spam-ham classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1c7e8c9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#basics\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle as pkl\n",
    "from collections import Counter\n",
    "\n",
    "#language processors\n",
    "import nltk\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.corpus import stopwords\n",
    "from wordcloud import WordCloud\n",
    "from string import punctuation\n",
    "\n",
    "#metrics and preprocessors\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler,LabelEncoder  \n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,precision_score\n",
    "\n",
    "#models\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier,ExtraTreeClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB,MultinomialNB,GaussianNB\n",
    "from sklearn.ensemble import AdaBoostClassifier,BaggingClassifier,ExtraTreesClassifier,GradientBoostingClassifier,RandomForestClassifier,VotingClassifier,StackingClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "803dfb82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n",
      "Requirement already satisfied: numexpr in c:\\users\\nikhi\\anaconda3\\lib\\site-packages (2.10.1)\n",
      "Requirement already satisfied: numpy>=1.23.0 in c:\\users\\nikhi\\anaconda3\\lib\\site-packages (from numexpr) (1.23.1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: bottleneck in c:\\users\\nikhi\\anaconda3\\lib\\site-packages (1.4.0)\n",
      "Requirement already satisfied: numpy in c:\\users\\nikhi\\anaconda3\\lib\\site-packages (from bottleneck) (1.23.1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade numexpr\n",
    "!pip install --upgrade bottleneck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2282caf3",
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "</table>\n",
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      ],
      "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",
       "  Unnamed: 3 Unnamed: 4  \n",
       "0        NaN        NaN  \n",
       "1        NaN        NaN  \n",
       "2        NaN        NaN  \n",
       "3        NaN        NaN  \n",
       "4        NaN        NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"spam.csv\",encoding='latin-1')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "42239919",
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1ab81c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Unnamed: 2','Unnamed: 3','Unnamed: 4'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bf7e811d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "403"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bd6939ea",
   "metadata": {},
   "outputs": [
    {
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       "  </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>5169 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",
       "[5169 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "281d27de",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'v1':'target','v2':'text'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b1e1f7d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>text</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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking 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",
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       "    <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",
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      "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..."
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = LabelEncoder()\n",
    "df['target'] = encoder.fit_transform(df['target'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ffee2d",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "52bb5d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    4825\n",
       "1     747\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f4cf0f3",
   "metadata": {},
   "source": [
    "#Data is imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3e9d9797",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3))\n",
    "plt.pie(x=df['target'].value_counts(),autopct='%0.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ee05480a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Nikhi\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Length of text messages:\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ab894eab",
   "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>target</th>\n",
       "      <th>text</th>\n",
       "      <th>num_char</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentnce</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "   num_words  num_sentnce  \n",
       "0         24            2  \n",
       "1          8            2  \n",
       "2         37            2  \n",
       "3         13            1  \n",
       "4         15            1  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num_char'] = df['text'].apply(len)\n",
    "df['num_words'] = df['text'].apply(lambda x: len(nltk.word_tokenize(x)))\n",
    "df['num_sentnce'] = df['text'].apply(lambda x: len(nltk.sent_tokenize(x)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "35c27071",
   "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>target</th>\n",
       "      <th>num_char</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentnce</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4825.0</td>\n",
       "      <td>4825.000000</td>\n",
       "      <td>4825.000000</td>\n",
       "      <td>4825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>71.023627</td>\n",
       "      <td>17.271917</td>\n",
       "      <td>1.808705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>58.016023</td>\n",
       "      <td>13.988885</td>\n",
       "      <td>1.328227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>910.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       target     num_char    num_words  num_sentnce\n",
       "count  4825.0  4825.000000  4825.000000  4825.000000\n",
       "mean      0.0    71.023627    17.271917     1.808705\n",
       "std       0.0    58.016023    13.988885     1.328227\n",
       "min       0.0     2.000000     1.000000     1.000000\n",
       "25%       0.0    33.000000     8.000000     1.000000\n",
       "50%       0.0    52.000000    13.000000     1.000000\n",
       "75%       0.0    92.000000    22.000000     2.000000\n",
       "max       0.0   910.000000   220.000000    28.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['target'] == 0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "747a5b05",
   "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>target</th>\n",
       "      <th>num_char</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentnce</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>747.0</td>\n",
       "      <td>747.000000</td>\n",
       "      <td>747.000000</td>\n",
       "      <td>747.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>138.866131</td>\n",
       "      <td>27.891566</td>\n",
       "      <td>3.017403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29.183082</td>\n",
       "      <td>6.867007</td>\n",
       "      <td>1.528449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\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>1.0</td>\n",
       "      <td>132.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\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>1.0</td>\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>1.0</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       target    num_char   num_words  num_sentnce\n",
       "count   747.0  747.000000  747.000000   747.000000\n",
       "mean      1.0  138.866131   27.891566     3.017403\n",
       "std       0.0   29.183082    6.867007     1.528449\n",
       "min       1.0   13.000000    2.000000     1.000000\n",
       "25%       1.0  132.500000   25.000000     2.000000\n",
       "50%       1.0  149.000000   29.000000     3.000000\n",
       "75%       1.0  157.000000   32.000000     4.000000\n",
       "max       1.0  224.000000   46.000000     8.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['target'] == 1].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ec8fe1b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.histplot(x = df[df['target'] == 0]['num_char'])\n",
    "sns.histplot(x = df[df['target'] == 1]['num_char'],color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cf287d31",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nikhi\\anaconda3\\lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\Nikhi\\anaconda3\\lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n",
      "C:\\Users\\Nikhi\\anaconda3\\lib\\site-packages\\seaborn\\_base.py:948: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1b84c255f10>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 808.875x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,hue='target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "28fc1257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+ZjemLpmMqPAoRIRF4tOhn8DQyBBHdh4FAPgGTkZiXBLWzc//Rek/5nNEXo/C0+in0NPXh8f7LdCltyeWTFumaLND9/ZIS0lDwpME1KpfE2P9R+Ps0XO4cqroDzUClixdi43rl+Bq6HVcvnwN474ehkqVjLDpl/yrOjZuWIqnT2MxfcZ8AMC3k0Zj9qxJGDBoLB5F/w07u/xx5xcvMpCRkQkAWLh4FX7dtgqnT19AyMlz8OraEf/r3gWdPT9Vz0mWIytXbMCqnxfgWugNXL0ajtFjvFHJ2FhxpcbqNQsR+zQO/rPz526NnzAc02aMx9AhExAT/VjRq5CRkal4P3p+8iGSklLw+O+naPhePcz/cSYOHfwLJ06cUc9JqosKhwwLmydnYGBQ6JfgnJwcXL16Fb6+vooyqVQKT09PnD9/vsj2X79yz8jICGfOFP89K3EykJubi/r16+PgwYMYOXJkSatXSDfv3MWQr6coHv+4fA0AoMeHnvh+xkR1hVVhBB84CTMrc3w5aTAsbSxw//Z9TBk4DalJaQAAW0dbpfFMQ2NDjJ83DjYO1sjOykbMvb8xb9x8BB84qdjHys4So2eNgIW1BZITUnBs11/YsnRbWZ9aufP773/AxtoSs/0mwd7eBuHht9D9fwMUk9iqV6sC+SvdqyOGD4KBgQF+37lWqZ05cxdhztzFAID9+49g9JipmDL5awQumYPIqAfo03cYzp67XHYnVk7t2X0IVtaWmDZjPOzsrHHjegR6feKNxIRkAEDVag5K78eQoV/AwMAAW7b9pNROwLylmD8vP1m2s7fF9wHTYWtrhbi4ROz4dS9+nL+i7E5KU6hwmCAgIAD+/v5KZbNmzcLs2bML7JuUlASZTFZgmN3Ozg537twptH0vLy8sXrwY7du3R+3atREUFIQ9e/ZAJiv+6oei1hlwdHTE8ePH0aBBg5JWLVJFWWegItC0dQa0maatM6DNNHGdAW1W6usM/Lns7TsVk/T9EcXuGXj69CkcHR1x7tw5tGrVSlE+efJknDx5EhcvXixQJzExEcOGDcOBAwcgkUhQu3ZteHp6YsOGDXj58mXxYizhOQEAxowZgx9++AF5eXliqhMREWk2Fa4zUJJ5ctbW1tDR0UF8fLxSeXx8POzt7QutY2Njg3379iEjIwPR0dG4c+cOKleujFq1ineJKSByzsDly5cRFBSEY8eOwcXFBZUqVVJ6fs+ePWKaJSIi0gxqusxYX18fzZo1Q1BQEHr27AkAkMvlCAoKwtixY99Y19DQEI6OjsjNzcXu3bvx2WefFfu4opIBc3Nz9O7dW0xVIiIizafGSwt9fHwwePBguLu7o0WLFggMDERGRga8vb0BAIMGDYKjoyMCAgIAABcvXsSTJ0/g5uaGJ0+eYPbs2ZDL5Zg8eXKxjykqGdi4caOYakRERPQWffv2RWJiIvz8/BAXFwc3NzccOXJEMakwJiZGsWAXAGRlZWHGjBl48OABKleujG7dumHLli0wNzcv9jFFTSAsDZxAqDk4gVBzcAKh5uAEQs1S6hMI9/+osraMehT/G7q6iOoZAIBdu3bht99+Q0xMDHJycpSeCw0NLaIWERFROVBObjCkKqKuJli2bBm8vb1hZ2eHa9euoUWLFrCyssKDBw/w4YcfqjpGIiIiKkWikoGffvoJa9aswfLly6Gvr4/Jkyfjr7/+wrhx45Cenq7qGImIiMqWmm9UVNZEJQMxMTFo3Tp/PWwjIyM8f/4cQP4SxL/++qvqoiMiIlIHFa4zUB6ISgbs7e2RkpICAKhevTouXMi/5/jDhw+hIfMRiYiIqJhEJQPvv/8+/vjjDwCAt7c3JkyYgC5duqBv37745JNPVBogERFRmdOyngFRVxNMnz4djo6OAPKXJrayssK5c+fw8ccf44MPPlBpgERERGVOy3q5RSUDzs7OiI2Nha1t/u15P//8c3z++edITk6Gra1tie6UREREROolKhkoal7AixcvCtxTmYiIqNwpJ937qlKiZMDHxwcAIJFI4OfnB2NjY8VzMpkMFy9ehJubm0oDJCIiKnNMBop27do1APk9Azdu3IC+vr7iOX19fbi6umLSpEmqjZCIiKislZP1AVSlRMlAcHAwgPwrCJYuXQpTU9NSCYqIiIjKDu9aSERE9DoOExAREWk5Lbu0UNSiQ0RERFRxsGeAiIjodRwmICIi0nJalgxwmICIiEjLsWeAiIjodVxngIiISLsJcu26moDJABER0es4Z4CIiIi0CXsGiIiIXsc5A0RERFpOy+YMcJiAiIhIy7FngIiI6HVaNoGQyQAREdHrtCwZ4DABERGRlmPPABER0eu07BbGTAaIiIhex2ECIiIi0ibsGSAiInqdlq0zwGSAiIjodVyBkIiISMtpWc8A5wwQERFpOY3pGejqNkLdIdA/joX9rO4Q6B/y1Dh1h0D/iOoaoO4QqAwJWnY1gcYkA0RERBqDwwRERESkTdgzQERE9Dotu5qAPQNERESvkwuq20RYuXIlnJycYGhoCA8PD1y6dOmN+wcGBqJevXowMjJCtWrVMGHCBGRlZRX7eEwGiIiINMjOnTvh4+ODWbNmITQ0FK6urvDy8kJCQkKh+2/fvh1Tp07FrFmzEBERgfXr12Pnzp2YNm1asY/JZICIiOh1crnqthJavHgxhg0bBm9vbzRs2BCrV6+GsbExNmzYUOj+586dQ5s2bdC/f384OTmha9eu6Nev31t7E17FZICIiOh1KhwmyM7OxrNnz5S27OzsQg+bk5ODq1evwtPTU1EmlUrh6emJ8+fPF1qndevWuHr1quLD/8GDBzh8+DC6detW7NNlMkBERFSKAgICYGZmprQFBBS+bkVSUhJkMhns7OyUyu3s7BAXV/i6I/3798ecOXPQtm1b6OnpoXbt2ujYsSOHCYiIiN6JIFfZ5uvri/T0dKXN19dXZaGGhIRg3rx5+OmnnxAaGoo9e/bg0KFDmDt3brHb4KWFREREr1PhokMGBgYwMDAo1r7W1tbQ0dFBfHy8Unl8fDzs7e0LrTNz5kwMHDgQQ4cOBQC4uLggIyMDw4cPx/Tp0yGVvv17P3sGiIiIXiPI5SrbSkJfXx/NmjVDUFCQokwulyMoKAitWrUqtE5mZmaBD3wdHZ388xCKl9SwZ4CIiEiD+Pj4YPDgwXB3d0eLFi0QGBiIjIwMeHt7AwAGDRoER0dHxbyDjz76CIsXL0aTJk3g4eGBe/fuYebMmfjoo48UScHbMBkgIiJ6nRrvTdC3b18kJibCz88PcXFxcHNzw5EjRxSTCmNiYpR6AmbMmAGJRIIZM2bgyZMnsLGxwUcffYTvv/++2MeUCMXtQyhlnap2UXcI9A/etVBz8K6FmoN3LdQsLg8PlGr7L779RGVtVV6wV2VtlZYSzxnIzc1F586dcffu3dKIh4iIiMpYiYcJ9PT0cP369dKIhYiISDPwRkVvN2DAAKxfv17VsRAREWkGNd+oqKyJmkCYl5eHDRs24Pjx42jWrBkqVaqk9PzixYtVEhwRERGVPlHJwM2bN9G0aVMAQFRUlNJzEonk3aMiIiJSI6GcfKNXFVHJQHBwsKrjICIi0hxalgxwBUIiIiItJ3rRoStXruC3335DTEwMcnJylJ7bs2fPOwdGRESkNiVcRri8E9UzsGPHDrRu3RoRERHYu3cvcnNzcevWLZw4cQJmZmaqjpGIiKhsadnVBKKSgXnz5mHJkiU4cOAA9PX1sXTpUty5cwefffYZqlevruoYiYiIyhaTgbe7f/8+unfvDiD/DksZGRmQSCSYMGEC1qxZo9IAiYiIqHSJSgYsLCzw/PlzAICjoyNu3rwJAEhLS0NmZqbqoiMiIlIDQRBUtpUHoiYQtm/fHn/99RdcXFzQp08ffPPNNzhx4gT++usvdO7cWdUxEhERla1y0r2vKqKSgRUrViArKwsAMH36dOjp6eHcuXPo3bs3ZsyYodIAiYiIqHSJSgYsLS0V/5dKpZg6darKAiIiIlI79gwUj1wux71795CQkAD5a9djtm/f/p0DIyIiUhcuR1wMFy5cQP/+/REdHV1gcoREIoFMJlNJcERERFT6RCUDI0eOhLu7Ow4dOgQHBwfenIiIiCoW9gy83d27d7Fr1y44OzurOh4iIiL1067ViMWtM+Dh4YF79+6pOhYiIiJSg2L3DFy/fl3x/6+//hoTJ05EXFwcXFxcoKenp7Rv48aNVRchERFRGeMEwiK4ublBIpEoTRgcMmSI4v//PscJhEREVO4xGSjcw4cPSzMOIiIizaFlcwaKnQzUqFGjNOMgIiIiNRE1gTAgIAAbNmwoUL5hwwb88MMP7xwUERGROglyQWVbeSAqGfj5559Rv379AuXvvfceVq9e/c5BERERqZVchVs5IGqdgbi4ODg4OBQot7GxQWxs7DsHpSl6Dv4YfUf2gaWNJe5H3MeymStxJyyy0H3bfdgWX4ztB0enKtDR08GTh0/x25pd+Gv3ccU+FtbmGD5tGNzbN0Nls0q4fvEGls1ciScPn5TVKVV4V8JuYOP2Xbh95x4Sk1OwNGAmOrdvre6wKpQdB4Owac+fSEpNR92a1eE74gu41KtV6L65eXlY//sh/BF0FgnJqXBydMB47z5o28xFsc8HQybhaUJygbp9u7+P6aMGltp5VBSWA7vBZngv6NpYICviIZ7O/hkvw+8Wub/UpBLsvx0IU69W0DEzQe7TBMTOWYvnIVcBALbf9IPd+P5KdbLuP8Zdz1Gleh6kXqKSgWrVquHs2bOoWbOmUvnZs2dRpUoVlQSmbp0+6oBRfiOwxHcZIq5F4NOhvfDj1gAM6jAEaclpBfZ/lvYMW5dvR8y9v5GXm4tWni0xZdEkpCWl4fLJKwCAuev9kZebhxlf+SHzeSb6DO+Nhb/+AO9OQ5H1MquMz7BievkyC/Wca+GT7l0xftp36g6nwjly6iIWrNuBmWMGwaVeLWzd/xdG+i3CHz8HwMrctMD+K7bswaHg85j19ZeoWc0BZ0NvYsL3y7F5wXQ0qJ0/D2n7Ej/IX+lKvRf9GMNnLETXNs3L7LzKK7PubeEwfSiezliJzLAoWA/5GDV/mYPIziMhS04vsL9ETxc1t8xFXnIaYkbPR25cMvSr2kL27IXSflmR0Xg44L870AqycvL1VoXKS/e+qogaJhg2bBjGjx+PjRs3Ijo6GtHR0diwYQMmTJiAYcOGqTpGtegzvDcO/fonjvx2FNF3Y7B46lJkZWXjw8+9Ct0//Px1nDlyFjH3YvA0Oha71+/F/YgHaNT8PQBA1ZqOeK9ZQwROW4bI8Cj8/eAxlvgug4GhPt7v2aksT61Ca9eqOcYNHwzPDm3UHUqFtHnfMfT2ao+eXdqhdnVHzBwzCEYG+tj31+lC9z8YfB5DP/sf2jV3RVV7W/Tt9j7aujfG5r1HFPtYmpnC2sJMsZ28FI5qDrZwd6lXVqdVblkP7YnUnUeRuisI2ff+xpPpP0H+MhuWfboUur9FH0/omFdG9IjvkXk1ArlPEpBx8SayIh4p7SfIZMhLSlNsstRnZXA2GobDBG/37bffIjk5GaNHj0ZOTg4AwNDQEFOmTIGvr69KA1QHXT1d1HWpi20rdijKBEFA6OlQvNe0YbHaaNqmCarVroo189YBAPQM8hdmysnOUWozNycXLs0b4fCvf6rwDIhULzc3DxH3HmFon+6KMqlUCg+3hgi/U/iKpDm5udDXV16UzFBfD9duF96NnZubh0Mh5zGwpxfvefIWEj1dGDVyRuJPu/4rFAS8OBsG46aFJ1Kmnh7IvHYHjnNGwqSLB2TJz5D2x0kkrt4NvHL3WQOnKqh/YROE7Fxkht5B3ILNyH2aWNqnRGokKhmQSCT44YcfMHPmTERERMDIyAh16tSBgYGB0n6PHz9GlSpVIJUqd0BkZ2cjOztbqUwuyCGViOqoUDkzSzPo6OogNTFVqTw1KRXVnasVWa+SiTF+v7IDevp6kMvkCJy+DFdPhwIAYu79jbjH8Rg29SssmhqIrMwsfDqsN2yr2MLK1rJUz4dIFVKfPYdMLi8wHGBlboaHj+MKrdO6aSNs2XcUzd6ri2oOtrgYHoGg86GQFdHtfOJCKJ6/yESPzuzZeRsdC1NIdHWQl6T8dyovKQ0GtasWWkevuj0qVW2MtH0heOTtD4MaDqgydxQkujpIWJb/5SczLAp/fxuInAdPoGtrAdtx/VDrt/m46zUW8oyXpX5emkIoJ9/oVUVUMvCvypUro3nzosf1GjZsiLCwMNSqpTy5KCAgAP7+/kplNUxqoqZp7XcJR+0yX7zEUK+RMDI2QtO2TTDabySexsQi/Px1yPJkmDXMH98unIgDt/ZClifD1TOhuHDiEvgFiCqqKcP7w3/5JvQYNQ0SSFDVwRY9PNsWOayw99gptGnmAlsrizKOVDtIpBLkJaXjybSVgFyOrJv3oWdvBevhvRTJwIuTV/+rcOcRMq9Fof6Z9TDr3hapv/2lpsjVgMmA6ry6dPGrfH194ePjo1T2UYNPSjOUEklPSYcsTwYLG+U/SBbWFkhJSC2iVv75Pn30FABw//Z91KhTHV+M6Yfw8/n3dYi6cRfDvEaikokxdPX0kJ6Sjp8OLEPkG2b+EmkKC1MT6EilSE5THj9OTkuHtUXByYNA/nyApTPGITsnF2nPXsDWyhyBm35HVXubAvs+TUjChfDbWDJtbKnEX9HIUp9ByJNB11r575SutTnyEgv/O5WbkArk5ikNCWTdeww9W0tI9HQh5OYVqCN/noHsh0+hX6PgFWRUcailX97AwACmpqZKm6YMEQBAXm4eom5EoWnbJooyiUSCpm2b4Fbo7WK3I5VIFHMFXpXxPBPpKelwrOmIuo3r4uyxcyqJm6g06enpooGzEy6G//c7IJfLcTE8Aq7133w7cwN9PdhZWyBPJsPxc1fR0aNJgX32/XUGlmamaNfcVeWxV0RCbh5e3ryHSm1euTGcRILKrV2RGVr4JdCZV25D38kBr3ZHGtSsgtz45EITAQCQGhtCv4Z9kQlGRSXIVbeVB6XaM1Ce/b5mN6YumYyo8ChEhEXi06GfwNDIEEd2HgUA+AZORmJcEtbNz1+Jsf+YzxF5PQpPo59CT18fHu+3QJfenlgybZmizQ7d2yMtJQ0JTxJQq35NjPUfjbNHz+HKqauFxkAll5n5EjGPnyoeP3kajztR92FmagIHe1s1RlYxDOrZFTOWrEPDOk5wqVsLW/cfw8usbPT0bAsAmLZoLeyszPHNl30AANcj7yMhORX1a1VHfFIaVm3fB7lcgHfvbkrtyuVy7D9+Bh93bgNdHZ0yP6/yKmndPlRdNAEvr9/Dy/AoWA3pAamxIVJ35a9vUnXRBOTGJSN+wWYAQMq2P2E16H9wmDUMyb8chIFTFdiM6YPkTQcVbdpPG4LnQZeQ8zgBenaWsJ3QH5DJkfbHSbWco9qUkw9xVWEyUITgAydhZmWOLycNhqWNBe7fvo8pA6chNSkNAGDraKt0bbShsSHGzxsHGwdrZGdlI+be35g3bj6CD/z3C2RlZ4nRs0bAwtoCyQkpOLbrL2xZuq2sT61Cu3nnLoZ8PUXx+MflawAAPT70xPczJqorrArjg/YeSE1/jp+27kNSajrq1aqOVXN8YGVhBgCIS0yGVPrft86cnFys2LIXj+MSYGxkiLbNGmPexGEwrWys1O6FsNuITUxGzy7tyvR8yrv0Q2ega2UGO58voGttgayIB3j45Szk/fN3Sq+KjdLd93Jjk/BosB8cZg6F5Z/LkRuXjOSNB/KvJviHnr0Vqi2dBB1zU8hS0pFx5Tbu95oEWYp2XV5YXr7Rq4pEKGpgXwVMTU0LnUBYmE5VC78ulsresbCf1R0C/UOeWvgsfSp7UV0D1B0CvcLl4YFSbT+xSweVtWXzl+b3qqhlAiEREZEm07aegVJNBm7fvl1hlicmIiLtwWSgGLKysrB8+XIEBwcjISEBcrnyqxYamr/QTrVqRS/QQ0RERJpBVDLw1Vdf4dixY/j000/RokULLhtKREQVi6Dez7WVK1diwYIFiIuLg6urK5YvX44WLVoUum/Hjh1x8mTBeQndunXDoUOHinU8UcnAwYMHcfjwYbRpwyVDiYio4lHnMMHOnTvh4+OD1atXw8PDA4GBgfDy8kJkZCRsbQteIr1nzx7FfYIAIDk5Ga6urujTp0+xjylqpR9HR0eYmJiIqUpERKRVsrOz8ezZM6Xt9fvzvGrx4sUYNmwYvL290bBhQ6xevRrGxsbYsGFDoftbWlrC3t5esf31118wNjYu/WRg0aJFmDJlCqKjo8VUJyIi0miCXKKyLSAgAGZmZkpbQEDhl6rm5OTg6tWr8PT0VJRJpVJ4enri/PnzxYp9/fr1+Pzzz1GpUqVin6+oYQJ3d3dkZWWhVq1aMDY2hp6e8pK7KSkpYpolIiLSCKocJijsfjyv3+X3X0lJSZDJZLCzs1Mqt7Ozw507d956rEuXLuHmzZtYv359iWIUlQz069cPT548wbx582BnZ8cJhEREREUwMDAo8sNf1davXw8XF5ciJxsWRVQycO7cOZw/fx6urryhCBERVTyCmq4msLa2ho6ODuLj45XK4+PjYW9v/8a6GRkZ2LFjB+bMmVPi44qaM1C/fn28fPlSTFUiIiKNp667Furr66NZs2YICgpSlMnlcgQFBaFVq1ZvrPv7778jOzsbAwYMKPH5ikoG5s+fj4kTJyIkJATJyckFZkkSERGVZ6qcQFhSPj4+WLt2LX755RdERERg1KhRyMjIgLe3NwBg0KBB8PX1LVBv/fr16NmzJ6ysrEp8TFHDBB988AEAoHPnzkrlgiBAIpFAJpOJaZaIiEjr9e3bF4mJifDz80NcXBzc3Nxw5MgRxaTCmJgYSKXK3+UjIyNx5swZHDt2TNQxRSUDwcHBog5GRERUHqj7Pntjx47F2LFjC30uJCSkQFm9evXe6eaAopKBDh1Ud2tHIiIiTSOme788E5UMnDp16o3Pt2/fXlQwREREVPZEJQMdO3YsUPbqWgOcM0BEROWZtvUMiLqaIDU1VWlLSEjAkSNH0Lx5c9GTF4iIiDSFIKhuKw9E9QyYmZkVKOvSpQv09fXh4+ODq1evvnNgREREVDZEJQNFsbOzQ2RkpCqbJCIiKnPaNkwgKhm4fv260mNBEBAbG4v58+fDzc1NFXERERGpjbqWI1YXUcmAm5sbJBJJgWsaW7ZsWeT9lomIiEgziUoGHj58qPRYKpXCxsYGhoaGKgmKiIhInVR5C+PyQFQyUKNGDQQFBSEoKAgJCQmQy5VfNfYOEBFReSbnMMHb+fv7Y86cOXB3d4eDg4PSGgNERETlHecMFMPq1auxadMmDBw4UNXxEBERURkTlQzk5OSgdevWqo6FiIhII2jbpYWiViAcOnQotm/frupYiIiINAJXICyGrKwsrFmzBsePH0fjxo2hp6en9PzixYtVEhwRERGVPtGLDv27uNDNmzeVnuNkQiIiKu+0bZhAVDIQHBys6jiIiIg0hrZdWihqzgARERFVHCq9UREREVFFwHUGiIiItFx5uQpAVThMQEREpOXYM0BERPQabZtAyGSAiIjoNZwzQEREpOU4Z4CIiIi0CnsGiIiIXsM5A2pyOuG2ukOgf8hT49QdAv1DamGv7hDoHxa2GeoOgcqQts0Z4DABERGRltOYngEiIiJNwWECIiIiLadlFxNwmICIiEjbsWeAiIjoNRwmICIi0nK8moCIiIi0CnsGiIiIXiNXdwBljMkAERHRawRo1zABkwEiIqLXyLXs2kJRcwZCQ0Nx48YNxeP9+/ejZ8+emDZtGnJyclQWHBEREZU+UcnAiBEjEBUVBQB48OABPv/8cxgbG+P333/H5MmTVRogERFRWZNDorKtPBCVDERFRcHNzQ0A8Pvvv6N9+/bYvn07Nm3ahN27d6syPiIiojInQKKyTYyVK1fCyckJhoaG8PDwwKVLl964f1paGsaMGQMHBwcYGBigbt26OHz4cLGPJ2rOgCAIkMvz51oeP34c//vf/wAA1apVQ1JSkpgmiYiICMDOnTvh4+OD1atXw8PDA4GBgfDy8kJkZCRsbW0L7J+Tk4MuXbrA1tYWu3btgqOjI6Kjo2Fubl7sY4pKBtzd3fHdd9/B09MTJ0+exKpVqwAADx8+hJ2dnZgmiYiINIYqLy3Mzs5Gdna2UpmBgQEMDAwK3X/x4sUYNmwYvL29AQCrV6/GoUOHsGHDBkydOrXA/hs2bEBKSgrOnTsHPT09AICTk1OJYhQ1TBAYGIjQ0FCMHTsW06dPh7OzMwBg165daN26tZgmiYiINIYqhwkCAgJgZmamtAUEBBR63JycHFy9ehWenp6KMqlUCk9PT5w/f77QOn/88QdatWqFMWPGwM7ODo0aNcK8efMgk8mKfb6iegYaN26sdDXBvxYsWAAdHR0xTRIREVVIvr6+8PHxUSorqlcgKSkJMpmsQC+7nZ0d7ty5U2idBw8e4MSJE/jiiy9w+PBh3Lt3D6NHj0Zubi5mzZpVrBhVus6AoaGhKpsjIiJSC1UOE7xpSEAV5HI5bG1tsWbNGujo6KBZs2Z48uQJFixYoPpkwMLCAhJJ8WZFpqSkFLdZIiIijaOu5Yitra2ho6OD+Ph4pfL4+HjY29sXWsfBwQF6enpKPfMNGjRAXFwccnJyoK+v/9bjFjsZCAwMVPw/OTkZ3333Hby8vNCqVSsAwPnz53H06FHMnDmzuE0SERHRK/T19dGsWTMEBQWhZ8+eAPK/+QcFBWHs2LGF1mnTpg22b98OuVwOqTR/KmBUVBQcHByKlQgAJUgGBg8erPh/7969MWfOHKXAxo0bhxUrVuD48eOYMGFCcZslIiLSOOq8N4GPjw8GDx4Md3d3tGjRAoGBgcjIyFBcXTBo0CA4OjoqJiGOGjUKK1aswDfffIOvv/4ad+/exbx58zBu3LhiH1PUnIGjR4/ihx9+KFD+wQcfFHrZAxERUXkiV+PCgX379kViYiL8/PwQFxcHNzc3HDlyRDGpMCYmRtEDAOSv8XP06FFMmDABjRs3hqOjI7755htMmTKl2McUlQxYWVlh//79mDhxolL5/v37YWVlJaZJIiIijaHuZYTHjh1b5LBASEhIgbJWrVrhwoULoo8nKhnw9/fH0KFDERISAg8PDwDAxYsXceTIEaxdu1Z0MERERFT2RCUDX375JRo0aIBly5Zhz549APJnLp45c0aRHBAREZVXWnYH45InA7m5uRgxYgRmzpyJbdu2lUZMREREaqWuSwvVpcTLEevp6fHOhERERBWIqHsT9OzZE/v27VNxKERERJpBLpGobCsPRM0ZqFOnDubMmYOzZ8+iWbNmqFSpktLzJbm2kYiISNNwzkAxrF+/Hubm5rh69SquXr2q9JxEImEyQEREVI6ISgYePnyo6jiIiIg0hrZNIHznuxYKQn5nSnFvYkRERKTp1LkCoTqImkAIAJs3b4aLiwuMjIxgZGSExo0bY8uWLaqMjYiIiMqAqJ6BxYsXY+bMmRg7dizatGkDADhz5gxGjhyJpKQk3qiIiIjKNXUvR1zWRCUDy5cvx6pVqzBo0CBF2ccff4z33nsPs2fPZjJARETlGq8mKIbY2Fi0bt26QHnr1q0RGxv7zkERERGpE+cMFIOzszN+++23AuU7d+5EnTp13jkoIiIiKjui71rYt29fnDp1SjFn4OzZswgKCio0SSAiIipPeGlhMfTu3RsXL17EkiVLFMsSN2jQAJcuXUKTJk1UGR8REVGZ45yBYmrWrBm2bt2qyliIiIhIDUQlA4MGDUKnTp3QoUMH1KpVS9UxaYxRIwdjos8o2Nvb4Pr12/hm/ExcvhJW6L5fDemPgQM+xXvv1QMAhIbewIyZ8wvsX7++MwLmTUf7di2hq6uL2xFR+KzvMPz999NSPpvybcfBIGza8yeSUtNRt2Z1+I74Ai71Cv/Zy83Lw/rfD+GPoLNISE6Fk6MDxnv3QdtmLop9PhgyCU8TkgvU7dv9fUwfNbDUzkObXAm7gY3bd+H2nXtITE7B0oCZ6Ny+4MRjejeVPu0Bky/6QsfKErl37yN10XLk3r5T5P6SypVgNuorGHVsB6mpCfLi4pG+5CdknbuY/7yxEUxHDIFRh7bQsTBHTtQ9pC1egdyIyLI6JY3ACYTFoK+vj4CAADg7O6NatWoYMGAA1q1bh7t376o6PrXp0+djLFwwC3O/W4zmHh8g/PptHD60DTY2VoXu36FDK+zYuR+eXT9D2/Yf4+/HT/Hn4e2oUsVesU+tWjVwMngfIiPvoXOXT9GkmSe+nxeIrKzssjqtcunIqYtYsG4HRvbrgZ1LZ6NezWoY6bcIyWnPCt1/xZY92PVnCHxHfIF9q75Hn24dMeH75Yi4H63YZ/sSP5zYEqjY1nw3CQDQtU3zMjknbfDyZRbqOdfC9Imj1R1KhWXk2RHm34zCs/WbET94BHLu3YfN0h8gtTAvvIKuLmyWL4COgz2SfWcj7rPBSJ23CLLERMUuFtMmwbBFM6TMDkDcF18h++IV2KxYAKmNddmclIaQq3ArDyTCv+sJi/DkyROcOnUKJ0+exMmTJxEVFQUHBwc8fvy4xG3p6juKDaNUnDtzAJevhOOb8TMA5C+3/OjBZaz8aSN+XLDyrfWlUimSEm5j3PgZ2Lp1FwBg29afkJubhy+9NftGThm3fld3CEr6+8xFozpOmPbPN3a5XI6uX05Ev4888VWf7gX27zxoAoZ99j98/r/OirIJ81bAUF8PAZNGFHqMH9Zsx6nL4Ti4Zr5GLa0ttbB/+07lQKM2H5b7noH47kPVHUIBtutXIiciEmkLl+UXSCRw+GMnXvy+F883/1pg/0qffASTAX0R99lgQCYr2KCBPhxPHELy5BnIOnvxv+P8shpZ5y7h2c8bSutUSqzqxROl2v7aqgNU1tawx5o/pC56OWIAsLCwgJWVFSwsLGBubg5dXV3Y2NioKja10dPTQ9OmjRF04rSiTBAEBJ04g5YtmxWrDWNjI+jp6SI1JQ1AfjLR7cPOuHv3AQ4f3Ianj8Nx7swBfPyxV2mcQoWRm5uHiHuP0NLtPUWZVCqFh1tDhN+5V2idnNxc6OvrKZUZ6uvh2u3Ce65yc/NwKOQ8enZpp1GJANEb6epCr35dZF165c6xgoCsy1eh79Kw0CqG7Vsj+8YtmE/+Bg5/7oLd9vUwGdwfkOZ/FEh0dCDR1YGQnaNUT8jOhoFro1I7FU2kbT0DopKBadOmoXXr1rCyssLUqVORlZWFqVOnIi4uDteuXVN1jGXO2toSurq6SIhPUipPSEiEvV3xkp2AedPx9Gk8jgflJxS2ttYwMamMyd+OwdFjIfiwe3/s238Eu35bh/btWqr8HCqK1GfPIZPLYWVuqlRuZW6GpNTChwlaN22ELfuOIvpJHORyOc5fu4Wg86FITEkvdP8TF0Lx/EUmenRuo/L4iUqL1NwMEl0dyFNSlcrlKanQsbQstI5uFQcYv98BEqkUSRN88WzDFlT+og9MvPO/BQuZL5F9/RZMhwyE1NoKkEph/IEn9Bs1zH+sRQSJ6rbyQNQEwvnz58PGxgazZs1Cr169ULdu3RLVz87ORna28ji5IAgV5lvZ5G/HoO9nH6Nzlz6K85T+k3n/ceAoli5bCwAID7+FVq3cMXz4QJw6fUFt8VY0U4b3h//yTegxahokkKCqgy16eLbFvr9OF7r/3mOn0KaZC2ytLMo4UqIyJpVAlpqK1IDFgFyO3Dt3oWNjDZMBffF8/WYAQMrsAFjO+BZVDv0OIU+G3Mi7yDx2Avr1S/Z3nsoXUcnAtWvXcPLkSYSEhGDRokXQ19dHhw4d0LFjR3Ts2PGtyUFAQAD8/f2VyiTSypDomBZRo2wlJaUgLy8PtnbKE2ZsbW0QF59YRK18PhNGYPK3Y+D1wee4cSNCqc3c3FxERCh3Vd+5cxdtWrdQXfAVjIWpCXSk0gKTBZPT0mFtUfjPi6WZKZbOGIfsnFykPXsBWytzBG76HVXtC/bqPE1IwoXw21gybWypxE9UWuRp6RDyZJBaKiexUksLyFJSCq+TlAIhLw+Q/9d5nfcoBjrWVoCuLpCXB9mTp0gcNQESQ0NIKhlDnpwCy+9mIu+pdi01X16691VF1DCBq6srxo0bhz179iAxMRGHDx+Gvr4+xowZgwYNGry1vq+vL9LT05U2idRETCilIjc3F6Gh1/F+p7aKMolEgvc7tcWFC1eLrDdp4ihMnzYe3f83AFdDrxdo88qVcNStW1upvE6dWoiOKfmES22hp6eLBs5OuBh+W1Eml8txMTwCrvWd31jXQF8PdtYWyJPJcPzcVXT0KLgg1r6/zsDSzBTtmruqPHaiUpWXh9w7UTBs3vS/MokEBs2bIufG7UKrZF+/Cd2qjsArvbC61atClpgE5OUp7StkZUGenAKJSWUYtmyOrFNnS+U0NJW2zRkQ1TMgCAKuXbuGkJAQhISE4MyZM3j27BkaN26MDh06vLW+gYEBDAwMlMo0bYhgydK12Lh+Ca6GXsfly9cw7uthqFTJCJt+2QkA2LhhKZ4+jcX0GfMBAN9OGo3ZsyZhwKCxeBT9N+z+mVvw4kUGMjIyAQALF6/Cr9tW4fTpCwg5eQ5eXTvif927oLPnp+o5yXJiUM+umLFkHRrWcYJL3VrYuv8YXmZlo6dnfrI2bdFa2FmZ45sv+wAArkfeR0JyKurXqo74pDSs2r4PcrkA797dlNqVy+XYf/wMPu7cBro6OmV+XhVdZuZLxDz+b/2MJ0/jcSfqPsxMTeBgb6vGyCqO57/+Dku/qciJiETO7Tuo/HlvSA0NkXHwCADAYtZUyBKT8OyndQCAjN1/oHKfnjD3GYsXv+2FbnVHmHzZHy927lW0aeDhDkgkyIv+G7rVHGH29QjkRscg48ARtZyjunAFwmKwtLTEixcv4Orqig4dOmDYsGFo164dzM3NVRye+vz++x+wsbbEbL9JsLe3QXj4LXT/3wAkJORPKqxerQrkr3S1jRg+CAYGBvh951qldubMXYQ5cxcDAPbvP4LRY6ZiyuSvEbhkDiKjHqBP32E4e+5y2Z1YOfRBew+kpj/HT1v3ISk1HfVqVceqOT6wsjADAMQlJkMq/S+ZzMnJxYote/E4LgHGRoZo26wx5k0cBtPKxkrtXgi7jdjEZPTs0q5Mz0db3LxzF0O+nqJ4/OPyNQCAHh964vsZE9UVVoXy8ngI0szNYTrcGzpWFsiNuo+k8VMUkwp17WyVhgRkCYlIGjcFZhNGw27bOsgSk/Bixx4837JDsY+0ciWYjR4GHVtryJ89x8vg00hftb7wSxGpwhC1zsChQ4fQrl07mJq+eYz/8ePHqFKlimLy3Jto2joD2kzT1hnQZhVlnYGKQBPXGdBmpb3OwNLqqltn4JuYCrrOQPfu3d+aCABAw4YN8ejRIzGHICIiUhttmzPwTosOvc07LG5IREREZUT0XQuJiIgqqvLyjV5VmAwQERG9Rtv6tUt1mICIiIg0X6n2DGja2gFERETFIdeyj69STQY4gZCIiMojzhlQodu3b6NKlSqleQgiIiJ6R6KSgaysLCxfvhzBwcFISEhQWokPAEJDQwEA1apVe/cIiYiIypi29WuLSga++uorHDt2DJ9++ilatGjBuQFERFShyLUsHRCVDBw8eBCHDx9GmzZtVB0PERGR2mnbnAFRlxY6OjrCxERzbjlMRERUkaxcuRJOTk4wNDSEh4cHLl26VOS+mzZtgkQiUdoMDQ1LdDxRycCiRYswZcoUREdHi6lORESk0QQVbiW1c+dO+Pj4YNasWQgNDYWrqyu8vLyQkJBQZB1TU1PExsYqtpJ+PosaJnB3d0dWVhZq1aoFY2Nj6OnpKT2fkpIiplkiIiKNoMphguzsbGRnZyuVGRgYwMDAoND9Fy9ejGHDhsHb2xsAsHr1ahw6dAgbNmzA1KlTC60jkUhgby/+LqeikoF+/frhyZMnmDdvHuzs7DiBkIiIqAgBAQHw9/dXKps1axZmz55dYN+cnBxcvXoVvr6+ijKpVApPT0+cP3++yGO8ePECNWrUgFwuR9OmTTFv3jy89957xY5RVDJw7tw5nD9/Hq6urmKqExERaTRVrkA43dcXPj4+SmVF9QokJSVBJpPBzs5OqdzOzg537twptE69evWwYcMGNG7cGOnp6Vi4cCFat26NW7duoWrVqsWKUVQyUL9+fbx8+VJMVSIiIo2nyksL3zQkoAqtWrVCq1atFI9bt26NBg0a4Oeff8bcuXOL1YaoCYTz58/HxIkTERISguTkZDx79kxpIyIiopKztraGjo4O4uPjlcrj4+OLPSdAT08PTZo0wb1794p9XFHJwAcffIDz58+jc+fOsLW1hYWFBSwsLGBubg4LCwsxTRIREWkMdV1NoK+vj2bNmiEoKEhRJpfLERQUpPTt/01kMhlu3LgBBweHYh9X1DBBcHCwmGpERETlgjoXHfLx8cHgwYPh7u6OFi1aIDAwEBkZGYqrCwYNGgRHR0cEBAQAAObMmYOWLVvC2dkZaWlpWLBgAaKjozF06NBiH1NUMtChQwcx1YiIiOgt+vbti8TERPj5+SEuLg5ubm44cuSIYlJhTEwMpNL/OvZTU1MxbNgwxMXFwcLCAs2aNcO5c+fQsGHDYh9TIoi4z/CpU6fe+Hz79u1L2iR09R1LXIdKR8at39UdAv1DaiH+umFSrfjuxf+WRaWv6sUTpdr+FKd+Kmvrh0e/qqyt0iKqZ6Bjx44Fyl5da0Amk4kOiIiISN206zZFIicQpqamKm0JCQk4cuQImjdvjmPHjqk6RiIiojIlV+FWHojqGTAzMytQ1qVLF+jr68PHxwdXr15958CIiIiobIhKBopiZ2eHyMhIVTZJRERU5lS56FB5ICoZuH79utJjQRAQGxuL+fPnw83NTRVxERERqY12pQIikwE3NzdIJBK8fiFCy5YtsWHDBpUERkRERGVDVDLw8OFDpcdSqRQ2NjYwNDRUSVBERETqVF4m/qmKqGSgRo0aCAoKQlBQEBISEiCXK79s7B0gIqLyTNCygQJRyYC/vz/mzJkDd3d3ODg4KK0xQEREROWLqGRg9erV2LRpEwYOHKjqeIiIiNSOwwTFkJOTg9atW6s6FiIiIo2gbZcWilqBcOjQodi+fbuqYyEiIiI1ENUzkJWVhTVr1uD48eNo3Lgx9PT0lJ5fvHixSoIjIiJSB+3qF3iHRYf+XVzo5s2bSs9xMiEREZV32jZMICoZCA4OVnUcREREGkPbJhCKmjNAREREFYdKb1RERERUEXDRISIiIi3HYQIiIiLSKhrTM1BJnzc50hRRXQPUHQL9w8I2Q90h0D/sDq1TdwhUhjhMQEREpOU4TEBERERa5Z17BrKysmBoyC5+IiKqOOSCdg0TiOoZkMvlmDt3LhwdHVG5cmU8ePAAADBz5kysX79epQESERGVNUGFW3kgKhn47rvvsGnTJvz444/Q19dXlDdq1Ajr1nGSDRERUXkiKhnYvHkz1qxZgy+++AI6OjqKcldXV9y5c0dlwREREamDHILKtvJA1JyBJ0+ewNnZuUC5XC5Hbm7uOwdFRESkTtp2aaGonoGGDRvi9OnTBcp37dqFJk2avHNQRERE6iRX4VYeiOoZ8PPzw+DBg/HkyRPI5XLs2bMHkZGR2Lx5Mw4ePKjqGImIiKgUieoZ6NGjBw4cOIDjx4+jUqVK8PPzQ0REBA4cOIAuXbqoOkYiIqIyxTkDxdSuXTv89ddfqoyFiIhII3DOQDFcvnwZFy9eLFB+8eJFXLly5Z2DIiIiorIjKhkYM2YM/v777wLlT548wZgxY945KCIiInXiBMJiuH37Npo2bVqgvEmTJrh9+/Y7B0VERKROApcjfjsDAwPEx8cXKI+NjYWuLm+ESEREVJ6ISga6du0KX19fpKenK8rS0tIwbdo0Xk1ARETlHq8mKIaFCxeiffv2qFGjhmKRobCwMNjZ2WHLli0qDZCIiKislZexflURlQw4Ojri+vXr2LZtG8LDw2FkZARvb2/069cPenp6qo6RiIiISpHoAf5KlSph+PDhqoyFiIhII2jbOgOik4G7d+8iODgYCQkJkMuVO1T8/PzeOTAiIiJ1UfdY/8qVK7FgwQLExcXB1dUVy5cvR4sWLd5ab8eOHejXrx969OiBffv2Fft4opKBtWvXYtSoUbC2toa9vT0kEoniOYlEwmSAiIjKNXVeWrhz5074+Phg9erV8PDwQGBgILy8vBAZGQlbW9si6z169AiTJk1Cu3btSnxMiSDijGvUqIHRo0djypQpJT5gUcwq11ZZW/Ruztg0VHcI9A8L2wx1h0D/sDu0Tt0h0Cv0rGuVavsfVvtQZW39+fefJdrfw8MDzZs3x4oVKwAAcrkc1apVw9dff42pU6cWWkcmk6F9+/YYMmQITp8+jbS0tBL1DIi6tDA1NRV9+vQRU5WIiEjjqXIFwuzsbDx79kxpy87OLvS4OTk5uHr1Kjw9PRVlUqkUnp6eOH/+fJHxzpkzB7a2tvjqq69Ena+oZKBPnz44duyYqAMSERFpOkGF/wICAmBmZqa0BQQEFHrcpKQkyGQy2NnZKZXb2dkhLi6u0DpnzpzB+vXrsXbtWtHnK2rOgLOzM2bOnIkLFy7AxcWlwOWE48aNEx0QERFRReLr6wsfHx+lMgMDA5W0/fz5cwwcOBBr166FtbW16HZEJQNr1qxB5cqVcfLkSZw8eVLpOYlEwmSAiIjKNVVeTWBgYFDsD39ra2vo6OgUWPI/Pj4e9vb2Bfa/f/8+Hj16hI8++khR9u8Vfrq6uoiMjETt2m+fkycqGXj48KGYakREROWCuq4m0NfXR7NmzRAUFISePXsCyP9wDwoKwtixYwvsX79+fdy4cUOpbMaMGXj+/DmWLl2KatWqFeu4ouYMzJkzB5mZmQXKX758iTlz5ohpkoiIiAD4+Phg7dq1+OWXXxAREYFRo0YhIyMD3t7eAIBBgwbB19cXAGBoaIhGjRopbebm5jAxMUGjRo2gr69frGOKSgb8/f3x4sWLAuWZmZnw9/cX0yQREZHGUOeNivr27YuFCxfCz88Pbm5uCAsLw5EjRxSTCmNiYhAbG6vS8xU1TCAIgtJCQ/8KDw+HpaXlOwdFRESkTupejnjs2LGFDgsAQEhIyBvrbtq0qcTHK1EyYGFhAYlEAolEgrp16yolBDKZDC9evMDIkSNLHAQREZEmkatxBUJ1KFEyEBgYCEEQMGTIEPj7+8PMzEzxnL6+PpycnNCqVSuVB0lERESlp0TJwODBgwEANWvWROvWrXm7YiIiqpC0q19A5JyBDh06QC6XIyoqqtC7FrZv314lwREREamDuu9aWNZEJQMXLlxA//79ER0dXeBaTIlEAplMppLgiIiIqPSJSgZGjhwJd3d3HDp0CA4ODoVeWUBERFResWegGO7evYtdu3bB2dlZ1fEQERGpnbpWIFQXUYsOeXh44N69e6qOhYiIiNRAVM/A119/jYkTJyIuLq7QuxY2btxYJcERERGpA4cJiqF3794AgCFDhijKJBKJYmVCTiAkIqLyTN0rEJY13rXwDYYOH4Bx3wyDnZ0Nbt6IwLeT/BF69Xqh+w7+si8+7/8JGjaoCwAIC7sJ/9kLlfa3sbWC/5wpeL9zW5iZmeLc2cv4dpI/Htx/VBanU65ZDuwGm+G9oGtjgayIh3g6+2e8DL9b5P5Sk0qw/3YgTL1aQcfMBLlPExA7Zy2eh1wFANh+0w924/sr1cm6/xh3PUeV6nlUBJU+7QGTL/pCx8oSuXfvI3XRcuTevlPk/pLKlWA26isYdWwHqakJ8uLikb7kJ2Sdu5j/vLERTEcMgVGHttCxMEdO1D2kLV6B3IjIsjqlCu9K2A1s3L4Lt+/cQ2JyCpYGzETn9q3VHRZpEFHJQI0aNVQdh8bp1bs75gVMw4RvZuLKlXCMHuONvfs2oVnTLkhKTC6wf9t2Htj9+wFMvhCKrOxsjJ8wAnv3/4KWzT9AbGz+fam3/7oauXl56N93BJ49f4GxX3+F/Qc2w8PdC5mZL8v6FMsNs+5t4TB9KJ7OWInMsChYD/kYNX+Zg8jOIyFLTi+wv0RPFzW3zEVechpiRs9Hblwy9KvaQvZM+eZaWZHReDhghuKxIJO/3hS9xsizI8y/GYXUHwKRcysClT/vDZulPyDus8GQp6YVrKCrC5vlCyBLTUOy72zIEpOgY28H4ZUbnVlMmwS92jWRMjsAsqQkVPqgC2xWLEDc50MgT0wqu5OrwF6+zEI951r4pHtXjJ/2nbrDKRc4gbCYtmzZgjZt2qBKlSqIjo4GkL9c8f79+1UWnDqNGTsEv2zaiW1bdyPyzj2MHzcDmS9fYuDATwvdf9hXPli3dhtu3IjA3agH+HqML6RSCTp0zM++azs7oYVHU/iM90No6A3cu/sQE76ZCSMjQ3za56OyPLVyx3poT6TuPIrUXUHIvvc3nkz/CfKX2bDs06XQ/S36eELHvDKiR3yPzKsRyH2SgIyLN5EV8UhpP0EmQ15SmmKTpT4rg7Mp30z69UHG/sPIPHgEeQ+jkTZ/CYSsbFT66MNC96/00YeQmpoi+duZyLl+C7LYeORcu47cuw/ydzDQh1Gn9khf8TNywq5D9vgpnq37BXmPn6Jyr4/L8MwqtnatmmPc8MHw7NBG3aGUG+q8a6E6iEoGVq1aBR8fH3Tr1g1paWmKOQLm5uYIDAxUZXxqoaenB7cmjRASfE5RJggCQoLPoXmLJsVqw9jYCHp6ekj959uSgUH+PaWzs7KV2szOzkHLVu6qC76CkejpwqiRM16cCf+vUBDw4mwYjJvWK7SOqacHMq/dgeOckah/eTPqHFkBm9F9AKnyj7uBUxXUv7AJ9U6uRbUlE6FXxaY0T6X809WFXv26yLp09b8yQUDW5avQd2lYaBXD9q2RfeMWzCd/A4c/d8Fu+3qYDO6veC8kOjqQ6OpAyM5RqidkZ8PAtVGpnQrR2wiCoLKtPBCVDCxfvhxr167F9OnToaOjoyh3d3fHjRs33lo/Ozsbz549U9o06QWzsrKArq4uEhKUuygTE5JgZ1e8Dwz/uZMRFxuPkOCzAICoyAeIiXmCWf6TYG5uCj09PYyfMBxVqzrA3p4fQkXRsTCFRFcHeUmpSuV5SWnQtbEotI5edXuYfdgGkErxyNsfCct3wHpoT9iO/UyxT2ZYFP7+NhCPvpyNJzN/gl41O9T6bT6klYxK9XzKM6m5GSS6OpCnKL8X8pRU6BRx63LdKg4wfr8DJFIpkib44tmGLaj8RR+YeA8AAAiZL5F9/RZMhwyE1NoKkEph/IEn9Bs1zH9MRGVCVDLw8OFDNGlS8BuygYEBMjIy3lo/ICAAZmZmSlt2bupb65UXE3xGoHfv/+GLfqOQ/c83nry8PAzsPxq1nWsi+vE1xCXeRLv2rXDsaAjkcs1JhCoCiVSCvKR0PJm2Elk37yP90BkkrvwNll/815X94uRVPDt8Fll3HuHFqWt45O0PHZNKMOveVo2RV0BSCWSpqUgNWIzcO3fx8ngInm/chsq9/hsaS5kdAEgkqHLodziePorKn/VC5rETgJxzOEh9tG2YQNQEwpo1ayIsLKzARMIjR46gQYMGb63v6+sLHx8fpbKqDm5iQikVycmpyMvLg62ttVK5ja014uMT31j363FDMd5nJHp+NAi3binPhg4Lu4l2rT+CqWll6OnrIzkpBUHBu3Ht2tt7U7SVLPUZhDwZdK2VewF0rc2Rl1h4ApmbkArk5il9mGTdeww9W0tI9HQh5OYVqCN/noHsh0+hX8NBtSdQgcjT0iHkySC1VH4vpJYWkKWkFF4nKQVCnvJ7kfcoBjrWVoCuLpCXB9mTp0gcNQESQ0NIKhlDnpwCy+9mIu9pbKmeD9GbaNulhaJ6Bnx8fDBmzBjs3LkTgiDg0qVL+P777+Hr64vJkye/tb6BgQFMTU2VNk26v0Fubi7Crt1UTP4D8tdR6NCxFS5fulZkvW/GD8e3U8ai9yfeb/yAf/bsBZKTUlCrthOaNHXB4YPHVRp/RSLk5uHlzXuo1OaVhawkElRu7YrM0MIvPcu8chv6Tg7AKz9TBjWrIDc+udBEAACkxobQr2FfZIJBAPLykHsnCobNm/5XJpHAoHlT5Ny4XWiV7Os3oVvVUem90K1eFbLEJCBP+b0QsrIgT06BxKQyDFs2R9aps6VyGkRUkKiegaFDh8LIyAgzZsxAZmYm+vfvjypVqmDp0qX4/PPPVR2jWqxcsQGrfl6Aa6E3cPVq/qWFlYyNsXXrLgDA6jULEfs0Dv6zFwIAxk8YjmkzxmPokAmIiX6s6FXIyMhERkYmAKDnJx8iKSkFj/9+iobv1cP8H2fi0MG/cOLEGfWcZDmRtG4fqi6agJfX7+FleBSshvSA1NgQqbvyk6iqiyYgNy4Z8Qs2AwBStv0Jq0H/g8OsYUj+5SAMnKrAZkwfJG86qGjTftoQPA+6hJzHCdCzs4TthP6ATI60P06q5RzLi+e//g5Lv6nIiYhEzu07qPx5b0gNDZFx8AgAwGLWVMgSk/Dsp3UAgIzdf6Byn54w9xmLF7/thW51R5h82R8vdu5VtGng4Q5IJMiL/hu61Rxh9vUI5EbHIOPAEbWcY0WUmfkSMY+fKh4/eRqPO1H3YWZqAgd7WzVGprnkGjSPrSyISgYA4IsvvsAXX3yBzMxMvHjxAra2FesHas/uQ7CytsS0GeNhZ2eNG9cj0OsTbyQm5K8xULWaA+SvdH0OGfoFDAwMsGXbT0rtBMxbivnzlgEA7Oxt8X3AdNjaWiEuLhE7ft2LH+evKLuTKqfSD52BrpUZ7Hy+gK61BbIiHuDhl7OQl5QGAPlXAbwy7yI3NgmPBvvBYeZQWP65HLlxyUjeeACJq3cr9tGzt0K1pZOgY24KWUo6Mq7cxv1ekyBL4eWFb/LyeAjSzM1hOtwbOlYWyI26j6TxUxSTCnXtbJWGBGQJiUgaNwVmE0bDbts6yBKT8GLHHjzfskOxj7RyJZiNHgYdW2vInz3Hy+DTSF+1HuBKpipz885dDPl6iuLxj8vXAAB6fOiJ72dMVFdYGk3bhgkkgohp/C9fvoQgCDA2NgYAREdHY+/evWjYsCG6du0qKhCzyrVF1SPVO2NT+GViVPYsbN8+IZfKht2hdeoOgV6hZ12rVNt/z85DZW3dir+osrZKi6g5Az169MDmzfldsmlpaWjRogUWLVqEHj16YNWqVSoNkIiIqKzJBUFlW3kgKhkIDQ1Fu3btAAC7du2Cvb09oqOjsXnzZixbtkylARIREZU1QYX/ygNRyUBmZiZMTEwAAMeOHUOvXr0glUrRsmVLxdLEREREVD6ISgacnZ2xb98+/P333zh69KhinkBCQgJMTU1VGiAREVFZ4zBBMfj5+WHSpElwcnKCh4cHWrVqBSC/l6CwlQmJiIjKE20bJhB1aeGnn36Ktm3bIjY2Fq6uroryzp0745NPPlE8fvz4MapUqQKpVPTNEYmIiMpceflGryqi1xmwt7eHvb29UlmLFi2UHjds2BBhYWGoVat0LwEhIiIi8UQnA8WhSXciJCIiKq7y0r2vKqWaDBAREZVHgqBdd83kYD4REZGWY88AERHRa+QcJlAdTbotMRERUXFp25y3Uh0m0LYXk4iIqDwq1Z6B27dvo0qVKqV5CCIiIpXjMEExZGVlYfny5QgODkZCQgLkcuVZl6GhoQCAatWqvXuEREREZUzberZFJQNfffUVjh07hk8//RQtWrTg3AAiIqJyTFQycPDgQRw+fBht2rRRdTxERERqx+WIi8HR0VFxC2MiIqKKRttWIBR1NcGiRYswZcoUREdHqzoeIiIitRMEQWWbGCtXroSTkxMMDQ3h4eGBS5cuFbnvnj174O7uDnNzc1SqVAlubm7YsmVLiY4nqmfA3d0dWVlZqFWrFoyNjaGnp6f0fEpKiphmiYiItN7OnTvh4+OD1atXw8PDA4GBgfDy8kJkZCRsbW0L7G9paYnp06ejfv360NfXx8GDB+Ht7Q1bW1t4eXkV65gSQUTa4unpiZiYGHz11Vews7MrMIFw8ODBJW0SZpVrl7gOlY4zNg3VHQL9w8I2Q90h0D/sDq1Tdwj0Cj3r0r0bro1ZPZW1lZgeWaL9PTw80Lx5c6xYsQIAIJfLUa1aNXz99deYOnVqsdpo2rQpunfvjrlz5xZrf1E9A+fOncP58+fh6uoqpjoREZFGU+WlhdnZ2cjOzlYqMzAwgIGBQYF9c3JycPXqVfj6+irKpFIpPD09cf78+bceSxAEnDhxApGRkfjhhx+KHaOoOQP169fHy5cvxVQlIiLSKgEBATAzM1PaAgICCt03KSkJMpkMdnZ2SuV2dnaIi4sr8hjp6emoXLky9PX10b17dyxfvhxdunQpdoyiegbmz5+PiRMn4vvvv4eLi0uBOQOmpqZimiUiItIIqry00NfXFz4+PkplhfUKvAsTExOEhYXhxYsXCAoKgo+PD2rVqoWOHTsWq76oZOCDDz4AAHTu3FmpXBAESCQSyGQyMc0SERFpBFUOExQ1JFAYa2tr6OjoID4+Xqk8Pj4e9vb2RdaTSqVwdnYGALi5uSEiIgIBAQGlmwwEBweLqUZERERvoK+vj2bNmiEoKAg9e/YEkD+BMCgoCGPHji12O3K5vMA8hTcRlQx06NBBTDUiIqJyQZ03KvLx8cHgwYPh7u6OFi1aIDAwEBkZGfD29gYADBo0CI6Ojop5BwEBAXB3d0ft2rWRnZ2Nw4cPY8uWLVi1alWxjykqGTh16tQbn2/fvr2YZomIiDSCOm9U1LdvXyQmJsLPzw9xcXFwc3PDkSNHFJMKY2JiIJX+N/8/IyMDo0ePxuPHj2FkZIT69etj69at6Nu3b7GPKWqdgVeDUDT0yloDYuYMcJ0BzcF1BjQH1xnQHFxnQLOU9joDppVU1/6zjAcqa6u0iLq0MDU1VWlLSEjAkSNH0Lx5cxw7dkzVMRIREZUpuSCobCsPRA0TmJmZFSjr0qUL9PX14ePjg6tXr75zYEREROqibTcqEpUMFMXOzg6RkSVbdpGIiEjTlJdv9KoiKhm4fv260mNBEBAbG4v58+fDzc1NFXERERFRGRGVDLi5uUEikRSYbdmyZUts2LBBJYERERGpizqvJlAHUcnAw4cPlR5LpVLY2NjA0NBQJUERERGpE+cMFEONGjUQFBSEoKAgJCQkQC6XKz3P3gEiIqLyQ1Qy4O/vjzlz5sDd3R0ODg5KawwQERGVdxwmKIbVq1dj06ZNGDhwoKrjISIiUjttSwZELTqUk5OD1q1bqzoWIiIiUgNRycDQoUOxfft2VcdCRESkEQQVbuWBqGGCrKwsrFmzBsePH0fjxo2hp6en9PzixYtL3Gb6i/tiQtEY2dnZCAgIgK+vb7HvW02lh++H5uB7oTn4XhRfXs4TdYdQpkTdqKhTp05FNyiR4MSJE+8UVHn07NkzmJmZIT09HaampuoOR+vx/dAcfC80B98LKoqonoHg4GBVx0FERERqImrOABEREVUcTAaIiIi0HJMBFTEwMMCsWbM4KUdD8P3QHHwvNAffCyqKqAmEREREVHGwZ4CIiEjLMRkgIiLSckwGiIiItByTASIiIi3HZIAIwKNHjyCRSBAWFqbuUEgFNm3aBHNzc3WHQVRuaH0y0LFjR4wfP17dYShoWjxERK9iolUxaX0yoAo5OTnqDoE0FH82ShdfXyLV0Opk4Msvv8TJkyexdOlSSCQSSCQS3L9/H1999RVq1qwJIyMj1KtXD0uXLi1Qr2fPnvj+++9RpUoV1KtXDwBw7tw5uLm5wdDQEO7u7ti3b1+BruebN2/iww8/ROXKlWFnZ4eBAwciKSmpyHgePXpUVi+HSnXs2BHjxo3D5MmTYWlpCXt7e8yePRtA4V3yaWlpkEgkCAkJAQCEhIRAIpHg6NGjaNKkCYyMjPD+++8jISEBf/75Jxo0aABTU1P0798fmZmZxYpJLpfjxx9/hLOzMwwMDFC9enV8//33Svs8ePAAnTp1grGxMVxdXXH+/HnFc8nJyejXrx8cHR1hbGwMFxcX/PrrrwXOe+zYsRg/fjysra3h5eVV8hdPJE17zQ8ePAhzc3PIZDIAQFhYGCQSCaZOnarYZ+jQoRgwYIDi8e7du/Hee+/BwMAATk5OWLRokVKbTk5OmDt3LgYNGgRTU1MMHz4cQP631erVq8PY2BiffPIJkpOTleqFh4ejU6dOMDExgampKZo1a4YrV64U+7Utiqa95gCwa9cuuLi4wMjICFZWVvD09ERGRobi+XXr1qFBgwYwNDRE/fr18dNPPyme+zfmPXv2FPp7EBISAm9vb6Snpyv+Rv17vk5OTpg3bx6GDBkCExMTVK9eHWvWrFGK7fHjx+jXrx8sLS1RqVIluLu74+LFi4rn9+/fj6ZNm8LQ0BC1atWCv78/8vLyivt20LsQtFhaWprQqlUrYdiwYUJsbKwQGxsrZGVlCX5+fsLly5eFBw8eCFu3bhWMjY2FnTt3KuoNHjxYqFy5sjBw4EDh5s2bws2bN4X09HTB0tJSGDBggHDr1i3h8OHDQt26dQUAwrVr1wRBEITU1FTBxsZG8PX1FSIiIoTQ0FChS5cuQqdOnYqMJy8vTx0vzTvr0KGDYGpqKsyePVuIiooSfvnlF0EikQjHjh0THj58qPS6CEL+awNACA4OFgRBEIKDgwUAQsuWLYUzZ84IoaGhgrOzs9ChQweha9euQmhoqHDq1CnByspKmD9/frFimjx5smBhYSFs2rRJuHfvnnD69Glh7dq1giAIipjq168vHDx4UIiMjBQ+/fRToUaNGkJubq4gCILw+PFjYcGCBcK1a9eE+/fvC8uWLRN0dHSEixcvKp135cqVhW+//Va4c+eOcOfOHdW8oMWgaa95WlqaIJVKhcuXLwuCIAiBgYGCtbW14OHhodjH2dlZ8R5cuXJFkEqlwpw5c4TIyEhh48aNgpGRkbBx40bF/jVq1BBMTU2FhQsXCvfu3RPu3bsnXLhwQZBKpcIPP/wgREZGCkuXLhXMzc0FMzMzRb333ntPGDBggBARESFERUUJv/32mxAWFib+xf6Hpr3mT58+FXR1dYXFixcLDx8+FK5fvy6sXLlSeP78uSAIgrB161bBwcFB2L17t/DgwQNh9+7dgqWlpbBp0yZBEN7+e5CdnS0EBgYKpqamir9R/7Zdo0YNwdLSUli5cqVw9+5dISAgQJBKpYrfgefPnwu1atUS2rVrJ5w+fVq4e/eusHPnTuHcuXOCIAjCqVOnBFNTU2HTpk3C/fv3hWPHjglOTk7C7Nmz3/l9orfT6mRAEPJ/mb/55ps37jNmzBihd+/eiseDBw8W7OzshOzsbEXZqlWrBCsrK+Hly5eKsrVr1yr9MZg7d67QtWtXpbb//vtvAYAQGRlZ7HjKgw4dOght27ZVKmvevLkwZcqUEv2RPH78uGKfgIAAAYBw//59RdmIESMELy+vt8bz7NkzwcDAQPHB87p/Y1q3bp2i7NatWwIAISIiosh2u3fvLkycOFHpvJs0afLWeEqDpr3mgiAITZs2FRYsWCAIgiD07NlT+P777wV9fX3h+fPnwuPHjwUAQlRUlCAIgtC/f3+hS5cuSvW//fZboWHDhorHNWrUEHr27Km0T79+/YRu3boplfXt21cpGTAxMVF84KmSpr3mV69eFQAIjx49KvT52rVrC9u3b1cqmzt3rtCqVStBEIr3e7Bx40al1/ZfNWrUEAYMGKB4LJfLBVtbW2HVqlWCIAjCzz//LJiYmAjJycmFxta5c2dh3rx5SmVbtmwRHBwc3nLWpApaPUxQlJUrV6JZs2awsbFB5cqVsWbNGsTExCjt4+LiAn19fcXjyMhING7cGIaGhoqyFi1aKNUJDw9HcHAwKleurNjq168PALh//34pnpF6NG7cWOmxg4MDEhISRLdhZ2cHY2Nj1KpVS6msOG1GREQgOzsbnTt3LvbxHBwcAEDRvkwmw9y5c+Hi4gJLS0tUrlwZR48eLfCz0axZs7efWCnRpNccADp06ICQkBAIgoDTp0+jV69eaNCgAc6cOYOTJ0+iSpUqqFOnDoD896hNmzZK9du0aYO7d+8qhhoAwN3dXWmfiIgIeHh4KJW1atVK6bGPjw+GDh0KT09PzJ8/X6W/b5r0mru6uqJz585wcXFBnz59sHbtWqSmpgIAMjIyFMOgr/4N+u677wq8Hm/6PSjueUgkEtjb2yvqhYWFoUmTJrC0tCy0bnh4OObMmaMU27BhwxAbG1vsIRIST1fdAWiaHTt2YNKkSVi0aBFatWoFExMTLFiwQGlcCwAqVapU4rZfvHiBjz76CD/88EOB5/79hatI9PT0lB5LJBLI5XJIpfk5qPDKbTFyc3Pf2oZEIimyzbcxMjIqccwSiQQAFO0vWLAAS5cuRWBgIFxcXFCpUiWMHz++wCQ2MT8bqqJJrzmQP6a+YcMGhIeHQ09PD/Xr10fHjh0REhKC1NRUdOjQoVjtvErM6zt79mz0798fhw4dwp9//olZs2Zhx44d+OSTT0rc1us06TXX0dHBX3/9hXPnzuHYsWNYvnw5pk+fjosXL8LY2BgAsHbt2gLJk46OzhvjAVCs478p7rf9Dr548QL+/v7o1atXgede/ZJFpUPrewb09fWVvnWcPXsWrVu3xujRo9GkSRM4OzsX61tEvXr1cOPGDWRnZyvKLl++rLRP06ZNcevWLTg5OcHZ2Vlp+/cP3OvxVEQ2NjYAgNjYWEVZaV/fX6dOHRgZGSEoKEh0G2fPnkWPHj0wYMAAuLq6olatWoiKilJhlKVHHa85ALRr1w7Pnz/HkiVLFB/8/yYDISEh6Nixo2LfBg0a4OzZs0r1z549i7p16xb4sHpVgwYNCiTrFy5cKLBf3bp1MWHCBBw7dgy9evXCxo0b3+HM3k5dr7lEIkGbNm3g7++Pa9euQV9fH3v37oWdnR2qVKmCBw8eFPj7U7NmzWK3L/ZvVOPGjREWFoaUlJRCn2/atCkiIyMLxObs7KxIrKj0aP0r7OTkhIsXL+LRo0dISkpCnTp1cOXKFRw9ehRRUVGYOXNmgQ/1wvTv3x9yuRzDhw9HREQEjh49ioULFwL4L7MeM2YMUlJS0K9fP1y+fBn379/H0aNH4e3trfjlej2e4n4DK0+MjIzQsmVLzJ8/HxERETh58iRmzJhRqsc0NDTElClTMHnyZGzevBn379/HhQsXsH79+mK3UadOHcW3roiICIwYMQLx8fGlGLXqqOM1BwALCws0btwY27ZtU3zwt2/fHqGhoYiKilLqGZg4cSKCgoIwd+5cREVF4ZdffsGKFSswadKkNx5j3LhxOHLkCBYuXIi7d+9ixYoVOHLkiOL5ly9fYuzYsQgJCUF0dDTOnj2Ly5cvo0GDBqVyzv9Sx2t+8eJFzJs3D1euXEFMTAz27NmDxMRExbn6+/sjICAAy5YtQ1RUFG7cuIGNGzdi8eLFxT6Gk5MTXrx4gaCgICQlJRW7C79fv36wt7dHz549cfbsWTx48AC7d+9WXKng5+eHzZs3w9/fH7du3UJERAR27NhRJj+nxGQAkyZNgo6ODho2bAgbGxt4eXmhV69e6Nu3Lzw8PJCcnIzRo0e/tR1TU1McOHAAYWFhcHNzw/Tp0+Hn5wfgvy6uKlWq4OzZs5DJZOjatStcXFwwfvx4mJubKzLf1+N5fTy6otiwYQPy8vLQrFkzjB8/Ht99912pH3PmzJmYOHEi/Pz80KBBA/Tt27dEY7szZsxA06ZN4eXlhY4dOyr+sJUX6njNgfx5AzKZTJEMWFpaomHDhrC3t1dclgvkfzP87bffsGPHDjRq1Ah+fn6YM2cOvvzyyze237JlS6xduxZLly6Fq6srjh07pvQBoqOjg+TkZAwaNAh169bFZ599hg8//BD+/v6lcbpKyvo1NzU1xalTp9CtWzfUrVsXM2bMwKJFi/Dhhx8CyL+Uc926ddi4cSNcXFzQoUMHbNq0qUQ9A61bt8bIkSPRt29f2NjY4McffyxWPX19fRw7dgy2trbo1q0bXFxcMH/+fEWvj5eXFw4ePIhjx46hefPmaNmyJZYsWYIaNWqU/IWgEpMIrw5okUpt27ZNcU1uccesiYiIyhonEKrQ5s2bUatWLTg6OiI8PBxTpkzBZ599xkSAiIg0GpMBFYqLi4Ofnx/i4uLg4OCAPn36FFjhjkpHTEwMGjZsWOTzt2/fRvXq1cswooqPr3nZ42tOpYXDBFQh5OXlvXHpZicnJ+jqMvdVJb7mZY+vOZUWJgNERERaTuuvJiAiItJ2TAaIiIi0HJMBIiIiLcdkgIiISMsxGSAiItJyTAaIiIi0HJMBIiIiLfd/OfkvOQnY6csAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(numeric_only = True),annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "540e0740",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Preprocessing\n",
    "ps = PorterStemmer()\n",
    "\n",
    "def transform_txt(txt):\n",
    "    txt = txt.lower()\n",
    "    txt = nltk.word_tokenize(txt)\n",
    "    \n",
    "    y = []\n",
    "    for i in txt:\n",
    "        if i.isalnum():\n",
    "            y.append(i)\n",
    "            \n",
    "    txt = y[:]\n",
    "    y.clear()\n",
    "        \n",
    "    for i in txt:\n",
    "        if i not in stopwords.words('english') and i not in punctuation:\n",
    "            y.append(i)\n",
    "            \n",
    "    txt = y[:]\n",
    "    y.clear()\n",
    "        \n",
    "    for i in txt:\n",
    "        y.append(ps.stem(i))\n",
    "        \n",
    "    return \" \".join(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "425da863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'porter stem algorithm introduc martin porter 1980 follow set rule perform stem'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform_txt(\"The Porter stemming algorithm, introduced by Martin Porter in 1980, follows a set of rules to perform stemming.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fb82da88",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['transformed_text'] = df['text'].apply(transform_txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0ffbcc37",
   "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>target</th>\n",
       "      <th>text</th>\n",
       "      <th>num_char</th>\n",
       "      <th>num_words</th>\n",
       "      <th>num_sentnce</th>\n",
       "      <th>transformed_text</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",
       "  </tbody>\n",
       "</table>\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",
       "   num_words  num_sentnce                                   transformed_text  \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  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2a11cada",
   "metadata": {},
   "outputs": [],
   "source": [
    "wc = WordCloud(width=500,height=500,font_step=10,background_color='black')\n",
    "spam_wc = wc.generate(df[df['target']==1]['transformed_text'].str.cat(sep=\" \"))\n",
    "ham_wc = wc.generate(df[df['target']==0]['transformed_text'].str.cat(sep=\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ea58232f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1b84d3288b0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mni70ttaZ7/q2TfHJZ3DGJ7DO9NP6Mz+F3tmOMzhM/D17qR89TvnFl0FVUdMpwjt3YPUPgO8HBBNNRYmGkJ6PX7UQqoIwdGpHzqAmY3iVKtJyrsmQpCvtBf6+9DoCVTevbU9qCdAMgaIKVF3MeW10Q8EIKXzx351k9EzjBfRiKOddem+Ikm418HxJacq5+DQhYeydcaQvGXt3gkJ/ECrs3tvJ7f/iZrZ/dDMnv3ua/JnA2xeKYOcnd2DEDV79rTfof2EQz/WItkRpuaEJq2A1NFBSBotm1VDY/uhWdv/YjQy+Mszrv/8Wtcnlk4iuayM1VRugWB/DkwvXMpXtKYZLhxfMWa026hPDOOUCnfd9DLdexc5PnOcZCTI77yTWs4VIey9mpo1IWw9Th1+7KKPOq9fIHniZzA17ifdsA+nj1soMv/ANpO+RP/4mzXvuI9TUjlMpUpuYZUpZuXFyR9+g9Zb34Vk16tlhvFpl+o4IUttuIbFhJ5HWHnzXIZRpJ39sP8W+Q5frNq0qVMUgGVpZvtOvW/jV4MXyq1X8SgU9k6YGIAThnTswN6xHjccw1/XMeFEAfrmC7wQhHK9QAM9DSyZxBocx2tvQmpvQ29sAgd7eijM6NuM51E8OIvpHwfPxbQctFccrlHEmcniFMn6lhl+zsE4OrGBJfemIdG5ENUKX9Rzhlm6Ecu1OUY3mjHOfRFMa7/1UGxt2ReneEuZDP93Jre/P8N0/Hmb4ZA0JJJp0fujneqmXPaZGbF782jijffWGedoLPzj0UoEb7kry47+ykUrB5cu/2ke1dPF4+OTRKSaPzo049b84SPddnWx4sJdEd3zGSAEBWUlK7JKNUwsWRKWhEqWh0sL3xfORnmT7o1u56Ud30v/iIPv/8G1qU8s3xnCdGynPt/FYPFZftMYo2xNcqeWoXcwy/NzfITQDpI/v2GQPvDSTi8ifeIti38E53/Hs2R/37Lf+GN9tZHQltbF+RqZGg2MTeAG+E3y3eOYwlZE+EArSc+eEDKXvkTv6OsXTB0AIfMdm8u3n8WwLkBRPH5zxumbHdPkqa7NjLuXi6nm3CbMNQ42u6LtCVRHa9Guiqghdw68F9zT+nr1EbtpF4alncbNTJB98YO53dR2hKjN/R1Hwp+W9vHIFq3+A2qEjM/t7lSrnEm9ebm5C3C5O5/Om5w9ZD55re2BuDvZKof3ODxFu6bys5xCKiligVlAIddEIyZXAd/9oeE408vgbRc4eLFMreyDg6b8aRTNmIxJSSqpFD0WFRz/bw1tP5zj+RgkE3HR/ig/+VCd/+V/62P9Elneey+E5wZxQyjr8xk8dmZOzGjtb548+fwrdEEif4JxLhGqqGDEd1VBRVAUEQShPgnr+eH3J0a+foHl7hnt/4S6GXxvh+LdPkzuVxyrU8d0F5kwB2z+6hd0/cSNnnunn9d97C6u48vniujZSS4PEv8IPu+/Y4MwaT++84h/fruPbC684LlZPdeGxZyAlXr26yBf9Odu98wyh71j4V1Du4X//6upOvM3R9SsOGynhMOFtW3DGx9FbW1ATySAkB+jt7TiTWezhYdREAr2zHe/Y7ApTTSYIb99K7dARzC2bkY4beEtA9eBhzN4efMvCr1RQEwn8+pWrp7pUKLp+2T2pOecTKppi4ksPKX2iZhMVK4uUHpoaQkoPz3fQ1BCeZyO5/CH8c8SGc/AcSdWZnksk03VL8+eWcFxl3Y4I+749yeDxKmZEwa75xKYrHRxL4pwXRpOShkIA9bLHcnwToQiatqbZ+P71tO9pxUyYgRcuIZQKoajz35HBV4d5+j+9wKYPbqB9dysP/+Z7yZ3O0/fUGU58+3RD76j1xhbSG1PoEY1Q0kQLq1iXQEJcM1JruK4hUEiHl1bb1QhuNgtCkHn0I4hQiOqBgzjjARml8ubbpB55mOYf+RRevoDVd3aWoSkl9tkB9LY2IjfuBF2n+NwLAeECqOx/CzUSJv3hRxCqiletUXzmWeziwmGUaxEySEBcnoOL4D9CCNKxdejxKr7vkCv3Ewu1ULPzmHqSWLiVYnWYqNlMxMxQswsUqkNck8k6AuPywt9P8NCPtqOoAiklhUmXF/52HNe5fGNOdMe569/cQbI3wcGvHGHyaBan6uI7Hjs+vo3e+xu/J+MHJ8meyBHvjNG6s5ktP7CJm//JTURbIrz2v97Cq881xJ7l8e5fHibRHeOGT2xjd/ZG9n1h/6IEi8VwXRuphNnOhtRtjJSPMV45cUXPrQoDTdFRFR1FqEER6bnVvJT4+Pi+iycdXN+64t7ccnBuFasKHWVaoeOcZyJl4In60sH1g2u5diYHQTLUganFVvRtv1Zj4s++jJQSoQa8Yuk4M/UIVt8Zxv/oS6Ao4HtIz5/5je2BQSa/8tXgVmgqSIm0z/NOKxXyTzwdhBKFCIqyndXVgbwSKJ05zPALX1+yYspyEOvaQvdDn0Q1QhhalHJtjKjZhKJoqIqBpppEQk3U7ByOV6cpvhFNDWM5lyaftdoQqob0PYRQUEMR3FqFZ786wUtfm0AoAkU3CLVvpjIaQTVcot2bqWdHsaeZuaszCGje0UTbTS28/acHePcvDs6E6xRdCZh+ixTLe5ZHvq9A/kyB/hcGefh/vJeuvZ0kv3WKqeO5OfvmzxQ48/RZfE+ih3W2fngT1WyNA18+vCJDdV0bqbCepCW2mcnamStyPkVoJMw2EqE24mYrET2FqcUwlPCMsQLwpYfrW9helbpTomxnsb1FQnEXwWS1j5K1ug0jBQpRI0My1B5ci5EhpMUx1SiqoqMKDYnEly6OZ1F3S9ScAiVrgpI1RqE+grMA7f9yQREqIS1BSIsT0hOE9SSpUCeaYjTcX1NMOhM7L8nTgoBFOl4+geOfZ2SkRE6TJmbqpi6E583UpgGosRB+3UG61+6C5UK4tTJutXxZwsFOtYicXhBU6hM4bo2qyCGEguNVURWNQmWIRKQDx6szWTpNNNRE1Z7iWlko6bEkZrqN+uQwQtNJbt5N7sjrmOlW7PwEimoghYliZnAqJzESGYxYisrAyQWPqcRiGO1tOBOTs3V3F4MEOU0hDzeFUHQV33UxYjob3tdL+82t8/g3RtygbXcr1YkqpaESdsVBM1RiHVE0U8P3/MbsPl8iJdglm7e/dBAzabLrUzuoTtY4+d3T+M7yQrHXtZFShYYiVErWKq5IGkBBJRNZx7rULcSMJkwtHnhPC+RBVKGgKjqmFiNuttLCpks6vztmTV/jpb+YAkHCbGNd+haSZgchPY4qGhdVCma9rLCeIBXqpD2+HdutUHVyDBcPMVo+umAh9aWNU0FTTWJGhrjZRsxoJmqk0dUIumKiq6EFx30OuhpiXeqWSx5LsT5Ovj6Ms0AuUWgqWiqCUATOZAmtKYZ0fLyqhRYL8jpOvoLREscazn9fGSnPrl+2Eg7fsWZCicXaGK5v4U4TT8YLs2zXbOnUzN8t59pSYJC+j5luxa2WcMoFvHoV364TyrThWTVUM4RTymOX83h2Hc+qYZfyeIsY/dCmDWQ++oP45TLW0DD1YyeonTgZaEQuojqQPZFj4vDkNIsvgVNzMKIGiioYPzBJ196OOfubCYNdn95BpDmMVbDxbA9FE4TTYSItYd76kwOUhhb3WitjFV7/vTeJ///v4+af2kV1osrgq8PLmqquayPl+nVc315wJb0aSJhtbMjspSW6CVXo38d1HYK42UJv6jbaYlvQlOXXqAghEAhCehxTi5EKd9GT2sPpqX1MVk4vWgqwHMTNVna1f4iIlp5ulqbMKElci/c/1JWh6X07qY/kqQ8GhdaJPevJ7zuB2Z5Cb4oz+b23Cfe24JXq2Nb3T9jPs2oNi8tXA77dOHSshMJo4Rh2bgKh6Ujfw8y04dXKuLXK8uRhLjOEpuPbFkJRplm5Ei0Sx8pPEOveTG1iCN+xccoFkD6ebeFWCgvWIQpNI3LDDrRMGjJpjJ5u4nfcjm/VqZ84Rf57T2APjzT8bqG/yFP/8Xm2fngTzTuaUTSFiUOTHPtGkApRDYV6ftY4VsaqvP2nB+i9t4d4Txw9rOHWPQb3DdP3TH9Qc+XN/j6e4zNxKEtxqDRHuaI0VOb5//Iyt//zm9n8yEbGD0xil5e+cL2ujVTRmqBojdEa2xIU9a5qCEDQHN3A1ub7SZitF9/9CkDoRvAyWMsLsylCoy22hY2Zu4ibLRf/wlLGIgSCQNT1xrYPMlB4h7P5N7DcS88XGGqImNE8Ez691iE9H6dYoz6QxWhNoEZNtHgIoSjUB7JIX6JGTNSwgdmZxp4qwyXoFF4J1CeHkZ6LnZu4bEbBd6yAmHE+FBU9mkCNxPBtCyPdDEKg6CZ6Io3v2FQHT10+Mscy4RSnyJVyM+PJHXk9+LycpzI06wFWR/qAoCi/Wl9EPkzTCG3eNLsYEwIUULUoRlcHaItP6eWRCm9+8d2G257++Rfm/Nt3fYZfH2X49dFFj3kOVsHiyf/wXMNtuVN5Hv83zyzpOBfiujZSllvi1NQrbGt+gPXpvQwXD+L69YamypfL6QEhaIluZHvLe4nomQX3Cl4wGfxv+u+cW/OLYPW/3JW/nKn0mz2uRBJqbifcvQGvUqJ48tCS+++oQmdd6hbWp2/HUCPLGstSoSkmvalbiegpjk0+S83JX5bzXKuwxgtMPXMIr2ZTH8wiNIXC6ypuoRqwALMl/LpD/vVTSMe7ZiZYgA0/egfpPT3Ux4qc+tNXsCaCRcboK99BqNriZQ0LQI0a9H7yVrL7zlA41HjVD+A5FtJ18N3zOo76Hk65QLi9JyAg2RZGsgmhG9hTY+jTWpiLlXFccazi76mlUmjpVINTSKz+QZzxcUwz6Od2taLGpgGOu3prl+vaSCVDnaxL7kFXTLY23cPmzF24vjXPo/J8hxfP/smSayviZivbmh8gomcaGhkpJa5fp2xnKdRHKdRHsLwKrm+hCh1dCZEw20iGO4gZzYHw6SLGyvUCkoU7zQR0vBp1p0jVyVN1ChTqI4hUDKcwhZ2fWvJLoQqd3vTtbG56zxzGXiNIKfF8G8ev4/nO9L0S0zkpA10NL+jZCCFQhUZbbCuGGuHA2HcuyVBJGSwq5q2yFzj3QtcWGHh/ScdZdDwsblik7eJMBZO7X2sQ5rCCBZIzee3Rz/v+6nVybw+y8cfuRDFmp4tzclsrgaIqRLpTFA4t3sZEug6DT/8fFE2jOnZec79p2R07N0m4Yx1SetjZUYxUc2DUruMWzqH165jXkgGQtkP9VB9YFv/795v4zhM1vvp3KydjrRSppOC//uc0X/xSmbfeWZ1c9HVtpM5NnjW3SM1dOKHq+y5LzeQJFDZm9hIzmxfcp2SN05d7jWz17IIN9cYrJ1CFHuRtkntoi22Z6Rh7ISr2FEcnnsbyqoGxasCaU6tgpFswkpmAunqRyxEodCZ2sjF9x6IGSkpJ0RplqtpPyZqg7hZxfAtfuggCAoihRojoKZKhdpoiGzC1xuoOQgjS4W62Nd/P0Ymnqbsrm+gq9hRHx59ekvhoOtRFR+IGBPMNqOPXGSy8Q/USPTvHq2FdR91958CTuBX7kg35SlEemCsHpphhQi0d1CeGcStFSidn1VnqY9eW0O7lgNbSOBzvW/UZcWJVEyhXKTdbq0v+/htVhoYvoTvpBbiujdRUbWBe646FsNR8VXN0A82RjY2PISVTtX6OTjy9JEq4Jx2y1TOUrUlsr0J34iaUBlplYSOFUFSq9fkq7+egmiFUM4SI6gihXFQyJhNZx4bMnahKYwaclJKak+dsfj9j5ePYXvWitVyaYhLR06xP30ZrbEtDIokQgpboZmpOkZPZl/DkEldbikDRgnoOyy0zWGwcV59/HR5t8W0NvTzPtxkvnyBfnxXCFQqYIYGmiaB7ih/09nFs2ZBJbpgCXYdwXAAKviexbTlH9ENVIRwNtlUr858zISAcFSiKWFU5qMsNIxOh85GdpG/qws7XGPruIfLvDgX5NEXQfOcGOj+wAyWkk913huHvHsKrXUAKERDb1ML6T93G2b/ZT+n44u+N71jUJ4anSRVXB117HqFWuHJGQPouA29+C6REb57f/FFKiV+tYY+OBqkEKdE0iMcEigqWBfX67HMXMsE0g+fbcaBaCyjjQkAkLPA8MM1gnVutzj734XAgwaRpwR/LCozSzHFDglBI8Pp+i0qD53yluK6NlC9d7EvpN34BVKHTlbixIVtQSknVyXFi8oVl1yxZXplT2VcI6ymaIxvmTey6EqI3dSvF+uh0sex8CEXFKRVQw9GLCo4aapTNTXcT1hILGqhs9QzHJp6lZF/8WgJPTMXzbYrWKAfHHqOjepYtTfcS0uPz9leESk9qDwVrlNHSkQZHnItQW5zWuzeQ3N5G8dg4g986RGJbK9WRAtbE6nkw4Yjg3ocj/OAn42zaZhCOCop5n5FBl5eeqvLXf1TEme4dJATs2RvikY/H2HWrSWuHBhLGRz1efa7K//nTIsP9wbO3YYvOL/xWC/Wa5F9+ehSrPvcFbu/W+PU/bsOxJf/8H41Qr107OamFIDSF9T98G1osxKkvvUq0J82mn7iTY7/zLOVTk2Ru6WHDP76DM19+DWuqyrqP70GLmZz9yhuzB5GQ2NpG7w/fxsQLJy9qoADw/WUTg1YbzZtvx6pcuY7Lvmsz+Oa3kUiUaOMohT0yGug+isCTeui9Id53f4hMWmFw2OO//Y8CA4Me4ZDgs/8szs27DQwjKOH7r79R4J2DDi3NCr/zmxnOnHXp7lJJJhUee6LGH/xxGdeFX/i5JNGIQFOhu0tjZMzjN/5nkVN9wXP+oYdD/NCjUXrXqfyb/5jj5X2rE+67Ku3jv18RNZqIGc0LhMbkvFX5cmB5ZfpzbzbMiwkhSJitJMyFlbztYg7pe1iTI3MKRBuhM3EDqUVaV+TrwxyZeGpJBkpVDFpT28nE12PogbKDL12Gigc5mX0Rx5uvRCCEQFMMNmbuvChZQ4uZbPjhW0lubcWeqhLf3ILQFJrv6KX51nUXHd9ycNd7w3z25zMIBb7yxwV+79dyfPtvyxQLPtt3mfNCXnfcG2bDFoN9z9X437+R40u/m2dyzOVjn07w459JEQoH93fgjMs7r1us26iz+/b5enc7bjJZt1HnqW9WsK1r30ABqKZO8sYuRr53mOKRUUafPkalP0fznUFvtpZ7NjP1Zj8TL52meGSUwX94l9SNnRhNs5OskQoHBIrXzzL+wsLFq9ceAsLTlfpzPhSzcTmNm83O/D0cCrz73/rdIp//z3nSKcFdd5gIAZYteeq5Op//z3n+9c/lmJj0+KH/a/YdXNetUixJPv+f8/zuH5R49MMRNvQGvoyqwLYtOl/8Upl//fkcvi959CMRdD347t9/o8a/+rkpxieCfPVq4br2pM7lTHzprorsUMzIENaTDbf50mOw8C6XUlCbrZ2lbE2SaNBWIqTFSYbaydUGGoYmjWSGcMe6IDadm4QFaldCWpLu5G4aPURBiK/AicnnqdjZ+V9uAEUJlCdKtTEc7/wVrmS4dJiQnmBDei9qgzBmzGiiK7GLvtxrLHTfjGQIsynCsd9/CT1msu7Rm0BCfbyM0bR6bERFhe03mYSjCn/+u3n2v1LH80DXIRRWUBRw5wpK8OU/zPN//rRIpexjWxJFgWe/W+G//kEbN+wx6erVOHXUwapLnnuswgOPRHjfh6O8/Vp9xhgpCjzy8Rijgy77X65dSyU+i0IN6yiqwCkGv7l0feypCqHWOEJTMJJhyqdmi+idYh2hKaghHa9qg4R1/+hmpITqP7yDb3//FDDXcsPUSwtPnVJ6uHYN33NItG9BM8IBScdz8D0H6Xl4ro3v2kjfQ9EMVMNEKBqKqqNoOiCwK3kmTrxKaezUbMH0ArlBrzIbUahbkhdesnj3kIOmwfETLr096jn1LQ4dcYhGBKoKR4+73HyTPpPezRd8XnylTv+Ax+iYx0/9aIw9NxmcOOUiJbxzwObtA8GL8N3H63z8oxFiUYVcPhif7YC/yuUT17WRSoY62NJ0D0PFgwyXLq33kUAlYjShiMa3rGxnLzkB70uPqVp/QyMlRNBdVlX0hgoO0g/afghVOyds3ACCjvgOwlqyoRflS4+h4gFytaUnoD3PBiTNic1kS6ep2/nzjucyWHiXTLiHdLhnfn4KhY74DsbKJ6g26KoM597J6Ss694IqglBLlPrk6oX6fA/6Tzu4juTDn4wjJZw4YpPP+jh2Y8tRLkrOV7n2PBjocxk867Bpm0E0NhuoeHtfnWMHbXbfbrJlh8Ght4Ow7YatBrtuNXnmuxX6T38fFfHWbHzPR48HnqHQFIx0BGuyHBisQpVQ62yoV4uZSM/Hq09fo4DBbxxAqIKej+3GGi9RG5lPbkptvQVFNyj1H8cpLZyTvZI4/tQXKU8unhPTzCgdux5EKCquXaM0epLC0FHKE2ewyjl8d+73haJiRFNEm3tJdm0n2bkNzQijqBq13CxN/1yrlwtxvi6k60KhGLwrUoLnByFAgO5OlX/6EzGaMgFZassmjeGR2ZSIbYM9HdJ2nMDzip/3HJ+fa6rWJKYZGLvLievaSAXeRycDhXcu+ViKUAkvQhUP8lCXuoKQFOsLh9gienraSM43Um6lhG9bSHdharahRkhHulGU+U+VlBLLKzNSOrKiNgeOV214b+pukZHSEVLhrnkMOyEEESNNOty1oJFyCjWsbIWt//Q91EaLRLqSbPjULSQ2NzP81PGG31kpXni8Sku7xqP/OM6u20KcPGzzxos1nnu8yuhg49zm1p0GN+wJclKxhIJhwKbtRtBZ97z74Xnwzb8u8Su/08ruO0IcPWjhufDAByO4Lux/qd6QVHHVoQjUUNAXSzWnxXClxLNc8geGaX//dtyaTaQ7RWxDE8PfCRaDEy+eYuNP3EXT3vXYU1W6fnAXxWNj2FMV1FAQH6qPlygcGiHSlWLdD93K8d97bkZfDgK1ho57PoIeTXDmO1+6ZozUxSFo2ngrLVvuRAjB0LtPkD31Bq618KJK+h5WKYtVylIYPEy6dze9d3yMtu334NRKjB97CZjrMZ0P5YIi3oXImA8/GGLbFp1/+/M5JiY8PvvP4uy8QZ/ZnkwqpFPBe5pJK6SSCmf6AwMoBKzrUVEEIGD9OpXJSW8OKeNy4Lo2UopQUYQyLTh5qcdSMBagVgPU3fIcExUw7CQCgaYaOF4g8TLbrK2xv2N55eB7DSZ8Qw0vSFM30gEl3q0UGx4XCARjzY4ZCaELMVE+RdXJNdy2EDQ1hO+7uJ6NqugN9xkpHWVj5i7CemLeNlXoNEc2MFY+3pAU4lZsTv/5a3S8fxvNd/QiVAU9bnL091+i0r+6k1Yh5/OXv5/nyW+Uue/hKO/7cJR//LNJPv7jCf7of+R44puVmZrSZEbhJz+b4v4PRqlVfbITHrmsR70qF4zGv/NanROHbR76wSjf+dsSZkhwy3vCjA+77Hv+2uslFd3YxIYfuYNwewItarDjcw9SGcxx+k9eoT5eov+r++n68C62/LN7cYp1znzlDUong0VW7q0B+iMG3R+5CTWkMfXWAEP/8C6+7aEYGnauild3ccsWfX/5Olv/+X203r+FsadmKeeKbi6pzOBagxFN0bHrQVTdZPLUG4wffQnpL53A5Tl1sqffINayjpYtd9Kx6yHyAwexqwWc0caLWCUxn6DUCKWyJB4T7Nyuk3mPyf33hMhOzUYDdB1+8h9HiUUFN+3ScRzJG2/OLop37jD47M/Gmcx6fOKjUf7gT0pUKgGbsK1FZcN6jURCYfNGjdFRj6FRlwWcvyXjujZStlfF8WqBcblUookQqKLxJAxM1y4FxsHQY0TNZsr1cVRFpy11A8NT74CUJKIdVGqTGFoU2w1WRfXzRDGDQlnZ0JCoihG0/FgAZlMbWiRGdaQf5uXgAuFYU1sojyMZLR9d8NgLwXbLeL6DoUcplxq/QK5vMVE52VDMVQhBOtKFroYXZC5aU1XOfPUtznz1rWWPb7lw7CBk9+U/LPC3f1bkvg9E+NGfTfHPfi7DkXctBvpchIAHPhjlw5+M8+x3K3zx/8kxNuLNrA3++x+1smHr/AR3uejzxDfK/OzPZdh1awhFhfWbdf7qDwuUCtdeMqpyOsvB//LdBbdb2Qqn/+zVhtukJxl//iTjz88nRLhli+O/Myuf4xRqHPq1783bTzWWrx95LSDVsxPNiCB9j+zp/csyUOcgfY/c2QM0b7odLRQltW4X40dfxBpsHIrXW4P6KSnh6edqnJwOHfs+vPm2jecFNPPvfK+GYcCDD4QYHPb4+V/Jc/NuY8bzGhn1eOKZOju261Srkv/4y3mKpWCjL+HZF+vU65Ibdxj8rz8q8b2nakgglVT4xMcCksWBgw637DbYulnjj/+iTN+ZS8s3XtdGqmiNk6+P0BHfTrZ69tK7dS76vkz7wNMGJhZuwXErWE4Zx6vjehbJSCfJaDee7+L7Ls3JLeTLA3CBcvNCp1nIiwKw81kq/afw7VpDSSRFKKTD3Qt+v+6WlkyWmHNcRcPQoyAlhhbB9RrRgyXZ6ll6kjc3nHRMNU7MaFpQhUKoCuG2GEZTbE7Pm9pokfrY6qk0TEeyZmDVJU99q8KuW0M89INROtfpDPS5aBq0d2mYIcGLT1YZG559CTt6NNq79Ya/oefB26/VGTzj8OFPxhg47c6QLdYwH4pustDbEO5Kkbqpi9HHjwR9vAAUQebWdXhVe1G5pcuNaFM3CIFn1XBqK1dld+plXLuGZkaJZLqAoE+ZX62iXkBF11tbEYaBtG3+5C9mnycp4dvfm/XSK1XJl79a5cvMqlEcODSbz1KUgBzxv/+08aq+WPT5wz+Zr785mfX57d+/PIop17WRcn2Lvtxr7Gh5kBta389Q8SCOV2vIjrtomEtKPH/hxLauzNZNKELDlx6KouP5DopQgr5SegTPd/B9h7pdJBXtnvGmzkFTFg5xBOdvHMrTo3H0WALFbKU+PjSPhh6IvbYvOP5ifQxvBSs+TQnadbu+jViAVAJQtXPYXrWhGoUQglS4i4nKqXnb1LDOph+9neY7evEvECMb/OZBBr99eNljboRkWuFH/n9JhCJ485Uak2Memi7YdavJnfeHmZrw6DsRvLiuC8MDLlZd8qFPxBkf8ahWfNZvNviBH4oRiwcFkY1w+pjN/pdrPPKJOOs2ujz/eJWJse8fZtuVxGLhPj0eIrapBZSjczq0R3rS+JZ7VY2UHgpCb1JeuuTW7DGnyzuqVWpHjhG9dXbBJ4RAjUYx1/dSP375mrteLZ/2ujZS6XA3G9NBLU5PcjddiV14DZhxrnR4vu8PF/W0fOnPMyjnI6wnZ7JMNTvH0GSecwZlcHI/APX8bIOyaKiZfGVw3jEjRnLBnJHtVfAXEI6Vvo9bq6C4jQ2poYYX7VBbtrMrounbbhnLraApOnV74QZsjm9RcwoLSiYtVANmpMMktrZy5Lefo3h8fM5LP7OCXgU4dqAG8YFHIzz6I3HMkMD3IZf1OHXU5m/+pMj4tMckJbzyTJV1G3Te+wNRfvvL7fg+jA25PPGNMvuerfGPfnJ+/g0CGvuT36jwgY/FSDepvPhEdV5tlFBUhKLi+27Qs0s3Z0RWhaYHXXx9j1BzF1Z+/KqIqZrpNhR94fD3aiDc3IW4gOSjGBrRjU3Et7cRao2Tvrlnpv+WYmgkb+hk8uX5i50rCd8L3kHVCGNEEtQLS1O9uRBGJIlqBOxJb/o3lo5L5a23CW/fhhqbfZeUcIjIDduxTvchF2qyeRFUq5JvfKfGyAKLphderlO/CkIf17WR8qWH5Zax3DK5RfLSvnQvKovkS5eqk1uQ1JAw2wNNnZmJfvHjWU55nsEMGg52LPANqDr5BdXa7WIuUEV33YbFvIERXVifr+4WVxQO1dQQSJ+QkcZ2q1QWeIp96S6qbxddQE1eCIGdr1IZzOFZq6ceciGqFcnf/0WRl56qEk8qxGNpLLtMqWgzNuySHZ97TyfHPP70Czme+naZWDwIw05NegyddQmFBSeP2Jw+3jhkMnDGwbYl/acdThyev4+ZbiXS2oNVmETRTYSiIBQNr1ZGMUMIoVIePkV83TbcaumqGKn2Ox/BbFq4uHw1oGrmPEMoFEF0XYaW92wi0puhNx6aidFKz6d0YoLcO1dXw69emgQkiqrRvOkOiqOnltyV4ByEotK86bagDtH3scqzJKF63xlqx44TvXk3YlpsVqgq4e3bKL/xJvbg0IrGXa5IvvSXC7+j3/7e1VH6uK6NVK42SL621B9scaMi8SnZkzh+HUMNz9se1pOkQ11M1fqXdLZGuRtdDdMcXd/4/NKnUB9t6AkCGKkmwm3dGOkWJvc9hbxADiqkJVjIYZd4K25fL4RCNNTCxdQPPd9d9ByaaqAroXkt552yhe/6tD+wham3BqebqU3XcRTqOKXVW9oV8z7FfHB/m5MxXE/DcirUrMYswmpFcvTd+b+HVZe8/doCXXoF3Pv+CLoh2PdcjbEGQpxC1RGqRrRzE3Yxix5N4JQLaJE41fEBQk0dSM/Fs2p49tVhBeqJNKH05TVSjeDVHcaeOkp1IEfzezbR/zdvzCkElq6/qh72SpAfOETrtrtRNYN07y66Kjkmjr+CXc1fvEOBEBiRJK3b7iHZvRMA37PJD82Gtf1KlfwTT2F0dqC3t80smvX2NhL330v2776OrF9DrUouEde1kTrXc2m1ULLGqdpTGOGuedsUodKd3EPRGl1hu3RBV2IXhto4HFZ3SxTrowtej/RcfNuiNtrYSBoLsvoCA+Itq5/WLBy3Sq58FterLxp/l3gLGlgICnt1NTzPSAlVwUxH6P34Htrfu2WOQOnw40cZWeVaqdnxCIRQSUW7sJ3SovnIi0FRAkULgBt2m/zgD8eZmvB4+juVhnOWU8lTGXHQQlFq2RFC6TY8qxqEdOsVrPw40nOpTw6jhSLY11LvpCsA6UmqQ3kmXjyJW7GvuQaRlewAxaGjpHtvQlF12m+4n3jrBvJDR6hNDWNXC7h2Fd9zEBKEqqGaEYxIkkimi1TXdqLNvSjTVbKFoaNUs3O9Q2dklNx3HqPpE4+iJQMVHCEEsVtvxhkdpfjciysO+11ruM6N1FyIGQbeuXX/8h7umlNksnqWRKh9vqq2gJboBjriNzBYPHBRFfILkQmvozt5U8OQnJSSQn2UorVwh0ynmMOtBOyaC70oYMEaJgjConKZ4YjzYTkXZ/VI6U+3RFkYjcboliyO/eFLDfe3c5evX44vPcJGCpCXLKm1fovOT/3faZpaVTLNKom0wv/4xeyMAO2FcCvF6Xq3AJWR03O2W9NG6cLPrwYqw6eZePOZmZzJaiLS1kvbHe+fZvnNhVusUyrWg9dZueCdkXK5r/aqQnouwweeRI+miDb1IBSVeNtGoi3rcOsVPMcK8lbTGlhCURCqjqqbaGYURdUCGSUpqWQHGT7wFLKBzFn14GGUSIT0hz6AmpgWi1YUkg++DxSF0ouv4Neuvfq75eL/E0bKUKOkw900hXsIaXE86VGxs0xUT1OyJpbRlVcyUHiLjvgOokZ6zhaBQFNMtjbfj6aYDBcPLqnHkKYYNEXWs7X5fiJ6umG+y/FqnM3vX9xDkxK5AGkCWLTVetD47/KGSIL2goudQzSUnPIdj/LpLFrUwMhEEIrALdtYU5XLOhFli6cRQpzXUXnlqNckk2Me4Yjg7CmHJ79R5q19lz6pR2JtqJpBtTSO512d1hVWIUt56NTlyYtJGeRXG62vFEG0J01yVxdm89zShMlX+ygevnrsPoDq1BBnXv4qXXs+SKJjC6oeCso1Io21Py+E71oURk4w/Pb3qOUXuBbfp7zvdbxiicxHfmAm9KdEwqQf+QDm+vUUnnwae2T0+zr8d90bqbCWYGvzA7RGNyOEwPUshFBpj22jK7GLs/k3GCi8gyeXFs6x3DJ9uVfZ0fLQvJW/EAJdDbG5+R7S4W4mq30U6iNU7KlpAzMtKipUwnqKuNlKU6SXtuiWBcNxvvQYKL67LD29Rvj+K4mcRXxzM+s+uotIdyqoP6naDD9xjLHnTyK9y2Wp5KrRh4f7Xf7nLy+/Bu1i6N3yENF4O0ff+SrlwtUhC/h2veEqf1WO7czvon0OZlOUjT91N27Npj5WRJ4f8rtKDRovRC0/St/Lf0Nm/W6SnduIta5HM2OLNhh162XKE2coDB9j6uw7eNZFogVSUjt8hMl6jdRDDxLetgWhaaCqRHbuwOzuonb0GPVTp7HOnMXJTrFgfcQ1iuvaSKnCYGPmLpoivfQX3mSsfGJaGUIhoidZn7qdjZm7qDi5hjU6C2G0dIyE2T4dnpvf1VYVGi3RTWQi64JCXt/Cky6eb6MIDVVoqIqBoYbQFLNhke651uajpaOczb3Bpa7mvUUmksU6827a8DCGkeDk6cfIpDfR2nIjhh7FdsqMTxxkMnt0WmR2cQQ5nsU6w0hkA49WT4ZY/0M3Uzk7Rf/XDyA9n3Bnkt7/6ybsfI2pt67/bqzXMjy7xuWSbvdsa0GDY6QieLbL6T9+CSt7QcTiGjFSAJ5dZeLEq+TOvosejmPGmgil2tBDcdTpMKbn1HHrZWqFMaxSFqdWwrWrS7sORQEhsAeGyP79P5C4/x4Sd9+FUNWgJU4qSWzv7UR378IrV/AqFdzJLG4+j1+vB3mry3S//Fqd8r7XL/k417WRSobaaY6s52x+P6emXp6zrWxPULImuKn9B1iX3MNE5TRLNQSub3Ey+wIg6UrsaiiXJIRAE8ZMg8TzV+VLkXo515PpVPalBVvQLweLdcBVhIpYIBwYCmVIJnrYvvWjRMJN1K0irmcTjbSxfetWBgZf4kz/MxfNNwmhoC5S7BuMsYGRiodQDY2h7x7BmgpWleWzOaI9KZLbW9eM1FWCUy5gmWGc6T5mlwO+Yy3ozXq2i2+5AQX7GiNOzIOUuFYF16pQy4/C4LmODOfmgWWMX1EIbViPMA3UWAw1mUBNxNGSKdRkAi2VCgzXeRBCIMJhlHAYrbkJs3d1+7AtBHdycs1IXQymFsVQo4yWGmvS1d0S+fowHfEdCMSymIC2V+NE9kUst8K61C0LFqmew3I0yGpOkYHCW/Tn31pQz265sN2Fwwaqoi1qQEwzge1UOHHqMfKFPkCSiHdzw/ZP0Na2m6GRfVjW4vIvQW+vxg3bIMiL2Q0aJErfR2gKSui8hYAIVtL1icsjw7KGi2Ns32MouoldWP0w5jn4zlxPSouZtNyzGQiUSMyWGBv/6d0UD48ELUCmdy0eGaFy9vtBMX35xlUxTVp/8sdQIuHAQC9T2/DKaiGuzrmuayMlpjtoLkSvlkik9BcVbV0MjlejL7ePbPUsGzJ7Z/Jey0XA5PFwpcNY6RgDhbcp2ZPLZgguhrpbIngp5o9PoKKr8zvGnoPve4xPHCCXPzlDJCiWBsnl+2hvuxlNDWOxuJFShIauLH4Op0HtmJ2rUR0psuNf3kd2/wC+7RHf3EK4LcbQdy6tR9jCEISjzbR130oyvR5VNXGcKtXSKJPjhylMnb4gRCIwQgk6191JIrMeVTWolscZG9pPYapvjqeRad3O+q0f4NCbf0Es3kFr1y2Ew2lsu8LU+BHGBt+YFz5VVIPWjt00d+xC16OUS8OM9r922ckucyCUOQWp9cnhwPu+jHOe7ziMv/4Eim5iZUdQIwZN051/AaTtocdDgerEed6Unat8nxipFUAIlGh0DlHkesd1baRsr4rt1WiJbmKgMF9BO6TFiJttlO0sK835SALq9PnafefgSw/Xt1GFjiIUzgnQ+tLHkw6uZ2H7VWpOkXxtiInK8ltlLBVVJ4cvfdQGeSEhRNAIEaWh6oTr1qjX8sTDHXieTcWaQEqJbZcDNpF6cXkcVdEJLSLLFFz3/N/Aqzmc/ovXaL9/C+k9XQhVoTqYp+/Lb1AbXbl452KIJtrZvvuTKIpOuTiM7+fQtBDJpo0IRaWU68c/j2iTSK1j840fQ1UNysURLD9PONrCjj2fpv/k0wydeWnm2lQtRDTezvrNDxFP9VAtj1MtjxOOtbBx+4cIRTL0Hf3uzAJFUXR6tzxEZ+9dVIojVMvjGEaczTc+2vB+XQ6YkTSJlo1MDrwzo+itaiatG/ZiVXNMDR24TGeWZA/MLT84+J+/dZnOtYZrFde1kSpZk5StCdanbkNKj8lqH5ZbQREqUSNDT3IPqVAHxyafXXHRb1d8Jxub7iSspeZ4UbZXY7DwDvnaMKqiTVPAFcDHlz6ub+P6dSy3St0tLoMGvzI4Xh3LLRG5gDp/DhEjjSJUvAarcyl9QnocVykvqL5wMWiKMa160Rgla2LBbU7RYuCbBxn89iEQ4jIrCghiiS4isXaOvv0VJkcPIqWHqhqEIk14bh3/vMJeTY/QteEedCPOsXf+mmLuDL7vEom3s2n7D9C98T5yk8eplue2MUk1b+HEwb8nP3kS33eJJTrZuuvjZFq2M9K/j1oluB+JdC+tHXsoZE9z8vA3qFenUDWTtq5b2LDtg1j1hfUSVxPpzp1IYPLsm+hmlI5tDxCKZsgfujzF1BeDkYkS7kxSODg8d4MQxDe3EF3fRP7AEPXLtJBZKYSioZkRVCOEUNSgKLs0eU2RPa41XNdGyvLKnJx6mRvbHmFb8wNslvfMhEhUEXQZHS4dYqy8/BdNoNCZ2Mm2lgfQL5BJqjslDo0/TrZ6ZsXGxzQSGFoE26ni+RamkcSyC2hqCFXRsZ0KEp+QkaRuF1AVE9eroygqjtsgt0Mgq7SQkUqYbdNGqjEV33LKhIwkEp9c+eyyryekxRcUuJVSUlikUFlPhklsbibUFp8T5igeG6d4YmHjtjJIPDegVccSXeSzp3DdWuBBlubXq4TCaTIt2xgd3E9h6vTM81UtjZKdOEos1UOmZfs8I5WbPM7U+FHOeUPl4hClwiCZlm2Y4dS0kRIkMxvQ9DBjg/upV4P8j+fWGR18g871d6/ytTeGVc0zdOQpunc+jBFOEkt14tg1Tr/597jW/LYNVwJmS4z1P3YntaE80vMZf/Y4hcMjxNY3seEn70LaHs13b+LkHzx/1Q2VHooTa9tIet2NRJvXoah6wHQVAqs4yfGnvojnzA11K5qJqhkgAnHZc6K1/1/EdW2kAPL1Id4e+TrdiZuIGc1oqjHdKr3CePkEY+XjS66ROh/pcA+bmu6eZ6Ast8KRiSeZqMxv9rYctKa2o6khKvVJDC1C1cph6jFi4VbqdhFDrxI2M2hqiGJlEFUx0LQQldpEQyPlS5+pWj/t8e0N82YRPU3EyFCoD8/bFlxXmXx+pQoHgubohgXzda5vUag3LljU4ybbf/ZuQm3xgGp83oLTLtQug5GCYu4so4Ov095zO+mWrWTHj1DInqJUGMRz504m4VgrimqgaSatXXObOkairQihEIo0zTtHKT/IheE616kFOYdppqWq6pihJCApl+Yacd+zqVenCIVTl3y9DSEEQp2dHqqlMQYOfJf1N38Uu15i8PATwb24IFd1pSCEINyeoPDuENL36frITVgTZYzmGNWBHGe//Bq9/3gvsc0tV81ICVUj2bGNjpseIprpniE5nP8eeFa1Ifkh3buLzl3vRygK48dfZvTgszAtIF158/I3/1wNeKXVWcBc90YKoGxPcmzyWQw1gqYYM0yylTLnNMWkK3kj4QvCV1L6jJWOMVnpu+Qxe76N61m4noUiNEw9Rt0u4vse5doYuhbB820MPYrrWdTtAm2ZG8mXFhK4lZSsCSyvTEhr3Gq6LbZ5QSN1KVCFSnNk4wKjkhStcZwGzD4APRFCT4Y58jvPU+mfm6+TS6AeLx7GFQ0nCNsqceb4E+QnT9LSeROd6/bS1nUL+exJBk8/T7U823pBUw2EEDR37KKp7cYGA1iAQr0UhQihBCEhwG+gNrKU+rSVwgyn2HLHgxd8KlFUk1g6xcZbP470fcb79pEfXX5H50uFRJJ97Qz9f/MG0pds/Kn3oMVMhBqEgz3bw5oooUXn54qvBISi0rTxVrpuehg9kjxPvWS2HGUxklV5vA89HNRSNW+8nbEjLyA9F2nbTP7VV6/INVwqViuAed0YKaHOqt4ZMR2r5MyZxCQ+llfGWgXCXEiLN2Ty2V6NyeqZFXlmF2I0exCYnWSDRoouhfIAvvQQIkexMhx87juYRoJydWxRHb2qM0WhNowZ29rwBWmNbeVs7k0sb3VDOM3RTYT1xnIwUkomq30NmX0QGCKnbOFWbKS7/BX7Yrp7QihoojEt3nUqTI4dZGryOIYZp737djp692KYCY68+ZczRsZxKkgpOXPse4wPv02jV9NvoKW4lFdY+h6eayMAXYtgkZ+zXdMWZkteKlynzuTAxVfs9fJKPNmVMNPm3i+v6qDFTOLb2pCeJLouQ/sHbkDRAmKQFtZRTA3pXB11hUTndrr3PIIWigES16rh1EvUC+M4tRJNG29B1Rf+/exKnurUEPG2jZixNJFUB5XsAEDDVjwLQdchlVLI5Xy+X/VmrxsjFUmbRJtDhDMhMuvjHP7mGaySgyLUeROVQCGsJ1GESs0pLNuoJMy2hpRt17dXbYK/MJflTV/DuVf13Grs3OdCCPLlfhab/M4Z0abohnmTc8DwS9CZuIEzuTdmWH5TU8epViewrLkJeil9CsWzDAy9gm0vfM2GGqE7edOC2oE1p0CuOjCHVagYKpmbg1b3akhHC2ls+OQtTL0ziFefvS/VwTzVocWJA65nL1gQqgoNQ11YHR7OhdWyDJx6GjOcpKV9F4qqzRipankc2yoST/Uw3P/qDPttNeD7DvVqFokkkVlPuTTr5epGjHAkc9Ei6pXCc2pMDb67KsdSQxFCzV0Y8RSKHkKoC+tILoTi6YPYhcmZf9dHi+TfHaL3U7cjVIXs62fA8zGaY2gRg/U/uhezOcbUa2dW5RqWA0UP0bnrfejhOFL6lEZPMX78FQpDR/Hd4LlJdm1f1EhJ6VMeP0O8bSMoKtGW3hkjtRx0d6v8i89G+cL/rNDf//0lh3QO142RqhdtFFXBtTzcmotn++hKmA3p26nYOYZKAU1WERo9iZvoSOxEESrZ6hlOT+3D8ZeuFrwQAUAVWtD+/SqgZi2Nuj5WPkFP6mbiRss8b0oRGh2JnUxW+ijZwQp5ZOzNBY4kmcweZTK7cKhHIGiNbSEV6mzouUkpydeHKFpzO5dqEYPej++ZHZehkmpPENvYhO94M3Z4+PGjFzVSlldesJmjphhEjczcQm6h0NS6HRAU8/04VkCzjyY6icbbseqFOfVJVi3P5MgBWrtuYd2m9zI+/BZWvYimhwhHmkikehkZ2Bfkm1aAfPYkHb130tZ9G5XSKMXcWTQjQveGe9GN6JVh9wkFRdXw3bnhRVUP43v2wooTQhDt3EDznvsJt3SjhWMIVVtRLaFdnJpjpHzbZfTxw0y9fgahKFhTlcBrEhBqS5Da3U3tqWOU+yYXOerlQbSph0gmWGTVCxOc3fd31IvL9DilpF4MyDZCCELx5hWNZWzM50/+uMrExPengYLryEh5tk95oopQFepFB8/1SRoZ2mPbGC4dmdkvFepiU9Pd2F4V17foSe6h6hQa1lEthIVCSKYWpSO+g7I1ueohs9WC7VU4m3udnW0fRDB3RSuEIG60sLXlfg6OPnbJ15AJr2NT5j0LGm7Hq3E2t3+e12gXarz9i9+56PF99+IvXtXOBb2gGjSqFEIhE+klXDxA1ckHnwGxRBftPbcHXVGlR1A6IHHsCmeOP457HhPL82z6Tz0DQtDWdSsd6+4MWp5LH9/3sK0io4NvXHScC6FcHOHsiSdYv+VhbrjlR/F9Byl9KsURxobeJNW0acXHXirCsWY6tt7P4OHHsWuBURSKSte2ByhN9ZMbblxUHWnfwLr3/2O0WHJlRe6+j+9YeFYtUJ+4cLvrY03MfUaFqmBPVRh9/PBVa9eR6tqOUBSklIweeW75BmoadrXAuQJ8PTw/jywENDcrNDUpaNMzeb0uOX3aw/ehq0shlVawLMn5VRuhEHR2qgwMeDjTQaSmJoVIRDAwELxT0aigo0NBNwS5KZ+xMf+qseSvGyMVaQoRaw0TbQ6RXhfj0DfPYPpRDDUyp1tuT3I3tlflwOi3qXtldrU+QntsG4OFt5dcK7VQG3khFDrjN6ApJqPlYzPGyvWsFbVmv1wYLR+nNbaV1tjmef2rhBA0RzayreUBTmZfWlFxsUAhE1nHjpYHCeuNa6Ok9BkovtO4R5YMOrCeDz0RItwWR2gK1lSV+tjSJJE86VCyxhccRzLUTkfiBk5lX4FpBZLRgdeolscIRTKoqomUPrZVpDB1ZppKPvc5cewyp498i8mxQ8TiHWhGBN+1sawipfwAznnh0Gp5jIHTz1EpzfUeAfJTp3HdOrXq+VJDkvGht6lVsiTS69E0k3o1S27yBEYoSa2axbYurzyUogXkEO88T0pKievUMEKN76tQVNrueBg9ngr29z3s4hRWIYt0LMxMG6F0G269QnX0LCBQzTBGqhktHEQqKsOnyB19g3p2FGtq/v1qhNimFoxUmOy+M5dyyZcEI5YBBL5rUZlcfojuHHzPmQ5VCxRtfu60vUPh3/7bGPW6pK1dZdcunb/5ao3f+UIZ24Y9Nxs89JDJxo0qn/tcgVMnAwPU3KzwG7+Z5Bd/ocihQy6KAv/kn0YIhQS/8sslwmH4F/8iyqbNGnVLomvwh39Q5Z13rg4N/roxUm7dpZ63sMsO9byFa3moho6i6DOr5JAWJxPuYbR0dFp2yKdkT9ARv4FzahBLQcWeoubkCRupeZO8omi0xbbSFFmH5VZmFNCDCXCxo8pphYo6lluh6uQp1Eep2NmZ768WPN/mTO51okaGqJ5pYGwF7fHthLQ4fbnXmKz0LdnIaopJT3IPPck9i5IlcrUhBgvvXPR4QhE0713Pukd3YTYF+oi+5TH6/EkGvv7unBzVQhivnKQluqnhal4RKr2p27DdKkPFA/jSw6oXmBhZXj5GSp9C9hSF7OJq+pXiCJViY7p9buIYuYljjY5OKd9PKT+XuWlbpSvSokNKH6FqKKqONx22FEKg6RHcBVpJhJo7CbcGIS/p+0y8/Tz5Y/txaxWk59Jy8/2Yt7Zi5SYYeu5rSM9B0Qy0aJzMDXeS3n4reiyFXchSGz9volfEjKCsUC/8PQWRrhR6cr7XfCVxLtfkOdYl5SiFonKOZHJhqBXgPe8xMAzBf/6lEm1tCr/wS3GefLKOPb3rt79V59Qpl//4H+d6YRMTPv39Hrtu0jl82KWtTeHGXTq/84VgMXXf/Sa33WHwc/+uQKHg88OfjvBDnwxz8qRLpXLl3anrxkjZFRe7EjwQ+elnWhpBM79zSfv22Dak9Jms9s2EmDzfXbRrbSPU3SKDxQNsytzV8LtBX6nwvBqqxTA3uS+ndQUltldlonKKkdJhStbEqgnO5moDnJx8kR2tDzUUx1WESjrcQzLUyVT1LCPlY5StcRy/ju+700ZLTKub65halFSom67EjUSM9IxuYqPrrNhZTmSfp+ZcPJ9iNkdZ/0N7GHnyOKPPnUR6PrHeNFv+yV3YU1WGH784/XmycpqqkyNqZBpu15UQ21veR3N0I4OFd6k6OVzfRkoPiZxpMyJQUISKIgIFEU0xCelxHK9+SYXb1zqcWlBn1Ln1PrKDB5DSJ5buJta0juxg44VGpL0XRQvejcKJtxjf9705E61n1cAPnnPPruFbgfGzi1nqk8O49TLNN91L94Of5Ox3/pR6dhSjKUrvD9/O0DfeAQmbfuYefHtuyDfcnmD0yStPiT8fnh0YbkUzEMrKp1gjmp6mrvs4tfnecjis4LrguOA4wZ8LF8KeO39xbNvw9NMW732fyXe/U2f7Dp16XXLyRPD83nWXweSkR3OzQnOzQj7vc//9Jum0WDNSqw3breJ4NdpiW8nVBmiPbaPq5MnVh4BgItZVE2+xjrcN4EuPoeJBonqazsTOi/RJWhrmTujT/pmAsJKgJ7mH9vg2JsqnOJt/c9E28svBaPkYphZlc9M9DdmKQoigN1ZsE83RjdhelZpTwPFr06wygaoEBiqsp2bakiwGyytzIvvCkps4KoaGna8x/vJp3HJgoAvHxpl49cyMZ3Xxc1YZLh5kY+Y9qA0mjeA6ddpiW2iJbKTq5LHcEq60p8O6ynQPMH1apzGEpoTQlEA5YKR0lHxtcI6RErqKMHT8an3WCVYEaiKKl78285ULwbHKjBx7jtaNe1m365HpzyoMH3uOSqFxXZ2RyCAUFd91KJx6d54nIH0v8NCEgqKoc/x037GZOrSPaOcmIq3dZG58DyMvfROv7lA4MIxbtgi1JlB0jclX+s7rZyVI3NBxGe7A8mCVpwCJqocIJ1up5VZQeygUkh1bgMATrebme98vvmjx4IMGP//zMVwHDh10OdN38TytlHD0iMsnPhGmo1Plllt0DrzjUCoFD6qmwboejY89OrvIfuYZi+V0otd0QdfmEAPHa/geJDIalYK7on6L17WRKtkTFKxRNqRvpye5G0ONcGj8ezPeiK6ECOspqnae5YbTLLfEscnnsL0qPamblzRBrxRCCAw1QmdiJ8lprcHl9L9aGJKBwjsIobAhfSeGGl4wwS2EwNSiF21JsuCZpKTuFjk28Szj5RNL/p5bsXGKdSKdSdxKMNGppkZsfYaJfWdR9MBL9j1/wb5CUnoMFQ+RjqyjKdy7aBJfUVRiZhMxc75KxKIQAjUVRToeQhEIVUGYBn7VQokYqPEIXrlG9MZeaieGcCaKF28WeJXUHBqhkh+i/91voxmRoDuyY+HaC/c5U41wsJ9VxanO9wL86WZ7QlUR2vxohF2YpDpyhkhrN9H29RiJDNbUGOPPBRJmRirC1BtnGPnOwTnfc0p1zOaFhYyvBIojJ2jbcR+KqtGy9S7yg4cbhusWQyTdQbJrBwDSdymNzVewyed8LEvyxhsOx4+5DA15FItz3wExXa9+4RM/MBDsf999Btu2a3zpT6szdVQnjntkmjx+/b+X8Lzg+74P1eo5kWRBNKlihhWyIzZNHQa1so/n+kQSGlbVo1r06N4UZqzfwrEkTZ0GlZIHK+ikfV0bKdurcmzyWTZm7sRUo/RVXmO0NBvz1xQTRSiMlo8uS2BWoBAxUsTNNkwtPr2CvnxGaua8QiFqNHFD6/s5OvHMtObgpRkqX7qczb1B1c6zpfkeYkbzqniGc8/hkasNcjL7Arna0PK+LCVmc5Sbfv4DVIfy+I5HuCOJUAVaPETnw9sBGHrsCBMvL6z0UXeLHB1/il3tP0DCbFv9vjpCkLz3RnzHw6/UqZ8eQW9L4+ZKhLd0oaaiVN7tQ01EiN22ldKrR3GzF8j1KNMKE56HYuhEN+6gfPwgsmEx8JWH51p47my4WSgaQlEaT8DTjfek7zdU3fAdC+l7KJqOaoZxShcQdKSknh1Beh56Io0eScwhT5RPT1Lum9/LqnhkFDW0vPD9aqM83kd1aohYSy/x1g2su+0jjBx6BqsUeFiLQSgq0eYeem77CKoRRkrJVN/b097ZXIRCgpZWFUMXtLQE7LyTJ10KBUksJti5U2PXTTqtbQr33W/S1ORw8GCQV6rX4fnnLH76Z6JMTfkcPjRLivja12vcepvOv/13cc70uSSSgr4+j29+o47nQbJJ4/aH07z9fIHNN0Xp2hxGCKiVPayaTySusu+xHFJOC7pIyfodEcb7LSorKK6+ro0UBEy8g2PfbdiGouYWOTz+JJa79PBLWE/RndhFS3QjMbNlwSLVywUhBGE9yeam91Csj1Fz85d8TIlkvHKCuluiN3ULbbGtaOql13tJJLZbYbh4mIHCWzMEluXAs11GnjrBCIt7X9WBix+7bE9yePxxNjfdQ1Okd9V/O79qozXFqQ5MoERCGG1p6qdG8Os24fYeakcHcAtVvEIFoc8/d7izF0XX8eo16mPDgAwm+2u0xCWa7kI3Yw0p6L5jg5Qouolo0MrFt+tIz0U1QmiRxjJdvl1HSh9VD6HoDRaBDYyfPXXpXawvFb7nMHroGXrv/EfooShNm24nlGonP3iYysRZ7EpuzkJQ0QxUPUQo0UyifQupdbsIJYI6xlpxnPETrza81nvvNRkZ9rhhp8aOGzTa2hQOHnT5oy9WCIUE63o1hIC/+9saqgrrejVOnvRm8kqvvmqTTiuc7ffI52ePXyxI/suvlHjP3QaZjEJ/v8e77zjnR1UZPVNnvN8iltSIpTTG+y2EAqoukBIiCZXWHoOWLpPsiE0io9HcaVAt1ZZNZb/ujdQ5NGKn+dJdMsVaESqt0c1syOwlbrbOm+CklEh8fOnh++60NqA9p+9QIwihIISKJgx0NTTdyj1I0AuUBVf8MaOFdalbODb59JLGvxQUrVGOTDzFcOkwG9J3kAp3oght0XGcj1ltMg9POoyWjzNYeIeyNbFg48k5UFWEIpDO9L5C4FUdRp85ce4EKPEoSIlfrs7EMpRICCVkBP+WEmFoQTuPBi09CvURDo5+h474DnrTt2Go0eC3FMxjai7len3pBZ609Cm+egShqfg1C6EquJMFvEoda2ACZ6KAV6xSKZ1E+n5DiScj3Yw1PoIWi6PoOkIJnoWrUp4y3SfsnKSTMi8kJwjHWhZUTXDK+cDAmCH06HyaulMp4js2WiyJmWqh3D+f1ajoAfV9JmZ1/jZDRYuHsKeq12Sbi8LQUUYPP0vHzveiGmHirRuINnXjWjV8z56pezJiGba9/58FeU/dQDWjKIoakKaqBUbefZLq1PzoQzIp+NQPh/nVXy1x/JiLEPDBR0weeG8IXRdMTvr8n79ZPImUz0u+8pXG+0xM+PzD1xtLlRUmXarFMlLC2SNVJoctHMtn+21xRs5YlHIOtbLPc1/LUit7OJbk+a9lqVW8Ff1U/58xUpcCRah0J/ewtfleVGHMm7Bd36JYH2O8fJKp2sA0bXz5NQUCganFiOgZ0uFOmqObSJitjdmHAtrj26Y9lNVrlOj6FtnqGaaq/SRCrbREt5AMdRDSouhKGE01Zwzo3AaOdWyvRs0tkqsNMFE5tST23uz1CELbelGTMar7j6C1pFGiYZzhCfT2JoRpYJ3oR03FkXULv26jdzajt6RxJ/PonS1orRWsUwNE77gRZzyLdaxxSxHLq3Am/wbDpcO0RjeTifQSMVIYSgRNNRs2qfSnDa/n2zh+HcerU3eLFOtj5GqDOJ4F5zGfJODXgjCY73r41SBMJi9SgGxkmrELORTdQNFNtHgCO9t4sricMEIJ1u++i5GTLwKwfs9H54X1zEiK8TONC5Xrk8NIz0VRQ0Q7N1I4+c4cZQq7kMWtV9ATGRIbbqRw+gBuefZ5EapGuG0dQtXxXTvIYZ2HyLoM6z55Gyf/13PYucY0+PMhFHFFu9n6nsPY4efw6hXadt5PKN6CouoYkbnvsqJqhJOtcz6TvketMMbQ24+RHzhMoxBhuSw5dcrl4x8Pc+KESzQm2H2TznPPWljW5TXanivx3OAcriMpTAa/zakDFcp5b2Zbbmx2DsyNr7zG6royUkKB1o0x4q0hnJrHxJkK1dylK0W3RjezuWm+csI5inhfbh8jpSPLChs2gkRSd0vU3RJTtbMMl46wMb2XzsSN81hpAoGuhshEeqgWLs1IRRIaUkpqJQ9VE3RtjzF6skKhPkqhPoqmmIS1BIYWxVDDgXcllMCjme4+bHs1bK9CzZ0rG7T0i5dI28UrVRC6ht7ThnQ89C6B1pxCqCrO8ASKaSB1DaVax+hpR2tK4YxmkbaL1pbBHhhFeh5e7uIFrrZXZbD4LkPFg4T1BKYWmzbEBgqBfI+cblLp+y6ub+H61sy1ustkhV4MTmGK6uAZmJ7M82+/sqrHXw48p0526CBOvUwo1oTv2kyc3T/Ha4m3NFa2B6iOD+DbFqoRItazFUU3Atr5ueNbVapjA4Rbe4h2bqD11geZePMZnFIOoemktt5MYv0NCEXBrZbw6heE8YTAq9qBTNYSoKgC5Qq3XJe+x8TJ16jmR0j13Eh63Y2EEq0LRiXktBRS7uy75PoPNPSgzsHz4Dd/o8xtt+kkUwqVUZ8v7rM5dHBlDLrVwDljtdq4boxUNGPw/n+5ja5dKaQfqEFYFZdXvtzH4SfHltTWoRFiRjPr03egK/NrnnzpcmziGUbKR2fafa8mak6ek9mXSITaGyb7VaGTMNtYrBBZMxRSrUEoLDdSJ5rSiaQ0Js7USLaZSF8STetEkzoTZ2vkRuokWw1yI3VcR9LUbeJYEruaw2ir4lgeU0MW/gpYOheDX69jdjZj9w2D46E1p6iPTODpGng+QlNR03HURAxvqoAwdXzLAgFuvojqBEXPfqWO3tWKO760LsISn6qTX1HO7GIQqkpq260omk7u6H58e2Gv6HwDdbXhuRaFsWPTf4+THz02r028L33McGPFCa9Wpjx4gvT229BjKSIdGyidOTxnn9zhfWS234ZimGR23kliw048q4ai6WjhGIoRChZP44NzdPsA7MkyXs0mur6J8um5nW19x5sXTg0Yl1fWSAWQVCb7qeaGGT/2EmY0TaSpGzOaQTXDCKHg2VWsSp7q5AD1cuBhLlYELPTAG5vIwXef8GFOKkNDXF3eyCxk0P/qUnHdGKmbP9KNHlH58mffoDBSQzUVdn2gg70/vJ7T+7LUCisJvym0xDaTCLXPMxBSSoaKBxkpHbmskkeWV2asfHzaGM2HoUbRFGPBIt9Um8GGPQkiCZ1Dz0+x6dYkIycqNK8L09wdItlqkhuzMEIKNz6Q4bV/GJtm5Qi6t8fo2RlDMxTyoxa9u+IMHa8AJSb7VyaYuhicwXGcoQmQkur+2QnNGZ7VPnMnAq9RScZwJ3IITcMrVfEHZzvf1t451rBP1NWAnsjQ+eAnwJcUzxxd1EhdKwbqQtSKo9SK82vzylNnqTYgRZxD9uArmOlWqqNnsQvzmXi17DDZgy/TdNM9KJqOHkuhx1IAM/2XnFKO7KFX53hhEGj0GZkoO/79w1T7p3Brs+93IDx79oL9r2y470JIz8WpFnCqBcoTZ1Z8HGEYZB79yOoN7DLCK1fIf/u7l3yc68ZIhVM6J16YID8cPMxu3efUq1l2Ptyx4odTVXRaohun8xNz4Xh1xsrHrogmX8kab/i5EEExrSo0XBZWojAjKpqpID1JccJirK9KLK2jh1VUXeA5PqGMjqIJ9JBKc3cIz/YpTtqEohrFrE296lHM2uRHLZTLSWhcYmbVL1Zw+sdAEfiFBmHWaySZHu3cGNDKL1NLjasJ11qcSVcbH2Dgib/CLk41ptH7PhNvP4dQNTI37J1TLyWlxC5MMvrqd6kMza8R8myXyVf7mHx1ftlBbWR+LlRRBcpV8aRWF0LTiN+596oa3KXCmZhcM1LnY+DtHNvub+Psm1M4dR+hwIbbMpSzNmZERUz3T6rk7CWXFmmKSdJsb7it4kxelvBQI1jewpOBWKC77Dn4PkycrdF/qES94lGYtHAdiV33KOcchADH9tHNwBBbFY9X/34U35O4jk92sI7vg+/69L1dxHP8a6O+VEq8wuUVVl0NxHu3Xe0hrApULUQs00M03YUQCrXSOMWJvkULeqXnYuUaL7DOwa0UGXn5W+SOvkFiww1okQS+a1MbG6Ay0odTzjdccLjFOuNPN9I5bAzNVNGM1a3/W8OVwXVjpDRTpeemFJ/+wm2UJq0gF9MRpjBS40Of3xmwk33J3/z7t3FqSwurRIwUimh8i+puBXcpLcBXAQoLuy6+9BYlKpQmbWoll1opuGbXPkcTB6s6ex+88xLQ9Urjv3OVupx+v0KLxAk1dy5DuvjahGZE6N75MOF4K1ZlCiklsaZemntuof/gd6iXL61nk3QdauMDc4VkVxl6SEULXdmaxjWsDq4bIzV8pMC3/9vhRfeRUuLZS3cDDGXhrq2e7yzamnw1EVlETdyTzqI1SI7l41jXguuzQigKZrqVSFsPZlM7ajg63YDPwatVsHLjVIZOY+cvbaLUE2li67YRau5AC0WRvodbr1AfG6Q8eBK3Urz4Qc6HEITb16GGIjOe7jWSJls2jEgKM5Lm7LvfopofASR6KEHPzg8Qb1p/yUbqSkA3VTTj6hspRdXRQlFUPYxQVaTnUiuMXTPh6WsR142RmuqvMtVfxYxpaIZCveTguZJpVY4VYTF5IHW66PbyL5EFzdGFqL4Syy0vWyB3ZcMQ9H74J4h0bgAE/d/+MyqD83MFRrKJDR//5zMKAcVTBxl94RvzEt8A3Q9/iviGGwDo/85fUhk4PrNNj6eJb7iB9A23Y6Zbgo6uihLUGZyD9IPGeLZF6cwRJt54+qJ9hzZ/+t+gx1N49Sonv/JbCEUlc9NdNO+5F8UIBe0RzlkTKZGei1urMPnms+QOv4ZvN/aeFSNEuK2HUHMH4ZZOQi3dGIk0ihEUuwpNY9MP/+tFH8bB7/0VpTNHFtx+1SAldq043fBwuj7GqmDVcsuSE7ua0MJXy5MSGLE0ibbNpNbdSKy5J1BGFwKBoF6a4NgTf4hnz30/VCOCZgSMYteq4DlXvlZuJZBSTpeTOHiV1VH/uG6MlKIJtt3Xyo4H20m0mjz+W0ep5h3atsQ5+fIEnrP8l2mxthiGFkNXQqvWOmMhJEPtpMM98xUiAU+6lKyVdf1cNqTELuWJh6KBNFNrd0MjFWrpxEikZ//d3IEajs0zUopuEGrqRA1F8e16kHuYhlA1MrvuouX2981ZKMhpoyE9N+hvpAWqDIqmB8asqY3Bx/5q0TyIGoqghaOoZohIRy/JLbtJbb91WlFB4rvB8RXdCBQfFANDN2i/58OooQiT+59t2CU2vmEHPQ9/GqE2ngiFEGihhT3z4Lqv/kq/ERyrgqLqtG++m2p+GCklZiRNonkjU/UDpNoDIdRqYXimc++1BjOiYYSv7P1VVJ1Uz4103PhewumOhoteVWssP5bq3kHXng+CUJg48Qoj7z7FtRo0lr7EzU3h5vJ4+QLuVA53agp7dGmNKi+G68ZIbbyjib2f6uXg4yPc+HAHobiOU/fZ84Nd9L+dWxEFfTFiRNxsIaynqLmX76U01Ci9qVsxtVhDyR7Xt8jXV9AGYIWojw9xTjUy1NyYUBLpWA/MSiQZiQxaOIqdn2tM9XgaxQwhhMDOT86hZkvPpTp6FrdaRgtFsQqTlAdOUBvtxykXwfcQmk64tYv0zjswks2B4WzppmnPvYw8/w8XF2UVCm3veYRQph2hqFSG+ygcexMrNxkYqVCYeO820jtuR2g6iqaT2XUX1eE+yv3H5x3OrZYp9R+bMxHpsSRmpjVg90mf6nAfvrPwc+hWr80WHqpmYIYTRBJtJJo2IAFNM5FIMp03ziygRo4/f4GRutRs3Lln/tIn51BCx4xduQIioWg0b9lL564H0ULxGUr9nH0Wif+Wxk6jmVFU3aR5422MHno2WKDZNlNf/8ZlGrRAaCpC11EiEbREAi2TQm9tRRjGeWHrC8btuRRffJnqOwfxKhVkfXW9vmUbqeeff57f+I3fYP/+/YyMjPC1r32Nj33sYzPbpZT80i/9El/84hfJ5/Pcfffd/P7v/z5btmyZ2WdqaorPfvazfPOb30RRFD7+8Y/z27/928RiK5fY33BHEwcfH2H/3w3QvSsFQK3gYIS1FVNPbbdKxZkiZsxv26ArITZm7qQ8Oom9CPtupQhrSbY030tHfEfDFZiUkmzlDBV7fv3J5UJ1rH+6dYRCqKWLeZOQEES7gtCklR3FTLeihqMYqRaqI2fmHMtINaNOh8LqU2OBIOn55xo6zcQbT2Flx6gOnwn6V13Q2qLcf4z88bfo/YGfCLrACkG0ezOh5g5qYxdPwodbe5Cuw+SbzzG+7/FA9ue8iaR85ii10X7a7/1BtHAMLRInufVmKsN9SHeusakMnqQ6PJcOndpxGx33fgTVDJTNh578P9jFhQuM5TVaJ1UvZzn60p9edD9/zvgFnfd9FLuUpzx4Ais7uvTrE4JY9xZiPVuRvkfpzBGqo2dWNHYIIsTRphCaeeXYfal1O+na/YGgZQkSp1bGqkxRz4/j1Eu0bL1rJpzXCE61QGWyn0THFoxIimimm/LEGaTrUnz+xcs7+HNGSAS1ZUI3MLo6CW/fSnj7NvSmJkTInDVWmkbygfsRQPHlV1d9OMv+1SqVCrt37+b3fu/3Gm7/9V//db7whS/wB3/wB+zbt49oNMoHPvAB6udZ1x/5kR/h0KFDPPHEE3zrW9/i+eef52d+5mdWfhWAXfWIN4dmF18CYi0mnuevWB3Bk/Z06/T53xdC0BTpZWvzfUT0xh1fVwJdDdMZ38mu9g/REb9hwbyY49XoL7y1auddCpziFG4tMMh6LIVqzn3JtGgCI5EBKckffxvPsQIPp617pnXDORiJDIphIqXEyo3PM1KeVSP71guU+48HxqNR7yUpcQpTTOx/Ft91go7I0QR6PD1/3wsQvGCSYt9hJvY/E4TwLljpSs+leOog5f4T080PBdHODShqg7XdeaHIc3/mFOdK8D1v3j5z9r9mk+cS33Mu+uf82gQtmiC15WY67v5Beh/5cdTwMvqQSYkaitK0625ab32Q9PbbEI3u+RKhaArx1tDqt2dZAKoRouOG96KZEUBSGD7GmVf+hmOP/yF9L/81g29+e14O6kJIGShVAKAoRJt7zt94ef/4fvDH85COi1+tUj9xktw3v8PYH/4xU9/4FtbZ/hnPUAiBlkyQ+uDDJN/3AEJf3QDdso/2yCOP8MgjjzTcJqXkf/7P/8l/+k//iY9+9KMA/Pmf/zltbW18/etf51Of+hRHjhzhscce4/XXX+e2224D4Hd+53f40Ic+xG/+5m/S2dm5ogs5+vQYH/75nSQ7Q3RsTyIUQSxjcmb/FHa1cejHaGnHr9dwy8XGPW+kx0TlFG2xrYS0+LyHXAhBZ2IncbOV4eIhRkqHcDxr2qgtZcIJkqeKUIjoGZqjG2iNbSFmZNCUhV8qX3oMFN5esMj3ckH6PvXsKHosiVA1jHQLtdHZyv5wSxdC1fEci/rEEHZ+Aq29l0hr97SadzCJCUUNOreqGr5tYRemuJTiKys7ilPKYaZbUQwzYNQtAV69RvHEO3i1hcNsnlWjNjZAYtONCE3HSGYQmsYitdPXBS5VQijc2oXQAjFmr15dkHCyEOz8BE4pR6ipHTPTjh5NLOqFLgZVV0i0L+y1rDaizb2E00GH4FpulLOv/h12Zbn6mpJaIXi/hRCY8eZVG5+qBo0LIcgnzUSgRdCV95zGoevKeSIoXrFI6ZV91E6cJPMDjxDZvSsgNAGKaZK87178coXiiy9fvKnnErGqJq+vr4/R0VEeeuihmc+SySR79+7llVde4VOf+hSvvPIKqVRqxkABPPTQQyiKwr59+3j00UfnHdeyLCxr9iEvFufTgcdOlvj7//QOt358HfnBKkKBN78+wPEXFyZNCCGIbtqGU8xTPT0/zwCQqw0wXDrEhvQdiAb1SopQSZhtJFpa2dT0Hgr1YYr1MSr2FJZXmVPHJIRAQUVVDEwtRlhPENHTJEPt5+WdxKIrPl96DBcPcza//4pR4M9B+j61sQHivdsQihKE1c43Uq09KJqGlZ/EqRSpjpwl0t6L2dyJaoRwp42BYobRExmEELi1Ms4KJ59z8B0Lt17FhKDFRINOr/MvRuJWilSGTl90V7uYQ3oeaDooKqoRxq1cm4XEq6WpeKl07VCmY4YIUh48EXhay4BdzmOXAyNlxFNokZUbKc1UaNncWGPwciDZuR2hKEgpGT387AoMVAC7eo5NKWZae1wq2jpV/tFPJNmy00BRYHzE43/84iSVsqS9U+On/u8UrR1BH6qDb1n8zZ8UKOQuMDZS4k5Mkv37f0D6HtE9u2d+axEySb7vfpyJSWpHjq7KmFfVSI2OBvpebW1zdeba2tpmto2OjtLaOleaXtM0MpnMzD4X4td+7df45V/+5YuePzdU48kvLL0K3c5N4tUqizo9vvQ4m9tPzGimLbal4T6BUREYapiW6CZaopvmfP+cMRFCQUFdcdhBSp/R0jFOZl/A9i7enmDVIf2gBYOUCEXFTM/+jkLTMTOtoKi4lSJetUxtfBAIehGZzZ240xRz1QzPMAC9Wnl+V9blDutciILZ3+Ki3yFQO1gKWUF6Duc/JI3anV8uqFoIKX1877xwqBBBYW01N699hmf709PaJZ73EtUZ9Gg88J6nO+wud1XtWVW8WhUpJWoogmKsvAmnGdNJdy8j3HiJMKNpQOA7FtXcyIqPI313OqQW9PaaOT5hmpQOwiKKi0vWH6Es8wAYhGhVujFEiKosMe4P4k93zQyFBZ/4iQSpJpXf+dUpKiWfdJNKpRw828WCx/e+XmZ4wKWjS+Of/ps0b7xU461XGxMhvGKRwlPPYvb0oLUE5CUhBFoqRfzuO6mf7kNalx5y+L5g933+85/nc5/73My/i8UiPT098/Yzoxobbs+QWRfl8JOjlLMWekilVnQaGiKjqZX4jpsAweSz310wJ2B7FY5OPA1IWqKbltwEEAJP61I7wEopcbwaA4W3OZt/87IQNZYKp5THq5VRwzHMZBNC1ZCeix5LzuSCnHIBt1amPjkS5IpUlUj7upk6KDUURo+nguuqFHEW80oUBUUzMBIZwm3dGMkmtFAMNRRB6Np0+/EIRmr54RCnUmRJYdkLo7eXObWhhxIYoRi18iR6OI5rV/F9F00Po5kR6uVJjHASz7WwLzBSru2vClPZjF3a1KAaIRAC6bl4tRUsqHwfz64DEkXTG+cBl4iWjfErSppQ9MCgeq51SZqNQlE597CdW4xo6OzQbsPFIedPoAsDdTrCYxBiq3YznnQoyTytSg9RkeC0dzBgYjarbLvR5Iv/T46TR4LjjQzOjq9alhzYb6FqgUeem/SIxhe/b/bwCOU33iT1yMPnDVwQ2b4ds6eb+slTK77+c1hVI9XeHtCSx8bG6OjomPl8bGyMPXv2zOwzPj43l+K6LlNTUzPfvxCmaWKai6+kMj0RPvjvdmBGNDLrooweKxLNGOz9VC/f/m+HscrzHxbpe9SHB9ASyZmurguh5uQ5OPYY65I305XcRVhLXvZEbNCvqkK+Pkzf1GvTdPNgjKHeFvTWJH7NoXZ6dKap3vkwuzJI18Mem0+TF7qK2dWEnonh1WzqZ8ZnmvQtBrdWxi5MEYnE0SJxtGgCpziFEU9jJNJI38PKTwSTU72KnZ/EbGon0tE7c4/1eAo1FA08s4nhxvmoaaWJ1LZbSGzehZlsBmVhL2klP4W3mCL5VYKqh0m0bCCcaKN29BnMcApF1ZG+T7rzBmrFc+oEjf0l1/Kmt1/CsykISEiXALnIv5Z+kHMGd3F9youh55bVy+csBeeEd1XNRFFWPsWascw0dd3HrgUpjhalC0VoHHdex2bu85tWWjEwecd7Aw+HCX+Ym/X7mPCHKMopdEOgKFApNfZqt+82+fAPxYhEFCIxwbYbTR772kUiDVJS2vc6yffehwifl/fTVOLvuXNVjNSqLi82bNhAe3s7Tz311MxnxWKRffv2cddddwFw1113kc/n2b9//8w+Tz/9NL7vs3fv3hWfe+f725nqr/LXn3uT068GMi2lcYtI0kDVG1+mPTlObfAMtf6+JYUjHK/GqalXODj2GAOFt6g5xXm1D6sBKX3Kdpaz+Tc4OPYYB0a/Q74+xPkvu5qIEN3RQ+dPvg+zPdXwOOn37iJ+e+MQZWRrFx0/9gDx2zYTvaEHNby0cIpXr86E59RwBC0SxMr1RAYtEkM6NlY2CNv6joWVG0cIMBJptHA0qLHKBK1PpB+ED+dBUUhs2kXPBz5Ny+3vI5RpC5Kzvo+dn6QyeJLCiXfIHX6dqXdfJnf4taB+arlYpcTuakL6HqoewnMtQKAaYSKJNhRVw64VqBbHEIqGGUkRSbbPVeAgMFKr8URGmy7NSPl2HaQMiq6NFRxLiOB7096YXGEnP0UT9N4+v4TkcsIqZwGJopuEU40X3heFUEh2bAWmc8FTwXsSFQlqsjzPQAGYIkydGnI6vGdTw8MlJIJQZ63qY1mSznWNw9Wf/ukEhSmP3/nVLL/++Synjy1NycavVqmfnV/yYa7rQYldeph12Wa+XC5z8uSs0kBfXx9vv/02mUyGdevW8a/+1b/iV3/1V9myZQsbNmzgF37hF+js7JyppdqxYwcf/OAH+emf/mn+4A/+AMdx+MxnPsOnPvWpFTP7AMJJg/FTJWpFJzAcAoTKgmZYMUMohgEI9HQT1vjIEinAkqnqWQq1Yc7kXqcpsoGW6EYSZhuqok+3VhcglIYFuOeOIZGBggI+Uvp40qXm5Jmq9k+3oJ/C9qr4C+jyVQ6cxR7LE93eteBIJ77xeuOJWFWI3NBNfTDL+Fdfwndc5BLFYz2rFrRekD5qKIoejVNXNUJNwYTpOza1ieCF8uw69akxEjIgSxipFtx6lVBT4GVL35vZ93xE2tfR+cCjMwbQrVUonHiH/JHXccqFQBXC96ZlkSRGMkOoqQM91ljj8PsJqm7i1Eu4dhXdjFIYOxEw5Jw6pckzMwSEqaFD0xI0c3/fWsFZFZX6dM/SGJILwa2UkNJHUTTCLV0UTh9Y1qJAC8fRo4mAXGPX5+XeloqWTXGSHZErRj8HKI6coP2GB1BUjZbtd5MfOnJRyvmFiDWvI9G1HQDfsymOBnOuj7+g4LQvvem2QrN1OAIxk5PKjnu8+kyNH/7pJJGoID/l096t8uQ3K1TLEseWJFIq3b06N94WYv0WY0ljlZ6HPThIZPvWmc+EECjhMEZnJ/XjJ5Z17Rdi2UbqjTfe4L3vfe/Mv8/lin78x3+cL33pS/z7f//vqVQq/MzP/Az5fJ577rmHxx57jFBodjX15S9/mc985jM8+OCDM8W8X/jCFy7pQvrfnuK2T6yjnLWIZkxaNsTovjFFYbiOU58/AZttnYGRkqDFlz+5edIJurkW3mKg8Baq0ImZTYS1FCE9gaFG0JXA3RcogAwS4NLDlQ6uV8f2qlhumZpbpGrn8eQyX0QpG0ZS1KhJ7Kb1CE2l3j+BV55ddRltScKbOoju6Mav2cRv24w9nqd6fBg8idBUQr0tGG0pvJpN7eQIXqk255xWbgLfsVHNMFo0gdB0Qi3BAsMp52fZer6PnZsI2oibYcx0C7WxAcxptQqnXMAp5eeMXSgqbXc9MmugqiWGn/5biqcPLbiIWOkq+1qEa1Wol7MoioZVnUsokeeRJ1y7cZ6nPFFfFe++eVPikgQjquMDQe2XqpHYuIvJd17ArS6dERlu6SKUCZ4Tu5Sbzh8uH723NWNErmzqvTxxlsrEWeLtm4g1raP3jkcZOfj0koRkhaIRb9tIz60fRtWDzsTZU29iV/IATPljbNU6aRIdlOQUKhoeLjYWZZmnXfSSEBmqskxaacHHp+wH3/U8+NpfFhkbcdl7XxhdF/SddHCn2c9//Ft5PvYjcT78qTjvvl7nf/23KSZGl/Bu+T5udj7zUugaekvzlTdSDzzwwKIvgRCCX/mVX+FXfuVXFtwnk8nwV3/1V8s99aI49UqWRGuIu39sI83royRaTYYOFXj9b/obGqna4NmZYktlwLjkQkpPOhTqoxRozFC8olAEaixE+sHdlN/po352VpJICZvozQnUsIlQFIzWJL4VFMJKRRC/fQuZ9+3CGsmhN8Vxb9nI8JeeBm92FWzlpw2PEQqKenUzYPYB1dH+OUOxi1N49Qp6PI2RaELRzZnuq0E+au59N1IthJraZ1KE5TNHKfYdXvT3Eaq6NNr59wGk9IO80wpRydaR/qU9ywJBNGMSSRlUcyvzYGrjg7i1MqoRwky30nTTPYy99viSug+rZoSmXe9BiyZmir3dyvLlx8y4TtfuzBUlTUDABh09/ByhZBt6OEa6dzehRCuF4WOUxk9jlbJzivRV3UQzIoSSrSS7tpPq2oExnY+q5UeZPPka51YLRZll3B9gnbqVIA7jMez3MekPU5Q5Rrw+1qs7Zo59xj1CndkFjevC89+r8vz35i9yhgdc/td/WwHTVkr82nxPUagaauLSqfPfF+y+pcCpe7zxtwMcenwU1VCQvsSquNg1r/Fq8LyXxbevgIr4FYRXqjP1xDuEN3XMu/b62XGskSn05jheqc7E119Fuj5IiZaK0vT+3Uw9/S7F105gtCTo+b9/kMjmDqrHhmaOYU17R1LKgDCRbEKLBHUoF9Yd2YUsbrWEnshgpJoItXTO1FTURvu5cIBGMjOtLhAs40v9xy4aJlJDEbTolauDWSrmXNkVijaVJ61LD/eJgN3XuiXBmddW1obDs6pMHX6N9r0fBCFo3n0viqqRPfAyTqU4Tes//5wCRTPQ4yna7/oQ8d4g1OXVKxROvjNPkWQpaNuSoGtX+oqG+s6hMHyM4QNP0LnrIbRQjGhzD+F0J63b3oP0PTQzyNWY8SZ2PPIvA/q2qqNq5kyNlVXKMvT296jmZ0PiPj5nvWMM04eCgkTiYE9vCwzWhD+MgoKHh3OFqs6l58+ossxAESgXIbwtBdeNkYKANlm5YOUXSRvUCva10U32iqOBdZaANy2nLyXS82e8FCVsYHSkCa1rQQkZCEWgRExC61vnGCmvWsIu5TDSLWixJOH2oBxAuvZMbdQ5uJUSdjFHuL0XPZ4m3NYzTZrwqE7M3Rc4z0AFuKhSwbRU0VJVJq4kAqmjWZUN5QqonJcn6rh1DzN6aa+2Gddp355asZECyB95nfi6rUQ7N6HoJs03P0Bi0y5KfYepT43hWVWk7yEUDS0SI9K2jljPVrTIrCBr4eS7lPuXXvt4Doom6L2jhWTn1XkupO8yfuxlPKtK+84HCKc6UFQVRZ07HqGoGJG56Qbf96j+v+z9d3il53XeC//euntF79P7kBzWYS+iRfUuy7Ijy45jO4rsxHHiVCex48tWku/LcU4SxcnJsSVbLrLVCyWKvbfhcMgZTm/oHbuXtz/njxcDDAZ7A3sDGDbn5jW8ALy9PetZa93rXpkxxl57iML48msXeDWJE/4yUXfZWqHKQVzvkpJObciBGvkrSfLVWdZ7/HXv4W2Og58d4IU/H/Rrpd7W8D2HQKwFs/jmicbWOg0lEkBL+jO97BPHqF5YHsI0ZsaI9u9AjcR8cVcueVhXfiACY2aMxPZr0CIJQi2dgIRdLuDWqI9yjQriMiUNPbEyMyuQaCW17+BbMlteDa5RXRBVlWSZQLpj1Xbq64VjuWRHy0Ra1jeDVXWZ9p0JAlG1ZvlGI7DLBaZe+gndd32cYEuXL++TaCVw3V0Iz/NZe57rt0RRtYVnKISfvy0Nn2b68KNrEt4NxXX2vq/nrX0vhMfcxSNUcpMke/eQ3nTtPNuvPqGqmpti7uKr5EaOY+Q3ptVFI9BknzPgCAtVDiABllsloESIBzrJGqP12xJJEnIkUvNeb0R+9B1tpLSQTGCFpKiiyXTvSSCrb92LGoi1oATC2JUCnmujhxNYpSxqKIqsaFjlPFooSri1j/zICWLdO5BnhjCLczX7Fl0OSZL8912uEXOX8GtLZKnhBLhXMTGGZiifHKN45DwIkIMaTn55/PpS7kmLJhYKLatTI3jO8slAZXIY4Xmo0Tihzn6QJKzsdM06JWN2HNeoogZ9I5ncdQOF82/MEyyWqj4EW7vovOPDfn3WlaGGtwHMzBSuUUEJRZEkidbr755vQVJcmmOTJL+dh+eumHtrBELA1Ok8vdeuT/RYkiR6rkmR6osweXKt7WgE5fELDP3oq3Tf9THCnQMogRCSJCPJMpK8fPYtPA+nWiR35jVmjjy5plwUElz78QFaNq29q8LGQVDNjmPkJpk+/ZyvaN7Six5NoephJEnGsSpY5RzluRHMUgbPNhek1AA//NoWw61YoMgomoJrOeAKtHQYSZGpjmb9sP0aEFTjpII9BNQIeWOCgBojoETIGqMElAixQNuKLYEkRSHQW4Nl7HkIc/2plHe0kdr/QDf739ft65XV+LYlBbp2vbW5isTAPiRJxizM+X2VihmUQJhwugerkkdWfUFWEP6sUlF8cUohqNaRVFHiYWLXbyE0X9Cbes9+Qpvbyb90BrdQJbKnl/DOXkJbu/AMi7aP3UL5+IjP4FsBTrFK9tHXSd6zn8juXvA8pIDG5J8+gVtealCMmTG/pkcPouhBv5ZjZqxmHydjZgzPtlBDkQUpJTM7g2cuN1KuUSF/5ghtN90/31yxh973/gz5M69jzddnqcEQoY5+4lv3ocVSmHOTeI69EEp8u8AuZikOnaZl/prDXZvo/8DPU7hw3Nf+k+aTy4EQSiBI9sQry/puNY15I7URSPVF6DvQwtTp/NrD5UJg5WcZ+tGfEuvfQbRvB8F0B2o4hqwF5hVLbFzLwi5mMbNTFC68QXlikLVSC1sGolz3sf43LQ/YCITwcK0qVatKNdekVJIkEe5PE2iJUjg1SXRrG+ZMEc9yiG7rQLgunmFjTK6NASlLMgE1giscQKJszSF0D1XWEQicVTp/y6EQgS2bl/1deB7eBnTnfUcbqURnkLPPzzB5uvbDUTSZu39525t8Vkvh2SaubeI5Jq6loEUSGLlpPNfGyE3NGykDWQsiqxqebWLkp1f8PIXjYs8V8aoW5VN+rsgzbYTjh0WckoE5nmH6G88tbHO5kRGuS+bRo/76lzPBPEHh1QuYE1n0jqS/r3wFt7rco3OqZexijsC8HJFrlOfVzJefuWv5Rb1qaPPC8a3CXN3al8yxFwl3bSLSuw0kmWjfdsJdA7im4Xt3ur4gPWPl5xh/4tuEOnp9VqDWWG3Hm4W5154h0rPF7781328r3LVpgTggySrIMsKxKQ2fXb+RAqbPFnBtr24Re6OQZYlrPz7A698bxiqvXd4HfMZb4eJxikOnUMMxX9ZKURc8SOHYONWS3wpmHQlkPaxw/ac2keh+c2ujrio8gTldJDLQilM0kDWVxDV95F4bRglpIGl4a/SiAKp2gamSTxO3PV+KynIruMJGVyIYTnHFDuTha/ahppLLFzgOTn79E6Z3tJHKjlcZPZpjdrC2tZYk2H1fx1vapmfu7CGA+XAUSJKC5zlUZkcWXXohmDt7COE5ZM6/utTVrwGvYlI+NlR3uTk8izm8QsJbgDlSZ7nrYY7OYY6unBcTrkPxwnG8Xl9M18xOY+Xr7FMICuffWKCJu2bVp5/XgV3MMvrI12m7/l5iW/agBCPIqurXTgkPz7GxC1nKYxeYPvQIVnYG1zaJjl9ADUUX1NZrwZibwDXKvm7gFTVa9eBaVaqz4yh6wKfbrtBd90pY+TmGf/w12m98D5G+bb5Yqqohqfp8MbKHZxg41VLNUOlaUJw2mDlfpHPX+oub27fF2PeBXl795uCGaAIKz/Vr6Uq5VdeVZAjHVMoFp6FjSxJsv6eLaz7W/5aG+K8KZJn8yXGsbJmpx08iKRKSIqMlQmQPD+GZa59ECLxlHcadecbgil6UJBHo7yNx7101Zas8296QFvKSuBq6PlcZhUKBRKKxD7BjR4zZi6W67Tr+D97GkCRC7b2E2nt9xpei4jk2TqVIdXoUc27inVPIK8uE2noJtXWjhGO+1+zYeLaJXcpj5WYxs9PLOv6uBXpE5b3/bD/XfXxg3fsSQjBzrsi3f+sQsxfW1p4kmlKxTQ+z4hEIy6i6jFFy6d8dYXrEwKy4uI5AUSVcRxBNaRglF+EJbnhfK0cencOqru4pdO5K8LH/cCOtWzamrUUt2IbDlz/wCKXZN7GhmATBjjhWpoJnXWaMZAktGsQuNKdmsTHnJBHcvo3U+99LYPOmZV6rEAJrdIzx//Lf/eKsFZDP54nH66dl3tGeVCOYOnOV+/4sPBxp/sfaM7jFuYBYd3L86uHSub/F53ep3YbkF/zW8roW7ucqwsBvG3ge1alhqlOLxc7hzdtJ3XwnAc8j+9LTiJnLDJSs0PWxz6IEw1SGzpN57rFlu5QDQRI33kbp5DHszGKY0Ko4TJ8t4FjuuvtCSZJEy6Yo1396E099+WTTTD89KNPaG6R3Z4SjT2TYen2c2VGDmWGDXbcmKRdm6doSYmbEoGNziMkLVbq2hgnHFY48ksFzxUITvpWQ7o/wnn+yj5bNbweyRC3ME5jWmCirmW/yxJtroPxQEGoqSfSG64nechNqS7puWLVy8tSqBqoRvGuMVDCqougy5YyFJEPrpih6SCEzWqGa3zj6uawF0EJ+TF2Ppgi1dKPHUmjhOFo4jqwHkJRLrQUEnuvg2ZafsynnsQpzVLOTmNlp7GrRD/OswuK72lD1CImObcRaNmGUZpm++DLBaCu2UcQ2/dCZpKq+2Kdtg6L4hEHX9f8uWKASI4Fo4sX0iQNh1GAEPZr0KcrxVvR4C1o4jqIHkFXd7/KK3/7Asy1cy8AqZrEKsxi5acz8HHalgGOU5mur3v6GqzJ4HnNynO6f/kWUyBWDq+cy+cNvkD54N1q6ta4xlqUaeScBY0czFKcMUn3rF/hUVJkDn9xEfqzCoa9fwHMav7fJdp3+PVHaB4IMHtOoFh3yMxZG2aWcd6gUHFp6gqS6AqQ6AiAk+nb756xoEqGYQiiqYJTre8zt2+Pc9xt72XRT6wp6mW8F/GaFoWQnkZZeAtEWn90oNzdx8BybC8/+hf/8ZZng1uUkhasGSUYO6MihEGprK8FNA+i93cihkM9Krdc9vFKhfOhwzWXN4l1jpHbd20EgovLS14fo2Zfkgd/chWt5DL2a5Zk/Oe/32VkrJJlQazfRzi2EWroItfQQSLT67KRVkrMKQAiIt3CpjE8IgXBsqtlJqnPj2JXa7es3Evmh41RrFM/Kik73zrsJxlpxjBKx1k3MDB0m3XsNViXL9MWXkcMRItt3ImwHc2YKJRRCicSonD+DlkyhtbZjjg6jtbahBIKUTh9fVSVCC8cId2wm2rWZYKKdQKodPZpaaEVdD0pgsR1ApL1/4WfXNjFzM1QzE1RnxyhNnKc6t1x26W0F4eFWy3V7DgnL9A1unUvwTIO5Zx+tuWzqTIHpswWSvRtAIJBACyrc/ss7KGdNTvxkrGFDZVRcxs6UmR0xmB4yCIQVYmmNwqzN8IkSkaTG2JkyXVvCZMZNChmboTcEri3QdJnMhIUakOs6zH0H0tz3G3vpva7+jP6tgCQrxLt20r7rNuId29Yl2+XaJhISAoEcDNL5D/4+UgPe5VsF4XmUDh3GyayvmeklvGuMVNuWKLODJdSAzIGP9HL22RkuHprjrr+7FS2krMlIyapGtHsb6Z03EW7tRYskfEbSOj8GSZKQNJ1Ie/+SgfZqwi7naxopVQ8STnQyfOzHCOHRu+d+wG83EIj6tTayrqOmWrAmxgj19iNHIuAJ1HgCvaOLUP9mnOyc703pAWQ9gGfU0PKSFfR4C617biPatQU96veV2ojBRdEChNt6Cbf24G29DrucpzIzSubsK5TGzq1ZRRsgdfBurLkZ9HQr4c3bsbMZZp/4EZ5lIqka0d3XENu1D+F5FI4epnz+lE/fVzXi+w4Q3roLWVUxJsfJvvRUzXvTLBLX3UJs73W+oXr6YV/F/zI4hsvpx8bZdmcHirYxA1ooqXPfP9qLGlB440ejODU0Ma9EYdamMLsYyTh/ZDH8PnisdNl6i4n7qYuL9+eNp2sPdHpYZed7urjt726ndUvsbWWgAOJd2xm45ePokUVZprWn/9/GE60rIITAHB6h+OLLTUVUVsK7xkh5rkBW/OLdVE+Ip//3OVzHQ1ZlZKW5F1jWg0Q7N9O67w7ivTsWeva83T6EjYGEwG8HwGUdhLVgdL6nEQjbwp6dxs5lcSsV5FIRSdN8zUPXxZqbwTNNME2f0n6FFyUpGpH2PlLbrie57ToU3feGrsr9lCRkVUOPt6DHW0hu2U9x7Byzx5+jNHkR12i+bkNLpols3UXx1DFmn/oJajiyINcU23sd8X0HyLz4NEogSPrg3XhGlerIxQWlhNwrz4Hn0XL3AwjXIVPH+2kG+ddfpjp8nvYPfrpua/XTT0xw59/fSapvY/I0kiQR6wjywL+4hrZtcV7+8/MUJipvqrOqhRRat8a57Re2sePeLmS1fsjprYKih+m94cMLBspzHcxSBruax7WqeE0qaAjXWVGS6O0EZ2aW7A9/hD2xcULb7xojNfRqhjt+YQueKxg5mqOctUj3hXFdzy/2bRCBRCvt19xDcss1vurB2+wD2Gg4toFVydKz+z1UizPooSQdWw4Sb9/KyBs/AcAtlym98XrN7Usnckt+tzNLaeh6LE3L7oOkd9yIPq9+/mZgYeCSFOJ9Owi391EcOc30609RmRleeeMacCol8oefB1iU7JQkYruvpXjqGJXzp5AUlVDfAOHN26iODILnkX/t5YV9lM6cINi59p5pSyAErmmuGFa1yg6vfnOQe//R3obIB41AkiS0oMJNn91C+/Y4x34wzKnHJtZdR7XqcWWJ/hta2HV/N9vu6NiYMOZVQqxjC6F424KBmjr1DJnB1zByUwv9wN5tEEJgjY+T/eFDGGfX3433crxrjNTFQ3OYZYdQXGPkaA7H8rAMl1e/M4pVaeADkmSiXVvoPvghwq09TSc336nwHJPxU0/StulGUl17UPUQkVQPYycfp5wbW30HdSDJCrH+XXRd/15ft+0tvZ8SaiBMcut1hFp7mX3jGTJnXmmqfbw9V7vIVm9tJ5VuIb7/BgDUSIzyhdM+mUtWie7cR2TrTpRIDD3VgjGxvIPp1YIQcObJSfY80Evn7sSGDuqyIrHp5la6dic58IlNHP7GRS6+OINZtHFsb90RKlmVkFWZYFxj6+0d7Ly3i649SSItOrLSfJGya3tYFYdAVGs6stIs4p1bFya32aGjjL/+CJ7z1pKjrgouiVSbJuXXj5F/8mnsDaiLuhLvCiMlRyN4psXI67klf8+NVcmNNRD/l2Radt5E540P+Hmnt+cE7arBrGQZPfEIoyce2ZD9qaEoLbsP0nHgfr9w9W1yQy8JnPbc9lHC7f1MvPITrEJjYr6ijsdiZ+conz9F8fQbCwOzmG+dHtt7HYnrbmH6ke9hz82QvOE2Ah1dG3U5DSEzXObYgyO0bI6ihzb2c5ckiWBco/dAmt7r0pRmDS6+NMPQoVny4xVKcyZGwcauOFhVZ7mQhOQzB9WAjB7RCERVAlGNWFuA9h0JOncl6L++hUBMW3LMZuG5HiceGuWR/3ycn/nyrXTvTa7vwleBHm0BJDzPZebsi+86AyU8D69SwZnLYI6OUT58BOP8hdU3XCPe+UZKktA625DDIZzZDPZ485Y8te06um7+AFr46hUBvl2hqAHSPfvJT5/Fqm6AhEk4TvctHyC5+RqUt5lEEVwS5VVIbb8BJRBm4pWHahJKGoIQlE4fIzSwjerYEG65jJ5uwZz24/FKKIxnm3jVMnprO+GtO3BLS+v2JE3323iomi8UfLkxlBWfqKMqSKrq0/8Xt0TWNF+oVdPrUtSFKzj5kzF23tNF/40tV2XCcEnoONYe4poP97PvA31U8xaFySqVrIlZsjFLDq7lzXtZAkVTUDQJRZPRQirBuEYooRNK6ETbAiiavGHnWpgyeOnPz1PJmEyfyV91I6Wo/nvvmhVso776yVogXBfj3LkN3eeq8DyE4+IZBm6hgJPN4WSyWFNTPoPvKhfUv/ONlBA4sxnU1jSS2mRISZJIDOyl6+YPoIbWkFwWfjpTuA6e6yA8v/WA5zgI11kYfCRFme8npCIpb45nIYSYl91x/X81hF/BD8u19F1D5/bbKMwOkhk9SrUwhXulEnMDUEMx+u/+aeJ9O9/24VJJkoj37UILxxh59ttUpurLTHmmUbevVeHYqyCg5Y6fQtY17GwWO+MTI4onj6K3dtD5kc9i5zIUXn+FYI/P5pT1AC13vZdg7wBqLE7q1ntI3XI3s48/SPnCaaI795G65S6UcARJUej9uV+lfPYE2ZefQQlFaLnjfgKd3SjhKG3v+SB2IcfsEz/GmlmesC5OGzz55ZN85r8fJBi9+h2MZcXv7BtJX0boEH6vIyH8ny+Vd13tb8E2HJ78byeYPOVPwCZP5uHjV/WQV9VzEqbJ9Ff//Krt/0qo6QTtv/k5qkfPkPv+Y3jliq/y8iayZd75RuoSZBnPac6ih1q66bzhvX59TpMfi+c6fl3O7Cjl6SGqcxOYhTkco7y0RbYko4Wi6LEUwXQ3kY4BQi1dBNNdCy0u1ge/YNg1qzhGBdeq4hoVHKOMXc5jFTNY5Vxdb8GxKpx+7quE4p2ke/bSs+s+PM+hNDdMcW6YSn4C1149ZKrHW+m59cPE+3atWuvU8JUJD8+2Fgwtkows+16FJG9AKYAsE2rtpf/uzzD85NepTNcmVMw+8eO6+/BMg9zh58nNkyouh1PIMfXgN5b8rXDML3D0LJOZR3+wZFnnHR8i2rWN8oXTlE6/Qen0GzWP6dg5pn70zRWv7UqMvp7hxT89x+1/dzvaBof9GoLkt6V/MyO/VsXh5b84z6lHxxdCsZOncnheYyoWa0U1P02idw+KHlrowLuR2Ahl8YaPFdaRI0EkVcYzzQ2jlTeDd76RkiTUthb0fr+fSXFiujErL8t0XHcfodbmGqMJIbAKc8wef57i2FmM7FTdYkx/Aw+7UsCuFChPDZE5/TKBRCvRnu207rmNYKpj/jIaOwchPMz8LEZm0ldcKGV89WijjGNWcE3fSLm20fBsRwiPSn6cSn6cUKyN9i230LXjTtpsg2p+iumLL5GbPEO9bLisBem66X3E+3evyUAt1I8ID6uUozI1RDU3hV0q4FqV+eZ4nk8vn2+Qp4Xj6DFf8SPc1regit6s4ZIkiWCqg55bP8Loc9+hOrt2ssiG4CqNncIVvPatQVo3x9j93m4UdWMmEm9XeK7H6ccneOXrF3DMxYhAadagmrXW3RRyJeQnztCx525kRSXRs4vS9NXL11wOraed4M5NFJ95Few335hcLbzzjZQQOHNZtK4OnKmZhgfm5Kb9JDbtbXxQE77Hkh86wfiLP8QqZdfUUkB4LkZ2CiM3Q2H4JB3X3Ud6+w1NtZiozk0w9vz3cIzShjTKU7QgeihBumc/ic7tuJbB0Os/oDA7RCTVQ9eOu0CSyU2cXLatJCt0HLiX1NZrmw7xCeH30HIqRYqjZ5g7cwgzN43nWIuGqR7mGwXKioYSDBPr2U56xw0E090oerApYyVJEpGOTXTd+D5Gnv3WygrdkowaieFWSgt1ULKmI+tBnHJhfhUFJRTxBXFtC7da5pKBlxR1oU2F8Fy/E/FlorKSJPtiuqqKZ9u4RmVdrSsuR2nW5PH/cpxIi86mm9veNoSWjYbneFx8cYZH/n/HqGSXFnHbVZe5odJVNVLl6UGKE2eJd++kbfst5MdOUpoZ2rDnWBOSRHDXZqJ330jphdcR/8dIvc0gBPboBG6hMTFZLZKgbf+dSErj8XnPdZh54xmmXntiTQWhyyA8rMIco89+G8co0bbvThQ9uOpmkiQT691OrHcHmTOH1m2gVD3Epus+ihaKU8lNMHLsIUqZkQXv0KrkCIQShBMdy4yUJMukth2gbe8da8pBWcUM2XNHyJx5BTPXZFt1IRCug+s6uFaVucIcmdOHSAzsJbX9emJ9u5piFkqyTHxgDx3lHGMvfL+uGrmsB+h//+eYevEhyiN+Ajux83qifdsZ/clfggSpPTcT27wH4XlIksTMq09SGbsASIS7N5PacxOSoqHoAcpjF5g9/MSCwQu29tB5xwd9yS1VY/bwE5RHN67upDBZ5aHfP8qHfvcAPdekrzod+82GY7mce2aKh//DcgMFvpHKDJXov77lqp2D59qMHvkxA4EwkXQvm2/7DJMnnqIwfhqzlLkqx5QCOoGtfUja2zsXvBa8K4yUsGzU1hSB7Zso/OSpVdePdm8jmO5sIsQmyF08ytSRx3HN5a3U1wPhuUy/9iSKHqZl9y0N5anUQJi2vbdRGDru58DWAc/zyE2dpZwdxSjO1CBLCAozF2qeVzDVSds1dyM3YFwX9iZ8FfjSxAWmjjxKafz8wgC9XgjPJXfxKKXJC6S2HqDjhvtRg9HGDZUkkd5xI5WZETKnXq65jmdWKY+eI9q/i/LYBWRVJ9w1QGnoFMJzCXdtIrnnJiae+i52IUt86z5ar7uLsblJXKOCOTvBzMuP4hgVor1bab3hXuaOPocwq4CEGgoz9fyD2KU8LdfeQXzrfiqTwxvSwuMS5oZKPPQHR7nvN/aw+WD7u8NQiUtU8zGe/qNTFKZq51Ft0yU7Up7v73YV81LZccZe/REDBz9JINZK3/Uforr1Rqq5KcziLLZRni/sXX2SKTyP7NDRmutqPe2Ert2JPtBFaO82JE2l7Vc/vdDCxitXKTzyIvbIIqFGCgYIXbOdwJZelFQcXA9nLk/1tZOY5xtkusoysftuIrhrC+WXj1F5+bL8qSSh93cRvmEPansKYbuY54epvHICr9T8+PmuMFJKMo6TzVM5dmr1dQNhEpv3LUjzNAIjO8X4Sz/CdUwiN+6kemoYr1QFVfEpw0KgxCJ4VQNhNz/gulaVqdcfJ9TSRaRzc0MfT6itl+TWa5k9vjxh3ww8x2R26FVW+lgq+eXtrmVVp+2auwm1dDWX03MdsmdfZfzQj3Eqq7e7lmUVWdaQkLCdxl5wp1pi5vhzVLOT9Bz80HzesbEcjKzqtO+/C2NunEodskn+7Ot03P4h9HgLkuyH5yqTPuki1NGHZ/kJZiUUwczNkL7mNrRY0s8VWlVQFJRgCM9xkFUNWZbxpwaCytQI1Sm/4LcyOURy5/XIioq7gUbqUov5H/zbI7z3n+1n210dG15D9WZCCIGRtznyrUGe+X9OY1frf4PCFeTGK1hlv7D3akCSFXbc/yuEEh2oAV9WWtGDRFoHiLT2+2HseZpjI3EQz7HIDR9brv0nSWi9HYT2bUPSNSTd7/IsR8OXlTJISMpl3pUs0/r3PkFgez/C8rt5S4pM6NqdRO+5kdzXH6L0/GsrRmikgE78gduIv+92yi8exTh1cXGZphK7/yDx990Orodn2UiSRPj6XURuvZbMnz+4xGA2gnfum3kZPMNESSZQ21uxh1aeCWiRBNGuLQ0PrK5tMnPsGexyzg8xeQJJU5GjIfTeNiRVwbw4Qezua6kcOYs1urb233Yxy/TRp9nU3o/UEOtPomXnzWTOvorXhHJCbTQfMoz37SS19UDDgz/4IdPMmUOMvfCDVduTKEqAVGIzqeQWAgFfLeH4qW/geTbxqK8IUiiO1qfJC4/S2FlGnv4mfXd9mnBbb0Pn6BMpOmndezujz30Pz15+b61CFqdcINTei6zr2MUsdtEXQpUUFT2epvWGe7l0X0vDZ/BsC0lRSO25mVBHH65poAbDKKEIl7MlPPMyD0CIxd5a89DSEezMxrC7SjMGP/ydI9z4M1s48MkBkj1vX6mhehBCMHEiz0tfO8fpR8cbEpIuTlWp5qyrZqSQZKJtA8hXpBOk+WcpNauYUc9gCEHlpWNUXjqGHI/Q8Zs/jxTQmPrPf4qo1vm+PI/iU69QPvQG1sUxnLk8cihA+Ma9pH/2/URuu5bq8XO4ueIVh/KNqhwOEn//HcTuvYniYy9ReOg5vPLiOxvcvYXkx+7DPD9C7luPYo1MIgU0ondcT/Jj95H82H3M/NFfQxNM7He+kZIk1HQSrbMNTWrHHh5bcRYQ6RhADTZWEyWEoDo3QWns7NJ9SqB3txDc2oOwHMwLEwjTxsms7hmshOLoaapz4w0po0uShB5vIdKxieLI6h5kM5A1GVmVcaq1k69KIETbNXcvnaGtAiE8CkMnmDz86KoGSpY1BnrvpLvzRnQ9Mr+9QJZ8j6OtdQ+J+ACnzn6XSrVOy/p5VGZGGHvxBwzc91m0cGPSQJIsk9xyLdlzr1EcPb1suWeZVCYuEunZjBqOUzh3FM/28x92MYtVmGPi6e9e1jVY4NkWSjBMYuf1zL32DMXBk4Taegh1Le2eu9rcOnFdP4WjozglA+EJAq1RzNkiSkBD1lXsQhUlpCOrCnahgqTIqLEQdq6MEgkiawp2roxw/MHcKju8/OfnGH1tjru/sJu+G1qQ5Le3mPIlj8IxXY4/NMZLf3aO2YslRIMancVpg0reItm78fTw+TNEeB5C2qAw9gYTLoxjZ5f87pUqlJ87QvyB25BjEeRIaJmRwnH9utKP3EPktusoPOobKGEs/Zaj992MZ1oUHn4e87wfERCWTenZIwT3bCF0zQ7UthTOxMrf7eV45xspIbCnZlHbW3Gm51YhEkjEB/Y0wejzKI2fwyz4N1QK6qiJKNKmLszBSZy2El7FwKuaOPkyem87xpm1a7N5tkXm7GHCbX0NnaOih4j37aQ4emZDmUN7f3oX/Xf08uAXHq6xVCK55TpCLd0Ns6WFEFRnx5k49GPfI10Fremd9PfeRq4wzOi5l0jGB+jruXVheb44SlfnjcSi3asaKYDS+HkmXn6Ints+shB+WQ2KHqTjwHsoTVxALBMFFZRHzxPfdg1qKEppZPGjLw6eItq/g46D76M6PeorlHsemWMv+Gw+s0Koow9Z1Qh3b1rSH6sRqNEg8Wt6sebKWHMlktcPUDo7SXRbJ07ZxBjPEt3ZhVsxqQzOEh5oRU2GKbw+TGxPD8LzyB8ZwhhdbIHhmB7Dh+f463/0Irvu7+LAJzfRuTPx1tRTrQIhBGbRZuxYlpe+dp7hw7NLKOaNoDhdpTxnXrW8lHAdXv/mv1++QAJJ03zmXVOEJ7HhhkqOhpEjIT9MqMgg+w1NJUWBWp6eqpD82H3E7rmJwk+eo/CjZxCWvWyfen8nXrmKpOvoWxejF5KmIgwLSVfRezv+lhkpQA4F8PKFVQdqSVEIt/Y0vF/PtiiNL0qQCMOi8MSrC7+Xnju28HP5pRMboJguqEwO4prlhrw9SZYJJjtQQ9GG8juNIhAPoNYZoLRInOSW/ShaoOHrFZ7L9NEnMbKNSVZ1dhygVJ7mzLkfUqnOEgwkliw3qhkUWUXXG1QJER75wTeIdW8ltf2Ghmu5wu19xAd2k79wdNkyq5DBs0zKmSmcyuKs07MMJp/5IbGte9FjSTzbojw14s+sTZPpF35CtH8HaiRO7uRhKhNDC72uSsNnuTz0auVmfS/tMiMpHI/SqQm0VITo9g4CHXEqg7NIqrxQSyYpMsL1mYVyUENWZDzLwa1YGGOZutdvlR2Ofm+Eiy/MsPu9PWy9vZ2ea9IEousvnF4vhBAYRZvBl2Y4+9QUpx8fb7qV/SU4pkd2tIzwlnSn2VC4NcLESjqG1tmCcWEcOajjlQ2EEAS39mCNTONV1hu2Xx2SqhA6sJvILfvRulp94+H56jRqWxpnqraWZWj/dj/Ppcio7S3IQR33SiMV9CW+tJ4U7b/2MzX3IzwPKdgc/f+db6QkCTkcxjMtnNmV6Z2BeCtKgzNp8KmklekmPKMNkApxjDJGdoZoV2MDsN+6PlbTSEmytKbiUD2q1bU/odYeIp2bGzdQQlAcPUN+8HjDxw+HWigWx6gatRve2a6BJMnIcuM5BdesMH30KeIDe/wWLA1AVnXS266nNHYW9/JcERJaLIUajjLzyuM1j5U7cajmPo3ZcYzZ8YXfKxODCz+XhpeGFs3sNGZ2KTU/88I53LKJnatgTOQonZrALlQJ9aUpnZrAKVSJbGmjeGJ8YR0lpGPlyjj5Kp7lINyVJ3PFaYNDf3meEz8Zo2VTlH0f6GXrHR2EU4H5/k1XPxwohC+hJBxBadbg9JMTnHx4nLmLRSo5a90q69NnClgVB0VrLj/kmN6aPnO9rx21I43WlsSeyqB1tyKHAlSPXyS0bwvOTG6DjNTKzyV0/W7SP/sB3HyJ/INPY0/OIiwHPI/WL/x03QmMV6qQ/fZjhHb7tVhuoUj2r38Cl71LnmmD52EOTZD//hO4+dq6hfZkY6LOl/DON1JCIAwTbXMfajJB+dBrdVcNJNuaSvSb+VmfjfUmwjWrWMU56Nrc0PpaJF5Xd3DPp3YS62lek7Drhg7cGgloSVZIbb0OWW288Ng1K8y88cyqeagl27gmsqIhyypuDfHKgB7H81wcp7mPujo3TubMK7Tvv6shIytJEuH2fsJt/Qu5KVkPkti2n3DPFqrTo5iZ5d6hElBRwxpmzkAJKLiW6zeD3ADY2XnShAVuxeLSXDb36iDmpK9Plzu8+LNn2DhF/z7ZVuP0X+H5xIrSjMHwq3PoIYWuvSm239lB554kkXSAYFwjGNNQ9PWLwYp5UpJZcqjmLUozBtNnC5x9apKR1zLYVdef8W8QXv/uEMd+sLbQvGs3H3pTkjG8QhkvFEDraiWwqQthu3hlA69cxTPW3jn6EoQnkFWFeoZK0lWCe7YixyLMfeV7VF9fnBRJoSByMLAshHcJ1tAExonzmGeGkHSd2D034WaLFB97aWEbr1jGGp1C627DK1Uwz9TXw2wG73wjBdgTU9gTq4eStEiiqZCcmW88brpRcG0Tu9JYUTL4kkT1PIPN9w/QeV1787NOCeZOL/dK1VDM1+ZrQqWjNHGhaamhXGGY1vQuEvF+Mtmlis+yrNLRtg/HqVKuNK94nznzCqntN6A1KCisReJEu7dSGj+3UM8lqRqVsQsUh04tE+6VVZnu2/sx8wazx6aI9sQpjuQRjocSVJE1GStvEkgGcU0HzxW07G0nd3YOu7T2gcqcWFSwv2SgNgrC9Y3H4EszDL40gx5RSXaHSfSESXSGSHSHibQECCV0wkmdUFJHD6kouoyi+f9kVUJ4As8VOKaHXXEwKw7VnEU5Y1KaNciPVShMGWRHSswNltYczmvomjxwV1I02WCYF8fRe9uxpzK4pSrCdhZUIcwL437KYj2elOPi5ovo/Z0Ed22meuwMeAIpqPtGxJk38o6DJEmo7Wm/hMZxUdvTRO+5ETkexZ2tHb3w3VrfEOV/8CRKMkb8/Xfg5kuUXzq64FEVH3uR1r//08Q/4KvU2KOTvvGMhtA6WpFCAb+mqol7/64wUo1CC8ebmvFZ5Y392BuC8HDNqh+7bSB3IkkSWihWu1WDEIw8N8Yr/+u1pk7hwC/uJ9q53PDF+3c1FS51HYvS2FmcanPtCiYmD9Pesodd2z/C1PRRAoGE3/OrZRepxGbaWvcwO3eKQqH5FhtWMUNp7BzJrdc2xvSTZBIDe5k59jROtYRnGWSO1q9NkzWFaF+C7JlZhCtIbGuhOlNGCah03z7A1KtjaGGd9N52sqdmMAsmbdd1URzOr8tISRJoGth2/aizLPv/1qsRapUdps8WmD7rh5gVTUYNygSjKsGIikDC83ylc0mW/H8SvhK6AM8TeI6H5wgcy8UxPRzTxTFd9ICMEGAZG2dAJBmCYQXH9rDNq6TePX999eCVqhinFj2Ly/2VNZOtLjumVzWpHD5BcPsALb/wEdxCGYTALZbJ/c1PsIYmwHGpvH6a0IHd8yy9a8ETyKEA9uQc1ddOofd2rHpYZyZL5i8epO3XP0vyk/fjzOUxT/u1UsaJC+S++ziJ999B6y9/wvcQhfDbzega1uA4lVdO/B8jVQ++7FDjRmpD5I/WAM8xEZ7bcIJfDoSRJBkhlobGhID8SJG5U81JsRTHS0Q6rjBSkkS8f3dTuSinUqQw3Dw9vlSe4vS5HzDQdxe9PbeiyH54ce/OT2HbFTLZc5y78BCeaH60dc0qhdFTxAd2++SPBhBMdxJMdlBqwNg6VRsjW6U8XkRSJJSAQiAdwrM8SuMFSsN51LCGHgsQ35Ri/NkhrKKJa63PcrS3y/yLfxnjf/2vMqdOLt+XJMFNN2ncfkeAr/xJmWx24wZr1/ZwbY8P/Hw7D3y+g2/84SiP/VXz9YK9O0L8yh9sxqi4/IdfPM0GCZHQvSXIl364n6e+OcOf/d5Q44ZKlpHDoWV1i26hsDgTkCDYFiXUHsUumJSG63giDUAJKgjXD3suMA+FQNFV36i7HrI6Xx5iOKQ2JymMFv1wshCUn38dN5MnfNM+lEQMYTtYIxNLckPGsXNM/+HXiN5+HUprChwX4/QglcPH0Xs7Cd+4Z0mNlTBtqkfPYA1PLOnu4EzNMffH3yH+/juI3LgH68LIgndYfPh5jKNnCN+0D623A0mWcQslrJFJn/7e5Czpb5WRkhW1KSLBcurxm4PFivTGINepV5o5MUf2XPMfjV1ePh3XI0kCibamCIxmbgazWCNJKklIuo4ky3jVWjk/wWzmNOXqDMn4AKFQGlnWcOwqpfIkucIQjrP2XKExN4FdzqMk2xtaX5IkYn07KE00pqE3d2zKD3FoMsWhHMLxsAoGhcHFjzxzYgan6ntOmePTKAEVu7h2TyoalXjf+wN8+1tVak0LhIC2Npm/87kQ3/1OlWy2cQsQjivc+bFWjj6bZ+JC/ZCUJPu9pNaan1JUCS0gY1uXqOEbZEilS15k4+clh8PE7rwNfaAXSdWWjBuzX/0LvLKf35MUmWh/ElxB7JquNRupYCpIalsSPaxRGC1iVxzCrSGMnEG0O4qsyGTOZGjf30ZuKE9huEh8IE5psuwbKQDPwzhxAePEyqrr9uiUT3q4AsbJCxgnl27rFkrM/Pe/qrkf6+IYs//jr5cvEGBPzJL//pMNXftq+FtlpCRZpRkrdekTUUMKnu2HJyRZ8mmjSb97aGW26lNZ5/9+5Xfl8zT8eLykSPN0T/80JGn+77W2beo7r73ya39yFM9pPmwycWQK84oBM9TaM0/QaPzECiOnahrbQG8/ge4enEKeysl6rD9BtTpHtTqHJCn+vRLehtSLGNkprGJm3ug2dj2x3p1MHPpJQ/VohYv+QOWaLrOvL0rAXArnORWb7OlFTyNzcm0qJc2iXBEkEjKa1pwRSXfofOCXOhk/b6xopNaL0bNV/v+/egbPEbjOm9dUrxZC1+0jsGWAwhPP4FWWToi8ywtYhfDDultbkFSZ9oMDzL02hms05y1Eu6O07GjBrtjMncnSursFPapRHFdIb01h5E30mI7woDhaRHgCLaSihlXsylszmX6z8K4zUnpIJhJXfars/N8cW5Cbtpoe4GRFRQkobHtgM5lzWWZPZUhuSqDHdMy8SSgVJBAPUJ6pkOiLIzzB7KnMAgtJC6uktyYxCialyQotO1I4hkN5qkJ6a5LyTAW74pAciFPNGuSGCiD8ei6akRty7JpKBVZpbS/vxOEpJg4vJSUEU52ozRSeCkFx/NyyP8uRCEo0gpPL4uRXn3XKsoamhpAkGdezcZzqug2V51hUpkeI9WxvuFBGj6YIJtsxss3pjoW0JIlQF+ttFOV4JrnKGI63NiOhKLBjh4plCbwmWXK920O0dDXO6FwrXFswN75+lttGQEunKb/6OsapMyuuJzxBZcLPzVn5KsULa1M5LwwXCMYDOKZDaaJEcnOC/GCeaqaKGlQx8yZG1kBSZFzLQ49qIElE2sMYGWNDmY9vN7yrjFTXliAf/JUeUh06iro4KMxNWPzvf3ZusRFgg7NnRQ/7sWDbozxTJZAIkOiP0b6nlTM/ukDndW1MHJlGj2q07Ezh2R7Zi/kFOaHUliSyplDNmkTawqQ2J1B0hbGXJoh0hJk7m6X3YBfh1hClyTKFsRKe7aFogaaaBwpn/XUjK0FWdfRYqql2HHa1WFNdQpIkP6YvSbDC/lQ1RFvLbtLJbeh6xDdSrkXVyDAze4Js/mLdbRtBdW7Mj/s3uL6sqmsyUi2Rzezpet+6Kdolc4ajY9+naCwaqUhE4lOfDhEMQkurgqZJfOjDQfbsXfpZSxKk0zIf/FCI8+ddSuXVX5ZIQuHAPUk6Nwe55k5fTur9v9jBLR9ILawzcrrKw1+bqsXXoaM/wA33p2jrDeC6gsHjZV57MkcptzTMGAjL3PmxVjbtXSTkZCYtvv3fxqmF7Qei3PrBNA//+RRm1ePAPUl6toeQJJi4aHDo4Sy56cYmZ7IC19yV5MA9CUbPVnnqm7MLhA17cgp9oJ/yq6/7bJR6+1AVIj0JnLJFanfHmo2UVbQYfWGRBTv6/OLPI88uEoSMnO/FWSWbcw+uHH6+6T1xtu4L8fg3M0yP1b6GG+6JcfC9i8Xyk8MWj34zQ362dl7zno+n2HPjYr76wokqT3w7i1G5ekzJd5WR+sAv91Apujz2FyNYhrswbjvWvNZXpdhUrkeLJn36bdEitSVJcaxEIKZjGw6e4zH+6jThliCZ8zlcy6M6Z+Bc5uaXp8r03d6N57g+7TOgYBsunuNRHC9hlWxygwVC6SDVOQPP8ZBkFSUQQWpw+BRCYFeLGyqLFEgE0CMaxXE/4aoEQuix1CpbLUV1bvwy7bpFuOUyWroFp1RCb2vHkieWMX00LcLObR+mJbXdbyHvOQgEkiSTTGyio+0aLgw9xvjkK2v2qiozoz6lvCExX5AUze+ivD7buKFQVTh4UGfvPnU+jAcf+nCwZl5akiCf9/jTr5aZnlr9nrV2B3jgFzrRdIlUu+9F9e4I09a3SDYRHstZbQK2XRflrk+2kmzVkGRQNZm7P9nK9fel+PM/GCYzuegtSUAgJNPaEyDdqdPeF2DiQrWukeoYCHDHx1sxKh5br43QuSmIhM8w1IMSt7wvzZ/+3hAjp1fOWcoKXH9fks/99gClnMPDX5tG3bGL6N7d/nkpKoHNmwhs6seenplvm+5faPa7DyKq/mTBs13MuTKpvR0UL16dXlFrgSTDvR9PseemCEOnDKbHajOVhYBwTCGWUth1fYSLJ6q88FCOetU3kgTRhEKiVWXX9RFijxZ49oc5jI3tYLQE7yojlerQ+clXhxg9XfuOmcUMQnhINOYRhNI+HXPo6VGQAQ/yI4WFjzI/vFjPlLu4XPGhPFPl1HcXZzuTR2cWti1O+MzBzLkcmfO5hb8rgQB6NNmwt+c5VtMU79Ww6+Pb2XR3H9/7xR/756QH0WPppvZh5qZr94mSZbS2drS2djzDqElF7eq4ntb0TmbmTjI++QqVygyu56BrEZKJAXq6bqG/9w5KpQnyxbXRd+1yHs8yGmb4SbJCINGKJCsb1v9qvcjnBf/gCzk6O2UeeCDIv/hXUf7661XOnl1upQxD8MYxm3Pn3IbmaUMnK/ybjx9HkuCz/7yfD/5SJ//7X17k2HMrl2XoQZkD9yZ4+aEs//trU+RmbLo2BfnMb/Vy03tTHH0mz1PfnFk4B6Pi8eAfT/LgH0+yaW+Yf/Cft656brIscf/PtfPGcwX++2+cZ+JClZbuAB/6lS4Ovj/NbR9q4dsXx7CtpRd66ZiKJnHg3iS/8O8GGD9f5Wu/P8zYuSqBLRWcmUWijz1Zpw7vsndWkiXMbJWRn5whkG68PKNZRBMKm3YFOXusitmA1yI8ePK7WYbPGpw9Wt+CvPpUkVef8sex//XErpX3KeDxb2V5/FtZgmGZr72yt7mLWCPeVUbq4hsldt4YZ+J8tWbi1cxNNzXz1qMplGDEp6Jf2mw9YbV62172dyUQRo833jXUqZZwNngao0e0JUZSVnXUUKzh7YUQWKVc7XvtupSOvY4Sisy3VV+OtpbdFIpjXBh8ZIk0kuNUqVRnMa0i+3Z9hlisZ81Gyj/HrF/g3QAkSUIJRVECoTVNCi4pd2+0nJAQMDHh8ehjJp//xTBPPG7yzDNvXV5HkmH4VIXv/dE4cxP+eZx9rcSDfzzJrptibLsuytPfnkGs084XMw7f+q+jjJ3zPZpSvsJ3vzzGLe9L078rTDCiYF9B63dsD1mRuO6eBJ/9rT4mLxp87feHGT3je13mhUHMC4NNnYeWCNL73h04RRMhSYw8eHL1jdaATbuCfO63uvjPvzHMdKWx5/vyowVefnTjND3fKrzjjdRHf62X1l5/NhwMy9z64TZuen8LcxMm3rx0f37a4pv/1whGdgrXKDc8e5a1ANHOzeQH31h95Q2CHvMT9I3CKmawK7Vnt1pYbb53DRBMBJc4crIebKi1/SIEdilXN7Sqtbahd3SihELkX3hu2XqaFqJQGKVq5Gpun8sPgSShKOtJ5gvM/CyRjk0Nb6HoIRQ92JSRqthZ5soX0ZQQqqwvMBVlFCTJlxOSmP+b5EvarMWQlcseh1+xMK5WsWqDcB3BudfKCwbqEmbHTIyyRyx1Sax2fec5fKrC+PmlJJK5CYtywSEcU5DV5ffQqnpce2eCn/+3A0xeNPij37qw7DwvQY5FEaaFsC5bLsvIkTBeqbzwztpFk8mnLmBXbDzz6ihkyDLsvD5CIq2uX8P6HYh3vJGavFilXFh8OU6/slxSqJL3lwvXpTh+npadjYWuZFUn3reLwshy+ZurAkkisXk/stqYcKoQHkZueokK9+W45nN7SW5qzFO4HG17WjDzix+nFo41NXB6toW7glafcF3ccqlu47NSaRJF0VEUHdddvp+AHsN1LAxjHYoggqbkp8APe8paM8YaMuVBMuVBACRJQZMDqEoAVQ6iKoH534OocoDW6BZaIptYCxMwlxX8+98tYhhvvZHKzS5P0gsPPFfQBB+oLoQQzE1aNedAl8pEat3B3u0hbv1wC/G0xmN/NU12ur5Hkrj/XqonTy9h98nhMKmPfpDs9x7EK/oTFeEJHMMm2BpGVhXyZ1YvJ9ACElv3heka0AmEZCzDo5h1GR80mRg0F6KJ8ZTC1n1hWrs1bn9/gnBM4d5PpCjM17i5tuDo80Umhhavo61b4/q74yjzZQZmxeX150rMTrxzaerveCP10oPNKOoKCkMnSO+4seHmd5GuzQRTHU3rz60FWjhBctO+htf3bIvy5GDdHEnXDZ10XtveND1VkiXMwqUksIQWije1vefYKxZCO9kMsq4vrdy/DKMTL7Nj6wdJJ7cymzm1JGyoqiF6ew5SrkyRWxfDT+CazSmKKHoARVu79yaEi+VWsNza4VlZkklHBtZEVhcCCoW3noYshG8orjbWUke1+2CcN57Lk+rQ+amf6+DiGxVeezJXfwNf7n3hHZVUBTm8tHuxrMjEt7YS25TCqToUzs2u+L1FEwof+but3PHBFKl2FU2XcWyPasljZsLiS39/kOy0PyHeui/M5/5ZF7GUQrJFRVEkPvjzrQvCD0bVIztjLzFSiRaVOz+UJN2h0dKpUi54zE4O/x8j9XZBJKHi2B5mxUPVJdr6giAE0yMmru2/OJWZEapz4w33lQqmOkjvuJHx7PRVVaCQFJX2a+5uOPcjhMAuFyiOLO8ce9lKnH/kIs/9h5dQVIkt10S5eKyM43hEEyp6UCY7bROMyMiy5HukAg7+xk2ktyfnTwyUYHMJYc8x8Zz690rv7iHQ0YWTy2JNTixbLkkyleoce3Z+kkJpjEplFuG5aHqUeKybgJ5gcvo1+ntuX5I7c12LC0OPNnyeTp2cWD3IaqApBfg3G5IELS0y7e0ysYSEqtQ2d0detalUmmu65x9g/ef4VuLwo1m+8u8G6d8V5hf+3Sb+zr/qp1J0OHuktECOVVrSxO44SHDnNrTODsLX7V/YXonHEJaNd5lSuGe7lIazFAczKAF11Qnh3psjfPgX2nj58QLf/B/T5Odski0qOw5EaOvWKecXJ5xHXyjyrz9bRtMl/ul/HaCjV+dLf3+QqdF5oyTAuqLh4/njVX7v711AkiS++Ae97LnpanUffvPwrjJS7/18F2cOFzj+XJ6bHkhz/+e6sE3Bw386zquP+gl4u1KgMHySULqzobofSZJp2XUL5cmL5Go0v9sQSDKJ/j2ktl3XVH1U9sIRHKN+fkQIqMwaWCUbVZfoHtBQCHLhaBldEwR0gVOxGbguTjCi8MrDGTwXjLyxxMFpNPy4cFzX9aWd6i23LNxKGc+sXZi6c9uHCQaSACRi/SRi/cvW6Wy/btnfHKfalJFaKSRZC5Isr1jbteK2SEjUf7Ye62MSSBIcOKDxa78e4bbbdcLh+sd67/2znDrVePj6Uu1QPK2uKqT6doZZ8bAMj1MvF/nWfx3lF393Ez/9m738yb8dXMhveYUixqmzaJ2duLkc9vQsly7YvDiIee4iwlj63hozjU92gmEFPSQzeKLK2HkDz4NCxmX47PJ30XWgWvJwdAnP9UOm1bLvddWD8MCsCkDg2O/QB3UF3lVGavM1EY48niEYVrjr0x088meTuK7g5g+2Lhgp4ToUhk6Q2noderylobCfrAXouun9ONVyw/ptzSDc1kvnjQ+ghhsNqwnsSoHM6cMrrjX6wji5QT9vIwG26bObEu0awoVw3H/81ZJLteQuxMKtso1wF1/wKwU2Vz074a1Yt+Vk5hAryHWfOP1NZLn5V7PZmqlmqeSSrDTVjwwgQIg+eQcxKYVSp/ShSonj7ktN7fdKxOMSv/T3wtx9T4AXX7R4/XUbo06p0Mxs4/dJCL9o13UF9/1MOxMXq8xNWATCCrIMk4PNGfor4dd0+9+gcllDRUWT5lXTBaI5KctVIQQcfjRHa88En/qHPfz0b/byP3/rAkbFQ9g2xumzqK0tWGPjWIPDG3dgYOh0laFTVT74+Vbae3We+1GO069VcCyxodf4bsK7ykh5rv/CH3hPimrJ5eUfz9IxEOS+n10qP1+eHiJ74XU6rr234eZ3gWQ7/fd8hsnDD5O7eKypJn71IGs60a5t9N35CbRoqmFygufYzBx9Gru0cvHg0a8dX6A+e54fGkh16Ay+UaZjU5DOTUGGT5Yp5x36doRRVQnHFgw/M0ru4iVSgoSsNOlJeStr7OmdXUT2X4ckScz+4DvLRqBCsfkWHGtBs2QYSZab8nQDhNiv3E5cSmFj4WLXdEJssX7KeCIhc/vtOt/5dpV/9299AkW9Qa/ZNkonXy7wzHdmOXBPkt/+890I/AL5lx7K8Cf/ZnDN5yzJcOfHWrn/59oJRRWiSZVoQkUI+MPHr8Uou+RnbR766iSHH82t+Ti14DqCx78+TTytcf/PtfPT/6SPb/zhKNWSP3EpvfDyxlrGeQyeNvi//vEwH/17bVxzW5Q7P5xietTk0W9kefmxPLPj79zc0dXCu8pInXwxz0e/2EsopvDQH0/gudDSHaCYuWIwEoLZ48+RGNhLKN3Z0L4lSSKQaKX3jk8Q7hgge/ZVKjMja2P9SRLh1l5S228gte0AaijasIESQlAav0D2/GsrhtSAJfFx1xE8/71Fkkluxub0IZ/dVs67TFxYDPFlzmbJnF2sT2pmYJ4/yRVDQk6hQOX0SbTk6ioW4VAroWAKSVZx7Crl6gy2vTEtVESzg5AkN9U0s0XqIirFmRIjTHgXMUW1psaiR5NWowYUFaIxmWeetig3IHvUDApzDl//TyMceSJH50AAWZGoFF0uvrH0OZx4sYhlCM4fXR6CLszZfP9/jTM7bi1qBwqYHjE58kSu7rFdW5CdWhy4h05W+N4fTTB4Yvk74FiCH/6/E1SKLkZ50UsuzDl867+OMXiisoRwYZQ9HvrTSeYmTGRZIhxTFoyU3tfrE4iGR6GGcsqaIWD0vMkf/fYom3aF2H1jhOvvjvF3/mknN9wT4//+rREKV45Xf8vxrjJSz3xrmplRA6sqOH3I9wSMssdTf7O8ctwu5Zh89RH67/oUshZs2EgoepDW3beS6N9DZW6M/IVjlCbO41SL80ZBLBn8/N1KIMkoepBo91YSA3uIdG5CjzanhwfgVApMHn4Yu7T2vjWLJ8dCQzrPrTewiTV5HCsN5k42g1sqYml63dlqQI/T13MbremdqGoQJAnhuZhWgfGJV5iYfm1Z/6xm0azxbbaFSkjyk9YX3eNUaI7u3iwcG3I5D02Xava/XC+KWYdXHl75nTt1qMipQ7Wvs5Bx+OH/Xqp7KMTK22jRJJ5l4lpVLiXDxgclxocKNXu9Obbgx19Z/q0XMw7f+e+1ZZZy0zaP/Pn08mN3dRC74yBONk/1jRNU3zjpt5XZoG6+ngsXjle5eKLKiz/J8/FfbuOBn21h780RXnhoeWmFEE3Pkd41eFcZqUrB5dVHln5I547UHxwKg8eZTrbTce29SE1QiyVZRo+l0KJJEv17EMLDLuUwcjM41QKuZSI8B0mSkVUdLRJHj6XnZXXUhTet2aJNu1pi9PnvUZ4abGq75RcA8Z4YbXtbSW9Lokd1Tn//HLMn5wgkAkTaQuSGCni2/0GuxNSrvX955WsTwidPWLXDXLoWZdf2j5KID1CuTFMsT+B5NpoWIRxqYce2D6FqIUbGnl+XInqzuTaE21QeS8z/Z3AVhc3mkct5PPG4yfs/EOC5Z00mJt681ugbDkkikGgj0rWFyuQgSiiKGoxQnR1FDccQ8yUOSiACkl+KoYWiyIEQlanhDdGxLL/0CtVjxwlfs4/Qnl1ED96IOTSKee4C9vgETi7ftMGSJGjv09E0icy0vUCAMA2PUmHegwss/26EgPycw+4bwnRvCTAzbuG5oOoSriM2rDnk2xXvKiNVD91bQ4yfX55F9hyL2ePPoUeSpHfd3LTRkObrKCRkAolWAonWjTrlZbArRSZffXRD1C+6DnRw0xcP0LqrBUVXEEIwcWSK2ZNztO9r5YZfvpYX/8srTL7mzzC9NXlSa6/abGvdQzzez/DYs4xNHFoI70mSTCTcwZZN76G780ayufMUS8sp7I1CbtKLXS3XdiUqooiHS1RKUhDN1PM1j3JF8OCDBv/m38b53X8f59lnLCYmXWx7eej10CF7w0OCGwlZ1Qi19oLnIikKwUTrfKTCRdGCoAVxrSrBZBuBVAel0TNEe3cgqxpmdhrX3IBJgRB45QqlF16mfOhVQnt3E7v3TqI3X481NoFx5hzlQ6/i5hovKFc0iZ/6dJrr744xcs5cCOulOzT23hxh8JTBGy8t9xBdR3DkmSI33x/n8/+si+P3xHEdgSTDo9/IMHjSZxuGozJ7borS2qURCMsM7AwSiijc8cEkfduCGGWPwZNVLpyo4nmQblfZe3OUaFIhEJKJxH2ljp/6TAsz4xZGRXDsxSIz8wrqnf06e26KEAwrRBMysiLRsznABz7XSiHjYFQ9Xn6ssIRGvxF41xspWYGP/6M+vvwPa/eFcaolxl9+EElVSW7xKeAbra+2HgghcI0y4y89SPbckXUrX0Taw9zyD28g0h7mxLdOUxwtces/vWlheWWuihpSGbi7b9FIOc0l9iVFbd5LuQztrfsoFkcZn3hlSf5JCI9SeYLB4Se5bt/nicV612ekmpJ68tmA9fKAEvIy5fqMmKIocmyR93HOfY0q5Zo5KRDrzkv1dCv8zu/GaW+X2bIlwB136hiGqDnZ/5nPZDh39u07/fbDqh56rI3q7BiuWcEqZJAkCS0SR4skMLKTCMCzDJxqCc8yEJ67IYQmYKF7tN7dSfj669AHerHHJ8k//DiiUiFy8w0kP/gAmb/5js9UbQCuLTj8VIF0p8bWvSESaRUBzE7Y/OTrGZ75fpa5qRpqHQJeeiSPpknc96kUtz6QwLEFI+eWUuETLSoP/GyaTTtDyKqEqkk4juDGe+McuDOG58KjfzPH0BkDzxJ0bwnwsV9uI572C4U94SvS3/OxFK4rcB0oZJ0FI7Vtf4hP/4MOtIDk5yVLrn/Mz7bgugLPFZw9Wvk/RupKdGwK4tqC2TGT3p1h9MDSGbwekkl1rBzKc6olRp/9DlYxS+vugyjByNvCUHmuQ3V2jPGXf0Rp7OyG7LP/zl6iXRFe+M+HOP/wINHOyBIjVZ2pYhZMkpvn5ZSEwKk2KR+k6k3XVl2OYCBBrjCMVYcgUSpPI8sqmro+1Wk1GG1qfdc26w6Cm+U9RKXlElQCj1apm5R6P0WRw8LkStfGEBXOeEeaOpcrYRiCF19sbDJRKr59vSjwWZe5869Rqygrf/HYws+l0cWJZ+b0K5e23pBzCO7eSfS2W5CDAcxzF5j787/BmV6UPHLyBVo++ykkTW3YSAkBJ1+pcPKVyrLc0mo5RKPs8eg3Mjz6zczCVEgs/M/HxJDFH/zK4KrncAlvvFjmn35s5XHl8vWffTDPcz9a2XO8GjT6d7yRuu2jbZTzDg9/dYJP/9N+VE1aMntUVInACoWNl+CaFaaOPIaRnaLjwHsIpTre0iyla5vMnXyRuZMvYeTqtAxYA2Jd/sA88kLtRLJtOLiWRyC2aNjtcnNKyrKqI6/Dk7KdKqq6knZfFE94uO56qNsSaqBJJQ3brOtVxqQUKam2MLA7X6gblZI1l5dZhwbhPGZmPP7FP3trFa8VSSMaaCWspwmoUTQ1hCKpIMl4noPjWZhOiaqVpWTOYrmrsTTXoIqxURCCyquvYV4YrBnS8yoVyq8cQdhri2yseTBfmTi7bL8SEiEtSSTQQkhLoKsRFFlDlhQ84eF5NqZbxrRLlKxZKmYGsYJX38x5S7pOYMsmnOkZnGxuzRf9jjdSj/zpxAKlNRhW+JN/fR6runiTA2GZX/qD1XvUgD8IZc+9SmV6iPTOm0nvvAktHH/TvCohPFzLoDI1xPTRpyhPDq4YalNbEkgBDTdXAiFQ25LYUxmUZBQ5qOPM+YOWkojgTOcQtrNAS5frSOaoAQVZk7HKi7NDxyjjuU7DhkdSNZRmWs1fgWzuPJ0dB0intjKXOYvn+Q3nJElGUQL09dyGY1col9dhvCXQos2J77qWgWfV9qROuoeQG+xTdiVWGhTWAkmCYBDUeSVwx2HFuql1HAlV1okHO+lK7CUZ6kFTQ8iSPwhK0uUhUJ9E4gl3wWCVjGnG88fIVkax3eqG3YegFmdf94cJajUkxgRMFU9zfuYZPFHfwCwIyyoKkrY0KiBsG2FalA8fAXflc5aQ2d5+N+3xnTWXO67Jq8N/XVfPcS2QJZWQFqctuo2O+C6CWnzeMKmXPRP/uQj8PKsnXFzPxnCKzJUuMFk4ScXK4Ym1121Jukbk5htQU0mE7WBNTGJPTGKPTWDPzCCMxkKz73gjVcotvmgv/XCWqcGlcVpJhrFzK3fpXALht3CYPPwIdjlP98EPr0tUdPXD+RRvMz9DZWaU7PkjlMbONcQii9y8C7dQxpktIGyb8LXbqLx2jsgtu/HKBs5MDiURRQ7plA+fwRqZJjeYR3gwcHcfZx+8sHSHErTtbSXWFeXEtxY1AT3bxDHK6E30XtKjaf/mr4FpNT75Ci0tu9i1/WNMzx6nVJrE82x0PUo6uY14vJex8ZfIFYaa3vdlZ0mgib5d4Bsp177s/ZLk+dmhwNEE3gbVb60HqZTEPfcGuO02nd4+BQSMj3u8+KLFE4+bzDahNrESAmqEtuh2upP7SYS6FySf6k/o/KFRlhSQdTQRIqQlaIttp2zNMpY7ylThNFU7t+5zM+wCllMmHe5fdj5CCNqiW5jIv0HJrK9YLukaoT270Af6kQM6lwsXZr8/35l3FQMFoKkhuhL7ahtMYCJ/HMfbmAJeWVKJBzvpTu6nM74LVfZzritNsiUUkBQUNFQlQECNkgh2MZC+ianiaSbyJ8hUhtZU7uGVysz92V8hBQIEtgwQ2LaFyA0H0D78fgqPPUXx8aca2s873khdjsf/avnMWnjwg//ZnIKBGorRtu8O0jtvWjW30nRB6KXtXAcjN0V5apjy5EWMzARGbqY5EVvHxRqaQg4F0Ac6URIRkCW8ioE9lQFP+G2vpQCe4XtkYy9PUJoqc90v7CfaESE/4uebYl1Rtr9/C3s+7c/4Lj6+aABc28Qu5xs2UgB6NIkky4gGPuQrUTVynD3/IAN9d9PTeSOXDxC2XWZs/CWGRp9ZV52UJMvo0dWLiS/hEoHFNRcnPFo4TrxzK3a1iKSoVHNTWMWry+JbCe3tMv/wH0X5xCeDBAIShbwHEtxyi8wHPxTgB9/X+MP/q8T4+HoMlUQq3MtA+mZao1tQ5LXlHi8fOKOBNra330NrZAtDmVeYLZ1ft1c1kT9OZ3x3zeOG9RZigfYVjVRw1w5id99B9fgJnNkrZvxNFPemwwME1Nq5T9ezmS1dAAQBNYbprL2WTlWCDKRvoiuxj7CWXFP0R0Ja+NRUJUB3Yj/pyADjuTcYyhzCbtLbk4JB4nffgdrWAp6HWy5TPXma0gsvYQ03Pia/q4wU+Gw+VZeXpJOKc43GjSWCqXZ67/gkkc5N81pttR+2EALXrFAYPoVrVtCjSdRQDFkL+Ow2SUJ4Dp7j4DkmTrWMVcphlTIY2Wms/AyaFEQ4NqZZxHGa8PbmUXj6dYTjgiRhjkxTfvkknmFhjc0uzvIkQFUQ80aqMlvluf/0Mnf99kH2/szuhft03S/uR7ge1YzBi//3KxRHF1UDXLOKVZgj0r5c6LUeAqkOJEleY6ZAkM1dpFgcJxJpJxrpRJY1TKtAsTiGaRXmQ4BrRyDW0pSiufBczPzsEg/3UpPCWMdWChNnCERTS4xUVErSK29j3L1AgeUSVnGphT55O2PeeXJi9T5EK0HX4bM/G+JjHw/yN39d5et/VWVuzn8HWlpkfvozIX76MyFmZjz+6/9dwqit7bsiZEmlO7GfrW23E1Cb6zG2+r4V0pFNxENdDM6+yFD2FVxv7TnHXHWUsjVLNNC2bJkiq7THdzJVPF035Bfo7aX8yhFKz7/kz3TX8CJLyHQnarfeEQhK5gz56jiKrJGO9lOoTlKxcgTUCIqkYThFZEkFBJ5wcGt6XBIhPcmezgdIhfuRZWUZy3StkCSJoBpnc+stxIMdnJl+YkXDfiXkUJDwDddiT81gXhjEGh3DzWTxKtW6YfNaeFcZqVSHxi0faqO9L4CqL5Il8jMW3/rDVdqMSzLR7q303v5xgqmOFT9A4bqUJi8wffRpCsMn1pQQ1NUIvb23kk5sZnL2GBfGnmx6H8JcfGmF4y58R+JKQsEVbbRnT8zy8G8+waZ7+2nZmUYPa7iOR34oz8XHh8mcyy75KF3LwCxmEEI0PDCF0p1IqgZN0tcXIXBcg3xhmHxhY0U+AUJtPTTTgU+4DkZ2qTKBWZpDDYSpzI2ihROUZ5eeZ5w0HVIfeWmWglhupDxcYlKKDql/3UYqnZb54AeDPPywyR/8fpHL66RnZjy+9AdFojGJ9z4Q5C//osroaHNeqCoH6EvfwNbWO1DWIP7bCCRJQlOCbG27A02LcHHm+QbIFbXhuCZThdNEWltrvrMtkc0E1RgVu7aKhpPNoiQTfoJvjU5dLNhBNNhW+5sRglxljIqdRZWDyJJKKtKPLGmE9SSKoiNXJ9GVCOFAivFc7frIZKibnR3vIRHquSq5c79ztEpbbBuaGubExI8oGssVOmrBzeaY+q//E62zA723h8iNB5DDIXBdyoeOUD1+sqH9vKuM1Hv+ThfdW0McfTqHbSy+WZVSA/mdzk303vax1Q2UEGTOvcrk4UewCnOslVVkOWVODT7I7s0fWtP2a4UW0XBNh9JkmTf+6iRKQEENKHiOh12p450ID6swh+dYKFqgoeMogRCBeAuVGvI1zUCRNV+lowY8z16zRxVq6WlK0dxzHcz80o9TCYQJpbtwjAql6Yvz8j2LCEghJCSKovZAaAkDW5hEpOaaStZCMCQxsEnlf3y5TC1GtG3Dk0+YfPSjIULh5gYzWVLpTR1gc8vBee+xNi6Fvm23StmapWLlcFyfcq/IOgEtRkT3WWaXUOtbk2WV3uS1SEicnXoCdw3Je4FHtjKC5ZQJaMvDbaqs0xbbzlDm5ZrbO3MZorcfRE2nsGdmffbJPIrPvoAwV598pcP96Ertfk6OZzFbvjBfHC6QJRVF1vCEjSRJqHIA0ymjqxE/alPDqwzrKXZ23k8i2F3zPl6einA8k5I5Q9Uu4LhVPOEiSyq6GiGipwnrqXmvrX4OKxHsYkf7fZyc/AkVqwFZNlVF7+lCbWtDbU2jxKJIsoLnNVfL9q4yUpv3RfjGfx5m6Hh5qelYxY7o8Vb67voUweTKBspzbOZOvcTEyz/CtVaOl8iyhqYEEXhoSghPuBhW4Yo8yiWdv+UnKEkKQT2OLKs4rolpFRfWU2QNTfOppJ7nYNnlJS+xqgTQtQiSpOB6NpZdWhjMr/38Prb+1CZmTswxe2qO6eOzlKfKOFVnvhV2bYNenR3DqZYaNlIgEe/bSWW6eS9IljU6266hu+smQqF03fDF4MjTDI8+0/z+9SDhtt6mSgycSgEjuzTnKckKjlmlkhnDdZZ/eAoqIM3XRi2Hi4OHiy41V1RcD0Is/qu3fC3oiO9iS+ttqHKg7mBouwb56hhjudfJVkZwhbMwAAsu9dOSkCSZaKCVrsQ+WqNbCGmJmpMFVdbpSx3AdEoMzr20pvxj0Zgib0zQpm6red6d8d2M5l6raQDkUAh7cgokCbX1CoJNAx54QI2SjgzUNeoVK0umPAj4Rn0s+zqSJOF6DlUrjyRJeJ6LQDBXurBse10Js6fzfSsaKMczyFXGGM+/QbYyjONZVzwTAAlZUtDVCB2xXXQl9xLWUjXDhpIk0RLZxNa2Ozk1+Qi2u3KKQgmHid5xG/bEJMaZc9jjk7jlCriOn6ZoEO8qIzV+oeq3nzhRbphUJutBum9+/6oGSnge+aHjTL766KoGCiAW7mRLzz0YVs6fReoxJufeYGz6lVW3lSWFnvYbSSe24Hk2sqwzPPkC2YLfLj0R7aOz9RpkSUHVQhRKY1wcexohXGRZY2vve9C18MIMbHT6FYplX5lh6vVpgskAsc4oOz60lQN/dz9WySY/XCB3MUduuEBpvExuuEBheLHuxshOYZdy6LF0w2GFWM8Opo483nTfptb0TnZs+xCua1EqT+LWCRkaZq6p/V5CMNXpi/s2YaSKY2eXXYdnGXiORTjdTWl6EOcKNRAHG4EgKIWxxPJ3RkVDQcXZgFYdRlUwPOxwz70BHn7YpFpdapECAXjPewIMDzlUKo3Hr2KBdra334Om1Dek+eo4g3MvMlu6UNfrEZf9kKuOkauOEQt2sLnlIG3R7ajK8vyghMzm9C1UzDmmiit0oK4Dy62QKQ/REtmEIi0leEiSREhPkAz1MFe+uGzbymtHqby29ianYT3lsx7rvGMT+eN4lxleTzgLN+nSz7KkUjEzmPZSVXlF1tnUcgupcF8dA+WRNyYYzrzCVP5U3Yaal56JJxwcy+Ti3PNMFk7QlzpAX/oGVLnGM5FkOmI7yVVGGcm+uuI9cAsFZv/4z1AScdSOdtS2ViS9gD0x2dSM6V1lpGaGDT74Kz3sPphgbsJaqAkqZW2e+HrtOGqifw+x3p2rDFgCxygx/dqTOJXGCyZDwRSj06+QKw6RjPWzuedOZrOnMe2VWTzxaC+drddwavCHGGaettQu+jsPUqpMYTsVipVJqmNZHNcgFdvEpp47GZ54Acd1CepxkrF+Tlz4LoZVQJE17MtIGSPPjTF+aJJgMkAgrhNMBYn3xkhtSZLakqT31h6UgML0sVke+5eLFFHPsShPDRHp2tLQtUuShB5LE0x1UJ2rXThcD50d12HZZS4MPkouP1g3pOfUKPRtBKGWLrSGG0z6s9Li6BWyWpKMQFDNjPsswRreQEUUEXi0S70URW4ZYy0upQlLMSa8wbVcxhJksx6PP2byc38nTDYb5VvfrDI46A9OmzYpfOKTofl8VIW5Bmno/mB4kKBamz4thCBTGeLM1OMUjMma66yEojHFqclHMFoK9KVvQJG0Jd+hJEmoSpD+9I0Ujem6+aOVMFM6x+bWgzVZiJoSJh0ZIFMeqs0mnNfmXOZxr8ruk0iHN6EptWsFLafKbOncqufuCQfDXj7eJEM9dCX21WwMKoQgWx3l5MTD8ySH5tznqp3j/Oxz2K7Btra7aupbKrLGppZbmC6exnRWDucHtm8lcf+9yJEwwvOQZJnK68coPPE0NOhNNWWkvvSlL/Htb3+bU6dOEQqFuO222/iP//E/snPnYqGaYRj8k3/yT/j617+OaZo88MAD/I//8T/o6FhsPDg8PMwXvvAFnnjiCaLRKJ///Of50pe+hKquz2YGwgrnXishhCDRpi08H0muU7gaipHadt2qhadC+HIslZnmQlemVaBYHsd2KszmzrCl9x4iodbVjVSkC9ez/KK8YBrLLhEKpgjqCWyngutZqEqAoO6HShRZR5oPKxhWnqqVY1PPXUzPnSBXGl7GCnItl8pcFcdwcAwX1/aQZMk3WskAWlhDCSx/OXMXj9K2/06fENEA1FCUeP9uqpmJpmZO4VArheIo07PH8TaohuQSlECYeN+uhq8BwCpmqcyOLd2PFkCPJAmne5C1AI5ZxrnCWcqISUoiT6+8HRmFKTGMIxwkJBJyC5vkPYDEhLd8Jt8sTBP+7E8rdPco/PRnQvzc3wkvFLnLsoRpCh591OQrf1JpmNnXHt1Oa7R2IbwQgnx1jNOTj1I0G0uk14LlVjg38wyypNKfvgFqhJhS4T66kvu5MPNs09T0ipUhUx6iM75n2URUkiTS4X6CWnxZfZaSSpL88PvQurtQolGEYSIFdDzDYOq//BFeuf7grMgaXYnlxwM/0DZbOoexRrq5KgfY3HKwJq3dZwzO8sbYD9dVb+Z6FiPZwwS1GL2pAzVDlkEtQX/6Rs5OP009QyhHo8Tuvp3yK69SOXochIeSTJL62IcIz8w17Kk2ZRWeeuopvvjFL3LTTTfhOA7/6l/9K9773vdy4sQJIhE/QfiP//E/5sEHH+Qb3/gGiUSCX/u1X+MTn/gEzz33nH8DXJcPfvCDdHZ28vzzzzMxMcHP//zPo2kaf/AHf9DM6SzDD/5obPWVLkMg3kKkc3NDYZ/sheZdfyG8BUFRMZ8skFZIPF/CpXxUT/v1C9vPZE/juOZ8KPAGoqEOLKeMpob8fkvz8DyHkxe+R2tyB62pHXS07OXC2FOUKv5MN94Xo31vK4mBBImBOMlNCcLpIJW5KrnBPINPjZAfLpA5s5yNVp0bpzIzQrRBb0pWNaLd28iceQW73Lj0j+uaeJ6z7n5RtRBItBLt2tJEqE9QGD6xpD4KwLWqGLZJNTfl9wSrca4ONue8o+ySb6Bf2UmP2IqDjYyCho5BhQvuUcpsjJzR+LjHv/s3BV55OcSB6zVaW/0SgJkZl8Ov2PzwhwaFfGOTBU0J0RHfja7WnsC5ns2F2RfWZaAuwRMOF2afIxHqJhnuWbZckmR6ktcwnju6psF3PPfGfM3UFUYKiViwnWigddl+w9fsRVIUst/8HrE7b6Py2jGUVAI5GETYK4dnU6FeQnqy5jLXtZktX6xDJ18dqXAfqcjyImXw5cQuzD5H1V6/zJbjWYzljpKObCIaWN7dwc9PbWFUe73uM7lUBF09dRZh+lEPZ3qG6snTaB3LSwPqoSkj9dBDDy35/atf/Srt7e0cPnyYu+66i3w+zx//8R/zl3/5l9x3330AfOUrX2H37t28+OKLHDx4kIcffpgTJ07w6KOP0tHRwXXXXcfv/d7v8c//+T/nd37nd9D1jVd32HFjnDOvLB8IQi3dKA3otwnPxcw1TxEO6DE0NYRpFQkHW5AkmaqVq7Hm0heuauYwzDznhh/BFc7Ccte10NQQHem9DE08z1z+PPFIN+n4liX7chyTidmjzOXPs2PgvaRiAwtGatv7t7D30zupzFWZPTnHiW+eJnMui5EzMHMmVsle0tH3yvuQPXekYcMOPmsy0rGJ3IXXG1ofIJu7QCLeTzCYolrdyOJYiZadN6MGG9fsc4wKxbFzNYustXAcWQtg5KfreooFMccb7gukRQcpqR2dIB4uBZFhxhunRK6OMvrakM0Kvva1Ct/5jkR4nsVXLgvK5eZkkSJ6mpbopprLhBDMlM8zW16e0F8rLLfCcOYQ0UArqrKcnBNUY3Ql9nBh9vmm910wJimaM8SDHcuWKbJOe2w7s6ULS7w0JR6jevwU5rkLhPbuwslkMM+dJ/nxDyMFAgirvpHpjO9ZUOC4HEIIytYcheralPslJPpSB+ruO1MZns+vbcz7VDSmyZQHiejpZeQWCYmwniIZ7qGaz9XcXrgukiKjJmJY856npMhoHe0+KaVBrCu+ls/7FjudTgNw+PBhbNvm/vvvX1hn165d9Pf388ILL3Dw4EFeeOEF9u/fvyT898ADD/CFL3yB48ePc+DAgWXHMU0T01zMPxQKjc88FU3i4/+wl//48yeWLpAkQq31E5uXQ3jumpvrbe65i3xxlFR8gEzhIpXqLAChQJpIqIVQMIWmhmhJbqdSnaFq5pjLnyUd38zWvvspVaZQFA3XcxidOoQQLpZdJhXbhKaGiUe6lySd45EuOtJ7MewCsqSiKiHK1cvUm6sOVslG0RVC6RCRthDV2Squ6eJUXWTDwbXq08NK4+cxMpOEWroaun5Z1WnbfxfFsTPLvBGQCAaTy7aZmTtJOrWdrZt+ipGxF7DtMkK4yz49x6niXBljWwHh9l5S2w5w5aSgHoQQVGbHqEzXll9ybZNk3170SJLy7EjdPkYVilS8ImOcX9z3OgYSWQZtlWilZQmsy57j5XM/y1o9+tqV2FtXTcL1LIbnDm24p5upDJOtjNAa3VqHjbeHkeyRVVllV8L2DKaLp4kF2moyCdtjOzk78zTWZfkVN19ESfh5S2d2jtCenRinz6FEwqz0/kQCrSTC3XWWCnKVEcrW8ihFI0iEuonVMLTgU8wnCyebvjcrQeAxWThFT/JalJoMzADJUA/ThTM1CTNuqYxx9jypT30Me2IK4TiorS14pkn12PGGz2PNRsrzPH7jN36D22+/nX37/KrqyclJdF0nmUwuWbejo4PJycmFdS43UJeWX1pWC1/60pf43d/93ZrLdtwYwzI8Bt8oc83dSULRpeE0PagQCNcKsUmowdo1DFdCVnX0SKIp0gRAxcwyNXecoJ5gJnuameyphWXBQIJouJNsYRDw2YCua1I1c7iuxbmRR2lJbieox3Bck3xpDCFcHNfl/OgTpOKbUGSN8ZkjZIuDCzTaqpmjVJ1G1yJ4nsPFsacplBbDoKe/d5bJ16dJbUqQ6I/TuruFbe/bAhIURorkhvLkhwrMnJhj5vjssmsyC7MUhk8QSLY1JDgrSRKRjgFS229g9o1nlyxTZI29Oz/F8o9eIMsq7a17SSW3Yhi5mrmp8clXmJhamWG0cKxghLb9dyM3TKH3C3gLg8frhipd2yQ/dgokBW+VEBCszzBdjr37VD7zmbW3Kflv/63E1GT9SZcsab6hqDMY56pjayIxrAbTKZGpDJOObPLV0y+DJEkE1CipcC/Txeba1gjhkq2MYjolgtpywoymBGmLbmUstxjSN86eQ231w1zVk2dIffxDBHfuwBoZXQhd1UIq1EtQrS1K7Xr2/Lmv7T1IhfvRlHBNPULLrZIpr0fLsjYKxiSOZ9acsEiSRCzQgaoEcGt173YcSs+9iD05TaC3GxSFyuvjVE+cwis0npNbs5H64he/yBtvvMGzzz67+srrxL/8l/+S3/zN31z4vVAo0NfXB8DugwkqBYfBN8p86Fd7MKoul5PBFFVC0+vMfJoo5kzvuMlPnjfhUUlI5IsjTNsnli3LFi4uUMprwXENpuaO1VxWrk5Tri7mAgrlRSNkOxUmZuuH1syCxfTRGaaPziBrMoF4gGAyQKQ9TNueFjquaWfL/ZvInM3y419/dNn2wnXInDtCcut1jdPRJYm2fXdQnRmhPHXZhyT5reLr1StVDX8gVNUAsNy4KDVoy7WPL5PcvJ9E/26/a3CDsIoZMmcP112uaEHi3TsQwqMw7mCVls6QVXRUVidoeHhYND4D7utT+NSnr8wV+ZUvgYB/O23b724uxKLnJQRkMh5f+ZMyKwVb4sEOdKW2ERTCI1cdW8IYBfyDCkGgqwdzctzPvyoqSjSKUygAAklRfC3JFeDX85g1VS1UJUg82MV08RzNDvRFY4qiMV1HzkmiI7ZrCS3cnpzGnvS/MTeTJfvN7yGHQriFIsKqPSFR5SCpSH9dD7RsZchVm9MRvXzfsWB73bqrojHZtLZeI3A9m7KVqas/GAm0oMp6nUpAEKaFceIUxolTC+9Is1iTkfq1X/s1fvjDH/L000/T29u78PfOzk4syyKXyy3xpqampujs7FxY5+WXl1Z5T01NLSyrhUAgQCBQewb8/S8vPnTL8Pjyr53BMi9r1RGS+cf/z3KhSRANt5mWJIn0jhsw89NkzhxepiywwpYNrvfmQdZkgokAwVSQQFwn1hUltTXpkyj6E6hhFatoUZ6uf2+MuXEyZ16h84b3NnRMSZIIJNrovOn9jD7zLcy8H350XYsXXvkv67iaxl74WM92um58X1OdeD3XZfrok3XfEUnRCMRacIxy3VDwZnkPA3LtFg2Xo0iOl5yfNHxuzz9n8ZlPLzWIQsDWbQq/+qsRTp5yePIJk4kJF9eB9g6Zu+4OsGuXyu//XpELF1YO06XCfTXpzQC2a1Ayppfmb8IRtNY2rJkpgv2bkDQNJ5dFuA5KLI5TLKC3dRLsG6B47MiKnkjBmMJyKug1PIZLRAddCTctl2S7VWZL52t6aeCL3CZC3WQr8/JpVwymbr6Am185khLSE6TDA3VZfeO5Y0tqo5pBQIsSDdSWeAIWz3vDIaiYWdLh2rqdmhJCV6M1Q5hSQCewqR9zcNhX6Hgz+kkJIfj1X/91vvOd7/Dkk0+yefPmJctvuOEGNE3jscce45Of/CQAp0+fZnh4mFtvvRWAW2+9ld///d9nenqa9na/SdwjjzxCPB5nz549TV/A5df90J+MY1aXDhhmxeP0oRovlwAzvzycVQ9KIEjnjQ8Qau0lc/Yw5cnBFRXLq2aW0elDOO4alDyvIrb+1Ca2PrCZaGeEcGvIL64cKpAbynPq1bMUx4uUJssUx0sr7mfu5EskNu0j3LqcjVULkiQR695G103vY/S57+BUL+3/6naJDbf3033wQ2iR5qSHyhPnyQ8u94AvQZL9NiRWxQ8FOubyQbMg5hgXy71lGYUIMaJykjlvsuk6qVxOkMstffficYlf+nthTp5y+O1/XaBcWnpfn3jc5N/9TpzP/0KYs+cK5HP173sk0FIzOQ/+YH8le0wOBgl0deOW/WcqBwKoqTT23AxaPIE1NYFwbDzDWLVJoBAuJXO6LqMsqMXRlOCaNP2mS+fY0rZce9APJUZIhfvIVUbXHJZNhftqSjABmE65ZtFwo9DkYM1Q5SWUzMbHsmaxUp7LF6GtXUcnRyPE7roDe+o7uA3ISNVDU0bqi1/8In/5l3/J9773PWKx2EIOKZFIEAqFSCQS/NIv/RK/+Zu/STqdJh6P8+u//uvceuutHDx4EID3vve97Nmzh8997nP8p//0n5icnOS3f/u3+eIXv1jXW2oUbzy7PHcgBPzof9cqJhVUZkbxHLvBVud+Diu940biA7uxSzkqMyNUpkewilnsagHPtvBcBzwPDz92L+kB1BqhqlrnIy6dsPD8PlOe56scrJG0UQux7iiSLHHhsUFmT2bIDxWwyrZfM1V16jL7roRdzjN15DH67vwUajBEI16jJMskt1yDJMuMvfhDrEKGq2ak5gWDew5+mFBLvUT2cgghsMt5Zo49c5khXQ7PNqlkVi55mBZjzLrLmVy+ZKdGl9hEh9xfV9uvGSSTMnfepfO7v1NcZqAADAOeetLkS/8xTktaJp+rPaNX5WDd0A74CfplNT6yjKRoyMEQnmliZ+aQgyHUaBytrQN1cgLPslDCYeRgEK+ysoFZsYWGGl9WJKtEArT+1H7i+3qRA0u/5cnvvkL+sG8cDDtPpjxIV2Lvsv3Kskoq3M+YehTTWXmCVguSpNAV31szjyeEYK50cU37vYSgFl/oD1ULrmfXDdGuD6t/17pWJ7fvCTzDWLse1zyaMlJ/9Ed/BMA999yz5O9f+cpX+IVf+AUA/vAP/xBZlvnkJz+5pJj3EhRF4Yc//CFf+MIXuPXWW4lEInz+85/n3//7f7+mC2jtDVCYtbEMDz0oYxnLB3SjXPuDNHPTVKaHiXY31rkX/IFWC8VQg1FCrb207L51Tee9FALhugjPwbUtXKOMY5Qxixms/Cxmfg6rlMEq5XAqixp+a8Gr/+/apV6uPOfC8EkyZw7Ruvf2xtvFSzKJzdegR9NMvvoohZFTzfXQagBqKEpq6wE6bvgp1GCkOXVo4ZE9e5jCyClWus/Sikvnd4WHW6f41MFmzLtAWuqkX97BKa9+7qsRKCrEYjKaJtUM/UsS6AGJWExG1erfD00Joir1Nfocz8R2loZA7dkZcplnwPMwx5aGnczJxQli/tALDQ1YK4mXakpomURTqL+F1I1bmP7J69iZpQbQGFu6r7HcUTriOxfEVC9HMtRDWE+vyZjEg53Egu01lzmeyVzpAk6TwqqLkAjr9XtESZLELZt+fo37Xj9UqXZu2CtXcGZmCR+4BvP8xSVetFsq4ZUa84abDvethmAwyJe//GW+/OUv111nYGCAH/3oR80cui7e/0vdPP6Xk4ydrfKhX+3hx388TrUB1XMAu1IgP3SCcFsfcpPddzdWFl9CUmVAQ9FDMN9c8JIT7bkOVjHjKx/MjFAYOkFldhThrk0BfKPg2SYzx54h3NbXcO3UpXXC7X303flJchdeZ+70IapzY+uecSHLJPr3kN5xA7G+Xciq3vRzKk1cZObYMwjPJRqVqFQEuu6LYKfTMtmsRzIps2O7yqtH7GUaec3AwsTBJiY13nyxHkxDMD7u8tGPBnnicXOhl9QlJBISH/94kMkJF8Oof86qHFimc3c5bNeoHQ7zGvD2G3y+1gpSO5IkoatLPQZrpkjp3CRqLIRbtRDuZcdRlj7/kjlD0ZghEVpeQqEqAdqi2+bzO809147YzrqkhqqVI2+srTYKfM+7npr6wjpXoU1Ho6iXv5QCAYI7tqG2tuLedCPiMnn+8kuHKL1QW4H+SrzjtfvaegMEwwqyIrHrYJzH/nISs3qFkRK1vyHhuWTOHCLWs51Y32r6fW8dZEUlmGwnmGwn1rON1j0Hqc6OM33sGUoT5/FWE7yVJLRUGDmo4+QruOW1zuiWwypmGHn222x54BebEp8FUMMxWvfeRnLbdZTGz5M59TLVuXFcx0I49srCtJKEJCvIqoYSCBPt3krLjhsJtfQg68Gmn6UQAiM7ydgL38OeLzW45WadQkHgOIKODpnODoXjJ2yKRcGO7Sqvvb4+D1C7xP7bgPdubs7je981+OVfCfMXf5niscdMhgZdhIC+foX73hNg21aVr361wsx0fYOiyGrdQQd8I3W1Ya1S6+OH+xZ9WTmkE9/vJ/btbHmJMZx68Aj23KJnZDkVZorniAc7atZMdcZ3c2H2uaa8nqCWIBXpo1ZoTAiPbHWksdYW9SBJ6Gpj5TJvJ3jlMrN/9le1lxl/i5oennutyAd/tYcLr5dIdejc+zMdmFeoPJfyNs98s3ac26mWGHvhe/QHP0u4rbaq8NsJkqygBiNEe7YT6dpCYegEsydeoDh2tm7uSo0FSd++k8rwLJ7pbKiRAjAyE4w9/316bvtIU4ZKkiSQFNRglOSWa0luuQa7XKAyPYyRncQq53GNCp7rIDzX1/qUVSRFRQ3F0GMpQukuwm19C/qLa3l+QgisYpaJl39MdXYxPHXkNZvP/WyYhx81SKdlolEJ2/YNQqW6uoJDgFDdNhwKKq1SNzEptSECs6YJf/rVCpIEH/5IkL//hQiXpDBdB8bGXf7szyr8yR+XV/T+JElBrkOaALHujsiNwK3TLfcSFElbEm6VdZXKhWnG/+YFrJmV62/8PlPDGPY1hPTEsuUBNUJLdAtThcYa8oEfJvRVGWrURgmHyfwJ1pt7vVqNJq8qPA83t36JpnfglS/Fk1+f5sB9Nq19ARRVIprS0EPN0TyN7BSjz36brpveT6xnG0jy29xY+XkHSVFJbN5PqK2XmaNPM3vihWX5HTmkEd3Vg6QoaKkI9myRjc0AAUJQGDmJ9LJK3x2fQAkspw+veDUL60ro0SR6NAlcgxACz7Hm83UuSL6RlhUVSdE25BkJIfAsg/GXfkhh+CSXDyaWJZid8xgecZmZ9WhrkxkbczFNwdGj9qop5U55gG55c81lCioBQliYjHsbIy80N+fx3/9bicceM9k0oJBukZEkvzZqeNjlxHFn1fCkRA3V78uwVuWVZrCakoWvzL1opoTjEupvYfOvvw+nbCyxB9MPvU7x6FJh6IIxRdmaI6gtL7qVJIX22HamC6cbErNVJI1UuLcuqaFszq5JIX7JOcGKzSbftpAkJF1fseSgEbzjjVRhzubpb00jKxI928I8+L/GKGaWDsONzGEq08MMPvbntO27g/b9d62qjP52gSRJ6NEUXTe/n0CilcnDj+BUF2eTnmFTODpEdXgGSVWw8xsnm3I5hOuQO/86CEHv7R9HDUXXbUQkSfKbLDYuWN4UhBDYlQJjL/yA3IWjyzzRvl6Fxx43FrTvZi9rcXHy1OoehSdcnDr9lUyqTIkRJr1BiuTWdR1L9mvCa0dsXjti+735JGimnZdgha6JvDm5j3r090vw5hv3XYKdKTHxrZcXux1cdvrmRG7Z9o5nMF08Szrcj1SDQBEPdhINtlE0VteX09UILZHa+Vgh1lcbtRQrTxwst9IQZ+BqoF5oVEnESX3kA+R++BBOZu3hzne8kQJ/bHE9wZNfn6Kcd3Ds5h+WFo4Tau1BeC5WKUvoHWKkYHEwb9l9K7KqMfb89xYbMwqQgxrJm7YSGmgl88xpCq9vvHyKfyyP3IWjCM+l64b3EmxpTBvxrYAQAiMzwcQrP6EwdKJmqLQRQ7QSRsRZRtzmJHw2Eo1wGa7E5cr9yyHVHNQ3GivlxMBX+r/8DJ2iQe7l88vWU8IB6tm76eIZtrbeTkBeSreXJImQliAV6qNoTLPaFDcZ7iEcSNdcZjgF5ua7764HAlYMs5pOmWNj38f2rs4EdDVYTp3Cf0UBRcGro9DRKN4VRuoSjj6da24DSSIQbyG57QDx3p0E4q2o4WjNhOo7AbKikNp+A1Ypx+SrjyyMUp5hk391ELdq4ZSaTXw3Qra+DMIjP3gcu5Sj945PEG73E9pvF2N1abZZnhpk7PnvUZkZWT+rsEHU08KDjdP1Wy884aw486/VQXejUasj7OVwG+xkHN3bg6ypZJ8/s2yZ6RSZqwzSndi3bJkia6QifUzk38D2Vv5eVqqNypSH11UbdTnqdTwGv5VJyZzBugqySJdDiyRwzAriMp0+LZrEcS7zpGR5YdwRVQM3n0dNp3wV9DW+4u8qI9UoJEUlkGilbe8dJLZcgxoIvQPyUI1BkhVa996OmZshe/4ICIFn2BjjGczJXFMhgd7Yfralb+ds5lnGim80fhLCozIzwoWffIWO6+4lte0AaqiWZtqbCyEETqVA9twRpo8+1VSPq7VAQaNH3kqXPECAcF1CQlnkOeQu10l8K+C45oJYcS3odbrNbiT0FYqJhRCYdglJldGSYexcBWTQU1dsI0mEN7fj5OsP3OO5Y3TGdtX03Foim/wmoyt0iIzorSTDvTWXOZ7JXPniOmqjLoMQK3bA1eRgQ33qmoYkE0i1ARJmbhotnsKdrYKiEUi3IysqaiiKY1Zwq2XsUp7U3lsonH8Du5jxu0d4HunPfAJ7bAK3sujpGafPYJxcPnmohb91RkoNx2jZdQstu25pmjL9ToAkSWihCK17b6M8NYhVnNfUEiDc5uI/sqSgyoE1J22dSoHxlx6kNH6Otv13E+nc1HjR7wbDcyxKk4PMHHvaL9RdSyysSXTKfWyR91IRBeYYx6O2h2KItyZMUwu2Z+DUMVKSJKHKAVQ5sDGDbx2EVpD/8YSD7RrorTF6fvY2xv/6BZRIkIFffQ/OFazVQHucye8cqruvkjlL3pggFe5btkxTQrRFt66Yl2qPbatZUyaEoGrlyG2Qnp5AYDj1dQMlSSagRjHX2O23HpRgGD3RQrhjgKkXH0KLpbCLWSRFI9TRhyQEdqWIrKgEOgcoXDiOLCt489qmkiwjLJvq8VPL9i0abB0Pf8uMVCDZRu9tHyPatbXh4t0Fz0P4EkV2uYBVzOIYJVzbnC+obdaPlfyQ4nydj6zpqIEwajCCFkkgKeo8PVuaX7dZQyoRbh8g3r+b2ePPr+H8Ng7CdcgPHqc6N0Gsbxdt+24nmGy/6p7r5c+tOjvGzBvPUhw/h13KXbVjXokgEUBwwn2ZKuW6bLGr8XQ6OmUqZUGx2NzebdfAcsoIIWo+H0XWCagRHGsdRkpVYIVBKqzXzvGAn39xPBM7W2LiO4ew5kpEW2NUh+eYfug1uCTrJUmkbt2+4mlYTplMeYhkqKdmiL8jvpuLsy/WfG6qHCAZ7quTGhBkq6NU7Y3puAyCqpWv+0wAooEWCusoGK4FNRRFj6d9Rq2mo0XiBFt7MOcm0SMJqjPjvgZqbhZZ1RGei+c6aNEkrlnFq1TJ/+jh9Z/HBlzL2x+STKxnGz23foRguqvhwdG1Tcz8DJWpYQojJylPDfsK6EIAzXU6rXtqEiwwd+YLVLVwnGC6k0jnZiLt/QTiLajh2j1q6kFWFNr230nmzCt49tWb9TYKq5hh7sQL5M4eJrX9BhKb9xNMtaOFE021z2gEnmNjV/JU5ybInX+d/NDxt+QeZMUMraKboBTFEiYuTs3ck3QVMlKbBlR0HbJZj8FBl/5+hWpVYNmQy3okUzLlkkd3j0K1Irh48ZLREBSNGTriHhLLPWhNCRHSU7VVr3UNva8NN1PEMyy0nla8YgUkCTkUwKuauBWDxN3XUnj2GG62Vr5GIhGs31DTsPNYbhXPdqhe9Gsf7VyF3KHzlE8vHaT1liiSVj8KIPCYKw/Sndhfs2YqrKdIRfrJ1CA/ROfbztf6Jj3hMJ47xkZOP0ynON8Pq7aYazLcy3i+iZB8I8fMTGHlZvyr8FxmX38GhN88tDB4EjUcpTI5iGtU/MmfEMwde27ZfqRgACUaBVlGGAZuE72k4G+JkYp0bKLnto8SSjfWTVZ4LpXZMTKnD1EYOo51FWffVxo64TqY+RnM/Az5i8eQ9SDRjk2kd95EfGCPT8luEHosTaRrC8XhxgsTry4Erm0ye+J5MmcPE27rI9q9lVC6i2C6i8D8rG1N+7VMzPws1cwExtw4pcmLVGfHVlatuMrIiimmxSi7lBsoiRwWBl6NWbkpqlz0Gu9U2gguTfD3X6PR0ipTKgm2b1eRFTj0ss3efSpDQy7792s88vDSvEvBmMATbs0wr6aE5j2d5Ww6OaQT3NSJqcgosTBaWxKnUEYOBbCnsqhtCaonh/3atGrtSUNIixPUaucvhRBUrNwy7cDq0CzVoeUq4MU3RkFeeWJXMCYpWbM1a6YUSaUtum2+meDlH6pEPNBRV5W8YExRNKdrLlsrLKdC1c7VNVKJUA+ypOKtUgjdHMTS72c+RF6dHUdPtFCdzuEa88/i0kB2RRhd6+4kevutaJ3tSEh4hkH58BEqrx1rOOT+rjdSWiRBz20fIZSu3avqSniOTeb0IaaPPoVZmH3TmF91z8cyKIycojwzQltuivZr721Yk06SZZKb9i0zUiE1Tk98P3G9A0VWqVhZJsqnyFbH6oSkJJLBHnpiewirKVxhkzVGGS0cW5X9VPe6bJPS+DlKExdQAiHUYAQ9miSQaCOQbCeYaEUNx1C0ILKmIyuaH05wLDzbwjErWMUMZmFu3qjP4VRLOGYZzzK5fFCRZGVpYll4K1J6JVldEsYRwkOsQWkhKbXRIfehEyQtdeDy/7H3nnGWXeWV93+fcHOunKtzVitLrYyEJIRIJphgA+OIcU7jMa9tbGzjODPM2AZsjwEzNggQGQnlhLK6W93qnKsrx5vzSfv9cCp25eoqCYlZPxpV1T33xH32s5+0ls1cq+siObpYXSPl2JLeQQefX5DPSdraVXRd0N1tccONHnw+wfkui94em1Rq5jlly/1YdmXOKjtFUYn4GubOSwkBikCriWIOpfC01OIUyghVxRhI4mmpQRomCIEWD2MOzvbGYv5WVGXuhZgjLfLVkQUr3abDyi+e67Mdg9H8aWqCnXN4joKovwmfHqEyTZ5EUzzEg+1zGnEpJUPZY4s2JC8XVatAoTJKzN86RwOyK5cR87eQKq1Re8k0SNuimlq8h0zx+4m86SasVJrM9/YibQetJkH4xj04hQKVU7MXOnPhDW2kFI+PpivfQqC2laVQzttmldFDP2Jo30OvSmf9cmBXigy9/CieYIzE1muW+C2Bv6YJ1RvArpYQCGoD69heexsgKFtZHGkT8zXTGNpKd3Y/XZm9MyYBAdT6O+iIXoZpV7ClhV+LUBO/jpivhWOjj1BdgbbPJKSDXSliV4pUMyPk+05NYzyY9swmfpwxn8rx3xdeSDRvvJGmTTeiKBqK5iE3epYz+7+JUc7M2lYoKp2XvI2all0oio6ieUj2HuTswW/jWMvr94iLBvyEOG6/xKgcwF59ro95sXeviZTQ3e3y9x06ZGI7bjvYgZdNHMddyAoxezK1HIPRwlna4pfN+kwgSAQ78emRWZIadqZA9omDYNturqJ7eDI0joRyys3RZO5/Yc5zVoVOTWjdnMZR4kqkr1YxwnQM506woe6GWRIlQgiCnhoivoYZRsqjBecVASwZ6TUxFI60SJd6aYxun8UCDy57fUNkC5ly/zzelEAZX3g50kYV+iT9lPuzCUgCegyBQsXK41WDlK3sioPRwu9D+P0UnntwUjDSHBhES8TwdHb8PyMFEG7eSKRj+5JyHtKx3dLkV578sTNQk3AcRg49RaRzJ7p//jLdCQghUL1BvJEaSqMlAnqcDfE9SODY6MOkKwM40iLoibM+dg3t0csommkGC9M9LzdHcC79AkOFk5hOlYAeY3vtbSR8LcR8zQwXV7lhddJ7nfZyXIRDmxw4SrmYRNO8dO5+J0Ko8y5ZpOMwcv4lcqPn8PjCdO5+h1vksQKV5ZLMYWKQkWNYXFxD43IxWTcy/t/pqu3Tf54vUDCUPU5zdNecnHE+PUxdeNPcuk/W7PDQLMyjWRby1REPzPYU3BOFQmVkvMF2dWHYZUYLZ2mN7Z71ma76ifqbGSucnewfiwVaZzGxg+tFpUu9q1gwMROpUjcVM4emzJZSEUKhJriesO8I2fJs/TyfFiLqbSRbHUIVHoJ6jLKVo2oViPqaKJkZVEUj7KmbLE7x61HKVh7mqUpdFFIiFAXhmbnoUEMh7OLSF7avz67VJUDx+EhsvhJtCZO5y4A9zOjhp5chDf/awMinXW9jiVA9XrSAGzuP+ZqIeOvpyx9mrHweWxpIHApGknPpFwFoCG5CV2b2wmQrg/TlD2M4ZSQORTPFUPE0muLDt0BPy3IRjDXTsettqPrq9uJUCqOk+g8z0r1vCQSpkmJmgGT/IUb7Dl7UcZPSVd3drl5Np7KdRtFJvWib9S8hlhaKfjVRqI6SLvXM2VcnELTFL8Ovzy42WCkUodEU3Ylfj82zhaQ/e2hJfHrLh2Q4exzbme3pCiGIB9rRpoUgG8Jb5tyL5VRJlboX7DO7GFStAiP5+d/9gCdGe/xK1Dk8UU3xYDsmVatI3NdCQI/hUf2EvHVEvQ34tDBhTz0lM4MjbSSMt5+s3EQ4pRLW2BiJ97yD8M03ELruGhLvfSeezvYl90jBG9hI+aJ1RNq3La0iTjqkTu2nklrdEs61gGOZFIe6luztKboXzR9EoBD21qMIlVSpZ9Z2BTOJYZcI6DG8MxQ+JZnq4CwWgomeDFc8bjVKyQWheDvxxq3jBKKvf7Qpm2hVNhIWMTqUrWxRL2ebetWsf+uV2UqxrzUMu8hA9jDWPDlHnxZmU93Ns1RyV4ra0AZaY5fOy/aSLvUxVlgdIt65UKiOkSnPrbTs5qVcg+zTo0T9s5WeJ3qj5qoEXE30pl+mZM7NgyeEQlN0Bx2Jq2b1b1mOMR66c0hVeqhYhXGPT1Kyshh2iVx1mICewJYW+ng/3FyhxaVCVg2yDz5G6chxvBs68e/YimNapL7+LcyhxXNaE3jDhvsiHTsQS2wctc0q6bMH1/aEVg2uvLljGqiexQeQq7nkAcTkwJ2PVsaWpiuDML1oAOZt7hw/wjLOfX4oqka4puN1S0k1F0acXgoys+h21quYq1oOxgpdpEt91IU2zhleqgtvomimOT/2wpKLGeZCPNDGprqb5m0aN+0KfekDc3o6q4WqVSBd7CUeaJt1HopQqQl2kKsMkgi0oypzMx6nSqtHgzQfDKtIb2o/m+pvmfc8OmuuRkqH3vTLk8UtlWmNviUzQ8nMuD9bmRnfzRtTIdyiObuwBcCjBkkEOxgrnJt3ETMBp1Si8MzzFJ59waVMspcfOnyDGilBpH3rkrcuDneviCJHU32oQsORDpZdXlaC0aMFECg40sZcROTtQthmBduoLM1ICWWc5UFOHkdXZsfTBQJd8WHalTm429auwjEYa6Vh3dUEoy0Eog0IRWXnLb82SfhqmRV6jvyQ7Oi0JKsQBCKN1HdcSTjRgaJ5KOdHGO3eT2bk9Ioq8dYCBbIU5NpSL60lTLvE2dFniPlb5szBaKqXzppr0BQP58aeGxdEXPpYUYRGbXA9mxpuJuiZu+dISoeR3ElGC2eXte/lQuKQKp2nxdrtlsBfsPiqCW2gO7WXeKB9TpYJR9oMLtqnNLP6R6BMhi+nmomFBAABAABJREFUfp7axu9LoOtBcvm+ye9IJIPZY8T8rTREtsxa1LmsID7W111PxN/EmZEfUTYzF1WarggNVdEJeBI0R3aQCHWiqz5e7PrygkZK6BrBa67Et2Uzitc7Yz1b3PsyxZf2L+n4b0gj5QnF8ARjS25+LQ51rajUvDm2k9rIJjTVx5Ge7y5LfbOz7jriwTYMq8Qr3d9a1iCa1FdaCsabhSUOmeoQlmPQENxIqjJTIjvma0FXfKQrAxdXrbdMKKqGY1sUM/14A1EU1a2+s8cr6RzbwKxOPx9BonE76y/7KRzbopQbwilb+II1bL7mZ+g78TiDZ59ZdiXe/8PcyFUGOTP6NJvqb54z9KMpHjoSVxHxNdGfOUi61EfZzLKQQdEUH2FfHQ3hLbTGL0URc2uDSSnJVYY4l3x+0RX7aiBbHqBQHcWnhWcFCELeWqL+FkLe2jm/myn1zV1IMg3hUDOKolGtZhEIfL44pUoSVWh4PGFK5SR+XxwhFErlURzHQlXcezM9N2jYRc6OPYvfEyPia5yzJF0THhrCW0gE2hjKnSBZPE+xOkbVKixIaaUKHU314lGDeLUgPk+UqK+JWKCNgCc+abyXMl8poTDBKy6j8NyLWMnUjCnWzmQW/f4E3phGKlKDUJeW15BSUk0vTsk/F3qS+8iU+tnUdCvLDXudGnyU5vhu6iIbl31cIZQlh8Wk44xTN7kTTrrcR2NoCzljlJHiaSxpEvM2siF+LQ4Ow4VTa8rLdiHyyfPkk+dRNS+BaAO6N0zP0Qcxq3OHTfzhWlq23optGXS98j3yyW4cx8IXqmHDpe+mcf0eCulesiOvnUTGTAg0xYOqeGb8V1M8qKoXVdEnf48HOubVUvKoIToSV1IyMtiOge0YWI6J7VSxHBPLqWI75vjfjfHQ2Op4HoPZIwS9CVpjl6EIdc7QXyLYTthXT6E6Sr4yTKE6RtnMYtlVJBJV0fBqIQKeBBFfA2Fv/XgD7dzXK6XEsIqcHnmK0hzsFitF+IqrMIaHqPbNLmV3pM1I7iS1wXVc+D4rQqUuvAm/Jzrr+qWUDOdPjOtczY9IqJliaRS/L04o0IgQClLaBPy1FEtjBPw1hIKNlMtJfN4YVWN+ZoZCdYTTI0+ws/lteLW5m5/FuOx8e+IKmqI7KBkpqlYBwyph2CXX0EhX6FFVNHTFh6Z60VQfHjWAVwuiq/65n9EShpZTLlM5dRYlHEIpl5HTQn3OEudneIMaKT0YWTpzgZQYpZWXjLqhsbmemEBXfaiKB4mDaZVnrT4kzoqmEaFpS8634Tg442q9FbvAydSP2FZ7K5sTN7Apcd3kuUrpcDr5DKOltUtOrwYidRsJRBroP/UUmeFTTNz7cm6Yoa4X2HTVB4jWbSA31vWahf2ao7toS1yOpvjQFN01PEKMr0JdLsYJBdyZf5t/4aGrPpqju5BSjoeV5fjP47II40KActrfbcfEsEp0p/Yykj85uS/VGyDQ1EG+5yQ4DkJRXc9cUVF1j6tFNm3ZazlVzo4+jSJUmqOXoM6jKaWrPmL+VmL+FhxpX/BuuHyVilARLMzbKKWkbGY5PfLErEIE4fGiJxIYw0Mrin7oNbVYufnf9+H8KTbW3YRXn1m1qikemqM78aizw55FI0mmNBWSmw+KUAn66ygUB8kXBwgFmjDMIkKohIMNZHI9qIqG35egks3g80YJB5solkcx5jBYyWI3RwcfYHP9rfNSNE1et+qbLPiYPm4mMH0crhaEruHduA7F68FKZ5mu2VY6eBhrLLmk/bwhjZTqmcf6zwFpW2symcWCrbTVXDGe3BTkSv10j720KslfzRdasnKwY1szeOsKxhgHhr5HfXAjIb0GRahUrDzJcveMpClA3kjSnz9MwZg9mMpWjt7coXH6l1dJj0nR8AVrUFQdjy9Cw7qrZ3zuC9YAAm8ggaKo2K+RkQp44kR9qyv4KCbJhpf3PVu3ZlHpOJaBomguT2QkgScYo5pLoqg64fYtpE/uw67OzJOadoWTw49hOxYtsV3zVvVNnKcqFNQVSioXqqOcHnmK0cIZLhxb3pYWQjt3k/zhDyYjBMtB6uEHFvzcsisM50/Snrhixt9dpvHgrO2llGRKfZOFCAuhUs0ymjrBxDVlcm7Tb6k8RjrrLg59vhhjqSnW8Gx+oeZlyVjhLJZdYWPdTSSCSys8chdEa19BKxQFO5Uh/diTy6rmuxBvSCMlFJWlvs1LICxYNhSh0VF7FbnyMAOpV/DpETY23ky2PEQyv7Qu6/kgFBVvtG7JkheOWcUszVyFWU6VgfziNDzpSi/pytwvSd4Y5djYI0s6h9WCEAJFdY1+Q+fVSK6atY2Ujtu8/QaTYFkrBJvWU0kO4o3UUM0lXW2gech4bcfk7OjTlIwk62qvxzdPmGklmFjdp4rdnB19es5y8OiNNxPYuh09nkCNREBK0o8+hDHiToCxN72Z8umT+DrW4W1ppdLdRX7/XqRl4W3vIHrt9QDk9r1E5dyZuc8Dh9HCWZqjO9HUxXkyLadKqri03qhU9hyLTTbpbNei+7kQmXI/x4YepD1xlXveczT7rhYmcmPjPvvC2zoSNR4l/r534ZRKM5q4SwcPUTpwaEnHfEMaKbeoYGmWR1E1lGXER5cCVfEQ8NTQNfI8VatA1SqQr4xSG1530UZK0b1E2jYveXvbqKy5uN+rBenY2EbZDU3uvWfevJPj2DjWa8/8/uMKPRjBE6vDkx6mmh7FX9tCebQXx6igqDqaL4Q1Twjccqr0pQ+SKnazof5mEoF2PGpgxZOilBJHWpTNLD2pfQxlj83LB1k8chihadCxnvQTjyJtGys9la/yb9iEt7GJctdZ8gf2gwA5Tq9R7e8j+eD91L/3/ejxOAuVYRQqI2Qrg65nspCaspRUzBzJ4tIMi20vPiaXss1cKBlpTg0/xlD2mFvZ52vEM18+aQWQUmJLk6pVIFvqpy9zkIq5MJu5rFTIPfbUnGPDHJ1NCDwf3pBGyrGMJSvQCkVBD8VhePX4toRQ3BdkWgzWkdYsJoeVIFDXir92bjXQCyGlxCznXzdGSjqO6ynNm0x3KOYGsM0y4ZoOUgNHf2zKzV9PMHIpRl9+fPL30tDUJJs6Njev3nRIJEUjxZH+7xMPtFEf3kI80EbQk5hT5XbOfUiJYRfJlgdJl3oYzp0YrwqcH1Y6hZ3NYVfKmMPDc4b7zLExci88N/vLto2dc/sLF0PVypMu9RAPtC2qeDtWOLvmsu1LhSNtMuU+DvZ+i3igndrQemLjFYmqsjRS6ulwn1GJkpGiUB0jVxkkWThPeQmhTQBpmlSOn0SNRtAa6hGqip3NYQ4uL5/4hjRSVqW0LOXVQH07mbOvsJK431yP3XYMbLuKzxMhXxlGVTz4PTGypbk72pd8LFWjbueNyyoKKY/2vi7KsaVjUymmCNeuo7ZtNyNde5HSRtX92GYFx3avITtyllzyPLWtl+JYBsn+w5iVPKrHjy9YQyjWQnLgCKXsdPYQMf6/aetiMR4SnCveO/4yT6xC3TTLAttfgIHsYTeR/mMQcZRSzqn9tBpwpE2yeJ50qY+AJ05AjxENtIxX7kXxasHJhlPbsbCcChUzR7GaJFsZpFhNUjLS45P8KsTcpYMxdvHcfhJJX/oVMqX+hSd2CflFys5fCzjSIlk8R6rU7UqfaBFC3loi/kZ8ehSfFhkv6nLZ/p1xVQDTrmDYRSpmjpKRplAdo2LmJqNBK+m18m7aQPTNb0IJBpCOG4ovvXKY3BM/WlD4cjrekEbKyKfcHoMlbh9t387QvoeWJYynKh6C3gSRQDO6GiAWbEVVdYqVJLZj0p9+hdbE5fg9cfx6FE3xMJRxiVt11U/QW0PYV49XC5EIdVAyMpSr6Xm5yYSiUrvjekItSy9Zl47tSqW/ivA1RrBKBlZueX0tjmMx0rOPYKyJtq2307btDrfyspzl7IFvkk+6nq5lFDl34Nusu+Qd1LVdTuP6PeOlvBLHsSjnR0gPT1WyhRIdtGy5BY83jKp70TwBQol2tt/wi1hG0W0WPvogxYy7gIg3badpw3Wouh9N9wOCeON2dt3ShGWUsc0KZ/Z/Y94SeXBDL5M9c4qC4vO5Mfnx3xFiRZ33rzWEqrl5CMdBC0Wwy0WkZSE1QcFMUqiOMVY+7y6iHActHMUuFlyPRzJnVeJy4e5DzL0AkCxrcboQXJHB1ZVjf7UhpT05FlOlHkRaTBXgTAqtMrk+mMgxSSRI56KlOJVQiPDN11Pc9zKlQ0dBOqixGPF3vY3AaJLSwZ/gnJSRT+EYVaQvtCQX1xNJEF23i/SpfUs+hq76iQc70DU/Y/kzBLwJNNVLxcjj2CUG00epmgUi/iZKRpre5D6MccoUrx4iEV6HI23SxW5iwTZ01U/VzGHP9ZIJQaRjO3U7b1iW6GE1O0ppZPWlDRZC5wevJHWgl5EnL8gXCdACHuyKhbTnnkjyY+c5s/frROrWo3lDSMemWspQzs+MX5uVPGf230s40U4g2oiq+3AsE6Oap5DqpVqcqka0zQrFdB+lBcJQ9rT8lVUtkk/1sJAbNFcjtdB1PC0tGIODyGoVJRjEKZdRPB58GzdSPnYMadvojY0IwBgcXFEJ9WsFxefH19iKlc9g5nNELrmC/PHDWLkM3oYWhKJgjA3ja+nASI5ilwoEd+0m98o+7NzqkTY75TJaKIReU4tdqeAUC5N5p8UgNLeiUaiau1hYJYP2+sBEe8KsP68ZFK9Lx1Y+cRpZdd8xa2SU8vGT6A11S97PG9JIOWaV0mgPnkjNkrYXikrt9j2UhrupZpfmvlfMLN1jL85/DtJiLH+WsTkKJQqVUQqVJYYJhCDStpWWPe/AE04s7Tu4YZ7Uqf041qvLC9f9jf1YhdkeqR720XjbFkafPUdlZL4VqqSUH6aUX7xc1bENsqNnyI7OXaU1gXJ+mL4TSy9/zae6yaeWn59UvF58mzZiJpN4WppRAwHsQhFjYIDxjkl8Gzag1SRwCgWMoZX1+bxWkLaNGg6j+LwYqSSOUcUqZNGiMbz1jSgeL1Y+g6JpWLmMK1BZrWKXVpfLrtLTTXXDALXveDdOuUzywfswx0bHz9FCzmF4hK4Tvf4m/Bs34alrQIsnCO68hMxTj1E+u/D4WS6EmHqs8/083Xt5I0PaNkJV0KJhjHFpDqEq6A31/49gFiDTdYTYhktZSmJACEGwvoOmK99C/4s/wFxDufjlQA9GSWy6gvpL34TqXU4FlaSSGiTbfZSlvg2BtjhmroKZKxNaV4tdtSj3Z/A1RpCWQzVVxFcbwlsbQqgCM1+l1Jue9Ir0qJ9gh2tE7crMla2vMUJ0RxN1N2zALFSpjhUoD2SpDL++wynTYZdKOKUS0jDQojGs5BjC70fx+VACAYTHgxaLYaXTPw6pqmVDaJrrGeo6SAcrn8Nb24C0bRyjilOtIE0LI5NCji+MnHIRPV6DMbryHpkL4RQLJO//wZQs/LSw6dB/fGlOwy9Nk8yPniDz9FMX7Gx1Q67xOo36Fp3es1U8XkHLOi/njleIRFVi9Ro9p6rEalRitRrnjleolt/YlsouFKmcPkv8ve/CHBxGWhZabQ1OtUr58NKVqN+wRqo41EU1m8QXW5pbKRSF2IZLQMDg3oeoZldfXG2pUHQv4dYt1G67hlDzRhRteU2RtmmSOrUfI7e0jm6A5rfuoNSTZuSZM2z8pesxi1WO/uWDtL/3MrJHBxl99ixNd27DWxMCRRBojdH9tX0kX3K9Dj3sJbaziYZbt9D77YMM/HBqEAZa48R3NeNvihK/pAWzUCX5UveqGilfWCXR7CdS6yEY1wnEdDw+FVUXaB4FpCupbpkO1ZJNtWhTTJsUMya50SrZkSq2eRGThnQwBgZRvB4q586h19RgjY4hHQc7k0Goqvv3ulqsdOaiQk3BmEaixU+03ku41oMvpKF7FVSPgnQkZtXBKNkUUiaZ4Qqp/gq50SoXo+XplEsUz56YNAKFE4cnP6sOT4nsWYWp0vXc4ZdXfkDAG1Bp3R6mYUOQUFxH0QTVok0+aTBwqsBoVwlzup1Z6J46DkzLg0XqPLRuj1Hb7scf1hAKGGWHfNJgpKvI0Jki1eLSjZgQsP2KAMO9BrYpad3m4/IbQ6RHLZo6dCzTjTBefVsEj0+QHrUY6l1ZlMMbUEm0+ok1eInUe/CHdXSvguZRkIBddTAqtju2R6qkBiuk+yvY1qtsFC2LwrMvYA6N4G1tBlWl9MoA5WMncHJLf/ffsEbKqhTJnHuFhktvXZIyL7hhv9j63XijtYwefprM+SNuMcWrEJYRioqie4l27iC+8TICta2ovuCKykbLY32kT+9fOgktUDyfJNhRg78xQnkwS2hDHVrER6A5Rv8PDuMYNv0/PIpTtUBKOj5wJfU3bZo0UqW+DOfv2UdkS8OsfacP9GDmyvibo/R88wCl/gxyHnXWpUBRBbpXobbdz+Y9CdZdFqWmzT85WWvjL6yiChRFTFa0S8dtMLRtiW1KLMPBMhzMik05bzHaXabvaI5zL2cZPV/CrDrYprO0xy+h2jVVym1np8qpyyemCjms1DIr7QRoukK41sOW6xNsviZO46YQvqCKx6+iexUUXUFRBIpri5G2xLbc6zPKNtWSzVhvmVPPpTj+dJL0QAXTcJYfclrueyDA43Ofw4VwbIlRmfscdJ/C9ptquO79rTSsD+CP6GgeBaGAY0nMik0pZzF4qsDz3xzg5LMpLGMJFlhApNbDdT/dws5b64g2ePEFVVTdbRlxLNfAV/IWqcEKB+4fYu/3h6iW7EXvlZSQz1is3+EjPWbR3OFB97jVoKWCQ2rYolqRZJImiTqdYmHpKwZVE/gjGusuj7H1hgTtOyMEIjoev4Luc89fUUFRhPv8HYkz8fwr7hjIJw3O7c9w9IkxBk4VMMr2RS1algpZNagcO0Hl2IkLYp5Lh5BLbSj6MUIulyMaXVwV1JdoouNNH8BfO48k9Txwb4mkNNJD8uReyqN9GIU0VqW4agZL0b1oviCaP4w3Vku4ZRPhls3owQgXw6FlFNJ0PfTvlEaXVzAR3lTPuo9cw9Cjx/E1RAhvrGP0mbM037WDQ396H45h460J4muOovl0Ele042+KcOjP7p/xAu/6s7tJvnR+hicFEFpfy6ZfvoET//gk5f7Miq7NF9Zo3Bhky3UJLnlzHQ3rg6jaVKXXxXTZT74G0n3EubEqXS9nOfFskqHTRUZ7ypQyr25+L1LnofPSKLvvrGf7TbV4g+okkcZKFi9IMMo2p1/KsO/7g3S9nCU7Wl2z/EgwrvPhv9vB5j2zc6nnD2b5jz84SnpgZhVorNHL7R/r5Nr3NKNo81NBTTwv25K8cO8AT3ypm2Tf/BWlmkdh41Ux3vFfN9G0KegWCC7CHygdOPdyhof/uYszL2Zw7MVv1EQ9hnCpGWc5d0K43QxLWT96Ayot20Lsuq2OS9/SQLTBe9HP33EkQ2eKvPjtAU49n2akq7Sk61oJhEdHa6jH7BuYMW+qsSgIgZ3OAJDNZolEIvPu5w3rSQFU0kMkT7xEy55GxDJCZhNlmsGGTgJ1bRiFDJXUENXsKNXsGNV8CquUw6qWcIyKKysxbTQKRRmvItJRdS+Kx4fm9aP5w2iBCJ5gFD0UdSVFwgn0QHTJ3t5CsCpFhvY9TGmsb8HtVBVuepOX62/08sV/LTLQb1PqS6NHfATaEpT7Mii6SmxXC+XhHNKRBDtr6Hj/5VRGC1i5Cp5EgFerEUjVBRuujHP1TzWy4co40QYvirK6x5586cdtXqzBx2V3+dh9Rz2ZoQrn9md4+J/PM9K19o2bmkdhxy21XPPuJtZdFsUfWRkH3nQI4V6YN6ix45YaNl4Vo+tAhr3fG+LgQyM4axAKcidkMacnVdPmxxec2SQSb/Lxtt/dwO47613vZsF9u/vUdMG172smUu/hO391mvTgbEOlaIJL76znrt9cT6LFt6QJXgiBUGHDlTHe9ydb+c7fnOLYU4uHzyemASnnXs9KCbPk2uZA6/Yw13+gha031BBr8CIucrxPPH9VEbRsDfOO/7qJ4TNFXv7hMM9/s59SZvWb4tVIhOitN5O8516kMbXA827cgBoJkX/8R0vazxvaSCElyZMvEajvILHp8hUZAqGoeCM1eMMJtxfHNpGWiWNbSMd2WSUc1/NCSqYtdcaZJ5RpRkt1iT1Vbfzz1eM9s6slhvY/Qur0y4t6e4oCl1/p4SM/H+T73ykz0G9jl03MTJlgW5z0gV5sw2Ldz65n+MnTSEcS392C6vfQ+80D2FWLdT9z9WShxNTNYt4VqnQcpADVoyJUt6+JRUJ+QoG6jgC3/VIH226oIVzjueiXdblQVEGixY/jQCm7xp6UgNp2P2/+pU523lpLMDa3ztJFH0YIfCGNrTfU0L4rysar4zzxxW5Ge8qvWtVZuNZDpM7L4Gm36svjV3jzL3ew+456tEUM1IXQdNeoG2Wbr/3xcSxj5kVsujrOO//bJkKJ5d9PIQR1nQHe9YebSQ8eZvDU2irv+iMaV72jkRt+po3aNv+cBn41oOkKLdvC1HUG2HJ9goc+20XXgeyqeVVC1xFeD6gqwuudYpERAi0aWRa35hvbSAHSMhl88T40n59w69aV8/SNGxVV8cJcvUrTa01fZZjFLMMvP8rYsee5mFmm1JcmvKkeI1XCLhn46sKUB3PgSEp9Gepu3EjL23YhVIG/ZSrcKhRBZFsjgfY4/qYosUtasE2bzKF+KkNuIt3IlDHGCrS9+1JKfRnSr/SRW6A03B/W2HFLLW/9rfXEm12xvbUizVwKXnlomEJq7YyU5hFsvCrOT31iM/XrXTmItb5eIQSBqMae9zWz7rIo3//705x8Lr1m4Z/pUBRBy7YwJ59PgYTL39rInve1rHhSVjWFS+9s4OzeDC98c2DydWzYEODdf7R5RQZqOura/dz5q53c+6mTFNNrMw5q2/3c9ZvruewtDeMEJ2s/3nWfwqZr4jRuDPLgP51j3/eHMMoXl6wSXi+By3fj374Vb0cbNR9472RrgFAEwuMh+8DSyanf8EYKwCzl6H/hPlr3KITbZksurwpegwlUSkk5OcDwy4+SPX+Yi10GDz56gtSBXqrJAkJTOflPT5E95tILpQ/145g2/sYIZr7C4MPH8bfE3EMqoOiuGF7fd18BQNEUhDp1n81sma6v7CW8uWGyhH0++EIad3y8k6ve1bRm3sRyYJRtDj64dtWeqkdwxdsauePj65YcjlotTByrcWOQ9/zJFh78bBcHfjh8cZWOS0TzlhBCQE2rnxt+pnU+ysYlQ9UFV72riZPPpUj1V/AGVW762TZq2/0XfU+FIth0dZwNV8Q49OjqUyG1bg9z92+vZ8t1NWvmPc2FifsSqfXytt/ZiDeg8dzX+91ikRVCGgaVE6fAslD8PooHXgHTDSdK6WAlU5gDQ0ve30+EkQKopofpfuIeWva8g9j6SxDqaz/5rRRSShyzSvb8EYb2PUw1n1x+Qcccmxe7khS7xuPuhs3IU1OsEXLcM8ocmuIfnCghl7YkfbCP9MEFcmESygNZygMLk4gGEzrv/sRmN+zjWYPFxDIhpeTUCyk3FLYGUDXBlW9v5J3/dRP+iLbsAh8p3ao0x3ET/RN5IFUTy1qNCyGoafXzzv+6Cd2j8NJ3B9fcULVuC6F5FK58ZyONG2ZWskrpVmA6tkQobnhqsVCvEILWbWG23lDDC9/sZ9M1cS65ox5VmzmOpHQr4CzTrS5UdTdvtti9CsR0dt9Zz4nnUhgXMYlfiKbNId73Z1tp2x5eloGa8fxtOZltEIrrWS7XG/NHNO781XXoPpUnvtSNWVmhRyUldjpD+fgphM9H+eDhJbOCzIWfGCMFYJUL9D79LcrJAWq2XoM3Vv+6M1S2WaU4eI7kyZfIdh1ZtMxcVaFzncrGzTpeH6SSDieOWTjz5IPiCYX1G1QaGlU8XoFpSAYHbE6dtCjk13bSijZ4ecfvb+TSt8yeWJYCKSWWKSkkDUpZk3Lewqw6OJZEOhJFE3h8Kt6QRiCiEUp48AYWDv+aFYcjj49RLax+YllRBZe+pZ53/eFmfMHZsuxzQUqJWXFI9pVJ9pbJDFfJjVap5C1Mw0HVFLwBlUi9l0ith5o2P7Vt/iUZQCEEoYTOO/5gE5bhsO8HQ2taphxv9rH+ihg7bqmdXJBIKRnrLdN9MMtIV4li2sQTVGnaGGLTtXHiTb4F96n7FLbeUMPxHyW59r0thOJTRSdSSqolm+5DOQZPFUj1lZESIvVe1l8epXV7ZMHxIIRg2401hBM6yVUyUnUdfj7w51tp37k0bS4pJdWizVhPmdRAmcxQlXzSoJK3sEwHRRV4AyrhWg/ROi+JFj91HQECUW1JRt4bVLn9lzvIjVZ58dsDF9dbVyhQeHoORvpl4ifKSIFLmTR6+GkKg13U7bqRaOcOFM0DvLY5j/kwKTJmWxSGukid3Eth4CxmMbPod4WA29/i4xc/HmT9Bg2vV5BOOex7yWB0ZPboa25R+ORfRtm4SaOuThk3UjA8bPP4I1X++Z8KpFNrM2vpXoXbfqGDXbfVLdlATdwby5AMnMxz6oU0/cfz5McMSjmTSsHGMsaNlJRuf5VPxRtQ8Yc1QjUeatv8tGwL07k76obaJvO7Aiklo90leg7n1qRVbt1lUe74lXWLGqjJcmtTcvzpJAcfHGbobJH0QIVSdn7j6fGrxJu91HUE2HJdgive3og/7L7y8x1PCIEvqHLnr64jM1Tl9Ivpi7jChaF7Vd7x+xtpWB8cL0qSHHsqyZP/3kPv0dyM3IjuU9h8bYK3/tb68TDh/Oe/7rIo22+qYfO18cntpJRkhqo8+NkuTjyTnNXcXNPm57qfbuGGD7bgCcz/PPwRjc7LYiT7lh6umg+hhM4dv7KOtp2RJT3/ct7i6BNjHHpkhLEe10CV8/M/f92nEGvwjhdH1HDl2xsJRBd//ppX4faPdTLWW+bMGj7/peJ1baSa3r4bRZ9Y+YyzI0+H45Dae35WX450bEoj3fQ+NcjY0Rbqdt1IsHEdmi/oVuH9GBgr6TjYZhW7UiDff4bUqf2UUwM4RpWl5p42bNL4k7+IUCpKPvVHWV563qS+UeHnfjHIe356trZVtQqD/TYvPW/wwrMGmYzNuvUav/+JMD/70QCHXzG473vLYzdfCoQC1763meve37LkEJ9jSwopg5PPpXj6K72MnC9jVuwlhKhmJr0VFVRdweNXqesIsO2mGnbeUus2eoY0uvZnGDpTXOGVzY9Yk5e7f3sD9esWp7tyLEn34RyP/Mt5zu3PLKm5FNxc2vDZEsNnS5x4NsUL3xrgtl/oYPsttXgXmIgnQn93/eZ68n96Yk2uH1xPsmWrK23v2JJDD4/y3b87TXZ4dr7SrDgce2oM23J43ye3kmidP3c34Q3qvinvbKSrxLc/fYpTL6Tm9A6SvWUe+tw5ovVernj77Ib06dhyXYL9P7g4I6V5FG74YCuX3lW/aIjPrDicfC7Fo/96noFThSWH4cyKw2h3mdHuMqdeSLP3u4Pc/rFOtt1Ug+5VFnz+iWYft/1CB6neMqmB1X/nl4PXtZHyt8RRvRqKrhLe0oiZLVMezADga4igBr2Tif+54FgGxaEuikPn8de1EmndQrChA19NE55gdOm6TasE6dgYhTTVzBiVzDDF4W6Kg+cw51FJXQzv+4Afv0/wuf9d4AffreA4MDho8+lP5di+s4ZQeKZBSI45fOqPZx5roN/A6yvwj/8S55JLPfzwB5VVJ4/u2B3l1p/vQNWXtjgwqzavPDTCc9/o5/zB3EVVozk2OLaDWXEoprOcP5jl8X/rpuOSCOuvjHH86dSqV7sJBW78UCvrr4gtuq1Zsdl/3zAP/OM5siMrVxu2qg79xwt87ZMn2PPeZm7/WCehhGeBc3Q9kps/3Ma3Pn1yVln3aiM9WOGH/3huTgM1ASnh1PNpDjw4zK0/38F8eoRCiBlhO6Nk8/gXuuc1UJPblR0e+dcutt9SQ2CB3rTW7WFUXSA1D1oijDGUQvF6kIaJnEMjSQ35sUuVGS0XHbsj3PgzbejeheeYQsrgmXv6ePyLPReVB7OqDn3H8nztk8e55aPtvOnn2vH4FghtKoLN18a54u2NPPZv3a9Kxed8eF0bqXP/8iQIQWhDHY137GDgvkOUBzIA+OrDNL1t94wKs/nhigOWR/vQ/CE84QS+aB3++lYCdW34Yg2oHt+4o7YaXtZ497dVxcinKaeGqKQGqWRGMQtpzFIWs1TgYgLCuge279RJpRyOHTFnGJZ8zuHgAZPO9XM//mmtXgCMjTgU8pJAYPU9zGBc58YPto530y+8fyklpazFk//ew3Pf6F+zUuBqyebUC2k31LUGTvW6y2Nc9c6mBbeZKBx46XuDPPCPXRSSqyNcaZRsnrmnD9t0eMuvrycYn7+ASAjBZW9t4MgToxx9cuk8kMuFlJJ9Pxhi9PzijdKOLXnp24Pc8KFWfMGlTV89R3IcfHBkSa9TsrfM+QNZtt9cO+fnQggCEY1YSxCzfR1Wpog5kkGvi2AMpd3m5YAPHAc7V0JviONtraV0rBun7D5DT0Dl9o91EojNf/5SSip5d6z/6Ct9q1aoUc5aPPHFbnxBlRt/pm1BL07VFa776Rb2fm+QzNDKF0gXi9e1kXKJqiR6NIDQVKqjeeQ442R1rIBQBd7aEEunMpRY5TxWOU9ppBtx9sBkI64ejOKJ1uINJdCDETRfENUXRNG9KKqKUDSEorhNq47t/rNtHMvAqpawqyWsSgmrXMAsZjByScxKERwHKR23j2AVs9TBoCAQEFTKkkxm5n5tB5Jjswe9zy/YfZnOLbd62bFLJxpV8HgEgaAgEhWrP2EL2HRNnB1vql005CGlJDdicP//Osv++4dmhvUmSetW18WTKO5JsnqVXL6wxg0fbCFcM78XM35wjjwxygP/cG7V+7NsU/LCtwbQfSpv+fV1ePzzh/68QZU3/3In3Ydya9YnVsyYHHxg6UzpY71lzu3PsP2muQ3JdDiO5Jmv9i25pNq2JCefT7Htptp5u0o0r0JNu5/RcIDKuUGk7eBpq8cuVNAb4qiRAFo0SLVnBDXgxdNcS+l4D+Au/C6/u4F1l0YXXJQ5tuTZr/fzxJd7sZfCS7gMVAo2T3yph0Srnx231C7I3hJr9HLTz7bx/f9x5jWTF3l9G6lxGMkCvsYIze+4lOLZUSSSQHsNwY4aRh5fuTLthLEBE9uoUEmvnuTAWsO2xgkwFFAvMAACd16/ED/70QC/+CshzndZvPSCweiITakoaWhU+aVfDa76OXr9Kjd+qBVfaPFhaFYdHvp8F3u/PzjTFglBoHMTVj6zqpIQAL6mVoSmUe45t2r7XH95lA1XxRettEoPVFwPao0Mg2VInvtGP52XRbnkzfMrBQghaNoUYteb63nhm/1rUu3XezRPfhnX6TiSMy8uzUilByp0HVi47WE6pAPDZ0vYpjNvflTTFcJRhaFsCXMki1AVFL8HvSGG0FWq3cPQ0YDi1V2x4GmUQJEGL5feWY9nkarSniN5nvhSz5wGStE8IBQcs4JQdRRVxTaWlzfKDFV5/hv9rLssSii+wIJJwPaba3jmq32vWW7qDWGkSr0per++l8Y7d5C4eh0IMEbz9N67n8LZ1W+8ez2gWJSkUw519Tr1DQpnTk19pmqC5paZjz4aFfzMRwMMDtj87V/meOXAVIjw8it1PvZroVU/x47dETp2L04ULB3JS98ZZN/3h2YZKKF7iOy8jPyxgxipMdcyT49tTjSOMNffpzGAXvi7ohDevhsjPUa5v9tdRV6k/pDuU9jxpjoitQt7UdKRPH1PHyPn1qZgYQLVos2j/3KezdfEF+QH9AZVdt1ay9EnRsmNrk7YcQJSuoSnleWU+EvoO55HOnJRY9/9SnbZjanlnCvhEq2fWwVb0QShuIfi011I0wJFUDrk/uyYFrJq4hQrSNNGjQQw+sZwKq6h6twdpXP3wl5UtWjx1Jd7KE4nNBYKocb12EYZxzLQA1Gq2VGEoqD5Qxi5JJZRWVY04dQLaXoO5dh2U82CId9Yo4/NexK88K2BObdZa7whjJS0HDIHe8ke6Z8kUpSORFqrsOwTEGwKo/l1yiNFjAWYEn6c4Djw2CNVrr3Oyx13+Tl90mJs1MHjgV2X6Fx73cyJ0uMV+PyC8oBDJuPgOK63VVev8FPv9eNduD1l2VA0wbXvbVm0WEJKSfehHD/6z16M8szJJtC5kdiV1xFYtwlfcyuJcplyzznGnngAAL2mjtiV1+NrbEZaFrkjL7saR45D9LJrCK7fzND938QxqtTceDuKrjH21MPo4RiJG24luHEbTrVMZOfl2MU8A9/8vxd1zeGEh1231S2ae+s+lOPQI6OTZe9KwIsaj7hCf7aDU6qg1UQxh5I4RXd1q4T8aHUxhKYhqwbWaAan7I5VoWto9XHUoH8yZGsls1hjWQbPFHnpu0Pc+LOt84Z9hBBsuCpG85YQudELpEYUBb2uFlQNO5PBKS2PgNc2JcmeElZ1ee9qPmlQSJsLhk2lI+k9msesLM9IVUs2pez8RkpVBYGojp0Z5/GzJebwzFJt23CN7sQzAHeRsvuOOrzB+b0oKSWnXkxzdl9mRnhNCIEnGKVqGUhFxROIEqxtJdd/En+sEW8oQbrrlWVdp1V1eOHbg2y7sWbBUL43qLLu8igHHhxelsbWauENYaQAUASeRBAt6J1xw6sj+TnlzJcKLaBTs6MBf12A3PkMg8/1rMLJvjp48P4Kd97l453v9lFbKzh72iYUcfNOR4+YXHfD1EuYTDq8vNfk+ps8/PbvhzlxzMTnV9i+U0PXBf19qzs46zsDtO9avIHRKDu8/MDwnEn1Us85qqNDNL/vo6Sfe5LS+TNTHGGaTvyqG0BKhu/7JnptHYnr3kRloBdjdJjc4Zfxt60juvsqjNQY/tYORh78DtIwMNJjjDz4XRp/Kkjp3Clyr+xdFYmWLTckCNcszGhumQ4nnk2Smcbm7V3fQvy9b8LoHcbb0Uj56Hm865spHzlH5gfPIHSNxPtuRfF7J0NL+acOUD3dB6pC8JodBHZvwM6V8F+yEadQInXvE1hjWayqW9p96Vvq552UAbwBjd131HPq+dQMh1Lx+/GuX4fR17+inGWlaJEbW753ZlbtRY1UtWyTHqjQvl5HW2QxVCk59J13DYtlOAtOxkIVi4br5kKk1sOW6xILjnmz4nD6+RT5CwplpGNjV8suObVlUM2N4ViGy5yjqEjpIFQNaS+v6bzr5QyFtEG4Zv5nL4SgZWuIaL13XhUADQ8KCgbzhwQVFOqUVkacPiRLX5S8IYyUGvTS+p7LSVy9blrflItz//Y06b3nV7xvu2JRGsoTqA9SGl5bBuTVRibt8Kk/zvK+DwZ481t8XHG1h7OnLP7P54ooCmzbrk1Gtxwb/vYvc3zoIwFuvMXL1dd6GRm2efiBCo8+VOFjvxHGmKMMWShiRQKGHbsXiYUz3oA5WOGVB4fnjmLYNk6lgnQcHNPAqU69IIrHg6+5jeTTj2LmMlilAs4Ve/C3dmKMDiNNg/RLT1N3293EonFGHv4+xtg4P5/juPtybOQF+70Y7Lx1cZXoQtLgzIvpmSqqwvWgMt9/hvqPvxs7VyT76F7Ct1wOP3gGNRbCt6WdkX/9HtZwypV6r7gLMzXkJ3jlVvJPHaB85CyRZBZvZxOVY1MCjf0n8wyczBOp8yw4gW6/qRbdp86YwOW4fLu3tRVZreIUl+dJTSgkLxe2IankF/5eKWNSSBn82h/VUlPvzguKIlBVt0Bies72+SdKfO6v3ArGCbHI+SCEK+a4XGy6Jk4gOv8iRUq39+/s/sycRQqF4S6EqmEbFYQQVLKjSMeikhlG0TzLEjmdgFGy6T2aZ/tN8xspgLrOIOFaz7xGKqrUklAaOGcdxma2oVTRaFU30q5tZaw6gP2TZqRCG+oIb23izGefoNw7MxxhVy4+8VzNVhg5MIBZvPh4vBDQ3KzS2qZgGHD2jEUut3ZlM+e7bP773+T5n3+bnxTGtG33PB55sMJ0Sq2ebpu/+3Sev/+rqW0dBzwxH5/+J5V8d94NA+qqG1K1HRqubiN7Nkl5tDQpX+6YCw9A3afQvjO8YNhjAq88MkJ2ZAX3XSjo8Rpq3/QWnOq4J62IKXFDwMqmsUtFtGiM6tDCGlwXi3izj+bNC+f1pJSkByr0Hp1dj+qUq1ijGexMAXMkjZ0roHjc19dK5aie7SfxnjdReuU05SPnsHPj+Swh3DAh486glG4eZRoKSZPzB3NsujaxoMcRjOusvzzG8aenytGlYVDt6UXxeLELy8+hGWV7RRIotuUsmmsq5y2KWYtP/urQpPF960+HqalTuf/ePNmUTV2Txjs/GOHwvqmFiGOP8/rNAyEEquYqDi+5f0gwb1n7dKQHKwydnvs+2sYUf6R71Kn3wjFXFi2yTIeBk4VFi1A8foWmjUHO7cvME1SQtKgbcaRNl30UZ1pFrAcf67QdtKgbSTvDy/Ki4A1ipBRdpdyTpNg1laBcLahejdimWoqDOeQqNLS1tqr8t/8vzC1v8lAuSb74hRL/51+KXAT/4qKQzuwiaimZ85hzNeqqXp3whloCrTFG9vUR7ojjS/gZeKYb27BRNJVAfZBQewzNpzH4XPeC9yqU8NBwAaHoXLBNyaFHllD4MiGFOv36HJvqyCCZ/c9TPn92atNpL3Nwyy4Qgkp/L7GrbyT51EMziDClY89dBrkCtG4PL8jy4B4Qug/n5p58J2cGibQnij3G/2Q7jP3nQwR2rMO/awOByzaT+e6PqJzswc6XKB04ReTOa/Dv2oAaDpB/6sCs3XcdzGJW7AW1nBRVsOma+AwjpQQDeBrqcYol9Noaqj3L86TMqkNl3DPr2Khz/W1B4jXqDEb0I/srPPnAzInbcVxvaiFUizbVokW5NK73Blx7S4DPfTpJX9d4WDRr8OgPCrzno1Geftg9xoT8+kJwyVuXfp3hhIfGTYsXH/Uczs/0otcYjiVJ9S8eKZio8hSKmPPdzjpJ+uzTtGmbMKnSZ5/BwcYvQqzXdtKgdDBkn+ecfQRnmUbqtaeZXgVUR/Po0QDBzhpUvwfVr0/+ExdJe+/YDo7lENtcS7A5fNHnummzyrV7dIJBhdo6lbfe7UP3vPY0TAtCQCVVQjqSUGuUxI4G/HUhNK+GogoUXcET9YEj0QI6enjh0EEwqlPTOpuW6UKMnCuS7FuEfVw62IU8wQ1b8Levw1PfCIBTrVA8fYzIjkvxtXbgbWwmsvsKFN0NMXrqGolecgWZfc8y+uh9+JpaCW3eOWPmMcZGCK7bhL9zA77WjkXPdyE0bgji8S/sOUrpyqovG4oAy6Z44BSpex/HSmbxbe90P7NdUU5rJE3+yZdJfe1RysfOz9rFwIn8onQ7QoGWrSFUbeoeOaUS0jBRwyGs3NI7EidgGQ5GycbnF/zcbyXYdqkXy5bsvsaPogh2XeljqH+O1dQ4199CMCr2nAZ/yy4vgaBbKBQMCXZe4cOoTu3LVdVdzEiJZUmLNGwI4A8txtEIfcdXxi6zUkjp9qnZi0Q/ABKt/nkNs4VBl3WUQbubTm0HTWonIRFjh34tDUo7vfZJTlkHqMjle9tvCE8KReCpDbL9j99GeSCDXbUmY7p99+4je3jloRxpOQw+34PqUdECFy/jbVluuG0C5fI8OtMXC0VB6Job3jFMhNfjUrbYtquY6ThI00L4xv9u2YiAD1k1wHZQAn6kbSOrBnbFpDxcQNFVKqkS6slRVI+GbdqoXo1gS4RqukygKYxVsTByC6/MwnUeInWLNLMC5w5mMRep+pK2TfLpR4hedg3Ry66hdO4UxsgQOA6Zfc9jZjKEtuxEKAJjbNj1lIQg0LmB3KG9lLvdHqjk04/gb1uP4vXhVFzDmHnxaWLX3ET00qsxkqNUR/pBKO490sYVhg1rMpyGBDUWBtvGzk7lLzWPoLbdv2glo+NIeo8sf6L3tNYTvuVy7HwJFIEWD1N88djk52o0iPB60JtrkZaNGgtR7RqYQdNTSJkk+ytE6hZeYIRqPMSafCR7xxcPQlA5340aCiLN5YdlbdPBqNh4PYJQWOF/fnKUalkSr1H53F+P8ZZ3h9lxuY8Th2aGs5ZiSMyKM6uy7xtfyPCzvxrn1reHKJccQhEFKeGf/3paCHMJr+Ryl5V1nYFFFylI1owncSGYFRuj4uBfRBE53uRd0DDbmJyxDiKADdruyb+fsQ7Ra59adphvAm8II2Vmy/R/Z3YIA5jk8lsMmga3vdnLjh06+/YZ/OgpA6EIQi0Rgs0R9JA7qfY+enaRPS2MkycsHn+syt13+0ilHO75SonqGlS16y0NeNe3Y2dyWOksaiQIQsHsG8LT1oQ5OII0TDwdLZgDw9j5It72FpfYNptHb6p3xcuOn6WarlBNTxme8sjUizTw9HkAAk1hRl/upziw8CQrhKtAuhjTuZQwdLo4ucKL7LoSb20D2cP7MMZmNu0aYyOMPvKD2fuwTAonDlE4cWjWZ5m9z874vdx9btJgTcAul0g++aB73h4d/85NCL+XytGzaA01aDVRykfOoNXE0GpjlA+fxre5A7tYwj58ZnI//ohOuGbhogSASt6ikJo90Rv9o2Qf3QtA7omXMQfGkJZF9ocvAGCOZigfOYcaDYIjSR88TbXL5axU4xHUeARZreLtbAJVwdveQPpbT1E5NbNSdbSryLpL5+9bE0LgD2vEGr2TRkr1B/BuXI9ek6B86gzGMgsnbEtiVR28HgXTkjgOWLb732hMZXTI5uqbFl/QzL1vB+sCwuF9z5QZ7LVo36ATCCoU8g7nThoMz+WtrSJqWv3oC3DlgRtmzF0EP+NK4diLe6XghuldV2r+bW0szllHAEGLuoF++wx99ukVGyh4oxipdInkc2cRmoKiqziW49IjLXw/Z6C2VuEDHwpw7R4P2hfhR08ZSEdSHh3vjZKg+i7+dg0PO/z1X+b57D8WsUxJMumsOmErgOL3ofh9WCNj+DZ2IDQNK19wV9KRkBvCO3EONRZBOq7npLc3IytV7FwBT3sTlZNdS/byllr5KBRINC/edGWULDJDlcmqvmDnRoLrt1A6f2aWkXo1IDQV4dFxihXUeATf5g6k4yBUFTUWRgkF3GbOUhk7NTNk4wuq+Beo6ppAsq8852Rhp3KUx/dZPjy1SCq9fBIAWa5O/nwhvBtbUIM+kl97FKdYRug6tT93N572htlGqntxYUdvQCM8jZjWLhaxUymM7l4cY3melJQSy3Cr7ExD0nvOoLZB5cwxm0pZ8vFP1BBNqDz/+PK9i0nRxAvyO44D/T0mwwPWjNCV7nG109YCuk9xFymLhAcVTfBb91y1omrZi4HHp05KuCy4nV9FAD4RxC/my69JHOkw6HThET4iSg21SgsmU8Y37YywHI6lN4SRQkCgo4a6m7YQXFdD8tkzjD17hvDWJopdo5iZxV++hkaVrVs1NG0qVy40hUBThEqy5IawMqtzurmcJJdb26Y4O5vH6BvESmWxCyW0eBQ775LWWuks0rIRuoaVyiCrrkG2RlPIchk1FMDOFdyVv6rOjE/Oh6W+WEIQa1jcSBUz5oqqvtYK0rIxh5NIw8QpVaic6EIJBXCqJnYq64ZLVRUrlUONRTD7p+TmvQEN/xKon3KjxqIhrOXCGk4hfF4ib74KWamixsIIj0bpyOyIwEIM5BPQ/QqB2JTBFbqOmUrj5FfWnmGN0/5UK5Iv/a801YrrRX3r37O8+R0hjr9S4cFvLz8ECsxp8BtbNG68I0hTu9v/NxG46ztv8PV/W0E+cAnw+FUCkcWVwIUQ1LYtnqt9reDmnwUt9gbWq7uW/L3d+o2TP0skT1S/MWeZ+nx4QxgpX0OEjg9dQzVZxMyW8TXHEbpK7XUbEALSLy/cgCsErFuvUlevzHAchBDENtUQuL6d0lCefE+W9MmxNb6a1YE1PIY1PHWuZu+UZEnl2JlJD6ly9PTkz2afu42nvRlrNIVTqax6vkwINye1GKole4bo3WsNaZgYXf2Tv9vpKW/JmOaBmH3DmP0zPT2PX8EXWrzcvpQ1Vz09afSPkv7m4+hNNQhdw+gfw+gbwRpOzdp2Bg3PPNA9yoxVt9A0Aju2Y/QPYKVSy+uTksxI2LtVeC76u02+/I8XJ7g3l5H66V+MUd+ocvClygzPKTmyduE+j09d0vN/PUDTFJL2EM4KSRwlctnVfW8II+VviWOVDHrueZH4FR0E2mqQpoNVMlBDi6/aNQ327PGgqgJrWnjAMW0Gn+3GG/VRzVWwq68+JciaYPpMOMesaPQOInTNLahY5VikECwptOAaqdfp/b7glqq6siQxx3LOWv0aGtvB6BnG6Fk8RLoUIyUUgcevIBS3tUFWKhgDg+O5iuVXqTrTPPBITGHTdi+NbRratKrc86cNXtm7zIbqqarzGdiw1cM//cUYZ08YM1+DZZ73cqB5lRUxVPxYQkBGjpC1V86JKpd5t98QRkraDormxksnrl/oKnrEv6S+qUBQcM2euVf3ZsHALKwuqeZqIxAQXHa5TlOzimVKzpyxOHHCwpp26U3NCrt364QjCsWiw8kTFue77LkjeeMVgRdCUaBznUpbm0oi4cp4GIYkn5f09tr0dFuUF42sikWF3sBdYV/YUCndr6Mn6vA1NKN4vDimQXV0yGWLuHB1N97Q66tvQvH6kLaFkRqlOjw4gz7G37YexeOhMtSHv209AkG5rwu7UibQuQktEKQ6OkRlsPeC/Qu89c14aupQdC+OUXHPJTU6w7grqkDRFp/ALdN5zeQQYCr0thg0j9vIajvudKPo+jgd1fJPfuKRebyCX/jdBJ2bPAz3WzjTLEil4vDK3mXvek4M9pnUNmicucBIrSUUVaAuUjn3esNyDc3F4A1hpEp9aYSusv5XbgEJesRH50evQ/XrFM5dYPEFRMKCdetVtm7V2bhJY906ldbWCdoUuP12H21t89+aQsHhP/9vicOHFg4RXHeDh/e+14/XN/8E9fSPqtz79fKS0j4Ad7/Nx1vf5sOxJZ8cV9H95V8J8s53+amrV7AsOH/e4j//b4lv3lumWoHLLtf5zd8OcfnlOoGgoFyWnD1j8e9fLHH/fZXFG4kFbNig8sEPBbjqag+NTQrRqIKug2m6jOvDQzanTlnc85UyL7248ASgeRefsB1bztk06O/YQM0Nb8Zb34yie5CWSXVshORzj1E4dWTKM1RUojsvJ3bFHjyJehSPF2nbmNk0uWMHSO97BqfshqYiu67A39RGqfcc0V1XghBkD76EVcyRuOZmVF+AysgAgz/4GsaoKxsudJ3YZdcS3X0NeiyBouk4poGZSZLe9wzZg3uZmLQVVcySS5kLr2YT55zHX0KvDLieoRiv8lIDAfS6WpSgWziy3Oq+icfl8QraN3j4/N8kOXu8OsOBX015kOE+i//yW3GuuMFPctie3PfIkMXj960N7dlSn///w9x4QxgpY6zAuX99isY7dxK9pBWhCMxsiZ57XsQYmznw7r7by6f+MoLXI9B0gaa5tQETSU1FEazfoLF+w/y3JpVyePjBKiyS/GtrU7n9Ti+h0PyrqFTK4Vv3zjRSkZjCui0etl7i4/jBCoemhTo2bda4661uL8t3vl1h+06N//LzQbxe9xo0DbZs0fj9PwgzNOjQ023xR38S5rLL9UmW63BYsPtSnT/9VITubpuDB+b3NnUd3vYOH7/9OyGaW9QZ9wrA6wWvV5BIKGzZqnHjTV7+778X+fcvlshm5550l/LCztWrogVCRC+5ktzh/Qze9w2kYxPevJO6m99CzfW3URnsxcplAAht3Eb9He/CGBtm4Lv/SWWoHy0YIn7NzSSuvQVF0xh96sFJj8dTU0+pt4ue//gctbfcRezya6mOjdD3tX/D39ZJ7Y13Etq0ndToECgK0d1XU3vjnZT7uxl55LsYqSS+xmYS191G/W3vwCrkKZ45vuh1zgXNG8QbTmBbJmYpiycQxTYrWNUSnmDM5Sq0DFRvAGmbVPOrpJq71Hl02nOxCwWsdAYVsDKZFR9aSshnbcpFhxW0Wy0ZgZBCzzmTcFQhOO29VNZwJhQKF00q8OMOnwjQoW4j64wx5HTP+tyDj836ZaSdEfrtc/zkVfcBRrJIz1dfhK++uOB2Hq+7qnH7V8dXugqEw+7kK6WkWoVKZf6bmMs6mObiN7m/z+bZZwxqahQCAUEwKAhHFCIRgbZA+Mc0JcGgyrmTBpnU/C7WO97l4/IrPJw7a3H0qEkioXD1NR7CYYVYTPDRnwtw8oTFpZfpHDlsceK4STyhcP0NXvx+QTwh+JkPBzg4jyicqsI73+XnTz8VJhRWXALMvOT4cZO+PptyWeL1ClpaFHbs1IlEFBIJwcc+HiIQVPiff5+fswfswt6VuaAoYk7piMKpYySfeRTHcHec2f8sgfb1hDZtR/UHsXIZhKaT2PMm7FKRkcd+QLnXJVO1i3lGH/0Bqj9I7Irryb7ykqtBBdjlIoXTR6mODJA/dpDw5u0UTh6iMtSHXS6RuO429EgcAC0YInbptdiVMkM/vHfSMBZyaaTj0PS29xO7bA+l7jNI08Sx5ZK8pAkmB3+imUjjRkrpAUJ1HVSyI/jjTZQzw4Tq2kn3HiVU14ltVtD9IYxSdtns13NhIUqk6bBNZ7JMWqgq0rax0xmEtvK8i2lIsimbD30sxt5nypQKU6HPoX6LcydXx3J99tOrZNCXAeksrQ9pgrV9tSs8Vwu2Of91+AhSr7bj4MxppCQSDS/N6noG7fMzuP0WwxvGSM0Fb10Y6TgYyak+i2d+ZPCxX5hZNVRTq/APn42hae7C+uGHKnzl/14Qtpj0HiSmBV1nF58U9r5kcOqUhc8n8HoEXq/rCf3m74To7Jz/1peLkkN7yxjjPSTz4c23ezl21OKvP53nxHGLUEjwm78d4oM/40dVBVdf42HXJTpP/8jg7/82z7lzFsGgwn/7wzDv/WkfILjyKp1QSFAozD7QZZfr/MqvBQmGXON98oTF//j7AidPWKTSDqYh0TTX2F12uYdf/bUgW7dp+P2CD37Iz6GDJvffd2HCWy7KIgFuiOTC1adjVCn3nZ80UOBOAMbYMGLrJSge18P0JGrxxBJUhvqojofnJmBXSpR7zro0Sm3rJ42UYxrYpeLkNtKRbm6Jcb4/6VayAWjhGN66RnJHX8Yul2bw+xnJUexyGT0aRwtHMVNjSzZSmkcZJ+61MCsFzFIOggI9GHXlGByLSj6JVc4Dkkp2FEXVEEJZlQyBuoTiDindRcakkfJ6UYJBnFIJNRLBzqysjFtVobZBo6ZepbFVx6hOGannnyytmpHafbVvvPR8JvJZh5NH1qaR1n3+i4/50fMlvvGnJxZ8P5r+y6342uuo9I4x8o1nsIvLPOdl9I7OgpTzSphoQseDl6wzd/WzjUVZFogq7csur1mWkfr85z/P5z//ec6fPw/Ajh07+OQnP8ldd90FQKVS4fd+7/f42te+RrVa5c477+Rzn/scDQ0Nk/vo6enh4x//OE888QShUIiPfvSj/PVf/zWatvr2Mn5VJ3axyuhTU7K0o6MOo6MzB0FT01TpuZQwOGjz0ktTITCh6eh1tTiVCnY+h6exGZs8ireMnqjBLhWxsplZx69WYWR45rEsG0qlhUdJJKawcZuXLbt9HDtQ5pUXZ1c2TXh9936jzIGX3XMtlyVf+Lci73q3j1BI4PUKslmH//yPEseOuka1Unb46ldKvOd9PrccPCxo71AnP5+8d3HBz/9ikPXr3dXxkcMWv/c7GU6fmjlIDUNSKkkG+it0n7f5l/8To6lZJRQSfOzjQZ57tko6PZMXbSkidLpfQb9ADkHaFnZpdt5ggnB1QqVVDYRAVbErldkyG1K6hsWy0KPxqb87zpQ3Mh5rnJT9ljNLxbRQBKGqRHZcRnjLBf0iQiA0HSM1iurxYeIWJCxF1M8f0RACCqPnKY71IKUDowKhKO75SYdKzjWcmd5jIB2y5dyqJW2C0SW8g1JiVuzJnJE0TYSqoNfWIpFI08QcHFp4H3OgXJL82W/MXYG4mrm6D/xSjHDUHVdCgM+vEK9Vefi7+TUzUrYlFyXDBdd+DJ0pLljVOvSpB4hcs5n4LTsZPFfGzi49Byg8Gr6OOqp9SZzy6sZUFVQU1BlNu9MhkdiYaHhYbhXosixDa2srf/M3f8OmTZuQUvLlL3+Zd77znRw4cIAdO3bwO7/zO9x///3ce++9RKNRfv3Xf513v/vdPPusS0Fj2zZ33303jY2NPPfccwwODvKRj3wEXdf5q7/6q2WdOEDs0jbyp4ZRfTqR7U0zPxSC6LZmUi/Pdj2XC29TC8Ljwc5m0SIxvA1NyNp6Kv09eBubKZw4ctHHmA7TkHh8gpOHKqRG5x+wpaLklYMz80m9vTYjI85kHmx4yOHIoQu26bEolyXBoBt2rKubvYLevEXj+htcKp98zjVsZ8/Mfy5SwuFDJg/8sMLP/6LLcN7ernL9DV7u+8FMQ7GUUmdvQMN7IdeZlEtqGpa2BVIiVAWhKJOaRxMQigqKgmOtrFl4wpiV+7op9Z6b00jYpSJWwS1sqZbtJcmjB6P6OOuMRMqJc57Geg5TibqJY65iVUFwEX0vcA1uOT+NKd4wKL58cFWOX10gxL5a+OSvDU3LP0OiTuXu90Xo6167xnGr6lAtLf78/WFtUWZ1WTWx8+UVhQT12gjx23Yz+q3nVt1I2VhYmPhFiDQjsz5XUPDgw8Rgua7csozU29/+9hm/f/rTn+bzn/88L7zwAq2trXzhC1/gq1/9KrfeeisAX/rSl9i2bRsvvPAC1157LQ8//DDHjh3j0UcfpaGhgUsvvZS/+Iu/4L/9t//Gn/3Zn+HxLI+jq/bmLZQHswRa43R8eA9Gqjj58IQQ+Bqiq2Kk7FIRf0MjTrmE4g+gBkNY+RxIiZEcw1m87npZKJckR192mw3na1OSUlI1YKDfvuDv7t/Wr9eQUjI2Zs/yHC0bMhmHYFBBUdwS/OkQAm6/w0ck4uahBgZtXnjOWFLL1GOPVfm5XwgghCAUFuzYqfHD+6cqsqVkSfxk/rCGdwksDXPBTCeRpokWjKCGIljZqfCuUDX0eAKhqJOVesvefzaNXa1gZtMkn3lkhrzHXDBKNuUlGKlwrWeyau61QGQBZd4JGFWHUu7HhwlkuXCLMqbu70CPxXOPF/ngL8d44JsrY7ZYDNWSTTm3+PMP1XiW1KowJ4TAv7GJ2M078DTGsVIFUo8epHxqAID4m3cTu3knvo46fJ31SMMi+cB+cs+7dFpaIkTN3Vfi76zHGMww+p3nMZPu/fA0xoneuJ3yuSHiN+9E8WqMfPM5yqenCAIMWaEiizQp6xi2e2YxSvhFiITSSN5Jv3p9UrZtc++991IsFtmzZw/79+/HNE3e/OY3T26zdetW2tvbef7557n22mt5/vnn2bVr14zw35133snHP/5xjh49ymWXXTbnsarVKtVpGfhczl2hnvnHx8CRBDtqGH70OP3fPTC10hSC5rfvnmt3y4aZHMVMJ6dYGrrOuqEYKTFZeVPbfIjEFO58T4TOTR6e/GGBvT+a26UvFZ3ZuSQ5FU6UEkZGZnMDSgkTaR0hwHOBVIiiuOXzExgccOjuXlqis7fHploFn88tfmht0wiHxWSln3QkmaHFjVQwpi8qtT4frEKO3PGDxC7bQ2T7pWQOvohTLiE0HX/bOsLbdlMdHaTUvTKyYCufpXDyMKHNOwmd3kGp5yxOtYoQAqF7UAMhHLM6aRzLeWtJ6rM1rX6U17AKrL5jcUoeo2RTTL1+jVT7Bn1GdamquRpT6eTaNY5XSzbFjImUckFqJM2jEG3wLsmgzYKUoAjKpwfI/Ogo0Wu30PRzt9H1p/cgDYvci6ewc2Vq33E1Q//xBGYyj11wIxxqNEDTz9+OOZpl5N7niFyzieZfvoOe//k9ZNVC8enEb9mFngiRevBlFJ8HY2Rm7rEos4w5A7SrW9ilX0+/fYaqLAMCvwixTtuBLrwMWGfXnnHi8OHD7Nmzh0qlQigU4jvf+Q7bt2/n4MGDeDweYrHYjO0bGhoYGnJXrENDQzMM1MTnE5/Nh7/+67/mU5/61OwPxkM/xfNJKqN5l1R2Gko9SRxjlQbftJl+KhSzNnBs2Pd0iWLeIZee/1hzFTtMfB/Gy3rzc2wjpy5HMKU0MYFEQqG1ZSrU1tSk8Pt/sLhgG0A4rEyGLISAYNDNjU2sXqWEsZ7FPU9FFdR1BFA1saKcRHrvM3jrm0nseRO+xlbMbBrF6yO4bhNIydhTDy6bEHUCTrVCeu/TaNE4DW95D+W+LqxCDqFqqP4Anngt2SMvk3ruMcBlksiOVJGOnMybzQV/RCNU46Famnl/hICOzV4uuzGExys4/GKJEwdKrEAtfEHUdQYW3aZSsMgsgePvxxX/5TfjhCJTY1sIKBcd/vNzF0fBtBAcy12Y2aZEW0g7TkDTxuC8yryLoXyyn/LJceouxyF8+QZXTgaw82XMTAHHMDFTecyxKVovX3sdeiLE4BcfwUoVqA6kWP+XP4uvvZ7yadcTk1KSevggla6584YODr3WSXzCT4PSSa3SjImBQKDhwcak2z5O0ll+9GLZRmrLli0cPHiQbDbLN7/5TT760Y/y1FNPLfvAy8EnPvEJfvd3f3fy91wuR1tb2+Tv1ZG5hcKyh/rWrqt8Ql99+s9iIgdyceXAxYJDpcsgnbQXLNeuVhe/OGMFzM71DQrq+HsshGDLVp0tW1fm1Xi9oE3/qnTZvqslC29g4eHXviOC5lGwLRvHNLGrVVctdwYk0jaxK+Vx1gMXVi7D0A/vJbLtUiI7LiXQudEVQuw6Tfbgi1SGB5g0nJbpVgyO53ek42BXK5OWXOJWFsppOazKUD+D37+HyM7LCW/agb+lw20WzmUonD5K/vgrk9s6tmSsp4RlOAvKNQghaN0WmtJqGkdTh4df/mQTR/cWKeZsPvDrdXzzX8Y49PzqaQ8FYjqJ5oU9KSklpaxJZmiZFEU/Rvi3/5Ga4Uk5jqRYkGTW0JMCGDlfwijbC9JjCQEt28IceGB2TmdRCPC11xO9YRve1hr0mjCehtiS5IPVoA9vWy0df/gepOWAAMWno0WnFi1WuoBdWnhxUqHECXM/w0ovCaUJvwgikZRkjhGnl7yTXhax7ASWbaQ8Hg8bN24E4IorrmDv3r387//9v3n/+9+PYRhkMpkZ3tTw8DCNja5aamNjIy+99NKM/Q0PD09+Nh+8Xi9e7wLxckW41W72TDdy1byoC6BH4uiRONWUO5i8NY1URvvxxurw1jSQPXlwxoS2HPj8gpZOHUWBeK1GqeBwZP/ck8JSckQryatHIsqCK/7lQAgxK8RRSJuk+is0LSKn3b47gj+iUS3ZDD/0bfePF646pCT53OMkn39i1mdWNk3qhSdIvfjktO0n/28SI498b8a+S12nOPOZT07+7pRLdP3L3806rpVNk3r2MVLPPT7r3HV15oQ/cLJAtWwvYqRg3eUxXnl4Zgj5rg/F6T1d4Z5/HMUlphdc/5YwJw6UMFap2KB1awiPfwkaX2eKP1bEv8vFQI+Fzy+oa9Lw+QWlAhTza88ROXS6QLVkE1hEsmXDlTE0r7KkatDp0KJBmn7hzeT3n2HgXx/C0xCn9dfvnrnR5FC5oLWjalDtGWXwy49j58cXSFJiZaYtgpw5uuvngEmVEaeXEad30W2Xiouu+3Ych2q1yhVXXIGu6zz22GO85z3vAeDkyZP09PSwZ88eAPbs2cOnP/1pRkZGqK+vB+CRRx4hEomwffv2FZ9DoDVOZEcLI0+cWBJX38UivGEHQtWQloXQdELtm7CKeRzbxK6WZ1WTLQeqJvB4XW2bStmhkH/1JwR12qhwHMmLL5g89eTKQjwD/TbZ7MxrKKYNxrrLixqpYExnw1Vx9v9gaPEXZKHPL/hMqAq+dfVUesZcZd25vjuHMVzq/hVFp6P2Gs4MPTH5t75jecpZi2BsAckGAe27IniD6ox+lI7NXnx+hV/6I3ch17rBi2VKdF2smpHqvDS6uCifLTm7P7Mqx3utEK9V+dDHYmze4arM2rbkyP4qX/l8mkp57QpWkn1lMoMVYo3eeZ+/EIJovZemjUF6j85TxKEIFI+GUBUUj4Y9HkkXqoLid3NFikcndNk6tNjM8K1TNREeDf+6elDALlRxihWqvUmsXJnA5hbyL59FaCqehijG2NwRqlcbyzJSn/jEJ7jrrrtob28nn8/z1a9+lSeffJKHHnqIaDTKL/zCL/C7v/u7JBIJIpEIv/Ebv8GePXu49tprAbjjjjvYvn07H/7wh/m7v/s7hoaG+OM//mN+7dd+bWFPaRF4GyIE19XCE4tvuxowMmN4E/XYlRL+5s7xcJ+DUzXR/CEU3YNjrCwkUsw7nDpSnfTSXwvOr0JejldJusc+fcrk//zLykJLc9EbFTMmfSfybL+lZlGF3ive1sDBB4ZXtVdGOg6e2gjmaA7LdvDUR3HKBtK0UcM+pO1gpgr4WhIgJZWBNEzz0lVFx5EOUtqoyuy+D68eJOyfmXstZS3Ov5KldoHiBCEE8SYfzVtDdO2fSkxXSw6jAyYHnnV7xA48WyCXsqmskkcTiGq07YwsKm9vVh3OvLh2uRsARXXH/PQzsR3JKhBqAPDen4vi8Qr+8S/GKBYcYgmV9/9SjLf+dJhvf3ntJmXLkJx4JkXnZdEFtwvXeth0TZy+Y/lZ741/czP177seT10UNeil/Q/eTfn0ACPfeBYrVyLzxGFq7r4SaVjkXz5L+cwg06MGxlCa7LPHqXn7VUjLYex7L1E4eA4zmWP4q09Re/dVRK/fhjQtikd7KBw455LJOw52sbKk9g+BQKAgFuiFWm7Ib1lGamRkhI985CMMDg4SjUa55JJLeOihh7j99tsB+MxnPoOiKLznPe+Z0cw7AVVVue+++/j4xz/Onj17CAaDfPSjH+XP//zPl3XSF8LKufkIxaO+Kp5UoesEha6TgMTIzOywTh95cUlu8UKIxlQ27vAyPGARCiscffnVzQGMjU1VBArhCkLOZWxWCulAz6EcxYxJpHb+xYkQgvZdETZfl+D4j1aRzkYynkMUBDY2ofh0PLURnKqJUzXRa0KUzg4TWN+AtGwqfTO1lzY03kKuNMBQ5hiXdL5n3KBP3RxV0d3qzwtw4MFhrnhbI2IBhyVS62HLngTdr+QmVWVferzAlbeEGDxvkEvbhGIq5YKzahN367YwbTvCi4rynd2XobBGlX1CQOdGnatvDlDfrM1YnB3aW1k18tddl/v4X382NslgMdhr8Z0vZ3n/L8XW1EgBHHlilNt+uQPPAh6r5lHYemMNLz8wTGZwZvSifGqA7k/fO+93kz/cT/KH+6d+v2/fjM+lYZF64GVSD7w884sSqr1j9P/zA3Put9ozRs/ffXve407AS4AGtY2oqEUVc5sWieSw+eza0SJ94QtfWPBzn8/HZz/7WT772c/Ou01HRwc//OEPl3PYRVEZyYPl0P7+q8mdHMSZpvtUODM8gxZp9TDPjL0KM3k+57Izt6/X6VolOpjlYGTEJpmcaAgWNDW70hzJ5OqFHrsP58gMVl1Z7QUmx0BE5/K3NtD9SpZSdnVmZcWn42mMY1dM7ELFZagY13mp9IwhNAVZNfE0xCge7WVWDit7EsNyx5RXC9M1/DSOnDo3XQvQEJsdvu45lGOkq0TjxuC856bqClv2JHjpO4Ok+t3FyXMP5Who8/CR32tA4no0j34rQ3r04vWnNI9g83UJInULRzIcW3LwoZEZ+k+rCY9P8OFfj2PbcOpIFXsaR1x6bPUECatVh0SdyrmTU3+rb9Yovgph9bHeMucPZNm8JzHvNkIIOi6JsuGKGPvvH36tWuaWDQ2dTm0bbepmAEwMdLw4WIBAFRolJ09aDv9k6kn5GqNEdrWgeHUiu1pmGIrzX35uUSN14Yu+hIKYNYVpwIEXSmi6eFW68Oc6/ksvGnR0uB3wTY0Kl12u8+gjq1d6XMqYHHxohLYd4QVZUhRVsPO2Ok4+l2L/fUOrQrDgVC1Sj76CtB0c08YYSiMdl9lBWjZmukBwWyvZF0+hRQOofu+MyqZMcULpWZAp9jCcncl2rileasIbZh23nLc4+NAwd6xbt2A/VPslEbbfXMuz97jVqcW8wzc+P0owrKJqYJtQyNmr4tkmmv1c/VNNi/ZnDZ4u0P1Kds0mTV0XROMqn/nkGP095ozjrOYhH/5ugZ//7QRXXl8hOWLR1K6z/VLvq0I8a5Rs9t83xLrLowtqqnkDKrd/rJNTL6TJj/14a9lNICgi1Cmt5GSKE+ZeqrLMlZ7b6bfPMGSfp0ldR43aTJ91BvmTqMybPz7Iwd/62oq/XypPEbkqCkTCr71AmWUujS18LSAl/PC+Km+920cwqFBTq3DX3T72vmTMK7+xErz8w2Fu/kgb0UWYDnxBlbf+5nqGz5XoPbIKIRkpZxBzXkjSKQ2L0ol+fB11lM8NL1B6KznR/+Csv1qOwfmR52b93TYlR58Y49I762ncOH/RiKopvOnn2zn1fIqRLreR26hIjMrqSpzrPoU3/0on4ZqFmV4sw+HIE2OTnt1awLElmZRNOKqsqn7UhXji/gL5rMO1NwfYsstLctTiH/58jGMH1773S0o3ZNpzOMf6K2ILRhDq1we581fXcf9nzs6gofpxhS58eIWfE+ZectINj9tYONhUKNFrnyKkxGlRN5C3Usvypt4QRupiYRqSVNKhsUlFCGjvUKmtVRgbe/2W2l4sjhw22fuSyc23eFAUwe23ezl+zM8X/620ZEX5SESQy80/GHOjVfZ+d5DbfqljwRdWCEG82cc7fn8j3/rLkwydWYvw7Uw4VZPSOKXM8iHJl+duWhw8XeT400nqOgMLFo3Em3zc/sudfOdvTq1amHM6hAKX3tXArltrF9xOSklmuMqhR0aWrNy7ElgWVMqSj/1BDYf3V8hl7EkX6uxJg5efWx3qMcuEF54o8eKTJVSVxQU/LxJ6YwJzaCqnmewrc/DBEdp2RPBcyE05DYoiuOLuBpK9ZZ65pw+z8uM9FykoKKiU5dS76WCj4pbcW5jknRSt6iYECvInRaojcd16Mvu6Uf0e1ICHytDKwhGW5Ya33v5OP0K4goAf+JCfr321TDbrYNuM96eApglUzSV3XajS3O8X6Pp4tZIiUFX35/p6Bc+0KqpQSNDcolKpSBzbLYl1HLBttxF3Lj2mVwPJpMMX/k+RznUqHR0q4YjCb/9uiLY2la/fU2ZoyKZquEUQQoCmueKHobBg926d2+/0USpJfv935pducCzJy/cPs/WGGlq2hRY1VBuuivHTn9rKff/jDOdfyS1Jo2c1oOrC5bado8JQU71E/M0EvHHEtIqIipFlNHdq1vaW4fDMV/vZ+aa6BRkehIDdd9aTG63y6L91r4wqZ759K7DzTXW85VfX4QtpC9536cC+7w8ycHJtVGsnoCju+5VOWjS1ajQ0T93LfG71+pg2bPPQ321SKcmLNlBaIoIaDOIUyliZAp6WOqxUDqGp2PkSiteDf8c6hKZiZYs4+RLSgZe+O8ilb6lf1JvyhTXu/LV1qLrCM/f0UXkVPSrdqxCu9dC4KcjJZ1KLVtfaWNhYeIWfonTf+aqs4BfBcaPk4ODgEd4FK//mwuvaSMV2tZE90Et4WxPhzQ30fOXFWQ29S4Flwfe/X+Gmm73E4grBoMJv/FaIq67ycOiQSakk0TSX4icaVRAK/Os/Fzl3du6XR9fhF385wJYtOv5xsUO/XxAICiIRQTw+tYK+8y0+rrjSQ6noUC5LikVJuSwpFSXPP2fwtXtWl7x2qZASnn3G4LP/UOQTfxQmUSMIBhU+/NEgb3+nn2NHTQYHHapViUeHaEyhsVGlc506SUz72KOLW9ihs0Wev7eft//+RnzBhYejogjWXx7jw3+/k0f/z3kOPzZKbnTtYvaROg+t28J07I5y9Mkxeg7PDjV21l1HPNRBqZrEmRanWuhFTPaVefifu3jfn26ddzUthMDjV7npI22oHoXHv9C9KteqeRV23VrLO/7rJuJNvkW37zmc4+n/7FvTEBy4XtTf/MHq82BeiN/4kxr+5W9THH/l4ld//p3rEbEIRt8YajqHp7UevSGBlcziXdeEUzYQqoJWG0NLRCgd6QLHoVq0uf8zZ/noZ3YtGOoWQuALatz1G+uobffzzD19DJworNniTAgmWyA2XhVn+801qLrC373rRWxr4YWCISuUZZEapYnUOPVR1hmjWV1HrdJMRRaJK/VUZeUnq3DCkwgS6KhFD/tQ/R48icAsI2UVqosyT0gJL71g8K1vlvmZDwfwel3S1Ztu8XLTLbMH0dCgzT1fmV/HRdPgTbd6uezyxVnd/X5BW5sKzJysHMf11F4rIwXuffned8vYjuTXfzNEZ6cbDo3HXXXf+b/n5viWEhZ0bMn++4fZvCfBJbfXLVoGDRBr8vL239vIrtvq2H/fEK88PIJRcS4uwy7c+o1AVGf9FVE2XBWndXuYuo4A4RoP/Sfy9Bye/aVYqJ0zg0+QLfXNkE9Y7EU88vgYm68b4Yq3Nc6pQDwB3aty/QdaSTT7ePyLPfQcXrkHGanzcOPPtHH1TzURqVt4bEopKaRMHvtCN8Xsq0MoKwS0dOhceo2PQEihv9tk3zPlVS0eMqqSUnF1LK6smtjpAna+hN5cixYPY1YNqucHib11D8UDp5BWHC0exkpmZ1RodR/O8cxX+7j9VzrRvcqC417VFK56RyMduyMcfGCEl74zSGaocvGFM8K959E6Lx2XRtmyJ0Hz1hA1rX5CCbfpPD2wtDxkSRbIO0k8YmpeGHP6ade2sEW7AhuLgAjTa5/+ySqcKJ0fY+Ovvwk96kfx6sQva5/2qduMeu4LT5Pee37RfeXzkv/+dwV6emw+9LN+amtd4T59nMXEslxZ93JZ0t9vLxiGcxw4d85G11f+cksJfX2zjevIiM3RI67bf/783Ma3p8fmyGETx4Hh4dkDwnbg9GlXU6pclmQy8w8a04TvfafC3pdM3v8BP3e+xUc84XpVHo/r3TiOxDShUnF50EZGbJ591uDery/NwFbyFt/729MkWny0bgsvSskkhMAX0thyfYINV8W44+PrOP50kpPPphjrKVEt21hVB8tw+70mVWQVgaq5/zSPgu5V8AY1alp9tF8SpWNXhMZNQbx+FVV3PebFjKZllXGkhbNM0uFy3uLhz3URqfWy+dr4gtesexV23lbHpmvjHHpklJe+M0iyr0wpay0okKdqAn9EI1zrYct1Ndz4oVZiTV5X9XihEJ+UVEs2j3+xm+M/GlvU+CuqB8e+OC9PUeCWu0K85+eiFLI25bLDjXcEues9YT7zp2MkR1Yn5Pf0w0VuvCOIUS3MMH6mKckv8B7MheK+E5imdJtcT/ZMapcpPg/mcAqjdxSjf2xK02yaVbFNyTNf7SNc6+G697egLdJIreoKjRtC3PErAa7/QAtn92U48vgoAycLVIo2ZsXGMiSO46omCwEI4YZRNcUd8153zPsjOk2bgjRvDtGxO0LduiCaLlB1ZcEF00KwMTlu7ZthgIoyy0lzPx3aFjQ8DDpd9Ngnlu1JCbkS9azXGLlcjmg0CsL1pmqu3UCgI8HQA0dcgsRpqCYL2KXlvUANjQqXXKLT0alOsnqXSpJczmFo0KHrnEVvr73mSdcfR9Q3KOzYodPe4fZOaZqbOyvkJaOjDufP25w5bVJaumCoCwEbrojxnj/ZQtOm4JI8qgshpSSfNEgPVMiPGRQzJpbhYFvuS6tqAk9Awx/SCMQ0IrVewnUedK+6aNvBF3/zEIceccNRNeGNKML9Tm14Ex4twGjuNKY9JUZnWEWypb5Fz7njkggf/PS2Bav9Zl4jGGWbgVMF+o/lSfaVKaQNqkUb25QIVaB7FYIxnWi9l+YtIVq3h4nULdyPNh2OI3nua/384H+ewSwLFEXDcSwcx0LXA1hWBaFoBEMNFAvDxGs2kk11YdvGDIWAUELnw3+/ky3Xzd0XJB3JS98b4p7/7xiBoOCP/1cD9389x/OPu8U5wbDCL/1egsE+k6//28zcZjCu8/5PbeWS2+vn3/d3Brnnj2e2B/zGn9Rwzc0BCnnHlecYn/26Thv869+lpvb951u55M1z7xtg73cH+fqfHseag8BZq4nglKs4ixCyThzrvZ/cwqV31C+bL1NKSTlnkeovkx01KKZNzKqDbToIAYqmoHsE3pCGL6gSrvEQqvEQjLvFUEsZDumBCn/zjhfmlY1fClS0BVV7s9kskUhk3u+/rj0pJBjJIsVzYzhVi1JPErkK8drhIYdHpmkeKdEw4Tddh97SgFkZI598GscqoMQiRN92K7n7n8BOuy+Rb8dmvJvXkf3uwyAlWlM94VuvQ42FqZ7ppvDkC8iqgRIOErrxKqxUFv+OTSAExRcPUjlyavWoHdYAI8MOI2sh1SCh62CW7/7NKd7/59tItPiWbaiEEERqvQuyWKwGmhO70NWJfI6Crvpoiu8ab+h1n12uNLgkI9V9KMd3/+Y07/nkFmrb/ItesxBuH826S6N07o4gHddoWYaDY8tJb9HjV1H1hT2mCyGlRDpua8DD/9xFtWgTr9uCquhYVgXHsdB0P5ZRolrJEqvZQKWcwuuPEa/bTKWUIp9dGbGoqrmSLqePTolrFvMOh/dV2HH56j3PJ+4v8PTDs6tDV5Mj00ouvU2imDa573+cAQmX3F63qJc7HUIIAlGdQFSndaUn+ypgoqhipXh9GylA0Tw0NN/FyIlnVsVAzdp/OEj8A2/DTmbIP/w0/st3EP/A20l96V6cXAEtHsW7ZT2lFw4gPDr+S7dhjaZcA1WXIPHRd1Ped5jS3lcI3XItkbfcTPa+xxG6TvDqS7GSafKPPYvWUEvsPXcx2juInfnxIHZUFA1/qI5SfmTNNbTArZ479UKaL/32YX76z7bSsiWEqr/2PWsX4kjP9xetT1pOgOLkcym+8acneNvvbKB9Z2TJK2ohBEIF3woVjKdDSolRdtj7vUEe+lzXZBOpxxMikzqHP1CLP1BDMT+E7gli21VMs4xtmziOTSE3gO4JuqWDK6iysExJIe9w1Y1+9j1TxrIkwZDCNbf4ObR39fqzjrz846eFleyr8K2/PEl+zOCqdzXhD79+p2UdD0ERJahE8eLHwaEiixRkmpIsLIsOaQKv37sxDUJR16wT3tPWhBqNkL7nBzi5AubwGPW/+4vozQ0Y3f2Uj5zCu7GD8oGjqLEIWm2CwtN7AfDt3IJTKJN/7DmQEmnaxN53F+oz+9zfbZv8ky9QOXYG0dVH4KrdaI11q2Kk9IBGtD2Cv8aP5tcQCEaPj5HvX3opsccfY+Ol7+X4i1/GqL5KhlO6jOH3/H/HuOkjbVz59iZ074+XoZLSXni4CQgkvFRyxpxl67P3B6dfSHPvn53g7t/ewJbra151hd5yzuLJL/fw9Ff6ZpS7F3IDWGaJSilJuTiKL5CgXEpiW1XKxVFU1UMh24dpFNy81AqjAJWK5IF787zrwxFue3sYy3LwehVOHqnyo4dWvy9uLmfltQxgFFIm933mLMm+Mjf9bBuJ1uVHEl5rBESEdeoO6tU2dDFVmCOlpCzz9Ntn6bNPY7K89Msbwki5o0tBUd0qB8exJ1dzQnGr5ibF8oRAKKorTCgUhFCQztRLeeH2SsCP3lhH7cd/lomGKcXnQQm7OYTqmfP4d21BjUfRm+pwyhXsVAYANR7B09lC/e/9ortvXQdFQfF7cUoVnGIJp+C+gNK0wHZQPCsTF5yAHtTYfPdG1t/eSaDOj+bTUDQFx3J48X/vn2WkgvUBIm1uPDh9Lk0l/WOw0pRu0+sP/vsZeo/mufXnO0g0+5ZUyLAmpyPljAkskPCOGx+JbUmCCS/5kTKKphCIeyglqzRsjTF2Lkc5Y2BVlrZ67Dte4BufOskNH2zh2vc0E1hI1mOVYFsOA6eKPPqv5zn21NisptFifnB8O3dcVCuZyc+yqXMAmIY7pixz5ZWo0oG9z5ToOm1Q16Dh8QpyGZuBXpPSPArUK0HnJg8f/OUYDc3uMWxboqqCE4cq/MOfrwU10pQq9WLbGGWbZ+7pGx/z7Wy5vgbNs7yQ7WpgIgpgW5J8yliSY6zjZZ26g0a1g7QzzKgzQEUWUVAIiAhN6jrWaTuwMOm1T7Mcr+INYaSEohLvuIS6zdeiev1k+44zfPxppG3RuOMWdF+Inr2usF2orpPG7TfT/cK3CNa1kVh3OT0vfRerUgAh6NzzPkqpAYaP/whwmQfMwREy33wApzwednAc7Kyr92KNpbHzRTydrXg3dVI91YVTcKsGnGIZ41wvmW9PUedIy8LO5FEjIbfqbLWWbwrUbk5w9W9cQdPlDYgLYtu2YaN6ZvfkhJpC3PmZW1F1hWPfPMnz/2PvvIfQPUF8wRqKuSF31SwUfP443kAMx7EoF8awjKlVr6YH8IfrUBQdo5yhXEwCElXz4vW73/H6Yzi2RSk/NDkRTqCUtXju6/0cfyrJde9vZuetddS2+RfVPVotTJRhj5wvkh6YmoC33N6CUATVvInmVfGENPr2j1G3MYovptP94iihOh/hBj+nHu9fspECN1F932fOcvzpJLf9UiftO8JrYqwswyHVX+bwo6M8/qUeiulXp8x8PigqNDRrDPRYDPfPzF+Eowo19RrppEU2dXG5o//ym3GKBYfv/EeWj/x6nG98IctNd4Z4+pHV9dYU1WXCjybWkUv3IB0LKZ3xSkgTkCiqB+nYBCNNVMa9U9t0OLc/Q/crWS57awPXvLuJ5i0hAtG1X7AAWKZDftRg6GyBVx4a4fDjYwtWkE4gpESpVZsZsM9ywto/q8x8yO5ip+d6WtVN9Ntn144F/ccVqsePYw3Td+RxvOEamnbdRm7wDKVkr/tgxbRwkRBuSShQTg+hbvLgDddgVQp4QzX4Yw0MHH5scnNzYBinVMbT3kzl+BmEpqHWxrGSGQBkuYJxthv/9k2otTEKT704+d3K8TP4d29Db6zD7B9CBPwoocCkp7WaCDeHuPZ3r6Jx91SVkJQSaUuUBeh3st1Zst1ZarYkaLm6CW/EQzU32x3XPUHatrwZ2zIoF8dwbINIooPmdddhmWVUzYdZLXD++IM4toGqeenYejuaJ4BlVvB4w/SffYpcqhtfsJZ1O+6mUkrh2Ab+YC25VDe9px6bfYIS0oMVfvi/z3HgwRE2Xxtn854E6y+PrUouZtbhJFhVm6GzRc7uzXBmX5ruV3IziD7NkkWozk+2v0iwxoeqCsyyDYpb5m6WbaSUCCnQvSoVlmcApANnXsowcPIoW65LsOu2WrZcV0MwfnFeNox7TicLHHl8jGNPjdG/hs2hy0EgqPDxP6zh8fsLeH0Kh/eV6e+2CAQFH/ilGBu3eRgdtvniZ1KkRleeH43Xqvz7P6ToOWty1/vCPPVgkaF+i9veHuLA86uT+/L6ogQjTRRyQ/gDtSiKRrWSQ0oHf6CGcmkM6Tj4AgkK2X68/hia7qOUH5kMq9uWZN/3hzj5bIoNV8XYeWsdG66IEW30rrhMfD5Ix62K7T6Uo+tAht4jeXqO5JZV0afhQcdLv31uzj6oCiWS9iCd2vafLMaJCThmhfzQGSq5UaqFFIl1lxGsbaOUXLjSyChlKaYGiLZspTjWQ6RpI5V8kmp+SiPKTmXIfu8RwrddR+DaS5GmRfVUF5XjZye3qZ4+T+CaS7GSGcz+4cm/m32DZH/wGOGbr0GJhnHKFUp7X8E41wPSQVaq05qPJU6lsiJVX6HArg9up+GSehCQHyxw+v5z9O8dJNoa5qY/uW7e75pli+HDo9RurcEX85LYlGBw/0zeOVX30dBxFY5j0Xf6CWyrgqLqNHZeSz7dw1D3Xjy+MOt3vYNIopPM6CkSTTvwh+o49fI3sG2Dxo6rqW+7kmLO3bfHF6H/zFPkUucJRppYv+sdjPYdoFJKzXWaSAkDJwoMnS6y7/tDxBp9bLgixsZr47Rtj+ALqSiqcKujlAnZemYxrEvpvpRSukqzjiNxLEkxa9J/vMD5V7Kc3ZchN1KlmDHn5Ew7/eQgiiawKjZCCHS/RiVnkBsqofs0yjmDwmgZoQis6son1FLW5MCDw5x4NklNq58te+JsvamW5s0hNI8yfr2uYZy+yHav0b1Ox3ZDktnhCmdeTHP0R0mGThfJjVWx15jAWDpQKVoUM3MbaSklRsm9P0LA5df78QUUchmba24J8KXPpKhWJR0bdb78T2nu/KkwV98U4MFvuYKA1ZI9774Z7/W6EMWCgy/gLlLzGZt1mz2YhqSmftpUKKFaXGDfQHUB7yIYaaZSSmEZRSSSajmL5vETDDchhIJllvF4wxRy/VhWBU3z4vPHJ9+N6cgnDQ4+OMLJZ1OEEjrtu6LsuKWWth1hQjUeVE0glGnjAGaOeTk15p1pY8Io24x2l+k+lOXs3gyj3SWKaZNy3sRZwZB1sLGx5m3UleP/b1L9yWKcmIDjODjjCnDSsXFsE80ztwKqokyFiqRjk+0/Qdvlb2UsGCdUv45096GZITgpMfuGSH3521MP/4J7bA6OMPL3/zrHiUmqx89QPX5mVmjaTucY++evTP3Bshn77H8s46qnEG2PsvEtrvxD5nyWRz/xFOkzGYBFK8VswyZzLoN0JJpPI9YZmWGkhBA0r78erz/GmVe+hW1VxverEoo0UymMEavbON44qBOKtZAZPUUk0Yltm4QTboO1omr4Q7VouvtcjEqWcnEM26qSS3W7q8xw/bxGagKO7YbgCimTvmN5nvrPXjSPQm27n9q2APEmL+FaL4Gohu5T0D3KZE7OttyJq1KwKGVNsiMGmaEKyb4y2eEqF2gXzotqfubkZZTcsWebDkbR/dkwV6mkWbpFDX3H8vQdy/P4F3sIJVxOtdr2ADUtPvxRbbIB2TYdqmWHSt4iO1xhrLfMSFeJ1EBlSUUcq4lixuRLv3V4wXXz9FftxCtV/ur3Rshlbd75MxHWb/Vw+mgVx4Gesyb7ny2zZZdbjl7KmHzlE8eWvO8JPP1QkWDIZVp/+bkKH//DGhRN8PgPpuTaiyvc9wQK2T6iNRuQ0qFSSmFUc0hpk6meIRhuoFpOU61kCcfacGyLYmEEAWi6fzK/dyHKeYty3mK0u8z++4ZQVEFNm5/6dQFiDV5ije448HgVdJ/bw2dbErPqjoVy3iI3ZpAdrvD/s/fe4ZVd53nvb9fTC3oHBtN75cywi00kRYrqsi3ZslyiJIqLbKU4urFv7LgpznWsKJZjRbEjW7JNW7IaiyhS7ORwyOm9AQMMejk4ve267h8bAwwGfQYYUhq9zyMRc3Zb+5y917fWt97vfZMDZRI9JU8weIkeiYKbJeMmqFVaKdgZ3KuCVUAKEZdrGHIu3VyKE5ehaDqqPzT+tx/NFyI35M10XNtC0SbrLPzRminHFpP9WEaB6lW3oGg+imNz1LZctvOIhom9511I10lymAvCcSnsO4zZPX+tTcOuOlS/iuu4nPrHM6QvphdxISinDWzDRtEVglVXB/dxvSBJIhRtxCxPsvwkWcEfqkSSvXRiNtlFPt0/fpSEqgUIx5sm9h/tP4ZjG6h6yFucvfIFudaXRYBtuAxdKDB04drWFfRwBbWb70Dzh7GNEpneU5SSA0iKRvXavWQHzmFkRkGSqV6zm2JykGLC85Sq3XQ3ucEOIvWr0MMVZAfOk+0/D8JF1vxUrNhCoLIRq5BmrOOQt/Z5HRDCG13nxkwu7E9d17lmgoSEJvsx3SWS4xIL+2mFC6WiwB+UyGehqkalrlFDCE+NQpJnsK5Z4LmvxA+/l58QS33pmTzplIOiMF1l/RrOfRmmkWN04CgApYJXAG6Nr9UW85OZlnLRI2pcSUZZKFxHMNpdZLR7sVXz14+gFCEoRaZ8JhCkxSiNyko0NLIiiSkMZGR8UpBquQEFjUHn4s05k5IkmcoVOxCOgz9ajeILkh/2mEeFsV4qV+6gevVuXMemsn0HtjH5wwrHItl1lOadj5DuPYVVys12mQnIwQDhu3YjB2eerS0FhG1jdl5aUJCqaI8hKRLF0RKjZ5KLLlOxDQfHcFB9Kmpw6iMhcOm/8AqhWCNNq++mVEhQLiQQrkM+009mrItE/3FAIMnqxEwrn+5D1QL0d77msSfH62cc28AH+AIxfIEYRjlDMFKLJCuUConpjVtmyKpO8+73UUoPkR+6iBaOo4zPwmVFJdaygXJ6GCMz6hULN63ziB6JHpAkqtfsIVDZSHG0h+JYH65teQFK1WnYdh96qIJ072ki9ato3vNeul/9p2uqI7pRCGoV1AfW0JU7NMVteLlRLrucP2Xwn79Yh2UKchmXsRGbn/qlOCODFnfcH2LLLj+nj13DutH4IArXxbIlT8YIMGwfB48GsdLJic9+gvlRK7ewQp3uPA3e2lSzsvaK1J80buKh4mCzRbuTN81nbi7ihHBdRjvewiykidStQpIk+g49iVn0Rvy54YsMHn+BaMNqHKtM3+GnCcTqEM5kyiY7eB54D7nhi+PMmx8t6FGfxzbLGljFa2j/XDMaAa5jMTZ4Et0foX3TI1w8+QRGMcVQ937qV9xKIFyD61hoeoje8y9gljOM9h8lHG9mxcb3YJQzKIqPfLqXxMBxAFzHpr79NuI1awjFGkiNnKOcv/FBSlJUFF8AM5+iMNqD1XNqhi9hjuNlhcJIN4lz+6d87ovVEKpdwaXXv4mRGSE32MGaBz9FsKp5YhY2F3Q5QMxXjyr7kABXOKSNIcpODr8SQZP9uMImrFcDgrFSD7bwyB0+JURUr0ORVApWiryVGB+9SgSUCGG9EkXSsVyDjDE4cVzc10htoJ0KXyOGW8AVNmPlPgxneW06wPN5evzLad58qUC8UuHscYNMykVRYOU6nQ99MkYm5fDaDGoRc0EJhtBrG0C4WKkkaqwCt1TANQ189U1ImoadzSAWapL2NsDvkzBMgap4upvhsEyh4M5pFbScyIsMw86lazrWew5vspmUcG1Gz70BQKbvzIz7pHqOk+o5PvHv/EjXlO3+aA1mIU1hbP7O450I1/Zyy94C+uKZP2pQnUgXmoXJIGcZOXrOPTde/yIY7jmAUUxNvNDZsS4sI08gUoskyVhGfiKt4dgGl848Q7iiBVXz41gGhezgRDLfLGcY6T2IovjJpXrIJruv+3u4EvGWTVS0bpn4dzmbYPTC/mnpNscoMnziRarX7iXetpnswAXGzu/3ZkQLgGObmPnp62iK5scXqaRp58O4tgmShBoI4wvH5w1SiqSxMrYHgYvllGmObGa42Ena8NYKY746mkIbKDl5LKeEpvhJlQdAmKiSj7XxO8ePLdEYWk939jBJow8JqPA3EdVrcIRNVK8jVe7nYvYtADTZj64EUWQdXfbjCheZG1NIHWmOkOvLce7EVGap48CF0yZf+M8JbHtuD7eZIAeC+BqasNNjyMEQdmoMraoGJRjCHBlCjcWX7iYWgYp3PYDiD5A58AZWcu7B2S07fPQP2oRDMsGARGWlTF+/zckz1ttSgJxw+0m4/Tfsej/yQep6oIfihKpaqVq9i2TXUaziO0OOaLEojhYRriBQGcAXnd8e5ErImkzlqgoUn4JVsMgNTHbijm2QGj478W/XsUgOTw4EhHAp5oYp5oaZCZZZmHL8FEgyxdwoxjxEiWuFcF0kRUX1hYjUraSQ6CXZdQSb6bOCbP85CqM9BCobqd96H6ruZ/Doc0yM+MYDv6zqyOpV368QM47Cheti5jOkuo5NBMbEuTcpZ0bmbXtIqyDua+DgyLdxXAufEsJySpSd3BX7VHI+tY+inUaSJJzx1FxjaB2a7OP42A9whUNLeDONofVkzRFsYTJUvMBwsQMhXGqDK1kR2TkRpEZLXSiSiirp9OSOYwuLa1+ZWRzqdtQhqzKlZAnhCMINYQrDBYQrCNWHyA/kCTUEqd5cTe/LvYvKGMi6D9e0cAp5fPWN2IUCxvAggaZW7FKRG3WPVyK4Zj1qJEr+9PF5g1RHl8VH3hfk4FGTVe0qJ0+bVFcqaKqF+aOX+Fk0fmyClBL245RMbz68QGjBGOG6dtK9pxnrmL2I9Z2O4eOjOJaLv8JH4+56hk+MLljHMNoUpv1+j4Fn5i0Sp5ej6v7GI9N/hky/F1C3fuS3Z91P0f3o4Upso4iRTVBM9KGFYoAXaByzRLCqiXJ6mFBNK76ol16bD2YhhVlMI6ka5WwCJAktGMUx5l/oFsIFBLocwBAuqqyTt6YG84KVpOyOa6Fd0Zyorw5JkqkPrgEkAmqUgBZDkXVsx0SVdKJ6DboSJKxVoik+Jqmnk//z1hNuXOetaAqR5gjBmiDh+jBaWCN5PolVsKhaX4WZMz0Ojyx5mYOFnjcYpNh5DknTMMdGMIYmZwDGwLWJ4d5opFIuQkhc7LIYTTisX6NxvvPmCFDwYxSkAivrsJI57HQRJ19Gq4rgFAxcw0Kvi2GnC7i2gxIcz/FbDuoqHwOnfoCTu7YivtlERG+0jMnw8VHyQ3kq2uOs/8BaBg8PM3ho5tnNlQhWB9j1L7cTa/VkkYaOjpDtXf7ZZLk4Rt/5F7CM+Ukqyw1F81O9di++SBUCr+Zu+OTLwPjM8eIRatbfRqRhNeXMCGYhddXvPvMzYBUzjJx+jeq1e6ls34EQLkYuQX9yYN6BVMFKMVrqYnPVuzGcPLZrMlLqnLKPI+w5edCq7M34yk6eTG4YxzXxKWFWxfZiuWUK1hiOsJYwDEm01O2mUB4jmemcf/erIIQgUBEgN5jDyBn4q/yYeRNhC/wVfnxRH+VUGTWgogZUnHmMTC/DGOhDCUe8GkTrR7NXb2tVeX1/mZGEy0jCpbPrnesRVNOgsmKtj+NvFTFK3tOlahIP/1SMTTsDjAzafOerSVKJm4g4cRmyX8ffVouyXsccTqPXxRG2S/5UD4GVdUiaSunCINFdK0m/cQ4JCX9LNfnTix9NOakMY1/9Z5RYBCUSQo6EkEMhlGgIORhA0lRQFCRFnvyv7P1XUhXvsyUMZGbO5Ohfn+DOz91KsCbAvb9/F+e+e4Hul3oJVo5bSkighzVCdUG0oEbj7nrWv38NFaviSJJEMVHk2NdO3hD1AccqL/ka1LXCLGToP/gUkuK9CsKxvTUkACHI9J4mN9gxqffIFTqQQnDhB1/GtWYQzBSCwnAXpbF+79xCIFxn4hxzwcXBr0QYKJxhuHgBRzgLZtqlygPUBFcwWDiH41rIkoLAxRYmYbWakBrjTOooJTtHY2jd9GsLB1lSUWUfwmXczHH+Z0KSJKLhJoRwuZYEbudTnUiKhGN5M8OBNwe8QCRB/h/z2IaNcAWdT3XizFIgLQeDKIEgkqIwrYpblr11xisXtWQZJRRG1n1Isoxr2x6pojz7oFXSdZRg2NPhFALXNHAKeWZdLJNl1HAESffKYNxyCadYYDGz1Itd1oJcrhcDRdKQJQVHWET0WrLGCAIHrwhBmmDmeRBIeDqnqqyhKUGKZgqBO07gcXCENwC47/1R7n1flD/73BDnjpVRFPjopyr5yKcqMUouPr/MinU6/+3fDpLPLuymfmyCFBIULwwQXFmHHPShxoIYfWP4GivxN1dhZ0sgQ6l7FCuRA1nCNaxrM9czTIoHp3mJj7dDQvLpyMEAcsCHHPB7gWv8b62+htCebSjx2U2+rgWXXumlYlWcjR9dT6gmyM5/sY0tH9+IkfM6UFmR2fiRdax5dBWR+hBaeFwLTEBxrMTBvzxKqjO9pG360YDwgpJj4QtXEapqRvWHEa5DOTdKYeQSrjVVU1BSNOKtG3Ftk9zQBfRwBaGqVlR/ENe2KGdHJ8g5rm2CvVjXWglF1ojpdaiShiMcsuYwGXOE+Tq3oeIFIno1a+N3YLkGsiSTLPcxVOzAcPIYbonWyDYs10CR1Gmny1tjOMJmdexWTKdIf+E0RTsz88WuxHWOba6eGdmlyaBsXUFiufLzCcgygfZVxPfeib+lDUlVkX0+TzwacIoFSh3nSfzgCeysdy+S7iO6cw+RLdvQa+uRNR07n6PU1Unmzdco900nt+j1jcR27fXWk+JxcAXmyBC5k8fIHTngBasrIGkaka07ie25Hb2m1lOyGOwnc+hNFhN17GVg8UX9dQTUGKOFi8T8DfjUEOnyAH41iq74yRqj6EoAIVxs18CvRgjqleSNESL+ekJaJWPFS9SEVlG2cyRL3ve1+ZYgyRGb4T7vN2td4+Nd741w7liZr38xwfbbg7z3Z+Ns3Rtk33MLY43+2ASp4tl+nJJJ4Ww/rmlj9CRwxp0xrUQW17Bx8iXs9PiagCvIHuxALPUTIASibOCUjRkrAbTmevzrVy15kLKKNse/fppy2mDHL29FD2uoARUt6BUcS7JEtHmyAM8zuBOUxsq8+eeH6H6xZ1n8uOaCFPChN9dz9ajXLZSwBuZPVy5dQyQq27ZRt+ld+MJVKJqOcAW2kSfde5rBky9gFSc7akXVqV1760RNVOO2B/HH6lA034SKSX60+5rFgyv9zYCgYKVwcdFkHytje7iQ3kfeGiNVHqBoZSZGr1fCESYdmf2EtUoUScMRNkUrDQhKdo4L6X0E1ChCOBTtDEOF81wZYYp2hvPp1wmoURAupjP7rEJTAzTU7CAWaiKT70OdMIL0EA7W01y7C00LksxcZHD0GK6wkZCIhpupq9yEzxehbGTpHzlIseyth6qKn9aGW0lmuqiv2oyqBugfOUQq2zVTM9Cqqql+8DGUUIjEs09hp5No8Uoq73sIJRJh9MlvY/T3YufHO0VFoeL2u4nfcQ9WYpjki8/hFPP46puIbN+FXlXN6NPfmRKolGiM2vd9BF9dPbnjRyj1dCMpCuENW6i6591o8QoS3//uFFmz8MatVL37EVzDIPny89iZFL6mFiruuAclFMI1Fzt4mQ6lKo5aGZ/6oRCe8PUclj+2a2I4BU/OSFieWLQaIajFsV2TkF6FKusIXEpWhqBeSUCLki0PYTtlVNmPIms4wqJsT6btq+pUus8bZNMOkgwbd/ppbNX5yz/o4/ThEv3dJh/+pUoaVyyc4PVjE6TsjBd8bNMbaRlXWMZf3uZh8sU2Bpa+Yv/thJExOPn4GQYODLL+g2up21aDP+5HC6gTIrOu7WKVbMqpMoNHhjn1+Fmy/bm3g+BEaM82Kn7msWnmPuXTHYx+8as3rB3R+tW03fphytlRul5/nPxIF6oepHrNHmrX34Gs6fS8+R3cq1Tag5WNNO98lOzgOXoPPoFjlQnE6jyJrmsMUBIy1f42cmaCvvxJBAK/EqbS34wme0HAdIuY7uwEDNs1SBuDM2wRlOwMpStmRoYzve7o6n1ma2dj7S4qIq30Dh0gHKwlGm4kkb4AQNBfxYb2xxhMHKOYTNBSvxdF1ukd2o8AVMVHttBPOZWjoXora1of5Nj5fwA8s82G6q349RgjydMAlI30LA2RCLavxtfYxOhT3yZ31PNqK8ldSD4fNY98ACUYnMKgC6xYRfSWWzFHhhj6x7/FzmVBCHLyEUrdndT/1CeI33YXI098C7fsKVFU3n0fvvpGxp5/hsybryNsy3PTPn2C6offR3TXXkoXL5A/7WVY1Fic6M49SKrKyON/Q+lSl3eN40eJ7txNzXvef91BSvLpVHz4YQI7N0/5XFgWmW8/R+6FfbMeazsGYb0aRVIp2zlMp4TjGliuQVCLMZrvJBZoIKhVUbLSyJKC7XrtLdt5FKmMEC6mUySkV1G0Upd/DhzHc3gOR2VuvT9C55ky54+PF/lnHWxbEAguvLThxyZI/QQehCMYO59i35+8SbAmSLw9RrA6iBZUkSQJq2hRTJRIdqQojpXeluB0Gf6Nq5H06RYEknpjrDjAK+atXX8nrmMxcPy5iVo7xywxePyH6MEYVSu2M3p+P4XRqQWMeriS0fNvMHjqpYmgtBCK+VwQuKSMfhpD61kdvxWQ0GQfGXOYvHXji51ngyQrVEbb6R85zFjmAtlCPxXRFRNz4rrKjZSMFH3DBwCBLKs0193C0NgJTCvPWKZj8lwSrG17D1MFLiWGxk4uiIShxrx1VXNkaHJw4LrYmTTCttHiFZM7yzL+llbUaIzUKy9MpP8uH1PsOI8xNEBg1VrUSASzXEIOBAmuWY+ZGKXYcc4LUABC4BQL5M+eJLh2PeGtOyeDVDSGr6ERc2hwIkB513AonD5B1b0PLvYrnwZJ1/GtWTHNg84FUOYOAiU7Qynn3ftYsXvi86KV5jK/t5z3Zkiq7MOwc0iSMk66mVx1TJX6uDIT0n/JpL5Zp65Zo32dj407A3z9iwnKJS+9qftkFFWaLnE1B34SpH5MIQQURooURm68ttdCIMciaA11b3cz0EMV+KPVmIUMhdGp6xCuY5EdOE/Vyp1E61dPC1LCtRm7ePiaZ02zIVG6RMFKoSuePJPtWpTsLI64/vTQUkFCQlV8WLY3E7MdA9ue1L/z++LEIy3sXP8JwJs5ucJBkb0ONRysp65yI8FANX49iq6FkCRpgjlpWUUsa2HqEnY+jxACNRq/ooGSR6JQVZzC5HkkVUOLVwHe+tDVEK6DMTSAv6UNJRyF0RG0eAWy7sMaS+DkpqfQ7HQKt1REq6hE9vlwDQM5EEQOBDFGhqYNBJ1iAadYmCBTXCu0hmqUith1nWMhsF2DdHkQCQnLnSn9O3mDLz+Z47Ofr+c//2UT4YjCQLfJ4dcKE8rqrat1XEeQTi6coXhTBKnYHRso94xijWZwyz+aNNQbDklCGVeKWA6tOb2tCSUaetstslU9iKRouOU8tjk9oNtmCcc20YPTOwPHLGNbS+NBdCUELkU7TdFOL/m5rweSrEwyGxE4joUiex2tImsoyuQ6g2WXSGcv0TXwKpc7Mce1McwcQX8V69oeYihxkt7ht4gE61nb9vCUawnErCUeU3cUlLo6sBIjVN59nxcA8jnUSJT4HXdjp1Pkz5684h4kZG3cwXsmViYgTBNJkpB93r1JmgaShHCcK+7/iv1te7x4XEFSNTAMJNXLEAjTZKZ0hWuaKNcZpAJb1k1LlS8XbHdhjt1vvpjnH/5ijFvvD5MYtPnGV8a41DH5Pa/e6KfrnEHHqYU7gN8UQarxUw/iFMoUTveSO3yRwqkenOw7c4bxToEkKYRq2nBsA6uQxswv4fqdBL6WhmUV6F0ohGuDcJEkGUlWp2g6AkiyjCQrM2s6vh2aNG8TFM1Pdfsuhi+8AcLFFS750hA1FWspGUlCgRpC/hoSeGm8ROo8q1rvx69HKZTH0NUgsqxRNjIoio4kqxTLYyiyRkV0Bapy7R22OTrM2PM/oOaRD9Dw0z+PUyqCJGEODZI5sA8rOVmgLhzX246n6zcNkoQSDHqF3EVvBuaWiuC6yJqOpOlwFUVd9vmQVBW3VMI1vM5XmAbCdZEDQS+QXPWsyNcZoJBl/OtWXt85lgFmWfCN/53kqX9IYxoCy5h634deLXDiQJGB7oVnBW6KIIUi4W+twddSTez29ZhDaXKHOki9eAIrkUPY9tu6NrMskDzauSRLEylj13YXzOATwsEspFD9IWTtOl+oqyAHAmjN9eP1LG8vjEIK2yii+oL4wpWUM1ewCiUZf7QGWVEpJgeW5frRutXkx3oma7PegdD8ERo3309V6zZGOvZ7JnrCoW/kEO2Nd7Gh/X1kCv1kC/24rpfGyeT76B18k+a63WhqEMsuMZg4Rq4wRKmcZCzdwcrme7GdMslMJyXjykGQwHHMRfkOqbEYwrEY/ta3sZIJhON4s6pCfkqAELaFOTqMcByC7SspXbww5TyyquFracNOp3DG12TMVBI7l0WNx9EqKqel/LTqWpRQmFJX58R61eUZnb+xGWQFrqiPU6MxlFAQ17r2oly1rgqlqmKRHrc3BkJAYZYaqKG+xWeyboogZVxKIOsackBHjQZRIgECaxqofv9e8se6yLx+lmLHANZIBmG9TdLCSwRJgmhrlOoNVdRuribSGEYP6SAEx79+mp7Xplp/KD5lgqZu5k3cy2Z9wqsfch0frrm0KS0lHkFrbrhhqYq54BhFkl1HadrxEDVr9jB0+hWsYhZJUQhVNlO9Zi+lzAjZwQvzn+waULvmNmL1axk4/QLOMqQOrxf+aB1tOx8j1rBu2iyzWEpwqvM7Vx1x2YPVZSR1mpHUmWnbbMegq/9luvpfmfi8d5xgAWBaBQ6f/RoLHTlqsTjhTdswR0coXDiLMOdIJQlBsfMCxmAfke27KfX2UO7tRlgWSjBEdPdtaPEK0m+85rH+AGyb9P7XqH3sw8R234ZTLGCn0yBL+Oobid1yKzgOmYOTSvhWOkWxq5Pwxs3Ed99K7sQRXMNACYaI7b0D2R/Ata5dccW3qg054H9HvEPLjZsiSPX9xdOE1jcR2txGZNcqtEqvXkgJ+ojdtp7ILaspXxymcLaP/OGL5E9eQpjvXOmR2aD6FVY92M76D62lam0lijbJ8HFMh8A0Q0OoWlPB9l/YggAuPNlJ90seeUBSVAIV9fhitTjlAmMdB5esnUplHLWmcsnOdzW0YIxo/WoUXxBF1VE0P3ooTu362zELaRzbJDtwHrPgjd4TnQcIVTVTvXoP/ng9Zm4MSdEI17QhKyp9h57Cuk6zwtkgIVG39g5c22To3Ks49sJz9csKSSJSs5LmrQ8RqWn31ldm3HH6p7KqI8kKjlma2C4pKormv0qFfh6PmAXCNU2cYoFAywpqH/vQuGKEQDgOdjZD4fwZrMToxP5WYoTkSz+k9rEPUfvYhyn39+AaBlosjr+ljeKFc2QP7p8io5Q/fRx/UwuR7bvQKquxxkZBlvE1NKGGIyRfeQFjYHIA6BYLZA/ux9/cQuW9DxFYtRanWPCYiIqCMTSIEgov+B6nQJHRW5sWbboqK57NBwhyWUG8QsZ1BcWCmMi2COHZpryTcFMEKXMgiTmYJPPGObRvRQhvX0nFPZvwtdQg+zQkVSG4ronA6gYq7tmM0TdG5vUzZPafw84UfyQClhpQ2fpzG9n8MxvQI/oUptRc5IRSskzNpmoCVX60oMqlV3sRjifhY+bT+GJ1lK6TVj0FsjQjbXYp4Y/WULfxblRf0FMdcGxkRaVyxXaEcL36jkJ6Ikg5ZomeA98lPtRB1cqdxJo9NYn8SBcj59+glBqcQh4RCGyzjG0UlmRdSlF1Gjbegytchs69siDppLnP5ycQqsYsZxACHMdEGr+ObRueavocwVCSFOLNG2nd8V58oYoFkFskfOFKZFXDyKdQfUFA4JhltEAEPRjDNksEKxoopYcx8mNE6lbhOtY025xrgaRqOLkMIAit33TFb+KluqM79zD8nW9g9E2yM4sXzjLw918ltmsvgfZVyD4/VirJ2A+fIXf88DT1CGEYjD33NKXuTqI79hBoX+2pkvReYvTwAco93dNUJErdFxl6/G+puPNefM0t3iyu4zyZg/sJrlxDZOv2a/KxUmIRtMbaCUfshaK2TmHnbp1oXOaZ7xVpaFLYe6ePZ58sUVklU9eg8MYrxqLo4VejdbXOxp0BXnsmNyF7FI7K/My/qWLH7UGGemz+5guj9HT8ZE1qOgS4JROjbwyjb4yxpw8S2tRKdM9aguub8LfWoAR9KNEgwY1BghtbqP3pO8m8cZ780YuUOgYxRzLv2MXyFfe0svUTm1H9Co7pku7KkB/Ko/pVmm9tnPW4csZg6OgwKx9YQaw1Srw9RqojjS9ShRqMUhrrQ5aX8DGRZQKb1izd+WZAbqiD00/+2aKOsY0CiY63SHS8Ne++jlHk4itfu9bmTYckIas+mrc+hHBtRjr2X9caVayinVCkjrHRs0RizRRyQyBcNF8ETQuSTV2aNUgpepCalbfQvOUhb0a0gHSSrGrUrL0Ns5hBy45glwsomg/HLBOpa8cfqyPZdQRFDxBrWk+y+wiK7scpXP+sUY3EaPjYJ8F1vUA0PDCuoych+/yENmyi8u77iN2yl5GBPrhCd9Ec7Gf0yW8t+FquUSZ/8hj5k8cWdoBwMQb7GfrG16dtMocGSO97ecHXvhJqdSVaQ83ij1M9beNySVDfqLJxq0Zzq8rwoMO6jRqVVQqFwvX1bw98IMZt7w5z8YzB+RNldJ/Ez/16NQ//VJzeiwYbd/n5zT+q5w9+bYCx4YUNxm6eIHU1XEHhxCUKp3rQ6+IE1zUR3raCyK7VqHGPGq3GQlQ9vIP4XRspXRyicKqH3MEOiucH3lHBSgtpbPnZjWgBFcdyOPa3J+n64SWy/Tlqt9TMGaQcwybZkWLlAyvQghoV7XFSHWmE66AFwpi5FIHKBvLDF5ekrUo0gtb49tdHvdPgBQOZxo33Ixybkc63PObhNcAVFoqqjwc6QaxiBdnUJYxSilC4Dtueee1L1YM0bXk31e23oCyGLCMEtlnEyI4iSQqqP4g/Uk0pM4wka7iOjRACs5hBknIgyThmGdUfvKb7uxKBVWvQq2sZ++H3KZw9Oe29dG2T+K13IvsDSKqKWKB6+vVCq64BSfLSjDP0FUokilsqeqStxUCS0OqqUaKR+fed+XA6z1v09dg4jqDzvI3tgKLCobeuf9CwepOfoT6LwV5vkNW+3sct7wrx1ot5vvbFBFv3BPm5z1Sz+ZYALz+1sDW5mzdIXYYrMAdTmEMpsvvPoUZfJbJ7DbHb1+NrqUYJ+pADOqHNrYQ2NFP1yC5KnUOknj9G4VQvdjqPWIS/zXKgYWcdsdYIQgjOP9nJ8a+dmlmIcwa4tiA/WMCxHFSfQqTBo+Wa+RTFRD/huhUUx5bOhdO/rt1Tif8JpkGSJFRfkOatD+O6LomuAzPW5cwHXY+QGutE1YMkBk9660OOSSBYSS7TN+60POXK6KE4K3Z9kFjDWmRlcb+P61iMnts3zuyTkCQoJHqRJBnHNnAyw4BEbrgTkBCujW0UlmSGLmkayNIEDXxiHUmSkHSd4Mo1SKrmqU/MUhe11JCDIcKbt5E7dhg1GsMpFJA0FWE7aJWV2LkcvoZGrOQYTiGPW7r695gdkqrgW7vCc1ZYJNIpl6MHDIYGvf7qwlmvj4hVyPR0O1xaAguQyjqVjlMl8hkXRYEtewLU1Gt8/jcG6O00SQzZ/NK/r6G+ZeHp/p/0FpchwC1bmOUMY08dZOz7hwhvXUHFu7cT2d6OEgkgqQpqNEh4ezvh7e0YfWOkXjpBdv95jL4EuG/P7Kp2Sw2KpmDmLS4+273gAHUZZsHCLtnoYR1fbHwELUkgXMrpEa+gd4ng37gGFplLv5kgSRKKHqBt53sRrsXYpaOLDlTpsU58/hjF/AiuY+IpHUsI4ZLL9DKFlCBJRGtX0rz1YcLVK2ZN7wkhKGaGZ+UzXMlMHNeMAEmilBpC0XSMXGLKdYVj41zn2htAqfM8djpNbO8dKKEwxvAgwnFQ/AH8LW0EV6/FTAyTPXLgxmU/hIuwbNxikfCW7RQ7z3uqFT4/rmViZzLIPh/BVWsonD+7uCClafjWXlt9VD4nyOemfweZlEsmtTQDbccSSJI3UAnHFG67P8LJg0X6LnoDBLPsYtsC3fcT7b5rhyQRWFlHdM8agutb0BsqkH1e1L+6At7fUk39x+4mdtt6kk8fIvn8sbclUAWr/EiyRDFRopxZfEBxbdfzkZKYEKKVJAU9XIFjlhfkJrsQyJEQWmPtkpzrxwlCiCnBQZpYo3oY4TqM9RxflOqHaWQxjavlewSlwnTtv4qmjbRsewR/tHbGAOU984LscCd9x76PEIsImEJg5Ebn3+86YCXHGHnim0R37Ca0biPR3bd5pqZGGXMsQebAfvInj2IOLU+d20xwDQO3VMQ1yrjFAsE163HyOdxyGTUaxfL7wRWefJLPP/8Jr4BaWzld9fwdhJ5Og6YVOi2rdNZu8bNyg4///ccjGONFvf6QjKpKmMYiauCWq7E/UlBktHiIwNomKt+9jeCGZhS/PiHSKAwLoz9Jdt8ZSp2DRG5ZTXjzCpR4CNmvEVhVT8On3o1WG2X0m2/gGjeWw6noCkgezXwx1tpXHi+rMsIR2GWvExLCxXVsQjWtqIEwmZ4FeArNA72lASUWfdulkJYSkiKjRAM4udKcaV+1IoQw7WlkgdGug4Sr26bJLkmShB6M07rzfbiuQ6pv+nrL9UBRfVSv3E3T5vtRfeHZZ1CuTbLnOL1Hn8YsXf8zsBwoXeyg3HvJkyK6PEsXHgV9mtHhjYDrkjt+GIDCudNImjYpnaRpCMumkDvlNXORp/ZvXgfyO/f9+cE3Mvyn/9nInz7ehqLCqYMlDr9WnBhjrdnsx7YEYyM/0e5bECSfSmBlPeEtK4jdvh7/itopuV47W6Rwqtcr+H3jHHbSW+jLvHEOrTpKZNdq4ndv8oJawEfVo7sxhtKkXzxxQ2dURsZAuMLzkPItUsVB9mzktaCKa7mUkuOpByGwillKsnL9MylJQomG8a9biRKZQYrmRxhaTZTGj93B4D/um936RZZo+vm7KXWNMvK9qfVm6f7TXFJ0Wrc/ii9cMWWbJEnogSgrbvkgwnVID5xdEh1F1RemafP9VK/cjarNPpK3jAKjFw/Qf+K5aTYlC4EsqbjC8SSnAEXWsRyvhmmxkPw6SjyGEg2PG4n6kdRxB2x5vNzCFQjLQlgWbsnALZRw8wWcTA5h3tiB42VChHCcKR5T4rJs0iLPJ6kKcjxKYPPapWrisuDkwSJ/8fsj7LwjSCHr8uw3MxMGiAC1jRpH3yhOWHcsBDdlkJL9GuGtK4jfu4XAqnr0mhiSqnhGgEJgZ4pk958ns+8MpYvDONnCtLpDazRL8pnD5I93U/Oh26i4bwtK2E/s1nXkDnTcUG3A9KUswhGEqoOEG8Ikzi7cwFsPajTeUo8kS1glm3TXuHOpoqCH4tjlPIHKRopjffOcaRyShKQqKFUV6M31aE31aA01KPEoWm3Vgmw49NZGan7t5xd8D9eC3PP7KJ/umH/HeWCn8gx/9yBWciHFvjN3Tam+E8iKQsu2R9AC02eamj9C6/ZHPUPFwfOznmch0INxWra9h8rWbbMSJIQQOGaR/hPPkeg6eE0BCqAi0ILpFDx7cdeiIthCqtRH2VrAjEwCOeINbHxr29HqqpEjIeSgH1nXkXy6J6ulyJPaeEJ4Jqa2g2uaiLIxHqyugUU3D9xiiewzr2D1L4M5pyQh+XW0xjq0hlr0xjq0hhrkcAitpWFeKSRJVQjftRv/+uXT9hO2Q/Lvvoubnfrc2xa89L0s+57NYVuCq5cd33whz+FXCyQTP5lJTYPs11GrIsT2riF+7xZ8DZVIujpR9OrkyxjDKbKvnyX14gnsdGFBrr3mQJLhv3+ZwIpaAmsbCa5pRI0GbmiQ6j8wiJm38Ff42PjRdQwfG6GUnH+kImsy7fe10XpXMwC5wTyjp7x1C+HYOFaJcP1K8sNXFVxKkucD5fch+3TkYACtsdYLSq1NaI21yH4fyLI3M5W9kfRCJVyUaJjgjk2L+QoWjdKxM959KBJaVQQl6PM8iLIlrJQnLCqpsrctoHuDl3QRO1uciBF6TRQ1GsA1bcTVM2dFRq8Mo4T9OCVzPA01W0rNIdF9GMcyWXHLB2ZM/fmjtazY/WG63voG2aFrkWiSCFe30br9EcLVK2b9LYRwMfIpug992wuI1zFzK1op6iPrGMlfIOavJ+qrI2+MMOuTKUnIoQB6ayOhO3bh37AaOegRlpCkudPE49skRQEfyKHlFS920jnyrx269hPIMpJfR/bpSH6fV5rRVIevtQmtpQG1pgJJG09fyjLI89z/FZBkGb25ftz1enngGibyN56eUV1RCDBKMw+kMsnFp15viiAV3buWyK5VhHesRK+LT/zYQgisZI7CqR6yb10gf6wLO7UwD5srYSfzFDsGCaxpRIkGkPQb+7VmLmXpfqmHDR9aS+Ouenb/6k7OfPMcY+dnST9JEGuNsuKeVrb87EZvPcoVnPnmeczCJIUX4QlyegoC3nH6ihb861eiVleg1lZ7/62Ke2mXH7W1JkkidssqKu7egDBsJE2h0DHEyHcPgAA1FqThZ25HkmVkv4Zr2gz83WuYQ2kAgmvqie9dg7+1mu4vPEX50iQxIbyukfqP3opbtrCzJYIrayl2DM3eFiG8GZWq0bLtPfhC01N//nAlK275EF1vfYPcyCLq1iSZeNMGWrc9QiA2e42acF1yiS76jj1DbrSb61VddlwT0ylh2Hk0xU+q1INpzzJ4UxWC2zYS3LuNwJZ1M5ph/jhA8un4N6xGb2tCrakcf4+qUKLhRStI3Cy4KYJU/Sfvw9dchYTk+dQ4LnamQOqFE2QPXMDoTeDkFk4DnQl2ugCIt+fFEnDy8TNUr6+iekMla96zkvpttaQupnFMb6wjyRJNexsI1QWpWBEj3h4j0hRB9SsgoPvFHi69fKXpn4Si+1H0ALJ6mZYuE9y1mehDd19TncY7DbKuEN3RTrFzmLHnjk/e03jfbGdKDH1zP3a2jBYL0vKvHiC4omYiSKX3nafcN0bLpx6Ycl5Jlal6YAvFjmFGnjqErz5OcE3DbBOpKUj2HEeSZFq2vWfG1J8/Uk3rjvdy6dB3yScuzXKWK+5R0ahq30nTpvvRgxWz7ieES3rwLD1HnqCcnUoXv1b41QjJYg+2a5Iuzc6uU+JRoo/cQ2j3VuTo7CSOHwcokTCRB+7Av2HVj/V9zgoJbrs/TD7rcOKthfW5N0WQUiJ+EGDni5R7EmReO0Xm9bNe6maJCA6y5q21OCUT4dz44t50V4Z9/9+b7P6VndRvqyXWGiXaEpnoayRFYuX9bR7deZwdJEkSjunQ/XIvB//XEYzs1GJHxzIwCykc40pnU/XHIkABuKZN7lQvNY/sQPZpZA93Ueqa1CkUrouwXXx1MZSQD1yBHNCnnEM4YjrrTpbxNVaSev0cdrqIUzQx+sdYSJQSrk2i6xCubdJ2y/vRA9NTf6HKFtr3fISL+/+JQrJ31nOpvhANG+6hbu0dyMrMMxMhBI5lMNZ9iN5jz+BMK/S9dmSNEeYLdr61K4h/6GF8q9vmT+n9OECWkDT1x/8+Z4Eswbs/GKXrvPmTIHUlrNEsuTcvkDvaRf7oRZz80lsiOGWL0sVhz/13CTTJrgUjJxK8+gdvsPGj62i7u4VIY3hKQPL0NsdTna4g05Oh4wfdnPnWeUpjVz0wwqWY6KWYmL0T/JGHgNRrZzEGU8T3rqHx5+4kd6SboW+9CQKiO9upumcTxmgWhECrXJhqtSR5g4IJwVEhJuqNFtqwZO8JZFWneetDM6b+gvEG2m75AN0H/pliavosxReupnHTvdS034Ikz05WcawSA6eeZ6TzrSUNUJfvYy741rZT+YkPojf9RCbrZkIoqiAtYpx7UwSpnv/vO9ipPG5p+WRRks8eIfPqKVzLGU/9vT3I9uU49OWjnPteB9XrK6nfUUe0OYovqiPJYGRMsn05Bg4OkTg3RrY3N+khdRNCkiSKF0coXUoQ37uGug/uZujbb4EQVL97K8XOYUaePITs0wivm00DUZoI/uANAOxMEb02BrKEFguiVSzWlkEwdukoIGjZ/iiaPzJt9B2uaqF1x2NcOvQ9SpnBic8D8Qbadr7Ps9iYJUAJIbDKOXqOPEmy9/h1K68vFnp7CxU/895FFXeLcQYfjqdkj2ByFnt5ECZdQTS4/Pk1wrseHnlECI8cIwRuoYTZ04+bv7ndvXW/zJodftJJh8FL3lp20wodTZ/9O5dliFcvrkzmpghS5sDCKdnXCidTxMksz0OraNJkRkmA5pcxSw6qLhOq9JEdKeM6Aj2g4FgubdtiXDqSIt2doeP748y8K58bwXQ22kLhOlN8dhYFWVlQqlC47rIXYArXRQn5qLpvs8fAK5QJrqwjf6Z/ouMr94wRWlNP5bs24quJosYmBVHlgE5wVR3BlbVolSGiO1egRP0UO4ZxyybJF09R8+hOtMowsk9Dr46wCKPZ8TaOp/4cm7Zd70cPRKdslySZaN1q2vd8iItvfgMjnyRat5IVt3wYX7hyDokjl8JYL73Hvk9u5CIyCg4SiqziuDZLsR41F9TqCuIffBC9rWlBaS8hBG6+iDUwjHGxB7OrD2tgBCebwy17A09Z15CjYbT6avS2Znyr29Ca6hZFSBBC4GbzOIXiOH29jJPMYCdS2Ikk9sgYdiKFky96geta3iEhwL7Wd0gCdX6Cklcz5k6zDllKCNtm7RYfv/NHlYz0W/ybx7oRAn7vK000tulzHqso8PqzC/dnuymC1PJBomL1DgrD3Zi5FMv1crdur0DVZYyiTWaoTMO6KImuApIMq++o5sj3+olU+4g3Bhg4laFuTQRfWKX/ZIb04BKmNoWgcOgk1vB0eZ15IcsEt28ksGXdvLtaQ6PknnvtGhq4cBgdPbhlk8K5AQLttci6SuZAJ7njlyZ+xpEnDhK7ZRVKyEfuZC+Zty4iF100fKCrROrrkVWd1AtnkCSFYH01Zk8Ks2SSOdiJUzLxN1VSODdA9mg39jUOYpK9x1FUH01bH8QXjE/ZJkkS4eoVtO36ALmRTmpX7UWfywNKCDJDF+g9+jTFVD9+LUZQi5MtDxLQ4+SNMSQkFFnDdW0EYuJvR1joShDbNRBCoCg6CBfbXUSGQpII371nwcQBYduUTpyjsP8o5TMds85eXNvGLZawh0YpHT2D5PfhX9dOcNcWQnu2ekK0C7he8dBJci+/iZsr4OQKSz5YcnIFss+9hvLW4g0PJVUl+ui9qPHo3Ds6LsUjpyifXh436cvXsAezPP24RWrUnjKhPX2oxMmDM/9OkiTx4EdiM26bDT8JUtcDCWIrNlOz5W5KiT5SHUcojvXjWuaSKANcRqzOT6TGRzFtEohoNG2OYRZtRjrzFFImVtGhfm2EgdNZjKKDVXYopi0CMW3Jg5TZ2YPZ2TP/vldDllEr4wsKUk4yQ/7l+X2dlgKFcwMUzs3MPLOzJcZeODnx77AUp1pupiz7yGVTFJ+/SFSqIiM8le+AFMJxvXUd17DJHu4ie/j6Tf0QgkT3YYRwad3+KKp/KgNOkiRi9auJ1KyYkyDhSRwdo+/Esxj5JBF/PQEtik+NkCsPE/HVUTIzVARbJtQiZMmrUXJcE8e1CGgxDDuHaReJBZsYzV1YVJDSmusJ37PXq2ea556FZZF56kVyL76JWygsagwoygalY2cpn+/GuNBN/KOPIIcCcwZGSZLwb1pD5skXcNJXax9eG97371dz/o0kZ19LgjTerqOnp+yjaBIrd8VZvaeC7KjBqRcTpIeNafcr6Trhe26FeYKUcF2MjkvL/g7lJfja/yjg2pMNdWx466U83/rrmbNXsiKxeffiathuviAlSSiRAErIh6yrLIgXfAXM0QxucZwYIQTdz38df7yGaNsm6rbfh3AdCiM95Ac7MdKeAdz1Yqy3SDFjUUyb+IIqyZ4ChZSJWXZwbUEgpjF4LucFr5LDWE+RzGBpSUWfK1rDqD6FZHcO5x22hqWrIXQ1SL58pZipRNBXge0YmPbSrBFKgItDUeQJSTE0dAJSiD43R4PchsDFYXnSlF7q7yDCdWjd+diMqT9FnT3N4lglhs69xtDZVyYUyzXFj+WUUGQdJBlF1tDVIKrsJ1W8RNhXg66GGc13EvJVEtBi6GqIspVFkmRy5REMe+FpG1SF6IN3IYfm95FyimUyTz5P7vl9sICi+tkgSmXyrx/CNUwqfvpRbxYyR6BSayqJPHQX6W9+33MIvE6s3BVH1WVcWxCt81HK2vSfyU0JQpvvreFjf7gB23RRNJmdj9bz+G+fYbT7Hb7mJcAsT+1khnpNhnqtWS3oZUdMOPYuFDdVkNLr41TctxX/ynrUaGBc3XxxQWrwr58jf6x78gPhUk4NU04Nk7pwmKr1e6hav5fKtbspJwdIdx0n1Xn0usRBe4+lZ9128gdegWh+zCTR5XXGudGlZxeue7CZdQ8203twlM5Xh+g7PIqzCCXj5UQkUE9FqJXO4Ze9BXW8UXFtbANFY4yRzNkluU5ZFBlzBzFEGUsYyJJCVqQQuEhIJN1rSIMuEsmeYx7rb8uD05QpZoNZyjJw+gVGOt5EOJO9R7Y0RMhXQd4YRQiHnDGCK1wy5QEsp0zeSFCyslhOibwxRq48il+LUrayE/e8GOgtDfhWt82/puK4lA6fpPDawesKUBNwXUpHT6M11hJ75B5P828WSJJEYPM6Cq8fwuqbo/h6gZAViR0P17Lz0TpCcQ2z5HD+jRT/9LtnySW8GejeDzeQHirzwl/3EIio3P+pNrbcV82LX+1ZyoTMDcFX/zQxp+OuEJ6+39jQT2SRpkKC4IYWGn/pAfwr6ydqmhYDMc4qurpORlI01ECISNNa4iu3IckKiVOvkek5iy9aSfWmO5FkheT5g7OceWkgq7KX0nHmDoaSDFpQQ/Wr48rpLlbRmpfhN3IuzZr7mtj4aBvrH2oh3Zvn2D93cemtEUopY6Jo+MbCWztRZA1ZVlFl/4SVhCyr+NQwJXMW1Y1rgIWJJcZ9cShPjIR9BMiKFGWubcYmqTqhpnbMbBIzlZjw8vI2ylP+FsIdn1HZtO54L5p/dodWIQTl3Ci9R58mPXBmmi+V7ZbJlCZZgeniVH1G54rh8OW/TecaZ6WShG9N+4JsJpxcnuwzr+AWlo4SL0yL3HOvE9y1ZW7KuySh1Vbh37Da0+W7znSEJIEWUDj+7CgdbyVpWBtm13vrufXDjTz35W4A6leH6D6S4fizI0iKRMvmKBvuquLlr/XivE0eddeKi2fnHiALAU98LbUoTsdNEaTUWIj6j7+LwNrGCa0+t2TilkzkgI4S9OFaDk6x7Hn5+LQJXT8EGEMpyl3DGH0JypeuSClJEo17HyFQ0YBVzDJ2eh+5gQ7ccZNAI+2JT8bbty57kFrz6Er8UR+DR0dInElMyRNPfA9+hZbbm9j4kXXUbq1BVmRy/Tk6n+vm/BOd5AZmT91cfHWIS/tHaN1Ty6q7GmjYWsm7/9NOiskyF57v5+JrQ4yez1BM3bgaMVXx0VS5g+roKvxalKCvYsLzS5IUhLDJj43Mc5brh0EJQ1x7h+qLV+GvrKc01Iseq0KLxCmP9qMGI2jhOOXEAEoghBaKURrpxTFKJLo83bjmbe+ZRqaYgHDpOfwE6YEz19y2pYIcCuBf0wYLEBguHjiONbj0v5tbKJJ7fh9Vn/jA3CQKVcG/YRWFN47g5q8vVSwEnH8jyTd//xxmyUFWJUBiw11VE0FK0xXPDNAS2EWHoY4Cq3dflm9bviAlaQqSruEWykgBT7tSWPa4c7aEsCy0+iqcbAE3v3QDhnJxcfd0UwSpwOoGgptaPIWFkkH6xZPkjnXhFsrE79lCxf1bKV8aYfjrL3kil0Ef/rYaKu7ZjFYTw8kWGfybF6ZT2QUY6RFSFw5TTg153jVXoZwaIr2YyrVrgKLJbP7YBuJtMVIX0zz5r5/FzF21mC1B610t3PobtxCsmVxAjrXF2Pbzm6lYVcErv79v+nFXwLFcul4f4tJbI1S0hmncVsnK2+vZ8oEVrH+ohZHzaTpfGaLjxX7yo0tfMH01bMcYT+UJooEGhjNnJtJ9rnAoGSmKSziTWi64poGdz+BaBv7qBiRJwnUcIu0bQJJxjCLh5tUgSVj5FI7hdRhjl44ihKB5y4P4wlXT02iSRLRuFenBc0tK5LkWyMEAWlP9AlJ9DsUDJ67pGr5IFUZubM59ymc7cbJ5lNjsM1BJktDbmpCD/usOUq7tkhowMEvO+L8FyYES6+6oHL/Y+I5i/P8EmEUbPahcT4nXvJD8Ov41zQjLwejsR6uJo69ooHyuB72pBjnkp3j4HMHtayid6sJcRJAKhCSaVuhEKxQUZeabGLhk0t+9MBr+TRGkIresRh634kg8cYCRf3wNYXo50cCaRs8ls1Amf6x7Qvk8qyqkXzpJ868+QnBtEw2/eD99X3gCp3Bl5ytInNk/Z0rAzKUw8+llvDuoXFtBuD6ErMoURoo4xvQ8frA6yC2f3k6wJgACypkyjumghXT0kEbb3c1s/PBajn715AxXmArXchnrzJK8mOX8s/1UtkfY/P4VrHugiZZdNez++TV0vDTIkcc7yAwUr70ma14ISmaKsVwnZTPDaOY8YrHFSO8AOKaBVcojXAfHNHDNEsK2MMZG0GOV2IUc5dQIeqQCuzS5mC5ch7FLRzELKdr3fGQGd12JqrYd5Ea7SPWdZrnrn+aCHAmhVlfOu581OIqd8AKNpGrowRjCdbBKefzRKqxyAcco4q+oxypkcKwyvkg1tlEgXL8KSdGwy3ns8sxZATdfwOjuI7htw5ztUOJR1Noq7JG5g958yI1ZVDb68YUUjIKDoktUNgXwhRRq2oIIIZBVCVmRxtVhvH8v90+lhAOgKFi9I8iRIL72RnwrG7H6RnAyBSS/DpKEWyjjpHMLPu+O24N8+j/XEYnKuF4N9NQ4PI7vfDXFN//PwupXb4og5W+rAcAtGIw9fWgiQIHniyJcFxQZSVMmgpSwHYzeBINffYGWzzxGZMdKKh/eyeh39k9h/aj+EOH6lSj+0JQOwswlyfaOL9gvJc1uBtRsqEbRvCDct38Ad4bF5o0fXku0yVMtGD2T4M0vHibbn6P51kZu+VfbCVT5WfPoKs59r2NBNh9IUNkeoXFbFavubqBlZw1G3uLSmyNoQZVN722l/fY6XvqzE3TtG1rWryBfHr2K2fejBadcoNDneVuVRyfXhXKXJtN0VtcsHkzCJTfaRce+v6Nt5/uJ1KyYUJnw3H2j1K29k0KyH7OYXrZ7mA9afc2CvMSsvqEJZ2stECXSuIZ090kijWvwhSuwSnnK6SFCVc040WrscgFJkrFKWSRZwRepRAtFyQ1cmPG9c8sm1sAIzBOkJElCb66nfPL8td3wOE69nOCx31zFez+7mp7jGapaAmx/qJZ8yuTn/3QTVtnFF1QIV2mEKjQKKYuq5gC5MXMidb0ccDIF1GoTvbkWs28UO5lF2A5OtuhpVroOwrIxBxOoVTGczMJmlD/7a9XousTTj2cY6rVwZlh2AOg6v/BlgZsiSF2WpDEGU7hX6fYJ2wFXICmyl4u9Sjqp1DFI/sQlKh/cTmTnStKvnsIaudxhSNRuvYdgbRtmNkGkaS2FkUsEa5rpf+OJG3FrAESawkiKhFW0SV1MT8vshOtDtN7dDBKYBYuDf3mUwUMec6nj6YtUtMfY/LEN+OM+qjdW0fta/6zXkhSJ6lVRNjzSyoq9tVS0RSgmDQ79/QUuvTnC6IUMql+haWsV7/rNrez9xbUku7KkB5ZaF26W9jE1tXr1zMovhzHdIu6P4IxrLhRTg3Qf/DYt2x8h3rjekwcCQCJS007Vih0Mnn6JpR6ih9ZuBAmKnRcQs/GOAbVqdgX2K2En0whrchBpZEaxyzkcs4is1uOYo/jClWjBKG7WwrEMAhX1qPkUCJdSagg9FMcbv89wr7aNk8p4Qsvz5NMWMvObDyeeG2XDHVXc/tFGbv1QA7Iq0300zRN/2smW+2tYeUuc1/6hj9oVQR79zCqyoyZbHqjhzCtjuMsouiIsm/LZSRX90lWzJSfp1YkZ5xen3em6gnPHSjz+v8awzKV51m6KICX5NADcojFtdOKWLITjIusqSlCfZlYoLIfi+X4q7tmMr6kKvSY2GaQkiWBNCwNvPoGRHqXtvp+l55V/It6+DV+06obcG4C/wo8kSZTGShiZ6SOUpr0NRBq9QH3plV5GTkzOOhzTYejoCGsfW40aUIm3xWYMUr6IRuPWSrZ/dBVN26twXUGqO89L//04F17op5w1J8gaZsHmwksDqH6Fh/7fXUQagssapBRZp6X6Fmpj69GUwAQ5RpIkzvY9QyLXgYRMUIkRV2tIWH2oku45xgoLTfYs1EtOjqASxRE2JXf2FIes+og3rl+2+5kJwnVxbAPHKuPaJkKM69e57sTftlGg58gTSEjEGtdNBCpZUWnccC+2UcCxrp3YIoRLqvckV3b+kq4T23M78TvupdTdQeHsKax0CrdcmpTlkbz02ULg5PITKg9mPomV99YUCyOXKI0NTDAUc0Od3mhfuJTTw17qszOFcBzMQnrONTi3WEJYFpI+j3xPxdQ2y4EASkXUk0tKLcBdGCikLf7ht8/QsjlC/aoQyYEy3UczFNIWvaeyqLqMYwm23F/Dw7/aTiCqkhoos+8f+3HnYeouF2QUauQmRt0+XFxCRCmSR+Aio+CO1wLKyNMGe1/7Hwl+4bM1vPfjcY68UcSapUwlm3bIpRc2ULwpgpQwvRGeHNS9DuyKbW7JQNgOcsCHGgtNeAVdCTtVQLgCJRJADvqmnlsIHKOE61i4VhlF81FO9lO79Z7lu6GroGgKSGCX7GmFtnpEp/GWerSghpm36H29HzM/dcRbTpcxswah+hD+uH/Ga2x+3wru/vXNFMbKdL46ROcrg3S/PoRZnKXeQUCyO4esSMjy8toShPzVVIZX0JN4C78WRVMC5ErDVIRbKRjemkJYiaNJAVRJJ6JU4goHvxLGwcYWFgE5jF8OEZSjCAQDRgcuM9+bHoyy5s5PLOs9zQ5PUd11LFzbxLFNXHv8b8f7W5IVhOtO0UlUfUFW7v2p67qya5sc7P+dKVT2/MmjFM6fIbBiJYG2dirvfRC3XMYcGiT95mvjsyvPDn3eOxMCYZiTaTohEEz+7dqTWY4r23Dl55f3nfM6tu3VX83TJNl/xbsgS+gtjUgBP2pVBbkXFi7bVc7bXNif4sL+qSQexxI4lncfx54dZvRSkcomPwNn8yT6bkzmYSa4OEhIyONZiYAcpuQWkFGJyVWYooyDQ4AgZUoUxeSALp9xKZcE//I/1eLYgnJR4M6wJv3N/5Pkn778kzWpCVjJPL7GKvTaOFwlcGqlC7iGhRoNotfFKZ6bPouYsLtQrxZIFRiZUXzRaozsGFYpR92OB5BVDauwNLIqC8HlB50ZHMojDSFqt3hrctne7IQ9/NTjXRzbRZIlFN/MTESrZHPo7y9w/vl+Ut15jPz8zBzXFlx6a2TZaemq7KNoJBnNnKci3EZQr2Akc46AL04kUEvJTOEIm7ASQJE0bGHhl0PIkoLlGhSdLIqk4ggbWVIpu3nEMilHXD88zyVF9aGoPrS3uzmAMA2K589Q6jyPXtdIbPdtxG+/m+zRAxP1VR6teb4TiSXzd5sTrljYGukV77re3IjWVI9w3CnpyCVrkgN9p3P0nV44SeFGwEWgoaOg4JdCRKQKyhSRkcmJNAEpRFkUJmZUP/Nvqtiww8+rT+foOFPGmIVufu74woPwdQWpz3/+83zuc5/jM5/5DF/4whcAKJfL/Nt/+295/PHHMQyDhx56iL/4i7+grm6ygK6np4dPf/rTvPjii4TDYT75yU/yx3/8x6hzVIJfD0odQ4Q3t6FE/PhX1FI8PZlnNYfSOPkyWnWU8I6VZA92TMoeAUgS/rZaj1Rh2RPECgCEYPDgM15dlHBJnHqdms1341omI8dfXpZ7mfH+kiWEI/DFfGiByW5LkiVa7mgm0hhGuIKBg0Nk+6a/BLIiIyuyx4CdJcVw8rvdAItKQSQ6Mnzns294s7tlpOE7rokkyV7QsUsEIu2E/NVoahBJ8hbri26WslnwnJlxkVCAyyN1QdnMIRBk7dHxT360iijfLkiqihKJ4qtrJLh6Hb6GJux0ksQz38MpT3ZEYiHKEZI0bRC5LFCUiYHnXLgyGJl9g7iWjV5fi1te3KDLF1KoaPATqtDQfPKM1PLkQJnhzneGDJKGD58UIChFKYsiuuQnIIVR0XCEjSVMTMpEpAoKIjsl5VdZo3Lo1QJ/9v8MYZTcWd+ixXBCrjkqHDhwgC9/+cts3bp1yue/+Zu/yVNPPcU3vvENYrEYv/qrv8qHPvQhXn/9dQAcx+HRRx+lvr6effv2MTg4yM///M+jaRp/9Ed/dK3NmROFk5eoft8er25kz5opQcpO5SlfGsG/opbo3rWUOodIPXfUYxhJEsH1zURvX4+kKliJ7FUUdPDHajALGaxCBiOToO/1by3LPcyF9MUMruMSrAxQva6C0dMJEBCsCbD2vZ7atFm06Hy2e8bjtaCKGlARrpiRvg6LC06XIQSTShTLmPErmWlS+UsIBEVjDCFc1jU+gOmU6C9PFoW6V8yOrp4pXQ5KP26EiuVGeNM2Ilt24JoG5f5esof2YwwPXmUTIXCL84+cvUJ6fVxxY/kGCbKuzSmNdBniqmCkVlciBwO4pYXXAPrDKo98ZiVb311LrEafNTi+8vVevvWH18ckXCpYGFxyJqXErvw7KYYn/s6L9LRjv/GVMR74QIw994boOmtQKroIZzqFpVRwKeaXcU0qn8/zsz/7s3zlK1/hD/7gDyY+z2Qy/NVf/RV///d/z3333QfA//2//5cNGzawf/9+br31Vp599llOnz7ND3/4Q+rq6ti+fTu///u/z2/91m/xu7/7u+jzLGZeC8rdI5gjafS6OOFt7aixEPZlSqUQpF8+RfzOjaiRAHU/ezex29ZhDqeRgz4CK+s98zqg3JPAHE5PnliSqFq/By1cgXAcSslByslBSmMDGNnEDTOSGzw0jF2y8cf9bP3EJsyChZExWf/BNUSbIwgh6H9zgFTnzIWtgUo/vqiOa7uU0rOPEmVVomZtnJo1UVS/OmPcMfIWp5+6BpX064BpFxnJnMUVLiDoGtmHX4thOUXK5o1Lu96MMBOjJF96DiuZwCkVZw4uggUbBMrRMJKqLEtKDfCK9cOhBSlfOKkrnh0hwLZR4rFFGSluf6iW2z7aRLK/xCs/GCE7i65m76kfj+d0zz1htuwJsu3WINm0g23NnFp9+vE0T/5dekHnvKYg9Su/8is8+uijPPDAA1OC1KFDh7AsiwceeGDis/Xr19Pa2sobb7zBrbfeyhtvvMGWLVumpP8eeughPv3pT3Pq1Cl27Ngx7XqGYWAYkz9uNru4H9RK5cm8dprY7Rsonu2bllLIH+sive8M8Ts2oIYChLa0EdrcNmX072RLpF84jp28okhQCHpe/SayohOobCDcuJJ4+xYabnmY/GAnPS//46Laea3IDeU5/0Qnmz++gUhThHt+905gfC1NgsJIkeNfOzWjVJLiU6heX4WsyhhFk/ws0kiqX+H2f72RXR9bPaH5p/oVXMtby5IUiVLaZPh06oYHqaCvkoaKLRSNFEVzDNsuY1g5bKfE21nAejPA6F/Yb231Do5Xds7dwWv1NUiatmxBSvLpaHXVC/KyMoeuqL0TAnNg2GP25RauQrH5/mrGeov83edOe+tNszyOPy5PaangcujV+ZXxU6PLKDD7+OOPc/jwYQ4cODBt29DQELquE4/Hp3xeV1fH0NDQxD5XBqjL2y9vmwl//Md/zO/93u8ttqkTEKZN8tmjZN44R6ljcNrirLAchv/+FWRVIbJ7DbKmTglQVipP4tv7ybwxXU27YtVO/PEaZM3nKaKnRymM9FAcuYEdtYBT3zhLvD1G862NyKoXhIUQGBmD4187RbIzPeOhWkClfrtn4W0VLMYuzMy4adhSyYaHW0j15Dn3XB9mweK2T23g9FM95BNlVt/TSCFR5rUvza9YsdSwnTIlI0lAjxEN1gNM0LKH0ifJlYbnOcPi4Nom6YGlUVb/UYLr2NNmSpKiElq/Eb2ucZpHVPLl5xCmx7yzRhK4ZRM5MJUdezX0lkYkvw8WkB68Fsh+Ha21Yd79hONg9U2K7yLL6A116C2NuKZJ/uU3FnS9YEwjNWgweD7/ditT3RD81Z8sfVH9ooJUb28vn/nMZ3juuefw+2emKi8HPve5z/HZz3524t/ZbJaWlpZFncMcTMHg7DpuZn+S/r/4PqEtZ4juWYNaEcI1bK+Y98hFSheHENZV6zWSTN32+3CMArmBTvIDnRjpYWyzdMNSfZeRHyrw2uf3s/L+NlrvbkEPaqQvZTj/RCfDx0dnXWuKt8eINIWxSzaDh4cpDM+clqlZHUMPqfzwj45w8fUh9JDKzo+tpmvfMN37hhg6neKuX91MpC7I2MUby1Ay7SLDmTPoapiKcBuxYCPRUAuKrJHML4Hp4NXXK+Xo3P/4kp93uSHBxHrPlaGmYUs16x5q5Y3/dQKjMAdrUzChjXgZwbUbqLjjXoqXLhJcsYpy3yX0mnqMwf4p61JuvoQ1OIJv5dzvrVIRxbeqlWIyvej7WwjUuhr0pvp597MTqam1UK6LnUqjtzbiJBZGnQbIjpoEIoo3cDRvMGNUktm89xdJjZxnoOt1hHCJVLTStu5BysUUXaefwrHL+AIVtK1/kFyql8HufQCEog1UNWwmHG9G04LYVpl8po+hS29ilNITl6iq30Rj+x0M9bzFaP/RqxtAbfMOGttvp+v002TGLl7TbSwqSB06dIiRkRF27tw58ZnjOLzyyiv8+Z//OT/4wQ8wTZN0Oj1lNjU8PEx9vfdg1NfX89ZbUx0jh4eHJ7bNBJ/Ph8839whsKWBnimReO03m9dNMTKXmWsAVLh1PfIlAVSOB6maqN9yKrOnY5QK5vvOkOg4ve5sn2wKF4SIn/v4MJx8/A3gFrfPlEdJdGZ741A8AKI7OrrPnC2tIskTvoVGPASg8MoXqkxEC+o8kyPQX2PaRlXS/sbQzl/kQDTawufV9gEQ630Myf4nukTcom+nlYekJd1ZtuHcytKBK7foKRs+lsAqTgyhJDROsEdhWAbu8CCt4wN/UQubwm2QPvon86AdJv/U6CKi4411IqubVJAFONk/5bCf6iiYkeXYGnyTLRO67jdLxs17N1FJCUYjcd9u88kxCCMrnL05P60kS1nBiUanIw08N8chnVrH+jkpOvpC4wQW63rUilW0ofYewzSLheDOhWCO6P4o/WEkhO4AvECNWtZLksJcdUFQfa7Z/FFlWMMs5zHIWzRemsf0OKmrWcO7w45QKXilLITuE7o9Q27yT1Mg5bGtyBqxqfmqatqPqYQq5a/fmWlSQuv/++zlxYqpC8S/+4i+yfv16fuu3fouWlhY0TeP555/nwx/+MADnzp2jp6eH2267DYDbbruNP/zDP2RkZITaWi/N9NxzzxGNRtm4ceM138iSQkz837zQwnEUn2eHbBtFVFlG9YfRggursF8OeIPdhbW/nDYopw20mH9OE0PHdsEFRVegYHtMQNPFH/WILo7pYuYtGrdev5TMYmHaRYbTZ5AkBUXWiAWb0NQA+dIIudIQtnvj7EPeyYjUB1n3cCuZvjxmYWlm+sJ1EY4DCJxiHq2iinJfD0ooPLWm0LYxOi/h5gso0dkVyAH0Fc0Ed2+l8PqhJWX5BTauIbBpzbzrYqJkYJzrnsruk2W0mmqUaATHXvh313Ukw9CFPO/5tZU0b4oweC6PWXKnKd8k+8sMdSyNg/QEBOQzA8SqVqKqfhzbJBCqIZfuJRxtwBeMU8gOoPnCqFqAYtYLJI5tcPHUEzhWmVIhgetYaL4wK9Y9RHXzNmLVqyeCVLk4RjrRQVX9JsKxZtKJCxOX9wUqiFauYKjnLRz72t/BRQWpSCTC5s2bp3wWCoWoqqqa+PyXf/mX+exnP0tlZSXRaJRf+7Vf47bbbuPWW28F4MEHH2Tjxo184hOf4E/+5E8YGhrit3/7t/mVX/mVGzJbWlJIMg27HsIspCmnRkh3HsUspHEtc8JT6m1plirjrwpiF0xkTUFSZSRZpjxWQI/40GJ+in0ZtKgfLeKj0JchUBOmOJhhtv48N1zCNhzqN8Tp2jeM6wiyAwUat1XR8dIAAMFK34QlwY1E2cxwaXQ/mhJAUwIEfVVURtpprtpBx+BLjGbfGdTey6haE2P7T68h1hTCKtqcffoSF573hGX9cZ1N719Jy95arLzN6Se7uLRvCD2ose2nV2MbDo3bqzn3TA9tdzRQSpY59DdnKaVNQtV+Nn1wJc27aikmyxz/pw4GjnqdydafWs3697RR0RahdkMFjuVy+Ovn6HzBK16XJNj+M2to2FqFbTgc/rtzDBye32nYHOrH19AMQLmnm4q77sNKjSFpOuIq+/XyuYsYlwYIbF47J3FB0jWiD92NnUhinLu4JKwCvbWR2GP3IgXmXqYQQmD2D1I+c+HqDQjTQA4FJ2aHC8FH/991tO+ME4prVLcGMEvujLOp/d8c4KkvdC74vAuDIJ/po7Z5J4rqR9NtAuFq0qMX8PmiBIJVSJJCIFSFaeSwrMkgmR2bmia3jDyjgyeobdmF7gtP2TZ06QB1LbuJ16wmk+xCuN73U92wGde1SY2cm2a4uRgsefXsn/3ZnyHLMh/+8IenFPNehqIoPPnkk3z605/mtttuIxQK8clPfpL/8l/+y1I3ZfkhXC7+4K9n3KT4Q3C1XMsNgh7z0/TAWqycgXBczGwZPRYgeWKAio115LpSKCGNutvaSJ8bRbguocYodtHEnmVNYuhUksJYmYYtlXTtG8YxXS6+PsRdv7YFze/l3Nv21nL48aV+0eZH0FdBc9UtaIoPVfHjCoeikeTicCeZ4uxiuW8HtKDK3k9tZOhkksNfP0eoJoB1WVpKgh0fX0e8Ncybf3mKYHWAHR9fh1VyGOvIULOuguHTSYbPpNj9yxt54y9OsPF97dSsq2Dw+Bi3/OIGVJ/Ca184Rt3mSu749a089e/3URwrc/bpS+QGC2z/2Fpe/bOjFBLlKZJWlatiJDoyvPbF47TsruWuX9/Gd379VYzs3M9w4fwZCuc9tfZi53mUcBhffRPJF5/FNa4Scy6WyT33Gv617Ui+2UtNJElCa6yl8uc+SOofn6R8umNCz2/RkCR8q1qJf+Q96Kvmt67Hssm/9OZU+jmAEFijYwjH9fQFF4hLx7NkR+fvB/rPLs86bimfQFZUdH8EJAgEK+lL9xEM1xKM1qPqAYKReorZQVxn8t2XJBl/qArdF0FR/UiyF8yQpCvEi8evURglNXqe6obNDHbvxyilUFQ/1Y1byaV6KWQHr27WonDdQeqll16a8m+/38+XvvQlvvSlL816TFtbG08//fT1XhrwZg3xd21eVO3C9SB/vPsKFfSZIWs+ajbfxdDBZ25Im6bBFZjpEpEVlfS/cIFAXQRZkxGOoJwokruUQtZkSqN58r1pZF1Fj/uJrqykNJyfNgIGSPcWeO0vTpHp90ZbwhVcfHWI1ltqWfvuZiRJYvDkGCfGlSluJBzHJF3owbBy3v/swoSN/DsNwhWYeZuKtgi9fpWBw6MTI2tFlWm7rY43//cpBo+PISsSdRsraL+zkWRnFkmW6N43RKjaT+WKCCNnUrTeWo8aUPHHdGrWxnn9z08wcjZFqifHugdbadhSRedLnl5jOWvhWA7ljEnpKqkqI2ty7PEL5EdKFEZLbHxsBeHawLxB6upZRe7YYXLHZl+LNc5dpHT8LKHdW2fdB8YDVUMNVZ/4APnXD5F//RBOYnEGlnIkTGjPVsLv2ovWVLcg2nnp1AWKh0/NcDIZrbYarbkRYdvkhxfGYnv+/1yaf6dlhGMbGMUUwXCt51aNwDRy5LMD1DbtQNUCBMI1pEfOe+xNQNUCNK26m3jNGjQtiOMYuI6NrGjTAhR4rM+xwZNU1KwhVtXOSF+KWFU7qh4g23MRy7i+NOaPvHafpKs0fupBmGMxdinR+6ffwU2bcwZFLRglUDU/zXVhkMZVvV0kLovjCiRkBAJJkqmMrSKT6x2vCwIjU6b/+QvImoJdNMlfSiHJMlbRpDSSx7UcnDKMHR3ANR2QoP/5Dk/TbIYABV7n2vny1BFRdrDID/7LId7623NIkkSmr0BxjmLg5YJh58cdet/5sMsO+/78BFs+vJJ7f2snueEi+//yJKnuHKpfQfUrEwHEdQSlpEHd5spx5wmBbXjrgUbexnXFuNq7585cuSrG/f9pF1Zp3NAz7sMfX1hxfCFRxi57gd02HJAkFG1h75QSjhJcvRa9qobskQPY+RyKP4Cdy0xbUxKWTebJF9DqqtFaGuZO+0kSSlUFsUfuJXzXbkonzlE6dgarfxjXMD1ft3ERWkmSQPbsdtSaSgKb1xHctRmlIoaka/M7AguB1TtI+jvPzkzYcF2sRBK1rmYqNX0JcLnfXw6Kum2VKBXGCEbqUFQfpXwC2yxSyo+i+6Noegh/IE6pMOoN7CSJplV307DiNkb6DjPYvR/bKiKEIBJvZuOeT85wFUE2dYlCZpD61j0kBk9QWbcB17EYG75+s80f+SAFErJfv0r4dXkghABZpvWenxmn8s68n6zpc3rrLBSypBIJ1iHJKoXiKMFAFYqskS+NEPRXIqGQzfchAbKsMqH04wqcso1T9joruzjZFvcKGuzE3wLswrWlJo28xdDJd75F+42ELMMde/x09dj0DUxfvygmy7z5ldOc+PZFdv/iBm7/lS089e/3YZUdrJJDoNJbN5EUiUClj0KiPPmsTTxzUx8+x3ZJdmY49LVzJM6nJrg/Ru6K51AIZtOnEu6VPMiFdyp6TR1VDz0Gto1e10CxqwPZ7yd+292MPvUt3PJ0CSGrb4jMky9Q8bHHUOLReQMVmopSESN89x7Cd+9BmBb2WAo3V0BYFsIVnoZgOIhSGUMOBacePw+EENjDY6S/9YPZA5AkIUyLwr6DCOv63+0rsfm+Gqyyw9nXFk5tXygc26BUSHjkCT1IMTeCbZcxy1lsu0S0cgWua2OUvOyQpgWJxFtw7DL9na9MoZvr/iizPT/lYpJU4gINbbdS07SdcLyJbLKbUv7666Z+9IOU61LuGZmd1io8cUunYIAQhLa0wRX6WW7ZwsmVvJEZEkpAR4kFJ2iqkiRRvDBI4XgXxmCK0oUB9HdVMnT4OYQz88Oq+iPE2jfPuG0x0LQQsh4jVxzE54uiqn40NUhM0XFcC10Loxmh677O1dDDGuoCR9FXw3UF5czbsxb3TkEoKPGnv1vDH38xOS1I+WM6DVurKI55MxfH9BToAVzbpfu1QdY/0ko5bRCs8tOwtZoDf3V6XpfWctpk+HSSttvryY+UEI5L1eoYF18emNjHKtloQZW6TZWo/gzltEl5nnTevPe6YTPGYD/p116k+j3vB8DOZZH9gQmH4GkQguLRM8h+HxUffz/45p/pXLld8unojXVz7L1wCCFws3lS//QkpZPnZ4/PEugNdUg+HTuRxBq4dkr11dj9/nrSQ8ayBCkhXMrFFNWNYRTVR2r4LMJ1sIwCRjFNZd0GTCOPWfbW4FzXxnUtT0UnXItRyiBJMrGqdupb98yY7hu/EMmh09S13EJdyy34g1V0n/nBktzDj3yQcssWFz/39bkFTAWolWHqP3nf+AJojsz+c2ReO405lPYUmi93ApKEHNQJrmmk4v5thDe3osaD5I51kz/WBa6gMHyJTNfxWS+n+IJEmlZf973ZdhmfLBOPtJLKdhMO1KCpAbL5PqKhRlTFhxAOPl+MmCSTSJ2f5kR7LXjgc9tpv33+gseZkO7N83c//+J1t+FHGbu2+WlpUlHV6Q+lEILKlTG2/fQaZFUi1Z3njT8fL+sQcOwfL7Dp/e3c+q83YxZtjj5+nr7Do+gBlfxoCddysUo2xTED4QhKyTJW0cYq2Rz8m7NsfO8K7vz1rSAg0ZGm86VJ4kjqUo7T3+1i8wdX4jqCI393nr6DI9gl2wts4zVywoXsQMFL+80DWfdhp5MeSWL8HZJVdTZf3EnYNvk3jiCA+PvuR6mMz1k/tdQQQoDrYnb3k/rm9zHOd81Nd3cF1vCoR59fYvHbQEQlPbR8afJyIYEsq0iSTHF8ZmOZBcqFMRrabyM5fAaj7M2kHNtgpPcwwUg9a3f8FJaRQ5ZVhBAMdu8nEK6e9Tr5zAC5VA/VDVvIZ/rIZ/qWpP0/8kEKmKZMfjXkgE71Y7uJ7lpF+dIo/V9+huLZ/lkfNidXIjOcIXugg9qP3kH1Y7tp+lcPcem/foty1zB9r31z7vaYZUZPvnrN9zNxHtdgNHlm4t8jydMTf5eN9MTf/cPTJaquB8muHL7QVKci4Qr0oErzrhoKY2XSfQWsoo0kQ6g6QFV7hNELGc4/f2PYdNWVMo88EOK5l4u0t2qsX6Nzsdvilf0l1q3W2LszQC7n8sr+EqNjUzvb2mqFnVt9NDeqIKB/yGb/wTKpzPQAL0nQ3Kiya6uP2moV2xFc7LY4eKxMvjD5/Ph0iVtv8dNUr/LBR0MEAzIP3RuktnpyNnHkhMG+Ax5l/NDfzLyGVs6YHPrbcxz623NTP7dMXvy8R0jI9BcYOOLRw9/6P5PPRylpzHjsZTimy5knuznzZPeUzweOJiao6uCtmz3xmwsz9St1XyR++124pokajeFvbEZeuQYzOYaw5pmlOQ6F1w9hDyeIPnIPgU1rFqROvhRwC0UK+4+Re/517OH5qfYATjqDk56dNNW0IYzuV+g7nZtwpG1cF0YPzF48LEkQrfExeGGJa6SuQDE/wtClN5E0GVNcbr9gbOg0Qrjk0r1TmH2JwROUS2kqatagagFsq0h6tIN8dgBZUSnmRma+EILkyFmqG7aQGDyFYy1cLX4u/FgEqfkQWFVP/O5NIEuMfOsNimcWFuGFYZH8/iFC65sIbW6j6uEdDP71856Nx5wHuhRHe+fe5x2MI493clyfWieh+hTu+tVNXNo/zJtfPUd2sIhtOEiyRCCms/b+Jlr31HLhBgWppgaV//JbVTQ1qPzcR6K0Nav09tv8yZdSfPCRMPfcHqBsCP78r9L81z9PYRheQNmyQef3/2MVO7f4qar0Ru7JlMtLrxf5/T9Lcr5z8reVZbjvzgD/4Vcr2bJBJxZVcB3B0KjDEz8o8Cd/nmRwxAuANVUKf/Afq6ivVamtVggGJD78aJhH3z2Zjv1f/zfDvgNL8+K+U1Dq7kAJBolsvwW9pg4lFKbU3UnmwL6Frd0IgXGhm+TXvkNw+wbC9+xFa6xfkN/TYnA5XSpsm/LpDvKvHKB8+sKSqVrICnzkd9YRjGr8zb89ycA5j6b+8K+upGFNaM40YkXD8krMuZrFcPItb41zZSVut4E5VqBgD1Do68cpmmgVQYQjsLMe+Sqf7iWfnt6H9Xe+Mue1IvEWjFKabLJ7moTWteKmCFLxuzahhPxYiSy5Qx2LOtYay1E8P0BoUyuhTa3ojZWUuyZlf2RV91QnVH0K488xSxiZhY3QlhJ6tBLZF0DYFmZ2bFwNYHGYyXV3zX2NNO+s4Tuf3cfwmfSUbYXRMsUxg4Ytldzyc2v44eePXmPrFwdJkvilj0X5F58dobFe4b//Xg2/9x+q+NJfp/mPv5/gS5+v4eMfjvAX/zeNYQjqaxW++sV6qipk/ux/p/ju9wvIMnzsgxE++6/j6D6JX/mtURJJ7zvbuFbn//z3ekpll3/3uwle3V8iFJT49X8R55d/NookwX/4/VFMEwaGbB75eD8g8TufreQXfibKv/vdUb799OQI2TSXQRJHklFUDUnWkBXVW7uZoIsJT2jXdby1BtuaKLRcKgjbJnfiCPkzJ5FkCSHwdCsX+dw5yTTFI6eR4xGiNVVz1lEtqn2Og1ss42TzmBd7yL92ELO7b8lV1oWAwfMFojU6RnHy3uP1PlRdZvDCbLVVEpGqpbcnugzZrxHf1uLJpl1K4KuNEFpVS/pAF776GHpVmGJ3Al91GC0eYuiZE7jlayCGSDLReAtV9RtJjV4gn1m6wepNEaQCqz06uDmSRtiLj+5WIotwXLSqKGp8KlGhasOtxFdtx7VMglWNlDOj+KJVDB95ntHM9af8FotgwwoibRvQo5X0PvcPmEsUKCvaIuhhlbGumYsOSxmDUsakcfvsOevlwOtvlXl1f4lgQOL/+fVKDEvwxLMFLlw0eeG1Ep/79Qo0zRs8/NxHorS3qvzp/0rxpb9KY46/i5//YpJN63UeuT/Elg0ZXnzdG03+yi/FCYclfue/JvmHb+cm9FL/839Lsm6Nzic+GuEL/ztFd6+NKyCX9zx9DdPz0CmVBbkFGrstBormJxCtxR+txReuxBeMo/kjqL6gV8siKx4bzXVwHQvHLGMbBcxSBqOQwsgnKaYHMUvTKeLXBCEQljllsqBEojj53ILPr69sIfa+BwhsWD2z1bwQCNvGTqSR/DpywO/tJ8vj7pqePJNbKuMWy7i5PPZYGnsshdkzgNnd5xXoLpOZonDhn3737DS/RuEKjv9wlG//0eyqJ7/xD7csS5tgnDRWsohubKTYM0Z5IIMaKiH7vHS+sGxkn4oAXMdZNEs6FGsiVrkCVQtSVb8B17UZ7H4D11k68tRNEaTU+GVK6jWmEMYXdGW/hqxf8ZVJMtHWDYwef4liYoCWuz5C76vfpHL1Tmzj7bGCTp87jJEcpv6O9y7tiYVXixJrnFnl3BfR8Uf0WQVqlws9/ZanUVYQFEuCfMFlLOngCsgXXHRdQpHB75O4dZefUlnw1hFvkVofX3aTZYk3D5X56GMRtm3y8dK+EqGAxO27/fT225w6Z3oeeeNLC4YpOHnG5M69AbZs8NHdewMU7yUZX6iCqrbtxBrWogdiaP4wsupbEM0avJSXcB1ss4hVzFDMDJPsOUZmuGNWpuq1In7b3WT2vYhKGdvyarms8gwBW5IIbF1P/IMPzlo3JYTASabJPPECxsUeJFX1ApSiTCQvhOsRIYRlI0wL1zBwCyUvnTdBipLH2WliaVJRkow0ThG5fL6rY6BrCwqpuTvscn75nh/hCgrdCYyRLMZoDitdRFJkHMNGTfiQNQUl6DkG588PeyzoGTBehjZtghyOVtO27k5UPUg2Ocilk8+STy9tyv+mCFKXv3hfYwVKQMeeb03pSigyvqZKJFXBNW2PCXgFJEmmONqPVczgGAVcs0S29ww1W+4mdeHQUt4GAHq8BmGbKL4gkqph5TPYhSxXJr2FcGccMcqaDy1SgaL7cB0bK5fCKXvBVAvHQQJZ0VD8QRyjPJ4u9F6gkXMZzILFu35jC2985Sy5oSK26SLLEr6ozoaHW2jeWc3Rf7qxskjF0mR9jysEtiOwLDFuK+F9LskSkbBMPCpTW63w7a82THU3B1TVS1XVVHmRqLJCIRSUaW1WeeU7zVdbkHmzsyv2Xy7IikYgVk/d2tupaNqEovnHpWkWP+CSJAlJUdEDUXR/hGBlM1Vt2yllhhi+8AaZwbOYxbnVVADkQBAlEJx1u6Qo+Oob8YU1Wtb4CVdonHl1bHqQkiSCu7dS8VOPeEW3V9+T8IrLSyfOkv72s1j9w9c8EwqG62hZ+S58gQpKhRF6Ol7EKKev6VyX0bTiTqrrNgFw4cQ/UyxMJxQ8/T8vkh6aex1ypLtIIbW0g4QJuAIrWcBKemln15gMiOZ4Wk8J+zBGcti52dt51wMBbrnNxxf/KM2VIiO793byvo9+jWhc5r/+TpLkLFY/14ObIkgZvQkC7XUo0RDxe7eQ+O6b04wPZ0OgvY7Q5lYkRcZOF3ByV5qxCcx8Cj1aiVVIYZeLVK7fi6JqOObyLJDX3foQEhJ2uYDiCwGC4TefwUzPn9bzVzVQsXEPCBc1GMHMJhne/31cy6Ri426CtS3YpTxCCPRoJckT+8h0ngQE/UcTnPl+Lzs/vpqadXHGOrMYeQtFlYk2hqheFWXkbJrj3156/6a5sNA+y7YFjgPZnMvXvpljNDHzmskbB0sIAea47XVvv80/P5knk50+8hbCY+wtF/RQBbWr9lLdvgs9GL+mwDQrJG8OICkqocpm2nd/iMzQBYbPv05m6MKca1eRrTsIb9w2q2eaJMvodQ0YBQfbgEhVaMbZgm9VKxUfeQ9qZXzG8wigeOQUqX96Cmcsvfh7vNweSaayZh1CuJw/8c8I18I0rt9qpb/rFVKJ86za8NisCjQX9s9f6P7SV3tusIXHVDj5+Z/hnosWlimmDe6e/Gae118s8t++XA0sz4zwpghS2TfPE7tzA0gSNR/YizAsUi+fxC2ZMwcrCSRFxtdcTcMvPYC/pcZzue0ZxRxOT+4nBCPHX8IuFRCuS6rjMHU77gckBg8sjTbhtKbJCsK2GHnrh0iyTN2tDxNt20Ai8/q8uirlsUGG3/oBrmkQrG2h7rb3IOsB3HGqsBapYPD1J7EKWSo23EKkfRO53gu4Zhmr5LD/r86S6smz+f1tNG6tQla8F7OYNDj0dxc4+cQl0n3LR6WdF3O855mcS++Axab1Ok89V+C5l+ce8SXGHAaHbaIRmX/4dm5RweiyS/q1xxSJYEUjK275IKGKJmRVm/+Q64QkK8Qa1hGMNzDSsZ/Bsy/jziKQrEZi5I4dojwwM4NVUlSq3/0IwhX0nc4x1FnEsab+OHIkROz9D6BUxWc8hxAC61I/6X9+5roDlKL68QcrKBVGscw8rmtP0XaUZAVJUkAIXNfmygdJkmRvmwTCdaalCcVV+18Lkv3Lz/iUZdB0CVnyujzb8gZtsgyq6s2yXSEQLqiahGlMBiS/X2J40GGgz54WpACs8TXYmaCoTKwJW5bgWrxgb4oglTvaRe5QJ5Fdq1ArwjT+y4eoeGA72TfPUe4ewSmUPUKFhCe2WhcnuKmV2J61yCHPPsTJFEn+8Bh2ZmrnVk5OVp4Xhrq4+My4KvoyeUUL16E0OoBd9CrES8O9BGo9I7nZdPcmjhUuih7AF61CC8dQNB/yFZbfpcQARspLWZRHBwi3rEVWNdzxWaGRszj+7S5OfLcbPaThj2jYpkM5beLa7jyzGrHgac81S1zNERRcF/7+Wznec3+If/XzMRJJh44uC9sW+H0SFXGFWETmbIdJqSywHfjK17N88Y9q+OWPR/kfX0kzMGQjBAT8ElWVCgG/xIkz5rQXd2TUJhiQ2LBGJxySKBTFxIs6H8NPkhUqmjfTuv1R9FDF0s6e5oEkSWiBKE1b3o0/Uk3v0ac9csVVMEYGKff1YCfHZj2XOTLkqR0UHMhfNWuVZUK37cC3esWsP5kolck88zL26OzXWAgqqtdS13wLkXgLrmMRr15LOnGB/u7XcF2bcLSJxhW34w9U4Lo2w32HGB08Bnjmf00r7iBW0Q6yTD7dR3/3a5jGtSmWSzITA7urIVzBvG4WC31/YAqFX5bhofcFefd7g/j8EkZZ8O1/yPPq82X23uXjo5+IUCq6+AMyJ48Y3P1AgH/46xzPPllCkuA3fyfO1p06QwMO//5fJ1io4lu8UubjvxRm83YfkgRHDhj841fzZNKL6xtviiDl5IqMfON11IowgVX1SIpMcE0DwTUNCNfFLVueNbwsIfs0JE2Z0jm4pk3iiQMLo68vU3Ca7Rri8rB9PsgyVZtvxV/VgJkZQ9Z8SNoV1FcBrjl1tjBjBylAOAIja86rkD3tuJmGYTM1dR7Pn2vFK/tL/M+/SvMb/zLOX3+hjuOnDQxTEA7JtDSq2Db84meGJogQ3/l+nj07fPzMBz1CxYWLFo4riEUVVjSrDI86/PS/HKRYmtp5PP9aiXTG5Zc/HmNFi0Yy5RAIyDzzQoHv/WDumWZlyxZatj2CL3zjzSPh8m8uUdm6DSEEvceexipNta3IHz8y73nSb74+o24fgBINE9i0FnkOmrnZP4zZ1Xe9kxRSiQvkMn20rXk35VKSoZ63cF0L17VRtSBN7XdSzI9w6cIPicSaaGq/i2z6EkYpTVXtRqIVK+g6/wNcx6J11b1U129moOfNRb3n1a0BNr6rmqrmAKomzTiY6ngrxZHvz1Yk60E4zsIClSIj6ZPfbTQu85GfC/M3X85y9oRFrEImN56+VlWJUFjir7+U5//5wwpOHIYnv1XgnocCPPukl/r+/G+neOyjIe64d3Hv5UOPBWlfrfGFP0yjqPBLvxrltnf5+cH3iotaWrwpghQCimf76f2z71L/iXuJbG9H0lUvLy/LKMHpZotCCITtYI1kSHznTVIvnfAC2dsMSVbwVdQgqR7V2FdZi5EZuyoAeB3NlcFLVlRiq7aROPYque4z+Ksbia68Wl9w/idH9clUrojQsquGUI0fq+gwfDZF/5ExzII1+8MnBNjOuGr33EFVDs7/MjgOpDMOpfLkBbNZl2xeTGRwy4YgmXYnZjq2Df/jK2kOHi3zsQ9GuH1PgFBQJplyOHHG5J+fzDE0Ovkb5/Iuv/35MX74apFPfCTC3bcF0DSJ4RGbIycM/ul7OcrG9Bs+12HyS78xzKd/IcY9dwRQZImBYZsfvjJ3gArXrKB1x3vRg/FZ95koSnVthOvgWGXK+RRmIYlVzuPYBsKxEYAsKyiqD9UXRAvG8EeqUfUgkqx4NVXMnpOUFZWqtu04Vpneo09NUSRYCKyx2YVFlXgUfUXTnPfojKWwk+lFXXPmczk4dhlXeHR82y5z+Tn3FMArx51jy2RTPTS3S4SjzV6QqttILt1Luehp6uWz/UTirch9hxZMsa5o8PPJP91MTXsQWZZwZ1kLN4rOvEEK1wVnAe+QLCP5J4NUMe/S32PzyAdCGOUCp44Z5HOT7UgmXE4fMxnoc+g8b5HPubzrgcDEdjGeHlzsgOHO+/28+ZrB8KCDJEN3h822W3R++FSRRfhG3iRBCrzK9p4EPf/t24Q2txG7bR2+5mrUaGBi9iRcgbBsnHwZO1Ugf6KbzGtnsBLZ+c9/oyAE/upG6vY85LGo4tWkTnkjO8UfJFjfRqC2GS0cI7ZyM6XEAMXBboTrYGRGCbetQw3H8MWqFr3WEakLcMenN7L+oRZkVcY2HBRVRlYlhs6k2PeXp+l+Y2RWGrpbKnuRQpv7upKuo1TGcJKzM81OnjXZfl/PlKD48McGLn9FAPzl32T48t9mpuxTKgl++EqJ518pTRvRzhRgM1mX736/wPeeuSrAzPHOui48+1Jx2rrXXKPHQKyetl3vRwvEZt3HdWzK+TEKyV5yI13kE5co50YX7noqyejBGKHKZmJ1awhVNhGI1SOr+oydnqyo1K25jVJmmJGO/Vz3tGYcalUcOTy3MLJr2Z4VxzJClhX8wUpWrH94IgjbdhnG16s0PUR1/RaiFW0Tx3hFqgv/Hu74WBM17UFO/HCUY8+OkEvMHNyys3x+NZxcgfneWglQYlEkvw9RNjBN+KP/lOLRDwX5mV8II0lhvvI/Mpw65t2zY4vx+CcwDcFCkzPzobpG4b0fDnLb3f6Jdh05sHii0c0TpMYhTJv84U7yRy+iVoTRKsLIAR1ZUxGuizBt7EwBK5n3iBXvMAjXIdd9hvLYEIovQOrMgYl1MUlWUPQAdqlA4rinvab4AiDLCMtg5K3nCDasQJIVkqffItt1Grvsdb65S2eQlMnH30gnGDv++gRLUfUr3PqpDay9v5kLLw3QdyiBkbNQdJmq9ggb3tPKvf9uG9/+zD5SPTMzp9xCCde0UeYLUpqG3txAaY4gBdM7/ZmCwGyBQUz838JwLcznhR4jqzq1q/cSjM1eJ2QUkiQuHiA9cJZievDa7LiFi1lIYRZSpPpO4gtVEK1dRc2qvYSrWmcWeJVk6tbeQX7sEsXUwPTt1wA5HJrfRyoSRg4GcIulWfe7XjiORTE/TE/ni1PcYy/Pkor5URy7l96LL09sE+MzsoWidXOU4c4C3/+fF5eEIOGk5i8RQJJQqytQIiHsshcUyiXBP/9dgee/X+IXPh3hgx8Pc+rY/MzD60HnBYuu8xb//HeFiVfNMsWiZlFwEwapCbgCeyyHPbY8ts3LCadcInvx5LTP7WKO9PnZXVHNbBIzO7MdQGlkqp6hXcySuzQ5g2zeUc3KO+o5/HgHb331HGZh8klTdJn+42Pc/x+2s/n9K3j1f05vG4CdziLKBoQCM26/DMmn41u7gtLJcwsuFbhe6EoQCQlH2NjujTVuDFU0Ud1+C7Iy/XV0XZvM4AV6jzxJOZ+4tuA0E4TAyCcZzafIDF2gdvWt1K29A1Wf+ttIkkQgWkN1+y76sqOLTvvNiAWsTeormvCvX0XxyKllU4kwyxkyyYs0NO9mdNBzNQhG6hi4tA9cm6HeN1m54b3UNm6nVEzgD1RRyA2STXUjSTKqHsIfrERRfPgDldhWEcssTGEAyqpEIW0tmcq5NbQwfyatsQ61uhJ7NElDs8Ld9wcYGnBwHEF9k8qF0wv7HXUfVFYp1NQrhCMyrStUxhIu2bSLLEMsLlPfpBAISDQ0q/T32iRHXWwbvvP3BX7p16Kkki5D/TatKzWOHDA4d3Jxz9CN08Z/G6H+/+39eZwc13XniX5jy4jcl9oLVQWgsBI7CYAkSFGURFIkRYla3JIl09OyW3Y/2fI8eZ6fe9Tutj3TPe/J8/zaM2637Z5uP8tut23KkrVZEimJpLiTAAFi34ECUAtqzX2LjO2+P6KQQKF2oLAQyu/nU2QiIzIy4mbEPfeee87vBFVCLaFrFpxoAM1r4gTCKge+2TfFQIGvrt2/e4zRY1l6drbMegxnPLOwkbEiY2xYi9bRer2nvSAkZFbH72d72ye4K/WBazqCIl1bmLgkyXTc9QE/SfcqPMdmom8vfW8/S7UwunQGagoCq5Jj8NDzDBz4AbY5wyxYkkl2bcaILc3v4RaK8wbRKPEoic98hPD925Bjkev6PiEEpdwA1fI4V06fPc9h8NzrpMeOkWxZR7JlDbZVqZdRLxUu0nf8BwT0KM1tm9ACIWzL9zzowQRdK95Ha8c2quVxWjo2s2zlQ2j61HO9eLKEEVGJNC1NGoF1bmHi2HI4SHjX3aAqlAoeliV44GGDhx4Jsud1k7/9//mD89FhlwPv+DPHd3fXmBjzyEx47HnDN6qr1mr86pdj9K7RmBh3+fyvxXjqkyFCYYl4UubnfjHCz/9SlL5TNve9z+ALX4rT1ukPtt7dU+M//1Gerh6VDz4RIhiSKCwysg/u4JmUFtZQAgpWyUINaiTXpDCzJpIqoccMqumKL8mSClLL+dNwLRKgljNvurTPYkgffgu3cv2JiItFDchIsjRrQUOn5mLXXAKh2W8pN1fASefQutrndfcElrUSvncbuX96AZwbG7Ai8DiWeYmVsR2EtcSiPx9SY6SMboZKRxGLXLcxYm1EW1ZOaw8hBIXxPoaOvIBTuzm5Z+Pn9qIFY77RVLT6woQkSb5rsG3VpMvv+p4PJ1fAK5ZR4tFZ95EkCbUlRfIXniZ05gLWuUGs84M44xncYglRs6bWgZsDIVzGLs4ckei5FuPDh+ozqaspFYYoFabL/JiVDOdOPjfvd7/znWHWPZBi59MdvPrfB2aWhloE9vAYXrmKEpld8QMmZ8DbNmC8fYDisdN8++/LwPT76NQxm1OTs6qv/9XlfuVv/otvxI4ftvn3//PMbsFySfBf/3j29XrPg0P7LA7tu75lkzvWSC1/ZCWO6VAaKlIYmGxIGUItYTrvX8bIOxcJtYVRNIWxg6N03reMYEuIwTcGKPbfRoESV1G52HdLvreareHaHq3rElw8OD13JdISJNIcJD88R5Ks62IePUVw8zqYJV/kEpKmEX3sQayhESrvHmXRjuxrYLaCkYqkock6kiTjCRfLrdb3DcghmoI9pPQusuYQHh41t4Q3ufguIRNQQsiSjOvZWN7UmWSqZzOyNj261LWrfp5SJbe0FzkHwnUYO/0mkVQ38c71UxwPkiTR1LON0VNvXPeMzs0WqJ29QPDujfOvTYVDBLesJ7hxDcJy/DBsz5scSC4i4kx4CMdF1CzcYhm3UMQZncAaGsO+OIpXKiPM2jWro0syPP5rK1EDU51TQsBEf5XH/m8r2PZEKxcOFXz1javO+9z+PEdfnl81xqua1E6dI3TPxnn3lcNBkp9+komvVbAHL9401/lSc8caKUVXMDNVZFVGj+mE28IEwgGaNzQT7YoxfnAUNahhZvxOw0gFkVX5mlTSfxa4eChDNVvjwS/exVv/5QTjp/NYlcuySJs/uYK29Qle/N8PzHmc6sETxJ9+FCU6f9l7SQ+Q+h8+gdqSovzWu3NG+90oJGQ6wutIGd3IkoIm6/QXDzFaOQ1AZ+QuWkOrCKoRVid2IRCcyb1Fxckho9AV3UxzcHl9neJcYS+5mr9IL6s6kVT3jCW50xcOUsktTaDCYrDNEuN9e4i2rfLLz1xBMNFOIBijVr6+BXevUKKy9wj6ul6U8NwzApjM3VLVG1YQUVg21sAwtdPnqZ05j3nmAl5hcd4KSZJ48LNdBEKzazm2rgzTunLm+17RpAUZKWE7VA8eJ7h5LdJ8AUiShNbTSfO/+Gfkf/BTqodOLFn9rJvJHWuk8ASl4RLVCX9kP35kHNdymTg6Tu5cntLFEuXxCkbSwLM9Lvz0PIFIgMrENUQTSRJSQPPLBxgBZMNACurIuo4c1P33db+8gNqUREnNHmZcR5YJP7gDracTUa3hmTVErYZnWnhVE1Gz8Ko1PNNEmJafOHkD3WITfQX2f+MsD//fN/PE/7qD3GAJu+oiqxKRZoPUyihnXxnm7OvDcx7HyeaoHj5JeNfd8+ZLXRpJx5/6IMHN66jsOUj1yCmcdHZh4cmyhGzoyPEoSjyGEo+ipuJIqkrpzX0LktsReGTMAdJmP67nsCy6ga7IxrqR6i8exBU2KWMZR9Mv4uHVZ1EJvYPOyHqOpV/CdEt0hNbRE91Gyc7geDX0cBItFJvWDq5jkR08suQBA4YBobBMNjNdHaS5RcasCkolQW74FHa1iBJtmrKPJCuEU93XbaQAKgePY2xYQ/iBe5a8wOFikQIa+qoe9N5u3Pdtxzo3SHn3QSp7DyGshS3ye57gb79yFFm9tmtZcOSfENT6+rEujhHo6VzQMxTo6ST1uY9h3r2Ryr4jmKfPLdwIKzJKJIwci9SfHyURx6tUKb745g0LarmSO9ZIjewbIX8+V/93Le8vBNrlqTddLevfHI7pUB2f2VUVWL6M4OZ1yNGw3+mFg0iGgRwykIMGsqGDokzmz0qXhdsm//wk/qvenw9JwtiwBuOuVb5rQAg/kbP+B5ekhoQQ4Pn1di7V0xFVE8+89LqGWypTemMfXv7aohmFKzj0zXNUMzXu/uxqmnpj/szT89Un3vovJzj83fPUCvM81K5H8advY6xfhboQYw3Iho6+ZgX6ym4Sn7JxcgWckXHcchVRnXy4VRU5oCEF/d9FiYSQoxG/rIM8WWdA9pO3nXSO6qETC9aEq7kVNCWIpuhYbpWAEoLJEg2ecPzCgkLgCmeKyzBudGC5VTzhEZCDlJ0snZG7MJQIJa+GFoyi6dNH1mZxnFrp+uSAwD/FtWtVgiGJwQEXVYVwRKKQh54VCoYhc67PIZmU2HmfzsEDNqWSg+fUKI73YVxtpJAIxlthCYpOi6pJ9uvfRzI0glvWI2vawp6LG4kkoUTCGJvWoq/rJXz/NnLf+CHWxdH5B0UCTrwxc+TsUmMPj1F55xBaRytSYGEBGUo8SujerQTv3oCwbZyRCZxsHq9c8WdXsoykqf5g2jCQIyH/GQqHfAULWa7X65BkGfNkH8WX3lyq1Lk5uWON1JUG6nrRVy8n9tEPIS/whlgKfGMH9SJGLDA4MTlzx+/VLMzjZ7Cu0UgBuLbHiR8NcvaVYRI9EYxoAKfmkhsoUZ0loGIm7IGLlN/cR+zDDy34IZMkCTS/jlAgFCTQ2Xatl1GvD7YQAkqI7ugWAnIITzgYahR5BvfcjF8jKYTUBL3xHVx6mtNmP67wDbkaCCHPENVXK2VxlkBFX5HhyY8apDMeTc0yfWcclnWpjI963LdLp1YTeJ5g9VoNw5CmNEs5O0QLO6ceUJLQw0sn1+SVK2T+5jtEH76P8Pu2o7Y03VStwtmQJj0jxsY1NDc/Q/57L1Lee+iGB/AsGE9QfnMfwS3r0desWFibTQ6WJT0AegBldZjpK6GL4Cb+TneskWpw47BNl/FT174+JCyb4su70deuXPhDdlPxZ0kAUa2FpN7JicwrVOwcbeE1hGL3TNlbCA8JmauHEVWnQNXJczzz8uWiePgzLvBrRcny9EfQqZWXJB9JCCgUBBcHXTzXVwBYu07l3FmHQsFj5KJbn5G3tClT+p3Z6kopgfnXkBaMJCFsB2twBH0ii9rSNP9nbiKSJKG1NZP4zEf8siFvz69ZeDWbPtRMpeDQtzc34/ZASGH1jgTDZ8pkLy58YOLmiuS/8xOaf/0Xfc/ObfcMLR0NI9VgQQQiKpqhUp6Y5UGSwIhqeK6Ylkc1E24mR/ovv0nLr/8CWlfHzGoHN4mAHCKsJQlrSQwlSsromjQwhcnEXomU0UVC75gMoJh6riU7S2dkAz3RzVieyUT1ArZXZaxyliajm9WJ+yla46iyjuPZ9VB1SVZmvG7PtZckJ8rz4G/+qjxFC/XgAQvHhuGLbt1zfOaMgyKDdcVk2LVnTj5dirIhciiI1tlK8J6NhHduQYlFfHf57YgkoSZipD77UbxiCfPYmUWtwzzyKysYPVue1UhpAZlP/du1vPa3g7zy14vzo5qnzpH9u++R+PSTKInpa5t3Cj8TybwNrp+1j3bxyL/aOusit6or3P251Wz79KoFH9MZmyD919/CPH7WDy2+RQTVGE3BHiy3St4apcnoJhpoQUKmYI3Rl9+DKuvIkkJffg8XCge40hlfsEboy+9GkTX0SeUKAMercSLzCgVrHF2JIBAUrNHL61b1tcWruL5iVFNwbF+M99KfPVlNfTKSe1I8FGq1qX3vTBGH9XO+VhQFfX0vyWeepuV//OfEH38/alMSSdOQZPm27mTlaJj4Rz+E2rI4d6eiSrOW5wCoFh0ChkK85Rqcb55Hed9hsv/ww+uquXW7c0fNpEJtPVTTw37F0JsQdfKzRLwjRPumFJI0c7/qOYKmlTGibUH2/NXJBR/XOj9E5q+/Rezxh4i8f6cfanyTO6u8NULeGpl1e8YcIGNeHuWW7KlBDQJB2hwgbU4fCdueyVDp6IzHFZ6L8NxpsylFDSDLMrfQbk+TR7rEbIUQ50MOh4g+9j7CD9ztG6Z5fuN6kJB3ZR2y2YvrzcTlr5Au/++q4KWF3muSJKH39hDauYXC86+yVD+OHvKT5K85wtF2qLxzCDdbIPHJx9BXLfcDHW5jg79Y7igjtexDn0aSZXKnDlDsP4mVG8et3TiBygaXEZ6H53oE47PXCJr5gwJnIkPm2e9TPXSCyAfuR1/ZhRyPLv2DNhkJKarmLZ25XcK1TVzHmuZC04IxZFWf1eV2MwjGZ5JAEtjm4gNv1NZmUs88jbFxzeQC/uy/q3AcnEweZyyNPTCMNTiMM5HFLVUQlrU4ZXRZRlJkf6ZmBJBDIZREFLU5hdbejNqSQknGUaKRBRXalDSVyPvvnTdnL7XMIBjzu9ZAUCacUFl213RpJ0WV2fShZhRVIjt8HYEynkftVB/j/+lvCN27hcj7dqK1NyMZ+o15hlz3clTtTeCOMlKZY+8QX72Zlh0fIrVpF+Whs5Qv9lG8cBK7eO3hobUzF8h/58eLigpbDJGkxpYPNnHk1cyCJfsXjevd0GTYWEeIWHuIWukaF/xdl+rhk5hnLmCsXYm+vhd9ZTeB7o7rKoIoPA+vWMZJZ/2Ob3gMa3AE5zZwjzhWBdc20YypYehGpAk1EJpWbPBmEm7qmf6mEJiLDI1XO1pJffajGBvXzLnuKITAGRmnvOcQ1SMnsfuHEfYSiNnOgRKPEujpxNi4hsiD2/1w63lQm5MY63opvzV7EMW9n+xgx8fakVWJWLNOstPgC+u2TNlHQkINyIQSKhdPljj+2vWnHHjlCqWfvk310EmMu1b5aRuretBampC0a+vqxWQdODdfwJnIYl8cwxmboHZ+6KYpWNxRRip96HXyZw6iJ5qJr9lGbOVGosvX0bz1fZQGzpA7uY9qegTh2otyB1oXhrAuTNfvWiqMFUHWPLyKQ2/1UTg9h6zQTaZrezN3PdGNosm0rk8STOh8+Pe3T7s5ZUWmqTdKU2+Mvf/99HV9p6iaVA8ep3rsNEokhBKNoHW2oXW1obW1oCSifv5TQJtUIBC+XI5t45k13EIRL1/yc6nG0jhjabxSGc+0EKaJV63dNq5gq1rwdfmuykfSw0mCiXaq+dldkDcSPZwinJxelFAIsahyHXIkROLpRzA2rJ7bQLke1cMnyX3zOeyxiZsW6u3mi/7A6GQf1vlBEp95CmUBM/jQ9k1zGqk3vz7E2XdyrL43yYOfXYZlugwcmT4DdR3BRH+Vd38wwviFpXvu3XSW8ut7qew9jBINoyRiaMvaCXR3oDYlUOJR5EjIn2Eqsj8zsh2E5eBWKniFkq+zmS3gjE34z1C15gsIVKoLTnBeKu4oI4XwcMp5nHKe8lAfo28/T3T5OuK9m4n0rCGxfjvV8SHypw9QGblALTvmr1/dBiy2vtHNwK15qLpC06oY8Y4Qqi6z4r7pbiAhwDFdzrw8zLt/f2Zpvtx2cLMF3GwBq3+yY7yy87iyH5mp3W4TQzQXViVHrZwh3NQ9pWOUZIXWVfeRGzp2zWtA14wk09y7A2WGNSnXrlLOLEyFGyDy4A5C926d271nO5T3HCT79e/jlW7NAE1YNuV3DqMkYsQ//pifSzQLEhDo6UQOh/DKM59vYdyiMG5xek+Wrg1Ryjmbv/+dYzN/d/0/S48wazhmDWc8Q+30+clnZuZnSEImoEep1QogBIoSwHVtpoSG3iLuLCM1BYFrlsmdfJdC31ES6+4hteFeIl2riHT2UsuPUzx/wi8amJ5byucSnWtCtK0M4bmCFVuimCWXd34wRuZiDVmBB/9ZB30HCgydLCPJcM/jLRQmLAaOl9j4UAq75tG5JsTRVzPc9UCSzHCNgy/503zhCVZsjbHjoy1YFY+9PxxnYtD3+0oSdG+IsOnhFFpA5tgbWc6+W8Bz/bv73o+1cvFMmbYVIXo2RpgYqPL2d8euW3F5+EiGkWMZ4svC3PtL61j1/g5+9O/fRbhTnyohBLWSQ/Z88drdfQvhSsNz+9ugeRGeS3H8PMmuTUhX1ZKKtqwk2bWJ9PnZ64PdCMLJZaR6tswY3VcYOYO7QKOpNCeJfGjX3AZKCKz+i+S//9ItM1B1XJfKviOE37eDQEfr7NGVkoSkaaitKaxz85yzgOFTJaLNgVs+ZtK0MJ5n47oWAT2G65i4ro2iBJAVFcc2iSdWMDF2DCEcjGCSSmn8tnjM7tgQdElWUIIRYqs2s/zJf07rjkfRIgmK545z8fXvYU4Mk7xrJ90f/gVC7ZfLQ3d27OS+HV/mvh1f5u6tvzLlYW3uDvLRL/Vw92PNDJ+p0LYiyCd/ayWqLiHLEls+1ERLz2SpZFli3f0JutaH0YMKOz/SwurtMVqXB/n5312N6wju/VgrHb2+HzzVabDxoSRj50yalhn8s/+5FzXgPygdq0N8+iurcB1BbqzGk1/sYe3Oy8oSGx5M8sSvdNO+KsjA8RKuI3DtpRkBCdoCKigAAD4JSURBVA9yA2WGD2eo5iwuvDXK+av+Lrw9xsiRzKIMVD2aSbrqtTL9dTARIJSaIUT3in38CCmQFYnunS2EmnRkVaLnvlaMxCKDOW4iuaGjuPb0RWhJVujc8CEiLSu5WYXQtGCMZZseIRhtnWZcPNchfeHAgkfW4Xu3oiZjc+/kuBRefANn9JKw6uKvU5IV1r7/l9n4+Jfp3vaRKdtUI8LK+z6z4DpYzkQWe2B4/o5ZVVAS81zbJG98fYiX/7p/QfveKEKhFhKpXlratqAFIoRCzbS2byGgR2nruBtVDSKEixCi/rtHo51+uZbbgDtuJqXoQfRUO9GetcTXbCMQTWBmRsmdOUD+1AGq44MgBOmDrxHpWUfn+z9B670f5vw//QUIgaaFCYVa/Ho2tekL14om853/4xy5UYsz+/J88U82kOow5hWI1AyZt78zSntviLv1Zvb9aJz2VSFCcRWz4uLaHi/+9RB9+wscfzPLr/3pRno2RujbX+Shn+/g/OEiP/6vA0iyhB5W2PZoE2f3F7BrfqfhOIIf/Gn/DZthjJ3MceHt0SUZEca7woRSBhOnc0Q7wgQTAdJn8oSaDEJJg9HjWaIdIUKJAMNHMoSaDDxHUMlcjnaTVYnk8iiBsMbo8SyJZWEibUH6d48hIaEFVSrpGrIsoRkqJkvnNgukwiS2LqN4ZpzqwPWJrdbKWTIDh2hb88CU9yVJIhhvY8X2jzNw8Dnyw6e4cdNHCSPaTPfWJ0ksm14+QwhBYeQ0pfTCOls5FsHYsHreBF17PE1132QVZ0ki2rwcq1KgVl54kJPwXE699td03vUB9GgTV6qFSJKCHkrMqOox88H8mV1o55Y589QkWUYOzV1d+hKLUZG4UaiagRFMYtUKBINJItEOFFVHkhSq1TTVygSyrBIIhNG0MJZXRFF0tEAEx7kNzv9Wn8BSEl+zjfjqrYRau1BDUUqDZ5g48CrlobPUchPTRoHlwTMUzx0jsX6Hf1MuoAdOD5mYJX9ht1p0kBUJTZ9/QioEmGUX1xGUsjaeIxCe4NJErZRzqJX94+YnLGoVl1SnQd/+Ih2rQgSCCs/8uzUgQdvyEJWig6JKXIpSHjpVntKHaVoY17XwvJlmN5cf5KmvZ2fsZJ7MhWNLUhDSiAZoXZegOFKhfUOSSrZGojtCMGlQK1oIIUh0halkaniOvy4mh+Upp2rEAyS6I4yfyqMFFZLLoyy/v43+3WPXfX7zEVndwqbffYpTf/YKg0O5645yGu97h2TXJgLBqaNzSZIIp7pYsf0TjJx6g/T5d3GspXWLSZJCsnsTbWsemLH4IvhrUen+gzNX7Z0BrbVpQblQ5tEzfuFCQJY1mlZsJ31h/6KMFADCq1fTvV7chaiDS9INKxtyIyiXRnFdG8+1cFwLz/PbyrHLlEuXglQkivlBhHBBkigWhmbpO24+752WXgDN296PFo5RGjxD5shbmJlRPHv2aC7huTi1Cp5tLniQ6tjTEwrrj6IQddeVpsvooSuM12SNtsndpqEG5LrMv6pJyKpUN1pm2WX4bIWDL14OUy3nHCzzchSUa001wOFQCwJBzcxTs4oEjRSuZ+E4Vdpat5HOnMJxKrS1bSOdPolllQgFm7DtCq5nEQhE8TyHWi0/2VYCu7IEHcGki04LKn6l34JFsjtC3/kikiJTHKngOYJqzqI4UvE76maDWEeIiTP5+jnYFQdJkujc0sTQwQmCSR276qAFVaKdIUJNBmbBJtoZJhDWKE9U8ZzbwcM+nUp2mPGzu+m464PIyvRHUo82073tIzSv3M543x5yQ8dxbRPPseqagAtGkpAVDUUziKS6aV2zi0jLismR9XSjIoQgN3ScTP9BFvqQKMk4SmL2qruTB6Z29gIIQaLzLlpX7yLasoJoy0pc2yQ/fJKhIz8BJKKtK2npvRc9nMCpVRg7u3vxM0tJJtbaS9vaBxk89DzV/OjM+y0wsnApZKtuFq5rUS5djhS1rdKUbQCeZ1PIX54pX/n6VnNHGanM4TcpD5/Hys9fPOwS5aE+nFLhuqPBhIDChE33ujDnDhToXB2mc02Y84cWlvwYa9JYd1+cUsZm+eYoakBm8IRf7vngS2k2vT9FYcKilLWJJDUqBYfLz8n0c5ckGUVWCadWY5o5jGAKhGBs/CiqquO6/oxFVQ1c1yKZ7CVopLCdKuXyGPF4D+PjMyslXBcCRo5mGD2WQXhQHLk8M8gPXS5vPXzoskHue3V6YItddTnz06H67OrId8/Vm+Hod8/X9zv8j7emkvFiEJ7D2Nk9hFNdxDvWTzMWkiShqAHCqS7CqS7sjQVK6QuU0wOYxTSOVcGxqr7R8lx/NAwgyciygqwEUDQdVQ8TCCcIJTqJtfb6iubzJNeWs0MMHPrRwgVvJQklHkEKzL0OKDyvLuVTGD1DNT/Kqgc+x8VjP6U0fqE+2geBa1VJ9x/AKmVIdm1i2abHKI71Leqc4u1raFvzACMnX5vdQAFScAHyRJ5A1BY+y5AlhaAaJ6jF6/JarnCw3SoVO4vpLKSPkEgYHYQDTTieRbY6gOX6z44mG0T0ZgJKGEXWEMLD8WqYTomyNVGvb7YYFFkjrKXQ1QiqrCMh4Qob0y5SsbPYXiOZ95rInti76M9Uhs9RGT63sJ1nsGOX5NdcV/DOD8Z49Je6+MV/FyU9ZJIbqeG54spJ1KzHGT5bobk7yGd/dzVGROGtb4+QG/V9eQd+MkGqQ+fjv7kCWZEwyy6v/8MwuTGLSxW0rz6kb4BCIMk4bg1V0THNHK5r4nkOuh6lXB7F81wCgQiuY6GqBlUzgxAelfI4tl2edp5LgrjuMcGUY035/3sUq5xl8NCPCMbaCIRndpVdei8QipMKbSHZtRnPsXCsqj+zcieNlOfV95dk1Vdb13TUQBBF02fX5bsCP2JzgsGDz2Etxv0mS8jB+VW5he0gHN8Qea6NY1UQnotrmzjW1PuukhuGnD9QkYZPkurZuqBruEQw2kxqxd2M971DYezsnPuqzUnmC+AQnoe3QMWFSKCZnsQ9xIw2gmrM7/AlBW/SSFXtPGOlMwwWDuPM0fFLkkxnbBM9ibsxnSJHRp4jXTlPc2gly+JbiOltBNQQiqQiEDieRc0pka0O0Z97l5I1vqDzlZBJBrvoTmwjEmieNFL+gMP1HGpuibKVZrhwjJHSjVwnvcwdZaQuISkaihFEVjRAYBWzfq8oTa5reNcW+XbsjQyn9+aoVSfXpEou/+cvH6Ja9EsenNyd48KREqomYVseCN94OTWPv/ztE5glh0La4sTbOayKy7f+8By25SFcwV9/5SSeJwgEFYQnMEtuvSOvFl1+9F8H0EMKsizhusJ3BU5u//r/dnaaKytf6EeWFV/+zLMpl0cni/N5XBzey6UPX7z4DuBR8SYolUcQwsHzPKrSEhTdWwKMjhhezcXK+mtukiKjN4dBlrEyZbya39FpMQM1YlAdydfXiGRdRYsayLoKQuDWHOx8FeFM//0DqRCSIlNLl5FVBS1uIAdU8ARuzcbKVue1rJIiE0iFkHUVK13GrS7Op1/ODHL27a+zcuenMGIt83bEkiShaDqKdl2VgaYhhMAsjnPh3e+RHzm1uA8v9XqNJBFtWUmqe4tfydiIokdSixLg7djwQVzH8hU85voNVYXAiq55gwyF4+BOzB0wI0sKHdENrGp6gKAaQ5JkhBB4wp/pypKCocUw1Bhxo5PmSC+nxl+hUJs/gVuTgxhqlGWxLaxr+QCq7P/+nnDwhIskyQSUoD/DCjSRDC7j6NiPyFXnFiRQZZ2VyZ10J+5Gk/2BhhCXK00rskZYThHWUjQFl5MwOjmbeRvbu7HSc3eckdKTbSTv2kG0Zx1aLIlwXU797R/immWiPesIJJrJHn8H7xqKyjmWwLGumDoLf22o/k/PD6aYiWrBmXYMs3z5WJdeW9WZDaj/uZmPbZamT+eFcHGv0Ke75HsGpiyIep51xT6Xo+fENbgIbgSbfv+jmKMFTv6HF7ALJqHuJBt/7ym0mMGJ//AC6bf6QJZY/gv30vbIet7+pb/CLVsElyVY9vGtNO1cjtEeQ3iC6mCOkRdPMPLjY9i5yw+WpMis+pX3YbTFOP1nr9D6wbW0vG81wbYYruWQP3KRY3/wPE5xDi09CZofXMWqLzyIla1w6k9+SunswkavV1Ic6+PcO/9I15bHibb03nShUCE8yukBBg7+kMLo3LOOWQ5QnyHNhaRp05JmhRBcbSE0PcKyTY+RvnCAocM/woi1svK+Ty/qlIaOvIARbaZ93UNcePd7s+oP6su70Fqb583tElUTJ5Ob4xslOqIbWdfyMAElhBAepdoE2eogZTuLJxw0WSeit5IMdqErYZpDKwi0BTk48k+UrbkHiIqs0hG7i6jehiJrFGqj5MwhKlYOITx0NUwiuIyE0Ykia0T1FtY2Pcy+oW/iipmjXCVJYWXqflYmdyIh4wmHbGWQQm1s0h0p0NUICWMZCaMDVdHpTtwDksTpiddnPe5ScEcZKSUYYdkHPkWoYwVWPo1TKaJFEvX7XgvHaNn2fsz0MOXBJVJGqDOfBMKV+1zerig6Tam1xKJd6HoUSZKxHZNKZZxsto9SeWSO481+LkEjSTLZSzjcjqb55SMsq0ixdJF05vQ8rryFXMvV+y1038VdS/HUKJHeFtSYgV0wCS5LoAY18AShrgRpQA3rGK1RqhdzuBULLRFkza8/THxjJ+Ovn+bcf3sbWVNoum8lK37xPgLxIOf/bg9ueeqDpTdHWPn5+wkkQ4y+cAIrVyHYEUd4AjGTsOkVo/LmXb2s+tX3YQ7nOfNfXrsmAzV5UIpjfZzf848s2/wYya7Nft2pq9UClhghBMJzyPQfYvj4y76L7VrwBJ5Zm5JzMxOSIqN1tFI7dW7y+z08xyLashzbLOE5NaxKzl9XUzQ810YLxkh2b0YzolcdS0NW/aRURQ1MJhxf/m1qpQzFsT70rU/ScdfDDBx4btogTNJUgtvumj/gA7AGhv2S67MQCaRY3fTApIESTJTPcSbzBsXa2JT1IVU2SBidbGh7jKAaJ6q3sjr1IEdGn5+3008FlyPwGC4e51xmD2UrfbkEDGCoMValdtEV9xOz40YHqWAX45WZ12hbw6vpjm9FlhQ8z+FM+nUuFo9Tc0pc2ZZBNU5P4h66E3ejSCrLYptJl8/Petyl4I4yUk2b7kdPtjL08j+SP3OIxLp76Hjwo/Xt5eFzyJqO0dSxZEYqaKRY1fsEyUQvSGDbZc72/ZjxialBB6tWPk5nxw6E8Dh56ruks6doadrA8uUPEzSakGWFKztyIQRuT42J9Aku9L9CpbqQYBCJULCJ7u730dq8CUXRJl1GVx7Xw7YrDAy9wcjoQSzr6lGlxF3rPkVz010gwclT32Vs/PC0b9K0MDu3fwlF8UfDudw5Tpz6LrY9PYR3zaqnaG/bBpI06/FmI3/kIs339xKIh6gO5QkvT1FLl7HzVcLLm1CCGlrcwGiPkT0wCJJE50c2kdiyjIFvvMuFZ/fiWQ5IMPbyadb9T4+w7GNbyB0aIr176lpkcFkCc6TAoX/7PZySOZki4LuIZ3IRCsdFVmVaHl5L7+fvp3BqlLP/12uYo4tXCr+aamGMvt3fIDl4lPZ1DxFMtKOoAZbaUAkh8BwLszjO8IlXyPQfRnjXEcUpBG6hCLYN8wRPBO/ZSOmNveC4eI7FxeMv07ZmFz3NK8gMHGai7x0cs8jIyVdpWn4Pqa5NZAYPkx0M1qMak8s20rbuQWQlgCRJrH34l8ldPMHoqTcQnotZnKiveQ0efI6ee54m3rGW3MXjU87F2LCGyEM7kBZQfLF6YGaJI/DXdJYnd2KofjpB2c5wZPR5au7058LxTCYqfRwdfZ5tHZ9AlXWawytpi6zhYnHuoCVJkshVhzk+9sJkYc6pmE6BUxOv0BxeSVCLI0syzeHeGY2Jrkbojm9Fkw084XE+t48LuXfxxPT7oOrkOZt5k6jeTFNoJZpisCJ1LxOV81OM5FJyRxmpSPdaKqP95E8fwLOtabp8TqUIsoyqLywRbz50Pc7qVU/6HTpQs4pc6H+VdGZ6PSVZ0VBV388bDreh63FWrvgQmhYCBJ7n4Xo1ZEmdNC4gyyE62u/B0OMcO/FNatZcqtgSqcQqVvU+TiTSUR/FCuHhuhaSJCHLfrlyXY/Ru+Ix4tEeTvc9h2le6V8XmGbWl0uRFSLhdsbGj3D1DCgSbkMPXK4GGgw2YeixaUZKljXC4VZUNYjrWvWQ9oVS6ptACQcIpEKo4QDh5U2UzoxRmyiR2rECNWqgxYIYrVEKJ0bQogaxuzrwHI/Rl0/6Bsq/LDzLYfi5I7Q9sp7k9h4yey9MmyENfvsAdv6yK3DGGdQkrunQ9sH1LP8f7iV/5CKn//OrU9yI14vnWKQvHKAwepZUz1bi7WuItKxADYSWwA0ocGoVihMXyA0dJzt0dMlU191cEbdQRm2e20gZa1dirFmBedx3K5bGz1EanzpwEMIj03+ITP+h+nvp85fFXbNDR8kOzdyhe67N+b3fqv/bsSr0vf3stP0Cq3pI/LMnUGLzz6LcdA7z1OyBVuFAkmSwy1/PQdCfe3dGA3UleXOYsfIZlsU2TRqqXsbLZ+eMoBPC43z2nRkN1CVsr0am0s+y+GZAIhRIzrhfTG8jbrQjSRIVK8dY6dSMBuoSjldjpHiSptAKQCKmtxPUElTsa680MRd3lJGSNR27mMWz554qL0U8iqEnWNX7OM1N6wF/Peds33OMTxy7Inx2ZtratqJpIVQ1SNXMMjyyj0JhANe1kGWNaKSTzs6dBI0UkiQRj69gWed99J1/YdazD4daWbP6I4RCvgSM7VQZnzjGxMRxbNvPNzKMJK0tm0kmepFllebmuxAIjp345pR1qtJkkAUohMOtkwuoU783FvNLOVx6Xw/E0PUYxdJUlWxdj6Kpfqdq1rLY9uKSUe28SW2iTKgrSensOKHlKS7+02FqEyWWPb0VLWqgp8JImkJlMIsaDqDFDITjYQ5P73StTAW3UiPYGQdZgiu8Pq5pY44tvKNObFlGavtyFF1l9KWTS2qgrsQ2i4yeeoNM/0H0aDOx1l5irb2EU93Imj6pG3rJaM3ighUgEHh2jXLuIvmLJyil+zEL49dUI2ounPEMTjqL0pSY2+UX0Ih95IM4E1mc8RvTwc2JImNsWEPikx9G62ybd3chBJV3j+AWZ3eVR/QWAoovdWa7VbLV+QV5Hc8iVx2iPbIeRVaJ6s0E1DD2HOvmplMkb84XZCGoOv6gUJIkFElDkpQprk4Jmajeiir7cm4VO0PFzs17zvmaLx8lAbIkEzfaGkZqIVj5CbRYCi2axC5Oj74Jd/YiXGdReVQzETSaJmdQ6wAJs5bjzFnfQM1qAq94OxRsBgTpzEnO9D1HpZKeskM218d4+hhbNj5DONyGJMk0Na1ldOwQ5cr0HA9FCbB61RN1A1Wtpjl99jky2dNTfe/584yOHaazYwerVj6Gqho0pdbR3fUA/QOv1/ctloYmX2tEwu2TkUlXzigkEnFf77BSmcAwEqiqMXldUxUsDD05OVv0z8t2FteRu6ZF+dwE4d5mAoeHCCRDlM9P4FQs3JpDqDtJeHmK6lAOp2gia/O4aybXdmbqOoXrTTPGc9F070oKx4aJrm+n9wsPUhnMUr04/0xRCch+6oJ3SXVEQniCeHeU8kQV13QItYSo5Ws49YRtv+CgbRYpTZxn+PjLSLKKEW3GiDYTCCUIGNHJtRkNJF9vz3MsbLOEVc1RzY9RK6XxXHsyTN2/1mVdMpomcfGii1UDw/AjSLu6FWo1wcS4h2VBMChh24L54iLcbJ7a2X701cvnlEaSJAnjrlUkP/cxct/6MfbgNa6DLRZVRW1OEn34XiIP3YsUnL84oBACe3iM8u6DYM/eACEtiVbv8HPY7sICtKp2HturoshRQlqSgBJkrlXjspXBXYAihHvFgFmSJGRk3CtGZrKkENPbrrh+iXCgiZBIzXlcXb3sjZIkGV2dfxZ6rdxRRip78l26H/0srTsfI3t8D4rud45qMEKotZuW7R/ELuUoX1xgXtQM+C6+J+ozqEsGaiJ9gjnnaFc9A6aZ49z5F6hUZjKYgmo1w8Dgm6xd8zSyrGDoCYKhphmNVCq5hkR8BZIk4QmXCwOvkc6cmPE0hHC4OLyXULCJrmX3oygabS1bSKdPUSr7nUStVsCySqiqUZ8JXelqDATCBI0kSDCRPkFb6yZ0PUE40o4sq1NmZboemxSwFFSr2UVrgblVm0p/htS9KwivaMJKl7FyVVzTpjKYJXZXO0ZLlMpAFrdi4Soydr5KqCeF0RGj0j91sBJIhlDCAarDheuWMxp54Tjn/2Y3bY+sp/eXdrHqVx/i9J+9TG18dveOHguQ7I0TTBkUBoqYuRrBpiCFwSKxZVHMvJ/7tuqx5fS/MUTu3AwzOyF8QVDPpZIdopK9vlpn8bhM7yqVtetUXnvVYsUKhc5lCvmcR1OzjG3D229arFqtkkhKvPbKPJFcQlDec5DIQztRouE5d5VkmdDWu1DiUYovvkn10Em80o3Jz5P0APqqHowNawhuWY+2rG3hblPHpfz2gctlY2Y6PjKqHKgf03bNBSfS2l6tbnQUWauHlc+G5VURLEEEriTVZ34ALeFeWsK9iz7MpVyqG8EdZaRKA6fIHNtNauN9RHvW+kKQaoDlT34eRTcQSAy9+A8zzrJm4lJe0SUMI8mqlZddfLZd4tTp75LJnl20PM1E+gTF0lzTdUGxNIxlFeszlYA2vQS1LGu0tmxGln3F4nJphPGJI/Ncl8Po+CGaUmsJhZp9leTEynokoRCCYnmYUKgZkAmHW6cYqXCoFUU1cF2bUnmESKUVXU8QCU81UpIkYxgJFCWA59mTicKLe7CE41EdyqEnw8TWt1EZzGLnq7g1h+pglujqVtSozvirZ3CrNkII8kcuktjWTevD67jw7DsI2/9OSVNof2IjXs0hu39gzvWmhVBLl3FNm5GfHEOLB1n+89tZ/tmd9P3Vm7OGq4dbQ37HEA0Qbgv7agg9MYrDZdSgQiCsUipaOFWH8ti1uQ+N1atIfuwj5F/8KZVDc98L4HsKx0ZdOrsUursV1qxT2bBB44WfmAwPe6zsVVi+QmHtOoWuLnV+IwXYg8NU3jlE5IP3z28IJInAii6Sn3ua6MOjVPYfpbL/GG427/9GwrucNT/ncSYvZvJPkmTkkIG2fBnBdb0EVvX4JeNjkQUFSFxCCIF5/Ayln74F7uz3ryTJyNLlLtUT7oKDCfz8qcv7ztfpe56zqFn/bEhI9e+6dDyxyAWRpTiPubijjJRwbEbffp7yUB/Ju3b4br9yEeHalIbO+np+Ewt3KThX5A0FjRRrVj1FU9NaQKJcGef02R+QzZ7lWla5RscPz/s5x61hWSUMI4EkyaiqztXutGCwiXCopT5TG08fx3HmyOeZpFweo1geJhj0IwtTydUMj+ydzKcSFAoDtLVsRpIkIuEOMtnL0ZCRcDuqYmBZRcxajnxhgFRyLeFQC5oWwpl06SlygFDQzztxHHOWWeP8VEcKeI5LYms3w88dxSn511e+kKHloTUAVIZydfHb4eePEt+8jK5PbUNvCpF+5wKyptD8QC/Nu3oZfv4oucMLL943H57p0P/sO+jJEO0f3oBdNLnwd3vwrOkdWnm8SnRZhGhHmInjGeLdUbSQiqLJqIZKvCdGebRCaaxCcmWcsSOLbzM5FMJYsZxSNLog4eRSUVAqCTwPZBmsmuDCeYdCXlAseAQ032tXqcDAwAIj/zxB/vlXCPT2EFjeOa+hkiQJJRxEXr2cwKoe4p/4MM5EBuvCRZyRcdxsHrdYRli235kLJg0RICvIuoYUNFDiUZR4FLU5idregppK+MnFkuTrRi4y4ER4Hta5QTJ/+z288tyDhisTX8F3pc3sWJ6OLMlTkrddb76BwNJJtlwZJJE3LzJRWZynSQhBdp5E4evhjjJSAMJ1KJ4/RvHCCdRgGElR8WwL11y8C8GddE0FAlFW9z5BU9M6ACpV30Dlcn1cy83iujXM6gIWGafd9Oq0IAZDjxMIRJDws8NLpeEFndOlcxDCQ5IUopEOZEnFnSxpUSoNT47sJMLhy/V4ZFklGGpGllVsu4xVK1As+jeoJClEwh1Uq34yoqxoBA3ft+04JlXz2hZWzbECdr6K0RGn0n850bEykPXXXiwXc/TyTM8umJz+05fp+tTdNO1cTvsTG8EVVIdzXPj6Xt/QzZWYew0Ix+P83+5BjRl0fWIr5liB4eePTXMp1vI1LrwySDVtUhgqkT6drf9cp394uXPof23oRqZFTeHCBf8eu3De//+JE86UW2h42B/hHzniLOp2d9M58t97gdQzT6OmEgtSiZAuzYICMoHONgJXBDQIIcD1fHHXSQUZSZJAkecsT3+tCCGonblA9us/wJmY/94V+Jp5l3LELun0LQRVDtRnYZ5wceY1UkuDEAL7igjBYm2cM+k3bsp3L5Q7zkjVEZ4fcn4dOG4NTQvTu+JRmpp84U/TzHPi5HfIF/q51tGMZZcX5KuedvQZnnE/StBfqLWdKra9cBeRaeYm3W8KgUAERdWxHT/6rlYrYllFAoEYwWBq0o3noGkhQsGm+nXUrKI/gvQcZFklHuuuuxsVRScYTPkVWO0yNXNx4eeXqI0VOfq//RBJVaiNXf5NC8dHOPD//Ee/Mxmb+lubIwX6/vINBr+1H8XQ/Lwz08bKVOruv0sI16Pva2/S//W9Cwp8yB8eYvev/A1WujTFCFmZMqf/08tcSIZwyrU517wmTmTwXG/uW+hW6RHO9r2LPR8hqB45SfYbz5H6xU8gh+fX9JsLSZJAVZBYuKvuWvCNoYt57AyZv/8ezlhm3tnoJap2HseroSkGQTWGssC1Gl2JoCn+c2zahSmG40Yi8ChbaZrDfpkWXY2gycEbLnW0GO48IyXJqMEISjA0q/aZUynhVBYWarxi+Qdpb9tWP5btVCY78mvvQVxn9vIhi0WR/bBS8PNC5spvuBrHrV0xK5PQ1BAm2cltJuXqBLoeR1NDGHqCSnWCgBYlOBmdWKmM43k2jlujXBkjGukkHu/hkktS12Nomr9wXixdvOZkP+GKGY2HZzn+bGoWPNPBHFnY71wbL7HQbsGt2pT7ZnbD2fnqlDyrWc9thuTgOxLHpbLnIMJ2JkO9W2/IrGepEELgjE5Qfms/hRfeQCxQSPYSxdoYNaeMKuuTMkKd88scSRqJ4DIUSUMgKFkTk0oPNx5PuOSqF+mOOyiyRiTQTERvWlDo/EzIwRBedTLNRJKQNA1hXd+s8I4yUpKskNp0P9EVG9BCUSRZmXH2kTm6m4kDr857vGikk0R85ZTKnpFwO70rHuXk6X+aUV1hISzlQqMvmXM5cXcxxs93513e/8rrrLskE6tQVB1dj1OpTqDrMfRAFM9zKZb89T3PcyiXR4hFO/18qUCUmlUgEm6ri1SWSrNHRd2uSLJEtD2EVbbxHA+n5tbDxY24jpn3zZqR0DFzFkh+MUYzV5u1dpXW3kZkx3YKr75GcO1aAp3tVM/2UT1+guDaNRjr1uJks5T37ccrX84pU2JRjFW9aB3tyMEgwrZxxsepHDuJm1/YDFUOh4ncuwM1Eaf49h7s4SsCdyQJY/UqjN6VyOEQXqWKebaP2rnzC9Lim4/qweO4+QKxR99HaOfmRQUu3Cy8SpXKgWOUXt5N7fzggmtLXUnZzpAzhwgH/BzHrvhWRksn53TfhQJJWsKrkCQJ17PJVAew3Zs3kynURinWxkgElxHUYnRGN5I3RxY14L1EeOUaKufP4NVMJEVFjcaxMuMgyWixOG61gldbnOG/o4xUrHcj7bueQngOtdw4bmnmh9c1F5ZQqmlhPM8hm+ujVBphWee9SJJCc9N6zFqe8+dfwllgHsSNwo/y8ZAkedJgLXyU6huly1b8SoFZ17WoVNMI4aEqvpECiUikA1lWcBybwuRalOc5FEsjtAs/Zyscbps0Uu2Ab5QvrVvNRygkoShQLC6tr0uWIR6XKBTEXAFaU+i4u5nUihiZs3linRGGD02gRzUibSHCLUFGj2ZQdZlQc5DBPaM0rY4TTOr0vzWCNUvHriaTRHfdC7JMeOtm5FCQ0JZNlJcvJ7h+LUoyiaxpKOEIued/DEIgh4K0/csvoEQjl9d1JtXGwzu2k/nWd7GG5h4EyMEgyY89SXjzJvIvv4qTvrzGIodCxB/5IJEdd/uVAhC++vgD91N+9wDZHz5/3aNhPA+rb4D0f/821SMniT7yIFpHC5IeuC4X4PUghPBLhlRNKgePU3rtHeyhUYQ585xaQqpHvl35euoxPS7k9tESXoWuhokb7axpfpiz6Tcn6z+JK44nY6hR7mp5FEONIoSgZKUZLZ5adITd9VCxcwwXjxPRW1DlAJ2xTThejXPZPThebdrShISMLCmocoC40UHNLZM3/QGrEo4S6l2Lnc1g5zIEWtux81mCPb0EmlqoXDiLNba4fLg7ykgl1+/ELucZevmblIf6rtul5nk2fedfYHjk3XrodGfHTmRZZVnHTmyrTP/ga4uvjrqEuG4Nz3NQlACqEpgyG5oPVTWuMGpiWqJttZrBdqpoahBDjyPLCrFIJ+BLQF0KkPDzuiZwnGpdBimTPV03UpZVonrVepSigBGUKJem/kbP/PMgy1eo/NuvLI1EzyU6OhX+j/8Y51/9P/KcP7/A3JWKQ6wjzPCBCSRFoml1HKtsE24NogVVPNejmrXpvLuV8eNZqrkaHVtbGDmcwSrPPQo1Vi5n5M/+LwJdy2h55rNEdm4n/e3vYl0coflznyG8bQu5H78ArotXsyi++TZepYJ57gJuqYQajxF/7BGiu+4jtHUL1sjo9PDoyZI0SiJO8iNPEFy3luxzP6b09p7LsyNFIbrrPmLvf5DyuwfIv/QKTiaD2pQi8eFHib5vF04mQ+HV1xfd5jMhKiblN9+lsv8Yoa13EbxnI4Gudr+Ok6LccINVVzFP57CHxzBP9mEeOTWj4kVMa6Xi5lGlACAIKjFKdhpV1gmpccpOFnMGyaNibZzTE6+ytuVhNDlId3wbMb2FkcIJynYOgYsiaUT1Njqid9VnXaZT4tTEy3WViJuHoD9/gHCgia74FhRZZUVyJ83hXiYq5yiaY7jCQUJCkTUMNUpEbyZhdGKoMY6O/ahupPA8vGoVSVGQVBU1GEY2DITroIajeNbi19ruKCOlReKYE8NULp5fkjUfx6kxdHF3Xeaof/B1DCNJc9M6FCVAd/eDVKoT08RkbyaWVca2KyhKAEUx0GbIpZoNQ09MCtv6CbzuVZVOzVoOx64S0MLoehRF0QlHfMNTKAwyRWnaKlKrFQiH2wgFm1AUHd2IA0zWqZragS5fqbB9e4Bv/kN12k91a8bV04l3RRg+lEaPaVzcP46R1CmPVcmeLxBMGhSGyqi6Qv9bw1RzNVTDf10rzj/rqJ46g5PO4JbKCMfBGh2ldqEfr1rFHhlB33735YZwXYpvvj3l804mS+HlV4nsuMcvL6EoiKuMlGfbKLGob6DWryX7Tz+gfODwFPedrOtE7t2Bk82R+9FPcDL+Gp89Mkrh1dfRe7oJ37ON0jv78KpL54ISVZPy2/upHDyO1tGK1tmKsWYFWncHWkcrUkBbuu+ybJzRCezRcazBEezBUdx0FnssPeusCSAWaMWu1TCUCJZbJhpopuZVaDaW+8oNwpnRSIFguHgCRQ6wqukBNNkgGewmYSzD9kw84aFI6mQBRD9at2LnOJN+g0zl1pRtF8LlTPoNhHBZFt+MIgWI6i1E9GZf91I4IEnIXLm8ML2PLZ8/g6wFcIo58ATm6BDCdVFjccyRQdRQGCe3uCjfO8pIOZNh5ks3VRZTZkm1Wp7TZ76PoceJRDrQ1BCrVn4Y266Qy5/nVoRjVc0MtVoBXY9P6vz1MJE+Nu/sTtPCk8m6/g1XKA5OUYoAMKu+1p4QgkAgRijYhB6IAoJc4fyUfWu1PGYtTzjchqEniEb8Wjbgyyxd2TYtrTLvf1hn0yaNPbstPA9Ghl3sya+XZGhqkjGCElZNkM16dSmeYBBiMV/Gx7b9bZc8UcmUhOuAbkgEAhKVskc+L2ascanrkEzJpNMes0k9jh/PklgepTBUpjRWJT94uUMqj/tuXqtsU8lMvi7ZVCYW5v51czkAhGUhHAevXEHULF+aybYnZxXyZVUBSULSdeTJpCVJkvwRqllDVmfu0CVFIfWpjxNcvYr0N79D+eChaQU/1UQcrbWFytFjSLqO2jRVDser1VCiEdRUEmto6ddJRNXE6uvH6uunsudgvc6U1t6M2pJCbW1CicdQIiHkcAgpoCGpiu+/9TzfMLsenmXjVap45QpeuYqbK+BMZHDSOdxsAWHbCMdB2A4sMIm76uRpNVZSdrKTwUkSiqRStCeIak318u0z4QqL/tx+CuYIq5oeJDrpSvMlkyQEHq6wcV2LdKWfc9ndlGoTN9XNdzWWW+bUxCuMl/tYmbqPSKAZRdaQJaUeHi/wfDHsycrCmUr/FMNqp8emHNMd8vvk2vAQWjyJnVuYkMKV3FFGKnf6AM1bHiLcvmJS+mjpf3CzluPkme9x17qfIxxqIRhsYtXKD3Pi9Hcol6dLFt1oTDNPoThILNYFSDQ3rad/4LUZSnBMJRJur6ul++tuZ6cJ49pOBdPMEot1EwhEiMa6kCQZ13UoFaeugdh2ZTIPShAIRIlN7usHTYzUjaaiwKc/E+RDj+mkUjL/029FEMD//v8uMjKZj7N8ucq/+p0o4TBEozL/7WsVXnqxhizDQw/rPP6kga5LRKMSL/zY5L//N3829ttfiaLrEp4niER8fbw/+5MSRw5Pva5IROKZfx5izRqVP/rDIhcvztxp5QdLUwzTUlKfzUwqKQjP5Wp9xPorXSe8bQuh9evQ2ltB9dcSJUVGjswuOxR7//tQU0mQJF/he4aRrxzxVS/CmzYS3rRxxuM4mSyStnQzm9kQlo2wbChXcOcsKnhzSNcGSNcG6v8u25m6EcnW5l9jFbhkzUHevfiPRPVWEkYnuhpBkVQcr0bVKZA3hynWxpm3rxIe2epgPTk4Wx1cULRssTbGQO4AABU7izfPZ1zhMFE5R6bST1RvIWa0Y6hRVEUH4Yvh+iXkMxTM0QWHqlsTo1gT19Y/vqeNVLBt+ZRwVqdUwKkU6Hjo4+TPHMAcv4jnWNOeTbuUW7A00kwUi0Ocv/Ayq3ufIBCIEIt1sWrFYxw98Y0pwQc3B4/RsYO0t9+DphoEjSQd7du50P8Ks934sqzR3n4PeiCGEIJyZZx8fua8r1J5hFY2E9DCxCJdAFTN9Ixq5uXyKJ7nEghE/PUoScKyStRql9eXXBf+8i/K2I5gzRqV/+V3i77szBUTkLZ2mf/w/yly4YLLxz9p8JGPGezebVEqCg4dtDl10qFYEDzyYZ3PfDbI3/1tFdcBTZNobZP5f/27Ivmcx5f+xwjv/4DOieOXjZSqSXz6s0HWrFP5P/+/pVkN1I1mMSPm2EMPEv/QB6gNDPhrRvkCwnGQgwYtz3x21s95lSrpF18m9uD9xB/5IE46Q+XIVNf0peJ9lWPHKe19FzFDRJuwbeyxay3ieOdwrbMcTzjkzYvkzWuPcBUIhovHGC7OXstqJjK1QQpuGqt21RqvJBOJdmBWs7iOSSS2jEp5vC5g4OGSr42QX0A5+xvNe9pILf/I51ECxpT3JEVBUlSMpvbJzPTpn5s48Aqju390zd8rhMfY+CE0LUjvisdQVZ1Uai2rVz7OmXM/uumGqlAcYujibnq63ockyXQvewDPtRgdP4RllbnUCJKkEAiE6V72YF3yyHFrDA/vpTTLLLBQHEIIj0AgUs+BKpVGZ4xqLJYu4nk2mhYmFutGlhSqZnZaFWDT9GviOQ5Uq2LaIOLEcZt39/m+v33v2Dz0fp1wSKJU9N17qZRMPCFRLHhEozKKAq4DrivY/67NiWMOkgTHjzmsW6/4Ew8ACR5/UmfnvQH+/f9SYHBgCQQ6bzByKEj4nm24hQLZ7z+HNXA5f0Vrb0PSZk8WrRw6TPnAQeyJCZo/83Mkn/4InlXDPH22PqtyMhncfAFJVamePI1XvjHirrcjmhZGD8Qwa1lkWSOghamaGVTFQNNCVM1MvSCpH0Q0s3tPkv3Bj10TKKov1eR5gg07IowN1kiP2Gy6P8LgWZP0yBUDJlVi20NRTh0oU8i6aAEJz/Xd09GkQrXkYVsCVfO9A54LsgKyLOHYCzOYqhYinlhOtZKmXBpFN/wE43J5FEXVUbUgjl1BUQ1UzcBzbYxQCoRHtTJ3ftfN4j1tpDLHds/qj5+LysiF6/5uITyGLu7G0BN0LbsfWVZpa9uKWcszMPTmtPWdG4tgcPBNwuFWmpJrCQTC9PZ+mKamtWRz5yaNhIyhx0kmVxGNdCLLCp7wGBs7xMWRfcw26yqVR/A8F1XVCSoBhPAoV0Zn1AeslMdxnNoUVQrTzC26hlQ+f/lcPM/vBCQJwmGJn/+FIL2rVMolQXOLTCR62S0mBBQL3pTPyvLUMIyNmzVcD5Z1KfSddZcqp3rxLPB7hRAgBJKqIgeDdfFUraWF+CMf9NdnZvus5+fNWf0DZL77fVp+4TOknnqS8a9/E/viZI5b1aT0zl5iDz1I7KEHKe8/gFsogKyghIKozU14Zo3aufNLcNG3F5FwK6piUDXTNCVXY9tVFEUjFGzGcWooSoDm1HrMWg5VDZHJnpp2DEWFnrVBgmGZ0wcrtCwL0L1KZ89LBRxHoOkyrguOIwjoU9NDHEfgugJNl1A1ia5VOvEmjeP7ynzgE0n2v1Ykn3boWuUPxAfPmvRuCJIesRk4s7CBsISfSxlLLgchiCZ7UNUgtYGrIwj96lChcAuaHkE34ji2OW2AeSt4TxupseuYDS0FQnj0D76OrsdpbdmIoup0dz1wSyL+LLvE2b4fwUpBU9M6ZEkllVxDMrEabzJ8VJKUejSR69qMjB3g3PkX5zSolzT3opGOyX/XqFb9taercT2LcmWUYNCvACqEh2lmpwj1Xj4uqJpvgKYpRM3Sgbe1yzzyqM5/+uMy775rcd/9AbZuuzxImezP5+Q//6cya9ap/PIXwpw57XBx6PZWfhBVk8qhw8Qfe4SmT30ce3wcSVFQ4nHssXFq/QPzHwQwz5wl+8Mfk3r6KVJPP8X4f/s7vEoF4TgU3ngLJZEg/vBDhLZsxKuaSJKEFAigRMKU3tl3Rxopz3WoWGlc18Z1bXQ9jlnL+68DUd8L4FSvqlw9lWBYoXWZxtkjVTRdome1ztb3Rdn7cmHSQPj7SZfEcK9CmhS9jadUulYZrLjL4NBbRWqmIDPqsPIugxV3BamWPIpZh0hc5eT+hQ/6BGDVijh2dXINS+C5fjkYI5QioMewrTJGqAlVC1IuDKNpIWRFm7d4683iPW2kbgQ1q0ChOABIC5oBWFaRvnM/BgSG4XfO7W3byBcuYFmXF91NM1s/bqUyvqDcKs9zKFfG6rlMtav9yldRqYxz9Pg/sKxjJ21t2zD0OKpqIEuqH03kWTiOiVnN0j/4Opns6XlvRCE8Jq6oNlyr5ecMEJlIH69LIXmuPXnN0y3H6ZMOH/2YweeeCVEqerz4Qo3SPAm8pimoVgV3b9fo6JTZeV+AYGgRAesCCnmP736rSnOTxL/+t1H+198rMjF+8wyVV61SGxia4lazBodwJtJ1rT8nk6PW31+3uPkXX8YeGyO0eRNKPI5bLFF++TUqx44TvW8nSiQyxTp7lQrm+Qu4xeLl94Wg9O5+pIBK+O5tRHbeQ+G1N8HzcLM5Jp79BpV1awlt2YTalEI4LvZ4mtr585QXUO7jvUi+eMl1KhhPH0NCxhMu5croFa/H5nxWzYqHWfXYsDPM4bdLaIZMZtRGC8gkWlSal2lkx2wSzSrxlMLYkF131RkhmUSTirIhyPCFGpIM2TEHz4XcuE33GoOL52uk2jVyEw6lgsvooEW1vPD71TLzWGahroZfKY2D5AfqjA3t969euIwO7uVSNLNZzdbfvx2QxI0uBnIDKBQKxOPxW30atzWBQJRodBkhowlF1RHCxbbLVKppisWLtyDAYyqqCg8+FGDDRo1qVfCtb1QpFAQ7dmokkjIv/Ng/v7Y2mQcf0vnx8ybVqmDzFo177w/g2IJ39lhs2KTxza9XcV147HGdTMZj3zv+zHDLVo2WVplXflojFJJ48imDHz1nkssJgkH41KeD7N1jc/LE7TFibPAeZmoFnWs7xFVVVer/XoJj387k83lisdis29+TRiqfz5NIJG71aTRo0KBBg+skl8vNOem4feWI5yCdvj2iTho0aNCgwfVRLM6d0/meXJNKpfys+P7+/obbbw4KhQLd3d0MDAzMOZ3+WafRTguj0U4Lo9FOC8MXni7S2dk5537vSSMlTybwxuPxxk2wAGKxWKOdFkCjnRZGo50WRqOd5mchk4z3pLuvQYMGDRr8bNAwUg0aNGjQ4LblPWmkdF3n93//99F1/Vafym1No50WRqOdFkajnRZGo52WlvdkCHqDBg0aNPjZ4D05k2rQoEGDBj8bNIxUgwYNGjS4bWkYqQYNGjRocNvSMFINGjRo0OC25T1ppP70T/+UFStWYBgG9913H3v27LnVp3RTefXVV/nYxz5GZ2cnkiTxne98Z8p2IQS/93u/R0dHB8FgkEcffZTTp09P2SeTyfDMM88Qi8VIJBJ84QtfoFS6MaXSbwVf/epX2blzJ9FolNbWVj7xiU9w8uTJKfuYpsmXvvQlmpqaiEQi/NzP/Ryjo1MV3vv7+3nqqacIhUK0trby27/92zjOnSNI++d//uds2bKlnni6a9cunnvuufr2RhvNzB/8wR8gSRK/+Zu/WX+v0VY3CPEe49lnnxWBQED85V/+pTh69Kj41V/9VZFIJMTo6OitPrWbxg9/+EPxb/7NvxHf+ta3BCC+/e1vT9n+B3/wByIej4vvfOc74uDBg+Lpp58WK1euFNVqtb7PE088IbZu3Srefvtt8dprr4nVq1eLz33uczf5Sm4cjz/+uPja174mjhw5Ig4cOCA+8pGPiJ6eHlEqler7fPGLXxTd3d3ixRdfFHv37hX333+/eOCBB+rbHccRmzZtEo8++qjYv3+/+OEPfyiam5vFv/7X//pWXNIN4Xvf+574wQ9+IE6dOiVOnjwpfud3fkdomiaOHDkihGi00Uzs2bNHrFixQmzZskV8+ctfrr/faKsbw3vOSN17773iS1/6Uv3fruuKzs5O8dWvfvUWntWt42oj5XmeaG9vF3/4h39Yfy+Xywld18Xf//3fCyGEOHbsmADEO++8U9/nueeeE5IkiaGhoZt27jeTsbExAYhXXnlFCOG3iaZp4hvf+EZ9n+PHjwtAvPXWW0IIfzAgy7IYGRmp7/Pnf/7nIhaLiVqtdnMv4CaSTCbFX/zFXzTaaAaKxaJYs2aN+MlPfiIefvjhupFqtNWN4z3l7rMsi3379vHoo4/W35NlmUcffZS33nrrFp7Z7cO5c+cYGRmZ0kbxeJz77ruv3kZvvfUWiUSCHTt21Pd59NFHkWWZ3bt33/Rzvhnk83657EvixPv27cO27SnttH79enp6eqa00+bNm2lra6vv8/jjj1MoFDh69OZWXr4ZuK7Ls88+S7lcZteuXY02moEvfelLPPXUU1PaBBr3043kPSUwOzExgeu6U35kgLa2Nk6cOHGLzur2YmRkBGDGNrq0bWRkhNbW1inbVVUllUrV97mT8DyP3/zN3+TBBx9k06ZNgN8GgUBgWl2yq9tppna8tO1O4fDhw+zatQvTNIlEInz7299mw4YNHDhwoNFGV/Dss8/y7rvv8s4770zb1rifbhzvKSPVoMG18KUvfYkjR47w+uuv3+pTuS1Zt24dBw4cIJ/P881vfpPPf/7zvPLKK7f6tG4rBgYG+PKXv8xPfvITDMO41afzM8V7yt3X3NyMoijTImZGR0dpb2+/RWd1e3GpHeZqo/b2dsbGxqZsdxyHTCZzx7Xjb/zGb/D973+fn/70p3R1ddXfb29vx7IscrnclP2vbqeZ2vHStjuFQCDA6tWr2b59O1/96lfZunUrf/zHf9xooyvYt28fY2Nj3HPPPaiqiqqqvPLKK/zH//gfUVWVtra2RlvdIN5TRioQCLB9+3ZefPHF+nue5/Hiiy+ya9euW3hmtw8rV66kvb19ShsVCgV2795db6Ndu3aRy+XYt29ffZ+XXnoJz/O47777bvo53wiEEPzGb/wG3/72t3nppZdYuXLllO3bt29H07Qp7XTy5En6+/untNPhw4enGPSf/OQnxGIxNmzYcHMu5BbgeR61Wq3RRlfwyCOPcPjwYQ4cOFD/27FjB88880z9daOtbhC3OnJjsTz77LNC13XxV3/1V+LYsWPiX/7LfykSicSUiJk7nWKxKPbv3y/2798vAPFHf/RHYv/+/eLChQtCCD8EPZFIiO9+97vi0KFD4uMf//iMIeh333232L17t3j99dfFmjVr7qgQ9F/7tV8T8XhcvPzyy2J4eLj+V6lU6vt88YtfFD09PeKll14Se/fuFbt27RK7du2qb78UMvzhD39YHDhwQDz//POipaXljgoZ/spXviJeeeUVce7cOXHo0CHxla98RUiSJH784x8LIRptNBdXRvcJ0WirG8V7zkgJIcSf/MmfiJ6eHhEIBMS9994r3n777Vt9SjeVn/70pwKY9vf5z39eCOGHof/u7/6uaGtrE7qui0ceeUScPHlyyjHS6bT43Oc+JyKRiIjFYuKXf/mXRbFYvAVXc2OYqX0A8bWvfa2+T7VaFb/+678uksmkCIVC4pOf/KQYHh6ecpzz58+LJ598UgSDQdHc3Cx+67d+S9i2fZOv5sbxL/7FvxDLly8XgUBAtLS0iEceeaRuoIRotNFcXG2kGm11Y2iU6mjQoEGDBrct76k1qQYNGjRo8LNFw0g1aNCgQYPbloaRatCgQYMGty0NI9WgQYMGDW5bGkaqQYMGDRrctjSMVIMGDRo0uG1pGKkGDRo0aHDb0jBSDRo0aNDgtqVhpBo0aNCgwW1Lw0g1aNCgQYPbloaRatCgQYMGty0NI9WgQYMGDW5b/v/m2YoqK25cqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(spam_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d34f70f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1b84d7071c0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(ham_wc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fe46915a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracting top 30 ham and spam words:\n",
    "spam_corpus = []\n",
    "for msg in df[df['target'] == 1]['transformed_text'].to_list():\n",
    "    for word in msg.split():\n",
    "        spam_corpus.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a701174d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ham_corpus = []\n",
    "for msg in df[df['target'] == 0]['transformed_text'].to_list():\n",
    "    for word in msg.split():\n",
    "        ham_corpus.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2bb4f3eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = pd.DataFrame(Counter(spam_corpus).most_common(30))[0],y = pd.DataFrame(Counter(spam_corpus).most_common(30))[1])\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "968b0fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = pd.DataFrame(Counter(ham_corpus).most_common(30))[0],y = pd.DataFrame(Counter(ham_corpus).most_common(30))[1])\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae06a65d",
   "metadata": {},
   "source": [
    "# MODEL BUILDING + TEXT VECTORIZATION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e9d36c5",
   "metadata": {},
   "source": [
    "# Bag of Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "7b92e468",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer()\n",
    "X = cv.fit_transform(df['transformed_text']).toarray()\n",
    "y = df['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e0d05007",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4457, 6708) (4457,)\n"
     ]
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)\n",
    "print(X_train.shape,y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f927c6f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Naive Bayes:\n",
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9d18f598",
   "metadata": {},
   "outputs": [],
   "source": [
    "naivebayes_clf = {'GaussianNB':gnb,\n",
    "                 'MultinomialNB':mnb,\n",
    "                 'BernoulliNB':bnb}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4b576c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_NB_clf(model,X_train,X_test,y_train,y_test):\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred = model.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": 114,
   "id": "5efb9ea4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For GaussianNB\n",
      "Accuracy_score 0.873542600896861\n",
      "Precision_score 0.5346938775510204\n",
      "For MultinomialNB\n",
      "Accuracy_score 0.9713004484304932\n",
      "Precision_score 0.9038461538461539\n",
      "For BernoulliNB\n",
      "Accuracy_score 0.9695067264573991\n",
      "Precision_score 0.9920634920634921\n"
     ]
    }
   ],
   "source": [
    "accuracy = []\n",
    "precision = []\n",
    "\n",
    "for name,classifer in naivebayes_clf.items():\n",
    "    currentAccuracy,currentPrecision = train_NB_clf(classifer,X_train,y_train,X_test,y_test)\n",
    "    print('For',name)\n",
    "    print('Accuracy_score',currentAccuracy)\n",
    "    print('Precision_score',currentPrecision)\n",
    "    \n",
    "    accuracy.append(currentAccuracy)\n",
    "    precision.append(currentPrecision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "7ccc48c0",
   "metadata": {
    "scrolled": true
   },
   "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_score</th>\n",
       "      <th>Precision_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.969507</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>0.971300</td>\n",
       "      <td>0.903846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.873543</td>\n",
       "      <td>0.534694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Algorithm  Accuracy_score  Precision_score\n",
       "2    BernoulliNB        0.969507         0.992063\n",
       "1  MultinomialNB        0.971300         0.903846\n",
       "0     GaussianNB        0.873543         0.534694"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NB_performanceCV = pd.DataFrame({'Algorithm':naivebayes_clf.keys(),'Accuracy_score':accuracy,'Precision_score':precision}).sort_values('Precision_score',ascending=False)\n",
    "NB_performanceCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "522debf6",
   "metadata": {},
   "source": [
    "# TF - IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f03dcff8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4457, 6708) (4457,)\n"
     ]
    }
   ],
   "source": [
    "tf = TfidfVectorizer()\n",
    "X = tf.fit_transform(df['transformed_text']).toarray()\n",
    "y = df['target'].values\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)\n",
    "print(X_train.shape,y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "848dc444",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For GaussianNB\n",
      "Accuracy_score 0.8690582959641255\n",
      "Precision_score 0.5252100840336135\n",
      "For MultinomialNB\n",
      "Accuracy_score 0.957847533632287\n",
      "Precision_score 1.0\n",
      "For BernoulliNB\n",
      "Accuracy_score 0.9695067264573991\n",
      "Precision_score 0.9920634920634921\n"
     ]
    }
   ],
   "source": [
    "accuracy = []\n",
    "precision = []\n",
    "\n",
    "for name,classifer in naivebayes_clf.items():\n",
    "    currentAccuracy,currentPrecision = train_NB_clf(classifer,X_train,X_test,y_train,y_test)\n",
    "    print('For',name)\n",
    "    print('Accuracy_score',currentAccuracy)\n",
    "    print('Precision_score',currentPrecision)\n",
    "    \n",
    "    accuracy.append(currentAccuracy)\n",
    "    precision.append(currentPrecision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d4226b12",
   "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_score</th>\n",
       "      <th>Precision_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>0.957848</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.969507</td>\n",
       "      <td>0.992063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.869058</td>\n",
       "      <td>0.525210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Algorithm  Accuracy_score  Precision_score\n",
       "1  MultinomialNB        0.957848         1.000000\n",
       "2    BernoulliNB        0.969507         0.992063\n",
       "0     GaussianNB        0.869058         0.525210"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NB_performanceTF = pd.DataFrame({'Algorithm':naivebayes_clf.keys(),'Accuracy_score':accuracy,'Precision_score':precision}).sort_values('Precision_score',ascending=False)\n",
    "NB_performanceTF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a22fc83a",
   "metadata": {},
   "source": [
    "# using every other classifiers :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e200cd15",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBClassifier()\n",
    "svc = SVC(kernel='sigmoid', gamma=1.0)\n",
    "kNc = KNeighborsClassifier()\n",
    "lrc = LogisticRegression(solver='liblinear', penalty='l1')\n",
    "dtc = DecisionTreeClassifier(max_depth=5)\n",
    "etc = ExtraTreeClassifier(max_depth=5, random_state=2)\n",
    "etsc = ExtraTreesClassifier(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",
    "gbc = GradientBoostingClassifier(n_estimators=50,random_state=2)\n",
    "rfc = RandomForestClassifier(n_estimators=50, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "cdcbd57c",
   "metadata": {},
   "outputs": [],
   "source": [
    "clfs = {'XGBoost':xgb,\n",
    "        'SVM':svc,\n",
    "        'multiNB':mnb,\n",
    "        'KnearestNeig':kNc,\n",
    "        'logisticReg':lrc,\n",
    "        'decisionTree':dtc,\n",
    "        'Xtratree':etc,\n",
    "        'Xtratrees':etsc,\n",
    "        'AdaBoost':abc,\n",
    "        'Bagging':bc,\n",
    "        'GradientBoost':gbc, \n",
    "        'RandomFor':rfc\n",
    "       }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "e37e515c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for BOW\n",
    "cv = CountVectorizer()\n",
    "X = cv.fit_transform(df['transformed_text']).toarray()\n",
    "y = df['target'].values\n",
    "\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": 132,
   "id": "2d24d4e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_clf(model,X_train,X_test,y_train,y_test):\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred = model.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": 134,
   "id": "206a5bec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For XGBoost\n",
      "Accuracy_score 0.968609865470852\n",
      "Precision_score 0.9694656488549618\n",
      "For SVM\n",
      "Accuracy_score 0.9273542600896861\n",
      "Precision_score 0.8031496062992126\n",
      "For multiNB\n",
      "Accuracy_score 0.9713004484304932\n",
      "Precision_score 0.9038461538461539\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nikhi\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For KnearestNeig\n",
      "Accuracy_score 0.905829596412556\n",
      "Precision_score 1.0\n",
      "For logisticReg\n",
      "Accuracy_score 0.9695067264573991\n",
      "Precision_score 0.984375\n",
      "For decisionTree\n",
      "Accuracy_score 0.905829596412556\n",
      "Precision_score 0.9206349206349206\n",
      "For Xtratree\n",
      "Accuracy_score 0.8968609865470852\n",
      "Precision_score 0.7866666666666666\n",
      "For Xtratrees\n",
      "Accuracy_score 0.967713004484305\n",
      "Precision_score 1.0\n",
      "For AdaBoost\n",
      "Accuracy_score 0.9560538116591928\n",
      "Precision_score 0.9291338582677166\n",
      "For Bagging\n",
      "Accuracy_score 0.9650224215246637\n",
      "Precision_score 0.976\n",
      "For GradientBoost\n",
      "Accuracy_score 0.9381165919282511\n",
      "Precision_score 0.9587628865979382\n",
      "For RandomFor\n",
      "Accuracy_score 0.9623318385650225\n",
      "Precision_score 1.0\n"
     ]
    }
   ],
   "source": [
    "accuracy = []\n",
    "precision = []\n",
    "\n",
    "for name,classifer in clfs.items():\n",
    "    currentAccuracy,currentPrecision = train_clf(classifer,X_train,X_test,y_train,y_test)\n",
    "    print('For',name)\n",
    "    print('Accuracy_score',currentAccuracy)\n",
    "    print('Precision_score',currentPrecision)\n",
    "    \n",
    "    accuracy.append(currentAccuracy)\n",
    "    precision.append(currentPrecision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "5e1cc84a",
   "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>3</th>\n",
       "      <td>KnearestNeig</td>\n",
       "      <td>0.905830</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Xtratrees</td>\n",
       "      <td>0.967713</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RandomFor</td>\n",
       "      <td>0.962332</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>logisticReg</td>\n",
       "      <td>0.969507</td>\n",
       "      <td>0.984375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.965022</td>\n",
       "      <td>0.976000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.968610</td>\n",
       "      <td>0.969466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GradientBoost</td>\n",
       "      <td>0.938117</td>\n",
       "      <td>0.958763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.956054</td>\n",
       "      <td>0.929134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>decisionTree</td>\n",
       "      <td>0.905830</td>\n",
       "      <td>0.920635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>multiNB</td>\n",
       "      <td>0.971300</td>\n",
       "      <td>0.903846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.927354</td>\n",
       "      <td>0.803150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Xtratree</td>\n",
       "      <td>0.896861</td>\n",
       "      <td>0.786667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Algorithm  Accuracy  Precision\n",
       "3    KnearestNeig  0.905830   1.000000\n",
       "7       Xtratrees  0.967713   1.000000\n",
       "11      RandomFor  0.962332   1.000000\n",
       "4     logisticReg  0.969507   0.984375\n",
       "9         Bagging  0.965022   0.976000\n",
       "0         XGBoost  0.968610   0.969466\n",
       "10  GradientBoost  0.938117   0.958763\n",
       "8        AdaBoost  0.956054   0.929134\n",
       "5    decisionTree  0.905830   0.920635\n",
       "2         multiNB  0.971300   0.903846\n",
       "1             SVM  0.927354   0.803150\n",
       "6        Xtratree  0.896861   0.786667"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performanceCV = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy':accuracy,'Precision':precision}).sort_values('Precision',ascending=False)\n",
    "performanceCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "c6a7a688",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for TFIDF\n",
    "tf = TfidfVectorizer()\n",
    "X = tf.fit_transform(df['transformed_text']).toarray()\n",
    "y = df['target'].values\n",
    "\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": 161,
   "id": "9e5dc7a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For XGBoost\n",
      "Accuracy_score 0.968609865470852\n",
      "Precision_score 0.9767441860465116\n",
      "For SVM\n",
      "Accuracy_score 0.9659192825112107\n",
      "Precision_score 0.9761904761904762\n",
      "For multiNB\n",
      "Accuracy_score 0.957847533632287\n",
      "Precision_score 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nikhi\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For KnearestNeig\n",
      "Accuracy_score 0.8986547085201794\n",
      "Precision_score 1.0\n",
      "For logisticReg\n",
      "Accuracy_score 0.9443946188340807\n",
      "Precision_score 0.9444444444444444\n",
      "For decisionTree\n",
      "Accuracy_score 0.9246636771300448\n",
      "Precision_score 0.87\n",
      "For Xtratree\n",
      "Accuracy_score 0.8753363228699551\n",
      "Precision_score 0.7209302325581395\n",
      "For Xtratrees\n",
      "Accuracy_score 0.9739910313901345\n",
      "Precision_score 1.0\n",
      "For AdaBoost\n",
      "Accuracy_score 0.9533632286995516\n",
      "Precision_score 0.9344262295081968\n",
      "For Bagging\n",
      "Accuracy_score 0.9587443946188341\n",
      "Precision_score 0.9242424242424242\n",
      "For GradientBoost\n",
      "Accuracy_score 0.9426008968609866\n",
      "Precision_score 0.9607843137254902\n",
      "For RandomFor\n",
      "Accuracy_score 0.9641255605381166\n",
      "Precision_score 0.9836065573770492\n"
     ]
    }
   ],
   "source": [
    "accuracy = []\n",
    "precision = []\n",
    "\n",
    "for name,classifer in clfs.items():\n",
    "    currentAccuracy,currentPrecision = train_clf(classifer,X_train,X_test,y_train,y_test)\n",
    "    print('For',name)\n",
    "    print('Accuracy_score',currentAccuracy)\n",
    "    print('Precision_score',currentPrecision)\n",
    "    \n",
    "    accuracy.append(currentAccuracy)\n",
    "    precision.append(currentPrecision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "e6a8ec9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>multiNB</td>\n",
       "      <td>0.957848</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KnearestNeig</td>\n",
       "      <td>0.898655</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Xtratrees</td>\n",
       "      <td>0.973991</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RandomFor</td>\n",
       "      <td>0.964126</td>\n",
       "      <td>0.983607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.968610</td>\n",
       "      <td>0.976744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.965919</td>\n",
       "      <td>0.976190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GradientBoost</td>\n",
       "      <td>0.942601</td>\n",
       "      <td>0.960784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>logisticReg</td>\n",
       "      <td>0.944395</td>\n",
       "      <td>0.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.953363</td>\n",
       "      <td>0.934426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.958744</td>\n",
       "      <td>0.924242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>decisionTree</td>\n",
       "      <td>0.924664</td>\n",
       "      <td>0.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Xtratree</td>\n",
       "      <td>0.875336</td>\n",
       "      <td>0.720930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Algorithm  Accuracy  Precision\n",
       "2         multiNB  0.957848   1.000000\n",
       "3    KnearestNeig  0.898655   1.000000\n",
       "7       Xtratrees  0.973991   1.000000\n",
       "11      RandomFor  0.964126   0.983607\n",
       "0         XGBoost  0.968610   0.976744\n",
       "1             SVM  0.965919   0.976190\n",
       "10  GradientBoost  0.942601   0.960784\n",
       "4     logisticReg  0.944395   0.944444\n",
       "8        AdaBoost  0.953363   0.934426\n",
       "9         Bagging  0.958744   0.924242\n",
       "5    decisionTree  0.924664   0.870000\n",
       "6        Xtratree  0.875336   0.720930"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performanceTF = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy':accuracy,'Precision':precision}).sort_values('Precision',ascending=False)\n",
    "performanceTF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "e66b6500",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TFIDF with max_features = 3000\n",
    "\n",
    "tf = TfidfVectorizer(max_features=3000)\n",
    "X = tf.fit_transform(df['transformed_text']).toarray()\n",
    "y = df['target'].values\n",
    "\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": 149,
   "id": "628d4cf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For XGBoost\n",
      "Accuracy_score 0.9650224215246637\n",
      "Precision_score 0.968503937007874\n",
      "For SVM\n",
      "Accuracy_score 0.9704035874439462\n",
      "Precision_score 0.9844961240310077\n",
      "For multiNB\n",
      "Accuracy_score 0.9650224215246637\n",
      "Precision_score 0.9917355371900827\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nikhi\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For KnearestNeig\n",
      "Accuracy_score 0.905829596412556\n",
      "Precision_score 1.0\n",
      "For logisticReg\n",
      "Accuracy_score 0.9515695067264573\n",
      "Precision_score 0.9642857142857143\n",
      "For decisionTree\n",
      "Accuracy_score 0.9246636771300448\n",
      "Precision_score 0.8775510204081632\n",
      "For Xtratree\n",
      "Accuracy_score 0.8654708520179372\n",
      "Precision_score 0.9\n",
      "For Xtratrees\n",
      "Accuracy_score 0.967713004484305\n",
      "Precision_score 0.9919354838709677\n",
      "For AdaBoost\n",
      "Accuracy_score 0.9596412556053812\n",
      "Precision_score 0.959349593495935\n",
      "For Bagging\n",
      "Accuracy_score 0.9596412556053812\n",
      "Precision_score 0.9312977099236641\n",
      "For GradientBoost\n",
      "Accuracy_score 0.9363228699551569\n",
      "Precision_score 0.9306930693069307\n",
      "For RandomFor\n",
      "Accuracy_score 0.9659192825112107\n",
      "Precision_score 0.9918032786885246\n"
     ]
    }
   ],
   "source": [
    "accuracy = []\n",
    "precision = []\n",
    "\n",
    "for name,classifer in clfs.items():\n",
    "    currentAccuracy,currentPrecision = train_clf(classifer,X_train,X_test,y_train,y_test)\n",
    "    print('For',name)\n",
    "    print('Accuracy_score',currentAccuracy)\n",
    "    print('Precision_score',currentPrecision)\n",
    "    \n",
    "    accuracy.append(currentAccuracy)\n",
    "    precision.append(currentPrecision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "145fbe4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KnearestNeig</td>\n",
       "      <td>0.905830</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Xtratrees</td>\n",
       "      <td>0.967713</td>\n",
       "      <td>0.991935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RandomFor</td>\n",
       "      <td>0.965919</td>\n",
       "      <td>0.991803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>multiNB</td>\n",
       "      <td>0.965022</td>\n",
       "      <td>0.991736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.970404</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.965022</td>\n",
       "      <td>0.968504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>logisticReg</td>\n",
       "      <td>0.951570</td>\n",
       "      <td>0.964286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.959641</td>\n",
       "      <td>0.959350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.959641</td>\n",
       "      <td>0.931298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GradientBoost</td>\n",
       "      <td>0.936323</td>\n",
       "      <td>0.930693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Xtratree</td>\n",
       "      <td>0.865471</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>decisionTree</td>\n",
       "      <td>0.924664</td>\n",
       "      <td>0.877551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Algorithm  Accuracy  Precision\n",
       "3    KnearestNeig  0.905830   1.000000\n",
       "7       Xtratrees  0.967713   0.991935\n",
       "11      RandomFor  0.965919   0.991803\n",
       "2         multiNB  0.965022   0.991736\n",
       "1             SVM  0.970404   0.984496\n",
       "0         XGBoost  0.965022   0.968504\n",
       "4     logisticReg  0.951570   0.964286\n",
       "8        AdaBoost  0.959641   0.959350\n",
       "9         Bagging  0.959641   0.931298\n",
       "10  GradientBoost  0.936323   0.930693\n",
       "6        Xtratree  0.865471   0.900000\n",
       "5    decisionTree  0.924664   0.877551"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performanceTF3k = pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy':accuracy,'Precision':precision}).sort_values('Precision',ascending=False)\n",
    "performanceTF3k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "afa44943",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy_x</th>\n",
       "      <th>Precision_x</th>\n",
       "      <th>Accuracy_y</th>\n",
       "      <th>Precision_y</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KnearestNeig</td>\n",
       "      <td>0.905830</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.898655</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905830</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Xtratrees</td>\n",
       "      <td>0.967713</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.973991</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967713</td>\n",
       "      <td>0.991935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomFor</td>\n",
       "      <td>0.962332</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.964126</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.965919</td>\n",
       "      <td>0.991803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>logisticReg</td>\n",
       "      <td>0.969507</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.944395</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.951570</td>\n",
       "      <td>0.964286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.965022</td>\n",
       "      <td>0.976000</td>\n",
       "      <td>0.958744</td>\n",
       "      <td>0.924242</td>\n",
       "      <td>0.959641</td>\n",
       "      <td>0.931298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.968610</td>\n",
       "      <td>0.969466</td>\n",
       "      <td>0.968610</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>0.965022</td>\n",
       "      <td>0.968504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GradientBoost</td>\n",
       "      <td>0.938117</td>\n",
       "      <td>0.958763</td>\n",
       "      <td>0.942601</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.936323</td>\n",
       "      <td>0.930693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.956054</td>\n",
       "      <td>0.929134</td>\n",
       "      <td>0.953363</td>\n",
       "      <td>0.934426</td>\n",
       "      <td>0.959641</td>\n",
       "      <td>0.959350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>decisionTree</td>\n",
       "      <td>0.905830</td>\n",
       "      <td>0.920635</td>\n",
       "      <td>0.928251</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>0.924664</td>\n",
       "      <td>0.877551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>multiNB</td>\n",
       "      <td>0.971300</td>\n",
       "      <td>0.903846</td>\n",
       "      <td>0.957848</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.965022</td>\n",
       "      <td>0.991736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.927354</td>\n",
       "      <td>0.803150</td>\n",
       "      <td>0.965919</td>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.970404</td>\n",
       "      <td>0.984496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Xtratree</td>\n",
       "      <td>0.896861</td>\n",
       "      <td>0.786667</td>\n",
       "      <td>0.875336</td>\n",
       "      <td>0.720930</td>\n",
       "      <td>0.865471</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Algorithm  Accuracy_x  Precision_x  Accuracy_y  Precision_y  Accuracy  \\\n",
       "0    KnearestNeig    0.905830     1.000000    0.898655     1.000000  0.905830   \n",
       "1       Xtratrees    0.967713     1.000000    0.973991     1.000000  0.967713   \n",
       "2       RandomFor    0.962332     1.000000    0.964126     0.983607  0.965919   \n",
       "3     logisticReg    0.969507     0.984375    0.944395     0.944444  0.951570   \n",
       "4         Bagging    0.965022     0.976000    0.958744     0.924242  0.959641   \n",
       "5         XGBoost    0.968610     0.969466    0.968610     0.976744  0.965022   \n",
       "6   GradientBoost    0.938117     0.958763    0.942601     0.960784  0.936323   \n",
       "7        AdaBoost    0.956054     0.929134    0.953363     0.934426  0.959641   \n",
       "8    decisionTree    0.905830     0.920635    0.928251     0.890000  0.924664   \n",
       "9         multiNB    0.971300     0.903846    0.957848     1.000000  0.965022   \n",
       "10            SVM    0.927354     0.803150    0.965919     0.976190  0.970404   \n",
       "11       Xtratree    0.896861     0.786667    0.875336     0.720930  0.865471   \n",
       "\n",
       "    Precision  \n",
       "0    1.000000  \n",
       "1    0.991935  \n",
       "2    0.991803  \n",
       "3    0.964286  \n",
       "4    0.931298  \n",
       "5    0.968504  \n",
       "6    0.930693  \n",
       "7    0.959350  \n",
       "8    0.877551  \n",
       "9    0.991736  \n",
       "10   0.984496  \n",
       "11   0.900000  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performanceCV.merge(performanceTF,on='Algorithm').merge(performanceTF3k,on='Algorithm')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "50aca4ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('svm',\n",
       "                              SVC(gamma=1.0, kernel='sigmoid',\n",
       "                                  probability=True)),\n",
       "                             ('nb', MultinomialNB()),\n",
       "                             ('et',\n",
       "                              ExtraTreesClassifier(n_estimators=50,\n",
       "                                                   random_state=2))],\n",
       "                 voting='soft')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Voting Classifier\n",
    "svc = SVC(kernel='sigmoid', gamma=1.0,probability=True)\n",
    "mnb = MultinomialNB()\n",
    "etc = ExtraTreesClassifier(n_estimators=50, random_state=2)\n",
    "\n",
    "voting = VotingClassifier(estimators=[('svm', svc), ('nb', mnb), ('et', etc)],voting='soft')\n",
    "voting.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "ec1e0411",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.9713004484304932\n",
      "Precision 1.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = voting.predict(X_test)\n",
    "print(\"Accuracy\",accuracy_score(y_test,y_pred))\n",
    "print(\"Precision\",precision_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "fad48fa6",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_18408\\2384194780.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStackingClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimators\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mestimators\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfinal_estimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfinal_estimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_stacking.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    486\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_le\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLabelEncoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    487\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_le\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 488\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_le\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    490\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mif_delegate_has_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdelegate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"final_estimator_\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_stacking.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    191\u001b[0m             \u001b[1;33m{\u001b[0m\u001b[1;34m\"sample_weight\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m}\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m         )\n\u001b[1;32m--> 193\u001b[1;33m         predictions = Parallel(n_jobs=self.n_jobs)(\n\u001b[0m\u001b[0;32m    194\u001b[0m             delayed(cross_val_predict)(\n\u001b[0;32m    195\u001b[0m                 \u001b[0mclone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1041\u001b[0m             \u001b[1;31m# remaining jobs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1043\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1044\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1045\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    859\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    860\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 861\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    862\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    863\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    777\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    778\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 779\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    780\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    781\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    214\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_val_predict\u001b[1;34m(estimator, X, y, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method)\u001b[0m\n\u001b[0;32m    960\u001b[0m     \u001b[1;31m# independent, and that it is pickle-able.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    961\u001b[0m     \u001b[0mparallel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpre_dispatch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 962\u001b[1;33m     predictions = parallel(\n\u001b[0m\u001b[0;32m    963\u001b[0m         delayed(_fit_and_predict)(\n\u001b[0;32m    964\u001b[0m             \u001b[0mclone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1044\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1045\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1046\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1047\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1048\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    859\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    860\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 861\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    862\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    863\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    777\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    778\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 779\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    780\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    781\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    214\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_predict\u001b[1;34m(estimator, X, y, train, test, verbose, fit_params, method)\u001b[0m\n\u001b[0;32m   1042\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1044\u001b[1;33m         \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1045\u001b[0m     \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1046\u001b[0m     \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    253\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    254\u001b[0m         \u001b[0mseed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"i\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 255\u001b[1;33m         \u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    256\u001b[0m         \u001b[1;31m# see comment on the other call to np.iinfo in this file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py\u001b[0m in \u001b[0;36m_dense_fit\u001b[1;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[0;32m    313\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_probB\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_status_\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 315\u001b[1;33m         \u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlibsvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    316\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    317\u001b[0m             \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Applying stacking\n",
    "estimators=[('svm', svc), ('nb', mnb), ('et', etc)]\n",
    "final_estimator=RandomForestClassifier()\n",
    "\n",
    "clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator)\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1f56919",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy\",accuracy_score(y_test,y_pred))\n",
    "print(\"Precision\",precision_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874a100f",
   "metadata": {},
   "source": [
    "# pickle dumping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8cff088f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl.dump(etsc,open('model.pkl','wb'))\n",
    "pkl.dump(tf,open('vectorizer.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50498890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5.1\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "print(sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "751b7ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "etsc = ExtraTreesClassifier(n_estimators=50,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0e439cbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = etsc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "04e0a5e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.9739910313901345\n",
      "precision 1.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "print('accuracy',accuracy_score(y_test,y_pred))\n",
    "print('precision',precision_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f9bea2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.9.13\n"
     ]
    }
   ],
   "source": [
    "!python --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9921a24a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in c:\\users\\nikhi\\anaconda3\\lib\\site-packages (1.5.1)\n",
      "Requirement already satisfied: scipy>=1.6.0 in c:\\users\\nikhi\\anaconda3\\lib\\site-packages (from scikit-learn) (1.9.1)\n",
      "Requirement already satisfied: numpy>=1.19.5 in c:\\users\\nikhi\\anaconda3\\lib\\site-packages (from scikit-learn) (1.23.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in c:\\users\\nikhi\\anaconda3\\lib\\site-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in c:\\users\\nikhi\\anaconda3\\lib\\site-packages (from scikit-learn) (3.5.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -atplotlib (c:\\users\\nikhi\\anaconda3\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
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 "nbformat": 4,
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