diff --git a/data_explore_hack_reg.ipynb b/data_explore_hack_reg.ipynb
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@@ -0,0 +1,898 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to\n",
+      "[nltk_data]     C:\\Users\\klara\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     C:\\Users\\klara\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to\n",
+      "[nltk_data]     C:\\Users\\klara\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Wichtige Bibliotheken importieren\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import textstat\n",
+    "import nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "from nltk.stem import WordNetLemmatizer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "# NLTK-Modelle herunterladen\n",
+    "nltk.download('punkt')\n",
+    "nltk.download('stopwords')\n",
+    "nltk.download('wordnet')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(8000, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the data from csv\n",
+    "df = pd.read_csv('data/hack.csv')\n",
+    "\n",
+    "print(df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "df = df.dropna(subset=['humor_rating'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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>text</th>\n",
+       "      <th>tokenized_text</th>\n",
+       "      <th>standardized_text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TENNESSEE: We're the best state. Nobody even c...</td>\n",
+       "      <td>[TENNESSEE, :, We, 're, the, best, state, ., N...</td>\n",
+       "      <td>[TENNESSEE, :, 're, best, state, ., Nobody, ev...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A man inserted an advertisement in the classif...</td>\n",
+       "      <td>[A, man, inserted, an, advertisement, in, the,...</td>\n",
+       "      <td>[man, inserted, advertisement, classified, ``,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>How many men does it take to open a can of bee...</td>\n",
+       "      <td>[How, many, men, does, it, take, to, open, a, ...</td>\n",
+       "      <td>[many, men, take, open, beer, ?, None, ., open...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Told my mom I hit 1200 Twitter followers. She ...</td>\n",
+       "      <td>[Told, my, mom, I, hit, 1200, Twitter, followe...</td>\n",
+       "      <td>[Told, mom, hit, 1200, Twitter, follower, ., p...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Roses are dead. Love is fake. Weddings are bas...</td>\n",
+       "      <td>[Roses, are, dead, ., Love, is, fake, ., Weddi...</td>\n",
+       "      <td>[Roses, dead, ., Love, fake, ., Weddings, basi...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  \\\n",
+       "0  TENNESSEE: We're the best state. Nobody even c...   \n",
+       "1  A man inserted an advertisement in the classif...   \n",
+       "2  How many men does it take to open a can of bee...   \n",
+       "3  Told my mom I hit 1200 Twitter followers. She ...   \n",
+       "4  Roses are dead. Love is fake. Weddings are bas...   \n",
+       "\n",
+       "                                      tokenized_text  \\\n",
+       "0  [TENNESSEE, :, We, 're, the, best, state, ., N...   \n",
+       "1  [A, man, inserted, an, advertisement, in, the,...   \n",
+       "2  [How, many, men, does, it, take, to, open, a, ...   \n",
+       "3  [Told, my, mom, I, hit, 1200, Twitter, followe...   \n",
+       "4  [Roses, are, dead, ., Love, is, fake, ., Weddi...   \n",
+       "\n",
+       "                                   standardized_text  \n",
+       "0  [TENNESSEE, :, 're, best, state, ., Nobody, ev...  \n",
+       "1  [man, inserted, advertisement, classified, ``,...  \n",
+       "2  [many, men, take, open, beer, ?, None, ., open...  \n",
+       "3  [Told, mom, hit, 1200, Twitter, follower, ., p...  \n",
+       "4  [Roses, dead, ., Love, fake, ., Weddings, basi...  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "stop_words = set(stopwords.words('english'))\n",
+    "lemmatizer = WordNetLemmatizer()\n",
+    "\n",
+    "\n",
+    "def preprocess_text(text):\n",
+    "    tokens = word_tokenize(text)\n",
+    "    tokens = [lemmatizer.lemmatize(word) for word in tokens if word.lower() not in stop_words]\n",
+    "    return tokens\n",
+    "\n",
+    "\n",
+    "df['tokenized_text'] = df['text'].apply(word_tokenize)\n",
+    "\n",
+    "\n",
+    "df['standardized_text'] = df['text'].apply(preprocess_text)\n",
+    "\n",
+    "\n",
+    "df[['text', 'tokenized_text', 'standardized_text']].head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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",
+       "        vertical-align: top;\n",
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+       "\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>text</th>\n",
+       "      <th>text_length</th>\n",
+       "      <th>word_count</th>\n",
+       "      <th>avg_word_length</th>\n",
+       "      <th>punctuation_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TENNESSEE: We're the best state. Nobody even c...</td>\n",
+       "      <td>114</td>\n",
+       "      <td>17</td>\n",
+       "      <td>5.764706</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A man inserted an advertisement in the classif...</td>\n",
+       "      <td>189</td>\n",
+       "      <td>32</td>\n",
+       "      <td>4.937500</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>How many men does it take to open a can of bee...</td>\n",
+       "      <td>112</td>\n",
+       "      <td>26</td>\n",
+       "      <td>3.346154</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Told my mom I hit 1200 Twitter followers. She ...</td>\n",
+       "      <td>139</td>\n",
+       "      <td>26</td>\n",
+       "      <td>4.384615</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Roses are dead. Love is fake. Weddings are bas...</td>\n",
+       "      <td>72</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5.083333</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  text_length  word_count  \\\n",
+       "0  TENNESSEE: We're the best state. Nobody even c...          114          17   \n",
+       "1  A man inserted an advertisement in the classif...          189          32   \n",
+       "2  How many men does it take to open a can of bee...          112          26   \n",
+       "3  Told my mom I hit 1200 Twitter followers. She ...          139          26   \n",
+       "4  Roses are dead. Love is fake. Weddings are bas...           72          12   \n",
+       "\n",
+       "   avg_word_length  punctuation_count  \n",
+       "0         5.764706                  5  \n",
+       "1         4.937500                  4  \n",
+       "2         3.346154                  3  \n",
+       "3         4.384615                  3  \n",
+       "4         5.083333                  3  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df['text_length'] = df['text'].apply(len)\n",
+    "df['word_count'] = df['text'].apply(lambda x: len(x.split()))\n",
+    "df['avg_word_length'] = df['text'].apply(lambda x: np.mean([len(word) for word in x.split()]))\n",
+    "df['punctuation_count'] = df['text'].apply(lambda x: len([c for c in x if c in ['.', ',', '!', '?']]))\n",
+    "\n",
+    "\n",
+    "df[['text', 'text_length', 'word_count', 'avg_word_length', 'punctuation_count']].head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .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>text</th>\n",
+       "      <th>stopword_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TENNESSEE: We're the best state. Nobody even c...</td>\n",
+       "      <td>0.160000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A man inserted an advertisement in the classif...</td>\n",
+       "      <td>0.317073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>How many men does it take to open a can of bee...</td>\n",
+       "      <td>0.551724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Told my mom I hit 1200 Twitter followers. She ...</td>\n",
+       "      <td>0.366667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Roses are dead. Love is fake. Weddings are bas...</td>\n",
+       "      <td>0.266667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  stopword_ratio\n",
+       "0  TENNESSEE: We're the best state. Nobody even c...        0.160000\n",
+       "1  A man inserted an advertisement in the classif...        0.317073\n",
+       "2  How many men does it take to open a can of bee...        0.551724\n",
+       "3  Told my mom I hit 1200 Twitter followers. She ...        0.366667\n",
+       "4  Roses are dead. Love is fake. Weddings are bas...        0.266667"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "def remove_stopwords(tokens):\n",
+    "    return [word for word in tokens if word.lower() not in stop_words]\n",
+    "\n",
+    "def calculate_stopword_ratio(tokens):\n",
+    "    non_stopwords = remove_stopwords(tokens)\n",
+    "    return 1 - (len(non_stopwords) / len(tokens)) if len(tokens) > 0 else 0\n",
+    "\n",
+    "df['stopword_ratio'] = df['tokenized_text'].apply(calculate_stopword_ratio)\n",
+    "\n",
+    "df[['text', 'stopword_ratio']].head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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>text</th>\n",
+       "      <th>richness</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TENNESSEE: We're the best state. Nobody even c...</td>\n",
+       "      <td>0.809524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A man inserted an advertisement in the classif...</td>\n",
+       "      <td>0.857143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>How many men does it take to open a can of bee...</td>\n",
+       "      <td>0.846154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Told my mom I hit 1200 Twitter followers. She ...</td>\n",
+       "      <td>0.947368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Roses are dead. Love is fake. Weddings are bas...</td>\n",
+       "      <td>0.818182</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  richness\n",
+       "0  TENNESSEE: We're the best state. Nobody even c...  0.809524\n",
+       "1  A man inserted an advertisement in the classif...  0.857143\n",
+       "2  How many men does it take to open a can of bee...  0.846154\n",
+       "3  Told my mom I hit 1200 Twitter followers. She ...  0.947368\n",
+       "4  Roses are dead. Love is fake. Weddings are bas...  0.818182"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "def calculate_richness(tokens):\n",
+    "    return len(set(tokens)) / len(tokens) if len(tokens) > 0 else 0\n",
+    "\n",
+    "df['richness'] = df['standardized_text'].apply(calculate_richness)\n",
+    "\n",
+    "df[['text', 'richness']].head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\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>text</th>\n",
+       "      <th>richness</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TENNESSEE: We're the best state. Nobody even c...</td>\n",
+       "      <td>0.809524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A man inserted an advertisement in the classif...</td>\n",
+       "      <td>0.857143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>How many men does it take to open a can of bee...</td>\n",
+       "      <td>0.846154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Told my mom I hit 1200 Twitter followers. She ...</td>\n",
+       "      <td>0.947368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Roses are dead. Love is fake. Weddings are bas...</td>\n",
+       "      <td>0.818182</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  richness\n",
+       "0  TENNESSEE: We're the best state. Nobody even c...  0.809524\n",
+       "1  A man inserted an advertisement in the classif...  0.857143\n",
+       "2  How many men does it take to open a can of bee...  0.846154\n",
+       "3  Told my mom I hit 1200 Twitter followers. She ...  0.947368\n",
+       "4  Roses are dead. Love is fake. Weddings are bas...  0.818182"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "def calculate_richness(tokens):\n",
+    "    return len(set(tokens)) / len(tokens) if len(tokens) > 0 else 0\n",
+    "\n",
+    "df['richness'] = df['standardized_text'].apply(calculate_richness)\n",
+    "\n",
+    "df[['text', 'richness']].head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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>text</th>\n",
+       "      <th>readability</th>\n",
+       "      <th>difficult_words</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TENNESSEE: We're the best state. Nobody even c...</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>0.117647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A man inserted an advertisement in the classif...</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>How many men does it take to open a can of bee...</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Told my mom I hit 1200 Twitter followers. She ...</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>0.230769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Roses are dead. Love is fake. Weddings are bas...</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  readability  \\\n",
+       "0  TENNESSEE: We're the best state. Nobody even c...          8.5   \n",
+       "1  A man inserted an advertisement in the classif...          7.2   \n",
+       "2  How many men does it take to open a can of bee...          0.8   \n",
+       "3  Told my mom I hit 1200 Twitter followers. She ...          5.7   \n",
+       "4  Roses are dead. Love is fake. Weddings are bas...          4.5   \n",
+       "\n",
+       "   difficult_words  \n",
+       "0         0.117647  \n",
+       "1         0.250000  \n",
+       "2         0.000000  \n",
+       "3         0.230769  \n",
+       "4         0.333333  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "def get_readability(text):\n",
+    "    return textstat.automated_readability_index(text)\n",
+    "\n",
+    "def get_difficult_words(text):\n",
+    "    return textstat.difficult_words(text) / len(text.split())\n",
+    "\n",
+    "df['readability'] = df['text'].apply(get_readability)\n",
+    "df['difficult_words'] = df['text'].apply(get_difficult_words)\n",
+    "\n",
+    "\n",
+    "df[['text', 'readability', 'difficult_words']].head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.hist(df['text_length'], bins=20, alpha=0.7)\n",
+    "plt.title('Text Length Distribution')\n",
+    "plt.xlabel('Text Length')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jtJyvzy8oKMgsAADAfj4NO7/88ovpW+NOm7Py8vLMzzokXQOL9utxhhtt9tK+OMOGDTPPo6OjJSMjQ1JTU6Vdu3Zm3fr1680xtG8Prg7xiSleO/aCwR28dmwAgOVhp0+fPqaPTlRUlNxwww3yxRdfyKxZs+Shhx4y2wMCAmTUqFHy7LPPStOmTU340Xl5dIRVv379zD4tWrSQu+66S4YMGWKGp58/f15GjBhhaouKMhILAADYzadhR+fT0fDy6KOPmqYoDSePPPKImUTQ6YknnpCzZ8+aeXO0Bqdr165maHmVKlVc+2i/Hw043bt3NzVFOnxd5+YBAADw6Tw7/oJ5dso/mrEA4OqTVR7m2QEAAPA2wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGoVfV0AwN/FJ6Z45bgLBnfwynEBAH5Us9O4cWMJCAi4aBk+fLjZnp2dbX6uXbu2VK9eXQYMGCAnTpzwOMaRI0ekV69eUq1aNQkPD5fx48fLhQsXfHRGAADA3/g07KSkpMjx48ddS1JSkll/7733msfRo0fLBx98IMuXL5eNGzfKsWPHpH///q7X5+bmmqBz7tw52bJliyxatEgSExNl0qRJPjsnAADgXwIcDodD/MSoUaNk5cqVcvDgQcnKypK6devKkiVL5J577jHb9+/fLy1atJDk5GTp1KmTrFq1Snr37m1CUEREhNknISFBnnzySTl58qRUrly5SO+r7xUSEiKZmZlSs2ZNr54jyldTkzfRjAUAV6ao399+00FZa2fefvtteeihh0xTVmpqqpw/f15iYmJc+zRv3lyioqJM2FH62Lp1a1fQUbGxsebk9+zZU+h75eTkmH3cFwAAYCe/CTvvvfeeZGRkyODBg83ztLQ0UzMTGhrqsZ8GG93m3Mc96Di3O7cVZvr06SYJOpeGDRt64YwAAIA/8Juws2DBAunZs6fUr1/f6+81YcIEU+XlXI4ePer19wQAAFfx0PPvv/9e1q5dK++++65rXWRkpGna0toe99odHY2l25z7bN++3eNYztFazn0KEhQUZBYAAGA/v6jZWbhwoRk2riOrnNq1ayeVKlWSdevWudYdOHDADDWPjo42z/Vx165dkp6e7tpHR3RpJ6WWLVuW8VkAAAB/5POanby8PBN24uLipGLF/xRH+9LEx8fLmDFjJCwszASYkSNHmoCjI7FUjx49TKgZNGiQzJw50/TTefrpp83cPNTcAAAAvwg72nyltTU6Ciu/2bNnS2BgoJlMUEdQ6UirefPmubZXqFDBDFUfNmyYCUHBwcEmNE2dOrWMzwIAAPgrv5pnx1eYZ6f8Y54dALj6ZJW3eXYAAAC8gbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwWkVfFwC4WsUnpnjt2AsGd/DasQGgvKFmBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwms/Dzo8//igPPPCA1K5dW6pWrSqtW7eWHTt2uLY7HA6ZNGmS1KtXz2yPiYmRgwcPehzj1KlTMnDgQKlZs6aEhoZKfHy8nDlzxgdnAwAA/I1Pw87PP/8sXbp0kUqVKsmqVatk79698tJLL0mtWrVc+8ycOVNeeeUVSUhIkG3btklwcLDExsZKdna2ax8NOnv27JGkpCRZuXKlbNq0SYYOHeqjswIAAP4kwKFVJz7y1FNPyebNm+XTTz8tcLsWrX79+jJ27FgZN26cWZeZmSkRERGSmJgo999/v+zbt09atmwpKSkp0r59e7PP6tWr5e6775YffvjBvP5ysrKyJCQkxBxba4dQ/njz1gvlEbeLAHA1yCri97dPa3b+/e9/m4By7733Snh4uNx0003y5ptvurYfPnxY0tLSTNOVk55Ux44dJTk52TzXR226cgYdpfsHBgaamiAAAHB182nY+fbbb2X+/PnStGlTWbNmjQwbNkwee+wxWbRokdmuQUdpTY47fe7cpo8alNxVrFhRwsLCXPvkl5OTY9Kg+wIAAOzk07ue5+XlmRqZ5557zjzXmp3du3eb/jlxcXFee9/p06fLlClTvHZ8AADgP3xas6MjrLS/jbsWLVrIkSNHzM+RkZHm8cSJEx776HPnNn1MT0/32H7hwgUzQsu5T34TJkww7XvO5ejRo6V6XgAAwH/4NOzoSKwDBw54rPv666+lUaNG5ucmTZqYwLJu3TrXdm1y0r440dHR5rk+ZmRkSGpqqmuf9evXm1oj7dtTkKCgINORyX0BAAB28mkz1ujRo6Vz586mGesPf/iDbN++Xd544w2zqICAABk1apQ8++yzpl+Php+JEyeaEVb9+vVz1QTdddddMmTIENP8df78eRkxYoQZqVWUkVgAAMBuPg07HTp0kBUrVphmpalTp5owM2fOHDNvjtMTTzwhZ8+eNfPmaA1O165dzdDyKlWquPZZvHixCTjdu3c3o7AGDBhg5uYBAADw6Tw7/oJ5dso/5tnxxDw7AK4GWeVhnh0AAABvI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsVtHXBQBQvsQnpnjluAsGd/DKcQGAmh0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDWfhp1nnnlGAgICPJbmzZu7tmdnZ8vw4cOldu3aUr16dRkwYICcOHHC4xhHjhyRXr16SbVq1SQ8PFzGjx8vFy5c8MHZAAAAf+Tz20XccMMNsnbtWtfzihX/U6TRo0fLhx9+KMuXL5eQkBAZMWKE9O/fXzZv3my25+bmmqATGRkpW7ZskePHj8uDDz4olSpVkueee84n5wMAAPyLz8OOhhsNK/llZmbKggULZMmSJdKtWzezbuHChdKiRQvZunWrdOrUST7++GPZu3evCUsRERHStm1bmTZtmjz55JOm1qhy5co+OCMAAOBPfN5n5+DBg1K/fn255pprZODAgaZZSqWmpsr58+clJibGta82cUVFRUlycrJ5ro+tW7c2QccpNjZWsrKyZM+ePYW+Z05OjtnHfQEAAHbyadjp2LGjJCYmyurVq2X+/Ply+PBhufXWW+X06dOSlpZmamZCQ0M9XqPBRrcpfXQPOs7tzm2FmT59umkWcy4NGzb0yvkBAICrvBmrZ8+erp/btGljwk+jRo1k2bJlUrVqVa+974QJE2TMmDGu51qzQ+ABAMBOPm/Gcqe1ONdff70cOnTI9OM5d+6cZGRkeOyjo7GcfXz0Mf/oLOfzgvoBOQUFBUnNmjU9FgAAYCe/CjtnzpyRb775RurVqyft2rUzo6rWrVvn2n7gwAHTpyc6Oto818ddu3ZJenq6a5+kpCQTXlq2bOmTcwAAAP7Fp81Y48aNkz59+pimq2PHjsnkyZOlQoUK8sc//tH0pYmPjzfNTWFhYSbAjBw50gQcHYmlevToYULNoEGDZObMmaafztNPP23m5tHaGwAAAJ+GnR9++MEEm//93/+VunXrSteuXc2wcv1ZzZ49WwIDA81kgjqCSkdazZs3z/V6DUYrV66UYcOGmRAUHBwscXFxMnXqVB+eFQAA8Cc+DTtLly695PYqVarI3LlzzVIYrRX66KOPvFA6AABgA7/qswMAAFDaCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsFqJws4111xj7lSeX0ZGhtkGAABQrsPOd999J7m5uRetz8nJkR9//LE0ygUAAFAqKhZn53//+9+un9esWSMhISGu5xp+1q1bJ40bNy6dkgEAAJR12OnXr595DAgIkLi4OI9tlSpVMkHnpZdeKo1yAQAAlH3YycvLM49NmjSRlJQUqVOnTumUAgAAwB/CjtPhw4dLvyQAAAD+EnaU9s/RJT093VXj4/TWW2+VRtkAAAB8E3amTJkiU6dOlfbt20u9evVMHx4AAABrwk5CQoIkJibKoEGDSr9EAAAAvp5n59y5c9K5c+fSLAcAAID/hJ2HH35YlixZUvqlAQAA8IdmrOzsbHnjjTdk7dq10qZNGzPHjrtZs2aVVvkAAADKPux89dVX0rZtW/Pz7t27PbbRWRkAAJT7sLNhw4bSLwkAAIC/9NkBAACwumbnjjvuuGRz1fr166+kTAAAAKWmRGHH2V/H6fz587Jz507Tfyf/DUIBAADKXdiZPXt2geufeeYZOXPmzJWWCQAAwPf3xirIAw88ILfccou8+OKLpXlYWCQ+McXXRQAAXGVKtYNycnKyVKlSpTQPCQAAUPY1O/379/d47nA45Pjx47Jjxw6ZOHHilZUIAADA1zU7ISEhHktYWJjcfvvt8tFHH8nkyZNLVJAZM2aYEV6jRo3ymKl5+PDhUrt2balevboMGDBATpw44fG6I0eOSK9evaRatWoSHh4u48ePlwsXLpSoDAAAwD4lqtlZuHBhqRYiJSVFXn/9dXPrCXejR4+WDz/8UJYvX25C1YgRI0yt0ubNm8323NxcE3QiIyNly5YtpnbpwQcfNLeveO6550q1jAAA4Crss5Oamipvv/22Wb744osSHUNHbw0cOFDefPNNqVWrlmt9ZmamLFiwwNxnq1u3btKuXTsTsjTUbN261ezz8ccfy969e83763D4nj17yrRp02Tu3LnmzuwAAAAlCjvp6ekmgHTo0EEee+wxs2gY6d69u5w8ebJYx9JmKq2diYmJuShI6fw97uubN28uUVFRpiO00sfWrVtLRESEa5/Y2FjJysqSPXv2FPqeOTk5Zh/3BQAA2KlEYWfkyJFy+vRpEyhOnTplFp1QUEODBp+iWrp0qXz++ecyffr0i7alpaVJ5cqVJTQ01GO9Bhvd5tzHPeg4tzu3FUbfz73PUcOGDYtcZgAAcBWEndWrV8u8efOkRYsWrnUtW7Y0zUerVq0q0jGOHj0qjz/+uCxevLjMh6tPmDDBNJM5Fy0LAACwU4nCTl5enukEnJ+u021Foc1U2hx28803S8WKFc2yceNGeeWVV8zPWkOj/W4yMjI8XqejsbRDstLH/KOznM+d+xQkKChIatas6bEAAAA7lSjsaH8drZU5duyYa92PP/5oRk9pv52i0P127dpl7qnlXNq3b286Kzt/1vC0bt0612sOHDhghppHR0eb5/qox9DQ5JSUlGTCi9Y0AQAAlGjo+WuvvSa/+93vpHHjxq7+LtoU1KpVKzMyqihq1Khh9ncXHBxs5tRxro+Pj5cxY8aYeXw0wGhfIQ04nTp1Mtt79OhhQs2gQYNk5syZpp/O008/bTo9a+0NAABAicKOBhztWLx27VrZv3+/Waf9d/KPqLpSesPRwMBAM5mgjqDSkVbaV8ipQoUKsnLlShk2bJgJQRqW9K7rU6dOLdVyAACA8ivAofd6KKL169ebif10npv8/Vy0o2/nzp0lISFBbr31VilPdBSZjsrSc6D/jndxI9CysWBwB68duzz+Dr15PQD4//d3sWp25syZI0OGDCnwgPpmjzzyiJkEsLyFHZT/LzMAAEqlg/KXX34pd911V6HbtQ+NjrICAAAol2FHh3UXNOTcSYeMF3cGZQAAAL8JO7/5zW/MTMmF+eqrr6RevXqlUS4AAIBSUaw+O3fffbdMnDjRNGXln/X4119/lcmTJ0vv3r1Lp2QASox+VwBQwrCjc9i8++67cv3115tRWc2aNTPrdfi53ioiNzdX/vrXvxbnkAAAAP4TdvQWDlu2bDHz2uj9pZyj1gMCAswcOBp48t+YEwAAoFxNKtioUSP56KOP5Oeff5ZDhw6ZwNO0aVOpVauWd0oIAABQ1jMoKw03HTowURcAALDwRqAAAADlBWEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNZ+Gnfnz50ubNm2kZs2aZomOjpZVq1a5tmdnZ8vw4cOldu3aUr16dRkwYICcOHHC4xhHjhyRXr16SbVq1SQ8PFzGjx8vFy5c8MHZAAAAf+TTsNOgQQOZMWOGpKamyo4dO6Rbt27St29f2bNnj9k+evRo+eCDD2T58uWyceNGOXbsmPTv39/1+tzcXBN0zp07J1u2bJFFixZJYmKiTJo0yYdnBQAA/EmAw+FwiB8JCwuTF154Qe655x6pW7euLFmyxPys9u/fLy1atJDk5GTp1KmTqQXq3bu3CUERERFmn4SEBHnyySfl5MmTUrly5SK9Z1ZWloSEhEhmZqapYbraxSem+LoIQKlaMLiDr4sAwAuK+v3tN312tJZm6dKlcvbsWdOcpbU958+fl5iYGNc+zZs3l6ioKBN2lD62bt3aFXRUbGysOXln7RAAALi6VfR1AXbt2mXCjfbP0X45K1askJYtW8rOnTtNzUxoaKjH/hps0tLSzM/66B50nNud2wqTk5NjFicNRwAAwE4+r9lp1qyZCTbbtm2TYcOGSVxcnOzdu9er7zl9+nRT7eVcGjZs6NX3AwAAV3HY0dqb6667Ttq1a2dCyI033igvv/yyREZGmo7HGRkZHvvraCzdpvQx/+gs53PnPgWZMGGCad9zLkePHvXKuQEAAN/zedjJLy8vzzQxafipVKmSrFu3zrXtwIEDZqi5NnspfdRmsPT0dNc+SUlJppOSNoUVJigoyDXc3bkAAAA7+bTPjtaw9OzZ03Q6Pn36tBl59cknn8iaNWtM81J8fLyMGTPGjNDSQDJy5EgTcHQklurRo4cJNYMGDZKZM2eafjpPP/20mZtHAw0AAIBPw47WyDz44INy/PhxE250gkENOnfeeafZPnv2bAkMDDSTCWptj460mjdvnuv1FSpUkJUrV5q+PhqCgoODTZ+fqVOn+vCsAACAP/G7eXZ8gXl2PDHPDmzDPDuAncrdPDsAAADeQNgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFar6Ms3nz59urz77ruyf/9+qVq1qnTu3Fmef/55adasmWuf7OxsGTt2rCxdulRycnIkNjZW5s2bJxEREa59jhw5IsOGDZMNGzZI9erVJS4uzhy7YkWfnh4APxGfmOKV4y4Y3MErxwVgUc3Oxo0bZfjw4bJ161ZJSkqS8+fPS48ePeTs2bOufUaPHi0ffPCBLF++3Ox/7Ngx6d+/v2t7bm6u9OrVS86dOydbtmyRRYsWSWJiokyaNMlHZwUAAPxJgMPhcIifOHnypISHh5tQ89vf/lYyMzOlbt26smTJErnnnnvMPloL1KJFC0lOTpZOnTrJqlWrpHfv3iYEOWt7EhIS5MknnzTHq1y58mXfNysrS0JCQsz71axZU6523vorGLANNTuAbxX1+9uv+uxoYVVYWJh5TE1NNbU9MTExrn2aN28uUVFRJuwofWzdurVHs5Y2dekF2LNnT4Hvo81hut19AQAAdvKbsJOXlyejRo2SLl26SKtWrcy6tLQ0UzMTGhrqsa8GG93m3Mc96Di3O7cVRPvzaBJ0Lg0bNvTSWQEAAF/zm7CjfXd2795tOiJ724QJE0wtknM5evSo198TAAD4hl8MVxoxYoSsXLlSNm3aJA0aNHCtj4yMNB2PMzIyPGp3Tpw4YbY599m+fbvH8XS7c1tBgoKCzAIAAOzn05od7RutQWfFihWyfv16adKkicf2du3aSaVKlWTdunWudQcOHDBDzaOjo81zfdy1a5ekp6e79tGRXdpRqWXLlmV4NgAAwB9V9HXTlY60ev/996VGjRquPjbaj0bn3dHH+Ph4GTNmjOm0rAFm5MiRJuDoSCylQ9U11AwaNEhmzpxpjvH000+bY1N7AwAAfBp25s+fbx5vv/12j/ULFy6UwYMHm59nz54tgYGBMmDAAI9JBZ0qVKhgmsB0UkENQcHBwWZSwalTp5bx2QAAAH/kV/Ps+Arz7Hhinh2gaJhnB/CtcjnPDgAAQGkj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArFbR1wUAAFwsPjHFK8ddMLiDV44L+DOf1uxs2rRJ+vTpI/Xr15eAgAB57733PLY7HA6ZNGmS1KtXT6pWrSoxMTFy8OBBj31OnTolAwcOlJo1a0poaKjEx8fLmTNnyvhMAACAv/Jp2Dl79qzceOONMnfu3AK3z5w5U1555RVJSEiQbdu2SXBwsMTGxkp2drZrHw06e/bskaSkJFm5cqUJUEOHDi3DswAAAP7Mp81YPXv2NEtBtFZnzpw58vTTT0vfvn3Nur///e8SERFhaoDuv/9+2bdvn6xevVpSUlKkffv2Zp9XX31V7r77bnnxxRdNjREAALi6+W0H5cOHD0taWpppunIKCQmRjh07SnJysnmuj9p05Qw6SvcPDAw0NUGFycnJkaysLI8FAADYyW/DjgYdpTU57vS5c5s+hoeHe2yvWLGihIWFufYpyPTp001wci4NGzb0yjkAAADf89uw400TJkyQzMxM13L06FFfFwkAAFxtYScyMtI8njhxwmO9Pndu08f09HSP7RcuXDAjtJz7FCQoKMiM3nJfAACAnfw27DRp0sQElnXr1rnWad8a7YsTHR1tnutjRkaGpKamuvZZv3695OXlmb49AAAAPh2NpfPhHDp0yKNT8s6dO02fm6ioKBk1apQ8++yz0rRpUxN+Jk6caEZY9evXz+zfokULueuuu2TIkCFmePr58+dlxIgRZqQWI7EAAIDPw86OHTvkjjvucD0fM2aMeYyLi5PExER54oknzFw8Om+O1uB07drVDDWvUqWK6zWLFy82Aad79+5mFNaAAQPM3DwAUF5nOQZQugIcOqHNVU6bx3RUlnZWpv8O/wMHbMbtInA1fn/7bZ8dAACA0kDYAQAAViPsAAAAq/m0gzKuDH1rAAC4PGp2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrMakgAFxFvDkZKTcZhb+iZgcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXm2QEA+PUcPszfgytFzQ4AALAaNTsAgKsWtVFXB2p2AACA1Qg7AADAaoQdAABgNfrsAABQyri7vH8h7AAArtrggKsDzVgAAMBqhB0AAGA1wg4AALAaYQcAAFiNDsoAAJQjjPS6imt25s6dK40bN5YqVapIx44dZfv27b4uEgAA8ANW1Oy88847MmbMGElISDBBZ86cORIbGysHDhyQ8PBwXxcPAIById7Se4VZUbMza9YsGTJkiPzpT3+Sli1bmtBTrVo1eeutt3xdNAAA4GPlPuycO3dOUlNTJSYmxrUuMDDQPE9OTvZp2QAAgO+V+2asn376SXJzcyUiIsJjvT7fv39/ga/Jyckxi1NmZqZ5zMrKKvXyDV+cWurHBACgPMnywver+3EdDofdYackpk+fLlOmTLlofcOGDX1SHgAAbPb2o949/unTpyUkJMTesFOnTh2pUKGCnDhxwmO9Po+MjCzwNRMmTDAdmp3y8vLk1KlTUrt2bQkICPBIjBqAjh49KjVr1vTiWdiN61g6uI6lg+tYOriOpYPreGW0RkeDTv369S+5X7kPO5UrV5Z27drJunXrpF+/fq7wos9HjBhR4GuCgoLM4i40NLTQ99APIB/CK8d1LB1cx9LBdSwdXMfSwXUsuUvV6FgTdpTW0sTFxUn79u3llltuMUPPz549a0ZnAQCAq5sVYee+++6TkydPyqRJkyQtLU3atm0rq1evvqjTMgAAuPpYEXaUNlkV1mxVUtrUNXny5IuavFA8XMfSwXUsHVzH0sF1LB1cx7IR4LjceC0AAIByrNxPKggAAHAphB0AAGA1wg4AALAaYQcAAFiNsFOIuXPnSuPGjaVKlSrSsWNH2b59u6+L5Pc2bdokffr0MTNZ6kzU7733nsd27Quv0wPUq1dPqlatam7WevDgQZ+V119vZdKhQwepUaOGhIeHm4kyDxw44LFPdna2DB8+3Mz4Xb16dRkwYMBFM4hf7ebPny9t2rRxTdQWHR0tq1atcm3nGpbMjBkzzL/tUaNGudZxLS/vmWeeMdfNfWnevLlrO9fQ+wg7BXjnnXfMRIU6HPDzzz+XG2+8UWJjYyU9Pd3XRfNrOpGjXisNigWZOXOmvPLKK5KQkCDbtm2T4OBgc131Hzr+z8aNG83/9LZu3SpJSUly/vx56dGjh7m2TqNHj5YPPvhAli9fbvY/duyY9O/f36fl9jcNGjQwX8ypqamyY8cO6datm/Tt21f27NljtnMNiy8lJUVef/11EyLdcS2L5oYbbpDjx4+7ls8++8y1jWtYBnToOTzdcsstjuHDh7ue5+bmOurXr++YPn26T8tVnuhHa8WKFa7neXl5jsjISMcLL7zgWpeRkeEICgpy/M///I+PSun/0tPTzbXcuHGj65pVqlTJsXz5ctc++/btM/skJyf7sKT+r1atWo7//u//5hqWwOnTpx1NmzZ1JCUlOW677TbH448/btZzLYtm8uTJjhtvvLHAbVzDskHNTj7nzp0zfw1qE4tTYGCgeZ6cnOzTspVnhw8fNrNbu19XvZ+JNhFyXQuXmZlpHsPCwsyjfja1tsf9Omp1eFRUFNexELm5ubJ06VJTO6bNWVzD4tPaxl69enlcM8W1LDptstcm/muuuUYGDhwoR44cMeu5hmXDmhmUS8tPP/1k/ueY/1YT+nz//v0+K1d5p0FHFXRdndvgSW9oq30junTpIq1atTLr9FrpzW/z37iW63ixXbt2mXCjzaTaD2LFihXSsmVL2blzJ9ewGDQoanO+NmPlx+exaPSPusTERGnWrJlpwpoyZYrceuutsnv3bq5hGSHsAH7817T+z9C9bR9Fp18sGmy0duyf//ynuVmw9odA0R09elQef/xx039MB2ugZHr27On6Wfs8afhp1KiRLFu2zAzWgPfRjJVPnTp1pEKFChf1hNfnkZGRPitXeee8dlzXotH7vK1cuVI2bNhgOts66bXSptaMjAyP/bmOF9O/lq+77jpp166dGeWmnedffvllrmExaBOLDsy4+eabpWLFimbRwKgDDfRnrX3gWhaf1uJcf/31cujQIT6PZYSwU8D/IPV/juvWrfNoTtDnWiWOkmnSpIn5h+t+XbOyssyoLK7rf2jfbg062uSyfv16c93c6WezUqVKHtdRh6Zr+z/X8dL033FOTg7XsBi6d+9umgO1hsy5tG/f3vQ5cf7MtSy+M2fOyDfffGOm4eDzWEbKqCN0ubJ06VIzSigxMdGxd+9ex9ChQx2hoaGOtLQ0XxfN70dsfPHFF2bRj9asWbPMz99//73ZPmPGDHMd33//fcdXX33l6Nu3r6NJkyaOX3/91ddF9xvDhg1zhISEOD755BPH8ePHXcsvv/zi2ufPf/6zIyoqyrF+/XrHjh07HNHR0WbBfzz11FNmBNvhw4fNZ02fBwQEOD7++GOznWtYcu6jsRTX8vLGjh1r/k3r53Hz5s2OmJgYR506dcxoS8U19D7CTiFeffVV8+GrXLmyGYq+detWXxfJ723YsMGEnPxLXFyca/j5xIkTHRERESZMdu/e3XHgwAFfF9uvFHT9dFm4cKFrHw2Hjz76qBlKXa1aNcfvf/97E4jwHw899JCjUaNG5t9v3bp1zWfNGXQU17D0wg7X8vLuu+8+R7169czn8Te/+Y15fujQIdd2rqH3Beh/yqoWCQAAoKzRZwcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDoBy7/bbbzd3iAeAghB2AFyRhIQEqVGjhly4cMHj3j96vx8NIe4++eQTCQgIMPcFKmt6s8WZM2eaG4JWq1bN3PS3S5cusnDhQjl//nyZloVwBpStimX8fgAsc8cdd5hws2PHDunUqZNZ9+mnn5obv+qNXrOzs6VKlSpmvd7FPSoqSq699tpiv49O9p6bm2vutl2SoBMbGytffvmlTJs2zYScmjVrytatW+XFF1+Um266Sdq2bVvs4wIoH6jZAXBFmjVrZu7erLU2Tvpz3759zV3bNVC4r9dwpPQO5I899piEh4ebMNS1a1dJSUm5qBZo1apV5s7QQUFB8tlnn8nZs2flwQcflOrVq5v3femlly5bxjlz5simTZvMnaWHDx9ugs0111wj//Vf/2UCWdOmTYtUpsTERAkNDfU49nvvvWfK6fTMM8+Y4//jH/+Qxo0bS0hIiNx///1y+vRps33w4MGyceNGefnll83rdPnuu+9KePUBFAVhB8AV0wCjtTZO+rM21dx2222u9b/++qsJFs6w88QTT8i//vUvWbRokXz++edy3XXXmdqXU6dOeRz7qaeekhkzZsi+ffukTZs2Mn78eBMW3n//ffn4449NKNLXX8rixYslJibG1ODkp81twcHBxSrT5WgznYaglStXmkXLq+egNORER0fLkCFD5Pjx42Zp2LBhsY4PoHgIOwCumAaYzZs3m347WoPxxRdfmKDz29/+1lXjk5ycbGpOdF+tnZk/f7688MIL0rNnT2nZsqW8+eabUrVqVVmwYIHHsadOnSp33nmnafqqXLmy2a5NT927d5fWrVubYOLeX6ggBw8elObNm19yn+KU6XLy8vJMLVCrVq3k1ltvlUGDBplaJaU1PXoe2m9Im/p0qVChQrGOD6B4CDsArpjW4mhY0CYf7a9z/fXXS926dU3gcfbb0dCjTUfaZ0drPrRTsPadca9hueWWW0wNjrv27du7ftbXaf+bjh07utaFhYWZprTL9fe5nOKU6XK0+Uo7bTtpc1t6enqxjgGg9NBBGcAV0+aeBg0amCarn3/+2YQcVb9+fdNEs2XLFrOtW7duxT62s4npSmj42r9//xUfJzAw8KLgVNBILg1J7rRfjtb2APANanYAlAptntLaG13ch5xrU5Z2Mt6+fburv46zSUqbvtxDg9YMafNRYfR1GiS0tshJw9XXX399ybJpR+S1a9ea5rX89H21VqooZdLaKm2m0/2ddu7cKcWl76MjywCUDcIOgFKhQUZHS+mXv7NmR+nPr7/+uml+coYdra0ZNmyY6Wy8evVq2bt3r+mw+8svv0h8fHyh76EjsHS7vm79+vWye/duM7pJa1wuRee00eYp7eczd+5cMwT922+/lWXLlpnh8tqnpyhl0uYz7Wvzl7/8xTR7LVmyxPTNKS5t5tLApqOwfvrpJ2p9AC+jGQtAqdAgoyOutCNwRESER9jR2hDnEHUnHZ2kX/LaeVe3a9+cNWvWSK1atS75PtqBWOf16dOnj+kXM3bsWMnMzLzka3TYelJSksyePdsEr3HjxpnQ0qJFCzPUXDsSF6VM2j/o7bffNoFIOy9reNKh5kOHDi3WtdL3j4uLMzVGes0OHz5sAhAA7whwFKXnHgAAQDlFMxYAALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAYrP/B3g0+to8aQs7AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.hist(df['word_count'], bins=20, alpha=0.7)\n",
+    "plt.title('Word Count Distribution')\n",
+    "plt.xlabel('Word Count')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.hist(df['avg_word_length'], bins=20, alpha=0.7)\n",
+    "plt.title('Average Word Length Distribution')\n",
+    "plt.xlabel('Avg Word Length')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.hist(df['punctuation_count'], bins=20, alpha=0.7)\n",
+    "plt.title('Punctuation Count Distribution')\n",
+    "plt.xlabel('Punctuation Count')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.hist(df['richness'], bins=20, alpha=0.7)\n",
+    "plt.title('Richness Distribution')\n",
+    "plt.xlabel('Richness')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.hist(df['readability'], bins=20, alpha=0.7)\n",
+    "plt.title('Readability Distribution')\n",
+    "plt.xlabel('Readability')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HlLqV4g1veEM+EAiMllRSwrpu3TotPfubv/mbsu/Zv39//sYbbxwtpXvPe96T/9a3vqXvuf/++yvaN7dE8MorrzzttW9/+9v6Wn19/WnljpQTfvCDH8xv2rRJ95t9ZVtuSaELyubYx3I4dOhQ/qUvfWm+trY2v2LFCi2tpJx1KmWq492OHDmi6504cSL/tre9TfeP/aQU9PnPf37+i1/84ui23DLVb37zm2M+wy0dLC0z/MxnPqPfi7LESy+9NP/rX/86f9FFF+Wvv/76MetRYnj22Wfn/X7/mO1QQnnOOedMWro4Ga666qqy5cLg9ttv13LOlStXamnn+vXr83/0R3+U7+zsnHS7bJNjNpXy4P/8z//UMmo+6/zzz9dy6/FKMT/+8Y+Pee94x3+8z6IslXJszifl1G9961vz/f39Y9YZ7xhPVqZKiWopBgcHtazZLcXlGudvjRJo/vY4D/fdd1/Zct3xroFKjw1gOftXjNtuu02PAdfgueeem//e976nJc4sM1QfPPw3F8TFYJhPkLbAAAqXTDPemR+gb2B2jSHSVFJUBsNsgugU1+F0S5MNcwfTYBgWHUp7eqDBQMRHdYqRi7kBx7h0LkI+nHREObtsg2G2Qcl6sQYIIMZG92LXYHXCNBiGRQdmzNTNM3NB5EbJHGK7hWigtlyAsJIIEWZRCD7RcyAyJP/NMoNhroGYFM0NmhNEn/zNo/PAh+aP//iPF3r3DGVgBMOw6ICo7t/+7d+UUFCOiYgRn43SigLD7AFDLQyhqGQgaoHAEqEqgtKpmjoZDNMtC0aQzd8+lTuIR6mg4Rp0ewUZqgumwTAYDAaDwTDrMA2GwWAwGAyGWYcRDIPBYDAYDLMO/3IsrcNJEQOYubQ1NhgMBoNhqQFVBQaKCG2Lu/uWw7IjGJCL0u6FBoPBYDAYKseRI0fULXciLDuCQeTCPTgNDQ0LvTsGg8FgMCwaYIPPJN0dSyfCsiMYbloEcmEEw2AwGAyGqaMSiYGJPA0Gg8FgMMw6jGAYDAaDwWCYdRjBMBgMBoPBMOswgmEwGAwGg2HWYQTDYDAYDAbDrMMIhsFgMBgMhlmHEQyDwWAwGAyzDiMYBoPBYDAYZh1GMAwGg8FgMMw6jGAYDAaDwWCYdRjBMBgMBoPBMOswgmEwGAwGg2HWYQTDYDAYDAbDrGPZdVM1LF3kcnk5NjAi0WRahhMZqQv5pT4ckDVNNeL1Tt75z2AwGAxLMILxsY99TNu/vutd75pwvW9+85uybds2CYfDsmPHDvnhD384b/toqF482x2Vz92zTz74/T3yrtsekfd841G95znLed1gMBgMy4xgPPDAA/KFL3xBdu7cOeF69957r9x0003y5je/WXbt2iUvf/nL9fb444/P274aqg+Qhy//+qDcv79XjvTFJZvNS33YL9lcTo72x+X+A736upEMg8FgWEYEY3h4WF73utfJv/7rv0pzc/OE637605+W66+/Xt773vfK9u3b5cMf/rBceOGF8tnPfnbe9tdQfWmRHz1+QnqHk5LJ5SSby0trXVBTI611Iclk85LJ5qR3OCU/3nNC1zcYDAbDMiAYb3vb2+TGG2+Ua6+9dtJ177vvvtPWe+ELX6jLx0MymZShoaExN8PSAZqLfSeHNWLRH09LXTigqTbAfV1hOa8/2z2s6xsMBoNhiROM2267TR5++GH56Ec/WtH6XV1d0t7ePmYZz1k+Hth2Y2Pj6G3dunUz3m9D9SCWykgikxWf16uRioBvrJgz4PNqqsTn9Ugyk9X1DQaDwbCECcaRI0fkne98p3z9619XweZc4ZZbbpHBwcHRG59rWDqIBP0S9vuURPh9Xklnx6ZA0lnIBSQjLyG/T9c3GAwGw9xjwX5tH3roIenu7lYNhYtsNiu/+MUvVFNBasPn8415T0dHh5w4cWLMMp6zfDyEQiG9GZYmKEHd0lYnu48NSHNtQE5GkxKMBDU9ks/ntVy1rT4k0URGdq5t1PUNBoPBsIQjGM9//vNl9+7d8sgjj4zeLr74YhV88riUXIArrrhC7r777jHL7rrrLl1uWJ7A3+KF57aroNPv9WoqBEFnNJFW4SfPiWwg/HzBOe3mh2EwGAxLPYJRX18v55577phlkUhEWltbR5e//vWvlzVr1oxqNEipXH311fKJT3xChaFoOB588EH54he/uCDfwVAdOGNlvbzxqo1aTbLrSL+WqhKxqA36ZF1LrVy4vlnJBesZDAaDYX5Q1Qnpw4cPi9d7Kshy5ZVXyq233ip/9Vd/JX/5l38pZ555pnznO985jagYlh8gD5uvqTMnT4PBYKgSePIkqpcRKFOlmgTBZ0NDw0LvjsFgMBgMS3IMXXAfDIPBYDAYDEsPRjAMBoPBYDDMOoxgGAwGg8FgmHUYwTAYDAaDwbC8qkgMCwMaglGNga12JOgftwqj0vUMBoPBsPxgBMMwBrQ0x0+CBmL0+Aj5vNJWH5aLNzbL9lUNoySidD3sunHUxPTK/CYMBoPBYATDMApIw5d/fVD6YilZ1RiWRNonT3UNyW8O9Mmdj3fKWR31csG6Ztm2ql5+urd7dL3aYI3EUxl5/PigHB8cUdMrIxkGg8GwvGEEwzCa7iAiAWk4c2WdtjjffWxQRlIZWam9PNLSH0/J7qMD8uMnuqQhHJAL1jeNtkbH0Apjq2e6h+XHe07I5hV1li4xGAyGZQwTeRoUaClIdxCRAM92Dyu5aIkEJRTwSX1NQGLJrETCPm0oFk+f3vYcssH7eS/bMxgMBsPyhREMgwKhJlqK2qBf+3gQragLB0YjFAEfLc9zkkjnJOD3qBU365WiJuiTZCar25tOFIU+Inu7hvSe5waDwWBYnLAUiUERCfpVqImWIpXNSSabk0D41OWRzubE5/UqgQj5IRE5Xa8UI6msvs72ymG8yhMTjRoMBsPSghEMg4KBngEdoWZ7fUhbnKezeQn5PUK7GiIWKxvCsqqhRg6EYtKZSEigRGPBep2DCdmxplG3V4rxSISJRg0Gg2HpwQiGQUEUgWgBA3rXUEIiQZ8MxNOSD/tUe0HkYktbRMiY1Ib80lYf0vV4H68RuYBcoNmgNXqpwLO0QsUlEbuPFUSjNQG5YJ2JRg0Gg2GpwDQYhlEQJSBasGNNkzRHgpLO5eRkNCWNtQGNSqDDYMBf31Ir73j+mboeJORgT0zvWadctKG0QgXy4PN69L6jIeyIRstoNkw0ajAYDIsXFsEwjAHkYPM1dTqgP9k1JA8e6FMCMDiSVoEnJIIIBes9d+vKipw8iytU3AiFi3QuL0G/d1Q0SiSjGERHTgwlpiUaNRgMBsPCwQiG4TRAEta11Ort2m3t45IId73KK1RO12UEfV4lGFSeIBpFxwHR4DGvieQnFI3OJsz63GAwGGYPRjAME6JSEjERIkUVKqRFilEf9kt9yC+diYzEkmk5cDImffGUZHI58XkQmIr8zpkryopGZ5MkWBWLwWAwzC6MYBjmtUIF4WZpmgTvjYawXx46NCBwATQfQfHLYDwtuXxeuqNJ2d8zPOlAP12SMJ4A1apYDAaDYfowkadh3ipUqDBBJIrtOBEK7nm+rqVGzl7doHqLmgCRjqykMjlZ01wjV5/VJtlcXitJJjLeckkCpKCpNqBVJ9zznOW8Xg4TCVB5zvLJPttgMBgMp8MiGIZ5rVBxIwwIN9FWIBrdubZRvv3wMblqSyu1I6P6C9InRDvQaLiVJOXSNaUkYSqlrhMJUEurWGaaKjIYDIblBCMYhgWpUHE1EqsawvKrfT1ychjvjYiorrMEk1WSzIQkTCRAreSzDQaDwVAeRjAMCyYaJW3xhV/sl8eODsjerqg8fmxI0xO1QZ/qMpprg3LGyjoJ+DwTVpLMhCRMJECtxPrcYDAYDOVhv5qGGVdkTKdyo1hYCaHwiEcS6awEfSIjktdlJ6MJ1WlANK7Y0npaJYn7uV2DCclm81qF0lATnBJJmEiAOpn1ucFgMBjGhxEMw4wqMqZTuVGsmTijLSIPHhqQcMAr+bxPMrm8pDKOF8aKSFC6okmNalx79soxpKX4c0fSWTnSH5cDvTG5dGOLtNaFKiYJxRbpaDVIp1RifW4wGAyGiWEEwzDtss3plncWayaGk1ltDd8SCUmuJq/bIhIyNJKWcMAna5trNIJREzh1qZZ+7upgjdQEvPLAwX75+dMn5ZKNzbKqqaZikjCRANV1LTUYDAbD1GAEwzClioynT0Tlmw8elRt3dsj/+81hOdw3ouu56YVKKjdczURNICxH+0cknsxI0OdRQrG6KaypkoGRtJxbiDoc6o2N6ifG2791LRFNq/z2QL/s7RrWbYQD/opJQjkBqjl5GgwGw/RhBMNQcUVGfzytfUkeOTIg/737uPQNp7SElGhFR0ONCjKJFkxWuREJ+tXn4v79vbpNnDuHEhklJryfQR2RZ0ttUIlCsX5iov1rrQvLVWe0yvHBhLz6kvWaqpkKSZgN11KDwWAwODCjLcM4FRljuScRA4hF91BShpMZSWfy4vN5EDloOuPYQFxfZz2AjoH+IuUqN0hdQFSODoxIfcgnjTUBwcZqOJmWzsERJS4QjbqQT1McEBdXPzHe/rmglbzf65GOxrCSBYtAGAwGw8LACIZhDCJFZZvFQkmiEfFkWlu4M2Q3Y+ft80ko4BdMLnHb5D1EF1h/vMoNUhx3PXFCu6a21QUllsqqoRYpkpySjIxGLdobQvLsydhp+oly+1cMKys1GAyG6oARDEPZsk0iBxAFQEUHQsyg36cEIBIOKEHA1ttx3aTENCchn1cjGEQ0SiMPLtwUB/qJC9Y3y8r6sLp3op/AvZNtwmCwC0c/USoULbd/pRUj5T7XYDAYDPMLm+YZJi3bHEk7UYVMNidej0dWRAKqfyC6AMFAT5GTvOQ9IolUVp49OSwbWiNlKzeKTbEoP23eGBxtzx7werRM9VBfTG66dL0858y2095vZaUGg8GwOGAEYwExHYOq6bYjL/debLo7Cw6Xxc8Z5G84t0MeOTwg+3ti0h9PagqkKRKQgN8rfp9Pt8fA3tEQVkMsUh3RkbQSj3NWN8qrLl5btnKDzyHS0T2U0G0V9xwBGGu11YU1SjGTstJKjtNMjqXBYDAYJoYRjAXCdAyqptuOvNx7iTokSWsEvFoJUvqcbW9ui8jvXbhGWuuC8p2Hj8mh3rgMJZxKkmChWgSDLISVTiTBL+esaZD3vmCr+P3ls29EQ3qGU7ofNUGvBHw+rRbZsjKifheVOmdOVFZayXGaybE0GAwGw+QwgrEAmI5B1XRNrcq9N5H2yUOH+tRrArHmGW11crQ/riWjTTUBuWhDixKHPceHdMBn26++ZJ1uA20EqY3e4ZSSkWQ6K36fV3UZ61tq5dUXrxuXXLAfX733kFaM4HkBofF6ctI1NCK9saSsbAjrNipNcZQrK63kOIHpHsvpwCIlBoNhOcIIxjxjOq3FZ9KOvPS94MnOfk15rG+ukb54Wh47NigBn1efQzIO9sbk4g3Nur677T++estoWmLXkX450hdX7QTiTAb5C9c3T2ho5e7H4b64ZLOq2JB4OiNDibyWlVLy2t4QlpuvmP7gXslx4nXEoNM5ltOBRUoMBsNyhRGMecZ0WovPpB156Xup8KAipC4cEK/XKyG/V1MeWHLzvC7s18EX8kClSPG2i9MS0WRahgvmWAzOk83KeQ/EBL1GJptX74vWiFOmynbomMoNXcdcHtvHjg0IIZQ1zTVTPpZTxUyiTvMFi64YDIa5ghGMecZ0WovPpB156Xup1qAaJBB2Tj1jLNEM7kl34HNBxQgmWSKB07Y9XbfLoXhanj0R1W031AYln8uLF4FnCA8Mr/QOJ+VAT0zXk5bpDYqVHCdSPERPxjPqmuhYTgUziTrNFyy6YjAY5hJGMOYZkSKjKAabSoyipvOe8d7LYI5mIp3NS8jvwYhTgUMnhlmQDxbt7Yyq1oLUyUyNqxjI/vmeZ5QUsO3eeFrTIgzy3DsdVLNKdP7zNwflzcHN0xK6VnKcSOmwE9M5llPBTKJO84HFEF0xGAyLG2a0Nc+YjlHUTMylSt+L/TaDLBGDkVRGy0KBijc9efEw8Id8MjiSkl2H+3WmPRXjKmbu6DP2dg3p/dNdUfnUT56Rhw4PqKEWPhp8TjqT08ENcWculxOf1ysBv0ebqX3yrqflV8+c1G2VGxQZBJtqAzr7557nLKdCZbLjtHNNk1apzLVR12SW5hNZqc81SqMrEC3IJPc8ZznRldLjbzAYDFOBRTDmGdMxipqJuVTxe3cdHtBZam8spWLO7mhCB3wIB2NtLJXT6o4VkZAOOF1DCfF7vXLt9sqqOkqjC/hdoO84MjAiYb9XaoNeSWRyks6KRksYvjI5kaFkRmrw1/D7dL+ODiTkUE9MbtixSq4/t2PU12KylMNPnuiW686e+DhxLAB+H3Np1BWZQdRprlHt0RWDwbA0YBGMBYBrFHXu6kYZoGqjJ6b35ayxZ/Ke4vc+b9tK9bBgEPVIXlbUBXX2n8vnJZnJKclgcG2o8Ss5YBnCzxX1oYqEl+WiC36fR57ujkp/LKnboH26OykunhvnciIjmZxGEBrCAe1DAhF54GCfbpNtVzoo8jmTHaeZHMtKUc2W5tUcXTEYDEsHFsFYIExkFDWb7wHM/tFUrGqskQvWNUk6l1ctRiKdkXv39ar2AQ+KKze3aBTD6S/iVSMszLUmG2jGiy7Qu4RZeiyZ0eoVeowoo6UJK+zW45ALmpyxgCgHJl/5wgyf7+aG66/Z2lax0HVbR8Okx2m6x7JSVLOleaSKoysGg2HpwH5BFhDTqciY6nsY/B881CcPH+6XlojTpKw4AoD7JmMcGgzIBa+7QJ8x3kBTXMkBeSDK4EYXmKFT5kopayab1fTLQCytaRcMvFKZvGQyOckWJvZ8fiTo03QJjqK6fU3ZZJ30x4moXLyxecqDIvvRNZgobC8v65rHtm+fbkVMpajE0nwh4EZXiDZxfIuvBze6UombqsFgMFQtwfjc5z6nt4MHD+rzc845R97//vfLDTfcUHb9r3zlK/LGN75xzLJQKCSJhDOIGMprIh4+3KeunHhPHO13QvPMoOkBgj03fUE8nrxGLioZaEq1FjhyIuiEBKSzOdnXHZNO3DmHkzKcyDoRigJ8HlGiodGLvAimnwg8EZZCPCAsuIQy+O85NjhKCIhkVDoosn+3/uaw3L+/VwbjaW3C1lQTlMs3tchrL18/rwP7XEdKllp0xWAwLB0sKMFYu3atfOxjH5MzzzxTB4mvfvWr8rKXvUx27dqlZKMcGhoa5Kmnnhp9XpqPN5xehtgaCSm5YGDH6IqW6+eva9KBBLLRF0tKNOH0J8nkchMONOXKG08MjciTnU66hbJW/DQGE2lJpPPq0JlzQxUiGrXAA8PdIq+wX9hu8NmdAwn9vNUNIY1UkF5BH/E/j3fJS89bPemguL9nWKtWHj0yoGSmtT4oHvHoNu568oR0DyflXdeeOa8kY64jJUspumIwGJYOFpRgvOQlLxnz/G//9m81onH//fePSzAgFB0dHfO0h4sT5ezBj/aHtGqE3iNUajCoNNc26/OV9WFZ2eB4YCB4HG+gGU9rgbZjfUuNPH58SFuuOyJB+ow4EQt0FkUcYzRy0RT263IISX/a6XFSE/DI+taIChAhnZCeDa21us5TXVG1Er/riVODIloR+pdctKFZyc33HjmupbFBn0da60Kj+9je4NXqGcpgf/R4l0YVlvsMvRqjKwaDYemgajQY2WxWvvnNb0osFpMrrrhi3PWGh4dlw4YN6p1w4YUXykc+8pFxyQhIJpN6czE0NCRLHeUqLuhWiiYCcoGQsmc4KZ2DIzKczMr61lq5+coNUhPwTzjQjFfJ4VRx1MgTnVGt/sAKHHJACWy6kB9xTb24ZfN5LYdF73FyOCmpAvsg0kHlCVEJyAYW4mhEGAixEadK5CXnrZa3XrNF9+XJriF58ECfpni+88gxyWbzsr+QtikmF+4+khJCG/LY0UErwazi6IrBYFgaWHCCsXv3biUU6Cjq6urkjjvukLPPPrvsulu3bpUvfelLsnPnThkcHJR/+Id/kCuvvFL27Nmj6ZZy+OhHPyof/OAHZTmhnGU2WgvSIftPxiQ6kpZ4Oit9sbTO/MtFKqZqxU3LdtIwg/GkrqM+Fx4iFc4gH/B61bGzJuQINYlwHO0f0fUgD05kJaV6jkN9cVlZH5INrRHVXZD+IH3iVomwL5RR/vypk0WpGr92hO2LpzTq0VgTlFDJ1U2EA9Bkza2MsV4cBoPBsEQJBqThkUceUcJw++23y8033yw///nPy5IMiEhxdANysX37dvnCF74gH/7wh8tu/5ZbbpF3v/vdYyIY69atk6WMSEkZIhoLhJcMvqRBiCDge3HjzlXy8vPXnKaxmI4VN6kKN1JA5ILPYKsEETzucxYQrCCKQZRDiYejwYiE/EpACHHE044nxoXrGsXn851WJTJeqgYSBcnBIAyHUL5jcRQDASqoDfh1O9aLw2AwGJaw0VYwGJQzzjhDLrroIo02nHfeefLpT3+6ovcGAgG54IIL5Nlnnx13HapMEIYW35Y6ik2eeocT8siRAdVfUCKKCRZGFPQjefBgn4oiK7biTmXHNY+ilwhGXlSZMsi7kQsG9WwO986cEo5hSlcLb8W/C2tynlLqimsoa0WCfk3ddA0lyxpTjZeqIQVCy/egz6fbI0riwi2dhczsXNuo32Wi78qxMBgMBsMiJhilQFtRrJmYTLdBimXVqlVzvl+LCW4ZIjP63x7o18HWIRai1RRoHy7d2KJ6DLfnRCX9KX7y5Am57pyVmrKgkgOfjHQ2K8f6Y/KrZ3s01bGizvHZIKLh2IE72guEngz4uYKhltIC+pLQ+yTgUx0GUQ5EoclMRtMckKKhkZR+VnFFy3hOlHwukQdcSvkc3k9/kpF0Vk4MJXWbZ7XXy3XntKtQdKa9OEr7rljvDoPBYKiSFAnpCzwv1q9fL9FoVG699Va555575Ec/+pG+/vrXv17WrFmjkQ3woQ99SC6//HKNeAwMDMjHP/5xOXTokPzhH/7hQn6NqgQD7Q07OjR6wcA9UIgQ4Njp6hoQe7o9J0AlVtyILN3yxl1H+nVghcAMjqSVYAzhzAlhyUN0nMcazPB4NEVSE/BJKOB12sGzLC9KADDCovlawO/VRmjoPp84PiQ9w2m5YnOL3HTZKf+KyASpGr4XEQp1ChWPemoANBnudki1zLQXh6VXDAaDoYoJRnd3t5KIzs5OaWxsVPEm5OK6667T1w8fPixeRqkC+vv75S1veYt0dXVJc3OzplXuvffecUWhywETiRTbVChZI211YSUZRBVII7iDKuWkOF0yWCK4PNIfk0iwQdMJpQMv6ZX+eFIeOzYgbXUh2dwWkV1H+rQ7KxUh0WRGoxVoPIgGtEacKEI2n9PoCSmJprBTEhtLU8kx5AgtvR71yvDmRVKQE8pOPB5pifjlqs2tMpAgApHTCAhkhvdAUvh8zMOKTbfYb8hOdzQpz9vaJtdsW6miVvbvko3Nsr4loseGiEOltuPljve9+3rk1t8e1iqX9vqQ9k9h+e5jTqtzSmnZhglHDQbDcoYnX5pMX+JA5AmZQVS62PUYk82iGZD/8a6ndYAvZ699pC8mjx4d1HQF+gQ0FAzGG1pqZcfaRmmJhHQ9UgZPHHdKO6nEwPFzJJlxqkI0vZFTq3HGecgFZMARdJL2KDwGGtXwaLSiSB6hnhiaRuF1+pV4Pfo9XnC20/l015EBfS+pj2Q2p9+zqSagpln0USHikCj4ZKg+RIWcXgkF/PqeFXWhKR0XUj+kkv7surPGRDA43nc+3iU/3N2pKRcnzSMqJiVd01wTkFQupyLS4n21yIbBYFiOY+iCV5EYpodyjpqkDBApMosmjYFwcTx7bcSfOG9qZKEGcSS+ESKDIxnZ3xPT2fflm1t1BN11uF8jHKQ1XIvvRNoRbUIWGORdggBfhWS43hZet3Ck0J4dxsEybi7HcMkGy6gmaa0L6nNID+Wp+FzwuKOxVdY01+n3hEjwOXz3w31xNdDCfwPSgOCUx7iA8p0Y7Cs9LuNZpLvHm1JYCEg+j+upQzBGUg7JgICRKgoGvHJNY9vovhZ/tpEMg8GwXFB1Ik/D5KhEkIlIETBzLhZlMmAjnPzNgX4lFw1UXjTWqKEV6QtSHhAG0gxPdQ3JnuMDashF51XgDMij1aZO47ICQaBSFSLhkgsEmzyCeHgKz3X/C+WrfkpYCxch97SK39pepxEH9hNCQ3ktaRcGcLqzFn9P9rOlNiCbVkTUzfO67SulUSMSHtWaQJpwAKUa5Yy2yKTHhftSQWnp8V7dGJZY0mngBhlymrTlNV3iVstQgkukZzrCUYPBYFgqsAjGIgQzZWbUkAHsr4u1FaUixXI9J9RvQvJaTUK0wJ3Bo7NAO5HPp9SMat/JmG4bcSjeFm46ATsJyALvcktOPQUzrRT0oSj94VpfKPlwIxeFaIeSC0hI3nH6hJdQnor4lM+k6gPvjlDAp+u7Xhu63cL33H1sSDdEdQjb6R9JS12RzoTHDO5sd7LjMp5FenFZrFOKm9d9dD7Co4+VdFAWHXAIh9sVdirCUYPBYFhKMIKxCIFF9p7OIR2gmcXjaeE6dTL7LhUplvacQNj5b7/cp6kA192SCg6cPanoyOayuoztQyqIJCQyzoycmT7OFU6DMsdMy41mUBLix1SL1z0iQb+zjmoyCsRCe5NAFghf6Jvyks46y+LJjBwbiOv3ISLBwE0ZLJag7Y1hJVHF4HtChAAaCJxA2b+A79R6fD+au6ETIX0y0XGJjCPILHYw5R47czUKw8ej0G8FEa2THvJqNQ37Xqlw1GAwGJYijGAsMhC5+O9HOzUkz4BJFCKdzY/pksoA57pelgORCmbVqXROdQqQBQY/tuNED/ySyzsz8kEEBhhzeT1KZiALedeh0yUWBTJCZMNNAfC6QyyctRxPDGcdhl6iEfhipBwu42ypkF5B18H3o8Q0nsxq+gbihA6DqA3RCAgDWosav0/NutA6uNEW0hR8f8Bjn8ejqZJj/RklPAhTK+3FwfdR0650VrUgtJXHSIxlkDElEoWDwH7Dmzgn7ue7cJ1I+Wy3GobzYxUmlcNs3Q2GxQUjGIsIrhaAgZlKDxqF1YVIL3glGAlqKgACwgC4c23TqEixuNqEJmcno0m9J6xPCoIBGEaAvoLRciSTU0KBbuFENKkDPd1JacFOHoJBurg7qu5bgWTopvKOcDMHsSi87qZSigfc4mUQDcYKIiANYUhERvcb0oA2Ym/nkOwpfDYRBPaZypUtbRGN3KjTZ1tEWmqDarAVjDgRhL6CDwYlpHiBrIgE5fuPHpfrz+2YVHDpHjfuj/TTkj6qXWPReRDBgFCwD1SLQHTCYb+Sjo6GsdEWVziKfoPPpnTWvDOmBvMdMRgWH4xgLCK4WoDVTWFpqw/KsKY1UqozIBXAbPpQb1wu2dQyKlJ0qx96h5M60LGNRCqrpaEM1EQtGOyJemiUQkP9tDcPy1kdjbK6KS137+2WqAobHUZQWtjs9BPxqsgRo6xa0hKIHifQM2bKbIPUiOo0PB5Z11wjxwYT0lEflL54WkkGfhgIPvme9SG/ikJhNCeHUxqFefZkTNobQ3JyOCFH+uMacaF5GlUdfC/0JVs76tU/gwF/oqqO0iqdize0yEOH+uRAb1yjEHxPV3zaHAgquYGwdUWT+j0gSKRFOLZuxQtk7fhgYtyqHxsop18xZcfOYKg+GMFYRCjWAjBgkQ5xm5g5JZlOS/Ibd6zSH1w34nG4N65aBgZgBjxSJAySkSDt1Bko0S7kdVDENGpNc1g2r6jXGTqVEnVBOqHSU4SUgxOVKE6PEPpHLErlCa8QJchmczKMc9YkcKMe/AeBSVHBMpTUShdM1tIqOhUZSmScdAclsJmceGo8cvHGFvWeeOzYoGpQsCDfc3xQS0UhOG4vkmDGI3XhgBITvs/aZqd6hKoOSlZLw+zlmqnVh0Uu3dSqgx0kTrdFCsqDPXpQIqGAnLeuWQnM3s7oGOHouasbVIwL0Shuzoa+hajRRPuy3DFeYzs7dgZD9cMIxiJCpMQiGyOs5o2ONsHRJDgz/O2rHPMTohXYeZMywLeC9EIk5NMIQSyFp4VHaiAbBctsfsy3r6pXjQNCUrYVK4g/0T4QQUi6UYwCOSASwnvXt0ZU7Mg6vOIKOvkMzFgzGad7KmNAcfSCqAmvqFjV79VIBiRmOJkTrxdtSVJNtdhPvrv2K8H9k++QyCjB4vs92TnkaDB8Xtm8IqKDOYSH8Yj3Eb2AdGCdDjGbqKpjvGZq6EAu2dgia5tr9Jj87+dsko7GGk2LFGsCnrt15RitAMTpUz95ZkbW5MsV450LYMfOYKhuGMFYhF1Siw2iuCEqZBBjNldsEBVNptWECr1AfdinJZaIIJ2eIF5ti46GgAGTCEg6L3Kgd0TyOdIKPjWTIrVClYd6WxQJO7mntBS+QUOxPccGlSSkR/uQOCkXbMAhPq6W09VouByD9XV5QaugQ0jeiZI4/hjoLhCDYnXukXBBHIqG5MFD/UpYGgot6UnThDweOdATd4hFwKdRjUSGaEpWNRDMhCm/hWQk0gkdvFwisKohLJ1DCT2+ffGkDl5uF1YInFsOjMcGx62xNigbWiOnnadS4ehMrMmXu4iyOGpXzcfOYDCcDiMYi7BLKnlnyAQDYHGeH4EjFt9Pd0d1gNBKh1RWB0XXy4IIgL9ATCAADP4djWE52EMkIKmaAiIERA4YlF1fKDQYxVCTraxTjkrqBJ3E6Gv5nH4Wn5EvsQVn3XJge4nMqbJXQCEG+oqTktKoRkwcJ1HXGTSdTWpEhsEew69VjQEJeD1Oa/qgb3RbpDM4DnxX1xcDYkE1x//7zeFREoReg0ZspIs0MjKU1Pc60Z/caDnwqsbQhFU6pZioOVtxhUml21tOIsrIIjh2BoOhPOyvcpFhPIMoyAaj6R0PHxsdICAWbokmEQ90F8z0fIUyTQZNxJ2kD2jdzmBOigOtA6+pAZaPKIPjU+HCndO6hlnuMjeyARlwoxETCT3LoXj1BCGVggModh2Qk+Lt4aUBcSIy4/Qx8TiRD4hUIeJBtINICxoSRLG0ho8l0vLokQGN/CCYTWbyKuCkyqS5NiAXrG/WktQDPTEVz65uqpHmSFC/F+QFu3D6pBRbiU818jSZNfl8oppFlNV+7AwGw/gwgrEIUWoQ1RNNagMuSELxAAEBSasZhbdQbeJT0yzSIYBIBQZVVDYgVOyOOqkWqkzi6az4CrP2TJnoxXhAk8EAT+pj7LsqR1FvtNH7fMF/oxiQB583p+keoh89saS014X0exGpqa8JSWo4pceF8H93gQ2RMmmsDcilG1t0Vrz3YL+md9Y31+i6BwvEAvIF0cILBA2K49VxSoMya5GnEmvy+US1iyir+dgZDIaJYb1IFincPP9ZK+vl0SODOjCW9iXZuaZRqyYYEyEQDN38OFNiyeDp2n9fsqFFLtrQopEOfqcZnNEvMNgQzahkMHV7i2CVjfHVTEDqphgaKSmzHpEHvuua5lrtqYKIE5DWgDA4jdYcczBfoTeIq/mIBJ2eKugr0G9wTCAe3ON0yraIXHD8hkac5moQL/q20PqdbqsQvKlGns5d3ajvhcRwz+x7ISMEUxFRLhSq9dgZDIaJYRGMRY6JBgjKPCmR3HV4QAf+HS0RZ3ZPh9IoJl1+ec5ZbdI9mJCf7OmSgXhKyQXum6RTNFUyjmaiWCtRTAKoQBlvXSpE2N5khEW9OIreW4zS9w7T2bRgbEV56tGBuIo9SY+QyqBipT7klaZISFMcjVrRkpNcLiePHR2UlkhAj4XbJVarXLRZmeOG6nSKdVSptSG/bG6LqJEZg9xUhYWVWpPPp1hzsYgo5+vYGQyG2YMRjEWOyQaIVU010jOc0o6jzMRJnTBwXrapVT0b7th1TF0u+2OOkNLROOQllZ14QJlKiuBUxUhl6xfrPSb7HFIjjx8fGu3cilBTCpoNIhcMQNm8Y+BFxId0yqNHByWVzOpxIYpDhQipotqgV4WrqWxW4qm8hPw5FYsyyJJSQTSLdoNI0XSFhZNZk8+3WDOyiESUc33sDAbD7GLhfzUMM0KkggGC9udvvGpTwf/iVEnmR374pDx4sE+rMIBbMbEYG4pDjBx30kKnVpqOeUWbpBGRoMQUMSvCTQzEyBsRKWHQIvDD8Qv4Ao4fB1EMj1NC68tkpb4m6Nioh0TLdh8/NiQvO39N1QkLpyPWNBGlwWCYK5gGY5HDHSAYCFwr79IBgl4d65prdfa3raNB748Njsh9+3tlJOW4Y0aCXsd3Yg4jznMdzObrh/1+JVQh7fzq2KB3NIT0MSZbdI1d0+iE1lmGmBMfDY4cVSQcM6/PK7Wq43DcS+swy9KyXCdlRKRn57rGqgrPl4o1i7U4PGc5Yk23GV2piBKxJCJKLNmpuOGe5yaiNBgM04URjEWO6Q4QzOR7Y47TJc3SXA0Fs/65wni6itmCeoFpRMIRs3oKpAFdCBEMNBoYiPEaIlaWYeRF1ILUieouAj6NeOCNgcBzTXON7jCt4Inu4BmyrqVG2upDUk2YiVjTRJQGg2EuYCmSJeyNwQABuSg3QKgBVtZxycziMUHKQAWNp28f0jHqP1FS0VEq9hwPaCQYwHUwD+IqWlkRK7wIzuPaixcPneU/Nz/awI0IBczpYG9MQj7SP1kZiqe1TJeoBftBF1TIBP9IJ6BVYSBm9k8qBZ0KRIT0Eff0a6FiBz0G0QCIWzU4YM5UrGkiSoPBMNswgrFEMJUBglz9vc/2qnslbp1oMCbSX1KNQvOykui6olK9BgQlmymUjjqu4xW9163sqPTz0pmcRhoY9NWgy+eUrA6knYGVDq0QptZIQEIBv7agx0IcvQWeH0QsMNl6pjumRl6PHOlXEsIxyOVzSi6aaoLqAPrbA30ayXjyeFQ6B0e050pNwL8gDpiRWRBrmojSYDDMJoxgLCFUMkAUt29vqg04duCTbBciMlvCz3xhsJvK9kpbu08EOrg6/heOniQD2ShZh0KTrijW5mmtNkGfQJnr5rY6Odo3Ivt6Yqq3yOWJuKSlJ5eUpLqTOj1cSJMQyfjmg0f1vRCRptqgtNWFpKbJtyAOmCbWNBgM1QYjGMsIxUJAKiqwBWccKpcWKcZskQsGfgZj0jHTtvmcZPuQiVFCUdRUbTxQyprMYY2ekp5wUvYOR52SVhrApZx0CKkkQJUK5az9sbQc6RvRqA7RkbDfcb2kARtdas9b26jt2efTAdMcLw0GQ7XBRJ7LCEf64/LokX7JZLNy3/4+ragITGO8cbqcTg3a2l1boBQMKwr6itlEaQVMJcSIdcIBxzJ9aCSlJGLLilpNfaC5oBsr2hHSKkG/T0kaBmToOYhykHaAVBDloY8Jx3R/T0wrV+bbAdPEmgaDoZpgEYwlgsmEhqRGPvvTZ7U0lQgChlTqFzGJUJPX0Ba4XVB1fa8j/Cy0NDltfSnuiEp7doSaHo86YTrN0Dza3wSNRDldx2SABBQ7jLrltY6bp7NTk222+Dvj/NkcCUjXYFIaa/yqzYAwEOFB3Ilo1DHswoQr53Sk9eIpwjHMaASIyEF9yK+VJycKEQPEpFTzzCdMrGkwGKoFRjCWACZzb+T1T/3kGXmYpl75vLY0TxcG98n0F26jMTBKRnLj6yJKF7OeTzMieYkl07qCaxde7OxZqehTtznOTmvVSIUoXlPt0bXtvNNBlgoRXZ5zCIZGRgi86PYd/wzt0aJ+XfRDIcpBV9eMpkZYNphIScDr1e62bGMm0YOpVqmYWNNgMFQDjGAsckzm3njzFRvlx090ydNdUQkHvdLiDWqDLyomqIqopJ16rphsuAunEHlw5RYFw9BZ13mwf5781MWjLohWnBxKKeEaSWcl1j+iaRHt1uohPeI81shFwCPRZEYjOvkC2fF4KPHNSTKTEapAGfpjiazUBEXuebpbOocS8q5rz5wWyZiq9bfBYDBUC0yDsYhRiXvj7Q8fkceODujg2FAT0NA9M2pm30spaq69R6bLUvJYhTvsJxzwaqUIUQh8MKKprBPdIJ3kIXrhuHvyUaR4SB9xHujzUqiELeg6vLKyIaz7RP8SylpLXTQrJY+QRSp+EIxyz3OW87rBYDBUK4xgLGJU4t64r3tYXSgZ9gjj19CHpDGsHUHn0hZ8vkHaZrr8gjQOaQUGb6/HOUaIPnE4pVIEJ1Cs1GkJn84WNCWFtvJUjwR8DgFxIz1EP1bUO8eYah30J4hqEdnOtfW3wWAwVAuMYCxinHJvLJ/pokyR8QdiQeAe3YCz3C8bWmtlZX14zvuDzBeKsy9T/U4cnfaGkOoWakM+rSCBHLREQrKiPqTEg3QEhIP27hCN1c1h7XnCp7kCWMChhggQ3dB98Xi0E+vgSErt2efD+ttgMBiqAabBWMSIVODe2FQTkBX1ddITPSlRenHUOZwymc6OaY42FZEllSFTNcCaSvmrSxaoas1WuD+uYNU12XLv82UqUEI+j5IAIgtoKwhJEGkgIsEx6WgIK9EgleT3eOXkcFKu3NIqP3qiSza1ROTZk8PSHU0qiRhOZVTk6cFQozD485mIPSlrheRNJ7ZSbP3NPqGboYKF0lmqZSaz/l4sqAabdYPBMDcwgrGIUal747Vnr9THaAGO9sd1UCXsX+zQOZUhMF9B9cl0ULoPlXpxFROdYkFq2XWpgMnRvt1x5cx7ZLTHSDzq2Kbv6RySmoBPWmqD0tEY0sf0JNl1eEACfq+01TupJxqpqZW55E+V5ZLGCPnVerwvnpLVgbAMqr04GopIxYMtlSz0Tzk+EJfOwaSmuegbgzi3uTYoqxpDk1p/VztMwGowLG0s3l8nQ8XujfxYU8XwLz97Vn7yZLcOpjNJ3c+BCaeiXMRhrpDK4mmR0SiE35tXZ05oAoP3yrqQpHM5OTE0orqJ685ulwvXNcsDB/rl/v29OthDJDj+XsSgJWW8dHQNeDzqg9E1yHY9ctnmVlnbXFvxYAu5ONwX194pkaBPI1QB1YDkpTuaUKL4grPbF63192TVT2YMZjAsfpgGY5GjUvdGKhA2rUAs6NeyS+1u6nVSBssZaCYQZUIuiMw4BluFtEMhIuSYjXnkunNWylAirSmTuhrSU1411goH/dIY9mvlSL6QfkppySoVKF45b12TvPay9eOG/serFukZTqlRF9s5Rb3UfMN9tChhAlaDYXnAIhhLAJW4N/La48cGR8sxGVT5UR9JzUWyY/GAw+FLZ7VZGceDqAPRHxqXtTeE1fKbfiQcPzqlIgAlesEy+pBAP7T8tzagGomeaEr1G/Qv4d8Lz2mXV1+ybtzZeOlgeyrN5dEUTmOYah+PJFJZieUdvUd7Y43u10BhvxabqdZUBKyL7bsZDIZTMIKxRDCZeyPEA00As2um5AxU+DxUM71AvMn3QjPhtnifi/2lymZja62KPjHRIsV07ppGWd1Uo/4hRIVcMSXrnr26QQZH0vL0Ca/2HllZH9J27qF8XnUSZ69uVMJwzupGee8Ltup3ONIXL0v+xhtsISt87xX1QUmlc3Lu2kbVXLgiz9L9WkwoFrCWw1IRsBoMyx1GMJYJECoy+FGqqhPvfHZGOoz5gIo3i6xG52p3iTZww88CPw0amUEgGPQhGwzqiC4Rdj5zIipPdRV6k+Tykkjn5NhAQgd9tzMt793QGpFXXbxWDvbF5M7Hu2S3Ro8yWlJM+ur6czs0qjHeYMtnIuj0UAabzyu5cMpiHQFv91BS95P9IgqymCovIhVUPy12AavBYDCCsSxAjv/r9x+S/Sdjo/0/snOl1FyEgHQd6ourqdZIJqcD9n37emVv55DU12KWFZIv/mKfPNU1LAPxlJpxrW0JK8HoG04pQWA5hK2+xq8aitddvl63TQ8YbNq1Rb1SJI8cOBmTvV1RFd5GxhlsISwITo8PjKi2A8IB+mJJefbEsO4v6+AQivh0MVVeVFr9tFgFrAaDwcEyl/gtfbiNzv5nzwmtQDCcDg4LpaSU7hKCYEAnFdIVdQbz3uGk9MfSEvR5ZE1zjWorDvbEpWswoYMjoll0GegiNrZGJJnJamXJrb85rKXB9CmBDGDcpemNXE6X8/qqhrAOtgyqY3xJPB7Z0hbRNJazOC8nown57YE+2d8bk/qagFy0oUWaI8FFZx3uVj9R5UT1E0JWLNi557lb/bSYojIGg+F0WARjCYOZ+J27u+Sprqik0lkJ+j0SDuAqma6oydlyAKZbycLBcNq+I371ao+RsN8jiUxeoxPZkF8HdUL3fo9Hnu2JSTqVk9qgVwJ+nwpEL9jQLOuaa3WQvP2hI3Lfvh6t1mmtC43O0lVHUedVjcFv9vdqSeZ4pcaYdVGBgsajP5ZSf47hREb9NIhWMBADogC8l8oLoieLYWB2q5/c0lyOB8eGyIVbWm0wGBY3jGAsYSAgJPdPmSPh+1DApzNtv8+jzpDoB5Yzz6BKw7VPBxCw1U21EilUgeA34fehqcgoQWiodQZ0tBGkNYg4UGmChTjVOegr3CqIx48PqdCzfZxKCezDe4dTsr8nJtdsXTnhYAtpePBQn3zh5/vVqnxVY82YbS7WyotKqp8MBsPihRGMJQxCzr3xlBKJbC4ryXReRjTeTgmkT6sy4q4oY4p24YsJxd/LfYz/B1UjlOuipXCiF0R9cpLPezXNQUopWHgdkkYUIRzwaYWHk7rAk8LRcLAO72EZEQgV0+rnOYMly/HYQItBC3h3h1hOhQkVIy85b5UupmV8pDDYAsy+9nYNSfdwQkkRwk5KY4tJxmKtvJis+slgMCxeGMFYoiAf/51dx+VYX1yFgQ6PmLjIcymSCynpa5IvsgzPFVqxa7GKDux5OdQ3om3sHbJxav1sPKfeF5ADlhHhgDsgtoRsMMA/fnRQjvcn1F4cgSa6DbwqGsJ5fS/EgXW13BZBaNgv9z7bK99/tPM0q2wGXc7hrfcflp8/c1L1Hrx/7/GoRMJ+2dBSKzvXNo2mSazywmAwVBsWVOT5uc99Tnbu3CkNDQ16u+KKK+R//ud/JnzPN7/5Tdm2bZuEw2HZsWOH/PCHP5y3/V0scJ0hD/fF1KmyKEixLOESiFLCwUhfqkVxK2PJnHDY3NoPV3/pWIw7vUcgCdosjT8kGIlH1F78gYP9sqY5LL97Rqu+DgkhmsS5oBqEKAZkYSiRkSe7hsa4d7qCzbufPKHi3B/u6ZLuoYS+F70IEQ/MwCiZxbacNIxbeXHGyjqrvDAYDFWDBSUYa9eulY997GPy0EMPyYMPPijPe97z5GUve5ns2bOn7Pr33nuv3HTTTfLmN79Zdu3aJS9/+cv19vjjj8/7vlcrip0ht6yolYH44gqZzwXKZvQLnhXjrV/6kisL8JS8joizLuToMWh+5n4aDha/f+l6aaxxTLHgH6RcSK8QBaG/CHoYUiuINIutsqla+fKvD8hTnUMqzuW1ulBARaZ0eM3n8torpSealD3HB+TpE1GrvDAYDFUHT764Nq4K0NLSIh//+MeVRJTi93//9yUWi8kPfvCD0WWXX365nH/++fL5z3++7PaSyaTeXAwNDcm6detkcHBQoyZLDeTz//Gup3U23DkwInc9eUKHvOUaxSglCzynBwvHo3g5RKFcZY0b6VBHUTXjcpqcZTI51WNAHkhtIJflL4koAq6gvOk1l66TL/5iv/QNJ2VgJKOlmH6vV0kHZAQdDFGQKza3qqbCBR1Uf/VMjzZNiyezEgp4lYwAtB0x7YSbVbEpXVpfev4aNfWyyguDwTDXYAxtbGysaAytGh+MbDYrt912mxIIUiXlcN9998m11147ZtkLX/hCXT4ePvrRj+rBcG+Qi6WMU86QfommMjroMQNe7PNanzvYTwEIOetDPiUPER2kcZH0yqqmGhVLYkUe8jvHhsoa7klDQEB0mRILZzs1fm8hWuGXuqBPBZb4ZdDwbGVDSDulNkcCsq2jXreP4JMyUyIOV2xZIVef1Sa/e2ab3m9f3aDnBN0GpIOoxpjv6vXosqwynoIotAD8ORpq/PpeSMnallp52fmrjVwYDIaqw4Irwnbv3q2EIpFISF1dndxxxx1y9tlnl123q6tL2tvbxyzjOcvHwy233CLvfve7T4tgVHuawy3dO1XtcaqygBl0uXWGUxnZT7vvVFa1AHUsL6QCmAC71uBVFbKaAqYchMkj5nTSE/pUdZkeJV9Bv2Pu5LZadzt3Fh8bPW4c63y+4MTpLMvr9hwtBvcILD2S0f3j2B9LjWi0o7k2oNEN9BZuhIKAYXQwreSBahA8NVyXThekTVhGBEMyjlU4640ehxyhR4cQ1SDsDI39My6+NiJW+mkwGJYrwdi6das88sgjGm65/fbb5eabb5af//zn45KMqSIUCultMQk0XT+EnuGktuxmEKMPBTeqDLatqpe9ndEx6yTTmYI4Ma+eDIwnm1fUahjfGQAdOEWqi49kZKfZyyRTyA1BJgCD/cmhERlJEzmQ0SPhppAc0nDqM3MFQSemWgCTMpcPYHrFa9HEiN6zuGe4S4J+n6xtCssDB/qcVNVgQiMf/fGU7OuOaVUP5l3sA91Zi704lIAkMrJlZZ2cGEwoYaFBnU89NkTSmZwMJtJKdDL5nPQMJ+T7jx4f7W1SfP2UVqZYlMNgMCwrghEMBuWMM87QxxdddJE88MAD8ulPf1q+8IUvnLZuR0eHnDhxYswynrN8KVV/INAkhE+InYoBCiN7PSIr6oJaOXDHI8fUWKmtLqjrMFgNJdI6AOH6yFA3NJKRZ0/GpTbo00oG7Uha+JzFRi5mEwzlvXGHGFSCYv8MiIWWtxJBKHMcee54juQ1YvFEZ1TTHdx2HRnQahAazmF4VhcOSF7S2oPkgYN9cuGGZo1EQUZw/nzVxevkjl3H5MFD/TKcSEuM8+sV1V/k8h4JBRxzrW2rGmTP8SF93/O2rZSf7u3W64fXaKDG9qlMwSkUMy8jGQaDYb5QNRoMFxgdFYsyi0Eq5e677x6z7K677hpXs7FYqz/OaItI52BSZ67tDSFZ2RCWZDqng0g6m9WSx3Qmq88TqYxGK8jNk6tPZvLq9IhTZK3qDrzSTM6+oCGouhM+T3C/N5mQ6RAsdBhKMtx0S2E5z93oEAQP/Qbn41BvTBrDPj1XbZGgVn4QmUC7wT7gc/Gcs9rkrJX1uvzhQ/1q5IV7J0Tg+dvbtRnai87pULdQiBG9UrjHP+Os9nrVdqxriRQqT1LylV8f1AoUnlORUlyZwnWFlbibCjIYDIYlHcFAH3HDDTfI+vXrJRqNyq233ir33HOP/OhHP9LXX//618uaNWtUqAne+c53ytVXXy2f+MQn5MYbb1RRKOWtX/ziF2Wxg5w5YW1mngwkhNOZ5bpujXVhv5wYSupg1hoJSnc0pY+DAZ9WKGBXrS6QKSoMclq2SHh988qIkpM3/c4mjXZg2NQ5EJM7Hj4uiUxOe20sdah40487p+Ou6RKGUf1FmfXzJeRkbXONdlHtiSV1kI4mswURaUDiaUiec554DY1GdzQpv3ymV8JBn3ZEbasLyVVntGr6BH2F097do83REGqSNrnpsvVy8YaWUb0E0Ya/evHZcnN/XH67v1due+CIWoWvaaod4+TJPdvbc3xQLt/cUtaafDFaiRsMhsWNBSUY3d3dSiI6Ozu1wgPTLcjFddddp68fPnxYvMj4C7jyyiuVhPzVX/2V/OVf/qWceeaZ8p3vfEfOPfdcWTrVHzVKLgilB8KnTg8RCtUR5EWaIgEZTmb0MQMYIkRngPNIKp9TnQEljBAIBkAEiITdr9i8Qo72x+W2Bw7ra8sJKugseo7soRJq5ZINNA+1YZ944x5NVRSbayG3JFpAaiSN7XhhwyG3K+tgQh09N7RGVEfjai0QeVItAvnhvRBACECxKJN73sc5/dnTJ9WQi88qBcvQctCorRzmykrcBKUGg6EqCca///u/T/g60YxSvOpVr9LbUkMk6FdBHjlzZrgQBIyZKKMEDB6kOzyFKIX72CUXRL5JnyTSWekeSqovAz/+uw4PaDTjyeND8l+/PSy/OdAnnQOJZRG5cKEizRI+NdG3H1NJUrjH2IoUFMdaK1MKr7HMrdSBFLLQ6xPJ50SdOrO5tKQyGcGD6+dPd8tzt7ZJPJXTJmeIRDmvkEUIBoP0uuZIWVFmpHB9oMkBEJPiSAjvh8zQCr4c5sJK3ASlBoOhqkWeBgfM/PhxRpCHBoNeFiejCQkWek0wGKHHYOZ7oDcum1prdWBBOKgNsBIZGUllxOfzalfQkRSiTnprxFXgt/9/hjVVQqfQ1jqnZfvyimHMDL3RlJa3cqz7CroXgPEWZM+t4kEIms060Q10MsQ3miMhTVtR7fPfu7t0Pcifika1cZpIOOCXeNIxJy8nyuT6oCJFDbjUFCyvn9tSG5TNbbWq4yDdwj3XSHGaxLUSR98xFSvxiaITxYJkE5QaDIZyMIJRJeCHm5kfP87PnozJqsaQVoagu2DQQYPBD/nJaEqFewG/T6tIlFikkxq5YKCKFFqHU27JWIAlNYMY4XjC50RFmHVjLFVckmmYGBw3XFLdiAHngLQLRA2SQUyDgZxzQArFcfx0UiStkZDqNBBxcr4CXo+SBcgAolzWaasPqjlX11BSLlrfpNcAokxSIlwb+3uGVddBqgTygoiX6+LYQFy7rZ63rknecNVGrSJ5ptvR8pAWIXIBuZiqlfhE0Qn2yRUkIyB1yQzHhHJcPr943w0Gw/KEEYwqAjM+Zn7uDztiTsccKq+DFI+u2NIqWztO+WCwDrbRgyMZQbJBqJwQPjl5jJ7qQ345PpjQWTNulrzmeDl4xJfDQGqhv/UigcfxyIBAvODsdmkm7dQ5JM+cGNaZu8/nGHJh6Q3BoBQVAywiDDh+dkcz4vdjkOaktSCBnAteg/jhicG5ZNBG5FssyiRywDXBucUJdN/JmKPTyeUcu3EM5+pD8tytK2VDa+3o9YPmgrQIkQvIRaURhcmiE3huuIJkE5QaDIbxYASjysAgsPmaukmdPBlM3HWoUvjqvQeloyGskYonu6JSq14Lfp3xMtNldu0tRC5Y5vozOHbUhnJALhn0OZoL7MC5Zxb/ruvOknXNtXr8h+JpOdA3rHqMXz/TI8+ciMqJaFJaakNKHhhwKSVGOwHh4HhDMJpqg0r0IkGnGRqRhnxtftQ6nAiHK8osrjAiSkA0guiHq8Pg/NIOnvVKr5+pCi+Ly6XHi07Q6XUknZHVwZp5FZQaDIbFBSMYVQgGg/FmfgwAhOrdwQMfBUgIIWzKMAN+r3qJZHIeHcCIfri6P50xe13L7FMdQg3lARkjagAHc5uOZXNZiSadwZxz0FAbkBetXq3rN9cE5d9/eUCO9I+o4Jb1iS71xTFB80hrbUhODie10ypkA9MsyAUpK6p/OH/oKiANxaLM4gojwLaKm6NBSkifuAP6RNfPZCgmM+NFJ1gHRkVUA+IxH4JSg8Gw+GC/AIsI5fLidNNk9kweHr2GW5VQ3Fbc9VbCETKh1Sk5JRkWvJgYxRqVgURGJCFqaPW2/3xYzczCQUzMgirI5WBTiprJO11Vj/aPSHNtVmpDfrUDh6wgENXz4hVtmBYN+JQUaN+RggvomuYabf+OBsMVZTKguxVGcz2gl5KZctEJCBHmb64F+mwISucCVkJrMCwsjGAsEpTLi9PWm3bsUjCC0shGMuv4NpQpxaRUk3JLw/RBde+hvhE5EU1IR0ONdAeT0hdzhJ6XbGyW89Y2qaX7b/b3aQpje0uNbG6LyG8P9MvRgRE1yvJ4vJrSgEgk0hkV80IQ6sI+6WgIKbkoFmUWVxjN9YAeKSqXHo/M0N/m2rNXyv883jUrgtK5gJXQGgwLDyMYiwDl8uLOwJLUduA8PtY/osspUaWTZ7HNxahN9kJ9gSWIVDov8WRa01CkURrDfq0AoW37+paIVu9AKo70JxzBbSSoKSpSG1T/MLN2Gtl5lFy01gVHhbylosziCqO5HtArJTNXblkhHY3hGQtK5wJWQmswVAeMYCwClMuLI/KjkoBZJnn+w9G4U75YE9BSSLeLqNs7Y7wWFIuxs2o1gKNLV1MGVZw8SY30DSf1vEAiWuvCag1OBc+rL1mvgzaE4K4nTlUINdUEZXVTjVP9saJWxbeRcUL5pRVGczWgT4XMzFRQOheoRKRqJbQGw/zACEaVYKJ8MQ2z+uKpQrTCaXZF+N21E09nHTGiBwfJgrun+9OJczQ5c2bP5dzBjVycAqapHI9KtSmYamayWa00QayJV8bjxwaksTaoRE+9RjI57XDrii7PWDn9AZlB8cXneeVAT6zwPKIRk9keKKdCZmYiKJ0LVCJStRJag2F+YASjCjBRvhh8Z9dx2dc9LAd7htXxEW8FXD1dO3FC14Th+UclQjpzqjU7VappoxEVYTru6ZCREd6Yyap/BWkPt0KHKpKGcEDPH+SQgXm6A/J8awqqMToxWyJVK6E1GOYHRjAWGBPli5/sGtJ1iFQwC6ZKgd4k3dGEDCVS6rNAdAOCAekYGEmrpbhpLRYWbhRkJJUTvycjTxwflC//OjPt3P9CaQqqLTpRCSIViFSthNZgmB+Ub71oWJB8MT+IRCK4px/J0yei8nRXVF87e3WjljxSJYKHgrZlz+Y0BE8U46z2em0fbqWn1QF3np8klZXLSe9wUnP/nPPZukZ4zvLpbHepwhWpoheBeBfDFamSplroElqDYTnACEaV5osJtzsmT3l9jLju/HVN0lYf1jQIRCI6kpHLt7TIdWe3q1U12wh4nfB8dQeylzbcY0/pKgP/8YGE6mbc3P9caQoMp0Sq/L0g6CTCB8HjnufVUEJrMCwXWJywSvPFNL5yDLHyKvBkgOLHEa8FKhWwaiaXfPOVG6U+FJBfPnNSzbYwc8JmejiZk4F4SoZGTk+ZWOXI3DJ2eAABBSIN6GGINPE4lmSQi2rKA8Ou1vqgCjexHS8d8FzRL2mQvnhSiUQ5mKbgdMxXxY3BYJgYRjAWEJFx8sWEvfd2RfWeKO/jRweleyipoV1IBmWQDGI0yfreruMqLISE9A+nJJHJSV1I1OGT6oZkOuuIEEtgJGNuAJnzFA6sVvZ46Gfi1U6qz3bH5K+/t0cfU9XjL/hjXLi+WV550Vq5assKJRrFgk7O677umEarSJNx/ifTFJiD5eIVqRoMSwlGMBYQ5UyNIBWPHBmQeDLjdObUGn6/nIwm1AKcNAldUuniiQMkvStWN4V1hquNzjqjcqA3LmG/0zk1UYZcGLGYW3B8OW9oYzA+43w9enRQU128ijcJYl1EoKRPuoe65IEDffLCczrkqjNXaMt1V9DJLTqSVhdQUmMXrG8eJRnlXDzNwXJxi1QNhqUEIxgLiFJTI2yinz4xpEQBwSZ6CwCxiIR8WiHyROegrIgElVwQyTir/ZSZEHbhdPOMpXKSJb2ywN9vOcMlGUQvIAtEGpg8Y4ZGySrnlBW8hbPE8188c1IePNQnjTVBuWB90+h5PXt1g6bMaJTG+b9sU4v2LSk1vjIHS4PBUE0wglFF+eLHjg5okyx6PbQ3hGVLW0TXIUROqDybz2mqZGt7vYo817fUjg5CzGZ7oiltvBXwpqVvJDNGF2Clq/MLjjlRioDPq8QCghhP5STk90o8ldUOt7xGCgwRIjQDEgFxpCNuMVoiIY1cPHF8SM//k51D0lwbGqMpMAdLg8FQbTCCUUX5Ymaw//6r/bKptU6Fmu4g0bwxWBB2ZlWwdtUZK+S/d3dKbVHendchIS11QakJerX7J+H5oM+nA1wsmZFs7vQmaITrGeSMgEwdVOzgyO4tOqZUjqCr4LhyvElrIPLMFVQvrAehcIzRxophSKmwnEiVazleTDIu29yq5OLVl6yTc1c7aRGXLJiDpcFgqDYYwagSMFCQK2+rC6vFdPEgwWNX2EmEgvbf5NYhDUQuemNJOdpH86yENtk6OZTSKoZEOi+pdGaUVHgKs+pkwemTibKPQdIKEKYMSAUEwoW2YVfykNcoAtU8nBtIQzzlHH3IB3xAxZ9exzl01Kshny9UoHg0XUI0Q2SsUVQindXzD7koJQnmYGkwGKoNRjCqCJV2srxwXbPc+XiX3P0kYsCkxAi5F8apHkmP2WZxZIJVikWf9Cax6MX0UHzMxhw/jmc2L16PEy7Cx8TpAUO5KmTDWVst3gtvcYhJdjQaQdSDSAVVQhMJOosRMQdLg8FQZTCjrUVoEnSwLzZa309lwkxMHM35c27+qIgSQSggiWG6qBXgRpOKD7vToM5JnRC9qg36tLrktwd61Ra+EpMoc7A0GAzVBiMYVSr6JAxO75GDPTG9Z+bKckR6d+7ukuP9I4WMvp3EhQbHH81FuYiG3+OkRYpfLwdejidzMpzISltdSCuK0GE8fKhffTPc8z9eFYg5WBoMhmqDxUsXmUnQkb647D42WKhEcHwWCLdbTer8ijtJewR8okJbyk7TmZwMFCp33FPBPQLPcpEil3C4y7kLBxydBmJeOuXuWNuoje5uumy9XLyhZVJyYA6WBoOhmmAEY5GZBEE4YumMigk1NWLEYl7ggwwUHjsuqjhmOlUjpCDcZmNlT0fRQreVO9Ui3OcKmhgtZQ369ZyioUDQOTiS1i65CHzHIxelrp1EuN46zw6W5hxqMBjKwQjGIkMk6JdIwD/q8pktEgsa5g4uuQCprEhtgEhDXnqHHVHteEJZty9JMVyJBMvdcdht8Y4RF+e2Tt1bk9JYExhXmFkNrp3VsA8Gg6E6YQRjkYHZISHv/SeHxeP1aMWCYf6RyuRHLSwmOgNjSlkLniNgtFzV7V9CJUk2J421QU255PJ5LUPGv6KcMHMmrp1uxCGaTKvnBhVLRE2mGnkw51CDwTARjGAsMjAAXL+jQ57sisqRgRFHf2GYd5DZ8FTQQC5fEIFqOqRIbwFBUXcMN4JR6L7aVONX3UZ/LC01Ab88f/vpwsyZuHa6EYddR/rlcF9cy1drAj5Z31orF6xrrjjyYM6hBoNhMlgBwiIEA8BrLl0nqxvD2jHVsDAoV27qovisEKGAXLhkAxMuxLlKSApEhdJUTLTohpvAsyLgledta9MOq6WYimtnuYjD/ft7VSxMeq0+5JdEOqOv3fN0t3zpVwf18WSY7j4YDIblA4tgLFLg5rm1o17OWd2oPUwoafT5PBLX8kTHfyGTzetM2zB3wA2VG7qMYq2F2/8FPYXzuhOxiAQ8sra5VjxeGp6l5eRQQm3EEfRetKFJfU364ylNV1A9Um72P5lrJ71s+uMxTVUAN8VCxKF3OKnlq6RnaoNe6Y9nNK0xks5Jfzytug/cXt/34nMmjDyYc6jBYJgMRjAWKSJBv4bQ6VnynLNWyu5jTqO0vMfjGDfl8qP5fsPsg6HXDR7RGDU/rig0r+JNR8Ap4vH65GQspRELSly3rmqQVDon0WRGZ/s0MbtsU+uEZaWRCVw7cXbVpmjRpPzXA4flrtqQii7PW9eoEYf6sF8O9sbV0OtENKldWXEbhRzFk1lJZRJyx67jsm1Vg/z+JevH/f4T7QMw51CDwWB//UvAVrw1EtRoBX1IWiMhyeVzml9n4LASk9mFq6WAW/h9WK9PvL7bDI31w36vpg4oQaVj6vZV9bKuuVYjTuM1MZuKpTzkYtfhfjkZTcnK+pC014eVZOKbsqdzUAWdq5tqVEwKAYBcOOXONFlzcjxEPyANX/rVAdVknNVRPyNbe3MONRiWL0yDUQUg2kBOfG/XkN67ngoTgQHounNWqg/DL585qW6PK+qCatbUE02OigYNs4vcmCqQyddnPaIFLnjE4M9gfmIoOaaJ2dmrGvT5093RSa+DnesaVSD66NEBGRpJSTqb08hF52BSHV4HRtJy3/5eeehwvwzEk5r60PRINqfXBg3VnMhFXnAyRxHCNeXzeFT0Sbrk9oePjLsP5hxqMBgmg0UwFhjT9RHgfXft6ZbeWEqV/E4js6zmvhl5gj6vpLLZspUNhunDHS69dKEtNscYBwzm6C8AOgf6yDSGA9rSnfM2NJLWVAWC3e8/elz2n4xNeB0UXy9EJHqGU0oeiCKQYkFbQTTLqS5yurhCPkmN4A5KbxM8No4PjUg+5xiGcb0g+ISsQjoiIcdnZd8k7d3NOdRgMEwEIxgLiOn6CBS/b2VdSGfEgUL7dibLIb9XO3JiZ13svWCYPjQS4fXoscWmHXLhLHOIRmkwo9QjAydQBvN0hnbuaYmnc1Ib8OpsH7IByTg+mJjwOii9Xkh3xJJp2d8Tk2wup9GITDYrAR9VKl6NRkAYUumsvgetRCQU0P33ikeSORgSmh26v3o0zcF7ISMELrhNJtKcyNbeYDAsb1iKZIFQ6iPAjz8pDe55Tjj7mw8elSeOD44Jl2cyOfnGA0fkYM+w1AZ8Oqhg1xTyeaSlNqDEAqKRLcTv1Zq6pGzSMHWoa2our+kMt+SUIAF24R6vI/j0FB3vUk5HxAO2B9lL5/Ja9dM7nJJEKuNEDnL5stcB1wd+Epx3mtwd7Y9Lc21At8MY3lATlJ1rGiU6ktFIhdPTxKs7QMqCe41qUfmRzMi121fKNVtXSmsk4HyHnEMuIE5Ykrc3hDQCAslomsBFdOx3c6pgtnU06P1ck4vppBQNBsP8wyIYC4SJfATccsE9x4fkqRNR7UdBuHzbqnr5zf4++d6jx9Ur4dGjg6Oz1OODeR0k8gyCqawOflpNYuRiVuD6mZX6mqUr6AfDeK/RC97sKbD6wsAPmXjk8IBcsL5pQj+J7z12XH74eJckM1k51j+i6Q50G5Qrc60QAUFQmkvnJJ1Jqcur/nM7uSq5yWu05PcuWCs71zbK3/z3EyoIbVPtjk+JBtoMCAoVLme211edSNOsyQ2GxQMjGAuE8XwEmLE+cmRA4qQ7vCIdDYTMfWqOdMcjx3S2O5TISC7niPVGck4ZpGM17Xgb4IeRKThFApvfzT443iF/+SqScsc7m3OSJpALPV8ex1xrRX1IDvWPaHpE/TFKSAYkBILx7V1HtUqkvSHsdG/N5uVYf1ye6opKXdinBEFtxwupDa/kxQlceCSZJUpBaalDIogwUNr8f7J5+czdzyhBwT006PdJY21AycX6ltqqE2maNbnBsLhgBGOBECnjI8AMk8FkJJXRQSOZ8aqiHwEf4W6qBVQ0mMnpAILuIse/gokTJYfRhKM8NFIx98hky9uDF8PviigLfUf4zy0qYUBnMOf8MsiTNiFNUQyIZs9wUlMWND6Df0ASIA+kx4ho1OS8UhuCPIxtsIYdOS3lKY9FaErUY0PrKcEmNuSUyd7+0BGNCPAe0iJELqpNpGnW5AbD4oMRjAVCOR8BBhhcHHlOqHplQ1gHFmd5WhpCATncH1dy4fV6x7QJL4bOkhfkWy0PuKSiXD8Sl1QgsHXhGGw5XVKlQAQhis2RgM7Ej/QH5VBvXMmCyCmCwflFwMmnbO9okKdPDGsVSDDiVZJJVQoRDnQhbA+BJukybQevGyAC4tftsNWGsF9TasXA5+Ivbthe9SLNqViTj1f1YjAY5hdGMOYRbhdL94ccHwtCu8y++IEcSWd0sEilMxqdaIkElFww8OBfIJ68zoSJajBIESYv1gRU0t3TMDO4FuAu8mX0FoQQIIGsx2ljgGfAdoShjvcE0YuO+hodHBnQ8cSgRJUKD0glRlgHe2P6eiTo1dLRLSsj2gGVWTzaDdfDAqJRG/IrYcE8awTCoQZajogTH472upBWncTL1Na6Ik33+sSHI1IlRMPdJ4h4XzypfyflYNbkBkP1YUEJxkc/+lH59re/LXv37pWamhq58sor5e/+7u9k69at477nK1/5irzxjW8csywUCkkikZBqxnjitOdtWyl7O6O6vD+OdXNWZ6BYfrP8WV9Mc/UMTIlkRgcNCIdWMJSMbi7BqMD/yTBNTHZsncqSUyfGJXyQjGShxTuDNlqaQ30xJQzoH3BjxRNjf8+wxJIOoYQY4CsBmWTZRRub5fx1TbKvOyZdQwldJ5fz6PVxdkeDHOqLS2NNXv0xqCpJ53Oa8ljbXCMdOjBDVvyLRjxZvE998ZR+b77X2asb1cirGGZNbjBUHxb0r/HnP/+5vO1tb5NLLrlEMpmM/OVf/qW84AUvkCeeeEIikci472toaJCnnnpq9HlpyLTaMJk47eYrNspLg6vVzvnTdz2j62HGhJ00UYrBkbQMjWRUgwHU36KkBTgwYrHwKPUc8RS6pxJ1Gk5lNKJRF/TLquawMg/SHhCFM1bWydaOOokdzmrUijfSKRd31hNDKTnQG1MicsWWVrl4Y7OSkV1HBnR2f0ZbnZzVXq/vI4XCNebzprRPzQXrmjQi8uzJ2LjW3dUonizdJ27RkbQcHRjRUuwL1jePkgyzJjcYqhMLSjDuvPPO06ITK1eulIceekie85znjPs+CEVHR4csBlQiTvvJkyfkf//uZvneI8e1qoDqETQYdR6/5tXrgj45PkCExiM+L2kSM9CqVpS2bNfqDTQ1iYx4PV5pqw+IH+KYyakeArOtqKa7cnKgx4loUG5MVILUB4Zc61rCcrhvRKMW+KJctnmFRkEgDlxDyWxOX9uwolZ6Y0k53D8iTViPr27Q9SAX41l3V6N4crx94vsQvTs5nJQnOgflsk0tmhKCXJg1ucFQfaiqeOLgoNNeuqWlZcL1hoeHZcOGDVqqeeGFF8pHPvIROeecc8qum0wm9eZiaGhIqlGc9vCRfl2PH9R0tlbDwYSFMUciSoE3gT/kkwTivmTG2rAvICqxX9dxrlCO6q4f8Hrkwg3MvEMajaBnzEOH+pzqoZPDuhK6CggJ1wblqIT+V0SCsroprDoNiCaN0dBguNcPdvGUMaNDINWysj4soYBXoxzJdG5C6+5qFE+Ot08cNyIX2i12KKnHge6zZk1uMFQnqoZgQBbe9a53yVVXXSXnnnvuuOuhz/jSl74kO3fuVELyD//wD6rd2LNnj6xdu7aszuODH/ygVJvfRak4jUHCXY/Bo3ljUEPeDEREMx4/OqClhlqRIH5JpDMawcCs0SpG5g4uOXDTUYgqXYGlWylSTDZ87uuUpXpFQpQZhwMajSAVsqdzSK7Y3Krb29cz7Ag0g17JZ0iHsU5eSQXb8Hu9qqNAf0NEgcqR1rqg/O5ZK+TRw4Pi9+aUeJB+ITXCTL61LiRvvXqLkpRKqkIqvT7nUzw50T5BMi7b3Dql7rMGg2GZW4WjxXj88cfltttum3C9K664Ql7/+tfL+eefL1dffbWKRNva2uQLX/hC2fVvueUWJSLu7ciRIzKfiBT5XZSDK05j5lm8HjM3PBFW1IXUyZOpMD+hkAxeoy07To6hIF0lxsJ+amcHrvW3Pi6UpLpVO5ScUo7qKVlP24vRo6Rgvc25DRYGP0gD55uIADfSJHhUQFSIYkBOWDVdEHWqd4bH6XAK+eBxc01QugYSSjzPanesxfG3WN1UKxeub1YS8tO93TroVmLdHanw+mS9+cJk++R2n4VczIc1ucFgWMQE40//9E/lBz/4gfzsZz8rG4WYCIFAQC644AJ59tlny75OhQmi0OLbQvhdMLtkECmGK05D4HfhuuZx16sL+XSQ4YfUCb07Aw8DGQOPZUvmBsUVOdniihDJK9Fze5IAyAb9YEiDhHxeaQg5DcOoBBkcSemgSHorlkir++bR/hGNPKApGIyntbQ0nspJKudYijOw03Kd91NxQjQLgrJxRURNuSpJaczm9Tmf4slq3CeDwTAPBKO5uVk1EqW31tZWWbNmjUYVvvzlL1e0LX4sIBd33HGH/PSnP5VNmzZN+Qtks1nZvXu3rFq1SqoRkAJK/RChIZiLJtLqysk9z11xGjPe8dZDpIchEvbNjtDTmVnimaDeTGVm0Ya5Q4ruqYVmYzhlMvBTgErVD2M+ostEodkcIsTBRFYNrxBN5jweTQGcjCaUZEA6HBfWseeNd5NSoa/MMSIWGSIW9XLxxhbdPg6g46U0IC2VpjQqvT7nM0pQjftkMBimjinHPd///vfL3/7t38oNN9wgl156qS777W9/qxUhpDkOHDggb33rW7Xs9C1vecuE22L9W2+9Vb773e9KfX29dHV16fLGxkb1xQCkQyAuaCnAhz70Ibn88svljDPOkIGBAfn4xz8uhw4dkj/8wz+UagXiM0r93Jp+ctqEnUvFaSj1rz+3Q+5+8oTOQIla0GPCXa9rMCEf+eGT+kOL+BMBnwtmuoTYE6b+nBc4ugyPDujoLIgqRZNZjSL4C2kNdBpoMSgXJaUFuocT2ul2JJWTpGS1FNXrc/rIuBbf7hmkgoiSzFAgJ1ee0SYvOKdDfS2IkBRbzM80pVHp9TmfqMZ9MhgMc0wwfvWrX8nf/M3fyB//8R+PWY4G4sc//rF861vfUgHmZz7zmUkJxuc+9zm9v+aaa8YsJwLyhje8QR8fPnxYbbFd9Pf363YhI0RTLrroIrn33nvl7LPPlmoGP4ibr6kb15K52FQIR09GGazCrz17pVy5xSlLhIC8aMcqeeBgnzTU+FXoF0tlVQTKAIfgs5zbpGHm8JUM/rrM6xBAOpqipTg+MKJk45INzepb0R1NSa2KPJ0/M8gC5AO9zdE04X/HaZNzRVQKUSef4PQN8ciF6xo1SkU10WAsJd/ZdUzJRc9wSnpiKfW4KE6TzMQPYrLrcyFQjftkMBgqhydfmuScBHV1dfLII49oBKEYaCAQXlJCum/fPiUZsRh9FKoLlKkSIUHwOd96jPFwutGRX2eobn1/sdGRu+6h3piG2PFLYGCDZGhVSSF0X9pW3DD7paloIyAWlJNStYEvBZGKP7h8g3znkWPy0MF+tXt3zdKILjh24TlJaJ93R7vBOSt0VNeoCAkXlnHusfZOZ/KyaUVESzQpV37mxLB0DjmGWpQ1E0Vh2+Wul0pt66cycC/Uew0Gw+IaQ6ccwUBv8f3vf1/+7M/+bMxylrn+FRALUh6GyTFVoyM3dPyNB45qgyyISHMkqILBaDIjw1QfGLmYdZQ7pNoLJpuVVCar6Yw1jTVKNLavatCmdQ8c6JODvXEV4gKiHZhoJVKOVNRTqEYhYuHYiZ8yUHMs4XP6ACIzEE/LQ4f65ZKNzXLB+iaRwwO6zf5YSk4M5aaUPpiJLfhCvddgMCw+TJlgvO9971ONBRUfrgbjgQcekB/+8Ify+c9/Xp/fddddKvY0TI7pGB3xY/z/vZB+LXl5onNIraIhJFQr/M/jXTIQT50mGjTMDXIF0ScRikeODsjWjgZ5qmtIHj0yoHoJSALplXDAr0SD8tOw3yPetHN+SIvgzgpZIeWiVUFuZ9RszvE+8SIWzajL58jTGXnOWW1yZnudkoubLluv5bCRCqMBM7EFX6j3Tnj8LSJiMCwdgoH+Ab3DZz/7WfWgcM2v6CuC4RV4z3veM/t7ukQxXaMjqk4wGuJH+0Q06VhH1wS0GdavnumRtCvIMMw5IAoYZjWEPdrF9DM/fVZ9MxB3ksrw+bzig2WIR2LJnMRzp8y6KF9Fo5HP5xyjrZwT1SBqgf6C9UiZhfwe8WXzTmTkYJ86guKFAbnA76ISzMQWfKHeOxEsImIwVDem5Z6D2yY3w8wRKTIVmmpVQDmlPdu4ZFOL3Le/1xEQWhhj3jAYT8rBHpETw6nRPiREJiAMDLI4cmqRDzoZddNwwOAYwLUz62gxqCxRgy2vRz1Q0F1IYTmlqxCPp09EZX1zrV4Xlc7iZ2ILvlDvHQ/V2KDNYDDMAsHA1htRZ3d3tz4uxkRNygzjmwrxw8hsjh9cdLcYK+FnwI/upRtbx60K4Ed043Mi2ssEu3F1BA165T3/9aj0DqekL56e9++0XEH04dhgstB3RKQ2HNAmZ7RbzxYEni6ISLCcSuNkOi/BkNMUDZEnXVXpO+JcA47FuM/rVYLCPddJ50BCLt7QogT0c/fsq2gWPxNb8IV6bzlUY4M2g8EwCwTj/vvvl9e+9rXqPVFagMIfOqI3w9RNhZh18cNIa28aWtExEq+LmoBftqxIyv6e4bIzsnJhYlp8QzQoZ6ykMZdhdlBsQULkiKwIok5IAloK4J6PkaKVeTSUzEok6NWoxEA8o6LObI5qk5y6fAZJs3g8Egk7RlqUsdKH5Kv3VT6Lj8wgWrZQ7y2HamzQZjAYZsHJE/+Liy++WPuG9PX1qS+Fe+O5YepwUx38MO46PCAHe53y3o2tlCY2ah6fcDBkolyYmAGFfD8zNu5p7T044swGg74F+UrLFp4izwyCe4g1iVRUAsy2OqNJOdQX14og/F/QWGA/jlZjJJNVkkHVEH1IjvUnRmfxDNykVLjnOcuZxTPbnw0L7oV6bzmciojMjpupwWCokgjGM888I7fffvtpPhiGmQFyQFOz9a21+kPLjA5fBTdlUhr2nShMfFa7X7trZjNZrUgwzC80LJ/DMCs76nHhIj8OISHAQVQikcrouaz1eyWND7zPqwMmRANg6tUWCcm61lo52DMstSG/plLca2WiWXxptIx1Sj00xrPgXqj3lkNkliMiBoOhSiIYl1122biNxQzTBwPB/p6YzvTa6sM6oIw3YFQSJmY7fr+vUAq5IF9p2YIoAykNLLPUzr2IVXiLO7SWdmClNJXUipdOukF1cqXVe3t9WNY21+hAjDYjlctrw7PHO4dk97FBFfQ+cLBfyeZks3g3WkYnUtIwB3tieo+HxmTCyIV6bymsGZrBsDgwZYr/9re/XctQseresWOHdjMtBg6ehqljqkK4ydZvqwvpjJifX9wmYynrujof0MZlGGtp2/WxugyQK9exVX0vnE6sDJBa0OoVOXt1owp1KU2lsyoCUFxD2T7LMFeLhHzi9Xi1eRpurpQpQ0Qmqz6argX3Qr13LiMiBoOhSgjGK1/5Sr1/05veNLrMDeObyHP6iEwx7DvR+r2xpOw61F+oYBAZpge4Yd4AqfAWLNuLUSq4dR+zHtUPhDH0b4lXCpGMre11uk46l5dkOit7jg9ptGPnmka1ECcV1hLx6aBKBIOoVlNN06Q9SRh8pyuALH0v6bojffGKSMNMPrcY1gzNYFiCBINuqYb5KVedqInVeOsf6BmWe57qlmhibFmkYX4BpSvVduopKmmYRsqkscavNuKQRVZJZvLqdfH40QEJ+H3SXBuULW0RrSxi3HZ0OF7ZsjKi7p4QC8y60GMw0D52bFDWNtfOyyx+Ic2urBmawbDECMaGDRvmZk+WOcqFfTFYItcOuWitC2lnVffHs9z6RDl+trfbaRs+ToMuw8IBwuGnoZlPpNAwV88x5HA4kVZnzlENh5+SVL/qOGhmd7Q/rg3PuA7wywAtkZCmRPZ1x7Tjajqb1bJWCMjrLl8/5wN8NZhdzVZExGAwLBDB+N73vic33HCD6i14PBFe+tKXzta+LTsUh313HenXsDMzWTqmMsO9a0+35uHdH+3i9fmxf+RIvzY7UxdJyEWZGbNh4UlGrd8vzTVe1VVAKagCAY5Bl0dLjelRwnJcQCEhaBk7GsOSzuTGpMUgGc0bg7ouugw8M7gm1rdG5vZ7mNmVwWCYDYLx8pe/XEWdK1eu1MfjwTQYMwc/yDvWjsgDh/q09HDn2kZZWR/WgaPczNANEz94qE/2dg45ZY9FLcCNXFQPOB+QhdqgV0uSeY5IEyEnxJGeJasawuL1eUbJxZnt9SrY5UxSdUGF0ZH++Ji0GPdcK11DCTlvbZOmR+YaZnZlMBhmhWAU24GXWoMbZg9EIe7c3SU/fLxL+mJJaawJqJkS0QtmquPNDLmnrJXyRc392ymqWpD6iI5kJJ7Oycq6oBpocd5w7cwkMnq/oi6kaZKhkbQcIzXSWqvntTualIs3NqveYKLqCVAqugSzqVWYbfvv2YR1WDUYFqkG42tf+5r8/u//voRCzKpOIZVKyW233Savf/3rZ3P/lg3cfDa5dvwL2huYGTKoJFTI55QfhsadGdIevGswsaDfwTAxiCYNjDgRPoblzsGk6i4w2kLIiwdGX5xUBy6epLk82l8GL41tq+q1SmL7qgbZ3BYZt3oClPYmIeXCh5OSmS0hZqRKza6sw6rBUD2Y8l//G9/4Rrn++us1XVKMaDSqrxnBmFk+m9nWsf4R9TtQ34OIt1B+GNNqgnIzw6e7ovL1+w9JxtJTiwaqkck73VazmGx5T6W0uKH39Ptw+MyrdThN627c0TE6Gy9XPUG/mlLR5fGBuNz1xAn9zEs2NmvkazaEmFOtepoPVIPo1GAwzIBguH4XpTh69Kg0NjZOdXOGknw2gw6Ojgw8dNzkWFOCyI8meXkOffHMEHJy+0NHtNqEmWQ2nj7N3MlQfYilcxIskAoYBa3aS0EPE/Q0pE3i6YwUW6WV86IoFV3yt9o1mNQ271w4XUNJ1WfMhhCz2syuTHRqMCxignHBBRfoHy235z//+eL3n3orwk78MYhsGGaWz+a3j0gFzozBSFCPNw2z6KxK6qS/YK9cnFeHnBBSz2fxVAjIUDJddsAyVBdw+lRB7lg38VGQMsnnREWhtQGfdtkdTzRZTnQJIaV8tb4moAPw0b64dnel0y7rzVSIOZdmV1PVUZjo1GBYxATDrR555JFH5IUvfKHU1TkOgyAYDMrGjRtHXT4NU0OkJJ9NHwVsn10DpVw+r1Uh/DiWGijxA8xAFPB6ZTCT0R9mE3nOLWarOkd7kEzyOVSZIPiEYPZEk+OKJsuJLtF3ZHI5GU7kpJu0WjqrZl0QVojojrWN+hkzEWLOhdnVdHQU1Sw6NRiWKyomGB/4wAf0HiKByDMcDs/lfi0rlOazCS8j6mTG1R9LqjiPWeelG1tP+5FlZhvSJll5DU+7wNBJZ8AL9J2WMvKTaCumwu+wci8HhmdSG7UB5090MJHSNNhAPCWZTE46C4NlpDCglxNd0nCNa6JnOCXZXF78pNtCNMDzKHn99bM9cvaqhnGFmJVGEaZjdjXetqeroyj3/YthHVYNhvnHlP/abr755rnZk2WMcvnshhq/bOuok/09Htm8sk5ee+l6uXLLijE/8G5Z6+G+uM5uiXLki0y2jFzMP6YaPBrvHLlN0NLZnA7CPMZI6zM/eUZ8Xq+EAl4VAruz++vOWXma6DIS9Dn9aKhU8eIO6pOAz6c6noCXipW06iXa1Wdj7MD/ZNeQPHigT0lNMpub1WqM8SIUfAfM5Kajo6hG0anBsNwxZYKB3uIf//Ef5Rvf+IYcPnxYy1OL0dfXN5v7t2wwXj77sk2tZfPZxTO99S01Sk6y+YwQxNBBztjFogcNziABaDRJldE9lfM8ks5JU01ALtrQosZd7uz+edtWjiGpAzG0OLnRpmt4cLji0VQmr9vDh+ORYwNy6abW0YEfF9mnT0Qlk83rdrZ21KsXy2xUY0wUoXi6O6qpoPUttVPWUVSb6NRgMDiT3Snhgx/8oHzyk5/UNMng4KC8+93vlle84hXafOmv//qv52Yvlwn40X7rNVvkz647S97+/DP1/o+v3nLaj3mpYn5FfVhz6ohDmZkWw35O5wZzeVzdPjIuIBHt9SElBlQZrW+u0ZTHwd6Yzta5BrgWnuqKys1XbJRzVzeq6+fBvphGPrguuLFVCAXEgX4mqwuzebw23IF/97EB6Y+lVNPTVh+UwZG07D42qETF/RyiCFyDU0XpdUtkgsgK9zzvHU6qQRjEpxwgDAidx9NRuCR99Pv3xPSeyIWVqBoMiyCC8fWvf13+9V//VW688UYlFDfddJNs2bJFdu7cKffff7+84x3vmJs9XSaYKJ/thq+JcDx2dEBWNzmKeXLttUG/Yz/t8WiePpHJK9lQHYZFM2YdxYfUJQP52SAWHpH6kE+JgaupQTsRSzqPMc2CzLuly7h9cs6pDnn06IC8eOcqJalcJ/fv75VP/vgpJResn8rkVKvjNFPzqpAYwWdzbWB04O9oCMvB3rhWnrBNomiuD8vFG4IzqsaopNLjUG9c0zKryqQyXB0FBGS89vDWYdVgWMQEg54kO3bs0MdUkhDFAC9+8Yvlfe973+zvoeG0vPXJ4YT+4DO7pFcFAwQ3yhiZ0dIgLZnJOMTCyMW8gPELMjcT6OnKiwwlsko03H4yMRrehQLa9A4dBYAYMPDvOjIgyUxutJPql351UP7gCqeTKn1NvvfIcU09UI0SKooMYPlP5GJre72SCnfgZ1toNgJh56eh1IelNjT9aozJKj3a6kP6HUlz0NitnI5idWNYvv/ocdl/MjZuhYl1WDUYFmmKZO3atdLZ2amPiVz8+Mc/1scPPPDAafbhhtmBG74mT91Y45e2SFhdPpnF7Trcr6FyZncQDqoLKE0EDFBWsTr3UOfN/Oz9MTKuulEnLWWlxbsXzwyPpipAdCSt0QuiVaRQIqGA6iQO9Dpunlwzfr9X3nDVRk1BHO4fkWgirdcG9zxvCAfk5is3SiKTKwz8fo2GuUZvLiAzmH1R9jqTaoziSo9ygCBBDFojIdVRFO8vz/n+J6JJ2XN8SCM5iD255+/C/c4Gg2ERE4zf+73fk7vvvlsfv/3tb9eoxZlnnqkW4W9605vmYh+XNYrz1pSq7u0alj2dgzKcSKuJ0qHemPz6mR79Ee5oDEkjM0/cQL0enVVP+QQbFgwuGQz7vVIT8Gj0gvPXVhfUdEU2m1diQfShezipJHNlfUhJQTyZ0f41O9c0jtFJPH97u9xywzY5a2W9RiCwoeeeyMVf3LBNX4+MKfH0a0qF64uoAYDUULlCK3miCPi0TKcaw630YBvutksjFBeub5a3PfeM03QU565u0O+K9qScfmMm2hCDwTA3mPI05GMf+9joY4SeGzZskHvvvVdJxkte8pLZ3r9lDzdvXRMgxz4ogyMprQDgB5lQdiJNGD2rYez6gE/SmZzUtwSlvSEoDx/slxGMFuw3d9EAUoGxGqcMkqgnz+PR6EQ8n9XlB3rjKtSEUFJpAhmoCfp18EafUaqTgERcfWabPHykX9MiENUL1zVrhKO0xJPButjoDTEo22+KBNVqvLWufDVGJZ4ZlVZ6kOpgH4q3x/X+qZ88Y06dBsMiwoxdZy6//HK9gQcffFAuvvji2dgvQwH8wI6kM9IfSyu5GEll1JwpHPSLz+fVUkRmvugv+N1d3VSrgwWD0zMnhiUxnDSCscjg9CDxqBcK0Ym2+rCMpNFYZGVja0SaarNypH9EG6KRVljZENZzzgA9nmslZIJS1PGwc12jRsYQim5eEZFz1zRoEz0Gfr/PI801QVnfWiMXb2zRFAmEwiUQU3HerNRevFRHsbdryJw6DYalTjCGh4fF5/NJTc2pP3Tsw0mV/PCHP1SfDMPsIRL0q/U3ynoiF5ALt4yPn1J6WfgK98w2L1rfpLPYnuGkEhAqBjLGMBYN8q67Zy4vSa/zt0RqgFk6kYR3XHumpkI+9sO9OqiSziCtUTyrn4pOopgcYMqF6yfX2oq6kPpRQCioVjrWn5CTQwn5zq5jcqe/a5RAgKk6b06n0oN1zKnTYFhcqDhFf+TIEbniiiu0Yyo3/C/i8bhqLy677DKJRCKaKjHMLvjhXdVYI4OJtKQyGS0vZIZLHl1dGnOnLMHJVQ8XyhmZ+XoFQmLkYqEwk8JIyGMshcAxI7890CcPHepXEe+DB/slncnLzrVNWvFRSi5cLUMlOoli8TBiSbZ52aZmreYg5fbKi9bKKy9aI0+fGJYj/XFpjgTHCCupWLn1/sPj+lpMpItwIxTbOhr0frIy0kr0G9PVhhgMhrlBxXT/ve99ryQSCfn0pz8t3/72t/X+l7/8pZKLffv2aXWJYfbBD++1Z6+UHz/ZJf2xjObjE6msit0YwUiF8ANLlGMontZmVg01WCs7UQ5+i2erOZehckw0XOLMyQp0Sp2oyodzhsbCm2F27pWz2uu1goLBtNS1c6quleO1N2+oCcp5awO63UePDOq1NZ51N+mUk9GUkpK51kWYU6fBsIQJxi9+8QslFugtXv3qV0tHR4e87nWvk3e9611zu4cG7UFy1ZYVcufjnRJLOH0pyIv7vV7xB0R9LwAmSsf641p1gCCvo7FG7xlMrPHZ/MLtCUPqivPk8eRlJJ0vLPNo6ipRQdtbCCIVHKS/mgrpEAZYde28coP27phOq/RKTK8eOzagX2RNc03ZdUjPsC+Fytk510XMZXt4g8GwgATjxIkTsmnTJn28cuVKqa2tlRtuuGEOdslQCmZlb/ndzfL4sUHZf3JYTZNIgfAjjzdBJOzXiAaD1omhpAo+z1vXpKFsPDJ4jdQJFSbmizE3cLuoMgxrx9I8xlFBNcYK+Z3upZwbt3sqdSKVED6iHZAU1uZcF0cGXnLe6lHXzqm6VlbS3hziyufij1EO+GjwSUOJtKZP5kMXYU6dBsPiwZT+8hEPFj8OBk//UTHMDc7qqJfXXLpOPvnjp7VEMS85JRRuigRr5zNWRiSZyctNl62X55zZJkf74/Kf9x/SBlJrm2q0ZHUwnpLuaMqiGbMEhrX6kFe7lKoRVdopJSVKURvyq0kVWpmmWkeoCfnDxApPifG2B9zzw2pOmsuxhC+NDEzXtTJSgWgSV012ZLx12B164NDltbRB2Vx2MDWnToNhiREMfjDOOuus0R8RqkkuuOCCMaRjOXZTraT+f7bwvG3t8ounT+rgwox4MJ5WF0Y+js8fSmS0HwnPEeU9ezKqngfdtHLP5aQuHNDBiVlxxhjGrMBx0fSrIBMBLlQgn6fnh0jfcEpTWXVBv0YzwgEnNdI1lFAiWA7q3Fn0nPdDMIhaJbNZ6Y/l5FBfXAfv7+46JoGLPbKpdWz78kquyUram+9c06SP93QOlV2H9Nvlm1u1fNZ0EQaDYdoE48tf/nKlqy4bTKX+fzbAoHDh+ha5e+8Jiadykigkv3F05IZHxolsXt7/3T2aFsFGmlk1lSS9umrSIhezjFQ6J93ZlBKHdNajkYlkNi8eT04GE3mt+BBJyL6emDp0UqHRVhdS0pfSSNTpKF6GbqMmkNdmZz/eg9A3PWpL/uyJYfnqfYfkys0t8hcv2q7XXKXXZCWiSbcMFfv58dZ57WXrdR3TRRgMhlJ48qU1X0scQ0NDWmZLk7aGhoZpb8ct8TtV/0+TsczoD+9ctYf+yRNd8n++tVvJAxNDZs2IPiERbjdOUii8hl+Gx4t/QmZ0UCJ6QUUCkQ+rYJ0dqKW3Hm+vHme3NLi4PFOt2wsz+faGkJpZ0bX06MCIkpCJTgW2J801AekZdkzV3M9kOZoOyOX56xrlrdecIT/d2z2la7KYkNAKHXJAuWcxOahknfmM5BkMhsUxhporzTQwXomfW77HbI/6fzwDZvNHls/FE4HhiAGLzyWIwceHtEEVnTDzo104QwGvOj26cCsYGsJ+ieTy0hc/NRs2VA5OacArQvGOe/iU2KkJmle1En0jaT3WNKfjPA2NUP1DNMKvj0fSOblxx2o53BeTe57qluHCxrSxWaGdejZLl1Sn/Kd7OD36+WRinGmBR2r8IolMXstXv/TL/VJfE5Sz2iu/JisRTVayjukiDAZDKYxgTAOVlPjNRV8E93PpS5HP5SWgrbuJXDj7QFQDcqF5fK/TTZUUydhBSSSedgSiVJkk07QG90g0mVVHUPNhPQWiPcWRH/f4kQZBE5FPZlWLwIyeKop+7fMR0n4fntHUVbFGyRHlUk5MRQn9PloiIfUtwe+CtulsC/Ekn9s9lBht157O5fQxr7FddgXCIh6f+H05SWVysrcrqn1HpnpNVkIOjEAYDIapwgjGNFBJid9M6/9LQ86rGsJKLhByqlFTYdaMxwIgeqEDTgH5rCh5oAMng5XPbd2OBXUmqwSDCagSEItijKLYlKw4echjtwwVfQs6BI4tcI47rqmOLkbPBezOS1QDEa5zwhxakNfX6StDlQ+W3M4HcN34NdoASG2wCXQbuXymqHW7UzXkEAzns7ke0gUyCRHgdRxAee5Wq5ASo9qDrruzjblIj8xVysVSOQbDMiEYH/3oR9W8a+/evdrb5Morr5S/+7u/k61bt074vm9+85va++TgwYPaxZX3vOhFL5q3/Y7McV+EUqEes9Nk2iEQGGmNIPDMUKmQl7pQQImGG8Vwx0SGpOHUqfSIG5lgTMyq8PB0VmHRi7FHJVfmsfYKQWdRVAVCRCg9mNB01cloQjUVSkayjv8IUQvtkJrPy8BIWteLJ7Ny375eTVepW2eBGLiAAPIce3DSXi547JIgblwbCvQ42vwuJYd640omuD5dE7agz6OGXd/ZdVzTL7OlD5oLofNciafnW5RtMCx3VNyLBKTTadmyZYs8+eSTs/LhP//5z+Vtb3ub3H///XLXXXfp9l/wghdILBYb9z30O7npppvkzW9+s+zatUte/vKX6+3xxx+X+cJc9kUo7Q/RVBPQme7T3VEdvOisSYSEQYY8PiZHzIhT2eyoiZNh/oHUhSuB8tPRCEghapEpaClYh3MEIawJerXEFSJAaguCSLmnez1BHpVYZnJOWqRom7kip1BnXSeysrI+qCWl3dGErkFVEeZqRDIgOvU1ftV8cH1xnc0UpddqcZ+S6X7GXGxzLrdrMBhmiWAEAgHtRzJbuPPOO+UNb3iDnHPOOXLeeefJV77yFTl8+LA89NBD476HHijXX3+99kbZvn27fPjDH5YLL7xQPvvZz8p8wS3xQ5mPeI6wM6Fw7nk+3fr/UvEo4fKDPXGdta5vrnHEmx7R7bfWhTR8ns0xeGRUOEi2xIK9C4dCi5ExjylF5byOGmcVvC2o8KDbbSjg1+uEZUQ+SK1BDNS7JO9ENjDxao2cHikDrqCX5mQer9t/Jq8eKVw36HXcP3IiHJM1IZvutTqVRmfzuc253K7BYJhFggGIOJCSyGRmp79AMSh7AS0tLeOuc99998m11147ZtkLX/hCXV4OyWRSy2qKb7PZF+Hc1Y3axfRgT0zvqf+fbolqqXiUPHpfPKWDB4Zm3FMVwo/i2uZabeONXTMlj0RLLljXqO6LYT+GTrPyNQ1TAJEFog3caoNerTRxtRuAU0LUojbgWLtDACCRuKxSOrxjbaOadp2IJmVoJK0kZEtbRKNhVIcE/WNPKtsl/bJpRa1cvqlFCe45qxu0ZwnCUF6nZBaDtdWNNXrtEMkoFnzOh9B5Ibc5l9s1GAwTY8oigQceeEDuvvtu+fGPfyw7duzQNu3FQFMxHeA0SeO0q666Ss4999xx1+vq6pL2dscAyAXPWT6ezuODH/ygzAVmuy9CqXjUMcnKScDnnCa1nU5m1IL6ko210h/HSyEm121vl7v3dmsontkrod98HoKSVn2AYW5RfLYjQb9GK1bU4dzplcGRjNppE4IngrChtVYjEqRA0Fmgh+AcDyYycsH6Jnn9lRvk6RNR+d6jx2V7e8Nojw90FaTCavwevQbQcvDahpZaOaO9XjU6vA65aKkLSm8sqfocRJ58BvtEOoZriutjpiLkuRA6z5V4ej5E2QaDYRYIRlNTk7zyla+U2QaREXQUv/rVr2Z1u7fccou8+93vHn1OBGPdunWztv3ZLN+LlIhHGZCoEmHgQDTKPc/dRmcQDpY/2RmV/SdjkkIQqhUKGf3RZH3D3MO193Z0MU4vEhw38SHhPEAqDvfFtZoEchHCIasAxxVzROLJjJJEziXkpKkmKAG/c56BOrWms9IXczw0iOaPDCSlazApjxwZkhV1TgqFMD9eG9wCPo9eH4Brw712ZqMJ2VwInedim3O5XYPBMDGm/Bc1F5bhf/qnfyo/+MEPtCX82rVrJ1yXNvF0di0Gz1leDqFQSG+LAaX9IWjNTW8RRHuBWseRc2VDWJcTqUDvQSiddAjpkr7hpMQCThdMBiHD/JMMx0zLp4M7KS6WN4UDOoAxi3b8SBwBJ2mM3mH8MNKqqTmjrU6jE/hZIPwkKkFUoz+elqdPDOkASQXRqLlX4R5x6NH+rPppdA6NyPlrm0avm2DEyYK61w6dXp89GZtxE7JKeplM9TPmYptzuV2DwTAxpk3ZT548KU899ZQ+pqy0ra1tytvgj/vtb3+73HHHHXLPPfeMtoOfCFdccYWmaEinuKACheWLHeX6Q2xcUavh7sP9Ixr+ZjbMIERXTsgFGoytHQ2yMp6WR44MaMdUtzTRML8gM6blpWh/sjnVw/D8oSMDcu7qBjnQG9cb0Qi0EXS5RYdBkAEx5oOH+jXNha5ipFBB8lTXkBzpH5Hj/SOqoSgmF4yT6rVFmXJhm4h9n+mOSUdjSAYTtIhHlO1R/U5HQ0jJxWw0Iaukl8lUP2MutjmX2zUYDLPci4QSUkjB1772NdVNAJ/PJ69//evln/7pn6S2tvJ0wZ/8yZ/IrbfeKt/97nfHeF/gc44vBmC7a9asUS2FW6Z69dVXy8c+9jG58cYb5bbbbpOPfOQj8vDDD0+o3ZjtXiRzidLeDww0zGYJuSMGZDa8oj6oaRHaZLthX9Twe44PyuPHBtTG2jC/IDrBYOUWI0SCuGx6tdrnlhdtlwM9Mfnnn+1Tky3+7LRtu98jXhw+fV5Z3VSj0SmW4wqKNmPLijp56LBDPGiiBlwfDJdkMC4iHIV+bGuvlws3NEvPcEp6hpN6Y02qUBCN8hnP375SrtyyYlYG1Er6lFTDNudyuwbDcsLQFMbQKROMP/qjP5Kf/OQnWhaKIBOgm3jHO94h1113nXzuc5+reFuliu7iNAzlq+Caa66RjRs3aglrsdHWX/3VX40abf393/99xUZbi4FgjOfkSVdL93k0mZbP/vRZreen5M4FXhl03eyNpccMQlaBN7fEArlLXdAnv3vWCllZH9blGGgRwWCQ/9PnnSE/23tSdh8dkEjYJ93RlOxT7wVHW8GfoVPt4VQ6UH58tBC14pzS46Rz0OmGq6dbXTydihWekh4hLba1o07+5vd2SH0ooNdKbcAnB3pj8rO93TpbJ3pCCmc2DabMydNgWD4YmstmZ9/61rfk9ttv14HfBYM7EYdXv/rVUyIYlXAbUieleNWrXqW3pYxy4tHi54d6Y2pVjQlXc21QZ76A3H4x4dCxyMjFnEIFt9685NVAy8nxI7ZFUAkTIP1BWktLJZsgHx7JZEUOer1a6aO9ZfJ51UkMBtNKAFz3TcgF1ana9KzoXHJ9ePJOYzs8NNymd3XBgJIL91ph1o7HA9EtBlO3wyp6BFIGs9H1dy76lIy3zZkSBOupYjDMH6ZMMOLx+GllomDlypX6mmHuwaBx5+NdcqQ/ro21aOUdDjJIOf1HyC27nMKknnMPHeh5kM/L7mMD6p5JCB7GwYB27faVqoEgknF8YERLTEl5IAKNJVkPl096xOQk059TvwsIA8QBnQ3cojgChUu5L5cf1WBAYyA0EJVLNzWPihUn6vpL+uaxY4Pyn/cfkjdetUnWNdeWHairacZvVt8GwxInGIgpP/CBD6gGIxx2QsEjIyPqNbEUhJbVDtfymEFjW0e9PHUiqiWRRwdGNPzdVhfU8lXrLDJ/0OiBlwZlOTlwMuZ0tC10tWUs/sFjXdrz9khfXF/DvyJQqASCQAwksqMVIRAF9DNUo7AMa3G24S0hixrQKJCLbOH+3DUNcv2OVaMEYDyDqb5YUvZ1x+RENKGix86BhOxc23TaQF1NA3rxdc/3wdNitiMxBoNhgQkGVt04Z1JOir03ePTRR5Vs/OhHP5rl3TMUo9yMFNOtXzx1UgcrBho8MDBWUhtxemBYemTOkXerOQqD/WgawyPa1p00yDcePKKpLLQUpD703AVxaOW8nuotwnu0pwwN1YpaxfvVkMsRhpaeUoy8nnPmCnnrc88YM8iWM5iCXGi1USqr+waoqCgdqKtpQJ8oEkPZKSSJNNDGlsgYnZLpKwyGRUYwqNR45pln5Otf/7p2QQU0H3vd6143WvlhmBuUm5EGvF4th6wLR5w24mmneyegXFVLIcnTF4SeljKZfbj9QAgcEXdAQsH5cSIZHh3IB0YymjZprvXrQMmy7kKFh/Muh4xADp3eNg7pgFzQs4RtEZnyeSCSOW2OxuuvuHCN/NHVW2RDS+S0wTRSYjAFCSVyAbmg2kXbufu8oxqe4oG6kgEdgfF8DOCVWH0/fLhf/v5HT2kaaqGjLQaDYQY+GJSivuUtb5nOWw0zQLkZqdqJ5/PSHHJKVSEY5PMxbsoMJSSedrIlOvgRbjeSMSeg7wjeZiE/2gnHmjuXzev5Ijbhdr9d04SPSVa6hhISHUlruIPeMYWKb/W6yOU94vdjxuVENBB94spKBMPJjHiksYYUi0eu3rpSNq2oq8hgqri3DSg2bmOgxifj0aMDcscjx+SxYwPav2Sy3h3zIZiczOobozHs1bk/q73e0icGw2ImGMePH9fS1O7u7lEvDBeUqxrmBpEylsfFduJuRINBiChGaySoos90lpszIzbMPnQIJk1SCOcn8zk10HL/MjJZR3SrVu8+j5zVUaceF44JFhoOJ73FSfLAKPJ5JYMuiDIg4NQGabmcaj5Ii9AannLWvV1DY1ICxcLM89Y1yrGBuEYd8FDhWiBKQnSCihcICKSB1MkzJ4ZVOIwDaPdgUjuyntleJy2R0IL27pjI6puozFNdUT3eOKG6ry9UtMVgMMyAYOBHgRdGMBiU1tbWMTMcHhvBmDuUszx27cRPDI1oneLKhpD+ENP7giGPgSvkc1wezQtj7pAtaGpTTjlJ2XXQT+CD4ff75ORQUlMmeVfIyT1/QwWNhcvbOX+QCNYgCoUANBL0qn04vhk/2XNC/vuxLhWFElnYtqpBeqJJNWFzUwU0N1vV4JNjg44bKJ/U3hDWa4lUiavLQHAKkdnUUqeOoF2DDkk5f13TGJIx3707JrL6Zp/x96D8Fy+QhY62GAyGU5jyL8T73vc+ef/7369NxGghbpg/jGd5jC00M08GNs3tR5OaOmGmy+srIgE51u/MlvlpRmSYKiMWNMwArr3mOGDyDME7NpCU1rqgepUQmXAG/KJNlGyD9/TFnX4zVAkRfYhp2Wpem6JxTfQPJeXkcFIeOtgn33romJKGizY06awdsskAjM7idZetl58+eVIO9A7LzjWN+vfr6jJotkZqB+KxphlTt7BGMli+72RM38+AXWnvjtksb53I6vvZk8MaFdra3lDWuM86pRoMi8wH4zWveY2RiwUCuWRyym75ID+ezCavO7tdf/x/c6BPhZ1taDA0apGXwURWKx08hdJJyAUoLX00TA9uL5CgNz+uRTtjK46fnADOD+SCdFbS46g59YwUyIVb3uo0Tzt1z7n05vJSH/bKqqYa2d5RL48eHdSBliZmIymvDqRDiZQ8fWJYIiG/Rh7cVMHjx4bktZevk6/ee0h7kjBQk1qgXJUoSkONkzLhbxsLbczBiBBwjQ3E0zqQV9K7Yy7KW8e77s9e3aBRlzAimDKwTqkGw8Jhyn91b37zm9Wq+y/+4i/mZo8MFf3Ybr6m7rQZIlEMmqAhdGPGSYtuGmhpjj/n09LV4VRW3T7docHSJtMHQ1o4iK4hL3VBv6RzeFiMZRgcZ8bhoM8nkRAVIhktLW5vCOngfqw/Ptp+nQEccL4YMGlkxyAPQTirA/GiTz0r0NXsWN2oJGK0IkTNvZwIFkSTSpTiyIObKnjJeavHDNREPhAGr2+ulTPb63VbgHtSI3RxxbIcu3FIK5GLiXp3zGV5a7nrHgv9L/xiv3VKNRiWAsGg6diLX/xiufPOO2XHjh0SCIzNe37yk5+czf0zTAB+QLsGndQHs1t+dH0+j6zFldEj8sDBqA5W7fUhOZKhFZZHe2MwUDkz5rwMO6IBwxTREPY5PhdBnzYWa4n4JZHOSyLlmGZ51b47LzX4kRB5wO+C6hDKigM+8Xs8ksg6xALALZiJc2JG0hkVYHJmiHSEAj49pyvqQjob/+3BPtVIuBUh2rtEbcPzEibtknHsyRnkITRoE4pTBds6GkYH6me6o/KVXx2U5khAhcFcU+4gDck4e1WDNNUE5abL1msUYqJUR6V+FTMRXJaz+rZOqQbDEiIYGGq53U9LRZ6GuQezxFt/c1ju39+rSn/SHwwC56yu15ksIXhmsJ0DcR3QYoWohVMySajeq6WsDICG6SGacI7lwIhzf6hnRN08C9kn8RYekI5i4M/TFTedk5Y6Z7B/qntYK7CKUyrM9MXVYeQpU81rB10iT05fE4hNQAnMwIjTbTXgc/6EIY5oNDiv3FMxQmQCLU65VAEDLiLTJ44PSU8sKU91R9VyvjkS0vQIA7MS2KGknLeuSZ5zZtukg3QlfhVzIbgcL30yWbRlIVFNFuwGQ9UQjE984hPypS99abTbqWH+ycWnfvKMPHpkQGe9rfVBNV0iR37//j4VD1JuSDniyWhKM/ouj9Dumx4GQUcAapg+SqkZ1IBYHscf7cso0dDUh1f7kxBDoiDk2EBC0yOeEh0MOgh0GgzGQ4mMY7zl80pLXWi0mR08gxJVjVZBKLI5HUwRf+ISSnUJVSOQDJ/XISblUgXlLOdjiYz2ShlKpGVre52mbqYSAZjMr2IuBZfjpQ2rcdCuJgt2g6GqCEYoFBpt026Y/1kPTc6e7opqJQhmWu5Msb2Bdt4JdTLMu4ZMzJwLxMJt3a7209amZE5AQQhCz2KQuoACrmgIKQE5MZzS89EQ9ko0CeUYuz6vsY1M3jHfImLhelW4EYXLN7dqROKhQ/3SH0tJW70j6HUiF6RuPNIfT6kQlDNP6qCYKJRLZSAIpZqkN5aU3uGk7M2L3LijQ154bkfFg15kAr+K+RBcLoZOqdVkwW4wzDWm/Jf+zne+U/7pn/5JPvOZz8zNHhnGBbOz3ccGddCqrwmcFoZmNotnAtUJGDYRQsdgy62gtITI3MNtTsYxRx9BBOHiDc2adiBK0PXUST0T7nlBzIlTJ6eSzqqcw7pwUNMXpFbWqL8DLpyO3wNE4bWXrR+NCPxsb7eKMCEIEIr6sE8O941oxITPpjdNaaqgXCqDapPmjUHVbEBOSK8gCF3fGpkVvwoTXM6PRsVgWNQE47e//a389Kc/lR/84AdyzjnnnCby/Pa3vz2b+2coAqFfzdMj6Ms5OXvC5ITH0Vhw83u8ah2+rqFGqw00HG9ai3kDA7x7uCEHVJiQ4mAw4THni2oR0hiktdBKEHGAbZASGU7mZdsqp5pjb2dUCcvBnthpmgIGq5uv3ChntNfJwwf7tZyU7YYDfnnZec2qm1hRHyqbKhgvlcE+IgitDfn0M+Pp7JS0AhP5VZjgcuE0KgbDoiEYTU1N8opXvGJu9sYwISJUFhSMl7gxK2RkYpDihzyjfcMdz2pIh2vMlBnPnMEw62CgYOhwhg90EB4lgrRxP9ofd9qxZ0inJFVkS/8Ml/65kSYMrjinG1dE5M2/s1Eaa4NjBnc3h899/0hKO51gkHXNtjY5e5UTIZhoEK80lXEympS7n+ieklZgMQou5wsLqVExGBYFwfjyl788N3timBSUL9KgijA6OgvSIDymasQTz2v1QcCH4M+jgwMRDV238H53yLF4xtxhJJXRcmAGeKIK6CMQ3UIktDt6odkcnhVAyYjn1HJEnniZHOkb0WZjDxzol+t3dIzOaN0c/uHeuBIEfDKSGfqRROWJziF5x/PPnHT2W0kqY3VjWP5nd5ej5ZiiVmAxCS7nE5EF1qgYDPMNu5IXCQhV37WnW0V/rZGQ/sjns3nx+6hSEJ0VM0B5cjnxZr0ymMlITYAZ9ClRpxGLuQcRJsy3KDGlooNIBKCqBJOs0uKdQkXqGPAOr/pfeHVA7xxK6IBObp7IAOSCgR/SQj+ShhqPai5Y7zN3PyPrmmvVmGs8TJbKaK4N6H7xGdPVCpQKLrl+j/TFlzXhMI2KYblhygRj06ZNE/pd7N+/f6b7ZJggf3vGyohEk2n1QUBj4ZabEor3FLwt4rmsDlKQjuX1E77w4G8DT5LWSEB6htNqwlWrzckyhQoRJ2SBHqOU8CmxwAgt5NdtsH5HQ1hORJM6oN+406sRDGbAkAs0De7fImZctFsncnX7w0fkL67fPuEAPlEqY+faRvn2w8dmTStgZZkOTKNiWG6YMsF417veNeZ5Op2WXbt2qbPne9/73tnct2UHZnnk6ff3xPT5phURnY3yg+Pmb+tyOEbmZFNrRAeowZG0DMRTGiof9V7gv0JzLcAkGiKSn1p/LsMUwLGESDBIMwPd2lGvEYD79/WoK+dQgty7V+KpnLp6Mm67aZJiMJMN+NDOOOcc4yx3QD/QE1PNhdqHY7hVMvgH1Q/DI/sqHPzHS2U83R2dNa2AlWWOhWlUDMsJ0ypTLYd//ud/lgcffHA29mlZYjx3zss3tchrL18/mr/FBAkxZyDsV2dICEY0mRlt762+F0Q0yPkGvCoqRKtBqqR0QDNyMbuIpXJ6e/BQn+ztGpKOxrCSPspPseHmH6XD+ezpaREpnDfXiKsvltaBnG6mTbU1OhABn8ermgvSIqVAbwPJgFhWKhQs5x0RmSWtgJVllodpVAzLBbPWEvWGG26Qb33rW7O1uWXpznnXEyd0QMGdk8ZSiDfvevKEvsaPOmFlZsUMVuTcGXSYzTJY8dMEiXBPKFxihKZmBYMmVnJfs5+x2UcxXyBahBMnEQfOG54SrIFlu56KCcid2zmV90EYGIQ7B0Z0QN+8IqLXgIp3qV8t/nz6yiQgBKRXAjMSCrpaAcL2TqXS2M9hOb4ek2kFplKWudzgEjv6wnBv5MKwFDFrBOP222+XlpaW2drcsnXnXNkQlpqAX5tV0XGTMtOnT0Tlrie65LpzVuqPOkMLXTAHR1JOq+88P1giAWbJBZMnwOzZU7AK10ZnCEJn7YwbygErdgggxxyhp3M+8hp1wI+EcywTEAzGmcIquh2IxuPHh2RLW0Qbnv2vi9doZQqCzkQaXQfbzmqUANOu2qBfu6LORCjoagXQBEBwMPki8sJ9qStoZWWZ5ckOERr23coyDYaliSlPcy644ILT1M9dXV1y8uRJ+Zd/+ZfZ3r9l7c7JY2akuCs+dnRQXnr+GnnT72zS8PV/7+7URloMRjoo0QnT45GA37GC5jXAnaPFgGLw75S40DQYsw/nsGPb7RA//j5CAb8k6GarZGDi6AWvYcYlHtw+8cnISDDvU+MsBvSz2hu0FJVqEQSdaC5IizTWBnQgX99SOytCwUq0ApOZcLHMyjINhuWLKf9lv/zlLx/z3Ov1Sltbm1xzzTWybdu22dy35eXOKYj7Tg8vuMvi6cxoq+2/evHZ0loXlH++51md5TJThqCEfZ6C4ZZTIslgNRpJL7g/eT1eCXqYTRPxcPpnQEDcAVFD9LL8UI5ssYwGYhwbBnuOsR6f3MTtXFyxLRhJ61kYo7lge8XraHrLI1o9QhUQ0QL9jFxe+8001QbVldPF87e3q/j39oeO6ODP+SMtQuRiNoWCE2kFKqkMsbJMg2F5Y8oE4wMf+MDc7MkyRSToL4SQT3XGLAbLQG3AP6bVNn0ifvDYcTVzqm3wa4g8m8056ZDCQKgDZNARBQLICK3aEQqiB6BXRTrnDJVKMAr5f1nGBIMUEqWiDO4OEchLTrD39iqBgwigr6iUheUL55DKkHyeKJMTSfJ68qMkj/t8gcwQiSJi5Qs4tt1ntdfrOYmUzPLxufiLG7aXHfxnsxV4ORFoucqQWDItvz3YK3s6B+W1l66XK7essLJMg2GZo2KCMTQ0VNF6DQ0NM9mfZQd+/JnFYSUdHUlLsM47OtODKOhg4/WoN0HxTI98/BVbVqgwlAGFwag3mtSSVTdqQeyjNuBV0SEajfqQX9el6oRECSSjuFX4cob79Yk0oJXgOVEGby4vkZBXB0PElVoOPMVjxdnUCh5t3U6SipJWv+oP0oXKHv7H2yKJPwbkhtLkZEYePToov3vmirKz/PEGfzQ9pN2IjPE5XF/XT6Er6lQrQ3iMWLM/lpT+kbQc6onJDTtWjX6mlWUaDMsT/qn0IJnIYEtnzcz8stb3YipgkOCHGKvnR48M6A8w+XSGJcpVEfGRf6dtdvFMj8d01eyOJmX30UH9cack1Y1UkJtnG8NJzLic2fJgnhJXp603M+qR5RqumABkpPwYYeXyTvoIfwmvR5ojQekeSugxnspRU32MzyvZjON/4b6XypLm2qBWBeUKJINzRYQk4Hf8NLhBOjjH+3uGJx2M3WokBMNEXpxP8yh55fp617VnznhAL60MgVw8cmRALdLx5mgPeLUs94GDfRqlcH0urCzTYFh+qJhg/OxnPxtDJl70ohfJv/3bv8maNWvmat+WDfgB5sff9cHoHU7p8saaoFyxuUVuumx92YGBZe943pnyoR/s0T4lnrRHZ9zNNX5prQvrj/7hvvhodAKiAUekksElIyb0HAuN9vicjqcBLNizOYmmsnqcSClB3pJTOGIq2Mw7qSvnWDs9Y9gCPibbO+plOJWV4/0jSmaoBKJyiIjUqsYaLU3tjaUm9YsgssD1A0nl/CIYRr9DeobIGMt5/a9uPHtGA3txwy5+B4hccJ25rqK5vFP5AoHoK9lv6xBqMCwvVEwwrr766jHPfT6fXH755bJ58+a52K9lB8gCP/44eT57clhJBkLOM9rqNB0yHshp45nREPbLE51RfU4qBLfPwURG+1kwD/cUhIqUv/Jjn0/ndLbMwEewgyFH9YhFcN65fOB6idQGfRrhITXl+Fjk5IZzV2l56Kd+8rTE+0YqohgcV40kZE9V+nDTFAyi3KBXo1MHe+P62ZANx43TiWBQQcSgzfPJrLm5biCnnFOEoaMW4myvzquRsd/s79X11rdGJtzviTQcxZUhfDUiMMWuohAan9fZ/1WNPms/bjAsY1h9WBWBH3FmzE8ej1bct4FBIJnF2TGos2vU+qAvltQf+0jQLx5xWoKnsxlNnaAl0ERWvuDb4PNq5ULpqLmcyAVQkWvO0WCQRoJsMEsnmrGmuUa2rKyTtS210juc1LLTcvbrLhrD2IOjdXHIBecGlse2eRfHPZ7KyrPdMWmqdaIV61vrlICM2ac85ao5OTmc0GtivNQC9vKk1DBpK2dqRdoN0sp6ExGMyapDiitDaIrmusoWm33h5QI5glxZ+3GDYfnCCEYVYTp9GyKFGWU2l1OiQJSCqSWzbjp5agtwn1cimBqlszqAMnt2IxSE0Z0Z9cJ852oDcgiEtYzRjOMcQzwciF5Egn4lcOgxKAOOJx3iVgrIA9GJwcSpbWa1LtXxIHG3ixvn+tZaedGOVXLHw8dO84twxZPd0YQkUln5f789LHuODY1LNrGXp0KlPDyzdv25lSFEQ/g+EFaOF+SCCBqmYJCakWTGfC4MhmWMGfk6TiT6NMxMnc9Aw2yWe567+WzWK25/jbviirqQlqsyoxxW10UnBM/ZSaWzGu7nOXnySMgnrbXB0eGG2XF2GROM8f4AnGoSxJg5aYmE5MJ1zaMVP+GA3yFnfspNRy1G9LF7Q/dQjNF1CusxMkNUXnf5erlqy4rTrLld8STkgvLjda21srqxRgd7SABCTs4/PU+439haq71rBvA/KWPvTXQDTQ8N9GZ6/bmVIZdsaFECQZSCVBKRi/PXNenxqsRS3L2G3e/gXtsGg2FpoOKpxSte8YoxzxOJhPzxH/+xRCJjf7C+/e1vz97eLSNMpW8D5Y3FYWxmkLg6EoZnUIBsQBogH0ycSaEws+U1PDFcnwxNgSzz3/RyaaDiQ6LmY578mIqfhw/3y7H+ERV91vgdEgKp4/ii30AP0z2cHLX8drUXelrzjhaG9dkss35Q7BdB2/WnTwzpecSXA8EmAzWARD5zIiof/sEeJZacWyJYiEG3ddTJAwf7VRRKimJU5JlwLMURDGPQNdPrDz0FJONPnlsn561v0sgKJbXsA2ZhXHeT+VxYC3eDYemjYoLR2Ng45vkf/MEfzMX+LFsUq/MnapH9ZNeQ/Pypk6eFsSEObhQjhb9CNqd24UEfosWAE6XI5XXAyhREhzZhnByUlh7tH5E7n+iUF527Wge/11y6XvafjCk54Jz5EIRqi3WHXEAm/B6v+J1qYyeSVPC3cJBX/Qat17/0ywPy0MEBHVhdv4jHjg7ojJ7UVl04KG31Qdl3Ylg9JjjXA7GUkqArt7TKme0NumxP55B+7ua2iA7ukAoXLEdMSjXSeBUklV5/xXoKtvW7Z7bpdeiSBUpqJ/O5sBbuBsPyQMUE48tf/vLc7skyR6SCvg3MmB880Fe2/fUF65pk1+EB7V2xobVWfRYGRhB1OuYLRC2oIEllCevn1Ochm56ap8NyBERgcCQjn//Zs3KoZ0SJwPaOBj3eDNyqefGINBUqPjiuJ6MJJR9ElBCDcu44/lpQknN0GAhIqb5gxl88sL71mi1y2wOHZe+JIS1x7Y+l5FBvTEWnK+tD+n7A3f6euKxsqNFIgdv+nKqjizc0y+PHhtReHgdYTNrwUZlo0I7MoG/IVHwurIW7wbB8YOqrKkG5vg2ukycpEX68t7bXayqkXBibckHy9ax//rpGaQgHdVAaGEmPiu98uFJSVeLJy/GBxGgUw0jG5Dg2kNAyUIjAzVdu0EF197EB6WgIK6mgJw9pCYD4kf4hiZSTmgCcLpdcuHoMtDGYbfE+d2C9drvIb/b3qcNnpMYvg2k0FY6AsyuaVD0GkQ2vVmykZc/xQblqS6t+PtcF4tPXX7lRXn7B2nEH+3JlqO7153wnTMaIfp36TpP1DanU52KqqRiDwbB4YQSjSlDat4G+FPzI9gyn1J66NuDTwYiZ35qSPDpEZF93TEsG0QAQpj7SH9fnbjdVKkja6pjpBnQAdJttITo0gjE5IHkcT0pUf/JEt2ztqJcfP9GlXW6JEnHDfwRr7nUtNSqm/P6jnVopwiCNRiFdIBv8z/EnjVUX8o0OrGgrsHon3bWhpVavBdIWlMliyuW2fMdYgwgI7491Z3WD56xpUHMuFVyms9oUrxwm0j5sWzX+d6LaZTb6hkwnFWMwGBYnjGBUEVx1/q33H5afPdWtA0Uk5NfBanVjWMV75OZX1AVlXcspcS1Ri754SkIBn+ownupMqPCTGXLA41HTLSoijvYnJBRISbpg564VDV7H+8EwMehYe3RgRNa21KjI86muqA7opEEggBCQzkRGy1mfv32jDs70iRlJeZQ0uMbdblM59cFIZlXfgS8FA+uBnpQMJdKyoTWiuouTsaTEo6RXnAJXupgUR0CIRBCNOjmc1IoTUg4TlYVOpH1A2wPG+07P27ZyVnQR7Ju1cDcYlgfsr7jKQO65pS6oM0bC0cWOjjzvHEjI48eHCmFvp8gScy5IA906IRuYZmnb9oI1OKtBMLS/RjI7WpqZd62xl6Gp1lSBl0XPcFIrJCB5DLoXrm/W1zjmnANsvruGkko+rtnWpufymVxU+mJU7rCmE8Go8XudrqLpnBpfkQpgYPV6vGpORcQAfce5qxvVQpzziTtmphABIXrBdaGddnN5aaE8OZlR3cXLzl9TNo0xkfYBj5QfPXFCd++F57Tra+W+03O3rpxxBMNauBsMywdGMKoMpEWoUOBHuHSGB6E4d02DijkfOzao6zBQEa0YSeV05gmpiIQDOlOmeRZh/dIIBYMXFQ3ubLj4fsznGfFQnPIMyetMP5fzqCeFOzgy63fBAIyG4OKNzVIb8mnKqrk2ovqLvlhaowIMrNpErWDqRdTpRDSppag0VHNn9zh8ul1XKTkdTubFV/gMyIV7vjj/EEj0HjvXNZYlARNpH9yGeO5jvk+57zQbughr4W4wLB/MyGjLMPs4laMuz/1WNdXoj/ym1jo1VTrY42gvIBuYaEETmHXyD7dIZsQMZsU/14TVwwGvhuldYjGeDsNT5kJZbj/9SsC0l4jIyaGUEjkiGGpgNZKSg70xvfGYSh2XRDB4YgdOmqs5EpLVTTX6OnoMbti2Z7IZ7T1DefFVZ7SqpTdEIJcjKpVXksJ7IBRwAM4pehwGYJcoUKnS0RhW7Qf7Vc7AaqLr6lTju/zo42JAAPhOs6WLcFOBRGjca5h7IhdWomowLB0saATjF7/4hXz84x+Xhx56SDo7O+WOO+6Ql7/85eOuf88998hzn/vc05bz3o6ODlkKiFSQo8Zg6U2/s1EHGLcSgG6qH7/zKS1dpKKEaDopEdd6oZhABHw08/IqMSltcOYC8qEDa6HKxB12lnP3Va26kbySgM5BJ9KELoOW6hwXNDCUkkIkOHfP396uniUnoylpjgQ0wpAvEALOS7ZQqREpkIjv7DquaRhIAamwTW0RXY6WBrLh8ThRC857aySoOg+2c9GGZk2jUU7bE03K3U90nybiJLIx3nXFPgBUHu7judZFWAt3g2HpY0EJRiwWk/POO0/e9KY3neYUOhGeeuopaWg4pZJfuXKlLBVUmqOmw2rpj/GrLl4r9+3vlVhy/MSGY2mdl+EkluLj74frNk15pMZFqEZZxuRCiVVepEZJRFju3cdxzmgpcCToV/kl/UIO9MRU1wLhwwIc3cKv9vWoVTciXdeyHUJAOgKvCyqA4smcnNlep+QEES86G3QPbgoEl04IBFoLNBeQC0pSiVxoaefJmN7/cHenLi8VcTKQN9VAjBKnXVdUsmiTtbzzuBhzqYuwFu4Gw9LGghKMG264QW9TBYSiqalJliKmm6MmDP7MiZic2VYnT3ZFNdRNu/BUCYngXcPJsQZbbtt24C530ypaxkrXVa9I0COScApQhC7wbgnsUkGAlvZF5bsBH9ECrw6ydWG/pjsoGa0PIdDM6ndHY8GxyeZx8/RqyIeIxl17uuWMa+rltZevV7Jx/4E+JSIr6tHHeCSWzGoUgrcgomzL5UYHfiqEGMzR2bTUBvU4484K+XjqxLAaqHGm2CdsxSEXrMfJg1yMZ2DFtaRW42Wuq7PanbQEr0F+IByO3XxG27+bLsJgMCwLDcb5558vq1atkuuuu05+/etfT7huMpmUoaGhMbdqx3Ry1K6I74z2OmmtC+rAV27sLxeFYDaM4WcpXL8G3sNgym1U8OgUMCwpFKeL+M7pLNU3OfGjZSn0GqkJ+qU7mpKGcEDqa/wqqtUy4KxjYra6Kawk5LFjA3pOOFc37lytkQ/0FKybzOS1MRgRCx5DNCAGxfbeCHqJZEEUiEztWAOhdlIjaDogDa2RkC7jurh+R4eaqk1kYMU1ROfWctfVu649U37vgjVKfO7f3yf3PHVS72Op7KyVqBoMhuWFRVVFAqn4/Oc/LxdffLESh3/7t3+Ta665Rn7zm9/IhRdeWPY9H/3oR+WDH/ygLDZMNUcdTaalL57UPDsixLNWRuToQEIHw4ngdvckrD8RX1BNR9EKy0GLocQqJ+LP5STk92sX2kjQq54jaBXaG0JKLBDSkuqAqHFM+vCvSJ0SRa6oDzneFnUhXZf3QhAO98c1hQFZIUJRKrB0TacQbmIh7l4LkBWOfxyflMJ18XR3tCIDqxUl23Lfv79nWH66t1vJyxWbcQb1aFRsKJHR5djPG8kwGAxLlmBs3bpVby6uvPJK2bdvn/zjP/6j/Md//EfZ99xyyy3y7ne/e/Q5EYx169bJYkClOWoMlL7z8DHN5RPOZ1AgVx918xkTwYNLZa5sOWoxiViq5ape18Z7AraUz4lEAj4dcHuHU3osstm8tmunBXoxIAocOSJIkYIokntIAZGN5jBurEl56FC/Dvj4UqDPCPi9Ek9mEEGUFVdOdi1MxcCqdFvFHhlntZ9Kr4COfN76gxgMhuWTIinGpZdeKs8+++y4r4dCIRWEFt+WElx3xsN9I1rBwCwam3FC3+VKDk+DVjNM/XOXSvRCyUKZL+MpulHBoSkSj8jGFRFZ21SjbdKPD4xIPJkefY/2jhlJazRj55qmUVGkK9xF69A7nFDXTfrGoHWg+yqdVlVD040pV3J0W6yPN0Yl4sriz+C9xZhsW1PpD2IwGAzLhmA88sgjmjpZjiideZ69ukFD3K4wsxzcQdPFRB4YS4VETAdOtc2pEl18KOprAiqGPHdNk6yiyVk2L0f6ExodQMhJRAKycFZHvQp13dm+K9yll8xvD/SrsRYmWnwIjctIX3De0GAQLRgaSen9VEyn3M/gPbwXx1H0I9xPtq3JvFdm2wfDYDAsDyxoimR4eHhM9OHAgQNKGFpaWmT9+vWa3jh27Jh87Wtf09c/9alPyaZNm+Scc86RRCKhGoyf/vSn8uMf/1iWI0pnni2RkJy/rkkeONAnJ8poL5RcuONLHnGn4+hZXGnidvlk1ouQsxRuB9alCL6bZ7zv6XXcO5tqgjpIX7FlhTx0qE+O9Y8osSD9QRnoZZtb5bWXrT9Nr8DzG3Z0aPQCHQaCTL/Xq+XG+JrQJZeoBh4YjTUBOW9tkxKCqegeXHGw28yM/SItgohzom1FrD+IwWCYAyzoL8aDDz44xjjL1UrcfPPN8pWvfEUNtA4fPjz6eiqVkve85z1KOmpra2Xnzp3yk5/8pKz51nLAuJ0pGSg9p0yyCO0zKOLwyWtEN/BWoIEag2jfcFLi6dzorB0BIuWT+DTQL6MY40VGFgLTtTIPQaAKqQMqQxI0GimK7LAMksUxctMngyNp9Rh5snNIzl/fJJtW1Mk1W9vkieODcu3ZHUoUNpM+KeNP4gKx5obWGmmrC4+KPd0+MxtaarTx2aG+mFy7vV1+7/w1Wr0yHwZW1h/EYDDMBTz50oTtEgciz8bGRhkcHFz0egxmu/9419MabmfmSf6eGfJgPCXdwykVIlJGqWMLhlkejzY+S2ecZY21QRUfbu2o05LF4wMJSWad1xApptJZGSkMvkupL0kk4BHoFMcGIpUosTN1x/ViI7KGQlt17VLr98rVZ62Qtnqn9PPPrjurIjFu6flyQQv4JzqHNIrB9olC7VzbpCmP+arcKO20Wuq9Ml0Lb9J45tZpMCzPMdRinosYxTNPOmJSRcKg0N4Q1sqQ3lhagj6Phu/xX2DWzIycCSp+DpguXbapVY2UVjXWyO6jg3KwN64ageFERmfxIT/ExKvbWyqiDHQSfDeODU3iirUpbofZ0q9KNEcjQ3irZHPyi6dPqjfFlWesqHhmXy5ScKBnWH6jfhMZFZJC7Gi/fv+BXjVbm6/eHNNNr0xGWtztFduWzydxMhgMCwcjGEvE9RPXxxPRhPa1oAeJkzN3JJwMZNyy2ZxqCNa21mq6BFMo1iDiQXRj+6p6tYpWkpHIyLrmsAynsjIUT5+WKlnM4Ks4LeBEAgGPbFkRkWP9CRVy0qMFwWaqpMJXnU2L3E6Hklk52BeTN3ZsqmhG7s7kMdfCs+LpE1Gt9rl/f6+KOzkfNZhn1QWV4DnlrjJpeehsRghmsz9IaUSk2LZ8PomTwWBYOBjBWORwZ57/ef8hrRYADJJrW2rl/LqQtv/uJvROY7NMVl5wbrtsbquTr//msPbGIOKBYRPpApqfJQtEglRKTyxNDk3JRrUFL4pTNtNJ3xCQ4X1NNX5t/ga5QrDZE0udRi5cuG3t3b4k9aGA9guh38hEg3DpTJ40CH1LHjuKk2ZKS4vDQZ+01Ia0bBUwMDMgP3MiOm6b9LmIEMxGf5Di6qbxbMvNV8NgWPowgrFkSMYm7cBJ7pxySFc8uLG1VmfIdFiFTDxve7t8+6FjmgJhBk1PDbWv1t4ajv7CEYES2Ujr9ujJkZ3nCkUddwo25eWAdgKXTVAsaC0HNoW7KSQqX7SMtw+nMurxAClb3xyWwURaEunTt1Xc1p70CoMoTp6uP8R4g/Jp2oZAWAkBTdHQXJCOoTU7bdshHsPJgGoe6DPCeaPapFx5aDVHCKbiq2HNzgyGpQsjGEsE65prVRjIAOOSC8A9z7uGqARokF8906MtwVfWBeVgb6zQsMxpMsKA7Xo/uBUlDHqlXVfnyia8OBJR3PekHIrNwVjXJUalhMTdV9JG5ZBM56Wxxqkq2XcyptU0ap1esnp+jEYjr1EH+onQUdUlAKXpCrwymMkj4uxoCGsZKp9xvD+uxM7NOlHlA/VRR9W8Y5CGaRppEvaFbS2mCMG41U0ltuXmq2EwzB2qQWBtBGOZdGGl9PJQ34jm/LU0M52VaDI7puzUGYhPkQsiG+WMuOYyXVJMXib6nFyZpmzlMJmJGPf0DaHHyEgmV9G2GNBpOsaxc/0hyqUr8LfYfWxASdpTJ6JqM85x5ztqe/TCB0GWqFxhe+o/ks1Jz3BCvB6vbCnjvlntEYKI+WoYDAuKahFYL3onT8PkXVjxuwCdgyOaWnC6embHDLLlSMR8yzrLdXqdbXhKG70Vqkro21KpgVhj2K8DNykM7LcRhZKuIHpECSqRA+4fPNSrlTndQ0mnWsVNQRU+0/3j0+cZYhg5JRcsGIhndBv/68J1p806qt15cya25QaDYWZw06elv0c8ZzmvzxdsCrHEUFoJgM/F9x89Lsf5UW+r07w/oXUGcz9pkUJqAV2BCj3dEs3CuMAgWEHLtAVDabqG7+SaiZWLRBRrMJj8o83A+6Ng9zHp5wS8Iq31IbX4puIDU6y79jjpijPaIlrOit6FqhD2gWMKAdH3+r1KLHweohRj90mf6n9U/WQlFPDK71+yTm3HSxGp8gjBZNG0qVigGwyGylFt6VMjGEsQxZUAmDvtPxnTH3kusEjQL0f646Mix1F9Rclo7D6rZnJRTruhRKGCSMQpx/RTLp4Ttqsv3NM9lQGSqpNXnL9W+uNJ+fW+k7rCr54d0UEUUkE0ZCjhiGSp0oHIhANoY4iUoPlwtugrEB2X4PCYtvBrmmtk+6oG/cEo/SFYDM6bc+GrYTAYJka1pU+NYCxxFAvuuMAaavzq5MlAhAEX947QU8YdaLV5WtFr6AVcMyr3Gl6o/iTufhWjqN3KGBB9YD/1+xZWyBYYVCW7v6ElrMw/7xHZ1z0s//c7uzX9gf+IU4HjkYZwQDoaQxqhgGwQGWLjEDnIAn4jrseF7pPfIz6vV3y5vLi0g0qW2rhP/t9vDstvD/TJeeua1GY8UiTUWgwRgtn01TAYDItPYG0EY4kjUhROZ7Y7NOI4dEI2tPoCcWEuP1rGWY4oFGsxeC9VKQgXWZfBcqE9uHIT+FUURzggFkoyspMTilKi5SlELza11an49b59PVqZo2vlSWs46wwlM5IbyGsVSDjg1eMUwuci4JNoMjOavnH3D9KRy+dOfV6hj8y2VQ16/71Hjsu3Hjqqsw1Eo8VCrcUQIZgNXw2DwVAZIlWWPjWCscRRHE5vrw8pc22oDaihFoMtjb6wp0a4yOwXDUHIj+tnXlKF8k+NUjAD10hFXtKZnDTUBJRcYBhVrUDQ6hGPU3Za8MAodjx3OsciuDz9vcURG5ekrG2uUZL2xPFe6Y+ntbyUtdKSF584luyZbF5LV/u9UvhD53h65dLNLbK3M6pN0/C9gJw5OhHngUYxvB5prvGLz+fV4/xMd0yyhXUhKo01/tN8LiqNEFRDyZrBYJhbVFv61AjGEkdxOP3ZQskSIfQTg0mJpbI6s6bksjee0hk+5lt08RzJZ8WbJZVS2FDBzIrBDk8Jep/gBMr1G/CM38NjoeDuB9EWKjOomoEQBPw+JRsMuJtWRNRz4mBPfNKKGa2+CfrlcF9cnuyKSiyRGR2g+d4eb17JWr5wDHoxKQs41uMOzfHIRRua5bGjg0oyiAQ11Tp6DsgaWhmIHutjena8P6GzDfrEsI8YbrEdhFulQq3JIgTVUrJmMBjmFtWWPjWCsYgw3VmoG07/xgNH5FBvXOLJrGoxIAk+n0fdLFlGHxN0BFyMLvF12po7M/iaoNdJreQcJpxK51SPEQ46gziz7WoDA3nA5xev0OvDpy3SIUhEXwghck/FRrleK8VkiWNwsHdY9p+Ma3kqREt1EwVypdbjxadCoz8eiYT8zh84xlrZnJZnQuKIXDCT4HhS2UN8pTbgUyIB4SAKgpsnr0M6hpP0R8lNWahVzY6fBoNh9lFN6VMjGIsEM52Fss7/98JtOgt+onNQS1aZ3ZMSQaS459igzuzdviSNNQEn3J/KOO3dvaIaAHQIVKEw4Pq8WZ2FM8DiQlkt0Qu3XJV9dLql5jUq01QT0O/KscOJkwE7FPBp5MA1FRsXHpFdhweVRGhqpNCV1uVUvJfX/KSXco4B14aWGjkxnFIfkluu3yaJbE6JIWWrn7n7WblvX58SDYgHBGAwnlEx5+qmGtnbNVSIfoi+7vd6lRxNRahVbSVrBoNheQmsjWAsAszWLJRB9tWXrJUv/zojJ6JJvdgw3YokfTqIYfbE4Ot4RHg0YoEuIJqgu2deLa+pgiAq0FIbkGMDTgt49AGlhkoLDXfwT1ImUih3wQuE4zbidUy1MCFDV8HgTSRmvI70aFIymbyW7Gq7e9eIrOB6qp9XiGKo2NODmZlXYumcEjBIDLqKbW11uu6o0U1hQxAJyANt4DmOeGiwT5wTltM3BltyCOFUhFrVVrJmMBiWl8DaCEaVp0DAZLNQXg+c5zQum4yplgufMYitaa7V9ElbXVB6Y06DLcSLpAPcgXc4lZPcUEIHunNWNUosmZXu4aRGPKoqfFGwPNf7DAJKkXgyo2SJ4+ZwIRqLZbXpGIM1UQHcPEuBEJbIznhS1mI3VI44hCvsIyril/aGsGxordX268X9Sjj2bPOFZ7frPhBJYf+ePjGkHWwP9cVUdEuVCoZebItoFed+KkKtaitZMxgMywtGMKo8BXLeusYJZ6Hk8/9793F57OiA6ikqSZ2UC5/tOT4oH/nhkzrgOeWsKRlKnj6s0gwskU7J/Qf7NM2gg2/RTH6uLlK8J6jSUKvtXF7YtfE6nhYjHPBIOusQBFrPqxcFLpvilYawVwWXVHpotUzR+wv2FU6305Jtjvp/lPAqHjeGA7JzbaNsWuGmoDJasRMpRBuKowper1caagqG4XUh1Vw8cXxIuqNJ53UPHhkIOyOqmSGSNBWhVrWVrBkMhuUF+2Wp8hTIns5BDZGTly9FXyypTbR431nt9bK2ubbi1Elp+IyZMc/7achF+WlpC9USTwnSJcysCeXn86RKZvGAlJhlBQuiSMgMgkyPzyeJMv3jS3ehJuCRoA+BZaaQuvCoVoRyT9ISCDvxqoil0DiQlvCMdlNlYKdUtJyFeEh9RJx+IlpZU1hOiSme/1TXrGtxlpZGGyaKKrREQnLZ5lZ5snNIXn3JOqkP+eWRwwOyvyemfWWmKtSqtpI1g8GwvGAEY4ExmRDv0aMD/3975wEeV32l/aPpRRpJlizJTe4VgynGxhBaMGsIKWRDCSQbE8r3pTwJZUMeyCawgSRkNwthE5wQElhgIaEkkIQS53MoIWBTbIxxw+AqW7Ykq/eZ0cx8z3vu3PFoPOpjzYzm/e2jlabd+58Z4v+557znPbqZo6sA3QXxG8Suug4NPiBeLPY4YiWAoQr4EOCs3lwjTR1BaejwG3oCzMzAlbwVfxubqDm/Azs5WjIteT2awoeHg4okk4xLHy7xGQJs+ibt/rDYbL3PgqeaRmHx8QCCCIhTjQ/MHOqGsegRqfA59LOu70C7ruHEifcENFhSZ80k64r+P/XViDsZdClo+cVn394dlI37m8TntGsXyYzxXs1cYCP3DpBVQDstvksMrEPAd/rM0mELtTKtZY0QklswwEgzAwnxZpR65XBbQHbXt8uiyUWx50AbgWAAu924fGdMADgYAV+81qO+zS8vvG8MQ5ugFtdhqYf1tek0GVeK0D0/blPtDEKQqMUHwzgqBZ9HTEAZSZ7NUEfOhMqN0SpqBATxa4UgVf/PLH9EjGchmEAQ0dMTlsb2gD4fgVJ8J0xfGRkHshcw10qwPMW5kPWpauqSUCgswRa/imSRddjX2CmV4zxy0pRiOf+4siFlFUYq1MqkljVCSG7BACPNDCTE8zhtUprv0Cvh+KtQTOyEfsC0j04MTvoS8MVrPSAK/bCmVZq7esTnsmnwEIw6R4LEPTZZdsJILkSSDh4bDokOmvFHV51E3NwUsx0VmzCyB4ah1pEgAe9PMywYJgadCLwprEaQVN/ul+bOgGYzsOYe7eAwzqHmmknWhuNYrHnqZBqPwwrXTsMfBIGH0VEDMzKbZp2gwYCRFn4jm/DxeWWjmlXIlJY1QkhuwQAjzXgHIcRDEPHZkyfJ+/tbYlehKAegZj+nPF83pcEI+BK1Hj3tETncHtA2U5wfV+fY3lEeGQzJRJUQYgLoE4ZLsmFr4YTgxRjLboxEx4aOEtJRwUC0bAIxZR4CJxVtYiR9SNq6wyr+RHtqngXdJmbWw3gP2n4bzZSY90NEG46+r/jBb/AGQYmmCW6bUSEp1oZx7vgOkOVp7AxKhz+oGaMdNW2yctk0WbNt9LIKmdCyRgjJLRhgpJnBCvHOmFmqP+ZVKFwfMQhr66FWfd5AqfZErQfYsK9Jr/rRUwGdBTYhn8sq/kCPdMCZegCShRAILEzZQ6owsxrRfdsAtt/IJqiD6BGNCIIkZC30fqwlOkHVtDrH6/F8ZDJsVgg2rdKB0adm8BLubaClNuh5EItaVGAKfYVZDsL7xPRUdNPgfPiM0eGCl8LLAtmUhvagZlKQIUHGCS2pCF4+tWiifPWcmcwqEELGLAww0sxQhXjxV6EXHF8hh1q7B/W6RK0HvBnQ9mjoEoyx4dgEMW0Vm+VgwSavsz3ioo1w9Io/VZ0l8V4T6qAZ/dw04xMJ65RSfzCibZ4Y6AbDKrSeIquB94R1QAB72tRi6QyFtZW0pSsgWw626TwWDVzi1o4MjhmwuBxWPVeFz6UaDYgwEXQg64MgD2vCffjckRGJ+WGEw/o94LwIQNDNApdUmHu1dvdopwi+S2YVCCFjlVRfbJIRCPHQOYANCC2J+I0MRH+tpkN53RGthxFT6pV0vNgC+ouQYVudIOdQkl1Yozzgc9p0Wqgx0st4HoILaEbw+FCwJjtv9DyJ4JzaWmq1iN2KIACZm7BMHufRTECp1yFlBS4pL3RrxubEKUXy+dOmqvkVfg40GZ0jWK/+WKIaDy3zGGsp8dq09RfBxbIZJbJ4arG+Frdxfohr1Wkzapal7z86GA0xGoIOBGBYJwIWtPQiKEIAg+yRllIIIWSMwgxGhjBcId5gX5eo9cDGCCMnbNAINHB1jiDDbPdMJC9qmQ3LcPPRfIdNz4NSRDRGUU2E2bFhBh1mOQH0lRsxSyCJmB0l8XoP/NaR8pFINHNgCDQ7A2HZVdcuk4rdMn+iT4MpY35HRFq6jDkfTqtFNlc3G2PrnTbNKmj3SLQ0osePij19boceGzbdEGviZ2Jzt1Q3d4rbYdMARYe92YwgB509RnklIuGoZTi8NtBhgowHhsthuNqEIpfUteI4tOgmhIxdmMHIIEwh3rwKn/4eit/BQK8ztR5I22PzR1trsdcR0y2Y7pQILpDJMFGBY1THEMt4xMSQouUJ9G0gA6BCyKgDZps/FLPrPrLO5I6fMX8N6TvASIw9YOsNe22UIkxhptFNYtHppGjRxceA91nT6tcppidPKZbxBS6pbTVmquD5CLI0uIlrTcVaHDZjsimON3O8V8tK2jY83qPr8Tpset6W6Nh2fG4op2jXCjIq0dZZjF5HGQbnwY/HaVdTNGQ8aNFNCBnLMMDIMa0HtBnQbMDCGh0oGMQFIytslmhVhWZAdQPYsKObrTF8y8hu6EYeDQqwySN7gXIBsiPQccSmjcZ1WZjHMaUd8f/RmY/hfHDe7LXmPsompgbDnIFit1nFZbdpqyraTlGeQfCx7VCrfFjbHtOjYNjbKVOL9XwoF0GHgmNAI2E3A6RoeQMmXeUFLin2OvXvnnBYNSuY07JoSpGcN69Myn2GgBY6D3xmOA8CmeklHu0KQUCBLAvOB6tvlG9QqnHbrbToJoSMefgvXA6RaLrk7wmpPgFX+xAeogMEWQqHzZjmmWc3MhpI6+PqHi2auPKfVOSU5s4eLS8gqEAZAi2cXYGIWG2YtmqMQoeRVZ4FJZhwLxdMUxhqzjDBBowgBSUEf08glklAsGKKLSMJvhNq6x0I6W+vBj8WGe+DzTo8QoJ6jLpWvwYUly2eou8dw8Re3VEnwR5DkIn4xCIRfQ8uh03nusAkC9mXyhKP/Pifj5dXPjictJUUDqnr9zXK/a/uFLfdJoUeuz6OjAkyRG/sbJCDLZ36WcBgqyTODA0BHi26CSFjHQYYOUYyzUZbV1Bu+9MW+bC2TXpQbshDh4ZNSrxOCYfDsqehQ3UKmt3IEyn2umRScZ68X92qV+mGyRXmhFhlvNcp9R0BKbBZJWAL63TWg83dGsxgpojaiUeMrAE0E2gQtUa7PLAxm1UYU5NhVlnMQAT3oXxR6LZr9wYyBCjTOG1o8RUtA2FT7wxgKmmnZhuw8a/ZViP3vbxTDrd1GwFMpHd2BEZZ+D/YhUNrgXVDq9JfK+niqePklKkl2mIMrxKzVRi/F0wskMPtcPO0qh+Gx2nVrNFIzLSSTdtlWyshJFNhgJGDJJouwYDr+MmFcqC5S7o7AlGxJjbuoDS0B7TrAUEE2jKR5YCfg7/HIuPznfqYulX2hNQTAhkP0xUTI1AhhkSGoCMgEgzEizKO5CSw4Td29sT0GUUem3RH2z5N74r4YABZCqwBrzN0FnY1HUOg9M7eJqkodOq68Zyn1x+QZzdUaxAAoSW6QDA7pdPfZQhSo+PYkcFBNgTrnT+hQIMgvH+LxehKMTd2c6aI6jf6aTE2Syll+U7VcmBC6kjMtPqattvf1FxCCEkneRHD1zhnaG1tlcLCQmlpaRGfzye5Try7JwSN6/c0SkNnQPd/bOCqxcjD2HMjuIC+YYLPqc6UhmU2Wj0NB1D4UMCpsrEzoFfbyFKgkwO25gN5YsT7ZphaCBwXAYwpwjSBIZlZN4G1+ezx+TpjBNNl8T5wHMhW0UZ61uzxsqW6RbMzhieF4RWObhqAjIh5LgRKXoctOqY9IhcurNDpph8caut3Y4/f/BFoIZCAFsMspYw063D0tF2brt/MhvTXykwIIenaQ5nByGGSTXL1Omzyzt5GacGsk+6emEsl2lPt8H7wONR6GyUUZAigfYDmAVf/rc1BzV6YIkwELK3dRqahL2vx2FriHsDzbdEBasgwRCIQlhqtsAh0zFZQlFcgmMT6IVSFENMwygpLvtOqa9pY1aTeIMhIoBXVGg5LvsMqPWEETGEVsOp+rzoQo/QCMWeJzynba1rl5R2HZYLPJbPL83VeDDZ2ZEOQtTA39oFahUfSijrQtN2hTM0lhJDRhF0kOUyySa4QIy6ZPk4NqnBVj3Q/NJouh0UNplACANA5ICOAKaGlXrtmM7C5osukyOvQzU+FoXGRw0CpMnRymJiumBCCmk6h2qnhsRs+FRYMMcvTMg2AEFVtusMRFZGW+dy6XgQEKFHg/RkdIsZxDSMww70UU1BxzPyoTgID5uZX+KSnJ6LBBgIXvB+cHxs7Nnps+NjYzfc33Bbj4XxHfU3NJYSQTIIZjBymr0mu0DOcNKVIN1cYRRW57Xq1j4yEPyqsRHsqNnhoLs6aU25c1TcbuoYij1038pqWqKByEBj7MbIJRkkEXSXQTJgmW+jwwCaPqgyO7XVZpNjj1OfgCTDRwmbf0BHQAAMdKdiAkZVAFwsyLQhA8Fy1RrcawQIswxFIIPjA2mGqBd8Lzcx0BaXE69AMDUy0UEJJtrEfS7Osgabt9jU1lxBC0g0DjDHKYDoO+pvkis202OOQ7pZumVuRL1sPtsnOw+2x6aXIMKBcAa3B4mnFcqC5U6aVeGVPfYdqLnDVX+CySmuXSC9tZx8YHSIIWowbsOgOhkJS3dyt55lQ6IxamRulGpRs0AYLUy2UcFDKsUiedrs47cYAMokGKkeyFyi/GNkQw3HUCGZwzhOnFMvssgIt/eD5GOeO94CSiSFqhW+GxMoqCHLQJYLsgvnZJvvMwUg0GP19R31NzSWEkEyA/yqNQQbbcdDfJFcAMSEyA8gKqB13tFSBrAXKBeZrcBWNcyDDceq0Yr3ax0YMO2wIEZN6gCegG77aaxtGX/gbMz6wkWNo2YEmuI/apTTfLl6nVfY1dMnuhs6YrXc4AkWG4UiK6aVdmDjrtOtt+HQg3sAyXOrBEZaO6CwWeFjku/JkYqE7lqGQ2GssuoHjedsPtelxsdF3Ru9DOeh3b1XJ1upWmTeh4CgxKDI5WABKNMPt/BjstF16ahBCMg0GGGOMozsOkgsTBzPJFWZT584tk0fW7tVSBPQZqlVw2WRGqVcmF7tl5+EOeX9/i97G6HjoE7BR4/x7Gjp72Yv3h3asRK/s0Yba0O7XjRxgT/U4LJpFqGtDycYh4bAxBdXsOEEuAjkLBDm4v7qlW6YUW9Qro9BtU4dPlz1Pj21OPEUA4HXYJA8C1e4eqYgbe69W6m67fFjXrloNiwQ0c4LABXoTLK3AatPjvbm7QZ59r1o/P7x/fOYHmztlzbZaPRaCLogw+/oeUjltlxBCMgUGGGOI4XQcJLp7JjpW4u/SfIdUFJZoiyeu7M0yAsCGh9f988mTYqPjK3xOdc2El8VgMYy1Irphw/AKJQVkMlDiwDkhtcBvBBkoOeA2Bp5heNm4fIdhWw4BZySiplkIWA61dGl5ZWqJV4MVfA5YL46L5yLTAhOsj88rk5c/qDtqA7fZjPdoiDwNAzGUZiAwzYf7p90iNa3GbBfoVWDOpS20IlLT4tcWX0RHmIWCks9wOz8G+o7YokoIyUQYYIwhhtJxEC9M7K/N8oOaVvGHwjKpOF835r5EhqUFztgm+P6BZjnQ1KVTRmFcFew2BIjGXNPk3SSYgwLdBIIMrAHtpyjPoCwBAy+0muJvBAbISmhHhxMTTY0AyudyqA8GBouhKwSmYGfOHi8rjquQ+RN80hXskTVb6/TzQSYBG/QJk49s0FNLPEdt4HgdfDiQvUGZA7NLMMwMHShGu67o4DR8KvFiUAAvkIJoyQUBnykSHa5AdLjTdgkhJF0wwBhDjKTjINHd08Q7BJEhXo9N8LWPDsuDr++W6SX5Oo/jb9sPx56vcU90Oiou8BEg2K0i3//0cToI7B876+VPm6plfrlPg4g39zTqpj6xyKUbPWzBq5u6NHuBgxkizTy1KZ9od2vwAYEmBKffPG+2Zi9MZo0v6HODTmqh7g9qoDS9xKsup++GwlLsdmjmAoECgiEYfcFttMhr1wAE2hMz62G3Gv/zQuuref9A30N/9PUdEUJIJsIAYwzh7ScYQBofosvuYFiaOwNS1dChw8oGuhI2RYabq5ulvCAszbhCj4gUe+xaKoEOYMEEn1Q3d8r71c16Ho/NJh67TbMGyEL4nFZp9YcMN84EQy2ctrLEK59YOEEcKI9Y8uT1j+o1kEB7LM6DThH8RocI2mTxGggsEdzgfrNjxMjaRFSEOrHYrWtJfJ/9bdCJGzhei4wJ2m+19dVuNUbOR7NDEL7aMRQuGmjBghxlHACBKB43/zbvT9b5wRkjhJCxSFoDjNdee01+8pOfyIYNG+TQoUPy7LPPysUXX9zva1599VW56aabZOvWrTJlyhT57ne/K1ddddWorTmT6avjACl6iD/3NXTqZnzHn7ep6AEDuvDTX2cDNjp0SPx5U7W8sbPe8KXQ8erQYtilrMApr7bWyeNv7dMrddPoCifAc73O6Obfj9GWPxCWH6/+QM6YXSrbD7XK/qZO7VyByFLHyQeiugvt/DBeA9tyZBJgBqato1aLZhCQaXBYLNou+7XHN+pZB/M+E8HntXpzjexv7NLSS6ELAVNYgxUYeIH27h4p90H4GlFBK4Su5sRUlFBq0deal6fzT8z7Ezs/OGOEEDJWSauTZ0dHhyxatEhWrVo1qOfv2bNHLrroIjn33HPlvffekxtuuEGuvfZa+etf/3rM15oNmB0H6CyAkBDCw7q2bnl7T4Psru8Qpx2tlxBQhqTT3yMNHX7dgBGQoPMEm10iuO/ZjdVqG44rdAQPuLjGZojMAjpH4I+B7IIt+jjaSxFs+EPG0C9kKtDBkfgfm9tuOIFi3S9uOSTff26bvLWnUeZVFKiwFOUOZA/gN4HyCAIUZC4QWGDdmkGwGmPbUTbZ39SlZRUERMjUoL11sO8z8T3jeXhv8yrytXsGU1ZR9tAgpqlTtRcIfqCnsFsxpt2upRy8b3htYOAagiCsG6JXnfraHdTvxez82F3frufButDRAtEnfg92nYQQksmkNYNx4YUX6s9guf/++2X69Oly99136+358+fL66+/Lj/96U9lxYoVx3Cl2UN8xwE2qG2HWlVgOL3Eo+JH86rbzGygw+GUyiJtN03sbEA2AlfxGBQGZ8wpRS41usJmiWfg/iBcMPMskmexqDCz3R/WjVYnqkbTHfCdgO6gJ2SUVxAfoCMFwk5kQHBVj/Njc55U6NSOCwgpP6ptk00HWrTk4bCgLOOQArdD1wITrn2NXfreEAQcCEWk3G6VZTOKZU99l5ZSBvs+B5zP4rSrKLOpA4EOdBcRKXJbVdiJ3MyymSUyt+KID4YpEj1/QXnMB2MvAry4zg+c/5ev7uKMEULImCWrNBjr1q2T5cuX97oPgQUyGX3h9/v1J34S3FjHFCyu39cov/r7LinxOg2/hj2Nku8yOhkA/CywwSHjkKyzAX9vrm5RHQI2PrRnRisesSFmOFR3MCKFHsN/AsEAsiTYeLEZ+1wQRMLW267BCTZ+BAnINCAroFbfUSDWrGszOi5gVz6pOCRbqlulwGnVIAbzUdB2CpA9qCzOU9fQM2aXybtVTWqWBXDfUN7nQJ04yDiYBmI4NrIiV585XYo8jl6aCXiGDNbJc39j57A6fgghJFvIqmFnNTU1Ul5e3us+3EbQ0NWVfNjTXXfdpaNlzR/oNnIBbGJoi8RGjPkauOpGVsHQRxhAt4DsAjQMyDDAYyK+swF/dwSN2/GvQ8bBGIIG3wnDftswnzLu02mq0U3TcOc01oP7TJMrHA7H0bkmOEj0uTgGWjzRCYLShumNgRfh73jcTsM0C8EKfvA8nayqWhAMMIsM6n0m78SxHbXp4/OcVOxWwy0EF4mDzZINPOtrCFpf54m9twHWSQghmU5WZTCGw6233qqiUBMEI7kSZHjjukrU+tqKzoaI0eIZ7YIwOx+SzbTA3167cdt8HZwsGzuC0twV6DXITDUaGjQYtt3mRTlGrWPzR7eFuVHrlFTNfuRp1sJ4npHNaOkM6oh1rAm3kT3pDBgizsTnIouCe8p8xtAzOGrCiwIBCpw5UWoo9hqD1+BnoRt2N8a5G4PYknVsjNbsj9E6DyGEpIus+teroqJCamsN+2UT3Pb5fOJ2J/d+cDqd+pPrXSWzxntVw3C4rVscqh0wuiCQ3cCYcmgTEmda4G/ct6ehQwWKIbtFatv8KqBENiQR0xUcVQ+dWGoxSib5LkxCtWrpBD/BnpCELBbdPNHVgowDujOg51Bb7q6gPobOC2z+0GZgJDyei41XsxSBoIpVMYxsw95GLTlA61BR4BR/j13LIZiUipIGAhJkBNbvaZQ2f0iFlE++s181IzPGe2XRlCJtp8U5J+g01WM/+4MzRgghY52sCjCWLVsmL774Yq/71qxZo/eT/udYIIDARFJc9aMDAiEAtAnocMBjyWZa4O8Ljq+QDyC23N8sDe0BLT0gaEAWAHtiQtWiVysqMiYICvCcOnhZeB3iRoDQGRBrJCwep0WDAnSAYPw7SijoEMHrOmMlBAg6IxoY1Ld3S2tXj3asICuB8sLxk3yybnejdnfA2RNBBwITdJZ04nmhiDhsearJqG8PaKlE54x47Nql8uf3DsofNhzQ8oXZyooulGM9+4MzRgghY520Bhjt7e2yc+fOXm2oaD8dN26cVFZWanmjurpaHn30UX38K1/5itx3333y7W9/W66++mp5+eWX5amnnpIXXnghje8is0mcY4HOB3NUOcSf+Ku/mRa474bls1Usiq4GaDm0tGLNE0wNwbwNbPhoyTSDC+yJCBRQFplc5DbKIThfvlMDDmQVmiG67O7RH4COkiKvQ7MYsARH9ILjYqNFl8Wuwx1S1xpQ50yUT2BNDudPvIfqpm61JUfgABFoU0dA9RrYmnG+vLyIBiAINCYWejUDg+4aBEkIOJB5QYkFQ9HMYWSYT5LYFZLq2R+cMUIIGcukNcBYv369elqYmFqJlStXysMPP6zmW1VVVbHH0aKKYOLGG2+U//7v/5bJkyfLb37zG7aoDkCiDTZKA9h8B+Pkab7+yx+brp4a0FR8VIe2VZsKMZFFwDYIfYM6a3rtYsmzyKLJhdLuD8rlS6aq02f8+VCGwCYOzwt4bKB8svtwh2Y48DzVXkD8ifHw4YjMKfdp4IAuDpREEHTgih9dLVgDMio+j12NsFDuUA+OvWFxFSIbkqeZC2hNSr0OFb3aLCG1AEe7K0ywIP5EJgVhEIIZZBR21LTJ/z1rhg5wO5YOm5wxQggZq6Q1wDjnnHNiSv9kIMhI9pqNG+HQSEZzjkWB0y4lXpdqJFDuwP6HTRuBgGorrBhshk4LI0OCdD+8LhZOLEx6XtiDI+B4ZUedlHqdUtXYFRWSWjQIAMhCwNYcZR0dbua2yYKJhb2GrpniVUwlCYVD0WNYNcOCsgiyLbit/htRS3FkLNAui2wOtA8QkJrzQuJbRBFcjEaLKGeMEELGIlnVppor4KodokVMMsVv3E7nuY12VHha2HU8eZHLpuULt93o9MDjAfN20JgPgmzDrLL8mEgx2XG90U4KjOmAALW9O9gr4ERwAF0GSiozy/J10Bi0GfEYegqHTlvFcxFwaNBhwdpCWoJBxgMlGwQfEs224C8NPKLniZ8XwhZRQgjJMZFnLpDO2RTJzo2OC9ONEuUIDQ6iXhaGyDOiLaGqdZA8sdlQgrCo3sIUKZrHxe+mroCaZuE9/fMpE2OdFDPHezWLgA4RiE+RFYGWAkHA5CKPXHLyFFmzrfaorgv8xmth343YBGUZBBp4fU2LX7tDUKKBhgMdNPaouBMlF0t0/WY3jTkvhC2ihBAycvgvaAZhzsDAJos0Pcau44rdFB5CEHisgoxk5z7Y3KmbOoCT5QmTi3RGCNw1cXWP0gQ2aqvFGHDmcVplSrFbTq4sjokUzeNWNXTqe1F9RE9Yh5q9tadBLjllsmYYMNxsdplXtQjQTGCOCLQiH5tdKlcurdRjwUojWdcFXot2UwQKb+5ujE0xRQyCkguCn2mlHh1aVtXUpVNaoeVAZgTvAToSBDrq0THEFlFOQiWEkOQwwMgQks3AGK3ZFMnOjY0WGQB0iWCnxiwPzAiZMs6rm+j71S0yrcQrFx5foetDWygyD9BqmJuseVwEF9jMkTmALsLnhhdGWDUOv99wQL7x8dkqqjS7XIrcDplY5Jbz5pfJ6TNLY++3r66LiYUu9ecwfS1UGxIOa6CAUk1VY6cGGtA5lPVAk2GUdvAY9BwIbHxum3p9DKVFlJNQCSGkbxhgZAjJZmCM1myKZOfG5gu/igK34TKJ4AP3wS4b3RvYSKF7mFTk6XM9OC42YfW0CIZ04zaPDyEnPDgwkfXtvQ3y7X+aN6iOjWQdMc9tOigHW7plTnlBr88OY9UxLA1lnotOmKDB2aRCd+w89W1+ea+qWSfNJg4jGyhASGe2iRBCsgEGGBnCkdkUydPyKAfgiv1YCA+TnRsdFRhPbrca/4mYXRbJ1tNXmQC3obnAa+OHj5mgywTeFLuG0LGReC50mqDFNTEww8aPgAyttTg+gqMTJhX1zi5UiGZIhlriSGe2iRBCsgUGGBmCN42zKZKd2+zEMPUM8V0W8etBBuKlbXVJywQ4LgSd0FygLJIIjo0gA6fA6wfa5JOVJFDagPgUJRUTbPzv7W/WuSkqCJWIdrggU7L1UItcsaRSzoiWXobTIprKbBM1HISQsQoDjAxhtGdTxG9spnZh68HW2LnxGzbdda3dqlPABm52WZjrgfbhL5trVF+RrEywctk0fU8QdEJzYfpbmMeAKBN6CGQZfvdWlXpX9KVj6KskgY0enS0Qn0IfguNic0dwgZIMsi7oToXPBoSjLV1B2VvfKZ9YWKE26MMpY6Qq25SpGg4GPYSQVMAAI0MYzdkUSdtR3XYNJHBuXO0fbO7Wtk50aCDg8DptcrjdEFJiPfC6gJcEgou+ygR/216rrajoFkEJBJoLZCyQuTAtwnF86DomFrnE67T3DlBOnypuu03a/EH547vVOgtlTnnvc50wqVAONXfLloOtGvBAjFrT0qXrBI3tAc2gWPKCGiAhyEGg8c6+Rl3TcLQS3hRkmzJVw5GpQQ8hJPtggJFBjMZsir42NgQNCDBgSLWxqlldNjHfo3KcRy27Ieh8Y2e9CinRhnrC5EJ55t3qo8oEyCC0RTMTmw40yydPmCDfPG+2/Oylj7ScAs0FggzM/ahvD6qL5pJp49QBND5AwRrufH672ntDx7GrrkPKCpwyvsAh43SGigEEpwsn+dR2/IUtNWqmBf8MtM1aWvO0VRXBEbpTAMSmyGqgTbWh3T8srcRIs02ZquHI1KCHEJKdMMDIMI7lbIqBNrYPa9ukubNHSgsc4g+GdeS5Cj0tFin3OfX5KKX8nzNnyM769qPKBPCZQCCA7pNgKKRDxR56fa98cVml/OSSRfL0+iotw2BgmhsZAEdY5k8oUFOueJo6g1o2QaAyYVapVPjcWtaAbTi0FRhyFh9kwIsDYk+8P3xM5pTXsER0mur4AquuBevDtFVkULZWt2p3ybtVTUPuzBlptimdHUN9kalBDyEke2GAkYEcq9kUA21sKCFgA4f2Am6YEFAiw4AN2fSMQMcGSgvehDIBNm9DWGn4YcDcSqRH9jS061UxppMWehza9orXYEBZKBIWl733f4KmhgKTTj0Oi84PgbgUQQQyKijJvH+gRU6qLFLrcgAPDbiInjNnvL6PjfubNeOS77TI3oYu3TQRcOCc2EiRvcB7xXNQ9tle0zrkz3sk2aZ0dgz1RSYGPYSQ7IYBRg4x0MaGoAItpbaoqDPmWYF2Uq9FSwoQVMKQal6FL1YmwFRSZC4QXODqHWBTx6RSaCTeO9CiJRJsUsjGwDkT4lF4T2zY1yhLppfEXodABkGE027RoWQILowx7BY1zEKAAN0GNBw4fpnPoVmDCUUuDWCwZjiOItjBiHb4hEHYieMgQIIGAyPe8RsD5g+3hWT93kZZPm/o+pbhZpvS2TGUTUEPISS74bCzHMIbt7ElA5s7NnBsJsmuYrEpw7ETQYhZJkBgAFfP2rZutQqHvgHBhTvOfrvT36P6CxhfYUPFRl9R6JKp4zw64wS1f3PImfpvhMI67RTHRnCBgAPCTGQ10DRrzUN7Ka66O9UaHK+cW+6LrRmvQxkFwlGsM9gT0eEp8OLAGvD+cL4Of0gDk8Otfg0SRpJtQsCF34MJUkwNBwKjxGnCpoYjflDcaOAd4L8NzmchhAwVBhg5xEAbW0tXQLML4bAkfRy6DJRPUAKJLxNML/VKVzCkGza0DhgcZugkHBq0tPmNYWjQXpggGJhVnq/dK/saOuVQS5fqPTABFUEMSiMYYgaQHcFyEJDg/DgKxJzoFEHpxGu3qKg0Hpz71GnjdNAZzl3gtKlgFJkRTEo1giCrzC0v0KBmNK/M44MzaBuQEcJ7x2/cTmXH0GDJxKCHEJLd8HIkhxhInFha4NKyBDZfc6qpqcFAScKGeR7Fbp03YmIEGdO1VRTHwuh0ZB3MbEIgmo1A1gKZD7Pkgcch1DxlarGs39ckjR1BDSzwOEazAxzLtCxXXYfVIt4AZo+4ZV5FgV5RRyJheXsv7L7bZdHkoqMyLwhUJhe7tRUWx0cggVklCIIQwOD9ISjyjvKV+Wh0DGVqmzQhJDdggJFj9LexLZ9frtNT39zdoFfU6OYwNBkWHXuO32hRTbyKnVLsUd0D9BjxwQXo8Ac1WMFGvqW6WexWq4zzOGRmmVcDDGQeMDTt/AXleo4ZpV7d8B9Zt1c3OjyOjhRkIXAcj9Mux00sjGk2sE6YbKEVNdnGaF6Zw9cDPhzIophBDsBrUmlglikdQ2Mh6CGEZDcMMHKQ/jY2cyQ6BJ248kfmAT4YyCSgnTTZVWxfV7+Hmrtkw75mfRylDGQk0MmhLaj+oEwr8ciOmnYdmf7yB7VqqmWaOpkb3fvVzRpwoCMFok48bgYXAIFEab5TPnvyJHl/f0vSjRGgkwUTV821IXDKhCvzY9UxNFaCHkJI9pIXSSy4jnFaW1ulsLBQWlpaxOfzpXs5GUm8myP0CtisUX8f6Cq2lwtkMCR7Gzo0oIDOYW9DpzEbxGXTLpWDzV3SHghJocsup04rlolFnpjhFzZ9BBjwXDjQ1KleGmh3RUcKjLVM8J+umYH4ytkz9b6+Nsbhvqe+rLP7OxchhIxVhrKHMsAgKZ1HYb4OGznmi6CTAy6d5nRTdISgSwS3oe04b165TC01xJz4TxGtpTsPt2sZ5OZ/mqu6j0SHyURtwGAdJofznvqyVUdXCnw0aKdNCMklWoewh7JEQoaVuu9rszZfp2JKK2aY2OO6Ooo1gKhu6dJsBmaeoLUVIHj4qLZN6tr8mv3AiPVwOCyXL6lMmTYg8T3hPcDXo6+A48OaNln1yk5p6PCrsHR6iVe7XdZsr9XH8X6QZaGdNiGEHA0DjBwjFZMy+xuIhQ0Xx69p6Vabbog8zTkjyF6g5RSbdGNnUEsl2w+1qSU5Jq5C9xGJZjKQ3fh/22rlUKtfblg+O+XagIGGen1Y26qzUPC422GR+vaAFLvt0tVjCE7RN4vBapOLPbTTJoSQJDDAyCFSMSmzv4FYsNzGQDKUDuCLsb+pU/Y0dOgwMzSWmFbicOWEeBSlDnhvVH3UoRNP0VKKrhHMFUENAoHGhr2NsurlnXLj+XN0Mx9sBqK/QGqgoV6wNf/zpoP6OWFeCTpUEPAg84JBahC7wo+jsd2v4le0wNJOmxBCesMAI0dIxaTM+IFYs8Z7NfOArATaPsd57PLaR/VqfnXGrFK1GkcJ5J29TfL3Dw9rWyj8MOCmiQ4Or8OmwQRsxg80dWkAUmAzWlwRbMAQS6e4dgU1k1HTigmlRXLJ4kkyp9zXb7AE+suwDDTw7eG1e/W9IohAcAELddzG+uGlEWzpUkMy6DAwywQBBqCddmZl2ggh6YUBRg6QqkmZpngTgQPaT2GABR8KWHcjM4GAwLTAQIZiyjivbtJv7GyQ6qYuzQYgeEC76XETHbKnvkPq2/yasbBKnnpUhMJh7NsS7AlLV9T5UzUZhzvU8fOtPQ1yySmT5YOatj6zKABrSRZIXbCwYsCBb5j4io4VzDyBY2g4HNIAB+2yeF8mgZ6w6kaKPXb19KCdduZk2ggh6Yf/EuYAqZqUiavJ+na//mA2CLIVHrtRPmiMZjJs1jx17zQpyXfJCVMK5c1dDXL85CIZn++MmXFhqun6vU0q7OyJ6i4Q8KC1FZs1ghdzBHuR26ZZA3SOrHpll8wq86rpV3ywhGzIX7fVCoQcK9TbwnJUIPXS9lrpCvbIxD6GeiGA0HW4bOrbAc+Obh3xHtHjo2yDQAPPQTcJ1orgB39jbeky7RorpCLTRgjJDDiLJAc4MikzeTyJ1D68IQZK7XvsVg1CDjZ1aZkDxlW4jc0AZQS0nyIwsGOEaRyFLoeaaUHUaeoVAK76oc8wLMnztFMD80KQvUBWw5yJAhdQDE/DsLVxXrvqNqDzSAQlG2QuQpGI/p0skEIQgOP2NdQLr8f5IAOBTwbW3Nod1MADy7Zajf/JILeS74Q+wyiLYOBbuk27xlqmzRyMh9+4jfuRacPzCCGZDwOMHMCbokmZMM5qaA9ohkJFmnarZiwg6IQ+AVmIZP/0Y09GtgJ6jUTbFZ/bJkUehzhscNcM6mh1lB4QZGDOCHb1Ardd9Rp4bU9YNPjAsdDyGo+ZOcmTSK8sSnwghXLOhEJ3n0O9INqcVuqV1u4eLX3MLi9Q8Ske6woak15LCxyaskctxxjwFtKBb7y6Hr1MGyEk82GJJAcw53EgzYxSQfw/3uakzIFS+7hqfHn7YQ0WjK4K6C0igmSFy2bRIEUw4VRnh0R6HR/tnKfNKNGNONm8EFiGY0VoU0VZxB80AhW7BSPWbVJe4NSyRGOHXzd+jG3H1ezG/c06A8W0DkeJRs8pebG/4zE6WKyyfEGZ/GVLTdK1oEPk0nll8vIHdfo4Pq8Sr0MzGtCPIAsE/wtzUiwCHbwWA9+mlhiGYWSkmbbk/x1SREtIdsEAIwdIxaRMXDXCvwLZBpQQ2rvRihqWYASBRp4UemzSFQiLPxTRDAQChfjjX7m0Uo+TaJY1sdClpRYMTMPzmjr8qsnAsbCe0gKn5iQgsoTuAStEmysCkObOgLa+mqPh851WQ4QZQfnCMPBKFkidPrNUKgpd/Rp3TS3x6OPQBOD9QXOC+/CYGdBAS4J1LZpSpOsnI8Mbl2lDWSQRimgJyS74v9QcYaRumLhqhGYSIk2IPBGkIFMBvQPKDkgYoMtjUrFLywh76zuSHj/eLAslluc2HZSDLd1yUmWRPo6sAISVb+5u1CCmpdMv7RZDI4LgAiZXHqtVdRQIJlAmQWvpggkFmimZU26cZ+fhjn4DqYGMu+IfhwnYC5sPaRkIWpHE4Im6i8zJtBFCMgcGGDnESNww8VxMO3UXWaUjAP+LoJYvcMWJzEJ9e1AKXA65YfkcbXft6/jxdt0wydodDQTMzQQiUPxANPr6zno9T15eSEsvbqdVsyfQc0DzUN8WkNq2bh2Ihi4OZBLM6al9jaPH3x/UtMbW1l/XjLlW/MwY740ds6alSyDxgNfHefPL9P2SzMi0EUIyBwYYOcZwx4PHX10umlyorZnQH6CbxJaXp8ZYZ84qlTNnjR/0BmDW3N12l2YiIMyEdsIIWALic6Hs0qNZEvwf9BMQaM4c79UOlGklEe0mgVvoFUsr5azZR86dGEihNXXN1uF7K5jB2Ru76rXVFRseyiPPvFstm/a30KMhRaRq7gwhJP0wwCBDvrqEAdW8inz1gDCFjghAsMkP5erS67CpXuPN3Q2aFUFpBcEE3DKdVotmSHAVG+iJCHpCEHzMiAYXAFkPdLGgbINgoa9MCXQUj6zdd2Qaq90lh9v8sm53vZZXvn7uLJlTMfDGtbu+XVZvqdHj4P1C8EmPhtST6rkzhJD0wDZVMuSry4UTC6Wlqyfmf7F0eolc/bHpQ95ckfrGRn+guUs7UeD0CXFne3ePtr7id0WhWypLPOoIigAEJRWzvdSsy8Ovoq+6fKK3ArIjG6ua1bcCotV39jbKnc9v0+Fm/UGPhtHFDBDnVfj0N4MLQrIPZjBIWq4usRGv2VaregvYg6PUAgFnIGQMQ0MWA8dEOSQvz6LGWSijIGWOsggyF4Opy0Of8X51s5qEYfjaR7Xt2i6L7Ag6TfIkqJqMn6zeITdfMFfnnBxLN1RCCMkVGGCQUdNxJNuwjayCJzbG3R8Ma4BR4IaoFKJOqwYhaEVFlgGD0aC5QFlkoLo8SiOPrauSLQda9Jgt3T0a2EwqdmsXSk1bt2ZK4LGBdleMZ//eRQuSlkvo0UAIIUODAQZJC/EbNkoNxdMccrDFJRv2NulmjZIIJqmajpzIVCyY4JMit0O1HtBc9Jc5MWdaIIPhchjdJ6Fw0JjS2WQ4QaKYgXM7o+WZXXXtsuqVnfKN82YdFbR46dFACCFDghoMkha8CfblKDNgFgm6RNQqPBQRm8VyxJ0z6giKVlR0iyTW5RE4oO0V5Y6qhg5ZvdkQY2IqalmBS42ycAy3wyKdwZBmLly2PJ094nHatPXV47BIQ4c/qZbC7KLpy2J8IC0IIYTkGrzcIhljqoSfmWWYAxLQFtDJxW4NCNq6g/3qLRLHeyNo2N/YpZ0umKiKjR8243D+9PdENCBA/NAZCBsD1DwO7YixWY022GRaCno0EELI0GAGg6QFc8PGxowNG0EEHDJRyij2OqXC59Jx6XAHhagTeotkbaBmKQSBCsocML2CoBMBxY7ads1i4BynThunNucQeCI7gSADvho4D/QZ6FjB88YXOPucLBvfRYM1wa20v7URQkguwwwGyThTpWUzStR1ExmC/jpVEltHze4OBBIoeSBowHGLPcU6xOzsOeNl7a4GOdjcpV0o5T6n2p/j9TgXOlYQgPSnpaBHAyGEDA4GGCStjGTD7qt1FO2nOE5rV5faerd25Uuhx6FBxrIZ42T11loJIEvh7xG7zSplPpcGF8iYIJsy0LyLZF00Kh5l0EEIIZkVYKxatUp+8pOfSE1NjSxatEh+/vOfy5IlS5I+9+GHH5Yvf/nLve5zOp3S3d09SqslmdL2mqx1FNkIaCiaugLSHoDLaFDe2tMoJ1YWadtrY2dQTpsxTkWeEJNCc4GyiDlKfjhaikQNyFBtyAkhZCyS9gDjySeflJtuuknuv/9+Wbp0qdx7772yYsUK2bFjh5SVlSV9jc/n08dNEo2PSG7gTWgdRXABPwvMLyl0O8RhzZO6toBam7+xs14nrZ5cWXzUQLR9Dcknvw4GUwNi2pAj2KF9OCGEZECAcc8998h1110Xy0og0HjhhRfkoYcekltuuSXpaxBQVFRUjPJKc49MT/vHd6J4HVbNXCC4QBYCtIdF5lf4ZHa5V4ezYY7J/zlzhthshrZ5pFqKvjQgCHbQGYOMCFpeITzNpM+NEELGfIARCARkw4YNcuutt8buQ1vh8uXLZd26dX2+rr29XaZOnSrhcFhOPvlk+dGPfiTHHXdc0uf6/X79MWlt7X/mBMmetH986yhmi9S1devGDnMuCDxVuFnmlSKPU+aUW3S8+6HW7lg5ZqSOpLQPJ4SQDG1Tra+vl1AoJOXlRsraBLehx0jG3LlzNbvxpz/9SR577DENMk4//XQ5cOBA0uffddddUlhYGPuZMmXKMXkv2Uq8QRV+43ay1k/8xm3cj8fTsa7+OlGml+RLdwDCzaBaf0O4CXtxc/Iqgo2+2k+HyxENSPI4/VickxBCsoW0l0iGyrJly/THBMHF/Pnz5Ve/+pXceeedRz0f2RFoPOIzGAwy+s5SoIzQ2B5Ia9p/qNkT3Hf1x6bJodYu9cBAm2qByzDvOpZW3jgW7cMJISQ5af2Xr7S0VKxWq9TW1va6H7cHq7Gw2+1y0kknyc6dO5M+jg4T/JDBiRMxvryqoVNOqixKS9p/uKLJycUeOWFSkT4vMbgwrbwHaj9NhRvpsT4nIYRkC2ktkTgcDjnllFPkpZdeit2Hkgdux2cp+gMlls2bN8uECROO4UrHFoniRFx9Y+gXfmMzRAvnweajZ24c67R/f+vCbdyfbE5If86g+D3c9tOBSMc5CSEkW0h77hbli5UrV8rixYvV+wJtqh0dHbGuki996UsyadIk1VKAO+64Q0477TSZNWuWNDc3q3/Gvn375Nprr03zO8ke+hMnakrfaZPD7X5p6+7RUemjlfYfqmgyscsFZZtkzqDDaT8dqRvpsTwnIYRkA2kPMC6//HI5fPiw3HbbbSrsPPHEE2X16tUx4WdVVZV2lpg0NTVpWyueW1xcrBmQtWvXyoIFC9L4LrKLZAZVJigvjM93yt6GDs1UiNhHLe3f37rM7Ak2cDyvP53GV8+ZOWD7aSpbcGkfTgghR5MXSZYHH8NA5IlukpaWFjXsykXQlfHTNR9qZ0gyceL+xg7ZWNUslSUe3bQTp4YeK/OogdaF0gOGi3325EmyektNnE7DpjqNwa4vG1pwCSEk2/dQTlPNQUxxIjbkxPgSt7uCYTl3XpmcOnXcqE4NHWhduB8zQzbtbx6WTgNkQgsuIYTkAmkvkZD0GlRBjIgsQGKW4sqllbr5jmbafzDrWjSlSJ55t3pY5lZ03iSEkNGDAUaOMlhx4mg7UA60rp5wZNA6jUTovEkIIaMHA4wcJlPFif2tCzqN4ZpbDUVESgghZGQwwMhxRjqPY7TXNRJzKy+dNwkhZNSgyJNkFSMxtxqMiHRWWT6dNwkhJAXwUo1kHcM1txqMiHSozpuZPtKeEELSBX0wSNYy3M093gcDZmIITpC5GKrzJv00CCG5RusQ9lBmMEjO6UdSIW4d7lA2QgjJFRhgkJwsA4xE3Eo/DUIIGRgGGKRfWAY4GvppEELIwLCLhPQJbbWTc8RPI3l8fixH2hNCSLbADEaWcqzLFqkoA8Sv0WO3CtTEXcFQ1pdZvPTTIISQAeG/gFnIaJQtRloGiF9jfbtff0TypDTfIaX5zqwus4zE7IsQQnIFlkiyjNEqW4ykDBC/RpGINHT4pdPfIx3+HmnoCOh92VxmGYnZFyGE5AoMMLKIxLLFUEeVDwVvXBkgGX2VAeLXOGu8V2pa/OIPhqXM55Jyn1P8wZDUtPr1sVSuN11mXwsnFo7qSHtCCMkWWCLJIkaze2G4ZYD4Nbb7Q9LYGZB815HX428EFngs27stMnVYHCGEZALMYGQRo9m9MNwyQPwaA6GwvsZuPfKfGf7GfXhsLHRbmH4a8yp8+pvBBSGEGDDAyCK8wyxbjGYZIH6NDqtFbBaLBEPh2OP4G/fhMXZbEELI2IX/smcR6eheGGoZIH6N0FmM8zikrq1bHF4jlm3v7lE9Rr7TKjsPd7DbghBCxigMMLKIYzENdH9+qYsAABDqSURBVLDnHaxGIn6NCCAqCp3S0h3QiadoU4UGo8Ln1MfYbUEIIWMXlkiyjGzoXohfI4KKEq9TvE6b/pR4HXpfJq2XEEJI6uG49iwlEwaQDbSGserkSQghuUorx7WPfUYyDXS03ETTvUZCCCHpgwEGGTKmUyf8LKAD8Tjc2jUCYSe0Fyx9EEIIoQaDZKybKCGEkOyFAQY5Zm6ihBBCchcGGCRj3UQJIYRkLwwwyJDwjrKbKCGEkOyEAQYZEqZTJ4y9EjucTTfRWWX5dOckhJAchwEGGZUhaIQQQnIL5rHJsJ06TR8M2ICjLAJ3TgQX2dqimirzskwwQSPJ4XdDyOjBAIOMyhC0TGcwxmGjeRySevjdEDK6MMAgw2asOHWmyjiMBmSZC78bQkYfajBITpMq4zAakGUu/G4ISQ8MMEhOkyrjMBqQZS78bghJDwwwSE6TKuMwGpBlLvxuCEkPDDBITuNNkXFYqo5DUo+X3w0haYEBBslpUmUcRgOyzIXfDSHpgQEGyWlSZRxGA7LMhd8NIekhL5IY0o9xWltbpbCwUFpaWsTn86V7OSQDPRJQj0fKHFe1QzUOS9VxSOrhd0PI6O6hDDAIiUInz7EPvxtCRm8PpaqJkBQbh40VA7KxCL8bQnJMg7Fq1SqZNm2auFwuWbp0qbz99tv9Pv/pp5+WefPm6fOPP/54efHFF0dtrYQQQgjJggDjySeflJtuukluv/12effdd2XRokWyYsUKqaurS/r8tWvXyhVXXCHXXHONbNy4US6++GL92bJly6ivnRBCCCEZqsFAxuLUU0+V++67T2+Hw2GZMmWKfOMb35BbbrnlqOdffvnl0tHRIc8//3zsvtNOO01OPPFEuf/++496vt/v15/4+hGOTw0GIYQQcuw0GGnNYAQCAdmwYYMsX778yIIsFr29bt26pK/B/fHPB8h49PX8u+66Sz8M8wfBBSGEEEKOLWkNMOrr6yUUCkl5eXmv+3G7pqYm6Wtw/1Cef+utt2qkZf7s378/he+AEEIIITnZReJ0OvWHEEIIITmSwSgtLRWr1Sq1tbW97sftioqKpK/B/UN5PiGEEEJyLMBwOBxyyimnyEsvvRS7DyJP3F62bFnS1+D++OeDNWvW9Pl8QgghhORgiQQtqitXrpTFixfLkiVL5N5779UukS9/+cv6+Je+9CWZNGmSijXB9ddfL2effbbcfffdctFFF8kTTzwh69evlwceeCDN74QQQgghGRNgoO308OHDctttt6lQE+2mq1evjgk5q6qqtLPE5PTTT5ff/va38t3vfle+853vyOzZs+WPf/yjLFy4MI3vghBCCCEZ5YMx2nAWCSGEEDLGfTAIIYQQMjZhgEEIIYSQsafBGG3MihDSPIQQQggZPObeORh1Rc4FGG1tbfqbluGEEELI8PdSaDH6I+dEnvDZOHjwoBQUFEheXl66lzNmMIfIwYqd4tn0w+8jc+B3kTnwuxg5CBkQXEycOLFXh2cyci6DgQ9k8uTJ6V7GmAX/o+X/cDMHfh+ZA7+LzIHfxcgYKHNhQpEnIYQQQlIOAwxCCCGEpBwGGCQlYGLt7bffzsm1GQK/j8yB30XmwO9idMk5kSchhBBCjj3MYBBCCCEk5TDAIIQQQkjKYYBBCCGEkJTDAIMQQgghKYcBBkkZP/7xj9Ud9YYbbkj3UnKW6upq+eIXvyglJSXidrvl+OOPl/Xr16d7WTlJKBSS733vezJ9+nT9LmbOnCl33nnnoGY4kJHx2muvyac+9Sl1m8S/SX/84x97PY7v4LbbbpMJEybod7N8+XL56KOP0rbesQoDDJIS3nnnHfnVr34lJ5xwQrqXkrM0NTXJGWecIXa7Xf7yl7/Itm3b5O6775bi4uJ0Ly0n+Y//+A/55S9/Kffdd59s375db//nf/6n/PznP0/30sY8HR0dsmjRIlm1alXSx/E9/OxnP5P7779f3nrrLfF6vbJixQrp7u4e9bWOZdimSkZMe3u7nHzyyfKLX/xCfvCDH8iJJ54o9957b7qXlXPccsst8sYbb8g//vGPdC+FiMgnP/lJKS8vlwcffDB23+c+9zm9Yn7sscfSurZcAhmMZ599Vi6++GK9jS0PmY1//dd/lW9961t6X0tLi35XDz/8sHz+859P84rHDsxgkBHz9a9/XS666CJNM5L08ec//1kWL14sl156qZSVlclJJ50kv/71r9O9rJzl9NNPl5deekk+/PBDvb1p0yZ5/fXX5cILL0z30nKaPXv2SE1NTa9/rzBbY+nSpbJu3bq0rm2skXPDzkhqeeKJJ+Tdd9/VEglJL7t379aU/E033STf+c539Dv55je/KQ6HQ1auXJnu5eVkRgnTO+fNmydWq1U1GT/84Q/lC1/4QrqXltMguADIWMSD2+ZjJDUwwCDDBiOPr7/+elmzZo24XK50LyfnCYfDmsH40Y9+pLeRwdiyZYvWmRlgjD5PPfWUPP744/Lb3/5WjjvuOHnvvfdUAI30PL8PkguwREKGzYYNG6Surk71FzabTX/+/ve/q3gKf+OKjYweUMQvWLCg133z58+XqqqqtK0pl7n55ps1i4GaPrp5/uVf/kVuvPFGueuuu9K9tJymoqJCf9fW1va6H7fNx0hqYIBBhs15550nmzdv1isz8wdX0EgB42+khcnogQ6SHTt29LoP9f+pU6embU25TGdnp1gsvf+Jxf8mkGki6QNtwwgkoI8xQSkL3STLli1L69rGGiyRkGFTUFAgCxcu7HUf2r3gwZB4Pzn24OoYwkKUSC677DJ5++235YEHHtAfMvrAhwGai8rKSi2RbNy4Ue655x65+uqr0720nOhs27lzZy9hJy56xo0bp98HSlXoeJs9e7YGHPArQenK7DQhKQJtqoSkirPPPjty/fXXp3sZOctzzz0XWbhwYcTpdEbmzZsXeeCBB9K9pJyltbVV/7dQWVkZcblckRkzZkT+7d/+LeL3+9O9tDHPK6+8AvuFo35Wrlypj4fD4cj3vve9SHl5uf5v5bzzzovs2LEj3csec9AHgxBCCCEphxoMQgghhKQcBhiEEEIISTkMMAghhBCSchhgEEIIISTlMMAghBBCSMphgEEIIYSQlMMAgxBCCCEphwEGIYQQQlIOAwxCSMbz7//+73LiiSdKNrJ3717Jy8tTq2pCcgkGGIRkIevWrdPBWRdddNExP9cHH3ygG+Sbb77Z6/7TTjtNXC6XdHd3x+7D37jvwQcflFzcxK+66irOsyAkCgMMQrIQbODf+MY35LXXXpODBw8e03PNmzdPp0+++uqrsfva2trk3XfflfHjx/cKPBD4+P1++fjHPz6scwWDwZSsmRCSfhhgEJKFkyKffPJJ+epXv6oZjIcffjj22JVXXimXX375UZt2aWmpPProo7Hg4Atf+IJOvp0wYYL89Kc/lXPOOUcnTPbFueee2yvAeP3112XOnDk6MTT+fvyN8fCYUAl++ctfysyZM8XhcMjcuXPlf//3f3sdF1kHPOfTn/60rgfTR8GPf/xjKS8v14m911xzTa8syXDAiPS77rpL1+V2u2XRokXy+9//vte6sRaM8F68eLF4PB6dTLtjx45ex8EEzrKyMl3XtddeK7fcckusdIMyziOPPCJ/+tOf9Fj4if9sdu/erZ8jjo3zIxgjZEyT7mlrhJCh8eCDD0YWL14cm546c+ZMnQ4Jnn/++Yjb7Y60tbXFno/n4D5M9wTXXnttZOrUqZG//e1vkc2bN0c++9nPRgoKCvqdgouprF6vNxIMBvX2zTffHPn6178eeeKJJyJnnXVW7Hlnnnlm5KqrrtK/n3nmmYjdbo+sWrVKJ1XefffdEavVGnn55Zdjz8c/QWVlZZGHHnoosmvXrsi+ffsiTz75pE64/M1vfhP54IMPdAIp1rdo0aI+17dnzx491saNG5M+/oMf/ECny65evVrP8z//8z96jldffbXX9M2lS5fqfVu3btX3cvrpp8eO8dhjj+lUVKwV7+f73/9+xOfzxdaFz/yyyy6LXHDBBZFDhw7pDyanmmvD+fH94LWXXHKJfgfm50nIWIQBBiFZBja9e++9V//GBlVaWqobZPztRx99NPb8K664InL55Zfr3wgysOk//fTTscebm5sjHo+n3wDjo48+0k1y7dq1evvUU0+NPPXUU5GDBw/qRt3V1RXp7OzUvx955JHYOq+77rpex7n00ksjn/jEJ2K3ccwbbrih13OWLVsW+drXvtbrPmz8ww0wuru79f2Zaze55ppr9LOJDzAQdJm88MILeh/em7kGBFXxnHHGGb3WhXHgn/nMZ5KuDQGTCQIY3Ld9+/Y+3xMh2Q5LJIRkEUjZv/3223LFFVfobZvNpiURU1SJ25dddpk8/vjjerujo0NT9iiJmGl6lEyWLFkSO2ZhYaGWL/pj1qxZMnnyZE35t7a2ysaNG+Xss8/WEktlZaWm+039BcoAYPv27XLGGWf0Og5u4/54UJKIB48vXbq0133Lli2T4bJz507p7OyU888/X/Lz82M/KBnt2rWr13NPOOGE2N94b6Curi722cd/biDxdn/0d2xCxiK2dC+AEDJ4EEj09PTIxIkTY/chEeB0OuW+++7TYAHBBDZ/bF5r1qxRzcEFF1ww4nNDp/HKK6/oRjl79mzVIgCcC/djHQhEpkyZMqTjQntxrDUr4IUXXpBJkyb1egyfWzx2uz32NzQUpn4jFRzLYxOSiTCDQUiWgMACV9133323tmOaP5s2bdKA43e/+50+D+JEbPIQgiKTcemll8Y2txkzZujf77zzTuy4LS0t8uGHHw54fmQm1q5dq0ELgg2Ts846SzMb+DGzF2D+/Pnyxhtv9DoGbi9YsKDf8+B1b731Vq/7EltkhwLOh0CiqqpKA6D4n6EEQ8jyxH9uIPE2xKyhUGjYayVkLMEMBiFZwvPPPy9NTU3aVYFMRTyf+9znNLvxla98JdZNcv/992vggOyCCbofVq5cKTfffLOMGzdOsxC33367WCyW2FV1XyB4QMnloYcekl//+tex+5HBQEcF+NrXvha7H+dAueakk06S5cuXy3PPPSfPPPOM/O1vf+v3PNdff736SaB0gpIKgqStW7dqcDQQiV0f4LjjjpNvfetbcuONN2rG4GMf+5gGVQh2fD6ffh6DAW3B1113na4LQRwCuPfff7/XuqZNmyZ//etfdR0lJSVHfU+E5BTpFoEQQgbHJz/5yV4CyXjeeustFQ1u2rRJb2/btk1vo1PB7DAxgdDzyiuvVOFjRUVF5J577oksWbIkcssttwy4BhwPx0WHRDzTpk3T+yH6jOcXv/hFZMaMGSosnTNnTi/xKcBrnn322aPO88Mf/lDFqvn5+Sqc/Pa3vz0okWeyn/379+tnAGHs3LlzdS3jx4+PrFixIvL3v/+9l8izqakpdkwIRnEfjm1yxx13xNZ19dVXR775zW9GTjvttNjjdXV1kfPPP18fx2tx3GQCVJzHfJyQsUoe/l+6gxxCSPpAVgLaBJRekB0hgwfCUZiQJfp7EEJYIiEk50AHCOy/0QGBUsEdd9yh93/mM59J99IyGnSioOy0YsUKtWmH5gXlHmhSCCFHwwCDkBzkv/7rv1QnAFHiKaecIv/4xz/U7ZP0DTQqL774orqNwlkUos8//OEPqi8hhBwNSySEEEIISTlsUyWEEEJIymGAQQghhJCUwwCDEEIIISmHAQYhhBBCUg4DDEIIIYSkHAYYhBBCCEk5DDAIIYQQknIYYBBCCCFEUs3/B7oZ358lABNUAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(df['avg_word_length'], df['humor_rating'], alpha=0.5)\n",
+    "plt.title('Avg Word Length vs Humor Rating')\n",
+    "plt.xlabel('Avg Word Length')\n",
+    "plt.ylabel('Humor Rating')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id                   0.020462\n",
+      "is_humor                  NaN\n",
+      "humor_rating         1.000000\n",
+      "humor_controversy    0.173650\n",
+      "offense_rating      -0.308529\n",
+      "text_length          0.073611\n",
+      "word_count           0.093041\n",
+      "avg_word_length     -0.090018\n",
+      "punctuation_count    0.057945\n",
+      "stopword_ratio       0.105719\n",
+      "richness            -0.065616\n",
+      "readability         -0.060221\n",
+      "difficult_words     -0.099532\n",
+      "Name: humor_rating, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "numeric_df = df.select_dtypes(include=[np.number])\n",
+    "print(numeric_df.corr()['humor_rating'])\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}