGSAextauLG0Ft0wuaWynnc1t3EhPMp4OoVdUQk4XNzbsByHpvHr67chEGSM15LL/gpIuHPHCnZua+HOnStorCuhqW4JzJxXwZte475yWstXEdXtstlYV1nOhUnek4tj43SOh1m+FIrTa4SUkucvdfG1w6/Mitd++6pW7KrKH+y8id/8yROkcjlODw3z+Rf38qe375qVvr4UvF6KgqoplNeEcHkc2B0aqqayaedyIqNx/AVuNJtKoNDSoj5876bJvlhl3j5839zl8ofv3ZR/clNd/pUHt836XV0a5FPvunFJ/duxvjH/d0ttCX/2q/fkfwugvGg6c+9dt60FYGXD4lzcEklkLE4uqy9p9eMv9LL+1lXTv0M+1u+a/i2EwOV1suOh2ea4wtIAd37o5qu27/Y5ueMDN83atunONWy6czrWfPv9G/J/3zDj7zU3t7Lm5rnUoteCkWicnxw8w/Lg0ulwrwxDXAqiyTRHL/WzorqUwUiMdDZHacBH71iEIp+HE90DrKgupa2mjJ3VDcRSGaIjaZwZjYPtPWiqSqOrEC2mUO8tIDKSIqsbvHyuC1VR+OiqDbO03LsbLC6cgMPJO5pXLn1ArgPDozGeefEsA8NRAj4X8UQGj8tOU/3S2CoXwptecCtC4JghzGKZbL4y+7VCAHe3NPOjU2fJGgbxbJZvvXKcP7lt12vq+ZZS0hud4H/vfp5kzsrAawoV8ts335DnQ9nRWM/7163mKwePIIEfnz7L+spy3tm2NK/yFFQxmw1wIp3BlPJVL/1UVaFxReWsbQVFvjzxUqh0dkWeKQ3JMExSmRyxRIZYPE08mSGdzpEzTJASVVWwaRaxj8dlx+tx4HbZcTps+WewlPufeYzFFPfqoQhB08oKnC47Xt90cQZg1n1NxNPE42lSk1VeBNZ4Oewabpcdn8eJ1+3A47Zjt2kIsfSQvJxusPvERW5b08RYPMnFgTE2NFby0tkucoZJJJ7kbVtW4rrCqW6YJi+d6aJraJya4iBtdeV0DYcp9LrpHh5neVUJ4/EU6VyO091DFPpc3L62mZxhcKi9j3gqg26Y3Le5laFwjD2nLmFT1WtaBaazOvtPd7Fz3eJ26ivRPRKmuiiI3+XgRNcAA5EJKgoDRBJp/C4nyytLGAjHaKuxPrxD0TjRZJpkJstQJI4iBI3lIYYnt6uKQs4wKAl4KfS63/DC2TNRVuLnfe/YzI8ef4V7bm3jUvcIsYQVWSalJGsYhNMpCl1uNEVZcl/f9IJbVRQq/H6mag13h8P0RKI0hQqv2SYshGBjVSXbaqt5/lIXAD86dZblJcW8e/WqRVPfwRroeDZLRtcJuRe2jad0nb957gU6wxHAil75w1tupsI/HfalCsEnt27iaP9ljvYPkDMMPvfCXlaUlLCidC6l60KwqSplPi9nhq0oi/axMQYmYlQH55Y6e60hpSSnGwyNxjjbMcjpCwNc7BlhaDTGRCJNNqtjmBLTnOZznhJiihCoqoLToeFxOygu8FJZFqSptpimumJqKwoJ+i3n2htl+1c1leZV1YAklcnR2z3C+c5hznYM0tk7Ovu+DCtC6cr7UhWBplofpmDARVVZAS0NJbQ1V9BYW4zP61z05dRNk73nurhldSPhWIoTXQOsrivnB3tP8tHbNtFaVTw/rw6CykI/dSUFfPWZQ5QV+Dl4oZfSoJczvUPkDJOgx0VJwMPOtga+//JJqouCVBQG+N5LJ/j0vdvwu5xICV/ffYT7N7XSOTRuhWUudfwUQf9IlNOdg9htKrVlhThsVxcxDaUhjnddxu2w43U5WO4pQRGCAq+LkM+NXVNxzyCsctk1inxuKgoDVBUGMKXE5bBZkVk2jZxhMDqRoLoowKWhcSpD/tcl5G8pEMKq11NbFeLbPzqITVO5/w6rJJspJQ+fPUVXJMyOmnqqAwHqg1fP5IZfAMEtgK01VfzkjBUDHE1n+Js9L/Int91Chd83zbaHNemzuoFT0xbUoB2aym/ffAPnhkcZisdJ6zp/tft5jl8e5D1r2mgqKsSpaShCYE59EVNpLo2Nc6Cnlxc6u7l/RQuf3DK/99yUkm8dPc4z7VZlaSHgQxvWclN97RwtMeh08tldO/j49x8hnEoxkkjwF7v38K9vfxsB59KqiitCsKWmmt0XOwEYTST5P3te5I927aDU65k9PoZphR/atOtOOJCTwmpodIKXDl/k+YMddHQNE09mlkQzPRUVYDKV7agTjaW5PBTl+Ll+wHIqBnwu6mtCbGyrZeOqGuqrQzjsU8V2r6vri94TQCyR4WzHIHuPXuLYmT76hyKk07klGefy92VKcrqlnY9FElzsHuX5A+1omkJpkZ+ta+u48+YVNNeXoF3loySZ/jAU+T201ZUvaEpL53LsO9+DbpiMTSSxqQqGaTIcjVMa9NF+eZS3bV7BwfZeook0A+EYyYy1GqwM+WmtKkVRBMlMlnQ2x/KqEnwuJ11D4SWPo6JY/oSRSAJNVagsDuJYQrStDUGl4iQ7nKAcO6Xlhbi9DqpC08pHaXDaN1FZGKCyMEB8IoUasZzYwpBsbazG7rDNOjbke+Mcs4uhpqqQ4dEYwYA7X8/UlBLdNCn3+hhKxqnwLZVu+RdBcAvBLU0NNIQKuThmOUr2XOzk7NAwK0tLKXA70Q2TWDZLJJUmlsnwt/feyaqy+UPnhBCsKCnmz++8lc8+8TSjySQZ3eCHp87w03PnKfF4CXlcaIpK1tCJpjJE0inimWyeJ/xuY/5Cn1JKDvX282/7D+aP3VhVyce3bJzXtCOEYHV5GZ/atpm/3vMChik53NvPv+4/yO/tuBFtiSaDu1qW8c2jx+ibrPT+xPl2jl0eZGVpCQGXk5xhEMtkiKTSJHM5/v5t99BUdO1lvExT0jsQ5kdPHWf3vvOMhRPX3MZSoBsmY5EEY5EEh0/04LBrNNQU8dF3bWPbunpeK3f1VKr8pZ5Rnt17npcOX6R/MIJuLJ0Rb6nQdZP+wQg/+NkxHt9zmhs2NPCht2+hoWZ2HLGqKBim5PJ4lFPdg/lsXeUq5qDu4Qj9Y1E+vGsD5/uGUVUFt8NGKpOjqSzEM8c7QMCRjj5+/b4bGJ2YfnbKjLlp11Tsmkbn0Dhdw2HLxLVEqIpCod9D9+A4y2tLsC3R/Nh9YZA//OC/Y+gmiiL4X//2y6y/sfmq5x154Tyf+4OHrcIibjt/991PUdP0BobMXgOee/k861fV0N03xtmOAVa3VqEpCtsqq9nddYmAw0ndErVt+AUQ3ABFbjf/3607+YPHn2RoMl18KJ5gKH5pzrE21bJvLQYhBLc01vPPb7+fv37uBY4PWBXfM7pBbzSaL0owHzRFmVMQASwhMBxP8BfP7slnR4bcbj67awd+x8JJEFOlxA719fPUhQ4k8K2jx1lXUc6dzU1L0ror/D7+eNcO/ueTzzCWtGhDB2KxfAjiTLg0bVbq/lIgpWQinuaHPzvGD588xnh0/uSY1wuZrE5H1wj2JSy7lwIpJelMjsMne3j0mRMcO9NHMp27+omvEVLpHM+8fJ6jp3v5lffcwD07V2Kb1KRtqsJD21fx9LF2KgsDrK2vQFMVNjRVoSwSgVJfWkBDWSF7Tl3i5lUN+FwO1jVUktV1qouCxDNZygp83NBaxzPH21lTX05p0IfTrrGuviK/ilEVhQ/uXM/ukx2UFfjYtKyKbHRpoZnprM6hsz0YhomiKLRUF1NVErzqeVJKclkdQzetgsFLzEo2TUkum0NKy5n+ZonKmg+KEPQNhBkcmSCezOBxOairCRFOp/jImvXEc1ki6RRF7qWtEH4hBLcQghvqavjyO9/Olw4eZl93L5FUCn0yDlQRAlUI3HYbVYEA3kUE5cw211eW85V3PchTFy7y6JlznBseIZbJ5NsVWBPZZdMo8/lYX1nObcsa2Vw9t9inlPC9E6dI5nLUTpY2+9jmDawsLbmq8HVqGr+/40aGY3HCKUvwfvfYSTZWVVLkuTqZjhCCW5c1UuLz8uUDhznY2080nc7HySqTtlePzU5tQRD3PB+ehSCl5GzHIH//9ec43T7wRlXdmoPG2iJWLCvLZ6NNrWgEYJgSTVXoi0Qp83mxLRKrDdYS9R//43l+uvvU66JdLxXjkSRf+OpuxiMJPvDgZmya1e91DZWsa5jtGL5v0+IRIk67jXffOJvZsMg/LQTePml2uHeedu7aMF1xXghBfVkhHyvbnN/29Etnl3Q/lukHekciGKZk0/LqJZ333wFtrZVEJlI01ZXgcGjYbSq6abK/v49LkTCVPj9FLvf/W4IbrAm1vKSIv733TkYTSXqjUcaTlvB2aCoFLhclXg+FLjeuJWpmQgj8TicPta3ggZXLGUkkGZiYYDyZImea2FSVoNOK/S7yuHHZLAfI/HzB8LHNG/jIJosMSWBVrFEmBU0kleZydILawgI8dtsce3dtQZC/vfcucoZBZdCfP38+ZHUjnx045Zm2qyqry0r5/NvuYSSeoG9yfAwpcWoahW4XxR4PhW4XTm2qIMH0ufNmEpomu/ee5x++vucN17KvxG3bl+crquimydMXLuafRdYwaCkO8fzFLm5vbqKuMLhoW4oQtDVX8JNnT77+Hb8KsjmDb/zgAAGfiwdvXzNHq5ZSEktnMaWJ3+lEUQS6YZAzTJy2q2faSSmJZ7J47PZFNfbXApqq8MCNbfSPRAgFPHhdb1y6/Zsd/YMRBoaiDI5M8OBda6muLCRrGKwpLWNNSRnfPn2c2+qXHo3zphPcCy13piaoIgQlXk8+kSZtJhjL9FPpKp913LVACIFNVanw+66hHuPcNly2hT0xpweHSWSy7O3s4UOb1xFLZ0hkc5T7vWiKwsBEnPaRUZyaRnNJUf48KSXjyRSxTIZyvw/DlHz/2CnWVJaxrDhEPJPlR8fPcEfrMmoKAuQMg6xusKqsFMM0iaWz2FTFyhgEJlIZYiJDkdfDpdFxXrrUzW0tjVQG/LPGzjBMHn3mBP/8ny+QegPNCPMh6Hdx0+Zps1HOMPMMe/FMhupggGQuR2XAT6HbddU5IITgho0N1FUWcqn32usLCmFlYKqqgqpaRWutDFDTygy9xlVJTjf4ysN7WdFURktD6az+H7zUxw8Pn6KqMMCHblhPwO3k4vA45wdGuH/d1eO0TSn53BMv8pnbtxPyTq/ept4zCUwk0gRfJW+2YZjEUxlAUF4U4GL/KNmcMSue/v91SClJ6FlG0wmCdhcB+zQVwo6tzZhScuHiELG4RQNhUxR21NShCMGvrN14Ta6bN53gzphJxrMD6GYOkCSNCWrcK3BrfnSZ5VT0RUxpUmgvJ6FH8GgB+lMdxPUIzb5NbxrmvFkQghvqa+iLTDA4EaNnPMK3Dh9nRVkJqqJQGfRzdnCYsUSSjTWzzTASODM4TH9kAk1RuKW5gfbhUcr9PmoKgowmklwaCzOWSFLm9/IfB1+hxOtlKBanIuCjZzxCRjfwOx1oqoLTZmMknuCBtlb6oxP0hqOEkykqAv78yJmm5Innz/BP33zBStW9llsFXE4bxSEf5SUBSot8FPjduJw2VFUhlzOIJzOMRxIMjsYYGpkgHE2SnrRVzoeNbbWUF08LAJdNo628FAmcHRrGbbOxvLSYcr+PZC63YEm5mfB5nNy3q41//I89iwpaIcDjclBe4qexppiGmiKqyoOEgh58Hif2ydR43TCJxdMMjcY4f2mIo6d76egeIZNdWlZeZCLF13+wnz/7rfuw2zTLr5DK8Py5S9zUUsfWxhr8LgepbA6/y8HNy+uBKY08g6YqJDI5gi4nmqpYEVjJNAKrLql5xU12D4a50DuCqggkcNvGqzsDF0N77wj/+eSRfMTE0HiMd+5c/ara/EVDUs/xxXP7KXJ4GE7H+UDTesrc1rx9bu95Lg9GkMB9t7WRNQxSuRyJXJacadIVCVPi8dJatLRkwDed4DalQTQ3StpI4FDcVLmb88RLpjQmK8BIupOnWOG/ga7ESQK2YtJGAkPmUMTrvzybuSqYqR1duX0qKQUEE5kMT55t54HVrYSTKVZVlLKruZHvv3KKtJ5j57IG+iLROS9YOqfTNRZGCMFoPEGh20VVQYBNNZUE3S4cmkp10M+G6gom0hnODAzjqrHlwxlXV5YznkwylkgiJdzQUMPFkXH6IlGaikL0hqOsKp/W8qSUHDnVwz9/8/lrEtpul53VyyvZsbmJtpYKSkI+nE5bPhxxZvtTMAyTVDrH8Hic9q5hjp/p48T5y1weipDNWQ5mTVW486bWWct8IQQ1BUEAClwuvA67xYNxDeFUQghu3d7Cw48fZXBkdkFcRREUF3pZ21rF5rV1rGgqoyTkw27XFjSVTWHFsnJ2bl1GJqtz7uIQ333sCPuOXlqSLf3g8W7OtA+yprUSU0qeP3+Js5eHiaUzpHM6D25YydBEnG/vO0bI6+aTt2xBNwz+4pHdhHwedMPAYdP4zTtu5NkzHbxwrpOQz81AZK6TurzIT3GBF6dNI5FeOi/MQqgsCfLBuzbmWReHxmOTvOv/PaCbBuFskgq3n3trVrB3sJN4bnpc25ZXksnqrG+rwemwkdJz9Mcm2N11iUqfn4F4jELX0lc9bzrB7VQ9OBU3dsVJwFaMU/XMSFqxUe5qQCKpdDUTzY3S6F1HODuEQ3WhicWdbrmsTjaj4/FdXSNbDCMDUQ6/dIG73zU7ltvQTX768EHuemgjDqeNWCTJI9/ej2ZX6atXKfS4uDA8SsjjtuzKWFEqjaFCnm/vJJbJsLZqdl3ERDbLwESMlpJi4hlrIvidTl642MVNjXV47HYyusHezh7WV1WwsryUkMdNyO0inEpbCSGKgioUMrrO8+2dhFNp3r56BS67jYGJGMf6BlhTVY4AhsdifOFrzzERn5/V70o47Ro7tjbz7nvW0VhbfNXY5Jn7NE3F51XxeZ00VIe486ZWEqksl3pG2Xv0Ei8fuYRNU1i9vHLBNv2L0OtOfSQWOjdU4OHOm1r5xg8PWG15naxfVc1t25ezprWSoN99TVmPM+/R6bCxprWS1qYyfvLsSb74nZeuSpyVyeo89eJZVi+vRFUU7l/bSvdIhK1N1WystxgNa0NBblvZxMGLFv+NxDJ//dqtWykP+vnDh39GOJHiyZMX+L17bsbndPDb3/7pnGs5bBrtvQP4PU5C/lcf6+xzO2ZR5do09bqym39R0T4xylO9F4hkU1xO7Cdl5FgTmi7i8cKBdgzDpKd/HLtNZX1bDb6iYip9fvwOBxPZzDVFxbxpBHcuq2OaEofTRoVmaViKqpDL6KRkFkM38PiclDhqSSYy5LIGJaIIr9tFob2cZDxDKpHF5bFPhglZ2W0CgctjxzBMTh3pYqB3nBtuX4nXP5fy0jQl2XQOwzSx2zVyOQOX246UkIynUTUFp8tOKpWlr2uURDyDpik4nJZmkUxkWLulMc+16wu6ueXeNfzg6y/x7od2kjUMHKpKic9DRcCHx27nwTUrCDgdVBcELD5ix+yPT5HHzYc2rwPAOWlDf/vqVgYm4rhsGjZV4aPbNhDLZHBoKh/avJaBiTgBp8VvrCgCw5QYpsljp86xurKMcr+P4kkfwYc2r8M0LQIfwzD5+vf309W3NLtvTUUhn/nQzWxZU/eqMxynzvW6HbS1VNLWUsEHHtxMNJbC4772KipSSg6e7aGtoRy3054PMVMUMesFuXvnSo6e7mXL2jpu3d5CZWkQRRFzVlJSyjkcHAutvGZuc9g13nHnGux2lc9/ZXd+JbEQDp/sIZZIE/DNttWLK1YuM+F22Am6XWiKQFMEOcNANyQehx2HTcM7D/c4QEmBl97hCMCrrlQzHI7zyEun8iusy6MT3L11Ob43kA/854nlgRKWB6b5R6LZ9KzELadDo6t3jEwmx87tLQghME2T7okIbcWlBBzXpky+aQT36aPdDPaHufMdG3jhyZMUhLxU1xfznS/uIVBgad3v+ZWbuXC6n4MvnCcxYYW7feL37uHY/g7OvNJDLmewbVcrFTUhvvaFpyivLmRkMMqDH9iG2+Pg2Z8cI5XIkExkuP3B9QQKZmsa8WiSr37+KUwpCRS4iYwl+Ohv38n+3WfpvjiMoRvsuGc1voCbi+cG+P5XX2R8ZIL3fmInpRVB9u85x/7nzvL7f/0unC47Qgg0zYr+qCkIzHrppoRwodtaHlUukKKezuQo8XpnmQqcNhv1oelg/QK3i4LJdpyKMmvfTNQWFlAdDFA4I8Sw1GcRt0spOXGun6deOrek57WmtZLP/tqdVJYFlyyw01mdC73DrGooxzBMznQNsqqhnJFwHN0wGRyPIaVk7bJKFEXQMxxmcCzG2d5h6soKaawMMRyOc75nmKKgh5bqEjI5nZ6hMJmcTjyVZXVjBW6HjVOdA3zzycPcsamFspCf6qCfC+cHuHnHckZHYlbRBL+LkkIv//ez78DpsE3GH4tZZp1sVkfTVI4f6yYQcNPQWEI2q2OzqWSzBsNDUaqqC8nljPzzvnI8FEXh7h0rOds+yKNXiWQZHovRczlMW8vcZfNUhMl4PEUsnWUsnpi3oIddUykLennxfBdFPs+8ppJ4KpM3kfQNR6gpXXryx3xIpDLUlRVSHLTmU9A7+oZygf+8MZyOo5sm7dERDCnpjUdYW1RJgcN6jnfsWMGFS8P4vU6qKyZZEJGcGh6iMViIpiiWorXEd+lNM7K5rE4mZdlUs+lcXmOORVK871dv4UO/fitOt52Thzq58faV3POezYRK/KiawjOPvsLabY2sXFfLUz86Qi6nI6XkA5/axS33ruHEoU4q64rYfHMLm29u4aFfvhF/cG58tCklqqaw4642Cov9VDcU03l+kFNHuvjob93BOz96E0/98AimYVJU6ueDn97F1lta2fvsGRRVYefdq3E4ba86EUBKSTyepv9ymOMne7nUPUI6naOnb5x4Ik137xixeJqR0Rij4/ElX++mxtq8gL8SOd3g248eXpJde8Wycv7kN+65JqENEEum+c4zRzFNSTqr859PHUHXTY53XObzDz9P73CY4XAcU0qOnO/jsb1nAPjOM0dRVcHA2AT/9shekpkcj+09w+6j7YxGE/zFN57mQu8IZ7uG+Mpj+zGllXaezuRwOmw4bBoFhR6yk47Cjo4hItEkhw938tzus5w60ctzu8/w6CNHGR21hJyUknNnL7N/XwfhcIJQyEs6nSMez7DnubMc2H+R8fE4ly6N0N01yvcePsCjjxwlmZzfHKKpCu+9fyNB/+J2zJxu0NE1nH+m6+srKQtOO2aP91ymZyyC3+Vg95lL5AyTXSsa8qGBt7Q24rbb+MTOzYSTKYYm4nz4xvW4ryClctg0VEWhuiRIUdA7x7dyragrD7G6sYLykI+SAi+bWqspK7y+CK0pSClJRJMY18CXspQ2h0Yn8hzxr1XSjiYUErksPfEIXpsdt2bLr2KTKauUXlNdMYoiONcxCFj8MjnT5CvHj/CNE68wklh6JvKbRuNGTC9hs5lpT3xBkRe7Q8s7+9ZsaeAn395PSUWQ7beuQM8ZZNM68Yk0qqpwx9s3IISgsMiHZlNxOG0Y+lzH0EICx+G0Cga43HZM3bS4mW0qmqbicjvIZnSkKXF7rNJUXr+L1NTLukQZdiEywn+cO4o5DwvGuqIK3tnYxrFTvbQ0laHrJiMjMUzDpKNzhKDfhW6Y9PaPYxgm69fUzjp/Ipvm2xeOEc2meXfTaup8BYsus8GazKfbBzh6uveqfS8q9PL7n7iNktDcOonXBpmvKSmB1rpSHrxpmst6JByntqyA9S1VPHnwHCG/hz2vdDA0HqOjb4RkOsvRC300V5dQUeTn7TevJhxL8n+/uwchBGuaKigMeNi0vIag10kmYykCUkIw6CaT0TENk9Vrqjl5opeRkRjNzWXY7dOvxNBQlJUrqwgG3UxEUxiGSW/vGKqqMDoSY/XqanTdIKebFBZ6UYQgm9XxeOZqwUIIqsqDbF5de9VVTVf/NAf2Dcumn68Qgpta6rmppX56FKXk3jXL88/i3rXLkVListt4/7a1Cz4jm6YyEomTTOdIZ60Irubq66caFcKK+3949zF6hyM4bBofuWczQd/1hxmGh6Lsf+wo2x/YiMvjIJfVSUykCIR8OD0ODN1YlEdmaHSCRDKLy2kjmcpimJKyYj+j43GCfjeP7znN2hVV1FVeO2HdlQg5PQTtLmp9BThUjVUF5Ugk5y8O8cL+drweyxQ1EUuzbpWVmKQIwbtaVzEYj+F3OAn9IjonQyU+nvvpcQ6/1M6JQ52UVRXOe1w2oxMo9LB8dTVurxOP10HzqipSiQxFpX4cLptlVpjnOQQKPRw/eInymhDNqyrztunFHlpBkRen284LT55ifGSCltVVqJpCd8cw+/ec4+jLHWzdtZxsRqe7Y4iJSJJL5wepby5Dzxl0dQwTGY/T1T5EdX0xmk3lciLGd9qPY8i5H5RULsdDjW0UhXwMDk9QVOTF43IQjiaxaSqBgBuvx0EymSWWSDMeTuCdISge6zrH3xzdgwQGkzH+7w33XfV7IqXkiT2nrxq6piqCX37HFppql85eeCVM07IXZ3LGrBqc3itItTa1VvPX/7mb8WiSu7e24nU7UBWFFfVlvHOHFWbmsNuIJdO4nFZUyZw+yfx/GBmewG7XiIQTJCZpNcsrgvh8LpY1l7GsuYzxsTiOGZEQq9fUcOH8II2ilOhEkmxGp7a0iNhEipqaECMjsTyd67JlpSAETufCkRSKEGxd38DTL59bNARxeCyGlEvlWpek9X6c2nQYac6MMJ7aT5F7ByqWMJjvefk9TvpHomRyOuX6q4+3zuoGbqed1Y0V2DSFZCb7qgS3YZj4i3xcvjiEzaHR3zGIoij4C72s2bmCRCy9YEFpgM7eMc5fGiLodxEq8HKxZ4SH7lpH9+UwLY1l2DWV4sKl1XhcCpJGjo7oKOuKKvHYLEFdXuLnAw9txjNp65+IpYnGrOxoU0q+c/oEPruD4UScdyxfQYVvac/hTSO465pKuf+9WwiPx/nAp3bhL/Dg8Tm57YF1+WMMw+TMsR5qm0pJJjJ885+f5VN/dB+/9MmdnH6lm2QiQ0lZEI/fyS33Wum/lXVFeAMuTvQPsnxtNaYpae8bpnFFOacGhqzKG5PVaNweBzvuXk1xWYBQqR8jZ1BQ5OP9n9rF8cOdpAIq77x7I7mszq/83l1ExxLsuKeN5aurSSWyDA9EufW+tYwPxyivKiSdzpJJ57jh9pUM9oUpry5Es6msKizl72+6n/F0inAmycXoGD/tPpdP4wZouYIsp6qygLYr+LHnQyybyc9lKxxpnoKGV2AskuTg8e6rtr1iWTl33Lxi3tA+uHr0hc/tQFUVvvXUEZKZLOYkZ4oVuTH72EQ6i5SSoqAH3bDMHltW1LDvdBeP77dSsNc3VxHyT/MtW6F6EpCoiqAo6OHh3cdYVlXMznWNVNdYxFqFodkvq28yyqihYVrjFEJQWOhly9YGQFBcPL3sLysL5v+uq196EQ4hBE21RTjsGunMwh/JiXga0zRRlKsX1cgaoyRy3SBUoulXcKglZM0wujlBRh8knmvHa2/BrdXOeT5NlUU0VIQwzVfP3w5WhNH65ipSmRyvXOi7hqSeGdeeUVPS43dRVleMy+vkwtFLeAMeiqtDpCcjngZ7F6/OEyrw0FBTRCarMxFLURjwEJlIMRGz/oUKPFweirCs7tUVNZhCzjTomBhlZUEZigBVKBRcUTZO05RJlkvrzdQUhc0VlRwZvEzPRBSv3Y5/CY7KN43gVlSF1rU1c7YvXz3Nd2AakpGBKE0rKjANE+ekWcPptrPhBouxT0rJixe76ctGCXdJoqk0Hoedrsth+iJRtqyqZsCnozk0fA4HsUlCKIC4nuOUESUUyeLUbBQHPRwYvMxwPEFxhZtU2MaeS11UFwQ4p0dZ3VrG2aER1NExlpcWc+Ptc6tpVFTPZeErcnm4r2466+3k2ABP9rZjzCihdL0a7e3Vy3hhoJNkLssvL99w1YQkKSUnz/dflelPUQTvuHMt7hka5bFxi4Z1IpemwRei2rO4g8th0/j99+2iezBMcdDDPdtWYNNUNi2vQZ9BDGaakueOdrBzXSMlBT5eae8jlkzz4E1t/MH7d9E1GEZVFOrLCy3a3Ls2IYSlQb5n1yCCJEJ4+dTbb+DS5TG8rqVR5M6HeOqnuB03oKrzrwCvFQV+q4r9YoI7nc6xFJ4lKSU2NUjODJMzoji1SpK5bnyOVqRWQSJ3Ca9tGbHMOdxa7ZzzxSTHz2vlQ5STK5yWmmJ87qWPuWZTUVQFQ7cyYuPRlGXu8TppXF0LAsrqihGKyM9nPWfQcbpv0XabaotprLE+rBKZfxPeefc6FEVh89q615SYyq6odERH+YfTL2JTVN5Vvxp7RiWVztHdN0YkmpzkKymmtNhKeCtyu9nX30vOMOiOWvbxVSW/QIJ7KbDZVT78G7dx8dwAdrvGL//mHbi9c+2JOcPAY7fTH5mg1O9lPJnCoWkUuN1MpDMIMRkyZZr59OkpzovKoJ/xRJKJdAbdNIgkU7htNlK6TsjjwpSSk5eHcGoWSYxdVVle+tqXPrte1PsL+NqudyGR2JXFyZamcOh491WdUxUlAbasrZvVniIEHRMjZE2DYufVl5xCCPweJysbStEUq7pKThoUTC6np56DRDI4HqOyOIDDppFM5yjwWYUrfG4nbQ2zY93rygrI6d1kciepKOpHkiOVPQRM0Fxdgl1rJJ76GQiBy74ew5zAMAax25oxjHEc9uX5tqTUSWZexjAjeBw3k9P7SMjnUBQ/HucOcnov6dwpHLaVSJlGU4vJ5jpR1UJsaiWKsnhMtNNhw+W0Awt/KC32xqUJlLQ+BEgUoWFXC1GEDbtSgMTAoZYQzZwi4GhDmpLu9gFqW8pftT13IWRyOofP9VJTWkB73wiNlUVUL4Ed0Bdw4XTZyU1+zNpP9bLz/kn7/BRr4Qwecikl3RcGuXj28qLtWuGb+V/57YoCyWQWu13N1xaNRpJILP/H9cKt2fnt1TtQhcCQEpuikNNMXE4b5zoGWLuqmrHxRH6lqSoK9za1XKXV+XFVwS2EqAb+AygDTOCLUsq/F0IUAv8F1AFdwLullOHJc/4I+BhgAL8hpXzyuno3ty+ESvyESha3A7WUFpEzTDRFYTyZpDLgJ63rpLI5Qh43XoedcDJNRreiT0wpUYXA73RQHyqY5AQxGU0kWFddMWk/hXAyjV1VqQj6uTg6RpnPO4cw6ucNIQT2q1TymYl0RufMpJd7MWxeU4vfO1sTKHX6iOcyVHsKKHcv/kxiuSQ5qaMJlbSRxau5iOlJLsUvs9xfSyyXwKHaKXEUoAjBJ962jSPne+kfjXLH5hZW1C1WL9Igmvwv/O53kEi/BBjEkj8m4PkAmloKCFS1gGT6BRQcZPVusnoHLmMIRfHhYFpwG2aUROo5fO77EcKOlBlUtZhM7ixqrphE6in87ncxkfw+mlqBEA4yuTPY1GoCnndfdRxVVUHTrqLiXiGzpZSMDUYpLPEzNhTFF3Sj5wwURUHKIvThLaTcdkJlAVQzRHw8ia4b6DmDksodSAmXzvTz4y89x0O/divBIh/BolfrXJ4Lh01DN0y+88wrpNJZNrXOXUHPh0DIS2llAbGIRWS27+nTPPChGympLJjTRyklyXiGb//zM6TimTltSSkZGoqSyeioqkIymaWsLMDly2GqqgoZGIgQCnkZHIhSV1/E0NAEhm7la/T1jrNufd113380m+LQSC93VLVwdLiHYpeXel8hdqmyc1sLiVSGgM+Vp0l4NViKxq0DvyOlPCqE8AFHhBBPA78MPCul/GshxB8Cfwj8gRBiBfBeYCVQATwjhGiWUi45pme+xIYpUqFELktCt6I43JoNt2ZHneGYEkJQNSMmurogkD/fkJJ4LsOyshBCQK03iFuz58912+2zKE8bigpn9aeucLo/BdWVpPQcdjQGkzE8mh2Pzf6G17e72lLvai+nxRkysegxiiLYvKZuzva+ZIQLE8M4VY0a77SZJJXOcvrSIK31ZfnEjhPRDgDqPRWEszG6k4Mk9BSGNDkz0YlTcVDiDOb7XBTwcOfm5XOuOT8kSBObVp03aahKAQ7bMkAjmXmenN4LqAjhwDCHcdhaSGWPEvR8YFZLqhLE73k38dRTCOFACBcOWwuGGcY044CKTasCNDS1jGT6BWxaLaacQIil2XSvdY5IKfnxl/fwwMd28O9/8gNufWgz48NR6ldU0n68l2Q8TW/7ILe/Zwul1SH+/ve+w6otjYwORFixsYHtd63m2MsX6O8c4eS+DprX1hIsenWhevNBVQTv2LGaaCKN12VfUtkysCK5tt66go7TlultsHecL3z2+/zKH91HTWOJpW1PcnZ3tw/x3X95lv27z6BqyrwRY4ODUVKpHPF4moDfxbmzl8npBoODUQIBFzU1IdLpHKlUjqHBKJ1dI9xyS+usyLbrgSEl/YkoKT3HQHICv33aGrD75XMMj8bIZHXuvmUlleWTsdyTq8xrxVVHVko5AAxM/h0TQpwFKoEHgJ2Th30D2AP8weT270opM0CnEKID2AzsW0qHpJQ82XuBZ3s7qPMX8qsrtyCE4Mz4EN88f5RDw32EM5ZX1m930OgP8f6WdeyqbJx3AKSUpPQcT/d18Ejnadojo8RyGRQEhU4364oreU/TatYVVcyboiul5Lvtxzk60s/qonLe37yO4VScb54/yu6+iwwn45hICh0utpTV8KGWDbQEi94wLXz/UC8/unRq7oQTcGtVE3fVLL4U6x+KkrpKKrbX7aBxnkiStoIKip1eylyzte2h8Rif/afH+Kc/eCfNtZbjp8pl1RHMmToZI0vWyGFXNEqchdiEhkO1kdTnalBLg4bTvoZI/D8QqAhsaGoZU8tjRQQwzJMI4UBRgmhqGU77enRjCE2rmNWSaU6QyuxHCAVFeNDUYkBDVYJoajEO23LC8a9i1+pw2leRyZ3BaW8jq3fBUuNBl4IZj1MIQVF5gEun+ygo9tN3cYhYNMXWO1cTDPnoPj9AKpHmwrEeSqtCeHxO3vsbdzLYM8YP/u1Zbn/PFm66dy3d5we475dvmlXx5rWGTVMpClxbCr0QgjvetZnnf3qc3ovDABzb284fvO9fqW0uo7DYZ4XFXg7T3zVKOpnF5bbzrk/ewvf+/bnpcNxJ+HwujEmmRt0wWbe+lsv9Yerqi+nuGmV8PEE2qzM2FkPXDWpqQkQiSZLJDMar4GcvdLgodnn5lzMvU+byUe8LYZgm2axOLJHhgTvX0NkzyngkSWV5gVXgubcbl2ajwuejzOtbcknBa7JxCyHqgHXAAaB0UqgjpRwQQky5ZiuB/TNO65vcdmVbnwA+AVBTM3tJdWpskO9dPMmyQBEfblnP7v6L/OmhZxhLz+aEDmdSdMci7KxsmLe/UkouJ2P8yYGneK7/Uj78borHejyToiM6xhPd5/jEyi18cuUWHOrcIdk/1MMjnWc4HxlhbVEFf7D3cc6Eh/NtmVISzqS4ODHOM70d/OXWO7mtamnVa14tOifG+V7HiXktoqUu36KCW0pJ32AY4yqesOKQl8LAXNufQ9Wo9c512kmmHFXTqPVMmzpq3KV0JQfImToNngoU8eoEiRACr+tuLMucABQCnvcBVhaj074Wp71tcp/AYWsBFBy25VyZg6YoQYLeD03+UrFpNYCC23ETAHatGWsRas2TQt+nAIHTbvGwX/kBnSooPJU2n51MDrv6Tc2+v+qmMg7tPs3y9XV0nOrFNCTJWJpv/u1Pue/DN1FcUUA8OqnQFHpRNRXNpiJnPts3b4EYisoC/I+/fCef/6OH6e8cBSARS3PmSNecY31BNx/53bu54c42nv7BYVI90xQNQggaGy1R1Nc7TkVlEEVRqJ4MEigttVbg5eXBOe3OjCq6Pghuq1jGrRXLiOUy2BWVgeEoz7xwlngiw389cphMTuf+21fn++rQNA4P9DN8McHH126k0v8ahwMKIbzAD4DflFJOLCKU5tsxZ8pIKb8IfBFg48aN806pSDbFc/0X+V8Hn0JKuLumhTVF5fjsDsbTKU6ODXAhMsqWspp5hWQkk+J3X36MfYM9aELhpvI67qhpptLjJ2MYHB8d4MedpxlMxviHEy/jVDV+ZcXmBZeylybG+a2XfkJ/YoIH6ldwc0UDhQ4Xo+kET3Sf54XLnQyn4vzx/iepujXA8oLrj3deKnZU1vNPNz9IJJMinElxanyIn/WcX/L5A8OLm0nAckxO2eWklEwk0riddmKJNJdHJ/C5HVSWBOcUaDal5PJIlLFogoDXRUVxIE9CVe+psGy30QSDYzGcdo2q0iAO23SyVSKVxaap5HSD3qEIAqgsDeJ1TZu35GSWZP9whHgqQ3GBl5IC36xQOuvY+ab6PNXS5xyrTgpdQU43SKayRGMpIrEUkWiSiXiaeDJDKp0jncmRyepksjrZrE42Z0z+08lN/p3J6fQORq465nOeQX0x7cd7uOv9N3DxdB8Ol4ZpmJimaeUVnB+gaB5hNAWn20EskuDEvg7Ka4vmtR//PCGEYMWGOv7y6x/n8W/v58BzZxkdiJBJ56zoGZtGoNBD25ZG7v/gdppWViGlZNPO5XSc7sfhtOG4gpOlqvq1iQRaKsKZJHuHusgYVljg+5vWU1US4IPv3MrlwQh7D18i4HdRVjJtvu2OWlSvK4tL5i2JuBCWJLiFEDYsof0tKeUPJzcPCSHKJ7XtcmB4cnsfMLNmURWwuPt3AYQzKf700DOUu/381da7aAuV5e3ZU07F8UwqzwcwE6aUfOXsYfYP9qAKwafbtvGrq7biVKerhtxV08x9dcv51T0/oi8R5d9O7WdnZSPNwaI57YEVF90Ti/DnW+7goca2WX25r66Vv3vlBb569hDDqTj/emofn7vx/iUV/H01qPQEqPRM2/T39F/kyZ7zS1auxiLxqx5TWjRdZCGnG3z2nx5jZUM5r5zvI5XJMRFPc8e2Fj7xjhumHS8CfrT7BKcvDYKESDzFvTeu4OPv2I42WYH8J8+f4ts/O5IXzpXFAX73Q7uoKLbu5wvf3oPTbqOzf4yJZJpEMkvQ7+JPPnEXNWWWjTA8keRz/7mHCz3D2G0aqUyOt9+ymvfeuX7Oh2SpmJpbE7E0Xf1jnLs4xIXOYXoujzMaTpBMZsjkDEzTfF1Luc3UzN0BNze9YxPlNSHKllcgJZRUF7LjHZsY6B7lzl/ajlDAX+hhxwMbrPDIAg87HrQyif0FHt77G3fSebaf4GT6/uWBCA2LxKG/0YJdCEFpZSG//Lt3855fu5XwaIx4NIlpWsWAQyV+vH6XFRYoBFLCJ//4bXnqZEWd31T6Rt5LV2wcl2bnhrI6ItkU1d4gUkr2HrnEzVuXcal7hPMXB1ndaiVMeWx2dIdpRYBdw3WWElUigK8AZ6WUn5ux61Hgw8BfT/7/kRnbvy2E+ByWc3IZcPAa+pSHbprkTJO/3nYXq0Ozw5imYlCLXfPb0waTsbwJYV1xJZ9YuQXnJH/AzDZWFpby4eUb+MsjuxnPpHi08wy/s/amBR/01rIaHmxYOcsWJYTApdn4tVVb2d3XQWcszAuXO+mNR6j3v7Ff/WuBlOSzuBZDaEYSgZQQiaV48ZWL/NVn7qOsyM/Rs7382ZeeZP3yaratrgMglzMYGo/xd7/5AAGfi2cOnOcfv/sCd21vpa6ikFMdA3zpR/v4zfft5Ia19STTWT7/rT18/lt7+KvP3IdNU4mnsuw50sH/9yt3snllLeFYkt/7wiM8suckv/7emzGl5Ms/3s9EIs0//N5DBHwuDp/p4a++8jTL60rYuGJpUQ3T9yYZjyQ5cqqHFw91cLp9gLFw4udWl7K7e4wzZ/upqixEUQTLtzaRzupcjiTZcfNyTp3uZyCW5p571uD1OpFScubsZerbqjl+vIdMVidpSC5eHMblspMSguU3NNPZNYpp13jppQt0d42yvLWC06f6KCsLks3ppFNZ1qypwet9dfTH1wshBG6vIx/qm05lOXTwEnXNZXNkgJhHWE9BSsn+ve20ra7B+yqpnJeCAoebXRXLKHX7SBs6Qfv0Ne02lfMXhxgenWBdmzUvFSEocDrpiobRVGXBUoXzYSlH3gB8EDgphDg2ue2zWAL7YSHEx4Ae4F0AUsrTQoiHgTNYxsBPX0tEyZXYWdnAqlDZNX0xpZQcGOphOGVpk3fXtORJX66EEIKtZTU4VI2MoXNgqIfcZHz2fLilshH7AhltIaebmyrq6TwfJppNc3x0YBZXyJsNpmkuqSyZdx7uje1r6qmrsDgeNq+qpbGqiL0nOvOCWxGCt9+ymtJJTpNNK2tQVYWRcJy6ikJ2H2qnpqyAnRubsGkqLoeN99yxnt/9/CP0DkZoqLJskivqy7hhTT2apuJ0+GlrqqB3KIKUkkgsxUvHLvGJd2zH47Kj6wat9WWEgh72nehiQ2v1ksbeNCW9A2F+8uxJntt/geHR2KsmXXotMB5OYJqSwkIPiqowOBDB73fR0FBCU1MJg4NRbHYtz40ihMDjtrN3bzvl5UEi4SRV1YW0dwxRV1vEwGCE4ZEJtm1tQkpJTXUIm13llaNdSAnpdJboRIotmxvn5Vt5NTAMk8OHLnHuzGWCQTd337eWRCLDc8+cJpszuPGmFsrKAzzz1ClMw2Q8nOCWXSsIBN385MdHOHTwEr09Y+zctYLiEj/PPXOawcEoNbUhbtphRR8dOnCR8+cGKCj0cNc9azh2tJv/+vZ+zpzup2V5BTfc1DxnPkSjSY4e6mTnrSvo6hxhInp1RWZhSEbSCV4YvES1N8gdldP+pU1r69g3aSopDlkRPbpp0hEe56NrNrC/v5euaJjlodeoAo6U8iUWdpffusA5fwn85ZJ6cBVsLqlGuQ5v/Ssjl/MVzgudbvri0QWPTRs5bIpCxrA09bSRmyW4p66uCkFTILSgMBBC0BaynHASuBAZveZ+v5EwpSR3FX5oAId9blHaohlauKYqFBd4GRmP5000iiooLZyOFVYVi97WmDQvDIxEKSvy580ZQgiKgx6EgOFwLC+4Swt9s+hBbZpi8Z1gaf4T8TRf+uFevvnYIcAa9/BEkuQSPkhSSiITKb7/xCs88swJwj/ngshXIpfVEUJYURA5nbHROJmMTnGxDyEUAkE3Fy4Mkkhk8tpxRUUBJ0/10dxcZq16BqOsXl3NmdP92DSVxsYSDh/poqmxhJJSP4oQVFUW0Nk5SlGRD4fTht2+tMSta0H7hUH2v9zOu39pK1Ja8+EHDx9g7bo6CkNeHv7OPj7+a7vY8+wZ3v/hGykpDfD4Y8f46Md3sn5DPaMjMe572zrcHisjs6W1nJWrq/jGV15gVVs1Q0NRDh28xDvfswUpJZqmsGJVJeUVQW6/azXFC4Q/ppJZTp/uY+etKxgenmBoMAre66vcM55JcTE2ykP1bbw42Mml2BjLg5bD8/l97RQVerFpCplMDrxOVEXBpWl87+xJkjmdtaXlV7nCNN7UmZOKEJS4vfNOonQ2RyydpdjvITlZGcbtsDMUiRPwOOhPWE43U0o+u+9ni1a4llKS1K0XPWPok5lrc6EKZV57+kyUuLz5SJMpjf/NCim5qmYpYN4wydwV8bM53cCmTdvpBGLBMRcC7DZtTlEBfTKL1TYjS26x56aqCjZN5RMP3cDKhtkJOt6rFF+QUnLqwgBf+Npuzl8ael1t1TDFxWIlbC9Jm5fWx8/rcaBpCm1tdflda9dYS22f18muXStmnWazqdx371oANm2aZhGsmOG4rJ/Hrl1bO79f57VCf984rSsrKSu3qIB13WB0NE7L8nLcHgeGKUkmsgSCbpY1lxEJJ9i/tx1FEXi8Dux2DZ/fNfkhi7Nn91kcDos0TNcN+nrHWLmqirKyad57t9uBzW5VWHItoRiHuRSegYXuLxHl4EgP3bEw+4a6aY+Osrl42lRnGAaj43EcdjU/7wVwW30T58dG8NoduJcY9w5vcsENFs/tfEhmcjx/8iLrGyvJ5AwKfW76RqMc7xrgtrVNJGbUe8uaOpiLaxDq5HXUea439TiFmF+IzYRdVVEQmEjSxtIKxf48cVUuE+YXNO09I5hSogCJVJauy+PcvmXp6burmsp5ZM9JJhJp/JMFZs91Ww7GqtLgktooDnooKfQyODrB3dtb51S3WUhrNE3JCwfb+b9ffpbx69SyNVXB6bDh8zjw+1wEfE68bgcetwOX04bLYcPpsOF02nA6NOtvuw3TNPm7Lz97de1ewMYN9RYBlDIP8+FCp71JzXLVNSEee/QoLcvLMU1JVXUhFRVBThzvoTDkRVUEHo9j0vE4+1zNphKNphi4HKEw5KX9wiC6brBz1wpOn7T4Smpqi3jyiRM0NpVimibVNSEURUFVFXp7x0BY6exXjo/DYSMaTdHXO86pE72UlPivKxrfpdmo8xZS6Z4MN3T7Zyl5LqedbE7HYjOw5qgxyQ5Y6fMxlkpS6fPj1Jam7b/pBfdCoyiBkM9N93CEqqIAsVSa3tEIBR4XIPLOQ7ui8qdbbqd2RmbfYrCpKj77/PY9U1qcw4tBN03kpKi3vclr7ilCLKlKyVRhipmT/viFfr71+GFa68vYc6SdRCrDLZuXLem6Qghu29LCk/vO8flv7eGu7a2EJ5J8/ScHeduOVZQULC2rz+2084F7NvIv33sJwzRprS8lmc5yoXuE+25aSX3lXIIvKSX7XrnE3/z700uuqwmWc6miJEBrUxkrlpVTXxWipMiH3+PEYddQVWWRFcb09mgsteSMQiEE6iLOt8UgpeTwpX4iiRS7VjX+3Os/Ni0rZddtq9i3t53CQi9V1SHe8c7NvPjCOUaGJ3jfh27A6bKxZWsTqqrg8TpYt9FaMRQX+9m6vYm9L11gxy2trGqrZmR4gjOn+7jz3jV4PA5KSv2kUzn2vXyBUJGP6morO/qBt2/k4P6LRBMpbtjWPEecBIJu7rirjQP7OljZVkVB0EP7wNJK981EocONJhS+1XGUlJ5jNB2n0hPAa7NkydvuWJNXgGyaVf+1dyKKIU1aQsU4VBW3bekmmje/4F4AHoedAq+b0qCXWCqDlNBcUcx4PInbYSPktBJGTCmp9xVasd6v8pqmNIlmF3/ZI5lUnp71amaVnzcUReQpJhdDYp6qLvfdvJKRcII9R16iwOfmf338LmrLCiaTYRzcvrUlr0mDRfl56+ZmigssMqpQwM2ff+oevv3EEb74w724HDbeffta7r1pZV7j2rC8CtcVlcJXNJRTURxAYGmhd2xdjs/j5CcvnGLv8U6cDhvL60oIzBMRIaWku3+cz31l95KFdijoYefWZdy6vYXG2iLcLsfkPDLRjWE01Y9uDKMoRQjx5nmdDNPk35/eT89YhI2NVZMKzeuPKWqKqXftzMgILUVFqEKwZm0Nq9da76EEulMT3HH36ll5Ezt3WayZXq+T7ZOMn4oi8g7IKdx7/zoAhhJxUhh4hWDdhjrWbaib1ZeauiJq64r4zsmTTGQyBByzlTIhYOOmBjZumk7iux7BDeCzOfjE8q1I4IXBS8RymclriHwd2imks1nOjo1Q4fVzbmwEVSgEnS6cS4wsefPMtGuE066xtsFKV55p0q8utuImWwtKeKTzDLo0OTU+yJbS6qUy0y8IQ0q6Y2G2l83lNgZrosx0SDa8iUMBwZpQviVED4xPzF3WB7wufuXt29B1E1WZbc8uCnp47x3rKQn58qYLr9vBb71/JzAdWzsVt23oJkKxzFBT42pKyW3bl+N12ElkcyAlmqqwfV09TrtGLJ3B47ATS2fY2lZHa2MZLruGXVUXXEXkdIMv/dfLDI5cPenIZlO56+YVvP+BTVSWBskZF8kZB5ByHVn9MnathmT2ID7XXaRzp3GJtWhqyOLEMUcAG6BjmGFsWg2KeGND61RF4aO3bCKaTON3vTEFeyXw1MUORhIJbqypRTdN/v3wQW5taOTOpmXs6+3l4vgYN9ZYVK3/dvggdzQ2cXtjE3s6OxlKxNlSVcXZkRFGk0k0ReHOpiaODQwykkywvbqGzkiYkUQCCdzf3MK3TpxAFYIHlrdSXzC9qjZMk0fPnyOZy3FbQyORdIofnj2DS9N4oGU5T3S0k8jl2FRRyXgqSW90glWlJa9KuYvnMnzn4itkDJ2knuMjLZsXPDbgdHJvUwsXw+N4bDYOD/Tj+n/Jxn29uKG8DrdmI6nn+GnXOd67bE1+2fJq8PJAN+9uWjNvYk3a0Hl5sBsAp6qxuuj1o9B8LSAEFC6BxnJ4dG6xWbBMLXabSjyZYTyaJJPN0TTJf9zZP0osmSaXM4gnM1SXF9A3GCHgcxJPZCgIuGmutfhLlHnY0nTD5EhXP267jXgmy+rqMk73D7GstIgDl0YYjydZVVVK52iYzfXVvNTexa0rGnE75ndCSSk5dqaPvUcuXfV+XU4bn/rAzdx/a1s+0xPDRGAjmTlMVr+IZMukMBYowg3I/AcpmTmMovgxzTg2rQxxRYamfANyz4UQbG+Zy8E9H6bS8cGyL1/3nJUQTafx2u0EnU58DgctRcXcs6wZVVEocDnxOxy8MjjAO1espLWomLuXNRNJpXihu4s1ZWU819mJfdJsEHA4OTE4xEs93bSVlvJ8VyeaorCzvoHnuzrJGAZtpaWUe33UBYNXdoVIOk2px4vHbkdVFO5qWsbjFy5wYngITVG4rb6Bn3W0Ww7PVIpYNsOOurrru3fAY3Pw3kZrJWBIk6B9nsRA08QwLQe8bprs7++dPNdGVyRMa9HS0u7f3EbY64QQgpZgcZ7D5MTYAP9+6gBpPTcvT8RUptxoKkHWWDw87oXLnRwe7pvTjpSSJ3sucHLMokhdFSpjWeD19dS/Figtujo3wsBwlNxUwVZh0XfOzErM5nT6hyP0DUXy2yKxNGORBPFUBr/XyfnOYVxOGz0DYS70jHD5KlpvLJ1hPJECYX0gMrqBTVVJ5XJ4HHaKfB5CXs8k66OJx2EnmlzY/GGaksd2n5oTyXIlVEXwsXdt54HbV1tRMpNCTFNLkTKNXavBplahKoUYZgzduIxhRtCNoRltFGKYY9i0MmxqDVbisQVpFehZEkwp+dHBU/z06Ln8fDvZM8jnHnuRkz0Dk+1JHn/lHI8ePmNFR2VyfOnZA3zusRf53GMv8o3nj5Cdp9juoYt9fPOFowxH43x59yE+87VH+Y2vPcKXdx9iPJ68LpY8iWR9eQUCeLm3xyqWa5r0TUwQSad5qqMDp2bL+0typknfRBSbqhJyu6n0+bmtoQFNUXBq1urJoVn7qv0BbqlvQJsU6rZJPneHqtE3ESWRmxv+ua2qmtFkkpNDg9bHQNOwqQpem53xVIpL4TBFbg9pXWd5URGd4TAlnusvZTaRTbN3qIsXBzv51zP7GEzFyOkG4Wgy/+9i1whnLljPTlMUPDYbJW4PDQWFeBfwrc2H/2c1bpui8Ntrb+LU2BA98Qj/emo/Z8PDPNTYRlMghFPVyJoG4+kk7dExDgz1cHZ8mK/e+i6qvIF521SEIG3k+J2XH+N/rLmR7WW1eG12EnqW3X0X+fyxl8iZBg5V4+MrNuOch7DKnPxIGNIkZxrkTJNwJp1/UdKGzng6iV1V0RQFTaioQsypqThlSzSliW5KdNMgJ02i2WmGvaSeZTxtLTltitWewnRbQgiqyoIIMZcUaiYGR2NMxNMUFXixayp/8el78bqnJ1nA66K4wIt7BlfEDWvrLWeMtOLAq8sKGBmPs31tA4lUJu+gMw0ToUzzjkz1q9Dj4oH1rWiKkr/XqgI/hmmyrKSInGGgqQp3rmrGpqmEPG7s2sI8x2ORBK+cuXox5A2ranjwjjVznHmK8BOLrqesNIBda7Tu2/0QADZXLRc7h6mpMrDbNdyOLfnzdN1kJBzLxxEbhkHuKsrBFARw7vIIZ/uHuWP1MjRV4Ylj5/n2S69gmCarqsvQDZPvvnycNXWWwVAIy4keSaTYe6Ebr9PBu7a2zRmbUz2DfOW5QzxzsgNTSlZWlzIcjfOlZw9wuneIv3n/3Ut2ok73V5AxdEo8XtpKS1GE4G3LlzOSSFDp93P3smWY0qr6ogrB21qWM5pIUuUP8J5VbXSFw/gdTnbU1VlzX1FwqBpNhSG6IxH8Dgc31dTidzi4saaGgNPJpspKjg0OkMrl8M7g+hBASs/RUhRidWkZZV4fbrudHXV1lHt9ODSNcDrFHU1NDMZjBBxOaoNB/I7rX5XnTIOeeBgJ3FRWz0g6wXhvnH1HLuGb9LlMxFKsarE49xQheHuLFc559QKDs/H/rOAWQtDoD/H5G+/jj/b9jAvRUZ7p62B3/0Vcqg1VUTClScbQyU1GivhtjnmXsVMDGrA7eX/zOr5y9hB/uO8JAnYnHs1OUs8SyaYxpURTFD7WupFdVfPTzD7d285Xzh4imcuRNnKk9ByJXJasab3Mz/Z1cHSkH6dmw6VqODUbJS4Pf73tbgqds80a/9V+nB9eOkVSz5HWc6QMnXhuuubkdy4c4/Hu8zhVDddke/WBQv58y524J8OOqsosYqf0IoWCJ2IpuvvHKSqwYuqnuESmoKpK3kQyhaKCuZpLgd/qf3BGxZun/uN5dr5nO6Zhcu5gB+t2rbLI8LtHKa4qnCNAp347FG3276sImYvdI0QmFs+KU1WFh+5eh9Mxuy1zMpW8p2+c6sr45ArN0s7jiQylJX6L/zmRofvsZUIhL+FwgkDAxcBgFFVV8oLbIqNaephoa2UJz526SDydweN0cKZviE2N1ZztH0Y3TGLpDAORGL9UadVYddltfOLWzUgJv/PNx+gcDi/YdjSZpsDj4s/fewdehx3DlHzhpy/y8P6TdI2Eaam4tspOQsCqktm1UuuCBdQFLdtzW+nsWPv6goK8XbrK76dqAWa8ApeL6kBgMt9imLQxTrm/FNvkHNhePZfaQJJhebEDlxbKX2uqPwDLQtMRR1PbCq6hyvp8KHS6WVFQSo23gKxpEHK4UW2C+prNuCd56WPx9CzHeL6OwDVe600puFWhYFMUVKFcV9bkFIQQrC+u5Bu3vZtvnD/K493nGEzESOrZ/BdOESLP6313bQtFzrncJ1OCMGcaPFi/ghUFJfzTyb10RMfy3OCaUKj1Bfnoik28u2n1gvHnA4kJXhm5zJXr5anQQYlkPJOEGdTU/XZXXrDPRFcszLHRufxdU23p0mQsPbtEViyXnRXSWFLkp7DAw+WhhTNLdcPkyKke1q9cOIVcSsnAxSFO7z1PWUMJzRsaOb33PBNjMZatqydQ7OfI0ydw+12s29VG7/nLdJ7s4dKJbm56xxbOHmjPt3354hDf/LPvs27XKra9bSP+V1mJW0rJxZ7RqyZYlIZ8tLVUzLlH05CMhxMoAqLRFOlsjkgkiaapVFYUMDZmZYxGoklSqSydXSPE4mmGhjWcTg2bNq3FjYzHyS7ykZwJIQTLyopIZnMMRRMUGAZD0Tgfu2UTX959kGgyzfBEnHROp7FsOqPX+r+8aoy+qgge2rIKr8NiW1QV2NhYxbdfPsbIROKaBfcbgViuhwvRh2n0P0Cl58YFjxvPnONyYi9riz79hvVNILArGkdG+hACbiproMA3W9kS4toLacyHN6Xgfl/zWm6vXoYQghpv8FW1JYSgzO3j99ft4JMrNtMZC9OfmCCl53AoKkUuD1XeAKUuLw51bmr3TOimiS5N7q5t4aaKei5ERuiNRzGlpNzjoyVYTIHDtWgb99W1srGk6pruQVWUfHjjTHykdSP3zyg6vBTYVS2vbQN43HaW1RYvKrgB9h3t5AMPTGsOV0LP6jzx1ee466O34Pa5yKVzvPTDg7z79+7HH/Lx1Deex+60ce5gB96Ah72PHubuj+3ixAtnEIqgqrmc5x/ex/rb2iiqKKCosoBNd63F4782LUjmjcizn8FS6GtrqwpnmYCmoKqCtpWVSGm1qmnqjJhcFVOaxOMZ3C47fp8Tl8tOIpHB7baTTGZxT7YppaSje+SaSKvKC3x4HHa6R8NEkk40RWF9vdWXnrEIA+EYfpeDssC1V7RxaBrlBf5ZAt+mqUiunq/wekNKiS5TJHIDCKHg1SpQFQelro2Mpk8gJ7n1pZQk9AFcaghF2EkbY6jCgcTEkFli2V4QAq9WgXiVvO9Xw3gmweGRXnZVLkNg1aAcGIry8qGLTC0coxNpli8ro7x0fnPsUvGmE9xCCEpcXkpc3vzvhXClA2XmsRkjRcZM4dOCMGnXLXC6KXC6WV88p67DkpAXCULgszvYUFLFhmsUwkUuD0Uuy6k2lBlmKD1Eo7cBr+ZFSklXspu4HqfF14JdmT8gP22kaY93ELD5WVm4NCKlKzGeDQOSAlsBG1fX8vzBjkWP7+wd49SFy2xaPX8opKGbIKC4KoSqKaRiaYqqCimttbS2eCRBWX0J629rI1RpVWcPVRTgmSzQ4HDZ87Zum8OGzWHD5XWiqApWxIaJxEBgm6T0NJHoCNTJ7Vr+dyKzH4etCUW4MGUaVRQTS1w9brs05Js3iUYIQUFw8aouXs/scD/PpLCeud00Ja+cWbwy+ZXwuRxUhQJcHBrDoWnUFgWpKPRTUeDnTN8woxMJaouCeJxL53KegiLEdVPfvt5IGaMcHf173JoVZdHkfxt+e928x54a/yqtBR/Ab6vl4sRPCDqasCluRtMnsat+YtkeKjzbqfXe8bpGedkUjaSRYyydwKao6NIkmcqyormcopAlz0avYcW1GN50ghss4fhPB/ZzW0MjK0rmD48xpeT44CAvdHVNhgjV01pcnF+GJI0Yzw5+F5fmpcW3gSr3MhzK4trwGw27Yud45AQu1UmzrxkAh+LgpyNPUOWqwm4PznueIhRSRpqTkVP8Us17ruva7bF2DGmyvWgrG1ZV4/U4iCcWLh2W0w1+8LNjrF1RhX0ee7LDbWfFtmZ+8Pc/pba1ijU7V1BcNR3HvvPd23j5kUPEwwlaNy+jaX09P/3iM7j9LvSszoHHX2G0b5wz+9tZsXUZ9W01PPGV3dz2gZvwFXqIpZ/FppbhtK0CBLo5Siz1HJoawjRjKIoXw4xiU8tRFC9SZsgZ45jkULVi9HkiK66E03Ft5ELXyvU8PBbj5Ln+a7qGpii0VBRzaWgcU0raaspwaCqtVSWc6h0kmcnRWlnyhtc6fT0hpaQv8QJFzhW0BH7p6sdjMGV6lBh5T7vfXsuqgo8Qy/VxfOxfqPHeingdRZ5DVdlQVEVSz6EIHcM0qa8pshzGpiST1XE5bFcvFr0EvDkFt5Ts7elFIDhy+TJjqSSrS8u4oaYmz9r3ysAAn3z0Eas0FPCNY6/whbvvYVu1pYEGbcU8WPWrDGf6OD9xlIPjT7G96F7qPCsWv/hrAEManI6eoSvZTaG9kG2hLYxmRnklctyq2lG4gSJHEYX2AkL20AwuFEGZsxSv5mWxmDG7YqfaVcXFuBWTLKVkKD3EscgJJJLNhRvx2XwcHj+KLnMkjRTbQlvwa37OTJylO9lDykhR7rScRRWlQdqaK9j3Suei93XweBf7X+nkpk3TZdmmQimRsHLnSpZtbyHod9HZP8aG+zeSzRnYbSrFdSW887fus6JGFMH/z95bh9mV3Gf+nzp4mZoZpBYzDUgzo2HwgGk8jiGGxCF7k2ziZMOw2djeeJNsNg7HjilmHLKHGTTSjJixQc1wGQ7V74/bqO6WumdkR7u/vM+jR9K959SpOrfO91R94X2vvmcLUkoOn+7HDPu5+QPXccN7t6NrZRKeHe/YRqnkYJoaIPFkAUVMuQNK9ik8mcJxJYoSxPXKrh5P5pBuOTVMwcBxB0HzZlWuzQXLnloJSSlJWkVUUc6U8GsGJdfGr+kUHQdNUTiTHmVptGJB9QFSSp5+5fibYiBc1VDN7lPdOJ7Hu65aA8Daplr+9enX8KTk3i2Lc5f93wDLTRPWyzvjS78YJ2oxwZNTaYG6EgQEuhLAlRaSn6z7x3JdjiUHKbo2GbvEylgNMdNfTtl8+hDHT/WjKIK337GB9pa3Fj+4Ig13GZK/e23XeCqbQsl1+dCGDfzm9h2oQvDw8WOEDIN/ue/tqELwB089yT/u3s2WhoZJ4+5Im5yTJu9mMBQTQ/npVK+dyJzkQOoQt9TcVE5xQ2AoJmuja+gr9PHs0PO8q+Edl3X1b6oma2NrOF84z/NDL3J77a08N/Q872l6NwPFAV4afoVtiS28NPIK99bfzZMDT02eq6kKd+1czWv7z11Uf9KyXf7p6y+xrK2GmsoyZatluxw+1YfnSeLRAJ7n0dU/RlffGNWJEIOjZf7tsUye+qooy9tqsB2Xp149TihgMpLMMZbOEw35yeZLGLpKJl+ipiJMZ+8orfUVrFteT9h3M2X96XHmN2MjPn0ZltOFED4UJYDt9BEw1uPK7HjRi4KqJsYrRC/92w+P5cp+bFE2A68NdlHlC9GdTaIqAlPVUIVCwbG5traVzswYS6Oz+VAuhJSS/uE03398/6JLb4QQLKmtoC+ZwadrtFeXg5AddZUMpcuB5/bqBBNKTJ3DSbqHk2SLFr1jaTKFIo/tP0k85Kci5Gd1Y+1FGRevBAghqPKt5WT6+5PuEb9WhalEsb0sjlfA8rJYbhZdCWKqUYaLZcHs4eIhKszVAIyWjpO0TjFSPELEaEH5CZu7sG7y0eXbkEie7ztDblz8WkoolWyu2dyOlJDOLJwjZz5ckYbbkxLHk1zX0sKvX3MtYdPg+XOd/N2uXbx79Rra43F6MxmWJBK0x+MoQvDhjZv4o6efYiSfpy4cZtQa4KmBr1NlNrIpvpMqsxH1TXJJLHaany/0siqyklpfOTVKSkl3vpszubNYnkXRfes/3IXozHdxLtdJyS3hyPLKscJI0BpsQVd0+kb7GbOSVBmV1JjVtAenKD+FEGzb0EpHazXHzgzMdwkAzvWM8L//7Wl+71fuIBw0MXSVsXSe2soIUkrG0mWpKb+pY9nuZDm8qiiUxle0UpZfAq7nUZUIEfCbZHJFFEXguB5tDRUMjmSwbZfAuL6kKkLAVHaJovgR0sBnRCb924baiBAaCjMDuVJKaubhY55xD8+PUihak/7p2kAY2/PIOxarEjXkbIu4Wc5Bjhk+WsNxCo5zyRW3bbt84VuvLChAOhfq4xFWN9YQC/qoGE+lrIuFWddSW5YwmxaYfGL/CZ44eApJWaQ4HgrwtZf2IhA0VkT59PvuwFQ0EiE/HXWVs/K7g6bB8roqQm/CZ345UeXfiCNLHBl7mIDmZ1n07TjC5FT6IVJ2Go+TeNKiPXI3y6MPcDr9EAVnhPbw3QT0WjRh0BF9J735l0FKVsY+yOKf5MUh51j84NwhLM9hsJBldby8oxUCtm1sQ9dUnnzhKLfvfOu7/ivScOdtm9FCnk9u386qqrLg7v2rQ3zvyGHOjo3RHo/jSomhqpMFG+2JOLbnkSwWqQuHieoJ7m34xUm/tiddXOmgCX1B1J9vBXW+Wg6kDtLob8DDo8JI8MrIq9xacwvDpWGOZ04A5SDjhCG3PBtdaBTcIrZXdm+EvTCaMvsncjyHglvA9iyKXhFd6Lwysos7a2+jvzgw6UK5MIoeM2IMW8MMlYbozHfREpjKfw36Dd57z2b+x+d+fMmshxf3nOYv/vkJfuPnbiYe8bN9YzuaqlAoOVTGQvh9OsWSTcBvkCtYhPwGuaJFyD+h1ALVFWHWr2hAnUZZWiw5+MxyZk9rQ4JiyZkkwZJSYnsZQKIKH47MU3QG0ZQQPrWCrN1F2GjHcpOowkQVvhnZEq2NFZM86fOhbzDF8bODbFzViCIE6xJ1ONKj1h+mITgzx1gIwbqKSxPfO67HNx99g8dfOHrJYy+ElJJDA4NUh4L87UfvYyCbRVfLFYMBU+evP3QPwAwZvY/cuJUP7dwyZ3sThT9SSu7evJI7N67g6NAgek6lKlgOvq5rqePLn3hgRpv/EVCESpXvKvLuMlqCcfKuBa5KS+jdhPQctf4IGbtEyZUEtBrWJn5+Vhthfark/6cR2/KrOrc3Lkci8ak64Wkv9GLJpqE2xu07p0jU3gquSMM9genFMBO80DnLKiu3uG45X3l8bzshtul4ZTJ+TTHQmFo1DJV6GLUGWRHewpHhIXrSKdZW11IfvvRKrCUcZ0NlPYai4lcvHcBaEVlOySvx8sgrRJUY19Vs58bqGziSPgqjBmsa1iCR7EsewMWjM9eJjo533kd/4ixRPcK+5H42xjZQ759tHPqKfRxJH8Wv+nltZDfXVF7NjVU3cCh1hFpfDWuiq9EUjVWRFQgEIS1Ee7CNCqOCqxJXsXv0ddqDbVSbU342IQTXbV3K1nUtl/R1SwnPvHKC4dEs/+VDO1m5pLz9Dk9bvU0Y3Ilim2hoKq1PUxW2rm2ZldFwYdBzQkT4dNcwI2Npqtv2oSl+QEEVOp50KLkjNIbuJGOfIag3kSwdwvYyNATvmNFWe3MloaB5UVZAy3b57o/2sqajDmNc9UcX6ryVtJfKeLJsl28+/Dpf/O6rb1q3UlcVRgsFDFXl1OgIfkPn1a4uakNhdFVhIJvjutYW/LqOBF7q6iRsmkR9Pk4MD9MWj3NqZITacJhksUjRdtjR0sLu8z0sq6xEV1XGCoVJw60IcdEK1J8mXCk5OHYeBTic7CvHbypbOTB2nqRV4Fx2BE0o3Nm4+opIOlAVhSr/7JoDKeGNg120NVcymsyRyRYn5cveLK5Iwx3Qddricf5lz+uEDJOIafLcubOcGRvj82+8Tk86zeHBQdrjibI+pBAM5fPldD1R4Gj6NRr8S9ibfHZydZ2yh2kJloM4fk3DVFWyVgm49A381XXb+cS6a8tqMPPkgkopOXNigIHeMdo6alD7Y1zb2M6el08yck0GxoKsNjZz/FQPSjGC3AhXV2yjr2eUnnMjNK5u4NhYD9vbdnDqeB/hsA+3pHI000044iebKbJsVT2KotAUaKIp0DTj+isiy1kRmSlksL3yWgASRpyEUa4OWxdbw7rYmjnHYBoav/i+HZw4N8jIWG7OY6bj4PFePvmp73HXjau575Z1NNTEFkT6L4RAm0eRWwL5gkVP3xivH+rmlb1nOXFmgG0bmvnEL9aiK35sL4UrLXQlhEBgeSlcL0/eOU/BGUBXZj881YkwHW3VvH6w66J9e/H10/zgif28646NixIwuHAcfUNp/vWbL/HUS8ffstiwlJKQUWZJzJUsqoJB+rIZetMZakMhCo6Df5zLeaxQZEdLC98/cgRFCPoyGapDIQayWZqiUfyaxmghT6pY5MzoKDWhEFJO0Q1cSfBrOhVmEA+JIkSZ2kF6JK0CtX6XVbE6TqQH/6O7eUlM7DC/+cPdFIo299y67i23eUUabk1R+MUtW/n1Hz3KA9/6JrqiYHseD6xZS20oxFf372dlVRV52+Yfdr/Ghto6vrRvL/XhME2RBCUJaWcUyyvRFiwHKsySf7KSLG/b5B2HhP/SzHgTavILWYOMjWbJZop0nR0mOZpF1RRiiRDhiJ8Th8+z5dqlnO8cwbFdSkWbQNBkZCiDqin0nR8rl/TmShw/2ENDSwW6oXG+a4QNW9sY6EuydEUdP8kdrBCCpS1V/PL7ruOz//IkpQXkm6azRb7x0Ov86NkjbF7TxI6tS1m5pIbKeAjT0CYDYdOzUMp/l5nSSpZDOlukfzjNqXNDHDnVz/EzA/QPpWdeXypU+bdNpr15uJPBJomHP1SDECpBvRkxB3eapincun0Few91X9Rd4jge//S1F0lni9x/1yYiId+CDFqZYQ+Gx7I8+eIxvvvYvnnpYw1dvSTZVXlcZbY9T0qG8/my3xpJYzRKxOejo6KCZKE4ydEhgOWVZZfQNc3NnBkdpSUWQ1UUwqZJTSjEsaEhmqJRKgIBGqNRRnL5yWDslWW2IWnlSVkFNiYaqTCD6Eq58Gln7TKihg9T0aj1X5okbTFYMLnWIqLMQgh2XruMsVQB09AILkBG7ZJtvhkWsMuNLVu2yD179sz4TErJmbExXuzsJF0qsaamhmubmtBVlVSxiF/TeKWnm9994gnGCgVifj9/dtPN3LqkzBGSc9LYXomYUXYHjFoDZOxRmgMr+NHpkzSEw7TFEjNIZaSUOI6HpilzPqyeJ0lnCsSisw2+lJLhwTSloo0QguRojrrGOIW8hc9vkMuWg2+arqKqKrFEEF1XObyvi2y2yIo1jZw7NUBDcwXpVJ5A0CSXKSJlWaUjOZZjyfJalAVa7sGRDIauYdsOuq5SKNqc7RlhSXMlFbEgh0/2oWkqK5bUzOIDcVyPL3/vVb703V2LXi0KAQG/QVUiRE1lhMp4iHDQxDQ0pCzng+eLFplsmT1wLJUnmS6QL1gXvdaNVy/jv//XuydfBFJK0vkSjuti6hqZfIlEJEC2UEJTVSIBc9ZvmEwX+MSffJOz3ZcmyhdC0N5UwW3XrWTruhbqqiL4TB1VVSaJnFy3/OIZGctxqnOQXfvOsedgF8Nj2XlJu9Ysr+euG1bzl59/Cnee8S5tqeJfPv2+OfPlF4sLX5SuJ3E9D9fxKNkOluVSsh2KJZt8wSr/Kdrk8iVy+RLZvMWJswO8tr/zotdpa6rg6g1tBANGWcLNbxDwG/h9E3/rmIaGoauYhoauqZOqQaqilCXLxtua+N2SpTx516bOH1nwbmDO8boejuth2Q6W5VCy3KnxFstjzhUssuNjPnKynzcOX5yQrKO1iq3rWgiOjzUYMAn4DQI+nYCvPF7D0DB1DcNQFzTeC7Flyxb27Nkz55dXrOGG8ZfaRQKJnpT0pFL0pNNlkppodEYhgitdCk4Gv1beUk+sxA4NDXA2OcaOplYS04hlpJR8/TuvceN1y6mtic64nm27PPzj/QwMpfmlj+68PAMHspkChln+kT1Zlj1TxVv3Mb70+mnikQCpTIFIyMfB473UVUdZ3VHHuZ4Rzp0fIZMrcf22pSxpnp1TalkOX/j2K3z9oT1veat/OXCh4XZcl8dfP4GmKli2S2g8++Rc/yg+Q+fd162bJaggpeSJF4/xqb9/bIqmdgEwDY14NEA8GiDoN1AUBcd1yectkpkCqUyBYsm+pOBwa2MFf/6b9xAMGHz0v32V0eTcOd2X03APDKf552+8RCpToFC0J/+ULBvbdrGd8h/X9fBkeXFyuWyCGOf2VkQ5s0jTygZM11QMQ8VnlrU5/b7yn2Vt1XzonVcvSE5vPvT0j/H5b71CJlssj7VkjY/XwbJdHMfFtt1yLMwrk4X9pMY7MVZNVzF0taxDOq5HGvAZrOqo5f33bZs3PfNihvuKdJVMQMBFVWsUIWiOxWi+gEQdwPEsnh74FmdzR7i34WOMlPqoMOup9bUwmMtR4Q8ylM/NMNwAo2M5PvWXj/L7v/U2aqrK27B83uLfvvYSTz93jF/9xZsu4wghFJ66/ulsF0fSJ7mv4da33K4iBJbtkM2VCId83LpjBa8d6GT/0R6EEBSLNk118RlBw+kwDI2PvucawiGTL37nVfLF2XzH/5EQlFcunicJmAZL6io52z9K0GeytKFi3tL1nVcvY9+RHh586sCCld1LlkP/UHpByjnzoaUhwR//6p20NCRwXI/Wxop5DfflRDpb5OmXT8woLvppYcJ37iHB5ZLuodFUng++46oFuSXnw1iqwNMvv/W4wpvBYsebL1q8796tvBkn1RVruKWUWK7LcD5P0Zl70pmqSkNk7m3UcKkPTdFZHb0aT7ooQmWw2E2tr4XGSJRToyOsnaOc/md/5hr+6nNP8Om/fJTf/+TbAPibf3yKc13D/N5v3sWm9XOripQJ/V0UoeBIF1WU1d4nCiMc6ZSNjVDn3RoVvRKjVgrHc5CANn7shFiAh4cuytkOrnSRcipY6kgHTUyRZC1trebpl4+jqQrLl9Rw/OwguXyJlqV1NNbFefbVEoWihSoEnueVi2lKDoY51Yaha7z37i001yf4u688R3df8qK/2U8TiiK4YV07rleWNFMVhZp4CPsSD6yuKfzS+3Ywmsrx4u7TPwUtGljRXsPvffx22psqxwOzCmuX1fPGoUvzg/8n/hNz4Yo03FJKutMp/uTpZ9jX30dpDsMthGBjXR1fePs70NXZ72hN0XE8B024WF6JgWIXzcHlSKA3k8bUNIbyeSKmb0ab4ZCP3/jErfz13z3Bn37mIUqWjWnq/I8/eDutzZUX9bU91Ps0lmcxYiUxFJ0Hmu4mqPl5dmgXR1Jl2tIbqq5idaRj3nZ6Cv188dx3yTp57qy9gWXhNs7le/hx3/O40qU12MjttdfTne/lxeE9vLf5Hg4mj9FV6ON642qskkO8IoSdLXHnjpXYtoNh6DRtjCOlZLA3hV2wuWHzEkolh/Nnh7Fqo0RiAXrODdO+vJbB3jFiFUH8ARNVVdixZQlLW6r4yvdf44mXjpEvzBYP/onjgtslhMBnXJCaqSpc+NGsZsZ/49/95dv52+BzPP7C0Xl9zW8Vqqpww7alfOJnd1JdEZqRV752RQOKIi5JNfuf+E/MhSuSGkwCf7drF7t6urmxrZ3/cvXVxHx+7l2xko9t2cLq6mrqw2F+a/uOeQsFEkYNMaOKE5m9PDv4HTw8mgMrODM2ykg+T962Mcblj6b/AQiHfPzXj99KXW2U0bE8v/1rd9DaXJYhu5g/bNgaJagF+Lm29xDVw7wy8gbd+T7eGDvMh9vexbsb7+Th3qfIufMT+kspeW/zPdxVt5MHe5+i6Fl8r+cxbq3dwc+1v4e+4iBH0idpCTZgKgYP9z7Ns0O7uL5yK4f3dnHu1CC7nj/Oay+eZM9LJ3nq4f0cO1he2TmOx+F9nRzZ38WBPec4fayPkaE0e3edRlUFA71jWCWHU8f7OHZwisVOCEFddZTf+Pmb+avffxe3XLt8TvrTnwT8ps6mNU3cecPlKVyA8ngiIR+/9bFb+PWP3Ej1W8ypnQv11VF+4+du4vc/fscMoz2BJU2V87qp/hP/iUvhilxx522L13t7eWDNWn7vhhvwpOTJ06e5pb2dm9rb+eimzfzWYz/m8VOnWFNTM6eHSKCwJXEz62PXIfHQFRMFlSXxBEviU6x1JcvhK98oBzOmQ1UUImEfQsA/fP5ZqqvKD/faVY3ceuPcJasCQWuwEVM1aA81czB1nKgept5XRVANEFD9qEIjZacJaXOnItb7qwmqfur9NRS9Iik7Q8Et0uCvxacYtAebOZc7z7roCm6t3cEfHvpr3tlwOwkjRnVdDp9fp1S00XWNuqY4voBBbFyIQBGCxtZKPE9iWw7Fgk1tQ5xiwSY1lqdYsBkdSmNbDoHQbG4PTVVYs6yOlUvvovP8KM++eoKXXz9DZ+8ohcvkA1cUQShg0tZUwdZ1LVy7qZ22xgp0fX4X05uBEALT0HjHbeu5ekMrP3zyIE+9fJyB4fSbXgXrmkpTXZxbd6zgjhtWUZWYbbAnEI8FaGlIMJb+yfu5/xP/7+GKNNy265GzLNbV1qIpCo7nYWoa6VKZtCVsGLxz1So+++KLfGzLFqK+2UYmZQ9zIrOXrYlyoO9YejdhPU6Df+mM4zxP0tuXJDkPa1tzY4JSyaa7ZxSAxvr4vP2WSEatJFJKRktJolqImBFhzE7j4VFyLWxpE1DnX2kl7TSu9Mg5eRShElT9qEIl5+QwDYPh0ij1/hokkpeH32BTfDUHUsfYlljP8jVTPONyVTmu27q0zJcihEDVFDpW15f/P8fr7qa3rSuvrpsqLrq6VZVyqlx70zW8776tnO9PcvRUP0dO9XG2Z4Th0SzZXGmSj2QiU2GCnkBRRJk8TFcJBUyiET8NNVFaGytY3lZDa1MFlfEQ+jxpmZcTE7uJX3rfDh64exMHj/Wy+0Anx88MMDCSIV+wsB13fAzle6oqAlUtp7XFIn4aamOsXFLLxtWNdLRWExzPcLkYNFXhY+/dTnffbGmxSMj3ljIrpqO6IswnP3bz/xUumXg0gPoWCbAaaqP81i/cetkyRabD8kqczh6hI7QGbR6u/MWgMh6c5KBfLK5Iw60pCn5dZ7RQQMpy1VRFIMCJkan824hpki6VKDgOcxUkZ50UBTc7+X+JZLjUR7GYYG9/H6amsaGmlqZIlD/4rbsXHKS62H0WCHaPHqAz30tvYYCfbXkHFWacqB7mi2e/S8mz2BhbRVSfe2uuoFBwS3y960GGrFG2V2wmpAW4qfoavtb1EFE9RN4t8rb6mzid7eJsroefb38Pr47s48f9z/GuxjtQJis75+KKluwZe40TmRO8o+FdBLWZ4gBTPtj5x1j0irww9By31NyGIhT8ps7SliqWtlTxtptW8/LgyxwcOcrtiXfjFVUKRYusVeCFwefZWXMjhqqXU6N8+mSer8/U0FR1Mp3qp4GS04tAxdBqJq8ZjwS4fttSrt+2FNt2yeZLpLJFsrkSJcspz0VFYOoaAb9BOGgSDJiThUZz9d1y+sjbRzDUBvz68vFgs02mtIe2Not1K65CeROslQvl24mG/bztxrkrZS83RkoDlLwi9f65A/g/DSSiQe6+6Scz3jFrmGTPU9za/HZC2sILfy72W0130S5m7l+Rhjug6yytqOC18z387IYNqEKwsbaOf9v7Bje2tdEYifDIiRMEdB2fNjWE8k0ogRCEtRgDxS5GrD50xeRM9hBrY9fSHEjQnU6VZcjGsynUecqvp2OhN/WWmu1UmxX4VJ3T2d1knSre23Q3I1YSVagkjOg04zoTS0LN/HrHRyh5FhJJhRErM/cl1rMs3EbRLVFhxtCFTrWvgg+23ItPMbmucguj1pT0WJmN8DjVvmZ86nSXjGBTbAtH00cnGQSllFiehS0tfEp5dW95FooQlDwLv+pDGU/QKrgFil6R3uL5eUSVBVdXXcWpwgmqKgKE9TJjYNbJ0tpxJ7Xj/N+WZ5WzWDwLv6qjjIdail4RkPh+CoIXeesgivBhaFPitnn7CKrw49PbMQyNhKGRuITyzaWhULBPkSq+QHPsj8c/K/NH96X/EV9FB6ZS/ybalSQLTxD134Rg8as/KT1KXhFXOuiKiS6M8VY9BMpkNpMc166cnh1leUUUoWAqvmlVqpL9qVfxKX5qfY3jo1SmnWdjeUUMxYcmpqsYQckrYCi+SSK4hQieSCmxpYXtlVCFhqH4LuinjeWVMBRz2vWmxuNKh9I4QZuumOOfTxGzTT+2PDpJRI/zgZZfw1DMWX0p76gLSCTm+HM0MYah0SxPPH+U++/eNCs337ZdHnrqIDuv7qBiDoHt+XBFGm5FCO5dvoKXujqxXRdN17m9o4OvHTzAR77/PUxNI2dZfGzLFnzqMAUriSLCCKFQsA6jCD8R33WsjV7LUwPfwJMe7aE1NAY6AOhIVBIxDULG7ACblJL+wTTPPH+Mc13DtLVU8sA7tyGlx+mzQ1RVhuesnCxDoCs6Nb4KOnNHydhjLA9vQeIS0gwsrwBIim6egpsholcgUEjbIxiKD78awqeahCkbC0+6JK1BDMVPRA+iK5KsM0pEq0DB4UD6BVZGriKmVxLSDNL2CD41gCJUTNWPJmY+0EIIFGa6HwpugScGHsOWNp70eGfDu3lm6CmS4y8aTdG4t/7t7Evu5Vj6CD7VPy8t7WT709wwrnTZm3yDk5njfKj1o6hC5anBJ8g4GRQUDMXg7vp7OZI+zOHUISSSVZHVrIuun7t6VdoUrCOU3D5MrZGAvpK8fQSftgTLLavLGGojRec0fn0pOesQjjuMT2vHpy8FBHn7CFLamMbKyd+8YJ9gIPMFTK2JgL6CsO9aNGXuVZWUHiWnk4J9AlUJETI3IzCx3D7y1mEMtYaAsQohNAythpC5mbH8I9Puk0bY3Iamxqe1KZGUyJb24ckCIXMTqihf3/FGyFkHAEnQ2IgqAqRLLzOQ+QKeLGCodYTMrbPYIOdD0c3z9OAPOJ0tMxb61SAPNP0SilD54fkvcm/DhwhpEVzp8IPzX2R75e3U+proKZzh8f7vYHlFPDw6Qmu5peadZJwkzw4+yKHUHnxqgOOZ/YT1GPfW/yw6Bmdzx3hq8AfYXgldMbip+u20B1eyZ+x5RqwBTmeP0BzooOjm6C/28K7Gn7/oql1KycnsQZ4dfAhXOnh4bIxt55qKsoukM3+CpwZ+gOUV0RSdm6rvo31cQOX5oUeI6HGOpfcyYg1SYVRzf9Mv8vrYCzjSZnvF7ePptg4P9n6ZzfHraQ4sZffoMxxN7wXggeZfxq8GJ/sybPXxWP+3GbOGAGgOdHB3/QdQxxc8yVSeR589xDvu2DAr68kbF1lorI3932+4hRDc2dHBHR0dqOMPb10oxN/ffQ/fPnyIZKHI5oZ67lm+nKL9JKoI48nzhHzXUE5/dwDB8si4ZJkaKL91xw3K0eFBgrrBysoq4hdUTp4+O8SffuZBikUbRJn0/IF3bkUCX//OLhrrE3zkA9vnNCq31FxLwoghkWSdJJZXxPJKJO0hjqdfpzHQga6Y7B55jLCeQBM6puonY4+Rcca4uuJtBMe3YFJKjqZfI++kSTujrIxs4/XRp6j3t6MKjVpfK0PFbpoCywlpMZ4d/BbNwRXU+loJalEOJF/g6oq3EVbm98kD+FQfV1dcS8pO8fjAjyl6RfJOnrXRdSwLL+fLnV8k7+TYl3yDdzc+gOPZfPf8txf8W6pCZWtiG6ezJyc/yzs51kfXsyTUwZc7/428k+fF4RfYlrgKT3q8MvISa6JrJyf+dLhehkxpD7paSW/qf1MX+Tip4vPE/IKR3PcBSUXwnaQKT6MrCXKl/Whqgu7kn9Mc/2NMrQ1VBMmUduPJAr5QmdpWYuN5ORRMFOGfk+9k4nfJlHYxkPkiUf9OLLefgL4axxugJ/UXxHw3MlJ6mYJ9nIrguxexc/DoTf0divChKmHG8o/QHP8zXC9J59gfETavRggdU2tGUX140sKTFooIIMTCM3yklLww/CN6C528t/lXCKghCm6WgBYm56TpKZzBndiNIektnqPolbOgXhp+jCWhVVxTcQslr4jtWQgEQTXM9VVvY8wapjmwlI3xHShCQRc6OTfDQ71f4eaat7M0tIazuWM83PtVPtr224xaQ6TtMe6r/zBfPPdZ3tv8cU5k9nMic4A6X/O8987D49mhh9iauIFVkS0U3ClCtLyb5ZG+r3FL9TtpCS7jZPYgD/V+lV9o/z38aoihUh8HU6/xjoaPEDeqsD0LTejU+Bp4qPcrbIlfj08NMFTq41zuBLfVvhuADbHt1Pqa+Ub33+PKqcIaW1r84PwXafIv4d76n0WgYEtrche50N+kWFpccP+KNNzALP4MIQRLEwl+97rrp30qKVk2LhkCxjoUEcKndyDQcaXDs4Pf4Uz2IPc0fIxRa4AKs44as5lVVdX0ZzOzdPo8Kfnat3expK2aT/zCTTz74jH27C1zNChC0LGkhtf3deJ5cpZ7RYhyRskE6vxtONKiwqwjm0vSGOhgeXgLSXuQEasXvxpCURXO509hqH40oeF4M/OjzxdO4VdDaELD9ixqfM0sj2zhQPIF4kYNcaOGJv8yXOngU4Osilwz6YYJa/E53RkX4mj6CIfTh1gVWV3eukqJIhQiegRFKCgoeJT9cKpQkUJOuk4WgrkePkUohPXIOAmBMq7I7VJ0i/hUk+urds4ZPAXQlAhh39VYTg+qEqXkdOHXOyhYxwAPKT0K1jF8egeaWknI3Izl9iGEieX249Pb8emt+PT2GX0MGmswtCaC5kYivqsvOqax/KPUhD9M2Jw6bjT/EBHzGiqC7yLiXkvn2J+SCN6HYGGEQo43Rrr4IpXB+xFCo2CfwnJ7yVn7CRrrqQl/dEZ/I+ZVjKjfJeq/AWURhtuRNsfSe7m55p1UGrXlsWsLS4es8TVyOL2HSrOWJaFVhLWyK08TOjG9srxr1ELEjcrJc3oL51CEwvLwBnTFoCO0jmeUB+kunAag2qwnblQS1mJUm/UMFHvI2MmL9kNBUGM28vrYiwS0MK2B5RhKmZumr9BJ2h5jzB4ilRql5BZJ22OMWkM0jFOudoTW0OBvmzE3G/xtaEKnO3+apaE1HErvpj20kqBaLvAzVR8hLTprXo5ZQ4xaQzzQ9MtE9JmLpOHRLOd6RujpS1Is2uw93D1JdwzlSsvjp/sZGM5QU7k4sqwr1nDPhQk/1dS/IWhuAgS62lAuytDL7pCBYhcCwaroVXjSRSAYKHRRYzZzdJyPO6gbMzJSbMvlbOcwv/iRG6hIBNGmFfYIIYiE/eRyJTwpL2m6FNRJqTRN6GUfnBCEtTiN/mUoQqXabKbSaKC7cIKAFiNwwQO0PLyZ84VThLQYQS2KTw2ioGAqgcmo9pH0q3SEN+JXy5NSSslw6TwjVh9nswdZFb0GXSkbD8uzOJI+xEhpmP3JvayNrsPDw/IshkqD5awPyoZ1YoKqQkUTGktDHfy4/1H8qn/SH3ghLM/icOogI9Yw+8bb1xSdg6kDjFgj7E2+wZroGhShXtC+zpb4FnoKPYS1MNVmzZyGW0rJaP4RMqXdJAJ3o4owIPFpSxjMfgVDrUdik7X2UhP6EEPZr2E5/cT8N6KKIJeidJvwZl4cEiltFGFc8KmLIibmkgrTBGwXAomHECq6WoEQBg3R/4qh1pCV9kUM8+IzJ1zpYHklQtrCiJumP287Ku8gplfw8sjjPDP4Q26teRfLwxsu2k7RzWMo5iT/jiIUTNVP3smO0yRr5Re4UCbn3aUWHEIo3F77HvYlX+Kpge8jhMJdtT9Dc2ApeTeLRJKyRyd3TddW3kZIm0ph8GvBWX3WhcHq6BYOpF6lKbCE4+n9vK3+/Ze8R0W3MPl8X4jewRRf+u6rnO0aJpUp8id//fCs9kxD4203rWHJIjUoF2y4hRAqsAc4L6W8WwiRAL4JtALngPdIKcfGj/1d4Ocoz95flVI+tphOyXEaS1VRiPt8kwY7b9u83N3FSL7Apvo6liYqMLSmOdtQUPHwJoMYg6UeGsdTAZsiEXRFoTIw01etKALT1MjlZ6udSynpPj9KPB5ckKJ2zKgmOr7yqPO3TU5FVehsq7gD27PQFQOBQp2/LCOmTTMGQgiaAyup8y8ZL5XXiBvVKKisj12Pgsr2yvvwcNGFydbE7ZPGLm7UcHPN+xCIGX5uVShUm7Xc3/gAIDAVH6sja6gxa1GFyub4FvxagBuqbsSv+lFQuLv+Xvyqn+uqbmCkNIyp+pDSm9OVoKBQ46vlPY3vLbevlnPnmwPNNDW9b3x1pnFj9U0E1CCqULm7/j58qo9tiatZZo9RckvEjfndO7Y7jKZEAYnl9gAbMbRaivYpor7r8WSJTPEVdK0WOz+IpibwpIXl9o//jh4lpxvb7ccVfop2J6bWhBAKulpNuvgyQugE9JWoylyBSUHUfyMDmS9TEUzjySIR8xoivh30pv4GQ20ga71O2LwKgYHl9FGyO7HdUYrOaUytGSkdSk4nrpemaJ9GEQa6kiBorKPkdOPXl+F6GYQwCJvb6Br7Mwy1HiEMfHo7fn0JQhhI6ZIqvICpNU5mrFwKmtDxq0FGSgM0+ZdccI4AympRALZnUXDz4/dNogmdDbFrWRPdyv7kKzzW/23aQ6swxJSyEXIq9RMgoicouHksr4RPDUzqwEb1CkatN8elLaXEVHxclbiZTfEdvDT8OE8MfIePtv02ET2Bqfi4rvIuAtrUYubC3/BCCCFYHdnMVzv/Dyezh1CFRoO/9ZJ9CWphbM8i52TKwdpp93Pt8nr+8g/exYGj5/nLf3mSP/zVu/CZU8+jGGfSrIyHFq0DupgV968BR4GJNf3vAE9JKT8jhPid8f//NyHEKuC9wGqgHnhSCLFMSrlgOjYJ/M0rr9CfzfJ3d9+NqWlYrst/f/YZfnD0KEIIoj4ff3PnXVzV2DjnhE2YtYS1OAdTL3Equ5/GQActwRUAnE9nODE6PKMQB0DXVa7ZuoRvfm831ZVh8gUL1/UYS+bZe6CLJ545wi9+5IYF3eSyy0IZ//fMlbuYthqXeHhyPGDpDVNwc5TcIo60J31pCgqaomEoJqbix68GMFUfpuKbNPbqNJ4STehoc2Qa2I6kp8+luSJGTWSqOKTaN5OzJapPrU4SRoJMoUTRcagO13AxaIpGvb9h1ucN/sYZ/48qUy+ohJGY9u+LC+8KIagM3c+p4QfRlPPURT6BqoRQRYjq8AcJmRsnXyqqCFIT/jmShadwvCSNsU+iq7VIXHLWXjSlCgHkrP0YWh0Cg8rQAyTzT1C0zxDQl8/bh5j/FjS1grx1BE2Jlg2qtoT6yH8hU9pN0FhLxLcDgIJ9HFdmCBrryJb2oCuVeLJAtvQ6Ud9Ois4ZFMVPyNhMQ/Q3SBVfoOicxa8vQ6Biaq00xX+fTGkXQgpUsRYARQRojP02mdJuVMUHzN1fmGm4VKGxOX49zw09jKGYRPQ4KXuUJcFV+FQ/QS3MG2MvsiqymcPp18m7mXIbeBxK7Saix/GpQbJOGr8anPECj+mVnM4doS1UVl6qMuup97cQ0eO8MPwj1kS2cCyzH1Px0xRo52xu8XJuUHb3HErtpsKswRAmeTdLQA0Dgnp/CwmjmscHvsPG2HZc6ZCyR1kXu+qSLr64UU2lWcszgz9kQ+zayUwbx3PoK3YyVOrF8oqczR0jrldS528mblTSEuzgR/1f59qK21CEStHNsSy8HkUoGLrG0tYqaqsitDZWEPBfHi3PBdG6CiEagS8Bfw78xviK+ziwU0rZJ4SoA56VUi4fX20jpfz0+LmPAX8ipXxlvvYvpHUt2Db3f/MbXNXYxB/ccAMAz3ee41ceeogPbtjArUuW8unnnyfiM/mne+6dk6ukHKX3yhFw6eFTg5Mr0r39fZxLjbG9sYWaUGjGOblcic/9yzO8vOsUrufh2B6hkEmp5HD3Hev4yAd2zPBTLQYTRFQpe5Tuwlk6c6foK3Qzag2Rc7NYXmmcPMqbJM0f/wWmuTAUNEXHp/gJjvsTq8066vxN1PoaqTCq8KuByVSsCTiux7PHzjCYznLbmg4ifh/ZYomQz0QIKNkOjucR8ZdfKOlCEU1RCJgGPaMpVCFoSMwt4XXpcXtYzgkKpdfxZA5NqSbo34mqxC44zqZo7adoHwLpoGtNBHw3TLogpJQcHhhkb28f17e10ptOs6K6iq5kkoBuoCllma/VNdUz0kT//w4pJXvf6KSvL8ltt69FqJIDqVc5nNqD5ZWoNGu5teZd+NUgPYUzvDj8I0pukWXhdahCpSO8lpheya7RpziROYgrHeJ6JddW3k61WT85z1L2KE8P/IAxe5haXyO31dw/Xik8yovDP2Kw1EuFUc2OyjtJGNWcyB5AFwaNgXZeG32GrfGd9BU7KboFlofnziqCsiF9fvgRuvIn8aRHtVnP9nE3DkDGSfLy8OP0Fs+hCZ2O0FquqrgJgcKB1C7CWpT20Mo571Nn/gRH0m9wbcVtxMYXEgU3x0vDj1Fyi2UBD6HiUwJsr7wdU/GRd7O8OvIkXflTgKAjtIZrK2+bjDd5nqR/KE1tVXjBfPpwGfi4hRDfAT5NWefrk+OGOymljE07ZkxKGRdCfA54VUr51fHPPw/8SEr5nQva/AXgFwCam5s3d3ZOEbWPFQrc/dWv8OvXXsv9q9fgeh6/9qNHOTE8zDff8wAxn49vHT7E3776Kg++7/0kArPT80ZL/ZzJHWZz/KYZE0BKyXOd5zg9NsrO1rZZq+4JMYUTpwc4eryXdKZINOxnzaoGlrRVo2mLr2iTUpK2xzic3su+5C568mfJu7kFBQ8XA13ohPUYdb5GloRWsil+zWSgKFss8ZWX9+LXde7ZuJLhTI4jvYPkShYbm+t54vBJQqbJ9mUt9Kcy9CcztFYl2NrWwI8OnCDi93HDirY3NfZc8Rn6R38dVYmhqpVIWaIu8TcY+tIZxyWzn2c49b/QtWYUEUQIk/rKf0JVyi+MkuPw2MlTCCBbKue6L0kkSBVLDOayVIeCrK+royq4MHeWJx1cWUQVxngutIflpbDcJCAw1BiGEgXmlzArPz9lIWPLHUPioisRDDWOYCqXV0qJJ0t4OKjChyKmXiyetHFladwl5p+RS+zKEhIHTfgRb5Kn3XFcfve/fYv+gRT/+E8fIRg0J/OUYaqKdiqGJMcVccSsZ2euc2beC2bkP08f/1yfv1kspC/Tv/9J1wX8JK73lvi4hRB3A4NSyteFEDsXcL25LjTLQkkp/xn45/EOzvh+ojR6IhWwO5Xi1e5uPrB+PbFxn3dlIEjBcbDcuT0wllci56Tm/K51nL/bUGY/CEIIdF1l1fI6Vi2vm/XdYiClJOOkeHn4KV4deZakPXLZjfV02NJmdDzKfSxzgObAkknDHfKZrK6vIRrwkQj6OdY7iADOj6ZZ21jLuqY6qsJBBlJZTvYP8/5rNxIwyoULS6or6E9l3mSvPFK5r6OpdTRW/TuqEkVKZ1YKm5R5krmvEfTtpDbxl+M+XAchpoI+mqIQ1HUypRJra2vozWSoCYcYyuWoDYWoDAaJmOaCjDbAYOEVDg5/htbI/TSG7uD42L8wkH8R20sxYbhrAtexPPYxTHU2M6SUkqI7wOnUV+nLPUvJHQU8NCVEwlzL0tiHiZtrJw3xqdRX6Mp8n1WJX6chdNtkG53p73Mq9UU0JchVNX9DQC8X5Ehc9g79ERnrNNtq/4rQNNXyxWBsNMeZs4MEphGDlV12c/t65yZEmP+cmefOffalzl0sFtKXy3m9t9KffMHi+JkBVnXUzdqtj6XynDo3yIZVTej6wl/MC9lPbgfuFULcBfiAiBDiq8CAEKJumqtkItLQA0yPGDYCvQvuERDQNRoiEV7q6uL6lla+emA/Ukretqzsx5NSkioW0BVlVtrgBCJ6glFrgNfHniKoRkFAna+VqF5JSzRGSzQ253lSSrK5EnsPdNHZNUJtTZRbdq5EAsMjGcJBHz7fpSvVPOlyJL2Ph3q/QX+x55LHX25E9QR1/pmBW01V0BQFCRzrG6I2GiZo6qiKgk/X0FQFXVVor67gob1Haa2Ms7aplqO9g4xk82xoqSMRLO9uJlZmTKoVzrfKcHHcfgx9CapSMf5Snn3/PJnDdUcxA2sRIjDncYoQ3LSkfby6D1bVVCOAljmENGb3sTxPpvfR9QrknfMM5J+nP/8cqdIxIkYHcXU1RXeYlHWcc+lvUXD62FL9P9FEYEbbObuLPYP/jZR1HL9WS6VvE0JoZO1z9OWfY6S4j41Vf0JN4HqEEPi1GvJOL2OlQ9QHbx3vi2So+Bp5p3e8GOv0pOF2vCxjpYPjMZGL5+PPByklJ08NkE4VZhjuN9PO1N+CN0tPUK5uhol5s5h2psuSwVQA9KdFk/BmcapziD/934/wd3/2XhpqYzO+y2SLfOrvfsz/+v13LSqz5JKGW0r5u8DvAoyvuD8ppfyAEOKzwIeAz4z//cPxUx4EviaE+CvKwckO4LUF9wjQFZX7V6/hT555mpe7uxkrFPjQho20x+OT27kDAwNUBAIEjbmd/a50qDYbKTjZyQT9hFFz0R9ZSsnAYJpP/eUjnDo7BBLWrWnk5htWICX8w78+y/KOWt7zji0X3Tq70uG5oR/xWP/3KXlzVxn+pNEaWEpAnZkVsbm1AWU85e/9126gYDncuLIdXS2LsCqKwKtKoKsqyUIBXVUxNY3rV7QhpZy811I65Esvk8n/ANvtQ1friQTehd+8anI7bzs95IpPYzu92E4XnpdhOPUpAHStjWiwnHlSso9QKO3Ccs7iyQy54rN4XhIAv7mNoO+Wyd/c85Kk8t+hUHoZKV38xiaiofeiKlO/q+ulSGa+RCTwbmznHKncN3G8IXS1kYrIr6Frs4OnI8W9+NRKttb8BZW+bShCx5M253M/Zv/wpxjMv8JIcS81ge2T57iyxKGRz5KyjtMUuoeViU/gU8s+UdvLciL5eU6nvsqhkf9F1FyBX6shYixFESYZ6/T4y0fF8XKkrZOE9aXknC6SpcPU+HcghKDoDGG5SSp8m9DmzHCZG67rMTiYpvf8GOfODfP8c8fKeqmpAl/64guzVna1tVHuetuGOYmtpJSMjeV5bddpDhzoYmQki6apNDUluPqapaxe3TinRquUkieeOETnuWFuvnk1rW1VnD07yAvPH+fM6UGKJYdo1M+aNY3suG4ZiYswKUopsW2Xw4d62LXrND3do9iOS0UixLr1zVx9zRKi0cCiDLiUkt7Cfmp8q9CUtx4wlFLSXzxMjW/FDDcYQP9gikjIRyI226WbiAXxmTr9g+nLa7gvgs8A3xJC/BzQBdw/PoDDQohvAUcAB/j4YjJKoPwGvW/FCnyaxu7z51leWcF9K1ZOrq4t18V2Xd6xciX+eYJQYT3O9qp7FjUgKeHfv70LRVH4359+L3v2nuPA4fJqWVEErS2V7DvQxbvv2zwnvwmUq7qeHnyYH/d/b7IC7acPwfLIuhlbNyklhlZ+YMv57jo+fY6dw/gzPbGyBqgKTxkNKT2SuX9nJPVZAuYO/MY2LPsovSO/RFXs94kE3oMQAscdpmgdROICHhIL1ysz4amykokVl+10UrKP4MkyHYCU+cnjyp8x/u8UfaP/BdvpIuS/HVBI579Ptvg0DZX/iqaWM2M8L81Y5l9x3VEKpV2YxjoMbQm2083FJKI6Yh+hetxgAqjCpCF0B12ZBxkpvs5Ycf+k4ZZSMlp8g6HCLoJaI6sSv4qpVkyea6hRlsU+Sn/+OXJ2F/3552gN349fq8NUYuScHlxZQBEh8k4fRWeQJdH30519hGTpMBIPpELWPocri0TN5fNWcs6Fvr4kn/yNrzE6mpshEpHNFvnud3bPOn7N2kbuvGv9rM89T/LartP80z8+TXf3CGV2xPIu4dVX4Ic/eIMbdq7gF37xRuLxmbnRUsKLzx/npZdOEgyaHD7cwxc+/zzpdGGyDSnh6aeO8OAP3+C3f+duli2rnfMFMDKS5R///ileeukEluWW0w4pv8wff/wgra1VfOJXb2X9+qlqyzIHTxYQGEoQRxbxpIuhBMeTFrL0Fw5RZXZge+7kdxf6yaefZ8sCuvBjywIqGi4OUnrj7ZfwqzEEClJ6ONLClRa6EsCTcrLPszD+keMtTsxjUYZbSvks8Oz4v0eAm+c57s8pZ6C8aeiqytuWLeNty5YBM7dDhqry32+6edIXfrlg2Q5HjvXy4fdvp2NJNUeOTXl4hBAk4kGS6fy8BThSSvaNvcrj/T/4DzTa4Ff9tAY6GOhPUVEZZoLV7vjRXpatqEdVBI7rTXJce54cV4PXcBwXVVXGV7iSYtHG8yTRqH/cIJ9nNP03RIL3UxX9PcpTyGYo+SmGU/+LgHkdulaPz1iPz1iPxKLbPo6utVET/+ysvob8dxLy34nrDZIvvkjYfzfx8C/NOEZKSTr3bYrWIZqqvo6hl9M6w4F76B56D6nct0iEPz710OKQKz5HQ+Xn0bWJgKrDfNNdV0JUB3bMmksKBkG9mZHi6+P+6ykM5F/Ew6bCvwlTTUz2c6rNMBFjKTm7k9HiflrD96MrIYJ6E0nrGCV3FF0JkbKO4UmbhG8DY6VDZKyzOF4WXYmQsso0AVFj5aLmeTQa4CMfvR7HKRuDF184zmuvnSEWC/CBD27HuMDPmkgEZ2U7lDNRzvE/P/Mw6XSBltZKbr11DU3NFVglm/37u3n2mSM88fghMpkiv/t79xCag8Md4LHHDjI6kiVREeJd795KS2slVslhz+4zPPvsMc6dG+bv/+5JPv2ZBwgEjBl9yGVL/K/PPsru8f7fc+9qVq1qQNMUzp0b5vHHDnL27BCf+dRD/I8/v58lS8sv8O78HkZLZwnrtVT5lnEq/TSaYlLtW0nS6sKVNkmrh5w7ypnM8yhCpSGwkWrfVFplzhnmePpxFBQag5sZs7oxlAB5Z5SIUU9f/gCq0GgJXYMmfBxJPsy2yo9gewX2jn6dhNlGY2AzDTUxRsZynOkaYlVH3YyXy+lzQ2SyRWoqFyfmcUXnTM03WacHLi/r9WDSlXAhJt78oaBvzuBXmWxmgIf6voEt/wOkvaah1tdIVEnw+MuHuW7nCvp6kySTOQp5C6vkICUM9KfYsXMF4bCPvvNjHDvaS0NjgkLBwvM8crkSLS2VJMfy2LbLpq1tCAGF0uu4XopI4O3TfNA6keA7SOa+SsHag67dO21yzuzb/AZITP49e9Vlky08gakvRVHCOOPFNIoIoam1FEqvQviXYdrrNOS/DV2bXtY8f1zCUGKTxvdCTGx7JVMrIolLxi6XbI8U9/HawG/McaYkVToOQMkdHXeNaESM5YwU95K3ewhqTYwVD6ApAUJ6G1FjOaPFfRScPnQjTNo6iSr8hI32Odq/4GrjL1oPSShkcvsd6yY/7+tL8tprZwgETW67fS3B4KV93ZlMkX/+52dIpwusXt3AH/zR26mqCk+6rXbeuJKrrlrCZz79ELtePc3DD+3lgfdePefv29M9SseyWv74T95BbW05SwckN+xcQUVVmK//+yscO9bHyZP9rF/fPOPchx7ay57dZ4nFA/zxn7yTtWunagK271jG9Tes4Pd/91v09ib58pdf5A//6O1omsJg8RhrYvdhKmG6cq9R7VtBzGjkdOY5Sl6WTYn3kXdGGSudI2sPEtKrydoDMwz3qHWWnDNEUKsk6wzRENjAs/2fZXv1x8nYg1T7VhDQEoyUztARvhljgngKj4CWYHnkdgCWtjosa6vhU5/7MQ/cs4WlrVUgy77vbzy4m5VLa2lrungNw4W4Yg339FLbCz+bDIddZuNtGBrr1jTyg0f20t5WheOWKy9Llsup0wM89vQh3nXP5jkLcCQeTw08xJg1/KavX2bLM/GrAQzVVxb/ncblYXsWllfC8kozCnQuREdoFT7DR1V1mVa1p7vMY67rKpbl4roupq8cjASwHbcclM0UGBxMEwz68Ad0mloqUDWV5FiOiVvtuL0IYaIqldOuKFCVSoQwcZzLH4iV0sZx+7HdHjoHZrq/PJlFUyoBj+mGW1PrFzw/FGGgLOJRkNLF9spc73m7l6IzfwWgKvwoQmcisSpqrkDikbFOU+HbRNI6il+rxVQTxMxVuNIiZZ0gqDeTszvxqZX41dmi1hciV7J4Yv9JAqbB9hUthHxvLRD5xuvnOHN6EF1X+dkPXzdptGEq6+vqa5aw88aVPPLwPh59ZD933rme6Bx+XFVV+MAHtlNbG532mwg0TeW2W9fww++/Tj5vcfbM0AzDnckU+fGPDyCl5I471rF27exiu6amBLfdvo4v/tvz7NvbSV9vkqbmBBXmEk6knyCs15Iw2jideY4xq5Mq33KSVhenMs9QdFPEjRbGrC6CWiWVZscMuxM3WhjSThLSKqkw2+nO7WZt/J305PcS1mroLezDVCI0BbcwUjpDxh5goHiUuNGMPo2a1mdqfPIXb+FzX3qOf/jKc9hO+bnVNJXNa5v5xId2zqJ7vRSuSMMtpWT3+fPYnsfVjY2oSnnrfnp0lH9943WGcjluamvn3atXY17mQov3vnsbn/6rR/kvv/U1NFWhULT5tf/2dXr7kmzd1Mrtt6yZ0w/XV+xhX/LVRV9PFzqNgTZWRtbTGuggYVbhV4PoQh/n9Cg/8p70cKWDI22KboGMk2LMGmaw1EdfoZuBUi9JaxRXOiwLl4nkN25pRRGC7dctR1UVFFWALPvrCwUbczw7xjA0WloraW2vpliw0A1tMtpfVx+b8TAKYZTLmpnpCpI4IL3y95cdAlAJmNdQGf1tLvQVKiLIhVN5cS/1RS4AxqtfAdoi76El8s6LHq4J/2ROdzlAaZC2T1NyR8nb56kJXI8qfESMDlThI1k6TIVvM0V3mIRvA5oyH43wFBzXw3JcGiv882ZaLQZ79pzF8yT1DVFWrKib834KIdi+Yxk/enQ//QMpTp8ZZNOm1lnHJRJBVq9pmLONaDRAMGiSz1tk0jO1WDvPDdPfl0RVFbZuLe865qo7Wb68FkUR5PMlznUO0dAQo9Hciu3L4jkSvxpjZfhuPBz8WpQa/yqKTorW4A504WepcRfJsRGC4Spy6QI9pwfoWNdMQFSyJvp2SnaOoJagLbQDQwlieTkGi8eo9a+l1rcanxqh5GXYWvGzKELHp0boiNwy4z7VVUf50/96N32DKYZGs4CkKhGmrjo6Z3D3UrgiDbcnJZ9/43WKjsO2hgZUIFks8puP/ZjOZJKaUIhPPf8cHpIPrJu7wurCFftcK/gLIYSgMhHiv//e29m1+wz7D3eTyRSJRPy8/z1XsW1zOz5z7lu2e/SFSV6HhUAgWBpayW2176AtuGyS7P3iKK+iInqMaupmFDxYXomUPcpAsY+mQHs5/Wy8vNacI31Rn+bnrK2LAWWDruszZdVUVRAOT/kuDX05EgvbPoOuNk9unW37DJISxjjJ1+WEEAaGvhTb6cbQlqIsIsPiJwEFDZ9WDSXwpEVYb1/wg1deXcfJ2d1k7LPYXpa4r1zG7tOq8WvVpErHydndOF6emLGChWh6B0ydoGnQO5phbXPtWxkejuPR01326dfWRCfn0YUQQlBfF8Pn08nnLbo6R9i4sWXWvaisDM/rnhGKmMxm8S4wyt3dIziOh6IIvvWtXTz66P4520iO5SbTDEdHsjz5rV3ohkbjkmpOHewmGPHjuR6KprDummXEqyPkBxWSwyOkR3NU1cdJj3icLw5xYm8nQhH0nBoo970uRmoky4rNKjXj7gxTDVFhll8k/nHyKp8aAXWK4c8QM1+2E/UhzQ0JmhvmdsstBlek4S7YNqdHR7lvxcpy3rGUPHryBKdGRvjLO+5kR0sL//2ZZ/ju4cO8e9Vq/HNkR4xa/aTsEdpD5dVnf7ETT7o0BJbMe10pJcdP9mMYGtu2tLHzuuXj28Ly9/M9nHk3x+HUGwsen0BwdcWN3Ff/Pnzq4tKYZrQzreDBp/rxqQ3U+Ganu10KiyG48Rsb8enrGM38Qzk3W63GdYcYzfwDpr4Kn7Fx0ddfQA+JBn+GvpFfYTTzj8RCH0QRITxZxHbOoqk16FrjpZu5bBBU+jbRl3uKkeIb2F4aQ10YHYCuhAlqzRScvnKOtlCJjQcfNQJEjA5Gi/tJWUcBj6g5uzR7LmQKFkFfuew/lS/iu5CxfxGYiHEABEPmReenz6+jjacXpi9YMU/A9OlvSkMzlSqM90ey69XTCzrHdTx8AYOKmig9pwdQVIVETZSju88gkWy7pfySjFWGeeVH++jY0IIvaHL+7CCelFQ1xBk8P0pyME3rqgaSwxkq6+IkhzOThhsgoC3O+Mpx8i3XnU5lUYZALHrVfUUabtvzyNk2TdHoeD6rww+PHmVDXR03trVhqCo3tLXyXOc5spY1y3C70iVtjzJc6qUp0IEEhko9gLyo4QZ44tkjvPjKKUxDpa42xtL2ajqW1NDSVEFVRQh/wJi1Fe0tdDIyrn6xEHSEV3Nfw/t/KhJdlxtCBKmJf4r+sd+ia/CdqEolrjeMqlRSm/gsipgZHS/Xk2mTroWLt63DHCouQgiCvuupjP4Oo5m/JZX7OooI4MkiUlrUVXxumuEW4+Xrl0dsd+5+CmqDN3I69VUy9llOpb7M8tjHUIQ5Y4cncSm5o5hqfNzPDQKVqLmMlHWM0eI+TLWCoD7V95i5mv7884wU96IKPyG9bUFzJBrwYWoajusSC84vRr3Q8U28zC8lMiw9Juui51sAvOkpPn6eaWr87IeuIxq99LhWrqwnZGoYPp3WlQ30dw9TWRensi6GlBLf+O5BM1Q237SaitooY4NpEtURGpbU0N85zIb2FdilcjZVJB5k8PwoLcvfjLxcGVJK9hzo5AeP72dkfHcwHaah8XufuJPaqoVzcl+RhltVFHyaRtaykFJycGCAI0ND/NHOGzHUsq/QVDUcz8OdI/9xoNjJKyOPkLSGOZ0tSz6B4PbaD1zy2r/w4Rt44B1b6R9Ica5rhNNnh3jw0X2MJXN4nuSu29bxwDu3Th4vpeR09tiC0/8MxeSO2nf9RIz2QnhnFosL+yiEwNBX0Fj1VQql13HcflSlBr+5eZI0yrZdbMvBHzAY7MtRU/lZmNA0nNbHCTcLgKLEqa/4NzS1mky6QGiae6a869GIhX6WkP9mitZ+XC+FqsQw9OUYWuvksapaTWPVV9HUmXQFlxt+tZaViV9l/9CfcSr5JZKlQ9QErsenVuLKEnmnj2TpMHm7h2vq/hG/NhVgjBorcbyvM1o8MF5cE54cZ8xchZQuI4XX8WlV+LTK+bowiYJlky1aaKpCMl9gJJOnPrE4Yv7pUFWF2HhcI5XK43keyhz0EADZXBHLKs/9ROLyurDi8an2tm5rY8mSi7NTzoWlsXKwMxIPsveVUzz5w71U1cW44W0baGiv5vDr5ygVLc4e7ePAnk7u+cC1ADz78D76u0eoro9z032bcB2Pp37wGpqmcP7cMBu3d7B2WzuO7fLCjw5w/tww2VSeVZvbuP7OdTPU2/sG03z67x6jqiLEiiW1PPfqCa7a2IZlObx+qIs7d64hFlncy/aKNNxBXWdlVRXfO3KYmlCIL+59g5pQiJva2yYf9t5MBp+mYczBDFjna+PmmvfSXzjH8siWcmhL6Kji4sMtGyWVRDyIYWjo45HekuWQyRZJpQqULpAYknh05he2jQNoD66gJbB0lkEs5EsUchaJqsXlc05H2knywtCP8VhcMv9cEAiuqbiJSnO2v7Scjhkj5L8Zq2Tz0tNHCYaHqK6z6T47REVVhO5zw1xzw3JOH+9nla+ZQ3s7icQ6GR5IE68IsmFb2Ud4ZH83tuXQ0FzBkf2Ctg6F1158g+03reLEkfNU1URYua5p3Hgr6FoT+jwc7FDOEPEZa9/y+C+GiZdNQ/BWVGFwdPTvGC7sZqiwCybDySDQiZrLJ1fbUL53YWMJitBxZZ64b82M4pqQ3oKuRLC8UcL6NjRx6QdaVxUUIYgETII+Y7LQ6s1CUQRLO2rYs+csfb1J0uninEZZSsm5s0OUSg6GodLaWnVZFyOtrZUYhoZlOZw6OUB7e/Wbbv/Y/i6e+eEbvPtjOzm46wzf/+LzvO/jt9B1aoA9zx/n/o/tRNNVNF3FcVzqWypYs6WV73/xRRLVYVZvbuOhr77Me3/5JrbduJJ//9snaF9Zz4mD3Zw6fJ57P7idf/yzH3Lne6+eFes+eXYA09D41G/fRzBgcuLsAB94x1U01MbYc6CTL377FYolewZX96VwRRpuRQg+tnkLv/boI3z84YcIGyZ/fOONVPjLqwBPSnaf76EpEiVkzg56CCGoMhtIGDUzxAkuBSkljzx2gGdfPM7oWA5dV2msj7NiWR133LKGxvr4rO2a5ZUYLvUv+BrrYlvKEmDjwZTJNDvbw7KcslGQ4wxrE19e8H85jah++v+zTopnhx65LMU/AoUV4fVzGu7pcD1JIOjDKjl0nhpksD9F65JqqmoimD6dbKbIyFAaf8Cgt3uUmroYuewUDUA+V2LV+ibOnRpkZChDU1slVbVRXNdjoDc5Y+U9xT1yeQqv4uYaNlb9GboSZi7+FNsbRTpniakBGoK3TX6eKe1lOP8jAnoHtaH7qfBtYqS4j1TpKJaXRBE6fq2OqLGcqLFsckU9gZDewoaqP8GTNhW+DTO+M9UKNlT9IZabAaeeXNEh6Jvyf06kkunTjLOqKJiaSiIUQADGBSXtEwU3VsnBspwF5XFfc20H3//eHkZGsry26zS337F21j23bZennjqClJLm5kpaWi+9O1gMWloqaW+v4tixPn70owNs37Fs3iKfC3dyF+LI6+fYtGMZLR01RGIB/vaPvodtuSAl669awvL1TZPnWZbDUF+So3s7GRvKkBodp8yoCrP1hhVouoovYFIsWNglB1/AIBA28YdMfH5j1vXzBYtEPEgk7EdKiaoo5AolFEWwYXUj8tuSE2cH2ba+dcH35oo03EIINtTW8rX738OZ0VEaIhHaxnlKAFwpubGtjYZIFH2e1KeMk+R09gAbYzsBOJF5g7CeoN7fNufxEzh45DzHT/azcV0zWze3saKjltqaKMHA7B8EyoHJrJNe0Lh0xaA1sJThgTT9PaMcP9TD+q3teJ5H5+lBEpVhquuiPPi1V1FUgWHqLFlRx75dZ/Bcj+tuW8NAb5KzJ/qoro+zfmsbTz64F1VVuGrnSnwVflaE188y3IpQ0ITOqexh8m4OvxokpicIapGy1qV0yI6nF5a8Ig3+VrYldlLru3TAzzA0Vq5rxJOSUtFm2ep6KqsjKKqCbTm0L6ulujaGaep0rKxHKALXmdoRLF/TgM9v0NRaSTQWoLImQjDkIxT2sePmVbOCY4O57xP334ChvnUjEdDradbn913qSowVlX/KieH/RmxakDBkrML2RhnNP01N6H4MNUZdcCd1wZ0Luq6m+GkM3THnd4rQqAveiGU7PPLqUY50vsDvvO+myYKzg2f68KRky/KZu44jPYMc6uqnIhxg55qpOI4QgqamBEIIxsZyvLbrNLfcumaGP3piATE9T3v58jq271jG008d4ctfeoG6uihr1jZNnlcs2nz/e3vYs/sMmqbwjndumVH1eDng8+nc/8BV/M9PP8yhgz38/eee5Gc/tIOq6shkP1zXI5+zOHN2kFy2xLXb585qCkb8ZFLlrK9i3kLT1ck2dFObsSh69sG9jAymuft910xWnwLlc9SJhABAwspNrTz27d18/wsvcM0tq6muj826djxWrrgulWwCfpNELMjBY70sb6/Bdcs8LMXi/yNiwUIIGiMRGiOzfXWGqvLOVasven7GHiNjj0225UqHwWL3JQ33r/z8Tt5221qOneznjX2dPPjoPgDqaqIs76jlqi3tLG2f8ldmnQyWt7BKyZAaIWZUIAMaxw/2kE7l2bfrNNfcvIplaxo5c6wPKWFkKMPPfOwGvveVl4lXhqhrjFNRHeHEoR72vnqalRua2fvKKZauqCOfK/HAz9+AoigIAR9t+81Z1/Wky4/6v83xzAFuqr6HqxI3EjMqxvX+ymtYx7MZsQZ4ZvBhjmX2E9MThLRyAY/jJRkrvIDtjQGSqLkVTYlSdM8TNbcRiqqM5p+lKnYjnrRIFp5GxAax9Tbal19Vrj70lSW68vYJXFEkJO9AxQ/mUQZzx1G1EDVNN6ApOmbiOFIEUWJHsJUorncDQghG88/Qm/53is55DLWaquDdqCJIye0lWXgFRZjE/dejKfPrKXrSJl16g7x1Ek2JUhG4CUX4yJT2k7OOoSoh4v7r0dUYQqjoSnxWYFVRfOPyaZde9UspGUrm6B/NUBkNUFcRYSiZxfUko5k8rbUJfLpG70gay3FRhKClJo6uqdy8qYPDZ/sng3/JbAEJtNbOzmhY21xLW02CgKHPcpWs39BMY2Oc7u5RPve5J3nttTPU1cWwbZdUKk99fZwPfHD7jCCipin8/Md20teb5OjRXv7wD77LuvXNNDcnsEoOR4/2cvLkAFJK7r5nIzfdvOqyx2yEEOzYsYzOn7mar3/tFR577CC7d59hydIaYrEAjuMxNpajry/JyHCWm29ZPa/hvurGlXzhsz/im//4DL2dw9z89k2omjLui57Z72giyMHdZ3j24X10nuifNMbTaQEUVYCAQq5EsWARjgUo5EokR3Oz3J1LmitBQndfkhVLati+ZQl//5Xn6OkbI5nOMziSWXSK4BVruCdwYcBtoZMjqEUYKp0n44yiCYOu/HGWhTdd9BwhyoLAa1c3smplA/l8id6+JIeO9rJn7zm+8o1XGBhK8xsfn9o2F5zcgl0TYT1a1qULqowOZ1h/VTsvPXmE26MBThzuYWwkSyFXKqdYGVp5YiEIhHxouopQBLWNcSLRANffvhbDp5e/m/agXliwL6XkXP4kLw4/xub4Du6qe2BOX7+qqtT7W3hX40f45zP/k4d6v0ZrcBlhLUJX8m8JGisxlAr6sl+nKnAXWesYyeKLRM1tuF6B/uy3iPm347hJHG8Mn9ZIb/rLKEIn6ttGqvgq6dLrVAZuH5faKmdd5O0zmFod6dI+cvYJWmO/wXDuEWw3SXXoPkbyj+N4aapDb8dQa1CEQchYiaHWoggD2xvm7Oj/pDJ4B5Y7yLmxv2BJxZ+MZ5Yw6170Z75FpvQGFYFbcWUeiTu7H9YxWuO/dVkMkZSw/3QvtuvyrWf38V/esYNHdx0lnStSXxnlyddP8uE7tvDZbzzDTZs6ONkzxI0bl7J52ezdjpSS3Ue7GEnluG3rVGm2EIJcyaJraAxNVamJhqiNTxmPeDzIr//XO/jb//M4XV0jPPvM0WnnwnXXLWeqHnmqzerqCH/0J+/g377wPC++eIJXXj7JKy9PfV9ZGeLt79jC29+xeW4u6WnpqhfFRb7WNJX3vf9aGhrifPMbu+jqGmb3a2dmHqOrVNdEWLVq/t1TvCrML//hvfR1jxCJbaSyLoYQgqtuWnVBlwVX37yapiXVeJ7kpvvK6a2BkMmHP3kHml4uTvvgr99OJBbkB196gR13rKW+uYJzJ/r5wb+9wEd+684Zc6ciHuJ//Na91NeUr3nD1R10nh/h+V2n8Jkav/zBG2iuj1/8Hl14XxZ19E8RUkpSpRJPnD7FnvPnyVoWn7jqalZWVeF6HqdHR0kE/FQG5o5kR/VKlobW8/D5zyORNAWW0RyYX5dv4pqv7j7D7jfO0dk9zNBwBseVxGMBWpoq+KWf28mGtTO3qAU3v2BphJAWLsuPCcGd795KRXWE6roYuqFimjrty2splWxuuGMdmqZw/W1rCITK3CiarpKoCrN5ewdnTwwQSwQJRfxcs3PFJa97KLUHx7NZF912Sd09U/GzLLyWx/u/y7ncCdZGN2G5g9SaP4OqBBjMPYimzJ+xoKsJDLWGktOLEApF5zwTGc4RcytVwfumHe0RNFaQG89ZLtqdTBiQquDdJPxlualkcRcCjZC5Gk2JEjLWYGrlrJF0cS+WO0TR6S4LBZcO4HhpDHU294PEZrTwNEsSf4BvWiYKyPF+HAEkRaeLC0vo3wpqE2E6B8bKK8RsASnh+vVLWNtey59/9SmyeYtYyM/brl7J8e5KXjl8bk7DHQ8HWNJQOWeKXjTgYzSbx/UkKxpm0oMKIVi/oZm/+uv3c/jwebq6himVHPx+g9raKEuWzk13LISgqirMb37yTt7znqs4evQ8w8PZcuynMcHKlfVUVM5NxyoEvP/913LnXeuIRgPzpgoGAga/+cm7KJVsGhvnXnXqusrNt6zm6muWcvrUAGfODpFOFVBVhUQiSHNzJc0tFZOFYhfGgCbGEoz4Wbp66r5KKYlWhGZxD6maQvPS2RksTdN22o1tVXieZGwoQyDow/UkY8NZquZwlSiKoK1pyrVnGho//94dfOCdV6EqCoauLnqRcEUabiklA9ksv/HjH/F6by+GqlJ0HH5mbZk4x5OST7/wfLmC8pZb5yR9UoTCutgOOsIb0ISOrpgLUsQ4fKyXYsnmumuW0d5WRX1tlEjYP7miuPAGW7IECzTdhuJjQguyqa38cLV1lIN/a7dMc+GMP3cNLTP9uMHxibl2c+vkZ7XzTPbpGLEGEUJZcLGPT/EjkYxaQ4BKZeAOzo39L3xaI3Xhn0GM5ytPFBJIHKQs++i6U/+EQKEieBsF5xzT782FLoxUcS/n01+gMfLz6Gqcgt05/o1AU8a5MYQKs7Jk5LR/OZhaHVGznKKZ8O+8yIulHNwUzKxSTRffoCf9r+P9SFCwz17yHi0U54dT/ODFQ3z4jq0cOts/WYiRyZewbBfPk6iqQsGysWyHbKE0WTwzXQZi5s5TzqoEdsbpW4fTOXJFC1OfTQEQjQW4dnvHvO6EuSBEmVOkrb2KtvaF8UVP9G3Z8tk0rRdC09Q5y+Snt+VKj5JXwhcwWLe+mWVra1CFii40bOngShdNqEgkp7KdSClpDTZiKDqWZyORmIqBLR086aEIgS50zhf6GS6NsSKyBFOZO4Z1MQgB7/vELRzec5b0aI6rblpJx5q5xctnnlcu6gv43nxM4Mo03MA/7dnDmbExPnPbbTSEI3zswR9Ofq8pCmtravjxyZPkbZvQHGIKrnR4cehBTmb2cnf9z5O0B6kw6qm6RGXhR96/fXJ1sJAf0lsE1bh6Cc3A8iQtjOsOTi/VX1gmhe1lQXpoSpiSO4KhRhFomIoPV7r0FbvmTEWc0QckvYWyATWUcvaB5Q3h19uImBvQ1QTgYqhV5K2TZKz9ZEsHcLwMILHdYcLmOqS0yFsnCRrz7wgcL40q/KhKkLHC88w20DMhUFEVH6niHoLGcvx6G2FzAwPZ72F7Y2hKDNdLzVvsIzCImJs5n/43qoJvw5U5wuZ6HC+NIvyoSoixwouTTICul6Pk9OHJIiW3DyEUVBHC9kax3CEcL4vl9qGrlSjzZC9Fgj4MXeXx3cfRtbLqtxCClw+fY8/xbrYsbyLo0ylZDl95/HVSuSLvv3UzQ6kcj756lOFUju+/cJC7rl7J4bP97D7WhZTldq9eNSVlpiiCWDBA0XJmlY7DwigfLhck8EjXEe5oWoE+bc67nkfKLpIwL829MgHLs3mk7xl8qsnK8BIsaXMsfRpXelxXtZVH+56l2qwg42S5s3Ynu0cPoAoVQ9Xxqz5eHn4DD49tifW8OrKPoOYnaaW5p/5m3kgeYaQ0hk81WR6+NAPjhRBCEI4GuPrmi8fbxlJ5du07y83bV8zIBoJyltBLe06zaXUTkfDCc7mvSMOdt21e7OzkfevW8fYVK+nNZGbVwTVFoowVi+Qsa07DPVLqo+TmWRbehDvOpNdXPDvLcE9oPAhR3q5cKJBQnvAeTFdNlxKJDeiLUpCcaLmsVTiE6+UI6A0UnWGEUDGUGMnSYRK+jRTsQTxp4dOq6c89Q8K3Ab9WP5nHbnlj2G4av1ZL3unFp1biyBwjhb00hO5gtLiXqLmCgNZIa3AZr4+9xDODD1HvaylzmVzwIphQ7jmQeo2Dqd0YikljoA3HS1GwO/FpDZTcAYZyD1MTup+4fwd14fcxln+WkLma+vAHUIRBU/SXGMw9jFvaS0P0o+NBPAgZq1GEH9fzJitPY75rsNxBRvJPEvFtI2AsAwRR31WTrhCf2kDEN1HwpNAU/QTD+R9je6P4tCZMtY72+O8wUngKzysS8W1mPqepEIKGyEcZyT/BaOFZdDVB2NxA1Hf1tH5sIWB0AIKcdYKxwvOEzfWM5B4j4ttMzHcNY4XnKNhnMbU6BrLfpzr0dnxzKOsARAImv3H/Dbiehzaeb/2CENx11QqWNVVh6Br5okVVLMTP3rEFTVXRVQVPSt6zcz3371yPEOAzdNYvrWd1W3mHps1RQj6Wy6MqCgFz5vPgSckLp87RkojRkoj9xIy3lJLz+RRHxgY4lhzkxvqlvDLQiSs9rqlpYc9QD8/1neZtzStZE69lz1APtudyVXUzhjq3KRoujRLQfNxecz0AP+h9gp1VV3G+MMCpbCeKULil5lp+2PskQsCSUDMxPUJbsImnB18hbWfxqSZ9hUFc6XJD1VU8P/QaRbfEslArKbOCFZGLV1O/VQyNZPjq917j+m0dswy363h85bu7CPpNtq5fuKboFWm4bdcla1s0R+efZK6UzI4Hz4SqlBn2XOkyZg1SbdYg3X5Q4iBzgIqUOQQGUhhImUUoFeClQKmkLPLq4FmvlT9XKsYrAAWetRvFvPZNj7E/9xQBvYG0dQpPlpB4VAd2kLXPEjPX0Jt7jIBWj4dNyR2eUcQhsenPPUOFfwu2l2WstB8Q4zqGCkIoGGoMxnO/10e38crwU/QWO/nnM59hRXg9baHlxPQEqtCwPYsRa5CTmUOcyh7BlhabYtup9zVTdE7jeClqQr+GxKPk9OLJAkKoVAZvo3JafrPreYzmo7jee9EVjZ5UnppQiJ7kEIilxHw+njx1jFs7lhA2TRTho2DdRs6yqAgkOJ8ao2hngGvJW5K4v0TEt4SAMfVgBY0OgsbMrX7AWErAWMpCoCp+qkP3zvq8NvzArM/C5gaCxhIUEZ4xD2tCF2cDnA6JhapKdG3K/9pWFyce9k+6RDRVYXVrLaauTb7UVCFmbaVNXWO+Go1MscRYpgACssUSgWkH9qUy7Ok6z9KqBC+e7iTsMwkYOt1jKVbWVnF8YJiY38dAJotA0F4Zp2ssxfqGWo4PDpMulLh15VK0aVkVpZLN3l1nKOQtrr9lFYMDaSprIjx47jC3NS5n12AXAFHDxwv9Z6gNhKn1h2kIRFkaqeTw2AB7R86jKyqaonJNzdxGK6QHGSklOZE9R8KIUmUmOJE5y4g1RkeojU5xHoGCMr6086kmPYV+anyV1PoqKbhF2oJN1Puq6cr3ogoFRShl94lq0J8ZZrg0SoVRDg5aRRvd0PBcb9y4CIRSdrIqb4JvZQLziYQLpZydkiuUFtXeFWm4fZpGVSDA0aEh7l0xe6vteB6v9nRTGwoRnqMAB6DCrEMXJkdSr3Iqs48qXyNboh141uugxJDuAKp5A3hpJAKhxPCsvQgRQrpnUcxbEVoDoCGUSpBF3NLzCDEhlpudrRKwCEhcpPQQQh132UqkdHG9IraXwqdW4R9fxRlqDE9emOdZPj7rnEGg4kkLy01huWM4Xh7LHUMROgGtkZAW5Weaf4lvdf8LPYWzvJF8iTeSL437/Cd81eOl50JlXXQb9zV8EFVoBPR2IuYmOpN/DSgE9KXE/TfMMyZ44VwnI7k8a2prSBWLnB4epeQ6RH0+/FWV+DVtBrfMG+f7qAmHePb0WfoyGYKGQcG2sV2Pu1cuJ/IWeKXfLKS0sZzTqEqCbOEJfMYGDH0JjtM77t83kLKIEL5xAWQF18vguOdRlQRlXvJedL0dyz6JqsTxhA/XHURVq9m6chTP68STO1CEialrvOO6NW+pz9GAj51rlnCyb5iIf+Y9qwoFWVZdQUUwyGudPVzd1sSXdu0l7DN55Ww3qUKRoKFP7uYGMjqpQpFkoUjXaJLKUHCGcImUkge/+Rq9PaOMDmXYfuNKHvzmLu569xZcKUmYAXyqxqnUMPtGenE8D8fziPn8aIqCT9UoujaW57IkUkFzKDbvuCJaiFtrdtBfHEITKlclNnAm10Wtr4rGQB1+1YciFK6u2IBPMVkVWcrJTCe257AivISA6ifr5DFVg2srN2MoOhtjq4jpYSqMOCXXpuiW03mHukd4/ckDbLl1Pa8/eQCAtjVNdB/vZesdG4hWLo5GIJMtMjya5Xx/Ett2Odczgt+cvgCDE2cHGBzOUBFbHF3AFWu471m+gn/as5uWWJTllVVIIGdbnB4d5XtHjvDk6dP85rXb8c3Dx60KjR1V97IpvhMPl4AaQbidSJlGEU2g6qAkkG4nyALSG0MoibLhllnERIBL5vHsN1CMrQitDaQNsgCU5iREWig0ESCktxLQGyi5owgUNCVIVeBaFHSqAztQhIFAwadW4U1T1RHo1AVvwfYyxMy1+LU6dCWEJx0q/VuReESMZYjxtD8hBA3+Vn5hye+wP/kqB1N7GCz2UvQKeNJDFSoBNUS9v5lN8e2sjGxAF+VgjcCgLvwBpgKC8/vaVSGI+nwEdANdVbBdj6jPpFoP4tM1QqaB43nkShZRf3kFWhsOURsJE9B1XClZUVXJ4YFBJBD3vzWypDcLxx0iX3yBoP8WPC+D4/ZhO+fKL1svh8TDcQfQtRYigbcDYNmncNxeXG83Ah1da8EpvYYiTBzXwfUGAJ2SfRRVrUKIKeKty+G60FWVmliImlho1neqImhOxNAUwZLKCjRFYXt7C/3pDMurKznSP0h1OITjlYVDdFUlV7LxpCRv2SDLO1xtogDO9eg+N8xHPn4zX/hc2UVh+g0K2RI31C/hifMnWBatoiYQptjroNgKAcVgNFnAVDX2DPewIVHPYCFLslTAULSyWPU8mS3VvgqqfVNZQtP90Y2Bsuuo3j+VBbI6OrUjaw1OZZE0jM+5Gt9U0H9FZKqtYDSAbur0nxskPZqlfW0zuk9n6Pwo+ptgWzx+doC///Lz9A2myOZK/PqffntOD8F125bS0XZpsYzpED8JYqLFYsuWLXLPnj0zPstZFp954Xm+f/QomqKQKZWI+/2TQsHvWr2a37vuegLzqLxfiOFSL2PFwywJLkeoTUzlrbrT/i0pv8vK+oRTJEg2Zba5CUM9sUJVeXX0Wb7R9c8LG2d8Ox9o+TgAJXcIU51wx/z0IKXEw6Po5il5RTzpoQkNU/VhKv5Zvu/Ftl1mxSs/cGVyoqnxCcoGQBECZfzeyvFbP/kdU6+IqQdZYo8LSOSdLAU3T8kr4UkHEOPBKB9+NYBfDeBT/OiKMZlFtNjxuF6GfPHFchxD2pjGOiz7OFJaSFli0kEnxKQ4cqH0BpZzCikdFOFDUaIowo/EQkoL0DD0JRRLezH15VhuJyHfTcwnPDHFte5heRZFN0/ezVF081iehSvdcQ4eDUM1CahB/GoQn+ovc7vPEcOYmM/Tyb0uhmShyCtnuzA1jRs62iZdJZ4n+cLfPsGKtU08/8Qh3vWBa/n2V17ilz95J/2lPH5dx3ZdLMeltSrO/q4+rlnazLNHz3BNRwsHu/tpqYwxkMqSCPo51jdEfTzCqvoyF8lQOkv3aIpltZUc6R2krSrBuaFRGuJR6uMzV71T45A40qHg5sf/5Ch5JVzPQSJRhYKumPhU/+S90hVj0sUihKCQLdJ3ZoC69hrSIxk818MMmGiGiqZpBBZJBOVJST5f4rX9nfzDV57n1z560yw+/0jIR0tjxZwpgVu2bGHPnj1zTt4rcsUNENB1/nDnjdy1bDnPnD1DVzKJJ6EhEuHGtjaubmqas9y94GY5ldk/y6c0UuojZlQj1AlOgon7MdctuPDtqpfLRaSLIy1Kbom8myPnZOjJLzx1zJYOOTcz/j8fjptd8LkLgV8NXjJzRQiBikpQCxPkzRFaSSkpeoVLFh75lMCsVZQ27f/Tuc6nfyelxJYWvYVezmSPcS53iv5SDxk7Rckr4koXbxqfaNlIKahCxVR8BLUwFUYVdb4mmgJt1PtbiBkV6AsSqwAhDExjFZpSicRBCB+62oAnswg0mIw3yMn+C6GiqTX4jI0IdFxvBE2twXEHkHioSgxF+FD9MRQRRFHCXBihKb/0PNJ2ip7COc7lTtBTOMdIaYick8HySniUxz4p3zdj7H7CeoRqs47mwBJagx3U+Zrwq9MVjBb+Mov5fdy5atkc9wfe/jNX852vvMzwUIZvf/kl3vauLYSifnqP93NtRwsvnexEVxXqYuEZkoO9Y2lO9A9huy6267Giroq+ZIba6NRcdDyP3mSGeNBP53CSTKFEz2gKT0JdbEr3suDmGSie51z+JJ250wyV+kjbyfE54ozfJzl5rxShoKCiK8bkHGnwt9AWXEZjoI1IMEbb2rI4iH8eTpTFQBGCUNDHhlWN1FSG2bSmicA8ohSLxRW74p4OKeVkipMiLr4i7Cuc5aXhh2n0zwxWjVmD1Ppb2BjfOdnmBFzp4koHyytRdAvk3AwZJ0XaTpKyx0jbY6TsJFknPbnis6WF49mLYuLThD7jIbqcUIXKz7X/Js2Bxac1LRae9Pha1z9yLH1g3mMEggeaP8aa6MWrVadDSsmYPcK+sVfZl3yVvmIPlre4oM1c/fCpfqrMWjpCq1kRWUdToA2f8uYFLCb6anvlnGCJRMocUgoUJYAmFkeKL6Uk52Y5kTnIvrFdnM2dIOuk3zLLoyY04kYVK8Jr2Ri/huZA+wKVli7d3/Lf4DguqiImA3cn+ocxdQ3Pk5Qch1jAR+dwko7aSo72DrKqoZqu4SRNFTH6UxliAT+6qpAtWrRVlfmIRrN5OkeS1McidI0kaa6IMpDKUhePEAvqdOfPsjf5CsfSBxm1hnBmxX8WB4FCSAvTGuxgY+xqlkfWEVTLLqfL4cZyXY9T54ZY2lq1KEGJ/ytX3Lbrcmp0lHPJMXKWPWdUNu7zc2Nb2wxhg6AWZXvlPdT5W2ccO1TsYcweQkrJ2dwJzuSOTxrljJ0i62QoeHlKbhFH2njSnTcS/GbhSJuMk7qsbU5AQZ1XPBgmXCQuSWuUlD1aVqK/2PCEoMnfRkCb7TMFyDu5S45ldIHiElJKMk6Kl4af5JWRZ0jZows6b0FtU16ZdeXP0JU/w7NDP6LKrOE9TT9He3DFm34wXSl5rOsEAV1ntFggZvpwPI91FbXUBxcWxJJSknaSvDbyPK+NPsdQaQB5GSh5J+BIh6FSH0OlPl4deYbW4DKur7qdFZF1kzGMNwPPlfzoB69zx9s3YRgaUkpefvYYy1bVs7xuqkjHk5KXh/fjRlwcLYiIJ+m2LQaMEYaz3dSHqjhR7GJFpI32yJTfOREKkAiVFzg10RBSSuJhgyPpfXzz9BOcy50sz9/LBIlHxklxMLWHQ6nXqTJruapiJ9sS1xPWom/ZeKuqwvI3wSV+MVyRhtv1PP5216t8ce/eyWDJXFhZVcUNra0zyi0ieoKIXq4mnNhya0KnwqynwiznBr8+9hIvDj/xkx7GFQMpJUOlPh7t/xanMofHy/QvbiAUofBL7b/H0vDFiwsuhqQ1Mmf58fR+STyOpPfxcO836CtefoX4C+FKhzFrBL8afGsrbiRZu1xDENB06gIRCq5NfXB+gqvJc2VZI3TP2Is8PfjIomiB3yxsaXMye5gzueMsC6/mjtp30RxYgrLIGEtZfsvj9PF+XLesByklnD7eT1VNlKqa6RJukqJbojFQzalMNwW3RF9xmIgewpUeu0cPU+uvJO/OLXk2MT/O5E7wWP/3OJ09etHFyeWARDJY6uOh3m+wa+Q5bqm5l43xaxbsZpuzTSkpWQ7HTvfTP5gui79MWzSpqsKOrUsJLYBudwJXpOHOWhY/OHqUOzuW8XObNxHSjTn1j3RFmZFbOh2udHhl+FGOZ17n7vqPkrSGqTDrqDB+ssooVyJyTpqvdn6O7sIZQGAqJrpSzkefDwqizOr3FpAaZ2ecD5ZX4qnBh3hm8JG37BJZDKp99VSab20FJBCsTlSzOlFuR8KcWREXQkpJb7GLB89/jROZQ5dF9GIxcKXD0fR+OnOnuLH6bVxfdUeZ+GyBRimdzPP1L7zAvt1n+Ys/+j6KEHhS4jou9z2wbcaxAsGSUCNhPYihGIxZaer9VfhUAwksj7QwUkpRac4mWJJSkndzPDXwIC8NP0nRm9u4/+QgGSz18s3uf+FQ6nXubXgflcbcnC6XQrHk8Bf/+Bgv7j6NaeizeFtMQ2PN8vr/+w23qWm0xuI0RMK0xeKTxnkxN22k1E/OSbE0tB7Hs3GkTW/hzP/vDLeUkoPpPfQUzuJXg9xW8w5WRjbiU/xzvgynY8LP92aRssfw8FAvKEEvB5ZyfP/8V9gz+uJP3XgtC61GX6DAxnwE/aoQrK24NBfHdLjSZV/yVX54/t8v+VL7SSPv5ni07zt05c/wrsYPE9MTCxpLJBbgfT93PZblcN8D29C0cjZENB4gcIHhEULQFirXIlSaMWB2dWlrcDajn5SS/mIP3+75N85kj112l+Vi4EqXA6nd9BW7ub/xoywLr1m08T5yso/X9p3jk79wK5vWNM3p544uotwdrlTDrar80Y07+b0nnmB/fz/t8QR+XZtFElUdCvLeNWtnifdCecujKyaa0PDwSNnDVBhvXvDz/2acy51AIrmh6i5uqLrrp5aCmHXSuNKZkekyYbS/2f2v7E++9lN/KFWhsjwyW83lYtjX08eGxjpKjsPJwRHW1C9u5TVBJ/DM4KM8PvD9n+ru4mKQeBxM7SFlj/H+ll+mxqxfEEFSOOrn/g9eS019fF7WvzfdJyk5lT3CN7r+hWFr4LK2/VYwVOrnS+f+D+9s/DCb4tcsysU0OJKhqT7Bjdcuw9Avj8m9Ig23KyVf2befQ4ODhAyDs2Njc25DOyoqeM/qNXNSClUadShC5VDqVU5m91Nh1LE5fvNPvvNXIHJOFlWoLA2t+qnmjefdHJZbmiSrAih5Rb7b80X2JV9joayKlxNRPUHDBYHri+F8Ms1jR05iu+XCoYFMljX1i3OzONLhsf7v8vTgwz9xH+2bQVf+NF88+zd8uO1XqTEbFvRS8gdNnv7xAXKZIshy6faOm1aRqHzzuzQpJUcz+/la5z+RcZJvup2fFHJulm93fx5PumxJ7Fiw8a6MBylZDrbt/r9tuLOWxdNnznD/6tX88rarCOr6nN5Y5SI+bkWoXF/1djbFb8STLiEtdkmx4P9XMZGCuBBa28uJklug4OUJUc6ycDyHH/d9h9fHXuY/wmgDtAWXEVAXXl6sKQq6qpIpltBUhdtXLZwSFco+5ScHfviWjbYmdEzFxFT96KLMwSPxsD2bklek5Baxpc2bva99xW6+2vkPfLTtvxLXKy7OICkl//4vzxEImtQ1xMsFVEK8pdX3xEr7rRptBRVDMfCpfgzFRBnf7U1P97U8601n7xS9At87/yV8aoC10c0z7pPr9ILQUJSZosmrl9VTUxnmK9/bxb23ricW8c/yUs7l+74YrkhLFtR1drQ0E/X5iJomhqqOl18vHJZXpK94lubAcg4kXyTrpNhWcRu6MGn0t7I+dtVl6etoaZDuwsKKcOJ6Bc3BJSxE8mqxUFDm9UmX2QFfpDN/irbg8p8KtSeUMxlyToYqsxYpJbtGn+OF4ccX5B4RCAJqkIRRRZWvjoRRRUSL4lMDaIqOlB6WVyLrpBmzRhgq9TNiDZKxU/OmigkEKyPrF/UCq4mE+Nj2LYR9JhLmFDGYD1J6vDLyDE8OPLhoo62gUGFWsyS4gvbQCmp9DUT0OKZioinlyshyUZiD5ZbIOGkGiuc5kzvOycxhhkoDeCzumt35M3yn+9/4YOvHL1pv4HkSy3L48K/cRCBovuX5JKWkr9jN17v++U0Z7ZAWoSWwlKWhlTQG2ogbFfjVALowJlfFrnSxPYuCm2PYGqQzd4oTmcOcL5yj5BUvcYWZKLh5vtPzb8SNChr9rZPj97xhbGsfmrEWXd8w+XkqXWAsleflPWd48MkDhIM+1GlG2hhXgK+viS24D1ek4VYVhW0NjXxu1y6eOXuW+nAYn6bNmiBNkQi/ds21M6rxJjBq9dOZO4ZPCXIud5SYUcW53FE6Qhu4uuJGrq648bL09dWRZ/hG978s6Nj20Ao+2PIr/CQM93wQQrA2upWXh5/ghaEf0xZcTnNgyVsqbV8oPOmStpNIKenMn+LRvm/iXKLaMqLFWBFZx+roJpr87UT0KNp4peJc/Z0IHk6U8Y9YQ3TmTnI8c4jO3CkyTmryReFXg7QFly163E8dP81da5ZTsGxePdvNHatnVxPO1a+T2SM80vvNRRWIqEJjaWgl2ytvYWlo1eTuYP4+m/jVIFEjQYO/hU3xaym4eU5lj/DC0GOcyh5blAE/kt7LM4OPcEftu+Z1BaiqQn1jgoe/s4d1m1tQVRUhoLGlEv8iBYOllGSdDN/q/jwj1uCizq0267im4v8j763DJDnPc+/fW9DM3cNMO7uzzCBeoQWWZcfMEDuJEzscB74k5yQ5cTh2EiexHTuOY0ZZkm0xs1arZR5mbqaqer8/anBnZrdnJTnrc+699pqZrurqquqqp573ee/nvvezObSLkCOKwsqNTzrgUt349SDlrmrW+TdzQ8WdDOX6eG7iUV6eeoasmSn5s+PFSb7X/598pPm38aqzLkACTW/DNHrR9S1z6zocGlfuaGHvtuWb4zRNwetZnZjaZRm4pZQcGR2hwmdftMl8nmR+6YSOV9dXVOgTCAxZ5Gj8GTaHrqJo5ckaydc+WK1ie2J2z0qkjS3/cavff6/m50017+Prvf/KFzv/mk2hXbT6Ogjo4SWMjwUfRKWr9lV1ekok8eIUGTPFD/q/SspIrrhu2BHjiugNbI9cMcdwKCUznj0fc238mp86dxNXxG4gUYxzLnWCg9PPcS51ghp3AyF9qaXZxTCZzjKdyZHKF4hnL56dSWkf9/f7/4uMmS75c8qcVdxa9VY2BLdfEm94dn2P5mVjcAdrA5s4MPUMPxn6TsksFonk8dGf0ObrmJkTWboPlmW3+vd2jTPYNwEIFFXwtvdduerAbWFy//D36EqfLvk9LsXNVWU3cXXZLatqkDnfTEITGnXuJmprG9kVuZq7B75OZ/pkyfvRlT7DIyP3cnv12xEIFLUSo3gM3bFl0XrRsJf3vmVPydstBZdl4FaE4A+uvuaiQjhCiEVykwsRdVYjpUQRKnWeNZxIvEDU+fPDKhnuneD5h45y23uvRCiCJ+85SGV9lHXbmy74PsuyyKbzeBfQi16cfJwHR35A0ojb/PaJh3l24mEUVva6U1D4aMunaPV1LLu8VEwURnlk9F56MmeXXa4JnZ2Rq7ix4k1EHLHX5MFqB32VkCPCtvA+toT3MJYfImdmL6rlshyuXdPEd14+gqoovGnzxc+HhcUDIz9gKNdX6h7TEdjMW2o/QNRRfsFzUCia6NqFW+rnGjwslT2Ra6lxN/C1nn9luMQGp5yV5cdD3+FjLb+La5kHt6II3vq+K7BMuegeVbXVN/OcSBziuYnHSn5P2BHjLTUfpNzZik/zz9m6FSyTrFEg5HDPaZnMKqQIBBa2bMbJ6RHWh6uwpGQ4m6DGE0QCDZ5WPtz8m/xwhqJaGttJ8szEQ2wK7aDB04qqlqGq1654rIWiSe/AJMNjcUBQVR6kvjqM/n+L56QQYtnyx2qgCZ3rK+bF8dcH9yBmlMCWeyCcr6R2/muvNaSU5HNFTr/Sg8vjpGV9LV0nByhki7RtrqeiLkI+W8QyLRy6Tl1rBcN9didiKp7l3NE+aprLiVWF6Ds7wvjgNGu21NN7epgXHz3OlbdtobnDZgikjRQZM42uONApLSNSUJbNeFerbfPcxKMUreUlC7yanzur38328BVoyutzKc6KalW6lprvloqqoJ9b1q+Z6eK9cHCSUnI2eZwXJ58sefubQzt5e91H8Ki+JddhPJWz74cZ95wXjvWwtrGCSMDDVDJDOOAhkcrhcuoUDQMhBJlcEYemcvjsIJtaq6kLNvH+xl/jP7r+nvF8aRS77vQZDk49x57odcveBwdf6OTH3z9AOpVDVRV0h8bHfvNmKlfhVp4xU/x46Lslt69HHeW8v/HXcIgyvt91hDc3buJEfARdqLg1nRPTI7y5cRNuTcdCcnfPUXSh0hyI0p2cpN4X5tT0GBmjSKM/wqn4GGUuH08OdxJ0uNgRq+MXaj+IJc2ZCfSLI2tmeHDkbj7Y+Mm5kt75kFLSNzjFZ//zUY6cHEBa9t2gKILN62r5tQ9eR23l6pyJLsvAnc0X+cHzR8kVDHa21rKhYflGh/FEmntfPIGUkus3t1FfFppbJoQgnSvww+ePoQi4c/d63A6F8USaJ451cqRnmGQmj8/tpK0qyt61jTRV2CI33YOT+L1OYiEfhmlx9NwQm9suznFdLZ740ctEK4LEKkP0nh3m9Cu9RMoDHHzyFDuv61igPGe7cAAg4Sdfe5q61gru/cpT3PKuvTx7/xF237gBRVVweZ24vU5ilfPnYl/seraEVjdUEwL8mr0NS1pkzRRpI07aiFNznoDXhbBS3TCghXh3wy/T7l8dp/p/At85cJS+qThep4P2ihi3bmhfcd2ClV8VV7vFu5a31n5oSdAGuwr47OEuastDTCYyuJ06o1MpTEsSDrjpHZpifUsVQ+NxOpoqefpwF3s3NjKdzKJrColUjoOn+tm5vp4qrx2Uvtz1jyVNxllYPD72UzaHdi3Rq7EsycM/Ocyb3rmHxx84ym1v2cFPfngA9yrqtFJKnpt4bM7f9GLwqj7eWf9R6j0t5EyDBn+EsMvD+EiaiNNDoztCzizinLFAkxIcikrE6eV0fIw1wTJ6U1P0pqeo84Xw6U4yxQJjuRTxQpZ4IcuOWB1OxcWbat7LaG6oZNLBqcRhzqVPssa3fHNOOpPn05+7H7db509+/XaqK4JIKekfmuY79x3gb/7tAT79qbtWpRxYUuAWQoSALwIbsAdhHwJOAd8CGoFu4G1SyqmZ9X8f+DC22PUnpJT3l7xHQDpf4Av3P89kKstvvPEqNjRULrveaDzFP9/3NKYlaa6MLgrcAMlsns/f/zyGaXJlRxOj8RR/8e1H6ByZWFIa/9ANGT5x+xWYpsXJnhFqykIEvG5yhSKPHTjLxtaqFcsyl4pUPMu2q9cSivk5dbCbaGWQyrooZ4/0zWWoUs5kuTMS4JaUTI0liVWFaNtURy6TJxTzUdtcjqIIAmEv3oAbf3ie8uae0R++VNha2QaGLNqUyld5Gtyql3fUf/SSgna8kOE/zj3KNeXr2B59/ZUQwZ4sv6q1EUtKxlIr16yllBxPvEJnifVavxbizbXvx6etrG9SEQ3YlmTAyGQSv8eJaVqEfG6G9SSmZVE0THIFg5qyILGQl77hKaaLBm6XnQHqM92N7f4N7Iley+NjPy1p/4ZzAxxLHGRH+MolIwFVVahrjKFpCuWVQaIxP6ND0wRDpc2JxItTPD3+YEklCQWFmyrfTKtvHUIInKpGnTdEPJ9lZ6wOC4i6PIxmUxQsA5eqowjBpkg1TlWjLRCjOzXJrrJ61ocrSRcKjGVShJxuHIpGoy+CaUDBMHHqGn4tyO3V7+CLnX9X0migKIs8Pf4wrb6OZeeMjp0eYjKe5p9/6x1Ew/MaOY21UVoby/jEn3yLs92jbFpX+qiw1Iz7M8BPpZS/IGzldw/wB8DDUspPCyE+BXwK+D0hRAfwDmA9UA08JIRYI+X/XOdBwTB58Uw/X3jgefJFg5u3ttNUEUFKSffoFMf7Rti1pg6AY53D3P/cKRQhCPpcmJbF1vbaknQoVotd16/nyfteobw6xNar1/Lkva8wNjDFvls2ceZwH+lElkNPn2b9rmZOHuwmFc8wMTzN/jfv4MSBLirrotQ0l3P2SD8/+drT7L1lE/6wB9OwOPTMabZccXH2QykQQrFNFjTwaeFX1e2ooPKGyrfYtLxLOKcZM8+9/Qeo9URe08Btt1nbDJhqT2TRsps72tBVhfuPn2Fnw8o3V1EWeHL8AawSLnWB4LryW6lxNyxbpgN71LOprWpGOtZ+iAth/1QUQW1FCE1VaKmNoQiomrHW2tRWPTcUl5I56pkQCleXvYFXpp8vabJSYvHCxBNsDe1ZVAZQVYV1G2sp5IvU1Ef56z/+PoZhcf2tmy+6zdljfHnqGSZKVI9s8a1lb/S6ueYxRQg2RpZKV2yNzbfUK0JQ75sv25S57VFDFC/Hhkbpm0ywsbqKyXSWRneU3qlpBuNJFMU2AGmJrKMjuJVD08+XtI+nk0cYyw1R6V56fUxOZ4iEvAQD7kXftRCCSNBDwOdmKl46owVKCNxCiABwNfABAGnbeRSEEHcC186s9hXgMeD3gDuBb0rbKqRLCHEW2AU8u6o9u0Qk0jlM08LvnRdCLxgmn7nnSZoro/x/b7uexvLIXBlCSkhkc3PO2Btbq3j3Ldvxuhw0VtvsBqe+lIr4aiGEoKa5jJrmef/Gm94+zy0PlwdYs6l+Lru99d1XzLwRyqrDtG6sm1v3hrcuFve54wNXLfuZs63XE4VRJgtj5MwsbtXDGv/GOerXStocEsm51GECepR6z1If0FKxPriVvbH9q1al+1ngvzqfoMod5n3NVy96fTKTZU15lDdvXU/f1PJStlJKutNn6UkvPwl7PipcNeyOXrskaHce7iGTyFLRUEZ5fQxtBWVMAGXGMVxTF1+bmrb8ewSCqCPG5tBunigx6+7JnGUkP0iNe97MVwjBzW/chhBw853bWL+lnkDQU3LXZM7K8MLkEyWta89VvXFR9+2FUJgxUXBfQGfHmrFoe/R0J+OpDFVBP61lUSSSAz2D+FwOol4PV8Su51j8wEUprGCXBI/ED1DhWtp5Wh7zMzaRZGQ8SU3FPAtGSsnASJzpRIby6OpMTUrJuJuBMeDLQojNwAHgk0CFlHJoZgeGhBCzpmk1wHML3t/PMuoyQoiPAh8FqK+vX9VOXwgHT/cTT+XYs2Gxa7QQgj98636aKiLnPfUg5F0s8LK5rdruylQVWxrWMNHF6md+L4aLbk+s8PslQEqLzvQpHhz5AT3pM+StPCCpcTfS4luHMiO61J/t4vnJx6h1N7E7Mh9YdMVBQI/i18OXvDNu1cvNlW++oMCTlBJDmkwV0uTMIg5FI+Tw4FQW0+MEULAMJvMpDGkScnjxqs4lgdCQJtOFDFmzgFPRCDt86Iq66OaRQKKY4ZWpHmJOP1nDHh4LAalskfuPn0YV7aTyBU4Mj9IcW5yRg/1ge2nyqZI523ui1y7bMNV/Zpj4WIJiwaS8PrbMO18dhFDYGtrDM+MPl7SveSvHycRhql31i87ZkZe7aV9fg8vtoKm1dAkAKSXnUicZyQ2WtH6jt22uRDL7flMWUYSKQJlzBNKEjsQiYyQwZRGX4kViYUkTVehYmCjY3aZBj4uwx43f5STgclEbDjCRzqIKQaZYJOxx49J1Gh2tVLhqGCixDn8s8TLXlr9hyfW9rrWS2qowf/L393D79RupqwrbD/r+Se556DAb2qtpaShbYavLo5TArQHbgF+TUj4vhPgMdllkJSx3Vy8ZW0spPw98HmwHnBL2oyTUV4aJJ3P4PS7imflJmJ2tdUuC9ko4em6Ikckk1+9cw6neMQ6e7OM9b9i5Gsr2a4JZPeKV2pjFBeh852/ncPxFvt33BTJmCoFAFRqGXMr2cKkeDk49w8nEITYGd+DV7Ewgb2YZyfUwVRghGLq0gLIltGtJaeD8/XxlqpsvnXuMM4khCpaJrijUe8v4yy3vIOaaNSgQjOYS/MHBb3A8PkDRMom5/Hys7QauKV83Rwd7ebKLL559hN70OIa0EECLv4LfWHcbLT5bKGo0F+dzpx/kbHKYrtQo3+p5lp8OHgIg6PDwqw23MZHKcLB/EE1RuGbN8nTMpBHnVHJlR6CF8GkBNgV3LnuntG9v5vCTJ2jZvHIyY5l2xqiukFnPQkqJUTTRzqObVbvriTkrSqYHnkoe4dryN6DOhAvTtLj/RwdpaqvAtUorLonk4NRzJTUFCQQ7I1fOlWnsctY5ejMnAFjr38OLk/fh1UJUupopc9VzNvkSPj2CX49ycMrW3K9zr2Us38ca/07Opg7Q7N+KRw1QEzrPvxIIe9y0lc9y/V20+zeWHLiHc/1MFcYpdy2mHbtdOp/65Zv5wjee4kvfeoZszn5g+rxOrt3Txgd+YS+6vjqaaimBux/ol1LOFnu+ix24R4QQVTPZdhUwumD9ugXvrwVKe7y+BhgYjWOYFlPJzJydEkBbdazkOvX65irODUzwx//+E8rDPt53266fadCWUpI2BjkX/z7xwjkMmV0Su4UQbI5+gojr4rzi6eIEPxr8b3Jmho3BneyMXIMuHHyl+x+WrBvWo1S4qunNdDKaH6RJsxkUDsVJuaueiKMCrURJ1IVwKE6bWrZCtm5zegf4g1e+SZU7zG+su41qT5jJfIrh7DQBx8JJL8kP+17kjtrtfLh1P1mzwL+dfpC/P3EvG4K1cwHeoWqsD9XxgZZriTp99KTH+YcT9/G50w/w11vfjSZUvJqLW2u2MF3I8Oljd3NV+VpuqNwIgK5odIQrqd8fmTGllssrUc64KpXa5NLsbSfkiC49FxLOHOyivD7GuVd62HaDvR+maTHaO4HmUIlUBDn81ClS8Qybr1qL2+ditHccT8BNqCxAYjJFLp3H7XMhpeThbz7Lrps2Ud1cPnc/OBUXDZ6WkgP3YLaXZDFByGGPNBRFobYhxsjg9KKWd0W5eHNZykhwNnWipM/1av4lMqpnky+jKjqp4hRpM45fi7AueAWnky/Q5NtEg3cjI7kupgsjeNQAzb4tHJl+jFrPWjrTh8gYCdyqf9n9FMCaisVJSYtvHY+O/rgkbZOsmaUv07UkcAshKI/5+f2P38L4VIqpeAYhBNGQl0jIs8hQu1RcNHBLKYeFEH1CiHYp5SngeuD4zP/3A5+e+Xn3zFt+BHxdCPH32JOTbcALq96zS4CUkq3ttWRzBRy6RqYwPxT0OEvvRMvkC4xOpWiqjjCVzDIRTxMNvj5ekctBYnJw7O+YKpymwr0Dn1LL+emZQKApF2eKSCk5Gj/AZGGMTcFdvKv+V3AoTuLFyWWVAlWhUeaspit9mrH8ME1eO3ALYZeNziQPsjW8f9XHVONuuHC2jeRb3c+iCoU/2/w2qt3hucx5ObQHqvlY2w04Zvjf722+mk8d/Drd6TFiLpulsSFYx4Zg3cz+C1p8FRyd7uPh4SNkzAIBxY1Pd7E71sZ4PomuqNR7Y+wtm5/UlVLyXFcfp0bG0RSFzbWVXN22NOs+kVhqUL0S1gY2zbmLL0TnkV4mh6fJJHPUts0zqbqP9/Pi/YdZu7OFYNTPcPcYxYJBIWfz/PvODHPihbO847du54efe5DWLQ00dtSiagr9Z4dZv6d1yZ7VeZp5fvLxkvY3Y6YYyw/NBW45Y5zwj39xDxVVIVTV9px838euo6Z+5c5UKSV9mS4SJT7gat2NBPXFZakKVxNpc5oyZx0eNYCuOFFm/i2ETwtzpniAztQrhB2VVLiaODz9CB3Bq1anVeOqxqW6SmyHt6UdtoX3LTvhoQVxRgAAqDxJREFUrKoKFbEAFbHAomUXcolaCaWySn4N+NoMo6QT+CCgAN8WQnwY6AXeOrMjx4QQ38YO7Abw8deLUWJZi2+VyUSGrsEJppJZOpoqF3Vyrea0DIzGuWpLMxuaqxibSvHMkS7a6sp+Zlm3lCY5c4K1offQFnwbr7bA3Zs5OzPsvAaHcnFRIJ9mX1iZBS3qUlr4tCBV7qY5xbXVoCOwdcUGBYCsWeRYvJ8t4UaqZoI2rDwPsCXSiEOZnzSOOn0IIGMspm8ljSw96XFGcwlyZoGBzCRFa9YlvjSMpzL84pU7cOvLP/zzVo7u9JmStqUrDho9rctup3lTPanpNF1H+xALsrDqpnIqG8voOTnA+r1tVM1QP6NVIY4/d5b4eJLkVAajaOLyOtl54yYcLh2jYFBeG6F1S+Mi5TkhBOWuKhSUkkwsTGkylOufa4FXFMEb7trODbctYJEIQbTs4hNsZ1PHSzbOaPatXRSQhRC0+rcxXRxFQcWvhVkb2ItT9bA2sJe8mSFtTKMKDbfqZ0v4evJmhpCjnIKVJaDHqHI1rypI+rQAPi1Qso7JYLZviXlIKp3n2YNdXLmjZQlXe3QiyYEjvezf147LufL9cT5KCtxSyleAHcssWlbgWkr5F8BflLwXl4h0rrAoI4sEbOdu/0wjwERydRSbWaxvriSZydM3MkV1WZBb93X8TEslinCwLvx+uhL34NbK8Go1qMsEPY9ehS7mRwJSGmANI9TFlKSsmUEVasm6DnImqM1m5JY0iRcnSBiTDOd6WBvYuarj0WaEky6EvFkka+SJOf0lPaYC+nnUqpl3zV4NUkoeHTnG5888jCFNqt0R/LqLweylOc989pFnCbidbK6p4qq2xkXLpgrjTBbGS9pOQAsRdqw8EWVZFsGon1xqfn5majRBLp2nkCsiLUmsKsxTP3qJUFmA5HSaYr5IqCyAogj8C3jCiqrg8jh55p6X2XvbVrQFddSgFkZT9JIbhUZyA3O/i5kgXSwYi9QStYu0vJvSLPkBp6BQ72lacr0qQiWywMXKo9j3hVv1M5Q7R8aM0+Szlfm8WhCvFpypjXfT5t+BQ1md04xDOPBrQcZK9AWdKoxTMHO4tfnR8JnuUf7lK4+xsb16SeA2DIsvfP0pWurLVmUofFl2TipivlaWLy5PxZFS0jM2hWnJudq1EIIzfWN43Q5aay99Rn5kMsnnf/AsU8kMv/mua3n85XO865btP2M1a4WpwhmGhv8IVXEuGd4JFPaVf4py9wYwegADZBasCfC8Y9G6Xs02Z00ZiYsOyywsRvKDCAThGUEmgYJfD+NWfRRlftW65j4tQJmz6oKfqysquqqRNFYnsbkSRnJx/u7EvWwM1fM7HXcQcnhREHzx7CN8v291lbu7tnRwYngUTVFZV7U46M5KkhZLDIBRZzlO1bXi8urmCjKJLHXt83XSysYYkcrdaLqGqinUtFZw16/chKqr1LZVUswbqJqCqqnc9O4rUWcCtFAEt//ifsyiuURHxKV60BVHyYF7ojCGnNH9kFLy1CMnuP9HB0klsjgcGqqm8sk/vP2CLe8ZM1lyy71DcRJzlB7IhBBUu1upXqarVwhBo3dDydtaCEUocyPQUpA2U2TNzKLAPTqRJBzwEF6m3BoKuHG7HYxOJH/+A7dDV/G7nUwkM5wbnsCScknXYtE0efxY55L3VkT8DI7HX5VO//GuYW7cvYZDZwZx6Bqjk0k7s3+N0m4pJZ3xSZLFAq2hqG2GvACWLHB86ksE9AZ2lH0KpxpaZisCv+YHK4ksHkToHbDCTH2jZw0vTD7BK9PP0u7feGFNhUwnXelTeFTfnFOMxGI8P4AlLdJGnEbv6pzfI47yi6oMulUnTd5yjk73Ei9mCOqeRRQwWJ12zGguzlQ+zf7KDUQcdju5aVl0pZaXDhXYWbs5w/Fd+FkPnTyLKhRyhsF0Nsv+9pZF7x3I9pRc3445KpatbwMUCwbHnjlNTVsl3cf7KKuzH5yKYmfOC+FckLkt/F1zzN/SQgg0TV2W160pesm+mwCpom1DpwgHlmnx3BOn+NCv3sCD97zCXe/aw93feh7nRYb6k4VxMmaqpM/zaL45RtP/LMSqFDINq0DaTBFh/gGvzFx7y2m5W5bEMldv6nBZBm63Q6e9pozu0SmePdnDS2f62dlWN9MJJskXDb73zBFeOL1Ufa1vdJpsrsDpvjGi4Utr866OBXnw+VOMx9M8+tIZyiP+17RzUgIvDA/g1jTqfMElgVsIFb9eR9i5lirPPlaSgrUDmolQaxCKd6ZUMnLetgQdwa2UjVVwcPpZfFqAK2M3LQozljTJmmnOpU5wz+DXyZpp9kVvnBvSW9JCF04cmgtVaMtOal4IMWf5RVX5VCF4S/0u/ujQt/jb4/fyrqYriDr8pI0cPelxdkZb8OkrZ6rnI+zw4dGcHJzsYme0Za508tJkJ+oy++9WnZS5/LwwcY6bqjbh0ZwY0iTq8JPI5rl1YzuZfJEXexYzMSSyZE6yvV8rjwSHOkdQNYWxvglq2l5fU2tVKKtSSsyaaUxpMhuaNU0lEvMhkThdOj6/i/HRBOHoyo0v4/mRkppZAHyav+Smm9cbulL6A86YuZcWor4mwuR0hsMnB9i9pXFRQnLoRD+pTJ7qiuCq9umyDNyKELxl30aePN5FIpvnd75yH1eta6K+LEQmX+Rw9xBHe4fZ2lzD8b4R0rn5CamOxgpS2Tyaql5yaWNNfTnZfJEDJ/vxeZxct73tNW2+EUC1z89ELrMsvUyg0ui/lSOT/8Zk/hgerQpVODi/I6fR3YJPr8I2/VuPEBqoS520A1qYO6vfyzd6/43Hxu7jxcknCOoR8maWifwo/9H1d0wXJ5gsjGFKgwZPGzdW3DXX3agpOkFHGScTzxPUY6u2QAs7Lq6BLYRgb9kafnvd7Xy583F+9YUvoQoFCZS7AmwM1eHTXSgI/Lp7jk0yC1XYr89a2VV7wry76Uq+0f00z46dRhEKFa4gv9R2I9/pfXbJMbhVnQ80X8s/nvwxH37u33EoGjGnn8/u/ABXtDbw3QNH0VWFOzYtrtWb0mCqxPo22EYFKxoGeIGZ5tk+XuG53pI3u2qY0iB9AX3081GUxZmGHTeKqrBtTzOmYdHaXsWn/+h7OJ06b7hr+4rvl1KWXCcGmC5O8e2+L10WAmTdq9AKB0neXFzua66PsWNTA5/+3P3cet161jTZJZFTnSP8+NGj7N7aRH3N0qauC+GyDNxCCHa01vLbb7qGf/3ps4zH09z70jz306Vr3LRlDR+/bR+f/MKPODs0f+NEg16iQTvTHp5KXlLwlkj8HiebWquQQO/wFGsbL6yTvLrtQ8E0CTvdJAt5Qs6lmeRY7hUECuniEOni0DJbEVQ5fHjNg3ZtW5+5y7U2hL7YF1EIQUdgGx9o+g1+PPQtejPnSOfspoKcleFM6ihg83u3hPZwW9U7COrztUqbsmSSMzOYcmTZOuKF4CtxUlQVCrdUb2FvWRuD2WmyRgGP5qTaHSbssL/TmDPAF/Z8FJ82f84MyyBjpvjPfb9CUPfMbet9zVezv2I9o/kEHtVJo68Mp6JR6/Wjn/fAFEKwv3IDG0N19GcmAShzBfBqTlSR4yNX7sAwLcbTiye881Z+VQGwK316VaYBlwssaS6yX7v6Brtcdt0tG9m0vRGP14HXd+ER0WoecIniFM9PPnZJ+/o/CYlcIkylayq//uH9fOW7z3L/48f57n0vI4GAz8X+fe28/xf2oF1ELvh8XJaBG2xFtrv2bmBPez0vne2na2SSomESC/rY2lxNR10Fuqrw23ddzUQiw9rapTP1Qa+LP3zb9RQMg4660gv/R88NcffjR1lTXwYCykM+2hvLX7PJSQGUebxMZDPLBm2ADZGPsj78kQtuRxECioeQhZdAnRlaK8sPVYUQtHjX8bHm36cv20lX6hQThVGKVgGn6qLcWU2Lr4Nqd90SgwULi/7sGXTFiUNxrTrjXlgjNKXJeH6ajJGj0h3DoWgMZsdwqy5Cup8XJ49RsIpsDLXZNWdp4lI1EkYaU1oki2mq3WUYlsFobpKsmafMGeZcqo+1gQYyZhbnDE1QFQrlLj+qYqEJlYKVR0HiUjVUoVCwikzm42TNPDWechyKToU7RIU7NLe/tqbFOd69awt5w+Cps928c+c8DS5v5shZ2VWdj59HSOZNEyxL8u2vPMXIkK3bYpkWuq7ytvdfSUV1aMX3Jy5D5/bXA7MPOCkluWwRRRV43Q4+8vYreN9b9jA8Esfp0m3hqRnDk3Q6j8ulUyyYKKq46HzBZRu4wQ5MNdEgNdGV6z+72uYbLM6H26Fz45YLu3IvJ6o0ncxy0552dq9vWOltrwoFyyRTLFLvD5Is5PE7FtfyhBAI9BXp21JKEoVO3Fo5ur4Noa1BiMBM3Xpl/QlbEtNFq6+DFu/skF8yZ6q2QlasoFDpaiRrppHSKpmHO4uFk6FZI893+x5iW3gdp5I9RB1BpooJxnJT3FJ1BfFiClUoWNLixcljjOenafBUYWExlpui1lNBd3qQMmeYI/GzbAq1IZHkrQIPj7zI9vBaWHDNd6UHOTx9hoIsEtL93Fixm2Pxc9R5KilYBX40+DhtvgaGcuPsiy2vbhdyu7n70AlyxSK14cXXYs7KYFil1W1/vjGjK4zdIbn3mrXksnZmOTWR4qlHTuC6gG2ZJU0yRuk2bv83oFg0eej+I2zcUg8SXn6pi3Xrazh1bICaugjN9bbj06kTg0xPZ1jTXslTj59i09Z6GhovrF1yWQfuUpDI5+mcmGRrzaXbkj1yrpPddXX4ZhQCy8I+Pv+DZznRNYKuqVRE/Fy/a81rlnGbliRdLOB3OKj1r25SAuzOypfH/5Z14fdR4d4JwokkB1YGigfAddNFtzEfpC9+VEIIPGqAJu8GFKFcsJFmOZyvBFjtLmdDsJWHR56nPzuCJSUB3YcqFMqcYdyqk6gjiGmZ+DUPZ1K9rA+2EHL42RBs4YHh5yhzhmnz1bEu0ETOzDOam0RBIeJYWpZp8FaRNrLkrDxO1UFA9zEbhBq91XQEmzg8vTK/+PaNazkzOm5LqJYtrtfnzdyqH2Q/7xBC0NhSPve3lJLOMyOMDcdX1OM2pVky9fDnH/MPuOqaMD1dYxQLJqZhkkxmcTg1yirmKYa5bAG/34XDoVFVHaK3e/znJ3BPZjIk8nlqAgGmczliHg+JfB6nppEuFEjlC9QEA+SKRTJFg5xRpCYQYCqbpdK/mDYkpWQqm8OpqeRn7Jzcus5QIkHU48HvdDKezpCd2Ubv9DRbq6sYSRWIeTxURPy879b5JhOfx/GacrjdmkZzMExfMkGtz8CtrS4QSmlStFI2f7uoI3M/AeEH8gil9JLQamA3NNgX22q6Ds+HIhRCug9VCIK6j1Z/PQcmj+PWnHg0FxWuKM+MH8Kve4k6Q3g0F7l4gbX+Rp6dOMwjoy+xMdSKtkCtUSDYEGylwVvFkfhZtoXXzi1zqQ6K0oUQAoepM5AdYzg3zuHpM7QHGgloXjSh4dOWDzhCCByayvrq5c9r0Sq8qvPx8wjLsnjhqTMkZjSk8zmD44f7FndSnv8eLIpWaRZl/7dgNnD7/C4M0yKfKxKJ+pgYT+IPzDcCdWysY3QkjqIoVNdG8Acuzp66LAK3YVl89eArlPt8BJ1ORtNpbl/bzr0nT7Gvvp6fnD5NmddLzOsBBL1T03RUlFPl9zOYSNI9NcW7tiy+aB7v6sKpqvTG4wScTgzLwu900jM1ze3r2vnpqTP4nQ5aohEk8FxfH5pQuL61ZdEE5+uFrsSUbZ9kFAnjJl7oYjx7kFrf9TiUABO5I2TN5dkHplUgb06B1gjaOoQ7AFoLyBwUDy1Zf1aH26byiSXLJJLp4gQpI45PDcwIIF3YkPZS4VadXF2+HQUx97Oq2qbIqUKhxl3Gm2uvQ5n5HWBDsAUFhZsr985w+pU5I1gpJQ5F57qKnSiIuex3tgTW6K2BubVtvK/xNkCgCpVyZxiBYHf00ho0TGks2PL/O8hmC6STdgatagof+OX9lFeuPHqcvQb/X4KiKFTVLG1IqjqvSUnXVWpqbVaJ+wLlpoW4bAL3YCJJ2O1GVRS2VFXxyLlOBIKCaTKcTBF2uWcmqyS76mrZWGnLcjaEQvRMLW1jLvd5ebFvAI+uI6Xk9PgEbdEIXodOz9Q0o+kUPmcEKSFbLPJMTy8f3rHjdXG6WQ5Rl4fhdIqWkP2F9SR/wqnp/8atlVHluZJT019jIP3EirogliwCAoQL1GoEClI4Qd+0ZN2kEeebvf/GGv9GrojduIiXWrDy3D/yPV6YeIy8lcOpuNga3setVW9fVeNBqRBCMEvUVBEUckWe+ckh1myp5+HvPA8S7vjQNZw62M0LDx1l2zXr2HvLZoQqQEI+lef5Bw5z8kA3+VyBSEWQjp3NbL6yHdWpo6LaLc69Ezz1o5cZ7BolGPMTrQohAI/fzVVv3IY+16gyn7VfCix72u41ODM/X6ioCnHFtetQNQVFEaSSOZ5+5ARVdRGa2yqWeej/v3meXi9cFoHboap0lJchsANxYzjEfadOcVdHBw3hEO2x2FyQHkmlcGl25pgtFnm2t5fOqSnOTUzQHLH1toUQ1AWDHBsepS4UxKVp1IVCnJuYpMrvZ0t1Fd1T02iKQl0oOJPhr+XJ7m4q/T7c+upKF5cCr+5AUxQyxSK4YU3w7VS4dxF1zXcltgbfQqP/tiXvNWWeF0b/F7YJ5RQUnkO6brHb3QvPg/uNc+tKaSuWnUweJmdluSJ246JlT48/wOOj9yGRuFQPOSvD0+MP4FCc3Fb1jtfdqSaXyfNfn76burZKPH43Bx8/Qefxfgq5Iqqm8tnf+Tq1rRU0tFdTyBX5x9/8b04f7Gbj3jZ0p8ax589y8LETtG9txDEzEz/cM86fvvdfiVYG2b5/PQOdo/zg3x+muqmcq+/cviqneiklpmXN8e1NKee44nDpAf/nGZYp+eaXnkTVFIIhD+/7peu47/sHKOSLPP7gUT748Ruorjuflyx41W4gPzd4/Y/zsgjcihC8Z+sWiqaJQ9MQwO9cdRWaqiCA926bX9YSnTdDcGoat61t51bal/Bya4NBPrhj2wxDw/6MXXW1qEKgKQrv374Vw7RwaCo1gQCaovD2TRuXbYh5PaAKhfZIjIjTzmpdWhSXZk982YFFEHS0EnauXZK9mLKArvhBmpB/EJl/EozTQBHhvJHz0Zs5i8RizXnt7tPFCZ4ct32cry+/kz3R6+jOnOG7ff/Bi5NPcGXspgt2+r1WMA2LmuYKPvwnb+aLf/o9Hvzms/zN3b9FMOrjkzd/mq7jAzS0VzM+NM3Bx0/wS3/xNq5/q92pYhRNMsnsnDmylJJnfvwKqUSGP/vmr1JWHcY0rDmd6rf+6k1LdDsuhp8cO81tG9eSLRZ58kw3t6yfl31VhGLrd5RoersjciV+bfUT0v/TcChOnOp8Xdbrd/Gej15DT+cYLz/fyeR4knd88CpOHRvg3KmhJYFbCLGqJKDJu4bmGUnh1wISGJyOUzAtGqNh8obB2dEJWsoirypRmy3CLXyAV7pKN/29VFwWgRts3vbCoOlYoK+gCjG/bEEQU2YmHZeDIgTKeX59Lm3+cLWZAA7MradfwN/vtYQEuhNT+B1OpJT4HEvrWm3Bt+LRK1cQfFfwalUoihucb0Roa0HvwH7SLz0fk/lRBII6d/NcMiCl5ND080wXJ6j3tHB9xRtxqR7CjhhHpl/kcPwFBrI9P5PAjYD69ioURVBZHyMY81NeG0EoArfXSTph86S9ATeBiI8n7j5Aw9pqGtdWo+kqwfP8+uITKTxeF94Zc1ZVUwiXBTjR04lpmItU8i6GwwPDPHDiLIPxJAXDXCK0rysOu0xVgqOLIlSujN1Ig/fCDUwHX+5mTXsVXu9r3/I9MZ4kkcjR2BS75DkMoQj8AReZVJ5MKs+xQ72MDsUpFgxUVaFYWHouFJRVaaO0+jq4vfrtq9qvWRu6XNFAVxU0RSFXNFAVBV1V6HfHuffwSd64cTdF0+Kbg4fY4W1YwhSSUlI0TUxL4tQ1DNNCVxWKMz9zRQNFEThUlXNjkxRNk9byqP15hoGuKJiWRdG0ZgTzQFNe2zmjyyZw/ywhZRGzeBiz8AoSE1VrAuyLStHqZ/6euRCsQYz8s1jmMIpaiea8AqEsH1BLhQC2lFcxlctS41uafQkhKPes3D4sUNlZ/oeoihtQQSYg83V7ob4VHFsWrZ+3cqhCxaf55zKDoizwyvSzgGB35DqcM3KXAoUaTyOH4s8zVaIL96uFQOByO+ysTBU4nNqMW4st7DVb2ghGfXz8r97BV/7Pj/iDt36GNVsauPEde9l90yZcHsfcd9Kxs5kff/VJnv3JIbZf28H40BSHnjrF1qvXoq9C8xhgfVU5d23pYEttFaqizFFGZ+FUXChCKcnZ3ZImhWWYFVJKRkcTjI7EqW+IceaM3RoeCHpoaooxMZFmeGiamtqwrSQ3kiCVytHYVIbb7aC/f5JEPENLawXuGcEpKSUTEyncbgfj40mCQTeqqjAwMEVkRk9kaGiadCqPpinUN8QWaXZfCIoi2P+GTfzwm88TCLrZc1U7w4NTfPPLT5JK5vjIJ5bSURWh4FBLfxDlzewlGQz85MgpprM5OqrKaYiGeOxUF8OJJB/Yt52ozzuXnDk0lah3eQLCwHSCHx85RUXAxxWtDTx+uoubOtp44PgZNtVW8tipLhqjYa5sbeCRk+cwLAu3Q2cyneH44CgS2FRbyeOnugAoD/h4+46NqzqOi+HnInDP3rjzrSLz8/hiweulaU2b5FP/TD79VTTHNsBJPvWvSCuBqnfg9H4IVWuy/fryj5KN/3+AhVAqkNYIJP8Jd+j/oDn2XXrGIgRRl4eo69Im/4QQOFSbmietOBSPgutOEMoMLXAxdMWBJeWijq6+TCeD2R4CWoh1gS2LjAucik1HKpZofPtaQJQQNIQQbLtmHWu3NXH46dM8/J3n+Off/QbP/PgVfv0f3ovHZ9P+tu9fz83vuoJ//YNvUV4TAQEb97bxzt+8ddUCj6qisLWumgO9A+iqys6GmhkbMxtu1YM24995MVjIJQJEABMTKe675yDbdzRRLJrkskUmJlIcPtSLy7mBbLZIIpHlhe+dY//1Hdzzo4Ns297ImTPDdHTUcOBANx3razDPU5k7c3oY07R44YVO1q+vYcuWBpLJHOPjSWpqwtx3z0HWtFdx5swwd7xxG+XlpcmXCiFoX1/Dmo5qOxGYOafjo0mEYFlDBVWoeNTSmVrpElUEF6JoWnRPTPOxq3ehKoLBeBJdVRhPpskUCou+twvh6MAIV7Q20FFVTjKXJ5UrYFqSZC5P0O3CoaooMzTRjupyvA4HDZEQj5zs5C1b1/PY6U56J6ZZUxEjns1hWNayCqevBj8XgXs4leKrh18hWcjTHI7w7o2b+erhg8RzeYIumz64s7qG65taLhpMLbOHfOpLOH0fxen7ZUChmLuXzPTv4gr8AZrDrp1Kc5Bs/I9Q9Q7cwT9HKFGkNUEm/odkp/8QX+xbCPX14UzPYt4seHmesEAFdKSVRBgnQThBbQBl8Y0Tc1RgYdKf7abJ246FyXMTj1CURbYHti6yh5JSkjPt0oS+ykabnwWEEHgDbvbcsomdN2zgse+/wD//3jc4fbCHLVfZNdFi3uDckT7e/slb2P8Lu3G4dHwBN6IET8TlcPehE7SWRcgWi9x//Cx3bZn3+XQqLpyqq8S2d0miOL0kk5wYT1FXH2XTjEGwy62zdVsDiiJIJLIcO9pPKOwllcohJbStqWTjpjp+fN8rDA5Os3FjLes3LK6rCiGorAzxwP2Hqa+LMDQ4xbXXraOszE9/v63F4vY42LK1gXQqRza7MsfasiRC2Nu0TGvuPJ5vzxUrX97LEWY03VdR208a8SVOMqZpcfzYAC6XjtfnpFgwmZpK4/O7mJ5KU10Txu9y8tDJszRGw/ROTpM3DFy6jpRwbmyC4USK/qk4fpeTvqlpVEVQGwri1OdDYX00xPOdfUyms2ysqSCezfHU2W4KhknRsKiPBDk8MMwVrQ34XU4O9w1TFfTTFA3z+JkuRpNpNtZUkM4XcWgqF2JBSinJ5oq4nNqqvCd/LgL3s/29qIrg13btmTNOODQyzPs3b+Ozzz/Dr+/ex3dPHGN/U8tF53MtcwApM6iOHbaaHqDqmwGBZXQinHvtGlf+USxrHI/vVxDKDL1JqcDl+zipiXdQzD2C4zzDgtcShpWlM3E3w5lnMazMkqkvgWBL7NcJO9sQzqtAJkFqLBfk1/g38tjYj3ls9F4UoTBVGOfQ9HO4FDd7Y9efx4yQjOYHAYHvMptEi0+kSCcyxKrDcxrT7pksW1HnjyGVyNBzapB1O5oY6h6z65EunYr6GP6QZ9XB26GpODUNw7KYziQ4PTJOYzRsv6668WqBko2Cl3PKqakN89yzZ7j3noNs3FSHy+VAURQcDvtmTiZz+PxuPF4nqqrgcukoisDl0mlfW8X9PzlMT/c4e/a2Eo3NP7RjZT6KRZN1HTW89FIX+bzBsaP9TEymWNdRg9vlsDNHp446I3IkpSSfLSKR6LpGIW8wNjiF1+8iFPMx1DtJdWOMfDaP7tTQdc1ueBtLYpoW4TI/hVwRh1Mnny3g8jrJZws4XDqRCzj/nI9EcRrDKqIumHcqFAzOnBmmra2S7u5xysr8dHaOUlMbwaGrjI0mefvOjYwkUgTdLppjEUaTKa5b20LA5cQwTd61azMehwNdVbl5fRuKUJaUiNZVlhHzeSiaFn6Xk/fs2UreMLiqrRGnptJsRdhaX41TU9lQXUHU68HndHD1mkaGEyl8TgcuTaNoWViWhYQVacaFoslf/stP+aX3XE1NZajk8/NzEbj3NzXztSOH+NtnnuK6xmZuaG7BpWnU+P1U+uz/s6abFxsLCxEGoWOZPUi5BwDL7AdMFHX+wjKLxxEiiKI2LCoj2H8HMItHX7fjlVJyLv59Dk9+jpBjDZriYjp/iphrM3lzinjhHHW+G3Br5YAAoxNpDiC87wFzCGhccLyCRu8aNgZ3cnD6Gb7X/yX7dRSujN1MrbtxUSDLWVn6Mp3oQqfMWcnPBOd9ZQvb8W1WkP13z8lB/upXvkRZdZhwWYBsOk/PqUH23LKZts22rsxsNlvfVsXdX3yMn3z1KaS0hZCiVUF+7W/eTcfO1fkONkXDnBufREqJ3+XkzOg4NaEADk1FExoRR4zBbE9J2xrLD805yczC63XyznfvI5st4PU6qagM4HQ42LW7BUURNDTGMAwTTVPRdZXKqhC6rvKGW7fgdGq84517KBRNfOdNZvp8Lj70kWtwOnWaW8rRdZXrru9ASvB4HNx6+xYcDo0rr1oz9yCUUvLUTw/jdOk43Q6MooHu0JgYSXDFLRvpPTNMeU2Yk6/0YhomO65ZC8BA1xiWJRnqnSAVt0cfhXyRYMRLf+cYdc3llK0t/XpKGXGyZnqRW5DLpXPV1bY3Y21dBK/XSVV1CJdLt/df2kyz+kho7j0LtWWqQotLQU3O5aVUhRCU+bwMZRNkzSIR72K7s4bofAONKsSiz6hZ8BmlVPQN02JgeBpjlWYKl33gltK+yN+/eRsTmQx/+vgj7K6tnbnwVz/sVfU2dNct5JL/gGX0IISLQvZHaI49aI49Cz44hxA6nN8AIzT7dfn6KcJJDAbSj1Pvu4HtZZ8iUejhpbE/Z2/lXyBQ6En+lO7kfQihgkwBEqGW2fRAowcciz0hNaHzltoPEnNWcCZ5FFVobAzuZE90/5IGn6FsHzkzQ72nhTLn6yvmD3bG/Et//XZa19aSMwtsvm4t9WuqcDjtS/OX/8/bqWyIIqWkfVsjv/WZ99F1YoBUPIvb4+Stv3YTa3Y2zgkcJafSfPqjX6RpfS2/+tfvxOm2h8nJqTT/8qlv8r3PPcja//goqlr6tTOcSJLKF1CEoDkWYXdT3dwygaDSVcvR+IGStjWaH5pTZJzbhhBoukCINMOFbgpWlibnNhwzTUJ2ULUbyUb7Jjn50jmuvHMHLpfOYOcoT9/zMgho3lDHtus6MIsmLz18lMmROLtu2oS7KjQ3aRkKLa0zL1aiE0TKA+i6SjZTQErQHRpOt51BZzMFJkbijA5MEZihYAohiFYGmRhJoMxMJjtdOlX1USZH42iaSqjMDy4xMx9w8Q7KrJllojBGaIGWuxCCsrLFwbdiBQMCKSUpI0/WKOLVnXhU+xgzRoGMWSTkcKMJm+lhWCbThSwCQdDhRhWClFHgq2de5JqqVpr9UaJOb8lUYSkl3f2TDI5Ms3tLI/mCwbHTQ8v2D2RyRZLp1dv1XfaBG+Dl4UEePHcWIQQ3NLfgczip9tuayjV+P7qqUO0PlJhFOXB634NZeAHLHEQoAZy+j6K7bl40sSfUcqRMg8wAofm3yzRSZuxA+TrBkiZ5c5qmwB2owoWqOLCkgZQmuuqhwX8LXckfMZJ5gXrffkAiCwfBGke47lyyPVtnxM8bKt/GjRVvRsy0ey93vqrdDXxyzZ/hUJw/EwcS3aHBRg3pU5gupMhEi7TWNZCjiCktGnfXMFlIMpKbwqU6aL2ikeZ99ShCYTA7QVD3ciTVxUZHEz7NTe+ZYbpODPDL/+ft1LXNZ3jlNRFqWysY6Z1AWhJWwfxM54soQuB16MtSRmvdjSVzuacLE0wXJ6lQF4uiCRR0xYVXC+G0VmA7nBvl2//4Y+ITKfbdtg1VVTj9it1BuvXaDsIzk4vP/fQQpw500bG7hf/+qx/xK3/9LqQlUTWFQq6I0+NEUcRMk9Pi14r5Ims21qI7dYr5Iqqu4nBotjagJdl743ocTp1r79i6iA9fWRclUhagkC9SK8vx+l0IRVDfWk6xaI8WMrjwav6SykqmNOjLdNHsbV91aUtKyf0DJ/lJ33ECDhf1vggfXrOHQ5MDfP3cAfy67eH6ifXX4NF0PnvsCVJGHsOyuK2ug52xeh4aOMXTI12kjAI1niDvbtmO31G6A9M9Dx3miefP8G9/+S4mp9L8/l/90NbcPu9QpLTP62px2QduIQTXNjRxZZ09FJ7lXn985x5UIfiVmZ+/tGNXifm3RT71eVTHTjyhv2O5O1gI0Bz7yKe/hJF/Ad19J2ImkzDyLyCtDJpjz+vmziEQqIrLFpICdMWHKfNkzTF0xYcidByKn0yxE8xWcOxGaK0g3KCWr7jdVLHAd44f5cbmVuoCwUUZgAQ6pyZpCUdwKq6fqfNI1sxzPNFDu78OKSXH4j0zJrwTeDUXYd2P5lI5mxrEsGxurK1XIgnoHjJmnmPxHnZH1+IPedA0lecfPEKkMmjXWjMFDj97hgOPHufW91+16gacomlSHfTjdugE3EsleKvd9TiV0iYo81aO3sw5ys8zT7avL4vRXDdR5/INHJGKIO/6nTv4yp//AJjJKqfSVNTHqKyPEprJRg8/dYpb3nsVjR01PPeTQ0wMTfOTrzyBEILUdJpoVYi3/fob+Mbf3ouUkIqnCZcHufNj1/Of//v7WJbEE3AzPRrntg9dR8eueY/NeakAg2OHesmkC+y+ag1T4ynCMR+Hnz9Hf9cYV9+2mdhMNqzOlGG80ke5s7rk+YBzqRNcXXbzqrtTU0ae73S9wp9tu5VKjx9rpoz6tbMv8YE1u+kIVfDFU8/xyOBpbqldx7nkOO9s3saWaC0eVUdVFN7UsJHnRrt5X+tOWgOr72V475t3cfv1G4kEPUxOpakqD/LHv34rTsfikJvOFPjfn/nxqrd/WQZuKSXWzH87iIklmY42c9Gf//PiEAglRjH3YzJTH7eZGKgoah0O9x0oWqs9dHXuQnfeSC7514CBoq3BMk6TS/49uuvaxWWV1xiK0Ak5WhnLvkJr8G04lRButYxTU//NuvD7yRijTOfPUOteB/nnkMWXEfpGpDmCcOwA1/XLbteyJG5NJ1MsYknJ/efOMJ3Lsb+pmaFkki8feplbW9dwQ1PLa0pduhjKXWGKlkHeKpCzCgR0D8lihrWBOnSh4dPdpI0cEYcfRSiY0sSh6LgUnaJlsMZfizWT7da2VPKe372dH37+UR765nNoukqxYCAUwf637OJNv7h/1fu3o6GG3snpmXmUpcvDjihlzir6skvNq8+HRHIicYjt4SuWBCRFqEjkimqDHr9rZrJrfieqm8s58Ohxjj9/lnB5gHf+9u1kUjncMxmv0+MgncgyNRrn+rftZf2eVj7z6//FYOcoU6MJrnnzTjZe0c5nf+Or9J0eJp3I8rZPvoG7P/8w1711D+cO9y4K3GDfn/d+7yXOnhwiEc+wY28L3/v6s7zxrTtRFIGmLd9soqDS6lvHmdSxi54ngJ7MWZJGfJEbUynIGEV0RSXm8tqTjwLypkGymKfCZfcy1HiC9Kancakav7vxeu7uPcL3uw/zwTW72RheXCJcbRIjhCAc9BIOzpeSyqN+GmtjOM5r/spkC3hLFJZaiMsycE+ns3zz8UMoiiDi93DX3g1oMzXJfL7IoUN9NDbG6B+YwuXUmJ7O2OtGfSQTWcrLAwwNx4mEvYyOJWhuKqey0s4wLePMTDNNPWCBzCFlgWLheYrZH+CNfh1VqwOcuIN/Ri71j2STf2vXtIUb3bkfl/83bHGn1w2CBv8tDGeeR0oTRei0hd7Oi6N/Tn/6USxp4NfrqfS/GRQngjx43o4w+21O9woIulxE3PZEiyUlvfE4LeEIPoeDtkiU9miM65taXlehrdmHcsE0cWoaihCsD84bVtS4yyiYJiyYDxJAuTO0hH6WKhSYyGZxaRoxjweEQNUEd3zwWq5+43ZGB6Yo5Iq4PA5iVSECUd+qKFezGEmkePpcL+/bs5X+qTht5Ys77XThoM2/rqTADXA2dZxEcWpR/RbsElnYUUXIURrNVAjB1ms72HptB4Vckb/6xS+QnErjdDsoZO36dDFXxOV2oOkaofIAmkPDH/aRms6gairh8iCarhKI+EhNp3F6nDi9DvxhLw6XjmUubSwyTYuuMyN8+BM38uV/fgghbIZLIp5FURW7yWkFPZg2/3oeHPlhSXXuRHGas8njbAuvrmcioLsQwMsT/awJllMwDcrdPhr9EQ5M9LG7rIGXJ/q5urIFQ1qoQvDBtt3cP3CSRwbPzAVuXVEZzSWp8gTwaI5Lvi/qq8P89sduQF9mpKdrKjs2NuB2rY56e1kF7tmMZjSeZn1DBXvWNiAEqMrCGxbS6Rz9/ZPkCwYDA5MIBIoiOHV6mGjEx/h4iurqEEeP9dO+poqBgSkqK4NAnmz8/0MoZXij37BLCwjAwiweJTXxdszCAVStbob+F8Yd+BNcvk8gZQohfAglDK+T5OkshBCUu3dQ7t7BLLOi1nstzqowY9mXcSgBqr1X4VbLAAuUMmTqnxA4wfMLpX0GcGNzC0/39XJoeJhtVdUUTZPpXJao+7VXBYT5YPu5F1/gmb5e/u32N1J1npb6g+fO8jfPPLUoG20IhfjcbXfMSRZIKXmip5u/eeYpJjJZNEVw+5q1fGL3Hty6jlAEobLAXOng1aJvOs6OxhryhsFIcvnGkPWBbTw+dn9J0qWJ4jRH4y9zReyGRdeRrjhJFMdQUIk56y+6HSklfaeHcLodTI0lUHUVl9fJup3NvPjQEQp5g2ymQLQ6RCFX5Mgzp9F0ldH+CcpqIxQLRY48fRqHS2e4Z4yb3rWvpKKEogi8fhdnTw5RKBj090zQ3ztBtMyPVTBwuXXy2aUNSbNlpTJnJUO5/osfH5IXJp9kc2jXqsw7XKrGb2y4lm91HuTuniNsCFfxzpbt/GL7Xv777Es8M9LF+nAV+yqaKFgm3+l+hclchlzW4Fe3XDW3nV9o2sJ3ug7y/FgPH23fh0+fL5OZlkXPyBS1ZSF0VWE6lSXkcy8bF5xOneqK0LL7qmkKH3v3VatuDLusAjfATw+comtkktF4iuN9I8T8Xt60IOMWQlBeHqSiIsDERIqqqhCKIuY6J9PpPNGoj+HhOLt3teDxOCgU7JtJyiym0Y3TewWIeT6vlAowo6u8QE/BXq4i1BjwM9DsWABxniCPECrl7m2Uu7edt6aKdN2O4FbAJJfoYmRokMa185NfRtHkwGPH2bRvDY0uP11PdVK938twKkW138/Gigqcqso1DU2cnZwkUuNhYmiaYMS76hbxlSClpD+R4H8//ijHx8bIGkWK1tKSQH8igWlJfnXXrrn5jIDTuUiRbzCZ5I8ffYRrGht53+YtdE5O8aePP0K13897Nm1+zR+qG6sr+f7Bo5waHuet25fqdgshqPU0UemqYaAEWqBE8uzEI2wL78OjzU9EFq08EknKmFwxcOu6xsa9a+Z46xPDcQ4+dhyHS+fdv3sHbq+TK9+4nYe+8QxP3/syb/vkLbg8ThwunUKuyIPfeIbbPngNsaqwPQFZMHjg60/zhvdfTWVjGet2teDxuWnb0ki4PICxZimzSAjBm96xm2/951MM9k3ylX99hDe8eTuRmB9pWjSvq0Z3LD/761LcbAhuLylwA5xLn6A7fYYW37oVv1cpJb2j0yQyOZoqI5wZGKcq4udNoU0UTZP6sjBnB8aRUvL+xl2cG5ygraKMvpFpMrkiv9iyj9P9Y5xOjlHvtUd2liXxZB28q2wHdWUhzvSMUxsLMh5PUzQt1taVcbp/nIDHhaYqnOobY/e6iz9slzuXl3K5XnaB+6ZtbRQNi6JpkskXESzOuJ1OjfXrawCIxZa21s5iuWVCeFH1DRQy30VR61G0JpAFTOM0+fRXUNVGtPOodJcj7O7GcXTyqFhgnAOKIHPkkyMcfLyF5FSaivooPSeHWL+rGSQUckWmjo5iTudxOjT21dWTy+Q5+tQ53D4nsYiP8aEMCVeK3tNDdOxsZuDkINNjSWpbK+g6MUBjezXR6tU35hiWxd8+8xRCCH7niiv58yceW/a4JrNZ6oJB7lrXsShYL1znka5OTGnxq7v2UObx0BKOcHB4iO8eP8YvdKzHrdt1/HShQMjlomt6ipFUmmq/n8ZQaNUKkGsrY/zJ7fa8gbkCA8CpuNga2ltS4AYYyPby8tTTXBG7cS4g6YoLUxbxaqEV3+dw69z6wWsA+6bfcvVatly9dm65EAKn28FtH7p20fuEEOy6cSO1C5g2Qgh23LCB+vb54HzTu68A4Oq7dgBQ27o897q8Msiv/u6tFAoGqqagqrbK+unDfXh8Llo31OAPLh25CSHYEtrDk2MPlDSZW7QKPDx6D/XeVhwriFTliwYHzw5wzeYWUtkCfaPTnO4fQ2C3wY9MphAC9nU08syxbpqrozx3ood4Ksu1W1rpHJzAOu97NS2LA6f7uWXXWl482cfgRIL+sWnSuQI1sSB9Y9N4ZpIat1NnaDKx4jHEk1lOd46waV3tksnJS8XPRsO0RAgh0FSVwckEh7uGuPeFE9z74okVb5bVQ8cT/F9ozr3kkp8hPflR0tO/Rj79FTTHbjyRf0MoSzPr2eae2frswr//JyAxeWH0z5jInwSKYHaBEgYlBKhEKoNMDE8z2DnKYNcYQz3jc3LIFfVRmjfUzrEDcpkC6USGoZ5xuk4MUFEXIRD2UsgXyabznDrYTfvWBk681MlQ9xg9p4cuaZ81ReF3rriSv7vpFmIez4rnzi7VuFesJ0rgleEhmsMRIm73XOv1jupq+hJxRtO2Fshj3V18+Ec/4K+efpJf+/F9/K/HH+Gd3/s2//rSCxjLZPorwZKS+46cQhGCXNHgp8dOL7+igG3hvQQuEHQXH4fFQyP3MJYfnruWMkYcgYJL8a34vtnjXdgUVsprG/etwRv0nPda21wn6YX+Lzknlq3HffLYAE6Xjq5rvPDUaQ48ewa314llWhekuFW5a2kPlC66dCp5hJennlnxmpl9EPeNTtE3Nj0TRxRCPjfRgBePy0E2X2RwIkHA66JvdJqgx0XQ5ybi9xDwuBidTs1ZaswiGvAQ9LqIBb1UhP2sb6wklS0wncqiqyqj0ymGJhPE0zmmUlmmV+BjHz01yKc/dz/J1Or52ivhssu4ATRV4aWz/exaU0f/eHzVAVJKiSFt2pjNTIGUUUAAfr0Gzf/nOPxZBtLD1HkjCOFBCjeWZC4wg91ab0pJT2qSmMuLW3Xw9Ggn11S2MpJNEHOtfIO9npDSIm9OIYUf1EbwVCOEGykNHP4GapoEhYJBJpkjVOZH01QSEylGeicIlwdwuh2LbsiR/knq2yqJVoYIRHwUCwbJqQyj/ZNUN5Zx9IVzNK6rBgF1rZemzyKEoDZgZ+qWXJ7xLIGpXI7JbIbPPv8sUsLaWIwr6xvwOex9tqRkOJWi0udbxHwp83opmCYT2QwNoRCGZXFibIy1sTK++uZfwKWpfO/4cf7xuWfZVVPLzuqakkoqhweGeexMF0OJJJYl2du8/HBYIAg7YmyPXMGjo/eVdE6miuPcPfDfvLfx4+iKg4Qxii4cJTvFSClJm2l60j20+9tRZ1gpZ1JnqHPXkTWzJI0kDZ4Grv2FXXPvmcW1b9ld0ucs+VzLNgceGZpmoHeC627eyMjQNLV1UabG4viDHuKT6bkGnfOhoHJV7CaOx1+hKC/uQ2lKkx8PfYdadyM17oYl35uiCG7e0U6uUMTt1GmqiuB26DMa/iqWBGakWhurIiQyObJGkU2+KlRF0FAZRnMomDPnpmiaCAGb2qqZTGfY2FRFY2UOVVGojPi5emMzHpeD/W4nmqagqyp37luP27F8WTGRyhH0u/G/hjK9l2XgrouFePtVm4n4PbRWxdBUBcuSFIsGSPsGN4omDqetpeDxOhaxBdJGgfsHTlDm8jGRTxF0uNEVFU2o+HQnyWKOTeEaTqckdX5bl3gkm+DpkU58upOCZaAKBa/mYCKfpszl41RihGsr2gA76z4dHyWou1c8htUiVewnZ0yWtK4pcxTMJGDXvmcpGEJouAP1tCyTzCwcDi+Epqts2reG9q0Ni16/4W1Lb+raFjtov54WVNuqqnimr4+e6Wmmczm+duQQHWXl/P3NtxDzeO0ykWHg1BZfuo6ZoXrOmJ8c1FWVd23cRJnHzirf0tHB144c4uHOTnZW15S0P1tqq/iVq3fTXlGGIrhgmUURClfGbuTg1LNMF0v7Lo8lDnLf4Le5o+Zd1HlW53spkRxPHMeUJqP5UQazgzR5mwDIWTncqpu+bB8RI8Lp5Gm8mhdL2nTCGncNYcfqaHYL4Qu4+NDHb+And7/M1774OJqu4vG7qG+KMT2RoqJ25W0LIWjytrM+uI1Xpp8r6fPixUm+2ft5PtD0SaKO8kXB+0DfINVBP8OJFD2TU6yvqmByIsvZsQm21FRxfHiUCr+PkWQKj0OnvbyMZ7t7ecvm9fZ2pKQvnmAincHp0Hiuq5edDbVMpDNoikrM5yXodSOl5NrNLXhddhIRCcyXgtwXmAuqrQqRLxhkcsXzulQvHZdl4E7l8tz34gmm0jmqI37ecfUWjILJE4+dIJMp0NBURm/3OIGgHbBqaiO0rZmvxRnSJGMWcGs65SKAKS3KXX7ypoGUkrZAOZqikCzmyJu2y7pH1cmaBSRybv1kMY9L1VGFQt40yFsG8YL9tC5YJoniazf0OTX1NToTP6TUNv6VhPtXOzHnDbhp37o0i7ngZ79OJSJFCD6ybQcf3rp9Lrt+caCfX7nvXr53/Dgf3W7XXbUZofqFMC37caIumNR1aRplHu/csXl0B5U+P73x6UUSwReDz+lAVxUMy6J/OkFdeOUaf9RRzjVlb+BHg18v6QEnkTw1/iBCCG6tetuqmp8EggpnBbqiM5IbodJVyWBuEF3o81LIUjKWH6PMWUZXpouMkcGUJvWe5UcOs+bRk4UxBIKoc5mGLgFenwuXx8Fb3r2X5588zbf+80l27G2lrCpEWVXoovuuCpUbK+7kTPIY6Zkk5GLoy3bxX93/zLsbfolyZ/XcecoXDfqm4uQNA5/TSXt5jEdOdxJyu+YkVYNuF0XTxKVreJ06VQE/rhlFQCEElQGf3dkooSYYoCYYIFMoMpnOzOnfCCHwLWjAklKSMdNMFsaWaP4sxNrmSvZua+bb9x7grbdtw+dxnnfxCfQVuO8r4bIM3IMTCRorIlQUihQNE0tKdIeGJW1H6anJFKZpkcsV0bSl6l5B3c0ddRvRFRUVgQR0RSFl5HGrDrSZJo59ZXUoooiU4NOd3FW/eU6kyrBMXKpO2sjj0TSQNVgI9pU3oSnKTJeVA14jyRILkyrvlTQF7rjouqZV4NDEZ16Tz/1ZdkiWAkWIue9AEYId1TWsiUY5NmYL1KuKQtTjYSqXWxR84/kcihCEXAs0QFisOSYARazOkz2Ry/PTY2e4Y9NaUvkCB/sGeefOzSuuL4Rgb2w/RxMvcy51oqTPsLB4Yux+Jgtj3FnzHmKO5cx2l/+soB60ta69HvoyfdR56ujL9pEwEhQsOxGJOWP0ZfpY519H0kgipcRznhG0lJKsmaE7c4YDk09zMnmE26vezt5lAreqKrzzQ1fPNdrsvaad9vXVeP1u8tkCulO/qCmDEIIadz3Xlt/Kj4e+XfIoridzli90/i131byXtYHNqEJlc20V/dNxyn1eZkvr6UKBgmESdLvYXldDxOumLhREUQR+p5NczKBomnONfbWhIIZlEXA5aSu357k0RSHodi/S0rbLsAYjuQEOTb/AoenniTor+MXm316xw3MqkWEqkeHpF89x/xPHiYa8i86PQ9f4w197A5WroK9eloG7Imzr+h7qGiRfNObsf/bfsJ5Z2wTTtOjpHiMS8S2SsgT7ogg5lpYxQo75i1UUTxDJ/iVkMhhKGC30Wbz60pqcQ1Ex0/8B1iiq//fwaPbJ9Wir73a6EAQQca6j2nPVRW9a08pzcvorS163s6wCQlxaLW1hJn25BHRLSoqWiVfX526LjrIy7jl1ikyxiM/hQErJqfFxQi4X5QtcTXKGwUQmS7U/MPf3aDrN3too0pKc6B2lvaGc/pFpAj4Xo5NJUpk8bqfO2iY7eI4m0/ROTfPo6U5UoXBFa8Mye7kYLsXNndXv5vOdf03KWJltsBASyZH4AfqzPVxffgfbw/twq96Lfg8hR2jJ7wtfq3LbJbJg0B4lVDBT7pJ2h2bGTNGf6eZ44iAnE4cZL4zMGW6s9IgTQuD1ORf9ncsWUVSFiaFpes+MUN9WSdPaqjm52OW3o3BV2U2cTR3nVPLIBY9zIcbyw/xn92fZGb6Ka8tvJeasYF1F2QJ6r+SqlkYyhSKVAd+y5a3W8+zKHJrKmpmAPXsFNccic/dE3swxmh/idPIox+Iv05/tJm/ZI+7wxeRqJQT9bm6+pmPZxbqmLumovBguy8DtcmhUhHy8ed9GhqeSM4FboC84OF1XaVtThZ2grT7ICK0VPfgXmNl7MbPfYiWzAgChRJCXqEZYKqq9V+NUwyVmWioOJWjLGlkLHFVkBooHwHXLJe2DlHDgsRNsu7p9Tl/iZ4mcYXB2coKmUHhO//rhznN0Tk3xi9vsMokQgv2NzXzllVd4qPMct7atYSKT4YcnT3BVfSPBBRl33jT54ckTtETC6IrKY91dDCaTXN1gmxQcOjVA2O/mkRdOc9f+TfzkqeNcu6ONSHA++LeWRfjN668k5iu9KUkIQb2nmdur3sF3+79ckjvOLKYK43yv/z95evwhdkWuZkNwOxFHGeqMiuOlXOuzwceUJlkzzURhlJ7MOc6lTtCX6WS6MIVVgmemlBLDMFFVBdNczKo6+GIXLWsqUSyLqgY7AKYT2RUnKGfhUty8ufb9fP7c3zBRGCn5mApWnqcnHuJw/EU2h3axPXwFNe56HDPuTWGPm7BndXNQ805bkryVI16YZCDbw7n0SbrSpxnPj1Cw8qvaJkB5zM8nPnjdqt93IZQUuIUQvwF8BPsRfAT4IOABvoUt/twNvE1KOTWz/u8DHwZM4BNSyvtXs1M9o1NMpbLsbq/nyWOdvOWKTYvMg2FWd9ma+X1pfWiWGWIHZIXZDsT5Y3KC1oA4T6VtybEDivuu2fOwmsNYFao8V5S8ru05+Uc4zFPI1D+CmL05CghlnvUhpaTrxCB9Z0do3VBLZX2UYy92kk7m2HLFGga7x4hVhRjtn6KqIcrESJxsOn9RTfNXC5ubryx5DOaMIp966AFMSxL1uEkVigwlE7x9/Ub2N81raLfHYnxw61b+8skn+MaRw4xn0vidTj62Y8eibQacTs5NTfLe738Pl6ZxamKcO9asZV+dXd/d3lHHg8+dwu3S8XmdeN1O2hvLcZ03gXR2bIIvP2vLtu5pquPqtqaLH6MQ7IpexURhhIdH7sG6QGJwPiSSoVwfdw9+jQdHfki1u4EWXzt17maiznJ8WgBdcdgKj3OMXjuDNqWJKQ1yVpaMkSJenGKiMMpIbpDR3CDjhVHSRqKklvMl+yXha194nD1Xt3Pf9w4wPZmay2XGRxJ84g9uJ5PKM9gzzs5rbcGvUs5ThbOat9d9mK/0/BNpo7R69yySRpynxh/kuYnHKHdW0eRbQ6OnjQpXNQE9hFNxoQptxmFeAPYDx8TElCYFK0fWyJA04kwWxhjNDzGSG2QsP0SiOE3eyrO64trPBhcN3EKIGuATQIeUMiuE+DbwDqADeFhK+WkhxKeATwG/J4TomFm+HqgGHhJCrJGyBDfVGUR8Hh4/0kkqmydfNBc14Fj5F5BmDyhhrMx3kTKH6tqP4nmb3fItbJU1WTyAmfk20hxEqLWonneBvqnk4CulRBaexco9bJ8HfQ2K+61LOhpfK6zmoSCEwKtXIoUBjo0gQvYCmYPi4bn1LEvyxI9eZtO+Ntw+F6cP9XLomTOEon6evO8VNuxq5vuff5RQzE/TumrKq8M8/L0X2XvzxgsOcV8NpJRsqqjk87ffSdS9OCMKOF38y613cGR0hNFUCr/TyfryctZEY4uof6qi8NHtO9ldU8eJ8TFCLhd7a+vmeN1z6wmFP77mWrqnpxlIJPilHbvYWVNjM1CEoLE6wrd++jJvvWkLAkHIv3zL8vGhUdZXlVMVDDAUL630ATbt7abKu8iaGZ4ef3jGhm51yJhpzqaOczZ1HIFAVxw4FRcu1W0HbzQQNkXUkAZFq0DBylO0ihiygCnN14wFJAS85T370HQVr8/JB35l/5zS4iM/tq87f8iDP+7BNK3SJ1mFYI1/A2+t/RDf6vvisr6cF4MhiwzmehnM9fI0D6EJDcfMeXIqTlShYyswSixpUpTFmfM0e66MS/p+SoElJaZhLSu+JaXEtCTqKm31Si2VaIBbCFHEzrQHgd8Hrp1Z/hXgMeD3gDuBb0op80CXEOIssAt4ttSdKgt6uWNXB6PxFHvXNixqxpDGCYzkZ1Gcu1Dct4M1jpH6N1SZR/V+BKREFp6nGP8dVNcdKK4bkIWXKU5/Ej30d6BvK/0EqZUIvQMz+x2E2YviLk0H5NXCvriKiJmMaumXbWJJA0W1KW1ztT0coM1ng4oiuPW9V/LiI8dJTKVxe20d4rKaMJV1EYJRH9MTKRraq1A1BaHYYkSvJwpFk77+KRrLg7xyYoDNa2rQNZVsroDH7SCkubihsZmDJ/rZ3FKD06EtW6PUFIXt1dVsr155xCSReHSd/U3NS5ZZUjI6kaQi6qepJooQ8Kb9G5f9rLbyKFGvh3uPnGRjTekuLkIIdBy8sfpdCARPjz9cUkniQsdTsPIUrDxJI37J27lUCCHwB2xa3Fves5dIzDd37W3e2cTkSJz4eBJVVReVNUvd9uaQzTX/Tt+XSmaarARDGhhmiswlmA6/1ugfnOI/vvU0v//xW5aM5vIFg3/44sO86007aaiJrrCFpbjoXSqlHBBC/C3Qi82heEBK+YAQokJKOTSzzpAQYnb6uQZYSM7sn3ltEYQQHwU+ClBfX3/+MqqjAaqjK82yWqj+30KottykKjNY2e+get4BwoWZ/iKKY5+9jtCQzuuR1jhm+gtooX8u5bBt+o/WjFSbsIpHwSxNW+G1wompL2PKAhujvzJjCjyPeKGLl8f+mh2xX8WvLhjIWVkwe8HzVvtP0+Lws2fIpvOU14ZZu7WB/nN2N2VNUxmHnz3LHe+7ks7jA0yOJug5NcTUaIIXHznO7hvWvy517kyuwNGzQ0RDXroHJ4mnsqxvqeLZw90010QYnkjidTlIZvI8d7ibq7a18Hok/9KSDI7Fue2q9eiaOte1uxyuam0EoC4cLNkpfBZCCByKkzfWvBuv5ufhkXtKajq53BGJLiYEtKyppKmlnOcftZk0hfzqSzGKUNgS2o1LdfPtvv9gsjD2muzr/zRy+SJne8aWtNWDfX2c6x2nb3DqtQ3cQogwdhbdBEwD3xFCvOdCb1nmtSV7LKX8PPB5gB07dqxqLCeUGEKZ53Eq+hbM1BfAmgQliFU8heb/DWbNgEFFcezGSP2LbfU1W1q4TCExGMm+SMi5ZkENcx5uLUbaGGYi9S387haYbdOXOWyxLBuKqnDdm7YjpZwLwnd95Fosy0LVVEK1QXpScW7o2AUCghEfG/e0IgSM5jOEcOHWXlund6/bSdDvQlXsUoVhWpzsGgEpyeUNWmtjDE0kEML241vuYi8FHl2nxh9YxOteCFVV2Lnh4gyRhaOdgPvSpHyFEDiEg5sq76LcWcXdg18nXmKDzuUIy5J840tP8Ma37SKwoI4tFEFlXYSBzjHCF9ARuhCEEKz1b+Kjzb/Dd/u/zLnUyde14ev1xPxk59LXZlEoGOTyxZVUcFdEKePiG4AuKeUYgBDi+8A+YEQIUTWTbVcBozPr9wN1C95fi11aKRmzB7diSUOosPCGFE7AQmIgpAEYi/SyhRD237Jg+zJe5rCkScGM49frll2uCTcOxUdOVIPn3XP0PymLYPbNrSeEQNVUJrJpnh/op9ztZSqfpc4X4mx8gl0VtUzk0kwX/RwYHSDkdJMq5Im5vRydGOGmhrbXPHDrmsKuDQ24HBqb19QgsaV8M9kCPo8TVRXUVYbn9v9ifOCVcE1DI3tq6/Dor+3+vxqoQmVbeB9V7nruGfwGJxOHX1Xp5H8KUkpGh+PLpmjDvRN4Ay4mRhLUNpfNrT8r2Wzz9O0Rj1Bm6Xuz04Z2HV1KqHDW8OGm3+SR0ft4avwBsmbmZ3eAq8RKV+iZ7lGefOEsE1NpphNZ/ut7z6EvGMVKCWd7xkgkczTULm9cvBJKCdy9wB4hhAe7VHI98BKQBt4PfHrm590z6/8I+LoQ4u+xJyfbgBdWs1MTyQxnBsfZ0FCJz+VYWuO1kmClQXXZF4Q5DMKNED5brlWJIs3+uY4ne51+hBLF1uC+vGHrizvJm8vXMU1ZxJR5VDUC6Mj882CcBSQ4ti9ZfyyXIVHI49Z0Ik4P/ak4OcP2UUwW83TFpxhIJSiaFmVuL92JqUW86dcaC1uFZ+Fzz5cgnDqkcwU8Lv2SmDymaXHiUD8bttS/KlOITDrP4w8eY9O2Bmrqlw5jDcPkxBH7c1YzEVflquWDTZ/kwNQzPDJyL6P5IS4n5oJX9bE2sJkW39pll6uqwvrNdTx4zyts3tlkzwsIqKoJ09xRTSaZJ1Y53106Nhzn6YePM9g3yS1v3k6xYHLsYA++gJu2jmpefPK07dbj0rnm5g089tMjFAsm19y8gVur3sr6wFbuH/4+p1PHStI7/1lBEzr1nmb2xvYv23zjcTkoFk1Od46QyRR44oWzi65HISAU8PDJD11HbeXq5AdKqXE/L4T4LvAy9jj8IHaJwwd8WwjxYezg/taZ9Y/NME+Oz6z/8dUwSgACbie6qvCdpw7j1FSu2dhMTTQ4f3NY41j5B2yansxiZX+Aom8GJQJoKK5bsbI/QnHdBGot0uzDyt6L4noDCPdMRm+ALM6UFyTIFFLqgAMhlJl1ivY6FGbWTyNxzqzz+lHmFKETdrQzkH6cluCbcasLmwssRrLPkTXGCTpaQSah+CISFaG1gDkM+rpF22sLRom6PHg0u33/xOQo0/ksiUKebeU1BHQnDYEQLk1HE4IOWU7GKBJchTlqqbCk5IVTfSSzOUI+D7qq4NQ1BibirK+v5ETfCM2VUR4/2smtO9ZSHloq5BWfSpNIZDGKJvVNZaSTOTKZPKlEjsbWcsaGEyAlQthD0c7TwxiGRU19BH/ATW/XGC6Xg6raxbx5KSWJ6QxDA1OUVwYJR30EQx7GRhNU10VIJXIM9E0QKw8QLfMzPDg99/7J8SSJeNZmXHidhCIrN8/YpRMneyLXsj6wlZcmn+LZiUdtpcDXidlwMejCQZW7js2hXWwK7iTmLF+2TAd2qWR6KkPX2RE6z4zAjIbLnW/fxamXe4hVBnF5HFTNPOx6O8corw6RzxtEon4S8QzlVSFeevoM5VVBKmsjjAxOYZoWLzx5mvGRBB6vk0MvdnHjG7fS6G3jw82/ycnEYZ4Yv5+u1On/sXkCBYWwI8a6wGa2hfdS52lGF8vHg5rKEB9791XcfHUH/+sz9/EPf/xW3C4d07QYG0lQURWiWDDwep2rHlmWRCGQUv4J8CfnvZzHzr6XW/8vgL9Y1Z4sgCkliYxNdC8L+nj40Flu2dZORXimbqZWY+Uexsx8165Zo6KF/hbQ7PKA931Isw9j8iO23Kk1hXBsQ/W+byYDNzETn8YqHrLr4laa4tTHQHhQvR9Fde0HmcCI/wHSHLYzegyKk+8H4UcL/ilCW8pUeO0gaAnexeDQkzw19Fs0+G/Fp9dgySITuSN0Je6jzL2FqGsDYIJSicACawLE0mxWVRTK3PONEBtjlbSFonj0eTsmH4u7Lb36a9sZuhCDkwmaKsJMp3Nc0dHIAy+fJl806B6ZZDSepizooyrsJ+BZvgP0lZe6mRhLoKgKyUSORDzDYO8E6zbaZsNCETz35GnWb6m3OeOaytOPnmT/GzZy5OUekoksY8MJbrhtE7UN8zK+2WyB73/9OTZua8DndxOO2iJcs5ieSjM9mebxB4/xoY/bl/7zT51m/eY6nn70JKlUDtO08Hic3PXOiyvvCSEI6CGuK7+NXdFrOJk4zIGpp+lOnyFjpnk9s3CBwK16qXLZEqtr/ZupcteuGIQWQlEEb3vfFcxXcOc7Fn1eJ76gB7d3/vqpro/wk+8dYOueFnwBF9/9r6e55uYNc1RCh1PD6dKRlsTjdVFRHaJjSz2RmTq5EAJdONgQ3M7awCZ6M528NPkUJ5OHmS5MrIojfylwKE6ijnJafGvpCGyhwduKV53ft5Uwu6w85mdNcwUup47LqWNZkv7ucWprI3T3TlDfVEYuU8Cy7DmdbLZAQ+OFuzEvy87JXKFIWdDL3rUNOHSVsXh6kQC5EG600F8jjS6QeYTWDkoEIQSmZaEIH1rwfyONc2CNgVJmZ6PoM8sVVN8vocpluqCU0MyH+BC+P0AT5988AtT5k1rrbuSWyreUNIFS4774ZJh9fIKIs4Pd5X/K0cl/58jE55gdtGiKmyrPPjZFP446W8d3vQEwoHgc9PUX3b6mKPgcly4xKRBsD++j1tNY0roVrnnKXsEwKZomfWNx9q5rQFdVrt3YzEg8TdjrpixkB+1owEO+aOCakco0TYtEJjdD15Os6ajB6dLpPjuC2+Nk/ZYG1m+25wSiMR/OGQ8/TVdJJbJs2FJPY0s5jz94DK/PRSjiXeI/mYpnCYQ8bN9js5XOvymPH+5DUQTpVA7TksTK/Dhm6JOartKyppJ8rsjYsF3iKhomqUweTVPxuVcOiEIIfJqf7eF9bA3vZrIwzrnUSc4kj9GX7SJemCRv5S85G1dQ0BUHPi1AzFlBrbuJRm8rNe4GgnoYVcyLLZWKTCbPU4+coLdzDF/AxRXXraOuMUbFebVaKSUDPRM4XTonD/fhdGrsvXYtg32T7L12LZU1YYyiSTjqQ0pJQ0s5Rw5003lqeO612X2bDeDN3naave2kjAS9mU5OJ4/QnT7LeGGErJle0LK/OggEmtBwq17CjhjV7noaPK3Ue+zGJ+dMV+ZqR9set4Pf+sUb5trahZhJCATkckWMosmJYwNomsLYSBxd16i8iFDXZRm4hRA8dbyb+148QXUkyLuv23oev1aC8KIsqOdKKSkaJg8+f4pta2uJBr0YVisux1oKRRPVEnQNjDM2lWJLew1uZ2zJMDmdK6AIgdspGRpPcbwrxZ4NjXjdDoqGSdEw7bqVYWJaRZB24K7zXLyTbiKfYigzzbHpARyqRou/fEXGg30OFCo9e4m5t5AuDpAzJ1FQ8eiVeLRKBDaFTVppyD9ht7lrzVA8BM6r5o7JlJKMmcehaDgV7ZJKPEOZaSbyi/mwTtFAvct+EAUdHmo9pbXrO3WN6za1oKsq4RmPPr/Hhd9j3xTRmfr3+SpsR84O8qf//lOaa6LcsraZF58+g8Opse+atYwOx9F1+1xalsXxI/2MDcc5caSfyuoQjz90jM07mujvm2D77hZOHuvH43UuYkQAhCI+8vkiP/nhyzS3VVBeGeLsqSE0TaW+sYyJsSTeoBunSyeTLXD62ACjw9OcOjaArtvcZcu0cDjt2+qZQ1387VcfZWt7DX/ysVsWNREtByEEKhplzkpijgp2R64mb+VJFKcZL4wwnh9hsjBGshgnY6bJGVksTCwsFASqouMQDlyqG4/mI6CFCDkihPUYIUcUvxbEqbpQXqVnqpTwrS8/haIKNm1vYHoyzX/804N84vfvIFq2lE0yPZmmrDJIOplDc2i0r6+ZmaxMYZqDaFobC6f4duzzggigKD6M4jGEcKLpbXPXwuxIxKd5WOstoyPwXgxZIGWkmCyMMZ4fYaIwSrw4RdpIkjOzGLKIJU1AoAoVXdFxKm7cqhe/FiDoiBDWo4QdMQJ6CLfqRbuEB9pyEEIsSjzzuSKFgkF8KmNr3yeyVNWEcLsdlJUHyOeLc9fQSrgsA/fwZJL2mjIy+QL5oq0OWAqjeHAszuMvnyVfMLhiSxMvHe+juizI4TOD3HZFB08d6mQynsHl1NjaXrvovYfPDHLgZB/RoJcbdrXz/NEejp4bwuNysLaxgh88ehinQ6O5JsroZIp4KksmV+SOq9dTWx666L49MnSCvzz6Y0BS743y31f+Ij79wjVkO8PwEHK2XWAtA8wBwLTLRubo3JK8ZfB3x+7nydHTVLgC/NGmO2j1l6/6Qvzvrmf5RtfzKy6/tWYTf7blrpK2pQhBRWj1VLEXj/cxNJ4g5HejKIKtu5ppaa/E53dRXR+Zy56FEDS2lPPBj19va0R7nbz3o9cylcjQPTbN3s1NNLSUUSiauD0O+6E+U19UdYVfeM8+0skcHp8Ty5Lsf8MmVFXB4da58x27ufvRw7ztQ1cxMp0krVi8/1f2IxRBQ3M5CFtedjZAP3+kh9HJJPFUdtVVD/s7EujCSVgrJ+aoxPRYqKpC0TARQvLM0W72bmiwLd4EMDdFJux/r8E8jJSS5545w9DgNHe8abv9cLIsEvEMv/jrN+H12QSBsdEk05OpJYFbCMF1t24iGc+i6ypev33Nm8ZZCoUDCOHBNM4ihAeEA1WtxiiewDLH0PQOFLUCa8E1bVnD5LL3oyg+dMdOTKMTlQJG4QBetYGQdyctvrX2CFjaTUv2aHjpyHn23/z5/tnA5XZw9X5bcGq5B10puCwDd1XEj6YqHOoemlMHnIXQO2a0QxaHciFsGllLbYwbd7fjdTu4ckszf/bF+3nz/s1Egh42tVWTTOeXBG2wZ4ANw6K6LIjLaVPVELB3YyMvHOultTbGrg0NfOnu5wgHPFy1tYV4Ksuxc8MlBW4LiTEzhDPla1iTE35QwsjUPwAS4X7n3KKzyVF+0PcyBctgMDvNPf2v8Bvrblr1R+iKhq5oFC1jxr1mcWHoNT2eZWCYFgdPzjdA1dRHiER8+AM2Q2ihOL0QguB5mXQ05gdd4dmTvSRfKOB3O8nkbbeUqUQGr9uB26kT8LqoLgsSjfooFE0eO3Ca2vIgfo+Lx57r5JqtLdRUh3G5daLCSzKTZzyd5ci5IZqro/SNTNFaV0ZHcyW5fJHDZ1fFgl0Wzx7tIRrwkM4VGByPs2NtHa+cGWTnujo0RUVBRVVeP0EwwzD5zjefZ3IyxRtu34Kuq6iKQnV9lB//4ADtHTVMT6XpPjfC2g01HH2ll5q6COHo/KSypqmL/gYwzT40rRXDOItQYljWFLNCb9JKo+qtmOYgihoDigsYYnmEcAAmQriRVgpppdC0dZhmz9z2BTbtcCWp1Vd9Xoom+XwRr++1n8AvBZdV4JZScuDsAOOJNIZl4dI1YgHvolOvOHairGDoO7veZMJ2suganKCtroyugQl2dtSjq+pcpuxxzd/sUkrKIz7ecMU6vvfIYRqrImiqQjKdJ5UtEPC56BuZYmg8MTe5MDSeIJHOEfwf+uJmIYSCdN+FII/9dc5/pYZlYi0IqkXr0mp/H2i5gttrN5M28iQKWaYLGb7a+QwnE8MrvmcymcHrcuCcqQFLKTFMC02dH6bPmiFczLx3Ip7mXP/43N/NbZWXZLoaDngwDIuJeIb6yjCdA+NsbK3mTO8o2bxBIp2nPGJnQEXDwLIsasqCvHSyj6JhMpW0r51s3iCTL5LJFUlnC+iqisup43Y5WN9ciRCCgbEE/SPTq97H86EqgtbaGM8c6ULXVIYnk0wnMyQzeRLpHNl8cRE3+LXGxHiK7u4xAoF5Gq0lJZqq0N87wcjgNNKSBENennviFADX37ppSaA+H7q+haJxEodzN5Y1jaLWgXBimgPojq2Y1ggOxzYsa1YbxgQ0FCWCw7EDEEiZRiheFCWMEF7EBbw6V4tctsDEuG0ybJoWRtEkGPYyOZEiVuan88wIiiLYsmNpmVRKiWVJ0qkcyUQOwzDRNFvfxetzoWmzoneXvn+XVeAGqAz7yRaK9I1Ns7GxkoOdg1yxrvGirsaWaWGaFvt3tHHwVD9uZxOZXJH33LqDk90jpDI5Kv0eesQkB4/2sHtz05xhLtjB/tDpQbavrSUc8BC0JJVRP0fODrKzo554MsvxzmHeePUGHnnxNMMTCfweJ9vWLt8ks1rMOo9cyhDXFr5ayk9v9pexI9rEwckeylx+bq5enTXWLEIOzyItc0taPDJ8YkngNkyL7pFJcgWDoYkElRE/jRUR/B4nBcPkR88cY09HA/mCgRCCockEmqqws71uxeAtpeRk1yiJV2m06vc4aW8ox+XQ8LmdjE6l2L9zDQ5NJeR3UyiaZHIFQj77PHpcDja1VZNI59nYUk0qkyca8pIvGraFHlAW9lJfGWFgdJrKaICysG9un4+cGSSbL13OdSVsbatBUxX2bmwilcnjdTlorIzgcTnwuR0XDdpzJtemnGtwUdXSatxSSk6fGiKZzC0K3IoiuOtde0o+htlAZlly7vMVNYJT3bdkXU1rtH/SCsyPq6WUmKaFZXlRlFaUGVEmbaE2DxEShRyGtAg53HNNPT2pSVShUOsNlczrLxQMnnn8JAjweJxEy/z0905Q2xCjr9tOIpYzMS8UDA48d44H7j3E2VNDJBM5TMNE1VQ8XgdVNWF27WvjrnfuxvEqHN8vq8AthKA2FsSSknNDEyQyeYrGfJZoGibjg9OEyvzEJ5J4Ax4KuQJCUTCLJkbRoLW+jNa6MkzDpCngQxgWNR43TkXl1OFertrayEjvBAPnRmhcNy/S1FQdpal6QZOFCjftmW9A2LOxce53p0NjZ0cDNeUrW1itFoY0eHnqIOsCa2ekKBUsLFShzmXNmtDmGoryVh5d0SlaRXRFx7CMObNYiURBwa1q/M32t9KXGafMGQSRZzg3QpW7dKGk1UAIOHR2kOl0jpDXxdBkgjW1NgNH11SCXjflIR8Pv3yGkM9NwOMk6F3s6G6YJvFUjtHJJN2DU5zrH+O5wz1YM+yCofEEf/2Vh1fkvVZE/bz/9l1LAlo8leOex4/OBdN1TRXcea0tKuVbhnYohKC2PMTgWIL/vu9FCjPX4Zb2Gja0VM3ts5SSltoY8VSW0ckkLxzt4WzfGE+/0jW3rXP9E/zllx9cMVg2Vkd4583blrBcgDlRIoemzjUuzY42XI6V66NSSoaHpnnqiVMcO9rPxEQK07DweBxUVAVZt66GzVsbqKkNL/pcwzDp7Zmgt2ecrs4xnnvmDNKSTE2m+ad/+CnqeXouTS3lvOnNO5Z8H7PB+tzZEZ547CRnTg+RSuZwux00t1ZwzXXrWLu2eo4SuBDjY0m+/tWn8QfcvPt9V5BJ53n4oWO8/FIX01NpvD4XGzbWcsPNG6mqCi06r6fio3zp1AtEnB7e0rQJr+bgM8eewKFofKh9F5siF5ZxnoXH66S1vQqJxO1ykE7nqaoJE435CQTdnDw2QE3dPINGSkkqmePzn3mQR356hGLRtCclXTq6Q8MwLCbHU4yPJvH6XLzl3aU/+JbDZRW4Z1EbC3LV+ibG4inu2NUxN3nUeayfw0+don5NFcWCQbFgYJkWlQ1lBKM+pkbjlNdFbeGWI30U8kWcHgcnX+rCE3DhmZlI8QRcGEVzrm62Wty0Z+0cDey1Qs7MkzLSmNLi5emXafO1cjR+jIAeIGtmUVDYHd2FQNCT6SVn5qhx13A0fhSX6mY0P0rUEQEEdZ4aBrJDSCzcqhtdVUmbJoO5IWrcpV24lwJFCDY2V2GYFm6njiIEmVwBp64hgKaqCGPTKdrryhECwj43A+OJRd/D4wfO8ff//SiJdI5i0VwypTSdzHLvk8dW3Ic19WW899alpbRIwIPLofHtB19BSslDz5+mKhZgz8bGFa+BfMHgn771BI++eAawHwrvvHnbkqrpPU8c5Ys/eJZEenGiMYuxqRT3PLHyPu/oqOMdN21bcflqIaXkqSdO8bl/epCx0cRclgv2sF8ehPt/fJim5jL+7rPvXZRNj40m+P3f+QbTUxksa94sIZ3O8+D9R5d81u69Ldx513YWskKklBQKBt/82rN8/7svkE7lbcaMKrAsySsHe/jJva9w+53beN8HrsLlXtwhm0xmeeCnh3G5dLZtb+SL//4oJ08M2g8HAaZh8fJLXTzw0yP85u/eytZt89+hBJoDUe6oX88Pu4+wNVbDdVVtrA2V89J435LALaUEOQ3Cv0DbyD5fW7ZHbFqwWPyw8vldXHHN4q5Sw7D40r88zAP3vgLA+s113P6WHbS1V+L2OMhmCvT3TnLwhU527mt91bLJl1XgllJSMEw0VaEy7Kcy7OfZkz1cvaHZHtJG/YRifoIxP90nBgiXBXD5nCSnUmi6ytRoAsu0BZQCER9dx/uZHI6Tzxb+//beOzyu87rz/7y3TJ/BYGbQO0CCBexFpEiKFCVZ1bbcLbeNHTve+OdsYseJnWyym7bJbpyNnZ443XZsx7Yk25Jsy5JMdYld7A0kUUiiA4Pp5Zb398cdgAABkJQsWeCz+D6PhOGdW869c++573vO93wP4Yog4yMpPD432XQeo2jStLTuNcWZfJ7XvzjFq3qo9lThUlxUuCrwawFsbIJagLSZpsx1WSnRlg4XPW7ESZtpVEWl3FWOKlRMaZVCJ85Dp6BQ561jpDCMJrTJTipvBIQQkyPs2b5bXBebsbw8OD2RWCgamJbtXONS+iBfMMgXnfCEqgiC/rnzCkG/Z1bxCEURfOS+jZzoGmTf8V6y+SJ/9a3naK2PUVkemOG8bSn5/jNHee7AOcAZ5f63999SkoCdvm6uYGDZEv+Usv1svkjRKHHv1dlH9ROY+G5quGwCr2VgMTiY4O/+6gmGh1OsWt3IPW9dQ3VNGEXA6Gia06f6eeVAFytWNRK4IkcTLvfzuc/fh2nZIOHJnxzl+WdPEY0F+NSvvGUGTa283D9jtG3bkm9/czff/PqLCAE7di7j1tuXE40ESCZzvPRiJ7uePMZD39mDZVp88v+7HW2WkE8ymeOL//tRkPCJ/7qTpctqURTByRN9PPzgXgb6x/nyn/2IL37pg1SXRt4BzY1X1VGFoC+bRIwKGvxluBQV054tiS4x019B838U1OkzUSv/Y1TPnSCuriMipeTYoV6e/NERpIRtO5fy2f/+NgKh6Y2fG5pj3Ly9HfjZWSzzynEDvHLuEiGfhydeOUPQ62ZwPM0tHU4cK1ZXztZ3bcDCpr6jBo+mgwQTm+FciroWHzlp4JcKVY1RKuojKEJQt7Sa0WKakGlS7vYR1CaI9Ne2R0qJjSRtFEgZOQxp41JUynQvPs1dakj7KmPSU2hIUkqKtsl4MYtbiTBayBFxVeJSXGyJ3owiFJr8TdjSntym2d9E3srjUlzEXFE0RQckBcvEkjYSSZu/fTLUIlCIuaqJumL4tNm1WuZLv8nbNrazaUXztGVf/+E+vvWTgwC01sf4s8/cP6Mj0gQ0VZn1OyEEAZ+bz37wVj7z5w8zNJbm/KVRvvLgi/zWx+6YNoOSUnLi3AD/9sgeLNtGCHjXzlXs3Lh41mvzzp2ruPvm6TIDf/Wfz/L4S6cAWN1ey+//13unNQSZCr3U8Dpt5tg7doqdlWtJGGlcio6/dK9O/X2yVgFdUdHFzMdXCMGZ0wOMjKQJhbx87gv3UVs3nWN/y46l5PPbsG05w+l6vS5u2rzo8nU4dnFy+aabF+H1Xn3QIqXkbOcgD313D7Zt8973b+ajn9iBrquTNmzc1EZrWyV//zdP8tijr7BhUxsbb2qd+fK0JdlMgT/6P++jY0X95PcrVjWwuL2a3/vdB+nvG+fRHxzkl375NgAWhaLsHurmX0/v4b7GZSSKec4khnl5qIePLNowh80FbPMMGCdQXGtBhJHmcYQSuS5tIylh1+NHKRZMQmVefuGXd85w2vD6PlfzznFvWtJIIpPno3dsoMzn4VjPwLQb/rsX9vGfXXtp9Ef40oYHSBg5vnziCV4eOYdhW9R6w/ziom3cXbcSRRF0pUf4i5NP8MpYL4ZtUeEJ8p6mDTzQfBPuq4w+na7XBs8PneHxS0c5lRwgWcxhSgtdUSl3+VkTaeT+hrWsKXeoWdcLTVFQhCBRzPLoxcM83neUi5k4OauIKhTCLh9rIo28t2kjq8rrZxTqKELBp/lK+3J+wpxV5AsHH6InPTrrMYUQfL7jHrZXtc9p1+NdndQFQqyqfGNi4NeDibLgqfBOYQBpqkIk5HtNrBIhBK31UT79vlv44395gqJh8cTu06xdWs9bb+mYzB8k0nn+4pvPMp7KAbC6vY6P3b9pVr1uIQQ+j2vaLExKOc0+XVOJlvlQVYVjiS5SRpZGXxWnUr1EXEHGMkm0rMbm6HJsaSOxOZO6yKJA7aTjTps59oyeZGmokcPj5wjrfqq9EXozQ1R6yunLjRDS/WyMLJ0cr9u2TWEWXWwhxDUd8M+Cp544SiZdoKq6jHe/f2YSTlUV7rpnFT998hgnj1/ih4+8wvoNLajqTMe2bkMLy5bXTXN6QghWrWlkw8YWnnvmFC+/2MmHPrIVf8CDrqh8rP2mKYX4kDWLpM0iFZ45WCcyh20cRyiVzug7+HlQIliZf0XRV4B6deddyBucPnEJgPZltdQ1RK4o7rPhdeLVT2BeOe6JE5vI7E98nnrCiWKOC9kxUkaevtw4XzrxBM8PnZn8vjM1yB8deZSQ7qU9VMUXDn6X01PYDz2ZUf7y5JNoQuGDLZtnvZhSSs6nh/ni8R+zd6RrBk85ZxkkjTw9mVGe6DvG2+vX8umlt1Gmz9766kroikpfbpz/deRRDo71ziiXT5sFLmbjPDNwik8s3s5HWregX+PFICUM5BJcyM6u8yyArFmYphE8MVtwpujQFApT7pl+k058N3FWr/bms0ozhatl850KTxtVvLaKPstOY1gDuLW2a24vhOD2m9o5dq6fB588hGFafOWhl1jeUk1rfRTLlnz10b0cO+dwsGNhP7/+4Z2ErhKeeTVIFNOsKGvhQnaYeDGFLlQKloFL10mZWcdxS/CqLjJmnogr5LAnhIoqFJJGhmpPhFpvlFPJXloDNRyMd1LhDhMvJrGlzaLF1UQifkZH03zxTx7hgQ9tYf3GVgKlzuxv5IyqWDQ5dsSRFl62vI7yOZoFezw66ze0cPL4JU6f7COZyFEemblux4r6WRPRiiJYs66Z5545xfBQkuFhJ+l3MZPg8Ngl7mtYPnmeft2NX7+KxINwo3reilBCGMn9gI1Qarhe3f5stsD4mNNurbElhqoqpefGBATjheMEXW1o+Hi9Wh/OK8ctpWTPmV7ipZEOwNm+ET55z+YZdLG0meebXbvZP9rFhmgzYZePA6PdxItZslaRr55/kbZAJWdTgywrq6HJH+Vkop+ezCimtPnP7r28tX41ZS7fDBvOpob4jQPfpivt0H5UoVDvK6clUIFfc5EwcpxLDTGQS5KzDL7Ts5fBfII/XPPO63LeBcvkDw8/wivxXjyqTluggjpfOapQuJSL05kcJGcZpM0Cf3t6F15V5/3Nm67q/FyKxq8uvYNL2TjjhsO1Hs6neGGok6J9edT1jROHUYTg9NgIreEIH1i2Cl1ReOzsKZ690M2HO1ZTE3DYCjnD4Lunj3FqdBivrvOptZuIeWdvAJuzDIq2iVvRKNgmfs1NysgzkEugCYWWYAW6omJLSdLIoSsqAkHRNtEVlaFcijpfmHEjS5nuJWsaBHQXunLlLWpRMM8jhYYiXBTNfnyuleSNLlQlNKn86NJmNF2ahKYqfPwdmznVPcTRzj6G42n+6j+f5U9+5a3sP3mBh58+jJQOk+NT79lKe2PF6+bsGv1VeFQ3bcFaCnaRsCvIwbEz1HijSClxKTqjxSQFyyDJZQ1qiSSgeSnTA1R6dPpyIywva6Y7M8CyUBOnkj0sDTWhCoXq6jI+/l938pW//SlnOwf533/0A+obImy9ZQnbb11Kc0vFdVMCXy2ymSKjo448Ql19ZM5wpBCC+gaHxZVK5RkbS8/quGMVwVntFEJMxrUNwyI+lqG5pYKxQpa+7PX3BXWgwoywk1Nt6SQvr05iMIoWpukM7vwBT2kwZDOS24MqvOSsAfLmAFW+HZf1hX5GzC/HDQzG0yxvrGRijJfI5GetfTKlzfd7X+FTS3aWRqQKLw2f5XP7v03OMjg42sPhsQu8o2Edn1t+F37NTW9mlF/a/VUGcgn6suOcTw+zNjJd+Cll5vnjo49NOu0Kd5D/tuwObqtaSkB3ejbaSOKFDI9cPMQ/dz5H2izw7OBp/u70Lj7fcQ/aNRKAXelhJNBRVstvdNxNR7gOl+IwL4q2yeH4Bf73sR9yLjWMYVt85cyzbIi2XLVcXVMUbq12Mt0To+qxYob3Pfv3DBcu9+87MjzIkkiMX1m3mf+79wX29l9ka10jd7e20xkfZTSXndzH9zpPcCmd5L+tvxnDtgjNIUwlkTw3eIYlZdVY0mbP8HlWhOvoTo9Q6Q0RL2RJmXnWR5vJWwa7Bk7hVXVA0BasIOYO0J0ZocobZPfweRr8EY7FL9EciLKt8spyf4W8cQ4bFU2Nki0exuNaAkgMaxjbziCRV3XcQgjCAS+//qFb+dyXvs9YMsveY7383XdfYM/RHvKl8MJbt3dw15Zlr6uDa/BVTn5eH1kyWSS1KOCEA3Z61gJQ5Smftp1P87AxepnJEHOXTa6XNDIENC/1vlJiWMAdd66kbVEVD393L7tf6qSne4Se7hG+/9A+1m1o4X0PbGbp8tpZKYg/C0zTolhKJPv8V1ca9PocNollWRTyMznvQoir9q70uHWE4tyr+RLNszFQzg96jrF3uJeyEpe7xhci5HIYZVnDwK1qaJOsDoElalDRHf0frQEwsHLfB3sEK/cdVO/bSyJ1DqSUFC1rsun01JdgsWBOPn+mnSFvD6EqPiQWNtZ1SXdcD96YVt6vEQK4d8NSFtXEqI+VEQl4uXvDklkzzgBV3hDvblyPW9VQhMJN0VaWh50H1pQ2Xs3FxxZtI6A7b8EGf5QN0WYADGnRfUU8WErJYxcOc2isFwC/5ub3V9/P/fVrCLm8KKWpvCoUYp4gH23bymeX34kmFCTwgwuvcHCsZ0Z7oishgUpPkD9e+27WRZrwqA51TgiBW9XZGG3hD1e/c7LoZayY4bs9+65b7mJCSW22cl9NCNZV1VDh87O2qobO+IjzgCgK+pQYrgSODg9yd8tiagJBGkNhXHP0ZASBS1HJmkW6UiOoQsGvuSftXVpWjVKaIkokWbOIrqiEXV5aAjGKtkm8kKU/l0AgCGoePKpOtXc2nrzA61qO17UcVQTwuVcjZQ5FuNHVCiTWVZ321Gu0rKWKT757C7qmYEvJQ08d5kKp2rGjrZpfeteWN7QqEUBTVBYH63+ml0NI91922iUoiqBtURW//vn7+Mu/+yif/NRttC+poVg0eeG50/z3L3ybn/z4yGtuDTcXFEVMOsUJyu1cMA0bcGR4Z+Nzg5wcyc66vWVNSpBMHDNl5DFti4e7j/L1zv18rXM/XakxiqaJZUuO9A/QOTqKZdsUTBMJxOV7MewyDOlFej6BxIPqfS96+EsI36cxaMSW0lm/5LR3nT2PVfrs8eoEShosA33xUhsyQaVvOzX+t1Dt20m1/w5U8doVOa/EvBpxCyHQNZVEJsd3XziCS9MI+dy8ffPsUqWLgpWEXJdjsrqisiRUxYHRbgDaAhXUeMOX9w80+S8X2QznU9M4xFmryPcuHMQu3Q1vqelgS+WiOR8qRSi8rX4NP+k7xt6RLids0r2P9ZHmayrB3V27kpZAbM5pYEe4ljtrOvhOzz4Anhs8wy+37yTinj1meL2wpGQom8GWksFMmuay8lnXE0DE46U3mWB1Zc3kdZotXCOA7VXt2FKyJFSFjURFocEfQVOUyQo2cMJOHeFaVobrJmPfMXeAe+pWoCkqtd4wLlWj0R9BVWafzrvUOtza9FtX91Y5f9XKGevPBSEE921bzrGz/Tz2/PFJG8NBL7/+4Z2UB+d/t6RrQVUV6urLee8Dm3nb/es5eKCLf/vnZ+nuGuZf/+kZVq9porZu9nvgtcDrdREMeRkfzzI8NHfIQkrJyHASKZ2Rc6hsZghOShgfn71lmZSSsdE0Uko0TaWstH2DP8wfrL972roXE0keP3OWtbU1RHw+DMuiL5niua5utjQ1cnxwiHV1NTzVeY6iZbGzrZW2aATTsvjR6U5qQ0GyRYNEPk/M76c/mWIok+FHp84wnstzT/tiGlti9F+Kc+p4H+PxDJFoAO0N7LY1r0bcExgcT7O0vpIP7FhDtmBgWbO/dWu8YZQrOK9R9+XMcYM/gjYlGSCEIDhFkS9rTe+icS41PBki0YTCPXUrryq9CuBWNO6uXTlpxYGxHkamhCZmgyoUtlQ4lKuCZWDaFpZtUbQcMZ28VQQEO6uXTR5/KJ+iK/2zd71WhODxrk7+10vP0JWIs62+iZxp8M2TR3hlsJ8nus7ySOdJLCl539KVPNVzjv/x/FP8wYu7GMyk59yvKhRcqoamqLgUDVVRcKsaqlAc/fPSebgVjdXl9WiKSsFyRjyaouLVXOilvxP7uta1vx5MaKTMNfLTNLXEzb68rCzgpToamnX9NxJOefrr34NyYgbm9bnYsq2dz/7mvXg8OuPxLGc759abcTaesI3rUjj0eHUWtzuspDOn+8nPEgIBh+p3vEQ1rKktnzOJea5zYM7f7sypfqSEUJmXWKWTl5HAkbE+vtdzlIe7j/K97qMcHRmg0u8n7PGQKRbJFIucGBzCtGziuRyaopDIFyjzeIh4vWSKjl8wbJuiadIYDhPP5agOBumOxynzuvFoGv3JFBGvF5emcsvty1EUwdBAgkcf3IdpWsxm9uX+mz/bTGdejbgnUB8Ls+d0L0e7B+hoqpoSj5qOcpdvxohsajJrqhOfwFRHbl/BFjmV6J9M5JW5vLQFr96FApyHYkW4Dreqk7cMxotZzqdHqJp1mu/Aq+o0+CPkrCJPDRxkXWQRp5MXSRpZ2gI1nEv3syhYS0sgRkBzkzAcGuL51DDrS6Ge1wpFCD60fDV1gRBlbg9eTcOWkp0NLWyvd/btUlVUIWgMlfHFW+8ins/hUjUinquPICxp052Ioysqft2FRGJLScYwiHi8DGUzlHs87B+4xJa6Js7GR1gRq+JSOoVP08mYRRQETaHwVUMHRdMiWzQIet2k8wV8bhdF0wQJ/hItL50vYtn2ZFu0xopyMvkCAY97MvwhpeT4uX6+/sN90x6ynv4x/unhl/iN/3Lb6x4qsaVJ0RpHV50Xg2U7mtpuJUzW7CNeOE6ldzO6EsKwU4BEV0JYMo8tHSeoK7Mn7CbOaYKfPddsrizsQ9PUWamCV8JXKg7KZgtkMgW8vqvTCIUQ7Ly9g+eePUXX+WH27TnHLTuWXkGPk3SeGeDggW7A4ZW759Cf3r+vi5Hh1LQkpZSS0ZEUL7/kVLQu76ifHHFfSMf5yqnduBWV5mCEU+NDfHbFDqwi5EwTv8uFLSVN5WGacnkqAn40RcGlqmxqrEdK8Lucc/RoGpubGkjm82xvbaY/meLty5dyYTxBaySC36Uzls3hc+ls3bGEJx5t5MjBHr779ZfJZorc+451VFaHUDUV07RIJ/P0nB9maCDB3fevnZX+eL2Yl45bUxXqY2WMpXOOOI2Us4YevNrMm2jqWh51tg7fc9/wU6l05S7/ZKHOtRB1B/BrLvKWgSVtLmbGoKJtzvV9mhu/5kZXVGLuEBeyw4wbaao9ETJWHreqU6b7CeoegrqHhOGwbAbyiddcpj8BAbgUdZI5AqAKQW1w9hGmV9PxBq6vU/poLsv5RJzBTJraQJC8adKXSaIJlY5YJZfSSW6uacSwbTyqxlA2w0guy5n4CFmjSNG28GsuavzBGaGQqTjS00+mWEQgGBhPURsJkczm2bS4Eb/HeTCfPNJJKldg0+IGuofi5A2TC6MJKkJ+NrTVI6Uknszxpf94ZpKv3VYfo284Qa5g8MMXTrBiUc0kv/v1gJSSC6kfAWDKDGWuJQzldqMKD9W+bWTNAcbyxwnqLRhKir7MLqS0qfHvZCS3HwSE9DYintVXPc73H9pPJpNnw02t1NaV4/W6UBSBadoMDyX5xtdeJJ3OEw77aFtUNed+hBAsWlyFqiqMx7N8/+F9vPeBzXi9LqdxSaky1O93T14jIQTrNjSzdVs7zz59kr//6yexbcn6DS24PTqGYXH6VB9//9dPkU7laVtUyV33rprzGg8NJvjLLz3Oxz95KzW1TiHR8FCSf/7KLvr7xvF4dN52/7pJyuBIIcNNFY34NZ3V0VqWhivpyyW4tcaZ4VYGLo/sq4POMxD1zc6UEkJQX3Z5AFbudQYuSysvD+gqA87gUPMr/OoX7uVPf+/7dJ7q53v/uYefPHqIaCyAy61RLJgkEjkyqTxLV9Rx19vXzP0DXgfmpePuGYozns6zvq0OXVPnpMFdayqtvMqpdrJ4mYbo19zXlBudgFvVcCuXndu4MXtcbnJ9RSvFfgW13igRd4hloUaSRpYqTzn9uVGi7hAKyrSXT9qYpdXaq8THVq6nyv+zxcnnQkB3lwpIJNLRsacjWoVp28S8fkZyGdyahk/XGS/kyFsmaaOIpihU+YN4Ne26uNymZZMvmsRCfty6RnU4iNelU1NeGpVJSSzop8znoWhaJHMFTNumvTZGIpOf3Mc/PvwSJ847oYKaWIg//vR9PP7SSb762F4M0+IfHnyR9sYK2pteffOJ2SCxGM0fpsy9GNN2RtBRzxpU4SFvjRFytVK0xgm5FjOQfY68OYJLDWPYKSQ2Nb7teLW5He0ELl0a45HvHeBb33iJsjIf4bAPXdfI5YqMDKdIp/O43RoPfHgLNbVXj2+vWNXA6rVNHNzfxXe+tZtnnz5JWdiRx82k89y0eRG/8mvTNd5dLo1P/cpbSKVyvHKgm//zv35AZWUZoTIv2WyBgf4ExaJJQ2OUz/7mfXOGSTRN4a57VvPTJ4/x2V/5GlXVYVRVMDiQIJHIoWkK73n/JlavbZr8faq8QfaYvSwvr+JrnQcoWibvall19d9FXm6P9lohhKChOcYf/vkDfP/be3lu1wlGhpJc7B1FylLSVlOpqAqxZkPLNG76azn+vHLcUkoOd/VzfmCUrsExgj43Qa/78gN5BV5vFqo9JYinzMHKmA1XSrFeq7GAEM42mqLSHLhcpRh2OW/viWU5qzjtpWVfN69kbrSVX1134WeBV9N4S9MiTo+NsjQyU5ekORQG4LYGp9Hy/W1OmfjicHTGutMx9SZ3xKmWN1YRDfowTGvGC1YRgi1LmkrrSxZVx9A1J84uK5z44uMvnZwUq3LrKp9+3y0010b48L0bONLZx8FTFxkZz/ClbzzDF3/t7ZQFXl2iSVxhs7NMpcK7EUvmCLibURUPUlooQkci0ZQA48ULBArHKXO1kzJ68KgR/HodaaMbcZ1ksm3blzDQP875c0Nk0nnG4xmkdBKVPr+bDTe1cv8717Nx06Jrdhf3el38xhfu42v/9jx795xjZCTF0GASTVPw+lz4/bMrK0ZjAX7399/Fo98/wE+fPMbQYJLBwQSaplAe8bN5y2Le876bJrnYs0FK2LZjCRtuauXb33yZnp4RikUTXVNpaa3gHe/ayFvuXjlNsKnWV8YvLN6IW1XJGkUsKVkfm9k4ZSoOxo+yNLQY/xQ5CCklaTOLT/Nct76PEIJILMAvfvo23vuRm+m/GGd0JIVp2rjdOpFYgKrqMgIh77ScylBhBMM2qffVXNdxYJ45boCw30N7XQWLax3GhUfXrk9U5HWAV70ceilYZikGfu0fzZTWtCYFPvXqcUDDtq+ra4wtHf2RCXhmFKPML0xQEJdFr50beDXwTol/pnMFmivKCfqc6bl7FpVGIQTalPjh1Di1lHCmd5h/ePBFpwUY8LbtK9i5YdGknsmvfXAHv/7n32M0keXwmUv8+yN7+fT7b5mWa8maBdJmDrtUpCFwkqwS0IQ6rWw/mcljWjZuVaM+8BYMO40i3ChTHELBMhkrGIwb2/HkQzQHK/DrdxPSNZJFlbB7OzlLJWdlHa3pKfFey7InucRCCFavaaJjRT25bJHx8Sy5XBHblrhcGuFyH+GwH02bObMxTRtFESiKmNyvadpUVIb4zG/cw8hIiuR4DsO0cLt1wmEfobLZX2hCCEIhLx/8yFbuf9cGBgcSZDIF/H43sViQYMl5XW2UadsOHXDbdsd5Dw0myWTyeDwuqqpC+KaEaCaPC/TnkpxPjk5KAQ/nM9T6pocCpZQkzTTdmYucSXXRGmhiODNKvJigLdCERPL9iz9hUbCZlWVLcCkuOlNdhPQAjb7pJfi2tOnOXCRppFgUbMGt6FzkEq5Gnc3L2xkujJIxcxTtDLbHz5gRJ+IK05u5RLWnkou5Aeq9jtMu2kU6U91zXpMJzCtWiRCC5qoIK5qqWdlcw4qmahbVxq5b/PxnRc2UhOJ4MUveunbyBhzuaM50MtECqPRcnZGQM4vkrWuL7OdMg4x5OTwScc9Usft/AfVV4cl399BYipNdg695X8lMgS//x9OMjDslyktbqvj4OzZPjtqEECxpquQT77wZTVWQEh7adZhn9ndOYwJoisrj/QfImgUuZIfZN9aJIhS+f/FlBNBQFZ5ct3cgzvlLI6X8hIpLLUNTPChCRxE6Ao29w5dIFHMUbJ3udJbHL57kTDLBT/svcXC0j5eGLvHiYDfnU9NrDwoFk8Ov9Exb1t01zOFDvYTL/TS3VLBseR0dK+pZ3F5NRUVosqjl0sUxcrnLzKrOM/3E4851MU2b73xrNy88d6rUpsukv2+cxUuq6VhRz6LFVU6Xe9fVG1ALIQgEPLjdGonxLG2LqigL++ZMns6AvKyt0tQcY3lHPa1tlZMVilfiYibBnx7+KedTo1zMjHMxMz75bE7freTJgedxKy4ShsMCM2yDjJnlueE9uBUXilCo9VTiVtw8PfQSY8VxnhnazXBh+m+QtfI8N7wHvST2tmf0EBdz/eyPH6E7c5EDY0e5lBsgoPmxpMXLowcZLY5zLHEGTVEp2gYXsn2OHO/wflJmBr82e9x9AvN7CPdzhBCC9lAVqlCwpE28mGEwn6DMde0pck96lKzlOFi3qtMcmBkmmIqsVWAon5yjwOQyhvJJUoYTk1UQ0zjo8xF2aeQ5AVn6X84w8Ln0Sc2TK/9e6wFe1lJNJORjNJGlaFh86T+e5lcf2E5HWw0uXcW2JUXDIpMrICU0VM8+/TYtm39/ZA+HzjiCQCG/h898cAfloensJIff3cGh05f4ycunKBRN/ubbz7OoIUZTjSMglDULtASqyJjO7xNxBYkX0ywN1ZMyc6xZUoff6yKTK5LJFfnTf/8pv/L+W1jcWIGuOTYXDJN0toCqKoRcHsYKzmi6whOgNx0naxpUegIkjbwjV6rpjBYyl6+vlBw+1EM24zimVCrPs0+fIJnIUVcfoe9SnP37zhONBliztpnnnztFsWDS1BKjrj7CN77+Ik1NMbbfuhSv18XxoxepqAhhWTYvvXCak8cv0dQcQyiCQwe7Sx10pl9Xp1Fwkpdf6iTg93DLrUt55UA3g4MJVq9poqIyyDO7TpBO5QmV+RgaTLBn9zlCIS9bb2mfs7jutWK0kGFLZQsfbd941ftKIinaRVoDjRxPnCZjZjk0foIqd4yclcetuAhoPqLuCC5FZyg/Sr2vhmZ/A251enjIp3rYGtvA4fGT+DQvQ4URPKqHGk8VAc2PKlTag61UeWIlLRrJwfhRloUWoaAQ0gLkS/4jbiRYV76CMv3qTYQXHPcULAnVUOEJMpBLkLMMdg+fY3Gw6urTOSl5fugMVmk01uCL0OC/ehy5aFscjl9kZXjuijkpJQfGeiiU6IkB3c2i0PUXl/y8kcwVONo7QEXIoVdliwZDiTRL6yrY03mB9a119MdTNFWEeaW7j5aKCMlcnrUtdXNKtE6gKhLk/ltX8u+P7MWWku6+Mb7wV49QHvLhdetOsrJgki8arFlSz5/92ttnUK2klDyzv5OHdjk6JKoi+C9v3ciqxXWz/gYuXeVT793G6Z4huvvG6B9J8hfffJY//vR9+L1uynQfW2MdXElubvVXOzTIWrjr5qV8b9cRJHCya5DP/vn3JpUNDdOmUDTIF0xu3bCI3/2lu0pCWwIQNAUi2CXhLUtKFCGcF+MV4YUlS2v40aOHADi4v4ua2nLCYR+GYfHoDw7S0lrBnt1nqawKce7sIB/88Fa+9+BeOlbU09QcY8fOZVRWliGE0/Ulnc4TjQVYs66Znu4RNt3siP63L6nh6V0nZv19nnriGLfsWEokGmB0JE1vzwh33LWSR763n/YlNYTDfiqryhgaTPKjxw5RWVXGoVe6aWmtoLHp6oOc60XaKHAiPkjSyHNg5AK1/tBkyXtbKEbMMz0BqqDQ5K/nyYHnSVs5FKFgSpOcnSeoOetWeyp4dng3W2Mb2BhdzZnUeQKqH7cyPRSas/J0ZS6iCCfEtL58JQfix0pdqNx4p8TJBYL2YCsvjx5kZ+UWEkaK06nzGLZBo7+WFWVLeGrwBcpdZTPE56ZiwXFPQcTt57bqZXyzazcAD/ce5J66VcTmCFFIKelKD/Nk/+Ub+q7aFdeMcQP8+NIR3tGwdlpB0NT9pow8P7x4eHLZ8rK6a47Q30zkDdMZRUtJ58AI0aCfXNFAEYJIwEeuaODSVAbH0ySzeVKlWLV+HZ1AhIAP37uRVLbAo88eI180MUybobGZBUGzFWtJKenuH+Nvvv08hZKOxtY1rbzn9tVzJueEEFRHg/zqA9v5nb/9IbmCwZ6jPXzjxwec0IoyUfo1W9JcIBTJL79nK/miyVN7TlM0LIqGxcDozOIsy3ZmKlcqQE7EwLXSvTd71WpJ3VE6Mq6aqqDrGoZhoQhBNBrg7nvXEA77iUQCBEMeFNVhNGmqgm1NVNY4zWsnSuA1VUFRlGkhpAmO+JUvD9uWTvf3kiqeoirOvm3n3HRNLelxO1crGg3QfNdKKipfvyKnlFHg+cHz2FLSEAhzaPRS6XoJQrpnhuMWQnBzdB2GbaAIFU2ovLv+Xuc6l37TTdG1FG0Dl6JTrpfR5m9CCNDFdHqsT/Wyo2ITAK4Su6zGWzUpGrYlun6S4SaEoD3YQmugEV1oBHU/99TcCjj7jbkitPjrnRDRVcgRC457CgTwgeZN7Bo4yUAuwfn0MH96/Ef89or7iLj80xJCAJey4/zx0ccYLTgOpMkf5f6GtdcVuzsx3sc/dT7HL7ffilfVp+27YJv8U+dznEr0A07R0Lsa16G/gd1rflZUhPz4XTouXaMuUoZLUynWVuB3uwj5PLg1bdJ5t1VHcesaainOaduS8ZEk4VhoVkcqhMDvdfGZD+zgjpvaeWb/Wc70DjOeymFLidelUV7mp7E6zKYVzbPuI5HO874710KJmrV9XdtkAlFKSSFv4PG6MIomRdmPxRgClVVLPfzuJ25jcCyFQMfn1SkUzWt2QRJCEPJ7+O2P3cE9W5bx7IGznLs4QqLU9Njr1omG/TTVlLN1detruuaGYbFv7znyeYOjR3pZu76Fp586ju5SaVtUxaLF1ezbcw5/wE1dfTmNTU73nuaWCoSAVWuaeOmFM2zbvoRCwWR4KEUh30M0GsDt0WlqdkbDhYLJ/n3nyaQLnDpxiWUd07Vgdt6+nBeeO02ozMuOncuoqirjySeOsW37Empqwjz1xDF0l0pDQ5Q1a5t4+aVOPKMuWtuuTW28XtT4Qnxu5a1Y0nb0Q9QJnXoD1xxJfUUo08IeriscskDgmfK9uzQgO5Y4SIOvhTLdoVI6GkPT74ep+1JRMaWBQC/JRii4So5cRcV7hQaQS1x74LfguKdACEGjP8Jnl93JHxz+AVmryJN9x+lKjfDW+tV0hOvwaS5SRp5DY708evEQF7NxAAKam88tv4tKz9VjUwAxd4CsWeTr51/kRKKPe+tW0hKoQBMKF7NxHrt4mJeGz07S/26pbGdH9ZI5Xwg5s8ilbJysZZCzimSMAmmzwHA+NVnWL4EXhjrJWkX8mrvU4smFV9MJu3xUe8pm7N8pJoqTNvPkLCdRmjEKpMw8vZnLxUpd6REe7NlPQHN0j/2qC4/mwq+68Loj+N3OjRj0Og/BlUwQs2jy9IN7edvHb8XlnrvYR9NUVrfXsbq9Dsu2sSxJajxDLl2gtvlyEnsG00AI1rTXsaZ9pviUlJJctsjeZ09xy50rOP5KD5FKF4GqBKoSwJZJVq1IoAg3Ue8Oruw/eCWMoomiKpMsD5eusbGjkY0djZiWjVVqn6UIZ1Q6YeprSTrrusrtb1kxbdk73j29y0v9lIa2W7Y5TTR27HRomIsWV7Fo8WXn+ZGPbpu27bbtzj3ndmvcdc/sXGghBLV15bz7fTdNLtt6y/RmHfe/a/20f7/z3TN7gk5FWZmPd79vE5ZlvWodlfPJUXb1neWTS51mvD+6cJLmQIQNFQ3AREer0m+AcnkmgT29MxWX28hduexs+hQRV2zScU/FlfsHyFhp9o2+wC0Vb0HBkYCYWG9iHedYDuV3wq6rYcFxXwEhBHfWdpC3DL504ickjBydqUG+fPIJ1JLmhiWtyZg2QMTl5zc77mZ7Vft1PYAfaNlEQPPwFyefYO/IefaNnJ8sPDFte1psa2W4ns+vuAePMrdDO57o41f3foOCbWLZE+5+ZoTskYuHeOTiISchWBJ4UoXCzRVtfHnjB1CvmJqljQK/tu+bXMzGsaRdolfN3O+JRB8njvZNhg4m9lvu8vH1bb80GeIxDZPEWAZFCNKJLFWNUVRVob9nhI13rEArsR0So2mEIkiMpPCHvJSXptTjIynGhx1hsFAkgNfvZs+PD5NJ5Nh4xwpqWypweXSS8QzxoSTR6jL8IS/p8azT2WYsjdfvIVo9/SV1eO95XnjqOJd6R0HC9ncJFOFDV8vJGxew7DSqeu0q2kLeYNejr1BVW87iFXX4Ah6G+uK4PS7C0QCpeJZ8rkgo7MMfnJ0V8WpweZZmY5rd2DKHrjWjKE5YwJZ5DLMLgYautV71pTN9H00oymW5iGvZOXsY0cS0LqGp9UgMTLMLUNG1tml22DKPbSdRlcua55FogI99Ysd1X4epSBoFpuYdNKEwnE+XbJKcz5zhUHwvEsn68ptp9i/iaOIAnemTqEJle8WdpI0kh8b3YUsLC4u7qt8BUvLU4GMoQmEo3z/rsaV0nPqR8f1IJBsiW6hwV7Fr8EecTh0jbSZZV76ZCk81zw8/yXgxTtgV4ZaKOzg8vo+x4igpI0G1p5YtsZ1XPc8bznGrQpmMBc5WGakKMfn9bGXyinAkSCVzV16qQuH+hrW0BmL8Y+dzHBjtJmsVseR0/nVAc7M51sYnFm9nWVnNVW9wVSi4FA1dUbkp2sqKcB0xd4CvdD7DudQwprSn5bmCmoc7apbzqSU7qfKE5oyxT3TinrDreqs9J2Ajr8Ipl5jSQuIkx14NLVMinXMq2SmEYHw4xd984Vu0dtSDENz9oa2EY0HOHe1l10N7+d1/+SQen5tnvrePE3vP0bK8njOHuvnF//FOBPCdv36C1hX1PP3QXn7pD96D1+/m5L7zCEUQCPsorwwxdHGM7/z1T6hrraS/e4QPfu5ejrx4hj1PHGHRqkY6D/fy4d+8j5bll4syNm5rp6o2TH1zDFVVEaoN2AgUvFojqeJxgq7lXIs9axoWF7tG8HjdGEWLPc+cIhnPkBzPsnnnMn7wHy+x9uZFnD/dzwOf3Dmj8e5rh6RgnCCZ+VfKQ7+B173FWWpnyeWfI5N7jJrYtxBijtZdk/s4WdrHr+N1b/2ZLLJlitHx36My8rdIWSSXf5507lFqYt9EiMuzUsPoJJt/gnDws7weJXV1vhD/NtbP4bE+3KrGcwPnJ0ffAJ2pE9T5GukIrcWjekiaCQ7EX+au6vvpzXbx8sjTLAouI2mM896Gj/L88BN0ZzoZL8ap9tSxpvwm/qPnK3Me/0zqOA2+ZpaFVuNRPWhCZ2NkKwU7z72170ZB5XjyFWwpeVvd+3lh+ClOJI4QL44R1ELcWnEX373wVZaEVl71PG84x/3epg3cVu1M9WYLS9xbt5qNUSdmGJulx9wdNctZWe4E/68mkaoIwaryBr684QG60sMcGb/IhcwYecskoLlpCkRZEa6j0R9Fu44y7YnjqggaA1FUReGOmuVsirVyZPwiJ8b7GC2kcSkqjf4oa6NNNPujVy0Bl1Ky/+kTLNvayte3/dKsamQTsEyLvu4RGmbRpvBrrmkqixMIaB7+auOHphUXvRqoQqAkbRKZNOFYEAnYls37fvVu3F5nBiGE4OZ71vDy44cnX1yWabNqSzv3/sItfO8rP6XnZB++oIeKunJue/dNnNrfRV1rJeFYkI7Ni3C5dW5/7yaklPzoq8+z7a3ruOktK3j+kYO8+MNX8AW9LFnXwns+/RYe/48XOXf0wqTjFsLRgh4ZSvLiT08QrQjylvvXoZeEhqSUePXljlDtNX5jf9BDTUOEpasbKI8F6D07xD3v20jXmQEunB/GH/Sweecyes8Nkc8Wrum4pbSwpUP/U4Qf58UhkTKDxEYRAUBBCBW/9z7yhRdhyktYVSME/R8gl9/FUD6BV1PImHlGCilqveWMFtP4NTd5y0AgaA3cO2MfTuIzj6SAIryAq2RDDiF0bJkt2TZxLo7NUhpIDECiquUE/R8gm//ptPOzZR5NrSHk/0UmnLaURSQ2SAuwECLAq2n3VeUN8qFF6/n2+UOYtsWddUtYXOYUhAkh2FZxBwfGXuLRvu+wJXYrbsVDwhjnWOIVbGlT43VCKtWeOlyKi4AWomgXSRhxloRW4FY8RFyzM2GEEGyvuJMD8Zd4rO87bIndRqOvBUU43Z4UnMYL8eIYFe4qdKFT6almOD+IQBBxxXApbgJakKw5txIn3GCOWwhBzBMkdpU4csTtv6pDDrt8hF0+p6Q1P5OYf+XxXKpGe6iaWleEgGf2jh65ooGqzN5d/MrjXrn/kMvLtsrFbKtcPGnTxHF6zwzQfbqffCbP5jtXkRzP0Nc1THwoydZ7V9PXPcLYQAKf5sbdK9F0laqGCK88f4ZFK+rZt+sEukvj5rtXsu/5E+z/0SFc96xmy92r0KbEmbOpHLse2odl2Wy9ZzVSSnY/cRQpJdvuW8vo0DhHXuokVlvOmm3t7N91gkwqz023d9Bzup+2lfWMDiQAuNA5QDZdIBwLsGRtM9/5xyfRdJXNd64kWl1GsNyP7r5W0QaUxZzfWHdpWJbNotWN/PjrL/CDf36arW9dS6jU5urKvRTyRbylMmyP300+W8Qf8hIuqcvpLnUG86RYMOnvHeP9H9/B3udOMdg3Tn0pMSeR7Bo8TIOvoqQAWWRZqNEJ9xg5TiYv0OyvoiszQFughnA0wIEXzrBp5zLaltXwwhPHSCVybL2jg927TvD8T45i23LSxrlg20niyS9jWpcAQcj/ETzuraSzD5IrvICUBi69jXDwM1zPY5y3DfYOnSDs8nMhM8JYMQ0IEkYWt6KxvXLZrNsZ5hnGU38HCKQsEA3/HkJ4GIl/AVWtxLbjCOEjWvb7gCCe/CKWPYwiypB2btZ9Tl734nGSma+hKuVEyn7PsbOwh1T2uwjhwbJG8Xl2EvR/6LrDSkIINlc2samyCSZi1FMS/0ljnOWh1bhVL52pE2yN3U7UFaM92IFLceNRvQwXBmccr8Zbz5nUcfxqgIH8pVmPLaUkZSboKFuDS3FzNn2SRl8LutDJmClGikOEtDIafM0cjO+mxtvA2dQpOsrW0pU5w9n0SUJ6mLgxSth1dUrxvHbcUkoGE2mGkxmaYmGCXjejqSz98SR10TLCPi8XRsdJ54ssqo5SNC16R8api4RwaRrZQpHygJfB8TQuTSVTKJIvmrRWR+gaHOOHB09x5+p2ltRWTCtntqWkd3icTKFIa1WEkWSGb790hLesWszSugoS2TyD42nqY2W4NZWH9xwjGvCxrrWOWMhP91AcRRHUlocYGC9VZVk2TRVhktk8A+NpmivLKRgmqVyBbMGgrTrKpbEED+8+xp1r2llaW8GFc4NkUznqWip54YevUFEX4dL5IW59x3o8PjfNS2p48YeHsCwbl0dn9xNHWbdjKUMXx1i2vpmqhgj7dp2gYXEVbSvquXhuiJtuX4F6xQtGUVUq6so5tuccJw920dc1QtOSahoXVSMEPP6tl7nvI9vwBTyc2HeeXKZAW0cdT313D5ZlT4YlELDnqeN88DN388OvPc+Stc00tVdT1RClbUU94yMprnS1uXSe3jP9pBM5ek7301BKll35nBbzBvlsAX/QSz5bIJvMEwj7CFeEOPD0caobo7R01LPhtg6e/f5+LMvm5R8d4s4PbqH3TP/VqVW6CgIef3g/2XSeNZsuKzsKBGFXgGpvOcP5BIP5cZYE61GEyoXcCGUuP17NTdrMEy+m2bxzGcMDCQIhD+u3LWZ0KIXLpeEPeohWlbF0dSObdi6bjOfPdd+nc48CUFH+FyCUSZ0Sv/cefJ7bsexxRsZ/m5B/HFW9Nhe6wh3CtAdZW96MW9Go9oa5lI3TFqjCrerT5B6mQtdaiJb9TyQm48m/pFA8gMd1M4Z5nnDo19C1VobHfhXDPIdtJ7DsEWLl/xfDOM3I+H+/qk1u1zpCWKSy351cZssCljVMVfSfsOxhRsZ/l4D/PQiuv1ejI70As4VeUmaSnsw5vKqXzdEdeFUfd1bfz7HEQUDQ5G8hrJej+Jxta7z1qEKjylODaRt0pk+yOboDvxaclmCccPRJY5zebBde1cdNkVsAKNo5VoU3cDi+l9XlN9Hoa6Vg5Tk6vp/2YAdtgXa6M2fxqn5OJA+zo+JOQlr4quc4rx03wKXRBEOJNC+e7OY9N6/k688eZH1bPSGfh7MDoxzrGaC9NkamEOTbLx6mo6GKZ46fY9vSZrqG4tyxahE/2Hd8kkssgXgmh8+tk84XZ6WODY6neHj3UTYubqCmPIhhWSSzedTSun1jSQYTaZ4+fo6P7dxAPJ0jGnCq71463UPv8DjJXJ722goOdfWRzheoDodY21LL7s5eWirLefFUN9XhIP3xJAGP25EnLQ+RyhWc2LxwnFe0qoxYbZjTh7oRAhoWVVFeUdJyVgRKqdCksq6cXDrPgadPsuG25bz8kyO43Dq+oAfTsHB7dDRdnQxRTEBKyeGXzjA2mCAY9mEUTDLJLHUtlYQrguSzjtBVZV05iqqQTuSIVoepqCsnk8zh8buxbZtCvojb4yJSEaSirhxf0Onxp7t1dLeGpqsEyrzc/r7pTY9TiSw9ZwbYet9aek73URYLsHrrEryljuQrNi/G5dF58YeH2HLvWhraqzm57xy7HtzD2z5+K+t2LEPaksELozQtraVjUxtur4ueU33c99HttK5owB+6rO2xZF3LDBF7RRFsuW05Z09cIrSkGt11+bEQQtAWqCFRzODXPNR6I5P2N/srOZvqo2AVqXSXEXYFUDWFmilMjopqJzFrWTaLltdSVRtGmYO7PtUuwziHx70RRfFN+d4gmf4qptWHopRh2wkk1xfGSho5tlYsIaB52BRz+ng2+adrylx5XaSU5Aovk8n+AFWtwjDP45EOI0RVYuhqIwIXilKGlHlMqw9da0HgRlPrUcTVedpzjaJd+lKE8KOI0oxYWjN8sJSSYtG8Zsn9xLpDhUtY0iTqiuBWNMp053eMF4dIGWNUeSpYFFhJf76XMr2cnJXiQu4sdd5mNEUnb2Wp8lThUwNoioukMYoAjiX20hboQC01G466onhVF5WeesaKQ+SsFP35bhp97ZMjcYAloRUsCa2Ydt0bfS0sDi6/6rlMYF477oJhcrR3gLDPy1g6y4XRBItqotyyrBmA5092cdvKNpoqyklk846a2LIWugbHGEvnJgsTLNtJjm1d2kyuaHBpLMmS2gqqywIsrZ3ZwTsS8NFcFeHUxSGW1FZQWx6iIuRnWX0lli052jtA0Osmns7hdWnUlIdYXBOjIuTnwZePoqkKfo8LVRHURULkiiYtleV0D8fpG0tSGfITDToP5M1LmvDoGsd6B9m4qJ7KsgBL6ysnncP+Z05y5nAPG3Z2UJgS2rFtyeEXzzB0Kc7+p0+w6Y4VLF7dyMFnT1HbHKO3c4CB7hFymQK6W8MX9JJOZHn+sVfYcvfqaSM+r8/N+HAK07Sob6ti3fal/PgbL1JeEeLWd6ynYXE1D/3DLqobo3Tc1MYT397NmUM9rN2+lHy2yJPf3k0uU2DdjmX4y3wIwBf0OrOOlhi7f3IUVVVYvLqRdTumT8kr6yLc+cDNc94DLcvrkFLy9INpyitDmEWT+HCK5RudPIbLo3PzPdP1qdvXNNG+5nIT6Mb2y6prDYuruRKmYfHjh/bR2FLB2GgawzBxey6/4GLuEBFXENuWqF7h3E+KREeno6wZW0rKXUES2TxpWXSE+MXlcn4pHRrZljs6nKrKksa8ogiQErvELT83OEp1OEjA7ULTGigUj+Hz3MGE17KsYbKFZ6iO/iu2zJErvADM7nAnjjuBKk8ZQvjndHKz7QMkqcw3CQU+ise14YoRtOBKb6qqVeQLuwETyx6ZjM9fj33T15v5YisUDGxLoqiCQt7E53dxYG8X6zY0O8VHUqIqYrKAyLRs/H63UxSETbw4RF+uG78WotJdx6XcecxSVXLYFSOghNEVN/HiECEtzMXsOQCCWphyVwUpc5yUGWcwf4GUOY4uXCwOrkZXdFShciyxpyRnLFkcXMnZ1DHyVoaV4c2cTiU4nznB+vLts157gNbAYsL69St3zmvHnSuaDCXStFY5J1RbHuSZY+fYfaaX2kiItqoozx4/z5K6ShbXRFEUwcune0jmCjTEyjjU3cfuMz2kcgWqw8HLPF9A1xTGs3mOXxhkaV3ltFBJrmhQW+6I83cPxVnbUkvRtDjS009TRTmXxhJsW9YyedsGvS6OlJx5R0MVffEkTRXlVIT8DCXSKKVKs4qQn+pwkKaKcsp8HrqG4pNNgoUATXXCOcd6BljW4GhAb7qjg2UbWvH4XFhTGqcKAcvWt9C+utGpcNMU1m1fyqqbF6NqKlvuXkU+W0TXVRRNRVEEH/rsPY6S3JTGrEIIVm5exKJVDWi6OslBblpai7QlHp+L299zE/lsAU1T0d0a7/7l27BNG7fPhZTQcVOrs62i0LGxFaEI7vnQFnRdY/GqRhraqqbF1F8L3vnLt3Ns91niw0l2vGMDbSt+tga7E8ik8owOJ9E0ldalNei6hnsWLvnhnn4s28bn1jnTN8Kyukr64knaa2Ic6LpEbXkITVGoCPk53TeMIgTL66vw6BqZQpHnT3VT5vMwnMpQXRZAVRRM29EVrw4HSebynB8a486VixEeQcB7P2PJP2F47DMgVAK+9+JxbURXmxhN/AmK8KCpVYDAskdIZb5B0TiBLTMYZjdB//vJFw+SzT2JaQ0RT/0FPs9teFw3z3rdLu/jOLZMY5g9BP3vx+u+hWT6q2TVx5HSQAgXCFGiHJaIpcIHQsOjbyCT+xHD8c+jKmWoSgUgKBT3kck/UbLjL/G5d+Jx30w6+xD5wksUzbOMp75MwPc+hNAQE70ahSglYOHQ/m4uXRwjXO4nkciyYlUDHo9TIfrKgW6WLK2h88wAUkI2UyCdyrNtxxKqasJY0iJlJghoZQR1xxGrhsZAvpcabxMBrYwL2bNkzRRZM03SGCfqdiofL1ev6kRd1WhilIBWhsQm7IoykO/FljZBzSlRD+kRRgoDVLhrKNgB3IqXmLsGBYWclcWvzczPObO6pa/qvhU/a++z1wMbNmyQ+/fvn7FcSkn3cJyiaRHwuKgtDzGUSNNX0rwo83noHoqTLRRZXFtB0TTpHopTHykjHPDSPTSGadn43C50VSXodU92d44GfFwYSZApFGivrZhGo8sbJp19w+ilKj9NURgYTzOSyrCktoKBeIpssUjA46Y2EqJomJzpH6GmPEQk4KVrcIycYdJcUU62UMSyJV6XjqoIipbFhZEEteVBVEXB49KcRFe+SEXIT99YkvFsniW1FQz2jKC7NCrr3zgN7f/X0XNuiJOHezGKFqqmoGkKN+9cRvCK5rX7z1+kvSbGqb5hZ+ZXFSVTKNIUC7PrxHlWNlQhJU7FqGkxmEhx1yqH1985MMLuzl7qImVcGksQDfooGCa6qhLyeti6pInnT3WTKRTZ0t5Eud/rsOWliWWPO4wEJQyoSIrYdqLk0EyE8AM2lj3KBC1HoKEoMaRMY8vL7ARFBEosjdmopcYs+4gCEtuOg9AQwl1S63Nj2+MlmwS2TJYYJzpgYNnjKEoQKQsoIoSUmSvs8CNEENseQ3J5FqkqkdJ5F0uUQTl5nFf29zA0mKCqJszQQILV65o4cfQiSzvq6Do7RFNLjEsXxjBMC01TMQ2LxUtrqKouK814bJiMfAtyVobTqVdYUbYJTeiTBTi2tJmQJ55Yd+rsYKIoZyK2fWVVw9TvJqQEJraBV1dotWHDBvbv3z/rBvPacS9gAfMFE+X6tpSMpXNE/F7i2Rwhj5tM0cCtqeSKJkJA0OPGsCwCHieeWTBMxrN5gh4Xpm2jCIVc0cCjayiKwOfSyRUN0oUi0YDvVXPx/1+AVWr4rCgKhmGiaSpG0UTVHA2Uy+EXh8orcdadS4vGlnZJxEt9XWZubwTmveMWQqSA02+2Ha8CMWDkzTbiOnEj2Qo3lr03kq1wY9l7I9kKb4y9TVLKWbuSzJcY92kp5YZrrzY/IITYf6PYeyPZCjeWvTeSrXBj2Xsj2Qo/f3sX5mQLWMACFnCDYcFxL2ABC1jADYb54rj/8c024FXiRrL3RrIVbix7byRb4cay90ayFX7O9s6L5OQCFrCABSzg+jFfRtwLWMACFrCA68SC417AAhawgBsMb7rjFkLcLYQ4LYQ4K4T4rXlgT4MQ4mkhxEkhxHEhxK+VlkeEEE8KITpLf8unbPPbJftPCyHuehNsVoUQrwghHrsBbA0LIR4UQpwqXeOb56u9QojPlu6BY0KIbwkhPPPJViHEvwohhoQQx6Yse9X2CSHWCyGOlr77K/EGVaTMYe+fle6FI0KI7wkhwvPB3tlsnfLdbwghpBAiNmXZz9dWR4jpzfkPUIFzQCuOQvthYPmbbFMNsK70OQicAZYDXwR+q7T8t4A/LX1eXrLbDbSUzkf9Odv868A3gcdK/57Ptn4V+ETpswsIz0d7gTqgC/CW/v0d4KPzyVZgO7AOODZl2au2D9gL3IxTE/5j4J6fo713Alrp85/OF3tns7W0vAH4CdADxN4sW9/sEfdNwFkp5XkpZRH4T+D+N9MgKWW/lPJg6XMKOInzEN+P43Qo/X1H6fP9wH9KKQtSyi7gLM55/VwghKgH7gP+ecri+WprCOeB+BcAKWVRSjk+X+3FKVDzCiE0wAf0zSdbpZTPAWNXLH5V9gkhaoCQlPJl6Xiar03Z5g23V0r5hJTSLP1zNzDRU+5NtXeOawvwZeDzTGs0+PO39c123HXAhSn/vlhaNi8ghGgG1gJ7gCopZT84zh2oLK32Zp/DX+DcSFPbusxXW1uBYeDfSqGdfxaOStK8s1dKeQn4v0Av0A8kpJRPzEdbr8Crta+u9PnK5W8GfhFnVArz0F4hxNuBS1LKw1d89XO39c123LPFe+YFP1E4nVUfAj4jpUxebdVZlv1czkEI8VZgSEp54Ho3mWXZz/N6azjTz7+XUq4FMjjT+bnwZl7bcpyRVAtQC/iFEB++2iazLJsX93IJc9k3L+wWQvwOYALfmFg0y2pvmr1CCB/wO8D/nO3rWZa9oba+2Y77Ik7MaAL1ONPRNxVCCB3HaX9DSvlwafFgaepD6e9QafmbeQ5bgbcLIbpxwky3CSH+Y57aOnH8i1LKPaV/P4jjyOejvXcAXVLKYSmlATwMbJmntk7Fq7XvIpfDE1OX/9wghPgF4K3Ah0ohBZh/9rbhvMQPl563euCgEKL6zbD1zXbc+4DFQogWIYQLeAB45M00qJT1/RfgpJTyS1O+egT4hdLnXwB+MGX5A0IItxCiBViMk5B4wyGl/G0pZb2Ushnn2u2SUn54PtpasncAuCCEWFJadDtwYp7a2wtsFkL4SvfE7Tj5jvlo61S8KvtK4ZSUEGJz6Tz/y5Rt3nAIIe4GvgC8XUqZnfLVvLJXSnlUSlkppWwuPW8XcUgMA2+Kra93NvY1ZG/vxWFunAN+Zx7Ysw1nOnMEOFT6714gCvwU6Cz9jUzZ5ndK9p/mDcrIX4fdt3KZVTJvbQXWAPtL1/f7QPl8tRf4A+AUcAz4Og5rYN7YCnwLJ/5u4DiSj78W+4ANpXM8B/wNpYrqn5O9Z3HiwxPP2j/MB3tns/WK77spsUreDFsXSt4XsIAFLOAGw5sdKlnAAhawgAW8Siw47gUsYAELuMGw4LgXsIAFLOAGw4LjXsACFrCAGwwLjnsBC1jAAm4wLDjuBSxgAQu4wbDguBewgAUs4AbD/w8klnlV89kT2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(spam_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e6cd0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x21fdce0e1f0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hCA2QrAl+HLsaIG1FEQgaPDeQskJIaVHp3IauuDBUL8Opk0iZhQnDLWAsdCIAgYIlUwwkdhHLtNIffxGJhUdvIpHtIW2NkMjmlg9sStFEzHP84wGwqQHS5jAtoe9T7NiIzzbJ4DEMbYJRY7dPXagq3PsQQuDSJheuHJqOg8lj2VRtZhZJAZESCTzafppKl5f/3HH7BJPDlJJwOpkXA72tYSm3NSzl8FAPu3rbuK1hKR+YI8Y9Hrduj47yTFfLrO1eC+RmZRq5N6qNORL5g+tUyCnb01YERegY6uwLuTbVP+Vkytjeuf9VhcoiTzMZmSGg59ptK97EaDqEW3NxbdmVhLNh1gZWYVfs7CzdgVOdfK6r/SsYTtfg1mZm3IBCsWMbhhIg6NiCJVNkrDBuo5lo+iwgcGrV2NXSGfduj4T42LP3E04nMaUkZZoITO5rOZ5nAMudHt69aC3Oi9ZTNaHwUOspvnPyAKa0cOkGiWyGl7pbuefcMT67/gp+a9Hqacb02FAfz3a1kMhmCKeTZCyLGrd/VsMdzaT43qkDVLl89CeifPXIbhQhcOo6sUya57sv8Itzx/i7LddxU/388X4pJUkzy5cPPM93Th5gZXE57160piCj/4Yx3DZVY2NJXd62xf7c6O7Rcx/ryFCUGpefBZX5rIqV/kr6u0epqCmaeFgBm5Md5U3TziNlLsiQMjOEM0nCmTihTIJoJknKymBKiQB0RcWhGnh1Bz7DiU934tJsaGNx2RwtUOC3LcBvm1zQKHWszjufpjhwXsQC8RiTHaPYvvyi3xqxqe8AwKXXU++9GyFUHFoFCwMfAcBQfAwkduO3LaPaczPJ7AAtoe+wovhzNPreDYBTq6LJ9x4AdMXPiuLPkTQHZvR4ZsL4c0qaGcKZBKF0nFAmTjSbIm1lx1g+CnZVx6PbCRgu/LoTt25HHxuEXqspY6nDzb6BTl7oucCV1U04VB1NUSiyTxqSuc4122/j298oEc1xYy2lJGNNri1oihNLZslacZgSHpmK8f44229+wzdxbEkW5CgeNYRlDqEoDor0IhSRe55uLX+RVhUqJbbgtONOOQMOvRpFGGMzvkn4bMsnzqvhmRLfngwr1Hp8/ODadyCBjmiI3336HpyazjeuuiNvYFaFMiMFM5ZN843je7mtcSnvXbKOEoeLUDrJt0/s4/unD/GfR17mqpomKlyTA58A/mzDlfzx2stImBk+t+sxnuo8P8c9TuL0yABtkRHeuWg171i4koDNwVAyzn8ceZlftpzg3w69yPbKujkXVseN9j8eeJ7vnjzA+rIqvrz9JqoKYEHBG8hwz4RoJEFv5wiqqlBeFaC7bYiGReUMD0QYHojgK3LhcBq0neun7XwfFTVF044xHocKZxKcjfRxaKSN46OddMSHGE7HSGTTZKU1saA2jpw/JFCEgk3V8Gh2KhwBFvsqWOmvxa3NzOH9VWBTNVYF6nDqOeOqCSeakvuYfLYleW1T5hC98afpjj5GPNtFwL4GVThx6rmPThV2nErVRHu7VjIrjRDGP2gIZ+K55zTcxvFQF+2xIUbSMRJmGlNaWNLK8xEFAlUIdEXDo9spt/tY5K1gTVE9K/zVlNl9qEJ51UZcAB9YupFTowP80a6HWBwo5ea6xVxfu6jgTv5Ghyps6Iqb0dRZgvblZK04vfGXGfeY3XoVDq2UC+FfsjjwHlRhI2slUIUxwTKaD7nF3Qgj8UcYif+SZOYMphVBYqEIG7pahtd+GUH3Xdi1BROMlNcCUpoMx+5hJP4QIBHCoNTzPty2LQAYqkadNzcgWTKXK6EqCjUeP0H7bF7+JCwp2VBWzZ9vvAqHmpvNBGwOPrVmBy/1tHEhPMKJ4f58wy0Edk3Drml4pW1a+GUuZKXF1TUL+KO1l6ErysT5PrPucnb3tNMWGaUlNMza0qpp+2pjIZCEmeUf9j/H908dZGtFHf+4/QZKHTOxz2bGG9pwt58f4OjeCyiqwtotTfR1jxAs9XDiUDu+gIvjB9uw2XUqa4Okk9m8faWUZKTJyVAXj3YfYdfAWXoSIbLSnOVs+RiLCGNJk2zWJJZN0ZsMcXCklR/x8uvipZXZffxo+8fwGfN3Vr9tJQ6tgrQVoly5Arta8qqMmJSSpJXh2Ggnj3Uf4ZWh8/QlQmTnWfmf2B9JVkqyZpqEmaY/GebIaAc/b9+LT3ewMlDD9ZWr2Fy8AJ/uuORrFELQ5C3iO1e+ncc7zvCLlmP88+EX+MbJvXx42SbeuXDNnAk/bwwIDMWdW3kQKrriIsf2saEKB4owWOC7kxMj36Yn/iKmzODSykmLMJAbwNcUf5KDg/9MT/xldMVF1oqzruQzBGyL0BQnqrBNOZuCoXgmqYJSksiconPkz4mm9sFFi+amTGJaIZKZMwzH7qPC9wcUu+9CFDgozAUpTQajP6Jr9G+xZByBjQrfJ3DZ1r9mg64AbmtcOmG0IddvvIadRm8R50PD9Cdir8m5IOf5v6Vp2YTRHj9f0O6iyu1lIBFjKDkz682lGyTMLF/Y9ww/On2YK6qb+MLW6wjanZf0PN7QhltRBMXlXpCQzZqAIBZNoesqtU2l9HYOY5kWhk2bYD+MJ94cHGnjey0vsm/4AkkzM/eJXgVeD6KSvISj5jyGUuzMHDec91xSkjAzvNh/mp+07eZ4qIu0lZ1/x0KPj2Q0E+f5/tO82H+GOncxd9Rs4MaqVfj0S+ukQgh8Njt3Nq3g1oalHBvq5Z8Pv8CXDjzHQn8JW8re2BQum+pja/mXsKleDNXPxrLPI1Cp99yANeZI1Htuoti+moQ5gEMtwamVkpWJCapo0L6Syyv/nUimHVOmcahBXHouW3FN8R8ghIYc81bdehWXV34Fm+rPDcyZM1wY/AipbC6Orwg3bts6HPoShLCTMXuIpvaRyl4gaw3SOfLXWDJOqecDiEtkWkzFNKMt7FT6/ohSz/tek0FhHJqi0uQLztgHjLG1FbNAR6QQODSNOk9g2vkUkTufRM5KZbSpKv926CV+ePoQ2yrq+YdtN+C32S+5/76hDXdlbZBA0A2Aoiok4hnMrMnCFdV4/U7WbFmA02Wju32IddubAehLhvmfc8/wSPfh18Vgv1GQm/pO0pDGX/x4yGM8W3QmmNLi4HAbXz/7NIdG2gr2rl8tLCQXogP808lHuL9zPx9YcAWXly1GF4VljMJkDNemaqwtqeJTq3Zw9xM/4tRIP1vKavP20YSCEJAyX7uB6FeBQMGu5UIBKirq2EKiJpx5rTxGDZ4peWuKNHh+11nKSr0sbi7HUL0E1fw1EYBoWOWp505wx63rEAIUoWHXcjFp04rQMfL5CaPt0JdSU/S3uIxVTP38TWuU/si36Av/F5IUPaF/xaEvxmO/7FXO5EwGoz+ka/TvsGQcRTip9H+WEvc7xxZpXzuoQuD4NUooaIqCrQC21Ux4pPU050M5jnpPPDK2wH7pEhVvOMMtZY4/HQklcDgNPF4Hkpz3XVKenwBQOcb68AfdSCk5MNzKF48/wPlo///Blb/2kFLSE40QsDtw6NM9lEO9PTzbdoGPb9yCPtaRYpk0j5w7y51Llk1rL6Ukkk3yv+df4GftrxDLpqa1eT0hkZyN9PHnh3/GdRUr+ejCqym1zx2nTplZfnDmEEuKSqnzBLCrGuF0kl+2nhwLo0xfNCtxuPAbDp7uPMf1tQsJ2JwkzQx+wzGhQ2FaFuF0kqSZpSsWxhpjqXTFQhiqhkszcI6lXGcsk3A6RcrM0hePjMkuxOmKhrCpGm7dwK7pE9cbSadIWVkGkrnpeV8iSlcsjE1V8ei2PIbNTOqc4/Q2gPJSLz5f/iLXxfskkxmOn+rirbeu5eLDjcYfJZp6BQBNKaYu+E849OlcdU0NUO77OKYVZiD6bSwZoyf0b7hs61HF3Noa03KCZXbMaP/9mNF2Ux34c4Kut/9KHvzcF/HrnnG9uvN1RkP8ztL1dMVCPHjhFJ97+XH+/fJbL9nrfsMZ7nQ6y/e//hyH9l3grvftoKYuyOnjXVx906pZb8ySkqd7T/CF479kJP3axbL+r9Ebi/KVV15mUXExV9Y34TYMXupoI+h0srmqhlXlFTzd2oIkJ5T0Unsbw8kEHaHQtGNJKWmLDfKF4w+wb+jCJYVlXmukLZMHug5yOtzDZ5ffykp/zezvFnii8yxfPvQ8Dk3DUFSSZhZFKPzu0o0zZrqVONx8cOlGvnL0Je549Ps4NA3Tknxpyw0TmiEXIiN8+Ll7iGbSpMwsGcvkobZTPNvVgqGqXF7ZyN9uug4BvNTTyl/seYKUmSVhZlGFwteO7eZ/T+3DUDTes2gtH1qW01B7sPUk/3L4RdKWSSKbQRUKXzjwDP9y+EVsqsonV27njqYVedd730MHMQyNcy391NUEuenalaiqwuNPH+fgkXZuvXE15aU5pyWZyvD408c5e74Pm6Hzjjum0B0lHDnRyfkL/dx6w2oUNctQ7OdALhxT5Lp9LDwyC/sEjVLv+xmJP0DWGiSePkwstR+vYwdzGSoh7BO875zR/sGY0U6gCi/VRX9NkfO2189o/wbhhrpF/PG6yxhNJRlKJnipp5W/3/cMf735Ghxa4eGjN5zhPn+6F0UV3Pq2DWTSWRxOgxNHOrj6xlUz9h0pJc/3n+Jvj91HOJP49V/w64igw0mFx8OO2nrK3W6+dWg/l9XWc7C3B49hY2nJZHz73PAQp4cGWV9ZxcmBgbzjSCk5OtrBXx65h9bY4K/7NmbFmUgvf3zgR3x22S1cXrZkRv6qQ9X46mW3czY0SG88QtJM4dZVlgVqKHPOzA9XhOB9i9ezo7KB0yMDZCyTUoebVcWTVMhql5evbL91Itx0MTyGbaK7rSmp4quX3z7rwkbxFLW3nVVNLPLPwt4ZUwq8GEeOd7J4YQV337GRb33/RSrL/WxYW88Vly2mrWOI4ZEcNVBKyWNPHaO9c5i779xENmviddsZGo4hhODoiU4ee+o473/39pyeTraXRObE2Kl1fI5rmdsACwy1CpdtLaHE40gyhJPP4rHvmNOhzdEI1elGW/FTG/g7/M6bCmapKEKgIMhaFlnr9Q3h/V/AUDUUBEU2B1/Ych0ffPoe7jl/nBq3j4+u3DLBOpkPbzjDbZoWLrcdw6YjpSQ0Ekc3tFmN9slwN1849stLNtqqEARtHhpcJTS4Syiz+3DrNjShkrayhDNJehOjXIgO0BobYCQdf828VAWBrqi4NDsBw0mp3UulI0Cztwy7OklL0hUFu6bjMWxoikIsnabOH2AgHmMwnj+zCKdSVLg91Pn8OPT8qfjBkTb+7NDP6E1O98T/rzGYivBXR+/lM1aWaytWTDPeQgiK7E422WuRUjKcOs1o+gJuw0ZvoosGz1UzHldVFBb5S2Y1onZNZ2nR9CxAKSWmmW8wfIYdX1FhglhBu7MgClvetaoKa1bWUlLsYdWKGs629LFhbT02Q0PXJ71UKeH4yW7ecstaKsv9eccYGIzw399+jj/8/esIFuVoZalsK+YYH1xVvNi0+gKm4wpOYzmhxOMAxNPHgCwwuzeoKi5AMhT7+YTR1pQgtUVfxOe45pKohT6bHb/NTk8swq6eNm5pWDKhCmpKa2z94lcPi0wNN1lTdGzG146mnuH1WPjO8df9fGnbDXz4mXv52tHdVLl9vKVp2W9WAs44Fiwq57nHj7H/5XMoqoKuq9z9O5fN2DacSfDlEw8xkIoUfHyborGxuIlbq9eyyl+L33CiTkmqGcf4i8xKk8FUlH1DF/hl534Oj7Rf0mKeR7dT5QhQZvdR5QxQ7Syi0hGgzOEjYLjwaPaJUfjia0hms1S43dx/+iSX19WztKSUHx07wmgywd3LV/Jc6wVODQ7wXOsFVpWX83xbK8PJBOqUhcpT4R4+f/gXr9poq0LBqzsos3spteeu2aHqKEJMDHADyTB9yRBDqSipV8FMCWcSfPH4A9gUjZ1ls0/lAYRQkVhkrQTljjWv6p7mw08eOYAiBHfdtO51Of7FkFKSSKQBiMfTOOwzG0khwGbTiEaT0+LchqFx1eVL+OXDh/jo+6/AbtdJm72Mh0lUxTdmYOdGzuuezA7OmANYMoU6BwtEES5CiafGFiITgEKF71OXbLQhN0jeVL+Yrx/bw1/sfoIHLpzEZ9iJZdM4NJ0vbLke5wzrPZcCKSVPdJzjha4LRDIpopk0hwdz4m3fP32Ql3vbcOkGHt3GLQ1LZs2k/FUhhGB1cQV/teka/ujFh/i7vU9T6fIUJHT1hjPcdofBBz5xLa3n+0nEUtTUFxMs8cyokfzz9j0cHumY5UjT0egu4eOLrmNzyYJ5GQ3jv+lCo8Lh5+aq1Vxdvowne4/z1TNP0legIVzoqeAf1t6FV3egMHt220zY3dVBqctNY6CIl7s6yJgW1zUt4GBvD53hMPedOcny0jIO9HVzRUMDH92wEdOSEzoHvckQf330XroSIwWfE3KTmwqHn+2li9hespAFnnL8hhNdUScGmHGMe0LxbIquxAgHhlt5tu8kx0c7L8mIhzMJvnD8AYI2NytmiXkLIfAZ9TjUIhLmCJJ8Tr6UkpFMP6Z1aWwiRagEjDKUMSMzPBovWGvitYCU8PATR2ltH2LvgQt89ANXkMmYHD3RSXvnMFnTwu9zsmxxJVfvXMqPf7GH1o4hslmLK3YsAsDrcXDtFcuIRlP87P59/Nadm7CsyVmZIuwT0sHzQZli4C2ZmPacL0Yqe56OkacxrfFvwmI4dg9+53Voyuz5BZaUSEuiqpPGXQAfW7kFXVF4sPUUe/s6kUicmsGakkqEgOF4nLRp4bRp1HsD+G32WYsolDvdLPAF8Rn5zI0zIwPs6m2b+Ntr2PAaNiSSl7s6UAQEXU5Wl1ROGG5NUWnwjmvFzDQgCapcXpp8QdxTEnqklDiETp3LT4nDhWVJMmYW29jMeGdlI59cuY2fnDvCfx/dQ707kBd+mwlvOMMN0N0xzOG9F0gk0kQjSTZua54QfxpHV2KEn7a9UnD4Ym1RPX+18q1UOqbzLwuBEAKHZnBz1WqaPWX8+eGfF8ReOTTSxhM9R3lb7aZLPu/K0nJ+cuIo5W4Pa8oreb7tAh6bDWWM6VDl8fLxjVv41z27iKbT+O2T7INENs2/nHyE0+GeSzpnpSPA3fVbuK5yBUHDPXHvs0GQM3w+w4nPcLLEW8nbajdydLSTH1x4iV0DZ8kUmPQ0mIrwd8d+yb+tfzdls7BN0maE7vheTJlCV5w0eK6Z+M2UWX7a/mX6ku2XdM8Bo5QPNX0Ruzr5sUgkvQNhegfDBP0uqsr9EwOilJKB4Sjd/SG8bju1FQE0TSVrmoQjSfxeB8q4yqRlEQon8HkceQZqKlRV4eqdS7FMi9//3auoLPeTTmfJZi2uujyXMZvJWkgJK5dVU1Lsoa1jCKfDIOB3kc5kuf6GFSCgbHERAcvIhXvyvF2r8FDflBmlYFxTZXZEU3sA0NVypMyQtYaIpffTMfKX1BX9I4pwTpP7sqRkcDhKa/cw65bVgMwxxzr7Rgn6XfzBmh18cPlGQqkkkpz+jdewYSgq3zp4gAvDI/zjzdfxzavuBOQEX3sqBPDpdTvHMhzzDfvvLt/E+5fNrGPzN088g13T+OMrduTtV+pw8aPr7gKYcW1FFYK/33odlpR5A8nzB85jdWf5g4qtXLmwmR8/fgApobrMT3NNMY/tPoUA/nXtzTx/4DxPvHCaSDSJOUeM/w1nuHs6R/jfrz7FtiuWUOkq4vD+Vvp6Qtzxzs15XOUHOw8WHCJpcpfyVyvvoNLh/5XjVUIIFnkr+KtVd/Cp/T+gPxmes70pLb7X8hI7y5ZSar+0ai9FDgeGqnK0v5cbFyzkpY42OsNhzo8Ms7aikqFEnI5wiFTWzOu4Ukru69zHM30nCz6XKhSurVjORxde8ys9pxzXWmfdWMr7E73H+I/TT8z7nMZxNtLLf5x+nD9f8ZYZPw5D9aAJG2WO1aTM0Yt+lWRlhqxMX9I1Z630NJN25HQXLR2DOO0G5zsGeectG7hpZ45i+dyec/zggb2UFXsYHI6xYlElH3r7NmKJNH/6z7/kU++7iub6XGz9zIV+vvLdZ/nSH9+O1z07XzcYcNFYPxmPt9l0Nq1vJJ01efFsK6NkODcwxNm+IXwOO3WNQY529mIfGmEwGufAQA/lo36OdPVRXxygMpVCU/yMC1mZMo6ksJlI1pqcTSqKe15PXaATcN5Cue8TpLIttA5+AlOGGY0/jE2tocL/R3kJN1JKnt97joGRKH6Pg4MnO7nQOcSOdU08+OwxljSWs21tI17DjvciT1lKSdY0yZg5Z2CqeuC06xICQ1W5WDBNCIGuqugzCKnJMQ2enG65lvcd5JLeZg/TjOcZzLRdVxRWNlTSOxjGYdO55bLl/Pc9u+gbirBtVSMep40n95zBzFjcctUynj94nmx2dsP9ehVSeNXo6xll/dYFXHvLarZfuYS7f2cHvd0jeSv6kWySx3uOFnQ8u6rzycXXTxgjKS0y5tCMYu6WlcS0Zi/QMA4hBEu8lXxowRUT8fG50J0Y4ZHuwzNydufDomAxCwJBbKrKrQsXc3pwgGUlpVS6PTg0nUN9Pdy6cDGOsQ4spaQl2s+3zj9fcLaYoWh8cMFO/nzFW16TwQ0mDfhNlav5l3XvotkzsxzoTHii5xhP9Byd9XllZYpwpr1gnY5Xg2Qqy+c+cj1//YmbeNetG3jo2WNkTYvRcIL/vXc3H3vnZfz1x2/mrz5xE7sOtHD8XA9ej53FjeU8+fKkxvdze86ypKkcj2t2bZvLti4k4J95QTNjmgjgcEcP7UOj1BcH6BwJsetcG5saa9jf1kWl38PyqjLKfR58TjvNZUHO9Q1iaNUTRtc0Q5jm/OE9KSWp7GQIwVArUMTcujx2fRE1RX+HTavHa7+CSv+nEdgAi/7ItxiK/iTvezNNi8HRKJtX1ecybMNxovEUqXSW+qog65bVoM0yOxmHAHojUV680Mbx3j4yppmnj90fibKnvZMXW9q4MDSS571KKclaFi1Dw7zQ0srutg66QuEZ+1tOLiBDdyg8MVhcCqSUrFpYydZVDdz7zBFURSGWSDMaSSAEeN12hsNxhkIxXHYDXVMxdBVVUeacH71hPO5YNElH6yCZjMnJI51UVBXhcBoc2nuBpoXlebO1Y6OddMZnqy6Tj+0li9hY3DRhjCyZonP0C9QF/gYh8hMbYpljWFYUn2PnvMcVQnBd5Uru79zP0dHOOdtK4JGuw9xRswG3XniW1MnBAfZ1d/Fby3Mc9mqvjzuX5vi8sXSaCreH2xbmL+ZlpcU3zz/HUGruyjXj0ITC+xdcznsbL3vNVuynQgjBYm8FX1jzDv7kwI9pKSC8lJEm3zj3LBuCjZQ58pOuMmYUUyaJZwdJmxECtkkFSCEUFnnW49dLiJtRUmactJUgbSXJyiymzGLKwrzOFQsrKfLlUvOry/3Ek2myWYu27mGGR+OcPN/L+fYBLEtiWhbn2gdYvaSaa7cv5h/+50neecsGVFVh37F2/uC9V875fHZsaZ7194FIjK7RMHZdw6ZpeOwGTkPH57Czr7UTv9OBx26jJxRhKBrH77TjMHQiyRQ2rR5dLSVtdmHKCInMCQxtds48gCRNPH1o4m+nsZr5/Dsh9LH/z4VVgu67SJud9IX/G0mK7tCXMLQqvPadOQEpVaEk4ObgyU6KfE4GhqN4XXbshkZ5sYd9x9vZsbYJTZud931+aJg/ffgJ7JpGy9Aw1y5q5hOXbUETgvaRUf7kwcdwGQaaonBucIgPbdnAXatXjLFtTP71+V08dfY85V4PGdOkyuflizddOy12HU6m+OsnnsFnt/PHV2yfSHS7FJzrGOR81yBbVzVQX1HEwEiU5w6c54atSykPenhuf272cfWmhRw600U0maa+smhOTe43jOEeGojw+AOHchXXbRovPn0CIQSWJQkEp8QepWT34LmCmB2aUHlLzboJKdaxI2DKBLHMcUwritu2BlV4SZudZM1BHHrh1eadqsEtVWs5Nto5b/TwQmyA0+Ee1hYVQsnKod4f4INr189Yddqh67xn5eq8bVJKjoy08+wlhEhurFrNexq2X7JQ02wZfzNBCEGDq4Q/W34bf3jghwUlSbXHh/hZ+yt8dOE1eQuFhurFoRYTyrRT786nAiqoXF32TuRYwWZTZslaudBJykqQNOM80ft9LsTmn6057VOf+RSmTyoDSOKJNKl0rl9dv2MpSxpzdMEFdSUEfE72H2/H7bTjcthYUDfzAp2UknBmiJSVo7I6NQ8u1ZfXtjbo5xbnEjRVQVMVVEWh1OPG0FQiyRRum4GqKNy8ajE2TePaZc3oqkql34umCDz2bQzFfgpYDMfvx+e4itmofVJKEumTYxRAUIQDn2PnJQ/mitAp936cdLaLkfgvMa0QHcOfo7HkGxMJQJetX4BlSYSSyxIdl2goDXqwLDlv5aXRRJKvvOVmav0+jvT08ol7H+KGJc0sLSulyuflP95yMwFnTtTsu/sO8vPDx3nriqUYqspTZ8/z+Jlz/NvtN7G4tDin55I184y2EBBKJvmrx5/BY7Pxqcu3YZ+BzZK2UoTSuQIqilAIGGWoU1L6hRCsW1LDuiU5KYPOoRBLm8pZv7SG1v6c8mlZuQ+HoeG0GwibwvefO8hVqxaga78Bhrumvpjf/8xNwDiXNqfPB/kGIWOZHBkpbPGpyhlgub96WsdLZ3uIpQ4BCuHkC9T4P4ciHMQzZ8iYg9j1+oKOL4Rgc8kCvLqD0Dw88oxlsneohbVFMx97vP7heKcVQsxJe1KEwGXkG/SstPhR68sFa7Q0ukv56MKrMZT8bhBNp3my9Tw3NDWTzGS5/+wpbmhqJmB38Oj5s2yvqWMgHuOBs6foCIfw2ezctnAxq8sqCKdSPNpylpsXLJq4vr5YlOfbW7mleTHvb7qcfz71yKyJL1Nxf+cBbqteR41rMq3dkhkS5iCqMIhle3HrkyGYSX1tASioQsNQJmc4Ulq4tF+tqnxp0IPDbnDTFcspC05PptE1lWu2LebJXafxeRxcsakZQ595UMzKND/t+Cd6EjkdkS3Ft3B12Tvz2ihC4HPmz9I0I/dB+52TM0avY/pMTkpJsfudjMYfwZQRQomnGI0/ht954zSaXk6cLUxP6F+wZG5g9di34TCmSycUAkU4qQ58nrTZSyy1h7TZSfvwp2ks/jq6Wj7meY+tWQE9/SHKS7woioKq5hy2voEQZSXeCQdu6me8sKSYWr8PVVFYWlZKsdvFsd5+lpaVoikKHruNztEwoWQSgHg6nau4pMLzLa1sa6hleXnpRJ8xpoQaIRdu+fxjT1PkcPDHV+zAoWszpPZLToX3cF/nfwISp+blg01fxKfPrl0+HIlz+EI3CytLONzazbH2PkzTonMohCkldl3DZdMp9bnnfr6Fv4rXF+MiQolEmv/5tyf4/Kd+xOc/9WP+9Pe+z70/2j3RLpxJFExvW+6vxqVN79C6GqTY/Q6CrreQMQeRmOhqMQ59euGF+VBq81Lvml3neiqOjXbmGSzLsrjnW8/zyjMnObjrHF/6gx/x6Xd9nS/+wQ/Z9/xpLPPSMsfORXrZPXiuoLaaUPjAgp2U2KZTLQF+cOwwQ/E4Rwf6+Pd9u9nb3UU4leK7xw7mYoixKEGHk7ctWU6py81nn32CkWQCQ1W598wJXu7qmBiMHjp3hl2d7eiKwm0161jlr53hiqZjKBXlgc6DU4reZgil2wCFUvtK4tmBuQ/wOqCmIsCapdV85bvPsuvgBXYfauWnjxxgJJRbGxFCsHlVPd19IU6e62XLmsZZPdbR9AB9yXYyMk1GpjHlayOKJaWkozMXSnQaKynxvBdQkDJJ+8jnGIh8i4zZhyXTSJnFtOLE04dpHfwk4eSzQE7XpNz7SQSF61RPhRAip41S9EVsWgMA8fRhOkY+jyWjeTM2KSUPPXmUaCxFJpuLVSdTGfoGI0iZi4m/vP883b2jZMe+CaeuT8zEVKHg1HWiqVQuS7i3jw/97H7+/qnn+MWR4+zr6J4sHE3OWy9xzU23e/z0OY719GEhUZXZabwt0SNkZCr3Dq0008RiLsKiqhLaB0Z55Uw76xdUc75nkHTWpKbYj9dhp8TnpsjjorJobgfjDeNxj+P8qV5UVeGK65Zj2HJxs2h40psdSEWIZpIFHWuRt2JGIpMibGOLNr/6h6IrKo2eUg6Pzj8L6IgPkzDTuMe8QGlJdj1xnEgojhCCtdubaVhcwSvPnORLf/gjPv+132b5+oaCpqpSSh7pPkLcLIxRscJfw+Wli2c8tlPXKHe56YiEOdLfxzUNTRwd6KPO58dns+O12dhaXcv6iiqi6TTFTif3nD7OYDxOc1GQWxYs5r4zJ9lZ10DWsniy9TwfXL0uV+ZJNXh343aOHegsiCb4WM8R7qrfTJHNPeZtD2NTvcSyvfiNhjn3jWcyJLNZihyzVyKZCRtX5ldiKgt6uO2qlWhaLlzxe++6nIefO87Dzx1HAIsay7DbJmdHAZ+TxU1lWJakpGhmz0lKSUfizESYZD709Yc4eLidgN/F+rX1nG/pp6V1gKWLKxFC0N4xRCSaZOP6Rrp7Rvn5vXvZtrmZbVuaKfN+hLTZw3DsHkxrhM7Rv6Uv8nUMrQYFG1lriFS2DUvmBh9V8VMT+Cucxopfac1DCIFNa6K26ItcGPwoWWuIUOJxekL/QqX/M3mDwmg4wSNPHyOezHDXbes509LH7v0tLF9UyeBwlCdfOMXChlK2bcxpzYwmk2QtC11VcyJgyRRFTieWlHxt1x4WBIv49FWXYVNVnjhznn9+7qXcNQHFbmduMZLZiY5b62v50JYN/OEvH+H7+w/z3g1rpsWc01aSjviZS3omhqZSXxrgQEsXdwVXs7apmq6hEB6Hgc9pJ5XN0jk0yuEL3XMe5w1nuLOmSXGZl7IKP73do2zc1swPvvkcUubiToOpSF4h4dkgENQ4i2boeLlpdI4kNVlUN5Y+Rix9FMuKEk0dwGWsLijrS4jceQpBKBMnkk1OW6Ds6xrhz//jPawdk6a9/OZV/OE7vsqux4+zfH2+cUqbJrqiYI6FVMa9jnAmwfP9pygECoI7ajdgV2fJ0EOwuLiE00ODnB4e4C0Ll/KTE0c5PTRIU6AIRQieam3hh8ePYNc0dFUhlEphkdODvqq+iR8cP0x7aJRYJk0ym2F9edXEu9gUbGKxr5Kjo/MnT3XFR9g71MK1FSvQFSfVri0F3SPA3p5Ojg3087F1mwreRwjBplX1edvKS7y89drVE3+7nTbedv0a7rw+l7k5Hp8d9yKj8RRdvaO8744tc2h8SC5Ej1Kosvsr+y7gdBjUVBcRiSR58NHDrF1Vx8/v28fqlbUMj8RoqCvmpZfPctm2hZSVetm0oRG7XUMInZrA32DTahmI/C9Za5iM2UvG7L3oLCpOYymV/s/isW19zdhFbtsmqgN/QfvwZ7BkgoHIdzG0Wkrc75n4xpx2g5uvWcnjz52guzfE0uYK9hxsBaC02MOixjKu3L6IkqAHzsPp/gEOdHazvKKMFy+0EU6lWF2ZW2dIZ82cmqaEoXicB0+czuOwX9O8gL947CmeO3+BddWVmJZkNJGkNuCb+J48Nhv1AT9/ee2V/MmDj1Hl83LdogV5z2Q43cNIuu+SnoclJW67jc0L61AVwWXLGugdiZC1LFx2A69i4+4dq+cNJb7hDHddQwl93aPUNZZw309e4cWnT7BqfcPEBzCSjmEV0NlVoRAwpk+HFGFQ4f0YQhioQqPC+1EEGgINn/1ycub80mhmQdvc8ahxpMwskUySioscwOqGEpatn1y0DJZ6CZb5GB7I5z5nTZOnzp5nY001x/v6WV5eSpHTiZSS46FOuuOFhZAqHH42FzfP+WEuLS7l4XOniWcyrCgt4/vHDrOvt4utVTWMJBN88eXn+ezWy9hRU08kneId9/5kYt9Sl4sNFdU82nKWeCbDztoG3FPi8XZV54bKlRwb7Zj3TVpInuw9xtUVy1Gn+EeWlETT6TmTSvpiMVLZ10eTW4jpFeRHIwl+9MA+zrUPUlMRYMXCylmfccpK0Fmgtyal5MrLFrN7bwtPPnOCKy5bjGVJ3G4bV1+xjJHRGI31JZSXeWlrH8Jm19F1DZfLNhGCVIWLcu/HCThvYiT+CNHUHjJmL1JmURUvdr0Jr/1KvI7LUMX8JeEcxhIq/Z8GCbpaNlHwYeZnpRBw3oJAJ5XNzUwF6ph6YO4bTaUzDI/GiEST2O06mayJaVpksiY2I1coJRRJEvC58Dsc3LZ8Cd/ac4DheJxIKs1Htm6kNuBHAO9at4ovP/sih7t7UBWFrfW1mNJCEbn3tq2hlvdvXMeXn31xIjFoUWkxX7jxWhRVEHQ6sY0xWlZUlPHHV2znu/sOsbSshNqAf+KdtMdPkZGXJo18qnOAzqEQt21aOvZeBFXBfOZUxTxhEngDGu5A0M11t6wBAb//mZuIhBJUVAWAXDZbvEANaU0o2GbwKIVQcRqLxv5ScRgLAXAahbNJLoZdNeao1z0JS1okZwhl+IvcE5XWx64SRcktyIzPNCA39T83NEwym8Vrs+GxTfJrdw2cLVhDZVNxE/45yqMJIWj0Bzg1NMiK0jL8NjsVbjf7e3v47RVryIwlQJS53IBkV2c7Q4lJ/rsA3rJoCX/94jMIBF+84tppiQxbSxbi0Z8uSBzs8EgHQ6loXgJTbzTC7zx0L6k5uLXRdJq7lq6Y9ffXGg6bzqrFVWxYWcfy5kq0OVgBg6luRjOFKzWeOtNLPJ6L4RYVuaiqDNDWMUxFmQ+bLSdGpShK7r81FV1XefLp41y2fREOR27QFELBrjdT7l0AmEiZQSIRQh1zVgqXZLDrCyjXF8zfcAxCaARcN8/ym2DT2gaOn+6hsa6EkqCbl/e14HIa7D/cxub1jWxZ38i+w2047Dp3r12JQm722R+N4TIMgi7nhLe8s6mBVZXlhBIpfA4bfoeDdNacMMa6qvKe9au5dfliRuNJNFUh6HSij3HHP7J144RBF0JwxYJGttTVjiXz5CCRtEQLyyWZiqU1pSyteXVVq6biDWe4pYRjh9rY9ewpUskMjQvLCZZ40XQVJAWFSYCx0Wz6yvnFbWa/jsLbqmI8LXhu050raTTduCqqUpAuu9du520rl1HicuXF21JWlkMjbXPsOeVcCLaVLJz3dCVOF+VuNxsqqlCEYENFNe3hUC7xR9e5pXkxf/7cUxQ5HFR5vFxR1ziRNSaEYElxCU7NwKnrNPj9045f4fCx0FPOvuEL817zcCrKyVB3nuHOWhZem50/2bx9Vl2RFzvayFyiNOjUijvztRsZiOAPunPvD7DbdLatm3+BW0pJW/zkJWV4rlxeTXNTKQ6Hga6rvO0t64nF09htOkLJXa8iBFdfsRRFEdz9tk0kUxlstpmcFwFovNaVaF4thBBs25A/CFy+ZWHe3zWVRdRU5ockNVWlvmj64qkQgiKnkyLnpHNi17VpbQIOB4EZ1j9sF2VjKkLgNPKfY8KM0p0orCr8xed9LfDGeHNT0NE6yL0/fIXrb1+D02Xj4J4W7vvxbn7r/TmFwEIyFSE3lb7YSCbNMPuH7yVpxbi89P3oYvZkmEh2gIPD9wOC7aXvRZ3jUeUGk/nDN7mK6L8akedYbz8ba6px6hrqWLHSgWSYjlhhCUle3cFi3+xT+HE4NI3/vO7miQrtNy1YyHVNCyY0GD65cSvvWr4aiaTYkftApg4mCgKHrnHbwsUz3rMmVNYW1RdkuC1y1Y0uK100Sd9SVdaXV7K2vHJWwz2cSHBs4NJikIO9ISKjcRqXVM7ZzjQtXnjkCNe+bQMO59yZhRdDYo3FtwuDEALD0PJmZZqm4vNONzrjeigXt///A3Jl+XIc/fGVxdwq1XR1z9cb/ckOItlLE297LfGGe7Mjw1HWbmpk47ZcDLayuoiffOdFYFLoqRCY0iSRzfdo7KqHNUW38Uj3P2JNoV7lvKzJNWYhBG6tmFWBm3m676u5dN05+kQ4kyxoiUkVCg711dGrxuE0dH5x9Dgeu40bFzXjNAwuxAYKLkNW7SyiaIbY/8XI6TxMdg9VUVCnsEc1ISh3T4/tJ7IZTg8Nsr+nm3gmw46amROOhBAs99egCFEQp/tEqIustNDHqqiUutx8cuPWOWcOq8rKqfP55z32OPq7R3jge7tIxtMsW1/Php2LMWw65451oqgKvR3DrN2+EKfbxv7nThMo8cyZ3TeOnMGxSFspYtlR+pLtdCWm0zaj2VF6ki0FXy+AKnRKbFUos1SXGa9BOt6/xxdSC8WETvUl7i+RjP0f4zQAXsW5LSxC6QE6E2foTrQwnO4lno2QlWkEApvqxKcHqbA3UuloQlMKW59ShEaJrRq1gKo8U+moCTNGKDPIodFnptE3LWnSl2ojVoC0wFT49GJcmm/+hlPwhjHcodE4p451Eoum2P/KeewOHYfLxpF9rTQ0TyZZBA03CmLeBUpTWgymIrkY3kSnUdAV+8QIDeOLDAc5G3kZVWgs911Lsa0eZaztfDEMKWXBEq82VcNzCSnvM6E5GMS0LDRFQVMUpJSci/QVtGAL0OgpnZZw81oimk7zw+NHMFSVv9pxJR5j9oGqzhXEqdqIZuend3bEh4hmkgRsuUFHESIv5jgTih3OidlAIdB0FU1X8Re7qagNoukaqWSG//3nR7n85tUEgm4sy0IogrLqAP/7T4+wdltzrtDHFFjSIm5GCGcGGUx10Ztsoy/ZznC6h2hmhJSVzHmNF+HI6PMcC71U8PUCBIwyfrfpS3nKhpDrl92JUZ7tO8WxkU5CmQRe3cGqQA1XlC+ZUX3RtCx+3r6XSCbJO+o3AZJnek+xZ6iFkVQMh2aw1FfJNRXLqJ6RsTV2HGlxJtzLM72nOBfpI2VlKbV52FjcyPbShbg127xhSolFe/wUu4ce5kL0KAkzOo+64dMozF98ehwerYgPL/jHGROypJRkZIpoZpThdC/9qXZ6k20MJDsJZQZImFGyM0gnJK0YP2j9wiV7/jdWfICNwesuaZ83jOGORZOcOppLHa9rLKGzfQgAh8vIk3Qtc/gwFI3kPJrLEmiJ9nOVXDaPt9zHgeFfsrPsQ6TMGLsGv8f1FX+ITZ3fK4XxCuaFFSf2G648KqBQFO780E4yWZN4PI3TaaAoCpqmcNfHrkKSq8Fps02qlO1qayeSSqMqgiWlJXhVldZo4YkoDe7CkoVeDZLZLAOxWG4xcmzbXJ04YLgIGM6CDHc4nWAgFZ4w3IWgIxyiLxZlQ2VhQvhFJV7Ka4owbDqLVuWShDLpLG6vg6tuW4vNMTkIVTeWYJ8lRDKQ6uAHbV8gkhm5pDh2bg3k0lgwM7U3pcWjXUf5yqkn6E2GUIWCLlQy0uTR7iN8/8IuPrX0eq64qFycicU97ftpiQ7Q5CnlR6272Tt0AVUoKAjSlskTPcf5adse/nrVW9kQnJ5jkDIzfOvcC/zgwstEsskJDfeMZXJfxwFWF9XyFytvo95VPKsMQNKK8Wz/T9k3/ARpq7CcDQALs1B25dhzm62x5LGe73Bk9AXSVgJrhkH2tbiGybNdeom2N4zhrqwu4rc/MrsYzzjK7F4CNhc9idF52x4d7RwrdzS7ZzaUbidgVOLXK5C6RKASzQ4VbLijmSRnI4XFUWudwYlQiZSSQ0faufeZ42QyJvc+dYx3/9ZWVq+oobN7hMdfOUcimeaB507y1tvWsX5NXU6wqbSEZCbLga5udFXFkhbdBTwLyI1f1Y7ZPaWLEUmliGXSGKqaC/PoOn3RKA5do8jhZDSZIJHJ4jYMvDYbfbEo5hTdiflgU3VK7F46ChAMS1tZehMhFnor5m07jlNDgxwf7C/YcMMkH3vq4rRh0yYWIAtB1soQyQzP6JW93pBS8kLfGf7u2AOkzSy3VK/mpqpVFBkuhtMxHuo6zKNdR/nLw/fhXGuweYoA2zjSVpa/P/YgGcvkI81XsqmkEUPROB3u5Rtnn6MjPsy/nHyM/9n8vjxHxJIW32vZxf+cew6f7uD3F1/NxmBu3wvRAb7b8hIHhtv4i0P38pWN75xG15VSkjCj3N/1VU6GZ9faV1AQQhkLpVy6Yh/AdDLnlOsgF7ZKWvNr6vxf4Q1juAuFR3ew0FNekOE+EeqiLxmmyhmYtY1NcZGy4oDEGlOP0+aRsRxHTqugm75EYaGSlYGaiQoyo6Nx/utbz/K+d21n9YoaUuksdrtO1rT4xndeYOumJnZetpjWtkH+/b+eorG+mGCRG01RONbXx+a6GlQhSFlZRlKFdTBNqBTb8/U1xvVRpsaZNTW3IPnA2VP0RqNkLBOXbnDLwsUc6OnmxGA/716xmq/seZkt1bWcGhrkU5u20hUOs6+ni+UlhdGdNKFQYpuu9zETLCS9ydGcIJEQxDMZdnd1zFlQdk9P5yVVzgaoaijmoR+8TDqZYfsNKwGmGe3IaJwDL51hoGeUXU8cZ822Zkoq/JMNRK64hDIHPXMmgzN1oa1QKBfxp6PZJF898xSxbIq76jfxh0uuR1fUiQFpXbAej2bnh627+Y/TT7LCXz2jYmUoHefv1tzJ1eVLJwz7Ym8FJTYPn9z3Q85G+jgb6WNN0WSWacuYcdYVlc+vvI3LyyYzcxd5y1nmr+KDu7/N0dEOHu46wm/Vb84bNCxMnur7ISfCr3Cx22oodhpdK2j2rCVoq8BQHJgyQzgzTGvsBKcjewlnhmZ8Rg7VjUv14tGL8BulFBlllNhqsCmzh9EUoU57tlMhZylMMdc+M0LMPYjMht84w60g2FrSzHMFZAmOpmM83XucdzVsA3Jhka7ECWLZYc5FdlPpXEKpvQkl9BSvDP2YtJWg2FaHRy9mKNU+1naIc5FdVDqX4tXzDZIlJQ92HSooddumaGwITlLFOrpGcNoN1q+pR9fVCa7t6Gic7t5R1q2px27TWdBYisNu0NE5TLDIzYm+fhQhODc4RKnLhdAkkQJCDZArveTVpzMRvv/wPp7bfw4pc0b7M++7mqaaYgSCDZVVDMbj9EQjdEfChNMpouk0oVSSYqeL2xYtoWvfK6RMk0XFxRzsnTtV92IUmrwEMDhFqnYwEeezzz5BidM1a7cfTSa5fdGSS7qeJWvqCAQ9pFIZ7A4DoQje+fvXoGjKxKCh6SqVdcX87uduBUFeCAWgxFbN+xr+etYpcDwb4ecd/zrNo1vh38Hm4I0z7iOlRcKMYo71NUOxYVOdaELPE9I6PNLJuUg/Xt3B3fWbJ4w2jC04C427GzbzcNcRToV6ODzSwdaSBdO87uX+ai4rXTiNf78iUE2JzU1XYpSu+MiE4ZZS8mTPCUKZBBuCDWwtaZ62b42ziC3FC7i3Yz/P9p7k7XUbJxabpZScjx7hwMjTXGy0i40qbq78IPWuZSgXlRyUUrLCt53tmdt4vPd7nAi9nGdQc4qR72Klfwe6YkOZgYFijc2MxvXdBYKryn6LrcW3IKWJxEQR+e94z9CjHBp9Nm+bXXHx1pqP477EhcaAXrhW/Th+4wy3EIItJc0EDNe88qAS+Fn7Hq6uWE653YcQCg7Vy47S98GYd6MJG5eXfZCBZMvYSnMDAgVFqHi0YraXvJdxXyjv2FJyLNRZsIRqs6ecZk/ZlI9okt6Ud39K7lyTq/m5AUKMyVyuqqzg4VOnCTqd+B12epMh0gXWdjQUbcY098vXLaChMsjLRy7w4AvHSaQyE9eoiMmkjFe6OtlUVUPb6CiQq7s3/lRMaTEQixFOpRhJJgnY7QWFS7x64YuHeTMLKVlZWsZXrrlpVt3iR86f4fxIYTTJcSiKQmV98ZTTSFqtOF9/9DDv27yWuqIADpeN5uXTwy+pbBZLShy6nWrn7Brb4czwjCwQjxag2rFw2nOTUrJ/+FHa4sfRxwxIg3sVCzxrprU7OtqBKS1qXUVUzFIUo9zuo9Yd5MhIB4dH2tlaMj2RZkWgesZFbEPRcGq5GWnKnOx3ppQcGs5lRdY4ixhIRWYcUP1GznHoio+SMNPoimNs/yy7Bx+atibgUr3cUfNxoj1ehv0JiovcM0oKB/Qybqv6CEkzyvnokYntFib7R55gpX/7rAyS0cRTZM0RfI7LUIQdTSkiaFSQypp0jH6RtNlHwHEt5d73oggjN9PW90w7jiJUKu2N+IziGc7y2uI3znBDLmV7R+kiftl5YN62nfFhvnrmST63/Da8euk0rxnAEA6qnPnylQGjioBRNeMxpZSMpuP826nHCqLhCQQ3V6/JM5q11UWkMyYvvHSWdavrJpIlvB47CxpLee7FM1x71TLOne8jnc5SV5OTiuwNR3AZBuFUilQ2S8rKkC0wKclQ1GkfoxCC+soi6iuLiKfSPPjC8YnfNlXV4NJ1arw+lgSL8drsnBwa4PZFS6j2+rhhwUIUIbimMcfv7otFWVlWTlckTMBeGHvGVSC9EyA65Vn77HbevmQ5Nk2blcdd4nQxmJi/otF82FRXzZ7WDiKpnFGRUpI2TRKZLA5dw1BVspbFc2cvIIRgY101HrsNQc6Yp00Lt8141QWIJRaD6Q5uqvzInNN7gL5ETiah2OZBn2VA0xSV4rGZzmzrI7OpRs6GjJVlcKyU4ANdh3is59gs7XJ9NWVl8qrSjKT76IhPn0WvDVxFlWMBL4daOXq6m7qqIipKvJw410tdVRHReIolCyo419rPysVVXF7ydtpjp8hMGQB6E61ciB1jsWfjjPdkWnE6Q/9Gd/irCGGnxv+HBBzX0Rf9LqBQ7nkf3eH/wmk047PvLPiZzAcpJSc6+znZ2c/S6lKW1hTuef9GGm4FwdtqN/JU7/GCDOej3UcoMtx8uPlK7Kp+SR3yYkgpCWUSfPHEAxwuMFuxwV3CNRXL887r9Tr4+Iev4qf37uPhx46gGyq/9bbNrFhWxft/ezvf+/FuvvDlh3A4DD7ygZ34vA5GEglahkfYWFON12bDpmlkkmbBJcpytfQKj6E2+PPXBqSUlLnchKIJoqEkZTYnliVZHMwxBC6vywliWZYklc5i6BoICEUSRBNpHIaG3+OY4D4LITAK5N1C7mMfZ9v7bHauaZg75XpDRRVrygtfzJwJ43oSU736rGXx/b2HCCWSjMQT/NFVO+gNR3jw2GlcNoP+SIw71yyjfXiUnxw8iq6o1BX5efvaFZdsvCOZEWLZUTRhcCayl1JbHUKAQ/XinUH3eTyUkkt6mv1c4wv2s60RaJdYWMNCkh07d4nNQ4l97rULv+7Me6ZdiXMkLyobqAmDZb4tE0JUqxZXcexMNx09I1y5ZRFP7zpNabGHVw5eoKw4N9BUOZsosdfkZTVamJwJ72exZ+OM12JaUfyOK6n0fohk9gK94W/js+8glekg6LqJIueNZK0RQoldr6nhBijxungxEmPX6bb//xtuIQSLfZXcULmSn7fvnbe9KS1+0PoSPYlRPrbwampcwVfl/VjS4nS4h3859Sj7hy4UxPrRhcp7G3bgvygkIIRg8aIK/uxPbiaTMVFUga7l4nfBIjef+MhVZDImqqpMZMN1jIbw2m20DA1j1zUCDgeWLJTBnR/2uFRIKRmNJPjWfbt5/uB54okMNkNjw7JafveOrZQFJz20nsEQf/afD/NH77mCvcfbuf/ZY4RjSWy6xsfesZ2bdkzObi5lIDHl+ILQzDSyRDZDNJ3JDWqqmuO6vw6S85qicOOyRQxEYnx/3yF6I1EWlhazqb6GmoCPHU25uO8vDh9nQXGQmoCP77xykOuXNuO/RInZvmQLrbGjSCQ9iXP0J1sBqHEumdFwj4eeotkklrRmDMlIJtdFfMalXc9s0KYkl91as4b3L7hszvYCMfHupZT0p6bLIrs1HwEjp/inayonz/eiqSrlpV72HW0j4HeytLmCH9y3hy1rt+faCRvl9vpp6ei9yVZMmUWboU5pKttBwHEVNq0OQy2nT3wPSyaQmDkBOiGwaTVEU/PP8GfD8Y4+LMvifO8QRR4nWxbWoWsqpT43tSUBuoYuLWlnXsMthKgBvguUAxbwdSnlvwkhioCfAPVAK/B2KeXI2D6fBd4PmMDHpZSPFXpBhRbUVYXCexsv45XB8wXRySyZU5k7PNLGTVWrubZiBfXuYmxjHt9snFKAhJmhJdrHQ12HeLT7KKFM4dPvayqWY98dZ2jrCMVV+VoL41VA1ItYC+OKbjZb/vaVFeWsrCgv+Nwz4dXONdIZk3/8ztMcPtPF+27bxKL6Mrr6Rvn2/a/w119/jC9+4ha8rlx4JJO1aOsZ5n/ueRlFEXzkbdvwuuxc6B5iUf1FoapLuKDZuoYlLR48d4ZvHNpHbzTKP1x5HZfX1vPzU8epdHvYWl37mqZDtw6N8K3d+7lyYROmZU0sWgrBmGiTGCu6axJLpwklkty5etmcFcJnQ5N7LU3utaSsGDYlR5+TWLNSDRd6yxBAZ3yEWDaFbwYxsVg2RdeYkuQCz68ueASgKxoN7hJOhLppifSjCuWS5B0imenp407Ni67k4umrl1azdEE5uq6iqSrxRBq7TePk+V7WLKvBbps0ZV59usxyzAyTlWm0GZQ/7Xotw/GHsWlVJDMXSKTP0hv5DsnMObLWyFhl+WEUUfh6zMXYc7adI229vHXzcp46co5IIs2Naxe96n5ZiMedBf5QSnlACOEB9gshngDeCzwlpfyiEOIzwGeATwshlgJ3AcuASuBJIcRCKQugXgD7j7Rx4OjsOs2VZT5uvGoFQuRi3Z9cfD1/dvjnJAosIDCQivC/LS/w07ZXqHMVs8hbQYO7hFK7F7duRxMqWWkSzSTpT4Y5H+3nVKib9vhQwSXBxrHMV8XHF1/HSy/uYu9jh7E5DbbcvI7+9kFO7ztPZVM5dUuqOP7yGWKhOEs3N5NOZehp6ScZS7HxhtWkkxkOPHUUX7GXpZubOfL8CaKjcaoWlLNoQxOKUAq2fZaUBaWXz4TDZ7p48WALn3zXTt5yRU5gf3lTOQGfkz/5l/t5dt85brls2URHtCxJKJrkX//4LRMGfdOKumnHLTTMAzkxr5nu9fjAAF/bv4cPrF7HA2dPkx5TDJTAA+dOs7W6sIo7M8G0LPa2d3F+MDfLceo6pmWRzJoMRGOks+bENdUEfDxx6hyRZIprlzRz49KFPHbyLJqi4DKMCfW5S4EQAkua7Bl6iG0ld6AKjUhmhFPh3WwM3jyt7bqiegKGi75EiFcGW7imYlmecZBSsm/oAt3xEby6g/UzJNG8GghgZ9liHu0+yt6hC5yP9Octxk/FdCEvmasecxFUoU+QAnRNRZ8iL+B22ZBSUlMRYElTeR5zRhPT100saWLNYoKKnDcRTu7hzMCHciXX/H9IMttCkfMmhuOPksi2Ek68SKXvY5fySC66Z9i+uJ7ti+vxOez87OUj3LB20at2pOY13FLKHqBn7L8jQoiTQBVwG7BzrNl3gGeBT49t/7GUMgVcEEKcAzYCLxdyQTYjt0AnJew73IrdprN4QTmZrMn+w22UlXgmZE6FEFxWtpgPLtjJ1848VRAtbxxxM83JcDcnwzn62thrn6LxN3eC7XxocpfylyvvoMTmIZvJ4g0GiUeSnHj5DJVNZQQri3jqhy9y64ev4dAzx7nx/Vfy6Heeo7q5ApB4Am5efmA/va0DlNUVs/uhA+iGxov37uWuT9/Gg//zJE2r6tCFWrBnY0rrkgzlOKSUHDrdha6rbFhak/eRLG0sp9jvZs+xNm7esYyp3+mWlfV4XbOzS3KLfIVnChqqNiPn9UBvN5fX1vPWRUt5uatj4tpKnS4G4/E5K53MByEEZR4Xv7ttAwAeu42gy8knd24lY5pctahporbm1oZaagM5KpimKKyvraI+GGA4nqBkiuzopcCUWVqjR+hNtnAqvBsFhZF076zaJJVOP2+tXcc3z73Av596kmKbmxWBGlShYEqL46Nd/NvJJzClxU1Vq6h1TQ+3vBoIIdhW2syGYAO7B8/zl4fv5Q+X3sASXyU2VcOSkrSVpT8Z5sBwG2uK6micyOIVqDMwWLLW3HrrQgj83nwvWEpJeoaqQqrQZn1mmhKgqfifyJojKMKOqkymwUdT+xmOP0y5930EnNdMzKZeDca/A0UpTJ9nLlxSjFsIUQ+sAV4BysaMOlLKHiHE+JyrCtg9ZbfOsW0XH+tDwIcAamsnPaIVS6pYsaSKoZEYp8/38pnfux7bmBbEZZuauf/xw3ka1apQ+K2GraSsLN8+/xzpAhkWF+O1MNbjWOar4i9XvpVGd666t27oNCyvZbBrmNBghCd/8CJLt+R4rqZpUVThp7yhBDOTq7dXUV+Cr8TL/iePMtIfon5ZDZtvXEOwMkBZfQnlDaXY7AaWJbGpWsGGOyvNV/18hkNx7IaGy5GfnGRoKh6XjeFQfKxDTxonv8cxrzdX6EwJwDmLQJdd04hl0nnvTkrJ+ZFhip3OV220Ibcu0BCcPvWu8k/XuFAVhbqiwMT5AYpdTkrchafpXwyBgqE6QEoS2QiKUPDoQRZ41s3cXgje27Sd9tgwT/Yc5/f2fp8l3gqCdjfDqRgnQ93Esim2lTTzoeadv7Ja5VQ4VYM/XX4zf3roFxwb7eSje75LjbMIv+EkKy1GUzEGUhGSZob/3PSeKYabGQvsxswQaSuBrhTOPJJIRtLTJSicqndGTxzGDKrU0dUSxgi4E7+5bWvx2NcXfP5ZIeCFkxdYVFXCowdPs7qhEgEMReKMRhOEEykGQlGKPM5Z6a1TUbDhFkK4gV8An5RShuf4IGf6YZo9lFJ+Hfg6wPr166f9ns5kiUSTZDLmhOFOJDOEQgm4yIcyFI3fabqcoOHmq2eeZPQSYtCvJTShcHX5cj655DpKbJMiPnangaIpaLqKbtMQiqCvbRBVVxGK4PzhNh78n6eoXVKFqirsfvggdpeNTTeuoWpBOS1H2vEG3VQvqsTuytHM7O5c+MGhGthUvaAknJSZow++GjjGsjqzFxUusKQkkzVzMcaL3nwhU/BwgfVDAXyzcL531NTx4xNH+dc9u+iKhDk60MeZ4UHuPX2SL1xxTcHHfy3RGmtn19ArvL3mLegzLIgVCkUoVDsWc1npXZTac+Jnc0EgcGt2Pr/ydpb5q7iv4wDHQ11kRkw0RaXc7uM9jdu4q34z3osyJgXgM5wUGS7sswiRTW1jU6dTS2tdQf5twzv5SesrPNFzjK74CBeiA2NKfhqldi+bihtpmmK0hRBUOhbk8hemmIpYNkRfso0GV+G1L5NmbEad7HJHPeos+uOWTDMUu4/RxHOYFzFb/I7LKPe+r6Bzz4fFVaXsPtNOU3mQG9YsImtZPHPsPAORGIoQPHLwNG/dvBy3ff7M7YIMtxBCJ2e0fyClvGdsc58QomLM264Axoe5TqBmyu7VwKWl0wHFRW6qygP86Rfvo6EmSDptcq61n3fdsWnGl6grKm+t3cBCbwX/cfpxDo60vaqwwKtFtbOI32m6nOsrV2JTtLxrvOIdW9FsOsUVAaSUrL1qOalEhivv3sZwzwiL1jdxzTt34PQ5ef7nu9l0wxqaVtXhHNNbXro555073HZu/uBVCEVw20euxbDrZE2JW7NNcGjnQkaaRC7BUE7FovpSfv7kIdp6RigJuMemjDAwEqV/OMr2NY2vKhQwnI7O32gMRbNkWVa4PXzpymv55qH9jCQTPHz+DLVeH3+x4wrWV1QV/NG/lhjOjHBo9Bh3VN92iYXwpkMi6UqcIWFGqXQ0YyhzJzcJIXDrNn67cRt31K6nNzFK3EzjVA3KHD482sz7a0LlH9e+HVNaE0k2F8NQNL605q1kZBaXZiNlJjAU2wRlTwhBsc3NRxdeybsbt9AVHySaSaIIgd9wUe7I6fVcfPYa50LcWoBIdpJoYMos+4afpNa5ZEY2yLTnJCWnwnum1YFUUFk4ywwFYCT+JJ2jX6HYdSuamp8849BnT6S6VNQU+7hxbX6lrTu3vLoKTYWwSgTwTeCklPKfp/z0S+C3gS+O/Xv/lO0/FEL8M7nFyWZgeprRfBemKnzsvTs5drqL1o4hDEPj7beuo75mZlUxyE1rV/ir+df17+KZvpN8r+VFzkQuLoj62kFBUOkMcHPVGm6vWTdr0oJtXEVuyuKUYc9N29wBN4s3NuEZqwZet6QKT5Ebl2/Su3T7J6fa9rGFPseYx21TNIpsblpj85fBMi2LoVS+ocytmFtkTYtUOhdvTqQyJFMZVFWZ0C3ZtLyO2vIA33lgD1WlPkoCbiKxFN99YA+qqnDN5ktfIbekZKiAAQdynt7UCjhTEU2n0RWVP9u2E0URIHNVTNRfgf4Ik3rQUkpUMV0yNPe7REoLMaag93oMErnC10s5E3mFo6PPUmKvZYF7HUW2ijmlTIUQeHQ7Hr0wJpIQYkYWylSY0uTJvm/Sk7yAJS0Mxc676j9LwMhXnRRCkLZGebr/K6TMnIxtjbOZO9wfn3GA92pFLPNtYffQQ3nbT4R3Uzu8iA3B62b1mCH3LroSZ3m6/yfT1PzKHfU0uJbN+pyiqQOUen6LSu9Hxi5+zkfwqlAZ8FLkfm2ol1CYx70NeDdwVAhxaGzbn5Iz2D8VQrwfaAfeBiClPC6E+Clwghwj5WOFMkqmIkeH01i3so51Kyf1EGLxNC6nMWdntas65Q7fJScRFILxIsTL/dVcVb6MzcULKDJcr/qD9Ra5Wbp5skxT48p85oWUkpSVQhUq+gzJKopQqHT4KYRhaiHpSYxM0NcATrf180/ffYZ4Ms1oJEHWtPjbbzyOx2nD47TxuQ9cS3WZH7/HwaffdzVf/NYT/O7f/ISSIjej4QQZ0+QP3rmTxup8T2WcHjcXcotVhRluVShjsgXTD9oeDvH5F3IaFxVuL0uCJSwrKaHBX0SZ0zWtFFUhGEgN8UTf07TGOjClSYW9jJsrr6PMVppbm5Ame4b3s2twDzEzjlN1sC6wistLtk8LZ0gpCWcjPNr7JMu8S6h1Xpo2hRCCElsNAaOM9tgJ9gw/SFvsGH6jlC3Fb8Gnzy3VG8oMEcuGqLD/6gwSVahcVfYOwtlh9g09yenIgbyiJFPh04u5rerDRLOjPNz9LcKZEWbXPBVsDt7EqfAeRjOTMsWmzPBY73fpS7Wzseh6graKsbT/MfYSJtHMKCfCu3lx4D7C2XyhKV3Y2Fn69jkzTjU1wHhc+7UeeHNKk3DtqoXzfg+XgkJYJS8y+xh01Sz7/B3wd7/Cdc2IRDLDt378Eh97705UdWaaUcrK8uPWl/n2+efnjPsaikaNs4hoNkXSTJO2zIlkFoVcppyhaDhUnYDhptLpp9lTxlJfFQs85bmCDr+iR1cIJJJvXfghCz1NXF12+bTfBdDgLpyLe7FnXlHs5QNv2ZzHkTalpC8aRQjwenKevRCCFQsq+PfP3Mm+4+30DkYI+JysXVxNVWm+QS0r8vClT9xCbUV+5uXFCGUSBYdKXJptVo97SXEJ37n5rQwl4rSGRjnS38d3jhzizPAg71u1lvevmn2aPBsyVhq7YueWyutRUHig51F+2P5zPtH8YVRUWmJt/KTjXt5WfTsltiADqZzBmIn1EsqE+caF7xI0imhyN5CxLm0NRkrJyfAuzkcP4NNLubLs3QSNKjriJzkZeonNxbfPue/+4acYTQ/wluqPXtJ5Z4IQghJ7NSVU0xo7yenI7C6DrhhUOxeQtbLY1bnFxIQQFBnlXFP+bu7v+mqeDndWptk3/DhHRl+gyCgnYJRgU5yYMkskO8xgqptYNjSNWqAKjR0lb2GRZ92c32mR8wYuDH0Wm1qFy7YCgTFh8VThRlenL04XCtOSPPbSCXRN45oti+bfoUC84TInR8Nx4vE0xUE3XT2jedSbSCzF4PDMH7qUuWywfz75CA91HZozvl3vKuZji65hS/EC0pZJPJsibWXHMvNyhltTVGyKhlMzMBQ9p7LGr7euHeQMdzgTIWHOPAgJIVjgKSuoKhDA+Uh/Xgkwn9vBphX1eW2S2Sw/O3aMX54+xeYV9XiZNN5Bn4vrts6tuOew62xcPp2zfTG6EyNEM4WVXCuxefHPUnItbWY5PTzI8YF+jg300R2NoCsq1zYsYG3Z3LUjZ0OFvZy3VOV40hJJwkzw445fkLYyOFSVRDZHOat2VFLjrKLZnVN+nNo/NEUllAnzvbYfU2Ev487q2zBU45INN0jcmp8ry96NU/UyblUqHQtwarOnlkspycoMF6LH8BslWFiIsS4iUGYM/WRlmoyVRhEqtrEKUL+uPi+EYLlvK2kryeO93yVh5n/raStBb/ICvcn565TaFAeXldzJ1uJb5pXKTWZaSWbbaB35KxRhzxt8g65bqQ18tuB7yGRNDpzrIBJPUVdZRF1FgDWLa3huf65UXTia5MDJDhw2nXVLa0ikMhw42YmuqWxYXktb9zDtPSMsb55bquENZ7j3HGzl+Olu3nrjGj73xftyPM2x55jNmjgd0xdNpJTEsim+cOyXPN5zdI7JGGwvXcSnl948oZzmBPzzxPVmgylzOiH6WOwtbWXQFBVVqGOcaXPit6SVJJSJYCg6Pt07Jgafu7HsmLqfEAqhdIi0zODTPdiV6UJN44qCGSuDrugoQqHBXYJbtxPOTOevXoz22BDhTJzgHDrYdk3j7pUream9LW/gtKRkIBYjY5mUuXLa4BIYSSSIpFIEHA68NhvRdBrTsvCNCU0NxeM4jVzF96n3cSrUPaFvMR8aPaUzKhsCHO3v4wMP38e68ipuaGpmfUUVlW7PvKXNZkOuCkuSPcMHOBM5R9xMMJoexZTmxPNo9jSx0reMfz37VZrdC7isZCuLPc2oU2LOpjT5duv3yVpZ3lp9Kzb10ooKT0KgK3Z2Dd6HOZaoUutaxlLfNsrUhhn3GE0P8MrQY/QkLtAeP0N/qpNvtXx+4njbS25liXfDRPuMlWLf8FMcHHmWSHYEQ7HR5F7J5SVvxasHf23GWxEq6wJXUWKr5pn+n9ASPXpJFWI0oVPrXMzlpW8bk4Gdn1rntW9kadmPZ/xNVQqXHQZIZbI89MJx7r5hHQ88e5QP3rl1THM7B4nE7bTx8uELBP0uXj58gSWN5ZQWuRkJx/nls0dZ2VzJjx7ZP/d9XtJV/Rpw1fbF7Ny6kN7+MFvWN/Khd+6YIG1Hokm+9p3npu2TlRZfO/sUj/ccm9PnvKp8OZ9bfitefTq/OJM1OXSsg86eURpqg6xaWk1r5xDHTnXjdBhsXd+Iw57PAz0dOc8z/S/wu43vJZqN8k9nvsrOkm1cWbqDQ6NHOTJ6gt+ufwdHQif4RecDZKwMlrRY4l3I22tux6k5EAieHXiJ4fQIEjgwcoS0lWZdYBXvrL1zhnvNcl/XIwykBnlP/Ttway5KbB5qXUGOjXbO+3xH0lHORwYoMtyX9DFaUvKzY8c43NeLpii4DYNPbtlKbzTKN/bvw65ptIyM8BeX72QwEeeBU6f4851XkLUs/u755/nEls3UTxGtspAcLFCkC2BVoHbWeN2iYAl/e/nVHOnv49GWszx07gw1Xh8rSsvYWlVDtffS9JEtLH7U/gs64l3cWnkDpfZizkUvcH/X5MKZXbHx2/V30x7vZNfQHr5x4btsDW7kzurbJjy2kfQoq/0rODByhFeG9nFZybZXxbyRSA6OPM5y3+W4NH/u/PNUaJJInJqbGlczHfHT+PQgCz1rJxLN/PrkmoQpTZ7u+yl7h59gbeBKqp0LGE33s3voUfqS7byz7tNzevavPQRl9loq7A1ciE5+0woqumLDlLnvSAgFVWjYFDt+o4Qa52IWezZS7VyAJoy8ZJm5+rqquGc00FKayFdRxSjoc9FQHcTQVdLpfMdkz9E2kqkMmqaSSGUIx5IsayrHbtO50DVEOmOiaSpbVjbwvTnO8YYz3OOiSiVBN7dfvwbDmEKtc9tZsaQqL8gvpeS5vpPc0753zvSZpb4q/mTZTTMabSklz+46TWvnMNs3NKHrY8ppWYsFDaXsOXCBl/ae56rti/P2Deg+2uNdJM0EXYkeEmaSk+EzXFG6nXPRC3g0F4OpYb7T+mNurriWTcF1xLJx/qfluzzY8zhvr74NBMSycZ7pf5FbKq/ns4s/gURiXRTqEQjSVoZ7Oh/kfLSVDzW+G5eamykYisbaQH1BhjsrLfYMnWNDcGZPbTaMJBI8cOY0n9y8BUNV+dKLL9A2OkpjIMDHN29mNJnkfw8e5OTgADvq6vl6dB+DsRijySQCqPLkx6dH03GOF3C9kGPOrPTPrjfiMQxuaFrIZbX1dEciHOrr4YWONv7h5Rf4nVVr+ei6TZd0rykzxbHQSe6svpXV/hxd62T4zLT+paBQ76yl3lnLAlcDP+64hxsrrsWt5YyqX/fxlqpbWOpdzHdbf0zQFmC5d+klXQvkvDWn5sOnl+DWiyaM71wIGKXsKLmdRDbK/uGnKbPXcVnJW2bs+72JVvYMPcZlpW9lR8ntKGNlwQJGOT/r+FdOhPewLnDlr8Xrzs2eQ9zf9VVOR/ZNPHOvFuTq8ndS41yUqwM5ZrgNYcOuunCoLtQplMGRdBSHZjCcihDJJljkyemnX8o9xNMnGUk8SbX/k5d0D539ozz43DFsho6iCA6f7qK9d4QLXUOkMlmS6SzJVAZDU1lUX8YvnjxMaZGbTSvqKfa7CEWTuBxzJx294Qz3OBx2g5rK/Is3dJXbrluVty2STfLN88+RmqOYgKFofKT5KoJzeJknz/VxyzUraKjJeSKWJekbCNPaOUTfYBi3e/o012/40ITKcCbEuegF1gVWcTbSQsJM0hnvZmfJNo6FT2JTDLYVb8Ku2nCpTq4qvYyfdz7AzRXX4tJyxten+7iydAf2i6fTY7lGQgh+0fkA7fFOPrLgvQT0fJH8rSXN/Kj15YLS/l8aOMvvNF0+K1d3JkTSaYbjcV5sb0MRgo1V1bh0nedaW3nk7BmWl5UxFI9jWhKHprG2ooKXOzrojUXZ2VCPNiUbTErJkZF2BgqkAta4gtS7ZxenPzcyzBdffp7hRAKbqlLr87Ohoor3rFjN4uCli9rrik7QVsSR0HHK7WX0pQZ4YXBXXqz0SOg4vcl+apxVOUmA0aOU2Usw8rL8ckUxVvqWcXPldXyv7af8/oIPzppINBskYFpZ7u/6V7xjnnKjezUr/Vdc8r3NhPPRI5gyS6WjkVBmcvHaowfQhEF77CTrAvPXg30tkJFpHuz5H05FJlU/vXqQd9T8MTXOHPvq2f4jDKUj+HUXxTYvPYlemj2VnAi3IxCs8jfwbP8RSux+6pylvDJ0ipZoL9eUr5kIXeY8cQsm3ul0py9rjZAxCysEPhV1FQG2rW7E47KjKoK1S6pZvbgal13nuq1LaBkcwhbVqa8O0lxXykg4jqIIvG47775lI6FoAs8shajH8YY13FJKEskMnT0jpDMm4w/W5bBRP1ZUQErJ7sFznJ2Hq73IW8G6ecR0yoo9nDjTQ5HfhWVaaJrKY8+e4KO/fTkv7jnHTB6OTTEotRXTnejlQqydK0t30BJtpSfRx2gmRKWjnJbBVny6d4LKJ4QgaCsibsZJmIkphtuDPku2GsDLg3vpTvbxjprbpxltIQRLfJXUuoKcL6DifEu0n6OjnWwMNs4qAmRJOZb8myucW+RwUOHxcPPCRVR5vSSyGXw2O1/bu4cbmheyuaaGwz29E9dzZUMj/7HnFbKWxVuXLM07jyUlj3YfKThBaktx86zp7gB+u533r1pHnc9P0OEY0zR59QvJmtB4d907+GX3I/y44xeU2Ut5d91dnAyfnqCYujUX56It7B0+gCIE1Y4qbq+6ecIwlNiK2RLckKuQLhQuK96GJS3ORS+wNjCb1z3zjFEg2F5yZ15Fd+0S0sDnw3C6j4xM8+O2L08k0kCuH2SsFCkrmVM9fD0IzlOQS6B5hZOhVya2CQSXl9xJjXPhROgjbqawKwZJM82ZSBfXVazj6b5DWBLWBJroS45S6yplmbeOcDbOUl8t4UyCaDZJwMiFRCKpvfRHf0h94C+JZ07TF/nutOvJmP3Y9aZp28evbIY7QNdUljVVEPRPUoTLgvmzzQWlxeyNtqOogq7EKC2JIVYHqzgTHmA0lWCxvwyH8RvqcWcyJl/+2uP09IcYGo5RWuyhtz/Ebdevpq46OCGh+VzfyXkFW1YFarHNYRQBbrxyOQ8/fYwf3LOHxQvKuGzzQjavbeCRZ45TVuyhtGQyxmdJCWNc6DpXDS3RVsKZCDXOKsodZZyInEZBwW/4cKgOUlYaKS0Y+yiSZo6XrU1JKJjzo5BQbi/l2vIruKfzQWoclSzx5pe4cmt2rihbWpDhzlgm93XsY11R/YSg/sSppOTc8DA/O3aM4USC/9q7lx21dVy7YAEfWr+B/9q7F1NKKjxuPr55C9c3L+Snx47yQlsbpW4XLiM3QNX5/Th1nSKHg6DTmXf8lmg/uwfPzXudADZF58ryucMLJU4XJc5XrwdyMYQQ1Diq+GjT+8cMVk6BcYG7caJNo6uejzT9zsRUXkEZW4TKvZNaZzW1ztz0XEpJVyzM5SU70BWVWDY046JZypqdvtqfbONY6AWyMoVEssiziUXeSwsBzXq/CAzFwe3VH8E1Q71El+Z93Y025ORqj4y+kFdI2a66aPasyevrHs0BY+nxHt3Jy4MnqXOWMZqJYld0bKqOXTU4MnqBelcZHs2BJWXe+oKm+HBojQihkcp2kTEH8Nq35V2PmCNbc6ZMTlOaqJrF9rWzGfvx4+aKc0COwdUTDyOBlsgQ11UtwlmA/O8b1nAPjcQIR5P8w5/dwde//wLvedtmzrb0c6FjcCLGnTQznAnPnxk5G/93HEII3C4bb79lHeFkioFIjIFojKsvX0Lr0AjlPg/JTJZEJks8laZlcJhIMsWWplrqnTXc1/0Ibs2FR3PT5Krn+YGXKbYFsSkGizxNPNb7NN3JXmocVVhYHB49Rq2zeiIWWgjqXDVsL95E3Ezwv60/5hPNH6LSkS9neV3lCn7a/kpB7JIX+s9wPNTFSn9N3kcRjaVI9Cb45NatE9u0sd9rVDd/efkVqJqCoaooQrC1pob1lTnBHGVKOERTFP5y5xW5RJwp57Wk5CdtuwsucLzMX8Vib8WvnYaZu+6ZzymlpDUyQmtkmCWBMmKZNAGbg1g2TV88goVkaaCM48O9qEJhUaCUs6EBKpy5wV8VOjbFSZTRvOMOp3qwMFEv+iwlkmOh52n2bKA3eR6/XjYtO3D2G5m/SbGtEktm8egB6pxL/k8kAiDHnR9O53/Pucqw+c7F1uLJgVwRgoxlYigaprRQhEKZI4BAsNBTNSUJT04UCgZw6Atx+BZOnMPvuIoK74fyzhNOvsxwPD+TE3J9w6lNtykZK8VoZpCgbW4KaiSTZDgVZygV48W+FpyagSUlbs2gxO5GLyBx8A1ruKWUOGw6DruOzdCIxVMsaCjliedPYFkSVRUkzExBRQ2iBehzjHfWI529RJMp+qMxgi4n0VSa1IUOVlaV83JLO9cvX0gslVOiU4RChaOc3mQ/O0u2oQqFOlc1bW2drPIvRyBocNWztXgj/3X+f1nhW8pIepQLsXY+1Pie/OKlc30sY0ZEEQpXl17GYGqIb7X+kI83fxCvNplmX+8qYUfpIh7qOjTv/cbNFN889yxfWnvXROUSgHQ6y4EDrYyOxFizuo7DRzrQdZXly6o4eaqbLZuaOHemj4GBCJUVfnRd5czZXqqri1i+tCpvIDEuyliUUnJ4tJ3Heo7Oe32Q+yjfUrN+xqK1/5cwpcU9F45S6nATz2aIZJL4DQc+m4Ndva1oioJD1TkTGuD2+hU4NZ3BZJy0ZWKoGrpi4NODDKXzJXx6kq2MpPsIGpXTjKcqNEpsNXQlTlNir+FsZG662DgUVAzFTiQzjCmzeYOCGEsgW+hZw/MD97Br8CFKq2pxqOMFGyQJM4KhOCYU+maTNJ2JvTHZNv/f2ZgeQog84wqQMKPsG3mCHSVvRR9jilw8S7SNSUlMbhdj22f3XKee2+e4DCmz067H0MpwGssu3hWAIqMcgZJHVcyVSNtHo2t5XsjpYphSsrMiJ6p1U81S4tk0RTYXaStbcLb3G+uLmIKA34lhaMQSaRYtKOO/v/cCPo8Dr9sxUfE8x5WeP+lkz1ALv924A0cBhWlVIVhYXky0LU0slcahaywsK0ZXVRLpLIaqEnS7iCRTqIogqAb42IL3U27PZS9WOSr4ePMHqXFUjh1P4c7qW1nqXcT56AWa3PXcUX0LpbZJzZVNwXUs9S6a1mkhNwW/o+pmvLp77Hgqd1bfypnIuYn6ghNtheCuus0813cyr7DubHh58Bz3deznHXWb86aRJSUeDEOjrX2Q/oEwN9+4akLEPpnK0tExxKqVtew/0IquqxiGlidyPxOklIxm4nylwALLAM2ecnaUvvoqIa8XhBAE7U7KHG6WFZVzcqSP8+Ehbiuu4uhwD4t8JQRsDortbjyGjWgmxWg6QV8iikszUFCpdi6kJZY/gCXMCC8M3MvNlR9EY1LWQSBY4tuKTXXiUD280P/TaUUUZoOh2FjoWcMrQ49xT+d/UmyrJGXGWe7fOrHYV2yr5Jryd/Joz3f4VstfUONciCJURjODDKa6uLv2jyh31APQET/Lmch+kmactvhJsjLFk30/wqsFcWoeNhRdg1PzYEmLw6PP05/sJGlGGUn3owqVB7u+iV11UWKrYnXg8ryQkSYMim1V9E0pYyaRPN//Cy5Ej7LAs5qgUYlNdc74rQBjiUMOnJoXl5qroDNf/9HVmTXJ7VojdvfM7KsSWzVO1UPMDOVtPzz6HMv926h2LJz1vEU2J0W2V19NB97Ahttu0/nkB6/C5bRx2eaFpNMmoXCCa3cunZj9GYpWkDd2dLSDH7bu4t2N29FnEAuaiqbSIA5dZ319FUUuJyd7Bgi6HGRNiw/sWE8onqS5NMiRzl6SmSweu40VvslMQkMYrPLnj9K60FjlXzZt+zjK7aUThv9iCCFo9jTm/W1Xbayc4VhC5GpxXle5kl8UWIvzv88+Tb2rmM3FC3IejxCMjMTIZkwqK+rw+RwYem7GMzAYoat7BLvdwOW0YdhyBntwKMKiheVzPteUleHfTz3O0dHZqxtNhSoU3tWwDY9WWLX4ceR7hHKCWmlhYsps3gLf1FYpM46CgiJUFKGO8UGme4QwVqy6cRV9iQhFNidby+tZXVxF0Obk9vrlJM0spQ43W8vrJ/a9snIBTnVygXqRZz0vDz6QV40c4NDIs4BkR8lbCRhlE2GCakdOVW590Q2s9l+NRBLNhnIhDq1ojmcvuKLsbThUN+ejRxlK9eDW/Xn3JoTC2sCVlNiqODjyLL3JdiQWPj3IluBNBIxJbZWkFSOSzZUZq3Q0UulonNiezaSnPF9JJDNC3MxVnV/qyxXqtTCJm2EiWScXL8YKBGsCV3I6sp/slOdiYdIWP0lb/OSUlrNBjOn62PDqQWqdi1nm20qtczGamLlQeK7PmGStEPKi96EIJ5o6Pe7v0YuodS3hZHh33vaYGeZnHf/CtWXvZoFnNTYlF4/POx8SU2bJygwpM45T82Iol5acJV5tNYfXEuvXr5f79u3L25Z/XYKpL3n84afMDB985ZsF8Zd1oXJ1xXLurt/CAk/ZxGLlG82b+1WRU0kb4cOvfIvuxGhB+5TZvfz1qjtZX9SQW7WPp5ESXC4b6XQWu10nkzEJhxNomoJh07EZKolEhsefOkawyI1pWlxx+fRUeCklSTPD184+yY9ad18Ck2QBX177WwXNkuLZMCfDr5AwoyTNOCkrMfZvnJSZIG0lycgUaStJNDMyzVgKFLx6EYbiwFBs6IoNQ7FjV5zYVCc2xTH2r5Mye02ePvRgLIbLMHBclBU6EIszmkjQXDxz1mHWyvCTji9zKjyzcKZD9VBur6fIKMdQbFhYpK0kSTNGPBshYUZImFGqHM3cXffpeTMEx5UOmVhsnZ7KPv7NjS8Oztbu9YQpszzf/wueH7gnz3j/KtCETqN7FdeUvZMye9305DtziLaRvyGa3I8kfxYbdN0yY8q7lJKW2BF+0PoFMnL6DFJBJWirpMxei1vzIVDIyDQpMzHx7uLZCGmZ5O7aT1Pnmv7trF+/nn379s348N+wHrdpWry09zyb1+VG9F88dIChkRh33bae4qJc+TJD0VjprynIcGekySPdh3m27wSN7lKW+KpodJdQYsvVmjQU9ZJWznMrw8qEnolLs+FQDXTlV6Oi/aoQQlDlCPCh5iv5u6P3F8Tr7kuG+ezBn/DJxddzXeUKXK7J6aXdnjNIuq4SDOZnlzmdBlftXEosniJYNFPmmWQgFeErpx7jsZ4jBYW1ICdB8NGFV8+a4n4xRtL9PNj9P7MW0J0PEiuPvzwXlnq3UO9aPtFXjvb0YVM1FhQX4XXY6RgZpcLrxaaq7GnrpClYRE8oTDprUlvkn+Czq0LjqtK76Um0/H/svXWcXOd5/v19Dg3jMvNKK2a0bMuWZabYIYcdbgNNmyZpkyZtk5TS5JcmbZqkaZhjO2ZmgcXMsMw8s8Nz4P3jLGp3pZXtpO776eWPLM2Zw3PO/dzPDdc147GTxghN8aM0xS+eD8hYc0vyCiGQuXg4a+w3vzA5einYBt9uOBjbx2TH63LeBQmJFeEtjOhD7Bt85rLa3WeDbmU5M7KPnlQLbyr9OFWeRVPOaSD+MGm9nercr6FIUwmlFGnmrlshBJWehawKX8eugcemNWeZGPSl2+hLX3yGKZDmnmiefF6XvcUfCUORBI88c4S1K6rYue88R062U1uZz+8e2c9H33MVY9OPawsX8kDrPlJzVHZJGlmORzo4HukAGJ8Q27/j5RnbMRZBVci4FY2w5qXME2a+v5jFwTJqfQX4VNcf3ZALIbihaAkHBpt5uH0uhK8wmInzlWMPsWegkffWbKLSk3tJb0sIgd/vwu+fyjNsszRm2d57hu+ffYFzsZ5Z9jAdspC4t+YqGgL/MwIIl4usYZLKpnji1BkCTicpXccwO7h76UIUWSJjGDx07CQ1OWFimQxLim1ubCEEBc4K7iz9GA+2f4fh7OU3esyGy5lFT24LH/t8uTCsLIeGfs+y0B0oYmLKfzzyOJWetXjVSzdBWZbFYKaLPYNPcSLyCtHswOtitCcjku3joY7v8O7KL9rJxbGZu95GjvtWfI6ZRVpmgywUril4O2kzyaGhF6eUMf6h8YY13Km0jtftQBKCF7af5u13rCE/x8d//PjF8aoSIQSLgqVcWTCfp+dYqXAhrLGxcuJ/c4YBZA2DFFlG9BQ9qSgno5083XUMRcgUugKsy63lxuKlLAyU2AyDl/FgGKZJRzRK+3CUAp+X6nCI7Kh0mCpffF+qJPPxedfREuvn8HDrrOtNRsbUebTjIDv6znBNwQJuKFnCPF8RbsVxycHHsiwMy6Q/HWPPwHkebj/A0aG2yxJwBri+aDF3l695VZwe/xNQZYl5+blEmlPE0hkCTidloQBpXSeV1ckYBookEXA5SetT4+tCCKo9S3h35d/wQu9vOTOyj/QMQrezQ0zpBYBR7u9sGy2x50no/bjkMPOCb6Ix+jTzg3eR0PvoSR4i5Kgmlu2hzHMFkUwzw5lGKryvrjvSwqQreZwlwVunLO9OnibXUXNJw21YBoeHX+K57l9O49N2SG5yHEWE1AJcsmdWwV8TE93MkDBixLKDDGf7SBqxaZ7wYKab7f0Pcmvxh8dnTS51HonsCew3+vJMokNycUvxByl11bFz4BEG0l2XNeDIQp410XoxvGENt9/rJBpLct9jB8hkdebVFNDVE0GSphL0K0LmT+u3cHakm6ZY3+w7/CNDtwzaE4Pc17qHR9sPsjq3mvdUb2JpqHxOSilZw+C/du/j14ePEktnuHvJQv5q85W82NjMgY5OPnP1povOD2x+Yy9fWHw7nz7wK1rmoJAzhqFMnPvb9vJIx0FK3CEW+Euo9xdS4g4T0jy4ZBVJCHTLJJZN0ZceoSnWx8lIB2dGuhlMx1+V7PLKcBWfarjxks1SFyKo5XFz8QftJqc/MEJawZSQ2ry8XHxOB6vKSshxuznR00vY7aIvFqck4Gcwnhy/E4uLpivRCCHIdZRwV9kn6E21cmbkAK2JUwxlekga8XFCpXTKRBEaTsmDX80h11FCkauKUlcd8YQdC5YlCZdTpWnkaYrcq0kbEWLZLiRUBtOnAYusmWA400SpZz2nhn9PsXs1rfGXKXDZVBJZ3SCVvvTs1elQUWSJ4WwHfalzxPVBmuK7xwV5s2aSgXQjjkvwcFuWyb7Bp3iq6ydTcg+q5GBlaAurwtcR1gpHG14uPZjbib8sMX2YU9G9vNR3H3F9auXHqege1gaXI1m2Q2NaSaKpV2gc+Axex3LEpFmDS6m+qFiwEAJVOFgV3sqCwDoaY0c5HztMd6qFuD5MIpNAyJbN/6JLqDgJeXIIawXkO8spdlVT5Lo83iB4Ixtun5O33LaK3fub+MA7NuHQFKIjSdYsr5zW7l3qDvPVpW/mS0fu5+zI3KflfyykzCzbek+zf6CJu8pXc2/NVTOSXU3GgY4unjh9lm/ceiMH2jvpi9v16qUBP999ZQ+prI5bu3gMWAhBtTefLy+9m786+Bs6kkOXdd4ZU6cp1kdTrI/HOu2wkjxaUy4EmNbYi/LaDebCQCl/u+RNl6UmNBhNIEuCgDfAqvD/jChwccBuxPA57Jd9Y9UED3lNbg5Zw+DK6koaCvJmvS47/qxQ5Kqm0FmFhUnWzJC10rZYQCzFp7/6e4aH06iSA1koo4NH8+gfGw21hfz9n99KjmM+jdEn0WQfld5rptg7O/lmoUl+/FopXcn9xLKdLAi+BSEE2/ae499/8iIXi7YIAR9799VsXl+PaRmjhnuAM9EXkEZnALJQWBq6k4A6O6+0ZVm0J8/xXM+vphptoXFL0Qepca3H0CFumLg0E1kSDMQS+F1OnKpCLJVBN00E4FAVMrpBOquT43MT0gpYl3MzbtnHA+3fnhLGiOsRGkeeIcix8WWKFCatd5DWO6acY9B1zayG27IsDMN+9mVZwil8zPesZ1FgI+lsGkNkeP6FI6xYVYnX66S3K8aRAx3ccftaQMIyLSRJwjRNdNNAlqdzpM+GN6zhFkKwdnkVa5dXjX9esqCUJQtKZyzcn+cv4pur3sV3zzzHM13H5hzz/mMiYWT4edMOzkS7+cLi2yl2hWYnvertY31FGStLijnTNzBuuD2aRtow0M25GUshBAsDJfzD8rfwxcP3X5bnfSEsLPSxN/p1LEZaGirn75fcRbErOKcH17IsMlmDR3ccp6IgxNK6ErxuB7IkMC2LRDKDosg4NQVzdF3TNHFoKql0FrfTLgszTYt4KoOmyjhUm4UyndXtbjzdwDQt3KMsbal0FnWUitOpqaiKPb1NpLI2hapTwzRMdNO0yyeTGTxODcOwZq0smQl2x6aMQ3bhYDR3ICdJjaiMDKeBsT/TUZjnH6VikHApORS6VuJWCpBRMTHoTR6lP3V8XGqszLOJPX3fpMyzEVnYZZepVJae/kuTfyVT2dFZXTnr8t5D1kqxIfdeVGlqvuOi4TVMXul/dJpgwsLARpYEr+J7z+4lmcni1lSKQ35SWR1FkuiPxbln43K+9+wuqvLDlIT9xJIZjrX34NZUFpUVsqHeZpOs968kqOUzmOmadFwLQ17I/NzPXfI6LybCMDQY57lnjuF0qaxdV8u2l09TXpFDOOTl7NluPB4H6WGVwzv6yC8MsHhJGWeUIbAkXnz+BPF4msKiIGdPd6EoMhs21VNWNnNN+YV4wxpumLmzaiZYlkXWspVs6v1FHBpqoS0xOOO6/9OwgN0D5/mrg7/ln5a/dVzQ4UKE3S72t3eQvcBAn+7rJ+B04pjU8DKXZNSiQCn/uuIevnLsQQ4PzS3m/YeGhODqggY+s/CWWYWWZ4JlwXP7zvDsnjMU5fo5eKaDt25ZTjjg5ieP7aVnMIpumNyycSGl+UG++/sdmKZFbtBL79AI166qY+3CSn7w8C5GEil0w+St1y5jfmUBz+09Q3PXEMMjCUzL4k/vugJZlvi337xETsBDz2CMTUuruGZ1PU/tOsWuY81IQrCopogltcXsONzETRsb+JvvPcHffuAGntt3hrULK6gru7gu5NTrm5osVBSJ5QvK6OgeJpZIk0zZYs6pjE4mMzVubmIwkD6DKnkZzjTSOPIka/I+xZLQu+lNHSPXuWDcK/aqRSjCQbF7IimXn+Nj5aJyovEUiURm9DhZUqkshjn9ORNCIFkKq3PuQb2E+vyFiOkRmuPHpiyTkEcbc2Q0Raa2IAePU+NkRy/xVIaP37CBB/Yco30wgixJ3Lx8Pi5N5YlDp7liXgVhr5v9jRNesya5CKp5Uww3QNKII43yjVhWdpSTZmqdt2UZ9gzFmtkT1nWD3DwfLpdGX98IPp+TVaurefH5E6xaXc3O7aeRZYm1G+rYse00i5eUgWUPjqdPdVFZmcvwUJy8fHvWNhJJQtnc7t0bxnAbhkkynsbhUlGUuSXx7Ex0nJd7TvFE52FORjvn3JX3P41jkXb+4djD/OPyt+BTp6s/b6ws51eHjvC5x5/GtCyiqRTf3rGLR0+e5mMb142ru1iWxZmj7RzZ3Uh+cZC1mxt46fHDRIcSbNy6CNM0OXGghfhIiitvWsK/rriH75x5lsc6DpIx/3hZ8AvhURy8o3ID76q+Arc8u/jzTBACblzfwKnmXq5cXs3K+fbTvudEK4PRBJ991xZ6h0b4f79+iY/euQHdMHnnDav4+RP7uOf6lTy79zTprIGmynzuXddyvmOAnz6+l7/94A0kUlmGY0n+/J6rkYSEqkhEYinOtQ9wz9aVVBSGABgYjvP07lN86QM3oCkyX/7h01SX5NDZH6GxY4CA10lT5wAdfRFyApdPgBVN78fnWIzAgdul8YWP3YhhmhiGSTqjk0rrnDzXzd/+26Po+sTgblk6sWwHdf7b0K0UQ5nzgCDoqCbomGjkShkRehIH8GmleCeFM1YuLmfZglIM00LXDVJpmzv6v36zg2e2nWQ2OGUfw9lOspOSqxIyYUf5+EBxIYYzvSSMqd69U3aTq5XY2482hElCoMoyiixxtnuA3micK9xOZEkaT2ILMZqwv+AYAmZMaE7OUwwlnyeVbaTI/5Ep6ySyp+iP3U956PMwSyll4/leiotD1NUXkh6dhdTPL2L/3kYKCoOk01n27j5PVXUezU19DA/HiUYSLF1WTiatU1gcJB63bZbbM/cmnDeM4X7l2ePEY2kM3WDrm1ahqLPXnNqZ8yQPtx/g/ta9tCUGX1Uy7H8ar/Sf45fNr/DB2s3TqijCLhdfv+UGfn7gMHvbOkhksxzt7uHTV13BNbUTdKzZjMG2J4/ylg9ehcOpIskShWVhkvE0h145R+W8QkYiCW5++zpUTUGSBJ9deAurwlV8/9zztMQHZjq1Pxgk7EqgP6nfwsqcKuQ5SEtdiPESttESzrF70d47TGVRCE2VyQ/ZTUGxVAaf24HbqRL0uXA5VAzDoq1niJqSHBRFpjgvQDSRIp21vdd55Xk4L8gfhPwuSvICKKMzncFoAq/bgd/tRAjIDXqIJzMIITje2M2Vy2o40dyDaVqzcivb9LlJJOHAtNKMc3lg0Zd4Go9WjyTsmnpZFsiyBCrjSkxDkcQMnCYOFoXexUDqFLJQWRq+F1lMb2LSzQQGOotC75gSDrCPJXGsv5v5ubnkuD1YloXfM3sHq4nOtt7v0ZE4MiVU4pR9bC36LE55ZvWchDGCeUHVkSo5UCUNAWyor8Dr1FBlmVyfm5DHxeGWLq5bXEtBwMfmhdUoozwlC0oKcDtUVFlmRVXJxHVaOjF9em7HM4kkyjDj6ObM+Z945iQWJmIGw60oMitWVrF0eQVCQHi0z6G4OERR0cRM2rLsgcWyoLauEEkSFBYFsUYZRl8N3jCGOz6Sorgil5OHWjh3ooPaBSUzGm/TMtk/0My/nX6Kk5HOyzbY9ghs03ReLlXlxGvFKF/1axssLCx+07yLawoXUOubWq0ghKDI5+Mvr7qCrGliWhbqqIcxE5GPw6WhqDKnDrfSdKqbovIcejuHEUBuYQDnJEUNTSjcULyElTlV/LZlNw+376c/PTe19VcLCUGFJ5e3Vq7jxuKleJVLc0hcCrIkSGWymJaFAErzgrx86Dy6bjAQtY2axzmzN1+aH6SlawjDMOkZHMHj1HCos3fTCpuzdRwhv5t4MkM8mUZVZfqH4xTl+An73TR1DXL9uvl85/7tlBeGx43LhTCsERoHv0aJ/12cG/wK0iQDmzUuL5E8GQGtnIBWPmWZZVkc7ulmOJ1iUV4BjUMGSb2WAoebl9ubccgKSwsK2dXRRsDh5NRAP63RCPNycqkPXzzuGtcH6U81clvpV3HJgYnbJATSRUzMaAcFkxMmupVFHyV8aiiZoIEoDNrGf8viuvFlS8onZgrlucGJ63c7x6+5P90+A+OgRI5WjGEmyRidZI0edGOYlH5+0v0yGUo8iyy5Z7UTobCHYMiNJE0P6U4toGDUSE/9bnIN/eW+C28Yw127sISzxzuoqCtEnoWwKGsa3Ne6h++eeW7OtKBOSaXMk0NDoJgabz6FziBEdAoKw+NsYqZpEo8k8IVmL12ye8Ps5FPazJLQM0SySfrTUToSQ7TE+2mLDzKUjV+SH3wyhrMJfteyh88tvGWaQdZNk7P9A5ztHyCtGxT4PCwqLCDsmqhI0RwKS9ZU88APX6agNMz8ZeXEogk6mk0Ky8I43Q58gemENkII8p1+/qR+C3eUruSxzkM83XmUtsQA+utYVueSNRoCxdxSspyrCuYTVN2vW2PN2oUV3Pf8YfadbOPtW1ewrL6Eg2fa+eefP086o/Omq5fgcWm4NNUulXPYUlJOh8KVy2r4r4de4V9+/jzJdIa3XbcCRZbQVHmGFxF7AJj0AucGPVyzqo6v/+pFJEmwqLqQiqIwlUVhugaiFIR9yLJE5WhoZSbIwkd1+LPoZoSgcwMl/neOfmPRPPzt1+UeTcbB7i5urptHxjB4vrkRr6aR6/ZgWbCtrQXDMnGpKvNz8zg90M+C3Dx2d7RTdwnDLRA4ZT8uOXBZAg8eJYAkpClVSUkjRm+qFZ8ye+J+rtCtDNv7HiRzAc+5R/FT6Kwka3TTNvQ1YpmDGGacSGr7lPVkyU/FaJgkm9WRJFtW8fzJTkoqcnG67Ws9c7SN6vnFF40SGLrJI7/exY13rx53oizL4tDuRlxujflL5hjcHsUbxnC3N/WRWxCgr2uYFRtqp90E3TT4SeM2fnDuhTnFZkOahxuKlnBjyVKqvfm4ZBVDNxnoHGTPS4eofcc80ok0DpeDrK4TTUsUeQrIpnUUVbZjifE0QhL4whcX1rUsC90yGMzEOTbcziPtB3il/xzZOcaQX+o5yftrrqLANdFem8zq/OMLL/PC+UaCLheaJDGcSuFUFL503WbWlJWOj96rr5rHyivq7M+S4O0fvdaudRe2qamoK5j12JIQlHrCfKh2M2+vWM+R4Va2957h4FALnckhknoG8zJmFqqQCWpuqr35rM6tZn1uHdXePDRJuawXMasbbDvRxPLqEkLe6TkAIQRrF1bQUFlIKpulJxIjP+Tlg7evJxpPoSoyHpeGaVq879a1uBwq7715DU6HyrtvXI3HpfHxt2wiGk/j0BTcDjsxdd2aedOO5XM7+ct3bkab9ExKQnDzhgVcuawG07RDCZIkuHplLRuWVKLIEn/5jmvGq1Vmgk1R6kUSDop9b0MZFay1LIs89/VI4tWqws8Mr6YRcDhIGwY5Ljc1oTBZw6AlMowiBH6HkzMD/YScTlyqSsjpGs+lzIRotodYthcTE0nI7O7/KWWeFeMiA5JQyHPWIc8S4w5qeXiV4JSWf9My2Nn/CGXuejTp4iWzs8Hu3E3yYu9vOR55Zdr3Nd6l+NQQghxq877NQPwhYulDFAc+OmktYYsICx/ZjM5Ljx6msCxM1fwiDm4/ixCCYNhDMNfH7hdOUVqVR3wkidfvmtHxNE2Tw3saue72FTDpce5uHySdyv7vNdwwOp2Qpv9QlmXxSMdBfnDuxUsabQnBxvx6Pj7vOqq9BeMdf5ZlceDZI6TiaSJ9EfY/cwTTMFmwrp5sOsuxHacJFQbZ88RBAnl+ZEXm7P5GjKzBzR++Dl9o9gSTXYSvUOAMkF/gZ1PePB7vPMw3Tj5BbA4zg/50jINDLVzvnCAv2t7cwuGuLr5/1+1UhUNIQhDPZPn14SN8a/sufviWO3EoE1P7yQ+LrFzgMV7yDEbb1zUXG/Pq2ZhXT9LI0puK0BLvpzneT1dymKF0nBE9RdbUsWC81d+vusl3+ih151DuyaHEFSKouZHF3OtSL4QiS8iSRDSRIpZKE/K46ByMYpgWAyNxyvNCOFWFUx29VOSF2HW6lUg8xYqaEkL+iRmGLIvxGLP3gr8VWSbsnzobcTmm18ZLkiAwy+BxYezXoSrjIZeLxYWn7F+oCMlPxhjEMGMokh+/Y8Xr3vK/taYWVZbRZJk3L1hEIpsh1+0h3+PBISsEnU7yPR5kIbimshq3qrK1pnbW56c9foiT0WdGP1nE9QG6UycZe+Kcko8tRX+BPEsTjlv2U+1dwsGh56csPxc7yCOd3+fagnsIqrPXv1+IMYPdEj/B9v4HaYmfmBbOdMle1uXcbBNoCYFAxetYgYWJJk/nQQdIxjOcPtqGrhvkFQVJpTLs33aakeEkW+5cgQDOHuugrbGX69+8elo0vK87QmfrACORJKeOtuMa9dQNw2T/zrNsuObyBaTfMIZ7yZpqRiJJKuunhkosy+LMSDf/cfpZMhcRBAbbaN9ZtopPzr8ezwwx1OhgjPmra+hts0f4hrV15JaGiQ3bnX4un4tsWqf1RAeLN80nFU9TubAUWZl7Ak0IgSYr3Fa6nLie5v+deuKSoRMLi30DjVxftHh8WVd0hA0V5TTkTzy4DkXh9gUNPHPmPBnDGDfcY/cpM1opAdh1xK7ZqzWimRQeRUOWpl7b2PpuRaPSm0elN48rrbHoPhMvwpiIMRO5gtfT0AghkCV7z+e7BqgrzuVwUxcZXWdNfTl7zrRhmCZXL67GoSqosoSmyJzr6mdp1YQCiWVZmKbFUCRBc/sArV1D9A/GxrsDPS4HuWEPxfkBSotC5IQ8qHOsahrbv26YDAzFae0YpK1riP4he/+yLOHzOCnI9VFRkkN5cQj3DL+JZRl0jPyMwcTL44nKAu8d5Htufd3uqRCCgGNiIAk6nQSdTgzTZPsr52moLiCntpgCz1QjG3A4Zy03bQhcx/zAFvsaZtGkFEizJuEEgnU5N3E6undKdYmFxeHhl2iOH6fBv4Zq71JytWKcsgdFUhEITMvEsHQyZoq4HmEg00V74gzN8RMMZDpnpO+VkNmUdyfFrpop5+NUqnAqlbPeO3/ITXlNAUvX1VJYFkZzKFx101KaznTT3xMlFk3y5G938+HP3zaevJ6Mwb4RnnvkIG3NffziP58ftyeSENQ0FLHmyumzvEvhDWG4TdNi+9PHWLauhsO7znHF9YvH40BZy+C/zr7AYObSybON+fWzGm0hBMuuXsipPeeoWVpJqCCAezT223G2G1mWGe6NsGjjPIb7olQuLCMy2ojgcE6N21mWxUAigUNRxjvmLoQkJG4vXcFjHQc5Fe2acZ3JODvSQ9Yy0EanlUuKCtnb3kE0ncY/egzDtDjS1U1dbs4UGlGw7+HjLx9nw/JqzrX0MRJPcf0V06kix3B4qIOGQAEBzYVyCc94PJky6f9zcePHqETHEjM2J4MYnwGN6QDOdOyMbtAXieNQFTRV4VR7L4OxBPkBL7l+N4psK+x0DEQpCHoJ+9wEvS4S6YkOPF03OHKqg4efPcLB420MRZPjnW4XQpYEHreDipIwm1bXcvdNK3Bos78epmnROzDCjn3neXnPWfuex1Iz1joDaKpMaWGI669cwK1bFuP3TtQ8Z4xBRtLHaMj7BrLkIWsMcn7on8h1b0EWr41w/1KwTIsX953DqSksrr245NaFEKNJft3McGz4MeYFrkVGGa8saYnvpSn2CoWuBdT7N08LmQghKHJWcU3B23iy68fT2B0j2X52DTzO7oEnUSUNTXIiC9twW9iGO2tm0K3MjIZ6MhShsiH3Ntbn3DKNAtf+HS7+QGsOhY6mPvwhN5IkIcmSHY60LFweB6uvns9Ljx3i5revnxbmrV9UQvX82xFfEbzvk1vx+OwBVDBaKSTAMLO0Jo5hWFnKPYvRpOkzvCnXc9Fv/1iwLEzDordzmOr5RTicE0bp+HAHO/vPXnIXQdXNx+qvm9FojyG3JMwVd66Ztnz+mlrmr6kd/1xSZ2erV163ZNbjfXfPXq6uruKKiopZ1/EoDjYXLJiT4e5LRUnoGbRRY1Hg89I9MsI7fvU75uXlokoyPbEYx7t7WVlazJeefg6A8mCQ969eSVY3WLWonJ8+tJvyohC3XG177yPZKMciB+2poKSxJLgaTdLwq04ebj2KV3WytWQ+QW3iQWlLNNOWaAIgR8uj3rdwZuNqpjk8vI+smaHAWUy1x1ZUMSyD5vg5jkT20ZlsI2kkUIRKUAtR7q5mvm8RRa4yfrr3EDc21FHon7lcbElVEaoskx/w0jM8wpLKIhRZxudycNWiGlyaQvfQCH63k6sWVeNU7U5Jy7IYjCT4wa938PS2E6TSF3+pwR4Uo7EUR0934nJqvOXmlbOua1kWT718gu/+4mUGhuJzygBksgaNbf1895cvs+tgI1/4+E0U5NpNR5LQ0OQchFBGp/AympQDiFH+ldfOiW1ZFlndIJ01cGoKygzt1Zms3TLuUOXLm3Vgcir6LKejzyEJhXW57yXHUcmegZ+zIHADZ6Iv4JKDVHhWzeBQSawKb8WyLJ7v/fW0Lsqx/WfM1LQk41wRUgvYXPBWlgQ3zRpvn3I8yyKV0bEsW7VdkSU2XLeQY/uaiY+kWHFFPV6/i6p5thaqP+Shoq4Af9BNJp1FViT7OTTtOkBZEiiKzJvftwmv3zXNK7csi4FMB4aVxSl76UqeocKz9KLn+IYw3JIssen6RUiKxPDAxA9nWhZPdh4mZVy6ff2awgXU+PJpi0TIGAb7OjrwORysKC7msdOnmZeby8aKCpscyTTZ19HB/o4O3KrG1dVVVAaD455g2jDY0dLC8d5ect1urq2pId9jc2gMJBKc6uvn5eZmfA6H3YYrS6wtLZ3mBQMsDpUhX5A5nwlxPU3SyBDE9rCyhsHy4uIpMTqvI0xNzlS+YIciE4kleejZI+iGiUNV6OkfYefBRrasn8dApo/ftP0Qw9LxK0HqfQvRJI1yT4jORITFoWKcF5A6HYsc4PGu+wFYGlxNnW/BjNPglJHkgfafE9OjLA+upaqqjoQR55GO37B3aAdZ8wIi/DgcGNrFsuAa3lv1MbqjI2QMg6xhEEmmyPG4MUyL/ngcl6pSXWBfa1o3UB0ybpeGW1WJZzKoqsRQMkVRjh9tElOiZVn09I/w5W8/zqET03naXQ4Vn9eJy6naohHJDCPxNOnRDkQhBFs3NaBcIjxWmOdnJJ6eYrRdTpWcoIfcsBePS0M3TPoGY3T2DI8PHpYFB0+0860fv8CXPnnzuFefyJ7nWM+HUeUQab0bIWRO938WIVSqQ3+JQ5lOUDVXmJbFjoON/PqpA8RTGUI+F++5dS1L60c9bAHn2vr5628/Qv9wHI9L46NvvoKFNRdXNRpD1kzhVwvYkPcBksYwx4YfZ1X4rWiShwWBrXiVHDqTR6jwzMz5IQuFNTk3Uuyq4cXe39EUP/aaRRQkZEJaAUuCV7AitIWAmjvngSgST/Hpf3+ISDzFPdet4I5NizE0ifkbqnCpKkeG+vEbfgJ5PgbjCUrrC0gbBjULS8ZnfI/tPMEj24+TG/TwhfduxePUKKnIRc8apJJTry0h+mmOHyRtxJGEQrV3xSXP8Q1huAEO7T6PnjHIZHQKS8M4XRpxPcWegcZLbqsImeuKFiMQPHv+PI+dPsOa0hIeP32GhQX5lPgD/OrIEX52990U+nz85MABHjl1ms3VVTQODvLbY0f5xo03Mj8vD900+dq2bZzo7eOKinL2tnfwwPET/Putt1Dg9bK7rZ1tLc30xeMc7uqiJxbDqSgsLSycZriFEOQ7/GiSQtK4+IOoW+aUAao8GOCvr7kSmKhyvdhj9743rSMykuJsSy+L6opIZy+exD0w0MZQOsGJ4W7mBwoo985etjYXRLJDJIw4v279bw4P27JpDsmJQ3YiEKSNFGkzhYVFnbcBCXuandENfrr3EFXhEJtqKvjBK/sxTJP+eJy3LF9MWTDAv2/bRdDlpGckxsc2reO5M+c52dNH7qj2559v3jge708kM/zrfz0zxWgLAXWV+dxyzWKWLSwlN+QdzwUk01n6B2OcOt/D7kNNdPeNsGZZ5SVDRwvri1i+sIyjpztYPK+ETatrWTK/hPxcH05NQZLt2G4mo3O+pZ8f/m4new43j5M37TzQyNFTHaxcXI4seagNfwkwZ4gVi1k1EeeKc619fOvXL/Mnb76CBdWF7DvRytd+8hz/+qnbyQt5wbLYfbSFz927haJcP794Yh/fu38H3/iLOy+pJQo2E6AmefGp+TgkD2kjRtKIMpYIccr+S3rLkpAoc8/jnorP0pE8z8nobprjxxnK9JA2k5iWMUvfhK3To0g2c2JIy6fEXUeNdwmlrnpco4nRy5mxGIZJS/cQQyNJhkdSxNMZ7t97lOWVJVTlhjjW3kN1fhjTstjf1GETXQnB0rJCzvUOcE1DDVnd4ODZDtxOlb6hGO6CEM89eogn7t9LKmE3ahmGieZQ+OTf3sG8yg2ci+1FESoO6dKdtm8Yw51fHBzv2pJHGxY6EkP0JCOX3Dbs8NgNLEJgWZDncfPnGzfSFolQHgzy8XXrONDZSefICBbwi8NH+PqNN7KsqBDDsvjrp5/hZ4cO85XrtnCsp5cXm5r4yV13UeL3k9J13v/A73n41Ck+uGoVN9bXcWVVJfs7OnnPiuVcWVl50XNzyiqqJJO8ZGWgrY04GYlslqfPnGN3azspXac8GODG+fXMy8ud1mlpmiaPvHiU/qE4ZUUhXt53jjdfv3zWo1X7cjk6ZKuM5xXUXOrkLokRPcITXQ9wZHg/hc4SNuVdR523Aa/iRwhI6HHaky2cih5lnn+RvZGAH+0+wJqKUq6uq6IzEmV/Wwd3LV2IhcVzZxpZXlpEfzzB8tJiWociHOroIq0brK8s57p5tfz9U8/THY1REbY70R557ii7DzWPn5ciS9x143Lec9e6KXFlsOO7Z3adpe1MFwtW17D4hhVEY0mMkRTbXziOL+ShvKGEIy+fpGJBKZpT5ez+JhrW15FXmsOfvusqTNOiojQ8Y+gBIXA5NRbWF/HFT9zMX3/tQQ6ftHk0slmDHfvPs3JxOQIZC53e2MNkjH6cSgkF3jvQ5ILXJUTy/N6zzKvI58qVNciSxNb183lyx0m2H2rizs2LsYDNq+tYNs8Wr9i8up5//OEz48Ral4JHCZM1Ezzd+U9kzCSyUNjZ9wMsoDN5nM7EUXzKpcUU7PJIjQpPA+Xu+ehWhrgeZUQfHNWvHEE3M3b5IdKosXbbwsBKAK8SwCG5bfbE1zFR7lRVlleUcLKzl0UlBRQGfZSEAuw612qLYCdSrK4q5bHDp7lteQNCCEI+N0JAOqPTPThC2OXk0d/s5m0fvIqTh9soKguTVxDg8fv24vW60K0YKWMEn5o3p/zRG8ZwL1xROW1Za2KA9BxY/gqdAfzqRMY8x+1GliS8mkah14ssSWiyTMYwODcwQDSd4qWmJna327JCffE4sUwGwzQ50tNN1jB56OQpZEmAZRvQ0312JYoQE/7Q66nHJxDjDUEAyWyWLzz1HM2DQywrLiTsdtE4OMTHH3yUL1x7NVdVT/UKxzy5nKCH7v4o5ixJsskocPlRJZmMqeNibjJhs6E/3cv2/ueo9y3gHRUfIqhOFbD1Kn7yHIUsC64Z9yh1w6QyHOR4Vw9b6u3BQ1Nk/KP81oV+H52RKC5VJeB0cNui+dTm5dAZGcGwbO90csHDUCTBA08emnLtN12ziI/cswlVnR6zNXSD84db0Fwa2YzOQNcweibLoTNdDHQOoWd0fGEvw31RqhWZSP8I8ZEE8mhopmqUye1Sz4AQgoDPyVtuXsnR053j53e2uc9OlkpJmoa+Tq57C0HXBuKZszQNfYP63K8gmHtDy0ywgM6+CEV5/vHBXpElCnP9tPcM2+eHoDB3Is+gjs4W5tpHJguNqws+TkfyCIpwUuJeTNZMEtMH2NX/E1ThZFP+h6c3mOkGiZEUhm6iOhTcHsdo0k+Mltg6CKi5eAjhS6YwDROHS8PlcYw3SY1RqwoY33byMUzTjjXLyqsvTdVNO5RXm59jC2cU5nKqq5fScIDWgWFKcwKUhQNoskzRaIen123n2gzTYmgkQTKRxh90s2pjPcMDcTxeJys21NLTOcSBXedYd0spitDsnMYc7vsbxnBPL5Gy6ElG5pT4yXH4pnBezPZvgJSuI7Dj3JlRNZnlxUUUeLxIQpDMZpGFIGsaGJZ9TtfUVFOX8+qmq0kjM6dGHEWScUzSWNzV2k5/PM4P33InQac9KJmWxROnzvDDvQdYX1E2pRxQkgRXrKzhqe0nOXK6gxuuuHhtaOPIAKtzywk7PHiU197oYWHhk73cXfbuaUZ7DPagN/HCeTSNGxrqOd3bz28OHuWdq5aysrSYnU2tKJLE9Q11LC0pYmdTK7ta2pAQ1OTlgIAXzjZxtm8Ar0OjyO/Dsiz2HW2lq3dihlaQ6+e9d62b0WgDCEmQTqRxeZ14g25O7TlHKpZi/ppa0ok0pfXF+EJeQvkBTu89x/w1tSiKQvPxVnKKL6+zTwjBvOoCvG4H0ZgdNoiMJMnqBpISxyEXke+5DSEk/I6lxDInMK3slDb4VwOBHdefnKC1sGlqc4ITU/LZqnvmdAwhcCkBan2bxpcpwoFLDnJzyZdsB2cSH4plWRzedZ7f/deLtJ3vI5vVcbk0yusKuPfTN1JeazeMZTM6j/96F9ueOEpPxxC6buDxOVlzdQP3/Om1eHxOTNPiv//lcYYHYvzZV+7CMYnawTQt/vufH2Oof4Q/++rdU767HDhVlWsWTMxKV1ROcKHUFuRgWha7zrVy7cKa8cFx8ow4k9VxeRxkMzqJeJri8jDPPHSQuoUldHcMUVAcRJPcmJgEtcI5Kei8YQz3TIhm55ZFdstzU8cAKPb5cSoKb1m8iLLAdCHQymAQp6pw74oVs5b6jRUPXaqj0LIsupOROc0avIoD1yTD3TI0zKKCfILOiem9LATLS4r5wZ79pPWpddwAmYyOy6Fy2+Yl9A3FyJtBwHcMeU4PO3ob8SgOriueR0C7ePnRXLA4uJICh12RMxcCnXvXrcSjaZQE/QwlkiiyzHvXrmAwYTPMhd0uJCH45NUbGEok0WSZgMuJAG5aUM+CwnxCLqftIQK7DzVNqZm/am0t+Tmz08XGhhM4PA47lDOS4pq3b0QIgdvvpmY0zu1wO1i4oR5f2ItpmKzcuoRArn/G/V0KHrcDp0MdN9z6KOe3JvkxrASdI7/CqRQRz5zFsjJEUnsRQibgWIE8h7jnbFjRUMZvnjrASDyNz+NgMBLnXFs/N11icH+tOBZ5nHrf1Tjkqefe1zXMNz73OyrrC/nIF25Fc6p0tQxw+kjbeBs52ANCe1M/85aUccd7r8DhVNnzwike/flO/EE3b/3IZiRJUD2/iO/83UOcffs6Fq6cmIn2d0d48dFDbLlzFZrztc0oLwYBrK8tHz9ny7LoHojaVSWApip4fS5uvHs1QkD9olKeeeggf/MnPyGU4+XmN68hrg/ikNxEsrYQTI7j4p2UczbcQggZ2Ad0WJZ1ixAiDPwGqMSW4XiLZVlDo+v+FfB+bBG3T1iW9dTcb8MEjDnqFV4O1VN9bg7Lior4l23bed+K5bhUlbZIhHyPh+XFxawrK+NHBw7yjR07edPCBUhC0Dg4xMKCfGrCdpWDKsvke7283NRMeSCAaVlUBIOoM7QHHxpqnRN3SY7Dh0uZeGjLggGeP9/IcCo1xeM+0NFJwDWVjxts7+KVw03IkkQ6q7PnaDO15bPHFYUQSEiY1uU0tF8MgjrvAgbTJ+hK7GRB6F7ExQiGhCDgmhTe8rjHviDPO/VF12SZAt/EIORQFNyqOmVZOp3lXPOEdJ0kCdYsrZx23LEQgBDgD3vZ9Ka1mIZJbkl4SuOXP2cidJBTPFHJ45xjN+TkphVrVM90rJ596nKwyOJUiknrnaT1LsBCU/KJpPchoeDVFiDz6gy3EIJNy6vZcbCRv/nO48yrzOfYuU4W1xWxoqEMBKNygJNOTDAthzLzNY4lU6XRxKE15bvOxFFqvBunbdfXFSEyGOP6N69m/Ra71NS6wuLme9ZNOQ9Zkfjw528dz3kBLFpdxekjrRzZ08ibP3gVsiKz6sp5BHO9vPDwQRasqEQI+/7v33aGdDLLphsXv+rZxFxwYXgmGk/z6M4TWNhhqYKQD7CoqMlHzxo43Rof/5vbiQ7H8ficOF0auuWmI3mKlBGjxrv6kse8HI/7k8BJYMzd+BzwnGVZ/ySE+Nzo588KIRYAbwMWAsXAs0KIesu6TNVYQJuj9uBQJj5K8ymoDAXxaDYt5KKCAsqCASQhWFNaiiIL+lIx/uaaq/nvffv53DNP41E0CrxePrzGvlkBp5Ov3XA939+7j0898ThuRaU6FGZx4QTfhyJJfGrjBr79yi7+8smnKPJ5+cqWLQRdU73WuJ5me9/pOV1DlTcPdVKMe115GQ8eP8m9v/09y4rtipXW4Qhn+vr5my1XT+OQEELgcqi0dg2xff/5GVu0J6MvFWNZTgk5Dg8e5bVNx8EOSYW0HOLZFrriO2kIvfc173M23LJwPsoF1AjxRJrBSHz8s8upUlo0PZyRzuj85KHdvPu2NbicGnmlr61iY8xAxxMZuvoitHcN0dUbZSiSIJHMkMpkyWR00lmDZDLDUCQxbR+y5KM88NEp1K4gkMTlCRPMBpdD5XP3bmHf8TY6+yK88+bVrJhfOl5Z84m3X0l4EhFZVXEOX/rwDePqP7Ph7MjL9KROsy73PTzf/U3i+uD4vNfCIqbPrLZUXJFDYWmYn3zjSaJDcVZfPZ9wnn88tj2GsWIFQzdJxFKkklmyGR2310k6mbFFw4FgjpeNWxfx0mOHeXtvlNzCAHrWYNuTR6hbUkpl/eyllMl0lsNnO6YoSo0k0mR121y1dA+y/cilK9vAdp56hkZ4evdpDp2zk9D5IS+VRWFMw+LpBw/QfK4HTVMoLA1RVV9EZV0BlbX5xOgnbSZwywHSZgIPwYsea06WUQhRCtwMfBX489HFtwNXj/77J8CLwGdHl//asqw00CSEOAesAaazvVwCAW1uXWPtiUHiRhq/5OKa6gmy+HcsnShi//CaVfy/I9tYYZSwubiGP12/lk115azMK0WRJCRJYJgmkhCUBQJ86ZrN/OjUPq4tqaXSH5rigQghWF5UxPfvuB3dNFEkCeWC1nHLsni59zTn5qCBKbDluybDo6n8443X8cyZc+xt72AwkaQ6HOJjG9YyL2/mmtS6inxkSSIv7GX14tkbgwAqvGFOR3rpTkZZm1uJql26euDi1yDZiuNjXqRlkjaHAYEmeRGTBiXLMsmaMUwMNMk3ievY3jhrxpCEhixUMuYIquRBoIxPQzVFRzeT6KYLedS4xZOZKXFcl0PDdwEx/Vj4ZsOyalRVuSABZ393sW7OC2EYJqcbe3jypePsPdJK70CUdEafc1Jv4rwMWiP/SSS1H9NKARZutYba8BfHJcVeC8YG9RV1xRRICqqqoEgSsUiC6GAcD5DoizGsWyiaQjySRI1lGeqJkFsUnHW/Ra6FBLVSwCJlRFkZfguyNKYqY7J/8LczbhfM8fK5b97Dr77zPD/82hP88j+eY901C7jt3RsorZqgeNB1g51PH+Op+/bS02HT3AoE/T0RqudN1bLcfOtynvztHva8eJIb37qW9qY+zh5r54OfvfmirH19wzH+6nuPEZ9UW20xMSA/tvMEj78yu4DEZFyYLBdCcOvGhQR9LgTw/j+/gVQyQ3f7IMcOtLDt6aP8+FtP84Vv3EN+g92BKuS5BX3n6nF/E/gMMLnFrcCyrC4Ay7K6hBBj5LklwK5J67WPLpsCIcSHgA8BlJeXX/i13Q7rCiIhLhlL7k5FOB3tYlW4asYXTjdNnmo7w9lIP4vChUhC8FJnI9FMio1FlTzZdpqWkSESepY31yzhfGSAk0O9nIv0c1PFvGl8HmPnp8ryjOERy7LoSg7zg3MvzElI16s4WRaqmOZteDWNOxct4I5Fdixy8pVdGEMWAhyagmFa7DzYSN9QjDdtmbn7KpZNE9bcLAgW0JWIMpJN4dcmOIxfKwwrzaH+bzKcPo1upShyb2BR+EPIkoOsmeDk0I/oTuwGwKeWsTjnT/EoRTSNPEoi201vch+q7CPPuZzW2JPkOVewNPeTYEl0JrZzeujnGFYKSWjMD76LYs8mdN2c0s6uqfKUKTbYycBfPLqP3sERPveB65AdKi/uOUNz5yD9Q3EqisO0dA7y0bddge8iIRHLsoiMpPjR73by+IvHSSSn1+hLo91ysiRQZAlVkZFkicHh+LSKn6wxSCrbRlXozxlO7SbHdTU98YenDHavBxqPt9PdMkDTqU4aVlbi8jjY9+JJ+juHqV1SRjKWZtXVDTz0w5dYt3UxD//wJd79mZvJK565xt+n5uFT89DNDBWe1ZR5lo2r3ViWybmRbTMaISEElfWFfOZf30ZbYy/bnjjKs7/fz76XT/PF/3z3uIe848mjfPML93PVTUt51ye2klPgR1FlvvbpX5NOZqfsr6KugIUrK3nh4UNce8dKdj13Apfbwcor5110EPa6HKxbWMGR810MROLoF1AiTDbilwNFlrhh7Xzeft0KpNGa7Sfv38vJI20k4xl8ARcrNtTylnuvpHpeIaacwCmPOTiXNt2XNNxCiFuAXsuy9gshrp7DOc901GlXblnW94HvA6xatcoaXTZlnXJ3Di5ZI25cXI4saxrc37qXZaGKKeGGMchCsKWsjuaRIW6tXIAA1hWUc9/5owD0JGKsK6ggns1wZKCLIwNdfGLxFXz/xO45XO606yKSTfDPJx6leY7CvIuCZZS6p74clmVxrLuXFxubiKbSU5oPqsMh3rZsyZQbbZoWOw40EkumWb24gsX1s/NOxPQ0J4e7sSzoT8cpck9N0uqX4H24FGLZNqp8t7Ao/CFi2TZe6fkCRZ6N5DmXcy5yHwOpo6wr+DKq5OHM8C851P8N1hd8laTeQ3/qCKvzP8+O7s/iUYpZlffX7O79O+Ybw2TNEY4M/DtLcz5OjnMhvcn9HBr4NwKOasQFnqlpWdOeuoDPxbtvX8MXv/3YOKdI72CMgNdFPJHGocm4nSotnYMsqrvI/Yun+Yf/eIKd+xsnAhsCivICLFtYyoLaIkoKgwR8LtwuzW6bViTi8TQf+9JvGJwxXOLGIRegG8M4lGJ0YwjTSo/rIr4eyCkM0N81jMOp0tnUR/XCUhpWVtHi6WLD9Ut44hc7MU2LuqXlbHnzGlKJNOePtc9quMfPXagsDd1xwVLB2tx345hB/WbM6VBUmcr6QirrC1l7TQOfv/cHvPLscSrrC7EseOW5E/gCbt7z5zcQzLE7l9OpLLGR1LT6clmR2PKmVXzzr3/HyYMtvPLsCVZdOY9w3sUTySGfi6986CYisRTnO/o5cLqdPSdbOXq+C9OybA4c39wS90LYYamKwjDXrKhj3cKK8XCUaZqcONRKb1eE2oYiGpaWU1VfSG5BAIdTJZJNkTSiBNQ84voQIa3ooseai8e9EbhNCHET4AT8QoifAz1CiKJRb7sI6B1dv52pkpelQOelDpIxdfYNniSSjSMQrAk3UOgKUOIJcSbafanNeannJM93H2dr0fRExOTa6zHY77VtDhVJwq85yJrGeCIxaxrolxmWH/O0/+n4I+zoOzOnbWQhcVvp8ik13ABn+gb4+EOPUpuTQ4HPw+TxMK1PPy9JElyzrp5DJ9t55VATA8PxWRtwHJJChTeMIiQqzDB5zqnVJ3H90irfF4NLzqPSfwsOOYAm+/AoRcSznYQdC2iLPcu84D34VFuFuzbwZl7o+AgjWbumPqBV41FLcSuF5DgX4VVLEUgYVpLO+DbcSgFhxwJAIse5GFmo9CcPo6kbURV5vHU9ndHJZKcOQELY3u+UvkRht6+nMlmK8gJERlJkLtJ1alkWv3viADsPTBhtl1PlnXeu5bZrFxP0240XM3l5sjRzLbEqh/Bqi1Ak28ic6P04bq32dQmTjCGTyvLzrz/Bmz64GcuC3g5bTFuWJYQskCbNTkaG4rbsWzRJac3sXO5TMUoXOQohBG4lOOOavZ3DpFNZCoqDqJqCaVqkU1ksC5yjlLtCgNNt19fHR5IEwh6ymSwvPXaIlrM91DZMHViFECxbX0NuYYBHfr6T7rYB3v+Zm6aJYkyGrhtIkq1p6VIVVs4vY9X8Mu64ajHv/vIvGYjEufuapbzz+tl5a6acAxO8JIKpz4CiyHzq795EdDhBa2Mvxw608MxDB4gOJ/jEF+8gp9oumexOnafWO51P6UJc0nBblvVXwF9hn8jVwKcty3qnEOJrwHuAfxr9+6HRTR4GfimE+AZ2crIO2HOp4zTFOulLR1gSqEEIcCsOHJLGhty6ORnutKnzrycfx6s6WZ9bNy0rLguJ2kAOAkjoWZ7rOEskk2JXTwulngBuRcN02PHNIreP+84fIcfhwaVc2uOxLIuMqbOt9zTfOfMczfG+S24zhoZAMRvz6qe90PvaO1hZUsw/3rQVdYZQzfRzsAVs6yvzuXJVLUG/7SVMNlMWJqZl0p+O81LXWcJOD0PpBLeULRxPUJoY9KUufb8vBlX2oYyzmwmEkLEwMKwUGSOKW5noCFQlL5JQSRm29qUsjb24EopwMmYMLCzi2U6GUifZ1vWp8WPpZhLDyhJ026K6sYQ9O0smMwxGEuRcRNVoDBOCs5eeog5HkzzxwvHxWKYQgvfctY57bl89Y0htMkzTnHHaLVAo9N4FQGXoE2SMAVQph9kEal8NFFWmtCaf7U8cRtMUcgoDON0akiTwBz0oqkwgxwsCuloH+Om/PIZlWcxbfvFcCUDKHGHfwK9Yl/se1DkMNicONPOfX36Y/OIggbCHTFqn7XwvVfWFXHnjBLHbtbevYPfzJ/m7j/6EqnlFDPWNYBgmazc3MNAzvaPa63dx5U1L+fV/Pk/1/CLql5Re9DyOnejg7Nke6moLOHW6izWrqqiuzifkdVEU9jEQiaPKMm7Ha0/em6bFzudPcOpoG72dw2TSOoGQh4al5QTDdi9FQ+BKJCT8av4l9/da6rj/CfitEOL9QCvwZgDLso4LIX4LnAB04E8vVVGStQx600NEs3GSRhpZ2OxaQgiuL17Cfa17iM1BvX0gHePzh37HvTVXcWfZqim6hk5Z4ZYKm+bUrai8rXbZtO0LXBMv+cq8i//oo9dK3EhzcLCF37bsYs9A45xVbwAcksoHaq6esQFGlWWCLifqLF7ahRAC1iyumLauJjuQhYxh6aTNNHEjRp2/nP5UjP50HKesTqG5HM4M0ZWaTs50OZjcbDEZklCRhYZuJsanyyY6pqWjiAlDP3lPk6FILvJcK1iZ95kp3ymSG8tQKcjz0ztgzxbSWZ0zTb3UVkwku1q7hth/vJW+oRhPbT/JsgZbRWis/G02T3kyWjsH6R2cmJHkhb3ctHnRJY022BzpYzOCqbAYTG5jIPk81ii1qSoFqQh+HFm89vp6sLsK3/aJ68lmdFRVHi2JFIDF0o31yIrE3R+9lhP7mli4uppb3n0FiipP8cRnQ9oYYSDdPKua+4VYc3UDTpdG85luevqiWIrgtndtZNn6Grx+F53DUXxOB4tWVfGV/34/O54/TjaRZfmGWtZubiCT1uluG5yRJ3/lpnp++/0X2XTjkik6qzPBoSkEA2727muioiKHSNTuH1AVmcriMMeaLu3AWKPas5fqorYsi5FokoXLK7jxTasJ5/twubRx4ZiBTBuGlSXPUTkjoduFuCzDbVnWi9jVI1iWNQBcO8t6X8WuQJkTDMsgY+oUOMN0pfoRCPKdIdw4qfHmc3VBA492HJrTvqLZJN869RTPdB3lrvI1bMirI8fhRXqN7eljnpJpWYzoSRpjfezqO8fLvadojPVddlhFALeVrmB9Xt1EFt00SY1O75cVF/LwiVNsa2phcWHBlCSoLAmcylQ+htmuzav48So+BjNpMmaa09GjFDvLWJ1bQU8yilNWCTvco9dmsmdwGyN69LKuZa5QhItc1xI6EzsocK8BS6E/eRhFcuLTyulJXjynkO9aRWd8G1krgVexqxnsJKWKUCQW1Rdx9JRdhmVZ8PKes1y/qWGcRtPtVKkpy+Uv77XJ/71uB5vX1uNQFWrLcnE6VCqKwhctgxsYDSOMobggQMB7aS/Tsiya2gZIpqY3Y2XNYbpGfkWJ/z3j8mVCqK+5a3Iy7NI6gTzDtY09WZIm4XRr+MOeKV2Gl0rOueQgmuQhqQ/jUaaXV05+NjO6ztnhQdzzwmxZW0nvSIyAy0lhwMfJrl6ifX3EM3apX11BLkqBm+yyENcvrKMk6B/fV2FZeNpxAI7vb8bl1sbrwy+G2poCnE6VpUvLaGkdoLp6wtOtLcm76LZjiOkptved4IaiizP6ybLETXevnvWcHJKbY5Hn6UicosKz5PVrwPlDwilprAzVE9OTxPQkZ0bacMn2gyMLiffVXMme/kZ603MzKCYWxyMdnDj6ILkOL4uCpSwLVVDvL6LQFcCvunBICookXxBKsKfkhmmStQzSRpaYnmYwHaMzOURjrI+zI900xfroT42QvfzS9HGsyqniI/XXoEzydve1d/DFp54br0mPplJ87MFHCbtdU8IlK0qL+ccbt86pbMgte6j01DGYsROlL/Q+SYWnhipPPcXuwHiJXdpIs29oB8/3PP6qrwnsEId0QbxeoIx64YL5wfewp/fveaX7C6iyj8HUcRaE3o8mBRDICIzRbSba1CWh2IO5exVFnivY2fU5Ao5aTCtLxoiypuCLuOQ8Nq6q4f4nDo7HqPcfaeXQyXZWLrLj6bkhL7mzhk5s43uxapKZINlu+iXXMwyTZ7afnKUZy8CplBJ0rnndK0kuF3WLy6hddOnZ5mSokpMcRwVPdH6FYtei8ZJAVbhYErptSvgkls5wsLUT04LykTglIT9N/UMoksTp7j4iyTRloQBuh8rOcy1c21CDU1UIuWeeeViWhaHbIaj2pj4e++UuNly3cFbDPoaRkRTZrE4qlaW3b4SA30VgNLwohGDD4kpiyTRLZ0lS66bBnsGzRLMJulNDxPQUewbO4lEcLA9Vc2ioiZieJKh5WBaq5lS0nbZEP8uCVRQ4g9MMuFfJYV3O3aOfXmeP+w+JjmQ/cT3FqZEWSlx5tCZ6qfeV2aVDnjz+ZN4W/uHYw5eUL5sMC4u+9Agv9JzkhZ6TyELCKat4FAee0RZzTVKQhE2qY1gmWdP2/lNGloSRIW1kSZv6nMr65opFgVL+ZvGd0xTP5+Xm8rdbr7nk9kGnc44N/nbCZFPutZyIHCJlJhnODvL9899ggX8ZZe5KZCEznB3kXOwUbYkmBBJLAqs4Gtk/C43mxVHgWk1Aq0EafbQEMivzPo0m24OEVy1lY9G/0J88hG4lmR98Jz7VLget8t863om3NPcTOKQgiuRmbcHf4VLykVBZkvMnDHm3EM00IQuNgFaDS85BCMGCmkKWzC9h39FWwG6u+NaPXuDLf3Eb5XPkFpnsXc60fjhoE5iNlY1190eJJ9IELlJ5YFkWL+85x879Uxs5TCtJT/xhnIZJUm+mcehruNVq+64JN7me615Xr3sumEto5EJYWLjkAOUX8G0LMfO+Qm736Fhnkc7qRJIpHKpC62CElRXFgKA8FKQnEsOl2YNA1jAQM9wL07T47lce5tzxDno7h8grCvLWj1xz0aTk2Dl3dUdsCbsZHvPq4hw+cseGWbc/H+uiPd5Pnb+Y5lgPT3YdYJ6/hI7EAAeHGnmu+zD3VF7F45378Ktunu46yKJABb9r28Gf1t00LRxiP2tzjwi8YQx3niPItr6XWBmahyQkNGkiKSiE4MbipXQkBvnR+ZfRX6URNSyTuJ4mPod4+R8CAlidU83fLL6TYtf0UTfkdrGhYqKm/VJGZE7HFIJq7zxuKrqLx7p+R9pMkzDi7Bvawb6hHVPW1SSN6wpuZ2FgGadGjpIxL/8+abIfTZ4owRJC4NMqpnx2yiFKvZunbetSJqanPnViquhXK9Ezu0ilnsLhuouQWkBQ1pCU2in3RdMU3nP3ek6e7yaesGurz7f287l/eZAPvnUja5dVzhoG0XWTwUicE2e7SKay3Hj1whnXKy0KEQq46Ru0BT+6+6I8ve0kb7ph2bTGnTHVmRdeOcO3fvzCtPi2hYVppbAsCLuuAhjtnmRSU9LMeD3q7V8rxvIUslBYHLx1xnUufG59TgcbasZ4PWAgnqQ8HKS5f4iycIDGviGurK+kwO9lU10l/tH1J1dS6Vmdp37yMgNdQ9Qtr2T1VfPJLwkRyvVSt6CYF3+1g3QizRV3rqZ68dQeEUM3eOG3r7Bqy2Lq6wppbbOT4qUlF/fQL0Q0m6TAFaTMnctBqZFoNkG5Ow+BoCXeS47DR7k7D5/qZihjd/T6VBdX5y/mcgz0bHhDGe57q29GEQppMzOqTzgBVZK5t+ZqAH7SuP2yPO83AhySyh1lK/lw3TUEVNclDXE2q/PSCyfp6Y5w512raWsboH5UKmkyJsfeGU04XZhkk4TEVflbKXSV8nLfU7TEG0nocQzTQJJk3IqbMncVV+VtZb5/MWkjxXzfIkb0KPmOIgQTFJkTiTyBLGQq3NUkjDiyUNDk1698bTJMvZFU/BdIch6G0Y4QblKJX+L2f3HKekIIljWU8L67N/C9X24bb1tu7Rjk7/7tMSpKwyyqL6a8OIzHrWEYtlxZV2+E5vYB2rqGiESTXLtxPjdcvXDG1ysccLNpdS0PPHXIPjfT4nu/3EZnT4Rr1teTG/YihGAknuJccx/P7TjFvmOtZLMGRXl+XC6NxlY7bCULN0Xet+Bxz0xmZjf6JImMJEmldVLpLIlkhngyQyKZprl9AHNSvL1vMMYDTx3C53HaakEuDbdTxeFQcWoK4ZAHt3PmgcswTfoGYiRTdgdqMp0lkUgTHz3e2ebeKevvOdxM1jDwjB3HpeFyqDg0BbdLIy/sRZohYavKMvn+iXBV3ijfTDydwTAtPA6VypwQsiSNe9tVuVONqiRLLFxfx5M/fol9zxzlY998D2uvsQsPMqksS69q4Mdf+h1l84pmMNwmp/c20rC2ju6hBD29EQzD7nisrbl0NccYqrwF3N+6k57UMIoksShQweOd+0iZWbYULGUgPQICnJJKmTuHoOalMzk4atxfO94QhtvCImsZpIwMkKE13oMqKczzT73pDlnhA7WbKXIF+Y/TzzIwBwHh/2kIBHW+Au6tvIorC+fjUOZG8r5/bxM93RG6u4bt+OiTR6msysPhGGspttXFDxxtZdvec3R0D2OaFkG/i7qqfNavqKa6fKI1XhIy832LKKSKh0/u4/n9h+mPRskL+lm7djm3XLEKn8seUFyym3urPwmAaVgcOtHB8ztO09ZpZ/LnVRew9coFVJSE+WDNn4+f84WD7RiyZpYDQ6fI0QLUeMsue/ZgGK2ojo2IMeFc4cCykjOuK0kSd9+0HLD40X2vjHveumFyvqWf8y1za4qaDZIkcc/tqzl4oo2mUW8tmcry28f28/unDuF0qAhhU3lObn8vyPXx+Y/dyPGzXfznz1+e8/F+fN8rPPb8MXTDRDfM0Tb9mbmyu3oj/NuPXgAmBnBJCBRFQpFlPvX+a7nhqpkZAYejSf7s739H/1DM7kI1bV5ou2piOp7dcYpnd5yynQRGjyVLNqlSro//+Pu3EfSPJr1NEz1jICRBf8cgsiKTUxxCHuX9To6kiPRFqQv48ed4x/MuekZnqDdCNq0TzPfj9tnPpyRJVC4so7SuiKbjbVPOS3OqzFtVTahgOvOnoRsYusH7/v7N9A3GOXKwBcMwsSyoqry00MNk5Gg+3lW1GQsLWUg4JJU6XzGykHDJGm8u34iE4NaSNThkhXdWXkVMT+GRXzuFMrxBDHfKyHBo6Cz7h07jU9wMZqJcUzBzllaVZG4vXUlDoITvnHmWXX3nXlOS8A8FCUG5J4e7ytdwc8lSzp/o50hfO6uXV2KY5niBvmla4x6yYZgg7CL+4aE4i5eUExtJYRijL9IkmKbFT+7bxa8e2ovf66QwP4BlWRw/08XLu8/R1RvlLz60ZYL0x7Lo7ovy1W8/wYmzXZQUBqn05tPXFeP7P9jH0YODfO5Pricw+nLIyOiGya8e2svPH9iNy6VRnB8gEzf43WMHeOKF43zuT66/pMwXQMJI851zv2FteDEfrb14tnwmyEoNmeTDCLkAITzomf0o6rJZ11dkibfespJ51QX8+L5dHD3dcdGmmslwaAp5Ye9FvaLCPD9/+8mb+df/epbjZ7ommrZ0Y9zLH4MkCRbPK+aT77uG+qp8JEmgKvK09WZDMpWdwqMxV4wZdxNrNB6vk83OPku1TItYIj1j1cvFj2M7XlhgmAbZrEEsnp4ysHSc6+G3//oIvpCX9nPdWKbFn/3HvYSLgpzZ38TPvnw/mlsjNpTgtg9vYeMdqzANk59+5QFajrcjJImRoRif/Pd7qWiYxp4xZzQda+N333iMjvM9/Pn3P8gN1y8e/262ENqYcHDfcIzBaMIejC8j96MpCktqi1BlhZB26Z6CueINYbhdsoNqbxH1vlICqpee1OBFk4FCCOp9hfzz8rexs+8sv2p+haPDbW+I8IlHcbAwUMLNJcu4In/eeAKyqDBLc0s/sXiabTvPIMkShfkBmlv70VSZ+tpCTp7uxAJuuX4py1ZU8ouf7aC5sY/h7zzHshWV4wrwYE+LH3z6MFVluXzl07eSG/ZiYWsuNrb0Ew55phifrG7wnZ++xOnzPXzqA9dyzYZ5ODSFWCLND3+zkwefPsxvHtnHB99+xbjHs/tAEz+5bxerl1byyXs3kxv2YpoWB4+18ZVvP86//fB5vv33b51Tk8trgSSX4/C8m0zy95hGL4pjHQ7XHbMOGGMsc8sXlrGgroiT57rZub+R42e76BscIZHMoBs2odjY1D4ccLOkoZQNK6qprcwjmzVmF2AQgpqKPP75s3fywiun2b7/PC0dg8TiaUzLslV8fE4qS3JYv6Kaq9fX43Fpdr6hLJfbty4hnsiQE/RMU/y+EIvnlUzjz3g1EEDZRVrXHZrCtRvmEUumaU8MkjUNKjy540IklmXRnx4hzzmRv7Asi67UMBnToMwdHl/X53GMt3oD6OksB547xl/814e49ytvQc8aaE6VbFrnZ1++n1s/vIVVW5dw9kAz//6pn7DkqgZ8IQ9v/rObcHqcSJLgu5/5BbsfP0j5/OJXne+pXlLBx/7tvXz+9q8hLPBfIqGcSGV4dOcJHtt5gtaeIVIZ3VbUuYxj5oe8/Opv34Xf8/pWC70hDDfYicOXew/jVd3kOgLM900nnpoMO8mlsrmggQ15dRwbbuOpzqPsHjhPd3L4VScwLxeykAhqbup8hazLrWV9bi0V3lxUMVV5fAxdPRGKi4LE4mnaO4dYML+YM+e6GRqOYwFFhUGEgILCAB/66LV0dw3j8TgoLApMrYfN6mQyOn6fk1DAbbcuC0HA52LZwtLxezSG8819vHKgkfUrqrlx8yKU0eqBgM/NPXes5vlXzvDcjtO87bbV+L1OdMPkgScPoioyH7rnCgrzJmpo1yyr5Mq1dTzy7BH2HW1l66aG11QjPxcoynwk70fBsmcllhkDSbvocYUQOB0qyxaUUlEbZm9fI+uDdaQzWQzDnukossRDDx5gZCTFgrJ8FtYXYRgm9923lzvuWIlzFgJ+IQSDAzF8iso/ffZOm8I1ncU0LRRFwu3U6O8f4aWXTo0bbcuy6I3EuPbKBhbXFJFMZ4nEU+TP4okJIbhp80Ju2jxzovT1RHdkhCUrytm8rIY9/Y38onknn1y6mYBqG7eUmeWR9oO8qWzVuIE+NNTCQ+0HWBQs5YaiJRdVUgoVBmlYU4uiKiiqbXaG+4ZoPd3Jke2nOHuomUwyw1BPhOHeCL6Qh1Qiw/5njzHUM0x3cx/e0KsXkwB79qM51UtWnIBN7fqVnzzDiwfOzYlPfzZM5WWfXmxgh6JMbBrfuVf0vGEMt0t24FPd9KSGaI13U+LKxa1cOtk1ZsBXhqtYEa4imk1ydqSbg4PNHBtupyXez2AmTsrIvmrRAIGd4HNICh7FQZ7TT7k7h3p/EQ2BIqq8+YQ1D7KYucvRtCwam/vo7olQU5XP+cZehBCUl4bxuDRCAfuBjMXSZEapSU+e6MDjcVI3C5dwfo6PBXWFHDjaype/9Ti3X7+MhXVFuJzqjAnMo6c7SaV18nJ8nGmcSjWbzui4nRqDw3EGh+P4vU4i0SRnm3sJ+F1ERpKcPDe1i8zpULEsON/SB5saphxrKBvlZKSRhJGi3F1EniM0A1eMRcrM0JbopiPZS8bIENL81HrLCWn+KddgmX3EI3+NZRnjJWayuoxB+R20xAdwyxoCQa0vn32DzWiSzLJQBedHelgULKU1PkBMT3FypBNVkVkQKKHA6UfXTRobezl9qot77llPRUUOlgVtbYMsXVY+2mFoMTgYtw31YIy8PB9+v4tEIkMylWXBghJkSeDzOHBqCh0dQ1gW+EocWKZFZDhBa+sAsixRXBwkJ+DhZFMPi2uKaO4axO3U0A2To+fsc6spyeVYYxe5QQ+VRWEM0ySRzqIpMo5Rg5fK6MiSIJ01cDtVJCHI6AaZrP07SkKQyuhoqmzL8aWzOB0q6YyOEHa8f2w9w7D3PziSpGMgiiQkVuVU8XjnIcaobjOGzo7es+Q6fOP0AKeiXTzacQiHpJA7KgKSMDLsHWjEtEzW5NTgmdS5rCjTuzBtLUiZ8nnFuEdrqOetriGnKER3cx//cu93ufKutdQsq6TjfM/lKaa8BpiWxS+e3s8LB86Oh3wUWSLodeF2alPK9k3TzjmMMVHqujnqRNnf5/g9E/qYWJyJPk+N7wpU4bSrjqwUBwfvZ75/CwFtdt7wC/GGMdxpI8tAJooqyeQ48nBKl1e/OkYkFdTcrM6pZlW4ChOLhJ5mKBOnNzVCbypCfzrGcCbOiJ4ipWfJWPr4iCoLgSopOCQFt+LAqzgIqG6Cmocch5ew5iGoufEoDlRJmUYkMxskIVi3qgawf+Drr104fs5ge9mv7D1PYYGfeNKOD7Y09VNWkTOrBJhDU/jLD2/l+7/czs7959mxr5HKshy2bmpg65UNhIOeKdv19NnNS/c9foAHnjg4bX+GYaKqCtnRWHA0liKRzDI4nOBTf/e7aeuP3bPUpJioZVmcHmnm3878gqFMFJ/qIWvqrAjNn1a3mjBS/NPJH3I21oJLdqIIiUg2RlD182f176DBXz3RUZo9jqKtxOn5ABOlVILjXScYziToTkXQJJkyTxi3rLG7/zw+xcmegUaKXEGe6T7O+txaotkkha4A97Xu5aP115DN6rS3D5JMZujqHh43yv0DMX7/+3184fO343KpPPHEEbp7IlRV5nLqdBef+rPryWYNDh1swTQt3vGO9fa9vX8vpmnh0BSEqEKSBCdPdVFYFOT8+V6uu24hdfMKx19ql0OltXuI4lw/mazBrmPN9A/HyQl42H+qncIcP0/tP01b3zD9kTgfvGkd+UEv33poOwGPk1Q6y9s3LydrmPzi+QM4NYWA28k916zgx8/s5Y4Niwj73PzHIzv5yM3r+X8PvEzY72Y4lmRdQwVXL6nhx8/sZSSRJppIUVkwc0mcLEmUucP88PxLbMqfhyIEIc1N3qjBrvDkYlkWP2vcTrnH1mD80fmX+dN5Wy7avh3I85FflkOoIMCq65ZgAZlEBqfXwbGdp3G4NG750LVYlsUTP3wB32hIbqzCyTRMrNG/Jw8Khm4vNwyb6ndCWNhOUNpNOwamYSIuVP8BBqMJHtt5wqYFAFY3lPPuG1dTV5pr/7YIdMPm/enoHmYknmZBbSGyJNHWPURhrh8LaG4foLYiD89oJY8AhjMd7On/OWtz303GjLO99/uokmtWQq7Z8IYx3ACqULAwxxW8XwvsBJvAp7rwqS7KPXbW+NXUv77aMEDWzHBs+EWqfSsIqBNZ6+nshbB+dTWZrIGqyAgBtXUFPP3UUZKJDIoq43AozJtfPD6yCyEoLgjwhU/cyNmmXp7ZdpLte8/znz9/mceeP8ZnP7qVRfMm4oFjhvZNNyxjScPMnXGSJCjMt2OY9n2yqCgJ8763bJh1ellSGBz/d9JI84PGB9Atg79d9FHK3YUMZCJ8//x9DGSmkgK5ZAdbCtby9vIbKXHnowiZMyPNfP30T3mg/Tn+akEl8mgtsyQXomf2Y1kpJtO3ykKiwmM33yT0NHsHmhjJJlElmbSpsyJcyXPdJ1CEREjzUOIOUecr5KnOYximidvtYMOGOnbvaWTz1QtwOOzXYdnScp55+hhjLp5pmmzaVM+a1dV889+eIhJJUlQUZMmSMg4eahk/H6/XSVfXMCuWV1BenkN7+yBVlbm86c6V7NvXzPnzveQU+hmKJkmms4zE0wxFE7T1DNPYOQDYggfDIwmwLCRJcOXianqGRnhq/xnOdfaTH/DQNxzjvdetIi/gRQj44VN7uXpJDavqS/n2Qzto6h4klrTL6yzLnvablkUknuJPbt1AMpPlF88fZEF5AR39UT7/9mt54fA5Ogdm7kyWhUSJO4QqyePPXqErSKEriFdxUOXNI66n2TfQRMLI2CGhdJSsaeKQJWRVxhf2Tmswdbg03vU3b+JnX36AZ362DdO0CBcG+fC/vIOqReVk01m+/YkfYZkWkizh9tu//Yl9jfz+20/S19rPQG+UxAe/z8bbV7F8y2L2P32E7Q/u5eTe8wx0D3N02ynu+uSNlNUX88j3nuHErnOMDMb52VceoKAij3f+9Z34c6aGqs539NM3bNdeL6wu4qsfvomgd6KEVzcMnn7uJLlhD+GAh5Nnu+noGmbrpgaOne6iOC9A72CMHfsbyQl48I2XewpW5ryVXf0/YUffDxjKtFLsWsyK8N3Il9lo9YYx3CHNx83F69Ekdc6e7KvBHzoWOxlZM8X2/vsIOYqnGO6ZIEkSTsckJWwgN9dH43m7ftbrc1JfXzSFME4Iu0KhobaQhtpC3nnnWh5+5jA/fWA3//mzl/l/X7obx2gtbP6ohmJe2Mfm9dPZCC+Ez+vE6bCnhetXVuFxXbqMqSneQWOsnXdU3Mw8X+VoaaGTu8uu41jk3JR1BYIr81aOXwfA0uA85vuq6E71kzF1XGP8LJZBJv0s2cxuhLBfMkVbhiy2IgsJVciokkLKyJA1DRJGBkVIzA8U85uW3by1Yi2yELQnhnio7QCFrsC4EZoLhBDjtdaSJM06+N9801La2gZ5/InD9PWPUFmRi9tjhwtkWcIyLVRFZvWCcgzTJOBzsaC6kMIcPxsWV6KpCrlBD139URbV2jHwbz24nU2Lq2wPb5RD3O3Q8Lkd44NpVjdwagqSmKhYEcIecAzTJK3b4TeXU8Xr0sbLCg3TRJFt9aexmumxaxtTkJzpWmeaBSpCIs/p486yVYQ1DxYW2ug9Lqkt5LM/+giqY2q+QAjBwvX1fOEXH6e7pR9Zlsgvz0FWJPJKw/zNrz9Jb2s//hwv4aIQpmHS0T7E+Y5htn74OoJBN4cPteJ2a8RVhX37mqhYWkH5cJKSKxawbHk5VVX5dhhGwJZ3XMHVb1435fje4HSVrc7+6Hjl1+2bFk0x2mCHTcuLQ3T0DBP0u1m6oISBIVsgI+R3YZgWhbk+astzKS8OYWGQnSQYvjL8Fl7p/zFOOcCS0G2jMe6ptLiXwhvGcCuSjDIHGkv7gbOmqYhcDGNJqFdjtGd7SS/skJtp+as9hhCCquo8Skonpq6SJKawoY1tM1ZBAXY79ttvX822PXZddyyewaHZMe8lDSU4NIVXDjTyphuX4bqgEWPy/gBCfjd1VXkcOdnBqXM9rFg0vf76wm3aEt2YmNT5yseXCSEoceXjmqE5R7cMOpK9tCW6iWRjpI0MvelBOxcx6Z7Iai3+8I/tB9wyQSgI4WCdyw4HLQiWYFoWTllhMB3HrWhokkLa1ClxhZjnL0KTFD7VcD0pI0tIm5rkmqyUE4ul2Lu3id6+KNu3n2HlyiokWRpncZNHp9bnG3s5cKCZlpYB9u9vZunScp555hipVBbDMHGMVgBNzJDsOufivIn6Yp974p54XBO/dXmhXf0xOJIgndWJxlOMJFPIY+cgiykhiKuX1vDgzmMca+4mkcpQXRSmtjiX+7YdIexzkxqNbY/L6wn7vPICXmRJ8IvnDtAxEKE0N8CInuKR9oP0paL8tmU3W4sW45AUnug8TF96hF81v8JNJUvtuLas4pBtY6xJCneWreJXzTtRJYWFgRJuLLYVmGRFxhucObEohMAX9uILT0/QBvP8BC8QQug73UX9/CJqawvYu6eRYL6fRCKNy6WxdGk5TqdKPKNTV1dITmGQQO6EkIPHPzcpxHQmO3qfJaqLwtOee9M0iSczSJKEx2k3OQkgnkzTNxTjXEsfyxpKsIDGtgFE6Ax7Bn4xcc121oy43s+DbZ9DERpbiz+HX50r9zmIN0Lr7KpVq6x9+/bNaV3TtPj332xj7/GWS688itqyXD7//q2XLL2aDMPSOT9yAJfs40R0B/N8azExODOyl0WBTZS45mFh0ZE4TWP8ECPZQTxKkPn+dRQ6q0en71H+u/HT3FryCSo9i7Asi9bECZLGCHW+VchCIWumOTOyl9b4cRRJY75/HaWueezedZ4Xnj0BWIxEUxQUBviTT1yHOpqgahnt9JtfU4jf60SWJbK6wfEzXXzx649QkOfjP778tnEDncnqfPXbT/LirjO86YZlvPXWVQT9LiwLkqmM3YVnWqxcXD5eAfHKgUa++PVHKSkM8qn3X0NdVT6yLKHrBn2DMc429bJpTR2u0cqL37c/x0+bH+Ffln6KOt9Em3skM8InDv4Ta8NL+GjtWwAYzET5r8b7OB45T54jRJ4jjEt2cDx6HkXIfH3Zp8eT05Zlkk2/RDr5AFhJZHUpTs97kSQvs3kpGVPnqc6jVHnzWBgomXVANU2T3t4R8vP9SJIgnc7S3j40Sits5x/S6Swul4bTqdLXP0Io6CYSSTI8bCvZaJpCaWmIaDTJ4GAct9tBfr4PXTeJRJPk5nhJJjOk0zqhy6iMsCyL/miCkUSKkM+NKtvVKj1DIxQEfZNitxaDIwmG4ymKw36cmoJhmrT3R3BqtlHJDXjoHYpREPJhmCaDIwnyg16S6SzdQyO2Vwn4vU4SeoaxMJFTtoW3k8ZELblbcSALiaypA2J89mJZFmlTRzcNXIp2SUZOy7I4NdTHUGaimarGn0OBe/by0ng8zZ4958nN8aGoMr29EcJhL5qqUFYexuXSOHasncGBGMtXVOL3T5T8JbIZ+lMT6kNuRSXH6Z52jg9vP8bf/+hpFFnie595C0trpxJNXcxmWqOO8+Q9ZswECWNo1m0EAp+aj3yB2tGqVavYt2/fjDfwDeNxXw46+yOcaZ27WIGmypcdMdfNLM/3/JwiVzWa5OLhjm9R6VmMLKk80/0j3ln5ZQRwePg5nLKXQmcVbYlT/K71n3hX1ZcJXZAhtiyTU9FdvNT7a64rfC8SMoZl8GLPL2hPnqbBv5GEEeHB9m9yQ9EHWbN2BStX2cLHsZEkv79/H5Mfh/buYf7m64/g9zrJz/HhdKjEk3aJoaYpvOeu9TgnTU1VReZj77mKRCrD7588xLPbT5ET8mCadlt1NJbirbesZOXiMR4JwdplVXzknZv44W928umv3k9+rh+HphBPpBmOJvH7nKxdXjVuuF2yEwtrtAN20r20DPQLeMofaH+W/YMn+GT9O1kdXogq2Y/iP5/8Ia2JqRUspt5IJvkwLu9HEMJDJvUs6cQvcXo+NB43tSyLbNaY8lJtzVsEQGZGDuwJhELuKc0ppaVT652V0TK3TEYn4HdhmhY+nxOfb8JjzmYNXC6NkhJt/DOA3+ckk9GRZQm3WyOdvrwGF79Twz9pdpTN6IQ9rmnNNF6HhneU8H/seouCE96mnjUIeye2C7pHz0sISsITXq2eMdAmzXzN0cYvbZKp0DMGOmO/p0Ua09bXlG0SN+S5ya1ZwNcOvsxLnTb5lkDw1XVbeWvdVK3Uyb+px+Ng8+aJ7s+GhunsfYsXz9zkdbC/k4+9/DDmaKnwqvwyvnv1HdPkDsvyQ6iKTSTW2RdhSc1UqomLl6BOX+aQPThkz/i12P9d0Kh1mab4f6Xh/mPBxGBB4AoKnFWciu5iWWgLHiXAb1q/StpM4JED3Fj8EaTRB31hYBP/3fiX9KZaphhuAZyM7mR7333cUPRBKjyLEELQl2zlRHQn91R8kVxHGWBhWga7Bx6hSFpAMm6/ZMlkhqHB2OhLZB9r8fwSPvm+zRw73UnfYIxs1iAc9LBmWSXXbphPzSQBAbAfttywl7/71C3s3N/IrgON9PSPIEmC+uoCFs0rZuPK6vH1x+KYb7phGUvml/DcztOca+olndEpzPVTVZ7LuuVVeCcpqZe5C5CQOBtrZVGgdtxz70r1k5ykG2pYJk3xDvKcYVaFF+AYpfBN6Cm6UwPTfgfDaEXRViIrCxBCoLluJjny71PWSSYz/P3n72Og/41Pg/D/NwgBH/7YdaxcYz8/A8Nx2rqHWDqvBMM02XO0hdULK0ikMuw51oIiSaxZUoHLqWFYJsa4YbaYKQKaNlPs6H8Rj+JlbfiKV52nCmhOknqWlGG/VycGe4hm0uQ4p4ZQakpyKMsP0dg5wPMHzrJldf00jctXA8uy6Ewe5dDQ70kZI4zNamShcW3hp/DNQflmDP/rDLcQsGFJFU5NIRpLMZJIM5JI2+Q4GZ1kKkv6Iq29lwMJGbfsRxEKmuTEowSQx1Ws7REzrkdojR9jMNNNyoiTNhLoVnbKPppiRzg8/Dy3lXx83GgDdKcayZhJ9g4+Pr7f3lQLkWwvh482sWebHQ6SJMGmq+ZP6Zz0e53cvnUpt21dijX2tAsQwv73hdJt9r0TuF0a126cxzUb5k3EqCUxLSGcSGb4xX27ee/bNlBfXUBdVT6GaRGJJggG3NOY8ACqPKVUe0t5oms78/1VVLiLGM5EeaD9WYxJtASSkMh1BGmOd9Ic76TKU0LCSPFI50t0JHvJd0z1eGWlhnTid0hyCUJ4yaafQ1Gncm6YpkVbywC9PX8YEYj/w8URj08MzIoi8dDzR5hXmU/PwAg7DzaxckEZP3pwFysayhiOJvj5I3v5wN0z06ZmzAyR7BCq0AioQRySkwX+Jewe3AbASDaKW/EQzUbwqwESehyn7GQ4O4wiZAJqiLgewym7UIRC3IjhkByEHC4csjJuuIfSSXoSsWmG2+d28LYty/mXXzzPjqNNPLz9GLdvWjye33i10K0Ur/T/hHrfVeQ5axibQQskXPJ0bpWL4X+d4Qa49cqF3HrlQszRek7DMEmPkvrsPtbCP/z3M6+p22kMQkziyRVTE0IAfekWHmj7OkWuWio9i8h1lHIq+sqUdQwry/nYARySi6b4Eco9C5FHb7tuplGESlDNH5d9Cqj5OGU3C2vqWL+6AYQdd9U0ZdyDzZgjCASq5MWyMmSs2Kh2o0JPcj+WZZLrXIgq2YQ9diLX9qANw0KWhc17PWqwDcPEJu9h/BiyJLFpXR3KpIToSCzFr+7fw4fefSXI0vj6Y3DJDj5Q/Sa+deYX/N2x/8SnejAsk425y4j4YhPajsCtxVdxOtrMl49/j7AjQNJIU+4u4vaSzewZPDplyinJ5Ti9HyKTfAjLSqBqa9Fct/xRK4T+D3OH3+OkojjM8fPdnG/tZ93SSiKxFEdOdyAQZHWDkXhqllZ+i5b4eTqSbXSm2rkidzPl7ioUSWHM0O0d2sl83yIe6vwNtxW/hcPD+2jwL6Yl3khPuotlwdWMZCNokoNFgaU83f0IWwpuwqVoeFSNSCYFQMYw6IxHWBCe6ukKIbhlwwLaeof59bMH+cZvXuJ0ay93bFpMRWEY5+i7OJcikPGuAyHQrSyK0GgIbEWVXhuT5hvScNueoDkqS6UhJp3m5JdVFgJZsuO3Y/HcvJAtePrH6LI6Fd2FQ/Zwc/FHkYVK0phJHV1wdcE78MgBHmj/OkE1n+Wh6xBCIqgVIgmZRcGr8CkT2WvLsjh8qBVFkdA0hd/88hUWLCrlzrtWM5A+Rm/qMFkzQZXvelLGIJFMM0l9gPnBt9Cd2I8sNJxyiJCjHoDGlj5Onetm3cpq/v0HL/BnH76WF3eeYfniMg4dbePk2W5M0+T6zQtZsaScgaE4v/79XiLRJH/5p1vRNImOrmF++cAeDh1tJZnWmV9bwC1bJ4RdLcuiLdlO1ozz5cUf4/RICwkjSbm7iGpPCb3pofEfRQhBrbecry75BKejTSSMNAXOMHW+CkzL5Mq8lTguaMCSlQoc7rfCaHuwafQiyaX/Z7zfgBBCcOXKWn7/3GFS6Sw3XrkAXTfIz/HxzltXjzYoiVnDDwXOIjJmhuHsIL2pbsrdVVO+D6k5NMXPkavl0xQ/h1v2kOcoIGUkSRoJulMdLAuu5tmexyh0lqBICl7FTxIdzyTxbxOL7sT00JphmrT1DlNfmkd5QYjzHf088NJRHn/lJIU5fgrDPnxux5x0Rv0eJx+/exMuh4omufGrhbQlDlHqWjJFKUoWKrMJT8yEN5zhtiyLeLaF00P/STRzirrgBynx3kx34gWccg5Bx5I3zMvqkNykjTjRbD+q5OTg0DPE9eEp69i81SoFziquL3w/T3R9D7+aR413OaXueQTUfF7q/SVrc25HkxxEs/0IIdHWmqCuvoiXnj/BO959BU8+fohMRqcj8QpgYVkmCb0X3bS9h1i2E0mohJ3zx4322H3ye52ca+ylMM/PSDxFa8cg55v7uGp9HetX13D1xnk0tvTz0BOHWL64jJyQh3fctYZ/+LcnxmcuxYVB7rplOYlEmk9+8JoZlVKCapAd/Ttp8M9nfc4SUmaKlJFGEhJB1cNLfduQQsvI0XJQJYVcR5DcvOXT9uNRppL/mEYn8chnEVJoXGBAVheOdlL+H96IKC8OMxJPU1IQxOd2YFmwfH4pP31oN36vi6rSHK5eUzdtO8MyeKzrAVaFNuBTAnYi74LZc76zgGd7Hmd5cA2HhveyLLiaJ7oeZFFgGQE1SMbM4FcCOGUXuwe3Mc+3EElIKJKEQ55q8gbTCS5EJJbik9/8PT1DI1NYDlMZneauQZq7Bud8H/JDXj5yxwZcDnWc9viZrn/Bp+QhCzs/pEgq1xV95rLKAd9whtu00pwY/DpBx2JUyYNu2h1MhpmkI/kkQceSS+zh9YEkJPKdlWiSE0nIFDqrUCQNgUSBswpJKCwMbqItcZLftf0zmuSkyrOU1Tk345J9o/uQKXRW45DsAv5q7zI25b2VI8MvUOyqxSX7uK3k47zY+yvua/tnLMvCKbtZl3sHhYVFPP7oIXw+JyWloVFVbvCqxWiSl4BWiUMOcGTgh9QH7mQgdQKwEx0JvZesGRsPlQQCbjJZg9PnetiwuoZTZ7uxLAuHQ+X57cfo6omQzRrEE3a7vSTZDSOTQ0OSJFBkGSEJm3figk5KIQTu0ZgiQFSP8mLvyzhkByE1SImrhPOxRvIcufgV33gVyVxg6KdRHZtxet7H66Ee8n/4w0MSgpDfzaaVNfZnSXD39cvpG4yRzujkz1C3DWPPkZem+FmieoRcRz5D2QEODu2lN93Fkch+qj31ZM0s5e4qtne8RDCYg1fx0ThyjqSeoMRrV5Us8C/h0c77ua7g5vFzUi5ovEro06t8LMuWVHv9K6UFK8NvZskFakFCSLjl0CzbzIw3nOHOmMMYZoqawLtpiv4KsH9Mh5xD2pj7SPdaoQiN20s+OSpyC3eW/vnM/y77C1JGHEnIOCX3lP4nh+TmrrK/HF9XCIklwc0sCl6FhN0QFNKKuKP0z0gZCSwMVMmJKhwYK02CIQ+FRQFkSeLGm5eiaQqV6ha6EnsZSp+n2L2GKt9WotlWqnw3IguVfOdSOhO7iOndhLRawC6H9HoctHYM8rY7V/Pz3+2ipChIe+cQB4+28td/dhOd3cP818+2XfymiFFSHSaI/Geb/bQl2ulL91HoLGREj1HoLKDQWcDS4BJUaY7lYpYBVgpJziOb3oll9iPEaB20kIHXh5T+//D6ondghAefP4KmydSUTYh5yJJEYe5E6eFMeSgJmZuK7iRlJHFITvSsgSxJrAltZLlnLU6HA5dw8+ai9+CRPGzM3kzTrmGu23oLXb29qIqDcMiHiYlu6izwL8ExGk82LWtaWepMIVWXQ+Wd168ieZmlmzPB49LQRnsvhBD41SIszNFuycnX/b+8HFASDiwMMuYEt4Vp6QylD+NWZ1Zc/kM0EdmkVROj82z/llHwKIFJ382+j7Fl8uR9ja7jVnxT1lMUeQozYHWNPY1ShJMy76bx5XmuxVO2UyUPZd5rxh/IMQWTovwA/YMxigoCxOJpSopCBAMuMhmdBx8/SN9AbLxq5XxzHweOtNI3MMITzx1jxZJyykvChIMeMhmdH/9qJ/NqC9m4pmb8uJZlETfipMw0cT1OUA2S78hnWXApLtmFJCRMTHrTfeQ78uZkvI3scRIj/wpYmGY/+tAhhLDDKIq2HJf3U5fcx//hj4+g38UtVy2y+cZfhfiwKqmokkrLuR6O72/G4dJYsLyCI3tayC30k0pkyaSzLFheQXlpEZGuJmQh03kygi/gpiA/zInoERrjZ7kq77rx/cayGQbTU5WTXMr059DlUHn3DaumLR9DXE8hjSrdXA4sy6I1vp/DQw+SMeOMkQs4JA/XFH4K7yVoMSbjDWe4NSlAgfsqDvZ+HgsdRXgYSO0nobezNPerpA0DSYjxP4ZpkjVNTg30sTB37nWQpmlxZu85Dr14nHRyopSpqLKA69591XiL8/82RLMpfnZ+D9W+XK4vaaAjPkxcz3DNpvmsW1WNQ1P4+AeuIRhw4XE7+MzHbmBwOE5Bnp9EMoMkCfw+JwvnF7NwXhEIgc9jeyxej4PPfvwGevqihC/oALS7SDupdJfTlmxnvm8+iwIL6Uh2UuWpRCDYmLuBrmQXYS2EyqUNt6wuwBuaXK9t2K1pQkbMYfv/w/8MNFWhOP/yyttmQuOpLrIZHc2h0tnSj6xIdLcN4fY4WL6hlkDYw9BAbEIerjTM8ICdbFzgX0KDf/GorJpdKXV0oJu+5NRkZL5rgnEwpqfIWvo4X9JwJk6eM8BINknayJDvDBLNJjgWaaHIFSakeZEQ+FQ30WwcwzLJcwRmnYXqVpp9g79inn8LcX0AScj41UIaR3agXGaVyRvOcAshURV4BwHHAvqSO8maI3iUchrCf0bHiINtrYfRZJkyf4CqYIhDPd1UB0M8df4cQcfcL771ZDvf+PD3WHHt4nG6SABJuXwP4UJcbAbwh0ysxrNpHmg5RFt8iLV5lWQMnf0DbSwIFuL3OTme6uJwRweqJHOVq5bTkR5OxLpZnFeM3+ckMNoenJfjIy/HN+MxggE3wcB0zgdJSCwINExZVu2totprVwRYlkWxs4hiZ9H45zHMrmSjIIQfy9LJJB8ik3oGiyyyUovL84Fx7/v/8P9PLFpVxeFd5yksC5NNZ5EkQUFJCM2hoGoKlgVNp7sZ7Isy1DdC67ke4rEU85aU4nI7xnM0lmUxnE7x3WO7yE6SAFSERJXfji2bWOzsP0lbop9iV5i4nqLMncupaDvN8R4qPPm0JProSA6MN6ftGTiDbhlszG1gz+BZ1ufMJ+8i0TvdTCMLlXn+zbTE95I2YtT7rmYo3Upv6jTlnpVzvjdvOMNtWSZpY4Cwczk5zonpihCCrNFLTSjMYDLJQDJBkdfHYDLBxtJy6sI5VAVD9LbPrQGj9WQ7a25czvv/4Z7XxZiOafwNjyQ43zFAY/sA3f1RYqP82l63RlFugNqyXGpKc/F7nZfNgmhZFsl0dppeoaYqODUFp6KyLFyKIsksChbhkFXynF6aYwPU+fN5pvMUb65czgtdZyly+/lt0wHW51fxs3N7+PzSG9AkmVRGJ3NBA5OqyLgc0wUa5nK+maxBR1+Es619tHYPMRiJk8kaaKpM2O+mvChMXXkeJXkBtFmkwozsMTLpl3H5PokQLrLpbaTiP8Tl+8xlnc//4Y+L2YSGJ38/bRnWeOw7p8DPNbcts78Y9ZovfD6Wra9h2foahBBsvH7R6Kpigi/e0Dk11Ms3D+9gd89UYeFcl4eagE3uJSHImjphzUtPagi/6qbMncfewbPkOvzM85VwItqGU1LxqS6SeoaMqVPrLUSRZEpdOVR68i/6jiiSAxCkjRh+tZCDI/dT5llOXB/AsC6vafANZ7hNdI72/wMCKPZeT65zLZocGp8OHentoSoYYlFePrs72/FoGm5NRZUlzgxeXMF78oOSX57L/meOkEll0WaQp5qrkRojdT/V3MODLx5l97EWBobjs+oEKrJEQY6PTctruOPqxVQUh2fscpwND754lF88PpWQa93iSv763uuQZQmP4sAlK7gUO/7mlFXius0d4ledVHlzOOrsJJJJopsmPsXJbeWLkYWEaVp87SfPsfvYVAKvt12/gjdduxSnppDMZNEUhaFYgpDXhSpPN7aWZQvPvrDvHI9tO87Z1j4S6cyMWXohbJrSuoo8br1yEVevqsXjnCpJZjfdrEJW5gOgOhRS8R/P+Z79H/5n0BmP8s3DO8hemBAchQWcHJrKOfTbc0fYc4GBfTWwsCtGOmIRmkaGSM5QPXJFUQXhSV2Ti4IVqEJhOBsnrHlpjHWzMa+B4UycgOZhUaCC/nSUpJFmgb+cPKct0B3WfMhiZqdjMhThYHHwViQhE9Yq0CQ3D7V9gYBWRKFz/mVd3xvOcEuoLMn9Av3JXXTEnuL88E8IOZdR7NmKUynh2spqGnLzAKgIBMe3u63OvvDdnfFZ921Z8Oj3nub/Y++/o+y4qvR/+HOq6ubcOeegnLOcZFnOOCdsbGMwOQzMMKQBDAwMDDAwQxgyBozB2MbgnCTLtmQr5yx1UOccb0513j9u9+2+fW+3WjYzX//WO89aWnbXrTqVd+2z97Of3XGmCz2uc3T7Cb583bcpm1uMqo1rBxdy7Yc3zcpwSykZGg3wm6d28dz247Pqxh2L63T0jvDIi/t5ccdJ3n3FMm7dtGRWHq0QguVzS/nlE28SmNR55o2DTXQPeCnOSzBQxqU2+0M+9g+04ouGmePKx6YlWnyZVQO5Zgc1zlwavH0UWl0oQtDVP8Ibh5oZ9k4kcCwmA8vnlnK6qx+jpnKmqx+b2YimKPjDEa5ZlvrA6brk4Kl2fvzoNk409ZyzglVK8IciHDzVweEznTz12lE+ecdFzK8uSF4PRS0k6PsxerwXoViIhvegqPmE/L9GUdwYLTec87r/H/5noKoKVpsxY2/OkUiIp8+eSJaYzwYH+7s42N/19zzEjHAaTdxdvwxlvOxcCCpsCQJAMdkAFFqykFJix8rwSJBASMes2zBjYzAQxkoixNrlS8TNT9Gbth9NVagoyEr2hK2yr03+dlH+RwjFRzEp9v/vNlIYhxACs5ZLsf1aiuxXEYi20R14lSMD/0aF43bm5tz6lkMbAsgqcBMfCzUU1aT3eMsqdM/YbmkcUkpauob4xq9e5EjDW3vQhkYD/Owvb3CqpZfP3H0pHqflnOdWWZTN/KpC9hxvTS4b9gZ583Azt2xcTLktixJrIjHkMlq4pSJR4GLVjLynehUmVeOK4rkYhEqFPRtfLIRZTSRj3jzUzIg3Nes+tzKf6tIconGdR944SF1RLoPeAKtrS3n1eFMK/TEe13lm2zF+/OdtjPpD5309dF1y6HQH//xfT/LZezZy8YqasdmIccw4Jz4CRsuVExuJt9dA9v8wS4hESM7hspCf76KsIoeqmjwqKnMpKHKTM0U3+50MTSh8aP5qFmTnT/u+SSkZ9Yf56+uH2bznNJ0Do0TeArc712Pn91+6E6ct0WMyLiP0h5sJxSdCugKFIut8DOeRs3nHGW4Yj43FCcQ66A2+QV9wBwbFgdXwNkucBay/YdXf5fg6+kb48n8/m1Fe1mzUKC3wUFGYhdtpAZnoY9fSNUhb93CKCJauS7bsPk0oEuWBD16J02ae8RwNmsKmtfXsPdGafIgksGXXaa67aAEmo4Y2xhs3KCpZpnTDZp4ku+k2JqaK4UiMLbtPp8QkhYBNa+oxaiqaqmA2aMwrzkOXksOtXaytK08abV2XPPfGcX7w8Ktp/FdVSYSHKouyyMtyYDSoBEJROvtGaOoYYHg0kLLfwZEA33rwZaxmA6sWlKNqxajabdNekwT+PsJi/4exIhirEU+2neISDxVVeVRV51FSlk1unhO7w5TUtp/pWS2wOrhv7gp2drfSODqANxL+3+r3Oy1smoEPzF/F/fNWJrvVZ8KIL8SXf/U8O4+dfVuFOPFJIVOJzht9v6YzeBSblsW4y6MKA9mmcgzK/4cNt5Q63YGttHufIRBrx21aSK37/bhNC1DF7DpYTIfJD5mu6xx+7ThHt58kOkmvuag6n8vvuWTGAr1AKML3fv9KmtE2aAqXLK/l9iuWUlOaO9ZYdPy8IBSJcqa1j0de3M/r+xtT4uBvHmrmvx/bzmfecykGw/QSkkII1i6sIMdtp29ogtp08mwPTR39zKmY3ouYCU0dA5w8mzrVy3LaWLe4EiEELb1D1BXlku1ICM9ftnCiXFlKyaEzHfzokddTjLYA5lYVcPfVK1g2txSHzZQSz4/HdfqGfLyw4ySPvLg/JUQz4gvxvYe28sPP3kRhzrmpZYoQCa/vHSKH8HYgxzqHx+M6sVg80fQ2pifal8X1/4GKvlRceMkc7vvQJXiy7JjNBpS3qIqXZbby2aUXEdbjdPlHOdTfzY7uFg70d9LmHSZ4HiGUtwOBwGk0sTKvhPfNXcGq/NKJbkAZIKXkkS0H2Hn0bPJDY9AUnFYzJqOBYDjCkDeIpipkOa1ICeFoDH8wQnyMtVKS5+aqNXNYOacMm2VMJ10P0Bdq4Nrir2HXstOO8nzwzjPcxBgI7qXAdgm5lrWY1Bw4RyeNt4LW4x3896ceZM21yznwylGWXrqQU3sasLmsM15DKWUiCXkkNYFnMmp85Jb13Lxx8VjD36kl4WA1G1lcV8ycinwee/kAP3/iTSJjgvtSwrPbjrNibimXra6f8Xxz3HbWLKzg6dePJpcFw1G27m1gTsXs9Q4mn9Ore8+kecqrF5STN0aVrMrPomqaLuC+QJgfP7KNEd9EeEQIuHzNHD591yW4HZlDQJqmUpjr4t53rWJhTSFf+enzDIxM5Chau4f4/TN7+cw9GzIK+kgpicUSbc7MFiPf+N4dSD1zUngqdF0SjcVRlOnFjmZCIBhNGLW/w2MZCccYGfCRXeBCUQRSTiS943GdSCRGKBglEAjjHQ0y2O+jt3eU7q5hujuH6esdZXQkeM6GEbPFmdPdtLUMUFjoQVGUGb+FUsqxHpdi2vaAJkWlwuGhwuHhusq5BGIRWrzD7Opp4ydHdjAwqSvN2oIyal2zL0SZDgIwqioek5VKp4e5njyK7U40ce4WhqP+EC/uOpkMA66eX849V66kqjgbo6ayee9p/u33m6kqyuY//+FGDKpCKBqjqXOAv752hO2Hm4hEYiytK2Fp3UT3JQUVo2pDE0berk17xxlugYH52Z8BUi+wlHEkMil/+nbRerKd1dcs571fvwPv4K+44eNXIoFf/PPviYSimCyZkwU9g14efelAStJNCLj98qXcumnprCrFTEaNO65cxpA3yMPP7016UNFYnN8+vZtVC8px2aefNiWMYj0vvHmcaGzCUL2+v5G7r1k5qav07OALRnh9f2PKMk1VuHztzB8QSLy4L+08xfGm1K41i2qL+Kf3bEjQHs8xhjKWdP3Qzev4999uJj5JTX/zrlPcumkJlUVZU54Hya5djbyy5ThOp5n33H0B7gyNX6dDS0s/f31iL4oq+PjHN6FM+jDouk4oHENKidViJBCMoGkqAjh8soO5NQUcOniWpQtKyXJZk2GDeExH13W0aWiN0+HYniZ+9uXH+dafP449A0d+Kiazo6LROH5/mL6eUZoaejh+tJ0Txzro6hgiHH5rhryrY4h/e+CvXHvjcu68dz32Ge6hLiWvHmhkcMTP7ZelC4bpuuRs1yBVxdljVcJgN5iYn5XPHHcur3U08Vpnc3L96yrmcseUDjhvF+e6F829g5zu7OeyRTWoikJb7zA9gwmlz3mVBfzrB65KaRicCGcmzt1hNWEZUyYtyHKwvL6U3zyzi98+v5t/ffAl/vMfbiAnN0pvKBGGNCsOtnT/gCr72qS0qxAqZdZlGNXZP79vv9rk74xE89v0B38ofIQO33N/t/2YbWYioQgIgcVupv1MFyaLEd+wP7E8A6SUbN51mu6BVPnWyqJs7rxyeUajneB3pyucqYrCXVevoKIw1Yttah/g9f2N5yzimV9dSEVR6nSrrXuIow2d5yUBIKXkaEMXrd2pPfHKC7NYWFN0zoc+EIrwt1cPp3zITAaV+29YOyujPQ4hBJetrqO6NDdl+ag/xCu7T6cdczgcY8vmY1x11SLuufdCnE5LMrww3rA32bFcSsLhKJFILLmsrCybm25eyfBwIC30MDgc4PHn9vPc1qMcO93F1h2nefG14/QMeNl5oJme/lFGvEH2HWnl1Z1nkvd336vH2fL47lmd72TocZ1QIHzuFSddq/F/RqOGx2Ojbk4hV1yzmE9/7hr+6+fv5T9+cg/33n8xtfUFM4bepkMoFOUvj+zkm19+gq6OoWmfKVVRWFZfQmjM2/cGwjz7xjFe2HmCQCjCG4eb+cVTO3hx18m0+gBFCOrduVNGFCnn9/f4dy4ca+3hodf2J8McHX0jROOJmfC71s9P6/Ju1FQEgkg0ntwGxogVRo33Xr2SJbXFdA2M8utndjEU7qLBu51G73biMooiVFr8e2jwbqfBu50m7xtE9FRSwLnwjvG4dRlHEkPBiCTGVOp+MNb9dxWZql5czrE3TqLHdVZetZSf/uNvsTos5JXlYrFlrsAMR2O8sud02vJrL5qP25HuIUspaTvTza4XD7P26iXEIjFKawtQx0IpHoeFay6cz4//PCHupEvJC2+e4Mp1c2ecwlvNBi5dWcuZSXH2WFzn5Z2nWL2gYtahXikTXu1U3vmGFTXJ2NxMONbUTVN7aruxOZX5LK6bvkHvdLCajaxfXMnpltRY+84jZ7nn2pVJsZ6R4QCP/2UPp051IYGqqlxuu201Q0MB/vzIToLBCA6Hmbvesw6r1cRLLx3h6JF2dF1n06YFLF1WQaJxxDRepC4pK04I5p9tH6CqLIfWjkHMRo2SAjdlxdmcaOhmzdIKtu9pRNclQV+IN184jDvHwUDPCAJweGwYTQaklAR8Ibpb+vF7Q7iz7RRV5KJO45nrus7IgA+TxYhlrDVcNBKjvaEHvzdEYXkO2QXppdXjf1utJurmFFI3p5Cbb1/N8aPtPPfUAfbsbCQ4C8rqOKSEfXuaeeALj/HP/3IdtfUF57ynW/acprzQQygcY/uhJpbUFnOypYdLltZkfJ7neHL/t+TzZ40RfwgpEx+l6rGZwmQk9MQT9iAWSw/NWUwGrlk7jwOn2tl9vIWPhS7gyqIv/l2PcVaGWwjhBn4FLCBxjd8HnAL+DFQAZ4HbpJRDY+t/AXg/EAc+KaV88Vz76PZvpi+4g/nZn+NQ31cIxDpTfo/Ehyl33jy7s5oFsgo93PPV21BUhcUXz+cff/FhhntHmLO6FnUaD6Wjd4TmjlQjZbMYWbeoMuMDHfCGePKXr+D02Ohp7efYrgauee/FZBe4gcSLtn5JFb95cheBSV7+qbO9dPePUlowvdSjEIJLVtTy8PP78E3y1nYfa6V/2EdeVuaS9akYGPGze0rBjc1iZMPK2lmFSXYePptm9FcvqMD4Frw8IQTzqgpQJlW+AbT1DDM0GiR/rAzf5bby3vdeSE/3CHe9Zz1lZdkIAX99Yg9z5xaxdFkFT/xlD6+/fop584rZu7eZD33oUgYHffz2wW3Mm1+SkXc8DrPZQGGeC01VKCn0cLq5F7fTisdtIz/HyanGbkoLPRiNGqWFHo7tauSJX7zCEVkA1gAAw75JREFU8b3NWG0mju5sQCiCD3zlRuqWlBP0h/mPf/gDI4M+rHYzXWf7WL1pIff9y3XJMMs4dF3n1b/t4/k/vMHH/u02yusL8Y0E+OmXHqezpR+bw8xg7yh3ffpK1l+9ZAa5gMRyi9XIspWVLF5WzqkTnTzy0Jvs3dWY0eBMh7NNfXzjK0/whQduYM68mWdhvlCYXLedUCRGR98IRoOGqiqYjOmmRghBtSsbo6ISnlSkI6WkpW+YzYfP0DPiw2OzsLqujKWVRShjFZQtfcO8eOg0g74AC0sL2LiwBovJQCyu88aps1TlZfH6iWZa+4apzM/i6qX1OCwmhBCEozE2Hz7D4dZuKnI9ifOZdErjXrQQZPzYWE2JArFAKEIgHElz2oQQ1JXlYjSoeANhWnuGKM5NFOv0hRvIMpajjTUKkVIyEu3EorowqdN3t5+K2Xrc/wW8IKW8RQhhBKzAF4EtUspvCyE+D3we+JwQYh5wBzAfKAI2CyHqpJSZy6fGkGVehs1QlmhtpI9S5/kw2iSObn9oN39PLWYhBEIRDHUP4xv2UzG/FHVJxUT/ximQUnK6pY9gKDWBV5TrSpGqnIxQIIw7x0nVghKkBD2eHjIpzHFSnOviTNuE5+wLhjnd2jej4QYoy3ezuLaINw5NxAj7h33sPtrCNRfOn5Xh3X2slb7h1KKlhTVFlBdmTkRORjSmp3HYFUUwr+r8E6TjyM+yo2lKMmkL4A+G6R3yJg130lsWoCiJfcbjOk1NfXT3jHDwUAuhYJSS0iw6O4doax3goYe2I3XIyrIRi8VhBpEqh82Eo3JCsKx40od2zbLKlHUXzyshHtOpWVTKtz/6W+Ysq+CmD2wAAYax2KfFauJDX78ZV7Ydg0Hl4PbT/Odn/sgNH7iE3CJPcmxdl2x9Yi9P//Z1PvrNhNEGeO6hN/CNBvnXhz6MxWZm2zMH+P13nmXB6hrcOef+QAuR0FCft6CEL/3rTby25Ti//eVr9PXOvj9nV8cQ//71J/nKN2+msnqitDsYjvLm4Wbae4c53NDJ2gUVvLz7FLqUXL6qHovZQDQW54WdJ9m4sg7jFENYZHPgMpnpDU48g4O+AJ996DmWVxczpziX9oERDjZ3srSyCCkljT0DfOHhF1hTW0ZlbhaP7zzC0bZuPnPdxUTjcX78/JuJhsS1pVTkeXhi11Eauwf4wo0bkEgeem0/z+4/yU2rF9A74uPVY03YzBOzy/H/13XJsC89hOG0mTAaNILhKN0DXooysJ4sJgOaqhKOxhkcTSRfJTp7B/7MxfkfQVMmwpwnR7ZQYJlDhX32VOVzGm4hhBO4CHgvgJQyAkSEENcDl4yt9jvgVeBzwPXAI1LKMNAshGgAVgGpzRinwKzlYtZy0WWULPNScswrUScpZsVlAG+keYYRzg+xaIzHvvc0Wx/ZTtAb4t9f/jJ6XOel377Kvf96B6qabvSaOvrTpnTlBR5MhsyX0ZXtQNUUXnx4O5pBo3ZxGZ4phQomo0ZZoSfFcEsJZ1r7uPQcXq+qKly+dg47Dp9NeqhSwsu7TnPFOUItkAitbN51KuVjogjB5WvrZ5Vk9QXDdPWnvvzGMV2TyQyT80E0rqdJAMTiOoMj6Z1KJkMIQV6ekwsvqmfZsgp0XSIEnDnTTWVlLh/72CYMBpV4XD9nzHema56RHWNQUVQFVVXQDComa2rJPgKyC1wM93vxDvmJxeLouiQ8KWyhGVS2P3OAFx/Zwae+eycVcxOebTQSY8+WY8xdUUlHYyKEZLYaGRn00XW2f1aGe/Kxm0wGNl21iNr6Qv7zu89x/Ej7rLfvaB/kP771DF/71q1k5zoS4xk0Nq6s49IVtWiqgsmgUZLrps/np8DlQFUE73/XGmJxHUOGZ8ppNFNkc04x3EFGgiFuWr2AmoKEgZNyoiPhH7cdZElFEZ+69gIUIVhUUcAnf/0Ud6xfTL7bga5LllYX8Q9Xr088F047P3z+DYLRhM7Pk3uO88lr1nPZwpqExxsIcapzQi6jINuJqgjiuqSpc4D1C1Nn1G67BZfNTNfAKPtPt6cwR8YRCEaScfJYXCcuY0T1ILqMEokH0ETCtkl0fLE+JHWzvg8wO4+7CugDHhRCLAb2Af8A5EspuxIXVXYJIcZdlGJg56Tt28eWpUAI8UHggwBlZWUTy9GodX+AqXlTj3kxTmP97M5qFjizv5ljO07xlcf+iV989g9IXeLMdnBy9xlCvlCCFjgFXX3pHkpBjnPaeLKqKdz2ySvoaR1ASsgvy05r+SVIeN1T0dk3klKVmAlCCFbOLyM/25FiQI82dNLWPURlhvjcZLT3DHPkTGpIKjfLzuoF5YTjA8R0LxKJKoxYtCKm9sQb8QZTwjSQKOT53A+fRn2LPLm4LpOJrnGMc+CnwmDQkucnBNxw43IefvhNdu5oIBqLc+ONK6ipyaew0M0Pf/giZpOBomIPN920kv37m9m37yxdncP85S97uOiiegrGPOvUfUv0uGSgbxQhBFm5DtTz1Jge7vPyq2/8jf7OYXKK3AkK3ZTz6e8e4akHX0fVFKyOSQ5LLM7osJ99r56g8VjH+FFRNa8Eg2n613fEH0IicdvScy9CCCqqcvnyv97M97/9DHt2NmYYITNOn+zi5z/ezGe++C5MYxxvk0FLaTGmqIIer48chw1VUVFVhbiUxHUdVVGI6YnEsUFVMSoqte4cjgx0AwJFQGmOm3V15Xzm989y8bwqrl5WT21hDopQiMZiHGvrwWWz8INntgMQjETxhSL0jPjIdztAwKKKwiRTyGO3EI0nePADvgD+cIS6wpxk8nJeST6nJxnu0jw3DquZYV+QA6c7uHPTcrRJjpzNYqKqOJuugVGe33mC6y6YT67bnnwWdSnZdbyFSDRBk3TazPSFGtgz8DCdgaM81/mvSXaclDoWzU2++fxs22wMtwYsAz4hpdwlhPgvEmGR6ZDpjU2LP0gpfwH8AmDFihVy8qrBWDcWrTBlKE3Y0UWUYKwHk5qN4PwoV1PR3z5A7dJKiqoL0MY8MDGJQzsVui4ZyTBtypSUHEc8rtPe0ENpbQG7Xz7CQPcQi9bVp12hTGMMjQYSx3GOc/Q4raxfUsXjmw8ml/mCEV7b30hl8VSS/wSklGw70IR3iuFdv7gSs3WQYwM/IqYHMKouTGo2c7M+lTbGqD+UplQoIaWQ5u+FqTFZIQTvf//FOMekaIUQVFbm8k//dDXDw35MJgMeT6JY6O57LmBw0EcspuPx2BACKitzyctzcdVViR6mrrEPtZSSiB7FoBgSnr+Ev/x+O4//djtCEdzz0Y1cfevKWT97UkpefnQX3S39fOU3H8TpsdHZ0sfhN86krGe1m/iXX7yfpx98jf/+l8f4zA/vwe6yoGoqTo+dC65ZwrX3XpiyzdQPiJQyUZodi+MLheka8DK3LI98j4OW3iFynDaisTgj/hCFWQ6yc+x85ovv4psP/JXDB1LzHDNh26snmb+olOtvXoEQgqcOn6DX68MfiXLrsgXE4jovHD9DbX42BlXhLweOMRwIUpuXw9zCXJ46fJJYPM7GOdXU5eXwsQVrua5iHgC17mxMmsoXbtrA4ZYunt57gk//9hnuWL+Yuy9ahi4THmyuw0px1oTD86lrL6Asx53826RlNm36WCh08qxu6kwgx2WjrjSX3SdaOdrURffgKCW5E2OriuDCRVW8eaSZlu4hHvj1i3zgXWsoK/AQjcXZcfQsf3hpHwA2s4HKwizyzR42FX6Wrd3/xZKsm7CoifEEAovmxiDOT497Nq5DO9Aupdw19vfjJAx5jxCiEGDsv72T1i+dtH0JkOrWzQBdRjk++AM6/S/Q4XueSHwYKSWDoQPs7Powu7s/zpnhX6a1/jlflM0t4cTO05zZ30Q0HGW4b5TND71OXlkOFnv6RdSlJBxND9OPd6zOhNF+L9ue2kfT0XaO7jjDG88cYHQwVchdCJExcROOxKaNt0+GIgSbVtelJQMzFdRMRigSY+veVONh1FQ2ra4nGO+k0HYZ+bZLqPd8HFWxZbzeoUgshXP9vwkhBNljMePJy6xWI0VFHrKz7WPFI4l4uOLU0V0RVKPAHw/iN/vIKrSQX+zEmqcyKIcIxyP4YkH+1rGVRl8roXiYSCTGq88fZnQ4wMign63PHUopY57YN9hdVrrO9hMORggHI8n1goEwFpsJo9lAKBhh+zMHCU75YBqMGp5cB/d89lpUTeW3336KaDiGZlC54JolvPq3vXSe7SMaiRHwhWhv7EGfUmwUjcV5af9pBrwBdF3iC4XZdrSJ1480MTDq5+X9p9l84DRDvgBbDjYA4Mmy8enPXk1x6blzGuOIx3X+9Ps3aDnbnwg1hEJcWl/NmspSGvsGqMzx4LGa0XXJUCBIz6iP969fwcV1lbzZ2MqgL0BclxxuT3D/K5weLiyq4MKiCgqsidCPQVVYXlXMV27dyCevXs/jO47gD0cwqArluR5ynDZuX7+Yd1+whHdfsITb1y2iwH3usJHbllC07Br2JmmcbQMjKZ6lpipcurwWQcI52X+qPU0//pKl1ZTkuQHYc6KVj3//L9z51Ye462t/4NsPbUmGCpfPKaU4N8EAMil25ruvJttYgdtYhNtYhMtYiFE5t0bRVJzTcEspu4E2IcS4L78ROA48Bdw7tuxe4Mmx/38KuEMIYRJCVAK1wHmQWyWBaDsdvufp9L3AsYHvIYnR5X+ZUsf1rMz/AQPBfQRjHeceagaUziniyvddyk//8bec2d/EDz74M46+cZK7H7g1Ywfz8WObihkvuBBEwzHeePYAm969juxCN6EMdKxMY8iMe8uM+op8aqfwn5va00vYk2NLyemWXhrbUkv2q0pymFOZj0XNR5dRLFoBTSN/IBwfINNEKmE40o9SU5W/+79zdSQK+MK88uxBdr56An2yPoSUnPQ283LPDpr87YTiYVr8XTT42niifTNtgW7+cPZZjo028WL3mwTiQdqDPQxFRonqicpK8yRapH0GIbDLb1/NiX3NfPGOn/DAvT+n5VQXQgguvm4Zo0N+vnL3T/n6+3/J6KCPeSuqkoqUBpNGVn7i5bY5LXzkG7fQ1TLAtmcOAHDlnWtZuLaWf/vQb/jsLf/F52/7EY/++GXiU2YhqqqwZk4Zh5s7icTi1BTm4LSaCUaiOCxmdCnRJdgtpiQTSAhBcWkWH/zYRkwzhF6mYnDAxx9/t514TEcVChajAU1R0GWClRGXkqiuJxJ0sRgjwTD+cASH2cS8wjxuXbaAK+ald3kHaOge4NE3D3PobBfH2no42NxJgceBUVNRhOCO9YvZcqSRP247wL7Gdl492sgftx8kEpuR/wBAlt3Cujnl/HLzbvY1dfDK0UZeO96UMrEVQnDRkmouXlrNv9y7iUuW1qSNk+2y8bEbL0gmMiOxRBJy1B9K5puKcpx86Pp1aKqSNPyl1qUYFEsyz/FWMds79Qng4TFGSRNwHwmj/6gQ4v1AK3ArgJTymBDiURLGPQZ87FyMksmI6l5shnKW5H4NhMKhvq8Sjg8Rk35sWgkWrRinsY5AtAOboezcA04DIQQX37aOVVcvY6R/FM2g4c5zTVv1JkTm0uhodPrqNFeOg+KqPCRQUlPA6QNncUxt+SUl0QyevGHsIZ0NzEaNjavrODapejESi7Nl92mW1BdnHOeVPafTZhAbV9WNVYGVYzWUAKAJG1ZDUVrvTCDR9R2Rwrl3Oyx89UNXYbeen0zluTDu3UyHU0fb+f6X/0plfQHL19WmfHxPe1u4MGc5RZZcdCSdwV50qTMYGSWmx6lzlLM+Zwl/63iFXJOHfHM2C911WFQTUkre85FLefy32zCaDLznI5dm5H8LIViwpoZvP/oJhvq8mCyGJGOkvL6Qb/7pYwx0j2C2msgtcidbcgHULizlgQc/iMWWoKvlFLr5ym8+wHhlkMli5L7Pv4vr33cx3mE/ZouJrHxnWoxbSghFY6yqK6MwK5F78TgSibTTHf1cML+SnSdbGPQGuGhhVcqxr1pbw4ZNC3jhmYOzvidvbjvN0SNt1OVnYzMayXPYcFrM7GvtRADbzpzlyvm1bKir4ukjJ6jLy+GC6nJeOtHA5pONXFJXidOSPrs1GzSOtHbz0qEzCAEVeR6+dPOlicIXIVhRU8IDt27k8R1HePlwA3aTkXVzylEVgZCCyxfVUjQpjJLjtHHV0vqk4f/EVev41ZY9/PcLOyjLdfPpay/kdGcfyqQcTq7bxrc+fO20ZfxCCDYsr0HTFH711E6auwaIxOIIBFazkaV1xXz4hnXUluQkGUMv/HUfF142n/7eER766Vaq6gq44/0XJUO154NZGW4p5UEgU/fMjdOs/03gm+d9NAAI4jJITAYQUiESH2AkfIxwrB+dxNRfCAXJrL8FGTHUPczuFw6y7LKF5JflIs4hpKMqAoc1/SEb9YczduaABE1tw62rQSaq4y6+cWXGpg2ZJFAdYy/xbCCE4KKl1fzu6T0pcfg3DzVz/w1r8DhTE60jvhDbD6YydJw2MxcvT3QS0WWMHv+r+GMJQXt/tIUSx7sQUx4Xq9mAqiroUzyd6pLsWfPIz4U2fy8xqeOxT18OLKXk8N7mBM0vQ36i0JzDoZFT+ONBck0emvztXJS7nFPeswAJEfxJ6ysIzvo7qLQVY1ZNLF1TzeKVVWP0w+mfEyEE7hxHGtNDCIHDbcPhnvhoq5OcAFVTsdpTQz7mKcVPQk0Y9JxC97TXQVMVllan8gCyHInrtqK2BCkliyuLqMj3pDkhqqpw251r2PnGGYaHpte0n4xwKMpfH93Nl/71ZgwGlaxJidDVlRPR0uXlxSwvL056ndctmjPjs12S7eLrd1yeDAUpipLSLUoRghXVJSyvKiEu9WT/2fHfP3T5mpTxynLcfOKqdcm/PTYLn7nuokSyVCS0WNbVl6dsM52jNhmqonDxkmpWzimlpWeIgZEABk2lMNtJUY4zxejHYnGOH2zl4isW8Oxje7nm1pXs2HqS7s4OcosigI4iHGjquYuc4B1UOTkOk+rBYaxmZ9eHEQhcprl0+V8GBN3+V9FllOHQEcodt76t/agGjdN7G3j+V5upWFDGJbetY86qGswzlGkXZKBe9Q56M6yZwOign99/60nCoQjhQAShCD7+nTtxZqUS7TONkedxzLr6ERJ88uVzS3hlz0TcuntglP0n21NohVJKjjR00tU3krL90jklFI95tSPhkwyGD1JgvRQhBKqwIDJE1dwOCyajlpKgDIWjjPhC5GU5kFJyyttOltHBQHgUk2rAabByfKQVh8HCInclTb5uArEQgXiY5Vl1nBptwxcL4jE6KLfmcdLbzhxnadq+JyMWjXNk39mMvwkhWOaZR4OvFX8sSKm1gCsK1uON+rmu+BKyjG5cRjtGxcgFOUsRCC4vWEurv4u4nAgnqNrfr4bg/xWEENQWZxZwSoRMstlw2Tz++tieWY+5f28zjWe6qZ97bnkEkJz1PkOJ7VIMMxSbCJHQNFHUmVUyhUgIN50vZjP++Yxls5iYV5Gu7T8Zyliu5eThdoKBMIuWV3BkbyNdAz/GmjUXIYwYtQo0deZxxvGOM9ygUO/5CMX2q5FSx26sQKAgkTQNPsfBjicptF+FQRTSPjCCy5qI3TksJrzB2es9OLPtfPxH72e4d5RDrx7j+d9s4bHvP83SDQtYd/1KCqvy07zwqgwsjbaeYaIxPWOloNNj4wNfvwUkRCIxnv7VK2kskVhcp61nOG3biqLZJ4sg4QlesXYOr+1rSCYMdV2yedcpLllek+SlS5kQo5qcVFQUweVr6pMUPiEUHMZaPOYlGQ32OFx2C1lOawolMByN09YzTE1pwkAcH2nBrlloC/TiMtgJxcMs8lTR4u9FEyqHhpqosOdTbS9ClzrPd+7hxtL1ZBsdmFQDJsVAe6CPUutUTYsJDPR5aW3MHM8H0BSVOc6JwpkKW1HK705DwhMuH1ueY/KQY5q5+OmtIBNbafLzNfX3801YTacnkrGqN+ZFU4wYxATnXAjYdNViXnj2EMHA7ErjQ8EoW148Sv3conNq5Eh0eoI7KbSux8DsqwT/txGOxDjc2ElBtpMcly0pz/x2WGyqprDu0nlseeYg77pjdYL3rylk5xTgtF6PIswgZv8heccZ7sTX0IjTmJ64GBhYRsvZPLo0DSsjdAyO0DEwQkm2i4o8D8faesk1zk6MfJzD6cl3seyyhUhd54XfvMJrj73Jruf2s/DCudzx+RuTKoFCCOrK8zAbtRSecaIcO5Cs6psMXdfxDSdoffFYQn8iIWA1MWUeHAmkGW6jplJfPnPj0UznM+41TxaMOnCqg94hX5IrPuILsvd4ak+/ohwXy+eVJvenCjPt3icZCO5BEUbMWh617g9kDJXUlOak7E/KROuyDSsSCZ1sk5MGbyd2zUJYjxLSo1TaCsbKf0cwKBq1jmLyzR4i8Sgek4MqewHq2ENs1UxE9am8bkksFsfvDdHfM8qOrSfwjiZCRAFfmAO7GtNKyZPnWpZNfpF72ms7NOCj+XR3xt8A8orclJSfW3Y0FIhw8mg7SEndghIsFiOnj3Ww+ekDDA/6qazNZ+O7lpJXmKi6CwWjbHvpCAfGONXzlpRx8ZWLcLhmZhyMX4uzZ3o4tKeZ1sZefN4QRqNGQbGH+cvKmbe4DKs9NfTmj43SEjiFJoxU2ObgMiQ4/xWVOcyZW8SBaWYwmbDrzTO8574LcbosNIw8gkn1kGdZydHBn6LLiQ+ARGckfGaGkaY/R11KvNEwvUE/vUEfQ6EggVgUicSkajiNJnLMNvKtdjwmC0blrdOFfcEwX/rl80SiMfI9DqqKs5lTnk99WS5l+R48Dkuy6O58wpmrL6pj1QW1yUT7HfdfzEjwNP3eH6MIM2bDPByWa2Y13jvCcEf0EYbDJ3AYq1GYvveiEFCa7UEiOdbWg6YqeENh5pXm85edR9iwoJrRodlxiOOxOE2HW3jt0R0c2X6CktpCbv/cDcxbW0c0HONHH/sVR7edYPnlExKTZQUeSgs8KcJOI94gh053sGlNugSq3xvirz/bTCQcBSGoX1KBJ2+iPHa8AcHUdmEFOU7KC8/f43PazFy0rJo/TGomPOwNsOdYK++6aD6QEIXqmaJueOHSKtyTZGRthlKW532PccaIEFrG5KQQgtULytm650wKt2TnkbOM+kO47BYKzVk0eDsosGQxHPFR6yjiha69+GMhrixcwY7ICZRxr14IzIqBcQbLYHiUk6NtxPQ41fYi8sxuAr4Qv//JFk4f66C3a5jR4UBCh3rsADpaB3jg43+Y9hrd9w+buPW+C6f9/ei+s3zrs3+etlnBzfeu5/5/vDLzj5PQ1z3C1z/1MJFQjK//5O5EI+YvPs7IWPx420tH2fbyUb78gzvJynHwk28+xSvPHUKPJ3b86vOH2bH1JJ//99twTiNXK6WktamP3/9kC/veOJORsaRqCpV1BbznI5ey8oK6JPdbVTRAEJNRGv1HqbLNx2PMRTOorLmg9rwMd2/PKCeOdbB6XQ1OYxWaYiUmA3ijZyl3XM34/ZRSxx+bHTN4vEN8l3+U1zqbebWjiRNDvQyEAoTjMXQ5kRIfmy9gUBQcRhNldjcr80vYWFLDouwCzOr0lN3poOs63kAYbyBMQ0c/L+0+haoI7FYThVlOqktymFueT11pLiV5btx2czImnpElJiWD/T4O7m4iHEzk6lRNYcWFl2IwbkcIM1bT2rTtpsM7wnCrwkiXfwtnRx8ny7yYXMtqzGouU8XGS7JdZNmtyZeqa2iUkuwyPDYLZoOBqvxsDg7NroS3p6WPX372IZZtWsxnH/wYBZV5KGPJBLNNMm9dPcEpSUOLycDFy6pTDLcuJc9uP8bFy2vS+NgOt5X7v3pL4o+x05h8PpFYnGe3HU9rqLtucSX289TUHh9746o6Ht9yiNAYh3s8NHL1BfNQFcH2A40pUpSmMUbK5ONShBFTWoeOzPtbvbCcLJeVgUkl6W3dw2zZfZobNyyiyl5IsTUHTajo6JgUI3WOEgyKhlU1cUXhckxqYlZjECo3lq5PNnC1aRYuL1gOgMOQMF7BYJTXXzrKyOBEAm2qKNV0qn/jxzwTzFYjuYXuBBc7HCMWiSWbNUDG3GdGSBKdbGKxRPx912unCIeiZOc5GBkMEIvFaT7dw2MPbqeyNp+tzx3GbDFithgZGfQTj+sc2NnIlmcOcsNda9OOW0rJ0f0t/MeX/kJ3R2LGo6gKdocZk9lANBLDNxoiFovTcLyTf//co7z/H6/k6ltWjFUUCootlZhUKxbVisKEB7loSTkms4FwaPo6gMmIx3X27mpk9boa8iwJvY1ArJs8y0oqHTdOqiiMMxA6eO5rJyVtvhF+d3I/T589QV/QNyM1duzOENHjDIQCDIQCHOjv5Hcn97Eou5D75i7n0uIaTOrsvHCbxcj7r13NseZumrsG6Rn04g2EicV1RnwhRnwhTrb28uybx9FUBafNTHGOi9rSXOaW51FTkktRrhOn1ZyUjtB1yR9+9gpOl5XGU93Uzy+mpbmL+pVdFDqvJ66PMhp8Fo/t7nMeH7xjDLeFWvf76A28SfPon+kOvI7LWEe1664UoanirFQxl7qinMQD3NrD+jnlmM6jk0leaQ5f/etnsUyTjLz8vZegTdEgEUJwxbq5PPHK4aRwDMC+E+28tr+BTVM61wghpq1Zl1KybX8j+06khi3sFiNXr5836/OYiurSHOZW5nPg5MQH7FhjF/3DPqwmY1qYpL48j7qyvKnDzBr5WU42rqrn0ZcPJJfpUvLbp3ezqLaI6pIcbFoqG8dtnIhvWif9JoTArk14/ibVQN5Yhdk4nC4LX/zuHcmGzwAHdjbyyK9eA6C4PJuPfuHaaUMlhSUz5w6WranmJ3/+KKFQlHAwQsAX5rc/3sy+SZWO4WCEWCSGxW4mGokRjcSw2EzocR1FVdL4uc/8eRd5hW6+/cv7KCjx8OYrJ/jZvz9LJBxj+0tH2fXaSZasruID/3QVriwbL/11H7/78WZ0XbLjlRNce/sqDJOeRSklna0D/OArT9DdMYQQgoUrKrjxPeuonlOIxWokEo7R2tTH3/7wJnu2nyYYiPCb/3yRwpIslq2tpit4lu5QC4pQWZm1EYs2cU8Ki9zk5jlpb01VwpwJx460Ew5HMY/xmq1aPnM896WsI1Cocd2BQZk+vh2Jx3my+Tj/eWg7Hf7Zi2BlQjgeZ09vOwf7O9lYUsPnl11CuWP6MNk4zEYDt29cmhgjGmfEF6RrYJTGjgHOtPXR2NlPZ/8ow94gkViMwdEAg6MBjjQlBNcMmorbbqEkz8WS2mLuv3YNQkIsqnPzvet57MHtvPuDF/PIr7YyOtxAQW4MiM+YT5qKd4ThjuqjHB34HnZDJYtzv4xFzafN9xSDocPkWWeePgghWFg+u0zsZGhGDS1DxeL4mDZn5ulpSZ6b6y9ZyG+f2pX0AqKxOD9+ZBtFOS7mV5+bziOl5HhTDz96ZFtayfimNfVUl+awrfMsK/NLsGjp9ME27wijkRDzs9NV+MarHw+eak96h8PeIMcau3HazCmaJmJsf29FgjU5hkh0/3ltf0NKCKZnwMvXfv4CX/rAFdSV5Z7XVHW8bVfPoJfOvhGWzS1NctENRo0Fy1KpWwO9o0kFIqvNxKIVlRiMGrFonDMnu6ifX5TS4WYmqJqK3WnBPlZKL6XEM5kFJCXPP/gqziw7zhwHziw7x3acZs7KavraB8kqcOHKdoBh4r75fWHe/cFLqF+YaHZ92buW8urzhzm8pxnvaBC708z9/3QV5TWJvMaVN6/gucf30NM5TGfbQELDe9IxxOM6D/9sK51tCX36dRvn8qmv3ojdkeqEZOU6qF9QzPcf+CvbXz5GwBfm4Z+9wrwlpdg0BxW2ubQFzqQlFS1WI2Xl2edluLs6h+jv81FcMh7iEyhjCoyTx/eYMjslUkqCsSg/OLSd353cT0R/e3TfyYjqOi+0nub0cD/fWXcVy3PPrRU//rvZqGHOcpCf5WBxTSJ5HYvr+IJhegZ9tPYM0dw5QEvPEJ39I/SP+Bn1h+kf9tE37KOjb4S7r1iBzWzEajMRjcSIxeJsfe4wbWcHuejKGwiEd6MoFpzWd836nN4RhlsIA/Oy/gGD4kxesHzrxZyvvPr5dH55qxAC7rhiGXuOtXK0cULStGfQyxd+/DQfueUCLlmR0AbONL0NhqO8vr+R/35se1qsuazAw73vWoWqCPb1dGDTjDiMJmrc2fQEfHT6R6lweugN+jg20EtUj1PrzkERgtND/VgNBqpd2axfXMmvnbZk/0ZdSvYeb8VqNqZoZ7udVi5YUvW2suVCCIrzXHzopnV8+7ebU+RYT7f28env/ZXbLl/C5WvmkJdlR1XSCxrGDXUwHKVn0MvRxi52HDrLwdMdzK8uYOmcknNqtmRCKBTlb3/ezWceuG7Whjsz0p+rBevr2bv5CAOdQ0iZoCRGwlEaDrVw9X0b6O4cTq7rcFqoX1CSPG+DUWXu4lIO70lw6StrCyipyEn+bnOYyS/20NM5jN8XTjPcZ8/0sOPVkwC4PFbe+4lNaUYbEvfGYjNxx/0Xs++NMwQDEU4f6+DUkXbyFpnpDXXgMeZhUtP1pCsqc3lzW3rTkOkQDERoa+kj4HiBkcj4diLt2hkUO/OzPoxBpHrd4XiMb+1/lYdPH0wLHU6GIgRGRcWoqmhjBTO6TIRJIvE4MTm9FEbT6CCf3PY0P7noOpbkzIa+mIrk/dNUPA4rbruFutJcguEo/SM+mjoHONbczaEznRxp6kp51xRFcMf9F2F3WnjX7at56cn9rLtkDln5USymGxjxP0E01onJkFkieireEYZbQaNx5A84jNU4jdWY1GwsWur0XUpJ76CXUX+YYDhKIBTBH4wk/+sLhPEFw7R0DqZpfHT0jvAfD23FYTVhs5qwW4zYLEas5vF/BswmA9kuKy77zFl8IQQuu5l/vvdSPv/Dp1M82N5BH9/89Us89vIB1i6upK4sNykgNewNcaatjx2HmjnV0pvWfMBlN/NPd2+gIDtx47zRREPVM8P93Fa7iMFwgG6/jy2tjVxSUsXJwT5cRhMH+7q4uqKe3qCfPc3tvLt+EWVZblYtKOP5N04kxz90ujMt9rtyXmlGNkwiMRSbxAhQUMX0/HYhBJevnUN77zC/f2ZPyrkNjPj52WNv8MgL+6kty6WqJIdctw2jUSMW1/EHwgyMBOgeGKWjd4S+IR+hcDT5ur/dj3E4HOX5Jw8QDES47KpFOJwWtrxwhE3XLCISjrHtlROsXFvNgT3NbLhiIbqu8/Kzh7hk0wIsyerP1POOhKIcev0EC9bW0d7QjdFiJLvQjcGk0dXUm1bR6PJYcbgmjKMQgtz8ibBfaVUumjbxYVFVBfvYcxOPxlNizVJKdr12iqA/QcFcsLyCorLpVSCFEJRW5lJUlk3jyS5i0TiH9jRz4fxC+iOduA25Ga9xUdJznh10XXK2uZ+KpeWowohOnDbv89gNZbhNcwAYCh8jFB9IxtOT20qdB0/u40+nD6UZbQHkWGwsyyliRV4Jte4c8i12bAYjBkVFCIjpOsFYlMFwgLOjQxzs72JPbztnR4fSDHmnf5Qv7HiRBzfeQoHVMWvjPX6NorE4w74gLd1DnGjp4XhzD40d/fQN+wiEoinHr6kKLps5yWBzuq20NfcRCce456OXEvD7CEUfJi470NQC/OHtmAxzZnU87wjDHYkP4TBW4Y+2oQoz/mg7hbYNKevEdck3f/MyB062o+sJelBCJObc4w95g/x16+G05UIkZCSFEKiqwp1XLudDN6/LMEL6dvXleTzwwSv5+i9foHOS3GssrnO8OXFDxRjpXowd/3RGyO2w8Nl7N7JqQTlCJDp82A1GNpQkvOH+kJ8DvZ1kma0MhYNIJKsLSriwuIKHTx7k1FA/Hb4Ronqc0Ug4oam9Zg4v75xoSdY0pXPPuJZ3pnL4iD7EycEfossEDc+i5VPn+TBihuYDBk3lvdetRlEUHnp2D+FJlElJ4h7sPtbK7mOt57y+f08MDfqpqStgaNDPY394k7s/cDEHdjex8aqFRMIxDu5t5qKN89i/u5mlK6sI+MM0nOzm8muXZB5QQOWCUpZumI+qKZSONTsI+cOc2tvE8ssWphkDu9OSUikJJMvdAbIyFHaNl0FLZIqolR7XOTZJyW/OwtKxzvDTvwgGo0pugYvGk4kZYmtTLxalDpNiYTjaT1ym0i2FEGTnOlEUcV56Gu2tAxRY34UQgoHQUYbDp1ie+8UkI0knxp7eB/DHunEaKxLnJyUH+7v42dFdaUa20Org3jnLuLq8nnyrHYOiokuJOuZpx6WORKIl+c/ZLMst4oaqefhjEfb1dvKr43vY09ueYlBPDvfx4yM7+NqqTWjTMEAg4ckHQlG6B0c509bHseZuTrT00t47zKg/lOKgCJEgL+R5HNQU5zC/qoC55flUFHiwW4xIKXnxb/vZ9dophIAvfvd2/viL7Wy8OYzT04jHfjfe4EjasUyHd4ThlkgchioC0Q5iuh/jlITU+FqhcCxlKv629yslCfaVJBbX05qZzgQhBEvqi/nOp67n+w9t5eCpjjRvIdG8dvoHXwioLcvl03dtYGl9atxNVZREtRWJhE2n30uNOzvl9/HjaBgeINdqo9U7nPy6L6otorzQQ+NYP8ipL3ZpvnvavpDeSAMe82KK7QlOqUCkcbgzwaip3HfdaqqKs/nZ42+kNSA+XwiR6EP5dmoWc/Oc1NQXEgpF2Pri0RR52LFbj9GosXh5Oft3N+H3hVixtnoGZopgxaaFY8c3sY7ZZuKSW9dk3MJkNpA23KS/zRZj2n2YzhEMh2P0dE5c1x1bT3C2oWeaY51A0yRuunckyHC4H6chiyrbfCyqLW19p9OMqinokdm/b309o8TjOpqmEtW9YzO2yYrNOjE9SEyfYARF9Tj/fWQnI5FUBtea/FK+seYKSuwO/tjyEsF4BLfRzlDEy70VV9Ho62DHwDEEsDF/BdX2Il7s3k1PaJBGXwcb8pZzWclS1uaX8Yvju/n50V0p7dGebD7O7TWLWJidnpMKR2P85dXDHDjdQVNnP31DfkKRaErQx6Aq5LhslOV7mFuRz/zKAmpKcsj3OMbCpKnPRzQS48ShNj7+L9fy0E+3JhQrFRURvZ4cZzGg4bLdOOtr/Y4w3AbFzpnh3xCK9+E2zafe86GM6/1vFh3PpgpNCEFNSQ7f/dT1PLv9GE9sOUxrz9A5vRRFERTmOLn2wvncsGERHkd6eObm6vnYDEbWF5VjUjVK7C4CsQgfW7wGp9FEdTwbh9HEDdXzcBnNNI0MsiingGxzIqlqsxi5ZEVt0nBPxSXLa3BMQzk0a/n0Bt7AF2lGFSYUYUjTR5/u2miq4NKVtSyqLeKp147wwpsn6egdSaEgzjwGOKxm6spzuXRlHRcvq35bMXifL0QoFME3GsJgTIj667okFo0zOhxINM8VsGJNNb/571eQEi69YqrXfO6Kxhl1blRlxhi9kqHb0nQIh6IE/BOVqscPtnL84PnNYuIxnUrbXFpCJ3m19wlKrDXMd63COKnjlMlsRFNVouehCTQ85CceSxhuj2kOJ4ceZHfvV/CY5iZ4zOEjSOI4jInkspSSY4O9vNHdkjLOwqx8fnDBtRRYHYT1CMNRP5fkLeXEaAs5JhcDkVGa/J3kmzxckLsIj9FBRI9xZLiJj9TcwJ9bt1BhSxAW7EYTn1i0DinhJ0d3JJ0rXzTC441HWZBdkPZU+4MRfvvcbobG6itURUnUJGQ7qCnJZU55HnWleZTmuXHZzdMKUU2GoggMBpXhAR96XGeo30dfzwC69hzwEYRQEMyeAvyOMNyaYmNRzr8QlwGMihtFpCvLKYrCB29eN6NIfyweZ8uRBnafacNuNnHDqvlU5mfx+vEmhgMhrluRyGgPeAM89Np+PrhpNSaDyrYTzWw/cZaj/X2c7OhjTnEuA94Af919jEK3g72N7WQ7rNxxwWJyHLY04223mrht01KuXDeXg6c62H20hTNtfQyMBAiFo4kkkclAltNKTWkOK+aVsbi+OMVg+2NBtvbuJxyPsCp7HqWOBGMkx5LwhhzGzDe12JaIiS/KSfccbrp0MTWlOWnhJCESfSVnMsSSGO2+pxEITGoOla67pqUrSSmJ6V50YhgUJ4rQyPXYed/1a7jlsiUca+xm/8l2zrT20TM4iC8YQuqJRgVGo4bdaiLXbaeyKIs5lfnUV+SRn+WY8YWI6QEESkp7u6kQQuD2WHnsDzsYGvCx8apFWG1GauoL+PWPt2CxGfFkJa6vJ8uOw2lJHI9z6phvz2V4Ox+eqdDjeopjYHOYz9mKbSrsTgvdoVZiMsrFeTcQjPvwx7wYjRPnbTSo5/VBgUS7rkgkhslswKi4WZ3/Tdp9mxmJNACCPMtqSuwbUyi+L7edIRibiOGbVY3PLrskJf5sVo2YFSMOzUJEjyGlztWFa9k/dIo/tW7m4twlLHLXUGTJ4U+tm/EYHRRZJpK9BkXlA/NXsb3rLAf6JwqAXu9sxhsJ4zJN/wypisLaBeVsXFHHwqpC8rMcmAxamkd9LiiqwpU3LefPv9lGa2Mf//n1J7n0mgW4s/cTih5FEQ5UxYaqzI6B9Y4w3FHdx7HBHzDu2ZQ7biLLvChlHUUIls+dXmxISslTe44jzQr/9bEbaekb4m+7j3HLpYspKnLz3SdfY+3iCixGA8/sO8HSeSVcs34erx1vYliP8B8fvo5+r59vPfEK373nGsKxOH/adpBb1y3klrUL+evuYzz4yl7++fqLM+4/kbS0cNGyai5aVk0srhOOxJJxMG2s0/U4IX/qzTEoGuXWAh5qeYFsk4tS6+wa7kb0KG/2H+HivIRA0uTjyXHbuHTl+fWyA7BpZdS6P4g30oCm2HEYqzJWTkLiuvcH36DN+xdMWh6VzruTkrDj12Td4krWLkr0gWwdeYFgZJQi2/UIEt6oQVMxaEoyzDMbtHv/ilnLo8C2adp1rDYjn/rCtQDE4xKrLeEQ3Hr3OgL+MCaTgXhcT8Ry4xJFVVh/yeySQ/+voGpqsvpRCPjwZ69m+frMutbTQdNURrRORgODnPIeYIFrDWYllf46kwridIiEY0mJYiEEFi2XGtcdKeukhA/0OLt7UusKluYWsSq/JNU5IiEoJcaecAkcGW5ElxKP0UF/OBEbDsRD5Jrc5JjchPUoJmWC2eUwGLmjdjEH+zuT86euwChnvUMsNhWmnbvZaECIIHFdZ/vhZnYea8FuMZKf5aSyKIv6sjzmludTXuDBbbdM+15PPu/aeUX88zduxjcaxGQxYLUZ8AZbCYR3I1AxGqqxmabX5JmMd4Thjule6tzvw6AkvEdVmZ3eyGTEdcnzB06xoroEXyiMy2pmyB+koXuAReUFWIwaJ9p7WVxRyCtHGrllbSJO+fz+k9QUZBOMRLEYDUTjOsfaeqgpzMFlNXH7usV47FaCkSgPvrJ3LDly7sq8hDFKNXbj4ZdMN9eoGFjgqiLH5ErbRkfiiwWI6jHsmgWTkjBAURnjtLeNV3r3MddZgaqoZBkdqEJFSklIj+CPhbBpZsxKIo6a0HzwYzdY8UYD6Oi4DLakNghARB/m1OBPsGj5RPQRrFoJFc7b045ZSp2IPkx3YAt51kvIHqt41WWMuB5ACAMx3YtBcaEIIzpecu1z0RQ7JtWcTMTqMkRE9ya8dUxIosRlGKSOLqMYVQ9i7Jxi+ig6ceIymKKDMd29mCqPCqCqAodz4hkb6Pfy2B924HRaqKrL/7t6yH9vmMwG7E4Lg31e5Jh4mSfbft7H3DYyQKV9HlbVgVFJZwy9FS5PLBpPSOuSeG590TYGw0eIT7pPqjBSbNuIppjxRsO0+VITchcWVmJUJp5Fg2LgqsI1ZBudeIxOpJS4DDZsmpm2QC+rsuZSbS+mJ5TgtJda8+gLD/NUx3beU355chwhBKvzS3Eazcl4ejgep2l0kMU5qYbbZTPzo0/fxKnWXo40dnH8bDetPcN4AyGGfb2cau3lhZ0nE6wRu5ny/CzmVxawoLqQ2uIccj12zMZ0LRMhBCazAdOYtPOBnY3kFc/BmdOGzbQOXc7cEHsy3hGGWwgDkfgIBsXBW52WxnUdbyjM2b4h/OHEg3LJ/CpyHFYMqsqGBTVsPtyAx25l2B9kUXkhupSMBsPIgYmHZ3VtGYWexAfEajJiNiamRaqipDQMmC4GPrVN5OS/W8/2Y7YYyct3nteL9rf21zg4fAYhBJF4lI/W3ESpNZ83+g6ztXc/jb4Ofnf2OcyqkfdWXIPTYOPoSBOPtb2CIhRiMsadZZcz11lBKB7mB6cfYa6zgmMjzUT0KB+svp4q+4SOszfSQLZlGUW2q5BEOTH4QyRRBKlGMC7DdPqeZjR8grjuJxzvo9x5J1F9mFOD/4lJzSGq+yiyX022eTUjkRO0eh8j27yCcue7kVISiLXSOPwrVGEmJv3UeT5BJD7EmaGfYNGKiOjDuIzzqHTdy2BoH2dHH8ai5eONNFLqmH0yZyZ4smzc/YGLMY/pi7+TYTJplFTkJNUQG07MuitgCjzGPDoCjRgUE/WOpahTuNyxaDytNdq5ENdlMowTjPeys+fz2AxFjEaacRmr8cc6sWlFFNsSMv4j4TD+2IRRV4Sg3p0q4KUKJRmvHpc9GBj1Y1IsLPfU4w2E0YRKVMYIxsOE9SiDES95GdQdcyxWcszWlERoZ4bqTEVRqCjMorzAw+Wr6omOdbdp6hrkaFMXR5u6aOoYYHA0wMBI4t/+0+2Jwj2zgcJsF/XluSysKmJ+ZT5O1UA8A6ni0N4G5mmvojmsmA3z8AZfxGO/L229THhHGG6AhpHfoSlWQFDuuJEs8+JzbjMZBlWlpiCbucV53LpuEYoQhKMxjGOdwNfPqeDZfSd48eApVtaUYh8rza0rysFmMvL+jYnCl3AsjkFV6BgcnbHmQ0p4fesJ8gtcdHUOY7EYyMlzcnDfWVRVYcNl83nxuUNomoLVamLN+lqeeHQ3dqeFCy6qZ+78tMb302Jj/kquLlqHKlR+1/wsb/Qf4Y6yfC7OW4rH6OTx9lf4x/p3J0ThEQTjYR5qeYF7Kq6i3lHGnsETPNTyAl9bcD8S6Az2s9RTxxfn3YsudYxKKs3PrOXR7d+CRSskFOtHFabMHXAUCxXOewjGesi3Xky2ZTUAkfgggVgHc7L+GZOaxbjmTI5lDaFYN1F94mVpGX0Ej3kpWebldPieptP3PNmWlcRliPqsTxHTAxzp/zJlzttp8z5Gles+XKb5HBv41+QYQpmYQusz0C6ng6Io2GwzJYb+3/TVzAShCJavrWHHK8eREg7uamJ40I8n+/xkUqN6hMFID4pQqbEvSvs9GIwSi771vq6jkUZcplpW5H6JA/3fY477HjTFxoH+7xDVR9EUM2E9RnQS00NB4DZZkjOxo83d9I/4KchyoCoKNrMRg6Zyqq2XupJcBuIB/vDSPjYsrWFhdSG3lm6gPzzCBTkLKbakx4qNiobNkOp8+KLTS0GPb280aBRkOynIdrJ2fjm6LhkNhGjvHeFUay8nW3pp6Oija2AUrz/MmfY+zrT38cwbx8l325kzqpHtsae13+tq62XeGg1F2AjHGpldC+AE3hGG26zmsDD7s0R1H2YtD01Yx4pAZDJuKyEj53gcQsB9G1by3SdfY19TB5qqoCoKn79xA3azkXyXjfJcDy8cOMV37rkmeVPuvGAp3/7bVj73h+cwaRoxXefzN16SEFoXqRdy8t9CwOhokJHhAN7RIGazgaOH2/Bk2enqHOb0yS76eka5/yOX8qeH3sBsNjBvQQnVtflU184ufj2OgcgwewZP4I0GOO1tY66zPHk849ck0QVEGVt/hPZAL892vsmLyi6C8TD94WGC8QiaUDEqBlZ45mJWM7cXs2mlFNmvpD+4E6Pipsr1Hs7noQIwq3nJEMf00AnE2onpPvzRs0gZx2lOtDa1aEWowopUEsZDyihR3YtZy0egYtEmPnx2x8TLPjLkx+8Lp/Ck3z7eOaETIQSrLqon99cuertG6G4f5Ok/7eSuD29I44pPxeQPmj8+Som1Bm90iKgMwxR97NHRQDLsMVsoipj0jgo0YUagogkzwVgf2eYiDIoNf6wLi5Y39m5nvrbRWJzjZ3uYV5FPQ0c/Rk0l22XDaTVjNRsZ9AaoKc6hMNvJ3PI8BFBkyaHIMpPkrnzbn+BEzYdIVk4uqCogGtMZ8Qdp6x3meHMPu0+0sPdEG5FYnGgsztzFlbz3Q5em9bJ97vE9uKwxYDfh6Blc1htmfRzvCMMd1X2cGPoJqrAgZYw5WR/FoLh4oWsnl+avoMHXhkFo1DvLpx1DCEF5rpvv3H01rf3DxHWdQo8D29gLLITgo1eupW/UT3X+BB86323nW3ddRWv/MJFYnHyXHbfNgs1k5Eu3XJrU3a0ryuEf33Vh8sEUQpCTY+fEsQ48WXbicR2T2UD93CLWX1iH2WLk+NF2TGYNzZCIzwpFEIvF08IpM6EnPMh/nv4zd5ZdToWtiJd7dhPTZ+abq0LBrlm4umgtVjWRMdeEgl0zE4pHUYRI8sAzIRBrx6A4qXTdhSbseKMNdPm3kGe9CLN6ProjE+tNGI1EG+TE3wp2QxUOQzWF9ivRZQyBijd6Oo3BIoSKUfUQiLZhVLMIRFuxjhnvwtIsLDYTfm+IwT4vr794hGtvX52SYJvapXs2SD3m9OX/r2LhOflObrx7Pb/6jxeIx3X+8vs3UFSFd92xOikBO/4hA9DjEu9IgNPHOujuGOLa21dTY19IXI/RG+7ArqXnVXq6Rs67ma2qKkkmisNQTjg+hC6j5JgXc2zoZ+SalzEYOk69+x4gwSAxKirheOJ51qVkMBxIOGlj0ghNnQOYDBr9IwEGRgMsrS2mZ9CLyaBRXZyDoghae4epLjq3mmUoHsMbSfWwHYZzU/CS11GXBCNRBkYCtPcN09Q5QFPHAK09Q/QO+fAGQoQiMeK6nmRyGQwqN961FlVLZ0it3TAHtKPEiaNLP7o+iqrMroHKO8Jwx3Q/C7L+CU2x0x3YylDoMPnWizCpRpr9nRwZbuSqwtlVNNrMRuaWpKvdCSHIc9nJc9nTlluMBuqLJrK5UkqMmkrdpGU2k5HK/NQHvLQsG+9oiLwCF5FwjNKyLF5/9SRGo8ZFG+aOlQ0LCos8KKrCnLlFvP7qiUTl5dzULixxqRPRo8T0OBE9SigewagY8McSXaMrbEVIdE6NtlA9KR5tVU2MRv30hAaxqCacBjs5Jjcl1jwavO2sz1lERI8SjIcndK/PAX+0laaRP2DW8qlxv4+W0UfJNq+gZfQx6j0fYaqXZFDsKRROIVQMqjtlLUmUltFHGAztR8oouoxQ7ryTCuedNAz/nL7QDgSCSte9KBjQ1ESeQSAwKh4EKuXOO2kaeZAu/4sIoaGIRFw2v8jNgmXl7HrtFLou+e0PX+bsmR4Wr6rCYjURDkfxDgcY7POy8sI65ixKZyeNa1t3tw/i94UJ+EIE/AmdkNPHOpLrHd7TxO9+tBmr3ZT4ZzNjtZvIL3RTUfu/k9gUQnDVzSs4e6aHl57cTzgU5eGfbWXrc4dYOFYCbzQZCIciDPR6aT/bT1tTH4P9XhatrOTa28c42wqUa/UZ93G2uS/j8plgMKhJVUarVsDSnM+hCCMFtgsI68MMhU8yP+vD2MY+uC6jCYfBiHcsXKEjOTnUx2UlNaiK4Jq1c2nsGMAfCrNmXjm6lFjNBjwO61jeSXDV6jlEY7ML6fQEfPSHJop/BFBsd6WtJ6UkEosz6g/RM+jlbNcgDR39NHYM0N47zOBogGAkmvZhEyKhLFjoclJRkEV9eR4LqgpwOa0IIZJSvt0dQzjdVmrm5hKI7ybLfjdx3Yc3tJks+/2zOpd3hOEWCEYip7BohXgjTeRYViKEYKGrmue63sSmWXAZ0qu7/l5I9awE4XiiMs2kepLLY3qAZu8zVDlvQBMJL7akLJuSstQv/e13rk2MIuDyqxKxw8uuWABAaXk2d95zQUZve9/gSbb27mMwMsq2vkMcGWnk3WWXU2rNZ33OQn7R+DfcRjvrcxYleyEClNkKWJU1n581/A230cEHqq7Drln4cPWNPNH+Kj888ygGobE+dxE19hIUISix5GEQ0996ITQqnO/GoNgZCR9HAnnWCxmNnEZmkJ+sct2HEBOhCbOaz9ysf2ZyeEVgoMRxI8X268f+TvSyVFUr87K/SFwGEWiowoxEp9bwUQBUYWN+zpdQhBmXcT6Lc76BRKKIRCMASJSH3/3RjZw900NP5zDBQITn/7KXF57Yy7jQkUzcWgpKszIaboDHH9zG5qcPJibU0zibZ453cub4pITgGE1t3ca5fOk/3j3tNf17w2Q28KHPXo3DZeGZR3cTCkToaBmgo2VmRb/p5G4nIxbTaTjVdc71psJo0pKcciGUsfxG4sJXOm6g0jExWwWwG0yUOtx0BibE1l7rbOL+eSuxaAZsZiP1ZblEY3HslgnPuCBr4lmbvHwmSCl5vbMZf3QiGWpWNWpc6Z66NxDm8z99hsaOfryBMJEMISNBQjkw22mjrMBDfVkuc8ryqSzKIs/jwGo2pDQwllLytz/u5NSRdkrKcxge9PHSkxFu/5gfg/oKceklGu/CG3wem3k9qpL+QZmMd4ThNqoe+oK7iMSHybYsw21KdGvJNrlwaFYWumv+h49A0u5/FV+0jVL7RobDp3EYylGERpvvFaxaPgXWVWPJ08n6BBkssJg5IjqdQ7Y8aw5LPamca1WoKELw7rLLick4yljycXKkzqgYuKt87HcScW4hBLlmNx+qvoGYjI8ZybEmEYoxmcicDnZDOaeHfo6UOgbVQTQ+Qpd/81goY2oII2GAU5cpaMKatp5BZO78LtBQJv0mUJPeNJBSsDF1X+NjV88p5Cv/dRd//PlWDu9pxu8Lo8d1QCar1hxuK4604poJONxW8orc0/4OoOshovooJjU35Wa63KmOhWZQyCtyEwpGE3S9KeNYrEbyixOOgc2efkyuLBv5xZ6EF2vQ0kI9QgisNhP3fXITKy+s49lH93B0/1lGhwPEJ4XjFFXBYjVSUJLFsjXVbLh68TlnBQP9XlrO9s+4TibYbCYMYzQ4KSXDkZOcGf4TgVgPy3O/iEXLo8P/KiX2jajCiKYorMwrYdckLvfh/m62dZ5lU2lNgj5n0JLhyrcKKSX9oQCPnDmc8j0ud3god6SzT6KxOGfa+5KVkwAmQ0IRsDTfTV1pHnPK86guzibf48BmMU3q1zpNwVg04W3/w1euw2Y3I3XJr/7zBUKj68jJSrwrFhbDLOUl3hGGW6Jj1QqxaHnEdD+BWAd2QzkKgptKLgFmF08cb3eUqemqmGEMiU4g1o1RcWJUXFi1AqK6H6N0oQojBdbMGhTp4wAZjmFyXHw6qEKZ1pgmjN7kWzVV12LidyllsrxcCIEmUrt+jC9LHvOU41WEwKwWMDfr00h0DIqDmO6nP7iTCudtnG+ScjIy3Z9z3ZtZjSn9CGGgqq6AL3zndvq6R+hsHcQ71pXHajORlesgO9eJ05NZZx3g3o9fxns+vGHG/fWHdtIw/HNW5X8JZdIsY2pisKDYww8e+hBImYhvTmEUrL1kLsvX1iSedUt6aO/9n7qC9378MhibYj/6513YbCaumSJ+pWoqi1ZUsmBZBcODPrrbhxjq9xGNxDAYNVweKzkFLrJyHBhN5+6TKKXk0P6WZA/P84HbbUvSKUPxfg70f5ci68X4Yx3EZQghVFq8z+IxzcFprARgY0kNvz6+h+BYnDuix/n3A69R48qm0ul526EnKSXBeIz/OLiNhpHUj9EVZXXYMujdCyESlbyF2cmuNtVjiVC71ZiiE5QJutQ56++lwpZPd3CIQosHVVMoLPHQdKqbsqpcRoeDhENxbJZFRANmrJM+erPBO8JwR+JDCTaJYgPEGJ/73C+zlJJgNErL8DDHe/poGBiky+tlJBQiEouPVW9puCxm8u12KjxuqrOzKHe7cVvMk6YykrgeRlMtRHUfo5FmdOJYtXxMauLhCcb6CcR6GIk0k2WalzIFisTjtAwPc7irm5O9/XSOevGGw+hSYjMayHc4mJObw5KiQqqzPJi08++BBzAaCtHvnyDp5zvs2IzG5HE0Dg7yzPFTHOnpIRqPU+hwcEFFOZdUV2I3pgoZSSkJxWJsa27hlcYmOkZHMSgq5R4XF1ZUsKa8FMvYcRpVF0X2K877eMcN9EgozMm+Pg51dtM0OEi/P0AoFkNVFNxmM6VuFwsK8lhUWECB3Z4yxZwNvKPfxGS6GLPlSjRNpbAk65ydbqZivDiCDAqIkz80FkXDFNax2I2owpTmJEgpEUKgKApWW+rv479BIryjanC675dUWd+HGUdyHV1PfOCMk3jla9bW8NST+1PGGm+pZjAkqimzchw4XIl4qqYpSJnonajrMimada7rGo3G2fry0Vm3aJuM3Hxncj8jkTO4jDXUu+9mKJyQF1bQMCh2ImN0UCEE87LyWFNQxtaOpuQ4jSMDfPz1J/nX1ZezNLdwrHLy/N8XKSW9QT/fO/A6TzQdTfG28612bqqen3E7l93Mf3/mFmxmY7IichytgT58sRBVtnzagwMoQqHYks1pbwdZRgceo40z3i5OjrbjMtjoC49QaPGMJYiD/P4nr2C2GolGYui65OfffR6A2+67MK1ByEx4RxhuITTafc8n6GNAif3qsfhYZuhS0j4ywtPHT/Hi6TM0Dw0RnKWyn0FVybZamJeXxyXVlVxeW43DHMOg2BBCJar7KbEnCgRUYcSsJehFJtXDHPfdiDGvePyjsaWhiT8fPsqR7h78kZkr+SwGjXl5edyxeCGX19VgNUzfGDkTnj91hm9seTUZQ//6po3cMH8uupQ8dfwk33ltG33+1Oqrvx47wfLiIr5xxWVUZXmSbINur4+vbd7Kq03NxCYXWjTDnw4eYX1FGV+97FJKXM6xbXQGw8cJxwfJNi+cFP/PDCklXV4vjx4+yrMnT9M2PJK6nylQhCDLYuGCynLuXraEBfl5MzJfUvaljyLl9HzctwMp4/QEttITeAWjmo1VK2V8xiOlJBTvos37BIFoG3ZjNaWOmzEqWUhidPlfxGaooMv/AuFYPx7zUkrs16MIE/5oM/3BHQyEdmP0ejCpuZi1PLK1Tfzx4Z1Eo3EcDjO33pZoW2YyGZLzLCklDQ09vPzSUaSULF9RyerV1ezc0cCBAy3ouuSqqxdhtZr48yM7cTot+P1h7nrPOrKypud7Syk5cayDo4fbpl1nJkzW8FZFwgnS5YQOSUQfJRwfxKS4k8uMisqHF6xhT287vknx5+NDvbzvlce5sWo+t1QvpMaVhWkWTX8Tip863QEfm9saeOjUfhpHB1PWUYXg/rkrKbW7M46nKkpK8+w2/xBRPU6FPYvXeo+yMX8x3aFhtvcdJy51rixcji8aZN9gI6XWHPLNbqJ6DItq5LS3g4XuClRN4f5PXz5NjYFI0WOfDd4RhhupU+G4GYOaCMhb1Mw9EKWUeMNh/nDgEH84cIhenz/jejMhGo/T7fXR7fWx/WwLFR43a8tKqXReO6bta52ik5CAIlSUsRCDlJLjPb1857Xt7Gprn9EgTUYwGmNfRycHO7v4y9HjfO6SC1mQnzdr4x3XdYKxiQ/U8d4+bpg/l9eazvK1zVvxZfhw6FKyp72Dzz73Ir+46XqyrBaGQyE+9/yLvNmS+QWN6TqvNZ3lc8+/xH/f8C5cZhPh+DDeaAseYz29wT2U2i/PuC0krvHTJ07xozd20DYyu76BupT0BwL87dgJtjQ0cdfSRXxw1UocpnTJUynjRMKvEg6/iUGrQ8rJjQaihEOvEI3sRlHzMVtuRFESH99Y9DDh0Cvo+giaoR6z5ToUxZYo3Y/sQFVLCQefI653Y7Zch8GwlL7gm5wa+k+qXfcjhMrZkT8kcwxRfYRDfV/EZVpAkf1a+gKvc6T/AZbk/jsgaBp5EFWYKHPchtu4gDPDP02UfNtvIKIPoxNFEkcVVjTFhiosRCIxerpHuOrqxVTX5GdMJkopefrJA6xaU43dZuJvf93HnDlFvPDCEW65ZSV9fV6ee/YQ19+wnFAoysc+fhlbt55g185Grrxq0bTPWyQc49GHdxAOz17eeByKIigrm+BQe0xzECjs7v0qo5FGGkefwBdtxW2qx2aYYEUJIViRW8z75q7gx0d2pEgjj0RC/PbkPh5rOEytO4fFOYXUu3MptDlwG80JQ44gosfxRsP0Bf00jgxwZKCbY4M9DIQCGXPMm0prubNuSTKEKaWkzT/EoaEO5rjyafENEohFWZpdQotvkIbRPhZ4iqiwZ+HQrBRaPHQFhzAIjbnOQjqDAwxEfMTH8knjeaWoHiMUjxKIh7GpqaGQoZEA/YM+airOr63fON4RhlsRRnqC25M96gptG3GpqYksKSUtQ8N85eUt7GxtT9O+fiuozs5iUUH+WNx3+tjnZOhSsvlMI1/d/Mpb+nAAxKVkZ2sbH/rLkzxw2QY21dXMWFw0gdR1zvT30+v3873Xt2c02pNxuKubhw8e4mNrV/Or3fvYMY3Rnow9be385egx3rOshg7/KwRiPYTjg9gNZdNuE4xG+eEbO/ndvgNE4m9NO90bDvPzXXto6B/kG1dsJNtqTQ1NhbfhG/0+NsfHiMfbiUb2YrZck5gFBf5EOLQNq+0OIpH9jA5/AbfnRyDMxGKNqFopBmURfu/PkDKE1fZeII7f+2MUxYXRtAGDWjgmsSnp9D1Noe2KJBsmHB+g0/ccAH3BN5BSp879cRRhIsu8lF1d72cwtI8s8wqk1CmyX5Pc1hs9w2DoAMX2G8gyL8NuqKTN+ziF9itwGuuS5/e++y9m544GXn/tJB/40AYsU/RWpEzI1Xa0D2Kzmbjgwjp0XScQCNPU1IuiKqxcVQWA1WJE01ScDgudk3S8p0JKyZaXjrJvT9O068wEs8VIcVlW8j6pwsyy3M/T6X8Ns+oGBFXOGym0XpRWhasIwYfmr6bTP8oTjcfQp5hbfyzKwf4uDvZ3JddXxbjkVKKsRpeS+CxswtqCMr62ahPWKbHtlzpP4jCY2N3XQliPsaGglj39LUT1OPPcBTBWDLg2px6BoNCSxYV584jpcfItHpyGfpZ6KskyOmgN9HFJ/gJCepSF7nKCsYThnowDR9t46bXj/Nvnb5h1TcdkvCMMt1F1M9fziWl/H4/ffuqp5zjZN32226AqOE1m3GZzsuN7IBplOBTCF46kecbXzKlLxohnAyklrzQ08YUXXmYkFEr73aiqlLqcVGR5yLIkqvmGQyFah4ZpGR4hGI2mrN/r9/OFF15GAlfU1cziy5v6YJ4dGuaRg0c40z+AUVVZUJBPVZaHoWCQ3W3teMORlC3/duwEq0pL+POhI4kYqqqyID+PquwsvOEwu1rbGZ50XuPb3L5oARWOa+kKvEko3o/LWJXx6CKxOD/Y9ia/23cg7SVShCDbaqEyK4tChx2LwUA4FqPb66NpcJA+fyDlYywlbG5oBOC711yREqMPBZ/BbL0Jk/kqIEo4+NLYRkGCgcexOz6DwbAAVatheOA+YvFmNG0uZsuNQAQpQ5gslxOLnpx0aWOYzJswW26adP4xArEO8qwbkvu2aeVJg+GLNGAzVKAIU4JdgxWLVow30kCWeQVCKNiNEz09NWFDl+0z3uGR4QB7djdhtZkIhaLE4zqDgz6OHGmjp3eUI0famTu3iPUX1NHS0o/LZcVsNuByWVmypJxQKIrdbsZuNyMENDX38eILRzh6tJ3rr1+WcZ9SSk4e6+C3v3yV+Cw50VORm+cgZ0onH03YKLNfTZn96uSyTM+4EAKrZuCBlZdh0Qw8cuYQ0RlmsbqU5+24KUKwqbSWr6+6jDyLCrFjSMUDSqKeotqRgy8WptaZy/HhbqyaEZtmoj/ko9HbzxxXPhAn3+AD3ChCUG6biAzMcZZk/P98szvj8fgD4ZTORueLd4ThhhkYH1IyEAjyhedfzmi0BVCVlcVVc2pZX1FOmduFzWjEMBYfjcTjeMMR2kdGONjZxY6WNo509yR41nW151VFd6qvn69ufiXNaBsUhUtrqrhn2RLm5edhNRhSpmGhWJzGgUH+fPgITx0/SWCSAR8Nh/na5q0UOh1J73+Gq5TyV+eol1/u3ovNaOSByzZwVX0dJk0lLiVvnG3hn555IcUQd4yM8vXNWxkOhXCbzXzlsg1cXluDSUu0hNrb3sknn3qGgcAEo6B5cIizQ8PU5BgASbHtUvpD+7EbUrnQupQ8euQoD+0/mGa0q7OzuG/FMi6pqiDbak00DB77LabrDAaDvNrYzC937+Xs0HDKtq80NvHzXXv49AXrxlQZdXS9D1XdOBZ7N6CouWPHEECP9+D3/hCEGZAIYQUZA+IEA38iEn4DIazEYy2oWuWkS6ugqqlyolIyRr/UUxKLyU2EhmRyWEEiiSdDagmf8Dy1sh1m5swtIhSMsPyDl2CzmYhG4uTnObnyhlp0ZUxAbcNcOjqGGB0JJAq8FMGtt63iUMNxrDKL8vIchob81NTkU1Wdx8KFJRQVp7M0pJQ0N/byvW89w9DgW5tBAtTNKUpRYgzGeugN7aPcflUyLzQThBA4jCa+tOJS5nny+dGRNzMKQL0VZJutvH/uCu6ZszzBIpF+ZPQwMt6OYv9nhBBcVFDDaCSERTNQbsvCqGpcXFCTmOHpcayaEaQX3f9zFOfXYMp9DYWjvPTaCYoLXCxbWMbwaJA9B89Oq5tz4OhbyyOM4x1juKdDXEr+e8cuDnamFwQ4TEbev3I5dy5ZjMeS4MJOfTBNmobDZKLI6WBlSTH3rVhG56iX5sEhytwzk9wnIxSL8Z3XttPt9aUstxgMfOai9dyxeCFGVc3wcgusRoUFBXnMzb+UCyvLeeClV+gPTCQR+/x+/u2V1/jlzTfgNM9e7EgfY4Z8dM0qrp8/N/mx0ITgwsoKbl+8kJ/v2pNcPy4lp/sHUITgo2tXce3c+uQ2qhCsLC3mhvlz+fWeCfZCKBbjRG8f8/Lr0YnR6d9KrmVF6lFJyZn+AX78xs40T2ljdRVf3XQpBY7M0qMGVSXfbue2RQtYW1bKPz/3Ivs6JgpcdCl5eP8hNlZXs6SogIRglQtdHxx7KeLounfsWptRlGwcri+hpTRdNRKLnSTg/z3urF+hqkUE/A8Rix6fcjRT7h0qNkM1I+GjCaVECd7IKeQYl99tWkhf4HViuhdNcRDRhwlEW6lwviftPDMjodWhy0jyw6BpKjU1qVo2niwbniwbDd5+JMOEGMUsrJjyvNQU5wGS3nALHmMBoqCTSmcFiojBkMBoVKmrK8i4d12XHDvcxvf//RnaWwczrjOrsxCwdEVFypR/JNJAb2AX5farzmssk6pxR+0iLigs549nDvJ080k6A6Pn7WELINdi4/LSOu6Zs4waV/YE7VTYwbgSGUxUxEopUeQoLn07RBSE6ULAgIleZHQXJiULIdaNvX6J2gAZ2QtKLkJLMEEGhvz88uFtzKkpYMn8Uto6B/nWj15IJB0zPPexWJwVixLbjowGcToS9svnD2O3mc7pUL6jDbeUkiPdPTxx9HhaksFlNvGNKy7j8tqaWbMPhBAYVJVyj5tyj/u8jmNLQxNvtqS2iFKE4CNrVvKepYvPeQyJOLrg8toa4rrkc8+9mJJoPNDZxd+OHefuZUtmuGnpy50mEzcsmEtc1wnpOmZNS0r3XF1fy+/3H0hj3BQ5HVw/b25aXF0RggsrKvjt3tRQR8NADzG9DKehEmlIaGRH4l4MSsIYx6XkF7v2pHyMABYXFvCNKy8jx2o954MohKDU7eJfL9/Iex99gl7/hPfnjUR4cN9+vl94FaoQmMwbCfofwmBYQDzeRSx2amwMGybLlfh9v8Rqfz8ChXi8E5P5ssQVkTpSHyaqDxIOvYCqnot+JSh13Mjhvi+hCjOKMDIQ2oMY48xnm1fSaajkSP8DeMzLGAjtwmVagNu0CEk84/2avExTbDgMtTQO/4Icy1oMiotC25XTeqhxGScmIxwffQOL6sBpyKY9cBqQZJuK6ffuJRwPcHx0BwtcF5CX5+C665enjSOlJBiM8MIzh/jj77YzMjx7HehMcDgtLFhUmnKPTaqbxAxEnrdElxCCUoebf156MffNXcHunna2dzZzdLCH7oAXXyRCbKxRcELxRqApChbNQJ7FxhxPHhcUVrCuoIwCmwPlnHRCHd33U4RxORBH9/8SxXY/uu9HKJYbkdETyPgAwpxgm8nwaxA7g7DdkxyhINfJd798M06HOUmJzM128JV/vAaLOZ1i+vLrJ2g820csFmfL9pO8a9Mi4rrOi68e56arl54z7v2ONty6lDx84FBa4k1VFD51wTquqKudZVLv7SEci/HwgUNpMfKlRYXcs2zprD4co9EAmlCxaiYur61h29yzPHbkWPJ3XUoePniYd82bg8cy+0YSFVkeip1OBoJBXmw4QyQex6RpXFVTS6nbTb7dnhZ+WFFSjMeaeR8lbhdWoyElPt45OkIw1sdw+CQ2QymSGMOB05SPNRM+3dfPlobUpJZZ0/jHC9fNymiPQwhBbU42ty5awE927Er5bXtzCy1Dw1RnZ2G2XInUh/H7foqm1eN0PYCqJXpT2uwfIBj4KwHfrwCJwbiKBN2qFqv9Pvy+n6Moudjsn0DK8TCSgtlyI6palHY8btMiFud9m/7gDkxqFotzv0ko1p1IpAvBgpwv0Rt4HX+0hWL7u8i1XIgijEh05mZ9DoexOjlevu0ysi0TxVwCjfnZX6Q7sHmsliFV0D/9+oBZseGLDRGTEayqk17ZgkDBqjoYjvSiE0eXifZeJqNGQcHErFJKSTgc49D+szz68A6OHm47byGpTJi/sJTcPGfKMpexFqPq5szIH8kxL0mGjBSh4TRWjUkWzAxFCPIsdq4pr+ea8nrC8RgtnX384acvYMyxsOHda5FIjKqKw2DCbbLgNpoxa4bzK+qSIdD7EKaLAYkMbYZ4GwgbGFYh1GJ03y8R5g0Qb0cG/oDi+jdg4h1SVYU5NakzG6fDTG1lHqYMhTWninpoPNvHwePt7NjXRP+gD13KMZbJuQ/5HW24O0e9bGs+m7Z8VUkxNy+c/79itKWUnOzr50h3aidtVQjuWbYEm9GQXG84OsIpb0NCEN5Ri0Oz0x1K0IZaAn3oUqfaXkiW0c7dy5bw/KkzKR+l5sEhdrS0cVX9dLH39JesKsuDQVXRpc5oOIxBUTBrKqoisBoMGQ334sL0BqnjcJqMWA3GFMPtjwisWglWQwGGMfrcaKQped7PnTyd9nFdWlzIipLMXeTPhSvra3lw7/60XMCbLa1jXHQTFts9WGx3M+7Bju9HCAsW67uxWMdbZk14WxbrXVisd6ZtAypWW2qLrXEIoeA2LcBtWpBcZplkYDVhp2hS8i25HSrZU0JKtilsHCEEJi2HcmfmfU9FgbmKwUgnVfYl2DU3XcEm5jgTH4L+cAf1jpX44yNYVDsRPYRZtY8V6sTp7Rll3+4mXnnpKKdPdiVbjL1dKIpgw6b5aQ0oIvoII5FGeoN7aPO9xPg1NyoOVuf/Gyb13GHKaCQGUhIJxzBZjZhUjbrSQu5992W8/uRe1hdONB0O+cMIKTBrBpAQCUfHqmolZmtq6EHKOMjIWO4jCsIAwpIw1jIOwgpKLkgf6P3I6GmEWgAooOQhLDei+3+H4vgkkJmNVlaUxUfuuSitC9Y4PG4rWW4byxeWYTYZmFtTAIK0gp/p8I413FJK9rZ3MBhILb1VheCupYuxjGln63pCye/vtc9wLI5JS41Vb2tuIRRLDTcUOBysKS9NFrT0hvt48OzD1NlriOhRtvZu4/7Ke2kPDHBspIUaexHeWJBmfw83laylJiebhQX57GidSFLoUrK5oZEr62tnPb0sdjoRgNVgpMLtpmVkmObhYQLRGG6zSPPeFSEo92QuPIAE08Q05XqGolFAw6Ak4u9CKLhMCf2YQCTK6xk+rptqqjGq539fhBCUuV0UO52cGUgVTNrd1sFdSxdP8qZSK0EzJ4IyLZfJbf6/BLvqwW6ZKHKpsC5gXA/Lanaj6xJr3EjQG2FoKMShthOcPtXNiaPtNDf14vOG3lJF5EwoKctm+cqqtOfJrOawvuD7aesLQBGzE4bas/kIB18/mTC8iuCuz1yL0WxAM6gp78eRN8+wd8tRvEN+LrphBbVLyvnZF/9MXkkWA90jXPmeC5i3amLmQ+w4euilRJIy+DjCcjOK7X3owWdAgGJ7Hyi5KJab0QO/RwgHwno7YEAYFiOMa0AGkZHDCOPqjDFst8vK8kXTh+JWLCpn0dxiFCVRfPPYM/uQQGGeiw3rzt0ndlaGWwjxaeB+Es/IEeA+Ep+aPwMVwFngNinl0Nj6XwDeD8SBT0opX5zNfqZid1t7mo9Z4HCwsjThye0608JIMMzVizNLU54v+rx+/rzzMB/btDb5YMT1xAdkKhYW5OM2T4gDbevfQYW1jI35F6NLnV83P8QrvTtRcBHWYwiRoAkdG2lFlxKjorKmvDTFcAMc6urGFw7jNGcSQ0p/QHJsiVCELxKhy+fFaTKxpKAQz9j25ikCPQZVIds6PWd9PA8wGTMVGHWMjtIyPJyyzKSqLCkqfMs6E2ZNo9iVbribBgcJx2JYDOkxw3Aoyo9/8CIjQ28vXvv/NUgSJfKRSIxgIILfH07I0gbCRCOxv7uhngwh4Mprl2QU7koIjaUuT3woZ39AIX+YkpoCrr3vYn75wOO0N/ZQNb8kbb26JeW4su2cOdTK4TdOUbOojIA3xG2fvJKGI20c2n4q1XBrC1DsC1IHUatQ7J+cdPwCaViJYliZel7WROJZmDbO+jwywWBQMRhUdF2y+8BZLr9oLkbjhLriuXBOwy2EKAY+CcyTUgaFEI8CdwDzgC1Sym8LIT4PfB74nBBi3tjv84EiYLMQok5KeV5zs0g8zun+dInKefm52I1GDrV28Zc9R7GPsTCybBZWVZciAG8ozEtHztA+OMLC0gIurK/EoCo09Q3iDYZZXJaY6p7q6ieu68wrzqO5b4iXj55hV2MbVXlZmDSVdXUVROKxNMMEMD8/LyVU0xPqpTPYTUcwwX4JxcNkmxzY1Ryq7AXkmVzYNTPzXWVJManxMSZnzPt8frq8vmkMdzrGWSguk4lIPM5oOMxoOMzigoIx7ybVeBoUFWsGwzeOVD82gZlet8aBQQKRVH66w2yiwJFZCXA2UIQgK0MMfigYxB+JZDTcsbjOgb3N9Pb8fShk/4dzo6Q0m8uuWHAeH2hJ4+hjlNmvxqjO7vkwWQwIRWA0GYhlkLXQ4zoPf+9ZSqrziEbjSW60w2PDZDFiMhvGVCIncD45l7eKWCxOKBzDZk2v/B1HKBzl4NE2hkYC7D/ahsdlwe20Mq9u5lwHzD5UogEWIUSUhKfdCXwBuGTs998BrwKfA64HHpEJ8YhmIUQDsArYMct9AYkpeKbKxPrcHOJxnabeQfq8fkAw4AugKSIRD9N1vv30a+Q4rCwuK+Sve4/RPeLlzrVLUIXCfzy/jS9etwGn2cS/PbWVf7r6QiTQ0j9Ex9AowUiUQV8Ak0FD13WGgyGGg6m8bQFp4YYso4diSyHXFF4xtoZEEUpK93QAl3FC/rPI4cCkaSmFOaFYjM7RUepzZ2rBNHEclrEKsMahQRbk5XNxeQVbzzbTPDTEwrz0FmmaoqSFQgbCozgMVozK+UfOzg4NpRn1WFznTwcPp+1ntpBAQ4aPdigaI3ye7bT+D/8zUFWF2+5ai9sz8TxPeNSZP/M6cfqDBymxXTbr/ezdcoxwMMJw/yjFVfl0t/Rz8PWTdDT1cnDbSeatrCboCxGNxBjsHcUwNsNMxtwFya48/5s4crKDH/3mVb72mWspLcqsuySEwGQysGpJBaaxpuRSSoKhaEYmymSc802VUnYIIb4HtAJB4CUp5UtCiHwpZdfYOl1CiPEyomJg56Qh2seWTT3oDwIfBCgrSy+h9kUiGcu4S1wurCYjN66Yz9H2buoKcrl9zUSz06bufpr6BvmHK9ZhMRpRFMGvtu7hlpULKc9x894Ll/NfL76B1Wjg2qVzWFhagCIEG+ZVE9clvlCYu9YvTXqqDYODaaXbqqKkeYQX5qzjwbMPowgVl8FBX3iADbkXkm2aXizLaTZh1tQUw61LOUMpfbpcrWHsAS12ONnS1ETryDADgSB3L16SeQgBPaEhVGOc3tAIAEdGznJlwXKyTOfnJSeEpHxpy4dDIX705s4MW7w9xHU9KVn7f/h/ixWrq7lk47w0b/LE0K+TaoBTIdHxRScotVJKnjl7khbvMACLcwq4oLAiZcwVGxdQt7ScDbesxuowEwlHmb+6hnmrqzGaDGhGjfu+dCO97QN48l1IXWJzWrj901chFEFZbSHZBe7MxzNWJu+PRvBFw8SlxKxq2I1GzOp5MlOmoKV9EK8vhNMxPUPMZNRYPK+YX//pTew2E5esq+O5LUfZsb+Zm69eOuP4swmVeEh40ZXAMPCYEGKmCoNZ0SGklL8AfgGwYsWKtN9DsSjRKQZTQEpcORN6Rnx0DI7w5cdfRgiIxnUcFhMxXcckNC6sr+Qve47SOTTKNUvmnJOZ4o9E0oyFIkRKqbwQgmJLIR+uvo/Dw8cYjfqotJXjMEwosUkp8Y0GEx3Fx8j2RlXDqKbfAm84nLFSL9OlHW8QnGO1cv2cOZwdGmZxfiG2acIhAgjEw+wZPIMmFEyKEV/0/LWXIXFTRzOU/v9P4fwipP+H/ynk5Tu5/6OXjsngToZkJNJIvnUNZjW9s4wkTsPIY5P+hscbj/BaZzMA76qYw/rCiglhN1XB6jBQOW8iru3JdeLJTaUe2t1W7O7UvE3BmOCV2WbCbEtNhkopGY2EebHtNM+3nOLMyAC+aDiZe8o2W1mUXcjV5fWsKSjDPAtVwqkwGhLxau0cCXoJWMwGli0sZc/BFmxWI/PqCunoHp5xu9nMjS8DmqWUfQBCiCeAdUCPEKJwzNsuBHrH1m8HJtdDl5AIrZwXInE9rVoqoQ099ULIFCPnspgoz/HwnTuuwjg2bVIA01gXkV2NrYRjcbIdNjYfa+C6pXNnvCkxfarkTQKaohCPxQmHY6iqgtGkYYvauShrPaqiEAyEUaVGOBQlFo1jsRnp6xqmtaGXS8bE8BNNe9P3HZlWwyD9SMa3bh4e4s9Hj1LudtHj91Fgt2PWMt1eQa2jCLOpBJtmRhUKc5wlWLXZZfrTj/X/Qhf//wSLxciHPrGJ8oqcjI6F01hJiX0T5gyyv7qM0xV4Y9qxO/yjxHQ9+Y6v2Lhg2nXfKqSUHBvs4YHdmznQ35mxIrM/FODUcD9/az7O+sJyPr/sEurdmc53eiyYU4SUkrNt/cyrmz5RrwhBfq6TrW+exm414Q+E2Xe4lUvXz0y4mI3hbgXWCCGsJEIlG4G9gB+4F/j22H+fHFv/KeCPQojvk0hO1gK7Z7GfFGRKkgEpFzrXYeNoew9rakYwagoFLgd1hbnYzUaeOnCCdbXleEMJ73VpeREdQ6P8cuse/vmai3BaTHzp8ZeoK8hhTmFCWtFjs9A57KWhuz/R9NPtGGsXlo64rnP2TA9H9zSjqAq184tpON6BoijkFDgJ+MIYjBpdbQN4su3kF2dRUpVLa0NvcoxxVbOpyGTMz4VwLM6ywkIuqahEiEQScjpIwKElpnBSSgLxMC45O3XEqRAZro5Z0yh02DPSpCZDl5JIPIZBUWdVxGTRtGRo6P/wvw+DUeXe+y9i/UX10xqiuZ73T6vPIlCod9+DYZrEZG/QTzAWTRpum3PiGR1rGTox1lsIYUgpOTXcz8defzIZnpkJUT3Oqx1NNI4M8P0LrmVFbmpdgi71ZJOHyf8PUFzo5r23reXnD23jlmuXUVWWk8Z1N5k0PC4r82oLKS3yoGkqQiQUHfNzUmcVUzGbGPcuIcTjwH4gBhwgEeKwA48KId5PwrjfOrb+sTHmyfGx9T92vowSSGiMTA1j6GPNC8Zx08oF/HLrbn7wwjaWlBVxzwVLsRoNPHDjRv604xD/9eIb2ExGrl2S0K042zfEPRcsZX5JPgL46GVraekfpr4wFwEsKMln04IafrJ5J3lOG5+4fD1WQ+I4JpeAj2uEGKM6pdV59HeP0Hi8g3hMx5ljpa9zhAuuXMBrzx5CEYL5Kyo5caCF4sqcJN9YCEEsrqeFg4AZWB/TP6w2o5E32lo50tuDKhRum7+AEmf6zZdINncfwGyaeIhaA73cW3EZVm12TJbJRzOVbgiJBPKDt96YRiucjLiu87NjuxgKB9lUWsvq/MwNfKfClHEW8X/4n4bRqHHXfRdw/S0rkyXdUyHEzP0ShRC4JlWSTsVoOMRQOIjdaKTL72Vvbzv/v/beOzyO8zr7/j3TtjcseiNAACTYexMpUr3LKi5S3OQat8QteV3jFr/O65K4xXacRHZcYyu2Jav3TkmUSLH3CpDoHVhsn5nn+2MWZQmARcUi8+G+LhK7s1POzDxz5jyn3Gd3Xyft8Rhpy3TSRH0hFhWWsqyokiKP76yK8JJmlm9seeKMlPZ4nBge5DPPP8gvLn0L1eOaL+wZOsgMbwVBPcDeoYNUeSsI6c5L6djxHn539xZa2vr50nfuwe3SJ1y3FYtm8NW/u47jbX309scZGEwgBLz9ppWvDVeJlPIrwFdOWpzGsb4nW/8bwDfOZN9TwaPruHUtj88DHEKmEcVXFPDx+TddPPp9JJhQGgrwqavWYUnJs8ea6E05Ptx1s2vy9rWmPj8o6tI13rd+Ofa4/YU9HnRVzSNPsqRkIJkiKAwO726hqq6Y2Ytms/PFI0RLQtQ2lvHyxkPMXVbDS0/uZ/fmY8xbVkPzoU6Gh5IM9sUJR/0MZzITsiQE5PFP52NqD69LVfnYilUU+3wc6O2ZckBLKUnbWTZExwK6hqKdUZntZCiYpDw/kc2iq+qkaXvguFcOD/Wyf7CLjy9YS22wAENV6U07OdjxbIYqfxhVCHpSCWLZFBW+EIaiIpG0Dg9hSUm5L4h2hjw103jl8PpcvPeDG7jupmVn1CUenHHWn95Hd2oLlj3WnUhTPMwM3pxrvJ2PhJlle08bP9+3hQea90/ZCEERglJvgLfWLeC2xqVEXJ7TKrqRLu8vdByf8JtLUSn0+PBoOgkzS08yTsbOfy6PDfXxve0b+c7aa9CFSl9mgJd6t3Fs+DhezUN7sotq75gvPhz0cv1lCxhhFBiRbvz5lBU73aWWzK/CtiXpjMn9j+1yWCnPV66SgMsg6HLTf1Iq3tG+MTL4EeU6/izTpsn+7h4EMLekmMXlZfxiyzbAURj7u7oBmFNchGnb7Ovqxq1pzC4qpC+Z5FhfP+XBgFORKARhj5ug25VXfi2lpHVoiDnRKlZfOpeqOiehZsO1i0bXqawtQkro64qx/MJZCEVQWlXAotVjFkd3PD6hIlNTFcqmzIGe+m7qqsodu3dRFgjQk4jzV/MXTrqeIhSuKl1GmSdIxjYxpcVFxQvwqGfv4x4hhjoZA8kksfTk+dYAKSvLvv4uBjMpDgx0U+z1YyP5ykuPMr+gFJeqckv9Ig4P9nB3015KPQGGs2k+sWgdDzQf4MBAN6oQlHkD/FXDqUi5pvFqUV4R4SOfuIKVa+pQTvGSPLkBdMJsY3P3Vyl0L6IvtYeoewHD2RNOD9cpXCmmtPnipoeJm9lJfx+BLSVt8SF+uPM5NrY38a0LrqYuWHDKcWBLyd3H9mHK/PjRyuIqPr14HbMjRbgUlZRlcmiwl//at4VHTxzGGrf+wycO8Y6edpYXVRDU/DQG64kaEfyal2WRhQS0sdTIaMTHzafJDAFHkT++cT/tnU6G19IF1WdkRJ2z5opH16kMTZzq7+3smjIgJqXkzt17OdbXT9qykFKiKcrohciYFn3JJM8ea+bl1jaeOHKUfV3dxDNZLCn59dbtxNIZhsfxdARcLqpCE5XT/q5uymZEqapzWo9N/g9WbJiNUCb+NkKFenJVYtDlpjw4leKe3OI+0NPDlrZWwm43d+/fR6k/QPoUQUNNUTkW7+BnRx/mt01P8kD75lecrVEfjU6wegdTaVoHB6fcJmi4ubJ6FjWBCDfOnEepNwDSsaTeP2c575+zAr9ucH/zfoKGm4jbw47edroSw9zfvI9ij49Ct4+n2o6StaeDo68HdF3lokvn8v+++1csX1OHhSRtmWQsk5RlkjCdbKuEmSFhOmx9L3YfZ9jM5LI2moi6FrKk8HNE3QuYFX43a0r/GQmkrKkpZE+ntMdDAi93t/KJZ++hNT50SgqDwUyKnb351NAlHj/fvuBqVpVUEXF58OoGBW4vK4sr+d6663jfnOV5M9ekmeWPh50mJJqisTyyCJdqMGwm6Un3YsoxI2xqnXDSPxyXSSjoIRT0UlU+Mag7Gc5Zi1sVgoVlJTx3EpXqod5ejg8M0lA4Md3IlpLWwSE+smblpH7i5oF+DvX0InFcHcsrK7h/3wH2dXezqKyUy+rreOZYE4pgtABGVxSWlJex+aSy950dnSSz2VN20DmVBSBhwj7BIY2airlvKnh0jbDb6fzz4chKhOC0LoSjwx1cX7GKck+Ue1o3kbBSBM+wfdt41EUjFHg9ebnnGcvixROtZ1327tV0NEUdfbG5VY1yb5DGcDGNi4sJuzx4VJ3qQISoy8vKkiq0UwRhp3H2UBRB/axSbnnnBaxe24Cuq7QnhtjR10ZHIkaBy0vCyqAIhWpfmF397ehC5ZKKBh5vO4hfM5gXKUURak4xCTTFRyLbhk8rRxMeklYXPv301YFnij19XXxr69P889prcE2SXgvQmRimN5VPh7C6tJrqwETeHiEEHk3n4wsvYFdvO5s6x2gpNrY30ZdKUOjxsS92iOZ4CyXuQlShYkmbEa1jmhaZrIXHfeqG4FLC4xsPsHppLbaUPL7xALe8aSIV78k4ZxU3wJrqam5/6eU8/3IsneH+/Qf4xNo1Ey7ICIHSffsOUBYMsLyigkM9vXQNxznW30/bUAy3ppE2TRQh6E8kqYtGeb65meFMhngmw6zCQvZ0drFhZu2oY2L9zBp+cVIPxab+fvZ1dbOsovwVTdW7h+OTcqCsq5kx2r1nIiY/TnUoTHUoPGH5qSwQv+bhd81PEdb9tCZ76M8MszBcw1x/zRlIP4ao18uS8jIePng4b/nDBw/xzqWL8E/xYlMQBPQx94wQ4Nfz3TU31y3gl/tfpinWR5Hbx7yCYm5pWMTDxw/g0XTmRkpoDBedlbzTmByarlLfUMK1Ny5l3fpGfP4xRr2g4ebgYDduVSeWTRFxeSnxBDgW62VuuITBTAq3olHjL2B22HEbBoxazFgSW2Yp8ixjV9+Pibga6U/vZY76/tPK41Y15hYUsyhaRrkviEvViJsZmob6ebm7lWNDfXkJA4+cOMimjgWsL6+Z9HnsTsUnzM7mFZSckszNrxt8YO4KtnS3js6MOxPD7B/oZqmukLLSVHsrqPM77ewMZcxY3H2gjZ/84mm+9KlrT2tFCwGptImU8owzys5ZxS2EYH5pCbUFkQmcJX/YuZvr5zTmKD5F3jY3zpvDsb5+FJEjV9d13rpwPiBYX1vD0b5+Ai4XAZeBbUuyts17li0l7HFTEQoylEqzurrSKVrPDYz5pSXMKoyyu3MslS9tWtyxYzeLy8vQzlJxSyl54MBBOk+qOvQbBpfWT2Raez2wKFyLS9UpdAXRhIpPc2EoOmfreVCE4Po5jTx2+AjWOG7nfV3dPHrwMDfOmzxP3qcbfHrxhWi5AiK/7uLvFl+Ya0/m3MvaQITPL72YhJnBqxmoQmFNSTWLomVkbAu/fub9QqcxEaoqiBYGWLK8losuncu8BVW4PRMtRK+mMytURJHbj183aI0P0pEcYmlhJZaU+HUXHk2nyhdmW08rK4qq8KhFLC36HIowKPGswgqnGcwcYknRZ/Fqk3fkGcGSwnI+s3Q9SworcI3rKjWSFhjLpLmveR/f3bZxNKCdtiz+5/AO1pXNGB1D4zGczeQpegGU+wKnfNaEEKwoqWKGP8yRIce9Y0qbHT3tCL2btmQnGTvD0XgzutC5tGQdXtUJlDad6KVvMEHQf+pMLSHg8vVzeG7zEYQQXHph4ynXH8E5q7jBCVDePH8e33rqmTwfbOdwnG888TT/ct1VhN3uvIuvqyqzxvF8zCnOt8hO/h72uHmmtYlHWg7h1w2WFpfz8PFD1IejNA31k7EtFhWWcevihXz5kcfz8q4fPniI6+fM5sLaGWesbKWUNPUP8Ist2yb4ldfPrKE+OnWJ/GtZN7h7sJnne/ayurCRrtQAFxUvwq0aJKxTd4s/GUII1tVUM7+khB3tHaPLTdvmB8+9wNySYmYVRiedHY23sE/+PrJvt6aNpgCO7MGnG/iYxtlC1RT8fjflFRHmzq9kyfIaZjWWEQr7EGJq155AcFXlmEKZHSqedL0LS8eaSAuhoAt/bnuNCt/FVPguzv3mHMeWckIsZkFBKT/ZcAOl3olKdcQnHHK5eXvDYqJuH5/aeB/JnF98c1cLPakERW4ve184ROuRDuZfMJuK+tIJsSSBIGicPv01oLtYWlQxqrgBDgx085H513ModpSUnWaGt4KeTD+PdDzNmsJllLqL0XUVI8cAeDrsO9RBe+cgQhFs2nqMqy+ed9ptzmnFPWJB37l7zwSr+9ljTXz2gUf4h0s3UBUKnZWVKqXEtG16EwmKfD7600ncqoYlJZ2JYTRFYVdPB6aUXFFdz/budq6ZPYs/7drDtnG9LxPZLF977El+8KZrmFdSfEYpSV3Dcb78yOO0DuWz2AXdLj6wYtkZt2E7GZZtI4TIYxs8lTRDZoLZwUqSVobB7KujQvUZBh9ZvZJP3vtAXpZMy+AQn7r3Qf7pqstYmGvecDYvOHAaKGxpaaU0EGBeyeQKA5wH0e0x8HhfKytcMsqJKqYqBzt3MKJ4NU3FMDQ8XgO/30W0MEBxSYjKGVGqqqOUlUcIR7yjCuVM7sdk62w50oLfbdBYMfU9Od0+EmYmryGwrih8YtHaSZX2ZPu7rLKOyyrrubfJ4UbpSyU5MtRL+vggO5/ZyyVvX4c/NBK3OcnoEWCcQXxEAPOjpfzhyK7RZS3DQ1i2TUe6m4yVoTXZji1tlkTms3vwAKXuYubPLse2Jc0tvTTWl055Pk51ZS9vv2klhjGxdmUqnNOKGyDq9fDpC9fy6fsezE/Jw+kAfrCnh3csWcSVs+opDQSm9A9bts1wJsuJwUG2trbxzNEmehMJfnXrWwjoBh5VwwaaYwOoQnHexsIp+PDqOgGXwf/ZsI6P3HVvXpf35oEBPvrne/nkugu4oqF+tCPO+OkdOAG7LS1t/PMzGyd001GE4D3LljC/5HRd3ieHlJJ9Pd0YqkpDQZS93V2jn6fC8kgD97RuojcTY3VhI/6zLL4ZDyEEG2bW8LaF8/n11u15j8jBnh4++Ke7uWXRfG6cO4eqcGjKBgt2rpFFTyLO/q4enms+zvPNxzkxMMg3r77ilIrb49X5v9++BXNKuoCzQyp7kPaB/4uUJgH3hRQFP8yrUd77BlspcYcocPlPu25fepjO1CBzQg43W2/6CIOZVtL2MLX+tdjS5ER8Cx4tTKGrgWGzgzLvPNrTOygPzKE7uw9LjVEXvgApMgxmW4hbndT46/CoY5laUkpMy0IIgWXbowVTpm2jKQoSsHMGwUhusS0lqqJwtKuPqN/LzJICNEVFUZyAsi0lli3RVWV0vwIwbTlK8TAyxocyaYYyY89SlT/MypLKM34GVKFw9YxZ3N+0HxuJKW2ahvoJ9Lgpm1lCSfXUDJtnakQIIagJhPMMov50krRtEjXCHE+00ZnqIWNnqPFVYefSByvLI7z7rav5918/yy1vWk5NVXRC5aRpWWze3szx1j7uuGcL4ZCXkqIg61ZMXaQ0gnNecQshuLiulo+sWckPN74woYt4y+AQ337qWf7jxc3UR6PURwsoCfhxazq2tImlM3TH47QMDtEyOEh3PEE6ZxVWhoIgYUNl7aiyEQhM20JT1FywQGF9hRPwWF5Zwd+vX8s3nng6z7JsG4rxhYce5RdbtnJhbQ1zi4uI+rwoQjCYSnG4p4/nmpvZ3tYxIW9bAFfOqud9K5ZNWZGWv/ZExDJpHjp8iLRpUR4I0DEc45b5C065p2EzyU1Va/GpI11tXp1FqSkKn1i3hvZYjMcOHclT3v3JJD/dtJnfbttJQ7SAhsIopYEAHl3HlpJENkNfIklHbJiWwUE6hocZTmfOqrO3oiiUVZxZKtVkcIp7s4ATlIunNZL6PiRZIr45VBZMdPecen+SmJmiNdFHoStAUqRJuNP43C5qfEXYUtIc7yZk+ChyBUhaGU4kegloHvyWm6aBbgrK/MTNFO50P248eNQy0vYRBjInqIpW0pnaSzhYy2D8MAWRhbT0NyG9UeLxQwT1Mg7HH6bEM5fu9EEaQ1dhnFT0YtmS7z/wHG5dozeWYHVDNasaqvj1M9v42JVraOkb5Ildh4kGfBzt6iWZMdEUhXlVJSDh6b1H2XKkhQK/l9suWkb30DC/f24HGdNiXlUJ1y2dw5O7j9DcPUBPLE59WSE3rxxzAyTNbJ6rpDYYwXcWcQshBHXBKC5VJWk5z1VXcpiaefW8/Ngu7v33x5i3poG6RTVnvM/JUOD2oivKqKyJbIaUadIYaCCoB1hbuJysbbJ36BBLIg6/StPxXu58YBtNLb1s/+af8bj1XOBxbAwtnl/JR969gcXzKp3CJonTFf4McM4rbnBoVN+/fBm2Lfm3TS9NUH4S6E+m2NzSOmmK3elwckrZyal0ihhpdCp464L5JLMm39v4fF75vWnb7O3qZm9Xd14DAwlTKiABXFpfx1cvv2RKNr8zgd9wcVFNLWnLojoYwqNrRD2nTu3bN3SCOcFqfB5HcU/ORnjmEEIQdLn4xpWX4VI1HjhwcMJ5x9Jptra1s7WtfYq9nB5n2m5sJKXwtMtw7pGZ3YVpHsbtefNr0tIsZWX51dFnmB+uRBGCrLTZN9jKoVgHfelhjsd7cKs6h2Od3FpzAfe0vMxMfzHF7iA+zU3CSnPn8ZdYX9KIgkLYqEJVdOLJXtLWEC53kLrABsJGJT3pKEdjz1Loqidjx9EVL2GjEo8aIWkNEDFm4NMmWp8SSVNXP5+6dh1+j4vv3/8si2vL6RocRiLJmBY9wwkQsGxmJQ9u2887LlzCg9sOUFkQor60kFvXLuSf73mWIx29PLzjILPKiphZUsC/PbKJC2bNoD+exJI2n7puHScbHqa08wpcIi4vkzMDTQ2vbmCo2qjiTppZ/GEfb//cDSSHU3iDr4yDZzz8upFrfuIo7oxtkbEtFKFQ4XECrYdix1gQaiRiODUfwYCby9Y1ntL4KC8JU1oU5LFn93PFhjnYtuSxZ/dz+YY5p70K54XiBtA1lQ+tXkFtQYTvPvvchAa4rxYZK85Qtp2oq46UNUhT/AWqfSvxqgX0ZY7Rmz5CXeBiNFXjtmWLKfb7+PbTz9I2FJuwLwl5EezJ4NE1bl20kL+9YBUBl+tVKU1FCBaXlrG7q5NYJo3fMMhY1il7PkaMAL87/hQVnihuVeeqshV41FfnHxbC6XH5T1ddxuziQn6++eUJla+vBIoQ6IqClBap5P2Y2R1YVgeqWoUv8DdkszuQdj/Z7D6kTOD3/w0oYdKph8mkN6EoETy+d6EoUTKZ50gnHwNMXO4rMFwXksk8R2L4P7DtONnMdtzem161zH2ZYcK6lw3Fc0E4rpJ1xY1kbZOmeDe7Bk7QGCon4vIRyyaxpc0lpfMRAo7Gunih+yDrihup8hbSNOxCERoKGrrioca3hu70IdxqkCLXbMo9i9nS+2vWFn8UgaArdYDe9FFKPfNRhY6mTH1fA26D4pDfcePL/BejZdvOrFMoBDwuAh43PpfhuP4FlIT96KpKQcDDQCJJ52AMVVEYSCRZ3VA12gt2ZnEBxiQcM0ouJjMCW9oTyKROB2ebSVhE3QaG+7WJd2iKOtq1ChwjbWv/LkrSfg7EjqAJlZ50H2+run50ncICP7fcsHyy3eVh2+4TPL3pIL39w1iWjculn9H5nzeKW+BYwlfPbmBJeRm/37GLe/bup3Vo6Kym1OBY8OWBANfNmY1bU5HSJmZ2InI3x6UGsGWWuNmDVy0gqJdzcOhRav3rUIWGqihc2ziL+SXF/OdLW3jo4OE8v/ep4NJUlpaX89erlrNmRvWE1KWTS4fzMfV5Ng308+SxY4TcbuoiEaJeL/OKpvYJzwlWUu0tGj2O6xV0v5kMQgi8hsGHVq3gopk1/GLLNp44coyBZPKscmKEcLjXF5eVcd3c2WyYWYNltZBK3kcw/A2S8V+hqKUI4cMyW0inHsAf/BxCuBBKgGxmC+nU0/gCf0MmvZH48I8IBL+MqpTh9d+GtAeJDX0Hw1iOoS/FdK1Hygxe39sRwks2s+NVXYew4aUvM8xLvUeIuvwIHCXl/FWYHSyj2B0ipHsp80SwkWzqOUjE8GEoOuuL5yKx2TNwgrmhNaNjM6iXoQidUq/jCtOFh7BRyYbST6ELp3BrWcHbMWUGXfHg2NWnuPInjTGXrpFIZ9hzopOXDp8YTfGcEJ6VsPnwCSoLQhzr7ONNy+eyfGYlg4kUy+sqSaaz+EYU5xRGiUfVMVSnzBygMzmMLeVZEUcNpFN5M/CTM5NGcKpK4tPh5HMXAhaF55JmkOvLL8ejujkYO4I27hk6E0NMSsncWWVs6G1gyYJqFCEIBs4s1nTeKO4RCCEoCwb45Lo1vGvpYl5ubeW5puPs7eqmMxYjnsmSsazRAaCrCi5NI+R2UxkMMrekmOWVFcwvLSbi8eSmypJYtpPBbCsFRi2KUEcbnQoh0DBQhDZBjhmRMP94xaW8d/lSHj9ylOebjtPU389QOk3GsjAtG1VRCHvcVIVCLK0o55K6mSwoLRlt69XRPUQ46MHtGnOVvLitieULq1FVhXTGxDRtfF6DBWWlfGLtmtEHcaTgCJyGEVGvl0Q2w/HBwVG+kysa6vOoAzy6js8w8GrGpGyAuqryzqWLaB7qwKv6UIVGWSAwaW7sVLClzd7BZoaVJB9Zv5iPrFnF803NvHD8BId7++hLJEhmTadBhRBoisClagRcLsqCAWYXFbKkvIxFZaWUBvw52gKBaY6YhSkkFkIJjj4gurEKTWscdYdkMpsxzUPEYz9EygTONNfEtjtJpzeCTGPbfUhMFCWIEB5ARYizy1CaCl7VxTtq19Ec78GnuVhd2IBfd2NLSZE7SED3cGioHV1R8WgG76xdx5FYJ17NTaErwIXFjYQMD52pIRQxjsg/57Yz8qpcRd53VRionN7aVITgsgX1GDk60csWNhD0uHjPxcvZ2dzO3MoSNFXBpWuUhPysbawh5HVzwewZRHweZhRF2Hm8nb9at5iycIDrl8/h+QPNvHComVmlhQgE86pKcOv6pAZJwHAR0F0MZRwSqiODvfSmEpR4Tx/ABUfx7extzyOEimczE5S/lJL2RP7MWHLmbresbeVxnKhCQVc0KjzV9GUGSJgJCowwAS1fbiklqbTJwSOdHD3eTTpjUVjgZ05DKWXFIRRF4DI0Ll8/Z7QE/kxx3inuEQghKPR5uaKhnisa6slYFolsllg6TdocU9yG6jTH9Ro67nFUsXmFOwgKjBkMZc+u34MQAlUI6guj1EULeP/ypQxnsgymHCvgm08/w7a2dr573dUsrShHVxQsy2bTtiYyWZM1S2eyc18rq5fWMhhLsW33CVRV0NU7TN9AnPqaIppaeunuG+aai+ezoLSEBaUT+0gCNESjHB8cYGfnMDMjBcyMOKQ7lzXUcVlDfpTaljZJK4EudEBgyiwuxY0ps9jYvH3xQrYPJClxlVPhnXF2NwYwpcVPDt/D/qHjvH3Gpbx/5tXMWLyQWxcvJJXNEks7VarZXMaBwySo4TMMPJo2mhJ58kBW1XIUNUoi/ms0rQ63a6x3oRD5ikoIPy73Bry+20a2RtpDDMd+SDD8TwjhIZvZOX4L4LVriyaEIOoKEJ2kHVxAdyOlZH64HFsmkfYgPlVlcaQScJS0L9fYYobv1L1HR/o82jKFlE4OvqK4EZw+6KwqCpcuqB/9PvJ5QXUpC6onFsmUhJ1zuWD22JhYPWuMYdPQNNbPraXjeAvFER0hYG5lCUPZBBt79iAQLAzXEtSdl4xfN6j2h2nNpQR2JeI82HyA2xqXnpESi5tZ7j62N2/ZHYd3sqFiJqtLqkb3Ydo2W7tOin1Jh43wTDCQTuXlgXtUDUNR2Td0mF2D+4gYITShESkMo+dUqpSS4619fP8/H2fX/jaUXDaNaVr4fC5uedNy3nbdMnRdfUWGwnmruEcwctKuXKFGZBKa0dPBkibDZjdJa5C0PYRAJWH1Y2R9mEYNaStG2ooxnO0iZFSNtgs7WQ5NVQl7VMIex7LyuwzSlonX0Ef9zVnTIb86dKyL1Utqcbt1UuksbR0Do29gj0tj2YJqtu4+zozKKGXFIcLBqc/L8UMKrqir54q6erricTqGY1QGJ5JjSSk5GNtNwhqm0lPLsfhB5yVolNCRakGg0BhcSEiPIF+FIhtrGytHr48AvIaB9xT8LqfeaRbbbEXoHiyrhWx2J7qxcsJqQgjc7ssYGvwqSXEHoKBqtRjGCkAjk34J22pHMubeUrUZJIZvR1FC6MbpfZOjIkmbWOoZhlPPARJNjRL1vwNVCeZ+lwwk7iKZ2YtbbyDieyu2TDKYeJCBxL2kzWNImUQIF4ZaTsBzCRHfm9GUU3dccXjdU8RSzzCQuI9Udj+WPQgoaGohPtcKIr6b8ehzESc1rE6ktzOQuB8hVKL+2zC0fN6QVPYIfcN3ADaaEqEw+H4U4c479nDqGWKpZ1GVAIWBD6AqIyVRgouKF9CdHhxd31A0qr1FaELLi6NoQmFFSSUvdDp8RDaSH+9+gdmRIlaVVJ2SmjhrW9y+9yW2decbW/3pJH/33P38w/KLubCsFl1RePTEYV7sOpG3no2kZXjwtEF5KSXNsf68cvmA4caj6WSSGZaE51Hjcxj9HEPIwXAizbd+/AhZ0+Jzf3MVtdVRdE1lYCjBs5sO89s7X6Ig7OWqi+b9/1NxvxawZRZLZih2N5I0B9EUg4hRhUAlK1PErT6qvCtIWP0EqeDVkCoOxlK0dw/iceskU1k6upzPXrdBS0c/S+dXk0qbuF06LpdO0O9m6+4TzKiMTlk++0LLCQ739TLiiesYjnFxTe2kihugN9PNkvAqFKFiSpNZ/nnsG9qBXwsQ1qMMZHtHPaOvNtvkbOFYkDaO2s+3RlKph9FdK3G51mPLGPHhHxOKzMXlugBJfqaRolYQDH8TM7sTEOj6AoQIEgz/E2Z2P4axFJf7spyLBAxjFSLgwbZ6EMIHdJ+BrDaDyYdo6fsslj2AphRRFf0Oihg/ZZYMJh9jMHEvbn0uPvcFtPV/laHk44xkKYwgYzYznN7EQPxuqgt/gEtrmPTaSynJWCdo6/8KseRTE849a7WSzOygf/gPFAU/QFHgQyiKm5HxYcsE3bHbARuvsRRDKx39TUpJLPkU3bF/c66jCBLyXoNLr807p774nxhI3IXHWEhR8EOjvwybSR5s34JEMj9UA0BPeoi7Wp5nXmgGFd6lebJeXtXAz/ZuHmUF7E7G+djTf+Y9jcu4rnYO5d4A+jjisbRlcmSoj1/sf5l7ju3LSwJQhdPwpC0+xCeevZdqfxiPpnNksHc062Q8Nned4B2zF6OeIhwogU2dJ/KiBJX+IIai4tO8PN31AlsHdqMrOleVXoxfc2YTu/a10tY5wPf/8W3MqBijnK0qjzBvVjmmZfPA47u5bN2cM6quPBnTihvQFQ9VvhV5ywL62FTRo4bAPes1OVZJYYBrL56PqipoqsJVF81DVRRe3H6M+hmOa+Tqi+Zh6Brrlteh6yrhkDfPB34yehIJVldWjVr1R/r6Tsma1+Cfw56hbVR76yhxldEcP8zC0Apak00MZPto8M9jf2w7qtApc1eiiVeeqvhK0Dv0bTLZQ5QU/ABVjLkabLsXpAVCwzZ7ESKAEDqKNrF7jhACVS1EVS/JW65p1Wha9STraxjGOKViNp1SRkdpP0BL3+ex7AF0tYyq6Hfxu9ZO+aLLmCdo6f0Mw+nn0NUSAu6LceuzQQhSmb0MJh/FsvtIZnfT2v81agv/E3ESY6OUkqzVwfGej5HIbAcELm0mAc9FuLRapMySzOxhKPUUlt1Lx+D3sewYpeHPouTcSYZWg6oEsew+UtkDBOXl4+KHkkRm69g1l8OksgfzFLeUGdJZh1TMo89BjPOnBzQPl5cu5a6W50eXVXiirI420pEaOOmaCxojRVxUUcf9zftHl/elk3x3x0Z+tm8Llf4QJR6nf2rSzNIWj3FieGCCm8OlqPz1/FX8cv/LDGXSZG07r0w977g4CnljezOHBnqn7Ccpgbb4EE+3Hs1bPq+gBEUIan1V9IcH6E730Riow6eOzYrbu4aIRvyUF+fHTJxxKZjfWM4LLx8lkzX/9ytuKSWxTIatrW0c6u3Fsm2qwqEcA51zcWYWRKgMjgWtbClpGxpiS2sr7UMxfIbBorIy5hYXjQa9Tj5GIptle3s7+7t7yFoWMwsiLK+oJOJxT3qDs5bFrs5Otre1Y0qbuUXFLK0on7QfoxACn3cs8h0KeJBSsm5FPUOxJAG/G5fh+DkNw7k9fu+pmxxcXd+Ql3te6g9MGXgRQlDkKqPI5UyPi11j0+SQPlbAsjxy4SmPOYLJcqVfLbLmMdLZPXCSJenx3kI69Qjp1FMoSiGB0JeAV9bk+NVASpuBxH209n8Byx7EUKuoin4fn2vFKc/fljGG0xvxuVZRVfBtDK2G8b1RIuktNPd8GNPuJp7aRCKzA797zUl7segc/JdRpR32Xk955MtoyvgMIkkqu4/jvX9HKruHntgv8BiLCHuvz7n0CjC0SpIZR3EzLgnPlnGSmb0I4cGl1ZLK7iWR2UHQc8U4n3E/WctxUXiMBePOYcwlNh7OdpNfF00ofHzhBbzc3UrHSQHEwUyKwb4Ue+icdNvxuGrGbD42fzVB3cV3tj0zoYPNCIrcPhYVlvFYy2H600n+75bH+ee111Li8efdOyklCTPL93dszAts6orCqhLnxb9v6DC2lCyLLOTFvq0Uu4sI6s5sKxRwExtOMZxIE9HzO0xJKWnrGMDvc6G9wh6q543illLSPDDAZx96mD1d3ZT6/ShC0BYbIpU1MVSVkNvNp9et5S3zneos07a5Y+cufrxpE/3JFG5NGw2IXTt7Np/dcGEeSZWUksO9fXz18cfZ2taOrqooAlKmycxIAV+65CJWV1Xl3eBkNsu/bHyO3+/YiS0lLk3DkjYbamonzeXO2Fme6dqFjc2GooUoQmHvUDPb+4/QnR5A79Go8ZWwvGA25Z6pq/WklFjS5mi8nW39h2lL9iAllHkKWBKpp95fMaWbY2TaaUqLQ7FWtg8cpiPZjxCCSk8hSyIN1PpL83JXJzt+V3qAF3v3cXS4HQnU+EpYFZ1DoRE860KKM4Gi+PF4b37N93sGRx795Cjte2nt+yKWHMSl1VId/QEeY/EZvbRUJUpF5P9iaLUnrS9yfuk30x37KZIM8fRmfK7V+eMts5eBxH0AuPR6yiNfncQfLnDrc6ks+DpHu96NLYfpHvopQc/FqCKAwIVbbySZ2Uk6ewQpM4icDztjtpK12tDVIkLeK0kNOorbcetouXVOYNlDCHQ8xty8Y/emh9g12ERnqp/dg83MCVbRnurjUKyVvkyMQ7FW6v1jVMhCCGaFC/nHlZfz2RcepD+dPLtbA6woruTzyy7CpWrc1rgMWzq+8pFslRFEXB6+uPxiij1+nmk7Rsa22NjezG2P/YF3zV7CiuJKwi43lpQcHXTcMU+0Hsnbx8xglAVRh5pCIkdz0U9+XS1orEAi+fdfP8s7bl5JYYEfRRGk0lm27TrBXQ/t4KarFo0aZ2eL80ZxW1Ly/edeYGdHJ1+59BKunT0LRQg2NjfzmQcfZk5REd+99moKvU6QRErJwwcP8U9PPU1DNMo3r7ySuoIChtIp7ti5m9/v3IlE8vXLLxt1MfQlk3zmoYc40tfHJy5Yw+UN9eiKwra2dv5l43P8/YMP8fObbx5lu5NScueevfx623YWlpbwmfUXUh4McnxggO8/93weDewIUlaG248+wFA2QaWniPvbXuSxzq1k7PypX9QI8uH667mkZMmEII2UkqFsgp8dfZDHO7eSsPIHqEc1uKRkKR+su4agNrF/pZSS3swQ/374PjZ27yJ10rF9mptrylZxW+0VeNWJxUFSSjb27OYnh+6mI9Wf91uR6wneP/MaXOrp3SujGRH2ALaMoYgAihI+xbomlj2IlHFAQ1UiCOHJe/FK0ggUYCI9qZRWTknpCHHmQ18RI2PKZiBxN619/4Alh3Dps5gR/SFufe4ZzzSC7g249cl91wA+10q6Y/8B2GTM/ICalJKh5CPY0mlaEfHePGUQ08mnX4zPtYpY6nFS2f2OBe9aC4DXWEB//H/IWu1Ydj+KUoaUkmR2D7ZM4tJq8blWAhrp7GEsexBNjTp+5uxBJFk0pQhjErdTiTvCzZVr88bt/HANEjnlLPSyqnp+qF3P17c8waGBnjPK+TcUlatmzOILyy4etZgNVeUD81awqrSKO4/s4cCAU8k8O1LEm2fOZ360lJSZZX60hK25wOaBgW6+9OIjeDQdj+bQMAxn0xPoNRQEtzYsHOWRnxNsYEvfDjb37WBecHZe67LiwgAfe89F/OD2x3n2xUNEI340TWFoOMVQLMVFaxq46eolr3iGet4o7lg6zZbWVmojEa5vnD3aeeaSmU5e9L6ubiSMVmsNZzL8x+bNuDSNf7rycuYUFSGEoFwG+NxF62mNDXHf/gPcPG8uKyudJp/37T/Ars5OPrh8OR9csXw0La0qFMKlqXzyvge4fcsWvnnlFahCkMhm+f3OXbg1jS9fcgnzcwyB5YEAX77kEt5+x/9M2WYtZWX44cG7OBZvZ1lkFquic/Drbo7Hu3i4YwudqX5+fOhu6vzl1PrzU7OSVobvHvgjz3bvJGIEuKZ8FbMClUgp2TPUzBOd23igbRNJM8X/mXML7pMqImNmgm/u/R0v9x+i2BXmppIlzPSXY0qLHQNHeLprJ3888QwZO8vHGm5AH6fkpJTsGzrOd/f/gYFsnBneEq4sW06JO0J3aoAnu3bwo0N35ZHKTwVJioHYvzMY/x22PYSqhAn6bp24npQkUk8wMPxzMuZ+bDsBQkVXK4kEPozf86ZcgUqGrr6/wyZNaeRfRwOPI/uIJf5I//B/UhL5F9zGognHmQqq4gckA4l7aOn7IraM4dHnnzKAOBV87lVMFdwWQqAqIcbcFinGuzHAIp5+2VkXHb971WnKDDX87jXEUo8jyRJPbx71wbv1OQh0LHuIjNWKnsssSaS3AhK3PgeXVoemhDCtXtJmM5rqkJYls04KnqFVoyqRvCNGXUGirvyWg1XeIqq8p254oQjBurIafnv5rdx5ZDf3NO3j2FAfSdPMKyBSEPgNg8WF5bxz1hI2VNRiKPlBbFUoLIqWsShaNpp/PcL7LoTT3eZD81bx8WfvJZ0LWkqc9MBTpQguLa7g5pnzR4/VluxgcXgeHtVNc6KVpJXCq3lGj7NhdQMzqwt5etMhjjR1k81aLJrrZ+WSGpYtnIHxCnzbIzhvFHfWsslaFi5Ny2MAVHI3ImNZZMZ1TG/q7+dwbx/LKsppiEbzpmYuVeWaWbN48shRnjp6jJWVDiH8U8eOOW/xWbPy6FWFEKyprqYsEGDT8RMMplIUeL20x2I09/dTH43mcU4LIaiLFjCzIML+7p5Jz0ciORRr4Z01l/GOGZeiK9qoFb8i2sjnd9zOQHaYZ7t3UuMrybMqH+3YwsbuXRQYQb624DbmBsf4wC8rXcrSSD3/b+/veKZ7J+uKFnBR8aK87f/c8jxb+w9R7ony9QXvpdY3Rjt5eeky5oVq+MGBO3mwfTPrihawLDKmnCxp89/NjzOQjVPtLeYbC9836tKRUnJl2Qq+vuc3bO0/dMr7KaVkcPjX9A19H7/3evyeN2HbAwzGf0XGPHpSZgZYdg9CuIkEPoGuVWFZXfTHfkLXwBcx9EZceiNg4DIW0Dv0HdLZnbiNleMe6CxDiT8CEl2bebI4p4Qi/MRST9Pa/2VsGcOtz2ZG4Y8ncXecdk8Y6mnY78TofxNgyxRZqz0nkxddLZ3Ugh3dlRC4tBk4LwqbdHYsyObSZqAqIUy7h1T2CF5jGZI0yYyTheMx5uV84VUkMttJZvbgNZYAJqnsQQDHTcJrF7gWQlDk8fHX81ZyS8187rzzGdxzC0n7BEnTxKWqlPuCzAoXUuUPjWabTLUvAF1MVI5CCC6prOPds5fy832bT0tPAVAbiPD1lZcTNBxrO24m2Nq/m2pvOQHdz66B/VxashYvY8aCEILqigLeefPKHEOwzNMRrwbnXLPgEWrIdNYkm2v4CxByu6gtiHB8YID9PT25PFbJ8cFB9nV3UxEMEvWNReDbhmJkLIsZ4fAE0ighBNW55cf6+3N0ouZo8LLEP7Fyy6vrlAT8DKRS9CYcP1xPPEHasigPBkYpMUdgqCpFvlPT/Vf7SnhL1XoMVc+7oXOCVcwNOkUOh4bb8qaNSSvDfW0vYiO5tnxVntIGp4v7msJ5zAlWY0qbxzq3Yo/bw2A2zsMdTnPgN1demKe0wbFWLilewgxfCRk7yxOd+Q0f2lN97BhwFMCNlWvz/PBCCEK6j1uqLzqlfxzAlo6SdhkLKA7/Ez73ZQS8b6E48u0xHuwchBAEvG+mLPrvhHzvwue+mID3bURDn8e2Y6Qy20bX83uuRhE+Yom7GE8RkDEPk87sxO+5dsJL4XRIm0dyKX/9Odmdys2zhUBFUc6+zmAEUmawpTP2HHfP6cujFSXAyIvAsocYKTJS1QhGLhsnlXX4rE2rm7TZjBBux52DgVufC0Aid40te4iM6eRdO4HJMdiWTfO+FizzlZeXO+cmIGnxxD8+wIJUgPc0LuMj81fxvjnLuXrGbOpCUQxVe1XKTxMKn168jr9ZcMGoMp5qvbWlM/jpRTfRGCnKD2BiM5AdpDfdz/zQbEL6xObmI+ejKAIlp4dGdNerwTlncaeyJv/17Ms0dfehqQpvWjKXVXVOqtvHVq/i7x54iI/efS9rqqvQFIWXWlpImSZfuGgDgXGFHZmc0p+KaMnI8QWPuDLsXHMFp+XZ5H44XVGxc8n/4JTCSinR1ckzQU9F8gSwKDwTvzbxQVZQKHKHAUiYqdxU0TlCe7KXE4kuNKGyJNLg5FufNAg0oVDrL2P7wBGa4h0kzTR+3TlOU7yDrtQAbsVgUbhu0u1dqk61t5gjw20cGW4ja5ujPutjw+0O1aiisyhcN+HhEUJQ5y8npPvoy0wk4BpB1mzGNFsJBG5EiLGIvqHVYej1mFZ+JoEQ2miOt21nAAtViSKEjm2PHUdTK/G6LyaeeowC+5NoailSSuLJRwCB33PNlDJNhf74nYCNppZgWt1kzGZa+j7LjMKfoilFZ6lAXo2lNT4740wf/PHrja8WNnDrc0hktpHKHgIsUtlDWPYQhlaFoTnuQ69rMX3x/yaV2YuUKbJWB6bdj8DlpAKOO/eB7iF+8qlf8ZU/fApv4JW/oE4W/fWoIxBC4FY1Pr7wAi6vqueeY/t4qauJ3lTccUPpBrPChVxZPYv15bV4tfyYiU/zck3ZpehCnzT1NpnK8PQLh1i9rJZw0IuUkp7+OHfcvYW2zgFWLanl6kuctN9XgnNOce9p7cTvMvjKTZcxlEzxs6e3sKy2Al1VWV1VxQ1zGvnz3n0MplLoisrVs2Zx9axZzCnOf4AiHrfTEDiZmpRxbDCVxrJtwm7PaFAj4DLoTSSIZ7OcXGhs2jaxdBpDVUcb4PoMA0UIYjnu6PF8HiNphadCpWfqh37EJydHaxAdtKd6ydgmArj9yAN4tMmrEFsSjosmaaVJ21n8OGmHLckeTGkhkfzw4F0YU3TFboo7bciGzSRZaeLKTYk7Uv1IJD7NQ1if3HL1am6Cp1HcptXtBLi0ipOugYaqFOYpbikllt1LLHEXifQzWFYXUqaxZQI56gcegULQ+xaGk/cSTz1J0HsrUiYYTj6E27UcQzv7np6K8FIYeC9R/6209X+DweT9xNObaev/GpUF30ZhYgD49YAQLhTFB5ZTNGbbCTiNm9Sppsz5eZUQ4yfZXmMBfXEnS8SWCZKZ3YCFR29Eyb1MPcY8hHCTsVrI2t25Ss80ulo6qtwt06L1cAcvPbSd1sMdbH5oB4ZHJxDxM3fNLKfJgi1p3tfCy4/tIpPMMG/tbOaunoWW8/NKW3J0VzNbHtmJy2Mw74LZeeRU6WSG/S8d5uDLR0knM1TPLmf5FYvwBNykEmn2vnCQxhX1+HIdb6SUtB7qYKhvmDkr6xFTGGOqEMyPljKvoIStvb9lOKuxuODdGKqGS9VO2XDBo0494zl6vIcf/vxJZlRGCQe9ZE2LH97+BC/vOs6MigL+7VdPo6oK1146/xWNnXNGcdu2ZDCZGu00EU9nGEqmifq9o9HpzS2t3LFrF1+99BJunDv3JMau/JOvjUSIeDzs7+4mnskQcI1Nh6SUbG9vx5aShaUlo3wZ80tK2NPZxf7ubqpPaofWOeyQ/FeGghR6ncFRFggQdLto6u9nOJMh5B67kYlslpbB/PZkJ+PkoOGZIJ6zwCWwP3b8lOuqQkFByQvuxLPOVNuSNnuGms5ge5GnF5O5DBZdUdGnKPJREbhOG5wca12RDwEnuVlsu5+O3g+RNg8Q8t6C2/d2VKUA02qjs+/T+VsLgdtYiqHNIZa4k4D3ZtLZPWTMwxT7PwCvwCcb9FxMaejTgEJFwdfIdneSyGxhIHEfhlZJaejvX9F+zxaKcOHSqklnD2LLBFmrFUOrPmXKqOPXdq61Sx/jrHGuk1M8Y1l9mFZvTnGD17WUEQVvqFXoShEZq4109ijp7BFA5op4nMrc+GCC+29/gqM7mxkeiPPc3ZtRdZWK+lLmrG5AStj88HZ++n9+w8IL5+ANevjRJ37BZe9Yx1s+eS1CEezddJBvvecnLFjXSLAwwMa7N5McHqMkaDnUzm+/cSc186txeXR++bU/svWJ3fztD9+LtCW/+PL/cO0HL+XK91yEEALbsvnZP/yeqlllzFk1xsdyKtQFL0RKm6Axeb3G2eB4Sx+hgJuq8rAT0D/YwUvbm/jUBy/lkrWz+ckvn+bRp/dy5UVz0bXzuABnOJ3mp09sIpkxiaczHO7sIWPZ1BZGRh/x4wMDJLMmxwcGOdjT4ygOAW5VI+rz5nWELvH7ubyhnj/s2s2de/byjkULRwOOh3v7+NPuPZT4/VxSl7PApOTGuXO4e99+/uvlrSwtLx9V0CnT5JdbtzGYTvOhOSvx5JoeFPl8LK+o4PEjR7n/wAFuWbBg9MXz+JGjHB8YOINA1NlBzQVb3IrBZ+feSqExuV9tdH1FJaT78r4DBDUvn5v7VwQmcdWMh65oeVa9lju+nYtFTIbxHCVTyxVFoGFZHSflm5vY9kDeuon0cyQzL1EY+jJh//sZoThNpl+e9DhCeAl4b8pVYO5nOPkgqhLB617/ih5IIXTAca1pSjFV0W9xrPv9ZMwmuod+hq5WEfW/fVSu1w8KftcahnJ84rHUc/hcJxfojEGSZTjtVDAKdHyufA4WQ6tCVSNYVi9ps4lU9jACDa8xFsxWlQAuvYGMdYJUdi9p8xjgBCZH1EegwM+Hv/1Onr/3ZX7+D7/nEz95Px6/ZzTOmoqn+c037uSq9140qqjXXLeUb73nJ6y+dhmVDaX8+ccPs2D9HD71bx9A1VSev2cL37ztx6Oy1s6r4ut3fwbD7Tx7jSvq+Y/P/ZZELIk/7GPDW9fwxO+f55K/Wofh1uls7uHw9ibe/rkbydoJLJmmP9OMRw1jKH5604cp8czHUPzYmAxnnRmmW82niZBSkrZj9KYOkLET+LQiClwz0RQXUoIp0/SmDpC0+nGrYQpcdbhUP4lkhoDPjWFoSAmPPruP8pIQF66qxzA0FjRW8OK2JjIZ6/xW3AG3i89eu2HCYygQOHpVsqqqiopgkB+9sIn/eGnz6OByaRoN0QI+ufaC0QIZRQg+tnoV+7u6+c4zz/LiiRPMLS6mL5nkscNHGEyl+Mqll1AVcm6UEILFZWV8eOUKfrTpRd7zhz+xfmYtblVlS2sbm1tauKRuJrcsWDA2qIXgQytXsrWtnW8+/SxbW9upjYQ51j/ASy0tNBYXcWCKrJJXikJXEFUoWNKm2BVmbujs2PuKXGGnPZu0qPAUUu07s2avIygwnBL0hJUmYaUJTdJv3ZQWcfPU/OS6VoOqlpJIPUPY/9cI4c2Vc7eSyR7MS+Wz7UFGrLwxTg2bRPppnHZj+XCClFfSH/sRscTdJFJP43NfiqoUIqVNX6aLAqOEpBXHxsK0swxm+yh0leJVT92o1snUaKCq4Fs093wU0+6lY/CbGFo5AffFr6vLRAhB0HMFXUM/xbS7GYj/maj/VvRJMlWklMRTm0bTB93GHDzGwvxAtBLGpVUTt7pIZnZi2l2oSkGeZQ4qXmMRsdQTJDN7clktIi8wKYRwJkpi7Pv4Nny9bf10Nvew9NIFKLlKwbrFNXj8bg5tPUphRYRju09wy99fj6o5xlfDklp847vXCOjrGODI9ib6OgdpOdhGNm1imU5PzLU3ruCenz7K0Z3NzF5Rx0sPbae8roQZ8yppTTzH3oG7COildKf2UeSeQ9zsoT25lZWFHyVrxdk7cCcdyR1UeFewovDDo4dN20M82f5V3GoElxpgKNvG0oL3UOyZi43JC13fI2UNEtDLiGU7mBm4mPrg5YRDXmLxFMlUlkQizvNbjnLTVYvwehwjaCy29MqClOeM4hZiaj5aKSXHBwb52hNP4NY03r10ySiXtmnbtA4N8djhI3z+4Uf5/a1vozTgPHylfj8/etN13L7lZZ44cpRNJ07gUjVmFxXyvmXLWDuj+qSBrPDBFcupDIX4zbbt/HHXbmwpKfb7+Nia1bxryWICrjHrUwjBwtISfnjdtfzohU08uvcAmktnZrSAr1xyMSnL5PbNL+OepPvHK0WVt5iIEaAnPcj2gSPMCU49VT4ZTuCwDL/mZthMsXvwGFXeswuuVfuK0YVGwkrTFO+g1B2ZsH1PepD+U/i3AVQlQsh3K31D36dn8B/xe2/AtmMMDv+Skwezy5iHEB4G4r9GVaIgNBKpJ4gl/pTHkzEemlqOz30JscRdSJnE772BEaW/a/BFloTXsT+2jWpvA2k7xWC2l/1DW7m89G2nTLGDHG2BazXlka/Q0vc5LHuQlr4vUlt0+1kV47wSGFo10cC76Rz8HhnrOC19n6ci8nUMbQZjUzgn37u1/0sO8yAuigMfHi0kGj0PdNz6XOLpzQynNmHZcXyuOXm52UIIvK5FgEoysxdbJhDChUeffcbnmUllQILLM27mpqvoLp1ELIlt2WTTWdy+MReF7tJRc5aolJInf/88v//W3Sy4cA7Vcyrw+PL9yyXVhSy8sJEn73ie2gXVPPfnzVzy9rXohobMWGiKmzXFn+Sh1r+n0rcan1bM1t6fIbFwqSFWF32cl3tvx7TzDY54touE2cf60i/gVQuxMXMFXoKsnaArtZcNpV+g0DV7HJumYE5DGZmsxf/714eIDadQFTHKu+1QvvYT8LudXpOvAOeM4j4VbCn5/vPPs7Ojk1+/9S3MLc5P5pfAd555lp9teZmDPb2U5poICCEo8fv5wkUb+Ns1q4lnMuiqStjtnpSnBBxf9/WNs7mioZ7BVApbSgIu12hPyNF8ZtOio6mb8roSVlRW8NPrr+frH/wJt/3jDdRUFePSNGwpuayubkKq4KtBxPCzrnA+f259jvtaX+CCwrnM8OZ3hx/JEjGlhUDkRb1L3QUsL5jNk13b+eOJZ1gSaZigfEe2z0oLFTHqXgGnrL3SW8SxeDsPtr/E0kgDhjKWmmVLm6e6dkyo5hy/bzvXVSXgeT+WPcRQ/E/0Df0RQw8R8r0Nt7Geofh9IBWHslaZR9j3aWLJn9PS/XYsW8Xrmk1J5F/oGfxXLMtLNmuhaePvqULA+2ZiiTsx9Dm49YWjD02dfx4HYtsZzPYRMYp4sfcxfFqAuDV0WhfPCIRQCHuvJ2O20DH4XbJWCyf6PktN4X86+dWvk/IWQqEo8H5S2QMMJu4nlnqaw11vxe9ag0urQWKSzOwjnn4JW8YAjcLA+wh6r5pUphHLOZ5+ETAdN8lJ/nqX3oCqBHJuEomulqOrFZMJN6nMwcIAukujt62fylllCCFIDCUZHohTXFWI7nICmT2tfaNus/hQwlH4QDad5c8/fphL/mott3zmTQgh2PLITh79zbNjh1YEl73jQn70yV+w8uol9HUMsOKKMZePRw2jCgND8ePVCtCEgZ0L0iu5cTHZCztoVFLons3jbV+ixr+BmYFL8GnOLNVQ/MwMXMLGzu9Q6VtFXeByIkYNAGXFQf7mvRdxx91bUDWFT/31pZQUOW5N07Q53NTF4nmVr7gI57xQ3GnLYl9XN8U+HzMLJlp4SDlq1U4YOxJSwylU26bY50PNdVE2MyapRAaP34WqqWTTWaQE27bRdA2ZMSnO5WFnMybDAwk8fjeqpmDbkrYjndz5rw/xnq++FcOtoxsaRtKmQHeTHc6g+gSaruJ6Da1tcFxHt1RfxPaBwzTFO/nSzl/wlqoLmR+qxaO5yNpmjhOihS19B3l37eXMD9WObq8KhXfVXM6+oWaOxTv4h50/581V62kMVuFWDTJ2lt70EAeGTrBt4DAfrb8hr3LTq7q5sfICfnjwLp7v2cO/H7mPGysuIKT7GTaTPNu9iztPPItL0UnbE90Y6bTJ//x5C8lUBpdLY+2qD3Lw6Fq6e1opKapk3arV3HX/FnRtFsYFNql0D89tOkwgsIrLL76Bbbt2cqxpkFBwBldctJSNG/+a2LBJJLSNN18/RsAvhEBVCxDCwO+9nhGWPSEEpe5qXux9jDr/fCSSgWwvxe4KzjboIIRGUfCDZKxW+ob/m2RmB639X6I6+j0U/K+b8laEn8qCb6KrxfQN34FpdTKQ+POE9TQlSlHwQxT634uY5FEXQjiWMy4kaUDB65rYxEBXijC0KpKZXQC49JmoysQGEcECP4mhJC0H26lsKEMC3oCbSHGI5Vcu4u5/e4SK+lJcXoMHf/4kvrCPxpX1GG6dZZcv5Mk7nmfVNUvwh7088bvnxoKTQqCqCqlEmnQiw/Bggod+8RS2NVaSLoSgcWU9wQI/f/iXe5m/djYFZeHxZ5v7X3A25SuacLOu+O/pSu3l8NDDPNL2OS4s+QyFrkaEUFhc8G5m+C/kyNBjPNn+VRZHb2Om/xKEEFy0ZhZrl9eBcHrmjrpYVYW///DleE7B+HlauV7xln9B6IpCgdfDvq5u9nR2saS8bDTTJGvb7Oro4M/79lHs91EfjY5uJ6XkkV89za6N+xGKQuOKOq75wCW0He7g99+5F0V1moq+4ws3sen+rRzbdZzu1j6qG8tpOdjOR793G4mhJH/83v1OTrhb591fegv9XYP86QcPsPeFQ/z3/7uLxhX1rLt5JWbG5I7v3EM2ncW2JR/+zjvxh09dhHO2EEJQ4o7whbnv4LsH/siBoRP84OCdGIqOrjgEVxk7iyVtdKHyjhmXTti+xlfCF+a+g+8d+CPH4u388/7/waU4+aiWtMjYJpa0casG9knNFIQQXFG6nEOxVh5sf4m7WjbySPsW3KpB2s6SsjJcVbaCjG3ySMeWCfLbtkRRBeGQlxlVUfYf7GLL9jgL581nz75uli+ySKVg1rwZFEYDHG3qxpY2c2eX43EXEwlK4kX9bN91gtXLUiSTKo31ZaOyjd17m1jiboRw4XePWZtSShShEtQLqPXNwa14WVt4NVk7zcXFN6LkHmpViRD0XgXSxGMsnPReOERfOmWhz6FgkLU6AIV4ejNCW8tgOkmFL4jXWAzSBKFNKBF3Uh2dFndO8LOAkPcqkBZeYxHJjMnu452UhP1UF4bpHopzpKOXuVUllIe/RNh7IwOJe0lmdmBafQihoqsl+FwrCXmvxaXN5MSJfro626idWUwo5OHQwQ6EIqitLeJ4k4eu3lVksgPU1VUSH6zlyL5jzMyte+RwF+l0loLyN9HZ5mF4OE3AvZSaqMLJE8n6xTUsv2Ih37ztx3gDHhpX1vPR774bRVW47Stv5T8//9988YZvoygCf9jHx//1vQSjTkrpjX9zJScOtPEPN3wbX8jLwgsbWXLpfAy3gW5ovOVT1/LLr/2R7U/tRdNV1ly3jEwqM+ozB3B5DTa8dTW3f+H33PKZG874xTlZMcyI5S+xUYRGqWcRJZ4FbOz8Di3xlyh0NY4WihUYdRQUzsSrRWmKPc1M/8WA4/pNx9O0HunEcOtk01nqF83g+IF2hvvjzFpaS3wwweHtzfgjPmrmVji/DcQpKJmcS38E54Xi1hSF9y5bymcfeoSP3H2Pk/Hh85I2TU4MDrG/uxtDVfnixRsoHVf1aFs2257cw8W3XsDC9XNQFAUp4a4fPcT6m1ey9LIFPPyLp3nkV0+jaiqzls3EG/RQ3ejkFrcd6eSJ3z3HsssW0riyjjt/+CAv3Pcyl79rPW/68OVYWYsPf+ddIMDMWphZk3U3rWDB2kb+9RP/xbHdJ1iwrjHvXFShMidYTYk7QvQUGSEVnkIWhmcy01/GyZagEIJ6fznfXPQBnu7awcbu3bQle0lZGbRcFkmdv4yVkUpmBSZyRAghmB+q4tuL38eTnbvZ1LuP9mQfGTuLrrgJGwEa/BWsKZw7KceES9H5WMMNzAlW82jHy7QlewGY6S/jitLlXFqyhBd69tKR6qPMXTBhe11T0XUVTVNQFEFxYYB1q+pZv6aBcMjLzdct5dlNh7C3N7NkYTXBgIe7H9yG12PwxLP7efP1y9i7vw2/30UqbWJaNssX1wCSrNkGWCQzWxiK/4aA9y3o2lgAV2Kzo/85il0VhHSH4L7QNbFNl0urozT8AzRFIWllMaWNbUsyloklJUHDTdLMclfTLi6rmEVJ+CsoQjCUSYFQaIsP8nJPC1dVNVLgfz9FgQ+O7nsomcK2JUGPm97hBE/tOcKlC+oJetxIOYMZ0Z+QSGfQVJWHtx+kbzhBSdhPPJ3h109vZeGMkReVhtdYjNdYjCTrvBwQuSwYx8JLpTLcc/fLrFs3G9O0eHHTEeLxNJZl0942wIED7WQz76a0LEyPGuGpXSeY3VjOHb/bxOo19TQd6yZS4KO9fS1Nx2bRMKuUI3t7qSjqpbY2f2y4vAYf//H76WvvxzJtglE/WfsE0rYIF9fy6X//IH3tA5imRaDIJCOeA2aSNo+Dfy+f+9XH6G3rR9NVCkrDWFkLLUdxvPbGFcxf10h8IIE/4iNQ4Of6D18+mmUygkDET83cChpXjC8OU0a7VokR3kohUHIZUm2JLZyIb6IjuRMbk03d/0qpZxE1/vV0pfawb+AuQkY1pp2iN32IhuBVAMTNLl7q+SlBvQKBQmtiM7ND1zH+eW090sG2J/fS3dpHpDhEfCjJnhcOUlAS5viBNlZdvRgza/HYfz/Hte+/mD987wGufs8G7vvZk0h7arfdeaG4hRBcWlfHz26+kbv27mN/dzcnBgfRVZVCn5f3L1/GVbMaqI/m06AqqsKtn3kT9/3n4zz1Py9w3QcvZfbyOnra+qlqLEfVVGoXVPHgz5+kZl4Vwagfb4eHUGEAw22QSWZoOdhOOpFh5zN7yaSy+EIjxRa5MLoYs/RcXhdVs8vRDI1AxEcmmZlwLl7VxT/Me6cj3ymm5m+uupCbqy7MrZFGShUnVzjDSPDOLfdzfflSrilbSsqyMWUGBYGhCAzVTyazCWluBu3SCftPmW2odjNvrlrNTZVrSZgxrFzYJWseIuJegCRDMnsAzZiNQMGWGRThRmKiC4ury1ZweekykpZznh7VQBOOwrioeBHrixdO8BuqmsKMqiiaqlAQ8eH3uSkrDfPUcweoqihgXmM5z7xwECklDTOLaWvvZ+vO49RWF1IUDTBnVhlbdzQzr7Gcnr5hdE1lcCjJvQ/t4NabF9HZ/wnS2b1ImcXrWkck8LeMnxoLFBZH1uWylU5tkb3Q1Yxfd/Hnpl1cWz3X4VFODNGRGGJlcTU+zcVzHccIGW42lNWxseMYx4f7KfMGqQ8V8lLXcZJmFp9ucMvMxQghONzRy8PbDlAaCXDZwgZ2H+9g8+EWioJ+6kujPHegiZtWzue3z27n2mWN7GnppCBXy3Css5+W3kFmlReN0jiMuoYwQEwM1BqGzgVrZ7Fjx3EMQ6O1tY8L1zdiWzaPPbqbwqgfoQiqZxTS3tpPT+8wNeks9Q0ltLb0MWduBaVlIe66cwtut8HiJU4QNB6fGMMQQqAbGiUzxhR6PH2Y4fQLObbBZejRF/BqZfiMJfQMpwEbQysjln4e3aPiLzuKafeCciGGJ5i373BRkHDR2LKRYKe0JZl0lljfMPff/jiXvWs9bt9Y3Ua5dymFrlkIFFYUfQivVoRAsLro4yiohIxqNMVDbeDi0W08qjMzirrqqQ9ewXC2C1UzmBW8hpDhZK55tChzQjcymHVYHFcXf5xCV37QViComlWGqikEIn6O728jlcigGRpFlQXsfuEgyVgK27JJJdIUV0eZd8Esdj534JRl8eeF4gaHTGpxWRmLy8owbRtbSgROJshkDYBHUDazhA9/513s33yYP37/Ab7wm78lWham/WgXxVWFnDjQRmnNyEDL34+iKpTVFrP+zatYfPE8LNMenZopqkI2k8W27FEOAicN6tT+MyHEKVsljawzovCkzJBKPYNpNeNxX00q/SSa1oCqFJJM3oci/GSye/G4NpDOvICqlpNJvYzquhhNrSCbIwQ6GRKTodRWktlmQu4VDKe2IIRC0LWc3tSjeLQQpj1Ef3IjuhJmKL0V044RcC0ilt6KSysn5F6FrgTQlcn9p5Odp6GrrFpWO2H5kgVjXWxuunbJ6OfCqJ+ZNWOK4LINc0Y/79nfRjDoJlrgI50xEUKnMPRVLLsLVYlgaI0IkV9MMf7angpCCEo8fp5pP0rY8LCjt411pbUcH3Zy848M9fLWmYuoCxVyVVUjAtjV184n5l+IS9U4ONjN0sJKbq5dwH/u3zRavet3GyAEAY8Lj6GzYEYpRzp72TC3lvb+GOmsCUiSmSwloQALq8uoLgxTGQ1REQ1RXxblkvl1eM/QP5rNmiQTGQoL/Zw40cvs2WU8/9whbMtmdmM5bW39Do+GEEQKfJSXh3G5NEIhL8GQh5dePILP56KhoZTm5p7RVL+zcd+79UayVieDyUew7QRpsxmvsZAxyzQX8JfDpM0mvMZC4ultBD0bzmj//V2DfPt9/0ZvWz/1i2u47B0X5t1ztxoazc8OG2Ozr4irBgC/XoJfn7wJty68VPkmz5VXhU6ZdzFlLJ5SNqEIFFVBUVUUVWHmgioGe2J4Ax6ipWGO7jpOtCyCZVooihMXEzh+8FPhvFHcMKZQzzRLw8xa/OYbd5KMJUnF0yy9dAGapnLDR6/kjn++l41/3kw2Y/LuL72ZTfdvRdNVDLeThmS4dXSXxs2fuJrff/senvnTi0gpedvfXU/lrDKKq6IIBD/+1C9ZfNE8Lrh+GR7/mJJweVyor4K2cQS2PYBt92NZ3ZjmMSdoZ6xAyhSaNgtNqyedeQlJCtvuz5WA29h2D6rqDMapGir4jDmOAk9vx5LDGEohhlqEV6/Hq9eTsbrwG40YWjHDsb34jFm5/oaCAs9FnLbeehK8lgG7ubPLKIj4yGYtVi2tRQgFtzH/Ndt/qSfIwcEebqiZx33Ne6kPFiKA2kABg5kUAoFl23QmYxS7/bhUjUNDPRS4vEhkrmQ6/3y9hs51yxq57+V91BYXUBT0EU9l6BtOoKkKA/EUzd0D9A/nNxQYyYgZ//1MoOsqtbVFmGYBZeVhVFWlpDSEQBAt9DMcKwMBhq4hgWXLZ9LVOUgo7CUc9hIMeslmTEpKQ8yZW47f72bJ0ho07cwCfEJoaEoY2x5GVcNkzBa8+iJMq4+s1UHGbEVikjU7cpwzkmR2H1593hntHyAYDfDR777bednOcLJUzhVUN5ZTXlfCnJV1qKqKJ+CmalY5Q70xiquiXPu+i+lu7WPRhY34Ql6ued/FCEVw5bvX8y+/n/oai1fLUvVaYPny5XLLlomBrFcLKSXpRJr4UBLd0PBHfDk/tySdzJAYcqqudJdGKp5G1VTMrIlmaJgZE93Q0AyNTCpLfDCB4dbxBb0IxXmIMkknwu0NeHD7XMT6hvGHfSiqQnwwge7SJ/jgzhaW1Usq9SgS8LgvJZV+GlUpxuVaQzJ5P5pWjy0HscwWFKUAKRNImcYwFmGaTVhWB17vTShKvj89bXbSl3wSVXgJu1czkHoBXS0k5F5Fb+JhPNoMPHodPYkHCbqWkbY6yJidhNwriWcOEvGs4/WvFHxjYdo2z7QfYVlRFdt6WlhaWMnT7UcIGm7KPEEaQoXs7u+gKdbHxeX1JM0sz3c2ETTczI+U0pOKUxcsZHtvKyuKnOl15+AwLx46TtDj4oLZM9AUlRcONpMxLdY21rDlSAvxVIaAx8XKhiqOdvYR9LgoDvmRUrL1WCsLq8teUbXdGwFbZgEJ0kIIw+mck6tEdZpneAGJLZMoIoBAYMsUqhJ+XfPhzwcsX76cLVu2THoR/lcr7v8NeK3uz2SVda/1PqcxjWm8djjnFbcQIgYceKPlOAsUAq9tLfvrh/NJVji/5D2fZIXzS97zSVZ4feSdIaWctHXQueLjPiClXH761c4NCCG2nC/ynk+ywvkl7/kkK5xf8p5PssJfXt7/3U7KaUxjGtP4X4hpxT2NaUxjGucZzhXF/R9vtABnifNJ3vNJVji/5D2fZIXzS97zSVb4C8t7TgQnpzGNaUxjGmeOc8XinsY0pjGNaZwhphX3NKYxjWmcZ3jDFbcQ4iohxAEhxGEhxOfOAXmqhBBPCiH2CSH2CCE+kVteIIR4VAhxKPc3Mm6bz+fkPyCEuPINkFkVQmwTQtx3HsgaFkL8UQixP3eN15yr8gohPpUbA7uFEL8TQrjPJVmFED8XQnQJIXaPW3bW8gkhlgkhduV++6F4nSqrppD3O7mxsFMIcZcQInwuyDuZrON++3shhBRCFL5hskop37B/OGQXR4CZgAHsAOa+wTKVAUtznwPAQWAu8G3gc7nlnwO+lfs8Nye3C6jNnY/6F5b508B/A/flvp/Lsv4S+EDuswGEz0V5gQrgGODJff8f4D3nkqzAemApsHvcsrOWD3gJWIPD9vQgcPVfUN4rAC33+VvniryTyZpbXgU8DDQDhW+UrG+0xb0SOCylPCqlzAC/B254IwWSUrZLKbfmPseAfTgP8Q04Sofc3xtzn28Afi+lTEspjwGHcc7rLwIhRCVwLXD7uMXnqqxBnAfiZwBSyoyUcuBclRenQM0jhNAAL9B2LskqpXwG6Dtp8VnJJ4QoA4JSyheko2l+NW6b111eKeUjUkoz93UTUHkuyDvFtQX4HvAZ8huj/sVlfaMVdwVwYtz3ltyycwJCiBpgCfAiUCKlbAdHuQMj7dHf6HP4Ps5AGt+q5lyVdSbQDfxXzrVzuxDCdy7KK6VsBf4ZOA60A4NSykfORVlPwtnKV5H7fPLyNwLvw7FK4RyUVwjxJqBVSrnjpJ/+4rK+0Yp7Mn/POZGfKITwA38CPimlHDrVqpMs+4ucgxDiOqBLSvnymW4yybK/5PXWcKaf/yalXALEcabzU+GNvLYRHEuqFigHfEKId55qk0mWnRNjOYep5Dsn5BZCfBEwgd+OLJpktTdMXuE0Lf0i8OXJfp5k2esq6xutuFtwfEYjqMSZjr6hEA7v5J+A30op78wt7sxNfcj97cotfyPPYS3wJiFEE46b6RIhxG/OUVlHjt8ipXwx9/2POIr8XJT3MuCYlLJbSpkF7gQuOEdlHY+zla+FMffE+OV/MQghbgOuA96RcynAuSdvHc5LfEfueasEtgohSt8IWd9oxb0ZaBBC1AohDOBW4J43UqBc1PdnwD4p5XfH/XQPcFvu823A3eOW3yqEcAkhaoEGnIDE6w4p5eellJVSyhqca/eElPKd56KsOXk7gBNCiNm5RZcCe89ReY8Dq4UQ3tyYuBQn3nEuyjoeZyVfzp0SE0Kszp3nu8dt87pDCHEV8FngTVLKxLifzil5pZS7pJTFUsqa3PPWgpPE0PGGyPpaR2NfQfT2GpzMjSPAF88BedbhTGd2Attz/64BosDjwKHc34Jx23wxJ/8BXqeI/BnIfRFjWSXnrKzAYmBL7vr+GYicq/ICXwP2A7uBX+NkDZwzsgK/w/G/Z3EUyftfiXzA8tw5HgF+RK6i+i8k72Ec//DIs/bTc0HeyWQ96fcmclklb4Ss0yXv05jGNKZxnuGNdpVMYxrTmMY0zhLTinsa05jGNM4zTCvuaUxjGtM4zzCtuKcxjWlM4zzDtOKexjSmMY3zDNOKexrTmMY0zjNMK+5pTGMa0zjP8P8BEiAcGk8EYzAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ham_wc = wc.generate(data[data['Target']==0]['Transformed'].str.cat(sep=\" \"))\n",
    "plt.imshow(ham_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "af8fb557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['free entri 2 wkli comp win fa cup final tkt 21st may text fa 87121 receiv entri question std txt rate c appli 08452810075over18',\n",
       " 'freemsg hey darl 3 week word back like fun still tb ok xxx std chg send rcv',\n",
       " 'winner valu network custom select receivea prize reward claim call claim code kl341 valid 12 hour',\n",
       " 'mobil 11 month u r entitl updat latest colour mobil camera free call mobil updat co free 08002986030',\n",
       " 'six chanc win cash 100 pound txt csh11 send cost 6day tsandc appli repli hl 4 info',\n",
       " 'urgent 1 week free membership prize jackpot txt word claim 81010 c lccltd pobox 4403ldnw1a7rw18',\n",
       " 'xxxmobilemovieclub use credit click wap link next txt messag click http',\n",
       " 'england v macedonia dont miss news txt ur nation team 87077 eg england 87077 tri wale scotland poboxox36504w45wq',\n",
       " 'thank subscript rington uk mobil charg pleas confirm repli ye repli charg',\n",
       " '07732584351 rodger burn msg tri call repli sm free nokia mobil free camcord pleas call 08000930705 deliveri tomorrow',\n",
       " 'sm ac sptv new jersey devil detroit red wing play ice hockey correct incorrect end repli end sptv',\n",
       " 'congrat 1 year special cinema pass 2 call 09061209465 c suprman v matrix3 starwars3 etc 4 free 150pm dont miss',\n",
       " 'valu custom pleas advis follow recent review mob award bonu prize call 09066364589',\n",
       " 'urgent ur award complimentari trip eurodisinc trav aco entry41 claim txt di 87121 morefrmmob shracomorsglsuplt 10 ls1 3aj',\n",
       " 'hear new come ken stuff',\n",
       " 'pleas call custom servic repres 0800 169 6031 guarante cash prize',\n",
       " 'free rington wait collect simpli text password 85069 verifi get usher britney fml',\n",
       " 'gent tri contact last weekend draw show prize guarante call claim code k52 valid 12hr 150ppm',\n",
       " 'winner u special select 2 receiv 4 holiday flight inc speak live oper 2 claim',\n",
       " 'privat 2004 account statement 07742676969 show 786 unredeem bonu point claim call 08719180248 identifi code 45239 expir',\n",
       " 'urgent mobil award bonu caller prize final tri contact u call landlin 09064019788 box42wr29c 150ppm',\n",
       " 'today voda number end 7548 select receiv 350 award match pleas call 08712300220 quot claim code 4041 standard rate app',\n",
       " 'sunshin quiz wkli q win top soni dvd player u know countri algarv txt ansr sp tyron',\n",
       " 'want 2 get laid tonight want real dog locat sent direct 2 ur mob join uk largest dog network bt txting gravel 69888 nt ec2a 150p',\n",
       " 'rcv msg chat svc free hardcor servic text go 69988 u get noth u must age verifi yr network tri',\n",
       " 'freemsg repli text randi sexi femal live local luv hear netcollex ltd 08700621170150p per msg repli stop end',\n",
       " 'custom servic annonc new year deliveri wait pleas call 07046744435 arrang deliveri',\n",
       " 'winner u special select 2 receiv cash 4 holiday flight inc speak live oper 2 claim 0871277810810',\n",
       " 'stop bootydeli invit friend repli see stop send stop frnd 62468',\n",
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       " 'freemsg hi babi wow got new cam mobi wan na c hot pic fanci chat im w8in 4utxt rpli chat 82242 hlp 08712317606 msg150p 2rcv',\n",
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       " 'contract mobil 11 mnth latest motorola nokia etc free doubl min text orang tariff text ye callback remov record',\n",
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       " 'today voda number end 7634 select receiv reward match pleas call 08712300220 quot claim code 7684 standard rate appli',\n",
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       " 'tri contact repli offer video phone 750 anytim network min half price line rental camcord repli call 08000930705',\n",
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       " 'want latest video handset 750 anytim network min half price line rental repli call 08000930705 deliveri tomorrow',\n",
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       " 'record indic u mayb entitl 5000 pound compens accid claim 4 free repli claim msg 2 stop txt stop',\n",
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       " 'prize go anoth custom c polo ltd suit 373 london w1j 6hl pleas call back busi',\n",
       " 'urgent mobil number award prize guarante call 09061790126 land line claim valid 12hr 150ppm',\n",
       " 'urgent pleas call 09061213237 landlin cash luxuri 4 canari island holiday await collect cs sae po box m227xi 150ppm',\n",
       " 'xma iscom ur award either cd gift voucher free entri 2 r weekli draw txt music 87066 tnc',\n",
       " 'u r subscrib 2 textcomp 250 wkli comp 1st wk free question follow subsequ wk charg unsubscrib txt stop 2 84128 custcar 08712405020',\n",
       " 'call 09095350301 send girl erot ecstaci stop text call 08712460324 nat rate',\n",
       " 'xma reward wait comput randomli pick loyal mobil custom receiv reward call 09066380611',\n",
       " 'import messag final contact attempt import messag wait custom claim dept expir call 08717507382',\n",
       " 'date two start sent text talk sport radio last week connect think coincid',\n",
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       " 'free entri gr8prize wkli comp 4 chanc win latest nokia 8800 psp cash everi great 80878 08715705022',\n",
       " '1 new messag call',\n",
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       " 'guarante 32000 award mayb even cash claim ur award call free 0800 legitimat efreefon number wat u think',\n",
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       " 'urgent tri contact last weekend draw show u prize guarante call 09064017295 claim code k52 valid 12hr 150p pm',\n",
       " '2p per min call germani 08448350055 bt line 2p per min check info c text stop opt',\n",
       " 'marvel mobil play offici ultim game ur mobil right text spider 83338 game send u free 8ball wallpap',\n",
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       " 'privat 2003 account statement show 800 point call 08718738001 identifi code 49557 expir',\n",
       " 'want explicit sex 30 sec ring 02073162414 cost gsex pobox 2667 wc1n 3xx',\n",
       " 'ask 3mobil 0870 chatlin inclu free min india cust serv sed ye l8er got mega bill 3 dont giv shit bailiff due day 3 want',\n",
       " 'contract mobil 11 mnth latest motorola nokia etc free doubl min text orang tariff text ye callback remov record',\n",
       " 'remind o2 get pound free call credit detail great offer pl repli 2 text valid name hous postcod',\n",
       " '2nd time tri 2 contact u pound prize 2 claim easi call 087187272008 now1 10p per minut']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Target']==1]['Transformed'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "532c4655",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " '09111032124',\n",
       " 'pobox12n146tf150p',\n",
       " 'urgent',\n",
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       " 'number',\n",
       " 'award',\n",
       " 'prize',\n",
       " 'guarante',\n",
       " 'call',\n",
       " '09058094455',\n",
       " 'land',\n",
       " 'line',\n",
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       " 'valid',\n",
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       " '3650',\n",
       " 'video',\n",
       " 'camera',\n",
       " ...]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_corpus = []\n",
    "for msg in data[data['Target']==1]['Transformed'].to_list():\n",
    "    for word in msg.split():\n",
    "        spam_corpus.append(word)\n",
    "spam_corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ffa70b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "sns.barplot(pd.DataFrame(Counter(spam_corpus).most_common(30))[0], pd.DataFrame(Counter(spam_corpus).most_common(30))[1])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c9b528f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35402"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ham_corpus = []\n",
    "for msg in data[data['Target']==0]['Transformed'].to_list():\n",
    "    for word in msg.split():\n",
    "        ham_corpus.append(word)\n",
    "len(ham_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b74fa46b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "cv = CountVectorizer()\n",
    "tfidf = TfidfVectorizer(max_features=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8d55777b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tfidf.fit_transform(data['Transformed']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "61f7dbc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5169, 3000)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "95325e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['Target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "96b30d33",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "89d2e1d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9c9ea53d",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9c6002e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8694390715667312\n",
      "[[788 108]\n",
      " [ 27 111]]\n",
      "0.5068493150684932\n"
     ]
    }
   ],
   "source": [
    "gnb.fit(x_train, y_train)\n",
    "ypred1 = gnb.predict(x_test)\n",
    "print(accuracy_score(y_test, ypred1))\n",
    "print(confusion_matrix(y_test, ypred1))\n",
    "print(precision_score(y_test, ypred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4721be23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8694390715667312"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "786e91ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9709864603481625\n",
      "[[896   0]\n",
      " [ 30 108]]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "mnb.fit(x_train, y_train)\n",
    "ypred2 = mnb.predict(x_test)\n",
    "print(accuracy_score(y_test, ypred2))\n",
    "print(confusion_matrix(y_test, ypred2))\n",
    "print(precision_score(y_test, ypred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ebda63d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9709864603481625"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnb.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f632f90e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9835589941972921\n",
      "[[895   1]\n",
      " [ 16 122]]\n",
      "0.991869918699187\n"
     ]
    }
   ],
   "source": [
    "bnb.fit(x_train, y_train)\n",
    "ypred3 = bnb.predict(x_test)\n",
    "print(accuracy_score(y_test, ypred3))\n",
    "print(confusion_matrix(y_test, ypred3))\n",
    "print(precision_score(y_test, ypred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f3f7d388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9835589941972921"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bnb.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72d85b20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4cd0dfb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6bdf5eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='sigmoid', gamma=1.0)\n",
    "knc = KNeighborsClassifier()\n",
    "mnb = MultinomialNB()\n",
    "dtc = DecisionTreeClassifier(max_depth=5)\n",
    "lrc = LogisticRegression(solver='liblinear', penalty='l1')\n",
    "rfc = RandomForestClassifier(n_estimators=50, random_state=2)\n",
    "abc = AdaBoostClassifier(n_estimators=50, random_state=2)\n",
    "bc = BaggingClassifier(n_estimators=50, random_state=2)\n",
    "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
    "gbdt = GradientBoostingClassifier(n_estimators=50,random_state=2)\n",
    "xgb = XGBClassifier(n_estimators=50,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "468b9bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "clfs = {\n",
    "    'SVC' : svc,\n",
    "    'KN' : knc, \n",
    "    'NB': mnb, \n",
    "    'DT': dtc, \n",
    "    'LR': lrc, \n",
    "    'RF': rfc, \n",
    "    'AdaBoost': abc, \n",
    "    'BgC': bc, \n",
    "    'ETC': etc,\n",
    "    'GBDT':gbdt,\n",
    "    'xgb':xgb\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b2ba90bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_classifier(clf,X_train,y_train,X_test,y_test):\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test,y_pred)\n",
    "    precision = precision_score(y_test,y_pred)\n",
    "    \n",
    "    return accuracy,precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a35ca030",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For  SVC\n",
      "Accuracy -  0.9758220502901354\n",
      "Precision -  0.9747899159663865\n",
      "For  KN\n",
      "Accuracy -  0.9052224371373307\n",
      "Precision -  1.0\n",
      "For  NB\n",
      "Accuracy -  0.9709864603481625\n",
      "Precision -  1.0\n",
      "For  DT\n",
      "Accuracy -  0.9303675048355899\n",
      "Precision -  0.8173076923076923\n",
      "For  LR\n",
      "Accuracy -  0.9584139264990329\n",
      "Precision -  0.9702970297029703\n",
      "For  RF\n",
      "Accuracy -  0.9758220502901354\n",
      "Precision -  0.9829059829059829\n",
      "For  AdaBoost\n",
      "Accuracy -  0.960348162475822\n",
      "Precision -  0.9292035398230089\n",
      "For  BgC\n",
      "Accuracy -  0.9584139264990329\n",
      "Precision -  0.8682170542635659\n",
      "For  ETC\n",
      "Accuracy -  0.9748549323017408\n",
      "Precision -  0.9745762711864406\n",
      "For  GBDT\n",
      "Accuracy -  0.9468085106382979\n",
      "Precision -  0.9191919191919192\n",
      "For  xgb\n",
      "Accuracy -  0.9671179883945842\n",
      "Precision -  0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "\n",
    "for name,clf in clfs.items():\n",
    "    \n",
    "    current_accuracy,current_precision = train_classifier(clf, x_train,y_train,x_test,y_test)\n",
    "    \n",
    "    print(\"For \",name)\n",
    "    print(\"Accuracy - \",current_accuracy)\n",
    "    print(\"Precision - \",current_precision)\n",
    "    \n",
    "    accuracy_scores.append(current_accuracy)\n",
    "    precision_scores.append(current_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9a881694",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_df = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy':accuracy_scores,'Precision':precision_scores}).sort_values('Precision',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5dbb2c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KN</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.970986</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.982906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.975822</td>\n",
       "      <td>0.974790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ETC</td>\n",
       "      <td>0.974855</td>\n",
       "      <td>0.974576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.970297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.967118</td>\n",
       "      <td>0.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.960348</td>\n",
       "      <td>0.929204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.946809</td>\n",
       "      <td>0.919192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BgC</td>\n",
       "      <td>0.958414</td>\n",
       "      <td>0.868217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.930368</td>\n",
       "      <td>0.817308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Algorithm  Accuracy  Precision\n",
       "1         KN  0.905222   1.000000\n",
       "2         NB  0.970986   1.000000\n",
       "5         RF  0.975822   0.982906\n",
       "0        SVC  0.975822   0.974790\n",
       "8        ETC  0.974855   0.974576\n",
       "4         LR  0.958414   0.970297\n",
       "10       xgb  0.967118   0.933333\n",
       "6   AdaBoost  0.960348   0.929204\n",
       "9       GBDT  0.946809   0.919192\n",
       "7        BgC  0.958414   0.868217\n",
       "3         DT  0.930368   0.817308"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63949a1e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f9b234ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='sigmoid', gamma=1.0, probability=True)\n",
    "mnb = MultinomialNB()\n",
    "rfc = RandomForestClassifier(n_estimators=50, random_state=2)\n",
    "\n",
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3afdaa1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting  = VotingClassifier(estimators=[('svm', svc), ('nb', mnb), ('rfc', rfc)], voting='soft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cc850890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('svm',\n",
       "                              SVC(gamma=1.0, kernel='sigmoid',\n",
       "                                  probability=True)),\n",
       "                             ('nb', MultinomialNB()),\n",
       "                             ('rfc',\n",
       "                              RandomForestClassifier(n_estimators=50,\n",
       "                                                     random_state=2))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6b5a095f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = voting.predict(x_test)\n",
    "# print(voting.accuracy_score(y_test, y_pred))\n",
    "# print(voting.precision_score(y_test, y_pred))\n",
    "# print(voting.score(x_test, y_test))\n",
    "# print(voting.precision_score(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "77485fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "pickle.dump(tfidf, open('vectorizer.pkl', 'wb'))\n",
    "pickle.dump(mnb, open('model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9debd5e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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